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stringlengths 2
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stringlengths 15
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stringlengths 0
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int64 0
58.4M
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float64 0
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int64 0
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cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/gym/utils/ezpickle.py
|
class EzPickle(object):
"""Objects that are pickled and unpickled via their constructor
arguments.
Example usage:
class Dog(Animal, EzPickle):
def __init__(self, furcolor, tailkind="bushy"):
Animal.__init__()
EzPickle.__init__(furcolor, tailkind)
...
When this object is unpickled, a new Dog will be constructed by passing the provided
furcolor and tailkind into the constructor. However, philosophers are still not sure
whether it is still the same dog.
This is generally needed only for environments which wrap C/C++ code, such as MuJoCo
and Atari.
"""
def __init__(self, *args, **kwargs):
self._ezpickle_args = args
self._ezpickle_kwargs = kwargs
def __getstate__(self):
return {"_ezpickle_args" : self._ezpickle_args, "_ezpickle_kwargs": self._ezpickle_kwargs}
def __setstate__(self, d):
out = type(self)(*d["_ezpickle_args"], **d["_ezpickle_kwargs"])
self.__dict__.update(out.__dict__)
| 1,051 | 36.571429 | 98 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/gym/utils/reraise.py
|
import sys
# We keep the actual reraising in different modules, since the
# reraising code uses syntax mutually exclusive to Python 2/3.
if sys.version_info[0] < 3:
from .reraise_impl_py2 import reraise_impl #pylint: disable=E0401
else:
from .reraise_impl_py3 import reraise_impl
def reraise(prefix=None, suffix=None):
old_exc_type, old_exc_value, traceback = sys.exc_info()
if old_exc_value is None:
old_exc_value = old_exc_type()
e = ReraisedException(old_exc_value, prefix, suffix)
reraise_impl(e, traceback)
# http://stackoverflow.com/a/13653312
def full_class_name(o):
module = o.__class__.__module__
if module is None or module == str.__class__.__module__:
return o.__class__.__name__
return module + '.' + o.__class__.__name__
class ReraisedException(Exception):
def __init__(self, old_exc, prefix, suffix):
self.old_exc = old_exc
self.prefix = prefix
self.suffix = suffix
def __str__(self):
klass = self.old_exc.__class__
orig = "%s: %s" % (full_class_name(self.old_exc), klass.__str__(self.old_exc))
prefixpart = suffixpart = ''
if self.prefix is not None:
prefixpart = self.prefix + "\n"
if self.suffix is not None:
suffixpart = "\n\n" + self.suffix
return "%sThe original exception was:\n\n%s%s" % (prefixpart, orig, suffixpart)
| 1,404 | 32.452381 | 87 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/gym/utils/play.py
|
import gym
import pygame
import sys
import time
import matplotlib
try:
matplotlib.use('GTK3Agg')
import matplotlib.pyplot as plt
except Exception:
pass
import pyglet.window as pw
from collections import deque
from pygame.locals import HWSURFACE, DOUBLEBUF, RESIZABLE, VIDEORESIZE
from threading import Thread
def display_arr(screen, arr, video_size, transpose):
arr_min, arr_max = arr.min(), arr.max()
arr = 255.0 * (arr - arr_min) / (arr_max - arr_min)
pyg_img = pygame.surfarray.make_surface(arr.swapaxes(0, 1) if transpose else arr)
pyg_img = pygame.transform.scale(pyg_img, video_size)
screen.blit(pyg_img, (0,0))
def play(env, transpose=True, fps=30, zoom=None, callback=None, keys_to_action=None):
"""Allows one to play the game using keyboard.
To simply play the game use:
play(gym.make("Pong-v3"))
Above code works also if env is wrapped, so it's particularly useful in
verifying that the frame-level preprocessing does not render the game
unplayable.
If you wish to plot real time statistics as you play, you can use
gym.utils.play.PlayPlot. Here's a sample code for plotting the reward
for last 5 second of gameplay.
def callback(obs_t, obs_tp1, rew, done, info):
return [rew,]
env_plotter = EnvPlotter(callback, 30 * 5, ["reward"])
env = gym.make("Pong-v3")
play(env, callback=env_plotter.callback)
Arguments
---------
env: gym.Env
Environment to use for playing.
transpose: bool
If True the output of observation is transposed.
Defaults to true.
fps: int
Maximum number of steps of the environment to execute every second.
Defaults to 30.
zoom: float
Make screen edge this many times bigger
callback: lambda or None
Callback if a callback is provided it will be executed after
every step. It takes the following input:
obs_t: observation before performing action
obs_tp1: observation after performing action
action: action that was executed
rew: reward that was received
done: whether the environemnt is done or not
info: debug info
keys_to_action: dict: tuple(int) -> int or None
Mapping from keys pressed to action performed.
For example if pressed 'w' and space at the same time is supposed
to trigger action number 2 then key_to_action dict would look like this:
{
# ...
sorted(ord('w'), ord(' ')) -> 2
# ...
}
If None, default key_to_action mapping for that env is used, if provided.
"""
obs_s = env.observation_space
assert type(obs_s) == gym.spaces.box.Box
assert len(obs_s.shape) == 2 or (len(obs_s.shape) == 3 and obs_s.shape[2] in [1,3])
if keys_to_action is None:
if hasattr(env, 'get_keys_to_action'):
keys_to_action = env.get_keys_to_action()
elif hasattr(env.unwrapped, 'get_keys_to_action'):
keys_to_action = env.unwrapped.get_keys_to_action()
else:
assert False, env.spec.id + " does not have explicit key to action mapping, " + \
"please specify one manually"
relevant_keys = set(sum(map(list, keys_to_action.keys()),[]))
if transpose:
video_size = env.observation_space.shape[1], env.observation_space.shape[0]
else:
video_size = env.observation_space.shape[0], env.observation_space.shape[1]
if zoom is not None:
video_size = int(video_size[0] * zoom), int(video_size[1] * zoom)
pressed_keys = []
running = True
env_done = True
screen = pygame.display.set_mode(video_size)
clock = pygame.time.Clock()
while running:
if env_done:
env_done = False
obs = env.reset()
else:
action = keys_to_action[tuple(sorted(pressed_keys))]
prev_obs = obs
obs, rew, env_done, info = env.step(action)
if callback is not None:
callback(prev_obs, obs, action, rew, env_done, info)
if obs is not None:
if len(obs.shape) == 2:
obs = obs[:, :, None]
if obs.shape[2] == 1:
obs = obs.repeat(3, axis=2)
display_arr(screen, obs, transpose=transpose, video_size=video_size)
# process pygame events
for event in pygame.event.get():
# test events, set key states
if event.type == pygame.KEYDOWN:
if event.key in relevant_keys:
pressed_keys.append(event.key)
elif event.key == 27:
running = False
elif event.type == pygame.KEYUP:
if event.key in relevant_keys:
pressed_keys.remove(event.key)
elif event.type == pygame.QUIT:
running = False
elif event.type == VIDEORESIZE:
video_size = event.size
screen = pygame.display.set_mode(video_size)
print(video_size)
pygame.display.flip()
clock.tick(fps)
pygame.quit()
class PlayPlot(object):
def __init__(self, callback, horizon_timesteps, plot_names):
self.data_callback = callback
self.horizon_timesteps = horizon_timesteps
self.plot_names = plot_names
num_plots = len(self.plot_names)
self.fig, self.ax = plt.subplots(num_plots)
if num_plots == 1:
self.ax = [self.ax]
for axis, name in zip(self.ax, plot_names):
axis.set_title(name)
self.t = 0
self.cur_plot = [None for _ in range(num_plots)]
self.data = [deque(maxlen=horizon_timesteps) for _ in range(num_plots)]
def callback(self, obs_t, obs_tp1, action, rew, done, info):
points = self.data_callback(obs_t, obs_tp1, action, rew, done, info)
for point, data_series in zip(points, self.data):
data_series.append(point)
self.t += 1
xmin, xmax = max(0, self.t - self.horizon_timesteps), self.t
for i, plot in enumerate(self.cur_plot):
if plot is not None:
plot.remove()
self.cur_plot[i] = self.ax[i].scatter(range(xmin, xmax), list(self.data[i]))
self.ax[i].set_xlim(xmin, xmax)
plt.pause(0.000001)
if __name__ == '__main__':
env = gym.make("MontezumaRevengeNoFrameskip-v4")
play(env, zoom=4, fps=60)
| 6,569 | 34.13369 | 93 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/gym/utils/closer.py
|
import atexit
import threading
import weakref
class Closer(object):
"""A registry that ensures your objects get closed, whether manually,
upon garbage collection, or upon exit. To work properly, your
objects need to cooperate and do something like the following:
```
closer = Closer()
class Example(object):
def __init__(self):
self._id = closer.register(self)
def close(self):
# Probably worth making idempotent too!
...
closer.unregister(self._id)
def __del__(self):
self.close()
```
That is, your objects should:
- register() themselves and save the returned ID
- unregister() themselves upon close()
- include a __del__ method which close()'s the object
"""
def __init__(self, atexit_register=True):
self.lock = threading.Lock()
self.next_id = -1
self.closeables = weakref.WeakValueDictionary()
if atexit_register:
atexit.register(self.close)
def generate_next_id(self):
with self.lock:
self.next_id += 1
return self.next_id
def register(self, closeable):
"""Registers an object with a 'close' method.
Returns:
int: The registration ID of this object. It is the caller's responsibility to save this ID if early closing is desired.
"""
assert hasattr(closeable, 'close'), 'No close method for {}'.format(closeable)
next_id = self.generate_next_id()
self.closeables[next_id] = closeable
return next_id
def unregister(self, id):
assert id is not None
if id in self.closeables:
del self.closeables[id]
def close(self):
# Explicitly fetch all monitors first so that they can't disappear while
# we iterate. cf. http://stackoverflow.com/a/12429620
closeables = list(self.closeables.values())
for closeable in closeables:
closeable.close()
| 2,019 | 28.705882 | 131 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/gym/utils/reraise_impl_py3.py
|
# http://stackoverflow.com/a/33822606 -- `from None` disables Python 3'
# semi-smart exception chaining, which we don't want in this case.
def reraise_impl(e, traceback):
raise e.with_traceback(traceback) from None
| 219 | 43 | 71 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/gym/utils/reraise_impl_py2.py
|
def reraise_impl(e, traceback):
raise e.__class__, e, traceback
| 68 | 22 | 35 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/gym/utils/seeding.py
|
import hashlib
import numpy as np
import os
import random as _random
from six import integer_types
import struct
import sys
from gym import error
def np_random(seed=None):
if seed is not None and not (isinstance(seed, integer_types) and 0 <= seed):
raise error.Error('Seed must be a non-negative integer or omitted, not {}'.format(seed))
seed = create_seed(seed)
rng = np.random.RandomState()
rng.seed(_int_list_from_bigint(hash_seed(seed)))
return rng, seed
def hash_seed(seed=None, max_bytes=8):
"""Any given evaluation is likely to have many PRNG's active at
once. (Most commonly, because the environment is running in
multiple processes.) There's literature indicating that having
linear correlations between seeds of multiple PRNG's can correlate
the outputs:
http://blogs.unity3d.com/2015/01/07/a-primer-on-repeatable-random-numbers/
http://stackoverflow.com/questions/1554958/how-different-do-random-seeds-need-to-be
http://dl.acm.org/citation.cfm?id=1276928
Thus, for sanity we hash the seeds before using them. (This scheme
is likely not crypto-strength, but it should be good enough to get
rid of simple correlations.)
Args:
seed (Optional[int]): None seeds from an operating system specific randomness source.
max_bytes: Maximum number of bytes to use in the hashed seed.
"""
if seed is None:
seed = create_seed(max_bytes=max_bytes)
hash = hashlib.sha512(str(seed).encode('utf8')).digest()
return _bigint_from_bytes(hash[:max_bytes])
def create_seed(a=None, max_bytes=8):
"""Create a strong random seed. Otherwise, Python 2 would seed using
the system time, which might be non-robust especially in the
presence of concurrency.
Args:
a (Optional[int, str]): None seeds from an operating system specific randomness source.
max_bytes: Maximum number of bytes to use in the seed.
"""
# Adapted from https://svn.python.org/projects/python/tags/r32/Lib/random.py
if a is None:
a = _bigint_from_bytes(os.urandom(max_bytes))
elif isinstance(a, str):
a = a.encode('utf8')
a += hashlib.sha512(a).digest()
a = _bigint_from_bytes(a[:max_bytes])
elif isinstance(a, integer_types):
a = a % 2**(8 * max_bytes)
else:
raise error.Error('Invalid type for seed: {} ({})'.format(type(a), a))
return a
# TODO: don't hardcode sizeof_int here
def _bigint_from_bytes(bytes):
sizeof_int = 4
padding = sizeof_int - len(bytes) % sizeof_int
bytes += b'\0' * padding
int_count = int(len(bytes) / sizeof_int)
unpacked = struct.unpack("{}I".format(int_count), bytes)
accum = 0
for i, val in enumerate(unpacked):
accum += 2 ** (sizeof_int * 8 * i) * val
return accum
def _int_list_from_bigint(bigint):
# Special case 0
if bigint < 0:
raise error.Error('Seed must be non-negative, not {}'.format(bigint))
elif bigint == 0:
return [0]
ints = []
while bigint > 0:
bigint, mod = divmod(bigint, 2 ** 32)
ints.append(mod)
return ints
| 3,141 | 33.152174 | 96 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/gym/utils/atomic_write.py
|
# Based on http://stackoverflow.com/questions/2333872/atomic-writing-to-file-with-python
import os
from contextlib import contextmanager
# We would ideally atomically replace any existing file with the new
# version. However, on Windows there's no Python-only solution prior
# to Python 3.3. (This library includes a C extension to do so:
# https://pypi.python.org/pypi/pyosreplace/0.1.)
#
# Correspondingly, we make a best effort, but on Python < 3.3 use a
# replace method which could result in the file temporarily
# disappearing.
import sys
if sys.version_info >= (3, 3):
# Python 3.3 and up have a native `replace` method
from os import replace
elif sys.platform.startswith("win"):
def replace(src, dst):
# TODO: on Windows, this will raise if the file is in use,
# which is possible. We'll need to make this more robust over
# time.
try:
os.remove(dst)
except OSError:
pass
os.rename(src, dst)
else:
# POSIX rename() is always atomic
from os import rename as replace
@contextmanager
def atomic_write(filepath, binary=False, fsync=False):
""" Writeable file object that atomically updates a file (using a temporary file). In some cases (namely Python < 3.3 on Windows), this could result in an existing file being temporarily unlinked.
:param filepath: the file path to be opened
:param binary: whether to open the file in a binary mode instead of textual
:param fsync: whether to force write the file to disk
"""
tmppath = filepath + '~'
while os.path.isfile(tmppath):
tmppath += '~'
try:
with open(tmppath, 'wb' if binary else 'w') as file:
yield file
if fsync:
file.flush()
os.fsync(file.fileno())
replace(tmppath, filepath)
finally:
try:
os.remove(tmppath)
except (IOError, OSError):
pass
| 1,951 | 33.857143 | 200 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/gym/utils/__init__.py
|
"""A set of common utilities used within the environments. These are
not intended as API functions, and will not remain stable over time.
"""
# These submodules should not have any import-time dependencies.
# We want this since we use `utils` during our import-time sanity checks
# that verify that our dependencies are actually present.
from .colorize import colorize
from .ezpickle import EzPickle
from .reraise import reraise
| 430 | 38.181818 | 72 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/gym/utils/colorize.py
|
"""A set of common utilities used within the environments. These are
not intended as API functions, and will not remain stable over time.
"""
color2num = dict(
gray=30,
red=31,
green=32,
yellow=33,
blue=34,
magenta=35,
cyan=36,
white=37,
crimson=38
)
def colorize(string, color, bold=False, highlight = False):
"""Return string surrounded by appropriate terminal color codes to
print colorized text. Valid colors: gray, red, green, yellow,
blue, magenta, cyan, white, crimson
"""
# Import six here so that `utils` has no import-time dependencies.
# We want this since we use `utils` during our import-time sanity checks
# that verify that our dependencies (including six) are actually present.
import six
attr = []
num = color2num[color]
if highlight: num += 10
attr.append(six.u(str(num)))
if bold: attr.append(six.u('1'))
attrs = six.u(';').join(attr)
return six.u('\x1b[%sm%s\x1b[0m') % (attrs, string)
| 1,008 | 27.027778 | 77 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/pytz/exceptions.py
|
'''
Custom exceptions raised by pytz.
'''
__all__ = [
'UnknownTimeZoneError', 'InvalidTimeError', 'AmbiguousTimeError',
'NonExistentTimeError',
]
class UnknownTimeZoneError(KeyError):
'''Exception raised when pytz is passed an unknown timezone.
>>> isinstance(UnknownTimeZoneError(), LookupError)
True
This class is actually a subclass of KeyError to provide backwards
compatibility with code relying on the undocumented behavior of earlier
pytz releases.
>>> isinstance(UnknownTimeZoneError(), KeyError)
True
'''
pass
class InvalidTimeError(Exception):
'''Base class for invalid time exceptions.'''
class AmbiguousTimeError(InvalidTimeError):
'''Exception raised when attempting to create an ambiguous wallclock time.
At the end of a DST transition period, a particular wallclock time will
occur twice (once before the clocks are set back, once after). Both
possibilities may be correct, unless further information is supplied.
See DstTzInfo.normalize() for more info
'''
class NonExistentTimeError(InvalidTimeError):
'''Exception raised when attempting to create a wallclock time that
cannot exist.
At the start of a DST transition period, the wallclock time jumps forward.
The instants jumped over never occur.
'''
| 1,329 | 26.142857 | 78 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/pytz/tzfile.py
|
#!/usr/bin/env python
'''
$Id: tzfile.py,v 1.8 2004/06/03 00:15:24 zenzen Exp $
'''
from datetime import datetime
from struct import unpack, calcsize
from pytz.tzinfo import StaticTzInfo, DstTzInfo, memorized_ttinfo
from pytz.tzinfo import memorized_datetime, memorized_timedelta
def _byte_string(s):
"""Cast a string or byte string to an ASCII byte string."""
return s.encode('ASCII')
_NULL = _byte_string('\0')
def _std_string(s):
"""Cast a string or byte string to an ASCII string."""
return str(s.decode('ASCII'))
def build_tzinfo(zone, fp):
head_fmt = '>4s c 15x 6l'
head_size = calcsize(head_fmt)
(magic, format, ttisgmtcnt, ttisstdcnt, leapcnt, timecnt,
typecnt, charcnt) = unpack(head_fmt, fp.read(head_size))
# Make sure it is a tzfile(5) file
assert magic == _byte_string('TZif'), 'Got magic %s' % repr(magic)
# Read out the transition times, localtime indices and ttinfo structures.
data_fmt = '>%(timecnt)dl %(timecnt)dB %(ttinfo)s %(charcnt)ds' % dict(
timecnt=timecnt, ttinfo='lBB' * typecnt, charcnt=charcnt)
data_size = calcsize(data_fmt)
data = unpack(data_fmt, fp.read(data_size))
# make sure we unpacked the right number of values
assert len(data) == 2 * timecnt + 3 * typecnt + 1
transitions = [memorized_datetime(trans)
for trans in data[:timecnt]]
lindexes = list(data[timecnt:2 * timecnt])
ttinfo_raw = data[2 * timecnt:-1]
tznames_raw = data[-1]
del data
# Process ttinfo into separate structs
ttinfo = []
tznames = {}
i = 0
while i < len(ttinfo_raw):
# have we looked up this timezone name yet?
tzname_offset = ttinfo_raw[i + 2]
if tzname_offset not in tznames:
nul = tznames_raw.find(_NULL, tzname_offset)
if nul < 0:
nul = len(tznames_raw)
tznames[tzname_offset] = _std_string(
tznames_raw[tzname_offset:nul])
ttinfo.append((ttinfo_raw[i],
bool(ttinfo_raw[i + 1]),
tznames[tzname_offset]))
i += 3
# Now build the timezone object
if len(ttinfo) == 1 or len(transitions) == 0:
ttinfo[0][0], ttinfo[0][2]
cls = type(zone, (StaticTzInfo,), dict(
zone=zone,
_utcoffset=memorized_timedelta(ttinfo[0][0]),
_tzname=ttinfo[0][2]))
else:
# Early dates use the first standard time ttinfo
i = 0
while ttinfo[i][1]:
i += 1
if ttinfo[i] == ttinfo[lindexes[0]]:
transitions[0] = datetime.min
else:
transitions.insert(0, datetime.min)
lindexes.insert(0, i)
# calculate transition info
transition_info = []
for i in range(len(transitions)):
inf = ttinfo[lindexes[i]]
utcoffset = inf[0]
if not inf[1]:
dst = 0
else:
for j in range(i - 1, -1, -1):
prev_inf = ttinfo[lindexes[j]]
if not prev_inf[1]:
break
dst = inf[0] - prev_inf[0] # dst offset
# Bad dst? Look further. DST > 24 hours happens when
# a timzone has moved across the international dateline.
if dst <= 0 or dst > 3600 * 3:
for j in range(i + 1, len(transitions)):
stdinf = ttinfo[lindexes[j]]
if not stdinf[1]:
dst = inf[0] - stdinf[0]
if dst > 0:
break # Found a useful std time.
tzname = inf[2]
# Round utcoffset and dst to the nearest minute or the
# datetime library will complain. Conversions to these timezones
# might be up to plus or minus 30 seconds out, but it is
# the best we can do.
utcoffset = int((utcoffset + 30) // 60) * 60
dst = int((dst + 30) // 60) * 60
transition_info.append(memorized_ttinfo(utcoffset, dst, tzname))
cls = type(zone, (DstTzInfo,), dict(
zone=zone,
_utc_transition_times=transitions,
_transition_info=transition_info))
return cls()
if __name__ == '__main__':
import os.path
from pprint import pprint
base = os.path.join(os.path.dirname(__file__), 'zoneinfo')
tz = build_tzinfo('Australia/Melbourne',
open(os.path.join(base, 'Australia', 'Melbourne'), 'rb'))
tz = build_tzinfo('US/Eastern',
open(os.path.join(base, 'US', 'Eastern'), 'rb'))
pprint(tz._utc_transition_times)
| 4,745 | 34.155556 | 79 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/pytz/__init__.py
|
'''
datetime.tzinfo timezone definitions generated from the
Olson timezone database:
ftp://elsie.nci.nih.gov/pub/tz*.tar.gz
See the datetime section of the Python Library Reference for information
on how to use these modules.
'''
import sys
import datetime
import os.path
from pytz.exceptions import AmbiguousTimeError
from pytz.exceptions import InvalidTimeError
from pytz.exceptions import NonExistentTimeError
from pytz.exceptions import UnknownTimeZoneError
from pytz.lazy import LazyDict, LazyList, LazySet
from pytz.tzinfo import unpickler, BaseTzInfo
from pytz.tzfile import build_tzinfo
# The IANA (nee Olson) database is updated several times a year.
OLSON_VERSION = '2018d'
VERSION = '2018.4' # pip compatible version number.
__version__ = VERSION
OLSEN_VERSION = OLSON_VERSION # Old releases had this misspelling
__all__ = [
'timezone', 'utc', 'country_timezones', 'country_names',
'AmbiguousTimeError', 'InvalidTimeError',
'NonExistentTimeError', 'UnknownTimeZoneError',
'all_timezones', 'all_timezones_set',
'common_timezones', 'common_timezones_set',
]
if sys.version_info[0] > 2: # Python 3.x
# Python 3.x doesn't have unicode(), making writing code
# for Python 2.3 and Python 3.x a pain.
unicode = str
def ascii(s):
r"""
>>> ascii('Hello')
'Hello'
>>> ascii('\N{TRADE MARK SIGN}') #doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
UnicodeEncodeError: ...
"""
if type(s) == bytes:
s = s.decode('ASCII')
else:
s.encode('ASCII') # Raise an exception if not ASCII
return s # But the string - not a byte string.
else: # Python 2.x
def ascii(s):
r"""
>>> ascii('Hello')
'Hello'
>>> ascii(u'Hello')
'Hello'
>>> ascii(u'\N{TRADE MARK SIGN}') #doctest: +IGNORE_EXCEPTION_DETAIL
Traceback (most recent call last):
...
UnicodeEncodeError: ...
"""
return s.encode('ASCII')
def open_resource(name):
"""Open a resource from the zoneinfo subdir for reading.
Uses the pkg_resources module if available and no standard file
found at the calculated location.
It is possible to specify different location for zoneinfo
subdir by using the PYTZ_TZDATADIR environment variable.
"""
name_parts = name.lstrip('/').split('/')
for part in name_parts:
if part == os.path.pardir or os.path.sep in part:
raise ValueError('Bad path segment: %r' % part)
zoneinfo_dir = os.environ.get('PYTZ_TZDATADIR', None)
if zoneinfo_dir is not None:
filename = os.path.join(zoneinfo_dir, *name_parts)
else:
filename = os.path.join(os.path.dirname(__file__),
'zoneinfo', *name_parts)
if not os.path.exists(filename):
# http://bugs.launchpad.net/bugs/383171 - we avoid using this
# unless absolutely necessary to help when a broken version of
# pkg_resources is installed.
try:
from pkg_resources import resource_stream
except ImportError:
resource_stream = None
if resource_stream is not None:
return resource_stream(__name__, 'zoneinfo/' + name)
return open(filename, 'rb')
def resource_exists(name):
"""Return true if the given resource exists"""
try:
open_resource(name).close()
return True
except IOError:
return False
_tzinfo_cache = {}
def timezone(zone):
r''' Return a datetime.tzinfo implementation for the given timezone
>>> from datetime import datetime, timedelta
>>> utc = timezone('UTC')
>>> eastern = timezone('US/Eastern')
>>> eastern.zone
'US/Eastern'
>>> timezone(unicode('US/Eastern')) is eastern
True
>>> utc_dt = datetime(2002, 10, 27, 6, 0, 0, tzinfo=utc)
>>> loc_dt = utc_dt.astimezone(eastern)
>>> fmt = '%Y-%m-%d %H:%M:%S %Z (%z)'
>>> loc_dt.strftime(fmt)
'2002-10-27 01:00:00 EST (-0500)'
>>> (loc_dt - timedelta(minutes=10)).strftime(fmt)
'2002-10-27 00:50:00 EST (-0500)'
>>> eastern.normalize(loc_dt - timedelta(minutes=10)).strftime(fmt)
'2002-10-27 01:50:00 EDT (-0400)'
>>> (loc_dt + timedelta(minutes=10)).strftime(fmt)
'2002-10-27 01:10:00 EST (-0500)'
Raises UnknownTimeZoneError if passed an unknown zone.
>>> try:
... timezone('Asia/Shangri-La')
... except UnknownTimeZoneError:
... print('Unknown')
Unknown
>>> try:
... timezone(unicode('\N{TRADE MARK SIGN}'))
... except UnknownTimeZoneError:
... print('Unknown')
Unknown
'''
if zone.upper() == 'UTC':
return utc
try:
zone = ascii(zone)
except UnicodeEncodeError:
# All valid timezones are ASCII
raise UnknownTimeZoneError(zone)
zone = _unmunge_zone(zone)
if zone not in _tzinfo_cache:
if zone in all_timezones_set:
fp = open_resource(zone)
try:
_tzinfo_cache[zone] = build_tzinfo(zone, fp)
finally:
fp.close()
else:
raise UnknownTimeZoneError(zone)
return _tzinfo_cache[zone]
def _unmunge_zone(zone):
"""Undo the time zone name munging done by older versions of pytz."""
return zone.replace('_plus_', '+').replace('_minus_', '-')
ZERO = datetime.timedelta(0)
HOUR = datetime.timedelta(hours=1)
class UTC(BaseTzInfo):
"""UTC
Optimized UTC implementation. It unpickles using the single module global
instance defined beneath this class declaration.
"""
zone = "UTC"
_utcoffset = ZERO
_dst = ZERO
_tzname = zone
def fromutc(self, dt):
if dt.tzinfo is None:
return self.localize(dt)
return super(utc.__class__, self).fromutc(dt)
def utcoffset(self, dt):
return ZERO
def tzname(self, dt):
return "UTC"
def dst(self, dt):
return ZERO
def __reduce__(self):
return _UTC, ()
def localize(self, dt, is_dst=False):
'''Convert naive time to local time'''
if dt.tzinfo is not None:
raise ValueError('Not naive datetime (tzinfo is already set)')
return dt.replace(tzinfo=self)
def normalize(self, dt, is_dst=False):
'''Correct the timezone information on the given datetime'''
if dt.tzinfo is self:
return dt
if dt.tzinfo is None:
raise ValueError('Naive time - no tzinfo set')
return dt.astimezone(self)
def __repr__(self):
return "<UTC>"
def __str__(self):
return "UTC"
UTC = utc = UTC() # UTC is a singleton
def _UTC():
"""Factory function for utc unpickling.
Makes sure that unpickling a utc instance always returns the same
module global.
These examples belong in the UTC class above, but it is obscured; or in
the README.txt, but we are not depending on Python 2.4 so integrating
the README.txt examples with the unit tests is not trivial.
>>> import datetime, pickle
>>> dt = datetime.datetime(2005, 3, 1, 14, 13, 21, tzinfo=utc)
>>> naive = dt.replace(tzinfo=None)
>>> p = pickle.dumps(dt, 1)
>>> naive_p = pickle.dumps(naive, 1)
>>> len(p) - len(naive_p)
17
>>> new = pickle.loads(p)
>>> new == dt
True
>>> new is dt
False
>>> new.tzinfo is dt.tzinfo
True
>>> utc is UTC is timezone('UTC')
True
>>> utc is timezone('GMT')
False
"""
return utc
_UTC.__safe_for_unpickling__ = True
def _p(*args):
"""Factory function for unpickling pytz tzinfo instances.
Just a wrapper around tzinfo.unpickler to save a few bytes in each pickle
by shortening the path.
"""
return unpickler(*args)
_p.__safe_for_unpickling__ = True
class _CountryTimezoneDict(LazyDict):
"""Map ISO 3166 country code to a list of timezone names commonly used
in that country.
iso3166_code is the two letter code used to identify the country.
>>> def print_list(list_of_strings):
... 'We use a helper so doctests work under Python 2.3 -> 3.x'
... for s in list_of_strings:
... print(s)
>>> print_list(country_timezones['nz'])
Pacific/Auckland
Pacific/Chatham
>>> print_list(country_timezones['ch'])
Europe/Zurich
>>> print_list(country_timezones['CH'])
Europe/Zurich
>>> print_list(country_timezones[unicode('ch')])
Europe/Zurich
>>> print_list(country_timezones['XXX'])
Traceback (most recent call last):
...
KeyError: 'XXX'
Previously, this information was exposed as a function rather than a
dictionary. This is still supported::
>>> print_list(country_timezones('nz'))
Pacific/Auckland
Pacific/Chatham
"""
def __call__(self, iso3166_code):
"""Backwards compatibility."""
return self[iso3166_code]
def _fill(self):
data = {}
zone_tab = open_resource('zone.tab')
try:
for line in zone_tab:
line = line.decode('UTF-8')
if line.startswith('#'):
continue
code, coordinates, zone = line.split(None, 4)[:3]
if zone not in all_timezones_set:
continue
try:
data[code].append(zone)
except KeyError:
data[code] = [zone]
self.data = data
finally:
zone_tab.close()
country_timezones = _CountryTimezoneDict()
class _CountryNameDict(LazyDict):
'''Dictionary proving ISO3166 code -> English name.
>>> print(country_names['au'])
Australia
'''
def _fill(self):
data = {}
zone_tab = open_resource('iso3166.tab')
try:
for line in zone_tab.readlines():
line = line.decode('UTF-8')
if line.startswith('#'):
continue
code, name = line.split(None, 1)
data[code] = name.strip()
self.data = data
finally:
zone_tab.close()
country_names = _CountryNameDict()
# Time-zone info based solely on fixed offsets
class _FixedOffset(datetime.tzinfo):
zone = None # to match the standard pytz API
def __init__(self, minutes):
if abs(minutes) >= 1440:
raise ValueError("absolute offset is too large", minutes)
self._minutes = minutes
self._offset = datetime.timedelta(minutes=minutes)
def utcoffset(self, dt):
return self._offset
def __reduce__(self):
return FixedOffset, (self._minutes, )
def dst(self, dt):
return ZERO
def tzname(self, dt):
return None
def __repr__(self):
return 'pytz.FixedOffset(%d)' % self._minutes
def localize(self, dt, is_dst=False):
'''Convert naive time to local time'''
if dt.tzinfo is not None:
raise ValueError('Not naive datetime (tzinfo is already set)')
return dt.replace(tzinfo=self)
def normalize(self, dt, is_dst=False):
'''Correct the timezone information on the given datetime'''
if dt.tzinfo is self:
return dt
if dt.tzinfo is None:
raise ValueError('Naive time - no tzinfo set')
return dt.astimezone(self)
def FixedOffset(offset, _tzinfos={}):
"""return a fixed-offset timezone based off a number of minutes.
>>> one = FixedOffset(-330)
>>> one
pytz.FixedOffset(-330)
>>> str(one.utcoffset(datetime.datetime.now()))
'-1 day, 18:30:00'
>>> str(one.dst(datetime.datetime.now()))
'0:00:00'
>>> two = FixedOffset(1380)
>>> two
pytz.FixedOffset(1380)
>>> str(two.utcoffset(datetime.datetime.now()))
'23:00:00'
>>> str(two.dst(datetime.datetime.now()))
'0:00:00'
The datetime.timedelta must be between the range of -1 and 1 day,
non-inclusive.
>>> FixedOffset(1440)
Traceback (most recent call last):
...
ValueError: ('absolute offset is too large', 1440)
>>> FixedOffset(-1440)
Traceback (most recent call last):
...
ValueError: ('absolute offset is too large', -1440)
An offset of 0 is special-cased to return UTC.
>>> FixedOffset(0) is UTC
True
There should always be only one instance of a FixedOffset per timedelta.
This should be true for multiple creation calls.
>>> FixedOffset(-330) is one
True
>>> FixedOffset(1380) is two
True
It should also be true for pickling.
>>> import pickle
>>> pickle.loads(pickle.dumps(one)) is one
True
>>> pickle.loads(pickle.dumps(two)) is two
True
"""
if offset == 0:
return UTC
info = _tzinfos.get(offset)
if info is None:
# We haven't seen this one before. we need to save it.
# Use setdefault to avoid a race condition and make sure we have
# only one
info = _tzinfos.setdefault(offset, _FixedOffset(offset))
return info
FixedOffset.__safe_for_unpickling__ = True
def _test():
import doctest
sys.path.insert(0, os.pardir)
import pytz
return doctest.testmod(pytz)
if __name__ == '__main__':
_test()
all_timezones = \
['Africa/Abidjan',
'Africa/Accra',
'Africa/Addis_Ababa',
'Africa/Algiers',
'Africa/Asmara',
'Africa/Asmera',
'Africa/Bamako',
'Africa/Bangui',
'Africa/Banjul',
'Africa/Bissau',
'Africa/Blantyre',
'Africa/Brazzaville',
'Africa/Bujumbura',
'Africa/Cairo',
'Africa/Casablanca',
'Africa/Ceuta',
'Africa/Conakry',
'Africa/Dakar',
'Africa/Dar_es_Salaam',
'Africa/Djibouti',
'Africa/Douala',
'Africa/El_Aaiun',
'Africa/Freetown',
'Africa/Gaborone',
'Africa/Harare',
'Africa/Johannesburg',
'Africa/Juba',
'Africa/Kampala',
'Africa/Khartoum',
'Africa/Kigali',
'Africa/Kinshasa',
'Africa/Lagos',
'Africa/Libreville',
'Africa/Lome',
'Africa/Luanda',
'Africa/Lubumbashi',
'Africa/Lusaka',
'Africa/Malabo',
'Africa/Maputo',
'Africa/Maseru',
'Africa/Mbabane',
'Africa/Mogadishu',
'Africa/Monrovia',
'Africa/Nairobi',
'Africa/Ndjamena',
'Africa/Niamey',
'Africa/Nouakchott',
'Africa/Ouagadougou',
'Africa/Porto-Novo',
'Africa/Sao_Tome',
'Africa/Timbuktu',
'Africa/Tripoli',
'Africa/Tunis',
'Africa/Windhoek',
'America/Adak',
'America/Anchorage',
'America/Anguilla',
'America/Antigua',
'America/Araguaina',
'America/Argentina/Buenos_Aires',
'America/Argentina/Catamarca',
'America/Argentina/ComodRivadavia',
'America/Argentina/Cordoba',
'America/Argentina/Jujuy',
'America/Argentina/La_Rioja',
'America/Argentina/Mendoza',
'America/Argentina/Rio_Gallegos',
'America/Argentina/Salta',
'America/Argentina/San_Juan',
'America/Argentina/San_Luis',
'America/Argentina/Tucuman',
'America/Argentina/Ushuaia',
'America/Aruba',
'America/Asuncion',
'America/Atikokan',
'America/Atka',
'America/Bahia',
'America/Bahia_Banderas',
'America/Barbados',
'America/Belem',
'America/Belize',
'America/Blanc-Sablon',
'America/Boa_Vista',
'America/Bogota',
'America/Boise',
'America/Buenos_Aires',
'America/Cambridge_Bay',
'America/Campo_Grande',
'America/Cancun',
'America/Caracas',
'America/Catamarca',
'America/Cayenne',
'America/Cayman',
'America/Chicago',
'America/Chihuahua',
'America/Coral_Harbour',
'America/Cordoba',
'America/Costa_Rica',
'America/Creston',
'America/Cuiaba',
'America/Curacao',
'America/Danmarkshavn',
'America/Dawson',
'America/Dawson_Creek',
'America/Denver',
'America/Detroit',
'America/Dominica',
'America/Edmonton',
'America/Eirunepe',
'America/El_Salvador',
'America/Ensenada',
'America/Fort_Nelson',
'America/Fort_Wayne',
'America/Fortaleza',
'America/Glace_Bay',
'America/Godthab',
'America/Goose_Bay',
'America/Grand_Turk',
'America/Grenada',
'America/Guadeloupe',
'America/Guatemala',
'America/Guayaquil',
'America/Guyana',
'America/Halifax',
'America/Havana',
'America/Hermosillo',
'America/Indiana/Indianapolis',
'America/Indiana/Knox',
'America/Indiana/Marengo',
'America/Indiana/Petersburg',
'America/Indiana/Tell_City',
'America/Indiana/Vevay',
'America/Indiana/Vincennes',
'America/Indiana/Winamac',
'America/Indianapolis',
'America/Inuvik',
'America/Iqaluit',
'America/Jamaica',
'America/Jujuy',
'America/Juneau',
'America/Kentucky/Louisville',
'America/Kentucky/Monticello',
'America/Knox_IN',
'America/Kralendijk',
'America/La_Paz',
'America/Lima',
'America/Los_Angeles',
'America/Louisville',
'America/Lower_Princes',
'America/Maceio',
'America/Managua',
'America/Manaus',
'America/Marigot',
'America/Martinique',
'America/Matamoros',
'America/Mazatlan',
'America/Mendoza',
'America/Menominee',
'America/Merida',
'America/Metlakatla',
'America/Mexico_City',
'America/Miquelon',
'America/Moncton',
'America/Monterrey',
'America/Montevideo',
'America/Montreal',
'America/Montserrat',
'America/Nassau',
'America/New_York',
'America/Nipigon',
'America/Nome',
'America/Noronha',
'America/North_Dakota/Beulah',
'America/North_Dakota/Center',
'America/North_Dakota/New_Salem',
'America/Ojinaga',
'America/Panama',
'America/Pangnirtung',
'America/Paramaribo',
'America/Phoenix',
'America/Port-au-Prince',
'America/Port_of_Spain',
'America/Porto_Acre',
'America/Porto_Velho',
'America/Puerto_Rico',
'America/Punta_Arenas',
'America/Rainy_River',
'America/Rankin_Inlet',
'America/Recife',
'America/Regina',
'America/Resolute',
'America/Rio_Branco',
'America/Rosario',
'America/Santa_Isabel',
'America/Santarem',
'America/Santiago',
'America/Santo_Domingo',
'America/Sao_Paulo',
'America/Scoresbysund',
'America/Shiprock',
'America/Sitka',
'America/St_Barthelemy',
'America/St_Johns',
'America/St_Kitts',
'America/St_Lucia',
'America/St_Thomas',
'America/St_Vincent',
'America/Swift_Current',
'America/Tegucigalpa',
'America/Thule',
'America/Thunder_Bay',
'America/Tijuana',
'America/Toronto',
'America/Tortola',
'America/Vancouver',
'America/Virgin',
'America/Whitehorse',
'America/Winnipeg',
'America/Yakutat',
'America/Yellowknife',
'Antarctica/Casey',
'Antarctica/Davis',
'Antarctica/DumontDUrville',
'Antarctica/Macquarie',
'Antarctica/Mawson',
'Antarctica/McMurdo',
'Antarctica/Palmer',
'Antarctica/Rothera',
'Antarctica/South_Pole',
'Antarctica/Syowa',
'Antarctica/Troll',
'Antarctica/Vostok',
'Arctic/Longyearbyen',
'Asia/Aden',
'Asia/Almaty',
'Asia/Amman',
'Asia/Anadyr',
'Asia/Aqtau',
'Asia/Aqtobe',
'Asia/Ashgabat',
'Asia/Ashkhabad',
'Asia/Atyrau',
'Asia/Baghdad',
'Asia/Bahrain',
'Asia/Baku',
'Asia/Bangkok',
'Asia/Barnaul',
'Asia/Beirut',
'Asia/Bishkek',
'Asia/Brunei',
'Asia/Calcutta',
'Asia/Chita',
'Asia/Choibalsan',
'Asia/Chongqing',
'Asia/Chungking',
'Asia/Colombo',
'Asia/Dacca',
'Asia/Damascus',
'Asia/Dhaka',
'Asia/Dili',
'Asia/Dubai',
'Asia/Dushanbe',
'Asia/Famagusta',
'Asia/Gaza',
'Asia/Harbin',
'Asia/Hebron',
'Asia/Ho_Chi_Minh',
'Asia/Hong_Kong',
'Asia/Hovd',
'Asia/Irkutsk',
'Asia/Istanbul',
'Asia/Jakarta',
'Asia/Jayapura',
'Asia/Jerusalem',
'Asia/Kabul',
'Asia/Kamchatka',
'Asia/Karachi',
'Asia/Kashgar',
'Asia/Kathmandu',
'Asia/Katmandu',
'Asia/Khandyga',
'Asia/Kolkata',
'Asia/Krasnoyarsk',
'Asia/Kuala_Lumpur',
'Asia/Kuching',
'Asia/Kuwait',
'Asia/Macao',
'Asia/Macau',
'Asia/Magadan',
'Asia/Makassar',
'Asia/Manila',
'Asia/Muscat',
'Asia/Nicosia',
'Asia/Novokuznetsk',
'Asia/Novosibirsk',
'Asia/Omsk',
'Asia/Oral',
'Asia/Phnom_Penh',
'Asia/Pontianak',
'Asia/Pyongyang',
'Asia/Qatar',
'Asia/Qyzylorda',
'Asia/Rangoon',
'Asia/Riyadh',
'Asia/Saigon',
'Asia/Sakhalin',
'Asia/Samarkand',
'Asia/Seoul',
'Asia/Shanghai',
'Asia/Singapore',
'Asia/Srednekolymsk',
'Asia/Taipei',
'Asia/Tashkent',
'Asia/Tbilisi',
'Asia/Tehran',
'Asia/Tel_Aviv',
'Asia/Thimbu',
'Asia/Thimphu',
'Asia/Tokyo',
'Asia/Tomsk',
'Asia/Ujung_Pandang',
'Asia/Ulaanbaatar',
'Asia/Ulan_Bator',
'Asia/Urumqi',
'Asia/Ust-Nera',
'Asia/Vientiane',
'Asia/Vladivostok',
'Asia/Yakutsk',
'Asia/Yangon',
'Asia/Yekaterinburg',
'Asia/Yerevan',
'Atlantic/Azores',
'Atlantic/Bermuda',
'Atlantic/Canary',
'Atlantic/Cape_Verde',
'Atlantic/Faeroe',
'Atlantic/Faroe',
'Atlantic/Jan_Mayen',
'Atlantic/Madeira',
'Atlantic/Reykjavik',
'Atlantic/South_Georgia',
'Atlantic/St_Helena',
'Atlantic/Stanley',
'Australia/ACT',
'Australia/Adelaide',
'Australia/Brisbane',
'Australia/Broken_Hill',
'Australia/Canberra',
'Australia/Currie',
'Australia/Darwin',
'Australia/Eucla',
'Australia/Hobart',
'Australia/LHI',
'Australia/Lindeman',
'Australia/Lord_Howe',
'Australia/Melbourne',
'Australia/NSW',
'Australia/North',
'Australia/Perth',
'Australia/Queensland',
'Australia/South',
'Australia/Sydney',
'Australia/Tasmania',
'Australia/Victoria',
'Australia/West',
'Australia/Yancowinna',
'Brazil/Acre',
'Brazil/DeNoronha',
'Brazil/East',
'Brazil/West',
'CET',
'CST6CDT',
'Canada/Atlantic',
'Canada/Central',
'Canada/Eastern',
'Canada/Mountain',
'Canada/Newfoundland',
'Canada/Pacific',
'Canada/Saskatchewan',
'Canada/Yukon',
'Chile/Continental',
'Chile/EasterIsland',
'Cuba',
'EET',
'EST',
'EST5EDT',
'Egypt',
'Eire',
'Etc/GMT',
'Etc/GMT+0',
'Etc/GMT+1',
'Etc/GMT+10',
'Etc/GMT+11',
'Etc/GMT+12',
'Etc/GMT+2',
'Etc/GMT+3',
'Etc/GMT+4',
'Etc/GMT+5',
'Etc/GMT+6',
'Etc/GMT+7',
'Etc/GMT+8',
'Etc/GMT+9',
'Etc/GMT-0',
'Etc/GMT-1',
'Etc/GMT-10',
'Etc/GMT-11',
'Etc/GMT-12',
'Etc/GMT-13',
'Etc/GMT-14',
'Etc/GMT-2',
'Etc/GMT-3',
'Etc/GMT-4',
'Etc/GMT-5',
'Etc/GMT-6',
'Etc/GMT-7',
'Etc/GMT-8',
'Etc/GMT-9',
'Etc/GMT0',
'Etc/Greenwich',
'Etc/UCT',
'Etc/UTC',
'Etc/Universal',
'Etc/Zulu',
'Europe/Amsterdam',
'Europe/Andorra',
'Europe/Astrakhan',
'Europe/Athens',
'Europe/Belfast',
'Europe/Belgrade',
'Europe/Berlin',
'Europe/Bratislava',
'Europe/Brussels',
'Europe/Bucharest',
'Europe/Budapest',
'Europe/Busingen',
'Europe/Chisinau',
'Europe/Copenhagen',
'Europe/Dublin',
'Europe/Gibraltar',
'Europe/Guernsey',
'Europe/Helsinki',
'Europe/Isle_of_Man',
'Europe/Istanbul',
'Europe/Jersey',
'Europe/Kaliningrad',
'Europe/Kiev',
'Europe/Kirov',
'Europe/Lisbon',
'Europe/Ljubljana',
'Europe/London',
'Europe/Luxembourg',
'Europe/Madrid',
'Europe/Malta',
'Europe/Mariehamn',
'Europe/Minsk',
'Europe/Monaco',
'Europe/Moscow',
'Europe/Nicosia',
'Europe/Oslo',
'Europe/Paris',
'Europe/Podgorica',
'Europe/Prague',
'Europe/Riga',
'Europe/Rome',
'Europe/Samara',
'Europe/San_Marino',
'Europe/Sarajevo',
'Europe/Saratov',
'Europe/Simferopol',
'Europe/Skopje',
'Europe/Sofia',
'Europe/Stockholm',
'Europe/Tallinn',
'Europe/Tirane',
'Europe/Tiraspol',
'Europe/Ulyanovsk',
'Europe/Uzhgorod',
'Europe/Vaduz',
'Europe/Vatican',
'Europe/Vienna',
'Europe/Vilnius',
'Europe/Volgograd',
'Europe/Warsaw',
'Europe/Zagreb',
'Europe/Zaporozhye',
'Europe/Zurich',
'GB',
'GB-Eire',
'GMT',
'GMT+0',
'GMT-0',
'GMT0',
'Greenwich',
'HST',
'Hongkong',
'Iceland',
'Indian/Antananarivo',
'Indian/Chagos',
'Indian/Christmas',
'Indian/Cocos',
'Indian/Comoro',
'Indian/Kerguelen',
'Indian/Mahe',
'Indian/Maldives',
'Indian/Mauritius',
'Indian/Mayotte',
'Indian/Reunion',
'Iran',
'Israel',
'Jamaica',
'Japan',
'Kwajalein',
'Libya',
'MET',
'MST',
'MST7MDT',
'Mexico/BajaNorte',
'Mexico/BajaSur',
'Mexico/General',
'NZ',
'NZ-CHAT',
'Navajo',
'PRC',
'PST8PDT',
'Pacific/Apia',
'Pacific/Auckland',
'Pacific/Bougainville',
'Pacific/Chatham',
'Pacific/Chuuk',
'Pacific/Easter',
'Pacific/Efate',
'Pacific/Enderbury',
'Pacific/Fakaofo',
'Pacific/Fiji',
'Pacific/Funafuti',
'Pacific/Galapagos',
'Pacific/Gambier',
'Pacific/Guadalcanal',
'Pacific/Guam',
'Pacific/Honolulu',
'Pacific/Johnston',
'Pacific/Kiritimati',
'Pacific/Kosrae',
'Pacific/Kwajalein',
'Pacific/Majuro',
'Pacific/Marquesas',
'Pacific/Midway',
'Pacific/Nauru',
'Pacific/Niue',
'Pacific/Norfolk',
'Pacific/Noumea',
'Pacific/Pago_Pago',
'Pacific/Palau',
'Pacific/Pitcairn',
'Pacific/Pohnpei',
'Pacific/Ponape',
'Pacific/Port_Moresby',
'Pacific/Rarotonga',
'Pacific/Saipan',
'Pacific/Samoa',
'Pacific/Tahiti',
'Pacific/Tarawa',
'Pacific/Tongatapu',
'Pacific/Truk',
'Pacific/Wake',
'Pacific/Wallis',
'Pacific/Yap',
'Poland',
'Portugal',
'ROC',
'ROK',
'Singapore',
'Turkey',
'UCT',
'US/Alaska',
'US/Aleutian',
'US/Arizona',
'US/Central',
'US/East-Indiana',
'US/Eastern',
'US/Hawaii',
'US/Indiana-Starke',
'US/Michigan',
'US/Mountain',
'US/Pacific',
'US/Samoa',
'UTC',
'Universal',
'W-SU',
'WET',
'Zulu']
all_timezones = LazyList(
tz for tz in all_timezones if resource_exists(tz))
all_timezones_set = LazySet(all_timezones)
common_timezones = \
['Africa/Abidjan',
'Africa/Accra',
'Africa/Addis_Ababa',
'Africa/Algiers',
'Africa/Asmara',
'Africa/Bamako',
'Africa/Bangui',
'Africa/Banjul',
'Africa/Bissau',
'Africa/Blantyre',
'Africa/Brazzaville',
'Africa/Bujumbura',
'Africa/Cairo',
'Africa/Casablanca',
'Africa/Ceuta',
'Africa/Conakry',
'Africa/Dakar',
'Africa/Dar_es_Salaam',
'Africa/Djibouti',
'Africa/Douala',
'Africa/El_Aaiun',
'Africa/Freetown',
'Africa/Gaborone',
'Africa/Harare',
'Africa/Johannesburg',
'Africa/Juba',
'Africa/Kampala',
'Africa/Khartoum',
'Africa/Kigali',
'Africa/Kinshasa',
'Africa/Lagos',
'Africa/Libreville',
'Africa/Lome',
'Africa/Luanda',
'Africa/Lubumbashi',
'Africa/Lusaka',
'Africa/Malabo',
'Africa/Maputo',
'Africa/Maseru',
'Africa/Mbabane',
'Africa/Mogadishu',
'Africa/Monrovia',
'Africa/Nairobi',
'Africa/Ndjamena',
'Africa/Niamey',
'Africa/Nouakchott',
'Africa/Ouagadougou',
'Africa/Porto-Novo',
'Africa/Sao_Tome',
'Africa/Tripoli',
'Africa/Tunis',
'Africa/Windhoek',
'America/Adak',
'America/Anchorage',
'America/Anguilla',
'America/Antigua',
'America/Araguaina',
'America/Argentina/Buenos_Aires',
'America/Argentina/Catamarca',
'America/Argentina/Cordoba',
'America/Argentina/Jujuy',
'America/Argentina/La_Rioja',
'America/Argentina/Mendoza',
'America/Argentina/Rio_Gallegos',
'America/Argentina/Salta',
'America/Argentina/San_Juan',
'America/Argentina/San_Luis',
'America/Argentina/Tucuman',
'America/Argentina/Ushuaia',
'America/Aruba',
'America/Asuncion',
'America/Atikokan',
'America/Bahia',
'America/Bahia_Banderas',
'America/Barbados',
'America/Belem',
'America/Belize',
'America/Blanc-Sablon',
'America/Boa_Vista',
'America/Bogota',
'America/Boise',
'America/Cambridge_Bay',
'America/Campo_Grande',
'America/Cancun',
'America/Caracas',
'America/Cayenne',
'America/Cayman',
'America/Chicago',
'America/Chihuahua',
'America/Costa_Rica',
'America/Creston',
'America/Cuiaba',
'America/Curacao',
'America/Danmarkshavn',
'America/Dawson',
'America/Dawson_Creek',
'America/Denver',
'America/Detroit',
'America/Dominica',
'America/Edmonton',
'America/Eirunepe',
'America/El_Salvador',
'America/Fort_Nelson',
'America/Fortaleza',
'America/Glace_Bay',
'America/Godthab',
'America/Goose_Bay',
'America/Grand_Turk',
'America/Grenada',
'America/Guadeloupe',
'America/Guatemala',
'America/Guayaquil',
'America/Guyana',
'America/Halifax',
'America/Havana',
'America/Hermosillo',
'America/Indiana/Indianapolis',
'America/Indiana/Knox',
'America/Indiana/Marengo',
'America/Indiana/Petersburg',
'America/Indiana/Tell_City',
'America/Indiana/Vevay',
'America/Indiana/Vincennes',
'America/Indiana/Winamac',
'America/Inuvik',
'America/Iqaluit',
'America/Jamaica',
'America/Juneau',
'America/Kentucky/Louisville',
'America/Kentucky/Monticello',
'America/Kralendijk',
'America/La_Paz',
'America/Lima',
'America/Los_Angeles',
'America/Lower_Princes',
'America/Maceio',
'America/Managua',
'America/Manaus',
'America/Marigot',
'America/Martinique',
'America/Matamoros',
'America/Mazatlan',
'America/Menominee',
'America/Merida',
'America/Metlakatla',
'America/Mexico_City',
'America/Miquelon',
'America/Moncton',
'America/Monterrey',
'America/Montevideo',
'America/Montserrat',
'America/Nassau',
'America/New_York',
'America/Nipigon',
'America/Nome',
'America/Noronha',
'America/North_Dakota/Beulah',
'America/North_Dakota/Center',
'America/North_Dakota/New_Salem',
'America/Ojinaga',
'America/Panama',
'America/Pangnirtung',
'America/Paramaribo',
'America/Phoenix',
'America/Port-au-Prince',
'America/Port_of_Spain',
'America/Porto_Velho',
'America/Puerto_Rico',
'America/Punta_Arenas',
'America/Rainy_River',
'America/Rankin_Inlet',
'America/Recife',
'America/Regina',
'America/Resolute',
'America/Rio_Branco',
'America/Santarem',
'America/Santiago',
'America/Santo_Domingo',
'America/Sao_Paulo',
'America/Scoresbysund',
'America/Sitka',
'America/St_Barthelemy',
'America/St_Johns',
'America/St_Kitts',
'America/St_Lucia',
'America/St_Thomas',
'America/St_Vincent',
'America/Swift_Current',
'America/Tegucigalpa',
'America/Thule',
'America/Thunder_Bay',
'America/Tijuana',
'America/Toronto',
'America/Tortola',
'America/Vancouver',
'America/Whitehorse',
'America/Winnipeg',
'America/Yakutat',
'America/Yellowknife',
'Antarctica/Casey',
'Antarctica/Davis',
'Antarctica/DumontDUrville',
'Antarctica/Macquarie',
'Antarctica/Mawson',
'Antarctica/McMurdo',
'Antarctica/Palmer',
'Antarctica/Rothera',
'Antarctica/Syowa',
'Antarctica/Troll',
'Antarctica/Vostok',
'Arctic/Longyearbyen',
'Asia/Aden',
'Asia/Almaty',
'Asia/Amman',
'Asia/Anadyr',
'Asia/Aqtau',
'Asia/Aqtobe',
'Asia/Ashgabat',
'Asia/Atyrau',
'Asia/Baghdad',
'Asia/Bahrain',
'Asia/Baku',
'Asia/Bangkok',
'Asia/Barnaul',
'Asia/Beirut',
'Asia/Bishkek',
'Asia/Brunei',
'Asia/Chita',
'Asia/Choibalsan',
'Asia/Colombo',
'Asia/Damascus',
'Asia/Dhaka',
'Asia/Dili',
'Asia/Dubai',
'Asia/Dushanbe',
'Asia/Famagusta',
'Asia/Gaza',
'Asia/Hebron',
'Asia/Ho_Chi_Minh',
'Asia/Hong_Kong',
'Asia/Hovd',
'Asia/Irkutsk',
'Asia/Jakarta',
'Asia/Jayapura',
'Asia/Jerusalem',
'Asia/Kabul',
'Asia/Kamchatka',
'Asia/Karachi',
'Asia/Kathmandu',
'Asia/Khandyga',
'Asia/Kolkata',
'Asia/Krasnoyarsk',
'Asia/Kuala_Lumpur',
'Asia/Kuching',
'Asia/Kuwait',
'Asia/Macau',
'Asia/Magadan',
'Asia/Makassar',
'Asia/Manila',
'Asia/Muscat',
'Asia/Nicosia',
'Asia/Novokuznetsk',
'Asia/Novosibirsk',
'Asia/Omsk',
'Asia/Oral',
'Asia/Phnom_Penh',
'Asia/Pontianak',
'Asia/Pyongyang',
'Asia/Qatar',
'Asia/Qyzylorda',
'Asia/Riyadh',
'Asia/Sakhalin',
'Asia/Samarkand',
'Asia/Seoul',
'Asia/Shanghai',
'Asia/Singapore',
'Asia/Srednekolymsk',
'Asia/Taipei',
'Asia/Tashkent',
'Asia/Tbilisi',
'Asia/Tehran',
'Asia/Thimphu',
'Asia/Tokyo',
'Asia/Tomsk',
'Asia/Ulaanbaatar',
'Asia/Urumqi',
'Asia/Ust-Nera',
'Asia/Vientiane',
'Asia/Vladivostok',
'Asia/Yakutsk',
'Asia/Yangon',
'Asia/Yekaterinburg',
'Asia/Yerevan',
'Atlantic/Azores',
'Atlantic/Bermuda',
'Atlantic/Canary',
'Atlantic/Cape_Verde',
'Atlantic/Faroe',
'Atlantic/Madeira',
'Atlantic/Reykjavik',
'Atlantic/South_Georgia',
'Atlantic/St_Helena',
'Atlantic/Stanley',
'Australia/Adelaide',
'Australia/Brisbane',
'Australia/Broken_Hill',
'Australia/Currie',
'Australia/Darwin',
'Australia/Eucla',
'Australia/Hobart',
'Australia/Lindeman',
'Australia/Lord_Howe',
'Australia/Melbourne',
'Australia/Perth',
'Australia/Sydney',
'Canada/Atlantic',
'Canada/Central',
'Canada/Eastern',
'Canada/Mountain',
'Canada/Newfoundland',
'Canada/Pacific',
'Europe/Amsterdam',
'Europe/Andorra',
'Europe/Astrakhan',
'Europe/Athens',
'Europe/Belgrade',
'Europe/Berlin',
'Europe/Bratislava',
'Europe/Brussels',
'Europe/Bucharest',
'Europe/Budapest',
'Europe/Busingen',
'Europe/Chisinau',
'Europe/Copenhagen',
'Europe/Dublin',
'Europe/Gibraltar',
'Europe/Guernsey',
'Europe/Helsinki',
'Europe/Isle_of_Man',
'Europe/Istanbul',
'Europe/Jersey',
'Europe/Kaliningrad',
'Europe/Kiev',
'Europe/Kirov',
'Europe/Lisbon',
'Europe/Ljubljana',
'Europe/London',
'Europe/Luxembourg',
'Europe/Madrid',
'Europe/Malta',
'Europe/Mariehamn',
'Europe/Minsk',
'Europe/Monaco',
'Europe/Moscow',
'Europe/Oslo',
'Europe/Paris',
'Europe/Podgorica',
'Europe/Prague',
'Europe/Riga',
'Europe/Rome',
'Europe/Samara',
'Europe/San_Marino',
'Europe/Sarajevo',
'Europe/Saratov',
'Europe/Simferopol',
'Europe/Skopje',
'Europe/Sofia',
'Europe/Stockholm',
'Europe/Tallinn',
'Europe/Tirane',
'Europe/Ulyanovsk',
'Europe/Uzhgorod',
'Europe/Vaduz',
'Europe/Vatican',
'Europe/Vienna',
'Europe/Vilnius',
'Europe/Volgograd',
'Europe/Warsaw',
'Europe/Zagreb',
'Europe/Zaporozhye',
'Europe/Zurich',
'GMT',
'Indian/Antananarivo',
'Indian/Chagos',
'Indian/Christmas',
'Indian/Cocos',
'Indian/Comoro',
'Indian/Kerguelen',
'Indian/Mahe',
'Indian/Maldives',
'Indian/Mauritius',
'Indian/Mayotte',
'Indian/Reunion',
'Pacific/Apia',
'Pacific/Auckland',
'Pacific/Bougainville',
'Pacific/Chatham',
'Pacific/Chuuk',
'Pacific/Easter',
'Pacific/Efate',
'Pacific/Enderbury',
'Pacific/Fakaofo',
'Pacific/Fiji',
'Pacific/Funafuti',
'Pacific/Galapagos',
'Pacific/Gambier',
'Pacific/Guadalcanal',
'Pacific/Guam',
'Pacific/Honolulu',
'Pacific/Kiritimati',
'Pacific/Kosrae',
'Pacific/Kwajalein',
'Pacific/Majuro',
'Pacific/Marquesas',
'Pacific/Midway',
'Pacific/Nauru',
'Pacific/Niue',
'Pacific/Norfolk',
'Pacific/Noumea',
'Pacific/Pago_Pago',
'Pacific/Palau',
'Pacific/Pitcairn',
'Pacific/Pohnpei',
'Pacific/Port_Moresby',
'Pacific/Rarotonga',
'Pacific/Saipan',
'Pacific/Tahiti',
'Pacific/Tarawa',
'Pacific/Tongatapu',
'Pacific/Wake',
'Pacific/Wallis',
'US/Alaska',
'US/Arizona',
'US/Central',
'US/Eastern',
'US/Hawaii',
'US/Mountain',
'US/Pacific',
'UTC']
common_timezones = LazyList(
tz for tz in common_timezones if tz in all_timezones)
common_timezones_set = LazySet(common_timezones)
| 34,156 | 21.368697 | 77 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/pytz/tzinfo.py
|
'''Base classes and helpers for building zone specific tzinfo classes'''
from datetime import datetime, timedelta, tzinfo
from bisect import bisect_right
try:
set
except NameError:
from sets import Set as set
import pytz
from pytz.exceptions import AmbiguousTimeError, NonExistentTimeError
__all__ = []
_timedelta_cache = {}
def memorized_timedelta(seconds):
'''Create only one instance of each distinct timedelta'''
try:
return _timedelta_cache[seconds]
except KeyError:
delta = timedelta(seconds=seconds)
_timedelta_cache[seconds] = delta
return delta
_epoch = datetime.utcfromtimestamp(0)
_datetime_cache = {0: _epoch}
def memorized_datetime(seconds):
'''Create only one instance of each distinct datetime'''
try:
return _datetime_cache[seconds]
except KeyError:
# NB. We can't just do datetime.utcfromtimestamp(seconds) as this
# fails with negative values under Windows (Bug #90096)
dt = _epoch + timedelta(seconds=seconds)
_datetime_cache[seconds] = dt
return dt
_ttinfo_cache = {}
def memorized_ttinfo(*args):
'''Create only one instance of each distinct tuple'''
try:
return _ttinfo_cache[args]
except KeyError:
ttinfo = (
memorized_timedelta(args[0]),
memorized_timedelta(args[1]),
args[2]
)
_ttinfo_cache[args] = ttinfo
return ttinfo
_notime = memorized_timedelta(0)
def _to_seconds(td):
'''Convert a timedelta to seconds'''
return td.seconds + td.days * 24 * 60 * 60
class BaseTzInfo(tzinfo):
# Overridden in subclass
_utcoffset = None
_tzname = None
zone = None
def __str__(self):
return self.zone
class StaticTzInfo(BaseTzInfo):
'''A timezone that has a constant offset from UTC
These timezones are rare, as most locations have changed their
offset at some point in their history
'''
def fromutc(self, dt):
'''See datetime.tzinfo.fromutc'''
if dt.tzinfo is not None and dt.tzinfo is not self:
raise ValueError('fromutc: dt.tzinfo is not self')
return (dt + self._utcoffset).replace(tzinfo=self)
def utcoffset(self, dt, is_dst=None):
'''See datetime.tzinfo.utcoffset
is_dst is ignored for StaticTzInfo, and exists only to
retain compatibility with DstTzInfo.
'''
return self._utcoffset
def dst(self, dt, is_dst=None):
'''See datetime.tzinfo.dst
is_dst is ignored for StaticTzInfo, and exists only to
retain compatibility with DstTzInfo.
'''
return _notime
def tzname(self, dt, is_dst=None):
'''See datetime.tzinfo.tzname
is_dst is ignored for StaticTzInfo, and exists only to
retain compatibility with DstTzInfo.
'''
return self._tzname
def localize(self, dt, is_dst=False):
'''Convert naive time to local time'''
if dt.tzinfo is not None:
raise ValueError('Not naive datetime (tzinfo is already set)')
return dt.replace(tzinfo=self)
def normalize(self, dt, is_dst=False):
'''Correct the timezone information on the given datetime.
This is normally a no-op, as StaticTzInfo timezones never have
ambiguous cases to correct:
>>> from pytz import timezone
>>> gmt = timezone('GMT')
>>> isinstance(gmt, StaticTzInfo)
True
>>> dt = datetime(2011, 5, 8, 1, 2, 3, tzinfo=gmt)
>>> gmt.normalize(dt) is dt
True
The supported method of converting between timezones is to use
datetime.astimezone(). Currently normalize() also works:
>>> la = timezone('America/Los_Angeles')
>>> dt = la.localize(datetime(2011, 5, 7, 1, 2, 3))
>>> fmt = '%Y-%m-%d %H:%M:%S %Z (%z)'
>>> gmt.normalize(dt).strftime(fmt)
'2011-05-07 08:02:03 GMT (+0000)'
'''
if dt.tzinfo is self:
return dt
if dt.tzinfo is None:
raise ValueError('Naive time - no tzinfo set')
return dt.astimezone(self)
def __repr__(self):
return '<StaticTzInfo %r>' % (self.zone,)
def __reduce__(self):
# Special pickle to zone remains a singleton and to cope with
# database changes.
return pytz._p, (self.zone,)
class DstTzInfo(BaseTzInfo):
'''A timezone that has a variable offset from UTC
The offset might change if daylight saving time comes into effect,
or at a point in history when the region decides to change their
timezone definition.
'''
# Overridden in subclass
# Sorted list of DST transition times, UTC
_utc_transition_times = None
# [(utcoffset, dstoffset, tzname)] corresponding to
# _utc_transition_times entries
_transition_info = None
zone = None
# Set in __init__
_tzinfos = None
_dst = None # DST offset
def __init__(self, _inf=None, _tzinfos=None):
if _inf:
self._tzinfos = _tzinfos
self._utcoffset, self._dst, self._tzname = _inf
else:
_tzinfos = {}
self._tzinfos = _tzinfos
self._utcoffset, self._dst, self._tzname = (
self._transition_info[0])
_tzinfos[self._transition_info[0]] = self
for inf in self._transition_info[1:]:
if inf not in _tzinfos:
_tzinfos[inf] = self.__class__(inf, _tzinfos)
def fromutc(self, dt):
'''See datetime.tzinfo.fromutc'''
if (dt.tzinfo is not None and
getattr(dt.tzinfo, '_tzinfos', None) is not self._tzinfos):
raise ValueError('fromutc: dt.tzinfo is not self')
dt = dt.replace(tzinfo=None)
idx = max(0, bisect_right(self._utc_transition_times, dt) - 1)
inf = self._transition_info[idx]
return (dt + inf[0]).replace(tzinfo=self._tzinfos[inf])
def normalize(self, dt):
'''Correct the timezone information on the given datetime
If date arithmetic crosses DST boundaries, the tzinfo
is not magically adjusted. This method normalizes the
tzinfo to the correct one.
To test, first we need to do some setup
>>> from pytz import timezone
>>> utc = timezone('UTC')
>>> eastern = timezone('US/Eastern')
>>> fmt = '%Y-%m-%d %H:%M:%S %Z (%z)'
We next create a datetime right on an end-of-DST transition point,
the instant when the wallclocks are wound back one hour.
>>> utc_dt = datetime(2002, 10, 27, 6, 0, 0, tzinfo=utc)
>>> loc_dt = utc_dt.astimezone(eastern)
>>> loc_dt.strftime(fmt)
'2002-10-27 01:00:00 EST (-0500)'
Now, if we subtract a few minutes from it, note that the timezone
information has not changed.
>>> before = loc_dt - timedelta(minutes=10)
>>> before.strftime(fmt)
'2002-10-27 00:50:00 EST (-0500)'
But we can fix that by calling the normalize method
>>> before = eastern.normalize(before)
>>> before.strftime(fmt)
'2002-10-27 01:50:00 EDT (-0400)'
The supported method of converting between timezones is to use
datetime.astimezone(). Currently, normalize() also works:
>>> th = timezone('Asia/Bangkok')
>>> am = timezone('Europe/Amsterdam')
>>> dt = th.localize(datetime(2011, 5, 7, 1, 2, 3))
>>> fmt = '%Y-%m-%d %H:%M:%S %Z (%z)'
>>> am.normalize(dt).strftime(fmt)
'2011-05-06 20:02:03 CEST (+0200)'
'''
if dt.tzinfo is None:
raise ValueError('Naive time - no tzinfo set')
# Convert dt in localtime to UTC
offset = dt.tzinfo._utcoffset
dt = dt.replace(tzinfo=None)
dt = dt - offset
# convert it back, and return it
return self.fromutc(dt)
def localize(self, dt, is_dst=False):
'''Convert naive time to local time.
This method should be used to construct localtimes, rather
than passing a tzinfo argument to a datetime constructor.
is_dst is used to determine the correct timezone in the ambigous
period at the end of daylight saving time.
>>> from pytz import timezone
>>> fmt = '%Y-%m-%d %H:%M:%S %Z (%z)'
>>> amdam = timezone('Europe/Amsterdam')
>>> dt = datetime(2004, 10, 31, 2, 0, 0)
>>> loc_dt1 = amdam.localize(dt, is_dst=True)
>>> loc_dt2 = amdam.localize(dt, is_dst=False)
>>> loc_dt1.strftime(fmt)
'2004-10-31 02:00:00 CEST (+0200)'
>>> loc_dt2.strftime(fmt)
'2004-10-31 02:00:00 CET (+0100)'
>>> str(loc_dt2 - loc_dt1)
'1:00:00'
Use is_dst=None to raise an AmbiguousTimeError for ambiguous
times at the end of daylight saving time
>>> try:
... loc_dt1 = amdam.localize(dt, is_dst=None)
... except AmbiguousTimeError:
... print('Ambiguous')
Ambiguous
is_dst defaults to False
>>> amdam.localize(dt) == amdam.localize(dt, False)
True
is_dst is also used to determine the correct timezone in the
wallclock times jumped over at the start of daylight saving time.
>>> pacific = timezone('US/Pacific')
>>> dt = datetime(2008, 3, 9, 2, 0, 0)
>>> ploc_dt1 = pacific.localize(dt, is_dst=True)
>>> ploc_dt2 = pacific.localize(dt, is_dst=False)
>>> ploc_dt1.strftime(fmt)
'2008-03-09 02:00:00 PDT (-0700)'
>>> ploc_dt2.strftime(fmt)
'2008-03-09 02:00:00 PST (-0800)'
>>> str(ploc_dt2 - ploc_dt1)
'1:00:00'
Use is_dst=None to raise a NonExistentTimeError for these skipped
times.
>>> try:
... loc_dt1 = pacific.localize(dt, is_dst=None)
... except NonExistentTimeError:
... print('Non-existent')
Non-existent
'''
if dt.tzinfo is not None:
raise ValueError('Not naive datetime (tzinfo is already set)')
# Find the two best possibilities.
possible_loc_dt = set()
for delta in [timedelta(days=-1), timedelta(days=1)]:
loc_dt = dt + delta
idx = max(0, bisect_right(
self._utc_transition_times, loc_dt) - 1)
inf = self._transition_info[idx]
tzinfo = self._tzinfos[inf]
loc_dt = tzinfo.normalize(dt.replace(tzinfo=tzinfo))
if loc_dt.replace(tzinfo=None) == dt:
possible_loc_dt.add(loc_dt)
if len(possible_loc_dt) == 1:
return possible_loc_dt.pop()
# If there are no possibly correct timezones, we are attempting
# to convert a time that never happened - the time period jumped
# during the start-of-DST transition period.
if len(possible_loc_dt) == 0:
# If we refuse to guess, raise an exception.
if is_dst is None:
raise NonExistentTimeError(dt)
# If we are forcing the pre-DST side of the DST transition, we
# obtain the correct timezone by winding the clock forward a few
# hours.
elif is_dst:
return self.localize(
dt + timedelta(hours=6), is_dst=True) - timedelta(hours=6)
# If we are forcing the post-DST side of the DST transition, we
# obtain the correct timezone by winding the clock back.
else:
return self.localize(
dt - timedelta(hours=6),
is_dst=False) + timedelta(hours=6)
# If we get this far, we have multiple possible timezones - this
# is an ambiguous case occuring during the end-of-DST transition.
# If told to be strict, raise an exception since we have an
# ambiguous case
if is_dst is None:
raise AmbiguousTimeError(dt)
# Filter out the possiblilities that don't match the requested
# is_dst
filtered_possible_loc_dt = [
p for p in possible_loc_dt if bool(p.tzinfo._dst) == is_dst
]
# Hopefully we only have one possibility left. Return it.
if len(filtered_possible_loc_dt) == 1:
return filtered_possible_loc_dt[0]
if len(filtered_possible_loc_dt) == 0:
filtered_possible_loc_dt = list(possible_loc_dt)
# If we get this far, we have in a wierd timezone transition
# where the clocks have been wound back but is_dst is the same
# in both (eg. Europe/Warsaw 1915 when they switched to CET).
# At this point, we just have to guess unless we allow more
# hints to be passed in (such as the UTC offset or abbreviation),
# but that is just getting silly.
#
# Choose the earliest (by UTC) applicable timezone if is_dst=True
# Choose the latest (by UTC) applicable timezone if is_dst=False
# i.e., behave like end-of-DST transition
dates = {} # utc -> local
for local_dt in filtered_possible_loc_dt:
utc_time = (
local_dt.replace(tzinfo=None) - local_dt.tzinfo._utcoffset)
assert utc_time not in dates
dates[utc_time] = local_dt
return dates[[min, max][not is_dst](dates)]
def utcoffset(self, dt, is_dst=None):
'''See datetime.tzinfo.utcoffset
The is_dst parameter may be used to remove ambiguity during DST
transitions.
>>> from pytz import timezone
>>> tz = timezone('America/St_Johns')
>>> ambiguous = datetime(2009, 10, 31, 23, 30)
>>> str(tz.utcoffset(ambiguous, is_dst=False))
'-1 day, 20:30:00'
>>> str(tz.utcoffset(ambiguous, is_dst=True))
'-1 day, 21:30:00'
>>> try:
... tz.utcoffset(ambiguous)
... except AmbiguousTimeError:
... print('Ambiguous')
Ambiguous
'''
if dt is None:
return None
elif dt.tzinfo is not self:
dt = self.localize(dt, is_dst)
return dt.tzinfo._utcoffset
else:
return self._utcoffset
def dst(self, dt, is_dst=None):
'''See datetime.tzinfo.dst
The is_dst parameter may be used to remove ambiguity during DST
transitions.
>>> from pytz import timezone
>>> tz = timezone('America/St_Johns')
>>> normal = datetime(2009, 9, 1)
>>> str(tz.dst(normal))
'1:00:00'
>>> str(tz.dst(normal, is_dst=False))
'1:00:00'
>>> str(tz.dst(normal, is_dst=True))
'1:00:00'
>>> ambiguous = datetime(2009, 10, 31, 23, 30)
>>> str(tz.dst(ambiguous, is_dst=False))
'0:00:00'
>>> str(tz.dst(ambiguous, is_dst=True))
'1:00:00'
>>> try:
... tz.dst(ambiguous)
... except AmbiguousTimeError:
... print('Ambiguous')
Ambiguous
'''
if dt is None:
return None
elif dt.tzinfo is not self:
dt = self.localize(dt, is_dst)
return dt.tzinfo._dst
else:
return self._dst
def tzname(self, dt, is_dst=None):
'''See datetime.tzinfo.tzname
The is_dst parameter may be used to remove ambiguity during DST
transitions.
>>> from pytz import timezone
>>> tz = timezone('America/St_Johns')
>>> normal = datetime(2009, 9, 1)
>>> tz.tzname(normal)
'NDT'
>>> tz.tzname(normal, is_dst=False)
'NDT'
>>> tz.tzname(normal, is_dst=True)
'NDT'
>>> ambiguous = datetime(2009, 10, 31, 23, 30)
>>> tz.tzname(ambiguous, is_dst=False)
'NST'
>>> tz.tzname(ambiguous, is_dst=True)
'NDT'
>>> try:
... tz.tzname(ambiguous)
... except AmbiguousTimeError:
... print('Ambiguous')
Ambiguous
'''
if dt is None:
return self.zone
elif dt.tzinfo is not self:
dt = self.localize(dt, is_dst)
return dt.tzinfo._tzname
else:
return self._tzname
def __repr__(self):
if self._dst:
dst = 'DST'
else:
dst = 'STD'
if self._utcoffset > _notime:
return '<DstTzInfo %r %s+%s %s>' % (
self.zone, self._tzname, self._utcoffset, dst
)
else:
return '<DstTzInfo %r %s%s %s>' % (
self.zone, self._tzname, self._utcoffset, dst
)
def __reduce__(self):
# Special pickle to zone remains a singleton and to cope with
# database changes.
return pytz._p, (
self.zone,
_to_seconds(self._utcoffset),
_to_seconds(self._dst),
self._tzname
)
def unpickler(zone, utcoffset=None, dstoffset=None, tzname=None):
"""Factory function for unpickling pytz tzinfo instances.
This is shared for both StaticTzInfo and DstTzInfo instances, because
database changes could cause a zones implementation to switch between
these two base classes and we can't break pickles on a pytz version
upgrade.
"""
# Raises a KeyError if zone no longer exists, which should never happen
# and would be a bug.
tz = pytz.timezone(zone)
# A StaticTzInfo - just return it
if utcoffset is None:
return tz
# This pickle was created from a DstTzInfo. We need to
# determine which of the list of tzinfo instances for this zone
# to use in order to restore the state of any datetime instances using
# it correctly.
utcoffset = memorized_timedelta(utcoffset)
dstoffset = memorized_timedelta(dstoffset)
try:
return tz._tzinfos[(utcoffset, dstoffset, tzname)]
except KeyError:
# The particular state requested in this timezone no longer exists.
# This indicates a corrupt pickle, or the timezone database has been
# corrected violently enough to make this particular
# (utcoffset,dstoffset) no longer exist in the zone, or the
# abbreviation has been changed.
pass
# See if we can find an entry differing only by tzname. Abbreviations
# get changed from the initial guess by the database maintainers to
# match reality when this information is discovered.
for localized_tz in tz._tzinfos.values():
if (localized_tz._utcoffset == utcoffset and
localized_tz._dst == dstoffset):
return localized_tz
# This (utcoffset, dstoffset) information has been removed from the
# zone. Add it back. This might occur when the database maintainers have
# corrected incorrect information. datetime instances using this
# incorrect information will continue to do so, exactly as they were
# before being pickled. This is purely an overly paranoid safety net - I
# doubt this will ever been needed in real life.
inf = (utcoffset, dstoffset, tzname)
tz._tzinfos[inf] = tz.__class__(inf, tz._tzinfos)
return tz._tzinfos[inf]
| 19,272 | 32.344291 | 78 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/pytz/lazy.py
|
from threading import RLock
try:
from collections.abc import Mapping as DictMixin
except ImportError: # Python < 3.3
try:
from UserDict import DictMixin # Python 2
except ImportError: # Python 3.0-3.3
from collections import Mapping as DictMixin
# With lazy loading, we might end up with multiple threads triggering
# it at the same time. We need a lock.
_fill_lock = RLock()
class LazyDict(DictMixin):
"""Dictionary populated on first use."""
data = None
def __getitem__(self, key):
if self.data is None:
_fill_lock.acquire()
try:
if self.data is None:
self._fill()
finally:
_fill_lock.release()
return self.data[key.upper()]
def __contains__(self, key):
if self.data is None:
_fill_lock.acquire()
try:
if self.data is None:
self._fill()
finally:
_fill_lock.release()
return key in self.data
def __iter__(self):
if self.data is None:
_fill_lock.acquire()
try:
if self.data is None:
self._fill()
finally:
_fill_lock.release()
return iter(self.data)
def __len__(self):
if self.data is None:
_fill_lock.acquire()
try:
if self.data is None:
self._fill()
finally:
_fill_lock.release()
return len(self.data)
def keys(self):
if self.data is None:
_fill_lock.acquire()
try:
if self.data is None:
self._fill()
finally:
_fill_lock.release()
return self.data.keys()
class LazyList(list):
"""List populated on first use."""
_props = [
'__str__', '__repr__', '__unicode__',
'__hash__', '__sizeof__', '__cmp__',
'__lt__', '__le__', '__eq__', '__ne__', '__gt__', '__ge__',
'append', 'count', 'index', 'extend', 'insert', 'pop', 'remove',
'reverse', 'sort', '__add__', '__radd__', '__iadd__', '__mul__',
'__rmul__', '__imul__', '__contains__', '__len__', '__nonzero__',
'__getitem__', '__setitem__', '__delitem__', '__iter__',
'__reversed__', '__getslice__', '__setslice__', '__delslice__']
def __new__(cls, fill_iter=None):
if fill_iter is None:
return list()
# We need a new class as we will be dynamically messing with its
# methods.
class LazyList(list):
pass
fill_iter = [fill_iter]
def lazy(name):
def _lazy(self, *args, **kw):
_fill_lock.acquire()
try:
if len(fill_iter) > 0:
list.extend(self, fill_iter.pop())
for method_name in cls._props:
delattr(LazyList, method_name)
finally:
_fill_lock.release()
return getattr(list, name)(self, *args, **kw)
return _lazy
for name in cls._props:
setattr(LazyList, name, lazy(name))
new_list = LazyList()
return new_list
# Not all versions of Python declare the same magic methods.
# Filter out properties that don't exist in this version of Python
# from the list.
LazyList._props = [prop for prop in LazyList._props if hasattr(list, prop)]
class LazySet(set):
"""Set populated on first use."""
_props = (
'__str__', '__repr__', '__unicode__',
'__hash__', '__sizeof__', '__cmp__',
'__lt__', '__le__', '__eq__', '__ne__', '__gt__', '__ge__',
'__contains__', '__len__', '__nonzero__',
'__getitem__', '__setitem__', '__delitem__', '__iter__',
'__sub__', '__and__', '__xor__', '__or__',
'__rsub__', '__rand__', '__rxor__', '__ror__',
'__isub__', '__iand__', '__ixor__', '__ior__',
'add', 'clear', 'copy', 'difference', 'difference_update',
'discard', 'intersection', 'intersection_update', 'isdisjoint',
'issubset', 'issuperset', 'pop', 'remove',
'symmetric_difference', 'symmetric_difference_update',
'union', 'update')
def __new__(cls, fill_iter=None):
if fill_iter is None:
return set()
class LazySet(set):
pass
fill_iter = [fill_iter]
def lazy(name):
def _lazy(self, *args, **kw):
_fill_lock.acquire()
try:
if len(fill_iter) > 0:
for i in fill_iter.pop():
set.add(self, i)
for method_name in cls._props:
delattr(LazySet, method_name)
finally:
_fill_lock.release()
return getattr(set, name)(self, *args, **kw)
return _lazy
for name in cls._props:
setattr(LazySet, name, lazy(name))
new_set = LazySet()
return new_set
# Not all versions of Python declare the same magic methods.
# Filter out properties that don't exist in this version of Python
# from the list.
LazySet._props = [prop for prop in LazySet._props if hasattr(set, prop)]
| 5,404 | 30.242775 | 75 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/pytz/reference.py
|
'''
Reference tzinfo implementations from the Python docs.
Used for testing against as they are only correct for the years
1987 to 2006. Do not use these for real code.
'''
from datetime import tzinfo, timedelta, datetime
from pytz import HOUR, ZERO, UTC
__all__ = [
'FixedOffset',
'LocalTimezone',
'USTimeZone',
'Eastern',
'Central',
'Mountain',
'Pacific',
'UTC'
]
# A class building tzinfo objects for fixed-offset time zones.
# Note that FixedOffset(0, "UTC") is a different way to build a
# UTC tzinfo object.
class FixedOffset(tzinfo):
"""Fixed offset in minutes east from UTC."""
def __init__(self, offset, name):
self.__offset = timedelta(minutes=offset)
self.__name = name
def utcoffset(self, dt):
return self.__offset
def tzname(self, dt):
return self.__name
def dst(self, dt):
return ZERO
import time as _time
STDOFFSET = timedelta(seconds=-_time.timezone)
if _time.daylight:
DSTOFFSET = timedelta(seconds=-_time.altzone)
else:
DSTOFFSET = STDOFFSET
DSTDIFF = DSTOFFSET - STDOFFSET
# A class capturing the platform's idea of local time.
class LocalTimezone(tzinfo):
def utcoffset(self, dt):
if self._isdst(dt):
return DSTOFFSET
else:
return STDOFFSET
def dst(self, dt):
if self._isdst(dt):
return DSTDIFF
else:
return ZERO
def tzname(self, dt):
return _time.tzname[self._isdst(dt)]
def _isdst(self, dt):
tt = (dt.year, dt.month, dt.day,
dt.hour, dt.minute, dt.second,
dt.weekday(), 0, -1)
stamp = _time.mktime(tt)
tt = _time.localtime(stamp)
return tt.tm_isdst > 0
Local = LocalTimezone()
def first_sunday_on_or_after(dt):
days_to_go = 6 - dt.weekday()
if days_to_go:
dt += timedelta(days_to_go)
return dt
# In the US, DST starts at 2am (standard time) on the first Sunday in April.
DSTSTART = datetime(1, 4, 1, 2)
# and ends at 2am (DST time; 1am standard time) on the last Sunday of Oct.
# which is the first Sunday on or after Oct 25.
DSTEND = datetime(1, 10, 25, 1)
# A complete implementation of current DST rules for major US time zones.
class USTimeZone(tzinfo):
def __init__(self, hours, reprname, stdname, dstname):
self.stdoffset = timedelta(hours=hours)
self.reprname = reprname
self.stdname = stdname
self.dstname = dstname
def __repr__(self):
return self.reprname
def tzname(self, dt):
if self.dst(dt):
return self.dstname
else:
return self.stdname
def utcoffset(self, dt):
return self.stdoffset + self.dst(dt)
def dst(self, dt):
if dt is None or dt.tzinfo is None:
# An exception may be sensible here, in one or both cases.
# It depends on how you want to treat them. The default
# fromutc() implementation (called by the default astimezone()
# implementation) passes a datetime with dt.tzinfo is self.
return ZERO
assert dt.tzinfo is self
# Find first Sunday in April & the last in October.
start = first_sunday_on_or_after(DSTSTART.replace(year=dt.year))
end = first_sunday_on_or_after(DSTEND.replace(year=dt.year))
# Can't compare naive to aware objects, so strip the timezone from
# dt first.
if start <= dt.replace(tzinfo=None) < end:
return HOUR
else:
return ZERO
Eastern = USTimeZone(-5, "Eastern", "EST", "EDT")
Central = USTimeZone(-6, "Central", "CST", "CDT")
Mountain = USTimeZone(-7, "Mountain", "MST", "MDT")
Pacific = USTimeZone(-8, "Pacific", "PST", "PDT")
| 3,778 | 25.801418 | 76 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/past/__init__.py
|
# coding=utf-8
"""
past: compatibility with Python 2 from Python 3
===============================================
``past`` is a package to aid with Python 2/3 compatibility. Whereas ``future``
contains backports of Python 3 constructs to Python 2, ``past`` provides
implementations of some Python 2 constructs in Python 3 and tools to import and
run Python 2 code in Python 3. It is intended to be used sparingly, as a way of
running old Python 2 code from Python 3 until the code is ported properly.
Potential uses for libraries:
- as a step in porting a Python 2 codebase to Python 3 (e.g. with the ``futurize`` script)
- to provide Python 3 support for previously Python 2-only libraries with the
same APIs as on Python 2 -- particularly with regard to 8-bit strings (the
``past.builtins.str`` type).
- to aid in providing minimal-effort Python 3 support for applications using
libraries that do not yet wish to upgrade their code properly to Python 3, or
wish to upgrade it gradually to Python 3 style.
Here are some code examples that run identically on Python 3 and 2::
>>> from past.builtins import str as oldstr
>>> philosopher = oldstr(u'\u5b54\u5b50'.encode('utf-8'))
>>> # This now behaves like a Py2 byte-string on both Py2 and Py3.
>>> # For example, indexing returns a Python 2-like string object, not
>>> # an integer:
>>> philosopher[0]
'\xe5'
>>> type(philosopher[0])
<past.builtins.oldstr>
>>> # List-producing versions of range, reduce, map, filter
>>> from past.builtins import range, reduce
>>> range(10)
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> reduce(lambda x, y: x+y, [1, 2, 3, 4, 5])
15
>>> # Other functions removed in Python 3 are resurrected ...
>>> from past.builtins import execfile
>>> execfile('myfile.py')
>>> from past.builtins import raw_input
>>> name = raw_input('What is your name? ')
What is your name? [cursor]
>>> from past.builtins import reload
>>> reload(mymodule) # equivalent to imp.reload(mymodule) in Python 3
>>> from past.builtins import xrange
>>> for i in xrange(10):
... pass
It also provides import hooks so you can import and use Python 2 modules like
this::
$ python3
>>> from past import autotranslate
>>> authotranslate('mypy2module')
>>> import mypy2module
until the authors of the Python 2 modules have upgraded their code. Then, for
example::
>>> mypy2module.func_taking_py2_string(oldstr(b'abcd'))
Credits
-------
:Author: Ed Schofield
:Sponsor: Python Charmers Pty Ltd, Australia: http://pythoncharmers.com
Licensing
---------
Copyright 2013-2016 Python Charmers Pty Ltd, Australia.
The software is distributed under an MIT licence. See LICENSE.txt.
"""
from past.translation import install_hooks as autotranslate
from future import __version__, __copyright__, __license__
__title__ = 'past'
__author__ = 'Ed Schofield'
| 2,948 | 30.37234 | 90 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/past/builtins/noniterators.py
|
"""
This module is designed to be used as follows::
from past.builtins.noniterators import filter, map, range, reduce, zip
And then, for example::
assert isinstance(range(5), list)
The list-producing functions this brings in are::
- ``filter``
- ``map``
- ``range``
- ``reduce``
- ``zip``
"""
from __future__ import division, absolute_import, print_function
from itertools import chain, starmap
import itertools # since zip_longest doesn't exist on Py2
from past.types import basestring
from past.utils import PY3
def flatmap(f, items):
return chain.from_iterable(map(f, items))
if PY3:
import builtins
# list-producing versions of the major Python iterating functions
def oldfilter(*args):
"""
filter(function or None, sequence) -> list, tuple, or string
Return those items of sequence for which function(item) is true.
If function is None, return the items that are true. If sequence
is a tuple or string, return the same type, else return a list.
"""
mytype = type(args[1])
if isinstance(args[1], basestring):
return mytype().join(builtins.filter(*args))
elif isinstance(args[1], (tuple, list)):
return mytype(builtins.filter(*args))
else:
# Fall back to list. Is this the right thing to do?
return list(builtins.filter(*args))
# This is surprisingly difficult to get right. For example, the
# solutions here fail with the test cases in the docstring below:
# http://stackoverflow.com/questions/8072755/
def oldmap(func, *iterables):
"""
map(function, sequence[, sequence, ...]) -> list
Return a list of the results of applying the function to the
items of the argument sequence(s). If more than one sequence is
given, the function is called with an argument list consisting of
the corresponding item of each sequence, substituting None for
missing values when not all sequences have the same length. If
the function is None, return a list of the items of the sequence
(or a list of tuples if more than one sequence).
Test cases:
>>> oldmap(None, 'hello world')
['h', 'e', 'l', 'l', 'o', ' ', 'w', 'o', 'r', 'l', 'd']
>>> oldmap(None, range(4))
[0, 1, 2, 3]
More test cases are in past.tests.test_builtins.
"""
zipped = itertools.zip_longest(*iterables)
l = list(zipped)
if len(l) == 0:
return []
if func is None:
result = l
else:
result = list(starmap(func, l))
# Inspect to see whether it's a simple sequence of tuples
try:
if max([len(item) for item in result]) == 1:
return list(chain.from_iterable(result))
# return list(flatmap(func, result))
except TypeError as e:
# Simple objects like ints have no len()
pass
return result
############################
### For reference, the source code for Py2.7 map function:
# static PyObject *
# builtin_map(PyObject *self, PyObject *args)
# {
# typedef struct {
# PyObject *it; /* the iterator object */
# int saw_StopIteration; /* bool: did the iterator end? */
# } sequence;
#
# PyObject *func, *result;
# sequence *seqs = NULL, *sqp;
# Py_ssize_t n, len;
# register int i, j;
#
# n = PyTuple_Size(args);
# if (n < 2) {
# PyErr_SetString(PyExc_TypeError,
# "map() requires at least two args");
# return NULL;
# }
#
# func = PyTuple_GetItem(args, 0);
# n--;
#
# if (func == Py_None) {
# if (PyErr_WarnPy3k("map(None, ...) not supported in 3.x; "
# "use list(...)", 1) < 0)
# return NULL;
# if (n == 1) {
# /* map(None, S) is the same as list(S). */
# return PySequence_List(PyTuple_GetItem(args, 1));
# }
# }
#
# /* Get space for sequence descriptors. Must NULL out the iterator
# * pointers so that jumping to Fail_2 later doesn't see trash.
# */
# if ((seqs = PyMem_NEW(sequence, n)) == NULL) {
# PyErr_NoMemory();
# return NULL;
# }
# for (i = 0; i < n; ++i) {
# seqs[i].it = (PyObject*)NULL;
# seqs[i].saw_StopIteration = 0;
# }
#
# /* Do a first pass to obtain iterators for the arguments, and set len
# * to the largest of their lengths.
# */
# len = 0;
# for (i = 0, sqp = seqs; i < n; ++i, ++sqp) {
# PyObject *curseq;
# Py_ssize_t curlen;
#
# /* Get iterator. */
# curseq = PyTuple_GetItem(args, i+1);
# sqp->it = PyObject_GetIter(curseq);
# if (sqp->it == NULL) {
# static char errmsg[] =
# "argument %d to map() must support iteration";
# char errbuf[sizeof(errmsg) + 25];
# PyOS_snprintf(errbuf, sizeof(errbuf), errmsg, i+2);
# PyErr_SetString(PyExc_TypeError, errbuf);
# goto Fail_2;
# }
#
# /* Update len. */
# curlen = _PyObject_LengthHint(curseq, 8);
# if (curlen > len)
# len = curlen;
# }
#
# /* Get space for the result list. */
# if ((result = (PyObject *) PyList_New(len)) == NULL)
# goto Fail_2;
#
# /* Iterate over the sequences until all have stopped. */
# for (i = 0; ; ++i) {
# PyObject *alist, *item=NULL, *value;
# int numactive = 0;
#
# if (func == Py_None && n == 1)
# alist = NULL;
# else if ((alist = PyTuple_New(n)) == NULL)
# goto Fail_1;
#
# for (j = 0, sqp = seqs; j < n; ++j, ++sqp) {
# if (sqp->saw_StopIteration) {
# Py_INCREF(Py_None);
# item = Py_None;
# }
# else {
# item = PyIter_Next(sqp->it);
# if (item)
# ++numactive;
# else {
# if (PyErr_Occurred()) {
# Py_XDECREF(alist);
# goto Fail_1;
# }
# Py_INCREF(Py_None);
# item = Py_None;
# sqp->saw_StopIteration = 1;
# }
# }
# if (alist)
# PyTuple_SET_ITEM(alist, j, item);
# else
# break;
# }
#
# if (!alist)
# alist = item;
#
# if (numactive == 0) {
# Py_DECREF(alist);
# break;
# }
#
# if (func == Py_None)
# value = alist;
# else {
# value = PyEval_CallObject(func, alist);
# Py_DECREF(alist);
# if (value == NULL)
# goto Fail_1;
# }
# if (i >= len) {
# int status = PyList_Append(result, value);
# Py_DECREF(value);
# if (status < 0)
# goto Fail_1;
# }
# else if (PyList_SetItem(result, i, value) < 0)
# goto Fail_1;
# }
#
# if (i < len && PyList_SetSlice(result, i, len, NULL) < 0)
# goto Fail_1;
#
# goto Succeed;
#
# Fail_1:
# Py_DECREF(result);
# Fail_2:
# result = NULL;
# Succeed:
# assert(seqs);
# for (i = 0; i < n; ++i)
# Py_XDECREF(seqs[i].it);
# PyMem_DEL(seqs);
# return result;
# }
def oldrange(*args, **kwargs):
return list(builtins.range(*args, **kwargs))
def oldzip(*args, **kwargs):
return list(builtins.zip(*args, **kwargs))
filter = oldfilter
map = oldmap
range = oldrange
from functools import reduce
zip = oldzip
__all__ = ['filter', 'map', 'range', 'reduce', 'zip']
else:
import __builtin__
# Python 2-builtin ranges produce lists
filter = __builtin__.filter
map = __builtin__.map
range = __builtin__.range
reduce = __builtin__.reduce
zip = __builtin__.zip
__all__ = []
| 9,422 | 33.390511 | 83 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/past/builtins/misc.py
|
from __future__ import unicode_literals
import sys
import inspect
from collections import Mapping
from future.utils import PY3, exec_
if PY3:
import builtins
def apply(f, *args, **kw):
return f(*args, **kw)
from past.builtins import str as oldstr
def chr(i):
"""
Return a byte-string of one character with ordinal i; 0 <= i <= 256
"""
return oldstr(bytes((i,)))
def cmp(x, y):
"""
cmp(x, y) -> integer
Return negative if x<y, zero if x==y, positive if x>y.
"""
return (x > y) - (x < y)
from sys import intern
def oct(number):
"""oct(number) -> string
Return the octal representation of an integer
"""
return '0' + builtins.oct(number)[2:]
raw_input = input
from imp import reload
unicode = str
unichr = chr
xrange = range
else:
import __builtin__
apply = __builtin__.apply
chr = __builtin__.chr
cmp = __builtin__.cmp
execfile = __builtin__.execfile
intern = __builtin__.intern
oct = __builtin__.oct
raw_input = __builtin__.raw_input
reload = __builtin__.reload
unicode = __builtin__.unicode
unichr = __builtin__.unichr
xrange = __builtin__.xrange
if PY3:
def execfile(filename, myglobals=None, mylocals=None):
"""
Read and execute a Python script from a file in the given namespaces.
The globals and locals are dictionaries, defaulting to the current
globals and locals. If only globals is given, locals defaults to it.
"""
if myglobals is None:
# There seems to be no alternative to frame hacking here.
caller_frame = inspect.stack()[1]
myglobals = caller_frame[0].f_globals
mylocals = caller_frame[0].f_locals
elif mylocals is None:
# Only if myglobals is given do we set mylocals to it.
mylocals = myglobals
if not isinstance(myglobals, Mapping):
raise TypeError('globals must be a mapping')
if not isinstance(mylocals, Mapping):
raise TypeError('locals must be a mapping')
with open(filename, "rbU") as fin:
source = fin.read()
code = compile(source, filename, "exec")
exec_(code, myglobals, mylocals)
if PY3:
__all__ = ['apply', 'chr', 'cmp', 'execfile', 'intern', 'raw_input',
'reload', 'unichr', 'unicode', 'xrange']
else:
__all__ = []
| 2,500 | 26.483516 | 77 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/past/builtins/__init__.py
|
"""
A resurrection of some old functions from Python 2 for use in Python 3. These
should be used sparingly, to help with porting efforts, since code using them
is no longer standard Python 3 code.
This module provides the following:
1. Implementations of these builtin functions which have no equivalent on Py3:
- apply
- chr
- cmp
- execfile
2. Aliases:
- intern <- sys.intern
- raw_input <- input
- reduce <- functools.reduce
- reload <- imp.reload
- unichr <- chr
- unicode <- str
- xrange <- range
3. List-producing versions of the corresponding Python 3 iterator-producing functions:
- filter
- map
- range
- zip
4. Forward-ported Py2 types:
- basestring
- dict
- str
- long
- unicode
"""
from future.utils import PY3
from past.builtins.noniterators import (filter, map, range, reduce, zip)
# from past.builtins.misc import (ascii, hex, input, oct, open)
if PY3:
from past.types import (basestring,
olddict as dict,
oldstr as str,
long,
unicode)
else:
from __builtin__ import (basestring, dict, str, long, unicode)
from past.builtins.misc import (apply, chr, cmp, execfile, intern, oct,
raw_input, reload, unichr, unicode, xrange)
from past import utils
if utils.PY3:
# We only import names that shadow the builtins on Py3. No other namespace
# pollution on Py3.
# Only shadow builtins on Py3; no new names
__all__ = ['filter', 'map', 'range', 'reduce', 'zip',
'basestring', 'dict', 'str', 'long', 'unicode',
'apply', 'chr', 'cmp', 'execfile', 'intern', 'raw_input',
'reload', 'unichr', 'xrange'
]
else:
# No namespace pollution on Py2
__all__ = []
| 1,810 | 23.808219 | 86 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/past/tests/__init__.py
| 0 | 0 | 0 |
py
|
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/past/types/oldstr.py
|
"""
Pure-Python implementation of a Python 2-like str object for Python 3.
"""
from collections import Iterable
from numbers import Integral
from past.utils import PY2, with_metaclass
_builtin_bytes = bytes
class BaseOldStr(type):
def __instancecheck__(cls, instance):
return isinstance(instance, _builtin_bytes)
def unescape(s):
"""
Interprets strings with escape sequences
Example:
>>> s = unescape(r'abc\\def') # i.e. 'abc\\\\def'
>>> print(s)
'abc\def'
>>> s2 = unescape('abc\\ndef')
>>> len(s2)
8
>>> print(s2)
abc
def
"""
return s.encode().decode('unicode_escape')
class oldstr(with_metaclass(BaseOldStr, _builtin_bytes)):
"""
A forward port of the Python 2 8-bit string object to Py3
"""
# Python 2 strings have no __iter__ method:
@property
def __iter__(self):
raise AttributeError
def __dir__(self):
return [thing for thing in dir(_builtin_bytes) if thing != '__iter__']
# def __new__(cls, *args, **kwargs):
# """
# From the Py3 bytes docstring:
# bytes(iterable_of_ints) -> bytes
# bytes(string, encoding[, errors]) -> bytes
# bytes(bytes_or_buffer) -> immutable copy of bytes_or_buffer
# bytes(int) -> bytes object of size given by the parameter initialized with null bytes
# bytes() -> empty bytes object
#
# Construct an immutable array of bytes from:
# - an iterable yielding integers in range(256)
# - a text string encoded using the specified encoding
# - any object implementing the buffer API.
# - an integer
# """
#
# if len(args) == 0:
# return super(newbytes, cls).__new__(cls)
# # Was: elif isinstance(args[0], newbytes):
# # We use type() instead of the above because we're redefining
# # this to be True for all unicode string subclasses. Warning:
# # This may render newstr un-subclassable.
# elif type(args[0]) == newbytes:
# return args[0]
# elif isinstance(args[0], _builtin_bytes):
# value = args[0]
# elif isinstance(args[0], unicode):
# if 'encoding' not in kwargs:
# raise TypeError('unicode string argument without an encoding')
# ###
# # Was: value = args[0].encode(**kwargs)
# # Python 2.6 string encode() method doesn't take kwargs:
# # Use this instead:
# newargs = [kwargs['encoding']]
# if 'errors' in kwargs:
# newargs.append(kwargs['errors'])
# value = args[0].encode(*newargs)
# ###
# elif isinstance(args[0], Iterable):
# if len(args[0]) == 0:
# # What is this?
# raise ValueError('unknown argument type')
# elif len(args[0]) > 0 and isinstance(args[0][0], Integral):
# # It's a list of integers
# value = b''.join([chr(x) for x in args[0]])
# else:
# raise ValueError('item cannot be interpreted as an integer')
# elif isinstance(args[0], Integral):
# if args[0] < 0:
# raise ValueError('negative count')
# value = b'\x00' * args[0]
# else:
# value = args[0]
# return super(newbytes, cls).__new__(cls, value)
def __repr__(self):
s = super(oldstr, self).__repr__() # e.g. b'abc' on Py3, b'abc' on Py3
return s[1:]
def __str__(self):
s = super(oldstr, self).__str__() # e.g. "b'abc'" or "b'abc\\ndef'
# TODO: fix this:
assert s[:2] == "b'" and s[-1] == "'"
return unescape(s[2:-1]) # e.g. 'abc' or 'abc\ndef'
def __getitem__(self, y):
if isinstance(y, Integral):
return super(oldstr, self).__getitem__(slice(y, y+1))
else:
return super(oldstr, self).__getitem__(y)
def __getslice__(self, *args):
return self.__getitem__(slice(*args))
def __contains__(self, key):
if isinstance(key, int):
return False
def __native__(self):
return bytes(self)
__all__ = ['oldstr']
| 4,300 | 31.338346 | 95 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/past/types/olddict.py
|
"""
A dict subclass for Python 3 that behaves like Python 2's dict
Example use:
>>> from past.builtins import dict
>>> d1 = dict() # instead of {} for an empty dict
>>> d2 = dict(key1='value1', key2='value2')
The keys, values and items methods now return lists on Python 3.x and there are
methods for iterkeys, itervalues, iteritems, and viewkeys etc.
>>> for d in (d1, d2):
... assert isinstance(d.keys(), list)
... assert isinstance(d.values(), list)
... assert isinstance(d.items(), list)
"""
import sys
from past.utils import with_metaclass
_builtin_dict = dict
ver = sys.version_info[:2]
class BaseOldDict(type):
def __instancecheck__(cls, instance):
return isinstance(instance, _builtin_dict)
class olddict(with_metaclass(BaseOldDict, _builtin_dict)):
"""
A backport of the Python 3 dict object to Py2
"""
iterkeys = _builtin_dict.keys
viewkeys = _builtin_dict.keys
def keys(self):
return list(super(olddict, self).keys())
itervalues = _builtin_dict.values
viewvalues = _builtin_dict.values
def values(self):
return list(super(olddict, self).values())
iteritems = _builtin_dict.items
viewitems = _builtin_dict.items
def items(self):
return list(super(olddict, self).items())
def has_key(self, k):
"""
D.has_key(k) -> True if D has a key k, else False
"""
return k in self
# def __new__(cls, *args, **kwargs):
# """
# dict() -> new empty dictionary
# dict(mapping) -> new dictionary initialized from a mapping object's
# (key, value) pairs
# dict(iterable) -> new dictionary initialized as if via:
# d = {}
# for k, v in iterable:
# d[k] = v
# dict(**kwargs) -> new dictionary initialized with the name=value pairs
# in the keyword argument list. For example: dict(one=1, two=2)
# """
#
# if len(args) == 0:
# return super(olddict, cls).__new__(cls)
# # Was: elif isinstance(args[0], newbytes):
# # We use type() instead of the above because we're redefining
# # this to be True for all unicode string subclasses. Warning:
# # This may render newstr un-subclassable.
# elif type(args[0]) == olddict:
# return args[0]
# # elif isinstance(args[0], _builtin_dict):
# # value = args[0]
# else:
# value = args[0]
# return super(olddict, cls).__new__(cls, value)
def __native__(self):
"""
Hook for the past.utils.native() function
"""
return super(oldbytes, self)
__all__ = ['olddict']
| 2,735 | 26.918367 | 80 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/past/types/__init__.py
|
"""
Forward-ports of types from Python 2 for use with Python 3:
- ``basestring``: equivalent to ``(str, bytes)`` in ``isinstance`` checks
- ``dict``: with list-producing .keys() etc. methods
- ``str``: bytes-like, but iterating over them doesn't product integers
- ``long``: alias of Py3 int with ``L`` suffix in the ``repr``
- ``unicode``: alias of Py3 str with ``u`` prefix in the ``repr``
"""
from past import utils
if utils.PY2:
import __builtin__
basestring = __builtin__.basestring
dict = __builtin__.dict
str = __builtin__.str
long = __builtin__.long
unicode = __builtin__.unicode
__all__ = []
else:
from .basestring import basestring
from .olddict import olddict
from .oldstr import oldstr
long = int
unicode = str
# from .unicode import unicode
__all__ = ['basestring', 'olddict', 'oldstr', 'long', 'unicode']
| 880 | 27.419355 | 73 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/past/types/basestring.py
|
"""
An implementation of the basestring type for Python 3
Example use:
>>> s = b'abc'
>>> assert isinstance(s, basestring)
>>> from past.types import str as oldstr
>>> s2 = oldstr(b'abc')
>>> assert isinstance(s2, basestring)
"""
import sys
from past.utils import with_metaclass, PY2
if PY2:
str = unicode
ver = sys.version_info[:2]
class BaseBaseString(type):
def __instancecheck__(cls, instance):
return isinstance(instance, (bytes, str))
def __subclasshook__(cls, thing):
# TODO: What should go here?
raise NotImplemented
class basestring(with_metaclass(BaseBaseString)):
"""
A minimal backport of the Python 2 basestring type to Py3
"""
__all__ = ['basestring']
| 729 | 16.804878 | 61 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/past/utils/__init__.py
|
"""
Various non-built-in utility functions and definitions for Py2
compatibility in Py3.
For example:
>>> # The old_div() function behaves like Python 2's / operator
>>> # without "from __future__ import division"
>>> from past.utils import old_div
>>> old_div(3, 2) # like 3/2 in Py2
0
>>> old_div(3, 2.0) # like 3/2.0 in Py2
1.5
"""
import sys
import numbers
PY3 = sys.version_info[0] == 3
PY2 = sys.version_info[0] == 2
PYPY = hasattr(sys, 'pypy_translation_info')
def with_metaclass(meta, *bases):
"""
Function from jinja2/_compat.py. License: BSD.
Use it like this::
class BaseForm(object):
pass
class FormType(type):
pass
class Form(with_metaclass(FormType, BaseForm)):
pass
This requires a bit of explanation: the basic idea is to make a
dummy metaclass for one level of class instantiation that replaces
itself with the actual metaclass. Because of internal type checks
we also need to make sure that we downgrade the custom metaclass
for one level to something closer to type (that's why __call__ and
__init__ comes back from type etc.).
This has the advantage over six.with_metaclass of not introducing
dummy classes into the final MRO.
"""
class metaclass(meta):
__call__ = type.__call__
__init__ = type.__init__
def __new__(cls, name, this_bases, d):
if this_bases is None:
return type.__new__(cls, name, (), d)
return meta(name, bases, d)
return metaclass('temporary_class', None, {})
def native(obj):
"""
On Py2, this is a no-op: native(obj) -> obj
On Py3, returns the corresponding native Py3 types that are
superclasses for forward-ported objects from Py2:
>>> from past.builtins import str, dict
>>> native(str(b'ABC')) # Output on Py3 follows. On Py2, output is 'ABC'
b'ABC'
>>> type(native(str(b'ABC')))
bytes
Existing native types on Py3 will be returned unchanged:
>>> type(native(b'ABC'))
bytes
"""
if hasattr(obj, '__native__'):
return obj.__native__()
else:
return obj
# An alias for future.utils.old_div():
def old_div(a, b):
"""
Equivalent to ``a / b`` on Python 2 without ``from __future__ import
division``.
TODO: generalize this to other objects (like arrays etc.)
"""
if isinstance(a, numbers.Integral) and isinstance(b, numbers.Integral):
return a // b
else:
return a / b
__all__ = ['PY3', 'PY2', 'PYPY', 'with_metaclass', 'native', 'old_div']
| 2,665 | 26.204082 | 78 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/past/translation/__init__.py
|
# -*- coding: utf-8 -*-
"""
past.translation
==================
The ``past.translation`` package provides an import hook for Python 3 which
transparently runs ``futurize`` fixers over Python 2 code on import to convert
print statements into functions, etc.
It is intended to assist users in migrating to Python 3.x even if some
dependencies still only support Python 2.x.
Usage
-----
Once your Py2 package is installed in the usual module search path, the import
hook is invoked as follows:
>>> from past import autotranslate
>>> autotranslate('mypackagename')
Or:
>>> autotranslate(['mypackage1', 'mypackage2'])
You can unregister the hook using::
>>> from past.translation import remove_hooks
>>> remove_hooks()
Author: Ed Schofield.
Inspired by and based on ``uprefix`` by Vinay M. Sajip.
"""
import imp
import logging
import marshal
import os
import sys
import copy
from lib2to3.pgen2.parse import ParseError
from lib2to3.refactor import RefactoringTool
from libfuturize import fixes
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
myfixes = (list(fixes.libfuturize_fix_names_stage1) +
list(fixes.lib2to3_fix_names_stage1) +
list(fixes.libfuturize_fix_names_stage2) +
list(fixes.lib2to3_fix_names_stage2))
# We detect whether the code is Py2 or Py3 by applying certain lib2to3 fixers
# to it. If the diff is empty, it's Python 3 code.
py2_detect_fixers = [
# From stage 1:
'lib2to3.fixes.fix_apply',
# 'lib2to3.fixes.fix_dict', # TODO: add support for utils.viewitems() etc. and move to stage2
'lib2to3.fixes.fix_except',
'lib2to3.fixes.fix_execfile',
'lib2to3.fixes.fix_exitfunc',
'lib2to3.fixes.fix_funcattrs',
'lib2to3.fixes.fix_filter',
'lib2to3.fixes.fix_has_key',
'lib2to3.fixes.fix_idioms',
'lib2to3.fixes.fix_import', # makes any implicit relative imports explicit. (Use with ``from __future__ import absolute_import)
'lib2to3.fixes.fix_intern',
'lib2to3.fixes.fix_isinstance',
'lib2to3.fixes.fix_methodattrs',
'lib2to3.fixes.fix_ne',
'lib2to3.fixes.fix_numliterals', # turns 1L into 1, 0755 into 0o755
'lib2to3.fixes.fix_paren',
'lib2to3.fixes.fix_print',
'lib2to3.fixes.fix_raise', # uses incompatible with_traceback() method on exceptions
'lib2to3.fixes.fix_renames',
'lib2to3.fixes.fix_reduce',
# 'lib2to3.fixes.fix_set_literal', # this is unnecessary and breaks Py2.6 support
'lib2to3.fixes.fix_repr',
'lib2to3.fixes.fix_standarderror',
'lib2to3.fixes.fix_sys_exc',
'lib2to3.fixes.fix_throw',
'lib2to3.fixes.fix_tuple_params',
'lib2to3.fixes.fix_types',
'lib2to3.fixes.fix_ws_comma',
'lib2to3.fixes.fix_xreadlines',
# From stage 2:
'lib2to3.fixes.fix_basestring',
# 'lib2to3.fixes.fix_buffer', # perhaps not safe. Test this.
# 'lib2to3.fixes.fix_callable', # not needed in Py3.2+
# 'lib2to3.fixes.fix_dict', # TODO: add support for utils.viewitems() etc.
'lib2to3.fixes.fix_exec',
# 'lib2to3.fixes.fix_future', # we don't want to remove __future__ imports
'lib2to3.fixes.fix_getcwdu',
# 'lib2to3.fixes.fix_imports', # called by libfuturize.fixes.fix_future_standard_library
# 'lib2to3.fixes.fix_imports2', # we don't handle this yet (dbm)
# 'lib2to3.fixes.fix_input',
# 'lib2to3.fixes.fix_itertools',
# 'lib2to3.fixes.fix_itertools_imports',
'lib2to3.fixes.fix_long',
# 'lib2to3.fixes.fix_map',
# 'lib2to3.fixes.fix_metaclass', # causes SyntaxError in Py2! Use the one from ``six`` instead
'lib2to3.fixes.fix_next',
'lib2to3.fixes.fix_nonzero', # TODO: add a decorator for mapping __bool__ to __nonzero__
# 'lib2to3.fixes.fix_operator', # we will need support for this by e.g. extending the Py2 operator module to provide those functions in Py3
'lib2to3.fixes.fix_raw_input',
# 'lib2to3.fixes.fix_unicode', # strips off the u'' prefix, which removes a potentially helpful source of information for disambiguating unicode/byte strings
# 'lib2to3.fixes.fix_urllib',
'lib2to3.fixes.fix_xrange',
# 'lib2to3.fixes.fix_zip',
]
class RTs:
"""
A namespace for the refactoring tools. This avoids creating these at
the module level, which slows down the module import. (See issue #117).
There are two possible grammars: with or without the print statement.
Hence we have two possible refactoring tool implementations.
"""
_rt = None
_rtp = None
_rt_py2_detect = None
_rtp_py2_detect = None
@staticmethod
def setup():
"""
Call this before using the refactoring tools to create them on demand
if needed.
"""
if None in [RTs._rt, RTs._rtp]:
RTs._rt = RefactoringTool(myfixes)
RTs._rtp = RefactoringTool(myfixes, {'print_function': True})
@staticmethod
def setup_detect_python2():
"""
Call this before using the refactoring tools to create them on demand
if needed.
"""
if None in [RTs._rt_py2_detect, RTs._rtp_py2_detect]:
RTs._rt_py2_detect = RefactoringTool(py2_detect_fixers)
RTs._rtp_py2_detect = RefactoringTool(py2_detect_fixers,
{'print_function': True})
# We need to find a prefix for the standard library, as we don't want to
# process any files there (they will already be Python 3).
#
# The following method is used by Sanjay Vinip in uprefix. This fails for
# ``conda`` environments:
# # In a non-pythonv virtualenv, sys.real_prefix points to the installed Python.
# # In a pythonv venv, sys.base_prefix points to the installed Python.
# # Outside a virtual environment, sys.prefix points to the installed Python.
# if hasattr(sys, 'real_prefix'):
# _syslibprefix = sys.real_prefix
# else:
# _syslibprefix = getattr(sys, 'base_prefix', sys.prefix)
# Instead, we use the portion of the path common to both the stdlib modules
# ``math`` and ``urllib``.
def splitall(path):
"""
Split a path into all components. From Python Cookbook.
"""
allparts = []
while True:
parts = os.path.split(path)
if parts[0] == path: # sentinel for absolute paths
allparts.insert(0, parts[0])
break
elif parts[1] == path: # sentinel for relative paths
allparts.insert(0, parts[1])
break
else:
path = parts[0]
allparts.insert(0, parts[1])
return allparts
def common_substring(s1, s2):
"""
Returns the longest common substring to the two strings, starting from the
left.
"""
chunks = []
path1 = splitall(s1)
path2 = splitall(s2)
for (dir1, dir2) in zip(path1, path2):
if dir1 != dir2:
break
chunks.append(dir1)
return os.path.join(*chunks)
# _stdlibprefix = common_substring(math.__file__, urllib.__file__)
def detect_python2(source, pathname):
"""
Returns a bool indicating whether we think the code is Py2
"""
RTs.setup_detect_python2()
try:
tree = RTs._rt_py2_detect.refactor_string(source, pathname)
except ParseError as e:
if e.msg != 'bad input' or e.value != '=':
raise
tree = RTs._rtp.refactor_string(source, pathname)
if source != str(tree)[:-1]: # remove added newline
# The above fixers made changes, so we conclude it's Python 2 code
logger.debug('Detected Python 2 code: {0}'.format(pathname))
with open('/tmp/original_code.py', 'w') as f:
f.write('### Original code (detected as py2): %s\n%s' %
(pathname, source))
with open('/tmp/py2_detection_code.py', 'w') as f:
f.write('### Code after running py3 detection (from %s)\n%s' %
(pathname, str(tree)[:-1]))
return True
else:
logger.debug('Detected Python 3 code: {0}'.format(pathname))
with open('/tmp/original_code.py', 'w') as f:
f.write('### Original code (detected as py3): %s\n%s' %
(pathname, source))
try:
os.remove('/tmp/futurize_code.py')
except OSError:
pass
return False
class Py2Fixer(object):
"""
An import hook class that uses lib2to3 for source-to-source translation of
Py2 code to Py3.
"""
# See the comments on :class:future.standard_library.RenameImport.
# We add this attribute here so remove_hooks() and install_hooks() can
# unambiguously detect whether the import hook is installed:
PY2FIXER = True
def __init__(self):
self.found = None
self.base_exclude_paths = ['future', 'past']
self.exclude_paths = copy.copy(self.base_exclude_paths)
self.include_paths = []
def include(self, paths):
"""
Pass in a sequence of module names such as 'plotrique.plotting' that,
if present at the leftmost side of the full package name, would
specify the module to be transformed from Py2 to Py3.
"""
self.include_paths += paths
def exclude(self, paths):
"""
Pass in a sequence of strings such as 'mymodule' that, if
present at the leftmost side of the full package name, would cause
the module not to undergo any source transformation.
"""
self.exclude_paths += paths
def find_module(self, fullname, path=None):
logger.debug('Running find_module: {0}...'.format(fullname))
if '.' in fullname:
parent, child = fullname.rsplit('.', 1)
if path is None:
loader = self.find_module(parent, path)
mod = loader.load_module(parent)
path = mod.__path__
fullname = child
# Perhaps we should try using the new importlib functionality in Python
# 3.3: something like this?
# thing = importlib.machinery.PathFinder.find_module(fullname, path)
try:
self.found = imp.find_module(fullname, path)
except Exception as e:
logger.debug('Py2Fixer could not find {0}')
logger.debug('Exception was: {0})'.format(fullname, e))
return None
self.kind = self.found[-1][-1]
if self.kind == imp.PKG_DIRECTORY:
self.pathname = os.path.join(self.found[1], '__init__.py')
elif self.kind == imp.PY_SOURCE:
self.pathname = self.found[1]
return self
def transform(self, source):
# This implementation uses lib2to3,
# you can override and use something else
# if that's better for you
# lib2to3 likes a newline at the end
RTs.setup()
source += '\n'
try:
tree = RTs._rt.refactor_string(source, self.pathname)
except ParseError as e:
if e.msg != 'bad input' or e.value != '=':
raise
tree = RTs._rtp.refactor_string(source, self.pathname)
# could optimise a bit for only doing str(tree) if
# getattr(tree, 'was_changed', False) returns True
return str(tree)[:-1] # remove added newline
def load_module(self, fullname):
logger.debug('Running load_module for {0}...'.format(fullname))
if fullname in sys.modules:
mod = sys.modules[fullname]
else:
if self.kind in (imp.PY_COMPILED, imp.C_EXTENSION, imp.C_BUILTIN,
imp.PY_FROZEN):
convert = False
# elif (self.pathname.startswith(_stdlibprefix)
# and 'site-packages' not in self.pathname):
# # We assume it's a stdlib package in this case. Is this too brittle?
# # Please file a bug report at https://github.com/PythonCharmers/python-future
# # if so.
# convert = False
# in theory, other paths could be configured to be excluded here too
elif any([fullname.startswith(path) for path in self.exclude_paths]):
convert = False
elif any([fullname.startswith(path) for path in self.include_paths]):
convert = True
else:
convert = False
if not convert:
logger.debug('Excluded {0} from translation'.format(fullname))
mod = imp.load_module(fullname, *self.found)
else:
logger.debug('Autoconverting {0} ...'.format(fullname))
mod = imp.new_module(fullname)
sys.modules[fullname] = mod
# required by PEP 302
mod.__file__ = self.pathname
mod.__name__ = fullname
mod.__loader__ = self
# This:
# mod.__package__ = '.'.join(fullname.split('.')[:-1])
# seems to result in "SystemError: Parent module '' not loaded,
# cannot perform relative import" for a package's __init__.py
# file. We use the approach below. Another option to try is the
# minimal load_module pattern from the PEP 302 text instead.
# Is the test in the next line more or less robust than the
# following one? Presumably less ...
# ispkg = self.pathname.endswith('__init__.py')
if self.kind == imp.PKG_DIRECTORY:
mod.__path__ = [ os.path.dirname(self.pathname) ]
mod.__package__ = fullname
else:
#else, regular module
mod.__path__ = []
mod.__package__ = fullname.rpartition('.')[0]
try:
cachename = imp.cache_from_source(self.pathname)
if not os.path.exists(cachename):
update_cache = True
else:
sourcetime = os.stat(self.pathname).st_mtime
cachetime = os.stat(cachename).st_mtime
update_cache = cachetime < sourcetime
# # Force update_cache to work around a problem with it being treated as Py3 code???
# update_cache = True
if not update_cache:
with open(cachename, 'rb') as f:
data = f.read()
try:
code = marshal.loads(data)
except Exception:
# pyc could be corrupt. Regenerate it
update_cache = True
if update_cache:
if self.found[0]:
source = self.found[0].read()
elif self.kind == imp.PKG_DIRECTORY:
with open(self.pathname) as f:
source = f.read()
if detect_python2(source, self.pathname):
source = self.transform(source)
with open('/tmp/futurized_code.py', 'w') as f:
f.write('### Futurized code (from %s)\n%s' %
(self.pathname, source))
code = compile(source, self.pathname, 'exec')
dirname = os.path.dirname(cachename)
if not os.path.exists(dirname):
os.makedirs(dirname)
try:
with open(cachename, 'wb') as f:
data = marshal.dumps(code)
f.write(data)
except Exception: # could be write-protected
pass
exec(code, mod.__dict__)
except Exception as e:
# must remove module from sys.modules
del sys.modules[fullname]
raise # keep it simple
if self.found[0]:
self.found[0].close()
return mod
_hook = Py2Fixer()
def install_hooks(include_paths=(), exclude_paths=()):
if isinstance(include_paths, str):
include_paths = (include_paths,)
if isinstance(exclude_paths, str):
exclude_paths = (exclude_paths,)
assert len(include_paths) + len(exclude_paths) > 0, 'Pass at least one argument'
_hook.include(include_paths)
_hook.exclude(exclude_paths)
# _hook.debug = debug
enable = sys.version_info[0] >= 3 # enabled for all 3.x
if enable and _hook not in sys.meta_path:
sys.meta_path.insert(0, _hook) # insert at beginning. This could be made a parameter
# We could return the hook when there are ways of configuring it
#return _hook
def remove_hooks():
if _hook in sys.meta_path:
sys.meta_path.remove(_hook)
def detect_hooks():
"""
Returns True if the import hooks are installed, False if not.
"""
return _hook in sys.meta_path
# present = any([hasattr(hook, 'PY2FIXER') for hook in sys.meta_path])
# return present
class hooks(object):
"""
Acts as a context manager. Use like this:
>>> from past import translation
>>> with translation.hooks():
... import mypy2module
>>> import requests # py2/3 compatible anyway
>>> # etc.
"""
def __enter__(self):
self.hooks_were_installed = detect_hooks()
install_hooks()
return self
def __exit__(self, *args):
if not self.hooks_were_installed:
remove_hooks()
class suspend_hooks(object):
"""
Acts as a context manager. Use like this:
>>> from past import translation
>>> translation.install_hooks()
>>> import http.client
>>> # ...
>>> with translation.suspend_hooks():
>>> import requests # or others that support Py2/3
If the hooks were disabled before the context, they are not installed when
the context is left.
"""
def __enter__(self):
self.hooks_were_installed = detect_hooks()
remove_hooks()
return self
def __exit__(self, *args):
if self.hooks_were_installed:
install_hooks()
| 18,459 | 35.993988 | 163 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/conftest.py
|
"""
Pytest configuration and fixtures for the Numpy test suite.
"""
from __future__ import division, absolute_import, print_function
import warnings
import pytest
from numpy.core.multiarray_tests import get_fpu_mode
_old_fpu_mode = None
_collect_results = {}
@pytest.hookimpl()
def pytest_itemcollected(item):
"""
Check FPU precision mode was not changed during test collection.
The clumsy way we do it here is mainly necessary because numpy
still uses yield tests, which can execute code at test collection
time.
"""
global _old_fpu_mode
mode = get_fpu_mode()
if _old_fpu_mode is None:
_old_fpu_mode = mode
elif mode != _old_fpu_mode:
_collect_results[item] = (_old_fpu_mode, mode)
_old_fpu_mode = mode
@pytest.fixture(scope="function", autouse=True)
def check_fpu_mode(request):
"""
Check FPU precision mode was not changed during the test.
"""
old_mode = get_fpu_mode()
yield
new_mode = get_fpu_mode()
if old_mode != new_mode:
raise AssertionError("FPU precision mode changed from {0:#x} to {1:#x}"
" during the test".format(old_mode, new_mode))
collect_result = _collect_results.get(request.node)
if collect_result is not None:
old_mode, new_mode = collect_result
raise AssertionError("FPU precision mode changed from {0:#x} to {1:#x}"
" when collecting the test".format(old_mode,
new_mode))
| 1,557 | 27.327273 | 79 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/setup.py
|
#!/usr/bin/env python
from __future__ import division, print_function
def configuration(parent_package='',top_path=None):
from numpy.distutils.misc_util import Configuration
config = Configuration('numpy', parent_package, top_path)
config.add_subpackage('compat')
config.add_subpackage('core')
config.add_subpackage('distutils')
config.add_subpackage('doc')
config.add_subpackage('f2py')
config.add_subpackage('fft')
config.add_subpackage('lib')
config.add_subpackage('linalg')
config.add_subpackage('ma')
config.add_subpackage('matrixlib')
config.add_subpackage('polynomial')
config.add_subpackage('random')
config.add_subpackage('testing')
config.add_data_dir('doc')
config.add_data_dir('tests')
config.make_config_py() # installs __config__.py
return config
if __name__ == '__main__':
print('This is the wrong setup.py file to run')
| 920 | 30.758621 | 61 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/_import_tools.py
|
from __future__ import division, absolute_import, print_function
import os
import sys
import warnings
__all__ = ['PackageLoader']
class PackageLoader(object):
def __init__(self, verbose=False, infunc=False):
""" Manages loading packages.
"""
if infunc:
_level = 2
else:
_level = 1
self.parent_frame = frame = sys._getframe(_level)
self.parent_name = eval('__name__', frame.f_globals, frame.f_locals)
parent_path = eval('__path__', frame.f_globals, frame.f_locals)
if isinstance(parent_path, str):
parent_path = [parent_path]
self.parent_path = parent_path
if '__all__' not in frame.f_locals:
exec('__all__ = []', frame.f_globals, frame.f_locals)
self.parent_export_names = eval('__all__', frame.f_globals, frame.f_locals)
self.info_modules = {}
self.imported_packages = []
self.verbose = None
def _get_info_files(self, package_dir, parent_path, parent_package=None):
""" Return list of (package name,info.py file) from parent_path subdirectories.
"""
from glob import glob
files = glob(os.path.join(parent_path, package_dir, 'info.py'))
for info_file in glob(os.path.join(parent_path, package_dir, 'info.pyc')):
if info_file[:-1] not in files:
files.append(info_file)
info_files = []
for info_file in files:
package_name = os.path.dirname(info_file[len(parent_path)+1:])\
.replace(os.sep, '.')
if parent_package:
package_name = parent_package + '.' + package_name
info_files.append((package_name, info_file))
info_files.extend(self._get_info_files('*',
os.path.dirname(info_file),
package_name))
return info_files
def _init_info_modules(self, packages=None):
"""Initialize info_modules = {<package_name>: <package info.py module>}.
"""
from numpy.compat import npy_load_module
info_files = []
info_modules = self.info_modules
if packages is None:
for path in self.parent_path:
info_files.extend(self._get_info_files('*', path))
else:
for package_name in packages:
package_dir = os.path.join(*package_name.split('.'))
for path in self.parent_path:
names_files = self._get_info_files(package_dir, path)
if names_files:
info_files.extend(names_files)
break
else:
try:
exec('import %s.info as info' % (package_name))
info_modules[package_name] = info
except ImportError as msg:
self.warn('No scipy-style subpackage %r found in %s. '\
'Ignoring: %s'\
% (package_name, ':'.join(self.parent_path), msg))
for package_name, info_file in info_files:
if package_name in info_modules:
continue
fullname = self.parent_name +'.'+ package_name
if info_file[-1]=='c':
filedescriptor = ('.pyc', 'rb', 2)
else:
filedescriptor = ('.py', 'U', 1)
try:
info_module = npy_load_module(fullname + '.info',
info_file,
filedescriptor)
except Exception as msg:
self.error(msg)
info_module = None
if info_module is None or getattr(info_module, 'ignore', False):
info_modules.pop(package_name, None)
else:
self._init_info_modules(getattr(info_module, 'depends', []))
info_modules[package_name] = info_module
return
def _get_sorted_names(self):
""" Return package names sorted in the order as they should be
imported due to dependence relations between packages.
"""
depend_dict = {}
for name, info_module in self.info_modules.items():
depend_dict[name] = getattr(info_module, 'depends', [])
package_names = []
for name in list(depend_dict.keys()):
if not depend_dict[name]:
package_names.append(name)
del depend_dict[name]
while depend_dict:
for name, lst in list(depend_dict.items()):
new_lst = [n for n in lst if n in depend_dict]
if not new_lst:
package_names.append(name)
del depend_dict[name]
else:
depend_dict[name] = new_lst
return package_names
def __call__(self,*packages, **options):
"""Load one or more packages into parent package top-level namespace.
This function is intended to shorten the need to import many
subpackages, say of scipy, constantly with statements such as
import scipy.linalg, scipy.fftpack, scipy.etc...
Instead, you can say:
import scipy
scipy.pkgload('linalg','fftpack',...)
or
scipy.pkgload()
to load all of them in one call.
If a name which doesn't exist in scipy's namespace is
given, a warning is shown.
Parameters
----------
*packages : arg-tuple
the names (one or more strings) of all the modules one
wishes to load into the top-level namespace.
verbose= : integer
verbosity level [default: -1].
verbose=-1 will suspend also warnings.
force= : bool
when True, force reloading loaded packages [default: False].
postpone= : bool
when True, don't load packages [default: False]
"""
# 2014-10-29, 1.10
warnings.warn('pkgload and PackageLoader are obsolete '
'and will be removed in a future version of numpy',
DeprecationWarning, stacklevel=2)
frame = self.parent_frame
self.info_modules = {}
if options.get('force', False):
self.imported_packages = []
self.verbose = verbose = options.get('verbose', -1)
postpone = options.get('postpone', None)
self._init_info_modules(packages or None)
self.log('Imports to %r namespace\n----------------------------'\
% self.parent_name)
for package_name in self._get_sorted_names():
if package_name in self.imported_packages:
continue
info_module = self.info_modules[package_name]
global_symbols = getattr(info_module, 'global_symbols', [])
postpone_import = getattr(info_module, 'postpone_import', False)
if (postpone and not global_symbols) \
or (postpone_import and postpone is not None):
continue
old_object = frame.f_locals.get(package_name, None)
cmdstr = 'import '+package_name
if self._execcmd(cmdstr):
continue
self.imported_packages.append(package_name)
if verbose!=-1:
new_object = frame.f_locals.get(package_name)
if old_object is not None and old_object is not new_object:
self.warn('Overwriting %s=%s (was %s)' \
% (package_name, self._obj2repr(new_object),
self._obj2repr(old_object)))
if '.' not in package_name:
self.parent_export_names.append(package_name)
for symbol in global_symbols:
if symbol=='*':
symbols = eval('getattr(%s,"__all__",None)'\
% (package_name),
frame.f_globals, frame.f_locals)
if symbols is None:
symbols = eval('dir(%s)' % (package_name),
frame.f_globals, frame.f_locals)
symbols = [s for s in symbols if not s.startswith('_')]
else:
symbols = [symbol]
if verbose!=-1:
old_objects = {}
for s in symbols:
if s in frame.f_locals:
old_objects[s] = frame.f_locals[s]
cmdstr = 'from '+package_name+' import '+symbol
if self._execcmd(cmdstr):
continue
if verbose!=-1:
for s, old_object in old_objects.items():
new_object = frame.f_locals[s]
if new_object is not old_object:
self.warn('Overwriting %s=%s (was %s)' \
% (s, self._obj2repr(new_object),
self._obj2repr(old_object)))
if symbol=='*':
self.parent_export_names.extend(symbols)
else:
self.parent_export_names.append(symbol)
return
def _execcmd(self, cmdstr):
""" Execute command in parent_frame."""
frame = self.parent_frame
try:
exec (cmdstr, frame.f_globals, frame.f_locals)
except Exception as msg:
self.error('%s -> failed: %s' % (cmdstr, msg))
return True
else:
self.log('%s -> success' % (cmdstr))
return
def _obj2repr(self, obj):
""" Return repr(obj) with"""
module = getattr(obj, '__module__', None)
file = getattr(obj, '__file__', None)
if module is not None:
return repr(obj) + ' from ' + module
if file is not None:
return repr(obj) + ' from ' + file
return repr(obj)
def log(self, mess):
if self.verbose>1:
print(str(mess), file=sys.stderr)
def warn(self, mess):
if self.verbose>=0:
print(str(mess), file=sys.stderr)
def error(self, mess):
if self.verbose!=-1:
print(str(mess), file=sys.stderr)
def _get_doc_title(self, info_module):
""" Get the title from a package info.py file.
"""
title = getattr(info_module, '__doc_title__', None)
if title is not None:
return title
title = getattr(info_module, '__doc__', None)
if title is not None:
title = title.lstrip().split('\n', 1)[0]
return title
return '* Not Available *'
def _format_titles(self,titles,colsep='---'):
display_window_width = 70 # How to determine the correct value in runtime??
lengths = [len(name)-name.find('.')-1 for (name, title) in titles]+[0]
max_length = max(lengths)
lines = []
for (name, title) in titles:
name = name[name.find('.')+1:]
w = max_length - len(name)
words = title.split()
line = '%s%s %s' % (name, w*' ', colsep)
tab = len(line) * ' '
while words:
word = words.pop(0)
if len(line)+len(word)>display_window_width:
lines.append(line)
line = tab
line += ' ' + word
lines.append(line)
return '\n'.join(lines)
def get_pkgdocs(self):
""" Return documentation summary of subpackages.
"""
import sys
self.info_modules = {}
self._init_info_modules(None)
titles = []
symbols = []
for package_name, info_module in self.info_modules.items():
global_symbols = getattr(info_module, 'global_symbols', [])
fullname = self.parent_name +'.'+ package_name
note = ''
if fullname not in sys.modules:
note = ' [*]'
titles.append((fullname, self._get_doc_title(info_module) + note))
if global_symbols:
symbols.append((package_name, ', '.join(global_symbols)))
retstr = self._format_titles(titles) +\
'\n [*] - using a package requires explicit import (see pkgload)'
if symbols:
retstr += """\n\nGlobal symbols from subpackages"""\
"""\n-------------------------------\n""" +\
self._format_titles(symbols, '-->')
return retstr
class PackageLoaderDebug(PackageLoader):
def _execcmd(self, cmdstr):
""" Execute command in parent_frame."""
frame = self.parent_frame
print('Executing', repr(cmdstr), '...', end=' ')
sys.stdout.flush()
exec (cmdstr, frame.f_globals, frame.f_locals)
print('ok')
sys.stdout.flush()
return
if int(os.environ.get('NUMPY_IMPORT_DEBUG', '0')):
PackageLoader = PackageLoaderDebug
| 13,234 | 36.599432 | 87 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/_distributor_init.py
|
""" Distributor init file
Distributors: you can add custom code here to support particular distributions
of numpy.
For example, this is a good place to put any checks for hardware requirements.
The numpy standard source distribution will not put code in this file, so you
can safely replace this file with your own version.
"""
| 331 | 29.181818 | 78 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/dual.py
|
"""
Aliases for functions which may be accelerated by Scipy.
Scipy_ can be built to use accelerated or otherwise improved libraries
for FFTs, linear algebra, and special functions. This module allows
developers to transparently support these accelerated functions when
scipy is available but still support users who have only installed
NumPy.
.. _Scipy : http://www.scipy.org
"""
from __future__ import division, absolute_import, print_function
# This module should be used for functions both in numpy and scipy if
# you want to use the numpy version if available but the scipy version
# otherwise.
# Usage --- from numpy.dual import fft, inv
__all__ = ['fft', 'ifft', 'fftn', 'ifftn', 'fft2', 'ifft2',
'norm', 'inv', 'svd', 'solve', 'det', 'eig', 'eigvals',
'eigh', 'eigvalsh', 'lstsq', 'pinv', 'cholesky', 'i0']
import numpy.linalg as linpkg
import numpy.fft as fftpkg
from numpy.lib import i0
import sys
fft = fftpkg.fft
ifft = fftpkg.ifft
fftn = fftpkg.fftn
ifftn = fftpkg.ifftn
fft2 = fftpkg.fft2
ifft2 = fftpkg.ifft2
norm = linpkg.norm
inv = linpkg.inv
svd = linpkg.svd
solve = linpkg.solve
det = linpkg.det
eig = linpkg.eig
eigvals = linpkg.eigvals
eigh = linpkg.eigh
eigvalsh = linpkg.eigvalsh
lstsq = linpkg.lstsq
pinv = linpkg.pinv
cholesky = linpkg.cholesky
_restore_dict = {}
def register_func(name, func):
if name not in __all__:
raise ValueError("%s not a dual function." % name)
f = sys._getframe(0).f_globals
_restore_dict[name] = f[name]
f[name] = func
def restore_func(name):
if name not in __all__:
raise ValueError("%s not a dual function." % name)
try:
val = _restore_dict[name]
except KeyError:
return
else:
sys._getframe(0).f_globals[name] = val
def restore_all():
for name in _restore_dict.keys():
restore_func(name)
| 1,864 | 24.902778 | 71 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/ctypeslib.py
|
"""
============================
``ctypes`` Utility Functions
============================
See Also
---------
load_library : Load a C library.
ndpointer : Array restype/argtype with verification.
as_ctypes : Create a ctypes array from an ndarray.
as_array : Create an ndarray from a ctypes array.
References
----------
.. [1] "SciPy Cookbook: ctypes", http://www.scipy.org/Cookbook/Ctypes
Examples
--------
Load the C library:
>>> _lib = np.ctypeslib.load_library('libmystuff', '.') #doctest: +SKIP
Our result type, an ndarray that must be of type double, be 1-dimensional
and is C-contiguous in memory:
>>> array_1d_double = np.ctypeslib.ndpointer(
... dtype=np.double,
... ndim=1, flags='CONTIGUOUS') #doctest: +SKIP
Our C-function typically takes an array and updates its values
in-place. For example::
void foo_func(double* x, int length)
{
int i;
for (i = 0; i < length; i++) {
x[i] = i*i;
}
}
We wrap it using:
>>> _lib.foo_func.restype = None #doctest: +SKIP
>>> _lib.foo_func.argtypes = [array_1d_double, c_int] #doctest: +SKIP
Then, we're ready to call ``foo_func``:
>>> out = np.empty(15, dtype=np.double)
>>> _lib.foo_func(out, len(out)) #doctest: +SKIP
"""
from __future__ import division, absolute_import, print_function
__all__ = ['load_library', 'ndpointer', 'test', 'ctypes_load_library',
'c_intp', 'as_ctypes', 'as_array']
import sys, os
from numpy import integer, ndarray, dtype as _dtype, deprecate, array
from numpy.core.multiarray import _flagdict, flagsobj
try:
import ctypes
except ImportError:
ctypes = None
if ctypes is None:
def _dummy(*args, **kwds):
"""
Dummy object that raises an ImportError if ctypes is not available.
Raises
------
ImportError
If ctypes is not available.
"""
raise ImportError("ctypes is not available.")
ctypes_load_library = _dummy
load_library = _dummy
as_ctypes = _dummy
as_array = _dummy
from numpy import intp as c_intp
_ndptr_base = object
else:
import numpy.core._internal as nic
c_intp = nic._getintp_ctype()
del nic
_ndptr_base = ctypes.c_void_p
# Adapted from Albert Strasheim
def load_library(libname, loader_path):
"""
It is possible to load a library using
>>> lib = ctypes.cdll[<full_path_name>]
But there are cross-platform considerations, such as library file extensions,
plus the fact Windows will just load the first library it finds with that name.
NumPy supplies the load_library function as a convenience.
Parameters
----------
libname : str
Name of the library, which can have 'lib' as a prefix,
but without an extension.
loader_path : str
Where the library can be found.
Returns
-------
ctypes.cdll[libpath] : library object
A ctypes library object
Raises
------
OSError
If there is no library with the expected extension, or the
library is defective and cannot be loaded.
"""
if ctypes.__version__ < '1.0.1':
import warnings
warnings.warn("All features of ctypes interface may not work " \
"with ctypes < 1.0.1", stacklevel=2)
ext = os.path.splitext(libname)[1]
if not ext:
# Try to load library with platform-specific name, otherwise
# default to libname.[so|pyd]. Sometimes, these files are built
# erroneously on non-linux platforms.
from numpy.distutils.misc_util import get_shared_lib_extension
so_ext = get_shared_lib_extension()
libname_ext = [libname + so_ext]
# mac, windows and linux >= py3.2 shared library and loadable
# module have different extensions so try both
so_ext2 = get_shared_lib_extension(is_python_ext=True)
if not so_ext2 == so_ext:
libname_ext.insert(0, libname + so_ext2)
else:
libname_ext = [libname]
loader_path = os.path.abspath(loader_path)
if not os.path.isdir(loader_path):
libdir = os.path.dirname(loader_path)
else:
libdir = loader_path
for ln in libname_ext:
libpath = os.path.join(libdir, ln)
if os.path.exists(libpath):
try:
return ctypes.cdll[libpath]
except OSError:
## defective lib file
raise
## if no successful return in the libname_ext loop:
raise OSError("no file with expected extension")
ctypes_load_library = deprecate(load_library, 'ctypes_load_library',
'load_library')
def _num_fromflags(flaglist):
num = 0
for val in flaglist:
num += _flagdict[val]
return num
_flagnames = ['C_CONTIGUOUS', 'F_CONTIGUOUS', 'ALIGNED', 'WRITEABLE',
'OWNDATA', 'UPDATEIFCOPY', 'WRITEBACKIFCOPY']
def _flags_fromnum(num):
res = []
for key in _flagnames:
value = _flagdict[key]
if (num & value):
res.append(key)
return res
class _ndptr(_ndptr_base):
def _check_retval_(self):
"""This method is called when this class is used as the .restype
attribute for a shared-library function. It constructs a numpy
array from a void pointer."""
return array(self)
@property
def __array_interface__(self):
return {'descr': self._dtype_.descr,
'__ref': self,
'strides': None,
'shape': self._shape_,
'version': 3,
'typestr': self._dtype_.descr[0][1],
'data': (self.value, False),
}
@classmethod
def from_param(cls, obj):
if not isinstance(obj, ndarray):
raise TypeError("argument must be an ndarray")
if cls._dtype_ is not None \
and obj.dtype != cls._dtype_:
raise TypeError("array must have data type %s" % cls._dtype_)
if cls._ndim_ is not None \
and obj.ndim != cls._ndim_:
raise TypeError("array must have %d dimension(s)" % cls._ndim_)
if cls._shape_ is not None \
and obj.shape != cls._shape_:
raise TypeError("array must have shape %s" % str(cls._shape_))
if cls._flags_ is not None \
and ((obj.flags.num & cls._flags_) != cls._flags_):
raise TypeError("array must have flags %s" %
_flags_fromnum(cls._flags_))
return obj.ctypes
# Factory for an array-checking class with from_param defined for
# use with ctypes argtypes mechanism
_pointer_type_cache = {}
def ndpointer(dtype=None, ndim=None, shape=None, flags=None):
"""
Array-checking restype/argtypes.
An ndpointer instance is used to describe an ndarray in restypes
and argtypes specifications. This approach is more flexible than
using, for example, ``POINTER(c_double)``, since several restrictions
can be specified, which are verified upon calling the ctypes function.
These include data type, number of dimensions, shape and flags. If a
given array does not satisfy the specified restrictions,
a ``TypeError`` is raised.
Parameters
----------
dtype : data-type, optional
Array data-type.
ndim : int, optional
Number of array dimensions.
shape : tuple of ints, optional
Array shape.
flags : str or tuple of str
Array flags; may be one or more of:
- C_CONTIGUOUS / C / CONTIGUOUS
- F_CONTIGUOUS / F / FORTRAN
- OWNDATA / O
- WRITEABLE / W
- ALIGNED / A
- WRITEBACKIFCOPY / X
- UPDATEIFCOPY / U
Returns
-------
klass : ndpointer type object
A type object, which is an ``_ndtpr`` instance containing
dtype, ndim, shape and flags information.
Raises
------
TypeError
If a given array does not satisfy the specified restrictions.
Examples
--------
>>> clib.somefunc.argtypes = [np.ctypeslib.ndpointer(dtype=np.float64,
... ndim=1,
... flags='C_CONTIGUOUS')]
... #doctest: +SKIP
>>> clib.somefunc(np.array([1, 2, 3], dtype=np.float64))
... #doctest: +SKIP
"""
if dtype is not None:
dtype = _dtype(dtype)
num = None
if flags is not None:
if isinstance(flags, str):
flags = flags.split(',')
elif isinstance(flags, (int, integer)):
num = flags
flags = _flags_fromnum(num)
elif isinstance(flags, flagsobj):
num = flags.num
flags = _flags_fromnum(num)
if num is None:
try:
flags = [x.strip().upper() for x in flags]
except Exception:
raise TypeError("invalid flags specification")
num = _num_fromflags(flags)
try:
return _pointer_type_cache[(dtype, ndim, shape, num)]
except KeyError:
pass
if dtype is None:
name = 'any'
elif dtype.names:
name = str(id(dtype))
else:
name = dtype.str
if ndim is not None:
name += "_%dd" % ndim
if shape is not None:
try:
strshape = [str(x) for x in shape]
except TypeError:
strshape = [str(shape)]
shape = (shape,)
shape = tuple(shape)
name += "_"+"x".join(strshape)
if flags is not None:
name += "_"+"_".join(flags)
else:
flags = []
klass = type("ndpointer_%s"%name, (_ndptr,),
{"_dtype_": dtype,
"_shape_" : shape,
"_ndim_" : ndim,
"_flags_" : num})
_pointer_type_cache[(dtype, shape, ndim, num)] = klass
return klass
if ctypes is not None:
ct = ctypes
################################################################
# simple types
# maps the numpy typecodes like '<f8' to simple ctypes types like
# c_double. Filled in by prep_simple.
_typecodes = {}
def prep_simple(simple_type, dtype):
"""Given a ctypes simple type, construct and attach an
__array_interface__ property to it if it does not yet have one.
"""
try: simple_type.__array_interface__
except AttributeError: pass
else: return
typestr = _dtype(dtype).str
_typecodes[typestr] = simple_type
def __array_interface__(self):
return {'descr': [('', typestr)],
'__ref': self,
'strides': None,
'shape': (),
'version': 3,
'typestr': typestr,
'data': (ct.addressof(self), False),
}
simple_type.__array_interface__ = property(__array_interface__)
simple_types = [
((ct.c_byte, ct.c_short, ct.c_int, ct.c_long, ct.c_longlong), "i"),
((ct.c_ubyte, ct.c_ushort, ct.c_uint, ct.c_ulong, ct.c_ulonglong), "u"),
((ct.c_float, ct.c_double), "f"),
]
# Prep that numerical ctypes types:
for types, code in simple_types:
for tp in types:
prep_simple(tp, "%c%d" % (code, ct.sizeof(tp)))
################################################################
# array types
_ARRAY_TYPE = type(ct.c_int * 1)
def prep_array(array_type):
"""Given a ctypes array type, construct and attach an
__array_interface__ property to it if it does not yet have one.
"""
try: array_type.__array_interface__
except AttributeError: pass
else: return
shape = []
ob = array_type
while type(ob) is _ARRAY_TYPE:
shape.append(ob._length_)
ob = ob._type_
shape = tuple(shape)
ai = ob().__array_interface__
descr = ai['descr']
typestr = ai['typestr']
def __array_interface__(self):
return {'descr': descr,
'__ref': self,
'strides': None,
'shape': shape,
'version': 3,
'typestr': typestr,
'data': (ct.addressof(self), False),
}
array_type.__array_interface__ = property(__array_interface__)
def prep_pointer(pointer_obj, shape):
"""Given a ctypes pointer object, construct and
attach an __array_interface__ property to it if it does not
yet have one.
"""
try: pointer_obj.__array_interface__
except AttributeError: pass
else: return
contents = pointer_obj.contents
dtype = _dtype(type(contents))
inter = {'version': 3,
'typestr': dtype.str,
'data': (ct.addressof(contents), False),
'shape': shape}
pointer_obj.__array_interface__ = inter
################################################################
# public functions
def as_array(obj, shape=None):
"""Create a numpy array from a ctypes array or a ctypes POINTER.
The numpy array shares the memory with the ctypes object.
The size parameter must be given if converting from a ctypes POINTER.
The size parameter is ignored if converting from a ctypes array
"""
tp = type(obj)
try: tp.__array_interface__
except AttributeError:
if hasattr(obj, 'contents'):
prep_pointer(obj, shape)
else:
prep_array(tp)
return array(obj, copy=False)
def as_ctypes(obj):
"""Create and return a ctypes object from a numpy array. Actually
anything that exposes the __array_interface__ is accepted."""
ai = obj.__array_interface__
if ai["strides"]:
raise TypeError("strided arrays not supported")
if ai["version"] != 3:
raise TypeError("only __array_interface__ version 3 supported")
addr, readonly = ai["data"]
if readonly:
raise TypeError("readonly arrays unsupported")
tp = _typecodes[ai["typestr"]]
for dim in ai["shape"][::-1]:
tp = tp * dim
result = tp.from_address(addr)
result.__keep = ai
return result
| 14,730 | 31.375824 | 89 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/version.py
|
# THIS FILE IS GENERATED FROM NUMPY SETUP.PY
#
# To compare versions robustly, use `numpy.lib.NumpyVersion`
short_version = '1.14.5'
version = '1.14.5'
full_version = '1.14.5'
git_revision = 'd3348c1123d3862a42d50a7fee14e50b268944a4'
release = True
if not release:
version = full_version
| 294 | 21.692308 | 60 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/add_newdocs.py
|
"""
This is only meant to add docs to objects defined in C-extension modules.
The purpose is to allow easier editing of the docstrings without
requiring a re-compile.
NOTE: Many of the methods of ndarray have corresponding functions.
If you update these docstrings, please keep also the ones in
core/fromnumeric.py, core/defmatrix.py up-to-date.
"""
from __future__ import division, absolute_import, print_function
from numpy.lib import add_newdoc
###############################################################################
#
# flatiter
#
# flatiter needs a toplevel description
#
###############################################################################
add_newdoc('numpy.core', 'flatiter',
"""
Flat iterator object to iterate over arrays.
A `flatiter` iterator is returned by ``x.flat`` for any array `x`.
It allows iterating over the array as if it were a 1-D array,
either in a for-loop or by calling its `next` method.
Iteration is done in row-major, C-style order (the last
index varying the fastest). The iterator can also be indexed using
basic slicing or advanced indexing.
See Also
--------
ndarray.flat : Return a flat iterator over an array.
ndarray.flatten : Returns a flattened copy of an array.
Notes
-----
A `flatiter` iterator can not be constructed directly from Python code
by calling the `flatiter` constructor.
Examples
--------
>>> x = np.arange(6).reshape(2, 3)
>>> fl = x.flat
>>> type(fl)
<type 'numpy.flatiter'>
>>> for item in fl:
... print(item)
...
0
1
2
3
4
5
>>> fl[2:4]
array([2, 3])
""")
# flatiter attributes
add_newdoc('numpy.core', 'flatiter', ('base',
"""
A reference to the array that is iterated over.
Examples
--------
>>> x = np.arange(5)
>>> fl = x.flat
>>> fl.base is x
True
"""))
add_newdoc('numpy.core', 'flatiter', ('coords',
"""
An N-dimensional tuple of current coordinates.
Examples
--------
>>> x = np.arange(6).reshape(2, 3)
>>> fl = x.flat
>>> fl.coords
(0, 0)
>>> fl.next()
0
>>> fl.coords
(0, 1)
"""))
add_newdoc('numpy.core', 'flatiter', ('index',
"""
Current flat index into the array.
Examples
--------
>>> x = np.arange(6).reshape(2, 3)
>>> fl = x.flat
>>> fl.index
0
>>> fl.next()
0
>>> fl.index
1
"""))
# flatiter functions
add_newdoc('numpy.core', 'flatiter', ('__array__',
"""__array__(type=None) Get array from iterator
"""))
add_newdoc('numpy.core', 'flatiter', ('copy',
"""
copy()
Get a copy of the iterator as a 1-D array.
Examples
--------
>>> x = np.arange(6).reshape(2, 3)
>>> x
array([[0, 1, 2],
[3, 4, 5]])
>>> fl = x.flat
>>> fl.copy()
array([0, 1, 2, 3, 4, 5])
"""))
###############################################################################
#
# nditer
#
###############################################################################
add_newdoc('numpy.core', 'nditer',
"""
Efficient multi-dimensional iterator object to iterate over arrays.
To get started using this object, see the
:ref:`introductory guide to array iteration <arrays.nditer>`.
Parameters
----------
op : ndarray or sequence of array_like
The array(s) to iterate over.
flags : sequence of str, optional
Flags to control the behavior of the iterator.
* "buffered" enables buffering when required.
* "c_index" causes a C-order index to be tracked.
* "f_index" causes a Fortran-order index to be tracked.
* "multi_index" causes a multi-index, or a tuple of indices
with one per iteration dimension, to be tracked.
* "common_dtype" causes all the operands to be converted to
a common data type, with copying or buffering as necessary.
* "copy_if_overlap" causes the iterator to determine if read
operands have overlap with write operands, and make temporary
copies as necessary to avoid overlap. False positives (needless
copying) are possible in some cases.
* "delay_bufalloc" delays allocation of the buffers until
a reset() call is made. Allows "allocate" operands to
be initialized before their values are copied into the buffers.
* "external_loop" causes the `values` given to be
one-dimensional arrays with multiple values instead of
zero-dimensional arrays.
* "grow_inner" allows the `value` array sizes to be made
larger than the buffer size when both "buffered" and
"external_loop" is used.
* "ranged" allows the iterator to be restricted to a sub-range
of the iterindex values.
* "refs_ok" enables iteration of reference types, such as
object arrays.
* "reduce_ok" enables iteration of "readwrite" operands
which are broadcasted, also known as reduction operands.
* "zerosize_ok" allows `itersize` to be zero.
op_flags : list of list of str, optional
This is a list of flags for each operand. At minimum, one of
"readonly", "readwrite", or "writeonly" must be specified.
* "readonly" indicates the operand will only be read from.
* "readwrite" indicates the operand will be read from and written to.
* "writeonly" indicates the operand will only be written to.
* "no_broadcast" prevents the operand from being broadcasted.
* "contig" forces the operand data to be contiguous.
* "aligned" forces the operand data to be aligned.
* "nbo" forces the operand data to be in native byte order.
* "copy" allows a temporary read-only copy if required.
* "updateifcopy" allows a temporary read-write copy if required.
* "allocate" causes the array to be allocated if it is None
in the `op` parameter.
* "no_subtype" prevents an "allocate" operand from using a subtype.
* "arraymask" indicates that this operand is the mask to use
for selecting elements when writing to operands with the
'writemasked' flag set. The iterator does not enforce this,
but when writing from a buffer back to the array, it only
copies those elements indicated by this mask.
* 'writemasked' indicates that only elements where the chosen
'arraymask' operand is True will be written to.
* "overlap_assume_elementwise" can be used to mark operands that are
accessed only in the iterator order, to allow less conservative
copying when "copy_if_overlap" is present.
op_dtypes : dtype or tuple of dtype(s), optional
The required data type(s) of the operands. If copying or buffering
is enabled, the data will be converted to/from their original types.
order : {'C', 'F', 'A', 'K'}, optional
Controls the iteration order. 'C' means C order, 'F' means
Fortran order, 'A' means 'F' order if all the arrays are Fortran
contiguous, 'C' order otherwise, and 'K' means as close to the
order the array elements appear in memory as possible. This also
affects the element memory order of "allocate" operands, as they
are allocated to be compatible with iteration order.
Default is 'K'.
casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
Controls what kind of data casting may occur when making a copy
or buffering. Setting this to 'unsafe' is not recommended,
as it can adversely affect accumulations.
* 'no' means the data types should not be cast at all.
* 'equiv' means only byte-order changes are allowed.
* 'safe' means only casts which can preserve values are allowed.
* 'same_kind' means only safe casts or casts within a kind,
like float64 to float32, are allowed.
* 'unsafe' means any data conversions may be done.
op_axes : list of list of ints, optional
If provided, is a list of ints or None for each operands.
The list of axes for an operand is a mapping from the dimensions
of the iterator to the dimensions of the operand. A value of
-1 can be placed for entries, causing that dimension to be
treated as "newaxis".
itershape : tuple of ints, optional
The desired shape of the iterator. This allows "allocate" operands
with a dimension mapped by op_axes not corresponding to a dimension
of a different operand to get a value not equal to 1 for that
dimension.
buffersize : int, optional
When buffering is enabled, controls the size of the temporary
buffers. Set to 0 for the default value.
Attributes
----------
dtypes : tuple of dtype(s)
The data types of the values provided in `value`. This may be
different from the operand data types if buffering is enabled.
finished : bool
Whether the iteration over the operands is finished or not.
has_delayed_bufalloc : bool
If True, the iterator was created with the "delay_bufalloc" flag,
and no reset() function was called on it yet.
has_index : bool
If True, the iterator was created with either the "c_index" or
the "f_index" flag, and the property `index` can be used to
retrieve it.
has_multi_index : bool
If True, the iterator was created with the "multi_index" flag,
and the property `multi_index` can be used to retrieve it.
index
When the "c_index" or "f_index" flag was used, this property
provides access to the index. Raises a ValueError if accessed
and `has_index` is False.
iterationneedsapi : bool
Whether iteration requires access to the Python API, for example
if one of the operands is an object array.
iterindex : int
An index which matches the order of iteration.
itersize : int
Size of the iterator.
itviews
Structured view(s) of `operands` in memory, matching the reordered
and optimized iterator access pattern.
multi_index
When the "multi_index" flag was used, this property
provides access to the index. Raises a ValueError if accessed
accessed and `has_multi_index` is False.
ndim : int
The iterator's dimension.
nop : int
The number of iterator operands.
operands : tuple of operand(s)
The array(s) to be iterated over.
shape : tuple of ints
Shape tuple, the shape of the iterator.
value
Value of `operands` at current iteration. Normally, this is a
tuple of array scalars, but if the flag "external_loop" is used,
it is a tuple of one dimensional arrays.
Notes
-----
`nditer` supersedes `flatiter`. The iterator implementation behind
`nditer` is also exposed by the NumPy C API.
The Python exposure supplies two iteration interfaces, one which follows
the Python iterator protocol, and another which mirrors the C-style
do-while pattern. The native Python approach is better in most cases, but
if you need the iterator's coordinates or index, use the C-style pattern.
Examples
--------
Here is how we might write an ``iter_add`` function, using the
Python iterator protocol::
def iter_add_py(x, y, out=None):
addop = np.add
it = np.nditer([x, y, out], [],
[['readonly'], ['readonly'], ['writeonly','allocate']])
for (a, b, c) in it:
addop(a, b, out=c)
return it.operands[2]
Here is the same function, but following the C-style pattern::
def iter_add(x, y, out=None):
addop = np.add
it = np.nditer([x, y, out], [],
[['readonly'], ['readonly'], ['writeonly','allocate']])
while not it.finished:
addop(it[0], it[1], out=it[2])
it.iternext()
return it.operands[2]
Here is an example outer product function::
def outer_it(x, y, out=None):
mulop = np.multiply
it = np.nditer([x, y, out], ['external_loop'],
[['readonly'], ['readonly'], ['writeonly', 'allocate']],
op_axes=[range(x.ndim)+[-1]*y.ndim,
[-1]*x.ndim+range(y.ndim),
None])
for (a, b, c) in it:
mulop(a, b, out=c)
return it.operands[2]
>>> a = np.arange(2)+1
>>> b = np.arange(3)+1
>>> outer_it(a,b)
array([[1, 2, 3],
[2, 4, 6]])
Here is an example function which operates like a "lambda" ufunc::
def luf(lamdaexpr, *args, **kwargs):
"luf(lambdaexpr, op1, ..., opn, out=None, order='K', casting='safe', buffersize=0)"
nargs = len(args)
op = (kwargs.get('out',None),) + args
it = np.nditer(op, ['buffered','external_loop'],
[['writeonly','allocate','no_broadcast']] +
[['readonly','nbo','aligned']]*nargs,
order=kwargs.get('order','K'),
casting=kwargs.get('casting','safe'),
buffersize=kwargs.get('buffersize',0))
while not it.finished:
it[0] = lamdaexpr(*it[1:])
it.iternext()
return it.operands[0]
>>> a = np.arange(5)
>>> b = np.ones(5)
>>> luf(lambda i,j:i*i + j/2, a, b)
array([ 0.5, 1.5, 4.5, 9.5, 16.5])
""")
# nditer methods
add_newdoc('numpy.core', 'nditer', ('copy',
"""
copy()
Get a copy of the iterator in its current state.
Examples
--------
>>> x = np.arange(10)
>>> y = x + 1
>>> it = np.nditer([x, y])
>>> it.next()
(array(0), array(1))
>>> it2 = it.copy()
>>> it2.next()
(array(1), array(2))
"""))
add_newdoc('numpy.core', 'nditer', ('debug_print',
"""
debug_print()
Print the current state of the `nditer` instance and debug info to stdout.
"""))
add_newdoc('numpy.core', 'nditer', ('enable_external_loop',
"""
enable_external_loop()
When the "external_loop" was not used during construction, but
is desired, this modifies the iterator to behave as if the flag
was specified.
"""))
add_newdoc('numpy.core', 'nditer', ('iternext',
"""
iternext()
Check whether iterations are left, and perform a single internal iteration
without returning the result. Used in the C-style pattern do-while
pattern. For an example, see `nditer`.
Returns
-------
iternext : bool
Whether or not there are iterations left.
"""))
add_newdoc('numpy.core', 'nditer', ('remove_axis',
"""
remove_axis(i)
Removes axis `i` from the iterator. Requires that the flag "multi_index"
be enabled.
"""))
add_newdoc('numpy.core', 'nditer', ('remove_multi_index',
"""
remove_multi_index()
When the "multi_index" flag was specified, this removes it, allowing
the internal iteration structure to be optimized further.
"""))
add_newdoc('numpy.core', 'nditer', ('reset',
"""
reset()
Reset the iterator to its initial state.
"""))
###############################################################################
#
# broadcast
#
###############################################################################
add_newdoc('numpy.core', 'broadcast',
"""
Produce an object that mimics broadcasting.
Parameters
----------
in1, in2, ... : array_like
Input parameters.
Returns
-------
b : broadcast object
Broadcast the input parameters against one another, and
return an object that encapsulates the result.
Amongst others, it has ``shape`` and ``nd`` properties, and
may be used as an iterator.
See Also
--------
broadcast_arrays
broadcast_to
Examples
--------
Manually adding two vectors, using broadcasting:
>>> x = np.array([[1], [2], [3]])
>>> y = np.array([4, 5, 6])
>>> b = np.broadcast(x, y)
>>> out = np.empty(b.shape)
>>> out.flat = [u+v for (u,v) in b]
>>> out
array([[ 5., 6., 7.],
[ 6., 7., 8.],
[ 7., 8., 9.]])
Compare against built-in broadcasting:
>>> x + y
array([[5, 6, 7],
[6, 7, 8],
[7, 8, 9]])
""")
# attributes
add_newdoc('numpy.core', 'broadcast', ('index',
"""
current index in broadcasted result
Examples
--------
>>> x = np.array([[1], [2], [3]])
>>> y = np.array([4, 5, 6])
>>> b = np.broadcast(x, y)
>>> b.index
0
>>> b.next(), b.next(), b.next()
((1, 4), (1, 5), (1, 6))
>>> b.index
3
"""))
add_newdoc('numpy.core', 'broadcast', ('iters',
"""
tuple of iterators along ``self``'s "components."
Returns a tuple of `numpy.flatiter` objects, one for each "component"
of ``self``.
See Also
--------
numpy.flatiter
Examples
--------
>>> x = np.array([1, 2, 3])
>>> y = np.array([[4], [5], [6]])
>>> b = np.broadcast(x, y)
>>> row, col = b.iters
>>> row.next(), col.next()
(1, 4)
"""))
add_newdoc('numpy.core', 'broadcast', ('ndim',
"""
Number of dimensions of broadcasted result. Alias for `nd`.
.. versionadded:: 1.12.0
Examples
--------
>>> x = np.array([1, 2, 3])
>>> y = np.array([[4], [5], [6]])
>>> b = np.broadcast(x, y)
>>> b.ndim
2
"""))
add_newdoc('numpy.core', 'broadcast', ('nd',
"""
Number of dimensions of broadcasted result. For code intended for NumPy
1.12.0 and later the more consistent `ndim` is preferred.
Examples
--------
>>> x = np.array([1, 2, 3])
>>> y = np.array([[4], [5], [6]])
>>> b = np.broadcast(x, y)
>>> b.nd
2
"""))
add_newdoc('numpy.core', 'broadcast', ('numiter',
"""
Number of iterators possessed by the broadcasted result.
Examples
--------
>>> x = np.array([1, 2, 3])
>>> y = np.array([[4], [5], [6]])
>>> b = np.broadcast(x, y)
>>> b.numiter
2
"""))
add_newdoc('numpy.core', 'broadcast', ('shape',
"""
Shape of broadcasted result.
Examples
--------
>>> x = np.array([1, 2, 3])
>>> y = np.array([[4], [5], [6]])
>>> b = np.broadcast(x, y)
>>> b.shape
(3, 3)
"""))
add_newdoc('numpy.core', 'broadcast', ('size',
"""
Total size of broadcasted result.
Examples
--------
>>> x = np.array([1, 2, 3])
>>> y = np.array([[4], [5], [6]])
>>> b = np.broadcast(x, y)
>>> b.size
9
"""))
add_newdoc('numpy.core', 'broadcast', ('reset',
"""
reset()
Reset the broadcasted result's iterator(s).
Parameters
----------
None
Returns
-------
None
Examples
--------
>>> x = np.array([1, 2, 3])
>>> y = np.array([[4], [5], [6]]
>>> b = np.broadcast(x, y)
>>> b.index
0
>>> b.next(), b.next(), b.next()
((1, 4), (2, 4), (3, 4))
>>> b.index
3
>>> b.reset()
>>> b.index
0
"""))
###############################################################################
#
# numpy functions
#
###############################################################################
add_newdoc('numpy.core.multiarray', 'array',
"""
array(object, dtype=None, copy=True, order='K', subok=False, ndmin=0)
Create an array.
Parameters
----------
object : array_like
An array, any object exposing the array interface, an object whose
__array__ method returns an array, or any (nested) sequence.
dtype : data-type, optional
The desired data-type for the array. If not given, then the type will
be determined as the minimum type required to hold the objects in the
sequence. This argument can only be used to 'upcast' the array. For
downcasting, use the .astype(t) method.
copy : bool, optional
If true (default), then the object is copied. Otherwise, a copy will
only be made if __array__ returns a copy, if obj is a nested sequence,
or if a copy is needed to satisfy any of the other requirements
(`dtype`, `order`, etc.).
order : {'K', 'A', 'C', 'F'}, optional
Specify the memory layout of the array. If object is not an array, the
newly created array will be in C order (row major) unless 'F' is
specified, in which case it will be in Fortran order (column major).
If object is an array the following holds.
===== ========= ===================================================
order no copy copy=True
===== ========= ===================================================
'K' unchanged F & C order preserved, otherwise most similar order
'A' unchanged F order if input is F and not C, otherwise C order
'C' C order C order
'F' F order F order
===== ========= ===================================================
When ``copy=False`` and a copy is made for other reasons, the result is
the same as if ``copy=True``, with some exceptions for `A`, see the
Notes section. The default order is 'K'.
subok : bool, optional
If True, then sub-classes will be passed-through, otherwise
the returned array will be forced to be a base-class array (default).
ndmin : int, optional
Specifies the minimum number of dimensions that the resulting
array should have. Ones will be pre-pended to the shape as
needed to meet this requirement.
Returns
-------
out : ndarray
An array object satisfying the specified requirements.
See Also
--------
empty, empty_like, zeros, zeros_like, ones, ones_like, full, full_like
Notes
-----
When order is 'A' and `object` is an array in neither 'C' nor 'F' order,
and a copy is forced by a change in dtype, then the order of the result is
not necessarily 'C' as expected. This is likely a bug.
Examples
--------
>>> np.array([1, 2, 3])
array([1, 2, 3])
Upcasting:
>>> np.array([1, 2, 3.0])
array([ 1., 2., 3.])
More than one dimension:
>>> np.array([[1, 2], [3, 4]])
array([[1, 2],
[3, 4]])
Minimum dimensions 2:
>>> np.array([1, 2, 3], ndmin=2)
array([[1, 2, 3]])
Type provided:
>>> np.array([1, 2, 3], dtype=complex)
array([ 1.+0.j, 2.+0.j, 3.+0.j])
Data-type consisting of more than one element:
>>> x = np.array([(1,2),(3,4)],dtype=[('a','<i4'),('b','<i4')])
>>> x['a']
array([1, 3])
Creating an array from sub-classes:
>>> np.array(np.mat('1 2; 3 4'))
array([[1, 2],
[3, 4]])
>>> np.array(np.mat('1 2; 3 4'), subok=True)
matrix([[1, 2],
[3, 4]])
""")
add_newdoc('numpy.core.multiarray', 'empty',
"""
empty(shape, dtype=float, order='C')
Return a new array of given shape and type, without initializing entries.
Parameters
----------
shape : int or tuple of int
Shape of the empty array
dtype : data-type, optional
Desired output data-type.
order : {'C', 'F'}, optional
Whether to store multi-dimensional data in row-major
(C-style) or column-major (Fortran-style) order in
memory.
Returns
-------
out : ndarray
Array of uninitialized (arbitrary) data of the given shape, dtype, and
order. Object arrays will be initialized to None.
See Also
--------
empty_like, zeros, ones
Notes
-----
`empty`, unlike `zeros`, does not set the array values to zero,
and may therefore be marginally faster. On the other hand, it requires
the user to manually set all the values in the array, and should be
used with caution.
Examples
--------
>>> np.empty([2, 2])
array([[ -9.74499359e+001, 6.69583040e-309],
[ 2.13182611e-314, 3.06959433e-309]]) #random
>>> np.empty([2, 2], dtype=int)
array([[-1073741821, -1067949133],
[ 496041986, 19249760]]) #random
""")
add_newdoc('numpy.core.multiarray', 'empty_like',
"""
empty_like(a, dtype=None, order='K', subok=True)
Return a new array with the same shape and type as a given array.
Parameters
----------
a : array_like
The shape and data-type of `a` define these same attributes of the
returned array.
dtype : data-type, optional
Overrides the data type of the result.
.. versionadded:: 1.6.0
order : {'C', 'F', 'A', or 'K'}, optional
Overrides the memory layout of the result. 'C' means C-order,
'F' means F-order, 'A' means 'F' if ``a`` is Fortran contiguous,
'C' otherwise. 'K' means match the layout of ``a`` as closely
as possible.
.. versionadded:: 1.6.0
subok : bool, optional.
If True, then the newly created array will use the sub-class
type of 'a', otherwise it will be a base-class array. Defaults
to True.
Returns
-------
out : ndarray
Array of uninitialized (arbitrary) data with the same
shape and type as `a`.
See Also
--------
ones_like : Return an array of ones with shape and type of input.
zeros_like : Return an array of zeros with shape and type of input.
empty : Return a new uninitialized array.
ones : Return a new array setting values to one.
zeros : Return a new array setting values to zero.
Notes
-----
This function does *not* initialize the returned array; to do that use
`zeros_like` or `ones_like` instead. It may be marginally faster than
the functions that do set the array values.
Examples
--------
>>> a = ([1,2,3], [4,5,6]) # a is array-like
>>> np.empty_like(a)
array([[-1073741821, -1073741821, 3], #random
[ 0, 0, -1073741821]])
>>> a = np.array([[1., 2., 3.],[4.,5.,6.]])
>>> np.empty_like(a)
array([[ -2.00000715e+000, 1.48219694e-323, -2.00000572e+000],#random
[ 4.38791518e-305, -2.00000715e+000, 4.17269252e-309]])
""")
add_newdoc('numpy.core.multiarray', 'scalar',
"""
scalar(dtype, obj)
Return a new scalar array of the given type initialized with obj.
This function is meant mainly for pickle support. `dtype` must be a
valid data-type descriptor. If `dtype` corresponds to an object
descriptor, then `obj` can be any object, otherwise `obj` must be a
string. If `obj` is not given, it will be interpreted as None for object
type and as zeros for all other types.
""")
add_newdoc('numpy.core.multiarray', 'zeros',
"""
zeros(shape, dtype=float, order='C')
Return a new array of given shape and type, filled with zeros.
Parameters
----------
shape : int or sequence of ints
Shape of the new array, e.g., ``(2, 3)`` or ``2``.
dtype : data-type, optional
The desired data-type for the array, e.g., `numpy.int8`. Default is
`numpy.float64`.
order : {'C', 'F'}, optional
Whether to store multidimensional data in C- or Fortran-contiguous
(row- or column-wise) order in memory.
Returns
-------
out : ndarray
Array of zeros with the given shape, dtype, and order.
See Also
--------
zeros_like : Return an array of zeros with shape and type of input.
ones_like : Return an array of ones with shape and type of input.
empty_like : Return an empty array with shape and type of input.
ones : Return a new array setting values to one.
empty : Return a new uninitialized array.
Examples
--------
>>> np.zeros(5)
array([ 0., 0., 0., 0., 0.])
>>> np.zeros((5,), dtype=int)
array([0, 0, 0, 0, 0])
>>> np.zeros((2, 1))
array([[ 0.],
[ 0.]])
>>> s = (2,2)
>>> np.zeros(s)
array([[ 0., 0.],
[ 0., 0.]])
>>> np.zeros((2,), dtype=[('x', 'i4'), ('y', 'i4')]) # custom dtype
array([(0, 0), (0, 0)],
dtype=[('x', '<i4'), ('y', '<i4')])
""")
add_newdoc('numpy.core.multiarray', 'set_typeDict',
"""set_typeDict(dict)
Set the internal dictionary that can look up an array type using a
registered code.
""")
add_newdoc('numpy.core.multiarray', 'fromstring',
"""
fromstring(string, dtype=float, count=-1, sep='')
A new 1-D array initialized from text data in a string.
Parameters
----------
string : str
A string containing the data.
dtype : data-type, optional
The data type of the array; default: float. For binary input data,
the data must be in exactly this format.
count : int, optional
Read this number of `dtype` elements from the data. If this is
negative (the default), the count will be determined from the
length of the data.
sep : str, optional
The string separating numbers in the data; extra whitespace between
elements is also ignored.
.. deprecated:: 1.14
If this argument is not provided, `fromstring` falls back on the
behaviour of `frombuffer` after encoding unicode string inputs as
either utf-8 (python 3), or the default encoding (python 2).
Returns
-------
arr : ndarray
The constructed array.
Raises
------
ValueError
If the string is not the correct size to satisfy the requested
`dtype` and `count`.
See Also
--------
frombuffer, fromfile, fromiter
Examples
--------
>>> np.fromstring('1 2', dtype=int, sep=' ')
array([1, 2])
>>> np.fromstring('1, 2', dtype=int, sep=',')
array([1, 2])
""")
add_newdoc('numpy.core.multiarray', 'fromiter',
"""
fromiter(iterable, dtype, count=-1)
Create a new 1-dimensional array from an iterable object.
Parameters
----------
iterable : iterable object
An iterable object providing data for the array.
dtype : data-type
The data-type of the returned array.
count : int, optional
The number of items to read from *iterable*. The default is -1,
which means all data is read.
Returns
-------
out : ndarray
The output array.
Notes
-----
Specify `count` to improve performance. It allows ``fromiter`` to
pre-allocate the output array, instead of resizing it on demand.
Examples
--------
>>> iterable = (x*x for x in range(5))
>>> np.fromiter(iterable, float)
array([ 0., 1., 4., 9., 16.])
""")
add_newdoc('numpy.core.multiarray', 'fromfile',
"""
fromfile(file, dtype=float, count=-1, sep='')
Construct an array from data in a text or binary file.
A highly efficient way of reading binary data with a known data-type,
as well as parsing simply formatted text files. Data written using the
`tofile` method can be read using this function.
Parameters
----------
file : file or str
Open file object or filename.
dtype : data-type
Data type of the returned array.
For binary files, it is used to determine the size and byte-order
of the items in the file.
count : int
Number of items to read. ``-1`` means all items (i.e., the complete
file).
sep : str
Separator between items if file is a text file.
Empty ("") separator means the file should be treated as binary.
Spaces (" ") in the separator match zero or more whitespace characters.
A separator consisting only of spaces must match at least one
whitespace.
See also
--------
load, save
ndarray.tofile
loadtxt : More flexible way of loading data from a text file.
Notes
-----
Do not rely on the combination of `tofile` and `fromfile` for
data storage, as the binary files generated are are not platform
independent. In particular, no byte-order or data-type information is
saved. Data can be stored in the platform independent ``.npy`` format
using `save` and `load` instead.
Examples
--------
Construct an ndarray:
>>> dt = np.dtype([('time', [('min', int), ('sec', int)]),
... ('temp', float)])
>>> x = np.zeros((1,), dtype=dt)
>>> x['time']['min'] = 10; x['temp'] = 98.25
>>> x
array([((10, 0), 98.25)],
dtype=[('time', [('min', '<i4'), ('sec', '<i4')]), ('temp', '<f8')])
Save the raw data to disk:
>>> import os
>>> fname = os.tmpnam()
>>> x.tofile(fname)
Read the raw data from disk:
>>> np.fromfile(fname, dtype=dt)
array([((10, 0), 98.25)],
dtype=[('time', [('min', '<i4'), ('sec', '<i4')]), ('temp', '<f8')])
The recommended way to store and load data:
>>> np.save(fname, x)
>>> np.load(fname + '.npy')
array([((10, 0), 98.25)],
dtype=[('time', [('min', '<i4'), ('sec', '<i4')]), ('temp', '<f8')])
""")
add_newdoc('numpy.core.multiarray', 'frombuffer',
"""
frombuffer(buffer, dtype=float, count=-1, offset=0)
Interpret a buffer as a 1-dimensional array.
Parameters
----------
buffer : buffer_like
An object that exposes the buffer interface.
dtype : data-type, optional
Data-type of the returned array; default: float.
count : int, optional
Number of items to read. ``-1`` means all data in the buffer.
offset : int, optional
Start reading the buffer from this offset (in bytes); default: 0.
Notes
-----
If the buffer has data that is not in machine byte-order, this should
be specified as part of the data-type, e.g.::
>>> dt = np.dtype(int)
>>> dt = dt.newbyteorder('>')
>>> np.frombuffer(buf, dtype=dt)
The data of the resulting array will not be byteswapped, but will be
interpreted correctly.
Examples
--------
>>> s = 'hello world'
>>> np.frombuffer(s, dtype='S1', count=5, offset=6)
array(['w', 'o', 'r', 'l', 'd'],
dtype='|S1')
>>> np.frombuffer(b'\\x01\\x02', dtype=np.uint8)
array([1, 2], dtype=uint8)
>>> np.frombuffer(b'\\x01\\x02\\x03\\x04\\x05', dtype=np.uint8, count=3)
array([1, 2, 3], dtype=uint8)
""")
add_newdoc('numpy.core.multiarray', 'concatenate',
"""
concatenate((a1, a2, ...), axis=0, out=None)
Join a sequence of arrays along an existing axis.
Parameters
----------
a1, a2, ... : sequence of array_like
The arrays must have the same shape, except in the dimension
corresponding to `axis` (the first, by default).
axis : int, optional
The axis along which the arrays will be joined. Default is 0.
out : ndarray, optional
If provided, the destination to place the result. The shape must be
correct, matching that of what concatenate would have returned if no
out argument were specified.
Returns
-------
res : ndarray
The concatenated array.
See Also
--------
ma.concatenate : Concatenate function that preserves input masks.
array_split : Split an array into multiple sub-arrays of equal or
near-equal size.
split : Split array into a list of multiple sub-arrays of equal size.
hsplit : Split array into multiple sub-arrays horizontally (column wise)
vsplit : Split array into multiple sub-arrays vertically (row wise)
dsplit : Split array into multiple sub-arrays along the 3rd axis (depth).
stack : Stack a sequence of arrays along a new axis.
hstack : Stack arrays in sequence horizontally (column wise)
vstack : Stack arrays in sequence vertically (row wise)
dstack : Stack arrays in sequence depth wise (along third dimension)
Notes
-----
When one or more of the arrays to be concatenated is a MaskedArray,
this function will return a MaskedArray object instead of an ndarray,
but the input masks are *not* preserved. In cases where a MaskedArray
is expected as input, use the ma.concatenate function from the masked
array module instead.
Examples
--------
>>> a = np.array([[1, 2], [3, 4]])
>>> b = np.array([[5, 6]])
>>> np.concatenate((a, b), axis=0)
array([[1, 2],
[3, 4],
[5, 6]])
>>> np.concatenate((a, b.T), axis=1)
array([[1, 2, 5],
[3, 4, 6]])
This function will not preserve masking of MaskedArray inputs.
>>> a = np.ma.arange(3)
>>> a[1] = np.ma.masked
>>> b = np.arange(2, 5)
>>> a
masked_array(data = [0 -- 2],
mask = [False True False],
fill_value = 999999)
>>> b
array([2, 3, 4])
>>> np.concatenate([a, b])
masked_array(data = [0 1 2 2 3 4],
mask = False,
fill_value = 999999)
>>> np.ma.concatenate([a, b])
masked_array(data = [0 -- 2 2 3 4],
mask = [False True False False False False],
fill_value = 999999)
""")
add_newdoc('numpy.core', 'inner',
"""
inner(a, b)
Inner product of two arrays.
Ordinary inner product of vectors for 1-D arrays (without complex
conjugation), in higher dimensions a sum product over the last axes.
Parameters
----------
a, b : array_like
If `a` and `b` are nonscalar, their last dimensions must match.
Returns
-------
out : ndarray
`out.shape = a.shape[:-1] + b.shape[:-1]`
Raises
------
ValueError
If the last dimension of `a` and `b` has different size.
See Also
--------
tensordot : Sum products over arbitrary axes.
dot : Generalised matrix product, using second last dimension of `b`.
einsum : Einstein summation convention.
Notes
-----
For vectors (1-D arrays) it computes the ordinary inner-product::
np.inner(a, b) = sum(a[:]*b[:])
More generally, if `ndim(a) = r > 0` and `ndim(b) = s > 0`::
np.inner(a, b) = np.tensordot(a, b, axes=(-1,-1))
or explicitly::
np.inner(a, b)[i0,...,ir-1,j0,...,js-1]
= sum(a[i0,...,ir-1,:]*b[j0,...,js-1,:])
In addition `a` or `b` may be scalars, in which case::
np.inner(a,b) = a*b
Examples
--------
Ordinary inner product for vectors:
>>> a = np.array([1,2,3])
>>> b = np.array([0,1,0])
>>> np.inner(a, b)
2
A multidimensional example:
>>> a = np.arange(24).reshape((2,3,4))
>>> b = np.arange(4)
>>> np.inner(a, b)
array([[ 14, 38, 62],
[ 86, 110, 134]])
An example where `b` is a scalar:
>>> np.inner(np.eye(2), 7)
array([[ 7., 0.],
[ 0., 7.]])
""")
add_newdoc('numpy.core', 'fastCopyAndTranspose',
"""_fastCopyAndTranspose(a)""")
add_newdoc('numpy.core.multiarray', 'correlate',
"""cross_correlate(a,v, mode=0)""")
add_newdoc('numpy.core.multiarray', 'arange',
"""
arange([start,] stop[, step,], dtype=None)
Return evenly spaced values within a given interval.
Values are generated within the half-open interval ``[start, stop)``
(in other words, the interval including `start` but excluding `stop`).
For integer arguments the function is equivalent to the Python built-in
`range <http://docs.python.org/lib/built-in-funcs.html>`_ function,
but returns an ndarray rather than a list.
When using a non-integer step, such as 0.1, the results will often not
be consistent. It is better to use ``linspace`` for these cases.
Parameters
----------
start : number, optional
Start of interval. The interval includes this value. The default
start value is 0.
stop : number
End of interval. The interval does not include this value, except
in some cases where `step` is not an integer and floating point
round-off affects the length of `out`.
step : number, optional
Spacing between values. For any output `out`, this is the distance
between two adjacent values, ``out[i+1] - out[i]``. The default
step size is 1. If `step` is specified as a position argument,
`start` must also be given.
dtype : dtype
The type of the output array. If `dtype` is not given, infer the data
type from the other input arguments.
Returns
-------
arange : ndarray
Array of evenly spaced values.
For floating point arguments, the length of the result is
``ceil((stop - start)/step)``. Because of floating point overflow,
this rule may result in the last element of `out` being greater
than `stop`.
See Also
--------
linspace : Evenly spaced numbers with careful handling of endpoints.
ogrid: Arrays of evenly spaced numbers in N-dimensions.
mgrid: Grid-shaped arrays of evenly spaced numbers in N-dimensions.
Examples
--------
>>> np.arange(3)
array([0, 1, 2])
>>> np.arange(3.0)
array([ 0., 1., 2.])
>>> np.arange(3,7)
array([3, 4, 5, 6])
>>> np.arange(3,7,2)
array([3, 5])
""")
add_newdoc('numpy.core.multiarray', '_get_ndarray_c_version',
"""_get_ndarray_c_version()
Return the compile time NDARRAY_VERSION number.
""")
add_newdoc('numpy.core.multiarray', '_reconstruct',
"""_reconstruct(subtype, shape, dtype)
Construct an empty array. Used by Pickles.
""")
add_newdoc('numpy.core.multiarray', 'set_string_function',
"""
set_string_function(f, repr=1)
Internal method to set a function to be used when pretty printing arrays.
""")
add_newdoc('numpy.core.multiarray', 'set_numeric_ops',
"""
set_numeric_ops(op1=func1, op2=func2, ...)
Set numerical operators for array objects.
Parameters
----------
op1, op2, ... : callable
Each ``op = func`` pair describes an operator to be replaced.
For example, ``add = lambda x, y: np.add(x, y) % 5`` would replace
addition by modulus 5 addition.
Returns
-------
saved_ops : list of callables
A list of all operators, stored before making replacements.
Notes
-----
.. WARNING::
Use with care! Incorrect usage may lead to memory errors.
A function replacing an operator cannot make use of that operator.
For example, when replacing add, you may not use ``+``. Instead,
directly call ufuncs.
Examples
--------
>>> def add_mod5(x, y):
... return np.add(x, y) % 5
...
>>> old_funcs = np.set_numeric_ops(add=add_mod5)
>>> x = np.arange(12).reshape((3, 4))
>>> x + x
array([[0, 2, 4, 1],
[3, 0, 2, 4],
[1, 3, 0, 2]])
>>> ignore = np.set_numeric_ops(**old_funcs) # restore operators
""")
add_newdoc('numpy.core.multiarray', 'where',
"""
where(condition, [x, y])
Return elements, either from `x` or `y`, depending on `condition`.
If only `condition` is given, return ``condition.nonzero()``.
Parameters
----------
condition : array_like, bool
When True, yield `x`, otherwise yield `y`.
x, y : array_like, optional
Values from which to choose. `x`, `y` and `condition` need to be
broadcastable to some shape.
Returns
-------
out : ndarray or tuple of ndarrays
If both `x` and `y` are specified, the output array contains
elements of `x` where `condition` is True, and elements from
`y` elsewhere.
If only `condition` is given, return the tuple
``condition.nonzero()``, the indices where `condition` is True.
See Also
--------
nonzero, choose
Notes
-----
If `x` and `y` are given and input arrays are 1-D, `where` is
equivalent to::
[xv if c else yv for (c,xv,yv) in zip(condition,x,y)]
Examples
--------
>>> np.where([[True, False], [True, True]],
... [[1, 2], [3, 4]],
... [[9, 8], [7, 6]])
array([[1, 8],
[3, 4]])
>>> np.where([[0, 1], [1, 0]])
(array([0, 1]), array([1, 0]))
>>> x = np.arange(9.).reshape(3, 3)
>>> np.where( x > 5 )
(array([2, 2, 2]), array([0, 1, 2]))
>>> x[np.where( x > 3.0 )] # Note: result is 1D.
array([ 4., 5., 6., 7., 8.])
>>> np.where(x < 5, x, -1) # Note: broadcasting.
array([[ 0., 1., 2.],
[ 3., 4., -1.],
[-1., -1., -1.]])
Find the indices of elements of `x` that are in `goodvalues`.
>>> goodvalues = [3, 4, 7]
>>> ix = np.isin(x, goodvalues)
>>> ix
array([[False, False, False],
[ True, True, False],
[False, True, False]])
>>> np.where(ix)
(array([1, 1, 2]), array([0, 1, 1]))
""")
add_newdoc('numpy.core.multiarray', 'lexsort',
"""
lexsort(keys, axis=-1)
Perform an indirect sort using a sequence of keys.
Given multiple sorting keys, which can be interpreted as columns in a
spreadsheet, lexsort returns an array of integer indices that describes
the sort order by multiple columns. The last key in the sequence is used
for the primary sort order, the second-to-last key for the secondary sort
order, and so on. The keys argument must be a sequence of objects that
can be converted to arrays of the same shape. If a 2D array is provided
for the keys argument, it's rows are interpreted as the sorting keys and
sorting is according to the last row, second last row etc.
Parameters
----------
keys : (k, N) array or tuple containing k (N,)-shaped sequences
The `k` different "columns" to be sorted. The last column (or row if
`keys` is a 2D array) is the primary sort key.
axis : int, optional
Axis to be indirectly sorted. By default, sort over the last axis.
Returns
-------
indices : (N,) ndarray of ints
Array of indices that sort the keys along the specified axis.
See Also
--------
argsort : Indirect sort.
ndarray.sort : In-place sort.
sort : Return a sorted copy of an array.
Examples
--------
Sort names: first by surname, then by name.
>>> surnames = ('Hertz', 'Galilei', 'Hertz')
>>> first_names = ('Heinrich', 'Galileo', 'Gustav')
>>> ind = np.lexsort((first_names, surnames))
>>> ind
array([1, 2, 0])
>>> [surnames[i] + ", " + first_names[i] for i in ind]
['Galilei, Galileo', 'Hertz, Gustav', 'Hertz, Heinrich']
Sort two columns of numbers:
>>> a = [1,5,1,4,3,4,4] # First column
>>> b = [9,4,0,4,0,2,1] # Second column
>>> ind = np.lexsort((b,a)) # Sort by a, then by b
>>> print(ind)
[2 0 4 6 5 3 1]
>>> [(a[i],b[i]) for i in ind]
[(1, 0), (1, 9), (3, 0), (4, 1), (4, 2), (4, 4), (5, 4)]
Note that sorting is first according to the elements of ``a``.
Secondary sorting is according to the elements of ``b``.
A normal ``argsort`` would have yielded:
>>> [(a[i],b[i]) for i in np.argsort(a)]
[(1, 9), (1, 0), (3, 0), (4, 4), (4, 2), (4, 1), (5, 4)]
Structured arrays are sorted lexically by ``argsort``:
>>> x = np.array([(1,9), (5,4), (1,0), (4,4), (3,0), (4,2), (4,1)],
... dtype=np.dtype([('x', int), ('y', int)]))
>>> np.argsort(x) # or np.argsort(x, order=('x', 'y'))
array([2, 0, 4, 6, 5, 3, 1])
""")
add_newdoc('numpy.core.multiarray', 'can_cast',
"""
can_cast(from_, to, casting='safe')
Returns True if cast between data types can occur according to the
casting rule. If from is a scalar or array scalar, also returns
True if the scalar value can be cast without overflow or truncation
to an integer.
Parameters
----------
from_ : dtype, dtype specifier, scalar, or array
Data type, scalar, or array to cast from.
to : dtype or dtype specifier
Data type to cast to.
casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
Controls what kind of data casting may occur.
* 'no' means the data types should not be cast at all.
* 'equiv' means only byte-order changes are allowed.
* 'safe' means only casts which can preserve values are allowed.
* 'same_kind' means only safe casts or casts within a kind,
like float64 to float32, are allowed.
* 'unsafe' means any data conversions may be done.
Returns
-------
out : bool
True if cast can occur according to the casting rule.
Notes
-----
Starting in NumPy 1.9, can_cast function now returns False in 'safe'
casting mode for integer/float dtype and string dtype if the string dtype
length is not long enough to store the max integer/float value converted
to a string. Previously can_cast in 'safe' mode returned True for
integer/float dtype and a string dtype of any length.
See also
--------
dtype, result_type
Examples
--------
Basic examples
>>> np.can_cast(np.int32, np.int64)
True
>>> np.can_cast(np.float64, complex)
True
>>> np.can_cast(complex, float)
False
>>> np.can_cast('i8', 'f8')
True
>>> np.can_cast('i8', 'f4')
False
>>> np.can_cast('i4', 'S4')
False
Casting scalars
>>> np.can_cast(100, 'i1')
True
>>> np.can_cast(150, 'i1')
False
>>> np.can_cast(150, 'u1')
True
>>> np.can_cast(3.5e100, np.float32)
False
>>> np.can_cast(1000.0, np.float32)
True
Array scalar checks the value, array does not
>>> np.can_cast(np.array(1000.0), np.float32)
True
>>> np.can_cast(np.array([1000.0]), np.float32)
False
Using the casting rules
>>> np.can_cast('i8', 'i8', 'no')
True
>>> np.can_cast('<i8', '>i8', 'no')
False
>>> np.can_cast('<i8', '>i8', 'equiv')
True
>>> np.can_cast('<i4', '>i8', 'equiv')
False
>>> np.can_cast('<i4', '>i8', 'safe')
True
>>> np.can_cast('<i8', '>i4', 'safe')
False
>>> np.can_cast('<i8', '>i4', 'same_kind')
True
>>> np.can_cast('<i8', '>u4', 'same_kind')
False
>>> np.can_cast('<i8', '>u4', 'unsafe')
True
""")
add_newdoc('numpy.core.multiarray', 'promote_types',
"""
promote_types(type1, type2)
Returns the data type with the smallest size and smallest scalar
kind to which both ``type1`` and ``type2`` may be safely cast.
The returned data type is always in native byte order.
This function is symmetric and associative.
Parameters
----------
type1 : dtype or dtype specifier
First data type.
type2 : dtype or dtype specifier
Second data type.
Returns
-------
out : dtype
The promoted data type.
Notes
-----
.. versionadded:: 1.6.0
Starting in NumPy 1.9, promote_types function now returns a valid string
length when given an integer or float dtype as one argument and a string
dtype as another argument. Previously it always returned the input string
dtype, even if it wasn't long enough to store the max integer/float value
converted to a string.
See Also
--------
result_type, dtype, can_cast
Examples
--------
>>> np.promote_types('f4', 'f8')
dtype('float64')
>>> np.promote_types('i8', 'f4')
dtype('float64')
>>> np.promote_types('>i8', '<c8')
dtype('complex128')
>>> np.promote_types('i4', 'S8')
dtype('S11')
""")
add_newdoc('numpy.core.multiarray', 'min_scalar_type',
"""
min_scalar_type(a)
For scalar ``a``, returns the data type with the smallest size
and smallest scalar kind which can hold its value. For non-scalar
array ``a``, returns the vector's dtype unmodified.
Floating point values are not demoted to integers,
and complex values are not demoted to floats.
Parameters
----------
a : scalar or array_like
The value whose minimal data type is to be found.
Returns
-------
out : dtype
The minimal data type.
Notes
-----
.. versionadded:: 1.6.0
See Also
--------
result_type, promote_types, dtype, can_cast
Examples
--------
>>> np.min_scalar_type(10)
dtype('uint8')
>>> np.min_scalar_type(-260)
dtype('int16')
>>> np.min_scalar_type(3.1)
dtype('float16')
>>> np.min_scalar_type(1e50)
dtype('float64')
>>> np.min_scalar_type(np.arange(4,dtype='f8'))
dtype('float64')
""")
add_newdoc('numpy.core.multiarray', 'result_type',
"""
result_type(*arrays_and_dtypes)
Returns the type that results from applying the NumPy
type promotion rules to the arguments.
Type promotion in NumPy works similarly to the rules in languages
like C++, with some slight differences. When both scalars and
arrays are used, the array's type takes precedence and the actual value
of the scalar is taken into account.
For example, calculating 3*a, where a is an array of 32-bit floats,
intuitively should result in a 32-bit float output. If the 3 is a
32-bit integer, the NumPy rules indicate it can't convert losslessly
into a 32-bit float, so a 64-bit float should be the result type.
By examining the value of the constant, '3', we see that it fits in
an 8-bit integer, which can be cast losslessly into the 32-bit float.
Parameters
----------
arrays_and_dtypes : list of arrays and dtypes
The operands of some operation whose result type is needed.
Returns
-------
out : dtype
The result type.
See also
--------
dtype, promote_types, min_scalar_type, can_cast
Notes
-----
.. versionadded:: 1.6.0
The specific algorithm used is as follows.
Categories are determined by first checking which of boolean,
integer (int/uint), or floating point (float/complex) the maximum
kind of all the arrays and the scalars are.
If there are only scalars or the maximum category of the scalars
is higher than the maximum category of the arrays,
the data types are combined with :func:`promote_types`
to produce the return value.
Otherwise, `min_scalar_type` is called on each array, and
the resulting data types are all combined with :func:`promote_types`
to produce the return value.
The set of int values is not a subset of the uint values for types
with the same number of bits, something not reflected in
:func:`min_scalar_type`, but handled as a special case in `result_type`.
Examples
--------
>>> np.result_type(3, np.arange(7, dtype='i1'))
dtype('int8')
>>> np.result_type('i4', 'c8')
dtype('complex128')
>>> np.result_type(3.0, -2)
dtype('float64')
""")
add_newdoc('numpy.core.multiarray', 'newbuffer',
"""
newbuffer(size)
Return a new uninitialized buffer object.
Parameters
----------
size : int
Size in bytes of returned buffer object.
Returns
-------
newbuffer : buffer object
Returned, uninitialized buffer object of `size` bytes.
""")
add_newdoc('numpy.core.multiarray', 'getbuffer',
"""
getbuffer(obj [,offset[, size]])
Create a buffer object from the given object referencing a slice of
length size starting at offset.
Default is the entire buffer. A read-write buffer is attempted followed
by a read-only buffer.
Parameters
----------
obj : object
offset : int, optional
size : int, optional
Returns
-------
buffer_obj : buffer
Examples
--------
>>> buf = np.getbuffer(np.ones(5), 1, 3)
>>> len(buf)
3
>>> buf[0]
'\\x00'
>>> buf
<read-write buffer for 0x8af1e70, size 3, offset 1 at 0x8ba4ec0>
""")
add_newdoc('numpy.core', 'dot',
"""
dot(a, b, out=None)
Dot product of two arrays. Specifically,
- If both `a` and `b` are 1-D arrays, it is inner product of vectors
(without complex conjugation).
- If both `a` and `b` are 2-D arrays, it is matrix multiplication,
but using :func:`matmul` or ``a @ b`` is preferred.
- If either `a` or `b` is 0-D (scalar), it is equivalent to :func:`multiply`
and using ``numpy.multiply(a, b)`` or ``a * b`` is preferred.
- If `a` is an N-D array and `b` is a 1-D array, it is a sum product over
the last axis of `a` and `b`.
- If `a` is an N-D array and `b` is an M-D array (where ``M>=2``), it is a
sum product over the last axis of `a` and the second-to-last axis of `b`::
dot(a, b)[i,j,k,m] = sum(a[i,j,:] * b[k,:,m])
Parameters
----------
a : array_like
First argument.
b : array_like
Second argument.
out : ndarray, optional
Output argument. This must have the exact kind that would be returned
if it was not used. In particular, it must have the right type, must be
C-contiguous, and its dtype must be the dtype that would be returned
for `dot(a,b)`. This is a performance feature. Therefore, if these
conditions are not met, an exception is raised, instead of attempting
to be flexible.
Returns
-------
output : ndarray
Returns the dot product of `a` and `b`. If `a` and `b` are both
scalars or both 1-D arrays then a scalar is returned; otherwise
an array is returned.
If `out` is given, then it is returned.
Raises
------
ValueError
If the last dimension of `a` is not the same size as
the second-to-last dimension of `b`.
See Also
--------
vdot : Complex-conjugating dot product.
tensordot : Sum products over arbitrary axes.
einsum : Einstein summation convention.
matmul : '@' operator as method with out parameter.
Examples
--------
>>> np.dot(3, 4)
12
Neither argument is complex-conjugated:
>>> np.dot([2j, 3j], [2j, 3j])
(-13+0j)
For 2-D arrays it is the matrix product:
>>> a = [[1, 0], [0, 1]]
>>> b = [[4, 1], [2, 2]]
>>> np.dot(a, b)
array([[4, 1],
[2, 2]])
>>> a = np.arange(3*4*5*6).reshape((3,4,5,6))
>>> b = np.arange(3*4*5*6)[::-1].reshape((5,4,6,3))
>>> np.dot(a, b)[2,3,2,1,2,2]
499128
>>> sum(a[2,3,2,:] * b[1,2,:,2])
499128
""")
add_newdoc('numpy.core', 'matmul',
"""
matmul(a, b, out=None)
Matrix product of two arrays.
The behavior depends on the arguments in the following way.
- If both arguments are 2-D they are multiplied like conventional
matrices.
- If either argument is N-D, N > 2, it is treated as a stack of
matrices residing in the last two indexes and broadcast accordingly.
- If the first argument is 1-D, it is promoted to a matrix by
prepending a 1 to its dimensions. After matrix multiplication
the prepended 1 is removed.
- If the second argument is 1-D, it is promoted to a matrix by
appending a 1 to its dimensions. After matrix multiplication
the appended 1 is removed.
Multiplication by a scalar is not allowed, use ``*`` instead. Note that
multiplying a stack of matrices with a vector will result in a stack of
vectors, but matmul will not recognize it as such.
``matmul`` differs from ``dot`` in two important ways.
- Multiplication by scalars is not allowed.
- Stacks of matrices are broadcast together as if the matrices
were elements.
.. warning::
This function is preliminary and included in NumPy 1.10.0 for testing
and documentation. Its semantics will not change, but the number and
order of the optional arguments will.
.. versionadded:: 1.10.0
Parameters
----------
a : array_like
First argument.
b : array_like
Second argument.
out : ndarray, optional
Output argument. This must have the exact kind that would be returned
if it was not used. In particular, it must have the right type, must be
C-contiguous, and its dtype must be the dtype that would be returned
for `dot(a,b)`. This is a performance feature. Therefore, if these
conditions are not met, an exception is raised, instead of attempting
to be flexible.
Returns
-------
output : ndarray
Returns the dot product of `a` and `b`. If `a` and `b` are both
1-D arrays then a scalar is returned; otherwise an array is
returned. If `out` is given, then it is returned.
Raises
------
ValueError
If the last dimension of `a` is not the same size as
the second-to-last dimension of `b`.
If scalar value is passed.
See Also
--------
vdot : Complex-conjugating dot product.
tensordot : Sum products over arbitrary axes.
einsum : Einstein summation convention.
dot : alternative matrix product with different broadcasting rules.
Notes
-----
The matmul function implements the semantics of the `@` operator introduced
in Python 3.5 following PEP465.
Examples
--------
For 2-D arrays it is the matrix product:
>>> a = [[1, 0], [0, 1]]
>>> b = [[4, 1], [2, 2]]
>>> np.matmul(a, b)
array([[4, 1],
[2, 2]])
For 2-D mixed with 1-D, the result is the usual.
>>> a = [[1, 0], [0, 1]]
>>> b = [1, 2]
>>> np.matmul(a, b)
array([1, 2])
>>> np.matmul(b, a)
array([1, 2])
Broadcasting is conventional for stacks of arrays
>>> a = np.arange(2*2*4).reshape((2,2,4))
>>> b = np.arange(2*2*4).reshape((2,4,2))
>>> np.matmul(a,b).shape
(2, 2, 2)
>>> np.matmul(a,b)[0,1,1]
98
>>> sum(a[0,1,:] * b[0,:,1])
98
Vector, vector returns the scalar inner product, but neither argument
is complex-conjugated:
>>> np.matmul([2j, 3j], [2j, 3j])
(-13+0j)
Scalar multiplication raises an error.
>>> np.matmul([1,2], 3)
Traceback (most recent call last):
...
ValueError: Scalar operands are not allowed, use '*' instead
""")
add_newdoc('numpy.core', 'c_einsum',
"""
c_einsum(subscripts, *operands, out=None, dtype=None, order='K', casting='safe')
Evaluates the Einstein summation convention on the operands.
Using the Einstein summation convention, many common multi-dimensional
array operations can be represented in a simple fashion. This function
provides a way to compute such summations. The best way to understand this
function is to try the examples below, which show how many common NumPy
functions can be implemented as calls to `einsum`.
This is the core C function.
Parameters
----------
subscripts : str
Specifies the subscripts for summation.
operands : list of array_like
These are the arrays for the operation.
out : ndarray, optional
If provided, the calculation is done into this array.
dtype : {data-type, None}, optional
If provided, forces the calculation to use the data type specified.
Note that you may have to also give a more liberal `casting`
parameter to allow the conversions. Default is None.
order : {'C', 'F', 'A', 'K'}, optional
Controls the memory layout of the output. 'C' means it should
be C contiguous. 'F' means it should be Fortran contiguous,
'A' means it should be 'F' if the inputs are all 'F', 'C' otherwise.
'K' means it should be as close to the layout as the inputs as
is possible, including arbitrarily permuted axes.
Default is 'K'.
casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
Controls what kind of data casting may occur. Setting this to
'unsafe' is not recommended, as it can adversely affect accumulations.
* 'no' means the data types should not be cast at all.
* 'equiv' means only byte-order changes are allowed.
* 'safe' means only casts which can preserve values are allowed.
* 'same_kind' means only safe casts or casts within a kind,
like float64 to float32, are allowed.
* 'unsafe' means any data conversions may be done.
Default is 'safe'.
Returns
-------
output : ndarray
The calculation based on the Einstein summation convention.
See Also
--------
einsum, dot, inner, outer, tensordot
Notes
-----
.. versionadded:: 1.6.0
The subscripts string is a comma-separated list of subscript labels,
where each label refers to a dimension of the corresponding operand.
Repeated subscripts labels in one operand take the diagonal. For example,
``np.einsum('ii', a)`` is equivalent to ``np.trace(a)``.
Whenever a label is repeated, it is summed, so ``np.einsum('i,i', a, b)``
is equivalent to ``np.inner(a,b)``. If a label appears only once,
it is not summed, so ``np.einsum('i', a)`` produces a view of ``a``
with no changes.
The order of labels in the output is by default alphabetical. This
means that ``np.einsum('ij', a)`` doesn't affect a 2D array, while
``np.einsum('ji', a)`` takes its transpose.
The output can be controlled by specifying output subscript labels
as well. This specifies the label order, and allows summing to
be disallowed or forced when desired. The call ``np.einsum('i->', a)``
is like ``np.sum(a, axis=-1)``, and ``np.einsum('ii->i', a)``
is like ``np.diag(a)``. The difference is that `einsum` does not
allow broadcasting by default.
To enable and control broadcasting, use an ellipsis. Default
NumPy-style broadcasting is done by adding an ellipsis
to the left of each term, like ``np.einsum('...ii->...i', a)``.
To take the trace along the first and last axes,
you can do ``np.einsum('i...i', a)``, or to do a matrix-matrix
product with the left-most indices instead of rightmost, you can do
``np.einsum('ij...,jk...->ik...', a, b)``.
When there is only one operand, no axes are summed, and no output
parameter is provided, a view into the operand is returned instead
of a new array. Thus, taking the diagonal as ``np.einsum('ii->i', a)``
produces a view.
An alternative way to provide the subscripts and operands is as
``einsum(op0, sublist0, op1, sublist1, ..., [sublistout])``. The examples
below have corresponding `einsum` calls with the two parameter methods.
.. versionadded:: 1.10.0
Views returned from einsum are now writeable whenever the input array
is writeable. For example, ``np.einsum('ijk...->kji...', a)`` will now
have the same effect as ``np.swapaxes(a, 0, 2)`` and
``np.einsum('ii->i', a)`` will return a writeable view of the diagonal
of a 2D array.
Examples
--------
>>> a = np.arange(25).reshape(5,5)
>>> b = np.arange(5)
>>> c = np.arange(6).reshape(2,3)
>>> np.einsum('ii', a)
60
>>> np.einsum(a, [0,0])
60
>>> np.trace(a)
60
>>> np.einsum('ii->i', a)
array([ 0, 6, 12, 18, 24])
>>> np.einsum(a, [0,0], [0])
array([ 0, 6, 12, 18, 24])
>>> np.diag(a)
array([ 0, 6, 12, 18, 24])
>>> np.einsum('ij,j', a, b)
array([ 30, 80, 130, 180, 230])
>>> np.einsum(a, [0,1], b, [1])
array([ 30, 80, 130, 180, 230])
>>> np.dot(a, b)
array([ 30, 80, 130, 180, 230])
>>> np.einsum('...j,j', a, b)
array([ 30, 80, 130, 180, 230])
>>> np.einsum('ji', c)
array([[0, 3],
[1, 4],
[2, 5]])
>>> np.einsum(c, [1,0])
array([[0, 3],
[1, 4],
[2, 5]])
>>> c.T
array([[0, 3],
[1, 4],
[2, 5]])
>>> np.einsum('..., ...', 3, c)
array([[ 0, 3, 6],
[ 9, 12, 15]])
>>> np.einsum(3, [Ellipsis], c, [Ellipsis])
array([[ 0, 3, 6],
[ 9, 12, 15]])
>>> np.multiply(3, c)
array([[ 0, 3, 6],
[ 9, 12, 15]])
>>> np.einsum('i,i', b, b)
30
>>> np.einsum(b, [0], b, [0])
30
>>> np.inner(b,b)
30
>>> np.einsum('i,j', np.arange(2)+1, b)
array([[0, 1, 2, 3, 4],
[0, 2, 4, 6, 8]])
>>> np.einsum(np.arange(2)+1, [0], b, [1])
array([[0, 1, 2, 3, 4],
[0, 2, 4, 6, 8]])
>>> np.outer(np.arange(2)+1, b)
array([[0, 1, 2, 3, 4],
[0, 2, 4, 6, 8]])
>>> np.einsum('i...->...', a)
array([50, 55, 60, 65, 70])
>>> np.einsum(a, [0,Ellipsis], [Ellipsis])
array([50, 55, 60, 65, 70])
>>> np.sum(a, axis=0)
array([50, 55, 60, 65, 70])
>>> a = np.arange(60.).reshape(3,4,5)
>>> b = np.arange(24.).reshape(4,3,2)
>>> np.einsum('ijk,jil->kl', a, b)
array([[ 4400., 4730.],
[ 4532., 4874.],
[ 4664., 5018.],
[ 4796., 5162.],
[ 4928., 5306.]])
>>> np.einsum(a, [0,1,2], b, [1,0,3], [2,3])
array([[ 4400., 4730.],
[ 4532., 4874.],
[ 4664., 5018.],
[ 4796., 5162.],
[ 4928., 5306.]])
>>> np.tensordot(a,b, axes=([1,0],[0,1]))
array([[ 4400., 4730.],
[ 4532., 4874.],
[ 4664., 5018.],
[ 4796., 5162.],
[ 4928., 5306.]])
>>> a = np.arange(6).reshape((3,2))
>>> b = np.arange(12).reshape((4,3))
>>> np.einsum('ki,jk->ij', a, b)
array([[10, 28, 46, 64],
[13, 40, 67, 94]])
>>> np.einsum('ki,...k->i...', a, b)
array([[10, 28, 46, 64],
[13, 40, 67, 94]])
>>> np.einsum('k...,jk', a, b)
array([[10, 28, 46, 64],
[13, 40, 67, 94]])
>>> # since version 1.10.0
>>> a = np.zeros((3, 3))
>>> np.einsum('ii->i', a)[:] = 1
>>> a
array([[ 1., 0., 0.],
[ 0., 1., 0.],
[ 0., 0., 1.]])
""")
add_newdoc('numpy.core', 'vdot',
"""
vdot(a, b)
Return the dot product of two vectors.
The vdot(`a`, `b`) function handles complex numbers differently than
dot(`a`, `b`). If the first argument is complex the complex conjugate
of the first argument is used for the calculation of the dot product.
Note that `vdot` handles multidimensional arrays differently than `dot`:
it does *not* perform a matrix product, but flattens input arguments
to 1-D vectors first. Consequently, it should only be used for vectors.
Parameters
----------
a : array_like
If `a` is complex the complex conjugate is taken before calculation
of the dot product.
b : array_like
Second argument to the dot product.
Returns
-------
output : ndarray
Dot product of `a` and `b`. Can be an int, float, or
complex depending on the types of `a` and `b`.
See Also
--------
dot : Return the dot product without using the complex conjugate of the
first argument.
Examples
--------
>>> a = np.array([1+2j,3+4j])
>>> b = np.array([5+6j,7+8j])
>>> np.vdot(a, b)
(70-8j)
>>> np.vdot(b, a)
(70+8j)
Note that higher-dimensional arrays are flattened!
>>> a = np.array([[1, 4], [5, 6]])
>>> b = np.array([[4, 1], [2, 2]])
>>> np.vdot(a, b)
30
>>> np.vdot(b, a)
30
>>> 1*4 + 4*1 + 5*2 + 6*2
30
""")
##############################################################################
#
# Documentation for ndarray attributes and methods
#
##############################################################################
##############################################################################
#
# ndarray object
#
##############################################################################
add_newdoc('numpy.core.multiarray', 'ndarray',
"""
ndarray(shape, dtype=float, buffer=None, offset=0,
strides=None, order=None)
An array object represents a multidimensional, homogeneous array
of fixed-size items. An associated data-type object describes the
format of each element in the array (its byte-order, how many bytes it
occupies in memory, whether it is an integer, a floating point number,
or something else, etc.)
Arrays should be constructed using `array`, `zeros` or `empty` (refer
to the See Also section below). The parameters given here refer to
a low-level method (`ndarray(...)`) for instantiating an array.
For more information, refer to the `numpy` module and examine the
methods and attributes of an array.
Parameters
----------
(for the __new__ method; see Notes below)
shape : tuple of ints
Shape of created array.
dtype : data-type, optional
Any object that can be interpreted as a numpy data type.
buffer : object exposing buffer interface, optional
Used to fill the array with data.
offset : int, optional
Offset of array data in buffer.
strides : tuple of ints, optional
Strides of data in memory.
order : {'C', 'F'}, optional
Row-major (C-style) or column-major (Fortran-style) order.
Attributes
----------
T : ndarray
Transpose of the array.
data : buffer
The array's elements, in memory.
dtype : dtype object
Describes the format of the elements in the array.
flags : dict
Dictionary containing information related to memory use, e.g.,
'C_CONTIGUOUS', 'OWNDATA', 'WRITEABLE', etc.
flat : numpy.flatiter object
Flattened version of the array as an iterator. The iterator
allows assignments, e.g., ``x.flat = 3`` (See `ndarray.flat` for
assignment examples; TODO).
imag : ndarray
Imaginary part of the array.
real : ndarray
Real part of the array.
size : int
Number of elements in the array.
itemsize : int
The memory use of each array element in bytes.
nbytes : int
The total number of bytes required to store the array data,
i.e., ``itemsize * size``.
ndim : int
The array's number of dimensions.
shape : tuple of ints
Shape of the array.
strides : tuple of ints
The step-size required to move from one element to the next in
memory. For example, a contiguous ``(3, 4)`` array of type
``int16`` in C-order has strides ``(8, 2)``. This implies that
to move from element to element in memory requires jumps of 2 bytes.
To move from row-to-row, one needs to jump 8 bytes at a time
(``2 * 4``).
ctypes : ctypes object
Class containing properties of the array needed for interaction
with ctypes.
base : ndarray
If the array is a view into another array, that array is its `base`
(unless that array is also a view). The `base` array is where the
array data is actually stored.
See Also
--------
array : Construct an array.
zeros : Create an array, each element of which is zero.
empty : Create an array, but leave its allocated memory unchanged (i.e.,
it contains "garbage").
dtype : Create a data-type.
Notes
-----
There are two modes of creating an array using ``__new__``:
1. If `buffer` is None, then only `shape`, `dtype`, and `order`
are used.
2. If `buffer` is an object exposing the buffer interface, then
all keywords are interpreted.
No ``__init__`` method is needed because the array is fully initialized
after the ``__new__`` method.
Examples
--------
These examples illustrate the low-level `ndarray` constructor. Refer
to the `See Also` section above for easier ways of constructing an
ndarray.
First mode, `buffer` is None:
>>> np.ndarray(shape=(2,2), dtype=float, order='F')
array([[ -1.13698227e+002, 4.25087011e-303],
[ 2.88528414e-306, 3.27025015e-309]]) #random
Second mode:
>>> np.ndarray((2,), buffer=np.array([1,2,3]),
... offset=np.int_().itemsize,
... dtype=int) # offset = 1*itemsize, i.e. skip first element
array([2, 3])
""")
##############################################################################
#
# ndarray attributes
#
##############################################################################
add_newdoc('numpy.core.multiarray', 'ndarray', ('__array_interface__',
"""Array protocol: Python side."""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('__array_finalize__',
"""None."""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('__array_priority__',
"""Array priority."""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('__array_struct__',
"""Array protocol: C-struct side."""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('_as_parameter_',
"""Allow the array to be interpreted as a ctypes object by returning the
data-memory location as an integer
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('base',
"""
Base object if memory is from some other object.
Examples
--------
The base of an array that owns its memory is None:
>>> x = np.array([1,2,3,4])
>>> x.base is None
True
Slicing creates a view, whose memory is shared with x:
>>> y = x[2:]
>>> y.base is x
True
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('ctypes',
"""
An object to simplify the interaction of the array with the ctypes
module.
This attribute creates an object that makes it easier to use arrays
when calling shared libraries with the ctypes module. The returned
object has, among others, data, shape, and strides attributes (see
Notes below) which themselves return ctypes objects that can be used
as arguments to a shared library.
Parameters
----------
None
Returns
-------
c : Python object
Possessing attributes data, shape, strides, etc.
See Also
--------
numpy.ctypeslib
Notes
-----
Below are the public attributes of this object which were documented
in "Guide to NumPy" (we have omitted undocumented public attributes,
as well as documented private attributes):
* data: A pointer to the memory area of the array as a Python integer.
This memory area may contain data that is not aligned, or not in correct
byte-order. The memory area may not even be writeable. The array
flags and data-type of this array should be respected when passing this
attribute to arbitrary C-code to avoid trouble that can include Python
crashing. User Beware! The value of this attribute is exactly the same
as self._array_interface_['data'][0].
* shape (c_intp*self.ndim): A ctypes array of length self.ndim where
the basetype is the C-integer corresponding to dtype('p') on this
platform. This base-type could be c_int, c_long, or c_longlong
depending on the platform. The c_intp type is defined accordingly in
numpy.ctypeslib. The ctypes array contains the shape of the underlying
array.
* strides (c_intp*self.ndim): A ctypes array of length self.ndim where
the basetype is the same as for the shape attribute. This ctypes array
contains the strides information from the underlying array. This strides
information is important for showing how many bytes must be jumped to
get to the next element in the array.
* data_as(obj): Return the data pointer cast to a particular c-types object.
For example, calling self._as_parameter_ is equivalent to
self.data_as(ctypes.c_void_p). Perhaps you want to use the data as a
pointer to a ctypes array of floating-point data:
self.data_as(ctypes.POINTER(ctypes.c_double)).
* shape_as(obj): Return the shape tuple as an array of some other c-types
type. For example: self.shape_as(ctypes.c_short).
* strides_as(obj): Return the strides tuple as an array of some other
c-types type. For example: self.strides_as(ctypes.c_longlong).
Be careful using the ctypes attribute - especially on temporary
arrays or arrays constructed on the fly. For example, calling
``(a+b).ctypes.data_as(ctypes.c_void_p)`` returns a pointer to memory
that is invalid because the array created as (a+b) is deallocated
before the next Python statement. You can avoid this problem using
either ``c=a+b`` or ``ct=(a+b).ctypes``. In the latter case, ct will
hold a reference to the array until ct is deleted or re-assigned.
If the ctypes module is not available, then the ctypes attribute
of array objects still returns something useful, but ctypes objects
are not returned and errors may be raised instead. In particular,
the object will still have the as parameter attribute which will
return an integer equal to the data attribute.
Examples
--------
>>> import ctypes
>>> x
array([[0, 1],
[2, 3]])
>>> x.ctypes.data
30439712
>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long))
<ctypes.LP_c_long object at 0x01F01300>
>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_long)).contents
c_long(0)
>>> x.ctypes.data_as(ctypes.POINTER(ctypes.c_longlong)).contents
c_longlong(4294967296L)
>>> x.ctypes.shape
<numpy.core._internal.c_long_Array_2 object at 0x01FFD580>
>>> x.ctypes.shape_as(ctypes.c_long)
<numpy.core._internal.c_long_Array_2 object at 0x01FCE620>
>>> x.ctypes.strides
<numpy.core._internal.c_long_Array_2 object at 0x01FCE620>
>>> x.ctypes.strides_as(ctypes.c_longlong)
<numpy.core._internal.c_longlong_Array_2 object at 0x01F01300>
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('data',
"""Python buffer object pointing to the start of the array's data."""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('dtype',
"""
Data-type of the array's elements.
Parameters
----------
None
Returns
-------
d : numpy dtype object
See Also
--------
numpy.dtype
Examples
--------
>>> x
array([[0, 1],
[2, 3]])
>>> x.dtype
dtype('int32')
>>> type(x.dtype)
<type 'numpy.dtype'>
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('imag',
"""
The imaginary part of the array.
Examples
--------
>>> x = np.sqrt([1+0j, 0+1j])
>>> x.imag
array([ 0. , 0.70710678])
>>> x.imag.dtype
dtype('float64')
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('itemsize',
"""
Length of one array element in bytes.
Examples
--------
>>> x = np.array([1,2,3], dtype=np.float64)
>>> x.itemsize
8
>>> x = np.array([1,2,3], dtype=np.complex128)
>>> x.itemsize
16
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('flags',
"""
Information about the memory layout of the array.
Attributes
----------
C_CONTIGUOUS (C)
The data is in a single, C-style contiguous segment.
F_CONTIGUOUS (F)
The data is in a single, Fortran-style contiguous segment.
OWNDATA (O)
The array owns the memory it uses or borrows it from another object.
WRITEABLE (W)
The data area can be written to. Setting this to False locks
the data, making it read-only. A view (slice, etc.) inherits WRITEABLE
from its base array at creation time, but a view of a writeable
array may be subsequently locked while the base array remains writeable.
(The opposite is not true, in that a view of a locked array may not
be made writeable. However, currently, locking a base object does not
lock any views that already reference it, so under that circumstance it
is possible to alter the contents of a locked array via a previously
created writeable view onto it.) Attempting to change a non-writeable
array raises a RuntimeError exception.
ALIGNED (A)
The data and all elements are aligned appropriately for the hardware.
WRITEBACKIFCOPY (X)
This array is a copy of some other array. The C-API function
PyArray_ResolveWritebackIfCopy must be called before deallocating
to the base array will be updated with the contents of this array.
UPDATEIFCOPY (U)
(Deprecated, use WRITEBACKIFCOPY) This array is a copy of some other array.
When this array is
deallocated, the base array will be updated with the contents of
this array.
FNC
F_CONTIGUOUS and not C_CONTIGUOUS.
FORC
F_CONTIGUOUS or C_CONTIGUOUS (one-segment test).
BEHAVED (B)
ALIGNED and WRITEABLE.
CARRAY (CA)
BEHAVED and C_CONTIGUOUS.
FARRAY (FA)
BEHAVED and F_CONTIGUOUS and not C_CONTIGUOUS.
Notes
-----
The `flags` object can be accessed dictionary-like (as in ``a.flags['WRITEABLE']``),
or by using lowercased attribute names (as in ``a.flags.writeable``). Short flag
names are only supported in dictionary access.
Only the WRITEBACKIFCOPY, UPDATEIFCOPY, WRITEABLE, and ALIGNED flags can be
changed by the user, via direct assignment to the attribute or dictionary
entry, or by calling `ndarray.setflags`.
The array flags cannot be set arbitrarily:
- UPDATEIFCOPY can only be set ``False``.
- WRITEBACKIFCOPY can only be set ``False``.
- ALIGNED can only be set ``True`` if the data is truly aligned.
- WRITEABLE can only be set ``True`` if the array owns its own memory
or the ultimate owner of the memory exposes a writeable buffer
interface or is a string.
Arrays can be both C-style and Fortran-style contiguous simultaneously.
This is clear for 1-dimensional arrays, but can also be true for higher
dimensional arrays.
Even for contiguous arrays a stride for a given dimension
``arr.strides[dim]`` may be *arbitrary* if ``arr.shape[dim] == 1``
or the array has no elements.
It does *not* generally hold that ``self.strides[-1] == self.itemsize``
for C-style contiguous arrays or ``self.strides[0] == self.itemsize`` for
Fortran-style contiguous arrays is true.
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('flat',
"""
A 1-D iterator over the array.
This is a `numpy.flatiter` instance, which acts similarly to, but is not
a subclass of, Python's built-in iterator object.
See Also
--------
flatten : Return a copy of the array collapsed into one dimension.
flatiter
Examples
--------
>>> x = np.arange(1, 7).reshape(2, 3)
>>> x
array([[1, 2, 3],
[4, 5, 6]])
>>> x.flat[3]
4
>>> x.T
array([[1, 4],
[2, 5],
[3, 6]])
>>> x.T.flat[3]
5
>>> type(x.flat)
<type 'numpy.flatiter'>
An assignment example:
>>> x.flat = 3; x
array([[3, 3, 3],
[3, 3, 3]])
>>> x.flat[[1,4]] = 1; x
array([[3, 1, 3],
[3, 1, 3]])
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('nbytes',
"""
Total bytes consumed by the elements of the array.
Notes
-----
Does not include memory consumed by non-element attributes of the
array object.
Examples
--------
>>> x = np.zeros((3,5,2), dtype=np.complex128)
>>> x.nbytes
480
>>> np.prod(x.shape) * x.itemsize
480
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('ndim',
"""
Number of array dimensions.
Examples
--------
>>> x = np.array([1, 2, 3])
>>> x.ndim
1
>>> y = np.zeros((2, 3, 4))
>>> y.ndim
3
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('real',
"""
The real part of the array.
Examples
--------
>>> x = np.sqrt([1+0j, 0+1j])
>>> x.real
array([ 1. , 0.70710678])
>>> x.real.dtype
dtype('float64')
See Also
--------
numpy.real : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('shape',
"""
Tuple of array dimensions.
The shape property is usually used to get the current shape of an array,
but may also be used to reshape the array in-place by assigning a tuple of
array dimensions to it. As with `numpy.reshape`, one of the new shape
dimensions can be -1, in which case its value is inferred from the size of
the array and the remaining dimensions. Reshaping an array in-place will
fail if a copy is required.
Examples
--------
>>> x = np.array([1, 2, 3, 4])
>>> x.shape
(4,)
>>> y = np.zeros((2, 3, 4))
>>> y.shape
(2, 3, 4)
>>> y.shape = (3, 8)
>>> y
array([[ 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0.],
[ 0., 0., 0., 0., 0., 0., 0., 0.]])
>>> y.shape = (3, 6)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: total size of new array must be unchanged
>>> np.zeros((4,2))[::2].shape = (-1,)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
AttributeError: incompatible shape for a non-contiguous array
See Also
--------
numpy.reshape : similar function
ndarray.reshape : similar method
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('size',
"""
Number of elements in the array.
Equivalent to ``np.prod(a.shape)``, i.e., the product of the array's
dimensions.
Examples
--------
>>> x = np.zeros((3, 5, 2), dtype=np.complex128)
>>> x.size
30
>>> np.prod(x.shape)
30
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('strides',
"""
Tuple of bytes to step in each dimension when traversing an array.
The byte offset of element ``(i[0], i[1], ..., i[n])`` in an array `a`
is::
offset = sum(np.array(i) * a.strides)
A more detailed explanation of strides can be found in the
"ndarray.rst" file in the NumPy reference guide.
Notes
-----
Imagine an array of 32-bit integers (each 4 bytes)::
x = np.array([[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9]], dtype=np.int32)
This array is stored in memory as 40 bytes, one after the other
(known as a contiguous block of memory). The strides of an array tell
us how many bytes we have to skip in memory to move to the next position
along a certain axis. For example, we have to skip 4 bytes (1 value) to
move to the next column, but 20 bytes (5 values) to get to the same
position in the next row. As such, the strides for the array `x` will be
``(20, 4)``.
See Also
--------
numpy.lib.stride_tricks.as_strided
Examples
--------
>>> y = np.reshape(np.arange(2*3*4), (2,3,4))
>>> y
array([[[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]],
[[12, 13, 14, 15],
[16, 17, 18, 19],
[20, 21, 22, 23]]])
>>> y.strides
(48, 16, 4)
>>> y[1,1,1]
17
>>> offset=sum(y.strides * np.array((1,1,1)))
>>> offset/y.itemsize
17
>>> x = np.reshape(np.arange(5*6*7*8), (5,6,7,8)).transpose(2,3,1,0)
>>> x.strides
(32, 4, 224, 1344)
>>> i = np.array([3,5,2,2])
>>> offset = sum(i * x.strides)
>>> x[3,5,2,2]
813
>>> offset / x.itemsize
813
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('T',
"""
Same as self.transpose(), except that self is returned if
self.ndim < 2.
Examples
--------
>>> x = np.array([[1.,2.],[3.,4.]])
>>> x
array([[ 1., 2.],
[ 3., 4.]])
>>> x.T
array([[ 1., 3.],
[ 2., 4.]])
>>> x = np.array([1.,2.,3.,4.])
>>> x
array([ 1., 2., 3., 4.])
>>> x.T
array([ 1., 2., 3., 4.])
"""))
##############################################################################
#
# ndarray methods
#
##############################################################################
add_newdoc('numpy.core.multiarray', 'ndarray', ('__array__',
""" a.__array__(|dtype) -> reference if type unchanged, copy otherwise.
Returns either a new reference to self if dtype is not given or a new array
of provided data type if dtype is different from the current dtype of the
array.
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('__array_prepare__',
"""a.__array_prepare__(obj) -> Object of same type as ndarray object obj.
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('__array_wrap__',
"""a.__array_wrap__(obj) -> Object of same type as ndarray object a.
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('__copy__',
"""a.__copy__()
Used if :func:`copy.copy` is called on an array. Returns a copy of the array.
Equivalent to ``a.copy(order='K')``.
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('__deepcopy__',
"""a.__deepcopy__(memo, /) -> Deep copy of array.
Used if :func:`copy.deepcopy` is called on an array.
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('__reduce__',
"""a.__reduce__()
For pickling.
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('__setstate__',
"""a.__setstate__(state, /)
For unpickling.
The `state` argument must be a sequence that contains the following
elements:
Parameters
----------
version : int
optional pickle version. If omitted defaults to 0.
shape : tuple
dtype : data-type
isFortran : bool
rawdata : string or list
a binary string with the data (or a list if 'a' is an object array)
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('all',
"""
a.all(axis=None, out=None, keepdims=False)
Returns True if all elements evaluate to True.
Refer to `numpy.all` for full documentation.
See Also
--------
numpy.all : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('any',
"""
a.any(axis=None, out=None, keepdims=False)
Returns True if any of the elements of `a` evaluate to True.
Refer to `numpy.any` for full documentation.
See Also
--------
numpy.any : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('argmax',
"""
a.argmax(axis=None, out=None)
Return indices of the maximum values along the given axis.
Refer to `numpy.argmax` for full documentation.
See Also
--------
numpy.argmax : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('argmin',
"""
a.argmin(axis=None, out=None)
Return indices of the minimum values along the given axis of `a`.
Refer to `numpy.argmin` for detailed documentation.
See Also
--------
numpy.argmin : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('argsort',
"""
a.argsort(axis=-1, kind='quicksort', order=None)
Returns the indices that would sort this array.
Refer to `numpy.argsort` for full documentation.
See Also
--------
numpy.argsort : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('argpartition',
"""
a.argpartition(kth, axis=-1, kind='introselect', order=None)
Returns the indices that would partition this array.
Refer to `numpy.argpartition` for full documentation.
.. versionadded:: 1.8.0
See Also
--------
numpy.argpartition : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('astype',
"""
a.astype(dtype, order='K', casting='unsafe', subok=True, copy=True)
Copy of the array, cast to a specified type.
Parameters
----------
dtype : str or dtype
Typecode or data-type to which the array is cast.
order : {'C', 'F', 'A', 'K'}, optional
Controls the memory layout order of the result.
'C' means C order, 'F' means Fortran order, 'A'
means 'F' order if all the arrays are Fortran contiguous,
'C' order otherwise, and 'K' means as close to the
order the array elements appear in memory as possible.
Default is 'K'.
casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
Controls what kind of data casting may occur. Defaults to 'unsafe'
for backwards compatibility.
* 'no' means the data types should not be cast at all.
* 'equiv' means only byte-order changes are allowed.
* 'safe' means only casts which can preserve values are allowed.
* 'same_kind' means only safe casts or casts within a kind,
like float64 to float32, are allowed.
* 'unsafe' means any data conversions may be done.
subok : bool, optional
If True, then sub-classes will be passed-through (default), otherwise
the returned array will be forced to be a base-class array.
copy : bool, optional
By default, astype always returns a newly allocated array. If this
is set to false, and the `dtype`, `order`, and `subok`
requirements are satisfied, the input array is returned instead
of a copy.
Returns
-------
arr_t : ndarray
Unless `copy` is False and the other conditions for returning the input
array are satisfied (see description for `copy` input parameter), `arr_t`
is a new array of the same shape as the input array, with dtype, order
given by `dtype`, `order`.
Notes
-----
Starting in NumPy 1.9, astype method now returns an error if the string
dtype to cast to is not long enough in 'safe' casting mode to hold the max
value of integer/float array that is being casted. Previously the casting
was allowed even if the result was truncated.
Raises
------
ComplexWarning
When casting from complex to float or int. To avoid this,
one should use ``a.real.astype(t)``.
Examples
--------
>>> x = np.array([1, 2, 2.5])
>>> x
array([ 1. , 2. , 2.5])
>>> x.astype(int)
array([1, 2, 2])
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('byteswap',
"""
a.byteswap(inplace=False)
Swap the bytes of the array elements
Toggle between low-endian and big-endian data representation by
returning a byteswapped array, optionally swapped in-place.
Parameters
----------
inplace : bool, optional
If ``True``, swap bytes in-place, default is ``False``.
Returns
-------
out : ndarray
The byteswapped array. If `inplace` is ``True``, this is
a view to self.
Examples
--------
>>> A = np.array([1, 256, 8755], dtype=np.int16)
>>> map(hex, A)
['0x1', '0x100', '0x2233']
>>> A.byteswap(inplace=True)
array([ 256, 1, 13090], dtype=int16)
>>> map(hex, A)
['0x100', '0x1', '0x3322']
Arrays of strings are not swapped
>>> A = np.array(['ceg', 'fac'])
>>> A.byteswap()
array(['ceg', 'fac'],
dtype='|S3')
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('choose',
"""
a.choose(choices, out=None, mode='raise')
Use an index array to construct a new array from a set of choices.
Refer to `numpy.choose` for full documentation.
See Also
--------
numpy.choose : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('clip',
"""
a.clip(min=None, max=None, out=None)
Return an array whose values are limited to ``[min, max]``.
One of max or min must be given.
Refer to `numpy.clip` for full documentation.
See Also
--------
numpy.clip : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('compress',
"""
a.compress(condition, axis=None, out=None)
Return selected slices of this array along given axis.
Refer to `numpy.compress` for full documentation.
See Also
--------
numpy.compress : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('conj',
"""
a.conj()
Complex-conjugate all elements.
Refer to `numpy.conjugate` for full documentation.
See Also
--------
numpy.conjugate : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('conjugate',
"""
a.conjugate()
Return the complex conjugate, element-wise.
Refer to `numpy.conjugate` for full documentation.
See Also
--------
numpy.conjugate : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('copy',
"""
a.copy(order='C')
Return a copy of the array.
Parameters
----------
order : {'C', 'F', 'A', 'K'}, optional
Controls the memory layout of the copy. 'C' means C-order,
'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,
'C' otherwise. 'K' means match the layout of `a` as closely
as possible. (Note that this function and :func:`numpy.copy` are very
similar, but have different default values for their order=
arguments.)
See also
--------
numpy.copy
numpy.copyto
Examples
--------
>>> x = np.array([[1,2,3],[4,5,6]], order='F')
>>> y = x.copy()
>>> x.fill(0)
>>> x
array([[0, 0, 0],
[0, 0, 0]])
>>> y
array([[1, 2, 3],
[4, 5, 6]])
>>> y.flags['C_CONTIGUOUS']
True
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('cumprod',
"""
a.cumprod(axis=None, dtype=None, out=None)
Return the cumulative product of the elements along the given axis.
Refer to `numpy.cumprod` for full documentation.
See Also
--------
numpy.cumprod : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('cumsum',
"""
a.cumsum(axis=None, dtype=None, out=None)
Return the cumulative sum of the elements along the given axis.
Refer to `numpy.cumsum` for full documentation.
See Also
--------
numpy.cumsum : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('diagonal',
"""
a.diagonal(offset=0, axis1=0, axis2=1)
Return specified diagonals. In NumPy 1.9 the returned array is a
read-only view instead of a copy as in previous NumPy versions. In
a future version the read-only restriction will be removed.
Refer to :func:`numpy.diagonal` for full documentation.
See Also
--------
numpy.diagonal : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('dot',
"""
a.dot(b, out=None)
Dot product of two arrays.
Refer to `numpy.dot` for full documentation.
See Also
--------
numpy.dot : equivalent function
Examples
--------
>>> a = np.eye(2)
>>> b = np.ones((2, 2)) * 2
>>> a.dot(b)
array([[ 2., 2.],
[ 2., 2.]])
This array method can be conveniently chained:
>>> a.dot(b).dot(b)
array([[ 8., 8.],
[ 8., 8.]])
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('dump',
"""a.dump(file)
Dump a pickle of the array to the specified file.
The array can be read back with pickle.load or numpy.load.
Parameters
----------
file : str
A string naming the dump file.
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('dumps',
"""
a.dumps()
Returns the pickle of the array as a string.
pickle.loads or numpy.loads will convert the string back to an array.
Parameters
----------
None
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('fill',
"""
a.fill(value)
Fill the array with a scalar value.
Parameters
----------
value : scalar
All elements of `a` will be assigned this value.
Examples
--------
>>> a = np.array([1, 2])
>>> a.fill(0)
>>> a
array([0, 0])
>>> a = np.empty(2)
>>> a.fill(1)
>>> a
array([ 1., 1.])
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('flatten',
"""
a.flatten(order='C')
Return a copy of the array collapsed into one dimension.
Parameters
----------
order : {'C', 'F', 'A', 'K'}, optional
'C' means to flatten in row-major (C-style) order.
'F' means to flatten in column-major (Fortran-
style) order. 'A' means to flatten in column-major
order if `a` is Fortran *contiguous* in memory,
row-major order otherwise. 'K' means to flatten
`a` in the order the elements occur in memory.
The default is 'C'.
Returns
-------
y : ndarray
A copy of the input array, flattened to one dimension.
See Also
--------
ravel : Return a flattened array.
flat : A 1-D flat iterator over the array.
Examples
--------
>>> a = np.array([[1,2], [3,4]])
>>> a.flatten()
array([1, 2, 3, 4])
>>> a.flatten('F')
array([1, 3, 2, 4])
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('getfield',
"""
a.getfield(dtype, offset=0)
Returns a field of the given array as a certain type.
A field is a view of the array data with a given data-type. The values in
the view are determined by the given type and the offset into the current
array in bytes. The offset needs to be such that the view dtype fits in the
array dtype; for example an array of dtype complex128 has 16-byte elements.
If taking a view with a 32-bit integer (4 bytes), the offset needs to be
between 0 and 12 bytes.
Parameters
----------
dtype : str or dtype
The data type of the view. The dtype size of the view can not be larger
than that of the array itself.
offset : int
Number of bytes to skip before beginning the element view.
Examples
--------
>>> x = np.diag([1.+1.j]*2)
>>> x[1, 1] = 2 + 4.j
>>> x
array([[ 1.+1.j, 0.+0.j],
[ 0.+0.j, 2.+4.j]])
>>> x.getfield(np.float64)
array([[ 1., 0.],
[ 0., 2.]])
By choosing an offset of 8 bytes we can select the complex part of the
array for our view:
>>> x.getfield(np.float64, offset=8)
array([[ 1., 0.],
[ 0., 4.]])
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('item',
"""
a.item(*args)
Copy an element of an array to a standard Python scalar and return it.
Parameters
----------
\\*args : Arguments (variable number and type)
* none: in this case, the method only works for arrays
with one element (`a.size == 1`), which element is
copied into a standard Python scalar object and returned.
* int_type: this argument is interpreted as a flat index into
the array, specifying which element to copy and return.
* tuple of int_types: functions as does a single int_type argument,
except that the argument is interpreted as an nd-index into the
array.
Returns
-------
z : Standard Python scalar object
A copy of the specified element of the array as a suitable
Python scalar
Notes
-----
When the data type of `a` is longdouble or clongdouble, item() returns
a scalar array object because there is no available Python scalar that
would not lose information. Void arrays return a buffer object for item(),
unless fields are defined, in which case a tuple is returned.
`item` is very similar to a[args], except, instead of an array scalar,
a standard Python scalar is returned. This can be useful for speeding up
access to elements of the array and doing arithmetic on elements of the
array using Python's optimized math.
Examples
--------
>>> x = np.random.randint(9, size=(3, 3))
>>> x
array([[3, 1, 7],
[2, 8, 3],
[8, 5, 3]])
>>> x.item(3)
2
>>> x.item(7)
5
>>> x.item((0, 1))
1
>>> x.item((2, 2))
3
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('itemset',
"""
a.itemset(*args)
Insert scalar into an array (scalar is cast to array's dtype, if possible)
There must be at least 1 argument, and define the last argument
as *item*. Then, ``a.itemset(*args)`` is equivalent to but faster
than ``a[args] = item``. The item should be a scalar value and `args`
must select a single item in the array `a`.
Parameters
----------
\\*args : Arguments
If one argument: a scalar, only used in case `a` is of size 1.
If two arguments: the last argument is the value to be set
and must be a scalar, the first argument specifies a single array
element location. It is either an int or a tuple.
Notes
-----
Compared to indexing syntax, `itemset` provides some speed increase
for placing a scalar into a particular location in an `ndarray`,
if you must do this. However, generally this is discouraged:
among other problems, it complicates the appearance of the code.
Also, when using `itemset` (and `item`) inside a loop, be sure
to assign the methods to a local variable to avoid the attribute
look-up at each loop iteration.
Examples
--------
>>> x = np.random.randint(9, size=(3, 3))
>>> x
array([[3, 1, 7],
[2, 8, 3],
[8, 5, 3]])
>>> x.itemset(4, 0)
>>> x.itemset((2, 2), 9)
>>> x
array([[3, 1, 7],
[2, 0, 3],
[8, 5, 9]])
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('max',
"""
a.max(axis=None, out=None, keepdims=False)
Return the maximum along a given axis.
Refer to `numpy.amax` for full documentation.
See Also
--------
numpy.amax : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('mean',
"""
a.mean(axis=None, dtype=None, out=None, keepdims=False)
Returns the average of the array elements along given axis.
Refer to `numpy.mean` for full documentation.
See Also
--------
numpy.mean : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('min',
"""
a.min(axis=None, out=None, keepdims=False)
Return the minimum along a given axis.
Refer to `numpy.amin` for full documentation.
See Also
--------
numpy.amin : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'shares_memory',
"""
shares_memory(a, b, max_work=None)
Determine if two arrays share memory
Parameters
----------
a, b : ndarray
Input arrays
max_work : int, optional
Effort to spend on solving the overlap problem (maximum number
of candidate solutions to consider). The following special
values are recognized:
max_work=MAY_SHARE_EXACT (default)
The problem is solved exactly. In this case, the function returns
True only if there is an element shared between the arrays.
max_work=MAY_SHARE_BOUNDS
Only the memory bounds of a and b are checked.
Raises
------
numpy.TooHardError
Exceeded max_work.
Returns
-------
out : bool
See Also
--------
may_share_memory
Examples
--------
>>> np.may_share_memory(np.array([1,2]), np.array([5,8,9]))
False
""")
add_newdoc('numpy.core.multiarray', 'may_share_memory',
"""
may_share_memory(a, b, max_work=None)
Determine if two arrays might share memory
A return of True does not necessarily mean that the two arrays
share any element. It just means that they *might*.
Only the memory bounds of a and b are checked by default.
Parameters
----------
a, b : ndarray
Input arrays
max_work : int, optional
Effort to spend on solving the overlap problem. See
`shares_memory` for details. Default for ``may_share_memory``
is to do a bounds check.
Returns
-------
out : bool
See Also
--------
shares_memory
Examples
--------
>>> np.may_share_memory(np.array([1,2]), np.array([5,8,9]))
False
>>> x = np.zeros([3, 4])
>>> np.may_share_memory(x[:,0], x[:,1])
True
""")
add_newdoc('numpy.core.multiarray', 'ndarray', ('newbyteorder',
"""
arr.newbyteorder(new_order='S')
Return the array with the same data viewed with a different byte order.
Equivalent to::
arr.view(arr.dtype.newbytorder(new_order))
Changes are also made in all fields and sub-arrays of the array data
type.
Parameters
----------
new_order : string, optional
Byte order to force; a value from the byte order specifications
below. `new_order` codes can be any of:
* 'S' - swap dtype from current to opposite endian
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* {'|', 'I'} - ignore (no change to byte order)
The default value ('S') results in swapping the current
byte order. The code does a case-insensitive check on the first
letter of `new_order` for the alternatives above. For example,
any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
Returns
-------
new_arr : array
New array object with the dtype reflecting given change to the
byte order.
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('nonzero',
"""
a.nonzero()
Return the indices of the elements that are non-zero.
Refer to `numpy.nonzero` for full documentation.
See Also
--------
numpy.nonzero : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('prod',
"""
a.prod(axis=None, dtype=None, out=None, keepdims=False)
Return the product of the array elements over the given axis
Refer to `numpy.prod` for full documentation.
See Also
--------
numpy.prod : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('ptp',
"""
a.ptp(axis=None, out=None)
Peak to peak (maximum - minimum) value along a given axis.
Refer to `numpy.ptp` for full documentation.
See Also
--------
numpy.ptp : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('put',
"""
a.put(indices, values, mode='raise')
Set ``a.flat[n] = values[n]`` for all `n` in indices.
Refer to `numpy.put` for full documentation.
See Also
--------
numpy.put : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'copyto',
"""
copyto(dst, src, casting='same_kind', where=True)
Copies values from one array to another, broadcasting as necessary.
Raises a TypeError if the `casting` rule is violated, and if
`where` is provided, it selects which elements to copy.
.. versionadded:: 1.7.0
Parameters
----------
dst : ndarray
The array into which values are copied.
src : array_like
The array from which values are copied.
casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
Controls what kind of data casting may occur when copying.
* 'no' means the data types should not be cast at all.
* 'equiv' means only byte-order changes are allowed.
* 'safe' means only casts which can preserve values are allowed.
* 'same_kind' means only safe casts or casts within a kind,
like float64 to float32, are allowed.
* 'unsafe' means any data conversions may be done.
where : array_like of bool, optional
A boolean array which is broadcasted to match the dimensions
of `dst`, and selects elements to copy from `src` to `dst`
wherever it contains the value True.
""")
add_newdoc('numpy.core.multiarray', 'putmask',
"""
putmask(a, mask, values)
Changes elements of an array based on conditional and input values.
Sets ``a.flat[n] = values[n]`` for each n where ``mask.flat[n]==True``.
If `values` is not the same size as `a` and `mask` then it will repeat.
This gives behavior different from ``a[mask] = values``.
Parameters
----------
a : array_like
Target array.
mask : array_like
Boolean mask array. It has to be the same shape as `a`.
values : array_like
Values to put into `a` where `mask` is True. If `values` is smaller
than `a` it will be repeated.
See Also
--------
place, put, take, copyto
Examples
--------
>>> x = np.arange(6).reshape(2, 3)
>>> np.putmask(x, x>2, x**2)
>>> x
array([[ 0, 1, 2],
[ 9, 16, 25]])
If `values` is smaller than `a` it is repeated:
>>> x = np.arange(5)
>>> np.putmask(x, x>1, [-33, -44])
>>> x
array([ 0, 1, -33, -44, -33])
""")
add_newdoc('numpy.core.multiarray', 'ndarray', ('ravel',
"""
a.ravel([order])
Return a flattened array.
Refer to `numpy.ravel` for full documentation.
See Also
--------
numpy.ravel : equivalent function
ndarray.flat : a flat iterator on the array.
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('repeat',
"""
a.repeat(repeats, axis=None)
Repeat elements of an array.
Refer to `numpy.repeat` for full documentation.
See Also
--------
numpy.repeat : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('reshape',
"""
a.reshape(shape, order='C')
Returns an array containing the same data with a new shape.
Refer to `numpy.reshape` for full documentation.
See Also
--------
numpy.reshape : equivalent function
Notes
-----
Unlike the free function `numpy.reshape`, this method on `ndarray` allows
the elements of the shape parameter to be passed in as separate arguments.
For example, ``a.reshape(10, 11)`` is equivalent to
``a.reshape((10, 11))``.
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('resize',
"""
a.resize(new_shape, refcheck=True)
Change shape and size of array in-place.
Parameters
----------
new_shape : tuple of ints, or `n` ints
Shape of resized array.
refcheck : bool, optional
If False, reference count will not be checked. Default is True.
Returns
-------
None
Raises
------
ValueError
If `a` does not own its own data or references or views to it exist,
and the data memory must be changed.
PyPy only: will always raise if the data memory must be changed, since
there is no reliable way to determine if references or views to it
exist.
SystemError
If the `order` keyword argument is specified. This behaviour is a
bug in NumPy.
See Also
--------
resize : Return a new array with the specified shape.
Notes
-----
This reallocates space for the data area if necessary.
Only contiguous arrays (data elements consecutive in memory) can be
resized.
The purpose of the reference count check is to make sure you
do not use this array as a buffer for another Python object and then
reallocate the memory. However, reference counts can increase in
other ways so if you are sure that you have not shared the memory
for this array with another Python object, then you may safely set
`refcheck` to False.
Examples
--------
Shrinking an array: array is flattened (in the order that the data are
stored in memory), resized, and reshaped:
>>> a = np.array([[0, 1], [2, 3]], order='C')
>>> a.resize((2, 1))
>>> a
array([[0],
[1]])
>>> a = np.array([[0, 1], [2, 3]], order='F')
>>> a.resize((2, 1))
>>> a
array([[0],
[2]])
Enlarging an array: as above, but missing entries are filled with zeros:
>>> b = np.array([[0, 1], [2, 3]])
>>> b.resize(2, 3) # new_shape parameter doesn't have to be a tuple
>>> b
array([[0, 1, 2],
[3, 0, 0]])
Referencing an array prevents resizing...
>>> c = a
>>> a.resize((1, 1))
Traceback (most recent call last):
...
ValueError: cannot resize an array that has been referenced ...
Unless `refcheck` is False:
>>> a.resize((1, 1), refcheck=False)
>>> a
array([[0]])
>>> c
array([[0]])
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('round',
"""
a.round(decimals=0, out=None)
Return `a` with each element rounded to the given number of decimals.
Refer to `numpy.around` for full documentation.
See Also
--------
numpy.around : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('searchsorted',
"""
a.searchsorted(v, side='left', sorter=None)
Find indices where elements of v should be inserted in a to maintain order.
For full documentation, see `numpy.searchsorted`
See Also
--------
numpy.searchsorted : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('setfield',
"""
a.setfield(val, dtype, offset=0)
Put a value into a specified place in a field defined by a data-type.
Place `val` into `a`'s field defined by `dtype` and beginning `offset`
bytes into the field.
Parameters
----------
val : object
Value to be placed in field.
dtype : dtype object
Data-type of the field in which to place `val`.
offset : int, optional
The number of bytes into the field at which to place `val`.
Returns
-------
None
See Also
--------
getfield
Examples
--------
>>> x = np.eye(3)
>>> x.getfield(np.float64)
array([[ 1., 0., 0.],
[ 0., 1., 0.],
[ 0., 0., 1.]])
>>> x.setfield(3, np.int32)
>>> x.getfield(np.int32)
array([[3, 3, 3],
[3, 3, 3],
[3, 3, 3]])
>>> x
array([[ 1.00000000e+000, 1.48219694e-323, 1.48219694e-323],
[ 1.48219694e-323, 1.00000000e+000, 1.48219694e-323],
[ 1.48219694e-323, 1.48219694e-323, 1.00000000e+000]])
>>> x.setfield(np.eye(3), np.int32)
>>> x
array([[ 1., 0., 0.],
[ 0., 1., 0.],
[ 0., 0., 1.]])
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('setflags',
"""
a.setflags(write=None, align=None, uic=None)
Set array flags WRITEABLE, ALIGNED, (WRITEBACKIFCOPY and UPDATEIFCOPY),
respectively.
These Boolean-valued flags affect how numpy interprets the memory
area used by `a` (see Notes below). The ALIGNED flag can only
be set to True if the data is actually aligned according to the type.
The WRITEBACKIFCOPY and (deprecated) UPDATEIFCOPY flags can never be set
to True. The flag WRITEABLE can only be set to True if the array owns its
own memory, or the ultimate owner of the memory exposes a writeable buffer
interface, or is a string. (The exception for string is made so that
unpickling can be done without copying memory.)
Parameters
----------
write : bool, optional
Describes whether or not `a` can be written to.
align : bool, optional
Describes whether or not `a` is aligned properly for its type.
uic : bool, optional
Describes whether or not `a` is a copy of another "base" array.
Notes
-----
Array flags provide information about how the memory area used
for the array is to be interpreted. There are 7 Boolean flags
in use, only four of which can be changed by the user:
WRITEBACKIFCOPY, UPDATEIFCOPY, WRITEABLE, and ALIGNED.
WRITEABLE (W) the data area can be written to;
ALIGNED (A) the data and strides are aligned appropriately for the hardware
(as determined by the compiler);
UPDATEIFCOPY (U) (deprecated), replaced by WRITEBACKIFCOPY;
WRITEBACKIFCOPY (X) this array is a copy of some other array (referenced
by .base). When the C-API function PyArray_ResolveWritebackIfCopy is
called, the base array will be updated with the contents of this array.
All flags can be accessed using the single (upper case) letter as well
as the full name.
Examples
--------
>>> y
array([[3, 1, 7],
[2, 0, 0],
[8, 5, 9]])
>>> y.flags
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : True
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
>>> y.setflags(write=0, align=0)
>>> y.flags
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : True
WRITEABLE : False
ALIGNED : False
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
>>> y.setflags(uic=1)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: cannot set WRITEBACKIFCOPY flag to True
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('sort',
"""
a.sort(axis=-1, kind='quicksort', order=None)
Sort an array, in-place.
Parameters
----------
axis : int, optional
Axis along which to sort. Default is -1, which means sort along the
last axis.
kind : {'quicksort', 'mergesort', 'heapsort'}, optional
Sorting algorithm. Default is 'quicksort'.
order : str or list of str, optional
When `a` is an array with fields defined, this argument specifies
which fields to compare first, second, etc. A single field can
be specified as a string, and not all fields need be specified,
but unspecified fields will still be used, in the order in which
they come up in the dtype, to break ties.
See Also
--------
numpy.sort : Return a sorted copy of an array.
argsort : Indirect sort.
lexsort : Indirect stable sort on multiple keys.
searchsorted : Find elements in sorted array.
partition: Partial sort.
Notes
-----
See ``sort`` for notes on the different sorting algorithms.
Examples
--------
>>> a = np.array([[1,4], [3,1]])
>>> a.sort(axis=1)
>>> a
array([[1, 4],
[1, 3]])
>>> a.sort(axis=0)
>>> a
array([[1, 3],
[1, 4]])
Use the `order` keyword to specify a field to use when sorting a
structured array:
>>> a = np.array([('a', 2), ('c', 1)], dtype=[('x', 'S1'), ('y', int)])
>>> a.sort(order='y')
>>> a
array([('c', 1), ('a', 2)],
dtype=[('x', '|S1'), ('y', '<i4')])
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('partition',
"""
a.partition(kth, axis=-1, kind='introselect', order=None)
Rearranges the elements in the array in such a way that value of the
element in kth position is in the position it would be in a sorted array.
All elements smaller than the kth element are moved before this element and
all equal or greater are moved behind it. The ordering of the elements in
the two partitions is undefined.
.. versionadded:: 1.8.0
Parameters
----------
kth : int or sequence of ints
Element index to partition by. The kth element value will be in its
final sorted position and all smaller elements will be moved before it
and all equal or greater elements behind it.
The order all elements in the partitions is undefined.
If provided with a sequence of kth it will partition all elements
indexed by kth of them into their sorted position at once.
axis : int, optional
Axis along which to sort. Default is -1, which means sort along the
last axis.
kind : {'introselect'}, optional
Selection algorithm. Default is 'introselect'.
order : str or list of str, optional
When `a` is an array with fields defined, this argument specifies
which fields to compare first, second, etc. A single field can
be specified as a string, and not all fields need be specified,
but unspecified fields will still be used, in the order in which
they come up in the dtype, to break ties.
See Also
--------
numpy.partition : Return a parititioned copy of an array.
argpartition : Indirect partition.
sort : Full sort.
Notes
-----
See ``np.partition`` for notes on the different algorithms.
Examples
--------
>>> a = np.array([3, 4, 2, 1])
>>> a.partition(3)
>>> a
array([2, 1, 3, 4])
>>> a.partition((1, 3))
array([1, 2, 3, 4])
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('squeeze',
"""
a.squeeze(axis=None)
Remove single-dimensional entries from the shape of `a`.
Refer to `numpy.squeeze` for full documentation.
See Also
--------
numpy.squeeze : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('std',
"""
a.std(axis=None, dtype=None, out=None, ddof=0, keepdims=False)
Returns the standard deviation of the array elements along given axis.
Refer to `numpy.std` for full documentation.
See Also
--------
numpy.std : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('sum',
"""
a.sum(axis=None, dtype=None, out=None, keepdims=False)
Return the sum of the array elements over the given axis.
Refer to `numpy.sum` for full documentation.
See Also
--------
numpy.sum : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('swapaxes',
"""
a.swapaxes(axis1, axis2)
Return a view of the array with `axis1` and `axis2` interchanged.
Refer to `numpy.swapaxes` for full documentation.
See Also
--------
numpy.swapaxes : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('take',
"""
a.take(indices, axis=None, out=None, mode='raise')
Return an array formed from the elements of `a` at the given indices.
Refer to `numpy.take` for full documentation.
See Also
--------
numpy.take : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('tofile',
"""
a.tofile(fid, sep="", format="%s")
Write array to a file as text or binary (default).
Data is always written in 'C' order, independent of the order of `a`.
The data produced by this method can be recovered using the function
fromfile().
Parameters
----------
fid : file or str
An open file object, or a string containing a filename.
sep : str
Separator between array items for text output.
If "" (empty), a binary file is written, equivalent to
``file.write(a.tobytes())``.
format : str
Format string for text file output.
Each entry in the array is formatted to text by first converting
it to the closest Python type, and then using "format" % item.
Notes
-----
This is a convenience function for quick storage of array data.
Information on endianness and precision is lost, so this method is not a
good choice for files intended to archive data or transport data between
machines with different endianness. Some of these problems can be overcome
by outputting the data as text files, at the expense of speed and file
size.
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('tolist',
"""
a.tolist()
Return the array as a (possibly nested) list.
Return a copy of the array data as a (nested) Python list.
Data items are converted to the nearest compatible Python type.
Parameters
----------
none
Returns
-------
y : list
The possibly nested list of array elements.
Notes
-----
The array may be recreated, ``a = np.array(a.tolist())``.
Examples
--------
>>> a = np.array([1, 2])
>>> a.tolist()
[1, 2]
>>> a = np.array([[1, 2], [3, 4]])
>>> list(a)
[array([1, 2]), array([3, 4])]
>>> a.tolist()
[[1, 2], [3, 4]]
"""))
tobytesdoc = """
a.{name}(order='C')
Construct Python bytes containing the raw data bytes in the array.
Constructs Python bytes showing a copy of the raw contents of
data memory. The bytes object can be produced in either 'C' or 'Fortran',
or 'Any' order (the default is 'C'-order). 'Any' order means C-order
unless the F_CONTIGUOUS flag in the array is set, in which case it
means 'Fortran' order.
{deprecated}
Parameters
----------
order : {{'C', 'F', None}}, optional
Order of the data for multidimensional arrays:
C, Fortran, or the same as for the original array.
Returns
-------
s : bytes
Python bytes exhibiting a copy of `a`'s raw data.
Examples
--------
>>> x = np.array([[0, 1], [2, 3]])
>>> x.tobytes()
b'\\x00\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x03\\x00\\x00\\x00'
>>> x.tobytes('C') == x.tobytes()
True
>>> x.tobytes('F')
b'\\x00\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\x01\\x00\\x00\\x00\\x03\\x00\\x00\\x00'
"""
add_newdoc('numpy.core.multiarray', 'ndarray',
('tostring', tobytesdoc.format(name='tostring',
deprecated=
'This function is a compatibility '
'alias for tobytes. Despite its '
'name it returns bytes not '
'strings.')))
add_newdoc('numpy.core.multiarray', 'ndarray',
('tobytes', tobytesdoc.format(name='tobytes',
deprecated='.. versionadded:: 1.9.0')))
add_newdoc('numpy.core.multiarray', 'ndarray', ('trace',
"""
a.trace(offset=0, axis1=0, axis2=1, dtype=None, out=None)
Return the sum along diagonals of the array.
Refer to `numpy.trace` for full documentation.
See Also
--------
numpy.trace : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('transpose',
"""
a.transpose(*axes)
Returns a view of the array with axes transposed.
For a 1-D array, this has no effect. (To change between column and
row vectors, first cast the 1-D array into a matrix object.)
For a 2-D array, this is the usual matrix transpose.
For an n-D array, if axes are given, their order indicates how the
axes are permuted (see Examples). If axes are not provided and
``a.shape = (i[0], i[1], ... i[n-2], i[n-1])``, then
``a.transpose().shape = (i[n-1], i[n-2], ... i[1], i[0])``.
Parameters
----------
axes : None, tuple of ints, or `n` ints
* None or no argument: reverses the order of the axes.
* tuple of ints: `i` in the `j`-th place in the tuple means `a`'s
`i`-th axis becomes `a.transpose()`'s `j`-th axis.
* `n` ints: same as an n-tuple of the same ints (this form is
intended simply as a "convenience" alternative to the tuple form)
Returns
-------
out : ndarray
View of `a`, with axes suitably permuted.
See Also
--------
ndarray.T : Array property returning the array transposed.
Examples
--------
>>> a = np.array([[1, 2], [3, 4]])
>>> a
array([[1, 2],
[3, 4]])
>>> a.transpose()
array([[1, 3],
[2, 4]])
>>> a.transpose((1, 0))
array([[1, 3],
[2, 4]])
>>> a.transpose(1, 0)
array([[1, 3],
[2, 4]])
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('var',
"""
a.var(axis=None, dtype=None, out=None, ddof=0, keepdims=False)
Returns the variance of the array elements, along given axis.
Refer to `numpy.var` for full documentation.
See Also
--------
numpy.var : equivalent function
"""))
add_newdoc('numpy.core.multiarray', 'ndarray', ('view',
"""
a.view(dtype=None, type=None)
New view of array with the same data.
Parameters
----------
dtype : data-type or ndarray sub-class, optional
Data-type descriptor of the returned view, e.g., float32 or int16. The
default, None, results in the view having the same data-type as `a`.
This argument can also be specified as an ndarray sub-class, which
then specifies the type of the returned object (this is equivalent to
setting the ``type`` parameter).
type : Python type, optional
Type of the returned view, e.g., ndarray or matrix. Again, the
default None results in type preservation.
Notes
-----
``a.view()`` is used two different ways:
``a.view(some_dtype)`` or ``a.view(dtype=some_dtype)`` constructs a view
of the array's memory with a different data-type. This can cause a
reinterpretation of the bytes of memory.
``a.view(ndarray_subclass)`` or ``a.view(type=ndarray_subclass)`` just
returns an instance of `ndarray_subclass` that looks at the same array
(same shape, dtype, etc.) This does not cause a reinterpretation of the
memory.
For ``a.view(some_dtype)``, if ``some_dtype`` has a different number of
bytes per entry than the previous dtype (for example, converting a
regular array to a structured array), then the behavior of the view
cannot be predicted just from the superficial appearance of ``a`` (shown
by ``print(a)``). It also depends on exactly how ``a`` is stored in
memory. Therefore if ``a`` is C-ordered versus fortran-ordered, versus
defined as a slice or transpose, etc., the view may give different
results.
Examples
--------
>>> x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])
Viewing array data using a different type and dtype:
>>> y = x.view(dtype=np.int16, type=np.matrix)
>>> y
matrix([[513]], dtype=int16)
>>> print(type(y))
<class 'numpy.matrixlib.defmatrix.matrix'>
Creating a view on a structured array so it can be used in calculations
>>> x = np.array([(1, 2),(3,4)], dtype=[('a', np.int8), ('b', np.int8)])
>>> xv = x.view(dtype=np.int8).reshape(-1,2)
>>> xv
array([[1, 2],
[3, 4]], dtype=int8)
>>> xv.mean(0)
array([ 2., 3.])
Making changes to the view changes the underlying array
>>> xv[0,1] = 20
>>> print(x)
[(1, 20) (3, 4)]
Using a view to convert an array to a recarray:
>>> z = x.view(np.recarray)
>>> z.a
array([1], dtype=int8)
Views share data:
>>> x[0] = (9, 10)
>>> z[0]
(9, 10)
Views that change the dtype size (bytes per entry) should normally be
avoided on arrays defined by slices, transposes, fortran-ordering, etc.:
>>> x = np.array([[1,2,3],[4,5,6]], dtype=np.int16)
>>> y = x[:, 0:2]
>>> y
array([[1, 2],
[4, 5]], dtype=int16)
>>> y.view(dtype=[('width', np.int16), ('length', np.int16)])
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
ValueError: new type not compatible with array.
>>> z = y.copy()
>>> z.view(dtype=[('width', np.int16), ('length', np.int16)])
array([[(1, 2)],
[(4, 5)]], dtype=[('width', '<i2'), ('length', '<i2')])
"""))
##############################################################################
#
# umath functions
#
##############################################################################
add_newdoc('numpy.core.umath', 'frompyfunc',
"""
frompyfunc(func, nin, nout)
Takes an arbitrary Python function and returns a NumPy ufunc.
Can be used, for example, to add broadcasting to a built-in Python
function (see Examples section).
Parameters
----------
func : Python function object
An arbitrary Python function.
nin : int
The number of input arguments.
nout : int
The number of objects returned by `func`.
Returns
-------
out : ufunc
Returns a NumPy universal function (``ufunc``) object.
See Also
--------
vectorize : evaluates pyfunc over input arrays using broadcasting rules of numpy
Notes
-----
The returned ufunc always returns PyObject arrays.
Examples
--------
Use frompyfunc to add broadcasting to the Python function ``oct``:
>>> oct_array = np.frompyfunc(oct, 1, 1)
>>> oct_array(np.array((10, 30, 100)))
array([012, 036, 0144], dtype=object)
>>> np.array((oct(10), oct(30), oct(100))) # for comparison
array(['012', '036', '0144'],
dtype='|S4')
""")
add_newdoc('numpy.core.umath', 'geterrobj',
"""
geterrobj()
Return the current object that defines floating-point error handling.
The error object contains all information that defines the error handling
behavior in NumPy. `geterrobj` is used internally by the other
functions that get and set error handling behavior (`geterr`, `seterr`,
`geterrcall`, `seterrcall`).
Returns
-------
errobj : list
The error object, a list containing three elements:
[internal numpy buffer size, error mask, error callback function].
The error mask is a single integer that holds the treatment information
on all four floating point errors. The information for each error type
is contained in three bits of the integer. If we print it in base 8, we
can see what treatment is set for "invalid", "under", "over", and
"divide" (in that order). The printed string can be interpreted with
* 0 : 'ignore'
* 1 : 'warn'
* 2 : 'raise'
* 3 : 'call'
* 4 : 'print'
* 5 : 'log'
See Also
--------
seterrobj, seterr, geterr, seterrcall, geterrcall
getbufsize, setbufsize
Notes
-----
For complete documentation of the types of floating-point exceptions and
treatment options, see `seterr`.
Examples
--------
>>> np.geterrobj() # first get the defaults
[10000, 0, None]
>>> def err_handler(type, flag):
... print("Floating point error (%s), with flag %s" % (type, flag))
...
>>> old_bufsize = np.setbufsize(20000)
>>> old_err = np.seterr(divide='raise')
>>> old_handler = np.seterrcall(err_handler)
>>> np.geterrobj()
[20000, 2, <function err_handler at 0x91dcaac>]
>>> old_err = np.seterr(all='ignore')
>>> np.base_repr(np.geterrobj()[1], 8)
'0'
>>> old_err = np.seterr(divide='warn', over='log', under='call',
invalid='print')
>>> np.base_repr(np.geterrobj()[1], 8)
'4351'
""")
add_newdoc('numpy.core.umath', 'seterrobj',
"""
seterrobj(errobj)
Set the object that defines floating-point error handling.
The error object contains all information that defines the error handling
behavior in NumPy. `seterrobj` is used internally by the other
functions that set error handling behavior (`seterr`, `seterrcall`).
Parameters
----------
errobj : list
The error object, a list containing three elements:
[internal numpy buffer size, error mask, error callback function].
The error mask is a single integer that holds the treatment information
on all four floating point errors. The information for each error type
is contained in three bits of the integer. If we print it in base 8, we
can see what treatment is set for "invalid", "under", "over", and
"divide" (in that order). The printed string can be interpreted with
* 0 : 'ignore'
* 1 : 'warn'
* 2 : 'raise'
* 3 : 'call'
* 4 : 'print'
* 5 : 'log'
See Also
--------
geterrobj, seterr, geterr, seterrcall, geterrcall
getbufsize, setbufsize
Notes
-----
For complete documentation of the types of floating-point exceptions and
treatment options, see `seterr`.
Examples
--------
>>> old_errobj = np.geterrobj() # first get the defaults
>>> old_errobj
[10000, 0, None]
>>> def err_handler(type, flag):
... print("Floating point error (%s), with flag %s" % (type, flag))
...
>>> new_errobj = [20000, 12, err_handler]
>>> np.seterrobj(new_errobj)
>>> np.base_repr(12, 8) # int for divide=4 ('print') and over=1 ('warn')
'14'
>>> np.geterr()
{'over': 'warn', 'divide': 'print', 'invalid': 'ignore', 'under': 'ignore'}
>>> np.geterrcall() is err_handler
True
""")
##############################################################################
#
# compiled_base functions
#
##############################################################################
add_newdoc('numpy.core.multiarray', 'digitize',
"""
digitize(x, bins, right=False)
Return the indices of the bins to which each value in input array belongs.
Each index ``i`` returned is such that ``bins[i-1] <= x < bins[i]`` if
`bins` is monotonically increasing, or ``bins[i-1] > x >= bins[i]`` if
`bins` is monotonically decreasing. If values in `x` are beyond the
bounds of `bins`, 0 or ``len(bins)`` is returned as appropriate. If right
is True, then the right bin is closed so that the index ``i`` is such
that ``bins[i-1] < x <= bins[i]`` or ``bins[i-1] >= x > bins[i]`` if `bins`
is monotonically increasing or decreasing, respectively.
Parameters
----------
x : array_like
Input array to be binned. Prior to NumPy 1.10.0, this array had to
be 1-dimensional, but can now have any shape.
bins : array_like
Array of bins. It has to be 1-dimensional and monotonic.
right : bool, optional
Indicating whether the intervals include the right or the left bin
edge. Default behavior is (right==False) indicating that the interval
does not include the right edge. The left bin end is open in this
case, i.e., bins[i-1] <= x < bins[i] is the default behavior for
monotonically increasing bins.
Returns
-------
out : ndarray of ints
Output array of indices, of same shape as `x`.
Raises
------
ValueError
If `bins` is not monotonic.
TypeError
If the type of the input is complex.
See Also
--------
bincount, histogram, unique, searchsorted
Notes
-----
If values in `x` are such that they fall outside the bin range,
attempting to index `bins` with the indices that `digitize` returns
will result in an IndexError.
.. versionadded:: 1.10.0
`np.digitize` is implemented in terms of `np.searchsorted`. This means
that a binary search is used to bin the values, which scales much better
for larger number of bins than the previous linear search. It also removes
the requirement for the input array to be 1-dimensional.
Examples
--------
>>> x = np.array([0.2, 6.4, 3.0, 1.6])
>>> bins = np.array([0.0, 1.0, 2.5, 4.0, 10.0])
>>> inds = np.digitize(x, bins)
>>> inds
array([1, 4, 3, 2])
>>> for n in range(x.size):
... print(bins[inds[n]-1], "<=", x[n], "<", bins[inds[n]])
...
0.0 <= 0.2 < 1.0
4.0 <= 6.4 < 10.0
2.5 <= 3.0 < 4.0
1.0 <= 1.6 < 2.5
>>> x = np.array([1.2, 10.0, 12.4, 15.5, 20.])
>>> bins = np.array([0, 5, 10, 15, 20])
>>> np.digitize(x,bins,right=True)
array([1, 2, 3, 4, 4])
>>> np.digitize(x,bins,right=False)
array([1, 3, 3, 4, 5])
""")
add_newdoc('numpy.core.multiarray', 'bincount',
"""
bincount(x, weights=None, minlength=0)
Count number of occurrences of each value in array of non-negative ints.
The number of bins (of size 1) is one larger than the largest value in
`x`. If `minlength` is specified, there will be at least this number
of bins in the output array (though it will be longer if necessary,
depending on the contents of `x`).
Each bin gives the number of occurrences of its index value in `x`.
If `weights` is specified the input array is weighted by it, i.e. if a
value ``n`` is found at position ``i``, ``out[n] += weight[i]`` instead
of ``out[n] += 1``.
Parameters
----------
x : array_like, 1 dimension, nonnegative ints
Input array.
weights : array_like, optional
Weights, array of the same shape as `x`.
minlength : int, optional
A minimum number of bins for the output array.
.. versionadded:: 1.6.0
Returns
-------
out : ndarray of ints
The result of binning the input array.
The length of `out` is equal to ``np.amax(x)+1``.
Raises
------
ValueError
If the input is not 1-dimensional, or contains elements with negative
values, or if `minlength` is negative.
TypeError
If the type of the input is float or complex.
See Also
--------
histogram, digitize, unique
Examples
--------
>>> np.bincount(np.arange(5))
array([1, 1, 1, 1, 1])
>>> np.bincount(np.array([0, 1, 1, 3, 2, 1, 7]))
array([1, 3, 1, 1, 0, 0, 0, 1])
>>> x = np.array([0, 1, 1, 3, 2, 1, 7, 23])
>>> np.bincount(x).size == np.amax(x)+1
True
The input array needs to be of integer dtype, otherwise a
TypeError is raised:
>>> np.bincount(np.arange(5, dtype=float))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: array cannot be safely cast to required type
A possible use of ``bincount`` is to perform sums over
variable-size chunks of an array, using the ``weights`` keyword.
>>> w = np.array([0.3, 0.5, 0.2, 0.7, 1., -0.6]) # weights
>>> x = np.array([0, 1, 1, 2, 2, 2])
>>> np.bincount(x, weights=w)
array([ 0.3, 0.7, 1.1])
""")
add_newdoc('numpy.core.multiarray', 'ravel_multi_index',
"""
ravel_multi_index(multi_index, dims, mode='raise', order='C')
Converts a tuple of index arrays into an array of flat
indices, applying boundary modes to the multi-index.
Parameters
----------
multi_index : tuple of array_like
A tuple of integer arrays, one array for each dimension.
dims : tuple of ints
The shape of array into which the indices from ``multi_index`` apply.
mode : {'raise', 'wrap', 'clip'}, optional
Specifies how out-of-bounds indices are handled. Can specify
either one mode or a tuple of modes, one mode per index.
* 'raise' -- raise an error (default)
* 'wrap' -- wrap around
* 'clip' -- clip to the range
In 'clip' mode, a negative index which would normally
wrap will clip to 0 instead.
order : {'C', 'F'}, optional
Determines whether the multi-index should be viewed as
indexing in row-major (C-style) or column-major
(Fortran-style) order.
Returns
-------
raveled_indices : ndarray
An array of indices into the flattened version of an array
of dimensions ``dims``.
See Also
--------
unravel_index
Notes
-----
.. versionadded:: 1.6.0
Examples
--------
>>> arr = np.array([[3,6,6],[4,5,1]])
>>> np.ravel_multi_index(arr, (7,6))
array([22, 41, 37])
>>> np.ravel_multi_index(arr, (7,6), order='F')
array([31, 41, 13])
>>> np.ravel_multi_index(arr, (4,6), mode='clip')
array([22, 23, 19])
>>> np.ravel_multi_index(arr, (4,4), mode=('clip','wrap'))
array([12, 13, 13])
>>> np.ravel_multi_index((3,1,4,1), (6,7,8,9))
1621
""")
add_newdoc('numpy.core.multiarray', 'unravel_index',
"""
unravel_index(indices, dims, order='C')
Converts a flat index or array of flat indices into a tuple
of coordinate arrays.
Parameters
----------
indices : array_like
An integer array whose elements are indices into the flattened
version of an array of dimensions ``dims``. Before version 1.6.0,
this function accepted just one index value.
dims : tuple of ints
The shape of the array to use for unraveling ``indices``.
order : {'C', 'F'}, optional
Determines whether the indices should be viewed as indexing in
row-major (C-style) or column-major (Fortran-style) order.
.. versionadded:: 1.6.0
Returns
-------
unraveled_coords : tuple of ndarray
Each array in the tuple has the same shape as the ``indices``
array.
See Also
--------
ravel_multi_index
Examples
--------
>>> np.unravel_index([22, 41, 37], (7,6))
(array([3, 6, 6]), array([4, 5, 1]))
>>> np.unravel_index([31, 41, 13], (7,6), order='F')
(array([3, 6, 6]), array([4, 5, 1]))
>>> np.unravel_index(1621, (6,7,8,9))
(3, 1, 4, 1)
""")
add_newdoc('numpy.core.multiarray', 'add_docstring',
"""
add_docstring(obj, docstring)
Add a docstring to a built-in obj if possible.
If the obj already has a docstring raise a RuntimeError
If this routine does not know how to add a docstring to the object
raise a TypeError
""")
add_newdoc('numpy.core.umath', '_add_newdoc_ufunc',
"""
add_ufunc_docstring(ufunc, new_docstring)
Replace the docstring for a ufunc with new_docstring.
This method will only work if the current docstring for
the ufunc is NULL. (At the C level, i.e. when ufunc->doc is NULL.)
Parameters
----------
ufunc : numpy.ufunc
A ufunc whose current doc is NULL.
new_docstring : string
The new docstring for the ufunc.
Notes
-----
This method allocates memory for new_docstring on
the heap. Technically this creates a mempory leak, since this
memory will not be reclaimed until the end of the program
even if the ufunc itself is removed. However this will only
be a problem if the user is repeatedly creating ufuncs with
no documentation, adding documentation via add_newdoc_ufunc,
and then throwing away the ufunc.
""")
add_newdoc('numpy.core.multiarray', 'packbits',
"""
packbits(myarray, axis=None)
Packs the elements of a binary-valued array into bits in a uint8 array.
The result is padded to full bytes by inserting zero bits at the end.
Parameters
----------
myarray : array_like
An array of integers or booleans whose elements should be packed to
bits.
axis : int, optional
The dimension over which bit-packing is done.
``None`` implies packing the flattened array.
Returns
-------
packed : ndarray
Array of type uint8 whose elements represent bits corresponding to the
logical (0 or nonzero) value of the input elements. The shape of
`packed` has the same number of dimensions as the input (unless `axis`
is None, in which case the output is 1-D).
See Also
--------
unpackbits: Unpacks elements of a uint8 array into a binary-valued output
array.
Examples
--------
>>> a = np.array([[[1,0,1],
... [0,1,0]],
... [[1,1,0],
... [0,0,1]]])
>>> b = np.packbits(a, axis=-1)
>>> b
array([[[160],[64]],[[192],[32]]], dtype=uint8)
Note that in binary 160 = 1010 0000, 64 = 0100 0000, 192 = 1100 0000,
and 32 = 0010 0000.
""")
add_newdoc('numpy.core.multiarray', 'unpackbits',
"""
unpackbits(myarray, axis=None)
Unpacks elements of a uint8 array into a binary-valued output array.
Each element of `myarray` represents a bit-field that should be unpacked
into a binary-valued output array. The shape of the output array is either
1-D (if `axis` is None) or the same shape as the input array with unpacking
done along the axis specified.
Parameters
----------
myarray : ndarray, uint8 type
Input array.
axis : int, optional
The dimension over which bit-unpacking is done.
``None`` implies unpacking the flattened array.
Returns
-------
unpacked : ndarray, uint8 type
The elements are binary-valued (0 or 1).
See Also
--------
packbits : Packs the elements of a binary-valued array into bits in a uint8
array.
Examples
--------
>>> a = np.array([[2], [7], [23]], dtype=np.uint8)
>>> a
array([[ 2],
[ 7],
[23]], dtype=uint8)
>>> b = np.unpackbits(a, axis=1)
>>> b
array([[0, 0, 0, 0, 0, 0, 1, 0],
[0, 0, 0, 0, 0, 1, 1, 1],
[0, 0, 0, 1, 0, 1, 1, 1]], dtype=uint8)
""")
##############################################################################
#
# Documentation for ufunc attributes and methods
#
##############################################################################
##############################################################################
#
# ufunc object
#
##############################################################################
add_newdoc('numpy.core', 'ufunc',
"""
Functions that operate element by element on whole arrays.
To see the documentation for a specific ufunc, use `info`. For
example, ``np.info(np.sin)``. Because ufuncs are written in C
(for speed) and linked into Python with NumPy's ufunc facility,
Python's help() function finds this page whenever help() is called
on a ufunc.
A detailed explanation of ufuncs can be found in the docs for :ref:`ufuncs`.
Calling ufuncs:
===============
op(*x[, out], where=True, **kwargs)
Apply `op` to the arguments `*x` elementwise, broadcasting the arguments.
The broadcasting rules are:
* Dimensions of length 1 may be prepended to either array.
* Arrays may be repeated along dimensions of length 1.
Parameters
----------
*x : array_like
Input arrays.
out : ndarray, None, or tuple of ndarray and None, optional
Alternate array object(s) in which to put the result; if provided, it
must have a shape that the inputs broadcast to. A tuple of arrays
(possible only as a keyword argument) must have length equal to the
number of outputs; use `None` for outputs to be allocated by the ufunc.
where : array_like, optional
Values of True indicate to calculate the ufunc at that position, values
of False indicate to leave the value in the output alone.
**kwargs
For other keyword-only arguments, see the :ref:`ufunc docs <ufuncs.kwargs>`.
Returns
-------
r : ndarray or tuple of ndarray
`r` will have the shape that the arrays in `x` broadcast to; if `out` is
provided, `r` will be equal to `out`. If the function has more than one
output, then the result will be a tuple of arrays.
""")
##############################################################################
#
# ufunc attributes
#
##############################################################################
add_newdoc('numpy.core', 'ufunc', ('identity',
"""
The identity value.
Data attribute containing the identity element for the ufunc, if it has one.
If it does not, the attribute value is None.
Examples
--------
>>> np.add.identity
0
>>> np.multiply.identity
1
>>> np.power.identity
1
>>> print(np.exp.identity)
None
"""))
add_newdoc('numpy.core', 'ufunc', ('nargs',
"""
The number of arguments.
Data attribute containing the number of arguments the ufunc takes, including
optional ones.
Notes
-----
Typically this value will be one more than what you might expect because all
ufuncs take the optional "out" argument.
Examples
--------
>>> np.add.nargs
3
>>> np.multiply.nargs
3
>>> np.power.nargs
3
>>> np.exp.nargs
2
"""))
add_newdoc('numpy.core', 'ufunc', ('nin',
"""
The number of inputs.
Data attribute containing the number of arguments the ufunc treats as input.
Examples
--------
>>> np.add.nin
2
>>> np.multiply.nin
2
>>> np.power.nin
2
>>> np.exp.nin
1
"""))
add_newdoc('numpy.core', 'ufunc', ('nout',
"""
The number of outputs.
Data attribute containing the number of arguments the ufunc treats as output.
Notes
-----
Since all ufuncs can take output arguments, this will always be (at least) 1.
Examples
--------
>>> np.add.nout
1
>>> np.multiply.nout
1
>>> np.power.nout
1
>>> np.exp.nout
1
"""))
add_newdoc('numpy.core', 'ufunc', ('ntypes',
"""
The number of types.
The number of numerical NumPy types - of which there are 18 total - on which
the ufunc can operate.
See Also
--------
numpy.ufunc.types
Examples
--------
>>> np.add.ntypes
18
>>> np.multiply.ntypes
18
>>> np.power.ntypes
17
>>> np.exp.ntypes
7
>>> np.remainder.ntypes
14
"""))
add_newdoc('numpy.core', 'ufunc', ('types',
"""
Returns a list with types grouped input->output.
Data attribute listing the data-type "Domain-Range" groupings the ufunc can
deliver. The data-types are given using the character codes.
See Also
--------
numpy.ufunc.ntypes
Examples
--------
>>> np.add.types
['??->?', 'bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l',
'LL->L', 'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'FF->F', 'DD->D',
'GG->G', 'OO->O']
>>> np.multiply.types
['??->?', 'bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l',
'LL->L', 'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'FF->F', 'DD->D',
'GG->G', 'OO->O']
>>> np.power.types
['bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l', 'LL->L',
'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'FF->F', 'DD->D', 'GG->G',
'OO->O']
>>> np.exp.types
['f->f', 'd->d', 'g->g', 'F->F', 'D->D', 'G->G', 'O->O']
>>> np.remainder.types
['bb->b', 'BB->B', 'hh->h', 'HH->H', 'ii->i', 'II->I', 'll->l', 'LL->L',
'qq->q', 'QQ->Q', 'ff->f', 'dd->d', 'gg->g', 'OO->O']
"""))
add_newdoc('numpy.core', 'ufunc', ('signature',
"""
Definition of the core elements a generalized ufunc operates on.
The signature determines how the dimensions of each input/output array
are split into core and loop dimensions:
1. Each dimension in the signature is matched to a dimension of the
corresponding passed-in array, starting from the end of the shape tuple.
2. Core dimensions assigned to the same label in the signature must have
exactly matching sizes, no broadcasting is performed.
3. The core dimensions are removed from all inputs and the remaining
dimensions are broadcast together, defining the loop dimensions.
Notes
-----
Generalized ufuncs are used internally in many linalg functions, and in
the testing suite; the examples below are taken from these.
For ufuncs that operate on scalars, the signature is `None`, which is
equivalent to '()' for every argument.
Examples
--------
>>> np.core.umath_tests.matrix_multiply.signature
'(m,n),(n,p)->(m,p)'
>>> np.linalg._umath_linalg.det.signature
'(m,m)->()'
>>> np.add.signature is None
True # equivalent to '(),()->()'
"""))
##############################################################################
#
# ufunc methods
#
##############################################################################
add_newdoc('numpy.core', 'ufunc', ('reduce',
"""
reduce(a, axis=0, dtype=None, out=None, keepdims=False)
Reduces `a`'s dimension by one, by applying ufunc along one axis.
Let :math:`a.shape = (N_0, ..., N_i, ..., N_{M-1})`. Then
:math:`ufunc.reduce(a, axis=i)[k_0, ..,k_{i-1}, k_{i+1}, .., k_{M-1}]` =
the result of iterating `j` over :math:`range(N_i)`, cumulatively applying
ufunc to each :math:`a[k_0, ..,k_{i-1}, j, k_{i+1}, .., k_{M-1}]`.
For a one-dimensional array, reduce produces results equivalent to:
::
r = op.identity # op = ufunc
for i in range(len(A)):
r = op(r, A[i])
return r
For example, add.reduce() is equivalent to sum().
Parameters
----------
a : array_like
The array to act on.
axis : None or int or tuple of ints, optional
Axis or axes along which a reduction is performed.
The default (`axis` = 0) is perform a reduction over the first
dimension of the input array. `axis` may be negative, in
which case it counts from the last to the first axis.
.. versionadded:: 1.7.0
If this is `None`, a reduction is performed over all the axes.
If this is a tuple of ints, a reduction is performed on multiple
axes, instead of a single axis or all the axes as before.
For operations which are either not commutative or not associative,
doing a reduction over multiple axes is not well-defined. The
ufuncs do not currently raise an exception in this case, but will
likely do so in the future.
dtype : data-type code, optional
The type used to represent the intermediate results. Defaults
to the data-type of the output array if this is provided, or
the data-type of the input array if no output array is provided.
out : ndarray, None, or tuple of ndarray and None, optional
A location into which the result is stored. If not provided or `None`,
a freshly-allocated array is returned. For consistency with
:ref:`ufunc.__call__`, if given as a keyword, this may be wrapped in a
1-element tuple.
.. versionchanged:: 1.13.0
Tuples are allowed for keyword argument.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the original `arr`.
.. versionadded:: 1.7.0
Returns
-------
r : ndarray
The reduced array. If `out` was supplied, `r` is a reference to it.
Examples
--------
>>> np.multiply.reduce([2,3,5])
30
A multi-dimensional array example:
>>> X = np.arange(8).reshape((2,2,2))
>>> X
array([[[0, 1],
[2, 3]],
[[4, 5],
[6, 7]]])
>>> np.add.reduce(X, 0)
array([[ 4, 6],
[ 8, 10]])
>>> np.add.reduce(X) # confirm: default axis value is 0
array([[ 4, 6],
[ 8, 10]])
>>> np.add.reduce(X, 1)
array([[ 2, 4],
[10, 12]])
>>> np.add.reduce(X, 2)
array([[ 1, 5],
[ 9, 13]])
"""))
add_newdoc('numpy.core', 'ufunc', ('accumulate',
"""
accumulate(array, axis=0, dtype=None, out=None)
Accumulate the result of applying the operator to all elements.
For a one-dimensional array, accumulate produces results equivalent to::
r = np.empty(len(A))
t = op.identity # op = the ufunc being applied to A's elements
for i in range(len(A)):
t = op(t, A[i])
r[i] = t
return r
For example, add.accumulate() is equivalent to np.cumsum().
For a multi-dimensional array, accumulate is applied along only one
axis (axis zero by default; see Examples below) so repeated use is
necessary if one wants to accumulate over multiple axes.
Parameters
----------
array : array_like
The array to act on.
axis : int, optional
The axis along which to apply the accumulation; default is zero.
dtype : data-type code, optional
The data-type used to represent the intermediate results. Defaults
to the data-type of the output array if such is provided, or the
the data-type of the input array if no output array is provided.
out : ndarray, None, or tuple of ndarray and None, optional
A location into which the result is stored. If not provided or `None`,
a freshly-allocated array is returned. For consistency with
:ref:`ufunc.__call__`, if given as a keyword, this may be wrapped in a
1-element tuple.
.. versionchanged:: 1.13.0
Tuples are allowed for keyword argument.
Returns
-------
r : ndarray
The accumulated values. If `out` was supplied, `r` is a reference to
`out`.
Examples
--------
1-D array examples:
>>> np.add.accumulate([2, 3, 5])
array([ 2, 5, 10])
>>> np.multiply.accumulate([2, 3, 5])
array([ 2, 6, 30])
2-D array examples:
>>> I = np.eye(2)
>>> I
array([[ 1., 0.],
[ 0., 1.]])
Accumulate along axis 0 (rows), down columns:
>>> np.add.accumulate(I, 0)
array([[ 1., 0.],
[ 1., 1.]])
>>> np.add.accumulate(I) # no axis specified = axis zero
array([[ 1., 0.],
[ 1., 1.]])
Accumulate along axis 1 (columns), through rows:
>>> np.add.accumulate(I, 1)
array([[ 1., 1.],
[ 0., 1.]])
"""))
add_newdoc('numpy.core', 'ufunc', ('reduceat',
"""
reduceat(a, indices, axis=0, dtype=None, out=None)
Performs a (local) reduce with specified slices over a single axis.
For i in ``range(len(indices))``, `reduceat` computes
``ufunc.reduce(a[indices[i]:indices[i+1]])``, which becomes the i-th
generalized "row" parallel to `axis` in the final result (i.e., in a
2-D array, for example, if `axis = 0`, it becomes the i-th row, but if
`axis = 1`, it becomes the i-th column). There are three exceptions to this:
* when ``i = len(indices) - 1`` (so for the last index),
``indices[i+1] = a.shape[axis]``.
* if ``indices[i] >= indices[i + 1]``, the i-th generalized "row" is
simply ``a[indices[i]]``.
* if ``indices[i] >= len(a)`` or ``indices[i] < 0``, an error is raised.
The shape of the output depends on the size of `indices`, and may be
larger than `a` (this happens if ``len(indices) > a.shape[axis]``).
Parameters
----------
a : array_like
The array to act on.
indices : array_like
Paired indices, comma separated (not colon), specifying slices to
reduce.
axis : int, optional
The axis along which to apply the reduceat.
dtype : data-type code, optional
The type used to represent the intermediate results. Defaults
to the data type of the output array if this is provided, or
the data type of the input array if no output array is provided.
out : ndarray, None, or tuple of ndarray and None, optional
A location into which the result is stored. If not provided or `None`,
a freshly-allocated array is returned. For consistency with
:ref:`ufunc.__call__`, if given as a keyword, this may be wrapped in a
1-element tuple.
.. versionchanged:: 1.13.0
Tuples are allowed for keyword argument.
Returns
-------
r : ndarray
The reduced values. If `out` was supplied, `r` is a reference to
`out`.
Notes
-----
A descriptive example:
If `a` is 1-D, the function `ufunc.accumulate(a)` is the same as
``ufunc.reduceat(a, indices)[::2]`` where `indices` is
``range(len(array) - 1)`` with a zero placed
in every other element:
``indices = zeros(2 * len(a) - 1)``, ``indices[1::2] = range(1, len(a))``.
Don't be fooled by this attribute's name: `reduceat(a)` is not
necessarily smaller than `a`.
Examples
--------
To take the running sum of four successive values:
>>> np.add.reduceat(np.arange(8),[0,4, 1,5, 2,6, 3,7])[::2]
array([ 6, 10, 14, 18])
A 2-D example:
>>> x = np.linspace(0, 15, 16).reshape(4,4)
>>> x
array([[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.],
[ 12., 13., 14., 15.]])
::
# reduce such that the result has the following five rows:
# [row1 + row2 + row3]
# [row4]
# [row2]
# [row3]
# [row1 + row2 + row3 + row4]
>>> np.add.reduceat(x, [0, 3, 1, 2, 0])
array([[ 12., 15., 18., 21.],
[ 12., 13., 14., 15.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.],
[ 24., 28., 32., 36.]])
::
# reduce such that result has the following two columns:
# [col1 * col2 * col3, col4]
>>> np.multiply.reduceat(x, [0, 3], 1)
array([[ 0., 3.],
[ 120., 7.],
[ 720., 11.],
[ 2184., 15.]])
"""))
add_newdoc('numpy.core', 'ufunc', ('outer',
"""
outer(A, B, **kwargs)
Apply the ufunc `op` to all pairs (a, b) with a in `A` and b in `B`.
Let ``M = A.ndim``, ``N = B.ndim``. Then the result, `C`, of
``op.outer(A, B)`` is an array of dimension M + N such that:
.. math:: C[i_0, ..., i_{M-1}, j_0, ..., j_{N-1}] =
op(A[i_0, ..., i_{M-1}], B[j_0, ..., j_{N-1}])
For `A` and `B` one-dimensional, this is equivalent to::
r = empty(len(A),len(B))
for i in range(len(A)):
for j in range(len(B)):
r[i,j] = op(A[i], B[j]) # op = ufunc in question
Parameters
----------
A : array_like
First array
B : array_like
Second array
kwargs : any
Arguments to pass on to the ufunc. Typically `dtype` or `out`.
Returns
-------
r : ndarray
Output array
See Also
--------
numpy.outer
Examples
--------
>>> np.multiply.outer([1, 2, 3], [4, 5, 6])
array([[ 4, 5, 6],
[ 8, 10, 12],
[12, 15, 18]])
A multi-dimensional example:
>>> A = np.array([[1, 2, 3], [4, 5, 6]])
>>> A.shape
(2, 3)
>>> B = np.array([[1, 2, 3, 4]])
>>> B.shape
(1, 4)
>>> C = np.multiply.outer(A, B)
>>> C.shape; C
(2, 3, 1, 4)
array([[[[ 1, 2, 3, 4]],
[[ 2, 4, 6, 8]],
[[ 3, 6, 9, 12]]],
[[[ 4, 8, 12, 16]],
[[ 5, 10, 15, 20]],
[[ 6, 12, 18, 24]]]])
"""))
add_newdoc('numpy.core', 'ufunc', ('at',
"""
at(a, indices, b=None)
Performs unbuffered in place operation on operand 'a' for elements
specified by 'indices'. For addition ufunc, this method is equivalent to
`a[indices] += b`, except that results are accumulated for elements that
are indexed more than once. For example, `a[[0,0]] += 1` will only
increment the first element once because of buffering, whereas
`add.at(a, [0,0], 1)` will increment the first element twice.
.. versionadded:: 1.8.0
Parameters
----------
a : array_like
The array to perform in place operation on.
indices : array_like or tuple
Array like index object or slice object for indexing into first
operand. If first operand has multiple dimensions, indices can be a
tuple of array like index objects or slice objects.
b : array_like
Second operand for ufuncs requiring two operands. Operand must be
broadcastable over first operand after indexing or slicing.
Examples
--------
Set items 0 and 1 to their negative values:
>>> a = np.array([1, 2, 3, 4])
>>> np.negative.at(a, [0, 1])
>>> print(a)
array([-1, -2, 3, 4])
::
Increment items 0 and 1, and increment item 2 twice:
>>> a = np.array([1, 2, 3, 4])
>>> np.add.at(a, [0, 1, 2, 2], 1)
>>> print(a)
array([2, 3, 5, 4])
::
Add items 0 and 1 in first array to second array,
and store results in first array:
>>> a = np.array([1, 2, 3, 4])
>>> b = np.array([1, 2])
>>> np.add.at(a, [0, 1], b)
>>> print(a)
array([2, 4, 3, 4])
"""))
##############################################################################
#
# Documentation for dtype attributes and methods
#
##############################################################################
##############################################################################
#
# dtype object
#
##############################################################################
add_newdoc('numpy.core.multiarray', 'dtype',
"""
dtype(obj, align=False, copy=False)
Create a data type object.
A numpy array is homogeneous, and contains elements described by a
dtype object. A dtype object can be constructed from different
combinations of fundamental numeric types.
Parameters
----------
obj
Object to be converted to a data type object.
align : bool, optional
Add padding to the fields to match what a C compiler would output
for a similar C-struct. Can be ``True`` only if `obj` is a dictionary
or a comma-separated string. If a struct dtype is being created,
this also sets a sticky alignment flag ``isalignedstruct``.
copy : bool, optional
Make a new copy of the data-type object. If ``False``, the result
may just be a reference to a built-in data-type object.
See also
--------
result_type
Examples
--------
Using array-scalar type:
>>> np.dtype(np.int16)
dtype('int16')
Structured type, one field name 'f1', containing int16:
>>> np.dtype([('f1', np.int16)])
dtype([('f1', '<i2')])
Structured type, one field named 'f1', in itself containing a structured
type with one field:
>>> np.dtype([('f1', [('f1', np.int16)])])
dtype([('f1', [('f1', '<i2')])])
Structured type, two fields: the first field contains an unsigned int, the
second an int32:
>>> np.dtype([('f1', np.uint), ('f2', np.int32)])
dtype([('f1', '<u4'), ('f2', '<i4')])
Using array-protocol type strings:
>>> np.dtype([('a','f8'),('b','S10')])
dtype([('a', '<f8'), ('b', '|S10')])
Using comma-separated field formats. The shape is (2,3):
>>> np.dtype("i4, (2,3)f8")
dtype([('f0', '<i4'), ('f1', '<f8', (2, 3))])
Using tuples. ``int`` is a fixed type, 3 the field's shape. ``void``
is a flexible type, here of size 10:
>>> np.dtype([('hello',(int,3)),('world',np.void,10)])
dtype([('hello', '<i4', 3), ('world', '|V10')])
Subdivide ``int16`` into 2 ``int8``'s, called x and y. 0 and 1 are
the offsets in bytes:
>>> np.dtype((np.int16, {'x':(np.int8,0), 'y':(np.int8,1)}))
dtype(('<i2', [('x', '|i1'), ('y', '|i1')]))
Using dictionaries. Two fields named 'gender' and 'age':
>>> np.dtype({'names':['gender','age'], 'formats':['S1',np.uint8]})
dtype([('gender', '|S1'), ('age', '|u1')])
Offsets in bytes, here 0 and 25:
>>> np.dtype({'surname':('S25',0),'age':(np.uint8,25)})
dtype([('surname', '|S25'), ('age', '|u1')])
""")
##############################################################################
#
# dtype attributes
#
##############################################################################
add_newdoc('numpy.core.multiarray', 'dtype', ('alignment',
"""
The required alignment (bytes) of this data-type according to the compiler.
More information is available in the C-API section of the manual.
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('byteorder',
"""
A character indicating the byte-order of this data-type object.
One of:
=== ==============
'=' native
'<' little-endian
'>' big-endian
'|' not applicable
=== ==============
All built-in data-type objects have byteorder either '=' or '|'.
Examples
--------
>>> dt = np.dtype('i2')
>>> dt.byteorder
'='
>>> # endian is not relevant for 8 bit numbers
>>> np.dtype('i1').byteorder
'|'
>>> # or ASCII strings
>>> np.dtype('S2').byteorder
'|'
>>> # Even if specific code is given, and it is native
>>> # '=' is the byteorder
>>> import sys
>>> sys_is_le = sys.byteorder == 'little'
>>> native_code = sys_is_le and '<' or '>'
>>> swapped_code = sys_is_le and '>' or '<'
>>> dt = np.dtype(native_code + 'i2')
>>> dt.byteorder
'='
>>> # Swapped code shows up as itself
>>> dt = np.dtype(swapped_code + 'i2')
>>> dt.byteorder == swapped_code
True
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('char',
"""A unique character code for each of the 21 different built-in types."""))
add_newdoc('numpy.core.multiarray', 'dtype', ('descr',
"""
PEP3118 interface description of the data-type.
The format is that required by the 'descr' key in the
PEP3118 `__array_interface__` attribute.
Warning: This attribute exists specifically for PEP3118 compliance, and
is not a datatype description compatible with `np.dtype`.
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('fields',
"""
Dictionary of named fields defined for this data type, or ``None``.
The dictionary is indexed by keys that are the names of the fields.
Each entry in the dictionary is a tuple fully describing the field::
(dtype, offset[, title])
If present, the optional title can be any object (if it is a string
or unicode then it will also be a key in the fields dictionary,
otherwise it's meta-data). Notice also that the first two elements
of the tuple can be passed directly as arguments to the ``ndarray.getfield``
and ``ndarray.setfield`` methods.
See Also
--------
ndarray.getfield, ndarray.setfield
Examples
--------
>>> dt = np.dtype([('name', np.str_, 16), ('grades', np.float64, (2,))])
>>> print(dt.fields)
{'grades': (dtype(('float64',(2,))), 16), 'name': (dtype('|S16'), 0)}
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('flags',
"""
Bit-flags describing how this data type is to be interpreted.
Bit-masks are in `numpy.core.multiarray` as the constants
`ITEM_HASOBJECT`, `LIST_PICKLE`, `ITEM_IS_POINTER`, `NEEDS_INIT`,
`NEEDS_PYAPI`, `USE_GETITEM`, `USE_SETITEM`. A full explanation
of these flags is in C-API documentation; they are largely useful
for user-defined data-types.
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('hasobject',
"""
Boolean indicating whether this dtype contains any reference-counted
objects in any fields or sub-dtypes.
Recall that what is actually in the ndarray memory representing
the Python object is the memory address of that object (a pointer).
Special handling may be required, and this attribute is useful for
distinguishing data types that may contain arbitrary Python objects
and data-types that won't.
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('isbuiltin',
"""
Integer indicating how this dtype relates to the built-in dtypes.
Read-only.
= ========================================================================
0 if this is a structured array type, with fields
1 if this is a dtype compiled into numpy (such as ints, floats etc)
2 if the dtype is for a user-defined numpy type
A user-defined type uses the numpy C-API machinery to extend
numpy to handle a new array type. See
:ref:`user.user-defined-data-types` in the NumPy manual.
= ========================================================================
Examples
--------
>>> dt = np.dtype('i2')
>>> dt.isbuiltin
1
>>> dt = np.dtype('f8')
>>> dt.isbuiltin
1
>>> dt = np.dtype([('field1', 'f8')])
>>> dt.isbuiltin
0
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('isnative',
"""
Boolean indicating whether the byte order of this dtype is native
to the platform.
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('isalignedstruct',
"""
Boolean indicating whether the dtype is a struct which maintains
field alignment. This flag is sticky, so when combining multiple
structs together, it is preserved and produces new dtypes which
are also aligned.
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('itemsize',
"""
The element size of this data-type object.
For 18 of the 21 types this number is fixed by the data-type.
For the flexible data-types, this number can be anything.
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('kind',
"""
A character code (one of 'biufcmMOSUV') identifying the general kind of data.
= ======================
b boolean
i signed integer
u unsigned integer
f floating-point
c complex floating-point
m timedelta
M datetime
O object
S (byte-)string
U Unicode
V void
= ======================
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('name',
"""
A bit-width name for this data-type.
Un-sized flexible data-type objects do not have this attribute.
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('names',
"""
Ordered list of field names, or ``None`` if there are no fields.
The names are ordered according to increasing byte offset. This can be
used, for example, to walk through all of the named fields in offset order.
Examples
--------
>>> dt = np.dtype([('name', np.str_, 16), ('grades', np.float64, (2,))])
>>> dt.names
('name', 'grades')
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('num',
"""
A unique number for each of the 21 different built-in types.
These are roughly ordered from least-to-most precision.
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('shape',
"""
Shape tuple of the sub-array if this data type describes a sub-array,
and ``()`` otherwise.
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('ndim',
"""
Number of dimensions of the sub-array if this data type describes a
sub-array, and ``0`` otherwise.
.. versionadded:: 1.13.0
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('str',
"""The array-protocol typestring of this data-type object."""))
add_newdoc('numpy.core.multiarray', 'dtype', ('subdtype',
"""
Tuple ``(item_dtype, shape)`` if this `dtype` describes a sub-array, and
None otherwise.
The *shape* is the fixed shape of the sub-array described by this
data type, and *item_dtype* the data type of the array.
If a field whose dtype object has this attribute is retrieved,
then the extra dimensions implied by *shape* are tacked on to
the end of the retrieved array.
"""))
add_newdoc('numpy.core.multiarray', 'dtype', ('type',
"""The type object used to instantiate a scalar of this data-type."""))
##############################################################################
#
# dtype methods
#
##############################################################################
add_newdoc('numpy.core.multiarray', 'dtype', ('newbyteorder',
"""
newbyteorder(new_order='S')
Return a new dtype with a different byte order.
Changes are also made in all fields and sub-arrays of the data type.
Parameters
----------
new_order : string, optional
Byte order to force; a value from the byte order specifications
below. The default value ('S') results in swapping the current
byte order. `new_order` codes can be any of:
* 'S' - swap dtype from current to opposite endian
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* {'|', 'I'} - ignore (no change to byte order)
The code does a case-insensitive check on the first letter of
`new_order` for these alternatives. For example, any of '>'
or 'B' or 'b' or 'brian' are valid to specify big-endian.
Returns
-------
new_dtype : dtype
New dtype object with the given change to the byte order.
Notes
-----
Changes are also made in all fields and sub-arrays of the data type.
Examples
--------
>>> import sys
>>> sys_is_le = sys.byteorder == 'little'
>>> native_code = sys_is_le and '<' or '>'
>>> swapped_code = sys_is_le and '>' or '<'
>>> native_dt = np.dtype(native_code+'i2')
>>> swapped_dt = np.dtype(swapped_code+'i2')
>>> native_dt.newbyteorder('S') == swapped_dt
True
>>> native_dt.newbyteorder() == swapped_dt
True
>>> native_dt == swapped_dt.newbyteorder('S')
True
>>> native_dt == swapped_dt.newbyteorder('=')
True
>>> native_dt == swapped_dt.newbyteorder('N')
True
>>> native_dt == native_dt.newbyteorder('|')
True
>>> np.dtype('<i2') == native_dt.newbyteorder('<')
True
>>> np.dtype('<i2') == native_dt.newbyteorder('L')
True
>>> np.dtype('>i2') == native_dt.newbyteorder('>')
True
>>> np.dtype('>i2') == native_dt.newbyteorder('B')
True
"""))
##############################################################################
#
# Datetime-related Methods
#
##############################################################################
add_newdoc('numpy.core.multiarray', 'busdaycalendar',
"""
busdaycalendar(weekmask='1111100', holidays=None)
A business day calendar object that efficiently stores information
defining valid days for the busday family of functions.
The default valid days are Monday through Friday ("business days").
A busdaycalendar object can be specified with any set of weekly
valid days, plus an optional "holiday" dates that always will be invalid.
Once a busdaycalendar object is created, the weekmask and holidays
cannot be modified.
.. versionadded:: 1.7.0
Parameters
----------
weekmask : str or array_like of bool, optional
A seven-element array indicating which of Monday through Sunday are
valid days. May be specified as a length-seven list or array, like
[1,1,1,1,1,0,0]; a length-seven string, like '1111100'; or a string
like "Mon Tue Wed Thu Fri", made up of 3-character abbreviations for
weekdays, optionally separated by white space. Valid abbreviations
are: Mon Tue Wed Thu Fri Sat Sun
holidays : array_like of datetime64[D], optional
An array of dates to consider as invalid dates, no matter which
weekday they fall upon. Holiday dates may be specified in any
order, and NaT (not-a-time) dates are ignored. This list is
saved in a normalized form that is suited for fast calculations
of valid days.
Returns
-------
out : busdaycalendar
A business day calendar object containing the specified
weekmask and holidays values.
See Also
--------
is_busday : Returns a boolean array indicating valid days.
busday_offset : Applies an offset counted in valid days.
busday_count : Counts how many valid days are in a half-open date range.
Attributes
----------
Note: once a busdaycalendar object is created, you cannot modify the
weekmask or holidays. The attributes return copies of internal data.
weekmask : (copy) seven-element array of bool
holidays : (copy) sorted array of datetime64[D]
Examples
--------
>>> # Some important days in July
... bdd = np.busdaycalendar(
... holidays=['2011-07-01', '2011-07-04', '2011-07-17'])
>>> # Default is Monday to Friday weekdays
... bdd.weekmask
array([ True, True, True, True, True, False, False], dtype='bool')
>>> # Any holidays already on the weekend are removed
... bdd.holidays
array(['2011-07-01', '2011-07-04'], dtype='datetime64[D]')
""")
add_newdoc('numpy.core.multiarray', 'busdaycalendar', ('weekmask',
"""A copy of the seven-element boolean mask indicating valid days."""))
add_newdoc('numpy.core.multiarray', 'busdaycalendar', ('holidays',
"""A copy of the holiday array indicating additional invalid days."""))
add_newdoc('numpy.core.multiarray', 'is_busday',
"""
is_busday(dates, weekmask='1111100', holidays=None, busdaycal=None, out=None)
Calculates which of the given dates are valid days, and which are not.
.. versionadded:: 1.7.0
Parameters
----------
dates : array_like of datetime64[D]
The array of dates to process.
weekmask : str or array_like of bool, optional
A seven-element array indicating which of Monday through Sunday are
valid days. May be specified as a length-seven list or array, like
[1,1,1,1,1,0,0]; a length-seven string, like '1111100'; or a string
like "Mon Tue Wed Thu Fri", made up of 3-character abbreviations for
weekdays, optionally separated by white space. Valid abbreviations
are: Mon Tue Wed Thu Fri Sat Sun
holidays : array_like of datetime64[D], optional
An array of dates to consider as invalid dates. They may be
specified in any order, and NaT (not-a-time) dates are ignored.
This list is saved in a normalized form that is suited for
fast calculations of valid days.
busdaycal : busdaycalendar, optional
A `busdaycalendar` object which specifies the valid days. If this
parameter is provided, neither weekmask nor holidays may be
provided.
out : array of bool, optional
If provided, this array is filled with the result.
Returns
-------
out : array of bool
An array with the same shape as ``dates``, containing True for
each valid day, and False for each invalid day.
See Also
--------
busdaycalendar: An object that specifies a custom set of valid days.
busday_offset : Applies an offset counted in valid days.
busday_count : Counts how many valid days are in a half-open date range.
Examples
--------
>>> # The weekdays are Friday, Saturday, and Monday
... np.is_busday(['2011-07-01', '2011-07-02', '2011-07-18'],
... holidays=['2011-07-01', '2011-07-04', '2011-07-17'])
array([False, False, True], dtype='bool')
""")
add_newdoc('numpy.core.multiarray', 'busday_offset',
"""
busday_offset(dates, offsets, roll='raise', weekmask='1111100', holidays=None, busdaycal=None, out=None)
First adjusts the date to fall on a valid day according to
the ``roll`` rule, then applies offsets to the given dates
counted in valid days.
.. versionadded:: 1.7.0
Parameters
----------
dates : array_like of datetime64[D]
The array of dates to process.
offsets : array_like of int
The array of offsets, which is broadcast with ``dates``.
roll : {'raise', 'nat', 'forward', 'following', 'backward', 'preceding', 'modifiedfollowing', 'modifiedpreceding'}, optional
How to treat dates that do not fall on a valid day. The default
is 'raise'.
* 'raise' means to raise an exception for an invalid day.
* 'nat' means to return a NaT (not-a-time) for an invalid day.
* 'forward' and 'following' mean to take the first valid day
later in time.
* 'backward' and 'preceding' mean to take the first valid day
earlier in time.
* 'modifiedfollowing' means to take the first valid day
later in time unless it is across a Month boundary, in which
case to take the first valid day earlier in time.
* 'modifiedpreceding' means to take the first valid day
earlier in time unless it is across a Month boundary, in which
case to take the first valid day later in time.
weekmask : str or array_like of bool, optional
A seven-element array indicating which of Monday through Sunday are
valid days. May be specified as a length-seven list or array, like
[1,1,1,1,1,0,0]; a length-seven string, like '1111100'; or a string
like "Mon Tue Wed Thu Fri", made up of 3-character abbreviations for
weekdays, optionally separated by white space. Valid abbreviations
are: Mon Tue Wed Thu Fri Sat Sun
holidays : array_like of datetime64[D], optional
An array of dates to consider as invalid dates. They may be
specified in any order, and NaT (not-a-time) dates are ignored.
This list is saved in a normalized form that is suited for
fast calculations of valid days.
busdaycal : busdaycalendar, optional
A `busdaycalendar` object which specifies the valid days. If this
parameter is provided, neither weekmask nor holidays may be
provided.
out : array of datetime64[D], optional
If provided, this array is filled with the result.
Returns
-------
out : array of datetime64[D]
An array with a shape from broadcasting ``dates`` and ``offsets``
together, containing the dates with offsets applied.
See Also
--------
busdaycalendar: An object that specifies a custom set of valid days.
is_busday : Returns a boolean array indicating valid days.
busday_count : Counts how many valid days are in a half-open date range.
Examples
--------
>>> # First business day in October 2011 (not accounting for holidays)
... np.busday_offset('2011-10', 0, roll='forward')
numpy.datetime64('2011-10-03','D')
>>> # Last business day in February 2012 (not accounting for holidays)
... np.busday_offset('2012-03', -1, roll='forward')
numpy.datetime64('2012-02-29','D')
>>> # Third Wednesday in January 2011
... np.busday_offset('2011-01', 2, roll='forward', weekmask='Wed')
numpy.datetime64('2011-01-19','D')
>>> # 2012 Mother's Day in Canada and the U.S.
... np.busday_offset('2012-05', 1, roll='forward', weekmask='Sun')
numpy.datetime64('2012-05-13','D')
>>> # First business day on or after a date
... np.busday_offset('2011-03-20', 0, roll='forward')
numpy.datetime64('2011-03-21','D')
>>> np.busday_offset('2011-03-22', 0, roll='forward')
numpy.datetime64('2011-03-22','D')
>>> # First business day after a date
... np.busday_offset('2011-03-20', 1, roll='backward')
numpy.datetime64('2011-03-21','D')
>>> np.busday_offset('2011-03-22', 1, roll='backward')
numpy.datetime64('2011-03-23','D')
""")
add_newdoc('numpy.core.multiarray', 'busday_count',
"""
busday_count(begindates, enddates, weekmask='1111100', holidays=[], busdaycal=None, out=None)
Counts the number of valid days between `begindates` and
`enddates`, not including the day of `enddates`.
If ``enddates`` specifies a date value that is earlier than the
corresponding ``begindates`` date value, the count will be negative.
.. versionadded:: 1.7.0
Parameters
----------
begindates : array_like of datetime64[D]
The array of the first dates for counting.
enddates : array_like of datetime64[D]
The array of the end dates for counting, which are excluded
from the count themselves.
weekmask : str or array_like of bool, optional
A seven-element array indicating which of Monday through Sunday are
valid days. May be specified as a length-seven list or array, like
[1,1,1,1,1,0,0]; a length-seven string, like '1111100'; or a string
like "Mon Tue Wed Thu Fri", made up of 3-character abbreviations for
weekdays, optionally separated by white space. Valid abbreviations
are: Mon Tue Wed Thu Fri Sat Sun
holidays : array_like of datetime64[D], optional
An array of dates to consider as invalid dates. They may be
specified in any order, and NaT (not-a-time) dates are ignored.
This list is saved in a normalized form that is suited for
fast calculations of valid days.
busdaycal : busdaycalendar, optional
A `busdaycalendar` object which specifies the valid days. If this
parameter is provided, neither weekmask nor holidays may be
provided.
out : array of int, optional
If provided, this array is filled with the result.
Returns
-------
out : array of int
An array with a shape from broadcasting ``begindates`` and ``enddates``
together, containing the number of valid days between
the begin and end dates.
See Also
--------
busdaycalendar: An object that specifies a custom set of valid days.
is_busday : Returns a boolean array indicating valid days.
busday_offset : Applies an offset counted in valid days.
Examples
--------
>>> # Number of weekdays in January 2011
... np.busday_count('2011-01', '2011-02')
21
>>> # Number of weekdays in 2011
... np.busday_count('2011', '2012')
260
>>> # Number of Saturdays in 2011
... np.busday_count('2011', '2012', weekmask='Sat')
53
""")
add_newdoc('numpy.core.multiarray', 'normalize_axis_index',
"""
normalize_axis_index(axis, ndim, msg_prefix=None)
Normalizes an axis index, `axis`, such that is a valid positive index into
the shape of array with `ndim` dimensions. Raises an AxisError with an
appropriate message if this is not possible.
Used internally by all axis-checking logic.
.. versionadded:: 1.13.0
Parameters
----------
axis : int
The un-normalized index of the axis. Can be negative
ndim : int
The number of dimensions of the array that `axis` should be normalized
against
msg_prefix : str
A prefix to put before the message, typically the name of the argument
Returns
-------
normalized_axis : int
The normalized axis index, such that `0 <= normalized_axis < ndim`
Raises
------
AxisError
If the axis index is invalid, when `-ndim <= axis < ndim` is false.
Examples
--------
>>> normalize_axis_index(0, ndim=3)
0
>>> normalize_axis_index(1, ndim=3)
1
>>> normalize_axis_index(-1, ndim=3)
2
>>> normalize_axis_index(3, ndim=3)
Traceback (most recent call last):
...
AxisError: axis 3 is out of bounds for array of dimension 3
>>> normalize_axis_index(-4, ndim=3, msg_prefix='axes_arg')
Traceback (most recent call last):
...
AxisError: axes_arg: axis -4 is out of bounds for array of dimension 3
""")
add_newdoc('numpy.core.multiarray', 'datetime_as_string',
"""
datetime_as_string(arr, unit=None, timezone='naive', casting='same_kind')
Convert an array of datetimes into an array of strings.
Parameters
----------
arr : array_like of datetime64
The array of UTC timestamps to format.
unit : str
One of None, 'auto', or a datetime unit.
timezone : {'naive', 'UTC', 'local'} or tzinfo
Timezone information to use when displaying the datetime. If 'UTC', end
with a Z to indicate UTC time. If 'local', convert to the local timezone
first, and suffix with a +-#### timezone offset. If a tzinfo object,
then do as with 'local', but use the specified timezone.
casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}
Casting to allow when changing between datetime units.
Returns
-------
str_arr : ndarray
An array of strings the same shape as `arr`.
Examples
--------
>>> d = np.arange('2002-10-27T04:30', 4*60, 60, dtype='M8[m]')
>>> d
array(['2002-10-27T04:30', '2002-10-27T05:30', '2002-10-27T06:30',
'2002-10-27T07:30'], dtype='datetime64[m]')
Setting the timezone to UTC shows the same information, but with a Z suffix
>>> np.datetime_as_string(d, timezone='UTC')
array(['2002-10-27T04:30Z', '2002-10-27T05:30Z', '2002-10-27T06:30Z',
'2002-10-27T07:30Z'], dtype='<U35')
Note that we picked datetimes that cross a DST boundary. Passing in a
``pytz`` timezone object will print the appropriate offset::
>>> np.datetime_as_string(d, timezone=pytz.timezone('US/Eastern'))
array(['2002-10-27T00:30-0400', '2002-10-27T01:30-0400',
'2002-10-27T01:30-0500', '2002-10-27T02:30-0500'], dtype='<U39')
Passing in a unit will change the precision::
>>> np.datetime_as_string(d, unit='h')
array(['2002-10-27T04', '2002-10-27T05', '2002-10-27T06', '2002-10-27T07'],
dtype='<U32')
>>> np.datetime_as_string(d, unit='s')
array(['2002-10-27T04:30:00', '2002-10-27T05:30:00', '2002-10-27T06:30:00',
'2002-10-27T07:30:00'], dtype='<U38')
But can be made to not lose precision::
>>> np.datetime_as_string(d, unit='h', casting='safe')
TypeError: Cannot create a datetime string as units 'h' from a NumPy
datetime with units 'm' according to the rule 'safe'
""")
add_newdoc('numpy.core.multiarray', 'datetime_data',
"""
datetime_data(dtype, /)
Get information about the step size of a date or time type.
The returned tuple can be passed as the second argument of `datetime64` and
`timedelta64`.
Parameters
----------
dtype : dtype
The dtype object, which must be a `datetime64` or `timedelta64` type.
Returns
-------
unit : str
The :ref:`datetime unit <arrays.dtypes.dateunits>` on which this dtype
is based.
count : int
The number of base units in a step.
Examples
--------
>>> dt_25s = np.dtype('timedelta64[25s]')
>>> np.datetime_data(dt_25s)
('s', 25)
>>> np.array(10, dt_25s).astype('timedelta64[s]')
array(250, dtype='timedelta64[s]')
The result can be used to construct a datetime that uses the same units
as a timedelta::
>>> np.datetime64('2010', np.datetime_data(dt_25s))
numpy.datetime64('2010-01-01T00:00:00','25s')
""")
##############################################################################
#
# nd_grid instances
#
##############################################################################
add_newdoc('numpy.lib.index_tricks', 'mgrid',
"""
`nd_grid` instance which returns a dense multi-dimensional "meshgrid".
An instance of `numpy.lib.index_tricks.nd_grid` which returns an dense
(or fleshed out) mesh-grid when indexed, so that each returned argument
has the same shape. The dimensions and number of the output arrays are
equal to the number of indexing dimensions. If the step length is not a
complex number, then the stop is not inclusive.
However, if the step length is a **complex number** (e.g. 5j), then
the integer part of its magnitude is interpreted as specifying the
number of points to create between the start and stop values, where
the stop value **is inclusive**.
Returns
----------
mesh-grid `ndarrays` all of the same dimensions
See Also
--------
numpy.lib.index_tricks.nd_grid : class of `ogrid` and `mgrid` objects
ogrid : like mgrid but returns open (not fleshed out) mesh grids
r_ : array concatenator
Examples
--------
>>> np.mgrid[0:5,0:5]
array([[[0, 0, 0, 0, 0],
[1, 1, 1, 1, 1],
[2, 2, 2, 2, 2],
[3, 3, 3, 3, 3],
[4, 4, 4, 4, 4]],
[[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4],
[0, 1, 2, 3, 4]]])
>>> np.mgrid[-1:1:5j]
array([-1. , -0.5, 0. , 0.5, 1. ])
""")
add_newdoc('numpy.lib.index_tricks', 'ogrid',
"""
`nd_grid` instance which returns an open multi-dimensional "meshgrid".
An instance of `numpy.lib.index_tricks.nd_grid` which returns an open
(i.e. not fleshed out) mesh-grid when indexed, so that only one dimension
of each returned array is greater than 1. The dimension and number of the
output arrays are equal to the number of indexing dimensions. If the step
length is not a complex number, then the stop is not inclusive.
However, if the step length is a **complex number** (e.g. 5j), then
the integer part of its magnitude is interpreted as specifying the
number of points to create between the start and stop values, where
the stop value **is inclusive**.
Returns
----------
mesh-grid `ndarrays` with only one dimension :math:`\\neq 1`
See Also
--------
np.lib.index_tricks.nd_grid : class of `ogrid` and `mgrid` objects
mgrid : like `ogrid` but returns dense (or fleshed out) mesh grids
r_ : array concatenator
Examples
--------
>>> from numpy import ogrid
>>> ogrid[-1:1:5j]
array([-1. , -0.5, 0. , 0.5, 1. ])
>>> ogrid[0:5,0:5]
[array([[0],
[1],
[2],
[3],
[4]]), array([[0, 1, 2, 3, 4]])]
""")
##############################################################################
#
# Documentation for `generic` attributes and methods
#
##############################################################################
add_newdoc('numpy.core.numerictypes', 'generic',
"""
Base class for numpy scalar types.
Class from which most (all?) numpy scalar types are derived. For
consistency, exposes the same API as `ndarray`, despite many
consequent attributes being either "get-only," or completely irrelevant.
This is the class from which it is strongly suggested users should derive
custom scalar types.
""")
# Attributes
add_newdoc('numpy.core.numerictypes', 'generic', ('T',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class so as to
provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('base',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class so as to
a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('data',
"""Pointer to start of data."""))
add_newdoc('numpy.core.numerictypes', 'generic', ('dtype',
"""Get array data-descriptor."""))
add_newdoc('numpy.core.numerictypes', 'generic', ('flags',
"""The integer value of flags."""))
add_newdoc('numpy.core.numerictypes', 'generic', ('flat',
"""A 1-D view of the scalar."""))
add_newdoc('numpy.core.numerictypes', 'generic', ('imag',
"""The imaginary part of the scalar."""))
add_newdoc('numpy.core.numerictypes', 'generic', ('itemsize',
"""The length of one element in bytes."""))
add_newdoc('numpy.core.numerictypes', 'generic', ('nbytes',
"""The length of the scalar in bytes."""))
add_newdoc('numpy.core.numerictypes', 'generic', ('ndim',
"""The number of array dimensions."""))
add_newdoc('numpy.core.numerictypes', 'generic', ('real',
"""The real part of the scalar."""))
add_newdoc('numpy.core.numerictypes', 'generic', ('shape',
"""Tuple of array dimensions."""))
add_newdoc('numpy.core.numerictypes', 'generic', ('size',
"""The number of elements in the gentype."""))
add_newdoc('numpy.core.numerictypes', 'generic', ('strides',
"""Tuple of bytes steps in each dimension."""))
# Methods
add_newdoc('numpy.core.numerictypes', 'generic', ('all',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('any',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('argmax',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('argmin',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('argsort',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('astype',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('byteswap',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class so as to
provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('choose',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('clip',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('compress',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('conjugate',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('copy',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('cumprod',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('cumsum',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('diagonal',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('dump',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('dumps',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('fill',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('flatten',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('getfield',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('item',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('itemset',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('max',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('mean',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('min',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('newbyteorder',
"""
newbyteorder(new_order='S')
Return a new `dtype` with a different byte order.
Changes are also made in all fields and sub-arrays of the data type.
The `new_order` code can be any from the following:
* 'S' - swap dtype from current to opposite endian
* {'<', 'L'} - little endian
* {'>', 'B'} - big endian
* {'=', 'N'} - native order
* {'|', 'I'} - ignore (no change to byte order)
Parameters
----------
new_order : str, optional
Byte order to force; a value from the byte order specifications
above. The default value ('S') results in swapping the current
byte order. The code does a case-insensitive check on the first
letter of `new_order` for the alternatives above. For example,
any of 'B' or 'b' or 'biggish' are valid to specify big-endian.
Returns
-------
new_dtype : dtype
New `dtype` object with the given change to the byte order.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('nonzero',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('prod',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('ptp',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('put',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('ravel',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('repeat',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('reshape',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('resize',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('round',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('searchsorted',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('setfield',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('setflags',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class so as to
provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('sort',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('squeeze',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('std',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('sum',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('swapaxes',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('take',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('tofile',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('tolist',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('tostring',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('trace',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('transpose',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('var',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
add_newdoc('numpy.core.numerictypes', 'generic', ('view',
"""
Not implemented (virtual attribute)
Class generic exists solely to derive numpy scalars from, and possesses,
albeit unimplemented, all the attributes of the ndarray class
so as to provide a uniform API.
See Also
--------
The corresponding attribute of the derived class of interest.
"""))
##############################################################################
#
# Documentation for other scalar classes
#
##############################################################################
add_newdoc('numpy.core.numerictypes', 'bool_',
"""NumPy's Boolean type. Character code: ``?``. Alias: bool8""")
add_newdoc('numpy.core.numerictypes', 'complex64',
"""
Complex number type composed of two 32 bit floats. Character code: 'F'.
""")
add_newdoc('numpy.core.numerictypes', 'complex128',
"""
Complex number type composed of two 64 bit floats. Character code: 'D'.
Python complex compatible.
""")
add_newdoc('numpy.core.numerictypes', 'complex256',
"""
Complex number type composed of two 128-bit floats. Character code: 'G'.
""")
add_newdoc('numpy.core.numerictypes', 'float32',
"""
32-bit floating-point number. Character code 'f'. C float compatible.
""")
add_newdoc('numpy.core.numerictypes', 'float64',
"""
64-bit floating-point number. Character code 'd'. Python float compatible.
""")
add_newdoc('numpy.core.numerictypes', 'float96',
"""
""")
add_newdoc('numpy.core.numerictypes', 'float128',
"""
128-bit floating-point number. Character code: 'g'. C long float
compatible.
""")
add_newdoc('numpy.core.numerictypes', 'int8',
"""8-bit integer. Character code ``b``. C char compatible.""")
add_newdoc('numpy.core.numerictypes', 'int16',
"""16-bit integer. Character code ``h``. C short compatible.""")
add_newdoc('numpy.core.numerictypes', 'int32',
"""32-bit integer. Character code 'i'. C int compatible.""")
add_newdoc('numpy.core.numerictypes', 'int64',
"""64-bit integer. Character code 'l'. Python int compatible.""")
add_newdoc('numpy.core.numerictypes', 'object_',
"""Any Python object. Character code: 'O'.""")
| 234,747 | 28.576414 | 128 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/_globals.py
|
"""
Module defining global singleton classes.
This module raises a RuntimeError if an attempt to reload it is made. In that
way the identities of the classes defined here are fixed and will remain so
even if numpy itself is reloaded. In particular, a function like the following
will still work correctly after numpy is reloaded::
def foo(arg=np._NoValue):
if arg is np._NoValue:
...
That was not the case when the singleton classes were defined in the numpy
``__init__.py`` file. See gh-7844 for a discussion of the reload problem that
motivated this module.
"""
from __future__ import division, absolute_import, print_function
__ALL__ = [
'ModuleDeprecationWarning', 'VisibleDeprecationWarning', '_NoValue'
]
# Disallow reloading this module so as to preserve the identities of the
# classes defined here.
if '_is_loaded' in globals():
raise RuntimeError('Reloading numpy._globals is not allowed')
_is_loaded = True
class ModuleDeprecationWarning(DeprecationWarning):
"""Module deprecation warning.
The nose tester turns ordinary Deprecation warnings into test failures.
That makes it hard to deprecate whole modules, because they get
imported by default. So this is a special Deprecation warning that the
nose tester will let pass without making tests fail.
"""
pass
class VisibleDeprecationWarning(UserWarning):
"""Visible deprecation warning.
By default, python will not show deprecation warnings, so this class
can be used when a very visible warning is helpful, for example because
the usage is most likely a user bug.
"""
pass
class _NoValue(object):
"""Special keyword value.
This class may be used as the default value assigned to a deprecated
keyword in order to check if it has been given a user defined value.
"""
pass
| 1,859 | 28.52381 | 78 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/__init__.py
|
"""
NumPy
=====
Provides
1. An array object of arbitrary homogeneous items
2. Fast mathematical operations over arrays
3. Linear Algebra, Fourier Transforms, Random Number Generation
How to use the documentation
----------------------------
Documentation is available in two forms: docstrings provided
with the code, and a loose standing reference guide, available from
`the NumPy homepage <http://www.scipy.org>`_.
We recommend exploring the docstrings using
`IPython <http://ipython.scipy.org>`_, an advanced Python shell with
TAB-completion and introspection capabilities. See below for further
instructions.
The docstring examples assume that `numpy` has been imported as `np`::
>>> import numpy as np
Code snippets are indicated by three greater-than signs::
>>> x = 42
>>> x = x + 1
Use the built-in ``help`` function to view a function's docstring::
>>> help(np.sort)
... # doctest: +SKIP
For some objects, ``np.info(obj)`` may provide additional help. This is
particularly true if you see the line "Help on ufunc object:" at the top
of the help() page. Ufuncs are implemented in C, not Python, for speed.
The native Python help() does not know how to view their help, but our
np.info() function does.
To search for documents containing a keyword, do::
>>> np.lookfor('keyword')
... # doctest: +SKIP
General-purpose documents like a glossary and help on the basic concepts
of numpy are available under the ``doc`` sub-module::
>>> from numpy import doc
>>> help(doc)
... # doctest: +SKIP
Available subpackages
---------------------
doc
Topical documentation on broadcasting, indexing, etc.
lib
Basic functions used by several sub-packages.
random
Core Random Tools
linalg
Core Linear Algebra Tools
fft
Core FFT routines
polynomial
Polynomial tools
testing
NumPy testing tools
f2py
Fortran to Python Interface Generator.
distutils
Enhancements to distutils with support for
Fortran compilers support and more.
Utilities
---------
test
Run numpy unittests
show_config
Show numpy build configuration
dual
Overwrite certain functions with high-performance Scipy tools
matlib
Make everything matrices.
__version__
NumPy version string
Viewing documentation using IPython
-----------------------------------
Start IPython with the NumPy profile (``ipython -p numpy``), which will
import `numpy` under the alias `np`. Then, use the ``cpaste`` command to
paste examples into the shell. To see which functions are available in
`numpy`, type ``np.<TAB>`` (where ``<TAB>`` refers to the TAB key), or use
``np.*cos*?<ENTER>`` (where ``<ENTER>`` refers to the ENTER key) to narrow
down the list. To view the docstring for a function, use
``np.cos?<ENTER>`` (to view the docstring) and ``np.cos??<ENTER>`` (to view
the source code).
Copies vs. in-place operation
-----------------------------
Most of the functions in `numpy` return a copy of the array argument
(e.g., `np.sort`). In-place versions of these functions are often
available as array methods, i.e. ``x = np.array([1,2,3]); x.sort()``.
Exceptions to this rule are documented.
"""
from __future__ import division, absolute_import, print_function
import sys
import warnings
from ._globals import ModuleDeprecationWarning, VisibleDeprecationWarning
from ._globals import _NoValue
# We first need to detect if we're being called as part of the numpy setup
# procedure itself in a reliable manner.
try:
__NUMPY_SETUP__
except NameError:
__NUMPY_SETUP__ = False
if __NUMPY_SETUP__:
sys.stderr.write('Running from numpy source directory.\n')
else:
try:
from numpy.__config__ import show as show_config
except ImportError:
msg = """Error importing numpy: you should not try to import numpy from
its source directory; please exit the numpy source tree, and relaunch
your python interpreter from there."""
raise ImportError(msg)
from .version import git_revision as __git_revision__
from .version import version as __version__
from ._import_tools import PackageLoader
def pkgload(*packages, **options):
loader = PackageLoader(infunc=True)
return loader(*packages, **options)
from . import add_newdocs
__all__ = ['add_newdocs',
'ModuleDeprecationWarning',
'VisibleDeprecationWarning']
pkgload.__doc__ = PackageLoader.__call__.__doc__
# We don't actually use this ourselves anymore, but I'm not 100% sure that
# no-one else in the world is using it (though I hope not)
from .testing import Tester, _numpy_tester
test = _numpy_tester().test
bench = _numpy_tester().bench
# Allow distributors to run custom init code
from . import _distributor_init
from . import core
from .core import *
from . import compat
from . import lib
from .lib import *
from . import linalg
from . import fft
from . import polynomial
from . import random
from . import ctypeslib
from . import ma
from . import matrixlib as _mat
from .matrixlib import *
from .compat import long
# Make these accessible from numpy name-space
# but not imported in from numpy import *
if sys.version_info[0] >= 3:
from builtins import bool, int, float, complex, object, str
unicode = str
else:
from __builtin__ import bool, int, float, complex, object, unicode, str
from .core import round, abs, max, min
__all__.extend(['__version__', 'pkgload', 'PackageLoader',
'show_config'])
__all__.extend(core.__all__)
__all__.extend(_mat.__all__)
__all__.extend(lib.__all__)
__all__.extend(['linalg', 'fft', 'random', 'ctypeslib', 'ma'])
# Filter annoying Cython warnings that serve no good purpose.
warnings.filterwarnings("ignore", message="numpy.dtype size changed")
warnings.filterwarnings("ignore", message="numpy.ufunc size changed")
warnings.filterwarnings("ignore", message="numpy.ndarray size changed")
# oldnumeric and numarray were removed in 1.9. In case some packages import
# but do not use them, we define them here for backward compatibility.
oldnumeric = 'removed'
numarray = 'removed'
def _sanity_check():
"""
Quick sanity checks for common bugs caused by environment.
There are some cases (e.g., the wrong BLAS ABI) that cause wrong
results under specific runtime conditions that are not necessarily
achieved during test suite runs, and it is useful to catch those early.
See https://github.com/numpy/numpy/issues/8577 and other
similar bug reports.
"""
try:
x = ones(2, dtype=float32)
if not abs(x.dot(x) - 2.0) < 1e-5:
raise AssertionError()
except AssertionError:
msg = ("The current Numpy installation ({!r}) fails to "
"pass simple sanity checks. This can be caused for example "
"by incorrect BLAS library being linked in.")
raise RuntimeError(msg.format(__file__))
_sanity_check()
del _sanity_check
| 7,171 | 31.017857 | 79 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/__config__.py
|
# This file is generated by numpy's setup.py
# It contains system_info results at the time of building this package.
__all__ = ["get_info","show"]
import os
import sys
extra_dll_dir = os.path.join(os.path.dirname(__file__), '.libs')
if sys.platform == 'win32' and os.path.isdir(extra_dll_dir):
os.environ.setdefault('PATH', '')
os.environ['PATH'] += os.pathsep + extra_dll_dir
blas_mkl_info={}
blis_info={}
openblas_info={}
atlas_3_10_blas_threads_info={}
atlas_3_10_blas_info={}
atlas_blas_threads_info={}
atlas_blas_info={}
blas_opt_info={'extra_compile_args': ['-msse3', '-I/System/Library/Frameworks/vecLib.framework/Headers'], 'extra_link_args': ['-Wl,-framework', '-Wl,Accelerate'], 'define_macros': [('NO_ATLAS_INFO', 3), ('HAVE_CBLAS', None)]}
lapack_mkl_info={}
openblas_lapack_info={}
openblas_clapack_info={}
atlas_3_10_threads_info={}
atlas_3_10_info={}
atlas_threads_info={}
atlas_info={}
lapack_opt_info={'extra_compile_args': ['-msse3'], 'extra_link_args': ['-Wl,-framework', '-Wl,Accelerate'], 'define_macros': [('NO_ATLAS_INFO', 3), ('HAVE_CBLAS', None)]}
def get_info(name):
g = globals()
return g.get(name, g.get(name + "_info", {}))
def show():
for name,info_dict in globals().items():
if name[0] == "_" or type(info_dict) is not type({}): continue
print(name + ":")
if not info_dict:
print(" NOT AVAILABLE")
for k,v in info_dict.items():
v = str(v)
if k == "sources" and len(v) > 200:
v = v[:60] + " ...\n... " + v[-60:]
print(" %s = %s" % (k,v))
| 1,597 | 34.511111 | 225 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/matlib.py
|
from __future__ import division, absolute_import, print_function
import numpy as np
from numpy.matrixlib.defmatrix import matrix, asmatrix
# need * as we're copying the numpy namespace
from numpy import *
__version__ = np.__version__
__all__ = np.__all__[:] # copy numpy namespace
__all__ += ['rand', 'randn', 'repmat']
def empty(shape, dtype=None, order='C'):
"""Return a new matrix of given shape and type, without initializing entries.
Parameters
----------
shape : int or tuple of int
Shape of the empty matrix.
dtype : data-type, optional
Desired output data-type.
order : {'C', 'F'}, optional
Whether to store multi-dimensional data in row-major
(C-style) or column-major (Fortran-style) order in
memory.
See Also
--------
empty_like, zeros
Notes
-----
`empty`, unlike `zeros`, does not set the matrix values to zero,
and may therefore be marginally faster. On the other hand, it requires
the user to manually set all the values in the array, and should be
used with caution.
Examples
--------
>>> import numpy.matlib
>>> np.matlib.empty((2, 2)) # filled with random data
matrix([[ 6.76425276e-320, 9.79033856e-307],
[ 7.39337286e-309, 3.22135945e-309]]) #random
>>> np.matlib.empty((2, 2), dtype=int)
matrix([[ 6600475, 0],
[ 6586976, 22740995]]) #random
"""
return ndarray.__new__(matrix, shape, dtype, order=order)
def ones(shape, dtype=None, order='C'):
"""
Matrix of ones.
Return a matrix of given shape and type, filled with ones.
Parameters
----------
shape : {sequence of ints, int}
Shape of the matrix
dtype : data-type, optional
The desired data-type for the matrix, default is np.float64.
order : {'C', 'F'}, optional
Whether to store matrix in C- or Fortran-contiguous order,
default is 'C'.
Returns
-------
out : matrix
Matrix of ones of given shape, dtype, and order.
See Also
--------
ones : Array of ones.
matlib.zeros : Zero matrix.
Notes
-----
If `shape` has length one i.e. ``(N,)``, or is a scalar ``N``,
`out` becomes a single row matrix of shape ``(1,N)``.
Examples
--------
>>> np.matlib.ones((2,3))
matrix([[ 1., 1., 1.],
[ 1., 1., 1.]])
>>> np.matlib.ones(2)
matrix([[ 1., 1.]])
"""
a = ndarray.__new__(matrix, shape, dtype, order=order)
a.fill(1)
return a
def zeros(shape, dtype=None, order='C'):
"""
Return a matrix of given shape and type, filled with zeros.
Parameters
----------
shape : int or sequence of ints
Shape of the matrix
dtype : data-type, optional
The desired data-type for the matrix, default is float.
order : {'C', 'F'}, optional
Whether to store the result in C- or Fortran-contiguous order,
default is 'C'.
Returns
-------
out : matrix
Zero matrix of given shape, dtype, and order.
See Also
--------
numpy.zeros : Equivalent array function.
matlib.ones : Return a matrix of ones.
Notes
-----
If `shape` has length one i.e. ``(N,)``, or is a scalar ``N``,
`out` becomes a single row matrix of shape ``(1,N)``.
Examples
--------
>>> import numpy.matlib
>>> np.matlib.zeros((2, 3))
matrix([[ 0., 0., 0.],
[ 0., 0., 0.]])
>>> np.matlib.zeros(2)
matrix([[ 0., 0.]])
"""
a = ndarray.__new__(matrix, shape, dtype, order=order)
a.fill(0)
return a
def identity(n,dtype=None):
"""
Returns the square identity matrix of given size.
Parameters
----------
n : int
Size of the returned identity matrix.
dtype : data-type, optional
Data-type of the output. Defaults to ``float``.
Returns
-------
out : matrix
`n` x `n` matrix with its main diagonal set to one,
and all other elements zero.
See Also
--------
numpy.identity : Equivalent array function.
matlib.eye : More general matrix identity function.
Examples
--------
>>> import numpy.matlib
>>> np.matlib.identity(3, dtype=int)
matrix([[1, 0, 0],
[0, 1, 0],
[0, 0, 1]])
"""
a = array([1]+n*[0], dtype=dtype)
b = empty((n, n), dtype=dtype)
b.flat = a
return b
def eye(n,M=None, k=0, dtype=float, order='C'):
"""
Return a matrix with ones on the diagonal and zeros elsewhere.
Parameters
----------
n : int
Number of rows in the output.
M : int, optional
Number of columns in the output, defaults to `n`.
k : int, optional
Index of the diagonal: 0 refers to the main diagonal,
a positive value refers to an upper diagonal,
and a negative value to a lower diagonal.
dtype : dtype, optional
Data-type of the returned matrix.
order : {'C', 'F'}, optional
Whether the output should be stored in row-major (C-style) or
column-major (Fortran-style) order in memory.
.. versionadded:: 1.14.0
Returns
-------
I : matrix
A `n` x `M` matrix where all elements are equal to zero,
except for the `k`-th diagonal, whose values are equal to one.
See Also
--------
numpy.eye : Equivalent array function.
identity : Square identity matrix.
Examples
--------
>>> import numpy.matlib
>>> np.matlib.eye(3, k=1, dtype=float)
matrix([[ 0., 1., 0.],
[ 0., 0., 1.],
[ 0., 0., 0.]])
"""
return asmatrix(np.eye(n, M=M, k=k, dtype=dtype, order=order))
def rand(*args):
"""
Return a matrix of random values with given shape.
Create a matrix of the given shape and propagate it with
random samples from a uniform distribution over ``[0, 1)``.
Parameters
----------
\\*args : Arguments
Shape of the output.
If given as N integers, each integer specifies the size of one
dimension.
If given as a tuple, this tuple gives the complete shape.
Returns
-------
out : ndarray
The matrix of random values with shape given by `\\*args`.
See Also
--------
randn, numpy.random.rand
Examples
--------
>>> import numpy.matlib
>>> np.matlib.rand(2, 3)
matrix([[ 0.68340382, 0.67926887, 0.83271405],
[ 0.00793551, 0.20468222, 0.95253525]]) #random
>>> np.matlib.rand((2, 3))
matrix([[ 0.84682055, 0.73626594, 0.11308016],
[ 0.85429008, 0.3294825 , 0.89139555]]) #random
If the first argument is a tuple, other arguments are ignored:
>>> np.matlib.rand((2, 3), 4)
matrix([[ 0.46898646, 0.15163588, 0.95188261],
[ 0.59208621, 0.09561818, 0.00583606]]) #random
"""
if isinstance(args[0], tuple):
args = args[0]
return asmatrix(np.random.rand(*args))
def randn(*args):
"""
Return a random matrix with data from the "standard normal" distribution.
`randn` generates a matrix filled with random floats sampled from a
univariate "normal" (Gaussian) distribution of mean 0 and variance 1.
Parameters
----------
\\*args : Arguments
Shape of the output.
If given as N integers, each integer specifies the size of one
dimension. If given as a tuple, this tuple gives the complete shape.
Returns
-------
Z : matrix of floats
A matrix of floating-point samples drawn from the standard normal
distribution.
See Also
--------
rand, random.randn
Notes
-----
For random samples from :math:`N(\\mu, \\sigma^2)`, use:
``sigma * np.matlib.randn(...) + mu``
Examples
--------
>>> import numpy.matlib
>>> np.matlib.randn(1)
matrix([[-0.09542833]]) #random
>>> np.matlib.randn(1, 2, 3)
matrix([[ 0.16198284, 0.0194571 , 0.18312985],
[-0.7509172 , 1.61055 , 0.45298599]]) #random
Two-by-four matrix of samples from :math:`N(3, 6.25)`:
>>> 2.5 * np.matlib.randn((2, 4)) + 3
matrix([[ 4.74085004, 8.89381862, 4.09042411, 4.83721922],
[ 7.52373709, 5.07933944, -2.64043543, 0.45610557]]) #random
"""
if isinstance(args[0], tuple):
args = args[0]
return asmatrix(np.random.randn(*args))
def repmat(a, m, n):
"""
Repeat a 0-D to 2-D array or matrix MxN times.
Parameters
----------
a : array_like
The array or matrix to be repeated.
m, n : int
The number of times `a` is repeated along the first and second axes.
Returns
-------
out : ndarray
The result of repeating `a`.
Examples
--------
>>> import numpy.matlib
>>> a0 = np.array(1)
>>> np.matlib.repmat(a0, 2, 3)
array([[1, 1, 1],
[1, 1, 1]])
>>> a1 = np.arange(4)
>>> np.matlib.repmat(a1, 2, 2)
array([[0, 1, 2, 3, 0, 1, 2, 3],
[0, 1, 2, 3, 0, 1, 2, 3]])
>>> a2 = np.asmatrix(np.arange(6).reshape(2, 3))
>>> np.matlib.repmat(a2, 2, 3)
matrix([[0, 1, 2, 0, 1, 2, 0, 1, 2],
[3, 4, 5, 3, 4, 5, 3, 4, 5],
[0, 1, 2, 0, 1, 2, 0, 1, 2],
[3, 4, 5, 3, 4, 5, 3, 4, 5]])
"""
a = asanyarray(a)
ndim = a.ndim
if ndim == 0:
origrows, origcols = (1, 1)
elif ndim == 1:
origrows, origcols = (1, a.shape[0])
else:
origrows, origcols = a.shape
rows = origrows * m
cols = origcols * n
c = a.reshape(1, a.size).repeat(m, 0).reshape(rows, origcols).repeat(n, 0)
return c.reshape(rows, cols)
| 9,809 | 25.950549 | 81 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/random/setup.py
|
from __future__ import division, print_function
from os.path import join, split, dirname
import os
import sys
from distutils.dep_util import newer
from distutils.msvccompiler import get_build_version as get_msvc_build_version
def needs_mingw_ftime_workaround():
# We need the mingw workaround for _ftime if the msvc runtime version is
# 7.1 or above and we build with mingw ...
# ... but we can't easily detect compiler version outside distutils command
# context, so we will need to detect in randomkit whether we build with gcc
msver = get_msvc_build_version()
if msver and msver >= 8:
return True
return False
def configuration(parent_package='',top_path=None):
from numpy.distutils.misc_util import Configuration, get_mathlibs
config = Configuration('random', parent_package, top_path)
def generate_libraries(ext, build_dir):
config_cmd = config.get_config_cmd()
libs = get_mathlibs()
if sys.platform == 'win32':
libs.append('Advapi32')
ext.libraries.extend(libs)
return None
# enable unix large file support on 32 bit systems
# (64 bit off_t, lseek -> lseek64 etc.)
if sys.platform[:3] == "aix":
defs = [('_LARGE_FILES', None)]
else:
defs = [('_FILE_OFFSET_BITS', '64'),
('_LARGEFILE_SOURCE', '1'),
('_LARGEFILE64_SOURCE', '1')]
if needs_mingw_ftime_workaround():
defs.append(("NPY_NEEDS_MINGW_TIME_WORKAROUND", None))
libs = []
# Configure mtrand
config.add_extension('mtrand',
sources=[join('mtrand', x) for x in
['mtrand.c', 'randomkit.c', 'initarray.c',
'distributions.c']]+[generate_libraries],
libraries=libs,
depends=[join('mtrand', '*.h'),
join('mtrand', '*.pyx'),
join('mtrand', '*.pxi'),],
define_macros=defs,
)
config.add_data_files(('.', join('mtrand', 'randomkit.h')))
config.add_data_dir('tests')
return config
if __name__ == '__main__':
from numpy.distutils.core import setup
setup(configuration=configuration)
| 2,312 | 34.584615 | 79 |
py
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cba-pipeline-public
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cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/random/info.py
|
"""
========================
Random Number Generation
========================
==================== =========================================================
Utility functions
==============================================================================
random_sample Uniformly distributed floats over ``[0, 1)``.
random Alias for `random_sample`.
bytes Uniformly distributed random bytes.
random_integers Uniformly distributed integers in a given range.
permutation Randomly permute a sequence / generate a random sequence.
shuffle Randomly permute a sequence in place.
seed Seed the random number generator.
choice Random sample from 1-D array.
==================== =========================================================
==================== =========================================================
Compatibility functions
==============================================================================
rand Uniformly distributed values.
randn Normally distributed values.
ranf Uniformly distributed floating point numbers.
randint Uniformly distributed integers in a given range.
==================== =========================================================
==================== =========================================================
Univariate distributions
==============================================================================
beta Beta distribution over ``[0, 1]``.
binomial Binomial distribution.
chisquare :math:`\\chi^2` distribution.
exponential Exponential distribution.
f F (Fisher-Snedecor) distribution.
gamma Gamma distribution.
geometric Geometric distribution.
gumbel Gumbel distribution.
hypergeometric Hypergeometric distribution.
laplace Laplace distribution.
logistic Logistic distribution.
lognormal Log-normal distribution.
logseries Logarithmic series distribution.
negative_binomial Negative binomial distribution.
noncentral_chisquare Non-central chi-square distribution.
noncentral_f Non-central F distribution.
normal Normal / Gaussian distribution.
pareto Pareto distribution.
poisson Poisson distribution.
power Power distribution.
rayleigh Rayleigh distribution.
triangular Triangular distribution.
uniform Uniform distribution.
vonmises Von Mises circular distribution.
wald Wald (inverse Gaussian) distribution.
weibull Weibull distribution.
zipf Zipf's distribution over ranked data.
==================== =========================================================
==================== =========================================================
Multivariate distributions
==============================================================================
dirichlet Multivariate generalization of Beta distribution.
multinomial Multivariate generalization of the binomial distribution.
multivariate_normal Multivariate generalization of the normal distribution.
==================== =========================================================
==================== =========================================================
Standard distributions
==============================================================================
standard_cauchy Standard Cauchy-Lorentz distribution.
standard_exponential Standard exponential distribution.
standard_gamma Standard Gamma distribution.
standard_normal Standard normal distribution.
standard_t Standard Student's t-distribution.
==================== =========================================================
==================== =========================================================
Internal functions
==============================================================================
get_state Get tuple representing internal state of generator.
set_state Set state of generator.
==================== =========================================================
"""
from __future__ import division, absolute_import, print_function
depends = ['core']
__all__ = [
'beta',
'binomial',
'bytes',
'chisquare',
'choice',
'dirichlet',
'exponential',
'f',
'gamma',
'geometric',
'get_state',
'gumbel',
'hypergeometric',
'laplace',
'logistic',
'lognormal',
'logseries',
'multinomial',
'multivariate_normal',
'negative_binomial',
'noncentral_chisquare',
'noncentral_f',
'normal',
'pareto',
'permutation',
'poisson',
'power',
'rand',
'randint',
'randn',
'random_integers',
'random_sample',
'rayleigh',
'seed',
'set_state',
'shuffle',
'standard_cauchy',
'standard_exponential',
'standard_gamma',
'standard_normal',
'standard_t',
'triangular',
'uniform',
'vonmises',
'wald',
'weibull',
'zipf'
]
| 5,199 | 36.142857 | 78 |
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|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/random/randomkit.h
|
/* Random kit 1.3 */
/*
* Copyright (c) 2003-2005, Jean-Sebastien Roy ([email protected])
*
* Permission is hereby granted, free of charge, to any person obtaining a
* copy of this software and associated documentation files (the
* "Software"), to deal in the Software without restriction, including
* without limitation the rights to use, copy, modify, merge, publish,
* distribute, sublicense, and/or sell copies of the Software, and to
* permit persons to whom the Software is furnished to do so, subject to
* the following conditions:
*
* The above copyright notice and this permission notice shall be included
* in all copies or substantial portions of the Software.
*
* THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS
* OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
* MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
* IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
* CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
* TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
* SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
*/
/* @(#) $Jeannot: randomkit.h,v 1.24 2005/07/21 22:14:09 js Exp $ */
/*
* Typical use:
*
* {
* rk_state state;
* unsigned long seed = 1, random_value;
*
* rk_seed(seed, &state); // Initialize the RNG
* ...
* random_value = rk_random(&state); // Generate random values in [0..RK_MAX]
* }
*
* Instead of rk_seed, you can use rk_randomseed which will get a random seed
* from /dev/urandom (or the clock, if /dev/urandom is unavailable):
*
* {
* rk_state state;
* unsigned long random_value;
*
* rk_randomseed(&state); // Initialize the RNG with a random seed
* ...
* random_value = rk_random(&state); // Generate random values in [0..RK_MAX]
* }
*/
/*
* Useful macro:
* RK_DEV_RANDOM: the device used for random seeding.
* defaults to "/dev/urandom"
*/
#ifndef _RANDOMKIT_
#define _RANDOMKIT_
#include <stddef.h>
#include <numpy/npy_common.h>
#define RK_STATE_LEN 624
typedef struct rk_state_
{
unsigned long key[RK_STATE_LEN];
int pos;
int has_gauss; /* !=0: gauss contains a gaussian deviate */
double gauss;
/* The rk_state structure has been extended to store the following
* information for the binomial generator. If the input values of n or p
* are different than nsave and psave, then the other parameters will be
* recomputed. RTK 2005-09-02 */
int has_binomial; /* !=0: following parameters initialized for
binomial */
double psave;
long nsave;
double r;
double q;
double fm;
long m;
double p1;
double xm;
double xl;
double xr;
double c;
double laml;
double lamr;
double p2;
double p3;
double p4;
}
rk_state;
typedef enum {
RK_NOERR = 0, /* no error */
RK_ENODEV = 1, /* no RK_DEV_RANDOM device */
RK_ERR_MAX = 2
} rk_error;
/* error strings */
extern char *rk_strerror[RK_ERR_MAX];
/* Maximum generated random value */
#define RK_MAX 0xFFFFFFFFUL
#ifdef __cplusplus
extern "C" {
#endif
/*
* Initialize the RNG state using the given seed.
*/
extern void rk_seed(unsigned long seed, rk_state *state);
/*
* Initialize the RNG state using a random seed.
* Uses /dev/random or, when unavailable, the clock (see randomkit.c).
* Returns RK_NOERR when no errors occurs.
* Returns RK_ENODEV when the use of RK_DEV_RANDOM failed (for example because
* there is no such device). In this case, the RNG was initialized using the
* clock.
*/
extern rk_error rk_randomseed(rk_state *state);
/*
* Returns a random unsigned long between 0 and RK_MAX inclusive
*/
extern unsigned long rk_random(rk_state *state);
/*
* Returns a random long between 0 and LONG_MAX inclusive
*/
extern long rk_long(rk_state *state);
/*
* Returns a random unsigned long between 0 and ULONG_MAX inclusive
*/
extern unsigned long rk_ulong(rk_state *state);
/*
* Returns a random unsigned long between 0 and max inclusive.
*/
extern unsigned long rk_interval(unsigned long max, rk_state *state);
/*
* Fills an array with cnt random npy_uint64 between off and off + rng
* inclusive. The numbers wrap if rng is sufficiently large.
*/
extern void rk_random_uint64(npy_uint64 off, npy_uint64 rng, npy_intp cnt,
npy_uint64 *out, rk_state *state);
/*
* Fills an array with cnt random npy_uint32 between off and off + rng
* inclusive. The numbers wrap if rng is sufficiently large.
*/
extern void rk_random_uint32(npy_uint32 off, npy_uint32 rng, npy_intp cnt,
npy_uint32 *out, rk_state *state);
/*
* Fills an array with cnt random npy_uint16 between off and off + rng
* inclusive. The numbers wrap if rng is sufficiently large.
*/
extern void rk_random_uint16(npy_uint16 off, npy_uint16 rng, npy_intp cnt,
npy_uint16 *out, rk_state *state);
/*
* Fills an array with cnt random npy_uint8 between off and off + rng
* inclusive. The numbers wrap if rng is sufficiently large.
*/
extern void rk_random_uint8(npy_uint8 off, npy_uint8 rng, npy_intp cnt,
npy_uint8 *out, rk_state *state);
/*
* Fills an array with cnt random npy_bool between off and off + rng
* inclusive. It is assumed tha npy_bool as the same size as npy_uint8.
*/
extern void rk_random_bool(npy_bool off, npy_bool rng, npy_intp cnt,
npy_bool *out, rk_state *state);
/*
* Returns a random double between 0.0 and 1.0, 1.0 excluded.
*/
extern double rk_double(rk_state *state);
/*
* fill the buffer with size random bytes
*/
extern void rk_fill(void *buffer, size_t size, rk_state *state);
/*
* fill the buffer with randombytes from the random device
* Returns RK_ENODEV if the device is unavailable, or RK_NOERR if it is
* On Unix, if strong is defined, RK_DEV_RANDOM is used. If not, RK_DEV_URANDOM
* is used instead. This parameter has no effect on Windows.
* Warning: on most unixes RK_DEV_RANDOM will wait for enough entropy to answer
* which can take a very long time on quiet systems.
*/
extern rk_error rk_devfill(void *buffer, size_t size, int strong);
/*
* fill the buffer using rk_devfill if the random device is available and using
* rk_fill if is is not
* parameters have the same meaning as rk_fill and rk_devfill
* Returns RK_ENODEV if the device is unavailable, or RK_NOERR if it is
*/
extern rk_error rk_altfill(void *buffer, size_t size, int strong,
rk_state *state);
/*
* return a random gaussian deviate with variance unity and zero mean.
*/
extern double rk_gauss(rk_state *state);
#ifdef __cplusplus
}
#endif
#endif /* _RANDOMKIT_ */
| 6,799 | 28.955947 | 79 |
h
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/random/__init__.py
|
"""
========================
Random Number Generation
========================
==================== =========================================================
Utility functions
==============================================================================
random Uniformly distributed values of a given shape.
bytes Uniformly distributed random bytes.
random_integers Uniformly distributed integers in a given range.
random_sample Uniformly distributed floats in a given range.
random Alias for random_sample
ranf Alias for random_sample
sample Alias for random_sample
choice Generate a weighted random sample from a given array-like
permutation Randomly permute a sequence / generate a random sequence.
shuffle Randomly permute a sequence in place.
seed Seed the random number generator.
==================== =========================================================
==================== =========================================================
Compatibility functions
==============================================================================
rand Uniformly distributed values.
randn Normally distributed values.
ranf Uniformly distributed floating point numbers.
randint Uniformly distributed integers in a given range.
==================== =========================================================
==================== =========================================================
Univariate distributions
==============================================================================
beta Beta distribution over ``[0, 1]``.
binomial Binomial distribution.
chisquare :math:`\\chi^2` distribution.
exponential Exponential distribution.
f F (Fisher-Snedecor) distribution.
gamma Gamma distribution.
geometric Geometric distribution.
gumbel Gumbel distribution.
hypergeometric Hypergeometric distribution.
laplace Laplace distribution.
logistic Logistic distribution.
lognormal Log-normal distribution.
logseries Logarithmic series distribution.
negative_binomial Negative binomial distribution.
noncentral_chisquare Non-central chi-square distribution.
noncentral_f Non-central F distribution.
normal Normal / Gaussian distribution.
pareto Pareto distribution.
poisson Poisson distribution.
power Power distribution.
rayleigh Rayleigh distribution.
triangular Triangular distribution.
uniform Uniform distribution.
vonmises Von Mises circular distribution.
wald Wald (inverse Gaussian) distribution.
weibull Weibull distribution.
zipf Zipf's distribution over ranked data.
==================== =========================================================
==================== =========================================================
Multivariate distributions
==============================================================================
dirichlet Multivariate generalization of Beta distribution.
multinomial Multivariate generalization of the binomial distribution.
multivariate_normal Multivariate generalization of the normal distribution.
==================== =========================================================
==================== =========================================================
Standard distributions
==============================================================================
standard_cauchy Standard Cauchy-Lorentz distribution.
standard_exponential Standard exponential distribution.
standard_gamma Standard Gamma distribution.
standard_normal Standard normal distribution.
standard_t Standard Student's t-distribution.
==================== =========================================================
==================== =========================================================
Internal functions
==============================================================================
get_state Get tuple representing internal state of generator.
set_state Set state of generator.
==================== =========================================================
"""
from __future__ import division, absolute_import, print_function
import warnings
# To get sub-modules
from .info import __doc__, __all__
with warnings.catch_warnings():
warnings.filterwarnings("ignore", message="numpy.ndarray size changed")
from .mtrand import *
# Some aliases:
ranf = random = sample = random_sample
__all__.extend(['ranf', 'random', 'sample'])
def __RandomState_ctor():
"""Return a RandomState instance.
This function exists solely to assist (un)pickling.
Note that the state of the RandomState returned here is irrelevant, as this function's
entire purpose is to return a newly allocated RandomState whose state pickle can set.
Consequently the RandomState returned by this function is a freshly allocated copy
with a seed=0.
See https://github.com/numpy/numpy/issues/4763 for a detailed discussion
"""
return RandomState(seed=0)
from numpy.testing import _numpy_tester
test = _numpy_tester().test
bench = _numpy_tester().bench
| 5,481 | 43.569106 | 90 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/random/tests/test_regression.py
|
from __future__ import division, absolute_import, print_function
import sys
from numpy.testing import (
run_module_suite, assert_, assert_array_equal, assert_raises,
)
from numpy import random
from numpy.compat import long
import numpy as np
class TestRegression(object):
def test_VonMises_range(self):
# Make sure generated random variables are in [-pi, pi].
# Regression test for ticket #986.
for mu in np.linspace(-7., 7., 5):
r = random.mtrand.vonmises(mu, 1, 50)
assert_(np.all(r > -np.pi) and np.all(r <= np.pi))
def test_hypergeometric_range(self):
# Test for ticket #921
assert_(np.all(np.random.hypergeometric(3, 18, 11, size=10) < 4))
assert_(np.all(np.random.hypergeometric(18, 3, 11, size=10) > 0))
# Test for ticket #5623
args = [
(2**20 - 2, 2**20 - 2, 2**20 - 2), # Check for 32-bit systems
]
is_64bits = sys.maxsize > 2**32
if is_64bits and sys.platform != 'win32':
args.append((2**40 - 2, 2**40 - 2, 2**40 - 2)) # Check for 64-bit systems
for arg in args:
assert_(np.random.hypergeometric(*arg) > 0)
def test_logseries_convergence(self):
# Test for ticket #923
N = 1000
np.random.seed(0)
rvsn = np.random.logseries(0.8, size=N)
# these two frequency counts should be close to theoretical
# numbers with this large sample
# theoretical large N result is 0.49706795
freq = np.sum(rvsn == 1) / float(N)
msg = "Frequency was %f, should be > 0.45" % freq
assert_(freq > 0.45, msg)
# theoretical large N result is 0.19882718
freq = np.sum(rvsn == 2) / float(N)
msg = "Frequency was %f, should be < 0.23" % freq
assert_(freq < 0.23, msg)
def test_permutation_longs(self):
np.random.seed(1234)
a = np.random.permutation(12)
np.random.seed(1234)
b = np.random.permutation(long(12))
assert_array_equal(a, b)
def test_shuffle_mixed_dimension(self):
# Test for trac ticket #2074
for t in [[1, 2, 3, None],
[(1, 1), (2, 2), (3, 3), None],
[1, (2, 2), (3, 3), None],
[(1, 1), 2, 3, None]]:
np.random.seed(12345)
shuffled = list(t)
random.shuffle(shuffled)
assert_array_equal(shuffled, [t[0], t[3], t[1], t[2]])
def test_call_within_randomstate(self):
# Check that custom RandomState does not call into global state
m = np.random.RandomState()
res = np.array([0, 8, 7, 2, 1, 9, 4, 7, 0, 3])
for i in range(3):
np.random.seed(i)
m.seed(4321)
# If m.state is not honored, the result will change
assert_array_equal(m.choice(10, size=10, p=np.ones(10)/10.), res)
def test_multivariate_normal_size_types(self):
# Test for multivariate_normal issue with 'size' argument.
# Check that the multivariate_normal size argument can be a
# numpy integer.
np.random.multivariate_normal([0], [[0]], size=1)
np.random.multivariate_normal([0], [[0]], size=np.int_(1))
np.random.multivariate_normal([0], [[0]], size=np.int64(1))
def test_beta_small_parameters(self):
# Test that beta with small a and b parameters does not produce
# NaNs due to roundoff errors causing 0 / 0, gh-5851
np.random.seed(1234567890)
x = np.random.beta(0.0001, 0.0001, size=100)
assert_(not np.any(np.isnan(x)), 'Nans in np.random.beta')
def test_choice_sum_of_probs_tolerance(self):
# The sum of probs should be 1.0 with some tolerance.
# For low precision dtypes the tolerance was too tight.
# See numpy github issue 6123.
np.random.seed(1234)
a = [1, 2, 3]
counts = [4, 4, 2]
for dt in np.float16, np.float32, np.float64:
probs = np.array(counts, dtype=dt) / sum(counts)
c = np.random.choice(a, p=probs)
assert_(c in a)
assert_raises(ValueError, np.random.choice, a, p=probs*0.9)
def test_shuffle_of_array_of_different_length_strings(self):
# Test that permuting an array of different length strings
# will not cause a segfault on garbage collection
# Tests gh-7710
np.random.seed(1234)
a = np.array(['a', 'a' * 1000])
for _ in range(100):
np.random.shuffle(a)
# Force Garbage Collection - should not segfault.
import gc
gc.collect()
def test_shuffle_of_array_of_objects(self):
# Test that permuting an array of objects will not cause
# a segfault on garbage collection.
# See gh-7719
np.random.seed(1234)
a = np.array([np.arange(1), np.arange(4)])
for _ in range(1000):
np.random.shuffle(a)
# Force Garbage Collection - should not segfault.
import gc
gc.collect()
if __name__ == "__main__":
run_module_suite()
| 5,119 | 35.834532 | 85 |
py
|
cba-pipeline-public
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cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/random/tests/test_random.py
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from __future__ import division, absolute_import, print_function
import warnings
import numpy as np
from numpy.testing import (
run_module_suite, assert_, assert_raises, assert_equal, assert_warns,
assert_no_warnings, assert_array_equal, assert_array_almost_equal,
suppress_warnings
)
from numpy import random
import sys
import warnings
class TestSeed(object):
def test_scalar(self):
s = np.random.RandomState(0)
assert_equal(s.randint(1000), 684)
s = np.random.RandomState(4294967295)
assert_equal(s.randint(1000), 419)
def test_array(self):
s = np.random.RandomState(range(10))
assert_equal(s.randint(1000), 468)
s = np.random.RandomState(np.arange(10))
assert_equal(s.randint(1000), 468)
s = np.random.RandomState([0])
assert_equal(s.randint(1000), 973)
s = np.random.RandomState([4294967295])
assert_equal(s.randint(1000), 265)
def test_invalid_scalar(self):
# seed must be an unsigned 32 bit integer
assert_raises(TypeError, np.random.RandomState, -0.5)
assert_raises(ValueError, np.random.RandomState, -1)
def test_invalid_array(self):
# seed must be an unsigned 32 bit integer
assert_raises(TypeError, np.random.RandomState, [-0.5])
assert_raises(ValueError, np.random.RandomState, [-1])
assert_raises(ValueError, np.random.RandomState, [4294967296])
assert_raises(ValueError, np.random.RandomState, [1, 2, 4294967296])
assert_raises(ValueError, np.random.RandomState, [1, -2, 4294967296])
def test_invalid_array_shape(self):
# gh-9832
assert_raises(ValueError, np.random.RandomState, np.array([], dtype=np.int64))
assert_raises(ValueError, np.random.RandomState, [[1, 2, 3]])
assert_raises(ValueError, np.random.RandomState, [[1, 2, 3],
[4, 5, 6]])
class TestBinomial(object):
def test_n_zero(self):
# Tests the corner case of n == 0 for the binomial distribution.
# binomial(0, p) should be zero for any p in [0, 1].
# This test addresses issue #3480.
zeros = np.zeros(2, dtype='int')
for p in [0, .5, 1]:
assert_(random.binomial(0, p) == 0)
assert_array_equal(random.binomial(zeros, p), zeros)
def test_p_is_nan(self):
# Issue #4571.
assert_raises(ValueError, random.binomial, 1, np.nan)
class TestMultinomial(object):
def test_basic(self):
random.multinomial(100, [0.2, 0.8])
def test_zero_probability(self):
random.multinomial(100, [0.2, 0.8, 0.0, 0.0, 0.0])
def test_int_negative_interval(self):
assert_(-5 <= random.randint(-5, -1) < -1)
x = random.randint(-5, -1, 5)
assert_(np.all(-5 <= x))
assert_(np.all(x < -1))
def test_size(self):
# gh-3173
p = [0.5, 0.5]
assert_equal(np.random.multinomial(1, p, np.uint32(1)).shape, (1, 2))
assert_equal(np.random.multinomial(1, p, np.uint32(1)).shape, (1, 2))
assert_equal(np.random.multinomial(1, p, np.uint32(1)).shape, (1, 2))
assert_equal(np.random.multinomial(1, p, [2, 2]).shape, (2, 2, 2))
assert_equal(np.random.multinomial(1, p, (2, 2)).shape, (2, 2, 2))
assert_equal(np.random.multinomial(1, p, np.array((2, 2))).shape,
(2, 2, 2))
assert_raises(TypeError, np.random.multinomial, 1, p,
float(1))
class TestSetState(object):
def setup(self):
self.seed = 1234567890
self.prng = random.RandomState(self.seed)
self.state = self.prng.get_state()
def test_basic(self):
old = self.prng.tomaxint(16)
self.prng.set_state(self.state)
new = self.prng.tomaxint(16)
assert_(np.all(old == new))
def test_gaussian_reset(self):
# Make sure the cached every-other-Gaussian is reset.
old = self.prng.standard_normal(size=3)
self.prng.set_state(self.state)
new = self.prng.standard_normal(size=3)
assert_(np.all(old == new))
def test_gaussian_reset_in_media_res(self):
# When the state is saved with a cached Gaussian, make sure the
# cached Gaussian is restored.
self.prng.standard_normal()
state = self.prng.get_state()
old = self.prng.standard_normal(size=3)
self.prng.set_state(state)
new = self.prng.standard_normal(size=3)
assert_(np.all(old == new))
def test_backwards_compatibility(self):
# Make sure we can accept old state tuples that do not have the
# cached Gaussian value.
old_state = self.state[:-2]
x1 = self.prng.standard_normal(size=16)
self.prng.set_state(old_state)
x2 = self.prng.standard_normal(size=16)
self.prng.set_state(self.state)
x3 = self.prng.standard_normal(size=16)
assert_(np.all(x1 == x2))
assert_(np.all(x1 == x3))
def test_negative_binomial(self):
# Ensure that the negative binomial results take floating point
# arguments without truncation.
self.prng.negative_binomial(0.5, 0.5)
class TestRandint(object):
rfunc = np.random.randint
# valid integer/boolean types
itype = [np.bool_, np.int8, np.uint8, np.int16, np.uint16,
np.int32, np.uint32, np.int64, np.uint64]
def test_unsupported_type(self):
assert_raises(TypeError, self.rfunc, 1, dtype=float)
def test_bounds_checking(self):
for dt in self.itype:
lbnd = 0 if dt is np.bool_ else np.iinfo(dt).min
ubnd = 2 if dt is np.bool_ else np.iinfo(dt).max + 1
assert_raises(ValueError, self.rfunc, lbnd - 1, ubnd, dtype=dt)
assert_raises(ValueError, self.rfunc, lbnd, ubnd + 1, dtype=dt)
assert_raises(ValueError, self.rfunc, ubnd, lbnd, dtype=dt)
assert_raises(ValueError, self.rfunc, 1, 0, dtype=dt)
def test_rng_zero_and_extremes(self):
for dt in self.itype:
lbnd = 0 if dt is np.bool_ else np.iinfo(dt).min
ubnd = 2 if dt is np.bool_ else np.iinfo(dt).max + 1
tgt = ubnd - 1
assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt)
tgt = lbnd
assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt)
tgt = (lbnd + ubnd)//2
assert_equal(self.rfunc(tgt, tgt + 1, size=1000, dtype=dt), tgt)
def test_full_range(self):
# Test for ticket #1690
for dt in self.itype:
lbnd = 0 if dt is np.bool_ else np.iinfo(dt).min
ubnd = 2 if dt is np.bool_ else np.iinfo(dt).max + 1
try:
self.rfunc(lbnd, ubnd, dtype=dt)
except Exception as e:
raise AssertionError("No error should have been raised, "
"but one was with the following "
"message:\n\n%s" % str(e))
def test_in_bounds_fuzz(self):
# Don't use fixed seed
np.random.seed()
for dt in self.itype[1:]:
for ubnd in [4, 8, 16]:
vals = self.rfunc(2, ubnd, size=2**16, dtype=dt)
assert_(vals.max() < ubnd)
assert_(vals.min() >= 2)
vals = self.rfunc(0, 2, size=2**16, dtype=np.bool_)
assert_(vals.max() < 2)
assert_(vals.min() >= 0)
def test_repeatability(self):
import hashlib
# We use a md5 hash of generated sequences of 1000 samples
# in the range [0, 6) for all but bool, where the range
# is [0, 2). Hashes are for little endian numbers.
tgt = {'bool': '7dd3170d7aa461d201a65f8bcf3944b0',
'int16': '1b7741b80964bb190c50d541dca1cac1',
'int32': '4dc9fcc2b395577ebb51793e58ed1a05',
'int64': '17db902806f448331b5a758d7d2ee672',
'int8': '27dd30c4e08a797063dffac2490b0be6',
'uint16': '1b7741b80964bb190c50d541dca1cac1',
'uint32': '4dc9fcc2b395577ebb51793e58ed1a05',
'uint64': '17db902806f448331b5a758d7d2ee672',
'uint8': '27dd30c4e08a797063dffac2490b0be6'}
for dt in self.itype[1:]:
np.random.seed(1234)
# view as little endian for hash
if sys.byteorder == 'little':
val = self.rfunc(0, 6, size=1000, dtype=dt)
else:
val = self.rfunc(0, 6, size=1000, dtype=dt).byteswap()
res = hashlib.md5(val.view(np.int8)).hexdigest()
assert_(tgt[np.dtype(dt).name] == res)
# bools do not depend on endianess
np.random.seed(1234)
val = self.rfunc(0, 2, size=1000, dtype=bool).view(np.int8)
res = hashlib.md5(val).hexdigest()
assert_(tgt[np.dtype(bool).name] == res)
def test_int64_uint64_corner_case(self):
# When stored in Numpy arrays, `lbnd` is casted
# as np.int64, and `ubnd` is casted as np.uint64.
# Checking whether `lbnd` >= `ubnd` used to be
# done solely via direct comparison, which is incorrect
# because when Numpy tries to compare both numbers,
# it casts both to np.float64 because there is
# no integer superset of np.int64 and np.uint64. However,
# `ubnd` is too large to be represented in np.float64,
# causing it be round down to np.iinfo(np.int64).max,
# leading to a ValueError because `lbnd` now equals
# the new `ubnd`.
dt = np.int64
tgt = np.iinfo(np.int64).max
lbnd = np.int64(np.iinfo(np.int64).max)
ubnd = np.uint64(np.iinfo(np.int64).max + 1)
# None of these function calls should
# generate a ValueError now.
actual = np.random.randint(lbnd, ubnd, dtype=dt)
assert_equal(actual, tgt)
def test_respect_dtype_singleton(self):
# See gh-7203
for dt in self.itype:
lbnd = 0 if dt is np.bool_ else np.iinfo(dt).min
ubnd = 2 if dt is np.bool_ else np.iinfo(dt).max + 1
sample = self.rfunc(lbnd, ubnd, dtype=dt)
assert_equal(sample.dtype, np.dtype(dt))
for dt in (bool, int, np.long):
lbnd = 0 if dt is bool else np.iinfo(dt).min
ubnd = 2 if dt is bool else np.iinfo(dt).max + 1
# gh-7284: Ensure that we get Python data types
sample = self.rfunc(lbnd, ubnd, dtype=dt)
assert_(not hasattr(sample, 'dtype'))
assert_equal(type(sample), dt)
class TestRandomDist(object):
# Make sure the random distribution returns the correct value for a
# given seed
def setup(self):
self.seed = 1234567890
def test_rand(self):
np.random.seed(self.seed)
actual = np.random.rand(3, 2)
desired = np.array([[0.61879477158567997, 0.59162362775974664],
[0.88868358904449662, 0.89165480011560816],
[0.4575674820298663, 0.7781880808593471]])
assert_array_almost_equal(actual, desired, decimal=15)
def test_randn(self):
np.random.seed(self.seed)
actual = np.random.randn(3, 2)
desired = np.array([[1.34016345771863121, 1.73759122771936081],
[1.498988344300628, -0.2286433324536169],
[2.031033998682787, 2.17032494605655257]])
assert_array_almost_equal(actual, desired, decimal=15)
def test_randint(self):
np.random.seed(self.seed)
actual = np.random.randint(-99, 99, size=(3, 2))
desired = np.array([[31, 3],
[-52, 41],
[-48, -66]])
assert_array_equal(actual, desired)
def test_random_integers(self):
np.random.seed(self.seed)
with suppress_warnings() as sup:
w = sup.record(DeprecationWarning)
actual = np.random.random_integers(-99, 99, size=(3, 2))
assert_(len(w) == 1)
desired = np.array([[31, 3],
[-52, 41],
[-48, -66]])
assert_array_equal(actual, desired)
def test_random_integers_max_int(self):
# Tests whether random_integers can generate the
# maximum allowed Python int that can be converted
# into a C long. Previous implementations of this
# method have thrown an OverflowError when attempting
# to generate this integer.
with suppress_warnings() as sup:
w = sup.record(DeprecationWarning)
actual = np.random.random_integers(np.iinfo('l').max,
np.iinfo('l').max)
assert_(len(w) == 1)
desired = np.iinfo('l').max
assert_equal(actual, desired)
def test_random_integers_deprecated(self):
with warnings.catch_warnings():
warnings.simplefilter("error", DeprecationWarning)
# DeprecationWarning raised with high == None
assert_raises(DeprecationWarning,
np.random.random_integers,
np.iinfo('l').max)
# DeprecationWarning raised with high != None
assert_raises(DeprecationWarning,
np.random.random_integers,
np.iinfo('l').max, np.iinfo('l').max)
def test_random_sample(self):
np.random.seed(self.seed)
actual = np.random.random_sample((3, 2))
desired = np.array([[0.61879477158567997, 0.59162362775974664],
[0.88868358904449662, 0.89165480011560816],
[0.4575674820298663, 0.7781880808593471]])
assert_array_almost_equal(actual, desired, decimal=15)
def test_choice_uniform_replace(self):
np.random.seed(self.seed)
actual = np.random.choice(4, 4)
desired = np.array([2, 3, 2, 3])
assert_array_equal(actual, desired)
def test_choice_nonuniform_replace(self):
np.random.seed(self.seed)
actual = np.random.choice(4, 4, p=[0.4, 0.4, 0.1, 0.1])
desired = np.array([1, 1, 2, 2])
assert_array_equal(actual, desired)
def test_choice_uniform_noreplace(self):
np.random.seed(self.seed)
actual = np.random.choice(4, 3, replace=False)
desired = np.array([0, 1, 3])
assert_array_equal(actual, desired)
def test_choice_nonuniform_noreplace(self):
np.random.seed(self.seed)
actual = np.random.choice(4, 3, replace=False,
p=[0.1, 0.3, 0.5, 0.1])
desired = np.array([2, 3, 1])
assert_array_equal(actual, desired)
def test_choice_noninteger(self):
np.random.seed(self.seed)
actual = np.random.choice(['a', 'b', 'c', 'd'], 4)
desired = np.array(['c', 'd', 'c', 'd'])
assert_array_equal(actual, desired)
def test_choice_exceptions(self):
sample = np.random.choice
assert_raises(ValueError, sample, -1, 3)
assert_raises(ValueError, sample, 3., 3)
assert_raises(ValueError, sample, [[1, 2], [3, 4]], 3)
assert_raises(ValueError, sample, [], 3)
assert_raises(ValueError, sample, [1, 2, 3, 4], 3,
p=[[0.25, 0.25], [0.25, 0.25]])
assert_raises(ValueError, sample, [1, 2], 3, p=[0.4, 0.4, 0.2])
assert_raises(ValueError, sample, [1, 2], 3, p=[1.1, -0.1])
assert_raises(ValueError, sample, [1, 2], 3, p=[0.4, 0.4])
assert_raises(ValueError, sample, [1, 2, 3], 4, replace=False)
assert_raises(ValueError, sample, [1, 2, 3], 2,
replace=False, p=[1, 0, 0])
def test_choice_return_shape(self):
p = [0.1, 0.9]
# Check scalar
assert_(np.isscalar(np.random.choice(2, replace=True)))
assert_(np.isscalar(np.random.choice(2, replace=False)))
assert_(np.isscalar(np.random.choice(2, replace=True, p=p)))
assert_(np.isscalar(np.random.choice(2, replace=False, p=p)))
assert_(np.isscalar(np.random.choice([1, 2], replace=True)))
assert_(np.random.choice([None], replace=True) is None)
a = np.array([1, 2])
arr = np.empty(1, dtype=object)
arr[0] = a
assert_(np.random.choice(arr, replace=True) is a)
# Check 0-d array
s = tuple()
assert_(not np.isscalar(np.random.choice(2, s, replace=True)))
assert_(not np.isscalar(np.random.choice(2, s, replace=False)))
assert_(not np.isscalar(np.random.choice(2, s, replace=True, p=p)))
assert_(not np.isscalar(np.random.choice(2, s, replace=False, p=p)))
assert_(not np.isscalar(np.random.choice([1, 2], s, replace=True)))
assert_(np.random.choice([None], s, replace=True).ndim == 0)
a = np.array([1, 2])
arr = np.empty(1, dtype=object)
arr[0] = a
assert_(np.random.choice(arr, s, replace=True).item() is a)
# Check multi dimensional array
s = (2, 3)
p = [0.1, 0.1, 0.1, 0.1, 0.4, 0.2]
assert_equal(np.random.choice(6, s, replace=True).shape, s)
assert_equal(np.random.choice(6, s, replace=False).shape, s)
assert_equal(np.random.choice(6, s, replace=True, p=p).shape, s)
assert_equal(np.random.choice(6, s, replace=False, p=p).shape, s)
assert_equal(np.random.choice(np.arange(6), s, replace=True).shape, s)
def test_bytes(self):
np.random.seed(self.seed)
actual = np.random.bytes(10)
desired = b'\x82Ui\x9e\xff\x97+Wf\xa5'
assert_equal(actual, desired)
def test_shuffle(self):
# Test lists, arrays (of various dtypes), and multidimensional versions
# of both, c-contiguous or not:
for conv in [lambda x: np.array([]),
lambda x: x,
lambda x: np.asarray(x).astype(np.int8),
lambda x: np.asarray(x).astype(np.float32),
lambda x: np.asarray(x).astype(np.complex64),
lambda x: np.asarray(x).astype(object),
lambda x: [(i, i) for i in x],
lambda x: np.asarray([[i, i] for i in x]),
lambda x: np.vstack([x, x]).T,
# gh-4270
lambda x: np.asarray([(i, i) for i in x],
[("a", object, 1),
("b", np.int32, 1)])]:
np.random.seed(self.seed)
alist = conv([1, 2, 3, 4, 5, 6, 7, 8, 9, 0])
np.random.shuffle(alist)
actual = alist
desired = conv([0, 1, 9, 6, 2, 4, 5, 8, 7, 3])
assert_array_equal(actual, desired)
def test_shuffle_masked(self):
# gh-3263
a = np.ma.masked_values(np.reshape(range(20), (5, 4)) % 3 - 1, -1)
b = np.ma.masked_values(np.arange(20) % 3 - 1, -1)
a_orig = a.copy()
b_orig = b.copy()
for i in range(50):
np.random.shuffle(a)
assert_equal(
sorted(a.data[~a.mask]), sorted(a_orig.data[~a_orig.mask]))
np.random.shuffle(b)
assert_equal(
sorted(b.data[~b.mask]), sorted(b_orig.data[~b_orig.mask]))
def test_beta(self):
np.random.seed(self.seed)
actual = np.random.beta(.1, .9, size=(3, 2))
desired = np.array(
[[1.45341850513746058e-02, 5.31297615662868145e-04],
[1.85366619058432324e-06, 4.19214516800110563e-03],
[1.58405155108498093e-04, 1.26252891949397652e-04]])
assert_array_almost_equal(actual, desired, decimal=15)
def test_binomial(self):
np.random.seed(self.seed)
actual = np.random.binomial(100.123, .456, size=(3, 2))
desired = np.array([[37, 43],
[42, 48],
[46, 45]])
assert_array_equal(actual, desired)
def test_chisquare(self):
np.random.seed(self.seed)
actual = np.random.chisquare(50, size=(3, 2))
desired = np.array([[63.87858175501090585, 68.68407748911370447],
[65.77116116901505904, 47.09686762438974483],
[72.3828403199695174, 74.18408615260374006]])
assert_array_almost_equal(actual, desired, decimal=13)
def test_dirichlet(self):
np.random.seed(self.seed)
alpha = np.array([51.72840233779265162, 39.74494232180943953])
actual = np.random.mtrand.dirichlet(alpha, size=(3, 2))
desired = np.array([[[0.54539444573611562, 0.45460555426388438],
[0.62345816822039413, 0.37654183177960598]],
[[0.55206000085785778, 0.44793999914214233],
[0.58964023305154301, 0.41035976694845688]],
[[0.59266909280647828, 0.40733090719352177],
[0.56974431743975207, 0.43025568256024799]]])
assert_array_almost_equal(actual, desired, decimal=15)
def test_dirichlet_size(self):
# gh-3173
p = np.array([51.72840233779265162, 39.74494232180943953])
assert_equal(np.random.dirichlet(p, np.uint32(1)).shape, (1, 2))
assert_equal(np.random.dirichlet(p, np.uint32(1)).shape, (1, 2))
assert_equal(np.random.dirichlet(p, np.uint32(1)).shape, (1, 2))
assert_equal(np.random.dirichlet(p, [2, 2]).shape, (2, 2, 2))
assert_equal(np.random.dirichlet(p, (2, 2)).shape, (2, 2, 2))
assert_equal(np.random.dirichlet(p, np.array((2, 2))).shape, (2, 2, 2))
assert_raises(TypeError, np.random.dirichlet, p, float(1))
def test_dirichlet_bad_alpha(self):
# gh-2089
alpha = np.array([5.4e-01, -1.0e-16])
assert_raises(ValueError, np.random.mtrand.dirichlet, alpha)
def test_exponential(self):
np.random.seed(self.seed)
actual = np.random.exponential(1.1234, size=(3, 2))
desired = np.array([[1.08342649775011624, 1.00607889924557314],
[2.46628830085216721, 2.49668106809923884],
[0.68717433461363442, 1.69175666993575979]])
assert_array_almost_equal(actual, desired, decimal=15)
def test_exponential_0(self):
assert_equal(np.random.exponential(scale=0), 0)
assert_raises(ValueError, np.random.exponential, scale=-0.)
def test_f(self):
np.random.seed(self.seed)
actual = np.random.f(12, 77, size=(3, 2))
desired = np.array([[1.21975394418575878, 1.75135759791559775],
[1.44803115017146489, 1.22108959480396262],
[1.02176975757740629, 1.34431827623300415]])
assert_array_almost_equal(actual, desired, decimal=15)
def test_gamma(self):
np.random.seed(self.seed)
actual = np.random.gamma(5, 3, size=(3, 2))
desired = np.array([[24.60509188649287182, 28.54993563207210627],
[26.13476110204064184, 12.56988482927716078],
[31.71863275789960568, 33.30143302795922011]])
assert_array_almost_equal(actual, desired, decimal=14)
def test_gamma_0(self):
assert_equal(np.random.gamma(shape=0, scale=0), 0)
assert_raises(ValueError, np.random.gamma, shape=-0., scale=-0.)
def test_geometric(self):
np.random.seed(self.seed)
actual = np.random.geometric(.123456789, size=(3, 2))
desired = np.array([[8, 7],
[17, 17],
[5, 12]])
assert_array_equal(actual, desired)
def test_gumbel(self):
np.random.seed(self.seed)
actual = np.random.gumbel(loc=.123456789, scale=2.0, size=(3, 2))
desired = np.array([[0.19591898743416816, 0.34405539668096674],
[-1.4492522252274278, -1.47374816298446865],
[1.10651090478803416, -0.69535848626236174]])
assert_array_almost_equal(actual, desired, decimal=15)
def test_gumbel_0(self):
assert_equal(np.random.gumbel(scale=0), 0)
assert_raises(ValueError, np.random.gumbel, scale=-0.)
def test_hypergeometric(self):
np.random.seed(self.seed)
actual = np.random.hypergeometric(10.1, 5.5, 14, size=(3, 2))
desired = np.array([[10, 10],
[10, 10],
[9, 9]])
assert_array_equal(actual, desired)
# Test nbad = 0
actual = np.random.hypergeometric(5, 0, 3, size=4)
desired = np.array([3, 3, 3, 3])
assert_array_equal(actual, desired)
actual = np.random.hypergeometric(15, 0, 12, size=4)
desired = np.array([12, 12, 12, 12])
assert_array_equal(actual, desired)
# Test ngood = 0
actual = np.random.hypergeometric(0, 5, 3, size=4)
desired = np.array([0, 0, 0, 0])
assert_array_equal(actual, desired)
actual = np.random.hypergeometric(0, 15, 12, size=4)
desired = np.array([0, 0, 0, 0])
assert_array_equal(actual, desired)
def test_laplace(self):
np.random.seed(self.seed)
actual = np.random.laplace(loc=.123456789, scale=2.0, size=(3, 2))
desired = np.array([[0.66599721112760157, 0.52829452552221945],
[3.12791959514407125, 3.18202813572992005],
[-0.05391065675859356, 1.74901336242837324]])
assert_array_almost_equal(actual, desired, decimal=15)
def test_laplace_0(self):
assert_equal(np.random.laplace(scale=0), 0)
assert_raises(ValueError, np.random.laplace, scale=-0.)
def test_logistic(self):
np.random.seed(self.seed)
actual = np.random.logistic(loc=.123456789, scale=2.0, size=(3, 2))
desired = np.array([[1.09232835305011444, 0.8648196662399954],
[4.27818590694950185, 4.33897006346929714],
[-0.21682183359214885, 2.63373365386060332]])
assert_array_almost_equal(actual, desired, decimal=15)
def test_lognormal(self):
np.random.seed(self.seed)
actual = np.random.lognormal(mean=.123456789, sigma=2.0, size=(3, 2))
desired = np.array([[16.50698631688883822, 36.54846706092654784],
[22.67886599981281748, 0.71617561058995771],
[65.72798501792723869, 86.84341601437161273]])
assert_array_almost_equal(actual, desired, decimal=13)
def test_lognormal_0(self):
assert_equal(np.random.lognormal(sigma=0), 1)
assert_raises(ValueError, np.random.lognormal, sigma=-0.)
def test_logseries(self):
np.random.seed(self.seed)
actual = np.random.logseries(p=.923456789, size=(3, 2))
desired = np.array([[2, 2],
[6, 17],
[3, 6]])
assert_array_equal(actual, desired)
def test_multinomial(self):
np.random.seed(self.seed)
actual = np.random.multinomial(20, [1/6.]*6, size=(3, 2))
desired = np.array([[[4, 3, 5, 4, 2, 2],
[5, 2, 8, 2, 2, 1]],
[[3, 4, 3, 6, 0, 4],
[2, 1, 4, 3, 6, 4]],
[[4, 4, 2, 5, 2, 3],
[4, 3, 4, 2, 3, 4]]])
assert_array_equal(actual, desired)
def test_multivariate_normal(self):
np.random.seed(self.seed)
mean = (.123456789, 10)
cov = [[1, 0], [0, 1]]
size = (3, 2)
actual = np.random.multivariate_normal(mean, cov, size)
desired = np.array([[[1.463620246718631, 11.73759122771936 ],
[1.622445133300628, 9.771356667546383]],
[[2.154490787682787, 12.170324946056553],
[1.719909438201865, 9.230548443648306]],
[[0.689515026297799, 9.880729819607714],
[-0.023054015651998, 9.201096623542879]]])
assert_array_almost_equal(actual, desired, decimal=15)
# Check for default size, was raising deprecation warning
actual = np.random.multivariate_normal(mean, cov)
desired = np.array([0.895289569463708, 9.17180864067987])
assert_array_almost_equal(actual, desired, decimal=15)
# Check that non positive-semidefinite covariance warns with
# RuntimeWarning
mean = [0, 0]
cov = [[1, 2], [2, 1]]
assert_warns(RuntimeWarning, np.random.multivariate_normal, mean, cov)
# and that it doesn't warn with RuntimeWarning check_valid='ignore'
assert_no_warnings(np.random.multivariate_normal, mean, cov,
check_valid='ignore')
# and that it raises with RuntimeWarning check_valid='raises'
assert_raises(ValueError, np.random.multivariate_normal, mean, cov,
check_valid='raise')
def test_negative_binomial(self):
np.random.seed(self.seed)
actual = np.random.negative_binomial(n=100, p=.12345, size=(3, 2))
desired = np.array([[848, 841],
[892, 611],
[779, 647]])
assert_array_equal(actual, desired)
def test_noncentral_chisquare(self):
np.random.seed(self.seed)
actual = np.random.noncentral_chisquare(df=5, nonc=5, size=(3, 2))
desired = np.array([[23.91905354498517511, 13.35324692733826346],
[31.22452661329736401, 16.60047399466177254],
[5.03461598262724586, 17.94973089023519464]])
assert_array_almost_equal(actual, desired, decimal=14)
actual = np.random.noncentral_chisquare(df=.5, nonc=.2, size=(3, 2))
desired = np.array([[1.47145377828516666, 0.15052899268012659],
[0.00943803056963588, 1.02647251615666169],
[0.332334982684171, 0.15451287602753125]])
assert_array_almost_equal(actual, desired, decimal=14)
np.random.seed(self.seed)
actual = np.random.noncentral_chisquare(df=5, nonc=0, size=(3, 2))
desired = np.array([[9.597154162763948, 11.725484450296079],
[10.413711048138335, 3.694475922923986],
[13.484222138963087, 14.377255424602957]])
assert_array_almost_equal(actual, desired, decimal=14)
def test_noncentral_f(self):
np.random.seed(self.seed)
actual = np.random.noncentral_f(dfnum=5, dfden=2, nonc=1,
size=(3, 2))
desired = np.array([[1.40598099674926669, 0.34207973179285761],
[3.57715069265772545, 7.92632662577829805],
[0.43741599463544162, 1.1774208752428319]])
assert_array_almost_equal(actual, desired, decimal=14)
def test_normal(self):
np.random.seed(self.seed)
actual = np.random.normal(loc=.123456789, scale=2.0, size=(3, 2))
desired = np.array([[2.80378370443726244, 3.59863924443872163],
[3.121433477601256, -0.33382987590723379],
[4.18552478636557357, 4.46410668111310471]])
assert_array_almost_equal(actual, desired, decimal=15)
def test_normal_0(self):
assert_equal(np.random.normal(scale=0), 0)
assert_raises(ValueError, np.random.normal, scale=-0.)
def test_pareto(self):
np.random.seed(self.seed)
actual = np.random.pareto(a=.123456789, size=(3, 2))
desired = np.array(
[[2.46852460439034849e+03, 1.41286880810518346e+03],
[5.28287797029485181e+07, 6.57720981047328785e+07],
[1.40840323350391515e+02, 1.98390255135251704e+05]])
# For some reason on 32-bit x86 Ubuntu 12.10 the [1, 0] entry in this
# matrix differs by 24 nulps. Discussion:
# http://mail.python.org/pipermail/numpy-discussion/2012-September/063801.html
# Consensus is that this is probably some gcc quirk that affects
# rounding but not in any important way, so we just use a looser
# tolerance on this test:
np.testing.assert_array_almost_equal_nulp(actual, desired, nulp=30)
def test_poisson(self):
np.random.seed(self.seed)
actual = np.random.poisson(lam=.123456789, size=(3, 2))
desired = np.array([[0, 0],
[1, 0],
[0, 0]])
assert_array_equal(actual, desired)
def test_poisson_exceptions(self):
lambig = np.iinfo('l').max
lamneg = -1
assert_raises(ValueError, np.random.poisson, lamneg)
assert_raises(ValueError, np.random.poisson, [lamneg]*10)
assert_raises(ValueError, np.random.poisson, lambig)
assert_raises(ValueError, np.random.poisson, [lambig]*10)
def test_power(self):
np.random.seed(self.seed)
actual = np.random.power(a=.123456789, size=(3, 2))
desired = np.array([[0.02048932883240791, 0.01424192241128213],
[0.38446073748535298, 0.39499689943484395],
[0.00177699707563439, 0.13115505880863756]])
assert_array_almost_equal(actual, desired, decimal=15)
def test_rayleigh(self):
np.random.seed(self.seed)
actual = np.random.rayleigh(scale=10, size=(3, 2))
desired = np.array([[13.8882496494248393, 13.383318339044731],
[20.95413364294492098, 21.08285015800712614],
[11.06066537006854311, 17.35468505778271009]])
assert_array_almost_equal(actual, desired, decimal=14)
def test_rayleigh_0(self):
assert_equal(np.random.rayleigh(scale=0), 0)
assert_raises(ValueError, np.random.rayleigh, scale=-0.)
def test_standard_cauchy(self):
np.random.seed(self.seed)
actual = np.random.standard_cauchy(size=(3, 2))
desired = np.array([[0.77127660196445336, -6.55601161955910605],
[0.93582023391158309, -2.07479293013759447],
[-4.74601644297011926, 0.18338989290760804]])
assert_array_almost_equal(actual, desired, decimal=15)
def test_standard_exponential(self):
np.random.seed(self.seed)
actual = np.random.standard_exponential(size=(3, 2))
desired = np.array([[0.96441739162374596, 0.89556604882105506],
[2.1953785836319808, 2.22243285392490542],
[0.6116915921431676, 1.50592546727413201]])
assert_array_almost_equal(actual, desired, decimal=15)
def test_standard_gamma(self):
np.random.seed(self.seed)
actual = np.random.standard_gamma(shape=3, size=(3, 2))
desired = np.array([[5.50841531318455058, 6.62953470301903103],
[5.93988484943779227, 2.31044849402133989],
[7.54838614231317084, 8.012756093271868]])
assert_array_almost_equal(actual, desired, decimal=14)
def test_standard_gamma_0(self):
assert_equal(np.random.standard_gamma(shape=0), 0)
assert_raises(ValueError, np.random.standard_gamma, shape=-0.)
def test_standard_normal(self):
np.random.seed(self.seed)
actual = np.random.standard_normal(size=(3, 2))
desired = np.array([[1.34016345771863121, 1.73759122771936081],
[1.498988344300628, -0.2286433324536169],
[2.031033998682787, 2.17032494605655257]])
assert_array_almost_equal(actual, desired, decimal=15)
def test_standard_t(self):
np.random.seed(self.seed)
actual = np.random.standard_t(df=10, size=(3, 2))
desired = np.array([[0.97140611862659965, -0.08830486548450577],
[1.36311143689505321, -0.55317463909867071],
[-0.18473749069684214, 0.61181537341755321]])
assert_array_almost_equal(actual, desired, decimal=15)
def test_triangular(self):
np.random.seed(self.seed)
actual = np.random.triangular(left=5.12, mode=10.23, right=20.34,
size=(3, 2))
desired = np.array([[12.68117178949215784, 12.4129206149193152],
[16.20131377335158263, 16.25692138747600524],
[11.20400690911820263, 14.4978144835829923]])
assert_array_almost_equal(actual, desired, decimal=14)
def test_uniform(self):
np.random.seed(self.seed)
actual = np.random.uniform(low=1.23, high=10.54, size=(3, 2))
desired = np.array([[6.99097932346268003, 6.73801597444323974],
[9.50364421400426274, 9.53130618907631089],
[5.48995325769805476, 8.47493103280052118]])
assert_array_almost_equal(actual, desired, decimal=15)
def test_uniform_range_bounds(self):
fmin = np.finfo('float').min
fmax = np.finfo('float').max
func = np.random.uniform
assert_raises(OverflowError, func, -np.inf, 0)
assert_raises(OverflowError, func, 0, np.inf)
assert_raises(OverflowError, func, fmin, fmax)
assert_raises(OverflowError, func, [-np.inf], [0])
assert_raises(OverflowError, func, [0], [np.inf])
# (fmax / 1e17) - fmin is within range, so this should not throw
# account for i386 extended precision DBL_MAX / 1e17 + DBL_MAX >
# DBL_MAX by increasing fmin a bit
np.random.uniform(low=np.nextafter(fmin, 1), high=fmax / 1e17)
def test_scalar_exception_propagation(self):
# Tests that exceptions are correctly propagated in distributions
# when called with objects that throw exceptions when converted to
# scalars.
#
# Regression test for gh: 8865
class ThrowingFloat(np.ndarray):
def __float__(self):
raise TypeError
throwing_float = np.array(1.0).view(ThrowingFloat)
assert_raises(TypeError, np.random.uniform, throwing_float, throwing_float)
class ThrowingInteger(np.ndarray):
def __int__(self):
raise TypeError
throwing_int = np.array(1).view(ThrowingInteger)
assert_raises(TypeError, np.random.hypergeometric, throwing_int, 1, 1)
def test_vonmises(self):
np.random.seed(self.seed)
actual = np.random.vonmises(mu=1.23, kappa=1.54, size=(3, 2))
desired = np.array([[2.28567572673902042, 2.89163838442285037],
[0.38198375564286025, 2.57638023113890746],
[1.19153771588353052, 1.83509849681825354]])
assert_array_almost_equal(actual, desired, decimal=15)
def test_vonmises_small(self):
# check infinite loop, gh-4720
np.random.seed(self.seed)
r = np.random.vonmises(mu=0., kappa=1.1e-8, size=10**6)
np.testing.assert_(np.isfinite(r).all())
def test_wald(self):
np.random.seed(self.seed)
actual = np.random.wald(mean=1.23, scale=1.54, size=(3, 2))
desired = np.array([[3.82935265715889983, 5.13125249184285526],
[0.35045403618358717, 1.50832396872003538],
[0.24124319895843183, 0.22031101461955038]])
assert_array_almost_equal(actual, desired, decimal=14)
def test_weibull(self):
np.random.seed(self.seed)
actual = np.random.weibull(a=1.23, size=(3, 2))
desired = np.array([[0.97097342648766727, 0.91422896443565516],
[1.89517770034962929, 1.91414357960479564],
[0.67057783752390987, 1.39494046635066793]])
assert_array_almost_equal(actual, desired, decimal=15)
def test_weibull_0(self):
assert_equal(np.random.weibull(a=0), 0)
assert_raises(ValueError, np.random.weibull, a=-0.)
def test_zipf(self):
np.random.seed(self.seed)
actual = np.random.zipf(a=1.23, size=(3, 2))
desired = np.array([[66, 29],
[1, 1],
[3, 13]])
assert_array_equal(actual, desired)
class TestBroadcast(object):
# tests that functions that broadcast behave
# correctly when presented with non-scalar arguments
def setup(self):
self.seed = 123456789
def setSeed(self):
np.random.seed(self.seed)
# TODO: Include test for randint once it can broadcast
# Can steal the test written in PR #6938
def test_uniform(self):
low = [0]
high = [1]
uniform = np.random.uniform
desired = np.array([0.53283302478975902,
0.53413660089041659,
0.50955303552646702])
self.setSeed()
actual = uniform(low * 3, high)
assert_array_almost_equal(actual, desired, decimal=14)
self.setSeed()
actual = uniform(low, high * 3)
assert_array_almost_equal(actual, desired, decimal=14)
def test_normal(self):
loc = [0]
scale = [1]
bad_scale = [-1]
normal = np.random.normal
desired = np.array([2.2129019979039612,
2.1283977976520019,
1.8417114045748335])
self.setSeed()
actual = normal(loc * 3, scale)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, normal, loc * 3, bad_scale)
self.setSeed()
actual = normal(loc, scale * 3)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, normal, loc, bad_scale * 3)
def test_beta(self):
a = [1]
b = [2]
bad_a = [-1]
bad_b = [-2]
beta = np.random.beta
desired = np.array([0.19843558305989056,
0.075230336409423643,
0.24976865978980844])
self.setSeed()
actual = beta(a * 3, b)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, beta, bad_a * 3, b)
assert_raises(ValueError, beta, a * 3, bad_b)
self.setSeed()
actual = beta(a, b * 3)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, beta, bad_a, b * 3)
assert_raises(ValueError, beta, a, bad_b * 3)
def test_exponential(self):
scale = [1]
bad_scale = [-1]
exponential = np.random.exponential
desired = np.array([0.76106853658845242,
0.76386282278691653,
0.71243813125891797])
self.setSeed()
actual = exponential(scale * 3)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, exponential, bad_scale * 3)
def test_standard_gamma(self):
shape = [1]
bad_shape = [-1]
std_gamma = np.random.standard_gamma
desired = np.array([0.76106853658845242,
0.76386282278691653,
0.71243813125891797])
self.setSeed()
actual = std_gamma(shape * 3)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, std_gamma, bad_shape * 3)
def test_gamma(self):
shape = [1]
scale = [2]
bad_shape = [-1]
bad_scale = [-2]
gamma = np.random.gamma
desired = np.array([1.5221370731769048,
1.5277256455738331,
1.4248762625178359])
self.setSeed()
actual = gamma(shape * 3, scale)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, gamma, bad_shape * 3, scale)
assert_raises(ValueError, gamma, shape * 3, bad_scale)
self.setSeed()
actual = gamma(shape, scale * 3)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, gamma, bad_shape, scale * 3)
assert_raises(ValueError, gamma, shape, bad_scale * 3)
def test_f(self):
dfnum = [1]
dfden = [2]
bad_dfnum = [-1]
bad_dfden = [-2]
f = np.random.f
desired = np.array([0.80038951638264799,
0.86768719635363512,
2.7251095168386801])
self.setSeed()
actual = f(dfnum * 3, dfden)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, f, bad_dfnum * 3, dfden)
assert_raises(ValueError, f, dfnum * 3, bad_dfden)
self.setSeed()
actual = f(dfnum, dfden * 3)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, f, bad_dfnum, dfden * 3)
assert_raises(ValueError, f, dfnum, bad_dfden * 3)
def test_noncentral_f(self):
dfnum = [2]
dfden = [3]
nonc = [4]
bad_dfnum = [0]
bad_dfden = [-1]
bad_nonc = [-2]
nonc_f = np.random.noncentral_f
desired = np.array([9.1393943263705211,
13.025456344595602,
8.8018098359100545])
self.setSeed()
actual = nonc_f(dfnum * 3, dfden, nonc)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, nonc_f, bad_dfnum * 3, dfden, nonc)
assert_raises(ValueError, nonc_f, dfnum * 3, bad_dfden, nonc)
assert_raises(ValueError, nonc_f, dfnum * 3, dfden, bad_nonc)
self.setSeed()
actual = nonc_f(dfnum, dfden * 3, nonc)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, nonc_f, bad_dfnum, dfden * 3, nonc)
assert_raises(ValueError, nonc_f, dfnum, bad_dfden * 3, nonc)
assert_raises(ValueError, nonc_f, dfnum, dfden * 3, bad_nonc)
self.setSeed()
actual = nonc_f(dfnum, dfden, nonc * 3)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, nonc_f, bad_dfnum, dfden, nonc * 3)
assert_raises(ValueError, nonc_f, dfnum, bad_dfden, nonc * 3)
assert_raises(ValueError, nonc_f, dfnum, dfden, bad_nonc * 3)
def test_noncentral_f_small_df(self):
self.setSeed()
desired = np.array([6.869638627492048, 0.785880199263955])
actual = np.random.noncentral_f(0.9, 0.9, 2, size=2)
assert_array_almost_equal(actual, desired, decimal=14)
def test_chisquare(self):
df = [1]
bad_df = [-1]
chisquare = np.random.chisquare
desired = np.array([0.57022801133088286,
0.51947702108840776,
0.1320969254923558])
self.setSeed()
actual = chisquare(df * 3)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, chisquare, bad_df * 3)
def test_noncentral_chisquare(self):
df = [1]
nonc = [2]
bad_df = [-1]
bad_nonc = [-2]
nonc_chi = np.random.noncentral_chisquare
desired = np.array([9.0015599467913763,
4.5804135049718742,
6.0872302432834564])
self.setSeed()
actual = nonc_chi(df * 3, nonc)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, nonc_chi, bad_df * 3, nonc)
assert_raises(ValueError, nonc_chi, df * 3, bad_nonc)
self.setSeed()
actual = nonc_chi(df, nonc * 3)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, nonc_chi, bad_df, nonc * 3)
assert_raises(ValueError, nonc_chi, df, bad_nonc * 3)
def test_standard_t(self):
df = [1]
bad_df = [-1]
t = np.random.standard_t
desired = np.array([3.0702872575217643,
5.8560725167361607,
1.0274791436474273])
self.setSeed()
actual = t(df * 3)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, t, bad_df * 3)
def test_vonmises(self):
mu = [2]
kappa = [1]
bad_kappa = [-1]
vonmises = np.random.vonmises
desired = np.array([2.9883443664201312,
-2.7064099483995943,
-1.8672476700665914])
self.setSeed()
actual = vonmises(mu * 3, kappa)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, vonmises, mu * 3, bad_kappa)
self.setSeed()
actual = vonmises(mu, kappa * 3)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, vonmises, mu, bad_kappa * 3)
def test_pareto(self):
a = [1]
bad_a = [-1]
pareto = np.random.pareto
desired = np.array([1.1405622680198362,
1.1465519762044529,
1.0389564467453547])
self.setSeed()
actual = pareto(a * 3)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, pareto, bad_a * 3)
def test_weibull(self):
a = [1]
bad_a = [-1]
weibull = np.random.weibull
desired = np.array([0.76106853658845242,
0.76386282278691653,
0.71243813125891797])
self.setSeed()
actual = weibull(a * 3)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, weibull, bad_a * 3)
def test_power(self):
a = [1]
bad_a = [-1]
power = np.random.power
desired = np.array([0.53283302478975902,
0.53413660089041659,
0.50955303552646702])
self.setSeed()
actual = power(a * 3)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, power, bad_a * 3)
def test_laplace(self):
loc = [0]
scale = [1]
bad_scale = [-1]
laplace = np.random.laplace
desired = np.array([0.067921356028507157,
0.070715642226971326,
0.019290950698972624])
self.setSeed()
actual = laplace(loc * 3, scale)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, laplace, loc * 3, bad_scale)
self.setSeed()
actual = laplace(loc, scale * 3)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, laplace, loc, bad_scale * 3)
def test_gumbel(self):
loc = [0]
scale = [1]
bad_scale = [-1]
gumbel = np.random.gumbel
desired = np.array([0.2730318639556768,
0.26936705726291116,
0.33906220393037939])
self.setSeed()
actual = gumbel(loc * 3, scale)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, gumbel, loc * 3, bad_scale)
self.setSeed()
actual = gumbel(loc, scale * 3)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, gumbel, loc, bad_scale * 3)
def test_logistic(self):
loc = [0]
scale = [1]
bad_scale = [-1]
logistic = np.random.logistic
desired = np.array([0.13152135837586171,
0.13675915696285773,
0.038216792802833396])
self.setSeed()
actual = logistic(loc * 3, scale)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, logistic, loc * 3, bad_scale)
self.setSeed()
actual = logistic(loc, scale * 3)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, logistic, loc, bad_scale * 3)
def test_lognormal(self):
mean = [0]
sigma = [1]
bad_sigma = [-1]
lognormal = np.random.lognormal
desired = np.array([9.1422086044848427,
8.4013952870126261,
6.3073234116578671])
self.setSeed()
actual = lognormal(mean * 3, sigma)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, lognormal, mean * 3, bad_sigma)
self.setSeed()
actual = lognormal(mean, sigma * 3)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, lognormal, mean, bad_sigma * 3)
def test_rayleigh(self):
scale = [1]
bad_scale = [-1]
rayleigh = np.random.rayleigh
desired = np.array([1.2337491937897689,
1.2360119924878694,
1.1936818095781789])
self.setSeed()
actual = rayleigh(scale * 3)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, rayleigh, bad_scale * 3)
def test_wald(self):
mean = [0.5]
scale = [1]
bad_mean = [0]
bad_scale = [-2]
wald = np.random.wald
desired = np.array([0.11873681120271318,
0.12450084820795027,
0.9096122728408238])
self.setSeed()
actual = wald(mean * 3, scale)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, wald, bad_mean * 3, scale)
assert_raises(ValueError, wald, mean * 3, bad_scale)
self.setSeed()
actual = wald(mean, scale * 3)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, wald, bad_mean, scale * 3)
assert_raises(ValueError, wald, mean, bad_scale * 3)
def test_triangular(self):
left = [1]
right = [3]
mode = [2]
bad_left_one = [3]
bad_mode_one = [4]
bad_left_two, bad_mode_two = right * 2
triangular = np.random.triangular
desired = np.array([2.03339048710429,
2.0347400359389356,
2.0095991069536208])
self.setSeed()
actual = triangular(left * 3, mode, right)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, triangular, bad_left_one * 3, mode, right)
assert_raises(ValueError, triangular, left * 3, bad_mode_one, right)
assert_raises(ValueError, triangular, bad_left_two * 3, bad_mode_two, right)
self.setSeed()
actual = triangular(left, mode * 3, right)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, triangular, bad_left_one, mode * 3, right)
assert_raises(ValueError, triangular, left, bad_mode_one * 3, right)
assert_raises(ValueError, triangular, bad_left_two, bad_mode_two * 3, right)
self.setSeed()
actual = triangular(left, mode, right * 3)
assert_array_almost_equal(actual, desired, decimal=14)
assert_raises(ValueError, triangular, bad_left_one, mode, right * 3)
assert_raises(ValueError, triangular, left, bad_mode_one, right * 3)
assert_raises(ValueError, triangular, bad_left_two, bad_mode_two, right * 3)
def test_binomial(self):
n = [1]
p = [0.5]
bad_n = [-1]
bad_p_one = [-1]
bad_p_two = [1.5]
binom = np.random.binomial
desired = np.array([1, 1, 1])
self.setSeed()
actual = binom(n * 3, p)
assert_array_equal(actual, desired)
assert_raises(ValueError, binom, bad_n * 3, p)
assert_raises(ValueError, binom, n * 3, bad_p_one)
assert_raises(ValueError, binom, n * 3, bad_p_two)
self.setSeed()
actual = binom(n, p * 3)
assert_array_equal(actual, desired)
assert_raises(ValueError, binom, bad_n, p * 3)
assert_raises(ValueError, binom, n, bad_p_one * 3)
assert_raises(ValueError, binom, n, bad_p_two * 3)
def test_negative_binomial(self):
n = [1]
p = [0.5]
bad_n = [-1]
bad_p_one = [-1]
bad_p_two = [1.5]
neg_binom = np.random.negative_binomial
desired = np.array([1, 0, 1])
self.setSeed()
actual = neg_binom(n * 3, p)
assert_array_equal(actual, desired)
assert_raises(ValueError, neg_binom, bad_n * 3, p)
assert_raises(ValueError, neg_binom, n * 3, bad_p_one)
assert_raises(ValueError, neg_binom, n * 3, bad_p_two)
self.setSeed()
actual = neg_binom(n, p * 3)
assert_array_equal(actual, desired)
assert_raises(ValueError, neg_binom, bad_n, p * 3)
assert_raises(ValueError, neg_binom, n, bad_p_one * 3)
assert_raises(ValueError, neg_binom, n, bad_p_two * 3)
def test_poisson(self):
max_lam = np.random.RandomState().poisson_lam_max
lam = [1]
bad_lam_one = [-1]
bad_lam_two = [max_lam * 2]
poisson = np.random.poisson
desired = np.array([1, 1, 0])
self.setSeed()
actual = poisson(lam * 3)
assert_array_equal(actual, desired)
assert_raises(ValueError, poisson, bad_lam_one * 3)
assert_raises(ValueError, poisson, bad_lam_two * 3)
def test_zipf(self):
a = [2]
bad_a = [0]
zipf = np.random.zipf
desired = np.array([2, 2, 1])
self.setSeed()
actual = zipf(a * 3)
assert_array_equal(actual, desired)
assert_raises(ValueError, zipf, bad_a * 3)
with np.errstate(invalid='ignore'):
assert_raises(ValueError, zipf, np.nan)
assert_raises(ValueError, zipf, [0, 0, np.nan])
def test_geometric(self):
p = [0.5]
bad_p_one = [-1]
bad_p_two = [1.5]
geom = np.random.geometric
desired = np.array([2, 2, 2])
self.setSeed()
actual = geom(p * 3)
assert_array_equal(actual, desired)
assert_raises(ValueError, geom, bad_p_one * 3)
assert_raises(ValueError, geom, bad_p_two * 3)
def test_hypergeometric(self):
ngood = [1]
nbad = [2]
nsample = [2]
bad_ngood = [-1]
bad_nbad = [-2]
bad_nsample_one = [0]
bad_nsample_two = [4]
hypergeom = np.random.hypergeometric
desired = np.array([1, 1, 1])
self.setSeed()
actual = hypergeom(ngood * 3, nbad, nsample)
assert_array_equal(actual, desired)
assert_raises(ValueError, hypergeom, bad_ngood * 3, nbad, nsample)
assert_raises(ValueError, hypergeom, ngood * 3, bad_nbad, nsample)
assert_raises(ValueError, hypergeom, ngood * 3, nbad, bad_nsample_one)
assert_raises(ValueError, hypergeom, ngood * 3, nbad, bad_nsample_two)
self.setSeed()
actual = hypergeom(ngood, nbad * 3, nsample)
assert_array_equal(actual, desired)
assert_raises(ValueError, hypergeom, bad_ngood, nbad * 3, nsample)
assert_raises(ValueError, hypergeom, ngood, bad_nbad * 3, nsample)
assert_raises(ValueError, hypergeom, ngood, nbad * 3, bad_nsample_one)
assert_raises(ValueError, hypergeom, ngood, nbad * 3, bad_nsample_two)
self.setSeed()
actual = hypergeom(ngood, nbad, nsample * 3)
assert_array_equal(actual, desired)
assert_raises(ValueError, hypergeom, bad_ngood, nbad, nsample * 3)
assert_raises(ValueError, hypergeom, ngood, bad_nbad, nsample * 3)
assert_raises(ValueError, hypergeom, ngood, nbad, bad_nsample_one * 3)
assert_raises(ValueError, hypergeom, ngood, nbad, bad_nsample_two * 3)
def test_logseries(self):
p = [0.5]
bad_p_one = [2]
bad_p_two = [-1]
logseries = np.random.logseries
desired = np.array([1, 1, 1])
self.setSeed()
actual = logseries(p * 3)
assert_array_equal(actual, desired)
assert_raises(ValueError, logseries, bad_p_one * 3)
assert_raises(ValueError, logseries, bad_p_two * 3)
class TestThread(object):
# make sure each state produces the same sequence even in threads
def setup(self):
self.seeds = range(4)
def check_function(self, function, sz):
from threading import Thread
out1 = np.empty((len(self.seeds),) + sz)
out2 = np.empty((len(self.seeds),) + sz)
# threaded generation
t = [Thread(target=function, args=(np.random.RandomState(s), o))
for s, o in zip(self.seeds, out1)]
[x.start() for x in t]
[x.join() for x in t]
# the same serial
for s, o in zip(self.seeds, out2):
function(np.random.RandomState(s), o)
# these platforms change x87 fpu precision mode in threads
if np.intp().dtype.itemsize == 4 and sys.platform == "win32":
assert_array_almost_equal(out1, out2)
else:
assert_array_equal(out1, out2)
def test_normal(self):
def gen_random(state, out):
out[...] = state.normal(size=10000)
self.check_function(gen_random, sz=(10000,))
def test_exp(self):
def gen_random(state, out):
out[...] = state.exponential(scale=np.ones((100, 1000)))
self.check_function(gen_random, sz=(100, 1000))
def test_multinomial(self):
def gen_random(state, out):
out[...] = state.multinomial(10, [1/6.]*6, size=10000)
self.check_function(gen_random, sz=(10000, 6))
# See Issue #4263
class TestSingleEltArrayInput(object):
def setup(self):
self.argOne = np.array([2])
self.argTwo = np.array([3])
self.argThree = np.array([4])
self.tgtShape = (1,)
def test_one_arg_funcs(self):
funcs = (np.random.exponential, np.random.standard_gamma,
np.random.chisquare, np.random.standard_t,
np.random.pareto, np.random.weibull,
np.random.power, np.random.rayleigh,
np.random.poisson, np.random.zipf,
np.random.geometric, np.random.logseries)
probfuncs = (np.random.geometric, np.random.logseries)
for func in funcs:
if func in probfuncs: # p < 1.0
out = func(np.array([0.5]))
else:
out = func(self.argOne)
assert_equal(out.shape, self.tgtShape)
def test_two_arg_funcs(self):
funcs = (np.random.uniform, np.random.normal,
np.random.beta, np.random.gamma,
np.random.f, np.random.noncentral_chisquare,
np.random.vonmises, np.random.laplace,
np.random.gumbel, np.random.logistic,
np.random.lognormal, np.random.wald,
np.random.binomial, np.random.negative_binomial)
probfuncs = (np.random.binomial, np.random.negative_binomial)
for func in funcs:
if func in probfuncs: # p <= 1
argTwo = np.array([0.5])
else:
argTwo = self.argTwo
out = func(self.argOne, argTwo)
assert_equal(out.shape, self.tgtShape)
out = func(self.argOne[0], argTwo)
assert_equal(out.shape, self.tgtShape)
out = func(self.argOne, argTwo[0])
assert_equal(out.shape, self.tgtShape)
# TODO: Uncomment once randint can broadcast arguments
# def test_randint(self):
# itype = [bool, np.int8, np.uint8, np.int16, np.uint16,
# np.int32, np.uint32, np.int64, np.uint64]
# func = np.random.randint
# high = np.array([1])
# low = np.array([0])
#
# for dt in itype:
# out = func(low, high, dtype=dt)
# self.assert_equal(out.shape, self.tgtShape)
#
# out = func(low[0], high, dtype=dt)
# self.assert_equal(out.shape, self.tgtShape)
#
# out = func(low, high[0], dtype=dt)
# self.assert_equal(out.shape, self.tgtShape)
def test_three_arg_funcs(self):
funcs = [np.random.noncentral_f, np.random.triangular,
np.random.hypergeometric]
for func in funcs:
out = func(self.argOne, self.argTwo, self.argThree)
assert_equal(out.shape, self.tgtShape)
out = func(self.argOne[0], self.argTwo, self.argThree)
assert_equal(out.shape, self.tgtShape)
out = func(self.argOne, self.argTwo[0], self.argThree)
assert_equal(out.shape, self.tgtShape)
if __name__ == "__main__":
run_module_suite()
| 65,295 | 38.814634 | 88 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/random/tests/__init__.py
| 0 | 0 | 0 |
py
|
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/einsumfunc.py
|
"""
Implementation of optimized einsum.
"""
from __future__ import division, absolute_import, print_function
from numpy.compat import basestring
from numpy.core.multiarray import c_einsum
from numpy.core.numeric import asarray, asanyarray, result_type, tensordot, dot
__all__ = ['einsum', 'einsum_path']
einsum_symbols = 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ'
einsum_symbols_set = set(einsum_symbols)
def _compute_size_by_dict(indices, idx_dict):
"""
Computes the product of the elements in indices based on the dictionary
idx_dict.
Parameters
----------
indices : iterable
Indices to base the product on.
idx_dict : dictionary
Dictionary of index sizes
Returns
-------
ret : int
The resulting product.
Examples
--------
>>> _compute_size_by_dict('abbc', {'a': 2, 'b':3, 'c':5})
90
"""
ret = 1
for i in indices:
ret *= idx_dict[i]
return ret
def _find_contraction(positions, input_sets, output_set):
"""
Finds the contraction for a given set of input and output sets.
Parameters
----------
positions : iterable
Integer positions of terms used in the contraction.
input_sets : list
List of sets that represent the lhs side of the einsum subscript
output_set : set
Set that represents the rhs side of the overall einsum subscript
Returns
-------
new_result : set
The indices of the resulting contraction
remaining : list
List of sets that have not been contracted, the new set is appended to
the end of this list
idx_removed : set
Indices removed from the entire contraction
idx_contraction : set
The indices used in the current contraction
Examples
--------
# A simple dot product test case
>>> pos = (0, 1)
>>> isets = [set('ab'), set('bc')]
>>> oset = set('ac')
>>> _find_contraction(pos, isets, oset)
({'a', 'c'}, [{'a', 'c'}], {'b'}, {'a', 'b', 'c'})
# A more complex case with additional terms in the contraction
>>> pos = (0, 2)
>>> isets = [set('abd'), set('ac'), set('bdc')]
>>> oset = set('ac')
>>> _find_contraction(pos, isets, oset)
({'a', 'c'}, [{'a', 'c'}, {'a', 'c'}], {'b', 'd'}, {'a', 'b', 'c', 'd'})
"""
idx_contract = set()
idx_remain = output_set.copy()
remaining = []
for ind, value in enumerate(input_sets):
if ind in positions:
idx_contract |= value
else:
remaining.append(value)
idx_remain |= value
new_result = idx_remain & idx_contract
idx_removed = (idx_contract - new_result)
remaining.append(new_result)
return (new_result, remaining, idx_removed, idx_contract)
def _optimal_path(input_sets, output_set, idx_dict, memory_limit):
"""
Computes all possible pair contractions, sieves the results based
on ``memory_limit`` and returns the lowest cost path. This algorithm
scales factorial with respect to the elements in the list ``input_sets``.
Parameters
----------
input_sets : list
List of sets that represent the lhs side of the einsum subscript
output_set : set
Set that represents the rhs side of the overall einsum subscript
idx_dict : dictionary
Dictionary of index sizes
memory_limit : int
The maximum number of elements in a temporary array
Returns
-------
path : list
The optimal contraction order within the memory limit constraint.
Examples
--------
>>> isets = [set('abd'), set('ac'), set('bdc')]
>>> oset = set('')
>>> idx_sizes = {'a': 1, 'b':2, 'c':3, 'd':4}
>>> _path__optimal_path(isets, oset, idx_sizes, 5000)
[(0, 2), (0, 1)]
"""
full_results = [(0, [], input_sets)]
for iteration in range(len(input_sets) - 1):
iter_results = []
# Compute all unique pairs
comb_iter = []
for x in range(len(input_sets) - iteration):
for y in range(x + 1, len(input_sets) - iteration):
comb_iter.append((x, y))
for curr in full_results:
cost, positions, remaining = curr
for con in comb_iter:
# Find the contraction
cont = _find_contraction(con, remaining, output_set)
new_result, new_input_sets, idx_removed, idx_contract = cont
# Sieve the results based on memory_limit
new_size = _compute_size_by_dict(new_result, idx_dict)
if new_size > memory_limit:
continue
# Find cost
new_cost = _compute_size_by_dict(idx_contract, idx_dict)
if idx_removed:
new_cost *= 2
# Build (total_cost, positions, indices_remaining)
new_cost += cost
new_pos = positions + [con]
iter_results.append((new_cost, new_pos, new_input_sets))
# Update combinatorial list, if we did not find anything return best
# path + remaining contractions
if iter_results:
full_results = iter_results
else:
path = min(full_results, key=lambda x: x[0])[1]
path += [tuple(range(len(input_sets) - iteration))]
return path
# If we have not found anything return single einsum contraction
if len(full_results) == 0:
return [tuple(range(len(input_sets)))]
path = min(full_results, key=lambda x: x[0])[1]
return path
def _greedy_path(input_sets, output_set, idx_dict, memory_limit):
"""
Finds the path by contracting the best pair until the input list is
exhausted. The best pair is found by minimizing the tuple
``(-prod(indices_removed), cost)``. What this amounts to is prioritizing
matrix multiplication or inner product operations, then Hadamard like
operations, and finally outer operations. Outer products are limited by
``memory_limit``. This algorithm scales cubically with respect to the
number of elements in the list ``input_sets``.
Parameters
----------
input_sets : list
List of sets that represent the lhs side of the einsum subscript
output_set : set
Set that represents the rhs side of the overall einsum subscript
idx_dict : dictionary
Dictionary of index sizes
memory_limit_limit : int
The maximum number of elements in a temporary array
Returns
-------
path : list
The greedy contraction order within the memory limit constraint.
Examples
--------
>>> isets = [set('abd'), set('ac'), set('bdc')]
>>> oset = set('')
>>> idx_sizes = {'a': 1, 'b':2, 'c':3, 'd':4}
>>> _path__greedy_path(isets, oset, idx_sizes, 5000)
[(0, 2), (0, 1)]
"""
if len(input_sets) == 1:
return [(0,)]
path = []
for iteration in range(len(input_sets) - 1):
iteration_results = []
comb_iter = []
# Compute all unique pairs
for x in range(len(input_sets)):
for y in range(x + 1, len(input_sets)):
comb_iter.append((x, y))
for positions in comb_iter:
# Find the contraction
contract = _find_contraction(positions, input_sets, output_set)
idx_result, new_input_sets, idx_removed, idx_contract = contract
# Sieve the results based on memory_limit
if _compute_size_by_dict(idx_result, idx_dict) > memory_limit:
continue
# Build sort tuple
removed_size = _compute_size_by_dict(idx_removed, idx_dict)
cost = _compute_size_by_dict(idx_contract, idx_dict)
sort = (-removed_size, cost)
# Add contraction to possible choices
iteration_results.append([sort, positions, new_input_sets])
# If we did not find a new contraction contract remaining
if len(iteration_results) == 0:
path.append(tuple(range(len(input_sets))))
break
# Sort based on first index
best = min(iteration_results, key=lambda x: x[0])
path.append(best[1])
input_sets = best[2]
return path
def _can_dot(inputs, result, idx_removed):
"""
Checks if we can use BLAS (np.tensordot) call and its beneficial to do so.
Parameters
----------
inputs : list of str
Specifies the subscripts for summation.
result : str
Resulting summation.
idx_removed : set
Indices that are removed in the summation
Returns
-------
type : bool
Returns true if BLAS should and can be used, else False
Notes
-----
If the operations is BLAS level 1 or 2 and is not already aligned
we default back to einsum as the memory movement to copy is more
costly than the operation itself.
Examples
--------
# Standard GEMM operation
>>> _can_dot(['ij', 'jk'], 'ik', set('j'))
True
# Can use the standard BLAS, but requires odd data movement
>>> _can_dot(['ijj', 'jk'], 'ik', set('j'))
False
# DDOT where the memory is not aligned
>>> _can_dot(['ijk', 'ikj'], '', set('ijk'))
False
"""
# All `dot` calls remove indices
if len(idx_removed) == 0:
return False
# BLAS can only handle two operands
if len(inputs) != 2:
return False
# Build a few temporaries
input_left, input_right = inputs
set_left = set(input_left)
set_right = set(input_right)
keep_left = set_left - idx_removed
keep_right = set_right - idx_removed
rs = len(idx_removed)
# Indices must overlap between the two operands
if not len(set_left & set_right):
return False
# We cannot have duplicate indices ("ijj, jk -> ik")
if (len(set_left) != len(input_left)) or (len(set_right) != len(input_right)):
return False
# Cannot handle partial inner ("ij, ji -> i")
if len(keep_left & keep_right):
return False
# At this point we are a DOT, GEMV, or GEMM operation
# Handle inner products
# DDOT with aligned data
if input_left == input_right:
return True
# DDOT without aligned data (better to use einsum)
if set_left == set_right:
return False
# Handle the 4 possible (aligned) GEMV or GEMM cases
# GEMM or GEMV no transpose
if input_left[-rs:] == input_right[:rs]:
return True
# GEMM or GEMV transpose both
if input_left[:rs] == input_right[-rs:]:
return True
# GEMM or GEMV transpose right
if input_left[-rs:] == input_right[-rs:]:
return True
# GEMM or GEMV transpose left
if input_left[:rs] == input_right[:rs]:
return True
# Einsum is faster than GEMV if we have to copy data
if not keep_left or not keep_right:
return False
# We are a matrix-matrix product, but we need to copy data
return True
def _parse_einsum_input(operands):
"""
A reproduction of einsum c side einsum parsing in python.
Returns
-------
input_strings : str
Parsed input strings
output_string : str
Parsed output string
operands : list of array_like
The operands to use in the numpy contraction
Examples
--------
The operand list is simplified to reduce printing:
>>> a = np.random.rand(4, 4)
>>> b = np.random.rand(4, 4, 4)
>>> __parse_einsum_input(('...a,...a->...', a, b))
('za,xza', 'xz', [a, b])
>>> __parse_einsum_input((a, [Ellipsis, 0], b, [Ellipsis, 0]))
('za,xza', 'xz', [a, b])
"""
if len(operands) == 0:
raise ValueError("No input operands")
if isinstance(operands[0], basestring):
subscripts = operands[0].replace(" ", "")
operands = [asanyarray(v) for v in operands[1:]]
# Ensure all characters are valid
for s in subscripts:
if s in '.,->':
continue
if s not in einsum_symbols:
raise ValueError("Character %s is not a valid symbol." % s)
else:
tmp_operands = list(operands)
operand_list = []
subscript_list = []
for p in range(len(operands) // 2):
operand_list.append(tmp_operands.pop(0))
subscript_list.append(tmp_operands.pop(0))
output_list = tmp_operands[-1] if len(tmp_operands) else None
operands = [asanyarray(v) for v in operand_list]
subscripts = ""
last = len(subscript_list) - 1
for num, sub in enumerate(subscript_list):
for s in sub:
if s is Ellipsis:
subscripts += "..."
elif isinstance(s, int):
subscripts += einsum_symbols[s]
else:
raise TypeError("For this input type lists must contain "
"either int or Ellipsis")
if num != last:
subscripts += ","
if output_list is not None:
subscripts += "->"
for s in output_list:
if s is Ellipsis:
subscripts += "..."
elif isinstance(s, int):
subscripts += einsum_symbols[s]
else:
raise TypeError("For this input type lists must contain "
"either int or Ellipsis")
# Check for proper "->"
if ("-" in subscripts) or (">" in subscripts):
invalid = (subscripts.count("-") > 1) or (subscripts.count(">") > 1)
if invalid or (subscripts.count("->") != 1):
raise ValueError("Subscripts can only contain one '->'.")
# Parse ellipses
if "." in subscripts:
used = subscripts.replace(".", "").replace(",", "").replace("->", "")
unused = list(einsum_symbols_set - set(used))
ellipse_inds = "".join(unused)
longest = 0
if "->" in subscripts:
input_tmp, output_sub = subscripts.split("->")
split_subscripts = input_tmp.split(",")
out_sub = True
else:
split_subscripts = subscripts.split(',')
out_sub = False
for num, sub in enumerate(split_subscripts):
if "." in sub:
if (sub.count(".") != 3) or (sub.count("...") != 1):
raise ValueError("Invalid Ellipses.")
# Take into account numerical values
if operands[num].shape == ():
ellipse_count = 0
else:
ellipse_count = max(operands[num].ndim, 1)
ellipse_count -= (len(sub) - 3)
if ellipse_count > longest:
longest = ellipse_count
if ellipse_count < 0:
raise ValueError("Ellipses lengths do not match.")
elif ellipse_count == 0:
split_subscripts[num] = sub.replace('...', '')
else:
rep_inds = ellipse_inds[-ellipse_count:]
split_subscripts[num] = sub.replace('...', rep_inds)
subscripts = ",".join(split_subscripts)
if longest == 0:
out_ellipse = ""
else:
out_ellipse = ellipse_inds[-longest:]
if out_sub:
subscripts += "->" + output_sub.replace("...", out_ellipse)
else:
# Special care for outputless ellipses
output_subscript = ""
tmp_subscripts = subscripts.replace(",", "")
for s in sorted(set(tmp_subscripts)):
if s not in (einsum_symbols):
raise ValueError("Character %s is not a valid symbol." % s)
if tmp_subscripts.count(s) == 1:
output_subscript += s
normal_inds = ''.join(sorted(set(output_subscript) -
set(out_ellipse)))
subscripts += "->" + out_ellipse + normal_inds
# Build output string if does not exist
if "->" in subscripts:
input_subscripts, output_subscript = subscripts.split("->")
else:
input_subscripts = subscripts
# Build output subscripts
tmp_subscripts = subscripts.replace(",", "")
output_subscript = ""
for s in sorted(set(tmp_subscripts)):
if s not in einsum_symbols:
raise ValueError("Character %s is not a valid symbol." % s)
if tmp_subscripts.count(s) == 1:
output_subscript += s
# Make sure output subscripts are in the input
for char in output_subscript:
if char not in input_subscripts:
raise ValueError("Output character %s did not appear in the input"
% char)
# Make sure number operands is equivalent to the number of terms
if len(input_subscripts.split(',')) != len(operands):
raise ValueError("Number of einsum subscripts must be equal to the "
"number of operands.")
return (input_subscripts, output_subscript, operands)
def einsum_path(*operands, **kwargs):
"""
einsum_path(subscripts, *operands, optimize='greedy')
Evaluates the lowest cost contraction order for an einsum expression by
considering the creation of intermediate arrays.
Parameters
----------
subscripts : str
Specifies the subscripts for summation.
*operands : list of array_like
These are the arrays for the operation.
optimize : {bool, list, tuple, 'greedy', 'optimal'}
Choose the type of path. If a tuple is provided, the second argument is
assumed to be the maximum intermediate size created. If only a single
argument is provided the largest input or output array size is used
as a maximum intermediate size.
* if a list is given that starts with ``einsum_path``, uses this as the
contraction path
* if False no optimization is taken
* if True defaults to the 'greedy' algorithm
* 'optimal' An algorithm that combinatorially explores all possible
ways of contracting the listed tensors and choosest the least costly
path. Scales exponentially with the number of terms in the
contraction.
* 'greedy' An algorithm that chooses the best pair contraction
at each step. Effectively, this algorithm searches the largest inner,
Hadamard, and then outer products at each step. Scales cubically with
the number of terms in the contraction. Equivalent to the 'optimal'
path for most contractions.
Default is 'greedy'.
Returns
-------
path : list of tuples
A list representation of the einsum path.
string_repr : str
A printable representation of the einsum path.
Notes
-----
The resulting path indicates which terms of the input contraction should be
contracted first, the result of this contraction is then appended to the
end of the contraction list. This list can then be iterated over until all
intermediate contractions are complete.
See Also
--------
einsum, linalg.multi_dot
Examples
--------
We can begin with a chain dot example. In this case, it is optimal to
contract the ``b`` and ``c`` tensors first as reprsented by the first
element of the path ``(1, 2)``. The resulting tensor is added to the end
of the contraction and the remaining contraction ``(0, 1)`` is then
completed.
>>> a = np.random.rand(2, 2)
>>> b = np.random.rand(2, 5)
>>> c = np.random.rand(5, 2)
>>> path_info = np.einsum_path('ij,jk,kl->il', a, b, c, optimize='greedy')
>>> print(path_info[0])
['einsum_path', (1, 2), (0, 1)]
>>> print(path_info[1])
Complete contraction: ij,jk,kl->il
Naive scaling: 4
Optimized scaling: 3
Naive FLOP count: 1.600e+02
Optimized FLOP count: 5.600e+01
Theoretical speedup: 2.857
Largest intermediate: 4.000e+00 elements
-------------------------------------------------------------------------
scaling current remaining
-------------------------------------------------------------------------
3 kl,jk->jl ij,jl->il
3 jl,ij->il il->il
A more complex index transformation example.
>>> I = np.random.rand(10, 10, 10, 10)
>>> C = np.random.rand(10, 10)
>>> path_info = np.einsum_path('ea,fb,abcd,gc,hd->efgh', C, C, I, C, C,
optimize='greedy')
>>> print(path_info[0])
['einsum_path', (0, 2), (0, 3), (0, 2), (0, 1)]
>>> print(path_info[1])
Complete contraction: ea,fb,abcd,gc,hd->efgh
Naive scaling: 8
Optimized scaling: 5
Naive FLOP count: 8.000e+08
Optimized FLOP count: 8.000e+05
Theoretical speedup: 1000.000
Largest intermediate: 1.000e+04 elements
--------------------------------------------------------------------------
scaling current remaining
--------------------------------------------------------------------------
5 abcd,ea->bcde fb,gc,hd,bcde->efgh
5 bcde,fb->cdef gc,hd,cdef->efgh
5 cdef,gc->defg hd,defg->efgh
5 defg,hd->efgh efgh->efgh
"""
# Make sure all keywords are valid
valid_contract_kwargs = ['optimize', 'einsum_call']
unknown_kwargs = [k for (k, v) in kwargs.items() if k
not in valid_contract_kwargs]
if len(unknown_kwargs):
raise TypeError("Did not understand the following kwargs:"
" %s" % unknown_kwargs)
# Figure out what the path really is
path_type = kwargs.pop('optimize', True)
if path_type is True:
path_type = 'greedy'
if path_type is None:
path_type = False
memory_limit = None
# No optimization or a named path algorithm
if (path_type is False) or isinstance(path_type, basestring):
pass
# Given an explicit path
elif len(path_type) and (path_type[0] == 'einsum_path'):
pass
# Path tuple with memory limit
elif ((len(path_type) == 2) and isinstance(path_type[0], basestring) and
isinstance(path_type[1], (int, float))):
memory_limit = int(path_type[1])
path_type = path_type[0]
else:
raise TypeError("Did not understand the path: %s" % str(path_type))
# Hidden option, only einsum should call this
einsum_call_arg = kwargs.pop("einsum_call", False)
# Python side parsing
input_subscripts, output_subscript, operands = _parse_einsum_input(operands)
subscripts = input_subscripts + '->' + output_subscript
# Build a few useful list and sets
input_list = input_subscripts.split(',')
input_sets = [set(x) for x in input_list]
output_set = set(output_subscript)
indices = set(input_subscripts.replace(',', ''))
# Get length of each unique dimension and ensure all dimensions are correct
dimension_dict = {}
for tnum, term in enumerate(input_list):
sh = operands[tnum].shape
if len(sh) != len(term):
raise ValueError("Einstein sum subscript %s does not contain the "
"correct number of indices for operand %d."
% (input_subscripts[tnum], tnum))
for cnum, char in enumerate(term):
dim = sh[cnum]
if char in dimension_dict.keys():
# For broadcasting cases we always want the largest dim size
if dimension_dict[char] == 1:
dimension_dict[char] = dim
elif dim not in (1, dimension_dict[char]):
raise ValueError("Size of label '%s' for operand %d (%d) "
"does not match previous terms (%d)."
% (char, tnum, dimension_dict[char], dim))
else:
dimension_dict[char] = dim
# Compute size of each input array plus the output array
size_list = []
for term in input_list + [output_subscript]:
size_list.append(_compute_size_by_dict(term, dimension_dict))
max_size = max(size_list)
if memory_limit is None:
memory_arg = max_size
else:
memory_arg = memory_limit
# Compute naive cost
# This isnt quite right, need to look into exactly how einsum does this
naive_cost = _compute_size_by_dict(indices, dimension_dict)
indices_in_input = input_subscripts.replace(',', '')
mult = max(len(input_list) - 1, 1)
if (len(indices_in_input) - len(set(indices_in_input))):
mult *= 2
naive_cost *= mult
# Compute the path
if (path_type is False) or (len(input_list) in [1, 2]) or (indices == output_set):
# Nothing to be optimized, leave it to einsum
path = [tuple(range(len(input_list)))]
elif path_type == "greedy":
# Maximum memory should be at most out_size for this algorithm
memory_arg = min(memory_arg, max_size)
path = _greedy_path(input_sets, output_set, dimension_dict, memory_arg)
elif path_type == "optimal":
path = _optimal_path(input_sets, output_set, dimension_dict, memory_arg)
elif path_type[0] == 'einsum_path':
path = path_type[1:]
else:
raise KeyError("Path name %s not found", path_type)
cost_list, scale_list, size_list, contraction_list = [], [], [], []
# Build contraction tuple (positions, gemm, einsum_str, remaining)
for cnum, contract_inds in enumerate(path):
# Make sure we remove inds from right to left
contract_inds = tuple(sorted(list(contract_inds), reverse=True))
contract = _find_contraction(contract_inds, input_sets, output_set)
out_inds, input_sets, idx_removed, idx_contract = contract
cost = _compute_size_by_dict(idx_contract, dimension_dict)
if idx_removed:
cost *= 2
cost_list.append(cost)
scale_list.append(len(idx_contract))
size_list.append(_compute_size_by_dict(out_inds, dimension_dict))
tmp_inputs = []
for x in contract_inds:
tmp_inputs.append(input_list.pop(x))
do_blas = _can_dot(tmp_inputs, out_inds, idx_removed)
# Last contraction
if (cnum - len(path)) == -1:
idx_result = output_subscript
else:
sort_result = [(dimension_dict[ind], ind) for ind in out_inds]
idx_result = "".join([x[1] for x in sorted(sort_result)])
input_list.append(idx_result)
einsum_str = ",".join(tmp_inputs) + "->" + idx_result
contraction = (contract_inds, idx_removed, einsum_str, input_list[:], do_blas)
contraction_list.append(contraction)
opt_cost = sum(cost_list) + 1
if einsum_call_arg:
return (operands, contraction_list)
# Return the path along with a nice string representation
overall_contraction = input_subscripts + "->" + output_subscript
header = ("scaling", "current", "remaining")
speedup = naive_cost / opt_cost
max_i = max(size_list)
path_print = " Complete contraction: %s\n" % overall_contraction
path_print += " Naive scaling: %d\n" % len(indices)
path_print += " Optimized scaling: %d\n" % max(scale_list)
path_print += " Naive FLOP count: %.3e\n" % naive_cost
path_print += " Optimized FLOP count: %.3e\n" % opt_cost
path_print += " Theoretical speedup: %3.3f\n" % speedup
path_print += " Largest intermediate: %.3e elements\n" % max_i
path_print += "-" * 74 + "\n"
path_print += "%6s %24s %40s\n" % header
path_print += "-" * 74
for n, contraction in enumerate(contraction_list):
inds, idx_rm, einsum_str, remaining, blas = contraction
remaining_str = ",".join(remaining) + "->" + output_subscript
path_run = (scale_list[n], einsum_str, remaining_str)
path_print += "\n%4d %24s %40s" % path_run
path = ['einsum_path'] + path
return (path, path_print)
# Rewrite einsum to handle different cases
def einsum(*operands, **kwargs):
"""
einsum(subscripts, *operands, out=None, dtype=None, order='K',
casting='safe', optimize=False)
Evaluates the Einstein summation convention on the operands.
Using the Einstein summation convention, many common multi-dimensional
array operations can be represented in a simple fashion. This function
provides a way to compute such summations. The best way to understand this
function is to try the examples below, which show how many common NumPy
functions can be implemented as calls to `einsum`.
Parameters
----------
subscripts : str
Specifies the subscripts for summation.
operands : list of array_like
These are the arrays for the operation.
out : {ndarray, None}, optional
If provided, the calculation is done into this array.
dtype : {data-type, None}, optional
If provided, forces the calculation to use the data type specified.
Note that you may have to also give a more liberal `casting`
parameter to allow the conversions. Default is None.
order : {'C', 'F', 'A', 'K'}, optional
Controls the memory layout of the output. 'C' means it should
be C contiguous. 'F' means it should be Fortran contiguous,
'A' means it should be 'F' if the inputs are all 'F', 'C' otherwise.
'K' means it should be as close to the layout as the inputs as
is possible, including arbitrarily permuted axes.
Default is 'K'.
casting : {'no', 'equiv', 'safe', 'same_kind', 'unsafe'}, optional
Controls what kind of data casting may occur. Setting this to
'unsafe' is not recommended, as it can adversely affect accumulations.
* 'no' means the data types should not be cast at all.
* 'equiv' means only byte-order changes are allowed.
* 'safe' means only casts which can preserve values are allowed.
* 'same_kind' means only safe casts or casts within a kind,
like float64 to float32, are allowed.
* 'unsafe' means any data conversions may be done.
Default is 'safe'.
optimize : {False, True, 'greedy', 'optimal'}, optional
Controls if intermediate optimization should occur. No optimization
will occur if False and True will default to the 'greedy' algorithm.
Also accepts an explicit contraction list from the ``np.einsum_path``
function. See ``np.einsum_path`` for more details. Default is False.
Returns
-------
output : ndarray
The calculation based on the Einstein summation convention.
See Also
--------
einsum_path, dot, inner, outer, tensordot, linalg.multi_dot
Notes
-----
.. versionadded:: 1.6.0
The subscripts string is a comma-separated list of subscript labels,
where each label refers to a dimension of the corresponding operand.
Repeated subscripts labels in one operand take the diagonal. For example,
``np.einsum('ii', a)`` is equivalent to ``np.trace(a)``.
Whenever a label is repeated, it is summed, so ``np.einsum('i,i', a, b)``
is equivalent to ``np.inner(a,b)``. If a label appears only once,
it is not summed, so ``np.einsum('i', a)`` produces a view of ``a``
with no changes.
The order of labels in the output is by default alphabetical. This
means that ``np.einsum('ij', a)`` doesn't affect a 2D array, while
``np.einsum('ji', a)`` takes its transpose.
The output can be controlled by specifying output subscript labels
as well. This specifies the label order, and allows summing to
be disallowed or forced when desired. The call ``np.einsum('i->', a)``
is like ``np.sum(a, axis=-1)``, and ``np.einsum('ii->i', a)``
is like ``np.diag(a)``. The difference is that `einsum` does not
allow broadcasting by default.
To enable and control broadcasting, use an ellipsis. Default
NumPy-style broadcasting is done by adding an ellipsis
to the left of each term, like ``np.einsum('...ii->...i', a)``.
To take the trace along the first and last axes,
you can do ``np.einsum('i...i', a)``, or to do a matrix-matrix
product with the left-most indices instead of rightmost, you can do
``np.einsum('ij...,jk...->ik...', a, b)``.
When there is only one operand, no axes are summed, and no output
parameter is provided, a view into the operand is returned instead
of a new array. Thus, taking the diagonal as ``np.einsum('ii->i', a)``
produces a view.
An alternative way to provide the subscripts and operands is as
``einsum(op0, sublist0, op1, sublist1, ..., [sublistout])``. The examples
below have corresponding `einsum` calls with the two parameter methods.
.. versionadded:: 1.10.0
Views returned from einsum are now writeable whenever the input array
is writeable. For example, ``np.einsum('ijk...->kji...', a)`` will now
have the same effect as ``np.swapaxes(a, 0, 2)`` and
``np.einsum('ii->i', a)`` will return a writeable view of the diagonal
of a 2D array.
.. versionadded:: 1.12.0
Added the ``optimize`` argument which will optimize the contraction order
of an einsum expression. For a contraction with three or more operands this
can greatly increase the computational efficiency at the cost of a larger
memory footprint during computation.
See ``np.einsum_path`` for more details.
Examples
--------
>>> a = np.arange(25).reshape(5,5)
>>> b = np.arange(5)
>>> c = np.arange(6).reshape(2,3)
>>> np.einsum('ii', a)
60
>>> np.einsum(a, [0,0])
60
>>> np.trace(a)
60
>>> np.einsum('ii->i', a)
array([ 0, 6, 12, 18, 24])
>>> np.einsum(a, [0,0], [0])
array([ 0, 6, 12, 18, 24])
>>> np.diag(a)
array([ 0, 6, 12, 18, 24])
>>> np.einsum('ij,j', a, b)
array([ 30, 80, 130, 180, 230])
>>> np.einsum(a, [0,1], b, [1])
array([ 30, 80, 130, 180, 230])
>>> np.dot(a, b)
array([ 30, 80, 130, 180, 230])
>>> np.einsum('...j,j', a, b)
array([ 30, 80, 130, 180, 230])
>>> np.einsum('ji', c)
array([[0, 3],
[1, 4],
[2, 5]])
>>> np.einsum(c, [1,0])
array([[0, 3],
[1, 4],
[2, 5]])
>>> c.T
array([[0, 3],
[1, 4],
[2, 5]])
>>> np.einsum('..., ...', 3, c)
array([[ 0, 3, 6],
[ 9, 12, 15]])
>>> np.einsum(',ij', 3, C)
array([[ 0, 3, 6],
[ 9, 12, 15]])
>>> np.einsum(3, [Ellipsis], c, [Ellipsis])
array([[ 0, 3, 6],
[ 9, 12, 15]])
>>> np.multiply(3, c)
array([[ 0, 3, 6],
[ 9, 12, 15]])
>>> np.einsum('i,i', b, b)
30
>>> np.einsum(b, [0], b, [0])
30
>>> np.inner(b,b)
30
>>> np.einsum('i,j', np.arange(2)+1, b)
array([[0, 1, 2, 3, 4],
[0, 2, 4, 6, 8]])
>>> np.einsum(np.arange(2)+1, [0], b, [1])
array([[0, 1, 2, 3, 4],
[0, 2, 4, 6, 8]])
>>> np.outer(np.arange(2)+1, b)
array([[0, 1, 2, 3, 4],
[0, 2, 4, 6, 8]])
>>> np.einsum('i...->...', a)
array([50, 55, 60, 65, 70])
>>> np.einsum(a, [0,Ellipsis], [Ellipsis])
array([50, 55, 60, 65, 70])
>>> np.sum(a, axis=0)
array([50, 55, 60, 65, 70])
>>> a = np.arange(60.).reshape(3,4,5)
>>> b = np.arange(24.).reshape(4,3,2)
>>> np.einsum('ijk,jil->kl', a, b)
array([[ 4400., 4730.],
[ 4532., 4874.],
[ 4664., 5018.],
[ 4796., 5162.],
[ 4928., 5306.]])
>>> np.einsum(a, [0,1,2], b, [1,0,3], [2,3])
array([[ 4400., 4730.],
[ 4532., 4874.],
[ 4664., 5018.],
[ 4796., 5162.],
[ 4928., 5306.]])
>>> np.tensordot(a,b, axes=([1,0],[0,1]))
array([[ 4400., 4730.],
[ 4532., 4874.],
[ 4664., 5018.],
[ 4796., 5162.],
[ 4928., 5306.]])
>>> a = np.arange(6).reshape((3,2))
>>> b = np.arange(12).reshape((4,3))
>>> np.einsum('ki,jk->ij', a, b)
array([[10, 28, 46, 64],
[13, 40, 67, 94]])
>>> np.einsum('ki,...k->i...', a, b)
array([[10, 28, 46, 64],
[13, 40, 67, 94]])
>>> np.einsum('k...,jk', a, b)
array([[10, 28, 46, 64],
[13, 40, 67, 94]])
>>> # since version 1.10.0
>>> a = np.zeros((3, 3))
>>> np.einsum('ii->i', a)[:] = 1
>>> a
array([[ 1., 0., 0.],
[ 0., 1., 0.],
[ 0., 0., 1.]])
"""
# Grab non-einsum kwargs
optimize_arg = kwargs.pop('optimize', False)
# If no optimization, run pure einsum
if optimize_arg is False:
return c_einsum(*operands, **kwargs)
valid_einsum_kwargs = ['out', 'dtype', 'order', 'casting']
einsum_kwargs = {k: v for (k, v) in kwargs.items() if
k in valid_einsum_kwargs}
# Make sure all keywords are valid
valid_contract_kwargs = ['optimize'] + valid_einsum_kwargs
unknown_kwargs = [k for (k, v) in kwargs.items() if
k not in valid_contract_kwargs]
if len(unknown_kwargs):
raise TypeError("Did not understand the following kwargs: %s"
% unknown_kwargs)
# Special handeling if out is specified
specified_out = False
out_array = einsum_kwargs.pop('out', None)
if out_array is not None:
specified_out = True
# Build the contraction list and operand
operands, contraction_list = einsum_path(*operands, optimize=optimize_arg,
einsum_call=True)
handle_out = False
# Start contraction loop
for num, contraction in enumerate(contraction_list):
inds, idx_rm, einsum_str, remaining, blas = contraction
tmp_operands = []
for x in inds:
tmp_operands.append(operands.pop(x))
# Do we need to deal with the output?
if specified_out and ((num + 1) == len(contraction_list)):
handle_out = True
# Handle broadcasting vs BLAS cases
if blas:
# Checks have already been handled
input_str, results_index = einsum_str.split('->')
input_left, input_right = input_str.split(',')
if 1 in tmp_operands[0].shape or 1 in tmp_operands[1].shape:
left_dims = {dim: size for dim, size in
zip(input_left, tmp_operands[0].shape)}
right_dims = {dim: size for dim, size in
zip(input_right, tmp_operands[1].shape)}
# If dims do not match we are broadcasting, BLAS off
if any(left_dims[ind] != right_dims[ind] for ind in idx_rm):
blas = False
# Call tensordot if still possible
if blas:
tensor_result = input_left + input_right
for s in idx_rm:
tensor_result = tensor_result.replace(s, "")
# Find indices to contract over
left_pos, right_pos = [], []
for s in idx_rm:
left_pos.append(input_left.find(s))
right_pos.append(input_right.find(s))
# Contract!
new_view = tensordot(*tmp_operands, axes=(tuple(left_pos), tuple(right_pos)))
# Build a new view if needed
if (tensor_result != results_index) or handle_out:
if handle_out:
einsum_kwargs["out"] = out_array
new_view = c_einsum(tensor_result + '->' + results_index, new_view, **einsum_kwargs)
# Call einsum
else:
# If out was specified
if handle_out:
einsum_kwargs["out"] = out_array
# Do the contraction
new_view = c_einsum(einsum_str, *tmp_operands, **einsum_kwargs)
# Append new items and derefernce what we can
operands.append(new_view)
del tmp_operands, new_view
if specified_out:
return out_array
else:
return operands[0]
| 40,704 | 34.120794 | 100 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/arrayprint.py
|
"""Array printing function
$Id: arrayprint.py,v 1.9 2005/09/13 13:58:44 teoliphant Exp $
"""
from __future__ import division, absolute_import, print_function
__all__ = ["array2string", "array_str", "array_repr", "set_string_function",
"set_printoptions", "get_printoptions", "format_float_positional",
"format_float_scientific"]
__docformat__ = 'restructuredtext'
#
# Written by Konrad Hinsen <[email protected]>
# last revision: 1996-3-13
# modified by Jim Hugunin 1997-3-3 for repr's and str's (and other details)
# and by Perry Greenfield 2000-4-1 for numarray
# and by Travis Oliphant 2005-8-22 for numpy
# Note: Both scalartypes.c.src and arrayprint.py implement strs for numpy
# scalars but for different purposes. scalartypes.c.src has str/reprs for when
# the scalar is printed on its own, while arrayprint.py has strs for when
# scalars are printed inside an ndarray. Only the latter strs are currently
# user-customizable.
import sys
import functools
if sys.version_info[0] >= 3:
try:
from _thread import get_ident
except ImportError:
from _dummy_thread import get_ident
else:
try:
from thread import get_ident
except ImportError:
from dummy_thread import get_ident
import numpy as np
from . import numerictypes as _nt
from .umath import absolute, not_equal, isnan, isinf, isfinite, isnat
from . import multiarray
from .multiarray import (array, dragon4_positional, dragon4_scientific,
datetime_as_string, datetime_data, dtype, ndarray,
set_legacy_print_mode)
from .fromnumeric import ravel, any
from .numeric import concatenate, asarray, errstate
from .numerictypes import (longlong, intc, int_, float_, complex_, bool_,
flexible)
import warnings
_format_options = {
'edgeitems': 3, # repr N leading and trailing items of each dimension
'threshold': 1000, # total items > triggers array summarization
'floatmode': 'maxprec',
'precision': 8, # precision of floating point representations
'suppress': False, # suppress printing small floating values in exp format
'linewidth': 75,
'nanstr': 'nan',
'infstr': 'inf',
'sign': '-',
'formatter': None,
'legacy': False}
def _make_options_dict(precision=None, threshold=None, edgeitems=None,
linewidth=None, suppress=None, nanstr=None, infstr=None,
sign=None, formatter=None, floatmode=None, legacy=None):
""" make a dictionary out of the non-None arguments, plus sanity checks """
options = {k: v for k, v in locals().items() if v is not None}
if suppress is not None:
options['suppress'] = bool(suppress)
modes = ['fixed', 'unique', 'maxprec', 'maxprec_equal']
if floatmode not in modes + [None]:
raise ValueError("floatmode option must be one of " +
", ".join('"{}"'.format(m) for m in modes))
if sign not in [None, '-', '+', ' ']:
raise ValueError("sign option must be one of ' ', '+', or '-'")
if legacy not in [None, False, '1.13']:
warnings.warn("legacy printing option can currently only be '1.13' or "
"`False`", stacklevel=3)
return options
def set_printoptions(precision=None, threshold=None, edgeitems=None,
linewidth=None, suppress=None, nanstr=None, infstr=None,
formatter=None, sign=None, floatmode=None, **kwarg):
"""
Set printing options.
These options determine the way floating point numbers, arrays and
other NumPy objects are displayed.
Parameters
----------
precision : int or None, optional
Number of digits of precision for floating point output (default 8).
May be `None` if `floatmode` is not `fixed`, to print as many digits as
necessary to uniquely specify the value.
threshold : int, optional
Total number of array elements which trigger summarization
rather than full repr (default 1000).
edgeitems : int, optional
Number of array items in summary at beginning and end of
each dimension (default 3).
linewidth : int, optional
The number of characters per line for the purpose of inserting
line breaks (default 75).
suppress : bool, optional
If True, always print floating point numbers using fixed point
notation, in which case numbers equal to zero in the current precision
will print as zero. If False, then scientific notation is used when
absolute value of the smallest number is < 1e-4 or the ratio of the
maximum absolute value to the minimum is > 1e3. The default is False.
nanstr : str, optional
String representation of floating point not-a-number (default nan).
infstr : str, optional
String representation of floating point infinity (default inf).
sign : string, either '-', '+', or ' ', optional
Controls printing of the sign of floating-point types. If '+', always
print the sign of positive values. If ' ', always prints a space
(whitespace character) in the sign position of positive values. If
'-', omit the sign character of positive values. (default '-')
formatter : dict of callables, optional
If not None, the keys should indicate the type(s) that the respective
formatting function applies to. Callables should return a string.
Types that are not specified (by their corresponding keys) are handled
by the default formatters. Individual types for which a formatter
can be set are::
- 'bool'
- 'int'
- 'timedelta' : a `numpy.timedelta64`
- 'datetime' : a `numpy.datetime64`
- 'float'
- 'longfloat' : 128-bit floats
- 'complexfloat'
- 'longcomplexfloat' : composed of two 128-bit floats
- 'numpystr' : types `numpy.string_` and `numpy.unicode_`
- 'object' : `np.object_` arrays
- 'str' : all other strings
Other keys that can be used to set a group of types at once are::
- 'all' : sets all types
- 'int_kind' : sets 'int'
- 'float_kind' : sets 'float' and 'longfloat'
- 'complex_kind' : sets 'complexfloat' and 'longcomplexfloat'
- 'str_kind' : sets 'str' and 'numpystr'
floatmode : str, optional
Controls the interpretation of the `precision` option for
floating-point types. Can take the following values:
- 'fixed' : Always print exactly `precision` fractional digits,
even if this would print more or fewer digits than
necessary to specify the value uniquely.
- 'unique : Print the minimum number of fractional digits necessary
to represent each value uniquely. Different elements may
have a different number of digits. The value of the
`precision` option is ignored.
- 'maxprec' : Print at most `precision` fractional digits, but if
an element can be uniquely represented with fewer digits
only print it with that many.
- 'maxprec_equal' : Print at most `precision` fractional digits,
but if every element in the array can be uniquely
represented with an equal number of fewer digits, use that
many digits for all elements.
legacy : string or `False`, optional
If set to the string `'1.13'` enables 1.13 legacy printing mode. This
approximates numpy 1.13 print output by including a space in the sign
position of floats and different behavior for 0d arrays. If set to
`False`, disables legacy mode. Unrecognized strings will be ignored
with a warning for forward compatibility.
.. versionadded:: 1.14.0
See Also
--------
get_printoptions, set_string_function, array2string
Notes
-----
`formatter` is always reset with a call to `set_printoptions`.
Examples
--------
Floating point precision can be set:
>>> np.set_printoptions(precision=4)
>>> print(np.array([1.123456789]))
[ 1.1235]
Long arrays can be summarised:
>>> np.set_printoptions(threshold=5)
>>> print(np.arange(10))
[0 1 2 ..., 7 8 9]
Small results can be suppressed:
>>> eps = np.finfo(float).eps
>>> x = np.arange(4.)
>>> x**2 - (x + eps)**2
array([ -4.9304e-32, -4.4409e-16, 0.0000e+00, 0.0000e+00])
>>> np.set_printoptions(suppress=True)
>>> x**2 - (x + eps)**2
array([-0., -0., 0., 0.])
A custom formatter can be used to display array elements as desired:
>>> np.set_printoptions(formatter={'all':lambda x: 'int: '+str(-x)})
>>> x = np.arange(3)
>>> x
array([int: 0, int: -1, int: -2])
>>> np.set_printoptions() # formatter gets reset
>>> x
array([0, 1, 2])
To put back the default options, you can use:
>>> np.set_printoptions(edgeitems=3,infstr='inf',
... linewidth=75, nanstr='nan', precision=8,
... suppress=False, threshold=1000, formatter=None)
"""
legacy = kwarg.pop('legacy', None)
if kwarg:
msg = "set_printoptions() got unexpected keyword argument '{}'"
raise TypeError(msg.format(kwarg.popitem()[0]))
opt = _make_options_dict(precision, threshold, edgeitems, linewidth,
suppress, nanstr, infstr, sign, formatter,
floatmode, legacy)
# formatter is always reset
opt['formatter'] = formatter
_format_options.update(opt)
# set the C variable for legacy mode
if _format_options['legacy'] == '1.13':
set_legacy_print_mode(113)
# reset the sign option in legacy mode to avoid confusion
_format_options['sign'] = '-'
elif _format_options['legacy'] is False:
set_legacy_print_mode(0)
def get_printoptions():
"""
Return the current print options.
Returns
-------
print_opts : dict
Dictionary of current print options with keys
- precision : int
- threshold : int
- edgeitems : int
- linewidth : int
- suppress : bool
- nanstr : str
- infstr : str
- formatter : dict of callables
- sign : str
For a full description of these options, see `set_printoptions`.
See Also
--------
set_printoptions, set_string_function
"""
return _format_options.copy()
def _leading_trailing(a, edgeitems, index=()):
"""
Keep only the N-D corners (leading and trailing edges) of an array.
Should be passed a base-class ndarray, since it makes no guarantees about
preserving subclasses.
"""
axis = len(index)
if axis == a.ndim:
return a[index]
if a.shape[axis] > 2*edgeitems:
return concatenate((
_leading_trailing(a, edgeitems, index + np.index_exp[ :edgeitems]),
_leading_trailing(a, edgeitems, index + np.index_exp[-edgeitems:])
), axis=axis)
else:
return _leading_trailing(a, edgeitems, index + np.index_exp[:])
def _object_format(o):
""" Object arrays containing lists should be printed unambiguously """
if type(o) is list:
fmt = 'list({!r})'
else:
fmt = '{!r}'
return fmt.format(o)
def repr_format(x):
return repr(x)
def str_format(x):
return str(x)
def _get_formatdict(data, **opt):
prec, fmode = opt['precision'], opt['floatmode']
supp, sign = opt['suppress'], opt['sign']
legacy = opt['legacy']
# wrapped in lambdas to avoid taking a code path with the wrong type of data
formatdict = {
'bool': lambda: BoolFormat(data),
'int': lambda: IntegerFormat(data),
'float': lambda:
FloatingFormat(data, prec, fmode, supp, sign, legacy=legacy),
'longfloat': lambda:
FloatingFormat(data, prec, fmode, supp, sign, legacy=legacy),
'complexfloat': lambda:
ComplexFloatingFormat(data, prec, fmode, supp, sign, legacy=legacy),
'longcomplexfloat': lambda:
ComplexFloatingFormat(data, prec, fmode, supp, sign, legacy=legacy),
'datetime': lambda: DatetimeFormat(data, legacy=legacy),
'timedelta': lambda: TimedeltaFormat(data),
'object': lambda: _object_format,
'void': lambda: str_format,
'numpystr': lambda: repr_format,
'str': lambda: str}
# we need to wrap values in `formatter` in a lambda, so that the interface
# is the same as the above values.
def indirect(x):
return lambda: x
formatter = opt['formatter']
if formatter is not None:
fkeys = [k for k in formatter.keys() if formatter[k] is not None]
if 'all' in fkeys:
for key in formatdict.keys():
formatdict[key] = indirect(formatter['all'])
if 'int_kind' in fkeys:
for key in ['int']:
formatdict[key] = indirect(formatter['int_kind'])
if 'float_kind' in fkeys:
for key in ['float', 'longfloat']:
formatdict[key] = indirect(formatter['float_kind'])
if 'complex_kind' in fkeys:
for key in ['complexfloat', 'longcomplexfloat']:
formatdict[key] = indirect(formatter['complex_kind'])
if 'str_kind' in fkeys:
for key in ['numpystr', 'str']:
formatdict[key] = indirect(formatter['str_kind'])
for key in formatdict.keys():
if key in fkeys:
formatdict[key] = indirect(formatter[key])
return formatdict
def _get_format_function(data, **options):
"""
find the right formatting function for the dtype_
"""
dtype_ = data.dtype
dtypeobj = dtype_.type
formatdict = _get_formatdict(data, **options)
if issubclass(dtypeobj, _nt.bool_):
return formatdict['bool']()
elif issubclass(dtypeobj, _nt.integer):
if issubclass(dtypeobj, _nt.timedelta64):
return formatdict['timedelta']()
else:
return formatdict['int']()
elif issubclass(dtypeobj, _nt.floating):
if issubclass(dtypeobj, _nt.longfloat):
return formatdict['longfloat']()
else:
return formatdict['float']()
elif issubclass(dtypeobj, _nt.complexfloating):
if issubclass(dtypeobj, _nt.clongfloat):
return formatdict['longcomplexfloat']()
else:
return formatdict['complexfloat']()
elif issubclass(dtypeobj, (_nt.unicode_, _nt.string_)):
return formatdict['numpystr']()
elif issubclass(dtypeobj, _nt.datetime64):
return formatdict['datetime']()
elif issubclass(dtypeobj, _nt.object_):
return formatdict['object']()
elif issubclass(dtypeobj, _nt.void):
if dtype_.names is not None:
return StructuredVoidFormat.from_data(data, **options)
else:
return formatdict['void']()
else:
return formatdict['numpystr']()
def _recursive_guard(fillvalue='...'):
"""
Like the python 3.2 reprlib.recursive_repr, but forwards *args and **kwargs
Decorates a function such that if it calls itself with the same first
argument, it returns `fillvalue` instead of recursing.
Largely copied from reprlib.recursive_repr
"""
def decorating_function(f):
repr_running = set()
@functools.wraps(f)
def wrapper(self, *args, **kwargs):
key = id(self), get_ident()
if key in repr_running:
return fillvalue
repr_running.add(key)
try:
return f(self, *args, **kwargs)
finally:
repr_running.discard(key)
return wrapper
return decorating_function
# gracefully handle recursive calls, when object arrays contain themselves
@_recursive_guard()
def _array2string(a, options, separator=' ', prefix=""):
# The formatter __init__s in _get_format_function cannot deal with
# subclasses yet, and we also need to avoid recursion issues in
# _formatArray with subclasses which return 0d arrays in place of scalars
data = asarray(a)
if a.shape == ():
a = data
if a.size > options['threshold']:
summary_insert = "..."
data = _leading_trailing(data, options['edgeitems'])
else:
summary_insert = ""
# find the right formatting function for the array
format_function = _get_format_function(data, **options)
# skip over "["
next_line_prefix = " "
# skip over array(
next_line_prefix += " "*len(prefix)
lst = _formatArray(a, format_function, options['linewidth'],
next_line_prefix, separator, options['edgeitems'],
summary_insert, options['legacy'])
return lst
def array2string(a, max_line_width=None, precision=None,
suppress_small=None, separator=' ', prefix="",
style=np._NoValue, formatter=None, threshold=None,
edgeitems=None, sign=None, floatmode=None, suffix="",
**kwarg):
"""
Return a string representation of an array.
Parameters
----------
a : array_like
Input array.
max_line_width : int, optional
The maximum number of columns the string should span. Newline
characters splits the string appropriately after array elements.
precision : int or None, optional
Floating point precision. Default is the current printing
precision (usually 8), which can be altered using `set_printoptions`.
suppress_small : bool, optional
Represent very small numbers as zero. A number is "very small" if it
is smaller than the current printing precision.
separator : str, optional
Inserted between elements.
prefix : str, optional
suffix: str, optional
The length of the prefix and suffix strings are used to respectively
align and wrap the output. An array is typically printed as::
prefix + array2string(a) + suffix
The output is left-padded by the length of the prefix string, and
wrapping is forced at the column ``max_line_width - len(suffix)``.
style : _NoValue, optional
Has no effect, do not use.
.. deprecated:: 1.14.0
formatter : dict of callables, optional
If not None, the keys should indicate the type(s) that the respective
formatting function applies to. Callables should return a string.
Types that are not specified (by their corresponding keys) are handled
by the default formatters. Individual types for which a formatter
can be set are::
- 'bool'
- 'int'
- 'timedelta' : a `numpy.timedelta64`
- 'datetime' : a `numpy.datetime64`
- 'float'
- 'longfloat' : 128-bit floats
- 'complexfloat'
- 'longcomplexfloat' : composed of two 128-bit floats
- 'void' : type `numpy.void`
- 'numpystr' : types `numpy.string_` and `numpy.unicode_`
- 'str' : all other strings
Other keys that can be used to set a group of types at once are::
- 'all' : sets all types
- 'int_kind' : sets 'int'
- 'float_kind' : sets 'float' and 'longfloat'
- 'complex_kind' : sets 'complexfloat' and 'longcomplexfloat'
- 'str_kind' : sets 'str' and 'numpystr'
threshold : int, optional
Total number of array elements which trigger summarization
rather than full repr.
edgeitems : int, optional
Number of array items in summary at beginning and end of
each dimension.
sign : string, either '-', '+', or ' ', optional
Controls printing of the sign of floating-point types. If '+', always
print the sign of positive values. If ' ', always prints a space
(whitespace character) in the sign position of positive values. If
'-', omit the sign character of positive values.
floatmode : str, optional
Controls the interpretation of the `precision` option for
floating-point types. Can take the following values:
- 'fixed' : Always print exactly `precision` fractional digits,
even if this would print more or fewer digits than
necessary to specify the value uniquely.
- 'unique : Print the minimum number of fractional digits necessary
to represent each value uniquely. Different elements may
have a different number of digits. The value of the
`precision` option is ignored.
- 'maxprec' : Print at most `precision` fractional digits, but if
an element can be uniquely represented with fewer digits
only print it with that many.
- 'maxprec_equal' : Print at most `precision` fractional digits,
but if every element in the array can be uniquely
represented with an equal number of fewer digits, use that
many digits for all elements.
legacy : string or `False`, optional
If set to the string `'1.13'` enables 1.13 legacy printing mode. This
approximates numpy 1.13 print output by including a space in the sign
position of floats and different behavior for 0d arrays. If set to
`False`, disables legacy mode. Unrecognized strings will be ignored
with a warning for forward compatibility.
.. versionadded:: 1.14.0
Returns
-------
array_str : str
String representation of the array.
Raises
------
TypeError
if a callable in `formatter` does not return a string.
See Also
--------
array_str, array_repr, set_printoptions, get_printoptions
Notes
-----
If a formatter is specified for a certain type, the `precision` keyword is
ignored for that type.
This is a very flexible function; `array_repr` and `array_str` are using
`array2string` internally so keywords with the same name should work
identically in all three functions.
Examples
--------
>>> x = np.array([1e-16,1,2,3])
>>> print(np.array2string(x, precision=2, separator=',',
... suppress_small=True))
[ 0., 1., 2., 3.]
>>> x = np.arange(3.)
>>> np.array2string(x, formatter={'float_kind':lambda x: "%.2f" % x})
'[0.00 1.00 2.00]'
>>> x = np.arange(3)
>>> np.array2string(x, formatter={'int':lambda x: hex(x)})
'[0x0L 0x1L 0x2L]'
"""
legacy = kwarg.pop('legacy', None)
if kwarg:
msg = "array2string() got unexpected keyword argument '{}'"
raise TypeError(msg.format(kwarg.popitem()[0]))
overrides = _make_options_dict(precision, threshold, edgeitems,
max_line_width, suppress_small, None, None,
sign, formatter, floatmode, legacy)
options = _format_options.copy()
options.update(overrides)
if options['legacy'] == '1.13':
if style is np._NoValue:
style = repr
if a.shape == () and not a.dtype.names:
return style(a.item())
elif style is not np._NoValue:
# Deprecation 11-9-2017 v1.14
warnings.warn("'style' argument is deprecated and no longer functional"
" except in 1.13 'legacy' mode",
DeprecationWarning, stacklevel=3)
if options['legacy'] != '1.13':
options['linewidth'] -= len(suffix)
# treat as a null array if any of shape elements == 0
if a.size == 0:
return "[]"
return _array2string(a, options, separator, prefix)
def _extendLine(s, line, word, line_width, next_line_prefix, legacy):
needs_wrap = len(line) + len(word) > line_width
if legacy != '1.13':
s# don't wrap lines if it won't help
if len(line) <= len(next_line_prefix):
needs_wrap = False
if needs_wrap:
s += line.rstrip() + "\n"
line = next_line_prefix
line += word
return s, line
def _formatArray(a, format_function, line_width, next_line_prefix,
separator, edge_items, summary_insert, legacy):
"""formatArray is designed for two modes of operation:
1. Full output
2. Summarized output
"""
def recurser(index, hanging_indent, curr_width):
"""
By using this local function, we don't need to recurse with all the
arguments. Since this function is not created recursively, the cost is
not significant
"""
axis = len(index)
axes_left = a.ndim - axis
if axes_left == 0:
return format_function(a[index])
# when recursing, add a space to align with the [ added, and reduce the
# length of the line by 1
next_hanging_indent = hanging_indent + ' '
if legacy == '1.13':
next_width = curr_width
else:
next_width = curr_width - len(']')
a_len = a.shape[axis]
show_summary = summary_insert and 2*edge_items < a_len
if show_summary:
leading_items = edge_items
trailing_items = edge_items
else:
leading_items = 0
trailing_items = a_len
# stringify the array with the hanging indent on the first line too
s = ''
# last axis (rows) - wrap elements if they would not fit on one line
if axes_left == 1:
# the length up until the beginning of the separator / bracket
if legacy == '1.13':
elem_width = curr_width - len(separator.rstrip())
else:
elem_width = curr_width - max(len(separator.rstrip()), len(']'))
line = hanging_indent
for i in range(leading_items):
word = recurser(index + (i,), next_hanging_indent, next_width)
s, line = _extendLine(
s, line, word, elem_width, hanging_indent, legacy)
line += separator
if show_summary:
s, line = _extendLine(
s, line, summary_insert, elem_width, hanging_indent, legacy)
if legacy == '1.13':
line += ", "
else:
line += separator
for i in range(trailing_items, 1, -1):
word = recurser(index + (-i,), next_hanging_indent, next_width)
s, line = _extendLine(
s, line, word, elem_width, hanging_indent, legacy)
line += separator
if legacy == '1.13':
# width of the seperator is not considered on 1.13
elem_width = curr_width
word = recurser(index + (-1,), next_hanging_indent, next_width)
s, line = _extendLine(
s, line, word, elem_width, hanging_indent, legacy)
s += line
# other axes - insert newlines between rows
else:
s = ''
line_sep = separator.rstrip() + '\n'*(axes_left - 1)
for i in range(leading_items):
nested = recurser(index + (i,), next_hanging_indent, next_width)
s += hanging_indent + nested + line_sep
if show_summary:
if legacy == '1.13':
# trailing space, fixed nbr of newlines, and fixed separator
s += hanging_indent + summary_insert + ", \n"
else:
s += hanging_indent + summary_insert + line_sep
for i in range(trailing_items, 1, -1):
nested = recurser(index + (-i,), next_hanging_indent,
next_width)
s += hanging_indent + nested + line_sep
nested = recurser(index + (-1,), next_hanging_indent, next_width)
s += hanging_indent + nested
# remove the hanging indent, and wrap in []
s = '[' + s[len(hanging_indent):] + ']'
return s
try:
# invoke the recursive part with an initial index and prefix
return recurser(index=(),
hanging_indent=next_line_prefix,
curr_width=line_width)
finally:
# recursive closures have a cyclic reference to themselves, which
# requires gc to collect (gh-10620). To avoid this problem, for
# performance and PyPy friendliness, we break the cycle:
recurser = None
def _none_or_positive_arg(x, name):
if x is None:
return -1
if x < 0:
raise ValueError("{} must be >= 0".format(name))
return x
class FloatingFormat(object):
""" Formatter for subtypes of np.floating """
def __init__(self, data, precision, floatmode, suppress_small, sign=False,
**kwarg):
# for backcompatibility, accept bools
if isinstance(sign, bool):
sign = '+' if sign else '-'
self._legacy = kwarg.get('legacy', False)
if self._legacy == '1.13':
# when not 0d, legacy does not support '-'
if data.shape != () and sign == '-':
sign = ' '
self.floatmode = floatmode
if floatmode == 'unique':
self.precision = None
else:
self.precision = precision
self.precision = _none_or_positive_arg(self.precision, 'precision')
self.suppress_small = suppress_small
self.sign = sign
self.exp_format = False
self.large_exponent = False
self.fillFormat(data)
def fillFormat(self, data):
# only the finite values are used to compute the number of digits
finite_vals = data[isfinite(data)]
# choose exponential mode based on the non-zero finite values:
abs_non_zero = absolute(finite_vals[finite_vals != 0])
if len(abs_non_zero) != 0:
max_val = np.max(abs_non_zero)
min_val = np.min(abs_non_zero)
with errstate(over='ignore'): # division can overflow
if max_val >= 1.e8 or (not self.suppress_small and
(min_val < 0.0001 or max_val/min_val > 1000.)):
self.exp_format = True
# do a first pass of printing all the numbers, to determine sizes
if len(finite_vals) == 0:
self.pad_left = 0
self.pad_right = 0
self.trim = '.'
self.exp_size = -1
self.unique = True
elif self.exp_format:
trim, unique = '.', True
if self.floatmode == 'fixed' or self._legacy == '1.13':
trim, unique = 'k', False
strs = (dragon4_scientific(x, precision=self.precision,
unique=unique, trim=trim, sign=self.sign == '+')
for x in finite_vals)
frac_strs, _, exp_strs = zip(*(s.partition('e') for s in strs))
int_part, frac_part = zip(*(s.split('.') for s in frac_strs))
self.exp_size = max(len(s) for s in exp_strs) - 1
self.trim = 'k'
self.precision = max(len(s) for s in frac_part)
# for back-compat with np 1.13, use 2 spaces & sign and full prec
if self._legacy == '1.13':
self.pad_left = 3
else:
# this should be only 1 or 2. Can be calculated from sign.
self.pad_left = max(len(s) for s in int_part)
# pad_right is only needed for nan length calculation
self.pad_right = self.exp_size + 2 + self.precision
self.unique = False
else:
# first pass printing to determine sizes
trim, unique = '.', True
if self.floatmode == 'fixed':
trim, unique = 'k', False
strs = (dragon4_positional(x, precision=self.precision,
fractional=True,
unique=unique, trim=trim,
sign=self.sign == '+')
for x in finite_vals)
int_part, frac_part = zip(*(s.split('.') for s in strs))
if self._legacy == '1.13':
self.pad_left = 1 + max(len(s.lstrip('-+')) for s in int_part)
else:
self.pad_left = max(len(s) for s in int_part)
self.pad_right = max(len(s) for s in frac_part)
self.exp_size = -1
if self.floatmode in ['fixed', 'maxprec_equal']:
self.precision = self.pad_right
self.unique = False
self.trim = 'k'
else:
self.unique = True
self.trim = '.'
if self._legacy != '1.13':
# account for sign = ' ' by adding one to pad_left
if self.sign == ' ' and not any(np.signbit(finite_vals)):
self.pad_left += 1
# if there are non-finite values, may need to increase pad_left
if data.size != finite_vals.size:
neginf = self.sign != '-' or any(data[isinf(data)] < 0)
nanlen = len(_format_options['nanstr'])
inflen = len(_format_options['infstr']) + neginf
offset = self.pad_right + 1 # +1 for decimal pt
self.pad_left = max(self.pad_left, nanlen - offset, inflen - offset)
def __call__(self, x):
if not np.isfinite(x):
with errstate(invalid='ignore'):
if np.isnan(x):
sign = '+' if self.sign == '+' else ''
ret = sign + _format_options['nanstr']
else: # isinf
sign = '-' if x < 0 else '+' if self.sign == '+' else ''
ret = sign + _format_options['infstr']
return ' '*(self.pad_left + self.pad_right + 1 - len(ret)) + ret
if self.exp_format:
return dragon4_scientific(x,
precision=self.precision,
unique=self.unique,
trim=self.trim,
sign=self.sign == '+',
pad_left=self.pad_left,
exp_digits=self.exp_size)
else:
return dragon4_positional(x,
precision=self.precision,
unique=self.unique,
fractional=True,
trim=self.trim,
sign=self.sign == '+',
pad_left=self.pad_left,
pad_right=self.pad_right)
# for back-compatibility, we keep the classes for each float type too
class FloatFormat(FloatingFormat):
def __init__(self, *args, **kwargs):
warnings.warn("FloatFormat has been replaced by FloatingFormat",
DeprecationWarning, stacklevel=2)
super(FloatFormat, self).__init__(*args, **kwargs)
class LongFloatFormat(FloatingFormat):
def __init__(self, *args, **kwargs):
warnings.warn("LongFloatFormat has been replaced by FloatingFormat",
DeprecationWarning, stacklevel=2)
super(LongFloatFormat, self).__init__(*args, **kwargs)
def format_float_scientific(x, precision=None, unique=True, trim='k',
sign=False, pad_left=None, exp_digits=None):
"""
Format a floating-point scalar as a decimal string in scientific notation.
Provides control over rounding, trimming and padding. Uses and assumes
IEEE unbiased rounding. Uses the "Dragon4" algorithm.
Parameters
----------
x : python float or numpy floating scalar
Value to format.
precision : non-negative integer or None, optional
Maximum number of digits to print. May be None if `unique` is
`True`, but must be an integer if unique is `False`.
unique : boolean, optional
If `True`, use a digit-generation strategy which gives the shortest
representation which uniquely identifies the floating-point number from
other values of the same type, by judicious rounding. If `precision`
was omitted, print all necessary digits, otherwise digit generation is
cut off after `precision` digits and the remaining value is rounded.
If `False`, digits are generated as if printing an infinite-precision
value and stopping after `precision` digits, rounding the remaining
value.
trim : one of 'k', '.', '0', '-', optional
Controls post-processing trimming of trailing digits, as follows:
k : keep trailing zeros, keep decimal point (no trimming)
. : trim all trailing zeros, leave decimal point
0 : trim all but the zero before the decimal point. Insert the
zero if it is missing.
- : trim trailing zeros and any trailing decimal point
sign : boolean, optional
Whether to show the sign for positive values.
pad_left : non-negative integer, optional
Pad the left side of the string with whitespace until at least that
many characters are to the left of the decimal point.
exp_digits : non-negative integer, optional
Pad the exponent with zeros until it contains at least this many digits.
If omitted, the exponent will be at least 2 digits.
Returns
-------
rep : string
The string representation of the floating point value
See Also
--------
format_float_positional
Examples
--------
>>> np.format_float_scientific(np.float32(np.pi))
'3.1415927e+00'
>>> s = np.float32(1.23e24)
>>> np.format_float_scientific(s, unique=False, precision=15)
'1.230000071797338e+24'
>>> np.format_float_scientific(s, exp_digits=4)
'1.23e+0024'
"""
precision = _none_or_positive_arg(precision, 'precision')
pad_left = _none_or_positive_arg(pad_left, 'pad_left')
exp_digits = _none_or_positive_arg(exp_digits, 'exp_digits')
return dragon4_scientific(x, precision=precision, unique=unique,
trim=trim, sign=sign, pad_left=pad_left,
exp_digits=exp_digits)
def format_float_positional(x, precision=None, unique=True,
fractional=True, trim='k', sign=False,
pad_left=None, pad_right=None):
"""
Format a floating-point scalar as a decimal string in positional notation.
Provides control over rounding, trimming and padding. Uses and assumes
IEEE unbiased rounding. Uses the "Dragon4" algorithm.
Parameters
----------
x : python float or numpy floating scalar
Value to format.
precision : non-negative integer or None, optional
Maximum number of digits to print. May be None if `unique` is
`True`, but must be an integer if unique is `False`.
unique : boolean, optional
If `True`, use a digit-generation strategy which gives the shortest
representation which uniquely identifies the floating-point number from
other values of the same type, by judicious rounding. If `precision`
was omitted, print out all necessary digits, otherwise digit generation
is cut off after `precision` digits and the remaining value is rounded.
If `False`, digits are generated as if printing an infinite-precision
value and stopping after `precision` digits, rounding the remaining
value.
fractional : boolean, optional
If `True`, the cutoff of `precision` digits refers to the total number
of digits after the decimal point, including leading zeros.
If `False`, `precision` refers to the total number of significant
digits, before or after the decimal point, ignoring leading zeros.
trim : one of 'k', '.', '0', '-', optional
Controls post-processing trimming of trailing digits, as follows:
k : keep trailing zeros, keep decimal point (no trimming)
. : trim all trailing zeros, leave decimal point
0 : trim all but the zero before the decimal point. Insert the
zero if it is missing.
- : trim trailing zeros and any trailing decimal point
sign : boolean, optional
Whether to show the sign for positive values.
pad_left : non-negative integer, optional
Pad the left side of the string with whitespace until at least that
many characters are to the left of the decimal point.
pad_right : non-negative integer, optional
Pad the right side of the string with whitespace until at least that
many characters are to the right of the decimal point.
Returns
-------
rep : string
The string representation of the floating point value
See Also
--------
format_float_scientific
Examples
--------
>>> np.format_float_scientific(np.float32(np.pi))
'3.1415927'
>>> np.format_float_positional(np.float16(np.pi))
'3.14'
>>> np.format_float_positional(np.float16(0.3))
'0.3'
>>> np.format_float_positional(np.float16(0.3), unique=False, precision=10)
'0.3000488281'
"""
precision = _none_or_positive_arg(precision, 'precision')
pad_left = _none_or_positive_arg(pad_left, 'pad_left')
pad_right = _none_or_positive_arg(pad_right, 'pad_right')
return dragon4_positional(x, precision=precision, unique=unique,
fractional=fractional, trim=trim,
sign=sign, pad_left=pad_left,
pad_right=pad_right)
class IntegerFormat(object):
def __init__(self, data):
if data.size > 0:
max_str_len = max(len(str(np.max(data))),
len(str(np.min(data))))
else:
max_str_len = 0
self.format = '%{}d'.format(max_str_len)
def __call__(self, x):
return self.format % x
class BoolFormat(object):
def __init__(self, data, **kwargs):
# add an extra space so " True" and "False" have the same length and
# array elements align nicely when printed, except in 0d arrays
self.truestr = ' True' if data.shape != () else 'True'
def __call__(self, x):
return self.truestr if x else "False"
class ComplexFloatingFormat(object):
""" Formatter for subtypes of np.complexfloating """
def __init__(self, x, precision, floatmode, suppress_small,
sign=False, **kwarg):
# for backcompatibility, accept bools
if isinstance(sign, bool):
sign = '+' if sign else '-'
floatmode_real = floatmode_imag = floatmode
if kwarg.get('legacy', False) == '1.13':
floatmode_real = 'maxprec_equal'
floatmode_imag = 'maxprec'
self.real_format = FloatingFormat(x.real, precision, floatmode_real,
suppress_small, sign=sign, **kwarg)
self.imag_format = FloatingFormat(x.imag, precision, floatmode_imag,
suppress_small, sign='+', **kwarg)
def __call__(self, x):
r = self.real_format(x.real)
i = self.imag_format(x.imag)
# add the 'j' before the terminal whitespace in i
sp = len(i.rstrip())
i = i[:sp] + 'j' + i[sp:]
return r + i
# for back-compatibility, we keep the classes for each complex type too
class ComplexFormat(ComplexFloatingFormat):
def __init__(self, *args, **kwargs):
warnings.warn(
"ComplexFormat has been replaced by ComplexFloatingFormat",
DeprecationWarning, stacklevel=2)
super(ComplexFormat, self).__init__(*args, **kwargs)
class LongComplexFormat(ComplexFloatingFormat):
def __init__(self, *args, **kwargs):
warnings.warn(
"LongComplexFormat has been replaced by ComplexFloatingFormat",
DeprecationWarning, stacklevel=2)
super(LongComplexFormat, self).__init__(*args, **kwargs)
class _TimelikeFormat(object):
def __init__(self, data):
non_nat = data[~isnat(data)]
if len(non_nat) > 0:
# Max str length of non-NaT elements
max_str_len = max(len(self._format_non_nat(np.max(non_nat))),
len(self._format_non_nat(np.min(non_nat))))
else:
max_str_len = 0
if len(non_nat) < data.size:
# data contains a NaT
max_str_len = max(max_str_len, 5)
self._format = '%{}s'.format(max_str_len)
self._nat = "'NaT'".rjust(max_str_len)
def _format_non_nat(self, x):
# override in subclass
raise NotImplementedError
def __call__(self, x):
if isnat(x):
return self._nat
else:
return self._format % self._format_non_nat(x)
class DatetimeFormat(_TimelikeFormat):
def __init__(self, x, unit=None, timezone=None, casting='same_kind',
legacy=False):
# Get the unit from the dtype
if unit is None:
if x.dtype.kind == 'M':
unit = datetime_data(x.dtype)[0]
else:
unit = 's'
if timezone is None:
timezone = 'naive'
self.timezone = timezone
self.unit = unit
self.casting = casting
self.legacy = legacy
# must be called after the above are configured
super(DatetimeFormat, self).__init__(x)
def __call__(self, x):
if self.legacy == '1.13':
return self._format_non_nat(x)
return super(DatetimeFormat, self).__call__(x)
def _format_non_nat(self, x):
return "'%s'" % datetime_as_string(x,
unit=self.unit,
timezone=self.timezone,
casting=self.casting)
class TimedeltaFormat(_TimelikeFormat):
def _format_non_nat(self, x):
return str(x.astype('i8'))
class SubArrayFormat(object):
def __init__(self, format_function):
self.format_function = format_function
def __call__(self, arr):
if arr.ndim <= 1:
return "[" + ", ".join(self.format_function(a) for a in arr) + "]"
return "[" + ", ".join(self.__call__(a) for a in arr) + "]"
class StructuredVoidFormat(object):
"""
Formatter for structured np.void objects.
This does not work on structured alias types like np.dtype(('i4', 'i2,i2')),
as alias scalars lose their field information, and the implementation
relies upon np.void.__getitem__.
"""
def __init__(self, format_functions):
self.format_functions = format_functions
@classmethod
def from_data(cls, data, **options):
"""
This is a second way to initialize StructuredVoidFormat, using the raw data
as input. Added to avoid changing the signature of __init__.
"""
format_functions = []
for field_name in data.dtype.names:
format_function = _get_format_function(data[field_name], **options)
if data.dtype[field_name].shape != ():
format_function = SubArrayFormat(format_function)
format_functions.append(format_function)
return cls(format_functions)
def __call__(self, x):
str_fields = [
format_function(field)
for field, format_function in zip(x, self.format_functions)
]
if len(str_fields) == 1:
return "({},)".format(str_fields[0])
else:
return "({})".format(", ".join(str_fields))
# for backwards compatibility
class StructureFormat(StructuredVoidFormat):
def __init__(self, *args, **kwargs):
# NumPy 1.14, 2018-02-14
warnings.warn(
"StructureFormat has been replaced by StructuredVoidFormat",
DeprecationWarning, stacklevel=2)
super(StructureFormat, self).__init__(*args, **kwargs)
def _void_scalar_repr(x):
"""
Implements the repr for structured-void scalars. It is called from the
scalartypes.c.src code, and is placed here because it uses the elementwise
formatters defined above.
"""
return StructuredVoidFormat.from_data(array(x), **_format_options)(x)
_typelessdata = [int_, float_, complex_, bool_]
if issubclass(intc, int):
_typelessdata.append(intc)
if issubclass(longlong, int):
_typelessdata.append(longlong)
def dtype_is_implied(dtype):
"""
Determine if the given dtype is implied by the representation of its values.
Parameters
----------
dtype : dtype
Data type
Returns
-------
implied : bool
True if the dtype is implied by the representation of its values.
Examples
--------
>>> np.core.arrayprint.dtype_is_implied(int)
True
>>> np.array([1, 2, 3], int)
array([1, 2, 3])
>>> np.core.arrayprint.dtype_is_implied(np.int8)
False
>>> np.array([1, 2, 3], np.int8)
array([1, 2, 3], dtype=np.int8)
"""
dtype = np.dtype(dtype)
if _format_options['legacy'] == '1.13' and dtype.type == bool_:
return False
# not just void types can be structured, and names are not part of the repr
if dtype.names is not None:
return False
return dtype.type in _typelessdata
def dtype_short_repr(dtype):
"""
Convert a dtype to a short form which evaluates to the same dtype.
The intent is roughly that the following holds
>>> from numpy import *
>>> assert eval(dtype_short_repr(dt)) == dt
"""
if dtype.names is not None:
# structured dtypes give a list or tuple repr
return str(dtype)
elif issubclass(dtype.type, flexible):
# handle these separately so they don't give garbage like str256
return "'%s'" % str(dtype)
typename = dtype.name
# quote typenames which can't be represented as python variable names
if typename and not (typename[0].isalpha() and typename.isalnum()):
typename = repr(typename)
return typename
def array_repr(arr, max_line_width=None, precision=None, suppress_small=None):
"""
Return the string representation of an array.
Parameters
----------
arr : ndarray
Input array.
max_line_width : int, optional
The maximum number of columns the string should span. Newline
characters split the string appropriately after array elements.
precision : int, optional
Floating point precision. Default is the current printing precision
(usually 8), which can be altered using `set_printoptions`.
suppress_small : bool, optional
Represent very small numbers as zero, default is False. Very small
is defined by `precision`, if the precision is 8 then
numbers smaller than 5e-9 are represented as zero.
Returns
-------
string : str
The string representation of an array.
See Also
--------
array_str, array2string, set_printoptions
Examples
--------
>>> np.array_repr(np.array([1,2]))
'array([1, 2])'
>>> np.array_repr(np.ma.array([0.]))
'MaskedArray([ 0.])'
>>> np.array_repr(np.array([], np.int32))
'array([], dtype=int32)'
>>> x = np.array([1e-6, 4e-7, 2, 3])
>>> np.array_repr(x, precision=6, suppress_small=True)
'array([ 0.000001, 0. , 2. , 3. ])'
"""
if max_line_width is None:
max_line_width = _format_options['linewidth']
if type(arr) is not ndarray:
class_name = type(arr).__name__
else:
class_name = "array"
skipdtype = dtype_is_implied(arr.dtype) and arr.size > 0
prefix = class_name + "("
suffix = ")" if skipdtype else ","
if (_format_options['legacy'] == '1.13' and
arr.shape == () and not arr.dtype.names):
lst = repr(arr.item())
elif arr.size > 0 or arr.shape == (0,):
lst = array2string(arr, max_line_width, precision, suppress_small,
', ', prefix, suffix=suffix)
else: # show zero-length shape unless it is (0,)
lst = "[], shape=%s" % (repr(arr.shape),)
arr_str = prefix + lst + suffix
if skipdtype:
return arr_str
dtype_str = "dtype={})".format(dtype_short_repr(arr.dtype))
# compute whether we should put dtype on a new line: Do so if adding the
# dtype would extend the last line past max_line_width.
# Note: This line gives the correct result even when rfind returns -1.
last_line_len = len(arr_str) - (arr_str.rfind('\n') + 1)
spacer = " "
if _format_options['legacy'] == '1.13':
if issubclass(arr.dtype.type, flexible):
spacer = '\n' + ' '*len(class_name + "(")
elif last_line_len + len(dtype_str) + 1 > max_line_width:
spacer = '\n' + ' '*len(class_name + "(")
return arr_str + spacer + dtype_str
_guarded_str = _recursive_guard()(str)
def array_str(a, max_line_width=None, precision=None, suppress_small=None):
"""
Return a string representation of the data in an array.
The data in the array is returned as a single string. This function is
similar to `array_repr`, the difference being that `array_repr` also
returns information on the kind of array and its data type.
Parameters
----------
a : ndarray
Input array.
max_line_width : int, optional
Inserts newlines if text is longer than `max_line_width`. The
default is, indirectly, 75.
precision : int, optional
Floating point precision. Default is the current printing precision
(usually 8), which can be altered using `set_printoptions`.
suppress_small : bool, optional
Represent numbers "very close" to zero as zero; default is False.
Very close is defined by precision: if the precision is 8, e.g.,
numbers smaller (in absolute value) than 5e-9 are represented as
zero.
See Also
--------
array2string, array_repr, set_printoptions
Examples
--------
>>> np.array_str(np.arange(3))
'[0 1 2]'
"""
if (_format_options['legacy'] == '1.13' and
a.shape == () and not a.dtype.names):
return str(a.item())
# the str of 0d arrays is a special case: It should appear like a scalar,
# so floats are not truncated by `precision`, and strings are not wrapped
# in quotes. So we return the str of the scalar value.
if a.shape == ():
# obtain a scalar and call str on it, avoiding problems for subclasses
# for which indexing with () returns a 0d instead of a scalar by using
# ndarray's getindex. Also guard against recursive 0d object arrays.
return _guarded_str(np.ndarray.__getitem__(a, ()))
return array2string(a, max_line_width, precision, suppress_small, ' ', "")
def set_string_function(f, repr=True):
"""
Set a Python function to be used when pretty printing arrays.
Parameters
----------
f : function or None
Function to be used to pretty print arrays. The function should expect
a single array argument and return a string of the representation of
the array. If None, the function is reset to the default NumPy function
to print arrays.
repr : bool, optional
If True (default), the function for pretty printing (``__repr__``)
is set, if False the function that returns the default string
representation (``__str__``) is set.
See Also
--------
set_printoptions, get_printoptions
Examples
--------
>>> def pprint(arr):
... return 'HA! - What are you going to do now?'
...
>>> np.set_string_function(pprint)
>>> a = np.arange(10)
>>> a
HA! - What are you going to do now?
>>> print(a)
[0 1 2 3 4 5 6 7 8 9]
We can reset the function to the default:
>>> np.set_string_function(None)
>>> a
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
`repr` affects either pretty printing or normal string representation.
Note that ``__repr__`` is still affected by setting ``__str__``
because the width of each array element in the returned string becomes
equal to the length of the result of ``__str__()``.
>>> x = np.arange(4)
>>> np.set_string_function(lambda x:'random', repr=False)
>>> x.__str__()
'random'
>>> x.__repr__()
'array([ 0, 1, 2, 3])'
"""
if f is None:
if repr:
return multiarray.set_string_function(array_repr, 1)
else:
return multiarray.set_string_function(array_str, 0)
else:
return multiarray.set_string_function(f, repr)
set_string_function(array_str, 0)
set_string_function(array_repr, 1)
| 57,352 | 36.485621 | 83 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/records.py
|
"""
Record Arrays
=============
Record arrays expose the fields of structured arrays as properties.
Most commonly, ndarrays contain elements of a single type, e.g. floats,
integers, bools etc. However, it is possible for elements to be combinations
of these using structured types, such as::
>>> a = np.array([(1, 2.0), (1, 2.0)], dtype=[('x', int), ('y', float)])
>>> a
array([(1, 2.0), (1, 2.0)],
dtype=[('x', '<i4'), ('y', '<f8')])
Here, each element consists of two fields: x (and int), and y (a float).
This is known as a structured array. The different fields are analogous
to columns in a spread-sheet. The different fields can be accessed as
one would a dictionary::
>>> a['x']
array([1, 1])
>>> a['y']
array([ 2., 2.])
Record arrays allow us to access fields as properties::
>>> ar = np.rec.array(a)
>>> ar.x
array([1, 1])
>>> ar.y
array([ 2., 2.])
"""
from __future__ import division, absolute_import, print_function
import sys
import os
import warnings
from . import numeric as sb
from . import numerictypes as nt
from numpy.compat import isfileobj, bytes, long
from .arrayprint import get_printoptions
# All of the functions allow formats to be a dtype
__all__ = ['record', 'recarray', 'format_parser']
ndarray = sb.ndarray
_byteorderconv = {'b':'>',
'l':'<',
'n':'=',
'B':'>',
'L':'<',
'N':'=',
'S':'s',
's':'s',
'>':'>',
'<':'<',
'=':'=',
'|':'|',
'I':'|',
'i':'|'}
# formats regular expression
# allows multidimension spec with a tuple syntax in front
# of the letter code '(2,3)f4' and ' ( 2 , 3 ) f4 '
# are equally allowed
numfmt = nt.typeDict
def find_duplicate(list):
"""Find duplication in a list, return a list of duplicated elements"""
dup = []
for i in range(len(list)):
if (list[i] in list[i + 1:]):
if (list[i] not in dup):
dup.append(list[i])
return dup
class format_parser(object):
"""
Class to convert formats, names, titles description to a dtype.
After constructing the format_parser object, the dtype attribute is
the converted data-type:
``dtype = format_parser(formats, names, titles).dtype``
Attributes
----------
dtype : dtype
The converted data-type.
Parameters
----------
formats : str or list of str
The format description, either specified as a string with
comma-separated format descriptions in the form ``'f8, i4, a5'``, or
a list of format description strings in the form
``['f8', 'i4', 'a5']``.
names : str or list/tuple of str
The field names, either specified as a comma-separated string in the
form ``'col1, col2, col3'``, or as a list or tuple of strings in the
form ``['col1', 'col2', 'col3']``.
An empty list can be used, in that case default field names
('f0', 'f1', ...) are used.
titles : sequence
Sequence of title strings. An empty list can be used to leave titles
out.
aligned : bool, optional
If True, align the fields by padding as the C-compiler would.
Default is False.
byteorder : str, optional
If specified, all the fields will be changed to the
provided byte-order. Otherwise, the default byte-order is
used. For all available string specifiers, see `dtype.newbyteorder`.
See Also
--------
dtype, typename, sctype2char
Examples
--------
>>> np.format_parser(['f8', 'i4', 'a5'], ['col1', 'col2', 'col3'],
... ['T1', 'T2', 'T3']).dtype
dtype([(('T1', 'col1'), '<f8'), (('T2', 'col2'), '<i4'),
(('T3', 'col3'), '|S5')])
`names` and/or `titles` can be empty lists. If `titles` is an empty list,
titles will simply not appear. If `names` is empty, default field names
will be used.
>>> np.format_parser(['f8', 'i4', 'a5'], ['col1', 'col2', 'col3'],
... []).dtype
dtype([('col1', '<f8'), ('col2', '<i4'), ('col3', '|S5')])
>>> np.format_parser(['f8', 'i4', 'a5'], [], []).dtype
dtype([('f0', '<f8'), ('f1', '<i4'), ('f2', '|S5')])
"""
def __init__(self, formats, names, titles, aligned=False, byteorder=None):
self._parseFormats(formats, aligned)
self._setfieldnames(names, titles)
self._createdescr(byteorder)
self.dtype = self._descr
def _parseFormats(self, formats, aligned=0):
""" Parse the field formats """
if formats is None:
raise ValueError("Need formats argument")
if isinstance(formats, list):
if len(formats) < 2:
formats.append('')
formats = ','.join(formats)
dtype = sb.dtype(formats, aligned)
fields = dtype.fields
if fields is None:
dtype = sb.dtype([('f1', dtype)], aligned)
fields = dtype.fields
keys = dtype.names
self._f_formats = [fields[key][0] for key in keys]
self._offsets = [fields[key][1] for key in keys]
self._nfields = len(keys)
def _setfieldnames(self, names, titles):
"""convert input field names into a list and assign to the _names
attribute """
if (names):
if (type(names) in [list, tuple]):
pass
elif isinstance(names, str):
names = names.split(',')
else:
raise NameError("illegal input names %s" % repr(names))
self._names = [n.strip() for n in names[:self._nfields]]
else:
self._names = []
# if the names are not specified, they will be assigned as
# "f0, f1, f2,..."
# if not enough names are specified, they will be assigned as "f[n],
# f[n+1],..." etc. where n is the number of specified names..."
self._names += ['f%d' % i for i in range(len(self._names),
self._nfields)]
# check for redundant names
_dup = find_duplicate(self._names)
if _dup:
raise ValueError("Duplicate field names: %s" % _dup)
if (titles):
self._titles = [n.strip() for n in titles[:self._nfields]]
else:
self._titles = []
titles = []
if (self._nfields > len(titles)):
self._titles += [None] * (self._nfields - len(titles))
def _createdescr(self, byteorder):
descr = sb.dtype({'names':self._names,
'formats':self._f_formats,
'offsets':self._offsets,
'titles':self._titles})
if (byteorder is not None):
byteorder = _byteorderconv[byteorder[0]]
descr = descr.newbyteorder(byteorder)
self._descr = descr
class record(nt.void):
"""A data-type scalar that allows field access as attribute lookup.
"""
# manually set name and module so that this class's type shows up
# as numpy.record when printed
__name__ = 'record'
__module__ = 'numpy'
def __repr__(self):
if get_printoptions()['legacy'] == '1.13':
return self.__str__()
return super(record, self).__repr__()
def __str__(self):
if get_printoptions()['legacy'] == '1.13':
return str(self.item())
return super(record, self).__str__()
def __getattribute__(self, attr):
if attr in ['setfield', 'getfield', 'dtype']:
return nt.void.__getattribute__(self, attr)
try:
return nt.void.__getattribute__(self, attr)
except AttributeError:
pass
fielddict = nt.void.__getattribute__(self, 'dtype').fields
res = fielddict.get(attr, None)
if res:
obj = self.getfield(*res[:2])
# if it has fields return a record,
# otherwise return the object
try:
dt = obj.dtype
except AttributeError:
#happens if field is Object type
return obj
if dt.fields:
return obj.view((self.__class__, obj.dtype.fields))
return obj
else:
raise AttributeError("'record' object has no "
"attribute '%s'" % attr)
def __setattr__(self, attr, val):
if attr in ['setfield', 'getfield', 'dtype']:
raise AttributeError("Cannot set '%s' attribute" % attr)
fielddict = nt.void.__getattribute__(self, 'dtype').fields
res = fielddict.get(attr, None)
if res:
return self.setfield(val, *res[:2])
else:
if getattr(self, attr, None):
return nt.void.__setattr__(self, attr, val)
else:
raise AttributeError("'record' object has no "
"attribute '%s'" % attr)
def __getitem__(self, indx):
obj = nt.void.__getitem__(self, indx)
# copy behavior of record.__getattribute__,
if isinstance(obj, nt.void) and obj.dtype.fields:
return obj.view((self.__class__, obj.dtype.fields))
else:
# return a single element
return obj
def pprint(self):
"""Pretty-print all fields."""
# pretty-print all fields
names = self.dtype.names
maxlen = max(len(name) for name in names)
rows = []
fmt = '%% %ds: %%s' % maxlen
for name in names:
rows.append(fmt % (name, getattr(self, name)))
return "\n".join(rows)
# The recarray is almost identical to a standard array (which supports
# named fields already) The biggest difference is that it can use
# attribute-lookup to find the fields and it is constructed using
# a record.
# If byteorder is given it forces a particular byteorder on all
# the fields (and any subfields)
class recarray(ndarray):
"""Construct an ndarray that allows field access using attributes.
Arrays may have a data-types containing fields, analogous
to columns in a spread sheet. An example is ``[(x, int), (y, float)]``,
where each entry in the array is a pair of ``(int, float)``. Normally,
these attributes are accessed using dictionary lookups such as ``arr['x']``
and ``arr['y']``. Record arrays allow the fields to be accessed as members
of the array, using ``arr.x`` and ``arr.y``.
Parameters
----------
shape : tuple
Shape of output array.
dtype : data-type, optional
The desired data-type. By default, the data-type is determined
from `formats`, `names`, `titles`, `aligned` and `byteorder`.
formats : list of data-types, optional
A list containing the data-types for the different columns, e.g.
``['i4', 'f8', 'i4']``. `formats` does *not* support the new
convention of using types directly, i.e. ``(int, float, int)``.
Note that `formats` must be a list, not a tuple.
Given that `formats` is somewhat limited, we recommend specifying
`dtype` instead.
names : tuple of str, optional
The name of each column, e.g. ``('x', 'y', 'z')``.
buf : buffer, optional
By default, a new array is created of the given shape and data-type.
If `buf` is specified and is an object exposing the buffer interface,
the array will use the memory from the existing buffer. In this case,
the `offset` and `strides` keywords are available.
Other Parameters
----------------
titles : tuple of str, optional
Aliases for column names. For example, if `names` were
``('x', 'y', 'z')`` and `titles` is
``('x_coordinate', 'y_coordinate', 'z_coordinate')``, then
``arr['x']`` is equivalent to both ``arr.x`` and ``arr.x_coordinate``.
byteorder : {'<', '>', '='}, optional
Byte-order for all fields.
aligned : bool, optional
Align the fields in memory as the C-compiler would.
strides : tuple of ints, optional
Buffer (`buf`) is interpreted according to these strides (strides
define how many bytes each array element, row, column, etc.
occupy in memory).
offset : int, optional
Start reading buffer (`buf`) from this offset onwards.
order : {'C', 'F'}, optional
Row-major (C-style) or column-major (Fortran-style) order.
Returns
-------
rec : recarray
Empty array of the given shape and type.
See Also
--------
rec.fromrecords : Construct a record array from data.
record : fundamental data-type for `recarray`.
format_parser : determine a data-type from formats, names, titles.
Notes
-----
This constructor can be compared to ``empty``: it creates a new record
array but does not fill it with data. To create a record array from data,
use one of the following methods:
1. Create a standard ndarray and convert it to a record array,
using ``arr.view(np.recarray)``
2. Use the `buf` keyword.
3. Use `np.rec.fromrecords`.
Examples
--------
Create an array with two fields, ``x`` and ``y``:
>>> x = np.array([(1.0, 2), (3.0, 4)], dtype=[('x', float), ('y', int)])
>>> x
array([(1.0, 2), (3.0, 4)],
dtype=[('x', '<f8'), ('y', '<i4')])
>>> x['x']
array([ 1., 3.])
View the array as a record array:
>>> x = x.view(np.recarray)
>>> x.x
array([ 1., 3.])
>>> x.y
array([2, 4])
Create a new, empty record array:
>>> np.recarray((2,),
... dtype=[('x', int), ('y', float), ('z', int)]) #doctest: +SKIP
rec.array([(-1073741821, 1.2249118382103472e-301, 24547520),
(3471280, 1.2134086255804012e-316, 0)],
dtype=[('x', '<i4'), ('y', '<f8'), ('z', '<i4')])
"""
# manually set name and module so that this class's type shows
# up as "numpy.recarray" when printed
__name__ = 'recarray'
__module__ = 'numpy'
def __new__(subtype, shape, dtype=None, buf=None, offset=0, strides=None,
formats=None, names=None, titles=None,
byteorder=None, aligned=False, order='C'):
if dtype is not None:
descr = sb.dtype(dtype)
else:
descr = format_parser(formats, names, titles, aligned, byteorder)._descr
if buf is None:
self = ndarray.__new__(subtype, shape, (record, descr), order=order)
else:
self = ndarray.__new__(subtype, shape, (record, descr),
buffer=buf, offset=offset,
strides=strides, order=order)
return self
def __array_finalize__(self, obj):
if self.dtype.type is not record and self.dtype.fields:
# if self.dtype is not np.record, invoke __setattr__ which will
# convert it to a record if it is a void dtype.
self.dtype = self.dtype
def __getattribute__(self, attr):
# See if ndarray has this attr, and return it if so. (note that this
# means a field with the same name as an ndarray attr cannot be
# accessed by attribute).
try:
return object.__getattribute__(self, attr)
except AttributeError: # attr must be a fieldname
pass
# look for a field with this name
fielddict = ndarray.__getattribute__(self, 'dtype').fields
try:
res = fielddict[attr][:2]
except (TypeError, KeyError):
raise AttributeError("recarray has no attribute %s" % attr)
obj = self.getfield(*res)
# At this point obj will always be a recarray, since (see
# PyArray_GetField) the type of obj is inherited. Next, if obj.dtype is
# non-structured, convert it to an ndarray. Then if obj is structured
# with void type convert it to the same dtype.type (eg to preserve
# numpy.record type if present), since nested structured fields do not
# inherit type. Don't do this for non-void structures though.
if obj.dtype.fields:
if issubclass(obj.dtype.type, nt.void):
return obj.view(dtype=(self.dtype.type, obj.dtype))
return obj
else:
return obj.view(ndarray)
# Save the dictionary.
# If the attr is a field name and not in the saved dictionary
# Undo any "setting" of the attribute and do a setfield
# Thus, you can't create attributes on-the-fly that are field names.
def __setattr__(self, attr, val):
# Automatically convert (void) structured types to records
# (but not non-void structures, subarrays, or non-structured voids)
if attr == 'dtype' and issubclass(val.type, nt.void) and val.fields:
val = sb.dtype((record, val))
newattr = attr not in self.__dict__
try:
ret = object.__setattr__(self, attr, val)
except Exception:
fielddict = ndarray.__getattribute__(self, 'dtype').fields or {}
if attr not in fielddict:
exctype, value = sys.exc_info()[:2]
raise exctype(value)
else:
fielddict = ndarray.__getattribute__(self, 'dtype').fields or {}
if attr not in fielddict:
return ret
if newattr:
# We just added this one or this setattr worked on an
# internal attribute.
try:
object.__delattr__(self, attr)
except Exception:
return ret
try:
res = fielddict[attr][:2]
except (TypeError, KeyError):
raise AttributeError("record array has no attribute %s" % attr)
return self.setfield(val, *res)
def __getitem__(self, indx):
obj = super(recarray, self).__getitem__(indx)
# copy behavior of getattr, except that here
# we might also be returning a single element
if isinstance(obj, ndarray):
if obj.dtype.fields:
obj = obj.view(type(self))
if issubclass(obj.dtype.type, nt.void):
return obj.view(dtype=(self.dtype.type, obj.dtype))
return obj
else:
return obj.view(type=ndarray)
else:
# return a single element
return obj
def __repr__(self):
repr_dtype = self.dtype
if (self.dtype.type is record
or (not issubclass(self.dtype.type, nt.void))):
# If this is a full record array (has numpy.record dtype),
# or if it has a scalar (non-void) dtype with no records,
# represent it using the rec.array function. Since rec.array
# converts dtype to a numpy.record for us, convert back
# to non-record before printing
if repr_dtype.type is record:
repr_dtype = sb.dtype((nt.void, repr_dtype))
prefix = "rec.array("
fmt = 'rec.array(%s,%sdtype=%s)'
else:
# otherwise represent it using np.array plus a view
# This should only happen if the user is playing
# strange games with dtypes.
prefix = "array("
fmt = 'array(%s,%sdtype=%s).view(numpy.recarray)'
# get data/shape string. logic taken from numeric.array_repr
if self.size > 0 or self.shape == (0,):
lst = sb.array2string(
self, separator=', ', prefix=prefix, suffix=',')
else:
# show zero-length shape unless it is (0,)
lst = "[], shape=%s" % (repr(self.shape),)
lf = '\n'+' '*len(prefix)
if get_printoptions()['legacy'] == '1.13':
lf = ' ' + lf # trailing space
return fmt % (lst, lf, repr_dtype)
def field(self, attr, val=None):
if isinstance(attr, int):
names = ndarray.__getattribute__(self, 'dtype').names
attr = names[attr]
fielddict = ndarray.__getattribute__(self, 'dtype').fields
res = fielddict[attr][:2]
if val is None:
obj = self.getfield(*res)
if obj.dtype.fields:
return obj
return obj.view(ndarray)
else:
return self.setfield(val, *res)
def fromarrays(arrayList, dtype=None, shape=None, formats=None,
names=None, titles=None, aligned=False, byteorder=None):
""" create a record array from a (flat) list of arrays
>>> x1=np.array([1,2,3,4])
>>> x2=np.array(['a','dd','xyz','12'])
>>> x3=np.array([1.1,2,3,4])
>>> r = np.core.records.fromarrays([x1,x2,x3],names='a,b,c')
>>> print(r[1])
(2, 'dd', 2.0)
>>> x1[1]=34
>>> r.a
array([1, 2, 3, 4])
"""
arrayList = [sb.asarray(x) for x in arrayList]
if shape is None or shape == 0:
shape = arrayList[0].shape
if isinstance(shape, int):
shape = (shape,)
if formats is None and dtype is None:
# go through each object in the list to see if it is an ndarray
# and determine the formats.
formats = []
for obj in arrayList:
if not isinstance(obj, ndarray):
raise ValueError("item in the array list must be an ndarray.")
formats.append(obj.dtype.str)
formats = ','.join(formats)
if dtype is not None:
descr = sb.dtype(dtype)
_names = descr.names
else:
parsed = format_parser(formats, names, titles, aligned, byteorder)
_names = parsed._names
descr = parsed._descr
# Determine shape from data-type.
if len(descr) != len(arrayList):
raise ValueError("mismatch between the number of fields "
"and the number of arrays")
d0 = descr[0].shape
nn = len(d0)
if nn > 0:
shape = shape[:-nn]
for k, obj in enumerate(arrayList):
nn = descr[k].ndim
testshape = obj.shape[:obj.ndim - nn]
if testshape != shape:
raise ValueError("array-shape mismatch in array %d" % k)
_array = recarray(shape, descr)
# populate the record array (makes a copy)
for i in range(len(arrayList)):
_array[_names[i]] = arrayList[i]
return _array
def fromrecords(recList, dtype=None, shape=None, formats=None, names=None,
titles=None, aligned=False, byteorder=None):
""" create a recarray from a list of records in text form
The data in the same field can be heterogeneous, they will be promoted
to the highest data type. This method is intended for creating
smaller record arrays. If used to create large array without formats
defined
r=fromrecords([(2,3.,'abc')]*100000)
it can be slow.
If formats is None, then this will auto-detect formats. Use list of
tuples rather than list of lists for faster processing.
>>> r=np.core.records.fromrecords([(456,'dbe',1.2),(2,'de',1.3)],
... names='col1,col2,col3')
>>> print(r[0])
(456, 'dbe', 1.2)
>>> r.col1
array([456, 2])
>>> r.col2
array(['dbe', 'de'],
dtype='|S3')
>>> import pickle
>>> print(pickle.loads(pickle.dumps(r)))
[(456, 'dbe', 1.2) (2, 'de', 1.3)]
"""
if formats is None and dtype is None: # slower
obj = sb.array(recList, dtype=object)
arrlist = [sb.array(obj[..., i].tolist()) for i in range(obj.shape[-1])]
return fromarrays(arrlist, formats=formats, shape=shape, names=names,
titles=titles, aligned=aligned, byteorder=byteorder)
if dtype is not None:
descr = sb.dtype((record, dtype))
else:
descr = format_parser(formats, names, titles, aligned, byteorder)._descr
# deprecated back-compat block for numpy 1.14, to be removed in a later
# release. This converts list-of-list input to list-of-tuples in some
# cases, as done in numpy <= 1.13. In the future we will require tuples.
if (isinstance(recList, list) and len(recList) > 0
and isinstance(recList[0], list) and len(recList[0]) > 0
and not isinstance(recList[0][0], (list, tuple))):
try:
memoryview(recList[0][0])
except:
if (shape is None or shape == 0):
shape = len(recList)
if isinstance(shape, (int, long)):
shape = (shape,)
if len(shape) > 1:
raise ValueError("Can only deal with 1-d array.")
_array = recarray(shape, descr)
for k in range(_array.size):
_array[k] = tuple(recList[k])
# list of lists instead of list of tuples ?
# 2018-02-07, 1.14.1
warnings.warn(
"fromrecords expected a list of tuples, may have received a "
"list of lists instead. In the future that will raise an error",
FutureWarning, stacklevel=2)
return _array
else:
pass
retval = sb.array(recList, dtype=descr)
if shape is not None and retval.shape != shape:
retval.shape = shape
return retval.view(recarray)
def fromstring(datastring, dtype=None, shape=None, offset=0, formats=None,
names=None, titles=None, aligned=False, byteorder=None):
""" create a (read-only) record array from binary data contained in
a string"""
if dtype is None and formats is None:
raise ValueError("Must have dtype= or formats=")
if dtype is not None:
descr = sb.dtype(dtype)
else:
descr = format_parser(formats, names, titles, aligned, byteorder)._descr
itemsize = descr.itemsize
if (shape is None or shape == 0 or shape == -1):
shape = (len(datastring) - offset) // itemsize
_array = recarray(shape, descr, buf=datastring, offset=offset)
return _array
def get_remaining_size(fd):
try:
fn = fd.fileno()
except AttributeError:
return os.path.getsize(fd.name) - fd.tell()
st = os.fstat(fn)
size = st.st_size - fd.tell()
return size
def fromfile(fd, dtype=None, shape=None, offset=0, formats=None,
names=None, titles=None, aligned=False, byteorder=None):
"""Create an array from binary file data
If file is a string then that file is opened, else it is assumed
to be a file object. The file object must support random access
(i.e. it must have tell and seek methods).
>>> from tempfile import TemporaryFile
>>> a = np.empty(10,dtype='f8,i4,a5')
>>> a[5] = (0.5,10,'abcde')
>>>
>>> fd=TemporaryFile()
>>> a = a.newbyteorder('<')
>>> a.tofile(fd)
>>>
>>> fd.seek(0)
>>> r=np.core.records.fromfile(fd, formats='f8,i4,a5', shape=10,
... byteorder='<')
>>> print(r[5])
(0.5, 10, 'abcde')
>>> r.shape
(10,)
"""
if (shape is None or shape == 0):
shape = (-1,)
elif isinstance(shape, (int, long)):
shape = (shape,)
name = 0
if isinstance(fd, str):
name = 1
fd = open(fd, 'rb')
if (offset > 0):
fd.seek(offset, 1)
size = get_remaining_size(fd)
if dtype is not None:
descr = sb.dtype(dtype)
else:
descr = format_parser(formats, names, titles, aligned, byteorder)._descr
itemsize = descr.itemsize
shapeprod = sb.array(shape).prod()
shapesize = shapeprod * itemsize
if shapesize < 0:
shape = list(shape)
shape[shape.index(-1)] = size / -shapesize
shape = tuple(shape)
shapeprod = sb.array(shape).prod()
nbytes = shapeprod * itemsize
if nbytes > size:
raise ValueError(
"Not enough bytes left in file for specified shape and type")
# create the array
_array = recarray(shape, descr)
nbytesread = fd.readinto(_array.data)
if nbytesread != nbytes:
raise IOError("Didn't read as many bytes as expected")
if name:
fd.close()
return _array
def array(obj, dtype=None, shape=None, offset=0, strides=None, formats=None,
names=None, titles=None, aligned=False, byteorder=None, copy=True):
"""Construct a record array from a wide-variety of objects.
"""
if ((isinstance(obj, (type(None), str)) or isfileobj(obj)) and
(formats is None) and (dtype is None)):
raise ValueError("Must define formats (or dtype) if object is "
"None, string, or an open file")
kwds = {}
if dtype is not None:
dtype = sb.dtype(dtype)
elif formats is not None:
dtype = format_parser(formats, names, titles,
aligned, byteorder)._descr
else:
kwds = {'formats': formats,
'names': names,
'titles': titles,
'aligned': aligned,
'byteorder': byteorder
}
if obj is None:
if shape is None:
raise ValueError("Must define a shape if obj is None")
return recarray(shape, dtype, buf=obj, offset=offset, strides=strides)
elif isinstance(obj, bytes):
return fromstring(obj, dtype, shape=shape, offset=offset, **kwds)
elif isinstance(obj, (list, tuple)):
if isinstance(obj[0], (tuple, list)):
return fromrecords(obj, dtype=dtype, shape=shape, **kwds)
else:
return fromarrays(obj, dtype=dtype, shape=shape, **kwds)
elif isinstance(obj, recarray):
if dtype is not None and (obj.dtype != dtype):
new = obj.view(dtype)
else:
new = obj
if copy:
new = new.copy()
return new
elif isfileobj(obj):
return fromfile(obj, dtype=dtype, shape=shape, offset=offset)
elif isinstance(obj, ndarray):
if dtype is not None and (obj.dtype != dtype):
new = obj.view(dtype)
else:
new = obj
if copy:
new = new.copy()
return new.view(recarray)
else:
interface = getattr(obj, "__array_interface__", None)
if interface is None or not isinstance(interface, dict):
raise ValueError("Unknown input type")
obj = sb.array(obj)
if dtype is not None and (obj.dtype != dtype):
obj = obj.view(dtype)
return obj.view(recarray)
| 30,591 | 33.763636 | 84 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/shape_base.py
|
from __future__ import division, absolute_import, print_function
__all__ = ['atleast_1d', 'atleast_2d', 'atleast_3d', 'block', 'hstack',
'stack', 'vstack']
from . import numeric as _nx
from .numeric import array, asanyarray, newaxis
from .multiarray import normalize_axis_index
def atleast_1d(*arys):
"""
Convert inputs to arrays with at least one dimension.
Scalar inputs are converted to 1-dimensional arrays, whilst
higher-dimensional inputs are preserved.
Parameters
----------
arys1, arys2, ... : array_like
One or more input arrays.
Returns
-------
ret : ndarray
An array, or list of arrays, each with ``a.ndim >= 1``.
Copies are made only if necessary.
See Also
--------
atleast_2d, atleast_3d
Examples
--------
>>> np.atleast_1d(1.0)
array([ 1.])
>>> x = np.arange(9.0).reshape(3,3)
>>> np.atleast_1d(x)
array([[ 0., 1., 2.],
[ 3., 4., 5.],
[ 6., 7., 8.]])
>>> np.atleast_1d(x) is x
True
>>> np.atleast_1d(1, [3, 4])
[array([1]), array([3, 4])]
"""
res = []
for ary in arys:
ary = asanyarray(ary)
if ary.ndim == 0:
result = ary.reshape(1)
else:
result = ary
res.append(result)
if len(res) == 1:
return res[0]
else:
return res
def atleast_2d(*arys):
"""
View inputs as arrays with at least two dimensions.
Parameters
----------
arys1, arys2, ... : array_like
One or more array-like sequences. Non-array inputs are converted
to arrays. Arrays that already have two or more dimensions are
preserved.
Returns
-------
res, res2, ... : ndarray
An array, or list of arrays, each with ``a.ndim >= 2``.
Copies are avoided where possible, and views with two or more
dimensions are returned.
See Also
--------
atleast_1d, atleast_3d
Examples
--------
>>> np.atleast_2d(3.0)
array([[ 3.]])
>>> x = np.arange(3.0)
>>> np.atleast_2d(x)
array([[ 0., 1., 2.]])
>>> np.atleast_2d(x).base is x
True
>>> np.atleast_2d(1, [1, 2], [[1, 2]])
[array([[1]]), array([[1, 2]]), array([[1, 2]])]
"""
res = []
for ary in arys:
ary = asanyarray(ary)
if ary.ndim == 0:
result = ary.reshape(1, 1)
elif ary.ndim == 1:
result = ary[newaxis,:]
else:
result = ary
res.append(result)
if len(res) == 1:
return res[0]
else:
return res
def atleast_3d(*arys):
"""
View inputs as arrays with at least three dimensions.
Parameters
----------
arys1, arys2, ... : array_like
One or more array-like sequences. Non-array inputs are converted to
arrays. Arrays that already have three or more dimensions are
preserved.
Returns
-------
res1, res2, ... : ndarray
An array, or list of arrays, each with ``a.ndim >= 3``. Copies are
avoided where possible, and views with three or more dimensions are
returned. For example, a 1-D array of shape ``(N,)`` becomes a view
of shape ``(1, N, 1)``, and a 2-D array of shape ``(M, N)`` becomes a
view of shape ``(M, N, 1)``.
See Also
--------
atleast_1d, atleast_2d
Examples
--------
>>> np.atleast_3d(3.0)
array([[[ 3.]]])
>>> x = np.arange(3.0)
>>> np.atleast_3d(x).shape
(1, 3, 1)
>>> x = np.arange(12.0).reshape(4,3)
>>> np.atleast_3d(x).shape
(4, 3, 1)
>>> np.atleast_3d(x).base is x.base # x is a reshape, so not base itself
True
>>> for arr in np.atleast_3d([1, 2], [[1, 2]], [[[1, 2]]]):
... print(arr, arr.shape)
...
[[[1]
[2]]] (1, 2, 1)
[[[1]
[2]]] (1, 2, 1)
[[[1 2]]] (1, 1, 2)
"""
res = []
for ary in arys:
ary = asanyarray(ary)
if ary.ndim == 0:
result = ary.reshape(1, 1, 1)
elif ary.ndim == 1:
result = ary[newaxis,:, newaxis]
elif ary.ndim == 2:
result = ary[:,:, newaxis]
else:
result = ary
res.append(result)
if len(res) == 1:
return res[0]
else:
return res
def vstack(tup):
"""
Stack arrays in sequence vertically (row wise).
This is equivalent to concatenation along the first axis after 1-D arrays
of shape `(N,)` have been reshaped to `(1,N)`. Rebuilds arrays divided by
`vsplit`.
This function makes most sense for arrays with up to 3 dimensions. For
instance, for pixel-data with a height (first axis), width (second axis),
and r/g/b channels (third axis). The functions `concatenate`, `stack` and
`block` provide more general stacking and concatenation operations.
Parameters
----------
tup : sequence of ndarrays
The arrays must have the same shape along all but the first axis.
1-D arrays must have the same length.
Returns
-------
stacked : ndarray
The array formed by stacking the given arrays, will be at least 2-D.
See Also
--------
stack : Join a sequence of arrays along a new axis.
hstack : Stack arrays in sequence horizontally (column wise).
dstack : Stack arrays in sequence depth wise (along third dimension).
concatenate : Join a sequence of arrays along an existing axis.
vsplit : Split array into a list of multiple sub-arrays vertically.
block : Assemble arrays from blocks.
Examples
--------
>>> a = np.array([1, 2, 3])
>>> b = np.array([2, 3, 4])
>>> np.vstack((a,b))
array([[1, 2, 3],
[2, 3, 4]])
>>> a = np.array([[1], [2], [3]])
>>> b = np.array([[2], [3], [4]])
>>> np.vstack((a,b))
array([[1],
[2],
[3],
[2],
[3],
[4]])
"""
return _nx.concatenate([atleast_2d(_m) for _m in tup], 0)
def hstack(tup):
"""
Stack arrays in sequence horizontally (column wise).
This is equivalent to concatenation along the second axis, except for 1-D
arrays where it concatenates along the first axis. Rebuilds arrays divided
by `hsplit`.
This function makes most sense for arrays with up to 3 dimensions. For
instance, for pixel-data with a height (first axis), width (second axis),
and r/g/b channels (third axis). The functions `concatenate`, `stack` and
`block` provide more general stacking and concatenation operations.
Parameters
----------
tup : sequence of ndarrays
The arrays must have the same shape along all but the second axis,
except 1-D arrays which can be any length.
Returns
-------
stacked : ndarray
The array formed by stacking the given arrays.
See Also
--------
stack : Join a sequence of arrays along a new axis.
vstack : Stack arrays in sequence vertically (row wise).
dstack : Stack arrays in sequence depth wise (along third axis).
concatenate : Join a sequence of arrays along an existing axis.
hsplit : Split array along second axis.
block : Assemble arrays from blocks.
Examples
--------
>>> a = np.array((1,2,3))
>>> b = np.array((2,3,4))
>>> np.hstack((a,b))
array([1, 2, 3, 2, 3, 4])
>>> a = np.array([[1],[2],[3]])
>>> b = np.array([[2],[3],[4]])
>>> np.hstack((a,b))
array([[1, 2],
[2, 3],
[3, 4]])
"""
arrs = [atleast_1d(_m) for _m in tup]
# As a special case, dimension 0 of 1-dimensional arrays is "horizontal"
if arrs and arrs[0].ndim == 1:
return _nx.concatenate(arrs, 0)
else:
return _nx.concatenate(arrs, 1)
def stack(arrays, axis=0, out=None):
"""
Join a sequence of arrays along a new axis.
The `axis` parameter specifies the index of the new axis in the dimensions
of the result. For example, if ``axis=0`` it will be the first dimension
and if ``axis=-1`` it will be the last dimension.
.. versionadded:: 1.10.0
Parameters
----------
arrays : sequence of array_like
Each array must have the same shape.
axis : int, optional
The axis in the result array along which the input arrays are stacked.
out : ndarray, optional
If provided, the destination to place the result. The shape must be
correct, matching that of what stack would have returned if no
out argument were specified.
Returns
-------
stacked : ndarray
The stacked array has one more dimension than the input arrays.
See Also
--------
concatenate : Join a sequence of arrays along an existing axis.
split : Split array into a list of multiple sub-arrays of equal size.
block : Assemble arrays from blocks.
Examples
--------
>>> arrays = [np.random.randn(3, 4) for _ in range(10)]
>>> np.stack(arrays, axis=0).shape
(10, 3, 4)
>>> np.stack(arrays, axis=1).shape
(3, 10, 4)
>>> np.stack(arrays, axis=2).shape
(3, 4, 10)
>>> a = np.array([1, 2, 3])
>>> b = np.array([2, 3, 4])
>>> np.stack((a, b))
array([[1, 2, 3],
[2, 3, 4]])
>>> np.stack((a, b), axis=-1)
array([[1, 2],
[2, 3],
[3, 4]])
"""
arrays = [asanyarray(arr) for arr in arrays]
if not arrays:
raise ValueError('need at least one array to stack')
shapes = set(arr.shape for arr in arrays)
if len(shapes) != 1:
raise ValueError('all input arrays must have the same shape')
result_ndim = arrays[0].ndim + 1
axis = normalize_axis_index(axis, result_ndim)
sl = (slice(None),) * axis + (_nx.newaxis,)
expanded_arrays = [arr[sl] for arr in arrays]
return _nx.concatenate(expanded_arrays, axis=axis, out=out)
def _block_check_depths_match(arrays, parent_index=[]):
"""
Recursive function checking that the depths of nested lists in `arrays`
all match. Mismatch raises a ValueError as described in the block
docstring below.
The entire index (rather than just the depth) needs to be calculated
for each innermost list, in case an error needs to be raised, so that
the index of the offending list can be printed as part of the error.
The parameter `parent_index` is the full index of `arrays` within the
nested lists passed to _block_check_depths_match at the top of the
recursion.
The return value is a pair. The first item returned is the full index
of an element (specifically the first element) from the bottom of the
nesting in `arrays`. An empty list at the bottom of the nesting is
represented by a `None` index.
The second item is the maximum of the ndims of the arrays nested in
`arrays`.
"""
def format_index(index):
idx_str = ''.join('[{}]'.format(i) for i in index if i is not None)
return 'arrays' + idx_str
if type(arrays) is tuple:
# not strictly necessary, but saves us from:
# - more than one way to do things - no point treating tuples like
# lists
# - horribly confusing behaviour that results when tuples are
# treated like ndarray
raise TypeError(
'{} is a tuple. '
'Only lists can be used to arrange blocks, and np.block does '
'not allow implicit conversion from tuple to ndarray.'.format(
format_index(parent_index)
)
)
elif type(arrays) is list and len(arrays) > 0:
idxs_ndims = (_block_check_depths_match(arr, parent_index + [i])
for i, arr in enumerate(arrays))
first_index, max_arr_ndim = next(idxs_ndims)
for index, ndim in idxs_ndims:
if ndim > max_arr_ndim:
max_arr_ndim = ndim
if len(index) != len(first_index):
raise ValueError(
"List depths are mismatched. First element was at depth "
"{}, but there is an element at depth {} ({})".format(
len(first_index),
len(index),
format_index(index)
)
)
return first_index, max_arr_ndim
elif type(arrays) is list and len(arrays) == 0:
# We've 'bottomed out' on an empty list
return parent_index + [None], 0
else:
# We've 'bottomed out' - arrays is either a scalar or an array
return parent_index, _nx.ndim(arrays)
def _block(arrays, max_depth, result_ndim):
"""
Internal implementation of block. `arrays` is the argument passed to
block. `max_depth` is the depth of nested lists within `arrays` and
`result_ndim` is the greatest of the dimensions of the arrays in
`arrays` and the depth of the lists in `arrays` (see block docstring
for details).
"""
def atleast_nd(a, ndim):
# Ensures `a` has at least `ndim` dimensions by prepending
# ones to `a.shape` as necessary
return array(a, ndmin=ndim, copy=False, subok=True)
def block_recursion(arrays, depth=0):
if depth < max_depth:
if len(arrays) == 0:
raise ValueError('Lists cannot be empty')
arrs = [block_recursion(arr, depth+1) for arr in arrays]
return _nx.concatenate(arrs, axis=-(max_depth-depth))
else:
# We've 'bottomed out' - arrays is either a scalar or an array
# type(arrays) is not list
return atleast_nd(arrays, result_ndim)
try:
return block_recursion(arrays)
finally:
# recursive closures have a cyclic reference to themselves, which
# requires gc to collect (gh-10620). To avoid this problem, for
# performance and PyPy friendliness, we break the cycle:
block_recursion = None
def block(arrays):
"""
Assemble an nd-array from nested lists of blocks.
Blocks in the innermost lists are concatenated (see `concatenate`) along
the last dimension (-1), then these are concatenated along the
second-last dimension (-2), and so on until the outermost list is reached.
Blocks can be of any dimension, but will not be broadcasted using the normal
rules. Instead, leading axes of size 1 are inserted, to make ``block.ndim``
the same for all blocks. This is primarily useful for working with scalars,
and means that code like ``np.block([v, 1])`` is valid, where
``v.ndim == 1``.
When the nested list is two levels deep, this allows block matrices to be
constructed from their components.
.. versionadded:: 1.13.0
Parameters
----------
arrays : nested list of array_like or scalars (but not tuples)
If passed a single ndarray or scalar (a nested list of depth 0), this
is returned unmodified (and not copied).
Elements shapes must match along the appropriate axes (without
broadcasting), but leading 1s will be prepended to the shape as
necessary to make the dimensions match.
Returns
-------
block_array : ndarray
The array assembled from the given blocks.
The dimensionality of the output is equal to the greatest of:
* the dimensionality of all the inputs
* the depth to which the input list is nested
Raises
------
ValueError
* If list depths are mismatched - for instance, ``[[a, b], c]`` is
illegal, and should be spelt ``[[a, b], [c]]``
* If lists are empty - for instance, ``[[a, b], []]``
See Also
--------
concatenate : Join a sequence of arrays together.
stack : Stack arrays in sequence along a new dimension.
hstack : Stack arrays in sequence horizontally (column wise).
vstack : Stack arrays in sequence vertically (row wise).
dstack : Stack arrays in sequence depth wise (along third dimension).
vsplit : Split array into a list of multiple sub-arrays vertically.
Notes
-----
When called with only scalars, ``np.block`` is equivalent to an ndarray
call. So ``np.block([[1, 2], [3, 4]])`` is equivalent to
``np.array([[1, 2], [3, 4]])``.
This function does not enforce that the blocks lie on a fixed grid.
``np.block([[a, b], [c, d]])`` is not restricted to arrays of the form::
AAAbb
AAAbb
cccDD
But is also allowed to produce, for some ``a, b, c, d``::
AAAbb
AAAbb
cDDDD
Since concatenation happens along the last axis first, `block` is _not_
capable of producing the following directly::
AAAbb
cccbb
cccDD
Matlab's "square bracket stacking", ``[A, B, ...; p, q, ...]``, is
equivalent to ``np.block([[A, B, ...], [p, q, ...]])``.
Examples
--------
The most common use of this function is to build a block matrix
>>> A = np.eye(2) * 2
>>> B = np.eye(3) * 3
>>> np.block([
... [A, np.zeros((2, 3))],
... [np.ones((3, 2)), B ]
... ])
array([[ 2., 0., 0., 0., 0.],
[ 0., 2., 0., 0., 0.],
[ 1., 1., 3., 0., 0.],
[ 1., 1., 0., 3., 0.],
[ 1., 1., 0., 0., 3.]])
With a list of depth 1, `block` can be used as `hstack`
>>> np.block([1, 2, 3]) # hstack([1, 2, 3])
array([1, 2, 3])
>>> a = np.array([1, 2, 3])
>>> b = np.array([2, 3, 4])
>>> np.block([a, b, 10]) # hstack([a, b, 10])
array([1, 2, 3, 2, 3, 4, 10])
>>> A = np.ones((2, 2), int)
>>> B = 2 * A
>>> np.block([A, B]) # hstack([A, B])
array([[1, 1, 2, 2],
[1, 1, 2, 2]])
With a list of depth 2, `block` can be used in place of `vstack`:
>>> a = np.array([1, 2, 3])
>>> b = np.array([2, 3, 4])
>>> np.block([[a], [b]]) # vstack([a, b])
array([[1, 2, 3],
[2, 3, 4]])
>>> A = np.ones((2, 2), int)
>>> B = 2 * A
>>> np.block([[A], [B]]) # vstack([A, B])
array([[1, 1],
[1, 1],
[2, 2],
[2, 2]])
It can also be used in places of `atleast_1d` and `atleast_2d`
>>> a = np.array(0)
>>> b = np.array([1])
>>> np.block([a]) # atleast_1d(a)
array([0])
>>> np.block([b]) # atleast_1d(b)
array([1])
>>> np.block([[a]]) # atleast_2d(a)
array([[0]])
>>> np.block([[b]]) # atleast_2d(b)
array([[1]])
"""
bottom_index, arr_ndim = _block_check_depths_match(arrays)
list_ndim = len(bottom_index)
return _block(arrays, list_ndim, max(arr_ndim, list_ndim))
| 18,816 | 29.898194 | 80 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/_internal.py
|
"""
A place for code to be called from core C-code.
Some things are more easily handled Python.
"""
from __future__ import division, absolute_import, print_function
import re
import sys
from numpy.compat import basestring
from .multiarray import dtype, array, ndarray
try:
import ctypes
except ImportError:
ctypes = None
from .numerictypes import object_
if (sys.byteorder == 'little'):
_nbo = b'<'
else:
_nbo = b'>'
def _makenames_list(adict, align):
allfields = []
fnames = list(adict.keys())
for fname in fnames:
obj = adict[fname]
n = len(obj)
if not isinstance(obj, tuple) or n not in [2, 3]:
raise ValueError("entry not a 2- or 3- tuple")
if (n > 2) and (obj[2] == fname):
continue
num = int(obj[1])
if (num < 0):
raise ValueError("invalid offset.")
format = dtype(obj[0], align=align)
if (n > 2):
title = obj[2]
else:
title = None
allfields.append((fname, format, num, title))
# sort by offsets
allfields.sort(key=lambda x: x[2])
names = [x[0] for x in allfields]
formats = [x[1] for x in allfields]
offsets = [x[2] for x in allfields]
titles = [x[3] for x in allfields]
return names, formats, offsets, titles
# Called in PyArray_DescrConverter function when
# a dictionary without "names" and "formats"
# fields is used as a data-type descriptor.
def _usefields(adict, align):
try:
names = adict[-1]
except KeyError:
names = None
if names is None:
names, formats, offsets, titles = _makenames_list(adict, align)
else:
formats = []
offsets = []
titles = []
for name in names:
res = adict[name]
formats.append(res[0])
offsets.append(res[1])
if (len(res) > 2):
titles.append(res[2])
else:
titles.append(None)
return dtype({"names": names,
"formats": formats,
"offsets": offsets,
"titles": titles}, align)
# construct an array_protocol descriptor list
# from the fields attribute of a descriptor
# This calls itself recursively but should eventually hit
# a descriptor that has no fields and then return
# a simple typestring
def _array_descr(descriptor):
fields = descriptor.fields
if fields is None:
subdtype = descriptor.subdtype
if subdtype is None:
if descriptor.metadata is None:
return descriptor.str
else:
new = descriptor.metadata.copy()
if new:
return (descriptor.str, new)
else:
return descriptor.str
else:
return (_array_descr(subdtype[0]), subdtype[1])
names = descriptor.names
ordered_fields = [fields[x] + (x,) for x in names]
result = []
offset = 0
for field in ordered_fields:
if field[1] > offset:
num = field[1] - offset
result.append(('', '|V%d' % num))
offset += num
elif field[1] < offset:
raise ValueError(
"dtype.descr is not defined for types with overlapping or "
"out-of-order fields")
if len(field) > 3:
name = (field[2], field[3])
else:
name = field[2]
if field[0].subdtype:
tup = (name, _array_descr(field[0].subdtype[0]),
field[0].subdtype[1])
else:
tup = (name, _array_descr(field[0]))
offset += field[0].itemsize
result.append(tup)
if descriptor.itemsize > offset:
num = descriptor.itemsize - offset
result.append(('', '|V%d' % num))
return result
# Build a new array from the information in a pickle.
# Note that the name numpy.core._internal._reconstruct is embedded in
# pickles of ndarrays made with NumPy before release 1.0
# so don't remove the name here, or you'll
# break backward compatibility.
def _reconstruct(subtype, shape, dtype):
return ndarray.__new__(subtype, shape, dtype)
# format_re was originally from numarray by J. Todd Miller
format_re = re.compile(br'(?P<order1>[<>|=]?)'
br'(?P<repeats> *[(]?[ ,0-9L]*[)]? *)'
br'(?P<order2>[<>|=]?)'
br'(?P<dtype>[A-Za-z0-9.?]*(?:\[[a-zA-Z0-9,.]+\])?)')
sep_re = re.compile(br'\s*,\s*')
space_re = re.compile(br'\s+$')
# astr is a string (perhaps comma separated)
_convorder = {b'=': _nbo}
def _commastring(astr):
startindex = 0
result = []
while startindex < len(astr):
mo = format_re.match(astr, pos=startindex)
try:
(order1, repeats, order2, dtype) = mo.groups()
except (TypeError, AttributeError):
raise ValueError('format number %d of "%s" is not recognized' %
(len(result)+1, astr))
startindex = mo.end()
# Separator or ending padding
if startindex < len(astr):
if space_re.match(astr, pos=startindex):
startindex = len(astr)
else:
mo = sep_re.match(astr, pos=startindex)
if not mo:
raise ValueError(
'format number %d of "%s" is not recognized' %
(len(result)+1, astr))
startindex = mo.end()
if order2 == b'':
order = order1
elif order1 == b'':
order = order2
else:
order1 = _convorder.get(order1, order1)
order2 = _convorder.get(order2, order2)
if (order1 != order2):
raise ValueError(
'inconsistent byte-order specification %s and %s' %
(order1, order2))
order = order1
if order in [b'|', b'=', _nbo]:
order = b''
dtype = order + dtype
if (repeats == b''):
newitem = dtype
else:
newitem = (dtype, eval(repeats))
result.append(newitem)
return result
class dummy_ctype(object):
def __init__(self, cls):
self._cls = cls
def __mul__(self, other):
return self
def __call__(self, *other):
return self._cls(other)
def __eq__(self, other):
return self._cls == other._cls
def __ne__(self, other):
return self._cls != other._cls
def _getintp_ctype():
val = _getintp_ctype.cache
if val is not None:
return val
if ctypes is None:
import numpy as np
val = dummy_ctype(np.intp)
else:
char = dtype('p').char
if (char == 'i'):
val = ctypes.c_int
elif char == 'l':
val = ctypes.c_long
elif char == 'q':
val = ctypes.c_longlong
else:
val = ctypes.c_long
_getintp_ctype.cache = val
return val
_getintp_ctype.cache = None
# Used for .ctypes attribute of ndarray
class _missing_ctypes(object):
def cast(self, num, obj):
return num
def c_void_p(self, num):
return num
class _ctypes(object):
def __init__(self, array, ptr=None):
if ctypes:
self._ctypes = ctypes
else:
self._ctypes = _missing_ctypes()
self._arr = array
self._data = ptr
if self._arr.ndim == 0:
self._zerod = True
else:
self._zerod = False
def data_as(self, obj):
return self._ctypes.cast(self._data, obj)
def shape_as(self, obj):
if self._zerod:
return None
return (obj*self._arr.ndim)(*self._arr.shape)
def strides_as(self, obj):
if self._zerod:
return None
return (obj*self._arr.ndim)(*self._arr.strides)
def get_data(self):
return self._data
def get_shape(self):
return self.shape_as(_getintp_ctype())
def get_strides(self):
return self.strides_as(_getintp_ctype())
def get_as_parameter(self):
return self._ctypes.c_void_p(self._data)
data = property(get_data, None, doc="c-types data")
shape = property(get_shape, None, doc="c-types shape")
strides = property(get_strides, None, doc="c-types strides")
_as_parameter_ = property(get_as_parameter, None, doc="_as parameter_")
def _newnames(datatype, order):
"""
Given a datatype and an order object, return a new names tuple, with the
order indicated
"""
oldnames = datatype.names
nameslist = list(oldnames)
if isinstance(order, str):
order = [order]
seen = set()
if isinstance(order, (list, tuple)):
for name in order:
try:
nameslist.remove(name)
except ValueError:
if name in seen:
raise ValueError("duplicate field name: %s" % (name,))
else:
raise ValueError("unknown field name: %s" % (name,))
seen.add(name)
return tuple(list(order) + nameslist)
raise ValueError("unsupported order value: %s" % (order,))
def _copy_fields(ary):
"""Return copy of structured array with padding between fields removed.
Parameters
----------
ary : ndarray
Structured array from which to remove padding bytes
Returns
-------
ary_copy : ndarray
Copy of ary with padding bytes removed
"""
dt = ary.dtype
copy_dtype = {'names': dt.names,
'formats': [dt.fields[name][0] for name in dt.names]}
return array(ary, dtype=copy_dtype, copy=True)
def _getfield_is_safe(oldtype, newtype, offset):
""" Checks safety of getfield for object arrays.
As in _view_is_safe, we need to check that memory containing objects is not
reinterpreted as a non-object datatype and vice versa.
Parameters
----------
oldtype : data-type
Data type of the original ndarray.
newtype : data-type
Data type of the field being accessed by ndarray.getfield
offset : int
Offset of the field being accessed by ndarray.getfield
Raises
------
TypeError
If the field access is invalid
"""
if newtype.hasobject or oldtype.hasobject:
if offset == 0 and newtype == oldtype:
return
if oldtype.names:
for name in oldtype.names:
if (oldtype.fields[name][1] == offset and
oldtype.fields[name][0] == newtype):
return
raise TypeError("Cannot get/set field of an object array")
return
def _view_is_safe(oldtype, newtype):
""" Checks safety of a view involving object arrays, for example when
doing::
np.zeros(10, dtype=oldtype).view(newtype)
Parameters
----------
oldtype : data-type
Data type of original ndarray
newtype : data-type
Data type of the view
Raises
------
TypeError
If the new type is incompatible with the old type.
"""
# if the types are equivalent, there is no problem.
# for example: dtype((np.record, 'i4,i4')) == dtype((np.void, 'i4,i4'))
if oldtype == newtype:
return
if newtype.hasobject or oldtype.hasobject:
raise TypeError("Cannot change data-type for object array.")
return
# Given a string containing a PEP 3118 format specifier,
# construct a NumPy dtype
_pep3118_native_map = {
'?': '?',
'c': 'S1',
'b': 'b',
'B': 'B',
'h': 'h',
'H': 'H',
'i': 'i',
'I': 'I',
'l': 'l',
'L': 'L',
'q': 'q',
'Q': 'Q',
'e': 'e',
'f': 'f',
'd': 'd',
'g': 'g',
'Zf': 'F',
'Zd': 'D',
'Zg': 'G',
's': 'S',
'w': 'U',
'O': 'O',
'x': 'V', # padding
}
_pep3118_native_typechars = ''.join(_pep3118_native_map.keys())
_pep3118_standard_map = {
'?': '?',
'c': 'S1',
'b': 'b',
'B': 'B',
'h': 'i2',
'H': 'u2',
'i': 'i4',
'I': 'u4',
'l': 'i4',
'L': 'u4',
'q': 'i8',
'Q': 'u8',
'e': 'f2',
'f': 'f',
'd': 'd',
'Zf': 'F',
'Zd': 'D',
's': 'S',
'w': 'U',
'O': 'O',
'x': 'V', # padding
}
_pep3118_standard_typechars = ''.join(_pep3118_standard_map.keys())
def _dtype_from_pep3118(spec):
class Stream(object):
def __init__(self, s):
self.s = s
self.byteorder = '@'
def advance(self, n):
res = self.s[:n]
self.s = self.s[n:]
return res
def consume(self, c):
if self.s[:len(c)] == c:
self.advance(len(c))
return True
return False
def consume_until(self, c):
if callable(c):
i = 0
while i < len(self.s) and not c(self.s[i]):
i = i + 1
return self.advance(i)
else:
i = self.s.index(c)
res = self.advance(i)
self.advance(len(c))
return res
@property
def next(self):
return self.s[0]
def __bool__(self):
return bool(self.s)
__nonzero__ = __bool__
stream = Stream(spec)
dtype, align = __dtype_from_pep3118(stream, is_subdtype=False)
return dtype
def __dtype_from_pep3118(stream, is_subdtype):
field_spec = dict(
names=[],
formats=[],
offsets=[],
itemsize=0
)
offset = 0
common_alignment = 1
is_padding = False
# Parse spec
while stream:
value = None
# End of structure, bail out to upper level
if stream.consume('}'):
break
# Sub-arrays (1)
shape = None
if stream.consume('('):
shape = stream.consume_until(')')
shape = tuple(map(int, shape.split(',')))
# Byte order
if stream.next in ('@', '=', '<', '>', '^', '!'):
byteorder = stream.advance(1)
if byteorder == '!':
byteorder = '>'
stream.byteorder = byteorder
# Byte order characters also control native vs. standard type sizes
if stream.byteorder in ('@', '^'):
type_map = _pep3118_native_map
type_map_chars = _pep3118_native_typechars
else:
type_map = _pep3118_standard_map
type_map_chars = _pep3118_standard_typechars
# Item sizes
itemsize_str = stream.consume_until(lambda c: not c.isdigit())
if itemsize_str:
itemsize = int(itemsize_str)
else:
itemsize = 1
# Data types
is_padding = False
if stream.consume('T{'):
value, align = __dtype_from_pep3118(
stream, is_subdtype=True)
elif stream.next in type_map_chars:
if stream.next == 'Z':
typechar = stream.advance(2)
else:
typechar = stream.advance(1)
is_padding = (typechar == 'x')
dtypechar = type_map[typechar]
if dtypechar in 'USV':
dtypechar += '%d' % itemsize
itemsize = 1
numpy_byteorder = {'@': '=', '^': '='}.get(
stream.byteorder, stream.byteorder)
value = dtype(numpy_byteorder + dtypechar)
align = value.alignment
else:
raise ValueError("Unknown PEP 3118 data type specifier %r" % stream.s)
#
# Native alignment may require padding
#
# Here we assume that the presence of a '@' character implicitly implies
# that the start of the array is *already* aligned.
#
extra_offset = 0
if stream.byteorder == '@':
start_padding = (-offset) % align
intra_padding = (-value.itemsize) % align
offset += start_padding
if intra_padding != 0:
if itemsize > 1 or (shape is not None and _prod(shape) > 1):
# Inject internal padding to the end of the sub-item
value = _add_trailing_padding(value, intra_padding)
else:
# We can postpone the injection of internal padding,
# as the item appears at most once
extra_offset += intra_padding
# Update common alignment
common_alignment = _lcm(align, common_alignment)
# Convert itemsize to sub-array
if itemsize != 1:
value = dtype((value, (itemsize,)))
# Sub-arrays (2)
if shape is not None:
value = dtype((value, shape))
# Field name
if stream.consume(':'):
name = stream.consume_until(':')
else:
name = None
if not (is_padding and name is None):
if name is not None and name in field_spec['names']:
raise RuntimeError("Duplicate field name '%s' in PEP3118 format"
% name)
field_spec['names'].append(name)
field_spec['formats'].append(value)
field_spec['offsets'].append(offset)
offset += value.itemsize
offset += extra_offset
field_spec['itemsize'] = offset
# extra final padding for aligned types
if stream.byteorder == '@':
field_spec['itemsize'] += (-offset) % common_alignment
# Check if this was a simple 1-item type, and unwrap it
if (field_spec['names'] == [None]
and field_spec['offsets'][0] == 0
and field_spec['itemsize'] == field_spec['formats'][0].itemsize
and not is_subdtype):
ret = field_spec['formats'][0]
else:
_fix_names(field_spec)
ret = dtype(field_spec)
# Finished
return ret, common_alignment
def _fix_names(field_spec):
""" Replace names which are None with the next unused f%d name """
names = field_spec['names']
for i, name in enumerate(names):
if name is not None:
continue
j = 0
while True:
name = 'f{}'.format(j)
if name not in names:
break
j = j + 1
names[i] = name
def _add_trailing_padding(value, padding):
"""Inject the specified number of padding bytes at the end of a dtype"""
if value.fields is None:
field_spec = dict(
names=['f0'],
formats=[value],
offsets=[0],
itemsize=value.itemsize
)
else:
fields = value.fields
names = value.names
field_spec = dict(
names=names,
formats=[fields[name][0] for name in names],
offsets=[fields[name][1] for name in names],
itemsize=value.itemsize
)
field_spec['itemsize'] += padding
return dtype(field_spec)
def _prod(a):
p = 1
for x in a:
p *= x
return p
def _gcd(a, b):
"""Calculate the greatest common divisor of a and b"""
while b:
a, b = b, a % b
return a
def _lcm(a, b):
return a // _gcd(a, b) * b
# Exception used in shares_memory()
class TooHardError(RuntimeError):
pass
class AxisError(ValueError, IndexError):
""" Axis supplied was invalid. """
def __init__(self, axis, ndim=None, msg_prefix=None):
# single-argument form just delegates to base class
if ndim is None and msg_prefix is None:
msg = axis
# do the string formatting here, to save work in the C code
else:
msg = ("axis {} is out of bounds for array of dimension {}"
.format(axis, ndim))
if msg_prefix is not None:
msg = "{}: {}".format(msg_prefix, msg)
super(AxisError, self).__init__(msg)
def array_ufunc_errmsg_formatter(dummy, ufunc, method, *inputs, **kwargs):
""" Format the error message for when __array_ufunc__ gives up. """
args_string = ', '.join(['{!r}'.format(arg) for arg in inputs] +
['{}={!r}'.format(k, v)
for k, v in kwargs.items()])
args = inputs + kwargs.get('out', ())
types_string = ', '.join(repr(type(arg).__name__) for arg in args)
return ('operand type(s) all returned NotImplemented from '
'__array_ufunc__({!r}, {!r}, {}): {}'
.format(ufunc, method, args_string, types_string))
def _ufunc_doc_signature_formatter(ufunc):
"""
Builds a signature string which resembles PEP 457
This is used to construct the first line of the docstring
"""
# input arguments are simple
if ufunc.nin == 1:
in_args = 'x'
else:
in_args = ', '.join('x{}'.format(i+1) for i in range(ufunc.nin))
# output arguments are both keyword or positional
if ufunc.nout == 0:
out_args = ', /, out=()'
elif ufunc.nout == 1:
out_args = ', /, out=None'
else:
out_args = '[, {positional}], / [, out={default}]'.format(
positional=', '.join(
'out{}'.format(i+1) for i in range(ufunc.nout)),
default=repr((None,)*ufunc.nout)
)
# keyword only args depend on whether this is a gufunc
kwargs = (
", casting='same_kind'"
", order='K'"
", dtype=None"
", subok=True"
"[, signature"
", extobj]"
)
if ufunc.signature is None:
kwargs = ", where=True" + kwargs
# join all the parts together
return '{name}({in_args}{out_args}, *{kwargs})'.format(
name=ufunc.__name__,
in_args=in_args,
out_args=out_args,
kwargs=kwargs
)
| 21,816 | 27.744401 | 82 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/cversions.py
|
"""Simple script to compute the api hash of the current API.
The API has is defined by numpy_api_order and ufunc_api_order.
"""
from __future__ import division, absolute_import, print_function
from os.path import dirname
from code_generators.genapi import fullapi_hash
from code_generators.numpy_api import full_api
if __name__ == '__main__':
curdir = dirname(__file__)
print(fullapi_hash(full_api))
| 413 | 24.875 | 64 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/setup.py
|
from __future__ import division, print_function
import os
import sys
import pickle
import copy
import sysconfig
import warnings
import platform
from os.path import join
from numpy.distutils import log
from distutils.dep_util import newer
from distutils.sysconfig import get_config_var
from numpy._build_utils.apple_accelerate import (
uses_accelerate_framework, get_sgemv_fix
)
from numpy.compat import npy_load_module
from setup_common import *
# Set to True to enable relaxed strides checking. This (mostly) means
# that `strides[dim]` is ignored if `shape[dim] == 1` when setting flags.
NPY_RELAXED_STRIDES_CHECKING = (os.environ.get('NPY_RELAXED_STRIDES_CHECKING', "1") != "0")
# Put NPY_RELAXED_STRIDES_DEBUG=1 in the environment if you want numpy to use a
# bogus value for affected strides in order to help smoke out bad stride usage
# when relaxed stride checking is enabled.
NPY_RELAXED_STRIDES_DEBUG = (os.environ.get('NPY_RELAXED_STRIDES_DEBUG', "0") != "0")
NPY_RELAXED_STRIDES_DEBUG = NPY_RELAXED_STRIDES_DEBUG and NPY_RELAXED_STRIDES_CHECKING
# XXX: ugly, we use a class to avoid calling twice some expensive functions in
# config.h/numpyconfig.h. I don't see a better way because distutils force
# config.h generation inside an Extension class, and as such sharing
# configuration informations between extensions is not easy.
# Using a pickled-based memoize does not work because config_cmd is an instance
# method, which cPickle does not like.
#
# Use pickle in all cases, as cPickle is gone in python3 and the difference
# in time is only in build. -- Charles Harris, 2013-03-30
class CallOnceOnly(object):
def __init__(self):
self._check_types = None
self._check_ieee_macros = None
self._check_complex = None
def check_types(self, *a, **kw):
if self._check_types is None:
out = check_types(*a, **kw)
self._check_types = pickle.dumps(out)
else:
out = copy.deepcopy(pickle.loads(self._check_types))
return out
def check_ieee_macros(self, *a, **kw):
if self._check_ieee_macros is None:
out = check_ieee_macros(*a, **kw)
self._check_ieee_macros = pickle.dumps(out)
else:
out = copy.deepcopy(pickle.loads(self._check_ieee_macros))
return out
def check_complex(self, *a, **kw):
if self._check_complex is None:
out = check_complex(*a, **kw)
self._check_complex = pickle.dumps(out)
else:
out = copy.deepcopy(pickle.loads(self._check_complex))
return out
def pythonlib_dir():
"""return path where libpython* is."""
if sys.platform == 'win32':
return os.path.join(sys.prefix, "libs")
else:
return get_config_var('LIBDIR')
def is_npy_no_signal():
"""Return True if the NPY_NO_SIGNAL symbol must be defined in configuration
header."""
return sys.platform == 'win32'
def is_npy_no_smp():
"""Return True if the NPY_NO_SMP symbol must be defined in public
header (when SMP support cannot be reliably enabled)."""
# Perhaps a fancier check is in order here.
# so that threads are only enabled if there
# are actually multiple CPUS? -- but
# threaded code can be nice even on a single
# CPU so that long-calculating code doesn't
# block.
return 'NPY_NOSMP' in os.environ
def win32_checks(deflist):
from numpy.distutils.misc_util import get_build_architecture
a = get_build_architecture()
# Distutils hack on AMD64 on windows
print('BUILD_ARCHITECTURE: %r, os.name=%r, sys.platform=%r' %
(a, os.name, sys.platform))
if a == 'AMD64':
deflist.append('DISTUTILS_USE_SDK')
# On win32, force long double format string to be 'g', not
# 'Lg', since the MS runtime does not support long double whose
# size is > sizeof(double)
if a == "Intel" or a == "AMD64":
deflist.append('FORCE_NO_LONG_DOUBLE_FORMATTING')
def check_math_capabilities(config, moredefs, mathlibs):
def check_func(func_name):
return config.check_func(func_name, libraries=mathlibs,
decl=True, call=True)
def check_funcs_once(funcs_name):
decl = dict([(f, True) for f in funcs_name])
st = config.check_funcs_once(funcs_name, libraries=mathlibs,
decl=decl, call=decl)
if st:
moredefs.extend([(fname2def(f), 1) for f in funcs_name])
return st
def check_funcs(funcs_name):
# Use check_funcs_once first, and if it does not work, test func per
# func. Return success only if all the functions are available
if not check_funcs_once(funcs_name):
# Global check failed, check func per func
for f in funcs_name:
if check_func(f):
moredefs.append((fname2def(f), 1))
return 0
else:
return 1
#use_msvc = config.check_decl("_MSC_VER")
if not check_funcs_once(MANDATORY_FUNCS):
raise SystemError("One of the required function to build numpy is not"
" available (the list is %s)." % str(MANDATORY_FUNCS))
# Standard functions which may not be available and for which we have a
# replacement implementation. Note that some of these are C99 functions.
# XXX: hack to circumvent cpp pollution from python: python put its
# config.h in the public namespace, so we have a clash for the common
# functions we test. We remove every function tested by python's
# autoconf, hoping their own test are correct
for f in OPTIONAL_STDFUNCS_MAYBE:
if config.check_decl(fname2def(f),
headers=["Python.h", "math.h"]):
OPTIONAL_STDFUNCS.remove(f)
check_funcs(OPTIONAL_STDFUNCS)
for h in OPTIONAL_HEADERS:
if config.check_func("", decl=False, call=False, headers=[h]):
moredefs.append((fname2def(h).replace(".", "_"), 1))
for tup in OPTIONAL_INTRINSICS:
headers = None
if len(tup) == 2:
f, args, m = tup[0], tup[1], fname2def(tup[0])
elif len(tup) == 3:
f, args, headers, m = tup[0], tup[1], [tup[2]], fname2def(tup[0])
else:
f, args, headers, m = tup[0], tup[1], [tup[2]], fname2def(tup[3])
if config.check_func(f, decl=False, call=True, call_args=args,
headers=headers):
moredefs.append((m, 1))
for dec, fn in OPTIONAL_FUNCTION_ATTRIBUTES:
if config.check_gcc_function_attribute(dec, fn):
moredefs.append((fname2def(fn), 1))
for fn in OPTIONAL_VARIABLE_ATTRIBUTES:
if config.check_gcc_variable_attribute(fn):
m = fn.replace("(", "_").replace(")", "_")
moredefs.append((fname2def(m), 1))
# C99 functions: float and long double versions
check_funcs(C99_FUNCS_SINGLE)
check_funcs(C99_FUNCS_EXTENDED)
def check_complex(config, mathlibs):
priv = []
pub = []
try:
if os.uname()[0] == "Interix":
warnings.warn("Disabling broken complex support. See #1365", stacklevel=2)
return priv, pub
except Exception:
# os.uname not available on all platforms. blanket except ugly but safe
pass
# Check for complex support
st = config.check_header('complex.h')
if st:
priv.append(('HAVE_COMPLEX_H', 1))
pub.append(('NPY_USE_C99_COMPLEX', 1))
for t in C99_COMPLEX_TYPES:
st = config.check_type(t, headers=["complex.h"])
if st:
pub.append(('NPY_HAVE_%s' % type2def(t), 1))
def check_prec(prec):
flist = [f + prec for f in C99_COMPLEX_FUNCS]
decl = dict([(f, True) for f in flist])
if not config.check_funcs_once(flist, call=decl, decl=decl,
libraries=mathlibs):
for f in flist:
if config.check_func(f, call=True, decl=True,
libraries=mathlibs):
priv.append((fname2def(f), 1))
else:
priv.extend([(fname2def(f), 1) for f in flist])
check_prec('')
check_prec('f')
check_prec('l')
return priv, pub
def check_ieee_macros(config):
priv = []
pub = []
macros = []
def _add_decl(f):
priv.append(fname2def("decl_%s" % f))
pub.append('NPY_%s' % fname2def("decl_%s" % f))
# XXX: hack to circumvent cpp pollution from python: python put its
# config.h in the public namespace, so we have a clash for the common
# functions we test. We remove every function tested by python's
# autoconf, hoping their own test are correct
_macros = ["isnan", "isinf", "signbit", "isfinite"]
for f in _macros:
py_symbol = fname2def("decl_%s" % f)
already_declared = config.check_decl(py_symbol,
headers=["Python.h", "math.h"])
if already_declared:
if config.check_macro_true(py_symbol,
headers=["Python.h", "math.h"]):
pub.append('NPY_%s' % fname2def("decl_%s" % f))
else:
macros.append(f)
# Normally, isnan and isinf are macro (C99), but some platforms only have
# func, or both func and macro version. Check for macro only, and define
# replacement ones if not found.
# Note: including Python.h is necessary because it modifies some math.h
# definitions
for f in macros:
st = config.check_decl(f, headers=["Python.h", "math.h"])
if st:
_add_decl(f)
return priv, pub
def check_types(config_cmd, ext, build_dir):
private_defines = []
public_defines = []
# Expected size (in number of bytes) for each type. This is an
# optimization: those are only hints, and an exhaustive search for the size
# is done if the hints are wrong.
expected = {'short': [2], 'int': [4], 'long': [8, 4],
'float': [4], 'double': [8], 'long double': [16, 12, 8],
'Py_intptr_t': [8, 4], 'PY_LONG_LONG': [8], 'long long': [8],
'off_t': [8, 4]}
# Check we have the python header (-dev* packages on Linux)
result = config_cmd.check_header('Python.h')
if not result:
python = 'python'
if '__pypy__' in sys.builtin_module_names:
python = 'pypy'
raise SystemError(
"Cannot compile 'Python.h'. Perhaps you need to "
"install {0}-dev|{0}-devel.".format(python))
res = config_cmd.check_header("endian.h")
if res:
private_defines.append(('HAVE_ENDIAN_H', 1))
public_defines.append(('NPY_HAVE_ENDIAN_H', 1))
res = config_cmd.check_header("sys/endian.h")
if res:
private_defines.append(('HAVE_SYS_ENDIAN_H', 1))
public_defines.append(('NPY_HAVE_SYS_ENDIAN_H', 1))
# Check basic types sizes
for type in ('short', 'int', 'long'):
res = config_cmd.check_decl("SIZEOF_%s" % sym2def(type), headers=["Python.h"])
if res:
public_defines.append(('NPY_SIZEOF_%s' % sym2def(type), "SIZEOF_%s" % sym2def(type)))
else:
res = config_cmd.check_type_size(type, expected=expected[type])
if res >= 0:
public_defines.append(('NPY_SIZEOF_%s' % sym2def(type), '%d' % res))
else:
raise SystemError("Checking sizeof (%s) failed !" % type)
for type in ('float', 'double', 'long double'):
already_declared = config_cmd.check_decl("SIZEOF_%s" % sym2def(type),
headers=["Python.h"])
res = config_cmd.check_type_size(type, expected=expected[type])
if res >= 0:
public_defines.append(('NPY_SIZEOF_%s' % sym2def(type), '%d' % res))
if not already_declared and not type == 'long double':
private_defines.append(('SIZEOF_%s' % sym2def(type), '%d' % res))
else:
raise SystemError("Checking sizeof (%s) failed !" % type)
# Compute size of corresponding complex type: used to check that our
# definition is binary compatible with C99 complex type (check done at
# build time in npy_common.h)
complex_def = "struct {%s __x; %s __y;}" % (type, type)
res = config_cmd.check_type_size(complex_def,
expected=[2 * x for x in expected[type]])
if res >= 0:
public_defines.append(('NPY_SIZEOF_COMPLEX_%s' % sym2def(type), '%d' % res))
else:
raise SystemError("Checking sizeof (%s) failed !" % complex_def)
for type in ('Py_intptr_t', 'off_t'):
res = config_cmd.check_type_size(type, headers=["Python.h"],
library_dirs=[pythonlib_dir()],
expected=expected[type])
if res >= 0:
private_defines.append(('SIZEOF_%s' % sym2def(type), '%d' % res))
public_defines.append(('NPY_SIZEOF_%s' % sym2def(type), '%d' % res))
else:
raise SystemError("Checking sizeof (%s) failed !" % type)
# We check declaration AND type because that's how distutils does it.
if config_cmd.check_decl('PY_LONG_LONG', headers=['Python.h']):
res = config_cmd.check_type_size('PY_LONG_LONG', headers=['Python.h'],
library_dirs=[pythonlib_dir()],
expected=expected['PY_LONG_LONG'])
if res >= 0:
private_defines.append(('SIZEOF_%s' % sym2def('PY_LONG_LONG'), '%d' % res))
public_defines.append(('NPY_SIZEOF_%s' % sym2def('PY_LONG_LONG'), '%d' % res))
else:
raise SystemError("Checking sizeof (%s) failed !" % 'PY_LONG_LONG')
res = config_cmd.check_type_size('long long',
expected=expected['long long'])
if res >= 0:
#private_defines.append(('SIZEOF_%s' % sym2def('long long'), '%d' % res))
public_defines.append(('NPY_SIZEOF_%s' % sym2def('long long'), '%d' % res))
else:
raise SystemError("Checking sizeof (%s) failed !" % 'long long')
if not config_cmd.check_decl('CHAR_BIT', headers=['Python.h']):
raise RuntimeError(
"Config wo CHAR_BIT is not supported"
", please contact the maintainers")
return private_defines, public_defines
def check_mathlib(config_cmd):
# Testing the C math library
mathlibs = []
mathlibs_choices = [[], ['m'], ['cpml']]
mathlib = os.environ.get('MATHLIB')
if mathlib:
mathlibs_choices.insert(0, mathlib.split(','))
for libs in mathlibs_choices:
if config_cmd.check_func("exp", libraries=libs, decl=True, call=True):
mathlibs = libs
break
else:
raise EnvironmentError("math library missing; rerun "
"setup.py after setting the "
"MATHLIB env variable")
return mathlibs
def visibility_define(config):
"""Return the define value to use for NPY_VISIBILITY_HIDDEN (may be empty
string)."""
if config.check_compiler_gcc4():
return '__attribute__((visibility("hidden")))'
else:
return ''
def configuration(parent_package='',top_path=None):
from numpy.distutils.misc_util import Configuration, dot_join
from numpy.distutils.system_info import get_info
config = Configuration('core', parent_package, top_path)
local_dir = config.local_path
codegen_dir = join(local_dir, 'code_generators')
if is_released(config):
warnings.simplefilter('error', MismatchCAPIWarning)
# Check whether we have a mismatch between the set C API VERSION and the
# actual C API VERSION
check_api_version(C_API_VERSION, codegen_dir)
generate_umath_py = join(codegen_dir, 'generate_umath.py')
n = dot_join(config.name, 'generate_umath')
generate_umath = npy_load_module('_'.join(n.split('.')),
generate_umath_py, ('.py', 'U', 1))
header_dir = 'include/numpy' # this is relative to config.path_in_package
cocache = CallOnceOnly()
def generate_config_h(ext, build_dir):
target = join(build_dir, header_dir, 'config.h')
d = os.path.dirname(target)
if not os.path.exists(d):
os.makedirs(d)
if newer(__file__, target):
config_cmd = config.get_config_cmd()
log.info('Generating %s', target)
# Check sizeof
moredefs, ignored = cocache.check_types(config_cmd, ext, build_dir)
# Check math library and C99 math funcs availability
mathlibs = check_mathlib(config_cmd)
moredefs.append(('MATHLIB', ','.join(mathlibs)))
check_math_capabilities(config_cmd, moredefs, mathlibs)
moredefs.extend(cocache.check_ieee_macros(config_cmd)[0])
moredefs.extend(cocache.check_complex(config_cmd, mathlibs)[0])
# Signal check
if is_npy_no_signal():
moredefs.append('__NPY_PRIVATE_NO_SIGNAL')
# Windows checks
if sys.platform == 'win32' or os.name == 'nt':
win32_checks(moredefs)
# C99 restrict keyword
moredefs.append(('NPY_RESTRICT', config_cmd.check_restrict()))
# Inline check
inline = config_cmd.check_inline()
# Use relaxed stride checking
if NPY_RELAXED_STRIDES_CHECKING:
moredefs.append(('NPY_RELAXED_STRIDES_CHECKING', 1))
# Use bogus stride debug aid when relaxed strides are enabled
if NPY_RELAXED_STRIDES_DEBUG:
moredefs.append(('NPY_RELAXED_STRIDES_DEBUG', 1))
# Get long double representation
if sys.platform != 'darwin':
rep = check_long_double_representation(config_cmd)
if rep in ['INTEL_EXTENDED_12_BYTES_LE',
'INTEL_EXTENDED_16_BYTES_LE',
'MOTOROLA_EXTENDED_12_BYTES_BE',
'IEEE_QUAD_LE', 'IEEE_QUAD_BE',
'IEEE_DOUBLE_LE', 'IEEE_DOUBLE_BE',
'DOUBLE_DOUBLE_BE', 'DOUBLE_DOUBLE_LE']:
moredefs.append(('HAVE_LDOUBLE_%s' % rep, 1))
else:
raise ValueError("Unrecognized long double format: %s" % rep)
# Py3K check
if sys.version_info[0] == 3:
moredefs.append(('NPY_PY3K', 1))
# Generate the config.h file from moredefs
target_f = open(target, 'w')
for d in moredefs:
if isinstance(d, str):
target_f.write('#define %s\n' % (d))
else:
target_f.write('#define %s %s\n' % (d[0], d[1]))
# define inline to our keyword, or nothing
target_f.write('#ifndef __cplusplus\n')
if inline == 'inline':
target_f.write('/* #undef inline */\n')
else:
target_f.write('#define inline %s\n' % inline)
target_f.write('#endif\n')
# add the guard to make sure config.h is never included directly,
# but always through npy_config.h
target_f.write("""
#ifndef _NPY_NPY_CONFIG_H_
#error config.h should never be included directly, include npy_config.h instead
#endif
""")
target_f.close()
print('File:', target)
target_f = open(target)
print(target_f.read())
target_f.close()
print('EOF')
else:
mathlibs = []
target_f = open(target)
for line in target_f:
s = '#define MATHLIB'
if line.startswith(s):
value = line[len(s):].strip()
if value:
mathlibs.extend(value.split(','))
target_f.close()
# Ugly: this can be called within a library and not an extension,
# in which case there is no libraries attributes (and none is
# needed).
if hasattr(ext, 'libraries'):
ext.libraries.extend(mathlibs)
incl_dir = os.path.dirname(target)
if incl_dir not in config.numpy_include_dirs:
config.numpy_include_dirs.append(incl_dir)
return target
def generate_numpyconfig_h(ext, build_dir):
"""Depends on config.h: generate_config_h has to be called before !"""
# put private include directory in build_dir on search path
# allows using code generation in headers headers
config.add_include_dirs(join(build_dir, "src", "private"))
config.add_include_dirs(join(build_dir, "src", "npymath"))
target = join(build_dir, header_dir, '_numpyconfig.h')
d = os.path.dirname(target)
if not os.path.exists(d):
os.makedirs(d)
if newer(__file__, target):
config_cmd = config.get_config_cmd()
log.info('Generating %s', target)
# Check sizeof
ignored, moredefs = cocache.check_types(config_cmd, ext, build_dir)
if is_npy_no_signal():
moredefs.append(('NPY_NO_SIGNAL', 1))
if is_npy_no_smp():
moredefs.append(('NPY_NO_SMP', 1))
else:
moredefs.append(('NPY_NO_SMP', 0))
mathlibs = check_mathlib(config_cmd)
moredefs.extend(cocache.check_ieee_macros(config_cmd)[1])
moredefs.extend(cocache.check_complex(config_cmd, mathlibs)[1])
if NPY_RELAXED_STRIDES_CHECKING:
moredefs.append(('NPY_RELAXED_STRIDES_CHECKING', 1))
if NPY_RELAXED_STRIDES_DEBUG:
moredefs.append(('NPY_RELAXED_STRIDES_DEBUG', 1))
# Check wether we can use inttypes (C99) formats
if config_cmd.check_decl('PRIdPTR', headers=['inttypes.h']):
moredefs.append(('NPY_USE_C99_FORMATS', 1))
# visibility check
hidden_visibility = visibility_define(config_cmd)
moredefs.append(('NPY_VISIBILITY_HIDDEN', hidden_visibility))
# Add the C API/ABI versions
moredefs.append(('NPY_ABI_VERSION', '0x%.8X' % C_ABI_VERSION))
moredefs.append(('NPY_API_VERSION', '0x%.8X' % C_API_VERSION))
# Add moredefs to header
target_f = open(target, 'w')
for d in moredefs:
if isinstance(d, str):
target_f.write('#define %s\n' % (d))
else:
target_f.write('#define %s %s\n' % (d[0], d[1]))
# Define __STDC_FORMAT_MACROS
target_f.write("""
#ifndef __STDC_FORMAT_MACROS
#define __STDC_FORMAT_MACROS 1
#endif
""")
target_f.close()
# Dump the numpyconfig.h header to stdout
print('File: %s' % target)
target_f = open(target)
print(target_f.read())
target_f.close()
print('EOF')
config.add_data_files((header_dir, target))
return target
def generate_api_func(module_name):
def generate_api(ext, build_dir):
script = join(codegen_dir, module_name + '.py')
sys.path.insert(0, codegen_dir)
try:
m = __import__(module_name)
log.info('executing %s', script)
h_file, c_file, doc_file = m.generate_api(os.path.join(build_dir, header_dir))
finally:
del sys.path[0]
config.add_data_files((header_dir, h_file),
(header_dir, doc_file))
return (h_file,)
return generate_api
generate_numpy_api = generate_api_func('generate_numpy_api')
generate_ufunc_api = generate_api_func('generate_ufunc_api')
config.add_include_dirs(join(local_dir, "src", "private"))
config.add_include_dirs(join(local_dir, "src"))
config.add_include_dirs(join(local_dir))
config.add_data_files('include/numpy/*.h')
config.add_include_dirs(join('src', 'npymath'))
config.add_include_dirs(join('src', 'multiarray'))
config.add_include_dirs(join('src', 'umath'))
config.add_include_dirs(join('src', 'npysort'))
config.add_define_macros([("NPY_INTERNAL_BUILD", "1")]) # this macro indicates that Numpy build is in process
config.add_define_macros([("HAVE_NPY_CONFIG_H", "1")])
if sys.platform[:3] == "aix":
config.add_define_macros([("_LARGE_FILES", None)])
else:
config.add_define_macros([("_FILE_OFFSET_BITS", "64")])
config.add_define_macros([('_LARGEFILE_SOURCE', '1')])
config.add_define_macros([('_LARGEFILE64_SOURCE', '1')])
config.numpy_include_dirs.extend(config.paths('include'))
deps = [join('src', 'npymath', '_signbit.c'),
join('include', 'numpy', '*object.h'),
join(codegen_dir, 'genapi.py'),
]
#######################################################################
# dummy module #
#######################################################################
# npymath needs the config.h and numpyconfig.h files to be generated, but
# build_clib cannot handle generate_config_h and generate_numpyconfig_h
# (don't ask). Because clib are generated before extensions, we have to
# explicitly add an extension which has generate_config_h and
# generate_numpyconfig_h as sources *before* adding npymath.
config.add_extension('_dummy',
sources=[join('src', 'dummymodule.c'),
generate_config_h,
generate_numpyconfig_h,
generate_numpy_api]
)
#######################################################################
# npymath library #
#######################################################################
subst_dict = dict([("sep", os.path.sep), ("pkgname", "numpy.core")])
def get_mathlib_info(*args):
# Another ugly hack: the mathlib info is known once build_src is run,
# but we cannot use add_installed_pkg_config here either, so we only
# update the substition dictionary during npymath build
config_cmd = config.get_config_cmd()
# Check that the toolchain works, to fail early if it doesn't
# (avoid late errors with MATHLIB which are confusing if the
# compiler does not work).
st = config_cmd.try_link('int main(void) { return 0;}')
if not st:
raise RuntimeError("Broken toolchain: cannot link a simple C program")
mlibs = check_mathlib(config_cmd)
posix_mlib = ' '.join(['-l%s' % l for l in mlibs])
msvc_mlib = ' '.join(['%s.lib' % l for l in mlibs])
subst_dict["posix_mathlib"] = posix_mlib
subst_dict["msvc_mathlib"] = msvc_mlib
npymath_sources = [join('src', 'npymath', 'npy_math_internal.h.src'),
join('src', 'npymath', 'npy_math.c'),
join('src', 'npymath', 'ieee754.c.src'),
join('src', 'npymath', 'npy_math_complex.c.src'),
join('src', 'npymath', 'halffloat.c')
]
# Must be true for CRT compilers but not MinGW/cygwin. See gh-9977.
is_msvc = platform.system() == 'Windows'
config.add_installed_library('npymath',
sources=npymath_sources + [get_mathlib_info],
install_dir='lib',
build_info={
'include_dirs' : [], # empty list required for creating npy_math_internal.h
'extra_compiler_args' : (['/GL-'] if is_msvc else []),
})
config.add_npy_pkg_config("npymath.ini.in", "lib/npy-pkg-config",
subst_dict)
config.add_npy_pkg_config("mlib.ini.in", "lib/npy-pkg-config",
subst_dict)
#######################################################################
# npysort library #
#######################################################################
# This library is created for the build but it is not installed
npysort_sources = [join('src', 'npysort', 'quicksort.c.src'),
join('src', 'npysort', 'mergesort.c.src'),
join('src', 'npysort', 'heapsort.c.src'),
join('src', 'private', 'npy_partition.h.src'),
join('src', 'npysort', 'selection.c.src'),
join('src', 'private', 'npy_binsearch.h.src'),
join('src', 'npysort', 'binsearch.c.src'),
]
config.add_library('npysort',
sources=npysort_sources,
include_dirs=[])
#######################################################################
# multiarray module #
#######################################################################
multiarray_deps = [
join('src', 'multiarray', 'arrayobject.h'),
join('src', 'multiarray', 'arraytypes.h'),
join('src', 'multiarray', 'array_assign.h'),
join('src', 'multiarray', 'buffer.h'),
join('src', 'multiarray', 'calculation.h'),
join('src', 'multiarray', 'cblasfuncs.h'),
join('src', 'multiarray', 'common.h'),
join('src', 'multiarray', 'convert_datatype.h'),
join('src', 'multiarray', 'convert.h'),
join('src', 'multiarray', 'conversion_utils.h'),
join('src', 'multiarray', 'ctors.h'),
join('src', 'multiarray', 'descriptor.h'),
join('src', 'multiarray', 'dragon4.h'),
join('src', 'multiarray', 'getset.h'),
join('src', 'multiarray', 'hashdescr.h'),
join('src', 'multiarray', 'iterators.h'),
join('src', 'multiarray', 'mapping.h'),
join('src', 'multiarray', 'methods.h'),
join('src', 'multiarray', 'multiarraymodule.h'),
join('src', 'multiarray', 'nditer_impl.h'),
join('src', 'multiarray', 'number.h'),
join('src', 'multiarray', 'numpyos.h'),
join('src', 'multiarray', 'refcount.h'),
join('src', 'multiarray', 'scalartypes.h'),
join('src', 'multiarray', 'sequence.h'),
join('src', 'multiarray', 'shape.h'),
join('src', 'multiarray', 'strfuncs.h'),
join('src', 'multiarray', 'ucsnarrow.h'),
join('src', 'multiarray', 'usertypes.h'),
join('src', 'multiarray', 'vdot.h'),
join('src', 'private', 'npy_config.h'),
join('src', 'private', 'templ_common.h.src'),
join('src', 'private', 'lowlevel_strided_loops.h'),
join('src', 'private', 'mem_overlap.h'),
join('src', 'private', 'npy_longdouble.h'),
join('src', 'private', 'ufunc_override.h'),
join('src', 'private', 'binop_override.h'),
join('src', 'private', 'npy_extint128.h'),
join('include', 'numpy', 'arrayobject.h'),
join('include', 'numpy', '_neighborhood_iterator_imp.h'),
join('include', 'numpy', 'npy_endian.h'),
join('include', 'numpy', 'arrayscalars.h'),
join('include', 'numpy', 'noprefix.h'),
join('include', 'numpy', 'npy_interrupt.h'),
join('include', 'numpy', 'npy_3kcompat.h'),
join('include', 'numpy', 'npy_math.h'),
join('include', 'numpy', 'halffloat.h'),
join('include', 'numpy', 'npy_common.h'),
join('include', 'numpy', 'npy_os.h'),
join('include', 'numpy', 'utils.h'),
join('include', 'numpy', 'ndarrayobject.h'),
join('include', 'numpy', 'npy_cpu.h'),
join('include', 'numpy', 'numpyconfig.h'),
join('include', 'numpy', 'ndarraytypes.h'),
join('include', 'numpy', 'npy_1_7_deprecated_api.h'),
# add library sources as distuils does not consider libraries
# dependencies
] + npysort_sources + npymath_sources
multiarray_src = [
join('src', 'multiarray', 'alloc.c'),
join('src', 'multiarray', 'arrayobject.c'),
join('src', 'multiarray', 'arraytypes.c.src'),
join('src', 'multiarray', 'array_assign.c'),
join('src', 'multiarray', 'array_assign_scalar.c'),
join('src', 'multiarray', 'array_assign_array.c'),
join('src', 'multiarray', 'buffer.c'),
join('src', 'multiarray', 'calculation.c'),
join('src', 'multiarray', 'compiled_base.c'),
join('src', 'multiarray', 'common.c'),
join('src', 'multiarray', 'convert.c'),
join('src', 'multiarray', 'convert_datatype.c'),
join('src', 'multiarray', 'conversion_utils.c'),
join('src', 'multiarray', 'ctors.c'),
join('src', 'multiarray', 'datetime.c'),
join('src', 'multiarray', 'datetime_strings.c'),
join('src', 'multiarray', 'datetime_busday.c'),
join('src', 'multiarray', 'datetime_busdaycal.c'),
join('src', 'multiarray', 'descriptor.c'),
join('src', 'multiarray', 'dragon4.c'),
join('src', 'multiarray', 'dtype_transfer.c'),
join('src', 'multiarray', 'einsum.c.src'),
join('src', 'multiarray', 'flagsobject.c'),
join('src', 'multiarray', 'getset.c'),
join('src', 'multiarray', 'hashdescr.c'),
join('src', 'multiarray', 'item_selection.c'),
join('src', 'multiarray', 'iterators.c'),
join('src', 'multiarray', 'lowlevel_strided_loops.c.src'),
join('src', 'multiarray', 'mapping.c'),
join('src', 'multiarray', 'methods.c'),
join('src', 'multiarray', 'multiarraymodule.c'),
join('src', 'multiarray', 'nditer_templ.c.src'),
join('src', 'multiarray', 'nditer_api.c'),
join('src', 'multiarray', 'nditer_constr.c'),
join('src', 'multiarray', 'nditer_pywrap.c'),
join('src', 'multiarray', 'number.c'),
join('src', 'multiarray', 'numpyos.c'),
join('src', 'multiarray', 'refcount.c'),
join('src', 'multiarray', 'sequence.c'),
join('src', 'multiarray', 'shape.c'),
join('src', 'multiarray', 'scalarapi.c'),
join('src', 'multiarray', 'scalartypes.c.src'),
join('src', 'multiarray', 'strfuncs.c'),
join('src', 'multiarray', 'temp_elide.c'),
join('src', 'multiarray', 'usertypes.c'),
join('src', 'multiarray', 'ucsnarrow.c'),
join('src', 'multiarray', 'vdot.c'),
join('src', 'private', 'templ_common.h.src'),
join('src', 'private', 'mem_overlap.c'),
join('src', 'private', 'npy_longdouble.c'),
join('src', 'private', 'ufunc_override.c'),
]
blas_info = get_info('blas_opt', 0)
if blas_info and ('HAVE_CBLAS', None) in blas_info.get('define_macros', []):
extra_info = blas_info
# These files are also in MANIFEST.in so that they are always in
# the source distribution independently of HAVE_CBLAS.
multiarray_src.extend([join('src', 'multiarray', 'cblasfuncs.c'),
join('src', 'multiarray', 'python_xerbla.c'),
])
if uses_accelerate_framework(blas_info):
multiarray_src.extend(get_sgemv_fix())
else:
extra_info = {}
config.add_extension('multiarray',
sources=multiarray_src +
[generate_config_h,
generate_numpyconfig_h,
generate_numpy_api,
join(codegen_dir, 'generate_numpy_api.py'),
join('*.py')],
depends=deps + multiarray_deps,
libraries=['npymath', 'npysort'],
extra_info=extra_info)
#######################################################################
# umath module #
#######################################################################
def generate_umath_c(ext, build_dir):
target = join(build_dir, header_dir, '__umath_generated.c')
dir = os.path.dirname(target)
if not os.path.exists(dir):
os.makedirs(dir)
script = generate_umath_py
if newer(script, target):
f = open(target, 'w')
f.write(generate_umath.make_code(generate_umath.defdict,
generate_umath.__file__))
f.close()
return []
umath_src = [
join('src', 'umath', 'umathmodule.c'),
join('src', 'umath', 'reduction.c'),
join('src', 'umath', 'funcs.inc.src'),
join('src', 'umath', 'simd.inc.src'),
join('src', 'umath', 'loops.h.src'),
join('src', 'umath', 'loops.c.src'),
join('src', 'umath', 'ufunc_object.c'),
join('src', 'umath', 'extobj.c'),
join('src', 'umath', 'scalarmath.c.src'),
join('src', 'umath', 'ufunc_type_resolution.c'),
join('src', 'umath', 'override.c'),
join('src', 'private', 'mem_overlap.c'),
join('src', 'private', 'npy_longdouble.c'),
join('src', 'private', 'ufunc_override.c')]
umath_deps = [
generate_umath_py,
join('include', 'numpy', 'npy_math.h'),
join('include', 'numpy', 'halffloat.h'),
join('src', 'multiarray', 'common.h'),
join('src', 'private', 'templ_common.h.src'),
join('src', 'umath', 'simd.inc.src'),
join('src', 'umath', 'override.h'),
join(codegen_dir, 'generate_ufunc_api.py'),
join('src', 'private', 'lowlevel_strided_loops.h'),
join('src', 'private', 'mem_overlap.h'),
join('src', 'private', 'npy_longdouble.h'),
join('src', 'private', 'ufunc_override.h'),
join('src', 'private', 'binop_override.h')] + npymath_sources
config.add_extension('umath',
sources=umath_src +
[generate_config_h,
generate_numpyconfig_h,
generate_umath_c,
generate_ufunc_api],
depends=deps + umath_deps,
libraries=['npymath'],
)
#######################################################################
# umath_tests module #
#######################################################################
config.add_extension('umath_tests',
sources=[join('src', 'umath', 'umath_tests.c.src')])
#######################################################################
# custom rational dtype module #
#######################################################################
config.add_extension('test_rational',
sources=[join('src', 'umath', 'test_rational.c.src')])
#######################################################################
# struct_ufunc_test module #
#######################################################################
config.add_extension('struct_ufunc_test',
sources=[join('src', 'umath', 'struct_ufunc_test.c.src')])
#######################################################################
# multiarray_tests module #
#######################################################################
config.add_extension('multiarray_tests',
sources=[join('src', 'multiarray', 'multiarray_tests.c.src'),
join('src', 'private', 'mem_overlap.c')],
depends=[join('src', 'private', 'mem_overlap.h'),
join('src', 'private', 'npy_extint128.h')],
libraries=['npymath'])
#######################################################################
# operand_flag_tests module #
#######################################################################
config.add_extension('operand_flag_tests',
sources=[join('src', 'umath', 'operand_flag_tests.c.src')])
config.add_data_dir('tests')
config.add_data_dir('tests/data')
config.make_svn_version_py()
return config
if __name__ == '__main__':
from numpy.distutils.core import setup
setup(configuration=configuration)
| 41,477 | 41.760825 | 113 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/_methods.py
|
"""
Array methods which are called by both the C-code for the method
and the Python code for the NumPy-namespace function
"""
from __future__ import division, absolute_import, print_function
import warnings
from numpy.core import multiarray as mu
from numpy.core import umath as um
from numpy.core.numeric import asanyarray
from numpy.core import numerictypes as nt
# save those O(100) nanoseconds!
umr_maximum = um.maximum.reduce
umr_minimum = um.minimum.reduce
umr_sum = um.add.reduce
umr_prod = um.multiply.reduce
umr_any = um.logical_or.reduce
umr_all = um.logical_and.reduce
# avoid keyword arguments to speed up parsing, saves about 15%-20% for very
# small reductions
def _amax(a, axis=None, out=None, keepdims=False):
return umr_maximum(a, axis, None, out, keepdims)
def _amin(a, axis=None, out=None, keepdims=False):
return umr_minimum(a, axis, None, out, keepdims)
def _sum(a, axis=None, dtype=None, out=None, keepdims=False):
return umr_sum(a, axis, dtype, out, keepdims)
def _prod(a, axis=None, dtype=None, out=None, keepdims=False):
return umr_prod(a, axis, dtype, out, keepdims)
def _any(a, axis=None, dtype=None, out=None, keepdims=False):
return umr_any(a, axis, dtype, out, keepdims)
def _all(a, axis=None, dtype=None, out=None, keepdims=False):
return umr_all(a, axis, dtype, out, keepdims)
def _count_reduce_items(arr, axis):
if axis is None:
axis = tuple(range(arr.ndim))
if not isinstance(axis, tuple):
axis = (axis,)
items = 1
for ax in axis:
items *= arr.shape[ax]
return items
def _mean(a, axis=None, dtype=None, out=None, keepdims=False):
arr = asanyarray(a)
is_float16_result = False
rcount = _count_reduce_items(arr, axis)
# Make this warning show up first
if rcount == 0:
warnings.warn("Mean of empty slice.", RuntimeWarning, stacklevel=2)
# Cast bool, unsigned int, and int to float64 by default
if dtype is None:
if issubclass(arr.dtype.type, (nt.integer, nt.bool_)):
dtype = mu.dtype('f8')
elif issubclass(arr.dtype.type, nt.float16):
dtype = mu.dtype('f4')
is_float16_result = True
ret = umr_sum(arr, axis, dtype, out, keepdims)
if isinstance(ret, mu.ndarray):
ret = um.true_divide(
ret, rcount, out=ret, casting='unsafe', subok=False)
if is_float16_result and out is None:
ret = arr.dtype.type(ret)
elif hasattr(ret, 'dtype'):
if is_float16_result:
ret = arr.dtype.type(ret / rcount)
else:
ret = ret.dtype.type(ret / rcount)
else:
ret = ret / rcount
return ret
def _var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
arr = asanyarray(a)
rcount = _count_reduce_items(arr, axis)
# Make this warning show up on top.
if ddof >= rcount:
warnings.warn("Degrees of freedom <= 0 for slice", RuntimeWarning,
stacklevel=2)
# Cast bool, unsigned int, and int to float64 by default
if dtype is None and issubclass(arr.dtype.type, (nt.integer, nt.bool_)):
dtype = mu.dtype('f8')
# Compute the mean.
# Note that if dtype is not of inexact type then arraymean will
# not be either.
arrmean = umr_sum(arr, axis, dtype, keepdims=True)
if isinstance(arrmean, mu.ndarray):
arrmean = um.true_divide(
arrmean, rcount, out=arrmean, casting='unsafe', subok=False)
else:
arrmean = arrmean.dtype.type(arrmean / rcount)
# Compute sum of squared deviations from mean
# Note that x may not be inexact and that we need it to be an array,
# not a scalar.
x = asanyarray(arr - arrmean)
if issubclass(arr.dtype.type, nt.complexfloating):
x = um.multiply(x, um.conjugate(x), out=x).real
else:
x = um.multiply(x, x, out=x)
ret = umr_sum(x, axis, dtype, out, keepdims)
# Compute degrees of freedom and make sure it is not negative.
rcount = max([rcount - ddof, 0])
# divide by degrees of freedom
if isinstance(ret, mu.ndarray):
ret = um.true_divide(
ret, rcount, out=ret, casting='unsafe', subok=False)
elif hasattr(ret, 'dtype'):
ret = ret.dtype.type(ret / rcount)
else:
ret = ret / rcount
return ret
def _std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
keepdims=keepdims)
if isinstance(ret, mu.ndarray):
ret = um.sqrt(ret, out=ret)
elif hasattr(ret, 'dtype'):
ret = ret.dtype.type(um.sqrt(ret))
else:
ret = um.sqrt(ret)
return ret
| 4,704 | 31.448276 | 76 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/memmap.py
|
from __future__ import division, absolute_import, print_function
import numpy as np
from .numeric import uint8, ndarray, dtype
from numpy.compat import long, basestring, is_pathlib_path
__all__ = ['memmap']
dtypedescr = dtype
valid_filemodes = ["r", "c", "r+", "w+"]
writeable_filemodes = ["r+", "w+"]
mode_equivalents = {
"readonly":"r",
"copyonwrite":"c",
"readwrite":"r+",
"write":"w+"
}
class memmap(ndarray):
"""Create a memory-map to an array stored in a *binary* file on disk.
Memory-mapped files are used for accessing small segments of large files
on disk, without reading the entire file into memory. NumPy's
memmap's are array-like objects. This differs from Python's ``mmap``
module, which uses file-like objects.
This subclass of ndarray has some unpleasant interactions with
some operations, because it doesn't quite fit properly as a subclass.
An alternative to using this subclass is to create the ``mmap``
object yourself, then create an ndarray with ndarray.__new__ directly,
passing the object created in its 'buffer=' parameter.
This class may at some point be turned into a factory function
which returns a view into an mmap buffer.
Delete the memmap instance to close.
Parameters
----------
filename : str, file-like object, or pathlib.Path instance
The file name or file object to be used as the array data buffer.
dtype : data-type, optional
The data-type used to interpret the file contents.
Default is `uint8`.
mode : {'r+', 'r', 'w+', 'c'}, optional
The file is opened in this mode:
+------+-------------------------------------------------------------+
| 'r' | Open existing file for reading only. |
+------+-------------------------------------------------------------+
| 'r+' | Open existing file for reading and writing. |
+------+-------------------------------------------------------------+
| 'w+' | Create or overwrite existing file for reading and writing. |
+------+-------------------------------------------------------------+
| 'c' | Copy-on-write: assignments affect data in memory, but |
| | changes are not saved to disk. The file on disk is |
| | read-only. |
+------+-------------------------------------------------------------+
Default is 'r+'.
offset : int, optional
In the file, array data starts at this offset. Since `offset` is
measured in bytes, it should normally be a multiple of the byte-size
of `dtype`. When ``mode != 'r'``, even positive offsets beyond end of
file are valid; The file will be extended to accommodate the
additional data. By default, ``memmap`` will start at the beginning of
the file, even if ``filename`` is a file pointer ``fp`` and
``fp.tell() != 0``.
shape : tuple, optional
The desired shape of the array. If ``mode == 'r'`` and the number
of remaining bytes after `offset` is not a multiple of the byte-size
of `dtype`, you must specify `shape`. By default, the returned array
will be 1-D with the number of elements determined by file size
and data-type.
order : {'C', 'F'}, optional
Specify the order of the ndarray memory layout:
:term:`row-major`, C-style or :term:`column-major`,
Fortran-style. This only has an effect if the shape is
greater than 1-D. The default order is 'C'.
Attributes
----------
filename : str or pathlib.Path instance
Path to the mapped file.
offset : int
Offset position in the file.
mode : str
File mode.
Methods
-------
flush
Flush any changes in memory to file on disk.
When you delete a memmap object, flush is called first to write
changes to disk before removing the object.
See also
--------
lib.format.open_memmap : Create or load a memory-mapped ``.npy`` file.
Notes
-----
The memmap object can be used anywhere an ndarray is accepted.
Given a memmap ``fp``, ``isinstance(fp, numpy.ndarray)`` returns
``True``.
Memory-mapped files cannot be larger than 2GB on 32-bit systems.
When a memmap causes a file to be created or extended beyond its
current size in the filesystem, the contents of the new part are
unspecified. On systems with POSIX filesystem semantics, the extended
part will be filled with zero bytes.
Examples
--------
>>> data = np.arange(12, dtype='float32')
>>> data.resize((3,4))
This example uses a temporary file so that doctest doesn't write
files to your directory. You would use a 'normal' filename.
>>> from tempfile import mkdtemp
>>> import os.path as path
>>> filename = path.join(mkdtemp(), 'newfile.dat')
Create a memmap with dtype and shape that matches our data:
>>> fp = np.memmap(filename, dtype='float32', mode='w+', shape=(3,4))
>>> fp
memmap([[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.],
[ 0., 0., 0., 0.]], dtype=float32)
Write data to memmap array:
>>> fp[:] = data[:]
>>> fp
memmap([[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.]], dtype=float32)
>>> fp.filename == path.abspath(filename)
True
Deletion flushes memory changes to disk before removing the object:
>>> del fp
Load the memmap and verify data was stored:
>>> newfp = np.memmap(filename, dtype='float32', mode='r', shape=(3,4))
>>> newfp
memmap([[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.]], dtype=float32)
Read-only memmap:
>>> fpr = np.memmap(filename, dtype='float32', mode='r', shape=(3,4))
>>> fpr.flags.writeable
False
Copy-on-write memmap:
>>> fpc = np.memmap(filename, dtype='float32', mode='c', shape=(3,4))
>>> fpc.flags.writeable
True
It's possible to assign to copy-on-write array, but values are only
written into the memory copy of the array, and not written to disk:
>>> fpc
memmap([[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.]], dtype=float32)
>>> fpc[0,:] = 0
>>> fpc
memmap([[ 0., 0., 0., 0.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.]], dtype=float32)
File on disk is unchanged:
>>> fpr
memmap([[ 0., 1., 2., 3.],
[ 4., 5., 6., 7.],
[ 8., 9., 10., 11.]], dtype=float32)
Offset into a memmap:
>>> fpo = np.memmap(filename, dtype='float32', mode='r', offset=16)
>>> fpo
memmap([ 4., 5., 6., 7., 8., 9., 10., 11.], dtype=float32)
"""
__array_priority__ = -100.0
def __new__(subtype, filename, dtype=uint8, mode='r+', offset=0,
shape=None, order='C'):
# Import here to minimize 'import numpy' overhead
import mmap
import os.path
try:
mode = mode_equivalents[mode]
except KeyError:
if mode not in valid_filemodes:
raise ValueError("mode must be one of %s" %
(valid_filemodes + list(mode_equivalents.keys())))
if hasattr(filename, 'read'):
fid = filename
own_file = False
elif is_pathlib_path(filename):
fid = filename.open((mode == 'c' and 'r' or mode)+'b')
own_file = True
else:
fid = open(filename, (mode == 'c' and 'r' or mode)+'b')
own_file = True
if (mode == 'w+') and shape is None:
raise ValueError("shape must be given")
fid.seek(0, 2)
flen = fid.tell()
descr = dtypedescr(dtype)
_dbytes = descr.itemsize
if shape is None:
bytes = flen - offset
if (bytes % _dbytes):
fid.close()
raise ValueError("Size of available data is not a "
"multiple of the data-type size.")
size = bytes // _dbytes
shape = (size,)
else:
if not isinstance(shape, tuple):
shape = (shape,)
size = 1
for k in shape:
size *= k
bytes = long(offset + size*_dbytes)
if mode == 'w+' or (mode == 'r+' and flen < bytes):
fid.seek(bytes - 1, 0)
fid.write(b'\0')
fid.flush()
if mode == 'c':
acc = mmap.ACCESS_COPY
elif mode == 'r':
acc = mmap.ACCESS_READ
else:
acc = mmap.ACCESS_WRITE
start = offset - offset % mmap.ALLOCATIONGRANULARITY
bytes -= start
array_offset = offset - start
mm = mmap.mmap(fid.fileno(), bytes, access=acc, offset=start)
self = ndarray.__new__(subtype, shape, dtype=descr, buffer=mm,
offset=array_offset, order=order)
self._mmap = mm
self.offset = offset
self.mode = mode
if isinstance(filename, basestring):
self.filename = os.path.abspath(filename)
elif is_pathlib_path(filename):
self.filename = filename.resolve()
# py3 returns int for TemporaryFile().name
elif (hasattr(filename, "name") and
isinstance(filename.name, basestring)):
self.filename = os.path.abspath(filename.name)
# same as memmap copies (e.g. memmap + 1)
else:
self.filename = None
if own_file:
fid.close()
return self
def __array_finalize__(self, obj):
if hasattr(obj, '_mmap') and np.may_share_memory(self, obj):
self._mmap = obj._mmap
self.filename = obj.filename
self.offset = obj.offset
self.mode = obj.mode
else:
self._mmap = None
self.filename = None
self.offset = None
self.mode = None
def flush(self):
"""
Write any changes in the array to the file on disk.
For further information, see `memmap`.
Parameters
----------
None
See Also
--------
memmap
"""
if self.base is not None and hasattr(self.base, 'flush'):
self.base.flush()
def __array_wrap__(self, arr, context=None):
arr = super(memmap, self).__array_wrap__(arr, context)
# Return a memmap if a memmap was given as the output of the
# ufunc. Leave the arr class unchanged if self is not a memmap
# to keep original memmap subclasses behavior
if self is arr or type(self) is not memmap:
return arr
# Return scalar instead of 0d memmap, e.g. for np.sum with
# axis=None
if arr.shape == ():
return arr[()]
# Return ndarray otherwise
return arr.view(np.ndarray)
def __getitem__(self, index):
res = super(memmap, self).__getitem__(index)
if type(res) is memmap and res._mmap is None:
return res.view(type=ndarray)
return res
| 11,432 | 32.725664 | 83 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/getlimits.py
|
"""Machine limits for Float32 and Float64 and (long double) if available...
"""
from __future__ import division, absolute_import, print_function
__all__ = ['finfo', 'iinfo']
import warnings
from .machar import MachAr
from . import numeric
from . import numerictypes as ntypes
from .numeric import array, inf
from .umath import log10, exp2
from . import umath
def _fr0(a):
"""fix rank-0 --> rank-1"""
if a.ndim == 0:
a = a.copy()
a.shape = (1,)
return a
def _fr1(a):
"""fix rank > 0 --> rank-0"""
if a.size == 1:
a = a.copy()
a.shape = ()
return a
_convert_to_float = {
ntypes.csingle: ntypes.single,
ntypes.complex_: ntypes.float_,
ntypes.clongfloat: ntypes.longfloat
}
# Parameters for creating MachAr / MachAr-like objects
_title_fmt = 'numpy {} precision floating point number'
_MACHAR_PARAMS = {
ntypes.double: dict(
itype = ntypes.int64,
fmt = '%24.16e',
title = _title_fmt.format('double')),
ntypes.single: dict(
itype = ntypes.int32,
fmt = '%15.7e',
title = _title_fmt.format('single')),
ntypes.longdouble: dict(
itype = ntypes.longlong,
fmt = '%s',
title = _title_fmt.format('long double')),
ntypes.half: dict(
itype = ntypes.int16,
fmt = '%12.5e',
title = _title_fmt.format('half'))}
class MachArLike(object):
""" Object to simulate MachAr instance """
def __init__(self,
ftype,
**kwargs):
params = _MACHAR_PARAMS[ftype]
float_conv = lambda v: array([v], ftype)
float_to_float = lambda v : _fr1(float_conv(v))
self._float_to_str = lambda v: (params['fmt'] %
array(_fr0(v)[0], ftype))
self.title = params['title']
# Parameter types same as for discovered MachAr object.
self.epsilon = self.eps = float_to_float(kwargs.pop('eps'))
self.epsneg = float_to_float(kwargs.pop('epsneg'))
self.xmax = self.huge = float_to_float(kwargs.pop('huge'))
self.xmin = self.tiny = float_to_float(kwargs.pop('tiny'))
self.ibeta = params['itype'](kwargs.pop('ibeta'))
self.__dict__.update(kwargs)
self.precision = int(-log10(self.eps))
self.resolution = float_to_float(float_conv(10) ** (-self.precision))
# Properties below to delay need for float_to_str, and thus avoid circular
# imports during early numpy module loading.
# See: https://github.com/numpy/numpy/pull/8983#discussion_r115838683
@property
def _str_eps(self):
return self._float_to_str(self.eps)
@property
def _str_epsneg(self):
return self._float_to_str(self.epsneg)
@property
def _str_xmin(self):
return self._float_to_str(self.xmin)
@property
def _str_xmax(self):
return self._float_to_str(self.xmax)
@property
def _str_resolution(self):
return self._float_to_str(self.resolution)
# Known parameters for float16
# See docstring of MachAr class for description of parameters.
_f16 = ntypes.float16
_float16_ma = MachArLike(_f16,
machep=-10,
negep=-11,
minexp=-14,
maxexp=16,
it=10,
iexp=5,
ibeta=2,
irnd=5,
ngrd=0,
eps=exp2(_f16(-10)),
epsneg=exp2(_f16(-11)),
huge=_f16(65504),
tiny=_f16(2 ** -14))
# Known parameters for float32
_f32 = ntypes.float32
_float32_ma = MachArLike(_f32,
machep=-23,
negep=-24,
minexp=-126,
maxexp=128,
it=23,
iexp=8,
ibeta=2,
irnd=5,
ngrd=0,
eps=exp2(_f32(-23)),
epsneg=exp2(_f32(-24)),
huge=_f32((1 - 2 ** -24) * 2**128),
tiny=exp2(_f32(-126)))
# Known parameters for float64
_f64 = ntypes.float64
_epsneg_f64 = 2.0 ** -53.0
_tiny_f64 = 2.0 ** -1022.0
_float64_ma = MachArLike(_f64,
machep=-52,
negep=-53,
minexp=-1022,
maxexp=1024,
it=52,
iexp=11,
ibeta=2,
irnd=5,
ngrd=0,
eps=2.0 ** -52.0,
epsneg=_epsneg_f64,
huge=(1.0 - _epsneg_f64) / _tiny_f64 * _f64(4),
tiny=_tiny_f64)
# Known parameters for IEEE 754 128-bit binary float
_ld = ntypes.longdouble
_epsneg_f128 = exp2(_ld(-113))
_tiny_f128 = exp2(_ld(-16382))
# Ignore runtime error when this is not f128
with numeric.errstate(all='ignore'):
_huge_f128 = (_ld(1) - _epsneg_f128) / _tiny_f128 * _ld(4)
_float128_ma = MachArLike(_ld,
machep=-112,
negep=-113,
minexp=-16382,
maxexp=16384,
it=112,
iexp=15,
ibeta=2,
irnd=5,
ngrd=0,
eps=exp2(_ld(-112)),
epsneg=_epsneg_f128,
huge=_huge_f128,
tiny=_tiny_f128)
# Known parameters for float80 (Intel 80-bit extended precision)
_epsneg_f80 = exp2(_ld(-64))
_tiny_f80 = exp2(_ld(-16382))
# Ignore runtime error when this is not f80
with numeric.errstate(all='ignore'):
_huge_f80 = (_ld(1) - _epsneg_f80) / _tiny_f80 * _ld(4)
_float80_ma = MachArLike(_ld,
machep=-63,
negep=-64,
minexp=-16382,
maxexp=16384,
it=63,
iexp=15,
ibeta=2,
irnd=5,
ngrd=0,
eps=exp2(_ld(-63)),
epsneg=_epsneg_f80,
huge=_huge_f80,
tiny=_tiny_f80)
# Guessed / known parameters for double double; see:
# https://en.wikipedia.org/wiki/Quadruple-precision_floating-point_format#Double-double_arithmetic
# These numbers have the same exponent range as float64, but extended number of
# digits in the significand.
_huge_dd = (umath.nextafter(_ld(inf), _ld(0))
if hasattr(umath, 'nextafter') # Missing on some platforms?
else _float64_ma.huge)
_float_dd_ma = MachArLike(_ld,
machep=-105,
negep=-106,
minexp=-1022,
maxexp=1024,
it=105,
iexp=11,
ibeta=2,
irnd=5,
ngrd=0,
eps=exp2(_ld(-105)),
epsneg= exp2(_ld(-106)),
huge=_huge_dd,
tiny=exp2(_ld(-1022)))
# Key to identify the floating point type. Key is result of
# ftype('-0.1').newbyteorder('<').tobytes()
# See:
# https://perl5.git.perl.org/perl.git/blob/3118d7d684b56cbeb702af874f4326683c45f045:/Configure
_KNOWN_TYPES = {
b'\x9a\x99\x99\x99\x99\x99\xb9\xbf' : _float64_ma,
b'\xcd\xcc\xcc\xbd' : _float32_ma,
b'f\xae' : _float16_ma,
# float80, first 10 bytes containing actual storage
b'\xcd\xcc\xcc\xcc\xcc\xcc\xcc\xcc\xfb\xbf' : _float80_ma,
# double double; low, high order (e.g. PPC 64)
b'\x9a\x99\x99\x99\x99\x99Y<\x9a\x99\x99\x99\x99\x99\xb9\xbf' :
_float_dd_ma,
# double double; high, low order (e.g. PPC 64 le)
b'\x9a\x99\x99\x99\x99\x99\xb9\xbf\x9a\x99\x99\x99\x99\x99Y<' :
_float_dd_ma,
# IEEE 754 128-bit binary float
b'\x9a\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\x99\xfb\xbf' :
_float128_ma,
}
def _get_machar(ftype):
""" Get MachAr instance or MachAr-like instance
Get parameters for floating point type, by first trying signatures of
various known floating point types, then, if none match, attempting to
identify parameters by analysis.
Parameters
----------
ftype : class
Numpy floating point type class (e.g. ``np.float64``)
Returns
-------
ma_like : instance of :class:`MachAr` or :class:`MachArLike`
Object giving floating point parameters for `ftype`.
Warns
-----
UserWarning
If the binary signature of the float type is not in the dictionary of
known float types.
"""
params = _MACHAR_PARAMS.get(ftype)
if params is None:
raise ValueError(repr(ftype))
# Detect known / suspected types
key = ftype('-0.1').newbyteorder('<').tobytes()
ma_like = _KNOWN_TYPES.get(key)
# Could be 80 bit == 10 byte extended precision, where last bytes can be
# random garbage. Try comparing first 10 bytes to pattern.
if ma_like is None and ftype == ntypes.longdouble:
ma_like = _KNOWN_TYPES.get(key[:10])
if ma_like is not None:
return ma_like
# Fall back to parameter discovery
warnings.warn(
'Signature {} for {} does not match any known type: '
'falling back to type probe function'.format(key, ftype),
UserWarning, stacklevel=2)
return _discovered_machar(ftype)
def _discovered_machar(ftype):
""" Create MachAr instance with found information on float types
"""
params = _MACHAR_PARAMS[ftype]
return MachAr(lambda v: array([v], ftype),
lambda v:_fr0(v.astype(params['itype']))[0],
lambda v:array(_fr0(v)[0], ftype),
lambda v: params['fmt'] % array(_fr0(v)[0], ftype),
params['title'])
class finfo(object):
"""
finfo(dtype)
Machine limits for floating point types.
Attributes
----------
bits : int
The number of bits occupied by the type.
eps : float
The smallest representable positive number such that
``1.0 + eps != 1.0``. Type of `eps` is an appropriate floating
point type.
epsneg : floating point number of the appropriate type
The smallest representable positive number such that
``1.0 - epsneg != 1.0``.
iexp : int
The number of bits in the exponent portion of the floating point
representation.
machar : MachAr
The object which calculated these parameters and holds more
detailed information.
machep : int
The exponent that yields `eps`.
max : floating point number of the appropriate type
The largest representable number.
maxexp : int
The smallest positive power of the base (2) that causes overflow.
min : floating point number of the appropriate type
The smallest representable number, typically ``-max``.
minexp : int
The most negative power of the base (2) consistent with there
being no leading 0's in the mantissa.
negep : int
The exponent that yields `epsneg`.
nexp : int
The number of bits in the exponent including its sign and bias.
nmant : int
The number of bits in the mantissa.
precision : int
The approximate number of decimal digits to which this kind of
float is precise.
resolution : floating point number of the appropriate type
The approximate decimal resolution of this type, i.e.,
``10**-precision``.
tiny : float
The smallest positive usable number. Type of `tiny` is an
appropriate floating point type.
Parameters
----------
dtype : float, dtype, or instance
Kind of floating point data-type about which to get information.
See Also
--------
MachAr : The implementation of the tests that produce this information.
iinfo : The equivalent for integer data types.
Notes
-----
For developers of NumPy: do not instantiate this at the module level.
The initial calculation of these parameters is expensive and negatively
impacts import times. These objects are cached, so calling ``finfo()``
repeatedly inside your functions is not a problem.
"""
_finfo_cache = {}
def __new__(cls, dtype):
try:
dtype = numeric.dtype(dtype)
except TypeError:
# In case a float instance was given
dtype = numeric.dtype(type(dtype))
obj = cls._finfo_cache.get(dtype, None)
if obj is not None:
return obj
dtypes = [dtype]
newdtype = numeric.obj2sctype(dtype)
if newdtype is not dtype:
dtypes.append(newdtype)
dtype = newdtype
if not issubclass(dtype, numeric.inexact):
raise ValueError("data type %r not inexact" % (dtype))
obj = cls._finfo_cache.get(dtype, None)
if obj is not None:
return obj
if not issubclass(dtype, numeric.floating):
newdtype = _convert_to_float[dtype]
if newdtype is not dtype:
dtypes.append(newdtype)
dtype = newdtype
obj = cls._finfo_cache.get(dtype, None)
if obj is not None:
return obj
obj = object.__new__(cls)._init(dtype)
for dt in dtypes:
cls._finfo_cache[dt] = obj
return obj
def _init(self, dtype):
self.dtype = numeric.dtype(dtype)
machar = _get_machar(dtype)
for word in ['precision', 'iexp',
'maxexp', 'minexp', 'negep',
'machep']:
setattr(self, word, getattr(machar, word))
for word in ['tiny', 'resolution', 'epsneg']:
setattr(self, word, getattr(machar, word).flat[0])
self.bits = self.dtype.itemsize * 8
self.max = machar.huge.flat[0]
self.min = -self.max
self.eps = machar.eps.flat[0]
self.nexp = machar.iexp
self.nmant = machar.it
self.machar = machar
self._str_tiny = machar._str_xmin.strip()
self._str_max = machar._str_xmax.strip()
self._str_epsneg = machar._str_epsneg.strip()
self._str_eps = machar._str_eps.strip()
self._str_resolution = machar._str_resolution.strip()
return self
def __str__(self):
fmt = (
'Machine parameters for %(dtype)s\n'
'---------------------------------------------------------------\n'
'precision = %(precision)3s resolution = %(_str_resolution)s\n'
'machep = %(machep)6s eps = %(_str_eps)s\n'
'negep = %(negep)6s epsneg = %(_str_epsneg)s\n'
'minexp = %(minexp)6s tiny = %(_str_tiny)s\n'
'maxexp = %(maxexp)6s max = %(_str_max)s\n'
'nexp = %(nexp)6s min = -max\n'
'---------------------------------------------------------------\n'
)
return fmt % self.__dict__
def __repr__(self):
c = self.__class__.__name__
d = self.__dict__.copy()
d['klass'] = c
return (("%(klass)s(resolution=%(resolution)s, min=-%(_str_max)s,"
" max=%(_str_max)s, dtype=%(dtype)s)") % d)
class iinfo(object):
"""
iinfo(type)
Machine limits for integer types.
Attributes
----------
bits : int
The number of bits occupied by the type.
min : int
The smallest integer expressible by the type.
max : int
The largest integer expressible by the type.
Parameters
----------
int_type : integer type, dtype, or instance
The kind of integer data type to get information about.
See Also
--------
finfo : The equivalent for floating point data types.
Examples
--------
With types:
>>> ii16 = np.iinfo(np.int16)
>>> ii16.min
-32768
>>> ii16.max
32767
>>> ii32 = np.iinfo(np.int32)
>>> ii32.min
-2147483648
>>> ii32.max
2147483647
With instances:
>>> ii32 = np.iinfo(np.int32(10))
>>> ii32.min
-2147483648
>>> ii32.max
2147483647
"""
_min_vals = {}
_max_vals = {}
def __init__(self, int_type):
try:
self.dtype = numeric.dtype(int_type)
except TypeError:
self.dtype = numeric.dtype(type(int_type))
self.kind = self.dtype.kind
self.bits = self.dtype.itemsize * 8
self.key = "%s%d" % (self.kind, self.bits)
if self.kind not in 'iu':
raise ValueError("Invalid integer data type.")
def min(self):
"""Minimum value of given dtype."""
if self.kind == 'u':
return 0
else:
try:
val = iinfo._min_vals[self.key]
except KeyError:
val = int(-(1 << (self.bits-1)))
iinfo._min_vals[self.key] = val
return val
min = property(min)
def max(self):
"""Maximum value of given dtype."""
try:
val = iinfo._max_vals[self.key]
except KeyError:
if self.kind == 'u':
val = int((1 << self.bits) - 1)
else:
val = int((1 << (self.bits-1)) - 1)
iinfo._max_vals[self.key] = val
return val
max = property(max)
def __str__(self):
"""String representation."""
fmt = (
'Machine parameters for %(dtype)s\n'
'---------------------------------------------------------------\n'
'min = %(min)s\n'
'max = %(max)s\n'
'---------------------------------------------------------------\n'
)
return fmt % {'dtype': self.dtype, 'min': self.min, 'max': self.max}
def __repr__(self):
return "%s(min=%s, max=%s, dtype=%s)" % (self.__class__.__name__,
self.min, self.max, self.dtype)
| 18,422 | 31.839572 | 98 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/function_base.py
|
from __future__ import division, absolute_import, print_function
import warnings
import operator
from . import numeric as _nx
from .numeric import (result_type, NaN, shares_memory, MAY_SHARE_BOUNDS,
TooHardError,asanyarray)
__all__ = ['logspace', 'linspace', 'geomspace']
def _index_deprecate(i, stacklevel=2):
try:
i = operator.index(i)
except TypeError:
msg = ("object of type {} cannot be safely interpreted as "
"an integer.".format(type(i)))
i = int(i)
stacklevel += 1
warnings.warn(msg, DeprecationWarning, stacklevel=stacklevel)
return i
def linspace(start, stop, num=50, endpoint=True, retstep=False, dtype=None):
"""
Return evenly spaced numbers over a specified interval.
Returns `num` evenly spaced samples, calculated over the
interval [`start`, `stop`].
The endpoint of the interval can optionally be excluded.
Parameters
----------
start : scalar
The starting value of the sequence.
stop : scalar
The end value of the sequence, unless `endpoint` is set to False.
In that case, the sequence consists of all but the last of ``num + 1``
evenly spaced samples, so that `stop` is excluded. Note that the step
size changes when `endpoint` is False.
num : int, optional
Number of samples to generate. Default is 50. Must be non-negative.
endpoint : bool, optional
If True, `stop` is the last sample. Otherwise, it is not included.
Default is True.
retstep : bool, optional
If True, return (`samples`, `step`), where `step` is the spacing
between samples.
dtype : dtype, optional
The type of the output array. If `dtype` is not given, infer the data
type from the other input arguments.
.. versionadded:: 1.9.0
Returns
-------
samples : ndarray
There are `num` equally spaced samples in the closed interval
``[start, stop]`` or the half-open interval ``[start, stop)``
(depending on whether `endpoint` is True or False).
step : float, optional
Only returned if `retstep` is True
Size of spacing between samples.
See Also
--------
arange : Similar to `linspace`, but uses a step size (instead of the
number of samples).
logspace : Samples uniformly distributed in log space.
Examples
--------
>>> np.linspace(2.0, 3.0, num=5)
array([ 2. , 2.25, 2.5 , 2.75, 3. ])
>>> np.linspace(2.0, 3.0, num=5, endpoint=False)
array([ 2. , 2.2, 2.4, 2.6, 2.8])
>>> np.linspace(2.0, 3.0, num=5, retstep=True)
(array([ 2. , 2.25, 2.5 , 2.75, 3. ]), 0.25)
Graphical illustration:
>>> import matplotlib.pyplot as plt
>>> N = 8
>>> y = np.zeros(N)
>>> x1 = np.linspace(0, 10, N, endpoint=True)
>>> x2 = np.linspace(0, 10, N, endpoint=False)
>>> plt.plot(x1, y, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.plot(x2, y + 0.5, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.ylim([-0.5, 1])
(-0.5, 1)
>>> plt.show()
"""
# 2016-02-25, 1.12
num = _index_deprecate(num)
if num < 0:
raise ValueError("Number of samples, %s, must be non-negative." % num)
div = (num - 1) if endpoint else num
# Convert float/complex array scalars to float, gh-3504
# and make sure one can use variables that have an __array_interface__, gh-6634
start = asanyarray(start) * 1.0
stop = asanyarray(stop) * 1.0
dt = result_type(start, stop, float(num))
if dtype is None:
dtype = dt
y = _nx.arange(0, num, dtype=dt)
delta = stop - start
# In-place multiplication y *= delta/div is faster, but prevents the multiplicant
# from overriding what class is produced, and thus prevents, e.g. use of Quantities,
# see gh-7142. Hence, we multiply in place only for standard scalar types.
_mult_inplace = _nx.isscalar(delta)
if num > 1:
step = delta / div
if step == 0:
# Special handling for denormal numbers, gh-5437
y /= div
if _mult_inplace:
y *= delta
else:
y = y * delta
else:
if _mult_inplace:
y *= step
else:
y = y * step
else:
# 0 and 1 item long sequences have an undefined step
step = NaN
# Multiply with delta to allow possible override of output class.
y = y * delta
y += start
if endpoint and num > 1:
y[-1] = stop
if retstep:
return y.astype(dtype, copy=False), step
else:
return y.astype(dtype, copy=False)
def logspace(start, stop, num=50, endpoint=True, base=10.0, dtype=None):
"""
Return numbers spaced evenly on a log scale.
In linear space, the sequence starts at ``base ** start``
(`base` to the power of `start`) and ends with ``base ** stop``
(see `endpoint` below).
Parameters
----------
start : float
``base ** start`` is the starting value of the sequence.
stop : float
``base ** stop`` is the final value of the sequence, unless `endpoint`
is False. In that case, ``num + 1`` values are spaced over the
interval in log-space, of which all but the last (a sequence of
length `num`) are returned.
num : integer, optional
Number of samples to generate. Default is 50.
endpoint : boolean, optional
If true, `stop` is the last sample. Otherwise, it is not included.
Default is True.
base : float, optional
The base of the log space. The step size between the elements in
``ln(samples) / ln(base)`` (or ``log_base(samples)``) is uniform.
Default is 10.0.
dtype : dtype
The type of the output array. If `dtype` is not given, infer the data
type from the other input arguments.
Returns
-------
samples : ndarray
`num` samples, equally spaced on a log scale.
See Also
--------
arange : Similar to linspace, with the step size specified instead of the
number of samples. Note that, when used with a float endpoint, the
endpoint may or may not be included.
linspace : Similar to logspace, but with the samples uniformly distributed
in linear space, instead of log space.
geomspace : Similar to logspace, but with endpoints specified directly.
Notes
-----
Logspace is equivalent to the code
>>> y = np.linspace(start, stop, num=num, endpoint=endpoint)
... # doctest: +SKIP
>>> power(base, y).astype(dtype)
... # doctest: +SKIP
Examples
--------
>>> np.logspace(2.0, 3.0, num=4)
array([ 100. , 215.443469 , 464.15888336, 1000. ])
>>> np.logspace(2.0, 3.0, num=4, endpoint=False)
array([ 100. , 177.827941 , 316.22776602, 562.34132519])
>>> np.logspace(2.0, 3.0, num=4, base=2.0)
array([ 4. , 5.0396842 , 6.34960421, 8. ])
Graphical illustration:
>>> import matplotlib.pyplot as plt
>>> N = 10
>>> x1 = np.logspace(0.1, 1, N, endpoint=True)
>>> x2 = np.logspace(0.1, 1, N, endpoint=False)
>>> y = np.zeros(N)
>>> plt.plot(x1, y, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.plot(x2, y + 0.5, 'o')
[<matplotlib.lines.Line2D object at 0x...>]
>>> plt.ylim([-0.5, 1])
(-0.5, 1)
>>> plt.show()
"""
y = linspace(start, stop, num=num, endpoint=endpoint)
if dtype is None:
return _nx.power(base, y)
return _nx.power(base, y).astype(dtype)
def geomspace(start, stop, num=50, endpoint=True, dtype=None):
"""
Return numbers spaced evenly on a log scale (a geometric progression).
This is similar to `logspace`, but with endpoints specified directly.
Each output sample is a constant multiple of the previous.
Parameters
----------
start : scalar
The starting value of the sequence.
stop : scalar
The final value of the sequence, unless `endpoint` is False.
In that case, ``num + 1`` values are spaced over the
interval in log-space, of which all but the last (a sequence of
length `num`) are returned.
num : integer, optional
Number of samples to generate. Default is 50.
endpoint : boolean, optional
If true, `stop` is the last sample. Otherwise, it is not included.
Default is True.
dtype : dtype
The type of the output array. If `dtype` is not given, infer the data
type from the other input arguments.
Returns
-------
samples : ndarray
`num` samples, equally spaced on a log scale.
See Also
--------
logspace : Similar to geomspace, but with endpoints specified using log
and base.
linspace : Similar to geomspace, but with arithmetic instead of geometric
progression.
arange : Similar to linspace, with the step size specified instead of the
number of samples.
Notes
-----
If the inputs or dtype are complex, the output will follow a logarithmic
spiral in the complex plane. (There are an infinite number of spirals
passing through two points; the output will follow the shortest such path.)
Examples
--------
>>> np.geomspace(1, 1000, num=4)
array([ 1., 10., 100., 1000.])
>>> np.geomspace(1, 1000, num=3, endpoint=False)
array([ 1., 10., 100.])
>>> np.geomspace(1, 1000, num=4, endpoint=False)
array([ 1. , 5.62341325, 31.6227766 , 177.827941 ])
>>> np.geomspace(1, 256, num=9)
array([ 1., 2., 4., 8., 16., 32., 64., 128., 256.])
Note that the above may not produce exact integers:
>>> np.geomspace(1, 256, num=9, dtype=int)
array([ 1, 2, 4, 7, 16, 32, 63, 127, 256])
>>> np.around(np.geomspace(1, 256, num=9)).astype(int)
array([ 1, 2, 4, 8, 16, 32, 64, 128, 256])
Negative, decreasing, and complex inputs are allowed:
>>> np.geomspace(1000, 1, num=4)
array([ 1000., 100., 10., 1.])
>>> np.geomspace(-1000, -1, num=4)
array([-1000., -100., -10., -1.])
>>> np.geomspace(1j, 1000j, num=4) # Straight line
array([ 0. +1.j, 0. +10.j, 0. +100.j, 0.+1000.j])
>>> np.geomspace(-1+0j, 1+0j, num=5) # Circle
array([-1.00000000+0.j , -0.70710678+0.70710678j,
0.00000000+1.j , 0.70710678+0.70710678j,
1.00000000+0.j ])
Graphical illustration of ``endpoint`` parameter:
>>> import matplotlib.pyplot as plt
>>> N = 10
>>> y = np.zeros(N)
>>> plt.semilogx(np.geomspace(1, 1000, N, endpoint=True), y + 1, 'o')
>>> plt.semilogx(np.geomspace(1, 1000, N, endpoint=False), y + 2, 'o')
>>> plt.axis([0.5, 2000, 0, 3])
>>> plt.grid(True, color='0.7', linestyle='-', which='both', axis='both')
>>> plt.show()
"""
if start == 0 or stop == 0:
raise ValueError('Geometric sequence cannot include zero')
dt = result_type(start, stop, float(num))
if dtype is None:
dtype = dt
else:
# complex to dtype('complex128'), for instance
dtype = _nx.dtype(dtype)
# Avoid negligible real or imaginary parts in output by rotating to
# positive real, calculating, then undoing rotation
out_sign = 1
if start.real == stop.real == 0:
start, stop = start.imag, stop.imag
out_sign = 1j * out_sign
if _nx.sign(start) == _nx.sign(stop) == -1:
start, stop = -start, -stop
out_sign = -out_sign
# Promote both arguments to the same dtype in case, for instance, one is
# complex and another is negative and log would produce NaN otherwise
start = start + (stop - stop)
stop = stop + (start - start)
if _nx.issubdtype(dtype, _nx.complexfloating):
start = start + 0j
stop = stop + 0j
log_start = _nx.log10(start)
log_stop = _nx.log10(stop)
result = out_sign * logspace(log_start, log_stop, num=num,
endpoint=endpoint, base=10.0, dtype=dtype)
return result.astype(dtype)
| 12,340 | 33.376045 | 88 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/defchararray.py
|
"""
This module contains a set of functions for vectorized string
operations and methods.
.. note::
The `chararray` class exists for backwards compatibility with
Numarray, it is not recommended for new development. Starting from numpy
1.4, if one needs arrays of strings, it is recommended to use arrays of
`dtype` `object_`, `string_` or `unicode_`, and use the free functions
in the `numpy.char` module for fast vectorized string operations.
Some methods will only be available if the corresponding string method is
available in your version of Python.
The preferred alias for `defchararray` is `numpy.char`.
"""
from __future__ import division, absolute_import, print_function
import sys
from .numerictypes import string_, unicode_, integer, object_, bool_, character
from .numeric import ndarray, compare_chararrays
from .numeric import array as narray
from numpy.core.multiarray import _vec_string
from numpy.compat import asbytes, long
import numpy
__all__ = [
'chararray', 'equal', 'not_equal', 'greater_equal', 'less_equal',
'greater', 'less', 'str_len', 'add', 'multiply', 'mod', 'capitalize',
'center', 'count', 'decode', 'encode', 'endswith', 'expandtabs',
'find', 'index', 'isalnum', 'isalpha', 'isdigit', 'islower', 'isspace',
'istitle', 'isupper', 'join', 'ljust', 'lower', 'lstrip', 'partition',
'replace', 'rfind', 'rindex', 'rjust', 'rpartition', 'rsplit',
'rstrip', 'split', 'splitlines', 'startswith', 'strip', 'swapcase',
'title', 'translate', 'upper', 'zfill', 'isnumeric', 'isdecimal',
'array', 'asarray'
]
_globalvar = 0
if sys.version_info[0] >= 3:
_unicode = str
_bytes = bytes
else:
_unicode = unicode
_bytes = str
_len = len
def _use_unicode(*args):
"""
Helper function for determining the output type of some string
operations.
For an operation on two ndarrays, if at least one is unicode, the
result should be unicode.
"""
for x in args:
if (isinstance(x, _unicode) or
issubclass(numpy.asarray(x).dtype.type, unicode_)):
return unicode_
return string_
def _to_string_or_unicode_array(result):
"""
Helper function to cast a result back into a string or unicode array
if an object array must be used as an intermediary.
"""
return numpy.asarray(result.tolist())
def _clean_args(*args):
"""
Helper function for delegating arguments to Python string
functions.
Many of the Python string operations that have optional arguments
do not use 'None' to indicate a default value. In these cases,
we need to remove all `None` arguments, and those following them.
"""
newargs = []
for chk in args:
if chk is None:
break
newargs.append(chk)
return newargs
def _get_num_chars(a):
"""
Helper function that returns the number of characters per field in
a string or unicode array. This is to abstract out the fact that
for a unicode array this is itemsize / 4.
"""
if issubclass(a.dtype.type, unicode_):
return a.itemsize // 4
return a.itemsize
def equal(x1, x2):
"""
Return (x1 == x2) element-wise.
Unlike `numpy.equal`, this comparison is performed by first
stripping whitespace characters from the end of the string. This
behavior is provided for backward-compatibility with numarray.
Parameters
----------
x1, x2 : array_like of str or unicode
Input arrays of the same shape.
Returns
-------
out : ndarray or bool
Output array of bools, or a single bool if x1 and x2 are scalars.
See Also
--------
not_equal, greater_equal, less_equal, greater, less
"""
return compare_chararrays(x1, x2, '==', True)
def not_equal(x1, x2):
"""
Return (x1 != x2) element-wise.
Unlike `numpy.not_equal`, this comparison is performed by first
stripping whitespace characters from the end of the string. This
behavior is provided for backward-compatibility with numarray.
Parameters
----------
x1, x2 : array_like of str or unicode
Input arrays of the same shape.
Returns
-------
out : ndarray or bool
Output array of bools, or a single bool if x1 and x2 are scalars.
See Also
--------
equal, greater_equal, less_equal, greater, less
"""
return compare_chararrays(x1, x2, '!=', True)
def greater_equal(x1, x2):
"""
Return (x1 >= x2) element-wise.
Unlike `numpy.greater_equal`, this comparison is performed by
first stripping whitespace characters from the end of the string.
This behavior is provided for backward-compatibility with
numarray.
Parameters
----------
x1, x2 : array_like of str or unicode
Input arrays of the same shape.
Returns
-------
out : ndarray or bool
Output array of bools, or a single bool if x1 and x2 are scalars.
See Also
--------
equal, not_equal, less_equal, greater, less
"""
return compare_chararrays(x1, x2, '>=', True)
def less_equal(x1, x2):
"""
Return (x1 <= x2) element-wise.
Unlike `numpy.less_equal`, this comparison is performed by first
stripping whitespace characters from the end of the string. This
behavior is provided for backward-compatibility with numarray.
Parameters
----------
x1, x2 : array_like of str or unicode
Input arrays of the same shape.
Returns
-------
out : ndarray or bool
Output array of bools, or a single bool if x1 and x2 are scalars.
See Also
--------
equal, not_equal, greater_equal, greater, less
"""
return compare_chararrays(x1, x2, '<=', True)
def greater(x1, x2):
"""
Return (x1 > x2) element-wise.
Unlike `numpy.greater`, this comparison is performed by first
stripping whitespace characters from the end of the string. This
behavior is provided for backward-compatibility with numarray.
Parameters
----------
x1, x2 : array_like of str or unicode
Input arrays of the same shape.
Returns
-------
out : ndarray or bool
Output array of bools, or a single bool if x1 and x2 are scalars.
See Also
--------
equal, not_equal, greater_equal, less_equal, less
"""
return compare_chararrays(x1, x2, '>', True)
def less(x1, x2):
"""
Return (x1 < x2) element-wise.
Unlike `numpy.greater`, this comparison is performed by first
stripping whitespace characters from the end of the string. This
behavior is provided for backward-compatibility with numarray.
Parameters
----------
x1, x2 : array_like of str or unicode
Input arrays of the same shape.
Returns
-------
out : ndarray or bool
Output array of bools, or a single bool if x1 and x2 are scalars.
See Also
--------
equal, not_equal, greater_equal, less_equal, greater
"""
return compare_chararrays(x1, x2, '<', True)
def str_len(a):
"""
Return len(a) element-wise.
Parameters
----------
a : array_like of str or unicode
Returns
-------
out : ndarray
Output array of integers
See also
--------
__builtin__.len
"""
return _vec_string(a, integer, '__len__')
def add(x1, x2):
"""
Return element-wise string concatenation for two arrays of str or unicode.
Arrays `x1` and `x2` must have the same shape.
Parameters
----------
x1 : array_like of str or unicode
Input array.
x2 : array_like of str or unicode
Input array.
Returns
-------
add : ndarray
Output array of `string_` or `unicode_`, depending on input types
of the same shape as `x1` and `x2`.
"""
arr1 = numpy.asarray(x1)
arr2 = numpy.asarray(x2)
out_size = _get_num_chars(arr1) + _get_num_chars(arr2)
dtype = _use_unicode(arr1, arr2)
return _vec_string(arr1, (dtype, out_size), '__add__', (arr2,))
def multiply(a, i):
"""
Return (a * i), that is string multiple concatenation,
element-wise.
Values in `i` of less than 0 are treated as 0 (which yields an
empty string).
Parameters
----------
a : array_like of str or unicode
i : array_like of ints
Returns
-------
out : ndarray
Output array of str or unicode, depending on input types
"""
a_arr = numpy.asarray(a)
i_arr = numpy.asarray(i)
if not issubclass(i_arr.dtype.type, integer):
raise ValueError("Can only multiply by integers")
out_size = _get_num_chars(a_arr) * max(long(i_arr.max()), 0)
return _vec_string(
a_arr, (a_arr.dtype.type, out_size), '__mul__', (i_arr,))
def mod(a, values):
"""
Return (a % i), that is pre-Python 2.6 string formatting
(iterpolation), element-wise for a pair of array_likes of str
or unicode.
Parameters
----------
a : array_like of str or unicode
values : array_like of values
These values will be element-wise interpolated into the string.
Returns
-------
out : ndarray
Output array of str or unicode, depending on input types
See also
--------
str.__mod__
"""
return _to_string_or_unicode_array(
_vec_string(a, object_, '__mod__', (values,)))
def capitalize(a):
"""
Return a copy of `a` with only the first character of each element
capitalized.
Calls `str.capitalize` element-wise.
For 8-bit strings, this method is locale-dependent.
Parameters
----------
a : array_like of str or unicode
Input array of strings to capitalize.
Returns
-------
out : ndarray
Output array of str or unicode, depending on input
types
See also
--------
str.capitalize
Examples
--------
>>> c = np.array(['a1b2','1b2a','b2a1','2a1b'],'S4'); c
array(['a1b2', '1b2a', 'b2a1', '2a1b'],
dtype='|S4')
>>> np.char.capitalize(c)
array(['A1b2', '1b2a', 'B2a1', '2a1b'],
dtype='|S4')
"""
a_arr = numpy.asarray(a)
return _vec_string(a_arr, a_arr.dtype, 'capitalize')
def center(a, width, fillchar=' '):
"""
Return a copy of `a` with its elements centered in a string of
length `width`.
Calls `str.center` element-wise.
Parameters
----------
a : array_like of str or unicode
width : int
The length of the resulting strings
fillchar : str or unicode, optional
The padding character to use (default is space).
Returns
-------
out : ndarray
Output array of str or unicode, depending on input
types
See also
--------
str.center
"""
a_arr = numpy.asarray(a)
width_arr = numpy.asarray(width)
size = long(numpy.max(width_arr.flat))
if numpy.issubdtype(a_arr.dtype, numpy.string_):
fillchar = asbytes(fillchar)
return _vec_string(
a_arr, (a_arr.dtype.type, size), 'center', (width_arr, fillchar))
def count(a, sub, start=0, end=None):
"""
Returns an array with the number of non-overlapping occurrences of
substring `sub` in the range [`start`, `end`].
Calls `str.count` element-wise.
Parameters
----------
a : array_like of str or unicode
sub : str or unicode
The substring to search for.
start, end : int, optional
Optional arguments `start` and `end` are interpreted as slice
notation to specify the range in which to count.
Returns
-------
out : ndarray
Output array of ints.
See also
--------
str.count
Examples
--------
>>> c = np.array(['aAaAaA', ' aA ', 'abBABba'])
>>> c
array(['aAaAaA', ' aA ', 'abBABba'],
dtype='|S7')
>>> np.char.count(c, 'A')
array([3, 1, 1])
>>> np.char.count(c, 'aA')
array([3, 1, 0])
>>> np.char.count(c, 'A', start=1, end=4)
array([2, 1, 1])
>>> np.char.count(c, 'A', start=1, end=3)
array([1, 0, 0])
"""
return _vec_string(a, integer, 'count', [sub, start] + _clean_args(end))
def decode(a, encoding=None, errors=None):
"""
Calls `str.decode` element-wise.
The set of available codecs comes from the Python standard library,
and may be extended at runtime. For more information, see the
:mod:`codecs` module.
Parameters
----------
a : array_like of str or unicode
encoding : str, optional
The name of an encoding
errors : str, optional
Specifies how to handle encoding errors
Returns
-------
out : ndarray
See also
--------
str.decode
Notes
-----
The type of the result will depend on the encoding specified.
Examples
--------
>>> c = np.array(['aAaAaA', ' aA ', 'abBABba'])
>>> c
array(['aAaAaA', ' aA ', 'abBABba'],
dtype='|S7')
>>> np.char.encode(c, encoding='cp037')
array(['\\x81\\xc1\\x81\\xc1\\x81\\xc1', '@@\\x81\\xc1@@',
'\\x81\\x82\\xc2\\xc1\\xc2\\x82\\x81'],
dtype='|S7')
"""
return _to_string_or_unicode_array(
_vec_string(a, object_, 'decode', _clean_args(encoding, errors)))
def encode(a, encoding=None, errors=None):
"""
Calls `str.encode` element-wise.
The set of available codecs comes from the Python standard library,
and may be extended at runtime. For more information, see the codecs
module.
Parameters
----------
a : array_like of str or unicode
encoding : str, optional
The name of an encoding
errors : str, optional
Specifies how to handle encoding errors
Returns
-------
out : ndarray
See also
--------
str.encode
Notes
-----
The type of the result will depend on the encoding specified.
"""
return _to_string_or_unicode_array(
_vec_string(a, object_, 'encode', _clean_args(encoding, errors)))
def endswith(a, suffix, start=0, end=None):
"""
Returns a boolean array which is `True` where the string element
in `a` ends with `suffix`, otherwise `False`.
Calls `str.endswith` element-wise.
Parameters
----------
a : array_like of str or unicode
suffix : str
start, end : int, optional
With optional `start`, test beginning at that position. With
optional `end`, stop comparing at that position.
Returns
-------
out : ndarray
Outputs an array of bools.
See also
--------
str.endswith
Examples
--------
>>> s = np.array(['foo', 'bar'])
>>> s[0] = 'foo'
>>> s[1] = 'bar'
>>> s
array(['foo', 'bar'],
dtype='|S3')
>>> np.char.endswith(s, 'ar')
array([False, True])
>>> np.char.endswith(s, 'a', start=1, end=2)
array([False, True])
"""
return _vec_string(
a, bool_, 'endswith', [suffix, start] + _clean_args(end))
def expandtabs(a, tabsize=8):
"""
Return a copy of each string element where all tab characters are
replaced by one or more spaces.
Calls `str.expandtabs` element-wise.
Return a copy of each string element where all tab characters are
replaced by one or more spaces, depending on the current column
and the given `tabsize`. The column number is reset to zero after
each newline occurring in the string. This doesn't understand other
non-printing characters or escape sequences.
Parameters
----------
a : array_like of str or unicode
Input array
tabsize : int, optional
Replace tabs with `tabsize` number of spaces. If not given defaults
to 8 spaces.
Returns
-------
out : ndarray
Output array of str or unicode, depending on input type
See also
--------
str.expandtabs
"""
return _to_string_or_unicode_array(
_vec_string(a, object_, 'expandtabs', (tabsize,)))
def find(a, sub, start=0, end=None):
"""
For each element, return the lowest index in the string where
substring `sub` is found.
Calls `str.find` element-wise.
For each element, return the lowest index in the string where
substring `sub` is found, such that `sub` is contained in the
range [`start`, `end`].
Parameters
----------
a : array_like of str or unicode
sub : str or unicode
start, end : int, optional
Optional arguments `start` and `end` are interpreted as in
slice notation.
Returns
-------
out : ndarray or int
Output array of ints. Returns -1 if `sub` is not found.
See also
--------
str.find
"""
return _vec_string(
a, integer, 'find', [sub, start] + _clean_args(end))
def index(a, sub, start=0, end=None):
"""
Like `find`, but raises `ValueError` when the substring is not found.
Calls `str.index` element-wise.
Parameters
----------
a : array_like of str or unicode
sub : str or unicode
start, end : int, optional
Returns
-------
out : ndarray
Output array of ints. Returns -1 if `sub` is not found.
See also
--------
find, str.find
"""
return _vec_string(
a, integer, 'index', [sub, start] + _clean_args(end))
def isalnum(a):
"""
Returns true for each element if all characters in the string are
alphanumeric and there is at least one character, false otherwise.
Calls `str.isalnum` element-wise.
For 8-bit strings, this method is locale-dependent.
Parameters
----------
a : array_like of str or unicode
Returns
-------
out : ndarray
Output array of str or unicode, depending on input type
See also
--------
str.isalnum
"""
return _vec_string(a, bool_, 'isalnum')
def isalpha(a):
"""
Returns true for each element if all characters in the string are
alphabetic and there is at least one character, false otherwise.
Calls `str.isalpha` element-wise.
For 8-bit strings, this method is locale-dependent.
Parameters
----------
a : array_like of str or unicode
Returns
-------
out : ndarray
Output array of bools
See also
--------
str.isalpha
"""
return _vec_string(a, bool_, 'isalpha')
def isdigit(a):
"""
Returns true for each element if all characters in the string are
digits and there is at least one character, false otherwise.
Calls `str.isdigit` element-wise.
For 8-bit strings, this method is locale-dependent.
Parameters
----------
a : array_like of str or unicode
Returns
-------
out : ndarray
Output array of bools
See also
--------
str.isdigit
"""
return _vec_string(a, bool_, 'isdigit')
def islower(a):
"""
Returns true for each element if all cased characters in the
string are lowercase and there is at least one cased character,
false otherwise.
Calls `str.islower` element-wise.
For 8-bit strings, this method is locale-dependent.
Parameters
----------
a : array_like of str or unicode
Returns
-------
out : ndarray
Output array of bools
See also
--------
str.islower
"""
return _vec_string(a, bool_, 'islower')
def isspace(a):
"""
Returns true for each element if there are only whitespace
characters in the string and there is at least one character,
false otherwise.
Calls `str.isspace` element-wise.
For 8-bit strings, this method is locale-dependent.
Parameters
----------
a : array_like of str or unicode
Returns
-------
out : ndarray
Output array of bools
See also
--------
str.isspace
"""
return _vec_string(a, bool_, 'isspace')
def istitle(a):
"""
Returns true for each element if the element is a titlecased
string and there is at least one character, false otherwise.
Call `str.istitle` element-wise.
For 8-bit strings, this method is locale-dependent.
Parameters
----------
a : array_like of str or unicode
Returns
-------
out : ndarray
Output array of bools
See also
--------
str.istitle
"""
return _vec_string(a, bool_, 'istitle')
def isupper(a):
"""
Returns true for each element if all cased characters in the
string are uppercase and there is at least one character, false
otherwise.
Call `str.isupper` element-wise.
For 8-bit strings, this method is locale-dependent.
Parameters
----------
a : array_like of str or unicode
Returns
-------
out : ndarray
Output array of bools
See also
--------
str.isupper
"""
return _vec_string(a, bool_, 'isupper')
def join(sep, seq):
"""
Return a string which is the concatenation of the strings in the
sequence `seq`.
Calls `str.join` element-wise.
Parameters
----------
sep : array_like of str or unicode
seq : array_like of str or unicode
Returns
-------
out : ndarray
Output array of str or unicode, depending on input types
See also
--------
str.join
"""
return _to_string_or_unicode_array(
_vec_string(sep, object_, 'join', (seq,)))
def ljust(a, width, fillchar=' '):
"""
Return an array with the elements of `a` left-justified in a
string of length `width`.
Calls `str.ljust` element-wise.
Parameters
----------
a : array_like of str or unicode
width : int
The length of the resulting strings
fillchar : str or unicode, optional
The character to use for padding
Returns
-------
out : ndarray
Output array of str or unicode, depending on input type
See also
--------
str.ljust
"""
a_arr = numpy.asarray(a)
width_arr = numpy.asarray(width)
size = long(numpy.max(width_arr.flat))
if numpy.issubdtype(a_arr.dtype, numpy.string_):
fillchar = asbytes(fillchar)
return _vec_string(
a_arr, (a_arr.dtype.type, size), 'ljust', (width_arr, fillchar))
def lower(a):
"""
Return an array with the elements converted to lowercase.
Call `str.lower` element-wise.
For 8-bit strings, this method is locale-dependent.
Parameters
----------
a : array_like, {str, unicode}
Input array.
Returns
-------
out : ndarray, {str, unicode}
Output array of str or unicode, depending on input type
See also
--------
str.lower
Examples
--------
>>> c = np.array(['A1B C', '1BCA', 'BCA1']); c
array(['A1B C', '1BCA', 'BCA1'],
dtype='|S5')
>>> np.char.lower(c)
array(['a1b c', '1bca', 'bca1'],
dtype='|S5')
"""
a_arr = numpy.asarray(a)
return _vec_string(a_arr, a_arr.dtype, 'lower')
def lstrip(a, chars=None):
"""
For each element in `a`, return a copy with the leading characters
removed.
Calls `str.lstrip` element-wise.
Parameters
----------
a : array-like, {str, unicode}
Input array.
chars : {str, unicode}, optional
The `chars` argument is a string specifying the set of
characters to be removed. If omitted or None, the `chars`
argument defaults to removing whitespace. The `chars` argument
is not a prefix; rather, all combinations of its values are
stripped.
Returns
-------
out : ndarray, {str, unicode}
Output array of str or unicode, depending on input type
See also
--------
str.lstrip
Examples
--------
>>> c = np.array(['aAaAaA', ' aA ', 'abBABba'])
>>> c
array(['aAaAaA', ' aA ', 'abBABba'],
dtype='|S7')
The 'a' variable is unstripped from c[1] because whitespace leading.
>>> np.char.lstrip(c, 'a')
array(['AaAaA', ' aA ', 'bBABba'],
dtype='|S7')
>>> np.char.lstrip(c, 'A') # leaves c unchanged
array(['aAaAaA', ' aA ', 'abBABba'],
dtype='|S7')
>>> (np.char.lstrip(c, ' ') == np.char.lstrip(c, '')).all()
... # XXX: is this a regression? this line now returns False
... # np.char.lstrip(c,'') does not modify c at all.
True
>>> (np.char.lstrip(c, ' ') == np.char.lstrip(c, None)).all()
True
"""
a_arr = numpy.asarray(a)
return _vec_string(a_arr, a_arr.dtype, 'lstrip', (chars,))
def partition(a, sep):
"""
Partition each element in `a` around `sep`.
Calls `str.partition` element-wise.
For each element in `a`, split the element as the first
occurrence of `sep`, and return 3 strings containing the part
before the separator, the separator itself, and the part after
the separator. If the separator is not found, return 3 strings
containing the string itself, followed by two empty strings.
Parameters
----------
a : array_like, {str, unicode}
Input array
sep : {str, unicode}
Separator to split each string element in `a`.
Returns
-------
out : ndarray, {str, unicode}
Output array of str or unicode, depending on input type.
The output array will have an extra dimension with 3
elements per input element.
See also
--------
str.partition
"""
return _to_string_or_unicode_array(
_vec_string(a, object_, 'partition', (sep,)))
def replace(a, old, new, count=None):
"""
For each element in `a`, return a copy of the string with all
occurrences of substring `old` replaced by `new`.
Calls `str.replace` element-wise.
Parameters
----------
a : array-like of str or unicode
old, new : str or unicode
count : int, optional
If the optional argument `count` is given, only the first
`count` occurrences are replaced.
Returns
-------
out : ndarray
Output array of str or unicode, depending on input type
See also
--------
str.replace
"""
return _to_string_or_unicode_array(
_vec_string(
a, object_, 'replace', [old, new] + _clean_args(count)))
def rfind(a, sub, start=0, end=None):
"""
For each element in `a`, return the highest index in the string
where substring `sub` is found, such that `sub` is contained
within [`start`, `end`].
Calls `str.rfind` element-wise.
Parameters
----------
a : array-like of str or unicode
sub : str or unicode
start, end : int, optional
Optional arguments `start` and `end` are interpreted as in
slice notation.
Returns
-------
out : ndarray
Output array of ints. Return -1 on failure.
See also
--------
str.rfind
"""
return _vec_string(
a, integer, 'rfind', [sub, start] + _clean_args(end))
def rindex(a, sub, start=0, end=None):
"""
Like `rfind`, but raises `ValueError` when the substring `sub` is
not found.
Calls `str.rindex` element-wise.
Parameters
----------
a : array-like of str or unicode
sub : str or unicode
start, end : int, optional
Returns
-------
out : ndarray
Output array of ints.
See also
--------
rfind, str.rindex
"""
return _vec_string(
a, integer, 'rindex', [sub, start] + _clean_args(end))
def rjust(a, width, fillchar=' '):
"""
Return an array with the elements of `a` right-justified in a
string of length `width`.
Calls `str.rjust` element-wise.
Parameters
----------
a : array_like of str or unicode
width : int
The length of the resulting strings
fillchar : str or unicode, optional
The character to use for padding
Returns
-------
out : ndarray
Output array of str or unicode, depending on input type
See also
--------
str.rjust
"""
a_arr = numpy.asarray(a)
width_arr = numpy.asarray(width)
size = long(numpy.max(width_arr.flat))
if numpy.issubdtype(a_arr.dtype, numpy.string_):
fillchar = asbytes(fillchar)
return _vec_string(
a_arr, (a_arr.dtype.type, size), 'rjust', (width_arr, fillchar))
def rpartition(a, sep):
"""
Partition (split) each element around the right-most separator.
Calls `str.rpartition` element-wise.
For each element in `a`, split the element as the last
occurrence of `sep`, and return 3 strings containing the part
before the separator, the separator itself, and the part after
the separator. If the separator is not found, return 3 strings
containing the string itself, followed by two empty strings.
Parameters
----------
a : array_like of str or unicode
Input array
sep : str or unicode
Right-most separator to split each element in array.
Returns
-------
out : ndarray
Output array of string or unicode, depending on input
type. The output array will have an extra dimension with
3 elements per input element.
See also
--------
str.rpartition
"""
return _to_string_or_unicode_array(
_vec_string(a, object_, 'rpartition', (sep,)))
def rsplit(a, sep=None, maxsplit=None):
"""
For each element in `a`, return a list of the words in the
string, using `sep` as the delimiter string.
Calls `str.rsplit` element-wise.
Except for splitting from the right, `rsplit`
behaves like `split`.
Parameters
----------
a : array_like of str or unicode
sep : str or unicode, optional
If `sep` is not specified or `None`, any whitespace string
is a separator.
maxsplit : int, optional
If `maxsplit` is given, at most `maxsplit` splits are done,
the rightmost ones.
Returns
-------
out : ndarray
Array of list objects
See also
--------
str.rsplit, split
"""
# This will return an array of lists of different sizes, so we
# leave it as an object array
return _vec_string(
a, object_, 'rsplit', [sep] + _clean_args(maxsplit))
def rstrip(a, chars=None):
"""
For each element in `a`, return a copy with the trailing
characters removed.
Calls `str.rstrip` element-wise.
Parameters
----------
a : array-like of str or unicode
chars : str or unicode, optional
The `chars` argument is a string specifying the set of
characters to be removed. If omitted or None, the `chars`
argument defaults to removing whitespace. The `chars` argument
is not a suffix; rather, all combinations of its values are
stripped.
Returns
-------
out : ndarray
Output array of str or unicode, depending on input type
See also
--------
str.rstrip
Examples
--------
>>> c = np.array(['aAaAaA', 'abBABba'], dtype='S7'); c
array(['aAaAaA', 'abBABba'],
dtype='|S7')
>>> np.char.rstrip(c, 'a')
array(['aAaAaA', 'abBABb'],
dtype='|S7')
>>> np.char.rstrip(c, 'A')
array(['aAaAa', 'abBABba'],
dtype='|S7')
"""
a_arr = numpy.asarray(a)
return _vec_string(a_arr, a_arr.dtype, 'rstrip', (chars,))
def split(a, sep=None, maxsplit=None):
"""
For each element in `a`, return a list of the words in the
string, using `sep` as the delimiter string.
Calls `str.split` element-wise.
Parameters
----------
a : array_like of str or unicode
sep : str or unicode, optional
If `sep` is not specified or `None`, any whitespace string is a
separator.
maxsplit : int, optional
If `maxsplit` is given, at most `maxsplit` splits are done.
Returns
-------
out : ndarray
Array of list objects
See also
--------
str.split, rsplit
"""
# This will return an array of lists of different sizes, so we
# leave it as an object array
return _vec_string(
a, object_, 'split', [sep] + _clean_args(maxsplit))
def splitlines(a, keepends=None):
"""
For each element in `a`, return a list of the lines in the
element, breaking at line boundaries.
Calls `str.splitlines` element-wise.
Parameters
----------
a : array_like of str or unicode
keepends : bool, optional
Line breaks are not included in the resulting list unless
keepends is given and true.
Returns
-------
out : ndarray
Array of list objects
See also
--------
str.splitlines
"""
return _vec_string(
a, object_, 'splitlines', _clean_args(keepends))
def startswith(a, prefix, start=0, end=None):
"""
Returns a boolean array which is `True` where the string element
in `a` starts with `prefix`, otherwise `False`.
Calls `str.startswith` element-wise.
Parameters
----------
a : array_like of str or unicode
prefix : str
start, end : int, optional
With optional `start`, test beginning at that position. With
optional `end`, stop comparing at that position.
Returns
-------
out : ndarray
Array of booleans
See also
--------
str.startswith
"""
return _vec_string(
a, bool_, 'startswith', [prefix, start] + _clean_args(end))
def strip(a, chars=None):
"""
For each element in `a`, return a copy with the leading and
trailing characters removed.
Calls `str.strip` element-wise.
Parameters
----------
a : array-like of str or unicode
chars : str or unicode, optional
The `chars` argument is a string specifying the set of
characters to be removed. If omitted or None, the `chars`
argument defaults to removing whitespace. The `chars` argument
is not a prefix or suffix; rather, all combinations of its
values are stripped.
Returns
-------
out : ndarray
Output array of str or unicode, depending on input type
See also
--------
str.strip
Examples
--------
>>> c = np.array(['aAaAaA', ' aA ', 'abBABba'])
>>> c
array(['aAaAaA', ' aA ', 'abBABba'],
dtype='|S7')
>>> np.char.strip(c)
array(['aAaAaA', 'aA', 'abBABba'],
dtype='|S7')
>>> np.char.strip(c, 'a') # 'a' unstripped from c[1] because whitespace leads
array(['AaAaA', ' aA ', 'bBABb'],
dtype='|S7')
>>> np.char.strip(c, 'A') # 'A' unstripped from c[1] because (unprinted) ws trails
array(['aAaAa', ' aA ', 'abBABba'],
dtype='|S7')
"""
a_arr = numpy.asarray(a)
return _vec_string(a_arr, a_arr.dtype, 'strip', _clean_args(chars))
def swapcase(a):
"""
Return element-wise a copy of the string with
uppercase characters converted to lowercase and vice versa.
Calls `str.swapcase` element-wise.
For 8-bit strings, this method is locale-dependent.
Parameters
----------
a : array_like, {str, unicode}
Input array.
Returns
-------
out : ndarray, {str, unicode}
Output array of str or unicode, depending on input type
See also
--------
str.swapcase
Examples
--------
>>> c=np.array(['a1B c','1b Ca','b Ca1','cA1b'],'S5'); c
array(['a1B c', '1b Ca', 'b Ca1', 'cA1b'],
dtype='|S5')
>>> np.char.swapcase(c)
array(['A1b C', '1B cA', 'B cA1', 'Ca1B'],
dtype='|S5')
"""
a_arr = numpy.asarray(a)
return _vec_string(a_arr, a_arr.dtype, 'swapcase')
def title(a):
"""
Return element-wise title cased version of string or unicode.
Title case words start with uppercase characters, all remaining cased
characters are lowercase.
Calls `str.title` element-wise.
For 8-bit strings, this method is locale-dependent.
Parameters
----------
a : array_like, {str, unicode}
Input array.
Returns
-------
out : ndarray
Output array of str or unicode, depending on input type
See also
--------
str.title
Examples
--------
>>> c=np.array(['a1b c','1b ca','b ca1','ca1b'],'S5'); c
array(['a1b c', '1b ca', 'b ca1', 'ca1b'],
dtype='|S5')
>>> np.char.title(c)
array(['A1B C', '1B Ca', 'B Ca1', 'Ca1B'],
dtype='|S5')
"""
a_arr = numpy.asarray(a)
return _vec_string(a_arr, a_arr.dtype, 'title')
def translate(a, table, deletechars=None):
"""
For each element in `a`, return a copy of the string where all
characters occurring in the optional argument `deletechars` are
removed, and the remaining characters have been mapped through the
given translation table.
Calls `str.translate` element-wise.
Parameters
----------
a : array-like of str or unicode
table : str of length 256
deletechars : str
Returns
-------
out : ndarray
Output array of str or unicode, depending on input type
See also
--------
str.translate
"""
a_arr = numpy.asarray(a)
if issubclass(a_arr.dtype.type, unicode_):
return _vec_string(
a_arr, a_arr.dtype, 'translate', (table,))
else:
return _vec_string(
a_arr, a_arr.dtype, 'translate', [table] + _clean_args(deletechars))
def upper(a):
"""
Return an array with the elements converted to uppercase.
Calls `str.upper` element-wise.
For 8-bit strings, this method is locale-dependent.
Parameters
----------
a : array_like, {str, unicode}
Input array.
Returns
-------
out : ndarray, {str, unicode}
Output array of str or unicode, depending on input type
See also
--------
str.upper
Examples
--------
>>> c = np.array(['a1b c', '1bca', 'bca1']); c
array(['a1b c', '1bca', 'bca1'],
dtype='|S5')
>>> np.char.upper(c)
array(['A1B C', '1BCA', 'BCA1'],
dtype='|S5')
"""
a_arr = numpy.asarray(a)
return _vec_string(a_arr, a_arr.dtype, 'upper')
def zfill(a, width):
"""
Return the numeric string left-filled with zeros
Calls `str.zfill` element-wise.
Parameters
----------
a : array_like, {str, unicode}
Input array.
width : int
Width of string to left-fill elements in `a`.
Returns
-------
out : ndarray, {str, unicode}
Output array of str or unicode, depending on input type
See also
--------
str.zfill
"""
a_arr = numpy.asarray(a)
width_arr = numpy.asarray(width)
size = long(numpy.max(width_arr.flat))
return _vec_string(
a_arr, (a_arr.dtype.type, size), 'zfill', (width_arr,))
def isnumeric(a):
"""
For each element, return True if there are only numeric
characters in the element.
Calls `unicode.isnumeric` element-wise.
Numeric characters include digit characters, and all characters
that have the Unicode numeric value property, e.g. ``U+2155,
VULGAR FRACTION ONE FIFTH``.
Parameters
----------
a : array_like, unicode
Input array.
Returns
-------
out : ndarray, bool
Array of booleans of same shape as `a`.
See also
--------
unicode.isnumeric
"""
if _use_unicode(a) != unicode_:
raise TypeError("isnumeric is only available for Unicode strings and arrays")
return _vec_string(a, bool_, 'isnumeric')
def isdecimal(a):
"""
For each element, return True if there are only decimal
characters in the element.
Calls `unicode.isdecimal` element-wise.
Decimal characters include digit characters, and all characters
that that can be used to form decimal-radix numbers,
e.g. ``U+0660, ARABIC-INDIC DIGIT ZERO``.
Parameters
----------
a : array_like, unicode
Input array.
Returns
-------
out : ndarray, bool
Array of booleans identical in shape to `a`.
See also
--------
unicode.isdecimal
"""
if _use_unicode(a) != unicode_:
raise TypeError("isnumeric is only available for Unicode strings and arrays")
return _vec_string(a, bool_, 'isdecimal')
class chararray(ndarray):
"""
chararray(shape, itemsize=1, unicode=False, buffer=None, offset=0,
strides=None, order=None)
Provides a convenient view on arrays of string and unicode values.
.. note::
The `chararray` class exists for backwards compatibility with
Numarray, it is not recommended for new development. Starting from numpy
1.4, if one needs arrays of strings, it is recommended to use arrays of
`dtype` `object_`, `string_` or `unicode_`, and use the free functions
in the `numpy.char` module for fast vectorized string operations.
Versus a regular NumPy array of type `str` or `unicode`, this
class adds the following functionality:
1) values automatically have whitespace removed from the end
when indexed
2) comparison operators automatically remove whitespace from the
end when comparing values
3) vectorized string operations are provided as methods
(e.g. `.endswith`) and infix operators (e.g. ``"+", "*", "%"``)
chararrays should be created using `numpy.char.array` or
`numpy.char.asarray`, rather than this constructor directly.
This constructor creates the array, using `buffer` (with `offset`
and `strides`) if it is not ``None``. If `buffer` is ``None``, then
constructs a new array with `strides` in "C order", unless both
``len(shape) >= 2`` and ``order='Fortran'``, in which case `strides`
is in "Fortran order".
Methods
-------
astype
argsort
copy
count
decode
dump
dumps
encode
endswith
expandtabs
fill
find
flatten
getfield
index
isalnum
isalpha
isdecimal
isdigit
islower
isnumeric
isspace
istitle
isupper
item
join
ljust
lower
lstrip
nonzero
put
ravel
repeat
replace
reshape
resize
rfind
rindex
rjust
rsplit
rstrip
searchsorted
setfield
setflags
sort
split
splitlines
squeeze
startswith
strip
swapaxes
swapcase
take
title
tofile
tolist
tostring
translate
transpose
upper
view
zfill
Parameters
----------
shape : tuple
Shape of the array.
itemsize : int, optional
Length of each array element, in number of characters. Default is 1.
unicode : bool, optional
Are the array elements of type unicode (True) or string (False).
Default is False.
buffer : int, optional
Memory address of the start of the array data. Default is None,
in which case a new array is created.
offset : int, optional
Fixed stride displacement from the beginning of an axis?
Default is 0. Needs to be >=0.
strides : array_like of ints, optional
Strides for the array (see `ndarray.strides` for full description).
Default is None.
order : {'C', 'F'}, optional
The order in which the array data is stored in memory: 'C' ->
"row major" order (the default), 'F' -> "column major"
(Fortran) order.
Examples
--------
>>> charar = np.chararray((3, 3))
>>> charar[:] = 'a'
>>> charar
chararray([['a', 'a', 'a'],
['a', 'a', 'a'],
['a', 'a', 'a']],
dtype='|S1')
>>> charar = np.chararray(charar.shape, itemsize=5)
>>> charar[:] = 'abc'
>>> charar
chararray([['abc', 'abc', 'abc'],
['abc', 'abc', 'abc'],
['abc', 'abc', 'abc']],
dtype='|S5')
"""
def __new__(subtype, shape, itemsize=1, unicode=False, buffer=None,
offset=0, strides=None, order='C'):
global _globalvar
if unicode:
dtype = unicode_
else:
dtype = string_
# force itemsize to be a Python long, since using NumPy integer
# types results in itemsize.itemsize being used as the size of
# strings in the new array.
itemsize = long(itemsize)
if sys.version_info[0] >= 3 and isinstance(buffer, _unicode):
# On Py3, unicode objects do not have the buffer interface
filler = buffer
buffer = None
else:
filler = None
_globalvar = 1
if buffer is None:
self = ndarray.__new__(subtype, shape, (dtype, itemsize),
order=order)
else:
self = ndarray.__new__(subtype, shape, (dtype, itemsize),
buffer=buffer,
offset=offset, strides=strides,
order=order)
if filler is not None:
self[...] = filler
_globalvar = 0
return self
def __array_finalize__(self, obj):
# The b is a special case because it is used for reconstructing.
if not _globalvar and self.dtype.char not in 'SUbc':
raise ValueError("Can only create a chararray from string data.")
def __getitem__(self, obj):
val = ndarray.__getitem__(self, obj)
if isinstance(val, character):
temp = val.rstrip()
if _len(temp) == 0:
val = ''
else:
val = temp
return val
# IMPLEMENTATION NOTE: Most of the methods of this class are
# direct delegations to the free functions in this module.
# However, those that return an array of strings should instead
# return a chararray, so some extra wrapping is required.
def __eq__(self, other):
"""
Return (self == other) element-wise.
See also
--------
equal
"""
return equal(self, other)
def __ne__(self, other):
"""
Return (self != other) element-wise.
See also
--------
not_equal
"""
return not_equal(self, other)
def __ge__(self, other):
"""
Return (self >= other) element-wise.
See also
--------
greater_equal
"""
return greater_equal(self, other)
def __le__(self, other):
"""
Return (self <= other) element-wise.
See also
--------
less_equal
"""
return less_equal(self, other)
def __gt__(self, other):
"""
Return (self > other) element-wise.
See also
--------
greater
"""
return greater(self, other)
def __lt__(self, other):
"""
Return (self < other) element-wise.
See also
--------
less
"""
return less(self, other)
def __add__(self, other):
"""
Return (self + other), that is string concatenation,
element-wise for a pair of array_likes of str or unicode.
See also
--------
add
"""
return asarray(add(self, other))
def __radd__(self, other):
"""
Return (other + self), that is string concatenation,
element-wise for a pair of array_likes of `string_` or `unicode_`.
See also
--------
add
"""
return asarray(add(numpy.asarray(other), self))
def __mul__(self, i):
"""
Return (self * i), that is string multiple concatenation,
element-wise.
See also
--------
multiply
"""
return asarray(multiply(self, i))
def __rmul__(self, i):
"""
Return (self * i), that is string multiple concatenation,
element-wise.
See also
--------
multiply
"""
return asarray(multiply(self, i))
def __mod__(self, i):
"""
Return (self % i), that is pre-Python 2.6 string formatting
(iterpolation), element-wise for a pair of array_likes of `string_`
or `unicode_`.
See also
--------
mod
"""
return asarray(mod(self, i))
def __rmod__(self, other):
return NotImplemented
def argsort(self, axis=-1, kind='quicksort', order=None):
"""
Return the indices that sort the array lexicographically.
For full documentation see `numpy.argsort`, for which this method is
in fact merely a "thin wrapper."
Examples
--------
>>> c = np.array(['a1b c', '1b ca', 'b ca1', 'Ca1b'], 'S5')
>>> c = c.view(np.chararray); c
chararray(['a1b c', '1b ca', 'b ca1', 'Ca1b'],
dtype='|S5')
>>> c[c.argsort()]
chararray(['1b ca', 'Ca1b', 'a1b c', 'b ca1'],
dtype='|S5')
"""
return self.__array__().argsort(axis, kind, order)
argsort.__doc__ = ndarray.argsort.__doc__
def capitalize(self):
"""
Return a copy of `self` with only the first character of each element
capitalized.
See also
--------
char.capitalize
"""
return asarray(capitalize(self))
def center(self, width, fillchar=' '):
"""
Return a copy of `self` with its elements centered in a
string of length `width`.
See also
--------
center
"""
return asarray(center(self, width, fillchar))
def count(self, sub, start=0, end=None):
"""
Returns an array with the number of non-overlapping occurrences of
substring `sub` in the range [`start`, `end`].
See also
--------
char.count
"""
return count(self, sub, start, end)
def decode(self, encoding=None, errors=None):
"""
Calls `str.decode` element-wise.
See also
--------
char.decode
"""
return decode(self, encoding, errors)
def encode(self, encoding=None, errors=None):
"""
Calls `str.encode` element-wise.
See also
--------
char.encode
"""
return encode(self, encoding, errors)
def endswith(self, suffix, start=0, end=None):
"""
Returns a boolean array which is `True` where the string element
in `self` ends with `suffix`, otherwise `False`.
See also
--------
char.endswith
"""
return endswith(self, suffix, start, end)
def expandtabs(self, tabsize=8):
"""
Return a copy of each string element where all tab characters are
replaced by one or more spaces.
See also
--------
char.expandtabs
"""
return asarray(expandtabs(self, tabsize))
def find(self, sub, start=0, end=None):
"""
For each element, return the lowest index in the string where
substring `sub` is found.
See also
--------
char.find
"""
return find(self, sub, start, end)
def index(self, sub, start=0, end=None):
"""
Like `find`, but raises `ValueError` when the substring is not found.
See also
--------
char.index
"""
return index(self, sub, start, end)
def isalnum(self):
"""
Returns true for each element if all characters in the string
are alphanumeric and there is at least one character, false
otherwise.
See also
--------
char.isalnum
"""
return isalnum(self)
def isalpha(self):
"""
Returns true for each element if all characters in the string
are alphabetic and there is at least one character, false
otherwise.
See also
--------
char.isalpha
"""
return isalpha(self)
def isdigit(self):
"""
Returns true for each element if all characters in the string are
digits and there is at least one character, false otherwise.
See also
--------
char.isdigit
"""
return isdigit(self)
def islower(self):
"""
Returns true for each element if all cased characters in the
string are lowercase and there is at least one cased character,
false otherwise.
See also
--------
char.islower
"""
return islower(self)
def isspace(self):
"""
Returns true for each element if there are only whitespace
characters in the string and there is at least one character,
false otherwise.
See also
--------
char.isspace
"""
return isspace(self)
def istitle(self):
"""
Returns true for each element if the element is a titlecased
string and there is at least one character, false otherwise.
See also
--------
char.istitle
"""
return istitle(self)
def isupper(self):
"""
Returns true for each element if all cased characters in the
string are uppercase and there is at least one character, false
otherwise.
See also
--------
char.isupper
"""
return isupper(self)
def join(self, seq):
"""
Return a string which is the concatenation of the strings in the
sequence `seq`.
See also
--------
char.join
"""
return join(self, seq)
def ljust(self, width, fillchar=' '):
"""
Return an array with the elements of `self` left-justified in a
string of length `width`.
See also
--------
char.ljust
"""
return asarray(ljust(self, width, fillchar))
def lower(self):
"""
Return an array with the elements of `self` converted to
lowercase.
See also
--------
char.lower
"""
return asarray(lower(self))
def lstrip(self, chars=None):
"""
For each element in `self`, return a copy with the leading characters
removed.
See also
--------
char.lstrip
"""
return asarray(lstrip(self, chars))
def partition(self, sep):
"""
Partition each element in `self` around `sep`.
See also
--------
partition
"""
return asarray(partition(self, sep))
def replace(self, old, new, count=None):
"""
For each element in `self`, return a copy of the string with all
occurrences of substring `old` replaced by `new`.
See also
--------
char.replace
"""
return asarray(replace(self, old, new, count))
def rfind(self, sub, start=0, end=None):
"""
For each element in `self`, return the highest index in the string
where substring `sub` is found, such that `sub` is contained
within [`start`, `end`].
See also
--------
char.rfind
"""
return rfind(self, sub, start, end)
def rindex(self, sub, start=0, end=None):
"""
Like `rfind`, but raises `ValueError` when the substring `sub` is
not found.
See also
--------
char.rindex
"""
return rindex(self, sub, start, end)
def rjust(self, width, fillchar=' '):
"""
Return an array with the elements of `self`
right-justified in a string of length `width`.
See also
--------
char.rjust
"""
return asarray(rjust(self, width, fillchar))
def rpartition(self, sep):
"""
Partition each element in `self` around `sep`.
See also
--------
rpartition
"""
return asarray(rpartition(self, sep))
def rsplit(self, sep=None, maxsplit=None):
"""
For each element in `self`, return a list of the words in
the string, using `sep` as the delimiter string.
See also
--------
char.rsplit
"""
return rsplit(self, sep, maxsplit)
def rstrip(self, chars=None):
"""
For each element in `self`, return a copy with the trailing
characters removed.
See also
--------
char.rstrip
"""
return asarray(rstrip(self, chars))
def split(self, sep=None, maxsplit=None):
"""
For each element in `self`, return a list of the words in the
string, using `sep` as the delimiter string.
See also
--------
char.split
"""
return split(self, sep, maxsplit)
def splitlines(self, keepends=None):
"""
For each element in `self`, return a list of the lines in the
element, breaking at line boundaries.
See also
--------
char.splitlines
"""
return splitlines(self, keepends)
def startswith(self, prefix, start=0, end=None):
"""
Returns a boolean array which is `True` where the string element
in `self` starts with `prefix`, otherwise `False`.
See also
--------
char.startswith
"""
return startswith(self, prefix, start, end)
def strip(self, chars=None):
"""
For each element in `self`, return a copy with the leading and
trailing characters removed.
See also
--------
char.strip
"""
return asarray(strip(self, chars))
def swapcase(self):
"""
For each element in `self`, return a copy of the string with
uppercase characters converted to lowercase and vice versa.
See also
--------
char.swapcase
"""
return asarray(swapcase(self))
def title(self):
"""
For each element in `self`, return a titlecased version of the
string: words start with uppercase characters, all remaining cased
characters are lowercase.
See also
--------
char.title
"""
return asarray(title(self))
def translate(self, table, deletechars=None):
"""
For each element in `self`, return a copy of the string where
all characters occurring in the optional argument
`deletechars` are removed, and the remaining characters have
been mapped through the given translation table.
See also
--------
char.translate
"""
return asarray(translate(self, table, deletechars))
def upper(self):
"""
Return an array with the elements of `self` converted to
uppercase.
See also
--------
char.upper
"""
return asarray(upper(self))
def zfill(self, width):
"""
Return the numeric string left-filled with zeros in a string of
length `width`.
See also
--------
char.zfill
"""
return asarray(zfill(self, width))
def isnumeric(self):
"""
For each element in `self`, return True if there are only
numeric characters in the element.
See also
--------
char.isnumeric
"""
return isnumeric(self)
def isdecimal(self):
"""
For each element in `self`, return True if there are only
decimal characters in the element.
See also
--------
char.isdecimal
"""
return isdecimal(self)
def array(obj, itemsize=None, copy=True, unicode=None, order=None):
"""
Create a `chararray`.
.. note::
This class is provided for numarray backward-compatibility.
New code (not concerned with numarray compatibility) should use
arrays of type `string_` or `unicode_` and use the free functions
in :mod:`numpy.char <numpy.core.defchararray>` for fast
vectorized string operations instead.
Versus a regular NumPy array of type `str` or `unicode`, this
class adds the following functionality:
1) values automatically have whitespace removed from the end
when indexed
2) comparison operators automatically remove whitespace from the
end when comparing values
3) vectorized string operations are provided as methods
(e.g. `str.endswith`) and infix operators (e.g. ``+, *, %``)
Parameters
----------
obj : array of str or unicode-like
itemsize : int, optional
`itemsize` is the number of characters per scalar in the
resulting array. If `itemsize` is None, and `obj` is an
object array or a Python list, the `itemsize` will be
automatically determined. If `itemsize` is provided and `obj`
is of type str or unicode, then the `obj` string will be
chunked into `itemsize` pieces.
copy : bool, optional
If true (default), then the object is copied. Otherwise, a copy
will only be made if __array__ returns a copy, if obj is a
nested sequence, or if a copy is needed to satisfy any of the other
requirements (`itemsize`, unicode, `order`, etc.).
unicode : bool, optional
When true, the resulting `chararray` can contain Unicode
characters, when false only 8-bit characters. If unicode is
`None` and `obj` is one of the following:
- a `chararray`,
- an ndarray of type `str` or `unicode`
- a Python str or unicode object,
then the unicode setting of the output array will be
automatically determined.
order : {'C', 'F', 'A'}, optional
Specify the order of the array. If order is 'C' (default), then the
array will be in C-contiguous order (last-index varies the
fastest). If order is 'F', then the returned array
will be in Fortran-contiguous order (first-index varies the
fastest). If order is 'A', then the returned array may
be in any order (either C-, Fortran-contiguous, or even
discontiguous).
"""
if isinstance(obj, (_bytes, _unicode)):
if unicode is None:
if isinstance(obj, _unicode):
unicode = True
else:
unicode = False
if itemsize is None:
itemsize = _len(obj)
shape = _len(obj) // itemsize
if unicode:
if sys.maxunicode == 0xffff:
# On a narrow Python build, the buffer for Unicode
# strings is UCS2, which doesn't match the buffer for
# NumPy Unicode types, which is ALWAYS UCS4.
# Therefore, we need to convert the buffer. On Python
# 2.6 and later, we can use the utf_32 codec. Earlier
# versions don't have that codec, so we convert to a
# numerical array that matches the input buffer, and
# then use NumPy to convert it to UCS4. All of this
# should happen in native endianness.
obj = obj.encode('utf_32')
else:
obj = _unicode(obj)
else:
# Let the default Unicode -> string encoding (if any) take
# precedence.
obj = _bytes(obj)
return chararray(shape, itemsize=itemsize, unicode=unicode,
buffer=obj, order=order)
if isinstance(obj, (list, tuple)):
obj = numpy.asarray(obj)
if isinstance(obj, ndarray) and issubclass(obj.dtype.type, character):
# If we just have a vanilla chararray, create a chararray
# view around it.
if not isinstance(obj, chararray):
obj = obj.view(chararray)
if itemsize is None:
itemsize = obj.itemsize
# itemsize is in 8-bit chars, so for Unicode, we need
# to divide by the size of a single Unicode character,
# which for NumPy is always 4
if issubclass(obj.dtype.type, unicode_):
itemsize //= 4
if unicode is None:
if issubclass(obj.dtype.type, unicode_):
unicode = True
else:
unicode = False
if unicode:
dtype = unicode_
else:
dtype = string_
if order is not None:
obj = numpy.asarray(obj, order=order)
if (copy or
(itemsize != obj.itemsize) or
(not unicode and isinstance(obj, unicode_)) or
(unicode and isinstance(obj, string_))):
obj = obj.astype((dtype, long(itemsize)))
return obj
if isinstance(obj, ndarray) and issubclass(obj.dtype.type, object):
if itemsize is None:
# Since no itemsize was specified, convert the input array to
# a list so the ndarray constructor will automatically
# determine the itemsize for us.
obj = obj.tolist()
# Fall through to the default case
if unicode:
dtype = unicode_
else:
dtype = string_
if itemsize is None:
val = narray(obj, dtype=dtype, order=order, subok=True)
else:
val = narray(obj, dtype=(dtype, itemsize), order=order, subok=True)
return val.view(chararray)
def asarray(obj, itemsize=None, unicode=None, order=None):
"""
Convert the input to a `chararray`, copying the data only if
necessary.
Versus a regular NumPy array of type `str` or `unicode`, this
class adds the following functionality:
1) values automatically have whitespace removed from the end
when indexed
2) comparison operators automatically remove whitespace from the
end when comparing values
3) vectorized string operations are provided as methods
(e.g. `str.endswith`) and infix operators (e.g. ``+``, ``*``,``%``)
Parameters
----------
obj : array of str or unicode-like
itemsize : int, optional
`itemsize` is the number of characters per scalar in the
resulting array. If `itemsize` is None, and `obj` is an
object array or a Python list, the `itemsize` will be
automatically determined. If `itemsize` is provided and `obj`
is of type str or unicode, then the `obj` string will be
chunked into `itemsize` pieces.
unicode : bool, optional
When true, the resulting `chararray` can contain Unicode
characters, when false only 8-bit characters. If unicode is
`None` and `obj` is one of the following:
- a `chararray`,
- an ndarray of type `str` or 'unicode`
- a Python str or unicode object,
then the unicode setting of the output array will be
automatically determined.
order : {'C', 'F'}, optional
Specify the order of the array. If order is 'C' (default), then the
array will be in C-contiguous order (last-index varies the
fastest). If order is 'F', then the returned array
will be in Fortran-contiguous order (first-index varies the
fastest).
"""
return array(obj, itemsize, copy=False,
unicode=unicode, order=order)
| 67,369 | 24.13806 | 86 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/numerictypes.py
|
"""
numerictypes: Define the numeric type objects
This module is designed so "from numerictypes import \\*" is safe.
Exported symbols include:
Dictionary with all registered number types (including aliases):
typeDict
Type objects (not all will be available, depends on platform):
see variable sctypes for which ones you have
Bit-width names
int8 int16 int32 int64 int128
uint8 uint16 uint32 uint64 uint128
float16 float32 float64 float96 float128 float256
complex32 complex64 complex128 complex192 complex256 complex512
datetime64 timedelta64
c-based names
bool_
object_
void, str_, unicode_
byte, ubyte,
short, ushort
intc, uintc,
intp, uintp,
int_, uint,
longlong, ulonglong,
single, csingle,
float_, complex_,
longfloat, clongfloat,
As part of the type-hierarchy: xx -- is bit-width
generic
+-> bool_ (kind=b)
+-> number (kind=i)
| integer
| signedinteger (intxx)
| byte
| short
| intc
| intp int0
| int_
| longlong
+-> unsignedinteger (uintxx) (kind=u)
| ubyte
| ushort
| uintc
| uintp uint0
| uint_
| ulonglong
+-> inexact
| +-> floating (floatxx) (kind=f)
| | half
| | single
| | float_ (double)
| | longfloat
| \\-> complexfloating (complexxx) (kind=c)
| csingle (singlecomplex)
| complex_ (cfloat, cdouble)
| clongfloat (longcomplex)
+-> flexible
| character
| void (kind=V)
|
| str_ (string_, bytes_) (kind=S) [Python 2]
| unicode_ (kind=U) [Python 2]
|
| bytes_ (string_) (kind=S) [Python 3]
| str_ (unicode_) (kind=U) [Python 3]
|
\\-> object_ (not used much) (kind=O)
"""
from __future__ import division, absolute_import, print_function
import types as _types
import sys
import numbers
import warnings
from numpy.compat import bytes, long
from numpy.core.multiarray import (
typeinfo, ndarray, array, empty, dtype, datetime_data,
datetime_as_string, busday_offset, busday_count, is_busday,
busdaycalendar
)
# we add more at the bottom
__all__ = ['sctypeDict', 'sctypeNA', 'typeDict', 'typeNA', 'sctypes',
'ScalarType', 'obj2sctype', 'cast', 'nbytes', 'sctype2char',
'maximum_sctype', 'issctype', 'typecodes', 'find_common_type',
'issubdtype', 'datetime_data', 'datetime_as_string',
'busday_offset', 'busday_count', 'is_busday', 'busdaycalendar',
]
# we don't export these for import *, but we do want them accessible
# as numerictypes.bool, etc.
if sys.version_info[0] >= 3:
from builtins import bool, int, float, complex, object, str
unicode = str
else:
from __builtin__ import bool, int, float, complex, object, unicode, str
# String-handling utilities to avoid locale-dependence.
# "import string" is costly to import!
# Construct the translation tables directly
# "A" = chr(65), "a" = chr(97)
_all_chars = [chr(_m) for _m in range(256)]
_ascii_upper = _all_chars[65:65+26]
_ascii_lower = _all_chars[97:97+26]
LOWER_TABLE = "".join(_all_chars[:65] + _ascii_lower + _all_chars[65+26:])
UPPER_TABLE = "".join(_all_chars[:97] + _ascii_upper + _all_chars[97+26:])
def english_lower(s):
""" Apply English case rules to convert ASCII strings to all lower case.
This is an internal utility function to replace calls to str.lower() such
that we can avoid changing behavior with changing locales. In particular,
Turkish has distinct dotted and dotless variants of the Latin letter "I" in
both lowercase and uppercase. Thus, "I".lower() != "i" in a "tr" locale.
Parameters
----------
s : str
Returns
-------
lowered : str
Examples
--------
>>> from numpy.core.numerictypes import english_lower
>>> english_lower('ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789_')
'abcdefghijklmnopqrstuvwxyzabcdefghijklmnopqrstuvwxyz0123456789_'
>>> english_lower('')
''
"""
lowered = s.translate(LOWER_TABLE)
return lowered
def english_upper(s):
""" Apply English case rules to convert ASCII strings to all upper case.
This is an internal utility function to replace calls to str.upper() such
that we can avoid changing behavior with changing locales. In particular,
Turkish has distinct dotted and dotless variants of the Latin letter "I" in
both lowercase and uppercase. Thus, "i".upper() != "I" in a "tr" locale.
Parameters
----------
s : str
Returns
-------
uppered : str
Examples
--------
>>> from numpy.core.numerictypes import english_upper
>>> english_upper('ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789_')
'ABCDEFGHIJKLMNOPQRSTUVWXYZABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789_'
>>> english_upper('')
''
"""
uppered = s.translate(UPPER_TABLE)
return uppered
def english_capitalize(s):
""" Apply English case rules to convert the first character of an ASCII
string to upper case.
This is an internal utility function to replace calls to str.capitalize()
such that we can avoid changing behavior with changing locales.
Parameters
----------
s : str
Returns
-------
capitalized : str
Examples
--------
>>> from numpy.core.numerictypes import english_capitalize
>>> english_capitalize('int8')
'Int8'
>>> english_capitalize('Int8')
'Int8'
>>> english_capitalize('')
''
"""
if s:
return english_upper(s[0]) + s[1:]
else:
return s
sctypeDict = {} # Contains all leaf-node scalar types with aliases
sctypeNA = {} # Contails all leaf-node types -> numarray type equivalences
allTypes = {} # Collect the types we will add to the module here
def _evalname(name):
k = 0
for ch in name:
if ch in '0123456789':
break
k += 1
try:
bits = int(name[k:])
except ValueError:
bits = 0
base = name[:k]
return base, bits
def bitname(obj):
"""Return a bit-width name for a given type object"""
name = obj.__name__
base = ''
char = ''
try:
if name[-1] == '_':
newname = name[:-1]
else:
newname = name
info = typeinfo[english_upper(newname)]
assert(info[-1] == obj) # sanity check
bits = info[2]
except KeyError: # bit-width name
base, bits = _evalname(name)
char = base[0]
if name == 'bool_':
char = 'b'
base = 'bool'
elif name == 'void':
char = 'V'
base = 'void'
elif name == 'object_':
char = 'O'
base = 'object'
bits = 0
elif name == 'datetime64':
char = 'M'
elif name == 'timedelta64':
char = 'm'
if sys.version_info[0] >= 3:
if name == 'bytes_':
char = 'S'
base = 'bytes'
elif name == 'str_':
char = 'U'
base = 'str'
else:
if name == 'string_':
char = 'S'
base = 'string'
elif name == 'unicode_':
char = 'U'
base = 'unicode'
bytes = bits // 8
if char != '' and bytes != 0:
char = "%s%d" % (char, bytes)
return base, bits, char
def _add_types():
for a in typeinfo.keys():
name = english_lower(a)
if isinstance(typeinfo[a], tuple):
typeobj = typeinfo[a][-1]
# define C-name and insert typenum and typechar references also
allTypes[name] = typeobj
sctypeDict[name] = typeobj
sctypeDict[typeinfo[a][0]] = typeobj
sctypeDict[typeinfo[a][1]] = typeobj
else: # generic class
allTypes[name] = typeinfo[a]
_add_types()
def _add_aliases():
for a in typeinfo.keys():
name = english_lower(a)
if not isinstance(typeinfo[a], tuple):
continue
typeobj = typeinfo[a][-1]
# insert bit-width version for this class (if relevant)
base, bit, char = bitname(typeobj)
if base[-3:] == 'int' or char[0] in 'ui':
continue
if base != '':
myname = "%s%d" % (base, bit)
if ((name != 'longdouble' and name != 'clongdouble') or
myname not in allTypes.keys()):
allTypes[myname] = typeobj
sctypeDict[myname] = typeobj
if base == 'complex':
na_name = '%s%d' % (english_capitalize(base), bit//2)
elif base == 'bool':
na_name = english_capitalize(base)
sctypeDict[na_name] = typeobj
else:
na_name = "%s%d" % (english_capitalize(base), bit)
sctypeDict[na_name] = typeobj
sctypeNA[na_name] = typeobj
sctypeDict[na_name] = typeobj
sctypeNA[typeobj] = na_name
sctypeNA[typeinfo[a][0]] = na_name
if char != '':
sctypeDict[char] = typeobj
sctypeNA[char] = na_name
_add_aliases()
# Integers are handled so that the int32 and int64 types should agree
# exactly with NPY_INT32, NPY_INT64. We need to enforce the same checking
# as is done in arrayobject.h where the order of getting a bit-width match
# is long, longlong, int, short, char.
def _add_integer_aliases():
_ctypes = ['LONG', 'LONGLONG', 'INT', 'SHORT', 'BYTE']
for ctype in _ctypes:
val = typeinfo[ctype]
bits = val[2]
charname = 'i%d' % (bits//8,)
ucharname = 'u%d' % (bits//8,)
intname = 'int%d' % bits
UIntname = 'UInt%d' % bits
Intname = 'Int%d' % bits
uval = typeinfo['U'+ctype]
typeobj = val[-1]
utypeobj = uval[-1]
if intname not in allTypes.keys():
uintname = 'uint%d' % bits
allTypes[intname] = typeobj
allTypes[uintname] = utypeobj
sctypeDict[intname] = typeobj
sctypeDict[uintname] = utypeobj
sctypeDict[Intname] = typeobj
sctypeDict[UIntname] = utypeobj
sctypeDict[charname] = typeobj
sctypeDict[ucharname] = utypeobj
sctypeNA[Intname] = typeobj
sctypeNA[UIntname] = utypeobj
sctypeNA[charname] = typeobj
sctypeNA[ucharname] = utypeobj
sctypeNA[typeobj] = Intname
sctypeNA[utypeobj] = UIntname
sctypeNA[val[0]] = Intname
sctypeNA[uval[0]] = UIntname
_add_integer_aliases()
# We use these later
void = allTypes['void']
generic = allTypes['generic']
#
# Rework the Python names (so that float and complex and int are consistent
# with Python usage)
#
def _set_up_aliases():
type_pairs = [('complex_', 'cdouble'),
('int0', 'intp'),
('uint0', 'uintp'),
('single', 'float'),
('csingle', 'cfloat'),
('singlecomplex', 'cfloat'),
('float_', 'double'),
('intc', 'int'),
('uintc', 'uint'),
('int_', 'long'),
('uint', 'ulong'),
('cfloat', 'cdouble'),
('longfloat', 'longdouble'),
('clongfloat', 'clongdouble'),
('longcomplex', 'clongdouble'),
('bool_', 'bool'),
('unicode_', 'unicode'),
('object_', 'object')]
if sys.version_info[0] >= 3:
type_pairs.extend([('bytes_', 'string'),
('str_', 'unicode'),
('string_', 'string')])
else:
type_pairs.extend([('str_', 'string'),
('string_', 'string'),
('bytes_', 'string')])
for alias, t in type_pairs:
allTypes[alias] = allTypes[t]
sctypeDict[alias] = sctypeDict[t]
# Remove aliases overriding python types and modules
to_remove = ['ulong', 'object', 'unicode', 'int', 'long', 'float',
'complex', 'bool', 'string', 'datetime', 'timedelta']
if sys.version_info[0] >= 3:
# Py3K
to_remove.append('bytes')
to_remove.append('str')
to_remove.remove('unicode')
to_remove.remove('long')
for t in to_remove:
try:
del allTypes[t]
del sctypeDict[t]
except KeyError:
pass
_set_up_aliases()
# Now, construct dictionary to lookup character codes from types
_sctype2char_dict = {}
def _construct_char_code_lookup():
for name in typeinfo.keys():
tup = typeinfo[name]
if isinstance(tup, tuple):
if tup[0] not in ['p', 'P']:
_sctype2char_dict[tup[-1]] = tup[0]
_construct_char_code_lookup()
sctypes = {'int': [],
'uint':[],
'float':[],
'complex':[],
'others':[bool, object, bytes, unicode, void]}
def _add_array_type(typename, bits):
try:
t = allTypes['%s%d' % (typename, bits)]
except KeyError:
pass
else:
sctypes[typename].append(t)
def _set_array_types():
ibytes = [1, 2, 4, 8, 16, 32, 64]
fbytes = [2, 4, 8, 10, 12, 16, 32, 64]
for bytes in ibytes:
bits = 8*bytes
_add_array_type('int', bits)
_add_array_type('uint', bits)
for bytes in fbytes:
bits = 8*bytes
_add_array_type('float', bits)
_add_array_type('complex', 2*bits)
_gi = dtype('p')
if _gi.type not in sctypes['int']:
indx = 0
sz = _gi.itemsize
_lst = sctypes['int']
while (indx < len(_lst) and sz >= _lst[indx](0).itemsize):
indx += 1
sctypes['int'].insert(indx, _gi.type)
sctypes['uint'].insert(indx, dtype('P').type)
_set_array_types()
genericTypeRank = ['bool', 'int8', 'uint8', 'int16', 'uint16',
'int32', 'uint32', 'int64', 'uint64', 'int128',
'uint128', 'float16',
'float32', 'float64', 'float80', 'float96', 'float128',
'float256',
'complex32', 'complex64', 'complex128', 'complex160',
'complex192', 'complex256', 'complex512', 'object']
def maximum_sctype(t):
"""
Return the scalar type of highest precision of the same kind as the input.
Parameters
----------
t : dtype or dtype specifier
The input data type. This can be a `dtype` object or an object that
is convertible to a `dtype`.
Returns
-------
out : dtype
The highest precision data type of the same kind (`dtype.kind`) as `t`.
See Also
--------
obj2sctype, mintypecode, sctype2char
dtype
Examples
--------
>>> np.maximum_sctype(int)
<type 'numpy.int64'>
>>> np.maximum_sctype(np.uint8)
<type 'numpy.uint64'>
>>> np.maximum_sctype(complex)
<type 'numpy.complex192'>
>>> np.maximum_sctype(str)
<type 'numpy.string_'>
>>> np.maximum_sctype('i2')
<type 'numpy.int64'>
>>> np.maximum_sctype('f4')
<type 'numpy.float96'>
"""
g = obj2sctype(t)
if g is None:
return t
t = g
name = t.__name__
base, bits = _evalname(name)
if bits == 0:
return t
else:
return sctypes[base][-1]
def issctype(rep):
"""
Determines whether the given object represents a scalar data-type.
Parameters
----------
rep : any
If `rep` is an instance of a scalar dtype, True is returned. If not,
False is returned.
Returns
-------
out : bool
Boolean result of check whether `rep` is a scalar dtype.
See Also
--------
issubsctype, issubdtype, obj2sctype, sctype2char
Examples
--------
>>> np.issctype(np.int32)
True
>>> np.issctype(list)
False
>>> np.issctype(1.1)
False
Strings are also a scalar type:
>>> np.issctype(np.dtype('str'))
True
"""
if not isinstance(rep, (type, dtype)):
return False
try:
res = obj2sctype(rep)
if res and res != object_:
return True
return False
except Exception:
return False
def obj2sctype(rep, default=None):
"""
Return the scalar dtype or NumPy equivalent of Python type of an object.
Parameters
----------
rep : any
The object of which the type is returned.
default : any, optional
If given, this is returned for objects whose types can not be
determined. If not given, None is returned for those objects.
Returns
-------
dtype : dtype or Python type
The data type of `rep`.
See Also
--------
sctype2char, issctype, issubsctype, issubdtype, maximum_sctype
Examples
--------
>>> np.obj2sctype(np.int32)
<type 'numpy.int32'>
>>> np.obj2sctype(np.array([1., 2.]))
<type 'numpy.float64'>
>>> np.obj2sctype(np.array([1.j]))
<type 'numpy.complex128'>
>>> np.obj2sctype(dict)
<type 'numpy.object_'>
>>> np.obj2sctype('string')
<type 'numpy.string_'>
>>> np.obj2sctype(1, default=list)
<type 'list'>
"""
# prevent abtract classes being upcast
if isinstance(rep, type) and issubclass(rep, generic):
return rep
# extract dtype from arrays
if isinstance(rep, ndarray):
return rep.dtype.type
# fall back on dtype to convert
try:
res = dtype(rep)
except Exception:
return default
else:
return res.type
def issubclass_(arg1, arg2):
"""
Determine if a class is a subclass of a second class.
`issubclass_` is equivalent to the Python built-in ``issubclass``,
except that it returns False instead of raising a TypeError if one
of the arguments is not a class.
Parameters
----------
arg1 : class
Input class. True is returned if `arg1` is a subclass of `arg2`.
arg2 : class or tuple of classes.
Input class. If a tuple of classes, True is returned if `arg1` is a
subclass of any of the tuple elements.
Returns
-------
out : bool
Whether `arg1` is a subclass of `arg2` or not.
See Also
--------
issubsctype, issubdtype, issctype
Examples
--------
>>> np.issubclass_(np.int32, int)
True
>>> np.issubclass_(np.int32, float)
False
"""
try:
return issubclass(arg1, arg2)
except TypeError:
return False
def issubsctype(arg1, arg2):
"""
Determine if the first argument is a subclass of the second argument.
Parameters
----------
arg1, arg2 : dtype or dtype specifier
Data-types.
Returns
-------
out : bool
The result.
See Also
--------
issctype, issubdtype,obj2sctype
Examples
--------
>>> np.issubsctype('S8', str)
True
>>> np.issubsctype(np.array([1]), int)
True
>>> np.issubsctype(np.array([1]), float)
False
"""
return issubclass(obj2sctype(arg1), obj2sctype(arg2))
def issubdtype(arg1, arg2):
"""
Returns True if first argument is a typecode lower/equal in type hierarchy.
Parameters
----------
arg1, arg2 : dtype_like
dtype or string representing a typecode.
Returns
-------
out : bool
See Also
--------
issubsctype, issubclass_
numpy.core.numerictypes : Overview of numpy type hierarchy.
Examples
--------
>>> np.issubdtype('S1', np.string_)
True
>>> np.issubdtype(np.float64, np.float32)
False
"""
if not issubclass_(arg1, generic):
arg1 = dtype(arg1).type
if not issubclass_(arg2, generic):
arg2_orig = arg2
arg2 = dtype(arg2).type
if not isinstance(arg2_orig, dtype):
# weird deprecated behaviour, that tried to infer np.floating from
# float, and similar less obvious things, such as np.generic from
# basestring
mro = arg2.mro()
arg2 = mro[1] if len(mro) > 1 else mro[0]
def type_repr(x):
""" Helper to produce clear error messages """
if not isinstance(x, type):
return repr(x)
elif issubclass(x, generic):
return "np.{}".format(x.__name__)
else:
return x.__name__
# 1.14, 2017-08-01
warnings.warn(
"Conversion of the second argument of issubdtype from `{raw}` "
"to `{abstract}` is deprecated. In future, it will be treated "
"as `{concrete} == np.dtype({raw}).type`.".format(
raw=type_repr(arg2_orig),
abstract=type_repr(arg2),
concrete=type_repr(dtype(arg2_orig).type)
),
FutureWarning, stacklevel=2
)
return issubclass(arg1, arg2)
# This dictionary allows look up based on any alias for an array data-type
class _typedict(dict):
"""
Base object for a dictionary for look-up with any alias for an array dtype.
Instances of `_typedict` can not be used as dictionaries directly,
first they have to be populated.
"""
def __getitem__(self, obj):
return dict.__getitem__(self, obj2sctype(obj))
nbytes = _typedict()
_alignment = _typedict()
_maxvals = _typedict()
_minvals = _typedict()
def _construct_lookups():
for name, val in typeinfo.items():
if not isinstance(val, tuple):
continue
obj = val[-1]
nbytes[obj] = val[2] // 8
_alignment[obj] = val[3]
if (len(val) > 5):
_maxvals[obj] = val[4]
_minvals[obj] = val[5]
else:
_maxvals[obj] = None
_minvals[obj] = None
_construct_lookups()
def sctype2char(sctype):
"""
Return the string representation of a scalar dtype.
Parameters
----------
sctype : scalar dtype or object
If a scalar dtype, the corresponding string character is
returned. If an object, `sctype2char` tries to infer its scalar type
and then return the corresponding string character.
Returns
-------
typechar : str
The string character corresponding to the scalar type.
Raises
------
ValueError
If `sctype` is an object for which the type can not be inferred.
See Also
--------
obj2sctype, issctype, issubsctype, mintypecode
Examples
--------
>>> for sctype in [np.int32, float, complex, np.string_, np.ndarray]:
... print(np.sctype2char(sctype))
l
d
D
S
O
>>> x = np.array([1., 2-1.j])
>>> np.sctype2char(x)
'D'
>>> np.sctype2char(list)
'O'
"""
sctype = obj2sctype(sctype)
if sctype is None:
raise ValueError("unrecognized type")
return _sctype2char_dict[sctype]
# Create dictionary of casting functions that wrap sequences
# indexed by type or type character
cast = _typedict()
try:
ScalarType = [_types.IntType, _types.FloatType, _types.ComplexType,
_types.LongType, _types.BooleanType,
_types.StringType, _types.UnicodeType, _types.BufferType]
except AttributeError:
# Py3K
ScalarType = [int, float, complex, int, bool, bytes, str, memoryview]
ScalarType.extend(_sctype2char_dict.keys())
ScalarType = tuple(ScalarType)
for key in _sctype2char_dict.keys():
cast[key] = lambda x, k=key: array(x, copy=False).astype(k)
# Create the typestring lookup dictionary
_typestr = _typedict()
for key in _sctype2char_dict.keys():
if issubclass(key, allTypes['flexible']):
_typestr[key] = _sctype2char_dict[key]
else:
_typestr[key] = empty((1,), key).dtype.str[1:]
# Make sure all typestrings are in sctypeDict
for key, val in _typestr.items():
if val not in sctypeDict:
sctypeDict[val] = key
# Add additional strings to the sctypeDict
if sys.version_info[0] >= 3:
_toadd = ['int', 'float', 'complex', 'bool', 'object',
'str', 'bytes', 'object', ('a', allTypes['bytes_'])]
else:
_toadd = ['int', 'float', 'complex', 'bool', 'object', 'string',
('str', allTypes['string_']),
'unicode', 'object', ('a', allTypes['string_'])]
for name in _toadd:
if isinstance(name, tuple):
sctypeDict[name[0]] = name[1]
else:
sctypeDict[name] = allTypes['%s_' % name]
del _toadd, name
# Now add the types we've determined to this module
for key in allTypes:
globals()[key] = allTypes[key]
__all__.append(key)
del key
typecodes = {'Character':'c',
'Integer':'bhilqp',
'UnsignedInteger':'BHILQP',
'Float':'efdg',
'Complex':'FDG',
'AllInteger':'bBhHiIlLqQpP',
'AllFloat':'efdgFDG',
'Datetime': 'Mm',
'All':'?bhilqpBHILQPefdgFDGSUVOMm'}
# backwards compatibility --- deprecated name
typeDict = sctypeDict
typeNA = sctypeNA
# b -> boolean
# u -> unsigned integer
# i -> signed integer
# f -> floating point
# c -> complex
# M -> datetime
# m -> timedelta
# S -> string
# U -> Unicode string
# V -> record
# O -> Python object
_kind_list = ['b', 'u', 'i', 'f', 'c', 'S', 'U', 'V', 'O', 'M', 'm']
__test_types = '?'+typecodes['AllInteger'][:-2]+typecodes['AllFloat']+'O'
__len_test_types = len(__test_types)
# Keep incrementing until a common type both can be coerced to
# is found. Otherwise, return None
def _find_common_coerce(a, b):
if a > b:
return a
try:
thisind = __test_types.index(a.char)
except ValueError:
return None
return _can_coerce_all([a, b], start=thisind)
# Find a data-type that all data-types in a list can be coerced to
def _can_coerce_all(dtypelist, start=0):
N = len(dtypelist)
if N == 0:
return None
if N == 1:
return dtypelist[0]
thisind = start
while thisind < __len_test_types:
newdtype = dtype(__test_types[thisind])
numcoerce = len([x for x in dtypelist if newdtype >= x])
if numcoerce == N:
return newdtype
thisind += 1
return None
def _register_types():
numbers.Integral.register(integer)
numbers.Complex.register(inexact)
numbers.Real.register(floating)
numbers.Number.register(number)
_register_types()
def find_common_type(array_types, scalar_types):
"""
Determine common type following standard coercion rules.
Parameters
----------
array_types : sequence
A list of dtypes or dtype convertible objects representing arrays.
scalar_types : sequence
A list of dtypes or dtype convertible objects representing scalars.
Returns
-------
datatype : dtype
The common data type, which is the maximum of `array_types` ignoring
`scalar_types`, unless the maximum of `scalar_types` is of a
different kind (`dtype.kind`). If the kind is not understood, then
None is returned.
See Also
--------
dtype, common_type, can_cast, mintypecode
Examples
--------
>>> np.find_common_type([], [np.int64, np.float32, complex])
dtype('complex128')
>>> np.find_common_type([np.int64, np.float32], [])
dtype('float64')
The standard casting rules ensure that a scalar cannot up-cast an
array unless the scalar is of a fundamentally different kind of data
(i.e. under a different hierarchy in the data type hierarchy) then
the array:
>>> np.find_common_type([np.float32], [np.int64, np.float64])
dtype('float32')
Complex is of a different type, so it up-casts the float in the
`array_types` argument:
>>> np.find_common_type([np.float32], [complex])
dtype('complex128')
Type specifier strings are convertible to dtypes and can therefore
be used instead of dtypes:
>>> np.find_common_type(['f4', 'f4', 'i4'], ['c8'])
dtype('complex128')
"""
array_types = [dtype(x) for x in array_types]
scalar_types = [dtype(x) for x in scalar_types]
maxa = _can_coerce_all(array_types)
maxsc = _can_coerce_all(scalar_types)
if maxa is None:
return maxsc
if maxsc is None:
return maxa
try:
index_a = _kind_list.index(maxa.kind)
index_sc = _kind_list.index(maxsc.kind)
except ValueError:
return None
if index_sc > index_a:
return _find_common_coerce(maxsc, maxa)
else:
return maxa
| 29,102 | 27.118841 | 88 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/info.py
|
"""Defines a multi-dimensional array and useful procedures for Numerical computation.
Functions
- array - NumPy Array construction
- zeros - Return an array of all zeros
- empty - Return an uninitialized array
- shape - Return shape of sequence or array
- rank - Return number of dimensions
- size - Return number of elements in entire array or a
certain dimension
- fromstring - Construct array from (byte) string
- take - Select sub-arrays using sequence of indices
- put - Set sub-arrays using sequence of 1-D indices
- putmask - Set portion of arrays using a mask
- reshape - Return array with new shape
- repeat - Repeat elements of array
- choose - Construct new array from indexed array tuple
- correlate - Correlate two 1-d arrays
- searchsorted - Search for element in 1-d array
- sum - Total sum over a specified dimension
- average - Average, possibly weighted, over axis or array.
- cumsum - Cumulative sum over a specified dimension
- product - Total product over a specified dimension
- cumproduct - Cumulative product over a specified dimension
- alltrue - Logical and over an entire axis
- sometrue - Logical or over an entire axis
- allclose - Tests if sequences are essentially equal
More Functions:
- arange - Return regularly spaced array
- asarray - Guarantee NumPy array
- convolve - Convolve two 1-d arrays
- swapaxes - Exchange axes
- concatenate - Join arrays together
- transpose - Permute axes
- sort - Sort elements of array
- argsort - Indices of sorted array
- argmax - Index of largest value
- argmin - Index of smallest value
- inner - Innerproduct of two arrays
- dot - Dot product (matrix multiplication)
- outer - Outerproduct of two arrays
- resize - Return array with arbitrary new shape
- indices - Tuple of indices
- fromfunction - Construct array from universal function
- diagonal - Return diagonal array
- trace - Trace of array
- dump - Dump array to file object (pickle)
- dumps - Return pickled string representing data
- load - Return array stored in file object
- loads - Return array from pickled string
- ravel - Return array as 1-D
- nonzero - Indices of nonzero elements for 1-D array
- shape - Shape of array
- where - Construct array from binary result
- compress - Elements of array where condition is true
- clip - Clip array between two values
- ones - Array of all ones
- identity - 2-D identity array (matrix)
(Universal) Math Functions
add logical_or exp
subtract logical_xor log
multiply logical_not log10
divide maximum sin
divide_safe minimum sinh
conjugate bitwise_and sqrt
power bitwise_or tan
absolute bitwise_xor tanh
negative invert ceil
greater left_shift fabs
greater_equal right_shift floor
less arccos arctan2
less_equal arcsin fmod
equal arctan hypot
not_equal cos around
logical_and cosh sign
arccosh arcsinh arctanh
"""
from __future__ import division, absolute_import, print_function
depends = ['testing']
global_symbols = ['*']
| 4,692 | 52.329545 | 85 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/__init__.py
|
from __future__ import division, absolute_import, print_function
from .info import __doc__
from numpy.version import version as __version__
# disables OpenBLAS affinity setting of the main thread that limits
# python threads or processes to one core
import os
env_added = []
for envkey in ['OPENBLAS_MAIN_FREE', 'GOTOBLAS_MAIN_FREE']:
if envkey not in os.environ:
os.environ[envkey] = '1'
env_added.append(envkey)
try:
from . import multiarray
except ImportError as exc:
msg = """
Importing the multiarray numpy extension module failed. Most
likely you are trying to import a failed build of numpy.
If you're working with a numpy git repo, try `git clean -xdf` (removes all
files not under version control). Otherwise reinstall numpy.
Original error was: %s
""" % (exc,)
raise ImportError(msg)
finally:
for envkey in env_added:
del os.environ[envkey]
del envkey
del env_added
del os
from . import umath
from . import _internal # for freeze programs
from . import numerictypes as nt
multiarray.set_typeDict(nt.sctypeDict)
from . import numeric
from .numeric import *
from . import fromnumeric
from .fromnumeric import *
from . import defchararray as char
from . import records as rec
from .records import *
from .memmap import *
from .defchararray import chararray
from . import function_base
from .function_base import *
from . import machar
from .machar import *
from . import getlimits
from .getlimits import *
from . import shape_base
from .shape_base import *
from . import einsumfunc
from .einsumfunc import *
del nt
from .fromnumeric import amax as max, amin as min, round_ as round
from .numeric import absolute as abs
__all__ = ['char', 'rec', 'memmap']
__all__ += numeric.__all__
__all__ += fromnumeric.__all__
__all__ += rec.__all__
__all__ += ['chararray']
__all__ += function_base.__all__
__all__ += machar.__all__
__all__ += getlimits.__all__
__all__ += shape_base.__all__
__all__ += einsumfunc.__all__
from numpy.testing import _numpy_tester
test = _numpy_tester().test
bench = _numpy_tester().bench
# Make it possible so that ufuncs can be pickled
# Here are the loading and unloading functions
# The name numpy.core._ufunc_reconstruct must be
# available for unpickling to work.
def _ufunc_reconstruct(module, name):
# The `fromlist` kwarg is required to ensure that `mod` points to the
# inner-most module rather than the parent package when module name is
# nested. This makes it possible to pickle non-toplevel ufuncs such as
# scipy.special.expit for instance.
mod = __import__(module, fromlist=[name])
return getattr(mod, name)
def _ufunc_reduce(func):
from pickle import whichmodule
name = func.__name__
return _ufunc_reconstruct, (whichmodule(func, name), name)
import sys
if sys.version_info[0] >= 3:
import copyreg
else:
import copy_reg as copyreg
copyreg.pickle(ufunc, _ufunc_reduce, _ufunc_reconstruct)
# Unclutter namespace (must keep _ufunc_reconstruct for unpickling)
del copyreg
del sys
del _ufunc_reduce
| 3,044 | 27.457944 | 74 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/machar.py
|
"""
Machine arithmetics - determine the parameters of the
floating-point arithmetic system
Author: Pearu Peterson, September 2003
"""
from __future__ import division, absolute_import, print_function
__all__ = ['MachAr']
from numpy.core.fromnumeric import any
from numpy.core.numeric import errstate
# Need to speed this up...especially for longfloat
class MachAr(object):
"""
Diagnosing machine parameters.
Attributes
----------
ibeta : int
Radix in which numbers are represented.
it : int
Number of base-`ibeta` digits in the floating point mantissa M.
machep : int
Exponent of the smallest (most negative) power of `ibeta` that,
added to 1.0, gives something different from 1.0
eps : float
Floating-point number ``beta**machep`` (floating point precision)
negep : int
Exponent of the smallest power of `ibeta` that, subtracted
from 1.0, gives something different from 1.0.
epsneg : float
Floating-point number ``beta**negep``.
iexp : int
Number of bits in the exponent (including its sign and bias).
minexp : int
Smallest (most negative) power of `ibeta` consistent with there
being no leading zeros in the mantissa.
xmin : float
Floating point number ``beta**minexp`` (the smallest [in
magnitude] usable floating value).
maxexp : int
Smallest (positive) power of `ibeta` that causes overflow.
xmax : float
``(1-epsneg) * beta**maxexp`` (the largest [in magnitude]
usable floating value).
irnd : int
In ``range(6)``, information on what kind of rounding is done
in addition, and on how underflow is handled.
ngrd : int
Number of 'guard digits' used when truncating the product
of two mantissas to fit the representation.
epsilon : float
Same as `eps`.
tiny : float
Same as `xmin`.
huge : float
Same as `xmax`.
precision : float
``- int(-log10(eps))``
resolution : float
``- 10**(-precision)``
Parameters
----------
float_conv : function, optional
Function that converts an integer or integer array to a float
or float array. Default is `float`.
int_conv : function, optional
Function that converts a float or float array to an integer or
integer array. Default is `int`.
float_to_float : function, optional
Function that converts a float array to float. Default is `float`.
Note that this does not seem to do anything useful in the current
implementation.
float_to_str : function, optional
Function that converts a single float to a string. Default is
``lambda v:'%24.16e' %v``.
title : str, optional
Title that is printed in the string representation of `MachAr`.
See Also
--------
finfo : Machine limits for floating point types.
iinfo : Machine limits for integer types.
References
----------
.. [1] Press, Teukolsky, Vetterling and Flannery,
"Numerical Recipes in C++," 2nd ed,
Cambridge University Press, 2002, p. 31.
"""
def __init__(self, float_conv=float,int_conv=int,
float_to_float=float,
float_to_str=lambda v:'%24.16e' % v,
title='Python floating point number'):
"""
float_conv - convert integer to float (array)
int_conv - convert float (array) to integer
float_to_float - convert float array to float
float_to_str - convert array float to str
title - description of used floating point numbers
"""
# We ignore all errors here because we are purposely triggering
# underflow to detect the properties of the runninng arch.
with errstate(under='ignore'):
self._do_init(float_conv, int_conv, float_to_float, float_to_str, title)
def _do_init(self, float_conv, int_conv, float_to_float, float_to_str, title):
max_iterN = 10000
msg = "Did not converge after %d tries with %s"
one = float_conv(1)
two = one + one
zero = one - one
# Do we really need to do this? Aren't they 2 and 2.0?
# Determine ibeta and beta
a = one
for _ in range(max_iterN):
a = a + a
temp = a + one
temp1 = temp - a
if any(temp1 - one != zero):
break
else:
raise RuntimeError(msg % (_, one.dtype))
b = one
for _ in range(max_iterN):
b = b + b
temp = a + b
itemp = int_conv(temp-a)
if any(itemp != 0):
break
else:
raise RuntimeError(msg % (_, one.dtype))
ibeta = itemp
beta = float_conv(ibeta)
# Determine it and irnd
it = -1
b = one
for _ in range(max_iterN):
it = it + 1
b = b * beta
temp = b + one
temp1 = temp - b
if any(temp1 - one != zero):
break
else:
raise RuntimeError(msg % (_, one.dtype))
betah = beta / two
a = one
for _ in range(max_iterN):
a = a + a
temp = a + one
temp1 = temp - a
if any(temp1 - one != zero):
break
else:
raise RuntimeError(msg % (_, one.dtype))
temp = a + betah
irnd = 0
if any(temp-a != zero):
irnd = 1
tempa = a + beta
temp = tempa + betah
if irnd == 0 and any(temp-tempa != zero):
irnd = 2
# Determine negep and epsneg
negep = it + 3
betain = one / beta
a = one
for i in range(negep):
a = a * betain
b = a
for _ in range(max_iterN):
temp = one - a
if any(temp-one != zero):
break
a = a * beta
negep = negep - 1
# Prevent infinite loop on PPC with gcc 4.0:
if negep < 0:
raise RuntimeError("could not determine machine tolerance "
"for 'negep', locals() -> %s" % (locals()))
else:
raise RuntimeError(msg % (_, one.dtype))
negep = -negep
epsneg = a
# Determine machep and eps
machep = - it - 3
a = b
for _ in range(max_iterN):
temp = one + a
if any(temp-one != zero):
break
a = a * beta
machep = machep + 1
else:
raise RuntimeError(msg % (_, one.dtype))
eps = a
# Determine ngrd
ngrd = 0
temp = one + eps
if irnd == 0 and any(temp*one - one != zero):
ngrd = 1
# Determine iexp
i = 0
k = 1
z = betain
t = one + eps
nxres = 0
for _ in range(max_iterN):
y = z
z = y*y
a = z*one # Check here for underflow
temp = z*t
if any(a+a == zero) or any(abs(z) >= y):
break
temp1 = temp * betain
if any(temp1*beta == z):
break
i = i + 1
k = k + k
else:
raise RuntimeError(msg % (_, one.dtype))
if ibeta != 10:
iexp = i + 1
mx = k + k
else:
iexp = 2
iz = ibeta
while k >= iz:
iz = iz * ibeta
iexp = iexp + 1
mx = iz + iz - 1
# Determine minexp and xmin
for _ in range(max_iterN):
xmin = y
y = y * betain
a = y * one
temp = y * t
if any((a + a) != zero) and any(abs(y) < xmin):
k = k + 1
temp1 = temp * betain
if any(temp1*beta == y) and any(temp != y):
nxres = 3
xmin = y
break
else:
break
else:
raise RuntimeError(msg % (_, one.dtype))
minexp = -k
# Determine maxexp, xmax
if mx <= k + k - 3 and ibeta != 10:
mx = mx + mx
iexp = iexp + 1
maxexp = mx + minexp
irnd = irnd + nxres
if irnd >= 2:
maxexp = maxexp - 2
i = maxexp + minexp
if ibeta == 2 and not i:
maxexp = maxexp - 1
if i > 20:
maxexp = maxexp - 1
if any(a != y):
maxexp = maxexp - 2
xmax = one - epsneg
if any(xmax*one != xmax):
xmax = one - beta*epsneg
xmax = xmax / (xmin*beta*beta*beta)
i = maxexp + minexp + 3
for j in range(i):
if ibeta == 2:
xmax = xmax + xmax
else:
xmax = xmax * beta
self.ibeta = ibeta
self.it = it
self.negep = negep
self.epsneg = float_to_float(epsneg)
self._str_epsneg = float_to_str(epsneg)
self.machep = machep
self.eps = float_to_float(eps)
self._str_eps = float_to_str(eps)
self.ngrd = ngrd
self.iexp = iexp
self.minexp = minexp
self.xmin = float_to_float(xmin)
self._str_xmin = float_to_str(xmin)
self.maxexp = maxexp
self.xmax = float_to_float(xmax)
self._str_xmax = float_to_str(xmax)
self.irnd = irnd
self.title = title
# Commonly used parameters
self.epsilon = self.eps
self.tiny = self.xmin
self.huge = self.xmax
import math
self.precision = int(-math.log10(float_to_float(self.eps)))
ten = two + two + two + two + two
resolution = ten ** (-self.precision)
self.resolution = float_to_float(resolution)
self._str_resolution = float_to_str(resolution)
def __str__(self):
fmt = (
'Machine parameters for %(title)s\n'
'---------------------------------------------------------------------\n'
'ibeta=%(ibeta)s it=%(it)s iexp=%(iexp)s ngrd=%(ngrd)s irnd=%(irnd)s\n'
'machep=%(machep)s eps=%(_str_eps)s (beta**machep == epsilon)\n'
'negep =%(negep)s epsneg=%(_str_epsneg)s (beta**epsneg)\n'
'minexp=%(minexp)s xmin=%(_str_xmin)s (beta**minexp == tiny)\n'
'maxexp=%(maxexp)s xmax=%(_str_xmax)s ((1-epsneg)*beta**maxexp == huge)\n'
'---------------------------------------------------------------------\n'
)
return fmt % self.__dict__
if __name__ == '__main__':
print(MachAr())
| 10,789 | 30.457726 | 88 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/fromnumeric.py
|
"""Module containing non-deprecated functions borrowed from Numeric.
"""
from __future__ import division, absolute_import, print_function
import types
import warnings
import numpy as np
from .. import VisibleDeprecationWarning
from . import multiarray as mu
from . import umath as um
from . import numerictypes as nt
from .numeric import asarray, array, asanyarray, concatenate
from . import _methods
_dt_ = nt.sctype2char
# functions that are methods
__all__ = [
'alen', 'all', 'alltrue', 'amax', 'amin', 'any', 'argmax',
'argmin', 'argpartition', 'argsort', 'around', 'choose', 'clip',
'compress', 'cumprod', 'cumproduct', 'cumsum', 'diagonal', 'mean',
'ndim', 'nonzero', 'partition', 'prod', 'product', 'ptp', 'put',
'rank', 'ravel', 'repeat', 'reshape', 'resize', 'round_',
'searchsorted', 'shape', 'size', 'sometrue', 'sort', 'squeeze',
'std', 'sum', 'swapaxes', 'take', 'trace', 'transpose', 'var',
]
_gentype = types.GeneratorType
# save away Python sum
_sum_ = sum
# functions that are now methods
def _wrapit(obj, method, *args, **kwds):
try:
wrap = obj.__array_wrap__
except AttributeError:
wrap = None
result = getattr(asarray(obj), method)(*args, **kwds)
if wrap:
if not isinstance(result, mu.ndarray):
result = asarray(result)
result = wrap(result)
return result
def _wrapfunc(obj, method, *args, **kwds):
try:
return getattr(obj, method)(*args, **kwds)
# An AttributeError occurs if the object does not have
# such a method in its class.
# A TypeError occurs if the object does have such a method
# in its class, but its signature is not identical to that
# of NumPy's. This situation has occurred in the case of
# a downstream library like 'pandas'.
except (AttributeError, TypeError):
return _wrapit(obj, method, *args, **kwds)
def take(a, indices, axis=None, out=None, mode='raise'):
"""
Take elements from an array along an axis.
When axis is not None, this function does the same thing as "fancy"
indexing (indexing arrays using arrays); however, it can be easier to use
if you need elements along a given axis. A call such as
``np.take(arr, indices, axis=3)`` is equivalent to
``arr[:,:,:,indices,...]``.
Explained without fancy indexing, this is equivalent to the following use
of `ndindex`, which sets each of ``ii``, ``jj``, and ``kk`` to a tuple of
indices::
Ni, Nk = a.shape[:axis], a.shape[axis+1:]
Nj = indices.shape
for ii in ndindex(Ni):
for jj in ndindex(Nj):
for kk in ndindex(Nk):
out[ii + jj + kk] = a[ii + (indices[jj],) + kk]
Parameters
----------
a : array_like (Ni..., M, Nk...)
The source array.
indices : array_like (Nj...)
The indices of the values to extract.
.. versionadded:: 1.8.0
Also allow scalars for indices.
axis : int, optional
The axis over which to select values. By default, the flattened
input array is used.
out : ndarray, optional (Ni..., Nj..., Nk...)
If provided, the result will be placed in this array. It should
be of the appropriate shape and dtype.
mode : {'raise', 'wrap', 'clip'}, optional
Specifies how out-of-bounds indices will behave.
* 'raise' -- raise an error (default)
* 'wrap' -- wrap around
* 'clip' -- clip to the range
'clip' mode means that all indices that are too large are replaced
by the index that addresses the last element along that axis. Note
that this disables indexing with negative numbers.
Returns
-------
out : ndarray (Ni..., Nj..., Nk...)
The returned array has the same type as `a`.
See Also
--------
compress : Take elements using a boolean mask
ndarray.take : equivalent method
Notes
-----
By eliminating the inner loop in the description above, and using `s_` to
build simple slice objects, `take` can be expressed in terms of applying
fancy indexing to each 1-d slice::
Ni, Nk = a.shape[:axis], a.shape[axis+1:]
for ii in ndindex(Ni):
for kk in ndindex(Nj):
out[ii + s_[...,] + kk] = a[ii + s_[:,] + kk][indices]
For this reason, it is equivalent to (but faster than) the following use
of `apply_along_axis`::
out = np.apply_along_axis(lambda a_1d: a_1d[indices], axis, a)
Examples
--------
>>> a = [4, 3, 5, 7, 6, 8]
>>> indices = [0, 1, 4]
>>> np.take(a, indices)
array([4, 3, 6])
In this example if `a` is an ndarray, "fancy" indexing can be used.
>>> a = np.array(a)
>>> a[indices]
array([4, 3, 6])
If `indices` is not one dimensional, the output also has these dimensions.
>>> np.take(a, [[0, 1], [2, 3]])
array([[4, 3],
[5, 7]])
"""
return _wrapfunc(a, 'take', indices, axis=axis, out=out, mode=mode)
# not deprecated --- copy if necessary, view otherwise
def reshape(a, newshape, order='C'):
"""
Gives a new shape to an array without changing its data.
Parameters
----------
a : array_like
Array to be reshaped.
newshape : int or tuple of ints
The new shape should be compatible with the original shape. If
an integer, then the result will be a 1-D array of that length.
One shape dimension can be -1. In this case, the value is
inferred from the length of the array and remaining dimensions.
order : {'C', 'F', 'A'}, optional
Read the elements of `a` using this index order, and place the
elements into the reshaped array using this index order. 'C'
means to read / write the elements using C-like index order,
with the last axis index changing fastest, back to the first
axis index changing slowest. 'F' means to read / write the
elements using Fortran-like index order, with the first index
changing fastest, and the last index changing slowest. Note that
the 'C' and 'F' options take no account of the memory layout of
the underlying array, and only refer to the order of indexing.
'A' means to read / write the elements in Fortran-like index
order if `a` is Fortran *contiguous* in memory, C-like order
otherwise.
Returns
-------
reshaped_array : ndarray
This will be a new view object if possible; otherwise, it will
be a copy. Note there is no guarantee of the *memory layout* (C- or
Fortran- contiguous) of the returned array.
See Also
--------
ndarray.reshape : Equivalent method.
Notes
-----
It is not always possible to change the shape of an array without
copying the data. If you want an error to be raised when the data is copied,
you should assign the new shape to the shape attribute of the array::
>>> a = np.zeros((10, 2))
# A transpose makes the array non-contiguous
>>> b = a.T
# Taking a view makes it possible to modify the shape without modifying
# the initial object.
>>> c = b.view()
>>> c.shape = (20)
AttributeError: incompatible shape for a non-contiguous array
The `order` keyword gives the index ordering both for *fetching* the values
from `a`, and then *placing* the values into the output array.
For example, let's say you have an array:
>>> a = np.arange(6).reshape((3, 2))
>>> a
array([[0, 1],
[2, 3],
[4, 5]])
You can think of reshaping as first raveling the array (using the given
index order), then inserting the elements from the raveled array into the
new array using the same kind of index ordering as was used for the
raveling.
>>> np.reshape(a, (2, 3)) # C-like index ordering
array([[0, 1, 2],
[3, 4, 5]])
>>> np.reshape(np.ravel(a), (2, 3)) # equivalent to C ravel then C reshape
array([[0, 1, 2],
[3, 4, 5]])
>>> np.reshape(a, (2, 3), order='F') # Fortran-like index ordering
array([[0, 4, 3],
[2, 1, 5]])
>>> np.reshape(np.ravel(a, order='F'), (2, 3), order='F')
array([[0, 4, 3],
[2, 1, 5]])
Examples
--------
>>> a = np.array([[1,2,3], [4,5,6]])
>>> np.reshape(a, 6)
array([1, 2, 3, 4, 5, 6])
>>> np.reshape(a, 6, order='F')
array([1, 4, 2, 5, 3, 6])
>>> np.reshape(a, (3,-1)) # the unspecified value is inferred to be 2
array([[1, 2],
[3, 4],
[5, 6]])
"""
return _wrapfunc(a, 'reshape', newshape, order=order)
def choose(a, choices, out=None, mode='raise'):
"""
Construct an array from an index array and a set of arrays to choose from.
First of all, if confused or uncertain, definitely look at the Examples -
in its full generality, this function is less simple than it might
seem from the following code description (below ndi =
`numpy.lib.index_tricks`):
``np.choose(a,c) == np.array([c[a[I]][I] for I in ndi.ndindex(a.shape)])``.
But this omits some subtleties. Here is a fully general summary:
Given an "index" array (`a`) of integers and a sequence of `n` arrays
(`choices`), `a` and each choice array are first broadcast, as necessary,
to arrays of a common shape; calling these *Ba* and *Bchoices[i], i =
0,...,n-1* we have that, necessarily, ``Ba.shape == Bchoices[i].shape``
for each `i`. Then, a new array with shape ``Ba.shape`` is created as
follows:
* if ``mode=raise`` (the default), then, first of all, each element of
`a` (and thus `Ba`) must be in the range `[0, n-1]`; now, suppose that
`i` (in that range) is the value at the `(j0, j1, ..., jm)` position
in `Ba` - then the value at the same position in the new array is the
value in `Bchoices[i]` at that same position;
* if ``mode=wrap``, values in `a` (and thus `Ba`) may be any (signed)
integer; modular arithmetic is used to map integers outside the range
`[0, n-1]` back into that range; and then the new array is constructed
as above;
* if ``mode=clip``, values in `a` (and thus `Ba`) may be any (signed)
integer; negative integers are mapped to 0; values greater than `n-1`
are mapped to `n-1`; and then the new array is constructed as above.
Parameters
----------
a : int array
This array must contain integers in `[0, n-1]`, where `n` is the number
of choices, unless ``mode=wrap`` or ``mode=clip``, in which cases any
integers are permissible.
choices : sequence of arrays
Choice arrays. `a` and all of the choices must be broadcastable to the
same shape. If `choices` is itself an array (not recommended), then
its outermost dimension (i.e., the one corresponding to
``choices.shape[0]``) is taken as defining the "sequence".
out : array, optional
If provided, the result will be inserted into this array. It should
be of the appropriate shape and dtype.
mode : {'raise' (default), 'wrap', 'clip'}, optional
Specifies how indices outside `[0, n-1]` will be treated:
* 'raise' : an exception is raised
* 'wrap' : value becomes value mod `n`
* 'clip' : values < 0 are mapped to 0, values > n-1 are mapped to n-1
Returns
-------
merged_array : array
The merged result.
Raises
------
ValueError: shape mismatch
If `a` and each choice array are not all broadcastable to the same
shape.
See Also
--------
ndarray.choose : equivalent method
Notes
-----
To reduce the chance of misinterpretation, even though the following
"abuse" is nominally supported, `choices` should neither be, nor be
thought of as, a single array, i.e., the outermost sequence-like container
should be either a list or a tuple.
Examples
--------
>>> choices = [[0, 1, 2, 3], [10, 11, 12, 13],
... [20, 21, 22, 23], [30, 31, 32, 33]]
>>> np.choose([2, 3, 1, 0], choices
... # the first element of the result will be the first element of the
... # third (2+1) "array" in choices, namely, 20; the second element
... # will be the second element of the fourth (3+1) choice array, i.e.,
... # 31, etc.
... )
array([20, 31, 12, 3])
>>> np.choose([2, 4, 1, 0], choices, mode='clip') # 4 goes to 3 (4-1)
array([20, 31, 12, 3])
>>> # because there are 4 choice arrays
>>> np.choose([2, 4, 1, 0], choices, mode='wrap') # 4 goes to (4 mod 4)
array([20, 1, 12, 3])
>>> # i.e., 0
A couple examples illustrating how choose broadcasts:
>>> a = [[1, 0, 1], [0, 1, 0], [1, 0, 1]]
>>> choices = [-10, 10]
>>> np.choose(a, choices)
array([[ 10, -10, 10],
[-10, 10, -10],
[ 10, -10, 10]])
>>> # With thanks to Anne Archibald
>>> a = np.array([0, 1]).reshape((2,1,1))
>>> c1 = np.array([1, 2, 3]).reshape((1,3,1))
>>> c2 = np.array([-1, -2, -3, -4, -5]).reshape((1,1,5))
>>> np.choose(a, (c1, c2)) # result is 2x3x5, res[0,:,:]=c1, res[1,:,:]=c2
array([[[ 1, 1, 1, 1, 1],
[ 2, 2, 2, 2, 2],
[ 3, 3, 3, 3, 3]],
[[-1, -2, -3, -4, -5],
[-1, -2, -3, -4, -5],
[-1, -2, -3, -4, -5]]])
"""
return _wrapfunc(a, 'choose', choices, out=out, mode=mode)
def repeat(a, repeats, axis=None):
"""
Repeat elements of an array.
Parameters
----------
a : array_like
Input array.
repeats : int or array of ints
The number of repetitions for each element. `repeats` is broadcasted
to fit the shape of the given axis.
axis : int, optional
The axis along which to repeat values. By default, use the
flattened input array, and return a flat output array.
Returns
-------
repeated_array : ndarray
Output array which has the same shape as `a`, except along
the given axis.
See Also
--------
tile : Tile an array.
Examples
--------
>>> np.repeat(3, 4)
array([3, 3, 3, 3])
>>> x = np.array([[1,2],[3,4]])
>>> np.repeat(x, 2)
array([1, 1, 2, 2, 3, 3, 4, 4])
>>> np.repeat(x, 3, axis=1)
array([[1, 1, 1, 2, 2, 2],
[3, 3, 3, 4, 4, 4]])
>>> np.repeat(x, [1, 2], axis=0)
array([[1, 2],
[3, 4],
[3, 4]])
"""
return _wrapfunc(a, 'repeat', repeats, axis=axis)
def put(a, ind, v, mode='raise'):
"""
Replaces specified elements of an array with given values.
The indexing works on the flattened target array. `put` is roughly
equivalent to:
::
a.flat[ind] = v
Parameters
----------
a : ndarray
Target array.
ind : array_like
Target indices, interpreted as integers.
v : array_like
Values to place in `a` at target indices. If `v` is shorter than
`ind` it will be repeated as necessary.
mode : {'raise', 'wrap', 'clip'}, optional
Specifies how out-of-bounds indices will behave.
* 'raise' -- raise an error (default)
* 'wrap' -- wrap around
* 'clip' -- clip to the range
'clip' mode means that all indices that are too large are replaced
by the index that addresses the last element along that axis. Note
that this disables indexing with negative numbers.
See Also
--------
putmask, place
Examples
--------
>>> a = np.arange(5)
>>> np.put(a, [0, 2], [-44, -55])
>>> a
array([-44, 1, -55, 3, 4])
>>> a = np.arange(5)
>>> np.put(a, 22, -5, mode='clip')
>>> a
array([ 0, 1, 2, 3, -5])
"""
try:
put = a.put
except AttributeError:
raise TypeError("argument 1 must be numpy.ndarray, "
"not {name}".format(name=type(a).__name__))
return put(ind, v, mode=mode)
def swapaxes(a, axis1, axis2):
"""
Interchange two axes of an array.
Parameters
----------
a : array_like
Input array.
axis1 : int
First axis.
axis2 : int
Second axis.
Returns
-------
a_swapped : ndarray
For NumPy >= 1.10.0, if `a` is an ndarray, then a view of `a` is
returned; otherwise a new array is created. For earlier NumPy
versions a view of `a` is returned only if the order of the
axes is changed, otherwise the input array is returned.
Examples
--------
>>> x = np.array([[1,2,3]])
>>> np.swapaxes(x,0,1)
array([[1],
[2],
[3]])
>>> x = np.array([[[0,1],[2,3]],[[4,5],[6,7]]])
>>> x
array([[[0, 1],
[2, 3]],
[[4, 5],
[6, 7]]])
>>> np.swapaxes(x,0,2)
array([[[0, 4],
[2, 6]],
[[1, 5],
[3, 7]]])
"""
return _wrapfunc(a, 'swapaxes', axis1, axis2)
def transpose(a, axes=None):
"""
Permute the dimensions of an array.
Parameters
----------
a : array_like
Input array.
axes : list of ints, optional
By default, reverse the dimensions, otherwise permute the axes
according to the values given.
Returns
-------
p : ndarray
`a` with its axes permuted. A view is returned whenever
possible.
See Also
--------
moveaxis
argsort
Notes
-----
Use `transpose(a, argsort(axes))` to invert the transposition of tensors
when using the `axes` keyword argument.
Transposing a 1-D array returns an unchanged view of the original array.
Examples
--------
>>> x = np.arange(4).reshape((2,2))
>>> x
array([[0, 1],
[2, 3]])
>>> np.transpose(x)
array([[0, 2],
[1, 3]])
>>> x = np.ones((1, 2, 3))
>>> np.transpose(x, (1, 0, 2)).shape
(2, 1, 3)
"""
return _wrapfunc(a, 'transpose', axes)
def partition(a, kth, axis=-1, kind='introselect', order=None):
"""
Return a partitioned copy of an array.
Creates a copy of the array with its elements rearranged in such a
way that the value of the element in k-th position is in the
position it would be in a sorted array. All elements smaller than
the k-th element are moved before this element and all equal or
greater are moved behind it. The ordering of the elements in the two
partitions is undefined.
.. versionadded:: 1.8.0
Parameters
----------
a : array_like
Array to be sorted.
kth : int or sequence of ints
Element index to partition by. The k-th value of the element
will be in its final sorted position and all smaller elements
will be moved before it and all equal or greater elements behind
it. The order all elements in the partitions is undefined. If
provided with a sequence of k-th it will partition all elements
indexed by k-th of them into their sorted position at once.
axis : int or None, optional
Axis along which to sort. If None, the array is flattened before
sorting. The default is -1, which sorts along the last axis.
kind : {'introselect'}, optional
Selection algorithm. Default is 'introselect'.
order : str or list of str, optional
When `a` is an array with fields defined, this argument
specifies which fields to compare first, second, etc. A single
field can be specified as a string. Not all fields need be
specified, but unspecified fields will still be used, in the
order in which they come up in the dtype, to break ties.
Returns
-------
partitioned_array : ndarray
Array of the same type and shape as `a`.
See Also
--------
ndarray.partition : Method to sort an array in-place.
argpartition : Indirect partition.
sort : Full sorting
Notes
-----
The various selection algorithms are characterized by their average
speed, worst case performance, work space size, and whether they are
stable. A stable sort keeps items with the same key in the same
relative order. The available algorithms have the following
properties:
================= ======= ============= ============ =======
kind speed worst case work space stable
================= ======= ============= ============ =======
'introselect' 1 O(n) 0 no
================= ======= ============= ============ =======
All the partition algorithms make temporary copies of the data when
partitioning along any but the last axis. Consequently,
partitioning along the last axis is faster and uses less space than
partitioning along any other axis.
The sort order for complex numbers is lexicographic. If both the
real and imaginary parts are non-nan then the order is determined by
the real parts except when they are equal, in which case the order
is determined by the imaginary parts.
Examples
--------
>>> a = np.array([3, 4, 2, 1])
>>> np.partition(a, 3)
array([2, 1, 3, 4])
>>> np.partition(a, (1, 3))
array([1, 2, 3, 4])
"""
if axis is None:
a = asanyarray(a).flatten()
axis = 0
else:
a = asanyarray(a).copy(order="K")
a.partition(kth, axis=axis, kind=kind, order=order)
return a
def argpartition(a, kth, axis=-1, kind='introselect', order=None):
"""
Perform an indirect partition along the given axis using the
algorithm specified by the `kind` keyword. It returns an array of
indices of the same shape as `a` that index data along the given
axis in partitioned order.
.. versionadded:: 1.8.0
Parameters
----------
a : array_like
Array to sort.
kth : int or sequence of ints
Element index to partition by. The k-th element will be in its
final sorted position and all smaller elements will be moved
before it and all larger elements behind it. The order all
elements in the partitions is undefined. If provided with a
sequence of k-th it will partition all of them into their sorted
position at once.
axis : int or None, optional
Axis along which to sort. The default is -1 (the last axis). If
None, the flattened array is used.
kind : {'introselect'}, optional
Selection algorithm. Default is 'introselect'
order : str or list of str, optional
When `a` is an array with fields defined, this argument
specifies which fields to compare first, second, etc. A single
field can be specified as a string, and not all fields need be
specified, but unspecified fields will still be used, in the
order in which they come up in the dtype, to break ties.
Returns
-------
index_array : ndarray, int
Array of indices that partition `a` along the specified axis.
In other words, ``a[index_array]`` yields a partitioned `a`.
See Also
--------
partition : Describes partition algorithms used.
ndarray.partition : Inplace partition.
argsort : Full indirect sort
Notes
-----
See `partition` for notes on the different selection algorithms.
Examples
--------
One dimensional array:
>>> x = np.array([3, 4, 2, 1])
>>> x[np.argpartition(x, 3)]
array([2, 1, 3, 4])
>>> x[np.argpartition(x, (1, 3))]
array([1, 2, 3, 4])
>>> x = [3, 4, 2, 1]
>>> np.array(x)[np.argpartition(x, 3)]
array([2, 1, 3, 4])
"""
return _wrapfunc(a, 'argpartition', kth, axis=axis, kind=kind, order=order)
def sort(a, axis=-1, kind='quicksort', order=None):
"""
Return a sorted copy of an array.
Parameters
----------
a : array_like
Array to be sorted.
axis : int or None, optional
Axis along which to sort. If None, the array is flattened before
sorting. The default is -1, which sorts along the last axis.
kind : {'quicksort', 'mergesort', 'heapsort'}, optional
Sorting algorithm. Default is 'quicksort'.
order : str or list of str, optional
When `a` is an array with fields defined, this argument specifies
which fields to compare first, second, etc. A single field can
be specified as a string, and not all fields need be specified,
but unspecified fields will still be used, in the order in which
they come up in the dtype, to break ties.
Returns
-------
sorted_array : ndarray
Array of the same type and shape as `a`.
See Also
--------
ndarray.sort : Method to sort an array in-place.
argsort : Indirect sort.
lexsort : Indirect stable sort on multiple keys.
searchsorted : Find elements in a sorted array.
partition : Partial sort.
Notes
-----
The various sorting algorithms are characterized by their average speed,
worst case performance, work space size, and whether they are stable. A
stable sort keeps items with the same key in the same relative
order. The three available algorithms have the following
properties:
=========== ======= ============= ============ =======
kind speed worst case work space stable
=========== ======= ============= ============ =======
'quicksort' 1 O(n^2) 0 no
'mergesort' 2 O(n*log(n)) ~n/2 yes
'heapsort' 3 O(n*log(n)) 0 no
=========== ======= ============= ============ =======
All the sort algorithms make temporary copies of the data when
sorting along any but the last axis. Consequently, sorting along
the last axis is faster and uses less space than sorting along
any other axis.
The sort order for complex numbers is lexicographic. If both the real
and imaginary parts are non-nan then the order is determined by the
real parts except when they are equal, in which case the order is
determined by the imaginary parts.
Previous to numpy 1.4.0 sorting real and complex arrays containing nan
values led to undefined behaviour. In numpy versions >= 1.4.0 nan
values are sorted to the end. The extended sort order is:
* Real: [R, nan]
* Complex: [R + Rj, R + nanj, nan + Rj, nan + nanj]
where R is a non-nan real value. Complex values with the same nan
placements are sorted according to the non-nan part if it exists.
Non-nan values are sorted as before.
.. versionadded:: 1.12.0
quicksort has been changed to an introsort which will switch
heapsort when it does not make enough progress. This makes its
worst case O(n*log(n)).
Examples
--------
>>> a = np.array([[1,4],[3,1]])
>>> np.sort(a) # sort along the last axis
array([[1, 4],
[1, 3]])
>>> np.sort(a, axis=None) # sort the flattened array
array([1, 1, 3, 4])
>>> np.sort(a, axis=0) # sort along the first axis
array([[1, 1],
[3, 4]])
Use the `order` keyword to specify a field to use when sorting a
structured array:
>>> dtype = [('name', 'S10'), ('height', float), ('age', int)]
>>> values = [('Arthur', 1.8, 41), ('Lancelot', 1.9, 38),
... ('Galahad', 1.7, 38)]
>>> a = np.array(values, dtype=dtype) # create a structured array
>>> np.sort(a, order='height') # doctest: +SKIP
array([('Galahad', 1.7, 38), ('Arthur', 1.8, 41),
('Lancelot', 1.8999999999999999, 38)],
dtype=[('name', '|S10'), ('height', '<f8'), ('age', '<i4')])
Sort by age, then height if ages are equal:
>>> np.sort(a, order=['age', 'height']) # doctest: +SKIP
array([('Galahad', 1.7, 38), ('Lancelot', 1.8999999999999999, 38),
('Arthur', 1.8, 41)],
dtype=[('name', '|S10'), ('height', '<f8'), ('age', '<i4')])
"""
if axis is None:
a = asanyarray(a).flatten()
axis = 0
else:
a = asanyarray(a).copy(order="K")
a.sort(axis=axis, kind=kind, order=order)
return a
def argsort(a, axis=-1, kind='quicksort', order=None):
"""
Returns the indices that would sort an array.
Perform an indirect sort along the given axis using the algorithm specified
by the `kind` keyword. It returns an array of indices of the same shape as
`a` that index data along the given axis in sorted order.
Parameters
----------
a : array_like
Array to sort.
axis : int or None, optional
Axis along which to sort. The default is -1 (the last axis). If None,
the flattened array is used.
kind : {'quicksort', 'mergesort', 'heapsort'}, optional
Sorting algorithm.
order : str or list of str, optional
When `a` is an array with fields defined, this argument specifies
which fields to compare first, second, etc. A single field can
be specified as a string, and not all fields need be specified,
but unspecified fields will still be used, in the order in which
they come up in the dtype, to break ties.
Returns
-------
index_array : ndarray, int
Array of indices that sort `a` along the specified axis.
If `a` is one-dimensional, ``a[index_array]`` yields a sorted `a`.
See Also
--------
sort : Describes sorting algorithms used.
lexsort : Indirect stable sort with multiple keys.
ndarray.sort : Inplace sort.
argpartition : Indirect partial sort.
Notes
-----
See `sort` for notes on the different sorting algorithms.
As of NumPy 1.4.0 `argsort` works with real/complex arrays containing
nan values. The enhanced sort order is documented in `sort`.
Examples
--------
One dimensional array:
>>> x = np.array([3, 1, 2])
>>> np.argsort(x)
array([1, 2, 0])
Two-dimensional array:
>>> x = np.array([[0, 3], [2, 2]])
>>> x
array([[0, 3],
[2, 2]])
>>> np.argsort(x, axis=0) # sorts along first axis (down)
array([[0, 1],
[1, 0]])
>>> np.argsort(x, axis=1) # sorts along last axis (across)
array([[0, 1],
[0, 1]])
Indices of the sorted elements of a N-dimensional array:
>>> ind = np.unravel_index(np.argsort(x, axis=None), x.shape)
>>> ind
(array([0, 1, 1, 0]), array([0, 0, 1, 1]))
>>> x[ind] # same as np.sort(x, axis=None)
array([0, 2, 2, 3])
Sorting with keys:
>>> x = np.array([(1, 0), (0, 1)], dtype=[('x', '<i4'), ('y', '<i4')])
>>> x
array([(1, 0), (0, 1)],
dtype=[('x', '<i4'), ('y', '<i4')])
>>> np.argsort(x, order=('x','y'))
array([1, 0])
>>> np.argsort(x, order=('y','x'))
array([0, 1])
"""
return _wrapfunc(a, 'argsort', axis=axis, kind=kind, order=order)
def argmax(a, axis=None, out=None):
"""
Returns the indices of the maximum values along an axis.
Parameters
----------
a : array_like
Input array.
axis : int, optional
By default, the index is into the flattened array, otherwise
along the specified axis.
out : array, optional
If provided, the result will be inserted into this array. It should
be of the appropriate shape and dtype.
Returns
-------
index_array : ndarray of ints
Array of indices into the array. It has the same shape as `a.shape`
with the dimension along `axis` removed.
See Also
--------
ndarray.argmax, argmin
amax : The maximum value along a given axis.
unravel_index : Convert a flat index into an index tuple.
Notes
-----
In case of multiple occurrences of the maximum values, the indices
corresponding to the first occurrence are returned.
Examples
--------
>>> a = np.arange(6).reshape(2,3)
>>> a
array([[0, 1, 2],
[3, 4, 5]])
>>> np.argmax(a)
5
>>> np.argmax(a, axis=0)
array([1, 1, 1])
>>> np.argmax(a, axis=1)
array([2, 2])
Indexes of the maximal elements of a N-dimensional array:
>>> ind = np.unravel_index(np.argmax(a, axis=None), a.shape)
>>> ind
(1, 2)
>>> a[ind]
5
>>> b = np.arange(6)
>>> b[1] = 5
>>> b
array([0, 5, 2, 3, 4, 5])
>>> np.argmax(b) # Only the first occurrence is returned.
1
"""
return _wrapfunc(a, 'argmax', axis=axis, out=out)
def argmin(a, axis=None, out=None):
"""
Returns the indices of the minimum values along an axis.
Parameters
----------
a : array_like
Input array.
axis : int, optional
By default, the index is into the flattened array, otherwise
along the specified axis.
out : array, optional
If provided, the result will be inserted into this array. It should
be of the appropriate shape and dtype.
Returns
-------
index_array : ndarray of ints
Array of indices into the array. It has the same shape as `a.shape`
with the dimension along `axis` removed.
See Also
--------
ndarray.argmin, argmax
amin : The minimum value along a given axis.
unravel_index : Convert a flat index into an index tuple.
Notes
-----
In case of multiple occurrences of the minimum values, the indices
corresponding to the first occurrence are returned.
Examples
--------
>>> a = np.arange(6).reshape(2,3)
>>> a
array([[0, 1, 2],
[3, 4, 5]])
>>> np.argmin(a)
0
>>> np.argmin(a, axis=0)
array([0, 0, 0])
>>> np.argmin(a, axis=1)
array([0, 0])
Indices of the minimum elements of a N-dimensional array:
>>> ind = np.unravel_index(np.argmin(a, axis=None), a.shape)
>>> ind
(0, 0)
>>> a[ind]
0
>>> b = np.arange(6)
>>> b[4] = 0
>>> b
array([0, 1, 2, 3, 0, 5])
>>> np.argmin(b) # Only the first occurrence is returned.
0
"""
return _wrapfunc(a, 'argmin', axis=axis, out=out)
def searchsorted(a, v, side='left', sorter=None):
"""
Find indices where elements should be inserted to maintain order.
Find the indices into a sorted array `a` such that, if the
corresponding elements in `v` were inserted before the indices, the
order of `a` would be preserved.
Parameters
----------
a : 1-D array_like
Input array. If `sorter` is None, then it must be sorted in
ascending order, otherwise `sorter` must be an array of indices
that sort it.
v : array_like
Values to insert into `a`.
side : {'left', 'right'}, optional
If 'left', the index of the first suitable location found is given.
If 'right', return the last such index. If there is no suitable
index, return either 0 or N (where N is the length of `a`).
sorter : 1-D array_like, optional
Optional array of integer indices that sort array a into ascending
order. They are typically the result of argsort.
.. versionadded:: 1.7.0
Returns
-------
indices : array of ints
Array of insertion points with the same shape as `v`.
See Also
--------
sort : Return a sorted copy of an array.
histogram : Produce histogram from 1-D data.
Notes
-----
Binary search is used to find the required insertion points.
As of NumPy 1.4.0 `searchsorted` works with real/complex arrays containing
`nan` values. The enhanced sort order is documented in `sort`.
Examples
--------
>>> np.searchsorted([1,2,3,4,5], 3)
2
>>> np.searchsorted([1,2,3,4,5], 3, side='right')
3
>>> np.searchsorted([1,2,3,4,5], [-10, 10, 2, 3])
array([0, 5, 1, 2])
"""
return _wrapfunc(a, 'searchsorted', v, side=side, sorter=sorter)
def resize(a, new_shape):
"""
Return a new array with the specified shape.
If the new array is larger than the original array, then the new
array is filled with repeated copies of `a`. Note that this behavior
is different from a.resize(new_shape) which fills with zeros instead
of repeated copies of `a`.
Parameters
----------
a : array_like
Array to be resized.
new_shape : int or tuple of int
Shape of resized array.
Returns
-------
reshaped_array : ndarray
The new array is formed from the data in the old array, repeated
if necessary to fill out the required number of elements. The
data are repeated in the order that they are stored in memory.
See Also
--------
ndarray.resize : resize an array in-place.
Examples
--------
>>> a=np.array([[0,1],[2,3]])
>>> np.resize(a,(2,3))
array([[0, 1, 2],
[3, 0, 1]])
>>> np.resize(a,(1,4))
array([[0, 1, 2, 3]])
>>> np.resize(a,(2,4))
array([[0, 1, 2, 3],
[0, 1, 2, 3]])
"""
if isinstance(new_shape, (int, nt.integer)):
new_shape = (new_shape,)
a = ravel(a)
Na = len(a)
total_size = um.multiply.reduce(new_shape)
if Na == 0 or total_size == 0:
return mu.zeros(new_shape, a.dtype)
n_copies = int(total_size / Na)
extra = total_size % Na
if extra != 0:
n_copies = n_copies + 1
extra = Na - extra
a = concatenate((a,)*n_copies)
if extra > 0:
a = a[:-extra]
return reshape(a, new_shape)
def squeeze(a, axis=None):
"""
Remove single-dimensional entries from the shape of an array.
Parameters
----------
a : array_like
Input data.
axis : None or int or tuple of ints, optional
.. versionadded:: 1.7.0
Selects a subset of the single-dimensional entries in the
shape. If an axis is selected with shape entry greater than
one, an error is raised.
Returns
-------
squeezed : ndarray
The input array, but with all or a subset of the
dimensions of length 1 removed. This is always `a` itself
or a view into `a`.
Raises
------
ValueError
If `axis` is not `None`, and an axis being squeezed is not of length 1
See Also
--------
expand_dims : The inverse operation, adding singleton dimensions
reshape : Insert, remove, and combine dimensions, and resize existing ones
Examples
--------
>>> x = np.array([[[0], [1], [2]]])
>>> x.shape
(1, 3, 1)
>>> np.squeeze(x).shape
(3,)
>>> np.squeeze(x, axis=0).shape
(3, 1)
>>> np.squeeze(x, axis=1).shape
Traceback (most recent call last):
...
ValueError: cannot select an axis to squeeze out which has size not equal to one
>>> np.squeeze(x, axis=2).shape
(1, 3)
"""
try:
squeeze = a.squeeze
except AttributeError:
return _wrapit(a, 'squeeze')
try:
# First try to use the new axis= parameter
return squeeze(axis=axis)
except TypeError:
# For backwards compatibility
return squeeze()
def diagonal(a, offset=0, axis1=0, axis2=1):
"""
Return specified diagonals.
If `a` is 2-D, returns the diagonal of `a` with the given offset,
i.e., the collection of elements of the form ``a[i, i+offset]``. If
`a` has more than two dimensions, then the axes specified by `axis1`
and `axis2` are used to determine the 2-D sub-array whose diagonal is
returned. The shape of the resulting array can be determined by
removing `axis1` and `axis2` and appending an index to the right equal
to the size of the resulting diagonals.
In versions of NumPy prior to 1.7, this function always returned a new,
independent array containing a copy of the values in the diagonal.
In NumPy 1.7 and 1.8, it continues to return a copy of the diagonal,
but depending on this fact is deprecated. Writing to the resulting
array continues to work as it used to, but a FutureWarning is issued.
Starting in NumPy 1.9 it returns a read-only view on the original array.
Attempting to write to the resulting array will produce an error.
In some future release, it will return a read/write view and writing to
the returned array will alter your original array. The returned array
will have the same type as the input array.
If you don't write to the array returned by this function, then you can
just ignore all of the above.
If you depend on the current behavior, then we suggest copying the
returned array explicitly, i.e., use ``np.diagonal(a).copy()`` instead
of just ``np.diagonal(a)``. This will work with both past and future
versions of NumPy.
Parameters
----------
a : array_like
Array from which the diagonals are taken.
offset : int, optional
Offset of the diagonal from the main diagonal. Can be positive or
negative. Defaults to main diagonal (0).
axis1 : int, optional
Axis to be used as the first axis of the 2-D sub-arrays from which
the diagonals should be taken. Defaults to first axis (0).
axis2 : int, optional
Axis to be used as the second axis of the 2-D sub-arrays from
which the diagonals should be taken. Defaults to second axis (1).
Returns
-------
array_of_diagonals : ndarray
If `a` is 2-D and not a `matrix`, a 1-D array of the same type as `a`
containing the diagonal is returned. If `a` is a `matrix`, a 1-D
array containing the diagonal is returned in order to maintain
backward compatibility.
If ``a.ndim > 2``, then the dimensions specified by `axis1` and `axis2`
are removed, and a new axis inserted at the end corresponding to the
diagonal.
Raises
------
ValueError
If the dimension of `a` is less than 2.
See Also
--------
diag : MATLAB work-a-like for 1-D and 2-D arrays.
diagflat : Create diagonal arrays.
trace : Sum along diagonals.
Examples
--------
>>> a = np.arange(4).reshape(2,2)
>>> a
array([[0, 1],
[2, 3]])
>>> a.diagonal()
array([0, 3])
>>> a.diagonal(1)
array([1])
A 3-D example:
>>> a = np.arange(8).reshape(2,2,2); a
array([[[0, 1],
[2, 3]],
[[4, 5],
[6, 7]]])
>>> a.diagonal(0, # Main diagonals of two arrays created by skipping
... 0, # across the outer(left)-most axis last and
... 1) # the "middle" (row) axis first.
array([[0, 6],
[1, 7]])
The sub-arrays whose main diagonals we just obtained; note that each
corresponds to fixing the right-most (column) axis, and that the
diagonals are "packed" in rows.
>>> a[:,:,0] # main diagonal is [0 6]
array([[0, 2],
[4, 6]])
>>> a[:,:,1] # main diagonal is [1 7]
array([[1, 3],
[5, 7]])
"""
if isinstance(a, np.matrix):
# Make diagonal of matrix 1-D to preserve backward compatibility.
return asarray(a).diagonal(offset=offset, axis1=axis1, axis2=axis2)
else:
return asanyarray(a).diagonal(offset=offset, axis1=axis1, axis2=axis2)
def trace(a, offset=0, axis1=0, axis2=1, dtype=None, out=None):
"""
Return the sum along diagonals of the array.
If `a` is 2-D, the sum along its diagonal with the given offset
is returned, i.e., the sum of elements ``a[i,i+offset]`` for all i.
If `a` has more than two dimensions, then the axes specified by axis1 and
axis2 are used to determine the 2-D sub-arrays whose traces are returned.
The shape of the resulting array is the same as that of `a` with `axis1`
and `axis2` removed.
Parameters
----------
a : array_like
Input array, from which the diagonals are taken.
offset : int, optional
Offset of the diagonal from the main diagonal. Can be both positive
and negative. Defaults to 0.
axis1, axis2 : int, optional
Axes to be used as the first and second axis of the 2-D sub-arrays
from which the diagonals should be taken. Defaults are the first two
axes of `a`.
dtype : dtype, optional
Determines the data-type of the returned array and of the accumulator
where the elements are summed. If dtype has the value None and `a` is
of integer type of precision less than the default integer
precision, then the default integer precision is used. Otherwise,
the precision is the same as that of `a`.
out : ndarray, optional
Array into which the output is placed. Its type is preserved and
it must be of the right shape to hold the output.
Returns
-------
sum_along_diagonals : ndarray
If `a` is 2-D, the sum along the diagonal is returned. If `a` has
larger dimensions, then an array of sums along diagonals is returned.
See Also
--------
diag, diagonal, diagflat
Examples
--------
>>> np.trace(np.eye(3))
3.0
>>> a = np.arange(8).reshape((2,2,2))
>>> np.trace(a)
array([6, 8])
>>> a = np.arange(24).reshape((2,2,2,3))
>>> np.trace(a).shape
(2, 3)
"""
if isinstance(a, np.matrix):
# Get trace of matrix via an array to preserve backward compatibility.
return asarray(a).trace(offset=offset, axis1=axis1, axis2=axis2, dtype=dtype, out=out)
else:
return asanyarray(a).trace(offset=offset, axis1=axis1, axis2=axis2, dtype=dtype, out=out)
def ravel(a, order='C'):
"""Return a contiguous flattened array.
A 1-D array, containing the elements of the input, is returned. A copy is
made only if needed.
As of NumPy 1.10, the returned array will have the same type as the input
array. (for example, a masked array will be returned for a masked array
input)
Parameters
----------
a : array_like
Input array. The elements in `a` are read in the order specified by
`order`, and packed as a 1-D array.
order : {'C','F', 'A', 'K'}, optional
The elements of `a` are read using this index order. 'C' means
to index the elements in row-major, C-style order,
with the last axis index changing fastest, back to the first
axis index changing slowest. 'F' means to index the elements
in column-major, Fortran-style order, with the
first index changing fastest, and the last index changing
slowest. Note that the 'C' and 'F' options take no account of
the memory layout of the underlying array, and only refer to
the order of axis indexing. 'A' means to read the elements in
Fortran-like index order if `a` is Fortran *contiguous* in
memory, C-like order otherwise. 'K' means to read the
elements in the order they occur in memory, except for
reversing the data when strides are negative. By default, 'C'
index order is used.
Returns
-------
y : array_like
If `a` is a matrix, y is a 1-D ndarray, otherwise y is an array of
the same subtype as `a`. The shape of the returned array is
``(a.size,)``. Matrices are special cased for backward
compatibility.
See Also
--------
ndarray.flat : 1-D iterator over an array.
ndarray.flatten : 1-D array copy of the elements of an array
in row-major order.
ndarray.reshape : Change the shape of an array without changing its data.
Notes
-----
In row-major, C-style order, in two dimensions, the row index
varies the slowest, and the column index the quickest. This can
be generalized to multiple dimensions, where row-major order
implies that the index along the first axis varies slowest, and
the index along the last quickest. The opposite holds for
column-major, Fortran-style index ordering.
When a view is desired in as many cases as possible, ``arr.reshape(-1)``
may be preferable.
Examples
--------
It is equivalent to ``reshape(-1, order=order)``.
>>> x = np.array([[1, 2, 3], [4, 5, 6]])
>>> print(np.ravel(x))
[1 2 3 4 5 6]
>>> print(x.reshape(-1))
[1 2 3 4 5 6]
>>> print(np.ravel(x, order='F'))
[1 4 2 5 3 6]
When ``order`` is 'A', it will preserve the array's 'C' or 'F' ordering:
>>> print(np.ravel(x.T))
[1 4 2 5 3 6]
>>> print(np.ravel(x.T, order='A'))
[1 2 3 4 5 6]
When ``order`` is 'K', it will preserve orderings that are neither 'C'
nor 'F', but won't reverse axes:
>>> a = np.arange(3)[::-1]; a
array([2, 1, 0])
>>> a.ravel(order='C')
array([2, 1, 0])
>>> a.ravel(order='K')
array([2, 1, 0])
>>> a = np.arange(12).reshape(2,3,2).swapaxes(1,2); a
array([[[ 0, 2, 4],
[ 1, 3, 5]],
[[ 6, 8, 10],
[ 7, 9, 11]]])
>>> a.ravel(order='C')
array([ 0, 2, 4, 1, 3, 5, 6, 8, 10, 7, 9, 11])
>>> a.ravel(order='K')
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
"""
if isinstance(a, np.matrix):
return asarray(a).ravel(order=order)
else:
return asanyarray(a).ravel(order=order)
def nonzero(a):
"""
Return the indices of the elements that are non-zero.
Returns a tuple of arrays, one for each dimension of `a`,
containing the indices of the non-zero elements in that
dimension. The values in `a` are always tested and returned in
row-major, C-style order. The corresponding non-zero
values can be obtained with::
a[nonzero(a)]
To group the indices by element, rather than dimension, use::
transpose(nonzero(a))
The result of this is always a 2-D array, with a row for
each non-zero element.
Parameters
----------
a : array_like
Input array.
Returns
-------
tuple_of_arrays : tuple
Indices of elements that are non-zero.
See Also
--------
flatnonzero :
Return indices that are non-zero in the flattened version of the input
array.
ndarray.nonzero :
Equivalent ndarray method.
count_nonzero :
Counts the number of non-zero elements in the input array.
Examples
--------
>>> x = np.array([[1,0,0], [0,2,0], [1,1,0]])
>>> x
array([[1, 0, 0],
[0, 2, 0],
[1, 1, 0]])
>>> np.nonzero(x)
(array([0, 1, 2, 2]), array([0, 1, 0, 1]))
>>> x[np.nonzero(x)]
array([1, 2, 1, 1])
>>> np.transpose(np.nonzero(x))
array([[0, 0],
[1, 1],
[2, 0],
[2, 1])
A common use for ``nonzero`` is to find the indices of an array, where
a condition is True. Given an array `a`, the condition `a` > 3 is a
boolean array and since False is interpreted as 0, np.nonzero(a > 3)
yields the indices of the `a` where the condition is true.
>>> a = np.array([[1,2,3],[4,5,6],[7,8,9]])
>>> a > 3
array([[False, False, False],
[ True, True, True],
[ True, True, True]])
>>> np.nonzero(a > 3)
(array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))
The ``nonzero`` method of the boolean array can also be called.
>>> (a > 3).nonzero()
(array([1, 1, 1, 2, 2, 2]), array([0, 1, 2, 0, 1, 2]))
"""
return _wrapfunc(a, 'nonzero')
def shape(a):
"""
Return the shape of an array.
Parameters
----------
a : array_like
Input array.
Returns
-------
shape : tuple of ints
The elements of the shape tuple give the lengths of the
corresponding array dimensions.
See Also
--------
alen
ndarray.shape : Equivalent array method.
Examples
--------
>>> np.shape(np.eye(3))
(3, 3)
>>> np.shape([[1, 2]])
(1, 2)
>>> np.shape([0])
(1,)
>>> np.shape(0)
()
>>> a = np.array([(1, 2), (3, 4)], dtype=[('x', 'i4'), ('y', 'i4')])
>>> np.shape(a)
(2,)
>>> a.shape
(2,)
"""
try:
result = a.shape
except AttributeError:
result = asarray(a).shape
return result
def compress(condition, a, axis=None, out=None):
"""
Return selected slices of an array along given axis.
When working along a given axis, a slice along that axis is returned in
`output` for each index where `condition` evaluates to True. When
working on a 1-D array, `compress` is equivalent to `extract`.
Parameters
----------
condition : 1-D array of bools
Array that selects which entries to return. If len(condition)
is less than the size of `a` along the given axis, then output is
truncated to the length of the condition array.
a : array_like
Array from which to extract a part.
axis : int, optional
Axis along which to take slices. If None (default), work on the
flattened array.
out : ndarray, optional
Output array. Its type is preserved and it must be of the right
shape to hold the output.
Returns
-------
compressed_array : ndarray
A copy of `a` without the slices along axis for which `condition`
is false.
See Also
--------
take, choose, diag, diagonal, select
ndarray.compress : Equivalent method in ndarray
np.extract: Equivalent method when working on 1-D arrays
numpy.doc.ufuncs : Section "Output arguments"
Examples
--------
>>> a = np.array([[1, 2], [3, 4], [5, 6]])
>>> a
array([[1, 2],
[3, 4],
[5, 6]])
>>> np.compress([0, 1], a, axis=0)
array([[3, 4]])
>>> np.compress([False, True, True], a, axis=0)
array([[3, 4],
[5, 6]])
>>> np.compress([False, True], a, axis=1)
array([[2],
[4],
[6]])
Working on the flattened array does not return slices along an axis but
selects elements.
>>> np.compress([False, True], a)
array([2])
"""
return _wrapfunc(a, 'compress', condition, axis=axis, out=out)
def clip(a, a_min, a_max, out=None):
"""
Clip (limit) the values in an array.
Given an interval, values outside the interval are clipped to
the interval edges. For example, if an interval of ``[0, 1]``
is specified, values smaller than 0 become 0, and values larger
than 1 become 1.
Parameters
----------
a : array_like
Array containing elements to clip.
a_min : scalar or array_like or `None`
Minimum value. If `None`, clipping is not performed on lower
interval edge. Not more than one of `a_min` and `a_max` may be
`None`.
a_max : scalar or array_like or `None`
Maximum value. If `None`, clipping is not performed on upper
interval edge. Not more than one of `a_min` and `a_max` may be
`None`. If `a_min` or `a_max` are array_like, then the three
arrays will be broadcasted to match their shapes.
out : ndarray, optional
The results will be placed in this array. It may be the input
array for in-place clipping. `out` must be of the right shape
to hold the output. Its type is preserved.
Returns
-------
clipped_array : ndarray
An array with the elements of `a`, but where values
< `a_min` are replaced with `a_min`, and those > `a_max`
with `a_max`.
See Also
--------
numpy.doc.ufuncs : Section "Output arguments"
Examples
--------
>>> a = np.arange(10)
>>> np.clip(a, 1, 8)
array([1, 1, 2, 3, 4, 5, 6, 7, 8, 8])
>>> a
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> np.clip(a, 3, 6, out=a)
array([3, 3, 3, 3, 4, 5, 6, 6, 6, 6])
>>> a = np.arange(10)
>>> a
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> np.clip(a, [3, 4, 1, 1, 1, 4, 4, 4, 4, 4], 8)
array([3, 4, 2, 3, 4, 5, 6, 7, 8, 8])
"""
return _wrapfunc(a, 'clip', a_min, a_max, out=out)
def sum(a, axis=None, dtype=None, out=None, keepdims=np._NoValue):
"""
Sum of array elements over a given axis.
Parameters
----------
a : array_like
Elements to sum.
axis : None or int or tuple of ints, optional
Axis or axes along which a sum is performed. The default,
axis=None, will sum all of the elements of the input array. If
axis is negative it counts from the last to the first axis.
.. versionadded:: 1.7.0
If axis is a tuple of ints, a sum is performed on all of the axes
specified in the tuple instead of a single axis or all the axes as
before.
dtype : dtype, optional
The type of the returned array and of the accumulator in which the
elements are summed. The dtype of `a` is used by default unless `a`
has an integer dtype of less precision than the default platform
integer. In that case, if `a` is signed then the platform integer
is used while if `a` is unsigned then an unsigned integer of the
same precision as the platform integer is used.
out : ndarray, optional
Alternative output array in which to place the result. It must have
the same shape as the expected output, but the type of the output
values will be cast if necessary.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the input array.
If the default value is passed, then `keepdims` will not be
passed through to the `sum` method of sub-classes of
`ndarray`, however any non-default value will be. If the
sub-classes `sum` method does not implement `keepdims` any
exceptions will be raised.
Returns
-------
sum_along_axis : ndarray
An array with the same shape as `a`, with the specified
axis removed. If `a` is a 0-d array, or if `axis` is None, a scalar
is returned. If an output array is specified, a reference to
`out` is returned.
See Also
--------
ndarray.sum : Equivalent method.
cumsum : Cumulative sum of array elements.
trapz : Integration of array values using the composite trapezoidal rule.
mean, average
Notes
-----
Arithmetic is modular when using integer types, and no error is
raised on overflow.
The sum of an empty array is the neutral element 0:
>>> np.sum([])
0.0
Examples
--------
>>> np.sum([0.5, 1.5])
2.0
>>> np.sum([0.5, 0.7, 0.2, 1.5], dtype=np.int32)
1
>>> np.sum([[0, 1], [0, 5]])
6
>>> np.sum([[0, 1], [0, 5]], axis=0)
array([0, 6])
>>> np.sum([[0, 1], [0, 5]], axis=1)
array([1, 5])
If the accumulator is too small, overflow occurs:
>>> np.ones(128, dtype=np.int8).sum(dtype=np.int8)
-128
"""
kwargs = {}
if keepdims is not np._NoValue:
kwargs['keepdims'] = keepdims
if isinstance(a, _gentype):
res = _sum_(a)
if out is not None:
out[...] = res
return out
return res
if type(a) is not mu.ndarray:
try:
sum = a.sum
except AttributeError:
pass
else:
return sum(axis=axis, dtype=dtype, out=out, **kwargs)
return _methods._sum(a, axis=axis, dtype=dtype,
out=out, **kwargs)
def product(a, axis=None, dtype=None, out=None, keepdims=np._NoValue):
"""
Return the product of array elements over a given axis.
See Also
--------
prod : equivalent function; see for details.
"""
kwargs = {}
if keepdims is not np._NoValue:
kwargs['keepdims'] = keepdims
return um.multiply.reduce(a, axis=axis, dtype=dtype, out=out, **kwargs)
def sometrue(a, axis=None, out=None, keepdims=np._NoValue):
"""
Check whether some values are true.
Refer to `any` for full documentation.
See Also
--------
any : equivalent function
"""
arr = asanyarray(a)
kwargs = {}
if keepdims is not np._NoValue:
kwargs['keepdims'] = keepdims
return arr.any(axis=axis, out=out, **kwargs)
def alltrue(a, axis=None, out=None, keepdims=np._NoValue):
"""
Check if all elements of input array are true.
See Also
--------
numpy.all : Equivalent function; see for details.
"""
arr = asanyarray(a)
kwargs = {}
if keepdims is not np._NoValue:
kwargs['keepdims'] = keepdims
return arr.all(axis=axis, out=out, **kwargs)
def any(a, axis=None, out=None, keepdims=np._NoValue):
"""
Test whether any array element along a given axis evaluates to True.
Returns single boolean unless `axis` is not ``None``
Parameters
----------
a : array_like
Input array or object that can be converted to an array.
axis : None or int or tuple of ints, optional
Axis or axes along which a logical OR reduction is performed.
The default (`axis` = `None`) is to perform a logical OR over all
the dimensions of the input array. `axis` may be negative, in
which case it counts from the last to the first axis.
.. versionadded:: 1.7.0
If this is a tuple of ints, a reduction is performed on multiple
axes, instead of a single axis or all the axes as before.
out : ndarray, optional
Alternate output array in which to place the result. It must have
the same shape as the expected output and its type is preserved
(e.g., if it is of type float, then it will remain so, returning
1.0 for True and 0.0 for False, regardless of the type of `a`).
See `doc.ufuncs` (Section "Output arguments") for details.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the input array.
If the default value is passed, then `keepdims` will not be
passed through to the `any` method of sub-classes of
`ndarray`, however any non-default value will be. If the
sub-classes `sum` method does not implement `keepdims` any
exceptions will be raised.
Returns
-------
any : bool or ndarray
A new boolean or `ndarray` is returned unless `out` is specified,
in which case a reference to `out` is returned.
See Also
--------
ndarray.any : equivalent method
all : Test whether all elements along a given axis evaluate to True.
Notes
-----
Not a Number (NaN), positive infinity and negative infinity evaluate
to `True` because these are not equal to zero.
Examples
--------
>>> np.any([[True, False], [True, True]])
True
>>> np.any([[True, False], [False, False]], axis=0)
array([ True, False])
>>> np.any([-1, 0, 5])
True
>>> np.any(np.nan)
True
>>> o=np.array([False])
>>> z=np.any([-1, 4, 5], out=o)
>>> z, o
(array([ True]), array([ True]))
>>> # Check now that z is a reference to o
>>> z is o
True
>>> id(z), id(o) # identity of z and o # doctest: +SKIP
(191614240, 191614240)
"""
arr = asanyarray(a)
kwargs = {}
if keepdims is not np._NoValue:
kwargs['keepdims'] = keepdims
return arr.any(axis=axis, out=out, **kwargs)
def all(a, axis=None, out=None, keepdims=np._NoValue):
"""
Test whether all array elements along a given axis evaluate to True.
Parameters
----------
a : array_like
Input array or object that can be converted to an array.
axis : None or int or tuple of ints, optional
Axis or axes along which a logical AND reduction is performed.
The default (`axis` = `None`) is to perform a logical AND over all
the dimensions of the input array. `axis` may be negative, in
which case it counts from the last to the first axis.
.. versionadded:: 1.7.0
If this is a tuple of ints, a reduction is performed on multiple
axes, instead of a single axis or all the axes as before.
out : ndarray, optional
Alternate output array in which to place the result.
It must have the same shape as the expected output and its
type is preserved (e.g., if ``dtype(out)`` is float, the result
will consist of 0.0's and 1.0's). See `doc.ufuncs` (Section
"Output arguments") for more details.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the input array.
If the default value is passed, then `keepdims` will not be
passed through to the `all` method of sub-classes of
`ndarray`, however any non-default value will be. If the
sub-classes `sum` method does not implement `keepdims` any
exceptions will be raised.
Returns
-------
all : ndarray, bool
A new boolean or array is returned unless `out` is specified,
in which case a reference to `out` is returned.
See Also
--------
ndarray.all : equivalent method
any : Test whether any element along a given axis evaluates to True.
Notes
-----
Not a Number (NaN), positive infinity and negative infinity
evaluate to `True` because these are not equal to zero.
Examples
--------
>>> np.all([[True,False],[True,True]])
False
>>> np.all([[True,False],[True,True]], axis=0)
array([ True, False])
>>> np.all([-1, 4, 5])
True
>>> np.all([1.0, np.nan])
True
>>> o=np.array([False])
>>> z=np.all([-1, 4, 5], out=o)
>>> id(z), id(o), z # doctest: +SKIP
(28293632, 28293632, array([ True]))
"""
arr = asanyarray(a)
kwargs = {}
if keepdims is not np._NoValue:
kwargs['keepdims'] = keepdims
return arr.all(axis=axis, out=out, **kwargs)
def cumsum(a, axis=None, dtype=None, out=None):
"""
Return the cumulative sum of the elements along a given axis.
Parameters
----------
a : array_like
Input array.
axis : int, optional
Axis along which the cumulative sum is computed. The default
(None) is to compute the cumsum over the flattened array.
dtype : dtype, optional
Type of the returned array and of the accumulator in which the
elements are summed. If `dtype` is not specified, it defaults
to the dtype of `a`, unless `a` has an integer dtype with a
precision less than that of the default platform integer. In
that case, the default platform integer is used.
out : ndarray, optional
Alternative output array in which to place the result. It must
have the same shape and buffer length as the expected output
but the type will be cast if necessary. See `doc.ufuncs`
(Section "Output arguments") for more details.
Returns
-------
cumsum_along_axis : ndarray.
A new array holding the result is returned unless `out` is
specified, in which case a reference to `out` is returned. The
result has the same size as `a`, and the same shape as `a` if
`axis` is not None or `a` is a 1-d array.
See Also
--------
sum : Sum array elements.
trapz : Integration of array values using the composite trapezoidal rule.
diff : Calculate the n-th discrete difference along given axis.
Notes
-----
Arithmetic is modular when using integer types, and no error is
raised on overflow.
Examples
--------
>>> a = np.array([[1,2,3], [4,5,6]])
>>> a
array([[1, 2, 3],
[4, 5, 6]])
>>> np.cumsum(a)
array([ 1, 3, 6, 10, 15, 21])
>>> np.cumsum(a, dtype=float) # specifies type of output value(s)
array([ 1., 3., 6., 10., 15., 21.])
>>> np.cumsum(a,axis=0) # sum over rows for each of the 3 columns
array([[1, 2, 3],
[5, 7, 9]])
>>> np.cumsum(a,axis=1) # sum over columns for each of the 2 rows
array([[ 1, 3, 6],
[ 4, 9, 15]])
"""
return _wrapfunc(a, 'cumsum', axis=axis, dtype=dtype, out=out)
def cumproduct(a, axis=None, dtype=None, out=None):
"""
Return the cumulative product over the given axis.
See Also
--------
cumprod : equivalent function; see for details.
"""
return _wrapfunc(a, 'cumprod', axis=axis, dtype=dtype, out=out)
def ptp(a, axis=None, out=None):
"""
Range of values (maximum - minimum) along an axis.
The name of the function comes from the acronym for 'peak to peak'.
Parameters
----------
a : array_like
Input values.
axis : int, optional
Axis along which to find the peaks. By default, flatten the
array.
out : array_like
Alternative output array in which to place the result. It must
have the same shape and buffer length as the expected output,
but the type of the output values will be cast if necessary.
Returns
-------
ptp : ndarray
A new array holding the result, unless `out` was
specified, in which case a reference to `out` is returned.
Examples
--------
>>> x = np.arange(4).reshape((2,2))
>>> x
array([[0, 1],
[2, 3]])
>>> np.ptp(x, axis=0)
array([2, 2])
>>> np.ptp(x, axis=1)
array([1, 1])
"""
return _wrapfunc(a, 'ptp', axis=axis, out=out)
def amax(a, axis=None, out=None, keepdims=np._NoValue):
"""
Return the maximum of an array or maximum along an axis.
Parameters
----------
a : array_like
Input data.
axis : None or int or tuple of ints, optional
Axis or axes along which to operate. By default, flattened input is
used.
.. versionadded:: 1.7.0
If this is a tuple of ints, the maximum is selected over multiple axes,
instead of a single axis or all the axes as before.
out : ndarray, optional
Alternative output array in which to place the result. Must
be of the same shape and buffer length as the expected output.
See `doc.ufuncs` (Section "Output arguments") for more details.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the input array.
If the default value is passed, then `keepdims` will not be
passed through to the `amax` method of sub-classes of
`ndarray`, however any non-default value will be. If the
sub-classes `sum` method does not implement `keepdims` any
exceptions will be raised.
Returns
-------
amax : ndarray or scalar
Maximum of `a`. If `axis` is None, the result is a scalar value.
If `axis` is given, the result is an array of dimension
``a.ndim - 1``.
See Also
--------
amin :
The minimum value of an array along a given axis, propagating any NaNs.
nanmax :
The maximum value of an array along a given axis, ignoring any NaNs.
maximum :
Element-wise maximum of two arrays, propagating any NaNs.
fmax :
Element-wise maximum of two arrays, ignoring any NaNs.
argmax :
Return the indices of the maximum values.
nanmin, minimum, fmin
Notes
-----
NaN values are propagated, that is if at least one item is NaN, the
corresponding max value will be NaN as well. To ignore NaN values
(MATLAB behavior), please use nanmax.
Don't use `amax` for element-wise comparison of 2 arrays; when
``a.shape[0]`` is 2, ``maximum(a[0], a[1])`` is faster than
``amax(a, axis=0)``.
Examples
--------
>>> a = np.arange(4).reshape((2,2))
>>> a
array([[0, 1],
[2, 3]])
>>> np.amax(a) # Maximum of the flattened array
3
>>> np.amax(a, axis=0) # Maxima along the first axis
array([2, 3])
>>> np.amax(a, axis=1) # Maxima along the second axis
array([1, 3])
>>> b = np.arange(5, dtype=float)
>>> b[2] = np.NaN
>>> np.amax(b)
nan
>>> np.nanmax(b)
4.0
"""
kwargs = {}
if keepdims is not np._NoValue:
kwargs['keepdims'] = keepdims
if type(a) is not mu.ndarray:
try:
amax = a.max
except AttributeError:
pass
else:
return amax(axis=axis, out=out, **kwargs)
return _methods._amax(a, axis=axis,
out=out, **kwargs)
def amin(a, axis=None, out=None, keepdims=np._NoValue):
"""
Return the minimum of an array or minimum along an axis.
Parameters
----------
a : array_like
Input data.
axis : None or int or tuple of ints, optional
Axis or axes along which to operate. By default, flattened input is
used.
.. versionadded:: 1.7.0
If this is a tuple of ints, the minimum is selected over multiple axes,
instead of a single axis or all the axes as before.
out : ndarray, optional
Alternative output array in which to place the result. Must
be of the same shape and buffer length as the expected output.
See `doc.ufuncs` (Section "Output arguments") for more details.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the input array.
If the default value is passed, then `keepdims` will not be
passed through to the `amin` method of sub-classes of
`ndarray`, however any non-default value will be. If the
sub-classes `sum` method does not implement `keepdims` any
exceptions will be raised.
Returns
-------
amin : ndarray or scalar
Minimum of `a`. If `axis` is None, the result is a scalar value.
If `axis` is given, the result is an array of dimension
``a.ndim - 1``.
See Also
--------
amax :
The maximum value of an array along a given axis, propagating any NaNs.
nanmin :
The minimum value of an array along a given axis, ignoring any NaNs.
minimum :
Element-wise minimum of two arrays, propagating any NaNs.
fmin :
Element-wise minimum of two arrays, ignoring any NaNs.
argmin :
Return the indices of the minimum values.
nanmax, maximum, fmax
Notes
-----
NaN values are propagated, that is if at least one item is NaN, the
corresponding min value will be NaN as well. To ignore NaN values
(MATLAB behavior), please use nanmin.
Don't use `amin` for element-wise comparison of 2 arrays; when
``a.shape[0]`` is 2, ``minimum(a[0], a[1])`` is faster than
``amin(a, axis=0)``.
Examples
--------
>>> a = np.arange(4).reshape((2,2))
>>> a
array([[0, 1],
[2, 3]])
>>> np.amin(a) # Minimum of the flattened array
0
>>> np.amin(a, axis=0) # Minima along the first axis
array([0, 1])
>>> np.amin(a, axis=1) # Minima along the second axis
array([0, 2])
>>> b = np.arange(5, dtype=float)
>>> b[2] = np.NaN
>>> np.amin(b)
nan
>>> np.nanmin(b)
0.0
"""
kwargs = {}
if keepdims is not np._NoValue:
kwargs['keepdims'] = keepdims
if type(a) is not mu.ndarray:
try:
amin = a.min
except AttributeError:
pass
else:
return amin(axis=axis, out=out, **kwargs)
return _methods._amin(a, axis=axis,
out=out, **kwargs)
def alen(a):
"""
Return the length of the first dimension of the input array.
Parameters
----------
a : array_like
Input array.
Returns
-------
alen : int
Length of the first dimension of `a`.
See Also
--------
shape, size
Examples
--------
>>> a = np.zeros((7,4,5))
>>> a.shape[0]
7
>>> np.alen(a)
7
"""
try:
return len(a)
except TypeError:
return len(array(a, ndmin=1))
def prod(a, axis=None, dtype=None, out=None, keepdims=np._NoValue):
"""
Return the product of array elements over a given axis.
Parameters
----------
a : array_like
Input data.
axis : None or int or tuple of ints, optional
Axis or axes along which a product is performed. The default,
axis=None, will calculate the product of all the elements in the
input array. If axis is negative it counts from the last to the
first axis.
.. versionadded:: 1.7.0
If axis is a tuple of ints, a product is performed on all of the
axes specified in the tuple instead of a single axis or all the
axes as before.
dtype : dtype, optional
The type of the returned array, as well as of the accumulator in
which the elements are multiplied. The dtype of `a` is used by
default unless `a` has an integer dtype of less precision than the
default platform integer. In that case, if `a` is signed then the
platform integer is used while if `a` is unsigned then an unsigned
integer of the same precision as the platform integer is used.
out : ndarray, optional
Alternative output array in which to place the result. It must have
the same shape as the expected output, but the type of the output
values will be cast if necessary.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left in the
result as dimensions with size one. With this option, the result
will broadcast correctly against the input array.
If the default value is passed, then `keepdims` will not be
passed through to the `prod` method of sub-classes of
`ndarray`, however any non-default value will be. If the
sub-classes `sum` method does not implement `keepdims` any
exceptions will be raised.
Returns
-------
product_along_axis : ndarray, see `dtype` parameter above.
An array shaped as `a` but with the specified axis removed.
Returns a reference to `out` if specified.
See Also
--------
ndarray.prod : equivalent method
numpy.doc.ufuncs : Section "Output arguments"
Notes
-----
Arithmetic is modular when using integer types, and no error is
raised on overflow. That means that, on a 32-bit platform:
>>> x = np.array([536870910, 536870910, 536870910, 536870910])
>>> np.prod(x) # random
16
The product of an empty array is the neutral element 1:
>>> np.prod([])
1.0
Examples
--------
By default, calculate the product of all elements:
>>> np.prod([1.,2.])
2.0
Even when the input array is two-dimensional:
>>> np.prod([[1.,2.],[3.,4.]])
24.0
But we can also specify the axis over which to multiply:
>>> np.prod([[1.,2.],[3.,4.]], axis=1)
array([ 2., 12.])
If the type of `x` is unsigned, then the output type is
the unsigned platform integer:
>>> x = np.array([1, 2, 3], dtype=np.uint8)
>>> np.prod(x).dtype == np.uint
True
If `x` is of a signed integer type, then the output type
is the default platform integer:
>>> x = np.array([1, 2, 3], dtype=np.int8)
>>> np.prod(x).dtype == int
True
"""
kwargs = {}
if keepdims is not np._NoValue:
kwargs['keepdims'] = keepdims
if type(a) is not mu.ndarray:
try:
prod = a.prod
except AttributeError:
pass
else:
return prod(axis=axis, dtype=dtype, out=out, **kwargs)
return _methods._prod(a, axis=axis, dtype=dtype,
out=out, **kwargs)
def cumprod(a, axis=None, dtype=None, out=None):
"""
Return the cumulative product of elements along a given axis.
Parameters
----------
a : array_like
Input array.
axis : int, optional
Axis along which the cumulative product is computed. By default
the input is flattened.
dtype : dtype, optional
Type of the returned array, as well as of the accumulator in which
the elements are multiplied. If *dtype* is not specified, it
defaults to the dtype of `a`, unless `a` has an integer dtype with
a precision less than that of the default platform integer. In
that case, the default platform integer is used instead.
out : ndarray, optional
Alternative output array in which to place the result. It must
have the same shape and buffer length as the expected output
but the type of the resulting values will be cast if necessary.
Returns
-------
cumprod : ndarray
A new array holding the result is returned unless `out` is
specified, in which case a reference to out is returned.
See Also
--------
numpy.doc.ufuncs : Section "Output arguments"
Notes
-----
Arithmetic is modular when using integer types, and no error is
raised on overflow.
Examples
--------
>>> a = np.array([1,2,3])
>>> np.cumprod(a) # intermediate results 1, 1*2
... # total product 1*2*3 = 6
array([1, 2, 6])
>>> a = np.array([[1, 2, 3], [4, 5, 6]])
>>> np.cumprod(a, dtype=float) # specify type of output
array([ 1., 2., 6., 24., 120., 720.])
The cumulative product for each column (i.e., over the rows) of `a`:
>>> np.cumprod(a, axis=0)
array([[ 1, 2, 3],
[ 4, 10, 18]])
The cumulative product for each row (i.e. over the columns) of `a`:
>>> np.cumprod(a,axis=1)
array([[ 1, 2, 6],
[ 4, 20, 120]])
"""
return _wrapfunc(a, 'cumprod', axis=axis, dtype=dtype, out=out)
def ndim(a):
"""
Return the number of dimensions of an array.
Parameters
----------
a : array_like
Input array. If it is not already an ndarray, a conversion is
attempted.
Returns
-------
number_of_dimensions : int
The number of dimensions in `a`. Scalars are zero-dimensional.
See Also
--------
ndarray.ndim : equivalent method
shape : dimensions of array
ndarray.shape : dimensions of array
Examples
--------
>>> np.ndim([[1,2,3],[4,5,6]])
2
>>> np.ndim(np.array([[1,2,3],[4,5,6]]))
2
>>> np.ndim(1)
0
"""
try:
return a.ndim
except AttributeError:
return asarray(a).ndim
def rank(a):
"""
Return the number of dimensions of an array.
If `a` is not already an array, a conversion is attempted.
Scalars are zero dimensional.
.. note::
This function is deprecated in NumPy 1.9 to avoid confusion with
`numpy.linalg.matrix_rank`. The ``ndim`` attribute or function
should be used instead.
Parameters
----------
a : array_like
Array whose number of dimensions is desired. If `a` is not an array,
a conversion is attempted.
Returns
-------
number_of_dimensions : int
The number of dimensions in the array.
See Also
--------
ndim : equivalent function
ndarray.ndim : equivalent property
shape : dimensions of array
ndarray.shape : dimensions of array
Notes
-----
In the old Numeric package, `rank` was the term used for the number of
dimensions, but in NumPy `ndim` is used instead.
Examples
--------
>>> np.rank([1,2,3])
1
>>> np.rank(np.array([[1,2,3],[4,5,6]]))
2
>>> np.rank(1)
0
"""
# 2014-04-12, 1.9
warnings.warn(
"`rank` is deprecated; use the `ndim` attribute or function instead. "
"To find the rank of a matrix see `numpy.linalg.matrix_rank`.",
VisibleDeprecationWarning, stacklevel=2)
try:
return a.ndim
except AttributeError:
return asarray(a).ndim
def size(a, axis=None):
"""
Return the number of elements along a given axis.
Parameters
----------
a : array_like
Input data.
axis : int, optional
Axis along which the elements are counted. By default, give
the total number of elements.
Returns
-------
element_count : int
Number of elements along the specified axis.
See Also
--------
shape : dimensions of array
ndarray.shape : dimensions of array
ndarray.size : number of elements in array
Examples
--------
>>> a = np.array([[1,2,3],[4,5,6]])
>>> np.size(a)
6
>>> np.size(a,1)
3
>>> np.size(a,0)
2
"""
if axis is None:
try:
return a.size
except AttributeError:
return asarray(a).size
else:
try:
return a.shape[axis]
except AttributeError:
return asarray(a).shape[axis]
def around(a, decimals=0, out=None):
"""
Evenly round to the given number of decimals.
Parameters
----------
a : array_like
Input data.
decimals : int, optional
Number of decimal places to round to (default: 0). If
decimals is negative, it specifies the number of positions to
the left of the decimal point.
out : ndarray, optional
Alternative output array in which to place the result. It must have
the same shape as the expected output, but the type of the output
values will be cast if necessary. See `doc.ufuncs` (Section
"Output arguments") for details.
Returns
-------
rounded_array : ndarray
An array of the same type as `a`, containing the rounded values.
Unless `out` was specified, a new array is created. A reference to
the result is returned.
The real and imaginary parts of complex numbers are rounded
separately. The result of rounding a float is a float.
See Also
--------
ndarray.round : equivalent method
ceil, fix, floor, rint, trunc
Notes
-----
For values exactly halfway between rounded decimal values, NumPy
rounds to the nearest even value. Thus 1.5 and 2.5 round to 2.0,
-0.5 and 0.5 round to 0.0, etc. Results may also be surprising due
to the inexact representation of decimal fractions in the IEEE
floating point standard [1]_ and errors introduced when scaling
by powers of ten.
References
----------
.. [1] "Lecture Notes on the Status of IEEE 754", William Kahan,
http://www.cs.berkeley.edu/~wkahan/ieee754status/IEEE754.PDF
.. [2] "How Futile are Mindless Assessments of
Roundoff in Floating-Point Computation?", William Kahan,
http://www.cs.berkeley.edu/~wkahan/Mindless.pdf
Examples
--------
>>> np.around([0.37, 1.64])
array([ 0., 2.])
>>> np.around([0.37, 1.64], decimals=1)
array([ 0.4, 1.6])
>>> np.around([.5, 1.5, 2.5, 3.5, 4.5]) # rounds to nearest even value
array([ 0., 2., 2., 4., 4.])
>>> np.around([1,2,3,11], decimals=1) # ndarray of ints is returned
array([ 1, 2, 3, 11])
>>> np.around([1,2,3,11], decimals=-1)
array([ 0, 0, 0, 10])
"""
return _wrapfunc(a, 'round', decimals=decimals, out=out)
def round_(a, decimals=0, out=None):
"""
Round an array to the given number of decimals.
Refer to `around` for full documentation.
See Also
--------
around : equivalent function
"""
return around(a, decimals=decimals, out=out)
def mean(a, axis=None, dtype=None, out=None, keepdims=np._NoValue):
"""
Compute the arithmetic mean along the specified axis.
Returns the average of the array elements. The average is taken over
the flattened array by default, otherwise over the specified axis.
`float64` intermediate and return values are used for integer inputs.
Parameters
----------
a : array_like
Array containing numbers whose mean is desired. If `a` is not an
array, a conversion is attempted.
axis : None or int or tuple of ints, optional
Axis or axes along which the means are computed. The default is to
compute the mean of the flattened array.
.. versionadded:: 1.7.0
If this is a tuple of ints, a mean is performed over multiple axes,
instead of a single axis or all the axes as before.
dtype : data-type, optional
Type to use in computing the mean. For integer inputs, the default
is `float64`; for floating point inputs, it is the same as the
input dtype.
out : ndarray, optional
Alternate output array in which to place the result. The default
is ``None``; if provided, it must have the same shape as the
expected output, but the type will be cast if necessary.
See `doc.ufuncs` for details.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the input array.
If the default value is passed, then `keepdims` will not be
passed through to the `mean` method of sub-classes of
`ndarray`, however any non-default value will be. If the
sub-classes `sum` method does not implement `keepdims` any
exceptions will be raised.
Returns
-------
m : ndarray, see dtype parameter above
If `out=None`, returns a new array containing the mean values,
otherwise a reference to the output array is returned.
See Also
--------
average : Weighted average
std, var, nanmean, nanstd, nanvar
Notes
-----
The arithmetic mean is the sum of the elements along the axis divided
by the number of elements.
Note that for floating-point input, the mean is computed using the
same precision the input has. Depending on the input data, this can
cause the results to be inaccurate, especially for `float32` (see
example below). Specifying a higher-precision accumulator using the
`dtype` keyword can alleviate this issue.
By default, `float16` results are computed using `float32` intermediates
for extra precision.
Examples
--------
>>> a = np.array([[1, 2], [3, 4]])
>>> np.mean(a)
2.5
>>> np.mean(a, axis=0)
array([ 2., 3.])
>>> np.mean(a, axis=1)
array([ 1.5, 3.5])
In single precision, `mean` can be inaccurate:
>>> a = np.zeros((2, 512*512), dtype=np.float32)
>>> a[0, :] = 1.0
>>> a[1, :] = 0.1
>>> np.mean(a)
0.54999924
Computing the mean in float64 is more accurate:
>>> np.mean(a, dtype=np.float64)
0.55000000074505806
"""
kwargs = {}
if keepdims is not np._NoValue:
kwargs['keepdims'] = keepdims
if type(a) is not mu.ndarray:
try:
mean = a.mean
except AttributeError:
pass
else:
return mean(axis=axis, dtype=dtype, out=out, **kwargs)
return _methods._mean(a, axis=axis, dtype=dtype,
out=out, **kwargs)
def std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue):
"""
Compute the standard deviation along the specified axis.
Returns the standard deviation, a measure of the spread of a distribution,
of the array elements. The standard deviation is computed for the
flattened array by default, otherwise over the specified axis.
Parameters
----------
a : array_like
Calculate the standard deviation of these values.
axis : None or int or tuple of ints, optional
Axis or axes along which the standard deviation is computed. The
default is to compute the standard deviation of the flattened array.
.. versionadded:: 1.7.0
If this is a tuple of ints, a standard deviation is performed over
multiple axes, instead of a single axis or all the axes as before.
dtype : dtype, optional
Type to use in computing the standard deviation. For arrays of
integer type the default is float64, for arrays of float types it is
the same as the array type.
out : ndarray, optional
Alternative output array in which to place the result. It must have
the same shape as the expected output but the type (of the calculated
values) will be cast if necessary.
ddof : int, optional
Means Delta Degrees of Freedom. The divisor used in calculations
is ``N - ddof``, where ``N`` represents the number of elements.
By default `ddof` is zero.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the input array.
If the default value is passed, then `keepdims` will not be
passed through to the `std` method of sub-classes of
`ndarray`, however any non-default value will be. If the
sub-classes `sum` method does not implement `keepdims` any
exceptions will be raised.
Returns
-------
standard_deviation : ndarray, see dtype parameter above.
If `out` is None, return a new array containing the standard deviation,
otherwise return a reference to the output array.
See Also
--------
var, mean, nanmean, nanstd, nanvar
numpy.doc.ufuncs : Section "Output arguments"
Notes
-----
The standard deviation is the square root of the average of the squared
deviations from the mean, i.e., ``std = sqrt(mean(abs(x - x.mean())**2))``.
The average squared deviation is normally calculated as
``x.sum() / N``, where ``N = len(x)``. If, however, `ddof` is specified,
the divisor ``N - ddof`` is used instead. In standard statistical
practice, ``ddof=1`` provides an unbiased estimator of the variance
of the infinite population. ``ddof=0`` provides a maximum likelihood
estimate of the variance for normally distributed variables. The
standard deviation computed in this function is the square root of
the estimated variance, so even with ``ddof=1``, it will not be an
unbiased estimate of the standard deviation per se.
Note that, for complex numbers, `std` takes the absolute
value before squaring, so that the result is always real and nonnegative.
For floating-point input, the *std* is computed using the same
precision the input has. Depending on the input data, this can cause
the results to be inaccurate, especially for float32 (see example below).
Specifying a higher-accuracy accumulator using the `dtype` keyword can
alleviate this issue.
Examples
--------
>>> a = np.array([[1, 2], [3, 4]])
>>> np.std(a)
1.1180339887498949
>>> np.std(a, axis=0)
array([ 1., 1.])
>>> np.std(a, axis=1)
array([ 0.5, 0.5])
In single precision, std() can be inaccurate:
>>> a = np.zeros((2, 512*512), dtype=np.float32)
>>> a[0, :] = 1.0
>>> a[1, :] = 0.1
>>> np.std(a)
0.45000005
Computing the standard deviation in float64 is more accurate:
>>> np.std(a, dtype=np.float64)
0.44999999925494177
"""
kwargs = {}
if keepdims is not np._NoValue:
kwargs['keepdims'] = keepdims
if type(a) is not mu.ndarray:
try:
std = a.std
except AttributeError:
pass
else:
return std(axis=axis, dtype=dtype, out=out, ddof=ddof, **kwargs)
return _methods._std(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
**kwargs)
def var(a, axis=None, dtype=None, out=None, ddof=0, keepdims=np._NoValue):
"""
Compute the variance along the specified axis.
Returns the variance of the array elements, a measure of the spread of a
distribution. The variance is computed for the flattened array by
default, otherwise over the specified axis.
Parameters
----------
a : array_like
Array containing numbers whose variance is desired. If `a` is not an
array, a conversion is attempted.
axis : None or int or tuple of ints, optional
Axis or axes along which the variance is computed. The default is to
compute the variance of the flattened array.
.. versionadded:: 1.7.0
If this is a tuple of ints, a variance is performed over multiple axes,
instead of a single axis or all the axes as before.
dtype : data-type, optional
Type to use in computing the variance. For arrays of integer type
the default is `float32`; for arrays of float types it is the same as
the array type.
out : ndarray, optional
Alternate output array in which to place the result. It must have
the same shape as the expected output, but the type is cast if
necessary.
ddof : int, optional
"Delta Degrees of Freedom": the divisor used in the calculation is
``N - ddof``, where ``N`` represents the number of elements. By
default `ddof` is zero.
keepdims : bool, optional
If this is set to True, the axes which are reduced are left
in the result as dimensions with size one. With this option,
the result will broadcast correctly against the input array.
If the default value is passed, then `keepdims` will not be
passed through to the `var` method of sub-classes of
`ndarray`, however any non-default value will be. If the
sub-classes `sum` method does not implement `keepdims` any
exceptions will be raised.
Returns
-------
variance : ndarray, see dtype parameter above
If ``out=None``, returns a new array containing the variance;
otherwise, a reference to the output array is returned.
See Also
--------
std , mean, nanmean, nanstd, nanvar
numpy.doc.ufuncs : Section "Output arguments"
Notes
-----
The variance is the average of the squared deviations from the mean,
i.e., ``var = mean(abs(x - x.mean())**2)``.
The mean is normally calculated as ``x.sum() / N``, where ``N = len(x)``.
If, however, `ddof` is specified, the divisor ``N - ddof`` is used
instead. In standard statistical practice, ``ddof=1`` provides an
unbiased estimator of the variance of a hypothetical infinite population.
``ddof=0`` provides a maximum likelihood estimate of the variance for
normally distributed variables.
Note that for complex numbers, the absolute value is taken before
squaring, so that the result is always real and nonnegative.
For floating-point input, the variance is computed using the same
precision the input has. Depending on the input data, this can cause
the results to be inaccurate, especially for `float32` (see example
below). Specifying a higher-accuracy accumulator using the ``dtype``
keyword can alleviate this issue.
Examples
--------
>>> a = np.array([[1, 2], [3, 4]])
>>> np.var(a)
1.25
>>> np.var(a, axis=0)
array([ 1., 1.])
>>> np.var(a, axis=1)
array([ 0.25, 0.25])
In single precision, var() can be inaccurate:
>>> a = np.zeros((2, 512*512), dtype=np.float32)
>>> a[0, :] = 1.0
>>> a[1, :] = 0.1
>>> np.var(a)
0.20250003
Computing the variance in float64 is more accurate:
>>> np.var(a, dtype=np.float64)
0.20249999932944759
>>> ((1-0.55)**2 + (0.1-0.55)**2)/2
0.2025
"""
kwargs = {}
if keepdims is not np._NoValue:
kwargs['keepdims'] = keepdims
if type(a) is not mu.ndarray:
try:
var = a.var
except AttributeError:
pass
else:
return var(axis=axis, dtype=dtype, out=out, ddof=ddof, **kwargs)
return _methods._var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
**kwargs)
| 100,637 | 30.498592 | 97 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/numeric.py
|
from __future__ import division, absolute_import, print_function
import collections
import itertools
import operator
import sys
import warnings
import numbers
import numpy as np
from . import multiarray
from .multiarray import (
_fastCopyAndTranspose as fastCopyAndTranspose, ALLOW_THREADS,
BUFSIZE, CLIP, MAXDIMS, MAY_SHARE_BOUNDS, MAY_SHARE_EXACT, RAISE,
WRAP, arange, array, broadcast, can_cast, compare_chararrays,
concatenate, copyto, count_nonzero, dot, dtype, empty,
empty_like, flatiter, frombuffer, fromfile, fromiter, fromstring,
inner, int_asbuffer, lexsort, matmul, may_share_memory,
min_scalar_type, ndarray, nditer, nested_iters, promote_types,
putmask, result_type, set_numeric_ops, shares_memory, vdot, where,
zeros, normalize_axis_index)
if sys.version_info[0] < 3:
from .multiarray import newbuffer, getbuffer
from . import umath
from .umath import (multiply, invert, sin, UFUNC_BUFSIZE_DEFAULT,
ERR_IGNORE, ERR_WARN, ERR_RAISE, ERR_CALL, ERR_PRINT,
ERR_LOG, ERR_DEFAULT, PINF, NAN)
from . import numerictypes
from .numerictypes import longlong, intc, int_, float_, complex_, bool_
from ._internal import TooHardError, AxisError
bitwise_not = invert
ufunc = type(sin)
newaxis = None
if sys.version_info[0] >= 3:
import pickle
basestring = str
import builtins
else:
import cPickle as pickle
import __builtin__ as builtins
loads = pickle.loads
__all__ = [
'newaxis', 'ndarray', 'flatiter', 'nditer', 'nested_iters', 'ufunc',
'arange', 'array', 'zeros', 'count_nonzero', 'empty', 'broadcast', 'dtype',
'fromstring', 'fromfile', 'frombuffer', 'int_asbuffer', 'where',
'argwhere', 'copyto', 'concatenate', 'fastCopyAndTranspose', 'lexsort',
'set_numeric_ops', 'can_cast', 'promote_types', 'min_scalar_type',
'result_type', 'asarray', 'asanyarray', 'ascontiguousarray',
'asfortranarray', 'isfortran', 'empty_like', 'zeros_like', 'ones_like',
'correlate', 'convolve', 'inner', 'dot', 'outer', 'vdot', 'roll',
'rollaxis', 'moveaxis', 'cross', 'tensordot', 'little_endian', 'require',
'fromiter', 'array_equal', 'array_equiv', 'indices', 'fromfunction',
'isclose', 'load', 'loads', 'isscalar', 'binary_repr', 'base_repr', 'ones',
'identity', 'allclose', 'compare_chararrays', 'putmask', 'seterr',
'geterr', 'setbufsize', 'getbufsize', 'seterrcall', 'geterrcall',
'errstate', 'flatnonzero', 'Inf', 'inf', 'infty', 'Infinity', 'nan', 'NaN',
'False_', 'True_', 'bitwise_not', 'CLIP', 'RAISE', 'WRAP', 'MAXDIMS',
'BUFSIZE', 'ALLOW_THREADS', 'ComplexWarning', 'full', 'full_like',
'matmul', 'shares_memory', 'may_share_memory', 'MAY_SHARE_BOUNDS',
'MAY_SHARE_EXACT', 'TooHardError', 'AxisError' ]
if sys.version_info[0] < 3:
__all__.extend(['getbuffer', 'newbuffer'])
class ComplexWarning(RuntimeWarning):
"""
The warning raised when casting a complex dtype to a real dtype.
As implemented, casting a complex number to a real discards its imaginary
part, but this behavior may not be what the user actually wants.
"""
pass
def zeros_like(a, dtype=None, order='K', subok=True):
"""
Return an array of zeros with the same shape and type as a given array.
Parameters
----------
a : array_like
The shape and data-type of `a` define these same attributes of
the returned array.
dtype : data-type, optional
Overrides the data type of the result.
.. versionadded:: 1.6.0
order : {'C', 'F', 'A', or 'K'}, optional
Overrides the memory layout of the result. 'C' means C-order,
'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,
'C' otherwise. 'K' means match the layout of `a` as closely
as possible.
.. versionadded:: 1.6.0
subok : bool, optional.
If True, then the newly created array will use the sub-class
type of 'a', otherwise it will be a base-class array. Defaults
to True.
Returns
-------
out : ndarray
Array of zeros with the same shape and type as `a`.
See Also
--------
ones_like : Return an array of ones with shape and type of input.
empty_like : Return an empty array with shape and type of input.
zeros : Return a new array setting values to zero.
ones : Return a new array setting values to one.
empty : Return a new uninitialized array.
Examples
--------
>>> x = np.arange(6)
>>> x = x.reshape((2, 3))
>>> x
array([[0, 1, 2],
[3, 4, 5]])
>>> np.zeros_like(x)
array([[0, 0, 0],
[0, 0, 0]])
>>> y = np.arange(3, dtype=float)
>>> y
array([ 0., 1., 2.])
>>> np.zeros_like(y)
array([ 0., 0., 0.])
"""
res = empty_like(a, dtype=dtype, order=order, subok=subok)
# needed instead of a 0 to get same result as zeros for for string dtypes
z = zeros(1, dtype=res.dtype)
multiarray.copyto(res, z, casting='unsafe')
return res
def ones(shape, dtype=None, order='C'):
"""
Return a new array of given shape and type, filled with ones.
Parameters
----------
shape : int or sequence of ints
Shape of the new array, e.g., ``(2, 3)`` or ``2``.
dtype : data-type, optional
The desired data-type for the array, e.g., `numpy.int8`. Default is
`numpy.float64`.
order : {'C', 'F'}, optional
Whether to store multidimensional data in C- or Fortran-contiguous
(row- or column-wise) order in memory.
Returns
-------
out : ndarray
Array of ones with the given shape, dtype, and order.
See Also
--------
zeros, ones_like
Examples
--------
>>> np.ones(5)
array([ 1., 1., 1., 1., 1.])
>>> np.ones((5,), dtype=int)
array([1, 1, 1, 1, 1])
>>> np.ones((2, 1))
array([[ 1.],
[ 1.]])
>>> s = (2,2)
>>> np.ones(s)
array([[ 1., 1.],
[ 1., 1.]])
"""
a = empty(shape, dtype, order)
multiarray.copyto(a, 1, casting='unsafe')
return a
def ones_like(a, dtype=None, order='K', subok=True):
"""
Return an array of ones with the same shape and type as a given array.
Parameters
----------
a : array_like
The shape and data-type of `a` define these same attributes of
the returned array.
dtype : data-type, optional
Overrides the data type of the result.
.. versionadded:: 1.6.0
order : {'C', 'F', 'A', or 'K'}, optional
Overrides the memory layout of the result. 'C' means C-order,
'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,
'C' otherwise. 'K' means match the layout of `a` as closely
as possible.
.. versionadded:: 1.6.0
subok : bool, optional.
If True, then the newly created array will use the sub-class
type of 'a', otherwise it will be a base-class array. Defaults
to True.
Returns
-------
out : ndarray
Array of ones with the same shape and type as `a`.
See Also
--------
zeros_like : Return an array of zeros with shape and type of input.
empty_like : Return an empty array with shape and type of input.
zeros : Return a new array setting values to zero.
ones : Return a new array setting values to one.
empty : Return a new uninitialized array.
Examples
--------
>>> x = np.arange(6)
>>> x = x.reshape((2, 3))
>>> x
array([[0, 1, 2],
[3, 4, 5]])
>>> np.ones_like(x)
array([[1, 1, 1],
[1, 1, 1]])
>>> y = np.arange(3, dtype=float)
>>> y
array([ 0., 1., 2.])
>>> np.ones_like(y)
array([ 1., 1., 1.])
"""
res = empty_like(a, dtype=dtype, order=order, subok=subok)
multiarray.copyto(res, 1, casting='unsafe')
return res
def full(shape, fill_value, dtype=None, order='C'):
"""
Return a new array of given shape and type, filled with `fill_value`.
Parameters
----------
shape : int or sequence of ints
Shape of the new array, e.g., ``(2, 3)`` or ``2``.
fill_value : scalar
Fill value.
dtype : data-type, optional
The desired data-type for the array The default, `None`, means
`np.array(fill_value).dtype`.
order : {'C', 'F'}, optional
Whether to store multidimensional data in C- or Fortran-contiguous
(row- or column-wise) order in memory.
Returns
-------
out : ndarray
Array of `fill_value` with the given shape, dtype, and order.
See Also
--------
zeros_like : Return an array of zeros with shape and type of input.
ones_like : Return an array of ones with shape and type of input.
empty_like : Return an empty array with shape and type of input.
full_like : Fill an array with shape and type of input.
zeros : Return a new array setting values to zero.
ones : Return a new array setting values to one.
empty : Return a new uninitialized array.
Examples
--------
>>> np.full((2, 2), np.inf)
array([[ inf, inf],
[ inf, inf]])
>>> np.full((2, 2), 10)
array([[10, 10],
[10, 10]])
"""
if dtype is None:
dtype = array(fill_value).dtype
a = empty(shape, dtype, order)
multiarray.copyto(a, fill_value, casting='unsafe')
return a
def full_like(a, fill_value, dtype=None, order='K', subok=True):
"""
Return a full array with the same shape and type as a given array.
Parameters
----------
a : array_like
The shape and data-type of `a` define these same attributes of
the returned array.
fill_value : scalar
Fill value.
dtype : data-type, optional
Overrides the data type of the result.
order : {'C', 'F', 'A', or 'K'}, optional
Overrides the memory layout of the result. 'C' means C-order,
'F' means F-order, 'A' means 'F' if `a` is Fortran contiguous,
'C' otherwise. 'K' means match the layout of `a` as closely
as possible.
subok : bool, optional.
If True, then the newly created array will use the sub-class
type of 'a', otherwise it will be a base-class array. Defaults
to True.
Returns
-------
out : ndarray
Array of `fill_value` with the same shape and type as `a`.
See Also
--------
zeros_like : Return an array of zeros with shape and type of input.
ones_like : Return an array of ones with shape and type of input.
empty_like : Return an empty array with shape and type of input.
zeros : Return a new array setting values to zero.
ones : Return a new array setting values to one.
empty : Return a new uninitialized array.
full : Fill a new array.
Examples
--------
>>> x = np.arange(6, dtype=int)
>>> np.full_like(x, 1)
array([1, 1, 1, 1, 1, 1])
>>> np.full_like(x, 0.1)
array([0, 0, 0, 0, 0, 0])
>>> np.full_like(x, 0.1, dtype=np.double)
array([ 0.1, 0.1, 0.1, 0.1, 0.1, 0.1])
>>> np.full_like(x, np.nan, dtype=np.double)
array([ nan, nan, nan, nan, nan, nan])
>>> y = np.arange(6, dtype=np.double)
>>> np.full_like(y, 0.1)
array([ 0.1, 0.1, 0.1, 0.1, 0.1, 0.1])
"""
res = empty_like(a, dtype=dtype, order=order, subok=subok)
multiarray.copyto(res, fill_value, casting='unsafe')
return res
def count_nonzero(a, axis=None):
"""
Counts the number of non-zero values in the array ``a``.
The word "non-zero" is in reference to the Python 2.x
built-in method ``__nonzero__()`` (renamed ``__bool__()``
in Python 3.x) of Python objects that tests an object's
"truthfulness". For example, any number is considered
truthful if it is nonzero, whereas any string is considered
truthful if it is not the empty string. Thus, this function
(recursively) counts how many elements in ``a`` (and in
sub-arrays thereof) have their ``__nonzero__()`` or ``__bool__()``
method evaluated to ``True``.
Parameters
----------
a : array_like
The array for which to count non-zeros.
axis : int or tuple, optional
Axis or tuple of axes along which to count non-zeros.
Default is None, meaning that non-zeros will be counted
along a flattened version of ``a``.
.. versionadded:: 1.12.0
Returns
-------
count : int or array of int
Number of non-zero values in the array along a given axis.
Otherwise, the total number of non-zero values in the array
is returned.
See Also
--------
nonzero : Return the coordinates of all the non-zero values.
Examples
--------
>>> np.count_nonzero(np.eye(4))
4
>>> np.count_nonzero([[0,1,7,0,0],[3,0,0,2,19]])
5
>>> np.count_nonzero([[0,1,7,0,0],[3,0,0,2,19]], axis=0)
array([1, 1, 1, 1, 1])
>>> np.count_nonzero([[0,1,7,0,0],[3,0,0,2,19]], axis=1)
array([2, 3])
"""
if axis is None:
return multiarray.count_nonzero(a)
a = asanyarray(a)
# TODO: this works around .astype(bool) not working properly (gh-9847)
if np.issubdtype(a.dtype, np.character):
a_bool = a != a.dtype.type()
else:
a_bool = a.astype(np.bool_, copy=False)
return a_bool.sum(axis=axis, dtype=np.intp)
def asarray(a, dtype=None, order=None):
"""Convert the input to an array.
Parameters
----------
a : array_like
Input data, in any form that can be converted to an array. This
includes lists, lists of tuples, tuples, tuples of tuples, tuples
of lists and ndarrays.
dtype : data-type, optional
By default, the data-type is inferred from the input data.
order : {'C', 'F'}, optional
Whether to use row-major (C-style) or
column-major (Fortran-style) memory representation.
Defaults to 'C'.
Returns
-------
out : ndarray
Array interpretation of `a`. No copy is performed if the input
is already an ndarray with matching dtype and order. If `a` is a
subclass of ndarray, a base class ndarray is returned.
See Also
--------
asanyarray : Similar function which passes through subclasses.
ascontiguousarray : Convert input to a contiguous array.
asfarray : Convert input to a floating point ndarray.
asfortranarray : Convert input to an ndarray with column-major
memory order.
asarray_chkfinite : Similar function which checks input for NaNs and Infs.
fromiter : Create an array from an iterator.
fromfunction : Construct an array by executing a function on grid
positions.
Examples
--------
Convert a list into an array:
>>> a = [1, 2]
>>> np.asarray(a)
array([1, 2])
Existing arrays are not copied:
>>> a = np.array([1, 2])
>>> np.asarray(a) is a
True
If `dtype` is set, array is copied only if dtype does not match:
>>> a = np.array([1, 2], dtype=np.float32)
>>> np.asarray(a, dtype=np.float32) is a
True
>>> np.asarray(a, dtype=np.float64) is a
False
Contrary to `asanyarray`, ndarray subclasses are not passed through:
>>> issubclass(np.matrix, np.ndarray)
True
>>> a = np.matrix([[1, 2]])
>>> np.asarray(a) is a
False
>>> np.asanyarray(a) is a
True
"""
return array(a, dtype, copy=False, order=order)
def asanyarray(a, dtype=None, order=None):
"""Convert the input to an ndarray, but pass ndarray subclasses through.
Parameters
----------
a : array_like
Input data, in any form that can be converted to an array. This
includes scalars, lists, lists of tuples, tuples, tuples of tuples,
tuples of lists, and ndarrays.
dtype : data-type, optional
By default, the data-type is inferred from the input data.
order : {'C', 'F'}, optional
Whether to use row-major (C-style) or column-major
(Fortran-style) memory representation. Defaults to 'C'.
Returns
-------
out : ndarray or an ndarray subclass
Array interpretation of `a`. If `a` is an ndarray or a subclass
of ndarray, it is returned as-is and no copy is performed.
See Also
--------
asarray : Similar function which always returns ndarrays.
ascontiguousarray : Convert input to a contiguous array.
asfarray : Convert input to a floating point ndarray.
asfortranarray : Convert input to an ndarray with column-major
memory order.
asarray_chkfinite : Similar function which checks input for NaNs and
Infs.
fromiter : Create an array from an iterator.
fromfunction : Construct an array by executing a function on grid
positions.
Examples
--------
Convert a list into an array:
>>> a = [1, 2]
>>> np.asanyarray(a)
array([1, 2])
Instances of `ndarray` subclasses are passed through as-is:
>>> a = np.matrix([1, 2])
>>> np.asanyarray(a) is a
True
"""
return array(a, dtype, copy=False, order=order, subok=True)
def ascontiguousarray(a, dtype=None):
"""
Return a contiguous array in memory (C order).
Parameters
----------
a : array_like
Input array.
dtype : str or dtype object, optional
Data-type of returned array.
Returns
-------
out : ndarray
Contiguous array of same shape and content as `a`, with type `dtype`
if specified.
See Also
--------
asfortranarray : Convert input to an ndarray with column-major
memory order.
require : Return an ndarray that satisfies requirements.
ndarray.flags : Information about the memory layout of the array.
Examples
--------
>>> x = np.arange(6).reshape(2,3)
>>> np.ascontiguousarray(x, dtype=np.float32)
array([[ 0., 1., 2.],
[ 3., 4., 5.]], dtype=float32)
>>> x.flags['C_CONTIGUOUS']
True
"""
return array(a, dtype, copy=False, order='C', ndmin=1)
def asfortranarray(a, dtype=None):
"""
Return an array laid out in Fortran order in memory.
Parameters
----------
a : array_like
Input array.
dtype : str or dtype object, optional
By default, the data-type is inferred from the input data.
Returns
-------
out : ndarray
The input `a` in Fortran, or column-major, order.
See Also
--------
ascontiguousarray : Convert input to a contiguous (C order) array.
asanyarray : Convert input to an ndarray with either row or
column-major memory order.
require : Return an ndarray that satisfies requirements.
ndarray.flags : Information about the memory layout of the array.
Examples
--------
>>> x = np.arange(6).reshape(2,3)
>>> y = np.asfortranarray(x)
>>> x.flags['F_CONTIGUOUS']
False
>>> y.flags['F_CONTIGUOUS']
True
"""
return array(a, dtype, copy=False, order='F', ndmin=1)
def require(a, dtype=None, requirements=None):
"""
Return an ndarray of the provided type that satisfies requirements.
This function is useful to be sure that an array with the correct flags
is returned for passing to compiled code (perhaps through ctypes).
Parameters
----------
a : array_like
The object to be converted to a type-and-requirement-satisfying array.
dtype : data-type
The required data-type. If None preserve the current dtype. If your
application requires the data to be in native byteorder, include
a byteorder specification as a part of the dtype specification.
requirements : str or list of str
The requirements list can be any of the following
* 'F_CONTIGUOUS' ('F') - ensure a Fortran-contiguous array
* 'C_CONTIGUOUS' ('C') - ensure a C-contiguous array
* 'ALIGNED' ('A') - ensure a data-type aligned array
* 'WRITEABLE' ('W') - ensure a writable array
* 'OWNDATA' ('O') - ensure an array that owns its own data
* 'ENSUREARRAY', ('E') - ensure a base array, instead of a subclass
See Also
--------
asarray : Convert input to an ndarray.
asanyarray : Convert to an ndarray, but pass through ndarray subclasses.
ascontiguousarray : Convert input to a contiguous array.
asfortranarray : Convert input to an ndarray with column-major
memory order.
ndarray.flags : Information about the memory layout of the array.
Notes
-----
The returned array will be guaranteed to have the listed requirements
by making a copy if needed.
Examples
--------
>>> x = np.arange(6).reshape(2,3)
>>> x.flags
C_CONTIGUOUS : True
F_CONTIGUOUS : False
OWNDATA : False
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
>>> y = np.require(x, dtype=np.float32, requirements=['A', 'O', 'W', 'F'])
>>> y.flags
C_CONTIGUOUS : False
F_CONTIGUOUS : True
OWNDATA : True
WRITEABLE : True
ALIGNED : True
WRITEBACKIFCOPY : False
UPDATEIFCOPY : False
"""
possible_flags = {'C':'C', 'C_CONTIGUOUS':'C', 'CONTIGUOUS':'C',
'F':'F', 'F_CONTIGUOUS':'F', 'FORTRAN':'F',
'A':'A', 'ALIGNED':'A',
'W':'W', 'WRITEABLE':'W',
'O':'O', 'OWNDATA':'O',
'E':'E', 'ENSUREARRAY':'E'}
if not requirements:
return asanyarray(a, dtype=dtype)
else:
requirements = set(possible_flags[x.upper()] for x in requirements)
if 'E' in requirements:
requirements.remove('E')
subok = False
else:
subok = True
order = 'A'
if requirements >= set(['C', 'F']):
raise ValueError('Cannot specify both "C" and "F" order')
elif 'F' in requirements:
order = 'F'
requirements.remove('F')
elif 'C' in requirements:
order = 'C'
requirements.remove('C')
arr = array(a, dtype=dtype, order=order, copy=False, subok=subok)
for prop in requirements:
if not arr.flags[prop]:
arr = arr.copy(order)
break
return arr
def isfortran(a):
"""
Returns True if the array is Fortran contiguous but *not* C contiguous.
This function is obsolete and, because of changes due to relaxed stride
checking, its return value for the same array may differ for versions
of NumPy >= 1.10.0 and previous versions. If you only want to check if an
array is Fortran contiguous use ``a.flags.f_contiguous`` instead.
Parameters
----------
a : ndarray
Input array.
Examples
--------
np.array allows to specify whether the array is written in C-contiguous
order (last index varies the fastest), or FORTRAN-contiguous order in
memory (first index varies the fastest).
>>> a = np.array([[1, 2, 3], [4, 5, 6]], order='C')
>>> a
array([[1, 2, 3],
[4, 5, 6]])
>>> np.isfortran(a)
False
>>> b = np.array([[1, 2, 3], [4, 5, 6]], order='FORTRAN')
>>> b
array([[1, 2, 3],
[4, 5, 6]])
>>> np.isfortran(b)
True
The transpose of a C-ordered array is a FORTRAN-ordered array.
>>> a = np.array([[1, 2, 3], [4, 5, 6]], order='C')
>>> a
array([[1, 2, 3],
[4, 5, 6]])
>>> np.isfortran(a)
False
>>> b = a.T
>>> b
array([[1, 4],
[2, 5],
[3, 6]])
>>> np.isfortran(b)
True
C-ordered arrays evaluate as False even if they are also FORTRAN-ordered.
>>> np.isfortran(np.array([1, 2], order='FORTRAN'))
False
"""
return a.flags.fnc
def argwhere(a):
"""
Find the indices of array elements that are non-zero, grouped by element.
Parameters
----------
a : array_like
Input data.
Returns
-------
index_array : ndarray
Indices of elements that are non-zero. Indices are grouped by element.
See Also
--------
where, nonzero
Notes
-----
``np.argwhere(a)`` is the same as ``np.transpose(np.nonzero(a))``.
The output of ``argwhere`` is not suitable for indexing arrays.
For this purpose use ``nonzero(a)`` instead.
Examples
--------
>>> x = np.arange(6).reshape(2,3)
>>> x
array([[0, 1, 2],
[3, 4, 5]])
>>> np.argwhere(x>1)
array([[0, 2],
[1, 0],
[1, 1],
[1, 2]])
"""
return transpose(nonzero(a))
def flatnonzero(a):
"""
Return indices that are non-zero in the flattened version of a.
This is equivalent to a.ravel().nonzero()[0].
Parameters
----------
a : ndarray
Input array.
Returns
-------
res : ndarray
Output array, containing the indices of the elements of `a.ravel()`
that are non-zero.
See Also
--------
nonzero : Return the indices of the non-zero elements of the input array.
ravel : Return a 1-D array containing the elements of the input array.
Examples
--------
>>> x = np.arange(-2, 3)
>>> x
array([-2, -1, 0, 1, 2])
>>> np.flatnonzero(x)
array([0, 1, 3, 4])
Use the indices of the non-zero elements as an index array to extract
these elements:
>>> x.ravel()[np.flatnonzero(x)]
array([-2, -1, 1, 2])
"""
return a.ravel().nonzero()[0]
_mode_from_name_dict = {'v': 0,
's': 1,
'f': 2}
def _mode_from_name(mode):
if isinstance(mode, basestring):
return _mode_from_name_dict[mode.lower()[0]]
return mode
def correlate(a, v, mode='valid'):
"""
Cross-correlation of two 1-dimensional sequences.
This function computes the correlation as generally defined in signal
processing texts::
c_{av}[k] = sum_n a[n+k] * conj(v[n])
with a and v sequences being zero-padded where necessary and conj being
the conjugate.
Parameters
----------
a, v : array_like
Input sequences.
mode : {'valid', 'same', 'full'}, optional
Refer to the `convolve` docstring. Note that the default
is 'valid', unlike `convolve`, which uses 'full'.
old_behavior : bool
`old_behavior` was removed in NumPy 1.10. If you need the old
behavior, use `multiarray.correlate`.
Returns
-------
out : ndarray
Discrete cross-correlation of `a` and `v`.
See Also
--------
convolve : Discrete, linear convolution of two one-dimensional sequences.
multiarray.correlate : Old, no conjugate, version of correlate.
Notes
-----
The definition of correlation above is not unique and sometimes correlation
may be defined differently. Another common definition is::
c'_{av}[k] = sum_n a[n] conj(v[n+k])
which is related to ``c_{av}[k]`` by ``c'_{av}[k] = c_{av}[-k]``.
Examples
--------
>>> np.correlate([1, 2, 3], [0, 1, 0.5])
array([ 3.5])
>>> np.correlate([1, 2, 3], [0, 1, 0.5], "same")
array([ 2. , 3.5, 3. ])
>>> np.correlate([1, 2, 3], [0, 1, 0.5], "full")
array([ 0.5, 2. , 3.5, 3. , 0. ])
Using complex sequences:
>>> np.correlate([1+1j, 2, 3-1j], [0, 1, 0.5j], 'full')
array([ 0.5-0.5j, 1.0+0.j , 1.5-1.5j, 3.0-1.j , 0.0+0.j ])
Note that you get the time reversed, complex conjugated result
when the two input sequences change places, i.e.,
``c_{va}[k] = c^{*}_{av}[-k]``:
>>> np.correlate([0, 1, 0.5j], [1+1j, 2, 3-1j], 'full')
array([ 0.0+0.j , 3.0+1.j , 1.5+1.5j, 1.0+0.j , 0.5+0.5j])
"""
mode = _mode_from_name(mode)
return multiarray.correlate2(a, v, mode)
def convolve(a, v, mode='full'):
"""
Returns the discrete, linear convolution of two one-dimensional sequences.
The convolution operator is often seen in signal processing, where it
models the effect of a linear time-invariant system on a signal [1]_. In
probability theory, the sum of two independent random variables is
distributed according to the convolution of their individual
distributions.
If `v` is longer than `a`, the arrays are swapped before computation.
Parameters
----------
a : (N,) array_like
First one-dimensional input array.
v : (M,) array_like
Second one-dimensional input array.
mode : {'full', 'valid', 'same'}, optional
'full':
By default, mode is 'full'. This returns the convolution
at each point of overlap, with an output shape of (N+M-1,). At
the end-points of the convolution, the signals do not overlap
completely, and boundary effects may be seen.
'same':
Mode 'same' returns output of length ``max(M, N)``. Boundary
effects are still visible.
'valid':
Mode 'valid' returns output of length
``max(M, N) - min(M, N) + 1``. The convolution product is only given
for points where the signals overlap completely. Values outside
the signal boundary have no effect.
Returns
-------
out : ndarray
Discrete, linear convolution of `a` and `v`.
See Also
--------
scipy.signal.fftconvolve : Convolve two arrays using the Fast Fourier
Transform.
scipy.linalg.toeplitz : Used to construct the convolution operator.
polymul : Polynomial multiplication. Same output as convolve, but also
accepts poly1d objects as input.
Notes
-----
The discrete convolution operation is defined as
.. math:: (a * v)[n] = \\sum_{m = -\\infty}^{\\infty} a[m] v[n - m]
It can be shown that a convolution :math:`x(t) * y(t)` in time/space
is equivalent to the multiplication :math:`X(f) Y(f)` in the Fourier
domain, after appropriate padding (padding is necessary to prevent
circular convolution). Since multiplication is more efficient (faster)
than convolution, the function `scipy.signal.fftconvolve` exploits the
FFT to calculate the convolution of large data-sets.
References
----------
.. [1] Wikipedia, "Convolution", http://en.wikipedia.org/wiki/Convolution.
Examples
--------
Note how the convolution operator flips the second array
before "sliding" the two across one another:
>>> np.convolve([1, 2, 3], [0, 1, 0.5])
array([ 0. , 1. , 2.5, 4. , 1.5])
Only return the middle values of the convolution.
Contains boundary effects, where zeros are taken
into account:
>>> np.convolve([1,2,3],[0,1,0.5], 'same')
array([ 1. , 2.5, 4. ])
The two arrays are of the same length, so there
is only one position where they completely overlap:
>>> np.convolve([1,2,3],[0,1,0.5], 'valid')
array([ 2.5])
"""
a, v = array(a, copy=False, ndmin=1), array(v, copy=False, ndmin=1)
if (len(v) > len(a)):
a, v = v, a
if len(a) == 0:
raise ValueError('a cannot be empty')
if len(v) == 0:
raise ValueError('v cannot be empty')
mode = _mode_from_name(mode)
return multiarray.correlate(a, v[::-1], mode)
def outer(a, b, out=None):
"""
Compute the outer product of two vectors.
Given two vectors, ``a = [a0, a1, ..., aM]`` and
``b = [b0, b1, ..., bN]``,
the outer product [1]_ is::
[[a0*b0 a0*b1 ... a0*bN ]
[a1*b0 .
[ ... .
[aM*b0 aM*bN ]]
Parameters
----------
a : (M,) array_like
First input vector. Input is flattened if
not already 1-dimensional.
b : (N,) array_like
Second input vector. Input is flattened if
not already 1-dimensional.
out : (M, N) ndarray, optional
A location where the result is stored
.. versionadded:: 1.9.0
Returns
-------
out : (M, N) ndarray
``out[i, j] = a[i] * b[j]``
See also
--------
inner
einsum : ``einsum('i,j->ij', a.ravel(), b.ravel())`` is the equivalent.
ufunc.outer : A generalization to N dimensions and other operations.
``np.multiply.outer(a.ravel(), b.ravel())`` is the equivalent.
References
----------
.. [1] : G. H. Golub and C. F. van Loan, *Matrix Computations*, 3rd
ed., Baltimore, MD, Johns Hopkins University Press, 1996,
pg. 8.
Examples
--------
Make a (*very* coarse) grid for computing a Mandelbrot set:
>>> rl = np.outer(np.ones((5,)), np.linspace(-2, 2, 5))
>>> rl
array([[-2., -1., 0., 1., 2.],
[-2., -1., 0., 1., 2.],
[-2., -1., 0., 1., 2.],
[-2., -1., 0., 1., 2.],
[-2., -1., 0., 1., 2.]])
>>> im = np.outer(1j*np.linspace(2, -2, 5), np.ones((5,)))
>>> im
array([[ 0.+2.j, 0.+2.j, 0.+2.j, 0.+2.j, 0.+2.j],
[ 0.+1.j, 0.+1.j, 0.+1.j, 0.+1.j, 0.+1.j],
[ 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j, 0.+0.j],
[ 0.-1.j, 0.-1.j, 0.-1.j, 0.-1.j, 0.-1.j],
[ 0.-2.j, 0.-2.j, 0.-2.j, 0.-2.j, 0.-2.j]])
>>> grid = rl + im
>>> grid
array([[-2.+2.j, -1.+2.j, 0.+2.j, 1.+2.j, 2.+2.j],
[-2.+1.j, -1.+1.j, 0.+1.j, 1.+1.j, 2.+1.j],
[-2.+0.j, -1.+0.j, 0.+0.j, 1.+0.j, 2.+0.j],
[-2.-1.j, -1.-1.j, 0.-1.j, 1.-1.j, 2.-1.j],
[-2.-2.j, -1.-2.j, 0.-2.j, 1.-2.j, 2.-2.j]])
An example using a "vector" of letters:
>>> x = np.array(['a', 'b', 'c'], dtype=object)
>>> np.outer(x, [1, 2, 3])
array([[a, aa, aaa],
[b, bb, bbb],
[c, cc, ccc]], dtype=object)
"""
a = asarray(a)
b = asarray(b)
return multiply(a.ravel()[:, newaxis], b.ravel()[newaxis,:], out)
def tensordot(a, b, axes=2):
"""
Compute tensor dot product along specified axes for arrays >= 1-D.
Given two tensors (arrays of dimension greater than or equal to one),
`a` and `b`, and an array_like object containing two array_like
objects, ``(a_axes, b_axes)``, sum the products of `a`'s and `b`'s
elements (components) over the axes specified by ``a_axes`` and
``b_axes``. The third argument can be a single non-negative
integer_like scalar, ``N``; if it is such, then the last ``N``
dimensions of `a` and the first ``N`` dimensions of `b` are summed
over.
Parameters
----------
a, b : array_like, len(shape) >= 1
Tensors to "dot".
axes : int or (2,) array_like
* integer_like
If an int N, sum over the last N axes of `a` and the first N axes
of `b` in order. The sizes of the corresponding axes must match.
* (2,) array_like
Or, a list of axes to be summed over, first sequence applying to `a`,
second to `b`. Both elements array_like must be of the same length.
See Also
--------
dot, einsum
Notes
-----
Three common use cases are:
* ``axes = 0`` : tensor product :math:`a\\otimes b`
* ``axes = 1`` : tensor dot product :math:`a\\cdot b`
* ``axes = 2`` : (default) tensor double contraction :math:`a:b`
When `axes` is integer_like, the sequence for evaluation will be: first
the -Nth axis in `a` and 0th axis in `b`, and the -1th axis in `a` and
Nth axis in `b` last.
When there is more than one axis to sum over - and they are not the last
(first) axes of `a` (`b`) - the argument `axes` should consist of
two sequences of the same length, with the first axis to sum over given
first in both sequences, the second axis second, and so forth.
Examples
--------
A "traditional" example:
>>> a = np.arange(60.).reshape(3,4,5)
>>> b = np.arange(24.).reshape(4,3,2)
>>> c = np.tensordot(a,b, axes=([1,0],[0,1]))
>>> c.shape
(5, 2)
>>> c
array([[ 4400., 4730.],
[ 4532., 4874.],
[ 4664., 5018.],
[ 4796., 5162.],
[ 4928., 5306.]])
>>> # A slower but equivalent way of computing the same...
>>> d = np.zeros((5,2))
>>> for i in range(5):
... for j in range(2):
... for k in range(3):
... for n in range(4):
... d[i,j] += a[k,n,i] * b[n,k,j]
>>> c == d
array([[ True, True],
[ True, True],
[ True, True],
[ True, True],
[ True, True]])
An extended example taking advantage of the overloading of + and \\*:
>>> a = np.array(range(1, 9))
>>> a.shape = (2, 2, 2)
>>> A = np.array(('a', 'b', 'c', 'd'), dtype=object)
>>> A.shape = (2, 2)
>>> a; A
array([[[1, 2],
[3, 4]],
[[5, 6],
[7, 8]]])
array([[a, b],
[c, d]], dtype=object)
>>> np.tensordot(a, A) # third argument default is 2 for double-contraction
array([abbcccdddd, aaaaabbbbbbcccccccdddddddd], dtype=object)
>>> np.tensordot(a, A, 1)
array([[[acc, bdd],
[aaacccc, bbbdddd]],
[[aaaaacccccc, bbbbbdddddd],
[aaaaaaacccccccc, bbbbbbbdddddddd]]], dtype=object)
>>> np.tensordot(a, A, 0) # tensor product (result too long to incl.)
array([[[[[a, b],
[c, d]],
...
>>> np.tensordot(a, A, (0, 1))
array([[[abbbbb, cddddd],
[aabbbbbb, ccdddddd]],
[[aaabbbbbbb, cccddddddd],
[aaaabbbbbbbb, ccccdddddddd]]], dtype=object)
>>> np.tensordot(a, A, (2, 1))
array([[[abb, cdd],
[aaabbbb, cccdddd]],
[[aaaaabbbbbb, cccccdddddd],
[aaaaaaabbbbbbbb, cccccccdddddddd]]], dtype=object)
>>> np.tensordot(a, A, ((0, 1), (0, 1)))
array([abbbcccccddddddd, aabbbbccccccdddddddd], dtype=object)
>>> np.tensordot(a, A, ((2, 1), (1, 0)))
array([acccbbdddd, aaaaacccccccbbbbbbdddddddd], dtype=object)
"""
try:
iter(axes)
except Exception:
axes_a = list(range(-axes, 0))
axes_b = list(range(0, axes))
else:
axes_a, axes_b = axes
try:
na = len(axes_a)
axes_a = list(axes_a)
except TypeError:
axes_a = [axes_a]
na = 1
try:
nb = len(axes_b)
axes_b = list(axes_b)
except TypeError:
axes_b = [axes_b]
nb = 1
a, b = asarray(a), asarray(b)
as_ = a.shape
nda = a.ndim
bs = b.shape
ndb = b.ndim
equal = True
if na != nb:
equal = False
else:
for k in range(na):
if as_[axes_a[k]] != bs[axes_b[k]]:
equal = False
break
if axes_a[k] < 0:
axes_a[k] += nda
if axes_b[k] < 0:
axes_b[k] += ndb
if not equal:
raise ValueError("shape-mismatch for sum")
# Move the axes to sum over to the end of "a"
# and to the front of "b"
notin = [k for k in range(nda) if k not in axes_a]
newaxes_a = notin + axes_a
N2 = 1
for axis in axes_a:
N2 *= as_[axis]
newshape_a = (int(multiply.reduce([as_[ax] for ax in notin])), N2)
olda = [as_[axis] for axis in notin]
notin = [k for k in range(ndb) if k not in axes_b]
newaxes_b = axes_b + notin
N2 = 1
for axis in axes_b:
N2 *= bs[axis]
newshape_b = (N2, int(multiply.reduce([bs[ax] for ax in notin])))
oldb = [bs[axis] for axis in notin]
at = a.transpose(newaxes_a).reshape(newshape_a)
bt = b.transpose(newaxes_b).reshape(newshape_b)
res = dot(at, bt)
return res.reshape(olda + oldb)
def roll(a, shift, axis=None):
"""
Roll array elements along a given axis.
Elements that roll beyond the last position are re-introduced at
the first.
Parameters
----------
a : array_like
Input array.
shift : int or tuple of ints
The number of places by which elements are shifted. If a tuple,
then `axis` must be a tuple of the same size, and each of the
given axes is shifted by the corresponding number. If an int
while `axis` is a tuple of ints, then the same value is used for
all given axes.
axis : int or tuple of ints, optional
Axis or axes along which elements are shifted. By default, the
array is flattened before shifting, after which the original
shape is restored.
Returns
-------
res : ndarray
Output array, with the same shape as `a`.
See Also
--------
rollaxis : Roll the specified axis backwards, until it lies in a
given position.
Notes
-----
.. versionadded:: 1.12.0
Supports rolling over multiple dimensions simultaneously.
Examples
--------
>>> x = np.arange(10)
>>> np.roll(x, 2)
array([8, 9, 0, 1, 2, 3, 4, 5, 6, 7])
>>> x2 = np.reshape(x, (2,5))
>>> x2
array([[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9]])
>>> np.roll(x2, 1)
array([[9, 0, 1, 2, 3],
[4, 5, 6, 7, 8]])
>>> np.roll(x2, 1, axis=0)
array([[5, 6, 7, 8, 9],
[0, 1, 2, 3, 4]])
>>> np.roll(x2, 1, axis=1)
array([[4, 0, 1, 2, 3],
[9, 5, 6, 7, 8]])
"""
a = asanyarray(a)
if axis is None:
return roll(a.ravel(), shift, 0).reshape(a.shape)
else:
axis = normalize_axis_tuple(axis, a.ndim, allow_duplicate=True)
broadcasted = broadcast(shift, axis)
if broadcasted.ndim > 1:
raise ValueError(
"'shift' and 'axis' should be scalars or 1D sequences")
shifts = {ax: 0 for ax in range(a.ndim)}
for sh, ax in broadcasted:
shifts[ax] += sh
rolls = [((slice(None), slice(None)),)] * a.ndim
for ax, offset in shifts.items():
offset %= a.shape[ax] or 1 # If `a` is empty, nothing matters.
if offset:
# (original, result), (original, result)
rolls[ax] = ((slice(None, -offset), slice(offset, None)),
(slice(-offset, None), slice(None, offset)))
result = empty_like(a)
for indices in itertools.product(*rolls):
arr_index, res_index = zip(*indices)
result[res_index] = a[arr_index]
return result
def rollaxis(a, axis, start=0):
"""
Roll the specified axis backwards, until it lies in a given position.
This function continues to be supported for backward compatibility, but you
should prefer `moveaxis`. The `moveaxis` function was added in NumPy
1.11.
Parameters
----------
a : ndarray
Input array.
axis : int
The axis to roll backwards. The positions of the other axes do not
change relative to one another.
start : int, optional
The axis is rolled until it lies before this position. The default,
0, results in a "complete" roll.
Returns
-------
res : ndarray
For NumPy >= 1.10.0 a view of `a` is always returned. For earlier
NumPy versions a view of `a` is returned only if the order of the
axes is changed, otherwise the input array is returned.
See Also
--------
moveaxis : Move array axes to new positions.
roll : Roll the elements of an array by a number of positions along a
given axis.
Examples
--------
>>> a = np.ones((3,4,5,6))
>>> np.rollaxis(a, 3, 1).shape
(3, 6, 4, 5)
>>> np.rollaxis(a, 2).shape
(5, 3, 4, 6)
>>> np.rollaxis(a, 1, 4).shape
(3, 5, 6, 4)
"""
n = a.ndim
axis = normalize_axis_index(axis, n)
if start < 0:
start += n
msg = "'%s' arg requires %d <= %s < %d, but %d was passed in"
if not (0 <= start < n + 1):
raise AxisError(msg % ('start', -n, 'start', n + 1, start))
if axis < start:
# it's been removed
start -= 1
if axis == start:
return a[...]
axes = list(range(0, n))
axes.remove(axis)
axes.insert(start, axis)
return a.transpose(axes)
def normalize_axis_tuple(axis, ndim, argname=None, allow_duplicate=False):
"""
Normalizes an axis argument into a tuple of non-negative integer axes.
This handles shorthands such as ``1`` and converts them to ``(1,)``,
as well as performing the handling of negative indices covered by
`normalize_axis_index`.
By default, this forbids axes from being specified multiple times.
Used internally by multi-axis-checking logic.
.. versionadded:: 1.13.0
Parameters
----------
axis : int, iterable of int
The un-normalized index or indices of the axis.
ndim : int
The number of dimensions of the array that `axis` should be normalized
against.
argname : str, optional
A prefix to put before the error message, typically the name of the
argument.
allow_duplicate : bool, optional
If False, the default, disallow an axis from being specified twice.
Returns
-------
normalized_axes : tuple of int
The normalized axis index, such that `0 <= normalized_axis < ndim`
Raises
------
AxisError
If any axis provided is out of range
ValueError
If an axis is repeated
See also
--------
normalize_axis_index : normalizing a single scalar axis
"""
try:
axis = [operator.index(axis)]
except TypeError:
axis = tuple(axis)
axis = tuple(normalize_axis_index(ax, ndim, argname) for ax in axis)
if not allow_duplicate and len(set(axis)) != len(axis):
if argname:
raise ValueError('repeated axis in `{}` argument'.format(argname))
else:
raise ValueError('repeated axis')
return axis
def moveaxis(a, source, destination):
"""
Move axes of an array to new positions.
Other axes remain in their original order.
.. versionadded:: 1.11.0
Parameters
----------
a : np.ndarray
The array whose axes should be reordered.
source : int or sequence of int
Original positions of the axes to move. These must be unique.
destination : int or sequence of int
Destination positions for each of the original axes. These must also be
unique.
Returns
-------
result : np.ndarray
Array with moved axes. This array is a view of the input array.
See Also
--------
transpose: Permute the dimensions of an array.
swapaxes: Interchange two axes of an array.
Examples
--------
>>> x = np.zeros((3, 4, 5))
>>> np.moveaxis(x, 0, -1).shape
(4, 5, 3)
>>> np.moveaxis(x, -1, 0).shape
(5, 3, 4)
These all achieve the same result:
>>> np.transpose(x).shape
(5, 4, 3)
>>> np.swapaxes(x, 0, -1).shape
(5, 4, 3)
>>> np.moveaxis(x, [0, 1], [-1, -2]).shape
(5, 4, 3)
>>> np.moveaxis(x, [0, 1, 2], [-1, -2, -3]).shape
(5, 4, 3)
"""
try:
# allow duck-array types if they define transpose
transpose = a.transpose
except AttributeError:
a = asarray(a)
transpose = a.transpose
source = normalize_axis_tuple(source, a.ndim, 'source')
destination = normalize_axis_tuple(destination, a.ndim, 'destination')
if len(source) != len(destination):
raise ValueError('`source` and `destination` arguments must have '
'the same number of elements')
order = [n for n in range(a.ndim) if n not in source]
for dest, src in sorted(zip(destination, source)):
order.insert(dest, src)
result = transpose(order)
return result
# fix hack in scipy which imports this function
def _move_axis_to_0(a, axis):
return moveaxis(a, axis, 0)
def cross(a, b, axisa=-1, axisb=-1, axisc=-1, axis=None):
"""
Return the cross product of two (arrays of) vectors.
The cross product of `a` and `b` in :math:`R^3` is a vector perpendicular
to both `a` and `b`. If `a` and `b` are arrays of vectors, the vectors
are defined by the last axis of `a` and `b` by default, and these axes
can have dimensions 2 or 3. Where the dimension of either `a` or `b` is
2, the third component of the input vector is assumed to be zero and the
cross product calculated accordingly. In cases where both input vectors
have dimension 2, the z-component of the cross product is returned.
Parameters
----------
a : array_like
Components of the first vector(s).
b : array_like
Components of the second vector(s).
axisa : int, optional
Axis of `a` that defines the vector(s). By default, the last axis.
axisb : int, optional
Axis of `b` that defines the vector(s). By default, the last axis.
axisc : int, optional
Axis of `c` containing the cross product vector(s). Ignored if
both input vectors have dimension 2, as the return is scalar.
By default, the last axis.
axis : int, optional
If defined, the axis of `a`, `b` and `c` that defines the vector(s)
and cross product(s). Overrides `axisa`, `axisb` and `axisc`.
Returns
-------
c : ndarray
Vector cross product(s).
Raises
------
ValueError
When the dimension of the vector(s) in `a` and/or `b` does not
equal 2 or 3.
See Also
--------
inner : Inner product
outer : Outer product.
ix_ : Construct index arrays.
Notes
-----
.. versionadded:: 1.9.0
Supports full broadcasting of the inputs.
Examples
--------
Vector cross-product.
>>> x = [1, 2, 3]
>>> y = [4, 5, 6]
>>> np.cross(x, y)
array([-3, 6, -3])
One vector with dimension 2.
>>> x = [1, 2]
>>> y = [4, 5, 6]
>>> np.cross(x, y)
array([12, -6, -3])
Equivalently:
>>> x = [1, 2, 0]
>>> y = [4, 5, 6]
>>> np.cross(x, y)
array([12, -6, -3])
Both vectors with dimension 2.
>>> x = [1,2]
>>> y = [4,5]
>>> np.cross(x, y)
-3
Multiple vector cross-products. Note that the direction of the cross
product vector is defined by the `right-hand rule`.
>>> x = np.array([[1,2,3], [4,5,6]])
>>> y = np.array([[4,5,6], [1,2,3]])
>>> np.cross(x, y)
array([[-3, 6, -3],
[ 3, -6, 3]])
The orientation of `c` can be changed using the `axisc` keyword.
>>> np.cross(x, y, axisc=0)
array([[-3, 3],
[ 6, -6],
[-3, 3]])
Change the vector definition of `x` and `y` using `axisa` and `axisb`.
>>> x = np.array([[1,2,3], [4,5,6], [7, 8, 9]])
>>> y = np.array([[7, 8, 9], [4,5,6], [1,2,3]])
>>> np.cross(x, y)
array([[ -6, 12, -6],
[ 0, 0, 0],
[ 6, -12, 6]])
>>> np.cross(x, y, axisa=0, axisb=0)
array([[-24, 48, -24],
[-30, 60, -30],
[-36, 72, -36]])
"""
if axis is not None:
axisa, axisb, axisc = (axis,) * 3
a = asarray(a)
b = asarray(b)
# Check axisa and axisb are within bounds
axisa = normalize_axis_index(axisa, a.ndim, msg_prefix='axisa')
axisb = normalize_axis_index(axisb, b.ndim, msg_prefix='axisb')
# Move working axis to the end of the shape
a = moveaxis(a, axisa, -1)
b = moveaxis(b, axisb, -1)
msg = ("incompatible dimensions for cross product\n"
"(dimension must be 2 or 3)")
if a.shape[-1] not in (2, 3) or b.shape[-1] not in (2, 3):
raise ValueError(msg)
# Create the output array
shape = broadcast(a[..., 0], b[..., 0]).shape
if a.shape[-1] == 3 or b.shape[-1] == 3:
shape += (3,)
# Check axisc is within bounds
axisc = normalize_axis_index(axisc, len(shape), msg_prefix='axisc')
dtype = promote_types(a.dtype, b.dtype)
cp = empty(shape, dtype)
# create local aliases for readability
a0 = a[..., 0]
a1 = a[..., 1]
if a.shape[-1] == 3:
a2 = a[..., 2]
b0 = b[..., 0]
b1 = b[..., 1]
if b.shape[-1] == 3:
b2 = b[..., 2]
if cp.ndim != 0 and cp.shape[-1] == 3:
cp0 = cp[..., 0]
cp1 = cp[..., 1]
cp2 = cp[..., 2]
if a.shape[-1] == 2:
if b.shape[-1] == 2:
# a0 * b1 - a1 * b0
multiply(a0, b1, out=cp)
cp -= a1 * b0
return cp
else:
assert b.shape[-1] == 3
# cp0 = a1 * b2 - 0 (a2 = 0)
# cp1 = 0 - a0 * b2 (a2 = 0)
# cp2 = a0 * b1 - a1 * b0
multiply(a1, b2, out=cp0)
multiply(a0, b2, out=cp1)
negative(cp1, out=cp1)
multiply(a0, b1, out=cp2)
cp2 -= a1 * b0
else:
assert a.shape[-1] == 3
if b.shape[-1] == 3:
# cp0 = a1 * b2 - a2 * b1
# cp1 = a2 * b0 - a0 * b2
# cp2 = a0 * b1 - a1 * b0
multiply(a1, b2, out=cp0)
tmp = array(a2 * b1)
cp0 -= tmp
multiply(a2, b0, out=cp1)
multiply(a0, b2, out=tmp)
cp1 -= tmp
multiply(a0, b1, out=cp2)
multiply(a1, b0, out=tmp)
cp2 -= tmp
else:
assert b.shape[-1] == 2
# cp0 = 0 - a2 * b1 (b2 = 0)
# cp1 = a2 * b0 - 0 (b2 = 0)
# cp2 = a0 * b1 - a1 * b0
multiply(a2, b1, out=cp0)
negative(cp0, out=cp0)
multiply(a2, b0, out=cp1)
multiply(a0, b1, out=cp2)
cp2 -= a1 * b0
return moveaxis(cp, -1, axisc)
little_endian = (sys.byteorder == 'little')
def indices(dimensions, dtype=int):
"""
Return an array representing the indices of a grid.
Compute an array where the subarrays contain index values 0,1,...
varying only along the corresponding axis.
Parameters
----------
dimensions : sequence of ints
The shape of the grid.
dtype : dtype, optional
Data type of the result.
Returns
-------
grid : ndarray
The array of grid indices,
``grid.shape = (len(dimensions),) + tuple(dimensions)``.
See Also
--------
mgrid, meshgrid
Notes
-----
The output shape is obtained by prepending the number of dimensions
in front of the tuple of dimensions, i.e. if `dimensions` is a tuple
``(r0, ..., rN-1)`` of length ``N``, the output shape is
``(N,r0,...,rN-1)``.
The subarrays ``grid[k]`` contains the N-D array of indices along the
``k-th`` axis. Explicitly::
grid[k,i0,i1,...,iN-1] = ik
Examples
--------
>>> grid = np.indices((2, 3))
>>> grid.shape
(2, 2, 3)
>>> grid[0] # row indices
array([[0, 0, 0],
[1, 1, 1]])
>>> grid[1] # column indices
array([[0, 1, 2],
[0, 1, 2]])
The indices can be used as an index into an array.
>>> x = np.arange(20).reshape(5, 4)
>>> row, col = np.indices((2, 3))
>>> x[row, col]
array([[0, 1, 2],
[4, 5, 6]])
Note that it would be more straightforward in the above example to
extract the required elements directly with ``x[:2, :3]``.
"""
dimensions = tuple(dimensions)
N = len(dimensions)
shape = (1,)*N
res = empty((N,)+dimensions, dtype=dtype)
for i, dim in enumerate(dimensions):
res[i] = arange(dim, dtype=dtype).reshape(
shape[:i] + (dim,) + shape[i+1:]
)
return res
def fromfunction(function, shape, **kwargs):
"""
Construct an array by executing a function over each coordinate.
The resulting array therefore has a value ``fn(x, y, z)`` at
coordinate ``(x, y, z)``.
Parameters
----------
function : callable
The function is called with N parameters, where N is the rank of
`shape`. Each parameter represents the coordinates of the array
varying along a specific axis. For example, if `shape`
were ``(2, 2)``, then the parameters would be
``array([[0, 0], [1, 1]])`` and ``array([[0, 1], [0, 1]])``
shape : (N,) tuple of ints
Shape of the output array, which also determines the shape of
the coordinate arrays passed to `function`.
dtype : data-type, optional
Data-type of the coordinate arrays passed to `function`.
By default, `dtype` is float.
Returns
-------
fromfunction : any
The result of the call to `function` is passed back directly.
Therefore the shape of `fromfunction` is completely determined by
`function`. If `function` returns a scalar value, the shape of
`fromfunction` would match the `shape` parameter.
See Also
--------
indices, meshgrid
Notes
-----
Keywords other than `dtype` are passed to `function`.
Examples
--------
>>> np.fromfunction(lambda i, j: i == j, (3, 3), dtype=int)
array([[ True, False, False],
[False, True, False],
[False, False, True]])
>>> np.fromfunction(lambda i, j: i + j, (3, 3), dtype=int)
array([[0, 1, 2],
[1, 2, 3],
[2, 3, 4]])
"""
dtype = kwargs.pop('dtype', float)
args = indices(shape, dtype=dtype)
return function(*args, **kwargs)
def isscalar(num):
"""
Returns True if the type of `num` is a scalar type.
Parameters
----------
num : any
Input argument, can be of any type and shape.
Returns
-------
val : bool
True if `num` is a scalar type, False if it is not.
Examples
--------
>>> np.isscalar(3.1)
True
>>> np.isscalar([3.1])
False
>>> np.isscalar(False)
True
>>> np.isscalar('numpy')
True
NumPy supports PEP 3141 numbers:
>>> from fractions import Fraction
>>> isscalar(Fraction(5, 17))
True
>>> from numbers import Number
>>> isscalar(Number())
True
"""
return (isinstance(num, generic)
or type(num) in ScalarType
or isinstance(num, numbers.Number))
def binary_repr(num, width=None):
"""
Return the binary representation of the input number as a string.
For negative numbers, if width is not given, a minus sign is added to the
front. If width is given, the two's complement of the number is
returned, with respect to that width.
In a two's-complement system negative numbers are represented by the two's
complement of the absolute value. This is the most common method of
representing signed integers on computers [1]_. A N-bit two's-complement
system can represent every integer in the range
:math:`-2^{N-1}` to :math:`+2^{N-1}-1`.
Parameters
----------
num : int
Only an integer decimal number can be used.
width : int, optional
The length of the returned string if `num` is positive, or the length
of the two's complement if `num` is negative, provided that `width` is
at least a sufficient number of bits for `num` to be represented in the
designated form.
If the `width` value is insufficient, it will be ignored, and `num` will
be returned in binary (`num` > 0) or two's complement (`num` < 0) form
with its width equal to the minimum number of bits needed to represent
the number in the designated form. This behavior is deprecated and will
later raise an error.
.. deprecated:: 1.12.0
Returns
-------
bin : str
Binary representation of `num` or two's complement of `num`.
See Also
--------
base_repr: Return a string representation of a number in the given base
system.
bin: Python's built-in binary representation generator of an integer.
Notes
-----
`binary_repr` is equivalent to using `base_repr` with base 2, but about 25x
faster.
References
----------
.. [1] Wikipedia, "Two's complement",
http://en.wikipedia.org/wiki/Two's_complement
Examples
--------
>>> np.binary_repr(3)
'11'
>>> np.binary_repr(-3)
'-11'
>>> np.binary_repr(3, width=4)
'0011'
The two's complement is returned when the input number is negative and
width is specified:
>>> np.binary_repr(-3, width=3)
'101'
>>> np.binary_repr(-3, width=5)
'11101'
"""
def warn_if_insufficient(width, binwdith):
if width is not None and width < binwidth:
warnings.warn(
"Insufficient bit width provided. This behavior "
"will raise an error in the future.", DeprecationWarning,
stacklevel=3)
if num == 0:
return '0' * (width or 1)
elif num > 0:
binary = bin(num)[2:]
binwidth = len(binary)
outwidth = (binwidth if width is None
else max(binwidth, width))
warn_if_insufficient(width, binwidth)
return binary.zfill(outwidth)
else:
if width is None:
return '-' + bin(-num)[2:]
else:
poswidth = len(bin(-num)[2:])
# See gh-8679: remove extra digit
# for numbers at boundaries.
if 2**(poswidth - 1) == -num:
poswidth -= 1
twocomp = 2**(poswidth + 1) + num
binary = bin(twocomp)[2:]
binwidth = len(binary)
outwidth = max(binwidth, width)
warn_if_insufficient(width, binwidth)
return '1' * (outwidth - binwidth) + binary
def base_repr(number, base=2, padding=0):
"""
Return a string representation of a number in the given base system.
Parameters
----------
number : int
The value to convert. Positive and negative values are handled.
base : int, optional
Convert `number` to the `base` number system. The valid range is 2-36,
the default value is 2.
padding : int, optional
Number of zeros padded on the left. Default is 0 (no padding).
Returns
-------
out : str
String representation of `number` in `base` system.
See Also
--------
binary_repr : Faster version of `base_repr` for base 2.
Examples
--------
>>> np.base_repr(5)
'101'
>>> np.base_repr(6, 5)
'11'
>>> np.base_repr(7, base=5, padding=3)
'00012'
>>> np.base_repr(10, base=16)
'A'
>>> np.base_repr(32, base=16)
'20'
"""
digits = '0123456789ABCDEFGHIJKLMNOPQRSTUVWXYZ'
if base > len(digits):
raise ValueError("Bases greater than 36 not handled in base_repr.")
elif base < 2:
raise ValueError("Bases less than 2 not handled in base_repr.")
num = abs(number)
res = []
while num:
res.append(digits[num % base])
num //= base
if padding:
res.append('0' * padding)
if number < 0:
res.append('-')
return ''.join(reversed(res or '0'))
def load(file):
"""
Wrapper around cPickle.load which accepts either a file-like object or
a filename.
Note that the NumPy binary format is not based on pickle/cPickle anymore.
For details on the preferred way of loading and saving files, see `load`
and `save`.
See Also
--------
load, save
"""
if isinstance(file, type("")):
file = open(file, "rb")
return pickle.load(file)
# These are all essentially abbreviations
# These might wind up in a special abbreviations module
def _maketup(descr, val):
dt = dtype(descr)
# Place val in all scalar tuples:
fields = dt.fields
if fields is None:
return val
else:
res = [_maketup(fields[name][0], val) for name in dt.names]
return tuple(res)
def identity(n, dtype=None):
"""
Return the identity array.
The identity array is a square array with ones on
the main diagonal.
Parameters
----------
n : int
Number of rows (and columns) in `n` x `n` output.
dtype : data-type, optional
Data-type of the output. Defaults to ``float``.
Returns
-------
out : ndarray
`n` x `n` array with its main diagonal set to one,
and all other elements 0.
Examples
--------
>>> np.identity(3)
array([[ 1., 0., 0.],
[ 0., 1., 0.],
[ 0., 0., 1.]])
"""
from numpy import eye
return eye(n, dtype=dtype)
def allclose(a, b, rtol=1.e-5, atol=1.e-8, equal_nan=False):
"""
Returns True if two arrays are element-wise equal within a tolerance.
The tolerance values are positive, typically very small numbers. The
relative difference (`rtol` * abs(`b`)) and the absolute difference
`atol` are added together to compare against the absolute difference
between `a` and `b`.
If either array contains one or more NaNs, False is returned.
Infs are treated as equal if they are in the same place and of the same
sign in both arrays.
Parameters
----------
a, b : array_like
Input arrays to compare.
rtol : float
The relative tolerance parameter (see Notes).
atol : float
The absolute tolerance parameter (see Notes).
equal_nan : bool
Whether to compare NaN's as equal. If True, NaN's in `a` will be
considered equal to NaN's in `b` in the output array.
.. versionadded:: 1.10.0
Returns
-------
allclose : bool
Returns True if the two arrays are equal within the given
tolerance; False otherwise.
See Also
--------
isclose, all, any, equal
Notes
-----
If the following equation is element-wise True, then allclose returns
True.
absolute(`a` - `b`) <= (`atol` + `rtol` * absolute(`b`))
The above equation is not symmetric in `a` and `b`, so that
``allclose(a, b)`` might be different from ``allclose(b, a)`` in
some rare cases.
The comparison of `a` and `b` uses standard broadcasting, which
means that `a` and `b` need not have the same shape in order for
``allclose(a, b)`` to evaluate to True. The same is true for
`equal` but not `array_equal`.
Examples
--------
>>> np.allclose([1e10,1e-7], [1.00001e10,1e-8])
False
>>> np.allclose([1e10,1e-8], [1.00001e10,1e-9])
True
>>> np.allclose([1e10,1e-8], [1.0001e10,1e-9])
False
>>> np.allclose([1.0, np.nan], [1.0, np.nan])
False
>>> np.allclose([1.0, np.nan], [1.0, np.nan], equal_nan=True)
True
"""
res = all(isclose(a, b, rtol=rtol, atol=atol, equal_nan=equal_nan))
return bool(res)
def isclose(a, b, rtol=1.e-5, atol=1.e-8, equal_nan=False):
"""
Returns a boolean array where two arrays are element-wise equal within a
tolerance.
The tolerance values are positive, typically very small numbers. The
relative difference (`rtol` * abs(`b`)) and the absolute difference
`atol` are added together to compare against the absolute difference
between `a` and `b`.
Parameters
----------
a, b : array_like
Input arrays to compare.
rtol : float
The relative tolerance parameter (see Notes).
atol : float
The absolute tolerance parameter (see Notes).
equal_nan : bool
Whether to compare NaN's as equal. If True, NaN's in `a` will be
considered equal to NaN's in `b` in the output array.
Returns
-------
y : array_like
Returns a boolean array of where `a` and `b` are equal within the
given tolerance. If both `a` and `b` are scalars, returns a single
boolean value.
See Also
--------
allclose
Notes
-----
.. versionadded:: 1.7.0
For finite values, isclose uses the following equation to test whether
two floating point values are equivalent.
absolute(`a` - `b`) <= (`atol` + `rtol` * absolute(`b`))
The above equation is not symmetric in `a` and `b`, so that
`isclose(a, b)` might be different from `isclose(b, a)` in
some rare cases.
Examples
--------
>>> np.isclose([1e10,1e-7], [1.00001e10,1e-8])
array([True, False])
>>> np.isclose([1e10,1e-8], [1.00001e10,1e-9])
array([True, True])
>>> np.isclose([1e10,1e-8], [1.0001e10,1e-9])
array([False, True])
>>> np.isclose([1.0, np.nan], [1.0, np.nan])
array([True, False])
>>> np.isclose([1.0, np.nan], [1.0, np.nan], equal_nan=True)
array([True, True])
"""
def within_tol(x, y, atol, rtol):
with errstate(invalid='ignore'):
return less_equal(abs(x-y), atol + rtol * abs(y))
x = asanyarray(a)
y = asanyarray(b)
# Make sure y is an inexact type to avoid bad behavior on abs(MIN_INT).
# This will cause casting of x later. Also, make sure to allow subclasses
# (e.g., for numpy.ma).
dt = multiarray.result_type(y, 1.)
y = array(y, dtype=dt, copy=False, subok=True)
xfin = isfinite(x)
yfin = isfinite(y)
if all(xfin) and all(yfin):
return within_tol(x, y, atol, rtol)
else:
finite = xfin & yfin
cond = zeros_like(finite, subok=True)
# Because we're using boolean indexing, x & y must be the same shape.
# Ideally, we'd just do x, y = broadcast_arrays(x, y). It's in
# lib.stride_tricks, though, so we can't import it here.
x = x * ones_like(cond)
y = y * ones_like(cond)
# Avoid subtraction with infinite/nan values...
cond[finite] = within_tol(x[finite], y[finite], atol, rtol)
# Check for equality of infinite values...
cond[~finite] = (x[~finite] == y[~finite])
if equal_nan:
# Make NaN == NaN
both_nan = isnan(x) & isnan(y)
# Needed to treat masked arrays correctly. = True would not work.
cond[both_nan] = both_nan[both_nan]
return cond[()] # Flatten 0d arrays to scalars
def array_equal(a1, a2):
"""
True if two arrays have the same shape and elements, False otherwise.
Parameters
----------
a1, a2 : array_like
Input arrays.
Returns
-------
b : bool
Returns True if the arrays are equal.
See Also
--------
allclose: Returns True if two arrays are element-wise equal within a
tolerance.
array_equiv: Returns True if input arrays are shape consistent and all
elements equal.
Examples
--------
>>> np.array_equal([1, 2], [1, 2])
True
>>> np.array_equal(np.array([1, 2]), np.array([1, 2]))
True
>>> np.array_equal([1, 2], [1, 2, 3])
False
>>> np.array_equal([1, 2], [1, 4])
False
"""
try:
a1, a2 = asarray(a1), asarray(a2)
except Exception:
return False
if a1.shape != a2.shape:
return False
return bool(asarray(a1 == a2).all())
def array_equiv(a1, a2):
"""
Returns True if input arrays are shape consistent and all elements equal.
Shape consistent means they are either the same shape, or one input array
can be broadcasted to create the same shape as the other one.
Parameters
----------
a1, a2 : array_like
Input arrays.
Returns
-------
out : bool
True if equivalent, False otherwise.
Examples
--------
>>> np.array_equiv([1, 2], [1, 2])
True
>>> np.array_equiv([1, 2], [1, 3])
False
Showing the shape equivalence:
>>> np.array_equiv([1, 2], [[1, 2], [1, 2]])
True
>>> np.array_equiv([1, 2], [[1, 2, 1, 2], [1, 2, 1, 2]])
False
>>> np.array_equiv([1, 2], [[1, 2], [1, 3]])
False
"""
try:
a1, a2 = asarray(a1), asarray(a2)
except Exception:
return False
try:
multiarray.broadcast(a1, a2)
except Exception:
return False
return bool(asarray(a1 == a2).all())
_errdict = {"ignore":ERR_IGNORE,
"warn":ERR_WARN,
"raise":ERR_RAISE,
"call":ERR_CALL,
"print":ERR_PRINT,
"log":ERR_LOG}
_errdict_rev = {}
for key in _errdict.keys():
_errdict_rev[_errdict[key]] = key
del key
def seterr(all=None, divide=None, over=None, under=None, invalid=None):
"""
Set how floating-point errors are handled.
Note that operations on integer scalar types (such as `int16`) are
handled like floating point, and are affected by these settings.
Parameters
----------
all : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
Set treatment for all types of floating-point errors at once:
- ignore: Take no action when the exception occurs.
- warn: Print a `RuntimeWarning` (via the Python `warnings` module).
- raise: Raise a `FloatingPointError`.
- call: Call a function specified using the `seterrcall` function.
- print: Print a warning directly to ``stdout``.
- log: Record error in a Log object specified by `seterrcall`.
The default is not to change the current behavior.
divide : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
Treatment for division by zero.
over : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
Treatment for floating-point overflow.
under : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
Treatment for floating-point underflow.
invalid : {'ignore', 'warn', 'raise', 'call', 'print', 'log'}, optional
Treatment for invalid floating-point operation.
Returns
-------
old_settings : dict
Dictionary containing the old settings.
See also
--------
seterrcall : Set a callback function for the 'call' mode.
geterr, geterrcall, errstate
Notes
-----
The floating-point exceptions are defined in the IEEE 754 standard [1]:
- Division by zero: infinite result obtained from finite numbers.
- Overflow: result too large to be expressed.
- Underflow: result so close to zero that some precision
was lost.
- Invalid operation: result is not an expressible number, typically
indicates that a NaN was produced.
.. [1] http://en.wikipedia.org/wiki/IEEE_754
Examples
--------
>>> old_settings = np.seterr(all='ignore') #seterr to known value
>>> np.seterr(over='raise')
{'over': 'ignore', 'divide': 'ignore', 'invalid': 'ignore',
'under': 'ignore'}
>>> np.seterr(**old_settings) # reset to default
{'over': 'raise', 'divide': 'ignore', 'invalid': 'ignore', 'under': 'ignore'}
>>> np.int16(32000) * np.int16(3)
30464
>>> old_settings = np.seterr(all='warn', over='raise')
>>> np.int16(32000) * np.int16(3)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
FloatingPointError: overflow encountered in short_scalars
>>> old_settings = np.seterr(all='print')
>>> np.geterr()
{'over': 'print', 'divide': 'print', 'invalid': 'print', 'under': 'print'}
>>> np.int16(32000) * np.int16(3)
Warning: overflow encountered in short_scalars
30464
"""
pyvals = umath.geterrobj()
old = geterr()
if divide is None:
divide = all or old['divide']
if over is None:
over = all or old['over']
if under is None:
under = all or old['under']
if invalid is None:
invalid = all or old['invalid']
maskvalue = ((_errdict[divide] << SHIFT_DIVIDEBYZERO) +
(_errdict[over] << SHIFT_OVERFLOW) +
(_errdict[under] << SHIFT_UNDERFLOW) +
(_errdict[invalid] << SHIFT_INVALID))
pyvals[1] = maskvalue
umath.seterrobj(pyvals)
return old
def geterr():
"""
Get the current way of handling floating-point errors.
Returns
-------
res : dict
A dictionary with keys "divide", "over", "under", and "invalid",
whose values are from the strings "ignore", "print", "log", "warn",
"raise", and "call". The keys represent possible floating-point
exceptions, and the values define how these exceptions are handled.
See Also
--------
geterrcall, seterr, seterrcall
Notes
-----
For complete documentation of the types of floating-point exceptions and
treatment options, see `seterr`.
Examples
--------
>>> np.geterr()
{'over': 'warn', 'divide': 'warn', 'invalid': 'warn',
'under': 'ignore'}
>>> np.arange(3.) / np.arange(3.)
array([ NaN, 1., 1.])
>>> oldsettings = np.seterr(all='warn', over='raise')
>>> np.geterr()
{'over': 'raise', 'divide': 'warn', 'invalid': 'warn', 'under': 'warn'}
>>> np.arange(3.) / np.arange(3.)
__main__:1: RuntimeWarning: invalid value encountered in divide
array([ NaN, 1., 1.])
"""
maskvalue = umath.geterrobj()[1]
mask = 7
res = {}
val = (maskvalue >> SHIFT_DIVIDEBYZERO) & mask
res['divide'] = _errdict_rev[val]
val = (maskvalue >> SHIFT_OVERFLOW) & mask
res['over'] = _errdict_rev[val]
val = (maskvalue >> SHIFT_UNDERFLOW) & mask
res['under'] = _errdict_rev[val]
val = (maskvalue >> SHIFT_INVALID) & mask
res['invalid'] = _errdict_rev[val]
return res
def setbufsize(size):
"""
Set the size of the buffer used in ufuncs.
Parameters
----------
size : int
Size of buffer.
"""
if size > 10e6:
raise ValueError("Buffer size, %s, is too big." % size)
if size < 5:
raise ValueError("Buffer size, %s, is too small." % size)
if size % 16 != 0:
raise ValueError("Buffer size, %s, is not a multiple of 16." % size)
pyvals = umath.geterrobj()
old = getbufsize()
pyvals[0] = size
umath.seterrobj(pyvals)
return old
def getbufsize():
"""
Return the size of the buffer used in ufuncs.
Returns
-------
getbufsize : int
Size of ufunc buffer in bytes.
"""
return umath.geterrobj()[0]
def seterrcall(func):
"""
Set the floating-point error callback function or log object.
There are two ways to capture floating-point error messages. The first
is to set the error-handler to 'call', using `seterr`. Then, set
the function to call using this function.
The second is to set the error-handler to 'log', using `seterr`.
Floating-point errors then trigger a call to the 'write' method of
the provided object.
Parameters
----------
func : callable f(err, flag) or object with write method
Function to call upon floating-point errors ('call'-mode) or
object whose 'write' method is used to log such message ('log'-mode).
The call function takes two arguments. The first is a string describing the
type of error (such as "divide by zero", "overflow", "underflow", or "invalid value"),
and the second is the status flag. The flag is a byte, whose four
least-significant bits indicate the type of error, one of "divide", "over",
"under", "invalid"::
[0 0 0 0 divide over under invalid]
In other words, ``flags = divide + 2*over + 4*under + 8*invalid``.
If an object is provided, its write method should take one argument,
a string.
Returns
-------
h : callable, log instance or None
The old error handler.
See Also
--------
seterr, geterr, geterrcall
Examples
--------
Callback upon error:
>>> def err_handler(type, flag):
... print("Floating point error (%s), with flag %s" % (type, flag))
...
>>> saved_handler = np.seterrcall(err_handler)
>>> save_err = np.seterr(all='call')
>>> np.array([1, 2, 3]) / 0.0
Floating point error (divide by zero), with flag 1
array([ Inf, Inf, Inf])
>>> np.seterrcall(saved_handler)
<function err_handler at 0x...>
>>> np.seterr(**save_err)
{'over': 'call', 'divide': 'call', 'invalid': 'call', 'under': 'call'}
Log error message:
>>> class Log(object):
... def write(self, msg):
... print("LOG: %s" % msg)
...
>>> log = Log()
>>> saved_handler = np.seterrcall(log)
>>> save_err = np.seterr(all='log')
>>> np.array([1, 2, 3]) / 0.0
LOG: Warning: divide by zero encountered in divide
<BLANKLINE>
array([ Inf, Inf, Inf])
>>> np.seterrcall(saved_handler)
<__main__.Log object at 0x...>
>>> np.seterr(**save_err)
{'over': 'log', 'divide': 'log', 'invalid': 'log', 'under': 'log'}
"""
if func is not None and not isinstance(func, collections.Callable):
if not hasattr(func, 'write') or not isinstance(func.write, collections.Callable):
raise ValueError("Only callable can be used as callback")
pyvals = umath.geterrobj()
old = geterrcall()
pyvals[2] = func
umath.seterrobj(pyvals)
return old
def geterrcall():
"""
Return the current callback function used on floating-point errors.
When the error handling for a floating-point error (one of "divide",
"over", "under", or "invalid") is set to 'call' or 'log', the function
that is called or the log instance that is written to is returned by
`geterrcall`. This function or log instance has been set with
`seterrcall`.
Returns
-------
errobj : callable, log instance or None
The current error handler. If no handler was set through `seterrcall`,
``None`` is returned.
See Also
--------
seterrcall, seterr, geterr
Notes
-----
For complete documentation of the types of floating-point exceptions and
treatment options, see `seterr`.
Examples
--------
>>> np.geterrcall() # we did not yet set a handler, returns None
>>> oldsettings = np.seterr(all='call')
>>> def err_handler(type, flag):
... print("Floating point error (%s), with flag %s" % (type, flag))
>>> oldhandler = np.seterrcall(err_handler)
>>> np.array([1, 2, 3]) / 0.0
Floating point error (divide by zero), with flag 1
array([ Inf, Inf, Inf])
>>> cur_handler = np.geterrcall()
>>> cur_handler is err_handler
True
"""
return umath.geterrobj()[2]
class _unspecified(object):
pass
_Unspecified = _unspecified()
class errstate(object):
"""
errstate(**kwargs)
Context manager for floating-point error handling.
Using an instance of `errstate` as a context manager allows statements in
that context to execute with a known error handling behavior. Upon entering
the context the error handling is set with `seterr` and `seterrcall`, and
upon exiting it is reset to what it was before.
Parameters
----------
kwargs : {divide, over, under, invalid}
Keyword arguments. The valid keywords are the possible floating-point
exceptions. Each keyword should have a string value that defines the
treatment for the particular error. Possible values are
{'ignore', 'warn', 'raise', 'call', 'print', 'log'}.
See Also
--------
seterr, geterr, seterrcall, geterrcall
Notes
-----
The ``with`` statement was introduced in Python 2.5, and can only be used
there by importing it: ``from __future__ import with_statement``. In
earlier Python versions the ``with`` statement is not available.
For complete documentation of the types of floating-point exceptions and
treatment options, see `seterr`.
Examples
--------
>>> from __future__ import with_statement # use 'with' in Python 2.5
>>> olderr = np.seterr(all='ignore') # Set error handling to known state.
>>> np.arange(3) / 0.
array([ NaN, Inf, Inf])
>>> with np.errstate(divide='warn'):
... np.arange(3) / 0.
...
__main__:2: RuntimeWarning: divide by zero encountered in divide
array([ NaN, Inf, Inf])
>>> np.sqrt(-1)
nan
>>> with np.errstate(invalid='raise'):
... np.sqrt(-1)
Traceback (most recent call last):
File "<stdin>", line 2, in <module>
FloatingPointError: invalid value encountered in sqrt
Outside the context the error handling behavior has not changed:
>>> np.geterr()
{'over': 'warn', 'divide': 'warn', 'invalid': 'warn',
'under': 'ignore'}
"""
# Note that we don't want to run the above doctests because they will fail
# without a from __future__ import with_statement
def __init__(self, **kwargs):
self.call = kwargs.pop('call', _Unspecified)
self.kwargs = kwargs
def __enter__(self):
self.oldstate = seterr(**self.kwargs)
if self.call is not _Unspecified:
self.oldcall = seterrcall(self.call)
def __exit__(self, *exc_info):
seterr(**self.oldstate)
if self.call is not _Unspecified:
seterrcall(self.oldcall)
def _setdef():
defval = [UFUNC_BUFSIZE_DEFAULT, ERR_DEFAULT, None]
umath.seterrobj(defval)
# set the default values
_setdef()
Inf = inf = infty = Infinity = PINF
nan = NaN = NAN
False_ = bool_(False)
True_ = bool_(True)
def extend_all(module):
adict = {}
for a in __all__:
adict[a] = 1
try:
mall = getattr(module, '__all__')
except AttributeError:
mall = [k for k in module.__dict__.keys() if not k.startswith('_')]
for a in mall:
if a not in adict:
__all__.append(a)
from .umath import *
from .numerictypes import *
from . import fromnumeric
from .fromnumeric import *
from . import arrayprint
from .arrayprint import *
extend_all(fromnumeric)
extend_all(umath)
extend_all(numerictypes)
extend_all(arrayprint)
| 85,731 | 28.522039 | 94 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/setup_common.py
|
from __future__ import division, absolute_import, print_function
# Code common to build tools
import sys
import warnings
import copy
import binascii
from numpy.distutils.misc_util import mingw32
#-------------------
# Versioning support
#-------------------
# How to change C_API_VERSION ?
# - increase C_API_VERSION value
# - record the hash for the new C API with the script cversions.py
# and add the hash to cversions.txt
# The hash values are used to remind developers when the C API number was not
# updated - generates a MismatchCAPIWarning warning which is turned into an
# exception for released version.
# Binary compatibility version number. This number is increased whenever the
# C-API is changed such that binary compatibility is broken, i.e. whenever a
# recompile of extension modules is needed.
C_ABI_VERSION = 0x01000009
# Minor API version. This number is increased whenever a change is made to the
# C-API -- whether it breaks binary compatibility or not. Some changes, such
# as adding a function pointer to the end of the function table, can be made
# without breaking binary compatibility. In this case, only the C_API_VERSION
# (*not* C_ABI_VERSION) would be increased. Whenever binary compatibility is
# broken, both C_API_VERSION and C_ABI_VERSION should be increased.
#
# 0x00000008 - 1.7.x
# 0x00000009 - 1.8.x
# 0x00000009 - 1.9.x
# 0x0000000a - 1.10.x
# 0x0000000a - 1.11.x
# 0x0000000a - 1.12.x
# 0x0000000b - 1.13.x
# 0x0000000c - 1.14.x
C_API_VERSION = 0x0000000c
class MismatchCAPIWarning(Warning):
pass
def is_released(config):
"""Return True if a released version of numpy is detected."""
from distutils.version import LooseVersion
v = config.get_version('../version.py')
if v is None:
raise ValueError("Could not get version")
pv = LooseVersion(vstring=v).version
if len(pv) > 3:
return False
return True
def get_api_versions(apiversion, codegen_dir):
"""
Return current C API checksum and the recorded checksum.
Return current C API checksum and the recorded checksum for the given
version of the C API version.
"""
# Compute the hash of the current API as defined in the .txt files in
# code_generators
sys.path.insert(0, codegen_dir)
try:
m = __import__('genapi')
numpy_api = __import__('numpy_api')
curapi_hash = m.fullapi_hash(numpy_api.full_api)
apis_hash = m.get_versions_hash()
finally:
del sys.path[0]
return curapi_hash, apis_hash[apiversion]
def check_api_version(apiversion, codegen_dir):
"""Emits a MismacthCAPIWarning if the C API version needs updating."""
curapi_hash, api_hash = get_api_versions(apiversion, codegen_dir)
# If different hash, it means that the api .txt files in
# codegen_dir have been updated without the API version being
# updated. Any modification in those .txt files should be reflected
# in the api and eventually abi versions.
# To compute the checksum of the current API, use
# code_generators/cversions.py script
if not curapi_hash == api_hash:
msg = ("API mismatch detected, the C API version "
"numbers have to be updated. Current C api version is %d, "
"with checksum %s, but recorded checksum for C API version %d in "
"codegen_dir/cversions.txt is %s. If functions were added in the "
"C API, you have to update C_API_VERSION in %s."
)
warnings.warn(msg % (apiversion, curapi_hash, apiversion, api_hash,
__file__),
MismatchCAPIWarning, stacklevel=2)
# Mandatory functions: if not found, fail the build
MANDATORY_FUNCS = ["sin", "cos", "tan", "sinh", "cosh", "tanh", "fabs",
"floor", "ceil", "sqrt", "log10", "log", "exp", "asin",
"acos", "atan", "fmod", 'modf', 'frexp', 'ldexp']
# Standard functions which may not be available and for which we have a
# replacement implementation. Note that some of these are C99 functions.
OPTIONAL_STDFUNCS = ["expm1", "log1p", "acosh", "asinh", "atanh",
"rint", "trunc", "exp2", "log2", "hypot", "atan2", "pow",
"copysign", "nextafter", "ftello", "fseeko",
"strtoll", "strtoull", "cbrt", "strtold_l", "fallocate",
"backtrace"]
OPTIONAL_HEADERS = [
# sse headers only enabled automatically on amd64/x32 builds
"xmmintrin.h", # SSE
"emmintrin.h", # SSE2
"features.h", # for glibc version linux
"xlocale.h", # see GH#8367
"dlfcn.h", # dladdr
]
# optional gcc compiler builtins and their call arguments and optional a
# required header and definition name (HAVE_ prepended)
# call arguments are required as the compiler will do strict signature checking
OPTIONAL_INTRINSICS = [("__builtin_isnan", '5.'),
("__builtin_isinf", '5.'),
("__builtin_isfinite", '5.'),
("__builtin_bswap32", '5u'),
("__builtin_bswap64", '5u'),
("__builtin_expect", '5, 0'),
("__builtin_mul_overflow", '5, 5, (int*)5'),
# broken on OSX 10.11, make sure its not optimized away
("volatile int r = __builtin_cpu_supports", '"sse"',
"stdio.h", "__BUILTIN_CPU_SUPPORTS"),
# MMX only needed for icc, but some clangs don't have it
("_m_from_int64", '0', "emmintrin.h"),
("_mm_load_ps", '(float*)0', "xmmintrin.h"), # SSE
("_mm_prefetch", '(float*)0, _MM_HINT_NTA',
"xmmintrin.h"), # SSE
("_mm_load_pd", '(double*)0', "emmintrin.h"), # SSE2
("__builtin_prefetch", "(float*)0, 0, 3"),
# check that the linker can handle avx
("__asm__ volatile", '"vpand %xmm1, %xmm2, %xmm3"',
"stdio.h", "LINK_AVX"),
("__asm__ volatile", '"vpand %ymm1, %ymm2, %ymm3"',
"stdio.h", "LINK_AVX2"),
]
# function attributes
# tested via "int %s %s(void *);" % (attribute, name)
# function name will be converted to HAVE_<upper-case-name> preprocessor macro
OPTIONAL_FUNCTION_ATTRIBUTES = [('__attribute__((optimize("unroll-loops")))',
'attribute_optimize_unroll_loops'),
('__attribute__((optimize("O3")))',
'attribute_optimize_opt_3'),
('__attribute__((nonnull (1)))',
'attribute_nonnull'),
('__attribute__((target ("avx")))',
'attribute_target_avx'),
('__attribute__((target ("avx2")))',
'attribute_target_avx2'),
]
# variable attributes tested via "int %s a" % attribute
OPTIONAL_VARIABLE_ATTRIBUTES = ["__thread", "__declspec(thread)"]
# Subset of OPTIONAL_STDFUNCS which may alreay have HAVE_* defined by Python.h
OPTIONAL_STDFUNCS_MAYBE = [
"expm1", "log1p", "acosh", "atanh", "asinh", "hypot", "copysign",
"ftello", "fseeko"
]
# C99 functions: float and long double versions
C99_FUNCS = [
"sin", "cos", "tan", "sinh", "cosh", "tanh", "fabs", "floor", "ceil",
"rint", "trunc", "sqrt", "log10", "log", "log1p", "exp", "expm1",
"asin", "acos", "atan", "asinh", "acosh", "atanh", "hypot", "atan2",
"pow", "fmod", "modf", 'frexp', 'ldexp', "exp2", "log2", "copysign",
"nextafter", "cbrt"
]
C99_FUNCS_SINGLE = [f + 'f' for f in C99_FUNCS]
C99_FUNCS_EXTENDED = [f + 'l' for f in C99_FUNCS]
C99_COMPLEX_TYPES = [
'complex double', 'complex float', 'complex long double'
]
C99_COMPLEX_FUNCS = [
"cabs", "cacos", "cacosh", "carg", "casin", "casinh", "catan",
"catanh", "ccos", "ccosh", "cexp", "cimag", "clog", "conj", "cpow",
"cproj", "creal", "csin", "csinh", "csqrt", "ctan", "ctanh"
]
def fname2def(name):
return "HAVE_%s" % name.upper()
def sym2def(symbol):
define = symbol.replace(' ', '')
return define.upper()
def type2def(symbol):
define = symbol.replace(' ', '_')
return define.upper()
# Code to detect long double representation taken from MPFR m4 macro
def check_long_double_representation(cmd):
cmd._check_compiler()
body = LONG_DOUBLE_REPRESENTATION_SRC % {'type': 'long double'}
# Disable whole program optimization (the default on vs2015, with python 3.5+)
# which generates intermediary object files and prevents checking the
# float representation.
if sys.platform == "win32" and not mingw32():
try:
cmd.compiler.compile_options.remove("/GL")
except (AttributeError, ValueError):
pass
# Disable multi-file interprocedural optimization in the Intel compiler on Linux
# which generates intermediary object files and prevents checking the
# float representation.
elif (sys.platform != "win32"
and cmd.compiler.compiler_type.startswith('intel')
and '-ipo' in cmd.compiler.cc_exe):
newcompiler = cmd.compiler.cc_exe.replace(' -ipo', '')
cmd.compiler.set_executables(
compiler=newcompiler,
compiler_so=newcompiler,
compiler_cxx=newcompiler,
linker_exe=newcompiler,
linker_so=newcompiler + ' -shared'
)
# We need to use _compile because we need the object filename
src, obj = cmd._compile(body, None, None, 'c')
try:
ltype = long_double_representation(pyod(obj))
return ltype
except ValueError:
# try linking to support CC="gcc -flto" or icc -ipo
# struct needs to be volatile so it isn't optimized away
body = body.replace('struct', 'volatile struct')
body += "int main(void) { return 0; }\n"
src, obj = cmd._compile(body, None, None, 'c')
cmd.temp_files.append("_configtest")
cmd.compiler.link_executable([obj], "_configtest")
ltype = long_double_representation(pyod("_configtest"))
return ltype
finally:
cmd._clean()
LONG_DOUBLE_REPRESENTATION_SRC = r"""
/* "before" is 16 bytes to ensure there's no padding between it and "x".
* We're not expecting any "long double" bigger than 16 bytes or with
* alignment requirements stricter than 16 bytes. */
typedef %(type)s test_type;
struct {
char before[16];
test_type x;
char after[8];
} foo = {
{ '\0', '\0', '\0', '\0', '\0', '\0', '\0', '\0',
'\001', '\043', '\105', '\147', '\211', '\253', '\315', '\357' },
-123456789.0,
{ '\376', '\334', '\272', '\230', '\166', '\124', '\062', '\020' }
};
"""
def pyod(filename):
"""Python implementation of the od UNIX utility (od -b, more exactly).
Parameters
----------
filename : str
name of the file to get the dump from.
Returns
-------
out : seq
list of lines of od output
Note
----
We only implement enough to get the necessary information for long double
representation, this is not intended as a compatible replacement for od.
"""
def _pyod2():
out = []
fid = open(filename, 'rb')
try:
yo = [int(oct(int(binascii.b2a_hex(o), 16))) for o in fid.read()]
for i in range(0, len(yo), 16):
line = ['%07d' % int(oct(i))]
line.extend(['%03d' % c for c in yo[i:i+16]])
out.append(" ".join(line))
return out
finally:
fid.close()
def _pyod3():
out = []
fid = open(filename, 'rb')
try:
yo2 = [oct(o)[2:] for o in fid.read()]
for i in range(0, len(yo2), 16):
line = ['%07d' % int(oct(i)[2:])]
line.extend(['%03d' % int(c) for c in yo2[i:i+16]])
out.append(" ".join(line))
return out
finally:
fid.close()
if sys.version_info[0] < 3:
return _pyod2()
else:
return _pyod3()
_BEFORE_SEQ = ['000', '000', '000', '000', '000', '000', '000', '000',
'001', '043', '105', '147', '211', '253', '315', '357']
_AFTER_SEQ = ['376', '334', '272', '230', '166', '124', '062', '020']
_IEEE_DOUBLE_BE = ['301', '235', '157', '064', '124', '000', '000', '000']
_IEEE_DOUBLE_LE = _IEEE_DOUBLE_BE[::-1]
_INTEL_EXTENDED_12B = ['000', '000', '000', '000', '240', '242', '171', '353',
'031', '300', '000', '000']
_INTEL_EXTENDED_16B = ['000', '000', '000', '000', '240', '242', '171', '353',
'031', '300', '000', '000', '000', '000', '000', '000']
_MOTOROLA_EXTENDED_12B = ['300', '031', '000', '000', '353', '171',
'242', '240', '000', '000', '000', '000']
_IEEE_QUAD_PREC_BE = ['300', '031', '326', '363', '105', '100', '000', '000',
'000', '000', '000', '000', '000', '000', '000', '000']
_IEEE_QUAD_PREC_LE = _IEEE_QUAD_PREC_BE[::-1]
_DOUBLE_DOUBLE_BE = (['301', '235', '157', '064', '124', '000', '000', '000'] +
['000'] * 8)
_DOUBLE_DOUBLE_LE = (['000', '000', '000', '124', '064', '157', '235', '301'] +
['000'] * 8)
def long_double_representation(lines):
"""Given a binary dump as given by GNU od -b, look for long double
representation."""
# Read contains a list of 32 items, each item is a byte (in octal
# representation, as a string). We 'slide' over the output until read is of
# the form before_seq + content + after_sequence, where content is the long double
# representation:
# - content is 12 bytes: 80 bits Intel representation
# - content is 16 bytes: 80 bits Intel representation (64 bits) or quad precision
# - content is 8 bytes: same as double (not implemented yet)
read = [''] * 32
saw = None
for line in lines:
# we skip the first word, as od -b output an index at the beginning of
# each line
for w in line.split()[1:]:
read.pop(0)
read.append(w)
# If the end of read is equal to the after_sequence, read contains
# the long double
if read[-8:] == _AFTER_SEQ:
saw = copy.copy(read)
if read[:12] == _BEFORE_SEQ[4:]:
if read[12:-8] == _INTEL_EXTENDED_12B:
return 'INTEL_EXTENDED_12_BYTES_LE'
if read[12:-8] == _MOTOROLA_EXTENDED_12B:
return 'MOTOROLA_EXTENDED_12_BYTES_BE'
elif read[:8] == _BEFORE_SEQ[8:]:
if read[8:-8] == _INTEL_EXTENDED_16B:
return 'INTEL_EXTENDED_16_BYTES_LE'
elif read[8:-8] == _IEEE_QUAD_PREC_BE:
return 'IEEE_QUAD_BE'
elif read[8:-8] == _IEEE_QUAD_PREC_LE:
return 'IEEE_QUAD_LE'
elif read[8:-8] == _DOUBLE_DOUBLE_BE:
return 'DOUBLE_DOUBLE_BE'
elif read[8:-8] == _DOUBLE_DOUBLE_LE:
return 'DOUBLE_DOUBLE_LE'
elif read[:16] == _BEFORE_SEQ:
if read[16:-8] == _IEEE_DOUBLE_LE:
return 'IEEE_DOUBLE_LE'
elif read[16:-8] == _IEEE_DOUBLE_BE:
return 'IEEE_DOUBLE_BE'
if saw is not None:
raise ValueError("Unrecognized format (%s)" % saw)
else:
# We never detected the after_sequence
raise ValueError("Could not lock sequences (%s)" % saw)
| 15,953 | 39.69898 | 86 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/generate_numpy_api.py
|
from __future__ import division, print_function
import os
import genapi
from genapi import \
TypeApi, GlobalVarApi, FunctionApi, BoolValuesApi
import numpy_api
# use annotated api when running under cpychecker
h_template = r"""
#if defined(_MULTIARRAYMODULE) || defined(WITH_CPYCHECKER_STEALS_REFERENCE_TO_ARG_ATTRIBUTE)
typedef struct {
PyObject_HEAD
npy_bool obval;
} PyBoolScalarObject;
extern NPY_NO_EXPORT PyTypeObject PyArrayMapIter_Type;
extern NPY_NO_EXPORT PyTypeObject PyArrayNeighborhoodIter_Type;
extern NPY_NO_EXPORT PyBoolScalarObject _PyArrayScalar_BoolValues[2];
%s
#else
#if defined(PY_ARRAY_UNIQUE_SYMBOL)
#define PyArray_API PY_ARRAY_UNIQUE_SYMBOL
#endif
#if defined(NO_IMPORT) || defined(NO_IMPORT_ARRAY)
extern void **PyArray_API;
#else
#if defined(PY_ARRAY_UNIQUE_SYMBOL)
void **PyArray_API;
#else
static void **PyArray_API=NULL;
#endif
#endif
%s
#if !defined(NO_IMPORT_ARRAY) && !defined(NO_IMPORT)
static int
_import_array(void)
{
int st;
PyObject *numpy = PyImport_ImportModule("numpy.core.multiarray");
PyObject *c_api = NULL;
if (numpy == NULL) {
PyErr_SetString(PyExc_ImportError, "numpy.core.multiarray failed to import");
return -1;
}
c_api = PyObject_GetAttrString(numpy, "_ARRAY_API");
Py_DECREF(numpy);
if (c_api == NULL) {
PyErr_SetString(PyExc_AttributeError, "_ARRAY_API not found");
return -1;
}
#if PY_VERSION_HEX >= 0x03000000
if (!PyCapsule_CheckExact(c_api)) {
PyErr_SetString(PyExc_RuntimeError, "_ARRAY_API is not PyCapsule object");
Py_DECREF(c_api);
return -1;
}
PyArray_API = (void **)PyCapsule_GetPointer(c_api, NULL);
#else
if (!PyCObject_Check(c_api)) {
PyErr_SetString(PyExc_RuntimeError, "_ARRAY_API is not PyCObject object");
Py_DECREF(c_api);
return -1;
}
PyArray_API = (void **)PyCObject_AsVoidPtr(c_api);
#endif
Py_DECREF(c_api);
if (PyArray_API == NULL) {
PyErr_SetString(PyExc_RuntimeError, "_ARRAY_API is NULL pointer");
return -1;
}
/* Perform runtime check of C API version */
if (NPY_VERSION != PyArray_GetNDArrayCVersion()) {
PyErr_Format(PyExc_RuntimeError, "module compiled against "\
"ABI version 0x%%x but this version of numpy is 0x%%x", \
(int) NPY_VERSION, (int) PyArray_GetNDArrayCVersion());
return -1;
}
if (NPY_FEATURE_VERSION > PyArray_GetNDArrayCFeatureVersion()) {
PyErr_Format(PyExc_RuntimeError, "module compiled against "\
"API version 0x%%x but this version of numpy is 0x%%x", \
(int) NPY_FEATURE_VERSION, (int) PyArray_GetNDArrayCFeatureVersion());
return -1;
}
/*
* Perform runtime check of endianness and check it matches the one set by
* the headers (npy_endian.h) as a safeguard
*/
st = PyArray_GetEndianness();
if (st == NPY_CPU_UNKNOWN_ENDIAN) {
PyErr_Format(PyExc_RuntimeError, "FATAL: module compiled as unknown endian");
return -1;
}
#if NPY_BYTE_ORDER == NPY_BIG_ENDIAN
if (st != NPY_CPU_BIG) {
PyErr_Format(PyExc_RuntimeError, "FATAL: module compiled as "\
"big endian, but detected different endianness at runtime");
return -1;
}
#elif NPY_BYTE_ORDER == NPY_LITTLE_ENDIAN
if (st != NPY_CPU_LITTLE) {
PyErr_Format(PyExc_RuntimeError, "FATAL: module compiled as "\
"little endian, but detected different endianness at runtime");
return -1;
}
#endif
return 0;
}
#if PY_VERSION_HEX >= 0x03000000
#define NUMPY_IMPORT_ARRAY_RETVAL NULL
#else
#define NUMPY_IMPORT_ARRAY_RETVAL
#endif
#define import_array() {if (_import_array() < 0) {PyErr_Print(); PyErr_SetString(PyExc_ImportError, "numpy.core.multiarray failed to import"); return NUMPY_IMPORT_ARRAY_RETVAL; } }
#define import_array1(ret) {if (_import_array() < 0) {PyErr_Print(); PyErr_SetString(PyExc_ImportError, "numpy.core.multiarray failed to import"); return ret; } }
#define import_array2(msg, ret) {if (_import_array() < 0) {PyErr_Print(); PyErr_SetString(PyExc_ImportError, msg); return ret; } }
#endif
#endif
"""
c_template = r"""
/* These pointers will be stored in the C-object for use in other
extension modules
*/
void *PyArray_API[] = {
%s
};
"""
c_api_header = """
===========
NumPy C-API
===========
"""
def generate_api(output_dir, force=False):
basename = 'multiarray_api'
h_file = os.path.join(output_dir, '__%s.h' % basename)
c_file = os.path.join(output_dir, '__%s.c' % basename)
d_file = os.path.join(output_dir, '%s.txt' % basename)
targets = (h_file, c_file, d_file)
sources = numpy_api.multiarray_api
if (not force and not genapi.should_rebuild(targets, [numpy_api.__file__, __file__])):
return targets
else:
do_generate_api(targets, sources)
return targets
def do_generate_api(targets, sources):
header_file = targets[0]
c_file = targets[1]
doc_file = targets[2]
global_vars = sources[0]
scalar_bool_values = sources[1]
types_api = sources[2]
multiarray_funcs = sources[3]
multiarray_api = sources[:]
module_list = []
extension_list = []
init_list = []
# Check multiarray api indexes
multiarray_api_index = genapi.merge_api_dicts(multiarray_api)
genapi.check_api_dict(multiarray_api_index)
numpyapi_list = genapi.get_api_functions('NUMPY_API',
multiarray_funcs)
ordered_funcs_api = genapi.order_dict(multiarray_funcs)
# Create dict name -> *Api instance
api_name = 'PyArray_API'
multiarray_api_dict = {}
for f in numpyapi_list:
name = f.name
index = multiarray_funcs[name][0]
annotations = multiarray_funcs[name][1:]
multiarray_api_dict[f.name] = FunctionApi(f.name, index, annotations,
f.return_type,
f.args, api_name)
for name, val in global_vars.items():
index, type = val
multiarray_api_dict[name] = GlobalVarApi(name, index, type, api_name)
for name, val in scalar_bool_values.items():
index = val[0]
multiarray_api_dict[name] = BoolValuesApi(name, index, api_name)
for name, val in types_api.items():
index = val[0]
multiarray_api_dict[name] = TypeApi(name, index, 'PyTypeObject', api_name)
if len(multiarray_api_dict) != len(multiarray_api_index):
keys_dict = set(multiarray_api_dict.keys())
keys_index = set(multiarray_api_index.keys())
raise AssertionError(
"Multiarray API size mismatch - "
"index has extra keys {}, dict has extra keys {}"
.format(keys_index - keys_dict, keys_dict - keys_index)
)
extension_list = []
for name, index in genapi.order_dict(multiarray_api_index):
api_item = multiarray_api_dict[name]
extension_list.append(api_item.define_from_array_api_string())
init_list.append(api_item.array_api_define())
module_list.append(api_item.internal_define())
# Write to header
s = h_template % ('\n'.join(module_list), '\n'.join(extension_list))
genapi.write_file(header_file, s)
# Write to c-code
s = c_template % ',\n'.join(init_list)
genapi.write_file(c_file, s)
# write to documentation
s = c_api_header
for func in numpyapi_list:
s += func.to_ReST()
s += '\n\n'
genapi.write_file(doc_file, s)
return targets
| 7,506 | 28.555118 | 180 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/tests/test_print.py
|
from __future__ import division, absolute_import, print_function
import sys
import locale
import nose
import numpy as np
from numpy.testing import (
run_module_suite, assert_, assert_equal, SkipTest
)
if sys.version_info[0] >= 3:
from io import StringIO
else:
from StringIO import StringIO
_REF = {np.inf: 'inf', -np.inf: '-inf', np.nan: 'nan'}
def check_float_type(tp):
for x in [0, 1, -1, 1e20]:
assert_equal(str(tp(x)), str(float(x)),
err_msg='Failed str formatting for type %s' % tp)
if tp(1e16).itemsize > 4:
assert_equal(str(tp(1e16)), str(float('1e16')),
err_msg='Failed str formatting for type %s' % tp)
else:
ref = '1e+16'
assert_equal(str(tp(1e16)), ref,
err_msg='Failed str formatting for type %s' % tp)
def test_float_types():
""" Check formatting.
This is only for the str function, and only for simple types.
The precision of np.float32 and np.longdouble aren't the same as the
python float precision.
"""
for t in [np.float32, np.double, np.longdouble]:
yield check_float_type, t
def check_nan_inf_float(tp):
for x in [np.inf, -np.inf, np.nan]:
assert_equal(str(tp(x)), _REF[x],
err_msg='Failed str formatting for type %s' % tp)
def test_nan_inf_float():
""" Check formatting of nan & inf.
This is only for the str function, and only for simple types.
The precision of np.float32 and np.longdouble aren't the same as the
python float precision.
"""
for t in [np.float32, np.double, np.longdouble]:
yield check_nan_inf_float, t
def check_complex_type(tp):
for x in [0, 1, -1, 1e20]:
assert_equal(str(tp(x)), str(complex(x)),
err_msg='Failed str formatting for type %s' % tp)
assert_equal(str(tp(x*1j)), str(complex(x*1j)),
err_msg='Failed str formatting for type %s' % tp)
assert_equal(str(tp(x + x*1j)), str(complex(x + x*1j)),
err_msg='Failed str formatting for type %s' % tp)
if tp(1e16).itemsize > 8:
assert_equal(str(tp(1e16)), str(complex(1e16)),
err_msg='Failed str formatting for type %s' % tp)
else:
ref = '(1e+16+0j)'
assert_equal(str(tp(1e16)), ref,
err_msg='Failed str formatting for type %s' % tp)
def test_complex_types():
"""Check formatting of complex types.
This is only for the str function, and only for simple types.
The precision of np.float32 and np.longdouble aren't the same as the
python float precision.
"""
for t in [np.complex64, np.cdouble, np.clongdouble]:
yield check_complex_type, t
def test_complex_inf_nan():
"""Check inf/nan formatting of complex types."""
TESTS = {
complex(np.inf, 0): "(inf+0j)",
complex(0, np.inf): "infj",
complex(-np.inf, 0): "(-inf+0j)",
complex(0, -np.inf): "-infj",
complex(np.inf, 1): "(inf+1j)",
complex(1, np.inf): "(1+infj)",
complex(-np.inf, 1): "(-inf+1j)",
complex(1, -np.inf): "(1-infj)",
complex(np.nan, 0): "(nan+0j)",
complex(0, np.nan): "nanj",
complex(-np.nan, 0): "(nan+0j)",
complex(0, -np.nan): "nanj",
complex(np.nan, 1): "(nan+1j)",
complex(1, np.nan): "(1+nanj)",
complex(-np.nan, 1): "(nan+1j)",
complex(1, -np.nan): "(1+nanj)",
}
for tp in [np.complex64, np.cdouble, np.clongdouble]:
for c, s in TESTS.items():
yield _check_complex_inf_nan, c, s, tp
def _check_complex_inf_nan(c, s, dtype):
assert_equal(str(dtype(c)), s)
# print tests
def _test_redirected_print(x, tp, ref=None):
file = StringIO()
file_tp = StringIO()
stdout = sys.stdout
try:
sys.stdout = file_tp
print(tp(x))
sys.stdout = file
if ref:
print(ref)
else:
print(x)
finally:
sys.stdout = stdout
assert_equal(file.getvalue(), file_tp.getvalue(),
err_msg='print failed for type%s' % tp)
def check_float_type_print(tp):
for x in [0, 1, -1, 1e20]:
_test_redirected_print(float(x), tp)
for x in [np.inf, -np.inf, np.nan]:
_test_redirected_print(float(x), tp, _REF[x])
if tp(1e16).itemsize > 4:
_test_redirected_print(float(1e16), tp)
else:
ref = '1e+16'
_test_redirected_print(float(1e16), tp, ref)
def check_complex_type_print(tp):
# We do not create complex with inf/nan directly because the feature is
# missing in python < 2.6
for x in [0, 1, -1, 1e20]:
_test_redirected_print(complex(x), tp)
if tp(1e16).itemsize > 8:
_test_redirected_print(complex(1e16), tp)
else:
ref = '(1e+16+0j)'
_test_redirected_print(complex(1e16), tp, ref)
_test_redirected_print(complex(np.inf, 1), tp, '(inf+1j)')
_test_redirected_print(complex(-np.inf, 1), tp, '(-inf+1j)')
_test_redirected_print(complex(-np.nan, 1), tp, '(nan+1j)')
def test_float_type_print():
"""Check formatting when using print """
for t in [np.float32, np.double, np.longdouble]:
yield check_float_type_print, t
def test_complex_type_print():
"""Check formatting when using print """
for t in [np.complex64, np.cdouble, np.clongdouble]:
yield check_complex_type_print, t
def test_scalar_format():
"""Test the str.format method with NumPy scalar types"""
tests = [('{0}', True, np.bool_),
('{0}', False, np.bool_),
('{0:d}', 130, np.uint8),
('{0:d}', 50000, np.uint16),
('{0:d}', 3000000000, np.uint32),
('{0:d}', 15000000000000000000, np.uint64),
('{0:d}', -120, np.int8),
('{0:d}', -30000, np.int16),
('{0:d}', -2000000000, np.int32),
('{0:d}', -7000000000000000000, np.int64),
('{0:g}', 1.5, np.float16),
('{0:g}', 1.5, np.float32),
('{0:g}', 1.5, np.float64),
('{0:g}', 1.5, np.longdouble),
('{0:g}', 1.5+0.5j, np.complex64),
('{0:g}', 1.5+0.5j, np.complex128),
('{0:g}', 1.5+0.5j, np.clongdouble)]
for (fmat, val, valtype) in tests:
try:
assert_equal(fmat.format(val), fmat.format(valtype(val)),
"failed with val %s, type %s" % (val, valtype))
except ValueError as e:
assert_(False,
"format raised exception (fmt='%s', val=%s, type=%s, exc='%s')" %
(fmat, repr(val), repr(valtype), str(e)))
# Locale tests: scalar types formatting should be independent of the locale
def in_foreign_locale(func):
"""
Swap LC_NUMERIC locale to one in which the decimal point is ',' and not '.'
If not possible, raise SkipTest
"""
if sys.platform == 'win32':
locales = ['FRENCH']
else:
locales = ['fr_FR', 'fr_FR.UTF-8', 'fi_FI', 'fi_FI.UTF-8']
def wrapper(*args, **kwargs):
curloc = locale.getlocale(locale.LC_NUMERIC)
try:
for loc in locales:
try:
locale.setlocale(locale.LC_NUMERIC, loc)
break
except locale.Error:
pass
else:
raise SkipTest("Skipping locale test, because "
"French locale not found")
return func(*args, **kwargs)
finally:
locale.setlocale(locale.LC_NUMERIC, locale=curloc)
return nose.tools.make_decorator(func)(wrapper)
@in_foreign_locale
def test_locale_single():
assert_equal(str(np.float32(1.2)), str(float(1.2)))
@in_foreign_locale
def test_locale_double():
assert_equal(str(np.double(1.2)), str(float(1.2)))
@in_foreign_locale
def test_locale_longdouble():
assert_equal(str(np.longdouble('1.2')), str(float(1.2)))
if __name__ == "__main__":
run_module_suite()
| 8,089 | 31.753036 | 80 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/tests/test_nditer.py
|
from __future__ import division, absolute_import, print_function
import sys
import warnings
import numpy as np
from numpy import array, arange, nditer, all
from numpy.core.multiarray_tests import test_nditer_too_large
from numpy.testing import (
run_module_suite, assert_, assert_equal, assert_array_equal,
assert_raises, assert_warns, dec, HAS_REFCOUNT, suppress_warnings
)
def iter_multi_index(i):
ret = []
while not i.finished:
ret.append(i.multi_index)
i.iternext()
return ret
def iter_indices(i):
ret = []
while not i.finished:
ret.append(i.index)
i.iternext()
return ret
def iter_iterindices(i):
ret = []
while not i.finished:
ret.append(i.iterindex)
i.iternext()
return ret
@dec.skipif(not HAS_REFCOUNT, "python does not have sys.getrefcount")
def test_iter_refcount():
# Make sure the iterator doesn't leak
# Basic
a = arange(6)
dt = np.dtype('f4').newbyteorder()
rc_a = sys.getrefcount(a)
rc_dt = sys.getrefcount(dt)
it = nditer(a, [],
[['readwrite', 'updateifcopy']],
casting='unsafe',
op_dtypes=[dt])
assert_(not it.iterationneedsapi)
assert_(sys.getrefcount(a) > rc_a)
assert_(sys.getrefcount(dt) > rc_dt)
it = None
assert_equal(sys.getrefcount(a), rc_a)
assert_equal(sys.getrefcount(dt), rc_dt)
# With a copy
a = arange(6, dtype='f4')
dt = np.dtype('f4')
rc_a = sys.getrefcount(a)
rc_dt = sys.getrefcount(dt)
it = nditer(a, [],
[['readwrite']],
op_dtypes=[dt])
rc2_a = sys.getrefcount(a)
rc2_dt = sys.getrefcount(dt)
it2 = it.copy()
assert_(sys.getrefcount(a) > rc2_a)
assert_(sys.getrefcount(dt) > rc2_dt)
it = None
assert_equal(sys.getrefcount(a), rc2_a)
assert_equal(sys.getrefcount(dt), rc2_dt)
it2 = None
assert_equal(sys.getrefcount(a), rc_a)
assert_equal(sys.getrefcount(dt), rc_dt)
del it2 # avoid pyflakes unused variable warning
def test_iter_best_order():
# The iterator should always find the iteration order
# with increasing memory addresses
# Test the ordering for 1-D to 5-D shapes
for shape in [(5,), (3, 4), (2, 3, 4), (2, 3, 4, 3), (2, 3, 2, 2, 3)]:
a = arange(np.prod(shape))
# Test each combination of positive and negative strides
for dirs in range(2**len(shape)):
dirs_index = [slice(None)]*len(shape)
for bit in range(len(shape)):
if ((2**bit) & dirs):
dirs_index[bit] = slice(None, None, -1)
dirs_index = tuple(dirs_index)
aview = a.reshape(shape)[dirs_index]
# C-order
i = nditer(aview, [], [['readonly']])
assert_equal([x for x in i], a)
# Fortran-order
i = nditer(aview.T, [], [['readonly']])
assert_equal([x for x in i], a)
# Other order
if len(shape) > 2:
i = nditer(aview.swapaxes(0, 1), [], [['readonly']])
assert_equal([x for x in i], a)
def test_iter_c_order():
# Test forcing C order
# Test the ordering for 1-D to 5-D shapes
for shape in [(5,), (3, 4), (2, 3, 4), (2, 3, 4, 3), (2, 3, 2, 2, 3)]:
a = arange(np.prod(shape))
# Test each combination of positive and negative strides
for dirs in range(2**len(shape)):
dirs_index = [slice(None)]*len(shape)
for bit in range(len(shape)):
if ((2**bit) & dirs):
dirs_index[bit] = slice(None, None, -1)
dirs_index = tuple(dirs_index)
aview = a.reshape(shape)[dirs_index]
# C-order
i = nditer(aview, order='C')
assert_equal([x for x in i], aview.ravel(order='C'))
# Fortran-order
i = nditer(aview.T, order='C')
assert_equal([x for x in i], aview.T.ravel(order='C'))
# Other order
if len(shape) > 2:
i = nditer(aview.swapaxes(0, 1), order='C')
assert_equal([x for x in i],
aview.swapaxes(0, 1).ravel(order='C'))
def test_iter_f_order():
# Test forcing F order
# Test the ordering for 1-D to 5-D shapes
for shape in [(5,), (3, 4), (2, 3, 4), (2, 3, 4, 3), (2, 3, 2, 2, 3)]:
a = arange(np.prod(shape))
# Test each combination of positive and negative strides
for dirs in range(2**len(shape)):
dirs_index = [slice(None)]*len(shape)
for bit in range(len(shape)):
if ((2**bit) & dirs):
dirs_index[bit] = slice(None, None, -1)
dirs_index = tuple(dirs_index)
aview = a.reshape(shape)[dirs_index]
# C-order
i = nditer(aview, order='F')
assert_equal([x for x in i], aview.ravel(order='F'))
# Fortran-order
i = nditer(aview.T, order='F')
assert_equal([x for x in i], aview.T.ravel(order='F'))
# Other order
if len(shape) > 2:
i = nditer(aview.swapaxes(0, 1), order='F')
assert_equal([x for x in i],
aview.swapaxes(0, 1).ravel(order='F'))
def test_iter_c_or_f_order():
# Test forcing any contiguous (C or F) order
# Test the ordering for 1-D to 5-D shapes
for shape in [(5,), (3, 4), (2, 3, 4), (2, 3, 4, 3), (2, 3, 2, 2, 3)]:
a = arange(np.prod(shape))
# Test each combination of positive and negative strides
for dirs in range(2**len(shape)):
dirs_index = [slice(None)]*len(shape)
for bit in range(len(shape)):
if ((2**bit) & dirs):
dirs_index[bit] = slice(None, None, -1)
dirs_index = tuple(dirs_index)
aview = a.reshape(shape)[dirs_index]
# C-order
i = nditer(aview, order='A')
assert_equal([x for x in i], aview.ravel(order='A'))
# Fortran-order
i = nditer(aview.T, order='A')
assert_equal([x for x in i], aview.T.ravel(order='A'))
# Other order
if len(shape) > 2:
i = nditer(aview.swapaxes(0, 1), order='A')
assert_equal([x for x in i],
aview.swapaxes(0, 1).ravel(order='A'))
def test_iter_best_order_multi_index_1d():
# The multi-indices should be correct with any reordering
a = arange(4)
# 1D order
i = nditer(a, ['multi_index'], [['readonly']])
assert_equal(iter_multi_index(i), [(0,), (1,), (2,), (3,)])
# 1D reversed order
i = nditer(a[::-1], ['multi_index'], [['readonly']])
assert_equal(iter_multi_index(i), [(3,), (2,), (1,), (0,)])
def test_iter_best_order_multi_index_2d():
# The multi-indices should be correct with any reordering
a = arange(6)
# 2D C-order
i = nditer(a.reshape(2, 3), ['multi_index'], [['readonly']])
assert_equal(iter_multi_index(i), [(0, 0), (0, 1), (0, 2), (1, 0), (1, 1), (1, 2)])
# 2D Fortran-order
i = nditer(a.reshape(2, 3).copy(order='F'), ['multi_index'], [['readonly']])
assert_equal(iter_multi_index(i), [(0, 0), (1, 0), (0, 1), (1, 1), (0, 2), (1, 2)])
# 2D reversed C-order
i = nditer(a.reshape(2, 3)[::-1], ['multi_index'], [['readonly']])
assert_equal(iter_multi_index(i), [(1, 0), (1, 1), (1, 2), (0, 0), (0, 1), (0, 2)])
i = nditer(a.reshape(2, 3)[:, ::-1], ['multi_index'], [['readonly']])
assert_equal(iter_multi_index(i), [(0, 2), (0, 1), (0, 0), (1, 2), (1, 1), (1, 0)])
i = nditer(a.reshape(2, 3)[::-1, ::-1], ['multi_index'], [['readonly']])
assert_equal(iter_multi_index(i), [(1, 2), (1, 1), (1, 0), (0, 2), (0, 1), (0, 0)])
# 2D reversed Fortran-order
i = nditer(a.reshape(2, 3).copy(order='F')[::-1], ['multi_index'], [['readonly']])
assert_equal(iter_multi_index(i), [(1, 0), (0, 0), (1, 1), (0, 1), (1, 2), (0, 2)])
i = nditer(a.reshape(2, 3).copy(order='F')[:, ::-1],
['multi_index'], [['readonly']])
assert_equal(iter_multi_index(i), [(0, 2), (1, 2), (0, 1), (1, 1), (0, 0), (1, 0)])
i = nditer(a.reshape(2, 3).copy(order='F')[::-1, ::-1],
['multi_index'], [['readonly']])
assert_equal(iter_multi_index(i), [(1, 2), (0, 2), (1, 1), (0, 1), (1, 0), (0, 0)])
def test_iter_best_order_multi_index_3d():
# The multi-indices should be correct with any reordering
a = arange(12)
# 3D C-order
i = nditer(a.reshape(2, 3, 2), ['multi_index'], [['readonly']])
assert_equal(iter_multi_index(i),
[(0, 0, 0), (0, 0, 1), (0, 1, 0), (0, 1, 1), (0, 2, 0), (0, 2, 1),
(1, 0, 0), (1, 0, 1), (1, 1, 0), (1, 1, 1), (1, 2, 0), (1, 2, 1)])
# 3D Fortran-order
i = nditer(a.reshape(2, 3, 2).copy(order='F'), ['multi_index'], [['readonly']])
assert_equal(iter_multi_index(i),
[(0, 0, 0), (1, 0, 0), (0, 1, 0), (1, 1, 0), (0, 2, 0), (1, 2, 0),
(0, 0, 1), (1, 0, 1), (0, 1, 1), (1, 1, 1), (0, 2, 1), (1, 2, 1)])
# 3D reversed C-order
i = nditer(a.reshape(2, 3, 2)[::-1], ['multi_index'], [['readonly']])
assert_equal(iter_multi_index(i),
[(1, 0, 0), (1, 0, 1), (1, 1, 0), (1, 1, 1), (1, 2, 0), (1, 2, 1),
(0, 0, 0), (0, 0, 1), (0, 1, 0), (0, 1, 1), (0, 2, 0), (0, 2, 1)])
i = nditer(a.reshape(2, 3, 2)[:, ::-1], ['multi_index'], [['readonly']])
assert_equal(iter_multi_index(i),
[(0, 2, 0), (0, 2, 1), (0, 1, 0), (0, 1, 1), (0, 0, 0), (0, 0, 1),
(1, 2, 0), (1, 2, 1), (1, 1, 0), (1, 1, 1), (1, 0, 0), (1, 0, 1)])
i = nditer(a.reshape(2, 3, 2)[:,:, ::-1], ['multi_index'], [['readonly']])
assert_equal(iter_multi_index(i),
[(0, 0, 1), (0, 0, 0), (0, 1, 1), (0, 1, 0), (0, 2, 1), (0, 2, 0),
(1, 0, 1), (1, 0, 0), (1, 1, 1), (1, 1, 0), (1, 2, 1), (1, 2, 0)])
# 3D reversed Fortran-order
i = nditer(a.reshape(2, 3, 2).copy(order='F')[::-1],
['multi_index'], [['readonly']])
assert_equal(iter_multi_index(i),
[(1, 0, 0), (0, 0, 0), (1, 1, 0), (0, 1, 0), (1, 2, 0), (0, 2, 0),
(1, 0, 1), (0, 0, 1), (1, 1, 1), (0, 1, 1), (1, 2, 1), (0, 2, 1)])
i = nditer(a.reshape(2, 3, 2).copy(order='F')[:, ::-1],
['multi_index'], [['readonly']])
assert_equal(iter_multi_index(i),
[(0, 2, 0), (1, 2, 0), (0, 1, 0), (1, 1, 0), (0, 0, 0), (1, 0, 0),
(0, 2, 1), (1, 2, 1), (0, 1, 1), (1, 1, 1), (0, 0, 1), (1, 0, 1)])
i = nditer(a.reshape(2, 3, 2).copy(order='F')[:,:, ::-1],
['multi_index'], [['readonly']])
assert_equal(iter_multi_index(i),
[(0, 0, 1), (1, 0, 1), (0, 1, 1), (1, 1, 1), (0, 2, 1), (1, 2, 1),
(0, 0, 0), (1, 0, 0), (0, 1, 0), (1, 1, 0), (0, 2, 0), (1, 2, 0)])
def test_iter_best_order_c_index_1d():
# The C index should be correct with any reordering
a = arange(4)
# 1D order
i = nditer(a, ['c_index'], [['readonly']])
assert_equal(iter_indices(i), [0, 1, 2, 3])
# 1D reversed order
i = nditer(a[::-1], ['c_index'], [['readonly']])
assert_equal(iter_indices(i), [3, 2, 1, 0])
def test_iter_best_order_c_index_2d():
# The C index should be correct with any reordering
a = arange(6)
# 2D C-order
i = nditer(a.reshape(2, 3), ['c_index'], [['readonly']])
assert_equal(iter_indices(i), [0, 1, 2, 3, 4, 5])
# 2D Fortran-order
i = nditer(a.reshape(2, 3).copy(order='F'),
['c_index'], [['readonly']])
assert_equal(iter_indices(i), [0, 3, 1, 4, 2, 5])
# 2D reversed C-order
i = nditer(a.reshape(2, 3)[::-1], ['c_index'], [['readonly']])
assert_equal(iter_indices(i), [3, 4, 5, 0, 1, 2])
i = nditer(a.reshape(2, 3)[:, ::-1], ['c_index'], [['readonly']])
assert_equal(iter_indices(i), [2, 1, 0, 5, 4, 3])
i = nditer(a.reshape(2, 3)[::-1, ::-1], ['c_index'], [['readonly']])
assert_equal(iter_indices(i), [5, 4, 3, 2, 1, 0])
# 2D reversed Fortran-order
i = nditer(a.reshape(2, 3).copy(order='F')[::-1],
['c_index'], [['readonly']])
assert_equal(iter_indices(i), [3, 0, 4, 1, 5, 2])
i = nditer(a.reshape(2, 3).copy(order='F')[:, ::-1],
['c_index'], [['readonly']])
assert_equal(iter_indices(i), [2, 5, 1, 4, 0, 3])
i = nditer(a.reshape(2, 3).copy(order='F')[::-1, ::-1],
['c_index'], [['readonly']])
assert_equal(iter_indices(i), [5, 2, 4, 1, 3, 0])
def test_iter_best_order_c_index_3d():
# The C index should be correct with any reordering
a = arange(12)
# 3D C-order
i = nditer(a.reshape(2, 3, 2), ['c_index'], [['readonly']])
assert_equal(iter_indices(i),
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
# 3D Fortran-order
i = nditer(a.reshape(2, 3, 2).copy(order='F'),
['c_index'], [['readonly']])
assert_equal(iter_indices(i),
[0, 6, 2, 8, 4, 10, 1, 7, 3, 9, 5, 11])
# 3D reversed C-order
i = nditer(a.reshape(2, 3, 2)[::-1], ['c_index'], [['readonly']])
assert_equal(iter_indices(i),
[6, 7, 8, 9, 10, 11, 0, 1, 2, 3, 4, 5])
i = nditer(a.reshape(2, 3, 2)[:, ::-1], ['c_index'], [['readonly']])
assert_equal(iter_indices(i),
[4, 5, 2, 3, 0, 1, 10, 11, 8, 9, 6, 7])
i = nditer(a.reshape(2, 3, 2)[:,:, ::-1], ['c_index'], [['readonly']])
assert_equal(iter_indices(i),
[1, 0, 3, 2, 5, 4, 7, 6, 9, 8, 11, 10])
# 3D reversed Fortran-order
i = nditer(a.reshape(2, 3, 2).copy(order='F')[::-1],
['c_index'], [['readonly']])
assert_equal(iter_indices(i),
[6, 0, 8, 2, 10, 4, 7, 1, 9, 3, 11, 5])
i = nditer(a.reshape(2, 3, 2).copy(order='F')[:, ::-1],
['c_index'], [['readonly']])
assert_equal(iter_indices(i),
[4, 10, 2, 8, 0, 6, 5, 11, 3, 9, 1, 7])
i = nditer(a.reshape(2, 3, 2).copy(order='F')[:,:, ::-1],
['c_index'], [['readonly']])
assert_equal(iter_indices(i),
[1, 7, 3, 9, 5, 11, 0, 6, 2, 8, 4, 10])
def test_iter_best_order_f_index_1d():
# The Fortran index should be correct with any reordering
a = arange(4)
# 1D order
i = nditer(a, ['f_index'], [['readonly']])
assert_equal(iter_indices(i), [0, 1, 2, 3])
# 1D reversed order
i = nditer(a[::-1], ['f_index'], [['readonly']])
assert_equal(iter_indices(i), [3, 2, 1, 0])
def test_iter_best_order_f_index_2d():
# The Fortran index should be correct with any reordering
a = arange(6)
# 2D C-order
i = nditer(a.reshape(2, 3), ['f_index'], [['readonly']])
assert_equal(iter_indices(i), [0, 2, 4, 1, 3, 5])
# 2D Fortran-order
i = nditer(a.reshape(2, 3).copy(order='F'),
['f_index'], [['readonly']])
assert_equal(iter_indices(i), [0, 1, 2, 3, 4, 5])
# 2D reversed C-order
i = nditer(a.reshape(2, 3)[::-1], ['f_index'], [['readonly']])
assert_equal(iter_indices(i), [1, 3, 5, 0, 2, 4])
i = nditer(a.reshape(2, 3)[:, ::-1], ['f_index'], [['readonly']])
assert_equal(iter_indices(i), [4, 2, 0, 5, 3, 1])
i = nditer(a.reshape(2, 3)[::-1, ::-1], ['f_index'], [['readonly']])
assert_equal(iter_indices(i), [5, 3, 1, 4, 2, 0])
# 2D reversed Fortran-order
i = nditer(a.reshape(2, 3).copy(order='F')[::-1],
['f_index'], [['readonly']])
assert_equal(iter_indices(i), [1, 0, 3, 2, 5, 4])
i = nditer(a.reshape(2, 3).copy(order='F')[:, ::-1],
['f_index'], [['readonly']])
assert_equal(iter_indices(i), [4, 5, 2, 3, 0, 1])
i = nditer(a.reshape(2, 3).copy(order='F')[::-1, ::-1],
['f_index'], [['readonly']])
assert_equal(iter_indices(i), [5, 4, 3, 2, 1, 0])
def test_iter_best_order_f_index_3d():
# The Fortran index should be correct with any reordering
a = arange(12)
# 3D C-order
i = nditer(a.reshape(2, 3, 2), ['f_index'], [['readonly']])
assert_equal(iter_indices(i),
[0, 6, 2, 8, 4, 10, 1, 7, 3, 9, 5, 11])
# 3D Fortran-order
i = nditer(a.reshape(2, 3, 2).copy(order='F'),
['f_index'], [['readonly']])
assert_equal(iter_indices(i),
[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
# 3D reversed C-order
i = nditer(a.reshape(2, 3, 2)[::-1], ['f_index'], [['readonly']])
assert_equal(iter_indices(i),
[1, 7, 3, 9, 5, 11, 0, 6, 2, 8, 4, 10])
i = nditer(a.reshape(2, 3, 2)[:, ::-1], ['f_index'], [['readonly']])
assert_equal(iter_indices(i),
[4, 10, 2, 8, 0, 6, 5, 11, 3, 9, 1, 7])
i = nditer(a.reshape(2, 3, 2)[:,:, ::-1], ['f_index'], [['readonly']])
assert_equal(iter_indices(i),
[6, 0, 8, 2, 10, 4, 7, 1, 9, 3, 11, 5])
# 3D reversed Fortran-order
i = nditer(a.reshape(2, 3, 2).copy(order='F')[::-1],
['f_index'], [['readonly']])
assert_equal(iter_indices(i),
[1, 0, 3, 2, 5, 4, 7, 6, 9, 8, 11, 10])
i = nditer(a.reshape(2, 3, 2).copy(order='F')[:, ::-1],
['f_index'], [['readonly']])
assert_equal(iter_indices(i),
[4, 5, 2, 3, 0, 1, 10, 11, 8, 9, 6, 7])
i = nditer(a.reshape(2, 3, 2).copy(order='F')[:,:, ::-1],
['f_index'], [['readonly']])
assert_equal(iter_indices(i),
[6, 7, 8, 9, 10, 11, 0, 1, 2, 3, 4, 5])
def test_iter_no_inner_full_coalesce():
# Check no_inner iterators which coalesce into a single inner loop
for shape in [(5,), (3, 4), (2, 3, 4), (2, 3, 4, 3), (2, 3, 2, 2, 3)]:
size = np.prod(shape)
a = arange(size)
# Test each combination of forward and backwards indexing
for dirs in range(2**len(shape)):
dirs_index = [slice(None)]*len(shape)
for bit in range(len(shape)):
if ((2**bit) & dirs):
dirs_index[bit] = slice(None, None, -1)
dirs_index = tuple(dirs_index)
aview = a.reshape(shape)[dirs_index]
# C-order
i = nditer(aview, ['external_loop'], [['readonly']])
assert_equal(i.ndim, 1)
assert_equal(i[0].shape, (size,))
# Fortran-order
i = nditer(aview.T, ['external_loop'], [['readonly']])
assert_equal(i.ndim, 1)
assert_equal(i[0].shape, (size,))
# Other order
if len(shape) > 2:
i = nditer(aview.swapaxes(0, 1),
['external_loop'], [['readonly']])
assert_equal(i.ndim, 1)
assert_equal(i[0].shape, (size,))
def test_iter_no_inner_dim_coalescing():
# Check no_inner iterators whose dimensions may not coalesce completely
# Skipping the last element in a dimension prevents coalescing
# with the next-bigger dimension
a = arange(24).reshape(2, 3, 4)[:,:, :-1]
i = nditer(a, ['external_loop'], [['readonly']])
assert_equal(i.ndim, 2)
assert_equal(i[0].shape, (3,))
a = arange(24).reshape(2, 3, 4)[:, :-1,:]
i = nditer(a, ['external_loop'], [['readonly']])
assert_equal(i.ndim, 2)
assert_equal(i[0].shape, (8,))
a = arange(24).reshape(2, 3, 4)[:-1,:,:]
i = nditer(a, ['external_loop'], [['readonly']])
assert_equal(i.ndim, 1)
assert_equal(i[0].shape, (12,))
# Even with lots of 1-sized dimensions, should still coalesce
a = arange(24).reshape(1, 1, 2, 1, 1, 3, 1, 1, 4, 1, 1)
i = nditer(a, ['external_loop'], [['readonly']])
assert_equal(i.ndim, 1)
assert_equal(i[0].shape, (24,))
def test_iter_dim_coalescing():
# Check that the correct number of dimensions are coalesced
# Tracking a multi-index disables coalescing
a = arange(24).reshape(2, 3, 4)
i = nditer(a, ['multi_index'], [['readonly']])
assert_equal(i.ndim, 3)
# A tracked index can allow coalescing if it's compatible with the array
a3d = arange(24).reshape(2, 3, 4)
i = nditer(a3d, ['c_index'], [['readonly']])
assert_equal(i.ndim, 1)
i = nditer(a3d.swapaxes(0, 1), ['c_index'], [['readonly']])
assert_equal(i.ndim, 3)
i = nditer(a3d.T, ['c_index'], [['readonly']])
assert_equal(i.ndim, 3)
i = nditer(a3d.T, ['f_index'], [['readonly']])
assert_equal(i.ndim, 1)
i = nditer(a3d.T.swapaxes(0, 1), ['f_index'], [['readonly']])
assert_equal(i.ndim, 3)
# When C or F order is forced, coalescing may still occur
a3d = arange(24).reshape(2, 3, 4)
i = nditer(a3d, order='C')
assert_equal(i.ndim, 1)
i = nditer(a3d.T, order='C')
assert_equal(i.ndim, 3)
i = nditer(a3d, order='F')
assert_equal(i.ndim, 3)
i = nditer(a3d.T, order='F')
assert_equal(i.ndim, 1)
i = nditer(a3d, order='A')
assert_equal(i.ndim, 1)
i = nditer(a3d.T, order='A')
assert_equal(i.ndim, 1)
def test_iter_broadcasting():
# Standard NumPy broadcasting rules
# 1D with scalar
i = nditer([arange(6), np.int32(2)], ['multi_index'], [['readonly']]*2)
assert_equal(i.itersize, 6)
assert_equal(i.shape, (6,))
# 2D with scalar
i = nditer([arange(6).reshape(2, 3), np.int32(2)],
['multi_index'], [['readonly']]*2)
assert_equal(i.itersize, 6)
assert_equal(i.shape, (2, 3))
# 2D with 1D
i = nditer([arange(6).reshape(2, 3), arange(3)],
['multi_index'], [['readonly']]*2)
assert_equal(i.itersize, 6)
assert_equal(i.shape, (2, 3))
i = nditer([arange(2).reshape(2, 1), arange(3)],
['multi_index'], [['readonly']]*2)
assert_equal(i.itersize, 6)
assert_equal(i.shape, (2, 3))
# 2D with 2D
i = nditer([arange(2).reshape(2, 1), arange(3).reshape(1, 3)],
['multi_index'], [['readonly']]*2)
assert_equal(i.itersize, 6)
assert_equal(i.shape, (2, 3))
# 3D with scalar
i = nditer([np.int32(2), arange(24).reshape(4, 2, 3)],
['multi_index'], [['readonly']]*2)
assert_equal(i.itersize, 24)
assert_equal(i.shape, (4, 2, 3))
# 3D with 1D
i = nditer([arange(3), arange(24).reshape(4, 2, 3)],
['multi_index'], [['readonly']]*2)
assert_equal(i.itersize, 24)
assert_equal(i.shape, (4, 2, 3))
i = nditer([arange(3), arange(8).reshape(4, 2, 1)],
['multi_index'], [['readonly']]*2)
assert_equal(i.itersize, 24)
assert_equal(i.shape, (4, 2, 3))
# 3D with 2D
i = nditer([arange(6).reshape(2, 3), arange(24).reshape(4, 2, 3)],
['multi_index'], [['readonly']]*2)
assert_equal(i.itersize, 24)
assert_equal(i.shape, (4, 2, 3))
i = nditer([arange(2).reshape(2, 1), arange(24).reshape(4, 2, 3)],
['multi_index'], [['readonly']]*2)
assert_equal(i.itersize, 24)
assert_equal(i.shape, (4, 2, 3))
i = nditer([arange(3).reshape(1, 3), arange(8).reshape(4, 2, 1)],
['multi_index'], [['readonly']]*2)
assert_equal(i.itersize, 24)
assert_equal(i.shape, (4, 2, 3))
# 3D with 3D
i = nditer([arange(2).reshape(1, 2, 1), arange(3).reshape(1, 1, 3),
arange(4).reshape(4, 1, 1)],
['multi_index'], [['readonly']]*3)
assert_equal(i.itersize, 24)
assert_equal(i.shape, (4, 2, 3))
i = nditer([arange(6).reshape(1, 2, 3), arange(4).reshape(4, 1, 1)],
['multi_index'], [['readonly']]*2)
assert_equal(i.itersize, 24)
assert_equal(i.shape, (4, 2, 3))
i = nditer([arange(24).reshape(4, 2, 3), arange(12).reshape(4, 1, 3)],
['multi_index'], [['readonly']]*2)
assert_equal(i.itersize, 24)
assert_equal(i.shape, (4, 2, 3))
def test_iter_itershape():
# Check that allocated outputs work with a specified shape
a = np.arange(6, dtype='i2').reshape(2, 3)
i = nditer([a, None], [], [['readonly'], ['writeonly', 'allocate']],
op_axes=[[0, 1, None], None],
itershape=(-1, -1, 4))
assert_equal(i.operands[1].shape, (2, 3, 4))
assert_equal(i.operands[1].strides, (24, 8, 2))
i = nditer([a.T, None], [], [['readonly'], ['writeonly', 'allocate']],
op_axes=[[0, 1, None], None],
itershape=(-1, -1, 4))
assert_equal(i.operands[1].shape, (3, 2, 4))
assert_equal(i.operands[1].strides, (8, 24, 2))
i = nditer([a.T, None], [], [['readonly'], ['writeonly', 'allocate']],
order='F',
op_axes=[[0, 1, None], None],
itershape=(-1, -1, 4))
assert_equal(i.operands[1].shape, (3, 2, 4))
assert_equal(i.operands[1].strides, (2, 6, 12))
# If we specify 1 in the itershape, it shouldn't allow broadcasting
# of that dimension to a bigger value
assert_raises(ValueError, nditer, [a, None], [],
[['readonly'], ['writeonly', 'allocate']],
op_axes=[[0, 1, None], None],
itershape=(-1, 1, 4))
# Test bug that for no op_axes but itershape, they are NULLed correctly
i = np.nditer([np.ones(2), None, None], itershape=(2,))
def test_iter_broadcasting_errors():
# Check that errors are thrown for bad broadcasting shapes
# 1D with 1D
assert_raises(ValueError, nditer, [arange(2), arange(3)],
[], [['readonly']]*2)
# 2D with 1D
assert_raises(ValueError, nditer,
[arange(6).reshape(2, 3), arange(2)],
[], [['readonly']]*2)
# 2D with 2D
assert_raises(ValueError, nditer,
[arange(6).reshape(2, 3), arange(9).reshape(3, 3)],
[], [['readonly']]*2)
assert_raises(ValueError, nditer,
[arange(6).reshape(2, 3), arange(4).reshape(2, 2)],
[], [['readonly']]*2)
# 3D with 3D
assert_raises(ValueError, nditer,
[arange(36).reshape(3, 3, 4), arange(24).reshape(2, 3, 4)],
[], [['readonly']]*2)
assert_raises(ValueError, nditer,
[arange(8).reshape(2, 4, 1), arange(24).reshape(2, 3, 4)],
[], [['readonly']]*2)
# Verify that the error message mentions the right shapes
try:
nditer([arange(2).reshape(1, 2, 1),
arange(3).reshape(1, 3),
arange(6).reshape(2, 3)],
[],
[['readonly'], ['readonly'], ['writeonly', 'no_broadcast']])
raise AssertionError('Should have raised a broadcast error')
except ValueError as e:
msg = str(e)
# The message should contain the shape of the 3rd operand
assert_(msg.find('(2,3)') >= 0,
'Message "%s" doesn\'t contain operand shape (2,3)' % msg)
# The message should contain the broadcast shape
assert_(msg.find('(1,2,3)') >= 0,
'Message "%s" doesn\'t contain broadcast shape (1,2,3)' % msg)
try:
nditer([arange(6).reshape(2, 3), arange(2)],
[],
[['readonly'], ['readonly']],
op_axes=[[0, 1], [0, np.newaxis]],
itershape=(4, 3))
raise AssertionError('Should have raised a broadcast error')
except ValueError as e:
msg = str(e)
# The message should contain "shape->remappedshape" for each operand
assert_(msg.find('(2,3)->(2,3)') >= 0,
'Message "%s" doesn\'t contain operand shape (2,3)->(2,3)' % msg)
assert_(msg.find('(2,)->(2,newaxis)') >= 0,
('Message "%s" doesn\'t contain remapped operand shape' +
'(2,)->(2,newaxis)') % msg)
# The message should contain the itershape parameter
assert_(msg.find('(4,3)') >= 0,
'Message "%s" doesn\'t contain itershape parameter (4,3)' % msg)
try:
nditer([np.zeros((2, 1, 1)), np.zeros((2,))],
[],
[['writeonly', 'no_broadcast'], ['readonly']])
raise AssertionError('Should have raised a broadcast error')
except ValueError as e:
msg = str(e)
# The message should contain the shape of the bad operand
assert_(msg.find('(2,1,1)') >= 0,
'Message "%s" doesn\'t contain operand shape (2,1,1)' % msg)
# The message should contain the broadcast shape
assert_(msg.find('(2,1,2)') >= 0,
'Message "%s" doesn\'t contain the broadcast shape (2,1,2)' % msg)
def test_iter_flags_errors():
# Check that bad combinations of flags produce errors
a = arange(6)
# Not enough operands
assert_raises(ValueError, nditer, [], [], [])
# Too many operands
assert_raises(ValueError, nditer, [a]*100, [], [['readonly']]*100)
# Bad global flag
assert_raises(ValueError, nditer, [a], ['bad flag'], [['readonly']])
# Bad op flag
assert_raises(ValueError, nditer, [a], [], [['readonly', 'bad flag']])
# Bad order parameter
assert_raises(ValueError, nditer, [a], [], [['readonly']], order='G')
# Bad casting parameter
assert_raises(ValueError, nditer, [a], [], [['readonly']], casting='noon')
# op_flags must match ops
assert_raises(ValueError, nditer, [a]*3, [], [['readonly']]*2)
# Cannot track both a C and an F index
assert_raises(ValueError, nditer, a,
['c_index', 'f_index'], [['readonly']])
# Inner iteration and multi-indices/indices are incompatible
assert_raises(ValueError, nditer, a,
['external_loop', 'multi_index'], [['readonly']])
assert_raises(ValueError, nditer, a,
['external_loop', 'c_index'], [['readonly']])
assert_raises(ValueError, nditer, a,
['external_loop', 'f_index'], [['readonly']])
# Must specify exactly one of readwrite/readonly/writeonly per operand
assert_raises(ValueError, nditer, a, [], [[]])
assert_raises(ValueError, nditer, a, [], [['readonly', 'writeonly']])
assert_raises(ValueError, nditer, a, [], [['readonly', 'readwrite']])
assert_raises(ValueError, nditer, a, [], [['writeonly', 'readwrite']])
assert_raises(ValueError, nditer, a,
[], [['readonly', 'writeonly', 'readwrite']])
# Python scalars are always readonly
assert_raises(TypeError, nditer, 1.5, [], [['writeonly']])
assert_raises(TypeError, nditer, 1.5, [], [['readwrite']])
# Array scalars are always readonly
assert_raises(TypeError, nditer, np.int32(1), [], [['writeonly']])
assert_raises(TypeError, nditer, np.int32(1), [], [['readwrite']])
# Check readonly array
a.flags.writeable = False
assert_raises(ValueError, nditer, a, [], [['writeonly']])
assert_raises(ValueError, nditer, a, [], [['readwrite']])
a.flags.writeable = True
# Multi-indices available only with the multi_index flag
i = nditer(arange(6), [], [['readonly']])
assert_raises(ValueError, lambda i:i.multi_index, i)
# Index available only with an index flag
assert_raises(ValueError, lambda i:i.index, i)
# GotoCoords and GotoIndex incompatible with buffering or no_inner
def assign_multi_index(i):
i.multi_index = (0,)
def assign_index(i):
i.index = 0
def assign_iterindex(i):
i.iterindex = 0
def assign_iterrange(i):
i.iterrange = (0, 1)
i = nditer(arange(6), ['external_loop'])
assert_raises(ValueError, assign_multi_index, i)
assert_raises(ValueError, assign_index, i)
assert_raises(ValueError, assign_iterindex, i)
assert_raises(ValueError, assign_iterrange, i)
i = nditer(arange(6), ['buffered'])
assert_raises(ValueError, assign_multi_index, i)
assert_raises(ValueError, assign_index, i)
assert_raises(ValueError, assign_iterrange, i)
# Can't iterate if size is zero
assert_raises(ValueError, nditer, np.array([]))
def test_iter_slice():
a, b, c = np.arange(3), np.arange(3), np.arange(3.)
i = nditer([a, b, c], [], ['readwrite'])
i[0:2] = (3, 3)
assert_equal(a, [3, 1, 2])
assert_equal(b, [3, 1, 2])
assert_equal(c, [0, 1, 2])
i[1] = 12
assert_equal(i[0:2], [3, 12])
def test_iter_nbo_align_contig():
# Check that byte order, alignment, and contig changes work
# Byte order change by requesting a specific dtype
a = np.arange(6, dtype='f4')
au = a.byteswap().newbyteorder()
assert_(a.dtype.byteorder != au.dtype.byteorder)
i = nditer(au, [], [['readwrite', 'updateifcopy']],
casting='equiv',
op_dtypes=[np.dtype('f4')])
assert_equal(i.dtypes[0].byteorder, a.dtype.byteorder)
assert_equal(i.operands[0].dtype.byteorder, a.dtype.byteorder)
assert_equal(i.operands[0], a)
i.operands[0][:] = 2
i = None
assert_equal(au, [2]*6)
# Byte order change by requesting NBO
a = np.arange(6, dtype='f4')
au = a.byteswap().newbyteorder()
assert_(a.dtype.byteorder != au.dtype.byteorder)
i = nditer(au, [], [['readwrite', 'updateifcopy', 'nbo']], casting='equiv')
assert_equal(i.dtypes[0].byteorder, a.dtype.byteorder)
assert_equal(i.operands[0].dtype.byteorder, a.dtype.byteorder)
assert_equal(i.operands[0], a)
i.operands[0][:] = 2
i = None
assert_equal(au, [2]*6)
# Unaligned input
a = np.zeros((6*4+1,), dtype='i1')[1:]
a.dtype = 'f4'
a[:] = np.arange(6, dtype='f4')
assert_(not a.flags.aligned)
# Without 'aligned', shouldn't copy
i = nditer(a, [], [['readonly']])
assert_(not i.operands[0].flags.aligned)
assert_equal(i.operands[0], a)
# With 'aligned', should make a copy
i = nditer(a, [], [['readwrite', 'updateifcopy', 'aligned']])
assert_(i.operands[0].flags.aligned)
assert_equal(i.operands[0], a)
i.operands[0][:] = 3
i = None
assert_equal(a, [3]*6)
# Discontiguous input
a = arange(12)
# If it is contiguous, shouldn't copy
i = nditer(a[:6], [], [['readonly']])
assert_(i.operands[0].flags.contiguous)
assert_equal(i.operands[0], a[:6])
# If it isn't contiguous, should buffer
i = nditer(a[::2], ['buffered', 'external_loop'],
[['readonly', 'contig']],
buffersize=10)
assert_(i[0].flags.contiguous)
assert_equal(i[0], a[::2])
def test_iter_array_cast():
# Check that arrays are cast as requested
# No cast 'f4' -> 'f4'
a = np.arange(6, dtype='f4').reshape(2, 3)
i = nditer(a, [], [['readwrite']], op_dtypes=[np.dtype('f4')])
assert_equal(i.operands[0], a)
assert_equal(i.operands[0].dtype, np.dtype('f4'))
# Byte-order cast '<f4' -> '>f4'
a = np.arange(6, dtype='<f4').reshape(2, 3)
i = nditer(a, [], [['readwrite', 'updateifcopy']],
casting='equiv',
op_dtypes=[np.dtype('>f4')])
assert_equal(i.operands[0], a)
assert_equal(i.operands[0].dtype, np.dtype('>f4'))
# Safe case 'f4' -> 'f8'
a = np.arange(24, dtype='f4').reshape(2, 3, 4).swapaxes(1, 2)
i = nditer(a, [], [['readonly', 'copy']],
casting='safe',
op_dtypes=[np.dtype('f8')])
assert_equal(i.operands[0], a)
assert_equal(i.operands[0].dtype, np.dtype('f8'))
# The memory layout of the temporary should match a (a is (48,4,16))
# except negative strides get flipped to positive strides.
assert_equal(i.operands[0].strides, (96, 8, 32))
a = a[::-1,:, ::-1]
i = nditer(a, [], [['readonly', 'copy']],
casting='safe',
op_dtypes=[np.dtype('f8')])
assert_equal(i.operands[0], a)
assert_equal(i.operands[0].dtype, np.dtype('f8'))
assert_equal(i.operands[0].strides, (96, 8, 32))
# Same-kind cast 'f8' -> 'f4' -> 'f8'
a = np.arange(24, dtype='f8').reshape(2, 3, 4).T
i = nditer(a, [],
[['readwrite', 'updateifcopy']],
casting='same_kind',
op_dtypes=[np.dtype('f4')])
assert_equal(i.operands[0], a)
assert_equal(i.operands[0].dtype, np.dtype('f4'))
assert_equal(i.operands[0].strides, (4, 16, 48))
# Check that UPDATEIFCOPY is activated
i.operands[0][2, 1, 1] = -12.5
assert_(a[2, 1, 1] != -12.5)
i = None
assert_equal(a[2, 1, 1], -12.5)
a = np.arange(6, dtype='i4')[::-2]
i = nditer(a, [],
[['writeonly', 'updateifcopy']],
casting='unsafe',
op_dtypes=[np.dtype('f4')])
assert_equal(i.operands[0].dtype, np.dtype('f4'))
# Even though the stride was negative in 'a', it
# becomes positive in the temporary
assert_equal(i.operands[0].strides, (4,))
i.operands[0][:] = [1, 2, 3]
i = None
assert_equal(a, [1, 2, 3])
def test_iter_array_cast_errors():
# Check that invalid casts are caught
# Need to enable copying for casts to occur
assert_raises(TypeError, nditer, arange(2, dtype='f4'), [],
[['readonly']], op_dtypes=[np.dtype('f8')])
# Also need to allow casting for casts to occur
assert_raises(TypeError, nditer, arange(2, dtype='f4'), [],
[['readonly', 'copy']], casting='no',
op_dtypes=[np.dtype('f8')])
assert_raises(TypeError, nditer, arange(2, dtype='f4'), [],
[['readonly', 'copy']], casting='equiv',
op_dtypes=[np.dtype('f8')])
assert_raises(TypeError, nditer, arange(2, dtype='f8'), [],
[['writeonly', 'updateifcopy']],
casting='no',
op_dtypes=[np.dtype('f4')])
assert_raises(TypeError, nditer, arange(2, dtype='f8'), [],
[['writeonly', 'updateifcopy']],
casting='equiv',
op_dtypes=[np.dtype('f4')])
# '<f4' -> '>f4' should not work with casting='no'
assert_raises(TypeError, nditer, arange(2, dtype='<f4'), [],
[['readonly', 'copy']], casting='no',
op_dtypes=[np.dtype('>f4')])
# 'f4' -> 'f8' is a safe cast, but 'f8' -> 'f4' isn't
assert_raises(TypeError, nditer, arange(2, dtype='f4'), [],
[['readwrite', 'updateifcopy']],
casting='safe',
op_dtypes=[np.dtype('f8')])
assert_raises(TypeError, nditer, arange(2, dtype='f8'), [],
[['readwrite', 'updateifcopy']],
casting='safe',
op_dtypes=[np.dtype('f4')])
# 'f4' -> 'i4' is neither a safe nor a same-kind cast
assert_raises(TypeError, nditer, arange(2, dtype='f4'), [],
[['readonly', 'copy']],
casting='same_kind',
op_dtypes=[np.dtype('i4')])
assert_raises(TypeError, nditer, arange(2, dtype='i4'), [],
[['writeonly', 'updateifcopy']],
casting='same_kind',
op_dtypes=[np.dtype('f4')])
def test_iter_scalar_cast():
# Check that scalars are cast as requested
# No cast 'f4' -> 'f4'
i = nditer(np.float32(2.5), [], [['readonly']],
op_dtypes=[np.dtype('f4')])
assert_equal(i.dtypes[0], np.dtype('f4'))
assert_equal(i.value.dtype, np.dtype('f4'))
assert_equal(i.value, 2.5)
# Safe cast 'f4' -> 'f8'
i = nditer(np.float32(2.5), [],
[['readonly', 'copy']],
casting='safe',
op_dtypes=[np.dtype('f8')])
assert_equal(i.dtypes[0], np.dtype('f8'))
assert_equal(i.value.dtype, np.dtype('f8'))
assert_equal(i.value, 2.5)
# Same-kind cast 'f8' -> 'f4'
i = nditer(np.float64(2.5), [],
[['readonly', 'copy']],
casting='same_kind',
op_dtypes=[np.dtype('f4')])
assert_equal(i.dtypes[0], np.dtype('f4'))
assert_equal(i.value.dtype, np.dtype('f4'))
assert_equal(i.value, 2.5)
# Unsafe cast 'f8' -> 'i4'
i = nditer(np.float64(3.0), [],
[['readonly', 'copy']],
casting='unsafe',
op_dtypes=[np.dtype('i4')])
assert_equal(i.dtypes[0], np.dtype('i4'))
assert_equal(i.value.dtype, np.dtype('i4'))
assert_equal(i.value, 3)
# Readonly scalars may be cast even without setting COPY or BUFFERED
i = nditer(3, [], [['readonly']], op_dtypes=[np.dtype('f8')])
assert_equal(i[0].dtype, np.dtype('f8'))
assert_equal(i[0], 3.)
def test_iter_scalar_cast_errors():
# Check that invalid casts are caught
# Need to allow copying/buffering for write casts of scalars to occur
assert_raises(TypeError, nditer, np.float32(2), [],
[['readwrite']], op_dtypes=[np.dtype('f8')])
assert_raises(TypeError, nditer, 2.5, [],
[['readwrite']], op_dtypes=[np.dtype('f4')])
# 'f8' -> 'f4' isn't a safe cast if the value would overflow
assert_raises(TypeError, nditer, np.float64(1e60), [],
[['readonly']],
casting='safe',
op_dtypes=[np.dtype('f4')])
# 'f4' -> 'i4' is neither a safe nor a same-kind cast
assert_raises(TypeError, nditer, np.float32(2), [],
[['readonly']],
casting='same_kind',
op_dtypes=[np.dtype('i4')])
def test_iter_object_arrays_basic():
# Check that object arrays work
obj = {'a':3,'b':'d'}
a = np.array([[1, 2, 3], None, obj, None], dtype='O')
if HAS_REFCOUNT:
rc = sys.getrefcount(obj)
# Need to allow references for object arrays
assert_raises(TypeError, nditer, a)
if HAS_REFCOUNT:
assert_equal(sys.getrefcount(obj), rc)
i = nditer(a, ['refs_ok'], ['readonly'])
vals = [x_[()] for x_ in i]
assert_equal(np.array(vals, dtype='O'), a)
vals, i, x = [None]*3
if HAS_REFCOUNT:
assert_equal(sys.getrefcount(obj), rc)
i = nditer(a.reshape(2, 2).T, ['refs_ok', 'buffered'],
['readonly'], order='C')
assert_(i.iterationneedsapi)
vals = [x_[()] for x_ in i]
assert_equal(np.array(vals, dtype='O'), a.reshape(2, 2).ravel(order='F'))
vals, i, x = [None]*3
if HAS_REFCOUNT:
assert_equal(sys.getrefcount(obj), rc)
i = nditer(a.reshape(2, 2).T, ['refs_ok', 'buffered'],
['readwrite'], order='C')
for x in i:
x[...] = None
vals, i, x = [None]*3
if HAS_REFCOUNT:
assert_(sys.getrefcount(obj) == rc-1)
assert_equal(a, np.array([None]*4, dtype='O'))
def test_iter_object_arrays_conversions():
# Conversions to/from objects
a = np.arange(6, dtype='O')
i = nditer(a, ['refs_ok', 'buffered'], ['readwrite'],
casting='unsafe', op_dtypes='i4')
for x in i:
x[...] += 1
assert_equal(a, np.arange(6)+1)
a = np.arange(6, dtype='i4')
i = nditer(a, ['refs_ok', 'buffered'], ['readwrite'],
casting='unsafe', op_dtypes='O')
for x in i:
x[...] += 1
assert_equal(a, np.arange(6)+1)
# Non-contiguous object array
a = np.zeros((6,), dtype=[('p', 'i1'), ('a', 'O')])
a = a['a']
a[:] = np.arange(6)
i = nditer(a, ['refs_ok', 'buffered'], ['readwrite'],
casting='unsafe', op_dtypes='i4')
for x in i:
x[...] += 1
assert_equal(a, np.arange(6)+1)
#Non-contiguous value array
a = np.zeros((6,), dtype=[('p', 'i1'), ('a', 'i4')])
a = a['a']
a[:] = np.arange(6) + 98172488
i = nditer(a, ['refs_ok', 'buffered'], ['readwrite'],
casting='unsafe', op_dtypes='O')
ob = i[0][()]
if HAS_REFCOUNT:
rc = sys.getrefcount(ob)
for x in i:
x[...] += 1
if HAS_REFCOUNT:
assert_(sys.getrefcount(ob) == rc-1)
assert_equal(a, np.arange(6)+98172489)
def test_iter_common_dtype():
# Check that the iterator finds a common data type correctly
i = nditer([array([3], dtype='f4'), array([0], dtype='f8')],
['common_dtype'],
[['readonly', 'copy']]*2,
casting='safe')
assert_equal(i.dtypes[0], np.dtype('f8'))
assert_equal(i.dtypes[1], np.dtype('f8'))
i = nditer([array([3], dtype='i4'), array([0], dtype='f4')],
['common_dtype'],
[['readonly', 'copy']]*2,
casting='safe')
assert_equal(i.dtypes[0], np.dtype('f8'))
assert_equal(i.dtypes[1], np.dtype('f8'))
i = nditer([array([3], dtype='f4'), array(0, dtype='f8')],
['common_dtype'],
[['readonly', 'copy']]*2,
casting='same_kind')
assert_equal(i.dtypes[0], np.dtype('f4'))
assert_equal(i.dtypes[1], np.dtype('f4'))
i = nditer([array([3], dtype='u4'), array(0, dtype='i4')],
['common_dtype'],
[['readonly', 'copy']]*2,
casting='safe')
assert_equal(i.dtypes[0], np.dtype('u4'))
assert_equal(i.dtypes[1], np.dtype('u4'))
i = nditer([array([3], dtype='u4'), array(-12, dtype='i4')],
['common_dtype'],
[['readonly', 'copy']]*2,
casting='safe')
assert_equal(i.dtypes[0], np.dtype('i8'))
assert_equal(i.dtypes[1], np.dtype('i8'))
i = nditer([array([3], dtype='u4'), array(-12, dtype='i4'),
array([2j], dtype='c8'), array([9], dtype='f8')],
['common_dtype'],
[['readonly', 'copy']]*4,
casting='safe')
assert_equal(i.dtypes[0], np.dtype('c16'))
assert_equal(i.dtypes[1], np.dtype('c16'))
assert_equal(i.dtypes[2], np.dtype('c16'))
assert_equal(i.dtypes[3], np.dtype('c16'))
assert_equal(i.value, (3, -12, 2j, 9))
# When allocating outputs, other outputs aren't factored in
i = nditer([array([3], dtype='i4'), None, array([2j], dtype='c16')], [],
[['readonly', 'copy'],
['writeonly', 'allocate'],
['writeonly']],
casting='safe')
assert_equal(i.dtypes[0], np.dtype('i4'))
assert_equal(i.dtypes[1], np.dtype('i4'))
assert_equal(i.dtypes[2], np.dtype('c16'))
# But, if common data types are requested, they are
i = nditer([array([3], dtype='i4'), None, array([2j], dtype='c16')],
['common_dtype'],
[['readonly', 'copy'],
['writeonly', 'allocate'],
['writeonly']],
casting='safe')
assert_equal(i.dtypes[0], np.dtype('c16'))
assert_equal(i.dtypes[1], np.dtype('c16'))
assert_equal(i.dtypes[2], np.dtype('c16'))
def test_iter_copy_if_overlap():
# Ensure the iterator makes copies on read/write overlap, if requested
# Copy not needed, 1 op
for flag in ['readonly', 'writeonly', 'readwrite']:
a = arange(10)
i = nditer([a], ['copy_if_overlap'], [[flag]])
assert_(i.operands[0] is a)
# Copy needed, 2 ops, read-write overlap
x = arange(10)
a = x[1:]
b = x[:-1]
i = nditer([a, b], ['copy_if_overlap'], [['readonly'], ['readwrite']])
assert_(not np.shares_memory(*i.operands))
# Copy not needed with elementwise, 2 ops, exactly same arrays
x = arange(10)
a = x
b = x
i = nditer([a, b], ['copy_if_overlap'], [['readonly', 'overlap_assume_elementwise'],
['readwrite', 'overlap_assume_elementwise']])
assert_(i.operands[0] is a and i.operands[1] is b)
i = nditer([a, b], ['copy_if_overlap'], [['readonly'], ['readwrite']])
assert_(i.operands[0] is a and not np.shares_memory(i.operands[1], b))
# Copy not needed, 2 ops, no overlap
x = arange(10)
a = x[::2]
b = x[1::2]
i = nditer([a, b], ['copy_if_overlap'], [['readonly'], ['writeonly']])
assert_(i.operands[0] is a and i.operands[1] is b)
# Copy needed, 2 ops, read-write overlap
x = arange(4, dtype=np.int8)
a = x[3:]
b = x.view(np.int32)[:1]
i = nditer([a, b], ['copy_if_overlap'], [['readonly'], ['writeonly']])
assert_(not np.shares_memory(*i.operands))
# Copy needed, 3 ops, read-write overlap
for flag in ['writeonly', 'readwrite']:
x = np.ones([10, 10])
a = x
b = x.T
c = x
i = nditer([a, b, c], ['copy_if_overlap'],
[['readonly'], ['readonly'], [flag]])
a2, b2, c2 = i.operands
assert_(not np.shares_memory(a2, c2))
assert_(not np.shares_memory(b2, c2))
# Copy not needed, 3 ops, read-only overlap
x = np.ones([10, 10])
a = x
b = x.T
c = x
i = nditer([a, b, c], ['copy_if_overlap'],
[['readonly'], ['readonly'], ['readonly']])
a2, b2, c2 = i.operands
assert_(a is a2)
assert_(b is b2)
assert_(c is c2)
# Copy not needed, 3 ops, read-only overlap
x = np.ones([10, 10])
a = x
b = np.ones([10, 10])
c = x.T
i = nditer([a, b, c], ['copy_if_overlap'],
[['readonly'], ['writeonly'], ['readonly']])
a2, b2, c2 = i.operands
assert_(a is a2)
assert_(b is b2)
assert_(c is c2)
# Copy not needed, 3 ops, write-only overlap
x = np.arange(7)
a = x[:3]
b = x[3:6]
c = x[4:7]
i = nditer([a, b, c], ['copy_if_overlap'],
[['readonly'], ['writeonly'], ['writeonly']])
a2, b2, c2 = i.operands
assert_(a is a2)
assert_(b is b2)
assert_(c is c2)
def test_iter_op_axes():
# Check that custom axes work
# Reverse the axes
a = arange(6).reshape(2, 3)
i = nditer([a, a.T], [], [['readonly']]*2, op_axes=[[0, 1], [1, 0]])
assert_(all([x == y for (x, y) in i]))
a = arange(24).reshape(2, 3, 4)
i = nditer([a.T, a], [], [['readonly']]*2, op_axes=[[2, 1, 0], None])
assert_(all([x == y for (x, y) in i]))
# Broadcast 1D to any dimension
a = arange(1, 31).reshape(2, 3, 5)
b = arange(1, 3)
i = nditer([a, b], [], [['readonly']]*2, op_axes=[None, [0, -1, -1]])
assert_equal([x*y for (x, y) in i], (a*b.reshape(2, 1, 1)).ravel())
b = arange(1, 4)
i = nditer([a, b], [], [['readonly']]*2, op_axes=[None, [-1, 0, -1]])
assert_equal([x*y for (x, y) in i], (a*b.reshape(1, 3, 1)).ravel())
b = arange(1, 6)
i = nditer([a, b], [], [['readonly']]*2,
op_axes=[None, [np.newaxis, np.newaxis, 0]])
assert_equal([x*y for (x, y) in i], (a*b.reshape(1, 1, 5)).ravel())
# Inner product-style broadcasting
a = arange(24).reshape(2, 3, 4)
b = arange(40).reshape(5, 2, 4)
i = nditer([a, b], ['multi_index'], [['readonly']]*2,
op_axes=[[0, 1, -1, -1], [-1, -1, 0, 1]])
assert_equal(i.shape, (2, 3, 5, 2))
# Matrix product-style broadcasting
a = arange(12).reshape(3, 4)
b = arange(20).reshape(4, 5)
i = nditer([a, b], ['multi_index'], [['readonly']]*2,
op_axes=[[0, -1], [-1, 1]])
assert_equal(i.shape, (3, 5))
def test_iter_op_axes_errors():
# Check that custom axes throws errors for bad inputs
# Wrong number of items in op_axes
a = arange(6).reshape(2, 3)
assert_raises(ValueError, nditer, [a, a], [], [['readonly']]*2,
op_axes=[[0], [1], [0]])
# Out of bounds items in op_axes
assert_raises(ValueError, nditer, [a, a], [], [['readonly']]*2,
op_axes=[[2, 1], [0, 1]])
assert_raises(ValueError, nditer, [a, a], [], [['readonly']]*2,
op_axes=[[0, 1], [2, -1]])
# Duplicate items in op_axes
assert_raises(ValueError, nditer, [a, a], [], [['readonly']]*2,
op_axes=[[0, 0], [0, 1]])
assert_raises(ValueError, nditer, [a, a], [], [['readonly']]*2,
op_axes=[[0, 1], [1, 1]])
# Different sized arrays in op_axes
assert_raises(ValueError, nditer, [a, a], [], [['readonly']]*2,
op_axes=[[0, 1], [0, 1, 0]])
# Non-broadcastable dimensions in the result
assert_raises(ValueError, nditer, [a, a], [], [['readonly']]*2,
op_axes=[[0, 1], [1, 0]])
def test_iter_copy():
# Check that copying the iterator works correctly
a = arange(24).reshape(2, 3, 4)
# Simple iterator
i = nditer(a)
j = i.copy()
assert_equal([x[()] for x in i], [x[()] for x in j])
i.iterindex = 3
j = i.copy()
assert_equal([x[()] for x in i], [x[()] for x in j])
# Buffered iterator
i = nditer(a, ['buffered', 'ranged'], order='F', buffersize=3)
j = i.copy()
assert_equal([x[()] for x in i], [x[()] for x in j])
i.iterindex = 3
j = i.copy()
assert_equal([x[()] for x in i], [x[()] for x in j])
i.iterrange = (3, 9)
j = i.copy()
assert_equal([x[()] for x in i], [x[()] for x in j])
i.iterrange = (2, 18)
next(i)
next(i)
j = i.copy()
assert_equal([x[()] for x in i], [x[()] for x in j])
# Casting iterator
i = nditer(a, ['buffered'], order='F', casting='unsafe',
op_dtypes='f8', buffersize=5)
j = i.copy()
i = None
assert_equal([x[()] for x in j], a.ravel(order='F'))
a = arange(24, dtype='<i4').reshape(2, 3, 4)
i = nditer(a, ['buffered'], order='F', casting='unsafe',
op_dtypes='>f8', buffersize=5)
j = i.copy()
i = None
assert_equal([x[()] for x in j], a.ravel(order='F'))
def test_iter_allocate_output_simple():
# Check that the iterator will properly allocate outputs
# Simple case
a = arange(6)
i = nditer([a, None], [], [['readonly'], ['writeonly', 'allocate']],
op_dtypes=[None, np.dtype('f4')])
assert_equal(i.operands[1].shape, a.shape)
assert_equal(i.operands[1].dtype, np.dtype('f4'))
def test_iter_allocate_output_buffered_readwrite():
# Allocated output with buffering + delay_bufalloc
a = arange(6)
i = nditer([a, None], ['buffered', 'delay_bufalloc'],
[['readonly'], ['allocate', 'readwrite']])
i.operands[1][:] = 1
i.reset()
for x in i:
x[1][...] += x[0][...]
assert_equal(i.operands[1], a+1)
def test_iter_allocate_output_itorder():
# The allocated output should match the iteration order
# C-order input, best iteration order
a = arange(6, dtype='i4').reshape(2, 3)
i = nditer([a, None], [], [['readonly'], ['writeonly', 'allocate']],
op_dtypes=[None, np.dtype('f4')])
assert_equal(i.operands[1].shape, a.shape)
assert_equal(i.operands[1].strides, a.strides)
assert_equal(i.operands[1].dtype, np.dtype('f4'))
# F-order input, best iteration order
a = arange(24, dtype='i4').reshape(2, 3, 4).T
i = nditer([a, None], [], [['readonly'], ['writeonly', 'allocate']],
op_dtypes=[None, np.dtype('f4')])
assert_equal(i.operands[1].shape, a.shape)
assert_equal(i.operands[1].strides, a.strides)
assert_equal(i.operands[1].dtype, np.dtype('f4'))
# Non-contiguous input, C iteration order
a = arange(24, dtype='i4').reshape(2, 3, 4).swapaxes(0, 1)
i = nditer([a, None], [],
[['readonly'], ['writeonly', 'allocate']],
order='C',
op_dtypes=[None, np.dtype('f4')])
assert_equal(i.operands[1].shape, a.shape)
assert_equal(i.operands[1].strides, (32, 16, 4))
assert_equal(i.operands[1].dtype, np.dtype('f4'))
def test_iter_allocate_output_opaxes():
# Specifying op_axes should work
a = arange(24, dtype='i4').reshape(2, 3, 4)
i = nditer([None, a], [], [['writeonly', 'allocate'], ['readonly']],
op_dtypes=[np.dtype('u4'), None],
op_axes=[[1, 2, 0], None])
assert_equal(i.operands[0].shape, (4, 2, 3))
assert_equal(i.operands[0].strides, (4, 48, 16))
assert_equal(i.operands[0].dtype, np.dtype('u4'))
def test_iter_allocate_output_types_promotion():
# Check type promotion of automatic outputs
i = nditer([array([3], dtype='f4'), array([0], dtype='f8'), None], [],
[['readonly']]*2+[['writeonly', 'allocate']])
assert_equal(i.dtypes[2], np.dtype('f8'))
i = nditer([array([3], dtype='i4'), array([0], dtype='f4'), None], [],
[['readonly']]*2+[['writeonly', 'allocate']])
assert_equal(i.dtypes[2], np.dtype('f8'))
i = nditer([array([3], dtype='f4'), array(0, dtype='f8'), None], [],
[['readonly']]*2+[['writeonly', 'allocate']])
assert_equal(i.dtypes[2], np.dtype('f4'))
i = nditer([array([3], dtype='u4'), array(0, dtype='i4'), None], [],
[['readonly']]*2+[['writeonly', 'allocate']])
assert_equal(i.dtypes[2], np.dtype('u4'))
i = nditer([array([3], dtype='u4'), array(-12, dtype='i4'), None], [],
[['readonly']]*2+[['writeonly', 'allocate']])
assert_equal(i.dtypes[2], np.dtype('i8'))
def test_iter_allocate_output_types_byte_order():
# Verify the rules for byte order changes
# When there's just one input, the output type exactly matches
a = array([3], dtype='u4').newbyteorder()
i = nditer([a, None], [],
[['readonly'], ['writeonly', 'allocate']])
assert_equal(i.dtypes[0], i.dtypes[1])
# With two or more inputs, the output type is in native byte order
i = nditer([a, a, None], [],
[['readonly'], ['readonly'], ['writeonly', 'allocate']])
assert_(i.dtypes[0] != i.dtypes[2])
assert_equal(i.dtypes[0].newbyteorder('='), i.dtypes[2])
def test_iter_allocate_output_types_scalar():
# If the inputs are all scalars, the output should be a scalar
i = nditer([None, 1, 2.3, np.float32(12), np.complex128(3)], [],
[['writeonly', 'allocate']] + [['readonly']]*4)
assert_equal(i.operands[0].dtype, np.dtype('complex128'))
assert_equal(i.operands[0].ndim, 0)
def test_iter_allocate_output_subtype():
# Make sure that the subtype with priority wins
# matrix vs ndarray
a = np.matrix([[1, 2], [3, 4]])
b = np.arange(4).reshape(2, 2).T
i = nditer([a, b, None], [],
[['readonly'], ['readonly'], ['writeonly', 'allocate']])
assert_equal(type(a), type(i.operands[2]))
assert_(type(b) != type(i.operands[2]))
assert_equal(i.operands[2].shape, (2, 2))
# matrix always wants things to be 2D
b = np.arange(4).reshape(1, 2, 2)
assert_raises(RuntimeError, nditer, [a, b, None], [],
[['readonly'], ['readonly'], ['writeonly', 'allocate']])
# but if subtypes are disabled, the result can still work
i = nditer([a, b, None], [],
[['readonly'], ['readonly'], ['writeonly', 'allocate', 'no_subtype']])
assert_equal(type(b), type(i.operands[2]))
assert_(type(a) != type(i.operands[2]))
assert_equal(i.operands[2].shape, (1, 2, 2))
def test_iter_allocate_output_errors():
# Check that the iterator will throw errors for bad output allocations
# Need an input if no output data type is specified
a = arange(6)
assert_raises(TypeError, nditer, [a, None], [],
[['writeonly'], ['writeonly', 'allocate']])
# Allocated output should be flagged for writing
assert_raises(ValueError, nditer, [a, None], [],
[['readonly'], ['allocate', 'readonly']])
# Allocated output can't have buffering without delayed bufalloc
assert_raises(ValueError, nditer, [a, None], ['buffered'],
['allocate', 'readwrite'])
# Must specify at least one input
assert_raises(ValueError, nditer, [None, None], [],
[['writeonly', 'allocate'],
['writeonly', 'allocate']],
op_dtypes=[np.dtype('f4'), np.dtype('f4')])
# If using op_axes, must specify all the axes
a = arange(24, dtype='i4').reshape(2, 3, 4)
assert_raises(ValueError, nditer, [a, None], [],
[['readonly'], ['writeonly', 'allocate']],
op_dtypes=[None, np.dtype('f4')],
op_axes=[None, [0, np.newaxis, 1]])
# If using op_axes, the axes must be within bounds
assert_raises(ValueError, nditer, [a, None], [],
[['readonly'], ['writeonly', 'allocate']],
op_dtypes=[None, np.dtype('f4')],
op_axes=[None, [0, 3, 1]])
# If using op_axes, there can't be duplicates
assert_raises(ValueError, nditer, [a, None], [],
[['readonly'], ['writeonly', 'allocate']],
op_dtypes=[None, np.dtype('f4')],
op_axes=[None, [0, 2, 1, 0]])
def test_iter_remove_axis():
a = arange(24).reshape(2, 3, 4)
i = nditer(a, ['multi_index'])
i.remove_axis(1)
assert_equal([x for x in i], a[:, 0,:].ravel())
a = a[::-1,:,:]
i = nditer(a, ['multi_index'])
i.remove_axis(0)
assert_equal([x for x in i], a[0,:,:].ravel())
def test_iter_remove_multi_index_inner_loop():
# Check that removing multi-index support works
a = arange(24).reshape(2, 3, 4)
i = nditer(a, ['multi_index'])
assert_equal(i.ndim, 3)
assert_equal(i.shape, (2, 3, 4))
assert_equal(i.itviews[0].shape, (2, 3, 4))
# Removing the multi-index tracking causes all dimensions to coalesce
before = [x for x in i]
i.remove_multi_index()
after = [x for x in i]
assert_equal(before, after)
assert_equal(i.ndim, 1)
assert_raises(ValueError, lambda i:i.shape, i)
assert_equal(i.itviews[0].shape, (24,))
# Removing the inner loop means there's just one iteration
i.reset()
assert_equal(i.itersize, 24)
assert_equal(i[0].shape, tuple())
i.enable_external_loop()
assert_equal(i.itersize, 24)
assert_equal(i[0].shape, (24,))
assert_equal(i.value, arange(24))
def test_iter_iterindex():
# Make sure iterindex works
buffersize = 5
a = arange(24).reshape(4, 3, 2)
for flags in ([], ['buffered']):
i = nditer(a, flags, buffersize=buffersize)
assert_equal(iter_iterindices(i), list(range(24)))
i.iterindex = 2
assert_equal(iter_iterindices(i), list(range(2, 24)))
i = nditer(a, flags, order='F', buffersize=buffersize)
assert_equal(iter_iterindices(i), list(range(24)))
i.iterindex = 5
assert_equal(iter_iterindices(i), list(range(5, 24)))
i = nditer(a[::-1], flags, order='F', buffersize=buffersize)
assert_equal(iter_iterindices(i), list(range(24)))
i.iterindex = 9
assert_equal(iter_iterindices(i), list(range(9, 24)))
i = nditer(a[::-1, ::-1], flags, order='C', buffersize=buffersize)
assert_equal(iter_iterindices(i), list(range(24)))
i.iterindex = 13
assert_equal(iter_iterindices(i), list(range(13, 24)))
i = nditer(a[::1, ::-1], flags, buffersize=buffersize)
assert_equal(iter_iterindices(i), list(range(24)))
i.iterindex = 23
assert_equal(iter_iterindices(i), list(range(23, 24)))
i.reset()
i.iterindex = 2
assert_equal(iter_iterindices(i), list(range(2, 24)))
def test_iter_iterrange():
# Make sure getting and resetting the iterrange works
buffersize = 5
a = arange(24, dtype='i4').reshape(4, 3, 2)
a_fort = a.ravel(order='F')
i = nditer(a, ['ranged'], ['readonly'], order='F',
buffersize=buffersize)
assert_equal(i.iterrange, (0, 24))
assert_equal([x[()] for x in i], a_fort)
for r in [(0, 24), (1, 2), (3, 24), (5, 5), (0, 20), (23, 24)]:
i.iterrange = r
assert_equal(i.iterrange, r)
assert_equal([x[()] for x in i], a_fort[r[0]:r[1]])
i = nditer(a, ['ranged', 'buffered'], ['readonly'], order='F',
op_dtypes='f8', buffersize=buffersize)
assert_equal(i.iterrange, (0, 24))
assert_equal([x[()] for x in i], a_fort)
for r in [(0, 24), (1, 2), (3, 24), (5, 5), (0, 20), (23, 24)]:
i.iterrange = r
assert_equal(i.iterrange, r)
assert_equal([x[()] for x in i], a_fort[r[0]:r[1]])
def get_array(i):
val = np.array([], dtype='f8')
for x in i:
val = np.concatenate((val, x))
return val
i = nditer(a, ['ranged', 'buffered', 'external_loop'],
['readonly'], order='F',
op_dtypes='f8', buffersize=buffersize)
assert_equal(i.iterrange, (0, 24))
assert_equal(get_array(i), a_fort)
for r in [(0, 24), (1, 2), (3, 24), (5, 5), (0, 20), (23, 24)]:
i.iterrange = r
assert_equal(i.iterrange, r)
assert_equal(get_array(i), a_fort[r[0]:r[1]])
def test_iter_buffering():
# Test buffering with several buffer sizes and types
arrays = []
# F-order swapped array
arrays.append(np.arange(24,
dtype='c16').reshape(2, 3, 4).T.newbyteorder().byteswap())
# Contiguous 1-dimensional array
arrays.append(np.arange(10, dtype='f4'))
# Unaligned array
a = np.zeros((4*16+1,), dtype='i1')[1:]
a.dtype = 'i4'
a[:] = np.arange(16, dtype='i4')
arrays.append(a)
# 4-D F-order array
arrays.append(np.arange(120, dtype='i4').reshape(5, 3, 2, 4).T)
for a in arrays:
for buffersize in (1, 2, 3, 5, 8, 11, 16, 1024):
vals = []
i = nditer(a, ['buffered', 'external_loop'],
[['readonly', 'nbo', 'aligned']],
order='C',
casting='equiv',
buffersize=buffersize)
while not i.finished:
assert_(i[0].size <= buffersize)
vals.append(i[0].copy())
i.iternext()
assert_equal(np.concatenate(vals), a.ravel(order='C'))
def test_iter_write_buffering():
# Test that buffering of writes is working
# F-order swapped array
a = np.arange(24).reshape(2, 3, 4).T.newbyteorder().byteswap()
i = nditer(a, ['buffered'],
[['readwrite', 'nbo', 'aligned']],
casting='equiv',
order='C',
buffersize=16)
x = 0
while not i.finished:
i[0] = x
x += 1
i.iternext()
assert_equal(a.ravel(order='C'), np.arange(24))
def test_iter_buffering_delayed_alloc():
# Test that delaying buffer allocation works
a = np.arange(6)
b = np.arange(1, dtype='f4')
i = nditer([a, b], ['buffered', 'delay_bufalloc', 'multi_index', 'reduce_ok'],
['readwrite'],
casting='unsafe',
op_dtypes='f4')
assert_(i.has_delayed_bufalloc)
assert_raises(ValueError, lambda i:i.multi_index, i)
assert_raises(ValueError, lambda i:i[0], i)
assert_raises(ValueError, lambda i:i[0:2], i)
def assign_iter(i):
i[0] = 0
assert_raises(ValueError, assign_iter, i)
i.reset()
assert_(not i.has_delayed_bufalloc)
assert_equal(i.multi_index, (0,))
assert_equal(i[0], 0)
i[1] = 1
assert_equal(i[0:2], [0, 1])
assert_equal([[x[0][()], x[1][()]] for x in i], list(zip(range(6), [1]*6)))
def test_iter_buffered_cast_simple():
# Test that buffering can handle a simple cast
a = np.arange(10, dtype='f4')
i = nditer(a, ['buffered', 'external_loop'],
[['readwrite', 'nbo', 'aligned']],
casting='same_kind',
op_dtypes=[np.dtype('f8')],
buffersize=3)
for v in i:
v[...] *= 2
assert_equal(a, 2*np.arange(10, dtype='f4'))
def test_iter_buffered_cast_byteswapped():
# Test that buffering can handle a cast which requires swap->cast->swap
a = np.arange(10, dtype='f4').newbyteorder().byteswap()
i = nditer(a, ['buffered', 'external_loop'],
[['readwrite', 'nbo', 'aligned']],
casting='same_kind',
op_dtypes=[np.dtype('f8').newbyteorder()],
buffersize=3)
for v in i:
v[...] *= 2
assert_equal(a, 2*np.arange(10, dtype='f4'))
with suppress_warnings() as sup:
sup.filter(np.ComplexWarning)
a = np.arange(10, dtype='f8').newbyteorder().byteswap()
i = nditer(a, ['buffered', 'external_loop'],
[['readwrite', 'nbo', 'aligned']],
casting='unsafe',
op_dtypes=[np.dtype('c8').newbyteorder()],
buffersize=3)
for v in i:
v[...] *= 2
assert_equal(a, 2*np.arange(10, dtype='f8'))
def test_iter_buffered_cast_byteswapped_complex():
# Test that buffering can handle a cast which requires swap->cast->copy
a = np.arange(10, dtype='c8').newbyteorder().byteswap()
a += 2j
i = nditer(a, ['buffered', 'external_loop'],
[['readwrite', 'nbo', 'aligned']],
casting='same_kind',
op_dtypes=[np.dtype('c16')],
buffersize=3)
for v in i:
v[...] *= 2
assert_equal(a, 2*np.arange(10, dtype='c8') + 4j)
a = np.arange(10, dtype='c8')
a += 2j
i = nditer(a, ['buffered', 'external_loop'],
[['readwrite', 'nbo', 'aligned']],
casting='same_kind',
op_dtypes=[np.dtype('c16').newbyteorder()],
buffersize=3)
for v in i:
v[...] *= 2
assert_equal(a, 2*np.arange(10, dtype='c8') + 4j)
a = np.arange(10, dtype=np.clongdouble).newbyteorder().byteswap()
a += 2j
i = nditer(a, ['buffered', 'external_loop'],
[['readwrite', 'nbo', 'aligned']],
casting='same_kind',
op_dtypes=[np.dtype('c16')],
buffersize=3)
for v in i:
v[...] *= 2
assert_equal(a, 2*np.arange(10, dtype=np.clongdouble) + 4j)
a = np.arange(10, dtype=np.longdouble).newbyteorder().byteswap()
i = nditer(a, ['buffered', 'external_loop'],
[['readwrite', 'nbo', 'aligned']],
casting='same_kind',
op_dtypes=[np.dtype('f4')],
buffersize=7)
for v in i:
v[...] *= 2
assert_equal(a, 2*np.arange(10, dtype=np.longdouble))
def test_iter_buffered_cast_structured_type():
# Tests buffering of structured types
# simple -> struct type (duplicates the value)
sdt = [('a', 'f4'), ('b', 'i8'), ('c', 'c8', (2, 3)), ('d', 'O')]
a = np.arange(3, dtype='f4') + 0.5
i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
casting='unsafe',
op_dtypes=sdt)
vals = [np.array(x) for x in i]
assert_equal(vals[0]['a'], 0.5)
assert_equal(vals[0]['b'], 0)
assert_equal(vals[0]['c'], [[(0.5)]*3]*2)
assert_equal(vals[0]['d'], 0.5)
assert_equal(vals[1]['a'], 1.5)
assert_equal(vals[1]['b'], 1)
assert_equal(vals[1]['c'], [[(1.5)]*3]*2)
assert_equal(vals[1]['d'], 1.5)
assert_equal(vals[0].dtype, np.dtype(sdt))
# object -> struct type
sdt = [('a', 'f4'), ('b', 'i8'), ('c', 'c8', (2, 3)), ('d', 'O')]
a = np.zeros((3,), dtype='O')
a[0] = (0.5, 0.5, [[0.5, 0.5, 0.5], [0.5, 0.5, 0.5]], 0.5)
a[1] = (1.5, 1.5, [[1.5, 1.5, 1.5], [1.5, 1.5, 1.5]], 1.5)
a[2] = (2.5, 2.5, [[2.5, 2.5, 2.5], [2.5, 2.5, 2.5]], 2.5)
if HAS_REFCOUNT:
rc = sys.getrefcount(a[0])
i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
casting='unsafe',
op_dtypes=sdt)
vals = [x.copy() for x in i]
assert_equal(vals[0]['a'], 0.5)
assert_equal(vals[0]['b'], 0)
assert_equal(vals[0]['c'], [[(0.5)]*3]*2)
assert_equal(vals[0]['d'], 0.5)
assert_equal(vals[1]['a'], 1.5)
assert_equal(vals[1]['b'], 1)
assert_equal(vals[1]['c'], [[(1.5)]*3]*2)
assert_equal(vals[1]['d'], 1.5)
assert_equal(vals[0].dtype, np.dtype(sdt))
vals, i, x = [None]*3
if HAS_REFCOUNT:
assert_equal(sys.getrefcount(a[0]), rc)
# single-field struct type -> simple
sdt = [('a', 'f4')]
a = np.array([(5.5,), (8,)], dtype=sdt)
i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
casting='unsafe',
op_dtypes='i4')
assert_equal([x_[()] for x_ in i], [5, 8])
# make sure multi-field struct type -> simple doesn't work
sdt = [('a', 'f4'), ('b', 'i8'), ('d', 'O')]
a = np.array([(5.5, 7, 'test'), (8, 10, 11)], dtype=sdt)
assert_raises(ValueError, lambda: (
nditer(a, ['buffered', 'refs_ok'], ['readonly'],
casting='unsafe',
op_dtypes='i4')))
# struct type -> struct type (field-wise copy)
sdt1 = [('a', 'f4'), ('b', 'i8'), ('d', 'O')]
sdt2 = [('d', 'u2'), ('a', 'O'), ('b', 'f8')]
a = np.array([(1, 2, 3), (4, 5, 6)], dtype=sdt1)
i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
casting='unsafe',
op_dtypes=sdt2)
assert_equal(i[0].dtype, np.dtype(sdt2))
assert_equal([np.array(x_) for x_ in i],
[np.array((1, 2, 3), dtype=sdt2),
np.array((4, 5, 6), dtype=sdt2)])
# make sure struct type -> struct type with different
# number of fields fails
sdt1 = [('a', 'f4'), ('b', 'i8'), ('d', 'O')]
sdt2 = [('b', 'O'), ('a', 'f8')]
a = np.array([(1, 2, 3), (4, 5, 6)], dtype=sdt1)
assert_raises(ValueError, lambda : (
nditer(a, ['buffered', 'refs_ok'], ['readwrite'],
casting='unsafe',
op_dtypes=sdt2)))
def test_iter_buffered_cast_subarray():
# Tests buffering of subarrays
# one element -> many (copies it to all)
sdt1 = [('a', 'f4')]
sdt2 = [('a', 'f8', (3, 2, 2))]
a = np.zeros((6,), dtype=sdt1)
a['a'] = np.arange(6)
i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
casting='unsafe',
op_dtypes=sdt2)
assert_equal(i[0].dtype, np.dtype(sdt2))
for x, count in zip(i, list(range(6))):
assert_(np.all(x['a'] == count))
# one element -> many -> back (copies it to all)
sdt1 = [('a', 'O', (1, 1))]
sdt2 = [('a', 'O', (3, 2, 2))]
a = np.zeros((6,), dtype=sdt1)
a['a'][:, 0, 0] = np.arange(6)
i = nditer(a, ['buffered', 'refs_ok'], ['readwrite'],
casting='unsafe',
op_dtypes=sdt2)
assert_equal(i[0].dtype, np.dtype(sdt2))
count = 0
for x in i:
assert_(np.all(x['a'] == count))
x['a'][0] += 2
count += 1
assert_equal(a['a'], np.arange(6).reshape(6, 1, 1)+2)
# many -> one element -> back (copies just element 0)
sdt1 = [('a', 'O', (3, 2, 2))]
sdt2 = [('a', 'O', (1,))]
a = np.zeros((6,), dtype=sdt1)
a['a'][:, 0, 0, 0] = np.arange(6)
i = nditer(a, ['buffered', 'refs_ok'], ['readwrite'],
casting='unsafe',
op_dtypes=sdt2)
assert_equal(i[0].dtype, np.dtype(sdt2))
count = 0
for x in i:
assert_equal(x['a'], count)
x['a'] += 2
count += 1
assert_equal(a['a'], np.arange(6).reshape(6, 1, 1, 1)*np.ones((1, 3, 2, 2))+2)
# many -> one element -> back (copies just element 0)
sdt1 = [('a', 'f8', (3, 2, 2))]
sdt2 = [('a', 'O', (1,))]
a = np.zeros((6,), dtype=sdt1)
a['a'][:, 0, 0, 0] = np.arange(6)
i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
casting='unsafe',
op_dtypes=sdt2)
assert_equal(i[0].dtype, np.dtype(sdt2))
count = 0
for x in i:
assert_equal(x['a'], count)
count += 1
# many -> one element (copies just element 0)
sdt1 = [('a', 'O', (3, 2, 2))]
sdt2 = [('a', 'f4', (1,))]
a = np.zeros((6,), dtype=sdt1)
a['a'][:, 0, 0, 0] = np.arange(6)
i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
casting='unsafe',
op_dtypes=sdt2)
assert_equal(i[0].dtype, np.dtype(sdt2))
count = 0
for x in i:
assert_equal(x['a'], count)
count += 1
# many -> matching shape (straightforward copy)
sdt1 = [('a', 'O', (3, 2, 2))]
sdt2 = [('a', 'f4', (3, 2, 2))]
a = np.zeros((6,), dtype=sdt1)
a['a'] = np.arange(6*3*2*2).reshape(6, 3, 2, 2)
i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
casting='unsafe',
op_dtypes=sdt2)
assert_equal(i[0].dtype, np.dtype(sdt2))
count = 0
for x in i:
assert_equal(x['a'], a[count]['a'])
count += 1
# vector -> smaller vector (truncates)
sdt1 = [('a', 'f8', (6,))]
sdt2 = [('a', 'f4', (2,))]
a = np.zeros((6,), dtype=sdt1)
a['a'] = np.arange(6*6).reshape(6, 6)
i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
casting='unsafe',
op_dtypes=sdt2)
assert_equal(i[0].dtype, np.dtype(sdt2))
count = 0
for x in i:
assert_equal(x['a'], a[count]['a'][:2])
count += 1
# vector -> bigger vector (pads with zeros)
sdt1 = [('a', 'f8', (2,))]
sdt2 = [('a', 'f4', (6,))]
a = np.zeros((6,), dtype=sdt1)
a['a'] = np.arange(6*2).reshape(6, 2)
i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
casting='unsafe',
op_dtypes=sdt2)
assert_equal(i[0].dtype, np.dtype(sdt2))
count = 0
for x in i:
assert_equal(x['a'][:2], a[count]['a'])
assert_equal(x['a'][2:], [0, 0, 0, 0])
count += 1
# vector -> matrix (broadcasts)
sdt1 = [('a', 'f8', (2,))]
sdt2 = [('a', 'f4', (2, 2))]
a = np.zeros((6,), dtype=sdt1)
a['a'] = np.arange(6*2).reshape(6, 2)
i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
casting='unsafe',
op_dtypes=sdt2)
assert_equal(i[0].dtype, np.dtype(sdt2))
count = 0
for x in i:
assert_equal(x['a'][0], a[count]['a'])
assert_equal(x['a'][1], a[count]['a'])
count += 1
# vector -> matrix (broadcasts and zero-pads)
sdt1 = [('a', 'f8', (2, 1))]
sdt2 = [('a', 'f4', (3, 2))]
a = np.zeros((6,), dtype=sdt1)
a['a'] = np.arange(6*2).reshape(6, 2, 1)
i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
casting='unsafe',
op_dtypes=sdt2)
assert_equal(i[0].dtype, np.dtype(sdt2))
count = 0
for x in i:
assert_equal(x['a'][:2, 0], a[count]['a'][:, 0])
assert_equal(x['a'][:2, 1], a[count]['a'][:, 0])
assert_equal(x['a'][2,:], [0, 0])
count += 1
# matrix -> matrix (truncates and zero-pads)
sdt1 = [('a', 'f8', (2, 3))]
sdt2 = [('a', 'f4', (3, 2))]
a = np.zeros((6,), dtype=sdt1)
a['a'] = np.arange(6*2*3).reshape(6, 2, 3)
i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
casting='unsafe',
op_dtypes=sdt2)
assert_equal(i[0].dtype, np.dtype(sdt2))
count = 0
for x in i:
assert_equal(x['a'][:2, 0], a[count]['a'][:, 0])
assert_equal(x['a'][:2, 1], a[count]['a'][:, 1])
assert_equal(x['a'][2,:], [0, 0])
count += 1
def test_iter_buffering_badwriteback():
# Writing back from a buffer cannot combine elements
# a needs write buffering, but had a broadcast dimension
a = np.arange(6).reshape(2, 3, 1)
b = np.arange(12).reshape(2, 3, 2)
assert_raises(ValueError, nditer, [a, b],
['buffered', 'external_loop'],
[['readwrite'], ['writeonly']],
order='C')
# But if a is readonly, it's fine
nditer([a, b], ['buffered', 'external_loop'],
[['readonly'], ['writeonly']],
order='C')
# If a has just one element, it's fine too (constant 0 stride, a reduction)
a = np.arange(1).reshape(1, 1, 1)
nditer([a, b], ['buffered', 'external_loop', 'reduce_ok'],
[['readwrite'], ['writeonly']],
order='C')
# check that it fails on other dimensions too
a = np.arange(6).reshape(1, 3, 2)
assert_raises(ValueError, nditer, [a, b],
['buffered', 'external_loop'],
[['readwrite'], ['writeonly']],
order='C')
a = np.arange(4).reshape(2, 1, 2)
assert_raises(ValueError, nditer, [a, b],
['buffered', 'external_loop'],
[['readwrite'], ['writeonly']],
order='C')
def test_iter_buffering_string():
# Safe casting disallows shrinking strings
a = np.array(['abc', 'a', 'abcd'], dtype=np.bytes_)
assert_equal(a.dtype, np.dtype('S4'))
assert_raises(TypeError, nditer, a, ['buffered'], ['readonly'],
op_dtypes='S2')
i = nditer(a, ['buffered'], ['readonly'], op_dtypes='S6')
assert_equal(i[0], b'abc')
assert_equal(i[0].dtype, np.dtype('S6'))
a = np.array(['abc', 'a', 'abcd'], dtype=np.unicode)
assert_equal(a.dtype, np.dtype('U4'))
assert_raises(TypeError, nditer, a, ['buffered'], ['readonly'],
op_dtypes='U2')
i = nditer(a, ['buffered'], ['readonly'], op_dtypes='U6')
assert_equal(i[0], u'abc')
assert_equal(i[0].dtype, np.dtype('U6'))
def test_iter_buffering_growinner():
# Test that the inner loop grows when no buffering is needed
a = np.arange(30)
i = nditer(a, ['buffered', 'growinner', 'external_loop'],
buffersize=5)
# Should end up with just one inner loop here
assert_equal(i[0].size, a.size)
@dec.slow
def test_iter_buffered_reduce_reuse():
# large enough array for all views, including negative strides.
a = np.arange(2*3**5)[3**5:3**5+1]
flags = ['buffered', 'delay_bufalloc', 'multi_index', 'reduce_ok', 'refs_ok']
op_flags = [('readonly',), ('readwrite', 'allocate')]
op_axes_list = [[(0, 1, 2), (0, 1, -1)], [(0, 1, 2), (0, -1, -1)]]
# wrong dtype to force buffering
op_dtypes = [float, a.dtype]
def get_params():
for xs in range(-3**2, 3**2 + 1):
for ys in range(xs, 3**2 + 1):
for op_axes in op_axes_list:
# last stride is reduced and because of that not
# important for this test, as it is the inner stride.
strides = (xs * a.itemsize, ys * a.itemsize, a.itemsize)
arr = np.lib.stride_tricks.as_strided(a, (3, 3, 3), strides)
for skip in [0, 1]:
yield arr, op_axes, skip
for arr, op_axes, skip in get_params():
nditer2 = np.nditer([arr.copy(), None],
op_axes=op_axes, flags=flags, op_flags=op_flags,
op_dtypes=op_dtypes)
nditer2.operands[-1][...] = 0
nditer2.reset()
nditer2.iterindex = skip
for (a2_in, b2_in) in nditer2:
b2_in += a2_in.astype(np.int_)
comp_res = nditer2.operands[-1]
for bufsize in range(0, 3**3):
nditer1 = np.nditer([arr, None],
op_axes=op_axes, flags=flags, op_flags=op_flags,
buffersize=bufsize, op_dtypes=op_dtypes)
nditer1.operands[-1][...] = 0
nditer1.reset()
nditer1.iterindex = skip
for (a1_in, b1_in) in nditer1:
b1_in += a1_in.astype(np.int_)
res = nditer1.operands[-1]
assert_array_equal(res, comp_res)
def test_iter_no_broadcast():
# Test that the no_broadcast flag works
a = np.arange(24).reshape(2, 3, 4)
b = np.arange(6).reshape(2, 3, 1)
c = np.arange(12).reshape(3, 4)
nditer([a, b, c], [],
[['readonly', 'no_broadcast'],
['readonly'], ['readonly']])
assert_raises(ValueError, nditer, [a, b, c], [],
[['readonly'], ['readonly', 'no_broadcast'], ['readonly']])
assert_raises(ValueError, nditer, [a, b, c], [],
[['readonly'], ['readonly'], ['readonly', 'no_broadcast']])
class TestIterNested(object):
def test_basic(self):
# Test nested iteration basic usage
a = arange(12).reshape(2, 3, 2)
i, j = np.nested_iters(a, [[0], [1, 2]])
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[0, 1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11]])
i, j = np.nested_iters(a, [[0, 1], [2]])
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[0, 1], [2, 3], [4, 5], [6, 7], [8, 9], [10, 11]])
i, j = np.nested_iters(a, [[0, 2], [1]])
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[0, 2, 4], [1, 3, 5], [6, 8, 10], [7, 9, 11]])
def test_reorder(self):
# Test nested iteration basic usage
a = arange(12).reshape(2, 3, 2)
# In 'K' order (default), it gets reordered
i, j = np.nested_iters(a, [[0], [2, 1]])
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[0, 1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11]])
i, j = np.nested_iters(a, [[1, 0], [2]])
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[0, 1], [2, 3], [4, 5], [6, 7], [8, 9], [10, 11]])
i, j = np.nested_iters(a, [[2, 0], [1]])
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[0, 2, 4], [1, 3, 5], [6, 8, 10], [7, 9, 11]])
# In 'C' order, it doesn't
i, j = np.nested_iters(a, [[0], [2, 1]], order='C')
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[0, 2, 4, 1, 3, 5], [6, 8, 10, 7, 9, 11]])
i, j = np.nested_iters(a, [[1, 0], [2]], order='C')
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[0, 1], [6, 7], [2, 3], [8, 9], [4, 5], [10, 11]])
i, j = np.nested_iters(a, [[2, 0], [1]], order='C')
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[0, 2, 4], [6, 8, 10], [1, 3, 5], [7, 9, 11]])
def test_flip_axes(self):
# Test nested iteration with negative axes
a = arange(12).reshape(2, 3, 2)[::-1, ::-1, ::-1]
# In 'K' order (default), the axes all get flipped
i, j = np.nested_iters(a, [[0], [1, 2]])
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[0, 1, 2, 3, 4, 5], [6, 7, 8, 9, 10, 11]])
i, j = np.nested_iters(a, [[0, 1], [2]])
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[0, 1], [2, 3], [4, 5], [6, 7], [8, 9], [10, 11]])
i, j = np.nested_iters(a, [[0, 2], [1]])
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[0, 2, 4], [1, 3, 5], [6, 8, 10], [7, 9, 11]])
# In 'C' order, flipping axes is disabled
i, j = np.nested_iters(a, [[0], [1, 2]], order='C')
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[11, 10, 9, 8, 7, 6], [5, 4, 3, 2, 1, 0]])
i, j = np.nested_iters(a, [[0, 1], [2]], order='C')
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[11, 10], [9, 8], [7, 6], [5, 4], [3, 2], [1, 0]])
i, j = np.nested_iters(a, [[0, 2], [1]], order='C')
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[11, 9, 7], [10, 8, 6], [5, 3, 1], [4, 2, 0]])
def test_broadcast(self):
# Test nested iteration with broadcasting
a = arange(2).reshape(2, 1)
b = arange(3).reshape(1, 3)
i, j = np.nested_iters([a, b], [[0], [1]])
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[[0, 0], [0, 1], [0, 2]], [[1, 0], [1, 1], [1, 2]]])
i, j = np.nested_iters([a, b], [[1], [0]])
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[[0, 0], [1, 0]], [[0, 1], [1, 1]], [[0, 2], [1, 2]]])
def test_dtype_copy(self):
# Test nested iteration with a copy to change dtype
# copy
a = arange(6, dtype='i4').reshape(2, 3)
i, j = np.nested_iters(a, [[0], [1]],
op_flags=['readonly', 'copy'],
op_dtypes='f8')
assert_equal(j[0].dtype, np.dtype('f8'))
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[0, 1, 2], [3, 4, 5]])
vals = None
# updateifcopy
a = arange(6, dtype='f4').reshape(2, 3)
i, j = np.nested_iters(a, [[0], [1]],
op_flags=['readwrite', 'updateifcopy'],
casting='same_kind',
op_dtypes='f8')
assert_equal(j[0].dtype, np.dtype('f8'))
for x in i:
for y in j:
y[...] += 1
assert_equal(a, [[0, 1, 2], [3, 4, 5]])
i, j, x, y = (None,)*4 # force the updateifcopy
assert_equal(a, [[1, 2, 3], [4, 5, 6]])
def test_dtype_buffered(self):
# Test nested iteration with buffering to change dtype
a = arange(6, dtype='f4').reshape(2, 3)
i, j = np.nested_iters(a, [[0], [1]],
flags=['buffered'],
op_flags=['readwrite'],
casting='same_kind',
op_dtypes='f8')
assert_equal(j[0].dtype, np.dtype('f8'))
for x in i:
for y in j:
y[...] += 1
assert_equal(a, [[1, 2, 3], [4, 5, 6]])
def test_0d(self):
a = np.arange(12).reshape(2, 3, 2)
i, j = np.nested_iters(a, [[], [1, 0, 2]])
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11]])
i, j = np.nested_iters(a, [[1, 0, 2], []])
vals = []
for x in i:
vals.append([y for y in j])
assert_equal(vals, [[0], [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11]])
i, j, k = np.nested_iters(a, [[2, 0], [], [1]])
vals = []
for x in i:
for y in j:
vals.append([z for z in k])
assert_equal(vals, [[0, 2, 4], [1, 3, 5], [6, 8, 10], [7, 9, 11]])
def test_iter_reduction_error():
a = np.arange(6)
assert_raises(ValueError, nditer, [a, None], [],
[['readonly'], ['readwrite', 'allocate']],
op_axes=[[0], [-1]])
a = np.arange(6).reshape(2, 3)
assert_raises(ValueError, nditer, [a, None], ['external_loop'],
[['readonly'], ['readwrite', 'allocate']],
op_axes=[[0, 1], [-1, -1]])
def test_iter_reduction():
# Test doing reductions with the iterator
a = np.arange(6)
i = nditer([a, None], ['reduce_ok'],
[['readonly'], ['readwrite', 'allocate']],
op_axes=[[0], [-1]])
# Need to initialize the output operand to the addition unit
i.operands[1][...] = 0
# Do the reduction
for x, y in i:
y[...] += x
# Since no axes were specified, should have allocated a scalar
assert_equal(i.operands[1].ndim, 0)
assert_equal(i.operands[1], np.sum(a))
a = np.arange(6).reshape(2, 3)
i = nditer([a, None], ['reduce_ok', 'external_loop'],
[['readonly'], ['readwrite', 'allocate']],
op_axes=[[0, 1], [-1, -1]])
# Need to initialize the output operand to the addition unit
i.operands[1][...] = 0
# Reduction shape/strides for the output
assert_equal(i[1].shape, (6,))
assert_equal(i[1].strides, (0,))
# Do the reduction
for x, y in i:
# Use a for loop instead of ``y[...] += x``
# (equivalent to ``y[...] = y[...].copy() + x``),
# because y has zero strides we use for the reduction
for j in range(len(y)):
y[j] += x[j]
# Since no axes were specified, should have allocated a scalar
assert_equal(i.operands[1].ndim, 0)
assert_equal(i.operands[1], np.sum(a))
# This is a tricky reduction case for the buffering double loop
# to handle
a = np.ones((2, 3, 5))
it1 = nditer([a, None], ['reduce_ok', 'external_loop'],
[['readonly'], ['readwrite', 'allocate']],
op_axes=[None, [0, -1, 1]])
it2 = nditer([a, None], ['reduce_ok', 'external_loop',
'buffered', 'delay_bufalloc'],
[['readonly'], ['readwrite', 'allocate']],
op_axes=[None, [0, -1, 1]], buffersize=10)
it1.operands[1].fill(0)
it2.operands[1].fill(0)
it2.reset()
for x in it1:
x[1][...] += x[0]
for x in it2:
x[1][...] += x[0]
assert_equal(it1.operands[1], it2.operands[1])
assert_equal(it2.operands[1].sum(), a.size)
def test_iter_buffering_reduction():
# Test doing buffered reductions with the iterator
a = np.arange(6)
b = np.array(0., dtype='f8').byteswap().newbyteorder()
i = nditer([a, b], ['reduce_ok', 'buffered'],
[['readonly'], ['readwrite', 'nbo']],
op_axes=[[0], [-1]])
assert_equal(i[1].dtype, np.dtype('f8'))
assert_(i[1].dtype != b.dtype)
# Do the reduction
for x, y in i:
y[...] += x
# Since no axes were specified, should have allocated a scalar
assert_equal(b, np.sum(a))
a = np.arange(6).reshape(2, 3)
b = np.array([0, 0], dtype='f8').byteswap().newbyteorder()
i = nditer([a, b], ['reduce_ok', 'external_loop', 'buffered'],
[['readonly'], ['readwrite', 'nbo']],
op_axes=[[0, 1], [0, -1]])
# Reduction shape/strides for the output
assert_equal(i[1].shape, (3,))
assert_equal(i[1].strides, (0,))
# Do the reduction
for x, y in i:
# Use a for loop instead of ``y[...] += x``
# (equivalent to ``y[...] = y[...].copy() + x``),
# because y has zero strides we use for the reduction
for j in range(len(y)):
y[j] += x[j]
assert_equal(b, np.sum(a, axis=1))
# Iterator inner double loop was wrong on this one
p = np.arange(2) + 1
it = np.nditer([p, None],
['delay_bufalloc', 'reduce_ok', 'buffered', 'external_loop'],
[['readonly'], ['readwrite', 'allocate']],
op_axes=[[-1, 0], [-1, -1]],
itershape=(2, 2))
it.operands[1].fill(0)
it.reset()
assert_equal(it[0], [1, 2, 1, 2])
# Iterator inner loop should take argument contiguity into account
x = np.ones((7, 13, 8), np.int8)[4:6,1:11:6,1:5].transpose(1, 2, 0)
x[...] = np.arange(x.size).reshape(x.shape)
y_base = np.arange(4*4, dtype=np.int8).reshape(4, 4)
y_base_copy = y_base.copy()
y = y_base[::2,:,None]
it = np.nditer([y, x],
['buffered', 'external_loop', 'reduce_ok'],
[['readwrite'], ['readonly']])
for a, b in it:
a.fill(2)
assert_equal(y_base[1::2], y_base_copy[1::2])
assert_equal(y_base[::2], 2)
def test_iter_buffering_reduction_reuse_reduce_loops():
# There was a bug triggering reuse of the reduce loop inappropriately,
# which caused processing to happen in unnecessarily small chunks
# and overran the buffer.
a = np.zeros((2, 7))
b = np.zeros((1, 7))
it = np.nditer([a, b], flags=['reduce_ok', 'external_loop', 'buffered'],
op_flags=[['readonly'], ['readwrite']],
buffersize=5)
bufsizes = []
for x, y in it:
bufsizes.append(x.shape[0])
assert_equal(bufsizes, [5, 2, 5, 2])
assert_equal(sum(bufsizes), a.size)
def test_iter_writemasked_badinput():
a = np.zeros((2, 3))
b = np.zeros((3,))
m = np.array([[True, True, False], [False, True, False]])
m2 = np.array([True, True, False])
m3 = np.array([0, 1, 1], dtype='u1')
mbad1 = np.array([0, 1, 1], dtype='i1')
mbad2 = np.array([0, 1, 1], dtype='f4')
# Need an 'arraymask' if any operand is 'writemasked'
assert_raises(ValueError, nditer, [a, m], [],
[['readwrite', 'writemasked'], ['readonly']])
# A 'writemasked' operand must not be readonly
assert_raises(ValueError, nditer, [a, m], [],
[['readonly', 'writemasked'], ['readonly', 'arraymask']])
# 'writemasked' and 'arraymask' may not be used together
assert_raises(ValueError, nditer, [a, m], [],
[['readonly'], ['readwrite', 'arraymask', 'writemasked']])
# 'arraymask' may only be specified once
assert_raises(ValueError, nditer, [a, m, m2], [],
[['readwrite', 'writemasked'],
['readonly', 'arraymask'],
['readonly', 'arraymask']])
# An 'arraymask' with nothing 'writemasked' also doesn't make sense
assert_raises(ValueError, nditer, [a, m], [],
[['readwrite'], ['readonly', 'arraymask']])
# A writemasked reduction requires a similarly smaller mask
assert_raises(ValueError, nditer, [a, b, m], ['reduce_ok'],
[['readonly'],
['readwrite', 'writemasked'],
['readonly', 'arraymask']])
# But this should work with a smaller/equal mask to the reduction operand
np.nditer([a, b, m2], ['reduce_ok'],
[['readonly'],
['readwrite', 'writemasked'],
['readonly', 'arraymask']])
# The arraymask itself cannot be a reduction
assert_raises(ValueError, nditer, [a, b, m2], ['reduce_ok'],
[['readonly'],
['readwrite', 'writemasked'],
['readwrite', 'arraymask']])
# A uint8 mask is ok too
np.nditer([a, m3], ['buffered'],
[['readwrite', 'writemasked'],
['readonly', 'arraymask']],
op_dtypes=['f4', None],
casting='same_kind')
# An int8 mask isn't ok
assert_raises(TypeError, np.nditer, [a, mbad1], ['buffered'],
[['readwrite', 'writemasked'],
['readonly', 'arraymask']],
op_dtypes=['f4', None],
casting='same_kind')
# A float32 mask isn't ok
assert_raises(TypeError, np.nditer, [a, mbad2], ['buffered'],
[['readwrite', 'writemasked'],
['readonly', 'arraymask']],
op_dtypes=['f4', None],
casting='same_kind')
def test_iter_writemasked():
a = np.zeros((3,), dtype='f8')
msk = np.array([True, True, False])
# When buffering is unused, 'writemasked' effectively does nothing.
# It's up to the user of the iterator to obey the requested semantics.
it = np.nditer([a, msk], [],
[['readwrite', 'writemasked'],
['readonly', 'arraymask']])
for x, m in it:
x[...] = 1
# Because we violated the semantics, all the values became 1
assert_equal(a, [1, 1, 1])
# Even if buffering is enabled, we still may be accessing the array
# directly.
it = np.nditer([a, msk], ['buffered'],
[['readwrite', 'writemasked'],
['readonly', 'arraymask']])
for x, m in it:
x[...] = 2.5
# Because we violated the semantics, all the values became 2.5
assert_equal(a, [2.5, 2.5, 2.5])
# If buffering will definitely happening, for instance because of
# a cast, only the items selected by the mask will be copied back from
# the buffer.
it = np.nditer([a, msk], ['buffered'],
[['readwrite', 'writemasked'],
['readonly', 'arraymask']],
op_dtypes=['i8', None],
casting='unsafe')
for x, m in it:
x[...] = 3
# Even though we violated the semantics, only the selected values
# were copied back
assert_equal(a, [3, 3, 2.5])
def test_iter_non_writable_attribute_deletion():
it = np.nditer(np.ones(2))
attr = ["value", "shape", "operands", "itviews", "has_delayed_bufalloc",
"iterationneedsapi", "has_multi_index", "has_index", "dtypes",
"ndim", "nop", "itersize", "finished"]
for s in attr:
assert_raises(AttributeError, delattr, it, s)
def test_iter_writable_attribute_deletion():
it = np.nditer(np.ones(2))
attr = [ "multi_index", "index", "iterrange", "iterindex"]
for s in attr:
assert_raises(AttributeError, delattr, it, s)
def test_iter_element_deletion():
it = np.nditer(np.ones(3))
try:
del it[1]
del it[1:2]
except TypeError:
pass
except Exception:
raise AssertionError
def test_iter_allocated_array_dtypes():
# If the dtype of an allocated output has a shape, the shape gets
# tacked onto the end of the result.
it = np.nditer(([1, 3, 20], None), op_dtypes=[None, ('i4', (2,))])
for a, b in it:
b[0] = a - 1
b[1] = a + 1
assert_equal(it.operands[1], [[0, 2], [2, 4], [19, 21]])
# Make sure this works for scalars too
it = np.nditer((10, 2, None), op_dtypes=[None, None, ('i4', (2, 2))])
for a, b, c in it:
c[0, 0] = a - b
c[0, 1] = a + b
c[1, 0] = a * b
c[1, 1] = a / b
assert_equal(it.operands[2], [[8, 12], [20, 5]])
def test_0d_iter():
# Basic test for iteration of 0-d arrays:
i = nditer([2, 3], ['multi_index'], [['readonly']]*2)
assert_equal(i.ndim, 0)
assert_equal(next(i), (2, 3))
assert_equal(i.multi_index, ())
assert_equal(i.iterindex, 0)
assert_raises(StopIteration, next, i)
# test reset:
i.reset()
assert_equal(next(i), (2, 3))
assert_raises(StopIteration, next, i)
# test forcing to 0-d
i = nditer(np.arange(5), ['multi_index'], [['readonly']], op_axes=[()])
assert_equal(i.ndim, 0)
assert_equal(len(i), 1)
# note that itershape=(), still behaves like None due to the conversions
# Test a more complex buffered casting case (same as another test above)
sdt = [('a', 'f4'), ('b', 'i8'), ('c', 'c8', (2, 3)), ('d', 'O')]
a = np.array(0.5, dtype='f4')
i = nditer(a, ['buffered', 'refs_ok'], ['readonly'],
casting='unsafe', op_dtypes=sdt)
vals = next(i)
assert_equal(vals['a'], 0.5)
assert_equal(vals['b'], 0)
assert_equal(vals['c'], [[(0.5)]*3]*2)
assert_equal(vals['d'], 0.5)
def test_iter_too_large():
# The total size of the iterator must not exceed the maximum intp due
# to broadcasting. Dividing by 1024 will keep it small enough to
# give a legal array.
size = np.iinfo(np.intp).max // 1024
arr = np.lib.stride_tricks.as_strided(np.zeros(1), (size,), (0,))
assert_raises(ValueError, nditer, (arr, arr[:, None]))
# test the same for multiindex. That may get more interesting when
# removing 0 dimensional axis is allowed (since an iterator can grow then)
assert_raises(ValueError, nditer,
(arr, arr[:, None]), flags=['multi_index'])
def test_iter_too_large_with_multiindex():
# When a multi index is being tracked, the error is delayed this
# checks the delayed error messages and getting below that by
# removing an axis.
base_size = 2**10
num = 1
while base_size**num < np.iinfo(np.intp).max:
num += 1
shape_template = [1, 1] * num
arrays = []
for i in range(num):
shape = shape_template[:]
shape[i * 2] = 2**10
arrays.append(np.empty(shape))
arrays = tuple(arrays)
# arrays are now too large to be broadcast. The different modes test
# different nditer functionality with or without GIL.
for mode in range(6):
assert_raises(ValueError, test_nditer_too_large, arrays, -1, mode)
# but if we do nothing with the nditer, it can be constructed:
test_nditer_too_large(arrays, -1, 7)
# When an axis is removed, things should work again (half the time):
for i in range(num):
for mode in range(6):
# an axis with size 1024 is removed:
test_nditer_too_large(arrays, i*2, mode)
# an axis with size 1 is removed:
assert_raises(ValueError, test_nditer_too_large,
arrays, i*2 + 1, mode)
if __name__ == "__main__":
run_module_suite()
| 106,132 | 38.076951 | 95 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/tests/test_regression.py
|
from __future__ import division, absolute_import, print_function
import copy
import pickle
import sys
import platform
import gc
import warnings
import tempfile
from os import path
from io import BytesIO
from itertools import chain
import numpy as np
from numpy.testing import (
run_module_suite, assert_, assert_equal, IS_PYPY,
assert_almost_equal, assert_array_equal, assert_array_almost_equal,
assert_raises, assert_warns, dec, suppress_warnings,
_assert_valid_refcount, HAS_REFCOUNT,
)
from numpy.compat import asbytes, asunicode, long
try:
RecursionError
except NameError:
RecursionError = RuntimeError # python < 3.5
class TestRegression(object):
def test_invalid_round(self):
# Ticket #3
v = 4.7599999999999998
assert_array_equal(np.array([v]), np.array(v))
def test_mem_empty(self):
# Ticket #7
np.empty((1,), dtype=[('x', np.int64)])
def test_pickle_transposed(self):
# Ticket #16
a = np.transpose(np.array([[2, 9], [7, 0], [3, 8]]))
f = BytesIO()
pickle.dump(a, f)
f.seek(0)
b = pickle.load(f)
f.close()
assert_array_equal(a, b)
def test_typeNA(self):
# Ticket #31
assert_equal(np.typeNA[np.int64], 'Int64')
assert_equal(np.typeNA[np.uint64], 'UInt64')
def test_dtype_names(self):
# Ticket #35
# Should succeed
np.dtype([(('name', 'label'), np.int32, 3)])
def test_reduce(self):
# Ticket #40
assert_almost_equal(np.add.reduce([1., .5], dtype=None), 1.5)
def test_zeros_order(self):
# Ticket #43
np.zeros([3], int, 'C')
np.zeros([3], order='C')
np.zeros([3], int, order='C')
def test_asarray_with_order(self):
# Check that nothing is done when order='F' and array C/F-contiguous
a = np.ones(2)
assert_(a is np.asarray(a, order='F'))
def test_ravel_with_order(self):
# Check that ravel works when order='F' and array C/F-contiguous
a = np.ones(2)
assert_(not a.ravel('F').flags.owndata)
def test_sort_bigendian(self):
# Ticket #47
a = np.linspace(0, 10, 11)
c = a.astype(np.dtype('<f8'))
c.sort()
assert_array_almost_equal(c, a)
def test_negative_nd_indexing(self):
# Ticket #49
c = np.arange(125).reshape((5, 5, 5))
origidx = np.array([-1, 0, 1])
idx = np.array(origidx)
c[idx]
assert_array_equal(idx, origidx)
def test_char_dump(self):
# Ticket #50
f = BytesIO()
ca = np.char.array(np.arange(1000, 1010), itemsize=4)
ca.dump(f)
f.seek(0)
ca = np.load(f)
f.close()
def test_noncontiguous_fill(self):
# Ticket #58.
a = np.zeros((5, 3))
b = a[:, :2,]
def rs():
b.shape = (10,)
assert_raises(AttributeError, rs)
def test_bool(self):
# Ticket #60
np.bool_(1) # Should succeed
def test_indexing1(self):
# Ticket #64
descr = [('x', [('y', [('z', 'c16', (2,)),]),]),]
buffer = ((([6j, 4j],),),)
h = np.array(buffer, dtype=descr)
h['x']['y']['z']
def test_indexing2(self):
# Ticket #65
descr = [('x', 'i4', (2,))]
buffer = ([3, 2],)
h = np.array(buffer, dtype=descr)
h['x']
def test_round(self):
# Ticket #67
x = np.array([1+2j])
assert_almost_equal(x**(-1), [1/(1+2j)])
def test_scalar_compare(self):
# Trac Ticket #72
# https://github.com/numpy/numpy/issues/565
a = np.array(['test', 'auto'])
assert_array_equal(a == 'auto', np.array([False, True]))
assert_(a[1] == 'auto')
assert_(a[0] != 'auto')
b = np.linspace(0, 10, 11)
# This should return true for now, but will eventually raise an error:
with suppress_warnings() as sup:
sup.filter(FutureWarning)
assert_(b != 'auto')
assert_(b[0] != 'auto')
def test_unicode_swapping(self):
# Ticket #79
ulen = 1
ucs_value = u'\U0010FFFF'
ua = np.array([[[ucs_value*ulen]*2]*3]*4, dtype='U%s' % ulen)
ua.newbyteorder() # Should succeed.
def test_object_array_fill(self):
# Ticket #86
x = np.zeros(1, 'O')
x.fill([])
def test_mem_dtype_align(self):
# Ticket #93
assert_raises(TypeError, np.dtype,
{'names':['a'], 'formats':['foo']}, align=1)
def test_endian_bool_indexing(self):
# Ticket #105
a = np.arange(10., dtype='>f8')
b = np.arange(10., dtype='<f8')
xa = np.where((a > 2) & (a < 6))
xb = np.where((b > 2) & (b < 6))
ya = ((a > 2) & (a < 6))
yb = ((b > 2) & (b < 6))
assert_array_almost_equal(xa, ya.nonzero())
assert_array_almost_equal(xb, yb.nonzero())
assert_(np.all(a[ya] > 0.5))
assert_(np.all(b[yb] > 0.5))
def test_endian_where(self):
# GitHub issue #369
net = np.zeros(3, dtype='>f4')
net[1] = 0.00458849
net[2] = 0.605202
max_net = net.max()
test = np.where(net <= 0., max_net, net)
correct = np.array([ 0.60520202, 0.00458849, 0.60520202])
assert_array_almost_equal(test, correct)
def test_endian_recarray(self):
# Ticket #2185
dt = np.dtype([
('head', '>u4'),
('data', '>u4', 2),
])
buf = np.recarray(1, dtype=dt)
buf[0]['head'] = 1
buf[0]['data'][:] = [1, 1]
h = buf[0]['head']
d = buf[0]['data'][0]
buf[0]['head'] = h
buf[0]['data'][0] = d
assert_(buf[0]['head'] == 1)
def test_mem_dot(self):
# Ticket #106
x = np.random.randn(0, 1)
y = np.random.randn(10, 1)
# Dummy array to detect bad memory access:
_z = np.ones(10)
_dummy = np.empty((0, 10))
z = np.lib.stride_tricks.as_strided(_z, _dummy.shape, _dummy.strides)
np.dot(x, np.transpose(y), out=z)
assert_equal(_z, np.ones(10))
# Do the same for the built-in dot:
np.core.multiarray.dot(x, np.transpose(y), out=z)
assert_equal(_z, np.ones(10))
def test_arange_endian(self):
# Ticket #111
ref = np.arange(10)
x = np.arange(10, dtype='<f8')
assert_array_equal(ref, x)
x = np.arange(10, dtype='>f8')
assert_array_equal(ref, x)
def test_argmax(self):
# Ticket #119
a = np.random.normal(0, 1, (4, 5, 6, 7, 8))
for i in range(a.ndim):
a.argmax(i) # Should succeed
def test_mem_divmod(self):
# Ticket #126
for i in range(10):
divmod(np.array([i])[0], 10)
def test_hstack_invalid_dims(self):
# Ticket #128
x = np.arange(9).reshape((3, 3))
y = np.array([0, 0, 0])
assert_raises(ValueError, np.hstack, (x, y))
def test_squeeze_type(self):
# Ticket #133
a = np.array([3])
b = np.array(3)
assert_(type(a.squeeze()) is np.ndarray)
assert_(type(b.squeeze()) is np.ndarray)
def test_add_identity(self):
# Ticket #143
assert_equal(0, np.add.identity)
def test_numpy_float_python_long_addition(self):
# Check that numpy float and python longs can be added correctly.
a = np.float_(23.) + 2**135
assert_equal(a, 23. + 2**135)
def test_binary_repr_0(self):
# Ticket #151
assert_equal('0', np.binary_repr(0))
def test_rec_iterate(self):
# Ticket #160
descr = np.dtype([('i', int), ('f', float), ('s', '|S3')])
x = np.rec.array([(1, 1.1, '1.0'),
(2, 2.2, '2.0')], dtype=descr)
x[0].tolist()
[i for i in x[0]]
def test_unicode_string_comparison(self):
# Ticket #190
a = np.array('hello', np.unicode_)
b = np.array('world')
a == b
def test_tobytes_FORTRANORDER_discontiguous(self):
# Fix in r2836
# Create non-contiguous Fortran ordered array
x = np.array(np.random.rand(3, 3), order='F')[:, :2]
assert_array_almost_equal(x.ravel(), np.frombuffer(x.tobytes()))
def test_flat_assignment(self):
# Correct behaviour of ticket #194
x = np.empty((3, 1))
x.flat = np.arange(3)
assert_array_almost_equal(x, [[0], [1], [2]])
x.flat = np.arange(3, dtype=float)
assert_array_almost_equal(x, [[0], [1], [2]])
def test_broadcast_flat_assignment(self):
# Ticket #194
x = np.empty((3, 1))
def bfa():
x[:] = np.arange(3)
def bfb():
x[:] = np.arange(3, dtype=float)
assert_raises(ValueError, bfa)
assert_raises(ValueError, bfb)
def test_nonarray_assignment(self):
# See also Issue gh-2870, test for non-array assignment
# and equivalent unsafe casted array assignment
a = np.arange(10)
b = np.ones(10, dtype=bool)
r = np.arange(10)
def assign(a, b, c):
a[b] = c
assert_raises(ValueError, assign, a, b, np.nan)
a[b] = np.array(np.nan) # but not this.
assert_raises(ValueError, assign, a, r, np.nan)
a[r] = np.array(np.nan)
def test_unpickle_dtype_with_object(self):
# Implemented in r2840
dt = np.dtype([('x', int), ('y', np.object_), ('z', 'O')])
f = BytesIO()
pickle.dump(dt, f)
f.seek(0)
dt_ = pickle.load(f)
f.close()
assert_equal(dt, dt_)
def test_mem_array_creation_invalid_specification(self):
# Ticket #196
dt = np.dtype([('x', int), ('y', np.object_)])
# Wrong way
assert_raises(ValueError, np.array, [1, 'object'], dt)
# Correct way
np.array([(1, 'object')], dt)
def test_recarray_single_element(self):
# Ticket #202
a = np.array([1, 2, 3], dtype=np.int32)
b = a.copy()
r = np.rec.array(a, shape=1, formats=['3i4'], names=['d'])
assert_array_equal(a, b)
assert_equal(a, r[0][0])
def test_zero_sized_array_indexing(self):
# Ticket #205
tmp = np.array([])
def index_tmp():
tmp[np.array(10)]
assert_raises(IndexError, index_tmp)
def test_chararray_rstrip(self):
# Ticket #222
x = np.chararray((1,), 5)
x[0] = b'a '
x = x.rstrip()
assert_equal(x[0], b'a')
def test_object_array_shape(self):
# Ticket #239
assert_equal(np.array([[1, 2], 3, 4], dtype=object).shape, (3,))
assert_equal(np.array([[1, 2], [3, 4]], dtype=object).shape, (2, 2))
assert_equal(np.array([(1, 2), (3, 4)], dtype=object).shape, (2, 2))
assert_equal(np.array([], dtype=object).shape, (0,))
assert_equal(np.array([[], [], []], dtype=object).shape, (3, 0))
assert_equal(np.array([[3, 4], [5, 6], None], dtype=object).shape, (3,))
def test_mem_around(self):
# Ticket #243
x = np.zeros((1,))
y = [0]
decimal = 6
np.around(abs(x-y), decimal) <= 10.0**(-decimal)
def test_character_array_strip(self):
# Ticket #246
x = np.char.array(("x", "x ", "x "))
for c in x:
assert_equal(c, "x")
def test_lexsort(self):
# Lexsort memory error
v = np.array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10])
assert_equal(np.lexsort(v), 0)
def test_lexsort_invalid_sequence(self):
# Issue gh-4123
class BuggySequence(object):
def __len__(self):
return 4
def __getitem__(self, key):
raise KeyError
assert_raises(KeyError, np.lexsort, BuggySequence())
def test_pickle_py2_bytes_encoding(self):
# Check that arrays and scalars pickled on Py2 are
# unpickleable on Py3 using encoding='bytes'
test_data = [
# (original, py2_pickle)
(np.unicode_('\u6f2c'),
b"cnumpy.core.multiarray\nscalar\np0\n(cnumpy\ndtype\np1\n"
b"(S'U1'\np2\nI0\nI1\ntp3\nRp4\n(I3\nS'<'\np5\nNNNI4\nI4\n"
b"I0\ntp6\nbS',o\\x00\\x00'\np7\ntp8\nRp9\n."),
(np.array([9e123], dtype=np.float64),
b"cnumpy.core.multiarray\n_reconstruct\np0\n(cnumpy\nndarray\n"
b"p1\n(I0\ntp2\nS'b'\np3\ntp4\nRp5\n(I1\n(I1\ntp6\ncnumpy\ndtype\n"
b"p7\n(S'f8'\np8\nI0\nI1\ntp9\nRp10\n(I3\nS'<'\np11\nNNNI-1\nI-1\n"
b"I0\ntp12\nbI00\nS'O\\x81\\xb7Z\\xaa:\\xabY'\np13\ntp14\nb."),
(np.array([(9e123,)], dtype=[('name', float)]),
b"cnumpy.core.multiarray\n_reconstruct\np0\n(cnumpy\nndarray\np1\n"
b"(I0\ntp2\nS'b'\np3\ntp4\nRp5\n(I1\n(I1\ntp6\ncnumpy\ndtype\np7\n"
b"(S'V8'\np8\nI0\nI1\ntp9\nRp10\n(I3\nS'|'\np11\nN(S'name'\np12\ntp13\n"
b"(dp14\ng12\n(g7\n(S'f8'\np15\nI0\nI1\ntp16\nRp17\n(I3\nS'<'\np18\nNNNI-1\n"
b"I-1\nI0\ntp19\nbI0\ntp20\nsI8\nI1\nI0\ntp21\n"
b"bI00\nS'O\\x81\\xb7Z\\xaa:\\xabY'\np22\ntp23\nb."),
]
if sys.version_info[:2] >= (3, 4):
# encoding='bytes' was added in Py3.4
for original, data in test_data:
result = pickle.loads(data, encoding='bytes')
assert_equal(result, original)
if isinstance(result, np.ndarray) and result.dtype.names:
for name in result.dtype.names:
assert_(isinstance(name, str))
def test_pickle_dtype(self):
# Ticket #251
pickle.dumps(float)
def test_swap_real(self):
# Ticket #265
assert_equal(np.arange(4, dtype='>c8').imag.max(), 0.0)
assert_equal(np.arange(4, dtype='<c8').imag.max(), 0.0)
assert_equal(np.arange(4, dtype='>c8').real.max(), 3.0)
assert_equal(np.arange(4, dtype='<c8').real.max(), 3.0)
def test_object_array_from_list(self):
# Ticket #270
assert_(np.array([1, 'A', None]).shape == (3,))
def test_multiple_assign(self):
# Ticket #273
a = np.zeros((3, 1), int)
a[[1, 2]] = 1
def test_empty_array_type(self):
assert_equal(np.array([]).dtype, np.zeros(0).dtype)
def test_void_copyswap(self):
dt = np.dtype([('one', '<i4'), ('two', '<i4')])
x = np.array((1, 2), dtype=dt)
x = x.byteswap()
assert_(x['one'] > 1 and x['two'] > 2)
def test_method_args(self):
# Make sure methods and functions have same default axis
# keyword and arguments
funcs1 = ['argmax', 'argmin', 'sum', ('product', 'prod'),
('sometrue', 'any'),
('alltrue', 'all'), 'cumsum', ('cumproduct', 'cumprod'),
'ptp', 'cumprod', 'prod', 'std', 'var', 'mean',
'round', 'min', 'max', 'argsort', 'sort']
funcs2 = ['compress', 'take', 'repeat']
for func in funcs1:
arr = np.random.rand(8, 7)
arr2 = arr.copy()
if isinstance(func, tuple):
func_meth = func[1]
func = func[0]
else:
func_meth = func
res1 = getattr(arr, func_meth)()
res2 = getattr(np, func)(arr2)
if res1 is None:
res1 = arr
if res1.dtype.kind in 'uib':
assert_((res1 == res2).all(), func)
else:
assert_(abs(res1-res2).max() < 1e-8, func)
for func in funcs2:
arr1 = np.random.rand(8, 7)
arr2 = np.random.rand(8, 7)
res1 = None
if func == 'compress':
arr1 = arr1.ravel()
res1 = getattr(arr2, func)(arr1)
else:
arr2 = (15*arr2).astype(int).ravel()
if res1 is None:
res1 = getattr(arr1, func)(arr2)
res2 = getattr(np, func)(arr1, arr2)
assert_(abs(res1-res2).max() < 1e-8, func)
def test_mem_lexsort_strings(self):
# Ticket #298
lst = ['abc', 'cde', 'fgh']
np.lexsort((lst,))
def test_fancy_index(self):
# Ticket #302
x = np.array([1, 2])[np.array([0])]
assert_equal(x.shape, (1,))
def test_recarray_copy(self):
# Ticket #312
dt = [('x', np.int16), ('y', np.float64)]
ra = np.array([(1, 2.3)], dtype=dt)
rb = np.rec.array(ra, dtype=dt)
rb['x'] = 2.
assert_(ra['x'] != rb['x'])
def test_rec_fromarray(self):
# Ticket #322
x1 = np.array([[1, 2], [3, 4], [5, 6]])
x2 = np.array(['a', 'dd', 'xyz'])
x3 = np.array([1.1, 2, 3])
np.rec.fromarrays([x1, x2, x3], formats="(2,)i4,a3,f8")
def test_object_array_assign(self):
x = np.empty((2, 2), object)
x.flat[2] = (1, 2, 3)
assert_equal(x.flat[2], (1, 2, 3))
def test_ndmin_float64(self):
# Ticket #324
x = np.array([1, 2, 3], dtype=np.float64)
assert_equal(np.array(x, dtype=np.float32, ndmin=2).ndim, 2)
assert_equal(np.array(x, dtype=np.float64, ndmin=2).ndim, 2)
def test_ndmin_order(self):
# Issue #465 and related checks
assert_(np.array([1, 2], order='C', ndmin=3).flags.c_contiguous)
assert_(np.array([1, 2], order='F', ndmin=3).flags.f_contiguous)
assert_(np.array(np.ones((2, 2), order='F'), ndmin=3).flags.f_contiguous)
assert_(np.array(np.ones((2, 2), order='C'), ndmin=3).flags.c_contiguous)
def test_mem_axis_minimization(self):
# Ticket #327
data = np.arange(5)
data = np.add.outer(data, data)
def test_mem_float_imag(self):
# Ticket #330
np.float64(1.0).imag
def test_dtype_tuple(self):
# Ticket #334
assert_(np.dtype('i4') == np.dtype(('i4', ())))
def test_dtype_posttuple(self):
# Ticket #335
np.dtype([('col1', '()i4')])
def test_numeric_carray_compare(self):
# Ticket #341
assert_equal(np.array(['X'], 'c'), b'X')
def test_string_array_size(self):
# Ticket #342
assert_raises(ValueError,
np.array, [['X'], ['X', 'X', 'X']], '|S1')
def test_dtype_repr(self):
# Ticket #344
dt1 = np.dtype(('uint32', 2))
dt2 = np.dtype(('uint32', (2,)))
assert_equal(dt1.__repr__(), dt2.__repr__())
def test_reshape_order(self):
# Make sure reshape order works.
a = np.arange(6).reshape(2, 3, order='F')
assert_equal(a, [[0, 2, 4], [1, 3, 5]])
a = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
b = a[:, 1]
assert_equal(b.reshape(2, 2, order='F'), [[2, 6], [4, 8]])
def test_reshape_zero_strides(self):
# Issue #380, test reshaping of zero strided arrays
a = np.ones(1)
a = np.lib.stride_tricks.as_strided(a, shape=(5,), strides=(0,))
assert_(a.reshape(5, 1).strides[0] == 0)
def test_reshape_zero_size(self):
# GitHub Issue #2700, setting shape failed for 0-sized arrays
a = np.ones((0, 2))
a.shape = (-1, 2)
# Cannot test if NPY_RELAXED_STRIDES_CHECKING changes the strides.
# With NPY_RELAXED_STRIDES_CHECKING the test becomes superfluous.
@dec.skipif(np.ones(1).strides[0] == np.iinfo(np.intp).max)
def test_reshape_trailing_ones_strides(self):
# GitHub issue gh-2949, bad strides for trailing ones of new shape
a = np.zeros(12, dtype=np.int32)[::2] # not contiguous
strides_c = (16, 8, 8, 8)
strides_f = (8, 24, 48, 48)
assert_equal(a.reshape(3, 2, 1, 1).strides, strides_c)
assert_equal(a.reshape(3, 2, 1, 1, order='F').strides, strides_f)
assert_equal(np.array(0, dtype=np.int32).reshape(1, 1).strides, (4, 4))
def test_repeat_discont(self):
# Ticket #352
a = np.arange(12).reshape(4, 3)[:, 2]
assert_equal(a.repeat(3), [2, 2, 2, 5, 5, 5, 8, 8, 8, 11, 11, 11])
def test_array_index(self):
# Make sure optimization is not called in this case.
a = np.array([1, 2, 3])
a2 = np.array([[1, 2, 3]])
assert_equal(a[np.where(a == 3)], a2[np.where(a2 == 3)])
def test_object_argmax(self):
a = np.array([1, 2, 3], dtype=object)
assert_(a.argmax() == 2)
def test_recarray_fields(self):
# Ticket #372
dt0 = np.dtype([('f0', 'i4'), ('f1', 'i4')])
dt1 = np.dtype([('f0', 'i8'), ('f1', 'i8')])
for a in [np.array([(1, 2), (3, 4)], "i4,i4"),
np.rec.array([(1, 2), (3, 4)], "i4,i4"),
np.rec.array([(1, 2), (3, 4)]),
np.rec.fromarrays([(1, 2), (3, 4)], "i4,i4"),
np.rec.fromarrays([(1, 2), (3, 4)])]:
assert_(a.dtype in [dt0, dt1])
def test_random_shuffle(self):
# Ticket #374
a = np.arange(5).reshape((5, 1))
b = a.copy()
np.random.shuffle(b)
assert_equal(np.sort(b, axis=0), a)
def test_refcount_vdot(self):
# Changeset #3443
_assert_valid_refcount(np.vdot)
def test_startswith(self):
ca = np.char.array(['Hi', 'There'])
assert_equal(ca.startswith('H'), [True, False])
def test_noncommutative_reduce_accumulate(self):
# Ticket #413
tosubtract = np.arange(5)
todivide = np.array([2.0, 0.5, 0.25])
assert_equal(np.subtract.reduce(tosubtract), -10)
assert_equal(np.divide.reduce(todivide), 16.0)
assert_array_equal(np.subtract.accumulate(tosubtract),
np.array([0, -1, -3, -6, -10]))
assert_array_equal(np.divide.accumulate(todivide),
np.array([2., 4., 16.]))
def test_convolve_empty(self):
# Convolve should raise an error for empty input array.
assert_raises(ValueError, np.convolve, [], [1])
assert_raises(ValueError, np.convolve, [1], [])
def test_multidim_byteswap(self):
# Ticket #449
r = np.array([(1, (0, 1, 2))], dtype="i2,3i2")
assert_array_equal(r.byteswap(),
np.array([(256, (0, 256, 512))], r.dtype))
def test_string_NULL(self):
# Changeset 3557
assert_equal(np.array("a\x00\x0b\x0c\x00").item(),
'a\x00\x0b\x0c')
def test_junk_in_string_fields_of_recarray(self):
# Ticket #483
r = np.array([[b'abc']], dtype=[('var1', '|S20')])
assert_(asbytes(r['var1'][0][0]) == b'abc')
def test_take_output(self):
# Ensure that 'take' honours output parameter.
x = np.arange(12).reshape((3, 4))
a = np.take(x, [0, 2], axis=1)
b = np.zeros_like(a)
np.take(x, [0, 2], axis=1, out=b)
assert_array_equal(a, b)
def test_take_object_fail(self):
# Issue gh-3001
d = 123.
a = np.array([d, 1], dtype=object)
if HAS_REFCOUNT:
ref_d = sys.getrefcount(d)
try:
a.take([0, 100])
except IndexError:
pass
if HAS_REFCOUNT:
assert_(ref_d == sys.getrefcount(d))
def test_array_str_64bit(self):
# Ticket #501
s = np.array([1, np.nan], dtype=np.float64)
with np.errstate(all='raise'):
np.array_str(s) # Should succeed
def test_frompyfunc_endian(self):
# Ticket #503
from math import radians
uradians = np.frompyfunc(radians, 1, 1)
big_endian = np.array([83.4, 83.5], dtype='>f8')
little_endian = np.array([83.4, 83.5], dtype='<f8')
assert_almost_equal(uradians(big_endian).astype(float),
uradians(little_endian).astype(float))
def test_mem_string_arr(self):
# Ticket #514
s = "aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa"
t = []
np.hstack((t, s))
def test_arr_transpose(self):
# Ticket #516
x = np.random.rand(*(2,)*16)
x.transpose(list(range(16))) # Should succeed
def test_string_mergesort(self):
# Ticket #540
x = np.array(['a']*32)
assert_array_equal(x.argsort(kind='m'), np.arange(32))
def test_argmax_byteorder(self):
# Ticket #546
a = np.arange(3, dtype='>f')
assert_(a[a.argmax()] == a.max())
def test_rand_seed(self):
# Ticket #555
for l in np.arange(4):
np.random.seed(l)
def test_mem_deallocation_leak(self):
# Ticket #562
a = np.zeros(5, dtype=float)
b = np.array(a, dtype=float)
del a, b
def test_mem_on_invalid_dtype(self):
"Ticket #583"
assert_raises(ValueError, np.fromiter, [['12', ''], ['13', '']], str)
def test_dot_negative_stride(self):
# Ticket #588
x = np.array([[1, 5, 25, 125., 625]])
y = np.array([[20.], [160.], [640.], [1280.], [1024.]])
z = y[::-1].copy()
y2 = y[::-1]
assert_equal(np.dot(x, z), np.dot(x, y2))
def test_object_casting(self):
# This used to trigger the object-type version of
# the bitwise_or operation, because float64 -> object
# casting succeeds
def rs():
x = np.ones([484, 286])
y = np.zeros([484, 286])
x |= y
assert_raises(TypeError, rs)
def test_unicode_scalar(self):
# Ticket #600
x = np.array(["DROND", "DROND1"], dtype="U6")
el = x[1]
new = pickle.loads(pickle.dumps(el))
assert_equal(new, el)
def test_arange_non_native_dtype(self):
# Ticket #616
for T in ('>f4', '<f4'):
dt = np.dtype(T)
assert_equal(np.arange(0, dtype=dt).dtype, dt)
assert_equal(np.arange(0.5, dtype=dt).dtype, dt)
assert_equal(np.arange(5, dtype=dt).dtype, dt)
def test_bool_flat_indexing_invalid_nr_elements(self):
s = np.ones(10, dtype=float)
x = np.array((15,), dtype=float)
def ia(x, s, v):
x[(s > 0)] = v
assert_raises(IndexError, ia, x, s, np.zeros(9, dtype=float))
assert_raises(IndexError, ia, x, s, np.zeros(11, dtype=float))
# Old special case (different code path):
assert_raises(ValueError, ia, x.flat, s, np.zeros(9, dtype=float))
assert_raises(ValueError, ia, x.flat, s, np.zeros(11, dtype=float))
def test_mem_scalar_indexing(self):
# Ticket #603
x = np.array([0], dtype=float)
index = np.array(0, dtype=np.int32)
x[index]
def test_binary_repr_0_width(self):
assert_equal(np.binary_repr(0, width=3), '000')
def test_fromstring(self):
assert_equal(np.fromstring("12:09:09", dtype=int, sep=":"),
[12, 9, 9])
def test_searchsorted_variable_length(self):
x = np.array(['a', 'aa', 'b'])
y = np.array(['d', 'e'])
assert_equal(x.searchsorted(y), [3, 3])
def test_string_argsort_with_zeros(self):
# Check argsort for strings containing zeros.
x = np.frombuffer(b"\x00\x02\x00\x01", dtype="|S2")
assert_array_equal(x.argsort(kind='m'), np.array([1, 0]))
assert_array_equal(x.argsort(kind='q'), np.array([1, 0]))
def test_string_sort_with_zeros(self):
# Check sort for strings containing zeros.
x = np.frombuffer(b"\x00\x02\x00\x01", dtype="|S2")
y = np.frombuffer(b"\x00\x01\x00\x02", dtype="|S2")
assert_array_equal(np.sort(x, kind="q"), y)
def test_copy_detection_zero_dim(self):
# Ticket #658
np.indices((0, 3, 4)).T.reshape(-1, 3)
def test_flat_byteorder(self):
# Ticket #657
x = np.arange(10)
assert_array_equal(x.astype('>i4'), x.astype('<i4').flat[:])
assert_array_equal(x.astype('>i4').flat[:], x.astype('<i4'))
def test_sign_bit(self):
x = np.array([0, -0.0, 0])
assert_equal(str(np.abs(x)), '[0. 0. 0.]')
def test_flat_index_byteswap(self):
for dt in (np.dtype('<i4'), np.dtype('>i4')):
x = np.array([-1, 0, 1], dtype=dt)
assert_equal(x.flat[0].dtype, x[0].dtype)
def test_copy_detection_corner_case(self):
# Ticket #658
np.indices((0, 3, 4)).T.reshape(-1, 3)
# Cannot test if NPY_RELAXED_STRIDES_CHECKING changes the strides.
# With NPY_RELAXED_STRIDES_CHECKING the test becomes superfluous,
# 0-sized reshape itself is tested elsewhere.
@dec.skipif(np.ones(1).strides[0] == np.iinfo(np.intp).max)
def test_copy_detection_corner_case2(self):
# Ticket #771: strides are not set correctly when reshaping 0-sized
# arrays
b = np.indices((0, 3, 4)).T.reshape(-1, 3)
assert_equal(b.strides, (3 * b.itemsize, b.itemsize))
def test_object_array_refcounting(self):
# Ticket #633
if not hasattr(sys, 'getrefcount'):
return
# NB. this is probably CPython-specific
cnt = sys.getrefcount
a = object()
b = object()
c = object()
cnt0_a = cnt(a)
cnt0_b = cnt(b)
cnt0_c = cnt(c)
# -- 0d -> 1-d broadcast slice assignment
arr = np.zeros(5, dtype=np.object_)
arr[:] = a
assert_equal(cnt(a), cnt0_a + 5)
arr[:] = b
assert_equal(cnt(a), cnt0_a)
assert_equal(cnt(b), cnt0_b + 5)
arr[:2] = c
assert_equal(cnt(b), cnt0_b + 3)
assert_equal(cnt(c), cnt0_c + 2)
del arr
# -- 1-d -> 2-d broadcast slice assignment
arr = np.zeros((5, 2), dtype=np.object_)
arr0 = np.zeros(2, dtype=np.object_)
arr0[0] = a
assert_(cnt(a) == cnt0_a + 1)
arr0[1] = b
assert_(cnt(b) == cnt0_b + 1)
arr[:, :] = arr0
assert_(cnt(a) == cnt0_a + 6)
assert_(cnt(b) == cnt0_b + 6)
arr[:, 0] = None
assert_(cnt(a) == cnt0_a + 1)
del arr, arr0
# -- 2-d copying + flattening
arr = np.zeros((5, 2), dtype=np.object_)
arr[:, 0] = a
arr[:, 1] = b
assert_(cnt(a) == cnt0_a + 5)
assert_(cnt(b) == cnt0_b + 5)
arr2 = arr.copy()
assert_(cnt(a) == cnt0_a + 10)
assert_(cnt(b) == cnt0_b + 10)
arr2 = arr[:, 0].copy()
assert_(cnt(a) == cnt0_a + 10)
assert_(cnt(b) == cnt0_b + 5)
arr2 = arr.flatten()
assert_(cnt(a) == cnt0_a + 10)
assert_(cnt(b) == cnt0_b + 10)
del arr, arr2
# -- concatenate, repeat, take, choose
arr1 = np.zeros((5, 1), dtype=np.object_)
arr2 = np.zeros((5, 1), dtype=np.object_)
arr1[...] = a
arr2[...] = b
assert_(cnt(a) == cnt0_a + 5)
assert_(cnt(b) == cnt0_b + 5)
tmp = np.concatenate((arr1, arr2))
assert_(cnt(a) == cnt0_a + 5 + 5)
assert_(cnt(b) == cnt0_b + 5 + 5)
tmp = arr1.repeat(3, axis=0)
assert_(cnt(a) == cnt0_a + 5 + 3*5)
tmp = arr1.take([1, 2, 3], axis=0)
assert_(cnt(a) == cnt0_a + 5 + 3)
x = np.array([[0], [1], [0], [1], [1]], int)
tmp = x.choose(arr1, arr2)
assert_(cnt(a) == cnt0_a + 5 + 2)
assert_(cnt(b) == cnt0_b + 5 + 3)
del tmp # Avoid pyflakes unused variable warning
def test_mem_custom_float_to_array(self):
# Ticket 702
class MyFloat(object):
def __float__(self):
return 1.0
tmp = np.atleast_1d([MyFloat()])
tmp.astype(float) # Should succeed
def test_object_array_refcount_self_assign(self):
# Ticket #711
class VictimObject(object):
deleted = False
def __del__(self):
self.deleted = True
d = VictimObject()
arr = np.zeros(5, dtype=np.object_)
arr[:] = d
del d
arr[:] = arr # refcount of 'd' might hit zero here
assert_(not arr[0].deleted)
arr[:] = arr # trying to induce a segfault by doing it again...
assert_(not arr[0].deleted)
def test_mem_fromiter_invalid_dtype_string(self):
x = [1, 2, 3]
assert_raises(ValueError,
np.fromiter, [xi for xi in x], dtype='S')
def test_reduce_big_object_array(self):
# Ticket #713
oldsize = np.setbufsize(10*16)
a = np.array([None]*161, object)
assert_(not np.any(a))
np.setbufsize(oldsize)
def test_mem_0d_array_index(self):
# Ticket #714
np.zeros(10)[np.array(0)]
def test_nonnative_endian_fill(self):
# Non-native endian arrays were incorrectly filled with scalars
# before r5034.
if sys.byteorder == 'little':
dtype = np.dtype('>i4')
else:
dtype = np.dtype('<i4')
x = np.empty([1], dtype=dtype)
x.fill(1)
assert_equal(x, np.array([1], dtype=dtype))
def test_dot_alignment_sse2(self):
# Test for ticket #551, changeset r5140
x = np.zeros((30, 40))
y = pickle.loads(pickle.dumps(x))
# y is now typically not aligned on a 8-byte boundary
z = np.ones((1, y.shape[0]))
# This shouldn't cause a segmentation fault:
np.dot(z, y)
def test_astype_copy(self):
# Ticket #788, changeset r5155
# The test data file was generated by scipy.io.savemat.
# The dtype is float64, but the isbuiltin attribute is 0.
data_dir = path.join(path.dirname(__file__), 'data')
filename = path.join(data_dir, "astype_copy.pkl")
if sys.version_info[0] >= 3:
f = open(filename, 'rb')
xp = pickle.load(f, encoding='latin1')
f.close()
else:
f = open(filename)
xp = pickle.load(f)
f.close()
xpd = xp.astype(np.float64)
assert_((xp.__array_interface__['data'][0] !=
xpd.__array_interface__['data'][0]))
def test_compress_small_type(self):
# Ticket #789, changeset 5217.
# compress with out argument segfaulted if cannot cast safely
import numpy as np
a = np.array([[1, 2], [3, 4]])
b = np.zeros((2, 1), dtype=np.single)
try:
a.compress([True, False], axis=1, out=b)
raise AssertionError("compress with an out which cannot be "
"safely casted should not return "
"successfully")
except TypeError:
pass
def test_attributes(self):
# Ticket #791
class TestArray(np.ndarray):
def __new__(cls, data, info):
result = np.array(data)
result = result.view(cls)
result.info = info
return result
def __array_finalize__(self, obj):
self.info = getattr(obj, 'info', '')
dat = TestArray([[1, 2, 3, 4], [5, 6, 7, 8]], 'jubba')
assert_(dat.info == 'jubba')
dat.resize((4, 2))
assert_(dat.info == 'jubba')
dat.sort()
assert_(dat.info == 'jubba')
dat.fill(2)
assert_(dat.info == 'jubba')
dat.put([2, 3, 4], [6, 3, 4])
assert_(dat.info == 'jubba')
dat.setfield(4, np.int32, 0)
assert_(dat.info == 'jubba')
dat.setflags()
assert_(dat.info == 'jubba')
assert_(dat.all(1).info == 'jubba')
assert_(dat.any(1).info == 'jubba')
assert_(dat.argmax(1).info == 'jubba')
assert_(dat.argmin(1).info == 'jubba')
assert_(dat.argsort(1).info == 'jubba')
assert_(dat.astype(TestArray).info == 'jubba')
assert_(dat.byteswap().info == 'jubba')
assert_(dat.clip(2, 7).info == 'jubba')
assert_(dat.compress([0, 1, 1]).info == 'jubba')
assert_(dat.conj().info == 'jubba')
assert_(dat.conjugate().info == 'jubba')
assert_(dat.copy().info == 'jubba')
dat2 = TestArray([2, 3, 1, 0], 'jubba')
choices = [[0, 1, 2, 3], [10, 11, 12, 13],
[20, 21, 22, 23], [30, 31, 32, 33]]
assert_(dat2.choose(choices).info == 'jubba')
assert_(dat.cumprod(1).info == 'jubba')
assert_(dat.cumsum(1).info == 'jubba')
assert_(dat.diagonal().info == 'jubba')
assert_(dat.flatten().info == 'jubba')
assert_(dat.getfield(np.int32, 0).info == 'jubba')
assert_(dat.imag.info == 'jubba')
assert_(dat.max(1).info == 'jubba')
assert_(dat.mean(1).info == 'jubba')
assert_(dat.min(1).info == 'jubba')
assert_(dat.newbyteorder().info == 'jubba')
assert_(dat.prod(1).info == 'jubba')
assert_(dat.ptp(1).info == 'jubba')
assert_(dat.ravel().info == 'jubba')
assert_(dat.real.info == 'jubba')
assert_(dat.repeat(2).info == 'jubba')
assert_(dat.reshape((2, 4)).info == 'jubba')
assert_(dat.round().info == 'jubba')
assert_(dat.squeeze().info == 'jubba')
assert_(dat.std(1).info == 'jubba')
assert_(dat.sum(1).info == 'jubba')
assert_(dat.swapaxes(0, 1).info == 'jubba')
assert_(dat.take([2, 3, 5]).info == 'jubba')
assert_(dat.transpose().info == 'jubba')
assert_(dat.T.info == 'jubba')
assert_(dat.var(1).info == 'jubba')
assert_(dat.view(TestArray).info == 'jubba')
# These methods do not preserve subclasses
assert_(type(dat.nonzero()[0]) is np.ndarray)
assert_(type(dat.nonzero()[1]) is np.ndarray)
def test_recarray_tolist(self):
# Ticket #793, changeset r5215
# Comparisons fail for NaN, so we can't use random memory
# for the test.
buf = np.zeros(40, dtype=np.int8)
a = np.recarray(2, formats="i4,f8,f8", names="id,x,y", buf=buf)
b = a.tolist()
assert_( a[0].tolist() == b[0])
assert_( a[1].tolist() == b[1])
def test_nonscalar_item_method(self):
# Make sure that .item() fails graciously when it should
a = np.arange(5)
assert_raises(ValueError, a.item)
def test_char_array_creation(self):
a = np.array('123', dtype='c')
b = np.array([b'1', b'2', b'3'])
assert_equal(a, b)
def test_unaligned_unicode_access(self):
# Ticket #825
for i in range(1, 9):
msg = 'unicode offset: %d chars' % i
t = np.dtype([('a', 'S%d' % i), ('b', 'U2')])
x = np.array([(b'a', u'b')], dtype=t)
if sys.version_info[0] >= 3:
assert_equal(str(x), "[(b'a', 'b')]", err_msg=msg)
else:
assert_equal(str(x), "[('a', u'b')]", err_msg=msg)
def test_sign_for_complex_nan(self):
# Ticket 794.
with np.errstate(invalid='ignore'):
C = np.array([-np.inf, -2+1j, 0, 2-1j, np.inf, np.nan])
have = np.sign(C)
want = np.array([-1+0j, -1+0j, 0+0j, 1+0j, 1+0j, np.nan])
assert_equal(have, want)
def test_for_equal_names(self):
# Ticket #674
dt = np.dtype([('foo', float), ('bar', float)])
a = np.zeros(10, dt)
b = list(a.dtype.names)
b[0] = "notfoo"
a.dtype.names = b
assert_(a.dtype.names[0] == "notfoo")
assert_(a.dtype.names[1] == "bar")
def test_for_object_scalar_creation(self):
# Ticket #816
a = np.object_()
b = np.object_(3)
b2 = np.object_(3.0)
c = np.object_([4, 5])
d = np.object_([None, {}, []])
assert_(a is None)
assert_(type(b) is int)
assert_(type(b2) is float)
assert_(type(c) is np.ndarray)
assert_(c.dtype == object)
assert_(d.dtype == object)
def test_array_resize_method_system_error(self):
# Ticket #840 - order should be an invalid keyword.
x = np.array([[0, 1], [2, 3]])
assert_raises(TypeError, x.resize, (2, 2), order='C')
def test_for_zero_length_in_choose(self):
"Ticket #882"
a = np.array(1)
assert_raises(ValueError, lambda x: x.choose([]), a)
def test_array_ndmin_overflow(self):
"Ticket #947."
assert_raises(ValueError, lambda: np.array([1], ndmin=33))
def test_void_scalar_with_titles(self):
# No ticket
data = [('john', 4), ('mary', 5)]
dtype1 = [(('source:yy', 'name'), 'O'), (('source:xx', 'id'), int)]
arr = np.array(data, dtype=dtype1)
assert_(arr[0][0] == 'john')
assert_(arr[0][1] == 4)
def test_void_scalar_constructor(self):
#Issue #1550
#Create test string data, construct void scalar from data and assert
#that void scalar contains original data.
test_string = np.array("test")
test_string_void_scalar = np.core.multiarray.scalar(
np.dtype(("V", test_string.dtype.itemsize)), test_string.tobytes())
assert_(test_string_void_scalar.view(test_string.dtype) == test_string)
#Create record scalar, construct from data and assert that
#reconstructed scalar is correct.
test_record = np.ones((), "i,i")
test_record_void_scalar = np.core.multiarray.scalar(
test_record.dtype, test_record.tobytes())
assert_(test_record_void_scalar == test_record)
#Test pickle and unpickle of void and record scalars
assert_(pickle.loads(pickle.dumps(test_string)) == test_string)
assert_(pickle.loads(pickle.dumps(test_record)) == test_record)
def test_blasdot_uninitialized_memory(self):
# Ticket #950
for m in [0, 1, 2]:
for n in [0, 1, 2]:
for k in range(3):
# Try to ensure that x->data contains non-zero floats
x = np.array([123456789e199], dtype=np.float64)
if IS_PYPY:
x.resize((m, 0), refcheck=False)
else:
x.resize((m, 0))
y = np.array([123456789e199], dtype=np.float64)
if IS_PYPY:
y.resize((0, n), refcheck=False)
else:
y.resize((0, n))
# `dot` should just return zero (m, n) matrix
z = np.dot(x, y)
assert_(np.all(z == 0))
assert_(z.shape == (m, n))
def test_zeros(self):
# Regression test for #1061.
# Set a size which cannot fit into a 64 bits signed integer
sz = 2 ** 64
good = 'Maximum allowed dimension exceeded'
try:
np.empty(sz)
except ValueError as e:
if not str(e) == good:
self.fail("Got msg '%s', expected '%s'" % (e, good))
except Exception as e:
self.fail("Got exception of type %s instead of ValueError" % type(e))
def test_huge_arange(self):
# Regression test for #1062.
# Set a size which cannot fit into a 64 bits signed integer
sz = 2 ** 64
good = 'Maximum allowed size exceeded'
try:
np.arange(sz)
assert_(np.size == sz)
except ValueError as e:
if not str(e) == good:
self.fail("Got msg '%s', expected '%s'" % (e, good))
except Exception as e:
self.fail("Got exception of type %s instead of ValueError" % type(e))
def test_fromiter_bytes(self):
# Ticket #1058
a = np.fromiter(list(range(10)), dtype='b')
b = np.fromiter(list(range(10)), dtype='B')
assert_(np.alltrue(a == np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])))
assert_(np.alltrue(b == np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])))
def test_array_from_sequence_scalar_array(self):
# Ticket #1078: segfaults when creating an array with a sequence of
# 0d arrays.
a = np.array((np.ones(2), np.array(2)))
assert_equal(a.shape, (2,))
assert_equal(a.dtype, np.dtype(object))
assert_equal(a[0], np.ones(2))
assert_equal(a[1], np.array(2))
a = np.array(((1,), np.array(1)))
assert_equal(a.shape, (2,))
assert_equal(a.dtype, np.dtype(object))
assert_equal(a[0], (1,))
assert_equal(a[1], np.array(1))
def test_array_from_sequence_scalar_array2(self):
# Ticket #1081: weird array with strange input...
t = np.array([np.array([]), np.array(0, object)])
assert_equal(t.shape, (2,))
assert_equal(t.dtype, np.dtype(object))
def test_array_too_big(self):
# Ticket #1080.
assert_raises(ValueError, np.zeros, [975]*7, np.int8)
assert_raises(ValueError, np.zeros, [26244]*5, np.int8)
def test_dtype_keyerrors_(self):
# Ticket #1106.
dt = np.dtype([('f1', np.uint)])
assert_raises(KeyError, dt.__getitem__, "f2")
assert_raises(IndexError, dt.__getitem__, 1)
assert_raises(TypeError, dt.__getitem__, 0.0)
def test_lexsort_buffer_length(self):
# Ticket #1217, don't segfault.
a = np.ones(100, dtype=np.int8)
b = np.ones(100, dtype=np.int32)
i = np.lexsort((a[::-1], b))
assert_equal(i, np.arange(100, dtype=int))
def test_object_array_to_fixed_string(self):
# Ticket #1235.
a = np.array(['abcdefgh', 'ijklmnop'], dtype=np.object_)
b = np.array(a, dtype=(np.str_, 8))
assert_equal(a, b)
c = np.array(a, dtype=(np.str_, 5))
assert_equal(c, np.array(['abcde', 'ijklm']))
d = np.array(a, dtype=(np.str_, 12))
assert_equal(a, d)
e = np.empty((2, ), dtype=(np.str_, 8))
e[:] = a[:]
assert_equal(a, e)
def test_unicode_to_string_cast(self):
# Ticket #1240.
a = np.array([[u'abc', u'\u03a3'],
[u'asdf', u'erw']],
dtype='U')
assert_raises(UnicodeEncodeError, np.array, a, 'S4')
def test_mixed_string_unicode_array_creation(self):
a = np.array(['1234', u'123'])
assert_(a.itemsize == 16)
a = np.array([u'123', '1234'])
assert_(a.itemsize == 16)
a = np.array(['1234', u'123', '12345'])
assert_(a.itemsize == 20)
a = np.array([u'123', '1234', u'12345'])
assert_(a.itemsize == 20)
a = np.array([u'123', '1234', u'1234'])
assert_(a.itemsize == 16)
def test_misaligned_objects_segfault(self):
# Ticket #1198 and #1267
a1 = np.zeros((10,), dtype='O,c')
a2 = np.array(['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j'], 'S10')
a1['f0'] = a2
repr(a1)
np.argmax(a1['f0'])
a1['f0'][1] = "FOO"
a1['f0'] = "FOO"
np.array(a1['f0'], dtype='S')
np.nonzero(a1['f0'])
a1.sort()
copy.deepcopy(a1)
def test_misaligned_scalars_segfault(self):
# Ticket #1267
s1 = np.array(('a', 'Foo'), dtype='c,O')
s2 = np.array(('b', 'Bar'), dtype='c,O')
s1['f1'] = s2['f1']
s1['f1'] = 'Baz'
def test_misaligned_dot_product_objects(self):
# Ticket #1267
# This didn't require a fix, but it's worth testing anyway, because
# it may fail if .dot stops enforcing the arrays to be BEHAVED
a = np.array([[(1, 'a'), (0, 'a')], [(0, 'a'), (1, 'a')]], dtype='O,c')
b = np.array([[(4, 'a'), (1, 'a')], [(2, 'a'), (2, 'a')]], dtype='O,c')
np.dot(a['f0'], b['f0'])
def test_byteswap_complex_scalar(self):
# Ticket #1259 and gh-441
for dtype in [np.dtype('<'+t) for t in np.typecodes['Complex']]:
z = np.array([2.2-1.1j], dtype)
x = z[0] # always native-endian
y = x.byteswap()
if x.dtype.byteorder == z.dtype.byteorder:
# little-endian machine
assert_equal(x, np.frombuffer(y.tobytes(), dtype=dtype.newbyteorder()))
else:
# big-endian machine
assert_equal(x, np.frombuffer(y.tobytes(), dtype=dtype))
# double check real and imaginary parts:
assert_equal(x.real, y.real.byteswap())
assert_equal(x.imag, y.imag.byteswap())
def test_structured_arrays_with_objects1(self):
# Ticket #1299
stra = 'aaaa'
strb = 'bbbb'
x = np.array([[(0, stra), (1, strb)]], 'i8,O')
x[x.nonzero()] = x.ravel()[:1]
assert_(x[0, 1] == x[0, 0])
@dec.skipif(not HAS_REFCOUNT, "python has no sys.getrefcount")
def test_structured_arrays_with_objects2(self):
# Ticket #1299 second test
stra = 'aaaa'
strb = 'bbbb'
numb = sys.getrefcount(strb)
numa = sys.getrefcount(stra)
x = np.array([[(0, stra), (1, strb)]], 'i8,O')
x[x.nonzero()] = x.ravel()[:1]
assert_(sys.getrefcount(strb) == numb)
assert_(sys.getrefcount(stra) == numa + 2)
def test_duplicate_title_and_name(self):
# Ticket #1254
dtspec = [(('a', 'a'), 'i'), ('b', 'i')]
assert_raises(ValueError, np.dtype, dtspec)
def test_signed_integer_division_overflow(self):
# Ticket #1317.
def test_type(t):
min = np.array([np.iinfo(t).min])
min //= -1
with np.errstate(divide="ignore"):
for t in (np.int8, np.int16, np.int32, np.int64, int, np.long):
test_type(t)
def test_buffer_hashlib(self):
try:
from hashlib import md5
except ImportError:
from md5 import new as md5
x = np.array([1, 2, 3], dtype=np.dtype('<i4'))
assert_equal(md5(x).hexdigest(), '2a1dd1e1e59d0a384c26951e316cd7e6')
def test_0d_string_scalar(self):
# Bug #1436; the following should succeed
np.asarray('x', '>c')
def test_log1p_compiler_shenanigans(self):
# Check if log1p is behaving on 32 bit intel systems.
assert_(np.isfinite(np.log1p(np.exp2(-53))))
def test_fromiter_comparison(self):
a = np.fromiter(list(range(10)), dtype='b')
b = np.fromiter(list(range(10)), dtype='B')
assert_(np.alltrue(a == np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])))
assert_(np.alltrue(b == np.array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])))
def test_fromstring_crash(self):
# Ticket #1345: the following should not cause a crash
np.fromstring(b'aa, aa, 1.0', sep=',')
def test_ticket_1539(self):
dtypes = [x for x in np.typeDict.values()
if (issubclass(x, np.number)
and not issubclass(x, np.timedelta64))]
a = np.array([], np.bool_) # not x[0] because it is unordered
failures = []
for x in dtypes:
b = a.astype(x)
for y in dtypes:
c = a.astype(y)
try:
np.dot(b, c)
except TypeError:
failures.append((x, y))
if failures:
raise AssertionError("Failures: %r" % failures)
def test_ticket_1538(self):
x = np.finfo(np.float32)
for name in 'eps epsneg max min resolution tiny'.split():
assert_equal(type(getattr(x, name)), np.float32,
err_msg=name)
def test_ticket_1434(self):
# Check that the out= argument in var and std has an effect
data = np.array(((1, 2, 3), (4, 5, 6), (7, 8, 9)))
out = np.zeros((3,))
ret = data.var(axis=1, out=out)
assert_(ret is out)
assert_array_equal(ret, data.var(axis=1))
ret = data.std(axis=1, out=out)
assert_(ret is out)
assert_array_equal(ret, data.std(axis=1))
def test_complex_nan_maximum(self):
cnan = complex(0, np.nan)
assert_equal(np.maximum(1, cnan), cnan)
def test_subclass_int_tuple_assignment(self):
# ticket #1563
class Subclass(np.ndarray):
def __new__(cls, i):
return np.ones((i,)).view(cls)
x = Subclass(5)
x[(0,)] = 2 # shouldn't raise an exception
assert_equal(x[0], 2)
def test_ufunc_no_unnecessary_views(self):
# ticket #1548
class Subclass(np.ndarray):
pass
x = np.array([1, 2, 3]).view(Subclass)
y = np.add(x, x, x)
assert_equal(id(x), id(y))
@dec.skipif(not HAS_REFCOUNT, "python has no sys.getrefcount")
def test_take_refcount(self):
# ticket #939
a = np.arange(16, dtype=float)
a.shape = (4, 4)
lut = np.ones((5 + 3, 4), float)
rgba = np.empty(shape=a.shape + (4,), dtype=lut.dtype)
c1 = sys.getrefcount(rgba)
try:
lut.take(a, axis=0, mode='clip', out=rgba)
except TypeError:
pass
c2 = sys.getrefcount(rgba)
assert_equal(c1, c2)
def test_fromfile_tofile_seeks(self):
# On Python 3, tofile/fromfile used to get (#1610) the Python
# file handle out of sync
f0 = tempfile.NamedTemporaryFile()
f = f0.file
f.write(np.arange(255, dtype='u1').tobytes())
f.seek(20)
ret = np.fromfile(f, count=4, dtype='u1')
assert_equal(ret, np.array([20, 21, 22, 23], dtype='u1'))
assert_equal(f.tell(), 24)
f.seek(40)
np.array([1, 2, 3], dtype='u1').tofile(f)
assert_equal(f.tell(), 43)
f.seek(40)
data = f.read(3)
assert_equal(data, b"\x01\x02\x03")
f.seek(80)
f.read(4)
data = np.fromfile(f, dtype='u1', count=4)
assert_equal(data, np.array([84, 85, 86, 87], dtype='u1'))
f.close()
def test_complex_scalar_warning(self):
for tp in [np.csingle, np.cdouble, np.clongdouble]:
x = tp(1+2j)
assert_warns(np.ComplexWarning, float, x)
with suppress_warnings() as sup:
sup.filter(np.ComplexWarning)
assert_equal(float(x), float(x.real))
def test_complex_scalar_complex_cast(self):
for tp in [np.csingle, np.cdouble, np.clongdouble]:
x = tp(1+2j)
assert_equal(complex(x), 1+2j)
def test_complex_boolean_cast(self):
# Ticket #2218
for tp in [np.csingle, np.cdouble, np.clongdouble]:
x = np.array([0, 0+0.5j, 0.5+0j], dtype=tp)
assert_equal(x.astype(bool), np.array([0, 1, 1], dtype=bool))
assert_(np.any(x))
assert_(np.all(x[1:]))
def test_uint_int_conversion(self):
x = 2**64 - 1
assert_equal(int(np.uint64(x)), x)
def test_duplicate_field_names_assign(self):
ra = np.fromiter(((i*3, i*2) for i in range(10)), dtype='i8,f8')
ra.dtype.names = ('f1', 'f2')
repr(ra) # should not cause a segmentation fault
assert_raises(ValueError, setattr, ra.dtype, 'names', ('f1', 'f1'))
def test_eq_string_and_object_array(self):
# From e-mail thread "__eq__ with str and object" (Keith Goodman)
a1 = np.array(['a', 'b'], dtype=object)
a2 = np.array(['a', 'c'])
assert_array_equal(a1 == a2, [True, False])
assert_array_equal(a2 == a1, [True, False])
def test_nonzero_byteswap(self):
a = np.array([0x80000000, 0x00000080, 0], dtype=np.uint32)
a.dtype = np.float32
assert_equal(a.nonzero()[0], [1])
a = a.byteswap().newbyteorder()
assert_equal(a.nonzero()[0], [1]) # [0] if nonzero() ignores swap
def test_find_common_type_boolean(self):
# Ticket #1695
assert_(np.find_common_type([], ['?', '?']) == '?')
def test_empty_mul(self):
a = np.array([1.])
a[1:1] *= 2
assert_equal(a, [1.])
def test_array_side_effect(self):
# The second use of itemsize was throwing an exception because in
# ctors.c, discover_itemsize was calling PyObject_Length without
# checking the return code. This failed to get the length of the
# number 2, and the exception hung around until something checked
# PyErr_Occurred() and returned an error.
assert_equal(np.dtype('S10').itemsize, 10)
np.array([['abc', 2], ['long ', '0123456789']], dtype=np.string_)
assert_equal(np.dtype('S10').itemsize, 10)
def test_any_float(self):
# all and any for floats
a = np.array([0.1, 0.9])
assert_(np.any(a))
assert_(np.all(a))
def test_large_float_sum(self):
a = np.arange(10000, dtype='f')
assert_equal(a.sum(dtype='d'), a.astype('d').sum())
def test_ufunc_casting_out(self):
a = np.array(1.0, dtype=np.float32)
b = np.array(1.0, dtype=np.float64)
c = np.array(1.0, dtype=np.float32)
np.add(a, b, out=c)
assert_equal(c, 2.0)
def test_array_scalar_contiguous(self):
# Array scalars are both C and Fortran contiguous
assert_(np.array(1.0).flags.c_contiguous)
assert_(np.array(1.0).flags.f_contiguous)
assert_(np.array(np.float32(1.0)).flags.c_contiguous)
assert_(np.array(np.float32(1.0)).flags.f_contiguous)
def test_squeeze_contiguous(self):
# Similar to GitHub issue #387
a = np.zeros((1, 2)).squeeze()
b = np.zeros((2, 2, 2), order='F')[:, :, ::2].squeeze()
assert_(a.flags.c_contiguous)
assert_(a.flags.f_contiguous)
assert_(b.flags.f_contiguous)
def test_reduce_contiguous(self):
# GitHub issue #387
a = np.add.reduce(np.zeros((2, 1, 2)), (0, 1))
b = np.add.reduce(np.zeros((2, 1, 2)), 1)
assert_(a.flags.c_contiguous)
assert_(a.flags.f_contiguous)
assert_(b.flags.c_contiguous)
def test_object_array_self_reference(self):
# Object arrays with references to themselves can cause problems
a = np.array(0, dtype=object)
a[()] = a
assert_raises(RecursionError, int, a)
assert_raises(RecursionError, long, a)
assert_raises(RecursionError, float, a)
if sys.version_info.major == 2:
# in python 3, this falls back on operator.index, which fails on
# on dtype=object
assert_raises(RecursionError, oct, a)
assert_raises(RecursionError, hex, a)
a[()] = None
def test_object_array_circular_reference(self):
# Test the same for a circular reference.
a = np.array(0, dtype=object)
b = np.array(0, dtype=object)
a[()] = b
b[()] = a
assert_raises(RecursionError, int, a)
# NumPy has no tp_traverse currently, so circular references
# cannot be detected. So resolve it:
a[()] = None
# This was causing a to become like the above
a = np.array(0, dtype=object)
a[...] += 1
assert_equal(a, 1)
def test_object_array_nested(self):
# but is fine with a reference to a different array
a = np.array(0, dtype=object)
b = np.array(0, dtype=object)
a[()] = b
assert_equal(int(a), int(0))
assert_equal(long(a), long(0))
assert_equal(float(a), float(0))
if sys.version_info.major == 2:
# in python 3, this falls back on operator.index, which fails on
# on dtype=object
assert_equal(oct(a), oct(0))
assert_equal(hex(a), hex(0))
def test_object_array_self_copy(self):
# An object array being copied into itself DECREF'ed before INCREF'ing
# causing segmentation faults (gh-3787)
a = np.array(object(), dtype=object)
np.copyto(a, a)
if HAS_REFCOUNT:
assert_(sys.getrefcount(a[()]) == 2)
a[()].__class__ # will segfault if object was deleted
def test_zerosize_accumulate(self):
"Ticket #1733"
x = np.array([[42, 0]], dtype=np.uint32)
assert_equal(np.add.accumulate(x[:-1, 0]), [])
def test_objectarray_setfield(self):
# Setfield should not overwrite Object fields with non-Object data
x = np.array([1, 2, 3], dtype=object)
assert_raises(TypeError, x.setfield, 4, np.int32, 0)
def test_setting_rank0_string(self):
"Ticket #1736"
s1 = b"hello1"
s2 = b"hello2"
a = np.zeros((), dtype="S10")
a[()] = s1
assert_equal(a, np.array(s1))
a[()] = np.array(s2)
assert_equal(a, np.array(s2))
a = np.zeros((), dtype='f4')
a[()] = 3
assert_equal(a, np.array(3))
a[()] = np.array(4)
assert_equal(a, np.array(4))
def test_string_astype(self):
"Ticket #1748"
s1 = b'black'
s2 = b'white'
s3 = b'other'
a = np.array([[s1], [s2], [s3]])
assert_equal(a.dtype, np.dtype('S5'))
b = a.astype(np.dtype('S0'))
assert_equal(b.dtype, np.dtype('S5'))
def test_ticket_1756(self):
# Ticket #1756
s = b'0123456789abcdef'
a = np.array([s]*5)
for i in range(1, 17):
a1 = np.array(a, "|S%d" % i)
a2 = np.array([s[:i]]*5)
assert_equal(a1, a2)
def test_fields_strides(self):
"gh-2355"
r = np.frombuffer(b'abcdefghijklmnop'*4*3, dtype='i4,(2,3)u2')
assert_equal(r[0:3:2]['f1'], r['f1'][0:3:2])
assert_equal(r[0:3:2]['f1'][0], r[0:3:2][0]['f1'])
assert_equal(r[0:3:2]['f1'][0][()], r[0:3:2][0]['f1'][()])
assert_equal(r[0:3:2]['f1'][0].strides, r[0:3:2][0]['f1'].strides)
def test_alignment_update(self):
# Check that alignment flag is updated on stride setting
a = np.arange(10)
assert_(a.flags.aligned)
a.strides = 3
assert_(not a.flags.aligned)
def test_ticket_1770(self):
"Should not segfault on python 3k"
import numpy as np
try:
a = np.zeros((1,), dtype=[('f1', 'f')])
a['f1'] = 1
a['f2'] = 1
except ValueError:
pass
except Exception:
raise AssertionError
def test_ticket_1608(self):
"x.flat shouldn't modify data"
x = np.array([[1, 2], [3, 4]]).T
np.array(x.flat)
assert_equal(x, [[1, 3], [2, 4]])
def test_pickle_string_overwrite(self):
import re
data = np.array([1], dtype='b')
blob = pickle.dumps(data, protocol=1)
data = pickle.loads(blob)
# Check that loads does not clobber interned strings
s = re.sub("a(.)", "\x01\\1", "a_")
assert_equal(s[0], "\x01")
data[0] = 0xbb
s = re.sub("a(.)", "\x01\\1", "a_")
assert_equal(s[0], "\x01")
def test_pickle_bytes_overwrite(self):
if sys.version_info[0] >= 3:
data = np.array([1], dtype='b')
data = pickle.loads(pickle.dumps(data))
data[0] = 0xdd
bytestring = "\x01 ".encode('ascii')
assert_equal(bytestring[0:1], '\x01'.encode('ascii'))
def test_pickle_py2_array_latin1_hack(self):
# Check that unpickling hacks in Py3 that support
# encoding='latin1' work correctly.
# Python2 output for pickle.dumps(numpy.array([129], dtype='b'))
data = (b"cnumpy.core.multiarray\n_reconstruct\np0\n(cnumpy\nndarray\np1\n(I0\n"
b"tp2\nS'b'\np3\ntp4\nRp5\n(I1\n(I1\ntp6\ncnumpy\ndtype\np7\n(S'i1'\np8\n"
b"I0\nI1\ntp9\nRp10\n(I3\nS'|'\np11\nNNNI-1\nI-1\nI0\ntp12\nbI00\nS'\\x81'\n"
b"p13\ntp14\nb.")
if sys.version_info[0] >= 3:
# This should work:
result = pickle.loads(data, encoding='latin1')
assert_array_equal(result, np.array([129], dtype='b'))
# Should not segfault:
assert_raises(Exception, pickle.loads, data, encoding='koi8-r')
def test_pickle_py2_scalar_latin1_hack(self):
# Check that scalar unpickling hack in Py3 that supports
# encoding='latin1' work correctly.
# Python2 output for pickle.dumps(...)
datas = [
# (original, python2_pickle, koi8r_validity)
(np.unicode_('\u6bd2'),
(b"cnumpy.core.multiarray\nscalar\np0\n(cnumpy\ndtype\np1\n"
b"(S'U1'\np2\nI0\nI1\ntp3\nRp4\n(I3\nS'<'\np5\nNNNI4\nI4\nI0\n"
b"tp6\nbS'\\xd2k\\x00\\x00'\np7\ntp8\nRp9\n."),
'invalid'),
(np.float64(9e123),
(b"cnumpy.core.multiarray\nscalar\np0\n(cnumpy\ndtype\np1\n(S'f8'\n"
b"p2\nI0\nI1\ntp3\nRp4\n(I3\nS'<'\np5\nNNNI-1\nI-1\nI0\ntp6\n"
b"bS'O\\x81\\xb7Z\\xaa:\\xabY'\np7\ntp8\nRp9\n."),
'invalid'),
(np.bytes_(b'\x9c'), # different 8-bit code point in KOI8-R vs latin1
(b"cnumpy.core.multiarray\nscalar\np0\n(cnumpy\ndtype\np1\n(S'S1'\np2\n"
b"I0\nI1\ntp3\nRp4\n(I3\nS'|'\np5\nNNNI1\nI1\nI0\ntp6\nbS'\\x9c'\np7\n"
b"tp8\nRp9\n."),
'different'),
]
if sys.version_info[0] >= 3:
for original, data, koi8r_validity in datas:
result = pickle.loads(data, encoding='latin1')
assert_equal(result, original)
# Decoding under non-latin1 encoding (e.g.) KOI8-R can
# produce bad results, but should not segfault.
if koi8r_validity == 'different':
# Unicode code points happen to lie within latin1,
# but are different in koi8-r, resulting to silent
# bogus results
result = pickle.loads(data, encoding='koi8-r')
assert_(result != original)
elif koi8r_validity == 'invalid':
# Unicode code points outside latin1, so results
# to an encoding exception
assert_raises(ValueError, pickle.loads, data, encoding='koi8-r')
else:
raise ValueError(koi8r_validity)
def test_structured_type_to_object(self):
a_rec = np.array([(0, 1), (3, 2)], dtype='i4,i8')
a_obj = np.empty((2,), dtype=object)
a_obj[0] = (0, 1)
a_obj[1] = (3, 2)
# astype records -> object
assert_equal(a_rec.astype(object), a_obj)
# '=' records -> object
b = np.empty_like(a_obj)
b[...] = a_rec
assert_equal(b, a_obj)
# '=' object -> records
b = np.empty_like(a_rec)
b[...] = a_obj
assert_equal(b, a_rec)
def test_assign_obj_listoflists(self):
# Ticket # 1870
# The inner list should get assigned to the object elements
a = np.zeros(4, dtype=object)
b = a.copy()
a[0] = [1]
a[1] = [2]
a[2] = [3]
a[3] = [4]
b[...] = [[1], [2], [3], [4]]
assert_equal(a, b)
# The first dimension should get broadcast
a = np.zeros((2, 2), dtype=object)
a[...] = [[1, 2]]
assert_equal(a, [[1, 2], [1, 2]])
def test_memoryleak(self):
# Ticket #1917 - ensure that array data doesn't leak
for i in range(1000):
# 100MB times 1000 would give 100GB of memory usage if it leaks
a = np.empty((100000000,), dtype='i1')
del a
@dec.skipif(not HAS_REFCOUNT, "python has no sys.getrefcount")
def test_ufunc_reduce_memoryleak(self):
a = np.arange(6)
acnt = sys.getrefcount(a)
np.add.reduce(a)
assert_equal(sys.getrefcount(a), acnt)
def test_search_sorted_invalid_arguments(self):
# Ticket #2021, should not segfault.
x = np.arange(0, 4, dtype='datetime64[D]')
assert_raises(TypeError, x.searchsorted, 1)
def test_string_truncation(self):
# Ticket #1990 - Data can be truncated in creation of an array from a
# mixed sequence of numeric values and strings
for val in [True, 1234, 123.4, complex(1, 234)]:
for tostr in [asunicode, asbytes]:
b = np.array([val, tostr('xx')])
assert_equal(tostr(b[0]), tostr(val))
b = np.array([tostr('xx'), val])
assert_equal(tostr(b[1]), tostr(val))
# test also with longer strings
b = np.array([val, tostr('xxxxxxxxxx')])
assert_equal(tostr(b[0]), tostr(val))
b = np.array([tostr('xxxxxxxxxx'), val])
assert_equal(tostr(b[1]), tostr(val))
def test_string_truncation_ucs2(self):
# Ticket #2081. Python compiled with two byte unicode
# can lead to truncation if itemsize is not properly
# adjusted for NumPy's four byte unicode.
if sys.version_info[0] >= 3:
a = np.array(['abcd'])
else:
a = np.array([u'abcd'])
assert_equal(a.dtype.itemsize, 16)
def test_unique_stable(self):
# Ticket #2063 must always choose stable sort for argsort to
# get consistent results
v = np.array(([0]*5 + [1]*6 + [2]*6)*4)
res = np.unique(v, return_index=True)
tgt = (np.array([0, 1, 2]), np.array([ 0, 5, 11]))
assert_equal(res, tgt)
def test_unicode_alloc_dealloc_match(self):
# Ticket #1578, the mismatch only showed up when running
# python-debug for python versions >= 2.7, and then as
# a core dump and error message.
a = np.array(['abc'], dtype=np.unicode)[0]
del a
def test_refcount_error_in_clip(self):
# Ticket #1588
a = np.zeros((2,), dtype='>i2').clip(min=0)
x = a + a
# This used to segfault:
y = str(x)
# Check the final string:
assert_(y == "[0 0]")
def test_searchsorted_wrong_dtype(self):
# Ticket #2189, it used to segfault, so we check that it raises the
# proper exception.
a = np.array([('a', 1)], dtype='S1, int')
assert_raises(TypeError, np.searchsorted, a, 1.2)
# Ticket #2066, similar problem:
dtype = np.format_parser(['i4', 'i4'], [], [])
a = np.recarray((2, ), dtype)
assert_raises(TypeError, np.searchsorted, a, 1)
def test_complex64_alignment(self):
# Issue gh-2668 (trac 2076), segfault on sparc due to misalignment
dtt = np.complex64
arr = np.arange(10, dtype=dtt)
# 2D array
arr2 = np.reshape(arr, (2, 5))
# Fortran write followed by (C or F) read caused bus error
data_str = arr2.tobytes('F')
data_back = np.ndarray(arr2.shape,
arr2.dtype,
buffer=data_str,
order='F')
assert_array_equal(arr2, data_back)
def test_structured_count_nonzero(self):
arr = np.array([0, 1]).astype('i4, (2)i4')[:1]
count = np.count_nonzero(arr)
assert_equal(count, 0)
def test_copymodule_preserves_f_contiguity(self):
a = np.empty((2, 2), order='F')
b = copy.copy(a)
c = copy.deepcopy(a)
assert_(b.flags.fortran)
assert_(b.flags.f_contiguous)
assert_(c.flags.fortran)
assert_(c.flags.f_contiguous)
def test_fortran_order_buffer(self):
import numpy as np
a = np.array([['Hello', 'Foob']], dtype='U5', order='F')
arr = np.ndarray(shape=[1, 2, 5], dtype='U1', buffer=a)
arr2 = np.array([[[u'H', u'e', u'l', u'l', u'o'],
[u'F', u'o', u'o', u'b', u'']]])
assert_array_equal(arr, arr2)
def test_assign_from_sequence_error(self):
# Ticket #4024.
arr = np.array([1, 2, 3])
assert_raises(ValueError, arr.__setitem__, slice(None), [9, 9])
arr.__setitem__(slice(None), [9])
assert_equal(arr, [9, 9, 9])
def test_format_on_flex_array_element(self):
# Ticket #4369.
dt = np.dtype([('date', '<M8[D]'), ('val', '<f8')])
arr = np.array([('2000-01-01', 1)], dt)
formatted = '{0}'.format(arr[0])
assert_equal(formatted, str(arr[0]))
def test_deepcopy_on_0d_array(self):
# Ticket #3311.
arr = np.array(3)
arr_cp = copy.deepcopy(arr)
assert_equal(arr, arr_cp)
assert_equal(arr.shape, arr_cp.shape)
assert_equal(int(arr), int(arr_cp))
assert_(arr is not arr_cp)
assert_(isinstance(arr_cp, type(arr)))
def test_deepcopy_F_order_object_array(self):
# Ticket #6456.
a = {'a': 1}
b = {'b': 2}
arr = np.array([[a, b], [a, b]], order='F')
arr_cp = copy.deepcopy(arr)
assert_equal(arr, arr_cp)
assert_(arr is not arr_cp)
# Ensure that we have actually copied the item.
assert_(arr[0, 1] is not arr_cp[1, 1])
# Ensure we are allowed to have references to the same object.
assert_(arr[0, 1] is arr[1, 1])
# Check the references hold for the copied objects.
assert_(arr_cp[0, 1] is arr_cp[1, 1])
def test_deepcopy_empty_object_array(self):
# Ticket #8536.
# Deepcopy should succeed
a = np.array([], dtype=object)
b = copy.deepcopy(a)
assert_(a.shape == b.shape)
def test_bool_subscript_crash(self):
# gh-4494
c = np.rec.array([(1, 2, 3), (4, 5, 6)])
masked = c[np.array([True, False])]
base = masked.base
del masked, c
base.dtype
def test_richcompare_crash(self):
# gh-4613
import operator as op
# dummy class where __array__ throws exception
class Foo(object):
__array_priority__ = 1002
def __array__(self, *args, **kwargs):
raise Exception()
rhs = Foo()
lhs = np.array(1)
for f in [op.lt, op.le, op.gt, op.ge]:
if sys.version_info[0] >= 3:
assert_raises(TypeError, f, lhs, rhs)
elif not sys.py3kwarning:
# With -3 switch in python 2, DeprecationWarning is raised
# which we are not interested in
f(lhs, rhs)
assert_(not op.eq(lhs, rhs))
assert_(op.ne(lhs, rhs))
def test_richcompare_scalar_and_subclass(self):
# gh-4709
class Foo(np.ndarray):
def __eq__(self, other):
return "OK"
x = np.array([1, 2, 3]).view(Foo)
assert_equal(10 == x, "OK")
assert_equal(np.int32(10) == x, "OK")
assert_equal(np.array([10]) == x, "OK")
def test_pickle_empty_string(self):
# gh-3926
import pickle
test_string = np.string_('')
assert_equal(pickle.loads(pickle.dumps(test_string)), test_string)
def test_frompyfunc_many_args(self):
# gh-5672
def passer(*args):
pass
assert_raises(ValueError, np.frompyfunc, passer, 32, 1)
def test_repeat_broadcasting(self):
# gh-5743
a = np.arange(60).reshape(3, 4, 5)
for axis in chain(range(-a.ndim, a.ndim), [None]):
assert_equal(a.repeat(2, axis=axis), a.repeat([2], axis=axis))
def test_frompyfunc_nout_0(self):
# gh-2014
def f(x):
x[0], x[-1] = x[-1], x[0]
uf = np.frompyfunc(f, 1, 0)
a = np.array([[1, 2, 3], [4, 5], [6, 7, 8, 9]])
assert_equal(uf(a), ())
assert_array_equal(a, [[3, 2, 1], [5, 4], [9, 7, 8, 6]])
@dec.skipif(not HAS_REFCOUNT, "python has no sys.getrefcount")
def test_leak_in_structured_dtype_comparison(self):
# gh-6250
recordtype = np.dtype([('a', np.float64),
('b', np.int32),
('d', (str, 5))])
# Simple case
a = np.zeros(2, dtype=recordtype)
for i in range(100):
a == a
assert_(sys.getrefcount(a) < 10)
# The case in the bug report.
before = sys.getrefcount(a)
u, v = a[0], a[1]
u == v
del u, v
gc.collect()
after = sys.getrefcount(a)
assert_equal(before, after)
def test_empty_percentile(self):
# gh-6530 / gh-6553
assert_array_equal(np.percentile(np.arange(10), []), np.array([]))
def test_void_compare_segfault(self):
# gh-6922. The following should not segfault
a = np.ones(3, dtype=[('object', 'O'), ('int', '<i2')])
a.sort()
def test_reshape_size_overflow(self):
# gh-7455
a = np.ones(20)[::2]
if np.dtype(np.intp).itemsize == 8:
# 64 bit. The following are the prime factors of 2**63 + 5,
# plus a leading 2, so when multiplied together as int64,
# the result overflows to a total size of 10.
new_shape = (2, 13, 419, 691, 823, 2977518503)
else:
# 32 bit. The following are the prime factors of 2**31 + 5,
# plus a leading 2, so when multiplied together as int32,
# the result overflows to a total size of 10.
new_shape = (2, 7, 7, 43826197)
assert_raises(ValueError, a.reshape, new_shape)
def test_invalid_structured_dtypes(self):
# gh-2865
# mapping python objects to other dtypes
assert_raises(ValueError, np.dtype, ('O', [('name', 'i8')]))
assert_raises(ValueError, np.dtype, ('i8', [('name', 'O')]))
assert_raises(ValueError, np.dtype,
('i8', [('name', [('name', 'O')])]))
assert_raises(ValueError, np.dtype, ([('a', 'i4'), ('b', 'i4')], 'O'))
assert_raises(ValueError, np.dtype, ('i8', 'O'))
# wrong number/type of tuple elements in dict
assert_raises(ValueError, np.dtype,
('i', {'name': ('i', 0, 'title', 'oops')}))
assert_raises(ValueError, np.dtype,
('i', {'name': ('i', 'wrongtype', 'title')}))
# disallowed as of 1.13
assert_raises(ValueError, np.dtype,
([('a', 'O'), ('b', 'O')], [('c', 'O'), ('d', 'O')]))
# allowed as a special case due to existing use, see gh-2798
a = np.ones(1, dtype=('O', [('name', 'O')]))
assert_equal(a[0], 1)
def test_correct_hash_dict(self):
# gh-8887 - __hash__ would be None despite tp_hash being set
all_types = set(np.typeDict.values()) - {np.void}
for t in all_types:
val = t()
try:
hash(val)
except TypeError as e:
assert_equal(t.__hash__, None)
else:
assert_(t.__hash__ != None)
def test_scalar_copy(self):
scalar_types = set(np.sctypeDict.values())
values = {
np.void: b"a",
np.bytes_: b"a",
np.unicode_: "a",
np.datetime64: "2017-08-25",
}
for sctype in scalar_types:
item = sctype(values.get(sctype, 1))
item2 = copy.copy(item)
assert_equal(item, item2)
def test_void_item_memview(self):
va = np.zeros(10, 'V4')
# for now, there is just a futurewarning
assert_warns(FutureWarning, va[:1].item)
# in the future, test we got a bytes copy:
#x = va[:1].item()
#va[0] = b'\xff\xff\xff\xff'
#del va
#assert_equal(x, b'\x00\x00\x00\x00')
def test_structarray_title(self):
# The following used to segfault on pypy, due to NPY_TITLE_KEY
# not working properly and resulting to double-decref of the
# structured array field items:
# See: https://bitbucket.org/pypy/pypy/issues/2789
for j in range(5):
structure = np.array([1], dtype=[(('x', 'X'), np.object_)])
structure[0]['x'] = np.array([2])
gc.collect()
if __name__ == "__main__":
run_module_suite()
| 80,168 | 34.084902 | 93 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/tests/test_errstate.py
|
from __future__ import division, absolute_import, print_function
import platform
import numpy as np
from numpy.testing import assert_, run_module_suite, dec
class TestErrstate(object):
@dec.skipif(platform.machine() == "armv5tel", "See gh-413.")
def test_invalid(self):
with np.errstate(all='raise', under='ignore'):
a = -np.arange(3)
# This should work
with np.errstate(invalid='ignore'):
np.sqrt(a)
# While this should fail!
try:
np.sqrt(a)
except FloatingPointError:
pass
else:
self.fail("Did not raise an invalid error")
def test_divide(self):
with np.errstate(all='raise', under='ignore'):
a = -np.arange(3)
# This should work
with np.errstate(divide='ignore'):
a // 0
# While this should fail!
try:
a // 0
except FloatingPointError:
pass
else:
self.fail("Did not raise divide by zero error")
def test_errcall(self):
def foo(*args):
print(args)
olderrcall = np.geterrcall()
with np.errstate(call=foo):
assert_(np.geterrcall() is foo, 'call is not foo')
with np.errstate(call=None):
assert_(np.geterrcall() is None, 'call is not None')
assert_(np.geterrcall() is olderrcall, 'call is not olderrcall')
if __name__ == "__main__":
run_module_suite()
| 1,576 | 28.754717 | 72 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/tests/test_api.py
|
from __future__ import division, absolute_import, print_function
import sys
import numpy as np
from numpy.testing import (
run_module_suite, assert_, assert_equal, assert_array_equal,
assert_raises, HAS_REFCOUNT
)
# Switch between new behaviour when NPY_RELAXED_STRIDES_CHECKING is set.
NPY_RELAXED_STRIDES_CHECKING = np.ones((10, 1), order='C').flags.f_contiguous
def test_array_array():
tobj = type(object)
ones11 = np.ones((1, 1), np.float64)
tndarray = type(ones11)
# Test is_ndarray
assert_equal(np.array(ones11, dtype=np.float64), ones11)
if HAS_REFCOUNT:
old_refcount = sys.getrefcount(tndarray)
np.array(ones11)
assert_equal(old_refcount, sys.getrefcount(tndarray))
# test None
assert_equal(np.array(None, dtype=np.float64),
np.array(np.nan, dtype=np.float64))
if HAS_REFCOUNT:
old_refcount = sys.getrefcount(tobj)
np.array(None, dtype=np.float64)
assert_equal(old_refcount, sys.getrefcount(tobj))
# test scalar
assert_equal(np.array(1.0, dtype=np.float64),
np.ones((), dtype=np.float64))
if HAS_REFCOUNT:
old_refcount = sys.getrefcount(np.float64)
np.array(np.array(1.0, dtype=np.float64), dtype=np.float64)
assert_equal(old_refcount, sys.getrefcount(np.float64))
# test string
S2 = np.dtype((str, 2))
S3 = np.dtype((str, 3))
S5 = np.dtype((str, 5))
assert_equal(np.array("1.0", dtype=np.float64),
np.ones((), dtype=np.float64))
assert_equal(np.array("1.0").dtype, S3)
assert_equal(np.array("1.0", dtype=str).dtype, S3)
assert_equal(np.array("1.0", dtype=S2), np.array("1."))
assert_equal(np.array("1", dtype=S5), np.ones((), dtype=S5))
# test unicode
_unicode = globals().get("unicode")
if _unicode:
U2 = np.dtype((_unicode, 2))
U3 = np.dtype((_unicode, 3))
U5 = np.dtype((_unicode, 5))
assert_equal(np.array(_unicode("1.0"), dtype=np.float64),
np.ones((), dtype=np.float64))
assert_equal(np.array(_unicode("1.0")).dtype, U3)
assert_equal(np.array(_unicode("1.0"), dtype=_unicode).dtype, U3)
assert_equal(np.array(_unicode("1.0"), dtype=U2),
np.array(_unicode("1.")))
assert_equal(np.array(_unicode("1"), dtype=U5),
np.ones((), dtype=U5))
builtins = getattr(__builtins__, '__dict__', __builtins__)
assert_(hasattr(builtins, 'get'))
# test buffer
_buffer = builtins.get("buffer")
if _buffer and sys.version_info[:3] >= (2, 7, 5):
# This test fails for earlier versions of Python.
# Evidently a bug got fixed in 2.7.5.
dat = np.array(_buffer('1.0'), dtype=np.float64)
assert_equal(dat, [49.0, 46.0, 48.0])
assert_(dat.dtype.type is np.float64)
dat = np.array(_buffer(b'1.0'))
assert_equal(dat, [49, 46, 48])
assert_(dat.dtype.type is np.uint8)
# test memoryview, new version of buffer
_memoryview = builtins.get("memoryview")
if _memoryview:
dat = np.array(_memoryview(b'1.0'), dtype=np.float64)
assert_equal(dat, [49.0, 46.0, 48.0])
assert_(dat.dtype.type is np.float64)
dat = np.array(_memoryview(b'1.0'))
assert_equal(dat, [49, 46, 48])
assert_(dat.dtype.type is np.uint8)
# test array interface
a = np.array(100.0, dtype=np.float64)
o = type("o", (object,),
dict(__array_interface__=a.__array_interface__))
assert_equal(np.array(o, dtype=np.float64), a)
# test array_struct interface
a = np.array([(1, 4.0, 'Hello'), (2, 6.0, 'World')],
dtype=[('f0', int), ('f1', float), ('f2', str)])
o = type("o", (object,),
dict(__array_struct__=a.__array_struct__))
## wasn't what I expected... is np.array(o) supposed to equal a ?
## instead we get a array([...], dtype=">V18")
assert_equal(bytes(np.array(o).data), bytes(a.data))
# test array
o = type("o", (object,),
dict(__array__=lambda *x: np.array(100.0, dtype=np.float64)))()
assert_equal(np.array(o, dtype=np.float64), np.array(100.0, np.float64))
# test recursion
nested = 1.5
for i in range(np.MAXDIMS):
nested = [nested]
# no error
np.array(nested)
# Exceeds recursion limit
assert_raises(ValueError, np.array, [nested], dtype=np.float64)
# Try with lists...
assert_equal(np.array([None] * 10, dtype=np.float64),
np.full((10,), np.nan, dtype=np.float64))
assert_equal(np.array([[None]] * 10, dtype=np.float64),
np.full((10, 1), np.nan, dtype=np.float64))
assert_equal(np.array([[None] * 10], dtype=np.float64),
np.full((1, 10), np.nan, dtype=np.float64))
assert_equal(np.array([[None] * 10] * 10, dtype=np.float64),
np.full((10, 10), np.nan, dtype=np.float64))
assert_equal(np.array([1.0] * 10, dtype=np.float64),
np.ones((10,), dtype=np.float64))
assert_equal(np.array([[1.0]] * 10, dtype=np.float64),
np.ones((10, 1), dtype=np.float64))
assert_equal(np.array([[1.0] * 10], dtype=np.float64),
np.ones((1, 10), dtype=np.float64))
assert_equal(np.array([[1.0] * 10] * 10, dtype=np.float64),
np.ones((10, 10), dtype=np.float64))
# Try with tuples
assert_equal(np.array((None,) * 10, dtype=np.float64),
np.full((10,), np.nan, dtype=np.float64))
assert_equal(np.array([(None,)] * 10, dtype=np.float64),
np.full((10, 1), np.nan, dtype=np.float64))
assert_equal(np.array([(None,) * 10], dtype=np.float64),
np.full((1, 10), np.nan, dtype=np.float64))
assert_equal(np.array([(None,) * 10] * 10, dtype=np.float64),
np.full((10, 10), np.nan, dtype=np.float64))
assert_equal(np.array((1.0,) * 10, dtype=np.float64),
np.ones((10,), dtype=np.float64))
assert_equal(np.array([(1.0,)] * 10, dtype=np.float64),
np.ones((10, 1), dtype=np.float64))
assert_equal(np.array([(1.0,) * 10], dtype=np.float64),
np.ones((1, 10), dtype=np.float64))
assert_equal(np.array([(1.0,) * 10] * 10, dtype=np.float64),
np.ones((10, 10), dtype=np.float64))
def test_fastCopyAndTranspose():
# 0D array
a = np.array(2)
b = np.fastCopyAndTranspose(a)
assert_equal(b, a.T)
assert_(b.flags.owndata)
# 1D array
a = np.array([3, 2, 7, 0])
b = np.fastCopyAndTranspose(a)
assert_equal(b, a.T)
assert_(b.flags.owndata)
# 2D array
a = np.arange(6).reshape(2, 3)
b = np.fastCopyAndTranspose(a)
assert_equal(b, a.T)
assert_(b.flags.owndata)
def test_array_astype():
a = np.arange(6, dtype='f4').reshape(2, 3)
# Default behavior: allows unsafe casts, keeps memory layout,
# always copies.
b = a.astype('i4')
assert_equal(a, b)
assert_equal(b.dtype, np.dtype('i4'))
assert_equal(a.strides, b.strides)
b = a.T.astype('i4')
assert_equal(a.T, b)
assert_equal(b.dtype, np.dtype('i4'))
assert_equal(a.T.strides, b.strides)
b = a.astype('f4')
assert_equal(a, b)
assert_(not (a is b))
# copy=False parameter can sometimes skip a copy
b = a.astype('f4', copy=False)
assert_(a is b)
# order parameter allows overriding of the memory layout,
# forcing a copy if the layout is wrong
b = a.astype('f4', order='F', copy=False)
assert_equal(a, b)
assert_(not (a is b))
assert_(b.flags.f_contiguous)
b = a.astype('f4', order='C', copy=False)
assert_equal(a, b)
assert_(a is b)
assert_(b.flags.c_contiguous)
# casting parameter allows catching bad casts
b = a.astype('c8', casting='safe')
assert_equal(a, b)
assert_equal(b.dtype, np.dtype('c8'))
assert_raises(TypeError, a.astype, 'i4', casting='safe')
# subok=False passes through a non-subclassed array
b = a.astype('f4', subok=0, copy=False)
assert_(a is b)
a = np.matrix([[0, 1, 2], [3, 4, 5]], dtype='f4')
# subok=True passes through a matrix
b = a.astype('f4', subok=True, copy=False)
assert_(a is b)
# subok=True is default, and creates a subtype on a cast
b = a.astype('i4', copy=False)
assert_equal(a, b)
assert_equal(type(b), np.matrix)
# subok=False never returns a matrix
b = a.astype('f4', subok=False, copy=False)
assert_equal(a, b)
assert_(not (a is b))
assert_(type(b) is not np.matrix)
# Make sure converting from string object to fixed length string
# does not truncate.
a = np.array([b'a'*100], dtype='O')
b = a.astype('S')
assert_equal(a, b)
assert_equal(b.dtype, np.dtype('S100'))
a = np.array([u'a'*100], dtype='O')
b = a.astype('U')
assert_equal(a, b)
assert_equal(b.dtype, np.dtype('U100'))
# Same test as above but for strings shorter than 64 characters
a = np.array([b'a'*10], dtype='O')
b = a.astype('S')
assert_equal(a, b)
assert_equal(b.dtype, np.dtype('S10'))
a = np.array([u'a'*10], dtype='O')
b = a.astype('U')
assert_equal(a, b)
assert_equal(b.dtype, np.dtype('U10'))
a = np.array(123456789012345678901234567890, dtype='O').astype('S')
assert_array_equal(a, np.array(b'1234567890' * 3, dtype='S30'))
a = np.array(123456789012345678901234567890, dtype='O').astype('U')
assert_array_equal(a, np.array(u'1234567890' * 3, dtype='U30'))
a = np.array([123456789012345678901234567890], dtype='O').astype('S')
assert_array_equal(a, np.array(b'1234567890' * 3, dtype='S30'))
a = np.array([123456789012345678901234567890], dtype='O').astype('U')
assert_array_equal(a, np.array(u'1234567890' * 3, dtype='U30'))
a = np.array(123456789012345678901234567890, dtype='S')
assert_array_equal(a, np.array(b'1234567890' * 3, dtype='S30'))
a = np.array(123456789012345678901234567890, dtype='U')
assert_array_equal(a, np.array(u'1234567890' * 3, dtype='U30'))
a = np.array(u'a\u0140', dtype='U')
b = np.ndarray(buffer=a, dtype='uint32', shape=2)
assert_(b.size == 2)
a = np.array([1000], dtype='i4')
assert_raises(TypeError, a.astype, 'S1', casting='safe')
a = np.array(1000, dtype='i4')
assert_raises(TypeError, a.astype, 'U1', casting='safe')
def test_copyto_fromscalar():
a = np.arange(6, dtype='f4').reshape(2, 3)
# Simple copy
np.copyto(a, 1.5)
assert_equal(a, 1.5)
np.copyto(a.T, 2.5)
assert_equal(a, 2.5)
# Where-masked copy
mask = np.array([[0, 1, 0], [0, 0, 1]], dtype='?')
np.copyto(a, 3.5, where=mask)
assert_equal(a, [[2.5, 3.5, 2.5], [2.5, 2.5, 3.5]])
mask = np.array([[0, 1], [1, 1], [1, 0]], dtype='?')
np.copyto(a.T, 4.5, where=mask)
assert_equal(a, [[2.5, 4.5, 4.5], [4.5, 4.5, 3.5]])
def test_copyto():
a = np.arange(6, dtype='i4').reshape(2, 3)
# Simple copy
np.copyto(a, [[3, 1, 5], [6, 2, 1]])
assert_equal(a, [[3, 1, 5], [6, 2, 1]])
# Overlapping copy should work
np.copyto(a[:, :2], a[::-1, 1::-1])
assert_equal(a, [[2, 6, 5], [1, 3, 1]])
# Defaults to 'same_kind' casting
assert_raises(TypeError, np.copyto, a, 1.5)
# Force a copy with 'unsafe' casting, truncating 1.5 to 1
np.copyto(a, 1.5, casting='unsafe')
assert_equal(a, 1)
# Copying with a mask
np.copyto(a, 3, where=[True, False, True])
assert_equal(a, [[3, 1, 3], [3, 1, 3]])
# Casting rule still applies with a mask
assert_raises(TypeError, np.copyto, a, 3.5, where=[True, False, True])
# Lists of integer 0's and 1's is ok too
np.copyto(a, 4.0, casting='unsafe', where=[[0, 1, 1], [1, 0, 0]])
assert_equal(a, [[3, 4, 4], [4, 1, 3]])
# Overlapping copy with mask should work
np.copyto(a[:, :2], a[::-1, 1::-1], where=[[0, 1], [1, 1]])
assert_equal(a, [[3, 4, 4], [4, 3, 3]])
# 'dst' must be an array
assert_raises(TypeError, np.copyto, [1, 2, 3], [2, 3, 4])
def test_copyto_permut():
# test explicit overflow case
pad = 500
l = [True] * pad + [True, True, True, True]
r = np.zeros(len(l)-pad)
d = np.ones(len(l)-pad)
mask = np.array(l)[pad:]
np.copyto(r, d, where=mask[::-1])
# test all permutation of possible masks, 9 should be sufficient for
# current 4 byte unrolled code
power = 9
d = np.ones(power)
for i in range(2**power):
r = np.zeros(power)
l = [(i & x) != 0 for x in range(power)]
mask = np.array(l)
np.copyto(r, d, where=mask)
assert_array_equal(r == 1, l)
assert_equal(r.sum(), sum(l))
r = np.zeros(power)
np.copyto(r, d, where=mask[::-1])
assert_array_equal(r == 1, l[::-1])
assert_equal(r.sum(), sum(l))
r = np.zeros(power)
np.copyto(r[::2], d[::2], where=mask[::2])
assert_array_equal(r[::2] == 1, l[::2])
assert_equal(r[::2].sum(), sum(l[::2]))
r = np.zeros(power)
np.copyto(r[::2], d[::2], where=mask[::-2])
assert_array_equal(r[::2] == 1, l[::-2])
assert_equal(r[::2].sum(), sum(l[::-2]))
for c in [0xFF, 0x7F, 0x02, 0x10]:
r = np.zeros(power)
mask = np.array(l)
imask = np.array(l).view(np.uint8)
imask[mask != 0] = c
np.copyto(r, d, where=mask)
assert_array_equal(r == 1, l)
assert_equal(r.sum(), sum(l))
r = np.zeros(power)
np.copyto(r, d, where=True)
assert_equal(r.sum(), r.size)
r = np.ones(power)
d = np.zeros(power)
np.copyto(r, d, where=False)
assert_equal(r.sum(), r.size)
def test_copy_order():
a = np.arange(24).reshape(2, 1, 3, 4)
b = a.copy(order='F')
c = np.arange(24).reshape(2, 1, 4, 3).swapaxes(2, 3)
def check_copy_result(x, y, ccontig, fcontig, strides=False):
assert_(not (x is y))
assert_equal(x, y)
assert_equal(res.flags.c_contiguous, ccontig)
assert_equal(res.flags.f_contiguous, fcontig)
# This check is impossible only because
# NPY_RELAXED_STRIDES_CHECKING changes the strides actively
if not NPY_RELAXED_STRIDES_CHECKING:
if strides:
assert_equal(x.strides, y.strides)
else:
assert_(x.strides != y.strides)
# Validate the initial state of a, b, and c
assert_(a.flags.c_contiguous)
assert_(not a.flags.f_contiguous)
assert_(not b.flags.c_contiguous)
assert_(b.flags.f_contiguous)
assert_(not c.flags.c_contiguous)
assert_(not c.flags.f_contiguous)
# Copy with order='C'
res = a.copy(order='C')
check_copy_result(res, a, ccontig=True, fcontig=False, strides=True)
res = b.copy(order='C')
check_copy_result(res, b, ccontig=True, fcontig=False, strides=False)
res = c.copy(order='C')
check_copy_result(res, c, ccontig=True, fcontig=False, strides=False)
res = np.copy(a, order='C')
check_copy_result(res, a, ccontig=True, fcontig=False, strides=True)
res = np.copy(b, order='C')
check_copy_result(res, b, ccontig=True, fcontig=False, strides=False)
res = np.copy(c, order='C')
check_copy_result(res, c, ccontig=True, fcontig=False, strides=False)
# Copy with order='F'
res = a.copy(order='F')
check_copy_result(res, a, ccontig=False, fcontig=True, strides=False)
res = b.copy(order='F')
check_copy_result(res, b, ccontig=False, fcontig=True, strides=True)
res = c.copy(order='F')
check_copy_result(res, c, ccontig=False, fcontig=True, strides=False)
res = np.copy(a, order='F')
check_copy_result(res, a, ccontig=False, fcontig=True, strides=False)
res = np.copy(b, order='F')
check_copy_result(res, b, ccontig=False, fcontig=True, strides=True)
res = np.copy(c, order='F')
check_copy_result(res, c, ccontig=False, fcontig=True, strides=False)
# Copy with order='K'
res = a.copy(order='K')
check_copy_result(res, a, ccontig=True, fcontig=False, strides=True)
res = b.copy(order='K')
check_copy_result(res, b, ccontig=False, fcontig=True, strides=True)
res = c.copy(order='K')
check_copy_result(res, c, ccontig=False, fcontig=False, strides=True)
res = np.copy(a, order='K')
check_copy_result(res, a, ccontig=True, fcontig=False, strides=True)
res = np.copy(b, order='K')
check_copy_result(res, b, ccontig=False, fcontig=True, strides=True)
res = np.copy(c, order='K')
check_copy_result(res, c, ccontig=False, fcontig=False, strides=True)
def test_contiguous_flags():
a = np.ones((4, 4, 1))[::2,:,:]
if NPY_RELAXED_STRIDES_CHECKING:
a.strides = a.strides[:2] + (-123,)
b = np.ones((2, 2, 1, 2, 2)).swapaxes(3, 4)
def check_contig(a, ccontig, fcontig):
assert_(a.flags.c_contiguous == ccontig)
assert_(a.flags.f_contiguous == fcontig)
# Check if new arrays are correct:
check_contig(a, False, False)
check_contig(b, False, False)
if NPY_RELAXED_STRIDES_CHECKING:
check_contig(np.empty((2, 2, 0, 2, 2)), True, True)
check_contig(np.array([[[1], [2]]], order='F'), True, True)
else:
check_contig(np.empty((2, 2, 0, 2, 2)), True, False)
check_contig(np.array([[[1], [2]]], order='F'), False, True)
check_contig(np.empty((2, 2)), True, False)
check_contig(np.empty((2, 2), order='F'), False, True)
# Check that np.array creates correct contiguous flags:
check_contig(np.array(a, copy=False), False, False)
check_contig(np.array(a, copy=False, order='C'), True, False)
check_contig(np.array(a, ndmin=4, copy=False, order='F'), False, True)
if NPY_RELAXED_STRIDES_CHECKING:
# Check slicing update of flags and :
check_contig(a[0], True, True)
check_contig(a[None, ::4, ..., None], True, True)
check_contig(b[0, 0, ...], False, True)
check_contig(b[:,:, 0:0,:,:], True, True)
else:
# Check slicing update of flags:
check_contig(a[0], True, False)
# Would be nice if this was C-Contiguous:
check_contig(a[None, 0, ..., None], False, False)
check_contig(b[0, 0, 0, ...], False, True)
# Test ravel and squeeze.
check_contig(a.ravel(), True, True)
check_contig(np.ones((1, 3, 1)).squeeze(), True, True)
def test_broadcast_arrays():
# Test user defined dtypes
a = np.array([(1, 2, 3)], dtype='u4,u4,u4')
b = np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)], dtype='u4,u4,u4')
result = np.broadcast_arrays(a, b)
assert_equal(result[0], np.array([(1, 2, 3), (1, 2, 3), (1, 2, 3)], dtype='u4,u4,u4'))
assert_equal(result[1], np.array([(1, 2, 3), (4, 5, 6), (7, 8, 9)], dtype='u4,u4,u4'))
if __name__ == "__main__":
run_module_suite()
| 18,906 | 35.5 | 90 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/tests/test_deprecations.py
|
"""
Tests related to deprecation warnings. Also a convenient place
to document how deprecations should eventually be turned into errors.
"""
from __future__ import division, absolute_import, print_function
import datetime
import sys
import operator
import warnings
import numpy as np
from numpy.testing import (
run_module_suite, assert_raises, assert_warns, assert_no_warnings,
assert_array_equal, assert_, dec)
try:
import pytz
_has_pytz = True
except ImportError:
_has_pytz = False
class _DeprecationTestCase(object):
# Just as warning: warnings uses re.match, so the start of this message
# must match.
message = ''
warning_cls = DeprecationWarning
def setup(self):
self.warn_ctx = warnings.catch_warnings(record=True)
self.log = self.warn_ctx.__enter__()
# Do *not* ignore other DeprecationWarnings. Ignoring warnings
# can give very confusing results because of
# http://bugs.python.org/issue4180 and it is probably simplest to
# try to keep the tests cleanly giving only the right warning type.
# (While checking them set to "error" those are ignored anyway)
# We still have them show up, because otherwise they would be raised
warnings.filterwarnings("always", category=self.warning_cls)
warnings.filterwarnings("always", message=self.message,
category=self.warning_cls)
def teardown(self):
self.warn_ctx.__exit__()
def assert_deprecated(self, function, num=1, ignore_others=False,
function_fails=False,
exceptions=np._NoValue,
args=(), kwargs={}):
"""Test if DeprecationWarnings are given and raised.
This first checks if the function when called gives `num`
DeprecationWarnings, after that it tries to raise these
DeprecationWarnings and compares them with `exceptions`.
The exceptions can be different for cases where this code path
is simply not anticipated and the exception is replaced.
Parameters
----------
function : callable
The function to test
num : int
Number of DeprecationWarnings to expect. This should normally be 1.
ignore_others : bool
Whether warnings of the wrong type should be ignored (note that
the message is not checked)
function_fails : bool
If the function would normally fail, setting this will check for
warnings inside a try/except block.
exceptions : Exception or tuple of Exceptions
Exception to expect when turning the warnings into an error.
The default checks for DeprecationWarnings. If exceptions is
empty the function is expected to run successfully.
args : tuple
Arguments for `function`
kwargs : dict
Keyword arguments for `function`
"""
# reset the log
self.log[:] = []
if exceptions is np._NoValue:
exceptions = (self.warning_cls,)
try:
function(*args, **kwargs)
except (Exception if function_fails else tuple()):
pass
# just in case, clear the registry
num_found = 0
for warning in self.log:
if warning.category is self.warning_cls:
num_found += 1
elif not ignore_others:
raise AssertionError(
"expected %s but got: %s" %
(self.warning_cls.__name__, warning.category))
if num is not None and num_found != num:
msg = "%i warnings found but %i expected." % (len(self.log), num)
lst = [str(w.category) for w in self.log]
raise AssertionError("\n".join([msg] + lst))
with warnings.catch_warnings():
warnings.filterwarnings("error", message=self.message,
category=self.warning_cls)
try:
function(*args, **kwargs)
if exceptions != tuple():
raise AssertionError(
"No error raised during function call")
except exceptions:
if exceptions == tuple():
raise AssertionError(
"Error raised during function call")
def assert_not_deprecated(self, function, args=(), kwargs={}):
"""Test that warnings are not raised.
This is just a shorthand for:
self.assert_deprecated(function, num=0, ignore_others=True,
exceptions=tuple(), args=args, kwargs=kwargs)
"""
self.assert_deprecated(function, num=0, ignore_others=True,
exceptions=tuple(), args=args, kwargs=kwargs)
class _VisibleDeprecationTestCase(_DeprecationTestCase):
warning_cls = np.VisibleDeprecationWarning
class TestRankDeprecation(_DeprecationTestCase):
"""Test that np.rank is deprecated. The function should simply be
removed. The VisibleDeprecationWarning may become unnecessary.
"""
def test(self):
a = np.arange(10)
assert_warns(np.VisibleDeprecationWarning, np.rank, a)
class TestComparisonDeprecations(_DeprecationTestCase):
"""This tests the deprecation, for non-element-wise comparison logic.
This used to mean that when an error occurred during element-wise comparison
(i.e. broadcasting) NotImplemented was returned, but also in the comparison
itself, False was given instead of the error.
Also test FutureWarning for the None comparison.
"""
message = "elementwise.* comparison failed; .*"
def test_normal_types(self):
for op in (operator.eq, operator.ne):
# Broadcasting errors:
self.assert_deprecated(op, args=(np.zeros(3), []))
a = np.zeros(3, dtype='i,i')
# (warning is issued a couple of times here)
self.assert_deprecated(op, args=(a, a[:-1]), num=None)
# Element comparison error (numpy array can't be compared).
a = np.array([1, np.array([1,2,3])], dtype=object)
b = np.array([1, np.array([1,2,3])], dtype=object)
self.assert_deprecated(op, args=(a, b), num=None)
def test_string(self):
# For two string arrays, strings always raised the broadcasting error:
a = np.array(['a', 'b'])
b = np.array(['a', 'b', 'c'])
assert_raises(ValueError, lambda x, y: x == y, a, b)
# The empty list is not cast to string, as this is only to document
# that fact (it likely should be changed). This means that the
# following works (and returns False) due to dtype mismatch:
a == []
def test_void_dtype_equality_failures(self):
class NotArray(object):
def __array__(self):
raise TypeError
# Needed so Python 3 does not raise DeprecationWarning twice.
def __ne__(self, other):
return NotImplemented
self.assert_deprecated(lambda: np.arange(2) == NotArray())
self.assert_deprecated(lambda: np.arange(2) != NotArray())
struct1 = np.zeros(2, dtype="i4,i4")
struct2 = np.zeros(2, dtype="i4,i4,i4")
assert_warns(FutureWarning, lambda: struct1 == 1)
assert_warns(FutureWarning, lambda: struct1 == struct2)
assert_warns(FutureWarning, lambda: struct1 != 1)
assert_warns(FutureWarning, lambda: struct1 != struct2)
def test_array_richcompare_legacy_weirdness(self):
# It doesn't really work to use assert_deprecated here, b/c part of
# the point of assert_deprecated is to check that when warnings are
# set to "error" mode then the error is propagated -- which is good!
# But here we are testing a bunch of code that is deprecated *because*
# it has the habit of swallowing up errors and converting them into
# different warnings. So assert_warns will have to be sufficient.
assert_warns(FutureWarning, lambda: np.arange(2) == "a")
assert_warns(FutureWarning, lambda: np.arange(2) != "a")
# No warning for scalar comparisons
with warnings.catch_warnings():
warnings.filterwarnings("error")
assert_(not (np.array(0) == "a"))
assert_(np.array(0) != "a")
assert_(not (np.int16(0) == "a"))
assert_(np.int16(0) != "a")
for arg1 in [np.asarray(0), np.int16(0)]:
struct = np.zeros(2, dtype="i4,i4")
for arg2 in [struct, "a"]:
for f in [operator.lt, operator.le, operator.gt, operator.ge]:
if sys.version_info[0] >= 3:
# py3
with warnings.catch_warnings() as l:
warnings.filterwarnings("always")
assert_raises(TypeError, f, arg1, arg2)
assert_(not l)
else:
# py2
assert_warns(DeprecationWarning, f, arg1, arg2)
class TestDatetime64Timezone(_DeprecationTestCase):
"""Parsing of datetime64 with timezones deprecated in 1.11.0, because
datetime64 is now timezone naive rather than UTC only.
It will be quite a while before we can remove this, because, at the very
least, a lot of existing code uses the 'Z' modifier to avoid conversion
from local time to UTC, even if otherwise it handles time in a timezone
naive fashion.
"""
def test_string(self):
self.assert_deprecated(np.datetime64, args=('2000-01-01T00+01',))
self.assert_deprecated(np.datetime64, args=('2000-01-01T00Z',))
@dec.skipif(not _has_pytz, "The pytz module is not available.")
def test_datetime(self):
tz = pytz.timezone('US/Eastern')
dt = datetime.datetime(2000, 1, 1, 0, 0, tzinfo=tz)
self.assert_deprecated(np.datetime64, args=(dt,))
class TestNonCContiguousViewDeprecation(_DeprecationTestCase):
"""View of non-C-contiguous arrays deprecated in 1.11.0.
The deprecation will not be raised for arrays that are both C and F
contiguous, as C contiguous is dominant. There are more such arrays
with relaxed stride checking than without so the deprecation is not
as visible with relaxed stride checking in force.
"""
def test_fortran_contiguous(self):
self.assert_deprecated(np.ones((2,2)).T.view, args=(complex,))
self.assert_deprecated(np.ones((2,2)).T.view, args=(np.int8,))
class TestInvalidOrderParameterInputForFlattenArrayDeprecation(_DeprecationTestCase):
"""Invalid arguments to the ORDER parameter in array.flatten() should not be
allowed and should raise an error. However, in the interests of not breaking
code that may inadvertently pass invalid arguments to this parameter, a
DeprecationWarning will be issued instead for the time being to give developers
time to refactor relevant code.
"""
def test_flatten_array_non_string_arg(self):
x = np.zeros((3, 5))
self.message = ("Non-string object detected for "
"the array ordering. Please pass "
"in 'C', 'F', 'A', or 'K' instead")
self.assert_deprecated(x.flatten, args=(np.pi,))
def test_flatten_array_invalid_string_arg(self):
# Tests that a DeprecationWarning is raised
# when a string of length greater than one
# starting with "C", "F", "A", or "K" (case-
# and unicode-insensitive) is passed in for
# the ORDER parameter. Otherwise, a TypeError
# will be raised!
x = np.zeros((3, 5))
self.message = ("Non length-one string passed "
"in for the array ordering. Please "
"pass in 'C', 'F', 'A', or 'K' instead")
self.assert_deprecated(x.flatten, args=("FACK",))
class TestArrayDataAttributeAssignmentDeprecation(_DeprecationTestCase):
"""Assigning the 'data' attribute of an ndarray is unsafe as pointed
out in gh-7093. Eventually, such assignment should NOT be allowed, but
in the interests of maintaining backwards compatibility, only a Deprecation-
Warning will be raised instead for the time being to give developers time to
refactor relevant code.
"""
def test_data_attr_assignment(self):
a = np.arange(10)
b = np.linspace(0, 1, 10)
self.message = ("Assigning the 'data' attribute is an "
"inherently unsafe operation and will "
"be removed in the future.")
self.assert_deprecated(a.__setattr__, args=('data', b.data))
class TestLinspaceInvalidNumParameter(_DeprecationTestCase):
"""Argument to the num parameter in linspace that cannot be
safely interpreted as an integer is deprecated in 1.12.0.
Argument to the num parameter in linspace that cannot be
safely interpreted as an integer should not be allowed.
In the interest of not breaking code that passes
an argument that could still be interpreted as an integer, a
DeprecationWarning will be issued for the time being to give
developers time to refactor relevant code.
"""
def test_float_arg(self):
# 2016-02-25, PR#7328
self.assert_deprecated(np.linspace, args=(0, 10, 2.5))
class TestBinaryReprInsufficientWidthParameterForRepresentation(_DeprecationTestCase):
"""
If a 'width' parameter is passed into ``binary_repr`` that is insufficient to
represent the number in base 2 (positive) or 2's complement (negative) form,
the function used to silently ignore the parameter and return a representation
using the minimal number of bits needed for the form in question. Such behavior
is now considered unsafe from a user perspective and will raise an error in the future.
"""
def test_insufficient_width_positive(self):
args = (10,)
kwargs = {'width': 2}
self.message = ("Insufficient bit width provided. This behavior "
"will raise an error in the future.")
self.assert_deprecated(np.binary_repr, args=args, kwargs=kwargs)
def test_insufficient_width_negative(self):
args = (-5,)
kwargs = {'width': 2}
self.message = ("Insufficient bit width provided. This behavior "
"will raise an error in the future.")
self.assert_deprecated(np.binary_repr, args=args, kwargs=kwargs)
class TestNumericStyleTypecodes(_DeprecationTestCase):
"""
Deprecate the old numeric-style dtypes, which are especially
confusing for complex types, e.g. Complex32 -> complex64. When the
deprecation cycle is complete, the check for the strings should be
removed from PyArray_DescrConverter in descriptor.c, and the
deprecated keys should not be added as capitalized aliases in
_add_aliases in numerictypes.py.
"""
def test_all_dtypes(self):
deprecated_types = [
'Bool', 'Complex32', 'Complex64', 'Float16', 'Float32', 'Float64',
'Int8', 'Int16', 'Int32', 'Int64', 'Object0', 'Timedelta64',
'UInt8', 'UInt16', 'UInt32', 'UInt64', 'Void0'
]
if sys.version_info[0] < 3:
deprecated_types.extend(['Unicode0', 'String0'])
for dt in deprecated_types:
self.assert_deprecated(np.dtype, exceptions=(TypeError,),
args=(dt,))
class TestTestDeprecated(object):
def test_assert_deprecated(self):
test_case_instance = _DeprecationTestCase()
test_case_instance.setup()
assert_raises(AssertionError,
test_case_instance.assert_deprecated,
lambda: None)
def foo():
warnings.warn("foo", category=DeprecationWarning, stacklevel=2)
test_case_instance.assert_deprecated(foo)
test_case_instance.teardown()
class TestClassicIntDivision(_DeprecationTestCase):
"""
See #7949. Deprecate the numeric-style dtypes with -3 flag in python 2
if used for division
List of data types: http://docs.scipy.org/doc/numpy/user/basics.types.html
"""
def test_int_dtypes(self):
#scramble types and do some mix and match testing
deprecated_types = [
'bool_', 'int_', 'intc', 'uint8', 'int8', 'uint64', 'int32', 'uint16',
'intp', 'int64', 'uint32', 'int16'
]
if sys.version_info[0] < 3 and sys.py3kwarning:
import operator as op
dt2 = 'bool_'
for dt1 in deprecated_types:
a = np.array([1,2,3], dtype=dt1)
b = np.array([1,2,3], dtype=dt2)
self.assert_deprecated(op.div, args=(a,b))
dt2 = dt1
class TestNonNumericConjugate(_DeprecationTestCase):
"""
Deprecate no-op behavior of ndarray.conjugate on non-numeric dtypes,
which conflicts with the error behavior of np.conjugate.
"""
def test_conjugate(self):
for a in np.array(5), np.array(5j):
self.assert_not_deprecated(a.conjugate)
for a in (np.array('s'), np.array('2016', 'M'),
np.array((1, 2), [('a', int), ('b', int)])):
self.assert_deprecated(a.conjugate)
class TestNPY_CHAR(_DeprecationTestCase):
# 2017-05-03, 1.13.0
def test_npy_char_deprecation(self):
from numpy.core.multiarray_tests import npy_char_deprecation
self.assert_deprecated(npy_char_deprecation)
assert_(npy_char_deprecation() == 'S1')
class Test_UPDATEIFCOPY(_DeprecationTestCase):
"""
v1.14 deprecates creating an array with the UPDATEIFCOPY flag, use
WRITEBACKIFCOPY instead
"""
def test_npy_updateifcopy_deprecation(self):
from numpy.core.multiarray_tests import npy_updateifcopy_deprecation
arr = np.arange(9).reshape(3, 3)
v = arr.T
self.assert_deprecated(npy_updateifcopy_deprecation, args=(v,))
class TestDatetimeEvent(_DeprecationTestCase):
# 2017-08-11, 1.14.0
def test_3_tuple(self):
for cls in (np.datetime64, np.timedelta64):
# two valid uses - (unit, num) and (unit, num, den, None)
self.assert_not_deprecated(cls, args=(1, ('ms', 2)))
self.assert_not_deprecated(cls, args=(1, ('ms', 2, 1, None)))
# trying to use the event argument, removed in 1.7.0, is deprecated
# it used to be a uint8
self.assert_deprecated(cls, args=(1, ('ms', 2, 'event')))
self.assert_deprecated(cls, args=(1, ('ms', 2, 63)))
self.assert_deprecated(cls, args=(1, ('ms', 2, 1, 'event')))
self.assert_deprecated(cls, args=(1, ('ms', 2, 1, 63)))
class TestTruthTestingEmptyArrays(_DeprecationTestCase):
# 2017-09-25, 1.14.0
message = '.*truth value of an empty array is ambiguous.*'
def test_1d(self):
self.assert_deprecated(bool, args=(np.array([]),))
def test_2d(self):
self.assert_deprecated(bool, args=(np.zeros((1, 0)),))
self.assert_deprecated(bool, args=(np.zeros((0, 1)),))
self.assert_deprecated(bool, args=(np.zeros((0, 0)),))
class TestBincount(_DeprecationTestCase):
# 2017-06-01, 1.14.0
def test_bincount_minlength(self):
self.assert_deprecated(lambda: np.bincount([1, 2, 3], minlength=None))
if __name__ == "__main__":
run_module_suite()
| 19,598 | 39.32716 | 91 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/tests/test_indexerrors.py
|
from __future__ import division, absolute_import, print_function
import numpy as np
from numpy.testing import run_module_suite, assert_raises
class TestIndexErrors(object):
'''Tests to exercise indexerrors not covered by other tests.'''
def test_arraytypes_fasttake(self):
'take from a 0-length dimension'
x = np.empty((2, 3, 0, 4))
assert_raises(IndexError, x.take, [0], axis=2)
assert_raises(IndexError, x.take, [1], axis=2)
assert_raises(IndexError, x.take, [0], axis=2, mode='wrap')
assert_raises(IndexError, x.take, [0], axis=2, mode='clip')
def test_take_from_object(self):
# Check exception taking from object array
d = np.zeros(5, dtype=object)
assert_raises(IndexError, d.take, [6])
# Check exception taking from 0-d array
d = np.zeros((5, 0), dtype=object)
assert_raises(IndexError, d.take, [1], axis=1)
assert_raises(IndexError, d.take, [0], axis=1)
assert_raises(IndexError, d.take, [0])
assert_raises(IndexError, d.take, [0], mode='wrap')
assert_raises(IndexError, d.take, [0], mode='clip')
def test_multiindex_exceptions(self):
a = np.empty(5, dtype=object)
assert_raises(IndexError, a.item, 20)
a = np.empty((5, 0), dtype=object)
assert_raises(IndexError, a.item, (0, 0))
a = np.empty(5, dtype=object)
assert_raises(IndexError, a.itemset, 20, 0)
a = np.empty((5, 0), dtype=object)
assert_raises(IndexError, a.itemset, (0, 0), 0)
def test_put_exceptions(self):
a = np.zeros((5, 5))
assert_raises(IndexError, a.put, 100, 0)
a = np.zeros((5, 5), dtype=object)
assert_raises(IndexError, a.put, 100, 0)
a = np.zeros((5, 5, 0))
assert_raises(IndexError, a.put, 100, 0)
a = np.zeros((5, 5, 0), dtype=object)
assert_raises(IndexError, a.put, 100, 0)
def test_iterators_exceptions(self):
"cases in iterators.c"
def assign(obj, ind, val):
obj[ind] = val
a = np.zeros([1, 2, 3])
assert_raises(IndexError, lambda: a[0, 5, None, 2])
assert_raises(IndexError, lambda: a[0, 5, 0, 2])
assert_raises(IndexError, lambda: assign(a, (0, 5, None, 2), 1))
assert_raises(IndexError, lambda: assign(a, (0, 5, 0, 2), 1))
a = np.zeros([1, 0, 3])
assert_raises(IndexError, lambda: a[0, 0, None, 2])
assert_raises(IndexError, lambda: assign(a, (0, 0, None, 2), 1))
a = np.zeros([1, 2, 3])
assert_raises(IndexError, lambda: a.flat[10])
assert_raises(IndexError, lambda: assign(a.flat, 10, 5))
a = np.zeros([1, 0, 3])
assert_raises(IndexError, lambda: a.flat[10])
assert_raises(IndexError, lambda: assign(a.flat, 10, 5))
a = np.zeros([1, 2, 3])
assert_raises(IndexError, lambda: a.flat[np.array(10)])
assert_raises(IndexError, lambda: assign(a.flat, np.array(10), 5))
a = np.zeros([1, 0, 3])
assert_raises(IndexError, lambda: a.flat[np.array(10)])
assert_raises(IndexError, lambda: assign(a.flat, np.array(10), 5))
a = np.zeros([1, 2, 3])
assert_raises(IndexError, lambda: a.flat[np.array([10])])
assert_raises(IndexError, lambda: assign(a.flat, np.array([10]), 5))
a = np.zeros([1, 0, 3])
assert_raises(IndexError, lambda: a.flat[np.array([10])])
assert_raises(IndexError, lambda: assign(a.flat, np.array([10]), 5))
def test_mapping(self):
"cases from mapping.c"
def assign(obj, ind, val):
obj[ind] = val
a = np.zeros((0, 10))
assert_raises(IndexError, lambda: a[12])
a = np.zeros((3, 5))
assert_raises(IndexError, lambda: a[(10, 20)])
assert_raises(IndexError, lambda: assign(a, (10, 20), 1))
a = np.zeros((3, 0))
assert_raises(IndexError, lambda: a[(1, 0)])
assert_raises(IndexError, lambda: assign(a, (1, 0), 1))
a = np.zeros((10,))
assert_raises(IndexError, lambda: assign(a, 10, 1))
a = np.zeros((0,))
assert_raises(IndexError, lambda: assign(a, 10, 1))
a = np.zeros((3, 5))
assert_raises(IndexError, lambda: a[(1, [1, 20])])
assert_raises(IndexError, lambda: assign(a, (1, [1, 20]), 1))
a = np.zeros((3, 0))
assert_raises(IndexError, lambda: a[(1, [0, 1])])
assert_raises(IndexError, lambda: assign(a, (1, [0, 1]), 1))
def test_methods(self):
"cases from methods.c"
a = np.zeros((3, 3))
assert_raises(IndexError, lambda: a.item(100))
assert_raises(IndexError, lambda: a.itemset(100, 1))
a = np.zeros((0, 3))
assert_raises(IndexError, lambda: a.item(100))
assert_raises(IndexError, lambda: a.itemset(100, 1))
if __name__ == "__main__":
run_module_suite()
| 4,926 | 37.795276 | 76 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/tests/test_half.py
|
from __future__ import division, absolute_import, print_function
import platform
import numpy as np
from numpy import uint16, float16, float32, float64
from numpy.testing import run_module_suite, assert_, assert_equal, dec
def assert_raises_fpe(strmatch, callable, *args, **kwargs):
try:
callable(*args, **kwargs)
except FloatingPointError as exc:
assert_(str(exc).find(strmatch) >= 0,
"Did not raise floating point %s error" % strmatch)
else:
assert_(False,
"Did not raise floating point %s error" % strmatch)
class TestHalf(object):
def setup(self):
# An array of all possible float16 values
self.all_f16 = np.arange(0x10000, dtype=uint16)
self.all_f16.dtype = float16
self.all_f32 = np.array(self.all_f16, dtype=float32)
self.all_f64 = np.array(self.all_f16, dtype=float64)
# An array of all non-NaN float16 values, in sorted order
self.nonan_f16 = np.concatenate(
(np.arange(0xfc00, 0x7fff, -1, dtype=uint16),
np.arange(0x0000, 0x7c01, 1, dtype=uint16)))
self.nonan_f16.dtype = float16
self.nonan_f32 = np.array(self.nonan_f16, dtype=float32)
self.nonan_f64 = np.array(self.nonan_f16, dtype=float64)
# An array of all finite float16 values, in sorted order
self.finite_f16 = self.nonan_f16[1:-1]
self.finite_f32 = self.nonan_f32[1:-1]
self.finite_f64 = self.nonan_f64[1:-1]
def test_half_conversions(self):
"""Checks that all 16-bit values survive conversion
to/from 32-bit and 64-bit float"""
# Because the underlying routines preserve the NaN bits, every
# value is preserved when converting to/from other floats.
# Convert from float32 back to float16
b = np.array(self.all_f32, dtype=float16)
assert_equal(self.all_f16.view(dtype=uint16),
b.view(dtype=uint16))
# Convert from float64 back to float16
b = np.array(self.all_f64, dtype=float16)
assert_equal(self.all_f16.view(dtype=uint16),
b.view(dtype=uint16))
# Convert float16 to longdouble and back
# This doesn't necessarily preserve the extra NaN bits,
# so exclude NaNs.
a_ld = np.array(self.nonan_f16, dtype=np.longdouble)
b = np.array(a_ld, dtype=float16)
assert_equal(self.nonan_f16.view(dtype=uint16),
b.view(dtype=uint16))
# Check the range for which all integers can be represented
i_int = np.arange(-2048, 2049)
i_f16 = np.array(i_int, dtype=float16)
j = np.array(i_f16, dtype=int)
assert_equal(i_int, j)
def test_nans_infs(self):
with np.errstate(all='ignore'):
# Check some of the ufuncs
assert_equal(np.isnan(self.all_f16), np.isnan(self.all_f32))
assert_equal(np.isinf(self.all_f16), np.isinf(self.all_f32))
assert_equal(np.isfinite(self.all_f16), np.isfinite(self.all_f32))
assert_equal(np.signbit(self.all_f16), np.signbit(self.all_f32))
assert_equal(np.spacing(float16(65504)), np.inf)
# Check comparisons of all values with NaN
nan = float16(np.nan)
assert_(not (self.all_f16 == nan).any())
assert_(not (nan == self.all_f16).any())
assert_((self.all_f16 != nan).all())
assert_((nan != self.all_f16).all())
assert_(not (self.all_f16 < nan).any())
assert_(not (nan < self.all_f16).any())
assert_(not (self.all_f16 <= nan).any())
assert_(not (nan <= self.all_f16).any())
assert_(not (self.all_f16 > nan).any())
assert_(not (nan > self.all_f16).any())
assert_(not (self.all_f16 >= nan).any())
assert_(not (nan >= self.all_f16).any())
def test_half_values(self):
"""Confirms a small number of known half values"""
a = np.array([1.0, -1.0,
2.0, -2.0,
0.0999755859375, 0.333251953125, # 1/10, 1/3
65504, -65504, # Maximum magnitude
2.0**(-14), -2.0**(-14), # Minimum normal
2.0**(-24), -2.0**(-24), # Minimum subnormal
0, -1/1e1000, # Signed zeros
np.inf, -np.inf])
b = np.array([0x3c00, 0xbc00,
0x4000, 0xc000,
0x2e66, 0x3555,
0x7bff, 0xfbff,
0x0400, 0x8400,
0x0001, 0x8001,
0x0000, 0x8000,
0x7c00, 0xfc00], dtype=uint16)
b.dtype = float16
assert_equal(a, b)
def test_half_rounding(self):
"""Checks that rounding when converting to half is correct"""
a = np.array([2.0**-25 + 2.0**-35, # Rounds to minimum subnormal
2.0**-25, # Underflows to zero (nearest even mode)
2.0**-26, # Underflows to zero
1.0+2.0**-11 + 2.0**-16, # rounds to 1.0+2**(-10)
1.0+2.0**-11, # rounds to 1.0 (nearest even mode)
1.0+2.0**-12, # rounds to 1.0
65519, # rounds to 65504
65520], # rounds to inf
dtype=float64)
rounded = [2.0**-24,
0.0,
0.0,
1.0+2.0**(-10),
1.0,
1.0,
65504,
np.inf]
# Check float64->float16 rounding
b = np.array(a, dtype=float16)
assert_equal(b, rounded)
# Check float32->float16 rounding
a = np.array(a, dtype=float32)
b = np.array(a, dtype=float16)
assert_equal(b, rounded)
def test_half_correctness(self):
"""Take every finite float16, and check the casting functions with
a manual conversion."""
# Create an array of all finite float16s
a_bits = self.finite_f16.view(dtype=uint16)
# Convert to 64-bit float manually
a_sgn = (-1.0)**((a_bits & 0x8000) >> 15)
a_exp = np.array((a_bits & 0x7c00) >> 10, dtype=np.int32) - 15
a_man = (a_bits & 0x03ff) * 2.0**(-10)
# Implicit bit of normalized floats
a_man[a_exp != -15] += 1
# Denormalized exponent is -14
a_exp[a_exp == -15] = -14
a_manual = a_sgn * a_man * 2.0**a_exp
a32_fail = np.nonzero(self.finite_f32 != a_manual)[0]
if len(a32_fail) != 0:
bad_index = a32_fail[0]
assert_equal(self.finite_f32, a_manual,
"First non-equal is half value %x -> %g != %g" %
(self.finite_f16[bad_index],
self.finite_f32[bad_index],
a_manual[bad_index]))
a64_fail = np.nonzero(self.finite_f64 != a_manual)[0]
if len(a64_fail) != 0:
bad_index = a64_fail[0]
assert_equal(self.finite_f64, a_manual,
"First non-equal is half value %x -> %g != %g" %
(self.finite_f16[bad_index],
self.finite_f64[bad_index],
a_manual[bad_index]))
def test_half_ordering(self):
"""Make sure comparisons are working right"""
# All non-NaN float16 values in reverse order
a = self.nonan_f16[::-1].copy()
# 32-bit float copy
b = np.array(a, dtype=float32)
# Should sort the same
a.sort()
b.sort()
assert_equal(a, b)
# Comparisons should work
assert_((a[:-1] <= a[1:]).all())
assert_(not (a[:-1] > a[1:]).any())
assert_((a[1:] >= a[:-1]).all())
assert_(not (a[1:] < a[:-1]).any())
# All != except for +/-0
assert_equal(np.nonzero(a[:-1] < a[1:])[0].size, a.size-2)
assert_equal(np.nonzero(a[1:] > a[:-1])[0].size, a.size-2)
def test_half_funcs(self):
"""Test the various ArrFuncs"""
# fill
assert_equal(np.arange(10, dtype=float16),
np.arange(10, dtype=float32))
# fillwithscalar
a = np.zeros((5,), dtype=float16)
a.fill(1)
assert_equal(a, np.ones((5,), dtype=float16))
# nonzero and copyswap
a = np.array([0, 0, -1, -1/1e20, 0, 2.0**-24, 7.629e-6], dtype=float16)
assert_equal(a.nonzero()[0],
[2, 5, 6])
a = a.byteswap().newbyteorder()
assert_equal(a.nonzero()[0],
[2, 5, 6])
# dot
a = np.arange(0, 10, 0.5, dtype=float16)
b = np.ones((20,), dtype=float16)
assert_equal(np.dot(a, b),
95)
# argmax
a = np.array([0, -np.inf, -2, 0.5, 12.55, 7.3, 2.1, 12.4], dtype=float16)
assert_equal(a.argmax(),
4)
a = np.array([0, -np.inf, -2, np.inf, 12.55, np.nan, 2.1, 12.4], dtype=float16)
assert_equal(a.argmax(),
5)
# getitem
a = np.arange(10, dtype=float16)
for i in range(10):
assert_equal(a.item(i), i)
def test_spacing_nextafter(self):
"""Test np.spacing and np.nextafter"""
# All non-negative finite #'s
a = np.arange(0x7c00, dtype=uint16)
hinf = np.array((np.inf,), dtype=float16)
a_f16 = a.view(dtype=float16)
assert_equal(np.spacing(a_f16[:-1]), a_f16[1:]-a_f16[:-1])
assert_equal(np.nextafter(a_f16[:-1], hinf), a_f16[1:])
assert_equal(np.nextafter(a_f16[0], -hinf), -a_f16[1])
assert_equal(np.nextafter(a_f16[1:], -hinf), a_f16[:-1])
# switch to negatives
a |= 0x8000
assert_equal(np.spacing(a_f16[0]), np.spacing(a_f16[1]))
assert_equal(np.spacing(a_f16[1:]), a_f16[:-1]-a_f16[1:])
assert_equal(np.nextafter(a_f16[0], hinf), -a_f16[1])
assert_equal(np.nextafter(a_f16[1:], hinf), a_f16[:-1])
assert_equal(np.nextafter(a_f16[:-1], -hinf), a_f16[1:])
def test_half_ufuncs(self):
"""Test the various ufuncs"""
a = np.array([0, 1, 2, 4, 2], dtype=float16)
b = np.array([-2, 5, 1, 4, 3], dtype=float16)
c = np.array([0, -1, -np.inf, np.nan, 6], dtype=float16)
assert_equal(np.add(a, b), [-2, 6, 3, 8, 5])
assert_equal(np.subtract(a, b), [2, -4, 1, 0, -1])
assert_equal(np.multiply(a, b), [0, 5, 2, 16, 6])
assert_equal(np.divide(a, b), [0, 0.199951171875, 2, 1, 0.66650390625])
assert_equal(np.equal(a, b), [False, False, False, True, False])
assert_equal(np.not_equal(a, b), [True, True, True, False, True])
assert_equal(np.less(a, b), [False, True, False, False, True])
assert_equal(np.less_equal(a, b), [False, True, False, True, True])
assert_equal(np.greater(a, b), [True, False, True, False, False])
assert_equal(np.greater_equal(a, b), [True, False, True, True, False])
assert_equal(np.logical_and(a, b), [False, True, True, True, True])
assert_equal(np.logical_or(a, b), [True, True, True, True, True])
assert_equal(np.logical_xor(a, b), [True, False, False, False, False])
assert_equal(np.logical_not(a), [True, False, False, False, False])
assert_equal(np.isnan(c), [False, False, False, True, False])
assert_equal(np.isinf(c), [False, False, True, False, False])
assert_equal(np.isfinite(c), [True, True, False, False, True])
assert_equal(np.signbit(b), [True, False, False, False, False])
assert_equal(np.copysign(b, a), [2, 5, 1, 4, 3])
assert_equal(np.maximum(a, b), [0, 5, 2, 4, 3])
x = np.maximum(b, c)
assert_(np.isnan(x[3]))
x[3] = 0
assert_equal(x, [0, 5, 1, 0, 6])
assert_equal(np.minimum(a, b), [-2, 1, 1, 4, 2])
x = np.minimum(b, c)
assert_(np.isnan(x[3]))
x[3] = 0
assert_equal(x, [-2, -1, -np.inf, 0, 3])
assert_equal(np.fmax(a, b), [0, 5, 2, 4, 3])
assert_equal(np.fmax(b, c), [0, 5, 1, 4, 6])
assert_equal(np.fmin(a, b), [-2, 1, 1, 4, 2])
assert_equal(np.fmin(b, c), [-2, -1, -np.inf, 4, 3])
assert_equal(np.floor_divide(a, b), [0, 0, 2, 1, 0])
assert_equal(np.remainder(a, b), [0, 1, 0, 0, 2])
assert_equal(np.divmod(a, b), ([0, 0, 2, 1, 0], [0, 1, 0, 0, 2]))
assert_equal(np.square(b), [4, 25, 1, 16, 9])
assert_equal(np.reciprocal(b), [-0.5, 0.199951171875, 1, 0.25, 0.333251953125])
assert_equal(np.ones_like(b), [1, 1, 1, 1, 1])
assert_equal(np.conjugate(b), b)
assert_equal(np.absolute(b), [2, 5, 1, 4, 3])
assert_equal(np.negative(b), [2, -5, -1, -4, -3])
assert_equal(np.positive(b), b)
assert_equal(np.sign(b), [-1, 1, 1, 1, 1])
assert_equal(np.modf(b), ([0, 0, 0, 0, 0], b))
assert_equal(np.frexp(b), ([-0.5, 0.625, 0.5, 0.5, 0.75], [2, 3, 1, 3, 2]))
assert_equal(np.ldexp(b, [0, 1, 2, 4, 2]), [-2, 10, 4, 64, 12])
def test_half_coercion(self):
"""Test that half gets coerced properly with the other types"""
a16 = np.array((1,), dtype=float16)
a32 = np.array((1,), dtype=float32)
b16 = float16(1)
b32 = float32(1)
assert_equal(np.power(a16, 2).dtype, float16)
assert_equal(np.power(a16, 2.0).dtype, float16)
assert_equal(np.power(a16, b16).dtype, float16)
assert_equal(np.power(a16, b32).dtype, float16)
assert_equal(np.power(a16, a16).dtype, float16)
assert_equal(np.power(a16, a32).dtype, float32)
assert_equal(np.power(b16, 2).dtype, float64)
assert_equal(np.power(b16, 2.0).dtype, float64)
assert_equal(np.power(b16, b16).dtype, float16)
assert_equal(np.power(b16, b32).dtype, float32)
assert_equal(np.power(b16, a16).dtype, float16)
assert_equal(np.power(b16, a32).dtype, float32)
assert_equal(np.power(a32, a16).dtype, float32)
assert_equal(np.power(a32, b16).dtype, float32)
assert_equal(np.power(b32, a16).dtype, float16)
assert_equal(np.power(b32, b16).dtype, float32)
@dec.skipif(platform.machine() == "armv5tel", "See gh-413.")
def test_half_fpe(self):
with np.errstate(all='raise'):
sx16 = np.array((1e-4,), dtype=float16)
bx16 = np.array((1e4,), dtype=float16)
sy16 = float16(1e-4)
by16 = float16(1e4)
# Underflow errors
assert_raises_fpe('underflow', lambda a, b:a*b, sx16, sx16)
assert_raises_fpe('underflow', lambda a, b:a*b, sx16, sy16)
assert_raises_fpe('underflow', lambda a, b:a*b, sy16, sx16)
assert_raises_fpe('underflow', lambda a, b:a*b, sy16, sy16)
assert_raises_fpe('underflow', lambda a, b:a/b, sx16, bx16)
assert_raises_fpe('underflow', lambda a, b:a/b, sx16, by16)
assert_raises_fpe('underflow', lambda a, b:a/b, sy16, bx16)
assert_raises_fpe('underflow', lambda a, b:a/b, sy16, by16)
assert_raises_fpe('underflow', lambda a, b:a/b,
float16(2.**-14), float16(2**11))
assert_raises_fpe('underflow', lambda a, b:a/b,
float16(-2.**-14), float16(2**11))
assert_raises_fpe('underflow', lambda a, b:a/b,
float16(2.**-14+2**-24), float16(2))
assert_raises_fpe('underflow', lambda a, b:a/b,
float16(-2.**-14-2**-24), float16(2))
assert_raises_fpe('underflow', lambda a, b:a/b,
float16(2.**-14+2**-23), float16(4))
# Overflow errors
assert_raises_fpe('overflow', lambda a, b:a*b, bx16, bx16)
assert_raises_fpe('overflow', lambda a, b:a*b, bx16, by16)
assert_raises_fpe('overflow', lambda a, b:a*b, by16, bx16)
assert_raises_fpe('overflow', lambda a, b:a*b, by16, by16)
assert_raises_fpe('overflow', lambda a, b:a/b, bx16, sx16)
assert_raises_fpe('overflow', lambda a, b:a/b, bx16, sy16)
assert_raises_fpe('overflow', lambda a, b:a/b, by16, sx16)
assert_raises_fpe('overflow', lambda a, b:a/b, by16, sy16)
assert_raises_fpe('overflow', lambda a, b:a+b,
float16(65504), float16(17))
assert_raises_fpe('overflow', lambda a, b:a-b,
float16(-65504), float16(17))
assert_raises_fpe('overflow', np.nextafter, float16(65504), float16(np.inf))
assert_raises_fpe('overflow', np.nextafter, float16(-65504), float16(-np.inf))
assert_raises_fpe('overflow', np.spacing, float16(65504))
# Invalid value errors
assert_raises_fpe('invalid', np.divide, float16(np.inf), float16(np.inf))
assert_raises_fpe('invalid', np.spacing, float16(np.inf))
assert_raises_fpe('invalid', np.spacing, float16(np.nan))
assert_raises_fpe('invalid', np.nextafter, float16(np.inf), float16(0))
assert_raises_fpe('invalid', np.nextafter, float16(-np.inf), float16(0))
assert_raises_fpe('invalid', np.nextafter, float16(0), float16(np.nan))
# These should not raise
float16(65472)+float16(32)
float16(2**-13)/float16(2)
float16(2**-14)/float16(2**10)
np.spacing(float16(-65504))
np.nextafter(float16(65504), float16(-np.inf))
np.nextafter(float16(-65504), float16(np.inf))
float16(2**-14)/float16(2**10)
float16(-2**-14)/float16(2**10)
float16(2**-14+2**-23)/float16(2)
float16(-2**-14-2**-23)/float16(2)
def test_half_array_interface(self):
"""Test that half is compatible with __array_interface__"""
class Dummy:
pass
a = np.ones((1,), dtype=float16)
b = Dummy()
b.__array_interface__ = a.__array_interface__
c = np.array(b)
assert_(c.dtype == float16)
assert_equal(a, c)
if __name__ == "__main__":
run_module_suite()
| 18,627 | 41.52968 | 90 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/tests/test_memmap.py
|
from __future__ import division, absolute_import, print_function
import sys
import os
import shutil
from tempfile import NamedTemporaryFile, TemporaryFile, mktemp, mkdtemp
import mmap
from numpy import (
memmap, sum, average, product, ndarray, isscalar, add, subtract, multiply)
from numpy.compat import Path
from numpy import arange, allclose, asarray
from numpy.testing import (
run_module_suite, assert_, assert_equal, assert_array_equal,
dec, suppress_warnings
)
class TestMemmap(object):
def setup(self):
self.tmpfp = NamedTemporaryFile(prefix='mmap')
self.tempdir = mkdtemp()
self.shape = (3, 4)
self.dtype = 'float32'
self.data = arange(12, dtype=self.dtype)
self.data.resize(self.shape)
def teardown(self):
self.tmpfp.close()
shutil.rmtree(self.tempdir)
def test_roundtrip(self):
# Write data to file
fp = memmap(self.tmpfp, dtype=self.dtype, mode='w+',
shape=self.shape)
fp[:] = self.data[:]
del fp # Test __del__ machinery, which handles cleanup
# Read data back from file
newfp = memmap(self.tmpfp, dtype=self.dtype, mode='r',
shape=self.shape)
assert_(allclose(self.data, newfp))
assert_array_equal(self.data, newfp)
assert_equal(newfp.flags.writeable, False)
def test_open_with_filename(self):
tmpname = mktemp('', 'mmap', dir=self.tempdir)
fp = memmap(tmpname, dtype=self.dtype, mode='w+',
shape=self.shape)
fp[:] = self.data[:]
del fp
def test_unnamed_file(self):
with TemporaryFile() as f:
fp = memmap(f, dtype=self.dtype, shape=self.shape)
del fp
def test_attributes(self):
offset = 1
mode = "w+"
fp = memmap(self.tmpfp, dtype=self.dtype, mode=mode,
shape=self.shape, offset=offset)
assert_equal(offset, fp.offset)
assert_equal(mode, fp.mode)
del fp
def test_filename(self):
tmpname = mktemp('', 'mmap', dir=self.tempdir)
fp = memmap(tmpname, dtype=self.dtype, mode='w+',
shape=self.shape)
abspath = os.path.abspath(tmpname)
fp[:] = self.data[:]
assert_equal(abspath, fp.filename)
b = fp[:1]
assert_equal(abspath, b.filename)
del b
del fp
@dec.skipif(Path is None, "No pathlib.Path")
def test_path(self):
tmpname = mktemp('', 'mmap', dir=self.tempdir)
fp = memmap(Path(tmpname), dtype=self.dtype, mode='w+',
shape=self.shape)
abspath = os.path.realpath(os.path.abspath(tmpname))
fp[:] = self.data[:]
assert_equal(abspath, str(fp.filename.resolve()))
b = fp[:1]
assert_equal(abspath, str(b.filename.resolve()))
del b
del fp
def test_filename_fileobj(self):
fp = memmap(self.tmpfp, dtype=self.dtype, mode="w+",
shape=self.shape)
assert_equal(fp.filename, self.tmpfp.name)
@dec.knownfailureif(sys.platform == 'gnu0', "This test is known to fail on hurd")
def test_flush(self):
fp = memmap(self.tmpfp, dtype=self.dtype, mode='w+',
shape=self.shape)
fp[:] = self.data[:]
assert_equal(fp[0], self.data[0])
fp.flush()
def test_del(self):
# Make sure a view does not delete the underlying mmap
fp_base = memmap(self.tmpfp, dtype=self.dtype, mode='w+',
shape=self.shape)
fp_base[0] = 5
fp_view = fp_base[0:1]
assert_equal(fp_view[0], 5)
del fp_view
# Should still be able to access and assign values after
# deleting the view
assert_equal(fp_base[0], 5)
fp_base[0] = 6
assert_equal(fp_base[0], 6)
def test_arithmetic_drops_references(self):
fp = memmap(self.tmpfp, dtype=self.dtype, mode='w+',
shape=self.shape)
tmp = (fp + 10)
if isinstance(tmp, memmap):
assert_(tmp._mmap is not fp._mmap)
def test_indexing_drops_references(self):
fp = memmap(self.tmpfp, dtype=self.dtype, mode='w+',
shape=self.shape)
tmp = fp[[(1, 2), (2, 3)]]
if isinstance(tmp, memmap):
assert_(tmp._mmap is not fp._mmap)
def test_slicing_keeps_references(self):
fp = memmap(self.tmpfp, dtype=self.dtype, mode='w+',
shape=self.shape)
assert_(fp[:2, :2]._mmap is fp._mmap)
def test_view(self):
fp = memmap(self.tmpfp, dtype=self.dtype, shape=self.shape)
new1 = fp.view()
new2 = new1.view()
assert_(new1.base is fp)
assert_(new2.base is fp)
new_array = asarray(fp)
assert_(new_array.base is fp)
def test_ufunc_return_ndarray(self):
fp = memmap(self.tmpfp, dtype=self.dtype, shape=self.shape)
fp[:] = self.data
with suppress_warnings() as sup:
sup.filter(FutureWarning, "np.average currently does not preserve")
for unary_op in [sum, average, product]:
result = unary_op(fp)
assert_(isscalar(result))
assert_(result.__class__ is self.data[0, 0].__class__)
assert_(unary_op(fp, axis=0).__class__ is ndarray)
assert_(unary_op(fp, axis=1).__class__ is ndarray)
for binary_op in [add, subtract, multiply]:
assert_(binary_op(fp, self.data).__class__ is ndarray)
assert_(binary_op(self.data, fp).__class__ is ndarray)
assert_(binary_op(fp, fp).__class__ is ndarray)
fp += 1
assert(fp.__class__ is memmap)
add(fp, 1, out=fp)
assert(fp.__class__ is memmap)
def test_getitem(self):
fp = memmap(self.tmpfp, dtype=self.dtype, shape=self.shape)
fp[:] = self.data
assert_(fp[1:, :-1].__class__ is memmap)
# Fancy indexing returns a copy that is not memmapped
assert_(fp[[0, 1]].__class__ is ndarray)
def test_memmap_subclass(self):
class MemmapSubClass(memmap):
pass
fp = MemmapSubClass(self.tmpfp, dtype=self.dtype, shape=self.shape)
fp[:] = self.data
# We keep previous behavior for subclasses of memmap, i.e. the
# ufunc and __getitem__ output is never turned into a ndarray
assert_(sum(fp, axis=0).__class__ is MemmapSubClass)
assert_(sum(fp).__class__ is MemmapSubClass)
assert_(fp[1:, :-1].__class__ is MemmapSubClass)
assert(fp[[0, 1]].__class__ is MemmapSubClass)
def test_mmap_offset_greater_than_allocation_granularity(self):
size = 5 * mmap.ALLOCATIONGRANULARITY
offset = mmap.ALLOCATIONGRANULARITY + 1
fp = memmap(self.tmpfp, shape=size, mode='w+', offset=offset)
assert_(fp.offset == offset)
if __name__ == "__main__":
run_module_suite()
| 7,031 | 33.985075 | 85 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/tests/test_numeric.py
|
from __future__ import division, absolute_import, print_function
import sys
import warnings
import itertools
import platform
from decimal import Decimal
import numpy as np
from numpy.core import umath
from numpy.random import rand, randint, randn
from numpy.testing import (
run_module_suite, assert_, assert_equal, assert_raises,
assert_raises_regex, assert_array_equal, assert_almost_equal,
assert_array_almost_equal, dec, HAS_REFCOUNT, suppress_warnings
)
class TestResize(object):
def test_copies(self):
A = np.array([[1, 2], [3, 4]])
Ar1 = np.array([[1, 2, 3, 4], [1, 2, 3, 4]])
assert_equal(np.resize(A, (2, 4)), Ar1)
Ar2 = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
assert_equal(np.resize(A, (4, 2)), Ar2)
Ar3 = np.array([[1, 2, 3], [4, 1, 2], [3, 4, 1], [2, 3, 4]])
assert_equal(np.resize(A, (4, 3)), Ar3)
def test_zeroresize(self):
A = np.array([[1, 2], [3, 4]])
Ar = np.resize(A, (0,))
assert_array_equal(Ar, np.array([]))
assert_equal(A.dtype, Ar.dtype)
Ar = np.resize(A, (0, 2))
assert_equal(Ar.shape, (0, 2))
Ar = np.resize(A, (2, 0))
assert_equal(Ar.shape, (2, 0))
def test_reshape_from_zero(self):
# See also gh-6740
A = np.zeros(0, dtype=[('a', np.float32, 1)])
Ar = np.resize(A, (2, 1))
assert_array_equal(Ar, np.zeros((2, 1), Ar.dtype))
assert_equal(A.dtype, Ar.dtype)
class TestNonarrayArgs(object):
# check that non-array arguments to functions wrap them in arrays
def test_choose(self):
choices = [[0, 1, 2],
[3, 4, 5],
[5, 6, 7]]
tgt = [5, 1, 5]
a = [2, 0, 1]
out = np.choose(a, choices)
assert_equal(out, tgt)
def test_clip(self):
arr = [-1, 5, 2, 3, 10, -4, -9]
out = np.clip(arr, 2, 7)
tgt = [2, 5, 2, 3, 7, 2, 2]
assert_equal(out, tgt)
def test_compress(self):
arr = [[0, 1, 2, 3, 4],
[5, 6, 7, 8, 9]]
tgt = [[5, 6, 7, 8, 9]]
out = np.compress([0, 1], arr, axis=0)
assert_equal(out, tgt)
def test_count_nonzero(self):
arr = [[0, 1, 7, 0, 0],
[3, 0, 0, 2, 19]]
tgt = np.array([2, 3])
out = np.count_nonzero(arr, axis=1)
assert_equal(out, tgt)
def test_cumproduct(self):
A = [[1, 2, 3], [4, 5, 6]]
assert_(np.all(np.cumproduct(A) == np.array([1, 2, 6, 24, 120, 720])))
def test_diagonal(self):
a = [[0, 1, 2, 3],
[4, 5, 6, 7],
[8, 9, 10, 11]]
out = np.diagonal(a)
tgt = [0, 5, 10]
assert_equal(out, tgt)
def test_mean(self):
A = [[1, 2, 3], [4, 5, 6]]
assert_(np.mean(A) == 3.5)
assert_(np.all(np.mean(A, 0) == np.array([2.5, 3.5, 4.5])))
assert_(np.all(np.mean(A, 1) == np.array([2., 5.])))
with warnings.catch_warnings(record=True) as w:
warnings.filterwarnings('always', '', RuntimeWarning)
assert_(np.isnan(np.mean([])))
assert_(w[0].category is RuntimeWarning)
def test_ptp(self):
a = [3, 4, 5, 10, -3, -5, 6.0]
assert_equal(np.ptp(a, axis=0), 15.0)
def test_prod(self):
arr = [[1, 2, 3, 4],
[5, 6, 7, 9],
[10, 3, 4, 5]]
tgt = [24, 1890, 600]
assert_equal(np.prod(arr, axis=-1), tgt)
def test_ravel(self):
a = [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]]
tgt = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
assert_equal(np.ravel(a), tgt)
def test_repeat(self):
a = [1, 2, 3]
tgt = [1, 1, 2, 2, 3, 3]
out = np.repeat(a, 2)
assert_equal(out, tgt)
def test_reshape(self):
arr = [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]]
tgt = [[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]]
assert_equal(np.reshape(arr, (2, 6)), tgt)
def test_round(self):
arr = [1.56, 72.54, 6.35, 3.25]
tgt = [1.6, 72.5, 6.4, 3.2]
assert_equal(np.around(arr, decimals=1), tgt)
def test_searchsorted(self):
arr = [-8, -5, -1, 3, 6, 10]
out = np.searchsorted(arr, 0)
assert_equal(out, 3)
def test_size(self):
A = [[1, 2, 3], [4, 5, 6]]
assert_(np.size(A) == 6)
assert_(np.size(A, 0) == 2)
assert_(np.size(A, 1) == 3)
def test_squeeze(self):
A = [[[1, 1, 1], [2, 2, 2], [3, 3, 3]]]
assert_(np.squeeze(A).shape == (3, 3))
def test_std(self):
A = [[1, 2, 3], [4, 5, 6]]
assert_almost_equal(np.std(A), 1.707825127659933)
assert_almost_equal(np.std(A, 0), np.array([1.5, 1.5, 1.5]))
assert_almost_equal(np.std(A, 1), np.array([0.81649658, 0.81649658]))
with warnings.catch_warnings(record=True) as w:
warnings.filterwarnings('always', '', RuntimeWarning)
assert_(np.isnan(np.std([])))
assert_(w[0].category is RuntimeWarning)
def test_swapaxes(self):
tgt = [[[0, 4], [2, 6]], [[1, 5], [3, 7]]]
a = [[[0, 1], [2, 3]], [[4, 5], [6, 7]]]
out = np.swapaxes(a, 0, 2)
assert_equal(out, tgt)
def test_sum(self):
m = [[1, 2, 3],
[4, 5, 6],
[7, 8, 9]]
tgt = [[6], [15], [24]]
out = np.sum(m, axis=1, keepdims=True)
assert_equal(tgt, out)
def test_take(self):
tgt = [2, 3, 5]
indices = [1, 2, 4]
a = [1, 2, 3, 4, 5]
out = np.take(a, indices)
assert_equal(out, tgt)
def test_trace(self):
c = [[1, 2], [3, 4], [5, 6]]
assert_equal(np.trace(c), 5)
def test_transpose(self):
arr = [[1, 2], [3, 4], [5, 6]]
tgt = [[1, 3, 5], [2, 4, 6]]
assert_equal(np.transpose(arr, (1, 0)), tgt)
def test_var(self):
A = [[1, 2, 3], [4, 5, 6]]
assert_almost_equal(np.var(A), 2.9166666666666665)
assert_almost_equal(np.var(A, 0), np.array([2.25, 2.25, 2.25]))
assert_almost_equal(np.var(A, 1), np.array([0.66666667, 0.66666667]))
with warnings.catch_warnings(record=True) as w:
warnings.filterwarnings('always', '', RuntimeWarning)
assert_(np.isnan(np.var([])))
assert_(w[0].category is RuntimeWarning)
class TestIsscalar(object):
def test_isscalar(self):
assert_(np.isscalar(3.1))
assert_(np.isscalar(np.int16(12345)))
assert_(np.isscalar(False))
assert_(np.isscalar('numpy'))
assert_(not np.isscalar([3.1]))
assert_(not np.isscalar(None))
# PEP 3141
from fractions import Fraction
assert_(np.isscalar(Fraction(5, 17)))
from numbers import Number
assert_(np.isscalar(Number()))
class TestBoolScalar(object):
def test_logical(self):
f = np.False_
t = np.True_
s = "xyz"
assert_((t and s) is s)
assert_((f and s) is f)
def test_bitwise_or(self):
f = np.False_
t = np.True_
assert_((t | t) is t)
assert_((f | t) is t)
assert_((t | f) is t)
assert_((f | f) is f)
def test_bitwise_and(self):
f = np.False_
t = np.True_
assert_((t & t) is t)
assert_((f & t) is f)
assert_((t & f) is f)
assert_((f & f) is f)
def test_bitwise_xor(self):
f = np.False_
t = np.True_
assert_((t ^ t) is f)
assert_((f ^ t) is t)
assert_((t ^ f) is t)
assert_((f ^ f) is f)
class TestBoolArray(object):
def setup(self):
# offset for simd tests
self.t = np.array([True] * 41, dtype=bool)[1::]
self.f = np.array([False] * 41, dtype=bool)[1::]
self.o = np.array([False] * 42, dtype=bool)[2::]
self.nm = self.f.copy()
self.im = self.t.copy()
self.nm[3] = True
self.nm[-2] = True
self.im[3] = False
self.im[-2] = False
def test_all_any(self):
assert_(self.t.all())
assert_(self.t.any())
assert_(not self.f.all())
assert_(not self.f.any())
assert_(self.nm.any())
assert_(self.im.any())
assert_(not self.nm.all())
assert_(not self.im.all())
# check bad element in all positions
for i in range(256 - 7):
d = np.array([False] * 256, dtype=bool)[7::]
d[i] = True
assert_(np.any(d))
e = np.array([True] * 256, dtype=bool)[7::]
e[i] = False
assert_(not np.all(e))
assert_array_equal(e, ~d)
# big array test for blocked libc loops
for i in list(range(9, 6000, 507)) + [7764, 90021, -10]:
d = np.array([False] * 100043, dtype=bool)
d[i] = True
assert_(np.any(d), msg="%r" % i)
e = np.array([True] * 100043, dtype=bool)
e[i] = False
assert_(not np.all(e), msg="%r" % i)
def test_logical_not_abs(self):
assert_array_equal(~self.t, self.f)
assert_array_equal(np.abs(~self.t), self.f)
assert_array_equal(np.abs(~self.f), self.t)
assert_array_equal(np.abs(self.f), self.f)
assert_array_equal(~np.abs(self.f), self.t)
assert_array_equal(~np.abs(self.t), self.f)
assert_array_equal(np.abs(~self.nm), self.im)
np.logical_not(self.t, out=self.o)
assert_array_equal(self.o, self.f)
np.abs(self.t, out=self.o)
assert_array_equal(self.o, self.t)
def test_logical_and_or_xor(self):
assert_array_equal(self.t | self.t, self.t)
assert_array_equal(self.f | self.f, self.f)
assert_array_equal(self.t | self.f, self.t)
assert_array_equal(self.f | self.t, self.t)
np.logical_or(self.t, self.t, out=self.o)
assert_array_equal(self.o, self.t)
assert_array_equal(self.t & self.t, self.t)
assert_array_equal(self.f & self.f, self.f)
assert_array_equal(self.t & self.f, self.f)
assert_array_equal(self.f & self.t, self.f)
np.logical_and(self.t, self.t, out=self.o)
assert_array_equal(self.o, self.t)
assert_array_equal(self.t ^ self.t, self.f)
assert_array_equal(self.f ^ self.f, self.f)
assert_array_equal(self.t ^ self.f, self.t)
assert_array_equal(self.f ^ self.t, self.t)
np.logical_xor(self.t, self.t, out=self.o)
assert_array_equal(self.o, self.f)
assert_array_equal(self.nm & self.t, self.nm)
assert_array_equal(self.im & self.f, False)
assert_array_equal(self.nm & True, self.nm)
assert_array_equal(self.im & False, self.f)
assert_array_equal(self.nm | self.t, self.t)
assert_array_equal(self.im | self.f, self.im)
assert_array_equal(self.nm | True, self.t)
assert_array_equal(self.im | False, self.im)
assert_array_equal(self.nm ^ self.t, self.im)
assert_array_equal(self.im ^ self.f, self.im)
assert_array_equal(self.nm ^ True, self.im)
assert_array_equal(self.im ^ False, self.im)
class TestBoolCmp(object):
def setup(self):
self.f = np.ones(256, dtype=np.float32)
self.ef = np.ones(self.f.size, dtype=bool)
self.d = np.ones(128, dtype=np.float64)
self.ed = np.ones(self.d.size, dtype=bool)
# generate values for all permutation of 256bit simd vectors
s = 0
for i in range(32):
self.f[s:s+8] = [i & 2**x for x in range(8)]
self.ef[s:s+8] = [(i & 2**x) != 0 for x in range(8)]
s += 8
s = 0
for i in range(16):
self.d[s:s+4] = [i & 2**x for x in range(4)]
self.ed[s:s+4] = [(i & 2**x) != 0 for x in range(4)]
s += 4
self.nf = self.f.copy()
self.nd = self.d.copy()
self.nf[self.ef] = np.nan
self.nd[self.ed] = np.nan
self.inff = self.f.copy()
self.infd = self.d.copy()
self.inff[::3][self.ef[::3]] = np.inf
self.infd[::3][self.ed[::3]] = np.inf
self.inff[1::3][self.ef[1::3]] = -np.inf
self.infd[1::3][self.ed[1::3]] = -np.inf
self.inff[2::3][self.ef[2::3]] = np.nan
self.infd[2::3][self.ed[2::3]] = np.nan
self.efnonan = self.ef.copy()
self.efnonan[2::3] = False
self.ednonan = self.ed.copy()
self.ednonan[2::3] = False
self.signf = self.f.copy()
self.signd = self.d.copy()
self.signf[self.ef] *= -1.
self.signd[self.ed] *= -1.
self.signf[1::6][self.ef[1::6]] = -np.inf
self.signd[1::6][self.ed[1::6]] = -np.inf
self.signf[3::6][self.ef[3::6]] = -np.nan
self.signd[3::6][self.ed[3::6]] = -np.nan
self.signf[4::6][self.ef[4::6]] = -0.
self.signd[4::6][self.ed[4::6]] = -0.
def test_float(self):
# offset for alignment test
for i in range(4):
assert_array_equal(self.f[i:] > 0, self.ef[i:])
assert_array_equal(self.f[i:] - 1 >= 0, self.ef[i:])
assert_array_equal(self.f[i:] == 0, ~self.ef[i:])
assert_array_equal(-self.f[i:] < 0, self.ef[i:])
assert_array_equal(-self.f[i:] + 1 <= 0, self.ef[i:])
r = self.f[i:] != 0
assert_array_equal(r, self.ef[i:])
r2 = self.f[i:] != np.zeros_like(self.f[i:])
r3 = 0 != self.f[i:]
assert_array_equal(r, r2)
assert_array_equal(r, r3)
# check bool == 0x1
assert_array_equal(r.view(np.int8), r.astype(np.int8))
assert_array_equal(r2.view(np.int8), r2.astype(np.int8))
assert_array_equal(r3.view(np.int8), r3.astype(np.int8))
# isnan on amd64 takes the same code path
assert_array_equal(np.isnan(self.nf[i:]), self.ef[i:])
assert_array_equal(np.isfinite(self.nf[i:]), ~self.ef[i:])
assert_array_equal(np.isfinite(self.inff[i:]), ~self.ef[i:])
assert_array_equal(np.isinf(self.inff[i:]), self.efnonan[i:])
assert_array_equal(np.signbit(self.signf[i:]), self.ef[i:])
def test_double(self):
# offset for alignment test
for i in range(2):
assert_array_equal(self.d[i:] > 0, self.ed[i:])
assert_array_equal(self.d[i:] - 1 >= 0, self.ed[i:])
assert_array_equal(self.d[i:] == 0, ~self.ed[i:])
assert_array_equal(-self.d[i:] < 0, self.ed[i:])
assert_array_equal(-self.d[i:] + 1 <= 0, self.ed[i:])
r = self.d[i:] != 0
assert_array_equal(r, self.ed[i:])
r2 = self.d[i:] != np.zeros_like(self.d[i:])
r3 = 0 != self.d[i:]
assert_array_equal(r, r2)
assert_array_equal(r, r3)
# check bool == 0x1
assert_array_equal(r.view(np.int8), r.astype(np.int8))
assert_array_equal(r2.view(np.int8), r2.astype(np.int8))
assert_array_equal(r3.view(np.int8), r3.astype(np.int8))
# isnan on amd64 takes the same code path
assert_array_equal(np.isnan(self.nd[i:]), self.ed[i:])
assert_array_equal(np.isfinite(self.nd[i:]), ~self.ed[i:])
assert_array_equal(np.isfinite(self.infd[i:]), ~self.ed[i:])
assert_array_equal(np.isinf(self.infd[i:]), self.ednonan[i:])
assert_array_equal(np.signbit(self.signd[i:]), self.ed[i:])
class TestSeterr(object):
def test_default(self):
err = np.geterr()
assert_equal(err,
dict(divide='warn',
invalid='warn',
over='warn',
under='ignore')
)
def test_set(self):
with np.errstate():
err = np.seterr()
old = np.seterr(divide='print')
assert_(err == old)
new = np.seterr()
assert_(new['divide'] == 'print')
np.seterr(over='raise')
assert_(np.geterr()['over'] == 'raise')
assert_(new['divide'] == 'print')
np.seterr(**old)
assert_(np.geterr() == old)
@dec.skipif(platform.machine() == "armv5tel", "See gh-413.")
def test_divide_err(self):
with np.errstate(divide='raise'):
try:
np.array([1.]) / np.array([0.])
except FloatingPointError:
pass
else:
self.fail()
np.seterr(divide='ignore')
np.array([1.]) / np.array([0.])
def test_errobj(self):
olderrobj = np.geterrobj()
self.called = 0
try:
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
with np.errstate(divide='warn'):
np.seterrobj([20000, 1, None])
np.array([1.]) / np.array([0.])
assert_equal(len(w), 1)
def log_err(*args):
self.called += 1
extobj_err = args
assert_(len(extobj_err) == 2)
assert_("divide" in extobj_err[0])
with np.errstate(divide='ignore'):
np.seterrobj([20000, 3, log_err])
np.array([1.]) / np.array([0.])
assert_equal(self.called, 1)
np.seterrobj(olderrobj)
with np.errstate(divide='ignore'):
np.divide(1., 0., extobj=[20000, 3, log_err])
assert_equal(self.called, 2)
finally:
np.seterrobj(olderrobj)
del self.called
def test_errobj_noerrmask(self):
# errmask = 0 has a special code path for the default
olderrobj = np.geterrobj()
try:
# set errobj to something non default
np.seterrobj([umath.UFUNC_BUFSIZE_DEFAULT,
umath.ERR_DEFAULT + 1, None])
# call a ufunc
np.isnan(np.array([6]))
# same with the default, lots of times to get rid of possible
# pre-existing stack in the code
for i in range(10000):
np.seterrobj([umath.UFUNC_BUFSIZE_DEFAULT, umath.ERR_DEFAULT,
None])
np.isnan(np.array([6]))
finally:
np.seterrobj(olderrobj)
class TestFloatExceptions(object):
def assert_raises_fpe(self, fpeerr, flop, x, y):
ftype = type(x)
try:
flop(x, y)
assert_(False,
"Type %s did not raise fpe error '%s'." % (ftype, fpeerr))
except FloatingPointError as exc:
assert_(str(exc).find(fpeerr) >= 0,
"Type %s raised wrong fpe error '%s'." % (ftype, exc))
def assert_op_raises_fpe(self, fpeerr, flop, sc1, sc2):
# Check that fpe exception is raised.
#
# Given a floating operation `flop` and two scalar values, check that
# the operation raises the floating point exception specified by
# `fpeerr`. Tests all variants with 0-d array scalars as well.
self.assert_raises_fpe(fpeerr, flop, sc1, sc2)
self.assert_raises_fpe(fpeerr, flop, sc1[()], sc2)
self.assert_raises_fpe(fpeerr, flop, sc1, sc2[()])
self.assert_raises_fpe(fpeerr, flop, sc1[()], sc2[()])
@dec.knownfailureif(True, "See ticket #2350")
def test_floating_exceptions(self):
# Test basic arithmetic function errors
with np.errstate(all='raise'):
# Test for all real and complex float types
for typecode in np.typecodes['AllFloat']:
ftype = np.obj2sctype(typecode)
if np.dtype(ftype).kind == 'f':
# Get some extreme values for the type
fi = np.finfo(ftype)
ft_tiny = fi.tiny
ft_max = fi.max
ft_eps = fi.eps
underflow = 'underflow'
divbyzero = 'divide by zero'
else:
# 'c', complex, corresponding real dtype
rtype = type(ftype(0).real)
fi = np.finfo(rtype)
ft_tiny = ftype(fi.tiny)
ft_max = ftype(fi.max)
ft_eps = ftype(fi.eps)
# The complex types raise different exceptions
underflow = ''
divbyzero = ''
overflow = 'overflow'
invalid = 'invalid'
self.assert_raises_fpe(underflow,
lambda a, b: a/b, ft_tiny, ft_max)
self.assert_raises_fpe(underflow,
lambda a, b: a*b, ft_tiny, ft_tiny)
self.assert_raises_fpe(overflow,
lambda a, b: a*b, ft_max, ftype(2))
self.assert_raises_fpe(overflow,
lambda a, b: a/b, ft_max, ftype(0.5))
self.assert_raises_fpe(overflow,
lambda a, b: a+b, ft_max, ft_max*ft_eps)
self.assert_raises_fpe(overflow,
lambda a, b: a-b, -ft_max, ft_max*ft_eps)
self.assert_raises_fpe(overflow,
np.power, ftype(2), ftype(2**fi.nexp))
self.assert_raises_fpe(divbyzero,
lambda a, b: a/b, ftype(1), ftype(0))
self.assert_raises_fpe(invalid,
lambda a, b: a/b, ftype(np.inf), ftype(np.inf))
self.assert_raises_fpe(invalid,
lambda a, b: a/b, ftype(0), ftype(0))
self.assert_raises_fpe(invalid,
lambda a, b: a-b, ftype(np.inf), ftype(np.inf))
self.assert_raises_fpe(invalid,
lambda a, b: a+b, ftype(np.inf), ftype(-np.inf))
self.assert_raises_fpe(invalid,
lambda a, b: a*b, ftype(0), ftype(np.inf))
def test_warnings(self):
# test warning code path
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
with np.errstate(all="warn"):
np.divide(1, 0.)
assert_equal(len(w), 1)
assert_("divide by zero" in str(w[0].message))
np.array(1e300) * np.array(1e300)
assert_equal(len(w), 2)
assert_("overflow" in str(w[-1].message))
np.array(np.inf) - np.array(np.inf)
assert_equal(len(w), 3)
assert_("invalid value" in str(w[-1].message))
np.array(1e-300) * np.array(1e-300)
assert_equal(len(w), 4)
assert_("underflow" in str(w[-1].message))
class TestTypes(object):
def check_promotion_cases(self, promote_func):
# tests that the scalars get coerced correctly.
b = np.bool_(0)
i8, i16, i32, i64 = np.int8(0), np.int16(0), np.int32(0), np.int64(0)
u8, u16, u32, u64 = np.uint8(0), np.uint16(0), np.uint32(0), np.uint64(0)
f32, f64, fld = np.float32(0), np.float64(0), np.longdouble(0)
c64, c128, cld = np.complex64(0), np.complex128(0), np.clongdouble(0)
# coercion within the same kind
assert_equal(promote_func(i8, i16), np.dtype(np.int16))
assert_equal(promote_func(i32, i8), np.dtype(np.int32))
assert_equal(promote_func(i16, i64), np.dtype(np.int64))
assert_equal(promote_func(u8, u32), np.dtype(np.uint32))
assert_equal(promote_func(f32, f64), np.dtype(np.float64))
assert_equal(promote_func(fld, f32), np.dtype(np.longdouble))
assert_equal(promote_func(f64, fld), np.dtype(np.longdouble))
assert_equal(promote_func(c128, c64), np.dtype(np.complex128))
assert_equal(promote_func(cld, c128), np.dtype(np.clongdouble))
assert_equal(promote_func(c64, fld), np.dtype(np.clongdouble))
# coercion between kinds
assert_equal(promote_func(b, i32), np.dtype(np.int32))
assert_equal(promote_func(b, u8), np.dtype(np.uint8))
assert_equal(promote_func(i8, u8), np.dtype(np.int16))
assert_equal(promote_func(u8, i32), np.dtype(np.int32))
assert_equal(promote_func(i64, u32), np.dtype(np.int64))
assert_equal(promote_func(u64, i32), np.dtype(np.float64))
assert_equal(promote_func(i32, f32), np.dtype(np.float64))
assert_equal(promote_func(i64, f32), np.dtype(np.float64))
assert_equal(promote_func(f32, i16), np.dtype(np.float32))
assert_equal(promote_func(f32, u32), np.dtype(np.float64))
assert_equal(promote_func(f32, c64), np.dtype(np.complex64))
assert_equal(promote_func(c128, f32), np.dtype(np.complex128))
assert_equal(promote_func(cld, f64), np.dtype(np.clongdouble))
# coercion between scalars and 1-D arrays
assert_equal(promote_func(np.array([b]), i8), np.dtype(np.int8))
assert_equal(promote_func(np.array([b]), u8), np.dtype(np.uint8))
assert_equal(promote_func(np.array([b]), i32), np.dtype(np.int32))
assert_equal(promote_func(np.array([b]), u32), np.dtype(np.uint32))
assert_equal(promote_func(np.array([i8]), i64), np.dtype(np.int8))
assert_equal(promote_func(u64, np.array([i32])), np.dtype(np.int32))
assert_equal(promote_func(i64, np.array([u32])), np.dtype(np.uint32))
assert_equal(promote_func(np.int32(-1), np.array([u64])),
np.dtype(np.float64))
assert_equal(promote_func(f64, np.array([f32])), np.dtype(np.float32))
assert_equal(promote_func(fld, np.array([f32])), np.dtype(np.float32))
assert_equal(promote_func(np.array([f64]), fld), np.dtype(np.float64))
assert_equal(promote_func(fld, np.array([c64])),
np.dtype(np.complex64))
assert_equal(promote_func(c64, np.array([f64])),
np.dtype(np.complex128))
assert_equal(promote_func(np.complex64(3j), np.array([f64])),
np.dtype(np.complex128))
# coercion between scalars and 1-D arrays, where
# the scalar has greater kind than the array
assert_equal(promote_func(np.array([b]), f64), np.dtype(np.float64))
assert_equal(promote_func(np.array([b]), i64), np.dtype(np.int64))
assert_equal(promote_func(np.array([b]), u64), np.dtype(np.uint64))
assert_equal(promote_func(np.array([i8]), f64), np.dtype(np.float64))
assert_equal(promote_func(np.array([u16]), f64), np.dtype(np.float64))
# uint and int are treated as the same "kind" for
# the purposes of array-scalar promotion.
assert_equal(promote_func(np.array([u16]), i32), np.dtype(np.uint16))
# float and complex are treated as the same "kind" for
# the purposes of array-scalar promotion, so that you can do
# (0j + float32array) to get a complex64 array instead of
# a complex128 array.
assert_equal(promote_func(np.array([f32]), c128),
np.dtype(np.complex64))
def test_coercion(self):
def res_type(a, b):
return np.add(a, b).dtype
self.check_promotion_cases(res_type)
# Use-case: float/complex scalar * bool/int8 array
# shouldn't narrow the float/complex type
for a in [np.array([True, False]), np.array([-3, 12], dtype=np.int8)]:
b = 1.234 * a
assert_equal(b.dtype, np.dtype('f8'), "array type %s" % a.dtype)
b = np.longdouble(1.234) * a
assert_equal(b.dtype, np.dtype(np.longdouble),
"array type %s" % a.dtype)
b = np.float64(1.234) * a
assert_equal(b.dtype, np.dtype('f8'), "array type %s" % a.dtype)
b = np.float32(1.234) * a
assert_equal(b.dtype, np.dtype('f4'), "array type %s" % a.dtype)
b = np.float16(1.234) * a
assert_equal(b.dtype, np.dtype('f2'), "array type %s" % a.dtype)
b = 1.234j * a
assert_equal(b.dtype, np.dtype('c16'), "array type %s" % a.dtype)
b = np.clongdouble(1.234j) * a
assert_equal(b.dtype, np.dtype(np.clongdouble),
"array type %s" % a.dtype)
b = np.complex128(1.234j) * a
assert_equal(b.dtype, np.dtype('c16'), "array type %s" % a.dtype)
b = np.complex64(1.234j) * a
assert_equal(b.dtype, np.dtype('c8'), "array type %s" % a.dtype)
# The following use-case is problematic, and to resolve its
# tricky side-effects requires more changes.
#
# Use-case: (1-t)*a, where 't' is a boolean array and 'a' is
# a float32, shouldn't promote to float64
#
# a = np.array([1.0, 1.5], dtype=np.float32)
# t = np.array([True, False])
# b = t*a
# assert_equal(b, [1.0, 0.0])
# assert_equal(b.dtype, np.dtype('f4'))
# b = (1-t)*a
# assert_equal(b, [0.0, 1.5])
# assert_equal(b.dtype, np.dtype('f4'))
#
# Probably ~t (bitwise negation) is more proper to use here,
# but this is arguably less intuitive to understand at a glance, and
# would fail if 't' is actually an integer array instead of boolean:
#
# b = (~t)*a
# assert_equal(b, [0.0, 1.5])
# assert_equal(b.dtype, np.dtype('f4'))
def test_result_type(self):
self.check_promotion_cases(np.result_type)
assert_(np.result_type(None) == np.dtype(None))
def test_promote_types_endian(self):
# promote_types should always return native-endian types
assert_equal(np.promote_types('<i8', '<i8'), np.dtype('i8'))
assert_equal(np.promote_types('>i8', '>i8'), np.dtype('i8'))
assert_equal(np.promote_types('>i8', '>U16'), np.dtype('U21'))
assert_equal(np.promote_types('<i8', '<U16'), np.dtype('U21'))
assert_equal(np.promote_types('>U16', '>i8'), np.dtype('U21'))
assert_equal(np.promote_types('<U16', '<i8'), np.dtype('U21'))
assert_equal(np.promote_types('<S5', '<U8'), np.dtype('U8'))
assert_equal(np.promote_types('>S5', '>U8'), np.dtype('U8'))
assert_equal(np.promote_types('<U8', '<S5'), np.dtype('U8'))
assert_equal(np.promote_types('>U8', '>S5'), np.dtype('U8'))
assert_equal(np.promote_types('<U5', '<U8'), np.dtype('U8'))
assert_equal(np.promote_types('>U8', '>U5'), np.dtype('U8'))
assert_equal(np.promote_types('<M8', '<M8'), np.dtype('M8'))
assert_equal(np.promote_types('>M8', '>M8'), np.dtype('M8'))
assert_equal(np.promote_types('<m8', '<m8'), np.dtype('m8'))
assert_equal(np.promote_types('>m8', '>m8'), np.dtype('m8'))
def test_promote_types_strings(self):
assert_equal(np.promote_types('bool', 'S'), np.dtype('S5'))
assert_equal(np.promote_types('b', 'S'), np.dtype('S4'))
assert_equal(np.promote_types('u1', 'S'), np.dtype('S3'))
assert_equal(np.promote_types('u2', 'S'), np.dtype('S5'))
assert_equal(np.promote_types('u4', 'S'), np.dtype('S10'))
assert_equal(np.promote_types('u8', 'S'), np.dtype('S20'))
assert_equal(np.promote_types('i1', 'S'), np.dtype('S4'))
assert_equal(np.promote_types('i2', 'S'), np.dtype('S6'))
assert_equal(np.promote_types('i4', 'S'), np.dtype('S11'))
assert_equal(np.promote_types('i8', 'S'), np.dtype('S21'))
assert_equal(np.promote_types('bool', 'U'), np.dtype('U5'))
assert_equal(np.promote_types('b', 'U'), np.dtype('U4'))
assert_equal(np.promote_types('u1', 'U'), np.dtype('U3'))
assert_equal(np.promote_types('u2', 'U'), np.dtype('U5'))
assert_equal(np.promote_types('u4', 'U'), np.dtype('U10'))
assert_equal(np.promote_types('u8', 'U'), np.dtype('U20'))
assert_equal(np.promote_types('i1', 'U'), np.dtype('U4'))
assert_equal(np.promote_types('i2', 'U'), np.dtype('U6'))
assert_equal(np.promote_types('i4', 'U'), np.dtype('U11'))
assert_equal(np.promote_types('i8', 'U'), np.dtype('U21'))
assert_equal(np.promote_types('bool', 'S1'), np.dtype('S5'))
assert_equal(np.promote_types('bool', 'S30'), np.dtype('S30'))
assert_equal(np.promote_types('b', 'S1'), np.dtype('S4'))
assert_equal(np.promote_types('b', 'S30'), np.dtype('S30'))
assert_equal(np.promote_types('u1', 'S1'), np.dtype('S3'))
assert_equal(np.promote_types('u1', 'S30'), np.dtype('S30'))
assert_equal(np.promote_types('u2', 'S1'), np.dtype('S5'))
assert_equal(np.promote_types('u2', 'S30'), np.dtype('S30'))
assert_equal(np.promote_types('u4', 'S1'), np.dtype('S10'))
assert_equal(np.promote_types('u4', 'S30'), np.dtype('S30'))
assert_equal(np.promote_types('u8', 'S1'), np.dtype('S20'))
assert_equal(np.promote_types('u8', 'S30'), np.dtype('S30'))
def test_can_cast(self):
assert_(np.can_cast(np.int32, np.int64))
assert_(np.can_cast(np.float64, complex))
assert_(not np.can_cast(complex, float))
assert_(np.can_cast('i8', 'f8'))
assert_(not np.can_cast('i8', 'f4'))
assert_(np.can_cast('i4', 'S11'))
assert_(np.can_cast('i8', 'i8', 'no'))
assert_(not np.can_cast('<i8', '>i8', 'no'))
assert_(np.can_cast('<i8', '>i8', 'equiv'))
assert_(not np.can_cast('<i4', '>i8', 'equiv'))
assert_(np.can_cast('<i4', '>i8', 'safe'))
assert_(not np.can_cast('<i8', '>i4', 'safe'))
assert_(np.can_cast('<i8', '>i4', 'same_kind'))
assert_(not np.can_cast('<i8', '>u4', 'same_kind'))
assert_(np.can_cast('<i8', '>u4', 'unsafe'))
assert_(np.can_cast('bool', 'S5'))
assert_(not np.can_cast('bool', 'S4'))
assert_(np.can_cast('b', 'S4'))
assert_(not np.can_cast('b', 'S3'))
assert_(np.can_cast('u1', 'S3'))
assert_(not np.can_cast('u1', 'S2'))
assert_(np.can_cast('u2', 'S5'))
assert_(not np.can_cast('u2', 'S4'))
assert_(np.can_cast('u4', 'S10'))
assert_(not np.can_cast('u4', 'S9'))
assert_(np.can_cast('u8', 'S20'))
assert_(not np.can_cast('u8', 'S19'))
assert_(np.can_cast('i1', 'S4'))
assert_(not np.can_cast('i1', 'S3'))
assert_(np.can_cast('i2', 'S6'))
assert_(not np.can_cast('i2', 'S5'))
assert_(np.can_cast('i4', 'S11'))
assert_(not np.can_cast('i4', 'S10'))
assert_(np.can_cast('i8', 'S21'))
assert_(not np.can_cast('i8', 'S20'))
assert_(np.can_cast('bool', 'S5'))
assert_(not np.can_cast('bool', 'S4'))
assert_(np.can_cast('b', 'U4'))
assert_(not np.can_cast('b', 'U3'))
assert_(np.can_cast('u1', 'U3'))
assert_(not np.can_cast('u1', 'U2'))
assert_(np.can_cast('u2', 'U5'))
assert_(not np.can_cast('u2', 'U4'))
assert_(np.can_cast('u4', 'U10'))
assert_(not np.can_cast('u4', 'U9'))
assert_(np.can_cast('u8', 'U20'))
assert_(not np.can_cast('u8', 'U19'))
assert_(np.can_cast('i1', 'U4'))
assert_(not np.can_cast('i1', 'U3'))
assert_(np.can_cast('i2', 'U6'))
assert_(not np.can_cast('i2', 'U5'))
assert_(np.can_cast('i4', 'U11'))
assert_(not np.can_cast('i4', 'U10'))
assert_(np.can_cast('i8', 'U21'))
assert_(not np.can_cast('i8', 'U20'))
assert_raises(TypeError, np.can_cast, 'i4', None)
assert_raises(TypeError, np.can_cast, None, 'i4')
# Also test keyword arguments
assert_(np.can_cast(from_=np.int32, to=np.int64))
def test_can_cast_values(self):
# gh-5917
for dt in np.sctypes['int'] + np.sctypes['uint']:
ii = np.iinfo(dt)
assert_(np.can_cast(ii.min, dt))
assert_(np.can_cast(ii.max, dt))
assert_(not np.can_cast(ii.min - 1, dt))
assert_(not np.can_cast(ii.max + 1, dt))
for dt in np.sctypes['float']:
fi = np.finfo(dt)
assert_(np.can_cast(fi.min, dt))
assert_(np.can_cast(fi.max, dt))
# Custom exception class to test exception propagation in fromiter
class NIterError(Exception):
pass
class TestFromiter(object):
def makegen(self):
for x in range(24):
yield x**2
def test_types(self):
ai32 = np.fromiter(self.makegen(), np.int32)
ai64 = np.fromiter(self.makegen(), np.int64)
af = np.fromiter(self.makegen(), float)
assert_(ai32.dtype == np.dtype(np.int32))
assert_(ai64.dtype == np.dtype(np.int64))
assert_(af.dtype == np.dtype(float))
def test_lengths(self):
expected = np.array(list(self.makegen()))
a = np.fromiter(self.makegen(), int)
a20 = np.fromiter(self.makegen(), int, 20)
assert_(len(a) == len(expected))
assert_(len(a20) == 20)
assert_raises(ValueError, np.fromiter,
self.makegen(), int, len(expected) + 10)
def test_values(self):
expected = np.array(list(self.makegen()))
a = np.fromiter(self.makegen(), int)
a20 = np.fromiter(self.makegen(), int, 20)
assert_(np.alltrue(a == expected, axis=0))
assert_(np.alltrue(a20 == expected[:20], axis=0))
def load_data(self, n, eindex):
# Utility method for the issue 2592 tests.
# Raise an exception at the desired index in the iterator.
for e in range(n):
if e == eindex:
raise NIterError('error at index %s' % eindex)
yield e
def test_2592(self):
# Test iteration exceptions are correctly raised.
count, eindex = 10, 5
assert_raises(NIterError, np.fromiter,
self.load_data(count, eindex), dtype=int, count=count)
def test_2592_edge(self):
# Test iter. exceptions, edge case (exception at end of iterator).
count = 10
eindex = count-1
assert_raises(NIterError, np.fromiter,
self.load_data(count, eindex), dtype=int, count=count)
class TestNonzero(object):
def test_nonzero_trivial(self):
assert_equal(np.count_nonzero(np.array([])), 0)
assert_equal(np.count_nonzero(np.array([], dtype='?')), 0)
assert_equal(np.nonzero(np.array([])), ([],))
assert_equal(np.count_nonzero(np.array(0)), 0)
assert_equal(np.count_nonzero(np.array(0, dtype='?')), 0)
assert_equal(np.nonzero(np.array(0)), ([],))
assert_equal(np.count_nonzero(np.array(1)), 1)
assert_equal(np.count_nonzero(np.array(1, dtype='?')), 1)
assert_equal(np.nonzero(np.array(1)), ([0],))
def test_nonzero_onedim(self):
x = np.array([1, 0, 2, -1, 0, 0, 8])
assert_equal(np.count_nonzero(x), 4)
assert_equal(np.count_nonzero(x), 4)
assert_equal(np.nonzero(x), ([0, 2, 3, 6],))
x = np.array([(1, 2), (0, 0), (1, 1), (-1, 3), (0, 7)],
dtype=[('a', 'i4'), ('b', 'i2')])
assert_equal(np.count_nonzero(x['a']), 3)
assert_equal(np.count_nonzero(x['b']), 4)
assert_equal(np.nonzero(x['a']), ([0, 2, 3],))
assert_equal(np.nonzero(x['b']), ([0, 2, 3, 4],))
def test_nonzero_twodim(self):
x = np.array([[0, 1, 0], [2, 0, 3]])
assert_equal(np.count_nonzero(x), 3)
assert_equal(np.nonzero(x), ([0, 1, 1], [1, 0, 2]))
x = np.eye(3)
assert_equal(np.count_nonzero(x), 3)
assert_equal(np.nonzero(x), ([0, 1, 2], [0, 1, 2]))
x = np.array([[(0, 1), (0, 0), (1, 11)],
[(1, 1), (1, 0), (0, 0)],
[(0, 0), (1, 5), (0, 1)]], dtype=[('a', 'f4'), ('b', 'u1')])
assert_equal(np.count_nonzero(x['a']), 4)
assert_equal(np.count_nonzero(x['b']), 5)
assert_equal(np.nonzero(x['a']), ([0, 1, 1, 2], [2, 0, 1, 1]))
assert_equal(np.nonzero(x['b']), ([0, 0, 1, 2, 2], [0, 2, 0, 1, 2]))
assert_(not x['a'].T.flags.aligned)
assert_equal(np.count_nonzero(x['a'].T), 4)
assert_equal(np.count_nonzero(x['b'].T), 5)
assert_equal(np.nonzero(x['a'].T), ([0, 1, 1, 2], [1, 1, 2, 0]))
assert_equal(np.nonzero(x['b'].T), ([0, 0, 1, 2, 2], [0, 1, 2, 0, 2]))
def test_sparse(self):
# test special sparse condition boolean code path
for i in range(20):
c = np.zeros(200, dtype=bool)
c[i::20] = True
assert_equal(np.nonzero(c)[0], np.arange(i, 200 + i, 20))
c = np.zeros(400, dtype=bool)
c[10 + i:20 + i] = True
c[20 + i*2] = True
assert_equal(np.nonzero(c)[0],
np.concatenate((np.arange(10 + i, 20 + i), [20 + i*2])))
def test_return_type(self):
class C(np.ndarray):
pass
for view in (C, np.ndarray):
for nd in range(1, 4):
shape = tuple(range(2, 2+nd))
x = np.arange(np.prod(shape)).reshape(shape).view(view)
for nzx in (np.nonzero(x), x.nonzero()):
for nzx_i in nzx:
assert_(type(nzx_i) is np.ndarray)
assert_(nzx_i.flags.writeable)
def test_count_nonzero_axis(self):
# Basic check of functionality
m = np.array([[0, 1, 7, 0, 0], [3, 0, 0, 2, 19]])
expected = np.array([1, 1, 1, 1, 1])
assert_equal(np.count_nonzero(m, axis=0), expected)
expected = np.array([2, 3])
assert_equal(np.count_nonzero(m, axis=1), expected)
assert_raises(ValueError, np.count_nonzero, m, axis=(1, 1))
assert_raises(TypeError, np.count_nonzero, m, axis='foo')
assert_raises(np.AxisError, np.count_nonzero, m, axis=3)
assert_raises(TypeError, np.count_nonzero,
m, axis=np.array([[1], [2]]))
def test_count_nonzero_axis_all_dtypes(self):
# More thorough test that the axis argument is respected
# for all dtypes and responds correctly when presented with
# either integer or tuple arguments for axis
msg = "Mismatch for dtype: %s"
def assert_equal_w_dt(a, b, err_msg):
assert_equal(a.dtype, b.dtype, err_msg=err_msg)
assert_equal(a, b, err_msg=err_msg)
for dt in np.typecodes['All']:
err_msg = msg % (np.dtype(dt).name,)
if dt != 'V':
if dt != 'M':
m = np.zeros((3, 3), dtype=dt)
n = np.ones(1, dtype=dt)
m[0, 0] = n[0]
m[1, 0] = n[0]
else: # np.zeros doesn't work for np.datetime64
m = np.array(['1970-01-01'] * 9)
m = m.reshape((3, 3))
m[0, 0] = '1970-01-12'
m[1, 0] = '1970-01-12'
m = m.astype(dt)
expected = np.array([2, 0, 0], dtype=np.intp)
assert_equal_w_dt(np.count_nonzero(m, axis=0),
expected, err_msg=err_msg)
expected = np.array([1, 1, 0], dtype=np.intp)
assert_equal_w_dt(np.count_nonzero(m, axis=1),
expected, err_msg=err_msg)
expected = np.array(2)
assert_equal(np.count_nonzero(m, axis=(0, 1)),
expected, err_msg=err_msg)
assert_equal(np.count_nonzero(m, axis=None),
expected, err_msg=err_msg)
assert_equal(np.count_nonzero(m),
expected, err_msg=err_msg)
if dt == 'V':
# There are no 'nonzero' objects for np.void, so the testing
# setup is slightly different for this dtype
m = np.array([np.void(1)] * 6).reshape((2, 3))
expected = np.array([0, 0, 0], dtype=np.intp)
assert_equal_w_dt(np.count_nonzero(m, axis=0),
expected, err_msg=err_msg)
expected = np.array([0, 0], dtype=np.intp)
assert_equal_w_dt(np.count_nonzero(m, axis=1),
expected, err_msg=err_msg)
expected = np.array(0)
assert_equal(np.count_nonzero(m, axis=(0, 1)),
expected, err_msg=err_msg)
assert_equal(np.count_nonzero(m, axis=None),
expected, err_msg=err_msg)
assert_equal(np.count_nonzero(m),
expected, err_msg=err_msg)
def test_count_nonzero_axis_consistent(self):
# Check that the axis behaviour for valid axes in
# non-special cases is consistent (and therefore
# correct) by checking it against an integer array
# that is then casted to the generic object dtype
from itertools import combinations, permutations
axis = (0, 1, 2, 3)
size = (5, 5, 5, 5)
msg = "Mismatch for axis: %s"
rng = np.random.RandomState(1234)
m = rng.randint(-100, 100, size=size)
n = m.astype(object)
for length in range(len(axis)):
for combo in combinations(axis, length):
for perm in permutations(combo):
assert_equal(
np.count_nonzero(m, axis=perm),
np.count_nonzero(n, axis=perm),
err_msg=msg % (perm,))
def test_countnonzero_axis_empty(self):
a = np.array([[0, 0, 1], [1, 0, 1]])
assert_equal(np.count_nonzero(a, axis=()), a.astype(bool))
def test_array_method(self):
# Tests that the array method
# call to nonzero works
m = np.array([[1, 0, 0], [4, 0, 6]])
tgt = [[0, 1, 1], [0, 0, 2]]
assert_equal(m.nonzero(), tgt)
def test_nonzero_invalid_object(self):
# gh-9295
a = np.array([np.array([1, 2]), 3])
assert_raises(ValueError, np.nonzero, a)
class BoolErrors:
def __bool__(self):
raise ValueError("Not allowed")
def __nonzero__(self):
raise ValueError("Not allowed")
assert_raises(ValueError, np.nonzero, np.array([BoolErrors()]))
class TestIndex(object):
def test_boolean(self):
a = rand(3, 5, 8)
V = rand(5, 8)
g1 = randint(0, 5, size=15)
g2 = randint(0, 8, size=15)
V[g1, g2] = -V[g1, g2]
assert_((np.array([a[0][V > 0], a[1][V > 0], a[2][V > 0]]) == a[:, V > 0]).all())
def test_boolean_edgecase(self):
a = np.array([], dtype='int32')
b = np.array([], dtype='bool')
c = a[b]
assert_equal(c, [])
assert_equal(c.dtype, np.dtype('int32'))
class TestBinaryRepr(object):
def test_zero(self):
assert_equal(np.binary_repr(0), '0')
def test_positive(self):
assert_equal(np.binary_repr(10), '1010')
assert_equal(np.binary_repr(12522),
'11000011101010')
assert_equal(np.binary_repr(10736848),
'101000111101010011010000')
def test_negative(self):
assert_equal(np.binary_repr(-1), '-1')
assert_equal(np.binary_repr(-10), '-1010')
assert_equal(np.binary_repr(-12522),
'-11000011101010')
assert_equal(np.binary_repr(-10736848),
'-101000111101010011010000')
def test_sufficient_width(self):
assert_equal(np.binary_repr(0, width=5), '00000')
assert_equal(np.binary_repr(10, width=7), '0001010')
assert_equal(np.binary_repr(-5, width=7), '1111011')
def test_neg_width_boundaries(self):
# see gh-8670
# Ensure that the example in the issue does not
# break before proceeding to a more thorough test.
assert_equal(np.binary_repr(-128, width=8), '10000000')
for width in range(1, 11):
num = -2**(width - 1)
exp = '1' + (width - 1) * '0'
assert_equal(np.binary_repr(num, width=width), exp)
class TestBaseRepr(object):
def test_base3(self):
assert_equal(np.base_repr(3**5, 3), '100000')
def test_positive(self):
assert_equal(np.base_repr(12, 10), '12')
assert_equal(np.base_repr(12, 10, 4), '000012')
assert_equal(np.base_repr(12, 4), '30')
assert_equal(np.base_repr(3731624803700888, 36), '10QR0ROFCEW')
def test_negative(self):
assert_equal(np.base_repr(-12, 10), '-12')
assert_equal(np.base_repr(-12, 10, 4), '-000012')
assert_equal(np.base_repr(-12, 4), '-30')
def test_base_range(self):
with assert_raises(ValueError):
np.base_repr(1, 1)
with assert_raises(ValueError):
np.base_repr(1, 37)
class TestArrayComparisons(object):
def test_array_equal(self):
res = np.array_equal(np.array([1, 2]), np.array([1, 2]))
assert_(res)
assert_(type(res) is bool)
res = np.array_equal(np.array([1, 2]), np.array([1, 2, 3]))
assert_(not res)
assert_(type(res) is bool)
res = np.array_equal(np.array([1, 2]), np.array([3, 4]))
assert_(not res)
assert_(type(res) is bool)
res = np.array_equal(np.array([1, 2]), np.array([1, 3]))
assert_(not res)
assert_(type(res) is bool)
res = np.array_equal(np.array(['a'], dtype='S1'), np.array(['a'], dtype='S1'))
assert_(res)
assert_(type(res) is bool)
res = np.array_equal(np.array([('a', 1)], dtype='S1,u4'),
np.array([('a', 1)], dtype='S1,u4'))
assert_(res)
assert_(type(res) is bool)
def test_none_compares_elementwise(self):
a = np.array([None, 1, None], dtype=object)
assert_equal(a == None, [True, False, True])
assert_equal(a != None, [False, True, False])
a = np.ones(3)
assert_equal(a == None, [False, False, False])
assert_equal(a != None, [True, True, True])
def test_array_equiv(self):
res = np.array_equiv(np.array([1, 2]), np.array([1, 2]))
assert_(res)
assert_(type(res) is bool)
res = np.array_equiv(np.array([1, 2]), np.array([1, 2, 3]))
assert_(not res)
assert_(type(res) is bool)
res = np.array_equiv(np.array([1, 2]), np.array([3, 4]))
assert_(not res)
assert_(type(res) is bool)
res = np.array_equiv(np.array([1, 2]), np.array([1, 3]))
assert_(not res)
assert_(type(res) is bool)
res = np.array_equiv(np.array([1, 1]), np.array([1]))
assert_(res)
assert_(type(res) is bool)
res = np.array_equiv(np.array([1, 1]), np.array([[1], [1]]))
assert_(res)
assert_(type(res) is bool)
res = np.array_equiv(np.array([1, 2]), np.array([2]))
assert_(not res)
assert_(type(res) is bool)
res = np.array_equiv(np.array([1, 2]), np.array([[1], [2]]))
assert_(not res)
assert_(type(res) is bool)
res = np.array_equiv(np.array([1, 2]), np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]))
assert_(not res)
assert_(type(res) is bool)
def assert_array_strict_equal(x, y):
assert_array_equal(x, y)
# Check flags, 32 bit arches typically don't provide 16 byte alignment
if ((x.dtype.alignment <= 8 or
np.intp().dtype.itemsize != 4) and
sys.platform != 'win32'):
assert_(x.flags == y.flags)
else:
assert_(x.flags.owndata == y.flags.owndata)
assert_(x.flags.writeable == y.flags.writeable)
assert_(x.flags.c_contiguous == y.flags.c_contiguous)
assert_(x.flags.f_contiguous == y.flags.f_contiguous)
assert_(x.flags.writebackifcopy == y.flags.writebackifcopy)
# check endianness
assert_(x.dtype.isnative == y.dtype.isnative)
class TestClip(object):
def setup(self):
self.nr = 5
self.nc = 3
def fastclip(self, a, m, M, out=None):
if out is None:
return a.clip(m, M)
else:
return a.clip(m, M, out)
def clip(self, a, m, M, out=None):
# use slow-clip
selector = np.less(a, m) + 2*np.greater(a, M)
return selector.choose((a, m, M), out=out)
# Handy functions
def _generate_data(self, n, m):
return randn(n, m)
def _generate_data_complex(self, n, m):
return randn(n, m) + 1.j * rand(n, m)
def _generate_flt_data(self, n, m):
return (randn(n, m)).astype(np.float32)
def _neg_byteorder(self, a):
a = np.asarray(a)
if sys.byteorder == 'little':
a = a.astype(a.dtype.newbyteorder('>'))
else:
a = a.astype(a.dtype.newbyteorder('<'))
return a
def _generate_non_native_data(self, n, m):
data = randn(n, m)
data = self._neg_byteorder(data)
assert_(not data.dtype.isnative)
return data
def _generate_int_data(self, n, m):
return (10 * rand(n, m)).astype(np.int64)
def _generate_int32_data(self, n, m):
return (10 * rand(n, m)).astype(np.int32)
# Now the real test cases
def test_simple_double(self):
# Test native double input with scalar min/max.
a = self._generate_data(self.nr, self.nc)
m = 0.1
M = 0.6
ac = self.fastclip(a, m, M)
act = self.clip(a, m, M)
assert_array_strict_equal(ac, act)
def test_simple_int(self):
# Test native int input with scalar min/max.
a = self._generate_int_data(self.nr, self.nc)
a = a.astype(int)
m = -2
M = 4
ac = self.fastclip(a, m, M)
act = self.clip(a, m, M)
assert_array_strict_equal(ac, act)
def test_array_double(self):
# Test native double input with array min/max.
a = self._generate_data(self.nr, self.nc)
m = np.zeros(a.shape)
M = m + 0.5
ac = self.fastclip(a, m, M)
act = self.clip(a, m, M)
assert_array_strict_equal(ac, act)
def test_simple_nonnative(self):
# Test non native double input with scalar min/max.
# Test native double input with non native double scalar min/max.
a = self._generate_non_native_data(self.nr, self.nc)
m = -0.5
M = 0.6
ac = self.fastclip(a, m, M)
act = self.clip(a, m, M)
assert_array_equal(ac, act)
# Test native double input with non native double scalar min/max.
a = self._generate_data(self.nr, self.nc)
m = -0.5
M = self._neg_byteorder(0.6)
assert_(not M.dtype.isnative)
ac = self.fastclip(a, m, M)
act = self.clip(a, m, M)
assert_array_equal(ac, act)
def test_simple_complex(self):
# Test native complex input with native double scalar min/max.
# Test native input with complex double scalar min/max.
a = 3 * self._generate_data_complex(self.nr, self.nc)
m = -0.5
M = 1.
ac = self.fastclip(a, m, M)
act = self.clip(a, m, M)
assert_array_strict_equal(ac, act)
# Test native input with complex double scalar min/max.
a = 3 * self._generate_data(self.nr, self.nc)
m = -0.5 + 1.j
M = 1. + 2.j
ac = self.fastclip(a, m, M)
act = self.clip(a, m, M)
assert_array_strict_equal(ac, act)
def test_clip_complex(self):
# Address Issue gh-5354 for clipping complex arrays
# Test native complex input without explicit min/max
# ie, either min=None or max=None
a = np.ones(10, dtype=complex)
m = a.min()
M = a.max()
am = self.fastclip(a, m, None)
aM = self.fastclip(a, None, M)
assert_array_strict_equal(am, a)
assert_array_strict_equal(aM, a)
def test_clip_non_contig(self):
# Test clip for non contiguous native input and native scalar min/max.
a = self._generate_data(self.nr * 2, self.nc * 3)
a = a[::2, ::3]
assert_(not a.flags['F_CONTIGUOUS'])
assert_(not a.flags['C_CONTIGUOUS'])
ac = self.fastclip(a, -1.6, 1.7)
act = self.clip(a, -1.6, 1.7)
assert_array_strict_equal(ac, act)
def test_simple_out(self):
# Test native double input with scalar min/max.
a = self._generate_data(self.nr, self.nc)
m = -0.5
M = 0.6
ac = np.zeros(a.shape)
act = np.zeros(a.shape)
self.fastclip(a, m, M, ac)
self.clip(a, m, M, act)
assert_array_strict_equal(ac, act)
def test_simple_int32_inout(self):
# Test native int32 input with double min/max and int32 out.
a = self._generate_int32_data(self.nr, self.nc)
m = np.float64(0)
M = np.float64(2)
ac = np.zeros(a.shape, dtype=np.int32)
act = ac.copy()
self.fastclip(a, m, M, ac)
self.clip(a, m, M, act)
assert_array_strict_equal(ac, act)
def test_simple_int64_out(self):
# Test native int32 input with int32 scalar min/max and int64 out.
a = self._generate_int32_data(self.nr, self.nc)
m = np.int32(-1)
M = np.int32(1)
ac = np.zeros(a.shape, dtype=np.int64)
act = ac.copy()
self.fastclip(a, m, M, ac)
self.clip(a, m, M, act)
assert_array_strict_equal(ac, act)
def test_simple_int64_inout(self):
# Test native int32 input with double array min/max and int32 out.
a = self._generate_int32_data(self.nr, self.nc)
m = np.zeros(a.shape, np.float64)
M = np.float64(1)
ac = np.zeros(a.shape, dtype=np.int32)
act = ac.copy()
self.fastclip(a, m, M, ac)
self.clip(a, m, M, act)
assert_array_strict_equal(ac, act)
def test_simple_int32_out(self):
# Test native double input with scalar min/max and int out.
a = self._generate_data(self.nr, self.nc)
m = -1.0
M = 2.0
ac = np.zeros(a.shape, dtype=np.int32)
act = ac.copy()
self.fastclip(a, m, M, ac)
self.clip(a, m, M, act)
assert_array_strict_equal(ac, act)
def test_simple_inplace_01(self):
# Test native double input with array min/max in-place.
a = self._generate_data(self.nr, self.nc)
ac = a.copy()
m = np.zeros(a.shape)
M = 1.0
self.fastclip(a, m, M, a)
self.clip(a, m, M, ac)
assert_array_strict_equal(a, ac)
def test_simple_inplace_02(self):
# Test native double input with scalar min/max in-place.
a = self._generate_data(self.nr, self.nc)
ac = a.copy()
m = -0.5
M = 0.6
self.fastclip(a, m, M, a)
self.clip(a, m, M, ac)
assert_array_strict_equal(a, ac)
def test_noncontig_inplace(self):
# Test non contiguous double input with double scalar min/max in-place.
a = self._generate_data(self.nr * 2, self.nc * 3)
a = a[::2, ::3]
assert_(not a.flags['F_CONTIGUOUS'])
assert_(not a.flags['C_CONTIGUOUS'])
ac = a.copy()
m = -0.5
M = 0.6
self.fastclip(a, m, M, a)
self.clip(a, m, M, ac)
assert_array_equal(a, ac)
def test_type_cast_01(self):
# Test native double input with scalar min/max.
a = self._generate_data(self.nr, self.nc)
m = -0.5
M = 0.6
ac = self.fastclip(a, m, M)
act = self.clip(a, m, M)
assert_array_strict_equal(ac, act)
def test_type_cast_02(self):
# Test native int32 input with int32 scalar min/max.
a = self._generate_int_data(self.nr, self.nc)
a = a.astype(np.int32)
m = -2
M = 4
ac = self.fastclip(a, m, M)
act = self.clip(a, m, M)
assert_array_strict_equal(ac, act)
def test_type_cast_03(self):
# Test native int32 input with float64 scalar min/max.
a = self._generate_int32_data(self.nr, self.nc)
m = -2
M = 4
ac = self.fastclip(a, np.float64(m), np.float64(M))
act = self.clip(a, np.float64(m), np.float64(M))
assert_array_strict_equal(ac, act)
def test_type_cast_04(self):
# Test native int32 input with float32 scalar min/max.
a = self._generate_int32_data(self.nr, self.nc)
m = np.float32(-2)
M = np.float32(4)
act = self.fastclip(a, m, M)
ac = self.clip(a, m, M)
assert_array_strict_equal(ac, act)
def test_type_cast_05(self):
# Test native int32 with double arrays min/max.
a = self._generate_int_data(self.nr, self.nc)
m = -0.5
M = 1.
ac = self.fastclip(a, m * np.zeros(a.shape), M)
act = self.clip(a, m * np.zeros(a.shape), M)
assert_array_strict_equal(ac, act)
def test_type_cast_06(self):
# Test native with NON native scalar min/max.
a = self._generate_data(self.nr, self.nc)
m = 0.5
m_s = self._neg_byteorder(m)
M = 1.
act = self.clip(a, m_s, M)
ac = self.fastclip(a, m_s, M)
assert_array_strict_equal(ac, act)
def test_type_cast_07(self):
# Test NON native with native array min/max.
a = self._generate_data(self.nr, self.nc)
m = -0.5 * np.ones(a.shape)
M = 1.
a_s = self._neg_byteorder(a)
assert_(not a_s.dtype.isnative)
act = a_s.clip(m, M)
ac = self.fastclip(a_s, m, M)
assert_array_strict_equal(ac, act)
def test_type_cast_08(self):
# Test NON native with native scalar min/max.
a = self._generate_data(self.nr, self.nc)
m = -0.5
M = 1.
a_s = self._neg_byteorder(a)
assert_(not a_s.dtype.isnative)
ac = self.fastclip(a_s, m, M)
act = a_s.clip(m, M)
assert_array_strict_equal(ac, act)
def test_type_cast_09(self):
# Test native with NON native array min/max.
a = self._generate_data(self.nr, self.nc)
m = -0.5 * np.ones(a.shape)
M = 1.
m_s = self._neg_byteorder(m)
assert_(not m_s.dtype.isnative)
ac = self.fastclip(a, m_s, M)
act = self.clip(a, m_s, M)
assert_array_strict_equal(ac, act)
def test_type_cast_10(self):
# Test native int32 with float min/max and float out for output argument.
a = self._generate_int_data(self.nr, self.nc)
b = np.zeros(a.shape, dtype=np.float32)
m = np.float32(-0.5)
M = np.float32(1)
act = self.clip(a, m, M, out=b)
ac = self.fastclip(a, m, M, out=b)
assert_array_strict_equal(ac, act)
def test_type_cast_11(self):
# Test non native with native scalar, min/max, out non native
a = self._generate_non_native_data(self.nr, self.nc)
b = a.copy()
b = b.astype(b.dtype.newbyteorder('>'))
bt = b.copy()
m = -0.5
M = 1.
self.fastclip(a, m, M, out=b)
self.clip(a, m, M, out=bt)
assert_array_strict_equal(b, bt)
def test_type_cast_12(self):
# Test native int32 input and min/max and float out
a = self._generate_int_data(self.nr, self.nc)
b = np.zeros(a.shape, dtype=np.float32)
m = np.int32(0)
M = np.int32(1)
act = self.clip(a, m, M, out=b)
ac = self.fastclip(a, m, M, out=b)
assert_array_strict_equal(ac, act)
def test_clip_with_out_simple(self):
# Test native double input with scalar min/max
a = self._generate_data(self.nr, self.nc)
m = -0.5
M = 0.6
ac = np.zeros(a.shape)
act = np.zeros(a.shape)
self.fastclip(a, m, M, ac)
self.clip(a, m, M, act)
assert_array_strict_equal(ac, act)
def test_clip_with_out_simple2(self):
# Test native int32 input with double min/max and int32 out
a = self._generate_int32_data(self.nr, self.nc)
m = np.float64(0)
M = np.float64(2)
ac = np.zeros(a.shape, dtype=np.int32)
act = ac.copy()
self.fastclip(a, m, M, ac)
self.clip(a, m, M, act)
assert_array_strict_equal(ac, act)
def test_clip_with_out_simple_int32(self):
# Test native int32 input with int32 scalar min/max and int64 out
a = self._generate_int32_data(self.nr, self.nc)
m = np.int32(-1)
M = np.int32(1)
ac = np.zeros(a.shape, dtype=np.int64)
act = ac.copy()
self.fastclip(a, m, M, ac)
self.clip(a, m, M, act)
assert_array_strict_equal(ac, act)
def test_clip_with_out_array_int32(self):
# Test native int32 input with double array min/max and int32 out
a = self._generate_int32_data(self.nr, self.nc)
m = np.zeros(a.shape, np.float64)
M = np.float64(1)
ac = np.zeros(a.shape, dtype=np.int32)
act = ac.copy()
self.fastclip(a, m, M, ac)
self.clip(a, m, M, act)
assert_array_strict_equal(ac, act)
def test_clip_with_out_array_outint32(self):
# Test native double input with scalar min/max and int out
a = self._generate_data(self.nr, self.nc)
m = -1.0
M = 2.0
ac = np.zeros(a.shape, dtype=np.int32)
act = ac.copy()
self.fastclip(a, m, M, ac)
self.clip(a, m, M, act)
assert_array_strict_equal(ac, act)
def test_clip_inplace_array(self):
# Test native double input with array min/max
a = self._generate_data(self.nr, self.nc)
ac = a.copy()
m = np.zeros(a.shape)
M = 1.0
self.fastclip(a, m, M, a)
self.clip(a, m, M, ac)
assert_array_strict_equal(a, ac)
def test_clip_inplace_simple(self):
# Test native double input with scalar min/max
a = self._generate_data(self.nr, self.nc)
ac = a.copy()
m = -0.5
M = 0.6
self.fastclip(a, m, M, a)
self.clip(a, m, M, ac)
assert_array_strict_equal(a, ac)
def test_clip_func_takes_out(self):
# Ensure that the clip() function takes an out=argument.
a = self._generate_data(self.nr, self.nc)
ac = a.copy()
m = -0.5
M = 0.6
a2 = np.clip(a, m, M, out=a)
self.clip(a, m, M, ac)
assert_array_strict_equal(a2, ac)
assert_(a2 is a)
def test_clip_nan(self):
d = np.arange(7.)
assert_equal(d.clip(min=np.nan), d)
assert_equal(d.clip(max=np.nan), d)
assert_equal(d.clip(min=np.nan, max=np.nan), d)
assert_equal(d.clip(min=-2, max=np.nan), d)
assert_equal(d.clip(min=np.nan, max=10), d)
class TestAllclose(object):
rtol = 1e-5
atol = 1e-8
def setup(self):
self.olderr = np.seterr(invalid='ignore')
def teardown(self):
np.seterr(**self.olderr)
def tst_allclose(self, x, y):
assert_(np.allclose(x, y), "%s and %s not close" % (x, y))
def tst_not_allclose(self, x, y):
assert_(not np.allclose(x, y), "%s and %s shouldn't be close" % (x, y))
def test_ip_allclose(self):
# Parametric test factory.
arr = np.array([100, 1000])
aran = np.arange(125).reshape((5, 5, 5))
atol = self.atol
rtol = self.rtol
data = [([1, 0], [1, 0]),
([atol], [0]),
([1], [1+rtol+atol]),
(arr, arr + arr*rtol),
(arr, arr + arr*rtol + atol*2),
(aran, aran + aran*rtol),
(np.inf, np.inf),
(np.inf, [np.inf])]
for (x, y) in data:
yield (self.tst_allclose, x, y)
def test_ip_not_allclose(self):
# Parametric test factory.
aran = np.arange(125).reshape((5, 5, 5))
atol = self.atol
rtol = self.rtol
data = [([np.inf, 0], [1, np.inf]),
([np.inf, 0], [1, 0]),
([np.inf, np.inf], [1, np.inf]),
([np.inf, np.inf], [1, 0]),
([-np.inf, 0], [np.inf, 0]),
([np.nan, 0], [np.nan, 0]),
([atol*2], [0]),
([1], [1+rtol+atol*2]),
(aran, aran + aran*atol + atol*2),
(np.array([np.inf, 1]), np.array([0, np.inf]))]
for (x, y) in data:
yield (self.tst_not_allclose, x, y)
def test_no_parameter_modification(self):
x = np.array([np.inf, 1])
y = np.array([0, np.inf])
np.allclose(x, y)
assert_array_equal(x, np.array([np.inf, 1]))
assert_array_equal(y, np.array([0, np.inf]))
def test_min_int(self):
# Could make problems because of abs(min_int) == min_int
min_int = np.iinfo(np.int_).min
a = np.array([min_int], dtype=np.int_)
assert_(np.allclose(a, a))
def test_equalnan(self):
x = np.array([1.0, np.nan])
assert_(np.allclose(x, x, equal_nan=True))
def test_return_class_is_ndarray(self):
# Issue gh-6475
# Check that allclose does not preserve subtypes
class Foo(np.ndarray):
def __new__(cls, *args, **kwargs):
return np.array(*args, **kwargs).view(cls)
a = Foo([1])
assert_(type(np.allclose(a, a)) is bool)
class TestIsclose(object):
rtol = 1e-5
atol = 1e-8
def setup(self):
atol = self.atol
rtol = self.rtol
arr = np.array([100, 1000])
aran = np.arange(125).reshape((5, 5, 5))
self.all_close_tests = [
([1, 0], [1, 0]),
([atol], [0]),
([1], [1 + rtol + atol]),
(arr, arr + arr*rtol),
(arr, arr + arr*rtol + atol),
(aran, aran + aran*rtol),
(np.inf, np.inf),
(np.inf, [np.inf]),
([np.inf, -np.inf], [np.inf, -np.inf]),
]
self.none_close_tests = [
([np.inf, 0], [1, np.inf]),
([np.inf, -np.inf], [1, 0]),
([np.inf, np.inf], [1, -np.inf]),
([np.inf, np.inf], [1, 0]),
([np.nan, 0], [np.nan, -np.inf]),
([atol*2], [0]),
([1], [1 + rtol + atol*2]),
(aran, aran + rtol*1.1*aran + atol*1.1),
(np.array([np.inf, 1]), np.array([0, np.inf])),
]
self.some_close_tests = [
([np.inf, 0], [np.inf, atol*2]),
([atol, 1, 1e6*(1 + 2*rtol) + atol], [0, np.nan, 1e6]),
(np.arange(3), [0, 1, 2.1]),
(np.nan, [np.nan, np.nan, np.nan]),
([0], [atol, np.inf, -np.inf, np.nan]),
(0, [atol, np.inf, -np.inf, np.nan]),
]
self.some_close_results = [
[True, False],
[True, False, False],
[True, True, False],
[False, False, False],
[True, False, False, False],
[True, False, False, False],
]
def test_ip_isclose(self):
self.setup()
tests = self.some_close_tests
results = self.some_close_results
for (x, y), result in zip(tests, results):
yield (assert_array_equal, np.isclose(x, y), result)
def tst_all_isclose(self, x, y):
assert_(np.all(np.isclose(x, y)), "%s and %s not close" % (x, y))
def tst_none_isclose(self, x, y):
msg = "%s and %s shouldn't be close"
assert_(not np.any(np.isclose(x, y)), msg % (x, y))
def tst_isclose_allclose(self, x, y):
msg = "isclose.all() and allclose aren't same for %s and %s"
msg2 = "isclose and allclose aren't same for %s and %s"
if np.isscalar(x) and np.isscalar(y):
assert_(np.isclose(x, y) == np.allclose(x, y), msg=msg2 % (x, y))
else:
assert_array_equal(np.isclose(x, y).all(), np.allclose(x, y), msg % (x, y))
def test_ip_all_isclose(self):
self.setup()
for (x, y) in self.all_close_tests:
yield (self.tst_all_isclose, x, y)
def test_ip_none_isclose(self):
self.setup()
for (x, y) in self.none_close_tests:
yield (self.tst_none_isclose, x, y)
def test_ip_isclose_allclose(self):
self.setup()
tests = (self.all_close_tests + self.none_close_tests +
self.some_close_tests)
for (x, y) in tests:
yield (self.tst_isclose_allclose, x, y)
def test_equal_nan(self):
assert_array_equal(np.isclose(np.nan, np.nan, equal_nan=True), [True])
arr = np.array([1.0, np.nan])
assert_array_equal(np.isclose(arr, arr, equal_nan=True), [True, True])
def test_masked_arrays(self):
# Make sure to test the output type when arguments are interchanged.
x = np.ma.masked_where([True, True, False], np.arange(3))
assert_(type(x) is type(np.isclose(2, x)))
assert_(type(x) is type(np.isclose(x, 2)))
x = np.ma.masked_where([True, True, False], [np.nan, np.inf, np.nan])
assert_(type(x) is type(np.isclose(np.inf, x)))
assert_(type(x) is type(np.isclose(x, np.inf)))
x = np.ma.masked_where([True, True, False], [np.nan, np.nan, np.nan])
y = np.isclose(np.nan, x, equal_nan=True)
assert_(type(x) is type(y))
# Ensure that the mask isn't modified...
assert_array_equal([True, True, False], y.mask)
y = np.isclose(x, np.nan, equal_nan=True)
assert_(type(x) is type(y))
# Ensure that the mask isn't modified...
assert_array_equal([True, True, False], y.mask)
x = np.ma.masked_where([True, True, False], [np.nan, np.nan, np.nan])
y = np.isclose(x, x, equal_nan=True)
assert_(type(x) is type(y))
# Ensure that the mask isn't modified...
assert_array_equal([True, True, False], y.mask)
def test_scalar_return(self):
assert_(np.isscalar(np.isclose(1, 1)))
def test_no_parameter_modification(self):
x = np.array([np.inf, 1])
y = np.array([0, np.inf])
np.isclose(x, y)
assert_array_equal(x, np.array([np.inf, 1]))
assert_array_equal(y, np.array([0, np.inf]))
def test_non_finite_scalar(self):
# GH7014, when two scalars are compared the output should also be a
# scalar
assert_(np.isclose(np.inf, -np.inf) is np.False_)
assert_(np.isclose(0, np.inf) is np.False_)
assert_(type(np.isclose(0, np.inf)) is np.bool_)
class TestStdVar(object):
def setup(self):
self.A = np.array([1, -1, 1, -1])
self.real_var = 1
def test_basic(self):
assert_almost_equal(np.var(self.A), self.real_var)
assert_almost_equal(np.std(self.A)**2, self.real_var)
def test_scalars(self):
assert_equal(np.var(1), 0)
assert_equal(np.std(1), 0)
def test_ddof1(self):
assert_almost_equal(np.var(self.A, ddof=1),
self.real_var*len(self.A)/float(len(self.A)-1))
assert_almost_equal(np.std(self.A, ddof=1)**2,
self.real_var*len(self.A)/float(len(self.A)-1))
def test_ddof2(self):
assert_almost_equal(np.var(self.A, ddof=2),
self.real_var*len(self.A)/float(len(self.A)-2))
assert_almost_equal(np.std(self.A, ddof=2)**2,
self.real_var*len(self.A)/float(len(self.A)-2))
def test_out_scalar(self):
d = np.arange(10)
out = np.array(0.)
r = np.std(d, out=out)
assert_(r is out)
assert_array_equal(r, out)
r = np.var(d, out=out)
assert_(r is out)
assert_array_equal(r, out)
r = np.mean(d, out=out)
assert_(r is out)
assert_array_equal(r, out)
class TestStdVarComplex(object):
def test_basic(self):
A = np.array([1, 1.j, -1, -1.j])
real_var = 1
assert_almost_equal(np.var(A), real_var)
assert_almost_equal(np.std(A)**2, real_var)
def test_scalars(self):
assert_equal(np.var(1j), 0)
assert_equal(np.std(1j), 0)
class TestCreationFuncs(object):
# Test ones, zeros, empty and full.
def setup(self):
dtypes = {np.dtype(tp) for tp in itertools.chain(*np.sctypes.values())}
# void, bytes, str
variable_sized = {tp for tp in dtypes if tp.str.endswith('0')}
self.dtypes = sorted(dtypes - variable_sized |
{np.dtype(tp.str.replace("0", str(i)))
for tp in variable_sized for i in range(1, 10)},
key=lambda dtype: dtype.str)
self.orders = {'C': 'c_contiguous', 'F': 'f_contiguous'}
self.ndims = 10
def check_function(self, func, fill_value=None):
par = ((0, 1, 2),
range(self.ndims),
self.orders,
self.dtypes)
fill_kwarg = {}
if fill_value is not None:
fill_kwarg = {'fill_value': fill_value}
for size, ndims, order, dtype in itertools.product(*par):
shape = ndims * [size]
# do not fill void type
if fill_kwarg and dtype.str.startswith('|V'):
continue
arr = func(shape, order=order, dtype=dtype,
**fill_kwarg)
assert_equal(arr.dtype, dtype)
assert_(getattr(arr.flags, self.orders[order]))
if fill_value is not None:
if dtype.str.startswith('|S'):
val = str(fill_value)
else:
val = fill_value
assert_equal(arr, dtype.type(val))
def test_zeros(self):
self.check_function(np.zeros)
def test_ones(self):
self.check_function(np.zeros)
def test_empty(self):
self.check_function(np.empty)
def test_full(self):
self.check_function(np.full, 0)
self.check_function(np.full, 1)
@dec.skipif(not HAS_REFCOUNT, "python has no sys.getrefcount")
def test_for_reference_leak(self):
# Make sure we have an object for reference
dim = 1
beg = sys.getrefcount(dim)
np.zeros([dim]*10)
assert_(sys.getrefcount(dim) == beg)
np.ones([dim]*10)
assert_(sys.getrefcount(dim) == beg)
np.empty([dim]*10)
assert_(sys.getrefcount(dim) == beg)
np.full([dim]*10, 0)
assert_(sys.getrefcount(dim) == beg)
class TestLikeFuncs(object):
'''Test ones_like, zeros_like, empty_like and full_like'''
def setup(self):
self.data = [
# Array scalars
(np.array(3.), None),
(np.array(3), 'f8'),
# 1D arrays
(np.arange(6, dtype='f4'), None),
(np.arange(6), 'c16'),
# 2D C-layout arrays
(np.arange(6).reshape(2, 3), None),
(np.arange(6).reshape(3, 2), 'i1'),
# 2D F-layout arrays
(np.arange(6).reshape((2, 3), order='F'), None),
(np.arange(6).reshape((3, 2), order='F'), 'i1'),
# 3D C-layout arrays
(np.arange(24).reshape(2, 3, 4), None),
(np.arange(24).reshape(4, 3, 2), 'f4'),
# 3D F-layout arrays
(np.arange(24).reshape((2, 3, 4), order='F'), None),
(np.arange(24).reshape((4, 3, 2), order='F'), 'f4'),
# 3D non-C/F-layout arrays
(np.arange(24).reshape(2, 3, 4).swapaxes(0, 1), None),
(np.arange(24).reshape(4, 3, 2).swapaxes(0, 1), '?'),
]
def compare_array_value(self, dz, value, fill_value):
if value is not None:
if fill_value:
try:
z = dz.dtype.type(value)
except OverflowError:
pass
else:
assert_(np.all(dz == z))
else:
assert_(np.all(dz == value))
def check_like_function(self, like_function, value, fill_value=False):
if fill_value:
fill_kwarg = {'fill_value': value}
else:
fill_kwarg = {}
for d, dtype in self.data:
# default (K) order, dtype
dz = like_function(d, dtype=dtype, **fill_kwarg)
assert_equal(dz.shape, d.shape)
assert_equal(np.array(dz.strides)*d.dtype.itemsize,
np.array(d.strides)*dz.dtype.itemsize)
assert_equal(d.flags.c_contiguous, dz.flags.c_contiguous)
assert_equal(d.flags.f_contiguous, dz.flags.f_contiguous)
if dtype is None:
assert_equal(dz.dtype, d.dtype)
else:
assert_equal(dz.dtype, np.dtype(dtype))
self.compare_array_value(dz, value, fill_value)
# C order, default dtype
dz = like_function(d, order='C', dtype=dtype, **fill_kwarg)
assert_equal(dz.shape, d.shape)
assert_(dz.flags.c_contiguous)
if dtype is None:
assert_equal(dz.dtype, d.dtype)
else:
assert_equal(dz.dtype, np.dtype(dtype))
self.compare_array_value(dz, value, fill_value)
# F order, default dtype
dz = like_function(d, order='F', dtype=dtype, **fill_kwarg)
assert_equal(dz.shape, d.shape)
assert_(dz.flags.f_contiguous)
if dtype is None:
assert_equal(dz.dtype, d.dtype)
else:
assert_equal(dz.dtype, np.dtype(dtype))
self.compare_array_value(dz, value, fill_value)
# A order
dz = like_function(d, order='A', dtype=dtype, **fill_kwarg)
assert_equal(dz.shape, d.shape)
if d.flags.f_contiguous:
assert_(dz.flags.f_contiguous)
else:
assert_(dz.flags.c_contiguous)
if dtype is None:
assert_equal(dz.dtype, d.dtype)
else:
assert_equal(dz.dtype, np.dtype(dtype))
self.compare_array_value(dz, value, fill_value)
# Test the 'subok' parameter
a = np.matrix([[1, 2], [3, 4]])
b = like_function(a, **fill_kwarg)
assert_(type(b) is np.matrix)
b = like_function(a, subok=False, **fill_kwarg)
assert_(type(b) is not np.matrix)
def test_ones_like(self):
self.check_like_function(np.ones_like, 1)
def test_zeros_like(self):
self.check_like_function(np.zeros_like, 0)
def test_empty_like(self):
self.check_like_function(np.empty_like, None)
def test_filled_like(self):
self.check_like_function(np.full_like, 0, True)
self.check_like_function(np.full_like, 1, True)
self.check_like_function(np.full_like, 1000, True)
self.check_like_function(np.full_like, 123.456, True)
self.check_like_function(np.full_like, np.inf, True)
class TestCorrelate(object):
def _setup(self, dt):
self.x = np.array([1, 2, 3, 4, 5], dtype=dt)
self.xs = np.arange(1, 20)[::3]
self.y = np.array([-1, -2, -3], dtype=dt)
self.z1 = np.array([ -3., -8., -14., -20., -26., -14., -5.], dtype=dt)
self.z1_4 = np.array([-2., -5., -8., -11., -14., -5.], dtype=dt)
self.z1r = np.array([-15., -22., -22., -16., -10., -4., -1.], dtype=dt)
self.z2 = np.array([-5., -14., -26., -20., -14., -8., -3.], dtype=dt)
self.z2r = np.array([-1., -4., -10., -16., -22., -22., -15.], dtype=dt)
self.zs = np.array([-3., -14., -30., -48., -66., -84.,
-102., -54., -19.], dtype=dt)
def test_float(self):
self._setup(float)
z = np.correlate(self.x, self.y, 'full')
assert_array_almost_equal(z, self.z1)
z = np.correlate(self.x, self.y[:-1], 'full')
assert_array_almost_equal(z, self.z1_4)
z = np.correlate(self.y, self.x, 'full')
assert_array_almost_equal(z, self.z2)
z = np.correlate(self.x[::-1], self.y, 'full')
assert_array_almost_equal(z, self.z1r)
z = np.correlate(self.y, self.x[::-1], 'full')
assert_array_almost_equal(z, self.z2r)
z = np.correlate(self.xs, self.y, 'full')
assert_array_almost_equal(z, self.zs)
def test_object(self):
self._setup(Decimal)
z = np.correlate(self.x, self.y, 'full')
assert_array_almost_equal(z, self.z1)
z = np.correlate(self.y, self.x, 'full')
assert_array_almost_equal(z, self.z2)
def test_no_overwrite(self):
d = np.ones(100)
k = np.ones(3)
np.correlate(d, k)
assert_array_equal(d, np.ones(100))
assert_array_equal(k, np.ones(3))
def test_complex(self):
x = np.array([1, 2, 3, 4+1j], dtype=complex)
y = np.array([-1, -2j, 3+1j], dtype=complex)
r_z = np.array([3-1j, 6, 8+1j, 11+5j, -5+8j, -4-1j], dtype=complex)
r_z = r_z[::-1].conjugate()
z = np.correlate(y, x, mode='full')
assert_array_almost_equal(z, r_z)
class TestConvolve(object):
def test_object(self):
d = [1.] * 100
k = [1.] * 3
assert_array_almost_equal(np.convolve(d, k)[2:-2], np.full(98, 3))
def test_no_overwrite(self):
d = np.ones(100)
k = np.ones(3)
np.convolve(d, k)
assert_array_equal(d, np.ones(100))
assert_array_equal(k, np.ones(3))
class TestArgwhere(object):
def test_2D(self):
x = np.arange(6).reshape((2, 3))
assert_array_equal(np.argwhere(x > 1),
[[0, 2],
[1, 0],
[1, 1],
[1, 2]])
def test_list(self):
assert_equal(np.argwhere([4, 0, 2, 1, 3]), [[0], [2], [3], [4]])
class TestStringFunction(object):
def test_set_string_function(self):
a = np.array([1])
np.set_string_function(lambda x: "FOO", repr=True)
assert_equal(repr(a), "FOO")
np.set_string_function(None, repr=True)
assert_equal(repr(a), "array([1])")
np.set_string_function(lambda x: "FOO", repr=False)
assert_equal(str(a), "FOO")
np.set_string_function(None, repr=False)
assert_equal(str(a), "[1]")
class TestRoll(object):
def test_roll1d(self):
x = np.arange(10)
xr = np.roll(x, 2)
assert_equal(xr, np.array([8, 9, 0, 1, 2, 3, 4, 5, 6, 7]))
def test_roll2d(self):
x2 = np.reshape(np.arange(10), (2, 5))
x2r = np.roll(x2, 1)
assert_equal(x2r, np.array([[9, 0, 1, 2, 3], [4, 5, 6, 7, 8]]))
x2r = np.roll(x2, 1, axis=0)
assert_equal(x2r, np.array([[5, 6, 7, 8, 9], [0, 1, 2, 3, 4]]))
x2r = np.roll(x2, 1, axis=1)
assert_equal(x2r, np.array([[4, 0, 1, 2, 3], [9, 5, 6, 7, 8]]))
# Roll multiple axes at once.
x2r = np.roll(x2, 1, axis=(0, 1))
assert_equal(x2r, np.array([[9, 5, 6, 7, 8], [4, 0, 1, 2, 3]]))
x2r = np.roll(x2, (1, 0), axis=(0, 1))
assert_equal(x2r, np.array([[5, 6, 7, 8, 9], [0, 1, 2, 3, 4]]))
x2r = np.roll(x2, (-1, 0), axis=(0, 1))
assert_equal(x2r, np.array([[5, 6, 7, 8, 9], [0, 1, 2, 3, 4]]))
x2r = np.roll(x2, (0, 1), axis=(0, 1))
assert_equal(x2r, np.array([[4, 0, 1, 2, 3], [9, 5, 6, 7, 8]]))
x2r = np.roll(x2, (0, -1), axis=(0, 1))
assert_equal(x2r, np.array([[1, 2, 3, 4, 0], [6, 7, 8, 9, 5]]))
x2r = np.roll(x2, (1, 1), axis=(0, 1))
assert_equal(x2r, np.array([[9, 5, 6, 7, 8], [4, 0, 1, 2, 3]]))
x2r = np.roll(x2, (-1, -1), axis=(0, 1))
assert_equal(x2r, np.array([[6, 7, 8, 9, 5], [1, 2, 3, 4, 0]]))
# Roll the same axis multiple times.
x2r = np.roll(x2, 1, axis=(0, 0))
assert_equal(x2r, np.array([[0, 1, 2, 3, 4], [5, 6, 7, 8, 9]]))
x2r = np.roll(x2, 1, axis=(1, 1))
assert_equal(x2r, np.array([[3, 4, 0, 1, 2], [8, 9, 5, 6, 7]]))
# Roll more than one turn in either direction.
x2r = np.roll(x2, 6, axis=1)
assert_equal(x2r, np.array([[4, 0, 1, 2, 3], [9, 5, 6, 7, 8]]))
x2r = np.roll(x2, -4, axis=1)
assert_equal(x2r, np.array([[4, 0, 1, 2, 3], [9, 5, 6, 7, 8]]))
def test_roll_empty(self):
x = np.array([])
assert_equal(np.roll(x, 1), np.array([]))
class TestRollaxis(object):
# expected shape indexed by (axis, start) for array of
# shape (1, 2, 3, 4)
tgtshape = {(0, 0): (1, 2, 3, 4), (0, 1): (1, 2, 3, 4),
(0, 2): (2, 1, 3, 4), (0, 3): (2, 3, 1, 4),
(0, 4): (2, 3, 4, 1),
(1, 0): (2, 1, 3, 4), (1, 1): (1, 2, 3, 4),
(1, 2): (1, 2, 3, 4), (1, 3): (1, 3, 2, 4),
(1, 4): (1, 3, 4, 2),
(2, 0): (3, 1, 2, 4), (2, 1): (1, 3, 2, 4),
(2, 2): (1, 2, 3, 4), (2, 3): (1, 2, 3, 4),
(2, 4): (1, 2, 4, 3),
(3, 0): (4, 1, 2, 3), (3, 1): (1, 4, 2, 3),
(3, 2): (1, 2, 4, 3), (3, 3): (1, 2, 3, 4),
(3, 4): (1, 2, 3, 4)}
def test_exceptions(self):
a = np.arange(1*2*3*4).reshape(1, 2, 3, 4)
assert_raises(np.AxisError, np.rollaxis, a, -5, 0)
assert_raises(np.AxisError, np.rollaxis, a, 0, -5)
assert_raises(np.AxisError, np.rollaxis, a, 4, 0)
assert_raises(np.AxisError, np.rollaxis, a, 0, 5)
def test_results(self):
a = np.arange(1*2*3*4).reshape(1, 2, 3, 4).copy()
aind = np.indices(a.shape)
assert_(a.flags['OWNDATA'])
for (i, j) in self.tgtshape:
# positive axis, positive start
res = np.rollaxis(a, axis=i, start=j)
i0, i1, i2, i3 = aind[np.array(res.shape) - 1]
assert_(np.all(res[i0, i1, i2, i3] == a))
assert_(res.shape == self.tgtshape[(i, j)], str((i,j)))
assert_(not res.flags['OWNDATA'])
# negative axis, positive start
ip = i + 1
res = np.rollaxis(a, axis=-ip, start=j)
i0, i1, i2, i3 = aind[np.array(res.shape) - 1]
assert_(np.all(res[i0, i1, i2, i3] == a))
assert_(res.shape == self.tgtshape[(4 - ip, j)])
assert_(not res.flags['OWNDATA'])
# positive axis, negative start
jp = j + 1 if j < 4 else j
res = np.rollaxis(a, axis=i, start=-jp)
i0, i1, i2, i3 = aind[np.array(res.shape) - 1]
assert_(np.all(res[i0, i1, i2, i3] == a))
assert_(res.shape == self.tgtshape[(i, 4 - jp)])
assert_(not res.flags['OWNDATA'])
# negative axis, negative start
ip = i + 1
jp = j + 1 if j < 4 else j
res = np.rollaxis(a, axis=-ip, start=-jp)
i0, i1, i2, i3 = aind[np.array(res.shape) - 1]
assert_(np.all(res[i0, i1, i2, i3] == a))
assert_(res.shape == self.tgtshape[(4 - ip, 4 - jp)])
assert_(not res.flags['OWNDATA'])
class TestMoveaxis(object):
def test_move_to_end(self):
x = np.random.randn(5, 6, 7)
for source, expected in [(0, (6, 7, 5)),
(1, (5, 7, 6)),
(2, (5, 6, 7)),
(-1, (5, 6, 7))]:
actual = np.moveaxis(x, source, -1).shape
assert_(actual, expected)
def test_move_new_position(self):
x = np.random.randn(1, 2, 3, 4)
for source, destination, expected in [
(0, 1, (2, 1, 3, 4)),
(1, 2, (1, 3, 2, 4)),
(1, -1, (1, 3, 4, 2)),
]:
actual = np.moveaxis(x, source, destination).shape
assert_(actual, expected)
def test_preserve_order(self):
x = np.zeros((1, 2, 3, 4))
for source, destination in [
(0, 0),
(3, -1),
(-1, 3),
([0, -1], [0, -1]),
([2, 0], [2, 0]),
(range(4), range(4)),
]:
actual = np.moveaxis(x, source, destination).shape
assert_(actual, (1, 2, 3, 4))
def test_move_multiples(self):
x = np.zeros((0, 1, 2, 3))
for source, destination, expected in [
([0, 1], [2, 3], (2, 3, 0, 1)),
([2, 3], [0, 1], (2, 3, 0, 1)),
([0, 1, 2], [2, 3, 0], (2, 3, 0, 1)),
([3, 0], [1, 0], (0, 3, 1, 2)),
([0, 3], [0, 1], (0, 3, 1, 2)),
]:
actual = np.moveaxis(x, source, destination).shape
assert_(actual, expected)
def test_errors(self):
x = np.random.randn(1, 2, 3)
assert_raises_regex(np.AxisError, 'source.*out of bounds',
np.moveaxis, x, 3, 0)
assert_raises_regex(np.AxisError, 'source.*out of bounds',
np.moveaxis, x, -4, 0)
assert_raises_regex(np.AxisError, 'destination.*out of bounds',
np.moveaxis, x, 0, 5)
assert_raises_regex(ValueError, 'repeated axis in `source`',
np.moveaxis, x, [0, 0], [0, 1])
assert_raises_regex(ValueError, 'repeated axis in `destination`',
np.moveaxis, x, [0, 1], [1, 1])
assert_raises_regex(ValueError, 'must have the same number',
np.moveaxis, x, 0, [0, 1])
assert_raises_regex(ValueError, 'must have the same number',
np.moveaxis, x, [0, 1], [0])
def test_array_likes(self):
x = np.ma.zeros((1, 2, 3))
result = np.moveaxis(x, 0, 0)
assert_(x.shape, result.shape)
assert_(isinstance(result, np.ma.MaskedArray))
x = [1, 2, 3]
result = np.moveaxis(x, 0, 0)
assert_(x, list(result))
assert_(isinstance(result, np.ndarray))
class TestCross(object):
def test_2x2(self):
u = [1, 2]
v = [3, 4]
z = -2
cp = np.cross(u, v)
assert_equal(cp, z)
cp = np.cross(v, u)
assert_equal(cp, -z)
def test_2x3(self):
u = [1, 2]
v = [3, 4, 5]
z = np.array([10, -5, -2])
cp = np.cross(u, v)
assert_equal(cp, z)
cp = np.cross(v, u)
assert_equal(cp, -z)
def test_3x3(self):
u = [1, 2, 3]
v = [4, 5, 6]
z = np.array([-3, 6, -3])
cp = np.cross(u, v)
assert_equal(cp, z)
cp = np.cross(v, u)
assert_equal(cp, -z)
def test_broadcasting(self):
# Ticket #2624 (Trac #2032)
u = np.tile([1, 2], (11, 1))
v = np.tile([3, 4], (11, 1))
z = -2
assert_equal(np.cross(u, v), z)
assert_equal(np.cross(v, u), -z)
assert_equal(np.cross(u, u), 0)
u = np.tile([1, 2], (11, 1)).T
v = np.tile([3, 4, 5], (11, 1))
z = np.tile([10, -5, -2], (11, 1))
assert_equal(np.cross(u, v, axisa=0), z)
assert_equal(np.cross(v, u.T), -z)
assert_equal(np.cross(v, v), 0)
u = np.tile([1, 2, 3], (11, 1)).T
v = np.tile([3, 4], (11, 1)).T
z = np.tile([-12, 9, -2], (11, 1))
assert_equal(np.cross(u, v, axisa=0, axisb=0), z)
assert_equal(np.cross(v.T, u.T), -z)
assert_equal(np.cross(u.T, u.T), 0)
u = np.tile([1, 2, 3], (5, 1))
v = np.tile([4, 5, 6], (5, 1)).T
z = np.tile([-3, 6, -3], (5, 1))
assert_equal(np.cross(u, v, axisb=0), z)
assert_equal(np.cross(v.T, u), -z)
assert_equal(np.cross(u, u), 0)
def test_broadcasting_shapes(self):
u = np.ones((2, 1, 3))
v = np.ones((5, 3))
assert_equal(np.cross(u, v).shape, (2, 5, 3))
u = np.ones((10, 3, 5))
v = np.ones((2, 5))
assert_equal(np.cross(u, v, axisa=1, axisb=0).shape, (10, 5, 3))
assert_raises(np.AxisError, np.cross, u, v, axisa=1, axisb=2)
assert_raises(np.AxisError, np.cross, u, v, axisa=3, axisb=0)
u = np.ones((10, 3, 5, 7))
v = np.ones((5, 7, 2))
assert_equal(np.cross(u, v, axisa=1, axisc=2).shape, (10, 5, 3, 7))
assert_raises(np.AxisError, np.cross, u, v, axisa=-5, axisb=2)
assert_raises(np.AxisError, np.cross, u, v, axisa=1, axisb=-4)
# gh-5885
u = np.ones((3, 4, 2))
for axisc in range(-2, 2):
assert_equal(np.cross(u, u, axisc=axisc).shape, (3, 4))
def test_outer_out_param():
arr1 = np.ones((5,))
arr2 = np.ones((2,))
arr3 = np.linspace(-2, 2, 5)
out1 = np.ndarray(shape=(5,5))
out2 = np.ndarray(shape=(2, 5))
res1 = np.outer(arr1, arr3, out1)
assert_equal(res1, out1)
assert_equal(np.outer(arr2, arr3, out2), out2)
class TestRequire(object):
flag_names = ['C', 'C_CONTIGUOUS', 'CONTIGUOUS',
'F', 'F_CONTIGUOUS', 'FORTRAN',
'A', 'ALIGNED',
'W', 'WRITEABLE',
'O', 'OWNDATA']
def generate_all_false(self, dtype):
arr = np.zeros((2, 2), [('junk', 'i1'), ('a', dtype)])
arr.setflags(write=False)
a = arr['a']
assert_(not a.flags['C'])
assert_(not a.flags['F'])
assert_(not a.flags['O'])
assert_(not a.flags['W'])
assert_(not a.flags['A'])
return a
def set_and_check_flag(self, flag, dtype, arr):
if dtype is None:
dtype = arr.dtype
b = np.require(arr, dtype, [flag])
assert_(b.flags[flag])
assert_(b.dtype == dtype)
# a further call to np.require ought to return the same array
# unless OWNDATA is specified.
c = np.require(b, None, [flag])
if flag[0] != 'O':
assert_(c is b)
else:
assert_(c.flags[flag])
def test_require_each(self):
id = ['f8', 'i4']
fd = [None, 'f8', 'c16']
for idtype, fdtype, flag in itertools.product(id, fd, self.flag_names):
a = self.generate_all_false(idtype)
yield self.set_and_check_flag, flag, fdtype, a
def test_unknown_requirement(self):
a = self.generate_all_false('f8')
assert_raises(KeyError, np.require, a, None, 'Q')
def test_non_array_input(self):
a = np.require([1, 2, 3, 4], 'i4', ['C', 'A', 'O'])
assert_(a.flags['O'])
assert_(a.flags['C'])
assert_(a.flags['A'])
assert_(a.dtype == 'i4')
assert_equal(a, [1, 2, 3, 4])
def test_C_and_F_simul(self):
a = self.generate_all_false('f8')
assert_raises(ValueError, np.require, a, None, ['C', 'F'])
def test_ensure_array(self):
class ArraySubclass(np.ndarray):
pass
a = ArraySubclass((2, 2))
b = np.require(a, None, ['E'])
assert_(type(b) is np.ndarray)
def test_preserve_subtype(self):
class ArraySubclass(np.ndarray):
pass
for flag in self.flag_names:
a = ArraySubclass((2, 2))
yield self.set_and_check_flag, flag, None, a
class TestBroadcast(object):
def test_broadcast_in_args(self):
# gh-5881
arrs = [np.empty((6, 7)), np.empty((5, 6, 1)), np.empty((7,)),
np.empty((5, 1, 7))]
mits = [np.broadcast(*arrs),
np.broadcast(np.broadcast(*arrs[:2]), np.broadcast(*arrs[2:])),
np.broadcast(arrs[0], np.broadcast(*arrs[1:-1]), arrs[-1])]
for mit in mits:
assert_equal(mit.shape, (5, 6, 7))
assert_equal(mit.ndim, 3)
assert_equal(mit.nd, 3)
assert_equal(mit.numiter, 4)
for a, ia in zip(arrs, mit.iters):
assert_(a is ia.base)
def test_broadcast_single_arg(self):
# gh-6899
arrs = [np.empty((5, 6, 7))]
mit = np.broadcast(*arrs)
assert_equal(mit.shape, (5, 6, 7))
assert_equal(mit.ndim, 3)
assert_equal(mit.nd, 3)
assert_equal(mit.numiter, 1)
assert_(arrs[0] is mit.iters[0].base)
def test_number_of_arguments(self):
arr = np.empty((5,))
for j in range(35):
arrs = [arr] * j
if j < 1 or j > 32:
assert_raises(ValueError, np.broadcast, *arrs)
else:
mit = np.broadcast(*arrs)
assert_equal(mit.numiter, j)
class TestKeepdims(object):
class sub_array(np.ndarray):
def sum(self, axis=None, dtype=None, out=None):
return np.ndarray.sum(self, axis, dtype, out, keepdims=True)
def test_raise(self):
sub_class = self.sub_array
x = np.arange(30).view(sub_class)
assert_raises(TypeError, np.sum, x, keepdims=True)
class TestTensordot(object):
def test_zero_dimension(self):
# Test resolution to issue #5663
a = np.ndarray((3,0))
b = np.ndarray((0,4))
td = np.tensordot(a, b, (1, 0))
assert_array_equal(td, np.dot(a, b))
assert_array_equal(td, np.einsum('ij,jk', a, b))
if __name__ == "__main__":
run_module_suite()
| 102,126 | 36.272628 | 91 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/tests/test_dtype.py
|
from __future__ import division, absolute_import, print_function
import pickle
import sys
import operator
import numpy as np
from numpy.core.test_rational import rational
from numpy.testing import (
run_module_suite, assert_, assert_equal, assert_raises,
dec
)
def assert_dtype_equal(a, b):
assert_equal(a, b)
assert_equal(hash(a), hash(b),
"two equivalent types do not hash to the same value !")
def assert_dtype_not_equal(a, b):
assert_(a != b)
assert_(hash(a) != hash(b),
"two different types hash to the same value !")
class TestBuiltin(object):
def test_run(self):
"""Only test hash runs at all."""
for t in [int, float, complex, np.int32, str, object,
np.unicode]:
dt = np.dtype(t)
hash(dt)
def test_dtype(self):
# Make sure equivalent byte order char hash the same (e.g. < and = on
# little endian)
for t in [int, float]:
dt = np.dtype(t)
dt2 = dt.newbyteorder("<")
dt3 = dt.newbyteorder(">")
if dt == dt2:
assert_(dt.byteorder != dt2.byteorder, "bogus test")
assert_dtype_equal(dt, dt2)
else:
assert_(dt.byteorder != dt3.byteorder, "bogus test")
assert_dtype_equal(dt, dt3)
def test_equivalent_dtype_hashing(self):
# Make sure equivalent dtypes with different type num hash equal
uintp = np.dtype(np.uintp)
if uintp.itemsize == 4:
left = uintp
right = np.dtype(np.uint32)
else:
left = uintp
right = np.dtype(np.ulonglong)
assert_(left == right)
assert_(hash(left) == hash(right))
def test_invalid_types(self):
# Make sure invalid type strings raise an error
assert_raises(TypeError, np.dtype, 'O3')
assert_raises(TypeError, np.dtype, 'O5')
assert_raises(TypeError, np.dtype, 'O7')
assert_raises(TypeError, np.dtype, 'b3')
assert_raises(TypeError, np.dtype, 'h4')
assert_raises(TypeError, np.dtype, 'I5')
assert_raises(TypeError, np.dtype, 'e3')
assert_raises(TypeError, np.dtype, 'f5')
if np.dtype('g').itemsize == 8 or np.dtype('g').itemsize == 16:
assert_raises(TypeError, np.dtype, 'g12')
elif np.dtype('g').itemsize == 12:
assert_raises(TypeError, np.dtype, 'g16')
if np.dtype('l').itemsize == 8:
assert_raises(TypeError, np.dtype, 'l4')
assert_raises(TypeError, np.dtype, 'L4')
else:
assert_raises(TypeError, np.dtype, 'l8')
assert_raises(TypeError, np.dtype, 'L8')
if np.dtype('q').itemsize == 8:
assert_raises(TypeError, np.dtype, 'q4')
assert_raises(TypeError, np.dtype, 'Q4')
else:
assert_raises(TypeError, np.dtype, 'q8')
assert_raises(TypeError, np.dtype, 'Q8')
def test_bad_param(self):
# Can't give a size that's too small
assert_raises(ValueError, np.dtype,
{'names':['f0', 'f1'],
'formats':['i4', 'i1'],
'offsets':[0, 4],
'itemsize':4})
# If alignment is enabled, the alignment (4) must divide the itemsize
assert_raises(ValueError, np.dtype,
{'names':['f0', 'f1'],
'formats':['i4', 'i1'],
'offsets':[0, 4],
'itemsize':9}, align=True)
# If alignment is enabled, the individual fields must be aligned
assert_raises(ValueError, np.dtype,
{'names':['f0', 'f1'],
'formats':['i1', 'f4'],
'offsets':[0, 2]}, align=True)
def test_field_order_equality(self):
x = np.dtype({'names': ['A', 'B'],
'formats': ['i4', 'f4'],
'offsets': [0, 4]})
y = np.dtype({'names': ['B', 'A'],
'formats': ['f4', 'i4'],
'offsets': [4, 0]})
assert_equal(x == y, False)
class TestRecord(object):
def test_equivalent_record(self):
"""Test whether equivalent record dtypes hash the same."""
a = np.dtype([('yo', int)])
b = np.dtype([('yo', int)])
assert_dtype_equal(a, b)
def test_different_names(self):
# In theory, they may hash the same (collision) ?
a = np.dtype([('yo', int)])
b = np.dtype([('ye', int)])
assert_dtype_not_equal(a, b)
def test_different_titles(self):
# In theory, they may hash the same (collision) ?
a = np.dtype({'names': ['r', 'b'],
'formats': ['u1', 'u1'],
'titles': ['Red pixel', 'Blue pixel']})
b = np.dtype({'names': ['r', 'b'],
'formats': ['u1', 'u1'],
'titles': ['RRed pixel', 'Blue pixel']})
assert_dtype_not_equal(a, b)
def test_mutate(self):
# Mutating a dtype should reset the cached hash value
a = np.dtype([('yo', int)])
b = np.dtype([('yo', int)])
c = np.dtype([('ye', int)])
assert_dtype_equal(a, b)
assert_dtype_not_equal(a, c)
a.names = ['ye']
assert_dtype_equal(a, c)
assert_dtype_not_equal(a, b)
state = b.__reduce__()[2]
a.__setstate__(state)
assert_dtype_equal(a, b)
assert_dtype_not_equal(a, c)
def test_not_lists(self):
"""Test if an appropriate exception is raised when passing bad values to
the dtype constructor.
"""
assert_raises(TypeError, np.dtype,
dict(names=set(['A', 'B']), formats=['f8', 'i4']))
assert_raises(TypeError, np.dtype,
dict(names=['A', 'B'], formats=set(['f8', 'i4'])))
def test_aligned_size(self):
# Check that structured dtypes get padded to an aligned size
dt = np.dtype('i4, i1', align=True)
assert_equal(dt.itemsize, 8)
dt = np.dtype([('f0', 'i4'), ('f1', 'i1')], align=True)
assert_equal(dt.itemsize, 8)
dt = np.dtype({'names':['f0', 'f1'],
'formats':['i4', 'u1'],
'offsets':[0, 4]}, align=True)
assert_equal(dt.itemsize, 8)
dt = np.dtype({'f0': ('i4', 0), 'f1':('u1', 4)}, align=True)
assert_equal(dt.itemsize, 8)
# Nesting should preserve that alignment
dt1 = np.dtype([('f0', 'i4'),
('f1', [('f1', 'i1'), ('f2', 'i4'), ('f3', 'i1')]),
('f2', 'i1')], align=True)
assert_equal(dt1.itemsize, 20)
dt2 = np.dtype({'names':['f0', 'f1', 'f2'],
'formats':['i4',
[('f1', 'i1'), ('f2', 'i4'), ('f3', 'i1')],
'i1'],
'offsets':[0, 4, 16]}, align=True)
assert_equal(dt2.itemsize, 20)
dt3 = np.dtype({'f0': ('i4', 0),
'f1': ([('f1', 'i1'), ('f2', 'i4'), ('f3', 'i1')], 4),
'f2': ('i1', 16)}, align=True)
assert_equal(dt3.itemsize, 20)
assert_equal(dt1, dt2)
assert_equal(dt2, dt3)
# Nesting should preserve packing
dt1 = np.dtype([('f0', 'i4'),
('f1', [('f1', 'i1'), ('f2', 'i4'), ('f3', 'i1')]),
('f2', 'i1')], align=False)
assert_equal(dt1.itemsize, 11)
dt2 = np.dtype({'names':['f0', 'f1', 'f2'],
'formats':['i4',
[('f1', 'i1'), ('f2', 'i4'), ('f3', 'i1')],
'i1'],
'offsets':[0, 4, 10]}, align=False)
assert_equal(dt2.itemsize, 11)
dt3 = np.dtype({'f0': ('i4', 0),
'f1': ([('f1', 'i1'), ('f2', 'i4'), ('f3', 'i1')], 4),
'f2': ('i1', 10)}, align=False)
assert_equal(dt3.itemsize, 11)
assert_equal(dt1, dt2)
assert_equal(dt2, dt3)
def test_union_struct(self):
# Should be able to create union dtypes
dt = np.dtype({'names':['f0', 'f1', 'f2'], 'formats':['<u4', '<u2', '<u2'],
'offsets':[0, 0, 2]}, align=True)
assert_equal(dt.itemsize, 4)
a = np.array([3], dtype='<u4').view(dt)
a['f1'] = 10
a['f2'] = 36
assert_equal(a['f0'], 10 + 36*256*256)
# Should be able to specify fields out of order
dt = np.dtype({'names':['f0', 'f1', 'f2'], 'formats':['<u4', '<u2', '<u2'],
'offsets':[4, 0, 2]}, align=True)
assert_equal(dt.itemsize, 8)
# field name should not matter: assignment is by position
dt2 = np.dtype({'names':['f2', 'f0', 'f1'],
'formats':['<u4', '<u2', '<u2'],
'offsets':[4, 0, 2]}, align=True)
vals = [(0, 1, 2), (3, -1, 4)]
vals2 = [(0, 1, 2), (3, -1, 4)]
a = np.array(vals, dt)
b = np.array(vals2, dt2)
assert_equal(a.astype(dt2), b)
assert_equal(b.astype(dt), a)
assert_equal(a.view(dt2), b)
assert_equal(b.view(dt), a)
# Should not be able to overlap objects with other types
assert_raises(TypeError, np.dtype,
{'names':['f0', 'f1'],
'formats':['O', 'i1'],
'offsets':[0, 2]})
assert_raises(TypeError, np.dtype,
{'names':['f0', 'f1'],
'formats':['i4', 'O'],
'offsets':[0, 3]})
assert_raises(TypeError, np.dtype,
{'names':['f0', 'f1'],
'formats':[[('a', 'O')], 'i1'],
'offsets':[0, 2]})
assert_raises(TypeError, np.dtype,
{'names':['f0', 'f1'],
'formats':['i4', [('a', 'O')]],
'offsets':[0, 3]})
# Out of order should still be ok, however
dt = np.dtype({'names':['f0', 'f1'],
'formats':['i1', 'O'],
'offsets':[np.dtype('intp').itemsize, 0]})
def test_comma_datetime(self):
dt = np.dtype('M8[D],datetime64[Y],i8')
assert_equal(dt, np.dtype([('f0', 'M8[D]'),
('f1', 'datetime64[Y]'),
('f2', 'i8')]))
def test_from_dictproxy(self):
# Tests for PR #5920
dt = np.dtype({'names': ['a', 'b'], 'formats': ['i4', 'f4']})
assert_dtype_equal(dt, np.dtype(dt.fields))
dt2 = np.dtype((np.void, dt.fields))
assert_equal(dt2.fields, dt.fields)
def test_from_dict_with_zero_width_field(self):
# Regression test for #6430 / #2196
dt = np.dtype([('val1', np.float32, (0,)), ('val2', int)])
dt2 = np.dtype({'names': ['val1', 'val2'],
'formats': [(np.float32, (0,)), int]})
assert_dtype_equal(dt, dt2)
assert_equal(dt.fields['val1'][0].itemsize, 0)
assert_equal(dt.itemsize, dt.fields['val2'][0].itemsize)
def test_bool_commastring(self):
d = np.dtype('?,?,?') # raises?
assert_equal(len(d.names), 3)
for n in d.names:
assert_equal(d.fields[n][0], np.dtype('?'))
def test_nonint_offsets(self):
# gh-8059
def make_dtype(off):
return np.dtype({'names': ['A'], 'formats': ['i4'],
'offsets': [off]})
assert_raises(TypeError, make_dtype, 'ASD')
assert_raises(OverflowError, make_dtype, 2**70)
assert_raises(TypeError, make_dtype, 2.3)
assert_raises(ValueError, make_dtype, -10)
# no errors here:
dt = make_dtype(np.uint32(0))
np.zeros(1, dtype=dt)[0].item()
def test_fields_by_index(self):
dt = np.dtype([('a', np.int8), ('b', np.float32, 3)])
assert_dtype_equal(dt[0], np.dtype(np.int8))
assert_dtype_equal(dt[1], np.dtype((np.float32, 3)))
assert_dtype_equal(dt[-1], dt[1])
assert_dtype_equal(dt[-2], dt[0])
assert_raises(IndexError, lambda: dt[-3])
assert_raises(TypeError, operator.getitem, dt, 3.0)
assert_raises(TypeError, operator.getitem, dt, [])
assert_equal(dt[1], dt[np.int8(1)])
class TestSubarray(object):
def test_single_subarray(self):
a = np.dtype((int, (2)))
b = np.dtype((int, (2,)))
assert_dtype_equal(a, b)
assert_equal(type(a.subdtype[1]), tuple)
assert_equal(type(b.subdtype[1]), tuple)
def test_equivalent_record(self):
"""Test whether equivalent subarray dtypes hash the same."""
a = np.dtype((int, (2, 3)))
b = np.dtype((int, (2, 3)))
assert_dtype_equal(a, b)
def test_nonequivalent_record(self):
"""Test whether different subarray dtypes hash differently."""
a = np.dtype((int, (2, 3)))
b = np.dtype((int, (3, 2)))
assert_dtype_not_equal(a, b)
a = np.dtype((int, (2, 3)))
b = np.dtype((int, (2, 2)))
assert_dtype_not_equal(a, b)
a = np.dtype((int, (1, 2, 3)))
b = np.dtype((int, (1, 2)))
assert_dtype_not_equal(a, b)
def test_shape_equal(self):
"""Test some data types that are equal"""
assert_dtype_equal(np.dtype('f8'), np.dtype(('f8', tuple())))
assert_dtype_equal(np.dtype('f8'), np.dtype(('f8', 1)))
assert_dtype_equal(np.dtype((int, 2)), np.dtype((int, (2,))))
assert_dtype_equal(np.dtype(('<f4', (3, 2))), np.dtype(('<f4', (3, 2))))
d = ([('a', 'f4', (1, 2)), ('b', 'f8', (3, 1))], (3, 2))
assert_dtype_equal(np.dtype(d), np.dtype(d))
def test_shape_simple(self):
"""Test some simple cases that shouldn't be equal"""
assert_dtype_not_equal(np.dtype('f8'), np.dtype(('f8', (1,))))
assert_dtype_not_equal(np.dtype(('f8', (1,))), np.dtype(('f8', (1, 1))))
assert_dtype_not_equal(np.dtype(('f4', (3, 2))), np.dtype(('f4', (2, 3))))
def test_shape_monster(self):
"""Test some more complicated cases that shouldn't be equal"""
assert_dtype_not_equal(
np.dtype(([('a', 'f4', (2, 1)), ('b', 'f8', (1, 3))], (2, 2))),
np.dtype(([('a', 'f4', (1, 2)), ('b', 'f8', (1, 3))], (2, 2))))
assert_dtype_not_equal(
np.dtype(([('a', 'f4', (2, 1)), ('b', 'f8', (1, 3))], (2, 2))),
np.dtype(([('a', 'f4', (2, 1)), ('b', 'i8', (1, 3))], (2, 2))))
assert_dtype_not_equal(
np.dtype(([('a', 'f4', (2, 1)), ('b', 'f8', (1, 3))], (2, 2))),
np.dtype(([('e', 'f8', (1, 3)), ('d', 'f4', (2, 1))], (2, 2))))
assert_dtype_not_equal(
np.dtype(([('a', [('a', 'i4', 6)], (2, 1)), ('b', 'f8', (1, 3))], (2, 2))),
np.dtype(([('a', [('a', 'u4', 6)], (2, 1)), ('b', 'f8', (1, 3))], (2, 2))))
def test_shape_sequence(self):
# Any sequence of integers should work as shape, but the result
# should be a tuple (immutable) of base type integers.
a = np.array([1, 2, 3], dtype=np.int16)
l = [1, 2, 3]
# Array gets converted
dt = np.dtype([('a', 'f4', a)])
assert_(isinstance(dt['a'].shape, tuple))
assert_(isinstance(dt['a'].shape[0], int))
# List gets converted
dt = np.dtype([('a', 'f4', l)])
assert_(isinstance(dt['a'].shape, tuple))
#
class IntLike(object):
def __index__(self):
return 3
def __int__(self):
# (a PyNumber_Check fails without __int__)
return 3
dt = np.dtype([('a', 'f4', IntLike())])
assert_(isinstance(dt['a'].shape, tuple))
assert_(isinstance(dt['a'].shape[0], int))
dt = np.dtype([('a', 'f4', (IntLike(),))])
assert_(isinstance(dt['a'].shape, tuple))
assert_(isinstance(dt['a'].shape[0], int))
def test_shape_matches_ndim(self):
dt = np.dtype([('a', 'f4', ())])
assert_equal(dt['a'].shape, ())
assert_equal(dt['a'].ndim, 0)
dt = np.dtype([('a', 'f4')])
assert_equal(dt['a'].shape, ())
assert_equal(dt['a'].ndim, 0)
dt = np.dtype([('a', 'f4', 4)])
assert_equal(dt['a'].shape, (4,))
assert_equal(dt['a'].ndim, 1)
dt = np.dtype([('a', 'f4', (1, 2, 3))])
assert_equal(dt['a'].shape, (1, 2, 3))
assert_equal(dt['a'].ndim, 3)
def test_shape_invalid(self):
# Check that the shape is valid.
max_int = np.iinfo(np.intc).max
max_intp = np.iinfo(np.intp).max
# Too large values (the datatype is part of this)
assert_raises(ValueError, np.dtype, [('a', 'f4', max_int // 4 + 1)])
assert_raises(ValueError, np.dtype, [('a', 'f4', max_int + 1)])
assert_raises(ValueError, np.dtype, [('a', 'f4', (max_int, 2))])
# Takes a different code path (fails earlier:
assert_raises(ValueError, np.dtype, [('a', 'f4', max_intp + 1)])
# Negative values
assert_raises(ValueError, np.dtype, [('a', 'f4', -1)])
assert_raises(ValueError, np.dtype, [('a', 'f4', (-1, -1))])
def test_alignment(self):
#Check that subarrays are aligned
t1 = np.dtype('1i4', align=True)
t2 = np.dtype('2i4', align=True)
assert_equal(t1.alignment, t2.alignment)
class TestMonsterType(object):
"""Test deeply nested subtypes."""
def test1(self):
simple1 = np.dtype({'names': ['r', 'b'], 'formats': ['u1', 'u1'],
'titles': ['Red pixel', 'Blue pixel']})
a = np.dtype([('yo', int), ('ye', simple1),
('yi', np.dtype((int, (3, 2))))])
b = np.dtype([('yo', int), ('ye', simple1),
('yi', np.dtype((int, (3, 2))))])
assert_dtype_equal(a, b)
c = np.dtype([('yo', int), ('ye', simple1),
('yi', np.dtype((a, (3, 2))))])
d = np.dtype([('yo', int), ('ye', simple1),
('yi', np.dtype((a, (3, 2))))])
assert_dtype_equal(c, d)
class TestMetadata(object):
def test_no_metadata(self):
d = np.dtype(int)
assert_(d.metadata is None)
def test_metadata_takes_dict(self):
d = np.dtype(int, metadata={'datum': 1})
assert_(d.metadata == {'datum': 1})
def test_metadata_rejects_nondict(self):
assert_raises(TypeError, np.dtype, int, metadata='datum')
assert_raises(TypeError, np.dtype, int, metadata=1)
assert_raises(TypeError, np.dtype, int, metadata=None)
def test_nested_metadata(self):
d = np.dtype([('a', np.dtype(int, metadata={'datum': 1}))])
assert_(d['a'].metadata == {'datum': 1})
def test_base_metadata_copied(self):
d = np.dtype((np.void, np.dtype('i4,i4', metadata={'datum': 1})))
assert_(d.metadata == {'datum': 1})
class TestString(object):
def test_complex_dtype_str(self):
dt = np.dtype([('top', [('tiles', ('>f4', (64, 64)), (1,)),
('rtile', '>f4', (64, 36))], (3,)),
('bottom', [('bleft', ('>f4', (8, 64)), (1,)),
('bright', '>f4', (8, 36))])])
assert_equal(str(dt),
"[('top', [('tiles', ('>f4', (64, 64)), (1,)), "
"('rtile', '>f4', (64, 36))], (3,)), "
"('bottom', [('bleft', ('>f4', (8, 64)), (1,)), "
"('bright', '>f4', (8, 36))])]")
# If the sticky aligned flag is set to True, it makes the
# str() function use a dict representation with an 'aligned' flag
dt = np.dtype([('top', [('tiles', ('>f4', (64, 64)), (1,)),
('rtile', '>f4', (64, 36))],
(3,)),
('bottom', [('bleft', ('>f4', (8, 64)), (1,)),
('bright', '>f4', (8, 36))])],
align=True)
assert_equal(str(dt),
"{'names':['top','bottom'], "
"'formats':[([('tiles', ('>f4', (64, 64)), (1,)), "
"('rtile', '>f4', (64, 36))], (3,)),"
"[('bleft', ('>f4', (8, 64)), (1,)), "
"('bright', '>f4', (8, 36))]], "
"'offsets':[0,76800], "
"'itemsize':80000, "
"'aligned':True}")
assert_equal(np.dtype(eval(str(dt))), dt)
dt = np.dtype({'names': ['r', 'g', 'b'], 'formats': ['u1', 'u1', 'u1'],
'offsets': [0, 1, 2],
'titles': ['Red pixel', 'Green pixel', 'Blue pixel']})
assert_equal(str(dt),
"[(('Red pixel', 'r'), 'u1'), "
"(('Green pixel', 'g'), 'u1'), "
"(('Blue pixel', 'b'), 'u1')]")
dt = np.dtype({'names': ['rgba', 'r', 'g', 'b'],
'formats': ['<u4', 'u1', 'u1', 'u1'],
'offsets': [0, 0, 1, 2],
'titles': ['Color', 'Red pixel',
'Green pixel', 'Blue pixel']})
assert_equal(str(dt),
"{'names':['rgba','r','g','b'],"
" 'formats':['<u4','u1','u1','u1'],"
" 'offsets':[0,0,1,2],"
" 'titles':['Color','Red pixel',"
"'Green pixel','Blue pixel'],"
" 'itemsize':4}")
dt = np.dtype({'names': ['r', 'b'], 'formats': ['u1', 'u1'],
'offsets': [0, 2],
'titles': ['Red pixel', 'Blue pixel']})
assert_equal(str(dt),
"{'names':['r','b'],"
" 'formats':['u1','u1'],"
" 'offsets':[0,2],"
" 'titles':['Red pixel','Blue pixel'],"
" 'itemsize':3}")
dt = np.dtype([('a', '<m8[D]'), ('b', '<M8[us]')])
assert_equal(str(dt),
"[('a', '<m8[D]'), ('b', '<M8[us]')]")
def test_complex_dtype_repr(self):
dt = np.dtype([('top', [('tiles', ('>f4', (64, 64)), (1,)),
('rtile', '>f4', (64, 36))], (3,)),
('bottom', [('bleft', ('>f4', (8, 64)), (1,)),
('bright', '>f4', (8, 36))])])
assert_equal(repr(dt),
"dtype([('top', [('tiles', ('>f4', (64, 64)), (1,)), "
"('rtile', '>f4', (64, 36))], (3,)), "
"('bottom', [('bleft', ('>f4', (8, 64)), (1,)), "
"('bright', '>f4', (8, 36))])])")
dt = np.dtype({'names': ['r', 'g', 'b'], 'formats': ['u1', 'u1', 'u1'],
'offsets': [0, 1, 2],
'titles': ['Red pixel', 'Green pixel', 'Blue pixel']},
align=True)
assert_equal(repr(dt),
"dtype([(('Red pixel', 'r'), 'u1'), "
"(('Green pixel', 'g'), 'u1'), "
"(('Blue pixel', 'b'), 'u1')], align=True)")
dt = np.dtype({'names': ['rgba', 'r', 'g', 'b'],
'formats': ['<u4', 'u1', 'u1', 'u1'],
'offsets': [0, 0, 1, 2],
'titles': ['Color', 'Red pixel',
'Green pixel', 'Blue pixel']}, align=True)
assert_equal(repr(dt),
"dtype({'names':['rgba','r','g','b'],"
" 'formats':['<u4','u1','u1','u1'],"
" 'offsets':[0,0,1,2],"
" 'titles':['Color','Red pixel',"
"'Green pixel','Blue pixel'],"
" 'itemsize':4}, align=True)")
dt = np.dtype({'names': ['r', 'b'], 'formats': ['u1', 'u1'],
'offsets': [0, 2],
'titles': ['Red pixel', 'Blue pixel'],
'itemsize': 4})
assert_equal(repr(dt),
"dtype({'names':['r','b'], "
"'formats':['u1','u1'], "
"'offsets':[0,2], "
"'titles':['Red pixel','Blue pixel'], "
"'itemsize':4})")
dt = np.dtype([('a', '<M8[D]'), ('b', '<m8[us]')])
assert_equal(repr(dt),
"dtype([('a', '<M8[D]'), ('b', '<m8[us]')])")
@dec.skipif(sys.version_info[0] >= 3)
def test_dtype_str_with_long_in_shape(self):
# Pull request #376, should not error
np.dtype('(1L,)i4')
def test_base_dtype_with_object_type(self):
# Issue gh-2798, should not error.
np.array(['a'], dtype="O").astype(("O", [("name", "O")]))
def test_empty_string_to_object(self):
# Pull request #4722
np.array(["", ""]).astype(object)
class TestDtypeAttributeDeletion(object):
def test_dtype_non_writable_attributes_deletion(self):
dt = np.dtype(np.double)
attr = ["subdtype", "descr", "str", "name", "base", "shape",
"isbuiltin", "isnative", "isalignedstruct", "fields",
"metadata", "hasobject"]
for s in attr:
assert_raises(AttributeError, delattr, dt, s)
def test_dtype_writable_attributes_deletion(self):
dt = np.dtype(np.double)
attr = ["names"]
for s in attr:
assert_raises(AttributeError, delattr, dt, s)
class TestDtypeAttributes(object):
def test_descr_has_trailing_void(self):
# see gh-6359
dtype = np.dtype({
'names': ['A', 'B'],
'formats': ['f4', 'f4'],
'offsets': [0, 8],
'itemsize': 16})
new_dtype = np.dtype(dtype.descr)
assert_equal(new_dtype.itemsize, 16)
def test_name_builtin(self):
for t in np.typeDict.values():
name = t.__name__
if name.endswith('_'):
name = name[:-1]
assert_equal(np.dtype(t).name, name)
def test_name_dtype_subclass(self):
# Ticket #4357
class user_def_subcls(np.void):
pass
assert_equal(np.dtype(user_def_subcls).name, 'user_def_subcls')
class TestPickling(object):
def check_pickling(self, dtype):
for proto in range(pickle.HIGHEST_PROTOCOL + 1):
pickled = pickle.loads(pickle.dumps(dtype, proto))
assert_equal(pickled, dtype)
assert_equal(pickled.descr, dtype.descr)
if dtype.metadata is not None:
assert_equal(pickled.metadata, dtype.metadata)
# Check the reconstructed dtype is functional
x = np.zeros(3, dtype=dtype)
y = np.zeros(3, dtype=pickled)
assert_equal(x, y)
assert_equal(x[0], y[0])
def test_builtin(self):
for t in [int, float, complex, np.int32, str, object,
np.unicode, bool]:
self.check_pickling(np.dtype(t))
def test_structured(self):
dt = np.dtype(([('a', '>f4', (2, 1)), ('b', '<f8', (1, 3))], (2, 2)))
self.check_pickling(dt)
dt = np.dtype('i4, i1', align=True)
self.check_pickling(dt)
dt = np.dtype('i4, i1', align=False)
self.check_pickling(dt)
dt = np.dtype({
'names': ['A', 'B'],
'formats': ['f4', 'f4'],
'offsets': [0, 8],
'itemsize': 16})
self.check_pickling(dt)
dt = np.dtype({'names': ['r', 'b'],
'formats': ['u1', 'u1'],
'titles': ['Red pixel', 'Blue pixel']})
self.check_pickling(dt)
def test_datetime(self):
for base in ['m8', 'M8']:
for unit in ['', 'Y', 'M', 'W', 'D', 'h', 'm', 's', 'ms',
'us', 'ns', 'ps', 'fs', 'as']:
dt = np.dtype('%s[%s]' % (base, unit) if unit else base)
self.check_pickling(dt)
if unit:
dt = np.dtype('%s[7%s]' % (base, unit))
self.check_pickling(dt)
def test_metadata(self):
dt = np.dtype(int, metadata={'datum': 1})
self.check_pickling(dt)
def test_rational_dtype():
# test for bug gh-5719
a = np.array([1111], dtype=rational).astype
assert_raises(OverflowError, a, 'int8')
# test that dtype detection finds user-defined types
x = rational(1)
assert_equal(np.array([x,x]).dtype, np.dtype(rational))
def test_dtypes_are_true():
# test for gh-6294
assert bool(np.dtype('f8'))
assert bool(np.dtype('i8'))
assert bool(np.dtype([('a', 'i8'), ('b', 'f4')]))
def test_invalid_dtype_string():
# test for gh-10440
assert_raises(TypeError, np.dtype, 'f8,i8,[f8,i8]')
if __name__ == "__main__":
run_module_suite()
| 29,072 | 38.880658 | 87 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/tests/test_scalarmath.py
|
from __future__ import division, absolute_import, print_function
import sys
import warnings
import itertools
import operator
import platform
import numpy as np
from numpy.testing import (
run_module_suite,
assert_, assert_equal, assert_raises,
assert_almost_equal, assert_allclose, assert_array_equal,
IS_PYPY, suppress_warnings, dec, _gen_alignment_data,
)
types = [np.bool_, np.byte, np.ubyte, np.short, np.ushort, np.intc, np.uintc,
np.int_, np.uint, np.longlong, np.ulonglong,
np.single, np.double, np.longdouble, np.csingle,
np.cdouble, np.clongdouble]
floating_types = np.floating.__subclasses__()
complex_floating_types = np.complexfloating.__subclasses__()
# This compares scalarmath against ufuncs.
class TestTypes(object):
def test_types(self):
for atype in types:
a = atype(1)
assert_(a == 1, "error with %r: got %r" % (atype, a))
def test_type_add(self):
# list of types
for k, atype in enumerate(types):
a_scalar = atype(3)
a_array = np.array([3], dtype=atype)
for l, btype in enumerate(types):
b_scalar = btype(1)
b_array = np.array([1], dtype=btype)
c_scalar = a_scalar + b_scalar
c_array = a_array + b_array
# It was comparing the type numbers, but the new ufunc
# function-finding mechanism finds the lowest function
# to which both inputs can be cast - which produces 'l'
# when you do 'q' + 'b'. The old function finding mechanism
# skipped ahead based on the first argument, but that
# does not produce properly symmetric results...
assert_equal(c_scalar.dtype, c_array.dtype,
"error with types (%d/'%c' + %d/'%c')" %
(k, np.dtype(atype).char, l, np.dtype(btype).char))
def test_type_create(self):
for k, atype in enumerate(types):
a = np.array([1, 2, 3], atype)
b = atype([1, 2, 3])
assert_equal(a, b)
def test_leak(self):
# test leak of scalar objects
# a leak would show up in valgrind as still-reachable of ~2.6MB
for i in range(200000):
np.add(1, 1)
class TestBaseMath(object):
def test_blocked(self):
# test alignments offsets for simd instructions
# alignments for vz + 2 * (vs - 1) + 1
for dt, sz in [(np.float32, 11), (np.float64, 7), (np.int32, 11)]:
for out, inp1, inp2, msg in _gen_alignment_data(dtype=dt,
type='binary',
max_size=sz):
exp1 = np.ones_like(inp1)
inp1[...] = np.ones_like(inp1)
inp2[...] = np.zeros_like(inp2)
assert_almost_equal(np.add(inp1, inp2), exp1, err_msg=msg)
assert_almost_equal(np.add(inp1, 2), exp1 + 2, err_msg=msg)
assert_almost_equal(np.add(1, inp2), exp1, err_msg=msg)
np.add(inp1, inp2, out=out)
assert_almost_equal(out, exp1, err_msg=msg)
inp2[...] += np.arange(inp2.size, dtype=dt) + 1
assert_almost_equal(np.square(inp2),
np.multiply(inp2, inp2), err_msg=msg)
# skip true divide for ints
if dt != np.int32 or (sys.version_info.major < 3 and not sys.py3kwarning):
assert_almost_equal(np.reciprocal(inp2),
np.divide(1, inp2), err_msg=msg)
inp1[...] = np.ones_like(inp1)
np.add(inp1, 2, out=out)
assert_almost_equal(out, exp1 + 2, err_msg=msg)
inp2[...] = np.ones_like(inp2)
np.add(2, inp2, out=out)
assert_almost_equal(out, exp1 + 2, err_msg=msg)
def test_lower_align(self):
# check data that is not aligned to element size
# i.e doubles are aligned to 4 bytes on i386
d = np.zeros(23 * 8, dtype=np.int8)[4:-4].view(np.float64)
o = np.zeros(23 * 8, dtype=np.int8)[4:-4].view(np.float64)
assert_almost_equal(d + d, d * 2)
np.add(d, d, out=o)
np.add(np.ones_like(d), d, out=o)
np.add(d, np.ones_like(d), out=o)
np.add(np.ones_like(d), d)
np.add(d, np.ones_like(d))
class TestPower(object):
def test_small_types(self):
for t in [np.int8, np.int16, np.float16]:
a = t(3)
b = a ** 4
assert_(b == 81, "error with %r: got %r" % (t, b))
def test_large_types(self):
for t in [np.int32, np.int64, np.float32, np.float64, np.longdouble]:
a = t(51)
b = a ** 4
msg = "error with %r: got %r" % (t, b)
if np.issubdtype(t, np.integer):
assert_(b == 6765201, msg)
else:
assert_almost_equal(b, 6765201, err_msg=msg)
def test_integers_to_negative_integer_power(self):
# Note that the combination of uint64 with a signed integer
# has common type np.float64. The other combinations should all
# raise a ValueError for integer ** negative integer.
exp = [np.array(-1, dt)[()] for dt in 'bhilq']
# 1 ** -1 possible special case
base = [np.array(1, dt)[()] for dt in 'bhilqBHILQ']
for i1, i2 in itertools.product(base, exp):
if i1.dtype.name != 'uint64':
assert_raises(ValueError, operator.pow, i1, i2)
else:
res = operator.pow(i1, i2)
assert_(res.dtype.type is np.float64)
assert_almost_equal(res, 1.)
# -1 ** -1 possible special case
base = [np.array(-1, dt)[()] for dt in 'bhilq']
for i1, i2 in itertools.product(base, exp):
if i1.dtype.name != 'uint64':
assert_raises(ValueError, operator.pow, i1, i2)
else:
res = operator.pow(i1, i2)
assert_(res.dtype.type is np.float64)
assert_almost_equal(res, -1.)
# 2 ** -1 perhaps generic
base = [np.array(2, dt)[()] for dt in 'bhilqBHILQ']
for i1, i2 in itertools.product(base, exp):
if i1.dtype.name != 'uint64':
assert_raises(ValueError, operator.pow, i1, i2)
else:
res = operator.pow(i1, i2)
assert_(res.dtype.type is np.float64)
assert_almost_equal(res, .5)
def test_mixed_types(self):
typelist = [np.int8, np.int16, np.float16,
np.float32, np.float64, np.int8,
np.int16, np.int32, np.int64]
for t1 in typelist:
for t2 in typelist:
a = t1(3)
b = t2(2)
result = a**b
msg = ("error with %r and %r:"
"got %r, expected %r") % (t1, t2, result, 9)
if np.issubdtype(np.dtype(result), np.integer):
assert_(result == 9, msg)
else:
assert_almost_equal(result, 9, err_msg=msg)
def test_modular_power(self):
# modular power is not implemented, so ensure it errors
a = 5
b = 4
c = 10
expected = pow(a, b, c)
for t in (np.int32, np.float32, np.complex64):
# note that 3-operand power only dispatches on the first argument
assert_raises(TypeError, operator.pow, t(a), b, c)
assert_raises(TypeError, operator.pow, np.array(t(a)), b, c)
def floordiv_and_mod(x, y):
return (x // y, x % y)
def _signs(dt):
if dt in np.typecodes['UnsignedInteger']:
return (+1,)
else:
return (+1, -1)
class TestModulus(object):
def test_modulus_basic(self):
dt = np.typecodes['AllInteger'] + np.typecodes['Float']
for op in [floordiv_and_mod, divmod]:
for dt1, dt2 in itertools.product(dt, dt):
for sg1, sg2 in itertools.product(_signs(dt1), _signs(dt2)):
fmt = 'op: %s, dt1: %s, dt2: %s, sg1: %s, sg2: %s'
msg = fmt % (op.__name__, dt1, dt2, sg1, sg2)
a = np.array(sg1*71, dtype=dt1)[()]
b = np.array(sg2*19, dtype=dt2)[()]
div, rem = op(a, b)
assert_equal(div*b + rem, a, err_msg=msg)
if sg2 == -1:
assert_(b < rem <= 0, msg)
else:
assert_(b > rem >= 0, msg)
def test_float_modulus_exact(self):
# test that float results are exact for small integers. This also
# holds for the same integers scaled by powers of two.
nlst = list(range(-127, 0))
plst = list(range(1, 128))
dividend = nlst + [0] + plst
divisor = nlst + plst
arg = list(itertools.product(dividend, divisor))
tgt = list(divmod(*t) for t in arg)
a, b = np.array(arg, dtype=int).T
# convert exact integer results from Python to float so that
# signed zero can be used, it is checked.
tgtdiv, tgtrem = np.array(tgt, dtype=float).T
tgtdiv = np.where((tgtdiv == 0.0) & ((b < 0) ^ (a < 0)), -0.0, tgtdiv)
tgtrem = np.where((tgtrem == 0.0) & (b < 0), -0.0, tgtrem)
for op in [floordiv_and_mod, divmod]:
for dt in np.typecodes['Float']:
msg = 'op: %s, dtype: %s' % (op.__name__, dt)
fa = a.astype(dt)
fb = b.astype(dt)
# use list comprehension so a_ and b_ are scalars
div, rem = zip(*[op(a_, b_) for a_, b_ in zip(fa, fb)])
assert_equal(div, tgtdiv, err_msg=msg)
assert_equal(rem, tgtrem, err_msg=msg)
def test_float_modulus_roundoff(self):
# gh-6127
dt = np.typecodes['Float']
for op in [floordiv_and_mod, divmod]:
for dt1, dt2 in itertools.product(dt, dt):
for sg1, sg2 in itertools.product((+1, -1), (+1, -1)):
fmt = 'op: %s, dt1: %s, dt2: %s, sg1: %s, sg2: %s'
msg = fmt % (op.__name__, dt1, dt2, sg1, sg2)
a = np.array(sg1*78*6e-8, dtype=dt1)[()]
b = np.array(sg2*6e-8, dtype=dt2)[()]
div, rem = op(a, b)
# Equal assertion should hold when fmod is used
assert_equal(div*b + rem, a, err_msg=msg)
if sg2 == -1:
assert_(b < rem <= 0, msg)
else:
assert_(b > rem >= 0, msg)
def test_float_modulus_corner_cases(self):
# Check remainder magnitude.
for dt in np.typecodes['Float']:
b = np.array(1.0, dtype=dt)
a = np.nextafter(np.array(0.0, dtype=dt), -b)
rem = operator.mod(a, b)
assert_(rem <= b, 'dt: %s' % dt)
rem = operator.mod(-a, -b)
assert_(rem >= -b, 'dt: %s' % dt)
# Check nans, inf
with suppress_warnings() as sup:
sup.filter(RuntimeWarning, "invalid value encountered in remainder")
for dt in np.typecodes['Float']:
fone = np.array(1.0, dtype=dt)
fzer = np.array(0.0, dtype=dt)
finf = np.array(np.inf, dtype=dt)
fnan = np.array(np.nan, dtype=dt)
rem = operator.mod(fone, fzer)
assert_(np.isnan(rem), 'dt: %s' % dt)
# MSVC 2008 returns NaN here, so disable the check.
#rem = operator.mod(fone, finf)
#assert_(rem == fone, 'dt: %s' % dt)
rem = operator.mod(fone, fnan)
assert_(np.isnan(rem), 'dt: %s' % dt)
rem = operator.mod(finf, fone)
assert_(np.isnan(rem), 'dt: %s' % dt)
class TestComplexDivision(object):
def test_zero_division(self):
with np.errstate(all="ignore"):
for t in [np.complex64, np.complex128]:
a = t(0.0)
b = t(1.0)
assert_(np.isinf(b/a))
b = t(complex(np.inf, np.inf))
assert_(np.isinf(b/a))
b = t(complex(np.inf, np.nan))
assert_(np.isinf(b/a))
b = t(complex(np.nan, np.inf))
assert_(np.isinf(b/a))
b = t(complex(np.nan, np.nan))
assert_(np.isnan(b/a))
b = t(0.)
assert_(np.isnan(b/a))
def test_signed_zeros(self):
with np.errstate(all="ignore"):
for t in [np.complex64, np.complex128]:
# tupled (numerator, denominator, expected)
# for testing as expected == numerator/denominator
data = (
(( 0.0,-1.0), ( 0.0, 1.0), (-1.0,-0.0)),
(( 0.0,-1.0), ( 0.0,-1.0), ( 1.0,-0.0)),
(( 0.0,-1.0), (-0.0,-1.0), ( 1.0, 0.0)),
(( 0.0,-1.0), (-0.0, 1.0), (-1.0, 0.0)),
(( 0.0, 1.0), ( 0.0,-1.0), (-1.0, 0.0)),
(( 0.0,-1.0), ( 0.0,-1.0), ( 1.0,-0.0)),
((-0.0,-1.0), ( 0.0,-1.0), ( 1.0,-0.0)),
((-0.0, 1.0), ( 0.0,-1.0), (-1.0,-0.0))
)
for cases in data:
n = cases[0]
d = cases[1]
ex = cases[2]
result = t(complex(n[0], n[1])) / t(complex(d[0], d[1]))
# check real and imag parts separately to avoid comparison
# in array context, which does not account for signed zeros
assert_equal(result.real, ex[0])
assert_equal(result.imag, ex[1])
def test_branches(self):
with np.errstate(all="ignore"):
for t in [np.complex64, np.complex128]:
# tupled (numerator, denominator, expected)
# for testing as expected == numerator/denominator
data = list()
# trigger branch: real(fabs(denom)) > imag(fabs(denom))
# followed by else condition as neither are == 0
data.append((( 2.0, 1.0), ( 2.0, 1.0), (1.0, 0.0)))
# trigger branch: real(fabs(denom)) > imag(fabs(denom))
# followed by if condition as both are == 0
# is performed in test_zero_division(), so this is skipped
# trigger else if branch: real(fabs(denom)) < imag(fabs(denom))
data.append((( 1.0, 2.0), ( 1.0, 2.0), (1.0, 0.0)))
for cases in data:
n = cases[0]
d = cases[1]
ex = cases[2]
result = t(complex(n[0], n[1])) / t(complex(d[0], d[1]))
# check real and imag parts separately to avoid comparison
# in array context, which does not account for signed zeros
assert_equal(result.real, ex[0])
assert_equal(result.imag, ex[1])
class TestConversion(object):
def test_int_from_long(self):
l = [1e6, 1e12, 1e18, -1e6, -1e12, -1e18]
li = [10**6, 10**12, 10**18, -10**6, -10**12, -10**18]
for T in [None, np.float64, np.int64]:
a = np.array(l, dtype=T)
assert_equal([int(_m) for _m in a], li)
a = np.array(l[:3], dtype=np.uint64)
assert_equal([int(_m) for _m in a], li[:3])
def test_iinfo_long_values(self):
for code in 'bBhH':
res = np.array(np.iinfo(code).max + 1, dtype=code)
tgt = np.iinfo(code).min
assert_(res == tgt)
for code in np.typecodes['AllInteger']:
res = np.array(np.iinfo(code).max, dtype=code)
tgt = np.iinfo(code).max
assert_(res == tgt)
for code in np.typecodes['AllInteger']:
res = np.typeDict[code](np.iinfo(code).max)
tgt = np.iinfo(code).max
assert_(res == tgt)
def test_int_raise_behaviour(self):
def overflow_error_func(dtype):
np.typeDict[dtype](np.iinfo(dtype).max + 1)
for code in 'lLqQ':
assert_raises(OverflowError, overflow_error_func, code)
def test_int_from_infinite_longdouble(self):
# gh-627
x = np.longdouble(np.inf)
assert_raises(OverflowError, int, x)
with suppress_warnings() as sup:
sup.record(np.ComplexWarning)
x = np.clongdouble(np.inf)
assert_raises(OverflowError, int, x)
assert_equal(len(sup.log), 1)
@dec.knownfailureif(not IS_PYPY,
"__int__ is not the same as int in cpython (gh-9972)")
def test_int_from_infinite_longdouble___int__(self):
x = np.longdouble(np.inf)
assert_raises(OverflowError, x.__int__)
with suppress_warnings() as sup:
sup.record(np.ComplexWarning)
x = np.clongdouble(np.inf)
assert_raises(OverflowError, x.__int__)
assert_equal(len(sup.log), 1)
@dec.knownfailureif(platform.machine().startswith("ppc64"))
@dec.skipif(np.finfo(np.double) == np.finfo(np.longdouble))
def test_int_from_huge_longdouble(self):
# Produce a longdouble that would overflow a double,
# use exponent that avoids bug in Darwin pow function.
exp = np.finfo(np.double).maxexp - 1
huge_ld = 2 * 1234 * np.longdouble(2) ** exp
huge_i = 2 * 1234 * 2 ** exp
assert_(huge_ld != np.inf)
assert_equal(int(huge_ld), huge_i)
def test_int_from_longdouble(self):
x = np.longdouble(1.5)
assert_equal(int(x), 1)
x = np.longdouble(-10.5)
assert_equal(int(x), -10)
def test_numpy_scalar_relational_operators(self):
# All integer
for dt1 in np.typecodes['AllInteger']:
assert_(1 > np.array(0, dtype=dt1)[()], "type %s failed" % (dt1,))
assert_(not 1 < np.array(0, dtype=dt1)[()], "type %s failed" % (dt1,))
for dt2 in np.typecodes['AllInteger']:
assert_(np.array(1, dtype=dt1)[()] > np.array(0, dtype=dt2)[()],
"type %s and %s failed" % (dt1, dt2))
assert_(not np.array(1, dtype=dt1)[()] < np.array(0, dtype=dt2)[()],
"type %s and %s failed" % (dt1, dt2))
#Unsigned integers
for dt1 in 'BHILQP':
assert_(-1 < np.array(1, dtype=dt1)[()], "type %s failed" % (dt1,))
assert_(not -1 > np.array(1, dtype=dt1)[()], "type %s failed" % (dt1,))
assert_(-1 != np.array(1, dtype=dt1)[()], "type %s failed" % (dt1,))
#unsigned vs signed
for dt2 in 'bhilqp':
assert_(np.array(1, dtype=dt1)[()] > np.array(-1, dtype=dt2)[()],
"type %s and %s failed" % (dt1, dt2))
assert_(not np.array(1, dtype=dt1)[()] < np.array(-1, dtype=dt2)[()],
"type %s and %s failed" % (dt1, dt2))
assert_(np.array(1, dtype=dt1)[()] != np.array(-1, dtype=dt2)[()],
"type %s and %s failed" % (dt1, dt2))
#Signed integers and floats
for dt1 in 'bhlqp' + np.typecodes['Float']:
assert_(1 > np.array(-1, dtype=dt1)[()], "type %s failed" % (dt1,))
assert_(not 1 < np.array(-1, dtype=dt1)[()], "type %s failed" % (dt1,))
assert_(-1 == np.array(-1, dtype=dt1)[()], "type %s failed" % (dt1,))
for dt2 in 'bhlqp' + np.typecodes['Float']:
assert_(np.array(1, dtype=dt1)[()] > np.array(-1, dtype=dt2)[()],
"type %s and %s failed" % (dt1, dt2))
assert_(not np.array(1, dtype=dt1)[()] < np.array(-1, dtype=dt2)[()],
"type %s and %s failed" % (dt1, dt2))
assert_(np.array(-1, dtype=dt1)[()] == np.array(-1, dtype=dt2)[()],
"type %s and %s failed" % (dt1, dt2))
def test_scalar_comparison_to_none(self):
# Scalars should just return False and not give a warnings.
# The comparisons are flagged by pep8, ignore that.
with warnings.catch_warnings(record=True) as w:
warnings.filterwarnings('always', '', FutureWarning)
assert_(not np.float32(1) == None)
assert_(not np.str_('test') == None)
# This is dubious (see below):
assert_(not np.datetime64('NaT') == None)
assert_(np.float32(1) != None)
assert_(np.str_('test') != None)
# This is dubious (see below):
assert_(np.datetime64('NaT') != None)
assert_(len(w) == 0)
# For documentation purposes, this is why the datetime is dubious.
# At the time of deprecation this was no behaviour change, but
# it has to be considered when the deprecations are done.
assert_(np.equal(np.datetime64('NaT'), None))
#class TestRepr(object):
# def test_repr(self):
# for t in types:
# val = t(1197346475.0137341)
# val_repr = repr(val)
# val2 = eval(val_repr)
# assert_equal( val, val2 )
class TestRepr(object):
def _test_type_repr(self, t):
finfo = np.finfo(t)
last_fraction_bit_idx = finfo.nexp + finfo.nmant
last_exponent_bit_idx = finfo.nexp
storage_bytes = np.dtype(t).itemsize*8
# could add some more types to the list below
for which in ['small denorm', 'small norm']:
# Values from http://en.wikipedia.org/wiki/IEEE_754
constr = np.array([0x00]*storage_bytes, dtype=np.uint8)
if which == 'small denorm':
byte = last_fraction_bit_idx // 8
bytebit = 7-(last_fraction_bit_idx % 8)
constr[byte] = 1 << bytebit
elif which == 'small norm':
byte = last_exponent_bit_idx // 8
bytebit = 7-(last_exponent_bit_idx % 8)
constr[byte] = 1 << bytebit
else:
raise ValueError('hmm')
val = constr.view(t)[0]
val_repr = repr(val)
val2 = t(eval(val_repr))
if not (val2 == 0 and val < 1e-100):
assert_equal(val, val2)
def test_float_repr(self):
# long double test cannot work, because eval goes through a python
# float
for t in [np.float32, np.float64]:
yield self._test_type_repr, t
if not IS_PYPY:
# sys.getsizeof() is not valid on PyPy
class TestSizeOf(object):
def test_equal_nbytes(self):
for type in types:
x = type(0)
assert_(sys.getsizeof(x) > x.nbytes)
def test_error(self):
d = np.float32()
assert_raises(TypeError, d.__sizeof__, "a")
class TestMultiply(object):
def test_seq_repeat(self):
# Test that basic sequences get repeated when multiplied with
# numpy integers. And errors are raised when multiplied with others.
# Some of this behaviour may be controversial and could be open for
# change.
for seq_type in (list, tuple):
seq = seq_type([1, 2, 3])
for numpy_type in np.typecodes["AllInteger"]:
i = np.dtype(numpy_type).type(2)
assert_equal(seq * i, seq * int(i))
assert_equal(i * seq, int(i) * seq)
for numpy_type in np.typecodes["All"].replace("V", ""):
if numpy_type in np.typecodes["AllInteger"]:
continue
i = np.dtype(numpy_type).type()
assert_raises(TypeError, operator.mul, seq, i)
assert_raises(TypeError, operator.mul, i, seq)
def test_no_seq_repeat_basic_array_like(self):
# Test that an array-like which does not know how to be multiplied
# does not attempt sequence repeat (raise TypeError).
# See also gh-7428.
class ArrayLike(object):
def __init__(self, arr):
self.arr = arr
def __array__(self):
return self.arr
# Test for simple ArrayLike above and memoryviews (original report)
for arr_like in (ArrayLike(np.ones(3)), memoryview(np.ones(3))):
assert_array_equal(arr_like * np.float32(3.), np.full(3, 3.))
assert_array_equal(np.float32(3.) * arr_like, np.full(3, 3.))
assert_array_equal(arr_like * np.int_(3), np.full(3, 3))
assert_array_equal(np.int_(3) * arr_like, np.full(3, 3))
class TestNegative(object):
def test_exceptions(self):
a = np.ones((), dtype=np.bool_)[()]
assert_raises(TypeError, operator.neg, a)
def test_result(self):
types = np.typecodes['AllInteger'] + np.typecodes['AllFloat']
with suppress_warnings() as sup:
sup.filter(RuntimeWarning)
for dt in types:
a = np.ones((), dtype=dt)[()]
assert_equal(operator.neg(a) + a, 0)
class TestSubtract(object):
def test_exceptions(self):
a = np.ones((), dtype=np.bool_)[()]
assert_raises(TypeError, operator.sub, a, a)
def test_result(self):
types = np.typecodes['AllInteger'] + np.typecodes['AllFloat']
with suppress_warnings() as sup:
sup.filter(RuntimeWarning)
for dt in types:
a = np.ones((), dtype=dt)[()]
assert_equal(operator.sub(a, a), 0)
class TestAbs(object):
def _test_abs_func(self, absfunc):
for tp in floating_types + complex_floating_types:
x = tp(-1.5)
assert_equal(absfunc(x), 1.5)
x = tp(0.0)
res = absfunc(x)
# assert_equal() checks zero signedness
assert_equal(res, 0.0)
x = tp(-0.0)
res = absfunc(x)
assert_equal(res, 0.0)
x = tp(np.finfo(tp).max)
assert_equal(absfunc(x), x.real)
x = tp(np.finfo(tp).tiny)
assert_equal(absfunc(x), x.real)
x = tp(np.finfo(tp).min)
assert_equal(absfunc(x), -x.real)
def test_builtin_abs(self):
self._test_abs_func(abs)
def test_numpy_abs(self):
self._test_abs_func(np.abs)
if __name__ == "__main__":
run_module_suite()
| 26,706 | 39.649924 | 90 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/tests/test_scalar_ctors.py
|
"""
Test the scalar contructors, which also do type-coercion
"""
from __future__ import division, absolute_import, print_function
import sys
import platform
import numpy as np
from numpy.testing import (
run_module_suite,
assert_equal, assert_almost_equal, assert_raises, assert_warns,
dec
)
class TestFromString(object):
def test_floating(self):
# Ticket #640, floats from string
fsingle = np.single('1.234')
fdouble = np.double('1.234')
flongdouble = np.longdouble('1.234')
assert_almost_equal(fsingle, 1.234)
assert_almost_equal(fdouble, 1.234)
assert_almost_equal(flongdouble, 1.234)
def test_floating_overflow(self):
""" Strings containing an unrepresentable float overflow """
fhalf = np.half('1e10000')
assert_equal(fhalf, np.inf)
fsingle = np.single('1e10000')
assert_equal(fsingle, np.inf)
fdouble = np.double('1e10000')
assert_equal(fdouble, np.inf)
flongdouble = assert_warns(RuntimeWarning, np.longdouble, '1e10000')
assert_equal(flongdouble, np.inf)
fhalf = np.half('-1e10000')
assert_equal(fhalf, -np.inf)
fsingle = np.single('-1e10000')
assert_equal(fsingle, -np.inf)
fdouble = np.double('-1e10000')
assert_equal(fdouble, -np.inf)
flongdouble = assert_warns(RuntimeWarning, np.longdouble, '-1e10000')
assert_equal(flongdouble, -np.inf)
@dec.knownfailureif((sys.version_info[0] >= 3) or
(sys.platform == "win32" and
platform.architecture()[0] == "64bit"),
"numpy.intp('0xff', 16) not supported on Py3, "
"as it does not inherit from Python int")
def test_intp(self):
# Ticket #99
i_width = np.int_(0).nbytes*2 - 1
np.intp('0x' + 'f'*i_width, 16)
assert_raises(OverflowError, np.intp, '0x' + 'f'*(i_width+1), 16)
assert_raises(ValueError, np.intp, '0x1', 32)
assert_equal(255, np.intp('0xFF', 16))
class TestFromInt(object):
def test_intp(self):
# Ticket #99
assert_equal(1024, np.intp(1024))
def test_uint64_from_negative(self):
assert_equal(np.uint64(-2), np.uint64(18446744073709551614))
if __name__ == "__main__":
run_module_suite()
| 2,362 | 32.28169 | 77 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/tests/test_scalarinherit.py
|
# -*- coding: utf-8 -*-
""" Test printing of scalar types.
"""
from __future__ import division, absolute_import, print_function
import numpy as np
from numpy.testing import run_module_suite, assert_
class A(object):
pass
class B(A, np.float64):
pass
class C(B):
pass
class D(C, B):
pass
class B0(np.float64, A):
pass
class C0(B0):
pass
class TestInherit(object):
def test_init(self):
x = B(1.0)
assert_(str(x) == '1.0')
y = C(2.0)
assert_(str(y) == '2.0')
z = D(3.0)
assert_(str(z) == '3.0')
def test_init2(self):
x = B0(1.0)
assert_(str(x) == '1.0')
y = C0(2.0)
assert_(str(y) == '2.0')
class TestCharacter(object):
def test_char_radd(self):
# GH issue 9620, reached gentype_add and raise TypeError
np_s = np.string_('abc')
np_u = np.unicode_('abc')
s = b'def'
u = u'def'
assert_(np_s.__radd__(np_s) is NotImplemented)
assert_(np_s.__radd__(np_u) is NotImplemented)
assert_(np_s.__radd__(s) is NotImplemented)
assert_(np_s.__radd__(u) is NotImplemented)
assert_(np_u.__radd__(np_s) is NotImplemented)
assert_(np_u.__radd__(np_u) is NotImplemented)
assert_(np_u.__radd__(s) is NotImplemented)
assert_(np_u.__radd__(u) is NotImplemented)
assert_(s + np_s == b'defabc')
assert_(u + np_u == u'defabc')
class Mystr(str, np.generic):
# would segfault
pass
ret = s + Mystr('abc')
assert_(type(ret) is type(s))
def test_char_repeat(self):
np_s = np.string_('abc')
np_u = np.unicode_('abc')
np_i = np.int(5)
res_np = np_s * np_i
res_s = b'abc' * 5
assert_(res_np == res_s)
if __name__ == "__main__":
run_module_suite()
| 1,867 | 22.64557 | 64 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/tests/test_getlimits.py
|
""" Test functions for limits module.
"""
from __future__ import division, absolute_import, print_function
import numpy as np
from numpy.core import finfo, iinfo
from numpy import half, single, double, longdouble
from numpy.testing import (
run_module_suite, assert_equal, assert_, assert_raises
)
from numpy.core.getlimits import (_discovered_machar, _float16_ma, _float32_ma,
_float64_ma, _float128_ma, _float80_ma)
##################################################
class TestPythonFloat(object):
def test_singleton(self):
ftype = finfo(float)
ftype2 = finfo(float)
assert_equal(id(ftype), id(ftype2))
class TestHalf(object):
def test_singleton(self):
ftype = finfo(half)
ftype2 = finfo(half)
assert_equal(id(ftype), id(ftype2))
class TestSingle(object):
def test_singleton(self):
ftype = finfo(single)
ftype2 = finfo(single)
assert_equal(id(ftype), id(ftype2))
class TestDouble(object):
def test_singleton(self):
ftype = finfo(double)
ftype2 = finfo(double)
assert_equal(id(ftype), id(ftype2))
class TestLongdouble(object):
def test_singleton(self):
ftype = finfo(longdouble)
ftype2 = finfo(longdouble)
assert_equal(id(ftype), id(ftype2))
class TestFinfo(object):
def test_basic(self):
dts = list(zip(['f2', 'f4', 'f8', 'c8', 'c16'],
[np.float16, np.float32, np.float64, np.complex64,
np.complex128]))
for dt1, dt2 in dts:
for attr in ('bits', 'eps', 'epsneg', 'iexp', 'machar', 'machep',
'max', 'maxexp', 'min', 'minexp', 'negep', 'nexp',
'nmant', 'precision', 'resolution', 'tiny'):
assert_equal(getattr(finfo(dt1), attr),
getattr(finfo(dt2), attr), attr)
assert_raises(ValueError, finfo, 'i4')
class TestIinfo(object):
def test_basic(self):
dts = list(zip(['i1', 'i2', 'i4', 'i8',
'u1', 'u2', 'u4', 'u8'],
[np.int8, np.int16, np.int32, np.int64,
np.uint8, np.uint16, np.uint32, np.uint64]))
for dt1, dt2 in dts:
for attr in ('bits', 'min', 'max'):
assert_equal(getattr(iinfo(dt1), attr),
getattr(iinfo(dt2), attr), attr)
assert_raises(ValueError, iinfo, 'f4')
def test_unsigned_max(self):
types = np.sctypes['uint']
for T in types:
assert_equal(iinfo(T).max, T(-1))
class TestRepr(object):
def test_iinfo_repr(self):
expected = "iinfo(min=-32768, max=32767, dtype=int16)"
assert_equal(repr(np.iinfo(np.int16)), expected)
def test_finfo_repr(self):
expected = "finfo(resolution=1e-06, min=-3.4028235e+38," + \
" max=3.4028235e+38, dtype=float32)"
assert_equal(repr(np.finfo(np.float32)), expected)
def test_instances():
iinfo(10)
finfo(3.0)
def assert_ma_equal(discovered, ma_like):
# Check MachAr-like objects same as calculated MachAr instances
for key, value in discovered.__dict__.items():
assert_equal(value, getattr(ma_like, key))
if hasattr(value, 'shape'):
assert_equal(value.shape, getattr(ma_like, key).shape)
assert_equal(value.dtype, getattr(ma_like, key).dtype)
def test_known_types():
# Test we are correctly compiling parameters for known types
for ftype, ma_like in ((np.float16, _float16_ma),
(np.float32, _float32_ma),
(np.float64, _float64_ma)):
assert_ma_equal(_discovered_machar(ftype), ma_like)
# Suppress warning for broken discovery of double double on PPC
with np.errstate(all='ignore'):
ld_ma = _discovered_machar(np.longdouble)
bytes = np.dtype(np.longdouble).itemsize
if (ld_ma.it, ld_ma.maxexp) == (63, 16384) and bytes in (12, 16):
# 80-bit extended precision
assert_ma_equal(ld_ma, _float80_ma)
elif (ld_ma.it, ld_ma.maxexp) == (112, 16384) and bytes == 16:
# IEE 754 128-bit
assert_ma_equal(ld_ma, _float128_ma)
def test_plausible_finfo():
# Assert that finfo returns reasonable results for all types
for ftype in np.sctypes['float'] + np.sctypes['complex']:
info = np.finfo(ftype)
assert_(info.nmant > 1)
assert_(info.minexp < -1)
assert_(info.maxexp > 1)
if __name__ == "__main__":
run_module_suite()
| 4,586 | 34.015267 | 79 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/tests/test_unicode.py
|
from __future__ import division, absolute_import, print_function
import sys
import numpy as np
from numpy.compat import unicode
from numpy.testing import (
run_module_suite, assert_, assert_equal, assert_array_equal)
# Guess the UCS length for this python interpreter
if sys.version_info[:2] >= (3, 3):
# Python 3.3 uses a flexible string representation
ucs4 = False
def buffer_length(arr):
if isinstance(arr, unicode):
arr = str(arr)
if not arr:
charmax = 0
else:
charmax = max([ord(c) for c in arr])
if charmax < 256:
size = 1
elif charmax < 65536:
size = 2
else:
size = 4
return size * len(arr)
v = memoryview(arr)
if v.shape is None:
return len(v) * v.itemsize
else:
return np.prod(v.shape) * v.itemsize
else:
if len(buffer(u'u')) == 4:
ucs4 = True
else:
ucs4 = False
def buffer_length(arr):
if isinstance(arr, np.ndarray):
return len(arr.data)
return len(buffer(arr))
# In both cases below we need to make sure that the byte swapped value (as
# UCS4) is still a valid unicode:
# Value that can be represented in UCS2 interpreters
ucs2_value = u'\u0900'
# Value that cannot be represented in UCS2 interpreters (but can in UCS4)
ucs4_value = u'\U00100900'
def test_string_cast():
str_arr = np.array(["1234", "1234\0\0"], dtype='S')
uni_arr1 = str_arr.astype('>U')
uni_arr2 = str_arr.astype('<U')
if sys.version_info[0] < 3:
assert_array_equal(str_arr, uni_arr1)
assert_array_equal(str_arr, uni_arr2)
else:
assert_(str_arr != uni_arr1)
assert_(str_arr != uni_arr2)
assert_array_equal(uni_arr1, uni_arr2)
############################################################
# Creation tests
############################################################
class CreateZeros(object):
"""Check the creation of zero-valued arrays"""
def content_check(self, ua, ua_scalar, nbytes):
# Check the length of the unicode base type
assert_(int(ua.dtype.str[2:]) == self.ulen)
# Check the length of the data buffer
assert_(buffer_length(ua) == nbytes)
# Small check that data in array element is ok
assert_(ua_scalar == u'')
# Encode to ascii and double check
assert_(ua_scalar.encode('ascii') == b'')
# Check buffer lengths for scalars
if ucs4:
assert_(buffer_length(ua_scalar) == 0)
else:
assert_(buffer_length(ua_scalar) == 0)
def test_zeros0D(self):
# Check creation of 0-dimensional objects
ua = np.zeros((), dtype='U%s' % self.ulen)
self.content_check(ua, ua[()], 4*self.ulen)
def test_zerosSD(self):
# Check creation of single-dimensional objects
ua = np.zeros((2,), dtype='U%s' % self.ulen)
self.content_check(ua, ua[0], 4*self.ulen*2)
self.content_check(ua, ua[1], 4*self.ulen*2)
def test_zerosMD(self):
# Check creation of multi-dimensional objects
ua = np.zeros((2, 3, 4), dtype='U%s' % self.ulen)
self.content_check(ua, ua[0, 0, 0], 4*self.ulen*2*3*4)
self.content_check(ua, ua[-1, -1, -1], 4*self.ulen*2*3*4)
class TestCreateZeros_1(CreateZeros):
"""Check the creation of zero-valued arrays (size 1)"""
ulen = 1
class TestCreateZeros_2(CreateZeros):
"""Check the creation of zero-valued arrays (size 2)"""
ulen = 2
class TestCreateZeros_1009(CreateZeros):
"""Check the creation of zero-valued arrays (size 1009)"""
ulen = 1009
class CreateValues(object):
"""Check the creation of unicode arrays with values"""
def content_check(self, ua, ua_scalar, nbytes):
# Check the length of the unicode base type
assert_(int(ua.dtype.str[2:]) == self.ulen)
# Check the length of the data buffer
assert_(buffer_length(ua) == nbytes)
# Small check that data in array element is ok
assert_(ua_scalar == self.ucs_value*self.ulen)
# Encode to UTF-8 and double check
assert_(ua_scalar.encode('utf-8') ==
(self.ucs_value*self.ulen).encode('utf-8'))
# Check buffer lengths for scalars
if ucs4:
assert_(buffer_length(ua_scalar) == 4*self.ulen)
else:
if self.ucs_value == ucs4_value:
# In UCS2, the \U0010FFFF will be represented using a
# surrogate *pair*
assert_(buffer_length(ua_scalar) == 2*2*self.ulen)
else:
# In UCS2, the \uFFFF will be represented using a
# regular 2-byte word
assert_(buffer_length(ua_scalar) == 2*self.ulen)
def test_values0D(self):
# Check creation of 0-dimensional objects with values
ua = np.array(self.ucs_value*self.ulen, dtype='U%s' % self.ulen)
self.content_check(ua, ua[()], 4*self.ulen)
def test_valuesSD(self):
# Check creation of single-dimensional objects with values
ua = np.array([self.ucs_value*self.ulen]*2, dtype='U%s' % self.ulen)
self.content_check(ua, ua[0], 4*self.ulen*2)
self.content_check(ua, ua[1], 4*self.ulen*2)
def test_valuesMD(self):
# Check creation of multi-dimensional objects with values
ua = np.array([[[self.ucs_value*self.ulen]*2]*3]*4, dtype='U%s' % self.ulen)
self.content_check(ua, ua[0, 0, 0], 4*self.ulen*2*3*4)
self.content_check(ua, ua[-1, -1, -1], 4*self.ulen*2*3*4)
class TestCreateValues_1_UCS2(CreateValues):
"""Check the creation of valued arrays (size 1, UCS2 values)"""
ulen = 1
ucs_value = ucs2_value
class TestCreateValues_1_UCS4(CreateValues):
"""Check the creation of valued arrays (size 1, UCS4 values)"""
ulen = 1
ucs_value = ucs4_value
class TestCreateValues_2_UCS2(CreateValues):
"""Check the creation of valued arrays (size 2, UCS2 values)"""
ulen = 2
ucs_value = ucs2_value
class TestCreateValues_2_UCS4(CreateValues):
"""Check the creation of valued arrays (size 2, UCS4 values)"""
ulen = 2
ucs_value = ucs4_value
class TestCreateValues_1009_UCS2(CreateValues):
"""Check the creation of valued arrays (size 1009, UCS2 values)"""
ulen = 1009
ucs_value = ucs2_value
class TestCreateValues_1009_UCS4(CreateValues):
"""Check the creation of valued arrays (size 1009, UCS4 values)"""
ulen = 1009
ucs_value = ucs4_value
############################################################
# Assignment tests
############################################################
class AssignValues(object):
"""Check the assignment of unicode arrays with values"""
def content_check(self, ua, ua_scalar, nbytes):
# Check the length of the unicode base type
assert_(int(ua.dtype.str[2:]) == self.ulen)
# Check the length of the data buffer
assert_(buffer_length(ua) == nbytes)
# Small check that data in array element is ok
assert_(ua_scalar == self.ucs_value*self.ulen)
# Encode to UTF-8 and double check
assert_(ua_scalar.encode('utf-8') ==
(self.ucs_value*self.ulen).encode('utf-8'))
# Check buffer lengths for scalars
if ucs4:
assert_(buffer_length(ua_scalar) == 4*self.ulen)
else:
if self.ucs_value == ucs4_value:
# In UCS2, the \U0010FFFF will be represented using a
# surrogate *pair*
assert_(buffer_length(ua_scalar) == 2*2*self.ulen)
else:
# In UCS2, the \uFFFF will be represented using a
# regular 2-byte word
assert_(buffer_length(ua_scalar) == 2*self.ulen)
def test_values0D(self):
# Check assignment of 0-dimensional objects with values
ua = np.zeros((), dtype='U%s' % self.ulen)
ua[()] = self.ucs_value*self.ulen
self.content_check(ua, ua[()], 4*self.ulen)
def test_valuesSD(self):
# Check assignment of single-dimensional objects with values
ua = np.zeros((2,), dtype='U%s' % self.ulen)
ua[0] = self.ucs_value*self.ulen
self.content_check(ua, ua[0], 4*self.ulen*2)
ua[1] = self.ucs_value*self.ulen
self.content_check(ua, ua[1], 4*self.ulen*2)
def test_valuesMD(self):
# Check assignment of multi-dimensional objects with values
ua = np.zeros((2, 3, 4), dtype='U%s' % self.ulen)
ua[0, 0, 0] = self.ucs_value*self.ulen
self.content_check(ua, ua[0, 0, 0], 4*self.ulen*2*3*4)
ua[-1, -1, -1] = self.ucs_value*self.ulen
self.content_check(ua, ua[-1, -1, -1], 4*self.ulen*2*3*4)
class TestAssignValues_1_UCS2(AssignValues):
"""Check the assignment of valued arrays (size 1, UCS2 values)"""
ulen = 1
ucs_value = ucs2_value
class TestAssignValues_1_UCS4(AssignValues):
"""Check the assignment of valued arrays (size 1, UCS4 values)"""
ulen = 1
ucs_value = ucs4_value
class TestAssignValues_2_UCS2(AssignValues):
"""Check the assignment of valued arrays (size 2, UCS2 values)"""
ulen = 2
ucs_value = ucs2_value
class TestAssignValues_2_UCS4(AssignValues):
"""Check the assignment of valued arrays (size 2, UCS4 values)"""
ulen = 2
ucs_value = ucs4_value
class TestAssignValues_1009_UCS2(AssignValues):
"""Check the assignment of valued arrays (size 1009, UCS2 values)"""
ulen = 1009
ucs_value = ucs2_value
class TestAssignValues_1009_UCS4(AssignValues):
"""Check the assignment of valued arrays (size 1009, UCS4 values)"""
ulen = 1009
ucs_value = ucs4_value
############################################################
# Byteorder tests
############################################################
class ByteorderValues(object):
"""Check the byteorder of unicode arrays in round-trip conversions"""
def test_values0D(self):
# Check byteorder of 0-dimensional objects
ua = np.array(self.ucs_value*self.ulen, dtype='U%s' % self.ulen)
ua2 = ua.newbyteorder()
# This changes the interpretation of the data region (but not the
# actual data), therefore the returned scalars are not
# the same (they are byte-swapped versions of each other).
assert_(ua[()] != ua2[()])
ua3 = ua2.newbyteorder()
# Arrays must be equal after the round-trip
assert_equal(ua, ua3)
def test_valuesSD(self):
# Check byteorder of single-dimensional objects
ua = np.array([self.ucs_value*self.ulen]*2, dtype='U%s' % self.ulen)
ua2 = ua.newbyteorder()
assert_((ua != ua2).all())
assert_(ua[-1] != ua2[-1])
ua3 = ua2.newbyteorder()
# Arrays must be equal after the round-trip
assert_equal(ua, ua3)
def test_valuesMD(self):
# Check byteorder of multi-dimensional objects
ua = np.array([[[self.ucs_value*self.ulen]*2]*3]*4,
dtype='U%s' % self.ulen)
ua2 = ua.newbyteorder()
assert_((ua != ua2).all())
assert_(ua[-1, -1, -1] != ua2[-1, -1, -1])
ua3 = ua2.newbyteorder()
# Arrays must be equal after the round-trip
assert_equal(ua, ua3)
def test_values_cast(self):
# Check byteorder of when casting the array for a strided and
# contiguous array:
test1 = np.array([self.ucs_value*self.ulen]*2, dtype='U%s' % self.ulen)
test2 = np.repeat(test1, 2)[::2]
for ua in (test1, test2):
ua2 = ua.astype(dtype=ua.dtype.newbyteorder())
assert_((ua == ua2).all())
assert_(ua[-1] == ua2[-1])
ua3 = ua2.astype(dtype=ua.dtype)
# Arrays must be equal after the round-trip
assert_equal(ua, ua3)
def test_values_updowncast(self):
# Check byteorder of when casting the array to a longer and shorter
# string length for strided and contiguous arrays
test1 = np.array([self.ucs_value*self.ulen]*2, dtype='U%s' % self.ulen)
test2 = np.repeat(test1, 2)[::2]
for ua in (test1, test2):
# Cast to a longer type with zero padding
longer_type = np.dtype('U%s' % (self.ulen+1)).newbyteorder()
ua2 = ua.astype(dtype=longer_type)
assert_((ua == ua2).all())
assert_(ua[-1] == ua2[-1])
# Cast back again with truncating:
ua3 = ua2.astype(dtype=ua.dtype)
# Arrays must be equal after the round-trip
assert_equal(ua, ua3)
class TestByteorder_1_UCS2(ByteorderValues):
"""Check the byteorder in unicode (size 1, UCS2 values)"""
ulen = 1
ucs_value = ucs2_value
class TestByteorder_1_UCS4(ByteorderValues):
"""Check the byteorder in unicode (size 1, UCS4 values)"""
ulen = 1
ucs_value = ucs4_value
class TestByteorder_2_UCS2(ByteorderValues):
"""Check the byteorder in unicode (size 2, UCS2 values)"""
ulen = 2
ucs_value = ucs2_value
class TestByteorder_2_UCS4(ByteorderValues):
"""Check the byteorder in unicode (size 2, UCS4 values)"""
ulen = 2
ucs_value = ucs4_value
class TestByteorder_1009_UCS2(ByteorderValues):
"""Check the byteorder in unicode (size 1009, UCS2 values)"""
ulen = 1009
ucs_value = ucs2_value
class TestByteorder_1009_UCS4(ByteorderValues):
"""Check the byteorder in unicode (size 1009, UCS4 values)"""
ulen = 1009
ucs_value = ucs4_value
if __name__ == "__main__":
run_module_suite()
| 13,733 | 33.164179 | 84 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/tests/test_item_selection.py
|
from __future__ import division, absolute_import, print_function
import sys
import numpy as np
from numpy.testing import (
run_module_suite, assert_, assert_raises,
assert_array_equal, HAS_REFCOUNT
)
class TestTake(object):
def test_simple(self):
a = [[1, 2], [3, 4]]
a_str = [[b'1', b'2'], [b'3', b'4']]
modes = ['raise', 'wrap', 'clip']
indices = [-1, 4]
index_arrays = [np.empty(0, dtype=np.intp),
np.empty(tuple(), dtype=np.intp),
np.empty((1, 1), dtype=np.intp)]
real_indices = {'raise': {-1: 1, 4: IndexError},
'wrap': {-1: 1, 4: 0},
'clip': {-1: 0, 4: 1}}
# Currently all types but object, use the same function generation.
# So it should not be necessary to test all. However test also a non
# refcounted struct on top of object.
types = int, object, np.dtype([('', 'i', 2)])
for t in types:
# ta works, even if the array may be odd if buffer interface is used
ta = np.array(a if np.issubdtype(t, np.number) else a_str, dtype=t)
tresult = list(ta.T.copy())
for index_array in index_arrays:
if index_array.size != 0:
tresult[0].shape = (2,) + index_array.shape
tresult[1].shape = (2,) + index_array.shape
for mode in modes:
for index in indices:
real_index = real_indices[mode][index]
if real_index is IndexError and index_array.size != 0:
index_array.put(0, index)
assert_raises(IndexError, ta.take, index_array,
mode=mode, axis=1)
elif index_array.size != 0:
index_array.put(0, index)
res = ta.take(index_array, mode=mode, axis=1)
assert_array_equal(res, tresult[real_index])
else:
res = ta.take(index_array, mode=mode, axis=1)
assert_(res.shape == (2,) + index_array.shape)
def test_refcounting(self):
objects = [object() for i in range(10)]
for mode in ('raise', 'clip', 'wrap'):
a = np.array(objects)
b = np.array([2, 2, 4, 5, 3, 5])
a.take(b, out=a[:6], mode=mode)
del a
if HAS_REFCOUNT:
assert_(all(sys.getrefcount(o) == 3 for o in objects))
# not contiguous, example:
a = np.array(objects * 2)[::2]
a.take(b, out=a[:6], mode=mode)
del a
if HAS_REFCOUNT:
assert_(all(sys.getrefcount(o) == 3 for o in objects))
def test_unicode_mode(self):
d = np.arange(10)
k = b'\xc3\xa4'.decode("UTF8")
assert_raises(ValueError, d.take, 5, mode=k)
def test_empty_partition(self):
# In reference to github issue #6530
a_original = np.array([0, 2, 4, 6, 8, 10])
a = a_original.copy()
# An empty partition should be a successful no-op
a.partition(np.array([], dtype=np.int16))
assert_array_equal(a, a_original)
def test_empty_argpartition(self):
# In reference to github issue #6530
a = np.array([0, 2, 4, 6, 8, 10])
a = a.argpartition(np.array([], dtype=np.int16))
b = np.array([0, 1, 2, 3, 4, 5])
assert_array_equal(a, b)
if __name__ == "__main__":
run_module_suite()
| 3,669 | 38.462366 | 80 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/tests/test_indexing.py
|
from __future__ import division, absolute_import, print_function
import sys
import warnings
import functools
import operator
import numpy as np
from numpy.core.multiarray_tests import array_indexing
from itertools import product
from numpy.testing import (
run_module_suite, assert_, assert_equal, assert_raises,
assert_array_equal, assert_warns, dec, HAS_REFCOUNT, suppress_warnings,
)
try:
cdll = None
if hasattr(sys, 'gettotalrefcount'):
try:
cdll = np.ctypeslib.load_library('multiarray_d', np.core.multiarray.__file__)
except OSError:
pass
if cdll is None:
cdll = np.ctypeslib.load_library('multiarray', np.core.multiarray.__file__)
_HAS_CTYPE = True
except ImportError:
_HAS_CTYPE = False
class TestIndexing(object):
def test_index_no_floats(self):
a = np.array([[[5]]])
assert_raises(IndexError, lambda: a[0.0])
assert_raises(IndexError, lambda: a[0, 0.0])
assert_raises(IndexError, lambda: a[0.0, 0])
assert_raises(IndexError, lambda: a[0.0,:])
assert_raises(IndexError, lambda: a[:, 0.0])
assert_raises(IndexError, lambda: a[:, 0.0,:])
assert_raises(IndexError, lambda: a[0.0,:,:])
assert_raises(IndexError, lambda: a[0, 0, 0.0])
assert_raises(IndexError, lambda: a[0.0, 0, 0])
assert_raises(IndexError, lambda: a[0, 0.0, 0])
assert_raises(IndexError, lambda: a[-1.4])
assert_raises(IndexError, lambda: a[0, -1.4])
assert_raises(IndexError, lambda: a[-1.4, 0])
assert_raises(IndexError, lambda: a[-1.4,:])
assert_raises(IndexError, lambda: a[:, -1.4])
assert_raises(IndexError, lambda: a[:, -1.4,:])
assert_raises(IndexError, lambda: a[-1.4,:,:])
assert_raises(IndexError, lambda: a[0, 0, -1.4])
assert_raises(IndexError, lambda: a[-1.4, 0, 0])
assert_raises(IndexError, lambda: a[0, -1.4, 0])
assert_raises(IndexError, lambda: a[0.0:, 0.0])
assert_raises(IndexError, lambda: a[0.0:, 0.0,:])
def test_slicing_no_floats(self):
a = np.array([[5]])
# start as float.
assert_raises(TypeError, lambda: a[0.0:])
assert_raises(TypeError, lambda: a[0:, 0.0:2])
assert_raises(TypeError, lambda: a[0.0::2, :0])
assert_raises(TypeError, lambda: a[0.0:1:2,:])
assert_raises(TypeError, lambda: a[:, 0.0:])
# stop as float.
assert_raises(TypeError, lambda: a[:0.0])
assert_raises(TypeError, lambda: a[:0, 1:2.0])
assert_raises(TypeError, lambda: a[:0.0:2, :0])
assert_raises(TypeError, lambda: a[:0.0,:])
assert_raises(TypeError, lambda: a[:, 0:4.0:2])
# step as float.
assert_raises(TypeError, lambda: a[::1.0])
assert_raises(TypeError, lambda: a[0:, :2:2.0])
assert_raises(TypeError, lambda: a[1::4.0, :0])
assert_raises(TypeError, lambda: a[::5.0,:])
assert_raises(TypeError, lambda: a[:, 0:4:2.0])
# mixed.
assert_raises(TypeError, lambda: a[1.0:2:2.0])
assert_raises(TypeError, lambda: a[1.0::2.0])
assert_raises(TypeError, lambda: a[0:, :2.0:2.0])
assert_raises(TypeError, lambda: a[1.0:1:4.0, :0])
assert_raises(TypeError, lambda: a[1.0:5.0:5.0,:])
assert_raises(TypeError, lambda: a[:, 0.4:4.0:2.0])
# should still get the DeprecationWarning if step = 0.
assert_raises(TypeError, lambda: a[::0.0])
def test_index_no_array_to_index(self):
# No non-scalar arrays.
a = np.array([[[1]]])
assert_raises(TypeError, lambda: a[a:a:a])
def test_none_index(self):
# `None` index adds newaxis
a = np.array([1, 2, 3])
assert_equal(a[None], a[np.newaxis])
assert_equal(a[None].ndim, a.ndim + 1)
def test_empty_tuple_index(self):
# Empty tuple index creates a view
a = np.array([1, 2, 3])
assert_equal(a[()], a)
assert_(a[()].base is a)
a = np.array(0)
assert_(isinstance(a[()], np.int_))
def test_void_scalar_empty_tuple(self):
s = np.zeros((), dtype='V4')
assert_equal(s[()].dtype, s.dtype)
assert_equal(s[()], s)
assert_equal(type(s[...]), np.ndarray)
def test_same_kind_index_casting(self):
# Indexes should be cast with same-kind and not safe, even if that
# is somewhat unsafe. So test various different code paths.
index = np.arange(5)
u_index = index.astype(np.uintp)
arr = np.arange(10)
assert_array_equal(arr[index], arr[u_index])
arr[u_index] = np.arange(5)
assert_array_equal(arr, np.arange(10))
arr = np.arange(10).reshape(5, 2)
assert_array_equal(arr[index], arr[u_index])
arr[u_index] = np.arange(5)[:,None]
assert_array_equal(arr, np.arange(5)[:,None].repeat(2, axis=1))
arr = np.arange(25).reshape(5, 5)
assert_array_equal(arr[u_index, u_index], arr[index, index])
def test_empty_fancy_index(self):
# Empty list index creates an empty array
# with the same dtype (but with weird shape)
a = np.array([1, 2, 3])
assert_equal(a[[]], [])
assert_equal(a[[]].dtype, a.dtype)
b = np.array([], dtype=np.intp)
assert_equal(a[[]], [])
assert_equal(a[[]].dtype, a.dtype)
b = np.array([])
assert_raises(IndexError, a.__getitem__, b)
def test_ellipsis_index(self):
a = np.array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
assert_(a[...] is not a)
assert_equal(a[...], a)
# `a[...]` was `a` in numpy <1.9.
assert_(a[...].base is a)
# Slicing with ellipsis can skip an
# arbitrary number of dimensions
assert_equal(a[0, ...], a[0])
assert_equal(a[0, ...], a[0,:])
assert_equal(a[..., 0], a[:, 0])
# Slicing with ellipsis always results
# in an array, not a scalar
assert_equal(a[0, ..., 1], np.array(2))
# Assignment with `(Ellipsis,)` on 0-d arrays
b = np.array(1)
b[(Ellipsis,)] = 2
assert_equal(b, 2)
def test_single_int_index(self):
# Single integer index selects one row
a = np.array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
assert_equal(a[0], [1, 2, 3])
assert_equal(a[-1], [7, 8, 9])
# Index out of bounds produces IndexError
assert_raises(IndexError, a.__getitem__, 1 << 30)
# Index overflow produces IndexError
assert_raises(IndexError, a.__getitem__, 1 << 64)
def test_single_bool_index(self):
# Single boolean index
a = np.array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
assert_equal(a[np.array(True)], a[None])
assert_equal(a[np.array(False)], a[None][0:0])
def test_boolean_shape_mismatch(self):
arr = np.ones((5, 4, 3))
index = np.array([True])
assert_raises(IndexError, arr.__getitem__, index)
index = np.array([False] * 6)
assert_raises(IndexError, arr.__getitem__, index)
index = np.zeros((4, 4), dtype=bool)
assert_raises(IndexError, arr.__getitem__, index)
assert_raises(IndexError, arr.__getitem__, (slice(None), index))
def test_boolean_indexing_onedim(self):
# Indexing a 2-dimensional array with
# boolean array of length one
a = np.array([[ 0., 0., 0.]])
b = np.array([ True], dtype=bool)
assert_equal(a[b], a)
# boolean assignment
a[b] = 1.
assert_equal(a, [[1., 1., 1.]])
def test_boolean_assignment_value_mismatch(self):
# A boolean assignment should fail when the shape of the values
# cannot be broadcast to the subscription. (see also gh-3458)
a = np.arange(4)
def f(a, v):
a[a > -1] = v
assert_raises(ValueError, f, a, [])
assert_raises(ValueError, f, a, [1, 2, 3])
assert_raises(ValueError, f, a[:1], [1, 2, 3])
def test_boolean_assignment_needs_api(self):
# See also gh-7666
# This caused a segfault on Python 2 due to the GIL not being
# held when the iterator does not need it, but the transfer function
# does
arr = np.zeros(1000)
indx = np.zeros(1000, dtype=bool)
indx[:100] = True
arr[indx] = np.ones(100, dtype=object)
expected = np.zeros(1000)
expected[:100] = 1
assert_array_equal(arr, expected)
def test_boolean_indexing_twodim(self):
# Indexing a 2-dimensional array with
# 2-dimensional boolean array
a = np.array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]])
b = np.array([[ True, False, True],
[False, True, False],
[ True, False, True]])
assert_equal(a[b], [1, 3, 5, 7, 9])
assert_equal(a[b[1]], [[4, 5, 6]])
assert_equal(a[b[0]], a[b[2]])
# boolean assignment
a[b] = 0
assert_equal(a, [[0, 2, 0],
[4, 0, 6],
[0, 8, 0]])
def test_reverse_strides_and_subspace_bufferinit(self):
# This tests that the strides are not reversed for simple and
# subspace fancy indexing.
a = np.ones(5)
b = np.zeros(5, dtype=np.intp)[::-1]
c = np.arange(5)[::-1]
a[b] = c
# If the strides are not reversed, the 0 in the arange comes last.
assert_equal(a[0], 0)
# This also tests that the subspace buffer is initialized:
a = np.ones((5, 2))
c = np.arange(10).reshape(5, 2)[::-1]
a[b, :] = c
assert_equal(a[0], [0, 1])
def test_reversed_strides_result_allocation(self):
# Test a bug when calculating the output strides for a result array
# when the subspace size was 1 (and test other cases as well)
a = np.arange(10)[:, None]
i = np.arange(10)[::-1]
assert_array_equal(a[i], a[i.copy('C')])
a = np.arange(20).reshape(-1, 2)
def test_uncontiguous_subspace_assignment(self):
# During development there was a bug activating a skip logic
# based on ndim instead of size.
a = np.full((3, 4, 2), -1)
b = np.full((3, 4, 2), -1)
a[[0, 1]] = np.arange(2 * 4 * 2).reshape(2, 4, 2).T
b[[0, 1]] = np.arange(2 * 4 * 2).reshape(2, 4, 2).T.copy()
assert_equal(a, b)
def test_too_many_fancy_indices_special_case(self):
# Just documents behaviour, this is a small limitation.
a = np.ones((1,) * 32) # 32 is NPY_MAXDIMS
assert_raises(IndexError, a.__getitem__, (np.array([0]),) * 32)
def test_scalar_array_bool(self):
# NumPy bools can be used as boolean index (python ones as of yet not)
a = np.array(1)
assert_equal(a[np.bool_(True)], a[np.array(True)])
assert_equal(a[np.bool_(False)], a[np.array(False)])
# After deprecating bools as integers:
#a = np.array([0,1,2])
#assert_equal(a[True, :], a[None, :])
#assert_equal(a[:, True], a[:, None])
#
#assert_(not np.may_share_memory(a, a[True, :]))
def test_everything_returns_views(self):
# Before `...` would return a itself.
a = np.arange(5)
assert_(a is not a[()])
assert_(a is not a[...])
assert_(a is not a[:])
def test_broaderrors_indexing(self):
a = np.zeros((5, 5))
assert_raises(IndexError, a.__getitem__, ([0, 1], [0, 1, 2]))
assert_raises(IndexError, a.__setitem__, ([0, 1], [0, 1, 2]), 0)
def test_trivial_fancy_out_of_bounds(self):
a = np.zeros(5)
ind = np.ones(20, dtype=np.intp)
ind[-1] = 10
assert_raises(IndexError, a.__getitem__, ind)
assert_raises(IndexError, a.__setitem__, ind, 0)
ind = np.ones(20, dtype=np.intp)
ind[0] = 11
assert_raises(IndexError, a.__getitem__, ind)
assert_raises(IndexError, a.__setitem__, ind, 0)
def test_nonbaseclass_values(self):
class SubClass(np.ndarray):
def __array_finalize__(self, old):
# Have array finalize do funny things
self.fill(99)
a = np.zeros((5, 5))
s = a.copy().view(type=SubClass)
s.fill(1)
a[[0, 1, 2, 3, 4], :] = s
assert_((a == 1).all())
# Subspace is last, so transposing might want to finalize
a[:, [0, 1, 2, 3, 4]] = s
assert_((a == 1).all())
a.fill(0)
a[...] = s
assert_((a == 1).all())
def test_subclass_writeable(self):
d = np.rec.array([('NGC1001', 11), ('NGC1002', 1.), ('NGC1003', 1.)],
dtype=[('target', 'S20'), ('V_mag', '>f4')])
ind = np.array([False, True, True], dtype=bool)
assert_(d[ind].flags.writeable)
ind = np.array([0, 1])
assert_(d[ind].flags.writeable)
assert_(d[...].flags.writeable)
assert_(d[0].flags.writeable)
def test_memory_order(self):
# This is not necessary to preserve. Memory layouts for
# more complex indices are not as simple.
a = np.arange(10)
b = np.arange(10).reshape(5,2).T
assert_(a[b].flags.f_contiguous)
# Takes a different implementation branch:
a = a.reshape(-1, 1)
assert_(a[b, 0].flags.f_contiguous)
def test_scalar_return_type(self):
# Full scalar indices should return scalars and object
# arrays should not call PyArray_Return on their items
class Zero(object):
# The most basic valid indexing
def __index__(self):
return 0
z = Zero()
class ArrayLike(object):
# Simple array, should behave like the array
def __array__(self):
return np.array(0)
a = np.zeros(())
assert_(isinstance(a[()], np.float_))
a = np.zeros(1)
assert_(isinstance(a[z], np.float_))
a = np.zeros((1, 1))
assert_(isinstance(a[z, np.array(0)], np.float_))
assert_(isinstance(a[z, ArrayLike()], np.float_))
# And object arrays do not call it too often:
b = np.array(0)
a = np.array(0, dtype=object)
a[()] = b
assert_(isinstance(a[()], np.ndarray))
a = np.array([b, None])
assert_(isinstance(a[z], np.ndarray))
a = np.array([[b, None]])
assert_(isinstance(a[z, np.array(0)], np.ndarray))
assert_(isinstance(a[z, ArrayLike()], np.ndarray))
def test_small_regressions(self):
# Reference count of intp for index checks
a = np.array([0])
if HAS_REFCOUNT:
refcount = sys.getrefcount(np.dtype(np.intp))
# item setting always checks indices in separate function:
a[np.array([0], dtype=np.intp)] = 1
a[np.array([0], dtype=np.uint8)] = 1
assert_raises(IndexError, a.__setitem__,
np.array([1], dtype=np.intp), 1)
assert_raises(IndexError, a.__setitem__,
np.array([1], dtype=np.uint8), 1)
if HAS_REFCOUNT:
assert_equal(sys.getrefcount(np.dtype(np.intp)), refcount)
def test_unaligned(self):
v = (np.zeros(64, dtype=np.int8) + ord('a'))[1:-7]
d = v.view(np.dtype("S8"))
# unaligned source
x = (np.zeros(16, dtype=np.int8) + ord('a'))[1:-7]
x = x.view(np.dtype("S8"))
x[...] = np.array("b" * 8, dtype="S")
b = np.arange(d.size)
#trivial
assert_equal(d[b], d)
d[b] = x
# nontrivial
# unaligned index array
b = np.zeros(d.size + 1).view(np.int8)[1:-(np.intp(0).itemsize - 1)]
b = b.view(np.intp)[:d.size]
b[...] = np.arange(d.size)
assert_equal(d[b.astype(np.int16)], d)
d[b.astype(np.int16)] = x
# boolean
d[b % 2 == 0]
d[b % 2 == 0] = x[::2]
def test_tuple_subclass(self):
arr = np.ones((5, 5))
# A tuple subclass should also be an nd-index
class TupleSubclass(tuple):
pass
index = ([1], [1])
index = TupleSubclass(index)
assert_(arr[index].shape == (1,))
# Unlike the non nd-index:
assert_(arr[index,].shape != (1,))
def test_broken_sequence_not_nd_index(self):
# See gh-5063:
# If we have an object which claims to be a sequence, but fails
# on item getting, this should not be converted to an nd-index (tuple)
# If this object happens to be a valid index otherwise, it should work
# This object here is very dubious and probably bad though:
class SequenceLike(object):
def __index__(self):
return 0
def __len__(self):
return 1
def __getitem__(self, item):
raise IndexError('Not possible')
arr = np.arange(10)
assert_array_equal(arr[SequenceLike()], arr[SequenceLike(),])
# also test that field indexing does not segfault
# for a similar reason, by indexing a structured array
arr = np.zeros((1,), dtype=[('f1', 'i8'), ('f2', 'i8')])
assert_array_equal(arr[SequenceLike()], arr[SequenceLike(),])
def test_indexing_array_weird_strides(self):
# See also gh-6221
# the shapes used here come from the issue and create the correct
# size for the iterator buffering size.
x = np.ones(10)
x2 = np.ones((10, 2))
ind = np.arange(10)[:, None, None, None]
ind = np.broadcast_to(ind, (10, 55, 4, 4))
# single advanced index case
assert_array_equal(x[ind], x[ind.copy()])
# higher dimensional advanced index
zind = np.zeros(4, dtype=np.intp)
assert_array_equal(x2[ind, zind], x2[ind.copy(), zind])
def test_indexing_array_negative_strides(self):
# From gh-8264,
# core dumps if negative strides are used in iteration
arro = np.zeros((4, 4))
arr = arro[::-1, ::-1]
slices = [slice(None), [0, 1, 2, 3]]
arr[slices] = 10
assert_array_equal(arr, 10.)
class TestFieldIndexing(object):
def test_scalar_return_type(self):
# Field access on an array should return an array, even if it
# is 0-d.
a = np.zeros((), [('a','f8')])
assert_(isinstance(a['a'], np.ndarray))
assert_(isinstance(a[['a']], np.ndarray))
class TestBroadcastedAssignments(object):
def assign(self, a, ind, val):
a[ind] = val
return a
def test_prepending_ones(self):
a = np.zeros((3, 2))
a[...] = np.ones((1, 3, 2))
# Fancy with subspace with and without transpose
a[[0, 1, 2], :] = np.ones((1, 3, 2))
a[:, [0, 1]] = np.ones((1, 3, 2))
# Fancy without subspace (with broadcasting)
a[[[0], [1], [2]], [0, 1]] = np.ones((1, 3, 2))
def test_prepend_not_one(self):
assign = self.assign
s_ = np.s_
a = np.zeros(5)
# Too large and not only ones.
assert_raises(ValueError, assign, a, s_[...], np.ones((2, 1)))
assert_raises(ValueError, assign, a, s_[[1, 2, 3],], np.ones((2, 1)))
assert_raises(ValueError, assign, a, s_[[[1], [2]],], np.ones((2,2,1)))
def test_simple_broadcasting_errors(self):
assign = self.assign
s_ = np.s_
a = np.zeros((5, 1))
assert_raises(ValueError, assign, a, s_[...], np.zeros((5, 2)))
assert_raises(ValueError, assign, a, s_[...], np.zeros((5, 0)))
assert_raises(ValueError, assign, a, s_[:, [0]], np.zeros((5, 2)))
assert_raises(ValueError, assign, a, s_[:, [0]], np.zeros((5, 0)))
assert_raises(ValueError, assign, a, s_[[0], :], np.zeros((2, 1)))
def test_index_is_larger(self):
# Simple case of fancy index broadcasting of the index.
a = np.zeros((5, 5))
a[[[0], [1], [2]], [0, 1, 2]] = [2, 3, 4]
assert_((a[:3, :3] == [2, 3, 4]).all())
def test_broadcast_subspace(self):
a = np.zeros((100, 100))
v = np.arange(100)[:,None]
b = np.arange(100)[::-1]
a[b] = v
assert_((a[::-1] == v).all())
class TestSubclasses(object):
def test_basic(self):
class SubClass(np.ndarray):
pass
s = np.arange(5).view(SubClass)
assert_(isinstance(s[:3], SubClass))
assert_(s[:3].base is s)
assert_(isinstance(s[[0, 1, 2]], SubClass))
assert_(isinstance(s[s > 0], SubClass))
def test_matrix_fancy(self):
# The matrix class messes with the shape. While this is always
# weird (getitem is not used, it does not have setitem nor knows
# about fancy indexing), this tests gh-3110
m = np.matrix([[1, 2], [3, 4]])
assert_(isinstance(m[[0,1,0], :], np.matrix))
# gh-3110. Note the transpose currently because matrices do *not*
# support dimension fixing for fancy indexing correctly.
x = np.asmatrix(np.arange(50).reshape(5,10))
assert_equal(x[:2, np.array(-1)], x[:2, -1].T)
def test_finalize_gets_full_info(self):
# Array finalize should be called on the filled array.
class SubClass(np.ndarray):
def __array_finalize__(self, old):
self.finalize_status = np.array(self)
self.old = old
s = np.arange(10).view(SubClass)
new_s = s[:3]
assert_array_equal(new_s.finalize_status, new_s)
assert_array_equal(new_s.old, s)
new_s = s[[0,1,2,3]]
assert_array_equal(new_s.finalize_status, new_s)
assert_array_equal(new_s.old, s)
new_s = s[s > 0]
assert_array_equal(new_s.finalize_status, new_s)
assert_array_equal(new_s.old, s)
@dec.skipif(not HAS_REFCOUNT)
def test_slice_decref_getsetslice(self):
# See gh-10066, a temporary slice object should be discarted.
# This test is only really interesting on Python 2 since
# it goes through `__set/getslice__` here and can probably be
# removed. Use 0:7 to make sure it is never None:7.
class KeepIndexObject(np.ndarray):
def __getitem__(self, indx):
self.indx = indx
if indx == slice(0, 7):
raise ValueError
def __setitem__(self, indx, val):
self.indx = indx
if indx == slice(0, 4):
raise ValueError
k = np.array([1]).view(KeepIndexObject)
k[0:5]
assert_equal(k.indx, slice(0, 5))
assert_equal(sys.getrefcount(k.indx), 2)
try:
k[0:7]
raise AssertionError
except ValueError:
# The exception holds a reference to the slice so clear on Py2
if hasattr(sys, 'exc_clear'):
with suppress_warnings() as sup:
sup.filter(DeprecationWarning)
sys.exc_clear()
assert_equal(k.indx, slice(0, 7))
assert_equal(sys.getrefcount(k.indx), 2)
k[0:3] = 6
assert_equal(k.indx, slice(0, 3))
assert_equal(sys.getrefcount(k.indx), 2)
try:
k[0:4] = 2
raise AssertionError
except ValueError:
# The exception holds a reference to the slice so clear on Py2
if hasattr(sys, 'exc_clear'):
with suppress_warnings() as sup:
sup.filter(DeprecationWarning)
sys.exc_clear()
assert_equal(k.indx, slice(0, 4))
assert_equal(sys.getrefcount(k.indx), 2)
class TestFancyIndexingCast(object):
def test_boolean_index_cast_assign(self):
# Setup the boolean index and float arrays.
shape = (8, 63)
bool_index = np.zeros(shape).astype(bool)
bool_index[0, 1] = True
zero_array = np.zeros(shape)
# Assigning float is fine.
zero_array[bool_index] = np.array([1])
assert_equal(zero_array[0, 1], 1)
# Fancy indexing works, although we get a cast warning.
assert_warns(np.ComplexWarning,
zero_array.__setitem__, ([0], [1]), np.array([2 + 1j]))
assert_equal(zero_array[0, 1], 2) # No complex part
# Cast complex to float, throwing away the imaginary portion.
assert_warns(np.ComplexWarning,
zero_array.__setitem__, bool_index, np.array([1j]))
assert_equal(zero_array[0, 1], 0)
class TestFancyIndexingEquivalence(object):
def test_object_assign(self):
# Check that the field and object special case using copyto is active.
# The right hand side cannot be converted to an array here.
a = np.arange(5, dtype=object)
b = a.copy()
a[:3] = [1, (1,2), 3]
b[[0, 1, 2]] = [1, (1,2), 3]
assert_array_equal(a, b)
# test same for subspace fancy indexing
b = np.arange(5, dtype=object)[None, :]
b[[0], :3] = [[1, (1,2), 3]]
assert_array_equal(a, b[0])
# Check that swapping of axes works.
# There was a bug that made the later assignment throw a ValueError
# do to an incorrectly transposed temporary right hand side (gh-5714)
b = b.T
b[:3, [0]] = [[1], [(1,2)], [3]]
assert_array_equal(a, b[:, 0])
# Another test for the memory order of the subspace
arr = np.ones((3, 4, 5), dtype=object)
# Equivalent slicing assignment for comparison
cmp_arr = arr.copy()
cmp_arr[:1, ...] = [[[1], [2], [3], [4]]]
arr[[0], ...] = [[[1], [2], [3], [4]]]
assert_array_equal(arr, cmp_arr)
arr = arr.copy('F')
arr[[0], ...] = [[[1], [2], [3], [4]]]
assert_array_equal(arr, cmp_arr)
def test_cast_equivalence(self):
# Yes, normal slicing uses unsafe casting.
a = np.arange(5)
b = a.copy()
a[:3] = np.array(['2', '-3', '-1'])
b[[0, 2, 1]] = np.array(['2', '-1', '-3'])
assert_array_equal(a, b)
# test the same for subspace fancy indexing
b = np.arange(5)[None, :]
b[[0], :3] = np.array([['2', '-3', '-1']])
assert_array_equal(a, b[0])
class TestMultiIndexingAutomated(object):
"""
These tests use code to mimic the C-Code indexing for selection.
NOTE:
* This still lacks tests for complex item setting.
* If you change behavior of indexing, you might want to modify
these tests to try more combinations.
* Behavior was written to match numpy version 1.8. (though a
first version matched 1.7.)
* Only tuple indices are supported by the mimicking code.
(and tested as of writing this)
* Error types should match most of the time as long as there
is only one error. For multiple errors, what gets raised
will usually not be the same one. They are *not* tested.
Update 2016-11-30: It is probably not worth maintaining this test
indefinitely and it can be dropped if maintenance becomes a burden.
"""
def setup(self):
self.a = np.arange(np.prod([3, 1, 5, 6])).reshape(3, 1, 5, 6)
self.b = np.empty((3, 0, 5, 6))
self.complex_indices = ['skip', Ellipsis,
0,
# Boolean indices, up to 3-d for some special cases of eating up
# dimensions, also need to test all False
np.array([True, False, False]),
np.array([[True, False], [False, True]]),
np.array([[[False, False], [False, False]]]),
# Some slices:
slice(-5, 5, 2),
slice(1, 1, 100),
slice(4, -1, -2),
slice(None, None, -3),
# Some Fancy indexes:
np.empty((0, 1, 1), dtype=np.intp), # empty and can be broadcast
np.array([0, 1, -2]),
np.array([[2], [0], [1]]),
np.array([[0, -1], [0, 1]], dtype=np.dtype('intp').newbyteorder()),
np.array([2, -1], dtype=np.int8),
np.zeros([1]*31, dtype=int), # trigger too large array.
np.array([0., 1.])] # invalid datatype
# Some simpler indices that still cover a bit more
self.simple_indices = [Ellipsis, None, -1, [1], np.array([True]),
'skip']
# Very simple ones to fill the rest:
self.fill_indices = [slice(None, None), 0]
def _get_multi_index(self, arr, indices):
"""Mimic multi dimensional indexing.
Parameters
----------
arr : ndarray
Array to be indexed.
indices : tuple of index objects
Returns
-------
out : ndarray
An array equivalent to the indexing operation (but always a copy).
`arr[indices]` should be identical.
no_copy : bool
Whether the indexing operation requires a copy. If this is `True`,
`np.may_share_memory(arr, arr[indicies])` should be `True` (with
some exceptions for scalars and possibly 0-d arrays).
Notes
-----
While the function may mostly match the errors of normal indexing this
is generally not the case.
"""
in_indices = list(indices)
indices = []
# if False, this is a fancy or boolean index
no_copy = True
# number of fancy/scalar indexes that are not consecutive
num_fancy = 0
# number of dimensions indexed by a "fancy" index
fancy_dim = 0
# NOTE: This is a funny twist (and probably OK to change).
# The boolean array has illegal indexes, but this is
# allowed if the broadcast fancy-indices are 0-sized.
# This variable is to catch that case.
error_unless_broadcast_to_empty = False
# We need to handle Ellipsis and make arrays from indices, also
# check if this is fancy indexing (set no_copy).
ndim = 0
ellipsis_pos = None # define here mostly to replace all but first.
for i, indx in enumerate(in_indices):
if indx is None:
continue
if isinstance(indx, np.ndarray) and indx.dtype == bool:
no_copy = False
if indx.ndim == 0:
raise IndexError
# boolean indices can have higher dimensions
ndim += indx.ndim
fancy_dim += indx.ndim
continue
if indx is Ellipsis:
if ellipsis_pos is None:
ellipsis_pos = i
continue # do not increment ndim counter
raise IndexError
if isinstance(indx, slice):
ndim += 1
continue
if not isinstance(indx, np.ndarray):
# This could be open for changes in numpy.
# numpy should maybe raise an error if casting to intp
# is not safe. It rejects np.array([1., 2.]) but not
# [1., 2.] as index (same for ie. np.take).
# (Note the importance of empty lists if changing this here)
indx = np.array(indx, dtype=np.intp)
in_indices[i] = indx
elif indx.dtype.kind != 'b' and indx.dtype.kind != 'i':
raise IndexError('arrays used as indices must be of '
'integer (or boolean) type')
if indx.ndim != 0:
no_copy = False
ndim += 1
fancy_dim += 1
if arr.ndim - ndim < 0:
# we can't take more dimensions then we have, not even for 0-d
# arrays. since a[()] makes sense, but not a[(),]. We will
# raise an error later on, unless a broadcasting error occurs
# first.
raise IndexError
if ndim == 0 and None not in in_indices:
# Well we have no indexes or one Ellipsis. This is legal.
return arr.copy(), no_copy
if ellipsis_pos is not None:
in_indices[ellipsis_pos:ellipsis_pos+1] = ([slice(None, None)] *
(arr.ndim - ndim))
for ax, indx in enumerate(in_indices):
if isinstance(indx, slice):
# convert to an index array
indx = np.arange(*indx.indices(arr.shape[ax]))
indices.append(['s', indx])
continue
elif indx is None:
# this is like taking a slice with one element from a new axis:
indices.append(['n', np.array([0], dtype=np.intp)])
arr = arr.reshape((arr.shape[:ax] + (1,) + arr.shape[ax:]))
continue
if isinstance(indx, np.ndarray) and indx.dtype == bool:
if indx.shape != arr.shape[ax:ax+indx.ndim]:
raise IndexError
try:
flat_indx = np.ravel_multi_index(np.nonzero(indx),
arr.shape[ax:ax+indx.ndim], mode='raise')
except Exception:
error_unless_broadcast_to_empty = True
# fill with 0s instead, and raise error later
flat_indx = np.array([0]*indx.sum(), dtype=np.intp)
# concatenate axis into a single one:
if indx.ndim != 0:
arr = arr.reshape((arr.shape[:ax]
+ (np.prod(arr.shape[ax:ax+indx.ndim]),)
+ arr.shape[ax+indx.ndim:]))
indx = flat_indx
else:
# This could be changed, a 0-d boolean index can
# make sense (even outside the 0-d indexed array case)
# Note that originally this is could be interpreted as
# integer in the full integer special case.
raise IndexError
else:
# If the index is a singleton, the bounds check is done
# before the broadcasting. This used to be different in <1.9
if indx.ndim == 0:
if indx >= arr.shape[ax] or indx < -arr.shape[ax]:
raise IndexError
if indx.ndim == 0:
# The index is a scalar. This used to be two fold, but if
# fancy indexing was active, the check was done later,
# possibly after broadcasting it away (1.7. or earlier).
# Now it is always done.
if indx >= arr.shape[ax] or indx < - arr.shape[ax]:
raise IndexError
if (len(indices) > 0 and
indices[-1][0] == 'f' and
ax != ellipsis_pos):
# NOTE: There could still have been a 0-sized Ellipsis
# between them. Checked that with ellipsis_pos.
indices[-1].append(indx)
else:
# We have a fancy index that is not after an existing one.
# NOTE: A 0-d array triggers this as well, while one may
# expect it to not trigger it, since a scalar would not be
# considered fancy indexing.
num_fancy += 1
indices.append(['f', indx])
if num_fancy > 1 and not no_copy:
# We have to flush the fancy indexes left
new_indices = indices[:]
axes = list(range(arr.ndim))
fancy_axes = []
new_indices.insert(0, ['f'])
ni = 0
ai = 0
for indx in indices:
ni += 1
if indx[0] == 'f':
new_indices[0].extend(indx[1:])
del new_indices[ni]
ni -= 1
for ax in range(ai, ai + len(indx[1:])):
fancy_axes.append(ax)
axes.remove(ax)
ai += len(indx) - 1 # axis we are at
indices = new_indices
# and now we need to transpose arr:
arr = arr.transpose(*(fancy_axes + axes))
# We only have one 'f' index now and arr is transposed accordingly.
# Now handle newaxis by reshaping...
ax = 0
for indx in indices:
if indx[0] == 'f':
if len(indx) == 1:
continue
# First of all, reshape arr to combine fancy axes into one:
orig_shape = arr.shape
orig_slice = orig_shape[ax:ax + len(indx[1:])]
arr = arr.reshape((arr.shape[:ax]
+ (np.prod(orig_slice).astype(int),)
+ arr.shape[ax + len(indx[1:]):]))
# Check if broadcasting works
res = np.broadcast(*indx[1:])
# unfortunately the indices might be out of bounds. So check
# that first, and use mode='wrap' then. However only if
# there are any indices...
if res.size != 0:
if error_unless_broadcast_to_empty:
raise IndexError
for _indx, _size in zip(indx[1:], orig_slice):
if _indx.size == 0:
continue
if np.any(_indx >= _size) or np.any(_indx < -_size):
raise IndexError
if len(indx[1:]) == len(orig_slice):
if np.product(orig_slice) == 0:
# Work around for a crash or IndexError with 'wrap'
# in some 0-sized cases.
try:
mi = np.ravel_multi_index(indx[1:], orig_slice,
mode='raise')
except Exception:
# This happens with 0-sized orig_slice (sometimes?)
# here it is a ValueError, but indexing gives a:
raise IndexError('invalid index into 0-sized')
else:
mi = np.ravel_multi_index(indx[1:], orig_slice,
mode='wrap')
else:
# Maybe never happens...
raise ValueError
arr = arr.take(mi.ravel(), axis=ax)
arr = arr.reshape((arr.shape[:ax]
+ mi.shape
+ arr.shape[ax+1:]))
ax += mi.ndim
continue
# If we are here, we have a 1D array for take:
arr = arr.take(indx[1], axis=ax)
ax += 1
return arr, no_copy
def _check_multi_index(self, arr, index):
"""Check a multi index item getting and simple setting.
Parameters
----------
arr : ndarray
Array to be indexed, must be a reshaped arange.
index : tuple of indexing objects
Index being tested.
"""
# Test item getting
try:
mimic_get, no_copy = self._get_multi_index(arr, index)
except Exception as e:
if HAS_REFCOUNT:
prev_refcount = sys.getrefcount(arr)
assert_raises(Exception, arr.__getitem__, index)
assert_raises(Exception, arr.__setitem__, index, 0)
if HAS_REFCOUNT:
assert_equal(prev_refcount, sys.getrefcount(arr))
return
self._compare_index_result(arr, index, mimic_get, no_copy)
def _check_single_index(self, arr, index):
"""Check a single index item getting and simple setting.
Parameters
----------
arr : ndarray
Array to be indexed, must be an arange.
index : indexing object
Index being tested. Must be a single index and not a tuple
of indexing objects (see also `_check_multi_index`).
"""
try:
mimic_get, no_copy = self._get_multi_index(arr, (index,))
except Exception as e:
if HAS_REFCOUNT:
prev_refcount = sys.getrefcount(arr)
assert_raises(Exception, arr.__getitem__, index)
assert_raises(Exception, arr.__setitem__, index, 0)
if HAS_REFCOUNT:
assert_equal(prev_refcount, sys.getrefcount(arr))
return
self._compare_index_result(arr, index, mimic_get, no_copy)
def _compare_index_result(self, arr, index, mimic_get, no_copy):
"""Compare mimicked result to indexing result.
"""
arr = arr.copy()
indexed_arr = arr[index]
assert_array_equal(indexed_arr, mimic_get)
# Check if we got a view, unless its a 0-sized or 0-d array.
# (then its not a view, and that does not matter)
if indexed_arr.size != 0 and indexed_arr.ndim != 0:
assert_(np.may_share_memory(indexed_arr, arr) == no_copy)
# Check reference count of the original array
if HAS_REFCOUNT:
if no_copy:
# refcount increases by one:
assert_equal(sys.getrefcount(arr), 3)
else:
assert_equal(sys.getrefcount(arr), 2)
# Test non-broadcast setitem:
b = arr.copy()
b[index] = mimic_get + 1000
if b.size == 0:
return # nothing to compare here...
if no_copy and indexed_arr.ndim != 0:
# change indexed_arr in-place to manipulate original:
indexed_arr += 1000
assert_array_equal(arr, b)
return
# Use the fact that the array is originally an arange:
arr.flat[indexed_arr.ravel()] += 1000
assert_array_equal(arr, b)
def test_boolean(self):
a = np.array(5)
assert_equal(a[np.array(True)], 5)
a[np.array(True)] = 1
assert_equal(a, 1)
# NOTE: This is different from normal broadcasting, as
# arr[boolean_array] works like in a multi index. Which means
# it is aligned to the left. This is probably correct for
# consistency with arr[boolean_array,] also no broadcasting
# is done at all
self._check_multi_index(
self.a, (np.zeros_like(self.a, dtype=bool),))
self._check_multi_index(
self.a, (np.zeros_like(self.a, dtype=bool)[..., 0],))
self._check_multi_index(
self.a, (np.zeros_like(self.a, dtype=bool)[None, ...],))
def test_multidim(self):
# Automatically test combinations with complex indexes on 2nd (or 1st)
# spot and the simple ones in one other spot.
with warnings.catch_warnings():
# This is so that np.array(True) is not accepted in a full integer
# index, when running the file separately.
warnings.filterwarnings('error', '', DeprecationWarning)
warnings.filterwarnings('error', '', np.VisibleDeprecationWarning)
def isskip(idx):
return isinstance(idx, str) and idx == "skip"
for simple_pos in [0, 2, 3]:
tocheck = [self.fill_indices, self.complex_indices,
self.fill_indices, self.fill_indices]
tocheck[simple_pos] = self.simple_indices
for index in product(*tocheck):
index = tuple(i for i in index if not isskip(i))
self._check_multi_index(self.a, index)
self._check_multi_index(self.b, index)
# Check very simple item getting:
self._check_multi_index(self.a, (0, 0, 0, 0))
self._check_multi_index(self.b, (0, 0, 0, 0))
# Also check (simple cases of) too many indices:
assert_raises(IndexError, self.a.__getitem__, (0, 0, 0, 0, 0))
assert_raises(IndexError, self.a.__setitem__, (0, 0, 0, 0, 0), 0)
assert_raises(IndexError, self.a.__getitem__, (0, 0, [1], 0, 0))
assert_raises(IndexError, self.a.__setitem__, (0, 0, [1], 0, 0), 0)
def test_1d(self):
a = np.arange(10)
with warnings.catch_warnings():
warnings.filterwarnings('error', '', np.VisibleDeprecationWarning)
for index in self.complex_indices:
self._check_single_index(a, index)
class TestFloatNonIntegerArgument(object):
"""
These test that ``TypeError`` is raised when you try to use
non-integers as arguments to for indexing and slicing e.g. ``a[0.0:5]``
and ``a[0.5]``, or other functions like ``array.reshape(1., -1)``.
"""
def test_valid_indexing(self):
# These should raise no errors.
a = np.array([[[5]]])
a[np.array([0])]
a[[0, 0]]
a[:, [0, 0]]
a[:, 0,:]
a[:,:,:]
def test_valid_slicing(self):
# These should raise no errors.
a = np.array([[[5]]])
a[::]
a[0:]
a[:2]
a[0:2]
a[::2]
a[1::2]
a[:2:2]
a[1:2:2]
def test_non_integer_argument_errors(self):
a = np.array([[5]])
assert_raises(TypeError, np.reshape, a, (1., 1., -1))
assert_raises(TypeError, np.reshape, a, (np.array(1.), -1))
assert_raises(TypeError, np.take, a, [0], 1.)
assert_raises(TypeError, np.take, a, [0], np.float64(1.))
def test_non_integer_sequence_multiplication(self):
# NumPy scalar sequence multiply should not work with non-integers
def mult(a, b):
return a * b
assert_raises(TypeError, mult, [1], np.float_(3))
# following should be OK
mult([1], np.int_(3))
def test_reduce_axis_float_index(self):
d = np.zeros((3,3,3))
assert_raises(TypeError, np.min, d, 0.5)
assert_raises(TypeError, np.min, d, (0.5, 1))
assert_raises(TypeError, np.min, d, (1, 2.2))
assert_raises(TypeError, np.min, d, (.2, 1.2))
class TestBooleanIndexing(object):
# Using a boolean as integer argument/indexing is an error.
def test_bool_as_int_argument_errors(self):
a = np.array([[[1]]])
assert_raises(TypeError, np.reshape, a, (True, -1))
assert_raises(TypeError, np.reshape, a, (np.bool_(True), -1))
# Note that operator.index(np.array(True)) does not work, a boolean
# array is thus also deprecated, but not with the same message:
assert_raises(TypeError, operator.index, np.array(True))
assert_warns(DeprecationWarning, operator.index, np.True_)
assert_raises(TypeError, np.take, args=(a, [0], False))
def test_boolean_indexing_weirdness(self):
# Weird boolean indexing things
a = np.ones((2, 3, 4))
a[False, True, ...].shape == (0, 2, 3, 4)
a[True, [0, 1], True, True, [1], [[2]]] == (1, 2)
assert_raises(IndexError, lambda: a[False, [0, 1], ...])
class TestArrayToIndexDeprecation(object):
"""Creating an an index from array not 0-D is an error.
"""
def test_array_to_index_error(self):
# so no exception is expected. The raising is effectively tested above.
a = np.array([[[1]]])
assert_raises(TypeError, operator.index, np.array([1]))
assert_raises(TypeError, np.reshape, a, (a, -1))
assert_raises(TypeError, np.take, a, [0], a)
class TestNonIntegerArrayLike(object):
"""Tests that array_likes only valid if can safely cast to integer.
For instance, lists give IndexError when they cannot be safely cast to
an integer.
"""
def test_basic(self):
a = np.arange(10)
assert_raises(IndexError, a.__getitem__, [0.5, 1.5])
assert_raises(IndexError, a.__getitem__, (['1', '2'],))
# The following is valid
a.__getitem__([])
class TestMultipleEllipsisError(object):
"""An index can only have a single ellipsis.
"""
def test_basic(self):
a = np.arange(10)
assert_raises(IndexError, lambda: a[..., ...])
assert_raises(IndexError, a.__getitem__, ((Ellipsis,) * 2,))
assert_raises(IndexError, a.__getitem__, ((Ellipsis,) * 3,))
class TestCApiAccess(object):
def test_getitem(self):
subscript = functools.partial(array_indexing, 0)
# 0-d arrays don't work:
assert_raises(IndexError, subscript, np.ones(()), 0)
# Out of bound values:
assert_raises(IndexError, subscript, np.ones(10), 11)
assert_raises(IndexError, subscript, np.ones(10), -11)
assert_raises(IndexError, subscript, np.ones((10, 10)), 11)
assert_raises(IndexError, subscript, np.ones((10, 10)), -11)
a = np.arange(10)
assert_array_equal(a[4], subscript(a, 4))
a = a.reshape(5, 2)
assert_array_equal(a[-4], subscript(a, -4))
def test_setitem(self):
assign = functools.partial(array_indexing, 1)
# Deletion is impossible:
assert_raises(ValueError, assign, np.ones(10), 0)
# 0-d arrays don't work:
assert_raises(IndexError, assign, np.ones(()), 0, 0)
# Out of bound values:
assert_raises(IndexError, assign, np.ones(10), 11, 0)
assert_raises(IndexError, assign, np.ones(10), -11, 0)
assert_raises(IndexError, assign, np.ones((10, 10)), 11, 0)
assert_raises(IndexError, assign, np.ones((10, 10)), -11, 0)
a = np.arange(10)
assign(a, 4, 10)
assert_(a[4] == 10)
a = a.reshape(5, 2)
assign(a, 4, 10)
assert_array_equal(a[-1], [10, 10])
if __name__ == "__main__":
run_module_suite()
| 50,084 | 36.971948 | 89 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/tests/test_function_base.py
|
from __future__ import division, absolute_import, print_function
from numpy import (logspace, linspace, geomspace, dtype, array, sctypes,
arange, isnan, ndarray, sqrt, nextafter)
from numpy.testing import (
run_module_suite, assert_, assert_equal, assert_raises,
assert_array_equal, assert_allclose, suppress_warnings
)
class PhysicalQuantity(float):
def __new__(cls, value):
return float.__new__(cls, value)
def __add__(self, x):
assert_(isinstance(x, PhysicalQuantity))
return PhysicalQuantity(float(x) + float(self))
__radd__ = __add__
def __sub__(self, x):
assert_(isinstance(x, PhysicalQuantity))
return PhysicalQuantity(float(self) - float(x))
def __rsub__(self, x):
assert_(isinstance(x, PhysicalQuantity))
return PhysicalQuantity(float(x) - float(self))
def __mul__(self, x):
return PhysicalQuantity(float(x) * float(self))
__rmul__ = __mul__
def __div__(self, x):
return PhysicalQuantity(float(self) / float(x))
def __rdiv__(self, x):
return PhysicalQuantity(float(x) / float(self))
class PhysicalQuantity2(ndarray):
__array_priority__ = 10
class TestLogspace(object):
def test_basic(self):
y = logspace(0, 6)
assert_(len(y) == 50)
y = logspace(0, 6, num=100)
assert_(y[-1] == 10 ** 6)
y = logspace(0, 6, endpoint=0)
assert_(y[-1] < 10 ** 6)
y = logspace(0, 6, num=7)
assert_array_equal(y, [1, 10, 100, 1e3, 1e4, 1e5, 1e6])
def test_dtype(self):
y = logspace(0, 6, dtype='float32')
assert_equal(y.dtype, dtype('float32'))
y = logspace(0, 6, dtype='float64')
assert_equal(y.dtype, dtype('float64'))
y = logspace(0, 6, dtype='int32')
assert_equal(y.dtype, dtype('int32'))
def test_physical_quantities(self):
a = PhysicalQuantity(1.0)
b = PhysicalQuantity(5.0)
assert_equal(logspace(a, b), logspace(1.0, 5.0))
def test_subclass(self):
a = array(1).view(PhysicalQuantity2)
b = array(7).view(PhysicalQuantity2)
ls = logspace(a, b)
assert type(ls) is PhysicalQuantity2
assert_equal(ls, logspace(1.0, 7.0))
ls = logspace(a, b, 1)
assert type(ls) is PhysicalQuantity2
assert_equal(ls, logspace(1.0, 7.0, 1))
class TestGeomspace(object):
def test_basic(self):
y = geomspace(1, 1e6)
assert_(len(y) == 50)
y = geomspace(1, 1e6, num=100)
assert_(y[-1] == 10 ** 6)
y = geomspace(1, 1e6, endpoint=False)
assert_(y[-1] < 10 ** 6)
y = geomspace(1, 1e6, num=7)
assert_array_equal(y, [1, 10, 100, 1e3, 1e4, 1e5, 1e6])
y = geomspace(8, 2, num=3)
assert_allclose(y, [8, 4, 2])
assert_array_equal(y.imag, 0)
y = geomspace(-1, -100, num=3)
assert_array_equal(y, [-1, -10, -100])
assert_array_equal(y.imag, 0)
y = geomspace(-100, -1, num=3)
assert_array_equal(y, [-100, -10, -1])
assert_array_equal(y.imag, 0)
def test_complex(self):
# Purely imaginary
y = geomspace(1j, 16j, num=5)
assert_allclose(y, [1j, 2j, 4j, 8j, 16j])
assert_array_equal(y.real, 0)
y = geomspace(-4j, -324j, num=5)
assert_allclose(y, [-4j, -12j, -36j, -108j, -324j])
assert_array_equal(y.real, 0)
y = geomspace(1+1j, 1000+1000j, num=4)
assert_allclose(y, [1+1j, 10+10j, 100+100j, 1000+1000j])
y = geomspace(-1+1j, -1000+1000j, num=4)
assert_allclose(y, [-1+1j, -10+10j, -100+100j, -1000+1000j])
# Logarithmic spirals
y = geomspace(-1, 1, num=3, dtype=complex)
assert_allclose(y, [-1, 1j, +1])
y = geomspace(0+3j, -3+0j, 3)
assert_allclose(y, [0+3j, -3/sqrt(2)+3j/sqrt(2), -3+0j])
y = geomspace(0+3j, 3+0j, 3)
assert_allclose(y, [0+3j, 3/sqrt(2)+3j/sqrt(2), 3+0j])
y = geomspace(-3+0j, 0-3j, 3)
assert_allclose(y, [-3+0j, -3/sqrt(2)-3j/sqrt(2), 0-3j])
y = geomspace(0+3j, -3+0j, 3)
assert_allclose(y, [0+3j, -3/sqrt(2)+3j/sqrt(2), -3+0j])
y = geomspace(-2-3j, 5+7j, 7)
assert_allclose(y, [-2-3j, -0.29058977-4.15771027j,
2.08885354-4.34146838j, 4.58345529-3.16355218j,
6.41401745-0.55233457j, 6.75707386+3.11795092j,
5+7j])
# Type promotion should prevent the -5 from becoming a NaN
y = geomspace(3j, -5, 2)
assert_allclose(y, [3j, -5])
y = geomspace(-5, 3j, 2)
assert_allclose(y, [-5, 3j])
def test_dtype(self):
y = geomspace(1, 1e6, dtype='float32')
assert_equal(y.dtype, dtype('float32'))
y = geomspace(1, 1e6, dtype='float64')
assert_equal(y.dtype, dtype('float64'))
y = geomspace(1, 1e6, dtype='int32')
assert_equal(y.dtype, dtype('int32'))
# Native types
y = geomspace(1, 1e6, dtype=float)
assert_equal(y.dtype, dtype('float_'))
y = geomspace(1, 1e6, dtype=complex)
assert_equal(y.dtype, dtype('complex'))
def test_array_scalar(self):
lim1 = array([120, 100], dtype="int8")
lim2 = array([-120, -100], dtype="int8")
lim3 = array([1200, 1000], dtype="uint16")
t1 = geomspace(lim1[0], lim1[1], 5)
t2 = geomspace(lim2[0], lim2[1], 5)
t3 = geomspace(lim3[0], lim3[1], 5)
t4 = geomspace(120.0, 100.0, 5)
t5 = geomspace(-120.0, -100.0, 5)
t6 = geomspace(1200.0, 1000.0, 5)
# t3 uses float32, t6 uses float64
assert_allclose(t1, t4, rtol=1e-2)
assert_allclose(t2, t5, rtol=1e-2)
assert_allclose(t3, t6, rtol=1e-5)
def test_physical_quantities(self):
a = PhysicalQuantity(1.0)
b = PhysicalQuantity(5.0)
assert_equal(geomspace(a, b), geomspace(1.0, 5.0))
def test_subclass(self):
a = array(1).view(PhysicalQuantity2)
b = array(7).view(PhysicalQuantity2)
gs = geomspace(a, b)
assert type(gs) is PhysicalQuantity2
assert_equal(gs, geomspace(1.0, 7.0))
gs = geomspace(a, b, 1)
assert type(gs) is PhysicalQuantity2
assert_equal(gs, geomspace(1.0, 7.0, 1))
def test_bounds(self):
assert_raises(ValueError, geomspace, 0, 10)
assert_raises(ValueError, geomspace, 10, 0)
assert_raises(ValueError, geomspace, 0, 0)
class TestLinspace(object):
def test_basic(self):
y = linspace(0, 10)
assert_(len(y) == 50)
y = linspace(2, 10, num=100)
assert_(y[-1] == 10)
y = linspace(2, 10, endpoint=0)
assert_(y[-1] < 10)
assert_raises(ValueError, linspace, 0, 10, num=-1)
def test_corner(self):
y = list(linspace(0, 1, 1))
assert_(y == [0.0], y)
with suppress_warnings() as sup:
sup.filter(DeprecationWarning, ".*safely interpreted as an integer")
y = list(linspace(0, 1, 2.5))
assert_(y == [0.0, 1.0])
def test_type(self):
t1 = linspace(0, 1, 0).dtype
t2 = linspace(0, 1, 1).dtype
t3 = linspace(0, 1, 2).dtype
assert_equal(t1, t2)
assert_equal(t2, t3)
def test_dtype(self):
y = linspace(0, 6, dtype='float32')
assert_equal(y.dtype, dtype('float32'))
y = linspace(0, 6, dtype='float64')
assert_equal(y.dtype, dtype('float64'))
y = linspace(0, 6, dtype='int32')
assert_equal(y.dtype, dtype('int32'))
def test_array_scalar(self):
lim1 = array([-120, 100], dtype="int8")
lim2 = array([120, -100], dtype="int8")
lim3 = array([1200, 1000], dtype="uint16")
t1 = linspace(lim1[0], lim1[1], 5)
t2 = linspace(lim2[0], lim2[1], 5)
t3 = linspace(lim3[0], lim3[1], 5)
t4 = linspace(-120.0, 100.0, 5)
t5 = linspace(120.0, -100.0, 5)
t6 = linspace(1200.0, 1000.0, 5)
assert_equal(t1, t4)
assert_equal(t2, t5)
assert_equal(t3, t6)
def test_complex(self):
lim1 = linspace(1 + 2j, 3 + 4j, 5)
t1 = array([1.0+2.j, 1.5+2.5j, 2.0+3j, 2.5+3.5j, 3.0+4j])
lim2 = linspace(1j, 10, 5)
t2 = array([0.0+1.j, 2.5+0.75j, 5.0+0.5j, 7.5+0.25j, 10.0+0j])
assert_equal(lim1, t1)
assert_equal(lim2, t2)
def test_physical_quantities(self):
a = PhysicalQuantity(0.0)
b = PhysicalQuantity(1.0)
assert_equal(linspace(a, b), linspace(0.0, 1.0))
def test_subclass(self):
a = array(0).view(PhysicalQuantity2)
b = array(1).view(PhysicalQuantity2)
ls = linspace(a, b)
assert type(ls) is PhysicalQuantity2
assert_equal(ls, linspace(0.0, 1.0))
ls = linspace(a, b, 1)
assert type(ls) is PhysicalQuantity2
assert_equal(ls, linspace(0.0, 1.0, 1))
def test_array_interface(self):
# Regression test for https://github.com/numpy/numpy/pull/6659
# Ensure that start/stop can be objects that implement
# __array_interface__ and are convertible to numeric scalars
class Arrayish(object):
"""
A generic object that supports the __array_interface__ and hence
can in principle be converted to a numeric scalar, but is not
otherwise recognized as numeric, but also happens to support
multiplication by floats.
Data should be an object that implements the buffer interface,
and contains at least 4 bytes.
"""
def __init__(self, data):
self._data = data
@property
def __array_interface__(self):
# Ideally should be `'shape': ()` but the current interface
# does not allow that
return {'shape': (1,), 'typestr': '<i4', 'data': self._data,
'version': 3}
def __mul__(self, other):
# For the purposes of this test any multiplication is an
# identity operation :)
return self
one = Arrayish(array(1, dtype='<i4'))
five = Arrayish(array(5, dtype='<i4'))
assert_equal(linspace(one, five), linspace(1, 5))
def test_denormal_numbers(self):
# Regression test for gh-5437. Will probably fail when compiled
# with ICC, which flushes denormals to zero
for ftype in sctypes['float']:
stop = nextafter(ftype(0), ftype(1)) * 5 # A denormal number
assert_(any(linspace(0, stop, 10, endpoint=False, dtype=ftype)))
def test_equivalent_to_arange(self):
for j in range(1000):
assert_equal(linspace(0, j, j+1, dtype=int),
arange(j+1, dtype=int))
def test_retstep(self):
y = linspace(0, 1, 2, retstep=True)
assert_(isinstance(y, tuple) and len(y) == 2)
for num in (0, 1):
for ept in (False, True):
y = linspace(0, 1, num, endpoint=ept, retstep=True)
assert_(isinstance(y, tuple) and len(y) == 2 and
len(y[0]) == num and isnan(y[1]),
'num={0}, endpoint={1}'.format(num, ept))
if __name__ == "__main__":
run_module_suite()
| 11,413 | 34.01227 | 80 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/tests/test_einsum.py
|
from __future__ import division, absolute_import, print_function
import numpy as np
from numpy.testing import (
run_module_suite, assert_, assert_equal, assert_array_equal,
assert_almost_equal, assert_raises, suppress_warnings
)
# Setup for optimize einsum
chars = 'abcdefghij'
sizes = np.array([2, 3, 4, 5, 4, 3, 2, 6, 5, 4, 3])
global_size_dict = {}
for size, char in zip(sizes, chars):
global_size_dict[char] = size
class TestEinSum(object):
def test_einsum_errors(self):
for do_opt in [True, False]:
# Need enough arguments
assert_raises(ValueError, np.einsum, optimize=do_opt)
assert_raises(ValueError, np.einsum, "", optimize=do_opt)
# subscripts must be a string
assert_raises(TypeError, np.einsum, 0, 0, optimize=do_opt)
# out parameter must be an array
assert_raises(TypeError, np.einsum, "", 0, out='test',
optimize=do_opt)
# order parameter must be a valid order
assert_raises(TypeError, np.einsum, "", 0, order='W',
optimize=do_opt)
# casting parameter must be a valid casting
assert_raises(ValueError, np.einsum, "", 0, casting='blah',
optimize=do_opt)
# dtype parameter must be a valid dtype
assert_raises(TypeError, np.einsum, "", 0, dtype='bad_data_type',
optimize=do_opt)
# other keyword arguments are rejected
assert_raises(TypeError, np.einsum, "", 0, bad_arg=0,
optimize=do_opt)
# issue 4528 revealed a segfault with this call
assert_raises(TypeError, np.einsum, *(None,)*63, optimize=do_opt)
# number of operands must match count in subscripts string
assert_raises(ValueError, np.einsum, "", 0, 0, optimize=do_opt)
assert_raises(ValueError, np.einsum, ",", 0, [0], [0],
optimize=do_opt)
assert_raises(ValueError, np.einsum, ",", [0], optimize=do_opt)
# can't have more subscripts than dimensions in the operand
assert_raises(ValueError, np.einsum, "i", 0, optimize=do_opt)
assert_raises(ValueError, np.einsum, "ij", [0, 0], optimize=do_opt)
assert_raises(ValueError, np.einsum, "...i", 0, optimize=do_opt)
assert_raises(ValueError, np.einsum, "i...j", [0, 0], optimize=do_opt)
assert_raises(ValueError, np.einsum, "i...", 0, optimize=do_opt)
assert_raises(ValueError, np.einsum, "ij...", [0, 0], optimize=do_opt)
# invalid ellipsis
assert_raises(ValueError, np.einsum, "i..", [0, 0], optimize=do_opt)
assert_raises(ValueError, np.einsum, ".i...", [0, 0], optimize=do_opt)
assert_raises(ValueError, np.einsum, "j->..j", [0, 0], optimize=do_opt)
assert_raises(ValueError, np.einsum, "j->.j...", [0, 0], optimize=do_opt)
# invalid subscript character
assert_raises(ValueError, np.einsum, "i%...", [0, 0], optimize=do_opt)
assert_raises(ValueError, np.einsum, "...j$", [0, 0], optimize=do_opt)
assert_raises(ValueError, np.einsum, "i->&", [0, 0], optimize=do_opt)
# output subscripts must appear in input
assert_raises(ValueError, np.einsum, "i->ij", [0, 0], optimize=do_opt)
# output subscripts may only be specified once
assert_raises(ValueError, np.einsum, "ij->jij", [[0, 0], [0, 0]],
optimize=do_opt)
# dimensions much match when being collapsed
assert_raises(ValueError, np.einsum, "ii",
np.arange(6).reshape(2, 3), optimize=do_opt)
assert_raises(ValueError, np.einsum, "ii->i",
np.arange(6).reshape(2, 3), optimize=do_opt)
# broadcasting to new dimensions must be enabled explicitly
assert_raises(ValueError, np.einsum, "i", np.arange(6).reshape(2, 3),
optimize=do_opt)
assert_raises(ValueError, np.einsum, "i->i", [[0, 1], [0, 1]],
out=np.arange(4).reshape(2, 2), optimize=do_opt)
def test_einsum_views(self):
# pass-through
for do_opt in [True, False]:
a = np.arange(6)
a.shape = (2, 3)
b = np.einsum("...", a, optimize=do_opt)
assert_(b.base is a)
b = np.einsum(a, [Ellipsis], optimize=do_opt)
assert_(b.base is a)
b = np.einsum("ij", a, optimize=do_opt)
assert_(b.base is a)
assert_equal(b, a)
b = np.einsum(a, [0, 1], optimize=do_opt)
assert_(b.base is a)
assert_equal(b, a)
# output is writeable whenever input is writeable
b = np.einsum("...", a, optimize=do_opt)
assert_(b.flags['WRITEABLE'])
a.flags['WRITEABLE'] = False
b = np.einsum("...", a, optimize=do_opt)
assert_(not b.flags['WRITEABLE'])
# transpose
a = np.arange(6)
a.shape = (2, 3)
b = np.einsum("ji", a, optimize=do_opt)
assert_(b.base is a)
assert_equal(b, a.T)
b = np.einsum(a, [1, 0], optimize=do_opt)
assert_(b.base is a)
assert_equal(b, a.T)
# diagonal
a = np.arange(9)
a.shape = (3, 3)
b = np.einsum("ii->i", a, optimize=do_opt)
assert_(b.base is a)
assert_equal(b, [a[i, i] for i in range(3)])
b = np.einsum(a, [0, 0], [0], optimize=do_opt)
assert_(b.base is a)
assert_equal(b, [a[i, i] for i in range(3)])
# diagonal with various ways of broadcasting an additional dimension
a = np.arange(27)
a.shape = (3, 3, 3)
b = np.einsum("...ii->...i", a, optimize=do_opt)
assert_(b.base is a)
assert_equal(b, [[x[i, i] for i in range(3)] for x in a])
b = np.einsum(a, [Ellipsis, 0, 0], [Ellipsis, 0], optimize=do_opt)
assert_(b.base is a)
assert_equal(b, [[x[i, i] for i in range(3)] for x in a])
b = np.einsum("ii...->...i", a, optimize=do_opt)
assert_(b.base is a)
assert_equal(b, [[x[i, i] for i in range(3)]
for x in a.transpose(2, 0, 1)])
b = np.einsum(a, [0, 0, Ellipsis], [Ellipsis, 0], optimize=do_opt)
assert_(b.base is a)
assert_equal(b, [[x[i, i] for i in range(3)]
for x in a.transpose(2, 0, 1)])
b = np.einsum("...ii->i...", a, optimize=do_opt)
assert_(b.base is a)
assert_equal(b, [a[:, i, i] for i in range(3)])
b = np.einsum(a, [Ellipsis, 0, 0], [0, Ellipsis], optimize=do_opt)
assert_(b.base is a)
assert_equal(b, [a[:, i, i] for i in range(3)])
b = np.einsum("jii->ij", a, optimize=do_opt)
assert_(b.base is a)
assert_equal(b, [a[:, i, i] for i in range(3)])
b = np.einsum(a, [1, 0, 0], [0, 1], optimize=do_opt)
assert_(b.base is a)
assert_equal(b, [a[:, i, i] for i in range(3)])
b = np.einsum("ii...->i...", a, optimize=do_opt)
assert_(b.base is a)
assert_equal(b, [a.transpose(2, 0, 1)[:, i, i] for i in range(3)])
b = np.einsum(a, [0, 0, Ellipsis], [0, Ellipsis], optimize=do_opt)
assert_(b.base is a)
assert_equal(b, [a.transpose(2, 0, 1)[:, i, i] for i in range(3)])
b = np.einsum("i...i->i...", a, optimize=do_opt)
assert_(b.base is a)
assert_equal(b, [a.transpose(1, 0, 2)[:, i, i] for i in range(3)])
b = np.einsum(a, [0, Ellipsis, 0], [0, Ellipsis], optimize=do_opt)
assert_(b.base is a)
assert_equal(b, [a.transpose(1, 0, 2)[:, i, i] for i in range(3)])
b = np.einsum("i...i->...i", a, optimize=do_opt)
assert_(b.base is a)
assert_equal(b, [[x[i, i] for i in range(3)]
for x in a.transpose(1, 0, 2)])
b = np.einsum(a, [0, Ellipsis, 0], [Ellipsis, 0], optimize=do_opt)
assert_(b.base is a)
assert_equal(b, [[x[i, i] for i in range(3)]
for x in a.transpose(1, 0, 2)])
# triple diagonal
a = np.arange(27)
a.shape = (3, 3, 3)
b = np.einsum("iii->i", a, optimize=do_opt)
assert_(b.base is a)
assert_equal(b, [a[i, i, i] for i in range(3)])
b = np.einsum(a, [0, 0, 0], [0], optimize=do_opt)
assert_(b.base is a)
assert_equal(b, [a[i, i, i] for i in range(3)])
# swap axes
a = np.arange(24)
a.shape = (2, 3, 4)
b = np.einsum("ijk->jik", a, optimize=do_opt)
assert_(b.base is a)
assert_equal(b, a.swapaxes(0, 1))
b = np.einsum(a, [0, 1, 2], [1, 0, 2], optimize=do_opt)
assert_(b.base is a)
assert_equal(b, a.swapaxes(0, 1))
def check_einsum_sums(self, dtype, do_opt=False):
# Check various sums. Does many sizes to exercise unrolled loops.
# sum(a, axis=-1)
for n in range(1, 17):
a = np.arange(n, dtype=dtype)
assert_equal(np.einsum("i->", a, optimize=do_opt),
np.sum(a, axis=-1).astype(dtype))
assert_equal(np.einsum(a, [0], [], optimize=do_opt),
np.sum(a, axis=-1).astype(dtype))
for n in range(1, 17):
a = np.arange(2*3*n, dtype=dtype).reshape(2, 3, n)
assert_equal(np.einsum("...i->...", a, optimize=do_opt),
np.sum(a, axis=-1).astype(dtype))
assert_equal(np.einsum(a, [Ellipsis, 0], [Ellipsis], optimize=do_opt),
np.sum(a, axis=-1).astype(dtype))
# sum(a, axis=0)
for n in range(1, 17):
a = np.arange(2*n, dtype=dtype).reshape(2, n)
assert_equal(np.einsum("i...->...", a, optimize=do_opt),
np.sum(a, axis=0).astype(dtype))
assert_equal(np.einsum(a, [0, Ellipsis], [Ellipsis], optimize=do_opt),
np.sum(a, axis=0).astype(dtype))
for n in range(1, 17):
a = np.arange(2*3*n, dtype=dtype).reshape(2, 3, n)
assert_equal(np.einsum("i...->...", a, optimize=do_opt),
np.sum(a, axis=0).astype(dtype))
assert_equal(np.einsum(a, [0, Ellipsis], [Ellipsis], optimize=do_opt),
np.sum(a, axis=0).astype(dtype))
# trace(a)
for n in range(1, 17):
a = np.arange(n*n, dtype=dtype).reshape(n, n)
assert_equal(np.einsum("ii", a, optimize=do_opt),
np.trace(a).astype(dtype))
assert_equal(np.einsum(a, [0, 0], optimize=do_opt),
np.trace(a).astype(dtype))
# multiply(a, b)
assert_equal(np.einsum("..., ...", 3, 4), 12) # scalar case
for n in range(1, 17):
a = np.arange(3 * n, dtype=dtype).reshape(3, n)
b = np.arange(2 * 3 * n, dtype=dtype).reshape(2, 3, n)
assert_equal(np.einsum("..., ...", a, b, optimize=do_opt),
np.multiply(a, b))
assert_equal(np.einsum(a, [Ellipsis], b, [Ellipsis], optimize=do_opt),
np.multiply(a, b))
# inner(a,b)
for n in range(1, 17):
a = np.arange(2 * 3 * n, dtype=dtype).reshape(2, 3, n)
b = np.arange(n, dtype=dtype)
assert_equal(np.einsum("...i, ...i", a, b, optimize=do_opt), np.inner(a, b))
assert_equal(np.einsum(a, [Ellipsis, 0], b, [Ellipsis, 0], optimize=do_opt),
np.inner(a, b))
for n in range(1, 11):
a = np.arange(n * 3 * 2, dtype=dtype).reshape(n, 3, 2)
b = np.arange(n, dtype=dtype)
assert_equal(np.einsum("i..., i...", a, b, optimize=do_opt),
np.inner(a.T, b.T).T)
assert_equal(np.einsum(a, [0, Ellipsis], b, [0, Ellipsis], optimize=do_opt),
np.inner(a.T, b.T).T)
# outer(a,b)
for n in range(1, 17):
a = np.arange(3, dtype=dtype)+1
b = np.arange(n, dtype=dtype)+1
assert_equal(np.einsum("i,j", a, b, optimize=do_opt),
np.outer(a, b))
assert_equal(np.einsum(a, [0], b, [1], optimize=do_opt),
np.outer(a, b))
# Suppress the complex warnings for the 'as f8' tests
with suppress_warnings() as sup:
sup.filter(np.ComplexWarning)
# matvec(a,b) / a.dot(b) where a is matrix, b is vector
for n in range(1, 17):
a = np.arange(4*n, dtype=dtype).reshape(4, n)
b = np.arange(n, dtype=dtype)
assert_equal(np.einsum("ij, j", a, b, optimize=do_opt),
np.dot(a, b))
assert_equal(np.einsum(a, [0, 1], b, [1], optimize=do_opt),
np.dot(a, b))
c = np.arange(4, dtype=dtype)
np.einsum("ij,j", a, b, out=c,
dtype='f8', casting='unsafe', optimize=do_opt)
assert_equal(c,
np.dot(a.astype('f8'),
b.astype('f8')).astype(dtype))
c[...] = 0
np.einsum(a, [0, 1], b, [1], out=c,
dtype='f8', casting='unsafe', optimize=do_opt)
assert_equal(c,
np.dot(a.astype('f8'),
b.astype('f8')).astype(dtype))
for n in range(1, 17):
a = np.arange(4*n, dtype=dtype).reshape(4, n)
b = np.arange(n, dtype=dtype)
assert_equal(np.einsum("ji,j", a.T, b.T, optimize=do_opt),
np.dot(b.T, a.T))
assert_equal(np.einsum(a.T, [1, 0], b.T, [1], optimize=do_opt),
np.dot(b.T, a.T))
c = np.arange(4, dtype=dtype)
np.einsum("ji,j", a.T, b.T, out=c,
dtype='f8', casting='unsafe', optimize=do_opt)
assert_equal(c,
np.dot(b.T.astype('f8'),
a.T.astype('f8')).astype(dtype))
c[...] = 0
np.einsum(a.T, [1, 0], b.T, [1], out=c,
dtype='f8', casting='unsafe', optimize=do_opt)
assert_equal(c,
np.dot(b.T.astype('f8'),
a.T.astype('f8')).astype(dtype))
# matmat(a,b) / a.dot(b) where a is matrix, b is matrix
for n in range(1, 17):
if n < 8 or dtype != 'f2':
a = np.arange(4*n, dtype=dtype).reshape(4, n)
b = np.arange(n*6, dtype=dtype).reshape(n, 6)
assert_equal(np.einsum("ij,jk", a, b, optimize=do_opt),
np.dot(a, b))
assert_equal(np.einsum(a, [0, 1], b, [1, 2], optimize=do_opt),
np.dot(a, b))
for n in range(1, 17):
a = np.arange(4*n, dtype=dtype).reshape(4, n)
b = np.arange(n*6, dtype=dtype).reshape(n, 6)
c = np.arange(24, dtype=dtype).reshape(4, 6)
np.einsum("ij,jk", a, b, out=c, dtype='f8', casting='unsafe',
optimize=do_opt)
assert_equal(c,
np.dot(a.astype('f8'),
b.astype('f8')).astype(dtype))
c[...] = 0
np.einsum(a, [0, 1], b, [1, 2], out=c,
dtype='f8', casting='unsafe', optimize=do_opt)
assert_equal(c,
np.dot(a.astype('f8'),
b.astype('f8')).astype(dtype))
# matrix triple product (note this is not currently an efficient
# way to multiply 3 matrices)
a = np.arange(12, dtype=dtype).reshape(3, 4)
b = np.arange(20, dtype=dtype).reshape(4, 5)
c = np.arange(30, dtype=dtype).reshape(5, 6)
if dtype != 'f2':
assert_equal(np.einsum("ij,jk,kl", a, b, c, optimize=do_opt),
a.dot(b).dot(c))
assert_equal(np.einsum(a, [0, 1], b, [1, 2], c, [2, 3],
optimize=do_opt), a.dot(b).dot(c))
d = np.arange(18, dtype=dtype).reshape(3, 6)
np.einsum("ij,jk,kl", a, b, c, out=d,
dtype='f8', casting='unsafe', optimize=do_opt)
tgt = a.astype('f8').dot(b.astype('f8'))
tgt = tgt.dot(c.astype('f8')).astype(dtype)
assert_equal(d, tgt)
d[...] = 0
np.einsum(a, [0, 1], b, [1, 2], c, [2, 3], out=d,
dtype='f8', casting='unsafe', optimize=do_opt)
tgt = a.astype('f8').dot(b.astype('f8'))
tgt = tgt.dot(c.astype('f8')).astype(dtype)
assert_equal(d, tgt)
# tensordot(a, b)
if np.dtype(dtype) != np.dtype('f2'):
a = np.arange(60, dtype=dtype).reshape(3, 4, 5)
b = np.arange(24, dtype=dtype).reshape(4, 3, 2)
assert_equal(np.einsum("ijk, jil -> kl", a, b),
np.tensordot(a, b, axes=([1, 0], [0, 1])))
assert_equal(np.einsum(a, [0, 1, 2], b, [1, 0, 3], [2, 3]),
np.tensordot(a, b, axes=([1, 0], [0, 1])))
c = np.arange(10, dtype=dtype).reshape(5, 2)
np.einsum("ijk,jil->kl", a, b, out=c,
dtype='f8', casting='unsafe', optimize=do_opt)
assert_equal(c, np.tensordot(a.astype('f8'), b.astype('f8'),
axes=([1, 0], [0, 1])).astype(dtype))
c[...] = 0
np.einsum(a, [0, 1, 2], b, [1, 0, 3], [2, 3], out=c,
dtype='f8', casting='unsafe', optimize=do_opt)
assert_equal(c, np.tensordot(a.astype('f8'), b.astype('f8'),
axes=([1, 0], [0, 1])).astype(dtype))
# logical_and(logical_and(a!=0, b!=0), c!=0)
a = np.array([1, 3, -2, 0, 12, 13, 0, 1], dtype=dtype)
b = np.array([0, 3.5, 0., -2, 0, 1, 3, 12], dtype=dtype)
c = np.array([True, True, False, True, True, False, True, True])
assert_equal(np.einsum("i,i,i->i", a, b, c,
dtype='?', casting='unsafe', optimize=do_opt),
np.logical_and(np.logical_and(a != 0, b != 0), c != 0))
assert_equal(np.einsum(a, [0], b, [0], c, [0], [0],
dtype='?', casting='unsafe'),
np.logical_and(np.logical_and(a != 0, b != 0), c != 0))
a = np.arange(9, dtype=dtype)
assert_equal(np.einsum(",i->", 3, a), 3*np.sum(a))
assert_equal(np.einsum(3, [], a, [0], []), 3*np.sum(a))
assert_equal(np.einsum("i,->", a, 3), 3*np.sum(a))
assert_equal(np.einsum(a, [0], 3, [], []), 3*np.sum(a))
# Various stride0, contiguous, and SSE aligned variants
for n in range(1, 25):
a = np.arange(n, dtype=dtype)
if np.dtype(dtype).itemsize > 1:
assert_equal(np.einsum("...,...", a, a, optimize=do_opt),
np.multiply(a, a))
assert_equal(np.einsum("i,i", a, a, optimize=do_opt), np.dot(a, a))
assert_equal(np.einsum("i,->i", a, 2, optimize=do_opt), 2*a)
assert_equal(np.einsum(",i->i", 2, a, optimize=do_opt), 2*a)
assert_equal(np.einsum("i,->", a, 2, optimize=do_opt), 2*np.sum(a))
assert_equal(np.einsum(",i->", 2, a, optimize=do_opt), 2*np.sum(a))
assert_equal(np.einsum("...,...", a[1:], a[:-1], optimize=do_opt),
np.multiply(a[1:], a[:-1]))
assert_equal(np.einsum("i,i", a[1:], a[:-1], optimize=do_opt),
np.dot(a[1:], a[:-1]))
assert_equal(np.einsum("i,->i", a[1:], 2, optimize=do_opt), 2*a[1:])
assert_equal(np.einsum(",i->i", 2, a[1:], optimize=do_opt), 2*a[1:])
assert_equal(np.einsum("i,->", a[1:], 2, optimize=do_opt),
2*np.sum(a[1:]))
assert_equal(np.einsum(",i->", 2, a[1:], optimize=do_opt),
2*np.sum(a[1:]))
# An object array, summed as the data type
a = np.arange(9, dtype=object)
b = np.einsum("i->", a, dtype=dtype, casting='unsafe')
assert_equal(b, np.sum(a))
assert_equal(b.dtype, np.dtype(dtype))
b = np.einsum(a, [0], [], dtype=dtype, casting='unsafe')
assert_equal(b, np.sum(a))
assert_equal(b.dtype, np.dtype(dtype))
# A case which was failing (ticket #1885)
p = np.arange(2) + 1
q = np.arange(4).reshape(2, 2) + 3
r = np.arange(4).reshape(2, 2) + 7
assert_equal(np.einsum('z,mz,zm->', p, q, r), 253)
# singleton dimensions broadcast (gh-10343)
p = np.ones((10,2))
q = np.ones((1,2))
assert_array_equal(np.einsum('ij,ij->j', p, q, optimize=True),
np.einsum('ij,ij->j', p, q, optimize=False))
assert_array_equal(np.einsum('ij,ij->j', p, q, optimize=True),
[10.] * 2)
# a blas-compatible contraction broadcasting case which was failing
# for optimize=True (ticket #10930)
x = np.array([2., 3.])
y = np.array([4.])
assert_array_equal(np.einsum("i, i", x, y, optimize=False), 20.)
assert_array_equal(np.einsum("i, i", x, y, optimize=True), 20.)
# all-ones array was bypassing bug (ticket #10930)
p = np.ones((1, 5)) / 2
q = np.ones((5, 5)) / 2
for optimize in (True, False):
assert_array_equal(np.einsum("...ij,...jk->...ik", p, p,
optimize=optimize),
np.einsum("...ij,...jk->...ik", p, q,
optimize=optimize))
assert_array_equal(np.einsum("...ij,...jk->...ik", p, q,
optimize=optimize),
np.full((1, 5), 1.25))
def test_einsum_sums_int8(self):
self.check_einsum_sums('i1')
def test_einsum_sums_uint8(self):
self.check_einsum_sums('u1')
def test_einsum_sums_int16(self):
self.check_einsum_sums('i2')
def test_einsum_sums_uint16(self):
self.check_einsum_sums('u2')
def test_einsum_sums_int32(self):
self.check_einsum_sums('i4')
self.check_einsum_sums('i4', True)
def test_einsum_sums_uint32(self):
self.check_einsum_sums('u4')
self.check_einsum_sums('u4', True)
def test_einsum_sums_int64(self):
self.check_einsum_sums('i8')
def test_einsum_sums_uint64(self):
self.check_einsum_sums('u8')
def test_einsum_sums_float16(self):
self.check_einsum_sums('f2')
def test_einsum_sums_float32(self):
self.check_einsum_sums('f4')
def test_einsum_sums_float64(self):
self.check_einsum_sums('f8')
self.check_einsum_sums('f8', True)
def test_einsum_sums_longdouble(self):
self.check_einsum_sums(np.longdouble)
def test_einsum_sums_cfloat64(self):
self.check_einsum_sums('c8')
self.check_einsum_sums('c8', True)
def test_einsum_sums_cfloat128(self):
self.check_einsum_sums('c16')
def test_einsum_sums_clongdouble(self):
self.check_einsum_sums(np.clongdouble)
def test_einsum_misc(self):
# This call used to crash because of a bug in
# PyArray_AssignZero
a = np.ones((1, 2))
b = np.ones((2, 2, 1))
assert_equal(np.einsum('ij...,j...->i...', a, b), [[[2], [2]]])
assert_equal(np.einsum('ij...,j...->i...', a, b, optimize=True), [[[2], [2]]])
# Regression test for issue #10369 (test unicode inputs with Python 2)
assert_equal(np.einsum(u'ij...,j...->i...', a, b), [[[2], [2]]])
assert_equal(np.einsum('...i,...i', [1, 2, 3], [2, 3, 4]), 20)
assert_equal(np.einsum(u'...i,...i', [1, 2, 3], [2, 3, 4]), 20)
assert_equal(np.einsum('...i,...i', [1, 2, 3], [2, 3, 4],
optimize=u'greedy'), 20)
# The iterator had an issue with buffering this reduction
a = np.ones((5, 12, 4, 2, 3), np.int64)
b = np.ones((5, 12, 11), np.int64)
assert_equal(np.einsum('ijklm,ijn,ijn->', a, b, b),
np.einsum('ijklm,ijn->', a, b))
assert_equal(np.einsum('ijklm,ijn,ijn->', a, b, b, optimize=True),
np.einsum('ijklm,ijn->', a, b, optimize=True))
# Issue #2027, was a problem in the contiguous 3-argument
# inner loop implementation
a = np.arange(1, 3)
b = np.arange(1, 5).reshape(2, 2)
c = np.arange(1, 9).reshape(4, 2)
assert_equal(np.einsum('x,yx,zx->xzy', a, b, c),
[[[1, 3], [3, 9], [5, 15], [7, 21]],
[[8, 16], [16, 32], [24, 48], [32, 64]]])
assert_equal(np.einsum('x,yx,zx->xzy', a, b, c, optimize=True),
[[[1, 3], [3, 9], [5, 15], [7, 21]],
[[8, 16], [16, 32], [24, 48], [32, 64]]])
def test_einsum_broadcast(self):
# Issue #2455 change in handling ellipsis
# remove the 'middle broadcast' error
# only use the 'RIGHT' iteration in prepare_op_axes
# adds auto broadcast on left where it belongs
# broadcast on right has to be explicit
# We need to test the optimized parsing as well
A = np.arange(2 * 3 * 4).reshape(2, 3, 4)
B = np.arange(3)
ref = np.einsum('ijk,j->ijk', A, B, optimize=False)
for opt in [True, False]:
assert_equal(np.einsum('ij...,j...->ij...', A, B, optimize=opt), ref)
assert_equal(np.einsum('ij...,...j->ij...', A, B, optimize=opt), ref)
assert_equal(np.einsum('ij...,j->ij...', A, B, optimize=opt), ref) # used to raise error
A = np.arange(12).reshape((4, 3))
B = np.arange(6).reshape((3, 2))
ref = np.einsum('ik,kj->ij', A, B, optimize=False)
for opt in [True, False]:
assert_equal(np.einsum('ik...,k...->i...', A, B, optimize=opt), ref)
assert_equal(np.einsum('ik...,...kj->i...j', A, B, optimize=opt), ref)
assert_equal(np.einsum('...k,kj', A, B, optimize=opt), ref) # used to raise error
assert_equal(np.einsum('ik,k...->i...', A, B, optimize=opt), ref) # used to raise error
dims = [2, 3, 4, 5]
a = np.arange(np.prod(dims)).reshape(dims)
v = np.arange(dims[2])
ref = np.einsum('ijkl,k->ijl', a, v, optimize=False)
for opt in [True, False]:
assert_equal(np.einsum('ijkl,k', a, v, optimize=opt), ref)
assert_equal(np.einsum('...kl,k', a, v, optimize=opt), ref) # used to raise error
assert_equal(np.einsum('...kl,k...', a, v, optimize=opt), ref)
J, K, M = 160, 160, 120
A = np.arange(J * K * M).reshape(1, 1, 1, J, K, M)
B = np.arange(J * K * M * 3).reshape(J, K, M, 3)
ref = np.einsum('...lmn,...lmno->...o', A, B, optimize=False)
for opt in [True, False]:
assert_equal(np.einsum('...lmn,lmno->...o', A, B,
optimize=opt), ref) # used to raise error
def test_einsum_fixedstridebug(self):
# Issue #4485 obscure einsum bug
# This case revealed a bug in nditer where it reported a stride
# as 'fixed' (0) when it was in fact not fixed during processing
# (0 or 4). The reason for the bug was that the check for a fixed
# stride was using the information from the 2D inner loop reuse
# to restrict the iteration dimensions it had to validate to be
# the same, but that 2D inner loop reuse logic is only triggered
# during the buffer copying step, and hence it was invalid to
# rely on those values. The fix is to check all the dimensions
# of the stride in question, which in the test case reveals that
# the stride is not fixed.
#
# NOTE: This test is triggered by the fact that the default buffersize,
# used by einsum, is 8192, and 3*2731 = 8193, is larger than that
# and results in a mismatch between the buffering and the
# striding for operand A.
A = np.arange(2 * 3).reshape(2, 3).astype(np.float32)
B = np.arange(2 * 3 * 2731).reshape(2, 3, 2731).astype(np.int16)
es = np.einsum('cl, cpx->lpx', A, B)
tp = np.tensordot(A, B, axes=(0, 0))
assert_equal(es, tp)
# The following is the original test case from the bug report,
# made repeatable by changing random arrays to aranges.
A = np.arange(3 * 3).reshape(3, 3).astype(np.float64)
B = np.arange(3 * 3 * 64 * 64).reshape(3, 3, 64, 64).astype(np.float32)
es = np.einsum('cl, cpxy->lpxy', A, B)
tp = np.tensordot(A, B, axes=(0, 0))
assert_equal(es, tp)
def test_einsum_fixed_collapsingbug(self):
# Issue #5147.
# The bug only occurred when output argument of einssum was used.
x = np.random.normal(0, 1, (5, 5, 5, 5))
y1 = np.zeros((5, 5))
np.einsum('aabb->ab', x, out=y1)
idx = np.arange(5)
y2 = x[idx[:, None], idx[:, None], idx, idx]
assert_equal(y1, y2)
def test_einsum_all_contig_non_contig_output(self):
# Issue gh-5907, tests that the all contiguous special case
# actually checks the contiguity of the output
x = np.ones((5, 5))
out = np.ones(10)[::2]
correct_base = np.ones(10)
correct_base[::2] = 5
# Always worked (inner iteration is done with 0-stride):
np.einsum('mi,mi,mi->m', x, x, x, out=out)
assert_array_equal(out.base, correct_base)
# Example 1:
out = np.ones(10)[::2]
np.einsum('im,im,im->m', x, x, x, out=out)
assert_array_equal(out.base, correct_base)
# Example 2, buffering causes x to be contiguous but
# special cases do not catch the operation before:
out = np.ones((2, 2, 2))[..., 0]
correct_base = np.ones((2, 2, 2))
correct_base[..., 0] = 2
x = np.ones((2, 2), np.float32)
np.einsum('ij,jk->ik', x, x, out=out)
assert_array_equal(out.base, correct_base)
def test_small_boolean_arrays(self):
# See gh-5946.
# Use array of True embedded in False.
a = np.zeros((16, 1, 1), dtype=np.bool_)[:2]
a[...] = True
out = np.zeros((16, 1, 1), dtype=np.bool_)[:2]
tgt = np.ones((2, 1, 1), dtype=np.bool_)
res = np.einsum('...ij,...jk->...ik', a, a, out=out)
assert_equal(res, tgt)
def optimize_compare(self, string):
# Tests all paths of the optimization function against
# conventional einsum
operands = [string]
terms = string.split('->')[0].split(',')
for term in terms:
dims = [global_size_dict[x] for x in term]
operands.append(np.random.rand(*dims))
noopt = np.einsum(*operands, optimize=False)
opt = np.einsum(*operands, optimize='greedy')
assert_almost_equal(opt, noopt)
opt = np.einsum(*operands, optimize='optimal')
assert_almost_equal(opt, noopt)
def test_hadamard_like_products(self):
# Hadamard outer products
self.optimize_compare('a,ab,abc->abc')
self.optimize_compare('a,b,ab->ab')
def test_index_transformations(self):
# Simple index transformation cases
self.optimize_compare('ea,fb,gc,hd,abcd->efgh')
self.optimize_compare('ea,fb,abcd,gc,hd->efgh')
self.optimize_compare('abcd,ea,fb,gc,hd->efgh')
def test_complex(self):
# Long test cases
self.optimize_compare('acdf,jbje,gihb,hfac,gfac,gifabc,hfac')
self.optimize_compare('acdf,jbje,gihb,hfac,gfac,gifabc,hfac')
self.optimize_compare('cd,bdhe,aidb,hgca,gc,hgibcd,hgac')
self.optimize_compare('abhe,hidj,jgba,hiab,gab')
self.optimize_compare('bde,cdh,agdb,hica,ibd,hgicd,hiac')
self.optimize_compare('chd,bde,agbc,hiad,hgc,hgi,hiad')
self.optimize_compare('chd,bde,agbc,hiad,bdi,cgh,agdb')
self.optimize_compare('bdhe,acad,hiab,agac,hibd')
def test_collapse(self):
# Inner products
self.optimize_compare('ab,ab,c->')
self.optimize_compare('ab,ab,c->c')
self.optimize_compare('ab,ab,cd,cd->')
self.optimize_compare('ab,ab,cd,cd->ac')
self.optimize_compare('ab,ab,cd,cd->cd')
self.optimize_compare('ab,ab,cd,cd,ef,ef->')
def test_expand(self):
# Outer products
self.optimize_compare('ab,cd,ef->abcdef')
self.optimize_compare('ab,cd,ef->acdf')
self.optimize_compare('ab,cd,de->abcde')
self.optimize_compare('ab,cd,de->be')
self.optimize_compare('ab,bcd,cd->abcd')
self.optimize_compare('ab,bcd,cd->abd')
def test_edge_cases(self):
# Difficult edge cases for optimization
self.optimize_compare('eb,cb,fb->cef')
self.optimize_compare('dd,fb,be,cdb->cef')
self.optimize_compare('bca,cdb,dbf,afc->')
self.optimize_compare('dcc,fce,ea,dbf->ab')
self.optimize_compare('fdf,cdd,ccd,afe->ae')
self.optimize_compare('abcd,ad')
self.optimize_compare('ed,fcd,ff,bcf->be')
self.optimize_compare('baa,dcf,af,cde->be')
self.optimize_compare('bd,db,eac->ace')
self.optimize_compare('fff,fae,bef,def->abd')
self.optimize_compare('efc,dbc,acf,fd->abe')
self.optimize_compare('ba,ac,da->bcd')
def test_inner_product(self):
# Inner products
self.optimize_compare('ab,ab')
self.optimize_compare('ab,ba')
self.optimize_compare('abc,abc')
self.optimize_compare('abc,bac')
self.optimize_compare('abc,cba')
def test_random_cases(self):
# Randomly built test cases
self.optimize_compare('aab,fa,df,ecc->bde')
self.optimize_compare('ecb,fef,bad,ed->ac')
self.optimize_compare('bcf,bbb,fbf,fc->')
self.optimize_compare('bb,ff,be->e')
self.optimize_compare('bcb,bb,fc,fff->')
self.optimize_compare('fbb,dfd,fc,fc->')
self.optimize_compare('afd,ba,cc,dc->bf')
self.optimize_compare('adb,bc,fa,cfc->d')
self.optimize_compare('bbd,bda,fc,db->acf')
self.optimize_compare('dba,ead,cad->bce')
self.optimize_compare('aef,fbc,dca->bde')
class TestEinSumPath(object):
def build_operands(self, string, size_dict=global_size_dict):
# Builds views based off initial operands
operands = [string]
terms = string.split('->')[0].split(',')
for term in terms:
dims = [size_dict[x] for x in term]
operands.append(np.random.rand(*dims))
return operands
def assert_path_equal(self, comp, benchmark):
# Checks if list of tuples are equivalent
ret = (len(comp) == len(benchmark))
assert_(ret)
for pos in range(len(comp) - 1):
ret &= isinstance(comp[pos + 1], tuple)
ret &= (comp[pos + 1] == benchmark[pos + 1])
assert_(ret)
def test_memory_contraints(self):
# Ensure memory constraints are satisfied
outer_test = self.build_operands('a,b,c->abc')
path, path_str = np.einsum_path(*outer_test, optimize=('greedy', 0))
self.assert_path_equal(path, ['einsum_path', (0, 1, 2)])
path, path_str = np.einsum_path(*outer_test, optimize=('optimal', 0))
self.assert_path_equal(path, ['einsum_path', (0, 1, 2)])
long_test = self.build_operands('acdf,jbje,gihb,hfac')
path, path_str = np.einsum_path(*long_test, optimize=('greedy', 0))
self.assert_path_equal(path, ['einsum_path', (0, 1, 2, 3)])
path, path_str = np.einsum_path(*long_test, optimize=('optimal', 0))
self.assert_path_equal(path, ['einsum_path', (0, 1, 2, 3)])
def test_long_paths(self):
# Long complex cases
# Long test 1
long_test1 = self.build_operands('acdf,jbje,gihb,hfac,gfac,gifabc,hfac')
path, path_str = np.einsum_path(*long_test1, optimize='greedy')
self.assert_path_equal(path, ['einsum_path',
(1, 4), (2, 4), (1, 4), (1, 3), (1, 2), (0, 1)])
path, path_str = np.einsum_path(*long_test1, optimize='optimal')
self.assert_path_equal(path, ['einsum_path',
(3, 6), (3, 4), (2, 4), (2, 3), (0, 2), (0, 1)])
# Long test 2
long_test2 = self.build_operands('chd,bde,agbc,hiad,bdi,cgh,agdb')
path, path_str = np.einsum_path(*long_test2, optimize='greedy')
self.assert_path_equal(path, ['einsum_path',
(3, 4), (0, 3), (3, 4), (1, 3), (1, 2), (0, 1)])
path, path_str = np.einsum_path(*long_test2, optimize='optimal')
self.assert_path_equal(path, ['einsum_path',
(0, 5), (1, 4), (3, 4), (1, 3), (1, 2), (0, 1)])
def test_edge_paths(self):
# Difficult edge cases
# Edge test1
edge_test1 = self.build_operands('eb,cb,fb->cef')
path, path_str = np.einsum_path(*edge_test1, optimize='greedy')
self.assert_path_equal(path, ['einsum_path', (0, 2), (0, 1)])
path, path_str = np.einsum_path(*edge_test1, optimize='optimal')
self.assert_path_equal(path, ['einsum_path', (0, 2), (0, 1)])
# Edge test2
edge_test2 = self.build_operands('dd,fb,be,cdb->cef')
path, path_str = np.einsum_path(*edge_test2, optimize='greedy')
self.assert_path_equal(path, ['einsum_path', (0, 3), (0, 1), (0, 1)])
path, path_str = np.einsum_path(*edge_test2, optimize='optimal')
self.assert_path_equal(path, ['einsum_path', (0, 3), (0, 1), (0, 1)])
# Edge test3
edge_test3 = self.build_operands('bca,cdb,dbf,afc->')
path, path_str = np.einsum_path(*edge_test3, optimize='greedy')
self.assert_path_equal(path, ['einsum_path', (1, 2), (0, 2), (0, 1)])
path, path_str = np.einsum_path(*edge_test3, optimize='optimal')
self.assert_path_equal(path, ['einsum_path', (1, 2), (0, 2), (0, 1)])
# Edge test4
edge_test4 = self.build_operands('dcc,fce,ea,dbf->ab')
path, path_str = np.einsum_path(*edge_test4, optimize='greedy')
self.assert_path_equal(path, ['einsum_path', (0, 3), (0, 2), (0, 1)])
path, path_str = np.einsum_path(*edge_test4, optimize='optimal')
self.assert_path_equal(path, ['einsum_path', (1, 2), (0, 2), (0, 1)])
# Edge test5
edge_test4 = self.build_operands('a,ac,ab,ad,cd,bd,bc->',
size_dict={"a": 20, "b": 20, "c": 20, "d": 20})
path, path_str = np.einsum_path(*edge_test4, optimize='greedy')
self.assert_path_equal(path, ['einsum_path', (0, 1), (0, 1, 2, 3, 4, 5)])
path, path_str = np.einsum_path(*edge_test4, optimize='optimal')
self.assert_path_equal(path, ['einsum_path', (0, 1), (0, 1, 2, 3, 4, 5)])
def test_path_type_input(self):
# Test explicit path handeling
path_test = self.build_operands('dcc,fce,ea,dbf->ab')
path, path_str = np.einsum_path(*path_test, optimize=False)
self.assert_path_equal(path, ['einsum_path', (0, 1, 2, 3)])
path, path_str = np.einsum_path(*path_test, optimize=True)
self.assert_path_equal(path, ['einsum_path', (0, 3), (0, 2), (0, 1)])
exp_path = ['einsum_path', (0, 2), (0, 2), (0, 1)]
path, path_str = np.einsum_path(*path_test, optimize=exp_path)
self.assert_path_equal(path, exp_path)
# Double check einsum works on the input path
noopt = np.einsum(*path_test, optimize=False)
opt = np.einsum(*path_test, optimize=exp_path)
assert_almost_equal(noopt, opt)
if __name__ == "__main__":
run_module_suite()
| 41,230 | 43.23927 | 101 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/tests/test_datetime.py
|
from __future__ import division, absolute_import, print_function
import pickle
import numpy
import numpy as np
import datetime
from numpy.testing import (
run_module_suite, assert_, assert_equal, assert_raises,
assert_warns, dec, suppress_warnings
)
# Use pytz to test out various time zones if available
try:
from pytz import timezone as tz
_has_pytz = True
except ImportError:
_has_pytz = False
class TestDateTime(object):
def test_datetime_dtype_creation(self):
for unit in ['Y', 'M', 'W', 'D',
'h', 'm', 's', 'ms', 'us',
'ns', 'ps', 'fs', 'as']:
dt1 = np.dtype('M8[750%s]' % unit)
assert_(dt1 == np.dtype('datetime64[750%s]' % unit))
dt2 = np.dtype('m8[%s]' % unit)
assert_(dt2 == np.dtype('timedelta64[%s]' % unit))
# Generic units shouldn't add [] to the end
assert_equal(str(np.dtype("M8")), "datetime64")
# Should be possible to specify the endianness
assert_equal(np.dtype("=M8"), np.dtype("M8"))
assert_equal(np.dtype("=M8[s]"), np.dtype("M8[s]"))
assert_(np.dtype(">M8") == np.dtype("M8") or
np.dtype("<M8") == np.dtype("M8"))
assert_(np.dtype(">M8[D]") == np.dtype("M8[D]") or
np.dtype("<M8[D]") == np.dtype("M8[D]"))
assert_(np.dtype(">M8") != np.dtype("<M8"))
assert_equal(np.dtype("=m8"), np.dtype("m8"))
assert_equal(np.dtype("=m8[s]"), np.dtype("m8[s]"))
assert_(np.dtype(">m8") == np.dtype("m8") or
np.dtype("<m8") == np.dtype("m8"))
assert_(np.dtype(">m8[D]") == np.dtype("m8[D]") or
np.dtype("<m8[D]") == np.dtype("m8[D]"))
assert_(np.dtype(">m8") != np.dtype("<m8"))
# Check that the parser rejects bad datetime types
assert_raises(TypeError, np.dtype, 'M8[badunit]')
assert_raises(TypeError, np.dtype, 'm8[badunit]')
assert_raises(TypeError, np.dtype, 'M8[YY]')
assert_raises(TypeError, np.dtype, 'm8[YY]')
assert_raises(TypeError, np.dtype, 'm4')
assert_raises(TypeError, np.dtype, 'M7')
assert_raises(TypeError, np.dtype, 'm7')
assert_raises(TypeError, np.dtype, 'M16')
assert_raises(TypeError, np.dtype, 'm16')
def test_datetime_casting_rules(self):
# Cannot cast safely/same_kind between timedelta and datetime
assert_(not np.can_cast('m8', 'M8', casting='same_kind'))
assert_(not np.can_cast('M8', 'm8', casting='same_kind'))
assert_(not np.can_cast('m8', 'M8', casting='safe'))
assert_(not np.can_cast('M8', 'm8', casting='safe'))
# Can cast safely/same_kind from integer to timedelta
assert_(np.can_cast('i8', 'm8', casting='same_kind'))
assert_(np.can_cast('i8', 'm8', casting='safe'))
# Cannot cast safely/same_kind from float to timedelta
assert_(not np.can_cast('f4', 'm8', casting='same_kind'))
assert_(not np.can_cast('f4', 'm8', casting='safe'))
# Cannot cast safely/same_kind from integer to datetime
assert_(not np.can_cast('i8', 'M8', casting='same_kind'))
assert_(not np.can_cast('i8', 'M8', casting='safe'))
# Cannot cast safely/same_kind from bool to datetime
assert_(not np.can_cast('b1', 'M8', casting='same_kind'))
assert_(not np.can_cast('b1', 'M8', casting='safe'))
# Can cast safely/same_kind from bool to timedelta
assert_(np.can_cast('b1', 'm8', casting='same_kind'))
assert_(np.can_cast('b1', 'm8', casting='safe'))
# Can cast datetime safely from months/years to days
assert_(np.can_cast('M8[M]', 'M8[D]', casting='safe'))
assert_(np.can_cast('M8[Y]', 'M8[D]', casting='safe'))
# Cannot cast timedelta safely from months/years to days
assert_(not np.can_cast('m8[M]', 'm8[D]', casting='safe'))
assert_(not np.can_cast('m8[Y]', 'm8[D]', casting='safe'))
# Can cast datetime same_kind from months/years to days
assert_(np.can_cast('M8[M]', 'M8[D]', casting='same_kind'))
assert_(np.can_cast('M8[Y]', 'M8[D]', casting='same_kind'))
# Can't cast timedelta same_kind from months/years to days
assert_(not np.can_cast('m8[M]', 'm8[D]', casting='same_kind'))
assert_(not np.can_cast('m8[Y]', 'm8[D]', casting='same_kind'))
# Can cast datetime same_kind across the date/time boundary
assert_(np.can_cast('M8[D]', 'M8[h]', casting='same_kind'))
# Can cast timedelta same_kind across the date/time boundary
assert_(np.can_cast('m8[D]', 'm8[h]', casting='same_kind'))
assert_(np.can_cast('m8[h]', 'm8[D]', casting='same_kind'))
# Cannot cast safely if the integer multiplier doesn't divide
assert_(not np.can_cast('M8[7h]', 'M8[3h]', casting='safe'))
assert_(not np.can_cast('M8[3h]', 'M8[6h]', casting='safe'))
# But can cast same_kind
assert_(np.can_cast('M8[7h]', 'M8[3h]', casting='same_kind'))
# Can cast safely if the integer multiplier does divide
assert_(np.can_cast('M8[6h]', 'M8[3h]', casting='safe'))
# We can always cast types with generic units (corresponding to NaT) to
# more specific types
assert_(np.can_cast('m8', 'm8[h]', casting='same_kind'))
assert_(np.can_cast('m8', 'm8[h]', casting='safe'))
assert_(np.can_cast('M8', 'M8[h]', casting='same_kind'))
assert_(np.can_cast('M8', 'M8[h]', casting='safe'))
# but not the other way around
assert_(not np.can_cast('m8[h]', 'm8', casting='same_kind'))
assert_(not np.can_cast('m8[h]', 'm8', casting='safe'))
assert_(not np.can_cast('M8[h]', 'M8', casting='same_kind'))
assert_(not np.can_cast('M8[h]', 'M8', casting='safe'))
def test_compare_generic_nat(self):
# regression tests for GH6452
assert_equal(np.datetime64('NaT'),
np.datetime64('2000') + np.timedelta64('NaT'))
# nb. we may want to make NaT != NaT true in the future
with suppress_warnings() as sup:
sup.filter(FutureWarning, ".*NAT ==")
assert_(np.datetime64('NaT') == np.datetime64('NaT', 'us'))
assert_(np.datetime64('NaT', 'us') == np.datetime64('NaT'))
def test_datetime_scalar_construction(self):
# Construct with different units
assert_equal(np.datetime64('1950-03-12', 'D'),
np.datetime64('1950-03-12'))
assert_equal(np.datetime64('1950-03-12T13', 's'),
np.datetime64('1950-03-12T13', 'm'))
# Default construction means NaT
assert_equal(np.datetime64(), np.datetime64('NaT'))
# Some basic strings and repr
assert_equal(str(np.datetime64('NaT')), 'NaT')
assert_equal(repr(np.datetime64('NaT')),
"numpy.datetime64('NaT')")
assert_equal(str(np.datetime64('2011-02')), '2011-02')
assert_equal(repr(np.datetime64('2011-02')),
"numpy.datetime64('2011-02')")
# None gets constructed as NaT
assert_equal(np.datetime64(None), np.datetime64('NaT'))
# Default construction of NaT is in generic units
assert_equal(np.datetime64().dtype, np.dtype('M8'))
assert_equal(np.datetime64('NaT').dtype, np.dtype('M8'))
# Construction from integers requires a specified unit
assert_raises(ValueError, np.datetime64, 17)
# When constructing from a scalar or zero-dimensional array,
# it either keeps the units or you can override them.
a = np.datetime64('2000-03-18T16', 'h')
b = np.array('2000-03-18T16', dtype='M8[h]')
assert_equal(a.dtype, np.dtype('M8[h]'))
assert_equal(b.dtype, np.dtype('M8[h]'))
assert_equal(np.datetime64(a), a)
assert_equal(np.datetime64(a).dtype, np.dtype('M8[h]'))
assert_equal(np.datetime64(b), a)
assert_equal(np.datetime64(b).dtype, np.dtype('M8[h]'))
assert_equal(np.datetime64(a, 's'), a)
assert_equal(np.datetime64(a, 's').dtype, np.dtype('M8[s]'))
assert_equal(np.datetime64(b, 's'), a)
assert_equal(np.datetime64(b, 's').dtype, np.dtype('M8[s]'))
# Construction from datetime.date
assert_equal(np.datetime64('1945-03-25'),
np.datetime64(datetime.date(1945, 3, 25)))
assert_equal(np.datetime64('2045-03-25', 'D'),
np.datetime64(datetime.date(2045, 3, 25), 'D'))
# Construction from datetime.datetime
assert_equal(np.datetime64('1980-01-25T14:36:22.5'),
np.datetime64(datetime.datetime(1980, 1, 25,
14, 36, 22, 500000)))
# Construction with time units from a date is okay
assert_equal(np.datetime64('1920-03-13', 'h'),
np.datetime64('1920-03-13T00'))
assert_equal(np.datetime64('1920-03', 'm'),
np.datetime64('1920-03-01T00:00'))
assert_equal(np.datetime64('1920', 's'),
np.datetime64('1920-01-01T00:00:00'))
assert_equal(np.datetime64(datetime.date(2045, 3, 25), 'ms'),
np.datetime64('2045-03-25T00:00:00.000'))
# Construction with date units from a datetime is also okay
assert_equal(np.datetime64('1920-03-13T18', 'D'),
np.datetime64('1920-03-13'))
assert_equal(np.datetime64('1920-03-13T18:33:12', 'M'),
np.datetime64('1920-03'))
assert_equal(np.datetime64('1920-03-13T18:33:12.5', 'Y'),
np.datetime64('1920'))
def test_datetime_scalar_construction_timezone(self):
# verify that supplying an explicit timezone works, but is deprecated
with assert_warns(DeprecationWarning):
assert_equal(np.datetime64('2000-01-01T00Z'),
np.datetime64('2000-01-01T00'))
with assert_warns(DeprecationWarning):
assert_equal(np.datetime64('2000-01-01T00-08'),
np.datetime64('2000-01-01T08'))
def test_datetime_array_find_type(self):
dt = np.datetime64('1970-01-01', 'M')
arr = np.array([dt])
assert_equal(arr.dtype, np.dtype('M8[M]'))
# at the moment, we don't automatically convert these to datetime64
dt = datetime.date(1970, 1, 1)
arr = np.array([dt])
assert_equal(arr.dtype, np.dtype('O'))
dt = datetime.datetime(1970, 1, 1, 12, 30, 40)
arr = np.array([dt])
assert_equal(arr.dtype, np.dtype('O'))
# find "supertype" for non-dates and dates
b = np.bool_(True)
dt = np.datetime64('1970-01-01', 'M')
arr = np.array([b, dt])
assert_equal(arr.dtype, np.dtype('O'))
dt = datetime.date(1970, 1, 1)
arr = np.array([b, dt])
assert_equal(arr.dtype, np.dtype('O'))
dt = datetime.datetime(1970, 1, 1, 12, 30, 40)
arr = np.array([b, dt])
assert_equal(arr.dtype, np.dtype('O'))
def test_timedelta_scalar_construction(self):
# Construct with different units
assert_equal(np.timedelta64(7, 'D'),
np.timedelta64(1, 'W'))
assert_equal(np.timedelta64(120, 's'),
np.timedelta64(2, 'm'))
# Default construction means 0
assert_equal(np.timedelta64(), np.timedelta64(0))
# None gets constructed as NaT
assert_equal(np.timedelta64(None), np.timedelta64('NaT'))
# Some basic strings and repr
assert_equal(str(np.timedelta64('NaT')), 'NaT')
assert_equal(repr(np.timedelta64('NaT')),
"numpy.timedelta64('NaT')")
assert_equal(str(np.timedelta64(3, 's')), '3 seconds')
assert_equal(repr(np.timedelta64(-3, 's')),
"numpy.timedelta64(-3,'s')")
assert_equal(repr(np.timedelta64(12)),
"numpy.timedelta64(12)")
# Construction from an integer produces generic units
assert_equal(np.timedelta64(12).dtype, np.dtype('m8'))
# When constructing from a scalar or zero-dimensional array,
# it either keeps the units or you can override them.
a = np.timedelta64(2, 'h')
b = np.array(2, dtype='m8[h]')
assert_equal(a.dtype, np.dtype('m8[h]'))
assert_equal(b.dtype, np.dtype('m8[h]'))
assert_equal(np.timedelta64(a), a)
assert_equal(np.timedelta64(a).dtype, np.dtype('m8[h]'))
assert_equal(np.timedelta64(b), a)
assert_equal(np.timedelta64(b).dtype, np.dtype('m8[h]'))
assert_equal(np.timedelta64(a, 's'), a)
assert_equal(np.timedelta64(a, 's').dtype, np.dtype('m8[s]'))
assert_equal(np.timedelta64(b, 's'), a)
assert_equal(np.timedelta64(b, 's').dtype, np.dtype('m8[s]'))
# Construction from datetime.timedelta
assert_equal(np.timedelta64(5, 'D'),
np.timedelta64(datetime.timedelta(days=5)))
assert_equal(np.timedelta64(102347621, 's'),
np.timedelta64(datetime.timedelta(seconds=102347621)))
assert_equal(np.timedelta64(-10234760000, 'us'),
np.timedelta64(datetime.timedelta(
microseconds=-10234760000)))
assert_equal(np.timedelta64(10234760000, 'us'),
np.timedelta64(datetime.timedelta(
microseconds=10234760000)))
assert_equal(np.timedelta64(1023476, 'ms'),
np.timedelta64(datetime.timedelta(milliseconds=1023476)))
assert_equal(np.timedelta64(10, 'm'),
np.timedelta64(datetime.timedelta(minutes=10)))
assert_equal(np.timedelta64(281, 'h'),
np.timedelta64(datetime.timedelta(hours=281)))
assert_equal(np.timedelta64(28, 'W'),
np.timedelta64(datetime.timedelta(weeks=28)))
# Cannot construct across nonlinear time unit boundaries
a = np.timedelta64(3, 's')
assert_raises(TypeError, np.timedelta64, a, 'M')
assert_raises(TypeError, np.timedelta64, a, 'Y')
a = np.timedelta64(6, 'M')
assert_raises(TypeError, np.timedelta64, a, 'D')
assert_raises(TypeError, np.timedelta64, a, 'h')
a = np.timedelta64(1, 'Y')
assert_raises(TypeError, np.timedelta64, a, 'D')
assert_raises(TypeError, np.timedelta64, a, 'm')
def test_timedelta_scalar_construction_units(self):
# String construction detecting units
assert_equal(np.datetime64('2010').dtype,
np.dtype('M8[Y]'))
assert_equal(np.datetime64('2010-03').dtype,
np.dtype('M8[M]'))
assert_equal(np.datetime64('2010-03-12').dtype,
np.dtype('M8[D]'))
assert_equal(np.datetime64('2010-03-12T17').dtype,
np.dtype('M8[h]'))
assert_equal(np.datetime64('2010-03-12T17:15').dtype,
np.dtype('M8[m]'))
assert_equal(np.datetime64('2010-03-12T17:15:08').dtype,
np.dtype('M8[s]'))
assert_equal(np.datetime64('2010-03-12T17:15:08.1').dtype,
np.dtype('M8[ms]'))
assert_equal(np.datetime64('2010-03-12T17:15:08.12').dtype,
np.dtype('M8[ms]'))
assert_equal(np.datetime64('2010-03-12T17:15:08.123').dtype,
np.dtype('M8[ms]'))
assert_equal(np.datetime64('2010-03-12T17:15:08.1234').dtype,
np.dtype('M8[us]'))
assert_equal(np.datetime64('2010-03-12T17:15:08.12345').dtype,
np.dtype('M8[us]'))
assert_equal(np.datetime64('2010-03-12T17:15:08.123456').dtype,
np.dtype('M8[us]'))
assert_equal(np.datetime64('1970-01-01T00:00:02.1234567').dtype,
np.dtype('M8[ns]'))
assert_equal(np.datetime64('1970-01-01T00:00:02.12345678').dtype,
np.dtype('M8[ns]'))
assert_equal(np.datetime64('1970-01-01T00:00:02.123456789').dtype,
np.dtype('M8[ns]'))
assert_equal(np.datetime64('1970-01-01T00:00:02.1234567890').dtype,
np.dtype('M8[ps]'))
assert_equal(np.datetime64('1970-01-01T00:00:02.12345678901').dtype,
np.dtype('M8[ps]'))
assert_equal(np.datetime64('1970-01-01T00:00:02.123456789012').dtype,
np.dtype('M8[ps]'))
assert_equal(np.datetime64(
'1970-01-01T00:00:02.1234567890123').dtype,
np.dtype('M8[fs]'))
assert_equal(np.datetime64(
'1970-01-01T00:00:02.12345678901234').dtype,
np.dtype('M8[fs]'))
assert_equal(np.datetime64(
'1970-01-01T00:00:02.123456789012345').dtype,
np.dtype('M8[fs]'))
assert_equal(np.datetime64(
'1970-01-01T00:00:02.1234567890123456').dtype,
np.dtype('M8[as]'))
assert_equal(np.datetime64(
'1970-01-01T00:00:02.12345678901234567').dtype,
np.dtype('M8[as]'))
assert_equal(np.datetime64(
'1970-01-01T00:00:02.123456789012345678').dtype,
np.dtype('M8[as]'))
# Python date object
assert_equal(np.datetime64(datetime.date(2010, 4, 16)).dtype,
np.dtype('M8[D]'))
# Python datetime object
assert_equal(np.datetime64(
datetime.datetime(2010, 4, 16, 13, 45, 18)).dtype,
np.dtype('M8[us]'))
# 'today' special value
assert_equal(np.datetime64('today').dtype,
np.dtype('M8[D]'))
# 'now' special value
assert_equal(np.datetime64('now').dtype,
np.dtype('M8[s]'))
def test_datetime_nat_casting(self):
a = np.array('NaT', dtype='M8[D]')
b = np.datetime64('NaT', '[D]')
# Arrays
assert_equal(a.astype('M8[s]'), np.array('NaT', dtype='M8[s]'))
assert_equal(a.astype('M8[ms]'), np.array('NaT', dtype='M8[ms]'))
assert_equal(a.astype('M8[M]'), np.array('NaT', dtype='M8[M]'))
assert_equal(a.astype('M8[Y]'), np.array('NaT', dtype='M8[Y]'))
assert_equal(a.astype('M8[W]'), np.array('NaT', dtype='M8[W]'))
# Scalars -> Scalars
assert_equal(np.datetime64(b, '[s]'), np.datetime64('NaT', '[s]'))
assert_equal(np.datetime64(b, '[ms]'), np.datetime64('NaT', '[ms]'))
assert_equal(np.datetime64(b, '[M]'), np.datetime64('NaT', '[M]'))
assert_equal(np.datetime64(b, '[Y]'), np.datetime64('NaT', '[Y]'))
assert_equal(np.datetime64(b, '[W]'), np.datetime64('NaT', '[W]'))
# Arrays -> Scalars
assert_equal(np.datetime64(a, '[s]'), np.datetime64('NaT', '[s]'))
assert_equal(np.datetime64(a, '[ms]'), np.datetime64('NaT', '[ms]'))
assert_equal(np.datetime64(a, '[M]'), np.datetime64('NaT', '[M]'))
assert_equal(np.datetime64(a, '[Y]'), np.datetime64('NaT', '[Y]'))
assert_equal(np.datetime64(a, '[W]'), np.datetime64('NaT', '[W]'))
def test_days_creation(self):
assert_equal(np.array('1599', dtype='M8[D]').astype('i8'),
(1600-1970)*365 - (1972-1600)/4 + 3 - 365)
assert_equal(np.array('1600', dtype='M8[D]').astype('i8'),
(1600-1970)*365 - (1972-1600)/4 + 3)
assert_equal(np.array('1601', dtype='M8[D]').astype('i8'),
(1600-1970)*365 - (1972-1600)/4 + 3 + 366)
assert_equal(np.array('1900', dtype='M8[D]').astype('i8'),
(1900-1970)*365 - (1970-1900)//4)
assert_equal(np.array('1901', dtype='M8[D]').astype('i8'),
(1900-1970)*365 - (1970-1900)//4 + 365)
assert_equal(np.array('1967', dtype='M8[D]').astype('i8'), -3*365 - 1)
assert_equal(np.array('1968', dtype='M8[D]').astype('i8'), -2*365 - 1)
assert_equal(np.array('1969', dtype='M8[D]').astype('i8'), -1*365)
assert_equal(np.array('1970', dtype='M8[D]').astype('i8'), 0*365)
assert_equal(np.array('1971', dtype='M8[D]').astype('i8'), 1*365)
assert_equal(np.array('1972', dtype='M8[D]').astype('i8'), 2*365)
assert_equal(np.array('1973', dtype='M8[D]').astype('i8'), 3*365 + 1)
assert_equal(np.array('1974', dtype='M8[D]').astype('i8'), 4*365 + 1)
assert_equal(np.array('2000', dtype='M8[D]').astype('i8'),
(2000 - 1970)*365 + (2000 - 1972)//4)
assert_equal(np.array('2001', dtype='M8[D]').astype('i8'),
(2000 - 1970)*365 + (2000 - 1972)//4 + 366)
assert_equal(np.array('2400', dtype='M8[D]').astype('i8'),
(2400 - 1970)*365 + (2400 - 1972)//4 - 3)
assert_equal(np.array('2401', dtype='M8[D]').astype('i8'),
(2400 - 1970)*365 + (2400 - 1972)//4 - 3 + 366)
assert_equal(np.array('1600-02-29', dtype='M8[D]').astype('i8'),
(1600-1970)*365 - (1972-1600)//4 + 3 + 31 + 28)
assert_equal(np.array('1600-03-01', dtype='M8[D]').astype('i8'),
(1600-1970)*365 - (1972-1600)//4 + 3 + 31 + 29)
assert_equal(np.array('2000-02-29', dtype='M8[D]').astype('i8'),
(2000 - 1970)*365 + (2000 - 1972)//4 + 31 + 28)
assert_equal(np.array('2000-03-01', dtype='M8[D]').astype('i8'),
(2000 - 1970)*365 + (2000 - 1972)//4 + 31 + 29)
assert_equal(np.array('2001-03-22', dtype='M8[D]').astype('i8'),
(2000 - 1970)*365 + (2000 - 1972)//4 + 366 + 31 + 28 + 21)
def test_days_to_pydate(self):
assert_equal(np.array('1599', dtype='M8[D]').astype('O'),
datetime.date(1599, 1, 1))
assert_equal(np.array('1600', dtype='M8[D]').astype('O'),
datetime.date(1600, 1, 1))
assert_equal(np.array('1601', dtype='M8[D]').astype('O'),
datetime.date(1601, 1, 1))
assert_equal(np.array('1900', dtype='M8[D]').astype('O'),
datetime.date(1900, 1, 1))
assert_equal(np.array('1901', dtype='M8[D]').astype('O'),
datetime.date(1901, 1, 1))
assert_equal(np.array('2000', dtype='M8[D]').astype('O'),
datetime.date(2000, 1, 1))
assert_equal(np.array('2001', dtype='M8[D]').astype('O'),
datetime.date(2001, 1, 1))
assert_equal(np.array('1600-02-29', dtype='M8[D]').astype('O'),
datetime.date(1600, 2, 29))
assert_equal(np.array('1600-03-01', dtype='M8[D]').astype('O'),
datetime.date(1600, 3, 1))
assert_equal(np.array('2001-03-22', dtype='M8[D]').astype('O'),
datetime.date(2001, 3, 22))
def test_dtype_comparison(self):
assert_(not (np.dtype('M8[us]') == np.dtype('M8[ms]')))
assert_(np.dtype('M8[us]') != np.dtype('M8[ms]'))
assert_(np.dtype('M8[2D]') != np.dtype('M8[D]'))
assert_(np.dtype('M8[D]') != np.dtype('M8[2D]'))
def test_pydatetime_creation(self):
a = np.array(['1960-03-12', datetime.date(1960, 3, 12)], dtype='M8[D]')
assert_equal(a[0], a[1])
a = np.array(['1999-12-31', datetime.date(1999, 12, 31)], dtype='M8[D]')
assert_equal(a[0], a[1])
a = np.array(['2000-01-01', datetime.date(2000, 1, 1)], dtype='M8[D]')
assert_equal(a[0], a[1])
# Will fail if the date changes during the exact right moment
a = np.array(['today', datetime.date.today()], dtype='M8[D]')
assert_equal(a[0], a[1])
# datetime.datetime.now() returns local time, not UTC
#a = np.array(['now', datetime.datetime.now()], dtype='M8[s]')
#assert_equal(a[0], a[1])
# we can give a datetime.date time units
assert_equal(np.array(datetime.date(1960, 3, 12), dtype='M8[s]'),
np.array(np.datetime64('1960-03-12T00:00:00')))
def test_datetime_string_conversion(self):
a = ['2011-03-16', '1920-01-01', '2013-05-19']
str_a = np.array(a, dtype='S')
uni_a = np.array(a, dtype='U')
dt_a = np.array(a, dtype='M')
# String to datetime
assert_equal(dt_a, str_a.astype('M'))
assert_equal(dt_a.dtype, str_a.astype('M').dtype)
dt_b = np.empty_like(dt_a)
dt_b[...] = str_a
assert_equal(dt_a, dt_b)
# Datetime to string
assert_equal(str_a, dt_a.astype('S0'))
str_b = np.empty_like(str_a)
str_b[...] = dt_a
assert_equal(str_a, str_b)
# Unicode to datetime
assert_equal(dt_a, uni_a.astype('M'))
assert_equal(dt_a.dtype, uni_a.astype('M').dtype)
dt_b = np.empty_like(dt_a)
dt_b[...] = uni_a
assert_equal(dt_a, dt_b)
# Datetime to unicode
assert_equal(uni_a, dt_a.astype('U'))
uni_b = np.empty_like(uni_a)
uni_b[...] = dt_a
assert_equal(uni_a, uni_b)
# Datetime to long string - gh-9712
assert_equal(str_a, dt_a.astype((np.string_, 128)))
str_b = np.empty(str_a.shape, dtype=(np.string_, 128))
str_b[...] = dt_a
assert_equal(str_a, str_b)
def test_datetime_array_str(self):
a = np.array(['2011-03-16', '1920-01-01', '2013-05-19'], dtype='M')
assert_equal(str(a), "['2011-03-16' '1920-01-01' '2013-05-19']")
a = np.array(['2011-03-16T13:55', '1920-01-01T03:12'], dtype='M')
assert_equal(np.array2string(a, separator=', ',
formatter={'datetime': lambda x:
"'%s'" % np.datetime_as_string(x, timezone='UTC')}),
"['2011-03-16T13:55Z', '1920-01-01T03:12Z']")
# Check that one NaT doesn't corrupt subsequent entries
a = np.array(['2010', 'NaT', '2030']).astype('M')
assert_equal(str(a), "['2010' 'NaT' '2030']")
def test_timedelta_array_str(self):
a = np.array([-1, 0, 100], dtype='m')
assert_equal(str(a), "[ -1 0 100]")
a = np.array(['NaT', 'NaT'], dtype='m')
assert_equal(str(a), "['NaT' 'NaT']")
# Check right-alignment with NaTs
a = np.array([-1, 'NaT', 0], dtype='m')
assert_equal(str(a), "[ -1 'NaT' 0]")
a = np.array([-1, 'NaT', 1234567], dtype='m')
assert_equal(str(a), "[ -1 'NaT' 1234567]")
# Test with other byteorder:
a = np.array([-1, 'NaT', 1234567], dtype='>m')
assert_equal(str(a), "[ -1 'NaT' 1234567]")
a = np.array([-1, 'NaT', 1234567], dtype='<m')
assert_equal(str(a), "[ -1 'NaT' 1234567]")
def test_pickle(self):
# Check that pickle roundtripping works
dt = np.dtype('M8[7D]')
assert_equal(pickle.loads(pickle.dumps(dt)), dt)
dt = np.dtype('M8[W]')
assert_equal(pickle.loads(pickle.dumps(dt)), dt)
# Check that loading pickles from 1.6 works
pkl = b"cnumpy\ndtype\np0\n(S'M8'\np1\nI0\nI1\ntp2\nRp3\n" + \
b"(I4\nS'<'\np4\nNNNI-1\nI-1\nI0\n((dp5\n(S'D'\np6\n" + \
b"I7\nI1\nI1\ntp7\ntp8\ntp9\nb."
assert_equal(pickle.loads(pkl), np.dtype('<M8[7D]'))
pkl = b"cnumpy\ndtype\np0\n(S'M8'\np1\nI0\nI1\ntp2\nRp3\n" + \
b"(I4\nS'<'\np4\nNNNI-1\nI-1\nI0\n((dp5\n(S'W'\np6\n" + \
b"I1\nI1\nI1\ntp7\ntp8\ntp9\nb."
assert_equal(pickle.loads(pkl), np.dtype('<M8[W]'))
pkl = b"cnumpy\ndtype\np0\n(S'M8'\np1\nI0\nI1\ntp2\nRp3\n" + \
b"(I4\nS'>'\np4\nNNNI-1\nI-1\nI0\n((dp5\n(S'us'\np6\n" + \
b"I1\nI1\nI1\ntp7\ntp8\ntp9\nb."
assert_equal(pickle.loads(pkl), np.dtype('>M8[us]'))
def test_setstate(self):
"Verify that datetime dtype __setstate__ can handle bad arguments"
dt = np.dtype('>M8[us]')
assert_raises(ValueError, dt.__setstate__, (4, '>', None, None, None, -1, -1, 0, 1))
assert_(dt.__reduce__()[2] == np.dtype('>M8[us]').__reduce__()[2])
assert_raises(TypeError, dt.__setstate__, (4, '>', None, None, None, -1, -1, 0, ({}, 'xxx')))
assert_(dt.__reduce__()[2] == np.dtype('>M8[us]').__reduce__()[2])
def test_dtype_promotion(self):
# datetime <op> datetime computes the metadata gcd
# timedelta <op> timedelta computes the metadata gcd
for mM in ['m', 'M']:
assert_equal(
np.promote_types(np.dtype(mM+'8[2Y]'), np.dtype(mM+'8[2Y]')),
np.dtype(mM+'8[2Y]'))
assert_equal(
np.promote_types(np.dtype(mM+'8[12Y]'), np.dtype(mM+'8[15Y]')),
np.dtype(mM+'8[3Y]'))
assert_equal(
np.promote_types(np.dtype(mM+'8[62M]'), np.dtype(mM+'8[24M]')),
np.dtype(mM+'8[2M]'))
assert_equal(
np.promote_types(np.dtype(mM+'8[1W]'), np.dtype(mM+'8[2D]')),
np.dtype(mM+'8[1D]'))
assert_equal(
np.promote_types(np.dtype(mM+'8[W]'), np.dtype(mM+'8[13s]')),
np.dtype(mM+'8[s]'))
assert_equal(
np.promote_types(np.dtype(mM+'8[13W]'), np.dtype(mM+'8[49s]')),
np.dtype(mM+'8[7s]'))
# timedelta <op> timedelta raises when there is no reasonable gcd
assert_raises(TypeError, np.promote_types,
np.dtype('m8[Y]'), np.dtype('m8[D]'))
assert_raises(TypeError, np.promote_types,
np.dtype('m8[M]'), np.dtype('m8[W]'))
# timedelta <op> timedelta may overflow with big unit ranges
assert_raises(OverflowError, np.promote_types,
np.dtype('m8[W]'), np.dtype('m8[fs]'))
assert_raises(OverflowError, np.promote_types,
np.dtype('m8[s]'), np.dtype('m8[as]'))
def test_cast_overflow(self):
# gh-4486
def cast():
numpy.datetime64("1971-01-01 00:00:00.000000000000000").astype("<M8[D]")
assert_raises(OverflowError, cast)
def cast2():
numpy.datetime64("2014").astype("<M8[fs]")
assert_raises(OverflowError, cast2)
def test_pyobject_roundtrip(self):
# All datetime types should be able to roundtrip through object
a = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0,
-1020040340, -2942398, -1, 0, 1, 234523453, 1199164176],
dtype=np.int64)
# With date units
for unit in ['M8[D]', 'M8[W]', 'M8[M]', 'M8[Y]']:
b = a.copy().view(dtype=unit)
b[0] = '-0001-01-01'
b[1] = '-0001-12-31'
b[2] = '0000-01-01'
b[3] = '0001-01-01'
b[4] = '1969-12-31'
b[5] = '1970-01-01'
b[6] = '9999-12-31'
b[7] = '10000-01-01'
b[8] = 'NaT'
assert_equal(b.astype(object).astype(unit), b,
"Error roundtripping unit %s" % unit)
# With time units
for unit in ['M8[as]', 'M8[16fs]', 'M8[ps]', 'M8[us]',
'M8[300as]', 'M8[20us]']:
b = a.copy().view(dtype=unit)
b[0] = '-0001-01-01T00'
b[1] = '-0001-12-31T00'
b[2] = '0000-01-01T00'
b[3] = '0001-01-01T00'
b[4] = '1969-12-31T23:59:59.999999'
b[5] = '1970-01-01T00'
b[6] = '9999-12-31T23:59:59.999999'
b[7] = '10000-01-01T00'
b[8] = 'NaT'
assert_equal(b.astype(object).astype(unit), b,
"Error roundtripping unit %s" % unit)
def test_month_truncation(self):
# Make sure that months are truncating correctly
assert_equal(np.array('1945-03-01', dtype='M8[M]'),
np.array('1945-03-31', dtype='M8[M]'))
assert_equal(np.array('1969-11-01', dtype='M8[M]'),
np.array('1969-11-30T23:59:59.99999', dtype='M').astype('M8[M]'))
assert_equal(np.array('1969-12-01', dtype='M8[M]'),
np.array('1969-12-31T23:59:59.99999', dtype='M').astype('M8[M]'))
assert_equal(np.array('1970-01-01', dtype='M8[M]'),
np.array('1970-01-31T23:59:59.99999', dtype='M').astype('M8[M]'))
assert_equal(np.array('1980-02-01', dtype='M8[M]'),
np.array('1980-02-29T23:59:59.99999', dtype='M').astype('M8[M]'))
def test_different_unit_comparison(self):
# Check some years with date units
for unit1 in ['Y', 'M', 'D']:
dt1 = np.dtype('M8[%s]' % unit1)
for unit2 in ['Y', 'M', 'D']:
dt2 = np.dtype('M8[%s]' % unit2)
assert_equal(np.array('1945', dtype=dt1),
np.array('1945', dtype=dt2))
assert_equal(np.array('1970', dtype=dt1),
np.array('1970', dtype=dt2))
assert_equal(np.array('9999', dtype=dt1),
np.array('9999', dtype=dt2))
assert_equal(np.array('10000', dtype=dt1),
np.array('10000-01-01', dtype=dt2))
assert_equal(np.datetime64('1945', unit1),
np.datetime64('1945', unit2))
assert_equal(np.datetime64('1970', unit1),
np.datetime64('1970', unit2))
assert_equal(np.datetime64('9999', unit1),
np.datetime64('9999', unit2))
assert_equal(np.datetime64('10000', unit1),
np.datetime64('10000-01-01', unit2))
# Check some datetimes with time units
for unit1 in ['6h', 'h', 'm', 's', '10ms', 'ms', 'us']:
dt1 = np.dtype('M8[%s]' % unit1)
for unit2 in ['h', 'm', 's', 'ms', 'us']:
dt2 = np.dtype('M8[%s]' % unit2)
assert_equal(np.array('1945-03-12T18', dtype=dt1),
np.array('1945-03-12T18', dtype=dt2))
assert_equal(np.array('1970-03-12T18', dtype=dt1),
np.array('1970-03-12T18', dtype=dt2))
assert_equal(np.array('9999-03-12T18', dtype=dt1),
np.array('9999-03-12T18', dtype=dt2))
assert_equal(np.array('10000-01-01T00', dtype=dt1),
np.array('10000-01-01T00', dtype=dt2))
assert_equal(np.datetime64('1945-03-12T18', unit1),
np.datetime64('1945-03-12T18', unit2))
assert_equal(np.datetime64('1970-03-12T18', unit1),
np.datetime64('1970-03-12T18', unit2))
assert_equal(np.datetime64('9999-03-12T18', unit1),
np.datetime64('9999-03-12T18', unit2))
assert_equal(np.datetime64('10000-01-01T00', unit1),
np.datetime64('10000-01-01T00', unit2))
# Check some days with units that won't overflow
for unit1 in ['D', '12h', 'h', 'm', 's', '4s', 'ms', 'us']:
dt1 = np.dtype('M8[%s]' % unit1)
for unit2 in ['D', 'h', 'm', 's', 'ms', 'us']:
dt2 = np.dtype('M8[%s]' % unit2)
assert_(np.equal(np.array('1932-02-17', dtype='M').astype(dt1),
np.array('1932-02-17T00:00:00', dtype='M').astype(dt2),
casting='unsafe'))
assert_(np.equal(np.array('10000-04-27', dtype='M').astype(dt1),
np.array('10000-04-27T00:00:00', dtype='M').astype(dt2),
casting='unsafe'))
# Shouldn't be able to compare datetime and timedelta
# TODO: Changing to 'same_kind' or 'safe' casting in the ufuncs by
# default is needed to properly catch this kind of thing...
a = np.array('2012-12-21', dtype='M8[D]')
b = np.array(3, dtype='m8[D]')
#assert_raises(TypeError, np.less, a, b)
assert_raises(TypeError, np.less, a, b, casting='same_kind')
def test_datetime_like(self):
a = np.array([3], dtype='m8[4D]')
b = np.array(['2012-12-21'], dtype='M8[D]')
assert_equal(np.ones_like(a).dtype, a.dtype)
assert_equal(np.zeros_like(a).dtype, a.dtype)
assert_equal(np.empty_like(a).dtype, a.dtype)
assert_equal(np.ones_like(b).dtype, b.dtype)
assert_equal(np.zeros_like(b).dtype, b.dtype)
assert_equal(np.empty_like(b).dtype, b.dtype)
def test_datetime_unary(self):
for tda, tdb, tdzero, tdone, tdmone in \
[
# One-dimensional arrays
(np.array([3], dtype='m8[D]'),
np.array([-3], dtype='m8[D]'),
np.array([0], dtype='m8[D]'),
np.array([1], dtype='m8[D]'),
np.array([-1], dtype='m8[D]')),
# NumPy scalars
(np.timedelta64(3, '[D]'),
np.timedelta64(-3, '[D]'),
np.timedelta64(0, '[D]'),
np.timedelta64(1, '[D]'),
np.timedelta64(-1, '[D]'))]:
# negative ufunc
assert_equal(-tdb, tda)
assert_equal((-tdb).dtype, tda.dtype)
assert_equal(np.negative(tdb), tda)
assert_equal(np.negative(tdb).dtype, tda.dtype)
# positive ufunc
assert_equal(np.positive(tda), tda)
assert_equal(np.positive(tda).dtype, tda.dtype)
assert_equal(np.positive(tdb), tdb)
assert_equal(np.positive(tdb).dtype, tdb.dtype)
# absolute ufunc
assert_equal(np.absolute(tdb), tda)
assert_equal(np.absolute(tdb).dtype, tda.dtype)
# sign ufunc
assert_equal(np.sign(tda), tdone)
assert_equal(np.sign(tdb), tdmone)
assert_equal(np.sign(tdzero), tdzero)
assert_equal(np.sign(tda).dtype, tda.dtype)
# The ufuncs always produce native-endian results
assert_
def test_datetime_add(self):
for dta, dtb, dtc, dtnat, tda, tdb, tdc in \
[
# One-dimensional arrays
(np.array(['2012-12-21'], dtype='M8[D]'),
np.array(['2012-12-24'], dtype='M8[D]'),
np.array(['2012-12-21T11'], dtype='M8[h]'),
np.array(['NaT'], dtype='M8[D]'),
np.array([3], dtype='m8[D]'),
np.array([11], dtype='m8[h]'),
np.array([3*24 + 11], dtype='m8[h]')),
# NumPy scalars
(np.datetime64('2012-12-21', '[D]'),
np.datetime64('2012-12-24', '[D]'),
np.datetime64('2012-12-21T11', '[h]'),
np.datetime64('NaT', '[D]'),
np.timedelta64(3, '[D]'),
np.timedelta64(11, '[h]'),
np.timedelta64(3*24 + 11, '[h]'))]:
# m8 + m8
assert_equal(tda + tdb, tdc)
assert_equal((tda + tdb).dtype, np.dtype('m8[h]'))
# m8 + bool
assert_equal(tdb + True, tdb + 1)
assert_equal((tdb + True).dtype, np.dtype('m8[h]'))
# m8 + int
assert_equal(tdb + 3*24, tdc)
assert_equal((tdb + 3*24).dtype, np.dtype('m8[h]'))
# bool + m8
assert_equal(False + tdb, tdb)
assert_equal((False + tdb).dtype, np.dtype('m8[h]'))
# int + m8
assert_equal(3*24 + tdb, tdc)
assert_equal((3*24 + tdb).dtype, np.dtype('m8[h]'))
# M8 + bool
assert_equal(dta + True, dta + 1)
assert_equal(dtnat + True, dtnat)
assert_equal((dta + True).dtype, np.dtype('M8[D]'))
# M8 + int
assert_equal(dta + 3, dtb)
assert_equal(dtnat + 3, dtnat)
assert_equal((dta + 3).dtype, np.dtype('M8[D]'))
# bool + M8
assert_equal(False + dta, dta)
assert_equal(False + dtnat, dtnat)
assert_equal((False + dta).dtype, np.dtype('M8[D]'))
# int + M8
assert_equal(3 + dta, dtb)
assert_equal(3 + dtnat, dtnat)
assert_equal((3 + dta).dtype, np.dtype('M8[D]'))
# M8 + m8
assert_equal(dta + tda, dtb)
assert_equal(dtnat + tda, dtnat)
assert_equal((dta + tda).dtype, np.dtype('M8[D]'))
# m8 + M8
assert_equal(tda + dta, dtb)
assert_equal(tda + dtnat, dtnat)
assert_equal((tda + dta).dtype, np.dtype('M8[D]'))
# In M8 + m8, the result goes to higher precision
assert_equal(np.add(dta, tdb, casting='unsafe'), dtc)
assert_equal(np.add(dta, tdb, casting='unsafe').dtype,
np.dtype('M8[h]'))
assert_equal(np.add(tdb, dta, casting='unsafe'), dtc)
assert_equal(np.add(tdb, dta, casting='unsafe').dtype,
np.dtype('M8[h]'))
# M8 + M8
assert_raises(TypeError, np.add, dta, dtb)
def test_datetime_subtract(self):
for dta, dtb, dtc, dtd, dte, dtnat, tda, tdb, tdc in \
[
# One-dimensional arrays
(np.array(['2012-12-21'], dtype='M8[D]'),
np.array(['2012-12-24'], dtype='M8[D]'),
np.array(['1940-12-24'], dtype='M8[D]'),
np.array(['1940-12-24T00'], dtype='M8[h]'),
np.array(['1940-12-23T13'], dtype='M8[h]'),
np.array(['NaT'], dtype='M8[D]'),
np.array([3], dtype='m8[D]'),
np.array([11], dtype='m8[h]'),
np.array([3*24 - 11], dtype='m8[h]')),
# NumPy scalars
(np.datetime64('2012-12-21', '[D]'),
np.datetime64('2012-12-24', '[D]'),
np.datetime64('1940-12-24', '[D]'),
np.datetime64('1940-12-24T00', '[h]'),
np.datetime64('1940-12-23T13', '[h]'),
np.datetime64('NaT', '[D]'),
np.timedelta64(3, '[D]'),
np.timedelta64(11, '[h]'),
np.timedelta64(3*24 - 11, '[h]'))]:
# m8 - m8
assert_equal(tda - tdb, tdc)
assert_equal((tda - tdb).dtype, np.dtype('m8[h]'))
assert_equal(tdb - tda, -tdc)
assert_equal((tdb - tda).dtype, np.dtype('m8[h]'))
# m8 - bool
assert_equal(tdc - True, tdc - 1)
assert_equal((tdc - True).dtype, np.dtype('m8[h]'))
# m8 - int
assert_equal(tdc - 3*24, -tdb)
assert_equal((tdc - 3*24).dtype, np.dtype('m8[h]'))
# int - m8
assert_equal(False - tdb, -tdb)
assert_equal((False - tdb).dtype, np.dtype('m8[h]'))
# int - m8
assert_equal(3*24 - tdb, tdc)
assert_equal((3*24 - tdb).dtype, np.dtype('m8[h]'))
# M8 - bool
assert_equal(dtb - True, dtb - 1)
assert_equal(dtnat - True, dtnat)
assert_equal((dtb - True).dtype, np.dtype('M8[D]'))
# M8 - int
assert_equal(dtb - 3, dta)
assert_equal(dtnat - 3, dtnat)
assert_equal((dtb - 3).dtype, np.dtype('M8[D]'))
# M8 - m8
assert_equal(dtb - tda, dta)
assert_equal(dtnat - tda, dtnat)
assert_equal((dtb - tda).dtype, np.dtype('M8[D]'))
# In M8 - m8, the result goes to higher precision
assert_equal(np.subtract(dtc, tdb, casting='unsafe'), dte)
assert_equal(np.subtract(dtc, tdb, casting='unsafe').dtype,
np.dtype('M8[h]'))
# M8 - M8 with different goes to higher precision
assert_equal(np.subtract(dtc, dtd, casting='unsafe'),
np.timedelta64(0, 'h'))
assert_equal(np.subtract(dtc, dtd, casting='unsafe').dtype,
np.dtype('m8[h]'))
assert_equal(np.subtract(dtd, dtc, casting='unsafe'),
np.timedelta64(0, 'h'))
assert_equal(np.subtract(dtd, dtc, casting='unsafe').dtype,
np.dtype('m8[h]'))
# m8 - M8
assert_raises(TypeError, np.subtract, tda, dta)
# bool - M8
assert_raises(TypeError, np.subtract, False, dta)
# int - M8
assert_raises(TypeError, np.subtract, 3, dta)
def test_datetime_multiply(self):
for dta, tda, tdb, tdc in \
[
# One-dimensional arrays
(np.array(['2012-12-21'], dtype='M8[D]'),
np.array([6], dtype='m8[h]'),
np.array([9], dtype='m8[h]'),
np.array([12], dtype='m8[h]')),
# NumPy scalars
(np.datetime64('2012-12-21', '[D]'),
np.timedelta64(6, '[h]'),
np.timedelta64(9, '[h]'),
np.timedelta64(12, '[h]'))]:
# m8 * int
assert_equal(tda * 2, tdc)
assert_equal((tda * 2).dtype, np.dtype('m8[h]'))
# int * m8
assert_equal(2 * tda, tdc)
assert_equal((2 * tda).dtype, np.dtype('m8[h]'))
# m8 * float
assert_equal(tda * 1.5, tdb)
assert_equal((tda * 1.5).dtype, np.dtype('m8[h]'))
# float * m8
assert_equal(1.5 * tda, tdb)
assert_equal((1.5 * tda).dtype, np.dtype('m8[h]'))
# m8 * m8
assert_raises(TypeError, np.multiply, tda, tdb)
# m8 * M8
assert_raises(TypeError, np.multiply, dta, tda)
# M8 * m8
assert_raises(TypeError, np.multiply, tda, dta)
# M8 * int
assert_raises(TypeError, np.multiply, dta, 2)
# int * M8
assert_raises(TypeError, np.multiply, 2, dta)
# M8 * float
assert_raises(TypeError, np.multiply, dta, 1.5)
# float * M8
assert_raises(TypeError, np.multiply, 1.5, dta)
# NaTs
with suppress_warnings() as sup:
sup.filter(RuntimeWarning, "invalid value encountered in multiply")
nat = np.timedelta64('NaT')
def check(a, b, res):
assert_equal(a * b, res)
assert_equal(b * a, res)
for tp in (int, float):
check(nat, tp(2), nat)
check(nat, tp(0), nat)
for f in (float('inf'), float('nan')):
check(np.timedelta64(1), f, nat)
check(np.timedelta64(0), f, nat)
check(nat, f, nat)
def test_datetime_divide(self):
for dta, tda, tdb, tdc, tdd in \
[
# One-dimensional arrays
(np.array(['2012-12-21'], dtype='M8[D]'),
np.array([6], dtype='m8[h]'),
np.array([9], dtype='m8[h]'),
np.array([12], dtype='m8[h]'),
np.array([6], dtype='m8[m]')),
# NumPy scalars
(np.datetime64('2012-12-21', '[D]'),
np.timedelta64(6, '[h]'),
np.timedelta64(9, '[h]'),
np.timedelta64(12, '[h]'),
np.timedelta64(6, '[m]'))]:
# m8 / int
assert_equal(tdc / 2, tda)
assert_equal((tdc / 2).dtype, np.dtype('m8[h]'))
# m8 / float
assert_equal(tda / 0.5, tdc)
assert_equal((tda / 0.5).dtype, np.dtype('m8[h]'))
# m8 / m8
assert_equal(tda / tdb, 6.0 / 9.0)
assert_equal(np.divide(tda, tdb), 6.0 / 9.0)
assert_equal(np.true_divide(tda, tdb), 6.0 / 9.0)
assert_equal(tdb / tda, 9.0 / 6.0)
assert_equal((tda / tdb).dtype, np.dtype('f8'))
assert_equal(tda / tdd, 60.0)
assert_equal(tdd / tda, 1.0 / 60.0)
# m8 // m8
assert_raises(TypeError, np.floor_divide, tda, tdb)
# int / m8
assert_raises(TypeError, np.divide, 2, tdb)
# float / m8
assert_raises(TypeError, np.divide, 0.5, tdb)
# m8 / M8
assert_raises(TypeError, np.divide, dta, tda)
# M8 / m8
assert_raises(TypeError, np.divide, tda, dta)
# M8 / int
assert_raises(TypeError, np.divide, dta, 2)
# int / M8
assert_raises(TypeError, np.divide, 2, dta)
# M8 / float
assert_raises(TypeError, np.divide, dta, 1.5)
# float / M8
assert_raises(TypeError, np.divide, 1.5, dta)
# NaTs
with suppress_warnings() as sup:
sup.filter(RuntimeWarning, r".*encountered in true\_divide")
nat = np.timedelta64('NaT')
for tp in (int, float):
assert_equal(np.timedelta64(1) / tp(0), nat)
assert_equal(np.timedelta64(0) / tp(0), nat)
assert_equal(nat / tp(0), nat)
assert_equal(nat / tp(2), nat)
# Division by inf
assert_equal(np.timedelta64(1) / float('inf'), np.timedelta64(0))
assert_equal(np.timedelta64(0) / float('inf'), np.timedelta64(0))
assert_equal(nat / float('inf'), nat)
# Division by nan
assert_equal(np.timedelta64(1) / float('nan'), nat)
assert_equal(np.timedelta64(0) / float('nan'), nat)
assert_equal(nat / float('nan'), nat)
def test_datetime_compare(self):
# Test all the comparison operators
a = np.datetime64('2000-03-12T18:00:00.000000')
b = np.array(['2000-03-12T18:00:00.000000',
'2000-03-12T17:59:59.999999',
'2000-03-12T18:00:00.000001',
'1970-01-11T12:00:00.909090',
'2016-01-11T12:00:00.909090'],
dtype='datetime64[us]')
assert_equal(np.equal(a, b), [1, 0, 0, 0, 0])
assert_equal(np.not_equal(a, b), [0, 1, 1, 1, 1])
assert_equal(np.less(a, b), [0, 0, 1, 0, 1])
assert_equal(np.less_equal(a, b), [1, 0, 1, 0, 1])
assert_equal(np.greater(a, b), [0, 1, 0, 1, 0])
assert_equal(np.greater_equal(a, b), [1, 1, 0, 1, 0])
def test_datetime_compare_nat(self):
dt_nat = np.datetime64('NaT', 'D')
dt_other = np.datetime64('2000-01-01')
td_nat = np.timedelta64('NaT', 'h')
td_other = np.timedelta64(1, 'h')
with suppress_warnings() as sup:
# The assert warns contexts will again see the warning:
sup.filter(FutureWarning, ".*NAT")
for op in [np.equal, np.less, np.less_equal,
np.greater, np.greater_equal]:
if op(dt_nat, dt_nat):
assert_warns(FutureWarning, op, dt_nat, dt_nat)
if op(dt_nat, dt_other):
assert_warns(FutureWarning, op, dt_nat, dt_other)
if op(dt_other, dt_nat):
assert_warns(FutureWarning, op, dt_other, dt_nat)
if op(td_nat, td_nat):
assert_warns(FutureWarning, op, td_nat, td_nat)
if op(td_nat, td_other):
assert_warns(FutureWarning, op, td_nat, td_other)
if op(td_other, td_nat):
assert_warns(FutureWarning, op, td_other, td_nat)
assert_warns(FutureWarning, np.not_equal, dt_nat, dt_nat)
assert_warns(FutureWarning, np.not_equal, td_nat, td_nat)
with suppress_warnings() as sup:
sup.record(FutureWarning)
assert_(np.not_equal(dt_nat, dt_other))
assert_(np.not_equal(dt_other, dt_nat))
assert_(np.not_equal(td_nat, td_other))
assert_(np.not_equal(td_other, td_nat))
assert_equal(len(sup.log), 0)
def test_datetime_futurewarning_once_nat(self):
# Test that the futurewarning is only given once per inner loop
arr1 = np.array(['NaT', 'NaT', '2000-01-01'] * 2, dtype='M8[s]')
arr2 = np.array(['NaT', '2000-01-01', 'NaT'] * 2, dtype='M8[s]')
# All except less, because for less it can't be wrong (NaT is min)
for op in [np.equal, np.less, np.less_equal,
np.greater, np.greater_equal]:
with suppress_warnings() as sup:
rec = sup.record(FutureWarning, ".*NAT")
op(arr1, arr2)
assert_(len(rec) == 1, "failed for {}".format(op))
def test_datetime_minmax(self):
# The metadata of the result should become the GCD
# of the operand metadata
a = np.array('1999-03-12T13', dtype='M8[2m]')
b = np.array('1999-03-12T12', dtype='M8[s]')
assert_equal(np.minimum(a, b), b)
assert_equal(np.minimum(a, b).dtype, np.dtype('M8[s]'))
assert_equal(np.fmin(a, b), b)
assert_equal(np.fmin(a, b).dtype, np.dtype('M8[s]'))
assert_equal(np.maximum(a, b), a)
assert_equal(np.maximum(a, b).dtype, np.dtype('M8[s]'))
assert_equal(np.fmax(a, b), a)
assert_equal(np.fmax(a, b).dtype, np.dtype('M8[s]'))
# Viewed as integers, the comparison is opposite because
# of the units chosen
assert_equal(np.minimum(a.view('i8'), b.view('i8')), a.view('i8'))
# Interaction with NaT
a = np.array('1999-03-12T13', dtype='M8[2m]')
dtnat = np.array('NaT', dtype='M8[h]')
assert_equal(np.minimum(a, dtnat), a)
assert_equal(np.minimum(dtnat, a), a)
assert_equal(np.maximum(a, dtnat), a)
assert_equal(np.maximum(dtnat, a), a)
# Also do timedelta
a = np.array(3, dtype='m8[h]')
b = np.array(3*3600 - 3, dtype='m8[s]')
assert_equal(np.minimum(a, b), b)
assert_equal(np.minimum(a, b).dtype, np.dtype('m8[s]'))
assert_equal(np.fmin(a, b), b)
assert_equal(np.fmin(a, b).dtype, np.dtype('m8[s]'))
assert_equal(np.maximum(a, b), a)
assert_equal(np.maximum(a, b).dtype, np.dtype('m8[s]'))
assert_equal(np.fmax(a, b), a)
assert_equal(np.fmax(a, b).dtype, np.dtype('m8[s]'))
# Viewed as integers, the comparison is opposite because
# of the units chosen
assert_equal(np.minimum(a.view('i8'), b.view('i8')), a.view('i8'))
# should raise between datetime and timedelta
#
# TODO: Allowing unsafe casting by
# default in ufuncs strikes again... :(
a = np.array(3, dtype='m8[h]')
b = np.array('1999-03-12T12', dtype='M8[s]')
#assert_raises(TypeError, np.minimum, a, b)
#assert_raises(TypeError, np.maximum, a, b)
#assert_raises(TypeError, np.fmin, a, b)
#assert_raises(TypeError, np.fmax, a, b)
assert_raises(TypeError, np.minimum, a, b, casting='same_kind')
assert_raises(TypeError, np.maximum, a, b, casting='same_kind')
assert_raises(TypeError, np.fmin, a, b, casting='same_kind')
assert_raises(TypeError, np.fmax, a, b, casting='same_kind')
def test_hours(self):
t = np.ones(3, dtype='M8[s]')
t[0] = 60*60*24 + 60*60*10
assert_(t[0].item().hour == 10)
def test_divisor_conversion_year(self):
assert_(np.dtype('M8[Y/4]') == np.dtype('M8[3M]'))
assert_(np.dtype('M8[Y/13]') == np.dtype('M8[4W]'))
assert_(np.dtype('M8[3Y/73]') == np.dtype('M8[15D]'))
def test_divisor_conversion_month(self):
assert_(np.dtype('M8[M/2]') == np.dtype('M8[2W]'))
assert_(np.dtype('M8[M/15]') == np.dtype('M8[2D]'))
assert_(np.dtype('M8[3M/40]') == np.dtype('M8[54h]'))
def test_divisor_conversion_week(self):
assert_(np.dtype('m8[W/7]') == np.dtype('m8[D]'))
assert_(np.dtype('m8[3W/14]') == np.dtype('m8[36h]'))
assert_(np.dtype('m8[5W/140]') == np.dtype('m8[360m]'))
def test_divisor_conversion_day(self):
assert_(np.dtype('M8[D/12]') == np.dtype('M8[2h]'))
assert_(np.dtype('M8[D/120]') == np.dtype('M8[12m]'))
assert_(np.dtype('M8[3D/960]') == np.dtype('M8[270s]'))
def test_divisor_conversion_hour(self):
assert_(np.dtype('m8[h/30]') == np.dtype('m8[2m]'))
assert_(np.dtype('m8[3h/300]') == np.dtype('m8[36s]'))
def test_divisor_conversion_minute(self):
assert_(np.dtype('m8[m/30]') == np.dtype('m8[2s]'))
assert_(np.dtype('m8[3m/300]') == np.dtype('m8[600ms]'))
def test_divisor_conversion_second(self):
assert_(np.dtype('m8[s/100]') == np.dtype('m8[10ms]'))
assert_(np.dtype('m8[3s/10000]') == np.dtype('m8[300us]'))
def test_divisor_conversion_fs(self):
assert_(np.dtype('M8[fs/100]') == np.dtype('M8[10as]'))
assert_raises(ValueError, lambda: np.dtype('M8[3fs/10000]'))
def test_divisor_conversion_as(self):
assert_raises(ValueError, lambda: np.dtype('M8[as/10]'))
def test_string_parser_variants(self):
# Allow space instead of 'T' between date and time
assert_equal(np.array(['1980-02-29T01:02:03'], np.dtype('M8[s]')),
np.array(['1980-02-29 01:02:03'], np.dtype('M8[s]')))
# Allow negative years
assert_equal(np.array(['-1980-02-29T01:02:03'], np.dtype('M8[s]')),
np.array(['-1980-02-29 01:02:03'], np.dtype('M8[s]')))
# UTC specifier
with assert_warns(DeprecationWarning):
assert_equal(
np.array(['-1980-02-29T01:02:03'], np.dtype('M8[s]')),
np.array(['-1980-02-29 01:02:03Z'], np.dtype('M8[s]')))
# Time zone offset
with assert_warns(DeprecationWarning):
assert_equal(
np.array(['1980-02-29T02:02:03'], np.dtype('M8[s]')),
np.array(['1980-02-29 00:32:03-0130'], np.dtype('M8[s]')))
with assert_warns(DeprecationWarning):
assert_equal(
np.array(['1980-02-28T22:32:03'], np.dtype('M8[s]')),
np.array(['1980-02-29 00:02:03+01:30'], np.dtype('M8[s]')))
with assert_warns(DeprecationWarning):
assert_equal(
np.array(['1980-02-29T02:32:03.506'], np.dtype('M8[s]')),
np.array(['1980-02-29 00:32:03.506-02'], np.dtype('M8[s]')))
with assert_warns(DeprecationWarning):
assert_equal(np.datetime64('1977-03-02T12:30-0230'),
np.datetime64('1977-03-02T15:00'))
def test_string_parser_error_check(self):
# Arbitrary bad string
assert_raises(ValueError, np.array, ['badvalue'], np.dtype('M8[us]'))
# Character after year must be '-'
assert_raises(ValueError, np.array, ['1980X'], np.dtype('M8[us]'))
# Cannot have trailing '-'
assert_raises(ValueError, np.array, ['1980-'], np.dtype('M8[us]'))
# Month must be in range [1,12]
assert_raises(ValueError, np.array, ['1980-00'], np.dtype('M8[us]'))
assert_raises(ValueError, np.array, ['1980-13'], np.dtype('M8[us]'))
# Month must have two digits
assert_raises(ValueError, np.array, ['1980-1'], np.dtype('M8[us]'))
assert_raises(ValueError, np.array, ['1980-1-02'], np.dtype('M8[us]'))
# 'Mor' is not a valid month
assert_raises(ValueError, np.array, ['1980-Mor'], np.dtype('M8[us]'))
# Cannot have trailing '-'
assert_raises(ValueError, np.array, ['1980-01-'], np.dtype('M8[us]'))
# Day must be in range [1,len(month)]
assert_raises(ValueError, np.array, ['1980-01-0'], np.dtype('M8[us]'))
assert_raises(ValueError, np.array, ['1980-01-00'], np.dtype('M8[us]'))
assert_raises(ValueError, np.array, ['1980-01-32'], np.dtype('M8[us]'))
assert_raises(ValueError, np.array, ['1979-02-29'], np.dtype('M8[us]'))
assert_raises(ValueError, np.array, ['1980-02-30'], np.dtype('M8[us]'))
assert_raises(ValueError, np.array, ['1980-03-32'], np.dtype('M8[us]'))
assert_raises(ValueError, np.array, ['1980-04-31'], np.dtype('M8[us]'))
assert_raises(ValueError, np.array, ['1980-05-32'], np.dtype('M8[us]'))
assert_raises(ValueError, np.array, ['1980-06-31'], np.dtype('M8[us]'))
assert_raises(ValueError, np.array, ['1980-07-32'], np.dtype('M8[us]'))
assert_raises(ValueError, np.array, ['1980-08-32'], np.dtype('M8[us]'))
assert_raises(ValueError, np.array, ['1980-09-31'], np.dtype('M8[us]'))
assert_raises(ValueError, np.array, ['1980-10-32'], np.dtype('M8[us]'))
assert_raises(ValueError, np.array, ['1980-11-31'], np.dtype('M8[us]'))
assert_raises(ValueError, np.array, ['1980-12-32'], np.dtype('M8[us]'))
# Cannot have trailing characters
assert_raises(ValueError, np.array, ['1980-02-03%'],
np.dtype('M8[us]'))
assert_raises(ValueError, np.array, ['1980-02-03 q'],
np.dtype('M8[us]'))
# Hours must be in range [0, 23]
assert_raises(ValueError, np.array, ['1980-02-03 25'],
np.dtype('M8[us]'))
assert_raises(ValueError, np.array, ['1980-02-03T25'],
np.dtype('M8[us]'))
assert_raises(ValueError, np.array, ['1980-02-03 24:01'],
np.dtype('M8[us]'))
assert_raises(ValueError, np.array, ['1980-02-03T24:01'],
np.dtype('M8[us]'))
assert_raises(ValueError, np.array, ['1980-02-03 -1'],
np.dtype('M8[us]'))
# No trailing ':'
assert_raises(ValueError, np.array, ['1980-02-03 01:'],
np.dtype('M8[us]'))
# Minutes must be in range [0, 59]
assert_raises(ValueError, np.array, ['1980-02-03 01:-1'],
np.dtype('M8[us]'))
assert_raises(ValueError, np.array, ['1980-02-03 01:60'],
np.dtype('M8[us]'))
# No trailing ':'
assert_raises(ValueError, np.array, ['1980-02-03 01:60:'],
np.dtype('M8[us]'))
# Seconds must be in range [0, 59]
assert_raises(ValueError, np.array, ['1980-02-03 01:10:-1'],
np.dtype('M8[us]'))
assert_raises(ValueError, np.array, ['1980-02-03 01:01:60'],
np.dtype('M8[us]'))
# Timezone offset must within a reasonable range
with assert_warns(DeprecationWarning):
assert_raises(ValueError, np.array, ['1980-02-03 01:01:00+0661'],
np.dtype('M8[us]'))
with assert_warns(DeprecationWarning):
assert_raises(ValueError, np.array, ['1980-02-03 01:01:00+2500'],
np.dtype('M8[us]'))
with assert_warns(DeprecationWarning):
assert_raises(ValueError, np.array, ['1980-02-03 01:01:00-0070'],
np.dtype('M8[us]'))
with assert_warns(DeprecationWarning):
assert_raises(ValueError, np.array, ['1980-02-03 01:01:00-3000'],
np.dtype('M8[us]'))
with assert_warns(DeprecationWarning):
assert_raises(ValueError, np.array, ['1980-02-03 01:01:00-25:00'],
np.dtype('M8[us]'))
def test_creation_overflow(self):
date = '1980-03-23 20:00:00'
timesteps = np.array([date], dtype='datetime64[s]')[0].astype(np.int64)
for unit in ['ms', 'us', 'ns']:
timesteps *= 1000
x = np.array([date], dtype='datetime64[%s]' % unit)
assert_equal(timesteps, x[0].astype(np.int64),
err_msg='Datetime conversion error for unit %s' % unit)
assert_equal(x[0].astype(np.int64), 322689600000000000)
def test_datetime_as_string(self):
# Check all the units with default string conversion
date = '1959-10-13'
datetime = '1959-10-13T12:34:56.789012345678901234'
assert_equal(np.datetime_as_string(np.datetime64(date, 'Y')),
'1959')
assert_equal(np.datetime_as_string(np.datetime64(date, 'M')),
'1959-10')
assert_equal(np.datetime_as_string(np.datetime64(date, 'D')),
'1959-10-13')
assert_equal(np.datetime_as_string(np.datetime64(datetime, 'h')),
'1959-10-13T12')
assert_equal(np.datetime_as_string(np.datetime64(datetime, 'm')),
'1959-10-13T12:34')
assert_equal(np.datetime_as_string(np.datetime64(datetime, 's')),
'1959-10-13T12:34:56')
assert_equal(np.datetime_as_string(np.datetime64(datetime, 'ms')),
'1959-10-13T12:34:56.789')
assert_equal(np.datetime_as_string(np.datetime64(datetime, 'us')),
'1959-10-13T12:34:56.789012')
datetime = '1969-12-31T23:34:56.789012345678901234'
assert_equal(np.datetime_as_string(np.datetime64(datetime, 'ns')),
'1969-12-31T23:34:56.789012345')
assert_equal(np.datetime_as_string(np.datetime64(datetime, 'ps')),
'1969-12-31T23:34:56.789012345678')
assert_equal(np.datetime_as_string(np.datetime64(datetime, 'fs')),
'1969-12-31T23:34:56.789012345678901')
datetime = '1969-12-31T23:59:57.789012345678901234'
assert_equal(np.datetime_as_string(np.datetime64(datetime, 'as')),
datetime)
datetime = '1970-01-01T00:34:56.789012345678901234'
assert_equal(np.datetime_as_string(np.datetime64(datetime, 'ns')),
'1970-01-01T00:34:56.789012345')
assert_equal(np.datetime_as_string(np.datetime64(datetime, 'ps')),
'1970-01-01T00:34:56.789012345678')
assert_equal(np.datetime_as_string(np.datetime64(datetime, 'fs')),
'1970-01-01T00:34:56.789012345678901')
datetime = '1970-01-01T00:00:05.789012345678901234'
assert_equal(np.datetime_as_string(np.datetime64(datetime, 'as')),
datetime)
# String conversion with the unit= parameter
a = np.datetime64('2032-07-18T12:23:34.123456', 'us')
assert_equal(np.datetime_as_string(a, unit='Y', casting='unsafe'),
'2032')
assert_equal(np.datetime_as_string(a, unit='M', casting='unsafe'),
'2032-07')
assert_equal(np.datetime_as_string(a, unit='W', casting='unsafe'),
'2032-07-18')
assert_equal(np.datetime_as_string(a, unit='D', casting='unsafe'),
'2032-07-18')
assert_equal(np.datetime_as_string(a, unit='h'), '2032-07-18T12')
assert_equal(np.datetime_as_string(a, unit='m'),
'2032-07-18T12:23')
assert_equal(np.datetime_as_string(a, unit='s'),
'2032-07-18T12:23:34')
assert_equal(np.datetime_as_string(a, unit='ms'),
'2032-07-18T12:23:34.123')
assert_equal(np.datetime_as_string(a, unit='us'),
'2032-07-18T12:23:34.123456')
assert_equal(np.datetime_as_string(a, unit='ns'),
'2032-07-18T12:23:34.123456000')
assert_equal(np.datetime_as_string(a, unit='ps'),
'2032-07-18T12:23:34.123456000000')
assert_equal(np.datetime_as_string(a, unit='fs'),
'2032-07-18T12:23:34.123456000000000')
assert_equal(np.datetime_as_string(a, unit='as'),
'2032-07-18T12:23:34.123456000000000000')
# unit='auto' parameter
assert_equal(np.datetime_as_string(
np.datetime64('2032-07-18T12:23:34.123456', 'us'), unit='auto'),
'2032-07-18T12:23:34.123456')
assert_equal(np.datetime_as_string(
np.datetime64('2032-07-18T12:23:34.12', 'us'), unit='auto'),
'2032-07-18T12:23:34.120')
assert_equal(np.datetime_as_string(
np.datetime64('2032-07-18T12:23:34', 'us'), unit='auto'),
'2032-07-18T12:23:34')
assert_equal(np.datetime_as_string(
np.datetime64('2032-07-18T12:23:00', 'us'), unit='auto'),
'2032-07-18T12:23')
# 'auto' doesn't split up hour and minute
assert_equal(np.datetime_as_string(
np.datetime64('2032-07-18T12:00:00', 'us'), unit='auto'),
'2032-07-18T12:00')
assert_equal(np.datetime_as_string(
np.datetime64('2032-07-18T00:00:00', 'us'), unit='auto'),
'2032-07-18')
# 'auto' doesn't split up the date
assert_equal(np.datetime_as_string(
np.datetime64('2032-07-01T00:00:00', 'us'), unit='auto'),
'2032-07-01')
assert_equal(np.datetime_as_string(
np.datetime64('2032-01-01T00:00:00', 'us'), unit='auto'),
'2032-01-01')
@dec.skipif(not _has_pytz, "The pytz module is not available.")
def test_datetime_as_string_timezone(self):
# timezone='local' vs 'UTC'
a = np.datetime64('2010-03-15T06:30', 'm')
assert_equal(np.datetime_as_string(a),
'2010-03-15T06:30')
assert_equal(np.datetime_as_string(a, timezone='naive'),
'2010-03-15T06:30')
assert_equal(np.datetime_as_string(a, timezone='UTC'),
'2010-03-15T06:30Z')
assert_(np.datetime_as_string(a, timezone='local') !=
'2010-03-15T06:30')
b = np.datetime64('2010-02-15T06:30', 'm')
assert_equal(np.datetime_as_string(a, timezone=tz('US/Central')),
'2010-03-15T01:30-0500')
assert_equal(np.datetime_as_string(a, timezone=tz('US/Eastern')),
'2010-03-15T02:30-0400')
assert_equal(np.datetime_as_string(a, timezone=tz('US/Pacific')),
'2010-03-14T23:30-0700')
assert_equal(np.datetime_as_string(b, timezone=tz('US/Central')),
'2010-02-15T00:30-0600')
assert_equal(np.datetime_as_string(b, timezone=tz('US/Eastern')),
'2010-02-15T01:30-0500')
assert_equal(np.datetime_as_string(b, timezone=tz('US/Pacific')),
'2010-02-14T22:30-0800')
# Dates to strings with a timezone attached is disabled by default
assert_raises(TypeError, np.datetime_as_string, a, unit='D',
timezone=tz('US/Pacific'))
# Check that we can print out the date in the specified time zone
assert_equal(np.datetime_as_string(a, unit='D',
timezone=tz('US/Pacific'), casting='unsafe'),
'2010-03-14')
assert_equal(np.datetime_as_string(b, unit='D',
timezone=tz('US/Central'), casting='unsafe'),
'2010-02-15')
def test_datetime_arange(self):
# With two datetimes provided as strings
a = np.arange('2010-01-05', '2010-01-10', dtype='M8[D]')
assert_equal(a.dtype, np.dtype('M8[D]'))
assert_equal(a,
np.array(['2010-01-05', '2010-01-06', '2010-01-07',
'2010-01-08', '2010-01-09'], dtype='M8[D]'))
a = np.arange('1950-02-10', '1950-02-06', -1, dtype='M8[D]')
assert_equal(a.dtype, np.dtype('M8[D]'))
assert_equal(a,
np.array(['1950-02-10', '1950-02-09', '1950-02-08',
'1950-02-07'], dtype='M8[D]'))
# Unit should be detected as months here
a = np.arange('1969-05', '1970-05', 2, dtype='M8')
assert_equal(a.dtype, np.dtype('M8[M]'))
assert_equal(a,
np.datetime64('1969-05') + np.arange(12, step=2))
# datetime, integer|timedelta works as well
# produces arange (start, start + stop) in this case
a = np.arange('1969', 18, 3, dtype='M8')
assert_equal(a.dtype, np.dtype('M8[Y]'))
assert_equal(a,
np.datetime64('1969') + np.arange(18, step=3))
a = np.arange('1969-12-19', 22, np.timedelta64(2), dtype='M8')
assert_equal(a.dtype, np.dtype('M8[D]'))
assert_equal(a,
np.datetime64('1969-12-19') + np.arange(22, step=2))
# Step of 0 is disallowed
assert_raises(ValueError, np.arange, np.datetime64('today'),
np.datetime64('today') + 3, 0)
# Promotion across nonlinear unit boundaries is disallowed
assert_raises(TypeError, np.arange, np.datetime64('2011-03-01', 'D'),
np.timedelta64(5, 'M'))
assert_raises(TypeError, np.arange,
np.datetime64('2012-02-03T14', 's'),
np.timedelta64(5, 'Y'))
def test_datetime_arange_no_dtype(self):
d = np.array('2010-01-04', dtype="M8[D]")
assert_equal(np.arange(d, d + 1), d)
assert_raises(ValueError, np.arange, d)
def test_timedelta_arange(self):
a = np.arange(3, 10, dtype='m8')
assert_equal(a.dtype, np.dtype('m8'))
assert_equal(a, np.timedelta64(0) + np.arange(3, 10))
a = np.arange(np.timedelta64(3, 's'), 10, 2, dtype='m8')
assert_equal(a.dtype, np.dtype('m8[s]'))
assert_equal(a, np.timedelta64(0, 's') + np.arange(3, 10, 2))
# Step of 0 is disallowed
assert_raises(ValueError, np.arange, np.timedelta64(0),
np.timedelta64(5), 0)
# Promotion across nonlinear unit boundaries is disallowed
assert_raises(TypeError, np.arange, np.timedelta64(0, 'D'),
np.timedelta64(5, 'M'))
assert_raises(TypeError, np.arange, np.timedelta64(0, 'Y'),
np.timedelta64(5, 'D'))
def test_timedelta_arange_no_dtype(self):
d = np.array(5, dtype="m8[D]")
assert_equal(np.arange(d, d + 1), d)
assert_raises(ValueError, np.arange, d)
def test_datetime_maximum_reduce(self):
a = np.array(['2010-01-02', '1999-03-14', '1833-03'], dtype='M8[D]')
assert_equal(np.maximum.reduce(a).dtype, np.dtype('M8[D]'))
assert_equal(np.maximum.reduce(a),
np.datetime64('2010-01-02'))
a = np.array([1, 4, 0, 7, 2], dtype='m8[s]')
assert_equal(np.maximum.reduce(a).dtype, np.dtype('m8[s]'))
assert_equal(np.maximum.reduce(a),
np.timedelta64(7, 's'))
def test_datetime_busday_offset(self):
# First Monday in June
assert_equal(
np.busday_offset('2011-06', 0, roll='forward', weekmask='Mon'),
np.datetime64('2011-06-06'))
# Last Monday in June
assert_equal(
np.busday_offset('2011-07', -1, roll='forward', weekmask='Mon'),
np.datetime64('2011-06-27'))
assert_equal(
np.busday_offset('2011-07', -1, roll='forward', weekmask='Mon'),
np.datetime64('2011-06-27'))
# Default M-F business days, different roll modes
assert_equal(np.busday_offset('2010-08', 0, roll='backward'),
np.datetime64('2010-07-30'))
assert_equal(np.busday_offset('2010-08', 0, roll='preceding'),
np.datetime64('2010-07-30'))
assert_equal(np.busday_offset('2010-08', 0, roll='modifiedpreceding'),
np.datetime64('2010-08-02'))
assert_equal(np.busday_offset('2010-08', 0, roll='modifiedfollowing'),
np.datetime64('2010-08-02'))
assert_equal(np.busday_offset('2010-08', 0, roll='forward'),
np.datetime64('2010-08-02'))
assert_equal(np.busday_offset('2010-08', 0, roll='following'),
np.datetime64('2010-08-02'))
assert_equal(np.busday_offset('2010-10-30', 0, roll='following'),
np.datetime64('2010-11-01'))
assert_equal(
np.busday_offset('2010-10-30', 0, roll='modifiedfollowing'),
np.datetime64('2010-10-29'))
assert_equal(
np.busday_offset('2010-10-30', 0, roll='modifiedpreceding'),
np.datetime64('2010-10-29'))
assert_equal(
np.busday_offset('2010-10-16', 0, roll='modifiedfollowing'),
np.datetime64('2010-10-18'))
assert_equal(
np.busday_offset('2010-10-16', 0, roll='modifiedpreceding'),
np.datetime64('2010-10-15'))
# roll='raise' by default
assert_raises(ValueError, np.busday_offset, '2011-06-04', 0)
# Bigger offset values
assert_equal(np.busday_offset('2006-02-01', 25),
np.datetime64('2006-03-08'))
assert_equal(np.busday_offset('2006-03-08', -25),
np.datetime64('2006-02-01'))
assert_equal(np.busday_offset('2007-02-25', 11, weekmask='SatSun'),
np.datetime64('2007-04-07'))
assert_equal(np.busday_offset('2007-04-07', -11, weekmask='SatSun'),
np.datetime64('2007-02-25'))
# NaT values when roll is not raise
assert_equal(np.busday_offset(np.datetime64('NaT'), 1, roll='nat'),
np.datetime64('NaT'))
assert_equal(np.busday_offset(np.datetime64('NaT'), 1, roll='following'),
np.datetime64('NaT'))
assert_equal(np.busday_offset(np.datetime64('NaT'), 1, roll='preceding'),
np.datetime64('NaT'))
def test_datetime_busdaycalendar(self):
# Check that it removes NaT, duplicates, and weekends
# and sorts the result.
bdd = np.busdaycalendar(
holidays=['NaT', '2011-01-17', '2011-03-06', 'NaT',
'2011-12-26', '2011-05-30', '2011-01-17'])
assert_equal(bdd.holidays,
np.array(['2011-01-17', '2011-05-30', '2011-12-26'], dtype='M8'))
# Default M-F weekmask
assert_equal(bdd.weekmask, np.array([1, 1, 1, 1, 1, 0, 0], dtype='?'))
# Check string weekmask with varying whitespace.
bdd = np.busdaycalendar(weekmask="Sun TueWed Thu\tFri")
assert_equal(bdd.weekmask, np.array([0, 1, 1, 1, 1, 0, 1], dtype='?'))
# Check length 7 0/1 string
bdd = np.busdaycalendar(weekmask="0011001")
assert_equal(bdd.weekmask, np.array([0, 0, 1, 1, 0, 0, 1], dtype='?'))
# Check length 7 string weekmask.
bdd = np.busdaycalendar(weekmask="Mon Tue")
assert_equal(bdd.weekmask, np.array([1, 1, 0, 0, 0, 0, 0], dtype='?'))
# All-zeros weekmask should raise
assert_raises(ValueError, np.busdaycalendar, weekmask=[0, 0, 0, 0, 0, 0, 0])
# weekday names must be correct case
assert_raises(ValueError, np.busdaycalendar, weekmask="satsun")
# All-zeros weekmask should raise
assert_raises(ValueError, np.busdaycalendar, weekmask="")
# Invalid weekday name codes should raise
assert_raises(ValueError, np.busdaycalendar, weekmask="Mon Tue We")
assert_raises(ValueError, np.busdaycalendar, weekmask="Max")
assert_raises(ValueError, np.busdaycalendar, weekmask="Monday Tue")
def test_datetime_busday_holidays_offset(self):
# With exactly one holiday
assert_equal(
np.busday_offset('2011-11-10', 1, holidays=['2011-11-11']),
np.datetime64('2011-11-14'))
assert_equal(
np.busday_offset('2011-11-04', 5, holidays=['2011-11-11']),
np.datetime64('2011-11-14'))
assert_equal(
np.busday_offset('2011-11-10', 5, holidays=['2011-11-11']),
np.datetime64('2011-11-18'))
assert_equal(
np.busday_offset('2011-11-14', -1, holidays=['2011-11-11']),
np.datetime64('2011-11-10'))
assert_equal(
np.busday_offset('2011-11-18', -5, holidays=['2011-11-11']),
np.datetime64('2011-11-10'))
assert_equal(
np.busday_offset('2011-11-14', -5, holidays=['2011-11-11']),
np.datetime64('2011-11-04'))
# With the holiday appearing twice
assert_equal(
np.busday_offset('2011-11-10', 1,
holidays=['2011-11-11', '2011-11-11']),
np.datetime64('2011-11-14'))
assert_equal(
np.busday_offset('2011-11-14', -1,
holidays=['2011-11-11', '2011-11-11']),
np.datetime64('2011-11-10'))
# With a NaT holiday
assert_equal(
np.busday_offset('2011-11-10', 1,
holidays=['2011-11-11', 'NaT']),
np.datetime64('2011-11-14'))
assert_equal(
np.busday_offset('2011-11-14', -1,
holidays=['NaT', '2011-11-11']),
np.datetime64('2011-11-10'))
# With another holiday after
assert_equal(
np.busday_offset('2011-11-10', 1,
holidays=['2011-11-11', '2011-11-24']),
np.datetime64('2011-11-14'))
assert_equal(
np.busday_offset('2011-11-14', -1,
holidays=['2011-11-11', '2011-11-24']),
np.datetime64('2011-11-10'))
# With another holiday before
assert_equal(
np.busday_offset('2011-11-10', 1,
holidays=['2011-10-10', '2011-11-11']),
np.datetime64('2011-11-14'))
assert_equal(
np.busday_offset('2011-11-14', -1,
holidays=['2011-10-10', '2011-11-11']),
np.datetime64('2011-11-10'))
# With another holiday before and after
assert_equal(
np.busday_offset('2011-11-10', 1,
holidays=['2011-10-10', '2011-11-11', '2011-11-24']),
np.datetime64('2011-11-14'))
assert_equal(
np.busday_offset('2011-11-14', -1,
holidays=['2011-10-10', '2011-11-11', '2011-11-24']),
np.datetime64('2011-11-10'))
# A bigger forward jump across more than one week/holiday
holidays = ['2011-10-10', '2011-11-11', '2011-11-24',
'2011-12-25', '2011-05-30', '2011-02-21',
'2011-12-26', '2012-01-02']
bdd = np.busdaycalendar(weekmask='1111100', holidays=holidays)
assert_equal(
np.busday_offset('2011-10-03', 4, holidays=holidays),
np.busday_offset('2011-10-03', 4))
assert_equal(
np.busday_offset('2011-10-03', 5, holidays=holidays),
np.busday_offset('2011-10-03', 5 + 1))
assert_equal(
np.busday_offset('2011-10-03', 27, holidays=holidays),
np.busday_offset('2011-10-03', 27 + 1))
assert_equal(
np.busday_offset('2011-10-03', 28, holidays=holidays),
np.busday_offset('2011-10-03', 28 + 2))
assert_equal(
np.busday_offset('2011-10-03', 35, holidays=holidays),
np.busday_offset('2011-10-03', 35 + 2))
assert_equal(
np.busday_offset('2011-10-03', 36, holidays=holidays),
np.busday_offset('2011-10-03', 36 + 3))
assert_equal(
np.busday_offset('2011-10-03', 56, holidays=holidays),
np.busday_offset('2011-10-03', 56 + 3))
assert_equal(
np.busday_offset('2011-10-03', 57, holidays=holidays),
np.busday_offset('2011-10-03', 57 + 4))
assert_equal(
np.busday_offset('2011-10-03', 60, holidays=holidays),
np.busday_offset('2011-10-03', 60 + 4))
assert_equal(
np.busday_offset('2011-10-03', 61, holidays=holidays),
np.busday_offset('2011-10-03', 61 + 5))
assert_equal(
np.busday_offset('2011-10-03', 61, busdaycal=bdd),
np.busday_offset('2011-10-03', 61 + 5))
# A bigger backward jump across more than one week/holiday
assert_equal(
np.busday_offset('2012-01-03', -1, holidays=holidays),
np.busday_offset('2012-01-03', -1 - 1))
assert_equal(
np.busday_offset('2012-01-03', -4, holidays=holidays),
np.busday_offset('2012-01-03', -4 - 1))
assert_equal(
np.busday_offset('2012-01-03', -5, holidays=holidays),
np.busday_offset('2012-01-03', -5 - 2))
assert_equal(
np.busday_offset('2012-01-03', -25, holidays=holidays),
np.busday_offset('2012-01-03', -25 - 2))
assert_equal(
np.busday_offset('2012-01-03', -26, holidays=holidays),
np.busday_offset('2012-01-03', -26 - 3))
assert_equal(
np.busday_offset('2012-01-03', -33, holidays=holidays),
np.busday_offset('2012-01-03', -33 - 3))
assert_equal(
np.busday_offset('2012-01-03', -34, holidays=holidays),
np.busday_offset('2012-01-03', -34 - 4))
assert_equal(
np.busday_offset('2012-01-03', -56, holidays=holidays),
np.busday_offset('2012-01-03', -56 - 4))
assert_equal(
np.busday_offset('2012-01-03', -57, holidays=holidays),
np.busday_offset('2012-01-03', -57 - 5))
assert_equal(
np.busday_offset('2012-01-03', -57, busdaycal=bdd),
np.busday_offset('2012-01-03', -57 - 5))
# Can't supply both a weekmask/holidays and busdaycal
assert_raises(ValueError, np.busday_offset, '2012-01-03', -15,
weekmask='1111100', busdaycal=bdd)
assert_raises(ValueError, np.busday_offset, '2012-01-03', -15,
holidays=holidays, busdaycal=bdd)
# Roll with the holidays
assert_equal(
np.busday_offset('2011-12-25', 0,
roll='forward', holidays=holidays),
np.datetime64('2011-12-27'))
assert_equal(
np.busday_offset('2011-12-26', 0,
roll='forward', holidays=holidays),
np.datetime64('2011-12-27'))
assert_equal(
np.busday_offset('2011-12-26', 0,
roll='backward', holidays=holidays),
np.datetime64('2011-12-23'))
assert_equal(
np.busday_offset('2012-02-27', 0,
roll='modifiedfollowing',
holidays=['2012-02-27', '2012-02-26', '2012-02-28',
'2012-03-01', '2012-02-29']),
np.datetime64('2012-02-24'))
assert_equal(
np.busday_offset('2012-03-06', 0,
roll='modifiedpreceding',
holidays=['2012-03-02', '2012-03-03', '2012-03-01',
'2012-03-05', '2012-03-07', '2012-03-06']),
np.datetime64('2012-03-08'))
def test_datetime_busday_holidays_count(self):
holidays = ['2011-01-01', '2011-10-10', '2011-11-11', '2011-11-24',
'2011-12-25', '2011-05-30', '2011-02-21', '2011-01-17',
'2011-12-26', '2012-01-02', '2011-02-21', '2011-05-30',
'2011-07-01', '2011-07-04', '2011-09-05', '2011-10-10']
bdd = np.busdaycalendar(weekmask='1111100', holidays=holidays)
# Validate against busday_offset broadcast against
# a range of offsets
dates = np.busday_offset('2011-01-01', np.arange(366),
roll='forward', busdaycal=bdd)
assert_equal(np.busday_count('2011-01-01', dates, busdaycal=bdd),
np.arange(366))
# Returns negative value when reversed
assert_equal(np.busday_count(dates, '2011-01-01', busdaycal=bdd),
-np.arange(366))
dates = np.busday_offset('2011-12-31', -np.arange(366),
roll='forward', busdaycal=bdd)
assert_equal(np.busday_count(dates, '2011-12-31', busdaycal=bdd),
np.arange(366))
# Returns negative value when reversed
assert_equal(np.busday_count('2011-12-31', dates, busdaycal=bdd),
-np.arange(366))
# Can't supply both a weekmask/holidays and busdaycal
assert_raises(ValueError, np.busday_offset, '2012-01-03', '2012-02-03',
weekmask='1111100', busdaycal=bdd)
assert_raises(ValueError, np.busday_offset, '2012-01-03', '2012-02-03',
holidays=holidays, busdaycal=bdd)
# Number of Mondays in March 2011
assert_equal(np.busday_count('2011-03', '2011-04', weekmask='Mon'), 4)
# Returns negative value when reversed
assert_equal(np.busday_count('2011-04', '2011-03', weekmask='Mon'), -4)
def test_datetime_is_busday(self):
holidays = ['2011-01-01', '2011-10-10', '2011-11-11', '2011-11-24',
'2011-12-25', '2011-05-30', '2011-02-21', '2011-01-17',
'2011-12-26', '2012-01-02', '2011-02-21', '2011-05-30',
'2011-07-01', '2011-07-04', '2011-09-05', '2011-10-10',
'NaT']
bdd = np.busdaycalendar(weekmask='1111100', holidays=holidays)
# Weekend/weekday tests
assert_equal(np.is_busday('2011-01-01'), False)
assert_equal(np.is_busday('2011-01-02'), False)
assert_equal(np.is_busday('2011-01-03'), True)
# All the holidays are not business days
assert_equal(np.is_busday(holidays, busdaycal=bdd),
np.zeros(len(holidays), dtype='?'))
def test_datetime_y2038(self):
# Test parsing on either side of the Y2038 boundary
a = np.datetime64('2038-01-19T03:14:07')
assert_equal(a.view(np.int64), 2**31 - 1)
a = np.datetime64('2038-01-19T03:14:08')
assert_equal(a.view(np.int64), 2**31)
# Test parsing on either side of the Y2038 boundary with
# a manually specified timezone offset
with assert_warns(DeprecationWarning):
a = np.datetime64('2038-01-19T04:14:07+0100')
assert_equal(a.view(np.int64), 2**31 - 1)
with assert_warns(DeprecationWarning):
a = np.datetime64('2038-01-19T04:14:08+0100')
assert_equal(a.view(np.int64), 2**31)
# Test parsing a date after Y2038
a = np.datetime64('2038-01-20T13:21:14')
assert_equal(str(a), '2038-01-20T13:21:14')
def test_isnat(self):
assert_(np.isnat(np.datetime64('NaT', 'ms')))
assert_(np.isnat(np.datetime64('NaT', 'ns')))
assert_(not np.isnat(np.datetime64('2038-01-19T03:14:07')))
assert_(np.isnat(np.timedelta64('NaT', "ms")))
assert_(not np.isnat(np.timedelta64(34, "ms")))
res = np.array([False, False, True])
for unit in ['Y', 'M', 'W', 'D',
'h', 'm', 's', 'ms', 'us',
'ns', 'ps', 'fs', 'as']:
arr = np.array([123, -321, "NaT"], dtype='<datetime64[%s]' % unit)
assert_equal(np.isnat(arr), res)
arr = np.array([123, -321, "NaT"], dtype='>datetime64[%s]' % unit)
assert_equal(np.isnat(arr), res)
arr = np.array([123, -321, "NaT"], dtype='<timedelta64[%s]' % unit)
assert_equal(np.isnat(arr), res)
arr = np.array([123, -321, "NaT"], dtype='>timedelta64[%s]' % unit)
assert_equal(np.isnat(arr), res)
def test_isnat_error(self):
# Test that only datetime dtype arrays are accepted
for t in np.typecodes["All"]:
if t in np.typecodes["Datetime"]:
continue
assert_raises(TypeError, np.isnat, np.zeros(10, t))
class TestDateTimeData(object):
def test_basic(self):
a = np.array(['1980-03-23'], dtype=np.datetime64)
assert_equal(np.datetime_data(a.dtype), ('D', 1))
if __name__ == "__main__":
run_module_suite()
| 93,390 | 46.238746 | 101 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/tests/test_ufunc.py
|
from __future__ import division, absolute_import, print_function
import warnings
import itertools
import numpy as np
import numpy.core.umath_tests as umt
import numpy.core.operand_flag_tests as opflag_tests
from numpy.core.test_rational import rational, test_add, test_add_rationals
from numpy.testing import (
run_module_suite, assert_, assert_equal, assert_raises,
assert_array_equal, assert_almost_equal, assert_array_almost_equal,
assert_no_warnings, assert_allclose,
)
class TestUfuncKwargs(object):
def test_kwarg_exact(self):
assert_raises(TypeError, np.add, 1, 2, castingx='safe')
assert_raises(TypeError, np.add, 1, 2, dtypex=int)
assert_raises(TypeError, np.add, 1, 2, extobjx=[4096])
assert_raises(TypeError, np.add, 1, 2, outx=None)
assert_raises(TypeError, np.add, 1, 2, sigx='ii->i')
assert_raises(TypeError, np.add, 1, 2, signaturex='ii->i')
assert_raises(TypeError, np.add, 1, 2, subokx=False)
assert_raises(TypeError, np.add, 1, 2, wherex=[True])
def test_sig_signature(self):
assert_raises(ValueError, np.add, 1, 2, sig='ii->i',
signature='ii->i')
def test_sig_dtype(self):
assert_raises(RuntimeError, np.add, 1, 2, sig='ii->i',
dtype=int)
assert_raises(RuntimeError, np.add, 1, 2, signature='ii->i',
dtype=int)
class TestUfunc(object):
def test_pickle(self):
import pickle
assert_(pickle.loads(pickle.dumps(np.sin)) is np.sin)
# Check that ufunc not defined in the top level numpy namespace such as
# numpy.core.test_rational.test_add can also be pickled
assert_(pickle.loads(pickle.dumps(test_add)) is test_add)
def test_pickle_withstring(self):
import pickle
astring = (b"cnumpy.core\n_ufunc_reconstruct\np0\n"
b"(S'numpy.core.umath'\np1\nS'cos'\np2\ntp3\nRp4\n.")
assert_(pickle.loads(astring) is np.cos)
def test_reduceat_shifting_sum(self):
L = 6
x = np.arange(L)
idx = np.array(list(zip(np.arange(L - 2), np.arange(L - 2) + 2))).ravel()
assert_array_equal(np.add.reduceat(x, idx)[::2], [1, 3, 5, 7])
def test_generic_loops(self):
"""Test generic loops.
The loops to be tested are:
PyUFunc_ff_f_As_dd_d
PyUFunc_ff_f
PyUFunc_dd_d
PyUFunc_gg_g
PyUFunc_FF_F_As_DD_D
PyUFunc_DD_D
PyUFunc_FF_F
PyUFunc_GG_G
PyUFunc_OO_O
PyUFunc_OO_O_method
PyUFunc_f_f_As_d_d
PyUFunc_d_d
PyUFunc_f_f
PyUFunc_g_g
PyUFunc_F_F_As_D_D
PyUFunc_F_F
PyUFunc_D_D
PyUFunc_G_G
PyUFunc_O_O
PyUFunc_O_O_method
PyUFunc_On_Om
Where:
f -- float
d -- double
g -- long double
F -- complex float
D -- complex double
G -- complex long double
O -- python object
It is difficult to assure that each of these loops is entered from the
Python level as the special cased loops are a moving target and the
corresponding types are architecture dependent. We probably need to
define C level testing ufuncs to get at them. For the time being, I've
just looked at the signatures registered in the build directory to find
relevant functions.
Fixme, currently untested:
PyUFunc_ff_f_As_dd_d
PyUFunc_FF_F_As_DD_D
PyUFunc_f_f_As_d_d
PyUFunc_F_F_As_D_D
PyUFunc_On_Om
"""
fone = np.exp
ftwo = lambda x, y: x**y
fone_val = 1
ftwo_val = 1
# check unary PyUFunc_f_f.
msg = "PyUFunc_f_f"
x = np.zeros(10, dtype=np.single)[0::2]
assert_almost_equal(fone(x), fone_val, err_msg=msg)
# check unary PyUFunc_d_d.
msg = "PyUFunc_d_d"
x = np.zeros(10, dtype=np.double)[0::2]
assert_almost_equal(fone(x), fone_val, err_msg=msg)
# check unary PyUFunc_g_g.
msg = "PyUFunc_g_g"
x = np.zeros(10, dtype=np.longdouble)[0::2]
assert_almost_equal(fone(x), fone_val, err_msg=msg)
# check unary PyUFunc_F_F.
msg = "PyUFunc_F_F"
x = np.zeros(10, dtype=np.csingle)[0::2]
assert_almost_equal(fone(x), fone_val, err_msg=msg)
# check unary PyUFunc_D_D.
msg = "PyUFunc_D_D"
x = np.zeros(10, dtype=np.cdouble)[0::2]
assert_almost_equal(fone(x), fone_val, err_msg=msg)
# check unary PyUFunc_G_G.
msg = "PyUFunc_G_G"
x = np.zeros(10, dtype=np.clongdouble)[0::2]
assert_almost_equal(fone(x), fone_val, err_msg=msg)
# check binary PyUFunc_ff_f.
msg = "PyUFunc_ff_f"
x = np.ones(10, dtype=np.single)[0::2]
assert_almost_equal(ftwo(x, x), ftwo_val, err_msg=msg)
# check binary PyUFunc_dd_d.
msg = "PyUFunc_dd_d"
x = np.ones(10, dtype=np.double)[0::2]
assert_almost_equal(ftwo(x, x), ftwo_val, err_msg=msg)
# check binary PyUFunc_gg_g.
msg = "PyUFunc_gg_g"
x = np.ones(10, dtype=np.longdouble)[0::2]
assert_almost_equal(ftwo(x, x), ftwo_val, err_msg=msg)
# check binary PyUFunc_FF_F.
msg = "PyUFunc_FF_F"
x = np.ones(10, dtype=np.csingle)[0::2]
assert_almost_equal(ftwo(x, x), ftwo_val, err_msg=msg)
# check binary PyUFunc_DD_D.
msg = "PyUFunc_DD_D"
x = np.ones(10, dtype=np.cdouble)[0::2]
assert_almost_equal(ftwo(x, x), ftwo_val, err_msg=msg)
# check binary PyUFunc_GG_G.
msg = "PyUFunc_GG_G"
x = np.ones(10, dtype=np.clongdouble)[0::2]
assert_almost_equal(ftwo(x, x), ftwo_val, err_msg=msg)
# class to use in testing object method loops
class foo(object):
def conjugate(self):
return np.bool_(1)
def logical_xor(self, obj):
return np.bool_(1)
# check unary PyUFunc_O_O
msg = "PyUFunc_O_O"
x = np.ones(10, dtype=object)[0::2]
assert_(np.all(np.abs(x) == 1), msg)
# check unary PyUFunc_O_O_method
msg = "PyUFunc_O_O_method"
x = np.zeros(10, dtype=object)[0::2]
for i in range(len(x)):
x[i] = foo()
assert_(np.all(np.conjugate(x) == True), msg)
# check binary PyUFunc_OO_O
msg = "PyUFunc_OO_O"
x = np.ones(10, dtype=object)[0::2]
assert_(np.all(np.add(x, x) == 2), msg)
# check binary PyUFunc_OO_O_method
msg = "PyUFunc_OO_O_method"
x = np.zeros(10, dtype=object)[0::2]
for i in range(len(x)):
x[i] = foo()
assert_(np.all(np.logical_xor(x, x)), msg)
# check PyUFunc_On_Om
# fixme -- I don't know how to do this yet
def test_all_ufunc(self):
"""Try to check presence and results of all ufuncs.
The list of ufuncs comes from generate_umath.py and is as follows:
===== ==== ============= =============== ========================
done args function types notes
===== ==== ============= =============== ========================
n 1 conjugate nums + O
n 1 absolute nums + O complex -> real
n 1 negative nums + O
n 1 sign nums + O -> int
n 1 invert bool + ints + O flts raise an error
n 1 degrees real + M cmplx raise an error
n 1 radians real + M cmplx raise an error
n 1 arccos flts + M
n 1 arccosh flts + M
n 1 arcsin flts + M
n 1 arcsinh flts + M
n 1 arctan flts + M
n 1 arctanh flts + M
n 1 cos flts + M
n 1 sin flts + M
n 1 tan flts + M
n 1 cosh flts + M
n 1 sinh flts + M
n 1 tanh flts + M
n 1 exp flts + M
n 1 expm1 flts + M
n 1 log flts + M
n 1 log10 flts + M
n 1 log1p flts + M
n 1 sqrt flts + M real x < 0 raises error
n 1 ceil real + M
n 1 trunc real + M
n 1 floor real + M
n 1 fabs real + M
n 1 rint flts + M
n 1 isnan flts -> bool
n 1 isinf flts -> bool
n 1 isfinite flts -> bool
n 1 signbit real -> bool
n 1 modf real -> (frac, int)
n 1 logical_not bool + nums + M -> bool
n 2 left_shift ints + O flts raise an error
n 2 right_shift ints + O flts raise an error
n 2 add bool + nums + O boolean + is ||
n 2 subtract bool + nums + O boolean - is ^
n 2 multiply bool + nums + O boolean * is &
n 2 divide nums + O
n 2 floor_divide nums + O
n 2 true_divide nums + O bBhH -> f, iIlLqQ -> d
n 2 fmod nums + M
n 2 power nums + O
n 2 greater bool + nums + O -> bool
n 2 greater_equal bool + nums + O -> bool
n 2 less bool + nums + O -> bool
n 2 less_equal bool + nums + O -> bool
n 2 equal bool + nums + O -> bool
n 2 not_equal bool + nums + O -> bool
n 2 logical_and bool + nums + M -> bool
n 2 logical_or bool + nums + M -> bool
n 2 logical_xor bool + nums + M -> bool
n 2 maximum bool + nums + O
n 2 minimum bool + nums + O
n 2 bitwise_and bool + ints + O flts raise an error
n 2 bitwise_or bool + ints + O flts raise an error
n 2 bitwise_xor bool + ints + O flts raise an error
n 2 arctan2 real + M
n 2 remainder ints + real + O
n 2 hypot real + M
===== ==== ============= =============== ========================
Types other than those listed will be accepted, but they are cast to
the smallest compatible type for which the function is defined. The
casting rules are:
bool -> int8 -> float32
ints -> double
"""
pass
def test_signature(self):
# the arguments to test_signature are: nin, nout, core_signature
# pass
assert_equal(umt.test_signature(2, 1, "(i),(i)->()"), 1)
# pass. empty core signature; treat as plain ufunc (with trivial core)
assert_equal(umt.test_signature(2, 1, "(),()->()"), 0)
# in the following calls, a ValueError should be raised because
# of error in core signature
# FIXME These should be using assert_raises
# error: extra parenthesis
msg = "core_sig: extra parenthesis"
try:
ret = umt.test_signature(2, 1, "((i)),(i)->()")
assert_equal(ret, None, err_msg=msg)
except ValueError:
pass
# error: parenthesis matching
msg = "core_sig: parenthesis matching"
try:
ret = umt.test_signature(2, 1, "(i),)i(->()")
assert_equal(ret, None, err_msg=msg)
except ValueError:
pass
# error: incomplete signature. letters outside of parenthesis are ignored
msg = "core_sig: incomplete signature"
try:
ret = umt.test_signature(2, 1, "(i),->()")
assert_equal(ret, None, err_msg=msg)
except ValueError:
pass
# error: incomplete signature. 2 output arguments are specified
msg = "core_sig: incomplete signature"
try:
ret = umt.test_signature(2, 2, "(i),(i)->()")
assert_equal(ret, None, err_msg=msg)
except ValueError:
pass
# more complicated names for variables
assert_equal(umt.test_signature(2, 1, "(i1,i2),(J_1)->(_kAB)"), 1)
def test_get_signature(self):
assert_equal(umt.inner1d.signature, "(i),(i)->()")
def test_forced_sig(self):
a = 0.5*np.arange(3, dtype='f8')
assert_equal(np.add(a, 0.5), [0.5, 1, 1.5])
assert_equal(np.add(a, 0.5, sig='i', casting='unsafe'), [0, 0, 1])
assert_equal(np.add(a, 0.5, sig='ii->i', casting='unsafe'), [0, 0, 1])
assert_equal(np.add(a, 0.5, sig=('i4',), casting='unsafe'), [0, 0, 1])
assert_equal(np.add(a, 0.5, sig=('i4', 'i4', 'i4'),
casting='unsafe'), [0, 0, 1])
b = np.zeros((3,), dtype='f8')
np.add(a, 0.5, out=b)
assert_equal(b, [0.5, 1, 1.5])
b[:] = 0
np.add(a, 0.5, sig='i', out=b, casting='unsafe')
assert_equal(b, [0, 0, 1])
b[:] = 0
np.add(a, 0.5, sig='ii->i', out=b, casting='unsafe')
assert_equal(b, [0, 0, 1])
b[:] = 0
np.add(a, 0.5, sig=('i4',), out=b, casting='unsafe')
assert_equal(b, [0, 0, 1])
b[:] = 0
np.add(a, 0.5, sig=('i4', 'i4', 'i4'), out=b, casting='unsafe')
assert_equal(b, [0, 0, 1])
def test_true_divide(self):
a = np.array(10)
b = np.array(20)
tgt = np.array(0.5)
for tc in 'bhilqBHILQefdgFDG':
dt = np.dtype(tc)
aa = a.astype(dt)
bb = b.astype(dt)
# Check result value and dtype.
for x, y in itertools.product([aa, -aa], [bb, -bb]):
# Check with no output type specified
if tc in 'FDG':
tgt = complex(x)/complex(y)
else:
tgt = float(x)/float(y)
res = np.true_divide(x, y)
rtol = max(np.finfo(res).resolution, 1e-15)
assert_allclose(res, tgt, rtol=rtol)
if tc in 'bhilqBHILQ':
assert_(res.dtype.name == 'float64')
else:
assert_(res.dtype.name == dt.name )
# Check with output type specified. This also checks for the
# incorrect casts in issue gh-3484 because the unary '-' does
# not change types, even for unsigned types, Hence casts in the
# ufunc from signed to unsigned and vice versa will lead to
# errors in the values.
for tcout in 'bhilqBHILQ':
dtout = np.dtype(tcout)
assert_raises(TypeError, np.true_divide, x, y, dtype=dtout)
for tcout in 'efdg':
dtout = np.dtype(tcout)
if tc in 'FDG':
# Casting complex to float is not allowed
assert_raises(TypeError, np.true_divide, x, y, dtype=dtout)
else:
tgt = float(x)/float(y)
rtol = max(np.finfo(dtout).resolution, 1e-15)
atol = max(np.finfo(dtout).tiny, 3e-308)
# Some test values result in invalid for float16.
with np.errstate(invalid='ignore'):
res = np.true_divide(x, y, dtype=dtout)
if not np.isfinite(res) and tcout == 'e':
continue
assert_allclose(res, tgt, rtol=rtol, atol=atol)
assert_(res.dtype.name == dtout.name)
for tcout in 'FDG':
dtout = np.dtype(tcout)
tgt = complex(x)/complex(y)
rtol = max(np.finfo(dtout).resolution, 1e-15)
atol = max(np.finfo(dtout).tiny, 3e-308)
res = np.true_divide(x, y, dtype=dtout)
if not np.isfinite(res):
continue
assert_allclose(res, tgt, rtol=rtol, atol=atol)
assert_(res.dtype.name == dtout.name)
# Check booleans
a = np.ones((), dtype=np.bool_)
res = np.true_divide(a, a)
assert_(res == 1.0)
assert_(res.dtype.name == 'float64')
res = np.true_divide(~a, a)
assert_(res == 0.0)
assert_(res.dtype.name == 'float64')
def test_sum_stability(self):
a = np.ones(500, dtype=np.float32)
assert_almost_equal((a / 10.).sum() - a.size / 10., 0, 4)
a = np.ones(500, dtype=np.float64)
assert_almost_equal((a / 10.).sum() - a.size / 10., 0, 13)
def test_sum(self):
for dt in (int, np.float16, np.float32, np.float64, np.longdouble):
for v in (0, 1, 2, 7, 8, 9, 15, 16, 19, 127,
128, 1024, 1235):
tgt = dt(v * (v + 1) / 2)
d = np.arange(1, v + 1, dtype=dt)
# warning if sum overflows, which it does in float16
overflow = not np.isfinite(tgt)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
assert_almost_equal(np.sum(d), tgt)
assert_equal(len(w), 1 * overflow)
assert_almost_equal(np.sum(d[::-1]), tgt)
assert_equal(len(w), 2 * overflow)
d = np.ones(500, dtype=dt)
assert_almost_equal(np.sum(d[::2]), 250.)
assert_almost_equal(np.sum(d[1::2]), 250.)
assert_almost_equal(np.sum(d[::3]), 167.)
assert_almost_equal(np.sum(d[1::3]), 167.)
assert_almost_equal(np.sum(d[::-2]), 250.)
assert_almost_equal(np.sum(d[-1::-2]), 250.)
assert_almost_equal(np.sum(d[::-3]), 167.)
assert_almost_equal(np.sum(d[-1::-3]), 167.)
# sum with first reduction entry != 0
d = np.ones((1,), dtype=dt)
d += d
assert_almost_equal(d, 2.)
def test_sum_complex(self):
for dt in (np.complex64, np.complex128, np.clongdouble):
for v in (0, 1, 2, 7, 8, 9, 15, 16, 19, 127,
128, 1024, 1235):
tgt = dt(v * (v + 1) / 2) - dt((v * (v + 1) / 2) * 1j)
d = np.empty(v, dtype=dt)
d.real = np.arange(1, v + 1)
d.imag = -np.arange(1, v + 1)
assert_almost_equal(np.sum(d), tgt)
assert_almost_equal(np.sum(d[::-1]), tgt)
d = np.ones(500, dtype=dt) + 1j
assert_almost_equal(np.sum(d[::2]), 250. + 250j)
assert_almost_equal(np.sum(d[1::2]), 250. + 250j)
assert_almost_equal(np.sum(d[::3]), 167. + 167j)
assert_almost_equal(np.sum(d[1::3]), 167. + 167j)
assert_almost_equal(np.sum(d[::-2]), 250. + 250j)
assert_almost_equal(np.sum(d[-1::-2]), 250. + 250j)
assert_almost_equal(np.sum(d[::-3]), 167. + 167j)
assert_almost_equal(np.sum(d[-1::-3]), 167. + 167j)
# sum with first reduction entry != 0
d = np.ones((1,), dtype=dt) + 1j
d += d
assert_almost_equal(d, 2. + 2j)
def test_inner1d(self):
a = np.arange(6).reshape((2, 3))
assert_array_equal(umt.inner1d(a, a), np.sum(a*a, axis=-1))
a = np.arange(6)
assert_array_equal(umt.inner1d(a, a), np.sum(a*a))
def test_broadcast(self):
msg = "broadcast"
a = np.arange(4).reshape((2, 1, 2))
b = np.arange(4).reshape((1, 2, 2))
assert_array_equal(umt.inner1d(a, b), np.sum(a*b, axis=-1), err_msg=msg)
msg = "extend & broadcast loop dimensions"
b = np.arange(4).reshape((2, 2))
assert_array_equal(umt.inner1d(a, b), np.sum(a*b, axis=-1), err_msg=msg)
# Broadcast in core dimensions should fail
a = np.arange(8).reshape((4, 2))
b = np.arange(4).reshape((4, 1))
assert_raises(ValueError, umt.inner1d, a, b)
# Extend core dimensions should fail
a = np.arange(8).reshape((4, 2))
b = np.array(7)
assert_raises(ValueError, umt.inner1d, a, b)
# Broadcast should fail
a = np.arange(2).reshape((2, 1, 1))
b = np.arange(3).reshape((3, 1, 1))
assert_raises(ValueError, umt.inner1d, a, b)
def test_type_cast(self):
msg = "type cast"
a = np.arange(6, dtype='short').reshape((2, 3))
assert_array_equal(umt.inner1d(a, a), np.sum(a*a, axis=-1),
err_msg=msg)
msg = "type cast on one argument"
a = np.arange(6).reshape((2, 3))
b = a + 0.1
assert_array_almost_equal(umt.inner1d(a, b), np.sum(a*b, axis=-1),
err_msg=msg)
def test_endian(self):
msg = "big endian"
a = np.arange(6, dtype='>i4').reshape((2, 3))
assert_array_equal(umt.inner1d(a, a), np.sum(a*a, axis=-1),
err_msg=msg)
msg = "little endian"
a = np.arange(6, dtype='<i4').reshape((2, 3))
assert_array_equal(umt.inner1d(a, a), np.sum(a*a, axis=-1),
err_msg=msg)
# Output should always be native-endian
Ba = np.arange(1, dtype='>f8')
La = np.arange(1, dtype='<f8')
assert_equal((Ba+Ba).dtype, np.dtype('f8'))
assert_equal((Ba+La).dtype, np.dtype('f8'))
assert_equal((La+Ba).dtype, np.dtype('f8'))
assert_equal((La+La).dtype, np.dtype('f8'))
assert_equal(np.absolute(La).dtype, np.dtype('f8'))
assert_equal(np.absolute(Ba).dtype, np.dtype('f8'))
assert_equal(np.negative(La).dtype, np.dtype('f8'))
assert_equal(np.negative(Ba).dtype, np.dtype('f8'))
def test_incontiguous_array(self):
msg = "incontiguous memory layout of array"
x = np.arange(64).reshape((2, 2, 2, 2, 2, 2))
a = x[:, 0,:, 0,:, 0]
b = x[:, 1,:, 1,:, 1]
a[0, 0, 0] = -1
msg2 = "make sure it references to the original array"
assert_equal(x[0, 0, 0, 0, 0, 0], -1, err_msg=msg2)
assert_array_equal(umt.inner1d(a, b), np.sum(a*b, axis=-1), err_msg=msg)
x = np.arange(24).reshape(2, 3, 4)
a = x.T
b = x.T
a[0, 0, 0] = -1
assert_equal(x[0, 0, 0], -1, err_msg=msg2)
assert_array_equal(umt.inner1d(a, b), np.sum(a*b, axis=-1), err_msg=msg)
def test_output_argument(self):
msg = "output argument"
a = np.arange(12).reshape((2, 3, 2))
b = np.arange(4).reshape((2, 1, 2)) + 1
c = np.zeros((2, 3), dtype='int')
umt.inner1d(a, b, c)
assert_array_equal(c, np.sum(a*b, axis=-1), err_msg=msg)
c[:] = -1
umt.inner1d(a, b, out=c)
assert_array_equal(c, np.sum(a*b, axis=-1), err_msg=msg)
msg = "output argument with type cast"
c = np.zeros((2, 3), dtype='int16')
umt.inner1d(a, b, c)
assert_array_equal(c, np.sum(a*b, axis=-1), err_msg=msg)
c[:] = -1
umt.inner1d(a, b, out=c)
assert_array_equal(c, np.sum(a*b, axis=-1), err_msg=msg)
msg = "output argument with incontiguous layout"
c = np.zeros((2, 3, 4), dtype='int16')
umt.inner1d(a, b, c[..., 0])
assert_array_equal(c[..., 0], np.sum(a*b, axis=-1), err_msg=msg)
c[:] = -1
umt.inner1d(a, b, out=c[..., 0])
assert_array_equal(c[..., 0], np.sum(a*b, axis=-1), err_msg=msg)
def test_innerwt(self):
a = np.arange(6).reshape((2, 3))
b = np.arange(10, 16).reshape((2, 3))
w = np.arange(20, 26).reshape((2, 3))
assert_array_equal(umt.innerwt(a, b, w), np.sum(a*b*w, axis=-1))
a = np.arange(100, 124).reshape((2, 3, 4))
b = np.arange(200, 224).reshape((2, 3, 4))
w = np.arange(300, 324).reshape((2, 3, 4))
assert_array_equal(umt.innerwt(a, b, w), np.sum(a*b*w, axis=-1))
def test_innerwt_empty(self):
"""Test generalized ufunc with zero-sized operands"""
a = np.array([], dtype='f8')
b = np.array([], dtype='f8')
w = np.array([], dtype='f8')
assert_array_equal(umt.innerwt(a, b, w), np.sum(a*b*w, axis=-1))
def test_matrix_multiply(self):
self.compare_matrix_multiply_results(np.long)
self.compare_matrix_multiply_results(np.double)
def test_matrix_multiply_umath_empty(self):
res = umt.matrix_multiply(np.ones((0, 10)), np.ones((10, 0)))
assert_array_equal(res, np.zeros((0, 0)))
res = umt.matrix_multiply(np.ones((10, 0)), np.ones((0, 10)))
assert_array_equal(res, np.zeros((10, 10)))
def compare_matrix_multiply_results(self, tp):
d1 = np.array(np.random.rand(2, 3, 4), dtype=tp)
d2 = np.array(np.random.rand(2, 3, 4), dtype=tp)
msg = "matrix multiply on type %s" % d1.dtype.name
def permute_n(n):
if n == 1:
return ([0],)
ret = ()
base = permute_n(n-1)
for perm in base:
for i in range(n):
new = perm + [n-1]
new[n-1] = new[i]
new[i] = n-1
ret += (new,)
return ret
def slice_n(n):
if n == 0:
return ((),)
ret = ()
base = slice_n(n-1)
for sl in base:
ret += (sl+(slice(None),),)
ret += (sl+(slice(0, 1),),)
return ret
def broadcastable(s1, s2):
return s1 == s2 or s1 == 1 or s2 == 1
permute_3 = permute_n(3)
slice_3 = slice_n(3) + ((slice(None, None, -1),)*3,)
ref = True
for p1 in permute_3:
for p2 in permute_3:
for s1 in slice_3:
for s2 in slice_3:
a1 = d1.transpose(p1)[s1]
a2 = d2.transpose(p2)[s2]
ref = ref and a1.base is not None
ref = ref and a2.base is not None
if (a1.shape[-1] == a2.shape[-2] and
broadcastable(a1.shape[0], a2.shape[0])):
assert_array_almost_equal(
umt.matrix_multiply(a1, a2),
np.sum(a2[..., np.newaxis].swapaxes(-3, -1) *
a1[..., np.newaxis,:], axis=-1),
err_msg=msg + ' %s %s' % (str(a1.shape),
str(a2.shape)))
assert_equal(ref, True, err_msg="reference check")
def test_euclidean_pdist(self):
a = np.arange(12, dtype=float).reshape(4, 3)
out = np.empty((a.shape[0] * (a.shape[0] - 1) // 2,), dtype=a.dtype)
umt.euclidean_pdist(a, out)
b = np.sqrt(np.sum((a[:, None] - a)**2, axis=-1))
b = b[~np.tri(a.shape[0], dtype=bool)]
assert_almost_equal(out, b)
# An output array is required to determine p with signature (n,d)->(p)
assert_raises(ValueError, umt.euclidean_pdist, a)
def test_object_logical(self):
a = np.array([3, None, True, False, "test", ""], dtype=object)
assert_equal(np.logical_or(a, None),
np.array([x or None for x in a], dtype=object))
assert_equal(np.logical_or(a, True),
np.array([x or True for x in a], dtype=object))
assert_equal(np.logical_or(a, 12),
np.array([x or 12 for x in a], dtype=object))
assert_equal(np.logical_or(a, "blah"),
np.array([x or "blah" for x in a], dtype=object))
assert_equal(np.logical_and(a, None),
np.array([x and None for x in a], dtype=object))
assert_equal(np.logical_and(a, True),
np.array([x and True for x in a], dtype=object))
assert_equal(np.logical_and(a, 12),
np.array([x and 12 for x in a], dtype=object))
assert_equal(np.logical_and(a, "blah"),
np.array([x and "blah" for x in a], dtype=object))
assert_equal(np.logical_not(a),
np.array([not x for x in a], dtype=object))
assert_equal(np.logical_or.reduce(a), 3)
assert_equal(np.logical_and.reduce(a), None)
def test_object_array_reduction(self):
# Reductions on object arrays
a = np.array(['a', 'b', 'c'], dtype=object)
assert_equal(np.sum(a), 'abc')
assert_equal(np.max(a), 'c')
assert_equal(np.min(a), 'a')
a = np.array([True, False, True], dtype=object)
assert_equal(np.sum(a), 2)
assert_equal(np.prod(a), 0)
assert_equal(np.any(a), True)
assert_equal(np.all(a), False)
assert_equal(np.max(a), True)
assert_equal(np.min(a), False)
assert_equal(np.array([[1]], dtype=object).sum(), 1)
assert_equal(np.array([[[1, 2]]], dtype=object).sum((0, 1)), [1, 2])
def test_object_array_accumulate_inplace(self):
# Checks that in-place accumulates work, see also gh-7402
arr = np.ones(4, dtype=object)
arr[:] = [[1] for i in range(4)]
# Twice reproduced also for tuples:
np.add.accumulate(arr, out=arr)
np.add.accumulate(arr, out=arr)
assert_array_equal(arr, np.array([[1]*i for i in [1, 3, 6, 10]]))
# And the same if the axis argument is used
arr = np.ones((2, 4), dtype=object)
arr[0, :] = [[2] for i in range(4)]
np.add.accumulate(arr, out=arr, axis=-1)
np.add.accumulate(arr, out=arr, axis=-1)
assert_array_equal(arr[0, :], np.array([[2]*i for i in [1, 3, 6, 10]]))
def test_object_array_reduceat_inplace(self):
# Checks that in-place reduceats work, see also gh-7465
arr = np.empty(4, dtype=object)
arr[:] = [[1] for i in range(4)]
out = np.empty(4, dtype=object)
out[:] = [[1] for i in range(4)]
np.add.reduceat(arr, np.arange(4), out=arr)
np.add.reduceat(arr, np.arange(4), out=arr)
assert_array_equal(arr, out)
# And the same if the axis argument is used
arr = np.ones((2, 4), dtype=object)
arr[0, :] = [[2] for i in range(4)]
out = np.ones((2, 4), dtype=object)
out[0, :] = [[2] for i in range(4)]
np.add.reduceat(arr, np.arange(4), out=arr, axis=-1)
np.add.reduceat(arr, np.arange(4), out=arr, axis=-1)
assert_array_equal(arr, out)
def test_object_scalar_multiply(self):
# Tickets #2469 and #4482
arr = np.matrix([1, 2], dtype=object)
desired = np.matrix([[3, 6]], dtype=object)
assert_equal(np.multiply(arr, 3), desired)
assert_equal(np.multiply(3, arr), desired)
def test_zerosize_reduction(self):
# Test with default dtype and object dtype
for a in [[], np.array([], dtype=object)]:
assert_equal(np.sum(a), 0)
assert_equal(np.prod(a), 1)
assert_equal(np.any(a), False)
assert_equal(np.all(a), True)
assert_raises(ValueError, np.max, a)
assert_raises(ValueError, np.min, a)
def test_axis_out_of_bounds(self):
a = np.array([False, False])
assert_raises(np.AxisError, a.all, axis=1)
a = np.array([False, False])
assert_raises(np.AxisError, a.all, axis=-2)
a = np.array([False, False])
assert_raises(np.AxisError, a.any, axis=1)
a = np.array([False, False])
assert_raises(np.AxisError, a.any, axis=-2)
def test_scalar_reduction(self):
# The functions 'sum', 'prod', etc allow specifying axis=0
# even for scalars
assert_equal(np.sum(3, axis=0), 3)
assert_equal(np.prod(3.5, axis=0), 3.5)
assert_equal(np.any(True, axis=0), True)
assert_equal(np.all(False, axis=0), False)
assert_equal(np.max(3, axis=0), 3)
assert_equal(np.min(2.5, axis=0), 2.5)
# Check scalar behaviour for ufuncs without an identity
assert_equal(np.power.reduce(3), 3)
# Make sure that scalars are coming out from this operation
assert_(type(np.prod(np.float32(2.5), axis=0)) is np.float32)
assert_(type(np.sum(np.float32(2.5), axis=0)) is np.float32)
assert_(type(np.max(np.float32(2.5), axis=0)) is np.float32)
assert_(type(np.min(np.float32(2.5), axis=0)) is np.float32)
# check if scalars/0-d arrays get cast
assert_(type(np.any(0, axis=0)) is np.bool_)
# assert that 0-d arrays get wrapped
class MyArray(np.ndarray):
pass
a = np.array(1).view(MyArray)
assert_(type(np.any(a)) is MyArray)
def test_casting_out_param(self):
# Test that it's possible to do casts on output
a = np.ones((200, 100), np.int64)
b = np.ones((200, 100), np.int64)
c = np.ones((200, 100), np.float64)
np.add(a, b, out=c)
assert_equal(c, 2)
a = np.zeros(65536)
b = np.zeros(65536, dtype=np.float32)
np.subtract(a, 0, out=b)
assert_equal(b, 0)
def test_where_param(self):
# Test that the where= ufunc parameter works with regular arrays
a = np.arange(7)
b = np.ones(7)
c = np.zeros(7)
np.add(a, b, out=c, where=(a % 2 == 1))
assert_equal(c, [0, 2, 0, 4, 0, 6, 0])
a = np.arange(4).reshape(2, 2) + 2
np.power(a, [2, 3], out=a, where=[[0, 1], [1, 0]])
assert_equal(a, [[2, 27], [16, 5]])
# Broadcasting the where= parameter
np.subtract(a, 2, out=a, where=[True, False])
assert_equal(a, [[0, 27], [14, 5]])
def test_where_param_buffer_output(self):
# This test is temporarily skipped because it requires
# adding masking features to the nditer to work properly
# With casting on output
a = np.ones(10, np.int64)
b = np.ones(10, np.int64)
c = 1.5 * np.ones(10, np.float64)
np.add(a, b, out=c, where=[1, 0, 0, 1, 0, 0, 1, 1, 1, 0])
assert_equal(c, [2, 1.5, 1.5, 2, 1.5, 1.5, 2, 2, 2, 1.5])
def test_where_param_alloc(self):
# With casting and allocated output
a = np.array([1], dtype=np.int64)
m = np.array([True], dtype=bool)
assert_equal(np.sqrt(a, where=m), [1])
# No casting and allocated output
a = np.array([1], dtype=np.float64)
m = np.array([True], dtype=bool)
assert_equal(np.sqrt(a, where=m), [1])
def check_identityless_reduction(self, a):
# np.minimum.reduce is a identityless reduction
# Verify that it sees the zero at various positions
a[...] = 1
a[1, 0, 0] = 0
assert_equal(np.minimum.reduce(a, axis=None), 0)
assert_equal(np.minimum.reduce(a, axis=(0, 1)), [0, 1, 1, 1])
assert_equal(np.minimum.reduce(a, axis=(0, 2)), [0, 1, 1])
assert_equal(np.minimum.reduce(a, axis=(1, 2)), [1, 0])
assert_equal(np.minimum.reduce(a, axis=0),
[[0, 1, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]])
assert_equal(np.minimum.reduce(a, axis=1),
[[1, 1, 1, 1], [0, 1, 1, 1]])
assert_equal(np.minimum.reduce(a, axis=2),
[[1, 1, 1], [0, 1, 1]])
assert_equal(np.minimum.reduce(a, axis=()), a)
a[...] = 1
a[0, 1, 0] = 0
assert_equal(np.minimum.reduce(a, axis=None), 0)
assert_equal(np.minimum.reduce(a, axis=(0, 1)), [0, 1, 1, 1])
assert_equal(np.minimum.reduce(a, axis=(0, 2)), [1, 0, 1])
assert_equal(np.minimum.reduce(a, axis=(1, 2)), [0, 1])
assert_equal(np.minimum.reduce(a, axis=0),
[[1, 1, 1, 1], [0, 1, 1, 1], [1, 1, 1, 1]])
assert_equal(np.minimum.reduce(a, axis=1),
[[0, 1, 1, 1], [1, 1, 1, 1]])
assert_equal(np.minimum.reduce(a, axis=2),
[[1, 0, 1], [1, 1, 1]])
assert_equal(np.minimum.reduce(a, axis=()), a)
a[...] = 1
a[0, 0, 1] = 0
assert_equal(np.minimum.reduce(a, axis=None), 0)
assert_equal(np.minimum.reduce(a, axis=(0, 1)), [1, 0, 1, 1])
assert_equal(np.minimum.reduce(a, axis=(0, 2)), [0, 1, 1])
assert_equal(np.minimum.reduce(a, axis=(1, 2)), [0, 1])
assert_equal(np.minimum.reduce(a, axis=0),
[[1, 0, 1, 1], [1, 1, 1, 1], [1, 1, 1, 1]])
assert_equal(np.minimum.reduce(a, axis=1),
[[1, 0, 1, 1], [1, 1, 1, 1]])
assert_equal(np.minimum.reduce(a, axis=2),
[[0, 1, 1], [1, 1, 1]])
assert_equal(np.minimum.reduce(a, axis=()), a)
def test_identityless_reduction_corder(self):
a = np.empty((2, 3, 4), order='C')
self.check_identityless_reduction(a)
def test_identityless_reduction_forder(self):
a = np.empty((2, 3, 4), order='F')
self.check_identityless_reduction(a)
def test_identityless_reduction_otherorder(self):
a = np.empty((2, 4, 3), order='C').swapaxes(1, 2)
self.check_identityless_reduction(a)
def test_identityless_reduction_noncontig(self):
a = np.empty((3, 5, 4), order='C').swapaxes(1, 2)
a = a[1:, 1:, 1:]
self.check_identityless_reduction(a)
def test_identityless_reduction_noncontig_unaligned(self):
a = np.empty((3*4*5*8 + 1,), dtype='i1')
a = a[1:].view(dtype='f8')
a.shape = (3, 4, 5)
a = a[1:, 1:, 1:]
self.check_identityless_reduction(a)
def test_identityless_reduction_nonreorderable(self):
a = np.array([[8.0, 2.0, 2.0], [1.0, 0.5, 0.25]])
res = np.divide.reduce(a, axis=0)
assert_equal(res, [8.0, 4.0, 8.0])
res = np.divide.reduce(a, axis=1)
assert_equal(res, [2.0, 8.0])
res = np.divide.reduce(a, axis=())
assert_equal(res, a)
assert_raises(ValueError, np.divide.reduce, a, axis=(0, 1))
def test_reduce_zero_axis(self):
# If we have a n x m array and do a reduction with axis=1, then we are
# doing n reductions, and each reduction takes an m-element array. For
# a reduction operation without an identity, then:
# n > 0, m > 0: fine
# n = 0, m > 0: fine, doing 0 reductions of m-element arrays
# n > 0, m = 0: can't reduce a 0-element array, ValueError
# n = 0, m = 0: can't reduce a 0-element array, ValueError (for
# consistency with the above case)
# This test doesn't actually look at return values, it just checks to
# make sure that error we get an error in exactly those cases where we
# expect one, and assumes the calculations themselves are done
# correctly.
def ok(f, *args, **kwargs):
f(*args, **kwargs)
def err(f, *args, **kwargs):
assert_raises(ValueError, f, *args, **kwargs)
def t(expect, func, n, m):
expect(func, np.zeros((n, m)), axis=1)
expect(func, np.zeros((m, n)), axis=0)
expect(func, np.zeros((n // 2, n // 2, m)), axis=2)
expect(func, np.zeros((n // 2, m, n // 2)), axis=1)
expect(func, np.zeros((n, m // 2, m // 2)), axis=(1, 2))
expect(func, np.zeros((m // 2, n, m // 2)), axis=(0, 2))
expect(func, np.zeros((m // 3, m // 3, m // 3,
n // 2, n // 2)),
axis=(0, 1, 2))
# Check what happens if the inner (resp. outer) dimensions are a
# mix of zero and non-zero:
expect(func, np.zeros((10, m, n)), axis=(0, 1))
expect(func, np.zeros((10, n, m)), axis=(0, 2))
expect(func, np.zeros((m, 10, n)), axis=0)
expect(func, np.zeros((10, m, n)), axis=1)
expect(func, np.zeros((10, n, m)), axis=2)
# np.maximum is just an arbitrary ufunc with no reduction identity
assert_equal(np.maximum.identity, None)
t(ok, np.maximum.reduce, 30, 30)
t(ok, np.maximum.reduce, 0, 30)
t(err, np.maximum.reduce, 30, 0)
t(err, np.maximum.reduce, 0, 0)
err(np.maximum.reduce, [])
np.maximum.reduce(np.zeros((0, 0)), axis=())
# all of the combinations are fine for a reduction that has an
# identity
t(ok, np.add.reduce, 30, 30)
t(ok, np.add.reduce, 0, 30)
t(ok, np.add.reduce, 30, 0)
t(ok, np.add.reduce, 0, 0)
np.add.reduce([])
np.add.reduce(np.zeros((0, 0)), axis=())
# OTOH, accumulate always makes sense for any combination of n and m,
# because it maps an m-element array to an m-element array. These
# tests are simpler because accumulate doesn't accept multiple axes.
for uf in (np.maximum, np.add):
uf.accumulate(np.zeros((30, 0)), axis=0)
uf.accumulate(np.zeros((0, 30)), axis=0)
uf.accumulate(np.zeros((30, 30)), axis=0)
uf.accumulate(np.zeros((0, 0)), axis=0)
def test_safe_casting(self):
# In old versions of numpy, in-place operations used the 'unsafe'
# casting rules. In versions >= 1.10, 'same_kind' is the
# default and an exception is raised instead of a warning.
# when 'same_kind' is not satisfied.
a = np.array([1, 2, 3], dtype=int)
# Non-in-place addition is fine
assert_array_equal(assert_no_warnings(np.add, a, 1.1),
[2.1, 3.1, 4.1])
assert_raises(TypeError, np.add, a, 1.1, out=a)
def add_inplace(a, b):
a += b
assert_raises(TypeError, add_inplace, a, 1.1)
# Make sure that explicitly overriding the exception is allowed:
assert_no_warnings(np.add, a, 1.1, out=a, casting="unsafe")
assert_array_equal(a, [2, 3, 4])
def test_ufunc_custom_out(self):
# Test ufunc with built in input types and custom output type
a = np.array([0, 1, 2], dtype='i8')
b = np.array([0, 1, 2], dtype='i8')
c = np.empty(3, dtype=rational)
# Output must be specified so numpy knows what
# ufunc signature to look for
result = test_add(a, b, c)
assert_equal(result, np.array([0, 2, 4], dtype=rational))
# no output type should raise TypeError
assert_raises(TypeError, test_add, a, b)
def test_operand_flags(self):
a = np.arange(16, dtype='l').reshape(4, 4)
b = np.arange(9, dtype='l').reshape(3, 3)
opflag_tests.inplace_add(a[:-1, :-1], b)
assert_equal(a, np.array([[0, 2, 4, 3], [7, 9, 11, 7],
[14, 16, 18, 11], [12, 13, 14, 15]], dtype='l'))
a = np.array(0)
opflag_tests.inplace_add(a, 3)
assert_equal(a, 3)
opflag_tests.inplace_add(a, [3, 4])
assert_equal(a, 10)
def test_struct_ufunc(self):
import numpy.core.struct_ufunc_test as struct_ufunc
a = np.array([(1, 2, 3)], dtype='u8,u8,u8')
b = np.array([(1, 2, 3)], dtype='u8,u8,u8')
result = struct_ufunc.add_triplet(a, b)
assert_equal(result, np.array([(2, 4, 6)], dtype='u8,u8,u8'))
def test_custom_ufunc(self):
a = np.array([rational(1, 2), rational(1, 3), rational(1, 4)],
dtype=rational)
b = np.array([rational(1, 2), rational(1, 3), rational(1, 4)],
dtype=rational)
result = test_add_rationals(a, b)
expected = np.array([rational(1), rational(2, 3), rational(1, 2)],
dtype=rational)
assert_equal(result, expected)
def test_custom_ufunc_forced_sig(self):
# gh-9351 - looking for a non-first userloop would previously hang
assert_raises(TypeError,
np.multiply, rational(1), 1, signature=(rational, int, None))
def test_custom_array_like(self):
class MyThing(object):
__array_priority__ = 1000
rmul_count = 0
getitem_count = 0
def __init__(self, shape):
self.shape = shape
def __len__(self):
return self.shape[0]
def __getitem__(self, i):
MyThing.getitem_count += 1
if not isinstance(i, tuple):
i = (i,)
if len(i) > self.ndim:
raise IndexError("boo")
return MyThing(self.shape[len(i):])
def __rmul__(self, other):
MyThing.rmul_count += 1
return self
np.float64(5)*MyThing((3, 3))
assert_(MyThing.rmul_count == 1, MyThing.rmul_count)
assert_(MyThing.getitem_count <= 2, MyThing.getitem_count)
def test_inplace_fancy_indexing(self):
a = np.arange(10)
np.add.at(a, [2, 5, 2], 1)
assert_equal(a, [0, 1, 4, 3, 4, 6, 6, 7, 8, 9])
a = np.arange(10)
b = np.array([100, 100, 100])
np.add.at(a, [2, 5, 2], b)
assert_equal(a, [0, 1, 202, 3, 4, 105, 6, 7, 8, 9])
a = np.arange(9).reshape(3, 3)
b = np.array([[100, 100, 100], [200, 200, 200], [300, 300, 300]])
np.add.at(a, (slice(None), [1, 2, 1]), b)
assert_equal(a, [[0, 201, 102], [3, 404, 205], [6, 607, 308]])
a = np.arange(27).reshape(3, 3, 3)
b = np.array([100, 200, 300])
np.add.at(a, (slice(None), slice(None), [1, 2, 1]), b)
assert_equal(a,
[[[0, 401, 202],
[3, 404, 205],
[6, 407, 208]],
[[9, 410, 211],
[12, 413, 214],
[15, 416, 217]],
[[18, 419, 220],
[21, 422, 223],
[24, 425, 226]]])
a = np.arange(9).reshape(3, 3)
b = np.array([[100, 100, 100], [200, 200, 200], [300, 300, 300]])
np.add.at(a, ([1, 2, 1], slice(None)), b)
assert_equal(a, [[0, 1, 2], [403, 404, 405], [206, 207, 208]])
a = np.arange(27).reshape(3, 3, 3)
b = np.array([100, 200, 300])
np.add.at(a, (slice(None), [1, 2, 1], slice(None)), b)
assert_equal(a,
[[[0, 1, 2],
[203, 404, 605],
[106, 207, 308]],
[[9, 10, 11],
[212, 413, 614],
[115, 216, 317]],
[[18, 19, 20],
[221, 422, 623],
[124, 225, 326]]])
a = np.arange(9).reshape(3, 3)
b = np.array([100, 200, 300])
np.add.at(a, (0, [1, 2, 1]), b)
assert_equal(a, [[0, 401, 202], [3, 4, 5], [6, 7, 8]])
a = np.arange(27).reshape(3, 3, 3)
b = np.array([100, 200, 300])
np.add.at(a, ([1, 2, 1], 0, slice(None)), b)
assert_equal(a,
[[[0, 1, 2],
[3, 4, 5],
[6, 7, 8]],
[[209, 410, 611],
[12, 13, 14],
[15, 16, 17]],
[[118, 219, 320],
[21, 22, 23],
[24, 25, 26]]])
a = np.arange(27).reshape(3, 3, 3)
b = np.array([100, 200, 300])
np.add.at(a, (slice(None), slice(None), slice(None)), b)
assert_equal(a,
[[[100, 201, 302],
[103, 204, 305],
[106, 207, 308]],
[[109, 210, 311],
[112, 213, 314],
[115, 216, 317]],
[[118, 219, 320],
[121, 222, 323],
[124, 225, 326]]])
a = np.arange(10)
np.negative.at(a, [2, 5, 2])
assert_equal(a, [0, 1, 2, 3, 4, -5, 6, 7, 8, 9])
# Test 0-dim array
a = np.array(0)
np.add.at(a, (), 1)
assert_equal(a, 1)
assert_raises(IndexError, np.add.at, a, 0, 1)
assert_raises(IndexError, np.add.at, a, [], 1)
# Test mixed dtypes
a = np.arange(10)
np.power.at(a, [1, 2, 3, 2], 3.5)
assert_equal(a, np.array([0, 1, 4414, 46, 4, 5, 6, 7, 8, 9]))
# Test boolean indexing and boolean ufuncs
a = np.arange(10)
index = a % 2 == 0
np.equal.at(a, index, [0, 2, 4, 6, 8])
assert_equal(a, [1, 1, 1, 3, 1, 5, 1, 7, 1, 9])
# Test unary operator
a = np.arange(10, dtype='u4')
np.invert.at(a, [2, 5, 2])
assert_equal(a, [0, 1, 2, 3, 4, 5 ^ 0xffffffff, 6, 7, 8, 9])
# Test empty subspace
orig = np.arange(4)
a = orig[:, None][:, 0:0]
np.add.at(a, [0, 1], 3)
assert_array_equal(orig, np.arange(4))
# Test with swapped byte order
index = np.array([1, 2, 1], np.dtype('i').newbyteorder())
values = np.array([1, 2, 3, 4], np.dtype('f').newbyteorder())
np.add.at(values, index, 3)
assert_array_equal(values, [1, 8, 6, 4])
# Test exception thrown
values = np.array(['a', 1], dtype=object)
assert_raises(TypeError, np.add.at, values, [0, 1], 1)
assert_array_equal(values, np.array(['a', 1], dtype=object))
# Test multiple output ufuncs raise error, gh-5665
assert_raises(ValueError, np.modf.at, np.arange(10), [1])
def test_reduce_arguments(self):
f = np.add.reduce
d = np.ones((5,2), dtype=int)
o = np.ones((2,), dtype=d.dtype)
r = o * 5
assert_equal(f(d), r)
# a, axis=0, dtype=None, out=None, keepdims=False
assert_equal(f(d, axis=0), r)
assert_equal(f(d, 0), r)
assert_equal(f(d, 0, dtype=None), r)
assert_equal(f(d, 0, dtype='i'), r)
assert_equal(f(d, 0, 'i'), r)
assert_equal(f(d, 0, None), r)
assert_equal(f(d, 0, None, out=None), r)
assert_equal(f(d, 0, None, out=o), r)
assert_equal(f(d, 0, None, o), r)
assert_equal(f(d, 0, None, None), r)
assert_equal(f(d, 0, None, None, keepdims=False), r)
assert_equal(f(d, 0, None, None, True), r.reshape((1,) + r.shape))
# multiple keywords
assert_equal(f(d, axis=0, dtype=None, out=None, keepdims=False), r)
assert_equal(f(d, 0, dtype=None, out=None, keepdims=False), r)
assert_equal(f(d, 0, None, out=None, keepdims=False), r)
# too little
assert_raises(TypeError, f)
# too much
assert_raises(TypeError, f, d, 0, None, None, False, 1)
# invalid axis
assert_raises(TypeError, f, d, "invalid")
assert_raises(TypeError, f, d, axis="invalid")
assert_raises(TypeError, f, d, axis="invalid", dtype=None,
keepdims=True)
# invalid dtype
assert_raises(TypeError, f, d, 0, "invalid")
assert_raises(TypeError, f, d, dtype="invalid")
assert_raises(TypeError, f, d, dtype="invalid", out=None)
# invalid out
assert_raises(TypeError, f, d, 0, None, "invalid")
assert_raises(TypeError, f, d, out="invalid")
assert_raises(TypeError, f, d, out="invalid", dtype=None)
# keepdims boolean, no invalid value
# assert_raises(TypeError, f, d, 0, None, None, "invalid")
# assert_raises(TypeError, f, d, keepdims="invalid", axis=0, dtype=None)
# invalid mix
assert_raises(TypeError, f, d, 0, keepdims="invalid", dtype="invalid",
out=None)
# invalid keyord
assert_raises(TypeError, f, d, axis=0, dtype=None, invalid=0)
assert_raises(TypeError, f, d, invalid=0)
assert_raises(TypeError, f, d, 0, keepdims=True, invalid="invalid",
out=None)
assert_raises(TypeError, f, d, axis=0, dtype=None, keepdims=True,
out=None, invalid=0)
assert_raises(TypeError, f, d, axis=0, dtype=None,
out=None, invalid=0)
def test_structured_equal(self):
# https://github.com/numpy/numpy/issues/4855
class MyA(np.ndarray):
def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
return getattr(ufunc, method)(*(input.view(np.ndarray)
for input in inputs), **kwargs)
a = np.arange(12.).reshape(4,3)
ra = a.view(dtype=('f8,f8,f8')).squeeze()
mra = ra.view(MyA)
target = np.array([ True, False, False, False], dtype=bool)
assert_equal(np.all(target == (mra == ra[0])), True)
def test_NotImplemented_not_returned(self):
# See gh-5964 and gh-2091. Some of these functions are not operator
# related and were fixed for other reasons in the past.
binary_funcs = [
np.power, np.add, np.subtract, np.multiply, np.divide,
np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or,
np.bitwise_xor, np.left_shift, np.right_shift, np.fmax,
np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2,
np.logical_and, np.logical_or, np.logical_xor, np.maximum,
np.minimum, np.mod
]
# These functions still return NotImplemented. Will be fixed in
# future.
# bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal]
a = np.array('1')
b = 1
for f in binary_funcs:
assert_raises(TypeError, f, a, b)
def test_reduce_noncontig_output(self):
# Check that reduction deals with non-contiguous output arrays
# appropriately.
#
# gh-8036
x = np.arange(7*13*8, dtype=np.int16).reshape(7, 13, 8)
x = x[4:6,1:11:6,1:5].transpose(1, 2, 0)
y_base = np.arange(4*4, dtype=np.int16).reshape(4, 4)
y = y_base[::2,:]
y_base_copy = y_base.copy()
r0 = np.add.reduce(x, out=y.copy(), axis=2)
r1 = np.add.reduce(x, out=y, axis=2)
# The results should match, and y_base shouldn't get clobbered
assert_equal(r0, r1)
assert_equal(y_base[1,:], y_base_copy[1,:])
assert_equal(y_base[3,:], y_base_copy[3,:])
def test_no_doc_string(self):
# gh-9337
assert_('\n' not in umt.inner1d_no_doc.__doc__)
if __name__ == "__main__":
run_module_suite()
| 55,288 | 38.919856 | 84 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/tests/test_umath_complex.py
|
from __future__ import division, absolute_import, print_function
import sys
import platform
import numpy as np
import numpy.core.umath as ncu
from numpy.testing import (
run_module_suite, assert_raises, assert_equal, assert_array_equal,
assert_almost_equal, dec
)
# TODO: branch cuts (use Pauli code)
# TODO: conj 'symmetry'
# TODO: FPU exceptions
# At least on Windows the results of many complex functions are not conforming
# to the C99 standard. See ticket 1574.
# Ditto for Solaris (ticket 1642) and OS X on PowerPC.
with np.errstate(all='ignore'):
functions_seem_flaky = ((np.exp(complex(np.inf, 0)).imag != 0)
or (np.log(complex(np.NZERO, 0)).imag != np.pi))
# TODO: replace with a check on whether platform-provided C99 funcs are used
skip_complex_tests = (not sys.platform.startswith('linux') or functions_seem_flaky)
def platform_skip(func):
return dec.skipif(skip_complex_tests,
"Numpy is using complex functions (e.g. sqrt) provided by your"
"platform's C library. However, they do not seem to behave according"
"to C99 -- so C99 tests are skipped.")(func)
class TestCexp(object):
def test_simple(self):
check = check_complex_value
f = np.exp
yield check, f, 1, 0, np.exp(1), 0, False
yield check, f, 0, 1, np.cos(1), np.sin(1), False
ref = np.exp(1) * complex(np.cos(1), np.sin(1))
yield check, f, 1, 1, ref.real, ref.imag, False
@platform_skip
def test_special_values(self):
# C99: Section G 6.3.1
check = check_complex_value
f = np.exp
# cexp(+-0 + 0i) is 1 + 0i
yield check, f, np.PZERO, 0, 1, 0, False
yield check, f, np.NZERO, 0, 1, 0, False
# cexp(x + infi) is nan + nani for finite x and raises 'invalid' FPU
# exception
yield check, f, 1, np.inf, np.nan, np.nan
yield check, f, -1, np.inf, np.nan, np.nan
yield check, f, 0, np.inf, np.nan, np.nan
# cexp(inf + 0i) is inf + 0i
yield check, f, np.inf, 0, np.inf, 0
# cexp(-inf + yi) is +0 * (cos(y) + i sin(y)) for finite y
yield check, f, -np.inf, 1, np.PZERO, np.PZERO
yield check, f, -np.inf, 0.75 * np.pi, np.NZERO, np.PZERO
# cexp(inf + yi) is +inf * (cos(y) + i sin(y)) for finite y
yield check, f, np.inf, 1, np.inf, np.inf
yield check, f, np.inf, 0.75 * np.pi, -np.inf, np.inf
# cexp(-inf + inf i) is +-0 +- 0i (signs unspecified)
def _check_ninf_inf(dummy):
msgform = "cexp(-inf, inf) is (%f, %f), expected (+-0, +-0)"
with np.errstate(invalid='ignore'):
z = f(np.array(complex(-np.inf, np.inf)))
if z.real != 0 or z.imag != 0:
raise AssertionError(msgform % (z.real, z.imag))
yield _check_ninf_inf, None
# cexp(inf + inf i) is +-inf + NaNi and raised invalid FPU ex.
def _check_inf_inf(dummy):
msgform = "cexp(inf, inf) is (%f, %f), expected (+-inf, nan)"
with np.errstate(invalid='ignore'):
z = f(np.array(complex(np.inf, np.inf)))
if not np.isinf(z.real) or not np.isnan(z.imag):
raise AssertionError(msgform % (z.real, z.imag))
yield _check_inf_inf, None
# cexp(-inf + nan i) is +-0 +- 0i
def _check_ninf_nan(dummy):
msgform = "cexp(-inf, nan) is (%f, %f), expected (+-0, +-0)"
with np.errstate(invalid='ignore'):
z = f(np.array(complex(-np.inf, np.nan)))
if z.real != 0 or z.imag != 0:
raise AssertionError(msgform % (z.real, z.imag))
yield _check_ninf_nan, None
# cexp(inf + nan i) is +-inf + nan
def _check_inf_nan(dummy):
msgform = "cexp(-inf, nan) is (%f, %f), expected (+-inf, nan)"
with np.errstate(invalid='ignore'):
z = f(np.array(complex(np.inf, np.nan)))
if not np.isinf(z.real) or not np.isnan(z.imag):
raise AssertionError(msgform % (z.real, z.imag))
yield _check_inf_nan, None
# cexp(nan + yi) is nan + nani for y != 0 (optional: raises invalid FPU
# ex)
yield check, f, np.nan, 1, np.nan, np.nan
yield check, f, np.nan, -1, np.nan, np.nan
yield check, f, np.nan, np.inf, np.nan, np.nan
yield check, f, np.nan, -np.inf, np.nan, np.nan
# cexp(nan + nani) is nan + nani
yield check, f, np.nan, np.nan, np.nan, np.nan
@dec.knownfailureif(True, "cexp(nan + 0I) is wrong on most implementations")
def test_special_values2(self):
# XXX: most implementations get it wrong here (including glibc <= 2.10)
# cexp(nan + 0i) is nan + 0i
check = check_complex_value
f = np.exp
yield check, f, np.nan, 0, np.nan, 0
class TestClog(object):
def test_simple(self):
x = np.array([1+0j, 1+2j])
y_r = np.log(np.abs(x)) + 1j * np.angle(x)
y = np.log(x)
for i in range(len(x)):
assert_almost_equal(y[i], y_r[i])
@platform_skip
@dec.skipif(platform.machine() == "armv5tel", "See gh-413.")
def test_special_values(self):
xl = []
yl = []
# From C99 std (Sec 6.3.2)
# XXX: check exceptions raised
# --- raise for invalid fails.
# clog(-0 + i0) returns -inf + i pi and raises the 'divide-by-zero'
# floating-point exception.
with np.errstate(divide='raise'):
x = np.array([np.NZERO], dtype=complex)
y = complex(-np.inf, np.pi)
assert_raises(FloatingPointError, np.log, x)
with np.errstate(divide='ignore'):
assert_almost_equal(np.log(x), y)
xl.append(x)
yl.append(y)
# clog(+0 + i0) returns -inf + i0 and raises the 'divide-by-zero'
# floating-point exception.
with np.errstate(divide='raise'):
x = np.array([0], dtype=complex)
y = complex(-np.inf, 0)
assert_raises(FloatingPointError, np.log, x)
with np.errstate(divide='ignore'):
assert_almost_equal(np.log(x), y)
xl.append(x)
yl.append(y)
# clog(x + i inf returns +inf + i pi /2, for finite x.
x = np.array([complex(1, np.inf)], dtype=complex)
y = complex(np.inf, 0.5 * np.pi)
assert_almost_equal(np.log(x), y)
xl.append(x)
yl.append(y)
x = np.array([complex(-1, np.inf)], dtype=complex)
assert_almost_equal(np.log(x), y)
xl.append(x)
yl.append(y)
# clog(x + iNaN) returns NaN + iNaN and optionally raises the
# 'invalid' floating- point exception, for finite x.
with np.errstate(invalid='raise'):
x = np.array([complex(1., np.nan)], dtype=complex)
y = complex(np.nan, np.nan)
#assert_raises(FloatingPointError, np.log, x)
with np.errstate(invalid='ignore'):
assert_almost_equal(np.log(x), y)
xl.append(x)
yl.append(y)
with np.errstate(invalid='raise'):
x = np.array([np.inf + 1j * np.nan], dtype=complex)
#assert_raises(FloatingPointError, np.log, x)
with np.errstate(invalid='ignore'):
assert_almost_equal(np.log(x), y)
xl.append(x)
yl.append(y)
# clog(- inf + iy) returns +inf + ipi , for finite positive-signed y.
x = np.array([-np.inf + 1j], dtype=complex)
y = complex(np.inf, np.pi)
assert_almost_equal(np.log(x), y)
xl.append(x)
yl.append(y)
# clog(+ inf + iy) returns +inf + i0, for finite positive-signed y.
x = np.array([np.inf + 1j], dtype=complex)
y = complex(np.inf, 0)
assert_almost_equal(np.log(x), y)
xl.append(x)
yl.append(y)
# clog(- inf + i inf) returns +inf + i3pi /4.
x = np.array([complex(-np.inf, np.inf)], dtype=complex)
y = complex(np.inf, 0.75 * np.pi)
assert_almost_equal(np.log(x), y)
xl.append(x)
yl.append(y)
# clog(+ inf + i inf) returns +inf + ipi /4.
x = np.array([complex(np.inf, np.inf)], dtype=complex)
y = complex(np.inf, 0.25 * np.pi)
assert_almost_equal(np.log(x), y)
xl.append(x)
yl.append(y)
# clog(+/- inf + iNaN) returns +inf + iNaN.
x = np.array([complex(np.inf, np.nan)], dtype=complex)
y = complex(np.inf, np.nan)
assert_almost_equal(np.log(x), y)
xl.append(x)
yl.append(y)
x = np.array([complex(-np.inf, np.nan)], dtype=complex)
assert_almost_equal(np.log(x), y)
xl.append(x)
yl.append(y)
# clog(NaN + iy) returns NaN + iNaN and optionally raises the
# 'invalid' floating-point exception, for finite y.
x = np.array([complex(np.nan, 1)], dtype=complex)
y = complex(np.nan, np.nan)
assert_almost_equal(np.log(x), y)
xl.append(x)
yl.append(y)
# clog(NaN + i inf) returns +inf + iNaN.
x = np.array([complex(np.nan, np.inf)], dtype=complex)
y = complex(np.inf, np.nan)
assert_almost_equal(np.log(x), y)
xl.append(x)
yl.append(y)
# clog(NaN + iNaN) returns NaN + iNaN.
x = np.array([complex(np.nan, np.nan)], dtype=complex)
y = complex(np.nan, np.nan)
assert_almost_equal(np.log(x), y)
xl.append(x)
yl.append(y)
# clog(conj(z)) = conj(clog(z)).
xa = np.array(xl, dtype=complex)
ya = np.array(yl, dtype=complex)
with np.errstate(divide='ignore'):
for i in range(len(xa)):
assert_almost_equal(np.log(xa[i].conj()), ya[i].conj())
class TestCsqrt(object):
def test_simple(self):
# sqrt(1)
yield check_complex_value, np.sqrt, 1, 0, 1, 0
# sqrt(1i)
yield check_complex_value, np.sqrt, 0, 1, 0.5*np.sqrt(2), 0.5*np.sqrt(2), False
# sqrt(-1)
yield check_complex_value, np.sqrt, -1, 0, 0, 1
def test_simple_conjugate(self):
ref = np.conj(np.sqrt(complex(1, 1)))
def f(z):
return np.sqrt(np.conj(z))
yield check_complex_value, f, 1, 1, ref.real, ref.imag, False
#def test_branch_cut(self):
# _check_branch_cut(f, -1, 0, 1, -1)
@platform_skip
def test_special_values(self):
# C99: Sec G 6.4.2
check = check_complex_value
f = np.sqrt
# csqrt(+-0 + 0i) is 0 + 0i
yield check, f, np.PZERO, 0, 0, 0
yield check, f, np.NZERO, 0, 0, 0
# csqrt(x + infi) is inf + infi for any x (including NaN)
yield check, f, 1, np.inf, np.inf, np.inf
yield check, f, -1, np.inf, np.inf, np.inf
yield check, f, np.PZERO, np.inf, np.inf, np.inf
yield check, f, np.NZERO, np.inf, np.inf, np.inf
yield check, f, np.inf, np.inf, np.inf, np.inf
yield check, f, -np.inf, np.inf, np.inf, np.inf
yield check, f, -np.nan, np.inf, np.inf, np.inf
# csqrt(x + nani) is nan + nani for any finite x
yield check, f, 1, np.nan, np.nan, np.nan
yield check, f, -1, np.nan, np.nan, np.nan
yield check, f, 0, np.nan, np.nan, np.nan
# csqrt(-inf + yi) is +0 + infi for any finite y > 0
yield check, f, -np.inf, 1, np.PZERO, np.inf
# csqrt(inf + yi) is +inf + 0i for any finite y > 0
yield check, f, np.inf, 1, np.inf, np.PZERO
# csqrt(-inf + nani) is nan +- infi (both +i infi are valid)
def _check_ninf_nan(dummy):
msgform = "csqrt(-inf, nan) is (%f, %f), expected (nan, +-inf)"
z = np.sqrt(np.array(complex(-np.inf, np.nan)))
#Fixme: ugly workaround for isinf bug.
with np.errstate(invalid='ignore'):
if not (np.isnan(z.real) and np.isinf(z.imag)):
raise AssertionError(msgform % (z.real, z.imag))
yield _check_ninf_nan, None
# csqrt(+inf + nani) is inf + nani
yield check, f, np.inf, np.nan, np.inf, np.nan
# csqrt(nan + yi) is nan + nani for any finite y (infinite handled in x
# + nani)
yield check, f, np.nan, 0, np.nan, np.nan
yield check, f, np.nan, 1, np.nan, np.nan
yield check, f, np.nan, np.nan, np.nan, np.nan
# XXX: check for conj(csqrt(z)) == csqrt(conj(z)) (need to fix branch
# cuts first)
class TestCpow(object):
def setUp(self):
self.olderr = np.seterr(invalid='ignore')
def tearDown(self):
np.seterr(**self.olderr)
def test_simple(self):
x = np.array([1+1j, 0+2j, 1+2j, np.inf, np.nan])
y_r = x ** 2
y = np.power(x, 2)
for i in range(len(x)):
assert_almost_equal(y[i], y_r[i])
def test_scalar(self):
x = np.array([1, 1j, 2, 2.5+.37j, np.inf, np.nan])
y = np.array([1, 1j, -0.5+1.5j, -0.5+1.5j, 2, 3])
lx = list(range(len(x)))
# Compute the values for complex type in python
p_r = [complex(x[i]) ** complex(y[i]) for i in lx]
# Substitute a result allowed by C99 standard
p_r[4] = complex(np.inf, np.nan)
# Do the same with numpy complex scalars
n_r = [x[i] ** y[i] for i in lx]
for i in lx:
assert_almost_equal(n_r[i], p_r[i], err_msg='Loop %d\n' % i)
def test_array(self):
x = np.array([1, 1j, 2, 2.5+.37j, np.inf, np.nan])
y = np.array([1, 1j, -0.5+1.5j, -0.5+1.5j, 2, 3])
lx = list(range(len(x)))
# Compute the values for complex type in python
p_r = [complex(x[i]) ** complex(y[i]) for i in lx]
# Substitute a result allowed by C99 standard
p_r[4] = complex(np.inf, np.nan)
# Do the same with numpy arrays
n_r = x ** y
for i in lx:
assert_almost_equal(n_r[i], p_r[i], err_msg='Loop %d\n' % i)
class TestCabs(object):
def setUp(self):
self.olderr = np.seterr(invalid='ignore')
def tearDown(self):
np.seterr(**self.olderr)
def test_simple(self):
x = np.array([1+1j, 0+2j, 1+2j, np.inf, np.nan])
y_r = np.array([np.sqrt(2.), 2, np.sqrt(5), np.inf, np.nan])
y = np.abs(x)
for i in range(len(x)):
assert_almost_equal(y[i], y_r[i])
def test_fabs(self):
# Test that np.abs(x +- 0j) == np.abs(x) (as mandated by C99 for cabs)
x = np.array([1+0j], dtype=complex)
assert_array_equal(np.abs(x), np.real(x))
x = np.array([complex(1, np.NZERO)], dtype=complex)
assert_array_equal(np.abs(x), np.real(x))
x = np.array([complex(np.inf, np.NZERO)], dtype=complex)
assert_array_equal(np.abs(x), np.real(x))
x = np.array([complex(np.nan, np.NZERO)], dtype=complex)
assert_array_equal(np.abs(x), np.real(x))
def test_cabs_inf_nan(self):
x, y = [], []
# cabs(+-nan + nani) returns nan
x.append(np.nan)
y.append(np.nan)
yield check_real_value, np.abs, np.nan, np.nan, np.nan
x.append(np.nan)
y.append(-np.nan)
yield check_real_value, np.abs, -np.nan, np.nan, np.nan
# According to C99 standard, if exactly one of the real/part is inf and
# the other nan, then cabs should return inf
x.append(np.inf)
y.append(np.nan)
yield check_real_value, np.abs, np.inf, np.nan, np.inf
x.append(-np.inf)
y.append(np.nan)
yield check_real_value, np.abs, -np.inf, np.nan, np.inf
# cabs(conj(z)) == conj(cabs(z)) (= cabs(z))
def f(a):
return np.abs(np.conj(a))
def g(a, b):
return np.abs(complex(a, b))
xa = np.array(x, dtype=complex)
for i in range(len(xa)):
ref = g(x[i], y[i])
yield check_real_value, f, x[i], y[i], ref
class TestCarg(object):
def test_simple(self):
check_real_value(ncu._arg, 1, 0, 0, False)
check_real_value(ncu._arg, 0, 1, 0.5*np.pi, False)
check_real_value(ncu._arg, 1, 1, 0.25*np.pi, False)
check_real_value(ncu._arg, np.PZERO, np.PZERO, np.PZERO)
@dec.knownfailureif(True,
"Complex arithmetic with signed zero is buggy on most implementation")
def test_zero(self):
# carg(-0 +- 0i) returns +- pi
yield check_real_value, ncu._arg, np.NZERO, np.PZERO, np.pi, False
yield check_real_value, ncu._arg, np.NZERO, np.NZERO, -np.pi, False
# carg(+0 +- 0i) returns +- 0
yield check_real_value, ncu._arg, np.PZERO, np.PZERO, np.PZERO
yield check_real_value, ncu._arg, np.PZERO, np.NZERO, np.NZERO
# carg(x +- 0i) returns +- 0 for x > 0
yield check_real_value, ncu._arg, 1, np.PZERO, np.PZERO, False
yield check_real_value, ncu._arg, 1, np.NZERO, np.NZERO, False
# carg(x +- 0i) returns +- pi for x < 0
yield check_real_value, ncu._arg, -1, np.PZERO, np.pi, False
yield check_real_value, ncu._arg, -1, np.NZERO, -np.pi, False
# carg(+- 0 + yi) returns pi/2 for y > 0
yield check_real_value, ncu._arg, np.PZERO, 1, 0.5 * np.pi, False
yield check_real_value, ncu._arg, np.NZERO, 1, 0.5 * np.pi, False
# carg(+- 0 + yi) returns -pi/2 for y < 0
yield check_real_value, ncu._arg, np.PZERO, -1, 0.5 * np.pi, False
yield check_real_value, ncu._arg, np.NZERO, -1, -0.5 * np.pi, False
#def test_branch_cuts(self):
# _check_branch_cut(ncu._arg, -1, 1j, -1, 1)
def test_special_values(self):
# carg(-np.inf +- yi) returns +-pi for finite y > 0
yield check_real_value, ncu._arg, -np.inf, 1, np.pi, False
yield check_real_value, ncu._arg, -np.inf, -1, -np.pi, False
# carg(np.inf +- yi) returns +-0 for finite y > 0
yield check_real_value, ncu._arg, np.inf, 1, np.PZERO, False
yield check_real_value, ncu._arg, np.inf, -1, np.NZERO, False
# carg(x +- np.infi) returns +-pi/2 for finite x
yield check_real_value, ncu._arg, 1, np.inf, 0.5 * np.pi, False
yield check_real_value, ncu._arg, 1, -np.inf, -0.5 * np.pi, False
# carg(-np.inf +- np.infi) returns +-3pi/4
yield check_real_value, ncu._arg, -np.inf, np.inf, 0.75 * np.pi, False
yield check_real_value, ncu._arg, -np.inf, -np.inf, -0.75 * np.pi, False
# carg(np.inf +- np.infi) returns +-pi/4
yield check_real_value, ncu._arg, np.inf, np.inf, 0.25 * np.pi, False
yield check_real_value, ncu._arg, np.inf, -np.inf, -0.25 * np.pi, False
# carg(x + yi) returns np.nan if x or y is nan
yield check_real_value, ncu._arg, np.nan, 0, np.nan, False
yield check_real_value, ncu._arg, 0, np.nan, np.nan, False
yield check_real_value, ncu._arg, np.nan, np.inf, np.nan, False
yield check_real_value, ncu._arg, np.inf, np.nan, np.nan, False
def check_real_value(f, x1, y1, x, exact=True):
z1 = np.array([complex(x1, y1)])
if exact:
assert_equal(f(z1), x)
else:
assert_almost_equal(f(z1), x)
def check_complex_value(f, x1, y1, x2, y2, exact=True):
z1 = np.array([complex(x1, y1)])
z2 = complex(x2, y2)
with np.errstate(invalid='ignore'):
if exact:
assert_equal(f(z1), z2)
else:
assert_almost_equal(f(z1), z2)
if __name__ == "__main__":
run_module_suite()
| 19,656 | 35.469388 | 87 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/tests/test_records.py
|
from __future__ import division, absolute_import, print_function
import sys
import collections
import pickle
import warnings
import textwrap
from os import path
import numpy as np
from numpy.testing import (
run_module_suite, assert_, assert_equal, assert_array_equal,
assert_array_almost_equal, assert_raises, assert_warns
)
class TestFromrecords(object):
def test_fromrecords(self):
r = np.rec.fromrecords([[456, 'dbe', 1.2], [2, 'de', 1.3]],
names='col1,col2,col3')
assert_equal(r[0].item(), (456, 'dbe', 1.2))
assert_equal(r['col1'].dtype.kind, 'i')
if sys.version_info[0] >= 3:
assert_equal(r['col2'].dtype.kind, 'U')
assert_equal(r['col2'].dtype.itemsize, 12)
else:
assert_equal(r['col2'].dtype.kind, 'S')
assert_equal(r['col2'].dtype.itemsize, 3)
assert_equal(r['col3'].dtype.kind, 'f')
def test_fromrecords_0len(self):
""" Verify fromrecords works with a 0-length input """
dtype = [('a', float), ('b', float)]
r = np.rec.fromrecords([], dtype=dtype)
assert_equal(r.shape, (0,))
def test_fromrecords_2d(self):
data = [
[(1, 2), (3, 4), (5, 6)],
[(6, 5), (4, 3), (2, 1)]
]
expected_a = [[1, 3, 5], [6, 4, 2]]
expected_b = [[2, 4, 6], [5, 3, 1]]
# try with dtype
r1 = np.rec.fromrecords(data, dtype=[('a', int), ('b', int)])
assert_equal(r1['a'], expected_a)
assert_equal(r1['b'], expected_b)
# try with names
r2 = np.rec.fromrecords(data, names=['a', 'b'])
assert_equal(r2['a'], expected_a)
assert_equal(r2['b'], expected_b)
assert_equal(r1, r2)
def test_fromrecords_list_of_lists(self):
# gh-10870 : For numpy 1.14 we keep the deprecated behavior
# that 1d list-of-lists input is accepted by fromrecords
expected = np.rec.array([(1, 1.5), (2, 2.5)], dtype='i8,f8')
with assert_warns(FutureWarning):
r = np.rec.fromrecords([[1, 1.5], [2, 2.5]], dtype='i8,f8')
assert_equal(r, expected)
def test_method_array(self):
r = np.rec.array(b'abcdefg' * 100, formats='i2,a3,i4', shape=3, byteorder='big')
assert_equal(r[1].item(), (25444, b'efg', 1633837924))
def test_method_array2(self):
r = np.rec.array([(1, 11, 'a'), (2, 22, 'b'), (3, 33, 'c'), (4, 44, 'd'), (5, 55, 'ex'),
(6, 66, 'f'), (7, 77, 'g')], formats='u1,f4,a1')
assert_equal(r[1].item(), (2, 22.0, b'b'))
def test_recarray_slices(self):
r = np.rec.array([(1, 11, 'a'), (2, 22, 'b'), (3, 33, 'c'), (4, 44, 'd'), (5, 55, 'ex'),
(6, 66, 'f'), (7, 77, 'g')], formats='u1,f4,a1')
assert_equal(r[1::2][1].item(), (4, 44.0, b'd'))
def test_recarray_fromarrays(self):
x1 = np.array([1, 2, 3, 4])
x2 = np.array(['a', 'dd', 'xyz', '12'])
x3 = np.array([1.1, 2, 3, 4])
r = np.rec.fromarrays([x1, x2, x3], names='a,b,c')
assert_equal(r[1].item(), (2, 'dd', 2.0))
x1[1] = 34
assert_equal(r.a, np.array([1, 2, 3, 4]))
def test_recarray_fromfile(self):
data_dir = path.join(path.dirname(__file__), 'data')
filename = path.join(data_dir, 'recarray_from_file.fits')
fd = open(filename, 'rb')
fd.seek(2880 * 2)
r1 = np.rec.fromfile(fd, formats='f8,i4,a5', shape=3, byteorder='big')
fd.seek(2880 * 2)
r2 = np.rec.array(fd, formats='f8,i4,a5', shape=3, byteorder='big')
fd.close()
assert_equal(r1, r2)
def test_recarray_from_obj(self):
count = 10
a = np.zeros(count, dtype='O')
b = np.zeros(count, dtype='f8')
c = np.zeros(count, dtype='f8')
for i in range(len(a)):
a[i] = list(range(1, 10))
mine = np.rec.fromarrays([a, b, c], names='date,data1,data2')
for i in range(len(a)):
assert_((mine.date[i] == list(range(1, 10))))
assert_((mine.data1[i] == 0.0))
assert_((mine.data2[i] == 0.0))
def test_recarray_repr(self):
a = np.array([(1, 0.1), (2, 0.2)],
dtype=[('foo', '<i4'), ('bar', '<f8')])
a = np.rec.array(a)
assert_equal(
repr(a),
textwrap.dedent("""\
rec.array([(1, 0.1), (2, 0.2)],
dtype=[('foo', '<i4'), ('bar', '<f8')])""")
)
# make sure non-structured dtypes also show up as rec.array
a = np.array(np.ones(4, dtype='f8'))
assert_(repr(np.rec.array(a)).startswith('rec.array'))
# check that the 'np.record' part of the dtype isn't shown
a = np.rec.array(np.ones(3, dtype='i4,i4'))
assert_equal(repr(a).find('numpy.record'), -1)
a = np.rec.array(np.ones(3, dtype='i4'))
assert_(repr(a).find('dtype=int32') != -1)
def test_0d_recarray_repr(self):
arr_0d = np.rec.array((1, 2.0, '2003'), dtype='<i4,<f8,<M8[Y]')
assert_equal(repr(arr_0d), textwrap.dedent("""\
rec.array((1, 2., '2003'),
dtype=[('f0', '<i4'), ('f1', '<f8'), ('f2', '<M8[Y]')])"""))
record = arr_0d[()]
assert_equal(repr(record), "(1, 2., '2003')")
# 1.13 converted to python scalars before the repr
try:
np.set_printoptions(legacy='1.13')
assert_equal(repr(record), '(1, 2.0, datetime.date(2003, 1, 1))')
finally:
np.set_printoptions(legacy=False)
def test_recarray_from_repr(self):
a = np.array([(1,'ABC'), (2, "DEF")],
dtype=[('foo', int), ('bar', 'S4')])
recordarr = np.rec.array(a)
recarr = a.view(np.recarray)
recordview = a.view(np.dtype((np.record, a.dtype)))
recordarr_r = eval("numpy." + repr(recordarr), {'numpy': np})
recarr_r = eval("numpy." + repr(recarr), {'numpy': np})
recordview_r = eval("numpy." + repr(recordview), {'numpy': np})
assert_equal(type(recordarr_r), np.recarray)
assert_equal(recordarr_r.dtype.type, np.record)
assert_equal(recordarr, recordarr_r)
assert_equal(type(recarr_r), np.recarray)
assert_equal(recarr_r.dtype.type, np.record)
assert_equal(recarr, recarr_r)
assert_equal(type(recordview_r), np.ndarray)
assert_equal(recordview.dtype.type, np.record)
assert_equal(recordview, recordview_r)
def test_recarray_views(self):
a = np.array([(1,'ABC'), (2, "DEF")],
dtype=[('foo', int), ('bar', 'S4')])
b = np.array([1,2,3,4,5], dtype=np.int64)
#check that np.rec.array gives right dtypes
assert_equal(np.rec.array(a).dtype.type, np.record)
assert_equal(type(np.rec.array(a)), np.recarray)
assert_equal(np.rec.array(b).dtype.type, np.int64)
assert_equal(type(np.rec.array(b)), np.recarray)
#check that viewing as recarray does the same
assert_equal(a.view(np.recarray).dtype.type, np.record)
assert_equal(type(a.view(np.recarray)), np.recarray)
assert_equal(b.view(np.recarray).dtype.type, np.int64)
assert_equal(type(b.view(np.recarray)), np.recarray)
#check that view to non-structured dtype preserves type=np.recarray
r = np.rec.array(np.ones(4, dtype="f4,i4"))
rv = r.view('f8').view('f4,i4')
assert_equal(type(rv), np.recarray)
assert_equal(rv.dtype.type, np.record)
#check that getitem also preserves np.recarray and np.record
r = np.rec.array(np.ones(4, dtype=[('a', 'i4'), ('b', 'i4'),
('c', 'i4,i4')]))
assert_equal(r['c'].dtype.type, np.record)
assert_equal(type(r['c']), np.recarray)
#and that it preserves subclasses (gh-6949)
class C(np.recarray):
pass
c = r.view(C)
assert_equal(type(c['c']), C)
# check that accessing nested structures keep record type, but
# not for subarrays, non-void structures, non-structured voids
test_dtype = [('a', 'f4,f4'), ('b', 'V8'), ('c', ('f4',2)),
('d', ('i8', 'i4,i4'))]
r = np.rec.array([((1,1), b'11111111', [1,1], 1),
((1,1), b'11111111', [1,1], 1)], dtype=test_dtype)
assert_equal(r.a.dtype.type, np.record)
assert_equal(r.b.dtype.type, np.void)
assert_equal(r.c.dtype.type, np.float32)
assert_equal(r.d.dtype.type, np.int64)
# check the same, but for views
r = np.rec.array(np.ones(4, dtype='i4,i4'))
assert_equal(r.view('f4,f4').dtype.type, np.record)
assert_equal(r.view(('i4',2)).dtype.type, np.int32)
assert_equal(r.view('V8').dtype.type, np.void)
assert_equal(r.view(('i8', 'i4,i4')).dtype.type, np.int64)
#check that we can undo the view
arrs = [np.ones(4, dtype='f4,i4'), np.ones(4, dtype='f8')]
for arr in arrs:
rec = np.rec.array(arr)
# recommended way to view as an ndarray:
arr2 = rec.view(rec.dtype.fields or rec.dtype, np.ndarray)
assert_equal(arr2.dtype.type, arr.dtype.type)
assert_equal(type(arr2), type(arr))
def test_recarray_from_names(self):
ra = np.rec.array([
(1, 'abc', 3.7000002861022949, 0),
(2, 'xy', 6.6999998092651367, 1),
(0, ' ', 0.40000000596046448, 0)],
names='c1, c2, c3, c4')
pa = np.rec.fromrecords([
(1, 'abc', 3.7000002861022949, 0),
(2, 'xy', 6.6999998092651367, 1),
(0, ' ', 0.40000000596046448, 0)],
names='c1, c2, c3, c4')
assert_(ra.dtype == pa.dtype)
assert_(ra.shape == pa.shape)
for k in range(len(ra)):
assert_(ra[k].item() == pa[k].item())
def test_recarray_conflict_fields(self):
ra = np.rec.array([(1, 'abc', 2.3), (2, 'xyz', 4.2),
(3, 'wrs', 1.3)],
names='field, shape, mean')
ra.mean = [1.1, 2.2, 3.3]
assert_array_almost_equal(ra['mean'], [1.1, 2.2, 3.3])
assert_(type(ra.mean) is type(ra.var))
ra.shape = (1, 3)
assert_(ra.shape == (1, 3))
ra.shape = ['A', 'B', 'C']
assert_array_equal(ra['shape'], [['A', 'B', 'C']])
ra.field = 5
assert_array_equal(ra['field'], [[5, 5, 5]])
assert_(isinstance(ra.field, collections.Callable))
def test_fromrecords_with_explicit_dtype(self):
a = np.rec.fromrecords([(1, 'a'), (2, 'bbb')],
dtype=[('a', int), ('b', object)])
assert_equal(a.a, [1, 2])
assert_equal(a[0].a, 1)
assert_equal(a.b, ['a', 'bbb'])
assert_equal(a[-1].b, 'bbb')
#
ndtype = np.dtype([('a', int), ('b', object)])
a = np.rec.fromrecords([(1, 'a'), (2, 'bbb')], dtype=ndtype)
assert_equal(a.a, [1, 2])
assert_equal(a[0].a, 1)
assert_equal(a.b, ['a', 'bbb'])
assert_equal(a[-1].b, 'bbb')
def test_recarray_stringtypes(self):
# Issue #3993
a = np.array([('abc ', 1), ('abc', 2)],
dtype=[('foo', 'S4'), ('bar', int)])
a = a.view(np.recarray)
assert_equal(a.foo[0] == a.foo[1], False)
def test_recarray_returntypes(self):
qux_fields = {'C': (np.dtype('S5'), 0), 'D': (np.dtype('S5'), 6)}
a = np.rec.array([('abc ', (1,1), 1, ('abcde', 'fgehi')),
('abc', (2,3), 1, ('abcde', 'jklmn'))],
dtype=[('foo', 'S4'),
('bar', [('A', int), ('B', int)]),
('baz', int), ('qux', qux_fields)])
assert_equal(type(a.foo), np.ndarray)
assert_equal(type(a['foo']), np.ndarray)
assert_equal(type(a.bar), np.recarray)
assert_equal(type(a['bar']), np.recarray)
assert_equal(a.bar.dtype.type, np.record)
assert_equal(type(a['qux']), np.recarray)
assert_equal(a.qux.dtype.type, np.record)
assert_equal(dict(a.qux.dtype.fields), qux_fields)
assert_equal(type(a.baz), np.ndarray)
assert_equal(type(a['baz']), np.ndarray)
assert_equal(type(a[0].bar), np.record)
assert_equal(type(a[0]['bar']), np.record)
assert_equal(a[0].bar.A, 1)
assert_equal(a[0].bar['A'], 1)
assert_equal(a[0]['bar'].A, 1)
assert_equal(a[0]['bar']['A'], 1)
assert_equal(a[0].qux.D, b'fgehi')
assert_equal(a[0].qux['D'], b'fgehi')
assert_equal(a[0]['qux'].D, b'fgehi')
assert_equal(a[0]['qux']['D'], b'fgehi')
def test_zero_width_strings(self):
# Test for #6430, based on the test case from #1901
cols = [['test'] * 3, [''] * 3]
rec = np.rec.fromarrays(cols)
assert_equal(rec['f0'], ['test', 'test', 'test'])
assert_equal(rec['f1'], ['', '', ''])
dt = np.dtype([('f0', '|S4'), ('f1', '|S')])
rec = np.rec.fromarrays(cols, dtype=dt)
assert_equal(rec.itemsize, 4)
assert_equal(rec['f0'], [b'test', b'test', b'test'])
assert_equal(rec['f1'], [b'', b'', b''])
class TestRecord(object):
def setup(self):
self.data = np.rec.fromrecords([(1, 2, 3), (4, 5, 6)],
dtype=[("col1", "<i4"),
("col2", "<i4"),
("col3", "<i4")])
def test_assignment1(self):
a = self.data
assert_equal(a.col1[0], 1)
a[0].col1 = 0
assert_equal(a.col1[0], 0)
def test_assignment2(self):
a = self.data
assert_equal(a.col1[0], 1)
a.col1[0] = 0
assert_equal(a.col1[0], 0)
def test_invalid_assignment(self):
a = self.data
def assign_invalid_column(x):
x[0].col5 = 1
assert_raises(AttributeError, assign_invalid_column, a)
def test_nonwriteable_setfield(self):
# gh-8171
r = np.rec.array([(0,), (1,)], dtype=[('f', 'i4')])
r.flags.writeable = False
with assert_raises(ValueError):
r.f = [2, 3]
with assert_raises(ValueError):
r.setfield([2,3], *r.dtype.fields['f'])
def test_out_of_order_fields(self):
dt = np.dtype({'names': ['a', 'b'],
'formats': ['i4', 'i4'],
'offsets': [4, 0]})
y = np.rec.fromrecords([(1, 2), (4, 5)], dtype=dt)
assert_raises(ValueError, lambda: y.dtype.descr)
def test_pickle_1(self):
# Issue #1529
a = np.array([(1, [])], dtype=[('a', np.int32), ('b', np.int32, 0)])
assert_equal(a, pickle.loads(pickle.dumps(a)))
assert_equal(a[0], pickle.loads(pickle.dumps(a[0])))
def test_pickle_2(self):
a = self.data
assert_equal(a, pickle.loads(pickle.dumps(a)))
assert_equal(a[0], pickle.loads(pickle.dumps(a[0])))
def test_pickle_3(self):
# Issue #7140
a = self.data
pa = pickle.loads(pickle.dumps(a[0]))
assert_(pa.flags.c_contiguous)
assert_(pa.flags.f_contiguous)
assert_(pa.flags.writeable)
assert_(pa.flags.aligned)
def test_objview_record(self):
# https://github.com/numpy/numpy/issues/2599
dt = np.dtype([('foo', 'i8'), ('bar', 'O')])
r = np.zeros((1,3), dtype=dt).view(np.recarray)
r.foo = np.array([1, 2, 3]) # TypeError?
# https://github.com/numpy/numpy/issues/3256
ra = np.recarray((2,), dtype=[('x', object), ('y', float), ('z', int)])
with assert_warns(FutureWarning):
ra[['x','y']] # TypeError?
def test_record_scalar_setitem(self):
# https://github.com/numpy/numpy/issues/3561
rec = np.recarray(1, dtype=[('x', float, 5)])
rec[0].x = 1
assert_equal(rec[0].x, np.ones(5))
def test_missing_field(self):
# https://github.com/numpy/numpy/issues/4806
arr = np.zeros((3,), dtype=[('x', int), ('y', int)])
assert_raises(ValueError, lambda: arr[['nofield']])
def test_find_duplicate():
l1 = [1, 2, 3, 4, 5, 6]
assert_(np.rec.find_duplicate(l1) == [])
l2 = [1, 2, 1, 4, 5, 6]
assert_(np.rec.find_duplicate(l2) == [1])
l3 = [1, 2, 1, 4, 1, 6, 2, 3]
assert_(np.rec.find_duplicate(l3) == [1, 2])
l3 = [2, 2, 1, 4, 1, 6, 2, 3]
assert_(np.rec.find_duplicate(l3) == [2, 1])
if __name__ == "__main__":
run_module_suite()
| 16,692 | 37.82093 | 96 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/tests/test_machar.py
|
"""
Test machar. Given recent changes to hardcode type data, we might want to get
rid of both MachAr and this test at some point.
"""
from __future__ import division, absolute_import, print_function
from numpy.core.machar import MachAr
import numpy.core.numerictypes as ntypes
from numpy import errstate, array
from numpy.testing import run_module_suite
class TestMachAr(object):
def _run_machar_highprec(self):
# Instantiate MachAr instance with high enough precision to cause
# underflow
try:
hiprec = ntypes.float96
MachAr(lambda v:array([v], hiprec))
except AttributeError:
# Fixme, this needs to raise a 'skip' exception.
"Skipping test: no ntypes.float96 available on this platform."
def test_underlow(self):
# Regression test for #759:
# instantiating MachAr for dtype = np.float96 raises spurious warning.
with errstate(all='raise'):
try:
self._run_machar_highprec()
except FloatingPointError as e:
msg = "Caught %s exception, should not have been raised." % e
raise AssertionError(msg)
if __name__ == "__main__":
run_module_suite()
| 1,235 | 32.405405 | 78 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/tests/__init__.py
| 0 | 0 | 0 |
py
|
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/tests/test_numerictypes.py
|
from __future__ import division, absolute_import, print_function
import sys
import itertools
import numpy as np
from numpy.testing import (
run_module_suite, assert_, assert_equal, assert_raises
)
# This is the structure of the table used for plain objects:
#
# +-+-+-+
# |x|y|z|
# +-+-+-+
# Structure of a plain array description:
Pdescr = [
('x', 'i4', (2,)),
('y', 'f8', (2, 2)),
('z', 'u1')]
# A plain list of tuples with values for testing:
PbufferT = [
# x y z
([3, 2], [[6., 4.], [6., 4.]], 8),
([4, 3], [[7., 5.], [7., 5.]], 9),
]
# This is the structure of the table used for nested objects (DON'T PANIC!):
#
# +-+---------------------------------+-----+----------+-+-+
# |x|Info |color|info |y|z|
# | +-----+--+----------------+----+--+ +----+-----+ | |
# | |value|y2|Info2 |name|z2| |Name|Value| | |
# | | | +----+-----+--+--+ | | | | | | |
# | | | |name|value|y3|z3| | | | | | | |
# +-+-----+--+----+-----+--+--+----+--+-----+----+-----+-+-+
#
# The corresponding nested array description:
Ndescr = [
('x', 'i4', (2,)),
('Info', [
('value', 'c16'),
('y2', 'f8'),
('Info2', [
('name', 'S2'),
('value', 'c16', (2,)),
('y3', 'f8', (2,)),
('z3', 'u4', (2,))]),
('name', 'S2'),
('z2', 'b1')]),
('color', 'S2'),
('info', [
('Name', 'U8'),
('Value', 'c16')]),
('y', 'f8', (2, 2)),
('z', 'u1')]
NbufferT = [
# x Info color info y z
# value y2 Info2 name z2 Name Value
# name value y3 z3
([3, 2], (6j, 6., (b'nn', [6j, 4j], [6., 4.], [1, 2]), b'NN', True), b'cc', (u'NN', 6j), [[6., 4.], [6., 4.]], 8),
([4, 3], (7j, 7., (b'oo', [7j, 5j], [7., 5.], [2, 1]), b'OO', False), b'dd', (u'OO', 7j), [[7., 5.], [7., 5.]], 9),
]
byteorder = {'little':'<', 'big':'>'}[sys.byteorder]
def normalize_descr(descr):
"Normalize a description adding the platform byteorder."
out = []
for item in descr:
dtype = item[1]
if isinstance(dtype, str):
if dtype[0] not in ['|', '<', '>']:
onebyte = dtype[1:] == "1"
if onebyte or dtype[0] in ['S', 'V', 'b']:
dtype = "|" + dtype
else:
dtype = byteorder + dtype
if len(item) > 2 and np.prod(item[2]) > 1:
nitem = (item[0], dtype, item[2])
else:
nitem = (item[0], dtype)
out.append(nitem)
elif isinstance(item[1], list):
l = []
for j in normalize_descr(item[1]):
l.append(j)
out.append((item[0], l))
else:
raise ValueError("Expected a str or list and got %s" %
(type(item)))
return out
############################################################
# Creation tests
############################################################
class CreateZeros(object):
"""Check the creation of heterogeneous arrays zero-valued"""
def test_zeros0D(self):
"""Check creation of 0-dimensional objects"""
h = np.zeros((), dtype=self._descr)
assert_(normalize_descr(self._descr) == h.dtype.descr)
assert_(h.dtype.fields['x'][0].name[:4] == 'void')
assert_(h.dtype.fields['x'][0].char == 'V')
assert_(h.dtype.fields['x'][0].type == np.void)
# A small check that data is ok
assert_equal(h['z'], np.zeros((), dtype='u1'))
def test_zerosSD(self):
"""Check creation of single-dimensional objects"""
h = np.zeros((2,), dtype=self._descr)
assert_(normalize_descr(self._descr) == h.dtype.descr)
assert_(h.dtype['y'].name[:4] == 'void')
assert_(h.dtype['y'].char == 'V')
assert_(h.dtype['y'].type == np.void)
# A small check that data is ok
assert_equal(h['z'], np.zeros((2,), dtype='u1'))
def test_zerosMD(self):
"""Check creation of multi-dimensional objects"""
h = np.zeros((2, 3), dtype=self._descr)
assert_(normalize_descr(self._descr) == h.dtype.descr)
assert_(h.dtype['z'].name == 'uint8')
assert_(h.dtype['z'].char == 'B')
assert_(h.dtype['z'].type == np.uint8)
# A small check that data is ok
assert_equal(h['z'], np.zeros((2, 3), dtype='u1'))
class TestCreateZerosPlain(CreateZeros):
"""Check the creation of heterogeneous arrays zero-valued (plain)"""
_descr = Pdescr
class TestCreateZerosNested(CreateZeros):
"""Check the creation of heterogeneous arrays zero-valued (nested)"""
_descr = Ndescr
class CreateValues(object):
"""Check the creation of heterogeneous arrays with values"""
def test_tuple(self):
"""Check creation from tuples"""
h = np.array(self._buffer, dtype=self._descr)
assert_(normalize_descr(self._descr) == h.dtype.descr)
if self.multiple_rows:
assert_(h.shape == (2,))
else:
assert_(h.shape == ())
def test_list_of_tuple(self):
"""Check creation from list of tuples"""
h = np.array([self._buffer], dtype=self._descr)
assert_(normalize_descr(self._descr) == h.dtype.descr)
if self.multiple_rows:
assert_(h.shape == (1, 2))
else:
assert_(h.shape == (1,))
def test_list_of_list_of_tuple(self):
"""Check creation from list of list of tuples"""
h = np.array([[self._buffer]], dtype=self._descr)
assert_(normalize_descr(self._descr) == h.dtype.descr)
if self.multiple_rows:
assert_(h.shape == (1, 1, 2))
else:
assert_(h.shape == (1, 1))
class TestCreateValuesPlainSingle(CreateValues):
"""Check the creation of heterogeneous arrays (plain, single row)"""
_descr = Pdescr
multiple_rows = 0
_buffer = PbufferT[0]
class TestCreateValuesPlainMultiple(CreateValues):
"""Check the creation of heterogeneous arrays (plain, multiple rows)"""
_descr = Pdescr
multiple_rows = 1
_buffer = PbufferT
class TestCreateValuesNestedSingle(CreateValues):
"""Check the creation of heterogeneous arrays (nested, single row)"""
_descr = Ndescr
multiple_rows = 0
_buffer = NbufferT[0]
class TestCreateValuesNestedMultiple(CreateValues):
"""Check the creation of heterogeneous arrays (nested, multiple rows)"""
_descr = Ndescr
multiple_rows = 1
_buffer = NbufferT
############################################################
# Reading tests
############################################################
class ReadValuesPlain(object):
"""Check the reading of values in heterogeneous arrays (plain)"""
def test_access_fields(self):
h = np.array(self._buffer, dtype=self._descr)
if not self.multiple_rows:
assert_(h.shape == ())
assert_equal(h['x'], np.array(self._buffer[0], dtype='i4'))
assert_equal(h['y'], np.array(self._buffer[1], dtype='f8'))
assert_equal(h['z'], np.array(self._buffer[2], dtype='u1'))
else:
assert_(len(h) == 2)
assert_equal(h['x'], np.array([self._buffer[0][0],
self._buffer[1][0]], dtype='i4'))
assert_equal(h['y'], np.array([self._buffer[0][1],
self._buffer[1][1]], dtype='f8'))
assert_equal(h['z'], np.array([self._buffer[0][2],
self._buffer[1][2]], dtype='u1'))
class TestReadValuesPlainSingle(ReadValuesPlain):
"""Check the creation of heterogeneous arrays (plain, single row)"""
_descr = Pdescr
multiple_rows = 0
_buffer = PbufferT[0]
class TestReadValuesPlainMultiple(ReadValuesPlain):
"""Check the values of heterogeneous arrays (plain, multiple rows)"""
_descr = Pdescr
multiple_rows = 1
_buffer = PbufferT
class ReadValuesNested(object):
"""Check the reading of values in heterogeneous arrays (nested)"""
def test_access_top_fields(self):
"""Check reading the top fields of a nested array"""
h = np.array(self._buffer, dtype=self._descr)
if not self.multiple_rows:
assert_(h.shape == ())
assert_equal(h['x'], np.array(self._buffer[0], dtype='i4'))
assert_equal(h['y'], np.array(self._buffer[4], dtype='f8'))
assert_equal(h['z'], np.array(self._buffer[5], dtype='u1'))
else:
assert_(len(h) == 2)
assert_equal(h['x'], np.array([self._buffer[0][0],
self._buffer[1][0]], dtype='i4'))
assert_equal(h['y'], np.array([self._buffer[0][4],
self._buffer[1][4]], dtype='f8'))
assert_equal(h['z'], np.array([self._buffer[0][5],
self._buffer[1][5]], dtype='u1'))
def test_nested1_acessors(self):
"""Check reading the nested fields of a nested array (1st level)"""
h = np.array(self._buffer, dtype=self._descr)
if not self.multiple_rows:
assert_equal(h['Info']['value'],
np.array(self._buffer[1][0], dtype='c16'))
assert_equal(h['Info']['y2'],
np.array(self._buffer[1][1], dtype='f8'))
assert_equal(h['info']['Name'],
np.array(self._buffer[3][0], dtype='U2'))
assert_equal(h['info']['Value'],
np.array(self._buffer[3][1], dtype='c16'))
else:
assert_equal(h['Info']['value'],
np.array([self._buffer[0][1][0],
self._buffer[1][1][0]],
dtype='c16'))
assert_equal(h['Info']['y2'],
np.array([self._buffer[0][1][1],
self._buffer[1][1][1]],
dtype='f8'))
assert_equal(h['info']['Name'],
np.array([self._buffer[0][3][0],
self._buffer[1][3][0]],
dtype='U2'))
assert_equal(h['info']['Value'],
np.array([self._buffer[0][3][1],
self._buffer[1][3][1]],
dtype='c16'))
def test_nested2_acessors(self):
"""Check reading the nested fields of a nested array (2nd level)"""
h = np.array(self._buffer, dtype=self._descr)
if not self.multiple_rows:
assert_equal(h['Info']['Info2']['value'],
np.array(self._buffer[1][2][1], dtype='c16'))
assert_equal(h['Info']['Info2']['z3'],
np.array(self._buffer[1][2][3], dtype='u4'))
else:
assert_equal(h['Info']['Info2']['value'],
np.array([self._buffer[0][1][2][1],
self._buffer[1][1][2][1]],
dtype='c16'))
assert_equal(h['Info']['Info2']['z3'],
np.array([self._buffer[0][1][2][3],
self._buffer[1][1][2][3]],
dtype='u4'))
def test_nested1_descriptor(self):
"""Check access nested descriptors of a nested array (1st level)"""
h = np.array(self._buffer, dtype=self._descr)
assert_(h.dtype['Info']['value'].name == 'complex128')
assert_(h.dtype['Info']['y2'].name == 'float64')
if sys.version_info[0] >= 3:
assert_(h.dtype['info']['Name'].name == 'str256')
else:
assert_(h.dtype['info']['Name'].name == 'unicode256')
assert_(h.dtype['info']['Value'].name == 'complex128')
def test_nested2_descriptor(self):
"""Check access nested descriptors of a nested array (2nd level)"""
h = np.array(self._buffer, dtype=self._descr)
assert_(h.dtype['Info']['Info2']['value'].name == 'void256')
assert_(h.dtype['Info']['Info2']['z3'].name == 'void64')
class TestReadValuesNestedSingle(ReadValuesNested):
"""Check the values of heterogeneous arrays (nested, single row)"""
_descr = Ndescr
multiple_rows = False
_buffer = NbufferT[0]
class TestReadValuesNestedMultiple(ReadValuesNested):
"""Check the values of heterogeneous arrays (nested, multiple rows)"""
_descr = Ndescr
multiple_rows = True
_buffer = NbufferT
class TestEmptyField(object):
def test_assign(self):
a = np.arange(10, dtype=np.float32)
a.dtype = [("int", "<0i4"), ("float", "<2f4")]
assert_(a['int'].shape == (5, 0))
assert_(a['float'].shape == (5, 2))
class TestCommonType(object):
def test_scalar_loses1(self):
res = np.find_common_type(['f4', 'f4', 'i2'], ['f8'])
assert_(res == 'f4')
def test_scalar_loses2(self):
res = np.find_common_type(['f4', 'f4'], ['i8'])
assert_(res == 'f4')
def test_scalar_wins(self):
res = np.find_common_type(['f4', 'f4', 'i2'], ['c8'])
assert_(res == 'c8')
def test_scalar_wins2(self):
res = np.find_common_type(['u4', 'i4', 'i4'], ['f4'])
assert_(res == 'f8')
def test_scalar_wins3(self): # doesn't go up to 'f16' on purpose
res = np.find_common_type(['u8', 'i8', 'i8'], ['f8'])
assert_(res == 'f8')
class TestMultipleFields(object):
def setup(self):
self.ary = np.array([(1, 2, 3, 4), (5, 6, 7, 8)], dtype='i4,f4,i2,c8')
def _bad_call(self):
return self.ary['f0', 'f1']
def test_no_tuple(self):
assert_raises(IndexError, self._bad_call)
def test_return(self):
res = self.ary[['f0', 'f2']].tolist()
assert_(res == [(1, 3), (5, 7)])
class TestIsSubDType(object):
# scalar types can be promoted into dtypes
wrappers = [np.dtype, lambda x: x]
def test_both_abstract(self):
assert_(np.issubdtype(np.floating, np.inexact))
assert_(not np.issubdtype(np.inexact, np.floating))
def test_same(self):
for cls in (np.float32, np.int32):
for w1, w2 in itertools.product(self.wrappers, repeat=2):
assert_(np.issubdtype(w1(cls), w2(cls)))
def test_subclass(self):
# note we cannot promote floating to a dtype, as it would turn into a
# concrete type
for w in self.wrappers:
assert_(np.issubdtype(w(np.float32), np.floating))
assert_(np.issubdtype(w(np.float64), np.floating))
def test_subclass_backwards(self):
for w in self.wrappers:
assert_(not np.issubdtype(np.floating, w(np.float32)))
assert_(not np.issubdtype(np.floating, w(np.float64)))
def test_sibling_class(self):
for w1, w2 in itertools.product(self.wrappers, repeat=2):
assert_(not np.issubdtype(w1(np.float32), w2(np.float64)))
assert_(not np.issubdtype(w1(np.float64), w2(np.float32)))
if __name__ == "__main__":
run_module_suite()
| 15,412 | 36.229469 | 119 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/tests/test_scalarprint.py
|
# -*- coding: utf-8 -*-
""" Test printing of scalar types.
"""
from __future__ import division, absolute_import, print_function
import code, sys
from tempfile import TemporaryFile
import numpy as np
from numpy.testing import assert_, assert_equal, suppress_warnings,\
run_module_suite
import sys, tempfile
class TestRealScalars(object):
def test_str(self):
svals = [0.0, -0.0, 1, -1, np.inf, -np.inf, np.nan]
styps = [np.float16, np.float32, np.float64, np.longdouble]
wanted = [
['0.0', '0.0', '0.0', '0.0' ],
['-0.0', '-0.0', '-0.0', '-0.0'],
['1.0', '1.0', '1.0', '1.0' ],
['-1.0', '-1.0', '-1.0', '-1.0'],
['inf', 'inf', 'inf', 'inf' ],
['-inf', '-inf', '-inf', '-inf'],
['nan', 'nan', 'nan', 'nan']]
for wants, val in zip(wanted, svals):
for want, styp in zip(wants, styps):
msg = 'for str({}({}))'.format(np.dtype(styp).name, repr(val))
assert_equal(str(styp(val)), want, err_msg=msg)
def test_scalar_cutoffs(self):
# test that both the str and repr of np.float64 behaves
# like python floats in python3. Note that in python2
# the str has truncated digits, but we do not do this
def check(v):
# we compare str to repr, to avoid python2 truncation behavior
assert_equal(str(np.float64(v)), repr(v))
assert_equal(repr(np.float64(v)), repr(v))
# check we use the same number of significant digits
check(1.12345678901234567890)
check(0.0112345678901234567890)
# check switch from scientific output to positional and back
check(1e-5)
check(1e-4)
check(1e15)
check(1e16)
def test_py2_float_print(self):
# gh-10753
# In python2, the python float type implements an obsolte method
# tp_print, which overrides tp_repr and tp_str when using the "print"
# keyword/method to output to a "real file" (ie, not a StringIO). Make
# sure we don't inherit it.
x = np.double(0.1999999999999)
with TemporaryFile('r+t') as f:
print(x, file=f)
f.seek(0)
output = f.read()
assert_equal(output, str(x) + '\n')
# In python2 the value float('0.1999999999999') prints with reduced
# precision as '0.2', but we want numpy's np.double('0.1999999999999')
# to print the unique value, '0.1999999999999'.
# gh-11031
# Only in the python2 interactive shell and when stdout is a "real"
# file, the output of the last command is printed to stdout without
# Py_PRINT_RAW (unlike the print statement) so `>>> x` and `>>> print
# x` are potentially different. Make sure they are the same. The only
# way I found to get prompt-like output is using an actual prompt from
# the 'code' module. Again, must use tempfile to get a "real" file.
# dummy user-input which enters one line and then ctrl-Ds.
def userinput():
yield 'np.sqrt(2)'
raise EOFError
gen = userinput()
input_func = lambda prompt="": next(gen)
with TemporaryFile('r+t') as fo, TemporaryFile('r+t') as fe:
orig_stdout, orig_stderr = sys.stdout, sys.stderr
sys.stdout, sys.stderr = fo, fe
# py2 code.interact sends irrelevant internal DeprecationWarnings
with suppress_warnings() as sup:
sup.filter(DeprecationWarning)
code.interact(local={'np': np}, readfunc=input_func, banner='')
sys.stdout, sys.stderr = orig_stdout, orig_stderr
fo.seek(0)
capture = fo.read().strip()
assert_equal(capture, repr(np.sqrt(2)))
def test_dragon4(self):
# these tests are adapted from Ryan Juckett's dragon4 implementation,
# see dragon4.c for details.
fpos32 = lambda x, **k: np.format_float_positional(np.float32(x), **k)
fsci32 = lambda x, **k: np.format_float_scientific(np.float32(x), **k)
fpos64 = lambda x, **k: np.format_float_positional(np.float64(x), **k)
fsci64 = lambda x, **k: np.format_float_scientific(np.float64(x), **k)
preckwd = lambda prec: {'unique': False, 'precision': prec}
assert_equal(fpos32('1.0'), "1.")
assert_equal(fsci32('1.0'), "1.e+00")
assert_equal(fpos32('10.234'), "10.234")
assert_equal(fpos32('-10.234'), "-10.234")
assert_equal(fsci32('10.234'), "1.0234e+01")
assert_equal(fsci32('-10.234'), "-1.0234e+01")
assert_equal(fpos32('1000.0'), "1000.")
assert_equal(fpos32('1.0', precision=0), "1.")
assert_equal(fsci32('1.0', precision=0), "1.e+00")
assert_equal(fpos32('10.234', precision=0), "10.")
assert_equal(fpos32('-10.234', precision=0), "-10.")
assert_equal(fsci32('10.234', precision=0), "1.e+01")
assert_equal(fsci32('-10.234', precision=0), "-1.e+01")
assert_equal(fpos32('10.234', precision=2), "10.23")
assert_equal(fsci32('-10.234', precision=2), "-1.02e+01")
assert_equal(fsci64('9.9999999999999995e-08', **preckwd(16)),
'9.9999999999999995e-08')
assert_equal(fsci64('9.8813129168249309e-324', **preckwd(16)),
'9.8813129168249309e-324')
assert_equal(fsci64('9.9999999999999694e-311', **preckwd(16)),
'9.9999999999999694e-311')
# test rounding
# 3.1415927410 is closest float32 to np.pi
assert_equal(fpos32('3.14159265358979323846', **preckwd(10)),
"3.1415927410")
assert_equal(fsci32('3.14159265358979323846', **preckwd(10)),
"3.1415927410e+00")
assert_equal(fpos64('3.14159265358979323846', **preckwd(10)),
"3.1415926536")
assert_equal(fsci64('3.14159265358979323846', **preckwd(10)),
"3.1415926536e+00")
# 299792448 is closest float32 to 299792458
assert_equal(fpos32('299792458.0', **preckwd(5)), "299792448.00000")
assert_equal(fsci32('299792458.0', **preckwd(5)), "2.99792e+08")
assert_equal(fpos64('299792458.0', **preckwd(5)), "299792458.00000")
assert_equal(fsci64('299792458.0', **preckwd(5)), "2.99792e+08")
assert_equal(fpos32('3.14159265358979323846', **preckwd(25)),
"3.1415927410125732421875000")
assert_equal(fpos64('3.14159265358979323846', **preckwd(50)),
"3.14159265358979311599796346854418516159057617187500")
assert_equal(fpos64('3.14159265358979323846'), "3.141592653589793")
# smallest numbers
assert_equal(fpos32(0.5**(126 + 23), unique=False, precision=149),
"0.00000000000000000000000000000000000000000000140129846432"
"4817070923729583289916131280261941876515771757068283889791"
"08268586060148663818836212158203125")
assert_equal(fpos64(0.5**(1022 + 52), unique=False, precision=1074),
"0.00000000000000000000000000000000000000000000000000000000"
"0000000000000000000000000000000000000000000000000000000000"
"0000000000000000000000000000000000000000000000000000000000"
"0000000000000000000000000000000000000000000000000000000000"
"0000000000000000000000000000000000000000000000000000000000"
"0000000000000000000000000000000000049406564584124654417656"
"8792868221372365059802614324764425585682500675507270208751"
"8652998363616359923797965646954457177309266567103559397963"
"9877479601078187812630071319031140452784581716784898210368"
"8718636056998730723050006387409153564984387312473397273169"
"6151400317153853980741262385655911710266585566867681870395"
"6031062493194527159149245532930545654440112748012970999954"
"1931989409080416563324524757147869014726780159355238611550"
"1348035264934720193790268107107491703332226844753335720832"
"4319360923828934583680601060115061698097530783422773183292"
"4790498252473077637592724787465608477820373446969953364701"
"7972677717585125660551199131504891101451037862738167250955"
"8373897335989936648099411642057026370902792427675445652290"
"87538682506419718265533447265625")
# largest numbers
assert_equal(fpos32(np.finfo(np.float32).max, **preckwd(0)),
"340282346638528859811704183484516925440.")
assert_equal(fpos64(np.finfo(np.float64).max, **preckwd(0)),
"1797693134862315708145274237317043567980705675258449965989"
"1747680315726078002853876058955863276687817154045895351438"
"2464234321326889464182768467546703537516986049910576551282"
"0762454900903893289440758685084551339423045832369032229481"
"6580855933212334827479782620414472316873817718091929988125"
"0404026184124858368.")
# Warning: In unique mode only the integer digits necessary for
# uniqueness are computed, the rest are 0. Should we change this?
assert_equal(fpos32(np.finfo(np.float32).max, precision=0),
"340282350000000000000000000000000000000.")
# test trailing zeros
assert_equal(fpos32('1.0', unique=False, precision=3), "1.000")
assert_equal(fpos64('1.0', unique=False, precision=3), "1.000")
assert_equal(fsci32('1.0', unique=False, precision=3), "1.000e+00")
assert_equal(fsci64('1.0', unique=False, precision=3), "1.000e+00")
assert_equal(fpos32('1.5', unique=False, precision=3), "1.500")
assert_equal(fpos64('1.5', unique=False, precision=3), "1.500")
assert_equal(fsci32('1.5', unique=False, precision=3), "1.500e+00")
assert_equal(fsci64('1.5', unique=False, precision=3), "1.500e+00")
# gh-10713
assert_equal(fpos64('324', unique=False, precision=5, fractional=False), "324.00")
def test_dragon4_interface(self):
tps = [np.float16, np.float32, np.float64]
if hasattr(np, 'float128'):
tps.append(np.float128)
fpos = np.format_float_positional
fsci = np.format_float_scientific
for tp in tps:
# test padding
assert_equal(fpos(tp('1.0'), pad_left=4, pad_right=4), " 1. ")
assert_equal(fpos(tp('-1.0'), pad_left=4, pad_right=4), " -1. ")
assert_equal(fpos(tp('-10.2'),
pad_left=4, pad_right=4), " -10.2 ")
# test exp_digits
assert_equal(fsci(tp('1.23e1'), exp_digits=5), "1.23e+00001")
# test fixed (non-unique) mode
assert_equal(fpos(tp('1.0'), unique=False, precision=4), "1.0000")
assert_equal(fsci(tp('1.0'), unique=False, precision=4),
"1.0000e+00")
# test trimming
# trim of 'k' or '.' only affects non-unique mode, since unique
# mode will not output trailing 0s.
assert_equal(fpos(tp('1.'), unique=False, precision=4, trim='k'),
"1.0000")
assert_equal(fpos(tp('1.'), unique=False, precision=4, trim='.'),
"1.")
assert_equal(fpos(tp('1.2'), unique=False, precision=4, trim='.'),
"1.2" if tp != np.float16 else "1.2002")
assert_equal(fpos(tp('1.'), unique=False, precision=4, trim='0'),
"1.0")
assert_equal(fpos(tp('1.2'), unique=False, precision=4, trim='0'),
"1.2" if tp != np.float16 else "1.2002")
assert_equal(fpos(tp('1.'), trim='0'), "1.0")
assert_equal(fpos(tp('1.'), unique=False, precision=4, trim='-'),
"1")
assert_equal(fpos(tp('1.2'), unique=False, precision=4, trim='-'),
"1.2" if tp != np.float16 else "1.2002")
assert_equal(fpos(tp('1.'), trim='-'), "1")
def float32_roundtrip(self):
# gh-9360
x = np.float32(1024 - 2**-14)
y = np.float32(1024 - 2**-13)
assert_(repr(x) != repr(y))
assert_equal(np.float32(repr(x)), x)
assert_equal(np.float32(repr(y)), y)
def float64_vs_python(self):
# gh-2643, gh-6136, gh-6908
assert_equal(repr(np.float64(0.1)), repr(0.1))
assert_(repr(np.float64(0.20000000000000004)) != repr(0.2))
if __name__ == "__main__":
run_module_suite()
| 13,011 | 47.371747 | 90 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/tests/test_multiarray.py
|
from __future__ import division, absolute_import, print_function
import collections
import tempfile
import sys
import shutil
import warnings
import operator
import io
import itertools
import functools
import ctypes
import os
import gc
from contextlib import contextmanager
if sys.version_info[0] >= 3:
import builtins
else:
import __builtin__ as builtins
from decimal import Decimal
from unittest import TestCase
import numpy as np
from numpy.compat import strchar, unicode
from numpy.core.tests.test_print import in_foreign_locale
from numpy.core.multiarray_tests import (
test_neighborhood_iterator, test_neighborhood_iterator_oob,
test_pydatamem_seteventhook_start, test_pydatamem_seteventhook_end,
test_inplace_increment, get_buffer_info, test_as_c_array,
)
from numpy.testing import (
run_module_suite, assert_, assert_raises, assert_warns,
assert_equal, assert_almost_equal, assert_array_equal, assert_raises_regex,
assert_array_almost_equal, assert_allclose, IS_PYPY, HAS_REFCOUNT,
assert_array_less, runstring, dec, SkipTest, temppath, suppress_warnings
)
# Need to test an object that does not fully implement math interface
from datetime import timedelta, datetime
if sys.version_info[:2] > (3, 2):
# In Python 3.3 the representation of empty shape, strides and sub-offsets
# is an empty tuple instead of None.
# http://docs.python.org/dev/whatsnew/3.3.html#api-changes
EMPTY = ()
else:
EMPTY = None
def _aligned_zeros(shape, dtype=float, order="C", align=None):
"""Allocate a new ndarray with aligned memory."""
dtype = np.dtype(dtype)
if dtype == np.dtype(object):
# Can't do this, fall back to standard allocation (which
# should always be sufficiently aligned)
if align is not None:
raise ValueError("object array alignment not supported")
return np.zeros(shape, dtype=dtype, order=order)
if align is None:
align = dtype.alignment
if not hasattr(shape, '__len__'):
shape = (shape,)
size = functools.reduce(operator.mul, shape) * dtype.itemsize
buf = np.empty(size + align + 1, np.uint8)
offset = buf.__array_interface__['data'][0] % align
if offset != 0:
offset = align - offset
# Note: slices producing 0-size arrays do not necessarily change
# data pointer --- so we use and allocate size+1
buf = buf[offset:offset+size+1][:-1]
data = np.ndarray(shape, dtype, buf, order=order)
data.fill(0)
return data
class TestFlags(object):
def setup(self):
self.a = np.arange(10)
def test_writeable(self):
mydict = locals()
self.a.flags.writeable = False
assert_raises(ValueError, runstring, 'self.a[0] = 3', mydict)
assert_raises(ValueError, runstring, 'self.a[0:1].itemset(3)', mydict)
self.a.flags.writeable = True
self.a[0] = 5
self.a[0] = 0
def test_otherflags(self):
assert_equal(self.a.flags.carray, True)
assert_equal(self.a.flags['C'], True)
assert_equal(self.a.flags.farray, False)
assert_equal(self.a.flags.behaved, True)
assert_equal(self.a.flags.fnc, False)
assert_equal(self.a.flags.forc, True)
assert_equal(self.a.flags.owndata, True)
assert_equal(self.a.flags.writeable, True)
assert_equal(self.a.flags.aligned, True)
with assert_warns(DeprecationWarning):
assert_equal(self.a.flags.updateifcopy, False)
with assert_warns(DeprecationWarning):
assert_equal(self.a.flags['U'], False)
assert_equal(self.a.flags['UPDATEIFCOPY'], False)
assert_equal(self.a.flags.writebackifcopy, False)
assert_equal(self.a.flags['X'], False)
assert_equal(self.a.flags['WRITEBACKIFCOPY'], False)
def test_string_align(self):
a = np.zeros(4, dtype=np.dtype('|S4'))
assert_(a.flags.aligned)
# not power of two are accessed byte-wise and thus considered aligned
a = np.zeros(5, dtype=np.dtype('|S4'))
assert_(a.flags.aligned)
def test_void_align(self):
a = np.zeros(4, dtype=np.dtype([("a", "i4"), ("b", "i4")]))
assert_(a.flags.aligned)
class TestHash(object):
# see #3793
def test_int(self):
for st, ut, s in [(np.int8, np.uint8, 8),
(np.int16, np.uint16, 16),
(np.int32, np.uint32, 32),
(np.int64, np.uint64, 64)]:
for i in range(1, s):
assert_equal(hash(st(-2**i)), hash(-2**i),
err_msg="%r: -2**%d" % (st, i))
assert_equal(hash(st(2**(i - 1))), hash(2**(i - 1)),
err_msg="%r: 2**%d" % (st, i - 1))
assert_equal(hash(st(2**i - 1)), hash(2**i - 1),
err_msg="%r: 2**%d - 1" % (st, i))
i = max(i - 1, 1)
assert_equal(hash(ut(2**(i - 1))), hash(2**(i - 1)),
err_msg="%r: 2**%d" % (ut, i - 1))
assert_equal(hash(ut(2**i - 1)), hash(2**i - 1),
err_msg="%r: 2**%d - 1" % (ut, i))
class TestAttributes(object):
def setup(self):
self.one = np.arange(10)
self.two = np.arange(20).reshape(4, 5)
self.three = np.arange(60, dtype=np.float64).reshape(2, 5, 6)
def test_attributes(self):
assert_equal(self.one.shape, (10,))
assert_equal(self.two.shape, (4, 5))
assert_equal(self.three.shape, (2, 5, 6))
self.three.shape = (10, 3, 2)
assert_equal(self.three.shape, (10, 3, 2))
self.three.shape = (2, 5, 6)
assert_equal(self.one.strides, (self.one.itemsize,))
num = self.two.itemsize
assert_equal(self.two.strides, (5*num, num))
num = self.three.itemsize
assert_equal(self.three.strides, (30*num, 6*num, num))
assert_equal(self.one.ndim, 1)
assert_equal(self.two.ndim, 2)
assert_equal(self.three.ndim, 3)
num = self.two.itemsize
assert_equal(self.two.size, 20)
assert_equal(self.two.nbytes, 20*num)
assert_equal(self.two.itemsize, self.two.dtype.itemsize)
assert_equal(self.two.base, np.arange(20))
def test_dtypeattr(self):
assert_equal(self.one.dtype, np.dtype(np.int_))
assert_equal(self.three.dtype, np.dtype(np.float_))
assert_equal(self.one.dtype.char, 'l')
assert_equal(self.three.dtype.char, 'd')
assert_(self.three.dtype.str[0] in '<>')
assert_equal(self.one.dtype.str[1], 'i')
assert_equal(self.three.dtype.str[1], 'f')
def test_int_subclassing(self):
# Regression test for https://github.com/numpy/numpy/pull/3526
numpy_int = np.int_(0)
if sys.version_info[0] >= 3:
# On Py3k int_ should not inherit from int, because it's not
# fixed-width anymore
assert_equal(isinstance(numpy_int, int), False)
else:
# Otherwise, it should inherit from int...
assert_equal(isinstance(numpy_int, int), True)
# ... and fast-path checks on C-API level should also work
from numpy.core.multiarray_tests import test_int_subclass
assert_equal(test_int_subclass(numpy_int), True)
def test_stridesattr(self):
x = self.one
def make_array(size, offset, strides):
return np.ndarray(size, buffer=x, dtype=int,
offset=offset*x.itemsize,
strides=strides*x.itemsize)
assert_equal(make_array(4, 4, -1), np.array([4, 3, 2, 1]))
assert_raises(ValueError, make_array, 4, 4, -2)
assert_raises(ValueError, make_array, 4, 2, -1)
assert_raises(ValueError, make_array, 8, 3, 1)
assert_equal(make_array(8, 3, 0), np.array([3]*8))
# Check behavior reported in gh-2503:
assert_raises(ValueError, make_array, (2, 3), 5, np.array([-2, -3]))
make_array(0, 0, 10)
def test_set_stridesattr(self):
x = self.one
def make_array(size, offset, strides):
try:
r = np.ndarray([size], dtype=int, buffer=x,
offset=offset*x.itemsize)
except Exception as e:
raise RuntimeError(e)
r.strides = strides = strides*x.itemsize
return r
assert_equal(make_array(4, 4, -1), np.array([4, 3, 2, 1]))
assert_equal(make_array(7, 3, 1), np.array([3, 4, 5, 6, 7, 8, 9]))
assert_raises(ValueError, make_array, 4, 4, -2)
assert_raises(ValueError, make_array, 4, 2, -1)
assert_raises(RuntimeError, make_array, 8, 3, 1)
# Check that the true extent of the array is used.
# Test relies on as_strided base not exposing a buffer.
x = np.lib.stride_tricks.as_strided(np.arange(1), (10, 10), (0, 0))
def set_strides(arr, strides):
arr.strides = strides
assert_raises(ValueError, set_strides, x, (10*x.itemsize, x.itemsize))
# Test for offset calculations:
x = np.lib.stride_tricks.as_strided(np.arange(10, dtype=np.int8)[-1],
shape=(10,), strides=(-1,))
assert_raises(ValueError, set_strides, x[::-1], -1)
a = x[::-1]
a.strides = 1
a[::2].strides = 2
def test_fill(self):
for t in "?bhilqpBHILQPfdgFDGO":
x = np.empty((3, 2, 1), t)
y = np.empty((3, 2, 1), t)
x.fill(1)
y[...] = 1
assert_equal(x, y)
def test_fill_max_uint64(self):
x = np.empty((3, 2, 1), dtype=np.uint64)
y = np.empty((3, 2, 1), dtype=np.uint64)
value = 2**64 - 1
y[...] = value
x.fill(value)
assert_array_equal(x, y)
def test_fill_struct_array(self):
# Filling from a scalar
x = np.array([(0, 0.0), (1, 1.0)], dtype='i4,f8')
x.fill(x[0])
assert_equal(x['f1'][1], x['f1'][0])
# Filling from a tuple that can be converted
# to a scalar
x = np.zeros(2, dtype=[('a', 'f8'), ('b', 'i4')])
x.fill((3.5, -2))
assert_array_equal(x['a'], [3.5, 3.5])
assert_array_equal(x['b'], [-2, -2])
class TestArrayConstruction(object):
def test_array(self):
d = np.ones(6)
r = np.array([d, d])
assert_equal(r, np.ones((2, 6)))
d = np.ones(6)
tgt = np.ones((2, 6))
r = np.array([d, d])
assert_equal(r, tgt)
tgt[1] = 2
r = np.array([d, d + 1])
assert_equal(r, tgt)
d = np.ones(6)
r = np.array([[d, d]])
assert_equal(r, np.ones((1, 2, 6)))
d = np.ones(6)
r = np.array([[d, d], [d, d]])
assert_equal(r, np.ones((2, 2, 6)))
d = np.ones((6, 6))
r = np.array([d, d])
assert_equal(r, np.ones((2, 6, 6)))
d = np.ones((6, ))
r = np.array([[d, d + 1], d + 2])
assert_equal(len(r), 2)
assert_equal(r[0], [d, d + 1])
assert_equal(r[1], d + 2)
tgt = np.ones((2, 3), dtype=bool)
tgt[0, 2] = False
tgt[1, 0:2] = False
r = np.array([[True, True, False], [False, False, True]])
assert_equal(r, tgt)
r = np.array([[True, False], [True, False], [False, True]])
assert_equal(r, tgt.T)
def test_array_empty(self):
assert_raises(TypeError, np.array)
def test_array_copy_false(self):
d = np.array([1, 2, 3])
e = np.array(d, copy=False)
d[1] = 3
assert_array_equal(e, [1, 3, 3])
e = np.array(d, copy=False, order='F')
d[1] = 4
assert_array_equal(e, [1, 4, 3])
e[2] = 7
assert_array_equal(d, [1, 4, 7])
def test_array_copy_true(self):
d = np.array([[1,2,3], [1, 2, 3]])
e = np.array(d, copy=True)
d[0, 1] = 3
e[0, 2] = -7
assert_array_equal(e, [[1, 2, -7], [1, 2, 3]])
assert_array_equal(d, [[1, 3, 3], [1, 2, 3]])
e = np.array(d, copy=True, order='F')
d[0, 1] = 5
e[0, 2] = 7
assert_array_equal(e, [[1, 3, 7], [1, 2, 3]])
assert_array_equal(d, [[1, 5, 3], [1,2,3]])
def test_array_cont(self):
d = np.ones(10)[::2]
assert_(np.ascontiguousarray(d).flags.c_contiguous)
assert_(np.ascontiguousarray(d).flags.f_contiguous)
assert_(np.asfortranarray(d).flags.c_contiguous)
assert_(np.asfortranarray(d).flags.f_contiguous)
d = np.ones((10, 10))[::2,::2]
assert_(np.ascontiguousarray(d).flags.c_contiguous)
assert_(np.asfortranarray(d).flags.f_contiguous)
class TestAssignment(object):
def test_assignment_broadcasting(self):
a = np.arange(6).reshape(2, 3)
# Broadcasting the input to the output
a[...] = np.arange(3)
assert_equal(a, [[0, 1, 2], [0, 1, 2]])
a[...] = np.arange(2).reshape(2, 1)
assert_equal(a, [[0, 0, 0], [1, 1, 1]])
# For compatibility with <= 1.5, a limited version of broadcasting
# the output to the input.
#
# This behavior is inconsistent with NumPy broadcasting
# in general, because it only uses one of the two broadcasting
# rules (adding a new "1" dimension to the left of the shape),
# applied to the output instead of an input. In NumPy 2.0, this kind
# of broadcasting assignment will likely be disallowed.
a[...] = np.arange(6)[::-1].reshape(1, 2, 3)
assert_equal(a, [[5, 4, 3], [2, 1, 0]])
# The other type of broadcasting would require a reduction operation.
def assign(a, b):
a[...] = b
assert_raises(ValueError, assign, a, np.arange(12).reshape(2, 2, 3))
def test_assignment_errors(self):
# Address issue #2276
class C:
pass
a = np.zeros(1)
def assign(v):
a[0] = v
assert_raises((AttributeError, TypeError), assign, C())
assert_raises(ValueError, assign, [1])
def test_unicode_assignment(self):
# gh-5049
from numpy.core.numeric import set_string_function
@contextmanager
def inject_str(s):
""" replace ndarray.__str__ temporarily """
set_string_function(lambda x: s, repr=False)
try:
yield
finally:
set_string_function(None, repr=False)
a1d = np.array([u'test'])
a0d = np.array(u'done')
with inject_str(u'bad'):
a1d[0] = a0d # previously this would invoke __str__
assert_equal(a1d[0], u'done')
# this would crash for the same reason
np.array([np.array(u'\xe5\xe4\xf6')])
def test_stringlike_empty_list(self):
# gh-8902
u = np.array([u'done'])
b = np.array([b'done'])
class bad_sequence(object):
def __getitem__(self): pass
def __len__(self): raise RuntimeError
assert_raises(ValueError, operator.setitem, u, 0, [])
assert_raises(ValueError, operator.setitem, b, 0, [])
assert_raises(ValueError, operator.setitem, u, 0, bad_sequence())
assert_raises(ValueError, operator.setitem, b, 0, bad_sequence())
def test_longdouble_assignment(self):
# only relevant if longdouble is larger than float
# we're looking for loss of precision
for dtype in (np.longdouble, np.longcomplex):
# gh-8902
tinyb = np.nextafter(np.longdouble(0), 1).astype(dtype)
tinya = np.nextafter(np.longdouble(0), -1).astype(dtype)
# construction
tiny1d = np.array([tinya])
assert_equal(tiny1d[0], tinya)
# scalar = scalar
tiny1d[0] = tinyb
assert_equal(tiny1d[0], tinyb)
# 0d = scalar
tiny1d[0, ...] = tinya
assert_equal(tiny1d[0], tinya)
# 0d = 0d
tiny1d[0, ...] = tinyb[...]
assert_equal(tiny1d[0], tinyb)
# scalar = 0d
tiny1d[0] = tinyb[...]
assert_equal(tiny1d[0], tinyb)
arr = np.array([np.array(tinya)])
assert_equal(arr[0], tinya)
def test_cast_to_string(self):
# cast to str should do "str(scalar)", not "str(scalar.item())"
# Example: In python2, str(float) is truncated, so we want to avoid
# str(np.float64(...).item()) as this would incorrectly truncate.
a = np.zeros(1, dtype='S20')
a[:] = np.array(['1.12345678901234567890'], dtype='f8')
assert_equal(a[0], b"1.1234567890123457")
class TestDtypedescr(object):
def test_construction(self):
d1 = np.dtype('i4')
assert_equal(d1, np.dtype(np.int32))
d2 = np.dtype('f8')
assert_equal(d2, np.dtype(np.float64))
def test_byteorders(self):
assert_(np.dtype('<i4') != np.dtype('>i4'))
assert_(np.dtype([('a', '<i4')]) != np.dtype([('a', '>i4')]))
def test_structured_non_void(self):
fields = [('a', '<i2'), ('b', '<i2')]
dt_int = np.dtype(('i4', fields))
assert_equal(str(dt_int), "(numpy.int32, [('a', '<i2'), ('b', '<i2')])")
# gh-9821
arr_int = np.zeros(4, dt_int)
assert_equal(repr(arr_int),
"array([0, 0, 0, 0], dtype=(numpy.int32, [('a', '<i2'), ('b', '<i2')]))")
class TestZeroRank(object):
def setup(self):
self.d = np.array(0), np.array('x', object)
def test_ellipsis_subscript(self):
a, b = self.d
assert_equal(a[...], 0)
assert_equal(b[...], 'x')
assert_(a[...].base is a) # `a[...] is a` in numpy <1.9.
assert_(b[...].base is b) # `b[...] is b` in numpy <1.9.
def test_empty_subscript(self):
a, b = self.d
assert_equal(a[()], 0)
assert_equal(b[()], 'x')
assert_(type(a[()]) is a.dtype.type)
assert_(type(b[()]) is str)
def test_invalid_subscript(self):
a, b = self.d
assert_raises(IndexError, lambda x: x[0], a)
assert_raises(IndexError, lambda x: x[0], b)
assert_raises(IndexError, lambda x: x[np.array([], int)], a)
assert_raises(IndexError, lambda x: x[np.array([], int)], b)
def test_ellipsis_subscript_assignment(self):
a, b = self.d
a[...] = 42
assert_equal(a, 42)
b[...] = ''
assert_equal(b.item(), '')
def test_empty_subscript_assignment(self):
a, b = self.d
a[()] = 42
assert_equal(a, 42)
b[()] = ''
assert_equal(b.item(), '')
def test_invalid_subscript_assignment(self):
a, b = self.d
def assign(x, i, v):
x[i] = v
assert_raises(IndexError, assign, a, 0, 42)
assert_raises(IndexError, assign, b, 0, '')
assert_raises(ValueError, assign, a, (), '')
def test_newaxis(self):
a, b = self.d
assert_equal(a[np.newaxis].shape, (1,))
assert_equal(a[..., np.newaxis].shape, (1,))
assert_equal(a[np.newaxis, ...].shape, (1,))
assert_equal(a[..., np.newaxis].shape, (1,))
assert_equal(a[np.newaxis, ..., np.newaxis].shape, (1, 1))
assert_equal(a[..., np.newaxis, np.newaxis].shape, (1, 1))
assert_equal(a[np.newaxis, np.newaxis, ...].shape, (1, 1))
assert_equal(a[(np.newaxis,)*10].shape, (1,)*10)
def test_invalid_newaxis(self):
a, b = self.d
def subscript(x, i):
x[i]
assert_raises(IndexError, subscript, a, (np.newaxis, 0))
assert_raises(IndexError, subscript, a, (np.newaxis,)*50)
def test_constructor(self):
x = np.ndarray(())
x[()] = 5
assert_equal(x[()], 5)
y = np.ndarray((), buffer=x)
y[()] = 6
assert_equal(x[()], 6)
def test_output(self):
x = np.array(2)
assert_raises(ValueError, np.add, x, [1], x)
class TestScalarIndexing(object):
def setup(self):
self.d = np.array([0, 1])[0]
def test_ellipsis_subscript(self):
a = self.d
assert_equal(a[...], 0)
assert_equal(a[...].shape, ())
def test_empty_subscript(self):
a = self.d
assert_equal(a[()], 0)
assert_equal(a[()].shape, ())
def test_invalid_subscript(self):
a = self.d
assert_raises(IndexError, lambda x: x[0], a)
assert_raises(IndexError, lambda x: x[np.array([], int)], a)
def test_invalid_subscript_assignment(self):
a = self.d
def assign(x, i, v):
x[i] = v
assert_raises(TypeError, assign, a, 0, 42)
def test_newaxis(self):
a = self.d
assert_equal(a[np.newaxis].shape, (1,))
assert_equal(a[..., np.newaxis].shape, (1,))
assert_equal(a[np.newaxis, ...].shape, (1,))
assert_equal(a[..., np.newaxis].shape, (1,))
assert_equal(a[np.newaxis, ..., np.newaxis].shape, (1, 1))
assert_equal(a[..., np.newaxis, np.newaxis].shape, (1, 1))
assert_equal(a[np.newaxis, np.newaxis, ...].shape, (1, 1))
assert_equal(a[(np.newaxis,)*10].shape, (1,)*10)
def test_invalid_newaxis(self):
a = self.d
def subscript(x, i):
x[i]
assert_raises(IndexError, subscript, a, (np.newaxis, 0))
assert_raises(IndexError, subscript, a, (np.newaxis,)*50)
def test_overlapping_assignment(self):
# With positive strides
a = np.arange(4)
a[:-1] = a[1:]
assert_equal(a, [1, 2, 3, 3])
a = np.arange(4)
a[1:] = a[:-1]
assert_equal(a, [0, 0, 1, 2])
# With positive and negative strides
a = np.arange(4)
a[:] = a[::-1]
assert_equal(a, [3, 2, 1, 0])
a = np.arange(6).reshape(2, 3)
a[::-1,:] = a[:, ::-1]
assert_equal(a, [[5, 4, 3], [2, 1, 0]])
a = np.arange(6).reshape(2, 3)
a[::-1, ::-1] = a[:, ::-1]
assert_equal(a, [[3, 4, 5], [0, 1, 2]])
# With just one element overlapping
a = np.arange(5)
a[:3] = a[2:]
assert_equal(a, [2, 3, 4, 3, 4])
a = np.arange(5)
a[2:] = a[:3]
assert_equal(a, [0, 1, 0, 1, 2])
a = np.arange(5)
a[2::-1] = a[2:]
assert_equal(a, [4, 3, 2, 3, 4])
a = np.arange(5)
a[2:] = a[2::-1]
assert_equal(a, [0, 1, 2, 1, 0])
a = np.arange(5)
a[2::-1] = a[:1:-1]
assert_equal(a, [2, 3, 4, 3, 4])
a = np.arange(5)
a[:1:-1] = a[2::-1]
assert_equal(a, [0, 1, 0, 1, 2])
class TestCreation(object):
def test_from_attribute(self):
class x(object):
def __array__(self, dtype=None):
pass
assert_raises(ValueError, np.array, x())
def test_from_string(self):
types = np.typecodes['AllInteger'] + np.typecodes['Float']
nstr = ['123', '123']
result = np.array([123, 123], dtype=int)
for type in types:
msg = 'String conversion for %s' % type
assert_equal(np.array(nstr, dtype=type), result, err_msg=msg)
def test_void(self):
arr = np.array([], dtype='V')
assert_equal(arr.dtype.kind, 'V')
def test_too_big_error(self):
# 45341 is the smallest integer greater than sqrt(2**31 - 1).
# 3037000500 is the smallest integer greater than sqrt(2**63 - 1).
# We want to make sure that the square byte array with those dimensions
# is too big on 32 or 64 bit systems respectively.
if np.iinfo('intp').max == 2**31 - 1:
shape = (46341, 46341)
elif np.iinfo('intp').max == 2**63 - 1:
shape = (3037000500, 3037000500)
else:
return
assert_raises(ValueError, np.empty, shape, dtype=np.int8)
assert_raises(ValueError, np.zeros, shape, dtype=np.int8)
assert_raises(ValueError, np.ones, shape, dtype=np.int8)
def test_zeros(self):
types = np.typecodes['AllInteger'] + np.typecodes['AllFloat']
for dt in types:
d = np.zeros((13,), dtype=dt)
assert_equal(np.count_nonzero(d), 0)
# true for ieee floats
assert_equal(d.sum(), 0)
assert_(not d.any())
d = np.zeros(2, dtype='(2,4)i4')
assert_equal(np.count_nonzero(d), 0)
assert_equal(d.sum(), 0)
assert_(not d.any())
d = np.zeros(2, dtype='4i4')
assert_equal(np.count_nonzero(d), 0)
assert_equal(d.sum(), 0)
assert_(not d.any())
d = np.zeros(2, dtype='(2,4)i4, (2,4)i4')
assert_equal(np.count_nonzero(d), 0)
@dec.slow
def test_zeros_big(self):
# test big array as they might be allocated different by the system
types = np.typecodes['AllInteger'] + np.typecodes['AllFloat']
for dt in types:
d = np.zeros((30 * 1024**2,), dtype=dt)
assert_(not d.any())
# This test can fail on 32-bit systems due to insufficient
# contiguous memory. Deallocating the previous array increases the
# chance of success.
del(d)
def test_zeros_obj(self):
# test initialization from PyLong(0)
d = np.zeros((13,), dtype=object)
assert_array_equal(d, [0] * 13)
assert_equal(np.count_nonzero(d), 0)
def test_zeros_obj_obj(self):
d = np.zeros(10, dtype=[('k', object, 2)])
assert_array_equal(d['k'], 0)
def test_zeros_like_like_zeros(self):
# test zeros_like returns the same as zeros
for c in np.typecodes['All']:
if c == 'V':
continue
d = np.zeros((3,3), dtype=c)
assert_array_equal(np.zeros_like(d), d)
assert_equal(np.zeros_like(d).dtype, d.dtype)
# explicitly check some special cases
d = np.zeros((3,3), dtype='S5')
assert_array_equal(np.zeros_like(d), d)
assert_equal(np.zeros_like(d).dtype, d.dtype)
d = np.zeros((3,3), dtype='U5')
assert_array_equal(np.zeros_like(d), d)
assert_equal(np.zeros_like(d).dtype, d.dtype)
d = np.zeros((3,3), dtype='<i4')
assert_array_equal(np.zeros_like(d), d)
assert_equal(np.zeros_like(d).dtype, d.dtype)
d = np.zeros((3,3), dtype='>i4')
assert_array_equal(np.zeros_like(d), d)
assert_equal(np.zeros_like(d).dtype, d.dtype)
d = np.zeros((3,3), dtype='<M8[s]')
assert_array_equal(np.zeros_like(d), d)
assert_equal(np.zeros_like(d).dtype, d.dtype)
d = np.zeros((3,3), dtype='>M8[s]')
assert_array_equal(np.zeros_like(d), d)
assert_equal(np.zeros_like(d).dtype, d.dtype)
d = np.zeros((3,3), dtype='f4,f4')
assert_array_equal(np.zeros_like(d), d)
assert_equal(np.zeros_like(d).dtype, d.dtype)
def test_empty_unicode(self):
# don't throw decode errors on garbage memory
for i in range(5, 100, 5):
d = np.empty(i, dtype='U')
str(d)
def test_sequence_non_homogenous(self):
assert_equal(np.array([4, 2**80]).dtype, object)
assert_equal(np.array([4, 2**80, 4]).dtype, object)
assert_equal(np.array([2**80, 4]).dtype, object)
assert_equal(np.array([2**80] * 3).dtype, object)
assert_equal(np.array([[1, 1],[1j, 1j]]).dtype, complex)
assert_equal(np.array([[1j, 1j],[1, 1]]).dtype, complex)
assert_equal(np.array([[1, 1, 1],[1, 1j, 1.], [1, 1, 1]]).dtype, complex)
@dec.skipif(sys.version_info[0] >= 3)
def test_sequence_long(self):
assert_equal(np.array([long(4), long(4)]).dtype, np.long)
assert_equal(np.array([long(4), 2**80]).dtype, object)
assert_equal(np.array([long(4), 2**80, long(4)]).dtype, object)
assert_equal(np.array([2**80, long(4)]).dtype, object)
def test_non_sequence_sequence(self):
"""Should not segfault.
Class Fail breaks the sequence protocol for new style classes, i.e.,
those derived from object. Class Map is a mapping type indicated by
raising a ValueError. At some point we may raise a warning instead
of an error in the Fail case.
"""
class Fail(object):
def __len__(self):
return 1
def __getitem__(self, index):
raise ValueError()
class Map(object):
def __len__(self):
return 1
def __getitem__(self, index):
raise KeyError()
a = np.array([Map()])
assert_(a.shape == (1,))
assert_(a.dtype == np.dtype(object))
assert_raises(ValueError, np.array, [Fail()])
def test_no_len_object_type(self):
# gh-5100, want object array from iterable object without len()
class Point2:
def __init__(self):
pass
def __getitem__(self, ind):
if ind in [0, 1]:
return ind
else:
raise IndexError()
d = np.array([Point2(), Point2(), Point2()])
assert_equal(d.dtype, np.dtype(object))
def test_false_len_sequence(self):
# gh-7264, segfault for this example
class C:
def __getitem__(self, i):
raise IndexError
def __len__(self):
return 42
assert_raises(ValueError, np.array, C()) # segfault?
def test_failed_len_sequence(self):
# gh-7393
class A(object):
def __init__(self, data):
self._data = data
def __getitem__(self, item):
return type(self)(self._data[item])
def __len__(self):
return len(self._data)
# len(d) should give 3, but len(d[0]) will fail
d = A([1,2,3])
assert_equal(len(np.array(d)), 3)
def test_array_too_big(self):
# Test that array creation succeeds for arrays addressable by intp
# on the byte level and fails for too large arrays.
buf = np.zeros(100)
max_bytes = np.iinfo(np.intp).max
for dtype in ["intp", "S20", "b"]:
dtype = np.dtype(dtype)
itemsize = dtype.itemsize
np.ndarray(buffer=buf, strides=(0,),
shape=(max_bytes//itemsize,), dtype=dtype)
assert_raises(ValueError, np.ndarray, buffer=buf, strides=(0,),
shape=(max_bytes//itemsize + 1,), dtype=dtype)
class TestStructured(object):
def test_subarray_field_access(self):
a = np.zeros((3, 5), dtype=[('a', ('i4', (2, 2)))])
a['a'] = np.arange(60).reshape(3, 5, 2, 2)
# Since the subarray is always in C-order, a transpose
# does not swap the subarray:
assert_array_equal(a.T['a'], a['a'].transpose(1, 0, 2, 3))
# In Fortran order, the subarray gets appended
# like in all other cases, not prepended as a special case
b = a.copy(order='F')
assert_equal(a['a'].shape, b['a'].shape)
assert_equal(a.T['a'].shape, a.T.copy()['a'].shape)
def test_subarray_comparison(self):
# Check that comparisons between record arrays with
# multi-dimensional field types work properly
a = np.rec.fromrecords(
[([1, 2, 3], 'a', [[1, 2], [3, 4]]), ([3, 3, 3], 'b', [[0, 0], [0, 0]])],
dtype=[('a', ('f4', 3)), ('b', object), ('c', ('i4', (2, 2)))])
b = a.copy()
assert_equal(a == b, [True, True])
assert_equal(a != b, [False, False])
b[1].b = 'c'
assert_equal(a == b, [True, False])
assert_equal(a != b, [False, True])
for i in range(3):
b[0].a = a[0].a
b[0].a[i] = 5
assert_equal(a == b, [False, False])
assert_equal(a != b, [True, True])
for i in range(2):
for j in range(2):
b = a.copy()
b[0].c[i, j] = 10
assert_equal(a == b, [False, True])
assert_equal(a != b, [True, False])
# Check that broadcasting with a subarray works
a = np.array([[(0,)], [(1,)]], dtype=[('a', 'f8')])
b = np.array([(0,), (0,), (1,)], dtype=[('a', 'f8')])
assert_equal(a == b, [[True, True, False], [False, False, True]])
assert_equal(b == a, [[True, True, False], [False, False, True]])
a = np.array([[(0,)], [(1,)]], dtype=[('a', 'f8', (1,))])
b = np.array([(0,), (0,), (1,)], dtype=[('a', 'f8', (1,))])
assert_equal(a == b, [[True, True, False], [False, False, True]])
assert_equal(b == a, [[True, True, False], [False, False, True]])
a = np.array([[([0, 0],)], [([1, 1],)]], dtype=[('a', 'f8', (2,))])
b = np.array([([0, 0],), ([0, 1],), ([1, 1],)], dtype=[('a', 'f8', (2,))])
assert_equal(a == b, [[True, False, False], [False, False, True]])
assert_equal(b == a, [[True, False, False], [False, False, True]])
# Check that broadcasting Fortran-style arrays with a subarray work
a = np.array([[([0, 0],)], [([1, 1],)]], dtype=[('a', 'f8', (2,))], order='F')
b = np.array([([0, 0],), ([0, 1],), ([1, 1],)], dtype=[('a', 'f8', (2,))])
assert_equal(a == b, [[True, False, False], [False, False, True]])
assert_equal(b == a, [[True, False, False], [False, False, True]])
# Check that incompatible sub-array shapes don't result to broadcasting
x = np.zeros((1,), dtype=[('a', ('f4', (1, 2))), ('b', 'i1')])
y = np.zeros((1,), dtype=[('a', ('f4', (2,))), ('b', 'i1')])
# This comparison invokes deprecated behaviour, and will probably
# start raising an error eventually. What we really care about in this
# test is just that it doesn't return True.
with suppress_warnings() as sup:
sup.filter(FutureWarning, "elementwise == comparison failed")
assert_equal(x == y, False)
x = np.zeros((1,), dtype=[('a', ('f4', (2, 1))), ('b', 'i1')])
y = np.zeros((1,), dtype=[('a', ('f4', (2,))), ('b', 'i1')])
# This comparison invokes deprecated behaviour, and will probably
# start raising an error eventually. What we really care about in this
# test is just that it doesn't return True.
with suppress_warnings() as sup:
sup.filter(FutureWarning, "elementwise == comparison failed")
assert_equal(x == y, False)
# Check that structured arrays that are different only in
# byte-order work
a = np.array([(5, 42), (10, 1)], dtype=[('a', '>i8'), ('b', '<f8')])
b = np.array([(5, 43), (10, 1)], dtype=[('a', '<i8'), ('b', '>f8')])
assert_equal(a == b, [False, True])
def test_casting(self):
# Check that casting a structured array to change its byte order
# works
a = np.array([(1,)], dtype=[('a', '<i4')])
assert_(np.can_cast(a.dtype, [('a', '>i4')], casting='unsafe'))
b = a.astype([('a', '>i4')])
assert_equal(b, a.byteswap().newbyteorder())
assert_equal(a['a'][0], b['a'][0])
# Check that equality comparison works on structured arrays if
# they are 'equiv'-castable
a = np.array([(5, 42), (10, 1)], dtype=[('a', '>i4'), ('b', '<f8')])
b = np.array([(5, 42), (10, 1)], dtype=[('a', '<i4'), ('b', '>f8')])
assert_(np.can_cast(a.dtype, b.dtype, casting='equiv'))
assert_equal(a == b, [True, True])
# Check that 'equiv' casting can change byte order
assert_(np.can_cast(a.dtype, b.dtype, casting='equiv'))
c = a.astype(b.dtype, casting='equiv')
assert_equal(a == c, [True, True])
# Check that 'safe' casting can change byte order and up-cast
# fields
t = [('a', '<i8'), ('b', '>f8')]
assert_(np.can_cast(a.dtype, t, casting='safe'))
c = a.astype(t, casting='safe')
assert_equal((c == np.array([(5, 42), (10, 1)], dtype=t)),
[True, True])
# Check that 'same_kind' casting can change byte order and
# change field widths within a "kind"
t = [('a', '<i4'), ('b', '>f4')]
assert_(np.can_cast(a.dtype, t, casting='same_kind'))
c = a.astype(t, casting='same_kind')
assert_equal((c == np.array([(5, 42), (10, 1)], dtype=t)),
[True, True])
# Check that casting fails if the casting rule should fail on
# any of the fields
t = [('a', '>i8'), ('b', '<f4')]
assert_(not np.can_cast(a.dtype, t, casting='safe'))
assert_raises(TypeError, a.astype, t, casting='safe')
t = [('a', '>i2'), ('b', '<f8')]
assert_(not np.can_cast(a.dtype, t, casting='equiv'))
assert_raises(TypeError, a.astype, t, casting='equiv')
t = [('a', '>i8'), ('b', '<i2')]
assert_(not np.can_cast(a.dtype, t, casting='same_kind'))
assert_raises(TypeError, a.astype, t, casting='same_kind')
assert_(not np.can_cast(a.dtype, b.dtype, casting='no'))
assert_raises(TypeError, a.astype, b.dtype, casting='no')
# Check that non-'unsafe' casting can't change the set of field names
for casting in ['no', 'safe', 'equiv', 'same_kind']:
t = [('a', '>i4')]
assert_(not np.can_cast(a.dtype, t, casting=casting))
t = [('a', '>i4'), ('b', '<f8'), ('c', 'i4')]
assert_(not np.can_cast(a.dtype, t, casting=casting))
def test_objview(self):
# https://github.com/numpy/numpy/issues/3286
a = np.array([], dtype=[('a', 'f'), ('b', 'f'), ('c', 'O')])
a[['a', 'b']] # TypeError?
# https://github.com/numpy/numpy/issues/3253
dat2 = np.zeros(3, [('A', 'i'), ('B', '|O')])
dat2[['B', 'A']] # TypeError?
def test_setfield(self):
# https://github.com/numpy/numpy/issues/3126
struct_dt = np.dtype([('elem', 'i4', 5),])
dt = np.dtype([('field', 'i4', 10),('struct', struct_dt)])
x = np.zeros(1, dt)
x[0]['field'] = np.ones(10, dtype='i4')
x[0]['struct'] = np.ones(1, dtype=struct_dt)
assert_equal(x[0]['field'], np.ones(10, dtype='i4'))
def test_setfield_object(self):
# make sure object field assignment with ndarray value
# on void scalar mimics setitem behavior
b = np.zeros(1, dtype=[('x', 'O')])
# next line should work identically to b['x'][0] = np.arange(3)
b[0]['x'] = np.arange(3)
assert_equal(b[0]['x'], np.arange(3))
# check that broadcasting check still works
c = np.zeros(1, dtype=[('x', 'O', 5)])
def testassign():
c[0]['x'] = np.arange(3)
assert_raises(ValueError, testassign)
def test_zero_width_string(self):
# Test for PR #6430 / issues #473, #4955, #2585
dt = np.dtype([('I', int), ('S', 'S0')])
x = np.zeros(4, dtype=dt)
assert_equal(x['S'], [b'', b'', b'', b''])
assert_equal(x['S'].itemsize, 0)
x['S'] = ['a', 'b', 'c', 'd']
assert_equal(x['S'], [b'', b'', b'', b''])
assert_equal(x['I'], [0, 0, 0, 0])
# Variation on test case from #4955
x['S'][x['I'] == 0] = 'hello'
assert_equal(x['S'], [b'', b'', b'', b''])
assert_equal(x['I'], [0, 0, 0, 0])
# Variation on test case from #2585
x['S'] = 'A'
assert_equal(x['S'], [b'', b'', b'', b''])
assert_equal(x['I'], [0, 0, 0, 0])
# Allow zero-width dtypes in ndarray constructor
y = np.ndarray(4, dtype=x['S'].dtype)
assert_equal(y.itemsize, 0)
assert_equal(x['S'], y)
# More tests for indexing an array with zero-width fields
assert_equal(np.zeros(4, dtype=[('a', 'S0,S0'),
('b', 'u1')])['a'].itemsize, 0)
assert_equal(np.empty(3, dtype='S0,S0').itemsize, 0)
assert_equal(np.zeros(4, dtype='S0,u1')['f0'].itemsize, 0)
xx = x['S'].reshape((2, 2))
assert_equal(xx.itemsize, 0)
assert_equal(xx, [[b'', b''], [b'', b'']])
# check for no uninitialized memory due to viewing S0 array
assert_equal(xx[:].dtype, xx.dtype)
assert_array_equal(eval(repr(xx), dict(array=np.array)), xx)
b = io.BytesIO()
np.save(b, xx)
b.seek(0)
yy = np.load(b)
assert_equal(yy.itemsize, 0)
assert_equal(xx, yy)
with temppath(suffix='.npy') as tmp:
np.save(tmp, xx)
yy = np.load(tmp)
assert_equal(yy.itemsize, 0)
assert_equal(xx, yy)
def test_base_attr(self):
a = np.zeros(3, dtype='i4,f4')
b = a[0]
assert_(b.base is a)
def test_assignment(self):
def testassign(arr, v):
c = arr.copy()
c[0] = v # assign using setitem
c[1:] = v # assign using "dtype_transfer" code paths
return c
dt = np.dtype([('foo', 'i8'), ('bar', 'i8')])
arr = np.ones(2, dt)
v1 = np.array([(2,3)], dtype=[('foo', 'i8'), ('bar', 'i8')])
v2 = np.array([(2,3)], dtype=[('bar', 'i8'), ('foo', 'i8')])
v3 = np.array([(2,3)], dtype=[('bar', 'i8'), ('baz', 'i8')])
v4 = np.array([(2,)], dtype=[('bar', 'i8')])
v5 = np.array([(2,3)], dtype=[('foo', 'f8'), ('bar', 'f8')])
w = arr.view({'names': ['bar'], 'formats': ['i8'], 'offsets': [8]})
ans = np.array([(2,3),(2,3)], dtype=dt)
assert_equal(testassign(arr, v1), ans)
assert_equal(testassign(arr, v2), ans)
assert_equal(testassign(arr, v3), ans)
assert_raises(ValueError, lambda: testassign(arr, v4))
assert_equal(testassign(arr, v5), ans)
w[:] = 4
assert_equal(arr, np.array([(1,4),(1,4)], dtype=dt))
# test field-reordering, assignment by position, and self-assignment
a = np.array([(1,2,3)],
dtype=[('foo', 'i8'), ('bar', 'i8'), ('baz', 'f4')])
a[['foo', 'bar']] = a[['bar', 'foo']]
assert_equal(a[0].item(), (2,1,3))
# test that this works even for 'simple_unaligned' structs
# (ie, that PyArray_EquivTypes cares about field order too)
a = np.array([(1,2)], dtype=[('a', 'i4'), ('b', 'i4')])
a[['a', 'b']] = a[['b', 'a']]
assert_equal(a[0].item(), (2,1))
def test_structuredscalar_indexing(self):
# test gh-7262
x = np.empty(shape=1, dtype="(2)3S,(2)3U")
assert_equal(x[["f0","f1"]][0], x[0][["f0","f1"]])
assert_equal(x[0], x[0][()])
def test_multiindex_titles(self):
a = np.zeros(4, dtype=[(('a', 'b'), 'i'), ('c', 'i'), ('d', 'i')])
assert_raises(KeyError, lambda : a[['a','c']])
assert_raises(KeyError, lambda : a[['a','a']])
assert_raises(ValueError, lambda : a[['b','b']]) # field exists, but repeated
a[['b','c']] # no exception
class TestBool(object):
def test_test_interning(self):
a0 = np.bool_(0)
b0 = np.bool_(False)
assert_(a0 is b0)
a1 = np.bool_(1)
b1 = np.bool_(True)
assert_(a1 is b1)
assert_(np.array([True])[0] is a1)
assert_(np.array(True)[()] is a1)
def test_sum(self):
d = np.ones(101, dtype=bool)
assert_equal(d.sum(), d.size)
assert_equal(d[::2].sum(), d[::2].size)
assert_equal(d[::-2].sum(), d[::-2].size)
d = np.frombuffer(b'\xff\xff' * 100, dtype=bool)
assert_equal(d.sum(), d.size)
assert_equal(d[::2].sum(), d[::2].size)
assert_equal(d[::-2].sum(), d[::-2].size)
def check_count_nonzero(self, power, length):
powers = [2 ** i for i in range(length)]
for i in range(2**power):
l = [(i & x) != 0 for x in powers]
a = np.array(l, dtype=bool)
c = builtins.sum(l)
assert_equal(np.count_nonzero(a), c)
av = a.view(np.uint8)
av *= 3
assert_equal(np.count_nonzero(a), c)
av *= 4
assert_equal(np.count_nonzero(a), c)
av[av != 0] = 0xFF
assert_equal(np.count_nonzero(a), c)
def test_count_nonzero(self):
# check all 12 bit combinations in a length 17 array
# covers most cases of the 16 byte unrolled code
self.check_count_nonzero(12, 17)
@dec.slow
def test_count_nonzero_all(self):
# check all combinations in a length 17 array
# covers all cases of the 16 byte unrolled code
self.check_count_nonzero(17, 17)
def test_count_nonzero_unaligned(self):
# prevent mistakes as e.g. gh-4060
for o in range(7):
a = np.zeros((18,), dtype=bool)[o+1:]
a[:o] = True
assert_equal(np.count_nonzero(a), builtins.sum(a.tolist()))
a = np.ones((18,), dtype=bool)[o+1:]
a[:o] = False
assert_equal(np.count_nonzero(a), builtins.sum(a.tolist()))
def _test_cast_from_flexible(self, dtype):
# empty string -> false
for n in range(3):
v = np.array(b'', (dtype, n))
assert_equal(bool(v), False)
assert_equal(bool(v[()]), False)
assert_equal(v.astype(bool), False)
assert_(isinstance(v.astype(bool), np.ndarray))
assert_(v[()].astype(bool) is np.False_)
# anything else -> true
for n in range(1, 4):
for val in [b'a', b'0', b' ']:
v = np.array(val, (dtype, n))
assert_equal(bool(v), True)
assert_equal(bool(v[()]), True)
assert_equal(v.astype(bool), True)
assert_(isinstance(v.astype(bool), np.ndarray))
assert_(v[()].astype(bool) is np.True_)
def test_cast_from_void(self):
self._test_cast_from_flexible(np.void)
@dec.knownfailureif(True, "See gh-9847")
def test_cast_from_unicode(self):
self._test_cast_from_flexible(np.unicode_)
@dec.knownfailureif(True, "See gh-9847")
def test_cast_from_bytes(self):
self._test_cast_from_flexible(np.bytes_)
class TestZeroSizeFlexible(object):
@staticmethod
def _zeros(shape, dtype=str):
dtype = np.dtype(dtype)
if dtype == np.void:
return np.zeros(shape, dtype=(dtype, 0))
# not constructable directly
dtype = np.dtype([('x', dtype, 0)])
return np.zeros(shape, dtype=dtype)['x']
def test_create(self):
zs = self._zeros(10, bytes)
assert_equal(zs.itemsize, 0)
zs = self._zeros(10, np.void)
assert_equal(zs.itemsize, 0)
zs = self._zeros(10, unicode)
assert_equal(zs.itemsize, 0)
def _test_sort_partition(self, name, kinds, **kwargs):
# Previously, these would all hang
for dt in [bytes, np.void, unicode]:
zs = self._zeros(10, dt)
sort_method = getattr(zs, name)
sort_func = getattr(np, name)
for kind in kinds:
sort_method(kind=kind, **kwargs)
sort_func(zs, kind=kind, **kwargs)
def test_sort(self):
self._test_sort_partition('sort', kinds='qhm')
def test_argsort(self):
self._test_sort_partition('argsort', kinds='qhm')
def test_partition(self):
self._test_sort_partition('partition', kinds=['introselect'], kth=2)
def test_argpartition(self):
self._test_sort_partition('argpartition', kinds=['introselect'], kth=2)
def test_resize(self):
# previously an error
for dt in [bytes, np.void, unicode]:
zs = self._zeros(10, dt)
zs.resize(25)
zs.resize((10, 10))
def test_view(self):
for dt in [bytes, np.void, unicode]:
zs = self._zeros(10, dt)
# viewing as itself should be allowed
assert_equal(zs.view(dt).dtype, np.dtype(dt))
# viewing as any non-empty type gives an empty result
assert_equal(zs.view((dt, 1)).shape, (0,))
def test_pickle(self):
import pickle
for dt in [bytes, np.void, unicode]:
zs = self._zeros(10, dt)
p = pickle.dumps(zs)
zs2 = pickle.loads(p)
assert_equal(zs.dtype, zs2.dtype)
class TestMethods(object):
def test_compress(self):
tgt = [[5, 6, 7, 8, 9]]
arr = np.arange(10).reshape(2, 5)
out = arr.compress([0, 1], axis=0)
assert_equal(out, tgt)
tgt = [[1, 3], [6, 8]]
out = arr.compress([0, 1, 0, 1, 0], axis=1)
assert_equal(out, tgt)
tgt = [[1], [6]]
arr = np.arange(10).reshape(2, 5)
out = arr.compress([0, 1], axis=1)
assert_equal(out, tgt)
arr = np.arange(10).reshape(2, 5)
out = arr.compress([0, 1])
assert_equal(out, 1)
def test_choose(self):
x = 2*np.ones((3,), dtype=int)
y = 3*np.ones((3,), dtype=int)
x2 = 2*np.ones((2, 3), dtype=int)
y2 = 3*np.ones((2, 3), dtype=int)
ind = np.array([0, 0, 1])
A = ind.choose((x, y))
assert_equal(A, [2, 2, 3])
A = ind.choose((x2, y2))
assert_equal(A, [[2, 2, 3], [2, 2, 3]])
A = ind.choose((x, y2))
assert_equal(A, [[2, 2, 3], [2, 2, 3]])
def test_prod(self):
ba = [1, 2, 10, 11, 6, 5, 4]
ba2 = [[1, 2, 3, 4], [5, 6, 7, 9], [10, 3, 4, 5]]
for ctype in [np.int16, np.uint16, np.int32, np.uint32,
np.float32, np.float64, np.complex64, np.complex128]:
a = np.array(ba, ctype)
a2 = np.array(ba2, ctype)
if ctype in ['1', 'b']:
assert_raises(ArithmeticError, a.prod)
assert_raises(ArithmeticError, a2.prod, axis=1)
else:
assert_equal(a.prod(axis=0), 26400)
assert_array_equal(a2.prod(axis=0),
np.array([50, 36, 84, 180], ctype))
assert_array_equal(a2.prod(axis=-1),
np.array([24, 1890, 600], ctype))
def test_repeat(self):
m = np.array([1, 2, 3, 4, 5, 6])
m_rect = m.reshape((2, 3))
A = m.repeat([1, 3, 2, 1, 1, 2])
assert_equal(A, [1, 2, 2, 2, 3,
3, 4, 5, 6, 6])
A = m.repeat(2)
assert_equal(A, [1, 1, 2, 2, 3, 3,
4, 4, 5, 5, 6, 6])
A = m_rect.repeat([2, 1], axis=0)
assert_equal(A, [[1, 2, 3],
[1, 2, 3],
[4, 5, 6]])
A = m_rect.repeat([1, 3, 2], axis=1)
assert_equal(A, [[1, 2, 2, 2, 3, 3],
[4, 5, 5, 5, 6, 6]])
A = m_rect.repeat(2, axis=0)
assert_equal(A, [[1, 2, 3],
[1, 2, 3],
[4, 5, 6],
[4, 5, 6]])
A = m_rect.repeat(2, axis=1)
assert_equal(A, [[1, 1, 2, 2, 3, 3],
[4, 4, 5, 5, 6, 6]])
def test_reshape(self):
arr = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 11, 12]])
tgt = [[1, 2, 3, 4, 5, 6], [7, 8, 9, 10, 11, 12]]
assert_equal(arr.reshape(2, 6), tgt)
tgt = [[1, 2, 3, 4], [5, 6, 7, 8], [9, 10, 11, 12]]
assert_equal(arr.reshape(3, 4), tgt)
tgt = [[1, 10, 8, 6], [4, 2, 11, 9], [7, 5, 3, 12]]
assert_equal(arr.reshape((3, 4), order='F'), tgt)
tgt = [[1, 4, 7, 10], [2, 5, 8, 11], [3, 6, 9, 12]]
assert_equal(arr.T.reshape((3, 4), order='C'), tgt)
def test_round(self):
def check_round(arr, expected, *round_args):
assert_equal(arr.round(*round_args), expected)
# With output array
out = np.zeros_like(arr)
res = arr.round(*round_args, out=out)
assert_equal(out, expected)
assert_equal(out, res)
check_round(np.array([1.2, 1.5]), [1, 2])
check_round(np.array(1.5), 2)
check_round(np.array([12.2, 15.5]), [10, 20], -1)
check_round(np.array([12.15, 15.51]), [12.2, 15.5], 1)
# Complex rounding
check_round(np.array([4.5 + 1.5j]), [4 + 2j])
check_round(np.array([12.5 + 15.5j]), [10 + 20j], -1)
def test_squeeze(self):
a = np.array([[[1], [2], [3]]])
assert_equal(a.squeeze(), [1, 2, 3])
assert_equal(a.squeeze(axis=(0,)), [[1], [2], [3]])
assert_raises(ValueError, a.squeeze, axis=(1,))
assert_equal(a.squeeze(axis=(2,)), [[1, 2, 3]])
def test_transpose(self):
a = np.array([[1, 2], [3, 4]])
assert_equal(a.transpose(), [[1, 3], [2, 4]])
assert_raises(ValueError, lambda: a.transpose(0))
assert_raises(ValueError, lambda: a.transpose(0, 0))
assert_raises(ValueError, lambda: a.transpose(0, 1, 2))
def test_sort(self):
# test ordering for floats and complex containing nans. It is only
# necessary to check the less-than comparison, so sorts that
# only follow the insertion sort path are sufficient. We only
# test doubles and complex doubles as the logic is the same.
# check doubles
msg = "Test real sort order with nans"
a = np.array([np.nan, 1, 0])
b = np.sort(a)
assert_equal(b, a[::-1], msg)
# check complex
msg = "Test complex sort order with nans"
a = np.zeros(9, dtype=np.complex128)
a.real += [np.nan, np.nan, np.nan, 1, 0, 1, 1, 0, 0]
a.imag += [np.nan, 1, 0, np.nan, np.nan, 1, 0, 1, 0]
b = np.sort(a)
assert_equal(b, a[::-1], msg)
# all c scalar sorts use the same code with different types
# so it suffices to run a quick check with one type. The number
# of sorted items must be greater than ~50 to check the actual
# algorithm because quick and merge sort fall over to insertion
# sort for small arrays.
a = np.arange(101)
b = a[::-1].copy()
for kind in ['q', 'm', 'h']:
msg = "scalar sort, kind=%s" % kind
c = a.copy()
c.sort(kind=kind)
assert_equal(c, a, msg)
c = b.copy()
c.sort(kind=kind)
assert_equal(c, a, msg)
# test complex sorts. These use the same code as the scalars
# but the compare function differs.
ai = a*1j + 1
bi = b*1j + 1
for kind in ['q', 'm', 'h']:
msg = "complex sort, real part == 1, kind=%s" % kind
c = ai.copy()
c.sort(kind=kind)
assert_equal(c, ai, msg)
c = bi.copy()
c.sort(kind=kind)
assert_equal(c, ai, msg)
ai = a + 1j
bi = b + 1j
for kind in ['q', 'm', 'h']:
msg = "complex sort, imag part == 1, kind=%s" % kind
c = ai.copy()
c.sort(kind=kind)
assert_equal(c, ai, msg)
c = bi.copy()
c.sort(kind=kind)
assert_equal(c, ai, msg)
# test sorting of complex arrays requiring byte-swapping, gh-5441
for endianess in '<>':
for dt in np.typecodes['Complex']:
arr = np.array([1+3.j, 2+2.j, 3+1.j], dtype=endianess + dt)
c = arr.copy()
c.sort()
msg = 'byte-swapped complex sort, dtype={0}'.format(dt)
assert_equal(c, arr, msg)
# test string sorts.
s = 'aaaaaaaa'
a = np.array([s + chr(i) for i in range(101)])
b = a[::-1].copy()
for kind in ['q', 'm', 'h']:
msg = "string sort, kind=%s" % kind
c = a.copy()
c.sort(kind=kind)
assert_equal(c, a, msg)
c = b.copy()
c.sort(kind=kind)
assert_equal(c, a, msg)
# test unicode sorts.
s = 'aaaaaaaa'
a = np.array([s + chr(i) for i in range(101)], dtype=np.unicode)
b = a[::-1].copy()
for kind in ['q', 'm', 'h']:
msg = "unicode sort, kind=%s" % kind
c = a.copy()
c.sort(kind=kind)
assert_equal(c, a, msg)
c = b.copy()
c.sort(kind=kind)
assert_equal(c, a, msg)
# test object array sorts.
a = np.empty((101,), dtype=object)
a[:] = list(range(101))
b = a[::-1]
for kind in ['q', 'h', 'm']:
msg = "object sort, kind=%s" % kind
c = a.copy()
c.sort(kind=kind)
assert_equal(c, a, msg)
c = b.copy()
c.sort(kind=kind)
assert_equal(c, a, msg)
# test record array sorts.
dt = np.dtype([('f', float), ('i', int)])
a = np.array([(i, i) for i in range(101)], dtype=dt)
b = a[::-1]
for kind in ['q', 'h', 'm']:
msg = "object sort, kind=%s" % kind
c = a.copy()
c.sort(kind=kind)
assert_equal(c, a, msg)
c = b.copy()
c.sort(kind=kind)
assert_equal(c, a, msg)
# test datetime64 sorts.
a = np.arange(0, 101, dtype='datetime64[D]')
b = a[::-1]
for kind in ['q', 'h', 'm']:
msg = "datetime64 sort, kind=%s" % kind
c = a.copy()
c.sort(kind=kind)
assert_equal(c, a, msg)
c = b.copy()
c.sort(kind=kind)
assert_equal(c, a, msg)
# test timedelta64 sorts.
a = np.arange(0, 101, dtype='timedelta64[D]')
b = a[::-1]
for kind in ['q', 'h', 'm']:
msg = "timedelta64 sort, kind=%s" % kind
c = a.copy()
c.sort(kind=kind)
assert_equal(c, a, msg)
c = b.copy()
c.sort(kind=kind)
assert_equal(c, a, msg)
# check axis handling. This should be the same for all type
# specific sorts, so we only check it for one type and one kind
a = np.array([[3, 2], [1, 0]])
b = np.array([[1, 0], [3, 2]])
c = np.array([[2, 3], [0, 1]])
d = a.copy()
d.sort(axis=0)
assert_equal(d, b, "test sort with axis=0")
d = a.copy()
d.sort(axis=1)
assert_equal(d, c, "test sort with axis=1")
d = a.copy()
d.sort()
assert_equal(d, c, "test sort with default axis")
# check axis handling for multidimensional empty arrays
a = np.array([])
a.shape = (3, 2, 1, 0)
for axis in range(-a.ndim, a.ndim):
msg = 'test empty array sort with axis={0}'.format(axis)
assert_equal(np.sort(a, axis=axis), a, msg)
msg = 'test empty array sort with axis=None'
assert_equal(np.sort(a, axis=None), a.ravel(), msg)
# test generic class with bogus ordering,
# should not segfault.
class Boom(object):
def __lt__(self, other):
return True
a = np.array([Boom()]*100, dtype=object)
for kind in ['q', 'm', 'h']:
msg = "bogus comparison object sort, kind=%s" % kind
c.sort(kind=kind)
def test_void_sort(self):
# gh-8210 - previously segfaulted
for i in range(4):
arr = np.empty(1000, 'V4')
arr[::-1].sort()
dt = np.dtype([('val', 'i4', (1,))])
for i in range(4):
arr = np.empty(1000, dt)
arr[::-1].sort()
def test_sort_raises(self):
#gh-9404
arr = np.array([0, datetime.now(), 1], dtype=object)
for kind in ['q', 'm', 'h']:
assert_raises(TypeError, arr.sort, kind=kind)
#gh-3879
class Raiser(object):
def raises_anything(*args, **kwargs):
raise TypeError("SOMETHING ERRORED")
__eq__ = __ne__ = __lt__ = __gt__ = __ge__ = __le__ = raises_anything
arr = np.array([[Raiser(), n] for n in range(10)]).reshape(-1)
np.random.shuffle(arr)
for kind in ['q', 'm', 'h']:
assert_raises(TypeError, arr.sort, kind=kind)
def test_sort_degraded(self):
# test degraded dataset would take minutes to run with normal qsort
d = np.arange(1000000)
do = d.copy()
x = d
# create a median of 3 killer where each median is the sorted second
# last element of the quicksort partition
while x.size > 3:
mid = x.size // 2
x[mid], x[-2] = x[-2], x[mid]
x = x[:-2]
assert_equal(np.sort(d), do)
assert_equal(d[np.argsort(d)], do)
def test_copy(self):
def assert_fortran(arr):
assert_(arr.flags.fortran)
assert_(arr.flags.f_contiguous)
assert_(not arr.flags.c_contiguous)
def assert_c(arr):
assert_(not arr.flags.fortran)
assert_(not arr.flags.f_contiguous)
assert_(arr.flags.c_contiguous)
a = np.empty((2, 2), order='F')
# Test copying a Fortran array
assert_c(a.copy())
assert_c(a.copy('C'))
assert_fortran(a.copy('F'))
assert_fortran(a.copy('A'))
# Now test starting with a C array.
a = np.empty((2, 2), order='C')
assert_c(a.copy())
assert_c(a.copy('C'))
assert_fortran(a.copy('F'))
assert_c(a.copy('A'))
def test_sort_order(self):
# Test sorting an array with fields
x1 = np.array([21, 32, 14])
x2 = np.array(['my', 'first', 'name'])
x3 = np.array([3.1, 4.5, 6.2])
r = np.rec.fromarrays([x1, x2, x3], names='id,word,number')
r.sort(order=['id'])
assert_equal(r.id, np.array([14, 21, 32]))
assert_equal(r.word, np.array(['name', 'my', 'first']))
assert_equal(r.number, np.array([6.2, 3.1, 4.5]))
r.sort(order=['word'])
assert_equal(r.id, np.array([32, 21, 14]))
assert_equal(r.word, np.array(['first', 'my', 'name']))
assert_equal(r.number, np.array([4.5, 3.1, 6.2]))
r.sort(order=['number'])
assert_equal(r.id, np.array([21, 32, 14]))
assert_equal(r.word, np.array(['my', 'first', 'name']))
assert_equal(r.number, np.array([3.1, 4.5, 6.2]))
assert_raises_regex(ValueError, 'duplicate',
lambda: r.sort(order=['id', 'id']))
if sys.byteorder == 'little':
strtype = '>i2'
else:
strtype = '<i2'
mydtype = [('name', strchar + '5'), ('col2', strtype)]
r = np.array([('a', 1), ('b', 255), ('c', 3), ('d', 258)],
dtype=mydtype)
r.sort(order='col2')
assert_equal(r['col2'], [1, 3, 255, 258])
assert_equal(r, np.array([('a', 1), ('c', 3), ('b', 255), ('d', 258)],
dtype=mydtype))
def test_argsort(self):
# all c scalar argsorts use the same code with different types
# so it suffices to run a quick check with one type. The number
# of sorted items must be greater than ~50 to check the actual
# algorithm because quick and merge sort fall over to insertion
# sort for small arrays.
a = np.arange(101)
b = a[::-1].copy()
for kind in ['q', 'm', 'h']:
msg = "scalar argsort, kind=%s" % kind
assert_equal(a.copy().argsort(kind=kind), a, msg)
assert_equal(b.copy().argsort(kind=kind), b, msg)
# test complex argsorts. These use the same code as the scalars
# but the compare function differs.
ai = a*1j + 1
bi = b*1j + 1
for kind in ['q', 'm', 'h']:
msg = "complex argsort, kind=%s" % kind
assert_equal(ai.copy().argsort(kind=kind), a, msg)
assert_equal(bi.copy().argsort(kind=kind), b, msg)
ai = a + 1j
bi = b + 1j
for kind in ['q', 'm', 'h']:
msg = "complex argsort, kind=%s" % kind
assert_equal(ai.copy().argsort(kind=kind), a, msg)
assert_equal(bi.copy().argsort(kind=kind), b, msg)
# test argsort of complex arrays requiring byte-swapping, gh-5441
for endianess in '<>':
for dt in np.typecodes['Complex']:
arr = np.array([1+3.j, 2+2.j, 3+1.j], dtype=endianess + dt)
msg = 'byte-swapped complex argsort, dtype={0}'.format(dt)
assert_equal(arr.argsort(),
np.arange(len(arr), dtype=np.intp), msg)
# test string argsorts.
s = 'aaaaaaaa'
a = np.array([s + chr(i) for i in range(101)])
b = a[::-1].copy()
r = np.arange(101)
rr = r[::-1]
for kind in ['q', 'm', 'h']:
msg = "string argsort, kind=%s" % kind
assert_equal(a.copy().argsort(kind=kind), r, msg)
assert_equal(b.copy().argsort(kind=kind), rr, msg)
# test unicode argsorts.
s = 'aaaaaaaa'
a = np.array([s + chr(i) for i in range(101)], dtype=np.unicode)
b = a[::-1]
r = np.arange(101)
rr = r[::-1]
for kind in ['q', 'm', 'h']:
msg = "unicode argsort, kind=%s" % kind
assert_equal(a.copy().argsort(kind=kind), r, msg)
assert_equal(b.copy().argsort(kind=kind), rr, msg)
# test object array argsorts.
a = np.empty((101,), dtype=object)
a[:] = list(range(101))
b = a[::-1]
r = np.arange(101)
rr = r[::-1]
for kind in ['q', 'm', 'h']:
msg = "object argsort, kind=%s" % kind
assert_equal(a.copy().argsort(kind=kind), r, msg)
assert_equal(b.copy().argsort(kind=kind), rr, msg)
# test structured array argsorts.
dt = np.dtype([('f', float), ('i', int)])
a = np.array([(i, i) for i in range(101)], dtype=dt)
b = a[::-1]
r = np.arange(101)
rr = r[::-1]
for kind in ['q', 'm', 'h']:
msg = "structured array argsort, kind=%s" % kind
assert_equal(a.copy().argsort(kind=kind), r, msg)
assert_equal(b.copy().argsort(kind=kind), rr, msg)
# test datetime64 argsorts.
a = np.arange(0, 101, dtype='datetime64[D]')
b = a[::-1]
r = np.arange(101)
rr = r[::-1]
for kind in ['q', 'h', 'm']:
msg = "datetime64 argsort, kind=%s" % kind
assert_equal(a.copy().argsort(kind=kind), r, msg)
assert_equal(b.copy().argsort(kind=kind), rr, msg)
# test timedelta64 argsorts.
a = np.arange(0, 101, dtype='timedelta64[D]')
b = a[::-1]
r = np.arange(101)
rr = r[::-1]
for kind in ['q', 'h', 'm']:
msg = "timedelta64 argsort, kind=%s" % kind
assert_equal(a.copy().argsort(kind=kind), r, msg)
assert_equal(b.copy().argsort(kind=kind), rr, msg)
# check axis handling. This should be the same for all type
# specific argsorts, so we only check it for one type and one kind
a = np.array([[3, 2], [1, 0]])
b = np.array([[1, 1], [0, 0]])
c = np.array([[1, 0], [1, 0]])
assert_equal(a.copy().argsort(axis=0), b)
assert_equal(a.copy().argsort(axis=1), c)
assert_equal(a.copy().argsort(), c)
# check axis handling for multidimensional empty arrays
a = np.array([])
a.shape = (3, 2, 1, 0)
for axis in range(-a.ndim, a.ndim):
msg = 'test empty array argsort with axis={0}'.format(axis)
assert_equal(np.argsort(a, axis=axis),
np.zeros_like(a, dtype=np.intp), msg)
msg = 'test empty array argsort with axis=None'
assert_equal(np.argsort(a, axis=None),
np.zeros_like(a.ravel(), dtype=np.intp), msg)
# check that stable argsorts are stable
r = np.arange(100)
# scalars
a = np.zeros(100)
assert_equal(a.argsort(kind='m'), r)
# complex
a = np.zeros(100, dtype=complex)
assert_equal(a.argsort(kind='m'), r)
# string
a = np.array(['aaaaaaaaa' for i in range(100)])
assert_equal(a.argsort(kind='m'), r)
# unicode
a = np.array(['aaaaaaaaa' for i in range(100)], dtype=np.unicode)
assert_equal(a.argsort(kind='m'), r)
def test_sort_unicode_kind(self):
d = np.arange(10)
k = b'\xc3\xa4'.decode("UTF8")
assert_raises(ValueError, d.sort, kind=k)
assert_raises(ValueError, d.argsort, kind=k)
def test_searchsorted(self):
# test for floats and complex containing nans. The logic is the
# same for all float types so only test double types for now.
# The search sorted routines use the compare functions for the
# array type, so this checks if that is consistent with the sort
# order.
# check double
a = np.array([0, 1, np.nan])
msg = "Test real searchsorted with nans, side='l'"
b = a.searchsorted(a, side='l')
assert_equal(b, np.arange(3), msg)
msg = "Test real searchsorted with nans, side='r'"
b = a.searchsorted(a, side='r')
assert_equal(b, np.arange(1, 4), msg)
# check double complex
a = np.zeros(9, dtype=np.complex128)
a.real += [0, 0, 1, 1, 0, 1, np.nan, np.nan, np.nan]
a.imag += [0, 1, 0, 1, np.nan, np.nan, 0, 1, np.nan]
msg = "Test complex searchsorted with nans, side='l'"
b = a.searchsorted(a, side='l')
assert_equal(b, np.arange(9), msg)
msg = "Test complex searchsorted with nans, side='r'"
b = a.searchsorted(a, side='r')
assert_equal(b, np.arange(1, 10), msg)
msg = "Test searchsorted with little endian, side='l'"
a = np.array([0, 128], dtype='<i4')
b = a.searchsorted(np.array(128, dtype='<i4'))
assert_equal(b, 1, msg)
msg = "Test searchsorted with big endian, side='l'"
a = np.array([0, 128], dtype='>i4')
b = a.searchsorted(np.array(128, dtype='>i4'))
assert_equal(b, 1, msg)
# Check 0 elements
a = np.ones(0)
b = a.searchsorted([0, 1, 2], 'l')
assert_equal(b, [0, 0, 0])
b = a.searchsorted([0, 1, 2], 'r')
assert_equal(b, [0, 0, 0])
a = np.ones(1)
# Check 1 element
b = a.searchsorted([0, 1, 2], 'l')
assert_equal(b, [0, 0, 1])
b = a.searchsorted([0, 1, 2], 'r')
assert_equal(b, [0, 1, 1])
# Check all elements equal
a = np.ones(2)
b = a.searchsorted([0, 1, 2], 'l')
assert_equal(b, [0, 0, 2])
b = a.searchsorted([0, 1, 2], 'r')
assert_equal(b, [0, 2, 2])
# Test searching unaligned array
a = np.arange(10)
aligned = np.empty(a.itemsize * a.size + 1, 'uint8')
unaligned = aligned[1:].view(a.dtype)
unaligned[:] = a
# Test searching unaligned array
b = unaligned.searchsorted(a, 'l')
assert_equal(b, a)
b = unaligned.searchsorted(a, 'r')
assert_equal(b, a + 1)
# Test searching for unaligned keys
b = a.searchsorted(unaligned, 'l')
assert_equal(b, a)
b = a.searchsorted(unaligned, 'r')
assert_equal(b, a + 1)
# Test smart resetting of binsearch indices
a = np.arange(5)
b = a.searchsorted([6, 5, 4], 'l')
assert_equal(b, [5, 5, 4])
b = a.searchsorted([6, 5, 4], 'r')
assert_equal(b, [5, 5, 5])
# Test all type specific binary search functions
types = ''.join((np.typecodes['AllInteger'], np.typecodes['AllFloat'],
np.typecodes['Datetime'], '?O'))
for dt in types:
if dt == 'M':
dt = 'M8[D]'
if dt == '?':
a = np.arange(2, dtype=dt)
out = np.arange(2)
else:
a = np.arange(0, 5, dtype=dt)
out = np.arange(5)
b = a.searchsorted(a, 'l')
assert_equal(b, out)
b = a.searchsorted(a, 'r')
assert_equal(b, out + 1)
def test_searchsorted_unicode(self):
# Test searchsorted on unicode strings.
# 1.6.1 contained a string length miscalculation in
# arraytypes.c.src:UNICODE_compare() which manifested as
# incorrect/inconsistent results from searchsorted.
a = np.array(['P:\\20x_dapi_cy3\\20x_dapi_cy3_20100185_1',
'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100186_1',
'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100187_1',
'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100189_1',
'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100190_1',
'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100191_1',
'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100192_1',
'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100193_1',
'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100194_1',
'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100195_1',
'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100196_1',
'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100197_1',
'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100198_1',
'P:\\20x_dapi_cy3\\20x_dapi_cy3_20100199_1'],
dtype=np.unicode)
ind = np.arange(len(a))
assert_equal([a.searchsorted(v, 'left') for v in a], ind)
assert_equal([a.searchsorted(v, 'right') for v in a], ind + 1)
assert_equal([a.searchsorted(a[i], 'left') for i in ind], ind)
assert_equal([a.searchsorted(a[i], 'right') for i in ind], ind + 1)
def test_searchsorted_with_sorter(self):
a = np.array([5, 2, 1, 3, 4])
s = np.argsort(a)
assert_raises(TypeError, np.searchsorted, a, 0, sorter=(1, (2, 3)))
assert_raises(TypeError, np.searchsorted, a, 0, sorter=[1.1])
assert_raises(ValueError, np.searchsorted, a, 0, sorter=[1, 2, 3, 4])
assert_raises(ValueError, np.searchsorted, a, 0, sorter=[1, 2, 3, 4, 5, 6])
# bounds check
assert_raises(ValueError, np.searchsorted, a, 4, sorter=[0, 1, 2, 3, 5])
assert_raises(ValueError, np.searchsorted, a, 0, sorter=[-1, 0, 1, 2, 3])
assert_raises(ValueError, np.searchsorted, a, 0, sorter=[4, 0, -1, 2, 3])
a = np.random.rand(300)
s = a.argsort()
b = np.sort(a)
k = np.linspace(0, 1, 20)
assert_equal(b.searchsorted(k), a.searchsorted(k, sorter=s))
a = np.array([0, 1, 2, 3, 5]*20)
s = a.argsort()
k = [0, 1, 2, 3, 5]
expected = [0, 20, 40, 60, 80]
assert_equal(a.searchsorted(k, side='l', sorter=s), expected)
expected = [20, 40, 60, 80, 100]
assert_equal(a.searchsorted(k, side='r', sorter=s), expected)
# Test searching unaligned array
keys = np.arange(10)
a = keys.copy()
np.random.shuffle(s)
s = a.argsort()
aligned = np.empty(a.itemsize * a.size + 1, 'uint8')
unaligned = aligned[1:].view(a.dtype)
# Test searching unaligned array
unaligned[:] = a
b = unaligned.searchsorted(keys, 'l', s)
assert_equal(b, keys)
b = unaligned.searchsorted(keys, 'r', s)
assert_equal(b, keys + 1)
# Test searching for unaligned keys
unaligned[:] = keys
b = a.searchsorted(unaligned, 'l', s)
assert_equal(b, keys)
b = a.searchsorted(unaligned, 'r', s)
assert_equal(b, keys + 1)
# Test all type specific indirect binary search functions
types = ''.join((np.typecodes['AllInteger'], np.typecodes['AllFloat'],
np.typecodes['Datetime'], '?O'))
for dt in types:
if dt == 'M':
dt = 'M8[D]'
if dt == '?':
a = np.array([1, 0], dtype=dt)
# We want the sorter array to be of a type that is different
# from np.intp in all platforms, to check for #4698
s = np.array([1, 0], dtype=np.int16)
out = np.array([1, 0])
else:
a = np.array([3, 4, 1, 2, 0], dtype=dt)
# We want the sorter array to be of a type that is different
# from np.intp in all platforms, to check for #4698
s = np.array([4, 2, 3, 0, 1], dtype=np.int16)
out = np.array([3, 4, 1, 2, 0], dtype=np.intp)
b = a.searchsorted(a, 'l', s)
assert_equal(b, out)
b = a.searchsorted(a, 'r', s)
assert_equal(b, out + 1)
# Test non-contiguous sorter array
a = np.array([3, 4, 1, 2, 0])
srt = np.empty((10,), dtype=np.intp)
srt[1::2] = -1
srt[::2] = [4, 2, 3, 0, 1]
s = srt[::2]
out = np.array([3, 4, 1, 2, 0], dtype=np.intp)
b = a.searchsorted(a, 'l', s)
assert_equal(b, out)
b = a.searchsorted(a, 'r', s)
assert_equal(b, out + 1)
def test_searchsorted_return_type(self):
# Functions returning indices should always return base ndarrays
class A(np.ndarray):
pass
a = np.arange(5).view(A)
b = np.arange(1, 3).view(A)
s = np.arange(5).view(A)
assert_(not isinstance(a.searchsorted(b, 'l'), A))
assert_(not isinstance(a.searchsorted(b, 'r'), A))
assert_(not isinstance(a.searchsorted(b, 'l', s), A))
assert_(not isinstance(a.searchsorted(b, 'r', s), A))
def test_argpartition_out_of_range(self):
# Test out of range values in kth raise an error, gh-5469
d = np.arange(10)
assert_raises(ValueError, d.argpartition, 10)
assert_raises(ValueError, d.argpartition, -11)
# Test also for generic type argpartition, which uses sorting
# and used to not bound check kth
d_obj = np.arange(10, dtype=object)
assert_raises(ValueError, d_obj.argpartition, 10)
assert_raises(ValueError, d_obj.argpartition, -11)
def test_partition_out_of_range(self):
# Test out of range values in kth raise an error, gh-5469
d = np.arange(10)
assert_raises(ValueError, d.partition, 10)
assert_raises(ValueError, d.partition, -11)
# Test also for generic type partition, which uses sorting
# and used to not bound check kth
d_obj = np.arange(10, dtype=object)
assert_raises(ValueError, d_obj.partition, 10)
assert_raises(ValueError, d_obj.partition, -11)
def test_argpartition_integer(self):
# Test non-integer values in kth raise an error/
d = np.arange(10)
assert_raises(TypeError, d.argpartition, 9.)
# Test also for generic type argpartition, which uses sorting
# and used to not bound check kth
d_obj = np.arange(10, dtype=object)
assert_raises(TypeError, d_obj.argpartition, 9.)
def test_partition_integer(self):
# Test out of range values in kth raise an error, gh-5469
d = np.arange(10)
assert_raises(TypeError, d.partition, 9.)
# Test also for generic type partition, which uses sorting
# and used to not bound check kth
d_obj = np.arange(10, dtype=object)
assert_raises(TypeError, d_obj.partition, 9.)
def test_partition_empty_array(self):
# check axis handling for multidimensional empty arrays
a = np.array([])
a.shape = (3, 2, 1, 0)
for axis in range(-a.ndim, a.ndim):
msg = 'test empty array partition with axis={0}'.format(axis)
assert_equal(np.partition(a, 0, axis=axis), a, msg)
msg = 'test empty array partition with axis=None'
assert_equal(np.partition(a, 0, axis=None), a.ravel(), msg)
def test_argpartition_empty_array(self):
# check axis handling for multidimensional empty arrays
a = np.array([])
a.shape = (3, 2, 1, 0)
for axis in range(-a.ndim, a.ndim):
msg = 'test empty array argpartition with axis={0}'.format(axis)
assert_equal(np.partition(a, 0, axis=axis),
np.zeros_like(a, dtype=np.intp), msg)
msg = 'test empty array argpartition with axis=None'
assert_equal(np.partition(a, 0, axis=None),
np.zeros_like(a.ravel(), dtype=np.intp), msg)
def test_partition(self):
d = np.arange(10)
assert_raises(TypeError, np.partition, d, 2, kind=1)
assert_raises(ValueError, np.partition, d, 2, kind="nonsense")
assert_raises(ValueError, np.argpartition, d, 2, kind="nonsense")
assert_raises(ValueError, d.partition, 2, axis=0, kind="nonsense")
assert_raises(ValueError, d.argpartition, 2, axis=0, kind="nonsense")
for k in ("introselect",):
d = np.array([])
assert_array_equal(np.partition(d, 0, kind=k), d)
assert_array_equal(np.argpartition(d, 0, kind=k), d)
d = np.ones(1)
assert_array_equal(np.partition(d, 0, kind=k)[0], d)
assert_array_equal(d[np.argpartition(d, 0, kind=k)],
np.partition(d, 0, kind=k))
# kth not modified
kth = np.array([30, 15, 5])
okth = kth.copy()
np.partition(np.arange(40), kth)
assert_array_equal(kth, okth)
for r in ([2, 1], [1, 2], [1, 1]):
d = np.array(r)
tgt = np.sort(d)
assert_array_equal(np.partition(d, 0, kind=k)[0], tgt[0])
assert_array_equal(np.partition(d, 1, kind=k)[1], tgt[1])
assert_array_equal(d[np.argpartition(d, 0, kind=k)],
np.partition(d, 0, kind=k))
assert_array_equal(d[np.argpartition(d, 1, kind=k)],
np.partition(d, 1, kind=k))
for i in range(d.size):
d[i:].partition(0, kind=k)
assert_array_equal(d, tgt)
for r in ([3, 2, 1], [1, 2, 3], [2, 1, 3], [2, 3, 1],
[1, 1, 1], [1, 2, 2], [2, 2, 1], [1, 2, 1]):
d = np.array(r)
tgt = np.sort(d)
assert_array_equal(np.partition(d, 0, kind=k)[0], tgt[0])
assert_array_equal(np.partition(d, 1, kind=k)[1], tgt[1])
assert_array_equal(np.partition(d, 2, kind=k)[2], tgt[2])
assert_array_equal(d[np.argpartition(d, 0, kind=k)],
np.partition(d, 0, kind=k))
assert_array_equal(d[np.argpartition(d, 1, kind=k)],
np.partition(d, 1, kind=k))
assert_array_equal(d[np.argpartition(d, 2, kind=k)],
np.partition(d, 2, kind=k))
for i in range(d.size):
d[i:].partition(0, kind=k)
assert_array_equal(d, tgt)
d = np.ones(50)
assert_array_equal(np.partition(d, 0, kind=k), d)
assert_array_equal(d[np.argpartition(d, 0, kind=k)],
np.partition(d, 0, kind=k))
# sorted
d = np.arange(49)
assert_equal(np.partition(d, 5, kind=k)[5], 5)
assert_equal(np.partition(d, 15, kind=k)[15], 15)
assert_array_equal(d[np.argpartition(d, 5, kind=k)],
np.partition(d, 5, kind=k))
assert_array_equal(d[np.argpartition(d, 15, kind=k)],
np.partition(d, 15, kind=k))
# rsorted
d = np.arange(47)[::-1]
assert_equal(np.partition(d, 6, kind=k)[6], 6)
assert_equal(np.partition(d, 16, kind=k)[16], 16)
assert_array_equal(d[np.argpartition(d, 6, kind=k)],
np.partition(d, 6, kind=k))
assert_array_equal(d[np.argpartition(d, 16, kind=k)],
np.partition(d, 16, kind=k))
assert_array_equal(np.partition(d, -6, kind=k),
np.partition(d, 41, kind=k))
assert_array_equal(np.partition(d, -16, kind=k),
np.partition(d, 31, kind=k))
assert_array_equal(d[np.argpartition(d, -6, kind=k)],
np.partition(d, 41, kind=k))
# median of 3 killer, O(n^2) on pure median 3 pivot quickselect
# exercises the median of median of 5 code used to keep O(n)
d = np.arange(1000000)
x = np.roll(d, d.size // 2)
mid = x.size // 2 + 1
assert_equal(np.partition(x, mid)[mid], mid)
d = np.arange(1000001)
x = np.roll(d, d.size // 2 + 1)
mid = x.size // 2 + 1
assert_equal(np.partition(x, mid)[mid], mid)
# max
d = np.ones(10)
d[1] = 4
assert_equal(np.partition(d, (2, -1))[-1], 4)
assert_equal(np.partition(d, (2, -1))[2], 1)
assert_equal(d[np.argpartition(d, (2, -1))][-1], 4)
assert_equal(d[np.argpartition(d, (2, -1))][2], 1)
d[1] = np.nan
assert_(np.isnan(d[np.argpartition(d, (2, -1))][-1]))
assert_(np.isnan(np.partition(d, (2, -1))[-1]))
# equal elements
d = np.arange(47) % 7
tgt = np.sort(np.arange(47) % 7)
np.random.shuffle(d)
for i in range(d.size):
assert_equal(np.partition(d, i, kind=k)[i], tgt[i])
assert_array_equal(d[np.argpartition(d, 6, kind=k)],
np.partition(d, 6, kind=k))
assert_array_equal(d[np.argpartition(d, 16, kind=k)],
np.partition(d, 16, kind=k))
for i in range(d.size):
d[i:].partition(0, kind=k)
assert_array_equal(d, tgt)
d = np.array([0, 1, 2, 3, 4, 5, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 9])
kth = [0, 3, 19, 20]
assert_equal(np.partition(d, kth, kind=k)[kth], (0, 3, 7, 7))
assert_equal(d[np.argpartition(d, kth, kind=k)][kth], (0, 3, 7, 7))
d = np.array([2, 1])
d.partition(0, kind=k)
assert_raises(ValueError, d.partition, 2)
assert_raises(np.AxisError, d.partition, 3, axis=1)
assert_raises(ValueError, np.partition, d, 2)
assert_raises(np.AxisError, np.partition, d, 2, axis=1)
assert_raises(ValueError, d.argpartition, 2)
assert_raises(np.AxisError, d.argpartition, 3, axis=1)
assert_raises(ValueError, np.argpartition, d, 2)
assert_raises(np.AxisError, np.argpartition, d, 2, axis=1)
d = np.arange(10).reshape((2, 5))
d.partition(1, axis=0, kind=k)
d.partition(4, axis=1, kind=k)
np.partition(d, 1, axis=0, kind=k)
np.partition(d, 4, axis=1, kind=k)
np.partition(d, 1, axis=None, kind=k)
np.partition(d, 9, axis=None, kind=k)
d.argpartition(1, axis=0, kind=k)
d.argpartition(4, axis=1, kind=k)
np.argpartition(d, 1, axis=0, kind=k)
np.argpartition(d, 4, axis=1, kind=k)
np.argpartition(d, 1, axis=None, kind=k)
np.argpartition(d, 9, axis=None, kind=k)
assert_raises(ValueError, d.partition, 2, axis=0)
assert_raises(ValueError, d.partition, 11, axis=1)
assert_raises(TypeError, d.partition, 2, axis=None)
assert_raises(ValueError, np.partition, d, 9, axis=1)
assert_raises(ValueError, np.partition, d, 11, axis=None)
assert_raises(ValueError, d.argpartition, 2, axis=0)
assert_raises(ValueError, d.argpartition, 11, axis=1)
assert_raises(ValueError, np.argpartition, d, 9, axis=1)
assert_raises(ValueError, np.argpartition, d, 11, axis=None)
td = [(dt, s) for dt in [np.int32, np.float32, np.complex64]
for s in (9, 16)]
for dt, s in td:
aae = assert_array_equal
at = assert_
d = np.arange(s, dtype=dt)
np.random.shuffle(d)
d1 = np.tile(np.arange(s, dtype=dt), (4, 1))
map(np.random.shuffle, d1)
d0 = np.transpose(d1)
for i in range(d.size):
p = np.partition(d, i, kind=k)
assert_equal(p[i], i)
# all before are smaller
assert_array_less(p[:i], p[i])
# all after are larger
assert_array_less(p[i], p[i + 1:])
aae(p, d[np.argpartition(d, i, kind=k)])
p = np.partition(d1, i, axis=1, kind=k)
aae(p[:, i], np.array([i] * d1.shape[0], dtype=dt))
# array_less does not seem to work right
at((p[:, :i].T <= p[:, i]).all(),
msg="%d: %r <= %r" % (i, p[:, i], p[:, :i].T))
at((p[:, i + 1:].T > p[:, i]).all(),
msg="%d: %r < %r" % (i, p[:, i], p[:, i + 1:].T))
aae(p, d1[np.arange(d1.shape[0])[:, None],
np.argpartition(d1, i, axis=1, kind=k)])
p = np.partition(d0, i, axis=0, kind=k)
aae(p[i, :], np.array([i] * d1.shape[0], dtype=dt))
# array_less does not seem to work right
at((p[:i, :] <= p[i, :]).all(),
msg="%d: %r <= %r" % (i, p[i, :], p[:i, :]))
at((p[i + 1:, :] > p[i, :]).all(),
msg="%d: %r < %r" % (i, p[i, :], p[:, i + 1:]))
aae(p, d0[np.argpartition(d0, i, axis=0, kind=k),
np.arange(d0.shape[1])[None, :]])
# check inplace
dc = d.copy()
dc.partition(i, kind=k)
assert_equal(dc, np.partition(d, i, kind=k))
dc = d0.copy()
dc.partition(i, axis=0, kind=k)
assert_equal(dc, np.partition(d0, i, axis=0, kind=k))
dc = d1.copy()
dc.partition(i, axis=1, kind=k)
assert_equal(dc, np.partition(d1, i, axis=1, kind=k))
def assert_partitioned(self, d, kth):
prev = 0
for k in np.sort(kth):
assert_array_less(d[prev:k], d[k], err_msg='kth %d' % k)
assert_((d[k:] >= d[k]).all(),
msg="kth %d, %r not greater equal %d" % (k, d[k:], d[k]))
prev = k + 1
def test_partition_iterative(self):
d = np.arange(17)
kth = (0, 1, 2, 429, 231)
assert_raises(ValueError, d.partition, kth)
assert_raises(ValueError, d.argpartition, kth)
d = np.arange(10).reshape((2, 5))
assert_raises(ValueError, d.partition, kth, axis=0)
assert_raises(ValueError, d.partition, kth, axis=1)
assert_raises(ValueError, np.partition, d, kth, axis=1)
assert_raises(ValueError, np.partition, d, kth, axis=None)
d = np.array([3, 4, 2, 1])
p = np.partition(d, (0, 3))
self.assert_partitioned(p, (0, 3))
self.assert_partitioned(d[np.argpartition(d, (0, 3))], (0, 3))
assert_array_equal(p, np.partition(d, (-3, -1)))
assert_array_equal(p, d[np.argpartition(d, (-3, -1))])
d = np.arange(17)
np.random.shuffle(d)
d.partition(range(d.size))
assert_array_equal(np.arange(17), d)
np.random.shuffle(d)
assert_array_equal(np.arange(17), d[d.argpartition(range(d.size))])
# test unsorted kth
d = np.arange(17)
np.random.shuffle(d)
keys = np.array([1, 3, 8, -2])
np.random.shuffle(d)
p = np.partition(d, keys)
self.assert_partitioned(p, keys)
p = d[np.argpartition(d, keys)]
self.assert_partitioned(p, keys)
np.random.shuffle(keys)
assert_array_equal(np.partition(d, keys), p)
assert_array_equal(d[np.argpartition(d, keys)], p)
# equal kth
d = np.arange(20)[::-1]
self.assert_partitioned(np.partition(d, [5]*4), [5])
self.assert_partitioned(np.partition(d, [5]*4 + [6, 13]),
[5]*4 + [6, 13])
self.assert_partitioned(d[np.argpartition(d, [5]*4)], [5])
self.assert_partitioned(d[np.argpartition(d, [5]*4 + [6, 13])],
[5]*4 + [6, 13])
d = np.arange(12)
np.random.shuffle(d)
d1 = np.tile(np.arange(12), (4, 1))
map(np.random.shuffle, d1)
d0 = np.transpose(d1)
kth = (1, 6, 7, -1)
p = np.partition(d1, kth, axis=1)
pa = d1[np.arange(d1.shape[0])[:, None],
d1.argpartition(kth, axis=1)]
assert_array_equal(p, pa)
for i in range(d1.shape[0]):
self.assert_partitioned(p[i,:], kth)
p = np.partition(d0, kth, axis=0)
pa = d0[np.argpartition(d0, kth, axis=0),
np.arange(d0.shape[1])[None,:]]
assert_array_equal(p, pa)
for i in range(d0.shape[1]):
self.assert_partitioned(p[:, i], kth)
def test_partition_cdtype(self):
d = np.array([('Galahad', 1.7, 38), ('Arthur', 1.8, 41),
('Lancelot', 1.9, 38)],
dtype=[('name', '|S10'), ('height', '<f8'), ('age', '<i4')])
tgt = np.sort(d, order=['age', 'height'])
assert_array_equal(np.partition(d, range(d.size),
order=['age', 'height']),
tgt)
assert_array_equal(d[np.argpartition(d, range(d.size),
order=['age', 'height'])],
tgt)
for k in range(d.size):
assert_equal(np.partition(d, k, order=['age', 'height'])[k],
tgt[k])
assert_equal(d[np.argpartition(d, k, order=['age', 'height'])][k],
tgt[k])
d = np.array(['Galahad', 'Arthur', 'zebra', 'Lancelot'])
tgt = np.sort(d)
assert_array_equal(np.partition(d, range(d.size)), tgt)
for k in range(d.size):
assert_equal(np.partition(d, k)[k], tgt[k])
assert_equal(d[np.argpartition(d, k)][k], tgt[k])
def test_partition_unicode_kind(self):
d = np.arange(10)
k = b'\xc3\xa4'.decode("UTF8")
assert_raises(ValueError, d.partition, 2, kind=k)
assert_raises(ValueError, d.argpartition, 2, kind=k)
def test_partition_fuzz(self):
# a few rounds of random data testing
for j in range(10, 30):
for i in range(1, j - 2):
d = np.arange(j)
np.random.shuffle(d)
d = d % np.random.randint(2, 30)
idx = np.random.randint(d.size)
kth = [0, idx, i, i + 1]
tgt = np.sort(d)[kth]
assert_array_equal(np.partition(d, kth)[kth], tgt,
err_msg="data: %r\n kth: %r" % (d, kth))
def test_argpartition_gh5524(self):
# A test for functionality of argpartition on lists.
d = [6,7,3,2,9,0]
p = np.argpartition(d,1)
self.assert_partitioned(np.array(d)[p],[1])
def test_flatten(self):
x0 = np.array([[1, 2, 3], [4, 5, 6]], np.int32)
x1 = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]], np.int32)
y0 = np.array([1, 2, 3, 4, 5, 6], np.int32)
y0f = np.array([1, 4, 2, 5, 3, 6], np.int32)
y1 = np.array([1, 2, 3, 4, 5, 6, 7, 8], np.int32)
y1f = np.array([1, 5, 3, 7, 2, 6, 4, 8], np.int32)
assert_equal(x0.flatten(), y0)
assert_equal(x0.flatten('F'), y0f)
assert_equal(x0.flatten('F'), x0.T.flatten())
assert_equal(x1.flatten(), y1)
assert_equal(x1.flatten('F'), y1f)
assert_equal(x1.flatten('F'), x1.T.flatten())
def test_dot(self):
a = np.array([[1, 0], [0, 1]])
b = np.array([[0, 1], [1, 0]])
c = np.array([[9, 1], [1, -9]])
d = np.arange(24).reshape(4, 6)
ddt = np.array(
[[ 55, 145, 235, 325],
[ 145, 451, 757, 1063],
[ 235, 757, 1279, 1801],
[ 325, 1063, 1801, 2539]]
)
dtd = np.array(
[[504, 540, 576, 612, 648, 684],
[540, 580, 620, 660, 700, 740],
[576, 620, 664, 708, 752, 796],
[612, 660, 708, 756, 804, 852],
[648, 700, 752, 804, 856, 908],
[684, 740, 796, 852, 908, 964]]
)
# gemm vs syrk optimizations
for et in [np.float32, np.float64, np.complex64, np.complex128]:
eaf = a.astype(et)
assert_equal(np.dot(eaf, eaf), eaf)
assert_equal(np.dot(eaf.T, eaf), eaf)
assert_equal(np.dot(eaf, eaf.T), eaf)
assert_equal(np.dot(eaf.T, eaf.T), eaf)
assert_equal(np.dot(eaf.T.copy(), eaf), eaf)
assert_equal(np.dot(eaf, eaf.T.copy()), eaf)
assert_equal(np.dot(eaf.T.copy(), eaf.T.copy()), eaf)
# syrk validations
for et in [np.float32, np.float64, np.complex64, np.complex128]:
eaf = a.astype(et)
ebf = b.astype(et)
assert_equal(np.dot(ebf, ebf), eaf)
assert_equal(np.dot(ebf.T, ebf), eaf)
assert_equal(np.dot(ebf, ebf.T), eaf)
assert_equal(np.dot(ebf.T, ebf.T), eaf)
# syrk - different shape, stride, and view validations
for et in [np.float32, np.float64, np.complex64, np.complex128]:
edf = d.astype(et)
assert_equal(
np.dot(edf[::-1, :], edf.T),
np.dot(edf[::-1, :].copy(), edf.T.copy())
)
assert_equal(
np.dot(edf[:, ::-1], edf.T),
np.dot(edf[:, ::-1].copy(), edf.T.copy())
)
assert_equal(
np.dot(edf, edf[::-1, :].T),
np.dot(edf, edf[::-1, :].T.copy())
)
assert_equal(
np.dot(edf, edf[:, ::-1].T),
np.dot(edf, edf[:, ::-1].T.copy())
)
assert_equal(
np.dot(edf[:edf.shape[0] // 2, :], edf[::2, :].T),
np.dot(edf[:edf.shape[0] // 2, :].copy(), edf[::2, :].T.copy())
)
assert_equal(
np.dot(edf[::2, :], edf[:edf.shape[0] // 2, :].T),
np.dot(edf[::2, :].copy(), edf[:edf.shape[0] // 2, :].T.copy())
)
# syrk - different shape
for et in [np.float32, np.float64, np.complex64, np.complex128]:
edf = d.astype(et)
eddtf = ddt.astype(et)
edtdf = dtd.astype(et)
assert_equal(np.dot(edf, edf.T), eddtf)
assert_equal(np.dot(edf.T, edf), edtdf)
# function versus methods
assert_equal(np.dot(a, b), a.dot(b))
assert_equal(np.dot(np.dot(a, b), c), a.dot(b).dot(c))
# test passing in an output array
c = np.zeros_like(a)
a.dot(b, c)
assert_equal(c, np.dot(a, b))
# test keyword args
c = np.zeros_like(a)
a.dot(b=b, out=c)
assert_equal(c, np.dot(a, b))
def test_dot_type_mismatch(self):
c = 1.
A = np.array((1,1), dtype='i,i')
assert_raises(TypeError, np.dot, c, A)
assert_raises(TypeError, np.dot, A, c)
def test_dot_out_mem_overlap(self):
np.random.seed(1)
# Test BLAS and non-BLAS code paths, including all dtypes
# that dot() supports
dtypes = [np.dtype(code) for code in np.typecodes['All']
if code not in 'USVM']
for dtype in dtypes:
a = np.random.rand(3, 3).astype(dtype)
# Valid dot() output arrays must be aligned
b = _aligned_zeros((3, 3), dtype=dtype)
b[...] = np.random.rand(3, 3)
y = np.dot(a, b)
x = np.dot(a, b, out=b)
assert_equal(x, y, err_msg=repr(dtype))
# Check invalid output array
assert_raises(ValueError, np.dot, a, b, out=b[::2])
assert_raises(ValueError, np.dot, a, b, out=b.T)
def test_dot_matmul_out(self):
# gh-9641
class Sub(np.ndarray):
pass
a = np.ones((2, 2)).view(Sub)
b = np.ones((2, 2)).view(Sub)
out = np.ones((2, 2))
# make sure out can be any ndarray (not only subclass of inputs)
np.dot(a, b, out=out)
np.matmul(a, b, out=out)
def test_diagonal(self):
a = np.arange(12).reshape((3, 4))
assert_equal(a.diagonal(), [0, 5, 10])
assert_equal(a.diagonal(0), [0, 5, 10])
assert_equal(a.diagonal(1), [1, 6, 11])
assert_equal(a.diagonal(-1), [4, 9])
b = np.arange(8).reshape((2, 2, 2))
assert_equal(b.diagonal(), [[0, 6], [1, 7]])
assert_equal(b.diagonal(0), [[0, 6], [1, 7]])
assert_equal(b.diagonal(1), [[2], [3]])
assert_equal(b.diagonal(-1), [[4], [5]])
assert_raises(ValueError, b.diagonal, axis1=0, axis2=0)
assert_equal(b.diagonal(0, 1, 2), [[0, 3], [4, 7]])
assert_equal(b.diagonal(0, 0, 1), [[0, 6], [1, 7]])
assert_equal(b.diagonal(offset=1, axis1=0, axis2=2), [[1], [3]])
# Order of axis argument doesn't matter:
assert_equal(b.diagonal(0, 2, 1), [[0, 3], [4, 7]])
def test_diagonal_view_notwriteable(self):
# this test is only for 1.9, the diagonal view will be
# writeable in 1.10.
a = np.eye(3).diagonal()
assert_(not a.flags.writeable)
assert_(not a.flags.owndata)
a = np.diagonal(np.eye(3))
assert_(not a.flags.writeable)
assert_(not a.flags.owndata)
a = np.diag(np.eye(3))
assert_(not a.flags.writeable)
assert_(not a.flags.owndata)
def test_diagonal_memleak(self):
# Regression test for a bug that crept in at one point
a = np.zeros((100, 100))
if HAS_REFCOUNT:
assert_(sys.getrefcount(a) < 50)
for i in range(100):
a.diagonal()
if HAS_REFCOUNT:
assert_(sys.getrefcount(a) < 50)
def test_size_zero_memleak(self):
# Regression test for issue 9615
# Exercises a special-case code path for dot products of length
# zero in cblasfuncs (making it is specific to floating dtypes).
a = np.array([], dtype=np.float64)
x = np.array(2.0)
for _ in range(100):
np.dot(a, a, out=x)
if HAS_REFCOUNT:
assert_(sys.getrefcount(x) < 50)
def test_trace(self):
a = np.arange(12).reshape((3, 4))
assert_equal(a.trace(), 15)
assert_equal(a.trace(0), 15)
assert_equal(a.trace(1), 18)
assert_equal(a.trace(-1), 13)
b = np.arange(8).reshape((2, 2, 2))
assert_equal(b.trace(), [6, 8])
assert_equal(b.trace(0), [6, 8])
assert_equal(b.trace(1), [2, 3])
assert_equal(b.trace(-1), [4, 5])
assert_equal(b.trace(0, 0, 1), [6, 8])
assert_equal(b.trace(0, 0, 2), [5, 9])
assert_equal(b.trace(0, 1, 2), [3, 11])
assert_equal(b.trace(offset=1, axis1=0, axis2=2), [1, 3])
def test_trace_subclass(self):
# The class would need to overwrite trace to ensure single-element
# output also has the right subclass.
class MyArray(np.ndarray):
pass
b = np.arange(8).reshape((2, 2, 2)).view(MyArray)
t = b.trace()
assert_(isinstance(t, MyArray))
def test_put(self):
icodes = np.typecodes['AllInteger']
fcodes = np.typecodes['AllFloat']
for dt in icodes + fcodes + 'O':
tgt = np.array([0, 1, 0, 3, 0, 5], dtype=dt)
# test 1-d
a = np.zeros(6, dtype=dt)
a.put([1, 3, 5], [1, 3, 5])
assert_equal(a, tgt)
# test 2-d
a = np.zeros((2, 3), dtype=dt)
a.put([1, 3, 5], [1, 3, 5])
assert_equal(a, tgt.reshape(2, 3))
for dt in '?':
tgt = np.array([False, True, False, True, False, True], dtype=dt)
# test 1-d
a = np.zeros(6, dtype=dt)
a.put([1, 3, 5], [True]*3)
assert_equal(a, tgt)
# test 2-d
a = np.zeros((2, 3), dtype=dt)
a.put([1, 3, 5], [True]*3)
assert_equal(a, tgt.reshape(2, 3))
# check must be writeable
a = np.zeros(6)
a.flags.writeable = False
assert_raises(ValueError, a.put, [1, 3, 5], [1, 3, 5])
# when calling np.put, make sure a
# TypeError is raised if the object
# isn't an ndarray
bad_array = [1, 2, 3]
assert_raises(TypeError, np.put, bad_array, [0, 2], 5)
def test_ravel(self):
a = np.array([[0, 1], [2, 3]])
assert_equal(a.ravel(), [0, 1, 2, 3])
assert_(not a.ravel().flags.owndata)
assert_equal(a.ravel('F'), [0, 2, 1, 3])
assert_equal(a.ravel(order='C'), [0, 1, 2, 3])
assert_equal(a.ravel(order='F'), [0, 2, 1, 3])
assert_equal(a.ravel(order='A'), [0, 1, 2, 3])
assert_(not a.ravel(order='A').flags.owndata)
assert_equal(a.ravel(order='K'), [0, 1, 2, 3])
assert_(not a.ravel(order='K').flags.owndata)
assert_equal(a.ravel(), a.reshape(-1))
a = np.array([[0, 1], [2, 3]], order='F')
assert_equal(a.ravel(), [0, 1, 2, 3])
assert_equal(a.ravel(order='A'), [0, 2, 1, 3])
assert_equal(a.ravel(order='K'), [0, 2, 1, 3])
assert_(not a.ravel(order='A').flags.owndata)
assert_(not a.ravel(order='K').flags.owndata)
assert_equal(a.ravel(), a.reshape(-1))
assert_equal(a.ravel(order='A'), a.reshape(-1, order='A'))
a = np.array([[0, 1], [2, 3]])[::-1, :]
assert_equal(a.ravel(), [2, 3, 0, 1])
assert_equal(a.ravel(order='C'), [2, 3, 0, 1])
assert_equal(a.ravel(order='F'), [2, 0, 3, 1])
assert_equal(a.ravel(order='A'), [2, 3, 0, 1])
# 'K' doesn't reverse the axes of negative strides
assert_equal(a.ravel(order='K'), [2, 3, 0, 1])
assert_(a.ravel(order='K').flags.owndata)
# Test simple 1-d copy behaviour:
a = np.arange(10)[::2]
assert_(a.ravel('K').flags.owndata)
assert_(a.ravel('C').flags.owndata)
assert_(a.ravel('F').flags.owndata)
# Not contiguous and 1-sized axis with non matching stride
a = np.arange(2**3 * 2)[::2]
a = a.reshape(2, 1, 2, 2).swapaxes(-1, -2)
strides = list(a.strides)
strides[1] = 123
a.strides = strides
assert_(a.ravel(order='K').flags.owndata)
assert_equal(a.ravel('K'), np.arange(0, 15, 2))
# contiguous and 1-sized axis with non matching stride works:
a = np.arange(2**3)
a = a.reshape(2, 1, 2, 2).swapaxes(-1, -2)
strides = list(a.strides)
strides[1] = 123
a.strides = strides
assert_(np.may_share_memory(a.ravel(order='K'), a))
assert_equal(a.ravel(order='K'), np.arange(2**3))
# Test negative strides (not very interesting since non-contiguous):
a = np.arange(4)[::-1].reshape(2, 2)
assert_(a.ravel(order='C').flags.owndata)
assert_(a.ravel(order='K').flags.owndata)
assert_equal(a.ravel('C'), [3, 2, 1, 0])
assert_equal(a.ravel('K'), [3, 2, 1, 0])
# 1-element tidy strides test (NPY_RELAXED_STRIDES_CHECKING):
a = np.array([[1]])
a.strides = (123, 432)
# If the stride is not 8, NPY_RELAXED_STRIDES_CHECKING is messing
# them up on purpose:
if np.ones(1).strides == (8,):
assert_(np.may_share_memory(a.ravel('K'), a))
assert_equal(a.ravel('K').strides, (a.dtype.itemsize,))
for order in ('C', 'F', 'A', 'K'):
# 0-d corner case:
a = np.array(0)
assert_equal(a.ravel(order), [0])
assert_(np.may_share_memory(a.ravel(order), a))
# Test that certain non-inplace ravels work right (mostly) for 'K':
b = np.arange(2**4 * 2)[::2].reshape(2, 2, 2, 2)
a = b[..., ::2]
assert_equal(a.ravel('K'), [0, 4, 8, 12, 16, 20, 24, 28])
assert_equal(a.ravel('C'), [0, 4, 8, 12, 16, 20, 24, 28])
assert_equal(a.ravel('A'), [0, 4, 8, 12, 16, 20, 24, 28])
assert_equal(a.ravel('F'), [0, 16, 8, 24, 4, 20, 12, 28])
a = b[::2, ...]
assert_equal(a.ravel('K'), [0, 2, 4, 6, 8, 10, 12, 14])
assert_equal(a.ravel('C'), [0, 2, 4, 6, 8, 10, 12, 14])
assert_equal(a.ravel('A'), [0, 2, 4, 6, 8, 10, 12, 14])
assert_equal(a.ravel('F'), [0, 8, 4, 12, 2, 10, 6, 14])
def test_ravel_subclass(self):
class ArraySubclass(np.ndarray):
pass
a = np.arange(10).view(ArraySubclass)
assert_(isinstance(a.ravel('C'), ArraySubclass))
assert_(isinstance(a.ravel('F'), ArraySubclass))
assert_(isinstance(a.ravel('A'), ArraySubclass))
assert_(isinstance(a.ravel('K'), ArraySubclass))
a = np.arange(10)[::2].view(ArraySubclass)
assert_(isinstance(a.ravel('C'), ArraySubclass))
assert_(isinstance(a.ravel('F'), ArraySubclass))
assert_(isinstance(a.ravel('A'), ArraySubclass))
assert_(isinstance(a.ravel('K'), ArraySubclass))
def test_swapaxes(self):
a = np.arange(1*2*3*4).reshape(1, 2, 3, 4).copy()
idx = np.indices(a.shape)
assert_(a.flags['OWNDATA'])
b = a.copy()
# check exceptions
assert_raises(ValueError, a.swapaxes, -5, 0)
assert_raises(ValueError, a.swapaxes, 4, 0)
assert_raises(ValueError, a.swapaxes, 0, -5)
assert_raises(ValueError, a.swapaxes, 0, 4)
for i in range(-4, 4):
for j in range(-4, 4):
for k, src in enumerate((a, b)):
c = src.swapaxes(i, j)
# check shape
shape = list(src.shape)
shape[i] = src.shape[j]
shape[j] = src.shape[i]
assert_equal(c.shape, shape, str((i, j, k)))
# check array contents
i0, i1, i2, i3 = [dim-1 for dim in c.shape]
j0, j1, j2, j3 = [dim-1 for dim in src.shape]
assert_equal(src[idx[j0], idx[j1], idx[j2], idx[j3]],
c[idx[i0], idx[i1], idx[i2], idx[i3]],
str((i, j, k)))
# check a view is always returned, gh-5260
assert_(not c.flags['OWNDATA'], str((i, j, k)))
# check on non-contiguous input array
if k == 1:
b = c
def test_conjugate(self):
a = np.array([1-1j, 1+1j, 23+23.0j])
ac = a.conj()
assert_equal(a.real, ac.real)
assert_equal(a.imag, -ac.imag)
assert_equal(ac, a.conjugate())
assert_equal(ac, np.conjugate(a))
a = np.array([1-1j, 1+1j, 23+23.0j], 'F')
ac = a.conj()
assert_equal(a.real, ac.real)
assert_equal(a.imag, -ac.imag)
assert_equal(ac, a.conjugate())
assert_equal(ac, np.conjugate(a))
a = np.array([1, 2, 3])
ac = a.conj()
assert_equal(a, ac)
assert_equal(ac, a.conjugate())
assert_equal(ac, np.conjugate(a))
a = np.array([1.0, 2.0, 3.0])
ac = a.conj()
assert_equal(a, ac)
assert_equal(ac, a.conjugate())
assert_equal(ac, np.conjugate(a))
a = np.array([1-1j, 1+1j, 1, 2.0], object)
ac = a.conj()
assert_equal(ac, [k.conjugate() for k in a])
assert_equal(ac, a.conjugate())
assert_equal(ac, np.conjugate(a))
a = np.array([1-1j, 1, 2.0, 'f'], object)
assert_raises(AttributeError, lambda: a.conj())
assert_raises(AttributeError, lambda: a.conjugate())
def test__complex__(self):
dtypes = ['i1', 'i2', 'i4', 'i8',
'u1', 'u2', 'u4', 'u8',
'f', 'd', 'g', 'F', 'D', 'G',
'?', 'O']
for dt in dtypes:
a = np.array(7, dtype=dt)
b = np.array([7], dtype=dt)
c = np.array([[[[[7]]]]], dtype=dt)
msg = 'dtype: {0}'.format(dt)
ap = complex(a)
assert_equal(ap, a, msg)
bp = complex(b)
assert_equal(bp, b, msg)
cp = complex(c)
assert_equal(cp, c, msg)
def test__complex__should_not_work(self):
dtypes = ['i1', 'i2', 'i4', 'i8',
'u1', 'u2', 'u4', 'u8',
'f', 'd', 'g', 'F', 'D', 'G',
'?', 'O']
for dt in dtypes:
a = np.array([1, 2, 3], dtype=dt)
assert_raises(TypeError, complex, a)
dt = np.dtype([('a', 'f8'), ('b', 'i1')])
b = np.array((1.0, 3), dtype=dt)
assert_raises(TypeError, complex, b)
c = np.array([(1.0, 3), (2e-3, 7)], dtype=dt)
assert_raises(TypeError, complex, c)
d = np.array('1+1j')
assert_raises(TypeError, complex, d)
e = np.array(['1+1j'], 'U')
assert_raises(TypeError, complex, e)
class TestCequenceMethods(object):
def test_array_contains(self):
assert_(4.0 in np.arange(16.).reshape(4,4))
assert_(20.0 not in np.arange(16.).reshape(4,4))
class TestBinop(object):
def test_inplace(self):
# test refcount 1 inplace conversion
assert_array_almost_equal(np.array([0.5]) * np.array([1.0, 2.0]),
[0.5, 1.0])
d = np.array([0.5, 0.5])[::2]
assert_array_almost_equal(d * (d * np.array([1.0, 2.0])),
[0.25, 0.5])
a = np.array([0.5])
b = np.array([0.5])
c = a + b
c = a - b
c = a * b
c = a / b
assert_equal(a, b)
assert_almost_equal(c, 1.)
c = a + b * 2. / b * a - a / b
assert_equal(a, b)
assert_equal(c, 0.5)
# true divide
a = np.array([5])
b = np.array([3])
c = (a * a) / b
assert_almost_equal(c, 25 / 3)
assert_equal(a, 5)
assert_equal(b, 3)
# ndarray.__rop__ always calls ufunc
# ndarray.__iop__ always calls ufunc
# ndarray.__op__, __rop__:
# - defer if other has __array_ufunc__ and it is None
# or other is not a subclass and has higher array priority
# - else, call ufunc
def test_ufunc_binop_interaction(self):
# Python method name (without underscores)
# -> (numpy ufunc, has_in_place_version, preferred_dtype)
ops = {
'add': (np.add, True, float),
'sub': (np.subtract, True, float),
'mul': (np.multiply, True, float),
'truediv': (np.true_divide, True, float),
'floordiv': (np.floor_divide, True, float),
'mod': (np.remainder, True, float),
'divmod': (np.divmod, False, float),
'pow': (np.power, True, int),
'lshift': (np.left_shift, True, int),
'rshift': (np.right_shift, True, int),
'and': (np.bitwise_and, True, int),
'xor': (np.bitwise_xor, True, int),
'or': (np.bitwise_or, True, int),
# 'ge': (np.less_equal, False),
# 'gt': (np.less, False),
# 'le': (np.greater_equal, False),
# 'lt': (np.greater, False),
# 'eq': (np.equal, False),
# 'ne': (np.not_equal, False),
}
class Coerced(Exception):
pass
def array_impl(self):
raise Coerced
def op_impl(self, other):
return "forward"
def rop_impl(self, other):
return "reverse"
def iop_impl(self, other):
return "in-place"
def array_ufunc_impl(self, ufunc, method, *args, **kwargs):
return ("__array_ufunc__", ufunc, method, args, kwargs)
# Create an object with the given base, in the given module, with a
# bunch of placeholder __op__ methods, and optionally a
# __array_ufunc__ and __array_priority__.
def make_obj(base, array_priority=False, array_ufunc=False,
alleged_module="__main__"):
class_namespace = {"__array__": array_impl}
if array_priority is not False:
class_namespace["__array_priority__"] = array_priority
for op in ops:
class_namespace["__{0}__".format(op)] = op_impl
class_namespace["__r{0}__".format(op)] = rop_impl
class_namespace["__i{0}__".format(op)] = iop_impl
if array_ufunc is not False:
class_namespace["__array_ufunc__"] = array_ufunc
eval_namespace = {"base": base,
"class_namespace": class_namespace,
"__name__": alleged_module,
}
MyType = eval("type('MyType', (base,), class_namespace)",
eval_namespace)
if issubclass(MyType, np.ndarray):
# Use this range to avoid special case weirdnesses around
# divide-by-0, pow(x, 2), overflow due to pow(big, big), etc.
return np.arange(3, 5).view(MyType)
else:
return MyType()
def check(obj, binop_override_expected, ufunc_override_expected,
inplace_override_expected, check_scalar=True):
for op, (ufunc, has_inplace, dtype) in ops.items():
err_msg = ('op: %s, ufunc: %s, has_inplace: %s, dtype: %s'
% (op, ufunc, has_inplace, dtype))
check_objs = [np.arange(3, 5, dtype=dtype)]
if check_scalar:
check_objs.append(check_objs[0][0])
for arr in check_objs:
arr_method = getattr(arr, "__{0}__".format(op))
def first_out_arg(result):
if op == "divmod":
assert_(isinstance(result, tuple))
return result[0]
else:
return result
# arr __op__ obj
if binop_override_expected:
assert_equal(arr_method(obj), NotImplemented, err_msg)
elif ufunc_override_expected:
assert_equal(arr_method(obj)[0], "__array_ufunc__",
err_msg)
else:
if (isinstance(obj, np.ndarray) and
(type(obj).__array_ufunc__ is
np.ndarray.__array_ufunc__)):
# __array__ gets ignored
res = first_out_arg(arr_method(obj))
assert_(res.__class__ is obj.__class__, err_msg)
else:
assert_raises((TypeError, Coerced),
arr_method, obj, err_msg=err_msg)
# obj __op__ arr
arr_rmethod = getattr(arr, "__r{0}__".format(op))
if ufunc_override_expected:
res = arr_rmethod(obj)
assert_equal(res[0], "__array_ufunc__",
err_msg=err_msg)
assert_equal(res[1], ufunc, err_msg=err_msg)
else:
if (isinstance(obj, np.ndarray) and
(type(obj).__array_ufunc__ is
np.ndarray.__array_ufunc__)):
# __array__ gets ignored
res = first_out_arg(arr_rmethod(obj))
assert_(res.__class__ is obj.__class__, err_msg)
else:
# __array_ufunc__ = "asdf" creates a TypeError
assert_raises((TypeError, Coerced),
arr_rmethod, obj, err_msg=err_msg)
# arr __iop__ obj
# array scalars don't have in-place operators
if has_inplace and isinstance(arr, np.ndarray):
arr_imethod = getattr(arr, "__i{0}__".format(op))
if inplace_override_expected:
assert_equal(arr_method(obj), NotImplemented,
err_msg=err_msg)
elif ufunc_override_expected:
res = arr_imethod(obj)
assert_equal(res[0], "__array_ufunc__", err_msg)
assert_equal(res[1], ufunc, err_msg)
assert_(type(res[-1]["out"]) is tuple, err_msg)
assert_(res[-1]["out"][0] is arr, err_msg)
else:
if (isinstance(obj, np.ndarray) and
(type(obj).__array_ufunc__ is
np.ndarray.__array_ufunc__)):
# __array__ gets ignored
assert_(arr_imethod(obj) is arr, err_msg)
else:
assert_raises((TypeError, Coerced),
arr_imethod, obj,
err_msg=err_msg)
op_fn = getattr(operator, op, None)
if op_fn is None:
op_fn = getattr(operator, op + "_", None)
if op_fn is None:
op_fn = getattr(builtins, op)
assert_equal(op_fn(obj, arr), "forward", err_msg)
if not isinstance(obj, np.ndarray):
if binop_override_expected:
assert_equal(op_fn(arr, obj), "reverse", err_msg)
elif ufunc_override_expected:
assert_equal(op_fn(arr, obj)[0], "__array_ufunc__",
err_msg)
if ufunc_override_expected:
assert_equal(ufunc(obj, arr)[0], "__array_ufunc__",
err_msg)
# No array priority, no array_ufunc -> nothing called
check(make_obj(object), False, False, False)
# Negative array priority, no array_ufunc -> nothing called
# (has to be very negative, because scalar priority is -1000000.0)
check(make_obj(object, array_priority=-2**30), False, False, False)
# Positive array priority, no array_ufunc -> binops and iops only
check(make_obj(object, array_priority=1), True, False, True)
# ndarray ignores array_priority for ndarray subclasses
check(make_obj(np.ndarray, array_priority=1), False, False, False,
check_scalar=False)
# Positive array_priority and array_ufunc -> array_ufunc only
check(make_obj(object, array_priority=1,
array_ufunc=array_ufunc_impl), False, True, False)
check(make_obj(np.ndarray, array_priority=1,
array_ufunc=array_ufunc_impl), False, True, False)
# array_ufunc set to None -> defer binops only
check(make_obj(object, array_ufunc=None), True, False, False)
check(make_obj(np.ndarray, array_ufunc=None), True, False, False,
check_scalar=False)
def test_ufunc_override_normalize_signature(self):
# gh-5674
class SomeClass(object):
def __array_ufunc__(self, ufunc, method, *inputs, **kw):
return kw
a = SomeClass()
kw = np.add(a, [1])
assert_('sig' not in kw and 'signature' not in kw)
kw = np.add(a, [1], sig='ii->i')
assert_('sig' not in kw and 'signature' in kw)
assert_equal(kw['signature'], 'ii->i')
kw = np.add(a, [1], signature='ii->i')
assert_('sig' not in kw and 'signature' in kw)
assert_equal(kw['signature'], 'ii->i')
def test_array_ufunc_index(self):
# Check that index is set appropriately, also if only an output
# is passed on (latter is another regression tests for github bug 4753)
# This also checks implicitly that 'out' is always a tuple.
class CheckIndex(object):
def __array_ufunc__(self, ufunc, method, *inputs, **kw):
for i, a in enumerate(inputs):
if a is self:
return i
# calls below mean we must be in an output.
for j, a in enumerate(kw['out']):
if a is self:
return (j,)
a = CheckIndex()
dummy = np.arange(2.)
# 1 input, 1 output
assert_equal(np.sin(a), 0)
assert_equal(np.sin(dummy, a), (0,))
assert_equal(np.sin(dummy, out=a), (0,))
assert_equal(np.sin(dummy, out=(a,)), (0,))
assert_equal(np.sin(a, a), 0)
assert_equal(np.sin(a, out=a), 0)
assert_equal(np.sin(a, out=(a,)), 0)
# 1 input, 2 outputs
assert_equal(np.modf(dummy, a), (0,))
assert_equal(np.modf(dummy, None, a), (1,))
assert_equal(np.modf(dummy, dummy, a), (1,))
assert_equal(np.modf(dummy, out=(a, None)), (0,))
assert_equal(np.modf(dummy, out=(a, dummy)), (0,))
assert_equal(np.modf(dummy, out=(None, a)), (1,))
assert_equal(np.modf(dummy, out=(dummy, a)), (1,))
assert_equal(np.modf(a, out=(dummy, a)), 0)
with warnings.catch_warnings(record=True) as w:
warnings.filterwarnings('always', '', DeprecationWarning)
assert_equal(np.modf(dummy, out=a), (0,))
assert_(w[0].category is DeprecationWarning)
assert_raises(ValueError, np.modf, dummy, out=(a,))
# 2 inputs, 1 output
assert_equal(np.add(a, dummy), 0)
assert_equal(np.add(dummy, a), 1)
assert_equal(np.add(dummy, dummy, a), (0,))
assert_equal(np.add(dummy, a, a), 1)
assert_equal(np.add(dummy, dummy, out=a), (0,))
assert_equal(np.add(dummy, dummy, out=(a,)), (0,))
assert_equal(np.add(a, dummy, out=a), 0)
def test_out_override(self):
# regression test for github bug 4753
class OutClass(np.ndarray):
def __array_ufunc__(self, ufunc, method, *inputs, **kw):
if 'out' in kw:
tmp_kw = kw.copy()
tmp_kw.pop('out')
func = getattr(ufunc, method)
kw['out'][0][...] = func(*inputs, **tmp_kw)
A = np.array([0]).view(OutClass)
B = np.array([5])
C = np.array([6])
np.multiply(C, B, A)
assert_equal(A[0], 30)
assert_(isinstance(A, OutClass))
A[0] = 0
np.multiply(C, B, out=A)
assert_equal(A[0], 30)
assert_(isinstance(A, OutClass))
def test_pow_override_with_errors(self):
# regression test for gh-9112
class PowerOnly(np.ndarray):
def __array_ufunc__(self, ufunc, method, *inputs, **kw):
if ufunc is not np.power:
raise NotImplementedError
return "POWER!"
# explicit cast to float, to ensure the fast power path is taken.
a = np.array(5., dtype=np.float64).view(PowerOnly)
assert_equal(a ** 2.5, "POWER!")
with assert_raises(NotImplementedError):
a ** 0.5
with assert_raises(NotImplementedError):
a ** 0
with assert_raises(NotImplementedError):
a ** 1
with assert_raises(NotImplementedError):
a ** -1
with assert_raises(NotImplementedError):
a ** 2
class TestTemporaryElide(object):
# elision is only triggered on relatively large arrays
def test_extension_incref_elide(self):
# test extension (e.g. cython) calling PyNumber_* slots without
# increasing the reference counts
#
# def incref_elide(a):
# d = input.copy() # refcount 1
# return d, d + d # PyNumber_Add without increasing refcount
from numpy.core.multiarray_tests import incref_elide
d = np.ones(100000)
orig, res = incref_elide(d)
d + d
# the return original should not be changed to an inplace operation
assert_array_equal(orig, d)
assert_array_equal(res, d + d)
def test_extension_incref_elide_stack(self):
# scanning if the refcount == 1 object is on the python stack to check
# that we are called directly from python is flawed as object may still
# be above the stack pointer and we have no access to the top of it
#
# def incref_elide_l(d):
# return l[4] + l[4] # PyNumber_Add without increasing refcount
from numpy.core.multiarray_tests import incref_elide_l
# padding with 1 makes sure the object on the stack is not overwriten
l = [1, 1, 1, 1, np.ones(100000)]
res = incref_elide_l(l)
# the return original should not be changed to an inplace operation
assert_array_equal(l[4], np.ones(100000))
assert_array_equal(res, l[4] + l[4])
def test_temporary_with_cast(self):
# check that we don't elide into a temporary which would need casting
d = np.ones(200000, dtype=np.int64)
assert_equal(((d + d) + 2**222).dtype, np.dtype('O'))
r = ((d + d) / 2)
assert_equal(r.dtype, np.dtype('f8'))
r = np.true_divide((d + d), 2)
assert_equal(r.dtype, np.dtype('f8'))
r = ((d + d) / 2.)
assert_equal(r.dtype, np.dtype('f8'))
r = ((d + d) // 2)
assert_equal(r.dtype, np.dtype(np.int64))
# commutative elision into the astype result
f = np.ones(100000, dtype=np.float32)
assert_equal(((f + f) + f.astype(np.float64)).dtype, np.dtype('f8'))
# no elision into lower type
d = f.astype(np.float64)
assert_equal(((f + f) + d).dtype, d.dtype)
l = np.ones(100000, dtype=np.longdouble)
assert_equal(((d + d) + l).dtype, l.dtype)
# test unary abs with different output dtype
for dt in (np.complex64, np.complex128, np.clongdouble):
c = np.ones(100000, dtype=dt)
r = abs(c * 2.0)
assert_equal(r.dtype, np.dtype('f%d' % (c.itemsize // 2)))
def test_elide_broadcast(self):
# test no elision on broadcast to higher dimension
# only triggers elision code path in debug mode as triggering it in
# normal mode needs 256kb large matching dimension, so a lot of memory
d = np.ones((2000, 1), dtype=int)
b = np.ones((2000), dtype=bool)
r = (1 - d) + b
assert_equal(r, 1)
assert_equal(r.shape, (2000, 2000))
def test_elide_scalar(self):
# check inplace op does not create ndarray from scalars
a = np.bool_()
assert_(type(~(a & a)) is np.bool_)
def test_elide_scalar_readonly(self):
# The imaginary part of a real array is readonly. This needs to go
# through fast_scalar_power which is only called for powers of
# +1, -1, 0, 0.5, and 2, so use 2. Also need valid refcount for
# elision which can be gotten for the imaginary part of a real
# array. Should not error.
a = np.empty(100000, dtype=np.float64)
a.imag ** 2
def test_elide_readonly(self):
# don't try to elide readonly temporaries
r = np.asarray(np.broadcast_to(np.zeros(1), 100000).flat) * 0.0
assert_equal(r, 0)
def test_elide_updateifcopy(self):
a = np.ones(2**20)[::2]
b = a.flat.__array__() + 1
del b
assert_equal(a, 1)
class TestCAPI(object):
def test_IsPythonScalar(self):
from numpy.core.multiarray_tests import IsPythonScalar
assert_(IsPythonScalar(b'foobar'))
assert_(IsPythonScalar(1))
assert_(IsPythonScalar(2**80))
assert_(IsPythonScalar(2.))
assert_(IsPythonScalar("a"))
class TestSubscripting(object):
def test_test_zero_rank(self):
x = np.array([1, 2, 3])
assert_(isinstance(x[0], np.int_))
if sys.version_info[0] < 3:
assert_(isinstance(x[0], int))
assert_(type(x[0, ...]) is np.ndarray)
class TestPickling(object):
def test_roundtrip(self):
import pickle
carray = np.array([[2, 9], [7, 0], [3, 8]])
DATA = [
carray,
np.transpose(carray),
np.array([('xxx', 1, 2.0)], dtype=[('a', (str, 3)), ('b', int),
('c', float)])
]
for a in DATA:
assert_equal(a, pickle.loads(a.dumps()), err_msg="%r" % a)
def _loads(self, obj):
if sys.version_info[0] >= 3:
return np.loads(obj, encoding='latin1')
else:
return np.loads(obj)
# version 0 pickles, using protocol=2 to pickle
# version 0 doesn't have a version field
def test_version0_int8(self):
s = b'\x80\x02cnumpy.core._internal\n_reconstruct\nq\x01cnumpy\nndarray\nq\x02K\x00\x85U\x01b\x87Rq\x03(K\x04\x85cnumpy\ndtype\nq\x04U\x02i1K\x00K\x01\x87Rq\x05(U\x01|NNJ\xff\xff\xff\xffJ\xff\xff\xff\xfftb\x89U\x04\x01\x02\x03\x04tb.'
a = np.array([1, 2, 3, 4], dtype=np.int8)
p = self._loads(s)
assert_equal(a, p)
def test_version0_float32(self):
s = b'\x80\x02cnumpy.core._internal\n_reconstruct\nq\x01cnumpy\nndarray\nq\x02K\x00\x85U\x01b\x87Rq\x03(K\x04\x85cnumpy\ndtype\nq\x04U\x02f4K\x00K\x01\x87Rq\x05(U\x01<NNJ\xff\xff\xff\xffJ\xff\xff\xff\xfftb\x89U\x10\x00\x00\x80?\x00\x00\x00@\x00\x00@@\x00\x00\x80@tb.'
a = np.array([1.0, 2.0, 3.0, 4.0], dtype=np.float32)
p = self._loads(s)
assert_equal(a, p)
def test_version0_object(self):
s = b'\x80\x02cnumpy.core._internal\n_reconstruct\nq\x01cnumpy\nndarray\nq\x02K\x00\x85U\x01b\x87Rq\x03(K\x02\x85cnumpy\ndtype\nq\x04U\x02O8K\x00K\x01\x87Rq\x05(U\x01|NNJ\xff\xff\xff\xffJ\xff\xff\xff\xfftb\x89]q\x06(}q\x07U\x01aK\x01s}q\x08U\x01bK\x02setb.'
a = np.array([{'a': 1}, {'b': 2}])
p = self._loads(s)
assert_equal(a, p)
# version 1 pickles, using protocol=2 to pickle
def test_version1_int8(self):
s = b'\x80\x02cnumpy.core._internal\n_reconstruct\nq\x01cnumpy\nndarray\nq\x02K\x00\x85U\x01b\x87Rq\x03(K\x01K\x04\x85cnumpy\ndtype\nq\x04U\x02i1K\x00K\x01\x87Rq\x05(K\x01U\x01|NNJ\xff\xff\xff\xffJ\xff\xff\xff\xfftb\x89U\x04\x01\x02\x03\x04tb.'
a = np.array([1, 2, 3, 4], dtype=np.int8)
p = self._loads(s)
assert_equal(a, p)
def test_version1_float32(self):
s = b'\x80\x02cnumpy.core._internal\n_reconstruct\nq\x01cnumpy\nndarray\nq\x02K\x00\x85U\x01b\x87Rq\x03(K\x01K\x04\x85cnumpy\ndtype\nq\x04U\x02f4K\x00K\x01\x87Rq\x05(K\x01U\x01<NNJ\xff\xff\xff\xffJ\xff\xff\xff\xfftb\x89U\x10\x00\x00\x80?\x00\x00\x00@\x00\x00@@\x00\x00\x80@tb.'
a = np.array([1.0, 2.0, 3.0, 4.0], dtype=np.float32)
p = self._loads(s)
assert_equal(a, p)
def test_version1_object(self):
s = b'\x80\x02cnumpy.core._internal\n_reconstruct\nq\x01cnumpy\nndarray\nq\x02K\x00\x85U\x01b\x87Rq\x03(K\x01K\x02\x85cnumpy\ndtype\nq\x04U\x02O8K\x00K\x01\x87Rq\x05(K\x01U\x01|NNJ\xff\xff\xff\xffJ\xff\xff\xff\xfftb\x89]q\x06(}q\x07U\x01aK\x01s}q\x08U\x01bK\x02setb.'
a = np.array([{'a': 1}, {'b': 2}])
p = self._loads(s)
assert_equal(a, p)
def test_subarray_int_shape(self):
s = b"cnumpy.core.multiarray\n_reconstruct\np0\n(cnumpy\nndarray\np1\n(I0\ntp2\nS'b'\np3\ntp4\nRp5\n(I1\n(I1\ntp6\ncnumpy\ndtype\np7\n(S'V6'\np8\nI0\nI1\ntp9\nRp10\n(I3\nS'|'\np11\nN(S'a'\np12\ng3\ntp13\n(dp14\ng12\n(g7\n(S'V4'\np15\nI0\nI1\ntp16\nRp17\n(I3\nS'|'\np18\n(g7\n(S'i1'\np19\nI0\nI1\ntp20\nRp21\n(I3\nS'|'\np22\nNNNI-1\nI-1\nI0\ntp23\nb(I2\nI2\ntp24\ntp25\nNNI4\nI1\nI0\ntp26\nbI0\ntp27\nsg3\n(g7\n(S'V2'\np28\nI0\nI1\ntp29\nRp30\n(I3\nS'|'\np31\n(g21\nI2\ntp32\nNNI2\nI1\nI0\ntp33\nbI4\ntp34\nsI6\nI1\nI0\ntp35\nbI00\nS'\\x01\\x01\\x01\\x01\\x01\\x02'\np36\ntp37\nb."
a = np.array([(1, (1, 2))], dtype=[('a', 'i1', (2, 2)), ('b', 'i1', 2)])
p = self._loads(s)
assert_equal(a, p)
class TestFancyIndexing(object):
def test_list(self):
x = np.ones((1, 1))
x[:, [0]] = 2.0
assert_array_equal(x, np.array([[2.0]]))
x = np.ones((1, 1, 1))
x[:, :, [0]] = 2.0
assert_array_equal(x, np.array([[[2.0]]]))
def test_tuple(self):
x = np.ones((1, 1))
x[:, (0,)] = 2.0
assert_array_equal(x, np.array([[2.0]]))
x = np.ones((1, 1, 1))
x[:, :, (0,)] = 2.0
assert_array_equal(x, np.array([[[2.0]]]))
def test_mask(self):
x = np.array([1, 2, 3, 4])
m = np.array([0, 1, 0, 0], bool)
assert_array_equal(x[m], np.array([2]))
def test_mask2(self):
x = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
m = np.array([0, 1], bool)
m2 = np.array([[0, 1, 0, 0], [1, 0, 0, 0]], bool)
m3 = np.array([[0, 1, 0, 0], [0, 0, 0, 0]], bool)
assert_array_equal(x[m], np.array([[5, 6, 7, 8]]))
assert_array_equal(x[m2], np.array([2, 5]))
assert_array_equal(x[m3], np.array([2]))
def test_assign_mask(self):
x = np.array([1, 2, 3, 4])
m = np.array([0, 1, 0, 0], bool)
x[m] = 5
assert_array_equal(x, np.array([1, 5, 3, 4]))
def test_assign_mask2(self):
xorig = np.array([[1, 2, 3, 4], [5, 6, 7, 8]])
m = np.array([0, 1], bool)
m2 = np.array([[0, 1, 0, 0], [1, 0, 0, 0]], bool)
m3 = np.array([[0, 1, 0, 0], [0, 0, 0, 0]], bool)
x = xorig.copy()
x[m] = 10
assert_array_equal(x, np.array([[1, 2, 3, 4], [10, 10, 10, 10]]))
x = xorig.copy()
x[m2] = 10
assert_array_equal(x, np.array([[1, 10, 3, 4], [10, 6, 7, 8]]))
x = xorig.copy()
x[m3] = 10
assert_array_equal(x, np.array([[1, 10, 3, 4], [5, 6, 7, 8]]))
class TestStringCompare(object):
def test_string(self):
g1 = np.array(["This", "is", "example"])
g2 = np.array(["This", "was", "example"])
assert_array_equal(g1 == g2, [g1[i] == g2[i] for i in [0, 1, 2]])
assert_array_equal(g1 != g2, [g1[i] != g2[i] for i in [0, 1, 2]])
assert_array_equal(g1 <= g2, [g1[i] <= g2[i] for i in [0, 1, 2]])
assert_array_equal(g1 >= g2, [g1[i] >= g2[i] for i in [0, 1, 2]])
assert_array_equal(g1 < g2, [g1[i] < g2[i] for i in [0, 1, 2]])
assert_array_equal(g1 > g2, [g1[i] > g2[i] for i in [0, 1, 2]])
def test_mixed(self):
g1 = np.array(["spam", "spa", "spammer", "and eggs"])
g2 = "spam"
assert_array_equal(g1 == g2, [x == g2 for x in g1])
assert_array_equal(g1 != g2, [x != g2 for x in g1])
assert_array_equal(g1 < g2, [x < g2 for x in g1])
assert_array_equal(g1 > g2, [x > g2 for x in g1])
assert_array_equal(g1 <= g2, [x <= g2 for x in g1])
assert_array_equal(g1 >= g2, [x >= g2 for x in g1])
def test_unicode(self):
g1 = np.array([u"This", u"is", u"example"])
g2 = np.array([u"This", u"was", u"example"])
assert_array_equal(g1 == g2, [g1[i] == g2[i] for i in [0, 1, 2]])
assert_array_equal(g1 != g2, [g1[i] != g2[i] for i in [0, 1, 2]])
assert_array_equal(g1 <= g2, [g1[i] <= g2[i] for i in [0, 1, 2]])
assert_array_equal(g1 >= g2, [g1[i] >= g2[i] for i in [0, 1, 2]])
assert_array_equal(g1 < g2, [g1[i] < g2[i] for i in [0, 1, 2]])
assert_array_equal(g1 > g2, [g1[i] > g2[i] for i in [0, 1, 2]])
class TestArgmax(object):
nan_arr = [
([0, 1, 2, 3, np.nan], 4),
([0, 1, 2, np.nan, 3], 3),
([np.nan, 0, 1, 2, 3], 0),
([np.nan, 0, np.nan, 2, 3], 0),
([0, 1, 2, 3, complex(0, np.nan)], 4),
([0, 1, 2, 3, complex(np.nan, 0)], 4),
([0, 1, 2, complex(np.nan, 0), 3], 3),
([0, 1, 2, complex(0, np.nan), 3], 3),
([complex(0, np.nan), 0, 1, 2, 3], 0),
([complex(np.nan, np.nan), 0, 1, 2, 3], 0),
([complex(np.nan, 0), complex(np.nan, 2), complex(np.nan, 1)], 0),
([complex(np.nan, np.nan), complex(np.nan, 2), complex(np.nan, 1)], 0),
([complex(np.nan, 0), complex(np.nan, 2), complex(np.nan, np.nan)], 0),
([complex(0, 0), complex(0, 2), complex(0, 1)], 1),
([complex(1, 0), complex(0, 2), complex(0, 1)], 0),
([complex(1, 0), complex(0, 2), complex(1, 1)], 2),
([np.datetime64('1923-04-14T12:43:12'),
np.datetime64('1994-06-21T14:43:15'),
np.datetime64('2001-10-15T04:10:32'),
np.datetime64('1995-11-25T16:02:16'),
np.datetime64('2005-01-04T03:14:12'),
np.datetime64('2041-12-03T14:05:03')], 5),
([np.datetime64('1935-09-14T04:40:11'),
np.datetime64('1949-10-12T12:32:11'),
np.datetime64('2010-01-03T05:14:12'),
np.datetime64('2015-11-20T12:20:59'),
np.datetime64('1932-09-23T10:10:13'),
np.datetime64('2014-10-10T03:50:30')], 3),
# Assorted tests with NaTs
([np.datetime64('NaT'),
np.datetime64('NaT'),
np.datetime64('2010-01-03T05:14:12'),
np.datetime64('NaT'),
np.datetime64('2015-09-23T10:10:13'),
np.datetime64('1932-10-10T03:50:30')], 4),
([np.datetime64('2059-03-14T12:43:12'),
np.datetime64('1996-09-21T14:43:15'),
np.datetime64('NaT'),
np.datetime64('2022-12-25T16:02:16'),
np.datetime64('1963-10-04T03:14:12'),
np.datetime64('2013-05-08T18:15:23')], 0),
([np.timedelta64(2, 's'),
np.timedelta64(1, 's'),
np.timedelta64('NaT', 's'),
np.timedelta64(3, 's')], 3),
([np.timedelta64('NaT', 's')] * 3, 0),
([timedelta(days=5, seconds=14), timedelta(days=2, seconds=35),
timedelta(days=-1, seconds=23)], 0),
([timedelta(days=1, seconds=43), timedelta(days=10, seconds=5),
timedelta(days=5, seconds=14)], 1),
([timedelta(days=10, seconds=24), timedelta(days=10, seconds=5),
timedelta(days=10, seconds=43)], 2),
([False, False, False, False, True], 4),
([False, False, False, True, False], 3),
([True, False, False, False, False], 0),
([True, False, True, False, False], 0),
]
def test_all(self):
a = np.random.normal(0, 1, (4, 5, 6, 7, 8))
for i in range(a.ndim):
amax = a.max(i)
aargmax = a.argmax(i)
axes = list(range(a.ndim))
axes.remove(i)
assert_(np.all(amax == aargmax.choose(*a.transpose(i,*axes))))
def test_combinations(self):
for arr, pos in self.nan_arr:
with suppress_warnings() as sup:
sup.filter(RuntimeWarning,
"invalid value encountered in reduce")
max_val = np.max(arr)
assert_equal(np.argmax(arr), pos, err_msg="%r" % arr)
assert_equal(arr[np.argmax(arr)], max_val, err_msg="%r" % arr)
def test_output_shape(self):
# see also gh-616
a = np.ones((10, 5))
# Check some simple shape mismatches
out = np.ones(11, dtype=np.int_)
assert_raises(ValueError, a.argmax, -1, out)
out = np.ones((2, 5), dtype=np.int_)
assert_raises(ValueError, a.argmax, -1, out)
# these could be relaxed possibly (used to allow even the previous)
out = np.ones((1, 10), dtype=np.int_)
assert_raises(ValueError, a.argmax, -1, out)
out = np.ones(10, dtype=np.int_)
a.argmax(-1, out=out)
assert_equal(out, a.argmax(-1))
def test_argmax_unicode(self):
d = np.zeros(6031, dtype='<U9')
d[5942] = "as"
assert_equal(d.argmax(), 5942)
def test_np_vs_ndarray(self):
# make sure both ndarray.argmax and numpy.argmax support out/axis args
a = np.random.normal(size=(2,3))
# check positional args
out1 = np.zeros(2, dtype=int)
out2 = np.zeros(2, dtype=int)
assert_equal(a.argmax(1, out1), np.argmax(a, 1, out2))
assert_equal(out1, out2)
# check keyword args
out1 = np.zeros(3, dtype=int)
out2 = np.zeros(3, dtype=int)
assert_equal(a.argmax(out=out1, axis=0), np.argmax(a, out=out2, axis=0))
assert_equal(out1, out2)
def test_object_argmax_with_NULLs(self):
# See gh-6032
a = np.empty(4, dtype='O')
ctypes.memset(a.ctypes.data, 0, a.nbytes)
assert_equal(a.argmax(), 0)
a[3] = 10
assert_equal(a.argmax(), 3)
a[1] = 30
assert_equal(a.argmax(), 1)
class TestArgmin(object):
nan_arr = [
([0, 1, 2, 3, np.nan], 4),
([0, 1, 2, np.nan, 3], 3),
([np.nan, 0, 1, 2, 3], 0),
([np.nan, 0, np.nan, 2, 3], 0),
([0, 1, 2, 3, complex(0, np.nan)], 4),
([0, 1, 2, 3, complex(np.nan, 0)], 4),
([0, 1, 2, complex(np.nan, 0), 3], 3),
([0, 1, 2, complex(0, np.nan), 3], 3),
([complex(0, np.nan), 0, 1, 2, 3], 0),
([complex(np.nan, np.nan), 0, 1, 2, 3], 0),
([complex(np.nan, 0), complex(np.nan, 2), complex(np.nan, 1)], 0),
([complex(np.nan, np.nan), complex(np.nan, 2), complex(np.nan, 1)], 0),
([complex(np.nan, 0), complex(np.nan, 2), complex(np.nan, np.nan)], 0),
([complex(0, 0), complex(0, 2), complex(0, 1)], 0),
([complex(1, 0), complex(0, 2), complex(0, 1)], 2),
([complex(1, 0), complex(0, 2), complex(1, 1)], 1),
([np.datetime64('1923-04-14T12:43:12'),
np.datetime64('1994-06-21T14:43:15'),
np.datetime64('2001-10-15T04:10:32'),
np.datetime64('1995-11-25T16:02:16'),
np.datetime64('2005-01-04T03:14:12'),
np.datetime64('2041-12-03T14:05:03')], 0),
([np.datetime64('1935-09-14T04:40:11'),
np.datetime64('1949-10-12T12:32:11'),
np.datetime64('2010-01-03T05:14:12'),
np.datetime64('2014-11-20T12:20:59'),
np.datetime64('2015-09-23T10:10:13'),
np.datetime64('1932-10-10T03:50:30')], 5),
# Assorted tests with NaTs
([np.datetime64('NaT'),
np.datetime64('NaT'),
np.datetime64('2010-01-03T05:14:12'),
np.datetime64('NaT'),
np.datetime64('2015-09-23T10:10:13'),
np.datetime64('1932-10-10T03:50:30')], 5),
([np.datetime64('2059-03-14T12:43:12'),
np.datetime64('1996-09-21T14:43:15'),
np.datetime64('NaT'),
np.datetime64('2022-12-25T16:02:16'),
np.datetime64('1963-10-04T03:14:12'),
np.datetime64('2013-05-08T18:15:23')], 4),
([np.timedelta64(2, 's'),
np.timedelta64(1, 's'),
np.timedelta64('NaT', 's'),
np.timedelta64(3, 's')], 1),
([np.timedelta64('NaT', 's')] * 3, 0),
([timedelta(days=5, seconds=14), timedelta(days=2, seconds=35),
timedelta(days=-1, seconds=23)], 2),
([timedelta(days=1, seconds=43), timedelta(days=10, seconds=5),
timedelta(days=5, seconds=14)], 0),
([timedelta(days=10, seconds=24), timedelta(days=10, seconds=5),
timedelta(days=10, seconds=43)], 1),
([True, True, True, True, False], 4),
([True, True, True, False, True], 3),
([False, True, True, True, True], 0),
([False, True, False, True, True], 0),
]
def test_all(self):
a = np.random.normal(0, 1, (4, 5, 6, 7, 8))
for i in range(a.ndim):
amin = a.min(i)
aargmin = a.argmin(i)
axes = list(range(a.ndim))
axes.remove(i)
assert_(np.all(amin == aargmin.choose(*a.transpose(i,*axes))))
def test_combinations(self):
for arr, pos in self.nan_arr:
with suppress_warnings() as sup:
sup.filter(RuntimeWarning,
"invalid value encountered in reduce")
min_val = np.min(arr)
assert_equal(np.argmin(arr), pos, err_msg="%r" % arr)
assert_equal(arr[np.argmin(arr)], min_val, err_msg="%r" % arr)
def test_minimum_signed_integers(self):
a = np.array([1, -2**7, -2**7 + 1], dtype=np.int8)
assert_equal(np.argmin(a), 1)
a = np.array([1, -2**15, -2**15 + 1], dtype=np.int16)
assert_equal(np.argmin(a), 1)
a = np.array([1, -2**31, -2**31 + 1], dtype=np.int32)
assert_equal(np.argmin(a), 1)
a = np.array([1, -2**63, -2**63 + 1], dtype=np.int64)
assert_equal(np.argmin(a), 1)
def test_output_shape(self):
# see also gh-616
a = np.ones((10, 5))
# Check some simple shape mismatches
out = np.ones(11, dtype=np.int_)
assert_raises(ValueError, a.argmin, -1, out)
out = np.ones((2, 5), dtype=np.int_)
assert_raises(ValueError, a.argmin, -1, out)
# these could be relaxed possibly (used to allow even the previous)
out = np.ones((1, 10), dtype=np.int_)
assert_raises(ValueError, a.argmin, -1, out)
out = np.ones(10, dtype=np.int_)
a.argmin(-1, out=out)
assert_equal(out, a.argmin(-1))
def test_argmin_unicode(self):
d = np.ones(6031, dtype='<U9')
d[6001] = "0"
assert_equal(d.argmin(), 6001)
def test_np_vs_ndarray(self):
# make sure both ndarray.argmin and numpy.argmin support out/axis args
a = np.random.normal(size=(2, 3))
# check positional args
out1 = np.zeros(2, dtype=int)
out2 = np.ones(2, dtype=int)
assert_equal(a.argmin(1, out1), np.argmin(a, 1, out2))
assert_equal(out1, out2)
# check keyword args
out1 = np.zeros(3, dtype=int)
out2 = np.ones(3, dtype=int)
assert_equal(a.argmin(out=out1, axis=0), np.argmin(a, out=out2, axis=0))
assert_equal(out1, out2)
def test_object_argmin_with_NULLs(self):
# See gh-6032
a = np.empty(4, dtype='O')
ctypes.memset(a.ctypes.data, 0, a.nbytes)
assert_equal(a.argmin(), 0)
a[3] = 30
assert_equal(a.argmin(), 3)
a[1] = 10
assert_equal(a.argmin(), 1)
class TestMinMax(object):
def test_scalar(self):
assert_raises(np.AxisError, np.amax, 1, 1)
assert_raises(np.AxisError, np.amin, 1, 1)
assert_equal(np.amax(1, axis=0), 1)
assert_equal(np.amin(1, axis=0), 1)
assert_equal(np.amax(1, axis=None), 1)
assert_equal(np.amin(1, axis=None), 1)
def test_axis(self):
assert_raises(np.AxisError, np.amax, [1, 2, 3], 1000)
assert_equal(np.amax([[1, 2, 3]], axis=1), 3)
def test_datetime(self):
# NaTs are ignored
for dtype in ('m8[s]', 'm8[Y]'):
a = np.arange(10).astype(dtype)
a[3] = 'NaT'
assert_equal(np.amin(a), a[0])
assert_equal(np.amax(a), a[9])
a[0] = 'NaT'
assert_equal(np.amin(a), a[1])
assert_equal(np.amax(a), a[9])
a.fill('NaT')
assert_equal(np.amin(a), a[0])
assert_equal(np.amax(a), a[0])
class TestNewaxis(object):
def test_basic(self):
sk = np.array([0, -0.1, 0.1])
res = 250*sk[:, np.newaxis]
assert_almost_equal(res.ravel(), 250*sk)
class TestClip(object):
def _check_range(self, x, cmin, cmax):
assert_(np.all(x >= cmin))
assert_(np.all(x <= cmax))
def _clip_type(self, type_group, array_max,
clip_min, clip_max, inplace=False,
expected_min=None, expected_max=None):
if expected_min is None:
expected_min = clip_min
if expected_max is None:
expected_max = clip_max
for T in np.sctypes[type_group]:
if sys.byteorder == 'little':
byte_orders = ['=', '>']
else:
byte_orders = ['<', '=']
for byteorder in byte_orders:
dtype = np.dtype(T).newbyteorder(byteorder)
x = (np.random.random(1000) * array_max).astype(dtype)
if inplace:
x.clip(clip_min, clip_max, x)
else:
x = x.clip(clip_min, clip_max)
byteorder = '='
if x.dtype.byteorder == '|':
byteorder = '|'
assert_equal(x.dtype.byteorder, byteorder)
self._check_range(x, expected_min, expected_max)
return x
def test_basic(self):
for inplace in [False, True]:
self._clip_type(
'float', 1024, -12.8, 100.2, inplace=inplace)
self._clip_type(
'float', 1024, 0, 0, inplace=inplace)
self._clip_type(
'int', 1024, -120, 100.5, inplace=inplace)
self._clip_type(
'int', 1024, 0, 0, inplace=inplace)
self._clip_type(
'uint', 1024, 0, 0, inplace=inplace)
self._clip_type(
'uint', 1024, -120, 100, inplace=inplace, expected_min=0)
def test_record_array(self):
rec = np.array([(-5, 2.0, 3.0), (5.0, 4.0, 3.0)],
dtype=[('x', '<f8'), ('y', '<f8'), ('z', '<f8')])
y = rec['x'].clip(-0.3, 0.5)
self._check_range(y, -0.3, 0.5)
def test_max_or_min(self):
val = np.array([0, 1, 2, 3, 4, 5, 6, 7])
x = val.clip(3)
assert_(np.all(x >= 3))
x = val.clip(min=3)
assert_(np.all(x >= 3))
x = val.clip(max=4)
assert_(np.all(x <= 4))
def test_nan(self):
input_arr = np.array([-2., np.nan, 0.5, 3., 0.25, np.nan])
result = input_arr.clip(-1, 1)
expected = np.array([-1., np.nan, 0.5, 1., 0.25, np.nan])
assert_array_equal(result, expected)
class TestCompress(object):
def test_axis(self):
tgt = [[5, 6, 7, 8, 9]]
arr = np.arange(10).reshape(2, 5)
out = np.compress([0, 1], arr, axis=0)
assert_equal(out, tgt)
tgt = [[1, 3], [6, 8]]
out = np.compress([0, 1, 0, 1, 0], arr, axis=1)
assert_equal(out, tgt)
def test_truncate(self):
tgt = [[1], [6]]
arr = np.arange(10).reshape(2, 5)
out = np.compress([0, 1], arr, axis=1)
assert_equal(out, tgt)
def test_flatten(self):
arr = np.arange(10).reshape(2, 5)
out = np.compress([0, 1], arr)
assert_equal(out, 1)
class TestPutmask(object):
def tst_basic(self, x, T, mask, val):
np.putmask(x, mask, val)
assert_equal(x[mask], T(val))
assert_equal(x.dtype, T)
def test_ip_types(self):
unchecked_types = [bytes, unicode, np.void, object]
x = np.random.random(1000)*100
mask = x < 40
for val in [-100, 0, 15]:
for types in np.sctypes.values():
for T in types:
if T not in unchecked_types:
yield self.tst_basic, x.copy().astype(T), T, mask, val
def test_mask_size(self):
assert_raises(ValueError, np.putmask, np.array([1, 2, 3]), [True], 5)
def tst_byteorder(self, dtype):
x = np.array([1, 2, 3], dtype)
np.putmask(x, [True, False, True], -1)
assert_array_equal(x, [-1, 2, -1])
def test_ip_byteorder(self):
for dtype in ('>i4', '<i4'):
yield self.tst_byteorder, dtype
def test_record_array(self):
# Note mixed byteorder.
rec = np.array([(-5, 2.0, 3.0), (5.0, 4.0, 3.0)],
dtype=[('x', '<f8'), ('y', '>f8'), ('z', '<f8')])
np.putmask(rec['x'], [True, False], 10)
assert_array_equal(rec['x'], [10, 5])
assert_array_equal(rec['y'], [2, 4])
assert_array_equal(rec['z'], [3, 3])
np.putmask(rec['y'], [True, False], 11)
assert_array_equal(rec['x'], [10, 5])
assert_array_equal(rec['y'], [11, 4])
assert_array_equal(rec['z'], [3, 3])
class TestTake(object):
def tst_basic(self, x):
ind = list(range(x.shape[0]))
assert_array_equal(x.take(ind, axis=0), x)
def test_ip_types(self):
unchecked_types = [bytes, unicode, np.void, object]
x = np.random.random(24)*100
x.shape = 2, 3, 4
for types in np.sctypes.values():
for T in types:
if T not in unchecked_types:
yield self.tst_basic, x.copy().astype(T)
def test_raise(self):
x = np.random.random(24)*100
x.shape = 2, 3, 4
assert_raises(IndexError, x.take, [0, 1, 2], axis=0)
assert_raises(IndexError, x.take, [-3], axis=0)
assert_array_equal(x.take([-1], axis=0)[0], x[1])
def test_clip(self):
x = np.random.random(24)*100
x.shape = 2, 3, 4
assert_array_equal(x.take([-1], axis=0, mode='clip')[0], x[0])
assert_array_equal(x.take([2], axis=0, mode='clip')[0], x[1])
def test_wrap(self):
x = np.random.random(24)*100
x.shape = 2, 3, 4
assert_array_equal(x.take([-1], axis=0, mode='wrap')[0], x[1])
assert_array_equal(x.take([2], axis=0, mode='wrap')[0], x[0])
assert_array_equal(x.take([3], axis=0, mode='wrap')[0], x[1])
def tst_byteorder(self, dtype):
x = np.array([1, 2, 3], dtype)
assert_array_equal(x.take([0, 2, 1]), [1, 3, 2])
def test_ip_byteorder(self):
for dtype in ('>i4', '<i4'):
yield self.tst_byteorder, dtype
def test_record_array(self):
# Note mixed byteorder.
rec = np.array([(-5, 2.0, 3.0), (5.0, 4.0, 3.0)],
dtype=[('x', '<f8'), ('y', '>f8'), ('z', '<f8')])
rec1 = rec.take([1])
assert_(rec1['x'] == 5.0 and rec1['y'] == 4.0)
class TestLexsort(object):
def test_basic(self):
a = [1, 2, 1, 3, 1, 5]
b = [0, 4, 5, 6, 2, 3]
idx = np.lexsort((b, a))
expected_idx = np.array([0, 4, 2, 1, 3, 5])
assert_array_equal(idx, expected_idx)
x = np.vstack((b, a))
idx = np.lexsort(x)
assert_array_equal(idx, expected_idx)
assert_array_equal(x[1][idx], np.sort(x[1]))
def test_datetime(self):
a = np.array([0,0,0], dtype='datetime64[D]')
b = np.array([2,1,0], dtype='datetime64[D]')
idx = np.lexsort((b, a))
expected_idx = np.array([2, 1, 0])
assert_array_equal(idx, expected_idx)
a = np.array([0,0,0], dtype='timedelta64[D]')
b = np.array([2,1,0], dtype='timedelta64[D]')
idx = np.lexsort((b, a))
expected_idx = np.array([2, 1, 0])
assert_array_equal(idx, expected_idx)
def test_object(self): # gh-6312
a = np.random.choice(10, 1000)
b = np.random.choice(['abc', 'xy', 'wz', 'efghi', 'qwst', 'x'], 1000)
for u in a, b:
left = np.lexsort((u.astype('O'),))
right = np.argsort(u, kind='mergesort')
assert_array_equal(left, right)
for u, v in (a, b), (b, a):
idx = np.lexsort((u, v))
assert_array_equal(idx, np.lexsort((u.astype('O'), v)))
assert_array_equal(idx, np.lexsort((u, v.astype('O'))))
u, v = np.array(u, dtype='object'), np.array(v, dtype='object')
assert_array_equal(idx, np.lexsort((u, v)))
def test_invalid_axis(self): # gh-7528
x = np.linspace(0., 1., 42*3).reshape(42, 3)
assert_raises(np.AxisError, np.lexsort, x, axis=2)
class TestIO(object):
"""Test tofile, fromfile, tobytes, and fromstring"""
def setup(self):
shape = (2, 4, 3)
rand = np.random.random
self.x = rand(shape) + rand(shape).astype(complex)*1j
self.x[0,:, 1] = [np.nan, np.inf, -np.inf, np.nan]
self.dtype = self.x.dtype
self.tempdir = tempfile.mkdtemp()
self.filename = tempfile.mktemp(dir=self.tempdir)
def teardown(self):
shutil.rmtree(self.tempdir)
def test_nofile(self):
# this should probably be supported as a file
# but for now test for proper errors
b = io.BytesIO()
assert_raises(IOError, np.fromfile, b, np.uint8, 80)
d = np.ones(7)
assert_raises(IOError, lambda x: x.tofile(b), d)
def test_bool_fromstring(self):
v = np.array([True, False, True, False], dtype=np.bool_)
y = np.fromstring('1 0 -2.3 0.0', sep=' ', dtype=np.bool_)
assert_array_equal(v, y)
def test_uint64_fromstring(self):
d = np.fromstring("9923372036854775807 104783749223640",
dtype=np.uint64, sep=' ')
e = np.array([9923372036854775807, 104783749223640], dtype=np.uint64)
assert_array_equal(d, e)
def test_int64_fromstring(self):
d = np.fromstring("-25041670086757 104783749223640",
dtype=np.int64, sep=' ')
e = np.array([-25041670086757, 104783749223640], dtype=np.int64)
assert_array_equal(d, e)
def test_empty_files_binary(self):
f = open(self.filename, 'w')
f.close()
y = np.fromfile(self.filename)
assert_(y.size == 0, "Array not empty")
def test_empty_files_text(self):
f = open(self.filename, 'w')
f.close()
y = np.fromfile(self.filename, sep=" ")
assert_(y.size == 0, "Array not empty")
def test_roundtrip_file(self):
f = open(self.filename, 'wb')
self.x.tofile(f)
f.close()
# NB. doesn't work with flush+seek, due to use of C stdio
f = open(self.filename, 'rb')
y = np.fromfile(f, dtype=self.dtype)
f.close()
assert_array_equal(y, self.x.flat)
def test_roundtrip_filename(self):
self.x.tofile(self.filename)
y = np.fromfile(self.filename, dtype=self.dtype)
assert_array_equal(y, self.x.flat)
def test_roundtrip_binary_str(self):
s = self.x.tobytes()
y = np.frombuffer(s, dtype=self.dtype)
assert_array_equal(y, self.x.flat)
s = self.x.tobytes('F')
y = np.frombuffer(s, dtype=self.dtype)
assert_array_equal(y, self.x.flatten('F'))
def test_roundtrip_str(self):
x = self.x.real.ravel()
s = "@".join(map(str, x))
y = np.fromstring(s, sep="@")
# NB. str imbues less precision
nan_mask = ~np.isfinite(x)
assert_array_equal(x[nan_mask], y[nan_mask])
assert_array_almost_equal(x[~nan_mask], y[~nan_mask], decimal=5)
def test_roundtrip_repr(self):
x = self.x.real.ravel()
s = "@".join(map(repr, x))
y = np.fromstring(s, sep="@")
assert_array_equal(x, y)
def test_unseekable_fromfile(self):
# gh-6246
self.x.tofile(self.filename)
def fail(*args, **kwargs):
raise IOError('Can not tell or seek')
with io.open(self.filename, 'rb', buffering=0) as f:
f.seek = fail
f.tell = fail
assert_raises(IOError, np.fromfile, f, dtype=self.dtype)
def test_io_open_unbuffered_fromfile(self):
# gh-6632
self.x.tofile(self.filename)
with io.open(self.filename, 'rb', buffering=0) as f:
y = np.fromfile(f, dtype=self.dtype)
assert_array_equal(y, self.x.flat)
def test_largish_file(self):
# check the fallocate path on files > 16MB
d = np.zeros(4 * 1024 ** 2)
d.tofile(self.filename)
assert_equal(os.path.getsize(self.filename), d.nbytes)
assert_array_equal(d, np.fromfile(self.filename))
# check offset
with open(self.filename, "r+b") as f:
f.seek(d.nbytes)
d.tofile(f)
assert_equal(os.path.getsize(self.filename), d.nbytes * 2)
# check append mode (gh-8329)
open(self.filename, "w").close() # delete file contents
with open(self.filename, "ab") as f:
d.tofile(f)
assert_array_equal(d, np.fromfile(self.filename))
with open(self.filename, "ab") as f:
d.tofile(f)
assert_equal(os.path.getsize(self.filename), d.nbytes * 2)
def test_io_open_buffered_fromfile(self):
# gh-6632
self.x.tofile(self.filename)
with io.open(self.filename, 'rb', buffering=-1) as f:
y = np.fromfile(f, dtype=self.dtype)
assert_array_equal(y, self.x.flat)
def test_file_position_after_fromfile(self):
# gh-4118
sizes = [io.DEFAULT_BUFFER_SIZE//8,
io.DEFAULT_BUFFER_SIZE,
io.DEFAULT_BUFFER_SIZE*8]
for size in sizes:
f = open(self.filename, 'wb')
f.seek(size-1)
f.write(b'\0')
f.close()
for mode in ['rb', 'r+b']:
err_msg = "%d %s" % (size, mode)
f = open(self.filename, mode)
f.read(2)
np.fromfile(f, dtype=np.float64, count=1)
pos = f.tell()
f.close()
assert_equal(pos, 10, err_msg=err_msg)
def test_file_position_after_tofile(self):
# gh-4118
sizes = [io.DEFAULT_BUFFER_SIZE//8,
io.DEFAULT_BUFFER_SIZE,
io.DEFAULT_BUFFER_SIZE*8]
for size in sizes:
err_msg = "%d" % (size,)
f = open(self.filename, 'wb')
f.seek(size-1)
f.write(b'\0')
f.seek(10)
f.write(b'12')
np.array([0], dtype=np.float64).tofile(f)
pos = f.tell()
f.close()
assert_equal(pos, 10 + 2 + 8, err_msg=err_msg)
f = open(self.filename, 'r+b')
f.read(2)
f.seek(0, 1) # seek between read&write required by ANSI C
np.array([0], dtype=np.float64).tofile(f)
pos = f.tell()
f.close()
assert_equal(pos, 10, err_msg=err_msg)
def _check_from(self, s, value, **kw):
if 'sep' not in kw:
y = np.frombuffer(s, **kw)
else:
y = np.fromstring(s, **kw)
assert_array_equal(y, value)
f = open(self.filename, 'wb')
f.write(s)
f.close()
y = np.fromfile(self.filename, **kw)
assert_array_equal(y, value)
def test_nan(self):
self._check_from(
b"nan +nan -nan NaN nan(foo) +NaN(BAR) -NAN(q_u_u_x_)",
[np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
sep=' ')
def test_inf(self):
self._check_from(
b"inf +inf -inf infinity -Infinity iNfInItY -inF",
[np.inf, np.inf, -np.inf, np.inf, -np.inf, np.inf, -np.inf],
sep=' ')
def test_numbers(self):
self._check_from(b"1.234 -1.234 .3 .3e55 -123133.1231e+133",
[1.234, -1.234, .3, .3e55, -123133.1231e+133], sep=' ')
def test_binary(self):
self._check_from(b'\x00\x00\x80?\x00\x00\x00@\x00\x00@@\x00\x00\x80@',
np.array([1, 2, 3, 4]),
dtype='<f4')
@dec.slow # takes > 1 minute on mechanical hard drive
def test_big_binary(self):
"""Test workarounds for 32-bit limited fwrite, fseek, and ftell
calls in windows. These normally would hang doing something like this.
See http://projects.scipy.org/numpy/ticket/1660"""
if sys.platform != 'win32':
return
try:
# before workarounds, only up to 2**32-1 worked
fourgbplus = 2**32 + 2**16
testbytes = np.arange(8, dtype=np.int8)
n = len(testbytes)
flike = tempfile.NamedTemporaryFile()
f = flike.file
np.tile(testbytes, fourgbplus // testbytes.nbytes).tofile(f)
flike.seek(0)
a = np.fromfile(f, dtype=np.int8)
flike.close()
assert_(len(a) == fourgbplus)
# check only start and end for speed:
assert_((a[:n] == testbytes).all())
assert_((a[-n:] == testbytes).all())
except (MemoryError, ValueError):
pass
def test_string(self):
self._check_from(b'1,2,3,4', [1., 2., 3., 4.], sep=',')
def test_counted_string(self):
self._check_from(b'1,2,3,4', [1., 2., 3., 4.], count=4, sep=',')
self._check_from(b'1,2,3,4', [1., 2., 3.], count=3, sep=',')
self._check_from(b'1,2,3,4', [1., 2., 3., 4.], count=-1, sep=',')
def test_string_with_ws(self):
self._check_from(b'1 2 3 4 ', [1, 2, 3, 4], dtype=int, sep=' ')
def test_counted_string_with_ws(self):
self._check_from(b'1 2 3 4 ', [1, 2, 3], count=3, dtype=int,
sep=' ')
def test_ascii(self):
self._check_from(b'1 , 2 , 3 , 4', [1., 2., 3., 4.], sep=',')
self._check_from(b'1,2,3,4', [1., 2., 3., 4.], dtype=float, sep=',')
def test_malformed(self):
self._check_from(b'1.234 1,234', [1.234, 1.], sep=' ')
def test_long_sep(self):
self._check_from(b'1_x_3_x_4_x_5', [1, 3, 4, 5], sep='_x_')
def test_dtype(self):
v = np.array([1, 2, 3, 4], dtype=np.int_)
self._check_from(b'1,2,3,4', v, sep=',', dtype=np.int_)
def test_dtype_bool(self):
# can't use _check_from because fromstring can't handle True/False
v = np.array([True, False, True, False], dtype=np.bool_)
s = b'1,0,-2.3,0'
f = open(self.filename, 'wb')
f.write(s)
f.close()
y = np.fromfile(self.filename, sep=',', dtype=np.bool_)
assert_(y.dtype == '?')
assert_array_equal(y, v)
def test_tofile_sep(self):
x = np.array([1.51, 2, 3.51, 4], dtype=float)
f = open(self.filename, 'w')
x.tofile(f, sep=',')
f.close()
f = open(self.filename, 'r')
s = f.read()
f.close()
#assert_equal(s, '1.51,2.0,3.51,4.0')
y = np.array([float(p) for p in s.split(',')])
assert_array_equal(x,y)
def test_tofile_format(self):
x = np.array([1.51, 2, 3.51, 4], dtype=float)
f = open(self.filename, 'w')
x.tofile(f, sep=',', format='%.2f')
f.close()
f = open(self.filename, 'r')
s = f.read()
f.close()
assert_equal(s, '1.51,2.00,3.51,4.00')
def test_locale(self):
in_foreign_locale(self.test_numbers)()
in_foreign_locale(self.test_nan)()
in_foreign_locale(self.test_inf)()
in_foreign_locale(self.test_counted_string)()
in_foreign_locale(self.test_ascii)()
in_foreign_locale(self.test_malformed)()
in_foreign_locale(self.test_tofile_sep)()
in_foreign_locale(self.test_tofile_format)()
class TestFromBuffer(object):
def tst_basic(self, buffer, expected, kwargs):
assert_array_equal(np.frombuffer(buffer,**kwargs), expected)
def test_ip_basic(self):
for byteorder in ['<', '>']:
for dtype in [float, int, complex]:
dt = np.dtype(dtype).newbyteorder(byteorder)
x = (np.random.random((4, 7))*5).astype(dt)
buf = x.tobytes()
yield self.tst_basic, buf, x.flat, {'dtype':dt}
def test_empty(self):
yield self.tst_basic, b'', np.array([]), {}
class TestFlat(object):
def setup(self):
a0 = np.arange(20.0)
a = a0.reshape(4, 5)
a0.shape = (4, 5)
a.flags.writeable = False
self.a = a
self.b = a[::2, ::2]
self.a0 = a0
self.b0 = a0[::2, ::2]
def test_contiguous(self):
testpassed = False
try:
self.a.flat[12] = 100.0
except ValueError:
testpassed = True
assert_(testpassed)
assert_(self.a.flat[12] == 12.0)
def test_discontiguous(self):
testpassed = False
try:
self.b.flat[4] = 100.0
except ValueError:
testpassed = True
assert_(testpassed)
assert_(self.b.flat[4] == 12.0)
def test___array__(self):
c = self.a.flat.__array__()
d = self.b.flat.__array__()
e = self.a0.flat.__array__()
f = self.b0.flat.__array__()
assert_(c.flags.writeable is False)
assert_(d.flags.writeable is False)
# for 1.14 all are set to non-writeable on the way to replacing the
# UPDATEIFCOPY array returned for non-contiguous arrays.
assert_(e.flags.writeable is True)
assert_(f.flags.writeable is False)
with assert_warns(DeprecationWarning):
assert_(c.flags.updateifcopy is False)
with assert_warns(DeprecationWarning):
assert_(d.flags.updateifcopy is False)
with assert_warns(DeprecationWarning):
assert_(e.flags.updateifcopy is False)
with assert_warns(DeprecationWarning):
# UPDATEIFCOPY is removed.
assert_(f.flags.updateifcopy is False)
assert_(c.flags.writebackifcopy is False)
assert_(d.flags.writebackifcopy is False)
assert_(e.flags.writebackifcopy is False)
assert_(f.flags.writebackifcopy is False)
class TestResize(object):
def test_basic(self):
x = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
if IS_PYPY:
x.resize((5, 5), refcheck=False)
else:
x.resize((5, 5))
assert_array_equal(x.flat[:9],
np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]).flat)
assert_array_equal(x[9:].flat, 0)
def test_check_reference(self):
x = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])
y = x
assert_raises(ValueError, x.resize, (5, 1))
del y # avoid pyflakes unused variable warning.
def test_int_shape(self):
x = np.eye(3)
if IS_PYPY:
x.resize(3, refcheck=False)
else:
x.resize(3)
assert_array_equal(x, np.eye(3)[0,:])
def test_none_shape(self):
x = np.eye(3)
x.resize(None)
assert_array_equal(x, np.eye(3))
x.resize()
assert_array_equal(x, np.eye(3))
def test_0d_shape(self):
# to it multiple times to test it does not break alloc cache gh-9216
for i in range(10):
x = np.empty((1,))
x.resize(())
assert_equal(x.shape, ())
assert_equal(x.size, 1)
x = np.empty(())
x.resize((1,))
assert_equal(x.shape, (1,))
assert_equal(x.size, 1)
def test_invalid_arguments(self):
assert_raises(TypeError, np.eye(3).resize, 'hi')
assert_raises(ValueError, np.eye(3).resize, -1)
assert_raises(TypeError, np.eye(3).resize, order=1)
assert_raises(TypeError, np.eye(3).resize, refcheck='hi')
def test_freeform_shape(self):
x = np.eye(3)
if IS_PYPY:
x.resize(3, 2, 1, refcheck=False)
else:
x.resize(3, 2, 1)
assert_(x.shape == (3, 2, 1))
def test_zeros_appended(self):
x = np.eye(3)
if IS_PYPY:
x.resize(2, 3, 3, refcheck=False)
else:
x.resize(2, 3, 3)
assert_array_equal(x[0], np.eye(3))
assert_array_equal(x[1], np.zeros((3, 3)))
def test_obj_obj(self):
# check memory is initialized on resize, gh-4857
a = np.ones(10, dtype=[('k', object, 2)])
if IS_PYPY:
a.resize(15, refcheck=False)
else:
a.resize(15,)
assert_equal(a.shape, (15,))
assert_array_equal(a['k'][-5:], 0)
assert_array_equal(a['k'][:-5], 1)
def test_empty_view(self):
# check that sizes containing a zero don't trigger a reallocate for
# already empty arrays
x = np.zeros((10, 0), int)
x_view = x[...]
x_view.resize((0, 10))
x_view.resize((0, 100))
class TestRecord(object):
def test_field_rename(self):
dt = np.dtype([('f', float), ('i', int)])
dt.names = ['p', 'q']
assert_equal(dt.names, ['p', 'q'])
def test_multiple_field_name_occurrence(self):
def test_assign():
dtype = np.dtype([("A", "f8"), ("B", "f8"), ("A", "f8")])
# Error raised when multiple fields have the same name
assert_raises(ValueError, test_assign)
if sys.version_info[0] >= 3:
def test_bytes_fields(self):
# Bytes are not allowed in field names and not recognized in titles
# on Py3
assert_raises(TypeError, np.dtype, [(b'a', int)])
assert_raises(TypeError, np.dtype, [(('b', b'a'), int)])
dt = np.dtype([((b'a', 'b'), int)])
assert_raises(TypeError, dt.__getitem__, b'a')
x = np.array([(1,), (2,), (3,)], dtype=dt)
assert_raises(IndexError, x.__getitem__, b'a')
y = x[0]
assert_raises(IndexError, y.__getitem__, b'a')
def test_multiple_field_name_unicode(self):
def test_assign_unicode():
dt = np.dtype([("\u20B9", "f8"),
("B", "f8"),
("\u20B9", "f8")])
# Error raised when multiple fields have the same name(unicode included)
assert_raises(ValueError, test_assign_unicode)
else:
def test_unicode_field_titles(self):
# Unicode field titles are added to field dict on Py2
title = u'b'
dt = np.dtype([((title, 'a'), int)])
dt[title]
dt['a']
x = np.array([(1,), (2,), (3,)], dtype=dt)
x[title]
x['a']
y = x[0]
y[title]
y['a']
def test_unicode_field_names(self):
# Unicode field names are not allowed on Py2
title = u'b'
assert_raises(TypeError, np.dtype, [(title, int)])
assert_raises(TypeError, np.dtype, [(('a', title), int)])
def test_field_names(self):
# Test unicode and 8-bit / byte strings can be used
a = np.zeros((1,), dtype=[('f1', 'i4'),
('f2', 'i4'),
('f3', [('sf1', 'i4')])])
is_py3 = sys.version_info[0] >= 3
if is_py3:
funcs = (str,)
# byte string indexing fails gracefully
assert_raises(IndexError, a.__setitem__, b'f1', 1)
assert_raises(IndexError, a.__getitem__, b'f1')
assert_raises(IndexError, a['f1'].__setitem__, b'sf1', 1)
assert_raises(IndexError, a['f1'].__getitem__, b'sf1')
else:
funcs = (str, unicode)
for func in funcs:
b = a.copy()
fn1 = func('f1')
b[fn1] = 1
assert_equal(b[fn1], 1)
fnn = func('not at all')
assert_raises(ValueError, b.__setitem__, fnn, 1)
assert_raises(ValueError, b.__getitem__, fnn)
b[0][fn1] = 2
assert_equal(b[fn1], 2)
# Subfield
assert_raises(ValueError, b[0].__setitem__, fnn, 1)
assert_raises(ValueError, b[0].__getitem__, fnn)
# Subfield
fn3 = func('f3')
sfn1 = func('sf1')
b[fn3][sfn1] = 1
assert_equal(b[fn3][sfn1], 1)
assert_raises(ValueError, b[fn3].__setitem__, fnn, 1)
assert_raises(ValueError, b[fn3].__getitem__, fnn)
# multiple subfields
fn2 = func('f2')
b[fn2] = 3
with suppress_warnings() as sup:
sup.filter(FutureWarning,
"Numpy has detected that you .*")
assert_equal(b[['f1', 'f2']][0].tolist(), (2, 3))
assert_equal(b[['f2', 'f1']][0].tolist(), (3, 2))
assert_equal(b[['f1', 'f3']][0].tolist(), (2, (1,)))
# view of subfield view/copy
assert_equal(b[['f1', 'f2']][0].view(('i4', 2)).tolist(),
(2, 3))
assert_equal(b[['f2', 'f1']][0].view(('i4', 2)).tolist(),
(3, 2))
view_dtype = [('f1', 'i4'), ('f3', [('', 'i4')])]
assert_equal(b[['f1', 'f3']][0].view(view_dtype).tolist(),
(2, (1,)))
# non-ascii unicode field indexing is well behaved
if not is_py3:
raise SkipTest('non ascii unicode field indexing skipped; '
'raises segfault on python 2.x')
else:
assert_raises(ValueError, a.__setitem__, u'\u03e0', 1)
assert_raises(ValueError, a.__getitem__, u'\u03e0')
def test_field_names_deprecation(self):
def collect_warnings(f, *args, **kwargs):
with warnings.catch_warnings(record=True) as log:
warnings.simplefilter("always")
f(*args, **kwargs)
return [w.category for w in log]
a = np.zeros((1,), dtype=[('f1', 'i4'),
('f2', 'i4'),
('f3', [('sf1', 'i4')])])
a['f1'][0] = 1
a['f2'][0] = 2
a['f3'][0] = (3,)
b = np.zeros((1,), dtype=[('f1', 'i4'),
('f2', 'i4'),
('f3', [('sf1', 'i4')])])
b['f1'][0] = 1
b['f2'][0] = 2
b['f3'][0] = (3,)
# All the different functions raise a warning, but not an error
assert_equal(collect_warnings(a[['f1', 'f2']].__setitem__, 0, (10, 20)),
[FutureWarning])
# For <=1.12 a is not modified, but it will be in 1.13
assert_equal(a, b)
# Views also warn
subset = a[['f1', 'f2']]
subset_view = subset.view()
assert_equal(collect_warnings(subset_view['f1'].__setitem__, 0, 10),
[FutureWarning])
# But the write goes through:
assert_equal(subset['f1'][0], 10)
# Only one warning per multiple field indexing, though (even if there
# are multiple views involved):
assert_equal(collect_warnings(subset['f1'].__setitem__, 0, 10), [])
# make sure views of a multi-field index warn too
c = np.zeros(3, dtype='i8,i8,i8')
assert_equal(collect_warnings(c[['f0', 'f2']].view, 'i8,i8'),
[FutureWarning])
def test_record_hash(self):
a = np.array([(1, 2), (1, 2)], dtype='i1,i2')
a.flags.writeable = False
b = np.array([(1, 2), (3, 4)], dtype=[('num1', 'i1'), ('num2', 'i2')])
b.flags.writeable = False
c = np.array([(1, 2), (3, 4)], dtype='i1,i2')
c.flags.writeable = False
assert_(hash(a[0]) == hash(a[1]))
assert_(hash(a[0]) == hash(b[0]))
assert_(hash(a[0]) != hash(b[1]))
assert_(hash(c[0]) == hash(a[0]) and c[0] == a[0])
def test_record_no_hash(self):
a = np.array([(1, 2), (1, 2)], dtype='i1,i2')
assert_raises(TypeError, hash, a[0])
def test_empty_structure_creation(self):
# make sure these do not raise errors (gh-5631)
np.array([()], dtype={'names': [], 'formats': [],
'offsets': [], 'itemsize': 12})
np.array([(), (), (), (), ()], dtype={'names': [], 'formats': [],
'offsets': [], 'itemsize': 12})
class TestView(object):
def test_basic(self):
x = np.array([(1, 2, 3, 4), (5, 6, 7, 8)],
dtype=[('r', np.int8), ('g', np.int8),
('b', np.int8), ('a', np.int8)])
# We must be specific about the endianness here:
y = x.view(dtype='<i4')
# ... and again without the keyword.
z = x.view('<i4')
assert_array_equal(y, z)
assert_array_equal(y, [67305985, 134678021])
def _mean(a, **args):
return a.mean(**args)
def _var(a, **args):
return a.var(**args)
def _std(a, **args):
return a.std(**args)
class TestStats(object):
funcs = [_mean, _var, _std]
def setup(self):
np.random.seed(range(3))
self.rmat = np.random.random((4, 5))
self.cmat = self.rmat + 1j * self.rmat
self.omat = np.array([Decimal(repr(r)) for r in self.rmat.flat])
self.omat = self.omat.reshape(4, 5)
def test_python_type(self):
for x in (np.float16(1.), 1, 1., 1+0j):
assert_equal(np.mean([x]), 1.)
assert_equal(np.std([x]), 0.)
assert_equal(np.var([x]), 0.)
def test_keepdims(self):
mat = np.eye(3)
for f in self.funcs:
for axis in [0, 1]:
res = f(mat, axis=axis, keepdims=True)
assert_(res.ndim == mat.ndim)
assert_(res.shape[axis] == 1)
for axis in [None]:
res = f(mat, axis=axis, keepdims=True)
assert_(res.shape == (1, 1))
def test_out(self):
mat = np.eye(3)
for f in self.funcs:
out = np.zeros(3)
tgt = f(mat, axis=1)
res = f(mat, axis=1, out=out)
assert_almost_equal(res, out)
assert_almost_equal(res, tgt)
out = np.empty(2)
assert_raises(ValueError, f, mat, axis=1, out=out)
out = np.empty((2, 2))
assert_raises(ValueError, f, mat, axis=1, out=out)
def test_dtype_from_input(self):
icodes = np.typecodes['AllInteger']
fcodes = np.typecodes['AllFloat']
# object type
for f in self.funcs:
mat = np.array([[Decimal(1)]*3]*3)
tgt = mat.dtype.type
res = f(mat, axis=1).dtype.type
assert_(res is tgt)
# scalar case
res = type(f(mat, axis=None))
assert_(res is Decimal)
# integer types
for f in self.funcs:
for c in icodes:
mat = np.eye(3, dtype=c)
tgt = np.float64
res = f(mat, axis=1).dtype.type
assert_(res is tgt)
# scalar case
res = f(mat, axis=None).dtype.type
assert_(res is tgt)
# mean for float types
for f in [_mean]:
for c in fcodes:
mat = np.eye(3, dtype=c)
tgt = mat.dtype.type
res = f(mat, axis=1).dtype.type
assert_(res is tgt)
# scalar case
res = f(mat, axis=None).dtype.type
assert_(res is tgt)
# var, std for float types
for f in [_var, _std]:
for c in fcodes:
mat = np.eye(3, dtype=c)
# deal with complex types
tgt = mat.real.dtype.type
res = f(mat, axis=1).dtype.type
assert_(res is tgt)
# scalar case
res = f(mat, axis=None).dtype.type
assert_(res is tgt)
def test_dtype_from_dtype(self):
mat = np.eye(3)
# stats for integer types
# FIXME:
# this needs definition as there are lots places along the line
# where type casting may take place.
# for f in self.funcs:
# for c in np.typecodes['AllInteger']:
# tgt = np.dtype(c).type
# res = f(mat, axis=1, dtype=c).dtype.type
# assert_(res is tgt)
# # scalar case
# res = f(mat, axis=None, dtype=c).dtype.type
# assert_(res is tgt)
# stats for float types
for f in self.funcs:
for c in np.typecodes['AllFloat']:
tgt = np.dtype(c).type
res = f(mat, axis=1, dtype=c).dtype.type
assert_(res is tgt)
# scalar case
res = f(mat, axis=None, dtype=c).dtype.type
assert_(res is tgt)
def test_ddof(self):
for f in [_var]:
for ddof in range(3):
dim = self.rmat.shape[1]
tgt = f(self.rmat, axis=1) * dim
res = f(self.rmat, axis=1, ddof=ddof) * (dim - ddof)
for f in [_std]:
for ddof in range(3):
dim = self.rmat.shape[1]
tgt = f(self.rmat, axis=1) * np.sqrt(dim)
res = f(self.rmat, axis=1, ddof=ddof) * np.sqrt(dim - ddof)
assert_almost_equal(res, tgt)
assert_almost_equal(res, tgt)
def test_ddof_too_big(self):
dim = self.rmat.shape[1]
for f in [_var, _std]:
for ddof in range(dim, dim + 2):
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
res = f(self.rmat, axis=1, ddof=ddof)
assert_(not (res < 0).any())
assert_(len(w) > 0)
assert_(issubclass(w[0].category, RuntimeWarning))
def test_empty(self):
A = np.zeros((0, 3))
for f in self.funcs:
for axis in [0, None]:
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
assert_(np.isnan(f(A, axis=axis)).all())
assert_(len(w) > 0)
assert_(issubclass(w[0].category, RuntimeWarning))
for axis in [1]:
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
assert_equal(f(A, axis=axis), np.zeros([]))
def test_mean_values(self):
for mat in [self.rmat, self.cmat, self.omat]:
for axis in [0, 1]:
tgt = mat.sum(axis=axis)
res = _mean(mat, axis=axis) * mat.shape[axis]
assert_almost_equal(res, tgt)
for axis in [None]:
tgt = mat.sum(axis=axis)
res = _mean(mat, axis=axis) * np.prod(mat.shape)
assert_almost_equal(res, tgt)
def test_mean_float16(self):
# This fail if the sum inside mean is done in float16 instead
# of float32.
assert_(_mean(np.ones(100000, dtype='float16')) == 1)
def test_var_values(self):
for mat in [self.rmat, self.cmat, self.omat]:
for axis in [0, 1, None]:
msqr = _mean(mat * mat.conj(), axis=axis)
mean = _mean(mat, axis=axis)
tgt = msqr - mean * mean.conjugate()
res = _var(mat, axis=axis)
assert_almost_equal(res, tgt)
def test_std_values(self):
for mat in [self.rmat, self.cmat, self.omat]:
for axis in [0, 1, None]:
tgt = np.sqrt(_var(mat, axis=axis))
res = _std(mat, axis=axis)
assert_almost_equal(res, tgt)
def test_subclass(self):
class TestArray(np.ndarray):
def __new__(cls, data, info):
result = np.array(data)
result = result.view(cls)
result.info = info
return result
def __array_finalize__(self, obj):
self.info = getattr(obj, "info", '')
dat = TestArray([[1, 2, 3, 4], [5, 6, 7, 8]], 'jubba')
res = dat.mean(1)
assert_(res.info == dat.info)
res = dat.std(1)
assert_(res.info == dat.info)
res = dat.var(1)
assert_(res.info == dat.info)
class TestVdot(object):
def test_basic(self):
dt_numeric = np.typecodes['AllFloat'] + np.typecodes['AllInteger']
dt_complex = np.typecodes['Complex']
# test real
a = np.eye(3)
for dt in dt_numeric + 'O':
b = a.astype(dt)
res = np.vdot(b, b)
assert_(np.isscalar(res))
assert_equal(np.vdot(b, b), 3)
# test complex
a = np.eye(3) * 1j
for dt in dt_complex + 'O':
b = a.astype(dt)
res = np.vdot(b, b)
assert_(np.isscalar(res))
assert_equal(np.vdot(b, b), 3)
# test boolean
b = np.eye(3, dtype=bool)
res = np.vdot(b, b)
assert_(np.isscalar(res))
assert_equal(np.vdot(b, b), True)
def test_vdot_array_order(self):
a = np.array([[1, 2], [3, 4]], order='C')
b = np.array([[1, 2], [3, 4]], order='F')
res = np.vdot(a, a)
# integer arrays are exact
assert_equal(np.vdot(a, b), res)
assert_equal(np.vdot(b, a), res)
assert_equal(np.vdot(b, b), res)
def test_vdot_uncontiguous(self):
for size in [2, 1000]:
# Different sizes match different branches in vdot.
a = np.zeros((size, 2, 2))
b = np.zeros((size, 2, 2))
a[:, 0, 0] = np.arange(size)
b[:, 0, 0] = np.arange(size) + 1
# Make a and b uncontiguous:
a = a[..., 0]
b = b[..., 0]
assert_equal(np.vdot(a, b),
np.vdot(a.flatten(), b.flatten()))
assert_equal(np.vdot(a, b.copy()),
np.vdot(a.flatten(), b.flatten()))
assert_equal(np.vdot(a.copy(), b),
np.vdot(a.flatten(), b.flatten()))
assert_equal(np.vdot(a.copy('F'), b),
np.vdot(a.flatten(), b.flatten()))
assert_equal(np.vdot(a, b.copy('F')),
np.vdot(a.flatten(), b.flatten()))
class TestDot(object):
def setup(self):
np.random.seed(128)
self.A = np.random.rand(4, 2)
self.b1 = np.random.rand(2, 1)
self.b2 = np.random.rand(2)
self.b3 = np.random.rand(1, 2)
self.b4 = np.random.rand(4)
self.N = 7
def test_dotmatmat(self):
A = self.A
res = np.dot(A.transpose(), A)
tgt = np.array([[1.45046013, 0.86323640],
[0.86323640, 0.84934569]])
assert_almost_equal(res, tgt, decimal=self.N)
def test_dotmatvec(self):
A, b1 = self.A, self.b1
res = np.dot(A, b1)
tgt = np.array([[0.32114320], [0.04889721],
[0.15696029], [0.33612621]])
assert_almost_equal(res, tgt, decimal=self.N)
def test_dotmatvec2(self):
A, b2 = self.A, self.b2
res = np.dot(A, b2)
tgt = np.array([0.29677940, 0.04518649, 0.14468333, 0.31039293])
assert_almost_equal(res, tgt, decimal=self.N)
def test_dotvecmat(self):
A, b4 = self.A, self.b4
res = np.dot(b4, A)
tgt = np.array([1.23495091, 1.12222648])
assert_almost_equal(res, tgt, decimal=self.N)
def test_dotvecmat2(self):
b3, A = self.b3, self.A
res = np.dot(b3, A.transpose())
tgt = np.array([[0.58793804, 0.08957460, 0.30605758, 0.62716383]])
assert_almost_equal(res, tgt, decimal=self.N)
def test_dotvecmat3(self):
A, b4 = self.A, self.b4
res = np.dot(A.transpose(), b4)
tgt = np.array([1.23495091, 1.12222648])
assert_almost_equal(res, tgt, decimal=self.N)
def test_dotvecvecouter(self):
b1, b3 = self.b1, self.b3
res = np.dot(b1, b3)
tgt = np.array([[0.20128610, 0.08400440], [0.07190947, 0.03001058]])
assert_almost_equal(res, tgt, decimal=self.N)
def test_dotvecvecinner(self):
b1, b3 = self.b1, self.b3
res = np.dot(b3, b1)
tgt = np.array([[ 0.23129668]])
assert_almost_equal(res, tgt, decimal=self.N)
def test_dotcolumnvect1(self):
b1 = np.ones((3, 1))
b2 = [5.3]
res = np.dot(b1, b2)
tgt = np.array([5.3, 5.3, 5.3])
assert_almost_equal(res, tgt, decimal=self.N)
def test_dotcolumnvect2(self):
b1 = np.ones((3, 1)).transpose()
b2 = [6.2]
res = np.dot(b2, b1)
tgt = np.array([6.2, 6.2, 6.2])
assert_almost_equal(res, tgt, decimal=self.N)
def test_dotvecscalar(self):
np.random.seed(100)
b1 = np.random.rand(1, 1)
b2 = np.random.rand(1, 4)
res = np.dot(b1, b2)
tgt = np.array([[0.15126730, 0.23068496, 0.45905553, 0.00256425]])
assert_almost_equal(res, tgt, decimal=self.N)
def test_dotvecscalar2(self):
np.random.seed(100)
b1 = np.random.rand(4, 1)
b2 = np.random.rand(1, 1)
res = np.dot(b1, b2)
tgt = np.array([[0.00256425],[0.00131359],[0.00200324],[ 0.00398638]])
assert_almost_equal(res, tgt, decimal=self.N)
def test_all(self):
dims = [(), (1,), (1, 1)]
dout = [(), (1,), (1, 1), (1,), (), (1,), (1, 1), (1,), (1, 1)]
for dim, (dim1, dim2) in zip(dout, itertools.product(dims, dims)):
b1 = np.zeros(dim1)
b2 = np.zeros(dim2)
res = np.dot(b1, b2)
tgt = np.zeros(dim)
assert_(res.shape == tgt.shape)
assert_almost_equal(res, tgt, decimal=self.N)
def test_vecobject(self):
class Vec(object):
def __init__(self, sequence=None):
if sequence is None:
sequence = []
self.array = np.array(sequence)
def __add__(self, other):
out = Vec()
out.array = self.array + other.array
return out
def __sub__(self, other):
out = Vec()
out.array = self.array - other.array
return out
def __mul__(self, other): # with scalar
out = Vec(self.array.copy())
out.array *= other
return out
def __rmul__(self, other):
return self*other
U_non_cont = np.transpose([[1., 1.], [1., 2.]])
U_cont = np.ascontiguousarray(U_non_cont)
x = np.array([Vec([1., 0.]), Vec([0., 1.])])
zeros = np.array([Vec([0., 0.]), Vec([0., 0.])])
zeros_test = np.dot(U_cont, x) - np.dot(U_non_cont, x)
assert_equal(zeros[0].array, zeros_test[0].array)
assert_equal(zeros[1].array, zeros_test[1].array)
def test_dot_2args(self):
from numpy.core.multiarray import dot
a = np.array([[1, 2], [3, 4]], dtype=float)
b = np.array([[1, 0], [1, 1]], dtype=float)
c = np.array([[3, 2], [7, 4]], dtype=float)
d = dot(a, b)
assert_allclose(c, d)
def test_dot_3args(self):
from numpy.core.multiarray import dot
np.random.seed(22)
f = np.random.random_sample((1024, 16))
v = np.random.random_sample((16, 32))
r = np.empty((1024, 32))
for i in range(12):
dot(f, v, r)
if HAS_REFCOUNT:
assert_equal(sys.getrefcount(r), 2)
r2 = dot(f, v, out=None)
assert_array_equal(r2, r)
assert_(r is dot(f, v, out=r))
v = v[:, 0].copy() # v.shape == (16,)
r = r[:, 0].copy() # r.shape == (1024,)
r2 = dot(f, v)
assert_(r is dot(f, v, r))
assert_array_equal(r2, r)
def test_dot_3args_errors(self):
from numpy.core.multiarray import dot
np.random.seed(22)
f = np.random.random_sample((1024, 16))
v = np.random.random_sample((16, 32))
r = np.empty((1024, 31))
assert_raises(ValueError, dot, f, v, r)
r = np.empty((1024,))
assert_raises(ValueError, dot, f, v, r)
r = np.empty((32,))
assert_raises(ValueError, dot, f, v, r)
r = np.empty((32, 1024))
assert_raises(ValueError, dot, f, v, r)
assert_raises(ValueError, dot, f, v, r.T)
r = np.empty((1024, 64))
assert_raises(ValueError, dot, f, v, r[:, ::2])
assert_raises(ValueError, dot, f, v, r[:, :32])
r = np.empty((1024, 32), dtype=np.float32)
assert_raises(ValueError, dot, f, v, r)
r = np.empty((1024, 32), dtype=int)
assert_raises(ValueError, dot, f, v, r)
def test_dot_array_order(self):
a = np.array([[1, 2], [3, 4]], order='C')
b = np.array([[1, 2], [3, 4]], order='F')
res = np.dot(a, a)
# integer arrays are exact
assert_equal(np.dot(a, b), res)
assert_equal(np.dot(b, a), res)
assert_equal(np.dot(b, b), res)
def test_dot_scalar_and_matrix_of_objects(self):
# Ticket #2469
arr = np.matrix([1, 2], dtype=object)
desired = np.matrix([[3, 6]], dtype=object)
assert_equal(np.dot(arr, 3), desired)
assert_equal(np.dot(3, arr), desired)
def test_accelerate_framework_sgemv_fix(self):
def aligned_array(shape, align, dtype, order='C'):
d = dtype(0)
N = np.prod(shape)
tmp = np.zeros(N * d.nbytes + align, dtype=np.uint8)
address = tmp.__array_interface__["data"][0]
for offset in range(align):
if (address + offset) % align == 0:
break
tmp = tmp[offset:offset+N*d.nbytes].view(dtype=dtype)
return tmp.reshape(shape, order=order)
def as_aligned(arr, align, dtype, order='C'):
aligned = aligned_array(arr.shape, align, dtype, order)
aligned[:] = arr[:]
return aligned
def assert_dot_close(A, X, desired):
assert_allclose(np.dot(A, X), desired, rtol=1e-5, atol=1e-7)
m = aligned_array(100, 15, np.float32)
s = aligned_array((100, 100), 15, np.float32)
np.dot(s, m) # this will always segfault if the bug is present
testdata = itertools.product((15,32), (10000,), (200,89), ('C','F'))
for align, m, n, a_order in testdata:
# Calculation in double precision
A_d = np.random.rand(m, n)
X_d = np.random.rand(n)
desired = np.dot(A_d, X_d)
# Calculation with aligned single precision
A_f = as_aligned(A_d, align, np.float32, order=a_order)
X_f = as_aligned(X_d, align, np.float32)
assert_dot_close(A_f, X_f, desired)
# Strided A rows
A_d_2 = A_d[::2]
desired = np.dot(A_d_2, X_d)
A_f_2 = A_f[::2]
assert_dot_close(A_f_2, X_f, desired)
# Strided A columns, strided X vector
A_d_22 = A_d_2[:, ::2]
X_d_2 = X_d[::2]
desired = np.dot(A_d_22, X_d_2)
A_f_22 = A_f_2[:, ::2]
X_f_2 = X_f[::2]
assert_dot_close(A_f_22, X_f_2, desired)
# Check the strides are as expected
if a_order == 'F':
assert_equal(A_f_22.strides, (8, 8 * m))
else:
assert_equal(A_f_22.strides, (8 * n, 8))
assert_equal(X_f_2.strides, (8,))
# Strides in A rows + cols only
X_f_2c = as_aligned(X_f_2, align, np.float32)
assert_dot_close(A_f_22, X_f_2c, desired)
# Strides just in A cols
A_d_12 = A_d[:, ::2]
desired = np.dot(A_d_12, X_d_2)
A_f_12 = A_f[:, ::2]
assert_dot_close(A_f_12, X_f_2c, desired)
# Strides in A cols and X
assert_dot_close(A_f_12, X_f_2, desired)
class MatmulCommon(object):
"""Common tests for '@' operator and numpy.matmul.
Do not derive from TestCase to avoid nose running it.
"""
# Should work with these types. Will want to add
# "O" at some point
types = "?bhilqBHILQefdgFDG"
def test_exceptions(self):
dims = [
((1,), (2,)), # mismatched vector vector
((2, 1,), (2,)), # mismatched matrix vector
((2,), (1, 2)), # mismatched vector matrix
((1, 2), (3, 1)), # mismatched matrix matrix
((1,), ()), # vector scalar
((), (1)), # scalar vector
((1, 1), ()), # matrix scalar
((), (1, 1)), # scalar matrix
((2, 2, 1), (3, 1, 2)), # cannot broadcast
]
for dt, (dm1, dm2) in itertools.product(self.types, dims):
a = np.ones(dm1, dtype=dt)
b = np.ones(dm2, dtype=dt)
assert_raises(ValueError, self.matmul, a, b)
def test_shapes(self):
dims = [
((1, 1), (2, 1, 1)), # broadcast first argument
((2, 1, 1), (1, 1)), # broadcast second argument
((2, 1, 1), (2, 1, 1)), # matrix stack sizes match
]
for dt, (dm1, dm2) in itertools.product(self.types, dims):
a = np.ones(dm1, dtype=dt)
b = np.ones(dm2, dtype=dt)
res = self.matmul(a, b)
assert_(res.shape == (2, 1, 1))
# vector vector returns scalars.
for dt in self.types:
a = np.ones((2,), dtype=dt)
b = np.ones((2,), dtype=dt)
c = self.matmul(a, b)
assert_(np.array(c).shape == ())
def test_result_types(self):
mat = np.ones((1,1))
vec = np.ones((1,))
for dt in self.types:
m = mat.astype(dt)
v = vec.astype(dt)
for arg in [(m, v), (v, m), (m, m)]:
res = self.matmul(*arg)
assert_(res.dtype == dt)
# vector vector returns scalars
res = self.matmul(v, v)
assert_(type(res) is np.dtype(dt).type)
def test_vector_vector_values(self):
vec = np.array([1, 2])
tgt = 5
for dt in self.types[1:]:
v1 = vec.astype(dt)
res = self.matmul(v1, v1)
assert_equal(res, tgt)
# boolean type
vec = np.array([True, True], dtype='?')
res = self.matmul(vec, vec)
assert_equal(res, True)
def test_vector_matrix_values(self):
vec = np.array([1, 2])
mat1 = np.array([[1, 2], [3, 4]])
mat2 = np.stack([mat1]*2, axis=0)
tgt1 = np.array([7, 10])
tgt2 = np.stack([tgt1]*2, axis=0)
for dt in self.types[1:]:
v = vec.astype(dt)
m1 = mat1.astype(dt)
m2 = mat2.astype(dt)
res = self.matmul(v, m1)
assert_equal(res, tgt1)
res = self.matmul(v, m2)
assert_equal(res, tgt2)
# boolean type
vec = np.array([True, False])
mat1 = np.array([[True, False], [False, True]])
mat2 = np.stack([mat1]*2, axis=0)
tgt1 = np.array([True, False])
tgt2 = np.stack([tgt1]*2, axis=0)
res = self.matmul(vec, mat1)
assert_equal(res, tgt1)
res = self.matmul(vec, mat2)
assert_equal(res, tgt2)
def test_matrix_vector_values(self):
vec = np.array([1, 2])
mat1 = np.array([[1, 2], [3, 4]])
mat2 = np.stack([mat1]*2, axis=0)
tgt1 = np.array([5, 11])
tgt2 = np.stack([tgt1]*2, axis=0)
for dt in self.types[1:]:
v = vec.astype(dt)
m1 = mat1.astype(dt)
m2 = mat2.astype(dt)
res = self.matmul(m1, v)
assert_equal(res, tgt1)
res = self.matmul(m2, v)
assert_equal(res, tgt2)
# boolean type
vec = np.array([True, False])
mat1 = np.array([[True, False], [False, True]])
mat2 = np.stack([mat1]*2, axis=0)
tgt1 = np.array([True, False])
tgt2 = np.stack([tgt1]*2, axis=0)
res = self.matmul(vec, mat1)
assert_equal(res, tgt1)
res = self.matmul(vec, mat2)
assert_equal(res, tgt2)
def test_matrix_matrix_values(self):
mat1 = np.array([[1, 2], [3, 4]])
mat2 = np.array([[1, 0], [1, 1]])
mat12 = np.stack([mat1, mat2], axis=0)
mat21 = np.stack([mat2, mat1], axis=0)
tgt11 = np.array([[7, 10], [15, 22]])
tgt12 = np.array([[3, 2], [7, 4]])
tgt21 = np.array([[1, 2], [4, 6]])
tgt12_21 = np.stack([tgt12, tgt21], axis=0)
tgt11_12 = np.stack((tgt11, tgt12), axis=0)
tgt11_21 = np.stack((tgt11, tgt21), axis=0)
for dt in self.types[1:]:
m1 = mat1.astype(dt)
m2 = mat2.astype(dt)
m12 = mat12.astype(dt)
m21 = mat21.astype(dt)
# matrix @ matrix
res = self.matmul(m1, m2)
assert_equal(res, tgt12)
res = self.matmul(m2, m1)
assert_equal(res, tgt21)
# stacked @ matrix
res = self.matmul(m12, m1)
assert_equal(res, tgt11_21)
# matrix @ stacked
res = self.matmul(m1, m12)
assert_equal(res, tgt11_12)
# stacked @ stacked
res = self.matmul(m12, m21)
assert_equal(res, tgt12_21)
# boolean type
m1 = np.array([[1, 1], [0, 0]], dtype=np.bool_)
m2 = np.array([[1, 0], [1, 1]], dtype=np.bool_)
m12 = np.stack([m1, m2], axis=0)
m21 = np.stack([m2, m1], axis=0)
tgt11 = m1
tgt12 = m1
tgt21 = np.array([[1, 1], [1, 1]], dtype=np.bool_)
tgt12_21 = np.stack([tgt12, tgt21], axis=0)
tgt11_12 = np.stack((tgt11, tgt12), axis=0)
tgt11_21 = np.stack((tgt11, tgt21), axis=0)
# matrix @ matrix
res = self.matmul(m1, m2)
assert_equal(res, tgt12)
res = self.matmul(m2, m1)
assert_equal(res, tgt21)
# stacked @ matrix
res = self.matmul(m12, m1)
assert_equal(res, tgt11_21)
# matrix @ stacked
res = self.matmul(m1, m12)
assert_equal(res, tgt11_12)
# stacked @ stacked
res = self.matmul(m12, m21)
assert_equal(res, tgt12_21)
class TestMatmul(MatmulCommon):
matmul = np.matmul
def test_out_arg(self):
a = np.ones((2, 2), dtype=float)
b = np.ones((2, 2), dtype=float)
tgt = np.full((2,2), 2, dtype=float)
# test as positional argument
msg = "out positional argument"
out = np.zeros((2, 2), dtype=float)
self.matmul(a, b, out)
assert_array_equal(out, tgt, err_msg=msg)
# test as keyword argument
msg = "out keyword argument"
out = np.zeros((2, 2), dtype=float)
self.matmul(a, b, out=out)
assert_array_equal(out, tgt, err_msg=msg)
# test out with not allowed type cast (safe casting)
# einsum and cblas raise different error types, so
# use Exception.
msg = "out argument with illegal cast"
out = np.zeros((2, 2), dtype=np.int32)
assert_raises(Exception, self.matmul, a, b, out=out)
# skip following tests for now, cblas does not allow non-contiguous
# outputs and consistency with dot would require same type,
# dimensions, subtype, and c_contiguous.
# test out with allowed type cast
# msg = "out argument with allowed cast"
# out = np.zeros((2, 2), dtype=np.complex128)
# self.matmul(a, b, out=out)
# assert_array_equal(out, tgt, err_msg=msg)
# test out non-contiguous
# msg = "out argument with non-contiguous layout"
# c = np.zeros((2, 2, 2), dtype=float)
# self.matmul(a, b, out=c[..., 0])
# assert_array_equal(c, tgt, err_msg=msg)
if sys.version_info[:2] >= (3, 5):
class TestMatmulOperator(MatmulCommon):
import operator
matmul = operator.matmul
def test_array_priority_override(self):
class A(object):
__array_priority__ = 1000
def __matmul__(self, other):
return "A"
def __rmatmul__(self, other):
return "A"
a = A()
b = np.ones(2)
assert_equal(self.matmul(a, b), "A")
assert_equal(self.matmul(b, a), "A")
def test_matmul_inplace():
# It would be nice to support in-place matmul eventually, but for now
# we don't have a working implementation, so better just to error out
# and nudge people to writing "a = a @ b".
a = np.eye(3)
b = np.eye(3)
assert_raises(TypeError, a.__imatmul__, b)
import operator
assert_raises(TypeError, operator.imatmul, a, b)
# we avoid writing the token `exec` so as not to crash python 2's
# parser
exec_ = getattr(builtins, "exec")
assert_raises(TypeError, exec_, "a @= b", globals(), locals())
class TestInner(object):
def test_inner_type_mismatch(self):
c = 1.
A = np.array((1,1), dtype='i,i')
assert_raises(TypeError, np.inner, c, A)
assert_raises(TypeError, np.inner, A, c)
def test_inner_scalar_and_vector(self):
for dt in np.typecodes['AllInteger'] + np.typecodes['AllFloat'] + '?':
sca = np.array(3, dtype=dt)[()]
vec = np.array([1, 2], dtype=dt)
desired = np.array([3, 6], dtype=dt)
assert_equal(np.inner(vec, sca), desired)
assert_equal(np.inner(sca, vec), desired)
def test_inner_scalar_and_matrix(self):
for dt in np.typecodes['AllInteger'] + np.typecodes['AllFloat'] + '?':
sca = np.array(3, dtype=dt)[()]
arr = np.matrix([[1, 2], [3, 4]], dtype=dt)
desired = np.matrix([[3, 6], [9, 12]], dtype=dt)
assert_equal(np.inner(arr, sca), desired)
assert_equal(np.inner(sca, arr), desired)
def test_inner_scalar_and_matrix_of_objects(self):
# Ticket #4482
arr = np.matrix([1, 2], dtype=object)
desired = np.matrix([[3, 6]], dtype=object)
assert_equal(np.inner(arr, 3), desired)
assert_equal(np.inner(3, arr), desired)
def test_vecself(self):
# Ticket 844.
# Inner product of a vector with itself segfaults or give
# meaningless result
a = np.zeros(shape=(1, 80), dtype=np.float64)
p = np.inner(a, a)
assert_almost_equal(p, 0, decimal=14)
def test_inner_product_with_various_contiguities(self):
# github issue 6532
for dt in np.typecodes['AllInteger'] + np.typecodes['AllFloat'] + '?':
# check an inner product involving a matrix transpose
A = np.array([[1, 2], [3, 4]], dtype=dt)
B = np.array([[1, 3], [2, 4]], dtype=dt)
C = np.array([1, 1], dtype=dt)
desired = np.array([4, 6], dtype=dt)
assert_equal(np.inner(A.T, C), desired)
assert_equal(np.inner(C, A.T), desired)
assert_equal(np.inner(B, C), desired)
assert_equal(np.inner(C, B), desired)
# check a matrix product
desired = np.array([[7, 10], [15, 22]], dtype=dt)
assert_equal(np.inner(A, B), desired)
# check the syrk vs. gemm paths
desired = np.array([[5, 11], [11, 25]], dtype=dt)
assert_equal(np.inner(A, A), desired)
assert_equal(np.inner(A, A.copy()), desired)
# check an inner product involving an aliased and reversed view
a = np.arange(5).astype(dt)
b = a[::-1]
desired = np.array(10, dtype=dt).item()
assert_equal(np.inner(b, a), desired)
def test_3d_tensor(self):
for dt in np.typecodes['AllInteger'] + np.typecodes['AllFloat'] + '?':
a = np.arange(24).reshape(2,3,4).astype(dt)
b = np.arange(24, 48).reshape(2,3,4).astype(dt)
desired = np.array(
[[[[ 158, 182, 206],
[ 230, 254, 278]],
[[ 566, 654, 742],
[ 830, 918, 1006]],
[[ 974, 1126, 1278],
[1430, 1582, 1734]]],
[[[1382, 1598, 1814],
[2030, 2246, 2462]],
[[1790, 2070, 2350],
[2630, 2910, 3190]],
[[2198, 2542, 2886],
[3230, 3574, 3918]]]],
dtype=dt
)
assert_equal(np.inner(a, b), desired)
assert_equal(np.inner(b, a).transpose(2,3,0,1), desired)
class TestAlen(object):
def test_basic(self):
m = np.array([1, 2, 3])
assert_equal(np.alen(m), 3)
m = np.array([[1, 2, 3], [4, 5, 7]])
assert_equal(np.alen(m), 2)
m = [1, 2, 3]
assert_equal(np.alen(m), 3)
m = [[1, 2, 3], [4, 5, 7]]
assert_equal(np.alen(m), 2)
def test_singleton(self):
assert_equal(np.alen(5), 1)
class TestChoose(object):
def setup(self):
self.x = 2*np.ones((3,), dtype=int)
self.y = 3*np.ones((3,), dtype=int)
self.x2 = 2*np.ones((2, 3), dtype=int)
self.y2 = 3*np.ones((2, 3), dtype=int)
self.ind = [0, 0, 1]
def test_basic(self):
A = np.choose(self.ind, (self.x, self.y))
assert_equal(A, [2, 2, 3])
def test_broadcast1(self):
A = np.choose(self.ind, (self.x2, self.y2))
assert_equal(A, [[2, 2, 3], [2, 2, 3]])
def test_broadcast2(self):
A = np.choose(self.ind, (self.x, self.y2))
assert_equal(A, [[2, 2, 3], [2, 2, 3]])
class TestRepeat(object):
def setup(self):
self.m = np.array([1, 2, 3, 4, 5, 6])
self.m_rect = self.m.reshape((2, 3))
def test_basic(self):
A = np.repeat(self.m, [1, 3, 2, 1, 1, 2])
assert_equal(A, [1, 2, 2, 2, 3,
3, 4, 5, 6, 6])
def test_broadcast1(self):
A = np.repeat(self.m, 2)
assert_equal(A, [1, 1, 2, 2, 3, 3,
4, 4, 5, 5, 6, 6])
def test_axis_spec(self):
A = np.repeat(self.m_rect, [2, 1], axis=0)
assert_equal(A, [[1, 2, 3],
[1, 2, 3],
[4, 5, 6]])
A = np.repeat(self.m_rect, [1, 3, 2], axis=1)
assert_equal(A, [[1, 2, 2, 2, 3, 3],
[4, 5, 5, 5, 6, 6]])
def test_broadcast2(self):
A = np.repeat(self.m_rect, 2, axis=0)
assert_equal(A, [[1, 2, 3],
[1, 2, 3],
[4, 5, 6],
[4, 5, 6]])
A = np.repeat(self.m_rect, 2, axis=1)
assert_equal(A, [[1, 1, 2, 2, 3, 3],
[4, 4, 5, 5, 6, 6]])
# TODO: test for multidimensional
NEIGH_MODE = {'zero': 0, 'one': 1, 'constant': 2, 'circular': 3, 'mirror': 4}
class TestNeighborhoodIter(object):
# Simple, 2d tests
def _test_simple2d(self, dt):
# Test zero and one padding for simple data type
x = np.array([[0, 1], [2, 3]], dtype=dt)
r = [np.array([[0, 0, 0], [0, 0, 1]], dtype=dt),
np.array([[0, 0, 0], [0, 1, 0]], dtype=dt),
np.array([[0, 0, 1], [0, 2, 3]], dtype=dt),
np.array([[0, 1, 0], [2, 3, 0]], dtype=dt)]
l = test_neighborhood_iterator(x, [-1, 0, -1, 1], x[0],
NEIGH_MODE['zero'])
assert_array_equal(l, r)
r = [np.array([[1, 1, 1], [1, 0, 1]], dtype=dt),
np.array([[1, 1, 1], [0, 1, 1]], dtype=dt),
np.array([[1, 0, 1], [1, 2, 3]], dtype=dt),
np.array([[0, 1, 1], [2, 3, 1]], dtype=dt)]
l = test_neighborhood_iterator(x, [-1, 0, -1, 1], x[0],
NEIGH_MODE['one'])
assert_array_equal(l, r)
r = [np.array([[4, 4, 4], [4, 0, 1]], dtype=dt),
np.array([[4, 4, 4], [0, 1, 4]], dtype=dt),
np.array([[4, 0, 1], [4, 2, 3]], dtype=dt),
np.array([[0, 1, 4], [2, 3, 4]], dtype=dt)]
l = test_neighborhood_iterator(x, [-1, 0, -1, 1], 4,
NEIGH_MODE['constant'])
assert_array_equal(l, r)
def test_simple2d(self):
self._test_simple2d(float)
def test_simple2d_object(self):
self._test_simple2d(Decimal)
def _test_mirror2d(self, dt):
x = np.array([[0, 1], [2, 3]], dtype=dt)
r = [np.array([[0, 0, 1], [0, 0, 1]], dtype=dt),
np.array([[0, 1, 1], [0, 1, 1]], dtype=dt),
np.array([[0, 0, 1], [2, 2, 3]], dtype=dt),
np.array([[0, 1, 1], [2, 3, 3]], dtype=dt)]
l = test_neighborhood_iterator(x, [-1, 0, -1, 1], x[0],
NEIGH_MODE['mirror'])
assert_array_equal(l, r)
def test_mirror2d(self):
self._test_mirror2d(float)
def test_mirror2d_object(self):
self._test_mirror2d(Decimal)
# Simple, 1d tests
def _test_simple(self, dt):
# Test padding with constant values
x = np.linspace(1, 5, 5).astype(dt)
r = [[0, 1, 2], [1, 2, 3], [2, 3, 4], [3, 4, 5], [4, 5, 0]]
l = test_neighborhood_iterator(x, [-1, 1], x[0], NEIGH_MODE['zero'])
assert_array_equal(l, r)
r = [[1, 1, 2], [1, 2, 3], [2, 3, 4], [3, 4, 5], [4, 5, 1]]
l = test_neighborhood_iterator(x, [-1, 1], x[0], NEIGH_MODE['one'])
assert_array_equal(l, r)
r = [[x[4], 1, 2], [1, 2, 3], [2, 3, 4], [3, 4, 5], [4, 5, x[4]]]
l = test_neighborhood_iterator(x, [-1, 1], x[4], NEIGH_MODE['constant'])
assert_array_equal(l, r)
def test_simple_float(self):
self._test_simple(float)
def test_simple_object(self):
self._test_simple(Decimal)
# Test mirror modes
def _test_mirror(self, dt):
x = np.linspace(1, 5, 5).astype(dt)
r = np.array([[2, 1, 1, 2, 3], [1, 1, 2, 3, 4], [1, 2, 3, 4, 5],
[2, 3, 4, 5, 5], [3, 4, 5, 5, 4]], dtype=dt)
l = test_neighborhood_iterator(x, [-2, 2], x[1], NEIGH_MODE['mirror'])
assert_([i.dtype == dt for i in l])
assert_array_equal(l, r)
def test_mirror(self):
self._test_mirror(float)
def test_mirror_object(self):
self._test_mirror(Decimal)
# Circular mode
def _test_circular(self, dt):
x = np.linspace(1, 5, 5).astype(dt)
r = np.array([[4, 5, 1, 2, 3], [5, 1, 2, 3, 4], [1, 2, 3, 4, 5],
[2, 3, 4, 5, 1], [3, 4, 5, 1, 2]], dtype=dt)
l = test_neighborhood_iterator(x, [-2, 2], x[0], NEIGH_MODE['circular'])
assert_array_equal(l, r)
def test_circular(self):
self._test_circular(float)
def test_circular_object(self):
self._test_circular(Decimal)
# Test stacking neighborhood iterators
class TestStackedNeighborhoodIter(object):
# Simple, 1d test: stacking 2 constant-padded neigh iterators
def test_simple_const(self):
dt = np.float64
# Test zero and one padding for simple data type
x = np.array([1, 2, 3], dtype=dt)
r = [np.array([0], dtype=dt),
np.array([0], dtype=dt),
np.array([1], dtype=dt),
np.array([2], dtype=dt),
np.array([3], dtype=dt),
np.array([0], dtype=dt),
np.array([0], dtype=dt)]
l = test_neighborhood_iterator_oob(x, [-2, 4], NEIGH_MODE['zero'],
[0, 0], NEIGH_MODE['zero'])
assert_array_equal(l, r)
r = [np.array([1, 0, 1], dtype=dt),
np.array([0, 1, 2], dtype=dt),
np.array([1, 2, 3], dtype=dt),
np.array([2, 3, 0], dtype=dt),
np.array([3, 0, 1], dtype=dt)]
l = test_neighborhood_iterator_oob(x, [-1, 3], NEIGH_MODE['zero'],
[-1, 1], NEIGH_MODE['one'])
assert_array_equal(l, r)
# 2nd simple, 1d test: stacking 2 neigh iterators, mixing const padding and
# mirror padding
def test_simple_mirror(self):
dt = np.float64
# Stacking zero on top of mirror
x = np.array([1, 2, 3], dtype=dt)
r = [np.array([0, 1, 1], dtype=dt),
np.array([1, 1, 2], dtype=dt),
np.array([1, 2, 3], dtype=dt),
np.array([2, 3, 3], dtype=dt),
np.array([3, 3, 0], dtype=dt)]
l = test_neighborhood_iterator_oob(x, [-1, 3], NEIGH_MODE['mirror'],
[-1, 1], NEIGH_MODE['zero'])
assert_array_equal(l, r)
# Stacking mirror on top of zero
x = np.array([1, 2, 3], dtype=dt)
r = [np.array([1, 0, 0], dtype=dt),
np.array([0, 0, 1], dtype=dt),
np.array([0, 1, 2], dtype=dt),
np.array([1, 2, 3], dtype=dt),
np.array([2, 3, 0], dtype=dt)]
l = test_neighborhood_iterator_oob(x, [-1, 3], NEIGH_MODE['zero'],
[-2, 0], NEIGH_MODE['mirror'])
assert_array_equal(l, r)
# Stacking mirror on top of zero: 2nd
x = np.array([1, 2, 3], dtype=dt)
r = [np.array([0, 1, 2], dtype=dt),
np.array([1, 2, 3], dtype=dt),
np.array([2, 3, 0], dtype=dt),
np.array([3, 0, 0], dtype=dt),
np.array([0, 0, 3], dtype=dt)]
l = test_neighborhood_iterator_oob(x, [-1, 3], NEIGH_MODE['zero'],
[0, 2], NEIGH_MODE['mirror'])
assert_array_equal(l, r)
# Stacking mirror on top of zero: 3rd
x = np.array([1, 2, 3], dtype=dt)
r = [np.array([1, 0, 0, 1, 2], dtype=dt),
np.array([0, 0, 1, 2, 3], dtype=dt),
np.array([0, 1, 2, 3, 0], dtype=dt),
np.array([1, 2, 3, 0, 0], dtype=dt),
np.array([2, 3, 0, 0, 3], dtype=dt)]
l = test_neighborhood_iterator_oob(x, [-1, 3], NEIGH_MODE['zero'],
[-2, 2], NEIGH_MODE['mirror'])
assert_array_equal(l, r)
# 3rd simple, 1d test: stacking 2 neigh iterators, mixing const padding and
# circular padding
def test_simple_circular(self):
dt = np.float64
# Stacking zero on top of mirror
x = np.array([1, 2, 3], dtype=dt)
r = [np.array([0, 3, 1], dtype=dt),
np.array([3, 1, 2], dtype=dt),
np.array([1, 2, 3], dtype=dt),
np.array([2, 3, 1], dtype=dt),
np.array([3, 1, 0], dtype=dt)]
l = test_neighborhood_iterator_oob(x, [-1, 3], NEIGH_MODE['circular'],
[-1, 1], NEIGH_MODE['zero'])
assert_array_equal(l, r)
# Stacking mirror on top of zero
x = np.array([1, 2, 3], dtype=dt)
r = [np.array([3, 0, 0], dtype=dt),
np.array([0, 0, 1], dtype=dt),
np.array([0, 1, 2], dtype=dt),
np.array([1, 2, 3], dtype=dt),
np.array([2, 3, 0], dtype=dt)]
l = test_neighborhood_iterator_oob(x, [-1, 3], NEIGH_MODE['zero'],
[-2, 0], NEIGH_MODE['circular'])
assert_array_equal(l, r)
# Stacking mirror on top of zero: 2nd
x = np.array([1, 2, 3], dtype=dt)
r = [np.array([0, 1, 2], dtype=dt),
np.array([1, 2, 3], dtype=dt),
np.array([2, 3, 0], dtype=dt),
np.array([3, 0, 0], dtype=dt),
np.array([0, 0, 1], dtype=dt)]
l = test_neighborhood_iterator_oob(x, [-1, 3], NEIGH_MODE['zero'],
[0, 2], NEIGH_MODE['circular'])
assert_array_equal(l, r)
# Stacking mirror on top of zero: 3rd
x = np.array([1, 2, 3], dtype=dt)
r = [np.array([3, 0, 0, 1, 2], dtype=dt),
np.array([0, 0, 1, 2, 3], dtype=dt),
np.array([0, 1, 2, 3, 0], dtype=dt),
np.array([1, 2, 3, 0, 0], dtype=dt),
np.array([2, 3, 0, 0, 1], dtype=dt)]
l = test_neighborhood_iterator_oob(x, [-1, 3], NEIGH_MODE['zero'],
[-2, 2], NEIGH_MODE['circular'])
assert_array_equal(l, r)
# 4th simple, 1d test: stacking 2 neigh iterators, but with lower iterator
# being strictly within the array
def test_simple_strict_within(self):
dt = np.float64
# Stacking zero on top of zero, first neighborhood strictly inside the
# array
x = np.array([1, 2, 3], dtype=dt)
r = [np.array([1, 2, 3, 0], dtype=dt)]
l = test_neighborhood_iterator_oob(x, [1, 1], NEIGH_MODE['zero'],
[-1, 2], NEIGH_MODE['zero'])
assert_array_equal(l, r)
# Stacking mirror on top of zero, first neighborhood strictly inside the
# array
x = np.array([1, 2, 3], dtype=dt)
r = [np.array([1, 2, 3, 3], dtype=dt)]
l = test_neighborhood_iterator_oob(x, [1, 1], NEIGH_MODE['zero'],
[-1, 2], NEIGH_MODE['mirror'])
assert_array_equal(l, r)
# Stacking mirror on top of zero, first neighborhood strictly inside the
# array
x = np.array([1, 2, 3], dtype=dt)
r = [np.array([1, 2, 3, 1], dtype=dt)]
l = test_neighborhood_iterator_oob(x, [1, 1], NEIGH_MODE['zero'],
[-1, 2], NEIGH_MODE['circular'])
assert_array_equal(l, r)
class TestWarnings(object):
def test_complex_warning(self):
x = np.array([1, 2])
y = np.array([1-2j, 1+2j])
with warnings.catch_warnings():
warnings.simplefilter("error", np.ComplexWarning)
assert_raises(np.ComplexWarning, x.__setitem__, slice(None), y)
assert_equal(x, [1, 2])
class TestMinScalarType(object):
def test_usigned_shortshort(self):
dt = np.min_scalar_type(2**8-1)
wanted = np.dtype('uint8')
assert_equal(wanted, dt)
def test_usigned_short(self):
dt = np.min_scalar_type(2**16-1)
wanted = np.dtype('uint16')
assert_equal(wanted, dt)
def test_usigned_int(self):
dt = np.min_scalar_type(2**32-1)
wanted = np.dtype('uint32')
assert_equal(wanted, dt)
def test_usigned_longlong(self):
dt = np.min_scalar_type(2**63-1)
wanted = np.dtype('uint64')
assert_equal(wanted, dt)
def test_object(self):
dt = np.min_scalar_type(2**64)
wanted = np.dtype('O')
assert_equal(wanted, dt)
from numpy.core._internal import _dtype_from_pep3118
class TestPEP3118Dtype(object):
def _check(self, spec, wanted):
dt = np.dtype(wanted)
actual = _dtype_from_pep3118(spec)
assert_equal(actual, dt,
err_msg="spec %r != dtype %r" % (spec, wanted))
def test_native_padding(self):
align = np.dtype('i').alignment
for j in range(8):
if j == 0:
s = 'bi'
else:
s = 'b%dxi' % j
self._check('@'+s, {'f0': ('i1', 0),
'f1': ('i', align*(1 + j//align))})
self._check('='+s, {'f0': ('i1', 0),
'f1': ('i', 1+j)})
def test_native_padding_2(self):
# Native padding should work also for structs and sub-arrays
self._check('x3T{xi}', {'f0': (({'f0': ('i', 4)}, (3,)), 4)})
self._check('^x3T{xi}', {'f0': (({'f0': ('i', 1)}, (3,)), 1)})
def test_trailing_padding(self):
# Trailing padding should be included, *and*, the item size
# should match the alignment if in aligned mode
align = np.dtype('i').alignment
size = np.dtype('i').itemsize
def aligned(n):
return align*(1 + (n-1)//align)
base = dict(formats=['i'], names=['f0'])
self._check('ix', dict(itemsize=aligned(size + 1), **base))
self._check('ixx', dict(itemsize=aligned(size + 2), **base))
self._check('ixxx', dict(itemsize=aligned(size + 3), **base))
self._check('ixxxx', dict(itemsize=aligned(size + 4), **base))
self._check('i7x', dict(itemsize=aligned(size + 7), **base))
self._check('^ix', dict(itemsize=size + 1, **base))
self._check('^ixx', dict(itemsize=size + 2, **base))
self._check('^ixxx', dict(itemsize=size + 3, **base))
self._check('^ixxxx', dict(itemsize=size + 4, **base))
self._check('^i7x', dict(itemsize=size + 7, **base))
def test_native_padding_3(self):
dt = np.dtype(
[('a', 'b'), ('b', 'i'),
('sub', np.dtype('b,i')), ('c', 'i')],
align=True)
self._check("T{b:a:xxxi:b:T{b:f0:=i:f1:}:sub:xxxi:c:}", dt)
dt = np.dtype(
[('a', 'b'), ('b', 'i'), ('c', 'b'), ('d', 'b'),
('e', 'b'), ('sub', np.dtype('b,i', align=True))])
self._check("T{b:a:=i:b:b:c:b:d:b:e:T{b:f0:xxxi:f1:}:sub:}", dt)
def test_padding_with_array_inside_struct(self):
dt = np.dtype(
[('a', 'b'), ('b', 'i'), ('c', 'b', (3,)),
('d', 'i')],
align=True)
self._check("T{b:a:xxxi:b:3b:c:xi:d:}", dt)
def test_byteorder_inside_struct(self):
# The byte order after @T{=i} should be '=', not '@'.
# Check this by noting the absence of native alignment.
self._check('@T{^i}xi', {'f0': ({'f0': ('i', 0)}, 0),
'f1': ('i', 5)})
def test_intra_padding(self):
# Natively aligned sub-arrays may require some internal padding
align = np.dtype('i').alignment
size = np.dtype('i').itemsize
def aligned(n):
return (align*(1 + (n-1)//align))
self._check('(3)T{ix}', (dict(
names=['f0'],
formats=['i'],
offsets=[0],
itemsize=aligned(size + 1)
), (3,)))
def test_char_vs_string(self):
dt = np.dtype('c')
self._check('c', dt)
dt = np.dtype([('f0', 'S1', (4,)), ('f1', 'S4')])
self._check('4c4s', dt)
def test_field_order(self):
# gh-9053 - previously, we relied on dictionary key order
self._check("(0)I:a:f:b:", [('a', 'I', (0,)), ('b', 'f')])
self._check("(0)I:b:f:a:", [('b', 'I', (0,)), ('a', 'f')])
def test_unnamed_fields(self):
self._check('ii', [('f0', 'i'), ('f1', 'i')])
self._check('ii:f0:', [('f1', 'i'), ('f0', 'i')])
self._check('i', 'i')
self._check('i:f0:', [('f0', 'i')])
class TestNewBufferProtocol(object):
def _check_roundtrip(self, obj):
obj = np.asarray(obj)
x = memoryview(obj)
y = np.asarray(x)
y2 = np.array(x)
assert_(not y.flags.owndata)
assert_(y2.flags.owndata)
assert_equal(y.dtype, obj.dtype)
assert_equal(y.shape, obj.shape)
assert_array_equal(obj, y)
assert_equal(y2.dtype, obj.dtype)
assert_equal(y2.shape, obj.shape)
assert_array_equal(obj, y2)
def test_roundtrip(self):
x = np.array([1, 2, 3, 4, 5], dtype='i4')
self._check_roundtrip(x)
x = np.array([[1, 2], [3, 4]], dtype=np.float64)
self._check_roundtrip(x)
x = np.zeros((3, 3, 3), dtype=np.float32)[:, 0,:]
self._check_roundtrip(x)
dt = [('a', 'b'),
('b', 'h'),
('c', 'i'),
('d', 'l'),
('dx', 'q'),
('e', 'B'),
('f', 'H'),
('g', 'I'),
('h', 'L'),
('hx', 'Q'),
('i', np.single),
('j', np.double),
('k', np.longdouble),
('ix', np.csingle),
('jx', np.cdouble),
('kx', np.clongdouble),
('l', 'S4'),
('m', 'U4'),
('n', 'V3'),
('o', '?'),
('p', np.half),
]
x = np.array(
[(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
b'aaaa', 'bbbb', b'xxx', True, 1.0)],
dtype=dt)
self._check_roundtrip(x)
x = np.array(([[1, 2], [3, 4]],), dtype=[('a', (int, (2, 2)))])
self._check_roundtrip(x)
x = np.array([1, 2, 3], dtype='>i2')
self._check_roundtrip(x)
x = np.array([1, 2, 3], dtype='<i2')
self._check_roundtrip(x)
x = np.array([1, 2, 3], dtype='>i4')
self._check_roundtrip(x)
x = np.array([1, 2, 3], dtype='<i4')
self._check_roundtrip(x)
# check long long can be represented as non-native
x = np.array([1, 2, 3], dtype='>q')
self._check_roundtrip(x)
# Native-only data types can be passed through the buffer interface
# only in native byte order
if sys.byteorder == 'little':
x = np.array([1, 2, 3], dtype='>g')
assert_raises(ValueError, self._check_roundtrip, x)
x = np.array([1, 2, 3], dtype='<g')
self._check_roundtrip(x)
else:
x = np.array([1, 2, 3], dtype='>g')
self._check_roundtrip(x)
x = np.array([1, 2, 3], dtype='<g')
assert_raises(ValueError, self._check_roundtrip, x)
def test_roundtrip_half(self):
half_list = [
1.0,
-2.0,
6.5504 * 10**4, # (max half precision)
2**-14, # ~= 6.10352 * 10**-5 (minimum positive normal)
2**-24, # ~= 5.96046 * 10**-8 (minimum strictly positive subnormal)
0.0,
-0.0,
float('+inf'),
float('-inf'),
0.333251953125, # ~= 1/3
]
x = np.array(half_list, dtype='>e')
self._check_roundtrip(x)
x = np.array(half_list, dtype='<e')
self._check_roundtrip(x)
def test_roundtrip_single_types(self):
for typ in np.typeDict.values():
dtype = np.dtype(typ)
if dtype.char in 'Mm':
# datetimes cannot be used in buffers
continue
if dtype.char == 'V':
# skip void
continue
x = np.zeros(4, dtype=dtype)
self._check_roundtrip(x)
if dtype.char not in 'qQgG':
dt = dtype.newbyteorder('<')
x = np.zeros(4, dtype=dt)
self._check_roundtrip(x)
dt = dtype.newbyteorder('>')
x = np.zeros(4, dtype=dt)
self._check_roundtrip(x)
def test_roundtrip_scalar(self):
# Issue #4015.
self._check_roundtrip(0)
def test_export_simple_1d(self):
x = np.array([1, 2, 3, 4, 5], dtype='i')
y = memoryview(x)
assert_equal(y.format, 'i')
assert_equal(y.shape, (5,))
assert_equal(y.ndim, 1)
assert_equal(y.strides, (4,))
assert_equal(y.suboffsets, EMPTY)
assert_equal(y.itemsize, 4)
def test_export_simple_nd(self):
x = np.array([[1, 2], [3, 4]], dtype=np.float64)
y = memoryview(x)
assert_equal(y.format, 'd')
assert_equal(y.shape, (2, 2))
assert_equal(y.ndim, 2)
assert_equal(y.strides, (16, 8))
assert_equal(y.suboffsets, EMPTY)
assert_equal(y.itemsize, 8)
def test_export_discontiguous(self):
x = np.zeros((3, 3, 3), dtype=np.float32)[:, 0,:]
y = memoryview(x)
assert_equal(y.format, 'f')
assert_equal(y.shape, (3, 3))
assert_equal(y.ndim, 2)
assert_equal(y.strides, (36, 4))
assert_equal(y.suboffsets, EMPTY)
assert_equal(y.itemsize, 4)
def test_export_record(self):
dt = [('a', 'b'),
('b', 'h'),
('c', 'i'),
('d', 'l'),
('dx', 'q'),
('e', 'B'),
('f', 'H'),
('g', 'I'),
('h', 'L'),
('hx', 'Q'),
('i', np.single),
('j', np.double),
('k', np.longdouble),
('ix', np.csingle),
('jx', np.cdouble),
('kx', np.clongdouble),
('l', 'S4'),
('m', 'U4'),
('n', 'V3'),
('o', '?'),
('p', np.half),
]
x = np.array(
[(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
b'aaaa', 'bbbb', b' ', True, 1.0)],
dtype=dt)
y = memoryview(x)
assert_equal(y.shape, (1,))
assert_equal(y.ndim, 1)
assert_equal(y.suboffsets, EMPTY)
sz = sum([np.dtype(b).itemsize for a, b in dt])
if np.dtype('l').itemsize == 4:
assert_equal(y.format, 'T{b:a:=h:b:i:c:l:d:q:dx:B:e:@H:f:=I:g:L:h:Q:hx:f:i:d:j:^g:k:=Zf:ix:Zd:jx:^Zg:kx:4s:l:=4w:m:3x:n:?:o:@e:p:}')
else:
assert_equal(y.format, 'T{b:a:=h:b:i:c:q:d:q:dx:B:e:@H:f:=I:g:Q:h:Q:hx:f:i:d:j:^g:k:=Zf:ix:Zd:jx:^Zg:kx:4s:l:=4w:m:3x:n:?:o:@e:p:}')
# Cannot test if NPY_RELAXED_STRIDES_CHECKING changes the strides
if not (np.ones(1).strides[0] == np.iinfo(np.intp).max):
assert_equal(y.strides, (sz,))
assert_equal(y.itemsize, sz)
def test_export_subarray(self):
x = np.array(([[1, 2], [3, 4]],), dtype=[('a', ('i', (2, 2)))])
y = memoryview(x)
assert_equal(y.format, 'T{(2,2)i:a:}')
assert_equal(y.shape, EMPTY)
assert_equal(y.ndim, 0)
assert_equal(y.strides, EMPTY)
assert_equal(y.suboffsets, EMPTY)
assert_equal(y.itemsize, 16)
def test_export_endian(self):
x = np.array([1, 2, 3], dtype='>i')
y = memoryview(x)
if sys.byteorder == 'little':
assert_equal(y.format, '>i')
else:
assert_equal(y.format, 'i')
x = np.array([1, 2, 3], dtype='<i')
y = memoryview(x)
if sys.byteorder == 'little':
assert_equal(y.format, 'i')
else:
assert_equal(y.format, '<i')
def test_export_flags(self):
# Check SIMPLE flag, see also gh-3613 (exception should be BufferError)
assert_raises(ValueError, get_buffer_info, np.arange(5)[::2], ('SIMPLE',))
def test_padding(self):
for j in range(8):
x = np.array([(1,), (2,)], dtype={'f0': (int, j)})
self._check_roundtrip(x)
def test_reference_leak(self):
if HAS_REFCOUNT:
count_1 = sys.getrefcount(np.core._internal)
a = np.zeros(4)
b = memoryview(a)
c = np.asarray(b)
if HAS_REFCOUNT:
count_2 = sys.getrefcount(np.core._internal)
assert_equal(count_1, count_2)
del c # avoid pyflakes unused variable warning.
def test_padded_struct_array(self):
dt1 = np.dtype(
[('a', 'b'), ('b', 'i'), ('sub', np.dtype('b,i')), ('c', 'i')],
align=True)
x1 = np.arange(dt1.itemsize, dtype=np.int8).view(dt1)
self._check_roundtrip(x1)
dt2 = np.dtype(
[('a', 'b'), ('b', 'i'), ('c', 'b', (3,)), ('d', 'i')],
align=True)
x2 = np.arange(dt2.itemsize, dtype=np.int8).view(dt2)
self._check_roundtrip(x2)
dt3 = np.dtype(
[('a', 'b'), ('b', 'i'), ('c', 'b'), ('d', 'b'),
('e', 'b'), ('sub', np.dtype('b,i', align=True))])
x3 = np.arange(dt3.itemsize, dtype=np.int8).view(dt3)
self._check_roundtrip(x3)
def test_relaxed_strides(self):
# Test that relaxed strides are converted to non-relaxed
c = np.ones((1, 10, 10), dtype='i8')
# Check for NPY_RELAXED_STRIDES_CHECKING:
if np.ones((10, 1), order="C").flags.f_contiguous:
c.strides = (-1, 80, 8)
assert_(memoryview(c).strides == (800, 80, 8))
# Writing C-contiguous data to a BytesIO buffer should work
fd = io.BytesIO()
fd.write(c.data)
fortran = c.T
assert_(memoryview(fortran).strides == (8, 80, 800))
arr = np.ones((1, 10))
if arr.flags.f_contiguous:
shape, strides = get_buffer_info(arr, ['F_CONTIGUOUS'])
assert_(strides[0] == 8)
arr = np.ones((10, 1), order='F')
shape, strides = get_buffer_info(arr, ['C_CONTIGUOUS'])
assert_(strides[-1] == 8)
def test_out_of_order_fields(self):
dt = np.dtype(dict(
formats=['<i4', '<i4'],
names=['one', 'two'],
offsets=[4, 0],
itemsize=8
))
# overlapping fields cannot be represented by PEP3118
arr = np.empty(1, dt)
with assert_raises(ValueError):
memoryview(arr)
class TestArrayAttributeDeletion(object):
def test_multiarray_writable_attributes_deletion(self):
# ticket #2046, should not seqfault, raise AttributeError
a = np.ones(2)
attr = ['shape', 'strides', 'data', 'dtype', 'real', 'imag', 'flat']
with suppress_warnings() as sup:
sup.filter(DeprecationWarning, "Assigning the 'data' attribute")
for s in attr:
assert_raises(AttributeError, delattr, a, s)
def test_multiarray_not_writable_attributes_deletion(self):
a = np.ones(2)
attr = ["ndim", "flags", "itemsize", "size", "nbytes", "base",
"ctypes", "T", "__array_interface__", "__array_struct__",
"__array_priority__", "__array_finalize__"]
for s in attr:
assert_raises(AttributeError, delattr, a, s)
def test_multiarray_flags_writable_attribute_deletion(self):
a = np.ones(2).flags
attr = ['writebackifcopy', 'updateifcopy', 'aligned', 'writeable']
for s in attr:
assert_raises(AttributeError, delattr, a, s)
def test_multiarray_flags_not_writable_attribute_deletion(self):
a = np.ones(2).flags
attr = ["contiguous", "c_contiguous", "f_contiguous", "fortran",
"owndata", "fnc", "forc", "behaved", "carray", "farray",
"num"]
for s in attr:
assert_raises(AttributeError, delattr, a, s)
def test_array_interface():
# Test scalar coercion within the array interface
class Foo(object):
def __init__(self, value):
self.value = value
self.iface = {'typestr': '=f8'}
def __float__(self):
return float(self.value)
@property
def __array_interface__(self):
return self.iface
f = Foo(0.5)
assert_equal(np.array(f), 0.5)
assert_equal(np.array([f]), [0.5])
assert_equal(np.array([f, f]), [0.5, 0.5])
assert_equal(np.array(f).dtype, np.dtype('=f8'))
# Test various shape definitions
f.iface['shape'] = ()
assert_equal(np.array(f), 0.5)
f.iface['shape'] = None
assert_raises(TypeError, np.array, f)
f.iface['shape'] = (1, 1)
assert_equal(np.array(f), [[0.5]])
f.iface['shape'] = (2,)
assert_raises(ValueError, np.array, f)
# test scalar with no shape
class ArrayLike(object):
array = np.array(1)
__array_interface__ = array.__array_interface__
assert_equal(np.array(ArrayLike()), 1)
def test_array_interface_itemsize():
# See gh-6361
my_dtype = np.dtype({'names': ['A', 'B'], 'formats': ['f4', 'f4'],
'offsets': [0, 8], 'itemsize': 16})
a = np.ones(10, dtype=my_dtype)
descr_t = np.dtype(a.__array_interface__['descr'])
typestr_t = np.dtype(a.__array_interface__['typestr'])
assert_equal(descr_t.itemsize, typestr_t.itemsize)
def test_array_interface_empty_shape():
# See gh-7994
arr = np.array([1, 2, 3])
interface1 = dict(arr.__array_interface__)
interface1['shape'] = ()
class DummyArray1(object):
__array_interface__ = interface1
# NOTE: Because Py2 str/Py3 bytes supports the buffer interface, setting
# the interface data to bytes would invoke the bug this tests for, that
# __array_interface__ with shape=() is not allowed if the data is an object
# exposing the buffer interface
interface2 = dict(interface1)
interface2['data'] = arr[0].tobytes()
class DummyArray2(object):
__array_interface__ = interface2
arr1 = np.asarray(DummyArray1())
arr2 = np.asarray(DummyArray2())
arr3 = arr[:1].reshape(())
assert_equal(arr1, arr2)
assert_equal(arr1, arr3)
def test_flat_element_deletion():
it = np.ones(3).flat
try:
del it[1]
del it[1:2]
except TypeError:
pass
except Exception:
raise AssertionError
def test_scalar_element_deletion():
a = np.zeros(2, dtype=[('x', 'int'), ('y', 'int')])
assert_raises(ValueError, a[0].__delitem__, 'x')
class TestMemEventHook(object):
def test_mem_seteventhook(self):
# The actual tests are within the C code in
# multiarray/multiarray_tests.c.src
test_pydatamem_seteventhook_start()
# force an allocation and free of a numpy array
# needs to be larger then limit of small memory cacher in ctors.c
a = np.zeros(1000)
del a
gc.collect()
test_pydatamem_seteventhook_end()
class TestMapIter(object):
def test_mapiter(self):
# The actual tests are within the C code in
# multiarray/multiarray_tests.c.src
a = np.arange(12).reshape((3, 4)).astype(float)
index = ([1, 1, 2, 0],
[0, 0, 2, 3])
vals = [50, 50, 30, 16]
test_inplace_increment(a, index, vals)
assert_equal(a, [[0.00, 1., 2.0, 19.],
[104., 5., 6.0, 7.0],
[8.00, 9., 40., 11.]])
b = np.arange(6).astype(float)
index = (np.array([1, 2, 0]),)
vals = [50, 4, 100.1]
test_inplace_increment(b, index, vals)
assert_equal(b, [100.1, 51., 6., 3., 4., 5.])
class TestAsCArray(object):
def test_1darray(self):
array = np.arange(24, dtype=np.double)
from_c = test_as_c_array(array, 3)
assert_equal(array[3], from_c)
def test_2darray(self):
array = np.arange(24, dtype=np.double).reshape(3, 8)
from_c = test_as_c_array(array, 2, 4)
assert_equal(array[2, 4], from_c)
def test_3darray(self):
array = np.arange(24, dtype=np.double).reshape(2, 3, 4)
from_c = test_as_c_array(array, 1, 2, 3)
assert_equal(array[1, 2, 3], from_c)
class TestConversion(object):
def test_array_scalar_relational_operation(self):
# All integer
for dt1 in np.typecodes['AllInteger']:
assert_(1 > np.array(0, dtype=dt1), "type %s failed" % (dt1,))
assert_(not 1 < np.array(0, dtype=dt1), "type %s failed" % (dt1,))
for dt2 in np.typecodes['AllInteger']:
assert_(np.array(1, dtype=dt1) > np.array(0, dtype=dt2),
"type %s and %s failed" % (dt1, dt2))
assert_(not np.array(1, dtype=dt1) < np.array(0, dtype=dt2),
"type %s and %s failed" % (dt1, dt2))
# Unsigned integers
for dt1 in 'BHILQP':
assert_(-1 < np.array(1, dtype=dt1), "type %s failed" % (dt1,))
assert_(not -1 > np.array(1, dtype=dt1), "type %s failed" % (dt1,))
assert_(-1 != np.array(1, dtype=dt1), "type %s failed" % (dt1,))
# Unsigned vs signed
for dt2 in 'bhilqp':
assert_(np.array(1, dtype=dt1) > np.array(-1, dtype=dt2),
"type %s and %s failed" % (dt1, dt2))
assert_(not np.array(1, dtype=dt1) < np.array(-1, dtype=dt2),
"type %s and %s failed" % (dt1, dt2))
assert_(np.array(1, dtype=dt1) != np.array(-1, dtype=dt2),
"type %s and %s failed" % (dt1, dt2))
# Signed integers and floats
for dt1 in 'bhlqp' + np.typecodes['Float']:
assert_(1 > np.array(-1, dtype=dt1), "type %s failed" % (dt1,))
assert_(not 1 < np.array(-1, dtype=dt1), "type %s failed" % (dt1,))
assert_(-1 == np.array(-1, dtype=dt1), "type %s failed" % (dt1,))
for dt2 in 'bhlqp' + np.typecodes['Float']:
assert_(np.array(1, dtype=dt1) > np.array(-1, dtype=dt2),
"type %s and %s failed" % (dt1, dt2))
assert_(not np.array(1, dtype=dt1) < np.array(-1, dtype=dt2),
"type %s and %s failed" % (dt1, dt2))
assert_(np.array(-1, dtype=dt1) == np.array(-1, dtype=dt2),
"type %s and %s failed" % (dt1, dt2))
def test_to_bool_scalar(self):
assert_equal(bool(np.array([False])), False)
assert_equal(bool(np.array([True])), True)
assert_equal(bool(np.array([[42]])), True)
assert_raises(ValueError, bool, np.array([1, 2]))
class NotConvertible(object):
def __bool__(self):
raise NotImplementedError
__nonzero__ = __bool__ # python 2
assert_raises(NotImplementedError, bool, np.array(NotConvertible()))
assert_raises(NotImplementedError, bool, np.array([NotConvertible()]))
self_containing = np.array([None])
self_containing[0] = self_containing
try:
Error = RecursionError
except NameError:
Error = RuntimeError # python < 3.5
assert_raises(Error, bool, self_containing) # previously stack overflow
def test_to_int_scalar(self):
# gh-9972 means that these aren't always the same
int_funcs = (int, lambda x: x.__int__())
for int_func in int_funcs:
assert_equal(int_func(np.array([1])), 1)
assert_equal(int_func(np.array([0])), 0)
assert_equal(int_func(np.array([[42]])), 42)
assert_raises(TypeError, int_func, np.array([1, 2]))
# gh-9972
assert_equal(4, int_func(np.array('4')))
assert_equal(5, int_func(np.bytes_(b'5')))
assert_equal(6, int_func(np.unicode_(u'6')))
class HasTrunc:
def __trunc__(self):
return 3
assert_equal(3, int_func(np.array(HasTrunc())))
assert_equal(3, int_func(np.array([HasTrunc()])))
class NotConvertible(object):
def __int__(self):
raise NotImplementedError
assert_raises(NotImplementedError,
int_func, np.array(NotConvertible()))
assert_raises(NotImplementedError,
int_func, np.array([NotConvertible()]))
class TestWhere(object):
def test_basic(self):
dts = [bool, np.int16, np.int32, np.int64, np.double, np.complex128,
np.longdouble, np.clongdouble]
for dt in dts:
c = np.ones(53, dtype=bool)
assert_equal(np.where( c, dt(0), dt(1)), dt(0))
assert_equal(np.where(~c, dt(0), dt(1)), dt(1))
assert_equal(np.where(True, dt(0), dt(1)), dt(0))
assert_equal(np.where(False, dt(0), dt(1)), dt(1))
d = np.ones_like(c).astype(dt)
e = np.zeros_like(d)
r = d.astype(dt)
c[7] = False
r[7] = e[7]
assert_equal(np.where(c, e, e), e)
assert_equal(np.where(c, d, e), r)
assert_equal(np.where(c, d, e[0]), r)
assert_equal(np.where(c, d[0], e), r)
assert_equal(np.where(c[::2], d[::2], e[::2]), r[::2])
assert_equal(np.where(c[1::2], d[1::2], e[1::2]), r[1::2])
assert_equal(np.where(c[::3], d[::3], e[::3]), r[::3])
assert_equal(np.where(c[1::3], d[1::3], e[1::3]), r[1::3])
assert_equal(np.where(c[::-2], d[::-2], e[::-2]), r[::-2])
assert_equal(np.where(c[::-3], d[::-3], e[::-3]), r[::-3])
assert_equal(np.where(c[1::-3], d[1::-3], e[1::-3]), r[1::-3])
def test_exotic(self):
# object
assert_array_equal(np.where(True, None, None), np.array(None))
# zero sized
m = np.array([], dtype=bool).reshape(0, 3)
b = np.array([], dtype=np.float64).reshape(0, 3)
assert_array_equal(np.where(m, 0, b), np.array([]).reshape(0, 3))
# object cast
d = np.array([-1.34, -0.16, -0.54, -0.31, -0.08, -0.95, 0.000, 0.313,
0.547, -0.18, 0.876, 0.236, 1.969, 0.310, 0.699, 1.013,
1.267, 0.229, -1.39, 0.487])
nan = float('NaN')
e = np.array(['5z', '0l', nan, 'Wz', nan, nan, 'Xq', 'cs', nan, nan,
'QN', nan, nan, 'Fd', nan, nan, 'kp', nan, '36', 'i1'],
dtype=object)
m = np.array([0, 0, 1, 0, 1, 1, 0, 0, 1, 1,
0, 1, 1, 0, 1, 1, 0, 1, 0, 0], dtype=bool)
r = e[:]
r[np.where(m)] = d[np.where(m)]
assert_array_equal(np.where(m, d, e), r)
r = e[:]
r[np.where(~m)] = d[np.where(~m)]
assert_array_equal(np.where(m, e, d), r)
assert_array_equal(np.where(m, e, e), e)
# minimal dtype result with NaN scalar (e.g required by pandas)
d = np.array([1., 2.], dtype=np.float32)
e = float('NaN')
assert_equal(np.where(True, d, e).dtype, np.float32)
e = float('Infinity')
assert_equal(np.where(True, d, e).dtype, np.float32)
e = float('-Infinity')
assert_equal(np.where(True, d, e).dtype, np.float32)
# also check upcast
e = float(1e150)
assert_equal(np.where(True, d, e).dtype, np.float64)
def test_ndim(self):
c = [True, False]
a = np.zeros((2, 25))
b = np.ones((2, 25))
r = np.where(np.array(c)[:,np.newaxis], a, b)
assert_array_equal(r[0], a[0])
assert_array_equal(r[1], b[0])
a = a.T
b = b.T
r = np.where(c, a, b)
assert_array_equal(r[:,0], a[:,0])
assert_array_equal(r[:,1], b[:,0])
def test_dtype_mix(self):
c = np.array([False, True, False, False, False, False, True, False,
False, False, True, False])
a = np.uint32(1)
b = np.array([5., 0., 3., 2., -1., -4., 0., -10., 10., 1., 0., 3.],
dtype=np.float64)
r = np.array([5., 1., 3., 2., -1., -4., 1., -10., 10., 1., 1., 3.],
dtype=np.float64)
assert_equal(np.where(c, a, b), r)
a = a.astype(np.float32)
b = b.astype(np.int64)
assert_equal(np.where(c, a, b), r)
# non bool mask
c = c.astype(int)
c[c != 0] = 34242324
assert_equal(np.where(c, a, b), r)
# invert
tmpmask = c != 0
c[c == 0] = 41247212
c[tmpmask] = 0
assert_equal(np.where(c, b, a), r)
def test_foreign(self):
c = np.array([False, True, False, False, False, False, True, False,
False, False, True, False])
r = np.array([5., 1., 3., 2., -1., -4., 1., -10., 10., 1., 1., 3.],
dtype=np.float64)
a = np.ones(1, dtype='>i4')
b = np.array([5., 0., 3., 2., -1., -4., 0., -10., 10., 1., 0., 3.],
dtype=np.float64)
assert_equal(np.where(c, a, b), r)
b = b.astype('>f8')
assert_equal(np.where(c, a, b), r)
a = a.astype('<i4')
assert_equal(np.where(c, a, b), r)
c = c.astype('>i4')
assert_equal(np.where(c, a, b), r)
def test_error(self):
c = [True, True]
a = np.ones((4, 5))
b = np.ones((5, 5))
assert_raises(ValueError, np.where, c, a, a)
assert_raises(ValueError, np.where, c[0], a, b)
def test_string(self):
# gh-4778 check strings are properly filled with nulls
a = np.array("abc")
b = np.array("x" * 753)
assert_equal(np.where(True, a, b), "abc")
assert_equal(np.where(False, b, a), "abc")
# check native datatype sized strings
a = np.array("abcd")
b = np.array("x" * 8)
assert_equal(np.where(True, a, b), "abcd")
assert_equal(np.where(False, b, a), "abcd")
def test_empty_result(self):
# pass empty where result through an assignment which reads the data of
# empty arrays, error detectable with valgrind, see gh-8922
x = np.zeros((1, 1))
ibad = np.vstack(np.where(x == 99.))
assert_array_equal(ibad,
np.atleast_2d(np.array([[],[]], dtype=np.intp)))
def test_largedim(self):
# invalid read regression gh-9304
shape = [10, 2, 3, 4, 5, 6]
np.random.seed(2)
array = np.random.rand(*shape)
for i in range(10):
benchmark = array.nonzero()
result = array.nonzero()
assert_array_equal(benchmark, result)
if not IS_PYPY:
# sys.getsizeof() is not valid on PyPy
class TestSizeOf(object):
def test_empty_array(self):
x = np.array([])
assert_(sys.getsizeof(x) > 0)
def check_array(self, dtype):
elem_size = dtype(0).itemsize
for length in [10, 50, 100, 500]:
x = np.arange(length, dtype=dtype)
assert_(sys.getsizeof(x) > length * elem_size)
def test_array_int32(self):
self.check_array(np.int32)
def test_array_int64(self):
self.check_array(np.int64)
def test_array_float32(self):
self.check_array(np.float32)
def test_array_float64(self):
self.check_array(np.float64)
def test_view(self):
d = np.ones(100)
assert_(sys.getsizeof(d[...]) < sys.getsizeof(d))
def test_reshape(self):
d = np.ones(100)
assert_(sys.getsizeof(d) < sys.getsizeof(d.reshape(100, 1, 1).copy()))
def test_resize(self):
d = np.ones(100)
old = sys.getsizeof(d)
d.resize(50)
assert_(old > sys.getsizeof(d))
d.resize(150)
assert_(old < sys.getsizeof(d))
def test_error(self):
d = np.ones(100)
assert_raises(TypeError, d.__sizeof__, "a")
class TestHashing(object):
def test_arrays_not_hashable(self):
x = np.ones(3)
assert_raises(TypeError, hash, x)
def test_collections_hashable(self):
x = np.array([])
assert_(not isinstance(x, collections.Hashable))
class TestArrayPriority(object):
# This will go away when __array_priority__ is settled, meanwhile
# it serves to check unintended changes.
op = operator
binary_ops = [
op.pow, op.add, op.sub, op.mul, op.floordiv, op.truediv, op.mod,
op.and_, op.or_, op.xor, op.lshift, op.rshift, op.mod, op.gt,
op.ge, op.lt, op.le, op.ne, op.eq
]
# See #7949. Dont use "/" operator With -3 switch, since python reports it
# as a DeprecationWarning
if sys.version_info[0] < 3 and not sys.py3kwarning:
binary_ops.append(op.div)
class Foo(np.ndarray):
__array_priority__ = 100.
def __new__(cls, *args, **kwargs):
return np.array(*args, **kwargs).view(cls)
class Bar(np.ndarray):
__array_priority__ = 101.
def __new__(cls, *args, **kwargs):
return np.array(*args, **kwargs).view(cls)
class Other(object):
__array_priority__ = 1000.
def _all(self, other):
return self.__class__()
__add__ = __radd__ = _all
__sub__ = __rsub__ = _all
__mul__ = __rmul__ = _all
__pow__ = __rpow__ = _all
__div__ = __rdiv__ = _all
__mod__ = __rmod__ = _all
__truediv__ = __rtruediv__ = _all
__floordiv__ = __rfloordiv__ = _all
__and__ = __rand__ = _all
__xor__ = __rxor__ = _all
__or__ = __ror__ = _all
__lshift__ = __rlshift__ = _all
__rshift__ = __rrshift__ = _all
__eq__ = _all
__ne__ = _all
__gt__ = _all
__ge__ = _all
__lt__ = _all
__le__ = _all
def test_ndarray_subclass(self):
a = np.array([1, 2])
b = self.Bar([1, 2])
for f in self.binary_ops:
msg = repr(f)
assert_(isinstance(f(a, b), self.Bar), msg)
assert_(isinstance(f(b, a), self.Bar), msg)
def test_ndarray_other(self):
a = np.array([1, 2])
b = self.Other()
for f in self.binary_ops:
msg = repr(f)
assert_(isinstance(f(a, b), self.Other), msg)
assert_(isinstance(f(b, a), self.Other), msg)
def test_subclass_subclass(self):
a = self.Foo([1, 2])
b = self.Bar([1, 2])
for f in self.binary_ops:
msg = repr(f)
assert_(isinstance(f(a, b), self.Bar), msg)
assert_(isinstance(f(b, a), self.Bar), msg)
def test_subclass_other(self):
a = self.Foo([1, 2])
b = self.Other()
for f in self.binary_ops:
msg = repr(f)
assert_(isinstance(f(a, b), self.Other), msg)
assert_(isinstance(f(b, a), self.Other), msg)
class TestBytestringArrayNonzero(object):
def test_empty_bstring_array_is_falsey(self):
assert_(not np.array([''], dtype=str))
def test_whitespace_bstring_array_is_falsey(self):
a = np.array(['spam'], dtype=str)
a[0] = ' \0\0'
assert_(not a)
def test_all_null_bstring_array_is_falsey(self):
a = np.array(['spam'], dtype=str)
a[0] = '\0\0\0\0'
assert_(not a)
def test_null_inside_bstring_array_is_truthy(self):
a = np.array(['spam'], dtype=str)
a[0] = ' \0 \0'
assert_(a)
class TestUnicodeArrayNonzero(object):
def test_empty_ustring_array_is_falsey(self):
assert_(not np.array([''], dtype=np.unicode))
def test_whitespace_ustring_array_is_falsey(self):
a = np.array(['eggs'], dtype=np.unicode)
a[0] = ' \0\0'
assert_(not a)
def test_all_null_ustring_array_is_falsey(self):
a = np.array(['eggs'], dtype=np.unicode)
a[0] = '\0\0\0\0'
assert_(not a)
def test_null_inside_ustring_array_is_truthy(self):
a = np.array(['eggs'], dtype=np.unicode)
a[0] = ' \0 \0'
assert_(a)
class TestFormat(object):
def test_0d(self):
a = np.array(np.pi)
assert_equal('{:0.3g}'.format(a), '3.14')
assert_equal('{:0.3g}'.format(a[()]), '3.14')
def test_1d_no_format(self):
a = np.array([np.pi])
assert_equal('{}'.format(a), str(a))
def test_1d_format(self):
# until gh-5543, ensure that the behaviour matches what it used to be
a = np.array([np.pi])
if sys.version_info[:2] >= (3, 4):
assert_raises(TypeError, '{:30}'.format, a)
else:
with suppress_warnings() as sup:
sup.filter(PendingDeprecationWarning)
res = '{:30}'.format(a)
dst = object.__format__(a, '30')
assert_equal(res, dst)
class TestCTypes(object):
def test_ctypes_is_available(self):
test_arr = np.array([[1, 2, 3], [4, 5, 6]])
assert_equal(ctypes, test_arr.ctypes._ctypes)
assert_equal(tuple(test_arr.ctypes.shape), (2, 3))
def test_ctypes_is_not_available(self):
from numpy.core import _internal
_internal.ctypes = None
try:
test_arr = np.array([[1, 2, 3], [4, 5, 6]])
assert_(isinstance(test_arr.ctypes._ctypes,
_internal._missing_ctypes))
assert_equal(tuple(test_arr.ctypes.shape), (2, 3))
finally:
_internal.ctypes = ctypes
class TestWritebackIfCopy(TestCase):
# all these tests use the WRITEBACKIFCOPY mechanism
def test_argmax_with_out(self):
mat = np.eye(5)
out = np.empty(5, dtype='i2')
res = np.argmax(mat, 0, out=out)
assert_equal(res, range(5))
def test_argmin_with_out(self):
mat = -np.eye(5)
out = np.empty(5, dtype='i2')
res = np.argmin(mat, 0, out=out)
assert_equal(res, range(5))
def test_clip_with_out(self):
mat = np.eye(5)
out = np.eye(5, dtype='i2')
res = np.clip(mat, a_min=-10, a_max=0, out=out)
assert_equal(np.sum(out), 0)
def test_insert_noncontiguous(self):
a = np.arange(6).reshape(2,3).T # force non-c-contiguous
# uses arr_insert
np.place(a, a>2, [44, 55])
assert_equal(a, np.array([[0, 44], [1, 55], [2, 44]]))
def test_put_noncontiguous(self):
a = np.arange(6).reshape(2,3).T # force non-c-contiguous
np.put(a, [0, 2], [44, 55])
assert_equal(a, np.array([[44, 3], [55, 4], [2, 5]]))
def test_putmask_noncontiguous(self):
a = np.arange(6).reshape(2,3).T # force non-c-contiguous
# uses arr_putmask
np.putmask(a, a>2, a**2)
assert_equal(a, np.array([[0, 9], [1, 16], [2, 25]]))
def test_take_mode_raise(self):
a = np.arange(6, dtype='int')
out = np.empty(2, dtype='int')
np.take(a, [0, 2], out=out, mode='raise')
assert_equal(out, np.array([0, 2]))
def test_choose_mod_raise(self):
a = np.array([[1, 0, 1], [0, 1, 0], [1, 0, 1]])
out = np.empty((3,3), dtype='int')
choices = [-10, 10]
np.choose(a, choices, out=out, mode='raise')
assert_equal(out, np.array([[ 10, -10, 10],
[-10, 10, -10],
[ 10, -10, 10]]))
def test_flatiter__array__(self):
a = np.arange(9).reshape(3,3)
b = a.T.flat
c = b.__array__()
# triggers the WRITEBACKIFCOPY resolution, assuming refcount semantics
del c
def test_dot_out(self):
# if HAVE_CBLAS, will use WRITEBACKIFCOPY
a = np.arange(9, dtype=float).reshape(3,3)
b = np.dot(a, a, out=a)
assert_equal(b, np.array([[15, 18, 21], [42, 54, 66], [69, 90, 111]]))
def test_view_assign(self):
from numpy.core.multiarray_tests import npy_create_writebackifcopy, npy_resolve
arr = np.arange(9).reshape(3, 3).T
arr_wb = npy_create_writebackifcopy(arr)
assert_(arr_wb.flags.writebackifcopy)
assert_(arr_wb.base is arr)
arr_wb[...] = -100
npy_resolve(arr_wb)
# arr changes after resolve, even though we assigned to arr_wb
assert_equal(arr, -100)
# after resolve, the two arrays no longer reference each other
assert_(arr_wb.ctypes.data != 0)
assert_equal(arr_wb.base, None)
# assigning to arr_wb does not get transfered to arr
arr_wb[...] = 100
assert_equal(arr, -100)
def test_view_discard_refcount(self):
from numpy.core.multiarray_tests import npy_create_writebackifcopy, npy_discard
arr = np.arange(9).reshape(3, 3).T
orig = arr.copy()
if HAS_REFCOUNT:
arr_cnt = sys.getrefcount(arr)
arr_wb = npy_create_writebackifcopy(arr)
assert_(arr_wb.flags.writebackifcopy)
assert_(arr_wb.base is arr)
arr_wb[...] = -100
npy_discard(arr_wb)
# arr remains unchanged after discard
assert_equal(arr, orig)
# after discard, the two arrays no longer reference each other
assert_(arr_wb.ctypes.data != 0)
assert_equal(arr_wb.base, None)
if HAS_REFCOUNT:
assert_equal(arr_cnt, sys.getrefcount(arr))
# assigning to arr_wb does not get transfered to arr
arr_wb[...] = 100
assert_equal(arr, orig)
class TestArange(object):
def test_infinite(self):
assert_raises_regex(
ValueError, "size exceeded",
np.arange, 0, np.inf
)
def test_nan_step(self):
assert_raises_regex(
ValueError, "cannot compute length",
np.arange, 0, 1, np.nan
)
def test_zero_step(self):
assert_raises(ZeroDivisionError, np.arange, 0, 10, 0)
assert_raises(ZeroDivisionError, np.arange, 0.0, 10.0, 0.0)
# empty range
assert_raises(ZeroDivisionError, np.arange, 0, 0, 0)
assert_raises(ZeroDivisionError, np.arange, 0.0, 0.0, 0.0)
def test_orderconverter_with_nonASCII_unicode_ordering():
# gh-7475
a = np.arange(5)
assert_raises(ValueError, a.flatten, order=u'\xe2')
def test_equal_override():
# gh-9153: ndarray.__eq__ uses special logic for structured arrays, which
# did not respect overrides with __array_priority__ or __array_ufunc__.
# The PR fixed this for __array_priority__ and __array_ufunc__ = None.
class MyAlwaysEqual(object):
def __eq__(self, other):
return "eq"
def __ne__(self, other):
return "ne"
class MyAlwaysEqualOld(MyAlwaysEqual):
__array_priority__ = 10000
class MyAlwaysEqualNew(MyAlwaysEqual):
__array_ufunc__ = None
array = np.array([(0, 1), (2, 3)], dtype='i4,i4')
for my_always_equal_cls in MyAlwaysEqualOld, MyAlwaysEqualNew:
my_always_equal = my_always_equal_cls()
assert_equal(my_always_equal == array, 'eq')
assert_equal(array == my_always_equal, 'eq')
assert_equal(my_always_equal != array, 'ne')
assert_equal(array != my_always_equal, 'ne')
def test_npymath_complex():
# Smoketest npymath functions
from numpy.core.multiarray_tests import (
npy_cabs, npy_carg)
funcs = {npy_cabs: np.absolute,
npy_carg: np.angle}
vals = (1, np.inf, -np.inf, np.nan)
types = (np.complex64, np.complex128, np.clongdouble)
for fun, npfun in funcs.items():
for x, y in itertools.product(vals, vals):
for t in types:
z = t(complex(x, y))
got = fun(z)
expected = npfun(z)
assert_allclose(got, expected)
def test_npymath_real():
# Smoketest npymath functions
from numpy.core.multiarray_tests import (
npy_log10, npy_cosh, npy_sinh, npy_tan, npy_tanh)
funcs = {npy_log10: np.log10,
npy_cosh: np.cosh,
npy_sinh: np.sinh,
npy_tan: np.tan,
npy_tanh: np.tanh}
vals = (1, np.inf, -np.inf, np.nan)
types = (np.float32, np.float64, np.longdouble)
with np.errstate(all='ignore'):
for fun, npfun in funcs.items():
for x, t in itertools.product(vals, types):
z = t(x)
got = fun(z)
expected = npfun(z)
assert_allclose(got, expected)
if __name__ == "__main__":
run_module_suite()
| 276,524 | 36.332928 | 588 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/tests/test_mem_overlap.py
|
from __future__ import division, absolute_import, print_function
import sys
import itertools
import numpy as np
from numpy.testing import (run_module_suite, assert_, assert_raises, assert_equal,
assert_array_equal, assert_allclose, dec)
from numpy.core.multiarray_tests import solve_diophantine, internal_overlap
from numpy.core import umath_tests
from numpy.lib.stride_tricks import as_strided
from numpy.compat import long
if sys.version_info[0] >= 3:
xrange = range
ndims = 2
size = 10
shape = tuple([size] * ndims)
MAY_SHARE_BOUNDS = 0
MAY_SHARE_EXACT = -1
def _indices_for_nelems(nelems):
"""Returns slices of length nelems, from start onwards, in direction sign."""
if nelems == 0:
return [size // 2] # int index
res = []
for step in (1, 2):
for sign in (-1, 1):
start = size // 2 - nelems * step * sign // 2
stop = start + nelems * step * sign
res.append(slice(start, stop, step * sign))
return res
def _indices_for_axis():
"""Returns (src, dst) pairs of indices."""
res = []
for nelems in (0, 2, 3):
ind = _indices_for_nelems(nelems)
# no itertools.product available in Py2.4
res.extend([(a, b) for a in ind for b in ind]) # all assignments of size "nelems"
return res
def _indices(ndims):
"""Returns ((axis0_src, axis0_dst), (axis1_src, axis1_dst), ... ) index pairs."""
ind = _indices_for_axis()
# no itertools.product available in Py2.4
res = [[]]
for i in range(ndims):
newres = []
for elem in ind:
for others in res:
newres.append([elem] + others)
res = newres
return res
def _check_assignment(srcidx, dstidx):
"""Check assignment arr[dstidx] = arr[srcidx] works."""
arr = np.arange(np.product(shape)).reshape(shape)
cpy = arr.copy()
cpy[dstidx] = arr[srcidx]
arr[dstidx] = arr[srcidx]
assert_(np.all(arr == cpy),
'assigning arr[%s] = arr[%s]' % (dstidx, srcidx))
def test_overlapping_assignments():
# Test automatically generated assignments which overlap in memory.
inds = _indices(ndims)
for ind in inds:
srcidx = tuple([a[0] for a in ind])
dstidx = tuple([a[1] for a in ind])
yield _check_assignment, srcidx, dstidx
@dec.slow
def test_diophantine_fuzz():
# Fuzz test the diophantine solver
rng = np.random.RandomState(1234)
max_int = np.iinfo(np.intp).max
for ndim in range(10):
feasible_count = 0
infeasible_count = 0
min_count = 500//(ndim + 1)
while min(feasible_count, infeasible_count) < min_count:
# Ensure big and small integer problems
A_max = 1 + rng.randint(0, 11, dtype=np.intp)**6
U_max = rng.randint(0, 11, dtype=np.intp)**6
A_max = min(max_int, A_max)
U_max = min(max_int-1, U_max)
A = tuple(int(rng.randint(1, A_max+1, dtype=np.intp))
for j in range(ndim))
U = tuple(int(rng.randint(0, U_max+2, dtype=np.intp))
for j in range(ndim))
b_ub = min(max_int-2, sum(a*ub for a, ub in zip(A, U)))
b = rng.randint(-1, b_ub+2, dtype=np.intp)
if ndim == 0 and feasible_count < min_count:
b = 0
X = solve_diophantine(A, U, b)
if X is None:
# Check the simplified decision problem agrees
X_simplified = solve_diophantine(A, U, b, simplify=1)
assert_(X_simplified is None, (A, U, b, X_simplified))
# Check no solution exists (provided the problem is
# small enough so that brute force checking doesn't
# take too long)
try:
ranges = tuple(xrange(0, a*ub+1, a) for a, ub in zip(A, U))
except OverflowError:
# xrange on 32-bit Python 2 may overflow
continue
size = 1
for r in ranges:
size *= len(r)
if size < 100000:
assert_(not any(sum(w) == b for w in itertools.product(*ranges)))
infeasible_count += 1
else:
# Check the simplified decision problem agrees
X_simplified = solve_diophantine(A, U, b, simplify=1)
assert_(X_simplified is not None, (A, U, b, X_simplified))
# Check validity
assert_(sum(a*x for a, x in zip(A, X)) == b)
assert_(all(0 <= x <= ub for x, ub in zip(X, U)))
feasible_count += 1
def test_diophantine_overflow():
# Smoke test integer overflow detection
max_intp = np.iinfo(np.intp).max
max_int64 = np.iinfo(np.int64).max
if max_int64 <= max_intp:
# Check that the algorithm works internally in 128-bit;
# solving this problem requires large intermediate numbers
A = (max_int64//2, max_int64//2 - 10)
U = (max_int64//2, max_int64//2 - 10)
b = 2*(max_int64//2) - 10
assert_equal(solve_diophantine(A, U, b), (1, 1))
def check_may_share_memory_exact(a, b):
got = np.may_share_memory(a, b, max_work=MAY_SHARE_EXACT)
assert_equal(np.may_share_memory(a, b),
np.may_share_memory(a, b, max_work=MAY_SHARE_BOUNDS))
a.fill(0)
b.fill(0)
a.fill(1)
exact = b.any()
err_msg = ""
if got != exact:
err_msg = " " + "\n ".join([
"base_a - base_b = %r" % (a.__array_interface__['data'][0] - b.__array_interface__['data'][0],),
"shape_a = %r" % (a.shape,),
"shape_b = %r" % (b.shape,),
"strides_a = %r" % (a.strides,),
"strides_b = %r" % (b.strides,),
"size_a = %r" % (a.size,),
"size_b = %r" % (b.size,)
])
assert_equal(got, exact, err_msg=err_msg)
def test_may_share_memory_manual():
# Manual test cases for may_share_memory
# Base arrays
xs0 = [
np.zeros([13, 21, 23, 22], dtype=np.int8),
np.zeros([13, 21, 23*2, 22], dtype=np.int8)[:,:,::2,:]
]
# Generate all negative stride combinations
xs = []
for x in xs0:
for ss in itertools.product(*(([slice(None), slice(None, None, -1)],)*4)):
xp = x[ss]
xs.append(xp)
for x in xs:
# The default is a simple extent check
assert_(np.may_share_memory(x[:,0,:], x[:,1,:]))
assert_(np.may_share_memory(x[:,0,:], x[:,1,:], max_work=None))
# Exact checks
check_may_share_memory_exact(x[:,0,:], x[:,1,:])
check_may_share_memory_exact(x[:,::7], x[:,3::3])
try:
xp = x.ravel()
if xp.flags.owndata:
continue
xp = xp.view(np.int16)
except ValueError:
continue
# 0-size arrays cannot overlap
check_may_share_memory_exact(x.ravel()[6:6],
xp.reshape(13, 21, 23, 11)[:,::7])
# Test itemsize is dealt with
check_may_share_memory_exact(x[:,::7],
xp.reshape(13, 21, 23, 11))
check_may_share_memory_exact(x[:,::7],
xp.reshape(13, 21, 23, 11)[:,3::3])
check_may_share_memory_exact(x.ravel()[6:7],
xp.reshape(13, 21, 23, 11)[:,::7])
# Check unit size
x = np.zeros([1], dtype=np.int8)
check_may_share_memory_exact(x, x)
check_may_share_memory_exact(x, x.copy())
def iter_random_view_pairs(x, same_steps=True, equal_size=False):
rng = np.random.RandomState(1234)
if equal_size and same_steps:
raise ValueError()
def random_slice(n, step):
start = rng.randint(0, n+1, dtype=np.intp)
stop = rng.randint(start, n+1, dtype=np.intp)
if rng.randint(0, 2, dtype=np.intp) == 0:
stop, start = start, stop
step *= -1
return slice(start, stop, step)
def random_slice_fixed_size(n, step, size):
start = rng.randint(0, n+1 - size*step)
stop = start + (size-1)*step + 1
if rng.randint(0, 2) == 0:
stop, start = start-1, stop-1
if stop < 0:
stop = None
step *= -1
return slice(start, stop, step)
# First a few regular views
yield x, x
for j in range(1, 7, 3):
yield x[j:], x[:-j]
yield x[...,j:], x[...,:-j]
# An array with zero stride internal overlap
strides = list(x.strides)
strides[0] = 0
xp = as_strided(x, shape=x.shape, strides=strides)
yield x, xp
yield xp, xp
# An array with non-zero stride internal overlap
strides = list(x.strides)
if strides[0] > 1:
strides[0] = 1
xp = as_strided(x, shape=x.shape, strides=strides)
yield x, xp
yield xp, xp
# Then discontiguous views
while True:
steps = tuple(rng.randint(1, 11, dtype=np.intp)
if rng.randint(0, 5, dtype=np.intp) == 0 else 1
for j in range(x.ndim))
s1 = tuple(random_slice(p, s) for p, s in zip(x.shape, steps))
t1 = np.arange(x.ndim)
rng.shuffle(t1)
if equal_size:
t2 = t1
else:
t2 = np.arange(x.ndim)
rng.shuffle(t2)
a = x[s1]
if equal_size:
if a.size == 0:
continue
steps2 = tuple(rng.randint(1, max(2, p//(1+pa)))
if rng.randint(0, 5) == 0 else 1
for p, s, pa in zip(x.shape, s1, a.shape))
s2 = tuple(random_slice_fixed_size(p, s, pa)
for p, s, pa in zip(x.shape, steps2, a.shape))
elif same_steps:
steps2 = steps
else:
steps2 = tuple(rng.randint(1, 11, dtype=np.intp)
if rng.randint(0, 5, dtype=np.intp) == 0 else 1
for j in range(x.ndim))
if not equal_size:
s2 = tuple(random_slice(p, s) for p, s in zip(x.shape, steps2))
a = a.transpose(t1)
b = x[s2].transpose(t2)
yield a, b
def check_may_share_memory_easy_fuzz(get_max_work, same_steps, min_count):
# Check that overlap problems with common strides are solved with
# little work.
x = np.zeros([17,34,71,97], dtype=np.int16)
feasible = 0
infeasible = 0
pair_iter = iter_random_view_pairs(x, same_steps)
while min(feasible, infeasible) < min_count:
a, b = next(pair_iter)
bounds_overlap = np.may_share_memory(a, b)
may_share_answer = np.may_share_memory(a, b)
easy_answer = np.may_share_memory(a, b, max_work=get_max_work(a, b))
exact_answer = np.may_share_memory(a, b, max_work=MAY_SHARE_EXACT)
if easy_answer != exact_answer:
# assert_equal is slow...
assert_equal(easy_answer, exact_answer)
if may_share_answer != bounds_overlap:
assert_equal(may_share_answer, bounds_overlap)
if bounds_overlap:
if exact_answer:
feasible += 1
else:
infeasible += 1
@dec.slow
def test_may_share_memory_easy_fuzz():
# Check that overlap problems with common strides are always
# solved with little work.
check_may_share_memory_easy_fuzz(get_max_work=lambda a, b: 1,
same_steps=True,
min_count=2000)
@dec.slow
def test_may_share_memory_harder_fuzz():
# Overlap problems with not necessarily common strides take more
# work.
#
# The work bound below can't be reduced much. Harder problems can
# also exist but not be detected here, as the set of problems
# comes from RNG.
check_may_share_memory_easy_fuzz(get_max_work=lambda a, b: max(a.size, b.size)//2,
same_steps=False,
min_count=2000)
def test_shares_memory_api():
x = np.zeros([4, 5, 6], dtype=np.int8)
assert_equal(np.shares_memory(x, x), True)
assert_equal(np.shares_memory(x, x.copy()), False)
a = x[:,::2,::3]
b = x[:,::3,::2]
assert_equal(np.shares_memory(a, b), True)
assert_equal(np.shares_memory(a, b, max_work=None), True)
assert_raises(np.TooHardError, np.shares_memory, a, b, max_work=1)
assert_raises(np.TooHardError, np.shares_memory, a, b, max_work=long(1))
def test_may_share_memory_bad_max_work():
x = np.zeros([1])
assert_raises(OverflowError, np.may_share_memory, x, x, max_work=10**100)
assert_raises(OverflowError, np.shares_memory, x, x, max_work=10**100)
def test_internal_overlap_diophantine():
def check(A, U, exists=None):
X = solve_diophantine(A, U, 0, require_ub_nontrivial=1)
if exists is None:
exists = (X is not None)
if X is not None:
assert_(sum(a*x for a, x in zip(A, X)) == sum(a*u//2 for a, u in zip(A, U)))
assert_(all(0 <= x <= u for x, u in zip(X, U)))
assert_(any(x != u//2 for x, u in zip(X, U)))
if exists:
assert_(X is not None, repr(X))
else:
assert_(X is None, repr(X))
# Smoke tests
check((3, 2), (2*2, 3*2), exists=True)
check((3*2, 2), (15*2, (3-1)*2), exists=False)
def test_internal_overlap_slices():
# Slicing an array never generates internal overlap
x = np.zeros([17,34,71,97], dtype=np.int16)
rng = np.random.RandomState(1234)
def random_slice(n, step):
start = rng.randint(0, n+1, dtype=np.intp)
stop = rng.randint(start, n+1, dtype=np.intp)
if rng.randint(0, 2, dtype=np.intp) == 0:
stop, start = start, stop
step *= -1
return slice(start, stop, step)
cases = 0
min_count = 5000
while cases < min_count:
steps = tuple(rng.randint(1, 11, dtype=np.intp)
if rng.randint(0, 5, dtype=np.intp) == 0 else 1
for j in range(x.ndim))
t1 = np.arange(x.ndim)
rng.shuffle(t1)
s1 = tuple(random_slice(p, s) for p, s in zip(x.shape, steps))
a = x[s1].transpose(t1)
assert_(not internal_overlap(a))
cases += 1
def check_internal_overlap(a, manual_expected=None):
got = internal_overlap(a)
# Brute-force check
m = set()
ranges = tuple(xrange(n) for n in a.shape)
for v in itertools.product(*ranges):
offset = sum(s*w for s, w in zip(a.strides, v))
if offset in m:
expected = True
break
else:
m.add(offset)
else:
expected = False
# Compare
if got != expected:
assert_equal(got, expected, err_msg=repr((a.strides, a.shape)))
if manual_expected is not None and expected != manual_expected:
assert_equal(expected, manual_expected)
return got
def test_internal_overlap_manual():
# Stride tricks can construct arrays with internal overlap
# We don't care about memory bounds, the array is not
# read/write accessed
x = np.arange(1).astype(np.int8)
# Check low-dimensional special cases
check_internal_overlap(x, False) # 1-dim
check_internal_overlap(x.reshape([]), False) # 0-dim
a = as_strided(x, strides=(3, 4), shape=(4, 4))
check_internal_overlap(a, False)
a = as_strided(x, strides=(3, 4), shape=(5, 4))
check_internal_overlap(a, True)
a = as_strided(x, strides=(0,), shape=(0,))
check_internal_overlap(a, False)
a = as_strided(x, strides=(0,), shape=(1,))
check_internal_overlap(a, False)
a = as_strided(x, strides=(0,), shape=(2,))
check_internal_overlap(a, True)
a = as_strided(x, strides=(0, -9993), shape=(87, 22))
check_internal_overlap(a, True)
a = as_strided(x, strides=(0, -9993), shape=(1, 22))
check_internal_overlap(a, False)
a = as_strided(x, strides=(0, -9993), shape=(0, 22))
check_internal_overlap(a, False)
def test_internal_overlap_fuzz():
# Fuzz check; the brute-force check is fairly slow
x = np.arange(1).astype(np.int8)
overlap = 0
no_overlap = 0
min_count = 100
rng = np.random.RandomState(1234)
while min(overlap, no_overlap) < min_count:
ndim = rng.randint(1, 4, dtype=np.intp)
strides = tuple(rng.randint(-1000, 1000, dtype=np.intp)
for j in range(ndim))
shape = tuple(rng.randint(1, 30, dtype=np.intp)
for j in range(ndim))
a = as_strided(x, strides=strides, shape=shape)
result = check_internal_overlap(a)
if result:
overlap += 1
else:
no_overlap += 1
def test_non_ndarray_inputs():
# Regression check for gh-5604
class MyArray(object):
def __init__(self, data):
self.data = data
@property
def __array_interface__(self):
return self.data.__array_interface__
class MyArray2(object):
def __init__(self, data):
self.data = data
def __array__(self):
return self.data
for cls in [MyArray, MyArray2]:
x = np.arange(5)
assert_(np.may_share_memory(cls(x[::2]), x[1::2]))
assert_(not np.shares_memory(cls(x[::2]), x[1::2]))
assert_(np.shares_memory(cls(x[1::3]), x[::2]))
assert_(np.may_share_memory(cls(x[1::3]), x[::2]))
def view_element_first_byte(x):
"""Construct an array viewing the first byte of each element of `x`"""
from numpy.lib.stride_tricks import DummyArray
interface = dict(x.__array_interface__)
interface['typestr'] = '|b1'
interface['descr'] = [('', '|b1')]
return np.asarray(DummyArray(interface, x))
def assert_copy_equivalent(operation, args, out, **kwargs):
"""
Check that operation(*args, out=out) produces results
equivalent to out[...] = operation(*args, out=out.copy())
"""
kwargs['out'] = out
kwargs2 = dict(kwargs)
kwargs2['out'] = out.copy()
out_orig = out.copy()
out[...] = operation(*args, **kwargs2)
expected = out.copy()
out[...] = out_orig
got = operation(*args, **kwargs).copy()
if (got != expected).any():
assert_equal(got, expected)
class TestUFunc(object):
"""
Test ufunc call memory overlap handling
"""
def check_unary_fuzz(self, operation, get_out_axis_size, dtype=np.int16,
count=5000):
shapes = [7, 13, 8, 21, 29, 32]
rng = np.random.RandomState(1234)
for ndim in range(1, 6):
x = rng.randint(0, 2**16, size=shapes[:ndim]).astype(dtype)
it = iter_random_view_pairs(x, same_steps=False, equal_size=True)
min_count = count // (ndim + 1)**2
overlapping = 0
while overlapping < min_count:
a, b = next(it)
a_orig = a.copy()
b_orig = b.copy()
if get_out_axis_size is None:
assert_copy_equivalent(operation, [a], out=b)
if np.shares_memory(a, b):
overlapping += 1
else:
for axis in itertools.chain(range(ndim), [None]):
a[...] = a_orig
b[...] = b_orig
# Determine size for reduction axis (None if scalar)
outsize, scalarize = get_out_axis_size(a, b, axis)
if outsize == 'skip':
continue
# Slice b to get an output array of the correct size
sl = [slice(None)] * ndim
if axis is None:
if outsize is None:
sl = [slice(0, 1)] + [0]*(ndim - 1)
else:
sl = [slice(0, outsize)] + [0]*(ndim - 1)
else:
if outsize is None:
k = b.shape[axis]//2
if ndim == 1:
sl[axis] = slice(k, k + 1)
else:
sl[axis] = k
else:
assert b.shape[axis] >= outsize
sl[axis] = slice(0, outsize)
b_out = b[tuple(sl)]
if scalarize:
b_out = b_out.reshape([])
if np.shares_memory(a, b_out):
overlapping += 1
# Check result
assert_copy_equivalent(operation, [a], out=b_out, axis=axis)
@dec.slow
def test_unary_ufunc_call_fuzz(self):
self.check_unary_fuzz(np.invert, None, np.int16)
def test_binary_ufunc_accumulate_fuzz(self):
def get_out_axis_size(a, b, axis):
if axis is None:
if a.ndim == 1:
return a.size, False
else:
return 'skip', False # accumulate doesn't support this
else:
return a.shape[axis], False
self.check_unary_fuzz(np.add.accumulate, get_out_axis_size,
dtype=np.int16, count=500)
def test_binary_ufunc_reduce_fuzz(self):
def get_out_axis_size(a, b, axis):
return None, (axis is None or a.ndim == 1)
self.check_unary_fuzz(np.add.reduce, get_out_axis_size,
dtype=np.int16, count=500)
def test_binary_ufunc_reduceat_fuzz(self):
def get_out_axis_size(a, b, axis):
if axis is None:
if a.ndim == 1:
return a.size, False
else:
return 'skip', False # reduceat doesn't support this
else:
return a.shape[axis], False
def do_reduceat(a, out, axis):
if axis is None:
size = len(a)
step = size//len(out)
else:
size = a.shape[axis]
step = a.shape[axis] // out.shape[axis]
idx = np.arange(0, size, step)
return np.add.reduceat(a, idx, out=out, axis=axis)
self.check_unary_fuzz(do_reduceat, get_out_axis_size,
dtype=np.int16, count=500)
def test_binary_ufunc_reduceat_manual(self):
def check(ufunc, a, ind, out):
c1 = ufunc.reduceat(a.copy(), ind.copy(), out=out.copy())
c2 = ufunc.reduceat(a, ind, out=out)
assert_array_equal(c1, c2)
# Exactly same input/output arrays
a = np.arange(10000, dtype=np.int16)
check(np.add, a, a[::-1].copy(), a)
# Overlap with index
a = np.arange(10000, dtype=np.int16)
check(np.add, a, a[::-1], a)
def test_unary_gufunc_fuzz(self):
shapes = [7, 13, 8, 21, 29, 32]
gufunc = umath_tests.euclidean_pdist
rng = np.random.RandomState(1234)
for ndim in range(2, 6):
x = rng.rand(*shapes[:ndim])
it = iter_random_view_pairs(x, same_steps=False, equal_size=True)
min_count = 500 // (ndim + 1)**2
overlapping = 0
while overlapping < min_count:
a, b = next(it)
if min(a.shape[-2:]) < 2 or min(b.shape[-2:]) < 2 or a.shape[-1] < 2:
continue
# Ensure the shapes are so that euclidean_pdist is happy
if b.shape[-1] > b.shape[-2]:
b = b[...,0,:]
else:
b = b[...,:,0]
n = a.shape[-2]
p = n * (n - 1) // 2
if p <= b.shape[-1] and p > 0:
b = b[...,:p]
else:
n = max(2, int(np.sqrt(b.shape[-1]))//2)
p = n * (n - 1) // 2
a = a[...,:n,:]
b = b[...,:p]
# Call
if np.shares_memory(a, b):
overlapping += 1
with np.errstate(over='ignore', invalid='ignore'):
assert_copy_equivalent(gufunc, [a], out=b)
def test_ufunc_at_manual(self):
def check(ufunc, a, ind, b=None):
a0 = a.copy()
if b is None:
ufunc.at(a0, ind.copy())
c1 = a0.copy()
ufunc.at(a, ind)
c2 = a.copy()
else:
ufunc.at(a0, ind.copy(), b.copy())
c1 = a0.copy()
ufunc.at(a, ind, b)
c2 = a.copy()
assert_array_equal(c1, c2)
# Overlap with index
a = np.arange(10000, dtype=np.int16)
check(np.invert, a[::-1], a)
# Overlap with second data array
a = np.arange(100, dtype=np.int16)
ind = np.arange(0, 100, 2, dtype=np.int16)
check(np.add, a, ind, a[25:75])
def test_unary_ufunc_1d_manual(self):
# Exercise branches in PyArray_EQUIVALENTLY_ITERABLE
def check(a, b):
a_orig = a.copy()
b_orig = b.copy()
b0 = b.copy()
c1 = ufunc(a, out=b0)
c2 = ufunc(a, out=b)
assert_array_equal(c1, c2)
# Trigger "fancy ufunc loop" code path
mask = view_element_first_byte(b).view(np.bool_)
a[...] = a_orig
b[...] = b_orig
c1 = ufunc(a, out=b.copy(), where=mask.copy()).copy()
a[...] = a_orig
b[...] = b_orig
c2 = ufunc(a, out=b, where=mask.copy()).copy()
# Also, mask overlapping with output
a[...] = a_orig
b[...] = b_orig
c3 = ufunc(a, out=b, where=mask).copy()
assert_array_equal(c1, c2)
assert_array_equal(c1, c3)
dtypes = [np.int8, np.int16, np.int32, np.int64, np.float32,
np.float64, np.complex64, np.complex128]
dtypes = [np.dtype(x) for x in dtypes]
for dtype in dtypes:
if np.issubdtype(dtype, np.integer):
ufunc = np.invert
else:
ufunc = np.reciprocal
n = 1000
k = 10
indices = [
np.index_exp[:n],
np.index_exp[k:k+n],
np.index_exp[n-1::-1],
np.index_exp[k+n-1:k-1:-1],
np.index_exp[:2*n:2],
np.index_exp[k:k+2*n:2],
np.index_exp[2*n-1::-2],
np.index_exp[k+2*n-1:k-1:-2],
]
for xi, yi in itertools.product(indices, indices):
v = np.arange(1, 1 + n*2 + k, dtype=dtype)
x = v[xi]
y = v[yi]
with np.errstate(all='ignore'):
check(x, y)
# Scalar cases
check(x[:1], y)
check(x[-1:], y)
check(x[:1].reshape([]), y)
check(x[-1:].reshape([]), y)
def test_unary_ufunc_where_same(self):
# Check behavior at wheremask overlap
ufunc = np.invert
def check(a, out, mask):
c1 = ufunc(a, out=out.copy(), where=mask.copy())
c2 = ufunc(a, out=out, where=mask)
assert_array_equal(c1, c2)
# Check behavior with same input and output arrays
x = np.arange(100).astype(np.bool_)
check(x, x, x)
check(x, x.copy(), x)
check(x, x, x.copy())
@dec.slow
def test_binary_ufunc_1d_manual(self):
ufunc = np.add
def check(a, b, c):
c0 = c.copy()
c1 = ufunc(a, b, out=c0)
c2 = ufunc(a, b, out=c)
assert_array_equal(c1, c2)
for dtype in [np.int8, np.int16, np.int32, np.int64,
np.float32, np.float64, np.complex64, np.complex128]:
# Check different data dependency orders
n = 1000
k = 10
indices = []
for p in [1, 2]:
indices.extend([
np.index_exp[:p*n:p],
np.index_exp[k:k+p*n:p],
np.index_exp[p*n-1::-p],
np.index_exp[k+p*n-1:k-1:-p],
])
for x, y, z in itertools.product(indices, indices, indices):
v = np.arange(6*n).astype(dtype)
x = v[x]
y = v[y]
z = v[z]
check(x, y, z)
# Scalar cases
check(x[:1], y, z)
check(x[-1:], y, z)
check(x[:1].reshape([]), y, z)
check(x[-1:].reshape([]), y, z)
check(x, y[:1], z)
check(x, y[-1:], z)
check(x, y[:1].reshape([]), z)
check(x, y[-1:].reshape([]), z)
def test_inplace_op_simple_manual(self):
rng = np.random.RandomState(1234)
x = rng.rand(200, 200) # bigger than bufsize
x += x.T
assert_array_equal(x - x.T, 0)
if __name__ == "__main__":
run_module_suite()
| 29,564 | 29.990566 | 108 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/tests/test_longdouble.py
|
from __future__ import division, absolute_import, print_function
import locale
import numpy as np
from numpy.testing import (
run_module_suite, assert_, assert_equal, dec, assert_raises,
assert_array_equal, temppath,
)
from .test_print import in_foreign_locale
LD_INFO = np.finfo(np.longdouble)
longdouble_longer_than_double = (LD_INFO.eps < np.finfo(np.double).eps)
_o = 1 + LD_INFO.eps
string_to_longdouble_inaccurate = (_o != np.longdouble(repr(_o)))
del _o
def test_scalar_extraction():
"""Confirm that extracting a value doesn't convert to python float"""
o = 1 + LD_INFO.eps
a = np.array([o, o, o])
assert_equal(a[1], o)
# Conversions string -> long double
# 0.1 not exactly representable in base 2 floating point.
repr_precision = len(repr(np.longdouble(0.1)))
# +2 from macro block starting around line 842 in scalartypes.c.src.
@dec.skipif(LD_INFO.precision + 2 >= repr_precision,
"repr precision not enough to show eps")
def test_repr_roundtrip():
# We will only see eps in repr if within printing precision.
o = 1 + LD_INFO.eps
assert_equal(np.longdouble(repr(o)), o, "repr was %s" % repr(o))
def test_unicode():
np.longdouble(u"1.2")
def test_string():
np.longdouble("1.2")
def test_bytes():
np.longdouble(b"1.2")
@in_foreign_locale
def test_fromstring_foreign_repr():
f = 1.234
a = np.fromstring(repr(f), dtype=float, sep=" ")
assert_equal(a[0], f)
@dec.knownfailureif(string_to_longdouble_inaccurate, "Need strtold_l")
def test_repr_roundtrip_bytes():
o = 1 + LD_INFO.eps
assert_equal(np.longdouble(repr(o).encode("ascii")), o)
@in_foreign_locale
def test_repr_roundtrip_foreign():
o = 1.5
assert_equal(o, np.longdouble(repr(o)))
def test_bogus_string():
assert_raises(ValueError, np.longdouble, "spam")
assert_raises(ValueError, np.longdouble, "1.0 flub")
@dec.knownfailureif(string_to_longdouble_inaccurate, "Need strtold_l")
def test_fromstring():
o = 1 + LD_INFO.eps
s = (" " + repr(o))*5
a = np.array([o]*5)
assert_equal(np.fromstring(s, sep=" ", dtype=np.longdouble), a,
err_msg="reading '%s'" % s)
@in_foreign_locale
def test_fromstring_best_effort_float():
assert_equal(np.fromstring("1,234", dtype=float, sep=" "),
np.array([1.]))
@in_foreign_locale
def test_fromstring_best_effort():
assert_equal(np.fromstring("1,234", dtype=np.longdouble, sep=" "),
np.array([1.]))
def test_fromstring_bogus():
assert_equal(np.fromstring("1. 2. 3. flop 4.", dtype=float, sep=" "),
np.array([1., 2., 3.]))
def test_fromstring_empty():
assert_equal(np.fromstring("xxxxx", sep="x"),
np.array([]))
def test_fromstring_missing():
assert_equal(np.fromstring("1xx3x4x5x6", sep="x"),
np.array([1]))
class TestFileBased(object):
ldbl = 1 + LD_INFO.eps
tgt = np.array([ldbl]*5)
out = ''.join([repr(t) + '\n' for t in tgt])
def test_fromfile_bogus(self):
with temppath() as path:
with open(path, 'wt') as f:
f.write("1. 2. 3. flop 4.\n")
res = np.fromfile(path, dtype=float, sep=" ")
assert_equal(res, np.array([1., 2., 3.]))
@dec.knownfailureif(string_to_longdouble_inaccurate, "Need strtold_l")
def test_fromfile(self):
with temppath() as path:
with open(path, 'wt') as f:
f.write(self.out)
res = np.fromfile(path, dtype=np.longdouble, sep="\n")
assert_equal(res, self.tgt)
@dec.knownfailureif(string_to_longdouble_inaccurate, "Need strtold_l")
def test_genfromtxt(self):
with temppath() as path:
with open(path, 'wt') as f:
f.write(self.out)
res = np.genfromtxt(path, dtype=np.longdouble)
assert_equal(res, self.tgt)
@dec.knownfailureif(string_to_longdouble_inaccurate, "Need strtold_l")
def test_loadtxt(self):
with temppath() as path:
with open(path, 'wt') as f:
f.write(self.out)
res = np.loadtxt(path, dtype=np.longdouble)
assert_equal(res, self.tgt)
@dec.knownfailureif(string_to_longdouble_inaccurate, "Need strtold_l")
def test_tofile_roundtrip(self):
with temppath() as path:
self.tgt.tofile(path, sep=" ")
res = np.fromfile(path, dtype=np.longdouble, sep=" ")
assert_equal(res, self.tgt)
@in_foreign_locale
def test_fromstring_foreign():
s = "1.234"
a = np.fromstring(s, dtype=np.longdouble, sep=" ")
assert_equal(a[0], np.longdouble(s))
@in_foreign_locale
def test_fromstring_foreign_sep():
a = np.array([1, 2, 3, 4])
b = np.fromstring("1,2,3,4,", dtype=np.longdouble, sep=",")
assert_array_equal(a, b)
@in_foreign_locale
def test_fromstring_foreign_value():
b = np.fromstring("1,234", dtype=np.longdouble, sep=" ")
assert_array_equal(b[0], 1)
# Conversions long double -> string
def test_repr_exact():
o = 1 + LD_INFO.eps
assert_(repr(o) != '1')
@dec.knownfailureif(longdouble_longer_than_double, "BUG #2376")
@dec.knownfailureif(string_to_longdouble_inaccurate, "Need strtold_l")
def test_format():
o = 1 + LD_INFO.eps
assert_("{0:.40g}".format(o) != '1')
@dec.knownfailureif(longdouble_longer_than_double, "BUG #2376")
@dec.knownfailureif(string_to_longdouble_inaccurate, "Need strtold_l")
def test_percent():
o = 1 + LD_INFO.eps
assert_("%.40g" % o != '1')
@dec.knownfailureif(longdouble_longer_than_double, "array repr problem")
@dec.knownfailureif(string_to_longdouble_inaccurate, "Need strtold_l")
def test_array_repr():
o = 1 + LD_INFO.eps
a = np.array([o])
b = np.array([1], dtype=np.longdouble)
if not np.all(a != b):
raise ValueError("precision loss creating arrays")
assert_(repr(a) != repr(b))
if __name__ == "__main__":
run_module_suite()
| 5,960 | 26.985915 | 74 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/tests/test_defchararray.py
|
from __future__ import division, absolute_import, print_function
import sys
import numpy as np
from numpy.core.multiarray import _vec_string
from numpy.testing import (
run_module_suite, assert_, assert_equal, assert_array_equal, assert_raises,
suppress_warnings,
)
kw_unicode_true = {'unicode': True} # make 2to3 work properly
kw_unicode_false = {'unicode': False}
class TestBasic(object):
def test_from_object_array(self):
A = np.array([['abc', 2],
['long ', '0123456789']], dtype='O')
B = np.char.array(A)
assert_equal(B.dtype.itemsize, 10)
assert_array_equal(B, [[b'abc', b'2'],
[b'long', b'0123456789']])
def test_from_object_array_unicode(self):
A = np.array([['abc', u'Sigma \u03a3'],
['long ', '0123456789']], dtype='O')
assert_raises(ValueError, np.char.array, (A,))
B = np.char.array(A, **kw_unicode_true)
assert_equal(B.dtype.itemsize, 10 * np.array('a', 'U').dtype.itemsize)
assert_array_equal(B, [['abc', u'Sigma \u03a3'],
['long', '0123456789']])
def test_from_string_array(self):
A = np.array([[b'abc', b'foo'],
[b'long ', b'0123456789']])
assert_equal(A.dtype.type, np.string_)
B = np.char.array(A)
assert_array_equal(B, A)
assert_equal(B.dtype, A.dtype)
assert_equal(B.shape, A.shape)
B[0, 0] = 'changed'
assert_(B[0, 0] != A[0, 0])
C = np.char.asarray(A)
assert_array_equal(C, A)
assert_equal(C.dtype, A.dtype)
C[0, 0] = 'changed again'
assert_(C[0, 0] != B[0, 0])
assert_(C[0, 0] == A[0, 0])
def test_from_unicode_array(self):
A = np.array([['abc', u'Sigma \u03a3'],
['long ', '0123456789']])
assert_equal(A.dtype.type, np.unicode_)
B = np.char.array(A)
assert_array_equal(B, A)
assert_equal(B.dtype, A.dtype)
assert_equal(B.shape, A.shape)
B = np.char.array(A, **kw_unicode_true)
assert_array_equal(B, A)
assert_equal(B.dtype, A.dtype)
assert_equal(B.shape, A.shape)
def fail():
np.char.array(A, **kw_unicode_false)
assert_raises(UnicodeEncodeError, fail)
def test_unicode_upconvert(self):
A = np.char.array(['abc'])
B = np.char.array([u'\u03a3'])
assert_(issubclass((A + B).dtype.type, np.unicode_))
def test_from_string(self):
A = np.char.array(b'abc')
assert_equal(len(A), 1)
assert_equal(len(A[0]), 3)
assert_(issubclass(A.dtype.type, np.string_))
def test_from_unicode(self):
A = np.char.array(u'\u03a3')
assert_equal(len(A), 1)
assert_equal(len(A[0]), 1)
assert_equal(A.itemsize, 4)
assert_(issubclass(A.dtype.type, np.unicode_))
class TestVecString(object):
def test_non_existent_method(self):
def fail():
_vec_string('a', np.string_, 'bogus')
assert_raises(AttributeError, fail)
def test_non_string_array(self):
def fail():
_vec_string(1, np.string_, 'strip')
assert_raises(TypeError, fail)
def test_invalid_args_tuple(self):
def fail():
_vec_string(['a'], np.string_, 'strip', 1)
assert_raises(TypeError, fail)
def test_invalid_type_descr(self):
def fail():
_vec_string(['a'], 'BOGUS', 'strip')
assert_raises(TypeError, fail)
def test_invalid_function_args(self):
def fail():
_vec_string(['a'], np.string_, 'strip', (1,))
assert_raises(TypeError, fail)
def test_invalid_result_type(self):
def fail():
_vec_string(['a'], np.integer, 'strip')
assert_raises(TypeError, fail)
def test_broadcast_error(self):
def fail():
_vec_string([['abc', 'def']], np.integer, 'find', (['a', 'd', 'j'],))
assert_raises(ValueError, fail)
class TestWhitespace(object):
def setup(self):
self.A = np.array([['abc ', '123 '],
['789 ', 'xyz ']]).view(np.chararray)
self.B = np.array([['abc', '123'],
['789', 'xyz']]).view(np.chararray)
def test1(self):
assert_(np.all(self.A == self.B))
assert_(np.all(self.A >= self.B))
assert_(np.all(self.A <= self.B))
assert_(not np.any(self.A > self.B))
assert_(not np.any(self.A < self.B))
assert_(not np.any(self.A != self.B))
class TestChar(object):
def setup(self):
self.A = np.array('abc1', dtype='c').view(np.chararray)
def test_it(self):
assert_equal(self.A.shape, (4,))
assert_equal(self.A.upper()[:2].tobytes(), b'AB')
class TestComparisons(object):
def setup(self):
self.A = np.array([['abc', '123'],
['789', 'xyz']]).view(np.chararray)
self.B = np.array([['efg', '123 '],
['051', 'tuv']]).view(np.chararray)
def test_not_equal(self):
assert_array_equal((self.A != self.B), [[True, False], [True, True]])
def test_equal(self):
assert_array_equal((self.A == self.B), [[False, True], [False, False]])
def test_greater_equal(self):
assert_array_equal((self.A >= self.B), [[False, True], [True, True]])
def test_less_equal(self):
assert_array_equal((self.A <= self.B), [[True, True], [False, False]])
def test_greater(self):
assert_array_equal((self.A > self.B), [[False, False], [True, True]])
def test_less(self):
assert_array_equal((self.A < self.B), [[True, False], [False, False]])
class TestComparisonsMixed1(TestComparisons):
"""Ticket #1276"""
def setup(self):
TestComparisons.setup(self)
self.B = np.array([['efg', '123 '],
['051', 'tuv']], np.unicode_).view(np.chararray)
class TestComparisonsMixed2(TestComparisons):
"""Ticket #1276"""
def setup(self):
TestComparisons.setup(self)
self.A = np.array([['abc', '123'],
['789', 'xyz']], np.unicode_).view(np.chararray)
class TestInformation(object):
def setup(self):
self.A = np.array([[' abc ', ''],
['12345', 'MixedCase'],
['123 \t 345 \0 ', 'UPPER']]).view(np.chararray)
self.B = np.array([[u' \u03a3 ', u''],
[u'12345', u'MixedCase'],
[u'123 \t 345 \0 ', u'UPPER']]).view(np.chararray)
def test_len(self):
assert_(issubclass(np.char.str_len(self.A).dtype.type, np.integer))
assert_array_equal(np.char.str_len(self.A), [[5, 0], [5, 9], [12, 5]])
assert_array_equal(np.char.str_len(self.B), [[3, 0], [5, 9], [12, 5]])
def test_count(self):
assert_(issubclass(self.A.count('').dtype.type, np.integer))
assert_array_equal(self.A.count('a'), [[1, 0], [0, 1], [0, 0]])
assert_array_equal(self.A.count('123'), [[0, 0], [1, 0], [1, 0]])
# Python doesn't seem to like counting NULL characters
# assert_array_equal(self.A.count('\0'), [[0, 0], [0, 0], [1, 0]])
assert_array_equal(self.A.count('a', 0, 2), [[1, 0], [0, 0], [0, 0]])
assert_array_equal(self.B.count('a'), [[0, 0], [0, 1], [0, 0]])
assert_array_equal(self.B.count('123'), [[0, 0], [1, 0], [1, 0]])
# assert_array_equal(self.B.count('\0'), [[0, 0], [0, 0], [1, 0]])
def test_endswith(self):
assert_(issubclass(self.A.endswith('').dtype.type, np.bool_))
assert_array_equal(self.A.endswith(' '), [[1, 0], [0, 0], [1, 0]])
assert_array_equal(self.A.endswith('3', 0, 3), [[0, 0], [1, 0], [1, 0]])
def fail():
self.A.endswith('3', 'fdjk')
assert_raises(TypeError, fail)
def test_find(self):
assert_(issubclass(self.A.find('a').dtype.type, np.integer))
assert_array_equal(self.A.find('a'), [[1, -1], [-1, 6], [-1, -1]])
assert_array_equal(self.A.find('3'), [[-1, -1], [2, -1], [2, -1]])
assert_array_equal(self.A.find('a', 0, 2), [[1, -1], [-1, -1], [-1, -1]])
assert_array_equal(self.A.find(['1', 'P']), [[-1, -1], [0, -1], [0, 1]])
def test_index(self):
def fail():
self.A.index('a')
assert_raises(ValueError, fail)
assert_(np.char.index('abcba', 'b') == 1)
assert_(issubclass(np.char.index('abcba', 'b').dtype.type, np.integer))
def test_isalnum(self):
assert_(issubclass(self.A.isalnum().dtype.type, np.bool_))
assert_array_equal(self.A.isalnum(), [[False, False], [True, True], [False, True]])
def test_isalpha(self):
assert_(issubclass(self.A.isalpha().dtype.type, np.bool_))
assert_array_equal(self.A.isalpha(), [[False, False], [False, True], [False, True]])
def test_isdigit(self):
assert_(issubclass(self.A.isdigit().dtype.type, np.bool_))
assert_array_equal(self.A.isdigit(), [[False, False], [True, False], [False, False]])
def test_islower(self):
assert_(issubclass(self.A.islower().dtype.type, np.bool_))
assert_array_equal(self.A.islower(), [[True, False], [False, False], [False, False]])
def test_isspace(self):
assert_(issubclass(self.A.isspace().dtype.type, np.bool_))
assert_array_equal(self.A.isspace(), [[False, False], [False, False], [False, False]])
def test_istitle(self):
assert_(issubclass(self.A.istitle().dtype.type, np.bool_))
assert_array_equal(self.A.istitle(), [[False, False], [False, False], [False, False]])
def test_isupper(self):
assert_(issubclass(self.A.isupper().dtype.type, np.bool_))
assert_array_equal(self.A.isupper(), [[False, False], [False, False], [False, True]])
def test_rfind(self):
assert_(issubclass(self.A.rfind('a').dtype.type, np.integer))
assert_array_equal(self.A.rfind('a'), [[1, -1], [-1, 6], [-1, -1]])
assert_array_equal(self.A.rfind('3'), [[-1, -1], [2, -1], [6, -1]])
assert_array_equal(self.A.rfind('a', 0, 2), [[1, -1], [-1, -1], [-1, -1]])
assert_array_equal(self.A.rfind(['1', 'P']), [[-1, -1], [0, -1], [0, 2]])
def test_rindex(self):
def fail():
self.A.rindex('a')
assert_raises(ValueError, fail)
assert_(np.char.rindex('abcba', 'b') == 3)
assert_(issubclass(np.char.rindex('abcba', 'b').dtype.type, np.integer))
def test_startswith(self):
assert_(issubclass(self.A.startswith('').dtype.type, np.bool_))
assert_array_equal(self.A.startswith(' '), [[1, 0], [0, 0], [0, 0]])
assert_array_equal(self.A.startswith('1', 0, 3), [[0, 0], [1, 0], [1, 0]])
def fail():
self.A.startswith('3', 'fdjk')
assert_raises(TypeError, fail)
class TestMethods(object):
def setup(self):
self.A = np.array([[' abc ', ''],
['12345', 'MixedCase'],
['123 \t 345 \0 ', 'UPPER']],
dtype='S').view(np.chararray)
self.B = np.array([[u' \u03a3 ', u''],
[u'12345', u'MixedCase'],
[u'123 \t 345 \0 ', u'UPPER']]).view(np.chararray)
def test_capitalize(self):
tgt = [[b' abc ', b''],
[b'12345', b'Mixedcase'],
[b'123 \t 345 \0 ', b'Upper']]
assert_(issubclass(self.A.capitalize().dtype.type, np.string_))
assert_array_equal(self.A.capitalize(), tgt)
tgt = [[u' \u03c3 ', ''],
['12345', 'Mixedcase'],
['123 \t 345 \0 ', 'Upper']]
assert_(issubclass(self.B.capitalize().dtype.type, np.unicode_))
assert_array_equal(self.B.capitalize(), tgt)
def test_center(self):
assert_(issubclass(self.A.center(10).dtype.type, np.string_))
C = self.A.center([10, 20])
assert_array_equal(np.char.str_len(C), [[10, 20], [10, 20], [12, 20]])
C = self.A.center(20, b'#')
assert_(np.all(C.startswith(b'#')))
assert_(np.all(C.endswith(b'#')))
C = np.char.center(b'FOO', [[10, 20], [15, 8]])
tgt = [[b' FOO ', b' FOO '],
[b' FOO ', b' FOO ']]
assert_(issubclass(C.dtype.type, np.string_))
assert_array_equal(C, tgt)
def test_decode(self):
if sys.version_info[0] >= 3:
A = np.char.array([b'\\u03a3'])
assert_(A.decode('unicode-escape')[0] == '\u03a3')
else:
with suppress_warnings() as sup:
if sys.py3kwarning:
sup.filter(DeprecationWarning, "'hex_codec'")
A = np.char.array(['736563726574206d657373616765'])
assert_(A.decode('hex_codec')[0] == 'secret message')
def test_encode(self):
B = self.B.encode('unicode_escape')
assert_(B[0][0] == str(' \\u03a3 ').encode('latin1'))
def test_expandtabs(self):
T = self.A.expandtabs()
assert_(T[2, 0] == b'123 345 \0')
def test_join(self):
if sys.version_info[0] >= 3:
# NOTE: list(b'123') == [49, 50, 51]
# so that b','.join(b'123') results to an error on Py3
A0 = self.A.decode('ascii')
else:
A0 = self.A
A = np.char.join([',', '#'], A0)
if sys.version_info[0] >= 3:
assert_(issubclass(A.dtype.type, np.unicode_))
else:
assert_(issubclass(A.dtype.type, np.string_))
tgt = np.array([[' ,a,b,c, ', ''],
['1,2,3,4,5', 'M#i#x#e#d#C#a#s#e'],
['1,2,3, ,\t, ,3,4,5, ,\x00, ', 'U#P#P#E#R']])
assert_array_equal(np.char.join([',', '#'], A0), tgt)
def test_ljust(self):
assert_(issubclass(self.A.ljust(10).dtype.type, np.string_))
C = self.A.ljust([10, 20])
assert_array_equal(np.char.str_len(C), [[10, 20], [10, 20], [12, 20]])
C = self.A.ljust(20, b'#')
assert_array_equal(C.startswith(b'#'), [
[False, True], [False, False], [False, False]])
assert_(np.all(C.endswith(b'#')))
C = np.char.ljust(b'FOO', [[10, 20], [15, 8]])
tgt = [[b'FOO ', b'FOO '],
[b'FOO ', b'FOO ']]
assert_(issubclass(C.dtype.type, np.string_))
assert_array_equal(C, tgt)
def test_lower(self):
tgt = [[b' abc ', b''],
[b'12345', b'mixedcase'],
[b'123 \t 345 \0 ', b'upper']]
assert_(issubclass(self.A.lower().dtype.type, np.string_))
assert_array_equal(self.A.lower(), tgt)
tgt = [[u' \u03c3 ', u''],
[u'12345', u'mixedcase'],
[u'123 \t 345 \0 ', u'upper']]
assert_(issubclass(self.B.lower().dtype.type, np.unicode_))
assert_array_equal(self.B.lower(), tgt)
def test_lstrip(self):
tgt = [[b'abc ', b''],
[b'12345', b'MixedCase'],
[b'123 \t 345 \0 ', b'UPPER']]
assert_(issubclass(self.A.lstrip().dtype.type, np.string_))
assert_array_equal(self.A.lstrip(), tgt)
tgt = [[b' abc', b''],
[b'2345', b'ixedCase'],
[b'23 \t 345 \x00', b'UPPER']]
assert_array_equal(self.A.lstrip([b'1', b'M']), tgt)
tgt = [[u'\u03a3 ', ''],
['12345', 'MixedCase'],
['123 \t 345 \0 ', 'UPPER']]
assert_(issubclass(self.B.lstrip().dtype.type, np.unicode_))
assert_array_equal(self.B.lstrip(), tgt)
def test_partition(self):
P = self.A.partition([b'3', b'M'])
tgt = [[(b' abc ', b'', b''), (b'', b'', b'')],
[(b'12', b'3', b'45'), (b'', b'M', b'ixedCase')],
[(b'12', b'3', b' \t 345 \0 '), (b'UPPER', b'', b'')]]
assert_(issubclass(P.dtype.type, np.string_))
assert_array_equal(P, tgt)
def test_replace(self):
R = self.A.replace([b'3', b'a'],
[b'##########', b'@'])
tgt = [[b' abc ', b''],
[b'12##########45', b'MixedC@se'],
[b'12########## \t ##########45 \x00', b'UPPER']]
assert_(issubclass(R.dtype.type, np.string_))
assert_array_equal(R, tgt)
if sys.version_info[0] < 3:
# NOTE: b'abc'.replace(b'a', 'b') is not allowed on Py3
R = self.A.replace(b'a', u'\u03a3')
tgt = [[u' \u03a3bc ', ''],
['12345', u'MixedC\u03a3se'],
['123 \t 345 \x00', 'UPPER']]
assert_(issubclass(R.dtype.type, np.unicode_))
assert_array_equal(R, tgt)
def test_rjust(self):
assert_(issubclass(self.A.rjust(10).dtype.type, np.string_))
C = self.A.rjust([10, 20])
assert_array_equal(np.char.str_len(C), [[10, 20], [10, 20], [12, 20]])
C = self.A.rjust(20, b'#')
assert_(np.all(C.startswith(b'#')))
assert_array_equal(C.endswith(b'#'),
[[False, True], [False, False], [False, False]])
C = np.char.rjust(b'FOO', [[10, 20], [15, 8]])
tgt = [[b' FOO', b' FOO'],
[b' FOO', b' FOO']]
assert_(issubclass(C.dtype.type, np.string_))
assert_array_equal(C, tgt)
def test_rpartition(self):
P = self.A.rpartition([b'3', b'M'])
tgt = [[(b'', b'', b' abc '), (b'', b'', b'')],
[(b'12', b'3', b'45'), (b'', b'M', b'ixedCase')],
[(b'123 \t ', b'3', b'45 \0 '), (b'', b'', b'UPPER')]]
assert_(issubclass(P.dtype.type, np.string_))
assert_array_equal(P, tgt)
def test_rsplit(self):
A = self.A.rsplit(b'3')
tgt = [[[b' abc '], [b'']],
[[b'12', b'45'], [b'MixedCase']],
[[b'12', b' \t ', b'45 \x00 '], [b'UPPER']]]
assert_(issubclass(A.dtype.type, np.object_))
assert_equal(A.tolist(), tgt)
def test_rstrip(self):
assert_(issubclass(self.A.rstrip().dtype.type, np.string_))
tgt = [[b' abc', b''],
[b'12345', b'MixedCase'],
[b'123 \t 345', b'UPPER']]
assert_array_equal(self.A.rstrip(), tgt)
tgt = [[b' abc ', b''],
[b'1234', b'MixedCase'],
[b'123 \t 345 \x00', b'UPP']
]
assert_array_equal(self.A.rstrip([b'5', b'ER']), tgt)
tgt = [[u' \u03a3', ''],
['12345', 'MixedCase'],
['123 \t 345', 'UPPER']]
assert_(issubclass(self.B.rstrip().dtype.type, np.unicode_))
assert_array_equal(self.B.rstrip(), tgt)
def test_strip(self):
tgt = [[b'abc', b''],
[b'12345', b'MixedCase'],
[b'123 \t 345', b'UPPER']]
assert_(issubclass(self.A.strip().dtype.type, np.string_))
assert_array_equal(self.A.strip(), tgt)
tgt = [[b' abc ', b''],
[b'234', b'ixedCas'],
[b'23 \t 345 \x00', b'UPP']]
assert_array_equal(self.A.strip([b'15', b'EReM']), tgt)
tgt = [[u'\u03a3', ''],
['12345', 'MixedCase'],
['123 \t 345', 'UPPER']]
assert_(issubclass(self.B.strip().dtype.type, np.unicode_))
assert_array_equal(self.B.strip(), tgt)
def test_split(self):
A = self.A.split(b'3')
tgt = [
[[b' abc '], [b'']],
[[b'12', b'45'], [b'MixedCase']],
[[b'12', b' \t ', b'45 \x00 '], [b'UPPER']]]
assert_(issubclass(A.dtype.type, np.object_))
assert_equal(A.tolist(), tgt)
def test_splitlines(self):
A = np.char.array(['abc\nfds\nwer']).splitlines()
assert_(issubclass(A.dtype.type, np.object_))
assert_(A.shape == (1,))
assert_(len(A[0]) == 3)
def test_swapcase(self):
tgt = [[b' ABC ', b''],
[b'12345', b'mIXEDcASE'],
[b'123 \t 345 \0 ', b'upper']]
assert_(issubclass(self.A.swapcase().dtype.type, np.string_))
assert_array_equal(self.A.swapcase(), tgt)
tgt = [[u' \u03c3 ', u''],
[u'12345', u'mIXEDcASE'],
[u'123 \t 345 \0 ', u'upper']]
assert_(issubclass(self.B.swapcase().dtype.type, np.unicode_))
assert_array_equal(self.B.swapcase(), tgt)
def test_title(self):
tgt = [[b' Abc ', b''],
[b'12345', b'Mixedcase'],
[b'123 \t 345 \0 ', b'Upper']]
assert_(issubclass(self.A.title().dtype.type, np.string_))
assert_array_equal(self.A.title(), tgt)
tgt = [[u' \u03a3 ', u''],
[u'12345', u'Mixedcase'],
[u'123 \t 345 \0 ', u'Upper']]
assert_(issubclass(self.B.title().dtype.type, np.unicode_))
assert_array_equal(self.B.title(), tgt)
def test_upper(self):
tgt = [[b' ABC ', b''],
[b'12345', b'MIXEDCASE'],
[b'123 \t 345 \0 ', b'UPPER']]
assert_(issubclass(self.A.upper().dtype.type, np.string_))
assert_array_equal(self.A.upper(), tgt)
tgt = [[u' \u03a3 ', u''],
[u'12345', u'MIXEDCASE'],
[u'123 \t 345 \0 ', u'UPPER']]
assert_(issubclass(self.B.upper().dtype.type, np.unicode_))
assert_array_equal(self.B.upper(), tgt)
def test_isnumeric(self):
def fail():
self.A.isnumeric()
assert_raises(TypeError, fail)
assert_(issubclass(self.B.isnumeric().dtype.type, np.bool_))
assert_array_equal(self.B.isnumeric(), [
[False, False], [True, False], [False, False]])
def test_isdecimal(self):
def fail():
self.A.isdecimal()
assert_raises(TypeError, fail)
assert_(issubclass(self.B.isdecimal().dtype.type, np.bool_))
assert_array_equal(self.B.isdecimal(), [
[False, False], [True, False], [False, False]])
class TestOperations(object):
def setup(self):
self.A = np.array([['abc', '123'],
['789', 'xyz']]).view(np.chararray)
self.B = np.array([['efg', '456'],
['051', 'tuv']]).view(np.chararray)
def test_add(self):
AB = np.array([['abcefg', '123456'],
['789051', 'xyztuv']]).view(np.chararray)
assert_array_equal(AB, (self.A + self.B))
assert_(len((self.A + self.B)[0][0]) == 6)
def test_radd(self):
QA = np.array([['qabc', 'q123'],
['q789', 'qxyz']]).view(np.chararray)
assert_array_equal(QA, ('q' + self.A))
def test_mul(self):
A = self.A
for r in (2, 3, 5, 7, 197):
Ar = np.array([[A[0, 0]*r, A[0, 1]*r],
[A[1, 0]*r, A[1, 1]*r]]).view(np.chararray)
assert_array_equal(Ar, (self.A * r))
for ob in [object(), 'qrs']:
try:
A * ob
except ValueError:
pass
else:
self.fail("chararray can only be multiplied by integers")
def test_rmul(self):
A = self.A
for r in (2, 3, 5, 7, 197):
Ar = np.array([[A[0, 0]*r, A[0, 1]*r],
[A[1, 0]*r, A[1, 1]*r]]).view(np.chararray)
assert_array_equal(Ar, (r * self.A))
for ob in [object(), 'qrs']:
try:
ob * A
except ValueError:
pass
else:
self.fail("chararray can only be multiplied by integers")
def test_mod(self):
"""Ticket #856"""
F = np.array([['%d', '%f'], ['%s', '%r']]).view(np.chararray)
C = np.array([[3, 7], [19, 1]])
FC = np.array([['3', '7.000000'],
['19', '1']]).view(np.chararray)
assert_array_equal(FC, F % C)
A = np.array([['%.3f', '%d'], ['%s', '%r']]).view(np.chararray)
A1 = np.array([['1.000', '1'], ['1', '1']]).view(np.chararray)
assert_array_equal(A1, (A % 1))
A2 = np.array([['1.000', '2'], ['3', '4']]).view(np.chararray)
assert_array_equal(A2, (A % [[1, 2], [3, 4]]))
def test_rmod(self):
assert_(("%s" % self.A) == str(self.A))
assert_(("%r" % self.A) == repr(self.A))
for ob in [42, object()]:
try:
ob % self.A
except TypeError:
pass
else:
self.fail("chararray __rmod__ should fail with "
"non-string objects")
def test_slice(self):
"""Regression test for https://github.com/numpy/numpy/issues/5982"""
arr = np.array([['abc ', 'def '], ['geh ', 'ijk ']],
dtype='S4').view(np.chararray)
sl1 = arr[:]
assert_array_equal(sl1, arr)
assert_(sl1.base is arr)
assert_(sl1.base.base is arr.base)
sl2 = arr[:, :]
assert_array_equal(sl2, arr)
assert_(sl2.base is arr)
assert_(sl2.base.base is arr.base)
assert_(arr[0, 0] == b'abc')
def test_empty_indexing():
"""Regression test for ticket 1948."""
# Check that indexing a chararray with an empty list/array returns an
# empty chararray instead of a chararray with a single empty string in it.
s = np.chararray((4,))
assert_(s[[]].size == 0)
if __name__ == "__main__":
run_module_suite()
| 25,693 | 35.342291 | 94 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/tests/test_shape_base.py
|
from __future__ import division, absolute_import, print_function
import warnings
import numpy as np
from numpy.core import (array, arange, atleast_1d, atleast_2d, atleast_3d,
block, vstack, hstack, newaxis, concatenate, stack)
from numpy.testing import (assert_, assert_raises,
assert_array_equal, assert_equal, run_module_suite,
assert_raises_regex, assert_almost_equal)
from numpy.compat import long
class TestAtleast1d(object):
def test_0D_array(self):
a = array(1)
b = array(2)
res = [atleast_1d(a), atleast_1d(b)]
desired = [array([1]), array([2])]
assert_array_equal(res, desired)
def test_1D_array(self):
a = array([1, 2])
b = array([2, 3])
res = [atleast_1d(a), atleast_1d(b)]
desired = [array([1, 2]), array([2, 3])]
assert_array_equal(res, desired)
def test_2D_array(self):
a = array([[1, 2], [1, 2]])
b = array([[2, 3], [2, 3]])
res = [atleast_1d(a), atleast_1d(b)]
desired = [a, b]
assert_array_equal(res, desired)
def test_3D_array(self):
a = array([[1, 2], [1, 2]])
b = array([[2, 3], [2, 3]])
a = array([a, a])
b = array([b, b])
res = [atleast_1d(a), atleast_1d(b)]
desired = [a, b]
assert_array_equal(res, desired)
def test_r1array(self):
""" Test to make sure equivalent Travis O's r1array function
"""
assert_(atleast_1d(3).shape == (1,))
assert_(atleast_1d(3j).shape == (1,))
assert_(atleast_1d(long(3)).shape == (1,))
assert_(atleast_1d(3.0).shape == (1,))
assert_(atleast_1d([[2, 3], [4, 5]]).shape == (2, 2))
class TestAtleast2d(object):
def test_0D_array(self):
a = array(1)
b = array(2)
res = [atleast_2d(a), atleast_2d(b)]
desired = [array([[1]]), array([[2]])]
assert_array_equal(res, desired)
def test_1D_array(self):
a = array([1, 2])
b = array([2, 3])
res = [atleast_2d(a), atleast_2d(b)]
desired = [array([[1, 2]]), array([[2, 3]])]
assert_array_equal(res, desired)
def test_2D_array(self):
a = array([[1, 2], [1, 2]])
b = array([[2, 3], [2, 3]])
res = [atleast_2d(a), atleast_2d(b)]
desired = [a, b]
assert_array_equal(res, desired)
def test_3D_array(self):
a = array([[1, 2], [1, 2]])
b = array([[2, 3], [2, 3]])
a = array([a, a])
b = array([b, b])
res = [atleast_2d(a), atleast_2d(b)]
desired = [a, b]
assert_array_equal(res, desired)
def test_r2array(self):
""" Test to make sure equivalent Travis O's r2array function
"""
assert_(atleast_2d(3).shape == (1, 1))
assert_(atleast_2d([3j, 1]).shape == (1, 2))
assert_(atleast_2d([[[3, 1], [4, 5]], [[3, 5], [1, 2]]]).shape == (2, 2, 2))
class TestAtleast3d(object):
def test_0D_array(self):
a = array(1)
b = array(2)
res = [atleast_3d(a), atleast_3d(b)]
desired = [array([[[1]]]), array([[[2]]])]
assert_array_equal(res, desired)
def test_1D_array(self):
a = array([1, 2])
b = array([2, 3])
res = [atleast_3d(a), atleast_3d(b)]
desired = [array([[[1], [2]]]), array([[[2], [3]]])]
assert_array_equal(res, desired)
def test_2D_array(self):
a = array([[1, 2], [1, 2]])
b = array([[2, 3], [2, 3]])
res = [atleast_3d(a), atleast_3d(b)]
desired = [a[:,:, newaxis], b[:,:, newaxis]]
assert_array_equal(res, desired)
def test_3D_array(self):
a = array([[1, 2], [1, 2]])
b = array([[2, 3], [2, 3]])
a = array([a, a])
b = array([b, b])
res = [atleast_3d(a), atleast_3d(b)]
desired = [a, b]
assert_array_equal(res, desired)
class TestHstack(object):
def test_non_iterable(self):
assert_raises(TypeError, hstack, 1)
def test_empty_input(self):
assert_raises(ValueError, hstack, ())
def test_0D_array(self):
a = array(1)
b = array(2)
res = hstack([a, b])
desired = array([1, 2])
assert_array_equal(res, desired)
def test_1D_array(self):
a = array([1])
b = array([2])
res = hstack([a, b])
desired = array([1, 2])
assert_array_equal(res, desired)
def test_2D_array(self):
a = array([[1], [2]])
b = array([[1], [2]])
res = hstack([a, b])
desired = array([[1, 1], [2, 2]])
assert_array_equal(res, desired)
class TestVstack(object):
def test_non_iterable(self):
assert_raises(TypeError, vstack, 1)
def test_empty_input(self):
assert_raises(ValueError, vstack, ())
def test_0D_array(self):
a = array(1)
b = array(2)
res = vstack([a, b])
desired = array([[1], [2]])
assert_array_equal(res, desired)
def test_1D_array(self):
a = array([1])
b = array([2])
res = vstack([a, b])
desired = array([[1], [2]])
assert_array_equal(res, desired)
def test_2D_array(self):
a = array([[1], [2]])
b = array([[1], [2]])
res = vstack([a, b])
desired = array([[1], [2], [1], [2]])
assert_array_equal(res, desired)
def test_2D_array2(self):
a = array([1, 2])
b = array([1, 2])
res = vstack([a, b])
desired = array([[1, 2], [1, 2]])
assert_array_equal(res, desired)
class TestConcatenate(object):
def test_exceptions(self):
# test axis must be in bounds
for ndim in [1, 2, 3]:
a = np.ones((1,)*ndim)
np.concatenate((a, a), axis=0) # OK
assert_raises(np.AxisError, np.concatenate, (a, a), axis=ndim)
assert_raises(np.AxisError, np.concatenate, (a, a), axis=-(ndim + 1))
# Scalars cannot be concatenated
assert_raises(ValueError, concatenate, (0,))
assert_raises(ValueError, concatenate, (np.array(0),))
# test shapes must match except for concatenation axis
a = np.ones((1, 2, 3))
b = np.ones((2, 2, 3))
axis = list(range(3))
for i in range(3):
np.concatenate((a, b), axis=axis[0]) # OK
assert_raises(ValueError, np.concatenate, (a, b), axis=axis[1])
assert_raises(ValueError, np.concatenate, (a, b), axis=axis[2])
a = np.moveaxis(a, -1, 0)
b = np.moveaxis(b, -1, 0)
axis.append(axis.pop(0))
# No arrays to concatenate raises ValueError
assert_raises(ValueError, concatenate, ())
def test_concatenate_axis_None(self):
a = np.arange(4, dtype=np.float64).reshape((2, 2))
b = list(range(3))
c = ['x']
r = np.concatenate((a, a), axis=None)
assert_equal(r.dtype, a.dtype)
assert_equal(r.ndim, 1)
r = np.concatenate((a, b), axis=None)
assert_equal(r.size, a.size + len(b))
assert_equal(r.dtype, a.dtype)
r = np.concatenate((a, b, c), axis=None)
d = array(['0.0', '1.0', '2.0', '3.0',
'0', '1', '2', 'x'])
assert_array_equal(r, d)
out = np.zeros(a.size + len(b))
r = np.concatenate((a, b), axis=None)
rout = np.concatenate((a, b), axis=None, out=out)
assert_(out is rout)
assert_equal(r, rout)
def test_large_concatenate_axis_None(self):
# When no axis is given, concatenate uses flattened versions.
# This also had a bug with many arrays (see gh-5979).
x = np.arange(1, 100)
r = np.concatenate(x, None)
assert_array_equal(x, r)
# This should probably be deprecated:
r = np.concatenate(x, 100) # axis is >= MAXDIMS
assert_array_equal(x, r)
def test_concatenate(self):
# Test concatenate function
# One sequence returns unmodified (but as array)
r4 = list(range(4))
assert_array_equal(concatenate((r4,)), r4)
# Any sequence
assert_array_equal(concatenate((tuple(r4),)), r4)
assert_array_equal(concatenate((array(r4),)), r4)
# 1D default concatenation
r3 = list(range(3))
assert_array_equal(concatenate((r4, r3)), r4 + r3)
# Mixed sequence types
assert_array_equal(concatenate((tuple(r4), r3)), r4 + r3)
assert_array_equal(concatenate((array(r4), r3)), r4 + r3)
# Explicit axis specification
assert_array_equal(concatenate((r4, r3), 0), r4 + r3)
# Including negative
assert_array_equal(concatenate((r4, r3), -1), r4 + r3)
# 2D
a23 = array([[10, 11, 12], [13, 14, 15]])
a13 = array([[0, 1, 2]])
res = array([[10, 11, 12], [13, 14, 15], [0, 1, 2]])
assert_array_equal(concatenate((a23, a13)), res)
assert_array_equal(concatenate((a23, a13), 0), res)
assert_array_equal(concatenate((a23.T, a13.T), 1), res.T)
assert_array_equal(concatenate((a23.T, a13.T), -1), res.T)
# Arrays much match shape
assert_raises(ValueError, concatenate, (a23.T, a13.T), 0)
# 3D
res = arange(2 * 3 * 7).reshape((2, 3, 7))
a0 = res[..., :4]
a1 = res[..., 4:6]
a2 = res[..., 6:]
assert_array_equal(concatenate((a0, a1, a2), 2), res)
assert_array_equal(concatenate((a0, a1, a2), -1), res)
assert_array_equal(concatenate((a0.T, a1.T, a2.T), 0), res.T)
out = res.copy()
rout = concatenate((a0, a1, a2), 2, out=out)
assert_(out is rout)
assert_equal(res, rout)
def test_bad_out_shape(self):
a = array([1, 2])
b = array([3, 4])
assert_raises(ValueError, concatenate, (a, b), out=np.empty(5))
assert_raises(ValueError, concatenate, (a, b), out=np.empty((4,1)))
assert_raises(ValueError, concatenate, (a, b), out=np.empty((1,4)))
concatenate((a, b), out=np.empty(4))
def test_out_dtype(self):
out = np.empty(4, np.float32)
res = concatenate((array([1, 2]), array([3, 4])), out=out)
assert_(out is res)
out = np.empty(4, np.complex64)
res = concatenate((array([0.1, 0.2]), array([0.3, 0.4])), out=out)
assert_(out is res)
# invalid cast
out = np.empty(4, np.int32)
assert_raises(TypeError, concatenate,
(array([0.1, 0.2]), array([0.3, 0.4])), out=out)
def test_stack():
# non-iterable input
assert_raises(TypeError, stack, 1)
# 0d input
for input_ in [(1, 2, 3),
[np.int32(1), np.int32(2), np.int32(3)],
[np.array(1), np.array(2), np.array(3)]]:
assert_array_equal(stack(input_), [1, 2, 3])
# 1d input examples
a = np.array([1, 2, 3])
b = np.array([4, 5, 6])
r1 = array([[1, 2, 3], [4, 5, 6]])
assert_array_equal(np.stack((a, b)), r1)
assert_array_equal(np.stack((a, b), axis=1), r1.T)
# all input types
assert_array_equal(np.stack(list([a, b])), r1)
assert_array_equal(np.stack(array([a, b])), r1)
# all shapes for 1d input
arrays = [np.random.randn(3) for _ in range(10)]
axes = [0, 1, -1, -2]
expected_shapes = [(10, 3), (3, 10), (3, 10), (10, 3)]
for axis, expected_shape in zip(axes, expected_shapes):
assert_equal(np.stack(arrays, axis).shape, expected_shape)
assert_raises_regex(np.AxisError, 'out of bounds', stack, arrays, axis=2)
assert_raises_regex(np.AxisError, 'out of bounds', stack, arrays, axis=-3)
# all shapes for 2d input
arrays = [np.random.randn(3, 4) for _ in range(10)]
axes = [0, 1, 2, -1, -2, -3]
expected_shapes = [(10, 3, 4), (3, 10, 4), (3, 4, 10),
(3, 4, 10), (3, 10, 4), (10, 3, 4)]
for axis, expected_shape in zip(axes, expected_shapes):
assert_equal(np.stack(arrays, axis).shape, expected_shape)
# empty arrays
assert_(stack([[], [], []]).shape == (3, 0))
assert_(stack([[], [], []], axis=1).shape == (0, 3))
# edge cases
assert_raises_regex(ValueError, 'need at least one array', stack, [])
assert_raises_regex(ValueError, 'must have the same shape',
stack, [1, np.arange(3)])
assert_raises_regex(ValueError, 'must have the same shape',
stack, [np.arange(3), 1])
assert_raises_regex(ValueError, 'must have the same shape',
stack, [np.arange(3), 1], axis=1)
assert_raises_regex(ValueError, 'must have the same shape',
stack, [np.zeros((3, 3)), np.zeros(3)], axis=1)
assert_raises_regex(ValueError, 'must have the same shape',
stack, [np.arange(2), np.arange(3)])
# np.matrix
m = np.matrix([[1, 2], [3, 4]])
assert_raises_regex(ValueError, 'shape too large to be a matrix',
stack, [m, m])
class TestBlock(object):
def test_block_simple_row_wise(self):
a_2d = np.ones((2, 2))
b_2d = 2 * a_2d
desired = np.array([[1, 1, 2, 2],
[1, 1, 2, 2]])
result = block([a_2d, b_2d])
assert_equal(desired, result)
def test_block_simple_column_wise(self):
a_2d = np.ones((2, 2))
b_2d = 2 * a_2d
expected = np.array([[1, 1],
[1, 1],
[2, 2],
[2, 2]])
result = block([[a_2d], [b_2d]])
assert_equal(expected, result)
def test_block_with_1d_arrays_row_wise(self):
# # # 1-D vectors are treated as row arrays
a = np.array([1, 2, 3])
b = np.array([2, 3, 4])
expected = np.array([1, 2, 3, 2, 3, 4])
result = block([a, b])
assert_equal(expected, result)
def test_block_with_1d_arrays_multiple_rows(self):
a = np.array([1, 2, 3])
b = np.array([2, 3, 4])
expected = np.array([[1, 2, 3, 2, 3, 4],
[1, 2, 3, 2, 3, 4]])
result = block([[a, b], [a, b]])
assert_equal(expected, result)
def test_block_with_1d_arrays_column_wise(self):
# # # 1-D vectors are treated as row arrays
a_1d = np.array([1, 2, 3])
b_1d = np.array([2, 3, 4])
expected = np.array([[1, 2, 3],
[2, 3, 4]])
result = block([[a_1d], [b_1d]])
assert_equal(expected, result)
def test_block_mixed_1d_and_2d(self):
a_2d = np.ones((2, 2))
b_1d = np.array([2, 2])
result = block([[a_2d], [b_1d]])
expected = np.array([[1, 1],
[1, 1],
[2, 2]])
assert_equal(expected, result)
def test_block_complicated(self):
# a bit more complicated
one_2d = np.array([[1, 1, 1]])
two_2d = np.array([[2, 2, 2]])
three_2d = np.array([[3, 3, 3, 3, 3, 3]])
four_1d = np.array([4, 4, 4, 4, 4, 4])
five_0d = np.array(5)
six_1d = np.array([6, 6, 6, 6, 6])
zero_2d = np.zeros((2, 6))
expected = np.array([[1, 1, 1, 2, 2, 2],
[3, 3, 3, 3, 3, 3],
[4, 4, 4, 4, 4, 4],
[5, 6, 6, 6, 6, 6],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0]])
result = block([[one_2d, two_2d],
[three_2d],
[four_1d],
[five_0d, six_1d],
[zero_2d]])
assert_equal(result, expected)
def test_nested(self):
one = np.array([1, 1, 1])
two = np.array([[2, 2, 2], [2, 2, 2], [2, 2, 2]])
three = np.array([3, 3, 3])
four = np.array([4, 4, 4])
five = np.array(5)
six = np.array([6, 6, 6, 6, 6])
zero = np.zeros((2, 6))
result = np.block([
[
np.block([
[one],
[three],
[four]
]),
two
],
[five, six],
[zero]
])
expected = np.array([[1, 1, 1, 2, 2, 2],
[3, 3, 3, 2, 2, 2],
[4, 4, 4, 2, 2, 2],
[5, 6, 6, 6, 6, 6],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0]])
assert_equal(result, expected)
def test_3d(self):
a000 = np.ones((2, 2, 2), int) * 1
a100 = np.ones((3, 2, 2), int) * 2
a010 = np.ones((2, 3, 2), int) * 3
a001 = np.ones((2, 2, 3), int) * 4
a011 = np.ones((2, 3, 3), int) * 5
a101 = np.ones((3, 2, 3), int) * 6
a110 = np.ones((3, 3, 2), int) * 7
a111 = np.ones((3, 3, 3), int) * 8
result = np.block([
[
[a000, a001],
[a010, a011],
],
[
[a100, a101],
[a110, a111],
]
])
expected = array([[[1, 1, 4, 4, 4],
[1, 1, 4, 4, 4],
[3, 3, 5, 5, 5],
[3, 3, 5, 5, 5],
[3, 3, 5, 5, 5]],
[[1, 1, 4, 4, 4],
[1, 1, 4, 4, 4],
[3, 3, 5, 5, 5],
[3, 3, 5, 5, 5],
[3, 3, 5, 5, 5]],
[[2, 2, 6, 6, 6],
[2, 2, 6, 6, 6],
[7, 7, 8, 8, 8],
[7, 7, 8, 8, 8],
[7, 7, 8, 8, 8]],
[[2, 2, 6, 6, 6],
[2, 2, 6, 6, 6],
[7, 7, 8, 8, 8],
[7, 7, 8, 8, 8],
[7, 7, 8, 8, 8]],
[[2, 2, 6, 6, 6],
[2, 2, 6, 6, 6],
[7, 7, 8, 8, 8],
[7, 7, 8, 8, 8],
[7, 7, 8, 8, 8]]])
assert_array_equal(result, expected)
def test_block_with_mismatched_shape(self):
a = np.array([0, 0])
b = np.eye(2)
assert_raises(ValueError, np.block, [a, b])
assert_raises(ValueError, np.block, [b, a])
def test_no_lists(self):
assert_equal(np.block(1), np.array(1))
assert_equal(np.block(np.eye(3)), np.eye(3))
def test_invalid_nesting(self):
msg = 'depths are mismatched'
assert_raises_regex(ValueError, msg, np.block, [1, [2]])
assert_raises_regex(ValueError, msg, np.block, [1, []])
assert_raises_regex(ValueError, msg, np.block, [[1], 2])
assert_raises_regex(ValueError, msg, np.block, [[], 2])
assert_raises_regex(ValueError, msg, np.block, [
[[1], [2]],
[[3, 4]],
[5] # missing brackets
])
def test_empty_lists(self):
assert_raises_regex(ValueError, 'empty', np.block, [])
assert_raises_regex(ValueError, 'empty', np.block, [[]])
assert_raises_regex(ValueError, 'empty', np.block, [[1], []])
def test_tuple(self):
assert_raises_regex(TypeError, 'tuple', np.block, ([1, 2], [3, 4]))
assert_raises_regex(TypeError, 'tuple', np.block, [(1, 2), (3, 4)])
def test_different_ndims(self):
a = 1.
b = 2 * np.ones((1, 2))
c = 3 * np.ones((1, 1, 3))
result = np.block([a, b, c])
expected = np.array([[[1., 2., 2., 3., 3., 3.]]])
assert_equal(result, expected)
def test_different_ndims_depths(self):
a = 1.
b = 2 * np.ones((1, 2))
c = 3 * np.ones((1, 2, 3))
result = np.block([[a, b], [c]])
expected = np.array([[[1., 2., 2.],
[3., 3., 3.],
[3., 3., 3.]]])
assert_equal(result, expected)
if __name__ == "__main__":
run_module_suite()
| 20,299 | 33.52381 | 84 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/core/tests/test_extint128.py
|
from __future__ import division, absolute_import, print_function
import sys
import itertools
import contextlib
import operator
import numpy as np
import numpy.core.multiarray_tests as mt
from numpy.compat import long
from numpy.testing import assert_raises, assert_equal, dec
INT64_MAX = np.iinfo(np.int64).max
INT64_MIN = np.iinfo(np.int64).min
INT64_MID = 2**32
# int128 is not two's complement, the sign bit is separate
INT128_MAX = 2**128 - 1
INT128_MIN = -INT128_MAX
INT128_MID = 2**64
INT64_VALUES = (
[INT64_MIN + j for j in range(20)] +
[INT64_MAX - j for j in range(20)] +
[INT64_MID + j for j in range(-20, 20)] +
[2*INT64_MID + j for j in range(-20, 20)] +
[INT64_MID//2 + j for j in range(-20, 20)] +
list(range(-70, 70))
)
INT128_VALUES = (
[INT128_MIN + j for j in range(20)] +
[INT128_MAX - j for j in range(20)] +
[INT128_MID + j for j in range(-20, 20)] +
[2*INT128_MID + j for j in range(-20, 20)] +
[INT128_MID//2 + j for j in range(-20, 20)] +
list(range(-70, 70)) +
[False] # negative zero
)
INT64_POS_VALUES = [x for x in INT64_VALUES if x > 0]
@contextlib.contextmanager
def exc_iter(*args):
"""
Iterate over Cartesian product of *args, and if an exception is raised,
add information of the current iterate.
"""
value = [None]
def iterate():
for v in itertools.product(*args):
value[0] = v
yield v
try:
yield iterate()
except Exception:
import traceback
msg = "At: %r\n%s" % (repr(value[0]),
traceback.format_exc())
raise AssertionError(msg)
def test_safe_binop():
# Test checked arithmetic routines
ops = [
(operator.add, 1),
(operator.sub, 2),
(operator.mul, 3)
]
with exc_iter(ops, INT64_VALUES, INT64_VALUES) as it:
for xop, a, b in it:
pyop, op = xop
c = pyop(a, b)
if not (INT64_MIN <= c <= INT64_MAX):
assert_raises(OverflowError, mt.extint_safe_binop, a, b, op)
else:
d = mt.extint_safe_binop(a, b, op)
if c != d:
# assert_equal is slow
assert_equal(d, c)
def test_to_128():
with exc_iter(INT64_VALUES) as it:
for a, in it:
b = mt.extint_to_128(a)
if a != b:
assert_equal(b, a)
def test_to_64():
with exc_iter(INT128_VALUES) as it:
for a, in it:
if not (INT64_MIN <= a <= INT64_MAX):
assert_raises(OverflowError, mt.extint_to_64, a)
else:
b = mt.extint_to_64(a)
if a != b:
assert_equal(b, a)
def test_mul_64_64():
with exc_iter(INT64_VALUES, INT64_VALUES) as it:
for a, b in it:
c = a * b
d = mt.extint_mul_64_64(a, b)
if c != d:
assert_equal(d, c)
def test_add_128():
with exc_iter(INT128_VALUES, INT128_VALUES) as it:
for a, b in it:
c = a + b
if not (INT128_MIN <= c <= INT128_MAX):
assert_raises(OverflowError, mt.extint_add_128, a, b)
else:
d = mt.extint_add_128(a, b)
if c != d:
assert_equal(d, c)
def test_sub_128():
with exc_iter(INT128_VALUES, INT128_VALUES) as it:
for a, b in it:
c = a - b
if not (INT128_MIN <= c <= INT128_MAX):
assert_raises(OverflowError, mt.extint_sub_128, a, b)
else:
d = mt.extint_sub_128(a, b)
if c != d:
assert_equal(d, c)
def test_neg_128():
with exc_iter(INT128_VALUES) as it:
for a, in it:
b = -a
c = mt.extint_neg_128(a)
if b != c:
assert_equal(c, b)
def test_shl_128():
with exc_iter(INT128_VALUES) as it:
for a, in it:
if a < 0:
b = -(((-a) << 1) & (2**128-1))
else:
b = (a << 1) & (2**128-1)
c = mt.extint_shl_128(a)
if b != c:
assert_equal(c, b)
def test_shr_128():
with exc_iter(INT128_VALUES) as it:
for a, in it:
if a < 0:
b = -((-a) >> 1)
else:
b = a >> 1
c = mt.extint_shr_128(a)
if b != c:
assert_equal(c, b)
def test_gt_128():
with exc_iter(INT128_VALUES, INT128_VALUES) as it:
for a, b in it:
c = a > b
d = mt.extint_gt_128(a, b)
if c != d:
assert_equal(d, c)
@dec.slow
def test_divmod_128_64():
with exc_iter(INT128_VALUES, INT64_POS_VALUES) as it:
for a, b in it:
if a >= 0:
c, cr = divmod(a, b)
else:
c, cr = divmod(-a, b)
c = -c
cr = -cr
d, dr = mt.extint_divmod_128_64(a, b)
if c != d or d != dr or b*d + dr != a:
assert_equal(d, c)
assert_equal(dr, cr)
assert_equal(b*d + dr, a)
def test_floordiv_128_64():
with exc_iter(INT128_VALUES, INT64_POS_VALUES) as it:
for a, b in it:
c = a // b
d = mt.extint_floordiv_128_64(a, b)
if c != d:
assert_equal(d, c)
def test_ceildiv_128_64():
with exc_iter(INT128_VALUES, INT64_POS_VALUES) as it:
for a, b in it:
c = (a + b - 1) // b
d = mt.extint_ceildiv_128_64(a, b)
if c != d:
assert_equal(d, c)
if __name__ == "__main__":
run_module_suite()
| 5,784 | 24.484581 | 76 |
py
|
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