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stringlengths 2
152
⌀ | file
stringlengths 15
239
| code
stringlengths 0
58.4M
| file_length
int64 0
58.4M
| avg_line_length
float64 0
1.81M
| max_line_length
int64 0
12.7M
| extension_type
stringclasses 364
values |
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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/testing/print_coercion_tables.py
|
#!/usr/bin/env python
"""Prints type-coercion tables for the built-in NumPy types
"""
from __future__ import division, absolute_import, print_function
import numpy as np
# Generic object that can be added, but doesn't do anything else
class GenericObject(object):
def __init__(self, v):
self.v = v
def __add__(self, other):
return self
def __radd__(self, other):
return self
dtype = np.dtype('O')
def print_cancast_table(ntypes):
print('X', end=' ')
for char in ntypes:
print(char, end=' ')
print()
for row in ntypes:
print(row, end=' ')
for col in ntypes:
print(int(np.can_cast(row, col)), end=' ')
print()
def print_coercion_table(ntypes, inputfirstvalue, inputsecondvalue, firstarray, use_promote_types=False):
print('+', end=' ')
for char in ntypes:
print(char, end=' ')
print()
for row in ntypes:
if row == 'O':
rowtype = GenericObject
else:
rowtype = np.obj2sctype(row)
print(row, end=' ')
for col in ntypes:
if col == 'O':
coltype = GenericObject
else:
coltype = np.obj2sctype(col)
try:
if firstarray:
rowvalue = np.array([rowtype(inputfirstvalue)], dtype=rowtype)
else:
rowvalue = rowtype(inputfirstvalue)
colvalue = coltype(inputsecondvalue)
if use_promote_types:
char = np.promote_types(rowvalue.dtype, colvalue.dtype).char
else:
value = np.add(rowvalue, colvalue)
if isinstance(value, np.ndarray):
char = value.dtype.char
else:
char = np.dtype(type(value)).char
except ValueError:
char = '!'
except OverflowError:
char = '@'
except TypeError:
char = '#'
print(char, end=' ')
print()
print("can cast")
print_cancast_table(np.typecodes['All'])
print()
print("In these tables, ValueError is '!', OverflowError is '@', TypeError is '#'")
print()
print("scalar + scalar")
print_coercion_table(np.typecodes['All'], 0, 0, False)
print()
print("scalar + neg scalar")
print_coercion_table(np.typecodes['All'], 0, -1, False)
print()
print("array + scalar")
print_coercion_table(np.typecodes['All'], 0, 0, True)
print()
print("array + neg scalar")
print_coercion_table(np.typecodes['All'], 0, -1, True)
print()
print("promote_types")
print_coercion_table(np.typecodes['All'], 0, 0, False, True)
| 2,705 | 28.413043 | 105 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/testing/__init__.py
|
"""Common test support for all numpy test scripts.
This single module should provide all the common functionality for numpy tests
in a single location, so that test scripts can just import it and work right
away.
"""
from __future__ import division, absolute_import, print_function
from unittest import TestCase
from . import decorators as dec
from .nosetester import run_module_suite, NoseTester as Tester, _numpy_tester
from .utils import *
test = _numpy_tester().test
| 475 | 28.75 | 78 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/testing/nosetester.py
|
"""
Back compatibility nosetester module. It will import the appropriate
set of tools
"""
from .nose_tools.nosetester import *
__all__ = ['get_package_name', 'run_module_suite', 'NoseTester',
'_numpy_tester', 'get_package_name', 'import_nose',
'suppress_warnings']
| 289 | 25.363636 | 68 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/testing/decorators.py
|
"""
Back compatibility decorators module. It will import the appropriate
set of tools
"""
from .nose_tools.decorators import *
| 128 | 17.428571 | 68 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/testing/tests/test_decorators.py
|
"""
Test the decorators from ``testing.decorators``.
"""
from __future__ import division, absolute_import, print_function
import warnings
from numpy.testing import (dec, assert_, assert_raises, run_module_suite,
SkipTest, KnownFailureException)
def test_slow():
@dec.slow
def slow_func(x, y, z):
pass
assert_(slow_func.slow)
def test_setastest():
@dec.setastest()
def f_default(a):
pass
@dec.setastest(True)
def f_istest(a):
pass
@dec.setastest(False)
def f_isnottest(a):
pass
assert_(f_default.__test__)
assert_(f_istest.__test__)
assert_(not f_isnottest.__test__)
class DidntSkipException(Exception):
pass
def test_skip_functions_hardcoded():
@dec.skipif(True)
def f1(x):
raise DidntSkipException
try:
f1('a')
except DidntSkipException:
raise Exception('Failed to skip')
except SkipTest:
pass
@dec.skipif(False)
def f2(x):
raise DidntSkipException
try:
f2('a')
except DidntSkipException:
pass
except SkipTest:
raise Exception('Skipped when not expected to')
def test_skip_functions_callable():
def skip_tester():
return skip_flag == 'skip me!'
@dec.skipif(skip_tester)
def f1(x):
raise DidntSkipException
try:
skip_flag = 'skip me!'
f1('a')
except DidntSkipException:
raise Exception('Failed to skip')
except SkipTest:
pass
@dec.skipif(skip_tester)
def f2(x):
raise DidntSkipException
try:
skip_flag = 'five is right out!'
f2('a')
except DidntSkipException:
pass
except SkipTest:
raise Exception('Skipped when not expected to')
def test_skip_generators_hardcoded():
@dec.knownfailureif(True, "This test is known to fail")
def g1(x):
for i in range(x):
yield i
try:
for j in g1(10):
pass
except KnownFailureException:
pass
else:
raise Exception('Failed to mark as known failure')
@dec.knownfailureif(False, "This test is NOT known to fail")
def g2(x):
for i in range(x):
yield i
raise DidntSkipException('FAIL')
try:
for j in g2(10):
pass
except KnownFailureException:
raise Exception('Marked incorrectly as known failure')
except DidntSkipException:
pass
def test_skip_generators_callable():
def skip_tester():
return skip_flag == 'skip me!'
@dec.knownfailureif(skip_tester, "This test is known to fail")
def g1(x):
for i in range(x):
yield i
try:
skip_flag = 'skip me!'
for j in g1(10):
pass
except KnownFailureException:
pass
else:
raise Exception('Failed to mark as known failure')
@dec.knownfailureif(skip_tester, "This test is NOT known to fail")
def g2(x):
for i in range(x):
yield i
raise DidntSkipException('FAIL')
try:
skip_flag = 'do not skip'
for j in g2(10):
pass
except KnownFailureException:
raise Exception('Marked incorrectly as known failure')
except DidntSkipException:
pass
def test_deprecated():
@dec.deprecated(True)
def non_deprecated_func():
pass
@dec.deprecated()
def deprecated_func():
import warnings
warnings.warn("TEST: deprecated func", DeprecationWarning)
@dec.deprecated()
def deprecated_func2():
import warnings
warnings.warn("AHHHH")
raise ValueError
@dec.deprecated()
def deprecated_func3():
import warnings
warnings.warn("AHHHH")
# marked as deprecated, but does not raise DeprecationWarning
assert_raises(AssertionError, non_deprecated_func)
# should be silent
deprecated_func()
with warnings.catch_warnings(record=True):
warnings.simplefilter("always") # do not propagate unrelated warnings
# fails if deprecated decorator just disables test. See #1453.
assert_raises(ValueError, deprecated_func2)
# warning is not a DeprecationWarning
assert_raises(AssertionError, deprecated_func3)
@dec.parametrize('base, power, expected',
[(1, 1, 1),
(2, 1, 2),
(2, 2, 4)])
def test_parametrize(base, power, expected):
assert_(base**power == expected)
if __name__ == '__main__':
run_module_suite()
| 4,554 | 21.661692 | 78 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/testing/tests/test_doctesting.py
|
""" Doctests for NumPy-specific nose/doctest modifications
"""
from __future__ import division, absolute_import, print_function
# try the #random directive on the output line
def check_random_directive():
'''
>>> 2+2
<BadExample object at 0x084D05AC> #random: may vary on your system
'''
# check the implicit "import numpy as np"
def check_implicit_np():
'''
>>> np.array([1,2,3])
array([1, 2, 3])
'''
# there's some extraneous whitespace around the correct responses
def check_whitespace_enabled():
'''
# whitespace after the 3
>>> 1+2
3
# whitespace before the 7
>>> 3+4
7
'''
def check_empty_output():
""" Check that no output does not cause an error.
This is related to nose bug 445; the numpy plugin changed the
doctest-result-variable default and therefore hit this bug:
http://code.google.com/p/python-nose/issues/detail?id=445
>>> a = 10
"""
def check_skip():
""" Check skip directive
The test below should not run
>>> 1/0 #doctest: +SKIP
"""
if __name__ == '__main__':
# Run tests outside numpy test rig
import nose
from numpy.testing.noseclasses import NumpyDoctest
argv = ['', __file__, '--with-numpydoctest']
nose.core.TestProgram(argv=argv, addplugins=[NumpyDoctest()])
| 1,322 | 22.210526 | 71 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/testing/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/testing/tests/test_utils.py
|
from __future__ import division, absolute_import, print_function
import warnings
import sys
import os
import itertools
import textwrap
import numpy as np
from numpy.testing import (
assert_equal, assert_array_equal, assert_almost_equal,
assert_array_almost_equal, assert_array_less, build_err_msg,
raises, assert_raises, assert_warns, assert_no_warnings,
assert_allclose, assert_approx_equal,
assert_array_almost_equal_nulp, assert_array_max_ulp,
clear_and_catch_warnings, suppress_warnings, run_module_suite,
assert_string_equal, assert_, tempdir, temppath,
)
import unittest
class _GenericTest(object):
def _test_equal(self, a, b):
self._assert_func(a, b)
def _test_not_equal(self, a, b):
try:
self._assert_func(a, b)
except AssertionError:
pass
else:
raise AssertionError("a and b are found equal but are not")
def test_array_rank1_eq(self):
"""Test two equal array of rank 1 are found equal."""
a = np.array([1, 2])
b = np.array([1, 2])
self._test_equal(a, b)
def test_array_rank1_noteq(self):
"""Test two different array of rank 1 are found not equal."""
a = np.array([1, 2])
b = np.array([2, 2])
self._test_not_equal(a, b)
def test_array_rank2_eq(self):
"""Test two equal array of rank 2 are found equal."""
a = np.array([[1, 2], [3, 4]])
b = np.array([[1, 2], [3, 4]])
self._test_equal(a, b)
def test_array_diffshape(self):
"""Test two arrays with different shapes are found not equal."""
a = np.array([1, 2])
b = np.array([[1, 2], [1, 2]])
self._test_not_equal(a, b)
def test_objarray(self):
"""Test object arrays."""
a = np.array([1, 1], dtype=object)
self._test_equal(a, 1)
def test_array_likes(self):
self._test_equal([1, 2, 3], (1, 2, 3))
class TestArrayEqual(_GenericTest, unittest.TestCase):
def setUp(self):
self._assert_func = assert_array_equal
def test_generic_rank1(self):
"""Test rank 1 array for all dtypes."""
def foo(t):
a = np.empty(2, t)
a.fill(1)
b = a.copy()
c = a.copy()
c.fill(0)
self._test_equal(a, b)
self._test_not_equal(c, b)
# Test numeric types and object
for t in '?bhilqpBHILQPfdgFDG':
foo(t)
# Test strings
for t in ['S1', 'U1']:
foo(t)
def test_generic_rank3(self):
"""Test rank 3 array for all dtypes."""
def foo(t):
a = np.empty((4, 2, 3), t)
a.fill(1)
b = a.copy()
c = a.copy()
c.fill(0)
self._test_equal(a, b)
self._test_not_equal(c, b)
# Test numeric types and object
for t in '?bhilqpBHILQPfdgFDG':
foo(t)
# Test strings
for t in ['S1', 'U1']:
foo(t)
def test_nan_array(self):
"""Test arrays with nan values in them."""
a = np.array([1, 2, np.nan])
b = np.array([1, 2, np.nan])
self._test_equal(a, b)
c = np.array([1, 2, 3])
self._test_not_equal(c, b)
def test_string_arrays(self):
"""Test two arrays with different shapes are found not equal."""
a = np.array(['floupi', 'floupa'])
b = np.array(['floupi', 'floupa'])
self._test_equal(a, b)
c = np.array(['floupipi', 'floupa'])
self._test_not_equal(c, b)
def test_recarrays(self):
"""Test record arrays."""
a = np.empty(2, [('floupi', float), ('floupa', float)])
a['floupi'] = [1, 2]
a['floupa'] = [1, 2]
b = a.copy()
self._test_equal(a, b)
c = np.empty(2, [('floupipi', float), ('floupa', float)])
c['floupipi'] = a['floupi'].copy()
c['floupa'] = a['floupa'].copy()
with suppress_warnings() as sup:
l = sup.record(FutureWarning, message="elementwise == ")
self._test_not_equal(c, b)
assert_(len(l) == 1)
class TestBuildErrorMessage(unittest.TestCase):
def test_build_err_msg_defaults(self):
x = np.array([1.00001, 2.00002, 3.00003])
y = np.array([1.00002, 2.00003, 3.00004])
err_msg = 'There is a mismatch'
a = build_err_msg([x, y], err_msg)
b = ('\nItems are not equal: There is a mismatch\n ACTUAL: array(['
'1.00001, 2.00002, 3.00003])\n DESIRED: array([1.00002, '
'2.00003, 3.00004])')
self.assertEqual(a, b)
def test_build_err_msg_no_verbose(self):
x = np.array([1.00001, 2.00002, 3.00003])
y = np.array([1.00002, 2.00003, 3.00004])
err_msg = 'There is a mismatch'
a = build_err_msg([x, y], err_msg, verbose=False)
b = '\nItems are not equal: There is a mismatch'
self.assertEqual(a, b)
def test_build_err_msg_custom_names(self):
x = np.array([1.00001, 2.00002, 3.00003])
y = np.array([1.00002, 2.00003, 3.00004])
err_msg = 'There is a mismatch'
a = build_err_msg([x, y], err_msg, names=('FOO', 'BAR'))
b = ('\nItems are not equal: There is a mismatch\n FOO: array(['
'1.00001, 2.00002, 3.00003])\n BAR: array([1.00002, 2.00003, '
'3.00004])')
self.assertEqual(a, b)
def test_build_err_msg_custom_precision(self):
x = np.array([1.000000001, 2.00002, 3.00003])
y = np.array([1.000000002, 2.00003, 3.00004])
err_msg = 'There is a mismatch'
a = build_err_msg([x, y], err_msg, precision=10)
b = ('\nItems are not equal: There is a mismatch\n ACTUAL: array(['
'1.000000001, 2.00002 , 3.00003 ])\n DESIRED: array(['
'1.000000002, 2.00003 , 3.00004 ])')
self.assertEqual(a, b)
class TestEqual(TestArrayEqual):
def setUp(self):
self._assert_func = assert_equal
def test_nan_items(self):
self._assert_func(np.nan, np.nan)
self._assert_func([np.nan], [np.nan])
self._test_not_equal(np.nan, [np.nan])
self._test_not_equal(np.nan, 1)
def test_inf_items(self):
self._assert_func(np.inf, np.inf)
self._assert_func([np.inf], [np.inf])
self._test_not_equal(np.inf, [np.inf])
def test_datetime(self):
self._test_equal(
np.datetime64("2017-01-01", "s"),
np.datetime64("2017-01-01", "s")
)
self._test_equal(
np.datetime64("2017-01-01", "s"),
np.datetime64("2017-01-01", "m")
)
# gh-10081
self._test_not_equal(
np.datetime64("2017-01-01", "s"),
np.datetime64("2017-01-02", "s")
)
self._test_not_equal(
np.datetime64("2017-01-01", "s"),
np.datetime64("2017-01-02", "m")
)
def test_nat_items(self):
# not a datetime
nadt_no_unit = np.datetime64("NaT")
nadt_s = np.datetime64("NaT", "s")
nadt_d = np.datetime64("NaT", "ns")
# not a timedelta
natd_no_unit = np.timedelta64("NaT")
natd_s = np.timedelta64("NaT", "s")
natd_d = np.timedelta64("NaT", "ns")
dts = [nadt_no_unit, nadt_s, nadt_d]
tds = [natd_no_unit, natd_s, natd_d]
for a, b in itertools.product(dts, dts):
self._assert_func(a, b)
self._assert_func([a], [b])
self._test_not_equal([a], b)
for a, b in itertools.product(tds, tds):
self._assert_func(a, b)
self._assert_func([a], [b])
self._test_not_equal([a], b)
for a, b in itertools.product(tds, dts):
self._test_not_equal(a, b)
self._test_not_equal(a, [b])
self._test_not_equal([a], [b])
self._test_not_equal([a], np.datetime64("2017-01-01", "s"))
self._test_not_equal([b], np.datetime64("2017-01-01", "s"))
self._test_not_equal([a], np.timedelta64(123, "s"))
self._test_not_equal([b], np.timedelta64(123, "s"))
def test_non_numeric(self):
self._assert_func('ab', 'ab')
self._test_not_equal('ab', 'abb')
def test_complex_item(self):
self._assert_func(complex(1, 2), complex(1, 2))
self._assert_func(complex(1, np.nan), complex(1, np.nan))
self._test_not_equal(complex(1, np.nan), complex(1, 2))
self._test_not_equal(complex(np.nan, 1), complex(1, np.nan))
self._test_not_equal(complex(np.nan, np.inf), complex(np.nan, 2))
def test_negative_zero(self):
self._test_not_equal(np.PZERO, np.NZERO)
def test_complex(self):
x = np.array([complex(1, 2), complex(1, np.nan)])
y = np.array([complex(1, 2), complex(1, 2)])
self._assert_func(x, x)
self._test_not_equal(x, y)
def test_error_message(self):
try:
self._assert_func(np.array([1, 2]), np.matrix([1, 2]))
except AssertionError as e:
msg = str(e)
msg2 = msg.replace("shapes (2L,), (1L, 2L)", "shapes (2,), (1, 2)")
msg_reference = textwrap.dedent("""\
Arrays are not equal
(shapes (2,), (1, 2) mismatch)
x: array([1, 2])
y: matrix([[1, 2]])""")
try:
self.assertEqual(msg, msg_reference)
except AssertionError:
self.assertEqual(msg2, msg_reference)
else:
raise AssertionError("Did not raise")
class TestArrayAlmostEqual(_GenericTest, unittest.TestCase):
def setUp(self):
self._assert_func = assert_array_almost_equal
def test_closeness(self):
# Note that in the course of time we ended up with
# `abs(x - y) < 1.5 * 10**(-decimal)`
# instead of the previously documented
# `abs(x - y) < 0.5 * 10**(-decimal)`
# so this check serves to preserve the wrongness.
# test scalars
self._assert_func(1.499999, 0.0, decimal=0)
self.assertRaises(AssertionError,
lambda: self._assert_func(1.5, 0.0, decimal=0))
# test arrays
self._assert_func([1.499999], [0.0], decimal=0)
self.assertRaises(AssertionError,
lambda: self._assert_func([1.5], [0.0], decimal=0))
def test_simple(self):
x = np.array([1234.2222])
y = np.array([1234.2223])
self._assert_func(x, y, decimal=3)
self._assert_func(x, y, decimal=4)
self.assertRaises(AssertionError,
lambda: self._assert_func(x, y, decimal=5))
def test_nan(self):
anan = np.array([np.nan])
aone = np.array([1])
ainf = np.array([np.inf])
self._assert_func(anan, anan)
self.assertRaises(AssertionError,
lambda: self._assert_func(anan, aone))
self.assertRaises(AssertionError,
lambda: self._assert_func(anan, ainf))
self.assertRaises(AssertionError,
lambda: self._assert_func(ainf, anan))
def test_inf(self):
a = np.array([[1., 2.], [3., 4.]])
b = a.copy()
a[0, 0] = np.inf
self.assertRaises(AssertionError,
lambda: self._assert_func(a, b))
b[0, 0] = -np.inf
self.assertRaises(AssertionError,
lambda: self._assert_func(a, b))
def test_subclass(self):
a = np.array([[1., 2.], [3., 4.]])
b = np.ma.masked_array([[1., 2.], [0., 4.]],
[[False, False], [True, False]])
self._assert_func(a, b)
self._assert_func(b, a)
self._assert_func(b, b)
def test_matrix(self):
# Matrix slicing keeps things 2-D, while array does not necessarily.
# See gh-8452.
m1 = np.matrix([[1., 2.]])
m2 = np.matrix([[1., np.nan]])
m3 = np.matrix([[1., -np.inf]])
m4 = np.matrix([[np.nan, np.inf]])
m5 = np.matrix([[1., 2.], [np.nan, np.inf]])
for m in m1, m2, m3, m4, m5:
self._assert_func(m, m)
a = np.array(m)
self._assert_func(a, m)
self._assert_func(m, a)
def test_subclass_that_cannot_be_bool(self):
# While we cannot guarantee testing functions will always work for
# subclasses, the tests should ideally rely only on subclasses having
# comparison operators, not on them being able to store booleans
# (which, e.g., astropy Quantity cannot usefully do). See gh-8452.
class MyArray(np.ndarray):
def __lt__(self, other):
return super(MyArray, self).__lt__(other).view(np.ndarray)
def all(self, *args, **kwargs):
raise NotImplementedError
a = np.array([1., 2.]).view(MyArray)
self._assert_func(a, a)
class TestAlmostEqual(_GenericTest, unittest.TestCase):
def setUp(self):
self._assert_func = assert_almost_equal
def test_closeness(self):
# Note that in the course of time we ended up with
# `abs(x - y) < 1.5 * 10**(-decimal)`
# instead of the previously documented
# `abs(x - y) < 0.5 * 10**(-decimal)`
# so this check serves to preserve the wrongness.
# test scalars
self._assert_func(1.499999, 0.0, decimal=0)
self.assertRaises(AssertionError,
lambda: self._assert_func(1.5, 0.0, decimal=0))
# test arrays
self._assert_func([1.499999], [0.0], decimal=0)
self.assertRaises(AssertionError,
lambda: self._assert_func([1.5], [0.0], decimal=0))
def test_nan_item(self):
self._assert_func(np.nan, np.nan)
self.assertRaises(AssertionError,
lambda: self._assert_func(np.nan, 1))
self.assertRaises(AssertionError,
lambda: self._assert_func(np.nan, np.inf))
self.assertRaises(AssertionError,
lambda: self._assert_func(np.inf, np.nan))
def test_inf_item(self):
self._assert_func(np.inf, np.inf)
self._assert_func(-np.inf, -np.inf)
self.assertRaises(AssertionError,
lambda: self._assert_func(np.inf, 1))
self.assertRaises(AssertionError,
lambda: self._assert_func(-np.inf, np.inf))
def test_simple_item(self):
self._test_not_equal(1, 2)
def test_complex_item(self):
self._assert_func(complex(1, 2), complex(1, 2))
self._assert_func(complex(1, np.nan), complex(1, np.nan))
self._assert_func(complex(np.inf, np.nan), complex(np.inf, np.nan))
self._test_not_equal(complex(1, np.nan), complex(1, 2))
self._test_not_equal(complex(np.nan, 1), complex(1, np.nan))
self._test_not_equal(complex(np.nan, np.inf), complex(np.nan, 2))
def test_complex(self):
x = np.array([complex(1, 2), complex(1, np.nan)])
z = np.array([complex(1, 2), complex(np.nan, 1)])
y = np.array([complex(1, 2), complex(1, 2)])
self._assert_func(x, x)
self._test_not_equal(x, y)
self._test_not_equal(x, z)
def test_error_message(self):
"""Check the message is formatted correctly for the decimal value"""
x = np.array([1.00000000001, 2.00000000002, 3.00003])
y = np.array([1.00000000002, 2.00000000003, 3.00004])
# test with a different amount of decimal digits
# note that we only check for the formatting of the arrays themselves
b = ('x: array([1.00000000001, 2.00000000002, 3.00003 '
' ])\n y: array([1.00000000002, 2.00000000003, 3.00004 ])')
try:
self._assert_func(x, y, decimal=12)
except AssertionError as e:
# remove anything that's not the array string
self.assertEqual(str(e).split('%)\n ')[1], b)
# with the default value of decimal digits, only the 3rd element differs
# note that we only check for the formatting of the arrays themselves
b = ('x: array([1. , 2. , 3.00003])\n y: array([1. , '
'2. , 3.00004])')
try:
self._assert_func(x, y)
except AssertionError as e:
# remove anything that's not the array string
self.assertEqual(str(e).split('%)\n ')[1], b)
def test_matrix(self):
# Matrix slicing keeps things 2-D, while array does not necessarily.
# See gh-8452.
m1 = np.matrix([[1., 2.]])
m2 = np.matrix([[1., np.nan]])
m3 = np.matrix([[1., -np.inf]])
m4 = np.matrix([[np.nan, np.inf]])
m5 = np.matrix([[1., 2.], [np.nan, np.inf]])
for m in m1, m2, m3, m4, m5:
self._assert_func(m, m)
a = np.array(m)
self._assert_func(a, m)
self._assert_func(m, a)
def test_subclass_that_cannot_be_bool(self):
# While we cannot guarantee testing functions will always work for
# subclasses, the tests should ideally rely only on subclasses having
# comparison operators, not on them being able to store booleans
# (which, e.g., astropy Quantity cannot usefully do). See gh-8452.
class MyArray(np.ndarray):
def __lt__(self, other):
return super(MyArray, self).__lt__(other).view(np.ndarray)
def all(self, *args, **kwargs):
raise NotImplementedError
a = np.array([1., 2.]).view(MyArray)
self._assert_func(a, a)
class TestApproxEqual(unittest.TestCase):
def setUp(self):
self._assert_func = assert_approx_equal
def test_simple_arrays(self):
x = np.array([1234.22])
y = np.array([1234.23])
self._assert_func(x, y, significant=5)
self._assert_func(x, y, significant=6)
self.assertRaises(AssertionError,
lambda: self._assert_func(x, y, significant=7))
def test_simple_items(self):
x = 1234.22
y = 1234.23
self._assert_func(x, y, significant=4)
self._assert_func(x, y, significant=5)
self._assert_func(x, y, significant=6)
self.assertRaises(AssertionError,
lambda: self._assert_func(x, y, significant=7))
def test_nan_array(self):
anan = np.array(np.nan)
aone = np.array(1)
ainf = np.array(np.inf)
self._assert_func(anan, anan)
self.assertRaises(AssertionError,
lambda: self._assert_func(anan, aone))
self.assertRaises(AssertionError,
lambda: self._assert_func(anan, ainf))
self.assertRaises(AssertionError,
lambda: self._assert_func(ainf, anan))
def test_nan_items(self):
anan = np.array(np.nan)
aone = np.array(1)
ainf = np.array(np.inf)
self._assert_func(anan, anan)
self.assertRaises(AssertionError,
lambda: self._assert_func(anan, aone))
self.assertRaises(AssertionError,
lambda: self._assert_func(anan, ainf))
self.assertRaises(AssertionError,
lambda: self._assert_func(ainf, anan))
class TestArrayAssertLess(unittest.TestCase):
def setUp(self):
self._assert_func = assert_array_less
def test_simple_arrays(self):
x = np.array([1.1, 2.2])
y = np.array([1.2, 2.3])
self._assert_func(x, y)
self.assertRaises(AssertionError,
lambda: self._assert_func(y, x))
y = np.array([1.0, 2.3])
self.assertRaises(AssertionError,
lambda: self._assert_func(x, y))
self.assertRaises(AssertionError,
lambda: self._assert_func(y, x))
def test_rank2(self):
x = np.array([[1.1, 2.2], [3.3, 4.4]])
y = np.array([[1.2, 2.3], [3.4, 4.5]])
self._assert_func(x, y)
self.assertRaises(AssertionError,
lambda: self._assert_func(y, x))
y = np.array([[1.0, 2.3], [3.4, 4.5]])
self.assertRaises(AssertionError,
lambda: self._assert_func(x, y))
self.assertRaises(AssertionError,
lambda: self._assert_func(y, x))
def test_rank3(self):
x = np.ones(shape=(2, 2, 2))
y = np.ones(shape=(2, 2, 2))+1
self._assert_func(x, y)
self.assertRaises(AssertionError,
lambda: self._assert_func(y, x))
y[0, 0, 0] = 0
self.assertRaises(AssertionError,
lambda: self._assert_func(x, y))
self.assertRaises(AssertionError,
lambda: self._assert_func(y, x))
def test_simple_items(self):
x = 1.1
y = 2.2
self._assert_func(x, y)
self.assertRaises(AssertionError,
lambda: self._assert_func(y, x))
y = np.array([2.2, 3.3])
self._assert_func(x, y)
self.assertRaises(AssertionError,
lambda: self._assert_func(y, x))
y = np.array([1.0, 3.3])
self.assertRaises(AssertionError,
lambda: self._assert_func(x, y))
def test_nan_noncompare(self):
anan = np.array(np.nan)
aone = np.array(1)
ainf = np.array(np.inf)
self._assert_func(anan, anan)
self.assertRaises(AssertionError,
lambda: self._assert_func(aone, anan))
self.assertRaises(AssertionError,
lambda: self._assert_func(anan, aone))
self.assertRaises(AssertionError,
lambda: self._assert_func(anan, ainf))
self.assertRaises(AssertionError,
lambda: self._assert_func(ainf, anan))
def test_nan_noncompare_array(self):
x = np.array([1.1, 2.2, 3.3])
anan = np.array(np.nan)
self.assertRaises(AssertionError,
lambda: self._assert_func(x, anan))
self.assertRaises(AssertionError,
lambda: self._assert_func(anan, x))
x = np.array([1.1, 2.2, np.nan])
self.assertRaises(AssertionError,
lambda: self._assert_func(x, anan))
self.assertRaises(AssertionError,
lambda: self._assert_func(anan, x))
y = np.array([1.0, 2.0, np.nan])
self._assert_func(y, x)
self.assertRaises(AssertionError,
lambda: self._assert_func(x, y))
def test_inf_compare(self):
aone = np.array(1)
ainf = np.array(np.inf)
self._assert_func(aone, ainf)
self._assert_func(-ainf, aone)
self._assert_func(-ainf, ainf)
self.assertRaises(AssertionError,
lambda: self._assert_func(ainf, aone))
self.assertRaises(AssertionError,
lambda: self._assert_func(aone, -ainf))
self.assertRaises(AssertionError,
lambda: self._assert_func(ainf, ainf))
self.assertRaises(AssertionError,
lambda: self._assert_func(ainf, -ainf))
self.assertRaises(AssertionError,
lambda: self._assert_func(-ainf, -ainf))
def test_inf_compare_array(self):
x = np.array([1.1, 2.2, np.inf])
ainf = np.array(np.inf)
self.assertRaises(AssertionError,
lambda: self._assert_func(x, ainf))
self.assertRaises(AssertionError,
lambda: self._assert_func(ainf, x))
self.assertRaises(AssertionError,
lambda: self._assert_func(x, -ainf))
self.assertRaises(AssertionError,
lambda: self._assert_func(-x, -ainf))
self.assertRaises(AssertionError,
lambda: self._assert_func(-ainf, -x))
self._assert_func(-ainf, x)
class TestRaises(unittest.TestCase):
def setUp(self):
class MyException(Exception):
pass
self.e = MyException
def raises_exception(self, e):
raise e
def does_not_raise_exception(self):
pass
def test_correct_catch(self):
raises(self.e)(self.raises_exception)(self.e) # raises?
def test_wrong_exception(self):
try:
raises(self.e)(self.raises_exception)(RuntimeError) # raises?
except RuntimeError:
return
else:
raise AssertionError("should have caught RuntimeError")
def test_catch_no_raise(self):
try:
raises(self.e)(self.does_not_raise_exception)() # raises?
except AssertionError:
return
else:
raise AssertionError("should have raised an AssertionError")
class TestWarns(unittest.TestCase):
def test_warn(self):
def f():
warnings.warn("yo")
return 3
before_filters = sys.modules['warnings'].filters[:]
assert_equal(assert_warns(UserWarning, f), 3)
after_filters = sys.modules['warnings'].filters
assert_raises(AssertionError, assert_no_warnings, f)
assert_equal(assert_no_warnings(lambda x: x, 1), 1)
# Check that the warnings state is unchanged
assert_equal(before_filters, after_filters,
"assert_warns does not preserver warnings state")
def test_context_manager(self):
before_filters = sys.modules['warnings'].filters[:]
with assert_warns(UserWarning):
warnings.warn("yo")
after_filters = sys.modules['warnings'].filters
def no_warnings():
with assert_no_warnings():
warnings.warn("yo")
assert_raises(AssertionError, no_warnings)
assert_equal(before_filters, after_filters,
"assert_warns does not preserver warnings state")
def test_warn_wrong_warning(self):
def f():
warnings.warn("yo", DeprecationWarning)
failed = False
with warnings.catch_warnings():
warnings.simplefilter("error", DeprecationWarning)
try:
# Should raise a DeprecationWarning
assert_warns(UserWarning, f)
failed = True
except DeprecationWarning:
pass
if failed:
raise AssertionError("wrong warning caught by assert_warn")
class TestAssertAllclose(unittest.TestCase):
def test_simple(self):
x = 1e-3
y = 1e-9
assert_allclose(x, y, atol=1)
self.assertRaises(AssertionError, assert_allclose, x, y)
a = np.array([x, y, x, y])
b = np.array([x, y, x, x])
assert_allclose(a, b, atol=1)
self.assertRaises(AssertionError, assert_allclose, a, b)
b[-1] = y * (1 + 1e-8)
assert_allclose(a, b)
self.assertRaises(AssertionError, assert_allclose, a, b,
rtol=1e-9)
assert_allclose(6, 10, rtol=0.5)
self.assertRaises(AssertionError, assert_allclose, 10, 6, rtol=0.5)
def test_min_int(self):
a = np.array([np.iinfo(np.int_).min], dtype=np.int_)
# Should not raise:
assert_allclose(a, a)
def test_report_fail_percentage(self):
a = np.array([1, 1, 1, 1])
b = np.array([1, 1, 1, 2])
try:
assert_allclose(a, b)
msg = ''
except AssertionError as exc:
msg = exc.args[0]
self.assertTrue("mismatch 25.0%" in msg)
def test_equal_nan(self):
a = np.array([np.nan])
b = np.array([np.nan])
# Should not raise:
assert_allclose(a, b, equal_nan=True)
def test_not_equal_nan(self):
a = np.array([np.nan])
b = np.array([np.nan])
self.assertRaises(AssertionError, assert_allclose, a, b,
equal_nan=False)
def test_equal_nan_default(self):
# Make sure equal_nan default behavior remains unchanged. (All
# of these functions use assert_array_compare under the hood.)
# None of these should raise.
a = np.array([np.nan])
b = np.array([np.nan])
assert_array_equal(a, b)
assert_array_almost_equal(a, b)
assert_array_less(a, b)
assert_allclose(a, b)
class TestArrayAlmostEqualNulp(unittest.TestCase):
def test_float64_pass(self):
# The number of units of least precision
# In this case, use a few places above the lowest level (ie nulp=1)
nulp = 5
x = np.linspace(-20, 20, 50, dtype=np.float64)
x = 10**x
x = np.r_[-x, x]
# Addition
eps = np.finfo(x.dtype).eps
y = x + x*eps*nulp/2.
assert_array_almost_equal_nulp(x, y, nulp)
# Subtraction
epsneg = np.finfo(x.dtype).epsneg
y = x - x*epsneg*nulp/2.
assert_array_almost_equal_nulp(x, y, nulp)
def test_float64_fail(self):
nulp = 5
x = np.linspace(-20, 20, 50, dtype=np.float64)
x = 10**x
x = np.r_[-x, x]
eps = np.finfo(x.dtype).eps
y = x + x*eps*nulp*2.
self.assertRaises(AssertionError, assert_array_almost_equal_nulp,
x, y, nulp)
epsneg = np.finfo(x.dtype).epsneg
y = x - x*epsneg*nulp*2.
self.assertRaises(AssertionError, assert_array_almost_equal_nulp,
x, y, nulp)
def test_float32_pass(self):
nulp = 5
x = np.linspace(-20, 20, 50, dtype=np.float32)
x = 10**x
x = np.r_[-x, x]
eps = np.finfo(x.dtype).eps
y = x + x*eps*nulp/2.
assert_array_almost_equal_nulp(x, y, nulp)
epsneg = np.finfo(x.dtype).epsneg
y = x - x*epsneg*nulp/2.
assert_array_almost_equal_nulp(x, y, nulp)
def test_float32_fail(self):
nulp = 5
x = np.linspace(-20, 20, 50, dtype=np.float32)
x = 10**x
x = np.r_[-x, x]
eps = np.finfo(x.dtype).eps
y = x + x*eps*nulp*2.
self.assertRaises(AssertionError, assert_array_almost_equal_nulp,
x, y, nulp)
epsneg = np.finfo(x.dtype).epsneg
y = x - x*epsneg*nulp*2.
self.assertRaises(AssertionError, assert_array_almost_equal_nulp,
x, y, nulp)
def test_complex128_pass(self):
nulp = 5
x = np.linspace(-20, 20, 50, dtype=np.float64)
x = 10**x
x = np.r_[-x, x]
xi = x + x*1j
eps = np.finfo(x.dtype).eps
y = x + x*eps*nulp/2.
assert_array_almost_equal_nulp(xi, x + y*1j, nulp)
assert_array_almost_equal_nulp(xi, y + x*1j, nulp)
# The test condition needs to be at least a factor of sqrt(2) smaller
# because the real and imaginary parts both change
y = x + x*eps*nulp/4.
assert_array_almost_equal_nulp(xi, y + y*1j, nulp)
epsneg = np.finfo(x.dtype).epsneg
y = x - x*epsneg*nulp/2.
assert_array_almost_equal_nulp(xi, x + y*1j, nulp)
assert_array_almost_equal_nulp(xi, y + x*1j, nulp)
y = x - x*epsneg*nulp/4.
assert_array_almost_equal_nulp(xi, y + y*1j, nulp)
def test_complex128_fail(self):
nulp = 5
x = np.linspace(-20, 20, 50, dtype=np.float64)
x = 10**x
x = np.r_[-x, x]
xi = x + x*1j
eps = np.finfo(x.dtype).eps
y = x + x*eps*nulp*2.
self.assertRaises(AssertionError, assert_array_almost_equal_nulp,
xi, x + y*1j, nulp)
self.assertRaises(AssertionError, assert_array_almost_equal_nulp,
xi, y + x*1j, nulp)
# The test condition needs to be at least a factor of sqrt(2) smaller
# because the real and imaginary parts both change
y = x + x*eps*nulp
self.assertRaises(AssertionError, assert_array_almost_equal_nulp,
xi, y + y*1j, nulp)
epsneg = np.finfo(x.dtype).epsneg
y = x - x*epsneg*nulp*2.
self.assertRaises(AssertionError, assert_array_almost_equal_nulp,
xi, x + y*1j, nulp)
self.assertRaises(AssertionError, assert_array_almost_equal_nulp,
xi, y + x*1j, nulp)
y = x - x*epsneg*nulp
self.assertRaises(AssertionError, assert_array_almost_equal_nulp,
xi, y + y*1j, nulp)
def test_complex64_pass(self):
nulp = 5
x = np.linspace(-20, 20, 50, dtype=np.float32)
x = 10**x
x = np.r_[-x, x]
xi = x + x*1j
eps = np.finfo(x.dtype).eps
y = x + x*eps*nulp/2.
assert_array_almost_equal_nulp(xi, x + y*1j, nulp)
assert_array_almost_equal_nulp(xi, y + x*1j, nulp)
y = x + x*eps*nulp/4.
assert_array_almost_equal_nulp(xi, y + y*1j, nulp)
epsneg = np.finfo(x.dtype).epsneg
y = x - x*epsneg*nulp/2.
assert_array_almost_equal_nulp(xi, x + y*1j, nulp)
assert_array_almost_equal_nulp(xi, y + x*1j, nulp)
y = x - x*epsneg*nulp/4.
assert_array_almost_equal_nulp(xi, y + y*1j, nulp)
def test_complex64_fail(self):
nulp = 5
x = np.linspace(-20, 20, 50, dtype=np.float32)
x = 10**x
x = np.r_[-x, x]
xi = x + x*1j
eps = np.finfo(x.dtype).eps
y = x + x*eps*nulp*2.
self.assertRaises(AssertionError, assert_array_almost_equal_nulp,
xi, x + y*1j, nulp)
self.assertRaises(AssertionError, assert_array_almost_equal_nulp,
xi, y + x*1j, nulp)
y = x + x*eps*nulp
self.assertRaises(AssertionError, assert_array_almost_equal_nulp,
xi, y + y*1j, nulp)
epsneg = np.finfo(x.dtype).epsneg
y = x - x*epsneg*nulp*2.
self.assertRaises(AssertionError, assert_array_almost_equal_nulp,
xi, x + y*1j, nulp)
self.assertRaises(AssertionError, assert_array_almost_equal_nulp,
xi, y + x*1j, nulp)
y = x - x*epsneg*nulp
self.assertRaises(AssertionError, assert_array_almost_equal_nulp,
xi, y + y*1j, nulp)
class TestULP(unittest.TestCase):
def test_equal(self):
x = np.random.randn(10)
assert_array_max_ulp(x, x, maxulp=0)
def test_single(self):
# Generate 1 + small deviation, check that adding eps gives a few UNL
x = np.ones(10).astype(np.float32)
x += 0.01 * np.random.randn(10).astype(np.float32)
eps = np.finfo(np.float32).eps
assert_array_max_ulp(x, x+eps, maxulp=20)
def test_double(self):
# Generate 1 + small deviation, check that adding eps gives a few UNL
x = np.ones(10).astype(np.float64)
x += 0.01 * np.random.randn(10).astype(np.float64)
eps = np.finfo(np.float64).eps
assert_array_max_ulp(x, x+eps, maxulp=200)
def test_inf(self):
for dt in [np.float32, np.float64]:
inf = np.array([np.inf]).astype(dt)
big = np.array([np.finfo(dt).max])
assert_array_max_ulp(inf, big, maxulp=200)
def test_nan(self):
# Test that nan is 'far' from small, tiny, inf, max and min
for dt in [np.float32, np.float64]:
if dt == np.float32:
maxulp = 1e6
else:
maxulp = 1e12
inf = np.array([np.inf]).astype(dt)
nan = np.array([np.nan]).astype(dt)
big = np.array([np.finfo(dt).max])
tiny = np.array([np.finfo(dt).tiny])
zero = np.array([np.PZERO]).astype(dt)
nzero = np.array([np.NZERO]).astype(dt)
self.assertRaises(AssertionError,
lambda: assert_array_max_ulp(nan, inf,
maxulp=maxulp))
self.assertRaises(AssertionError,
lambda: assert_array_max_ulp(nan, big,
maxulp=maxulp))
self.assertRaises(AssertionError,
lambda: assert_array_max_ulp(nan, tiny,
maxulp=maxulp))
self.assertRaises(AssertionError,
lambda: assert_array_max_ulp(nan, zero,
maxulp=maxulp))
self.assertRaises(AssertionError,
lambda: assert_array_max_ulp(nan, nzero,
maxulp=maxulp))
class TestStringEqual(unittest.TestCase):
def test_simple(self):
assert_string_equal("hello", "hello")
assert_string_equal("hello\nmultiline", "hello\nmultiline")
try:
assert_string_equal("foo\nbar", "hello\nbar")
except AssertionError as exc:
assert_equal(str(exc), "Differences in strings:\n- foo\n+ hello")
else:
raise AssertionError("exception not raised")
self.assertRaises(AssertionError,
lambda: assert_string_equal("foo", "hello"))
def assert_warn_len_equal(mod, n_in_context, py3_n_in_context=None):
mod_warns = mod.__warningregistry__
# Python 3.4 appears to clear any pre-existing warnings of the same type,
# when raising warnings inside a catch_warnings block. So, there is a
# warning generated by the tests within the context manager, but no
# previous warnings.
if 'version' in mod_warns:
if py3_n_in_context is None:
py3_n_in_context = n_in_context
assert_equal(len(mod_warns) - 1, py3_n_in_context)
else:
assert_equal(len(mod_warns), n_in_context)
def _get_fresh_mod():
# Get this module, with warning registry empty
my_mod = sys.modules[__name__]
try:
my_mod.__warningregistry__.clear()
except AttributeError:
pass
return my_mod
def test_clear_and_catch_warnings():
# Initial state of module, no warnings
my_mod = _get_fresh_mod()
assert_equal(getattr(my_mod, '__warningregistry__', {}), {})
with clear_and_catch_warnings(modules=[my_mod]):
warnings.simplefilter('ignore')
warnings.warn('Some warning')
assert_equal(my_mod.__warningregistry__, {})
# Without specified modules, don't clear warnings during context
with clear_and_catch_warnings():
warnings.simplefilter('ignore')
warnings.warn('Some warning')
assert_warn_len_equal(my_mod, 1)
# Confirm that specifying module keeps old warning, does not add new
with clear_and_catch_warnings(modules=[my_mod]):
warnings.simplefilter('ignore')
warnings.warn('Another warning')
assert_warn_len_equal(my_mod, 1)
# Another warning, no module spec does add to warnings dict, except on
# Python 3.4 (see comments in `assert_warn_len_equal`)
with clear_and_catch_warnings():
warnings.simplefilter('ignore')
warnings.warn('Another warning')
assert_warn_len_equal(my_mod, 2, 1)
def test_suppress_warnings_module():
# Initial state of module, no warnings
my_mod = _get_fresh_mod()
assert_equal(getattr(my_mod, '__warningregistry__', {}), {})
def warn_other_module():
# Apply along axis is implemented in python; stacklevel=2 means
# we end up inside its module, not ours.
def warn(arr):
warnings.warn("Some warning 2", stacklevel=2)
return arr
np.apply_along_axis(warn, 0, [0])
# Test module based warning suppression:
with suppress_warnings() as sup:
sup.record(UserWarning)
# suppress warning from other module (may have .pyc ending),
# if apply_along_axis is moved, had to be changed.
sup.filter(module=np.lib.shape_base)
warnings.warn("Some warning")
warn_other_module()
# Check that the suppression did test the file correctly (this module
# got filtered)
assert_(len(sup.log) == 1)
assert_(sup.log[0].message.args[0] == "Some warning")
assert_warn_len_equal(my_mod, 0)
sup = suppress_warnings()
# Will have to be changed if apply_along_axis is moved:
sup.filter(module=my_mod)
with sup:
warnings.warn('Some warning')
assert_warn_len_equal(my_mod, 0)
# And test repeat works:
sup.filter(module=my_mod)
with sup:
warnings.warn('Some warning')
assert_warn_len_equal(my_mod, 0)
# Without specified modules, don't clear warnings during context
with suppress_warnings():
warnings.simplefilter('ignore')
warnings.warn('Some warning')
assert_warn_len_equal(my_mod, 1)
def test_suppress_warnings_type():
# Initial state of module, no warnings
my_mod = _get_fresh_mod()
assert_equal(getattr(my_mod, '__warningregistry__', {}), {})
# Test module based warning suppression:
with suppress_warnings() as sup:
sup.filter(UserWarning)
warnings.warn('Some warning')
assert_warn_len_equal(my_mod, 0)
sup = suppress_warnings()
sup.filter(UserWarning)
with sup:
warnings.warn('Some warning')
assert_warn_len_equal(my_mod, 0)
# And test repeat works:
sup.filter(module=my_mod)
with sup:
warnings.warn('Some warning')
assert_warn_len_equal(my_mod, 0)
# Without specified modules, don't clear warnings during context
with suppress_warnings():
warnings.simplefilter('ignore')
warnings.warn('Some warning')
assert_warn_len_equal(my_mod, 1)
def test_suppress_warnings_decorate_no_record():
sup = suppress_warnings()
sup.filter(UserWarning)
@sup
def warn(category):
warnings.warn('Some warning', category)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always")
warn(UserWarning) # should be supppressed
warn(RuntimeWarning)
assert_(len(w) == 1)
def test_suppress_warnings_record():
sup = suppress_warnings()
log1 = sup.record()
with sup:
log2 = sup.record(message='Some other warning 2')
sup.filter(message='Some warning')
warnings.warn('Some warning')
warnings.warn('Some other warning')
warnings.warn('Some other warning 2')
assert_(len(sup.log) == 2)
assert_(len(log1) == 1)
assert_(len(log2) == 1)
assert_(log2[0].message.args[0] == 'Some other warning 2')
# Do it again, with the same context to see if some warnings survived:
with sup:
log2 = sup.record(message='Some other warning 2')
sup.filter(message='Some warning')
warnings.warn('Some warning')
warnings.warn('Some other warning')
warnings.warn('Some other warning 2')
assert_(len(sup.log) == 2)
assert_(len(log1) == 1)
assert_(len(log2) == 1)
assert_(log2[0].message.args[0] == 'Some other warning 2')
# Test nested:
with suppress_warnings() as sup:
sup.record()
with suppress_warnings() as sup2:
sup2.record(message='Some warning')
warnings.warn('Some warning')
warnings.warn('Some other warning')
assert_(len(sup2.log) == 1)
assert_(len(sup.log) == 1)
def test_suppress_warnings_forwarding():
def warn_other_module():
# Apply along axis is implemented in python; stacklevel=2 means
# we end up inside its module, not ours.
def warn(arr):
warnings.warn("Some warning", stacklevel=2)
return arr
np.apply_along_axis(warn, 0, [0])
with suppress_warnings() as sup:
sup.record()
with suppress_warnings("always"):
for i in range(2):
warnings.warn("Some warning")
assert_(len(sup.log) == 2)
with suppress_warnings() as sup:
sup.record()
with suppress_warnings("location"):
for i in range(2):
warnings.warn("Some warning")
warnings.warn("Some warning")
assert_(len(sup.log) == 2)
with suppress_warnings() as sup:
sup.record()
with suppress_warnings("module"):
for i in range(2):
warnings.warn("Some warning")
warnings.warn("Some warning")
warn_other_module()
assert_(len(sup.log) == 2)
with suppress_warnings() as sup:
sup.record()
with suppress_warnings("once"):
for i in range(2):
warnings.warn("Some warning")
warnings.warn("Some other warning")
warn_other_module()
assert_(len(sup.log) == 2)
def test_tempdir():
with tempdir() as tdir:
fpath = os.path.join(tdir, 'tmp')
with open(fpath, 'w'):
pass
assert_(not os.path.isdir(tdir))
raised = False
try:
with tempdir() as tdir:
raise ValueError()
except ValueError:
raised = True
assert_(raised)
assert_(not os.path.isdir(tdir))
def test_temppath():
with temppath() as fpath:
with open(fpath, 'w') as f:
pass
assert_(not os.path.isfile(fpath))
raised = False
try:
with temppath() as fpath:
raise ValueError()
except ValueError:
raised = True
assert_(raised)
assert_(not os.path.isfile(fpath))
class my_cacw(clear_and_catch_warnings):
class_modules = (sys.modules[__name__],)
def test_clear_and_catch_warnings_inherit():
# Test can subclass and add default modules
my_mod = _get_fresh_mod()
with my_cacw():
warnings.simplefilter('ignore')
warnings.warn('Some warning')
assert_equal(my_mod.__warningregistry__, {})
if __name__ == '__main__':
run_module_suite()
| 46,239 | 32.950073 | 80 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/testing/nose_tools/noseclasses.py
|
# These classes implement a doctest runner plugin for nose, a "known failure"
# error class, and a customized TestProgram for NumPy.
# Because this module imports nose directly, it should not
# be used except by nosetester.py to avoid a general NumPy
# dependency on nose.
from __future__ import division, absolute_import, print_function
import os
import sys
import doctest
import inspect
import numpy
import nose
from nose.plugins import doctests as npd
from nose.plugins.errorclass import ErrorClass, ErrorClassPlugin
from nose.plugins.base import Plugin
from nose.util import src
from .nosetester import get_package_name
from .utils import KnownFailureException, KnownFailureTest
# Some of the classes in this module begin with 'Numpy' to clearly distinguish
# them from the plethora of very similar names from nose/unittest/doctest
#-----------------------------------------------------------------------------
# Modified version of the one in the stdlib, that fixes a python bug (doctests
# not found in extension modules, http://bugs.python.org/issue3158)
class NumpyDocTestFinder(doctest.DocTestFinder):
def _from_module(self, module, object):
"""
Return true if the given object is defined in the given
module.
"""
if module is None:
return True
elif inspect.isfunction(object):
return module.__dict__ is object.__globals__
elif inspect.isbuiltin(object):
return module.__name__ == object.__module__
elif inspect.isclass(object):
return module.__name__ == object.__module__
elif inspect.ismethod(object):
# This one may be a bug in cython that fails to correctly set the
# __module__ attribute of methods, but since the same error is easy
# to make by extension code writers, having this safety in place
# isn't such a bad idea
return module.__name__ == object.__self__.__class__.__module__
elif inspect.getmodule(object) is not None:
return module is inspect.getmodule(object)
elif hasattr(object, '__module__'):
return module.__name__ == object.__module__
elif isinstance(object, property):
return True # [XX] no way not be sure.
else:
raise ValueError("object must be a class or function")
def _find(self, tests, obj, name, module, source_lines, globs, seen):
"""
Find tests for the given object and any contained objects, and
add them to `tests`.
"""
doctest.DocTestFinder._find(self, tests, obj, name, module,
source_lines, globs, seen)
# Below we re-run pieces of the above method with manual modifications,
# because the original code is buggy and fails to correctly identify
# doctests in extension modules.
# Local shorthands
from inspect import (
isroutine, isclass, ismodule, isfunction, ismethod
)
# Look for tests in a module's contained objects.
if ismodule(obj) and self._recurse:
for valname, val in obj.__dict__.items():
valname1 = '%s.%s' % (name, valname)
if ( (isroutine(val) or isclass(val))
and self._from_module(module, val)):
self._find(tests, val, valname1, module, source_lines,
globs, seen)
# Look for tests in a class's contained objects.
if isclass(obj) and self._recurse:
for valname, val in obj.__dict__.items():
# Special handling for staticmethod/classmethod.
if isinstance(val, staticmethod):
val = getattr(obj, valname)
if isinstance(val, classmethod):
val = getattr(obj, valname).__func__
# Recurse to methods, properties, and nested classes.
if ((isfunction(val) or isclass(val) or
ismethod(val) or isinstance(val, property)) and
self._from_module(module, val)):
valname = '%s.%s' % (name, valname)
self._find(tests, val, valname, module, source_lines,
globs, seen)
# second-chance checker; if the default comparison doesn't
# pass, then see if the expected output string contains flags that
# tell us to ignore the output
class NumpyOutputChecker(doctest.OutputChecker):
def check_output(self, want, got, optionflags):
ret = doctest.OutputChecker.check_output(self, want, got,
optionflags)
if not ret:
if "#random" in want:
return True
# it would be useful to normalize endianness so that
# bigendian machines don't fail all the tests (and there are
# actually some bigendian examples in the doctests). Let's try
# making them all little endian
got = got.replace("'>", "'<")
want = want.replace("'>", "'<")
# try to normalize out 32 and 64 bit default int sizes
for sz in [4, 8]:
got = got.replace("'<i%d'" % sz, "int")
want = want.replace("'<i%d'" % sz, "int")
ret = doctest.OutputChecker.check_output(self, want,
got, optionflags)
return ret
# Subclass nose.plugins.doctests.DocTestCase to work around a bug in
# its constructor that blocks non-default arguments from being passed
# down into doctest.DocTestCase
class NumpyDocTestCase(npd.DocTestCase):
def __init__(self, test, optionflags=0, setUp=None, tearDown=None,
checker=None, obj=None, result_var='_'):
self._result_var = result_var
self._nose_obj = obj
doctest.DocTestCase.__init__(self, test,
optionflags=optionflags,
setUp=setUp, tearDown=tearDown,
checker=checker)
print_state = numpy.get_printoptions()
class NumpyDoctest(npd.Doctest):
name = 'numpydoctest' # call nosetests with --with-numpydoctest
score = 1000 # load late, after doctest builtin
# always use whitespace and ellipsis options for doctests
doctest_optflags = doctest.NORMALIZE_WHITESPACE | doctest.ELLIPSIS
# files that should be ignored for doctests
doctest_ignore = ['generate_numpy_api.py',
'setup.py']
# Custom classes; class variables to allow subclassing
doctest_case_class = NumpyDocTestCase
out_check_class = NumpyOutputChecker
test_finder_class = NumpyDocTestFinder
# Don't use the standard doctest option handler; hard-code the option values
def options(self, parser, env=os.environ):
Plugin.options(self, parser, env)
# Test doctests in 'test' files / directories. Standard plugin default
# is False
self.doctest_tests = True
# Variable name; if defined, doctest results stored in this variable in
# the top-level namespace. None is the standard default
self.doctest_result_var = None
def configure(self, options, config):
# parent method sets enabled flag from command line --with-numpydoctest
Plugin.configure(self, options, config)
self.finder = self.test_finder_class()
self.parser = doctest.DocTestParser()
if self.enabled:
# Pull standard doctest out of plugin list; there's no reason to run
# both. In practice the Unplugger plugin above would cover us when
# run from a standard numpy.test() call; this is just in case
# someone wants to run our plugin outside the numpy.test() machinery
config.plugins.plugins = [p for p in config.plugins.plugins
if p.name != 'doctest']
def set_test_context(self, test):
""" Configure `test` object to set test context
We set the numpy / scipy standard doctest namespace
Parameters
----------
test : test object
with ``globs`` dictionary defining namespace
Returns
-------
None
Notes
-----
`test` object modified in place
"""
# set the namespace for tests
pkg_name = get_package_name(os.path.dirname(test.filename))
# Each doctest should execute in an environment equivalent to
# starting Python and executing "import numpy as np", and,
# for SciPy packages, an additional import of the local
# package (so that scipy.linalg.basic.py's doctests have an
# implicit "from scipy import linalg" as well.
#
# Note: __file__ allows the doctest in NoseTester to run
# without producing an error
test.globs = {'__builtins__':__builtins__,
'__file__':'__main__',
'__name__':'__main__',
'np':numpy}
# add appropriate scipy import for SciPy tests
if 'scipy' in pkg_name:
p = pkg_name.split('.')
p2 = p[-1]
test.globs[p2] = __import__(pkg_name, test.globs, {}, [p2])
# Override test loading to customize test context (with set_test_context
# method), set standard docstring options, and install our own test output
# checker
def loadTestsFromModule(self, module):
if not self.matches(module.__name__):
npd.log.debug("Doctest doesn't want module %s", module)
return
try:
tests = self.finder.find(module)
except AttributeError:
# nose allows module.__test__ = False; doctest does not and
# throws AttributeError
return
if not tests:
return
tests.sort()
module_file = src(module.__file__)
for test in tests:
if not test.examples:
continue
if not test.filename:
test.filename = module_file
# Set test namespace; test altered in place
self.set_test_context(test)
yield self.doctest_case_class(test,
optionflags=self.doctest_optflags,
checker=self.out_check_class(),
result_var=self.doctest_result_var)
# Add an afterContext method to nose.plugins.doctests.Doctest in order
# to restore print options to the original state after each doctest
def afterContext(self):
numpy.set_printoptions(**print_state)
# Ignore NumPy-specific build files that shouldn't be searched for tests
def wantFile(self, file):
bn = os.path.basename(file)
if bn in self.doctest_ignore:
return False
return npd.Doctest.wantFile(self, file)
class Unplugger(object):
""" Nose plugin to remove named plugin late in loading
By default it removes the "doctest" plugin.
"""
name = 'unplugger'
enabled = True # always enabled
score = 4000 # load late in order to be after builtins
def __init__(self, to_unplug='doctest'):
self.to_unplug = to_unplug
def options(self, parser, env):
pass
def configure(self, options, config):
# Pull named plugin out of plugins list
config.plugins.plugins = [p for p in config.plugins.plugins
if p.name != self.to_unplug]
class KnownFailurePlugin(ErrorClassPlugin):
'''Plugin that installs a KNOWNFAIL error class for the
KnownFailureClass exception. When KnownFailure is raised,
the exception will be logged in the knownfail attribute of the
result, 'K' or 'KNOWNFAIL' (verbose) will be output, and the
exception will not be counted as an error or failure.'''
enabled = True
knownfail = ErrorClass(KnownFailureException,
label='KNOWNFAIL',
isfailure=False)
def options(self, parser, env=os.environ):
env_opt = 'NOSE_WITHOUT_KNOWNFAIL'
parser.add_option('--no-knownfail', action='store_true',
dest='noKnownFail', default=env.get(env_opt, False),
help='Disable special handling of KnownFailure '
'exceptions')
def configure(self, options, conf):
if not self.can_configure:
return
self.conf = conf
disable = getattr(options, 'noKnownFail', False)
if disable:
self.enabled = False
KnownFailure = KnownFailurePlugin # backwards compat
class FPUModeCheckPlugin(Plugin):
"""
Plugin that checks the FPU mode before and after each test,
raising failures if the test changed the mode.
"""
def prepareTestCase(self, test):
from numpy.core.multiarray_tests import get_fpu_mode
def run(result):
old_mode = get_fpu_mode()
test.test(result)
new_mode = get_fpu_mode()
if old_mode != new_mode:
try:
raise AssertionError(
"FPU mode changed from {0:#x} to {1:#x} during the "
"test".format(old_mode, new_mode))
except AssertionError:
result.addFailure(test, sys.exc_info())
return run
# Class allows us to save the results of the tests in runTests - see runTests
# method docstring for details
class NumpyTestProgram(nose.core.TestProgram):
def runTests(self):
"""Run Tests. Returns true on success, false on failure, and
sets self.success to the same value.
Because nose currently discards the test result object, but we need
to return it to the user, override TestProgram.runTests to retain
the result
"""
if self.testRunner is None:
self.testRunner = nose.core.TextTestRunner(stream=self.config.stream,
verbosity=self.config.verbosity,
config=self.config)
plug_runner = self.config.plugins.prepareTestRunner(self.testRunner)
if plug_runner is not None:
self.testRunner = plug_runner
self.result = self.testRunner.run(self.test)
self.success = self.result.wasSuccessful()
return self.success
| 14,599 | 38.782016 | 87 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/testing/nose_tools/utils.py
|
"""
Utility function to facilitate testing.
"""
from __future__ import division, absolute_import, print_function
import os
import sys
import re
import operator
import warnings
from functools import partial, wraps
import shutil
import contextlib
from tempfile import mkdtemp, mkstemp
from unittest.case import SkipTest
from numpy.core import(
float32, empty, arange, array_repr, ndarray, isnat, array)
from numpy.lib.utils import deprecate
if sys.version_info[0] >= 3:
from io import StringIO
else:
from StringIO import StringIO
__all__ = [
'assert_equal', 'assert_almost_equal', 'assert_approx_equal',
'assert_array_equal', 'assert_array_less', 'assert_string_equal',
'assert_array_almost_equal', 'assert_raises', 'build_err_msg',
'decorate_methods', 'jiffies', 'memusage', 'print_assert_equal',
'raises', 'rand', 'rundocs', 'runstring', 'verbose', 'measure',
'assert_', 'assert_array_almost_equal_nulp', 'assert_raises_regex',
'assert_array_max_ulp', 'assert_warns', 'assert_no_warnings',
'assert_allclose', 'IgnoreException', 'clear_and_catch_warnings',
'SkipTest', 'KnownFailureException', 'temppath', 'tempdir', 'IS_PYPY',
'HAS_REFCOUNT', 'suppress_warnings', 'assert_array_compare',
'_assert_valid_refcount', '_gen_alignment_data',
]
class KnownFailureException(Exception):
'''Raise this exception to mark a test as a known failing test.'''
pass
KnownFailureTest = KnownFailureException # backwards compat
verbose = 0
IS_PYPY = '__pypy__' in sys.modules
HAS_REFCOUNT = getattr(sys, 'getrefcount', None) is not None
def import_nose():
""" Import nose only when needed.
"""
nose_is_good = True
minimum_nose_version = (1, 0, 0)
try:
import nose
except ImportError:
nose_is_good = False
else:
if nose.__versioninfo__ < minimum_nose_version:
nose_is_good = False
if not nose_is_good:
msg = ('Need nose >= %d.%d.%d for tests - see '
'http://nose.readthedocs.io' %
minimum_nose_version)
raise ImportError(msg)
return nose
def assert_(val, msg=''):
"""
Assert that works in release mode.
Accepts callable msg to allow deferring evaluation until failure.
The Python built-in ``assert`` does not work when executing code in
optimized mode (the ``-O`` flag) - no byte-code is generated for it.
For documentation on usage, refer to the Python documentation.
"""
__tracebackhide__ = True # Hide traceback for py.test
if not val:
try:
smsg = msg()
except TypeError:
smsg = msg
raise AssertionError(smsg)
def gisnan(x):
"""like isnan, but always raise an error if type not supported instead of
returning a TypeError object.
Notes
-----
isnan and other ufunc sometimes return a NotImplementedType object instead
of raising any exception. This function is a wrapper to make sure an
exception is always raised.
This should be removed once this problem is solved at the Ufunc level."""
from numpy.core import isnan
st = isnan(x)
if isinstance(st, type(NotImplemented)):
raise TypeError("isnan not supported for this type")
return st
def gisfinite(x):
"""like isfinite, but always raise an error if type not supported instead of
returning a TypeError object.
Notes
-----
isfinite and other ufunc sometimes return a NotImplementedType object instead
of raising any exception. This function is a wrapper to make sure an
exception is always raised.
This should be removed once this problem is solved at the Ufunc level."""
from numpy.core import isfinite, errstate
with errstate(invalid='ignore'):
st = isfinite(x)
if isinstance(st, type(NotImplemented)):
raise TypeError("isfinite not supported for this type")
return st
def gisinf(x):
"""like isinf, but always raise an error if type not supported instead of
returning a TypeError object.
Notes
-----
isinf and other ufunc sometimes return a NotImplementedType object instead
of raising any exception. This function is a wrapper to make sure an
exception is always raised.
This should be removed once this problem is solved at the Ufunc level."""
from numpy.core import isinf, errstate
with errstate(invalid='ignore'):
st = isinf(x)
if isinstance(st, type(NotImplemented)):
raise TypeError("isinf not supported for this type")
return st
@deprecate(message="numpy.testing.rand is deprecated in numpy 1.11. "
"Use numpy.random.rand instead.")
def rand(*args):
"""Returns an array of random numbers with the given shape.
This only uses the standard library, so it is useful for testing purposes.
"""
import random
from numpy.core import zeros, float64
results = zeros(args, float64)
f = results.flat
for i in range(len(f)):
f[i] = random.random()
return results
if os.name == 'nt':
# Code "stolen" from enthought/debug/memusage.py
def GetPerformanceAttributes(object, counter, instance=None,
inum=-1, format=None, machine=None):
# NOTE: Many counters require 2 samples to give accurate results,
# including "% Processor Time" (as by definition, at any instant, a
# thread's CPU usage is either 0 or 100). To read counters like this,
# you should copy this function, but keep the counter open, and call
# CollectQueryData() each time you need to know.
# See http://msdn.microsoft.com/library/en-us/dnperfmo/html/perfmonpt2.asp
# My older explanation for this was that the "AddCounter" process forced
# the CPU to 100%, but the above makes more sense :)
import win32pdh
if format is None:
format = win32pdh.PDH_FMT_LONG
path = win32pdh.MakeCounterPath( (machine, object, instance, None, inum, counter))
hq = win32pdh.OpenQuery()
try:
hc = win32pdh.AddCounter(hq, path)
try:
win32pdh.CollectQueryData(hq)
type, val = win32pdh.GetFormattedCounterValue(hc, format)
return val
finally:
win32pdh.RemoveCounter(hc)
finally:
win32pdh.CloseQuery(hq)
def memusage(processName="python", instance=0):
# from win32pdhutil, part of the win32all package
import win32pdh
return GetPerformanceAttributes("Process", "Virtual Bytes",
processName, instance,
win32pdh.PDH_FMT_LONG, None)
elif sys.platform[:5] == 'linux':
def memusage(_proc_pid_stat='/proc/%s/stat' % (os.getpid())):
"""
Return virtual memory size in bytes of the running python.
"""
try:
f = open(_proc_pid_stat, 'r')
l = f.readline().split(' ')
f.close()
return int(l[22])
except Exception:
return
else:
def memusage():
"""
Return memory usage of running python. [Not implemented]
"""
raise NotImplementedError
if sys.platform[:5] == 'linux':
def jiffies(_proc_pid_stat='/proc/%s/stat' % (os.getpid()),
_load_time=[]):
"""
Return number of jiffies elapsed.
Return number of jiffies (1/100ths of a second) that this
process has been scheduled in user mode. See man 5 proc.
"""
import time
if not _load_time:
_load_time.append(time.time())
try:
f = open(_proc_pid_stat, 'r')
l = f.readline().split(' ')
f.close()
return int(l[13])
except Exception:
return int(100*(time.time()-_load_time[0]))
else:
# os.getpid is not in all platforms available.
# Using time is safe but inaccurate, especially when process
# was suspended or sleeping.
def jiffies(_load_time=[]):
"""
Return number of jiffies elapsed.
Return number of jiffies (1/100ths of a second) that this
process has been scheduled in user mode. See man 5 proc.
"""
import time
if not _load_time:
_load_time.append(time.time())
return int(100*(time.time()-_load_time[0]))
def build_err_msg(arrays, err_msg, header='Items are not equal:',
verbose=True, names=('ACTUAL', 'DESIRED'), precision=8):
msg = ['\n' + header]
if err_msg:
if err_msg.find('\n') == -1 and len(err_msg) < 79-len(header):
msg = [msg[0] + ' ' + err_msg]
else:
msg.append(err_msg)
if verbose:
for i, a in enumerate(arrays):
if isinstance(a, ndarray):
# precision argument is only needed if the objects are ndarrays
r_func = partial(array_repr, precision=precision)
else:
r_func = repr
try:
r = r_func(a)
except Exception as exc:
r = '[repr failed for <{}>: {}]'.format(type(a).__name__, exc)
if r.count('\n') > 3:
r = '\n'.join(r.splitlines()[:3])
r += '...'
msg.append(' %s: %s' % (names[i], r))
return '\n'.join(msg)
def assert_equal(actual, desired, err_msg='', verbose=True):
"""
Raises an AssertionError if two objects are not equal.
Given two objects (scalars, lists, tuples, dictionaries or numpy arrays),
check that all elements of these objects are equal. An exception is raised
at the first conflicting values.
Parameters
----------
actual : array_like
The object to check.
desired : array_like
The expected object.
err_msg : str, optional
The error message to be printed in case of failure.
verbose : bool, optional
If True, the conflicting values are appended to the error message.
Raises
------
AssertionError
If actual and desired are not equal.
Examples
--------
>>> np.testing.assert_equal([4,5], [4,6])
...
<type 'exceptions.AssertionError'>:
Items are not equal:
item=1
ACTUAL: 5
DESIRED: 6
"""
__tracebackhide__ = True # Hide traceback for py.test
if isinstance(desired, dict):
if not isinstance(actual, dict):
raise AssertionError(repr(type(actual)))
assert_equal(len(actual), len(desired), err_msg, verbose)
for k, i in desired.items():
if k not in actual:
raise AssertionError(repr(k))
assert_equal(actual[k], desired[k], 'key=%r\n%s' % (k, err_msg), verbose)
return
if isinstance(desired, (list, tuple)) and isinstance(actual, (list, tuple)):
assert_equal(len(actual), len(desired), err_msg, verbose)
for k in range(len(desired)):
assert_equal(actual[k], desired[k], 'item=%r\n%s' % (k, err_msg), verbose)
return
from numpy.core import ndarray, isscalar, signbit
from numpy.lib import iscomplexobj, real, imag
if isinstance(actual, ndarray) or isinstance(desired, ndarray):
return assert_array_equal(actual, desired, err_msg, verbose)
msg = build_err_msg([actual, desired], err_msg, verbose=verbose)
# Handle complex numbers: separate into real/imag to handle
# nan/inf/negative zero correctly
# XXX: catch ValueError for subclasses of ndarray where iscomplex fail
try:
usecomplex = iscomplexobj(actual) or iscomplexobj(desired)
except ValueError:
usecomplex = False
if usecomplex:
if iscomplexobj(actual):
actualr = real(actual)
actuali = imag(actual)
else:
actualr = actual
actuali = 0
if iscomplexobj(desired):
desiredr = real(desired)
desiredi = imag(desired)
else:
desiredr = desired
desiredi = 0
try:
assert_equal(actualr, desiredr)
assert_equal(actuali, desiredi)
except AssertionError:
raise AssertionError(msg)
# isscalar test to check cases such as [np.nan] != np.nan
if isscalar(desired) != isscalar(actual):
raise AssertionError(msg)
# Inf/nan/negative zero handling
try:
isdesnan = gisnan(desired)
isactnan = gisnan(actual)
if isdesnan and isactnan:
return # both nan, so equal
# handle signed zero specially for floats
if desired == 0 and actual == 0:
if not signbit(desired) == signbit(actual):
raise AssertionError(msg)
except (TypeError, ValueError, NotImplementedError):
pass
try:
isdesnat = isnat(desired)
isactnat = isnat(actual)
dtypes_match = array(desired).dtype.type == array(actual).dtype.type
if isdesnat and isactnat:
# If both are NaT (and have the same dtype -- datetime or
# timedelta) they are considered equal.
if dtypes_match:
return
else:
raise AssertionError(msg)
except (TypeError, ValueError, NotImplementedError):
pass
try:
# Explicitly use __eq__ for comparison, gh-2552
if not (desired == actual):
raise AssertionError(msg)
except (DeprecationWarning, FutureWarning) as e:
# this handles the case when the two types are not even comparable
if 'elementwise == comparison' in e.args[0]:
raise AssertionError(msg)
else:
raise
def print_assert_equal(test_string, actual, desired):
"""
Test if two objects are equal, and print an error message if test fails.
The test is performed with ``actual == desired``.
Parameters
----------
test_string : str
The message supplied to AssertionError.
actual : object
The object to test for equality against `desired`.
desired : object
The expected result.
Examples
--------
>>> np.testing.print_assert_equal('Test XYZ of func xyz', [0, 1], [0, 1])
>>> np.testing.print_assert_equal('Test XYZ of func xyz', [0, 1], [0, 2])
Traceback (most recent call last):
...
AssertionError: Test XYZ of func xyz failed
ACTUAL:
[0, 1]
DESIRED:
[0, 2]
"""
__tracebackhide__ = True # Hide traceback for py.test
import pprint
if not (actual == desired):
msg = StringIO()
msg.write(test_string)
msg.write(' failed\nACTUAL: \n')
pprint.pprint(actual, msg)
msg.write('DESIRED: \n')
pprint.pprint(desired, msg)
raise AssertionError(msg.getvalue())
def assert_almost_equal(actual,desired,decimal=7,err_msg='',verbose=True):
"""
Raises an AssertionError if two items are not equal up to desired
precision.
.. note:: It is recommended to use one of `assert_allclose`,
`assert_array_almost_equal_nulp` or `assert_array_max_ulp`
instead of this function for more consistent floating point
comparisons.
The test verifies that the elements of ``actual`` and ``desired`` satisfy.
``abs(desired-actual) < 1.5 * 10**(-decimal)``
That is a looser test than originally documented, but agrees with what the
actual implementation in `assert_array_almost_equal` did up to rounding
vagaries. An exception is raised at conflicting values. For ndarrays this
delegates to assert_array_almost_equal
Parameters
----------
actual : array_like
The object to check.
desired : array_like
The expected object.
decimal : int, optional
Desired precision, default is 7.
err_msg : str, optional
The error message to be printed in case of failure.
verbose : bool, optional
If True, the conflicting values are appended to the error message.
Raises
------
AssertionError
If actual and desired are not equal up to specified precision.
See Also
--------
assert_allclose: Compare two array_like objects for equality with desired
relative and/or absolute precision.
assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
Examples
--------
>>> import numpy.testing as npt
>>> npt.assert_almost_equal(2.3333333333333, 2.33333334)
>>> npt.assert_almost_equal(2.3333333333333, 2.33333334, decimal=10)
...
<type 'exceptions.AssertionError'>:
Items are not equal:
ACTUAL: 2.3333333333333002
DESIRED: 2.3333333399999998
>>> npt.assert_almost_equal(np.array([1.0,2.3333333333333]),
... np.array([1.0,2.33333334]), decimal=9)
...
<type 'exceptions.AssertionError'>:
Arrays are not almost equal
<BLANKLINE>
(mismatch 50.0%)
x: array([ 1. , 2.33333333])
y: array([ 1. , 2.33333334])
"""
__tracebackhide__ = True # Hide traceback for py.test
from numpy.core import ndarray
from numpy.lib import iscomplexobj, real, imag
# Handle complex numbers: separate into real/imag to handle
# nan/inf/negative zero correctly
# XXX: catch ValueError for subclasses of ndarray where iscomplex fail
try:
usecomplex = iscomplexobj(actual) or iscomplexobj(desired)
except ValueError:
usecomplex = False
def _build_err_msg():
header = ('Arrays are not almost equal to %d decimals' % decimal)
return build_err_msg([actual, desired], err_msg, verbose=verbose,
header=header)
if usecomplex:
if iscomplexobj(actual):
actualr = real(actual)
actuali = imag(actual)
else:
actualr = actual
actuali = 0
if iscomplexobj(desired):
desiredr = real(desired)
desiredi = imag(desired)
else:
desiredr = desired
desiredi = 0
try:
assert_almost_equal(actualr, desiredr, decimal=decimal)
assert_almost_equal(actuali, desiredi, decimal=decimal)
except AssertionError:
raise AssertionError(_build_err_msg())
if isinstance(actual, (ndarray, tuple, list)) \
or isinstance(desired, (ndarray, tuple, list)):
return assert_array_almost_equal(actual, desired, decimal, err_msg)
try:
# If one of desired/actual is not finite, handle it specially here:
# check that both are nan if any is a nan, and test for equality
# otherwise
if not (gisfinite(desired) and gisfinite(actual)):
if gisnan(desired) or gisnan(actual):
if not (gisnan(desired) and gisnan(actual)):
raise AssertionError(_build_err_msg())
else:
if not desired == actual:
raise AssertionError(_build_err_msg())
return
except (NotImplementedError, TypeError):
pass
if abs(desired - actual) >= 1.5 * 10.0**(-decimal):
raise AssertionError(_build_err_msg())
def assert_approx_equal(actual,desired,significant=7,err_msg='',verbose=True):
"""
Raises an AssertionError if two items are not equal up to significant
digits.
.. note:: It is recommended to use one of `assert_allclose`,
`assert_array_almost_equal_nulp` or `assert_array_max_ulp`
instead of this function for more consistent floating point
comparisons.
Given two numbers, check that they are approximately equal.
Approximately equal is defined as the number of significant digits
that agree.
Parameters
----------
actual : scalar
The object to check.
desired : scalar
The expected object.
significant : int, optional
Desired precision, default is 7.
err_msg : str, optional
The error message to be printed in case of failure.
verbose : bool, optional
If True, the conflicting values are appended to the error message.
Raises
------
AssertionError
If actual and desired are not equal up to specified precision.
See Also
--------
assert_allclose: Compare two array_like objects for equality with desired
relative and/or absolute precision.
assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
Examples
--------
>>> np.testing.assert_approx_equal(0.12345677777777e-20, 0.1234567e-20)
>>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345671e-20,
significant=8)
>>> np.testing.assert_approx_equal(0.12345670e-20, 0.12345672e-20,
significant=8)
...
<type 'exceptions.AssertionError'>:
Items are not equal to 8 significant digits:
ACTUAL: 1.234567e-021
DESIRED: 1.2345672000000001e-021
the evaluated condition that raises the exception is
>>> abs(0.12345670e-20/1e-21 - 0.12345672e-20/1e-21) >= 10**-(8-1)
True
"""
__tracebackhide__ = True # Hide traceback for py.test
import numpy as np
(actual, desired) = map(float, (actual, desired))
if desired == actual:
return
# Normalized the numbers to be in range (-10.0,10.0)
# scale = float(pow(10,math.floor(math.log10(0.5*(abs(desired)+abs(actual))))))
with np.errstate(invalid='ignore'):
scale = 0.5*(np.abs(desired) + np.abs(actual))
scale = np.power(10, np.floor(np.log10(scale)))
try:
sc_desired = desired/scale
except ZeroDivisionError:
sc_desired = 0.0
try:
sc_actual = actual/scale
except ZeroDivisionError:
sc_actual = 0.0
msg = build_err_msg([actual, desired], err_msg,
header='Items are not equal to %d significant digits:' %
significant,
verbose=verbose)
try:
# If one of desired/actual is not finite, handle it specially here:
# check that both are nan if any is a nan, and test for equality
# otherwise
if not (gisfinite(desired) and gisfinite(actual)):
if gisnan(desired) or gisnan(actual):
if not (gisnan(desired) and gisnan(actual)):
raise AssertionError(msg)
else:
if not desired == actual:
raise AssertionError(msg)
return
except (TypeError, NotImplementedError):
pass
if np.abs(sc_desired - sc_actual) >= np.power(10., -(significant-1)):
raise AssertionError(msg)
def assert_array_compare(comparison, x, y, err_msg='', verbose=True,
header='', precision=6, equal_nan=True,
equal_inf=True):
__tracebackhide__ = True # Hide traceback for py.test
from numpy.core import array, isnan, isinf, any, inf
x = array(x, copy=False, subok=True)
y = array(y, copy=False, subok=True)
def isnumber(x):
return x.dtype.char in '?bhilqpBHILQPefdgFDG'
def istime(x):
return x.dtype.char in "Mm"
def chk_same_position(x_id, y_id, hasval='nan'):
"""Handling nan/inf: check that x and y have the nan/inf at the same
locations."""
try:
assert_array_equal(x_id, y_id)
except AssertionError:
msg = build_err_msg([x, y],
err_msg + '\nx and y %s location mismatch:'
% (hasval), verbose=verbose, header=header,
names=('x', 'y'), precision=precision)
raise AssertionError(msg)
try:
cond = (x.shape == () or y.shape == ()) or x.shape == y.shape
if not cond:
msg = build_err_msg([x, y],
err_msg
+ '\n(shapes %s, %s mismatch)' % (x.shape,
y.shape),
verbose=verbose, header=header,
names=('x', 'y'), precision=precision)
raise AssertionError(msg)
if isnumber(x) and isnumber(y):
has_nan = has_inf = False
if equal_nan:
x_isnan, y_isnan = isnan(x), isnan(y)
# Validate that NaNs are in the same place
has_nan = any(x_isnan) or any(y_isnan)
if has_nan:
chk_same_position(x_isnan, y_isnan, hasval='nan')
if equal_inf:
x_isinf, y_isinf = isinf(x), isinf(y)
# Validate that infinite values are in the same place
has_inf = any(x_isinf) or any(y_isinf)
if has_inf:
# Check +inf and -inf separately, since they are different
chk_same_position(x == +inf, y == +inf, hasval='+inf')
chk_same_position(x == -inf, y == -inf, hasval='-inf')
if has_nan and has_inf:
x = x[~(x_isnan | x_isinf)]
y = y[~(y_isnan | y_isinf)]
elif has_nan:
x = x[~x_isnan]
y = y[~y_isnan]
elif has_inf:
x = x[~x_isinf]
y = y[~y_isinf]
# Only do the comparison if actual values are left
if x.size == 0:
return
elif istime(x) and istime(y):
# If one is datetime64 and the other timedelta64 there is no point
if equal_nan and x.dtype.type == y.dtype.type:
x_isnat, y_isnat = isnat(x), isnat(y)
if any(x_isnat) or any(y_isnat):
chk_same_position(x_isnat, y_isnat, hasval="NaT")
if any(x_isnat) or any(y_isnat):
x = x[~x_isnat]
y = y[~y_isnat]
val = comparison(x, y)
if isinstance(val, bool):
cond = val
reduced = [0]
else:
reduced = val.ravel()
cond = reduced.all()
reduced = reduced.tolist()
if not cond:
match = 100-100.0*reduced.count(1)/len(reduced)
msg = build_err_msg([x, y],
err_msg
+ '\n(mismatch %s%%)' % (match,),
verbose=verbose, header=header,
names=('x', 'y'), precision=precision)
raise AssertionError(msg)
except ValueError:
import traceback
efmt = traceback.format_exc()
header = 'error during assertion:\n\n%s\n\n%s' % (efmt, header)
msg = build_err_msg([x, y], err_msg, verbose=verbose, header=header,
names=('x', 'y'), precision=precision)
raise ValueError(msg)
def assert_array_equal(x, y, err_msg='', verbose=True):
"""
Raises an AssertionError if two array_like objects are not equal.
Given two array_like objects, check that the shape is equal and all
elements of these objects are equal. An exception is raised at
shape mismatch or conflicting values. In contrast to the standard usage
in numpy, NaNs are compared like numbers, no assertion is raised if
both objects have NaNs in the same positions.
The usual caution for verifying equality with floating point numbers is
advised.
Parameters
----------
x : array_like
The actual object to check.
y : array_like
The desired, expected object.
err_msg : str, optional
The error message to be printed in case of failure.
verbose : bool, optional
If True, the conflicting values are appended to the error message.
Raises
------
AssertionError
If actual and desired objects are not equal.
See Also
--------
assert_allclose: Compare two array_like objects for equality with desired
relative and/or absolute precision.
assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
Examples
--------
The first assert does not raise an exception:
>>> np.testing.assert_array_equal([1.0,2.33333,np.nan],
... [np.exp(0),2.33333, np.nan])
Assert fails with numerical inprecision with floats:
>>> np.testing.assert_array_equal([1.0,np.pi,np.nan],
... [1, np.sqrt(np.pi)**2, np.nan])
...
<type 'exceptions.ValueError'>:
AssertionError:
Arrays are not equal
<BLANKLINE>
(mismatch 50.0%)
x: array([ 1. , 3.14159265, NaN])
y: array([ 1. , 3.14159265, NaN])
Use `assert_allclose` or one of the nulp (number of floating point values)
functions for these cases instead:
>>> np.testing.assert_allclose([1.0,np.pi,np.nan],
... [1, np.sqrt(np.pi)**2, np.nan],
... rtol=1e-10, atol=0)
"""
__tracebackhide__ = True # Hide traceback for py.test
assert_array_compare(operator.__eq__, x, y, err_msg=err_msg,
verbose=verbose, header='Arrays are not equal')
def assert_array_almost_equal(x, y, decimal=6, err_msg='', verbose=True):
"""
Raises an AssertionError if two objects are not equal up to desired
precision.
.. note:: It is recommended to use one of `assert_allclose`,
`assert_array_almost_equal_nulp` or `assert_array_max_ulp`
instead of this function for more consistent floating point
comparisons.
The test verifies identical shapes and that the elements of ``actual`` and
``desired`` satisfy.
``abs(desired-actual) < 1.5 * 10**(-decimal)``
That is a looser test than originally documented, but agrees with what the
actual implementation did up to rounding vagaries. An exception is raised
at shape mismatch or conflicting values. In contrast to the standard usage
in numpy, NaNs are compared like numbers, no assertion is raised if both
objects have NaNs in the same positions.
Parameters
----------
x : array_like
The actual object to check.
y : array_like
The desired, expected object.
decimal : int, optional
Desired precision, default is 6.
err_msg : str, optional
The error message to be printed in case of failure.
verbose : bool, optional
If True, the conflicting values are appended to the error message.
Raises
------
AssertionError
If actual and desired are not equal up to specified precision.
See Also
--------
assert_allclose: Compare two array_like objects for equality with desired
relative and/or absolute precision.
assert_array_almost_equal_nulp, assert_array_max_ulp, assert_equal
Examples
--------
the first assert does not raise an exception
>>> np.testing.assert_array_almost_equal([1.0,2.333,np.nan],
[1.0,2.333,np.nan])
>>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan],
... [1.0,2.33339,np.nan], decimal=5)
...
<type 'exceptions.AssertionError'>:
AssertionError:
Arrays are not almost equal
<BLANKLINE>
(mismatch 50.0%)
x: array([ 1. , 2.33333, NaN])
y: array([ 1. , 2.33339, NaN])
>>> np.testing.assert_array_almost_equal([1.0,2.33333,np.nan],
... [1.0,2.33333, 5], decimal=5)
<type 'exceptions.ValueError'>:
ValueError:
Arrays are not almost equal
x: array([ 1. , 2.33333, NaN])
y: array([ 1. , 2.33333, 5. ])
"""
__tracebackhide__ = True # Hide traceback for py.test
from numpy.core import around, number, float_, result_type, array
from numpy.core.numerictypes import issubdtype
from numpy.core.fromnumeric import any as npany
def compare(x, y):
try:
if npany(gisinf(x)) or npany( gisinf(y)):
xinfid = gisinf(x)
yinfid = gisinf(y)
if not (xinfid == yinfid).all():
return False
# if one item, x and y is +- inf
if x.size == y.size == 1:
return x == y
x = x[~xinfid]
y = y[~yinfid]
except (TypeError, NotImplementedError):
pass
# make sure y is an inexact type to avoid abs(MIN_INT); will cause
# casting of x later.
dtype = result_type(y, 1.)
y = array(y, dtype=dtype, copy=False, subok=True)
z = abs(x - y)
if not issubdtype(z.dtype, number):
z = z.astype(float_) # handle object arrays
return z < 1.5 * 10.0**(-decimal)
assert_array_compare(compare, x, y, err_msg=err_msg, verbose=verbose,
header=('Arrays are not almost equal to %d decimals' % decimal),
precision=decimal)
def assert_array_less(x, y, err_msg='', verbose=True):
"""
Raises an AssertionError if two array_like objects are not ordered by less
than.
Given two array_like objects, check that the shape is equal and all
elements of the first object are strictly smaller than those of the
second object. An exception is raised at shape mismatch or incorrectly
ordered values. Shape mismatch does not raise if an object has zero
dimension. In contrast to the standard usage in numpy, NaNs are
compared, no assertion is raised if both objects have NaNs in the same
positions.
Parameters
----------
x : array_like
The smaller object to check.
y : array_like
The larger object to compare.
err_msg : string
The error message to be printed in case of failure.
verbose : bool
If True, the conflicting values are appended to the error message.
Raises
------
AssertionError
If actual and desired objects are not equal.
See Also
--------
assert_array_equal: tests objects for equality
assert_array_almost_equal: test objects for equality up to precision
Examples
--------
>>> np.testing.assert_array_less([1.0, 1.0, np.nan], [1.1, 2.0, np.nan])
>>> np.testing.assert_array_less([1.0, 1.0, np.nan], [1, 2.0, np.nan])
...
<type 'exceptions.ValueError'>:
Arrays are not less-ordered
(mismatch 50.0%)
x: array([ 1., 1., NaN])
y: array([ 1., 2., NaN])
>>> np.testing.assert_array_less([1.0, 4.0], 3)
...
<type 'exceptions.ValueError'>:
Arrays are not less-ordered
(mismatch 50.0%)
x: array([ 1., 4.])
y: array(3)
>>> np.testing.assert_array_less([1.0, 2.0, 3.0], [4])
...
<type 'exceptions.ValueError'>:
Arrays are not less-ordered
(shapes (3,), (1,) mismatch)
x: array([ 1., 2., 3.])
y: array([4])
"""
__tracebackhide__ = True # Hide traceback for py.test
assert_array_compare(operator.__lt__, x, y, err_msg=err_msg,
verbose=verbose,
header='Arrays are not less-ordered',
equal_inf=False)
def runstring(astr, dict):
exec(astr, dict)
def assert_string_equal(actual, desired):
"""
Test if two strings are equal.
If the given strings are equal, `assert_string_equal` does nothing.
If they are not equal, an AssertionError is raised, and the diff
between the strings is shown.
Parameters
----------
actual : str
The string to test for equality against the expected string.
desired : str
The expected string.
Examples
--------
>>> np.testing.assert_string_equal('abc', 'abc')
>>> np.testing.assert_string_equal('abc', 'abcd')
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
...
AssertionError: Differences in strings:
- abc+ abcd? +
"""
# delay import of difflib to reduce startup time
__tracebackhide__ = True # Hide traceback for py.test
import difflib
if not isinstance(actual, str):
raise AssertionError(repr(type(actual)))
if not isinstance(desired, str):
raise AssertionError(repr(type(desired)))
if re.match(r'\A'+desired+r'\Z', actual, re.M):
return
diff = list(difflib.Differ().compare(actual.splitlines(1), desired.splitlines(1)))
diff_list = []
while diff:
d1 = diff.pop(0)
if d1.startswith(' '):
continue
if d1.startswith('- '):
l = [d1]
d2 = diff.pop(0)
if d2.startswith('? '):
l.append(d2)
d2 = diff.pop(0)
if not d2.startswith('+ '):
raise AssertionError(repr(d2))
l.append(d2)
if diff:
d3 = diff.pop(0)
if d3.startswith('? '):
l.append(d3)
else:
diff.insert(0, d3)
if re.match(r'\A'+d2[2:]+r'\Z', d1[2:]):
continue
diff_list.extend(l)
continue
raise AssertionError(repr(d1))
if not diff_list:
return
msg = 'Differences in strings:\n%s' % (''.join(diff_list)).rstrip()
if actual != desired:
raise AssertionError(msg)
def rundocs(filename=None, raise_on_error=True):
"""
Run doctests found in the given file.
By default `rundocs` raises an AssertionError on failure.
Parameters
----------
filename : str
The path to the file for which the doctests are run.
raise_on_error : bool
Whether to raise an AssertionError when a doctest fails. Default is
True.
Notes
-----
The doctests can be run by the user/developer by adding the ``doctests``
argument to the ``test()`` call. For example, to run all tests (including
doctests) for `numpy.lib`:
>>> np.lib.test(doctests=True) #doctest: +SKIP
"""
from numpy.compat import npy_load_module
import doctest
if filename is None:
f = sys._getframe(1)
filename = f.f_globals['__file__']
name = os.path.splitext(os.path.basename(filename))[0]
m = npy_load_module(name, filename)
tests = doctest.DocTestFinder().find(m)
runner = doctest.DocTestRunner(verbose=False)
msg = []
if raise_on_error:
out = lambda s: msg.append(s)
else:
out = None
for test in tests:
runner.run(test, out=out)
if runner.failures > 0 and raise_on_error:
raise AssertionError("Some doctests failed:\n%s" % "\n".join(msg))
def raises(*args,**kwargs):
nose = import_nose()
return nose.tools.raises(*args,**kwargs)
def assert_raises(*args, **kwargs):
"""
assert_raises(exception_class, callable, *args, **kwargs)
assert_raises(exception_class)
Fail unless an exception of class exception_class is thrown
by callable when invoked with arguments args and keyword
arguments kwargs. If a different type of exception is
thrown, it will not be caught, and the test case will be
deemed to have suffered an error, exactly as for an
unexpected exception.
Alternatively, `assert_raises` can be used as a context manager:
>>> from numpy.testing import assert_raises
>>> with assert_raises(ZeroDivisionError):
... 1 / 0
is equivalent to
>>> def div(x, y):
... return x / y
>>> assert_raises(ZeroDivisionError, div, 1, 0)
"""
__tracebackhide__ = True # Hide traceback for py.test
nose = import_nose()
return nose.tools.assert_raises(*args,**kwargs)
def assert_raises_regex(exception_class, expected_regexp, *args, **kwargs):
"""
assert_raises_regex(exception_class, expected_regexp, callable, *args,
**kwargs)
assert_raises_regex(exception_class, expected_regexp)
Fail unless an exception of class exception_class and with message that
matches expected_regexp is thrown by callable when invoked with arguments
args and keyword arguments kwargs.
Alternatively, can be used as a context manager like `assert_raises`.
Name of this function adheres to Python 3.2+ reference, but should work in
all versions down to 2.6.
Notes
-----
.. versionadded:: 1.9.0
"""
__tracebackhide__ = True # Hide traceback for py.test
nose = import_nose()
if sys.version_info.major >= 3:
funcname = nose.tools.assert_raises_regex
else:
# Only present in Python 2.7, missing from unittest in 2.6
funcname = nose.tools.assert_raises_regexp
return funcname(exception_class, expected_regexp, *args, **kwargs)
def decorate_methods(cls, decorator, testmatch=None):
"""
Apply a decorator to all methods in a class matching a regular expression.
The given decorator is applied to all public methods of `cls` that are
matched by the regular expression `testmatch`
(``testmatch.search(methodname)``). Methods that are private, i.e. start
with an underscore, are ignored.
Parameters
----------
cls : class
Class whose methods to decorate.
decorator : function
Decorator to apply to methods
testmatch : compiled regexp or str, optional
The regular expression. Default value is None, in which case the
nose default (``re.compile(r'(?:^|[\\b_\\.%s-])[Tt]est' % os.sep)``)
is used.
If `testmatch` is a string, it is compiled to a regular expression
first.
"""
if testmatch is None:
testmatch = re.compile(r'(?:^|[\\b_\\.%s-])[Tt]est' % os.sep)
else:
testmatch = re.compile(testmatch)
cls_attr = cls.__dict__
# delayed import to reduce startup time
from inspect import isfunction
methods = [_m for _m in cls_attr.values() if isfunction(_m)]
for function in methods:
try:
if hasattr(function, 'compat_func_name'):
funcname = function.compat_func_name
else:
funcname = function.__name__
except AttributeError:
# not a function
continue
if testmatch.search(funcname) and not funcname.startswith('_'):
setattr(cls, funcname, decorator(function))
return
def measure(code_str,times=1,label=None):
"""
Return elapsed time for executing code in the namespace of the caller.
The supplied code string is compiled with the Python builtin ``compile``.
The precision of the timing is 10 milli-seconds. If the code will execute
fast on this timescale, it can be executed many times to get reasonable
timing accuracy.
Parameters
----------
code_str : str
The code to be timed.
times : int, optional
The number of times the code is executed. Default is 1. The code is
only compiled once.
label : str, optional
A label to identify `code_str` with. This is passed into ``compile``
as the second argument (for run-time error messages).
Returns
-------
elapsed : float
Total elapsed time in seconds for executing `code_str` `times` times.
Examples
--------
>>> etime = np.testing.measure('for i in range(1000): np.sqrt(i**2)',
... times=times)
>>> print("Time for a single execution : ", etime / times, "s")
Time for a single execution : 0.005 s
"""
frame = sys._getframe(1)
locs, globs = frame.f_locals, frame.f_globals
code = compile(code_str,
'Test name: %s ' % label,
'exec')
i = 0
elapsed = jiffies()
while i < times:
i += 1
exec(code, globs, locs)
elapsed = jiffies() - elapsed
return 0.01*elapsed
def _assert_valid_refcount(op):
"""
Check that ufuncs don't mishandle refcount of object `1`.
Used in a few regression tests.
"""
if not HAS_REFCOUNT:
return True
import numpy as np
b = np.arange(100*100).reshape(100, 100)
c = b
i = 1
rc = sys.getrefcount(i)
for j in range(15):
d = op(b, c)
assert_(sys.getrefcount(i) >= rc)
del d # for pyflakes
def assert_allclose(actual, desired, rtol=1e-7, atol=0, equal_nan=True,
err_msg='', verbose=True):
"""
Raises an AssertionError if two objects are not equal up to desired
tolerance.
The test is equivalent to ``allclose(actual, desired, rtol, atol)``.
It compares the difference between `actual` and `desired` to
``atol + rtol * abs(desired)``.
.. versionadded:: 1.5.0
Parameters
----------
actual : array_like
Array obtained.
desired : array_like
Array desired.
rtol : float, optional
Relative tolerance.
atol : float, optional
Absolute tolerance.
equal_nan : bool, optional.
If True, NaNs will compare equal.
err_msg : str, optional
The error message to be printed in case of failure.
verbose : bool, optional
If True, the conflicting values are appended to the error message.
Raises
------
AssertionError
If actual and desired are not equal up to specified precision.
See Also
--------
assert_array_almost_equal_nulp, assert_array_max_ulp
Examples
--------
>>> x = [1e-5, 1e-3, 1e-1]
>>> y = np.arccos(np.cos(x))
>>> assert_allclose(x, y, rtol=1e-5, atol=0)
"""
__tracebackhide__ = True # Hide traceback for py.test
import numpy as np
def compare(x, y):
return np.core.numeric.isclose(x, y, rtol=rtol, atol=atol,
equal_nan=equal_nan)
actual, desired = np.asanyarray(actual), np.asanyarray(desired)
header = 'Not equal to tolerance rtol=%g, atol=%g' % (rtol, atol)
assert_array_compare(compare, actual, desired, err_msg=str(err_msg),
verbose=verbose, header=header, equal_nan=equal_nan)
def assert_array_almost_equal_nulp(x, y, nulp=1):
"""
Compare two arrays relatively to their spacing.
This is a relatively robust method to compare two arrays whose amplitude
is variable.
Parameters
----------
x, y : array_like
Input arrays.
nulp : int, optional
The maximum number of unit in the last place for tolerance (see Notes).
Default is 1.
Returns
-------
None
Raises
------
AssertionError
If the spacing between `x` and `y` for one or more elements is larger
than `nulp`.
See Also
--------
assert_array_max_ulp : Check that all items of arrays differ in at most
N Units in the Last Place.
spacing : Return the distance between x and the nearest adjacent number.
Notes
-----
An assertion is raised if the following condition is not met::
abs(x - y) <= nulps * spacing(maximum(abs(x), abs(y)))
Examples
--------
>>> x = np.array([1., 1e-10, 1e-20])
>>> eps = np.finfo(x.dtype).eps
>>> np.testing.assert_array_almost_equal_nulp(x, x*eps/2 + x)
>>> np.testing.assert_array_almost_equal_nulp(x, x*eps + x)
Traceback (most recent call last):
...
AssertionError: X and Y are not equal to 1 ULP (max is 2)
"""
__tracebackhide__ = True # Hide traceback for py.test
import numpy as np
ax = np.abs(x)
ay = np.abs(y)
ref = nulp * np.spacing(np.where(ax > ay, ax, ay))
if not np.all(np.abs(x-y) <= ref):
if np.iscomplexobj(x) or np.iscomplexobj(y):
msg = "X and Y are not equal to %d ULP" % nulp
else:
max_nulp = np.max(nulp_diff(x, y))
msg = "X and Y are not equal to %d ULP (max is %g)" % (nulp, max_nulp)
raise AssertionError(msg)
def assert_array_max_ulp(a, b, maxulp=1, dtype=None):
"""
Check that all items of arrays differ in at most N Units in the Last Place.
Parameters
----------
a, b : array_like
Input arrays to be compared.
maxulp : int, optional
The maximum number of units in the last place that elements of `a` and
`b` can differ. Default is 1.
dtype : dtype, optional
Data-type to convert `a` and `b` to if given. Default is None.
Returns
-------
ret : ndarray
Array containing number of representable floating point numbers between
items in `a` and `b`.
Raises
------
AssertionError
If one or more elements differ by more than `maxulp`.
See Also
--------
assert_array_almost_equal_nulp : Compare two arrays relatively to their
spacing.
Examples
--------
>>> a = np.linspace(0., 1., 100)
>>> res = np.testing.assert_array_max_ulp(a, np.arcsin(np.sin(a)))
"""
__tracebackhide__ = True # Hide traceback for py.test
import numpy as np
ret = nulp_diff(a, b, dtype)
if not np.all(ret <= maxulp):
raise AssertionError("Arrays are not almost equal up to %g ULP" %
maxulp)
return ret
def nulp_diff(x, y, dtype=None):
"""For each item in x and y, return the number of representable floating
points between them.
Parameters
----------
x : array_like
first input array
y : array_like
second input array
dtype : dtype, optional
Data-type to convert `x` and `y` to if given. Default is None.
Returns
-------
nulp : array_like
number of representable floating point numbers between each item in x
and y.
Examples
--------
# By definition, epsilon is the smallest number such as 1 + eps != 1, so
# there should be exactly one ULP between 1 and 1 + eps
>>> nulp_diff(1, 1 + np.finfo(x.dtype).eps)
1.0
"""
import numpy as np
if dtype:
x = np.array(x, dtype=dtype)
y = np.array(y, dtype=dtype)
else:
x = np.array(x)
y = np.array(y)
t = np.common_type(x, y)
if np.iscomplexobj(x) or np.iscomplexobj(y):
raise NotImplementedError("_nulp not implemented for complex array")
x = np.array(x, dtype=t)
y = np.array(y, dtype=t)
if not x.shape == y.shape:
raise ValueError("x and y do not have the same shape: %s - %s" %
(x.shape, y.shape))
def _diff(rx, ry, vdt):
diff = np.array(rx-ry, dtype=vdt)
return np.abs(diff)
rx = integer_repr(x)
ry = integer_repr(y)
return _diff(rx, ry, t)
def _integer_repr(x, vdt, comp):
# Reinterpret binary representation of the float as sign-magnitude:
# take into account two-complement representation
# See also
# http://www.cygnus-software.com/papers/comparingfloats/comparingfloats.htm
rx = x.view(vdt)
if not (rx.size == 1):
rx[rx < 0] = comp - rx[rx < 0]
else:
if rx < 0:
rx = comp - rx
return rx
def integer_repr(x):
"""Return the signed-magnitude interpretation of the binary representation of
x."""
import numpy as np
if x.dtype == np.float32:
return _integer_repr(x, np.int32, np.int32(-2**31))
elif x.dtype == np.float64:
return _integer_repr(x, np.int64, np.int64(-2**63))
else:
raise ValueError("Unsupported dtype %s" % x.dtype)
# The following two classes are copied from python 2.6 warnings module (context
# manager)
class WarningMessage(object):
"""
Holds the result of a single showwarning() call.
Deprecated in 1.8.0
Notes
-----
`WarningMessage` is copied from the Python 2.6 warnings module,
so it can be used in NumPy with older Python versions.
"""
_WARNING_DETAILS = ("message", "category", "filename", "lineno", "file",
"line")
def __init__(self, message, category, filename, lineno, file=None,
line=None):
local_values = locals()
for attr in self._WARNING_DETAILS:
setattr(self, attr, local_values[attr])
if category:
self._category_name = category.__name__
else:
self._category_name = None
def __str__(self):
return ("{message : %r, category : %r, filename : %r, lineno : %s, "
"line : %r}" % (self.message, self._category_name,
self.filename, self.lineno, self.line))
class WarningManager(object):
"""
A context manager that copies and restores the warnings filter upon
exiting the context.
The 'record' argument specifies whether warnings should be captured by a
custom implementation of ``warnings.showwarning()`` and be appended to a
list returned by the context manager. Otherwise None is returned by the
context manager. The objects appended to the list are arguments whose
attributes mirror the arguments to ``showwarning()``.
The 'module' argument is to specify an alternative module to the module
named 'warnings' and imported under that name. This argument is only useful
when testing the warnings module itself.
Deprecated in 1.8.0
Notes
-----
`WarningManager` is a copy of the ``catch_warnings`` context manager
from the Python 2.6 warnings module, with slight modifications.
It is copied so it can be used in NumPy with older Python versions.
"""
def __init__(self, record=False, module=None):
self._record = record
if module is None:
self._module = sys.modules['warnings']
else:
self._module = module
self._entered = False
def __enter__(self):
if self._entered:
raise RuntimeError("Cannot enter %r twice" % self)
self._entered = True
self._filters = self._module.filters
self._module.filters = self._filters[:]
self._showwarning = self._module.showwarning
if self._record:
log = []
def showwarning(*args, **kwargs):
log.append(WarningMessage(*args, **kwargs))
self._module.showwarning = showwarning
return log
else:
return None
def __exit__(self):
if not self._entered:
raise RuntimeError("Cannot exit %r without entering first" % self)
self._module.filters = self._filters
self._module.showwarning = self._showwarning
@contextlib.contextmanager
def _assert_warns_context(warning_class, name=None):
__tracebackhide__ = True # Hide traceback for py.test
with suppress_warnings() as sup:
l = sup.record(warning_class)
yield
if not len(l) > 0:
name_str = " when calling %s" % name if name is not None else ""
raise AssertionError("No warning raised" + name_str)
def assert_warns(warning_class, *args, **kwargs):
"""
Fail unless the given callable throws the specified warning.
A warning of class warning_class should be thrown by the callable when
invoked with arguments args and keyword arguments kwargs.
If a different type of warning is thrown, it will not be caught.
If called with all arguments other than the warning class omitted, may be
used as a context manager:
with assert_warns(SomeWarning):
do_something()
The ability to be used as a context manager is new in NumPy v1.11.0.
.. versionadded:: 1.4.0
Parameters
----------
warning_class : class
The class defining the warning that `func` is expected to throw.
func : callable
The callable to test.
\\*args : Arguments
Arguments passed to `func`.
\\*\\*kwargs : Kwargs
Keyword arguments passed to `func`.
Returns
-------
The value returned by `func`.
"""
if not args:
return _assert_warns_context(warning_class)
func = args[0]
args = args[1:]
with _assert_warns_context(warning_class, name=func.__name__):
return func(*args, **kwargs)
@contextlib.contextmanager
def _assert_no_warnings_context(name=None):
__tracebackhide__ = True # Hide traceback for py.test
with warnings.catch_warnings(record=True) as l:
warnings.simplefilter('always')
yield
if len(l) > 0:
name_str = " when calling %s" % name if name is not None else ""
raise AssertionError("Got warnings%s: %s" % (name_str, l))
def assert_no_warnings(*args, **kwargs):
"""
Fail if the given callable produces any warnings.
If called with all arguments omitted, may be used as a context manager:
with assert_no_warnings():
do_something()
The ability to be used as a context manager is new in NumPy v1.11.0.
.. versionadded:: 1.7.0
Parameters
----------
func : callable
The callable to test.
\\*args : Arguments
Arguments passed to `func`.
\\*\\*kwargs : Kwargs
Keyword arguments passed to `func`.
Returns
-------
The value returned by `func`.
"""
if not args:
return _assert_no_warnings_context()
func = args[0]
args = args[1:]
with _assert_no_warnings_context(name=func.__name__):
return func(*args, **kwargs)
def _gen_alignment_data(dtype=float32, type='binary', max_size=24):
"""
generator producing data with different alignment and offsets
to test simd vectorization
Parameters
----------
dtype : dtype
data type to produce
type : string
'unary': create data for unary operations, creates one input
and output array
'binary': create data for unary operations, creates two input
and output array
max_size : integer
maximum size of data to produce
Returns
-------
if type is 'unary' yields one output, one input array and a message
containing information on the data
if type is 'binary' yields one output array, two input array and a message
containing information on the data
"""
ufmt = 'unary offset=(%d, %d), size=%d, dtype=%r, %s'
bfmt = 'binary offset=(%d, %d, %d), size=%d, dtype=%r, %s'
for o in range(3):
for s in range(o + 2, max(o + 3, max_size)):
if type == 'unary':
inp = lambda: arange(s, dtype=dtype)[o:]
out = empty((s,), dtype=dtype)[o:]
yield out, inp(), ufmt % (o, o, s, dtype, 'out of place')
d = inp()
yield d, d, ufmt % (o, o, s, dtype, 'in place')
yield out[1:], inp()[:-1], ufmt % \
(o + 1, o, s - 1, dtype, 'out of place')
yield out[:-1], inp()[1:], ufmt % \
(o, o + 1, s - 1, dtype, 'out of place')
yield inp()[:-1], inp()[1:], ufmt % \
(o, o + 1, s - 1, dtype, 'aliased')
yield inp()[1:], inp()[:-1], ufmt % \
(o + 1, o, s - 1, dtype, 'aliased')
if type == 'binary':
inp1 = lambda: arange(s, dtype=dtype)[o:]
inp2 = lambda: arange(s, dtype=dtype)[o:]
out = empty((s,), dtype=dtype)[o:]
yield out, inp1(), inp2(), bfmt % \
(o, o, o, s, dtype, 'out of place')
d = inp1()
yield d, d, inp2(), bfmt % \
(o, o, o, s, dtype, 'in place1')
d = inp2()
yield d, inp1(), d, bfmt % \
(o, o, o, s, dtype, 'in place2')
yield out[1:], inp1()[:-1], inp2()[:-1], bfmt % \
(o + 1, o, o, s - 1, dtype, 'out of place')
yield out[:-1], inp1()[1:], inp2()[:-1], bfmt % \
(o, o + 1, o, s - 1, dtype, 'out of place')
yield out[:-1], inp1()[:-1], inp2()[1:], bfmt % \
(o, o, o + 1, s - 1, dtype, 'out of place')
yield inp1()[1:], inp1()[:-1], inp2()[:-1], bfmt % \
(o + 1, o, o, s - 1, dtype, 'aliased')
yield inp1()[:-1], inp1()[1:], inp2()[:-1], bfmt % \
(o, o + 1, o, s - 1, dtype, 'aliased')
yield inp1()[:-1], inp1()[:-1], inp2()[1:], bfmt % \
(o, o, o + 1, s - 1, dtype, 'aliased')
class IgnoreException(Exception):
"Ignoring this exception due to disabled feature"
@contextlib.contextmanager
def tempdir(*args, **kwargs):
"""Context manager to provide a temporary test folder.
All arguments are passed as this to the underlying tempfile.mkdtemp
function.
"""
tmpdir = mkdtemp(*args, **kwargs)
try:
yield tmpdir
finally:
shutil.rmtree(tmpdir)
@contextlib.contextmanager
def temppath(*args, **kwargs):
"""Context manager for temporary files.
Context manager that returns the path to a closed temporary file. Its
parameters are the same as for tempfile.mkstemp and are passed directly
to that function. The underlying file is removed when the context is
exited, so it should be closed at that time.
Windows does not allow a temporary file to be opened if it is already
open, so the underlying file must be closed after opening before it
can be opened again.
"""
fd, path = mkstemp(*args, **kwargs)
os.close(fd)
try:
yield path
finally:
os.remove(path)
class clear_and_catch_warnings(warnings.catch_warnings):
""" Context manager that resets warning registry for catching warnings
Warnings can be slippery, because, whenever a warning is triggered, Python
adds a ``__warningregistry__`` member to the *calling* module. This makes
it impossible to retrigger the warning in this module, whatever you put in
the warnings filters. This context manager accepts a sequence of `modules`
as a keyword argument to its constructor and:
* stores and removes any ``__warningregistry__`` entries in given `modules`
on entry;
* resets ``__warningregistry__`` to its previous state on exit.
This makes it possible to trigger any warning afresh inside the context
manager without disturbing the state of warnings outside.
For compatibility with Python 3.0, please consider all arguments to be
keyword-only.
Parameters
----------
record : bool, optional
Specifies whether warnings should be captured by a custom
implementation of ``warnings.showwarning()`` and be appended to a list
returned by the context manager. Otherwise None is returned by the
context manager. The objects appended to the list are arguments whose
attributes mirror the arguments to ``showwarning()``.
modules : sequence, optional
Sequence of modules for which to reset warnings registry on entry and
restore on exit. To work correctly, all 'ignore' filters should
filter by one of these modules.
Examples
--------
>>> import warnings
>>> with clear_and_catch_warnings(modules=[np.core.fromnumeric]):
... warnings.simplefilter('always')
... warnings.filterwarnings('ignore', module='np.core.fromnumeric')
... # do something that raises a warning but ignore those in
... # np.core.fromnumeric
"""
class_modules = ()
def __init__(self, record=False, modules=()):
self.modules = set(modules).union(self.class_modules)
self._warnreg_copies = {}
super(clear_and_catch_warnings, self).__init__(record=record)
def __enter__(self):
for mod in self.modules:
if hasattr(mod, '__warningregistry__'):
mod_reg = mod.__warningregistry__
self._warnreg_copies[mod] = mod_reg.copy()
mod_reg.clear()
return super(clear_and_catch_warnings, self).__enter__()
def __exit__(self, *exc_info):
super(clear_and_catch_warnings, self).__exit__(*exc_info)
for mod in self.modules:
if hasattr(mod, '__warningregistry__'):
mod.__warningregistry__.clear()
if mod in self._warnreg_copies:
mod.__warningregistry__.update(self._warnreg_copies[mod])
class suppress_warnings(object):
"""
Context manager and decorator doing much the same as
``warnings.catch_warnings``.
However, it also provides a filter mechanism to work around
http://bugs.python.org/issue4180.
This bug causes Python before 3.4 to not reliably show warnings again
after they have been ignored once (even within catch_warnings). It
means that no "ignore" filter can be used easily, since following
tests might need to see the warning. Additionally it allows easier
specificity for testing warnings and can be nested.
Parameters
----------
forwarding_rule : str, optional
One of "always", "once", "module", or "location". Analogous to
the usual warnings module filter mode, it is useful to reduce
noise mostly on the outmost level. Unsuppressed and unrecorded
warnings will be forwarded based on this rule. Defaults to "always".
"location" is equivalent to the warnings "default", match by exact
location the warning warning originated from.
Notes
-----
Filters added inside the context manager will be discarded again
when leaving it. Upon entering all filters defined outside a
context will be applied automatically.
When a recording filter is added, matching warnings are stored in the
``log`` attribute as well as in the list returned by ``record``.
If filters are added and the ``module`` keyword is given, the
warning registry of this module will additionally be cleared when
applying it, entering the context, or exiting it. This could cause
warnings to appear a second time after leaving the context if they
were configured to be printed once (default) and were already
printed before the context was entered.
Nesting this context manager will work as expected when the
forwarding rule is "always" (default). Unfiltered and unrecorded
warnings will be passed out and be matched by the outer level.
On the outmost level they will be printed (or caught by another
warnings context). The forwarding rule argument can modify this
behaviour.
Like ``catch_warnings`` this context manager is not threadsafe.
Examples
--------
>>> with suppress_warnings() as sup:
... sup.filter(DeprecationWarning, "Some text")
... sup.filter(module=np.ma.core)
... log = sup.record(FutureWarning, "Does this occur?")
... command_giving_warnings()
... # The FutureWarning was given once, the filtered warnings were
... # ignored. All other warnings abide outside settings (may be
... # printed/error)
... assert_(len(log) == 1)
... assert_(len(sup.log) == 1) # also stored in log attribute
Or as a decorator:
>>> sup = suppress_warnings()
>>> sup.filter(module=np.ma.core) # module must match exact
>>> @sup
>>> def some_function():
... # do something which causes a warning in np.ma.core
... pass
"""
def __init__(self, forwarding_rule="always"):
self._entered = False
# Suppressions are either instance or defined inside one with block:
self._suppressions = []
if forwarding_rule not in {"always", "module", "once", "location"}:
raise ValueError("unsupported forwarding rule.")
self._forwarding_rule = forwarding_rule
def _clear_registries(self):
if hasattr(warnings, "_filters_mutated"):
# clearing the registry should not be necessary on new pythons,
# instead the filters should be mutated.
warnings._filters_mutated()
return
# Simply clear the registry, this should normally be harmless,
# note that on new pythons it would be invalidated anyway.
for module in self._tmp_modules:
if hasattr(module, "__warningregistry__"):
module.__warningregistry__.clear()
def _filter(self, category=Warning, message="", module=None, record=False):
if record:
record = [] # The log where to store warnings
else:
record = None
if self._entered:
if module is None:
warnings.filterwarnings(
"always", category=category, message=message)
else:
module_regex = module.__name__.replace('.', r'\.') + '$'
warnings.filterwarnings(
"always", category=category, message=message,
module=module_regex)
self._tmp_modules.add(module)
self._clear_registries()
self._tmp_suppressions.append(
(category, message, re.compile(message, re.I), module, record))
else:
self._suppressions.append(
(category, message, re.compile(message, re.I), module, record))
return record
def filter(self, category=Warning, message="", module=None):
"""
Add a new suppressing filter or apply it if the state is entered.
Parameters
----------
category : class, optional
Warning class to filter
message : string, optional
Regular expression matching the warning message.
module : module, optional
Module to filter for. Note that the module (and its file)
must match exactly and cannot be a submodule. This may make
it unreliable for external modules.
Notes
-----
When added within a context, filters are only added inside
the context and will be forgotten when the context is exited.
"""
self._filter(category=category, message=message, module=module,
record=False)
def record(self, category=Warning, message="", module=None):
"""
Append a new recording filter or apply it if the state is entered.
All warnings matching will be appended to the ``log`` attribute.
Parameters
----------
category : class, optional
Warning class to filter
message : string, optional
Regular expression matching the warning message.
module : module, optional
Module to filter for. Note that the module (and its file)
must match exactly and cannot be a submodule. This may make
it unreliable for external modules.
Returns
-------
log : list
A list which will be filled with all matched warnings.
Notes
-----
When added within a context, filters are only added inside
the context and will be forgotten when the context is exited.
"""
return self._filter(category=category, message=message, module=module,
record=True)
def __enter__(self):
if self._entered:
raise RuntimeError("cannot enter suppress_warnings twice.")
self._orig_show = warnings.showwarning
self._filters = warnings.filters
warnings.filters = self._filters[:]
self._entered = True
self._tmp_suppressions = []
self._tmp_modules = set()
self._forwarded = set()
self.log = [] # reset global log (no need to keep same list)
for cat, mess, _, mod, log in self._suppressions:
if log is not None:
del log[:] # clear the log
if mod is None:
warnings.filterwarnings(
"always", category=cat, message=mess)
else:
module_regex = mod.__name__.replace('.', r'\.') + '$'
warnings.filterwarnings(
"always", category=cat, message=mess,
module=module_regex)
self._tmp_modules.add(mod)
warnings.showwarning = self._showwarning
self._clear_registries()
return self
def __exit__(self, *exc_info):
warnings.showwarning = self._orig_show
warnings.filters = self._filters
self._clear_registries()
self._entered = False
del self._orig_show
del self._filters
def _showwarning(self, message, category, filename, lineno,
*args, **kwargs):
use_warnmsg = kwargs.pop("use_warnmsg", None)
for cat, _, pattern, mod, rec in (
self._suppressions + self._tmp_suppressions)[::-1]:
if (issubclass(category, cat) and
pattern.match(message.args[0]) is not None):
if mod is None:
# Message and category match, either recorded or ignored
if rec is not None:
msg = WarningMessage(message, category, filename,
lineno, **kwargs)
self.log.append(msg)
rec.append(msg)
return
# Use startswith, because warnings strips the c or o from
# .pyc/.pyo files.
elif mod.__file__.startswith(filename):
# The message and module (filename) match
if rec is not None:
msg = WarningMessage(message, category, filename,
lineno, **kwargs)
self.log.append(msg)
rec.append(msg)
return
# There is no filter in place, so pass to the outside handler
# unless we should only pass it once
if self._forwarding_rule == "always":
if use_warnmsg is None:
self._orig_show(message, category, filename, lineno,
*args, **kwargs)
else:
self._orig_showmsg(use_warnmsg)
return
if self._forwarding_rule == "once":
signature = (message.args, category)
elif self._forwarding_rule == "module":
signature = (message.args, category, filename)
elif self._forwarding_rule == "location":
signature = (message.args, category, filename, lineno)
if signature in self._forwarded:
return
self._forwarded.add(signature)
if use_warnmsg is None:
self._orig_show(message, category, filename, lineno, *args,
**kwargs)
else:
self._orig_showmsg(use_warnmsg)
def __call__(self, func):
"""
Function decorator to apply certain suppressions to a whole
function.
"""
@wraps(func)
def new_func(*args, **kwargs):
with self:
return func(*args, **kwargs)
return new_func
| 75,434 | 32.827354 | 90 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/testing/nose_tools/__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/testing/nose_tools/parameterized.py
|
"""
tl;dr: all code code is licensed under simplified BSD, unless stated otherwise.
Unless stated otherwise in the source files, all code is copyright 2010 David
Wolever <[email protected]>. All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice,
this list of conditions and the following disclaimer.
2. Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY <COPYRIGHT HOLDER> ``AS IS'' AND ANY EXPRESS OR
IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF
MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO
EVENT SHALL <COPYRIGHT HOLDER> OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT,
INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE
OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
The views and conclusions contained in the software and documentation are those
of the authors and should not be interpreted as representing official policies,
either expressed or implied, of David Wolever.
"""
import re
import sys
import inspect
import warnings
from functools import wraps
from types import MethodType as MethodType
from collections import namedtuple
try:
from collections import OrderedDict as MaybeOrderedDict
except ImportError:
MaybeOrderedDict = dict
from unittest import TestCase
PY3 = sys.version_info[0] == 3
PY2 = sys.version_info[0] == 2
if PY3:
# Python 3 doesn't have an InstanceType, so just use a dummy type.
class InstanceType():
pass
lzip = lambda *a: list(zip(*a))
text_type = str
string_types = str,
bytes_type = bytes
def make_method(func, instance, type):
if instance is None:
return func
return MethodType(func, instance)
else:
from types import InstanceType
lzip = zip
text_type = unicode
bytes_type = str
string_types = basestring,
def make_method(func, instance, type):
return MethodType(func, instance, type)
_param = namedtuple("param", "args kwargs")
class param(_param):
""" Represents a single parameter to a test case.
For example::
>>> p = param("foo", bar=16)
>>> p
param("foo", bar=16)
>>> p.args
('foo', )
>>> p.kwargs
{'bar': 16}
Intended to be used as an argument to ``@parameterized``::
@parameterized([
param("foo", bar=16),
])
def test_stuff(foo, bar=16):
pass
"""
def __new__(cls, *args , **kwargs):
return _param.__new__(cls, args, kwargs)
@classmethod
def explicit(cls, args=None, kwargs=None):
""" Creates a ``param`` by explicitly specifying ``args`` and
``kwargs``::
>>> param.explicit([1,2,3])
param(*(1, 2, 3))
>>> param.explicit(kwargs={"foo": 42})
param(*(), **{"foo": "42"})
"""
args = args or ()
kwargs = kwargs or {}
return cls(*args, **kwargs)
@classmethod
def from_decorator(cls, args):
""" Returns an instance of ``param()`` for ``@parameterized`` argument
``args``::
>>> param.from_decorator((42, ))
param(args=(42, ), kwargs={})
>>> param.from_decorator("foo")
param(args=("foo", ), kwargs={})
"""
if isinstance(args, param):
return args
elif isinstance(args, string_types):
args = (args, )
try:
return cls(*args)
except TypeError as e:
if "after * must be" not in str(e):
raise
raise TypeError(
"Parameters must be tuples, but %r is not (hint: use '(%r, )')"
%(args, args),
)
def __repr__(self):
return "param(*%r, **%r)" %self
class QuietOrderedDict(MaybeOrderedDict):
""" When OrderedDict is available, use it to make sure that the kwargs in
doc strings are consistently ordered. """
__str__ = dict.__str__
__repr__ = dict.__repr__
def parameterized_argument_value_pairs(func, p):
"""Return tuples of parameterized arguments and their values.
This is useful if you are writing your own doc_func
function and need to know the values for each parameter name::
>>> def func(a, foo=None, bar=42, **kwargs): pass
>>> p = param(1, foo=7, extra=99)
>>> parameterized_argument_value_pairs(func, p)
[("a", 1), ("foo", 7), ("bar", 42), ("**kwargs", {"extra": 99})]
If the function's first argument is named ``self`` then it will be
ignored::
>>> def func(self, a): pass
>>> p = param(1)
>>> parameterized_argument_value_pairs(func, p)
[("a", 1)]
Additionally, empty ``*args`` or ``**kwargs`` will be ignored::
>>> def func(foo, *args): pass
>>> p = param(1)
>>> parameterized_argument_value_pairs(func, p)
[("foo", 1)]
>>> p = param(1, 16)
>>> parameterized_argument_value_pairs(func, p)
[("foo", 1), ("*args", (16, ))]
"""
argspec = inspect.getargspec(func)
arg_offset = 1 if argspec.args[:1] == ["self"] else 0
named_args = argspec.args[arg_offset:]
result = lzip(named_args, p.args)
named_args = argspec.args[len(result) + arg_offset:]
varargs = p.args[len(result):]
result.extend([
(name, p.kwargs.get(name, default))
for (name, default)
in zip(named_args, argspec.defaults or [])
])
seen_arg_names = set([ n for (n, _) in result ])
keywords = QuietOrderedDict(sorted([
(name, p.kwargs[name])
for name in p.kwargs
if name not in seen_arg_names
]))
if varargs:
result.append(("*%s" %(argspec.varargs, ), tuple(varargs)))
if keywords:
result.append(("**%s" %(argspec.keywords, ), keywords))
return result
def short_repr(x, n=64):
""" A shortened repr of ``x`` which is guaranteed to be ``unicode``::
>>> short_repr("foo")
u"foo"
>>> short_repr("123456789", n=4)
u"12...89"
"""
x_repr = repr(x)
if isinstance(x_repr, bytes_type):
try:
x_repr = text_type(x_repr, "utf-8")
except UnicodeDecodeError:
x_repr = text_type(x_repr, "latin1")
if len(x_repr) > n:
x_repr = x_repr[:n//2] + "..." + x_repr[len(x_repr) - n//2:]
return x_repr
def default_doc_func(func, num, p):
if func.__doc__ is None:
return None
all_args_with_values = parameterized_argument_value_pairs(func, p)
# Assumes that the function passed is a bound method.
descs = ["%s=%s" %(n, short_repr(v)) for n, v in all_args_with_values]
# The documentation might be a multiline string, so split it
# and just work with the first string, ignoring the period
# at the end if there is one.
first, nl, rest = func.__doc__.lstrip().partition("\n")
suffix = ""
if first.endswith("."):
suffix = "."
first = first[:-1]
args = "%s[with %s]" %(len(first) and " " or "", ", ".join(descs))
return "".join([first.rstrip(), args, suffix, nl, rest])
def default_name_func(func, num, p):
base_name = func.__name__
name_suffix = "_%s" %(num, )
if len(p.args) > 0 and isinstance(p.args[0], string_types):
name_suffix += "_" + parameterized.to_safe_name(p.args[0])
return base_name + name_suffix
_test_runner_override = None
_test_runner_guess = False
_test_runners = set(["unittest", "unittest2", "nose", "nose2", "pytest"])
_test_runner_aliases = {
"_pytest": "pytest",
}
def set_test_runner(name):
global _test_runner_override
if name not in _test_runners:
raise TypeError(
"Invalid test runner: %r (must be one of: %s)"
%(name, ", ".join(_test_runners)),
)
_test_runner_override = name
def detect_runner():
""" Guess which test runner we're using by traversing the stack and looking
for the first matching module. This *should* be reasonably safe, as
it's done during test disocvery where the test runner should be the
stack frame immediately outside. """
if _test_runner_override is not None:
return _test_runner_override
global _test_runner_guess
if _test_runner_guess is False:
stack = inspect.stack()
for record in reversed(stack):
frame = record[0]
module = frame.f_globals.get("__name__").partition(".")[0]
if module in _test_runner_aliases:
module = _test_runner_aliases[module]
if module in _test_runners:
_test_runner_guess = module
break
if record[1].endswith("python2.6/unittest.py"):
_test_runner_guess = "unittest"
break
else:
_test_runner_guess = None
return _test_runner_guess
class parameterized(object):
""" Parameterize a test case::
class TestInt(object):
@parameterized([
("A", 10),
("F", 15),
param("10", 42, base=42)
])
def test_int(self, input, expected, base=16):
actual = int(input, base=base)
assert_equal(actual, expected)
@parameterized([
(2, 3, 5)
(3, 5, 8),
])
def test_add(a, b, expected):
assert_equal(a + b, expected)
"""
def __init__(self, input, doc_func=None):
self.get_input = self.input_as_callable(input)
self.doc_func = doc_func or default_doc_func
def __call__(self, test_func):
self.assert_not_in_testcase_subclass()
@wraps(test_func)
def wrapper(test_self=None):
test_cls = test_self and type(test_self)
if test_self is not None:
if issubclass(test_cls, InstanceType):
raise TypeError((
"@parameterized can't be used with old-style classes, but "
"%r has an old-style class. Consider using a new-style "
"class, or '@parameterized.expand' "
"(see http://stackoverflow.com/q/54867/71522 for more "
"information on old-style classes)."
) %(test_self, ))
original_doc = wrapper.__doc__
for num, args in enumerate(wrapper.parameterized_input):
p = param.from_decorator(args)
unbound_func, nose_tuple = self.param_as_nose_tuple(test_self, test_func, num, p)
try:
wrapper.__doc__ = nose_tuple[0].__doc__
# Nose uses `getattr(instance, test_func.__name__)` to get
# a method bound to the test instance (as opposed to a
# method bound to the instance of the class created when
# tests were being enumerated). Set a value here to make
# sure nose can get the correct test method.
if test_self is not None:
setattr(test_cls, test_func.__name__, unbound_func)
yield nose_tuple
finally:
if test_self is not None:
delattr(test_cls, test_func.__name__)
wrapper.__doc__ = original_doc
wrapper.parameterized_input = self.get_input()
wrapper.parameterized_func = test_func
test_func.__name__ = "_parameterized_original_%s" %(test_func.__name__, )
return wrapper
def param_as_nose_tuple(self, test_self, func, num, p):
nose_func = wraps(func)(lambda *args: func(*args[:-1], **args[-1]))
nose_func.__doc__ = self.doc_func(func, num, p)
# Track the unbound function because we need to setattr the unbound
# function onto the class for nose to work (see comments above), and
# Python 3 doesn't let us pull the function out of a bound method.
unbound_func = nose_func
if test_self is not None:
# Under nose on Py2 we need to return an unbound method to make
# sure that the `self` in the method is properly shared with the
# `self` used in `setUp` and `tearDown`. But only there. Everyone
# else needs a bound method.
func_self = (
None if PY2 and detect_runner() == "nose" else
test_self
)
nose_func = make_method(nose_func, func_self, type(test_self))
return unbound_func, (nose_func, ) + p.args + (p.kwargs or {}, )
def assert_not_in_testcase_subclass(self):
parent_classes = self._terrible_magic_get_defining_classes()
if any(issubclass(cls, TestCase) for cls in parent_classes):
raise Exception("Warning: '@parameterized' tests won't work "
"inside subclasses of 'TestCase' - use "
"'@parameterized.expand' instead.")
def _terrible_magic_get_defining_classes(self):
""" Returns the set of parent classes of the class currently being defined.
Will likely only work if called from the ``parameterized`` decorator.
This function is entirely @brandon_rhodes's fault, as he suggested
the implementation: http://stackoverflow.com/a/8793684/71522
"""
stack = inspect.stack()
if len(stack) <= 4:
return []
frame = stack[4]
code_context = frame[4] and frame[4][0].strip()
if not (code_context and code_context.startswith("class ")):
return []
_, _, parents = code_context.partition("(")
parents, _, _ = parents.partition(")")
return eval("[" + parents + "]", frame[0].f_globals, frame[0].f_locals)
@classmethod
def input_as_callable(cls, input):
if callable(input):
return lambda: cls.check_input_values(input())
input_values = cls.check_input_values(input)
return lambda: input_values
@classmethod
def check_input_values(cls, input_values):
# Explicitly convery non-list inputs to a list so that:
# 1. A helpful exception will be raised if they aren't iterable, and
# 2. Generators are unwrapped exactly once (otherwise `nosetests
# --processes=n` has issues; see:
# https://github.com/wolever/nose-parameterized/pull/31)
if not isinstance(input_values, list):
input_values = list(input_values)
return [ param.from_decorator(p) for p in input_values ]
@classmethod
def expand(cls, input, name_func=None, doc_func=None, **legacy):
""" A "brute force" method of parameterizing test cases. Creates new
test cases and injects them into the namespace that the wrapped
function is being defined in. Useful for parameterizing tests in
subclasses of 'UnitTest', where Nose test generators don't work.
>>> @parameterized.expand([("foo", 1, 2)])
... def test_add1(name, input, expected):
... actual = add1(input)
... assert_equal(actual, expected)
...
>>> locals()
... 'test_add1_foo_0': <function ...> ...
>>>
"""
if "testcase_func_name" in legacy:
warnings.warn("testcase_func_name= is deprecated; use name_func=",
DeprecationWarning, stacklevel=2)
if not name_func:
name_func = legacy["testcase_func_name"]
if "testcase_func_doc" in legacy:
warnings.warn("testcase_func_doc= is deprecated; use doc_func=",
DeprecationWarning, stacklevel=2)
if not doc_func:
doc_func = legacy["testcase_func_doc"]
doc_func = doc_func or default_doc_func
name_func = name_func or default_name_func
def parameterized_expand_wrapper(f, instance=None):
stack = inspect.stack()
frame = stack[1]
frame_locals = frame[0].f_locals
paramters = cls.input_as_callable(input)()
for num, p in enumerate(paramters):
name = name_func(f, num, p)
frame_locals[name] = cls.param_as_standalone_func(p, f, name)
frame_locals[name].__doc__ = doc_func(f, num, p)
f.__test__ = False
return parameterized_expand_wrapper
@classmethod
def param_as_standalone_func(cls, p, func, name):
@wraps(func)
def standalone_func(*a):
return func(*(a + p.args), **p.kwargs)
standalone_func.__name__ = name
# place_as is used by py.test to determine what source file should be
# used for this test.
standalone_func.place_as = func
# Remove __wrapped__ because py.test will try to look at __wrapped__
# to determine which parameters should be used with this test case,
# and obviously we don't need it to do any parameterization.
try:
del standalone_func.__wrapped__
except AttributeError:
pass
return standalone_func
@classmethod
def to_safe_name(cls, s):
return str(re.sub("[^a-zA-Z0-9_]+", "_", s))
| 18,286 | 36.320408 | 97 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/testing/nose_tools/nosetester.py
|
"""
Nose test running.
This module implements ``test()`` and ``bench()`` functions for NumPy modules.
"""
from __future__ import division, absolute_import, print_function
import os
import sys
import warnings
from numpy.compat import basestring
import numpy as np
from .utils import import_nose, suppress_warnings
__all__ = ['get_package_name', 'run_module_suite', 'NoseTester',
'_numpy_tester', 'get_package_name', 'import_nose',
'suppress_warnings']
def get_package_name(filepath):
"""
Given a path where a package is installed, determine its name.
Parameters
----------
filepath : str
Path to a file. If the determination fails, "numpy" is returned.
Examples
--------
>>> np.testing.nosetester.get_package_name('nonsense')
'numpy'
"""
fullpath = filepath[:]
pkg_name = []
while 'site-packages' in filepath or 'dist-packages' in filepath:
filepath, p2 = os.path.split(filepath)
if p2 in ('site-packages', 'dist-packages'):
break
pkg_name.append(p2)
# if package name determination failed, just default to numpy/scipy
if not pkg_name:
if 'scipy' in fullpath:
return 'scipy'
else:
return 'numpy'
# otherwise, reverse to get correct order and return
pkg_name.reverse()
# don't include the outer egg directory
if pkg_name[0].endswith('.egg'):
pkg_name.pop(0)
return '.'.join(pkg_name)
def run_module_suite(file_to_run=None, argv=None):
"""
Run a test module.
Equivalent to calling ``$ nosetests <argv> <file_to_run>`` from
the command line
Parameters
----------
file_to_run : str, optional
Path to test module, or None.
By default, run the module from which this function is called.
argv : list of strings
Arguments to be passed to the nose test runner. ``argv[0]`` is
ignored. All command line arguments accepted by ``nosetests``
will work. If it is the default value None, sys.argv is used.
.. versionadded:: 1.9.0
Examples
--------
Adding the following::
if __name__ == "__main__" :
run_module_suite(argv=sys.argv)
at the end of a test module will run the tests when that module is
called in the python interpreter.
Alternatively, calling::
>>> run_module_suite(file_to_run="numpy/tests/test_matlib.py")
from an interpreter will run all the test routine in 'test_matlib.py'.
"""
if file_to_run is None:
f = sys._getframe(1)
file_to_run = f.f_locals.get('__file__', None)
if file_to_run is None:
raise AssertionError
if argv is None:
argv = sys.argv + [file_to_run]
else:
argv = argv + [file_to_run]
nose = import_nose()
from .noseclasses import KnownFailurePlugin
nose.run(argv=argv, addplugins=[KnownFailurePlugin()])
class NoseTester(object):
"""
Nose test runner.
This class is made available as numpy.testing.Tester, and a test function
is typically added to a package's __init__.py like so::
from numpy.testing import Tester
test = Tester().test
Calling this test function finds and runs all tests associated with the
package and all its sub-packages.
Attributes
----------
package_path : str
Full path to the package to test.
package_name : str
Name of the package to test.
Parameters
----------
package : module, str or None, optional
The package to test. If a string, this should be the full path to
the package. If None (default), `package` is set to the module from
which `NoseTester` is initialized.
raise_warnings : None, str or sequence of warnings, optional
This specifies which warnings to configure as 'raise' instead
of being shown once during the test execution. Valid strings are:
- "develop" : equals ``(Warning,)``
- "release" : equals ``()``, don't raise on any warnings.
Default is "release".
depth : int, optional
If `package` is None, then this can be used to initialize from the
module of the caller of (the caller of (...)) the code that
initializes `NoseTester`. Default of 0 means the module of the
immediate caller; higher values are useful for utility routines that
want to initialize `NoseTester` objects on behalf of other code.
"""
def __init__(self, package=None, raise_warnings="release", depth=0,
check_fpu_mode=False):
# Back-compat: 'None' used to mean either "release" or "develop"
# depending on whether this was a release or develop version of
# numpy. Those semantics were fine for testing numpy, but not so
# helpful for downstream projects like scipy that use
# numpy.testing. (They want to set this based on whether *they* are a
# release or develop version, not whether numpy is.) So we continue to
# accept 'None' for back-compat, but it's now just an alias for the
# default "release".
if raise_warnings is None:
raise_warnings = "release"
package_name = None
if package is None:
f = sys._getframe(1 + depth)
package_path = f.f_locals.get('__file__', None)
if package_path is None:
raise AssertionError
package_path = os.path.dirname(package_path)
package_name = f.f_locals.get('__name__', None)
elif isinstance(package, type(os)):
package_path = os.path.dirname(package.__file__)
package_name = getattr(package, '__name__', None)
else:
package_path = str(package)
self.package_path = package_path
# Find the package name under test; this name is used to limit coverage
# reporting (if enabled).
if package_name is None:
package_name = get_package_name(package_path)
self.package_name = package_name
# Set to "release" in constructor in maintenance branches.
self.raise_warnings = raise_warnings
# Whether to check for FPU mode changes
self.check_fpu_mode = check_fpu_mode
def _test_argv(self, label, verbose, extra_argv):
''' Generate argv for nosetest command
Parameters
----------
label : {'fast', 'full', '', attribute identifier}, optional
see ``test`` docstring
verbose : int, optional
Verbosity value for test outputs, in the range 1-10. Default is 1.
extra_argv : list, optional
List with any extra arguments to pass to nosetests.
Returns
-------
argv : list
command line arguments that will be passed to nose
'''
argv = [__file__, self.package_path, '-s']
if label and label != 'full':
if not isinstance(label, basestring):
raise TypeError('Selection label should be a string')
if label == 'fast':
label = 'not slow'
argv += ['-A', label]
argv += ['--verbosity', str(verbose)]
# When installing with setuptools, and also in some other cases, the
# test_*.py files end up marked +x executable. Nose, by default, does
# not run files marked with +x as they might be scripts. However, in
# our case nose only looks for test_*.py files under the package
# directory, which should be safe.
argv += ['--exe']
if extra_argv:
argv += extra_argv
return argv
def _show_system_info(self):
nose = import_nose()
import numpy
print("NumPy version %s" % numpy.__version__)
relaxed_strides = numpy.ones((10, 1), order="C").flags.f_contiguous
print("NumPy relaxed strides checking option:", relaxed_strides)
npdir = os.path.dirname(numpy.__file__)
print("NumPy is installed in %s" % npdir)
if 'scipy' in self.package_name:
import scipy
print("SciPy version %s" % scipy.__version__)
spdir = os.path.dirname(scipy.__file__)
print("SciPy is installed in %s" % spdir)
pyversion = sys.version.replace('\n', '')
print("Python version %s" % pyversion)
print("nose version %d.%d.%d" % nose.__versioninfo__)
def _get_custom_doctester(self):
""" Return instantiated plugin for doctests
Allows subclassing of this class to override doctester
A return value of None means use the nose builtin doctest plugin
"""
from .noseclasses import NumpyDoctest
return NumpyDoctest()
def prepare_test_args(self, label='fast', verbose=1, extra_argv=None,
doctests=False, coverage=False, timer=False):
"""
Run tests for module using nose.
This method does the heavy lifting for the `test` method. It takes all
the same arguments, for details see `test`.
See Also
--------
test
"""
# fail with nice error message if nose is not present
import_nose()
# compile argv
argv = self._test_argv(label, verbose, extra_argv)
# our way of doing coverage
if coverage:
argv += ['--cover-package=%s' % self.package_name, '--with-coverage',
'--cover-tests', '--cover-erase']
if timer:
if timer is True:
argv += ['--with-timer']
elif isinstance(timer, int):
argv += ['--with-timer', '--timer-top-n', str(timer)]
# construct list of plugins
import nose.plugins.builtin
from nose.plugins import EntryPointPluginManager
from .noseclasses import (KnownFailurePlugin, Unplugger,
FPUModeCheckPlugin)
plugins = [KnownFailurePlugin()]
plugins += [p() for p in nose.plugins.builtin.plugins]
if self.check_fpu_mode:
plugins += [FPUModeCheckPlugin()]
argv += ["--with-fpumodecheckplugin"]
try:
# External plugins (like nose-timer)
entrypoint_manager = EntryPointPluginManager()
entrypoint_manager.loadPlugins()
plugins += [p for p in entrypoint_manager.plugins]
except ImportError:
# Relies on pkg_resources, not a hard dependency
pass
# add doctesting if required
doctest_argv = '--with-doctest' in argv
if doctests == False and doctest_argv:
doctests = True
plug = self._get_custom_doctester()
if plug is None:
# use standard doctesting
if doctests and not doctest_argv:
argv += ['--with-doctest']
else: # custom doctesting
if doctest_argv: # in fact the unplugger would take care of this
argv.remove('--with-doctest')
plugins += [Unplugger('doctest'), plug]
if doctests:
argv += ['--with-' + plug.name]
return argv, plugins
def test(self, label='fast', verbose=1, extra_argv=None,
doctests=False, coverage=False, raise_warnings=None,
timer=False):
"""
Run tests for module using nose.
Parameters
----------
label : {'fast', 'full', '', attribute identifier}, optional
Identifies the tests to run. This can be a string to pass to
the nosetests executable with the '-A' option, or one of several
special values. Special values are:
* 'fast' - the default - which corresponds to the ``nosetests -A``
option of 'not slow'.
* 'full' - fast (as above) and slow tests as in the
'no -A' option to nosetests - this is the same as ''.
* None or '' - run all tests.
attribute_identifier - string passed directly to nosetests as '-A'.
verbose : int, optional
Verbosity value for test outputs, in the range 1-10. Default is 1.
extra_argv : list, optional
List with any extra arguments to pass to nosetests.
doctests : bool, optional
If True, run doctests in module. Default is False.
coverage : bool, optional
If True, report coverage of NumPy code. Default is False.
(This requires the `coverage module:
<http://nedbatchelder.com/code/modules/coverage.html>`_).
raise_warnings : None, str or sequence of warnings, optional
This specifies which warnings to configure as 'raise' instead
of being shown once during the test execution. Valid strings are:
- "develop" : equals ``(Warning,)``
- "release" : equals ``()``, don't raise on any warnings.
The default is to use the class initialization value.
timer : bool or int, optional
Timing of individual tests with ``nose-timer`` (which needs to be
installed). If True, time tests and report on all of them.
If an integer (say ``N``), report timing results for ``N`` slowest
tests.
Returns
-------
result : object
Returns the result of running the tests as a
``nose.result.TextTestResult`` object.
Notes
-----
Each NumPy module exposes `test` in its namespace to run all tests for it.
For example, to run all tests for numpy.lib:
>>> np.lib.test() #doctest: +SKIP
Examples
--------
>>> result = np.lib.test() #doctest: +SKIP
Running unit tests for numpy.lib
...
Ran 976 tests in 3.933s
OK
>>> result.errors #doctest: +SKIP
[]
>>> result.knownfail #doctest: +SKIP
[]
"""
# cap verbosity at 3 because nose becomes *very* verbose beyond that
verbose = min(verbose, 3)
from . import utils
utils.verbose = verbose
argv, plugins = self.prepare_test_args(
label, verbose, extra_argv, doctests, coverage, timer)
if doctests:
print("Running unit tests and doctests for %s" % self.package_name)
else:
print("Running unit tests for %s" % self.package_name)
self._show_system_info()
# reset doctest state on every run
import doctest
doctest.master = None
if raise_warnings is None:
raise_warnings = self.raise_warnings
_warn_opts = dict(develop=(Warning,),
release=())
if isinstance(raise_warnings, basestring):
raise_warnings = _warn_opts[raise_warnings]
with suppress_warnings("location") as sup:
# Reset the warning filters to the default state,
# so that running the tests is more repeatable.
warnings.resetwarnings()
# Set all warnings to 'warn', this is because the default 'once'
# has the bad property of possibly shadowing later warnings.
warnings.filterwarnings('always')
# Force the requested warnings to raise
for warningtype in raise_warnings:
warnings.filterwarnings('error', category=warningtype)
# Filter out annoying import messages.
sup.filter(message='Not importing directory')
sup.filter(message="numpy.dtype size changed")
sup.filter(message="numpy.ufunc size changed")
sup.filter(category=np.ModuleDeprecationWarning)
# Filter out boolean '-' deprecation messages. This allows
# older versions of scipy to test without a flood of messages.
sup.filter(message=".*boolean negative.*")
sup.filter(message=".*boolean subtract.*")
# Filter out distutils cpu warnings (could be localized to
# distutils tests). ASV has problems with top level import,
# so fetch module for suppression here.
with warnings.catch_warnings():
warnings.simplefilter("always")
from ...distutils import cpuinfo
sup.filter(category=UserWarning, module=cpuinfo)
# See #7949: Filter out deprecation warnings due to the -3 flag to
# python 2
if sys.version_info.major == 2 and sys.py3kwarning:
# This is very specific, so using the fragile module filter
# is fine
import threading
sup.filter(DeprecationWarning,
r"sys\.exc_clear\(\) not supported in 3\.x",
module=threading)
sup.filter(DeprecationWarning, message=r"in 3\.x, __setslice__")
sup.filter(DeprecationWarning, message=r"in 3\.x, __getslice__")
sup.filter(DeprecationWarning, message=r"buffer\(\) not supported in 3\.x")
sup.filter(DeprecationWarning, message=r"CObject type is not supported in 3\.x")
sup.filter(DeprecationWarning, message=r"comparing unequal types not supported in 3\.x")
# Filter out some deprecation warnings inside nose 1.3.7 when run
# on python 3.5b2. See
# https://github.com/nose-devs/nose/issues/929
# Note: it is hard to filter based on module for sup (lineno could
# be implemented).
warnings.filterwarnings("ignore", message=".*getargspec.*",
category=DeprecationWarning,
module=r"nose\.")
from .noseclasses import NumpyTestProgram
t = NumpyTestProgram(argv=argv, exit=False, plugins=plugins)
return t.result
def bench(self, label='fast', verbose=1, extra_argv=None):
"""
Run benchmarks for module using nose.
Parameters
----------
label : {'fast', 'full', '', attribute identifier}, optional
Identifies the benchmarks to run. This can be a string to pass to
the nosetests executable with the '-A' option, or one of several
special values. Special values are:
* 'fast' - the default - which corresponds to the ``nosetests -A``
option of 'not slow'.
* 'full' - fast (as above) and slow benchmarks as in the
'no -A' option to nosetests - this is the same as ''.
* None or '' - run all tests.
attribute_identifier - string passed directly to nosetests as '-A'.
verbose : int, optional
Verbosity value for benchmark outputs, in the range 1-10. Default is 1.
extra_argv : list, optional
List with any extra arguments to pass to nosetests.
Returns
-------
success : bool
Returns True if running the benchmarks works, False if an error
occurred.
Notes
-----
Benchmarks are like tests, but have names starting with "bench" instead
of "test", and can be found under the "benchmarks" sub-directory of the
module.
Each NumPy module exposes `bench` in its namespace to run all benchmarks
for it.
Examples
--------
>>> success = np.lib.bench() #doctest: +SKIP
Running benchmarks for numpy.lib
...
using 562341 items:
unique:
0.11
unique1d:
0.11
ratio: 1.0
nUnique: 56230 == 56230
...
OK
>>> success #doctest: +SKIP
True
"""
print("Running benchmarks for %s" % self.package_name)
self._show_system_info()
argv = self._test_argv(label, verbose, extra_argv)
argv += ['--match', r'(?:^|[\\b_\\.%s-])[Bb]ench' % os.sep]
# import nose or make informative error
nose = import_nose()
# get plugin to disable doctests
from .noseclasses import Unplugger
add_plugins = [Unplugger('doctest')]
return nose.run(argv=argv, addplugins=add_plugins)
def _numpy_tester():
if hasattr(np, "__version__") and ".dev0" in np.__version__:
mode = "develop"
else:
mode = "release"
return NoseTester(raise_warnings=mode, depth=1,
check_fpu_mode=True)
| 20,562 | 35.654189 | 104 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/testing/nose_tools/decorators.py
|
"""
Decorators for labeling and modifying behavior of test objects.
Decorators that merely return a modified version of the original
function object are straightforward. Decorators that return a new
function object need to use
::
nose.tools.make_decorator(original_function)(decorator)
in returning the decorator, in order to preserve meta-data such as
function name, setup and teardown functions and so on - see
``nose.tools`` for more information.
"""
from __future__ import division, absolute_import, print_function
import collections
from .utils import SkipTest, assert_warns
def slow(t):
"""
Label a test as 'slow'.
The exact definition of a slow test is obviously both subjective and
hardware-dependent, but in general any individual test that requires more
than a second or two should be labeled as slow (the whole suite consits of
thousands of tests, so even a second is significant).
Parameters
----------
t : callable
The test to label as slow.
Returns
-------
t : callable
The decorated test `t`.
Examples
--------
The `numpy.testing` module includes ``import decorators as dec``.
A test can be decorated as slow like this::
from numpy.testing import *
@dec.slow
def test_big(self):
print('Big, slow test')
"""
t.slow = True
return t
def setastest(tf=True):
"""
Signals to nose that this function is or is not a test.
Parameters
----------
tf : bool
If True, specifies that the decorated callable is a test.
If False, specifies that the decorated callable is not a test.
Default is True.
Notes
-----
This decorator can't use the nose namespace, because it can be
called from a non-test module. See also ``istest`` and ``nottest`` in
``nose.tools``.
Examples
--------
`setastest` can be used in the following way::
from numpy.testing import dec
@dec.setastest(False)
def func_with_test_in_name(arg1, arg2):
pass
"""
def set_test(t):
t.__test__ = tf
return t
return set_test
def skipif(skip_condition, msg=None):
"""
Make function raise SkipTest exception if a given condition is true.
If the condition is a callable, it is used at runtime to dynamically
make the decision. This is useful for tests that may require costly
imports, to delay the cost until the test suite is actually executed.
Parameters
----------
skip_condition : bool or callable
Flag to determine whether to skip the decorated test.
msg : str, optional
Message to give on raising a SkipTest exception. Default is None.
Returns
-------
decorator : function
Decorator which, when applied to a function, causes SkipTest
to be raised when `skip_condition` is True, and the function
to be called normally otherwise.
Notes
-----
The decorator itself is decorated with the ``nose.tools.make_decorator``
function in order to transmit function name, and various other metadata.
"""
def skip_decorator(f):
# Local import to avoid a hard nose dependency and only incur the
# import time overhead at actual test-time.
import nose
# Allow for both boolean or callable skip conditions.
if isinstance(skip_condition, collections.Callable):
skip_val = lambda: skip_condition()
else:
skip_val = lambda: skip_condition
def get_msg(func,msg=None):
"""Skip message with information about function being skipped."""
if msg is None:
out = 'Test skipped due to test condition'
else:
out = msg
return "Skipping test: %s: %s" % (func.__name__, out)
# We need to define *two* skippers because Python doesn't allow both
# return with value and yield inside the same function.
def skipper_func(*args, **kwargs):
"""Skipper for normal test functions."""
if skip_val():
raise SkipTest(get_msg(f, msg))
else:
return f(*args, **kwargs)
def skipper_gen(*args, **kwargs):
"""Skipper for test generators."""
if skip_val():
raise SkipTest(get_msg(f, msg))
else:
for x in f(*args, **kwargs):
yield x
# Choose the right skipper to use when building the actual decorator.
if nose.util.isgenerator(f):
skipper = skipper_gen
else:
skipper = skipper_func
return nose.tools.make_decorator(f)(skipper)
return skip_decorator
def knownfailureif(fail_condition, msg=None):
"""
Make function raise KnownFailureException exception if given condition is true.
If the condition is a callable, it is used at runtime to dynamically
make the decision. This is useful for tests that may require costly
imports, to delay the cost until the test suite is actually executed.
Parameters
----------
fail_condition : bool or callable
Flag to determine whether to mark the decorated test as a known
failure (if True) or not (if False).
msg : str, optional
Message to give on raising a KnownFailureException exception.
Default is None.
Returns
-------
decorator : function
Decorator, which, when applied to a function, causes
KnownFailureException to be raised when `fail_condition` is True,
and the function to be called normally otherwise.
Notes
-----
The decorator itself is decorated with the ``nose.tools.make_decorator``
function in order to transmit function name, and various other metadata.
"""
if msg is None:
msg = 'Test skipped due to known failure'
# Allow for both boolean or callable known failure conditions.
if isinstance(fail_condition, collections.Callable):
fail_val = lambda: fail_condition()
else:
fail_val = lambda: fail_condition
def knownfail_decorator(f):
# Local import to avoid a hard nose dependency and only incur the
# import time overhead at actual test-time.
import nose
from .noseclasses import KnownFailureException
def knownfailer(*args, **kwargs):
if fail_val():
raise KnownFailureException(msg)
else:
return f(*args, **kwargs)
return nose.tools.make_decorator(f)(knownfailer)
return knownfail_decorator
def deprecated(conditional=True):
"""
Filter deprecation warnings while running the test suite.
This decorator can be used to filter DeprecationWarning's, to avoid
printing them during the test suite run, while checking that the test
actually raises a DeprecationWarning.
Parameters
----------
conditional : bool or callable, optional
Flag to determine whether to mark test as deprecated or not. If the
condition is a callable, it is used at runtime to dynamically make the
decision. Default is True.
Returns
-------
decorator : function
The `deprecated` decorator itself.
Notes
-----
.. versionadded:: 1.4.0
"""
def deprecate_decorator(f):
# Local import to avoid a hard nose dependency and only incur the
# import time overhead at actual test-time.
import nose
def _deprecated_imp(*args, **kwargs):
# Poor man's replacement for the with statement
with assert_warns(DeprecationWarning):
f(*args, **kwargs)
if isinstance(conditional, collections.Callable):
cond = conditional()
else:
cond = conditional
if cond:
return nose.tools.make_decorator(f)(_deprecated_imp)
else:
return f
return deprecate_decorator
def parametrize(vars, input):
"""
Pytest compatibility class. This implements the simplest level of
pytest.mark.parametrize for use in nose as an aid in making the transition
to pytest. It achieves that by adding a dummy var parameter and ignoring
the doc_func parameter of the base class. It does not support variable
substitution by name, nor does it support nesting or classes. See the
pytest documentation for usage.
.. versionadded:: 1.14.0
"""
from .parameterized import parameterized
return parameterized(input)
| 8,591 | 29.360424 | 83 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/compat/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('compat', parent_package, top_path)
return config
if __name__ == '__main__':
from numpy.distutils.core import setup
setup(configuration=configuration)
| 371 | 27.615385 | 62 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/compat/py3k.py
|
"""
Python 3 compatibility tools.
"""
from __future__ import division, absolute_import, print_function
__all__ = ['bytes', 'asbytes', 'isfileobj', 'getexception', 'strchar',
'unicode', 'asunicode', 'asbytes_nested', 'asunicode_nested',
'asstr', 'open_latin1', 'long', 'basestring', 'sixu',
'integer_types', 'is_pathlib_path', 'npy_load_module', 'Path']
import sys
try:
from pathlib import Path
except ImportError:
Path = None
if sys.version_info[0] >= 3:
import io
long = int
integer_types = (int,)
basestring = str
unicode = str
bytes = bytes
def asunicode(s):
if isinstance(s, bytes):
return s.decode('latin1')
return str(s)
def asbytes(s):
if isinstance(s, bytes):
return s
return str(s).encode('latin1')
def asstr(s):
if isinstance(s, bytes):
return s.decode('latin1')
return str(s)
def isfileobj(f):
return isinstance(f, (io.FileIO, io.BufferedReader, io.BufferedWriter))
def open_latin1(filename, mode='r'):
return open(filename, mode=mode, encoding='iso-8859-1')
def sixu(s):
return s
strchar = 'U'
else:
bytes = str
long = long
basestring = basestring
unicode = unicode
integer_types = (int, long)
asbytes = str
asstr = str
strchar = 'S'
def isfileobj(f):
return isinstance(f, file)
def asunicode(s):
if isinstance(s, unicode):
return s
return str(s).decode('ascii')
def open_latin1(filename, mode='r'):
return open(filename, mode=mode)
def sixu(s):
return unicode(s, 'unicode_escape')
def getexception():
return sys.exc_info()[1]
def asbytes_nested(x):
if hasattr(x, '__iter__') and not isinstance(x, (bytes, unicode)):
return [asbytes_nested(y) for y in x]
else:
return asbytes(x)
def asunicode_nested(x):
if hasattr(x, '__iter__') and not isinstance(x, (bytes, unicode)):
return [asunicode_nested(y) for y in x]
else:
return asunicode(x)
def is_pathlib_path(obj):
"""
Check whether obj is a pathlib.Path object.
"""
return Path is not None and isinstance(obj, Path)
if sys.version_info[0] >= 3 and sys.version_info[1] >= 4:
def npy_load_module(name, fn, info=None):
"""
Load a module.
.. versionadded:: 1.11.2
Parameters
----------
name : str
Full module name.
fn : str
Path to module file.
info : tuple, optional
Only here for backward compatibility with Python 2.*.
Returns
-------
mod : module
"""
import importlib.machinery
return importlib.machinery.SourceFileLoader(name, fn).load_module()
else:
def npy_load_module(name, fn, info=None):
"""
Load a module.
.. versionadded:: 1.11.2
Parameters
----------
name : str
Full module name.
fn : str
Path to module file.
info : tuple, optional
Information as returned by `imp.find_module`
(suffix, mode, type).
Returns
-------
mod : module
"""
import imp
import os
if info is None:
path = os.path.dirname(fn)
fo, fn, info = imp.find_module(name, [path])
else:
fo = open(fn, info[1])
try:
mod = imp.load_module(name, fo, fn, info)
finally:
fo.close()
return mod
| 3,637 | 22.171975 | 79 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/compat/_inspect.py
|
"""Subset of inspect module from upstream python
We use this instead of upstream because upstream inspect is slow to import, and
significantly contributes to numpy import times. Importing this copy has almost
no overhead.
"""
from __future__ import division, absolute_import, print_function
import types
__all__ = ['getargspec', 'formatargspec']
# ----------------------------------------------------------- type-checking
def ismethod(object):
"""Return true if the object is an instance method.
Instance method objects provide these attributes:
__doc__ documentation string
__name__ name with which this method was defined
im_class class object in which this method belongs
im_func function object containing implementation of method
im_self instance to which this method is bound, or None
"""
return isinstance(object, types.MethodType)
def isfunction(object):
"""Return true if the object is a user-defined function.
Function objects provide these attributes:
__doc__ documentation string
__name__ name with which this function was defined
func_code code object containing compiled function bytecode
func_defaults tuple of any default values for arguments
func_doc (same as __doc__)
func_globals global namespace in which this function was defined
func_name (same as __name__)
"""
return isinstance(object, types.FunctionType)
def iscode(object):
"""Return true if the object is a code object.
Code objects provide these attributes:
co_argcount number of arguments (not including * or ** args)
co_code string of raw compiled bytecode
co_consts tuple of constants used in the bytecode
co_filename name of file in which this code object was created
co_firstlineno number of first line in Python source code
co_flags bitmap: 1=optimized | 2=newlocals | 4=*arg | 8=**arg
co_lnotab encoded mapping of line numbers to bytecode indices
co_name name with which this code object was defined
co_names tuple of names of local variables
co_nlocals number of local variables
co_stacksize virtual machine stack space required
co_varnames tuple of names of arguments and local variables
"""
return isinstance(object, types.CodeType)
# ------------------------------------------------ argument list extraction
# These constants are from Python's compile.h.
CO_OPTIMIZED, CO_NEWLOCALS, CO_VARARGS, CO_VARKEYWORDS = 1, 2, 4, 8
def getargs(co):
"""Get information about the arguments accepted by a code object.
Three things are returned: (args, varargs, varkw), where 'args' is
a list of argument names (possibly containing nested lists), and
'varargs' and 'varkw' are the names of the * and ** arguments or None.
"""
if not iscode(co):
raise TypeError('arg is not a code object')
nargs = co.co_argcount
names = co.co_varnames
args = list(names[:nargs])
# The following acrobatics are for anonymous (tuple) arguments.
# Which we do not need to support, so remove to avoid importing
# the dis module.
for i in range(nargs):
if args[i][:1] in ['', '.']:
raise TypeError("tuple function arguments are not supported")
varargs = None
if co.co_flags & CO_VARARGS:
varargs = co.co_varnames[nargs]
nargs = nargs + 1
varkw = None
if co.co_flags & CO_VARKEYWORDS:
varkw = co.co_varnames[nargs]
return args, varargs, varkw
def getargspec(func):
"""Get the names and default values of a function's arguments.
A tuple of four things is returned: (args, varargs, varkw, defaults).
'args' is a list of the argument names (it may contain nested lists).
'varargs' and 'varkw' are the names of the * and ** arguments or None.
'defaults' is an n-tuple of the default values of the last n arguments.
"""
if ismethod(func):
func = func.__func__
if not isfunction(func):
raise TypeError('arg is not a Python function')
args, varargs, varkw = getargs(func.__code__)
return args, varargs, varkw, func.__defaults__
def getargvalues(frame):
"""Get information about arguments passed into a particular frame.
A tuple of four things is returned: (args, varargs, varkw, locals).
'args' is a list of the argument names (it may contain nested lists).
'varargs' and 'varkw' are the names of the * and ** arguments or None.
'locals' is the locals dictionary of the given frame.
"""
args, varargs, varkw = getargs(frame.f_code)
return args, varargs, varkw, frame.f_locals
def joinseq(seq):
if len(seq) == 1:
return '(' + seq[0] + ',)'
else:
return '(' + ', '.join(seq) + ')'
def strseq(object, convert, join=joinseq):
"""Recursively walk a sequence, stringifying each element.
"""
if type(object) in [list, tuple]:
return join([strseq(_o, convert, join) for _o in object])
else:
return convert(object)
def formatargspec(args, varargs=None, varkw=None, defaults=None,
formatarg=str,
formatvarargs=lambda name: '*' + name,
formatvarkw=lambda name: '**' + name,
formatvalue=lambda value: '=' + repr(value),
join=joinseq):
"""Format an argument spec from the 4 values returned by getargspec.
The first four arguments are (args, varargs, varkw, defaults). The
other four arguments are the corresponding optional formatting functions
that are called to turn names and values into strings. The ninth
argument is an optional function to format the sequence of arguments.
"""
specs = []
if defaults:
firstdefault = len(args) - len(defaults)
for i in range(len(args)):
spec = strseq(args[i], formatarg, join)
if defaults and i >= firstdefault:
spec = spec + formatvalue(defaults[i - firstdefault])
specs.append(spec)
if varargs is not None:
specs.append(formatvarargs(varargs))
if varkw is not None:
specs.append(formatvarkw(varkw))
return '(' + ', '.join(specs) + ')'
def formatargvalues(args, varargs, varkw, locals,
formatarg=str,
formatvarargs=lambda name: '*' + name,
formatvarkw=lambda name: '**' + name,
formatvalue=lambda value: '=' + repr(value),
join=joinseq):
"""Format an argument spec from the 4 values returned by getargvalues.
The first four arguments are (args, varargs, varkw, locals). The
next four arguments are the corresponding optional formatting functions
that are called to turn names and values into strings. The ninth
argument is an optional function to format the sequence of arguments.
"""
def convert(name, locals=locals,
formatarg=formatarg, formatvalue=formatvalue):
return formatarg(name) + formatvalue(locals[name])
specs = []
for i in range(len(args)):
specs.append(strseq(args[i], convert, join))
if varargs:
specs.append(formatvarargs(varargs) + formatvalue(locals[varargs]))
if varkw:
specs.append(formatvarkw(varkw) + formatvalue(locals[varkw]))
return '(' + ', '.join(specs) + ')'
| 7,554 | 37.74359 | 79 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/compat/__init__.py
|
"""
Compatibility module.
This module contains duplicated code from Python itself or 3rd party
extensions, which may be included for the following reasons:
* compatibility
* we may only need a small subset of the copied library/module
"""
from __future__ import division, absolute_import, print_function
from . import _inspect
from . import py3k
from ._inspect import getargspec, formatargspec
from .py3k import *
__all__ = []
__all__.extend(_inspect.__all__)
__all__.extend(py3k.__all__)
| 498 | 22.761905 | 68 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/cb_rules.py
|
#!/usr/bin/env python
"""
Build call-back mechanism for f2py2e.
Copyright 2000 Pearu Peterson all rights reserved,
Pearu Peterson <[email protected]>
Permission to use, modify, and distribute this software is given under the
terms of the NumPy License.
NO WARRANTY IS EXPRESSED OR IMPLIED. USE AT YOUR OWN RISK.
$Date: 2005/07/20 11:27:58 $
Pearu Peterson
"""
from __future__ import division, absolute_import, print_function
from . import __version__
from .auxfuncs import (
applyrules, debugcapi, dictappend, errmess, getargs, hasnote, isarray,
iscomplex, iscomplexarray, iscomplexfunction, isfunction, isintent_c,
isintent_hide, isintent_in, isintent_inout, isintent_nothide,
isintent_out, isoptional, isrequired, isscalar, isstring,
isstringfunction, issubroutine, l_and, l_not, l_or, outmess, replace,
stripcomma, throw_error
)
from . import cfuncs
f2py_version = __version__.version
################## Rules for callback function ##############
cb_routine_rules = {
'cbtypedefs': 'typedef #rctype#(*#name#_typedef)(#optargs_td##args_td##strarglens_td##noargs#);',
'body': """
#begintitle#
PyObject *#name#_capi = NULL;/*was Py_None*/
PyTupleObject *#name#_args_capi = NULL;
int #name#_nofargs = 0;
jmp_buf #name#_jmpbuf;
/*typedef #rctype#(*#name#_typedef)(#optargs_td##args_td##strarglens_td##noargs#);*/
#static# #rctype# #callbackname# (#optargs##args##strarglens##noargs#) {
\tPyTupleObject *capi_arglist = #name#_args_capi;
\tPyObject *capi_return = NULL;
\tPyObject *capi_tmp = NULL;
\tPyObject *capi_arglist_list = NULL;
\tint capi_j,capi_i = 0;
\tint capi_longjmp_ok = 1;
#decl#
#ifdef F2PY_REPORT_ATEXIT
f2py_cb_start_clock();
#endif
\tCFUNCSMESS(\"cb:Call-back function #name# (maxnofargs=#maxnofargs#(-#nofoptargs#))\\n\");
\tCFUNCSMESSPY(\"cb:#name#_capi=\",#name#_capi);
\tif (#name#_capi==NULL) {
\t\tcapi_longjmp_ok = 0;
\t\t#name#_capi = PyObject_GetAttrString(#modulename#_module,\"#argname#\");
\t}
\tif (#name#_capi==NULL) {
\t\tPyErr_SetString(#modulename#_error,\"cb: Callback #argname# not defined (as an argument or module #modulename# attribute).\\n\");
\t\tgoto capi_fail;
\t}
\tif (F2PyCapsule_Check(#name#_capi)) {
\t#name#_typedef #name#_cptr;
\t#name#_cptr = F2PyCapsule_AsVoidPtr(#name#_capi);
\t#returncptr#(*#name#_cptr)(#optargs_nm##args_nm##strarglens_nm#);
\t#return#
\t}
\tif (capi_arglist==NULL) {
\t\tcapi_longjmp_ok = 0;
\t\tcapi_tmp = PyObject_GetAttrString(#modulename#_module,\"#argname#_extra_args\");
\t\tif (capi_tmp) {
\t\t\tcapi_arglist = (PyTupleObject *)PySequence_Tuple(capi_tmp);
\t\t\tif (capi_arglist==NULL) {
\t\t\t\tPyErr_SetString(#modulename#_error,\"Failed to convert #modulename#.#argname#_extra_args to tuple.\\n\");
\t\t\t\tgoto capi_fail;
\t\t\t}
\t\t} else {
\t\t\tPyErr_Clear();
\t\t\tcapi_arglist = (PyTupleObject *)Py_BuildValue(\"()\");
\t\t}
\t}
\tif (capi_arglist == NULL) {
\t\tPyErr_SetString(#modulename#_error,\"Callback #argname# argument list is not set.\\n\");
\t\tgoto capi_fail;
\t}
#setdims#
#ifdef PYPY_VERSION
#define CAPI_ARGLIST_SETITEM(idx, value) PyList_SetItem((PyObject *)capi_arglist_list, idx, value)
\tcapi_arglist_list = PySequence_List(capi_arglist);
\tif (capi_arglist_list == NULL) goto capi_fail;
#else
#define CAPI_ARGLIST_SETITEM(idx, value) PyTuple_SetItem((PyObject *)capi_arglist, idx, value)
#endif
#pyobjfrom#
#undef CAPI_ARGLIST_SETITEM
#ifdef PYPY_VERSION
\tCFUNCSMESSPY(\"cb:capi_arglist=\",capi_arglist_list);
#else
\tCFUNCSMESSPY(\"cb:capi_arglist=\",capi_arglist);
#endif
\tCFUNCSMESS(\"cb:Call-back calling Python function #argname#.\\n\");
#ifdef F2PY_REPORT_ATEXIT
f2py_cb_start_call_clock();
#endif
#ifdef PYPY_VERSION
\tcapi_return = PyObject_CallObject(#name#_capi,(PyObject *)capi_arglist_list);
\tPy_DECREF(capi_arglist_list);
\tcapi_arglist_list = NULL;
#else
\tcapi_return = PyObject_CallObject(#name#_capi,(PyObject *)capi_arglist);
#endif
#ifdef F2PY_REPORT_ATEXIT
f2py_cb_stop_call_clock();
#endif
\tCFUNCSMESSPY(\"cb:capi_return=\",capi_return);
\tif (capi_return == NULL) {
\t\tfprintf(stderr,\"capi_return is NULL\\n\");
\t\tgoto capi_fail;
\t}
\tif (capi_return == Py_None) {
\t\tPy_DECREF(capi_return);
\t\tcapi_return = Py_BuildValue(\"()\");
\t}
\telse if (!PyTuple_Check(capi_return)) {
\t\tcapi_return = Py_BuildValue(\"(N)\",capi_return);
\t}
\tcapi_j = PyTuple_Size(capi_return);
\tcapi_i = 0;
#frompyobj#
\tCFUNCSMESS(\"cb:#name#:successful\\n\");
\tPy_DECREF(capi_return);
#ifdef F2PY_REPORT_ATEXIT
f2py_cb_stop_clock();
#endif
\tgoto capi_return_pt;
capi_fail:
\tfprintf(stderr,\"Call-back #name# failed.\\n\");
\tPy_XDECREF(capi_return);
\tPy_XDECREF(capi_arglist_list);
\tif (capi_longjmp_ok)
\t\tlongjmp(#name#_jmpbuf,-1);
capi_return_pt:
\t;
#return#
}
#endtitle#
""",
'need': ['setjmp.h', 'CFUNCSMESS'],
'maxnofargs': '#maxnofargs#',
'nofoptargs': '#nofoptargs#',
'docstr': """\
\tdef #argname#(#docsignature#): return #docreturn#\\n\\
#docstrsigns#""",
'latexdocstr': """
{{}\\verb@def #argname#(#latexdocsignature#): return #docreturn#@{}}
#routnote#
#latexdocstrsigns#""",
'docstrshort': 'def #argname#(#docsignature#): return #docreturn#'
}
cb_rout_rules = [
{ # Init
'separatorsfor': {'decl': '\n',
'args': ',', 'optargs': '', 'pyobjfrom': '\n', 'freemem': '\n',
'args_td': ',', 'optargs_td': '',
'args_nm': ',', 'optargs_nm': '',
'frompyobj': '\n', 'setdims': '\n',
'docstrsigns': '\\n"\n"',
'latexdocstrsigns': '\n',
'latexdocstrreq': '\n', 'latexdocstropt': '\n',
'latexdocstrout': '\n', 'latexdocstrcbs': '\n',
},
'decl': '/*decl*/', 'pyobjfrom': '/*pyobjfrom*/', 'frompyobj': '/*frompyobj*/',
'args': [], 'optargs': '', 'return': '', 'strarglens': '', 'freemem': '/*freemem*/',
'args_td': [], 'optargs_td': '', 'strarglens_td': '',
'args_nm': [], 'optargs_nm': '', 'strarglens_nm': '',
'noargs': '',
'setdims': '/*setdims*/',
'docstrsigns': '', 'latexdocstrsigns': '',
'docstrreq': '\tRequired arguments:',
'docstropt': '\tOptional arguments:',
'docstrout': '\tReturn objects:',
'docstrcbs': '\tCall-back functions:',
'docreturn': '', 'docsign': '', 'docsignopt': '',
'latexdocstrreq': '\\noindent Required arguments:',
'latexdocstropt': '\\noindent Optional arguments:',
'latexdocstrout': '\\noindent Return objects:',
'latexdocstrcbs': '\\noindent Call-back functions:',
'routnote': {hasnote: '--- #note#', l_not(hasnote): ''},
}, { # Function
'decl': '\t#ctype# return_value;',
'frompyobj': [{debugcapi: '\tCFUNCSMESS("cb:Getting return_value->");'},
'\tif (capi_j>capi_i)\n\t\tGETSCALARFROMPYTUPLE(capi_return,capi_i++,&return_value,#ctype#,"#ctype#_from_pyobj failed in converting return_value of call-back function #name# to C #ctype#\\n");',
{debugcapi:
'\tfprintf(stderr,"#showvalueformat#.\\n",return_value);'}
],
'need': ['#ctype#_from_pyobj', {debugcapi: 'CFUNCSMESS'}, 'GETSCALARFROMPYTUPLE'],
'return': '\treturn return_value;',
'_check': l_and(isfunction, l_not(isstringfunction), l_not(iscomplexfunction))
},
{ # String function
'pyobjfrom': {debugcapi: '\tfprintf(stderr,"debug-capi:cb:#name#:%d:\\n",return_value_len);'},
'args': '#ctype# return_value,int return_value_len',
'args_nm': 'return_value,&return_value_len',
'args_td': '#ctype# ,int',
'frompyobj': [{debugcapi: '\tCFUNCSMESS("cb:Getting return_value->\\"");'},
"""\tif (capi_j>capi_i)
\t\tGETSTRFROMPYTUPLE(capi_return,capi_i++,return_value,return_value_len);""",
{debugcapi:
'\tfprintf(stderr,"#showvalueformat#\\".\\n",return_value);'}
],
'need': ['#ctype#_from_pyobj', {debugcapi: 'CFUNCSMESS'},
'string.h', 'GETSTRFROMPYTUPLE'],
'return': 'return;',
'_check': isstringfunction
},
{ # Complex function
'optargs': """
#ifndef F2PY_CB_RETURNCOMPLEX
#ctype# *return_value
#endif
""",
'optargs_nm': """
#ifndef F2PY_CB_RETURNCOMPLEX
return_value
#endif
""",
'optargs_td': """
#ifndef F2PY_CB_RETURNCOMPLEX
#ctype# *
#endif
""",
'decl': """
#ifdef F2PY_CB_RETURNCOMPLEX
\t#ctype# return_value;
#endif
""",
'frompyobj': [{debugcapi: '\tCFUNCSMESS("cb:Getting return_value->");'},
"""\
\tif (capi_j>capi_i)
#ifdef F2PY_CB_RETURNCOMPLEX
\t\tGETSCALARFROMPYTUPLE(capi_return,capi_i++,&return_value,#ctype#,\"#ctype#_from_pyobj failed in converting return_value of call-back function #name# to C #ctype#\\n\");
#else
\t\tGETSCALARFROMPYTUPLE(capi_return,capi_i++,return_value,#ctype#,\"#ctype#_from_pyobj failed in converting return_value of call-back function #name# to C #ctype#\\n\");
#endif
""",
{debugcapi: """
#ifdef F2PY_CB_RETURNCOMPLEX
\tfprintf(stderr,\"#showvalueformat#.\\n\",(return_value).r,(return_value).i);
#else
\tfprintf(stderr,\"#showvalueformat#.\\n\",(*return_value).r,(*return_value).i);
#endif
"""}
],
'return': """
#ifdef F2PY_CB_RETURNCOMPLEX
\treturn return_value;
#else
\treturn;
#endif
""",
'need': ['#ctype#_from_pyobj', {debugcapi: 'CFUNCSMESS'},
'string.h', 'GETSCALARFROMPYTUPLE', '#ctype#'],
'_check': iscomplexfunction
},
{'docstrout': '\t\t#pydocsignout#',
'latexdocstrout': ['\\item[]{{}\\verb@#pydocsignout#@{}}',
{hasnote: '--- #note#'}],
'docreturn': '#rname#,',
'_check': isfunction},
{'_check': issubroutine, 'return': 'return;'}
]
cb_arg_rules = [
{ # Doc
'docstropt': {l_and(isoptional, isintent_nothide): '\t\t#pydocsign#'},
'docstrreq': {l_and(isrequired, isintent_nothide): '\t\t#pydocsign#'},
'docstrout': {isintent_out: '\t\t#pydocsignout#'},
'latexdocstropt': {l_and(isoptional, isintent_nothide): ['\\item[]{{}\\verb@#pydocsign#@{}}',
{hasnote: '--- #note#'}]},
'latexdocstrreq': {l_and(isrequired, isintent_nothide): ['\\item[]{{}\\verb@#pydocsign#@{}}',
{hasnote: '--- #note#'}]},
'latexdocstrout': {isintent_out: ['\\item[]{{}\\verb@#pydocsignout#@{}}',
{l_and(hasnote, isintent_hide): '--- #note#',
l_and(hasnote, isintent_nothide): '--- See above.'}]},
'docsign': {l_and(isrequired, isintent_nothide): '#varname#,'},
'docsignopt': {l_and(isoptional, isintent_nothide): '#varname#,'},
'depend': ''
},
{
'args': {
l_and(isscalar, isintent_c): '#ctype# #varname_i#',
l_and(isscalar, l_not(isintent_c)): '#ctype# *#varname_i#_cb_capi',
isarray: '#ctype# *#varname_i#',
isstring: '#ctype# #varname_i#'
},
'args_nm': {
l_and(isscalar, isintent_c): '#varname_i#',
l_and(isscalar, l_not(isintent_c)): '#varname_i#_cb_capi',
isarray: '#varname_i#',
isstring: '#varname_i#'
},
'args_td': {
l_and(isscalar, isintent_c): '#ctype#',
l_and(isscalar, l_not(isintent_c)): '#ctype# *',
isarray: '#ctype# *',
isstring: '#ctype#'
},
# untested with multiple args
'strarglens': {isstring: ',int #varname_i#_cb_len'},
'strarglens_td': {isstring: ',int'}, # untested with multiple args
# untested with multiple args
'strarglens_nm': {isstring: ',#varname_i#_cb_len'},
},
{ # Scalars
'decl': {l_not(isintent_c): '\t#ctype# #varname_i#=(*#varname_i#_cb_capi);'},
'error': {l_and(isintent_c, isintent_out,
throw_error('intent(c,out) is forbidden for callback scalar arguments')):
''},
'frompyobj': [{debugcapi: '\tCFUNCSMESS("cb:Getting #varname#->");'},
{isintent_out:
'\tif (capi_j>capi_i)\n\t\tGETSCALARFROMPYTUPLE(capi_return,capi_i++,#varname_i#_cb_capi,#ctype#,"#ctype#_from_pyobj failed in converting argument #varname# of call-back function #name# to C #ctype#\\n");'},
{l_and(debugcapi, l_and(l_not(iscomplex), isintent_c)):
'\tfprintf(stderr,"#showvalueformat#.\\n",#varname_i#);'},
{l_and(debugcapi, l_and(l_not(iscomplex), l_not( isintent_c))):
'\tfprintf(stderr,"#showvalueformat#.\\n",*#varname_i#_cb_capi);'},
{l_and(debugcapi, l_and(iscomplex, isintent_c)):
'\tfprintf(stderr,"#showvalueformat#.\\n",(#varname_i#).r,(#varname_i#).i);'},
{l_and(debugcapi, l_and(iscomplex, l_not( isintent_c))):
'\tfprintf(stderr,"#showvalueformat#.\\n",(*#varname_i#_cb_capi).r,(*#varname_i#_cb_capi).i);'},
],
'need': [{isintent_out: ['#ctype#_from_pyobj', 'GETSCALARFROMPYTUPLE']},
{debugcapi: 'CFUNCSMESS'}],
'_check': isscalar
}, {
'pyobjfrom': [{isintent_in: """\
\tif (#name#_nofargs>capi_i)
\t\tif (CAPI_ARGLIST_SETITEM(capi_i++,pyobj_from_#ctype#1(#varname_i#)))
\t\t\tgoto capi_fail;"""},
{isintent_inout: """\
\tif (#name#_nofargs>capi_i)
\t\tif (CAPI_ARGLIST_SETITEM(capi_i++,pyarr_from_p_#ctype#1(#varname_i#_cb_capi)))
\t\t\tgoto capi_fail;"""}],
'need': [{isintent_in: 'pyobj_from_#ctype#1'},
{isintent_inout: 'pyarr_from_p_#ctype#1'},
{iscomplex: '#ctype#'}],
'_check': l_and(isscalar, isintent_nothide),
'_optional': ''
}, { # String
'frompyobj': [{debugcapi: '\tCFUNCSMESS("cb:Getting #varname#->\\"");'},
"""\tif (capi_j>capi_i)
\t\tGETSTRFROMPYTUPLE(capi_return,capi_i++,#varname_i#,#varname_i#_cb_len);""",
{debugcapi:
'\tfprintf(stderr,"#showvalueformat#\\":%d:.\\n",#varname_i#,#varname_i#_cb_len);'},
],
'need': ['#ctype#', 'GETSTRFROMPYTUPLE',
{debugcapi: 'CFUNCSMESS'}, 'string.h'],
'_check': l_and(isstring, isintent_out)
}, {
'pyobjfrom': [{debugcapi: '\tfprintf(stderr,"debug-capi:cb:#varname#=\\"#showvalueformat#\\":%d:\\n",#varname_i#,#varname_i#_cb_len);'},
{isintent_in: """\
\tif (#name#_nofargs>capi_i)
\t\tif (CAPI_ARGLIST_SETITEM(capi_i++,pyobj_from_#ctype#1size(#varname_i#,#varname_i#_cb_len)))
\t\t\tgoto capi_fail;"""},
{isintent_inout: """\
\tif (#name#_nofargs>capi_i) {
\t\tint #varname_i#_cb_dims[] = {#varname_i#_cb_len};
\t\tif (CAPI_ARGLIST_SETITEM(capi_i++,pyarr_from_p_#ctype#1(#varname_i#,#varname_i#_cb_dims)))
\t\t\tgoto capi_fail;
\t}"""}],
'need': [{isintent_in: 'pyobj_from_#ctype#1size'},
{isintent_inout: 'pyarr_from_p_#ctype#1'}],
'_check': l_and(isstring, isintent_nothide),
'_optional': ''
},
# Array ...
{
'decl': '\tnpy_intp #varname_i#_Dims[#rank#] = {#rank*[-1]#};',
'setdims': '\t#cbsetdims#;',
'_check': isarray,
'_depend': ''
},
{
'pyobjfrom': [{debugcapi: '\tfprintf(stderr,"debug-capi:cb:#varname#\\n");'},
{isintent_c: """\
\tif (#name#_nofargs>capi_i) {
\t\tint itemsize_ = #atype# == NPY_STRING ? 1 : 0;
\t\t/*XXX: Hmm, what will destroy this array??? */
\t\tPyArrayObject *tmp_arr = (PyArrayObject *)PyArray_New(&PyArray_Type,#rank#,#varname_i#_Dims,#atype#,NULL,(char*)#varname_i#,itemsize_,NPY_ARRAY_CARRAY,NULL);
""",
l_not(isintent_c): """\
\tif (#name#_nofargs>capi_i) {
\t\tint itemsize_ = #atype# == NPY_STRING ? 1 : 0;
\t\t/*XXX: Hmm, what will destroy this array??? */
\t\tPyArrayObject *tmp_arr = (PyArrayObject *)PyArray_New(&PyArray_Type,#rank#,#varname_i#_Dims,#atype#,NULL,(char*)#varname_i#,itemsize_,NPY_ARRAY_FARRAY,NULL);
""",
},
"""
\t\tif (tmp_arr==NULL)
\t\t\tgoto capi_fail;
\t\tif (CAPI_ARGLIST_SETITEM(capi_i++,(PyObject *)tmp_arr))
\t\t\tgoto capi_fail;
}"""],
'_check': l_and(isarray, isintent_nothide, l_or(isintent_in, isintent_inout)),
'_optional': '',
}, {
'frompyobj': [{debugcapi: '\tCFUNCSMESS("cb:Getting #varname#->");'},
"""\tif (capi_j>capi_i) {
\t\tPyArrayObject *rv_cb_arr = NULL;
\t\tif ((capi_tmp = PyTuple_GetItem(capi_return,capi_i++))==NULL) goto capi_fail;
\t\trv_cb_arr = array_from_pyobj(#atype#,#varname_i#_Dims,#rank#,F2PY_INTENT_IN""",
{isintent_c: '|F2PY_INTENT_C'},
""",capi_tmp);
\t\tif (rv_cb_arr == NULL) {
\t\t\tfprintf(stderr,\"rv_cb_arr is NULL\\n\");
\t\t\tgoto capi_fail;
\t\t}
\t\tMEMCOPY(#varname_i#,PyArray_DATA(rv_cb_arr),PyArray_NBYTES(rv_cb_arr));
\t\tif (capi_tmp != (PyObject *)rv_cb_arr) {
\t\t\tPy_DECREF(rv_cb_arr);
\t\t}
\t}""",
{debugcapi: '\tfprintf(stderr,"<-.\\n");'},
],
'need': ['MEMCOPY', {iscomplexarray: '#ctype#'}],
'_check': l_and(isarray, isintent_out)
}, {
'docreturn': '#varname#,',
'_check': isintent_out
}
]
################## Build call-back module #############
cb_map = {}
def buildcallbacks(m):
global cb_map
cb_map[m['name']] = []
for bi in m['body']:
if bi['block'] == 'interface':
for b in bi['body']:
if b:
buildcallback(b, m['name'])
else:
errmess('warning: empty body for %s\n' % (m['name']))
def buildcallback(rout, um):
global cb_map
from . import capi_maps
outmess('\tConstructing call-back function "cb_%s_in_%s"\n' %
(rout['name'], um))
args, depargs = getargs(rout)
capi_maps.depargs = depargs
var = rout['vars']
vrd = capi_maps.cb_routsign2map(rout, um)
rd = dictappend({}, vrd)
cb_map[um].append([rout['name'], rd['name']])
for r in cb_rout_rules:
if ('_check' in r and r['_check'](rout)) or ('_check' not in r):
ar = applyrules(r, vrd, rout)
rd = dictappend(rd, ar)
savevrd = {}
for i, a in enumerate(args):
vrd = capi_maps.cb_sign2map(a, var[a], index=i)
savevrd[a] = vrd
for r in cb_arg_rules:
if '_depend' in r:
continue
if '_optional' in r and isoptional(var[a]):
continue
if ('_check' in r and r['_check'](var[a])) or ('_check' not in r):
ar = applyrules(r, vrd, var[a])
rd = dictappend(rd, ar)
if '_break' in r:
break
for a in args:
vrd = savevrd[a]
for r in cb_arg_rules:
if '_depend' in r:
continue
if ('_optional' not in r) or ('_optional' in r and isrequired(var[a])):
continue
if ('_check' in r and r['_check'](var[a])) or ('_check' not in r):
ar = applyrules(r, vrd, var[a])
rd = dictappend(rd, ar)
if '_break' in r:
break
for a in depargs:
vrd = savevrd[a]
for r in cb_arg_rules:
if '_depend' not in r:
continue
if '_optional' in r:
continue
if ('_check' in r and r['_check'](var[a])) or ('_check' not in r):
ar = applyrules(r, vrd, var[a])
rd = dictappend(rd, ar)
if '_break' in r:
break
if 'args' in rd and 'optargs' in rd:
if isinstance(rd['optargs'], list):
rd['optargs'] = rd['optargs'] + ["""
#ifndef F2PY_CB_RETURNCOMPLEX
,
#endif
"""]
rd['optargs_nm'] = rd['optargs_nm'] + ["""
#ifndef F2PY_CB_RETURNCOMPLEX
,
#endif
"""]
rd['optargs_td'] = rd['optargs_td'] + ["""
#ifndef F2PY_CB_RETURNCOMPLEX
,
#endif
"""]
if isinstance(rd['docreturn'], list):
rd['docreturn'] = stripcomma(
replace('#docreturn#', {'docreturn': rd['docreturn']}))
optargs = stripcomma(replace('#docsignopt#',
{'docsignopt': rd['docsignopt']}
))
if optargs == '':
rd['docsignature'] = stripcomma(
replace('#docsign#', {'docsign': rd['docsign']}))
else:
rd['docsignature'] = replace('#docsign#[#docsignopt#]',
{'docsign': rd['docsign'],
'docsignopt': optargs,
})
rd['latexdocsignature'] = rd['docsignature'].replace('_', '\\_')
rd['latexdocsignature'] = rd['latexdocsignature'].replace(',', ', ')
rd['docstrsigns'] = []
rd['latexdocstrsigns'] = []
for k in ['docstrreq', 'docstropt', 'docstrout', 'docstrcbs']:
if k in rd and isinstance(rd[k], list):
rd['docstrsigns'] = rd['docstrsigns'] + rd[k]
k = 'latex' + k
if k in rd and isinstance(rd[k], list):
rd['latexdocstrsigns'] = rd['latexdocstrsigns'] + rd[k][0:1] +\
['\\begin{description}'] + rd[k][1:] +\
['\\end{description}']
if 'args' not in rd:
rd['args'] = ''
rd['args_td'] = ''
rd['args_nm'] = ''
if not (rd.get('args') or rd.get('optargs') or rd.get('strarglens')):
rd['noargs'] = 'void'
ar = applyrules(cb_routine_rules, rd)
cfuncs.callbacks[rd['name']] = ar['body']
if isinstance(ar['need'], str):
ar['need'] = [ar['need']]
if 'need' in rd:
for t in cfuncs.typedefs.keys():
if t in rd['need']:
ar['need'].append(t)
cfuncs.typedefs_generated[rd['name'] + '_typedef'] = ar['cbtypedefs']
ar['need'].append(rd['name'] + '_typedef')
cfuncs.needs[rd['name']] = ar['need']
capi_maps.lcb2_map[rd['name']] = {'maxnofargs': ar['maxnofargs'],
'nofoptargs': ar['nofoptargs'],
'docstr': ar['docstr'],
'latexdocstr': ar['latexdocstr'],
'argname': rd['argname']
}
outmess('\t %s\n' % (ar['docstrshort']))
return
################## Build call-back function #############
| 22,946 | 38.632124 | 230 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/__main__.py
|
# See http://cens.ioc.ee/projects/f2py2e/
from __future__ import division, print_function
import os
import sys
for mode in ["g3-numpy", "2e-numeric", "2e-numarray", "2e-numpy"]:
try:
i = sys.argv.index("--" + mode)
del sys.argv[i]
break
except ValueError:
pass
os.environ["NO_SCIPY_IMPORT"] = "f2py"
if mode == "g3-numpy":
sys.stderr.write("G3 f2py support is not implemented, yet.\\n")
sys.exit(1)
elif mode == "2e-numeric":
from f2py2e import main
elif mode == "2e-numarray":
sys.argv.append("-DNUMARRAY")
from f2py2e import main
elif mode == "2e-numpy":
from numpy.f2py import main
else:
sys.stderr.write("Unknown mode: " + repr(mode) + "\\n")
sys.exit(1)
main()
| 739 | 25.428571 | 67 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/common_rules.py
|
#!/usr/bin/env python
"""
Build common block mechanism for f2py2e.
Copyright 2000 Pearu Peterson all rights reserved,
Pearu Peterson <[email protected]>
Permission to use, modify, and distribute this software is given under the
terms of the NumPy License
NO WARRANTY IS EXPRESSED OR IMPLIED. USE AT YOUR OWN RISK.
$Date: 2005/05/06 10:57:33 $
Pearu Peterson
"""
from __future__ import division, absolute_import, print_function
__version__ = "$Revision: 1.19 $"[10:-1]
from . import __version__
f2py_version = __version__.version
from .auxfuncs import (
hasbody, hascommon, hasnote, isintent_hide, outmess
)
from . import capi_maps
from . import func2subr
from .crackfortran import rmbadname
def findcommonblocks(block, top=1):
ret = []
if hascommon(block):
for n in block['common'].keys():
vars = {}
for v in block['common'][n]:
vars[v] = block['vars'][v]
ret.append((n, block['common'][n], vars))
elif hasbody(block):
for b in block['body']:
ret = ret + findcommonblocks(b, 0)
if top:
tret = []
names = []
for t in ret:
if t[0] not in names:
names.append(t[0])
tret.append(t)
return tret
return ret
def buildhooks(m):
ret = {'commonhooks': [], 'initcommonhooks': [],
'docs': ['"COMMON blocks:\\n"']}
fwrap = ['']
def fadd(line, s=fwrap):
s[0] = '%s\n %s' % (s[0], line)
chooks = ['']
def cadd(line, s=chooks):
s[0] = '%s\n%s' % (s[0], line)
ihooks = ['']
def iadd(line, s=ihooks):
s[0] = '%s\n%s' % (s[0], line)
doc = ['']
def dadd(line, s=doc):
s[0] = '%s\n%s' % (s[0], line)
for (name, vnames, vars) in findcommonblocks(m):
lower_name = name.lower()
hnames, inames = [], []
for n in vnames:
if isintent_hide(vars[n]):
hnames.append(n)
else:
inames.append(n)
if hnames:
outmess('\t\tConstructing COMMON block support for "%s"...\n\t\t %s\n\t\t Hidden: %s\n' % (
name, ','.join(inames), ','.join(hnames)))
else:
outmess('\t\tConstructing COMMON block support for "%s"...\n\t\t %s\n' % (
name, ','.join(inames)))
fadd('subroutine f2pyinit%s(setupfunc)' % name)
fadd('external setupfunc')
for n in vnames:
fadd(func2subr.var2fixfortran(vars, n))
if name == '_BLNK_':
fadd('common %s' % (','.join(vnames)))
else:
fadd('common /%s/ %s' % (name, ','.join(vnames)))
fadd('call setupfunc(%s)' % (','.join(inames)))
fadd('end\n')
cadd('static FortranDataDef f2py_%s_def[] = {' % (name))
idims = []
for n in inames:
ct = capi_maps.getctype(vars[n])
at = capi_maps.c2capi_map[ct]
dm = capi_maps.getarrdims(n, vars[n])
if dm['dims']:
idims.append('(%s)' % (dm['dims']))
else:
idims.append('')
dms = dm['dims'].strip()
if not dms:
dms = '-1'
cadd('\t{\"%s\",%s,{{%s}},%s},' % (n, dm['rank'], dms, at))
cadd('\t{NULL}\n};')
inames1 = rmbadname(inames)
inames1_tps = ','.join(['char *' + s for s in inames1])
cadd('static void f2py_setup_%s(%s) {' % (name, inames1_tps))
cadd('\tint i_f2py=0;')
for n in inames1:
cadd('\tf2py_%s_def[i_f2py++].data = %s;' % (name, n))
cadd('}')
if '_' in lower_name:
F_FUNC = 'F_FUNC_US'
else:
F_FUNC = 'F_FUNC'
cadd('extern void %s(f2pyinit%s,F2PYINIT%s)(void(*)(%s));'
% (F_FUNC, lower_name, name.upper(),
','.join(['char*'] * len(inames1))))
cadd('static void f2py_init_%s(void) {' % name)
cadd('\t%s(f2pyinit%s,F2PYINIT%s)(f2py_setup_%s);'
% (F_FUNC, lower_name, name.upper(), name))
cadd('}\n')
iadd('\tF2PyDict_SetItemString(d, \"%s\", PyFortranObject_New(f2py_%s_def,f2py_init_%s));' % (
name, name, name))
tname = name.replace('_', '\\_')
dadd('\\subsection{Common block \\texttt{%s}}\n' % (tname))
dadd('\\begin{description}')
for n in inames:
dadd('\\item[]{{}\\verb@%s@{}}' %
(capi_maps.getarrdocsign(n, vars[n])))
if hasnote(vars[n]):
note = vars[n]['note']
if isinstance(note, list):
note = '\n'.join(note)
dadd('--- %s' % (note))
dadd('\\end{description}')
ret['docs'].append(
'"\t/%s/ %s\\n"' % (name, ','.join(map(lambda v, d: v + d, inames, idims))))
ret['commonhooks'] = chooks
ret['initcommonhooks'] = ihooks
ret['latexdoc'] = doc[0]
if len(ret['docs']) <= 1:
ret['docs'] = ''
return ret, fwrap[0]
| 5,030 | 32.317881 | 105 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/f2py2e.py
|
#!/usr/bin/env python
"""
f2py2e - Fortran to Python C/API generator. 2nd Edition.
See __usage__ below.
Copyright 1999--2011 Pearu Peterson all rights reserved,
Pearu Peterson <[email protected]>
Permission to use, modify, and distribute this software is given under the
terms of the NumPy License.
NO WARRANTY IS EXPRESSED OR IMPLIED. USE AT YOUR OWN RISK.
$Date: 2005/05/06 08:31:19 $
Pearu Peterson
"""
from __future__ import division, absolute_import, print_function
import sys
import os
import pprint
import re
from . import crackfortran
from . import rules
from . import cb_rules
from . import auxfuncs
from . import cfuncs
from . import f90mod_rules
from . import __version__
f2py_version = __version__.version
errmess = sys.stderr.write
# outmess=sys.stdout.write
show = pprint.pprint
outmess = auxfuncs.outmess
try:
from numpy import __version__ as numpy_version
except ImportError:
numpy_version = 'N/A'
__usage__ = """\
Usage:
1) To construct extension module sources:
f2py [<options>] <fortran files> [[[only:]||[skip:]] \\
<fortran functions> ] \\
[: <fortran files> ...]
2) To compile fortran files and build extension modules:
f2py -c [<options>, <build_flib options>, <extra options>] <fortran files>
3) To generate signature files:
f2py -h <filename.pyf> ...< same options as in (1) >
Description: This program generates a Python C/API file (<modulename>module.c)
that contains wrappers for given fortran functions so that they
can be called from Python. With the -c option the corresponding
extension modules are built.
Options:
--2d-numpy Use numpy.f2py tool with NumPy support. [DEFAULT]
--2d-numeric Use f2py2e tool with Numeric support.
--2d-numarray Use f2py2e tool with Numarray support.
--g3-numpy Use 3rd generation f2py from the separate f2py package.
[NOT AVAILABLE YET]
-h <filename> Write signatures of the fortran routines to file <filename>
and exit. You can then edit <filename> and use it instead
of <fortran files>. If <filename>==stdout then the
signatures are printed to stdout.
<fortran functions> Names of fortran routines for which Python C/API
functions will be generated. Default is all that are found
in <fortran files>.
<fortran files> Paths to fortran/signature files that will be scanned for
<fortran functions> in order to determine their signatures.
skip: Ignore fortran functions that follow until `:'.
only: Use only fortran functions that follow until `:'.
: Get back to <fortran files> mode.
-m <modulename> Name of the module; f2py generates a Python/C API
file <modulename>module.c or extension module <modulename>.
Default is 'untitled'.
--[no-]lower Do [not] lower the cases in <fortran files>. By default,
--lower is assumed with -h key, and --no-lower without -h key.
--build-dir <dirname> All f2py generated files are created in <dirname>.
Default is tempfile.mkdtemp().
--overwrite-signature Overwrite existing signature file.
--[no-]latex-doc Create (or not) <modulename>module.tex.
Default is --no-latex-doc.
--short-latex Create 'incomplete' LaTeX document (without commands
\\documentclass, \\tableofcontents, and \\begin{document},
\\end{document}).
--[no-]rest-doc Create (or not) <modulename>module.rst.
Default is --no-rest-doc.
--debug-capi Create C/API code that reports the state of the wrappers
during runtime. Useful for debugging.
--[no-]wrap-functions Create Fortran subroutine wrappers to Fortran 77
functions. --wrap-functions is default because it ensures
maximum portability/compiler independence.
--include-paths <path1>:<path2>:... Search include files from the given
directories.
--help-link [..] List system resources found by system_info.py. See also
--link-<resource> switch below. [..] is optional list
of resources names. E.g. try 'f2py --help-link lapack_opt'.
--quiet Run quietly.
--verbose Run with extra verbosity.
-v Print f2py version ID and exit.
numpy.distutils options (only effective with -c):
--fcompiler= Specify Fortran compiler type by vendor
--compiler= Specify C compiler type (as defined by distutils)
--help-fcompiler List available Fortran compilers and exit
--f77exec= Specify the path to F77 compiler
--f90exec= Specify the path to F90 compiler
--f77flags= Specify F77 compiler flags
--f90flags= Specify F90 compiler flags
--opt= Specify optimization flags
--arch= Specify architecture specific optimization flags
--noopt Compile without optimization
--noarch Compile without arch-dependent optimization
--debug Compile with debugging information
Extra options (only effective with -c):
--link-<resource> Link extension module with <resource> as defined
by numpy.distutils/system_info.py. E.g. to link
with optimized LAPACK libraries (vecLib on MacOSX,
ATLAS elsewhere), use --link-lapack_opt.
See also --help-link switch.
-L/path/to/lib/ -l<libname>
-D<define> -U<name>
-I/path/to/include/
<filename>.o <filename>.so <filename>.a
Using the following macros may be required with non-gcc Fortran
compilers:
-DPREPEND_FORTRAN -DNO_APPEND_FORTRAN -DUPPERCASE_FORTRAN
-DUNDERSCORE_G77
When using -DF2PY_REPORT_ATEXIT, a performance report of F2PY
interface is printed out at exit (platforms: Linux).
When using -DF2PY_REPORT_ON_ARRAY_COPY=<int>, a message is
sent to stderr whenever F2PY interface makes a copy of an
array. Integer <int> sets the threshold for array sizes when
a message should be shown.
Version: %s
numpy Version: %s
Requires: Python 2.3 or higher.
License: NumPy license (see LICENSE.txt in the NumPy source code)
Copyright 1999 - 2011 Pearu Peterson all rights reserved.
http://cens.ioc.ee/projects/f2py2e/""" % (f2py_version, numpy_version)
def scaninputline(inputline):
files, skipfuncs, onlyfuncs, debug = [], [], [], []
f, f2, f3, f5, f6, f7, f8, f9 = 1, 0, 0, 0, 0, 0, 0, 0
verbose = 1
dolc = -1
dolatexdoc = 0
dorestdoc = 0
wrapfuncs = 1
buildpath = '.'
include_paths = []
signsfile, modulename = None, None
options = {'buildpath': buildpath,
'coutput': None,
'f2py_wrapper_output': None}
for l in inputline:
if l == '':
pass
elif l == 'only:':
f = 0
elif l == 'skip:':
f = -1
elif l == ':':
f = 1
elif l[:8] == '--debug-':
debug.append(l[8:])
elif l == '--lower':
dolc = 1
elif l == '--build-dir':
f6 = 1
elif l == '--no-lower':
dolc = 0
elif l == '--quiet':
verbose = 0
elif l == '--verbose':
verbose += 1
elif l == '--latex-doc':
dolatexdoc = 1
elif l == '--no-latex-doc':
dolatexdoc = 0
elif l == '--rest-doc':
dorestdoc = 1
elif l == '--no-rest-doc':
dorestdoc = 0
elif l == '--wrap-functions':
wrapfuncs = 1
elif l == '--no-wrap-functions':
wrapfuncs = 0
elif l == '--short-latex':
options['shortlatex'] = 1
elif l == '--coutput':
f8 = 1
elif l == '--f2py-wrapper-output':
f9 = 1
elif l == '--overwrite-signature':
options['h-overwrite'] = 1
elif l == '-h':
f2 = 1
elif l == '-m':
f3 = 1
elif l[:2] == '-v':
print(f2py_version)
sys.exit()
elif l == '--show-compilers':
f5 = 1
elif l[:8] == '-include':
cfuncs.outneeds['userincludes'].append(l[9:-1])
cfuncs.userincludes[l[9:-1]] = '#include ' + l[8:]
elif l[:15] in '--include_paths':
outmess(
'f2py option --include_paths is deprecated, use --include-paths instead.\n')
f7 = 1
elif l[:15] in '--include-paths':
f7 = 1
elif l[0] == '-':
errmess('Unknown option %s\n' % repr(l))
sys.exit()
elif f2:
f2 = 0
signsfile = l
elif f3:
f3 = 0
modulename = l
elif f6:
f6 = 0
buildpath = l
elif f7:
f7 = 0
include_paths.extend(l.split(os.pathsep))
elif f8:
f8 = 0
options["coutput"] = l
elif f9:
f9 = 0
options["f2py_wrapper_output"] = l
elif f == 1:
try:
open(l).close()
files.append(l)
except IOError as detail:
errmess('IOError: %s. Skipping file "%s".\n' %
(str(detail), l))
elif f == -1:
skipfuncs.append(l)
elif f == 0:
onlyfuncs.append(l)
if not f5 and not files and not modulename:
print(__usage__)
sys.exit()
if not os.path.isdir(buildpath):
if not verbose:
outmess('Creating build directory %s' % (buildpath))
os.mkdir(buildpath)
if signsfile:
signsfile = os.path.join(buildpath, signsfile)
if signsfile and os.path.isfile(signsfile) and 'h-overwrite' not in options:
errmess(
'Signature file "%s" exists!!! Use --overwrite-signature to overwrite.\n' % (signsfile))
sys.exit()
options['debug'] = debug
options['verbose'] = verbose
if dolc == -1 and not signsfile:
options['do-lower'] = 0
else:
options['do-lower'] = dolc
if modulename:
options['module'] = modulename
if signsfile:
options['signsfile'] = signsfile
if onlyfuncs:
options['onlyfuncs'] = onlyfuncs
if skipfuncs:
options['skipfuncs'] = skipfuncs
options['dolatexdoc'] = dolatexdoc
options['dorestdoc'] = dorestdoc
options['wrapfuncs'] = wrapfuncs
options['buildpath'] = buildpath
options['include_paths'] = include_paths
return files, options
def callcrackfortran(files, options):
rules.options = options
crackfortran.debug = options['debug']
crackfortran.verbose = options['verbose']
if 'module' in options:
crackfortran.f77modulename = options['module']
if 'skipfuncs' in options:
crackfortran.skipfuncs = options['skipfuncs']
if 'onlyfuncs' in options:
crackfortran.onlyfuncs = options['onlyfuncs']
crackfortran.include_paths[:] = options['include_paths']
crackfortran.dolowercase = options['do-lower']
postlist = crackfortran.crackfortran(files)
if 'signsfile' in options:
outmess('Saving signatures to file "%s"\n' % (options['signsfile']))
pyf = crackfortran.crack2fortran(postlist)
if options['signsfile'][-6:] == 'stdout':
sys.stdout.write(pyf)
else:
f = open(options['signsfile'], 'w')
f.write(pyf)
f.close()
if options["coutput"] is None:
for mod in postlist:
mod["coutput"] = "%smodule.c" % mod["name"]
else:
for mod in postlist:
mod["coutput"] = options["coutput"]
if options["f2py_wrapper_output"] is None:
for mod in postlist:
mod["f2py_wrapper_output"] = "%s-f2pywrappers.f" % mod["name"]
else:
for mod in postlist:
mod["f2py_wrapper_output"] = options["f2py_wrapper_output"]
return postlist
def buildmodules(lst):
cfuncs.buildcfuncs()
outmess('Building modules...\n')
modules, mnames, isusedby = [], [], {}
for i in range(len(lst)):
if '__user__' in lst[i]['name']:
cb_rules.buildcallbacks(lst[i])
else:
if 'use' in lst[i]:
for u in lst[i]['use'].keys():
if u not in isusedby:
isusedby[u] = []
isusedby[u].append(lst[i]['name'])
modules.append(lst[i])
mnames.append(lst[i]['name'])
ret = {}
for i in range(len(mnames)):
if mnames[i] in isusedby:
outmess('\tSkipping module "%s" which is used by %s.\n' % (
mnames[i], ','.join(['"%s"' % s for s in isusedby[mnames[i]]])))
else:
um = []
if 'use' in modules[i]:
for u in modules[i]['use'].keys():
if u in isusedby and u in mnames:
um.append(modules[mnames.index(u)])
else:
outmess(
'\tModule "%s" uses nonexisting "%s" which will be ignored.\n' % (mnames[i], u))
ret[mnames[i]] = {}
dict_append(ret[mnames[i]], rules.buildmodule(modules[i], um))
return ret
def dict_append(d_out, d_in):
for (k, v) in d_in.items():
if k not in d_out:
d_out[k] = []
if isinstance(v, list):
d_out[k] = d_out[k] + v
else:
d_out[k].append(v)
def run_main(comline_list):
"""Run f2py as if string.join(comline_list,' ') is used as a command line.
In case of using -h flag, return None.
"""
crackfortran.reset_global_f2py_vars()
f2pydir = os.path.dirname(os.path.abspath(cfuncs.__file__))
fobjhsrc = os.path.join(f2pydir, 'src', 'fortranobject.h')
fobjcsrc = os.path.join(f2pydir, 'src', 'fortranobject.c')
files, options = scaninputline(comline_list)
auxfuncs.options = options
postlist = callcrackfortran(files, options)
isusedby = {}
for i in range(len(postlist)):
if 'use' in postlist[i]:
for u in postlist[i]['use'].keys():
if u not in isusedby:
isusedby[u] = []
isusedby[u].append(postlist[i]['name'])
for i in range(len(postlist)):
if postlist[i]['block'] == 'python module' and '__user__' in postlist[i]['name']:
if postlist[i]['name'] in isusedby:
# if not quiet:
outmess('Skipping Makefile build for module "%s" which is used by %s\n' % (
postlist[i]['name'], ','.join(['"%s"' % s for s in isusedby[postlist[i]['name']]])))
if 'signsfile' in options:
if options['verbose'] > 1:
outmess(
'Stopping. Edit the signature file and then run f2py on the signature file: ')
outmess('%s %s\n' %
(os.path.basename(sys.argv[0]), options['signsfile']))
return
for i in range(len(postlist)):
if postlist[i]['block'] != 'python module':
if 'python module' not in options:
errmess(
'Tip: If your original code is Fortran source then you must use -m option.\n')
raise TypeError('All blocks must be python module blocks but got %s' % (
repr(postlist[i]['block'])))
auxfuncs.debugoptions = options['debug']
f90mod_rules.options = options
auxfuncs.wrapfuncs = options['wrapfuncs']
ret = buildmodules(postlist)
for mn in ret.keys():
dict_append(ret[mn], {'csrc': fobjcsrc, 'h': fobjhsrc})
return ret
def filter_files(prefix, suffix, files, remove_prefix=None):
"""
Filter files by prefix and suffix.
"""
filtered, rest = [], []
match = re.compile(prefix + r'.*' + suffix + r'\Z').match
if remove_prefix:
ind = len(prefix)
else:
ind = 0
for file in [x.strip() for x in files]:
if match(file):
filtered.append(file[ind:])
else:
rest.append(file)
return filtered, rest
def get_prefix(module):
p = os.path.dirname(os.path.dirname(module.__file__))
return p
def run_compile():
"""
Do it all in one call!
"""
import tempfile
i = sys.argv.index('-c')
del sys.argv[i]
remove_build_dir = 0
try:
i = sys.argv.index('--build-dir')
except ValueError:
i = None
if i is not None:
build_dir = sys.argv[i + 1]
del sys.argv[i + 1]
del sys.argv[i]
else:
remove_build_dir = 1
build_dir = tempfile.mkdtemp()
_reg1 = re.compile(r'[-][-]link[-]')
sysinfo_flags = [_m for _m in sys.argv[1:] if _reg1.match(_m)]
sys.argv = [_m for _m in sys.argv if _m not in sysinfo_flags]
if sysinfo_flags:
sysinfo_flags = [f[7:] for f in sysinfo_flags]
_reg2 = re.compile(
r'[-][-]((no[-]|)(wrap[-]functions|lower)|debug[-]capi|quiet)|[-]include')
f2py_flags = [_m for _m in sys.argv[1:] if _reg2.match(_m)]
sys.argv = [_m for _m in sys.argv if _m not in f2py_flags]
f2py_flags2 = []
fl = 0
for a in sys.argv[1:]:
if a in ['only:', 'skip:']:
fl = 1
elif a == ':':
fl = 0
if fl or a == ':':
f2py_flags2.append(a)
if f2py_flags2 and f2py_flags2[-1] != ':':
f2py_flags2.append(':')
f2py_flags.extend(f2py_flags2)
sys.argv = [_m for _m in sys.argv if _m not in f2py_flags2]
_reg3 = re.compile(
r'[-][-]((f(90)?compiler([-]exec|)|compiler)=|help[-]compiler)')
flib_flags = [_m for _m in sys.argv[1:] if _reg3.match(_m)]
sys.argv = [_m for _m in sys.argv if _m not in flib_flags]
_reg4 = re.compile(
r'[-][-]((f(77|90)(flags|exec)|opt|arch)=|(debug|noopt|noarch|help[-]fcompiler))')
fc_flags = [_m for _m in sys.argv[1:] if _reg4.match(_m)]
sys.argv = [_m for _m in sys.argv if _m not in fc_flags]
if 1:
del_list = []
for s in flib_flags:
v = '--fcompiler='
if s[:len(v)] == v:
from numpy.distutils import fcompiler
fcompiler.load_all_fcompiler_classes()
allowed_keys = list(fcompiler.fcompiler_class.keys())
nv = ov = s[len(v):].lower()
if ov not in allowed_keys:
vmap = {} # XXX
try:
nv = vmap[ov]
except KeyError:
if ov not in vmap.values():
print('Unknown vendor: "%s"' % (s[len(v):]))
nv = ov
i = flib_flags.index(s)
flib_flags[i] = '--fcompiler=' + nv
continue
for s in del_list:
i = flib_flags.index(s)
del flib_flags[i]
assert len(flib_flags) <= 2, repr(flib_flags)
_reg5 = re.compile(r'[-][-](verbose)')
setup_flags = [_m for _m in sys.argv[1:] if _reg5.match(_m)]
sys.argv = [_m for _m in sys.argv if _m not in setup_flags]
if '--quiet' in f2py_flags:
setup_flags.append('--quiet')
modulename = 'untitled'
sources = sys.argv[1:]
for optname in ['--include_paths', '--include-paths']:
if optname in sys.argv:
i = sys.argv.index(optname)
f2py_flags.extend(sys.argv[i:i + 2])
del sys.argv[i + 1], sys.argv[i]
sources = sys.argv[1:]
if '-m' in sys.argv:
i = sys.argv.index('-m')
modulename = sys.argv[i + 1]
del sys.argv[i + 1], sys.argv[i]
sources = sys.argv[1:]
else:
from numpy.distutils.command.build_src import get_f2py_modulename
pyf_files, sources = filter_files('', '[.]pyf([.]src|)', sources)
sources = pyf_files + sources
for f in pyf_files:
modulename = get_f2py_modulename(f)
if modulename:
break
extra_objects, sources = filter_files('', '[.](o|a|so)', sources)
include_dirs, sources = filter_files('-I', '', sources, remove_prefix=1)
library_dirs, sources = filter_files('-L', '', sources, remove_prefix=1)
libraries, sources = filter_files('-l', '', sources, remove_prefix=1)
undef_macros, sources = filter_files('-U', '', sources, remove_prefix=1)
define_macros, sources = filter_files('-D', '', sources, remove_prefix=1)
for i in range(len(define_macros)):
name_value = define_macros[i].split('=', 1)
if len(name_value) == 1:
name_value.append(None)
if len(name_value) == 2:
define_macros[i] = tuple(name_value)
else:
print('Invalid use of -D:', name_value)
from numpy.distutils.system_info import get_info
num_info = {}
if num_info:
include_dirs.extend(num_info.get('include_dirs', []))
from numpy.distutils.core import setup, Extension
ext_args = {'name': modulename, 'sources': sources,
'include_dirs': include_dirs,
'library_dirs': library_dirs,
'libraries': libraries,
'define_macros': define_macros,
'undef_macros': undef_macros,
'extra_objects': extra_objects,
'f2py_options': f2py_flags,
}
if sysinfo_flags:
from numpy.distutils.misc_util import dict_append
for n in sysinfo_flags:
i = get_info(n)
if not i:
outmess('No %s resources found in system'
' (try `f2py --help-link`)\n' % (repr(n)))
dict_append(ext_args, **i)
ext = Extension(**ext_args)
sys.argv = [sys.argv[0]] + setup_flags
sys.argv.extend(['build',
'--build-temp', build_dir,
'--build-base', build_dir,
'--build-platlib', '.'])
if fc_flags:
sys.argv.extend(['config_fc'] + fc_flags)
if flib_flags:
sys.argv.extend(['build_ext'] + flib_flags)
setup(ext_modules=[ext])
if remove_build_dir and os.path.exists(build_dir):
import shutil
outmess('Removing build directory %s\n' % (build_dir))
shutil.rmtree(build_dir)
def main():
if '--help-link' in sys.argv[1:]:
sys.argv.remove('--help-link')
from numpy.distutils.system_info import show_all
show_all()
return
if '-c' in sys.argv[1:]:
run_compile()
else:
run_main(sys.argv[1:])
# if __name__ == "__main__":
# main()
# EOF
| 22,908 | 33.869102 | 108 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/setup.py
|
#!/usr/bin/env python
"""
setup.py for installing F2PY
Usage:
python setup.py install
Copyright 2001-2005 Pearu Peterson all rights reserved,
Pearu Peterson <[email protected]>
Permission to use, modify, and distribute this software is given under the
terms of the NumPy License.
NO WARRANTY IS EXPRESSED OR IMPLIED. USE AT YOUR OWN RISK.
$Revision: 1.32 $
$Date: 2005/01/30 17:22:14 $
Pearu Peterson
"""
from __future__ import division, print_function
__version__ = "$Id: setup.py,v 1.32 2005/01/30 17:22:14 pearu Exp $"
import os
import sys
from distutils.dep_util import newer
from numpy.distutils import log
from numpy.distutils.core import setup
from numpy.distutils.misc_util import Configuration
from __version__ import version
def _get_f2py_shebang():
""" Return shebang line for f2py script
If we are building a binary distribution format, then the shebang line
should be ``#!python`` rather than ``#!`` followed by the contents of
``sys.executable``.
"""
if set(('bdist_wheel', 'bdist_egg', 'bdist_wininst',
'bdist_rpm')).intersection(sys.argv):
return '#!python'
return '#!' + sys.executable
def configuration(parent_package='', top_path=None):
config = Configuration('f2py', parent_package, top_path)
config.add_data_dir('tests')
config.add_data_files('src/fortranobject.c',
'src/fortranobject.h',
)
config.make_svn_version_py()
def generate_f2py_py(build_dir):
f2py_exe = 'f2py' + os.path.basename(sys.executable)[6:]
if f2py_exe[-4:] == '.exe':
f2py_exe = f2py_exe[:-4] + '.py'
if 'bdist_wininst' in sys.argv and f2py_exe[-3:] != '.py':
f2py_exe = f2py_exe + '.py'
target = os.path.join(build_dir, f2py_exe)
if newer(__file__, target):
log.info('Creating %s', target)
f = open(target, 'w')
f.write(_get_f2py_shebang() + '\n')
mainloc = os.path.join(os.path.dirname(__file__), "__main__.py")
with open(mainloc) as mf:
f.write(mf.read())
f.close()
return target
config.add_scripts(generate_f2py_py)
log.info('F2PY Version %s', config.get_version())
return config
if __name__ == "__main__":
config = configuration(top_path='')
print('F2PY Version', version)
config = config.todict()
config['download_url'] = "http://cens.ioc.ee/projects/f2py2e/2.x"\
"/F2PY-2-latest.tar.gz"
config['classifiers'] = [
'Development Status :: 5 - Production/Stable',
'Intended Audience :: Developers',
'Intended Audience :: Science/Research',
'License :: OSI Approved :: NumPy License',
'Natural Language :: English',
'Operating System :: OS Independent',
'Programming Language :: C',
'Programming Language :: Fortran',
'Programming Language :: Python',
'Topic :: Scientific/Engineering',
'Topic :: Software Development :: Code Generators',
]
setup(version=version,
description="F2PY - Fortran to Python Interface Generaton",
author="Pearu Peterson",
author_email="[email protected]",
maintainer="Pearu Peterson",
maintainer_email="[email protected]",
license="BSD",
platforms="Unix, Windows (mingw|cygwin), Mac OSX",
long_description="""\
The Fortran to Python Interface Generator, or F2PY for short, is a
command line tool (f2py) for generating Python C/API modules for
wrapping Fortran 77/90/95 subroutines, accessing common blocks from
Python, and calling Python functions from Fortran (call-backs).
Interfacing subroutines/data from Fortran 90/95 modules is supported.""",
url="http://cens.ioc.ee/projects/f2py2e/",
keywords=['Fortran', 'f2py'],
**config)
| 3,925 | 32.271186 | 76 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/crackfortran.py
|
#!/usr/bin/env python
"""
crackfortran --- read fortran (77,90) code and extract declaration information.
Copyright 1999-2004 Pearu Peterson all rights reserved,
Pearu Peterson <[email protected]>
Permission to use, modify, and distribute this software is given under the
terms of the NumPy License.
NO WARRANTY IS EXPRESSED OR IMPLIED. USE AT YOUR OWN RISK.
$Date: 2005/09/27 07:13:49 $
Pearu Peterson
Usage of crackfortran:
======================
Command line keys: -quiet,-verbose,-fix,-f77,-f90,-show,-h <pyffilename>
-m <module name for f77 routines>,--ignore-contains
Functions: crackfortran, crack2fortran
The following Fortran statements/constructions are supported
(or will be if needed):
block data,byte,call,character,common,complex,contains,data,
dimension,double complex,double precision,end,external,function,
implicit,integer,intent,interface,intrinsic,
logical,module,optional,parameter,private,public,
program,real,(sequence?),subroutine,type,use,virtual,
include,pythonmodule
Note: 'virtual' is mapped to 'dimension'.
Note: 'implicit integer (z) static (z)' is 'implicit static (z)' (this is minor bug).
Note: code after 'contains' will be ignored until its scope ends.
Note: 'common' statement is extended: dimensions are moved to variable definitions
Note: f2py directive: <commentchar>f2py<line> is read as <line>
Note: pythonmodule is introduced to represent Python module
Usage:
`postlist=crackfortran(files,funcs)`
`postlist` contains declaration information read from the list of files `files`.
`crack2fortran(postlist)` returns a fortran code to be saved to pyf-file
`postlist` has the following structure:
*** it is a list of dictionaries containing `blocks':
B = {'block','body','vars','parent_block'[,'name','prefix','args','result',
'implicit','externals','interfaced','common','sortvars',
'commonvars','note']}
B['block'] = 'interface' | 'function' | 'subroutine' | 'module' |
'program' | 'block data' | 'type' | 'pythonmodule'
B['body'] --- list containing `subblocks' with the same structure as `blocks'
B['parent_block'] --- dictionary of a parent block:
C['body'][<index>]['parent_block'] is C
B['vars'] --- dictionary of variable definitions
B['sortvars'] --- dictionary of variable definitions sorted by dependence (independent first)
B['name'] --- name of the block (not if B['block']=='interface')
B['prefix'] --- prefix string (only if B['block']=='function')
B['args'] --- list of argument names if B['block']== 'function' | 'subroutine'
B['result'] --- name of the return value (only if B['block']=='function')
B['implicit'] --- dictionary {'a':<variable definition>,'b':...} | None
B['externals'] --- list of variables being external
B['interfaced'] --- list of variables being external and defined
B['common'] --- dictionary of common blocks (list of objects)
B['commonvars'] --- list of variables used in common blocks (dimensions are moved to variable definitions)
B['from'] --- string showing the 'parents' of the current block
B['use'] --- dictionary of modules used in current block:
{<modulename>:{['only':<0|1>],['map':{<local_name1>:<use_name1>,...}]}}
B['note'] --- list of LaTeX comments on the block
B['f2pyenhancements'] --- optional dictionary
{'threadsafe':'','fortranname':<name>,
'callstatement':<C-expr>|<multi-line block>,
'callprotoargument':<C-expr-list>,
'usercode':<multi-line block>|<list of multi-line blocks>,
'pymethoddef:<multi-line block>'
}
B['entry'] --- dictionary {entryname:argslist,..}
B['varnames'] --- list of variable names given in the order of reading the
Fortran code, useful for derived types.
B['saved_interface'] --- a string of scanned routine signature, defines explicit interface
*** Variable definition is a dictionary
D = B['vars'][<variable name>] =
{'typespec'[,'attrspec','kindselector','charselector','=','typename']}
D['typespec'] = 'byte' | 'character' | 'complex' | 'double complex' |
'double precision' | 'integer' | 'logical' | 'real' | 'type'
D['attrspec'] --- list of attributes (e.g. 'dimension(<arrayspec>)',
'external','intent(in|out|inout|hide|c|callback|cache|aligned4|aligned8|aligned16)',
'optional','required', etc)
K = D['kindselector'] = {['*','kind']} (only if D['typespec'] =
'complex' | 'integer' | 'logical' | 'real' )
C = D['charselector'] = {['*','len','kind']}
(only if D['typespec']=='character')
D['='] --- initialization expression string
D['typename'] --- name of the type if D['typespec']=='type'
D['dimension'] --- list of dimension bounds
D['intent'] --- list of intent specifications
D['depend'] --- list of variable names on which current variable depends on
D['check'] --- list of C-expressions; if C-expr returns zero, exception is raised
D['note'] --- list of LaTeX comments on the variable
*** Meaning of kind/char selectors (few examples):
D['typespec>']*K['*']
D['typespec'](kind=K['kind'])
character*C['*']
character(len=C['len'],kind=C['kind'])
(see also fortran type declaration statement formats below)
Fortran 90 type declaration statement format (F77 is subset of F90)
====================================================================
(Main source: IBM XL Fortran 5.1 Language Reference Manual)
type declaration = <typespec> [[<attrspec>]::] <entitydecl>
<typespec> = byte |
character[<charselector>] |
complex[<kindselector>] |
double complex |
double precision |
integer[<kindselector>] |
logical[<kindselector>] |
real[<kindselector>] |
type(<typename>)
<charselector> = * <charlen> |
([len=]<len>[,[kind=]<kind>]) |
(kind=<kind>[,len=<len>])
<kindselector> = * <intlen> |
([kind=]<kind>)
<attrspec> = comma separated list of attributes.
Only the following attributes are used in
building up the interface:
external
(parameter --- affects '=' key)
optional
intent
Other attributes are ignored.
<intentspec> = in | out | inout
<arrayspec> = comma separated list of dimension bounds.
<entitydecl> = <name> [[*<charlen>][(<arrayspec>)] | [(<arrayspec>)]*<charlen>]
[/<init_expr>/ | =<init_expr>] [,<entitydecl>]
In addition, the following attributes are used: check,depend,note
TODO:
* Apply 'parameter' attribute (e.g. 'integer parameter :: i=2' 'real x(i)'
-> 'real x(2)')
The above may be solved by creating appropriate preprocessor program, for example.
"""
from __future__ import division, absolute_import, print_function
import sys
import string
import fileinput
import re
import os
import copy
import platform
from . import __version__
# The eviroment provided by auxfuncs.py is needed for some calls to eval.
# As the needed functions cannot be determined by static inspection of the
# code, it is safest to use import * pending a major refactoring of f2py.
from .auxfuncs import *
f2py_version = __version__.version
# Global flags:
strictf77 = 1 # Ignore `!' comments unless line[0]=='!'
sourcecodeform = 'fix' # 'fix','free'
quiet = 0 # Be verbose if 0 (Obsolete: not used any more)
verbose = 1 # Be quiet if 0, extra verbose if > 1.
tabchar = 4 * ' '
pyffilename = ''
f77modulename = ''
skipemptyends = 0 # for old F77 programs without 'program' statement
ignorecontains = 1
dolowercase = 1
debug = []
# Global variables
beginpattern = ''
currentfilename = ''
expectbegin = 1
f90modulevars = {}
filepositiontext = ''
gotnextfile = 1
groupcache = None
groupcounter = 0
grouplist = {groupcounter: []}
groupname = ''
include_paths = []
neededmodule = -1
onlyfuncs = []
previous_context = None
skipblocksuntil = -1
skipfuncs = []
skipfunctions = []
usermodules = []
def reset_global_f2py_vars():
global groupcounter, grouplist, neededmodule, expectbegin
global skipblocksuntil, usermodules, f90modulevars, gotnextfile
global filepositiontext, currentfilename, skipfunctions, skipfuncs
global onlyfuncs, include_paths, previous_context
global strictf77, sourcecodeform, quiet, verbose, tabchar, pyffilename
global f77modulename, skipemptyends, ignorecontains, dolowercase, debug
# flags
strictf77 = 1
sourcecodeform = 'fix'
quiet = 0
verbose = 1
tabchar = 4 * ' '
pyffilename = ''
f77modulename = ''
skipemptyends = 0
ignorecontains = 1
dolowercase = 1
debug = []
# variables
groupcounter = 0
grouplist = {groupcounter: []}
neededmodule = -1
expectbegin = 1
skipblocksuntil = -1
usermodules = []
f90modulevars = {}
gotnextfile = 1
filepositiontext = ''
currentfilename = ''
skipfunctions = []
skipfuncs = []
onlyfuncs = []
include_paths = []
previous_context = None
def outmess(line, flag=1):
global filepositiontext
if not verbose:
return
if not quiet:
if flag:
sys.stdout.write(filepositiontext)
sys.stdout.write(line)
re._MAXCACHE = 50
defaultimplicitrules = {}
for c in "abcdefghopqrstuvwxyz$_":
defaultimplicitrules[c] = {'typespec': 'real'}
for c in "ijklmn":
defaultimplicitrules[c] = {'typespec': 'integer'}
del c
badnames = {}
invbadnames = {}
for n in ['int', 'double', 'float', 'char', 'short', 'long', 'void', 'case', 'while',
'return', 'signed', 'unsigned', 'if', 'for', 'typedef', 'sizeof', 'union',
'struct', 'static', 'register', 'new', 'break', 'do', 'goto', 'switch',
'continue', 'else', 'inline', 'extern', 'delete', 'const', 'auto',
'len', 'rank', 'shape', 'index', 'slen', 'size', '_i',
'max', 'min',
'flen', 'fshape',
'string', 'complex_double', 'float_double', 'stdin', 'stderr', 'stdout',
'type', 'default']:
badnames[n] = n + '_bn'
invbadnames[n + '_bn'] = n
def rmbadname1(name):
if name in badnames:
errmess('rmbadname1: Replacing "%s" with "%s".\n' %
(name, badnames[name]))
return badnames[name]
return name
def rmbadname(names):
return [rmbadname1(_m) for _m in names]
def undo_rmbadname1(name):
if name in invbadnames:
errmess('undo_rmbadname1: Replacing "%s" with "%s".\n'
% (name, invbadnames[name]))
return invbadnames[name]
return name
def undo_rmbadname(names):
return [undo_rmbadname1(_m) for _m in names]
def getextension(name):
i = name.rfind('.')
if i == -1:
return ''
if '\\' in name[i:]:
return ''
if '/' in name[i:]:
return ''
return name[i + 1:]
is_f_file = re.compile(r'.*[.](for|ftn|f77|f)\Z', re.I).match
_has_f_header = re.compile(r'-[*]-\s*fortran\s*-[*]-', re.I).search
_has_f90_header = re.compile(r'-[*]-\s*f90\s*-[*]-', re.I).search
_has_fix_header = re.compile(r'-[*]-\s*fix\s*-[*]-', re.I).search
_free_f90_start = re.compile(r'[^c*]\s*[^\s\d\t]', re.I).match
def is_free_format(file):
"""Check if file is in free format Fortran."""
# f90 allows both fixed and free format, assuming fixed unless
# signs of free format are detected.
result = 0
with open(file, 'r') as f:
line = f.readline()
n = 15 # the number of non-comment lines to scan for hints
if _has_f_header(line):
n = 0
elif _has_f90_header(line):
n = 0
result = 1
while n > 0 and line:
if line[0] != '!' and line.strip():
n -= 1
if (line[0] != '\t' and _free_f90_start(line[:5])) or line[-2:-1] == '&':
result = 1
break
line = f.readline()
return result
# Read fortran (77,90) code
def readfortrancode(ffile, dowithline=show, istop=1):
"""
Read fortran codes from files and
1) Get rid of comments, line continuations, and empty lines; lower cases.
2) Call dowithline(line) on every line.
3) Recursively call itself when statement \"include '<filename>'\" is met.
"""
global gotnextfile, filepositiontext, currentfilename, sourcecodeform, strictf77
global beginpattern, quiet, verbose, dolowercase, include_paths
if not istop:
saveglobals = gotnextfile, filepositiontext, currentfilename, sourcecodeform, strictf77,\
beginpattern, quiet, verbose, dolowercase
if ffile == []:
return
localdolowercase = dolowercase
cont = 0
finalline = ''
ll = ''
commentline = re.compile(
r'(?P<line>([^"]*["][^"]*["][^"!]*|[^\']*\'[^\']*\'[^\'!]*|[^!\'"]*))!{1}(?P<rest>.*)')
includeline = re.compile(
r'\s*include\s*(\'|")(?P<name>[^\'"]*)(\'|")', re.I)
cont1 = re.compile(r'(?P<line>.*)&\s*\Z')
cont2 = re.compile(r'(\s*&|)(?P<line>.*)')
mline_mark = re.compile(r".*?'''")
if istop:
dowithline('', -1)
ll, l1 = '', ''
spacedigits = [' '] + [str(_m) for _m in range(10)]
filepositiontext = ''
fin = fileinput.FileInput(ffile)
while True:
l = fin.readline()
if not l:
break
if fin.isfirstline():
filepositiontext = ''
currentfilename = fin.filename()
gotnextfile = 1
l1 = l
strictf77 = 0
sourcecodeform = 'fix'
ext = os.path.splitext(currentfilename)[1]
if is_f_file(currentfilename) and \
not (_has_f90_header(l) or _has_fix_header(l)):
strictf77 = 1
elif is_free_format(currentfilename) and not _has_fix_header(l):
sourcecodeform = 'free'
if strictf77:
beginpattern = beginpattern77
else:
beginpattern = beginpattern90
outmess('\tReading file %s (format:%s%s)\n'
% (repr(currentfilename), sourcecodeform,
strictf77 and ',strict' or ''))
l = l.expandtabs().replace('\xa0', ' ')
# Get rid of newline characters
while not l == '':
if l[-1] not in "\n\r\f":
break
l = l[:-1]
if not strictf77:
r = commentline.match(l)
if r:
l = r.group('line') + ' ' # Strip comments starting with `!'
rl = r.group('rest')
if rl[:4].lower() == 'f2py': # f2py directive
l = l + 4 * ' '
r = commentline.match(rl[4:])
if r:
l = l + r.group('line')
else:
l = l + rl[4:]
if l.strip() == '': # Skip empty line
cont = 0
continue
if sourcecodeform == 'fix':
if l[0] in ['*', 'c', '!', 'C', '#']:
if l[1:5].lower() == 'f2py': # f2py directive
l = ' ' + l[5:]
else: # Skip comment line
cont = 0
continue
elif strictf77:
if len(l) > 72:
l = l[:72]
if not (l[0] in spacedigits):
raise Exception('readfortrancode: Found non-(space,digit) char '
'in the first column.\n\tAre you sure that '
'this code is in fix form?\n\tline=%s' % repr(l))
if (not cont or strictf77) and (len(l) > 5 and not l[5] == ' '):
# Continuation of a previous line
ll = ll + l[6:]
finalline = ''
origfinalline = ''
else:
if not strictf77:
# F90 continuation
r = cont1.match(l)
if r:
l = r.group('line') # Continuation follows ..
if cont:
ll = ll + cont2.match(l).group('line')
finalline = ''
origfinalline = ''
else:
# clean up line beginning from possible digits.
l = ' ' + l[5:]
if localdolowercase:
finalline = ll.lower()
else:
finalline = ll
origfinalline = ll
ll = l
cont = (r is not None)
else:
# clean up line beginning from possible digits.
l = ' ' + l[5:]
if localdolowercase:
finalline = ll.lower()
else:
finalline = ll
origfinalline = ll
ll = l
elif sourcecodeform == 'free':
if not cont and ext == '.pyf' and mline_mark.match(l):
l = l + '\n'
while True:
lc = fin.readline()
if not lc:
errmess(
'Unexpected end of file when reading multiline\n')
break
l = l + lc
if mline_mark.match(lc):
break
l = l.rstrip()
r = cont1.match(l)
if r:
l = r.group('line') # Continuation follows ..
if cont:
ll = ll + cont2.match(l).group('line')
finalline = ''
origfinalline = ''
else:
if localdolowercase:
finalline = ll.lower()
else:
finalline = ll
origfinalline = ll
ll = l
cont = (r is not None)
else:
raise ValueError(
"Flag sourcecodeform must be either 'fix' or 'free': %s" % repr(sourcecodeform))
filepositiontext = 'Line #%d in %s:"%s"\n\t' % (
fin.filelineno() - 1, currentfilename, l1)
m = includeline.match(origfinalline)
if m:
fn = m.group('name')
if os.path.isfile(fn):
readfortrancode(fn, dowithline=dowithline, istop=0)
else:
include_dirs = [
os.path.dirname(currentfilename)] + include_paths
foundfile = 0
for inc_dir in include_dirs:
fn1 = os.path.join(inc_dir, fn)
if os.path.isfile(fn1):
foundfile = 1
readfortrancode(fn1, dowithline=dowithline, istop=0)
break
if not foundfile:
outmess('readfortrancode: could not find include file %s in %s. Ignoring.\n' % (
repr(fn), os.pathsep.join(include_dirs)))
else:
dowithline(finalline)
l1 = ll
if localdolowercase:
finalline = ll.lower()
else:
finalline = ll
origfinalline = ll
filepositiontext = 'Line #%d in %s:"%s"\n\t' % (
fin.filelineno() - 1, currentfilename, l1)
m = includeline.match(origfinalline)
if m:
fn = m.group('name')
if os.path.isfile(fn):
readfortrancode(fn, dowithline=dowithline, istop=0)
else:
include_dirs = [os.path.dirname(currentfilename)] + include_paths
foundfile = 0
for inc_dir in include_dirs:
fn1 = os.path.join(inc_dir, fn)
if os.path.isfile(fn1):
foundfile = 1
readfortrancode(fn1, dowithline=dowithline, istop=0)
break
if not foundfile:
outmess('readfortrancode: could not find include file %s in %s. Ignoring.\n' % (
repr(fn), os.pathsep.join(include_dirs)))
else:
dowithline(finalline)
filepositiontext = ''
fin.close()
if istop:
dowithline('', 1)
else:
gotnextfile, filepositiontext, currentfilename, sourcecodeform, strictf77,\
beginpattern, quiet, verbose, dolowercase = saveglobals
# Crack line
beforethisafter = r'\s*(?P<before>%s(?=\s*(\b(%s)\b)))' + \
r'\s*(?P<this>(\b(%s)\b))' + \
r'\s*(?P<after>%s)\s*\Z'
##
fortrantypes = r'character|logical|integer|real|complex|double\s*(precision\s*(complex|)|complex)|type(?=\s*\([\w\s,=(*)]*\))|byte'
typespattern = re.compile(
beforethisafter % ('', fortrantypes, fortrantypes, '.*'), re.I), 'type'
typespattern4implicit = re.compile(beforethisafter % (
'', fortrantypes + '|static|automatic|undefined', fortrantypes + '|static|automatic|undefined', '.*'), re.I)
#
functionpattern = re.compile(beforethisafter % (
r'([a-z]+[\w\s(=*+-/)]*?|)', 'function', 'function', '.*'), re.I), 'begin'
subroutinepattern = re.compile(beforethisafter % (
r'[a-z\s]*?', 'subroutine', 'subroutine', '.*'), re.I), 'begin'
# modulepattern=re.compile(beforethisafter%('[a-z\s]*?','module','module','.*'),re.I),'begin'
#
groupbegins77 = r'program|block\s*data'
beginpattern77 = re.compile(
beforethisafter % ('', groupbegins77, groupbegins77, '.*'), re.I), 'begin'
groupbegins90 = groupbegins77 + \
r'|module(?!\s*procedure)|python\s*module|interface|type(?!\s*\()'
beginpattern90 = re.compile(
beforethisafter % ('', groupbegins90, groupbegins90, '.*'), re.I), 'begin'
groupends = r'end|endprogram|endblockdata|endmodule|endpythonmodule|endinterface'
endpattern = re.compile(
beforethisafter % ('', groupends, groupends, r'[\w\s]*'), re.I), 'end'
# endifs='end\s*(if|do|where|select|while|forall)'
endifs = r'(end\s*(if|do|where|select|while|forall))|(module\s*procedure)'
endifpattern = re.compile(
beforethisafter % (r'[\w]*?', endifs, endifs, r'[\w\s]*'), re.I), 'endif'
#
implicitpattern = re.compile(
beforethisafter % ('', 'implicit', 'implicit', '.*'), re.I), 'implicit'
dimensionpattern = re.compile(beforethisafter % (
'', 'dimension|virtual', 'dimension|virtual', '.*'), re.I), 'dimension'
externalpattern = re.compile(
beforethisafter % ('', 'external', 'external', '.*'), re.I), 'external'
optionalpattern = re.compile(
beforethisafter % ('', 'optional', 'optional', '.*'), re.I), 'optional'
requiredpattern = re.compile(
beforethisafter % ('', 'required', 'required', '.*'), re.I), 'required'
publicpattern = re.compile(
beforethisafter % ('', 'public', 'public', '.*'), re.I), 'public'
privatepattern = re.compile(
beforethisafter % ('', 'private', 'private', '.*'), re.I), 'private'
intrisicpattern = re.compile(
beforethisafter % ('', 'intrisic', 'intrisic', '.*'), re.I), 'intrisic'
intentpattern = re.compile(beforethisafter % (
'', 'intent|depend|note|check', 'intent|depend|note|check', r'\s*\(.*?\).*'), re.I), 'intent'
parameterpattern = re.compile(
beforethisafter % ('', 'parameter', 'parameter', r'\s*\(.*'), re.I), 'parameter'
datapattern = re.compile(
beforethisafter % ('', 'data', 'data', '.*'), re.I), 'data'
callpattern = re.compile(
beforethisafter % ('', 'call', 'call', '.*'), re.I), 'call'
entrypattern = re.compile(
beforethisafter % ('', 'entry', 'entry', '.*'), re.I), 'entry'
callfunpattern = re.compile(
beforethisafter % ('', 'callfun', 'callfun', '.*'), re.I), 'callfun'
commonpattern = re.compile(
beforethisafter % ('', 'common', 'common', '.*'), re.I), 'common'
usepattern = re.compile(
beforethisafter % ('', 'use', 'use', '.*'), re.I), 'use'
containspattern = re.compile(
beforethisafter % ('', 'contains', 'contains', ''), re.I), 'contains'
formatpattern = re.compile(
beforethisafter % ('', 'format', 'format', '.*'), re.I), 'format'
# Non-fortran and f2py-specific statements
f2pyenhancementspattern = re.compile(beforethisafter % ('', 'threadsafe|fortranname|callstatement|callprotoargument|usercode|pymethoddef',
'threadsafe|fortranname|callstatement|callprotoargument|usercode|pymethoddef', '.*'), re.I | re.S), 'f2pyenhancements'
multilinepattern = re.compile(
r"\s*(?P<before>''')(?P<this>.*?)(?P<after>''')\s*\Z", re.S), 'multiline'
##
def _simplifyargs(argsline):
a = []
for n in markoutercomma(argsline).split('@,@'):
for r in '(),':
n = n.replace(r, '_')
a.append(n)
return ','.join(a)
crackline_re_1 = re.compile(r'\s*(?P<result>\b[a-z]+[\w]*\b)\s*[=].*', re.I)
def crackline(line, reset=0):
"""
reset=-1 --- initialize
reset=0 --- crack the line
reset=1 --- final check if mismatch of blocks occurred
Cracked data is saved in grouplist[0].
"""
global beginpattern, groupcounter, groupname, groupcache, grouplist
global filepositiontext, currentfilename, neededmodule, expectbegin
global skipblocksuntil, skipemptyends, previous_context, gotnextfile
if ';' in line and not (f2pyenhancementspattern[0].match(line) or
multilinepattern[0].match(line)):
for l in line.split(';'):
# XXX: non-zero reset values need testing
assert reset == 0, repr(reset)
crackline(l, reset)
return
if reset < 0:
groupcounter = 0
groupname = {groupcounter: ''}
groupcache = {groupcounter: {}}
grouplist = {groupcounter: []}
groupcache[groupcounter]['body'] = []
groupcache[groupcounter]['vars'] = {}
groupcache[groupcounter]['block'] = ''
groupcache[groupcounter]['name'] = ''
neededmodule = -1
skipblocksuntil = -1
return
if reset > 0:
fl = 0
if f77modulename and neededmodule == groupcounter:
fl = 2
while groupcounter > fl:
outmess('crackline: groupcounter=%s groupname=%s\n' %
(repr(groupcounter), repr(groupname)))
outmess(
'crackline: Mismatch of blocks encountered. Trying to fix it by assuming "end" statement.\n')
grouplist[groupcounter - 1].append(groupcache[groupcounter])
grouplist[groupcounter - 1][-1]['body'] = grouplist[groupcounter]
del grouplist[groupcounter]
groupcounter = groupcounter - 1
if f77modulename and neededmodule == groupcounter:
grouplist[groupcounter - 1].append(groupcache[groupcounter])
grouplist[groupcounter - 1][-1]['body'] = grouplist[groupcounter]
del grouplist[groupcounter]
groupcounter = groupcounter - 1 # end interface
grouplist[groupcounter - 1].append(groupcache[groupcounter])
grouplist[groupcounter - 1][-1]['body'] = grouplist[groupcounter]
del grouplist[groupcounter]
groupcounter = groupcounter - 1 # end module
neededmodule = -1
return
if line == '':
return
flag = 0
for pat in [dimensionpattern, externalpattern, intentpattern, optionalpattern,
requiredpattern,
parameterpattern, datapattern, publicpattern, privatepattern,
intrisicpattern,
endifpattern, endpattern,
formatpattern,
beginpattern, functionpattern, subroutinepattern,
implicitpattern, typespattern, commonpattern,
callpattern, usepattern, containspattern,
entrypattern,
f2pyenhancementspattern,
multilinepattern
]:
m = pat[0].match(line)
if m:
break
flag = flag + 1
if not m:
re_1 = crackline_re_1
if 0 <= skipblocksuntil <= groupcounter:
return
if 'externals' in groupcache[groupcounter]:
for name in groupcache[groupcounter]['externals']:
if name in invbadnames:
name = invbadnames[name]
if 'interfaced' in groupcache[groupcounter] and name in groupcache[groupcounter]['interfaced']:
continue
m1 = re.match(
r'(?P<before>[^"]*)\b%s\b\s*@\(@(?P<args>[^@]*)@\)@.*\Z' % name, markouterparen(line), re.I)
if m1:
m2 = re_1.match(m1.group('before'))
a = _simplifyargs(m1.group('args'))
if m2:
line = 'callfun %s(%s) result (%s)' % (
name, a, m2.group('result'))
else:
line = 'callfun %s(%s)' % (name, a)
m = callfunpattern[0].match(line)
if not m:
outmess(
'crackline: could not resolve function call for line=%s.\n' % repr(line))
return
analyzeline(m, 'callfun', line)
return
if verbose > 1 or (verbose == 1 and currentfilename.lower().endswith('.pyf')):
previous_context = None
outmess('crackline:%d: No pattern for line\n' % (groupcounter))
return
elif pat[1] == 'end':
if 0 <= skipblocksuntil < groupcounter:
groupcounter = groupcounter - 1
if skipblocksuntil <= groupcounter:
return
if groupcounter <= 0:
raise Exception('crackline: groupcounter(=%s) is nonpositive. '
'Check the blocks.'
% (groupcounter))
m1 = beginpattern[0].match((line))
if (m1) and (not m1.group('this') == groupname[groupcounter]):
raise Exception('crackline: End group %s does not match with '
'previous Begin group %s\n\t%s' %
(repr(m1.group('this')), repr(groupname[groupcounter]),
filepositiontext)
)
if skipblocksuntil == groupcounter:
skipblocksuntil = -1
grouplist[groupcounter - 1].append(groupcache[groupcounter])
grouplist[groupcounter - 1][-1]['body'] = grouplist[groupcounter]
del grouplist[groupcounter]
groupcounter = groupcounter - 1
if not skipemptyends:
expectbegin = 1
elif pat[1] == 'begin':
if 0 <= skipblocksuntil <= groupcounter:
groupcounter = groupcounter + 1
return
gotnextfile = 0
analyzeline(m, pat[1], line)
expectbegin = 0
elif pat[1] == 'endif':
pass
elif pat[1] == 'contains':
if ignorecontains:
return
if 0 <= skipblocksuntil <= groupcounter:
return
skipblocksuntil = groupcounter
else:
if 0 <= skipblocksuntil <= groupcounter:
return
analyzeline(m, pat[1], line)
def markouterparen(line):
l = ''
f = 0
for c in line:
if c == '(':
f = f + 1
if f == 1:
l = l + '@(@'
continue
elif c == ')':
f = f - 1
if f == 0:
l = l + '@)@'
continue
l = l + c
return l
def markoutercomma(line, comma=','):
l = ''
f = 0
cc = ''
for c in line:
if (not cc or cc == ')') and c == '(':
f = f + 1
cc = ')'
elif not cc and c == '\'' and (not l or l[-1] != '\\'):
f = f + 1
cc = '\''
elif c == cc:
f = f - 1
if f == 0:
cc = ''
elif c == comma and f == 0:
l = l + '@' + comma + '@'
continue
l = l + c
assert not f, repr((f, line, l, cc))
return l
def unmarkouterparen(line):
r = line.replace('@(@', '(').replace('@)@', ')')
return r
def appenddecl(decl, decl2, force=1):
if not decl:
decl = {}
if not decl2:
return decl
if decl is decl2:
return decl
for k in list(decl2.keys()):
if k == 'typespec':
if force or k not in decl:
decl[k] = decl2[k]
elif k == 'attrspec':
for l in decl2[k]:
decl = setattrspec(decl, l, force)
elif k == 'kindselector':
decl = setkindselector(decl, decl2[k], force)
elif k == 'charselector':
decl = setcharselector(decl, decl2[k], force)
elif k in ['=', 'typename']:
if force or k not in decl:
decl[k] = decl2[k]
elif k == 'note':
pass
elif k in ['intent', 'check', 'dimension', 'optional', 'required']:
errmess('appenddecl: "%s" not implemented.\n' % k)
else:
raise Exception('appenddecl: Unknown variable definition key:' +
str(k))
return decl
selectpattern = re.compile(
r'\s*(?P<this>(@\(@.*?@\)@|[*][\d*]+|[*]\s*@\(@.*?@\)@|))(?P<after>.*)\Z', re.I)
nameargspattern = re.compile(
r'\s*(?P<name>\b[\w$]+\b)\s*(@\(@\s*(?P<args>[\w\s,]*)\s*@\)@|)\s*((result(\s*@\(@\s*(?P<result>\b[\w$]+\b)\s*@\)@|))|(bind\s*@\(@\s*(?P<bind>.*)\s*@\)@))*\s*\Z', re.I)
callnameargspattern = re.compile(
r'\s*(?P<name>\b[\w$]+\b)\s*@\(@\s*(?P<args>.*)\s*@\)@\s*\Z', re.I)
real16pattern = re.compile(
r'([-+]?(?:\d+(?:\.\d*)?|\d*\.\d+))[dD]((?:[-+]?\d+)?)')
real8pattern = re.compile(
r'([-+]?((?:\d+(?:\.\d*)?|\d*\.\d+))[eE]((?:[-+]?\d+)?)|(\d+\.\d*))')
_intentcallbackpattern = re.compile(r'intent\s*\(.*?\bcallback\b', re.I)
def _is_intent_callback(vdecl):
for a in vdecl.get('attrspec', []):
if _intentcallbackpattern.match(a):
return 1
return 0
def _resolvenameargspattern(line):
line = markouterparen(line)
m1 = nameargspattern.match(line)
if m1:
return m1.group('name'), m1.group('args'), m1.group('result'), m1.group('bind')
m1 = callnameargspattern.match(line)
if m1:
return m1.group('name'), m1.group('args'), None, None
return None, [], None, None
def analyzeline(m, case, line):
global groupcounter, groupname, groupcache, grouplist, filepositiontext
global currentfilename, f77modulename, neededinterface, neededmodule
global expectbegin, gotnextfile, previous_context
block = m.group('this')
if case != 'multiline':
previous_context = None
if expectbegin and case not in ['begin', 'call', 'callfun', 'type'] \
and not skipemptyends and groupcounter < 1:
newname = os.path.basename(currentfilename).split('.')[0]
outmess(
'analyzeline: no group yet. Creating program group with name "%s".\n' % newname)
gotnextfile = 0
groupcounter = groupcounter + 1
groupname[groupcounter] = 'program'
groupcache[groupcounter] = {}
grouplist[groupcounter] = []
groupcache[groupcounter]['body'] = []
groupcache[groupcounter]['vars'] = {}
groupcache[groupcounter]['block'] = 'program'
groupcache[groupcounter]['name'] = newname
groupcache[groupcounter]['from'] = 'fromsky'
expectbegin = 0
if case in ['begin', 'call', 'callfun']:
# Crack line => block,name,args,result
block = block.lower()
if re.match(r'block\s*data', block, re.I):
block = 'block data'
if re.match(r'python\s*module', block, re.I):
block = 'python module'
name, args, result, bind = _resolvenameargspattern(m.group('after'))
if name is None:
if block == 'block data':
name = '_BLOCK_DATA_'
else:
name = ''
if block not in ['interface', 'block data']:
outmess('analyzeline: No name/args pattern found for line.\n')
previous_context = (block, name, groupcounter)
if args:
args = rmbadname([x.strip()
for x in markoutercomma(args).split('@,@')])
else:
args = []
if '' in args:
while '' in args:
args.remove('')
outmess(
'analyzeline: argument list is malformed (missing argument).\n')
# end of crack line => block,name,args,result
needmodule = 0
needinterface = 0
if case in ['call', 'callfun']:
needinterface = 1
if 'args' not in groupcache[groupcounter]:
return
if name not in groupcache[groupcounter]['args']:
return
for it in grouplist[groupcounter]:
if it['name'] == name:
return
if name in groupcache[groupcounter]['interfaced']:
return
block = {'call': 'subroutine', 'callfun': 'function'}[case]
if f77modulename and neededmodule == -1 and groupcounter <= 1:
neededmodule = groupcounter + 2
needmodule = 1
if block != 'interface':
needinterface = 1
# Create new block(s)
groupcounter = groupcounter + 1
groupcache[groupcounter] = {}
grouplist[groupcounter] = []
if needmodule:
if verbose > 1:
outmess('analyzeline: Creating module block %s\n' %
repr(f77modulename), 0)
groupname[groupcounter] = 'module'
groupcache[groupcounter]['block'] = 'python module'
groupcache[groupcounter]['name'] = f77modulename
groupcache[groupcounter]['from'] = ''
groupcache[groupcounter]['body'] = []
groupcache[groupcounter]['externals'] = []
groupcache[groupcounter]['interfaced'] = []
groupcache[groupcounter]['vars'] = {}
groupcounter = groupcounter + 1
groupcache[groupcounter] = {}
grouplist[groupcounter] = []
if needinterface:
if verbose > 1:
outmess('analyzeline: Creating additional interface block (groupcounter=%s).\n' % (
groupcounter), 0)
groupname[groupcounter] = 'interface'
groupcache[groupcounter]['block'] = 'interface'
groupcache[groupcounter]['name'] = 'unknown_interface'
groupcache[groupcounter]['from'] = '%s:%s' % (
groupcache[groupcounter - 1]['from'], groupcache[groupcounter - 1]['name'])
groupcache[groupcounter]['body'] = []
groupcache[groupcounter]['externals'] = []
groupcache[groupcounter]['interfaced'] = []
groupcache[groupcounter]['vars'] = {}
groupcounter = groupcounter + 1
groupcache[groupcounter] = {}
grouplist[groupcounter] = []
groupname[groupcounter] = block
groupcache[groupcounter]['block'] = block
if not name:
name = 'unknown_' + block
groupcache[groupcounter]['prefix'] = m.group('before')
groupcache[groupcounter]['name'] = rmbadname1(name)
groupcache[groupcounter]['result'] = result
if groupcounter == 1:
groupcache[groupcounter]['from'] = currentfilename
else:
if f77modulename and groupcounter == 3:
groupcache[groupcounter]['from'] = '%s:%s' % (
groupcache[groupcounter - 1]['from'], currentfilename)
else:
groupcache[groupcounter]['from'] = '%s:%s' % (
groupcache[groupcounter - 1]['from'], groupcache[groupcounter - 1]['name'])
for k in list(groupcache[groupcounter].keys()):
if not groupcache[groupcounter][k]:
del groupcache[groupcounter][k]
groupcache[groupcounter]['args'] = args
groupcache[groupcounter]['body'] = []
groupcache[groupcounter]['externals'] = []
groupcache[groupcounter]['interfaced'] = []
groupcache[groupcounter]['vars'] = {}
groupcache[groupcounter]['entry'] = {}
# end of creation
if block == 'type':
groupcache[groupcounter]['varnames'] = []
if case in ['call', 'callfun']: # set parents variables
if name not in groupcache[groupcounter - 2]['externals']:
groupcache[groupcounter - 2]['externals'].append(name)
groupcache[groupcounter]['vars'] = copy.deepcopy(
groupcache[groupcounter - 2]['vars'])
try:
del groupcache[groupcounter]['vars'][name][
groupcache[groupcounter]['vars'][name]['attrspec'].index('external')]
except Exception:
pass
if block in ['function', 'subroutine']: # set global attributes
try:
groupcache[groupcounter]['vars'][name] = appenddecl(
groupcache[groupcounter]['vars'][name], groupcache[groupcounter - 2]['vars'][''])
except Exception:
pass
if case == 'callfun': # return type
if result and result in groupcache[groupcounter]['vars']:
if not name == result:
groupcache[groupcounter]['vars'][name] = appenddecl(
groupcache[groupcounter]['vars'][name], groupcache[groupcounter]['vars'][result])
# if groupcounter>1: # name is interfaced
try:
groupcache[groupcounter - 2]['interfaced'].append(name)
except Exception:
pass
if block == 'function':
t = typespattern[0].match(m.group('before') + ' ' + name)
if t:
typespec, selector, attr, edecl = cracktypespec0(
t.group('this'), t.group('after'))
updatevars(typespec, selector, attr, edecl)
if case in ['call', 'callfun']:
grouplist[groupcounter - 1].append(groupcache[groupcounter])
grouplist[groupcounter - 1][-1]['body'] = grouplist[groupcounter]
del grouplist[groupcounter]
groupcounter = groupcounter - 1 # end routine
grouplist[groupcounter - 1].append(groupcache[groupcounter])
grouplist[groupcounter - 1][-1]['body'] = grouplist[groupcounter]
del grouplist[groupcounter]
groupcounter = groupcounter - 1 # end interface
elif case == 'entry':
name, args, result, bind = _resolvenameargspattern(m.group('after'))
if name is not None:
if args:
args = rmbadname([x.strip()
for x in markoutercomma(args).split('@,@')])
else:
args = []
assert result is None, repr(result)
groupcache[groupcounter]['entry'][name] = args
previous_context = ('entry', name, groupcounter)
elif case == 'type':
typespec, selector, attr, edecl = cracktypespec0(
block, m.group('after'))
last_name = updatevars(typespec, selector, attr, edecl)
if last_name is not None:
previous_context = ('variable', last_name, groupcounter)
elif case in ['dimension', 'intent', 'optional', 'required', 'external', 'public', 'private', 'intrisic']:
edecl = groupcache[groupcounter]['vars']
ll = m.group('after').strip()
i = ll.find('::')
if i < 0 and case == 'intent':
i = markouterparen(ll).find('@)@') - 2
ll = ll[:i + 1] + '::' + ll[i + 1:]
i = ll.find('::')
if ll[i:] == '::' and 'args' in groupcache[groupcounter]:
outmess('All arguments will have attribute %s%s\n' %
(m.group('this'), ll[:i]))
ll = ll + ','.join(groupcache[groupcounter]['args'])
if i < 0:
i = 0
pl = ''
else:
pl = ll[:i].strip()
ll = ll[i + 2:]
ch = markoutercomma(pl).split('@,@')
if len(ch) > 1:
pl = ch[0]
outmess('analyzeline: cannot handle multiple attributes without type specification. Ignoring %r.\n' % (
','.join(ch[1:])))
last_name = None
for e in [x.strip() for x in markoutercomma(ll).split('@,@')]:
m1 = namepattern.match(e)
if not m1:
if case in ['public', 'private']:
k = ''
else:
print(m.groupdict())
outmess('analyzeline: no name pattern found in %s statement for %s. Skipping.\n' % (
case, repr(e)))
continue
else:
k = rmbadname1(m1.group('name'))
if k not in edecl:
edecl[k] = {}
if case == 'dimension':
ap = case + m1.group('after')
if case == 'intent':
ap = m.group('this') + pl
if _intentcallbackpattern.match(ap):
if k not in groupcache[groupcounter]['args']:
if groupcounter > 1:
if '__user__' not in groupcache[groupcounter - 2]['name']:
outmess(
'analyzeline: missing __user__ module (could be nothing)\n')
# fixes ticket 1693
if k != groupcache[groupcounter]['name']:
outmess('analyzeline: appending intent(callback) %s'
' to %s arguments\n' % (k, groupcache[groupcounter]['name']))
groupcache[groupcounter]['args'].append(k)
else:
errmess(
'analyzeline: intent(callback) %s is ignored' % (k))
else:
errmess('analyzeline: intent(callback) %s is already'
' in argument list' % (k))
if case in ['optional', 'required', 'public', 'external', 'private', 'intrisic']:
ap = case
if 'attrspec' in edecl[k]:
edecl[k]['attrspec'].append(ap)
else:
edecl[k]['attrspec'] = [ap]
if case == 'external':
if groupcache[groupcounter]['block'] == 'program':
outmess('analyzeline: ignoring program arguments\n')
continue
if k not in groupcache[groupcounter]['args']:
continue
if 'externals' not in groupcache[groupcounter]:
groupcache[groupcounter]['externals'] = []
groupcache[groupcounter]['externals'].append(k)
last_name = k
groupcache[groupcounter]['vars'] = edecl
if last_name is not None:
previous_context = ('variable', last_name, groupcounter)
elif case == 'parameter':
edecl = groupcache[groupcounter]['vars']
ll = m.group('after').strip()[1:-1]
last_name = None
for e in markoutercomma(ll).split('@,@'):
try:
k, initexpr = [x.strip() for x in e.split('=')]
except Exception:
outmess(
'analyzeline: could not extract name,expr in parameter statement "%s" of "%s"\n' % (e, ll))
continue
params = get_parameters(edecl)
k = rmbadname1(k)
if k not in edecl:
edecl[k] = {}
if '=' in edecl[k] and (not edecl[k]['='] == initexpr):
outmess('analyzeline: Overwriting the value of parameter "%s" ("%s") with "%s".\n' % (
k, edecl[k]['='], initexpr))
t = determineexprtype(initexpr, params)
if t:
if t.get('typespec') == 'real':
tt = list(initexpr)
for m in real16pattern.finditer(initexpr):
tt[m.start():m.end()] = list(
initexpr[m.start():m.end()].lower().replace('d', 'e'))
initexpr = ''.join(tt)
elif t.get('typespec') == 'complex':
initexpr = initexpr[1:].lower().replace('d', 'e').\
replace(',', '+1j*(')
try:
v = eval(initexpr, {}, params)
except (SyntaxError, NameError, TypeError) as msg:
errmess('analyzeline: Failed to evaluate %r. Ignoring: %s\n'
% (initexpr, msg))
continue
edecl[k]['='] = repr(v)
if 'attrspec' in edecl[k]:
edecl[k]['attrspec'].append('parameter')
else:
edecl[k]['attrspec'] = ['parameter']
last_name = k
groupcache[groupcounter]['vars'] = edecl
if last_name is not None:
previous_context = ('variable', last_name, groupcounter)
elif case == 'implicit':
if m.group('after').strip().lower() == 'none':
groupcache[groupcounter]['implicit'] = None
elif m.group('after'):
if 'implicit' in groupcache[groupcounter]:
impl = groupcache[groupcounter]['implicit']
else:
impl = {}
if impl is None:
outmess(
'analyzeline: Overwriting earlier "implicit none" statement.\n')
impl = {}
for e in markoutercomma(m.group('after')).split('@,@'):
decl = {}
m1 = re.match(
r'\s*(?P<this>.*?)\s*(\(\s*(?P<after>[a-z-, ]+)\s*\)\s*|)\Z', e, re.I)
if not m1:
outmess(
'analyzeline: could not extract info of implicit statement part "%s"\n' % (e))
continue
m2 = typespattern4implicit.match(m1.group('this'))
if not m2:
outmess(
'analyzeline: could not extract types pattern of implicit statement part "%s"\n' % (e))
continue
typespec, selector, attr, edecl = cracktypespec0(
m2.group('this'), m2.group('after'))
kindselect, charselect, typename = cracktypespec(
typespec, selector)
decl['typespec'] = typespec
decl['kindselector'] = kindselect
decl['charselector'] = charselect
decl['typename'] = typename
for k in list(decl.keys()):
if not decl[k]:
del decl[k]
for r in markoutercomma(m1.group('after')).split('@,@'):
if '-' in r:
try:
begc, endc = [x.strip() for x in r.split('-')]
except Exception:
outmess(
'analyzeline: expected "<char>-<char>" instead of "%s" in range list of implicit statement\n' % r)
continue
else:
begc = endc = r.strip()
if not len(begc) == len(endc) == 1:
outmess(
'analyzeline: expected "<char>-<char>" instead of "%s" in range list of implicit statement (2)\n' % r)
continue
for o in range(ord(begc), ord(endc) + 1):
impl[chr(o)] = decl
groupcache[groupcounter]['implicit'] = impl
elif case == 'data':
ll = []
dl = ''
il = ''
f = 0
fc = 1
inp = 0
for c in m.group('after'):
if not inp:
if c == "'":
fc = not fc
if c == '/' and fc:
f = f + 1
continue
if c == '(':
inp = inp + 1
elif c == ')':
inp = inp - 1
if f == 0:
dl = dl + c
elif f == 1:
il = il + c
elif f == 2:
dl = dl.strip()
if dl.startswith(','):
dl = dl[1:].strip()
ll.append([dl, il])
dl = c
il = ''
f = 0
if f == 2:
dl = dl.strip()
if dl.startswith(','):
dl = dl[1:].strip()
ll.append([dl, il])
vars = {}
if 'vars' in groupcache[groupcounter]:
vars = groupcache[groupcounter]['vars']
last_name = None
for l in ll:
l = [x.strip() for x in l]
if l[0][0] == ',':
l[0] = l[0][1:]
if l[0][0] == '(':
outmess(
'analyzeline: implied-DO list "%s" is not supported. Skipping.\n' % l[0])
continue
i = 0
j = 0
llen = len(l[1])
for v in rmbadname([x.strip() for x in markoutercomma(l[0]).split('@,@')]):
if v[0] == '(':
outmess(
'analyzeline: implied-DO list "%s" is not supported. Skipping.\n' % v)
# XXX: subsequent init expressions may get wrong values.
# Ignoring since data statements are irrelevant for
# wrapping.
continue
fc = 0
while (i < llen) and (fc or not l[1][i] == ','):
if l[1][i] == "'":
fc = not fc
i = i + 1
i = i + 1
if v not in vars:
vars[v] = {}
if '=' in vars[v] and not vars[v]['='] == l[1][j:i - 1]:
outmess('analyzeline: changing init expression of "%s" ("%s") to "%s"\n' % (
v, vars[v]['='], l[1][j:i - 1]))
vars[v]['='] = l[1][j:i - 1]
j = i
last_name = v
groupcache[groupcounter]['vars'] = vars
if last_name is not None:
previous_context = ('variable', last_name, groupcounter)
elif case == 'common':
line = m.group('after').strip()
if not line[0] == '/':
line = '//' + line
cl = []
f = 0
bn = ''
ol = ''
for c in line:
if c == '/':
f = f + 1
continue
if f >= 3:
bn = bn.strip()
if not bn:
bn = '_BLNK_'
cl.append([bn, ol])
f = f - 2
bn = ''
ol = ''
if f % 2:
bn = bn + c
else:
ol = ol + c
bn = bn.strip()
if not bn:
bn = '_BLNK_'
cl.append([bn, ol])
commonkey = {}
if 'common' in groupcache[groupcounter]:
commonkey = groupcache[groupcounter]['common']
for c in cl:
if c[0] not in commonkey:
commonkey[c[0]] = []
for i in [x.strip() for x in markoutercomma(c[1]).split('@,@')]:
if i:
commonkey[c[0]].append(i)
groupcache[groupcounter]['common'] = commonkey
previous_context = ('common', bn, groupcounter)
elif case == 'use':
m1 = re.match(
r'\A\s*(?P<name>\b[\w]+\b)\s*((,(\s*\bonly\b\s*:|(?P<notonly>))\s*(?P<list>.*))|)\s*\Z', m.group('after'), re.I)
if m1:
mm = m1.groupdict()
if 'use' not in groupcache[groupcounter]:
groupcache[groupcounter]['use'] = {}
name = m1.group('name')
groupcache[groupcounter]['use'][name] = {}
isonly = 0
if 'list' in mm and mm['list'] is not None:
if 'notonly' in mm and mm['notonly'] is None:
isonly = 1
groupcache[groupcounter]['use'][name]['only'] = isonly
ll = [x.strip() for x in mm['list'].split(',')]
rl = {}
for l in ll:
if '=' in l:
m2 = re.match(
r'\A\s*(?P<local>\b[\w]+\b)\s*=\s*>\s*(?P<use>\b[\w]+\b)\s*\Z', l, re.I)
if m2:
rl[m2.group('local').strip()] = m2.group(
'use').strip()
else:
outmess(
'analyzeline: Not local=>use pattern found in %s\n' % repr(l))
else:
rl[l] = l
groupcache[groupcounter]['use'][name]['map'] = rl
else:
pass
else:
print(m.groupdict())
outmess('analyzeline: Could not crack the use statement.\n')
elif case in ['f2pyenhancements']:
if 'f2pyenhancements' not in groupcache[groupcounter]:
groupcache[groupcounter]['f2pyenhancements'] = {}
d = groupcache[groupcounter]['f2pyenhancements']
if m.group('this') == 'usercode' and 'usercode' in d:
if isinstance(d['usercode'], str):
d['usercode'] = [d['usercode']]
d['usercode'].append(m.group('after'))
else:
d[m.group('this')] = m.group('after')
elif case == 'multiline':
if previous_context is None:
if verbose:
outmess('analyzeline: No context for multiline block.\n')
return
gc = groupcounter
appendmultiline(groupcache[gc],
previous_context[:2],
m.group('this'))
else:
if verbose > 1:
print(m.groupdict())
outmess('analyzeline: No code implemented for line.\n')
def appendmultiline(group, context_name, ml):
if 'f2pymultilines' not in group:
group['f2pymultilines'] = {}
d = group['f2pymultilines']
if context_name not in d:
d[context_name] = []
d[context_name].append(ml)
return
def cracktypespec0(typespec, ll):
selector = None
attr = None
if re.match(r'double\s*complex', typespec, re.I):
typespec = 'double complex'
elif re.match(r'double\s*precision', typespec, re.I):
typespec = 'double precision'
else:
typespec = typespec.strip().lower()
m1 = selectpattern.match(markouterparen(ll))
if not m1:
outmess(
'cracktypespec0: no kind/char_selector pattern found for line.\n')
return
d = m1.groupdict()
for k in list(d.keys()):
d[k] = unmarkouterparen(d[k])
if typespec in ['complex', 'integer', 'logical', 'real', 'character', 'type']:
selector = d['this']
ll = d['after']
i = ll.find('::')
if i >= 0:
attr = ll[:i].strip()
ll = ll[i + 2:]
return typespec, selector, attr, ll
#####
namepattern = re.compile(r'\s*(?P<name>\b[\w]+\b)\s*(?P<after>.*)\s*\Z', re.I)
kindselector = re.compile(
r'\s*(\(\s*(kind\s*=)?\s*(?P<kind>.*)\s*\)|[*]\s*(?P<kind2>.*?))\s*\Z', re.I)
charselector = re.compile(
r'\s*(\((?P<lenkind>.*)\)|[*]\s*(?P<charlen>.*))\s*\Z', re.I)
lenkindpattern = re.compile(
r'\s*(kind\s*=\s*(?P<kind>.*?)\s*(@,@\s*len\s*=\s*(?P<len>.*)|)|(len\s*=\s*|)(?P<len2>.*?)\s*(@,@\s*(kind\s*=\s*|)(?P<kind2>.*)|))\s*\Z', re.I)
lenarraypattern = re.compile(
r'\s*(@\(@\s*(?!/)\s*(?P<array>.*?)\s*@\)@\s*[*]\s*(?P<len>.*?)|([*]\s*(?P<len2>.*?)|)\s*(@\(@\s*(?!/)\s*(?P<array2>.*?)\s*@\)@|))\s*(=\s*(?P<init>.*?)|(@\(@|)/\s*(?P<init2>.*?)\s*/(@\)@|)|)\s*\Z', re.I)
def removespaces(expr):
expr = expr.strip()
if len(expr) <= 1:
return expr
expr2 = expr[0]
for i in range(1, len(expr) - 1):
if (expr[i] == ' ' and
((expr[i + 1] in "()[]{}=+-/* ") or
(expr[i - 1] in "()[]{}=+-/* "))):
continue
expr2 = expr2 + expr[i]
expr2 = expr2 + expr[-1]
return expr2
def markinnerspaces(line):
l = ''
f = 0
cc = '\''
cb = ''
for c in line:
if cb == '\\' and c in ['\\', '\'', '"']:
l = l + c
cb = c
continue
if f == 0 and c in ['\'', '"']:
cc = c
if c == cc:
f = f + 1
elif c == cc:
f = f - 1
elif c == ' ' and f == 1:
l = l + '@_@'
continue
l = l + c
cb = c
return l
def updatevars(typespec, selector, attrspec, entitydecl):
global groupcache, groupcounter
last_name = None
kindselect, charselect, typename = cracktypespec(typespec, selector)
if attrspec:
attrspec = [x.strip() for x in markoutercomma(attrspec).split('@,@')]
l = []
c = re.compile(r'(?P<start>[a-zA-Z]+)')
for a in attrspec:
if not a:
continue
m = c.match(a)
if m:
s = m.group('start').lower()
a = s + a[len(s):]
l.append(a)
attrspec = l
el = [x.strip() for x in markoutercomma(entitydecl).split('@,@')]
el1 = []
for e in el:
for e1 in [x.strip() for x in markoutercomma(removespaces(markinnerspaces(e)), comma=' ').split('@ @')]:
if e1:
el1.append(e1.replace('@_@', ' '))
for e in el1:
m = namepattern.match(e)
if not m:
outmess(
'updatevars: no name pattern found for entity=%s. Skipping.\n' % (repr(e)))
continue
ename = rmbadname1(m.group('name'))
edecl = {}
if ename in groupcache[groupcounter]['vars']:
edecl = groupcache[groupcounter]['vars'][ename].copy()
not_has_typespec = 'typespec' not in edecl
if not_has_typespec:
edecl['typespec'] = typespec
elif typespec and (not typespec == edecl['typespec']):
outmess('updatevars: attempt to change the type of "%s" ("%s") to "%s". Ignoring.\n' % (
ename, edecl['typespec'], typespec))
if 'kindselector' not in edecl:
edecl['kindselector'] = copy.copy(kindselect)
elif kindselect:
for k in list(kindselect.keys()):
if k in edecl['kindselector'] and (not kindselect[k] == edecl['kindselector'][k]):
outmess('updatevars: attempt to change the kindselector "%s" of "%s" ("%s") to "%s". Ignoring.\n' % (
k, ename, edecl['kindselector'][k], kindselect[k]))
else:
edecl['kindselector'][k] = copy.copy(kindselect[k])
if 'charselector' not in edecl and charselect:
if not_has_typespec:
edecl['charselector'] = charselect
else:
errmess('updatevars:%s: attempt to change empty charselector to %r. Ignoring.\n'
% (ename, charselect))
elif charselect:
for k in list(charselect.keys()):
if k in edecl['charselector'] and (not charselect[k] == edecl['charselector'][k]):
outmess('updatevars: attempt to change the charselector "%s" of "%s" ("%s") to "%s". Ignoring.\n' % (
k, ename, edecl['charselector'][k], charselect[k]))
else:
edecl['charselector'][k] = copy.copy(charselect[k])
if 'typename' not in edecl:
edecl['typename'] = typename
elif typename and (not edecl['typename'] == typename):
outmess('updatevars: attempt to change the typename of "%s" ("%s") to "%s". Ignoring.\n' % (
ename, edecl['typename'], typename))
if 'attrspec' not in edecl:
edecl['attrspec'] = copy.copy(attrspec)
elif attrspec:
for a in attrspec:
if a not in edecl['attrspec']:
edecl['attrspec'].append(a)
else:
edecl['typespec'] = copy.copy(typespec)
edecl['kindselector'] = copy.copy(kindselect)
edecl['charselector'] = copy.copy(charselect)
edecl['typename'] = typename
edecl['attrspec'] = copy.copy(attrspec)
if m.group('after'):
m1 = lenarraypattern.match(markouterparen(m.group('after')))
if m1:
d1 = m1.groupdict()
for lk in ['len', 'array', 'init']:
if d1[lk + '2'] is not None:
d1[lk] = d1[lk + '2']
del d1[lk + '2']
for k in list(d1.keys()):
if d1[k] is not None:
d1[k] = unmarkouterparen(d1[k])
else:
del d1[k]
if 'len' in d1 and 'array' in d1:
if d1['len'] == '':
d1['len'] = d1['array']
del d1['array']
else:
d1['array'] = d1['array'] + ',' + d1['len']
del d1['len']
errmess('updatevars: "%s %s" is mapped to "%s %s(%s)"\n' % (
typespec, e, typespec, ename, d1['array']))
if 'array' in d1:
dm = 'dimension(%s)' % d1['array']
if 'attrspec' not in edecl or (not edecl['attrspec']):
edecl['attrspec'] = [dm]
else:
edecl['attrspec'].append(dm)
for dm1 in edecl['attrspec']:
if dm1[:9] == 'dimension' and dm1 != dm:
del edecl['attrspec'][-1]
errmess('updatevars:%s: attempt to change %r to %r. Ignoring.\n'
% (ename, dm1, dm))
break
if 'len' in d1:
if typespec in ['complex', 'integer', 'logical', 'real']:
if ('kindselector' not in edecl) or (not edecl['kindselector']):
edecl['kindselector'] = {}
edecl['kindselector']['*'] = d1['len']
elif typespec == 'character':
if ('charselector' not in edecl) or (not edecl['charselector']):
edecl['charselector'] = {}
if 'len' in edecl['charselector']:
del edecl['charselector']['len']
edecl['charselector']['*'] = d1['len']
if 'init' in d1:
if '=' in edecl and (not edecl['='] == d1['init']):
outmess('updatevars: attempt to change the init expression of "%s" ("%s") to "%s". Ignoring.\n' % (
ename, edecl['='], d1['init']))
else:
edecl['='] = d1['init']
else:
outmess('updatevars: could not crack entity declaration "%s". Ignoring.\n' % (
ename + m.group('after')))
for k in list(edecl.keys()):
if not edecl[k]:
del edecl[k]
groupcache[groupcounter]['vars'][ename] = edecl
if 'varnames' in groupcache[groupcounter]:
groupcache[groupcounter]['varnames'].append(ename)
last_name = ename
return last_name
def cracktypespec(typespec, selector):
kindselect = None
charselect = None
typename = None
if selector:
if typespec in ['complex', 'integer', 'logical', 'real']:
kindselect = kindselector.match(selector)
if not kindselect:
outmess(
'cracktypespec: no kindselector pattern found for %s\n' % (repr(selector)))
return
kindselect = kindselect.groupdict()
kindselect['*'] = kindselect['kind2']
del kindselect['kind2']
for k in list(kindselect.keys()):
if not kindselect[k]:
del kindselect[k]
for k, i in list(kindselect.items()):
kindselect[k] = rmbadname1(i)
elif typespec == 'character':
charselect = charselector.match(selector)
if not charselect:
outmess(
'cracktypespec: no charselector pattern found for %s\n' % (repr(selector)))
return
charselect = charselect.groupdict()
charselect['*'] = charselect['charlen']
del charselect['charlen']
if charselect['lenkind']:
lenkind = lenkindpattern.match(
markoutercomma(charselect['lenkind']))
lenkind = lenkind.groupdict()
for lk in ['len', 'kind']:
if lenkind[lk + '2']:
lenkind[lk] = lenkind[lk + '2']
charselect[lk] = lenkind[lk]
del lenkind[lk + '2']
del charselect['lenkind']
for k in list(charselect.keys()):
if not charselect[k]:
del charselect[k]
for k, i in list(charselect.items()):
charselect[k] = rmbadname1(i)
elif typespec == 'type':
typename = re.match(r'\s*\(\s*(?P<name>\w+)\s*\)', selector, re.I)
if typename:
typename = typename.group('name')
else:
outmess('cracktypespec: no typename found in %s\n' %
(repr(typespec + selector)))
else:
outmess('cracktypespec: no selector used for %s\n' %
(repr(selector)))
return kindselect, charselect, typename
######
def setattrspec(decl, attr, force=0):
if not decl:
decl = {}
if not attr:
return decl
if 'attrspec' not in decl:
decl['attrspec'] = [attr]
return decl
if force:
decl['attrspec'].append(attr)
if attr in decl['attrspec']:
return decl
if attr == 'static' and 'automatic' not in decl['attrspec']:
decl['attrspec'].append(attr)
elif attr == 'automatic' and 'static' not in decl['attrspec']:
decl['attrspec'].append(attr)
elif attr == 'public' and 'private' not in decl['attrspec']:
decl['attrspec'].append(attr)
elif attr == 'private' and 'public' not in decl['attrspec']:
decl['attrspec'].append(attr)
else:
decl['attrspec'].append(attr)
return decl
def setkindselector(decl, sel, force=0):
if not decl:
decl = {}
if not sel:
return decl
if 'kindselector' not in decl:
decl['kindselector'] = sel
return decl
for k in list(sel.keys()):
if force or k not in decl['kindselector']:
decl['kindselector'][k] = sel[k]
return decl
def setcharselector(decl, sel, force=0):
if not decl:
decl = {}
if not sel:
return decl
if 'charselector' not in decl:
decl['charselector'] = sel
return decl
for k in list(sel.keys()):
if force or k not in decl['charselector']:
decl['charselector'][k] = sel[k]
return decl
def getblockname(block, unknown='unknown'):
if 'name' in block:
return block['name']
return unknown
# post processing
def setmesstext(block):
global filepositiontext
try:
filepositiontext = 'In: %s:%s\n' % (block['from'], block['name'])
except Exception:
pass
def get_usedict(block):
usedict = {}
if 'parent_block' in block:
usedict = get_usedict(block['parent_block'])
if 'use' in block:
usedict.update(block['use'])
return usedict
def get_useparameters(block, param_map=None):
global f90modulevars
if param_map is None:
param_map = {}
usedict = get_usedict(block)
if not usedict:
return param_map
for usename, mapping in list(usedict.items()):
usename = usename.lower()
if usename not in f90modulevars:
outmess('get_useparameters: no module %s info used by %s\n' %
(usename, block.get('name')))
continue
mvars = f90modulevars[usename]
params = get_parameters(mvars)
if not params:
continue
# XXX: apply mapping
if mapping:
errmess('get_useparameters: mapping for %s not impl.' % (mapping))
for k, v in list(params.items()):
if k in param_map:
outmess('get_useparameters: overriding parameter %s with'
' value from module %s' % (repr(k), repr(usename)))
param_map[k] = v
return param_map
def postcrack2(block, tab='', param_map=None):
global f90modulevars
if not f90modulevars:
return block
if isinstance(block, list):
ret = []
for g in block:
g = postcrack2(g, tab=tab + '\t', param_map=param_map)
ret.append(g)
return ret
setmesstext(block)
outmess('%sBlock: %s\n' % (tab, block['name']), 0)
if param_map is None:
param_map = get_useparameters(block)
if param_map is not None and 'vars' in block:
vars = block['vars']
for n in list(vars.keys()):
var = vars[n]
if 'kindselector' in var:
kind = var['kindselector']
if 'kind' in kind:
val = kind['kind']
if val in param_map:
kind['kind'] = param_map[val]
new_body = []
for b in block['body']:
b = postcrack2(b, tab=tab + '\t', param_map=param_map)
new_body.append(b)
block['body'] = new_body
return block
def postcrack(block, args=None, tab=''):
"""
TODO:
function return values
determine expression types if in argument list
"""
global usermodules, onlyfunctions
if isinstance(block, list):
gret = []
uret = []
for g in block:
setmesstext(g)
g = postcrack(g, tab=tab + '\t')
# sort user routines to appear first
if 'name' in g and '__user__' in g['name']:
uret.append(g)
else:
gret.append(g)
return uret + gret
setmesstext(block)
if not isinstance(block, dict) and 'block' not in block:
raise Exception('postcrack: Expected block dictionary instead of ' +
str(block))
if 'name' in block and not block['name'] == 'unknown_interface':
outmess('%sBlock: %s\n' % (tab, block['name']), 0)
block = analyzeargs(block)
block = analyzecommon(block)
block['vars'] = analyzevars(block)
block['sortvars'] = sortvarnames(block['vars'])
if 'args' in block and block['args']:
args = block['args']
block['body'] = analyzebody(block, args, tab=tab)
userisdefined = []
if 'use' in block:
useblock = block['use']
for k in list(useblock.keys()):
if '__user__' in k:
userisdefined.append(k)
else:
useblock = {}
name = ''
if 'name' in block:
name = block['name']
# and not userisdefined: # Build a __user__ module
if 'externals' in block and block['externals']:
interfaced = []
if 'interfaced' in block:
interfaced = block['interfaced']
mvars = copy.copy(block['vars'])
if name:
mname = name + '__user__routines'
else:
mname = 'unknown__user__routines'
if mname in userisdefined:
i = 1
while '%s_%i' % (mname, i) in userisdefined:
i = i + 1
mname = '%s_%i' % (mname, i)
interface = {'block': 'interface', 'body': [],
'vars': {}, 'name': name + '_user_interface'}
for e in block['externals']:
if e in interfaced:
edef = []
j = -1
for b in block['body']:
j = j + 1
if b['block'] == 'interface':
i = -1
for bb in b['body']:
i = i + 1
if 'name' in bb and bb['name'] == e:
edef = copy.copy(bb)
del b['body'][i]
break
if edef:
if not b['body']:
del block['body'][j]
del interfaced[interfaced.index(e)]
break
interface['body'].append(edef)
else:
if e in mvars and not isexternal(mvars[e]):
interface['vars'][e] = mvars[e]
if interface['vars'] or interface['body']:
block['interfaced'] = interfaced
mblock = {'block': 'python module', 'body': [
interface], 'vars': {}, 'name': mname, 'interfaced': block['externals']}
useblock[mname] = {}
usermodules.append(mblock)
if useblock:
block['use'] = useblock
return block
def sortvarnames(vars):
indep = []
dep = []
for v in list(vars.keys()):
if 'depend' in vars[v] and vars[v]['depend']:
dep.append(v)
else:
indep.append(v)
n = len(dep)
i = 0
while dep: # XXX: How to catch dependence cycles correctly?
v = dep[0]
fl = 0
for w in dep[1:]:
if w in vars[v]['depend']:
fl = 1
break
if fl:
dep = dep[1:] + [v]
i = i + 1
if i > n:
errmess('sortvarnames: failed to compute dependencies because'
' of cyclic dependencies between '
+ ', '.join(dep) + '\n')
indep = indep + dep
break
else:
indep.append(v)
dep = dep[1:]
n = len(dep)
i = 0
return indep
def analyzecommon(block):
if not hascommon(block):
return block
commonvars = []
for k in list(block['common'].keys()):
comvars = []
for e in block['common'][k]:
m = re.match(
r'\A\s*\b(?P<name>.*?)\b\s*(\((?P<dims>.*?)\)|)\s*\Z', e, re.I)
if m:
dims = []
if m.group('dims'):
dims = [x.strip()
for x in markoutercomma(m.group('dims')).split('@,@')]
n = rmbadname1(m.group('name').strip())
if n in block['vars']:
if 'attrspec' in block['vars'][n]:
block['vars'][n]['attrspec'].append(
'dimension(%s)' % (','.join(dims)))
else:
block['vars'][n]['attrspec'] = [
'dimension(%s)' % (','.join(dims))]
else:
if dims:
block['vars'][n] = {
'attrspec': ['dimension(%s)' % (','.join(dims))]}
else:
block['vars'][n] = {}
if n not in commonvars:
commonvars.append(n)
else:
n = e
errmess(
'analyzecommon: failed to extract "<name>[(<dims>)]" from "%s" in common /%s/.\n' % (e, k))
comvars.append(n)
block['common'][k] = comvars
if 'commonvars' not in block:
block['commonvars'] = commonvars
else:
block['commonvars'] = block['commonvars'] + commonvars
return block
def analyzebody(block, args, tab=''):
global usermodules, skipfuncs, onlyfuncs, f90modulevars
setmesstext(block)
body = []
for b in block['body']:
b['parent_block'] = block
if b['block'] in ['function', 'subroutine']:
if args is not None and b['name'] not in args:
continue
else:
as_ = b['args']
if b['name'] in skipfuncs:
continue
if onlyfuncs and b['name'] not in onlyfuncs:
continue
b['saved_interface'] = crack2fortrangen(
b, '\n' + ' ' * 6, as_interface=True)
else:
as_ = args
b = postcrack(b, as_, tab=tab + '\t')
if b['block'] == 'interface' and not b['body']:
if 'f2pyenhancements' not in b:
continue
if b['block'].replace(' ', '') == 'pythonmodule':
usermodules.append(b)
else:
if b['block'] == 'module':
f90modulevars[b['name']] = b['vars']
body.append(b)
return body
def buildimplicitrules(block):
setmesstext(block)
implicitrules = defaultimplicitrules
attrrules = {}
if 'implicit' in block:
if block['implicit'] is None:
implicitrules = None
if verbose > 1:
outmess(
'buildimplicitrules: no implicit rules for routine %s.\n' % repr(block['name']))
else:
for k in list(block['implicit'].keys()):
if block['implicit'][k].get('typespec') not in ['static', 'automatic']:
implicitrules[k] = block['implicit'][k]
else:
attrrules[k] = block['implicit'][k]['typespec']
return implicitrules, attrrules
def myeval(e, g=None, l=None):
r = eval(e, g, l)
if type(r) in [type(0), type(0.0)]:
return r
raise ValueError('r=%r' % (r))
getlincoef_re_1 = re.compile(r'\A\b\w+\b\Z', re.I)
def getlincoef(e, xset): # e = a*x+b ; x in xset
try:
c = int(myeval(e, {}, {}))
return 0, c, None
except Exception:
pass
if getlincoef_re_1.match(e):
return 1, 0, e
len_e = len(e)
for x in xset:
if len(x) > len_e:
continue
if re.search(r'\w\s*\([^)]*\b' + x + r'\b', e):
# skip function calls having x as an argument, e.g max(1, x)
continue
re_1 = re.compile(r'(?P<before>.*?)\b' + x + r'\b(?P<after>.*)', re.I)
m = re_1.match(e)
if m:
try:
m1 = re_1.match(e)
while m1:
ee = '%s(%s)%s' % (
m1.group('before'), 0, m1.group('after'))
m1 = re_1.match(ee)
b = myeval(ee, {}, {})
m1 = re_1.match(e)
while m1:
ee = '%s(%s)%s' % (
m1.group('before'), 1, m1.group('after'))
m1 = re_1.match(ee)
a = myeval(ee, {}, {}) - b
m1 = re_1.match(e)
while m1:
ee = '%s(%s)%s' % (
m1.group('before'), 0.5, m1.group('after'))
m1 = re_1.match(ee)
c = myeval(ee, {}, {})
# computing another point to be sure that expression is linear
m1 = re_1.match(e)
while m1:
ee = '%s(%s)%s' % (
m1.group('before'), 1.5, m1.group('after'))
m1 = re_1.match(ee)
c2 = myeval(ee, {}, {})
if (a * 0.5 + b == c and a * 1.5 + b == c2):
return a, b, x
except Exception:
pass
break
return None, None, None
_varname_match = re.compile(r'\A[a-z]\w*\Z').match
def getarrlen(dl, args, star='*'):
edl = []
try:
edl.append(myeval(dl[0], {}, {}))
except Exception:
edl.append(dl[0])
try:
edl.append(myeval(dl[1], {}, {}))
except Exception:
edl.append(dl[1])
if isinstance(edl[0], int):
p1 = 1 - edl[0]
if p1 == 0:
d = str(dl[1])
elif p1 < 0:
d = '%s-%s' % (dl[1], -p1)
else:
d = '%s+%s' % (dl[1], p1)
elif isinstance(edl[1], int):
p1 = 1 + edl[1]
if p1 == 0:
d = '-(%s)' % (dl[0])
else:
d = '%s-(%s)' % (p1, dl[0])
else:
d = '%s-(%s)+1' % (dl[1], dl[0])
try:
return repr(myeval(d, {}, {})), None, None
except Exception:
pass
d1, d2 = getlincoef(dl[0], args), getlincoef(dl[1], args)
if None not in [d1[0], d2[0]]:
if (d1[0], d2[0]) == (0, 0):
return repr(d2[1] - d1[1] + 1), None, None
b = d2[1] - d1[1] + 1
d1 = (d1[0], 0, d1[2])
d2 = (d2[0], b, d2[2])
if d1[0] == 0 and d2[2] in args:
if b < 0:
return '%s * %s - %s' % (d2[0], d2[2], -b), d2[2], '+%s)/(%s)' % (-b, d2[0])
elif b:
return '%s * %s + %s' % (d2[0], d2[2], b), d2[2], '-%s)/(%s)' % (b, d2[0])
else:
return '%s * %s' % (d2[0], d2[2]), d2[2], ')/(%s)' % (d2[0])
if d2[0] == 0 and d1[2] in args:
if b < 0:
return '%s * %s - %s' % (-d1[0], d1[2], -b), d1[2], '+%s)/(%s)' % (-b, -d1[0])
elif b:
return '%s * %s + %s' % (-d1[0], d1[2], b), d1[2], '-%s)/(%s)' % (b, -d1[0])
else:
return '%s * %s' % (-d1[0], d1[2]), d1[2], ')/(%s)' % (-d1[0])
if d1[2] == d2[2] and d1[2] in args:
a = d2[0] - d1[0]
if not a:
return repr(b), None, None
if b < 0:
return '%s * %s - %s' % (a, d1[2], -b), d2[2], '+%s)/(%s)' % (-b, a)
elif b:
return '%s * %s + %s' % (a, d1[2], b), d2[2], '-%s)/(%s)' % (b, a)
else:
return '%s * %s' % (a, d1[2]), d2[2], ')/(%s)' % (a)
if d1[0] == d2[0] == 1:
c = str(d1[2])
if c not in args:
if _varname_match(c):
outmess('\tgetarrlen:variable "%s" undefined\n' % (c))
c = '(%s)' % c
if b == 0:
d = '%s-%s' % (d2[2], c)
elif b < 0:
d = '%s-%s-%s' % (d2[2], c, -b)
else:
d = '%s-%s+%s' % (d2[2], c, b)
elif d1[0] == 0:
c2 = str(d2[2])
if c2 not in args:
if _varname_match(c2):
outmess('\tgetarrlen:variable "%s" undefined\n' % (c2))
c2 = '(%s)' % c2
if d2[0] == 1:
pass
elif d2[0] == -1:
c2 = '-%s' % c2
else:
c2 = '%s*%s' % (d2[0], c2)
if b == 0:
d = c2
elif b < 0:
d = '%s-%s' % (c2, -b)
else:
d = '%s+%s' % (c2, b)
elif d2[0] == 0:
c1 = str(d1[2])
if c1 not in args:
if _varname_match(c1):
outmess('\tgetarrlen:variable "%s" undefined\n' % (c1))
c1 = '(%s)' % c1
if d1[0] == 1:
c1 = '-%s' % c1
elif d1[0] == -1:
c1 = '+%s' % c1
elif d1[0] < 0:
c1 = '+%s*%s' % (-d1[0], c1)
else:
c1 = '-%s*%s' % (d1[0], c1)
if b == 0:
d = c1
elif b < 0:
d = '%s-%s' % (c1, -b)
else:
d = '%s+%s' % (c1, b)
else:
c1 = str(d1[2])
if c1 not in args:
if _varname_match(c1):
outmess('\tgetarrlen:variable "%s" undefined\n' % (c1))
c1 = '(%s)' % c1
if d1[0] == 1:
c1 = '-%s' % c1
elif d1[0] == -1:
c1 = '+%s' % c1
elif d1[0] < 0:
c1 = '+%s*%s' % (-d1[0], c1)
else:
c1 = '-%s*%s' % (d1[0], c1)
c2 = str(d2[2])
if c2 not in args:
if _varname_match(c2):
outmess('\tgetarrlen:variable "%s" undefined\n' % (c2))
c2 = '(%s)' % c2
if d2[0] == 1:
pass
elif d2[0] == -1:
c2 = '-%s' % c2
else:
c2 = '%s*%s' % (d2[0], c2)
if b == 0:
d = '%s%s' % (c2, c1)
elif b < 0:
d = '%s%s-%s' % (c2, c1, -b)
else:
d = '%s%s+%s' % (c2, c1, b)
return d, None, None
word_pattern = re.compile(r'\b[a-z][\w$]*\b', re.I)
def _get_depend_dict(name, vars, deps):
if name in vars:
words = vars[name].get('depend', [])
if '=' in vars[name] and not isstring(vars[name]):
for word in word_pattern.findall(vars[name]['=']):
if word not in words and word in vars:
words.append(word)
for word in words[:]:
for w in deps.get(word, []) \
or _get_depend_dict(word, vars, deps):
if w not in words:
words.append(w)
else:
outmess('_get_depend_dict: no dependence info for %s\n' % (repr(name)))
words = []
deps[name] = words
return words
def _calc_depend_dict(vars):
names = list(vars.keys())
depend_dict = {}
for n in names:
_get_depend_dict(n, vars, depend_dict)
return depend_dict
def get_sorted_names(vars):
"""
"""
depend_dict = _calc_depend_dict(vars)
names = []
for name in list(depend_dict.keys()):
if not depend_dict[name]:
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:
names.append(name)
del depend_dict[name]
else:
depend_dict[name] = new_lst
return [name for name in names if name in vars]
def _kind_func(string):
# XXX: return something sensible.
if string[0] in "'\"":
string = string[1:-1]
if real16pattern.match(string):
return 8
elif real8pattern.match(string):
return 4
return 'kind(' + string + ')'
def _selected_int_kind_func(r):
# XXX: This should be processor dependent
m = 10 ** r
if m <= 2 ** 8:
return 1
if m <= 2 ** 16:
return 2
if m <= 2 ** 32:
return 4
if m <= 2 ** 63:
return 8
if m <= 2 ** 128:
return 16
return -1
def _selected_real_kind_func(p, r=0, radix=0):
# XXX: This should be processor dependent
# This is only good for 0 <= p <= 20
if p < 7:
return 4
if p < 16:
return 8
machine = platform.machine().lower()
if machine.startswith('power') or machine.startswith('ppc64'):
if p <= 20:
return 16
else:
if p < 19:
return 10
elif p <= 20:
return 16
return -1
def get_parameters(vars, global_params={}):
params = copy.copy(global_params)
g_params = copy.copy(global_params)
for name, func in [('kind', _kind_func),
('selected_int_kind', _selected_int_kind_func),
('selected_real_kind', _selected_real_kind_func), ]:
if name not in g_params:
g_params[name] = func
param_names = []
for n in get_sorted_names(vars):
if 'attrspec' in vars[n] and 'parameter' in vars[n]['attrspec']:
param_names.append(n)
kind_re = re.compile(r'\bkind\s*\(\s*(?P<value>.*)\s*\)', re.I)
selected_int_kind_re = re.compile(
r'\bselected_int_kind\s*\(\s*(?P<value>.*)\s*\)', re.I)
selected_kind_re = re.compile(
r'\bselected_(int|real)_kind\s*\(\s*(?P<value>.*)\s*\)', re.I)
for n in param_names:
if '=' in vars[n]:
v = vars[n]['=']
if islogical(vars[n]):
v = v.lower()
for repl in [
('.false.', 'False'),
('.true.', 'True'),
# TODO: test .eq., .neq., etc replacements.
]:
v = v.replace(*repl)
v = kind_re.sub(r'kind("\1")', v)
v = selected_int_kind_re.sub(r'selected_int_kind(\1)', v)
# We need to act according to the data.
# The easy case is if the data has a kind-specifier,
# then we may easily remove those specifiers.
# However, it may be that the user uses other specifiers...(!)
is_replaced = False
if 'kindselector' in vars[n]:
if 'kind' in vars[n]['kindselector']:
orig_v_len = len(v)
v = v.replace('_' + vars[n]['kindselector']['kind'], '')
# Again, this will be true if even a single specifier
# has been replaced, see comment above.
is_replaced = len(v) < orig_v_len
if not is_replaced:
if not selected_kind_re.match(v):
v_ = v.split('_')
# In case there are additive parameters
if len(v_) > 1:
v = ''.join(v_[:-1]).lower().replace(v_[-1].lower(), '')
# Currently this will not work for complex numbers.
# There is missing code for extracting a complex number,
# which may be defined in either of these:
# a) (Re, Im)
# b) cmplx(Re, Im)
# c) dcmplx(Re, Im)
# d) cmplx(Re, Im, <prec>)
if isdouble(vars[n]):
tt = list(v)
for m in real16pattern.finditer(v):
tt[m.start():m.end()] = list(
v[m.start():m.end()].lower().replace('d', 'e'))
v = ''.join(tt)
elif iscomplex(vars[n]):
# FIXME complex numbers may also have exponents
if v[0] == '(' and v[-1] == ')':
# FIXME, unused l looks like potential bug
l = markoutercomma(v[1:-1]).split('@,@')
try:
params[n] = eval(v, g_params, params)
except Exception as msg:
params[n] = v
outmess('get_parameters: got "%s" on %s\n' % (msg, repr(v)))
if isstring(vars[n]) and isinstance(params[n], int):
params[n] = chr(params[n])
nl = n.lower()
if nl != n:
params[nl] = params[n]
else:
print(vars[n])
outmess(
'get_parameters:parameter %s does not have value?!\n' % (repr(n)))
return params
def _eval_length(length, params):
if length in ['(:)', '(*)', '*']:
return '(*)'
return _eval_scalar(length, params)
_is_kind_number = re.compile(r'\d+_').match
def _eval_scalar(value, params):
if _is_kind_number(value):
value = value.split('_')[0]
try:
value = str(eval(value, {}, params))
except (NameError, SyntaxError):
return value
except Exception as msg:
errmess('"%s" in evaluating %r '
'(available names: %s)\n'
% (msg, value, list(params.keys())))
return value
def analyzevars(block):
global f90modulevars
setmesstext(block)
implicitrules, attrrules = buildimplicitrules(block)
vars = copy.copy(block['vars'])
if block['block'] == 'function' and block['name'] not in vars:
vars[block['name']] = {}
if '' in block['vars']:
del vars['']
if 'attrspec' in block['vars']['']:
gen = block['vars']['']['attrspec']
for n in list(vars.keys()):
for k in ['public', 'private']:
if k in gen:
vars[n] = setattrspec(vars[n], k)
svars = []
args = block['args']
for a in args:
try:
vars[a]
svars.append(a)
except KeyError:
pass
for n in list(vars.keys()):
if n not in args:
svars.append(n)
params = get_parameters(vars, get_useparameters(block))
dep_matches = {}
name_match = re.compile(r'\w[\w\d_$]*').match
for v in list(vars.keys()):
m = name_match(v)
if m:
n = v[m.start():m.end()]
try:
dep_matches[n]
except KeyError:
dep_matches[n] = re.compile(r'.*\b%s\b' % (v), re.I).match
for n in svars:
if n[0] in list(attrrules.keys()):
vars[n] = setattrspec(vars[n], attrrules[n[0]])
if 'typespec' not in vars[n]:
if not('attrspec' in vars[n] and 'external' in vars[n]['attrspec']):
if implicitrules:
ln0 = n[0].lower()
for k in list(implicitrules[ln0].keys()):
if k == 'typespec' and implicitrules[ln0][k] == 'undefined':
continue
if k not in vars[n]:
vars[n][k] = implicitrules[ln0][k]
elif k == 'attrspec':
for l in implicitrules[ln0][k]:
vars[n] = setattrspec(vars[n], l)
elif n in block['args']:
outmess('analyzevars: typespec of variable %s is not defined in routine %s.\n' % (
repr(n), block['name']))
if 'charselector' in vars[n]:
if 'len' in vars[n]['charselector']:
l = vars[n]['charselector']['len']
try:
l = str(eval(l, {}, params))
except Exception:
pass
vars[n]['charselector']['len'] = l
if 'kindselector' in vars[n]:
if 'kind' in vars[n]['kindselector']:
l = vars[n]['kindselector']['kind']
try:
l = str(eval(l, {}, params))
except Exception:
pass
vars[n]['kindselector']['kind'] = l
savelindims = {}
if 'attrspec' in vars[n]:
attr = vars[n]['attrspec']
attr.reverse()
vars[n]['attrspec'] = []
dim, intent, depend, check, note = None, None, None, None, None
for a in attr:
if a[:9] == 'dimension':
dim = (a[9:].strip())[1:-1]
elif a[:6] == 'intent':
intent = (a[6:].strip())[1:-1]
elif a[:6] == 'depend':
depend = (a[6:].strip())[1:-1]
elif a[:5] == 'check':
check = (a[5:].strip())[1:-1]
elif a[:4] == 'note':
note = (a[4:].strip())[1:-1]
else:
vars[n] = setattrspec(vars[n], a)
if intent:
if 'intent' not in vars[n]:
vars[n]['intent'] = []
for c in [x.strip() for x in markoutercomma(intent).split('@,@')]:
# Remove spaces so that 'in out' becomes 'inout'
tmp = c.replace(' ', '')
if tmp not in vars[n]['intent']:
vars[n]['intent'].append(tmp)
intent = None
if note:
note = note.replace('\\n\\n', '\n\n')
note = note.replace('\\n ', '\n')
if 'note' not in vars[n]:
vars[n]['note'] = [note]
else:
vars[n]['note'].append(note)
note = None
if depend is not None:
if 'depend' not in vars[n]:
vars[n]['depend'] = []
for c in rmbadname([x.strip() for x in markoutercomma(depend).split('@,@')]):
if c not in vars[n]['depend']:
vars[n]['depend'].append(c)
depend = None
if check is not None:
if 'check' not in vars[n]:
vars[n]['check'] = []
for c in [x.strip() for x in markoutercomma(check).split('@,@')]:
if c not in vars[n]['check']:
vars[n]['check'].append(c)
check = None
if dim and 'dimension' not in vars[n]:
vars[n]['dimension'] = []
for d in rmbadname([x.strip() for x in markoutercomma(dim).split('@,@')]):
star = '*'
if d == ':':
star = ':'
if d in params:
d = str(params[d])
for p in list(params.keys()):
re_1 = re.compile(r'(?P<before>.*?)\b' + p + r'\b(?P<after>.*)', re.I)
m = re_1.match(d)
while m:
d = m.group('before') + \
str(params[p]) + m.group('after')
m = re_1.match(d)
if d == star:
dl = [star]
else:
dl = markoutercomma(d, ':').split('@:@')
if len(dl) == 2 and '*' in dl: # e.g. dimension(5:*)
dl = ['*']
d = '*'
if len(dl) == 1 and not dl[0] == star:
dl = ['1', dl[0]]
if len(dl) == 2:
d, v, di = getarrlen(dl, list(block['vars'].keys()))
if d[:4] == '1 * ':
d = d[4:]
if di and di[-4:] == '/(1)':
di = di[:-4]
if v:
savelindims[d] = v, di
vars[n]['dimension'].append(d)
if 'dimension' in vars[n]:
if isintent_c(vars[n]):
shape_macro = 'shape'
else:
shape_macro = 'shape' # 'fshape'
if isstringarray(vars[n]):
if 'charselector' in vars[n]:
d = vars[n]['charselector']
if '*' in d:
d = d['*']
errmess('analyzevars: character array "character*%s %s(%s)" is considered as "character %s(%s)"; "intent(c)" is forced.\n'
% (d, n,
','.join(vars[n]['dimension']),
n, ','.join(vars[n]['dimension'] + [d])))
vars[n]['dimension'].append(d)
del vars[n]['charselector']
if 'intent' not in vars[n]:
vars[n]['intent'] = []
if 'c' not in vars[n]['intent']:
vars[n]['intent'].append('c')
else:
errmess(
"analyzevars: charselector=%r unhandled." % (d))
if 'check' not in vars[n] and 'args' in block and n in block['args']:
flag = 'depend' not in vars[n]
if flag:
vars[n]['depend'] = []
vars[n]['check'] = []
if 'dimension' in vars[n]:
#/----< no check
i = -1
ni = len(vars[n]['dimension'])
for d in vars[n]['dimension']:
ddeps = [] # dependecies of 'd'
ad = ''
pd = ''
if d not in vars:
if d in savelindims:
pd, ad = '(', savelindims[d][1]
d = savelindims[d][0]
else:
for r in block['args']:
if r not in vars:
continue
if re.match(r'.*?\b' + r + r'\b', d, re.I):
ddeps.append(r)
if d in vars:
if 'attrspec' in vars[d]:
for aa in vars[d]['attrspec']:
if aa[:6] == 'depend':
ddeps += aa[6:].strip()[1:-1].split(',')
if 'depend' in vars[d]:
ddeps = ddeps + vars[d]['depend']
i = i + 1
if d in vars and ('depend' not in vars[d]) \
and ('=' not in vars[d]) and (d not in vars[n]['depend']) \
and l_or(isintent_in, isintent_inout, isintent_inplace)(vars[n]):
vars[d]['depend'] = [n]
if ni > 1:
vars[d]['='] = '%s%s(%s,%s)%s' % (
pd, shape_macro, n, i, ad)
else:
vars[d]['='] = '%slen(%s)%s' % (pd, n, ad)
# /---< no check
if 1 and 'check' not in vars[d]:
if ni > 1:
vars[d]['check'] = ['%s%s(%s,%i)%s==%s'
% (pd, shape_macro, n, i, ad, d)]
else:
vars[d]['check'] = [
'%slen(%s)%s>=%s' % (pd, n, ad, d)]
if 'attrspec' not in vars[d]:
vars[d]['attrspec'] = ['optional']
if ('optional' not in vars[d]['attrspec']) and\
('required' not in vars[d]['attrspec']):
vars[d]['attrspec'].append('optional')
elif d not in ['*', ':']:
#/----< no check
if flag:
if d in vars:
if n not in ddeps:
vars[n]['depend'].append(d)
else:
vars[n]['depend'] = vars[n]['depend'] + ddeps
elif isstring(vars[n]):
length = '1'
if 'charselector' in vars[n]:
if '*' in vars[n]['charselector']:
length = _eval_length(vars[n]['charselector']['*'],
params)
vars[n]['charselector']['*'] = length
elif 'len' in vars[n]['charselector']:
length = _eval_length(vars[n]['charselector']['len'],
params)
del vars[n]['charselector']['len']
vars[n]['charselector']['*'] = length
if not vars[n]['check']:
del vars[n]['check']
if flag and not vars[n]['depend']:
del vars[n]['depend']
if '=' in vars[n]:
if 'attrspec' not in vars[n]:
vars[n]['attrspec'] = []
if ('optional' not in vars[n]['attrspec']) and \
('required' not in vars[n]['attrspec']):
vars[n]['attrspec'].append('optional')
if 'depend' not in vars[n]:
vars[n]['depend'] = []
for v, m in list(dep_matches.items()):
if m(vars[n]['=']):
vars[n]['depend'].append(v)
if not vars[n]['depend']:
del vars[n]['depend']
if isscalar(vars[n]):
vars[n]['='] = _eval_scalar(vars[n]['='], params)
for n in list(vars.keys()):
if n == block['name']: # n is block name
if 'note' in vars[n]:
block['note'] = vars[n]['note']
if block['block'] == 'function':
if 'result' in block and block['result'] in vars:
vars[n] = appenddecl(vars[n], vars[block['result']])
if 'prefix' in block:
pr = block['prefix']
ispure = 0
isrec = 1
pr1 = pr.replace('pure', '')
ispure = (not pr == pr1)
pr = pr1.replace('recursive', '')
isrec = (not pr == pr1)
m = typespattern[0].match(pr)
if m:
typespec, selector, attr, edecl = cracktypespec0(
m.group('this'), m.group('after'))
kindselect, charselect, typename = cracktypespec(
typespec, selector)
vars[n]['typespec'] = typespec
if kindselect:
if 'kind' in kindselect:
try:
kindselect['kind'] = eval(
kindselect['kind'], {}, params)
except Exception:
pass
vars[n]['kindselector'] = kindselect
if charselect:
vars[n]['charselector'] = charselect
if typename:
vars[n]['typename'] = typename
if ispure:
vars[n] = setattrspec(vars[n], 'pure')
if isrec:
vars[n] = setattrspec(vars[n], 'recursive')
else:
outmess(
'analyzevars: prefix (%s) were not used\n' % repr(block['prefix']))
if not block['block'] in ['module', 'pythonmodule', 'python module', 'block data']:
if 'commonvars' in block:
neededvars = copy.copy(block['args'] + block['commonvars'])
else:
neededvars = copy.copy(block['args'])
for n in list(vars.keys()):
if l_or(isintent_callback, isintent_aux)(vars[n]):
neededvars.append(n)
if 'entry' in block:
neededvars.extend(list(block['entry'].keys()))
for k in list(block['entry'].keys()):
for n in block['entry'][k]:
if n not in neededvars:
neededvars.append(n)
if block['block'] == 'function':
if 'result' in block:
neededvars.append(block['result'])
else:
neededvars.append(block['name'])
if block['block'] in ['subroutine', 'function']:
name = block['name']
if name in vars and 'intent' in vars[name]:
block['intent'] = vars[name]['intent']
if block['block'] == 'type':
neededvars.extend(list(vars.keys()))
for n in list(vars.keys()):
if n not in neededvars:
del vars[n]
return vars
analyzeargs_re_1 = re.compile(r'\A[a-z]+[\w$]*\Z', re.I)
def expr2name(a, block, args=[]):
orig_a = a
a_is_expr = not analyzeargs_re_1.match(a)
if a_is_expr: # `a` is an expression
implicitrules, attrrules = buildimplicitrules(block)
at = determineexprtype(a, block['vars'], implicitrules)
na = 'e_'
for c in a:
c = c.lower()
if c not in string.ascii_lowercase + string.digits:
c = '_'
na = na + c
if na[-1] == '_':
na = na + 'e'
else:
na = na + '_e'
a = na
while a in block['vars'] or a in block['args']:
a = a + 'r'
if a in args:
k = 1
while a + str(k) in args:
k = k + 1
a = a + str(k)
if a_is_expr:
block['vars'][a] = at
else:
if a not in block['vars']:
if orig_a in block['vars']:
block['vars'][a] = block['vars'][orig_a]
else:
block['vars'][a] = {}
if 'externals' in block and orig_a in block['externals'] + block['interfaced']:
block['vars'][a] = setattrspec(block['vars'][a], 'external')
return a
def analyzeargs(block):
setmesstext(block)
implicitrules, attrrules = buildimplicitrules(block)
if 'args' not in block:
block['args'] = []
args = []
for a in block['args']:
a = expr2name(a, block, args)
args.append(a)
block['args'] = args
if 'entry' in block:
for k, args1 in list(block['entry'].items()):
for a in args1:
if a not in block['vars']:
block['vars'][a] = {}
for b in block['body']:
if b['name'] in args:
if 'externals' not in block:
block['externals'] = []
if b['name'] not in block['externals']:
block['externals'].append(b['name'])
if 'result' in block and block['result'] not in block['vars']:
block['vars'][block['result']] = {}
return block
determineexprtype_re_1 = re.compile(r'\A\(.+?[,].+?\)\Z', re.I)
determineexprtype_re_2 = re.compile(r'\A[+-]?\d+(_(?P<name>[\w]+)|)\Z', re.I)
determineexprtype_re_3 = re.compile(
r'\A[+-]?[\d.]+[\d+\-de.]*(_(?P<name>[\w]+)|)\Z', re.I)
determineexprtype_re_4 = re.compile(r'\A\(.*\)\Z', re.I)
determineexprtype_re_5 = re.compile(r'\A(?P<name>\w+)\s*\(.*?\)\s*\Z', re.I)
def _ensure_exprdict(r):
if isinstance(r, int):
return {'typespec': 'integer'}
if isinstance(r, float):
return {'typespec': 'real'}
if isinstance(r, complex):
return {'typespec': 'complex'}
if isinstance(r, dict):
return r
raise AssertionError(repr(r))
def determineexprtype(expr, vars, rules={}):
if expr in vars:
return _ensure_exprdict(vars[expr])
expr = expr.strip()
if determineexprtype_re_1.match(expr):
return {'typespec': 'complex'}
m = determineexprtype_re_2.match(expr)
if m:
if 'name' in m.groupdict() and m.group('name'):
outmess(
'determineexprtype: selected kind types not supported (%s)\n' % repr(expr))
return {'typespec': 'integer'}
m = determineexprtype_re_3.match(expr)
if m:
if 'name' in m.groupdict() and m.group('name'):
outmess(
'determineexprtype: selected kind types not supported (%s)\n' % repr(expr))
return {'typespec': 'real'}
for op in ['+', '-', '*', '/']:
for e in [x.strip() for x in markoutercomma(expr, comma=op).split('@' + op + '@')]:
if e in vars:
return _ensure_exprdict(vars[e])
t = {}
if determineexprtype_re_4.match(expr): # in parenthesis
t = determineexprtype(expr[1:-1], vars, rules)
else:
m = determineexprtype_re_5.match(expr)
if m:
rn = m.group('name')
t = determineexprtype(m.group('name'), vars, rules)
if t and 'attrspec' in t:
del t['attrspec']
if not t:
if rn[0] in rules:
return _ensure_exprdict(rules[rn[0]])
if expr[0] in '\'"':
return {'typespec': 'character', 'charselector': {'*': '*'}}
if not t:
outmess(
'determineexprtype: could not determine expressions (%s) type.\n' % (repr(expr)))
return t
######
def crack2fortrangen(block, tab='\n', as_interface=False):
global skipfuncs, onlyfuncs
setmesstext(block)
ret = ''
if isinstance(block, list):
for g in block:
if g and g['block'] in ['function', 'subroutine']:
if g['name'] in skipfuncs:
continue
if onlyfuncs and g['name'] not in onlyfuncs:
continue
ret = ret + crack2fortrangen(g, tab, as_interface=as_interface)
return ret
prefix = ''
name = ''
args = ''
blocktype = block['block']
if blocktype == 'program':
return ''
argsl = []
if 'name' in block:
name = block['name']
if 'args' in block:
vars = block['vars']
for a in block['args']:
a = expr2name(a, block, argsl)
if not isintent_callback(vars[a]):
argsl.append(a)
if block['block'] == 'function' or argsl:
args = '(%s)' % ','.join(argsl)
f2pyenhancements = ''
if 'f2pyenhancements' in block:
for k in list(block['f2pyenhancements'].keys()):
f2pyenhancements = '%s%s%s %s' % (
f2pyenhancements, tab + tabchar, k, block['f2pyenhancements'][k])
intent_lst = block.get('intent', [])[:]
if blocktype == 'function' and 'callback' in intent_lst:
intent_lst.remove('callback')
if intent_lst:
f2pyenhancements = '%s%sintent(%s) %s' %\
(f2pyenhancements, tab + tabchar,
','.join(intent_lst), name)
use = ''
if 'use' in block:
use = use2fortran(block['use'], tab + tabchar)
common = ''
if 'common' in block:
common = common2fortran(block['common'], tab + tabchar)
if name == 'unknown_interface':
name = ''
result = ''
if 'result' in block:
result = ' result (%s)' % block['result']
if block['result'] not in argsl:
argsl.append(block['result'])
body = crack2fortrangen(block['body'], tab + tabchar)
vars = vars2fortran(
block, block['vars'], argsl, tab + tabchar, as_interface=as_interface)
mess = ''
if 'from' in block and not as_interface:
mess = '! in %s' % block['from']
if 'entry' in block:
entry_stmts = ''
for k, i in list(block['entry'].items()):
entry_stmts = '%s%sentry %s(%s)' \
% (entry_stmts, tab + tabchar, k, ','.join(i))
body = body + entry_stmts
if blocktype == 'block data' and name == '_BLOCK_DATA_':
name = ''
ret = '%s%s%s %s%s%s %s%s%s%s%s%s%send %s %s' % (
tab, prefix, blocktype, name, args, result, mess, f2pyenhancements, use, vars, common, body, tab, blocktype, name)
return ret
def common2fortran(common, tab=''):
ret = ''
for k in list(common.keys()):
if k == '_BLNK_':
ret = '%s%scommon %s' % (ret, tab, ','.join(common[k]))
else:
ret = '%s%scommon /%s/ %s' % (ret, tab, k, ','.join(common[k]))
return ret
def use2fortran(use, tab=''):
ret = ''
for m in list(use.keys()):
ret = '%s%suse %s,' % (ret, tab, m)
if use[m] == {}:
if ret and ret[-1] == ',':
ret = ret[:-1]
continue
if 'only' in use[m] and use[m]['only']:
ret = '%s only:' % (ret)
if 'map' in use[m] and use[m]['map']:
c = ' '
for k in list(use[m]['map'].keys()):
if k == use[m]['map'][k]:
ret = '%s%s%s' % (ret, c, k)
c = ','
else:
ret = '%s%s%s=>%s' % (ret, c, k, use[m]['map'][k])
c = ','
if ret and ret[-1] == ',':
ret = ret[:-1]
return ret
def true_intent_list(var):
lst = var['intent']
ret = []
for intent in lst:
try:
c = eval('isintent_%s(var)' % intent)
except NameError:
c = 0
if c:
ret.append(intent)
return ret
def vars2fortran(block, vars, args, tab='', as_interface=False):
"""
TODO:
public sub
...
"""
setmesstext(block)
ret = ''
nout = []
for a in args:
if a in block['vars']:
nout.append(a)
if 'commonvars' in block:
for a in block['commonvars']:
if a in vars:
if a not in nout:
nout.append(a)
else:
errmess(
'vars2fortran: Confused?!: "%s" is not defined in vars.\n' % a)
if 'varnames' in block:
nout.extend(block['varnames'])
if not as_interface:
for a in list(vars.keys()):
if a not in nout:
nout.append(a)
for a in nout:
if 'depend' in vars[a]:
for d in vars[a]['depend']:
if d in vars and 'depend' in vars[d] and a in vars[d]['depend']:
errmess(
'vars2fortran: Warning: cross-dependence between variables "%s" and "%s"\n' % (a, d))
if 'externals' in block and a in block['externals']:
if isintent_callback(vars[a]):
ret = '%s%sintent(callback) %s' % (ret, tab, a)
ret = '%s%sexternal %s' % (ret, tab, a)
if isoptional(vars[a]):
ret = '%s%soptional %s' % (ret, tab, a)
if a in vars and 'typespec' not in vars[a]:
continue
cont = 1
for b in block['body']:
if a == b['name'] and b['block'] == 'function':
cont = 0
break
if cont:
continue
if a not in vars:
show(vars)
outmess('vars2fortran: No definition for argument "%s".\n' % a)
continue
if a == block['name'] and not block['block'] == 'function':
continue
if 'typespec' not in vars[a]:
if 'attrspec' in vars[a] and 'external' in vars[a]['attrspec']:
if a in args:
ret = '%s%sexternal %s' % (ret, tab, a)
continue
show(vars[a])
outmess('vars2fortran: No typespec for argument "%s".\n' % a)
continue
vardef = vars[a]['typespec']
if vardef == 'type' and 'typename' in vars[a]:
vardef = '%s(%s)' % (vardef, vars[a]['typename'])
selector = {}
if 'kindselector' in vars[a]:
selector = vars[a]['kindselector']
elif 'charselector' in vars[a]:
selector = vars[a]['charselector']
if '*' in selector:
if selector['*'] in ['*', ':']:
vardef = '%s*(%s)' % (vardef, selector['*'])
else:
vardef = '%s*%s' % (vardef, selector['*'])
else:
if 'len' in selector:
vardef = '%s(len=%s' % (vardef, selector['len'])
if 'kind' in selector:
vardef = '%s,kind=%s)' % (vardef, selector['kind'])
else:
vardef = '%s)' % (vardef)
elif 'kind' in selector:
vardef = '%s(kind=%s)' % (vardef, selector['kind'])
c = ' '
if 'attrspec' in vars[a]:
attr = []
for l in vars[a]['attrspec']:
if l not in ['external']:
attr.append(l)
if attr:
vardef = '%s, %s' % (vardef, ','.join(attr))
c = ','
if 'dimension' in vars[a]:
vardef = '%s%sdimension(%s)' % (
vardef, c, ','.join(vars[a]['dimension']))
c = ','
if 'intent' in vars[a]:
lst = true_intent_list(vars[a])
if lst:
vardef = '%s%sintent(%s)' % (vardef, c, ','.join(lst))
c = ','
if 'check' in vars[a]:
vardef = '%s%scheck(%s)' % (vardef, c, ','.join(vars[a]['check']))
c = ','
if 'depend' in vars[a]:
vardef = '%s%sdepend(%s)' % (
vardef, c, ','.join(vars[a]['depend']))
c = ','
if '=' in vars[a]:
v = vars[a]['=']
if vars[a]['typespec'] in ['complex', 'double complex']:
try:
v = eval(v)
v = '(%s,%s)' % (v.real, v.imag)
except Exception:
pass
vardef = '%s :: %s=%s' % (vardef, a, v)
else:
vardef = '%s :: %s' % (vardef, a)
ret = '%s%s%s' % (ret, tab, vardef)
return ret
######
def crackfortran(files):
global usermodules
outmess('Reading fortran codes...\n', 0)
readfortrancode(files, crackline)
outmess('Post-processing...\n', 0)
usermodules = []
postlist = postcrack(grouplist[0])
outmess('Post-processing (stage 2)...\n', 0)
postlist = postcrack2(postlist)
return usermodules + postlist
def crack2fortran(block):
global f2py_version
pyf = crack2fortrangen(block) + '\n'
header = """! -*- f90 -*-
! Note: the context of this file is case sensitive.
"""
footer = """
! This file was auto-generated with f2py (version:%s).
! See http://cens.ioc.ee/projects/f2py2e/
""" % (f2py_version)
return header + pyf + footer
if __name__ == "__main__":
files = []
funcs = []
f = 1
f2 = 0
f3 = 0
showblocklist = 0
for l in sys.argv[1:]:
if l == '':
pass
elif l[0] == ':':
f = 0
elif l == '-quiet':
quiet = 1
verbose = 0
elif l == '-verbose':
verbose = 2
quiet = 0
elif l == '-fix':
if strictf77:
outmess(
'Use option -f90 before -fix if Fortran 90 code is in fix form.\n', 0)
skipemptyends = 1
sourcecodeform = 'fix'
elif l == '-skipemptyends':
skipemptyends = 1
elif l == '--ignore-contains':
ignorecontains = 1
elif l == '-f77':
strictf77 = 1
sourcecodeform = 'fix'
elif l == '-f90':
strictf77 = 0
sourcecodeform = 'free'
skipemptyends = 1
elif l == '-h':
f2 = 1
elif l == '-show':
showblocklist = 1
elif l == '-m':
f3 = 1
elif l[0] == '-':
errmess('Unknown option %s\n' % repr(l))
elif f2:
f2 = 0
pyffilename = l
elif f3:
f3 = 0
f77modulename = l
elif f:
try:
open(l).close()
files.append(l)
except IOError as detail:
errmess('IOError: %s\n' % str(detail))
else:
funcs.append(l)
if not strictf77 and f77modulename and not skipemptyends:
outmess("""\
Warning: You have specifyied module name for non Fortran 77 code
that should not need one (expect if you are scanning F90 code
for non module blocks but then you should use flag -skipemptyends
and also be sure that the files do not contain programs without program statement).
""", 0)
postlist = crackfortran(files, funcs)
if pyffilename:
outmess('Writing fortran code to file %s\n' % repr(pyffilename), 0)
pyf = crack2fortran(postlist)
with open(pyffilename, 'w') as f:
f.write(pyf)
if showblocklist:
show(postlist)
| 128,404 | 37.433104 | 207 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/use_rules.py
|
#!/usr/bin/env python
"""
Build 'use others module data' mechanism for f2py2e.
Unfinished.
Copyright 2000 Pearu Peterson all rights reserved,
Pearu Peterson <[email protected]>
Permission to use, modify, and distribute this software is given under the
terms of the NumPy License.
NO WARRANTY IS EXPRESSED OR IMPLIED. USE AT YOUR OWN RISK.
$Date: 2000/09/10 12:35:43 $
Pearu Peterson
"""
from __future__ import division, absolute_import, print_function
__version__ = "$Revision: 1.3 $"[10:-1]
f2py_version = 'See `f2py -v`'
from .auxfuncs import (
applyrules, dictappend, gentitle, hasnote, outmess
)
usemodule_rules = {
'body': """
#begintitle#
static char doc_#apiname#[] = \"\\\nVariable wrapper signature:\\n\\
\t #name# = get_#name#()\\n\\
Arguments:\\n\\
#docstr#\";
extern F_MODFUNC(#usemodulename#,#USEMODULENAME#,#realname#,#REALNAME#);
static PyObject *#apiname#(PyObject *capi_self, PyObject *capi_args) {
/*#decl#*/
\tif (!PyArg_ParseTuple(capi_args, \"\")) goto capi_fail;
printf(\"c: %d\\n\",F_MODFUNC(#usemodulename#,#USEMODULENAME#,#realname#,#REALNAME#));
\treturn Py_BuildValue(\"\");
capi_fail:
\treturn NULL;
}
""",
'method': '\t{\"get_#name#\",#apiname#,METH_VARARGS|METH_KEYWORDS,doc_#apiname#},',
'need': ['F_MODFUNC']
}
################
def buildusevars(m, r):
ret = {}
outmess(
'\t\tBuilding use variable hooks for module "%s" (feature only for F90/F95)...\n' % (m['name']))
varsmap = {}
revmap = {}
if 'map' in r:
for k in r['map'].keys():
if r['map'][k] in revmap:
outmess('\t\t\tVariable "%s<=%s" is already mapped by "%s". Skipping.\n' % (
r['map'][k], k, revmap[r['map'][k]]))
else:
revmap[r['map'][k]] = k
if 'only' in r and r['only']:
for v in r['map'].keys():
if r['map'][v] in m['vars']:
if revmap[r['map'][v]] == v:
varsmap[v] = r['map'][v]
else:
outmess('\t\t\tIgnoring map "%s=>%s". See above.\n' %
(v, r['map'][v]))
else:
outmess(
'\t\t\tNo definition for variable "%s=>%s". Skipping.\n' % (v, r['map'][v]))
else:
for v in m['vars'].keys():
if v in revmap:
varsmap[v] = revmap[v]
else:
varsmap[v] = v
for v in varsmap.keys():
ret = dictappend(ret, buildusevar(v, varsmap[v], m['vars'], m['name']))
return ret
def buildusevar(name, realname, vars, usemodulename):
outmess('\t\t\tConstructing wrapper function for variable "%s=>%s"...\n' % (
name, realname))
ret = {}
vrd = {'name': name,
'realname': realname,
'REALNAME': realname.upper(),
'usemodulename': usemodulename,
'USEMODULENAME': usemodulename.upper(),
'texname': name.replace('_', '\\_'),
'begintitle': gentitle('%s=>%s' % (name, realname)),
'endtitle': gentitle('end of %s=>%s' % (name, realname)),
'apiname': '#modulename#_use_%s_from_%s' % (realname, usemodulename)
}
nummap = {0: 'Ro', 1: 'Ri', 2: 'Rii', 3: 'Riii', 4: 'Riv',
5: 'Rv', 6: 'Rvi', 7: 'Rvii', 8: 'Rviii', 9: 'Rix'}
vrd['texnamename'] = name
for i in nummap.keys():
vrd['texnamename'] = vrd['texnamename'].replace(repr(i), nummap[i])
if hasnote(vars[realname]):
vrd['note'] = vars[realname]['note']
rd = dictappend({}, vrd)
print(name, realname, vars[realname])
ret = applyrules(usemodule_rules, rd)
return ret
| 3,652 | 30.491379 | 104 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/capi_maps.py
|
#!/usr/bin/env python
"""
Copyright 1999,2000 Pearu Peterson all rights reserved,
Pearu Peterson <[email protected]>
Permission to use, modify, and distribute this software is given under the
terms of the NumPy License.
NO WARRANTY IS EXPRESSED OR IMPLIED. USE AT YOUR OWN RISK.
$Date: 2005/05/06 10:57:33 $
Pearu Peterson
"""
from __future__ import division, absolute_import, print_function
__version__ = "$Revision: 1.60 $"[10:-1]
from . import __version__
f2py_version = __version__.version
import copy
import re
import os
import sys
from .crackfortran import markoutercomma
from . import cb_rules
# The eviroment provided by auxfuncs.py is needed for some calls to eval.
# As the needed functions cannot be determined by static inspection of the
# code, it is safest to use import * pending a major refactoring of f2py.
from .auxfuncs import *
__all__ = [
'getctype', 'getstrlength', 'getarrdims', 'getpydocsign',
'getarrdocsign', 'getinit', 'sign2map', 'routsign2map', 'modsign2map',
'cb_sign2map', 'cb_routsign2map', 'common_sign2map'
]
# Numarray and Numeric users should set this False
using_newcore = True
depargs = []
lcb_map = {}
lcb2_map = {}
# forced casting: mainly caused by the fact that Python or Numeric
# C/APIs do not support the corresponding C types.
c2py_map = {'double': 'float',
'float': 'float', # forced casting
'long_double': 'float', # forced casting
'char': 'int', # forced casting
'signed_char': 'int', # forced casting
'unsigned_char': 'int', # forced casting
'short': 'int', # forced casting
'unsigned_short': 'int', # forced casting
'int': 'int', # (forced casting)
'long': 'int',
'long_long': 'long',
'unsigned': 'int', # forced casting
'complex_float': 'complex', # forced casting
'complex_double': 'complex',
'complex_long_double': 'complex', # forced casting
'string': 'string',
}
c2capi_map = {'double': 'NPY_DOUBLE',
'float': 'NPY_FLOAT',
'long_double': 'NPY_DOUBLE', # forced casting
'char': 'NPY_STRING',
'unsigned_char': 'NPY_UBYTE',
'signed_char': 'NPY_BYTE',
'short': 'NPY_SHORT',
'unsigned_short': 'NPY_USHORT',
'int': 'NPY_INT',
'unsigned': 'NPY_UINT',
'long': 'NPY_LONG',
'long_long': 'NPY_LONG', # forced casting
'complex_float': 'NPY_CFLOAT',
'complex_double': 'NPY_CDOUBLE',
'complex_long_double': 'NPY_CDOUBLE', # forced casting
'string': 'NPY_STRING'}
# These new maps aren't used anyhere yet, but should be by default
# unless building numeric or numarray extensions.
if using_newcore:
c2capi_map = {'double': 'NPY_DOUBLE',
'float': 'NPY_FLOAT',
'long_double': 'NPY_LONGDOUBLE',
'char': 'NPY_BYTE',
'unsigned_char': 'NPY_UBYTE',
'signed_char': 'NPY_BYTE',
'short': 'NPY_SHORT',
'unsigned_short': 'NPY_USHORT',
'int': 'NPY_INT',
'unsigned': 'NPY_UINT',
'long': 'NPY_LONG',
'unsigned_long': 'NPY_ULONG',
'long_long': 'NPY_LONGLONG',
'unsigned_long_long': 'NPY_ULONGLONG',
'complex_float': 'NPY_CFLOAT',
'complex_double': 'NPY_CDOUBLE',
'complex_long_double': 'NPY_CDOUBLE',
'string':'NPY_STRING'
}
c2pycode_map = {'double': 'd',
'float': 'f',
'long_double': 'd', # forced casting
'char': '1',
'signed_char': '1',
'unsigned_char': 'b',
'short': 's',
'unsigned_short': 'w',
'int': 'i',
'unsigned': 'u',
'long': 'l',
'long_long': 'L',
'complex_float': 'F',
'complex_double': 'D',
'complex_long_double': 'D', # forced casting
'string': 'c'
}
if using_newcore:
c2pycode_map = {'double': 'd',
'float': 'f',
'long_double': 'g',
'char': 'b',
'unsigned_char': 'B',
'signed_char': 'b',
'short': 'h',
'unsigned_short': 'H',
'int': 'i',
'unsigned': 'I',
'long': 'l',
'unsigned_long': 'L',
'long_long': 'q',
'unsigned_long_long': 'Q',
'complex_float': 'F',
'complex_double': 'D',
'complex_long_double': 'G',
'string': 'S'}
c2buildvalue_map = {'double': 'd',
'float': 'f',
'char': 'b',
'signed_char': 'b',
'short': 'h',
'int': 'i',
'long': 'l',
'long_long': 'L',
'complex_float': 'N',
'complex_double': 'N',
'complex_long_double': 'N',
'string': 'z'}
if sys.version_info[0] >= 3:
# Bytes, not Unicode strings
c2buildvalue_map['string'] = 'y'
if using_newcore:
# c2buildvalue_map=???
pass
f2cmap_all = {'real': {'': 'float', '4': 'float', '8': 'double',
'12': 'long_double', '16': 'long_double'},
'integer': {'': 'int', '1': 'signed_char', '2': 'short',
'4': 'int', '8': 'long_long',
'-1': 'unsigned_char', '-2': 'unsigned_short',
'-4': 'unsigned', '-8': 'unsigned_long_long'},
'complex': {'': 'complex_float', '8': 'complex_float',
'16': 'complex_double', '24': 'complex_long_double',
'32': 'complex_long_double'},
'complexkind': {'': 'complex_float', '4': 'complex_float',
'8': 'complex_double', '12': 'complex_long_double',
'16': 'complex_long_double'},
'logical': {'': 'int', '1': 'char', '2': 'short', '4': 'int',
'8': 'long_long'},
'double complex': {'': 'complex_double'},
'double precision': {'': 'double'},
'byte': {'': 'char'},
'character': {'': 'string'}
}
if os.path.isfile('.f2py_f2cmap'):
# User defined additions to f2cmap_all.
# .f2py_f2cmap must contain a dictionary of dictionaries, only. For
# example, {'real':{'low':'float'}} means that Fortran 'real(low)' is
# interpreted as C 'float'. This feature is useful for F90/95 users if
# they use PARAMETERSs in type specifications.
try:
outmess('Reading .f2py_f2cmap ...\n')
f = open('.f2py_f2cmap', 'r')
d = eval(f.read(), {}, {})
f.close()
for k, d1 in list(d.items()):
for k1 in list(d1.keys()):
d1[k1.lower()] = d1[k1]
d[k.lower()] = d[k]
for k in list(d.keys()):
if k not in f2cmap_all:
f2cmap_all[k] = {}
for k1 in list(d[k].keys()):
if d[k][k1] in c2py_map:
if k1 in f2cmap_all[k]:
outmess(
"\tWarning: redefinition of {'%s':{'%s':'%s'->'%s'}}\n" % (k, k1, f2cmap_all[k][k1], d[k][k1]))
f2cmap_all[k][k1] = d[k][k1]
outmess('\tMapping "%s(kind=%s)" to "%s"\n' %
(k, k1, d[k][k1]))
else:
errmess("\tIgnoring map {'%s':{'%s':'%s'}}: '%s' must be in %s\n" % (
k, k1, d[k][k1], d[k][k1], list(c2py_map.keys())))
outmess('Successfully applied user defined changes from .f2py_f2cmap\n')
except Exception as msg:
errmess(
'Failed to apply user defined changes from .f2py_f2cmap: %s. Skipping.\n' % (msg))
cformat_map = {'double': '%g',
'float': '%g',
'long_double': '%Lg',
'char': '%d',
'signed_char': '%d',
'unsigned_char': '%hhu',
'short': '%hd',
'unsigned_short': '%hu',
'int': '%d',
'unsigned': '%u',
'long': '%ld',
'unsigned_long': '%lu',
'long_long': '%ld',
'complex_float': '(%g,%g)',
'complex_double': '(%g,%g)',
'complex_long_double': '(%Lg,%Lg)',
'string': '%s',
}
# Auxiliary functions
def getctype(var):
"""
Determines C type
"""
ctype = 'void'
if isfunction(var):
if 'result' in var:
a = var['result']
else:
a = var['name']
if a in var['vars']:
return getctype(var['vars'][a])
else:
errmess('getctype: function %s has no return value?!\n' % a)
elif issubroutine(var):
return ctype
elif 'typespec' in var and var['typespec'].lower() in f2cmap_all:
typespec = var['typespec'].lower()
f2cmap = f2cmap_all[typespec]
ctype = f2cmap[''] # default type
if 'kindselector' in var:
if '*' in var['kindselector']:
try:
ctype = f2cmap[var['kindselector']['*']]
except KeyError:
errmess('getctype: "%s %s %s" not supported.\n' %
(var['typespec'], '*', var['kindselector']['*']))
elif 'kind' in var['kindselector']:
if typespec + 'kind' in f2cmap_all:
f2cmap = f2cmap_all[typespec + 'kind']
try:
ctype = f2cmap[var['kindselector']['kind']]
except KeyError:
if typespec in f2cmap_all:
f2cmap = f2cmap_all[typespec]
try:
ctype = f2cmap[str(var['kindselector']['kind'])]
except KeyError:
errmess('getctype: "%s(kind=%s)" is mapped to C "%s" (to override define dict(%s = dict(%s="<C typespec>")) in %s/.f2py_f2cmap file).\n'
% (typespec, var['kindselector']['kind'], ctype,
typespec, var['kindselector']['kind'], os.getcwd()))
else:
if not isexternal(var):
errmess(
'getctype: No C-type found in "%s", assuming void.\n' % var)
return ctype
def getstrlength(var):
if isstringfunction(var):
if 'result' in var:
a = var['result']
else:
a = var['name']
if a in var['vars']:
return getstrlength(var['vars'][a])
else:
errmess('getstrlength: function %s has no return value?!\n' % a)
if not isstring(var):
errmess(
'getstrlength: expected a signature of a string but got: %s\n' % (repr(var)))
len = '1'
if 'charselector' in var:
a = var['charselector']
if '*' in a:
len = a['*']
elif 'len' in a:
len = a['len']
if re.match(r'\(\s*([*]|[:])\s*\)', len) or re.match(r'([*]|[:])', len):
if isintent_hide(var):
errmess('getstrlength:intent(hide): expected a string with defined length but got: %s\n' % (
repr(var)))
len = '-1'
return len
def getarrdims(a, var, verbose=0):
global depargs
ret = {}
if isstring(var) and not isarray(var):
ret['dims'] = getstrlength(var)
ret['size'] = ret['dims']
ret['rank'] = '1'
elif isscalar(var):
ret['size'] = '1'
ret['rank'] = '0'
ret['dims'] = ''
elif isarray(var):
dim = copy.copy(var['dimension'])
ret['size'] = '*'.join(dim)
try:
ret['size'] = repr(eval(ret['size']))
except Exception:
pass
ret['dims'] = ','.join(dim)
ret['rank'] = repr(len(dim))
ret['rank*[-1]'] = repr(len(dim) * [-1])[1:-1]
for i in range(len(dim)): # solve dim for dependecies
v = []
if dim[i] in depargs:
v = [dim[i]]
else:
for va in depargs:
if re.match(r'.*?\b%s\b.*' % va, dim[i]):
v.append(va)
for va in v:
if depargs.index(va) > depargs.index(a):
dim[i] = '*'
break
ret['setdims'], i = '', -1
for d in dim:
i = i + 1
if d not in ['*', ':', '(*)', '(:)']:
ret['setdims'] = '%s#varname#_Dims[%d]=%s,' % (
ret['setdims'], i, d)
if ret['setdims']:
ret['setdims'] = ret['setdims'][:-1]
ret['cbsetdims'], i = '', -1
for d in var['dimension']:
i = i + 1
if d not in ['*', ':', '(*)', '(:)']:
ret['cbsetdims'] = '%s#varname#_Dims[%d]=%s,' % (
ret['cbsetdims'], i, d)
elif isintent_in(var):
outmess('getarrdims:warning: assumed shape array, using 0 instead of %r\n'
% (d))
ret['cbsetdims'] = '%s#varname#_Dims[%d]=%s,' % (
ret['cbsetdims'], i, 0)
elif verbose:
errmess(
'getarrdims: If in call-back function: array argument %s must have bounded dimensions: got %s\n' % (repr(a), repr(d)))
if ret['cbsetdims']:
ret['cbsetdims'] = ret['cbsetdims'][:-1]
# if not isintent_c(var):
# var['dimension'].reverse()
return ret
def getpydocsign(a, var):
global lcb_map
if isfunction(var):
if 'result' in var:
af = var['result']
else:
af = var['name']
if af in var['vars']:
return getpydocsign(af, var['vars'][af])
else:
errmess('getctype: function %s has no return value?!\n' % af)
return '', ''
sig, sigout = a, a
opt = ''
if isintent_in(var):
opt = 'input'
elif isintent_inout(var):
opt = 'in/output'
out_a = a
if isintent_out(var):
for k in var['intent']:
if k[:4] == 'out=':
out_a = k[4:]
break
init = ''
ctype = getctype(var)
if hasinitvalue(var):
init, showinit = getinit(a, var)
init = ', optional\\n Default: %s' % showinit
if isscalar(var):
if isintent_inout(var):
sig = '%s : %s rank-0 array(%s,\'%s\')%s' % (a, opt, c2py_map[ctype],
c2pycode_map[ctype], init)
else:
sig = '%s : %s %s%s' % (a, opt, c2py_map[ctype], init)
sigout = '%s : %s' % (out_a, c2py_map[ctype])
elif isstring(var):
if isintent_inout(var):
sig = '%s : %s rank-0 array(string(len=%s),\'c\')%s' % (
a, opt, getstrlength(var), init)
else:
sig = '%s : %s string(len=%s)%s' % (
a, opt, getstrlength(var), init)
sigout = '%s : string(len=%s)' % (out_a, getstrlength(var))
elif isarray(var):
dim = var['dimension']
rank = repr(len(dim))
sig = '%s : %s rank-%s array(\'%s\') with bounds (%s)%s' % (a, opt, rank,
c2pycode_map[
ctype],
','.join(dim), init)
if a == out_a:
sigout = '%s : rank-%s array(\'%s\') with bounds (%s)'\
% (a, rank, c2pycode_map[ctype], ','.join(dim))
else:
sigout = '%s : rank-%s array(\'%s\') with bounds (%s) and %s storage'\
% (out_a, rank, c2pycode_map[ctype], ','.join(dim), a)
elif isexternal(var):
ua = ''
if a in lcb_map and lcb_map[a] in lcb2_map and 'argname' in lcb2_map[lcb_map[a]]:
ua = lcb2_map[lcb_map[a]]['argname']
if not ua == a:
ua = ' => %s' % ua
else:
ua = ''
sig = '%s : call-back function%s' % (a, ua)
sigout = sig
else:
errmess(
'getpydocsign: Could not resolve docsignature for "%s".\\n' % a)
return sig, sigout
def getarrdocsign(a, var):
ctype = getctype(var)
if isstring(var) and (not isarray(var)):
sig = '%s : rank-0 array(string(len=%s),\'c\')' % (a,
getstrlength(var))
elif isscalar(var):
sig = '%s : rank-0 array(%s,\'%s\')' % (a, c2py_map[ctype],
c2pycode_map[ctype],)
elif isarray(var):
dim = var['dimension']
rank = repr(len(dim))
sig = '%s : rank-%s array(\'%s\') with bounds (%s)' % (a, rank,
c2pycode_map[
ctype],
','.join(dim))
return sig
def getinit(a, var):
if isstring(var):
init, showinit = '""', "''"
else:
init, showinit = '', ''
if hasinitvalue(var):
init = var['=']
showinit = init
if iscomplex(var) or iscomplexarray(var):
ret = {}
try:
v = var["="]
if ',' in v:
ret['init.r'], ret['init.i'] = markoutercomma(
v[1:-1]).split('@,@')
else:
v = eval(v, {}, {})
ret['init.r'], ret['init.i'] = str(v.real), str(v.imag)
except Exception:
raise ValueError(
'getinit: expected complex number `(r,i)\' but got `%s\' as initial value of %r.' % (init, a))
if isarray(var):
init = '(capi_c.r=%s,capi_c.i=%s,capi_c)' % (
ret['init.r'], ret['init.i'])
elif isstring(var):
if not init:
init, showinit = '""', "''"
if init[0] == "'":
init = '"%s"' % (init[1:-1].replace('"', '\\"'))
if init[0] == '"':
showinit = "'%s'" % (init[1:-1])
return init, showinit
def sign2map(a, var):
"""
varname,ctype,atype
init,init.r,init.i,pytype
vardebuginfo,vardebugshowvalue,varshowvalue
varrfromat
intent
"""
global lcb_map, cb_map
out_a = a
if isintent_out(var):
for k in var['intent']:
if k[:4] == 'out=':
out_a = k[4:]
break
ret = {'varname': a, 'outvarname': out_a, 'ctype': getctype(var)}
intent_flags = []
for f, s in isintent_dict.items():
if f(var):
intent_flags.append('F2PY_%s' % s)
if intent_flags:
# XXX: Evaluate intent_flags here.
ret['intent'] = '|'.join(intent_flags)
else:
ret['intent'] = 'F2PY_INTENT_IN'
if isarray(var):
ret['varrformat'] = 'N'
elif ret['ctype'] in c2buildvalue_map:
ret['varrformat'] = c2buildvalue_map[ret['ctype']]
else:
ret['varrformat'] = 'O'
ret['init'], ret['showinit'] = getinit(a, var)
if hasinitvalue(var) and iscomplex(var) and not isarray(var):
ret['init.r'], ret['init.i'] = markoutercomma(
ret['init'][1:-1]).split('@,@')
if isexternal(var):
ret['cbnamekey'] = a
if a in lcb_map:
ret['cbname'] = lcb_map[a]
ret['maxnofargs'] = lcb2_map[lcb_map[a]]['maxnofargs']
ret['nofoptargs'] = lcb2_map[lcb_map[a]]['nofoptargs']
ret['cbdocstr'] = lcb2_map[lcb_map[a]]['docstr']
ret['cblatexdocstr'] = lcb2_map[lcb_map[a]]['latexdocstr']
else:
ret['cbname'] = a
errmess('sign2map: Confused: external %s is not in lcb_map%s.\n' % (
a, list(lcb_map.keys())))
if isstring(var):
ret['length'] = getstrlength(var)
if isarray(var):
ret = dictappend(ret, getarrdims(a, var))
dim = copy.copy(var['dimension'])
if ret['ctype'] in c2capi_map:
ret['atype'] = c2capi_map[ret['ctype']]
# Debug info
if debugcapi(var):
il = [isintent_in, 'input', isintent_out, 'output',
isintent_inout, 'inoutput', isrequired, 'required',
isoptional, 'optional', isintent_hide, 'hidden',
iscomplex, 'complex scalar',
l_and(isscalar, l_not(iscomplex)), 'scalar',
isstring, 'string', isarray, 'array',
iscomplexarray, 'complex array', isstringarray, 'string array',
iscomplexfunction, 'complex function',
l_and(isfunction, l_not(iscomplexfunction)), 'function',
isexternal, 'callback',
isintent_callback, 'callback',
isintent_aux, 'auxiliary',
]
rl = []
for i in range(0, len(il), 2):
if il[i](var):
rl.append(il[i + 1])
if isstring(var):
rl.append('slen(%s)=%s' % (a, ret['length']))
if isarray(var):
ddim = ','.join(
map(lambda x, y: '%s|%s' % (x, y), var['dimension'], dim))
rl.append('dims(%s)' % ddim)
if isexternal(var):
ret['vardebuginfo'] = 'debug-capi:%s=>%s:%s' % (
a, ret['cbname'], ','.join(rl))
else:
ret['vardebuginfo'] = 'debug-capi:%s %s=%s:%s' % (
ret['ctype'], a, ret['showinit'], ','.join(rl))
if isscalar(var):
if ret['ctype'] in cformat_map:
ret['vardebugshowvalue'] = 'debug-capi:%s=%s' % (
a, cformat_map[ret['ctype']])
if isstring(var):
ret['vardebugshowvalue'] = 'debug-capi:slen(%s)=%%d %s=\\"%%s\\"' % (
a, a)
if isexternal(var):
ret['vardebugshowvalue'] = 'debug-capi:%s=%%p' % (a)
if ret['ctype'] in cformat_map:
ret['varshowvalue'] = '#name#:%s=%s' % (a, cformat_map[ret['ctype']])
ret['showvalueformat'] = '%s' % (cformat_map[ret['ctype']])
if isstring(var):
ret['varshowvalue'] = '#name#:slen(%s)=%%d %s=\\"%%s\\"' % (a, a)
ret['pydocsign'], ret['pydocsignout'] = getpydocsign(a, var)
if hasnote(var):
ret['note'] = var['note']
return ret
def routsign2map(rout):
"""
name,NAME,begintitle,endtitle
rname,ctype,rformat
routdebugshowvalue
"""
global lcb_map
name = rout['name']
fname = getfortranname(rout)
ret = {'name': name,
'texname': name.replace('_', '\\_'),
'name_lower': name.lower(),
'NAME': name.upper(),
'begintitle': gentitle(name),
'endtitle': gentitle('end of %s' % name),
'fortranname': fname,
'FORTRANNAME': fname.upper(),
'callstatement': getcallstatement(rout) or '',
'usercode': getusercode(rout) or '',
'usercode1': getusercode1(rout) or '',
}
if '_' in fname:
ret['F_FUNC'] = 'F_FUNC_US'
else:
ret['F_FUNC'] = 'F_FUNC'
if '_' in name:
ret['F_WRAPPEDFUNC'] = 'F_WRAPPEDFUNC_US'
else:
ret['F_WRAPPEDFUNC'] = 'F_WRAPPEDFUNC'
lcb_map = {}
if 'use' in rout:
for u in rout['use'].keys():
if u in cb_rules.cb_map:
for un in cb_rules.cb_map[u]:
ln = un[0]
if 'map' in rout['use'][u]:
for k in rout['use'][u]['map'].keys():
if rout['use'][u]['map'][k] == un[0]:
ln = k
break
lcb_map[ln] = un[1]
elif 'externals' in rout and rout['externals']:
errmess('routsign2map: Confused: function %s has externals %s but no "use" statement.\n' % (
ret['name'], repr(rout['externals'])))
ret['callprotoargument'] = getcallprotoargument(rout, lcb_map) or ''
if isfunction(rout):
if 'result' in rout:
a = rout['result']
else:
a = rout['name']
ret['rname'] = a
ret['pydocsign'], ret['pydocsignout'] = getpydocsign(a, rout)
ret['ctype'] = getctype(rout['vars'][a])
if hasresultnote(rout):
ret['resultnote'] = rout['vars'][a]['note']
rout['vars'][a]['note'] = ['See elsewhere.']
if ret['ctype'] in c2buildvalue_map:
ret['rformat'] = c2buildvalue_map[ret['ctype']]
else:
ret['rformat'] = 'O'
errmess('routsign2map: no c2buildvalue key for type %s\n' %
(repr(ret['ctype'])))
if debugcapi(rout):
if ret['ctype'] in cformat_map:
ret['routdebugshowvalue'] = 'debug-capi:%s=%s' % (
a, cformat_map[ret['ctype']])
if isstringfunction(rout):
ret['routdebugshowvalue'] = 'debug-capi:slen(%s)=%%d %s=\\"%%s\\"' % (
a, a)
if isstringfunction(rout):
ret['rlength'] = getstrlength(rout['vars'][a])
if ret['rlength'] == '-1':
errmess('routsign2map: expected explicit specification of the length of the string returned by the fortran function %s; taking 10.\n' % (
repr(rout['name'])))
ret['rlength'] = '10'
if hasnote(rout):
ret['note'] = rout['note']
rout['note'] = ['See elsewhere.']
return ret
def modsign2map(m):
"""
modulename
"""
if ismodule(m):
ret = {'f90modulename': m['name'],
'F90MODULENAME': m['name'].upper(),
'texf90modulename': m['name'].replace('_', '\\_')}
else:
ret = {'modulename': m['name'],
'MODULENAME': m['name'].upper(),
'texmodulename': m['name'].replace('_', '\\_')}
ret['restdoc'] = getrestdoc(m) or []
if hasnote(m):
ret['note'] = m['note']
ret['usercode'] = getusercode(m) or ''
ret['usercode1'] = getusercode1(m) or ''
if m['body']:
ret['interface_usercode'] = getusercode(m['body'][0]) or ''
else:
ret['interface_usercode'] = ''
ret['pymethoddef'] = getpymethoddef(m) or ''
if 'coutput' in m:
ret['coutput'] = m['coutput']
if 'f2py_wrapper_output' in m:
ret['f2py_wrapper_output'] = m['f2py_wrapper_output']
return ret
def cb_sign2map(a, var, index=None):
ret = {'varname': a}
if index is None or 1: # disable 7712 patch
ret['varname_i'] = ret['varname']
else:
ret['varname_i'] = ret['varname'] + '_' + str(index)
ret['ctype'] = getctype(var)
if ret['ctype'] in c2capi_map:
ret['atype'] = c2capi_map[ret['ctype']]
if ret['ctype'] in cformat_map:
ret['showvalueformat'] = '%s' % (cformat_map[ret['ctype']])
if isarray(var):
ret = dictappend(ret, getarrdims(a, var))
ret['pydocsign'], ret['pydocsignout'] = getpydocsign(a, var)
if hasnote(var):
ret['note'] = var['note']
var['note'] = ['See elsewhere.']
return ret
def cb_routsign2map(rout, um):
"""
name,begintitle,endtitle,argname
ctype,rctype,maxnofargs,nofoptargs,returncptr
"""
ret = {'name': 'cb_%s_in_%s' % (rout['name'], um),
'returncptr': ''}
if isintent_callback(rout):
if '_' in rout['name']:
F_FUNC = 'F_FUNC_US'
else:
F_FUNC = 'F_FUNC'
ret['callbackname'] = '%s(%s,%s)' \
% (F_FUNC,
rout['name'].lower(),
rout['name'].upper(),
)
ret['static'] = 'extern'
else:
ret['callbackname'] = ret['name']
ret['static'] = 'static'
ret['argname'] = rout['name']
ret['begintitle'] = gentitle(ret['name'])
ret['endtitle'] = gentitle('end of %s' % ret['name'])
ret['ctype'] = getctype(rout)
ret['rctype'] = 'void'
if ret['ctype'] == 'string':
ret['rctype'] = 'void'
else:
ret['rctype'] = ret['ctype']
if ret['rctype'] != 'void':
if iscomplexfunction(rout):
ret['returncptr'] = """
#ifdef F2PY_CB_RETURNCOMPLEX
return_value=
#endif
"""
else:
ret['returncptr'] = 'return_value='
if ret['ctype'] in cformat_map:
ret['showvalueformat'] = '%s' % (cformat_map[ret['ctype']])
if isstringfunction(rout):
ret['strlength'] = getstrlength(rout)
if isfunction(rout):
if 'result' in rout:
a = rout['result']
else:
a = rout['name']
if hasnote(rout['vars'][a]):
ret['note'] = rout['vars'][a]['note']
rout['vars'][a]['note'] = ['See elsewhere.']
ret['rname'] = a
ret['pydocsign'], ret['pydocsignout'] = getpydocsign(a, rout)
if iscomplexfunction(rout):
ret['rctype'] = """
#ifdef F2PY_CB_RETURNCOMPLEX
#ctype#
#else
void
#endif
"""
else:
if hasnote(rout):
ret['note'] = rout['note']
rout['note'] = ['See elsewhere.']
nofargs = 0
nofoptargs = 0
if 'args' in rout and 'vars' in rout:
for a in rout['args']:
var = rout['vars'][a]
if l_or(isintent_in, isintent_inout)(var):
nofargs = nofargs + 1
if isoptional(var):
nofoptargs = nofoptargs + 1
ret['maxnofargs'] = repr(nofargs)
ret['nofoptargs'] = repr(nofoptargs)
if hasnote(rout) and isfunction(rout) and 'result' in rout:
ret['routnote'] = rout['note']
rout['note'] = ['See elsewhere.']
return ret
def common_sign2map(a, var): # obsolute
ret = {'varname': a, 'ctype': getctype(var)}
if isstringarray(var):
ret['ctype'] = 'char'
if ret['ctype'] in c2capi_map:
ret['atype'] = c2capi_map[ret['ctype']]
if ret['ctype'] in cformat_map:
ret['showvalueformat'] = '%s' % (cformat_map[ret['ctype']])
if isarray(var):
ret = dictappend(ret, getarrdims(a, var))
elif isstring(var):
ret['size'] = getstrlength(var)
ret['rank'] = '1'
ret['pydocsign'], ret['pydocsignout'] = getpydocsign(a, var)
if hasnote(var):
ret['note'] = var['note']
var['note'] = ['See elsewhere.']
# for strings this returns 0-rank but actually is 1-rank
ret['arrdocstr'] = getarrdocsign(a, var)
return ret
| 31,539 | 36.502973 | 160 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/__version__.py
|
from __future__ import division, absolute_import, print_function
major = 2
try:
from __svn_version__ import version
version_info = (major, version)
version = '%s_%s' % version_info
except (ImportError, ValueError):
version = str(major)
| 254 | 22.181818 | 64 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/diagnose.py
|
#!/usr/bin/env python
from __future__ import division, absolute_import, print_function
import os
import sys
import tempfile
def run_command(cmd):
print('Running %r:' % (cmd))
os.system(cmd)
print('------')
def run():
_path = os.getcwd()
os.chdir(tempfile.gettempdir())
print('------')
print('os.name=%r' % (os.name))
print('------')
print('sys.platform=%r' % (sys.platform))
print('------')
print('sys.version:')
print(sys.version)
print('------')
print('sys.prefix:')
print(sys.prefix)
print('------')
print('sys.path=%r' % (':'.join(sys.path)))
print('------')
try:
import numpy
has_newnumpy = 1
except ImportError:
print('Failed to import new numpy:', sys.exc_info()[1])
has_newnumpy = 0
try:
from numpy.f2py import f2py2e
has_f2py2e = 1
except ImportError:
print('Failed to import f2py2e:', sys.exc_info()[1])
has_f2py2e = 0
try:
import numpy.distutils
has_numpy_distutils = 2
except ImportError:
try:
import numpy_distutils
has_numpy_distutils = 1
except ImportError:
print('Failed to import numpy_distutils:', sys.exc_info()[1])
has_numpy_distutils = 0
if has_newnumpy:
try:
print('Found new numpy version %r in %s' %
(numpy.__version__, numpy.__file__))
except Exception as msg:
print('error:', msg)
print('------')
if has_f2py2e:
try:
print('Found f2py2e version %r in %s' %
(f2py2e.__version__.version, f2py2e.__file__))
except Exception as msg:
print('error:', msg)
print('------')
if has_numpy_distutils:
try:
if has_numpy_distutils == 2:
print('Found numpy.distutils version %r in %r' % (
numpy.distutils.__version__,
numpy.distutils.__file__))
else:
print('Found numpy_distutils version %r in %r' % (
numpy_distutils.numpy_distutils_version.numpy_distutils_version,
numpy_distutils.__file__))
print('------')
except Exception as msg:
print('error:', msg)
print('------')
try:
if has_numpy_distutils == 1:
print(
'Importing numpy_distutils.command.build_flib ...', end=' ')
import numpy_distutils.command.build_flib as build_flib
print('ok')
print('------')
try:
print(
'Checking availability of supported Fortran compilers:')
for compiler_class in build_flib.all_compilers:
compiler_class(verbose=1).is_available()
print('------')
except Exception as msg:
print('error:', msg)
print('------')
except Exception as msg:
print(
'error:', msg, '(ignore it, build_flib is obsolute for numpy.distutils 0.2.2 and up)')
print('------')
try:
if has_numpy_distutils == 2:
print('Importing numpy.distutils.fcompiler ...', end=' ')
import numpy.distutils.fcompiler as fcompiler
else:
print('Importing numpy_distutils.fcompiler ...', end=' ')
import numpy_distutils.fcompiler as fcompiler
print('ok')
print('------')
try:
print('Checking availability of supported Fortran compilers:')
fcompiler.show_fcompilers()
print('------')
except Exception as msg:
print('error:', msg)
print('------')
except Exception as msg:
print('error:', msg)
print('------')
try:
if has_numpy_distutils == 2:
print('Importing numpy.distutils.cpuinfo ...', end=' ')
from numpy.distutils.cpuinfo import cpuinfo
print('ok')
print('------')
else:
try:
print(
'Importing numpy_distutils.command.cpuinfo ...', end=' ')
from numpy_distutils.command.cpuinfo import cpuinfo
print('ok')
print('------')
except Exception as msg:
print('error:', msg, '(ignore it)')
print('Importing numpy_distutils.cpuinfo ...', end=' ')
from numpy_distutils.cpuinfo import cpuinfo
print('ok')
print('------')
cpu = cpuinfo()
print('CPU information:', end=' ')
for name in dir(cpuinfo):
if name[0] == '_' and name[1] != '_' and getattr(cpu, name[1:])():
print(name[1:], end=' ')
print('------')
except Exception as msg:
print('error:', msg)
print('------')
os.chdir(_path)
if __name__ == "__main__":
run()
| 5,295 | 32.732484 | 102 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/rules.py
|
#!/usr/bin/env python
"""
Rules for building C/API module with f2py2e.
Here is a skeleton of a new wrapper function (13Dec2001):
wrapper_function(args)
declarations
get_python_arguments, say, `a' and `b'
get_a_from_python
if (successful) {
get_b_from_python
if (successful) {
callfortran
if (successful) {
put_a_to_python
if (successful) {
put_b_to_python
if (successful) {
buildvalue = ...
}
}
}
}
cleanup_b
}
cleanup_a
return buildvalue
Copyright 1999,2000 Pearu Peterson all rights reserved,
Pearu Peterson <[email protected]>
Permission to use, modify, and distribute this software is given under the
terms of the NumPy License.
NO WARRANTY IS EXPRESSED OR IMPLIED. USE AT YOUR OWN RISK.
$Date: 2005/08/30 08:58:42 $
Pearu Peterson
"""
from __future__ import division, absolute_import, print_function
__version__ = "$Revision: 1.129 $"[10:-1]
from . import __version__
f2py_version = __version__.version
import os
import time
import copy
from .auxfuncs import (
applyrules, debugcapi, dictappend, errmess, gentitle, getargs2,
hascallstatement, hasexternals, hasinitvalue, hasnote, hasresultnote,
isarray, isarrayofstrings, iscomplex, iscomplexarray,
iscomplexfunction, iscomplexfunction_warn, isdummyroutine, isexternal,
isfunction, isfunction_wrap, isint1array, isintent_aux, isintent_c,
isintent_callback, isintent_copy, isintent_hide, isintent_inout,
isintent_nothide, isintent_out, isintent_overwrite, islogical,
islong_complex, islong_double, islong_doublefunction, islong_long,
islong_longfunction, ismoduleroutine, isoptional, isrequired, isscalar,
issigned_long_longarray, isstring, isstringarray, isstringfunction,
issubroutine, issubroutine_wrap, isthreadsafe, isunsigned,
isunsigned_char, isunsigned_chararray, isunsigned_long_long,
isunsigned_long_longarray, isunsigned_short, isunsigned_shortarray,
l_and, l_not, l_or, outmess, replace, stripcomma,
)
from . import capi_maps
from . import cfuncs
from . import common_rules
from . import use_rules
from . import f90mod_rules
from . import func2subr
options = {}
sepdict = {}
#for k in ['need_cfuncs']: sepdict[k]=','
for k in ['decl',
'frompyobj',
'cleanupfrompyobj',
'topyarr', 'method',
'pyobjfrom', 'closepyobjfrom',
'freemem',
'userincludes',
'includes0', 'includes', 'typedefs', 'typedefs_generated',
'cppmacros', 'cfuncs', 'callbacks',
'latexdoc',
'restdoc',
'routine_defs', 'externroutines',
'initf2pywraphooks',
'commonhooks', 'initcommonhooks',
'f90modhooks', 'initf90modhooks']:
sepdict[k] = '\n'
#################### Rules for C/API module #################
generationtime = int(os.environ.get('SOURCE_DATE_EPOCH', time.time()))
module_rules = {
'modulebody': """\
/* File: #modulename#module.c
* This file is auto-generated with f2py (version:#f2py_version#).
* f2py is a Fortran to Python Interface Generator (FPIG), Second Edition,
* written by Pearu Peterson <[email protected]>.
* Generation date: """ + time.asctime(time.gmtime(generationtime)) + """
* Do not edit this file directly unless you know what you are doing!!!
*/
#ifdef __cplusplus
extern \"C\" {
#endif
""" + gentitle("See f2py2e/cfuncs.py: includes") + """
#includes#
#includes0#
""" + gentitle("See f2py2e/rules.py: mod_rules['modulebody']") + """
static PyObject *#modulename#_error;
static PyObject *#modulename#_module;
""" + gentitle("See f2py2e/cfuncs.py: typedefs") + """
#typedefs#
""" + gentitle("See f2py2e/cfuncs.py: typedefs_generated") + """
#typedefs_generated#
""" + gentitle("See f2py2e/cfuncs.py: cppmacros") + """
#cppmacros#
""" + gentitle("See f2py2e/cfuncs.py: cfuncs") + """
#cfuncs#
""" + gentitle("See f2py2e/cfuncs.py: userincludes") + """
#userincludes#
""" + gentitle("See f2py2e/capi_rules.py: usercode") + """
#usercode#
/* See f2py2e/rules.py */
#externroutines#
""" + gentitle("See f2py2e/capi_rules.py: usercode1") + """
#usercode1#
""" + gentitle("See f2py2e/cb_rules.py: buildcallback") + """
#callbacks#
""" + gentitle("See f2py2e/rules.py: buildapi") + """
#body#
""" + gentitle("See f2py2e/f90mod_rules.py: buildhooks") + """
#f90modhooks#
""" + gentitle("See f2py2e/rules.py: module_rules['modulebody']") + """
""" + gentitle("See f2py2e/common_rules.py: buildhooks") + """
#commonhooks#
""" + gentitle("See f2py2e/rules.py") + """
static FortranDataDef f2py_routine_defs[] = {
#routine_defs#
\t{NULL}
};
static PyMethodDef f2py_module_methods[] = {
#pymethoddef#
\t{NULL,NULL}
};
#if PY_VERSION_HEX >= 0x03000000
static struct PyModuleDef moduledef = {
\tPyModuleDef_HEAD_INIT,
\t"#modulename#",
\tNULL,
\t-1,
\tf2py_module_methods,
\tNULL,
\tNULL,
\tNULL,
\tNULL
};
#endif
#if PY_VERSION_HEX >= 0x03000000
#define RETVAL m
PyMODINIT_FUNC PyInit_#modulename#(void) {
#else
#define RETVAL
PyMODINIT_FUNC init#modulename#(void) {
#endif
\tint i;
\tPyObject *m,*d, *s;
#if PY_VERSION_HEX >= 0x03000000
\tm = #modulename#_module = PyModule_Create(&moduledef);
#else
\tm = #modulename#_module = Py_InitModule(\"#modulename#\", f2py_module_methods);
#endif
\tPy_TYPE(&PyFortran_Type) = &PyType_Type;
\timport_array();
\tif (PyErr_Occurred())
\t\t{PyErr_SetString(PyExc_ImportError, \"can't initialize module #modulename# (failed to import numpy)\"); return RETVAL;}
\td = PyModule_GetDict(m);
\ts = PyString_FromString(\"$R""" + """evision: $\");
\tPyDict_SetItemString(d, \"__version__\", s);
#if PY_VERSION_HEX >= 0x03000000
\ts = PyUnicode_FromString(
#else
\ts = PyString_FromString(
#endif
\t\t\"This module '#modulename#' is auto-generated with f2py (version:#f2py_version#).\\nFunctions:\\n\"\n#docs#\".\");
\tPyDict_SetItemString(d, \"__doc__\", s);
\t#modulename#_error = PyErr_NewException (\"#modulename#.error\", NULL, NULL);
\tPy_DECREF(s);
\tfor(i=0;f2py_routine_defs[i].name!=NULL;i++)
\t\tPyDict_SetItemString(d, f2py_routine_defs[i].name,PyFortranObject_NewAsAttr(&f2py_routine_defs[i]));
#initf2pywraphooks#
#initf90modhooks#
#initcommonhooks#
#interface_usercode#
#ifdef F2PY_REPORT_ATEXIT
\tif (! PyErr_Occurred())
\t\ton_exit(f2py_report_on_exit,(void*)\"#modulename#\");
#endif
\treturn RETVAL;
}
#ifdef __cplusplus
}
#endif
""",
'separatorsfor': {'latexdoc': '\n\n',
'restdoc': '\n\n'},
'latexdoc': ['\\section{Module \\texttt{#texmodulename#}}\n',
'#modnote#\n',
'#latexdoc#'],
'restdoc': ['Module #modulename#\n' + '=' * 80,
'\n#restdoc#']
}
defmod_rules = [
{'body': '/*eof body*/',
'method': '/*eof method*/',
'externroutines': '/*eof externroutines*/',
'routine_defs': '/*eof routine_defs*/',
'initf90modhooks': '/*eof initf90modhooks*/',
'initf2pywraphooks': '/*eof initf2pywraphooks*/',
'initcommonhooks': '/*eof initcommonhooks*/',
'latexdoc': '',
'restdoc': '',
'modnote': {hasnote: '#note#', l_not(hasnote): ''},
}
]
routine_rules = {
'separatorsfor': sepdict,
'body': """
#begintitle#
static char doc_#apiname#[] = \"\\\n#docreturn##name#(#docsignatureshort#)\\n\\nWrapper for ``#name#``.\\\n\\n#docstrsigns#\";
/* #declfortranroutine# */
static PyObject *#apiname#(const PyObject *capi_self,
PyObject *capi_args,
PyObject *capi_keywds,
#functype# (*f2py_func)(#callprotoargument#)) {
\tPyObject * volatile capi_buildvalue = NULL;
\tvolatile int f2py_success = 1;
#decl#
\tstatic char *capi_kwlist[] = {#kwlist##kwlistopt##kwlistxa#NULL};
#usercode#
#routdebugenter#
#ifdef F2PY_REPORT_ATEXIT
f2py_start_clock();
#endif
\tif (!PyArg_ParseTupleAndKeywords(capi_args,capi_keywds,\\
\t\t\"#argformat##keyformat##xaformat#:#pyname#\",\\
\t\tcapi_kwlist#args_capi##keys_capi##keys_xa#))\n\t\treturn NULL;
#frompyobj#
/*end of frompyobj*/
#ifdef F2PY_REPORT_ATEXIT
f2py_start_call_clock();
#endif
#callfortranroutine#
if (PyErr_Occurred())
f2py_success = 0;
#ifdef F2PY_REPORT_ATEXIT
f2py_stop_call_clock();
#endif
/*end of callfortranroutine*/
\t\tif (f2py_success) {
#pyobjfrom#
/*end of pyobjfrom*/
\t\tCFUNCSMESS(\"Building return value.\\n\");
\t\tcapi_buildvalue = Py_BuildValue(\"#returnformat#\"#return#);
/*closepyobjfrom*/
#closepyobjfrom#
\t\t} /*if (f2py_success) after callfortranroutine*/
/*cleanupfrompyobj*/
#cleanupfrompyobj#
\tif (capi_buildvalue == NULL) {
#routdebugfailure#
\t} else {
#routdebugleave#
\t}
\tCFUNCSMESS(\"Freeing memory.\\n\");
#freemem#
#ifdef F2PY_REPORT_ATEXIT
f2py_stop_clock();
#endif
\treturn capi_buildvalue;
}
#endtitle#
""",
'routine_defs': '#routine_def#',
'initf2pywraphooks': '#initf2pywraphook#',
'externroutines': '#declfortranroutine#',
'doc': '#docreturn##name#(#docsignature#)',
'docshort': '#docreturn##name#(#docsignatureshort#)',
'docs': '"\t#docreturn##name#(#docsignature#)\\n"\n',
'need': ['arrayobject.h', 'CFUNCSMESS', 'MINMAX'],
'cppmacros': {debugcapi: '#define DEBUGCFUNCS'},
'latexdoc': ['\\subsection{Wrapper function \\texttt{#texname#}}\n',
"""
\\noindent{{}\\verb@#docreturn##name#@{}}\\texttt{(#latexdocsignatureshort#)}
#routnote#
#latexdocstrsigns#
"""],
'restdoc': ['Wrapped function ``#name#``\n' + '-' * 80,
]
}
################## Rules for C/API function ##############
rout_rules = [
{ # Init
'separatorsfor': {'callfortranroutine': '\n', 'routdebugenter': '\n', 'decl': '\n',
'routdebugleave': '\n', 'routdebugfailure': '\n',
'setjmpbuf': ' || ',
'docstrreq': '\n', 'docstropt': '\n', 'docstrout': '\n',
'docstrcbs': '\n', 'docstrsigns': '\\n"\n"',
'latexdocstrsigns': '\n',
'latexdocstrreq': '\n', 'latexdocstropt': '\n',
'latexdocstrout': '\n', 'latexdocstrcbs': '\n',
},
'kwlist': '', 'kwlistopt': '', 'callfortran': '', 'callfortranappend': '',
'docsign': '', 'docsignopt': '', 'decl': '/*decl*/',
'freemem': '/*freemem*/',
'docsignshort': '', 'docsignoptshort': '',
'docstrsigns': '', 'latexdocstrsigns': '',
'docstrreq': '\\nParameters\\n----------',
'docstropt': '\\nOther Parameters\\n----------------',
'docstrout': '\\nReturns\\n-------',
'docstrcbs': '\\nNotes\\n-----\\nCall-back functions::\\n',
'latexdocstrreq': '\\noindent Required arguments:',
'latexdocstropt': '\\noindent Optional arguments:',
'latexdocstrout': '\\noindent Return objects:',
'latexdocstrcbs': '\\noindent Call-back functions:',
'args_capi': '', 'keys_capi': '', 'functype': '',
'frompyobj': '/*frompyobj*/',
# this list will be reversed
'cleanupfrompyobj': ['/*end of cleanupfrompyobj*/'],
'pyobjfrom': '/*pyobjfrom*/',
# this list will be reversed
'closepyobjfrom': ['/*end of closepyobjfrom*/'],
'topyarr': '/*topyarr*/', 'routdebugleave': '/*routdebugleave*/',
'routdebugenter': '/*routdebugenter*/',
'routdebugfailure': '/*routdebugfailure*/',
'callfortranroutine': '/*callfortranroutine*/',
'argformat': '', 'keyformat': '', 'need_cfuncs': '',
'docreturn': '', 'return': '', 'returnformat': '', 'rformat': '',
'kwlistxa': '', 'keys_xa': '', 'xaformat': '', 'docsignxa': '', 'docsignxashort': '',
'initf2pywraphook': '',
'routnote': {hasnote: '--- #note#', l_not(hasnote): ''},
}, {
'apiname': 'f2py_rout_#modulename#_#name#',
'pyname': '#modulename#.#name#',
'decl': '',
'_check': l_not(ismoduleroutine)
}, {
'apiname': 'f2py_rout_#modulename#_#f90modulename#_#name#',
'pyname': '#modulename#.#f90modulename#.#name#',
'decl': '',
'_check': ismoduleroutine
}, { # Subroutine
'functype': 'void',
'declfortranroutine': {l_and(l_not(l_or(ismoduleroutine, isintent_c)), l_not(isdummyroutine)): 'extern void #F_FUNC#(#fortranname#,#FORTRANNAME#)(#callprotoargument#);',
l_and(l_not(ismoduleroutine), isintent_c, l_not(isdummyroutine)): 'extern void #fortranname#(#callprotoargument#);',
ismoduleroutine: '',
isdummyroutine: ''
},
'routine_def': {l_not(l_or(ismoduleroutine, isintent_c, isdummyroutine)): '\t{\"#name#\",-1,{{-1}},0,(char *)#F_FUNC#(#fortranname#,#FORTRANNAME#),(f2py_init_func)#apiname#,doc_#apiname#},',
l_and(l_not(ismoduleroutine), isintent_c, l_not(isdummyroutine)): '\t{\"#name#\",-1,{{-1}},0,(char *)#fortranname#,(f2py_init_func)#apiname#,doc_#apiname#},',
l_and(l_not(ismoduleroutine), isdummyroutine): '\t{\"#name#\",-1,{{-1}},0,NULL,(f2py_init_func)#apiname#,doc_#apiname#},',
},
'need': {l_and(l_not(l_or(ismoduleroutine, isintent_c)), l_not(isdummyroutine)): 'F_FUNC'},
'callfortranroutine': [
{debugcapi: [
"""\tfprintf(stderr,\"debug-capi:Fortran subroutine `#fortranname#(#callfortran#)\'\\n\");"""]},
{hasexternals: """\
\t\tif (#setjmpbuf#) {
\t\t\tf2py_success = 0;
\t\t} else {"""},
{isthreadsafe: '\t\t\tPy_BEGIN_ALLOW_THREADS'},
{hascallstatement: '''\t\t\t\t#callstatement#;
\t\t\t\t/*(*f2py_func)(#callfortran#);*/'''},
{l_not(l_or(hascallstatement, isdummyroutine))
: '\t\t\t\t(*f2py_func)(#callfortran#);'},
{isthreadsafe: '\t\t\tPy_END_ALLOW_THREADS'},
{hasexternals: """\t\t}"""}
],
'_check': l_and(issubroutine, l_not(issubroutine_wrap)),
}, { # Wrapped function
'functype': 'void',
'declfortranroutine': {l_not(l_or(ismoduleroutine, isdummyroutine)): 'extern void #F_WRAPPEDFUNC#(#name_lower#,#NAME#)(#callprotoargument#);',
isdummyroutine: '',
},
'routine_def': {l_not(l_or(ismoduleroutine, isdummyroutine)): '\t{\"#name#\",-1,{{-1}},0,(char *)#F_WRAPPEDFUNC#(#name_lower#,#NAME#),(f2py_init_func)#apiname#,doc_#apiname#},',
isdummyroutine: '\t{\"#name#\",-1,{{-1}},0,NULL,(f2py_init_func)#apiname#,doc_#apiname#},',
},
'initf2pywraphook': {l_not(l_or(ismoduleroutine, isdummyroutine)): '''
{
extern #ctype# #F_FUNC#(#name_lower#,#NAME#)(void);
PyObject* o = PyDict_GetItemString(d,"#name#");
PyObject_SetAttrString(o,"_cpointer", F2PyCapsule_FromVoidPtr((void*)#F_FUNC#(#name_lower#,#NAME#),NULL));
#if PY_VERSION_HEX >= 0x03000000
PyObject_SetAttrString(o,"__name__", PyUnicode_FromString("#name#"));
#else
PyObject_SetAttrString(o,"__name__", PyString_FromString("#name#"));
#endif
}
'''},
'need': {l_not(l_or(ismoduleroutine, isdummyroutine)): ['F_WRAPPEDFUNC', 'F_FUNC']},
'callfortranroutine': [
{debugcapi: [
"""\tfprintf(stderr,\"debug-capi:Fortran subroutine `f2pywrap#name_lower#(#callfortran#)\'\\n\");"""]},
{hasexternals: """\
\tif (#setjmpbuf#) {
\t\tf2py_success = 0;
\t} else {"""},
{isthreadsafe: '\tPy_BEGIN_ALLOW_THREADS'},
{l_not(l_or(hascallstatement, isdummyroutine))
: '\t(*f2py_func)(#callfortran#);'},
{hascallstatement:
'\t#callstatement#;\n\t/*(*f2py_func)(#callfortran#);*/'},
{isthreadsafe: '\tPy_END_ALLOW_THREADS'},
{hasexternals: '\t}'}
],
'_check': isfunction_wrap,
}, { # Wrapped subroutine
'functype': 'void',
'declfortranroutine': {l_not(l_or(ismoduleroutine, isdummyroutine)): 'extern void #F_WRAPPEDFUNC#(#name_lower#,#NAME#)(#callprotoargument#);',
isdummyroutine: '',
},
'routine_def': {l_not(l_or(ismoduleroutine, isdummyroutine)): '\t{\"#name#\",-1,{{-1}},0,(char *)#F_WRAPPEDFUNC#(#name_lower#,#NAME#),(f2py_init_func)#apiname#,doc_#apiname#},',
isdummyroutine: '\t{\"#name#\",-1,{{-1}},0,NULL,(f2py_init_func)#apiname#,doc_#apiname#},',
},
'initf2pywraphook': {l_not(l_or(ismoduleroutine, isdummyroutine)): '''
{
extern void #F_FUNC#(#name_lower#,#NAME#)(void);
PyObject* o = PyDict_GetItemString(d,"#name#");
PyObject_SetAttrString(o,"_cpointer", F2PyCapsule_FromVoidPtr((void*)#F_FUNC#(#name_lower#,#NAME#),NULL));
#if PY_VERSION_HEX >= 0x03000000
PyObject_SetAttrString(o,"__name__", PyUnicode_FromString("#name#"));
#else
PyObject_SetAttrString(o,"__name__", PyString_FromString("#name#"));
#endif
}
'''},
'need': {l_not(l_or(ismoduleroutine, isdummyroutine)): ['F_WRAPPEDFUNC', 'F_FUNC']},
'callfortranroutine': [
{debugcapi: [
"""\tfprintf(stderr,\"debug-capi:Fortran subroutine `f2pywrap#name_lower#(#callfortran#)\'\\n\");"""]},
{hasexternals: """\
\tif (#setjmpbuf#) {
\t\tf2py_success = 0;
\t} else {"""},
{isthreadsafe: '\tPy_BEGIN_ALLOW_THREADS'},
{l_not(l_or(hascallstatement, isdummyroutine))
: '\t(*f2py_func)(#callfortran#);'},
{hascallstatement:
'\t#callstatement#;\n\t/*(*f2py_func)(#callfortran#);*/'},
{isthreadsafe: '\tPy_END_ALLOW_THREADS'},
{hasexternals: '\t}'}
],
'_check': issubroutine_wrap,
}, { # Function
'functype': '#ctype#',
'docreturn': {l_not(isintent_hide): '#rname#,'},
'docstrout': '#pydocsignout#',
'latexdocstrout': ['\\item[]{{}\\verb@#pydocsignout#@{}}',
{hasresultnote: '--- #resultnote#'}],
'callfortranroutine': [{l_and(debugcapi, isstringfunction): """\
#ifdef USESCOMPAQFORTRAN
\tfprintf(stderr,\"debug-capi:Fortran function #ctype# #fortranname#(#callcompaqfortran#)\\n\");
#else
\tfprintf(stderr,\"debug-capi:Fortran function #ctype# #fortranname#(#callfortran#)\\n\");
#endif
"""},
{l_and(debugcapi, l_not(isstringfunction)): """\
\tfprintf(stderr,\"debug-capi:Fortran function #ctype# #fortranname#(#callfortran#)\\n\");
"""}
],
'_check': l_and(isfunction, l_not(isfunction_wrap))
}, { # Scalar function
'declfortranroutine': {l_and(l_not(l_or(ismoduleroutine, isintent_c)), l_not(isdummyroutine)): 'extern #ctype# #F_FUNC#(#fortranname#,#FORTRANNAME#)(#callprotoargument#);',
l_and(l_not(ismoduleroutine), isintent_c, l_not(isdummyroutine)): 'extern #ctype# #fortranname#(#callprotoargument#);',
isdummyroutine: ''
},
'routine_def': {l_and(l_not(l_or(ismoduleroutine, isintent_c)), l_not(isdummyroutine)): '\t{\"#name#\",-1,{{-1}},0,(char *)#F_FUNC#(#fortranname#,#FORTRANNAME#),(f2py_init_func)#apiname#,doc_#apiname#},',
l_and(l_not(ismoduleroutine), isintent_c, l_not(isdummyroutine)): '\t{\"#name#\",-1,{{-1}},0,(char *)#fortranname#,(f2py_init_func)#apiname#,doc_#apiname#},',
isdummyroutine: '\t{\"#name#\",-1,{{-1}},0,NULL,(f2py_init_func)#apiname#,doc_#apiname#},',
},
'decl': [{iscomplexfunction_warn: '\t#ctype# #name#_return_value={0,0};',
l_not(iscomplexfunction): '\t#ctype# #name#_return_value=0;'},
{iscomplexfunction:
'\tPyObject *#name#_return_value_capi = Py_None;'}
],
'callfortranroutine': [
{hasexternals: """\
\tif (#setjmpbuf#) {
\t\tf2py_success = 0;
\t} else {"""},
{isthreadsafe: '\tPy_BEGIN_ALLOW_THREADS'},
{hascallstatement: '''\t#callstatement#;
/*\t#name#_return_value = (*f2py_func)(#callfortran#);*/
'''},
{l_not(l_or(hascallstatement, isdummyroutine))
: '\t#name#_return_value = (*f2py_func)(#callfortran#);'},
{isthreadsafe: '\tPy_END_ALLOW_THREADS'},
{hasexternals: '\t}'},
{l_and(debugcapi, iscomplexfunction)
: '\tfprintf(stderr,"#routdebugshowvalue#\\n",#name#_return_value.r,#name#_return_value.i);'},
{l_and(debugcapi, l_not(iscomplexfunction)): '\tfprintf(stderr,"#routdebugshowvalue#\\n",#name#_return_value);'}],
'pyobjfrom': {iscomplexfunction: '\t#name#_return_value_capi = pyobj_from_#ctype#1(#name#_return_value);'},
'need': [{l_not(isdummyroutine): 'F_FUNC'},
{iscomplexfunction: 'pyobj_from_#ctype#1'},
{islong_longfunction: 'long_long'},
{islong_doublefunction: 'long_double'}],
'returnformat': {l_not(isintent_hide): '#rformat#'},
'return': {iscomplexfunction: ',#name#_return_value_capi',
l_not(l_or(iscomplexfunction, isintent_hide)): ',#name#_return_value'},
'_check': l_and(isfunction, l_not(isstringfunction), l_not(isfunction_wrap))
}, { # String function # in use for --no-wrap
'declfortranroutine': 'extern void #F_FUNC#(#fortranname#,#FORTRANNAME#)(#callprotoargument#);',
'routine_def': {l_not(l_or(ismoduleroutine, isintent_c)):
'\t{\"#name#\",-1,{{-1}},0,(char *)#F_FUNC#(#fortranname#,#FORTRANNAME#),(f2py_init_func)#apiname#,doc_#apiname#},',
l_and(l_not(ismoduleroutine), isintent_c):
'\t{\"#name#\",-1,{{-1}},0,(char *)#fortranname#,(f2py_init_func)#apiname#,doc_#apiname#},'
},
'decl': ['\t#ctype# #name#_return_value = NULL;',
'\tint #name#_return_value_len = 0;'],
'callfortran':'#name#_return_value,#name#_return_value_len,',
'callfortranroutine':['\t#name#_return_value_len = #rlength#;',
'\tif ((#name#_return_value = (string)malloc(sizeof(char)*(#name#_return_value_len+1))) == NULL) {',
'\t\tPyErr_SetString(PyExc_MemoryError, \"out of memory\");',
'\t\tf2py_success = 0;',
'\t} else {',
"\t\t(#name#_return_value)[#name#_return_value_len] = '\\0';",
'\t}',
'\tif (f2py_success) {',
{hasexternals: """\
\t\tif (#setjmpbuf#) {
\t\t\tf2py_success = 0;
\t\t} else {"""},
{isthreadsafe: '\t\tPy_BEGIN_ALLOW_THREADS'},
"""\
#ifdef USESCOMPAQFORTRAN
\t\t(*f2py_func)(#callcompaqfortran#);
#else
\t\t(*f2py_func)(#callfortran#);
#endif
""",
{isthreadsafe: '\t\tPy_END_ALLOW_THREADS'},
{hasexternals: '\t\t}'},
{debugcapi:
'\t\tfprintf(stderr,"#routdebugshowvalue#\\n",#name#_return_value_len,#name#_return_value);'},
'\t} /* if (f2py_success) after (string)malloc */',
],
'returnformat': '#rformat#',
'return': ',#name#_return_value',
'freemem': '\tSTRINGFREE(#name#_return_value);',
'need': ['F_FUNC', '#ctype#', 'STRINGFREE'],
'_check':l_and(isstringfunction, l_not(isfunction_wrap)) # ???obsolete
},
{ # Debugging
'routdebugenter': '\tfprintf(stderr,"debug-capi:Python C/API function #modulename#.#name#(#docsignature#)\\n");',
'routdebugleave': '\tfprintf(stderr,"debug-capi:Python C/API function #modulename#.#name#: successful.\\n");',
'routdebugfailure': '\tfprintf(stderr,"debug-capi:Python C/API function #modulename#.#name#: failure.\\n");',
'_check': debugcapi
}
]
################ Rules for arguments ##################
typedef_need_dict = {islong_long: 'long_long',
islong_double: 'long_double',
islong_complex: 'complex_long_double',
isunsigned_char: 'unsigned_char',
isunsigned_short: 'unsigned_short',
isunsigned: 'unsigned',
isunsigned_long_long: 'unsigned_long_long',
isunsigned_chararray: 'unsigned_char',
isunsigned_shortarray: 'unsigned_short',
isunsigned_long_longarray: 'unsigned_long_long',
issigned_long_longarray: 'long_long',
}
aux_rules = [
{
'separatorsfor': sepdict
},
{ # Common
'frompyobj': ['\t/* Processing auxiliary variable #varname# */',
{debugcapi: '\tfprintf(stderr,"#vardebuginfo#\\n");'}, ],
'cleanupfrompyobj': '\t/* End of cleaning variable #varname# */',
'need': typedef_need_dict,
},
# Scalars (not complex)
{ # Common
'decl': '\t#ctype# #varname# = 0;',
'need': {hasinitvalue: 'math.h'},
'frompyobj': {hasinitvalue: '\t#varname# = #init#;'},
'_check': l_and(isscalar, l_not(iscomplex)),
},
{
'return': ',#varname#',
'docstrout': '#pydocsignout#',
'docreturn': '#outvarname#,',
'returnformat': '#varrformat#',
'_check': l_and(isscalar, l_not(iscomplex), isintent_out),
},
# Complex scalars
{ # Common
'decl': '\t#ctype# #varname#;',
'frompyobj': {hasinitvalue: '\t#varname#.r = #init.r#, #varname#.i = #init.i#;'},
'_check': iscomplex
},
# String
{ # Common
'decl': ['\t#ctype# #varname# = NULL;',
'\tint slen(#varname#);',
],
'need':['len..'],
'_check':isstring
},
# Array
{ # Common
'decl': ['\t#ctype# *#varname# = NULL;',
'\tnpy_intp #varname#_Dims[#rank#] = {#rank*[-1]#};',
'\tconst int #varname#_Rank = #rank#;',
],
'need':['len..', {hasinitvalue: 'forcomb'}, {hasinitvalue: 'CFUNCSMESS'}],
'_check': isarray
},
# Scalararray
{ # Common
'_check': l_and(isarray, l_not(iscomplexarray))
}, { # Not hidden
'_check': l_and(isarray, l_not(iscomplexarray), isintent_nothide)
},
# Integer*1 array
{'need': '#ctype#',
'_check': isint1array,
'_depend': ''
},
# Integer*-1 array
{'need': '#ctype#',
'_check': isunsigned_chararray,
'_depend': ''
},
# Integer*-2 array
{'need': '#ctype#',
'_check': isunsigned_shortarray,
'_depend': ''
},
# Integer*-8 array
{'need': '#ctype#',
'_check': isunsigned_long_longarray,
'_depend': ''
},
# Complexarray
{'need': '#ctype#',
'_check': iscomplexarray,
'_depend': ''
},
# Stringarray
{
'callfortranappend': {isarrayofstrings: 'flen(#varname#),'},
'need': 'string',
'_check': isstringarray
}
]
arg_rules = [
{
'separatorsfor': sepdict
},
{ # Common
'frompyobj': ['\t/* Processing variable #varname# */',
{debugcapi: '\tfprintf(stderr,"#vardebuginfo#\\n");'}, ],
'cleanupfrompyobj': '\t/* End of cleaning variable #varname# */',
'_depend': '',
'need': typedef_need_dict,
},
# Doc signatures
{
'docstropt': {l_and(isoptional, isintent_nothide): '#pydocsign#'},
'docstrreq': {l_and(isrequired, isintent_nothide): '#pydocsign#'},
'docstrout': {isintent_out: '#pydocsignout#'},
'latexdocstropt': {l_and(isoptional, isintent_nothide): ['\\item[]{{}\\verb@#pydocsign#@{}}',
{hasnote: '--- #note#'}]},
'latexdocstrreq': {l_and(isrequired, isintent_nothide): ['\\item[]{{}\\verb@#pydocsign#@{}}',
{hasnote: '--- #note#'}]},
'latexdocstrout': {isintent_out: ['\\item[]{{}\\verb@#pydocsignout#@{}}',
{l_and(hasnote, isintent_hide): '--- #note#',
l_and(hasnote, isintent_nothide): '--- See above.'}]},
'depend': ''
},
# Required/Optional arguments
{
'kwlist': '"#varname#",',
'docsign': '#varname#,',
'_check': l_and(isintent_nothide, l_not(isoptional))
},
{
'kwlistopt': '"#varname#",',
'docsignopt': '#varname#=#showinit#,',
'docsignoptshort': '#varname#,',
'_check': l_and(isintent_nothide, isoptional)
},
# Docstring/BuildValue
{
'docreturn': '#outvarname#,',
'returnformat': '#varrformat#',
'_check': isintent_out
},
# Externals (call-back functions)
{ # Common
'docsignxa': {isintent_nothide: '#varname#_extra_args=(),'},
'docsignxashort': {isintent_nothide: '#varname#_extra_args,'},
'docstropt': {isintent_nothide: '#varname#_extra_args : input tuple, optional\\n Default: ()'},
'docstrcbs': '#cbdocstr#',
'latexdocstrcbs': '\\item[] #cblatexdocstr#',
'latexdocstropt': {isintent_nothide: '\\item[]{{}\\verb@#varname#_extra_args := () input tuple@{}} --- Extra arguments for call-back function {{}\\verb@#varname#@{}}.'},
'decl': ['\tPyObject *#varname#_capi = Py_None;',
'\tPyTupleObject *#varname#_xa_capi = NULL;',
'\tPyTupleObject *#varname#_args_capi = NULL;',
'\tint #varname#_nofargs_capi = 0;',
{l_not(isintent_callback):
'\t#cbname#_typedef #varname#_cptr;'}
],
'kwlistxa': {isintent_nothide: '"#varname#_extra_args",'},
'argformat': {isrequired: 'O'},
'keyformat': {isoptional: 'O'},
'xaformat': {isintent_nothide: 'O!'},
'args_capi': {isrequired: ',&#varname#_capi'},
'keys_capi': {isoptional: ',&#varname#_capi'},
'keys_xa': ',&PyTuple_Type,&#varname#_xa_capi',
'setjmpbuf': '(setjmp(#cbname#_jmpbuf))',
'callfortran': {l_not(isintent_callback): '#varname#_cptr,'},
'need': ['#cbname#', 'setjmp.h'],
'_check':isexternal
},
{
'frompyobj': [{l_not(isintent_callback): """\
if(F2PyCapsule_Check(#varname#_capi)) {
#varname#_cptr = F2PyCapsule_AsVoidPtr(#varname#_capi);
} else {
#varname#_cptr = #cbname#;
}
"""}, {isintent_callback: """\
if (#varname#_capi==Py_None) {
#varname#_capi = PyObject_GetAttrString(#modulename#_module,\"#varname#\");
if (#varname#_capi) {
if (#varname#_xa_capi==NULL) {
if (PyObject_HasAttrString(#modulename#_module,\"#varname#_extra_args\")) {
PyObject* capi_tmp = PyObject_GetAttrString(#modulename#_module,\"#varname#_extra_args\");
if (capi_tmp)
#varname#_xa_capi = (PyTupleObject *)PySequence_Tuple(capi_tmp);
else
#varname#_xa_capi = (PyTupleObject *)Py_BuildValue(\"()\");
if (#varname#_xa_capi==NULL) {
PyErr_SetString(#modulename#_error,\"Failed to convert #modulename#.#varname#_extra_args to tuple.\\n\");
return NULL;
}
}
}
}
if (#varname#_capi==NULL) {
PyErr_SetString(#modulename#_error,\"Callback #varname# not defined (as an argument or module #modulename# attribute).\\n\");
return NULL;
}
}
"""},
"""\
\t#varname#_nofargs_capi = #cbname#_nofargs;
\tif (create_cb_arglist(#varname#_capi,#varname#_xa_capi,#maxnofargs#,#nofoptargs#,&#cbname#_nofargs,&#varname#_args_capi,\"failed in processing argument list for call-back #varname#.\")) {
\t\tjmp_buf #varname#_jmpbuf;""",
{debugcapi: ["""\
\t\tfprintf(stderr,\"debug-capi:Assuming %d arguments; at most #maxnofargs#(-#nofoptargs#) is expected.\\n\",#cbname#_nofargs);
\t\tCFUNCSMESSPY(\"for #varname#=\",#cbname#_capi);""",
{l_not(isintent_callback): """\t\tfprintf(stderr,\"#vardebugshowvalue# (call-back in C).\\n\",#cbname#);"""}]},
"""\
\t\tCFUNCSMESS(\"Saving jmpbuf for `#varname#`.\\n\");
\t\tSWAP(#varname#_capi,#cbname#_capi,PyObject);
\t\tSWAP(#varname#_args_capi,#cbname#_args_capi,PyTupleObject);
\t\tmemcpy(&#varname#_jmpbuf,&#cbname#_jmpbuf,sizeof(jmp_buf));""",
],
'cleanupfrompyobj':
"""\
\t\tCFUNCSMESS(\"Restoring jmpbuf for `#varname#`.\\n\");
\t\t#cbname#_capi = #varname#_capi;
\t\tPy_DECREF(#cbname#_args_capi);
\t\t#cbname#_args_capi = #varname#_args_capi;
\t\t#cbname#_nofargs = #varname#_nofargs_capi;
\t\tmemcpy(&#cbname#_jmpbuf,&#varname#_jmpbuf,sizeof(jmp_buf));
\t}""",
'need': ['SWAP', 'create_cb_arglist'],
'_check':isexternal,
'_depend':''
},
# Scalars (not complex)
{ # Common
'decl': '\t#ctype# #varname# = 0;',
'pyobjfrom': {debugcapi: '\tfprintf(stderr,"#vardebugshowvalue#\\n",#varname#);'},
'callfortran': {isintent_c: '#varname#,', l_not(isintent_c): '&#varname#,'},
'return': {isintent_out: ',#varname#'},
'_check': l_and(isscalar, l_not(iscomplex))
}, {
'need': {hasinitvalue: 'math.h'},
'_check': l_and(isscalar, l_not(iscomplex)),
}, { # Not hidden
'decl': '\tPyObject *#varname#_capi = Py_None;',
'argformat': {isrequired: 'O'},
'keyformat': {isoptional: 'O'},
'args_capi': {isrequired: ',&#varname#_capi'},
'keys_capi': {isoptional: ',&#varname#_capi'},
'pyobjfrom': {isintent_inout: """\
\tf2py_success = try_pyarr_from_#ctype#(#varname#_capi,&#varname#);
\tif (f2py_success) {"""},
'closepyobjfrom': {isintent_inout: "\t} /*if (f2py_success) of #varname# pyobjfrom*/"},
'need': {isintent_inout: 'try_pyarr_from_#ctype#'},
'_check': l_and(isscalar, l_not(iscomplex), isintent_nothide)
}, {
'frompyobj': [
# hasinitvalue...
# if pyobj is None:
# varname = init
# else
# from_pyobj(varname)
#
# isoptional and noinitvalue...
# if pyobj is not None:
# from_pyobj(varname)
# else:
# varname is uninitialized
#
# ...
# from_pyobj(varname)
#
{hasinitvalue: '\tif (#varname#_capi == Py_None) #varname# = #init#; else',
'_depend': ''},
{l_and(isoptional, l_not(hasinitvalue)): '\tif (#varname#_capi != Py_None)',
'_depend': ''},
{l_not(islogical): '''\
\t\tf2py_success = #ctype#_from_pyobj(&#varname#,#varname#_capi,"#pyname#() #nth# (#varname#) can\'t be converted to #ctype#");
\tif (f2py_success) {'''},
{islogical: '''\
\t\t#varname# = (#ctype#)PyObject_IsTrue(#varname#_capi);
\t\tf2py_success = 1;
\tif (f2py_success) {'''},
],
'cleanupfrompyobj': '\t} /*if (f2py_success) of #varname#*/',
'need': {l_not(islogical): '#ctype#_from_pyobj'},
'_check': l_and(isscalar, l_not(iscomplex), isintent_nothide),
'_depend': ''
}, { # Hidden
'frompyobj': {hasinitvalue: '\t#varname# = #init#;'},
'need': typedef_need_dict,
'_check': l_and(isscalar, l_not(iscomplex), isintent_hide),
'_depend': ''
}, { # Common
'frompyobj': {debugcapi: '\tfprintf(stderr,"#vardebugshowvalue#\\n",#varname#);'},
'_check': l_and(isscalar, l_not(iscomplex)),
'_depend': ''
},
# Complex scalars
{ # Common
'decl': '\t#ctype# #varname#;',
'callfortran': {isintent_c: '#varname#,', l_not(isintent_c): '&#varname#,'},
'pyobjfrom': {debugcapi: '\tfprintf(stderr,"#vardebugshowvalue#\\n",#varname#.r,#varname#.i);'},
'return': {isintent_out: ',#varname#_capi'},
'_check': iscomplex
}, { # Not hidden
'decl': '\tPyObject *#varname#_capi = Py_None;',
'argformat': {isrequired: 'O'},
'keyformat': {isoptional: 'O'},
'args_capi': {isrequired: ',&#varname#_capi'},
'keys_capi': {isoptional: ',&#varname#_capi'},
'need': {isintent_inout: 'try_pyarr_from_#ctype#'},
'pyobjfrom': {isintent_inout: """\
\t\tf2py_success = try_pyarr_from_#ctype#(#varname#_capi,&#varname#);
\t\tif (f2py_success) {"""},
'closepyobjfrom': {isintent_inout: "\t\t} /*if (f2py_success) of #varname# pyobjfrom*/"},
'_check': l_and(iscomplex, isintent_nothide)
}, {
'frompyobj': [{hasinitvalue: '\tif (#varname#_capi==Py_None) {#varname#.r = #init.r#, #varname#.i = #init.i#;} else'},
{l_and(isoptional, l_not(hasinitvalue))
: '\tif (#varname#_capi != Py_None)'},
'\t\tf2py_success = #ctype#_from_pyobj(&#varname#,#varname#_capi,"#pyname#() #nth# (#varname#) can\'t be converted to #ctype#");'
'\n\tif (f2py_success) {'],
'cleanupfrompyobj': '\t} /*if (f2py_success) of #varname# frompyobj*/',
'need': ['#ctype#_from_pyobj'],
'_check': l_and(iscomplex, isintent_nothide),
'_depend': ''
}, { # Hidden
'decl': {isintent_out: '\tPyObject *#varname#_capi = Py_None;'},
'_check': l_and(iscomplex, isintent_hide)
}, {
'frompyobj': {hasinitvalue: '\t#varname#.r = #init.r#, #varname#.i = #init.i#;'},
'_check': l_and(iscomplex, isintent_hide),
'_depend': ''
}, { # Common
'pyobjfrom': {isintent_out: '\t#varname#_capi = pyobj_from_#ctype#1(#varname#);'},
'need': ['pyobj_from_#ctype#1'],
'_check': iscomplex
}, {
'frompyobj': {debugcapi: '\tfprintf(stderr,"#vardebugshowvalue#\\n",#varname#.r,#varname#.i);'},
'_check': iscomplex,
'_depend': ''
},
# String
{ # Common
'decl': ['\t#ctype# #varname# = NULL;',
'\tint slen(#varname#);',
'\tPyObject *#varname#_capi = Py_None;'],
'callfortran':'#varname#,',
'callfortranappend':'slen(#varname#),',
'pyobjfrom':{debugcapi: '\tfprintf(stderr,"#vardebugshowvalue#\\n",slen(#varname#),#varname#);'},
'return': {isintent_out: ',#varname#'},
'need': ['len..'], # 'STRINGFREE'],
'_check':isstring
}, { # Common
'frompyobj': """\
\tslen(#varname#) = #length#;
\tf2py_success = #ctype#_from_pyobj(&#varname#,&slen(#varname#),#init#,#varname#_capi,\"#ctype#_from_pyobj failed in converting #nth# `#varname#\' of #pyname# to C #ctype#\");
\tif (f2py_success) {""",
'cleanupfrompyobj': """\
\t\tSTRINGFREE(#varname#);
\t} /*if (f2py_success) of #varname#*/""",
'need': ['#ctype#_from_pyobj', 'len..', 'STRINGFREE'],
'_check':isstring,
'_depend':''
}, { # Not hidden
'argformat': {isrequired: 'O'},
'keyformat': {isoptional: 'O'},
'args_capi': {isrequired: ',&#varname#_capi'},
'keys_capi': {isoptional: ',&#varname#_capi'},
'pyobjfrom': {isintent_inout: '''\
\tf2py_success = try_pyarr_from_#ctype#(#varname#_capi,#varname#);
\tif (f2py_success) {'''},
'closepyobjfrom': {isintent_inout: '\t} /*if (f2py_success) of #varname# pyobjfrom*/'},
'need': {isintent_inout: 'try_pyarr_from_#ctype#'},
'_check': l_and(isstring, isintent_nothide)
}, { # Hidden
'_check': l_and(isstring, isintent_hide)
}, {
'frompyobj': {debugcapi: '\tfprintf(stderr,"#vardebugshowvalue#\\n",slen(#varname#),#varname#);'},
'_check': isstring,
'_depend': ''
},
# Array
{ # Common
'decl': ['\t#ctype# *#varname# = NULL;',
'\tnpy_intp #varname#_Dims[#rank#] = {#rank*[-1]#};',
'\tconst int #varname#_Rank = #rank#;',
'\tPyArrayObject *capi_#varname#_tmp = NULL;',
'\tint capi_#varname#_intent = 0;',
],
'callfortran':'#varname#,',
'return':{isintent_out: ',capi_#varname#_tmp'},
'need': 'len..',
'_check': isarray
}, { # intent(overwrite) array
'decl': '\tint capi_overwrite_#varname# = 1;',
'kwlistxa': '"overwrite_#varname#",',
'xaformat': 'i',
'keys_xa': ',&capi_overwrite_#varname#',
'docsignxa': 'overwrite_#varname#=1,',
'docsignxashort': 'overwrite_#varname#,',
'docstropt': 'overwrite_#varname# : input int, optional\\n Default: 1',
'_check': l_and(isarray, isintent_overwrite),
}, {
'frompyobj': '\tcapi_#varname#_intent |= (capi_overwrite_#varname#?0:F2PY_INTENT_COPY);',
'_check': l_and(isarray, isintent_overwrite),
'_depend': '',
},
{ # intent(copy) array
'decl': '\tint capi_overwrite_#varname# = 0;',
'kwlistxa': '"overwrite_#varname#",',
'xaformat': 'i',
'keys_xa': ',&capi_overwrite_#varname#',
'docsignxa': 'overwrite_#varname#=0,',
'docsignxashort': 'overwrite_#varname#,',
'docstropt': 'overwrite_#varname# : input int, optional\\n Default: 0',
'_check': l_and(isarray, isintent_copy),
}, {
'frompyobj': '\tcapi_#varname#_intent |= (capi_overwrite_#varname#?0:F2PY_INTENT_COPY);',
'_check': l_and(isarray, isintent_copy),
'_depend': '',
}, {
'need': [{hasinitvalue: 'forcomb'}, {hasinitvalue: 'CFUNCSMESS'}],
'_check': isarray,
'_depend': ''
}, { # Not hidden
'decl': '\tPyObject *#varname#_capi = Py_None;',
'argformat': {isrequired: 'O'},
'keyformat': {isoptional: 'O'},
'args_capi': {isrequired: ',&#varname#_capi'},
'keys_capi': {isoptional: ',&#varname#_capi'},
'_check': l_and(isarray, isintent_nothide)
}, {
'frompyobj': ['\t#setdims#;',
'\tcapi_#varname#_intent |= #intent#;',
{isintent_hide:
'\tcapi_#varname#_tmp = array_from_pyobj(#atype#,#varname#_Dims,#varname#_Rank,capi_#varname#_intent,Py_None);'},
{isintent_nothide:
'\tcapi_#varname#_tmp = array_from_pyobj(#atype#,#varname#_Dims,#varname#_Rank,capi_#varname#_intent,#varname#_capi);'},
"""\
\tif (capi_#varname#_tmp == NULL) {
\t\tif (!PyErr_Occurred())
\t\t\tPyErr_SetString(#modulename#_error,\"failed in converting #nth# `#varname#\' of #pyname# to C/Fortran array\" );
\t} else {
\t\t#varname# = (#ctype# *)(PyArray_DATA(capi_#varname#_tmp));
""",
{hasinitvalue: [
{isintent_nothide:
'\tif (#varname#_capi == Py_None) {'},
{isintent_hide: '\t{'},
{iscomplexarray: '\t\t#ctype# capi_c;'},
"""\
\t\tint *_i,capi_i=0;
\t\tCFUNCSMESS(\"#name#: Initializing #varname#=#init#\\n\");
\t\tif (initforcomb(PyArray_DIMS(capi_#varname#_tmp),PyArray_NDIM(capi_#varname#_tmp),1)) {
\t\t\twhile ((_i = nextforcomb()))
\t\t\t\t#varname#[capi_i++] = #init#; /* fortran way */
\t\t} else {
\t\t\tif (!PyErr_Occurred())
\t\t\t\tPyErr_SetString(#modulename#_error,\"Initialization of #nth# #varname# failed (initforcomb).\");
\t\t\tf2py_success = 0;
\t\t}
\t}
\tif (f2py_success) {"""]},
],
'cleanupfrompyobj': [ # note that this list will be reversed
'\t} /*if (capi_#varname#_tmp == NULL) ... else of #varname#*/',
{l_not(l_or(isintent_out, isintent_hide)): """\
\tif((PyObject *)capi_#varname#_tmp!=#varname#_capi) {
\t\tPy_XDECREF(capi_#varname#_tmp); }"""},
{l_and(isintent_hide, l_not(isintent_out))
: """\t\tPy_XDECREF(capi_#varname#_tmp);"""},
{hasinitvalue: '\t} /*if (f2py_success) of #varname# init*/'},
],
'_check': isarray,
'_depend': ''
},
# Scalararray
{ # Common
'_check': l_and(isarray, l_not(iscomplexarray))
}, { # Not hidden
'_check': l_and(isarray, l_not(iscomplexarray), isintent_nothide)
},
# Integer*1 array
{'need': '#ctype#',
'_check': isint1array,
'_depend': ''
},
# Integer*-1 array
{'need': '#ctype#',
'_check': isunsigned_chararray,
'_depend': ''
},
# Integer*-2 array
{'need': '#ctype#',
'_check': isunsigned_shortarray,
'_depend': ''
},
# Integer*-8 array
{'need': '#ctype#',
'_check': isunsigned_long_longarray,
'_depend': ''
},
# Complexarray
{'need': '#ctype#',
'_check': iscomplexarray,
'_depend': ''
},
# Stringarray
{
'callfortranappend': {isarrayofstrings: 'flen(#varname#),'},
'need': 'string',
'_check': isstringarray
}
]
################# Rules for checking ###############
check_rules = [
{
'frompyobj': {debugcapi: '\tfprintf(stderr,\"debug-capi:Checking `#check#\'\\n\");'},
'need': 'len..'
}, {
'frompyobj': '\tCHECKSCALAR(#check#,\"#check#\",\"#nth# #varname#\",\"#varshowvalue#\",#varname#) {',
'cleanupfrompyobj': '\t} /*CHECKSCALAR(#check#)*/',
'need': 'CHECKSCALAR',
'_check': l_and(isscalar, l_not(iscomplex)),
'_break': ''
}, {
'frompyobj': '\tCHECKSTRING(#check#,\"#check#\",\"#nth# #varname#\",\"#varshowvalue#\",#varname#) {',
'cleanupfrompyobj': '\t} /*CHECKSTRING(#check#)*/',
'need': 'CHECKSTRING',
'_check': isstring,
'_break': ''
}, {
'need': 'CHECKARRAY',
'frompyobj': '\tCHECKARRAY(#check#,\"#check#\",\"#nth# #varname#\") {',
'cleanupfrompyobj': '\t} /*CHECKARRAY(#check#)*/',
'_check': isarray,
'_break': ''
}, {
'need': 'CHECKGENERIC',
'frompyobj': '\tCHECKGENERIC(#check#,\"#check#\",\"#nth# #varname#\") {',
'cleanupfrompyobj': '\t} /*CHECKGENERIC(#check#)*/',
}
]
########## Applying the rules. No need to modify what follows #############
#################### Build C/API module #######################
def buildmodule(m, um):
"""
Return
"""
global f2py_version, options
outmess('\tBuilding module "%s"...\n' % (m['name']))
ret = {}
mod_rules = defmod_rules[:]
vrd = capi_maps.modsign2map(m)
rd = dictappend({'f2py_version': f2py_version}, vrd)
funcwrappers = []
funcwrappers2 = [] # F90 codes
for n in m['interfaced']:
nb = None
for bi in m['body']:
if not bi['block'] == 'interface':
errmess('buildmodule: Expected interface block. Skipping.\n')
continue
for b in bi['body']:
if b['name'] == n:
nb = b
break
if not nb:
errmess(
'buildmodule: Could not found the body of interfaced routine "%s". Skipping.\n' % (n))
continue
nb_list = [nb]
if 'entry' in nb:
for k, a in nb['entry'].items():
nb1 = copy.deepcopy(nb)
del nb1['entry']
nb1['name'] = k
nb1['args'] = a
nb_list.append(nb1)
for nb in nb_list:
api, wrap = buildapi(nb)
if wrap:
if ismoduleroutine(nb):
funcwrappers2.append(wrap)
else:
funcwrappers.append(wrap)
ar = applyrules(api, vrd)
rd = dictappend(rd, ar)
# Construct COMMON block support
cr, wrap = common_rules.buildhooks(m)
if wrap:
funcwrappers.append(wrap)
ar = applyrules(cr, vrd)
rd = dictappend(rd, ar)
# Construct F90 module support
mr, wrap = f90mod_rules.buildhooks(m)
if wrap:
funcwrappers2.append(wrap)
ar = applyrules(mr, vrd)
rd = dictappend(rd, ar)
for u in um:
ar = use_rules.buildusevars(u, m['use'][u['name']])
rd = dictappend(rd, ar)
needs = cfuncs.get_needs()
code = {}
for n in needs.keys():
code[n] = []
for k in needs[n]:
c = ''
if k in cfuncs.includes0:
c = cfuncs.includes0[k]
elif k in cfuncs.includes:
c = cfuncs.includes[k]
elif k in cfuncs.userincludes:
c = cfuncs.userincludes[k]
elif k in cfuncs.typedefs:
c = cfuncs.typedefs[k]
elif k in cfuncs.typedefs_generated:
c = cfuncs.typedefs_generated[k]
elif k in cfuncs.cppmacros:
c = cfuncs.cppmacros[k]
elif k in cfuncs.cfuncs:
c = cfuncs.cfuncs[k]
elif k in cfuncs.callbacks:
c = cfuncs.callbacks[k]
elif k in cfuncs.f90modhooks:
c = cfuncs.f90modhooks[k]
elif k in cfuncs.commonhooks:
c = cfuncs.commonhooks[k]
else:
errmess('buildmodule: unknown need %s.\n' % (repr(k)))
continue
code[n].append(c)
mod_rules.append(code)
for r in mod_rules:
if ('_check' in r and r['_check'](m)) or ('_check' not in r):
ar = applyrules(r, vrd, m)
rd = dictappend(rd, ar)
ar = applyrules(module_rules, rd)
fn = os.path.join(options['buildpath'], vrd['coutput'])
ret['csrc'] = fn
f = open(fn, 'w')
f.write(ar['modulebody'].replace('\t', 2 * ' '))
f.close()
outmess('\tWrote C/API module "%s" to file "%s"\n' % (m['name'], fn))
if options['dorestdoc']:
fn = os.path.join(
options['buildpath'], vrd['modulename'] + 'module.rest')
f = open(fn, 'w')
f.write('.. -*- rest -*-\n')
f.write('\n'.join(ar['restdoc']))
f.close()
outmess('\tReST Documentation is saved to file "%s/%smodule.rest"\n' %
(options['buildpath'], vrd['modulename']))
if options['dolatexdoc']:
fn = os.path.join(
options['buildpath'], vrd['modulename'] + 'module.tex')
ret['ltx'] = fn
f = open(fn, 'w')
f.write(
'%% This file is auto-generated with f2py (version:%s)\n' % (f2py_version))
if 'shortlatex' not in options:
f.write(
'\\documentclass{article}\n\\usepackage{a4wide}\n\\begin{document}\n\\tableofcontents\n\n')
f.write('\n'.join(ar['latexdoc']))
if 'shortlatex' not in options:
f.write('\\end{document}')
f.close()
outmess('\tDocumentation is saved to file "%s/%smodule.tex"\n' %
(options['buildpath'], vrd['modulename']))
if funcwrappers:
wn = os.path.join(options['buildpath'], vrd['f2py_wrapper_output'])
ret['fsrc'] = wn
f = open(wn, 'w')
f.write('C -*- fortran -*-\n')
f.write(
'C This file is autogenerated with f2py (version:%s)\n' % (f2py_version))
f.write(
'C It contains Fortran 77 wrappers to fortran functions.\n')
lines = []
for l in ('\n\n'.join(funcwrappers) + '\n').split('\n'):
if l and l[0] == ' ':
while len(l) >= 66:
lines.append(l[:66] + '\n &')
l = l[66:]
lines.append(l + '\n')
else:
lines.append(l + '\n')
lines = ''.join(lines).replace('\n &\n', '\n')
f.write(lines)
f.close()
outmess('\tFortran 77 wrappers are saved to "%s"\n' % (wn))
if funcwrappers2:
wn = os.path.join(
options['buildpath'], '%s-f2pywrappers2.f90' % (vrd['modulename']))
ret['fsrc'] = wn
f = open(wn, 'w')
f.write('! -*- f90 -*-\n')
f.write(
'! This file is autogenerated with f2py (version:%s)\n' % (f2py_version))
f.write(
'! It contains Fortran 90 wrappers to fortran functions.\n')
lines = []
for l in ('\n\n'.join(funcwrappers2) + '\n').split('\n'):
if len(l) > 72 and l[0] == ' ':
lines.append(l[:72] + '&\n &')
l = l[72:]
while len(l) > 66:
lines.append(l[:66] + '&\n &')
l = l[66:]
lines.append(l + '\n')
else:
lines.append(l + '\n')
lines = ''.join(lines).replace('\n &\n', '\n')
f.write(lines)
f.close()
outmess('\tFortran 90 wrappers are saved to "%s"\n' % (wn))
return ret
################## Build C/API function #############
stnd = {1: 'st', 2: 'nd', 3: 'rd', 4: 'th', 5: 'th',
6: 'th', 7: 'th', 8: 'th', 9: 'th', 0: 'th'}
def buildapi(rout):
rout, wrap = func2subr.assubr(rout)
args, depargs = getargs2(rout)
capi_maps.depargs = depargs
var = rout['vars']
if ismoduleroutine(rout):
outmess('\t\t\tConstructing wrapper function "%s.%s"...\n' %
(rout['modulename'], rout['name']))
else:
outmess('\t\tConstructing wrapper function "%s"...\n' % (rout['name']))
# Routine
vrd = capi_maps.routsign2map(rout)
rd = dictappend({}, vrd)
for r in rout_rules:
if ('_check' in r and r['_check'](rout)) or ('_check' not in r):
ar = applyrules(r, vrd, rout)
rd = dictappend(rd, ar)
# Args
nth, nthk = 0, 0
savevrd = {}
for a in args:
vrd = capi_maps.sign2map(a, var[a])
if isintent_aux(var[a]):
_rules = aux_rules
else:
_rules = arg_rules
if not isintent_hide(var[a]):
if not isoptional(var[a]):
nth = nth + 1
vrd['nth'] = repr(nth) + stnd[nth % 10] + ' argument'
else:
nthk = nthk + 1
vrd['nth'] = repr(nthk) + stnd[nthk % 10] + ' keyword'
else:
vrd['nth'] = 'hidden'
savevrd[a] = vrd
for r in _rules:
if '_depend' in r:
continue
if ('_check' in r and r['_check'](var[a])) or ('_check' not in r):
ar = applyrules(r, vrd, var[a])
rd = dictappend(rd, ar)
if '_break' in r:
break
for a in depargs:
if isintent_aux(var[a]):
_rules = aux_rules
else:
_rules = arg_rules
vrd = savevrd[a]
for r in _rules:
if '_depend' not in r:
continue
if ('_check' in r and r['_check'](var[a])) or ('_check' not in r):
ar = applyrules(r, vrd, var[a])
rd = dictappend(rd, ar)
if '_break' in r:
break
if 'check' in var[a]:
for c in var[a]['check']:
vrd['check'] = c
ar = applyrules(check_rules, vrd, var[a])
rd = dictappend(rd, ar)
if isinstance(rd['cleanupfrompyobj'], list):
rd['cleanupfrompyobj'].reverse()
if isinstance(rd['closepyobjfrom'], list):
rd['closepyobjfrom'].reverse()
rd['docsignature'] = stripcomma(replace('#docsign##docsignopt##docsignxa#',
{'docsign': rd['docsign'],
'docsignopt': rd['docsignopt'],
'docsignxa': rd['docsignxa']}))
optargs = stripcomma(replace('#docsignopt##docsignxa#',
{'docsignxa': rd['docsignxashort'],
'docsignopt': rd['docsignoptshort']}
))
if optargs == '':
rd['docsignatureshort'] = stripcomma(
replace('#docsign#', {'docsign': rd['docsign']}))
else:
rd['docsignatureshort'] = replace('#docsign#[#docsignopt#]',
{'docsign': rd['docsign'],
'docsignopt': optargs,
})
rd['latexdocsignatureshort'] = rd['docsignatureshort'].replace('_', '\\_')
rd['latexdocsignatureshort'] = rd[
'latexdocsignatureshort'].replace(',', ', ')
cfs = stripcomma(replace('#callfortran##callfortranappend#', {
'callfortran': rd['callfortran'], 'callfortranappend': rd['callfortranappend']}))
if len(rd['callfortranappend']) > 1:
rd['callcompaqfortran'] = stripcomma(replace('#callfortran# 0,#callfortranappend#', {
'callfortran': rd['callfortran'], 'callfortranappend': rd['callfortranappend']}))
else:
rd['callcompaqfortran'] = cfs
rd['callfortran'] = cfs
if isinstance(rd['docreturn'], list):
rd['docreturn'] = stripcomma(
replace('#docreturn#', {'docreturn': rd['docreturn']})) + ' = '
rd['docstrsigns'] = []
rd['latexdocstrsigns'] = []
for k in ['docstrreq', 'docstropt', 'docstrout', 'docstrcbs']:
if k in rd and isinstance(rd[k], list):
rd['docstrsigns'] = rd['docstrsigns'] + rd[k]
k = 'latex' + k
if k in rd and isinstance(rd[k], list):
rd['latexdocstrsigns'] = rd['latexdocstrsigns'] + rd[k][0:1] +\
['\\begin{description}'] + rd[k][1:] +\
['\\end{description}']
# Workaround for Python 2.6, 2.6.1 bug: http://bugs.python.org/issue4720
if rd['keyformat'] or rd['xaformat']:
argformat = rd['argformat']
if isinstance(argformat, list):
argformat.append('|')
else:
assert isinstance(argformat, str), repr(
(argformat, type(argformat)))
rd['argformat'] += '|'
ar = applyrules(routine_rules, rd)
if ismoduleroutine(rout):
outmess('\t\t\t %s\n' % (ar['docshort']))
else:
outmess('\t\t %s\n' % (ar['docshort']))
return ar, wrap
#################### EOF rules.py #######################
| 58,525 | 38.705563 | 212 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/f2py_testing.py
|
from __future__ import division, absolute_import, print_function
import sys
import re
from numpy.testing import jiffies, memusage
def cmdline():
m = re.compile(r'\A\d+\Z')
args = []
repeat = 1
for a in sys.argv[1:]:
if m.match(a):
repeat = eval(a)
else:
args.append(a)
f2py_opts = ' '.join(args)
return repeat, f2py_opts
def run(runtest, test_functions, repeat=1):
l = [(t, repr(t.__doc__.split('\n')[1].strip())) for t in test_functions]
start_memusage = memusage()
diff_memusage = None
start_jiffies = jiffies()
i = 0
while i < repeat:
i += 1
for t, fname in l:
runtest(t)
if start_memusage is None:
continue
if diff_memusage is None:
diff_memusage = memusage() - start_memusage
else:
diff_memusage2 = memusage() - start_memusage
if diff_memusage2 != diff_memusage:
print('memory usage change at step %i:' % i,
diff_memusage2 - diff_memusage,
fname)
diff_memusage = diff_memusage2
current_memusage = memusage()
print('run', repeat * len(test_functions), 'tests',
'in %.2f seconds' % ((jiffies() - start_jiffies) / 100.0))
if start_memusage:
print('initial virtual memory size:', start_memusage, 'bytes')
print('current virtual memory size:', current_memusage, 'bytes')
| 1,523 | 30.102041 | 77 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/func2subr.py
|
#!/usr/bin/env python
"""
Rules for building C/API module with f2py2e.
Copyright 1999,2000 Pearu Peterson all rights reserved,
Pearu Peterson <[email protected]>
Permission to use, modify, and distribute this software is given under the
terms of the NumPy License.
NO WARRANTY IS EXPRESSED OR IMPLIED. USE AT YOUR OWN RISK.
$Date: 2004/11/26 11:13:06 $
Pearu Peterson
"""
from __future__ import division, absolute_import, print_function
__version__ = "$Revision: 1.16 $"[10:-1]
f2py_version = 'See `f2py -v`'
import copy
from .auxfuncs import (
getfortranname, isexternal, isfunction, isfunction_wrap, isintent_in,
isintent_out, islogicalfunction, ismoduleroutine, isscalar,
issubroutine, issubroutine_wrap, outmess, show
)
def var2fixfortran(vars, a, fa=None, f90mode=None):
if fa is None:
fa = a
if a not in vars:
show(vars)
outmess('var2fixfortran: No definition for argument "%s".\n' % a)
return ''
if 'typespec' not in vars[a]:
show(vars[a])
outmess('var2fixfortran: No typespec for argument "%s".\n' % a)
return ''
vardef = vars[a]['typespec']
if vardef == 'type' and 'typename' in vars[a]:
vardef = '%s(%s)' % (vardef, vars[a]['typename'])
selector = {}
lk = ''
if 'kindselector' in vars[a]:
selector = vars[a]['kindselector']
lk = 'kind'
elif 'charselector' in vars[a]:
selector = vars[a]['charselector']
lk = 'len'
if '*' in selector:
if f90mode:
if selector['*'] in ['*', ':', '(*)']:
vardef = '%s(len=*)' % (vardef)
else:
vardef = '%s(%s=%s)' % (vardef, lk, selector['*'])
else:
if selector['*'] in ['*', ':']:
vardef = '%s*(%s)' % (vardef, selector['*'])
else:
vardef = '%s*%s' % (vardef, selector['*'])
else:
if 'len' in selector:
vardef = '%s(len=%s' % (vardef, selector['len'])
if 'kind' in selector:
vardef = '%s,kind=%s)' % (vardef, selector['kind'])
else:
vardef = '%s)' % (vardef)
elif 'kind' in selector:
vardef = '%s(kind=%s)' % (vardef, selector['kind'])
vardef = '%s %s' % (vardef, fa)
if 'dimension' in vars[a]:
vardef = '%s(%s)' % (vardef, ','.join(vars[a]['dimension']))
return vardef
def createfuncwrapper(rout, signature=0):
assert isfunction(rout)
extra_args = []
vars = rout['vars']
for a in rout['args']:
v = rout['vars'][a]
for i, d in enumerate(v.get('dimension', [])):
if d == ':':
dn = 'f2py_%s_d%s' % (a, i)
dv = dict(typespec='integer', intent=['hide'])
dv['='] = 'shape(%s, %s)' % (a, i)
extra_args.append(dn)
vars[dn] = dv
v['dimension'][i] = dn
rout['args'].extend(extra_args)
need_interface = bool(extra_args)
ret = ['']
def add(line, ret=ret):
ret[0] = '%s\n %s' % (ret[0], line)
name = rout['name']
fortranname = getfortranname(rout)
f90mode = ismoduleroutine(rout)
newname = '%sf2pywrap' % (name)
if newname not in vars:
vars[newname] = vars[name]
args = [newname] + rout['args'][1:]
else:
args = [newname] + rout['args']
l = var2fixfortran(vars, name, newname, f90mode)
if l[:13] == 'character*(*)':
if f90mode:
l = 'character(len=10)' + l[13:]
else:
l = 'character*10' + l[13:]
charselect = vars[name]['charselector']
if charselect.get('*', '') == '(*)':
charselect['*'] = '10'
sargs = ', '.join(args)
if f90mode:
add('subroutine f2pywrap_%s_%s (%s)' %
(rout['modulename'], name, sargs))
if not signature:
add('use %s, only : %s' % (rout['modulename'], fortranname))
else:
add('subroutine f2pywrap%s (%s)' % (name, sargs))
if not need_interface:
add('external %s' % (fortranname))
l = l + ', ' + fortranname
if need_interface:
for line in rout['saved_interface'].split('\n'):
if line.lstrip().startswith('use '):
add(line)
args = args[1:]
dumped_args = []
for a in args:
if isexternal(vars[a]):
add('external %s' % (a))
dumped_args.append(a)
for a in args:
if a in dumped_args:
continue
if isscalar(vars[a]):
add(var2fixfortran(vars, a, f90mode=f90mode))
dumped_args.append(a)
for a in args:
if a in dumped_args:
continue
if isintent_in(vars[a]):
add(var2fixfortran(vars, a, f90mode=f90mode))
dumped_args.append(a)
for a in args:
if a in dumped_args:
continue
add(var2fixfortran(vars, a, f90mode=f90mode))
add(l)
if need_interface:
if f90mode:
# f90 module already defines needed interface
pass
else:
add('interface')
add(rout['saved_interface'].lstrip())
add('end interface')
sargs = ', '.join([a for a in args if a not in extra_args])
if not signature:
if islogicalfunction(rout):
add('%s = .not.(.not.%s(%s))' % (newname, fortranname, sargs))
else:
add('%s = %s(%s)' % (newname, fortranname, sargs))
if f90mode:
add('end subroutine f2pywrap_%s_%s' % (rout['modulename'], name))
else:
add('end')
return ret[0]
def createsubrwrapper(rout, signature=0):
assert issubroutine(rout)
extra_args = []
vars = rout['vars']
for a in rout['args']:
v = rout['vars'][a]
for i, d in enumerate(v.get('dimension', [])):
if d == ':':
dn = 'f2py_%s_d%s' % (a, i)
dv = dict(typespec='integer', intent=['hide'])
dv['='] = 'shape(%s, %s)' % (a, i)
extra_args.append(dn)
vars[dn] = dv
v['dimension'][i] = dn
rout['args'].extend(extra_args)
need_interface = bool(extra_args)
ret = ['']
def add(line, ret=ret):
ret[0] = '%s\n %s' % (ret[0], line)
name = rout['name']
fortranname = getfortranname(rout)
f90mode = ismoduleroutine(rout)
args = rout['args']
sargs = ', '.join(args)
if f90mode:
add('subroutine f2pywrap_%s_%s (%s)' %
(rout['modulename'], name, sargs))
if not signature:
add('use %s, only : %s' % (rout['modulename'], fortranname))
else:
add('subroutine f2pywrap%s (%s)' % (name, sargs))
if not need_interface:
add('external %s' % (fortranname))
if need_interface:
for line in rout['saved_interface'].split('\n'):
if line.lstrip().startswith('use '):
add(line)
dumped_args = []
for a in args:
if isexternal(vars[a]):
add('external %s' % (a))
dumped_args.append(a)
for a in args:
if a in dumped_args:
continue
if isscalar(vars[a]):
add(var2fixfortran(vars, a, f90mode=f90mode))
dumped_args.append(a)
for a in args:
if a in dumped_args:
continue
add(var2fixfortran(vars, a, f90mode=f90mode))
if need_interface:
if f90mode:
# f90 module already defines needed interface
pass
else:
add('interface')
add(rout['saved_interface'].lstrip())
add('end interface')
sargs = ', '.join([a for a in args if a not in extra_args])
if not signature:
add('call %s(%s)' % (fortranname, sargs))
if f90mode:
add('end subroutine f2pywrap_%s_%s' % (rout['modulename'], name))
else:
add('end')
return ret[0]
def assubr(rout):
if isfunction_wrap(rout):
fortranname = getfortranname(rout)
name = rout['name']
outmess('\t\tCreating wrapper for Fortran function "%s"("%s")...\n' % (
name, fortranname))
rout = copy.copy(rout)
fname = name
rname = fname
if 'result' in rout:
rname = rout['result']
rout['vars'][fname] = rout['vars'][rname]
fvar = rout['vars'][fname]
if not isintent_out(fvar):
if 'intent' not in fvar:
fvar['intent'] = []
fvar['intent'].append('out')
flag = 1
for i in fvar['intent']:
if i.startswith('out='):
flag = 0
break
if flag:
fvar['intent'].append('out=%s' % (rname))
rout['args'][:] = [fname] + rout['args']
return rout, createfuncwrapper(rout)
if issubroutine_wrap(rout):
fortranname = getfortranname(rout)
name = rout['name']
outmess('\t\tCreating wrapper for Fortran subroutine "%s"("%s")...\n' % (
name, fortranname))
rout = copy.copy(rout)
return rout, createsubrwrapper(rout)
return rout, ''
| 9,224 | 29.75 | 81 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/info.py
|
"""Fortran to Python Interface Generator.
"""
from __future__ import division, absolute_import, print_function
postpone_import = True
| 136 | 18.571429 | 64 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/__init__.py
|
#!/usr/bin/env python
"""Fortran to Python Interface Generator.
"""
from __future__ import division, absolute_import, print_function
__all__ = ['run_main', 'compile', 'f2py_testing']
import sys
from . import f2py2e
from . import f2py_testing
from . import diagnose
run_main = f2py2e.run_main
main = f2py2e.main
def compile(source,
modulename='untitled',
extra_args='',
verbose=True,
source_fn=None,
extension='.f'
):
"""
Build extension module from processing source with f2py.
Parameters
----------
source : str
Fortran source of module / subroutine to compile
modulename : str, optional
The name of the compiled python module
extra_args : str, optional
Additional parameters passed to f2py
verbose : bool, optional
Print f2py output to screen
source_fn : str, optional
Name of the file where the fortran source is written.
The default is to use a temporary file with the extension
provided by the `extension` parameter
extension : {'.f', '.f90'}, optional
Filename extension if `source_fn` is not provided.
The extension tells which fortran standard is used.
The default is `.f`, which implies F77 standard.
.. versionadded:: 1.11.0
"""
from numpy.distutils.exec_command import exec_command
import tempfile
if source_fn is None:
f = tempfile.NamedTemporaryFile(suffix=extension)
else:
f = open(source_fn, 'w')
try:
f.write(source)
f.flush()
args = ' -c -m {} {} {}'.format(modulename, f.name, extra_args)
c = '{} -c "import numpy.f2py as f2py2e;f2py2e.main()" {}'
c = c.format(sys.executable, args)
status, output = exec_command(c)
if verbose:
print(output)
finally:
f.close()
return status
from numpy.testing import _numpy_tester
test = _numpy_tester().test
bench = _numpy_tester().bench
| 2,027 | 26.04 | 71 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/cfuncs.py
|
#!/usr/bin/env python
"""
C declarations, CPP macros, and C functions for f2py2e.
Only required declarations/macros/functions will be used.
Copyright 1999,2000 Pearu Peterson all rights reserved,
Pearu Peterson <[email protected]>
Permission to use, modify, and distribute this software is given under the
terms of the NumPy License.
NO WARRANTY IS EXPRESSED OR IMPLIED. USE AT YOUR OWN RISK.
$Date: 2005/05/06 11:42:34 $
Pearu Peterson
"""
from __future__ import division, absolute_import, print_function
import sys
import copy
from . import __version__
f2py_version = __version__.version
errmess = sys.stderr.write
##################### Definitions ##################
outneeds = {'includes0': [], 'includes': [], 'typedefs': [], 'typedefs_generated': [],
'userincludes': [],
'cppmacros': [], 'cfuncs': [], 'callbacks': [], 'f90modhooks': [],
'commonhooks': []}
needs = {}
includes0 = {'includes0': '/*need_includes0*/'}
includes = {'includes': '/*need_includes*/'}
userincludes = {'userincludes': '/*need_userincludes*/'}
typedefs = {'typedefs': '/*need_typedefs*/'}
typedefs_generated = {'typedefs_generated': '/*need_typedefs_generated*/'}
cppmacros = {'cppmacros': '/*need_cppmacros*/'}
cfuncs = {'cfuncs': '/*need_cfuncs*/'}
callbacks = {'callbacks': '/*need_callbacks*/'}
f90modhooks = {'f90modhooks': '/*need_f90modhooks*/',
'initf90modhooksstatic': '/*initf90modhooksstatic*/',
'initf90modhooksdynamic': '/*initf90modhooksdynamic*/',
}
commonhooks = {'commonhooks': '/*need_commonhooks*/',
'initcommonhooks': '/*need_initcommonhooks*/',
}
############ Includes ###################
includes0['math.h'] = '#include <math.h>'
includes0['string.h'] = '#include <string.h>'
includes0['setjmp.h'] = '#include <setjmp.h>'
includes['Python.h'] = '#include "Python.h"'
needs['arrayobject.h'] = ['Python.h']
includes['arrayobject.h'] = '''#define PY_ARRAY_UNIQUE_SYMBOL PyArray_API
#include "arrayobject.h"'''
includes['arrayobject.h'] = '#include "fortranobject.h"'
includes['stdarg.h'] = '#include <stdarg.h>'
############# Type definitions ###############
typedefs['unsigned_char'] = 'typedef unsigned char unsigned_char;'
typedefs['unsigned_short'] = 'typedef unsigned short unsigned_short;'
typedefs['unsigned_long'] = 'typedef unsigned long unsigned_long;'
typedefs['signed_char'] = 'typedef signed char signed_char;'
typedefs['long_long'] = """\
#ifdef _WIN32
typedef __int64 long_long;
#else
typedef long long long_long;
typedef unsigned long long unsigned_long_long;
#endif
"""
typedefs['unsigned_long_long'] = """\
#ifdef _WIN32
typedef __uint64 long_long;
#else
typedef unsigned long long unsigned_long_long;
#endif
"""
typedefs['long_double'] = """\
#ifndef _LONG_DOUBLE
typedef long double long_double;
#endif
"""
typedefs[
'complex_long_double'] = 'typedef struct {long double r,i;} complex_long_double;'
typedefs['complex_float'] = 'typedef struct {float r,i;} complex_float;'
typedefs['complex_double'] = 'typedef struct {double r,i;} complex_double;'
typedefs['string'] = """typedef char * string;"""
############### CPP macros ####################
cppmacros['CFUNCSMESS'] = """\
#ifdef DEBUGCFUNCS
#define CFUNCSMESS(mess) fprintf(stderr,\"debug-capi:\"mess);
#define CFUNCSMESSPY(mess,obj) CFUNCSMESS(mess) \\
PyObject_Print((PyObject *)obj,stderr,Py_PRINT_RAW);\\
fprintf(stderr,\"\\n\");
#else
#define CFUNCSMESS(mess)
#define CFUNCSMESSPY(mess,obj)
#endif
"""
cppmacros['F_FUNC'] = """\
#if defined(PREPEND_FORTRAN)
#if defined(NO_APPEND_FORTRAN)
#if defined(UPPERCASE_FORTRAN)
#define F_FUNC(f,F) _##F
#else
#define F_FUNC(f,F) _##f
#endif
#else
#if defined(UPPERCASE_FORTRAN)
#define F_FUNC(f,F) _##F##_
#else
#define F_FUNC(f,F) _##f##_
#endif
#endif
#else
#if defined(NO_APPEND_FORTRAN)
#if defined(UPPERCASE_FORTRAN)
#define F_FUNC(f,F) F
#else
#define F_FUNC(f,F) f
#endif
#else
#if defined(UPPERCASE_FORTRAN)
#define F_FUNC(f,F) F##_
#else
#define F_FUNC(f,F) f##_
#endif
#endif
#endif
#if defined(UNDERSCORE_G77)
#define F_FUNC_US(f,F) F_FUNC(f##_,F##_)
#else
#define F_FUNC_US(f,F) F_FUNC(f,F)
#endif
"""
cppmacros['F_WRAPPEDFUNC'] = """\
#if defined(PREPEND_FORTRAN)
#if defined(NO_APPEND_FORTRAN)
#if defined(UPPERCASE_FORTRAN)
#define F_WRAPPEDFUNC(f,F) _F2PYWRAP##F
#else
#define F_WRAPPEDFUNC(f,F) _f2pywrap##f
#endif
#else
#if defined(UPPERCASE_FORTRAN)
#define F_WRAPPEDFUNC(f,F) _F2PYWRAP##F##_
#else
#define F_WRAPPEDFUNC(f,F) _f2pywrap##f##_
#endif
#endif
#else
#if defined(NO_APPEND_FORTRAN)
#if defined(UPPERCASE_FORTRAN)
#define F_WRAPPEDFUNC(f,F) F2PYWRAP##F
#else
#define F_WRAPPEDFUNC(f,F) f2pywrap##f
#endif
#else
#if defined(UPPERCASE_FORTRAN)
#define F_WRAPPEDFUNC(f,F) F2PYWRAP##F##_
#else
#define F_WRAPPEDFUNC(f,F) f2pywrap##f##_
#endif
#endif
#endif
#if defined(UNDERSCORE_G77)
#define F_WRAPPEDFUNC_US(f,F) F_WRAPPEDFUNC(f##_,F##_)
#else
#define F_WRAPPEDFUNC_US(f,F) F_WRAPPEDFUNC(f,F)
#endif
"""
cppmacros['F_MODFUNC'] = """\
#if defined(F90MOD2CCONV1) /*E.g. Compaq Fortran */
#if defined(NO_APPEND_FORTRAN)
#define F_MODFUNCNAME(m,f) $ ## m ## $ ## f
#else
#define F_MODFUNCNAME(m,f) $ ## m ## $ ## f ## _
#endif
#endif
#if defined(F90MOD2CCONV2) /*E.g. IBM XL Fortran, not tested though */
#if defined(NO_APPEND_FORTRAN)
#define F_MODFUNCNAME(m,f) __ ## m ## _MOD_ ## f
#else
#define F_MODFUNCNAME(m,f) __ ## m ## _MOD_ ## f ## _
#endif
#endif
#if defined(F90MOD2CCONV3) /*E.g. MIPSPro Compilers */
#if defined(NO_APPEND_FORTRAN)
#define F_MODFUNCNAME(m,f) f ## .in. ## m
#else
#define F_MODFUNCNAME(m,f) f ## .in. ## m ## _
#endif
#endif
/*
#if defined(UPPERCASE_FORTRAN)
#define F_MODFUNC(m,M,f,F) F_MODFUNCNAME(M,F)
#else
#define F_MODFUNC(m,M,f,F) F_MODFUNCNAME(m,f)
#endif
*/
#define F_MODFUNC(m,f) (*(f2pymodstruct##m##.##f))
"""
cppmacros['SWAPUNSAFE'] = """\
#define SWAP(a,b) (size_t)(a) = ((size_t)(a) ^ (size_t)(b));\\
(size_t)(b) = ((size_t)(a) ^ (size_t)(b));\\
(size_t)(a) = ((size_t)(a) ^ (size_t)(b))
"""
cppmacros['SWAP'] = """\
#define SWAP(a,b,t) {\\
t *c;\\
c = a;\\
a = b;\\
b = c;}
"""
# cppmacros['ISCONTIGUOUS']='#define ISCONTIGUOUS(m) (PyArray_FLAGS(m) &
# NPY_ARRAY_C_CONTIGUOUS)'
cppmacros['PRINTPYOBJERR'] = """\
#define PRINTPYOBJERR(obj)\\
fprintf(stderr,\"#modulename#.error is related to \");\\
PyObject_Print((PyObject *)obj,stderr,Py_PRINT_RAW);\\
fprintf(stderr,\"\\n\");
"""
cppmacros['MINMAX'] = """\
#ifndef max
#define max(a,b) ((a > b) ? (a) : (b))
#endif
#ifndef min
#define min(a,b) ((a < b) ? (a) : (b))
#endif
#ifndef MAX
#define MAX(a,b) ((a > b) ? (a) : (b))
#endif
#ifndef MIN
#define MIN(a,b) ((a < b) ? (a) : (b))
#endif
"""
needs['len..'] = ['f2py_size']
cppmacros['len..'] = """\
#define rank(var) var ## _Rank
#define shape(var,dim) var ## _Dims[dim]
#define old_rank(var) (PyArray_NDIM((PyArrayObject *)(capi_ ## var ## _tmp)))
#define old_shape(var,dim) PyArray_DIM(((PyArrayObject *)(capi_ ## var ## _tmp)),dim)
#define fshape(var,dim) shape(var,rank(var)-dim-1)
#define len(var) shape(var,0)
#define flen(var) fshape(var,0)
#define old_size(var) PyArray_SIZE((PyArrayObject *)(capi_ ## var ## _tmp))
/* #define index(i) capi_i ## i */
#define slen(var) capi_ ## var ## _len
#define size(var, ...) f2py_size((PyArrayObject *)(capi_ ## var ## _tmp), ## __VA_ARGS__, -1)
"""
needs['f2py_size'] = ['stdarg.h']
cfuncs['f2py_size'] = """\
static int f2py_size(PyArrayObject* var, ...)
{
npy_int sz = 0;
npy_int dim;
npy_int rank;
va_list argp;
va_start(argp, var);
dim = va_arg(argp, npy_int);
if (dim==-1)
{
sz = PyArray_SIZE(var);
}
else
{
rank = PyArray_NDIM(var);
if (dim>=1 && dim<=rank)
sz = PyArray_DIM(var, dim-1);
else
fprintf(stderr, \"f2py_size: 2nd argument value=%d fails to satisfy 1<=value<=%d. Result will be 0.\\n\", dim, rank);
}
va_end(argp);
return sz;
}
"""
cppmacros[
'pyobj_from_char1'] = '#define pyobj_from_char1(v) (PyInt_FromLong(v))'
cppmacros[
'pyobj_from_short1'] = '#define pyobj_from_short1(v) (PyInt_FromLong(v))'
needs['pyobj_from_int1'] = ['signed_char']
cppmacros['pyobj_from_int1'] = '#define pyobj_from_int1(v) (PyInt_FromLong(v))'
cppmacros[
'pyobj_from_long1'] = '#define pyobj_from_long1(v) (PyLong_FromLong(v))'
needs['pyobj_from_long_long1'] = ['long_long']
cppmacros['pyobj_from_long_long1'] = """\
#ifdef HAVE_LONG_LONG
#define pyobj_from_long_long1(v) (PyLong_FromLongLong(v))
#else
#warning HAVE_LONG_LONG is not available. Redefining pyobj_from_long_long.
#define pyobj_from_long_long1(v) (PyLong_FromLong(v))
#endif
"""
needs['pyobj_from_long_double1'] = ['long_double']
cppmacros[
'pyobj_from_long_double1'] = '#define pyobj_from_long_double1(v) (PyFloat_FromDouble(v))'
cppmacros[
'pyobj_from_double1'] = '#define pyobj_from_double1(v) (PyFloat_FromDouble(v))'
cppmacros[
'pyobj_from_float1'] = '#define pyobj_from_float1(v) (PyFloat_FromDouble(v))'
needs['pyobj_from_complex_long_double1'] = ['complex_long_double']
cppmacros[
'pyobj_from_complex_long_double1'] = '#define pyobj_from_complex_long_double1(v) (PyComplex_FromDoubles(v.r,v.i))'
needs['pyobj_from_complex_double1'] = ['complex_double']
cppmacros[
'pyobj_from_complex_double1'] = '#define pyobj_from_complex_double1(v) (PyComplex_FromDoubles(v.r,v.i))'
needs['pyobj_from_complex_float1'] = ['complex_float']
cppmacros[
'pyobj_from_complex_float1'] = '#define pyobj_from_complex_float1(v) (PyComplex_FromDoubles(v.r,v.i))'
needs['pyobj_from_string1'] = ['string']
cppmacros[
'pyobj_from_string1'] = '#define pyobj_from_string1(v) (PyString_FromString((char *)v))'
needs['pyobj_from_string1size'] = ['string']
cppmacros[
'pyobj_from_string1size'] = '#define pyobj_from_string1size(v,len) (PyUString_FromStringAndSize((char *)v, len))'
needs['TRYPYARRAYTEMPLATE'] = ['PRINTPYOBJERR']
cppmacros['TRYPYARRAYTEMPLATE'] = """\
/* New SciPy */
#define TRYPYARRAYTEMPLATECHAR case NPY_STRING: *(char *)(PyArray_DATA(arr))=*v; break;
#define TRYPYARRAYTEMPLATELONG case NPY_LONG: *(long *)(PyArray_DATA(arr))=*v; break;
#define TRYPYARRAYTEMPLATEOBJECT case NPY_OBJECT: PyArray_SETITEM(arr,PyArray_DATA(arr),pyobj_from_ ## ctype ## 1(*v)); break;
#define TRYPYARRAYTEMPLATE(ctype,typecode) \\
PyArrayObject *arr = NULL;\\
if (!obj) return -2;\\
if (!PyArray_Check(obj)) return -1;\\
if (!(arr=(PyArrayObject *)obj)) {fprintf(stderr,\"TRYPYARRAYTEMPLATE:\");PRINTPYOBJERR(obj);return 0;}\\
if (PyArray_DESCR(arr)->type==typecode) {*(ctype *)(PyArray_DATA(arr))=*v; return 1;}\\
switch (PyArray_TYPE(arr)) {\\
case NPY_DOUBLE: *(double *)(PyArray_DATA(arr))=*v; break;\\
case NPY_INT: *(int *)(PyArray_DATA(arr))=*v; break;\\
case NPY_LONG: *(long *)(PyArray_DATA(arr))=*v; break;\\
case NPY_FLOAT: *(float *)(PyArray_DATA(arr))=*v; break;\\
case NPY_CDOUBLE: *(double *)(PyArray_DATA(arr))=*v; break;\\
case NPY_CFLOAT: *(float *)(PyArray_DATA(arr))=*v; break;\\
case NPY_BOOL: *(npy_bool *)(PyArray_DATA(arr))=(*v!=0); break;\\
case NPY_UBYTE: *(unsigned char *)(PyArray_DATA(arr))=*v; break;\\
case NPY_BYTE: *(signed char *)(PyArray_DATA(arr))=*v; break;\\
case NPY_SHORT: *(short *)(PyArray_DATA(arr))=*v; break;\\
case NPY_USHORT: *(npy_ushort *)(PyArray_DATA(arr))=*v; break;\\
case NPY_UINT: *(npy_uint *)(PyArray_DATA(arr))=*v; break;\\
case NPY_ULONG: *(npy_ulong *)(PyArray_DATA(arr))=*v; break;\\
case NPY_LONGLONG: *(npy_longlong *)(PyArray_DATA(arr))=*v; break;\\
case NPY_ULONGLONG: *(npy_ulonglong *)(PyArray_DATA(arr))=*v; break;\\
case NPY_LONGDOUBLE: *(npy_longdouble *)(PyArray_DATA(arr))=*v; break;\\
case NPY_CLONGDOUBLE: *(npy_longdouble *)(PyArray_DATA(arr))=*v; break;\\
case NPY_OBJECT: PyArray_SETITEM(arr, PyArray_DATA(arr), pyobj_from_ ## ctype ## 1(*v)); break;\\
default: return -2;\\
};\\
return 1
"""
needs['TRYCOMPLEXPYARRAYTEMPLATE'] = ['PRINTPYOBJERR']
cppmacros['TRYCOMPLEXPYARRAYTEMPLATE'] = """\
#define TRYCOMPLEXPYARRAYTEMPLATEOBJECT case NPY_OBJECT: PyArray_SETITEM(arr, PyArray_DATA(arr), pyobj_from_complex_ ## ctype ## 1((*v))); break;
#define TRYCOMPLEXPYARRAYTEMPLATE(ctype,typecode)\\
PyArrayObject *arr = NULL;\\
if (!obj) return -2;\\
if (!PyArray_Check(obj)) return -1;\\
if (!(arr=(PyArrayObject *)obj)) {fprintf(stderr,\"TRYCOMPLEXPYARRAYTEMPLATE:\");PRINTPYOBJERR(obj);return 0;}\\
if (PyArray_DESCR(arr)->type==typecode) {\\
*(ctype *)(PyArray_DATA(arr))=(*v).r;\\
*(ctype *)(PyArray_DATA(arr)+sizeof(ctype))=(*v).i;\\
return 1;\\
}\\
switch (PyArray_TYPE(arr)) {\\
case NPY_CDOUBLE: *(double *)(PyArray_DATA(arr))=(*v).r;*(double *)(PyArray_DATA(arr)+sizeof(double))=(*v).i;break;\\
case NPY_CFLOAT: *(float *)(PyArray_DATA(arr))=(*v).r;*(float *)(PyArray_DATA(arr)+sizeof(float))=(*v).i;break;\\
case NPY_DOUBLE: *(double *)(PyArray_DATA(arr))=(*v).r; break;\\
case NPY_LONG: *(long *)(PyArray_DATA(arr))=(*v).r; break;\\
case NPY_FLOAT: *(float *)(PyArray_DATA(arr))=(*v).r; break;\\
case NPY_INT: *(int *)(PyArray_DATA(arr))=(*v).r; break;\\
case NPY_SHORT: *(short *)(PyArray_DATA(arr))=(*v).r; break;\\
case NPY_UBYTE: *(unsigned char *)(PyArray_DATA(arr))=(*v).r; break;\\
case NPY_BYTE: *(signed char *)(PyArray_DATA(arr))=(*v).r; break;\\
case NPY_BOOL: *(npy_bool *)(PyArray_DATA(arr))=((*v).r!=0 && (*v).i!=0); break;\\
case NPY_USHORT: *(npy_ushort *)(PyArray_DATA(arr))=(*v).r; break;\\
case NPY_UINT: *(npy_uint *)(PyArray_DATA(arr))=(*v).r; break;\\
case NPY_ULONG: *(npy_ulong *)(PyArray_DATA(arr))=(*v).r; break;\\
case NPY_LONGLONG: *(npy_longlong *)(PyArray_DATA(arr))=(*v).r; break;\\
case NPY_ULONGLONG: *(npy_ulonglong *)(PyArray_DATA(arr))=(*v).r; break;\\
case NPY_LONGDOUBLE: *(npy_longdouble *)(PyArray_DATA(arr))=(*v).r; break;\\
case NPY_CLONGDOUBLE: *(npy_longdouble *)(PyArray_DATA(arr))=(*v).r;*(npy_longdouble *)(PyArray_DATA(arr)+sizeof(npy_longdouble))=(*v).i;break;\\
case NPY_OBJECT: PyArray_SETITEM(arr, PyArray_DATA(arr), pyobj_from_complex_ ## ctype ## 1((*v))); break;\\
default: return -2;\\
};\\
return -1;
"""
# cppmacros['NUMFROMARROBJ']="""\
# define NUMFROMARROBJ(typenum,ctype) \\
# if (PyArray_Check(obj)) arr = (PyArrayObject *)obj;\\
# else arr = (PyArrayObject *)PyArray_ContiguousFromObject(obj,typenum,0,0);\\
# if (arr) {\\
# if (PyArray_TYPE(arr)==NPY_OBJECT) {\\
# if (!ctype ## _from_pyobj(v,(PyArray_DESCR(arr)->getitem)(PyArray_DATA(arr)),\"\"))\\
# goto capi_fail;\\
# } else {\\
# (PyArray_DESCR(arr)->cast[typenum])(PyArray_DATA(arr),1,(char*)v,1,1);\\
# }\\
# if ((PyObject *)arr != obj) { Py_DECREF(arr); }\\
# return 1;\\
# }
# """
# XXX: Note that CNUMFROMARROBJ is identical with NUMFROMARROBJ
# cppmacros['CNUMFROMARROBJ']="""\
# define CNUMFROMARROBJ(typenum,ctype) \\
# if (PyArray_Check(obj)) arr = (PyArrayObject *)obj;\\
# else arr = (PyArrayObject *)PyArray_ContiguousFromObject(obj,typenum,0,0);\\
# if (arr) {\\
# if (PyArray_TYPE(arr)==NPY_OBJECT) {\\
# if (!ctype ## _from_pyobj(v,(PyArray_DESCR(arr)->getitem)(PyArray_DATA(arr)),\"\"))\\
# goto capi_fail;\\
# } else {\\
# (PyArray_DESCR(arr)->cast[typenum])((void *)(PyArray_DATA(arr)),1,(void *)(v),1,1);\\
# }\\
# if ((PyObject *)arr != obj) { Py_DECREF(arr); }\\
# return 1;\\
# }
# """
needs['GETSTRFROMPYTUPLE'] = ['STRINGCOPYN', 'PRINTPYOBJERR']
cppmacros['GETSTRFROMPYTUPLE'] = """\
#define GETSTRFROMPYTUPLE(tuple,index,str,len) {\\
PyObject *rv_cb_str = PyTuple_GetItem((tuple),(index));\\
if (rv_cb_str == NULL)\\
goto capi_fail;\\
if (PyString_Check(rv_cb_str)) {\\
str[len-1]='\\0';\\
STRINGCOPYN((str),PyString_AS_STRING((PyStringObject*)rv_cb_str),(len));\\
} else {\\
PRINTPYOBJERR(rv_cb_str);\\
PyErr_SetString(#modulename#_error,\"string object expected\");\\
goto capi_fail;\\
}\\
}
"""
cppmacros['GETSCALARFROMPYTUPLE'] = """\
#define GETSCALARFROMPYTUPLE(tuple,index,var,ctype,mess) {\\
if ((capi_tmp = PyTuple_GetItem((tuple),(index)))==NULL) goto capi_fail;\\
if (!(ctype ## _from_pyobj((var),capi_tmp,mess)))\\
goto capi_fail;\\
}
"""
cppmacros['FAILNULL'] = """\\
#define FAILNULL(p) do { \\
if ((p) == NULL) { \\
PyErr_SetString(PyExc_MemoryError, "NULL pointer found"); \\
goto capi_fail; \\
} \\
} while (0)
"""
needs['MEMCOPY'] = ['string.h', 'FAILNULL']
cppmacros['MEMCOPY'] = """\
#define MEMCOPY(to,from,n)\\
do { FAILNULL(to); FAILNULL(from); (void)memcpy(to,from,n); } while (0)
"""
cppmacros['STRINGMALLOC'] = """\
#define STRINGMALLOC(str,len)\\
if ((str = (string)malloc(sizeof(char)*(len+1))) == NULL) {\\
PyErr_SetString(PyExc_MemoryError, \"out of memory\");\\
goto capi_fail;\\
} else {\\
(str)[len] = '\\0';\\
}
"""
cppmacros['STRINGFREE'] = """\
#define STRINGFREE(str) do {if (!(str == NULL)) free(str);} while (0)
"""
needs['STRINGCOPYN'] = ['string.h', 'FAILNULL']
cppmacros['STRINGCOPYN'] = """\
#define STRINGCOPYN(to,from,buf_size) \\
do { \\
int _m = (buf_size); \\
char *_to = (to); \\
char *_from = (from); \\
FAILNULL(_to); FAILNULL(_from); \\
(void)strncpy(_to, _from, sizeof(char)*_m); \\
_to[_m-1] = '\\0'; \\
/* Padding with spaces instead of nulls */ \\
for (_m -= 2; _m >= 0 && _to[_m] == '\\0'; _m--) { \\
_to[_m] = ' '; \\
} \\
} while (0)
"""
needs['STRINGCOPY'] = ['string.h', 'FAILNULL']
cppmacros['STRINGCOPY'] = """\
#define STRINGCOPY(to,from)\\
do { FAILNULL(to); FAILNULL(from); (void)strcpy(to,from); } while (0)
"""
cppmacros['CHECKGENERIC'] = """\
#define CHECKGENERIC(check,tcheck,name) \\
if (!(check)) {\\
PyErr_SetString(#modulename#_error,\"(\"tcheck\") failed for \"name);\\
/*goto capi_fail;*/\\
} else """
cppmacros['CHECKARRAY'] = """\
#define CHECKARRAY(check,tcheck,name) \\
if (!(check)) {\\
PyErr_SetString(#modulename#_error,\"(\"tcheck\") failed for \"name);\\
/*goto capi_fail;*/\\
} else """
cppmacros['CHECKSTRING'] = """\
#define CHECKSTRING(check,tcheck,name,show,var)\\
if (!(check)) {\\
char errstring[256];\\
sprintf(errstring, \"%s: \"show, \"(\"tcheck\") failed for \"name, slen(var), var);\\
PyErr_SetString(#modulename#_error, errstring);\\
/*goto capi_fail;*/\\
} else """
cppmacros['CHECKSCALAR'] = """\
#define CHECKSCALAR(check,tcheck,name,show,var)\\
if (!(check)) {\\
char errstring[256];\\
sprintf(errstring, \"%s: \"show, \"(\"tcheck\") failed for \"name, var);\\
PyErr_SetString(#modulename#_error,errstring);\\
/*goto capi_fail;*/\\
} else """
# cppmacros['CHECKDIMS']="""\
# define CHECKDIMS(dims,rank) \\
# for (int i=0;i<(rank);i++)\\
# if (dims[i]<0) {\\
# fprintf(stderr,\"Unspecified array argument requires a complete dimension specification.\\n\");\\
# goto capi_fail;\\
# }
# """
cppmacros[
'ARRSIZE'] = '#define ARRSIZE(dims,rank) (_PyArray_multiply_list(dims,rank))'
cppmacros['OLDPYNUM'] = """\
#ifdef OLDPYNUM
#error You need to intall Numeric Python version 13 or higher. Get it from http:/sourceforge.net/project/?group_id=1369
#endif
"""
################# C functions ###############
cfuncs['calcarrindex'] = """\
static int calcarrindex(int *i,PyArrayObject *arr) {
int k,ii = i[0];
for (k=1; k < PyArray_NDIM(arr); k++)
ii += (ii*(PyArray_DIM(arr,k) - 1)+i[k]); /* assuming contiguous arr */
return ii;
}"""
cfuncs['calcarrindextr'] = """\
static int calcarrindextr(int *i,PyArrayObject *arr) {
int k,ii = i[PyArray_NDIM(arr)-1];
for (k=1; k < PyArray_NDIM(arr); k++)
ii += (ii*(PyArray_DIM(arr,PyArray_NDIM(arr)-k-1) - 1)+i[PyArray_NDIM(arr)-k-1]); /* assuming contiguous arr */
return ii;
}"""
cfuncs['forcomb'] = """\
static struct { int nd;npy_intp *d;int *i,*i_tr,tr; } forcombcache;
static int initforcomb(npy_intp *dims,int nd,int tr) {
int k;
if (dims==NULL) return 0;
if (nd<0) return 0;
forcombcache.nd = nd;
forcombcache.d = dims;
forcombcache.tr = tr;
if ((forcombcache.i = (int *)malloc(sizeof(int)*nd))==NULL) return 0;
if ((forcombcache.i_tr = (int *)malloc(sizeof(int)*nd))==NULL) return 0;
for (k=1;k<nd;k++) {
forcombcache.i[k] = forcombcache.i_tr[nd-k-1] = 0;
}
forcombcache.i[0] = forcombcache.i_tr[nd-1] = -1;
return 1;
}
static int *nextforcomb(void) {
int j,*i,*i_tr,k;
int nd=forcombcache.nd;
if ((i=forcombcache.i) == NULL) return NULL;
if ((i_tr=forcombcache.i_tr) == NULL) return NULL;
if (forcombcache.d == NULL) return NULL;
i[0]++;
if (i[0]==forcombcache.d[0]) {
j=1;
while ((j<nd) && (i[j]==forcombcache.d[j]-1)) j++;
if (j==nd) {
free(i);
free(i_tr);
return NULL;
}
for (k=0;k<j;k++) i[k] = i_tr[nd-k-1] = 0;
i[j]++;
i_tr[nd-j-1]++;
} else
i_tr[nd-1]++;
if (forcombcache.tr) return i_tr;
return i;
}"""
needs['try_pyarr_from_string'] = ['STRINGCOPYN', 'PRINTPYOBJERR', 'string']
cfuncs['try_pyarr_from_string'] = """\
static int try_pyarr_from_string(PyObject *obj,const string str) {
PyArrayObject *arr = NULL;
if (PyArray_Check(obj) && (!((arr = (PyArrayObject *)obj) == NULL)))
{ STRINGCOPYN(PyArray_DATA(arr),str,PyArray_NBYTES(arr)); }
return 1;
capi_fail:
PRINTPYOBJERR(obj);
PyErr_SetString(#modulename#_error,\"try_pyarr_from_string failed\");
return 0;
}
"""
needs['string_from_pyobj'] = ['string', 'STRINGMALLOC', 'STRINGCOPYN']
cfuncs['string_from_pyobj'] = """\
static int string_from_pyobj(string *str,int *len,const string inistr,PyObject *obj,const char *errmess) {
PyArrayObject *arr = NULL;
PyObject *tmp = NULL;
#ifdef DEBUGCFUNCS
fprintf(stderr,\"string_from_pyobj(str='%s',len=%d,inistr='%s',obj=%p)\\n\",(char*)str,*len,(char *)inistr,obj);
#endif
if (obj == Py_None) {
if (*len == -1)
*len = strlen(inistr); /* Will this cause problems? */
STRINGMALLOC(*str,*len);
STRINGCOPYN(*str,inistr,*len+1);
return 1;
}
if (PyArray_Check(obj)) {
if ((arr = (PyArrayObject *)obj) == NULL)
goto capi_fail;
if (!ISCONTIGUOUS(arr)) {
PyErr_SetString(PyExc_ValueError,\"array object is non-contiguous.\");
goto capi_fail;
}
if (*len == -1)
*len = (PyArray_ITEMSIZE(arr))*PyArray_SIZE(arr);
STRINGMALLOC(*str,*len);
STRINGCOPYN(*str,PyArray_DATA(arr),*len+1);
return 1;
}
if (PyString_Check(obj)) {
tmp = obj;
Py_INCREF(tmp);
}
#if PY_VERSION_HEX >= 0x03000000
else if (PyUnicode_Check(obj)) {
tmp = PyUnicode_AsASCIIString(obj);
}
else {
PyObject *tmp2;
tmp2 = PyObject_Str(obj);
if (tmp2) {
tmp = PyUnicode_AsASCIIString(tmp2);
Py_DECREF(tmp2);
}
else {
tmp = NULL;
}
}
#else
else {
tmp = PyObject_Str(obj);
}
#endif
if (tmp == NULL) goto capi_fail;
if (*len == -1)
*len = PyString_GET_SIZE(tmp);
STRINGMALLOC(*str,*len);
STRINGCOPYN(*str,PyString_AS_STRING(tmp),*len+1);
Py_DECREF(tmp);
return 1;
capi_fail:
Py_XDECREF(tmp);
{
PyObject* err = PyErr_Occurred();
if (err==NULL) err = #modulename#_error;
PyErr_SetString(err,errmess);
}
return 0;
}
"""
needs['char_from_pyobj'] = ['int_from_pyobj']
cfuncs['char_from_pyobj'] = """\
static int char_from_pyobj(char* v,PyObject *obj,const char *errmess) {
int i=0;
if (int_from_pyobj(&i,obj,errmess)) {
*v = (char)i;
return 1;
}
return 0;
}
"""
needs['signed_char_from_pyobj'] = ['int_from_pyobj', 'signed_char']
cfuncs['signed_char_from_pyobj'] = """\
static int signed_char_from_pyobj(signed_char* v,PyObject *obj,const char *errmess) {
int i=0;
if (int_from_pyobj(&i,obj,errmess)) {
*v = (signed_char)i;
return 1;
}
return 0;
}
"""
needs['short_from_pyobj'] = ['int_from_pyobj']
cfuncs['short_from_pyobj'] = """\
static int short_from_pyobj(short* v,PyObject *obj,const char *errmess) {
int i=0;
if (int_from_pyobj(&i,obj,errmess)) {
*v = (short)i;
return 1;
}
return 0;
}
"""
cfuncs['int_from_pyobj'] = """\
static int int_from_pyobj(int* v,PyObject *obj,const char *errmess) {
PyObject* tmp = NULL;
if (PyInt_Check(obj)) {
*v = (int)PyInt_AS_LONG(obj);
return 1;
}
tmp = PyNumber_Int(obj);
if (tmp) {
*v = PyInt_AS_LONG(tmp);
Py_DECREF(tmp);
return 1;
}
if (PyComplex_Check(obj))
tmp = PyObject_GetAttrString(obj,\"real\");
else if (PyString_Check(obj) || PyUnicode_Check(obj))
/*pass*/;
else if (PySequence_Check(obj))
tmp = PySequence_GetItem(obj,0);
if (tmp) {
PyErr_Clear();
if (int_from_pyobj(v,tmp,errmess)) {Py_DECREF(tmp); return 1;}
Py_DECREF(tmp);
}
{
PyObject* err = PyErr_Occurred();
if (err==NULL) err = #modulename#_error;
PyErr_SetString(err,errmess);
}
return 0;
}
"""
cfuncs['long_from_pyobj'] = """\
static int long_from_pyobj(long* v,PyObject *obj,const char *errmess) {
PyObject* tmp = NULL;
if (PyInt_Check(obj)) {
*v = PyInt_AS_LONG(obj);
return 1;
}
tmp = PyNumber_Int(obj);
if (tmp) {
*v = PyInt_AS_LONG(tmp);
Py_DECREF(tmp);
return 1;
}
if (PyComplex_Check(obj))
tmp = PyObject_GetAttrString(obj,\"real\");
else if (PyString_Check(obj) || PyUnicode_Check(obj))
/*pass*/;
else if (PySequence_Check(obj))
tmp = PySequence_GetItem(obj,0);
if (tmp) {
PyErr_Clear();
if (long_from_pyobj(v,tmp,errmess)) {Py_DECREF(tmp); return 1;}
Py_DECREF(tmp);
}
{
PyObject* err = PyErr_Occurred();
if (err==NULL) err = #modulename#_error;
PyErr_SetString(err,errmess);
}
return 0;
}
"""
needs['long_long_from_pyobj'] = ['long_long']
cfuncs['long_long_from_pyobj'] = """\
static int long_long_from_pyobj(long_long* v,PyObject *obj,const char *errmess) {
PyObject* tmp = NULL;
if (PyLong_Check(obj)) {
*v = PyLong_AsLongLong(obj);
return (!PyErr_Occurred());
}
if (PyInt_Check(obj)) {
*v = (long_long)PyInt_AS_LONG(obj);
return 1;
}
tmp = PyNumber_Long(obj);
if (tmp) {
*v = PyLong_AsLongLong(tmp);
Py_DECREF(tmp);
return (!PyErr_Occurred());
}
if (PyComplex_Check(obj))
tmp = PyObject_GetAttrString(obj,\"real\");
else if (PyString_Check(obj) || PyUnicode_Check(obj))
/*pass*/;
else if (PySequence_Check(obj))
tmp = PySequence_GetItem(obj,0);
if (tmp) {
PyErr_Clear();
if (long_long_from_pyobj(v,tmp,errmess)) {Py_DECREF(tmp); return 1;}
Py_DECREF(tmp);
}
{
PyObject* err = PyErr_Occurred();
if (err==NULL) err = #modulename#_error;
PyErr_SetString(err,errmess);
}
return 0;
}
"""
needs['long_double_from_pyobj'] = ['double_from_pyobj', 'long_double']
cfuncs['long_double_from_pyobj'] = """\
static int long_double_from_pyobj(long_double* v,PyObject *obj,const char *errmess) {
double d=0;
if (PyArray_CheckScalar(obj)){
if PyArray_IsScalar(obj, LongDouble) {
PyArray_ScalarAsCtype(obj, v);
return 1;
}
else if (PyArray_Check(obj) && PyArray_TYPE(obj)==NPY_LONGDOUBLE) {
(*v) = *((npy_longdouble *)PyArray_DATA(obj));
return 1;
}
}
if (double_from_pyobj(&d,obj,errmess)) {
*v = (long_double)d;
return 1;
}
return 0;
}
"""
cfuncs['double_from_pyobj'] = """\
static int double_from_pyobj(double* v,PyObject *obj,const char *errmess) {
PyObject* tmp = NULL;
if (PyFloat_Check(obj)) {
#ifdef __sgi
*v = PyFloat_AsDouble(obj);
#else
*v = PyFloat_AS_DOUBLE(obj);
#endif
return 1;
}
tmp = PyNumber_Float(obj);
if (tmp) {
#ifdef __sgi
*v = PyFloat_AsDouble(tmp);
#else
*v = PyFloat_AS_DOUBLE(tmp);
#endif
Py_DECREF(tmp);
return 1;
}
if (PyComplex_Check(obj))
tmp = PyObject_GetAttrString(obj,\"real\");
else if (PyString_Check(obj) || PyUnicode_Check(obj))
/*pass*/;
else if (PySequence_Check(obj))
tmp = PySequence_GetItem(obj,0);
if (tmp) {
PyErr_Clear();
if (double_from_pyobj(v,tmp,errmess)) {Py_DECREF(tmp); return 1;}
Py_DECREF(tmp);
}
{
PyObject* err = PyErr_Occurred();
if (err==NULL) err = #modulename#_error;
PyErr_SetString(err,errmess);
}
return 0;
}
"""
needs['float_from_pyobj'] = ['double_from_pyobj']
cfuncs['float_from_pyobj'] = """\
static int float_from_pyobj(float* v,PyObject *obj,const char *errmess) {
double d=0.0;
if (double_from_pyobj(&d,obj,errmess)) {
*v = (float)d;
return 1;
}
return 0;
}
"""
needs['complex_long_double_from_pyobj'] = ['complex_long_double', 'long_double',
'complex_double_from_pyobj']
cfuncs['complex_long_double_from_pyobj'] = """\
static int complex_long_double_from_pyobj(complex_long_double* v,PyObject *obj,const char *errmess) {
complex_double cd={0.0,0.0};
if (PyArray_CheckScalar(obj)){
if PyArray_IsScalar(obj, CLongDouble) {
PyArray_ScalarAsCtype(obj, v);
return 1;
}
else if (PyArray_Check(obj) && PyArray_TYPE(obj)==NPY_CLONGDOUBLE) {
(*v).r = ((npy_clongdouble *)PyArray_DATA(obj))->real;
(*v).i = ((npy_clongdouble *)PyArray_DATA(obj))->imag;
return 1;
}
}
if (complex_double_from_pyobj(&cd,obj,errmess)) {
(*v).r = (long_double)cd.r;
(*v).i = (long_double)cd.i;
return 1;
}
return 0;
}
"""
needs['complex_double_from_pyobj'] = ['complex_double']
cfuncs['complex_double_from_pyobj'] = """\
static int complex_double_from_pyobj(complex_double* v,PyObject *obj,const char *errmess) {
Py_complex c;
if (PyComplex_Check(obj)) {
c=PyComplex_AsCComplex(obj);
(*v).r=c.real, (*v).i=c.imag;
return 1;
}
if (PyArray_IsScalar(obj, ComplexFloating)) {
if (PyArray_IsScalar(obj, CFloat)) {
npy_cfloat new;
PyArray_ScalarAsCtype(obj, &new);
(*v).r = (double)new.real;
(*v).i = (double)new.imag;
}
else if (PyArray_IsScalar(obj, CLongDouble)) {
npy_clongdouble new;
PyArray_ScalarAsCtype(obj, &new);
(*v).r = (double)new.real;
(*v).i = (double)new.imag;
}
else { /* if (PyArray_IsScalar(obj, CDouble)) */
PyArray_ScalarAsCtype(obj, v);
}
return 1;
}
if (PyArray_CheckScalar(obj)) { /* 0-dim array or still array scalar */
PyObject *arr;
if (PyArray_Check(obj)) {
arr = PyArray_Cast((PyArrayObject *)obj, NPY_CDOUBLE);
}
else {
arr = PyArray_FromScalar(obj, PyArray_DescrFromType(NPY_CDOUBLE));
}
if (arr==NULL) return 0;
(*v).r = ((npy_cdouble *)PyArray_DATA(arr))->real;
(*v).i = ((npy_cdouble *)PyArray_DATA(arr))->imag;
return 1;
}
/* Python does not provide PyNumber_Complex function :-( */
(*v).i=0.0;
if (PyFloat_Check(obj)) {
#ifdef __sgi
(*v).r = PyFloat_AsDouble(obj);
#else
(*v).r = PyFloat_AS_DOUBLE(obj);
#endif
return 1;
}
if (PyInt_Check(obj)) {
(*v).r = (double)PyInt_AS_LONG(obj);
return 1;
}
if (PyLong_Check(obj)) {
(*v).r = PyLong_AsDouble(obj);
return (!PyErr_Occurred());
}
if (PySequence_Check(obj) && !(PyString_Check(obj) || PyUnicode_Check(obj))) {
PyObject *tmp = PySequence_GetItem(obj,0);
if (tmp) {
if (complex_double_from_pyobj(v,tmp,errmess)) {
Py_DECREF(tmp);
return 1;
}
Py_DECREF(tmp);
}
}
{
PyObject* err = PyErr_Occurred();
if (err==NULL)
err = PyExc_TypeError;
PyErr_SetString(err,errmess);
}
return 0;
}
"""
needs['complex_float_from_pyobj'] = [
'complex_float', 'complex_double_from_pyobj']
cfuncs['complex_float_from_pyobj'] = """\
static int complex_float_from_pyobj(complex_float* v,PyObject *obj,const char *errmess) {
complex_double cd={0.0,0.0};
if (complex_double_from_pyobj(&cd,obj,errmess)) {
(*v).r = (float)cd.r;
(*v).i = (float)cd.i;
return 1;
}
return 0;
}
"""
needs['try_pyarr_from_char'] = ['pyobj_from_char1', 'TRYPYARRAYTEMPLATE']
cfuncs[
'try_pyarr_from_char'] = 'static int try_pyarr_from_char(PyObject* obj,char* v) {\n TRYPYARRAYTEMPLATE(char,\'c\');\n}\n'
needs['try_pyarr_from_signed_char'] = ['TRYPYARRAYTEMPLATE', 'unsigned_char']
cfuncs[
'try_pyarr_from_unsigned_char'] = 'static int try_pyarr_from_unsigned_char(PyObject* obj,unsigned_char* v) {\n TRYPYARRAYTEMPLATE(unsigned_char,\'b\');\n}\n'
needs['try_pyarr_from_signed_char'] = ['TRYPYARRAYTEMPLATE', 'signed_char']
cfuncs[
'try_pyarr_from_signed_char'] = 'static int try_pyarr_from_signed_char(PyObject* obj,signed_char* v) {\n TRYPYARRAYTEMPLATE(signed_char,\'1\');\n}\n'
needs['try_pyarr_from_short'] = ['pyobj_from_short1', 'TRYPYARRAYTEMPLATE']
cfuncs[
'try_pyarr_from_short'] = 'static int try_pyarr_from_short(PyObject* obj,short* v) {\n TRYPYARRAYTEMPLATE(short,\'s\');\n}\n'
needs['try_pyarr_from_int'] = ['pyobj_from_int1', 'TRYPYARRAYTEMPLATE']
cfuncs[
'try_pyarr_from_int'] = 'static int try_pyarr_from_int(PyObject* obj,int* v) {\n TRYPYARRAYTEMPLATE(int,\'i\');\n}\n'
needs['try_pyarr_from_long'] = ['pyobj_from_long1', 'TRYPYARRAYTEMPLATE']
cfuncs[
'try_pyarr_from_long'] = 'static int try_pyarr_from_long(PyObject* obj,long* v) {\n TRYPYARRAYTEMPLATE(long,\'l\');\n}\n'
needs['try_pyarr_from_long_long'] = [
'pyobj_from_long_long1', 'TRYPYARRAYTEMPLATE', 'long_long']
cfuncs[
'try_pyarr_from_long_long'] = 'static int try_pyarr_from_long_long(PyObject* obj,long_long* v) {\n TRYPYARRAYTEMPLATE(long_long,\'L\');\n}\n'
needs['try_pyarr_from_float'] = ['pyobj_from_float1', 'TRYPYARRAYTEMPLATE']
cfuncs[
'try_pyarr_from_float'] = 'static int try_pyarr_from_float(PyObject* obj,float* v) {\n TRYPYARRAYTEMPLATE(float,\'f\');\n}\n'
needs['try_pyarr_from_double'] = ['pyobj_from_double1', 'TRYPYARRAYTEMPLATE']
cfuncs[
'try_pyarr_from_double'] = 'static int try_pyarr_from_double(PyObject* obj,double* v) {\n TRYPYARRAYTEMPLATE(double,\'d\');\n}\n'
needs['try_pyarr_from_complex_float'] = [
'pyobj_from_complex_float1', 'TRYCOMPLEXPYARRAYTEMPLATE', 'complex_float']
cfuncs[
'try_pyarr_from_complex_float'] = 'static int try_pyarr_from_complex_float(PyObject* obj,complex_float* v) {\n TRYCOMPLEXPYARRAYTEMPLATE(float,\'F\');\n}\n'
needs['try_pyarr_from_complex_double'] = [
'pyobj_from_complex_double1', 'TRYCOMPLEXPYARRAYTEMPLATE', 'complex_double']
cfuncs[
'try_pyarr_from_complex_double'] = 'static int try_pyarr_from_complex_double(PyObject* obj,complex_double* v) {\n TRYCOMPLEXPYARRAYTEMPLATE(double,\'D\');\n}\n'
needs['create_cb_arglist'] = ['CFUNCSMESS', 'PRINTPYOBJERR', 'MINMAX']
cfuncs['create_cb_arglist'] = """\
static int create_cb_arglist(PyObject* fun,PyTupleObject* xa,const int maxnofargs,const int nofoptargs,int *nofargs,PyTupleObject **args,const char *errmess) {
PyObject *tmp = NULL;
PyObject *tmp_fun = NULL;
int tot,opt,ext,siz,i,di=0;
CFUNCSMESS(\"create_cb_arglist\\n\");
tot=opt=ext=siz=0;
/* Get the total number of arguments */
if (PyFunction_Check(fun))
tmp_fun = fun;
else {
di = 1;
if (PyObject_HasAttrString(fun,\"im_func\")) {
tmp_fun = PyObject_GetAttrString(fun,\"im_func\");
}
else if (PyObject_HasAttrString(fun,\"__call__\")) {
tmp = PyObject_GetAttrString(fun,\"__call__\");
if (PyObject_HasAttrString(tmp,\"im_func\"))
tmp_fun = PyObject_GetAttrString(tmp,\"im_func\");
else {
tmp_fun = fun; /* built-in function */
tot = maxnofargs;
if (xa != NULL)
tot += PyTuple_Size((PyObject *)xa);
}
Py_XDECREF(tmp);
}
else if (PyFortran_Check(fun) || PyFortran_Check1(fun)) {
tot = maxnofargs;
if (xa != NULL)
tot += PyTuple_Size((PyObject *)xa);
tmp_fun = fun;
}
else if (F2PyCapsule_Check(fun)) {
tot = maxnofargs;
if (xa != NULL)
ext = PyTuple_Size((PyObject *)xa);
if(ext>0) {
fprintf(stderr,\"extra arguments tuple cannot be used with CObject call-back\\n\");
goto capi_fail;
}
tmp_fun = fun;
}
}
if (tmp_fun==NULL) {
fprintf(stderr,\"Call-back argument must be function|instance|instance.__call__|f2py-function but got %s.\\n\",(fun==NULL?\"NULL\":Py_TYPE(fun)->tp_name));
goto capi_fail;
}
#if PY_VERSION_HEX >= 0x03000000
if (PyObject_HasAttrString(tmp_fun,\"__code__\")) {
if (PyObject_HasAttrString(tmp = PyObject_GetAttrString(tmp_fun,\"__code__\"),\"co_argcount\"))
#else
if (PyObject_HasAttrString(tmp_fun,\"func_code\")) {
if (PyObject_HasAttrString(tmp = PyObject_GetAttrString(tmp_fun,\"func_code\"),\"co_argcount\"))
#endif
tot = PyInt_AsLong(PyObject_GetAttrString(tmp,\"co_argcount\")) - di;
Py_XDECREF(tmp);
}
/* Get the number of optional arguments */
#if PY_VERSION_HEX >= 0x03000000
if (PyObject_HasAttrString(tmp_fun,\"__defaults__\")) {
if (PyTuple_Check(tmp = PyObject_GetAttrString(tmp_fun,\"__defaults__\")))
#else
if (PyObject_HasAttrString(tmp_fun,\"func_defaults\")) {
if (PyTuple_Check(tmp = PyObject_GetAttrString(tmp_fun,\"func_defaults\")))
#endif
opt = PyTuple_Size(tmp);
Py_XDECREF(tmp);
}
/* Get the number of extra arguments */
if (xa != NULL)
ext = PyTuple_Size((PyObject *)xa);
/* Calculate the size of call-backs argument list */
siz = MIN(maxnofargs+ext,tot);
*nofargs = MAX(0,siz-ext);
#ifdef DEBUGCFUNCS
fprintf(stderr,\"debug-capi:create_cb_arglist:maxnofargs(-nofoptargs),tot,opt,ext,siz,nofargs=%d(-%d),%d,%d,%d,%d,%d\\n\",maxnofargs,nofoptargs,tot,opt,ext,siz,*nofargs);
#endif
if (siz<tot-opt) {
fprintf(stderr,\"create_cb_arglist: Failed to build argument list (siz) with enough arguments (tot-opt) required by user-supplied function (siz,tot,opt=%d,%d,%d).\\n\",siz,tot,opt);
goto capi_fail;
}
/* Initialize argument list */
*args = (PyTupleObject *)PyTuple_New(siz);
for (i=0;i<*nofargs;i++) {
Py_INCREF(Py_None);
PyTuple_SET_ITEM((PyObject *)(*args),i,Py_None);
}
if (xa != NULL)
for (i=(*nofargs);i<siz;i++) {
tmp = PyTuple_GetItem((PyObject *)xa,i-(*nofargs));
Py_INCREF(tmp);
PyTuple_SET_ITEM(*args,i,tmp);
}
CFUNCSMESS(\"create_cb_arglist-end\\n\");
return 1;
capi_fail:
if ((PyErr_Occurred())==NULL)
PyErr_SetString(#modulename#_error,errmess);
return 0;
}
"""
def buildcfuncs():
from .capi_maps import c2capi_map
for k in c2capi_map.keys():
m = 'pyarr_from_p_%s1' % k
cppmacros[
m] = '#define %s(v) (PyArray_SimpleNewFromData(0,NULL,%s,(char *)v))' % (m, c2capi_map[k])
k = 'string'
m = 'pyarr_from_p_%s1' % k
# NPY_CHAR compatibility, NPY_STRING with itemsize 1
cppmacros[
m] = '#define %s(v,dims) (PyArray_New(&PyArray_Type, 1, dims, NPY_STRING, NULL, v, 1, NPY_ARRAY_CARRAY, NULL))' % (m)
############ Auxiliary functions for sorting needs ###################
def append_needs(need, flag=1):
global outneeds, needs
if isinstance(need, list):
for n in need:
append_needs(n, flag)
elif isinstance(need, str):
if not need:
return
if need in includes0:
n = 'includes0'
elif need in includes:
n = 'includes'
elif need in typedefs:
n = 'typedefs'
elif need in typedefs_generated:
n = 'typedefs_generated'
elif need in cppmacros:
n = 'cppmacros'
elif need in cfuncs:
n = 'cfuncs'
elif need in callbacks:
n = 'callbacks'
elif need in f90modhooks:
n = 'f90modhooks'
elif need in commonhooks:
n = 'commonhooks'
else:
errmess('append_needs: unknown need %s\n' % (repr(need)))
return
if need in outneeds[n]:
return
if flag:
tmp = {}
if need in needs:
for nn in needs[need]:
t = append_needs(nn, 0)
if isinstance(t, dict):
for nnn in t.keys():
if nnn in tmp:
tmp[nnn] = tmp[nnn] + t[nnn]
else:
tmp[nnn] = t[nnn]
for nn in tmp.keys():
for nnn in tmp[nn]:
if nnn not in outneeds[nn]:
outneeds[nn] = [nnn] + outneeds[nn]
outneeds[n].append(need)
else:
tmp = {}
if need in needs:
for nn in needs[need]:
t = append_needs(nn, flag)
if isinstance(t, dict):
for nnn in t.keys():
if nnn in tmp:
tmp[nnn] = t[nnn] + tmp[nnn]
else:
tmp[nnn] = t[nnn]
if n not in tmp:
tmp[n] = []
tmp[n].append(need)
return tmp
else:
errmess('append_needs: expected list or string but got :%s\n' %
(repr(need)))
def get_needs():
global outneeds, needs
res = {}
for n in outneeds.keys():
out = []
saveout = copy.copy(outneeds[n])
while len(outneeds[n]) > 0:
if outneeds[n][0] not in needs:
out.append(outneeds[n][0])
del outneeds[n][0]
else:
flag = 0
for k in outneeds[n][1:]:
if k in needs[outneeds[n][0]]:
flag = 1
break
if flag:
outneeds[n] = outneeds[n][1:] + [outneeds[n][0]]
else:
out.append(outneeds[n][0])
del outneeds[n][0]
if saveout and (0 not in map(lambda x, y: x == y, saveout, outneeds[n])) \
and outneeds[n] != []:
print(n, saveout)
errmess(
'get_needs: no progress in sorting needs, probably circular dependence, skipping.\n')
out = out + saveout
break
saveout = copy.copy(outneeds[n])
if out == []:
out = [n]
res[n] = out
return res
| 45,113 | 34.719715 | 189 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/f90mod_rules.py
|
#!/usr/bin/env python
"""
Build F90 module support for f2py2e.
Copyright 2000 Pearu Peterson all rights reserved,
Pearu Peterson <[email protected]>
Permission to use, modify, and distribute this software is given under the
terms of the NumPy License.
NO WARRANTY IS EXPRESSED OR IMPLIED. USE AT YOUR OWN RISK.
$Date: 2005/02/03 19:30:23 $
Pearu Peterson
"""
from __future__ import division, absolute_import, print_function
__version__ = "$Revision: 1.27 $"[10:-1]
f2py_version = 'See `f2py -v`'
import numpy as np
from . import capi_maps
from . import func2subr
from .crackfortran import undo_rmbadname, undo_rmbadname1
# The eviroment provided by auxfuncs.py is needed for some calls to eval.
# As the needed functions cannot be determined by static inspection of the
# code, it is safest to use import * pending a major refactoring of f2py.
from .auxfuncs import *
options = {}
def findf90modules(m):
if ismodule(m):
return [m]
if not hasbody(m):
return []
ret = []
for b in m['body']:
if ismodule(b):
ret.append(b)
else:
ret = ret + findf90modules(b)
return ret
fgetdims1 = """\
external f2pysetdata
logical ns
integer r,i
integer(%d) s(*)
ns = .FALSE.
if (allocated(d)) then
do i=1,r
if ((size(d,i).ne.s(i)).and.(s(i).ge.0)) then
ns = .TRUE.
end if
end do
if (ns) then
deallocate(d)
end if
end if
if ((.not.allocated(d)).and.(s(1).ge.1)) then""" % np.intp().itemsize
fgetdims2 = """\
end if
if (allocated(d)) then
do i=1,r
s(i) = size(d,i)
end do
end if
flag = 1
call f2pysetdata(d,allocated(d))"""
fgetdims2_sa = """\
end if
if (allocated(d)) then
do i=1,r
s(i) = size(d,i)
end do
!s(r) must be equal to len(d(1))
end if
flag = 2
call f2pysetdata(d,allocated(d))"""
def buildhooks(pymod):
global fgetdims1, fgetdims2
from . import rules
ret = {'f90modhooks': [], 'initf90modhooks': [], 'body': [],
'need': ['F_FUNC', 'arrayobject.h'],
'separatorsfor': {'includes0': '\n', 'includes': '\n'},
'docs': ['"Fortran 90/95 modules:\\n"'],
'latexdoc': []}
fhooks = ['']
def fadd(line, s=fhooks):
s[0] = '%s\n %s' % (s[0], line)
doc = ['']
def dadd(line, s=doc):
s[0] = '%s\n%s' % (s[0], line)
for m in findf90modules(pymod):
sargs, fargs, efargs, modobjs, notvars, onlyvars = [], [], [], [], [
m['name']], []
sargsp = []
ifargs = []
mfargs = []
if hasbody(m):
for b in m['body']:
notvars.append(b['name'])
for n in m['vars'].keys():
var = m['vars'][n]
if (n not in notvars) and (not l_or(isintent_hide, isprivate)(var)):
onlyvars.append(n)
mfargs.append(n)
outmess('\t\tConstructing F90 module support for "%s"...\n' %
(m['name']))
if onlyvars:
outmess('\t\t Variables: %s\n' % (' '.join(onlyvars)))
chooks = ['']
def cadd(line, s=chooks):
s[0] = '%s\n%s' % (s[0], line)
ihooks = ['']
def iadd(line, s=ihooks):
s[0] = '%s\n%s' % (s[0], line)
vrd = capi_maps.modsign2map(m)
cadd('static FortranDataDef f2py_%s_def[] = {' % (m['name']))
dadd('\\subsection{Fortran 90/95 module \\texttt{%s}}\n' % (m['name']))
if hasnote(m):
note = m['note']
if isinstance(note, list):
note = '\n'.join(note)
dadd(note)
if onlyvars:
dadd('\\begin{description}')
for n in onlyvars:
var = m['vars'][n]
modobjs.append(n)
ct = capi_maps.getctype(var)
at = capi_maps.c2capi_map[ct]
dm = capi_maps.getarrdims(n, var)
dms = dm['dims'].replace('*', '-1').strip()
dms = dms.replace(':', '-1').strip()
if not dms:
dms = '-1'
use_fgetdims2 = fgetdims2
if isstringarray(var):
if 'charselector' in var and 'len' in var['charselector']:
cadd('\t{"%s",%s,{{%s,%s}},%s},'
% (undo_rmbadname1(n), dm['rank'], dms, var['charselector']['len'], at))
use_fgetdims2 = fgetdims2_sa
else:
cadd('\t{"%s",%s,{{%s}},%s},' %
(undo_rmbadname1(n), dm['rank'], dms, at))
else:
cadd('\t{"%s",%s,{{%s}},%s},' %
(undo_rmbadname1(n), dm['rank'], dms, at))
dadd('\\item[]{{}\\verb@%s@{}}' %
(capi_maps.getarrdocsign(n, var)))
if hasnote(var):
note = var['note']
if isinstance(note, list):
note = '\n'.join(note)
dadd('--- %s' % (note))
if isallocatable(var):
fargs.append('f2py_%s_getdims_%s' % (m['name'], n))
efargs.append(fargs[-1])
sargs.append(
'void (*%s)(int*,int*,void(*)(char*,int*),int*)' % (n))
sargsp.append('void (*)(int*,int*,void(*)(char*,int*),int*)')
iadd('\tf2py_%s_def[i_f2py++].func = %s;' % (m['name'], n))
fadd('subroutine %s(r,s,f2pysetdata,flag)' % (fargs[-1]))
fadd('use %s, only: d => %s\n' %
(m['name'], undo_rmbadname1(n)))
fadd('integer flag\n')
fhooks[0] = fhooks[0] + fgetdims1
dms = eval('range(1,%s+1)' % (dm['rank']))
fadd(' allocate(d(%s))\n' %
(','.join(['s(%s)' % i for i in dms])))
fhooks[0] = fhooks[0] + use_fgetdims2
fadd('end subroutine %s' % (fargs[-1]))
else:
fargs.append(n)
sargs.append('char *%s' % (n))
sargsp.append('char*')
iadd('\tf2py_%s_def[i_f2py++].data = %s;' % (m['name'], n))
if onlyvars:
dadd('\\end{description}')
if hasbody(m):
for b in m['body']:
if not isroutine(b):
print('Skipping', b['block'], b['name'])
continue
modobjs.append('%s()' % (b['name']))
b['modulename'] = m['name']
api, wrap = rules.buildapi(b)
if isfunction(b):
fhooks[0] = fhooks[0] + wrap
fargs.append('f2pywrap_%s_%s' % (m['name'], b['name']))
ifargs.append(func2subr.createfuncwrapper(b, signature=1))
else:
if wrap:
fhooks[0] = fhooks[0] + wrap
fargs.append('f2pywrap_%s_%s' % (m['name'], b['name']))
ifargs.append(
func2subr.createsubrwrapper(b, signature=1))
else:
fargs.append(b['name'])
mfargs.append(fargs[-1])
api['externroutines'] = []
ar = applyrules(api, vrd)
ar['docs'] = []
ar['docshort'] = []
ret = dictappend(ret, ar)
cadd('\t{"%s",-1,{{-1}},0,NULL,(void *)f2py_rout_#modulename#_%s_%s,doc_f2py_rout_#modulename#_%s_%s},' %
(b['name'], m['name'], b['name'], m['name'], b['name']))
sargs.append('char *%s' % (b['name']))
sargsp.append('char *')
iadd('\tf2py_%s_def[i_f2py++].data = %s;' %
(m['name'], b['name']))
cadd('\t{NULL}\n};\n')
iadd('}')
ihooks[0] = 'static void f2py_setup_%s(%s) {\n\tint i_f2py=0;%s' % (
m['name'], ','.join(sargs), ihooks[0])
if '_' in m['name']:
F_FUNC = 'F_FUNC_US'
else:
F_FUNC = 'F_FUNC'
iadd('extern void %s(f2pyinit%s,F2PYINIT%s)(void (*)(%s));'
% (F_FUNC, m['name'], m['name'].upper(), ','.join(sargsp)))
iadd('static void f2py_init_%s(void) {' % (m['name']))
iadd('\t%s(f2pyinit%s,F2PYINIT%s)(f2py_setup_%s);'
% (F_FUNC, m['name'], m['name'].upper(), m['name']))
iadd('}\n')
ret['f90modhooks'] = ret['f90modhooks'] + chooks + ihooks
ret['initf90modhooks'] = ['\tPyDict_SetItemString(d, "%s", PyFortranObject_New(f2py_%s_def,f2py_init_%s));' % (
m['name'], m['name'], m['name'])] + ret['initf90modhooks']
fadd('')
fadd('subroutine f2pyinit%s(f2pysetupfunc)' % (m['name']))
if mfargs:
for a in undo_rmbadname(mfargs):
fadd('use %s, only : %s' % (m['name'], a))
if ifargs:
fadd(' '.join(['interface'] + ifargs))
fadd('end interface')
fadd('external f2pysetupfunc')
if efargs:
for a in undo_rmbadname(efargs):
fadd('external %s' % (a))
fadd('call f2pysetupfunc(%s)' % (','.join(undo_rmbadname(fargs))))
fadd('end subroutine f2pyinit%s\n' % (m['name']))
dadd('\n'.join(ret['latexdoc']).replace(
r'\subsection{', r'\subsubsection{'))
ret['latexdoc'] = []
ret['docs'].append('"\t%s --- %s"' % (m['name'],
','.join(undo_rmbadname(modobjs))))
ret['routine_defs'] = ''
ret['doc'] = []
ret['docshort'] = []
ret['latexdoc'] = doc[0]
if len(ret['docs']) <= 1:
ret['docs'] = ''
return ret, fhooks[0]
| 9,850 | 35.084249 | 121 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/auxfuncs.py
|
#!/usr/bin/env python
"""
Auxiliary functions for f2py2e.
Copyright 1999,2000 Pearu Peterson all rights reserved,
Pearu Peterson <[email protected]>
Permission to use, modify, and distribute this software is given under the
terms of the NumPy (BSD style) LICENSE.
NO WARRANTY IS EXPRESSED OR IMPLIED. USE AT YOUR OWN RISK.
$Date: 2005/07/24 19:01:55 $
Pearu Peterson
"""
from __future__ import division, absolute_import, print_function
import pprint
import sys
import types
from functools import reduce
from . import __version__
from . import cfuncs
__all__ = [
'applyrules', 'debugcapi', 'dictappend', 'errmess', 'gentitle',
'getargs2', 'getcallprotoargument', 'getcallstatement',
'getfortranname', 'getpymethoddef', 'getrestdoc', 'getusercode',
'getusercode1', 'hasbody', 'hascallstatement', 'hascommon',
'hasexternals', 'hasinitvalue', 'hasnote', 'hasresultnote',
'isallocatable', 'isarray', 'isarrayofstrings', 'iscomplex',
'iscomplexarray', 'iscomplexfunction', 'iscomplexfunction_warn',
'isdouble', 'isdummyroutine', 'isexternal', 'isfunction',
'isfunction_wrap', 'isint1array', 'isinteger', 'isintent_aux',
'isintent_c', 'isintent_callback', 'isintent_copy', 'isintent_dict',
'isintent_hide', 'isintent_in', 'isintent_inout', 'isintent_inplace',
'isintent_nothide', 'isintent_out', 'isintent_overwrite', 'islogical',
'islogicalfunction', 'islong_complex', 'islong_double',
'islong_doublefunction', 'islong_long', 'islong_longfunction',
'ismodule', 'ismoduleroutine', 'isoptional', 'isprivate', 'isrequired',
'isroutine', 'isscalar', 'issigned_long_longarray', 'isstring',
'isstringarray', 'isstringfunction', 'issubroutine',
'issubroutine_wrap', 'isthreadsafe', 'isunsigned', 'isunsigned_char',
'isunsigned_chararray', 'isunsigned_long_long',
'isunsigned_long_longarray', 'isunsigned_short',
'isunsigned_shortarray', 'l_and', 'l_not', 'l_or', 'outmess',
'replace', 'show', 'stripcomma', 'throw_error',
]
f2py_version = __version__.version
errmess = sys.stderr.write
show = pprint.pprint
options = {}
debugoptions = []
wrapfuncs = 1
def outmess(t):
if options.get('verbose', 1):
sys.stdout.write(t)
def debugcapi(var):
return 'capi' in debugoptions
def _isstring(var):
return 'typespec' in var and var['typespec'] == 'character' and \
not isexternal(var)
def isstring(var):
return _isstring(var) and not isarray(var)
def ischaracter(var):
return isstring(var) and 'charselector' not in var
def isstringarray(var):
return isarray(var) and _isstring(var)
def isarrayofstrings(var):
# leaving out '*' for now so that `character*(*) a(m)` and `character
# a(m,*)` are treated differently. Luckily `character**` is illegal.
return isstringarray(var) and var['dimension'][-1] == '(*)'
def isarray(var):
return 'dimension' in var and not isexternal(var)
def isscalar(var):
return not (isarray(var) or isstring(var) or isexternal(var))
def iscomplex(var):
return isscalar(var) and \
var.get('typespec') in ['complex', 'double complex']
def islogical(var):
return isscalar(var) and var.get('typespec') == 'logical'
def isinteger(var):
return isscalar(var) and var.get('typespec') == 'integer'
def isreal(var):
return isscalar(var) and var.get('typespec') == 'real'
def get_kind(var):
try:
return var['kindselector']['*']
except KeyError:
try:
return var['kindselector']['kind']
except KeyError:
pass
def islong_long(var):
if not isscalar(var):
return 0
if var.get('typespec') not in ['integer', 'logical']:
return 0
return get_kind(var) == '8'
def isunsigned_char(var):
if not isscalar(var):
return 0
if var.get('typespec') != 'integer':
return 0
return get_kind(var) == '-1'
def isunsigned_short(var):
if not isscalar(var):
return 0
if var.get('typespec') != 'integer':
return 0
return get_kind(var) == '-2'
def isunsigned(var):
if not isscalar(var):
return 0
if var.get('typespec') != 'integer':
return 0
return get_kind(var) == '-4'
def isunsigned_long_long(var):
if not isscalar(var):
return 0
if var.get('typespec') != 'integer':
return 0
return get_kind(var) == '-8'
def isdouble(var):
if not isscalar(var):
return 0
if not var.get('typespec') == 'real':
return 0
return get_kind(var) == '8'
def islong_double(var):
if not isscalar(var):
return 0
if not var.get('typespec') == 'real':
return 0
return get_kind(var) == '16'
def islong_complex(var):
if not iscomplex(var):
return 0
return get_kind(var) == '32'
def iscomplexarray(var):
return isarray(var) and \
var.get('typespec') in ['complex', 'double complex']
def isint1array(var):
return isarray(var) and var.get('typespec') == 'integer' \
and get_kind(var) == '1'
def isunsigned_chararray(var):
return isarray(var) and var.get('typespec') in ['integer', 'logical']\
and get_kind(var) == '-1'
def isunsigned_shortarray(var):
return isarray(var) and var.get('typespec') in ['integer', 'logical']\
and get_kind(var) == '-2'
def isunsignedarray(var):
return isarray(var) and var.get('typespec') in ['integer', 'logical']\
and get_kind(var) == '-4'
def isunsigned_long_longarray(var):
return isarray(var) and var.get('typespec') in ['integer', 'logical']\
and get_kind(var) == '-8'
def issigned_chararray(var):
return isarray(var) and var.get('typespec') in ['integer', 'logical']\
and get_kind(var) == '1'
def issigned_shortarray(var):
return isarray(var) and var.get('typespec') in ['integer', 'logical']\
and get_kind(var) == '2'
def issigned_array(var):
return isarray(var) and var.get('typespec') in ['integer', 'logical']\
and get_kind(var) == '4'
def issigned_long_longarray(var):
return isarray(var) and var.get('typespec') in ['integer', 'logical']\
and get_kind(var) == '8'
def isallocatable(var):
return 'attrspec' in var and 'allocatable' in var['attrspec']
def ismutable(var):
return not ('dimension' not in var or isstring(var))
def ismoduleroutine(rout):
return 'modulename' in rout
def ismodule(rout):
return 'block' in rout and 'module' == rout['block']
def isfunction(rout):
return 'block' in rout and 'function' == rout['block']
def isfunction_wrap(rout):
if isintent_c(rout):
return 0
return wrapfuncs and isfunction(rout) and (not isexternal(rout))
def issubroutine(rout):
return 'block' in rout and 'subroutine' == rout['block']
def issubroutine_wrap(rout):
if isintent_c(rout):
return 0
return issubroutine(rout) and hasassumedshape(rout)
def hasassumedshape(rout):
if rout.get('hasassumedshape'):
return True
for a in rout['args']:
for d in rout['vars'].get(a, {}).get('dimension', []):
if d == ':':
rout['hasassumedshape'] = True
return True
return False
def isroutine(rout):
return isfunction(rout) or issubroutine(rout)
def islogicalfunction(rout):
if not isfunction(rout):
return 0
if 'result' in rout:
a = rout['result']
else:
a = rout['name']
if a in rout['vars']:
return islogical(rout['vars'][a])
return 0
def islong_longfunction(rout):
if not isfunction(rout):
return 0
if 'result' in rout:
a = rout['result']
else:
a = rout['name']
if a in rout['vars']:
return islong_long(rout['vars'][a])
return 0
def islong_doublefunction(rout):
if not isfunction(rout):
return 0
if 'result' in rout:
a = rout['result']
else:
a = rout['name']
if a in rout['vars']:
return islong_double(rout['vars'][a])
return 0
def iscomplexfunction(rout):
if not isfunction(rout):
return 0
if 'result' in rout:
a = rout['result']
else:
a = rout['name']
if a in rout['vars']:
return iscomplex(rout['vars'][a])
return 0
def iscomplexfunction_warn(rout):
if iscomplexfunction(rout):
outmess("""\
**************************************************************
Warning: code with a function returning complex value
may not work correctly with your Fortran compiler.
Run the following test before using it in your applications:
$(f2py install dir)/test-site/{b/runme_scalar,e/runme}
When using GNU gcc/g77 compilers, codes should work correctly.
**************************************************************\n""")
return 1
return 0
def isstringfunction(rout):
if not isfunction(rout):
return 0
if 'result' in rout:
a = rout['result']
else:
a = rout['name']
if a in rout['vars']:
return isstring(rout['vars'][a])
return 0
def hasexternals(rout):
return 'externals' in rout and rout['externals']
def isthreadsafe(rout):
return 'f2pyenhancements' in rout and \
'threadsafe' in rout['f2pyenhancements']
def hasvariables(rout):
return 'vars' in rout and rout['vars']
def isoptional(var):
return ('attrspec' in var and 'optional' in var['attrspec'] and
'required' not in var['attrspec']) and isintent_nothide(var)
def isexternal(var):
return 'attrspec' in var and 'external' in var['attrspec']
def isrequired(var):
return not isoptional(var) and isintent_nothide(var)
def isintent_in(var):
if 'intent' not in var:
return 1
if 'hide' in var['intent']:
return 0
if 'inplace' in var['intent']:
return 0
if 'in' in var['intent']:
return 1
if 'out' in var['intent']:
return 0
if 'inout' in var['intent']:
return 0
if 'outin' in var['intent']:
return 0
return 1
def isintent_inout(var):
return ('intent' in var and ('inout' in var['intent'] or
'outin' in var['intent']) and 'in' not in var['intent'] and
'hide' not in var['intent'] and 'inplace' not in var['intent'])
def isintent_out(var):
return 'out' in var.get('intent', [])
def isintent_hide(var):
return ('intent' in var and ('hide' in var['intent'] or
('out' in var['intent'] and 'in' not in var['intent'] and
(not l_or(isintent_inout, isintent_inplace)(var)))))
def isintent_nothide(var):
return not isintent_hide(var)
def isintent_c(var):
return 'c' in var.get('intent', [])
def isintent_cache(var):
return 'cache' in var.get('intent', [])
def isintent_copy(var):
return 'copy' in var.get('intent', [])
def isintent_overwrite(var):
return 'overwrite' in var.get('intent', [])
def isintent_callback(var):
return 'callback' in var.get('intent', [])
def isintent_inplace(var):
return 'inplace' in var.get('intent', [])
def isintent_aux(var):
return 'aux' in var.get('intent', [])
def isintent_aligned4(var):
return 'aligned4' in var.get('intent', [])
def isintent_aligned8(var):
return 'aligned8' in var.get('intent', [])
def isintent_aligned16(var):
return 'aligned16' in var.get('intent', [])
isintent_dict = {isintent_in: 'INTENT_IN', isintent_inout: 'INTENT_INOUT',
isintent_out: 'INTENT_OUT', isintent_hide: 'INTENT_HIDE',
isintent_cache: 'INTENT_CACHE',
isintent_c: 'INTENT_C', isoptional: 'OPTIONAL',
isintent_inplace: 'INTENT_INPLACE',
isintent_aligned4: 'INTENT_ALIGNED4',
isintent_aligned8: 'INTENT_ALIGNED8',
isintent_aligned16: 'INTENT_ALIGNED16',
}
def isprivate(var):
return 'attrspec' in var and 'private' in var['attrspec']
def hasinitvalue(var):
return '=' in var
def hasinitvalueasstring(var):
if not hasinitvalue(var):
return 0
return var['='][0] in ['"', "'"]
def hasnote(var):
return 'note' in var
def hasresultnote(rout):
if not isfunction(rout):
return 0
if 'result' in rout:
a = rout['result']
else:
a = rout['name']
if a in rout['vars']:
return hasnote(rout['vars'][a])
return 0
def hascommon(rout):
return 'common' in rout
def containscommon(rout):
if hascommon(rout):
return 1
if hasbody(rout):
for b in rout['body']:
if containscommon(b):
return 1
return 0
def containsmodule(block):
if ismodule(block):
return 1
if not hasbody(block):
return 0
for b in block['body']:
if containsmodule(b):
return 1
return 0
def hasbody(rout):
return 'body' in rout
def hascallstatement(rout):
return getcallstatement(rout) is not None
def istrue(var):
return 1
def isfalse(var):
return 0
class F2PYError(Exception):
pass
class throw_error(object):
def __init__(self, mess):
self.mess = mess
def __call__(self, var):
mess = '\n\n var = %s\n Message: %s\n' % (var, self.mess)
raise F2PYError(mess)
def l_and(*f):
l, l2 = 'lambda v', []
for i in range(len(f)):
l = '%s,f%d=f[%d]' % (l, i, i)
l2.append('f%d(v)' % (i))
return eval('%s:%s' % (l, ' and '.join(l2)))
def l_or(*f):
l, l2 = 'lambda v', []
for i in range(len(f)):
l = '%s,f%d=f[%d]' % (l, i, i)
l2.append('f%d(v)' % (i))
return eval('%s:%s' % (l, ' or '.join(l2)))
def l_not(f):
return eval('lambda v,f=f:not f(v)')
def isdummyroutine(rout):
try:
return rout['f2pyenhancements']['fortranname'] == ''
except KeyError:
return 0
def getfortranname(rout):
try:
name = rout['f2pyenhancements']['fortranname']
if name == '':
raise KeyError
if not name:
errmess('Failed to use fortranname from %s\n' %
(rout['f2pyenhancements']))
raise KeyError
except KeyError:
name = rout['name']
return name
def getmultilineblock(rout, blockname, comment=1, counter=0):
try:
r = rout['f2pyenhancements'].get(blockname)
except KeyError:
return
if not r:
return
if counter > 0 and isinstance(r, str):
return
if isinstance(r, list):
if counter >= len(r):
return
r = r[counter]
if r[:3] == "'''":
if comment:
r = '\t/* start ' + blockname + \
' multiline (' + repr(counter) + ') */\n' + r[3:]
else:
r = r[3:]
if r[-3:] == "'''":
if comment:
r = r[:-3] + '\n\t/* end multiline (' + repr(counter) + ')*/'
else:
r = r[:-3]
else:
errmess("%s multiline block should end with `'''`: %s\n"
% (blockname, repr(r)))
return r
def getcallstatement(rout):
return getmultilineblock(rout, 'callstatement')
def getcallprotoargument(rout, cb_map={}):
r = getmultilineblock(rout, 'callprotoargument', comment=0)
if r:
return r
if hascallstatement(rout):
outmess(
'warning: callstatement is defined without callprotoargument\n')
return
from .capi_maps import getctype
arg_types, arg_types2 = [], []
if l_and(isstringfunction, l_not(isfunction_wrap))(rout):
arg_types.extend(['char*', 'size_t'])
for n in rout['args']:
var = rout['vars'][n]
if isintent_callback(var):
continue
if n in cb_map:
ctype = cb_map[n] + '_typedef'
else:
ctype = getctype(var)
if l_and(isintent_c, l_or(isscalar, iscomplex))(var):
pass
elif isstring(var):
pass
else:
ctype = ctype + '*'
if isstring(var) or isarrayofstrings(var):
arg_types2.append('size_t')
arg_types.append(ctype)
proto_args = ','.join(arg_types + arg_types2)
if not proto_args:
proto_args = 'void'
return proto_args
def getusercode(rout):
return getmultilineblock(rout, 'usercode')
def getusercode1(rout):
return getmultilineblock(rout, 'usercode', counter=1)
def getpymethoddef(rout):
return getmultilineblock(rout, 'pymethoddef')
def getargs(rout):
sortargs, args = [], []
if 'args' in rout:
args = rout['args']
if 'sortvars' in rout:
for a in rout['sortvars']:
if a in args:
sortargs.append(a)
for a in args:
if a not in sortargs:
sortargs.append(a)
else:
sortargs = rout['args']
return args, sortargs
def getargs2(rout):
sortargs, args = [], rout.get('args', [])
auxvars = [a for a in rout['vars'].keys() if isintent_aux(rout['vars'][a])
and a not in args]
args = auxvars + args
if 'sortvars' in rout:
for a in rout['sortvars']:
if a in args:
sortargs.append(a)
for a in args:
if a not in sortargs:
sortargs.append(a)
else:
sortargs = auxvars + rout['args']
return args, sortargs
def getrestdoc(rout):
if 'f2pymultilines' not in rout:
return None
k = None
if rout['block'] == 'python module':
k = rout['block'], rout['name']
return rout['f2pymultilines'].get(k, None)
def gentitle(name):
l = (80 - len(name) - 6) // 2
return '/*%s %s %s*/' % (l * '*', name, l * '*')
def flatlist(l):
if isinstance(l, list):
return reduce(lambda x, y, f=flatlist: x + f(y), l, [])
return [l]
def stripcomma(s):
if s and s[-1] == ',':
return s[:-1]
return s
def replace(str, d, defaultsep=''):
if isinstance(d, list):
return [replace(str, _m, defaultsep) for _m in d]
if isinstance(str, list):
return [replace(_m, d, defaultsep) for _m in str]
for k in 2 * list(d.keys()):
if k == 'separatorsfor':
continue
if 'separatorsfor' in d and k in d['separatorsfor']:
sep = d['separatorsfor'][k]
else:
sep = defaultsep
if isinstance(d[k], list):
str = str.replace('#%s#' % (k), sep.join(flatlist(d[k])))
else:
str = str.replace('#%s#' % (k), d[k])
return str
def dictappend(rd, ar):
if isinstance(ar, list):
for a in ar:
rd = dictappend(rd, a)
return rd
for k in ar.keys():
if k[0] == '_':
continue
if k in rd:
if isinstance(rd[k], str):
rd[k] = [rd[k]]
if isinstance(rd[k], list):
if isinstance(ar[k], list):
rd[k] = rd[k] + ar[k]
else:
rd[k].append(ar[k])
elif isinstance(rd[k], dict):
if isinstance(ar[k], dict):
if k == 'separatorsfor':
for k1 in ar[k].keys():
if k1 not in rd[k]:
rd[k][k1] = ar[k][k1]
else:
rd[k] = dictappend(rd[k], ar[k])
else:
rd[k] = ar[k]
return rd
def applyrules(rules, d, var={}):
ret = {}
if isinstance(rules, list):
for r in rules:
rr = applyrules(r, d, var)
ret = dictappend(ret, rr)
if '_break' in rr:
break
return ret
if '_check' in rules and (not rules['_check'](var)):
return ret
if 'need' in rules:
res = applyrules({'needs': rules['need']}, d, var)
if 'needs' in res:
cfuncs.append_needs(res['needs'])
for k in rules.keys():
if k == 'separatorsfor':
ret[k] = rules[k]
continue
if isinstance(rules[k], str):
ret[k] = replace(rules[k], d)
elif isinstance(rules[k], list):
ret[k] = []
for i in rules[k]:
ar = applyrules({k: i}, d, var)
if k in ar:
ret[k].append(ar[k])
elif k[0] == '_':
continue
elif isinstance(rules[k], dict):
ret[k] = []
for k1 in rules[k].keys():
if isinstance(k1, types.FunctionType) and k1(var):
if isinstance(rules[k][k1], list):
for i in rules[k][k1]:
if isinstance(i, dict):
res = applyrules({'supertext': i}, d, var)
if 'supertext' in res:
i = res['supertext']
else:
i = ''
ret[k].append(replace(i, d))
else:
i = rules[k][k1]
if isinstance(i, dict):
res = applyrules({'supertext': i}, d)
if 'supertext' in res:
i = res['supertext']
else:
i = ''
ret[k].append(replace(i, d))
else:
errmess('applyrules: ignoring rule %s.\n' % repr(rules[k]))
if isinstance(ret[k], list):
if len(ret[k]) == 1:
ret[k] = ret[k][0]
if ret[k] == []:
del ret[k]
return ret
| 21,826 | 24.528655 | 78 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/src/fortranobject.c
|
#define FORTRANOBJECT_C
#include "fortranobject.h"
#ifdef __cplusplus
extern "C" {
#endif
#include <stdlib.h>
#include <string.h>
/*
This file implements: FortranObject, array_from_pyobj, copy_ND_array
Author: Pearu Peterson <[email protected]>
$Revision: 1.52 $
$Date: 2005/07/11 07:44:20 $
*/
int
F2PyDict_SetItemString(PyObject *dict, char *name, PyObject *obj)
{
if (obj==NULL) {
fprintf(stderr, "Error loading %s\n", name);
if (PyErr_Occurred()) {
PyErr_Print();
PyErr_Clear();
}
return -1;
}
return PyDict_SetItemString(dict, name, obj);
}
/************************* FortranObject *******************************/
typedef PyObject *(*fortranfunc)(PyObject *,PyObject *,PyObject *,void *);
PyObject *
PyFortranObject_New(FortranDataDef* defs, f2py_void_func init) {
int i;
PyFortranObject *fp = NULL;
PyObject *v = NULL;
if (init!=NULL) /* Initialize F90 module objects */
(*(init))();
if ((fp = PyObject_New(PyFortranObject, &PyFortran_Type))==NULL) return NULL;
if ((fp->dict = PyDict_New())==NULL) return NULL;
fp->len = 0;
while (defs[fp->len].name != NULL) fp->len++;
if (fp->len == 0) goto fail;
fp->defs = defs;
for (i=0;i<fp->len;i++)
if (fp->defs[i].rank == -1) { /* Is Fortran routine */
v = PyFortranObject_NewAsAttr(&(fp->defs[i]));
if (v==NULL) return NULL;
PyDict_SetItemString(fp->dict,fp->defs[i].name,v);
} else
if ((fp->defs[i].data)!=NULL) { /* Is Fortran variable or array (not allocatable) */
if (fp->defs[i].type == NPY_STRING) {
int n = fp->defs[i].rank-1;
v = PyArray_New(&PyArray_Type, n, fp->defs[i].dims.d,
NPY_STRING, NULL, fp->defs[i].data, fp->defs[i].dims.d[n],
NPY_ARRAY_FARRAY, NULL);
}
else {
v = PyArray_New(&PyArray_Type, fp->defs[i].rank, fp->defs[i].dims.d,
fp->defs[i].type, NULL, fp->defs[i].data, 0, NPY_ARRAY_FARRAY,
NULL);
}
if (v==NULL) return NULL;
PyDict_SetItemString(fp->dict,fp->defs[i].name,v);
}
Py_XDECREF(v);
return (PyObject *)fp;
fail:
Py_XDECREF(v);
return NULL;
}
PyObject *
PyFortranObject_NewAsAttr(FortranDataDef* defs) { /* used for calling F90 module routines */
PyFortranObject *fp = NULL;
fp = PyObject_New(PyFortranObject, &PyFortran_Type);
if (fp == NULL) return NULL;
if ((fp->dict = PyDict_New())==NULL) return NULL;
fp->len = 1;
fp->defs = defs;
return (PyObject *)fp;
}
/* Fortran methods */
static void
fortran_dealloc(PyFortranObject *fp) {
Py_XDECREF(fp->dict);
PyMem_Del(fp);
}
#if PY_VERSION_HEX >= 0x03000000
#else
static PyMethodDef fortran_methods[] = {
{NULL, NULL} /* sentinel */
};
#endif
/* Returns number of bytes consumed from buf, or -1 on error. */
static Py_ssize_t
format_def(char *buf, Py_ssize_t size, FortranDataDef def)
{
char *p = buf;
int i, n;
n = PyOS_snprintf(p, size, "array(%" NPY_INTP_FMT, def.dims.d[0]);
if (n < 0 || n >= size) {
return -1;
}
p += n;
size -= n;
for (i = 1; i < def.rank; i++) {
n = PyOS_snprintf(p, size, ",%" NPY_INTP_FMT, def.dims.d[i]);
if (n < 0 || n >= size) {
return -1;
}
p += n;
size -= n;
}
if (size <= 0) {
return -1;
}
*p++ = ')';
size--;
if (def.data == NULL) {
static const char notalloc[] = ", not allocated";
if (size < sizeof(notalloc)) {
return -1;
}
memcpy(p, notalloc, sizeof(notalloc));
}
return p - buf;
}
static PyObject *
fortran_doc(FortranDataDef def)
{
char *buf, *p;
PyObject *s = NULL;
Py_ssize_t n, origsize, size = 100;
if (def.doc != NULL) {
size += strlen(def.doc);
}
origsize = size;
buf = p = (char *)PyMem_Malloc(size);
if (buf == NULL) {
return PyErr_NoMemory();
}
if (def.rank == -1) {
if (def.doc) {
n = strlen(def.doc);
if (n > size) {
goto fail;
}
memcpy(p, def.doc, n);
p += n;
size -= n;
}
else {
n = PyOS_snprintf(p, size, "%s - no docs available", def.name);
if (n < 0 || n >= size) {
goto fail;
}
p += n;
size -= n;
}
}
else {
PyArray_Descr *d = PyArray_DescrFromType(def.type);
n = PyOS_snprintf(p, size, "'%c'-", d->type);
Py_DECREF(d);
if (n < 0 || n >= size) {
goto fail;
}
p += n;
size -= n;
if (def.data == NULL) {
n = format_def(p, size, def) == -1;
if (n < 0) {
goto fail;
}
p += n;
size -= n;
}
else if (def.rank > 0) {
n = format_def(p, size, def);
if (n < 0) {
goto fail;
}
p += n;
size -= n;
}
else {
n = strlen("scalar");
if (size < n) {
goto fail;
}
memcpy(p, "scalar", n);
p += n;
size -= n;
}
}
if (size <= 1) {
goto fail;
}
*p++ = '\n';
size--;
/* p now points one beyond the last character of the string in buf */
#if PY_VERSION_HEX >= 0x03000000
s = PyUnicode_FromStringAndSize(buf, p - buf);
#else
s = PyString_FromStringAndSize(buf, p - buf);
#endif
PyMem_Free(buf);
return s;
fail:
fprintf(stderr, "fortranobject.c: fortran_doc: len(p)=%zd>%zd=size:"
" too long docstring required, increase size\n",
p - buf, origsize);
PyMem_Free(buf);
return NULL;
}
static FortranDataDef *save_def; /* save pointer of an allocatable array */
static void set_data(char *d,npy_intp *f) { /* callback from Fortran */
if (*f) /* In fortran f=allocated(d) */
save_def->data = d;
else
save_def->data = NULL;
/* printf("set_data: d=%p,f=%d\n",d,*f); */
}
static PyObject *
fortran_getattr(PyFortranObject *fp, char *name) {
int i,j,k,flag;
if (fp->dict != NULL) {
PyObject *v = PyDict_GetItemString(fp->dict, name);
if (v != NULL) {
Py_INCREF(v);
return v;
}
}
for (i=0,j=1;i<fp->len && (j=strcmp(name,fp->defs[i].name));i++);
if (j==0)
if (fp->defs[i].rank!=-1) { /* F90 allocatable array */
if (fp->defs[i].func==NULL) return NULL;
for(k=0;k<fp->defs[i].rank;++k)
fp->defs[i].dims.d[k]=-1;
save_def = &fp->defs[i];
(*(fp->defs[i].func))(&fp->defs[i].rank,fp->defs[i].dims.d,set_data,&flag);
if (flag==2)
k = fp->defs[i].rank + 1;
else
k = fp->defs[i].rank;
if (fp->defs[i].data !=NULL) { /* array is allocated */
PyObject *v = PyArray_New(&PyArray_Type, k, fp->defs[i].dims.d,
fp->defs[i].type, NULL, fp->defs[i].data, 0, NPY_ARRAY_FARRAY,
NULL);
if (v==NULL) return NULL;
/* Py_INCREF(v); */
return v;
} else { /* array is not allocated */
Py_RETURN_NONE;
}
}
if (strcmp(name,"__dict__")==0) {
Py_INCREF(fp->dict);
return fp->dict;
}
if (strcmp(name,"__doc__")==0) {
#if PY_VERSION_HEX >= 0x03000000
PyObject *s = PyUnicode_FromString(""), *s2, *s3;
for (i=0;i<fp->len;i++) {
s2 = fortran_doc(fp->defs[i]);
s3 = PyUnicode_Concat(s, s2);
Py_DECREF(s2);
Py_DECREF(s);
s = s3;
}
#else
PyObject *s = PyString_FromString("");
for (i=0;i<fp->len;i++)
PyString_ConcatAndDel(&s,fortran_doc(fp->defs[i]));
#endif
if (PyDict_SetItemString(fp->dict, name, s))
return NULL;
return s;
}
if ((strcmp(name,"_cpointer")==0) && (fp->len==1)) {
PyObject *cobj = F2PyCapsule_FromVoidPtr((void *)(fp->defs[0].data),NULL);
if (PyDict_SetItemString(fp->dict, name, cobj))
return NULL;
return cobj;
}
#if PY_VERSION_HEX >= 0x03000000
if (1) {
PyObject *str, *ret;
str = PyUnicode_FromString(name);
ret = PyObject_GenericGetAttr((PyObject *)fp, str);
Py_DECREF(str);
return ret;
}
#else
return Py_FindMethod(fortran_methods, (PyObject *)fp, name);
#endif
}
static int
fortran_setattr(PyFortranObject *fp, char *name, PyObject *v) {
int i,j,flag;
PyArrayObject *arr = NULL;
for (i=0,j=1;i<fp->len && (j=strcmp(name,fp->defs[i].name));i++);
if (j==0) {
if (fp->defs[i].rank==-1) {
PyErr_SetString(PyExc_AttributeError,"over-writing fortran routine");
return -1;
}
if (fp->defs[i].func!=NULL) { /* is allocatable array */
npy_intp dims[F2PY_MAX_DIMS];
int k;
save_def = &fp->defs[i];
if (v!=Py_None) { /* set new value (reallocate if needed --
see f2py generated code for more
details ) */
for(k=0;k<fp->defs[i].rank;k++) dims[k]=-1;
if ((arr = array_from_pyobj(fp->defs[i].type,dims,fp->defs[i].rank,F2PY_INTENT_IN,v))==NULL)
return -1;
(*(fp->defs[i].func))(&fp->defs[i].rank,PyArray_DIMS(arr),set_data,&flag);
} else { /* deallocate */
for(k=0;k<fp->defs[i].rank;k++) dims[k]=0;
(*(fp->defs[i].func))(&fp->defs[i].rank,dims,set_data,&flag);
for(k=0;k<fp->defs[i].rank;k++) dims[k]=-1;
}
memcpy(fp->defs[i].dims.d,dims,fp->defs[i].rank*sizeof(npy_intp));
} else { /* not allocatable array */
if ((arr = array_from_pyobj(fp->defs[i].type,fp->defs[i].dims.d,fp->defs[i].rank,F2PY_INTENT_IN,v))==NULL)
return -1;
}
if (fp->defs[i].data!=NULL) { /* copy Python object to Fortran array */
npy_intp s = PyArray_MultiplyList(fp->defs[i].dims.d,PyArray_NDIM(arr));
if (s==-1)
s = PyArray_MultiplyList(PyArray_DIMS(arr),PyArray_NDIM(arr));
if (s<0 ||
(memcpy(fp->defs[i].data,PyArray_DATA(arr),s*PyArray_ITEMSIZE(arr)))==NULL) {
if ((PyObject*)arr!=v) {
Py_DECREF(arr);
}
return -1;
}
if ((PyObject*)arr!=v) {
Py_DECREF(arr);
}
} else return (fp->defs[i].func==NULL?-1:0);
return 0; /* successful */
}
if (fp->dict == NULL) {
fp->dict = PyDict_New();
if (fp->dict == NULL)
return -1;
}
if (v == NULL) {
int rv = PyDict_DelItemString(fp->dict, name);
if (rv < 0)
PyErr_SetString(PyExc_AttributeError,"delete non-existing fortran attribute");
return rv;
}
else
return PyDict_SetItemString(fp->dict, name, v);
}
static PyObject*
fortran_call(PyFortranObject *fp, PyObject *arg, PyObject *kw) {
int i = 0;
/* printf("fortran call
name=%s,func=%p,data=%p,%p\n",fp->defs[i].name,
fp->defs[i].func,fp->defs[i].data,&fp->defs[i].data); */
if (fp->defs[i].rank==-1) {/* is Fortran routine */
if (fp->defs[i].func==NULL) {
PyErr_Format(PyExc_RuntimeError, "no function to call");
return NULL;
}
else if (fp->defs[i].data==NULL)
/* dummy routine */
return (*((fortranfunc)(fp->defs[i].func)))((PyObject *)fp,arg,kw,NULL);
else
return (*((fortranfunc)(fp->defs[i].func)))((PyObject *)fp,arg,kw,
(void *)fp->defs[i].data);
}
PyErr_Format(PyExc_TypeError, "this fortran object is not callable");
return NULL;
}
static PyObject *
fortran_repr(PyFortranObject *fp)
{
PyObject *name = NULL, *repr = NULL;
name = PyObject_GetAttrString((PyObject *)fp, "__name__");
PyErr_Clear();
#if PY_VERSION_HEX >= 0x03000000
if (name != NULL && PyUnicode_Check(name)) {
repr = PyUnicode_FromFormat("<fortran %U>", name);
}
else {
repr = PyUnicode_FromString("<fortran object>");
}
#else
if (name != NULL && PyString_Check(name)) {
repr = PyString_FromFormat("<fortran %s>", PyString_AsString(name));
}
else {
repr = PyString_FromString("<fortran object>");
}
#endif
Py_XDECREF(name);
return repr;
}
PyTypeObject PyFortran_Type = {
#if PY_VERSION_HEX >= 0x03000000
PyVarObject_HEAD_INIT(NULL, 0)
#else
PyObject_HEAD_INIT(0)
0, /*ob_size*/
#endif
"fortran", /*tp_name*/
sizeof(PyFortranObject), /*tp_basicsize*/
0, /*tp_itemsize*/
/* methods */
(destructor)fortran_dealloc, /*tp_dealloc*/
0, /*tp_print*/
(getattrfunc)fortran_getattr, /*tp_getattr*/
(setattrfunc)fortran_setattr, /*tp_setattr*/
0, /*tp_compare/tp_reserved*/
(reprfunc)fortran_repr, /*tp_repr*/
0, /*tp_as_number*/
0, /*tp_as_sequence*/
0, /*tp_as_mapping*/
0, /*tp_hash*/
(ternaryfunc)fortran_call, /*tp_call*/
};
/************************* f2py_report_atexit *******************************/
#ifdef F2PY_REPORT_ATEXIT
static int passed_time = 0;
static int passed_counter = 0;
static int passed_call_time = 0;
static struct timeb start_time;
static struct timeb stop_time;
static struct timeb start_call_time;
static struct timeb stop_call_time;
static int cb_passed_time = 0;
static int cb_passed_counter = 0;
static int cb_passed_call_time = 0;
static struct timeb cb_start_time;
static struct timeb cb_stop_time;
static struct timeb cb_start_call_time;
static struct timeb cb_stop_call_time;
extern void f2py_start_clock(void) { ftime(&start_time); }
extern
void f2py_start_call_clock(void) {
f2py_stop_clock();
ftime(&start_call_time);
}
extern
void f2py_stop_clock(void) {
ftime(&stop_time);
passed_time += 1000*(stop_time.time - start_time.time);
passed_time += stop_time.millitm - start_time.millitm;
}
extern
void f2py_stop_call_clock(void) {
ftime(&stop_call_time);
passed_call_time += 1000*(stop_call_time.time - start_call_time.time);
passed_call_time += stop_call_time.millitm - start_call_time.millitm;
passed_counter += 1;
f2py_start_clock();
}
extern void f2py_cb_start_clock(void) { ftime(&cb_start_time); }
extern
void f2py_cb_start_call_clock(void) {
f2py_cb_stop_clock();
ftime(&cb_start_call_time);
}
extern
void f2py_cb_stop_clock(void) {
ftime(&cb_stop_time);
cb_passed_time += 1000*(cb_stop_time.time - cb_start_time.time);
cb_passed_time += cb_stop_time.millitm - cb_start_time.millitm;
}
extern
void f2py_cb_stop_call_clock(void) {
ftime(&cb_stop_call_time);
cb_passed_call_time += 1000*(cb_stop_call_time.time - cb_start_call_time.time);
cb_passed_call_time += cb_stop_call_time.millitm - cb_start_call_time.millitm;
cb_passed_counter += 1;
f2py_cb_start_clock();
}
static int f2py_report_on_exit_been_here = 0;
extern
void f2py_report_on_exit(int exit_flag,void *name) {
if (f2py_report_on_exit_been_here) {
fprintf(stderr," %s\n",(char*)name);
return;
}
f2py_report_on_exit_been_here = 1;
fprintf(stderr," /-----------------------\\\n");
fprintf(stderr," < F2PY performance report >\n");
fprintf(stderr," \\-----------------------/\n");
fprintf(stderr,"Overall time spent in ...\n");
fprintf(stderr,"(a) wrapped (Fortran/C) functions : %8d msec\n",
passed_call_time);
fprintf(stderr,"(b) f2py interface, %6d calls : %8d msec\n",
passed_counter,passed_time);
fprintf(stderr,"(c) call-back (Python) functions : %8d msec\n",
cb_passed_call_time);
fprintf(stderr,"(d) f2py call-back interface, %6d calls : %8d msec\n",
cb_passed_counter,cb_passed_time);
fprintf(stderr,"(e) wrapped (Fortran/C) functions (acctual) : %8d msec\n\n",
passed_call_time-cb_passed_call_time-cb_passed_time);
fprintf(stderr,"Use -DF2PY_REPORT_ATEXIT_DISABLE to disable this message.\n");
fprintf(stderr,"Exit status: %d\n",exit_flag);
fprintf(stderr,"Modules : %s\n",(char*)name);
}
#endif
/********************** report on array copy ****************************/
#ifdef F2PY_REPORT_ON_ARRAY_COPY
static void f2py_report_on_array_copy(PyArrayObject* arr) {
const npy_intp arr_size = PyArray_Size((PyObject *)arr);
if (arr_size>F2PY_REPORT_ON_ARRAY_COPY) {
fprintf(stderr,"copied an array: size=%ld, elsize=%"NPY_INTP_FMT"\n",
arr_size, (npy_intp)PyArray_ITEMSIZE(arr));
}
}
static void f2py_report_on_array_copy_fromany(void) {
fprintf(stderr,"created an array from object\n");
}
#define F2PY_REPORT_ON_ARRAY_COPY_FROMARR f2py_report_on_array_copy((PyArrayObject *)arr)
#define F2PY_REPORT_ON_ARRAY_COPY_FROMANY f2py_report_on_array_copy_fromany()
#else
#define F2PY_REPORT_ON_ARRAY_COPY_FROMARR
#define F2PY_REPORT_ON_ARRAY_COPY_FROMANY
#endif
/************************* array_from_obj *******************************/
/*
* File: array_from_pyobj.c
*
* Description:
* ------------
* Provides array_from_pyobj function that returns a contigious array
* object with the given dimensions and required storage order, either
* in row-major (C) or column-major (Fortran) order. The function
* array_from_pyobj is very flexible about its Python object argument
* that can be any number, list, tuple, or array.
*
* array_from_pyobj is used in f2py generated Python extension
* modules.
*
* Author: Pearu Peterson <[email protected]>
* Created: 13-16 January 2002
* $Id: fortranobject.c,v 1.52 2005/07/11 07:44:20 pearu Exp $
*/
static int check_and_fix_dimensions(const PyArrayObject* arr,
const int rank,
npy_intp *dims);
static int
count_negative_dimensions(const int rank,
const npy_intp *dims) {
int i=0,r=0;
while (i<rank) {
if (dims[i] < 0) ++r;
++i;
}
return r;
}
#ifdef DEBUG_COPY_ND_ARRAY
void dump_dims(int rank, npy_intp* dims) {
int i;
printf("[");
for(i=0;i<rank;++i) {
printf("%3" NPY_INTP_FMT, dims[i]);
}
printf("]\n");
}
void dump_attrs(const PyArrayObject* obj) {
const PyArrayObject_fields *arr = (const PyArrayObject_fields*) obj;
int rank = PyArray_NDIM(arr);
npy_intp size = PyArray_Size((PyObject *)arr);
printf("\trank = %d, flags = %d, size = %" NPY_INTP_FMT "\n",
rank,arr->flags,size);
printf("\tstrides = ");
dump_dims(rank,arr->strides);
printf("\tdimensions = ");
dump_dims(rank,arr->dimensions);
}
#endif
#define SWAPTYPE(a,b,t) {t c; c = (a); (a) = (b); (b) = c; }
static int swap_arrays(PyArrayObject* obj1, PyArrayObject* obj2) {
PyArrayObject_fields *arr1 = (PyArrayObject_fields*) obj1,
*arr2 = (PyArrayObject_fields*) obj2;
SWAPTYPE(arr1->data,arr2->data,char*);
SWAPTYPE(arr1->nd,arr2->nd,int);
SWAPTYPE(arr1->dimensions,arr2->dimensions,npy_intp*);
SWAPTYPE(arr1->strides,arr2->strides,npy_intp*);
SWAPTYPE(arr1->base,arr2->base,PyObject*);
SWAPTYPE(arr1->descr,arr2->descr,PyArray_Descr*);
SWAPTYPE(arr1->flags,arr2->flags,int);
/* SWAPTYPE(arr1->weakreflist,arr2->weakreflist,PyObject*); */
return 0;
}
#define ARRAY_ISCOMPATIBLE(arr,type_num) \
( (PyArray_ISINTEGER(arr) && PyTypeNum_ISINTEGER(type_num)) \
||(PyArray_ISFLOAT(arr) && PyTypeNum_ISFLOAT(type_num)) \
||(PyArray_ISCOMPLEX(arr) && PyTypeNum_ISCOMPLEX(type_num)) \
||(PyArray_ISBOOL(arr) && PyTypeNum_ISBOOL(type_num)) \
)
extern
PyArrayObject* array_from_pyobj(const int type_num,
npy_intp *dims,
const int rank,
const int intent,
PyObject *obj) {
/* Note about reference counting
-----------------------------
If the caller returns the array to Python, it must be done with
Py_BuildValue("N",arr).
Otherwise, if obj!=arr then the caller must call Py_DECREF(arr).
Note on intent(cache,out,..)
---------------------
Don't expect correct data when returning intent(cache) array.
*/
char mess[200];
PyArrayObject *arr = NULL;
PyArray_Descr *descr;
char typechar;
int elsize;
if ((intent & F2PY_INTENT_HIDE)
|| ((intent & F2PY_INTENT_CACHE) && (obj==Py_None))
|| ((intent & F2PY_OPTIONAL) && (obj==Py_None))
) {
/* intent(cache), optional, intent(hide) */
if (count_negative_dimensions(rank,dims) > 0) {
int i;
strcpy(mess, "failed to create intent(cache|hide)|optional array"
"-- must have defined dimensions but got (");
for(i=0;i<rank;++i)
sprintf(mess+strlen(mess),"%" NPY_INTP_FMT ",",dims[i]);
strcat(mess, ")");
PyErr_SetString(PyExc_ValueError,mess);
return NULL;
}
arr = (PyArrayObject *)
PyArray_New(&PyArray_Type, rank, dims, type_num,
NULL,NULL,1,
!(intent&F2PY_INTENT_C),
NULL);
if (arr==NULL) return NULL;
if (!(intent & F2PY_INTENT_CACHE))
PyArray_FILLWBYTE(arr, 0);
return arr;
}
descr = PyArray_DescrFromType(type_num);
/* compatibility with NPY_CHAR */
if (type_num == NPY_STRING) {
PyArray_DESCR_REPLACE(descr);
if (descr == NULL) {
return NULL;
}
descr->elsize = 1;
descr->type = NPY_CHARLTR;
}
elsize = descr->elsize;
typechar = descr->type;
Py_DECREF(descr);
if (PyArray_Check(obj)) {
arr = (PyArrayObject *)obj;
if (intent & F2PY_INTENT_CACHE) {
/* intent(cache) */
if (PyArray_ISONESEGMENT(arr)
&& PyArray_ITEMSIZE(arr)>=elsize) {
if (check_and_fix_dimensions(arr, rank, dims)) {
return NULL;
}
if (intent & F2PY_INTENT_OUT)
Py_INCREF(arr);
return arr;
}
strcpy(mess, "failed to initialize intent(cache) array");
if (!PyArray_ISONESEGMENT(arr))
strcat(mess, " -- input must be in one segment");
if (PyArray_ITEMSIZE(arr)<elsize)
sprintf(mess+strlen(mess),
" -- expected at least elsize=%d but got %" NPY_INTP_FMT,
elsize,
(npy_intp)PyArray_ITEMSIZE(arr)
);
PyErr_SetString(PyExc_ValueError,mess);
return NULL;
}
/* here we have always intent(in) or intent(inout) or intent(inplace) */
if (check_and_fix_dimensions(arr, rank, dims)) {
return NULL;
}
/*
printf("intent alignement=%d\n", F2PY_GET_ALIGNMENT(intent));
printf("alignement check=%d\n", F2PY_CHECK_ALIGNMENT(arr, intent));
int i;
for (i=1;i<=16;i++)
printf("i=%d isaligned=%d\n", i, ARRAY_ISALIGNED(arr, i));
*/
if ((! (intent & F2PY_INTENT_COPY))
&& PyArray_ITEMSIZE(arr)==elsize
&& ARRAY_ISCOMPATIBLE(arr,type_num)
&& F2PY_CHECK_ALIGNMENT(arr, intent)
) {
if ((intent & F2PY_INTENT_C)?PyArray_ISCARRAY(arr):PyArray_ISFARRAY(arr)) {
if ((intent & F2PY_INTENT_OUT)) {
Py_INCREF(arr);
}
/* Returning input array */
return arr;
}
}
if (intent & F2PY_INTENT_INOUT) {
strcpy(mess, "failed to initialize intent(inout) array");
if ((intent & F2PY_INTENT_C) && !PyArray_ISCARRAY(arr))
strcat(mess, " -- input not contiguous");
if (!(intent & F2PY_INTENT_C) && !PyArray_ISFARRAY(arr))
strcat(mess, " -- input not fortran contiguous");
if (PyArray_ITEMSIZE(arr)!=elsize)
sprintf(mess+strlen(mess),
" -- expected elsize=%d but got %" NPY_INTP_FMT,
elsize,
(npy_intp)PyArray_ITEMSIZE(arr)
);
if (!(ARRAY_ISCOMPATIBLE(arr,type_num)))
sprintf(mess+strlen(mess)," -- input '%c' not compatible to '%c'",
PyArray_DESCR(arr)->type,typechar);
if (!(F2PY_CHECK_ALIGNMENT(arr, intent)))
sprintf(mess+strlen(mess)," -- input not %d-aligned", F2PY_GET_ALIGNMENT(intent));
PyErr_SetString(PyExc_ValueError,mess);
return NULL;
}
/* here we have always intent(in) or intent(inplace) */
{
PyArrayObject * retarr;
retarr = (PyArrayObject *) \
PyArray_New(&PyArray_Type, PyArray_NDIM(arr), PyArray_DIMS(arr), type_num,
NULL,NULL,1,
!(intent&F2PY_INTENT_C),
NULL);
if (retarr==NULL)
return NULL;
F2PY_REPORT_ON_ARRAY_COPY_FROMARR;
if (PyArray_CopyInto(retarr, arr)) {
Py_DECREF(retarr);
return NULL;
}
if (intent & F2PY_INTENT_INPLACE) {
if (swap_arrays(arr,retarr))
return NULL; /* XXX: set exception */
Py_XDECREF(retarr);
if (intent & F2PY_INTENT_OUT)
Py_INCREF(arr);
} else {
arr = retarr;
}
}
return arr;
}
if ((intent & F2PY_INTENT_INOUT) ||
(intent & F2PY_INTENT_INPLACE) ||
(intent & F2PY_INTENT_CACHE)) {
PyErr_SetString(PyExc_TypeError,
"failed to initialize intent(inout|inplace|cache) "
"array, input not an array");
return NULL;
}
{
PyArray_Descr * descr = PyArray_DescrFromType(type_num);
/* compatibility with NPY_CHAR */
if (type_num == NPY_STRING) {
PyArray_DESCR_REPLACE(descr);
if (descr == NULL) {
return NULL;
}
descr->elsize = 1;
descr->type = NPY_CHARLTR;
}
F2PY_REPORT_ON_ARRAY_COPY_FROMANY;
arr = (PyArrayObject *) \
PyArray_FromAny(obj, descr, 0,0,
((intent & F2PY_INTENT_C)?NPY_ARRAY_CARRAY:NPY_ARRAY_FARRAY) \
| NPY_ARRAY_FORCECAST, NULL);
if (arr==NULL)
return NULL;
if (check_and_fix_dimensions(arr, rank, dims)) {
return NULL;
}
return arr;
}
}
/*****************************************/
/* Helper functions for array_from_pyobj */
/*****************************************/
static
int check_and_fix_dimensions(const PyArrayObject* arr, const int rank, npy_intp *dims)
{
/*
This function fills in blanks (that are -1's) in dims list using
the dimensions from arr. It also checks that non-blank dims will
match with the corresponding values in arr dimensions.
Returns 0 if the function is successful.
If an error condition is detected, an exception is set and 1 is returned.
*/
const npy_intp arr_size = (PyArray_NDIM(arr))?PyArray_Size((PyObject *)arr):1;
#ifdef DEBUG_COPY_ND_ARRAY
dump_attrs(arr);
printf("check_and_fix_dimensions:init: dims=");
dump_dims(rank,dims);
#endif
if (rank > PyArray_NDIM(arr)) { /* [1,2] -> [[1],[2]]; 1 -> [[1]] */
npy_intp new_size = 1;
int free_axe = -1;
int i;
npy_intp d;
/* Fill dims where -1 or 0; check dimensions; calc new_size; */
for(i=0;i<PyArray_NDIM(arr);++i) {
d = PyArray_DIM(arr,i);
if (dims[i] >= 0) {
if (d>1 && dims[i]!=d) {
PyErr_Format(PyExc_ValueError,
"%d-th dimension must be fixed to %"
NPY_INTP_FMT " but got %" NPY_INTP_FMT "\n",
i, dims[i], d);
return 1;
}
if (!dims[i]) dims[i] = 1;
} else {
dims[i] = d ? d : 1;
}
new_size *= dims[i];
}
for(i=PyArray_NDIM(arr);i<rank;++i)
if (dims[i]>1) {
PyErr_Format(PyExc_ValueError,
"%d-th dimension must be %" NPY_INTP_FMT
" but got 0 (not defined).\n",
i, dims[i]);
return 1;
} else if (free_axe<0)
free_axe = i;
else
dims[i] = 1;
if (free_axe>=0) {
dims[free_axe] = arr_size/new_size;
new_size *= dims[free_axe];
}
if (new_size != arr_size) {
PyErr_Format(PyExc_ValueError,
"unexpected array size: new_size=%" NPY_INTP_FMT
", got array with arr_size=%" NPY_INTP_FMT
" (maybe too many free indices)\n",
new_size, arr_size);
return 1;
}
} else if (rank==PyArray_NDIM(arr)) {
npy_intp new_size = 1;
int i;
npy_intp d;
for (i=0; i<rank; ++i) {
d = PyArray_DIM(arr,i);
if (dims[i]>=0) {
if (d > 1 && d!=dims[i]) {
PyErr_Format(PyExc_ValueError,
"%d-th dimension must be fixed to %"
NPY_INTP_FMT " but got %" NPY_INTP_FMT "\n",
i, dims[i], d);
return 1;
}
if (!dims[i]) dims[i] = 1;
} else dims[i] = d;
new_size *= dims[i];
}
if (new_size != arr_size) {
PyErr_Format(PyExc_ValueError,
"unexpected array size: new_size=%" NPY_INTP_FMT
", got array with arr_size=%" NPY_INTP_FMT "\n",
new_size, arr_size);
return 1;
}
} else { /* [[1,2]] -> [[1],[2]] */
int i,j;
npy_intp d;
int effrank;
npy_intp size;
for (i=0,effrank=0;i<PyArray_NDIM(arr);++i)
if (PyArray_DIM(arr,i)>1) ++effrank;
if (dims[rank-1]>=0)
if (effrank>rank) {
PyErr_Format(PyExc_ValueError,
"too many axes: %d (effrank=%d), "
"expected rank=%d\n",
PyArray_NDIM(arr), effrank, rank);
return 1;
}
for (i=0,j=0;i<rank;++i) {
while (j<PyArray_NDIM(arr) && PyArray_DIM(arr,j)<2) ++j;
if (j>=PyArray_NDIM(arr)) d = 1;
else d = PyArray_DIM(arr,j++);
if (dims[i]>=0) {
if (d>1 && d!=dims[i]) {
PyErr_Format(PyExc_ValueError,
"%d-th dimension must be fixed to %"
NPY_INTP_FMT " but got %" NPY_INTP_FMT
" (real index=%d)\n",
i, dims[i], d, j-1);
return 1;
}
if (!dims[i]) dims[i] = 1;
} else
dims[i] = d;
}
for (i=rank;i<PyArray_NDIM(arr);++i) { /* [[1,2],[3,4]] -> [1,2,3,4] */
while (j<PyArray_NDIM(arr) && PyArray_DIM(arr,j)<2) ++j;
if (j>=PyArray_NDIM(arr)) d = 1;
else d = PyArray_DIM(arr,j++);
dims[rank-1] *= d;
}
for (i=0,size=1;i<rank;++i) size *= dims[i];
if (size != arr_size) {
char msg[200];
int len;
snprintf(msg, sizeof(msg),
"unexpected array size: size=%" NPY_INTP_FMT
", arr_size=%" NPY_INTP_FMT
", rank=%d, effrank=%d, arr.nd=%d, dims=[",
size, arr_size, rank, effrank, PyArray_NDIM(arr));
for (i = 0; i < rank; ++i) {
len = strlen(msg);
snprintf(msg + len, sizeof(msg) - len,
" %" NPY_INTP_FMT, dims[i]);
}
len = strlen(msg);
snprintf(msg + len, sizeof(msg) - len, " ], arr.dims=[");
for (i = 0; i < PyArray_NDIM(arr); ++i) {
len = strlen(msg);
snprintf(msg + len, sizeof(msg) - len,
" %" NPY_INTP_FMT, PyArray_DIM(arr, i));
}
len = strlen(msg);
snprintf(msg + len, sizeof(msg) - len, " ]\n");
PyErr_SetString(PyExc_ValueError, msg);
return 1;
}
}
#ifdef DEBUG_COPY_ND_ARRAY
printf("check_and_fix_dimensions:end: dims=");
dump_dims(rank,dims);
#endif
return 0;
}
/* End of file: array_from_pyobj.c */
/************************* copy_ND_array *******************************/
extern
int copy_ND_array(const PyArrayObject *arr, PyArrayObject *out)
{
F2PY_REPORT_ON_ARRAY_COPY_FROMARR;
return PyArray_CopyInto(out, (PyArrayObject *)arr);
}
/*********************************************/
/* Compatibility functions for Python >= 3.0 */
/*********************************************/
#if PY_VERSION_HEX >= 0x03000000
PyObject *
F2PyCapsule_FromVoidPtr(void *ptr, void (*dtor)(PyObject *))
{
PyObject *ret = PyCapsule_New(ptr, NULL, dtor);
if (ret == NULL) {
PyErr_Clear();
}
return ret;
}
void *
F2PyCapsule_AsVoidPtr(PyObject *obj)
{
void *ret = PyCapsule_GetPointer(obj, NULL);
if (ret == NULL) {
PyErr_Clear();
}
return ret;
}
int
F2PyCapsule_Check(PyObject *ptr)
{
return PyCapsule_CheckExact(ptr);
}
#else
PyObject *
F2PyCapsule_FromVoidPtr(void *ptr, void (*dtor)(void *))
{
return PyCObject_FromVoidPtr(ptr, dtor);
}
void *
F2PyCapsule_AsVoidPtr(PyObject *ptr)
{
return PyCObject_AsVoidPtr(ptr);
}
int
F2PyCapsule_Check(PyObject *ptr)
{
return PyCObject_Check(ptr);
}
#endif
#ifdef __cplusplus
}
#endif
/************************* EOF fortranobject.c *******************************/
| 35,813 | 31.887052 | 118 |
c
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/src/fortranobject.h
|
#ifndef Py_FORTRANOBJECT_H
#define Py_FORTRANOBJECT_H
#ifdef __cplusplus
extern "C" {
#endif
#include "Python.h"
#ifdef FORTRANOBJECT_C
#define NO_IMPORT_ARRAY
#endif
#define PY_ARRAY_UNIQUE_SYMBOL _npy_f2py_ARRAY_API
#include "numpy/arrayobject.h"
/*
* Python 3 support macros
*/
#if PY_VERSION_HEX >= 0x03000000
#define PyString_Check PyBytes_Check
#define PyString_GET_SIZE PyBytes_GET_SIZE
#define PyString_AS_STRING PyBytes_AS_STRING
#define PyString_FromString PyBytes_FromString
#define PyUString_FromStringAndSize PyUnicode_FromStringAndSize
#define PyString_ConcatAndDel PyBytes_ConcatAndDel
#define PyString_AsString PyBytes_AsString
#define PyInt_Check PyLong_Check
#define PyInt_FromLong PyLong_FromLong
#define PyInt_AS_LONG PyLong_AsLong
#define PyInt_AsLong PyLong_AsLong
#define PyNumber_Int PyNumber_Long
#else
#define PyUString_FromStringAndSize PyString_FromStringAndSize
#endif
#ifdef F2PY_REPORT_ATEXIT
#include <sys/timeb.h>
extern void f2py_start_clock(void);
extern void f2py_stop_clock(void);
extern void f2py_start_call_clock(void);
extern void f2py_stop_call_clock(void);
extern void f2py_cb_start_clock(void);
extern void f2py_cb_stop_clock(void);
extern void f2py_cb_start_call_clock(void);
extern void f2py_cb_stop_call_clock(void);
extern void f2py_report_on_exit(int,void*);
#endif
#ifdef DMALLOC
#include "dmalloc.h"
#endif
/* Fortran object interface */
/*
123456789-123456789-123456789-123456789-123456789-123456789-123456789-12
PyFortranObject represents various Fortran objects:
Fortran (module) routines, COMMON blocks, module data.
Author: Pearu Peterson <[email protected]>
*/
#define F2PY_MAX_DIMS 40
typedef void (*f2py_set_data_func)(char*,npy_intp*);
typedef void (*f2py_void_func)(void);
typedef void (*f2py_init_func)(int*,npy_intp*,f2py_set_data_func,int*);
/*typedef void* (*f2py_c_func)(void*,...);*/
typedef void *(*f2pycfunc)(void);
typedef struct {
char *name; /* attribute (array||routine) name */
int rank; /* array rank, 0 for scalar, max is F2PY_MAX_DIMS,
|| rank=-1 for Fortran routine */
struct {npy_intp d[F2PY_MAX_DIMS];} dims; /* dimensions of the array, || not used */
int type; /* PyArray_<type> || not used */
char *data; /* pointer to array || Fortran routine */
f2py_init_func func; /* initialization function for
allocatable arrays:
func(&rank,dims,set_ptr_func,name,len(name))
|| C/API wrapper for Fortran routine */
char *doc; /* documentation string; only recommended
for routines. */
} FortranDataDef;
typedef struct {
PyObject_HEAD
int len; /* Number of attributes */
FortranDataDef *defs; /* An array of FortranDataDef's */
PyObject *dict; /* Fortran object attribute dictionary */
} PyFortranObject;
#define PyFortran_Check(op) (Py_TYPE(op) == &PyFortran_Type)
#define PyFortran_Check1(op) (0==strcmp(Py_TYPE(op)->tp_name,"fortran"))
extern PyTypeObject PyFortran_Type;
extern int F2PyDict_SetItemString(PyObject* dict, char *name, PyObject *obj);
extern PyObject * PyFortranObject_New(FortranDataDef* defs, f2py_void_func init);
extern PyObject * PyFortranObject_NewAsAttr(FortranDataDef* defs);
#if PY_VERSION_HEX >= 0x03000000
PyObject * F2PyCapsule_FromVoidPtr(void *ptr, void (*dtor)(PyObject *));
void * F2PyCapsule_AsVoidPtr(PyObject *obj);
int F2PyCapsule_Check(PyObject *ptr);
#else
PyObject * F2PyCapsule_FromVoidPtr(void *ptr, void (*dtor)(void *));
void * F2PyCapsule_AsVoidPtr(PyObject *ptr);
int F2PyCapsule_Check(PyObject *ptr);
#endif
#define ISCONTIGUOUS(m) (PyArray_FLAGS(m) & NPY_ARRAY_C_CONTIGUOUS)
#define F2PY_INTENT_IN 1
#define F2PY_INTENT_INOUT 2
#define F2PY_INTENT_OUT 4
#define F2PY_INTENT_HIDE 8
#define F2PY_INTENT_CACHE 16
#define F2PY_INTENT_COPY 32
#define F2PY_INTENT_C 64
#define F2PY_OPTIONAL 128
#define F2PY_INTENT_INPLACE 256
#define F2PY_INTENT_ALIGNED4 512
#define F2PY_INTENT_ALIGNED8 1024
#define F2PY_INTENT_ALIGNED16 2048
#define ARRAY_ISALIGNED(ARR, SIZE) ((size_t)(PyArray_DATA(ARR)) % (SIZE) == 0)
#define F2PY_ALIGN4(intent) (intent & F2PY_INTENT_ALIGNED4)
#define F2PY_ALIGN8(intent) (intent & F2PY_INTENT_ALIGNED8)
#define F2PY_ALIGN16(intent) (intent & F2PY_INTENT_ALIGNED16)
#define F2PY_GET_ALIGNMENT(intent) \
(F2PY_ALIGN4(intent) ? 4 : \
(F2PY_ALIGN8(intent) ? 8 : \
(F2PY_ALIGN16(intent) ? 16 : 1) ))
#define F2PY_CHECK_ALIGNMENT(arr, intent) ARRAY_ISALIGNED(arr, F2PY_GET_ALIGNMENT(intent))
extern PyArrayObject* array_from_pyobj(const int type_num,
npy_intp *dims,
const int rank,
const int intent,
PyObject *obj);
extern int copy_ND_array(const PyArrayObject *in, PyArrayObject *out);
#ifdef DEBUG_COPY_ND_ARRAY
extern void dump_attrs(const PyArrayObject* arr);
#endif
#ifdef __cplusplus
}
#endif
#endif /* !Py_FORTRANOBJECT_H */
| 4,931 | 29.257669 | 90 |
h
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/tests/test_return_real.py
|
from __future__ import division, absolute_import, print_function
from numpy import array
from numpy.compat import long
from numpy.testing import run_module_suite, assert_, assert_raises, dec
from . import util
class TestReturnReal(util.F2PyTest):
def check_function(self, t):
if t.__doc__.split()[0] in ['t0', 't4', 's0', 's4']:
err = 1e-5
else:
err = 0.0
assert_(abs(t(234) - 234.0) <= err)
assert_(abs(t(234.6) - 234.6) <= err)
assert_(abs(t(long(234)) - 234.0) <= err)
assert_(abs(t('234') - 234) <= err)
assert_(abs(t('234.6') - 234.6) <= err)
assert_(abs(t(-234) + 234) <= err)
assert_(abs(t([234]) - 234) <= err)
assert_(abs(t((234,)) - 234.) <= err)
assert_(abs(t(array(234)) - 234.) <= err)
assert_(abs(t(array([234])) - 234.) <= err)
assert_(abs(t(array([[234]])) - 234.) <= err)
assert_(abs(t(array([234], 'b')) + 22) <= err)
assert_(abs(t(array([234], 'h')) - 234.) <= err)
assert_(abs(t(array([234], 'i')) - 234.) <= err)
assert_(abs(t(array([234], 'l')) - 234.) <= err)
assert_(abs(t(array([234], 'B')) - 234.) <= err)
assert_(abs(t(array([234], 'f')) - 234.) <= err)
assert_(abs(t(array([234], 'd')) - 234.) <= err)
if t.__doc__.split()[0] in ['t0', 't4', 's0', 's4']:
assert_(t(1e200) == t(1e300)) # inf
#assert_raises(ValueError, t, array([234], 'S1'))
assert_raises(ValueError, t, 'abc')
assert_raises(IndexError, t, [])
assert_raises(IndexError, t, ())
assert_raises(Exception, t, t)
assert_raises(Exception, t, {})
try:
r = t(10 ** 400)
assert_(repr(r) in ['inf', 'Infinity'], repr(r))
except OverflowError:
pass
class TestCReturnReal(TestReturnReal):
suffix = ".pyf"
module_name = "c_ext_return_real"
code = """
python module c_ext_return_real
usercode \'\'\'
float t4(float value) { return value; }
void s4(float *t4, float value) { *t4 = value; }
double t8(double value) { return value; }
void s8(double *t8, double value) { *t8 = value; }
\'\'\'
interface
function t4(value)
real*4 intent(c) :: t4,value
end
function t8(value)
real*8 intent(c) :: t8,value
end
subroutine s4(t4,value)
intent(c) s4
real*4 intent(out) :: t4
real*4 intent(c) :: value
end
subroutine s8(t8,value)
intent(c) s8
real*8 intent(out) :: t8
real*8 intent(c) :: value
end
end interface
end python module c_ext_return_real
"""
@dec.slow
def test_all(self):
for name in "t4,t8,s4,s8".split(","):
self.check_function(getattr(self.module, name))
class TestF77ReturnReal(TestReturnReal):
code = """
function t0(value)
real value
real t0
t0 = value
end
function t4(value)
real*4 value
real*4 t4
t4 = value
end
function t8(value)
real*8 value
real*8 t8
t8 = value
end
function td(value)
double precision value
double precision td
td = value
end
subroutine s0(t0,value)
real value
real t0
cf2py intent(out) t0
t0 = value
end
subroutine s4(t4,value)
real*4 value
real*4 t4
cf2py intent(out) t4
t4 = value
end
subroutine s8(t8,value)
real*8 value
real*8 t8
cf2py intent(out) t8
t8 = value
end
subroutine sd(td,value)
double precision value
double precision td
cf2py intent(out) td
td = value
end
"""
@dec.slow
def test_all(self):
for name in "t0,t4,t8,td,s0,s4,s8,sd".split(","):
self.check_function(getattr(self.module, name))
class TestF90ReturnReal(TestReturnReal):
suffix = ".f90"
code = """
module f90_return_real
contains
function t0(value)
real :: value
real :: t0
t0 = value
end function t0
function t4(value)
real(kind=4) :: value
real(kind=4) :: t4
t4 = value
end function t4
function t8(value)
real(kind=8) :: value
real(kind=8) :: t8
t8 = value
end function t8
function td(value)
double precision :: value
double precision :: td
td = value
end function td
subroutine s0(t0,value)
real :: value
real :: t0
!f2py intent(out) t0
t0 = value
end subroutine s0
subroutine s4(t4,value)
real(kind=4) :: value
real(kind=4) :: t4
!f2py intent(out) t4
t4 = value
end subroutine s4
subroutine s8(t8,value)
real(kind=8) :: value
real(kind=8) :: t8
!f2py intent(out) t8
t8 = value
end subroutine s8
subroutine sd(td,value)
double precision :: value
double precision :: td
!f2py intent(out) td
td = value
end subroutine sd
end module f90_return_real
"""
@dec.slow
def test_all(self):
for name in "t0,t4,t8,td,s0,s4,s8,sd".split(","):
self.check_function(getattr(self.module.f90_return_real, name))
if __name__ == "__main__":
run_module_suite()
| 5,410 | 25.140097 | 75 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/tests/test_regression.py
|
from __future__ import division, absolute_import, print_function
import os
import math
import numpy as np
from numpy.testing import run_module_suite, dec, assert_raises, assert_equal
from . import util
def _path(*a):
return os.path.join(*((os.path.dirname(__file__),) + a))
class TestIntentInOut(util.F2PyTest):
# Check that intent(in out) translates as intent(inout)
sources = [_path('src', 'regression', 'inout.f90')]
@dec.slow
def test_inout(self):
# non-contiguous should raise error
x = np.arange(6, dtype=np.float32)[::2]
assert_raises(ValueError, self.module.foo, x)
# check values with contiguous array
x = np.arange(3, dtype=np.float32)
self.module.foo(x)
assert_equal(x, [3, 1, 2])
if __name__ == "__main__":
run_module_suite()
| 829 | 23.411765 | 76 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/tests/test_block_docstring.py
|
from __future__ import division, absolute_import, print_function
import textwrap
import sys
from . import util
from numpy.testing import run_module_suite, assert_equal, dec
class TestBlockDocString(util.F2PyTest):
code = """
SUBROUTINE FOO()
INTEGER BAR(2, 3)
COMMON /BLOCK/ BAR
RETURN
END
"""
@dec.knownfailureif(sys.platform=='win32', msg='Fails with MinGW64 Gfortran (Issue #9673)')
def test_block_docstring(self):
expected = "'i'-array(2,3)\n"
assert_equal(self.module.block.__doc__, expected)
if __name__ == "__main__":
run_module_suite()
| 623 | 23 | 95 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/tests/test_size.py
|
from __future__ import division, absolute_import, print_function
import os
from numpy.testing import run_module_suite, assert_equal, dec
from . import util
def _path(*a):
return os.path.join(*((os.path.dirname(__file__),) + a))
class TestSizeSumExample(util.F2PyTest):
sources = [_path('src', 'size', 'foo.f90')]
@dec.slow
def test_all(self):
r = self.module.foo([[]])
assert_equal(r, [0], repr(r))
r = self.module.foo([[1, 2]])
assert_equal(r, [3], repr(r))
r = self.module.foo([[1, 2], [3, 4]])
assert_equal(r, [3, 7], repr(r))
r = self.module.foo([[1, 2], [3, 4], [5, 6]])
assert_equal(r, [3, 7, 11], repr(r))
@dec.slow
def test_transpose(self):
r = self.module.trans([[]])
assert_equal(r.T, [[]], repr(r))
r = self.module.trans([[1, 2]])
assert_equal(r, [[1], [2]], repr(r))
r = self.module.trans([[1, 2, 3], [4, 5, 6]])
assert_equal(r, [[1, 4], [2, 5], [3, 6]], repr(r))
@dec.slow
def test_flatten(self):
r = self.module.flatten([[]])
assert_equal(r, [], repr(r))
r = self.module.flatten([[1, 2]])
assert_equal(r, [1, 2], repr(r))
r = self.module.flatten([[1, 2, 3], [4, 5, 6]])
assert_equal(r, [1, 2, 3, 4, 5, 6], repr(r))
if __name__ == "__main__":
run_module_suite()
| 1,388 | 24.722222 | 64 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/tests/test_array_from_pyobj.py
|
from __future__ import division, absolute_import, print_function
import unittest
import os
import sys
import copy
from numpy import (
array, alltrue, ndarray, zeros, dtype, intp, clongdouble
)
from numpy.testing import (
run_module_suite, assert_, assert_equal, SkipTest
)
from numpy.core.multiarray import typeinfo
from . import util
wrap = None
def setup_module():
"""
Build the required testing extension module
"""
global wrap
# Check compiler availability first
if not util.has_c_compiler():
raise SkipTest("No C compiler available")
if wrap is None:
config_code = """
config.add_extension('test_array_from_pyobj_ext',
sources=['wrapmodule.c', 'fortranobject.c'],
define_macros=[])
"""
d = os.path.dirname(__file__)
src = [os.path.join(d, 'src', 'array_from_pyobj', 'wrapmodule.c'),
os.path.join(d, '..', 'src', 'fortranobject.c'),
os.path.join(d, '..', 'src', 'fortranobject.h')]
wrap = util.build_module_distutils(src, config_code,
'test_array_from_pyobj_ext')
def flags_info(arr):
flags = wrap.array_attrs(arr)[6]
return flags2names(flags)
def flags2names(flags):
info = []
for flagname in ['CONTIGUOUS', 'FORTRAN', 'OWNDATA', 'ENSURECOPY',
'ENSUREARRAY', 'ALIGNED', 'NOTSWAPPED', 'WRITEABLE',
'WRITEBACKIFCOPY', 'UPDATEIFCOPY', 'BEHAVED', 'BEHAVED_RO',
'CARRAY', 'FARRAY'
]:
if abs(flags) & getattr(wrap, flagname, 0):
info.append(flagname)
return info
class Intent(object):
def __init__(self, intent_list=[]):
self.intent_list = intent_list[:]
flags = 0
for i in intent_list:
if i == 'optional':
flags |= wrap.F2PY_OPTIONAL
else:
flags |= getattr(wrap, 'F2PY_INTENT_' + i.upper())
self.flags = flags
def __getattr__(self, name):
name = name.lower()
if name == 'in_':
name = 'in'
return self.__class__(self.intent_list + [name])
def __str__(self):
return 'intent(%s)' % (','.join(self.intent_list))
def __repr__(self):
return 'Intent(%r)' % (self.intent_list)
def is_intent(self, *names):
for name in names:
if name not in self.intent_list:
return False
return True
def is_intent_exact(self, *names):
return len(self.intent_list) == len(names) and self.is_intent(*names)
intent = Intent()
_type_names = ['BOOL', 'BYTE', 'UBYTE', 'SHORT', 'USHORT', 'INT', 'UINT',
'LONG', 'ULONG', 'LONGLONG', 'ULONGLONG',
'FLOAT', 'DOUBLE', 'CFLOAT']
_cast_dict = {'BOOL': ['BOOL']}
_cast_dict['BYTE'] = _cast_dict['BOOL'] + ['BYTE']
_cast_dict['UBYTE'] = _cast_dict['BOOL'] + ['UBYTE']
_cast_dict['BYTE'] = ['BYTE']
_cast_dict['UBYTE'] = ['UBYTE']
_cast_dict['SHORT'] = _cast_dict['BYTE'] + ['UBYTE', 'SHORT']
_cast_dict['USHORT'] = _cast_dict['UBYTE'] + ['BYTE', 'USHORT']
_cast_dict['INT'] = _cast_dict['SHORT'] + ['USHORT', 'INT']
_cast_dict['UINT'] = _cast_dict['USHORT'] + ['SHORT', 'UINT']
_cast_dict['LONG'] = _cast_dict['INT'] + ['LONG']
_cast_dict['ULONG'] = _cast_dict['UINT'] + ['ULONG']
_cast_dict['LONGLONG'] = _cast_dict['LONG'] + ['LONGLONG']
_cast_dict['ULONGLONG'] = _cast_dict['ULONG'] + ['ULONGLONG']
_cast_dict['FLOAT'] = _cast_dict['SHORT'] + ['USHORT', 'FLOAT']
_cast_dict['DOUBLE'] = _cast_dict['INT'] + ['UINT', 'FLOAT', 'DOUBLE']
_cast_dict['CFLOAT'] = _cast_dict['FLOAT'] + ['CFLOAT']
# 32 bit system malloc typically does not provide the alignment required by
# 16 byte long double types this means the inout intent cannot be satisfied
# and several tests fail as the alignment flag can be randomly true or fals
# when numpy gains an aligned allocator the tests could be enabled again
if ((intp().dtype.itemsize != 4 or clongdouble().dtype.alignment <= 8) and
sys.platform != 'win32'):
_type_names.extend(['LONGDOUBLE', 'CDOUBLE', 'CLONGDOUBLE'])
_cast_dict['LONGDOUBLE'] = _cast_dict['LONG'] + \
['ULONG', 'FLOAT', 'DOUBLE', 'LONGDOUBLE']
_cast_dict['CLONGDOUBLE'] = _cast_dict['LONGDOUBLE'] + \
['CFLOAT', 'CDOUBLE', 'CLONGDOUBLE']
_cast_dict['CDOUBLE'] = _cast_dict['DOUBLE'] + ['CFLOAT', 'CDOUBLE']
class Type(object):
_type_cache = {}
def __new__(cls, name):
if isinstance(name, dtype):
dtype0 = name
name = None
for n, i in typeinfo.items():
if isinstance(i, tuple) and dtype0.type is i[-1]:
name = n
break
obj = cls._type_cache.get(name.upper(), None)
if obj is not None:
return obj
obj = object.__new__(cls)
obj._init(name)
cls._type_cache[name.upper()] = obj
return obj
def _init(self, name):
self.NAME = name.upper()
self.type_num = getattr(wrap, 'NPY_' + self.NAME)
assert_equal(self.type_num, typeinfo[self.NAME][1])
self.dtype = typeinfo[self.NAME][-1]
self.elsize = typeinfo[self.NAME][2] / 8
self.dtypechar = typeinfo[self.NAME][0]
def cast_types(self):
return [self.__class__(_m) for _m in _cast_dict[self.NAME]]
def all_types(self):
return [self.__class__(_m) for _m in _type_names]
def smaller_types(self):
bits = typeinfo[self.NAME][3]
types = []
for name in _type_names:
if typeinfo[name][3] < bits:
types.append(Type(name))
return types
def equal_types(self):
bits = typeinfo[self.NAME][3]
types = []
for name in _type_names:
if name == self.NAME:
continue
if typeinfo[name][3] == bits:
types.append(Type(name))
return types
def larger_types(self):
bits = typeinfo[self.NAME][3]
types = []
for name in _type_names:
if typeinfo[name][3] > bits:
types.append(Type(name))
return types
class Array(object):
def __init__(self, typ, dims, intent, obj):
self.type = typ
self.dims = dims
self.intent = intent
self.obj_copy = copy.deepcopy(obj)
self.obj = obj
# arr.dtypechar may be different from typ.dtypechar
self.arr = wrap.call(typ.type_num, dims, intent.flags, obj)
assert_(isinstance(self.arr, ndarray), repr(type(self.arr)))
self.arr_attr = wrap.array_attrs(self.arr)
if len(dims) > 1:
if self.intent.is_intent('c'):
assert_(intent.flags & wrap.F2PY_INTENT_C)
assert_(not self.arr.flags['FORTRAN'],
repr((self.arr.flags, getattr(obj, 'flags', None))))
assert_(self.arr.flags['CONTIGUOUS'])
assert_(not self.arr_attr[6] & wrap.FORTRAN)
else:
assert_(not intent.flags & wrap.F2PY_INTENT_C)
assert_(self.arr.flags['FORTRAN'])
assert_(not self.arr.flags['CONTIGUOUS'])
assert_(self.arr_attr[6] & wrap.FORTRAN)
if obj is None:
self.pyarr = None
self.pyarr_attr = None
return
if intent.is_intent('cache'):
assert_(isinstance(obj, ndarray), repr(type(obj)))
self.pyarr = array(obj).reshape(*dims).copy()
else:
self.pyarr = array(array(obj, dtype=typ.dtypechar).reshape(*dims),
order=self.intent.is_intent('c') and 'C' or 'F')
assert_(self.pyarr.dtype == typ,
repr((self.pyarr.dtype, typ)))
assert_(self.pyarr.flags['OWNDATA'], (obj, intent))
self.pyarr_attr = wrap.array_attrs(self.pyarr)
if len(dims) > 1:
if self.intent.is_intent('c'):
assert_(not self.pyarr.flags['FORTRAN'])
assert_(self.pyarr.flags['CONTIGUOUS'])
assert_(not self.pyarr_attr[6] & wrap.FORTRAN)
else:
assert_(self.pyarr.flags['FORTRAN'])
assert_(not self.pyarr.flags['CONTIGUOUS'])
assert_(self.pyarr_attr[6] & wrap.FORTRAN)
assert_(self.arr_attr[1] == self.pyarr_attr[1]) # nd
assert_(self.arr_attr[2] == self.pyarr_attr[2]) # dimensions
if self.arr_attr[1] <= 1:
assert_(self.arr_attr[3] == self.pyarr_attr[3],
repr((self.arr_attr[3], self.pyarr_attr[3],
self.arr.tobytes(), self.pyarr.tobytes()))) # strides
assert_(self.arr_attr[5][-2:] == self.pyarr_attr[5][-2:],
repr((self.arr_attr[5], self.pyarr_attr[5]))) # descr
assert_(self.arr_attr[6] == self.pyarr_attr[6],
repr((self.arr_attr[6], self.pyarr_attr[6],
flags2names(0 * self.arr_attr[6] - self.pyarr_attr[6]),
flags2names(self.arr_attr[6]), intent))) # flags
if intent.is_intent('cache'):
assert_(self.arr_attr[5][3] >= self.type.elsize,
repr((self.arr_attr[5][3], self.type.elsize)))
else:
assert_(self.arr_attr[5][3] == self.type.elsize,
repr((self.arr_attr[5][3], self.type.elsize)))
assert_(self.arr_equal(self.pyarr, self.arr))
if isinstance(self.obj, ndarray):
if typ.elsize == Type(obj.dtype).elsize:
if not intent.is_intent('copy') and self.arr_attr[1] <= 1:
assert_(self.has_shared_memory())
def arr_equal(self, arr1, arr2):
if arr1.shape != arr2.shape:
return False
s = arr1 == arr2
return alltrue(s.flatten())
def __str__(self):
return str(self.arr)
def has_shared_memory(self):
"""Check that created array shares data with input array.
"""
if self.obj is self.arr:
return True
if not isinstance(self.obj, ndarray):
return False
obj_attr = wrap.array_attrs(self.obj)
return obj_attr[0] == self.arr_attr[0]
class TestIntent(object):
def test_in_out(self):
assert_equal(str(intent.in_.out), 'intent(in,out)')
assert_(intent.in_.c.is_intent('c'))
assert_(not intent.in_.c.is_intent_exact('c'))
assert_(intent.in_.c.is_intent_exact('c', 'in'))
assert_(intent.in_.c.is_intent_exact('in', 'c'))
assert_(not intent.in_.is_intent('c'))
class _test_shared_memory(object):
num2seq = [1, 2]
num23seq = [[1, 2, 3], [4, 5, 6]]
def test_in_from_2seq(self):
a = self.array([2], intent.in_, self.num2seq)
assert_(not a.has_shared_memory())
def test_in_from_2casttype(self):
for t in self.type.cast_types():
obj = array(self.num2seq, dtype=t.dtype)
a = self.array([len(self.num2seq)], intent.in_, obj)
if t.elsize == self.type.elsize:
assert_(
a.has_shared_memory(), repr((self.type.dtype, t.dtype)))
else:
assert_(not a.has_shared_memory(), repr(t.dtype))
def test_inout_2seq(self):
obj = array(self.num2seq, dtype=self.type.dtype)
a = self.array([len(self.num2seq)], intent.inout, obj)
assert_(a.has_shared_memory())
try:
a = self.array([2], intent.in_.inout, self.num2seq)
except TypeError as msg:
if not str(msg).startswith('failed to initialize intent'
'(inout|inplace|cache) array'):
raise
else:
raise SystemError('intent(inout) should have failed on sequence')
def test_f_inout_23seq(self):
obj = array(self.num23seq, dtype=self.type.dtype, order='F')
shape = (len(self.num23seq), len(self.num23seq[0]))
a = self.array(shape, intent.in_.inout, obj)
assert_(a.has_shared_memory())
obj = array(self.num23seq, dtype=self.type.dtype, order='C')
shape = (len(self.num23seq), len(self.num23seq[0]))
try:
a = self.array(shape, intent.in_.inout, obj)
except ValueError as msg:
if not str(msg).startswith('failed to initialize intent'
'(inout) array'):
raise
else:
raise SystemError(
'intent(inout) should have failed on improper array')
def test_c_inout_23seq(self):
obj = array(self.num23seq, dtype=self.type.dtype)
shape = (len(self.num23seq), len(self.num23seq[0]))
a = self.array(shape, intent.in_.c.inout, obj)
assert_(a.has_shared_memory())
def test_in_copy_from_2casttype(self):
for t in self.type.cast_types():
obj = array(self.num2seq, dtype=t.dtype)
a = self.array([len(self.num2seq)], intent.in_.copy, obj)
assert_(not a.has_shared_memory(), repr(t.dtype))
def test_c_in_from_23seq(self):
a = self.array([len(self.num23seq), len(self.num23seq[0])],
intent.in_, self.num23seq)
assert_(not a.has_shared_memory())
def test_in_from_23casttype(self):
for t in self.type.cast_types():
obj = array(self.num23seq, dtype=t.dtype)
a = self.array([len(self.num23seq), len(self.num23seq[0])],
intent.in_, obj)
assert_(not a.has_shared_memory(), repr(t.dtype))
def test_f_in_from_23casttype(self):
for t in self.type.cast_types():
obj = array(self.num23seq, dtype=t.dtype, order='F')
a = self.array([len(self.num23seq), len(self.num23seq[0])],
intent.in_, obj)
if t.elsize == self.type.elsize:
assert_(a.has_shared_memory(), repr(t.dtype))
else:
assert_(not a.has_shared_memory(), repr(t.dtype))
def test_c_in_from_23casttype(self):
for t in self.type.cast_types():
obj = array(self.num23seq, dtype=t.dtype)
a = self.array([len(self.num23seq), len(self.num23seq[0])],
intent.in_.c, obj)
if t.elsize == self.type.elsize:
assert_(a.has_shared_memory(), repr(t.dtype))
else:
assert_(not a.has_shared_memory(), repr(t.dtype))
def test_f_copy_in_from_23casttype(self):
for t in self.type.cast_types():
obj = array(self.num23seq, dtype=t.dtype, order='F')
a = self.array([len(self.num23seq), len(self.num23seq[0])],
intent.in_.copy, obj)
assert_(not a.has_shared_memory(), repr(t.dtype))
def test_c_copy_in_from_23casttype(self):
for t in self.type.cast_types():
obj = array(self.num23seq, dtype=t.dtype)
a = self.array([len(self.num23seq), len(self.num23seq[0])],
intent.in_.c.copy, obj)
assert_(not a.has_shared_memory(), repr(t.dtype))
def test_in_cache_from_2casttype(self):
for t in self.type.all_types():
if t.elsize != self.type.elsize:
continue
obj = array(self.num2seq, dtype=t.dtype)
shape = (len(self.num2seq),)
a = self.array(shape, intent.in_.c.cache, obj)
assert_(a.has_shared_memory(), repr(t.dtype))
a = self.array(shape, intent.in_.cache, obj)
assert_(a.has_shared_memory(), repr(t.dtype))
obj = array(self.num2seq, dtype=t.dtype, order='F')
a = self.array(shape, intent.in_.c.cache, obj)
assert_(a.has_shared_memory(), repr(t.dtype))
a = self.array(shape, intent.in_.cache, obj)
assert_(a.has_shared_memory(), repr(t.dtype))
try:
a = self.array(shape, intent.in_.cache, obj[::-1])
except ValueError as msg:
if not str(msg).startswith('failed to initialize'
' intent(cache) array'):
raise
else:
raise SystemError(
'intent(cache) should have failed on multisegmented array')
def test_in_cache_from_2casttype_failure(self):
for t in self.type.all_types():
if t.elsize >= self.type.elsize:
continue
obj = array(self.num2seq, dtype=t.dtype)
shape = (len(self.num2seq),)
try:
self.array(shape, intent.in_.cache, obj) # Should succeed
except ValueError as msg:
if not str(msg).startswith('failed to initialize'
' intent(cache) array'):
raise
else:
raise SystemError(
'intent(cache) should have failed on smaller array')
def test_cache_hidden(self):
shape = (2,)
a = self.array(shape, intent.cache.hide, None)
assert_(a.arr.shape == shape)
shape = (2, 3)
a = self.array(shape, intent.cache.hide, None)
assert_(a.arr.shape == shape)
shape = (-1, 3)
try:
a = self.array(shape, intent.cache.hide, None)
except ValueError as msg:
if not str(msg).startswith('failed to create intent'
'(cache|hide)|optional array'):
raise
else:
raise SystemError(
'intent(cache) should have failed on undefined dimensions')
def test_hidden(self):
shape = (2,)
a = self.array(shape, intent.hide, None)
assert_(a.arr.shape == shape)
assert_(a.arr_equal(a.arr, zeros(shape, dtype=self.type.dtype)))
shape = (2, 3)
a = self.array(shape, intent.hide, None)
assert_(a.arr.shape == shape)
assert_(a.arr_equal(a.arr, zeros(shape, dtype=self.type.dtype)))
assert_(a.arr.flags['FORTRAN'] and not a.arr.flags['CONTIGUOUS'])
shape = (2, 3)
a = self.array(shape, intent.c.hide, None)
assert_(a.arr.shape == shape)
assert_(a.arr_equal(a.arr, zeros(shape, dtype=self.type.dtype)))
assert_(not a.arr.flags['FORTRAN'] and a.arr.flags['CONTIGUOUS'])
shape = (-1, 3)
try:
a = self.array(shape, intent.hide, None)
except ValueError as msg:
if not str(msg).startswith('failed to create intent'
'(cache|hide)|optional array'):
raise
else:
raise SystemError('intent(hide) should have failed'
' on undefined dimensions')
def test_optional_none(self):
shape = (2,)
a = self.array(shape, intent.optional, None)
assert_(a.arr.shape == shape)
assert_(a.arr_equal(a.arr, zeros(shape, dtype=self.type.dtype)))
shape = (2, 3)
a = self.array(shape, intent.optional, None)
assert_(a.arr.shape == shape)
assert_(a.arr_equal(a.arr, zeros(shape, dtype=self.type.dtype)))
assert_(a.arr.flags['FORTRAN'] and not a.arr.flags['CONTIGUOUS'])
shape = (2, 3)
a = self.array(shape, intent.c.optional, None)
assert_(a.arr.shape == shape)
assert_(a.arr_equal(a.arr, zeros(shape, dtype=self.type.dtype)))
assert_(not a.arr.flags['FORTRAN'] and a.arr.flags['CONTIGUOUS'])
def test_optional_from_2seq(self):
obj = self.num2seq
shape = (len(obj),)
a = self.array(shape, intent.optional, obj)
assert_(a.arr.shape == shape)
assert_(not a.has_shared_memory())
def test_optional_from_23seq(self):
obj = self.num23seq
shape = (len(obj), len(obj[0]))
a = self.array(shape, intent.optional, obj)
assert_(a.arr.shape == shape)
assert_(not a.has_shared_memory())
a = self.array(shape, intent.optional.c, obj)
assert_(a.arr.shape == shape)
assert_(not a.has_shared_memory())
def test_inplace(self):
obj = array(self.num23seq, dtype=self.type.dtype)
assert_(not obj.flags['FORTRAN'] and obj.flags['CONTIGUOUS'])
shape = obj.shape
a = self.array(shape, intent.inplace, obj)
assert_(obj[1][2] == a.arr[1][2], repr((obj, a.arr)))
a.arr[1][2] = 54
assert_(obj[1][2] == a.arr[1][2] ==
array(54, dtype=self.type.dtype), repr((obj, a.arr)))
assert_(a.arr is obj)
assert_(obj.flags['FORTRAN']) # obj attributes are changed inplace!
assert_(not obj.flags['CONTIGUOUS'])
def test_inplace_from_casttype(self):
for t in self.type.cast_types():
if t is self.type:
continue
obj = array(self.num23seq, dtype=t.dtype)
assert_(obj.dtype.type == t.dtype)
assert_(obj.dtype.type is not self.type.dtype)
assert_(not obj.flags['FORTRAN'] and obj.flags['CONTIGUOUS'])
shape = obj.shape
a = self.array(shape, intent.inplace, obj)
assert_(obj[1][2] == a.arr[1][2], repr((obj, a.arr)))
a.arr[1][2] = 54
assert_(obj[1][2] == a.arr[1][2] ==
array(54, dtype=self.type.dtype), repr((obj, a.arr)))
assert_(a.arr is obj)
assert_(obj.flags['FORTRAN']) # obj attributes changed inplace!
assert_(not obj.flags['CONTIGUOUS'])
assert_(obj.dtype.type is self.type.dtype) # obj changed inplace!
for t in _type_names:
exec('''\
class TestGen_%s(_test_shared_memory):
def setup(self):
self.type = Type(%r)
array = lambda self,dims,intent,obj: Array(Type(%r),dims,intent,obj)
''' % (t, t, t))
if __name__ == "__main__":
setup_module()
run_module_suite()
| 22,111 | 36.477966 | 80 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/tests/test_common.py
|
from __future__ import division, absolute_import, print_function
import os
import sys
import numpy as np
from . import util
from numpy.testing import run_module_suite, assert_array_equal, dec
def _path(*a):
return os.path.join(*((os.path.dirname(__file__),) + a))
class TestCommonBlock(util.F2PyTest):
sources = [_path('src', 'common', 'block.f')]
@dec.knownfailureif(sys.platform=='win32', msg='Fails with MinGW64 Gfortran (Issue #9673)')
def test_common_block(self):
self.module.initcb()
assert_array_equal(self.module.block.long_bn,
np.array(1.0, dtype=np.float64))
assert_array_equal(self.module.block.string_bn,
np.array('2', dtype='|S1'))
assert_array_equal(self.module.block.ok,
np.array(3, dtype=np.int32))
if __name__ == "__main__":
run_module_suite()
| 900 | 31.178571 | 95 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/tests/test_callback.py
|
from __future__ import division, absolute_import, print_function
import math
import textwrap
import sys
import numpy as np
from numpy.testing import run_module_suite, assert_, assert_equal, dec
from . import util
class TestF77Callback(util.F2PyTest):
code = """
subroutine t(fun,a)
integer a
cf2py intent(out) a
external fun
call fun(a)
end
subroutine func(a)
cf2py intent(in,out) a
integer a
a = a + 11
end
subroutine func0(a)
cf2py intent(out) a
integer a
a = 11
end
subroutine t2(a)
cf2py intent(callback) fun
integer a
cf2py intent(out) a
external fun
call fun(a)
end
subroutine string_callback(callback, a)
external callback
double precision callback
double precision a
character*1 r
cf2py intent(out) a
r = 'r'
a = callback(r)
end
subroutine string_callback_array(callback, cu, lencu, a)
external callback
integer callback
integer lencu
character*8 cu(lencu)
integer a
cf2py intent(out) a
a = callback(cu, lencu)
end
"""
@dec.slow
def test_all(self):
for name in "t,t2".split(","):
self.check_function(name)
@dec.slow
def test_docstring(self):
expected = """
a = t(fun,[fun_extra_args])
Wrapper for ``t``.
Parameters
----------
fun : call-back function
Other Parameters
----------------
fun_extra_args : input tuple, optional
Default: ()
Returns
-------
a : int
Notes
-----
Call-back functions::
def fun(): return a
Return objects:
a : int
"""
assert_equal(self.module.t.__doc__, textwrap.dedent(expected).lstrip())
def check_function(self, name):
t = getattr(self.module, name)
r = t(lambda: 4)
assert_(r == 4, repr(r))
r = t(lambda a: 5, fun_extra_args=(6,))
assert_(r == 5, repr(r))
r = t(lambda a: a, fun_extra_args=(6,))
assert_(r == 6, repr(r))
r = t(lambda a: 5 + a, fun_extra_args=(7,))
assert_(r == 12, repr(r))
r = t(lambda a: math.degrees(a), fun_extra_args=(math.pi,))
assert_(r == 180, repr(r))
r = t(math.degrees, fun_extra_args=(math.pi,))
assert_(r == 180, repr(r))
r = t(self.module.func, fun_extra_args=(6,))
assert_(r == 17, repr(r))
r = t(self.module.func0)
assert_(r == 11, repr(r))
r = t(self.module.func0._cpointer)
assert_(r == 11, repr(r))
class A(object):
def __call__(self):
return 7
def mth(self):
return 9
a = A()
r = t(a)
assert_(r == 7, repr(r))
r = t(a.mth)
assert_(r == 9, repr(r))
@dec.knownfailureif(sys.platform=='win32',
msg='Fails with MinGW64 Gfortran (Issue #9673)')
def test_string_callback(self):
def callback(code):
if code == 'r':
return 0
else:
return 1
f = getattr(self.module, 'string_callback')
r = f(callback)
assert_(r == 0, repr(r))
@dec.knownfailureif(sys.platform=='win32',
msg='Fails with MinGW64 Gfortran (Issue #9673)')
def test_string_callback_array(self):
# See gh-10027
cu = np.zeros((1, 8), 'S1')
def callback(cu, lencu):
if cu.shape != (lencu, 8):
return 1
if cu.dtype != 'S1':
return 2
if not np.all(cu == b''):
return 3
return 0
f = getattr(self.module, 'string_callback_array')
res = f(callback, cu, len(cu))
assert_(res == 0, repr(res))
if __name__ == "__main__":
run_module_suite()
| 4,014 | 22.757396 | 79 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/tests/test_assumed_shape.py
|
from __future__ import division, absolute_import, print_function
import os
from numpy.testing import run_module_suite, assert_, dec
from . import util
def _path(*a):
return os.path.join(*((os.path.dirname(__file__),) + a))
class TestAssumedShapeSumExample(util.F2PyTest):
sources = [_path('src', 'assumed_shape', 'foo_free.f90'),
_path('src', 'assumed_shape', 'foo_use.f90'),
_path('src', 'assumed_shape', 'precision.f90'),
_path('src', 'assumed_shape', 'foo_mod.f90'),
]
@dec.slow
def test_all(self):
r = self.module.fsum([1, 2])
assert_(r == 3, repr(r))
r = self.module.sum([1, 2])
assert_(r == 3, repr(r))
r = self.module.sum_with_use([1, 2])
assert_(r == 3, repr(r))
r = self.module.mod.sum([1, 2])
assert_(r == 3, repr(r))
r = self.module.mod.fsum([1, 2])
assert_(r == 3, repr(r))
if __name__ == "__main__":
run_module_suite()
| 1,001 | 26.833333 | 64 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/tests/test_string.py
|
from __future__ import division, absolute_import, print_function
import os
from numpy.testing import run_module_suite, assert_array_equal, dec
import numpy as np
from . import util
def _path(*a):
return os.path.join(*((os.path.dirname(__file__),) + a))
class TestString(util.F2PyTest):
sources = [_path('src', 'string', 'char.f90')]
@dec.slow
def test_char(self):
strings = np.array(['ab', 'cd', 'ef'], dtype='c').T
inp, out = self.module.char_test.change_strings(strings, strings.shape[1])
assert_array_equal(inp, strings)
expected = strings.copy()
expected[1, :] = 'AAA'
assert_array_equal(out, expected)
if __name__ == "__main__":
run_module_suite()
| 728 | 26 | 82 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/tests/test_parameter.py
|
from __future__ import division, absolute_import, print_function
import os
import math
import numpy as np
from numpy.testing import run_module_suite, dec, assert_raises, assert_equal
from . import util
def _path(*a):
return os.path.join(*((os.path.dirname(__file__),) + a))
class TestParameters(util.F2PyTest):
# Check that intent(in out) translates as intent(inout)
sources = [_path('src', 'parameter', 'constant_real.f90'),
_path('src', 'parameter', 'constant_integer.f90'),
_path('src', 'parameter', 'constant_both.f90'),
_path('src', 'parameter', 'constant_compound.f90'),
_path('src', 'parameter', 'constant_non_compound.f90'),
]
@dec.slow
def test_constant_real_single(self):
# non-contiguous should raise error
x = np.arange(6, dtype=np.float32)[::2]
assert_raises(ValueError, self.module.foo_single, x)
# check values with contiguous array
x = np.arange(3, dtype=np.float32)
self.module.foo_single(x)
assert_equal(x, [0 + 1 + 2*3, 1, 2])
@dec.slow
def test_constant_real_double(self):
# non-contiguous should raise error
x = np.arange(6, dtype=np.float64)[::2]
assert_raises(ValueError, self.module.foo_double, x)
# check values with contiguous array
x = np.arange(3, dtype=np.float64)
self.module.foo_double(x)
assert_equal(x, [0 + 1 + 2*3, 1, 2])
@dec.slow
def test_constant_compound_int(self):
# non-contiguous should raise error
x = np.arange(6, dtype=np.int32)[::2]
assert_raises(ValueError, self.module.foo_compound_int, x)
# check values with contiguous array
x = np.arange(3, dtype=np.int32)
self.module.foo_compound_int(x)
assert_equal(x, [0 + 1 + 2*6, 1, 2])
@dec.slow
def test_constant_non_compound_int(self):
# check values
x = np.arange(4, dtype=np.int32)
self.module.foo_non_compound_int(x)
assert_equal(x, [0 + 1 + 2 + 3*4, 1, 2, 3])
@dec.slow
def test_constant_integer_int(self):
# non-contiguous should raise error
x = np.arange(6, dtype=np.int32)[::2]
assert_raises(ValueError, self.module.foo_int, x)
# check values with contiguous array
x = np.arange(3, dtype=np.int32)
self.module.foo_int(x)
assert_equal(x, [0 + 1 + 2*3, 1, 2])
@dec.slow
def test_constant_integer_long(self):
# non-contiguous should raise error
x = np.arange(6, dtype=np.int64)[::2]
assert_raises(ValueError, self.module.foo_long, x)
# check values with contiguous array
x = np.arange(3, dtype=np.int64)
self.module.foo_long(x)
assert_equal(x, [0 + 1 + 2*3, 1, 2])
@dec.slow
def test_constant_both(self):
# non-contiguous should raise error
x = np.arange(6, dtype=np.float64)[::2]
assert_raises(ValueError, self.module.foo, x)
# check values with contiguous array
x = np.arange(3, dtype=np.float64)
self.module.foo(x)
assert_equal(x, [0 + 1*3*3 + 2*3*3, 1*3, 2*3])
@dec.slow
def test_constant_no(self):
# non-contiguous should raise error
x = np.arange(6, dtype=np.float64)[::2]
assert_raises(ValueError, self.module.foo_no, x)
# check values with contiguous array
x = np.arange(3, dtype=np.float64)
self.module.foo_no(x)
assert_equal(x, [0 + 1*3*3 + 2*3*3, 1*3, 2*3])
@dec.slow
def test_constant_sum(self):
# non-contiguous should raise error
x = np.arange(6, dtype=np.float64)[::2]
assert_raises(ValueError, self.module.foo_sum, x)
# check values with contiguous array
x = np.arange(3, dtype=np.float64)
self.module.foo_sum(x)
assert_equal(x, [0 + 1*3*3 + 2*3*3, 1*3, 2*3])
if __name__ == "__main__":
run_module_suite()
| 3,977 | 31.341463 | 76 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/tests/test_return_character.py
|
from __future__ import division, absolute_import, print_function
from numpy import array
from numpy.testing import run_module_suite, assert_, dec
from . import util
class TestReturnCharacter(util.F2PyTest):
def check_function(self, t):
tname = t.__doc__.split()[0]
if tname in ['t0', 't1', 's0', 's1']:
assert_(t(23) == b'2')
r = t('ab')
assert_(r == b'a', repr(r))
r = t(array('ab'))
assert_(r == b'a', repr(r))
r = t(array(77, 'u1'))
assert_(r == b'M', repr(r))
#assert_(_raises(ValueError, t, array([77,87])))
#assert_(_raises(ValueError, t, array(77)))
elif tname in ['ts', 'ss']:
assert_(t(23) == b'23 ', repr(t(23)))
assert_(t('123456789abcdef') == b'123456789a')
elif tname in ['t5', 's5']:
assert_(t(23) == b'23 ', repr(t(23)))
assert_(t('ab') == b'ab ', repr(t('ab')))
assert_(t('123456789abcdef') == b'12345')
else:
raise NotImplementedError
class TestF77ReturnCharacter(TestReturnCharacter):
code = """
function t0(value)
character value
character t0
t0 = value
end
function t1(value)
character*1 value
character*1 t1
t1 = value
end
function t5(value)
character*5 value
character*5 t5
t5 = value
end
function ts(value)
character*(*) value
character*(*) ts
ts = value
end
subroutine s0(t0,value)
character value
character t0
cf2py intent(out) t0
t0 = value
end
subroutine s1(t1,value)
character*1 value
character*1 t1
cf2py intent(out) t1
t1 = value
end
subroutine s5(t5,value)
character*5 value
character*5 t5
cf2py intent(out) t5
t5 = value
end
subroutine ss(ts,value)
character*(*) value
character*10 ts
cf2py intent(out) ts
ts = value
end
"""
@dec.slow
def test_all(self):
for name in "t0,t1,t5,s0,s1,s5,ss".split(","):
self.check_function(getattr(self.module, name))
class TestF90ReturnCharacter(TestReturnCharacter):
suffix = ".f90"
code = """
module f90_return_char
contains
function t0(value)
character :: value
character :: t0
t0 = value
end function t0
function t1(value)
character(len=1) :: value
character(len=1) :: t1
t1 = value
end function t1
function t5(value)
character(len=5) :: value
character(len=5) :: t5
t5 = value
end function t5
function ts(value)
character(len=*) :: value
character(len=10) :: ts
ts = value
end function ts
subroutine s0(t0,value)
character :: value
character :: t0
!f2py intent(out) t0
t0 = value
end subroutine s0
subroutine s1(t1,value)
character(len=1) :: value
character(len=1) :: t1
!f2py intent(out) t1
t1 = value
end subroutine s1
subroutine s5(t5,value)
character(len=5) :: value
character(len=5) :: t5
!f2py intent(out) t5
t5 = value
end subroutine s5
subroutine ss(ts,value)
character(len=*) :: value
character(len=10) :: ts
!f2py intent(out) ts
ts = value
end subroutine ss
end module f90_return_char
"""
@dec.slow
def test_all(self):
for name in "t0,t1,t5,ts,s0,s1,s5,ss".split(","):
self.check_function(getattr(self.module.f90_return_char, name))
if __name__ == "__main__":
run_module_suite()
| 3,869 | 25.148649 | 75 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/tests/test_mixed.py
|
from __future__ import division, absolute_import, print_function
import os
import textwrap
from numpy.testing import run_module_suite, assert_, assert_equal, dec
from . import util
def _path(*a):
return os.path.join(*((os.path.dirname(__file__),) + a))
class TestMixed(util.F2PyTest):
sources = [_path('src', 'mixed', 'foo.f'),
_path('src', 'mixed', 'foo_fixed.f90'),
_path('src', 'mixed', 'foo_free.f90')]
@dec.slow
def test_all(self):
assert_(self.module.bar11() == 11)
assert_(self.module.foo_fixed.bar12() == 12)
assert_(self.module.foo_free.bar13() == 13)
@dec.slow
def test_docstring(self):
expected = """
a = bar11()
Wrapper for ``bar11``.
Returns
-------
a : int
"""
assert_equal(self.module.bar11.__doc__,
textwrap.dedent(expected).lstrip())
if __name__ == "__main__":
run_module_suite()
| 975 | 22.804878 | 70 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/tests/util.py
|
"""
Utility functions for
- building and importing modules on test time, using a temporary location
- detecting if compilers are present
"""
from __future__ import division, absolute_import, print_function
import os
import sys
import subprocess
import tempfile
import shutil
import atexit
import textwrap
import re
import random
import numpy.f2py
from numpy.compat import asbytes, asstr
from numpy.testing import SkipTest, temppath, dec
from importlib import import_module
try:
from hashlib import md5
except ImportError:
from md5 import new as md5
#
# Maintaining a temporary module directory
#
_module_dir = None
def _cleanup():
global _module_dir
if _module_dir is not None:
try:
sys.path.remove(_module_dir)
except ValueError:
pass
try:
shutil.rmtree(_module_dir)
except (IOError, OSError):
pass
_module_dir = None
def get_module_dir():
global _module_dir
if _module_dir is None:
_module_dir = tempfile.mkdtemp()
atexit.register(_cleanup)
if _module_dir not in sys.path:
sys.path.insert(0, _module_dir)
return _module_dir
def get_temp_module_name():
# Assume single-threaded, and the module dir usable only by this thread
d = get_module_dir()
for j in range(5403, 9999999):
name = "_test_ext_module_%d" % j
fn = os.path.join(d, name)
if name not in sys.modules and not os.path.isfile(fn + '.py'):
return name
raise RuntimeError("Failed to create a temporary module name")
def _memoize(func):
memo = {}
def wrapper(*a, **kw):
key = repr((a, kw))
if key not in memo:
try:
memo[key] = func(*a, **kw)
except Exception as e:
memo[key] = e
raise
ret = memo[key]
if isinstance(ret, Exception):
raise ret
return ret
wrapper.__name__ = func.__name__
return wrapper
#
# Building modules
#
@_memoize
def build_module(source_files, options=[], skip=[], only=[], module_name=None):
"""
Compile and import a f2py module, built from the given files.
"""
code = ("import sys; sys.path = %s; import numpy.f2py as f2py2e; "
"f2py2e.main()" % repr(sys.path))
d = get_module_dir()
# Copy files
dst_sources = []
for fn in source_files:
if not os.path.isfile(fn):
raise RuntimeError("%s is not a file" % fn)
dst = os.path.join(d, os.path.basename(fn))
shutil.copyfile(fn, dst)
dst_sources.append(dst)
fn = os.path.join(os.path.dirname(fn), '.f2py_f2cmap')
if os.path.isfile(fn):
dst = os.path.join(d, os.path.basename(fn))
if not os.path.isfile(dst):
shutil.copyfile(fn, dst)
# Prepare options
if module_name is None:
module_name = get_temp_module_name()
f2py_opts = ['-c', '-m', module_name] + options + dst_sources
if skip:
f2py_opts += ['skip:'] + skip
if only:
f2py_opts += ['only:'] + only
# Build
cwd = os.getcwd()
try:
os.chdir(d)
cmd = [sys.executable, '-c', code] + f2py_opts
p = subprocess.Popen(cmd, stdout=subprocess.PIPE,
stderr=subprocess.STDOUT)
out, err = p.communicate()
if p.returncode != 0:
raise RuntimeError("Running f2py failed: %s\n%s"
% (cmd[4:], asstr(out)))
finally:
os.chdir(cwd)
# Partial cleanup
for fn in dst_sources:
os.unlink(fn)
# Import
return import_module(module_name)
@_memoize
def build_code(source_code, options=[], skip=[], only=[], suffix=None,
module_name=None):
"""
Compile and import Fortran code using f2py.
"""
if suffix is None:
suffix = '.f'
with temppath(suffix=suffix) as path:
with open(path, 'w') as f:
f.write(source_code)
return build_module([path], options=options, skip=skip, only=only,
module_name=module_name)
#
# Check if compilers are available at all...
#
_compiler_status = None
def _get_compiler_status():
global _compiler_status
if _compiler_status is not None:
return _compiler_status
_compiler_status = (False, False, False)
# XXX: this is really ugly. But I don't know how to invoke Distutils
# in a safer way...
code = """
import os
import sys
sys.path = %(syspath)s
def configuration(parent_name='',top_path=None):
global config
from numpy.distutils.misc_util import Configuration
config = Configuration('', parent_name, top_path)
return config
from numpy.distutils.core import setup
setup(configuration=configuration)
config_cmd = config.get_config_cmd()
have_c = config_cmd.try_compile('void foo() {}')
print('COMPILERS:%%d,%%d,%%d' %% (have_c,
config.have_f77c(),
config.have_f90c()))
sys.exit(99)
"""
code = code % dict(syspath=repr(sys.path))
with temppath(suffix='.py') as script:
with open(script, 'w') as f:
f.write(code)
cmd = [sys.executable, script, 'config']
p = subprocess.Popen(cmd, stdout=subprocess.PIPE,
stderr=subprocess.STDOUT)
out, err = p.communicate()
m = re.search(br'COMPILERS:(\d+),(\d+),(\d+)', out)
if m:
_compiler_status = (bool(int(m.group(1))), bool(int(m.group(2))),
bool(int(m.group(3))))
# Finished
return _compiler_status
def has_c_compiler():
return _get_compiler_status()[0]
def has_f77_compiler():
return _get_compiler_status()[1]
def has_f90_compiler():
return _get_compiler_status()[2]
#
# Building with distutils
#
@_memoize
def build_module_distutils(source_files, config_code, module_name, **kw):
"""
Build a module via distutils and import it.
"""
from numpy.distutils.misc_util import Configuration
from numpy.distutils.core import setup
d = get_module_dir()
# Copy files
dst_sources = []
for fn in source_files:
if not os.path.isfile(fn):
raise RuntimeError("%s is not a file" % fn)
dst = os.path.join(d, os.path.basename(fn))
shutil.copyfile(fn, dst)
dst_sources.append(dst)
# Build script
config_code = textwrap.dedent(config_code).replace("\n", "\n ")
code = """\
import os
import sys
sys.path = %(syspath)s
def configuration(parent_name='',top_path=None):
from numpy.distutils.misc_util import Configuration
config = Configuration('', parent_name, top_path)
%(config_code)s
return config
if __name__ == "__main__":
from numpy.distutils.core import setup
setup(configuration=configuration)
""" % dict(config_code=config_code, syspath=repr(sys.path))
script = os.path.join(d, get_temp_module_name() + '.py')
dst_sources.append(script)
f = open(script, 'wb')
f.write(asbytes(code))
f.close()
# Build
cwd = os.getcwd()
try:
os.chdir(d)
cmd = [sys.executable, script, 'build_ext', '-i']
p = subprocess.Popen(cmd, stdout=subprocess.PIPE,
stderr=subprocess.STDOUT)
out, err = p.communicate()
if p.returncode != 0:
raise RuntimeError("Running distutils build failed: %s\n%s"
% (cmd[4:], asstr(out)))
finally:
os.chdir(cwd)
# Partial cleanup
for fn in dst_sources:
os.unlink(fn)
# Import
__import__(module_name)
return sys.modules[module_name]
#
# Unittest convenience
#
class F2PyTest(object):
code = None
sources = None
options = []
skip = []
only = []
suffix = '.f'
module = None
module_name = None
@dec.knownfailureif(sys.platform=='win32', msg='Fails with MinGW64 Gfortran (Issue #9673)')
def setup(self):
if self.module is not None:
return
# Check compiler availability first
if not has_c_compiler():
raise SkipTest("No C compiler available")
codes = []
if self.sources:
codes.extend(self.sources)
if self.code is not None:
codes.append(self.suffix)
needs_f77 = False
needs_f90 = False
for fn in codes:
if fn.endswith('.f'):
needs_f77 = True
elif fn.endswith('.f90'):
needs_f90 = True
if needs_f77 and not has_f77_compiler():
raise SkipTest("No Fortran 77 compiler available")
if needs_f90 and not has_f90_compiler():
raise SkipTest("No Fortran 90 compiler available")
# Build the module
if self.code is not None:
self.module = build_code(self.code, options=self.options,
skip=self.skip, only=self.only,
suffix=self.suffix,
module_name=self.module_name)
if self.sources is not None:
self.module = build_module(self.sources, options=self.options,
skip=self.skip, only=self.only,
module_name=self.module_name)
| 9,454 | 25.263889 | 95 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/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/f2py/tests/test_return_integer.py
|
from __future__ import division, absolute_import, print_function
from numpy import array
from numpy.compat import long
from numpy.testing import run_module_suite, assert_, assert_raises, dec
from . import util
class TestReturnInteger(util.F2PyTest):
def check_function(self, t):
assert_(t(123) == 123, repr(t(123)))
assert_(t(123.6) == 123)
assert_(t(long(123)) == 123)
assert_(t('123') == 123)
assert_(t(-123) == -123)
assert_(t([123]) == 123)
assert_(t((123,)) == 123)
assert_(t(array(123)) == 123)
assert_(t(array([123])) == 123)
assert_(t(array([[123]])) == 123)
assert_(t(array([123], 'b')) == 123)
assert_(t(array([123], 'h')) == 123)
assert_(t(array([123], 'i')) == 123)
assert_(t(array([123], 'l')) == 123)
assert_(t(array([123], 'B')) == 123)
assert_(t(array([123], 'f')) == 123)
assert_(t(array([123], 'd')) == 123)
#assert_raises(ValueError, t, array([123],'S3'))
assert_raises(ValueError, t, 'abc')
assert_raises(IndexError, t, [])
assert_raises(IndexError, t, ())
assert_raises(Exception, t, t)
assert_raises(Exception, t, {})
if t.__doc__.split()[0] in ['t8', 's8']:
assert_raises(OverflowError, t, 100000000000000000000000)
assert_raises(OverflowError, t, 10000000011111111111111.23)
class TestF77ReturnInteger(TestReturnInteger):
code = """
function t0(value)
integer value
integer t0
t0 = value
end
function t1(value)
integer*1 value
integer*1 t1
t1 = value
end
function t2(value)
integer*2 value
integer*2 t2
t2 = value
end
function t4(value)
integer*4 value
integer*4 t4
t4 = value
end
function t8(value)
integer*8 value
integer*8 t8
t8 = value
end
subroutine s0(t0,value)
integer value
integer t0
cf2py intent(out) t0
t0 = value
end
subroutine s1(t1,value)
integer*1 value
integer*1 t1
cf2py intent(out) t1
t1 = value
end
subroutine s2(t2,value)
integer*2 value
integer*2 t2
cf2py intent(out) t2
t2 = value
end
subroutine s4(t4,value)
integer*4 value
integer*4 t4
cf2py intent(out) t4
t4 = value
end
subroutine s8(t8,value)
integer*8 value
integer*8 t8
cf2py intent(out) t8
t8 = value
end
"""
@dec.slow
def test_all(self):
for name in "t0,t1,t2,t4,t8,s0,s1,s2,s4,s8".split(","):
self.check_function(getattr(self.module, name))
class TestF90ReturnInteger(TestReturnInteger):
suffix = ".f90"
code = """
module f90_return_integer
contains
function t0(value)
integer :: value
integer :: t0
t0 = value
end function t0
function t1(value)
integer(kind=1) :: value
integer(kind=1) :: t1
t1 = value
end function t1
function t2(value)
integer(kind=2) :: value
integer(kind=2) :: t2
t2 = value
end function t2
function t4(value)
integer(kind=4) :: value
integer(kind=4) :: t4
t4 = value
end function t4
function t8(value)
integer(kind=8) :: value
integer(kind=8) :: t8
t8 = value
end function t8
subroutine s0(t0,value)
integer :: value
integer :: t0
!f2py intent(out) t0
t0 = value
end subroutine s0
subroutine s1(t1,value)
integer(kind=1) :: value
integer(kind=1) :: t1
!f2py intent(out) t1
t1 = value
end subroutine s1
subroutine s2(t2,value)
integer(kind=2) :: value
integer(kind=2) :: t2
!f2py intent(out) t2
t2 = value
end subroutine s2
subroutine s4(t4,value)
integer(kind=4) :: value
integer(kind=4) :: t4
!f2py intent(out) t4
t4 = value
end subroutine s4
subroutine s8(t8,value)
integer(kind=8) :: value
integer(kind=8) :: t8
!f2py intent(out) t8
t8 = value
end subroutine s8
end module f90_return_integer
"""
@dec.slow
def test_all(self):
for name in "t0,t1,t2,t4,t8,s0,s1,s2,s4,s8".split(","):
self.check_function(getattr(self.module.f90_return_integer, name))
if __name__ == "__main__":
run_module_suite()
| 4,696 | 24.950276 | 78 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/tests/test_kind.py
|
from __future__ import division, absolute_import, print_function
import os
from numpy.testing import run_module_suite, assert_, dec
from numpy.f2py.crackfortran import (
_selected_int_kind_func as selected_int_kind,
_selected_real_kind_func as selected_real_kind
)
from . import util
def _path(*a):
return os.path.join(*((os.path.dirname(__file__),) + a))
class TestKind(util.F2PyTest):
sources = [_path('src', 'kind', 'foo.f90')]
@dec.slow
def test_all(self):
selectedrealkind = self.module.selectedrealkind
selectedintkind = self.module.selectedintkind
for i in range(40):
assert_(selectedintkind(i) in [selected_int_kind(i), -1],
'selectedintkind(%s): expected %r but got %r' %
(i, selected_int_kind(i), selectedintkind(i)))
for i in range(20):
assert_(selectedrealkind(i) in [selected_real_kind(i), -1],
'selectedrealkind(%s): expected %r but got %r' %
(i, selected_real_kind(i), selectedrealkind(i)))
if __name__ == "__main__":
run_module_suite()
| 1,126 | 29.459459 | 71 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/tests/test_return_complex.py
|
from __future__ import division, absolute_import, print_function
from numpy import array
from numpy.compat import long
from numpy.testing import run_module_suite, assert_, assert_raises, dec
from . import util
class TestReturnComplex(util.F2PyTest):
def check_function(self, t):
tname = t.__doc__.split()[0]
if tname in ['t0', 't8', 's0', 's8']:
err = 1e-5
else:
err = 0.0
assert_(abs(t(234j) - 234.0j) <= err)
assert_(abs(t(234.6) - 234.6) <= err)
assert_(abs(t(long(234)) - 234.0) <= err)
assert_(abs(t(234.6 + 3j) - (234.6 + 3j)) <= err)
#assert_( abs(t('234')-234.)<=err)
#assert_( abs(t('234.6')-234.6)<=err)
assert_(abs(t(-234) + 234.) <= err)
assert_(abs(t([234]) - 234.) <= err)
assert_(abs(t((234,)) - 234.) <= err)
assert_(abs(t(array(234)) - 234.) <= err)
assert_(abs(t(array(23 + 4j, 'F')) - (23 + 4j)) <= err)
assert_(abs(t(array([234])) - 234.) <= err)
assert_(abs(t(array([[234]])) - 234.) <= err)
assert_(abs(t(array([234], 'b')) + 22.) <= err)
assert_(abs(t(array([234], 'h')) - 234.) <= err)
assert_(abs(t(array([234], 'i')) - 234.) <= err)
assert_(abs(t(array([234], 'l')) - 234.) <= err)
assert_(abs(t(array([234], 'q')) - 234.) <= err)
assert_(abs(t(array([234], 'f')) - 234.) <= err)
assert_(abs(t(array([234], 'd')) - 234.) <= err)
assert_(abs(t(array([234 + 3j], 'F')) - (234 + 3j)) <= err)
assert_(abs(t(array([234], 'D')) - 234.) <= err)
#assert_raises(TypeError, t, array([234], 'a1'))
assert_raises(TypeError, t, 'abc')
assert_raises(IndexError, t, [])
assert_raises(IndexError, t, ())
assert_raises(TypeError, t, t)
assert_raises(TypeError, t, {})
try:
r = t(10 ** 400)
assert_(repr(r) in ['(inf+0j)', '(Infinity+0j)'], repr(r))
except OverflowError:
pass
class TestF77ReturnComplex(TestReturnComplex):
code = """
function t0(value)
complex value
complex t0
t0 = value
end
function t8(value)
complex*8 value
complex*8 t8
t8 = value
end
function t16(value)
complex*16 value
complex*16 t16
t16 = value
end
function td(value)
double complex value
double complex td
td = value
end
subroutine s0(t0,value)
complex value
complex t0
cf2py intent(out) t0
t0 = value
end
subroutine s8(t8,value)
complex*8 value
complex*8 t8
cf2py intent(out) t8
t8 = value
end
subroutine s16(t16,value)
complex*16 value
complex*16 t16
cf2py intent(out) t16
t16 = value
end
subroutine sd(td,value)
double complex value
double complex td
cf2py intent(out) td
td = value
end
"""
@dec.slow
def test_all(self):
for name in "t0,t8,t16,td,s0,s8,s16,sd".split(","):
self.check_function(getattr(self.module, name))
class TestF90ReturnComplex(TestReturnComplex):
suffix = ".f90"
code = """
module f90_return_complex
contains
function t0(value)
complex :: value
complex :: t0
t0 = value
end function t0
function t8(value)
complex(kind=4) :: value
complex(kind=4) :: t8
t8 = value
end function t8
function t16(value)
complex(kind=8) :: value
complex(kind=8) :: t16
t16 = value
end function t16
function td(value)
double complex :: value
double complex :: td
td = value
end function td
subroutine s0(t0,value)
complex :: value
complex :: t0
!f2py intent(out) t0
t0 = value
end subroutine s0
subroutine s8(t8,value)
complex(kind=4) :: value
complex(kind=4) :: t8
!f2py intent(out) t8
t8 = value
end subroutine s8
subroutine s16(t16,value)
complex(kind=8) :: value
complex(kind=8) :: t16
!f2py intent(out) t16
t16 = value
end subroutine s16
subroutine sd(td,value)
double complex :: value
double complex :: td
!f2py intent(out) td
td = value
end subroutine sd
end module f90_return_complex
"""
@dec.slow
def test_all(self):
for name in "t0,t8,t16,td,s0,s8,s16,sd".split(","):
self.check_function(getattr(self.module.f90_return_complex, name))
if __name__ == "__main__":
run_module_suite()
| 4,784 | 26.982456 | 78 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/tests/test_return_logical.py
|
from __future__ import division, absolute_import, print_function
from numpy import array
from numpy.compat import long
from numpy.testing import run_module_suite, assert_, assert_raises, dec
from . import util
class TestReturnLogical(util.F2PyTest):
def check_function(self, t):
assert_(t(True) == 1, repr(t(True)))
assert_(t(False) == 0, repr(t(False)))
assert_(t(0) == 0)
assert_(t(None) == 0)
assert_(t(0.0) == 0)
assert_(t(0j) == 0)
assert_(t(1j) == 1)
assert_(t(234) == 1)
assert_(t(234.6) == 1)
assert_(t(long(234)) == 1)
assert_(t(234.6 + 3j) == 1)
assert_(t('234') == 1)
assert_(t('aaa') == 1)
assert_(t('') == 0)
assert_(t([]) == 0)
assert_(t(()) == 0)
assert_(t({}) == 0)
assert_(t(t) == 1)
assert_(t(-234) == 1)
assert_(t(10 ** 100) == 1)
assert_(t([234]) == 1)
assert_(t((234,)) == 1)
assert_(t(array(234)) == 1)
assert_(t(array([234])) == 1)
assert_(t(array([[234]])) == 1)
assert_(t(array([234], 'b')) == 1)
assert_(t(array([234], 'h')) == 1)
assert_(t(array([234], 'i')) == 1)
assert_(t(array([234], 'l')) == 1)
assert_(t(array([234], 'f')) == 1)
assert_(t(array([234], 'd')) == 1)
assert_(t(array([234 + 3j], 'F')) == 1)
assert_(t(array([234], 'D')) == 1)
assert_(t(array(0)) == 0)
assert_(t(array([0])) == 0)
assert_(t(array([[0]])) == 0)
assert_(t(array([0j])) == 0)
assert_(t(array([1])) == 1)
assert_raises(ValueError, t, array([0, 0]))
class TestF77ReturnLogical(TestReturnLogical):
code = """
function t0(value)
logical value
logical t0
t0 = value
end
function t1(value)
logical*1 value
logical*1 t1
t1 = value
end
function t2(value)
logical*2 value
logical*2 t2
t2 = value
end
function t4(value)
logical*4 value
logical*4 t4
t4 = value
end
c function t8(value)
c logical*8 value
c logical*8 t8
c t8 = value
c end
subroutine s0(t0,value)
logical value
logical t0
cf2py intent(out) t0
t0 = value
end
subroutine s1(t1,value)
logical*1 value
logical*1 t1
cf2py intent(out) t1
t1 = value
end
subroutine s2(t2,value)
logical*2 value
logical*2 t2
cf2py intent(out) t2
t2 = value
end
subroutine s4(t4,value)
logical*4 value
logical*4 t4
cf2py intent(out) t4
t4 = value
end
c subroutine s8(t8,value)
c logical*8 value
c logical*8 t8
cf2py intent(out) t8
c t8 = value
c end
"""
@dec.slow
def test_all(self):
for name in "t0,t1,t2,t4,s0,s1,s2,s4".split(","):
self.check_function(getattr(self.module, name))
class TestF90ReturnLogical(TestReturnLogical):
suffix = ".f90"
code = """
module f90_return_logical
contains
function t0(value)
logical :: value
logical :: t0
t0 = value
end function t0
function t1(value)
logical(kind=1) :: value
logical(kind=1) :: t1
t1 = value
end function t1
function t2(value)
logical(kind=2) :: value
logical(kind=2) :: t2
t2 = value
end function t2
function t4(value)
logical(kind=4) :: value
logical(kind=4) :: t4
t4 = value
end function t4
function t8(value)
logical(kind=8) :: value
logical(kind=8) :: t8
t8 = value
end function t8
subroutine s0(t0,value)
logical :: value
logical :: t0
!f2py intent(out) t0
t0 = value
end subroutine s0
subroutine s1(t1,value)
logical(kind=1) :: value
logical(kind=1) :: t1
!f2py intent(out) t1
t1 = value
end subroutine s1
subroutine s2(t2,value)
logical(kind=2) :: value
logical(kind=2) :: t2
!f2py intent(out) t2
t2 = value
end subroutine s2
subroutine s4(t4,value)
logical(kind=4) :: value
logical(kind=4) :: t4
!f2py intent(out) t4
t4 = value
end subroutine s4
subroutine s8(t8,value)
logical(kind=8) :: value
logical(kind=8) :: t8
!f2py intent(out) t8
t8 = value
end subroutine s8
end module f90_return_logical
"""
@dec.slow
def test_all(self):
for name in "t0,t1,t2,t4,t8,s0,s1,s2,s4,s8".split(","):
self.check_function(getattr(self.module.f90_return_logical, name))
if __name__ == "__main__":
run_module_suite()
| 4,950 | 25.057895 | 78 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/tests/src/mixed/foo_fixed.f90
|
module foo_fixed
contains
subroutine bar12(a)
!f2py intent(out) a
integer a
a = 12
end subroutine bar12
end module foo_fixed
| 179 | 19 | 28 |
f90
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/tests/src/mixed/foo.f
|
subroutine bar11(a)
cf2py intent(out) a
integer a
a = 11
end
| 85 | 13.333333 | 25 |
f
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/tests/src/mixed/foo_free.f90
|
module foo_free
contains
subroutine bar13(a)
!f2py intent(out) a
integer a
a = 13
end subroutine bar13
end module foo_free
| 139 | 14.555556 | 23 |
f90
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/tests/src/string/char.f90
|
MODULE char_test
CONTAINS
SUBROUTINE change_strings(strings, n_strs, out_strings)
IMPLICIT NONE
! Inputs
INTEGER, INTENT(IN) :: n_strs
CHARACTER, INTENT(IN), DIMENSION(2,n_strs) :: strings
CHARACTER, INTENT(OUT), DIMENSION(2,n_strs) :: out_strings
!f2py INTEGER, INTENT(IN) :: n_strs
!f2py CHARACTER, INTENT(IN), DIMENSION(2,n_strs) :: strings
!f2py CHARACTER, INTENT(OUT), DIMENSION(2,n_strs) :: strings
! Misc.
INTEGER*4 :: j
DO j=1, n_strs
out_strings(1,j) = strings(1,j)
out_strings(2,j) = 'A'
END DO
END SUBROUTINE change_strings
END MODULE char_test
| 618 | 19.633333 | 62 |
f90
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/tests/src/assumed_shape/precision.f90
|
module precision
integer, parameter :: rk = selected_real_kind(8)
integer, parameter :: ik = selected_real_kind(4)
end module
| 130 | 25.2 | 50 |
f90
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/tests/src/assumed_shape/foo_mod.f90
|
module mod
contains
subroutine sum(x, res)
implicit none
real, intent(in) :: x(:)
real, intent(out) :: res
integer :: i
!print *, "sum: size(x) = ", size(x)
res = 0.0
do i = 1, size(x)
res = res + x(i)
enddo
end subroutine sum
function fsum(x) result (res)
implicit none
real, intent(in) :: x(:)
real :: res
integer :: i
!print *, "fsum: size(x) = ", size(x)
res = 0.0
do i = 1, size(x)
res = res + x(i)
enddo
end function fsum
end module mod
| 499 | 10.904762 | 39 |
f90
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/tests/src/assumed_shape/foo_use.f90
|
subroutine sum_with_use(x, res)
use precision
implicit none
real(kind=rk), intent(in) :: x(:)
real(kind=rk), intent(out) :: res
integer :: i
!print *, "size(x) = ", size(x)
res = 0.0
do i = 1, size(x)
res = res + x(i)
enddo
end subroutine
| 269 | 12.5 | 35 |
f90
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/tests/src/assumed_shape/foo_free.f90
|
subroutine sum(x, res)
implicit none
real, intent(in) :: x(:)
real, intent(out) :: res
integer :: i
!print *, "sum: size(x) = ", size(x)
res = 0.0
do i = 1, size(x)
res = res + x(i)
enddo
end subroutine sum
function fsum(x) result (res)
implicit none
real, intent(in) :: x(:)
real :: res
integer :: i
!print *, "fsum: size(x) = ", size(x)
res = 0.0
do i = 1, size(x)
res = res + x(i)
enddo
end function fsum
| 460 | 12.171429 | 39 |
f90
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/tests/src/common/block.f
|
SUBROUTINE INITCB
DOUBLE PRECISION LONG
CHARACTER STRING
INTEGER OK
COMMON /BLOCK/ LONG, STRING, OK
LONG = 1.0
STRING = '2'
OK = 3
RETURN
END
| 224 | 17.75 | 38 |
f
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/tests/src/kind/foo.f90
|
subroutine selectedrealkind(p, r, res)
implicit none
integer, intent(in) :: p, r
!f2py integer :: r=0
integer, intent(out) :: res
res = selected_real_kind(p, r)
end subroutine
subroutine selectedintkind(p, res)
implicit none
integer, intent(in) :: p
integer, intent(out) :: res
res = selected_int_kind(p)
end subroutine
| 347 | 15.571429 | 38 |
f90
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/tests/src/parameter/constant_compound.f90
|
! Check that parameters are correct intercepted.
! Constants with comma separations are commonly
! used, for instance Pi = 3._dp
subroutine foo_compound_int(x)
implicit none
integer, parameter :: ii = selected_int_kind(9)
integer(ii), intent(inout) :: x
dimension x(3)
integer(ii), parameter :: three = 3_ii
integer(ii), parameter :: two = 2_ii
integer(ii), parameter :: six = three * 1_ii * two
x(1) = x(1) + x(2) + x(3) * six
return
end subroutine
| 469 | 28.375 | 52 |
f90
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/tests/src/parameter/constant_real.f90
|
! Check that parameters are correct intercepted.
! Constants with comma separations are commonly
! used, for instance Pi = 3._dp
subroutine foo_single(x)
implicit none
integer, parameter :: rp = selected_real_kind(6)
real(rp), intent(inout) :: x
dimension x(3)
real(rp), parameter :: three = 3._rp
x(1) = x(1) + x(2) + x(3) * three
return
end subroutine
subroutine foo_double(x)
implicit none
integer, parameter :: rp = selected_real_kind(15)
real(rp), intent(inout) :: x
dimension x(3)
real(rp), parameter :: three = 3._rp
x(1) = x(1) + x(2) + x(3) * three
return
end subroutine
| 610 | 24.458333 | 51 |
f90
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/tests/src/parameter/constant_non_compound.f90
|
! Check that parameters are correct intercepted.
! Specifically that types of constants without
! compound kind specs are correctly inferred
! adapted Gibbs iteration code from pymc
! for this test case
subroutine foo_non_compound_int(x)
implicit none
integer, parameter :: ii = selected_int_kind(9)
integer(ii) maxiterates
parameter (maxiterates=2)
integer(ii) maxseries
parameter (maxseries=2)
integer(ii) wasize
parameter (wasize=maxiterates*maxseries)
integer(ii), intent(inout) :: x
dimension x(wasize)
x(1) = x(1) + x(2) + x(3) + x(4) * wasize
return
end subroutine
| 609 | 24.416667 | 49 |
f90
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/tests/src/parameter/constant_integer.f90
|
! Check that parameters are correct intercepted.
! Constants with comma separations are commonly
! used, for instance Pi = 3._dp
subroutine foo_int(x)
implicit none
integer, parameter :: ii = selected_int_kind(9)
integer(ii), intent(inout) :: x
dimension x(3)
integer(ii), parameter :: three = 3_ii
x(1) = x(1) + x(2) + x(3) * three
return
end subroutine
subroutine foo_long(x)
implicit none
integer, parameter :: ii = selected_int_kind(18)
integer(ii), intent(inout) :: x
dimension x(3)
integer(ii), parameter :: three = 3_ii
x(1) = x(1) + x(2) + x(3) * three
return
end subroutine
| 612 | 25.652174 | 50 |
f90
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/tests/src/parameter/constant_both.f90
|
! Check that parameters are correct intercepted.
! Constants with comma separations are commonly
! used, for instance Pi = 3._dp
subroutine foo(x)
implicit none
integer, parameter :: sp = selected_real_kind(6)
integer, parameter :: dp = selected_real_kind(15)
integer, parameter :: ii = selected_int_kind(9)
integer, parameter :: il = selected_int_kind(18)
real(dp), intent(inout) :: x
dimension x(3)
real(sp), parameter :: three_s = 3._sp
real(dp), parameter :: three_d = 3._dp
integer(ii), parameter :: three_i = 3_ii
integer(il), parameter :: three_l = 3_il
x(1) = x(1) + x(2) * three_s * three_i + x(3) * three_d * three_l
x(2) = x(2) * three_s
x(3) = x(3) * three_l
return
end subroutine
subroutine foo_no(x)
implicit none
integer, parameter :: sp = selected_real_kind(6)
integer, parameter :: dp = selected_real_kind(15)
integer, parameter :: ii = selected_int_kind(9)
integer, parameter :: il = selected_int_kind(18)
real(dp), intent(inout) :: x
dimension x(3)
real(sp), parameter :: three_s = 3.
real(dp), parameter :: three_d = 3.
integer(ii), parameter :: three_i = 3
integer(il), parameter :: three_l = 3
x(1) = x(1) + x(2) * three_s * three_i + x(3) * three_d * three_l
x(2) = x(2) * three_s
x(3) = x(3) * three_l
return
end subroutine
subroutine foo_sum(x)
implicit none
integer, parameter :: sp = selected_real_kind(6)
integer, parameter :: dp = selected_real_kind(15)
integer, parameter :: ii = selected_int_kind(9)
integer, parameter :: il = selected_int_kind(18)
real(dp), intent(inout) :: x
dimension x(3)
real(sp), parameter :: three_s = 2._sp + 1._sp
real(dp), parameter :: three_d = 1._dp + 2._dp
integer(ii), parameter :: three_i = 2_ii + 1_ii
integer(il), parameter :: three_l = 1_il + 2_il
x(1) = x(1) + x(2) * three_s * three_i + x(3) * three_d * three_l
x(2) = x(2) * three_s
x(3) = x(3) * three_l
return
end subroutine
| 1,939 | 32.448276 | 67 |
f90
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/tests/src/size/foo.f90
|
subroutine foo(a, n, m, b)
implicit none
real, intent(in) :: a(n, m)
integer, intent(in) :: n, m
real, intent(out) :: b(size(a, 1))
integer :: i
do i = 1, size(b)
b(i) = sum(a(i,:))
enddo
end subroutine
subroutine trans(x,y)
implicit none
real, intent(in), dimension(:,:) :: x
real, intent(out), dimension( size(x,2), size(x,1) ) :: y
integer :: N, M, i, j
N = size(x,1)
M = size(x,2)
DO i=1,N
do j=1,M
y(j,i) = x(i,j)
END DO
END DO
end subroutine trans
subroutine flatten(x,y)
implicit none
real, intent(in), dimension(:,:) :: x
real, intent(out), dimension( size(x) ) :: y
integer :: N, M, i, j, k
N = size(x,1)
M = size(x,2)
k = 1
DO i=1,N
do j=1,M
y(k) = x(i,j)
k = k + 1
END DO
END DO
end subroutine flatten
| 815 | 17.133333 | 59 |
f90
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/tests/src/regression/inout.f90
|
! Check that intent(in out) translates as intent(inout).
! The separation seems to be a common usage.
subroutine foo(x)
implicit none
real(4), intent(in out) :: x
dimension x(3)
x(1) = x(1) + x(2) + x(3)
return
end
| 277 | 26.8 | 56 |
f90
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/f2py/tests/src/array_from_pyobj/wrapmodule.c
|
/* File: wrapmodule.c
* This file is auto-generated with f2py (version:2_1330).
* Hand edited by Pearu.
* f2py is a Fortran to Python Interface Generator (FPIG), Second Edition,
* written by Pearu Peterson <[email protected]>.
* See http://cens.ioc.ee/projects/f2py2e/
* Generation date: Fri Oct 21 22:41:12 2005
* $Revision:$
* $Date:$
* Do not edit this file directly unless you know what you are doing!!!
*/
#ifdef __cplusplus
extern "C" {
#endif
/*********************** See f2py2e/cfuncs.py: includes ***********************/
#include "Python.h"
#include "fortranobject.h"
#include <math.h>
static PyObject *wrap_error;
static PyObject *wrap_module;
/************************************ call ************************************/
static char doc_f2py_rout_wrap_call[] = "\
Function signature:\n\
arr = call(type_num,dims,intent,obj)\n\
Required arguments:\n"
" type_num : input int\n"
" dims : input int-sequence\n"
" intent : input int\n"
" obj : input python object\n"
"Return objects:\n"
" arr : array";
static PyObject *f2py_rout_wrap_call(PyObject *capi_self,
PyObject *capi_args) {
PyObject * volatile capi_buildvalue = NULL;
int type_num = 0;
npy_intp *dims = NULL;
PyObject *dims_capi = Py_None;
int rank = 0;
int intent = 0;
PyArrayObject *capi_arr_tmp = NULL;
PyObject *arr_capi = Py_None;
int i;
if (!PyArg_ParseTuple(capi_args,"iOiO|:wrap.call",\
&type_num,&dims_capi,&intent,&arr_capi))
return NULL;
rank = PySequence_Length(dims_capi);
dims = malloc(rank*sizeof(npy_intp));
for (i=0;i<rank;++i)
dims[i] = (npy_intp)PyInt_AsLong(PySequence_GetItem(dims_capi,i));
capi_arr_tmp = array_from_pyobj(type_num,dims,rank,intent|F2PY_INTENT_OUT,arr_capi);
if (capi_arr_tmp == NULL) {
free(dims);
return NULL;
}
capi_buildvalue = Py_BuildValue("N",capi_arr_tmp);
free(dims);
return capi_buildvalue;
}
static char doc_f2py_rout_wrap_attrs[] = "\
Function signature:\n\
arr = array_attrs(arr)\n\
Required arguments:\n"
" arr : input array object\n"
"Return objects:\n"
" data : data address in hex\n"
" nd : int\n"
" dimensions : tuple\n"
" strides : tuple\n"
" base : python object\n"
" (kind,type,type_num,elsize,alignment) : 4-tuple\n"
" flags : int\n"
" itemsize : int\n"
;
static PyObject *f2py_rout_wrap_attrs(PyObject *capi_self,
PyObject *capi_args) {
PyObject *arr_capi = Py_None;
PyArrayObject *arr = NULL;
PyObject *dimensions = NULL;
PyObject *strides = NULL;
char s[100];
int i;
memset(s,0,100*sizeof(char));
if (!PyArg_ParseTuple(capi_args,"O!|:wrap.attrs",
&PyArray_Type,&arr_capi))
return NULL;
arr = (PyArrayObject *)arr_capi;
sprintf(s,"%p",PyArray_DATA(arr));
dimensions = PyTuple_New(PyArray_NDIM(arr));
strides = PyTuple_New(PyArray_NDIM(arr));
for (i=0;i<PyArray_NDIM(arr);++i) {
PyTuple_SetItem(dimensions,i,PyInt_FromLong(PyArray_DIM(arr,i)));
PyTuple_SetItem(strides,i,PyInt_FromLong(PyArray_STRIDE(arr,i)));
}
return Py_BuildValue("siOOO(cciii)ii",s,PyArray_NDIM(arr),
dimensions,strides,
(PyArray_BASE(arr)==NULL?Py_None:PyArray_BASE(arr)),
PyArray_DESCR(arr)->kind,
PyArray_DESCR(arr)->type,
PyArray_TYPE(arr),
PyArray_ITEMSIZE(arr),
PyArray_DESCR(arr)->alignment,
PyArray_FLAGS(arr),
PyArray_ITEMSIZE(arr));
}
static PyMethodDef f2py_module_methods[] = {
{"call",f2py_rout_wrap_call,METH_VARARGS,doc_f2py_rout_wrap_call},
{"array_attrs",f2py_rout_wrap_attrs,METH_VARARGS,doc_f2py_rout_wrap_attrs},
{NULL,NULL}
};
#if PY_VERSION_HEX >= 0x03000000
static struct PyModuleDef moduledef = {
PyModuleDef_HEAD_INIT,
"test_array_from_pyobj_ext",
NULL,
-1,
f2py_module_methods,
NULL,
NULL,
NULL,
NULL
};
#endif
#if PY_VERSION_HEX >= 0x03000000
#define RETVAL m
PyMODINIT_FUNC PyInit_test_array_from_pyobj_ext(void) {
#else
#define RETVAL
PyMODINIT_FUNC inittest_array_from_pyobj_ext(void) {
#endif
PyObject *m,*d, *s;
#if PY_VERSION_HEX >= 0x03000000
m = wrap_module = PyModule_Create(&moduledef);
#else
m = wrap_module = Py_InitModule("test_array_from_pyobj_ext", f2py_module_methods);
#endif
Py_TYPE(&PyFortran_Type) = &PyType_Type;
import_array();
if (PyErr_Occurred())
Py_FatalError("can't initialize module wrap (failed to import numpy)");
d = PyModule_GetDict(m);
s = PyString_FromString("This module 'wrap' is auto-generated with f2py (version:2_1330).\nFunctions:\n"
" arr = call(type_num,dims,intent,obj)\n"
".");
PyDict_SetItemString(d, "__doc__", s);
wrap_error = PyErr_NewException ("wrap.error", NULL, NULL);
Py_DECREF(s);
PyDict_SetItemString(d, "F2PY_INTENT_IN", PyInt_FromLong(F2PY_INTENT_IN));
PyDict_SetItemString(d, "F2PY_INTENT_INOUT", PyInt_FromLong(F2PY_INTENT_INOUT));
PyDict_SetItemString(d, "F2PY_INTENT_OUT", PyInt_FromLong(F2PY_INTENT_OUT));
PyDict_SetItemString(d, "F2PY_INTENT_HIDE", PyInt_FromLong(F2PY_INTENT_HIDE));
PyDict_SetItemString(d, "F2PY_INTENT_CACHE", PyInt_FromLong(F2PY_INTENT_CACHE));
PyDict_SetItemString(d, "F2PY_INTENT_COPY", PyInt_FromLong(F2PY_INTENT_COPY));
PyDict_SetItemString(d, "F2PY_INTENT_C", PyInt_FromLong(F2PY_INTENT_C));
PyDict_SetItemString(d, "F2PY_OPTIONAL", PyInt_FromLong(F2PY_OPTIONAL));
PyDict_SetItemString(d, "F2PY_INTENT_INPLACE", PyInt_FromLong(F2PY_INTENT_INPLACE));
PyDict_SetItemString(d, "NPY_BOOL", PyInt_FromLong(NPY_BOOL));
PyDict_SetItemString(d, "NPY_BYTE", PyInt_FromLong(NPY_BYTE));
PyDict_SetItemString(d, "NPY_UBYTE", PyInt_FromLong(NPY_UBYTE));
PyDict_SetItemString(d, "NPY_SHORT", PyInt_FromLong(NPY_SHORT));
PyDict_SetItemString(d, "NPY_USHORT", PyInt_FromLong(NPY_USHORT));
PyDict_SetItemString(d, "NPY_INT", PyInt_FromLong(NPY_INT));
PyDict_SetItemString(d, "NPY_UINT", PyInt_FromLong(NPY_UINT));
PyDict_SetItemString(d, "NPY_INTP", PyInt_FromLong(NPY_INTP));
PyDict_SetItemString(d, "NPY_UINTP", PyInt_FromLong(NPY_UINTP));
PyDict_SetItemString(d, "NPY_LONG", PyInt_FromLong(NPY_LONG));
PyDict_SetItemString(d, "NPY_ULONG", PyInt_FromLong(NPY_ULONG));
PyDict_SetItemString(d, "NPY_LONGLONG", PyInt_FromLong(NPY_LONGLONG));
PyDict_SetItemString(d, "NPY_ULONGLONG", PyInt_FromLong(NPY_ULONGLONG));
PyDict_SetItemString(d, "NPY_FLOAT", PyInt_FromLong(NPY_FLOAT));
PyDict_SetItemString(d, "NPY_DOUBLE", PyInt_FromLong(NPY_DOUBLE));
PyDict_SetItemString(d, "NPY_LONGDOUBLE", PyInt_FromLong(NPY_LONGDOUBLE));
PyDict_SetItemString(d, "NPY_CFLOAT", PyInt_FromLong(NPY_CFLOAT));
PyDict_SetItemString(d, "NPY_CDOUBLE", PyInt_FromLong(NPY_CDOUBLE));
PyDict_SetItemString(d, "NPY_CLONGDOUBLE", PyInt_FromLong(NPY_CLONGDOUBLE));
PyDict_SetItemString(d, "NPY_OBJECT", PyInt_FromLong(NPY_OBJECT));
PyDict_SetItemString(d, "NPY_STRING", PyInt_FromLong(NPY_STRING));
PyDict_SetItemString(d, "NPY_UNICODE", PyInt_FromLong(NPY_UNICODE));
PyDict_SetItemString(d, "NPY_VOID", PyInt_FromLong(NPY_VOID));
PyDict_SetItemString(d, "NPY_NTYPES", PyInt_FromLong(NPY_NTYPES));
PyDict_SetItemString(d, "NPY_NOTYPE", PyInt_FromLong(NPY_NOTYPE));
PyDict_SetItemString(d, "NPY_USERDEF", PyInt_FromLong(NPY_USERDEF));
PyDict_SetItemString(d, "CONTIGUOUS", PyInt_FromLong(NPY_ARRAY_C_CONTIGUOUS));
PyDict_SetItemString(d, "FORTRAN", PyInt_FromLong(NPY_ARRAY_F_CONTIGUOUS));
PyDict_SetItemString(d, "OWNDATA", PyInt_FromLong(NPY_ARRAY_OWNDATA));
PyDict_SetItemString(d, "FORCECAST", PyInt_FromLong(NPY_ARRAY_FORCECAST));
PyDict_SetItemString(d, "ENSURECOPY", PyInt_FromLong(NPY_ARRAY_ENSURECOPY));
PyDict_SetItemString(d, "ENSUREARRAY", PyInt_FromLong(NPY_ARRAY_ENSUREARRAY));
PyDict_SetItemString(d, "ALIGNED", PyInt_FromLong(NPY_ARRAY_ALIGNED));
PyDict_SetItemString(d, "WRITEABLE", PyInt_FromLong(NPY_ARRAY_WRITEABLE));
PyDict_SetItemString(d, "UPDATEIFCOPY", PyInt_FromLong(NPY_ARRAY_UPDATEIFCOPY));
PyDict_SetItemString(d, "WRITEBACKIFCOPY", PyInt_FromLong(NPY_ARRAY_WRITEBACKIFCOPY));
PyDict_SetItemString(d, "BEHAVED", PyInt_FromLong(NPY_ARRAY_BEHAVED));
PyDict_SetItemString(d, "BEHAVED_NS", PyInt_FromLong(NPY_ARRAY_BEHAVED_NS));
PyDict_SetItemString(d, "CARRAY", PyInt_FromLong(NPY_ARRAY_CARRAY));
PyDict_SetItemString(d, "FARRAY", PyInt_FromLong(NPY_ARRAY_FARRAY));
PyDict_SetItemString(d, "CARRAY_RO", PyInt_FromLong(NPY_ARRAY_CARRAY_RO));
PyDict_SetItemString(d, "FARRAY_RO", PyInt_FromLong(NPY_ARRAY_FARRAY_RO));
PyDict_SetItemString(d, "DEFAULT", PyInt_FromLong(NPY_ARRAY_DEFAULT));
PyDict_SetItemString(d, "UPDATE_ALL", PyInt_FromLong(NPY_ARRAY_UPDATE_ALL));
if (PyErr_Occurred())
Py_FatalError("can't initialize module wrap");
#ifdef F2PY_REPORT_ATEXIT
on_exit(f2py_report_on_exit,(void*)"array_from_pyobj.wrap.call");
#endif
return RETVAL;
}
#ifdef __cplusplus
}
#endif
| 8,801 | 38.12 | 106 |
c
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/matrixlib/defmatrix.py
|
from __future__ import division, absolute_import, print_function
__all__ = ['matrix', 'bmat', 'mat', 'asmatrix']
import sys
import ast
import numpy.core.numeric as N
from numpy.core.numeric import concatenate, isscalar, binary_repr, identity, asanyarray
from numpy.core.numerictypes import issubdtype
def _convert_from_string(data):
for char in '[]':
data = data.replace(char, '')
rows = data.split(';')
newdata = []
count = 0
for row in rows:
trow = row.split(',')
newrow = []
for col in trow:
temp = col.split()
newrow.extend(map(ast.literal_eval, temp))
if count == 0:
Ncols = len(newrow)
elif len(newrow) != Ncols:
raise ValueError("Rows not the same size.")
count += 1
newdata.append(newrow)
return newdata
def asmatrix(data, dtype=None):
"""
Interpret the input as a matrix.
Unlike `matrix`, `asmatrix` does not make a copy if the input is already
a matrix or an ndarray. Equivalent to ``matrix(data, copy=False)``.
Parameters
----------
data : array_like
Input data.
dtype : data-type
Data-type of the output matrix.
Returns
-------
mat : matrix
`data` interpreted as a matrix.
Examples
--------
>>> x = np.array([[1, 2], [3, 4]])
>>> m = np.asmatrix(x)
>>> x[0,0] = 5
>>> m
matrix([[5, 2],
[3, 4]])
"""
return matrix(data, dtype=dtype, copy=False)
def matrix_power(M, n):
"""
Raise a square matrix to the (integer) power `n`.
For positive integers `n`, the power is computed by repeated matrix
squarings and matrix multiplications. If ``n == 0``, the identity matrix
of the same shape as M is returned. If ``n < 0``, the inverse
is computed and then raised to the ``abs(n)``.
Parameters
----------
M : ndarray or matrix object
Matrix to be "powered." Must be square, i.e. ``M.shape == (m, m)``,
with `m` a positive integer.
n : int
The exponent can be any integer or long integer, positive,
negative, or zero.
Returns
-------
M**n : ndarray or matrix object
The return value is the same shape and type as `M`;
if the exponent is positive or zero then the type of the
elements is the same as those of `M`. If the exponent is
negative the elements are floating-point.
Raises
------
LinAlgError
If the matrix is not numerically invertible.
See Also
--------
matrix
Provides an equivalent function as the exponentiation operator
(``**``, not ``^``).
Examples
--------
>>> from numpy import linalg as LA
>>> i = np.array([[0, 1], [-1, 0]]) # matrix equiv. of the imaginary unit
>>> LA.matrix_power(i, 3) # should = -i
array([[ 0, -1],
[ 1, 0]])
>>> LA.matrix_power(np.matrix(i), 3) # matrix arg returns matrix
matrix([[ 0, -1],
[ 1, 0]])
>>> LA.matrix_power(i, 0)
array([[1, 0],
[0, 1]])
>>> LA.matrix_power(i, -3) # should = 1/(-i) = i, but w/ f.p. elements
array([[ 0., 1.],
[-1., 0.]])
Somewhat more sophisticated example
>>> q = np.zeros((4, 4))
>>> q[0:2, 0:2] = -i
>>> q[2:4, 2:4] = i
>>> q # one of the three quaternion units not equal to 1
array([[ 0., -1., 0., 0.],
[ 1., 0., 0., 0.],
[ 0., 0., 0., 1.],
[ 0., 0., -1., 0.]])
>>> LA.matrix_power(q, 2) # = -np.eye(4)
array([[-1., 0., 0., 0.],
[ 0., -1., 0., 0.],
[ 0., 0., -1., 0.],
[ 0., 0., 0., -1.]])
"""
M = asanyarray(M)
if M.ndim != 2 or M.shape[0] != M.shape[1]:
raise ValueError("input must be a square array")
if not issubdtype(type(n), N.integer):
raise TypeError("exponent must be an integer")
from numpy.linalg import inv
if n==0:
M = M.copy()
M[:] = identity(M.shape[0])
return M
elif n<0:
M = inv(M)
n *= -1
result = M
if n <= 3:
for _ in range(n-1):
result=N.dot(result, M)
return result
# binary decomposition to reduce the number of Matrix
# multiplications for n > 3.
beta = binary_repr(n)
Z, q, t = M, 0, len(beta)
while beta[t-q-1] == '0':
Z = N.dot(Z, Z)
q += 1
result = Z
for k in range(q+1, t):
Z = N.dot(Z, Z)
if beta[t-k-1] == '1':
result = N.dot(result, Z)
return result
class matrix(N.ndarray):
"""
matrix(data, dtype=None, copy=True)
Returns a matrix from an array-like object, or from a string of data.
A matrix is a specialized 2-D array that retains its 2-D nature
through operations. It has certain special operators, such as ``*``
(matrix multiplication) and ``**`` (matrix power).
Parameters
----------
data : array_like or string
If `data` is a string, it is interpreted as a matrix with commas
or spaces separating columns, and semicolons separating rows.
dtype : data-type
Data-type of the output matrix.
copy : bool
If `data` is already an `ndarray`, then this flag determines
whether the data is copied (the default), or whether a view is
constructed.
See Also
--------
array
Examples
--------
>>> a = np.matrix('1 2; 3 4')
>>> print(a)
[[1 2]
[3 4]]
>>> np.matrix([[1, 2], [3, 4]])
matrix([[1, 2],
[3, 4]])
"""
__array_priority__ = 10.0
def __new__(subtype, data, dtype=None, copy=True):
if isinstance(data, matrix):
dtype2 = data.dtype
if (dtype is None):
dtype = dtype2
if (dtype2 == dtype) and (not copy):
return data
return data.astype(dtype)
if isinstance(data, N.ndarray):
if dtype is None:
intype = data.dtype
else:
intype = N.dtype(dtype)
new = data.view(subtype)
if intype != data.dtype:
return new.astype(intype)
if copy: return new.copy()
else: return new
if isinstance(data, str):
data = _convert_from_string(data)
# now convert data to an array
arr = N.array(data, dtype=dtype, copy=copy)
ndim = arr.ndim
shape = arr.shape
if (ndim > 2):
raise ValueError("matrix must be 2-dimensional")
elif ndim == 0:
shape = (1, 1)
elif ndim == 1:
shape = (1, shape[0])
order = 'C'
if (ndim == 2) and arr.flags.fortran:
order = 'F'
if not (order or arr.flags.contiguous):
arr = arr.copy()
ret = N.ndarray.__new__(subtype, shape, arr.dtype,
buffer=arr,
order=order)
return ret
def __array_finalize__(self, obj):
self._getitem = False
if (isinstance(obj, matrix) and obj._getitem): return
ndim = self.ndim
if (ndim == 2):
return
if (ndim > 2):
newshape = tuple([x for x in self.shape if x > 1])
ndim = len(newshape)
if ndim == 2:
self.shape = newshape
return
elif (ndim > 2):
raise ValueError("shape too large to be a matrix.")
else:
newshape = self.shape
if ndim == 0:
self.shape = (1, 1)
elif ndim == 1:
self.shape = (1, newshape[0])
return
def __getitem__(self, index):
self._getitem = True
try:
out = N.ndarray.__getitem__(self, index)
finally:
self._getitem = False
if not isinstance(out, N.ndarray):
return out
if out.ndim == 0:
return out[()]
if out.ndim == 1:
sh = out.shape[0]
# Determine when we should have a column array
try:
n = len(index)
except Exception:
n = 0
if n > 1 and isscalar(index[1]):
out.shape = (sh, 1)
else:
out.shape = (1, sh)
return out
def __mul__(self, other):
if isinstance(other, (N.ndarray, list, tuple)) :
# This promotes 1-D vectors to row vectors
return N.dot(self, asmatrix(other))
if isscalar(other) or not hasattr(other, '__rmul__') :
return N.dot(self, other)
return NotImplemented
def __rmul__(self, other):
return N.dot(other, self)
def __imul__(self, other):
self[:] = self * other
return self
def __pow__(self, other):
return matrix_power(self, other)
def __ipow__(self, other):
self[:] = self ** other
return self
def __rpow__(self, other):
return NotImplemented
def _align(self, axis):
"""A convenience function for operations that need to preserve axis
orientation.
"""
if axis is None:
return self[0, 0]
elif axis==0:
return self
elif axis==1:
return self.transpose()
else:
raise ValueError("unsupported axis")
def _collapse(self, axis):
"""A convenience function for operations that want to collapse
to a scalar like _align, but are using keepdims=True
"""
if axis is None:
return self[0, 0]
else:
return self
# Necessary because base-class tolist expects dimension
# reduction by x[0]
def tolist(self):
"""
Return the matrix as a (possibly nested) list.
See `ndarray.tolist` for full documentation.
See Also
--------
ndarray.tolist
Examples
--------
>>> x = np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> x.tolist()
[[0, 1, 2, 3], [4, 5, 6, 7], [8, 9, 10, 11]]
"""
return self.__array__().tolist()
# To preserve orientation of result...
def sum(self, axis=None, dtype=None, out=None):
"""
Returns the sum of the matrix elements, along the given axis.
Refer to `numpy.sum` for full documentation.
See Also
--------
numpy.sum
Notes
-----
This is the same as `ndarray.sum`, except that where an `ndarray` would
be returned, a `matrix` object is returned instead.
Examples
--------
>>> x = np.matrix([[1, 2], [4, 3]])
>>> x.sum()
10
>>> x.sum(axis=1)
matrix([[3],
[7]])
>>> x.sum(axis=1, dtype='float')
matrix([[ 3.],
[ 7.]])
>>> out = np.zeros((1, 2), dtype='float')
>>> x.sum(axis=1, dtype='float', out=out)
matrix([[ 3.],
[ 7.]])
"""
return N.ndarray.sum(self, axis, dtype, out, keepdims=True)._collapse(axis)
# To update docstring from array to matrix...
def squeeze(self, axis=None):
"""
Return a possibly reshaped matrix.
Refer to `numpy.squeeze` for more documentation.
Parameters
----------
axis : None or int or tuple of ints, optional
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 : matrix
The matrix, but as a (1, N) matrix if it had shape (N, 1).
See Also
--------
numpy.squeeze : related function
Notes
-----
If `m` has a single column then that column is returned
as the single row of a matrix. Otherwise `m` is returned.
The returned matrix is always either `m` itself or a view into `m`.
Supplying an axis keyword argument will not affect the returned matrix
but it may cause an error to be raised.
Examples
--------
>>> c = np.matrix([[1], [2]])
>>> c
matrix([[1],
[2]])
>>> c.squeeze()
matrix([[1, 2]])
>>> r = c.T
>>> r
matrix([[1, 2]])
>>> r.squeeze()
matrix([[1, 2]])
>>> m = np.matrix([[1, 2], [3, 4]])
>>> m.squeeze()
matrix([[1, 2],
[3, 4]])
"""
return N.ndarray.squeeze(self, axis=axis)
# To update docstring from array to matrix...
def flatten(self, order='C'):
"""
Return a flattened copy of the matrix.
All `N` elements of the matrix are placed into a single row.
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 `m` is Fortran *contiguous* in
memory, row-major order otherwise. 'K' means to flatten `m` in
the order the elements occur in memory. The default is 'C'.
Returns
-------
y : matrix
A copy of the matrix, flattened to a `(1, N)` matrix where `N`
is the number of elements in the original matrix.
See Also
--------
ravel : Return a flattened array.
flat : A 1-D flat iterator over the matrix.
Examples
--------
>>> m = np.matrix([[1,2], [3,4]])
>>> m.flatten()
matrix([[1, 2, 3, 4]])
>>> m.flatten('F')
matrix([[1, 3, 2, 4]])
"""
return N.ndarray.flatten(self, order=order)
def mean(self, axis=None, dtype=None, out=None):
"""
Returns the average of the matrix elements along the given axis.
Refer to `numpy.mean` for full documentation.
See Also
--------
numpy.mean
Notes
-----
Same as `ndarray.mean` except that, where that returns an `ndarray`,
this returns a `matrix` object.
Examples
--------
>>> x = np.matrix(np.arange(12).reshape((3, 4)))
>>> x
matrix([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> x.mean()
5.5
>>> x.mean(0)
matrix([[ 4., 5., 6., 7.]])
>>> x.mean(1)
matrix([[ 1.5],
[ 5.5],
[ 9.5]])
"""
return N.ndarray.mean(self, axis, dtype, out, keepdims=True)._collapse(axis)
def std(self, axis=None, dtype=None, out=None, ddof=0):
"""
Return the standard deviation of the array elements along the given axis.
Refer to `numpy.std` for full documentation.
See Also
--------
numpy.std
Notes
-----
This is the same as `ndarray.std`, except that where an `ndarray` would
be returned, a `matrix` object is returned instead.
Examples
--------
>>> x = np.matrix(np.arange(12).reshape((3, 4)))
>>> x
matrix([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> x.std()
3.4520525295346629
>>> x.std(0)
matrix([[ 3.26598632, 3.26598632, 3.26598632, 3.26598632]])
>>> x.std(1)
matrix([[ 1.11803399],
[ 1.11803399],
[ 1.11803399]])
"""
return N.ndarray.std(self, axis, dtype, out, ddof, keepdims=True)._collapse(axis)
def var(self, axis=None, dtype=None, out=None, ddof=0):
"""
Returns the variance of the matrix elements, along the given axis.
Refer to `numpy.var` for full documentation.
See Also
--------
numpy.var
Notes
-----
This is the same as `ndarray.var`, except that where an `ndarray` would
be returned, a `matrix` object is returned instead.
Examples
--------
>>> x = np.matrix(np.arange(12).reshape((3, 4)))
>>> x
matrix([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> x.var()
11.916666666666666
>>> x.var(0)
matrix([[ 10.66666667, 10.66666667, 10.66666667, 10.66666667]])
>>> x.var(1)
matrix([[ 1.25],
[ 1.25],
[ 1.25]])
"""
return N.ndarray.var(self, axis, dtype, out, ddof, keepdims=True)._collapse(axis)
def prod(self, axis=None, dtype=None, out=None):
"""
Return the product of the array elements over the given axis.
Refer to `prod` for full documentation.
See Also
--------
prod, ndarray.prod
Notes
-----
Same as `ndarray.prod`, except, where that returns an `ndarray`, this
returns a `matrix` object instead.
Examples
--------
>>> x = np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> x.prod()
0
>>> x.prod(0)
matrix([[ 0, 45, 120, 231]])
>>> x.prod(1)
matrix([[ 0],
[ 840],
[7920]])
"""
return N.ndarray.prod(self, axis, dtype, out, keepdims=True)._collapse(axis)
def any(self, axis=None, out=None):
"""
Test whether any array element along a given axis evaluates to True.
Refer to `numpy.any` for full documentation.
Parameters
----------
axis : int, optional
Axis along which logical OR is performed
out : ndarray, optional
Output to existing array instead of creating new one, must have
same shape as expected output
Returns
-------
any : bool, ndarray
Returns a single bool if `axis` is ``None``; otherwise,
returns `ndarray`
"""
return N.ndarray.any(self, axis, out, keepdims=True)._collapse(axis)
def all(self, axis=None, out=None):
"""
Test whether all matrix elements along a given axis evaluate to True.
Parameters
----------
See `numpy.all` for complete descriptions
See Also
--------
numpy.all
Notes
-----
This is the same as `ndarray.all`, but it returns a `matrix` object.
Examples
--------
>>> x = np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> y = x[0]; y
matrix([[0, 1, 2, 3]])
>>> (x == y)
matrix([[ True, True, True, True],
[False, False, False, False],
[False, False, False, False]])
>>> (x == y).all()
False
>>> (x == y).all(0)
matrix([[False, False, False, False]])
>>> (x == y).all(1)
matrix([[ True],
[False],
[False]])
"""
return N.ndarray.all(self, axis, out, keepdims=True)._collapse(axis)
def max(self, axis=None, out=None):
"""
Return the maximum value along an axis.
Parameters
----------
See `amax` for complete descriptions
See Also
--------
amax, ndarray.max
Notes
-----
This is the same as `ndarray.max`, but returns a `matrix` object
where `ndarray.max` would return an ndarray.
Examples
--------
>>> x = np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> x.max()
11
>>> x.max(0)
matrix([[ 8, 9, 10, 11]])
>>> x.max(1)
matrix([[ 3],
[ 7],
[11]])
"""
return N.ndarray.max(self, axis, out, keepdims=True)._collapse(axis)
def argmax(self, axis=None, out=None):
"""
Indexes of the maximum values along an axis.
Return the indexes of the first occurrences of the maximum values
along the specified axis. If axis is None, the index is for the
flattened matrix.
Parameters
----------
See `numpy.argmax` for complete descriptions
See Also
--------
numpy.argmax
Notes
-----
This is the same as `ndarray.argmax`, but returns a `matrix` object
where `ndarray.argmax` would return an `ndarray`.
Examples
--------
>>> x = np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> x.argmax()
11
>>> x.argmax(0)
matrix([[2, 2, 2, 2]])
>>> x.argmax(1)
matrix([[3],
[3],
[3]])
"""
return N.ndarray.argmax(self, axis, out)._align(axis)
def min(self, axis=None, out=None):
"""
Return the minimum value along an axis.
Parameters
----------
See `amin` for complete descriptions.
See Also
--------
amin, ndarray.min
Notes
-----
This is the same as `ndarray.min`, but returns a `matrix` object
where `ndarray.min` would return an ndarray.
Examples
--------
>>> x = -np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0, -1, -2, -3],
[ -4, -5, -6, -7],
[ -8, -9, -10, -11]])
>>> x.min()
-11
>>> x.min(0)
matrix([[ -8, -9, -10, -11]])
>>> x.min(1)
matrix([[ -3],
[ -7],
[-11]])
"""
return N.ndarray.min(self, axis, out, keepdims=True)._collapse(axis)
def argmin(self, axis=None, out=None):
"""
Indexes of the minimum values along an axis.
Return the indexes of the first occurrences of the minimum values
along the specified axis. If axis is None, the index is for the
flattened matrix.
Parameters
----------
See `numpy.argmin` for complete descriptions.
See Also
--------
numpy.argmin
Notes
-----
This is the same as `ndarray.argmin`, but returns a `matrix` object
where `ndarray.argmin` would return an `ndarray`.
Examples
--------
>>> x = -np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0, -1, -2, -3],
[ -4, -5, -6, -7],
[ -8, -9, -10, -11]])
>>> x.argmin()
11
>>> x.argmin(0)
matrix([[2, 2, 2, 2]])
>>> x.argmin(1)
matrix([[3],
[3],
[3]])
"""
return N.ndarray.argmin(self, axis, out)._align(axis)
def ptp(self, axis=None, out=None):
"""
Peak-to-peak (maximum - minimum) value along the given axis.
Refer to `numpy.ptp` for full documentation.
See Also
--------
numpy.ptp
Notes
-----
Same as `ndarray.ptp`, except, where that would return an `ndarray` object,
this returns a `matrix` object.
Examples
--------
>>> x = np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> x.ptp()
11
>>> x.ptp(0)
matrix([[8, 8, 8, 8]])
>>> x.ptp(1)
matrix([[3],
[3],
[3]])
"""
return N.ndarray.ptp(self, axis, out)._align(axis)
def getI(self):
"""
Returns the (multiplicative) inverse of invertible `self`.
Parameters
----------
None
Returns
-------
ret : matrix object
If `self` is non-singular, `ret` is such that ``ret * self`` ==
``self * ret`` == ``np.matrix(np.eye(self[0,:].size)`` all return
``True``.
Raises
------
numpy.linalg.LinAlgError: Singular matrix
If `self` is singular.
See Also
--------
linalg.inv
Examples
--------
>>> m = np.matrix('[1, 2; 3, 4]'); m
matrix([[1, 2],
[3, 4]])
>>> m.getI()
matrix([[-2. , 1. ],
[ 1.5, -0.5]])
>>> m.getI() * m
matrix([[ 1., 0.],
[ 0., 1.]])
"""
M, N = self.shape
if M == N:
from numpy.dual import inv as func
else:
from numpy.dual import pinv as func
return asmatrix(func(self))
def getA(self):
"""
Return `self` as an `ndarray` object.
Equivalent to ``np.asarray(self)``.
Parameters
----------
None
Returns
-------
ret : ndarray
`self` as an `ndarray`
Examples
--------
>>> x = np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> x.getA()
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
"""
return self.__array__()
def getA1(self):
"""
Return `self` as a flattened `ndarray`.
Equivalent to ``np.asarray(x).ravel()``
Parameters
----------
None
Returns
-------
ret : ndarray
`self`, 1-D, as an `ndarray`
Examples
--------
>>> x = np.matrix(np.arange(12).reshape((3,4))); x
matrix([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
>>> x.getA1()
array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])
"""
return self.__array__().ravel()
def ravel(self, order='C'):
"""
Return a flattened matrix.
Refer to `numpy.ravel` for more documentation.
Parameters
----------
order : {'C', 'F', 'A', 'K'}, optional
The elements of `m` are read using this index order. 'C' means to
index the elements in C-like order, with the last axis index
changing fastest, back to the first axis index changing slowest.
'F' means to index the elements in 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 axis indexing. 'A' means to read the elements in Fortran-like
index order if `m` 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
-------
ret : matrix
Return the matrix flattened to shape `(1, N)` where `N`
is the number of elements in the original matrix.
A copy is made only if necessary.
See Also
--------
matrix.flatten : returns a similar output matrix but always a copy
matrix.flat : a flat iterator on the array.
numpy.ravel : related function which returns an ndarray
"""
return N.ndarray.ravel(self, order=order)
def getT(self):
"""
Returns the transpose of the matrix.
Does *not* conjugate! For the complex conjugate transpose, use ``.H``.
Parameters
----------
None
Returns
-------
ret : matrix object
The (non-conjugated) transpose of the matrix.
See Also
--------
transpose, getH
Examples
--------
>>> m = np.matrix('[1, 2; 3, 4]')
>>> m
matrix([[1, 2],
[3, 4]])
>>> m.getT()
matrix([[1, 3],
[2, 4]])
"""
return self.transpose()
def getH(self):
"""
Returns the (complex) conjugate transpose of `self`.
Equivalent to ``np.transpose(self)`` if `self` is real-valued.
Parameters
----------
None
Returns
-------
ret : matrix object
complex conjugate transpose of `self`
Examples
--------
>>> x = np.matrix(np.arange(12).reshape((3,4)))
>>> z = x - 1j*x; z
matrix([[ 0. +0.j, 1. -1.j, 2. -2.j, 3. -3.j],
[ 4. -4.j, 5. -5.j, 6. -6.j, 7. -7.j],
[ 8. -8.j, 9. -9.j, 10.-10.j, 11.-11.j]])
>>> z.getH()
matrix([[ 0. +0.j, 4. +4.j, 8. +8.j],
[ 1. +1.j, 5. +5.j, 9. +9.j],
[ 2. +2.j, 6. +6.j, 10.+10.j],
[ 3. +3.j, 7. +7.j, 11.+11.j]])
"""
if issubclass(self.dtype.type, N.complexfloating):
return self.transpose().conjugate()
else:
return self.transpose()
T = property(getT, None)
A = property(getA, None)
A1 = property(getA1, None)
H = property(getH, None)
I = property(getI, None)
def _from_string(str, gdict, ldict):
rows = str.split(';')
rowtup = []
for row in rows:
trow = row.split(',')
newrow = []
for x in trow:
newrow.extend(x.split())
trow = newrow
coltup = []
for col in trow:
col = col.strip()
try:
thismat = ldict[col]
except KeyError:
try:
thismat = gdict[col]
except KeyError:
raise KeyError("%s not found" % (col,))
coltup.append(thismat)
rowtup.append(concatenate(coltup, axis=-1))
return concatenate(rowtup, axis=0)
def bmat(obj, ldict=None, gdict=None):
"""
Build a matrix object from a string, nested sequence, or array.
Parameters
----------
obj : str or array_like
Input data. If a string, variables in the current scope may be
referenced by name.
ldict : dict, optional
A dictionary that replaces local operands in current frame.
Ignored if `obj` is not a string or `gdict` is `None`.
gdict : dict, optional
A dictionary that replaces global operands in current frame.
Ignored if `obj` is not a string.
Returns
-------
out : matrix
Returns a matrix object, which is a specialized 2-D array.
See Also
--------
block :
A generalization of this function for N-d arrays, that returns normal
ndarrays.
Examples
--------
>>> A = np.mat('1 1; 1 1')
>>> B = np.mat('2 2; 2 2')
>>> C = np.mat('3 4; 5 6')
>>> D = np.mat('7 8; 9 0')
All the following expressions construct the same block matrix:
>>> np.bmat([[A, B], [C, D]])
matrix([[1, 1, 2, 2],
[1, 1, 2, 2],
[3, 4, 7, 8],
[5, 6, 9, 0]])
>>> np.bmat(np.r_[np.c_[A, B], np.c_[C, D]])
matrix([[1, 1, 2, 2],
[1, 1, 2, 2],
[3, 4, 7, 8],
[5, 6, 9, 0]])
>>> np.bmat('A,B; C,D')
matrix([[1, 1, 2, 2],
[1, 1, 2, 2],
[3, 4, 7, 8],
[5, 6, 9, 0]])
"""
if isinstance(obj, str):
if gdict is None:
# get previous frame
frame = sys._getframe().f_back
glob_dict = frame.f_globals
loc_dict = frame.f_locals
else:
glob_dict = gdict
loc_dict = ldict
return matrix(_from_string(obj, glob_dict, loc_dict))
if isinstance(obj, (tuple, list)):
# [[A,B],[C,D]]
arr_rows = []
for row in obj:
if isinstance(row, N.ndarray): # not 2-d
return matrix(concatenate(obj, axis=-1))
else:
arr_rows.append(concatenate(row, axis=-1))
return matrix(concatenate(arr_rows, axis=0))
if isinstance(obj, N.ndarray):
return matrix(obj)
mat = asmatrix
| 32,973 | 26.524207 | 89 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/matrixlib/setup.py
|
#!/usr/bin/env python
from __future__ import division, print_function
import os
def configuration(parent_package='', top_path=None):
from numpy.distutils.misc_util import Configuration
config = Configuration('matrixlib', parent_package, top_path)
config.add_data_dir('tests')
return config
if __name__ == "__main__":
from numpy.distutils.core import setup
config = configuration(top_path='').todict()
setup(**config)
| 448 | 27.0625 | 65 |
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/matrixlib/__init__.py
|
"""Sub-package containing the matrix class and related functions.
"""
from __future__ import division, absolute_import, print_function
from .defmatrix import *
__all__ = defmatrix.__all__
from numpy.testing import _numpy_tester
test = _numpy_tester().test
bench = _numpy_tester().bench
| 290 | 21.384615 | 65 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/matrixlib/tests/test_regression.py
|
from __future__ import division, absolute_import, print_function
import numpy as np
from numpy.testing import (
run_module_suite, assert_, assert_equal, assert_raises
)
class TestRegression(object):
def test_kron_matrix(self):
# Ticket #71
x = np.matrix('[1 0; 1 0]')
assert_equal(type(np.kron(x, x)), type(x))
def test_matrix_properties(self):
# Ticket #125
a = np.matrix([1.0], dtype=float)
assert_(type(a.real) is np.matrix)
assert_(type(a.imag) is np.matrix)
c, d = np.matrix([0.0]).nonzero()
assert_(type(c) is np.ndarray)
assert_(type(d) is np.ndarray)
def test_matrix_multiply_by_1d_vector(self):
# Ticket #473
def mul():
np.mat(np.eye(2))*np.ones(2)
assert_raises(ValueError, mul)
def test_matrix_std_argmax(self):
# Ticket #83
x = np.asmatrix(np.random.uniform(0, 1, (3, 3)))
assert_equal(x.std().shape, ())
assert_equal(x.argmax().shape, ())
if __name__ == "__main__":
run_module_suite()
| 1,082 | 26.769231 | 64 |
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/matrixlib/tests/test_defmatrix.py
|
from __future__ import division, absolute_import, print_function
import collections
import numpy as np
from numpy import matrix, asmatrix, bmat
from numpy.testing import (
run_module_suite, assert_, assert_equal, assert_almost_equal,
assert_array_equal, assert_array_almost_equal, assert_raises
)
from numpy.matrixlib.defmatrix import matrix_power
from numpy.matrixlib import mat
class TestCtor(object):
def test_basic(self):
A = np.array([[1, 2], [3, 4]])
mA = matrix(A)
assert_(np.all(mA.A == A))
B = bmat("A,A;A,A")
C = bmat([[A, A], [A, A]])
D = np.array([[1, 2, 1, 2],
[3, 4, 3, 4],
[1, 2, 1, 2],
[3, 4, 3, 4]])
assert_(np.all(B.A == D))
assert_(np.all(C.A == D))
E = np.array([[5, 6], [7, 8]])
AEresult = matrix([[1, 2, 5, 6], [3, 4, 7, 8]])
assert_(np.all(bmat([A, E]) == AEresult))
vec = np.arange(5)
mvec = matrix(vec)
assert_(mvec.shape == (1, 5))
def test_exceptions(self):
# Check for ValueError when called with invalid string data.
assert_raises(ValueError, matrix, "invalid")
def test_bmat_nondefault_str(self):
A = np.array([[1, 2], [3, 4]])
B = np.array([[5, 6], [7, 8]])
Aresult = np.array([[1, 2, 1, 2],
[3, 4, 3, 4],
[1, 2, 1, 2],
[3, 4, 3, 4]])
mixresult = np.array([[1, 2, 5, 6],
[3, 4, 7, 8],
[5, 6, 1, 2],
[7, 8, 3, 4]])
assert_(np.all(bmat("A,A;A,A") == Aresult))
assert_(np.all(bmat("A,A;A,A", ldict={'A':B}) == Aresult))
assert_raises(TypeError, bmat, "A,A;A,A", gdict={'A':B})
assert_(
np.all(bmat("A,A;A,A", ldict={'A':A}, gdict={'A':B}) == Aresult))
b2 = bmat("A,B;C,D", ldict={'A':A,'B':B}, gdict={'C':B,'D':A})
assert_(np.all(b2 == mixresult))
class TestProperties(object):
def test_sum(self):
"""Test whether matrix.sum(axis=1) preserves orientation.
Fails in NumPy <= 0.9.6.2127.
"""
M = matrix([[1, 2, 0, 0],
[3, 4, 0, 0],
[1, 2, 1, 2],
[3, 4, 3, 4]])
sum0 = matrix([8, 12, 4, 6])
sum1 = matrix([3, 7, 6, 14]).T
sumall = 30
assert_array_equal(sum0, M.sum(axis=0))
assert_array_equal(sum1, M.sum(axis=1))
assert_equal(sumall, M.sum())
assert_array_equal(sum0, np.sum(M, axis=0))
assert_array_equal(sum1, np.sum(M, axis=1))
assert_equal(sumall, np.sum(M))
def test_prod(self):
x = matrix([[1, 2, 3], [4, 5, 6]])
assert_equal(x.prod(), 720)
assert_equal(x.prod(0), matrix([[4, 10, 18]]))
assert_equal(x.prod(1), matrix([[6], [120]]))
assert_equal(np.prod(x), 720)
assert_equal(np.prod(x, axis=0), matrix([[4, 10, 18]]))
assert_equal(np.prod(x, axis=1), matrix([[6], [120]]))
y = matrix([0, 1, 3])
assert_(y.prod() == 0)
def test_max(self):
x = matrix([[1, 2, 3], [4, 5, 6]])
assert_equal(x.max(), 6)
assert_equal(x.max(0), matrix([[4, 5, 6]]))
assert_equal(x.max(1), matrix([[3], [6]]))
assert_equal(np.max(x), 6)
assert_equal(np.max(x, axis=0), matrix([[4, 5, 6]]))
assert_equal(np.max(x, axis=1), matrix([[3], [6]]))
def test_min(self):
x = matrix([[1, 2, 3], [4, 5, 6]])
assert_equal(x.min(), 1)
assert_equal(x.min(0), matrix([[1, 2, 3]]))
assert_equal(x.min(1), matrix([[1], [4]]))
assert_equal(np.min(x), 1)
assert_equal(np.min(x, axis=0), matrix([[1, 2, 3]]))
assert_equal(np.min(x, axis=1), matrix([[1], [4]]))
def test_ptp(self):
x = np.arange(4).reshape((2, 2))
assert_(x.ptp() == 3)
assert_(np.all(x.ptp(0) == np.array([2, 2])))
assert_(np.all(x.ptp(1) == np.array([1, 1])))
def test_var(self):
x = np.arange(9).reshape((3, 3))
mx = x.view(np.matrix)
assert_equal(x.var(ddof=0), mx.var(ddof=0))
assert_equal(x.var(ddof=1), mx.var(ddof=1))
def test_basic(self):
import numpy.linalg as linalg
A = np.array([[1., 2.],
[3., 4.]])
mA = matrix(A)
assert_(np.allclose(linalg.inv(A), mA.I))
assert_(np.all(np.array(np.transpose(A) == mA.T)))
assert_(np.all(np.array(np.transpose(A) == mA.H)))
assert_(np.all(A == mA.A))
B = A + 2j*A
mB = matrix(B)
assert_(np.allclose(linalg.inv(B), mB.I))
assert_(np.all(np.array(np.transpose(B) == mB.T)))
assert_(np.all(np.array(np.transpose(B).conj() == mB.H)))
def test_pinv(self):
x = matrix(np.arange(6).reshape(2, 3))
xpinv = matrix([[-0.77777778, 0.27777778],
[-0.11111111, 0.11111111],
[ 0.55555556, -0.05555556]])
assert_almost_equal(x.I, xpinv)
def test_comparisons(self):
A = np.arange(100).reshape(10, 10)
mA = matrix(A)
mB = matrix(A) + 0.1
assert_(np.all(mB == A+0.1))
assert_(np.all(mB == matrix(A+0.1)))
assert_(not np.any(mB == matrix(A-0.1)))
assert_(np.all(mA < mB))
assert_(np.all(mA <= mB))
assert_(np.all(mA <= mA))
assert_(not np.any(mA < mA))
assert_(not np.any(mB < mA))
assert_(np.all(mB >= mA))
assert_(np.all(mB >= mB))
assert_(not np.any(mB > mB))
assert_(np.all(mA == mA))
assert_(not np.any(mA == mB))
assert_(np.all(mB != mA))
assert_(not np.all(abs(mA) > 0))
assert_(np.all(abs(mB > 0)))
def test_asmatrix(self):
A = np.arange(100).reshape(10, 10)
mA = asmatrix(A)
A[0, 0] = -10
assert_(A[0, 0] == mA[0, 0])
def test_noaxis(self):
A = matrix([[1, 0], [0, 1]])
assert_(A.sum() == matrix(2))
assert_(A.mean() == matrix(0.5))
def test_repr(self):
A = matrix([[1, 0], [0, 1]])
assert_(repr(A) == "matrix([[1, 0],\n [0, 1]])")
def test_make_bool_matrix_from_str(self):
A = matrix('True; True; False')
B = matrix([[True], [True], [False]])
assert_array_equal(A, B)
class TestCasting(object):
def test_basic(self):
A = np.arange(100).reshape(10, 10)
mA = matrix(A)
mB = mA.copy()
O = np.ones((10, 10), np.float64) * 0.1
mB = mB + O
assert_(mB.dtype.type == np.float64)
assert_(np.all(mA != mB))
assert_(np.all(mB == mA+0.1))
mC = mA.copy()
O = np.ones((10, 10), np.complex128)
mC = mC * O
assert_(mC.dtype.type == np.complex128)
assert_(np.all(mA != mB))
class TestAlgebra(object):
def test_basic(self):
import numpy.linalg as linalg
A = np.array([[1., 2.], [3., 4.]])
mA = matrix(A)
B = np.identity(2)
for i in range(6):
assert_(np.allclose((mA ** i).A, B))
B = np.dot(B, A)
Ainv = linalg.inv(A)
B = np.identity(2)
for i in range(6):
assert_(np.allclose((mA ** -i).A, B))
B = np.dot(B, Ainv)
assert_(np.allclose((mA * mA).A, np.dot(A, A)))
assert_(np.allclose((mA + mA).A, (A + A)))
assert_(np.allclose((3*mA).A, (3*A)))
mA2 = matrix(A)
mA2 *= 3
assert_(np.allclose(mA2.A, 3*A))
def test_pow(self):
"""Test raising a matrix to an integer power works as expected."""
m = matrix("1. 2.; 3. 4.")
m2 = m.copy()
m2 **= 2
mi = m.copy()
mi **= -1
m4 = m2.copy()
m4 **= 2
assert_array_almost_equal(m2, m**2)
assert_array_almost_equal(m4, np.dot(m2, m2))
assert_array_almost_equal(np.dot(mi, m), np.eye(2))
def test_scalar_type_pow(self):
m = matrix([[1, 2], [3, 4]])
for scalar_t in [np.int8, np.uint8]:
two = scalar_t(2)
assert_array_almost_equal(m ** 2, m ** two)
def test_notimplemented(self):
'''Check that 'not implemented' operations produce a failure.'''
A = matrix([[1., 2.],
[3., 4.]])
# __rpow__
try:
1.0**A
except TypeError:
pass
else:
self.fail("matrix.__rpow__ doesn't raise a TypeError")
# __mul__ with something not a list, ndarray, tuple, or scalar
try:
A*object()
except TypeError:
pass
else:
self.fail("matrix.__mul__ with non-numeric object doesn't raise"
"a TypeError")
class TestMatrixReturn(object):
def test_instance_methods(self):
a = matrix([1.0], dtype='f8')
methodargs = {
'astype': ('intc',),
'clip': (0.0, 1.0),
'compress': ([1],),
'repeat': (1,),
'reshape': (1,),
'swapaxes': (0, 0),
'dot': np.array([1.0]),
}
excluded_methods = [
'argmin', 'choose', 'dump', 'dumps', 'fill', 'getfield',
'getA', 'getA1', 'item', 'nonzero', 'put', 'putmask', 'resize',
'searchsorted', 'setflags', 'setfield', 'sort',
'partition', 'argpartition',
'take', 'tofile', 'tolist', 'tostring', 'tobytes', 'all', 'any',
'sum', 'argmax', 'argmin', 'min', 'max', 'mean', 'var', 'ptp',
'prod', 'std', 'ctypes', 'itemset',
]
for attrib in dir(a):
if attrib.startswith('_') or attrib in excluded_methods:
continue
f = getattr(a, attrib)
if isinstance(f, collections.Callable):
# reset contents of a
a.astype('f8')
a.fill(1.0)
if attrib in methodargs:
args = methodargs[attrib]
else:
args = ()
b = f(*args)
assert_(type(b) is matrix, "%s" % attrib)
assert_(type(a.real) is matrix)
assert_(type(a.imag) is matrix)
c, d = matrix([0.0]).nonzero()
assert_(type(c) is np.ndarray)
assert_(type(d) is np.ndarray)
class TestIndexing(object):
def test_basic(self):
x = asmatrix(np.zeros((3, 2), float))
y = np.zeros((3, 1), float)
y[:, 0] = [0.8, 0.2, 0.3]
x[:, 1] = y > 0.5
assert_equal(x, [[0, 1], [0, 0], [0, 0]])
class TestNewScalarIndexing(object):
a = matrix([[1, 2], [3, 4]])
def test_dimesions(self):
a = self.a
x = a[0]
assert_equal(x.ndim, 2)
def test_array_from_matrix_list(self):
a = self.a
x = np.array([a, a])
assert_equal(x.shape, [2, 2, 2])
def test_array_to_list(self):
a = self.a
assert_equal(a.tolist(), [[1, 2], [3, 4]])
def test_fancy_indexing(self):
a = self.a
x = a[1, [0, 1, 0]]
assert_(isinstance(x, matrix))
assert_equal(x, matrix([[3, 4, 3]]))
x = a[[1, 0]]
assert_(isinstance(x, matrix))
assert_equal(x, matrix([[3, 4], [1, 2]]))
x = a[[[1], [0]], [[1, 0], [0, 1]]]
assert_(isinstance(x, matrix))
assert_equal(x, matrix([[4, 3], [1, 2]]))
def test_matrix_element(self):
x = matrix([[1, 2, 3], [4, 5, 6]])
assert_equal(x[0][0], matrix([[1, 2, 3]]))
assert_equal(x[0][0].shape, (1, 3))
assert_equal(x[0].shape, (1, 3))
assert_equal(x[:, 0].shape, (2, 1))
x = matrix(0)
assert_equal(x[0, 0], 0)
assert_equal(x[0], 0)
assert_equal(x[:, 0].shape, x.shape)
def test_scalar_indexing(self):
x = asmatrix(np.zeros((3, 2), float))
assert_equal(x[0, 0], x[0][0])
def test_row_column_indexing(self):
x = asmatrix(np.eye(2))
assert_array_equal(x[0,:], [[1, 0]])
assert_array_equal(x[1,:], [[0, 1]])
assert_array_equal(x[:, 0], [[1], [0]])
assert_array_equal(x[:, 1], [[0], [1]])
def test_boolean_indexing(self):
A = np.arange(6)
A.shape = (3, 2)
x = asmatrix(A)
assert_array_equal(x[:, np.array([True, False])], x[:, 0])
assert_array_equal(x[np.array([True, False, False]),:], x[0,:])
def test_list_indexing(self):
A = np.arange(6)
A.shape = (3, 2)
x = asmatrix(A)
assert_array_equal(x[:, [1, 0]], x[:, ::-1])
assert_array_equal(x[[2, 1, 0],:], x[::-1,:])
class TestPower(object):
def test_returntype(self):
a = np.array([[0, 1], [0, 0]])
assert_(type(matrix_power(a, 2)) is np.ndarray)
a = mat(a)
assert_(type(matrix_power(a, 2)) is matrix)
def test_list(self):
assert_array_equal(matrix_power([[0, 1], [0, 0]], 2), [[0, 0], [0, 0]])
class TestShape(object):
a = np.array([[1], [2]])
m = matrix([[1], [2]])
def test_shape(self):
assert_equal(self.a.shape, (2, 1))
assert_equal(self.m.shape, (2, 1))
def test_numpy_ravel(self):
assert_equal(np.ravel(self.a).shape, (2,))
assert_equal(np.ravel(self.m).shape, (2,))
def test_member_ravel(self):
assert_equal(self.a.ravel().shape, (2,))
assert_equal(self.m.ravel().shape, (1, 2))
def test_member_flatten(self):
assert_equal(self.a.flatten().shape, (2,))
assert_equal(self.m.flatten().shape, (1, 2))
def test_numpy_ravel_order(self):
x = np.array([[1, 2, 3], [4, 5, 6]])
assert_equal(np.ravel(x), [1, 2, 3, 4, 5, 6])
assert_equal(np.ravel(x, order='F'), [1, 4, 2, 5, 3, 6])
assert_equal(np.ravel(x.T), [1, 4, 2, 5, 3, 6])
assert_equal(np.ravel(x.T, order='A'), [1, 2, 3, 4, 5, 6])
x = matrix([[1, 2, 3], [4, 5, 6]])
assert_equal(np.ravel(x), [1, 2, 3, 4, 5, 6])
assert_equal(np.ravel(x, order='F'), [1, 4, 2, 5, 3, 6])
assert_equal(np.ravel(x.T), [1, 4, 2, 5, 3, 6])
assert_equal(np.ravel(x.T, order='A'), [1, 2, 3, 4, 5, 6])
def test_matrix_ravel_order(self):
x = matrix([[1, 2, 3], [4, 5, 6]])
assert_equal(x.ravel(), [[1, 2, 3, 4, 5, 6]])
assert_equal(x.ravel(order='F'), [[1, 4, 2, 5, 3, 6]])
assert_equal(x.T.ravel(), [[1, 4, 2, 5, 3, 6]])
assert_equal(x.T.ravel(order='A'), [[1, 2, 3, 4, 5, 6]])
def test_array_memory_sharing(self):
assert_(np.may_share_memory(self.a, self.a.ravel()))
assert_(not np.may_share_memory(self.a, self.a.flatten()))
def test_matrix_memory_sharing(self):
assert_(np.may_share_memory(self.m, self.m.ravel()))
assert_(not np.may_share_memory(self.m, self.m.flatten()))
if __name__ == "__main__":
run_module_suite()
| 15,078 | 31.780435 | 79 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/matrixlib/tests/test_numeric.py
|
from __future__ import division, absolute_import, print_function
import numpy as np
from numpy.testing import assert_equal, run_module_suite
class TestDot(object):
def test_matscalar(self):
b1 = np.matrix(np.ones((3, 3), dtype=complex))
assert_equal(b1*1.0, b1)
def test_diagonal():
b1 = np.matrix([[1,2],[3,4]])
diag_b1 = np.matrix([[1, 4]])
array_b1 = np.array([1, 4])
assert_equal(b1.diagonal(), diag_b1)
assert_equal(np.diagonal(b1), array_b1)
assert_equal(np.diag(b1), array_b1)
if __name__ == "__main__":
run_module_suite()
| 585 | 23.416667 | 64 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/matrixlib/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/matrixlib/tests/test_multiarray.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
)
class TestView(object):
def test_type(self):
x = np.array([1, 2, 3])
assert_(isinstance(x.view(np.matrix), np.matrix))
def test_keywords(self):
x = np.array([(1, 2)], dtype=[('a', np.int8), ('b', np.int8)])
# We must be specific about the endianness here:
y = x.view(dtype='<i2', type=np.matrix)
assert_array_equal(y, [[513]])
assert_(isinstance(y, np.matrix))
assert_equal(y.dtype, np.dtype('<i2'))
if __name__ == "__main__":
run_module_suite()
| 705 | 28.416667 | 70 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/tests/test_warnings.py
|
"""
Tests which scan for certain occurrences in the code, they may not find
all of these occurrences but should catch almost all.
"""
from __future__ import division, absolute_import, print_function
import sys
if sys.version_info >= (3, 4):
from pathlib import Path
import ast
import tokenize
import numpy
from numpy.testing import run_module_suite, dec
class ParseCall(ast.NodeVisitor):
def __init__(self):
self.ls = []
def visit_Attribute(self, node):
ast.NodeVisitor.generic_visit(self, node)
self.ls.append(node.attr)
def visit_Name(self, node):
self.ls.append(node.id)
class FindFuncs(ast.NodeVisitor):
def __init__(self, filename):
super().__init__()
self.__filename = filename
def visit_Call(self, node):
p = ParseCall()
p.visit(node.func)
ast.NodeVisitor.generic_visit(self, node)
if p.ls[-1] == 'simplefilter' or p.ls[-1] == 'filterwarnings':
if node.args[0].s == "ignore":
raise AssertionError(
"ignore filter should not be used; found in "
"{} on line {}".format(self.__filename, node.lineno))
if p.ls[-1] == 'warn' and (
len(p.ls) == 1 or p.ls[-2] == 'warnings'):
if "testing/tests/test_warnings.py" is self.__filename:
# This file
return
# See if stacklevel exists:
if len(node.args) == 3:
return
args = {kw.arg for kw in node.keywords}
if "stacklevel" in args:
return
raise AssertionError(
"warnings should have an appropriate stacklevel; found in "
"{} on line {}".format(self.__filename, node.lineno))
@dec.slow
def test_warning_calls():
# combined "ignore" and stacklevel error
base = Path(numpy.__file__).parent
for path in base.rglob("*.py"):
if base / "testing" in path.parents:
continue
if path == base / "__init__.py":
continue
if path == base / "random" / "__init__.py":
continue
# use tokenize to auto-detect encoding on systems where no
# default encoding is defined (e.g. LANG='C')
with tokenize.open(str(path)) as file:
tree = ast.parse(file.read())
FindFuncs(path).visit(tree)
if __name__ == "__main__":
run_module_suite()
| 2,686 | 30.611765 | 79 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/tests/test_matlib.py
|
from __future__ import division, absolute_import, print_function
import numpy as np
import numpy.matlib
from numpy.testing import assert_array_equal, assert_, run_module_suite
def test_empty():
x = numpy.matlib.empty((2,))
assert_(isinstance(x, np.matrix))
assert_(x.shape, (1, 2))
def test_ones():
assert_array_equal(numpy.matlib.ones((2, 3)),
np.matrix([[ 1., 1., 1.],
[ 1., 1., 1.]]))
assert_array_equal(numpy.matlib.ones(2), np.matrix([[ 1., 1.]]))
def test_zeros():
assert_array_equal(numpy.matlib.zeros((2, 3)),
np.matrix([[ 0., 0., 0.],
[ 0., 0., 0.]]))
assert_array_equal(numpy.matlib.zeros(2), np.matrix([[ 0., 0.]]))
def test_identity():
x = numpy.matlib.identity(2, dtype=int)
assert_array_equal(x, np.matrix([[1, 0], [0, 1]]))
def test_eye():
xc = numpy.matlib.eye(3, k=1, dtype=int)
assert_array_equal(xc, np.matrix([[ 0, 1, 0],
[ 0, 0, 1],
[ 0, 0, 0]]))
assert xc.flags.c_contiguous
assert not xc.flags.f_contiguous
xf = numpy.matlib.eye(3, 4, dtype=int, order='F')
assert_array_equal(xf, np.matrix([[ 1, 0, 0, 0],
[ 0, 1, 0, 0],
[ 0, 0, 1, 0]]))
assert not xf.flags.c_contiguous
assert xf.flags.f_contiguous
def test_rand():
x = numpy.matlib.rand(3)
# check matrix type, array would have shape (3,)
assert_(x.ndim == 2)
def test_randn():
x = np.matlib.randn(3)
# check matrix type, array would have shape (3,)
assert_(x.ndim == 2)
def test_repmat():
a1 = np.arange(4)
x = numpy.matlib.repmat(a1, 2, 2)
y = np.array([[0, 1, 2, 3, 0, 1, 2, 3],
[0, 1, 2, 3, 0, 1, 2, 3]])
assert_array_equal(x, y)
if __name__ == "__main__":
run_module_suite()
| 1,988 | 29.6 | 71 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/tests/test_ctypeslib.py
|
from __future__ import division, absolute_import, print_function
import sys
import numpy as np
from numpy.ctypeslib import ndpointer, load_library
from numpy.distutils.misc_util import get_shared_lib_extension
from numpy.testing import run_module_suite, assert_, assert_raises, dec
try:
cdll = None
if hasattr(sys, 'gettotalrefcount'):
try:
cdll = load_library('multiarray_d', np.core.multiarray.__file__)
except OSError:
pass
if cdll is None:
cdll = load_library('multiarray', np.core.multiarray.__file__)
_HAS_CTYPE = True
except ImportError:
_HAS_CTYPE = False
class TestLoadLibrary(object):
@dec.skipif(not _HAS_CTYPE,
"ctypes not available on this python installation")
@dec.knownfailureif(sys.platform ==
'cygwin', "This test is known to fail on cygwin")
def test_basic(self):
try:
# Should succeed
load_library('multiarray', np.core.multiarray.__file__)
except ImportError as e:
msg = ("ctypes is not available on this python: skipping the test"
" (import error was: %s)" % str(e))
print(msg)
@dec.skipif(not _HAS_CTYPE,
"ctypes not available on this python installation")
@dec.knownfailureif(sys.platform ==
'cygwin', "This test is known to fail on cygwin")
def test_basic2(self):
# Regression for #801: load_library with a full library name
# (including extension) does not work.
try:
try:
so = get_shared_lib_extension(is_python_ext=True)
# Should succeed
load_library('multiarray%s' % so, np.core.multiarray.__file__)
except ImportError:
print("No distutils available, skipping test.")
except ImportError as e:
msg = ("ctypes is not available on this python: skipping the test"
" (import error was: %s)" % str(e))
print(msg)
class TestNdpointer(object):
def test_dtype(self):
dt = np.intc
p = ndpointer(dtype=dt)
assert_(p.from_param(np.array([1], dt)))
dt = '<i4'
p = ndpointer(dtype=dt)
assert_(p.from_param(np.array([1], dt)))
dt = np.dtype('>i4')
p = ndpointer(dtype=dt)
p.from_param(np.array([1], dt))
assert_raises(TypeError, p.from_param,
np.array([1], dt.newbyteorder('swap')))
dtnames = ['x', 'y']
dtformats = [np.intc, np.float64]
dtdescr = {'names': dtnames, 'formats': dtformats}
dt = np.dtype(dtdescr)
p = ndpointer(dtype=dt)
assert_(p.from_param(np.zeros((10,), dt)))
samedt = np.dtype(dtdescr)
p = ndpointer(dtype=samedt)
assert_(p.from_param(np.zeros((10,), dt)))
dt2 = np.dtype(dtdescr, align=True)
if dt.itemsize != dt2.itemsize:
assert_raises(TypeError, p.from_param, np.zeros((10,), dt2))
else:
assert_(p.from_param(np.zeros((10,), dt2)))
def test_ndim(self):
p = ndpointer(ndim=0)
assert_(p.from_param(np.array(1)))
assert_raises(TypeError, p.from_param, np.array([1]))
p = ndpointer(ndim=1)
assert_raises(TypeError, p.from_param, np.array(1))
assert_(p.from_param(np.array([1])))
p = ndpointer(ndim=2)
assert_(p.from_param(np.array([[1]])))
def test_shape(self):
p = ndpointer(shape=(1, 2))
assert_(p.from_param(np.array([[1, 2]])))
assert_raises(TypeError, p.from_param, np.array([[1], [2]]))
p = ndpointer(shape=())
assert_(p.from_param(np.array(1)))
def test_flags(self):
x = np.array([[1, 2], [3, 4]], order='F')
p = ndpointer(flags='FORTRAN')
assert_(p.from_param(x))
p = ndpointer(flags='CONTIGUOUS')
assert_raises(TypeError, p.from_param, x)
p = ndpointer(flags=x.flags.num)
assert_(p.from_param(x))
assert_raises(TypeError, p.from_param, np.array([[1, 2], [3, 4]]))
def test_cache(self):
a1 = ndpointer(dtype=np.float64)
a2 = ndpointer(dtype=np.float64)
assert_(a1 == a2)
if __name__ == "__main__":
run_module_suite()
| 4,331 | 35.403361 | 78 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/tests/test_numpy_version.py
|
from __future__ import division, absolute_import, print_function
import re
import numpy as np
from numpy.testing import assert_, run_module_suite
def test_valid_numpy_version():
# Verify that the numpy version is a valid one (no .post suffix or other
# nonsense). See gh-6431 for an issue caused by an invalid version.
version_pattern = r"^[0-9]+\.[0-9]+\.[0-9]+(|a[0-9]|b[0-9]|rc[0-9])"
dev_suffix = r"(\.dev0\+([0-9a-f]{7}|Unknown))"
if np.version.release:
res = re.match(version_pattern, np.__version__)
else:
res = re.match(version_pattern + dev_suffix, np.__version__)
assert_(res is not None, np.__version__)
if __name__ == "__main__":
run_module_suite()
| 717 | 28.916667 | 76 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/tests/test_reloading.py
|
from __future__ import division, absolute_import, print_function
import sys
from numpy.testing import assert_raises, assert_, run_module_suite
if sys.version_info[:2] >= (3, 4):
from importlib import reload
else:
from imp import reload
def test_numpy_reloading():
# gh-7844. Also check that relevant globals retain their identity.
import numpy as np
import numpy._globals
_NoValue = np._NoValue
VisibleDeprecationWarning = np.VisibleDeprecationWarning
ModuleDeprecationWarning = np.ModuleDeprecationWarning
reload(np)
assert_(_NoValue is np._NoValue)
assert_(ModuleDeprecationWarning is np.ModuleDeprecationWarning)
assert_(VisibleDeprecationWarning is np.VisibleDeprecationWarning)
assert_raises(RuntimeError, reload, numpy._globals)
reload(np)
assert_(_NoValue is np._NoValue)
assert_(ModuleDeprecationWarning is np.ModuleDeprecationWarning)
assert_(VisibleDeprecationWarning is np.VisibleDeprecationWarning)
if __name__ == "__main__":
run_module_suite()
| 1,038 | 28.685714 | 70 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/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/tests/test_scripts.py
|
""" Test scripts
Test that we can run executable scripts that have been installed with numpy.
"""
from __future__ import division, print_function, absolute_import
import os
from os.path import join as pathjoin, isfile, dirname, basename
import sys
from subprocess import Popen, PIPE
import numpy as np
from numpy.compat.py3k import basestring
from nose.tools import assert_equal
from numpy.testing import assert_, dec
is_inplace = isfile(pathjoin(dirname(np.__file__), '..', 'setup.py'))
def run_command(cmd, check_code=True):
""" Run command sequence `cmd` returning exit code, stdout, stderr
Parameters
----------
cmd : str or sequence
string with command name or sequence of strings defining command
check_code : {True, False}, optional
If True, raise error for non-zero return code
Returns
-------
returncode : int
return code from execution of `cmd`
stdout : bytes (python 3) or str (python 2)
stdout from `cmd`
stderr : bytes (python 3) or str (python 2)
stderr from `cmd`
Raises
------
RuntimeError
If `check_code` is True, and return code !=0
"""
cmd = [cmd] if isinstance(cmd, basestring) else list(cmd)
if os.name == 'nt':
# Quote any arguments with spaces. The quotes delimit the arguments
# on Windows, and the arguments might be file paths with spaces.
# On Unix the list elements are each separate arguments.
cmd = ['"{0}"'.format(c) if ' ' in c else c for c in cmd]
proc = Popen(cmd, stdout=PIPE, stderr=PIPE)
stdout, stderr = proc.communicate()
if proc.poll() is None:
proc.terminate()
if check_code and proc.returncode != 0:
raise RuntimeError('\n'.join(
['Command "{0}" failed with',
'stdout', '------', '{1}', '',
'stderr', '------', '{2}']).format(cmd, stdout, stderr))
return proc.returncode, stdout, stderr
@dec.skipif(is_inplace)
def test_f2py():
# test that we can run f2py script
if sys.platform == 'win32':
exe_dir = dirname(sys.executable)
if exe_dir.endswith('Scripts'): # virtualenv
f2py_cmd = r"%s\f2py.py" % exe_dir
else:
f2py_cmd = r"%s\Scripts\f2py.py" % exe_dir
code, stdout, stderr = run_command([sys.executable, f2py_cmd, '-v'])
success = stdout.strip() == b'2'
assert_(success, "Warning: f2py not found in path")
else:
version = sys.version_info
major = str(version.major)
minor = str(version.minor)
f2py_cmds = ('f2py', 'f2py' + major, 'f2py' + major + '.' + minor)
success = False
for f2py_cmd in f2py_cmds:
try:
code, stdout, stderr = run_command([f2py_cmd, '-v'])
assert_equal(stdout.strip(), b'2')
success = True
break
except Exception:
pass
msg = "Warning: neither %s nor %s nor %s found in path" % f2py_cmds
assert_(success, msg)
| 3,059 | 31.903226 | 76 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/linalg/linalg.py
|
"""Lite version of scipy.linalg.
Notes
-----
This module is a lite version of the linalg.py module in SciPy which
contains high-level Python interface to the LAPACK library. The lite
version only accesses the following LAPACK functions: dgesv, zgesv,
dgeev, zgeev, dgesdd, zgesdd, dgelsd, zgelsd, dsyevd, zheevd, dgetrf,
zgetrf, dpotrf, zpotrf, dgeqrf, zgeqrf, zungqr, dorgqr.
"""
from __future__ import division, absolute_import, print_function
__all__ = ['matrix_power', 'solve', 'tensorsolve', 'tensorinv', 'inv',
'cholesky', 'eigvals', 'eigvalsh', 'pinv', 'slogdet', 'det',
'svd', 'eig', 'eigh', 'lstsq', 'norm', 'qr', 'cond', 'matrix_rank',
'LinAlgError', 'multi_dot']
import warnings
from numpy.core import (
array, asarray, zeros, empty, empty_like, intc, single, double,
csingle, cdouble, inexact, complexfloating, newaxis, ravel, all, Inf, dot,
add, multiply, sqrt, maximum, fastCopyAndTranspose, sum, isfinite, size,
finfo, errstate, geterrobj, longdouble, moveaxis, amin, amax, product, abs,
broadcast, atleast_2d, intp, asanyarray, object_, ones, matmul,
swapaxes, divide, count_nonzero
)
from numpy.core.multiarray import normalize_axis_index
from numpy.lib import triu, asfarray
from numpy.linalg import lapack_lite, _umath_linalg
from numpy.matrixlib.defmatrix import matrix_power
# For Python2/3 compatibility
_N = b'N'
_V = b'V'
_A = b'A'
_S = b'S'
_L = b'L'
fortran_int = intc
# Error object
class LinAlgError(Exception):
"""
Generic Python-exception-derived object raised by linalg functions.
General purpose exception class, derived from Python's exception.Exception
class, programmatically raised in linalg functions when a Linear
Algebra-related condition would prevent further correct execution of the
function.
Parameters
----------
None
Examples
--------
>>> from numpy import linalg as LA
>>> LA.inv(np.zeros((2,2)))
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "...linalg.py", line 350,
in inv return wrap(solve(a, identity(a.shape[0], dtype=a.dtype)))
File "...linalg.py", line 249,
in solve
raise LinAlgError('Singular matrix')
numpy.linalg.LinAlgError: Singular matrix
"""
pass
def _determine_error_states():
errobj = geterrobj()
bufsize = errobj[0]
with errstate(invalid='call', over='ignore',
divide='ignore', under='ignore'):
invalid_call_errmask = geterrobj()[1]
return [bufsize, invalid_call_errmask, None]
# Dealing with errors in _umath_linalg
_linalg_error_extobj = _determine_error_states()
del _determine_error_states
def _raise_linalgerror_singular(err, flag):
raise LinAlgError("Singular matrix")
def _raise_linalgerror_nonposdef(err, flag):
raise LinAlgError("Matrix is not positive definite")
def _raise_linalgerror_eigenvalues_nonconvergence(err, flag):
raise LinAlgError("Eigenvalues did not converge")
def _raise_linalgerror_svd_nonconvergence(err, flag):
raise LinAlgError("SVD did not converge")
def get_linalg_error_extobj(callback):
extobj = list(_linalg_error_extobj) # make a copy
extobj[2] = callback
return extobj
def _makearray(a):
new = asarray(a)
wrap = getattr(a, "__array_prepare__", new.__array_wrap__)
return new, wrap
def isComplexType(t):
return issubclass(t, complexfloating)
_real_types_map = {single : single,
double : double,
csingle : single,
cdouble : double}
_complex_types_map = {single : csingle,
double : cdouble,
csingle : csingle,
cdouble : cdouble}
def _realType(t, default=double):
return _real_types_map.get(t, default)
def _complexType(t, default=cdouble):
return _complex_types_map.get(t, default)
def _linalgRealType(t):
"""Cast the type t to either double or cdouble."""
return double
_complex_types_map = {single : csingle,
double : cdouble,
csingle : csingle,
cdouble : cdouble}
def _commonType(*arrays):
# in lite version, use higher precision (always double or cdouble)
result_type = single
is_complex = False
for a in arrays:
if issubclass(a.dtype.type, inexact):
if isComplexType(a.dtype.type):
is_complex = True
rt = _realType(a.dtype.type, default=None)
if rt is None:
# unsupported inexact scalar
raise TypeError("array type %s is unsupported in linalg" %
(a.dtype.name,))
else:
rt = double
if rt is double:
result_type = double
if is_complex:
t = cdouble
result_type = _complex_types_map[result_type]
else:
t = double
return t, result_type
# _fastCopyAndTranpose assumes the input is 2D (as all the calls in here are).
_fastCT = fastCopyAndTranspose
def _to_native_byte_order(*arrays):
ret = []
for arr in arrays:
if arr.dtype.byteorder not in ('=', '|'):
ret.append(asarray(arr, dtype=arr.dtype.newbyteorder('=')))
else:
ret.append(arr)
if len(ret) == 1:
return ret[0]
else:
return ret
def _fastCopyAndTranspose(type, *arrays):
cast_arrays = ()
for a in arrays:
if a.dtype.type is type:
cast_arrays = cast_arrays + (_fastCT(a),)
else:
cast_arrays = cast_arrays + (_fastCT(a.astype(type)),)
if len(cast_arrays) == 1:
return cast_arrays[0]
else:
return cast_arrays
def _assertRank2(*arrays):
for a in arrays:
if a.ndim != 2:
raise LinAlgError('%d-dimensional array given. Array must be '
'two-dimensional' % a.ndim)
def _assertRankAtLeast2(*arrays):
for a in arrays:
if a.ndim < 2:
raise LinAlgError('%d-dimensional array given. Array must be '
'at least two-dimensional' % a.ndim)
def _assertSquareness(*arrays):
for a in arrays:
if max(a.shape) != min(a.shape):
raise LinAlgError('Array must be square')
def _assertNdSquareness(*arrays):
for a in arrays:
if max(a.shape[-2:]) != min(a.shape[-2:]):
raise LinAlgError('Last 2 dimensions of the array must be square')
def _assertFinite(*arrays):
for a in arrays:
if not (isfinite(a).all()):
raise LinAlgError("Array must not contain infs or NaNs")
def _isEmpty2d(arr):
# check size first for efficiency
return arr.size == 0 and product(arr.shape[-2:]) == 0
def _assertNoEmpty2d(*arrays):
for a in arrays:
if _isEmpty2d(a):
raise LinAlgError("Arrays cannot be empty")
def transpose(a):
"""
Transpose each matrix in a stack of matrices.
Unlike np.transpose, this only swaps the last two axes, rather than all of
them
Parameters
----------
a : (...,M,N) array_like
Returns
-------
aT : (...,N,M) ndarray
"""
return swapaxes(a, -1, -2)
# Linear equations
def tensorsolve(a, b, axes=None):
"""
Solve the tensor equation ``a x = b`` for x.
It is assumed that all indices of `x` are summed over in the product,
together with the rightmost indices of `a`, as is done in, for example,
``tensordot(a, x, axes=b.ndim)``.
Parameters
----------
a : array_like
Coefficient tensor, of shape ``b.shape + Q``. `Q`, a tuple, equals
the shape of that sub-tensor of `a` consisting of the appropriate
number of its rightmost indices, and must be such that
``prod(Q) == prod(b.shape)`` (in which sense `a` is said to be
'square').
b : array_like
Right-hand tensor, which can be of any shape.
axes : tuple of ints, optional
Axes in `a` to reorder to the right, before inversion.
If None (default), no reordering is done.
Returns
-------
x : ndarray, shape Q
Raises
------
LinAlgError
If `a` is singular or not 'square' (in the above sense).
See Also
--------
numpy.tensordot, tensorinv, numpy.einsum
Examples
--------
>>> a = np.eye(2*3*4)
>>> a.shape = (2*3, 4, 2, 3, 4)
>>> b = np.random.randn(2*3, 4)
>>> x = np.linalg.tensorsolve(a, b)
>>> x.shape
(2, 3, 4)
>>> np.allclose(np.tensordot(a, x, axes=3), b)
True
"""
a, wrap = _makearray(a)
b = asarray(b)
an = a.ndim
if axes is not None:
allaxes = list(range(0, an))
for k in axes:
allaxes.remove(k)
allaxes.insert(an, k)
a = a.transpose(allaxes)
oldshape = a.shape[-(an-b.ndim):]
prod = 1
for k in oldshape:
prod *= k
a = a.reshape(-1, prod)
b = b.ravel()
res = wrap(solve(a, b))
res.shape = oldshape
return res
def solve(a, b):
"""
Solve a linear matrix equation, or system of linear scalar equations.
Computes the "exact" solution, `x`, of the well-determined, i.e., full
rank, linear matrix equation `ax = b`.
Parameters
----------
a : (..., M, M) array_like
Coefficient matrix.
b : {(..., M,), (..., M, K)}, array_like
Ordinate or "dependent variable" values.
Returns
-------
x : {(..., M,), (..., M, K)} ndarray
Solution to the system a x = b. Returned shape is identical to `b`.
Raises
------
LinAlgError
If `a` is singular or not square.
Notes
-----
.. versionadded:: 1.8.0
Broadcasting rules apply, see the `numpy.linalg` documentation for
details.
The solutions are computed using LAPACK routine _gesv
`a` must be square and of full-rank, i.e., all rows (or, equivalently,
columns) must be linearly independent; if either is not true, use
`lstsq` for the least-squares best "solution" of the
system/equation.
References
----------
.. [1] G. Strang, *Linear Algebra and Its Applications*, 2nd Ed., Orlando,
FL, Academic Press, Inc., 1980, pg. 22.
Examples
--------
Solve the system of equations ``3 * x0 + x1 = 9`` and ``x0 + 2 * x1 = 8``:
>>> a = np.array([[3,1], [1,2]])
>>> b = np.array([9,8])
>>> x = np.linalg.solve(a, b)
>>> x
array([ 2., 3.])
Check that the solution is correct:
>>> np.allclose(np.dot(a, x), b)
True
"""
a, _ = _makearray(a)
_assertRankAtLeast2(a)
_assertNdSquareness(a)
b, wrap = _makearray(b)
t, result_t = _commonType(a, b)
# We use the b = (..., M,) logic, only if the number of extra dimensions
# match exactly
if b.ndim == a.ndim - 1:
gufunc = _umath_linalg.solve1
else:
gufunc = _umath_linalg.solve
signature = 'DD->D' if isComplexType(t) else 'dd->d'
extobj = get_linalg_error_extobj(_raise_linalgerror_singular)
r = gufunc(a, b, signature=signature, extobj=extobj)
return wrap(r.astype(result_t, copy=False))
def tensorinv(a, ind=2):
"""
Compute the 'inverse' of an N-dimensional array.
The result is an inverse for `a` relative to the tensordot operation
``tensordot(a, b, ind)``, i. e., up to floating-point accuracy,
``tensordot(tensorinv(a), a, ind)`` is the "identity" tensor for the
tensordot operation.
Parameters
----------
a : array_like
Tensor to 'invert'. Its shape must be 'square', i. e.,
``prod(a.shape[:ind]) == prod(a.shape[ind:])``.
ind : int, optional
Number of first indices that are involved in the inverse sum.
Must be a positive integer, default is 2.
Returns
-------
b : ndarray
`a`'s tensordot inverse, shape ``a.shape[ind:] + a.shape[:ind]``.
Raises
------
LinAlgError
If `a` is singular or not 'square' (in the above sense).
See Also
--------
numpy.tensordot, tensorsolve
Examples
--------
>>> a = np.eye(4*6)
>>> a.shape = (4, 6, 8, 3)
>>> ainv = np.linalg.tensorinv(a, ind=2)
>>> ainv.shape
(8, 3, 4, 6)
>>> b = np.random.randn(4, 6)
>>> np.allclose(np.tensordot(ainv, b), np.linalg.tensorsolve(a, b))
True
>>> a = np.eye(4*6)
>>> a.shape = (24, 8, 3)
>>> ainv = np.linalg.tensorinv(a, ind=1)
>>> ainv.shape
(8, 3, 24)
>>> b = np.random.randn(24)
>>> np.allclose(np.tensordot(ainv, b, 1), np.linalg.tensorsolve(a, b))
True
"""
a = asarray(a)
oldshape = a.shape
prod = 1
if ind > 0:
invshape = oldshape[ind:] + oldshape[:ind]
for k in oldshape[ind:]:
prod *= k
else:
raise ValueError("Invalid ind argument.")
a = a.reshape(prod, -1)
ia = inv(a)
return ia.reshape(*invshape)
# Matrix inversion
def inv(a):
"""
Compute the (multiplicative) inverse of a matrix.
Given a square matrix `a`, return the matrix `ainv` satisfying
``dot(a, ainv) = dot(ainv, a) = eye(a.shape[0])``.
Parameters
----------
a : (..., M, M) array_like
Matrix to be inverted.
Returns
-------
ainv : (..., M, M) ndarray or matrix
(Multiplicative) inverse of the matrix `a`.
Raises
------
LinAlgError
If `a` is not square or inversion fails.
Notes
-----
.. versionadded:: 1.8.0
Broadcasting rules apply, see the `numpy.linalg` documentation for
details.
Examples
--------
>>> from numpy.linalg import inv
>>> a = np.array([[1., 2.], [3., 4.]])
>>> ainv = inv(a)
>>> np.allclose(np.dot(a, ainv), np.eye(2))
True
>>> np.allclose(np.dot(ainv, a), np.eye(2))
True
If a is a matrix object, then the return value is a matrix as well:
>>> ainv = inv(np.matrix(a))
>>> ainv
matrix([[-2. , 1. ],
[ 1.5, -0.5]])
Inverses of several matrices can be computed at once:
>>> a = np.array([[[1., 2.], [3., 4.]], [[1, 3], [3, 5]]])
>>> inv(a)
array([[[-2. , 1. ],
[ 1.5, -0.5]],
[[-5. , 2. ],
[ 3. , -1. ]]])
"""
a, wrap = _makearray(a)
_assertRankAtLeast2(a)
_assertNdSquareness(a)
t, result_t = _commonType(a)
signature = 'D->D' if isComplexType(t) else 'd->d'
extobj = get_linalg_error_extobj(_raise_linalgerror_singular)
ainv = _umath_linalg.inv(a, signature=signature, extobj=extobj)
return wrap(ainv.astype(result_t, copy=False))
# Cholesky decomposition
def cholesky(a):
"""
Cholesky decomposition.
Return the Cholesky decomposition, `L * L.H`, of the square matrix `a`,
where `L` is lower-triangular and .H is the conjugate transpose operator
(which is the ordinary transpose if `a` is real-valued). `a` must be
Hermitian (symmetric if real-valued) and positive-definite. Only `L` is
actually returned.
Parameters
----------
a : (..., M, M) array_like
Hermitian (symmetric if all elements are real), positive-definite
input matrix.
Returns
-------
L : (..., M, M) array_like
Upper or lower-triangular Cholesky factor of `a`. Returns a
matrix object if `a` is a matrix object.
Raises
------
LinAlgError
If the decomposition fails, for example, if `a` is not
positive-definite.
Notes
-----
.. versionadded:: 1.8.0
Broadcasting rules apply, see the `numpy.linalg` documentation for
details.
The Cholesky decomposition is often used as a fast way of solving
.. math:: A \\mathbf{x} = \\mathbf{b}
(when `A` is both Hermitian/symmetric and positive-definite).
First, we solve for :math:`\\mathbf{y}` in
.. math:: L \\mathbf{y} = \\mathbf{b},
and then for :math:`\\mathbf{x}` in
.. math:: L.H \\mathbf{x} = \\mathbf{y}.
Examples
--------
>>> A = np.array([[1,-2j],[2j,5]])
>>> A
array([[ 1.+0.j, 0.-2.j],
[ 0.+2.j, 5.+0.j]])
>>> L = np.linalg.cholesky(A)
>>> L
array([[ 1.+0.j, 0.+0.j],
[ 0.+2.j, 1.+0.j]])
>>> np.dot(L, L.T.conj()) # verify that L * L.H = A
array([[ 1.+0.j, 0.-2.j],
[ 0.+2.j, 5.+0.j]])
>>> A = [[1,-2j],[2j,5]] # what happens if A is only array_like?
>>> np.linalg.cholesky(A) # an ndarray object is returned
array([[ 1.+0.j, 0.+0.j],
[ 0.+2.j, 1.+0.j]])
>>> # But a matrix object is returned if A is a matrix object
>>> LA.cholesky(np.matrix(A))
matrix([[ 1.+0.j, 0.+0.j],
[ 0.+2.j, 1.+0.j]])
"""
extobj = get_linalg_error_extobj(_raise_linalgerror_nonposdef)
gufunc = _umath_linalg.cholesky_lo
a, wrap = _makearray(a)
_assertRankAtLeast2(a)
_assertNdSquareness(a)
t, result_t = _commonType(a)
signature = 'D->D' if isComplexType(t) else 'd->d'
r = gufunc(a, signature=signature, extobj=extobj)
return wrap(r.astype(result_t, copy=False))
# QR decompostion
def qr(a, mode='reduced'):
"""
Compute the qr factorization of a matrix.
Factor the matrix `a` as *qr*, where `q` is orthonormal and `r` is
upper-triangular.
Parameters
----------
a : array_like, shape (M, N)
Matrix to be factored.
mode : {'reduced', 'complete', 'r', 'raw', 'full', 'economic'}, optional
If K = min(M, N), then
* 'reduced' : returns q, r with dimensions (M, K), (K, N) (default)
* 'complete' : returns q, r with dimensions (M, M), (M, N)
* 'r' : returns r only with dimensions (K, N)
* 'raw' : returns h, tau with dimensions (N, M), (K,)
* 'full' : alias of 'reduced', deprecated
* 'economic' : returns h from 'raw', deprecated.
The options 'reduced', 'complete, and 'raw' are new in numpy 1.8,
see the notes for more information. The default is 'reduced', and to
maintain backward compatibility with earlier versions of numpy both
it and the old default 'full' can be omitted. Note that array h
returned in 'raw' mode is transposed for calling Fortran. The
'economic' mode is deprecated. The modes 'full' and 'economic' may
be passed using only the first letter for backwards compatibility,
but all others must be spelled out. See the Notes for more
explanation.
Returns
-------
q : ndarray of float or complex, optional
A matrix with orthonormal columns. When mode = 'complete' the
result is an orthogonal/unitary matrix depending on whether or not
a is real/complex. The determinant may be either +/- 1 in that
case.
r : ndarray of float or complex, optional
The upper-triangular matrix.
(h, tau) : ndarrays of np.double or np.cdouble, optional
The array h contains the Householder reflectors that generate q
along with r. The tau array contains scaling factors for the
reflectors. In the deprecated 'economic' mode only h is returned.
Raises
------
LinAlgError
If factoring fails.
Notes
-----
This is an interface to the LAPACK routines dgeqrf, zgeqrf,
dorgqr, and zungqr.
For more information on the qr factorization, see for example:
http://en.wikipedia.org/wiki/QR_factorization
Subclasses of `ndarray` are preserved except for the 'raw' mode. So if
`a` is of type `matrix`, all the return values will be matrices too.
New 'reduced', 'complete', and 'raw' options for mode were added in
NumPy 1.8.0 and the old option 'full' was made an alias of 'reduced'. In
addition the options 'full' and 'economic' were deprecated. Because
'full' was the previous default and 'reduced' is the new default,
backward compatibility can be maintained by letting `mode` default.
The 'raw' option was added so that LAPACK routines that can multiply
arrays by q using the Householder reflectors can be used. Note that in
this case the returned arrays are of type np.double or np.cdouble and
the h array is transposed to be FORTRAN compatible. No routines using
the 'raw' return are currently exposed by numpy, but some are available
in lapack_lite and just await the necessary work.
Examples
--------
>>> a = np.random.randn(9, 6)
>>> q, r = np.linalg.qr(a)
>>> np.allclose(a, np.dot(q, r)) # a does equal qr
True
>>> r2 = np.linalg.qr(a, mode='r')
>>> r3 = np.linalg.qr(a, mode='economic')
>>> np.allclose(r, r2) # mode='r' returns the same r as mode='full'
True
>>> # But only triu parts are guaranteed equal when mode='economic'
>>> np.allclose(r, np.triu(r3[:6,:6], k=0))
True
Example illustrating a common use of `qr`: solving of least squares
problems
What are the least-squares-best `m` and `y0` in ``y = y0 + mx`` for
the following data: {(0,1), (1,0), (1,2), (2,1)}. (Graph the points
and you'll see that it should be y0 = 0, m = 1.) The answer is provided
by solving the over-determined matrix equation ``Ax = b``, where::
A = array([[0, 1], [1, 1], [1, 1], [2, 1]])
x = array([[y0], [m]])
b = array([[1], [0], [2], [1]])
If A = qr such that q is orthonormal (which is always possible via
Gram-Schmidt), then ``x = inv(r) * (q.T) * b``. (In numpy practice,
however, we simply use `lstsq`.)
>>> A = np.array([[0, 1], [1, 1], [1, 1], [2, 1]])
>>> A
array([[0, 1],
[1, 1],
[1, 1],
[2, 1]])
>>> b = np.array([1, 0, 2, 1])
>>> q, r = LA.qr(A)
>>> p = np.dot(q.T, b)
>>> np.dot(LA.inv(r), p)
array([ 1.1e-16, 1.0e+00])
"""
if mode not in ('reduced', 'complete', 'r', 'raw'):
if mode in ('f', 'full'):
# 2013-04-01, 1.8
msg = "".join((
"The 'full' option is deprecated in favor of 'reduced'.\n",
"For backward compatibility let mode default."))
warnings.warn(msg, DeprecationWarning, stacklevel=2)
mode = 'reduced'
elif mode in ('e', 'economic'):
# 2013-04-01, 1.8
msg = "The 'economic' option is deprecated."
warnings.warn(msg, DeprecationWarning, stacklevel=2)
mode = 'economic'
else:
raise ValueError("Unrecognized mode '%s'" % mode)
a, wrap = _makearray(a)
_assertRank2(a)
_assertNoEmpty2d(a)
m, n = a.shape
t, result_t = _commonType(a)
a = _fastCopyAndTranspose(t, a)
a = _to_native_byte_order(a)
mn = min(m, n)
tau = zeros((mn,), t)
if isComplexType(t):
lapack_routine = lapack_lite.zgeqrf
routine_name = 'zgeqrf'
else:
lapack_routine = lapack_lite.dgeqrf
routine_name = 'dgeqrf'
# calculate optimal size of work data 'work'
lwork = 1
work = zeros((lwork,), t)
results = lapack_routine(m, n, a, m, tau, work, -1, 0)
if results['info'] != 0:
raise LinAlgError('%s returns %d' % (routine_name, results['info']))
# do qr decomposition
lwork = int(abs(work[0]))
work = zeros((lwork,), t)
results = lapack_routine(m, n, a, m, tau, work, lwork, 0)
if results['info'] != 0:
raise LinAlgError('%s returns %d' % (routine_name, results['info']))
# handle modes that don't return q
if mode == 'r':
r = _fastCopyAndTranspose(result_t, a[:, :mn])
return wrap(triu(r))
if mode == 'raw':
return a, tau
if mode == 'economic':
if t != result_t :
a = a.astype(result_t, copy=False)
return wrap(a.T)
# generate q from a
if mode == 'complete' and m > n:
mc = m
q = empty((m, m), t)
else:
mc = mn
q = empty((n, m), t)
q[:n] = a
if isComplexType(t):
lapack_routine = lapack_lite.zungqr
routine_name = 'zungqr'
else:
lapack_routine = lapack_lite.dorgqr
routine_name = 'dorgqr'
# determine optimal lwork
lwork = 1
work = zeros((lwork,), t)
results = lapack_routine(m, mc, mn, q, m, tau, work, -1, 0)
if results['info'] != 0:
raise LinAlgError('%s returns %d' % (routine_name, results['info']))
# compute q
lwork = int(abs(work[0]))
work = zeros((lwork,), t)
results = lapack_routine(m, mc, mn, q, m, tau, work, lwork, 0)
if results['info'] != 0:
raise LinAlgError('%s returns %d' % (routine_name, results['info']))
q = _fastCopyAndTranspose(result_t, q[:mc])
r = _fastCopyAndTranspose(result_t, a[:, :mc])
return wrap(q), wrap(triu(r))
# Eigenvalues
def eigvals(a):
"""
Compute the eigenvalues of a general matrix.
Main difference between `eigvals` and `eig`: the eigenvectors aren't
returned.
Parameters
----------
a : (..., M, M) array_like
A complex- or real-valued matrix whose eigenvalues will be computed.
Returns
-------
w : (..., M,) ndarray
The eigenvalues, each repeated according to its multiplicity.
They are not necessarily ordered, nor are they necessarily
real for real matrices.
Raises
------
LinAlgError
If the eigenvalue computation does not converge.
See Also
--------
eig : eigenvalues and right eigenvectors of general arrays
eigvalsh : eigenvalues of symmetric or Hermitian arrays.
eigh : eigenvalues and eigenvectors of symmetric/Hermitian arrays.
Notes
-----
.. versionadded:: 1.8.0
Broadcasting rules apply, see the `numpy.linalg` documentation for
details.
This is implemented using the _geev LAPACK routines which compute
the eigenvalues and eigenvectors of general square arrays.
Examples
--------
Illustration, using the fact that the eigenvalues of a diagonal matrix
are its diagonal elements, that multiplying a matrix on the left
by an orthogonal matrix, `Q`, and on the right by `Q.T` (the transpose
of `Q`), preserves the eigenvalues of the "middle" matrix. In other words,
if `Q` is orthogonal, then ``Q * A * Q.T`` has the same eigenvalues as
``A``:
>>> from numpy import linalg as LA
>>> x = np.random.random()
>>> Q = np.array([[np.cos(x), -np.sin(x)], [np.sin(x), np.cos(x)]])
>>> LA.norm(Q[0, :]), LA.norm(Q[1, :]), np.dot(Q[0, :],Q[1, :])
(1.0, 1.0, 0.0)
Now multiply a diagonal matrix by Q on one side and by Q.T on the other:
>>> D = np.diag((-1,1))
>>> LA.eigvals(D)
array([-1., 1.])
>>> A = np.dot(Q, D)
>>> A = np.dot(A, Q.T)
>>> LA.eigvals(A)
array([ 1., -1.])
"""
a, wrap = _makearray(a)
_assertRankAtLeast2(a)
_assertNdSquareness(a)
_assertFinite(a)
t, result_t = _commonType(a)
extobj = get_linalg_error_extobj(
_raise_linalgerror_eigenvalues_nonconvergence)
signature = 'D->D' if isComplexType(t) else 'd->D'
w = _umath_linalg.eigvals(a, signature=signature, extobj=extobj)
if not isComplexType(t):
if all(w.imag == 0):
w = w.real
result_t = _realType(result_t)
else:
result_t = _complexType(result_t)
return w.astype(result_t, copy=False)
def eigvalsh(a, UPLO='L'):
"""
Compute the eigenvalues of a Hermitian or real symmetric matrix.
Main difference from eigh: the eigenvectors are not computed.
Parameters
----------
a : (..., M, M) array_like
A complex- or real-valued matrix whose eigenvalues are to be
computed.
UPLO : {'L', 'U'}, optional
Specifies whether the calculation is done with the lower triangular
part of `a` ('L', default) or the upper triangular part ('U').
Irrespective of this value only the real parts of the diagonal will
be considered in the computation to preserve the notion of a Hermitian
matrix. It therefore follows that the imaginary part of the diagonal
will always be treated as zero.
Returns
-------
w : (..., M,) ndarray
The eigenvalues in ascending order, each repeated according to
its multiplicity.
Raises
------
LinAlgError
If the eigenvalue computation does not converge.
See Also
--------
eigh : eigenvalues and eigenvectors of symmetric/Hermitian arrays.
eigvals : eigenvalues of general real or complex arrays.
eig : eigenvalues and right eigenvectors of general real or complex
arrays.
Notes
-----
.. versionadded:: 1.8.0
Broadcasting rules apply, see the `numpy.linalg` documentation for
details.
The eigenvalues are computed using LAPACK routines _syevd, _heevd
Examples
--------
>>> from numpy import linalg as LA
>>> a = np.array([[1, -2j], [2j, 5]])
>>> LA.eigvalsh(a)
array([ 0.17157288, 5.82842712])
>>> # demonstrate the treatment of the imaginary part of the diagonal
>>> a = np.array([[5+2j, 9-2j], [0+2j, 2-1j]])
>>> a
array([[ 5.+2.j, 9.-2.j],
[ 0.+2.j, 2.-1.j]])
>>> # with UPLO='L' this is numerically equivalent to using LA.eigvals()
>>> # with:
>>> b = np.array([[5.+0.j, 0.-2.j], [0.+2.j, 2.-0.j]])
>>> b
array([[ 5.+0.j, 0.-2.j],
[ 0.+2.j, 2.+0.j]])
>>> wa = LA.eigvalsh(a)
>>> wb = LA.eigvals(b)
>>> wa; wb
array([ 1., 6.])
array([ 6.+0.j, 1.+0.j])
"""
UPLO = UPLO.upper()
if UPLO not in ('L', 'U'):
raise ValueError("UPLO argument must be 'L' or 'U'")
extobj = get_linalg_error_extobj(
_raise_linalgerror_eigenvalues_nonconvergence)
if UPLO == 'L':
gufunc = _umath_linalg.eigvalsh_lo
else:
gufunc = _umath_linalg.eigvalsh_up
a, wrap = _makearray(a)
_assertRankAtLeast2(a)
_assertNdSquareness(a)
t, result_t = _commonType(a)
signature = 'D->d' if isComplexType(t) else 'd->d'
w = gufunc(a, signature=signature, extobj=extobj)
return w.astype(_realType(result_t), copy=False)
def _convertarray(a):
t, result_t = _commonType(a)
a = _fastCT(a.astype(t))
return a, t, result_t
# Eigenvectors
def eig(a):
"""
Compute the eigenvalues and right eigenvectors of a square array.
Parameters
----------
a : (..., M, M) array
Matrices for which the eigenvalues and right eigenvectors will
be computed
Returns
-------
w : (..., M) array
The eigenvalues, each repeated according to its multiplicity.
The eigenvalues are not necessarily ordered. The resulting
array will be of complex type, unless the imaginary part is
zero in which case it will be cast to a real type. When `a`
is real the resulting eigenvalues will be real (0 imaginary
part) or occur in conjugate pairs
v : (..., M, M) array
The normalized (unit "length") eigenvectors, such that the
column ``v[:,i]`` is the eigenvector corresponding to the
eigenvalue ``w[i]``.
Raises
------
LinAlgError
If the eigenvalue computation does not converge.
See Also
--------
eigvals : eigenvalues of a non-symmetric array.
eigh : eigenvalues and eigenvectors of a symmetric or Hermitian
(conjugate symmetric) array.
eigvalsh : eigenvalues of a symmetric or Hermitian (conjugate symmetric)
array.
Notes
-----
.. versionadded:: 1.8.0
Broadcasting rules apply, see the `numpy.linalg` documentation for
details.
This is implemented using the _geev LAPACK routines which compute
the eigenvalues and eigenvectors of general square arrays.
The number `w` is an eigenvalue of `a` if there exists a vector
`v` such that ``dot(a,v) = w * v``. Thus, the arrays `a`, `w`, and
`v` satisfy the equations ``dot(a[:,:], v[:,i]) = w[i] * v[:,i]``
for :math:`i \\in \\{0,...,M-1\\}`.
The array `v` of eigenvectors may not be of maximum rank, that is, some
of the columns may be linearly dependent, although round-off error may
obscure that fact. If the eigenvalues are all different, then theoretically
the eigenvectors are linearly independent. Likewise, the (complex-valued)
matrix of eigenvectors `v` is unitary if the matrix `a` is normal, i.e.,
if ``dot(a, a.H) = dot(a.H, a)``, where `a.H` denotes the conjugate
transpose of `a`.
Finally, it is emphasized that `v` consists of the *right* (as in
right-hand side) eigenvectors of `a`. A vector `y` satisfying
``dot(y.T, a) = z * y.T`` for some number `z` is called a *left*
eigenvector of `a`, and, in general, the left and right eigenvectors
of a matrix are not necessarily the (perhaps conjugate) transposes
of each other.
References
----------
G. Strang, *Linear Algebra and Its Applications*, 2nd Ed., Orlando, FL,
Academic Press, Inc., 1980, Various pp.
Examples
--------
>>> from numpy import linalg as LA
(Almost) trivial example with real e-values and e-vectors.
>>> w, v = LA.eig(np.diag((1, 2, 3)))
>>> w; v
array([ 1., 2., 3.])
array([[ 1., 0., 0.],
[ 0., 1., 0.],
[ 0., 0., 1.]])
Real matrix possessing complex e-values and e-vectors; note that the
e-values are complex conjugates of each other.
>>> w, v = LA.eig(np.array([[1, -1], [1, 1]]))
>>> w; v
array([ 1. + 1.j, 1. - 1.j])
array([[ 0.70710678+0.j , 0.70710678+0.j ],
[ 0.00000000-0.70710678j, 0.00000000+0.70710678j]])
Complex-valued matrix with real e-values (but complex-valued e-vectors);
note that a.conj().T = a, i.e., a is Hermitian.
>>> a = np.array([[1, 1j], [-1j, 1]])
>>> w, v = LA.eig(a)
>>> w; v
array([ 2.00000000e+00+0.j, 5.98651912e-36+0.j]) # i.e., {2, 0}
array([[ 0.00000000+0.70710678j, 0.70710678+0.j ],
[ 0.70710678+0.j , 0.00000000+0.70710678j]])
Be careful about round-off error!
>>> a = np.array([[1 + 1e-9, 0], [0, 1 - 1e-9]])
>>> # Theor. e-values are 1 +/- 1e-9
>>> w, v = LA.eig(a)
>>> w; v
array([ 1., 1.])
array([[ 1., 0.],
[ 0., 1.]])
"""
a, wrap = _makearray(a)
_assertRankAtLeast2(a)
_assertNdSquareness(a)
_assertFinite(a)
t, result_t = _commonType(a)
extobj = get_linalg_error_extobj(
_raise_linalgerror_eigenvalues_nonconvergence)
signature = 'D->DD' if isComplexType(t) else 'd->DD'
w, vt = _umath_linalg.eig(a, signature=signature, extobj=extobj)
if not isComplexType(t) and all(w.imag == 0.0):
w = w.real
vt = vt.real
result_t = _realType(result_t)
else:
result_t = _complexType(result_t)
vt = vt.astype(result_t, copy=False)
return w.astype(result_t, copy=False), wrap(vt)
def eigh(a, UPLO='L'):
"""
Return the eigenvalues and eigenvectors of a Hermitian or symmetric matrix.
Returns two objects, a 1-D array containing the eigenvalues of `a`, and
a 2-D square array or matrix (depending on the input type) of the
corresponding eigenvectors (in columns).
Parameters
----------
a : (..., M, M) array
Hermitian/Symmetric matrices whose eigenvalues and
eigenvectors are to be computed.
UPLO : {'L', 'U'}, optional
Specifies whether the calculation is done with the lower triangular
part of `a` ('L', default) or the upper triangular part ('U').
Irrespective of this value only the real parts of the diagonal will
be considered in the computation to preserve the notion of a Hermitian
matrix. It therefore follows that the imaginary part of the diagonal
will always be treated as zero.
Returns
-------
w : (..., M) ndarray
The eigenvalues in ascending order, each repeated according to
its multiplicity.
v : {(..., M, M) ndarray, (..., M, M) matrix}
The column ``v[:, i]`` is the normalized eigenvector corresponding
to the eigenvalue ``w[i]``. Will return a matrix object if `a` is
a matrix object.
Raises
------
LinAlgError
If the eigenvalue computation does not converge.
See Also
--------
eigvalsh : eigenvalues of symmetric or Hermitian arrays.
eig : eigenvalues and right eigenvectors for non-symmetric arrays.
eigvals : eigenvalues of non-symmetric arrays.
Notes
-----
.. versionadded:: 1.8.0
Broadcasting rules apply, see the `numpy.linalg` documentation for
details.
The eigenvalues/eigenvectors are computed using LAPACK routines _syevd,
_heevd
The eigenvalues of real symmetric or complex Hermitian matrices are
always real. [1]_ The array `v` of (column) eigenvectors is unitary
and `a`, `w`, and `v` satisfy the equations
``dot(a, v[:, i]) = w[i] * v[:, i]``.
References
----------
.. [1] G. Strang, *Linear Algebra and Its Applications*, 2nd Ed., Orlando,
FL, Academic Press, Inc., 1980, pg. 222.
Examples
--------
>>> from numpy import linalg as LA
>>> a = np.array([[1, -2j], [2j, 5]])
>>> a
array([[ 1.+0.j, 0.-2.j],
[ 0.+2.j, 5.+0.j]])
>>> w, v = LA.eigh(a)
>>> w; v
array([ 0.17157288, 5.82842712])
array([[-0.92387953+0.j , -0.38268343+0.j ],
[ 0.00000000+0.38268343j, 0.00000000-0.92387953j]])
>>> np.dot(a, v[:, 0]) - w[0] * v[:, 0] # verify 1st e-val/vec pair
array([2.77555756e-17 + 0.j, 0. + 1.38777878e-16j])
>>> np.dot(a, v[:, 1]) - w[1] * v[:, 1] # verify 2nd e-val/vec pair
array([ 0.+0.j, 0.+0.j])
>>> A = np.matrix(a) # what happens if input is a matrix object
>>> A
matrix([[ 1.+0.j, 0.-2.j],
[ 0.+2.j, 5.+0.j]])
>>> w, v = LA.eigh(A)
>>> w; v
array([ 0.17157288, 5.82842712])
matrix([[-0.92387953+0.j , -0.38268343+0.j ],
[ 0.00000000+0.38268343j, 0.00000000-0.92387953j]])
>>> # demonstrate the treatment of the imaginary part of the diagonal
>>> a = np.array([[5+2j, 9-2j], [0+2j, 2-1j]])
>>> a
array([[ 5.+2.j, 9.-2.j],
[ 0.+2.j, 2.-1.j]])
>>> # with UPLO='L' this is numerically equivalent to using LA.eig() with:
>>> b = np.array([[5.+0.j, 0.-2.j], [0.+2.j, 2.-0.j]])
>>> b
array([[ 5.+0.j, 0.-2.j],
[ 0.+2.j, 2.+0.j]])
>>> wa, va = LA.eigh(a)
>>> wb, vb = LA.eig(b)
>>> wa; wb
array([ 1., 6.])
array([ 6.+0.j, 1.+0.j])
>>> va; vb
array([[-0.44721360-0.j , -0.89442719+0.j ],
[ 0.00000000+0.89442719j, 0.00000000-0.4472136j ]])
array([[ 0.89442719+0.j , 0.00000000-0.4472136j],
[ 0.00000000-0.4472136j, 0.89442719+0.j ]])
"""
UPLO = UPLO.upper()
if UPLO not in ('L', 'U'):
raise ValueError("UPLO argument must be 'L' or 'U'")
a, wrap = _makearray(a)
_assertRankAtLeast2(a)
_assertNdSquareness(a)
t, result_t = _commonType(a)
extobj = get_linalg_error_extobj(
_raise_linalgerror_eigenvalues_nonconvergence)
if UPLO == 'L':
gufunc = _umath_linalg.eigh_lo
else:
gufunc = _umath_linalg.eigh_up
signature = 'D->dD' if isComplexType(t) else 'd->dd'
w, vt = gufunc(a, signature=signature, extobj=extobj)
w = w.astype(_realType(result_t), copy=False)
vt = vt.astype(result_t, copy=False)
return w, wrap(vt)
# Singular value decomposition
def svd(a, full_matrices=True, compute_uv=True):
"""
Singular Value Decomposition.
When `a` is a 2D array, it is factorized as ``u @ np.diag(s) @ vh
= (u * s) @ vh``, where `u` and `vh` are 2D unitary arrays and `s` is a 1D
array of `a`'s singular values. When `a` is higher-dimensional, SVD is
applied in stacked mode as explained below.
Parameters
----------
a : (..., M, N) array_like
A real or complex array with ``a.ndim >= 2``.
full_matrices : bool, optional
If True (default), `u` and `vh` have the shapes ``(..., M, M)`` and
``(..., N, N)``, respectively. Otherwise, the shapes are
``(..., M, K)`` and ``(..., K, N)``, respectively, where
``K = min(M, N)``.
compute_uv : bool, optional
Whether or not to compute `u` and `vh` in addition to `s`. True
by default.
Returns
-------
u : { (..., M, M), (..., M, K) } array
Unitary array(s). The first ``a.ndim - 2`` dimensions have the same
size as those of the input `a`. The size of the last two dimensions
depends on the value of `full_matrices`. Only returned when
`compute_uv` is True.
s : (..., K) array
Vector(s) with the singular values, within each vector sorted in
descending order. The first ``a.ndim - 2`` dimensions have the same
size as those of the input `a`.
vh : { (..., N, N), (..., K, N) } array
Unitary array(s). The first ``a.ndim - 2`` dimensions have the same
size as those of the input `a`. The size of the last two dimensions
depends on the value of `full_matrices`. Only returned when
`compute_uv` is True.
Raises
------
LinAlgError
If SVD computation does not converge.
Notes
-----
.. versionchanged:: 1.8.0
Broadcasting rules apply, see the `numpy.linalg` documentation for
details.
The decomposition is performed using LAPACK routine ``_gesdd``.
SVD is usually described for the factorization of a 2D matrix :math:`A`.
The higher-dimensional case will be discussed below. In the 2D case, SVD is
written as :math:`A = U S V^H`, where :math:`A = a`, :math:`U= u`,
:math:`S= \\mathtt{np.diag}(s)` and :math:`V^H = vh`. The 1D array `s`
contains the singular values of `a` and `u` and `vh` are unitary. The rows
of `vh` are the eigenvectors of :math:`A^H A` and the columns of `u` are
the eigenvectors of :math:`A A^H`. In both cases the corresponding
(possibly non-zero) eigenvalues are given by ``s**2``.
If `a` has more than two dimensions, then broadcasting rules apply, as
explained in :ref:`routines.linalg-broadcasting`. This means that SVD is
working in "stacked" mode: it iterates over all indices of the first
``a.ndim - 2`` dimensions and for each combination SVD is applied to the
last two indices. The matrix `a` can be reconstructed from the
decomposition with either ``(u * s[..., None, :]) @ vh`` or
``u @ (s[..., None] * vh)``. (The ``@`` operator can be replaced by the
function ``np.matmul`` for python versions below 3.5.)
If `a` is a ``matrix`` object (as opposed to an ``ndarray``), then so are
all the return values.
Examples
--------
>>> a = np.random.randn(9, 6) + 1j*np.random.randn(9, 6)
>>> b = np.random.randn(2, 7, 8, 3) + 1j*np.random.randn(2, 7, 8, 3)
Reconstruction based on full SVD, 2D case:
>>> u, s, vh = np.linalg.svd(a, full_matrices=True)
>>> u.shape, s.shape, vh.shape
((9, 9), (6,), (6, 6))
>>> np.allclose(a, np.dot(u[:, :6] * s, vh))
True
>>> smat = np.zeros((9, 6), dtype=complex)
>>> smat[:6, :6] = np.diag(s)
>>> np.allclose(a, np.dot(u, np.dot(smat, vh)))
True
Reconstruction based on reduced SVD, 2D case:
>>> u, s, vh = np.linalg.svd(a, full_matrices=False)
>>> u.shape, s.shape, vh.shape
((9, 6), (6,), (6, 6))
>>> np.allclose(a, np.dot(u * s, vh))
True
>>> smat = np.diag(s)
>>> np.allclose(a, np.dot(u, np.dot(smat, vh)))
True
Reconstruction based on full SVD, 4D case:
>>> u, s, vh = np.linalg.svd(b, full_matrices=True)
>>> u.shape, s.shape, vh.shape
((2, 7, 8, 8), (2, 7, 3), (2, 7, 3, 3))
>>> np.allclose(b, np.matmul(u[..., :3] * s[..., None, :], vh))
True
>>> np.allclose(b, np.matmul(u[..., :3], s[..., None] * vh))
True
Reconstruction based on reduced SVD, 4D case:
>>> u, s, vh = np.linalg.svd(b, full_matrices=False)
>>> u.shape, s.shape, vh.shape
((2, 7, 8, 3), (2, 7, 3), (2, 7, 3, 3))
>>> np.allclose(b, np.matmul(u * s[..., None, :], vh))
True
>>> np.allclose(b, np.matmul(u, s[..., None] * vh))
True
"""
a, wrap = _makearray(a)
_assertNoEmpty2d(a)
_assertRankAtLeast2(a)
t, result_t = _commonType(a)
extobj = get_linalg_error_extobj(_raise_linalgerror_svd_nonconvergence)
m = a.shape[-2]
n = a.shape[-1]
if compute_uv:
if full_matrices:
if m < n:
gufunc = _umath_linalg.svd_m_f
else:
gufunc = _umath_linalg.svd_n_f
else:
if m < n:
gufunc = _umath_linalg.svd_m_s
else:
gufunc = _umath_linalg.svd_n_s
signature = 'D->DdD' if isComplexType(t) else 'd->ddd'
u, s, vh = gufunc(a, signature=signature, extobj=extobj)
u = u.astype(result_t, copy=False)
s = s.astype(_realType(result_t), copy=False)
vh = vh.astype(result_t, copy=False)
return wrap(u), s, wrap(vh)
else:
if m < n:
gufunc = _umath_linalg.svd_m
else:
gufunc = _umath_linalg.svd_n
signature = 'D->d' if isComplexType(t) else 'd->d'
s = gufunc(a, signature=signature, extobj=extobj)
s = s.astype(_realType(result_t), copy=False)
return s
def cond(x, p=None):
"""
Compute the condition number of a matrix.
This function is capable of returning the condition number using
one of seven different norms, depending on the value of `p` (see
Parameters below).
Parameters
----------
x : (..., M, N) array_like
The matrix whose condition number is sought.
p : {None, 1, -1, 2, -2, inf, -inf, 'fro'}, optional
Order of the norm:
===== ============================
p norm for matrices
===== ============================
None 2-norm, computed directly using the ``SVD``
'fro' Frobenius norm
inf max(sum(abs(x), axis=1))
-inf min(sum(abs(x), axis=1))
1 max(sum(abs(x), axis=0))
-1 min(sum(abs(x), axis=0))
2 2-norm (largest sing. value)
-2 smallest singular value
===== ============================
inf means the numpy.inf object, and the Frobenius norm is
the root-of-sum-of-squares norm.
Returns
-------
c : {float, inf}
The condition number of the matrix. May be infinite.
See Also
--------
numpy.linalg.norm
Notes
-----
The condition number of `x` is defined as the norm of `x` times the
norm of the inverse of `x` [1]_; the norm can be the usual L2-norm
(root-of-sum-of-squares) or one of a number of other matrix norms.
References
----------
.. [1] G. Strang, *Linear Algebra and Its Applications*, Orlando, FL,
Academic Press, Inc., 1980, pg. 285.
Examples
--------
>>> from numpy import linalg as LA
>>> a = np.array([[1, 0, -1], [0, 1, 0], [1, 0, 1]])
>>> a
array([[ 1, 0, -1],
[ 0, 1, 0],
[ 1, 0, 1]])
>>> LA.cond(a)
1.4142135623730951
>>> LA.cond(a, 'fro')
3.1622776601683795
>>> LA.cond(a, np.inf)
2.0
>>> LA.cond(a, -np.inf)
1.0
>>> LA.cond(a, 1)
2.0
>>> LA.cond(a, -1)
1.0
>>> LA.cond(a, 2)
1.4142135623730951
>>> LA.cond(a, -2)
0.70710678118654746
>>> min(LA.svd(a, compute_uv=0))*min(LA.svd(LA.inv(a), compute_uv=0))
0.70710678118654746
"""
x = asarray(x) # in case we have a matrix
if p is None:
s = svd(x, compute_uv=False)
return s[..., 0]/s[..., -1]
else:
return norm(x, p, axis=(-2, -1)) * norm(inv(x), p, axis=(-2, -1))
def matrix_rank(M, tol=None, hermitian=False):
"""
Return matrix rank of array using SVD method
Rank of the array is the number of singular values of the array that are
greater than `tol`.
.. versionchanged:: 1.14
Can now operate on stacks of matrices
Parameters
----------
M : {(M,), (..., M, N)} array_like
input vector or stack of matrices
tol : (...) array_like, float, optional
threshold below which SVD values are considered zero. If `tol` is
None, and ``S`` is an array with singular values for `M`, and
``eps`` is the epsilon value for datatype of ``S``, then `tol` is
set to ``S.max() * max(M.shape) * eps``.
.. versionchanged:: 1.14
Broadcasted against the stack of matrices
hermitian : bool, optional
If True, `M` is assumed to be Hermitian (symmetric if real-valued),
enabling a more efficient method for finding singular values.
Defaults to False.
.. versionadded:: 1.14
Notes
-----
The default threshold to detect rank deficiency is a test on the magnitude
of the singular values of `M`. By default, we identify singular values less
than ``S.max() * max(M.shape) * eps`` as indicating rank deficiency (with
the symbols defined above). This is the algorithm MATLAB uses [1]. It also
appears in *Numerical recipes* in the discussion of SVD solutions for linear
least squares [2].
This default threshold is designed to detect rank deficiency accounting for
the numerical errors of the SVD computation. Imagine that there is a column
in `M` that is an exact (in floating point) linear combination of other
columns in `M`. Computing the SVD on `M` will not produce a singular value
exactly equal to 0 in general: any difference of the smallest SVD value from
0 will be caused by numerical imprecision in the calculation of the SVD.
Our threshold for small SVD values takes this numerical imprecision into
account, and the default threshold will detect such numerical rank
deficiency. The threshold may declare a matrix `M` rank deficient even if
the linear combination of some columns of `M` is not exactly equal to
another column of `M` but only numerically very close to another column of
`M`.
We chose our default threshold because it is in wide use. Other thresholds
are possible. For example, elsewhere in the 2007 edition of *Numerical
recipes* there is an alternative threshold of ``S.max() *
np.finfo(M.dtype).eps / 2. * np.sqrt(m + n + 1.)``. The authors describe
this threshold as being based on "expected roundoff error" (p 71).
The thresholds above deal with floating point roundoff error in the
calculation of the SVD. However, you may have more information about the
sources of error in `M` that would make you consider other tolerance values
to detect *effective* rank deficiency. The most useful measure of the
tolerance depends on the operations you intend to use on your matrix. For
example, if your data come from uncertain measurements with uncertainties
greater than floating point epsilon, choosing a tolerance near that
uncertainty may be preferable. The tolerance may be absolute if the
uncertainties are absolute rather than relative.
References
----------
.. [1] MATLAB reference documention, "Rank"
http://www.mathworks.com/help/techdoc/ref/rank.html
.. [2] W. H. Press, S. A. Teukolsky, W. T. Vetterling and B. P. Flannery,
"Numerical Recipes (3rd edition)", Cambridge University Press, 2007,
page 795.
Examples
--------
>>> from numpy.linalg import matrix_rank
>>> matrix_rank(np.eye(4)) # Full rank matrix
4
>>> I=np.eye(4); I[-1,-1] = 0. # rank deficient matrix
>>> matrix_rank(I)
3
>>> matrix_rank(np.ones((4,))) # 1 dimension - rank 1 unless all 0
1
>>> matrix_rank(np.zeros((4,)))
0
"""
M = asarray(M)
if M.ndim < 2:
return int(not all(M==0))
if hermitian:
S = abs(eigvalsh(M))
else:
S = svd(M, compute_uv=False)
if tol is None:
tol = S.max(axis=-1, keepdims=True) * max(M.shape[-2:]) * finfo(S.dtype).eps
else:
tol = asarray(tol)[..., newaxis]
return count_nonzero(S > tol, axis=-1)
# Generalized inverse
def pinv(a, rcond=1e-15 ):
"""
Compute the (Moore-Penrose) pseudo-inverse of a matrix.
Calculate the generalized inverse of a matrix using its
singular-value decomposition (SVD) and including all
*large* singular values.
.. versionchanged:: 1.14
Can now operate on stacks of matrices
Parameters
----------
a : (..., M, N) array_like
Matrix or stack of matrices to be pseudo-inverted.
rcond : (...) array_like of float
Cutoff for small singular values.
Singular values smaller (in modulus) than
`rcond` * largest_singular_value (again, in modulus)
are set to zero. Broadcasts against the stack of matrices
Returns
-------
B : (..., N, M) ndarray
The pseudo-inverse of `a`. If `a` is a `matrix` instance, then so
is `B`.
Raises
------
LinAlgError
If the SVD computation does not converge.
Notes
-----
The pseudo-inverse of a matrix A, denoted :math:`A^+`, is
defined as: "the matrix that 'solves' [the least-squares problem]
:math:`Ax = b`," i.e., if :math:`\\bar{x}` is said solution, then
:math:`A^+` is that matrix such that :math:`\\bar{x} = A^+b`.
It can be shown that if :math:`Q_1 \\Sigma Q_2^T = A` is the singular
value decomposition of A, then
:math:`A^+ = Q_2 \\Sigma^+ Q_1^T`, where :math:`Q_{1,2}` are
orthogonal matrices, :math:`\\Sigma` is a diagonal matrix consisting
of A's so-called singular values, (followed, typically, by
zeros), and then :math:`\\Sigma^+` is simply the diagonal matrix
consisting of the reciprocals of A's singular values
(again, followed by zeros). [1]_
References
----------
.. [1] G. Strang, *Linear Algebra and Its Applications*, 2nd Ed., Orlando,
FL, Academic Press, Inc., 1980, pp. 139-142.
Examples
--------
The following example checks that ``a * a+ * a == a`` and
``a+ * a * a+ == a+``:
>>> a = np.random.randn(9, 6)
>>> B = np.linalg.pinv(a)
>>> np.allclose(a, np.dot(a, np.dot(B, a)))
True
>>> np.allclose(B, np.dot(B, np.dot(a, B)))
True
"""
a, wrap = _makearray(a)
rcond = asarray(rcond)
if _isEmpty2d(a):
res = empty(a.shape[:-2] + (a.shape[-1], a.shape[-2]), dtype=a.dtype)
return wrap(res)
a = a.conjugate()
u, s, vt = svd(a, full_matrices=False)
# discard small singular values
cutoff = rcond[..., newaxis] * amax(s, axis=-1, keepdims=True)
large = s > cutoff
s = divide(1, s, where=large, out=s)
s[~large] = 0
res = matmul(transpose(vt), multiply(s[..., newaxis], transpose(u)))
return wrap(res)
# Determinant
def slogdet(a):
"""
Compute the sign and (natural) logarithm of the determinant of an array.
If an array has a very small or very large determinant, then a call to
`det` may overflow or underflow. This routine is more robust against such
issues, because it computes the logarithm of the determinant rather than
the determinant itself.
Parameters
----------
a : (..., M, M) array_like
Input array, has to be a square 2-D array.
Returns
-------
sign : (...) array_like
A number representing the sign of the determinant. For a real matrix,
this is 1, 0, or -1. For a complex matrix, this is a complex number
with absolute value 1 (i.e., it is on the unit circle), or else 0.
logdet : (...) array_like
The natural log of the absolute value of the determinant.
If the determinant is zero, then `sign` will be 0 and `logdet` will be
-Inf. In all cases, the determinant is equal to ``sign * np.exp(logdet)``.
See Also
--------
det
Notes
-----
.. versionadded:: 1.8.0
Broadcasting rules apply, see the `numpy.linalg` documentation for
details.
.. versionadded:: 1.6.0
The determinant is computed via LU factorization using the LAPACK
routine z/dgetrf.
Examples
--------
The determinant of a 2-D array ``[[a, b], [c, d]]`` is ``ad - bc``:
>>> a = np.array([[1, 2], [3, 4]])
>>> (sign, logdet) = np.linalg.slogdet(a)
>>> (sign, logdet)
(-1, 0.69314718055994529)
>>> sign * np.exp(logdet)
-2.0
Computing log-determinants for a stack of matrices:
>>> a = np.array([ [[1, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]] ])
>>> a.shape
(3, 2, 2)
>>> sign, logdet = np.linalg.slogdet(a)
>>> (sign, logdet)
(array([-1., -1., -1.]), array([ 0.69314718, 1.09861229, 2.07944154]))
>>> sign * np.exp(logdet)
array([-2., -3., -8.])
This routine succeeds where ordinary `det` does not:
>>> np.linalg.det(np.eye(500) * 0.1)
0.0
>>> np.linalg.slogdet(np.eye(500) * 0.1)
(1, -1151.2925464970228)
"""
a = asarray(a)
_assertRankAtLeast2(a)
_assertNdSquareness(a)
t, result_t = _commonType(a)
real_t = _realType(result_t)
signature = 'D->Dd' if isComplexType(t) else 'd->dd'
sign, logdet = _umath_linalg.slogdet(a, signature=signature)
sign = sign.astype(result_t, copy=False)
logdet = logdet.astype(real_t, copy=False)
return sign, logdet
def det(a):
"""
Compute the determinant of an array.
Parameters
----------
a : (..., M, M) array_like
Input array to compute determinants for.
Returns
-------
det : (...) array_like
Determinant of `a`.
See Also
--------
slogdet : Another way to representing the determinant, more suitable
for large matrices where underflow/overflow may occur.
Notes
-----
.. versionadded:: 1.8.0
Broadcasting rules apply, see the `numpy.linalg` documentation for
details.
The determinant is computed via LU factorization using the LAPACK
routine z/dgetrf.
Examples
--------
The determinant of a 2-D array [[a, b], [c, d]] is ad - bc:
>>> a = np.array([[1, 2], [3, 4]])
>>> np.linalg.det(a)
-2.0
Computing determinants for a stack of matrices:
>>> a = np.array([ [[1, 2], [3, 4]], [[1, 2], [2, 1]], [[1, 3], [3, 1]] ])
>>> a.shape
(3, 2, 2)
>>> np.linalg.det(a)
array([-2., -3., -8.])
"""
a = asarray(a)
_assertRankAtLeast2(a)
_assertNdSquareness(a)
t, result_t = _commonType(a)
signature = 'D->D' if isComplexType(t) else 'd->d'
r = _umath_linalg.det(a, signature=signature)
r = r.astype(result_t, copy=False)
return r
# Linear Least Squares
def lstsq(a, b, rcond="warn"):
"""
Return the least-squares solution to a linear matrix equation.
Solves the equation `a x = b` by computing a vector `x` that
minimizes the Euclidean 2-norm `|| b - a x ||^2`. The equation may
be under-, well-, or over- determined (i.e., the number of
linearly independent rows of `a` can be less than, equal to, or
greater than its number of linearly independent columns). If `a`
is square and of full rank, then `x` (but for round-off error) is
the "exact" solution of the equation.
Parameters
----------
a : (M, N) array_like
"Coefficient" matrix.
b : {(M,), (M, K)} array_like
Ordinate or "dependent variable" values. If `b` is two-dimensional,
the least-squares solution is calculated for each of the `K` columns
of `b`.
rcond : float, optional
Cut-off ratio for small singular values of `a`.
For the purposes of rank determination, singular values are treated
as zero if they are smaller than `rcond` times the largest singular
value of `a`.
.. versionchanged:: 1.14.0
If not set, a FutureWarning is given. The previous default
of ``-1`` will use the machine precision as `rcond` parameter,
the new default will use the machine precision times `max(M, N)`.
To silence the warning and use the new default, use ``rcond=None``,
to keep using the old behavior, use ``rcond=-1``.
Returns
-------
x : {(N,), (N, K)} ndarray
Least-squares solution. If `b` is two-dimensional,
the solutions are in the `K` columns of `x`.
residuals : {(1,), (K,), (0,)} ndarray
Sums of residuals; squared Euclidean 2-norm for each column in
``b - a*x``.
If the rank of `a` is < N or M <= N, this is an empty array.
If `b` is 1-dimensional, this is a (1,) shape array.
Otherwise the shape is (K,).
rank : int
Rank of matrix `a`.
s : (min(M, N),) ndarray
Singular values of `a`.
Raises
------
LinAlgError
If computation does not converge.
Notes
-----
If `b` is a matrix, then all array results are returned as matrices.
Examples
--------
Fit a line, ``y = mx + c``, through some noisy data-points:
>>> x = np.array([0, 1, 2, 3])
>>> y = np.array([-1, 0.2, 0.9, 2.1])
By examining the coefficients, we see that the line should have a
gradient of roughly 1 and cut the y-axis at, more or less, -1.
We can rewrite the line equation as ``y = Ap``, where ``A = [[x 1]]``
and ``p = [[m], [c]]``. Now use `lstsq` to solve for `p`:
>>> A = np.vstack([x, np.ones(len(x))]).T
>>> A
array([[ 0., 1.],
[ 1., 1.],
[ 2., 1.],
[ 3., 1.]])
>>> m, c = np.linalg.lstsq(A, y)[0]
>>> print(m, c)
1.0 -0.95
Plot the data along with the fitted line:
>>> import matplotlib.pyplot as plt
>>> plt.plot(x, y, 'o', label='Original data', markersize=10)
>>> plt.plot(x, m*x + c, 'r', label='Fitted line')
>>> plt.legend()
>>> plt.show()
"""
import math
a, _ = _makearray(a)
b, wrap = _makearray(b)
is_1d = b.ndim == 1
if is_1d:
b = b[:, newaxis]
_assertRank2(a, b)
_assertNoEmpty2d(a, b) # TODO: relax this constraint
m = a.shape[0]
n = a.shape[1]
n_rhs = b.shape[1]
ldb = max(n, m)
if m != b.shape[0]:
raise LinAlgError('Incompatible dimensions')
t, result_t = _commonType(a, b)
real_t = _linalgRealType(t)
result_real_t = _realType(result_t)
# Determine default rcond value
if rcond == "warn":
# 2017-08-19, 1.14.0
warnings.warn("`rcond` parameter will change to the default of "
"machine precision times ``max(M, N)`` where M and N "
"are the input matrix dimensions.\n"
"To use the future default and silence this warning "
"we advise to pass `rcond=None`, to keep using the old, "
"explicitly pass `rcond=-1`.",
FutureWarning, stacklevel=2)
rcond = -1
if rcond is None:
rcond = finfo(t).eps * ldb
bstar = zeros((ldb, n_rhs), t)
bstar[:m, :n_rhs] = b
a, bstar = _fastCopyAndTranspose(t, a, bstar)
a, bstar = _to_native_byte_order(a, bstar)
s = zeros((min(m, n),), real_t)
# This line:
# * is incorrect, according to the LAPACK documentation
# * raises a ValueError if min(m,n) == 0
# * should not be calculated here anyway, as LAPACK should calculate
# `liwork` for us. But that only works if our version of lapack does
# not have this bug:
# http://icl.cs.utk.edu/lapack-forum/archives/lapack/msg00899.html
# Lapack_lite does have that bug...
nlvl = max( 0, int( math.log( float(min(m, n))/2. ) ) + 1 )
iwork = zeros((3*min(m, n)*nlvl+11*min(m, n),), fortran_int)
if isComplexType(t):
lapack_routine = lapack_lite.zgelsd
lwork = 1
rwork = zeros((lwork,), real_t)
work = zeros((lwork,), t)
results = lapack_routine(m, n, n_rhs, a, m, bstar, ldb, s, rcond,
0, work, -1, rwork, iwork, 0)
lrwork = int(rwork[0])
lwork = int(work[0].real)
work = zeros((lwork,), t)
rwork = zeros((lrwork,), real_t)
results = lapack_routine(m, n, n_rhs, a, m, bstar, ldb, s, rcond,
0, work, lwork, rwork, iwork, 0)
else:
lapack_routine = lapack_lite.dgelsd
lwork = 1
work = zeros((lwork,), t)
results = lapack_routine(m, n, n_rhs, a, m, bstar, ldb, s, rcond,
0, work, -1, iwork, 0)
lwork = int(work[0])
work = zeros((lwork,), t)
results = lapack_routine(m, n, n_rhs, a, m, bstar, ldb, s, rcond,
0, work, lwork, iwork, 0)
if results['info'] > 0:
raise LinAlgError('SVD did not converge in Linear Least Squares')
# undo transpose imposed by fortran-order arrays
b_out = bstar.T
# b_out contains both the solution and the components of the residuals
x = b_out[:n,:]
r_parts = b_out[n:,:]
if isComplexType(t):
resids = sum(abs(r_parts)**2, axis=-2)
else:
resids = sum(r_parts**2, axis=-2)
rank = results['rank']
# remove the axis we added
if is_1d:
x = x.squeeze(axis=-1)
# we probably should squeeze resids too, but we can't
# without breaking compatibility.
# as documented
if rank != n or m <= n:
resids = array([], result_real_t)
# coerce output arrays
s = s.astype(result_real_t, copy=False)
resids = resids.astype(result_real_t, copy=False)
x = x.astype(result_t, copy=True) # Copying lets the memory in r_parts be freed
return wrap(x), wrap(resids), rank, s
def _multi_svd_norm(x, row_axis, col_axis, op):
"""Compute a function of the singular values of the 2-D matrices in `x`.
This is a private utility function used by numpy.linalg.norm().
Parameters
----------
x : ndarray
row_axis, col_axis : int
The axes of `x` that hold the 2-D matrices.
op : callable
This should be either numpy.amin or numpy.amax or numpy.sum.
Returns
-------
result : float or ndarray
If `x` is 2-D, the return values is a float.
Otherwise, it is an array with ``x.ndim - 2`` dimensions.
The return values are either the minimum or maximum or sum of the
singular values of the matrices, depending on whether `op`
is `numpy.amin` or `numpy.amax` or `numpy.sum`.
"""
y = moveaxis(x, (row_axis, col_axis), (-2, -1))
result = op(svd(y, compute_uv=0), axis=-1)
return result
def norm(x, ord=None, axis=None, keepdims=False):
"""
Matrix or vector norm.
This function is able to return one of eight different matrix norms,
or one of an infinite number of vector norms (described below), depending
on the value of the ``ord`` parameter.
Parameters
----------
x : array_like
Input array. If `axis` is None, `x` must be 1-D or 2-D.
ord : {non-zero int, inf, -inf, 'fro', 'nuc'}, optional
Order of the norm (see table under ``Notes``). inf means numpy's
`inf` object.
axis : {int, 2-tuple of ints, None}, optional
If `axis` is an integer, it specifies the axis of `x` along which to
compute the vector norms. If `axis` is a 2-tuple, it specifies the
axes that hold 2-D matrices, and the matrix norms of these matrices
are computed. If `axis` is None then either a vector norm (when `x`
is 1-D) or a matrix norm (when `x` is 2-D) is returned.
keepdims : bool, optional
If this is set to True, the axes which are normed over are left in the
result as dimensions with size one. With this option the result will
broadcast correctly against the original `x`.
.. versionadded:: 1.10.0
Returns
-------
n : float or ndarray
Norm of the matrix or vector(s).
Notes
-----
For values of ``ord <= 0``, the result is, strictly speaking, not a
mathematical 'norm', but it may still be useful for various numerical
purposes.
The following norms can be calculated:
===== ============================ ==========================
ord norm for matrices norm for vectors
===== ============================ ==========================
None Frobenius norm 2-norm
'fro' Frobenius norm --
'nuc' nuclear norm --
inf max(sum(abs(x), axis=1)) max(abs(x))
-inf min(sum(abs(x), axis=1)) min(abs(x))
0 -- sum(x != 0)
1 max(sum(abs(x), axis=0)) as below
-1 min(sum(abs(x), axis=0)) as below
2 2-norm (largest sing. value) as below
-2 smallest singular value as below
other -- sum(abs(x)**ord)**(1./ord)
===== ============================ ==========================
The Frobenius norm is given by [1]_:
:math:`||A||_F = [\\sum_{i,j} abs(a_{i,j})^2]^{1/2}`
The nuclear norm is the sum of the singular values.
References
----------
.. [1] G. H. Golub and C. F. Van Loan, *Matrix Computations*,
Baltimore, MD, Johns Hopkins University Press, 1985, pg. 15
Examples
--------
>>> from numpy import linalg as LA
>>> a = np.arange(9) - 4
>>> a
array([-4, -3, -2, -1, 0, 1, 2, 3, 4])
>>> b = a.reshape((3, 3))
>>> b
array([[-4, -3, -2],
[-1, 0, 1],
[ 2, 3, 4]])
>>> LA.norm(a)
7.745966692414834
>>> LA.norm(b)
7.745966692414834
>>> LA.norm(b, 'fro')
7.745966692414834
>>> LA.norm(a, np.inf)
4.0
>>> LA.norm(b, np.inf)
9.0
>>> LA.norm(a, -np.inf)
0.0
>>> LA.norm(b, -np.inf)
2.0
>>> LA.norm(a, 1)
20.0
>>> LA.norm(b, 1)
7.0
>>> LA.norm(a, -1)
-4.6566128774142013e-010
>>> LA.norm(b, -1)
6.0
>>> LA.norm(a, 2)
7.745966692414834
>>> LA.norm(b, 2)
7.3484692283495345
>>> LA.norm(a, -2)
nan
>>> LA.norm(b, -2)
1.8570331885190563e-016
>>> LA.norm(a, 3)
5.8480354764257312
>>> LA.norm(a, -3)
nan
Using the `axis` argument to compute vector norms:
>>> c = np.array([[ 1, 2, 3],
... [-1, 1, 4]])
>>> LA.norm(c, axis=0)
array([ 1.41421356, 2.23606798, 5. ])
>>> LA.norm(c, axis=1)
array([ 3.74165739, 4.24264069])
>>> LA.norm(c, ord=1, axis=1)
array([ 6., 6.])
Using the `axis` argument to compute matrix norms:
>>> m = np.arange(8).reshape(2,2,2)
>>> LA.norm(m, axis=(1,2))
array([ 3.74165739, 11.22497216])
>>> LA.norm(m[0, :, :]), LA.norm(m[1, :, :])
(3.7416573867739413, 11.224972160321824)
"""
x = asarray(x)
if not issubclass(x.dtype.type, (inexact, object_)):
x = x.astype(float)
# Immediately handle some default, simple, fast, and common cases.
if axis is None:
ndim = x.ndim
if ((ord is None) or
(ord in ('f', 'fro') and ndim == 2) or
(ord == 2 and ndim == 1)):
x = x.ravel(order='K')
if isComplexType(x.dtype.type):
sqnorm = dot(x.real, x.real) + dot(x.imag, x.imag)
else:
sqnorm = dot(x, x)
ret = sqrt(sqnorm)
if keepdims:
ret = ret.reshape(ndim*[1])
return ret
# Normalize the `axis` argument to a tuple.
nd = x.ndim
if axis is None:
axis = tuple(range(nd))
elif not isinstance(axis, tuple):
try:
axis = int(axis)
except Exception:
raise TypeError("'axis' must be None, an integer or a tuple of integers")
axis = (axis,)
if len(axis) == 1:
if ord == Inf:
return abs(x).max(axis=axis, keepdims=keepdims)
elif ord == -Inf:
return abs(x).min(axis=axis, keepdims=keepdims)
elif ord == 0:
# Zero norm
return (x != 0).astype(x.real.dtype).sum(axis=axis, keepdims=keepdims)
elif ord == 1:
# special case for speedup
return add.reduce(abs(x), axis=axis, keepdims=keepdims)
elif ord is None or ord == 2:
# special case for speedup
s = (x.conj() * x).real
return sqrt(add.reduce(s, axis=axis, keepdims=keepdims))
else:
try:
ord + 1
except TypeError:
raise ValueError("Invalid norm order for vectors.")
absx = abs(x)
absx **= ord
ret = add.reduce(absx, axis=axis, keepdims=keepdims)
ret **= (1 / ord)
return ret
elif len(axis) == 2:
row_axis, col_axis = axis
row_axis = normalize_axis_index(row_axis, nd)
col_axis = normalize_axis_index(col_axis, nd)
if row_axis == col_axis:
raise ValueError('Duplicate axes given.')
if ord == 2:
ret = _multi_svd_norm(x, row_axis, col_axis, amax)
elif ord == -2:
ret = _multi_svd_norm(x, row_axis, col_axis, amin)
elif ord == 1:
if col_axis > row_axis:
col_axis -= 1
ret = add.reduce(abs(x), axis=row_axis).max(axis=col_axis)
elif ord == Inf:
if row_axis > col_axis:
row_axis -= 1
ret = add.reduce(abs(x), axis=col_axis).max(axis=row_axis)
elif ord == -1:
if col_axis > row_axis:
col_axis -= 1
ret = add.reduce(abs(x), axis=row_axis).min(axis=col_axis)
elif ord == -Inf:
if row_axis > col_axis:
row_axis -= 1
ret = add.reduce(abs(x), axis=col_axis).min(axis=row_axis)
elif ord in [None, 'fro', 'f']:
ret = sqrt(add.reduce((x.conj() * x).real, axis=axis))
elif ord == 'nuc':
ret = _multi_svd_norm(x, row_axis, col_axis, sum)
else:
raise ValueError("Invalid norm order for matrices.")
if keepdims:
ret_shape = list(x.shape)
ret_shape[axis[0]] = 1
ret_shape[axis[1]] = 1
ret = ret.reshape(ret_shape)
return ret
else:
raise ValueError("Improper number of dimensions to norm.")
# multi_dot
def multi_dot(arrays):
"""
Compute the dot product of two or more arrays in a single function call,
while automatically selecting the fastest evaluation order.
`multi_dot` chains `numpy.dot` and uses optimal parenthesization
of the matrices [1]_ [2]_. Depending on the shapes of the matrices,
this can speed up the multiplication a lot.
If the first argument is 1-D it is treated as a row vector.
If the last argument is 1-D it is treated as a column vector.
The other arguments must be 2-D.
Think of `multi_dot` as::
def multi_dot(arrays): return functools.reduce(np.dot, arrays)
Parameters
----------
arrays : sequence of array_like
If the first argument is 1-D it is treated as row vector.
If the last argument is 1-D it is treated as column vector.
The other arguments must be 2-D.
Returns
-------
output : ndarray
Returns the dot product of the supplied arrays.
See Also
--------
dot : dot multiplication with two arguments.
References
----------
.. [1] Cormen, "Introduction to Algorithms", Chapter 15.2, p. 370-378
.. [2] http://en.wikipedia.org/wiki/Matrix_chain_multiplication
Examples
--------
`multi_dot` allows you to write::
>>> from numpy.linalg import multi_dot
>>> # Prepare some data
>>> A = np.random.random(10000, 100)
>>> B = np.random.random(100, 1000)
>>> C = np.random.random(1000, 5)
>>> D = np.random.random(5, 333)
>>> # the actual dot multiplication
>>> multi_dot([A, B, C, D])
instead of::
>>> np.dot(np.dot(np.dot(A, B), C), D)
>>> # or
>>> A.dot(B).dot(C).dot(D)
Notes
-----
The cost for a matrix multiplication can be calculated with the
following function::
def cost(A, B):
return A.shape[0] * A.shape[1] * B.shape[1]
Let's assume we have three matrices
:math:`A_{10x100}, B_{100x5}, C_{5x50}`.
The costs for the two different parenthesizations are as follows::
cost((AB)C) = 10*100*5 + 10*5*50 = 5000 + 2500 = 7500
cost(A(BC)) = 10*100*50 + 100*5*50 = 50000 + 25000 = 75000
"""
n = len(arrays)
# optimization only makes sense for len(arrays) > 2
if n < 2:
raise ValueError("Expecting at least two arrays.")
elif n == 2:
return dot(arrays[0], arrays[1])
arrays = [asanyarray(a) for a in arrays]
# save original ndim to reshape the result array into the proper form later
ndim_first, ndim_last = arrays[0].ndim, arrays[-1].ndim
# Explicitly convert vectors to 2D arrays to keep the logic of the internal
# _multi_dot_* functions as simple as possible.
if arrays[0].ndim == 1:
arrays[0] = atleast_2d(arrays[0])
if arrays[-1].ndim == 1:
arrays[-1] = atleast_2d(arrays[-1]).T
_assertRank2(*arrays)
# _multi_dot_three is much faster than _multi_dot_matrix_chain_order
if n == 3:
result = _multi_dot_three(arrays[0], arrays[1], arrays[2])
else:
order = _multi_dot_matrix_chain_order(arrays)
result = _multi_dot(arrays, order, 0, n - 1)
# return proper shape
if ndim_first == 1 and ndim_last == 1:
return result[0, 0] # scalar
elif ndim_first == 1 or ndim_last == 1:
return result.ravel() # 1-D
else:
return result
def _multi_dot_three(A, B, C):
"""
Find the best order for three arrays and do the multiplication.
For three arguments `_multi_dot_three` is approximately 15 times faster
than `_multi_dot_matrix_chain_order`
"""
a0, a1b0 = A.shape
b1c0, c1 = C.shape
# cost1 = cost((AB)C) = a0*a1b0*b1c0 + a0*b1c0*c1
cost1 = a0 * b1c0 * (a1b0 + c1)
# cost2 = cost(A(BC)) = a1b0*b1c0*c1 + a0*a1b0*c1
cost2 = a1b0 * c1 * (a0 + b1c0)
if cost1 < cost2:
return dot(dot(A, B), C)
else:
return dot(A, dot(B, C))
def _multi_dot_matrix_chain_order(arrays, return_costs=False):
"""
Return a np.array that encodes the optimal order of mutiplications.
The optimal order array is then used by `_multi_dot()` to do the
multiplication.
Also return the cost matrix if `return_costs` is `True`
The implementation CLOSELY follows Cormen, "Introduction to Algorithms",
Chapter 15.2, p. 370-378. Note that Cormen uses 1-based indices.
cost[i, j] = min([
cost[prefix] + cost[suffix] + cost_mult(prefix, suffix)
for k in range(i, j)])
"""
n = len(arrays)
# p stores the dimensions of the matrices
# Example for p: A_{10x100}, B_{100x5}, C_{5x50} --> p = [10, 100, 5, 50]
p = [a.shape[0] for a in arrays] + [arrays[-1].shape[1]]
# m is a matrix of costs of the subproblems
# m[i,j]: min number of scalar multiplications needed to compute A_{i..j}
m = zeros((n, n), dtype=double)
# s is the actual ordering
# s[i, j] is the value of k at which we split the product A_i..A_j
s = empty((n, n), dtype=intp)
for l in range(1, n):
for i in range(n - l):
j = i + l
m[i, j] = Inf
for k in range(i, j):
q = m[i, k] + m[k+1, j] + p[i]*p[k+1]*p[j+1]
if q < m[i, j]:
m[i, j] = q
s[i, j] = k # Note that Cormen uses 1-based index
return (s, m) if return_costs else s
def _multi_dot(arrays, order, i, j):
"""Actually do the multiplication with the given order."""
if i == j:
return arrays[i]
else:
return dot(_multi_dot(arrays, order, i, order[i, j]),
_multi_dot(arrays, order, order[i, j] + 1, j))
| 80,437 | 30.894528 | 85 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/linalg/setup.py
|
from __future__ import division, print_function
import os
import sys
def configuration(parent_package='', top_path=None):
from numpy.distutils.misc_util import Configuration
from numpy.distutils.system_info import get_info
config = Configuration('linalg', parent_package, top_path)
config.add_data_dir('tests')
# Configure lapack_lite
src_dir = 'lapack_lite'
lapack_lite_src = [
os.path.join(src_dir, 'python_xerbla.c'),
os.path.join(src_dir, 'f2c_z_lapack.c'),
os.path.join(src_dir, 'f2c_c_lapack.c'),
os.path.join(src_dir, 'f2c_d_lapack.c'),
os.path.join(src_dir, 'f2c_s_lapack.c'),
os.path.join(src_dir, 'f2c_lapack.c'),
os.path.join(src_dir, 'f2c_blas.c'),
os.path.join(src_dir, 'f2c_config.c'),
os.path.join(src_dir, 'f2c.c'),
]
all_sources = config.paths(lapack_lite_src)
lapack_info = get_info('lapack_opt', 0) # and {}
def get_lapack_lite_sources(ext, build_dir):
if not lapack_info:
print("### Warning: Using unoptimized lapack ###")
return all_sources
else:
if sys.platform == 'win32':
print("### Warning: python_xerbla.c is disabled ###")
return []
return [all_sources[0]]
config.add_extension(
'lapack_lite',
sources=['lapack_litemodule.c', get_lapack_lite_sources],
depends=['lapack_lite/f2c.h'],
extra_info=lapack_info,
)
# umath_linalg module
config.add_extension(
'_umath_linalg',
sources=['umath_linalg.c.src', get_lapack_lite_sources],
depends=['lapack_lite/f2c.h'],
extra_info=lapack_info,
libraries=['npymath'],
)
return config
if __name__ == '__main__':
from numpy.distutils.core import setup
setup(configuration=configuration)
| 1,878 | 29.803279 | 70 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/linalg/info.py
|
"""\
Core Linear Algebra Tools
-------------------------
Linear algebra basics:
- norm Vector or matrix norm
- inv Inverse of a square matrix
- solve Solve a linear system of equations
- det Determinant of a square matrix
- lstsq Solve linear least-squares problem
- pinv Pseudo-inverse (Moore-Penrose) calculated using a singular
value decomposition
- matrix_power Integer power of a square matrix
Eigenvalues and decompositions:
- eig Eigenvalues and vectors of a square matrix
- eigh Eigenvalues and eigenvectors of a Hermitian matrix
- eigvals Eigenvalues of a square matrix
- eigvalsh Eigenvalues of a Hermitian matrix
- qr QR decomposition of a matrix
- svd Singular value decomposition of a matrix
- cholesky Cholesky decomposition of a matrix
Tensor operations:
- tensorsolve Solve a linear tensor equation
- tensorinv Calculate an inverse of a tensor
Exceptions:
- LinAlgError Indicates a failed linear algebra operation
"""
from __future__ import division, absolute_import, print_function
depends = ['core']
| 1,198 | 30.552632 | 76 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/linalg/__init__.py
|
"""
Core Linear Algebra Tools
=========================
=============== ==========================================================
Linear algebra basics
==========================================================================
norm Vector or matrix norm
inv Inverse of a square matrix
solve Solve a linear system of equations
det Determinant of a square matrix
slogdet Logarithm of the determinant of a square matrix
lstsq Solve linear least-squares problem
pinv Pseudo-inverse (Moore-Penrose) calculated using a singular
value decomposition
matrix_power Integer power of a square matrix
matrix_rank Calculate matrix rank using an SVD-based method
=============== ==========================================================
=============== ==========================================================
Eigenvalues and decompositions
==========================================================================
eig Eigenvalues and vectors of a square matrix
eigh Eigenvalues and eigenvectors of a Hermitian matrix
eigvals Eigenvalues of a square matrix
eigvalsh Eigenvalues of a Hermitian matrix
qr QR decomposition of a matrix
svd Singular value decomposition of a matrix
cholesky Cholesky decomposition of a matrix
=============== ==========================================================
=============== ==========================================================
Tensor operations
==========================================================================
tensorsolve Solve a linear tensor equation
tensorinv Calculate an inverse of a tensor
=============== ==========================================================
=============== ==========================================================
Exceptions
==========================================================================
LinAlgError Indicates a failed linear algebra operation
=============== ==========================================================
"""
from __future__ import division, absolute_import, print_function
# To get sub-modules
from .info import __doc__
from .linalg import *
from numpy.testing import _numpy_tester
test = _numpy_tester().test
bench = _numpy_tester().bench
| 2,332 | 40.660714 | 74 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/linalg/tests/test_regression.py
|
""" Test functions for linalg module
"""
from __future__ import division, absolute_import, print_function
import warnings
import numpy as np
from numpy import linalg, arange, float64, array, dot, transpose
from numpy.testing import (
run_module_suite, assert_, assert_raises, assert_equal, assert_array_equal,
assert_array_almost_equal, assert_array_less
)
class TestRegression(object):
def test_eig_build(self):
# Ticket #652
rva = array([1.03221168e+02 + 0.j,
-1.91843603e+01 + 0.j,
-6.04004526e-01 + 15.84422474j,
-6.04004526e-01 - 15.84422474j,
-1.13692929e+01 + 0.j,
-6.57612485e-01 + 10.41755503j,
-6.57612485e-01 - 10.41755503j,
1.82126812e+01 + 0.j,
1.06011014e+01 + 0.j,
7.80732773e+00 + 0.j,
-7.65390898e-01 + 0.j,
1.51971555e-15 + 0.j,
-1.51308713e-15 + 0.j])
a = arange(13 * 13, dtype=float64)
a.shape = (13, 13)
a = a % 17
va, ve = linalg.eig(a)
va.sort()
rva.sort()
assert_array_almost_equal(va, rva)
def test_eigh_build(self):
# Ticket 662.
rvals = [68.60568999, 89.57756725, 106.67185574]
cov = array([[77.70273908, 3.51489954, 15.64602427],
[3.51489954, 88.97013878, -1.07431931],
[15.64602427, -1.07431931, 98.18223512]])
vals, vecs = linalg.eigh(cov)
assert_array_almost_equal(vals, rvals)
def test_svd_build(self):
# Ticket 627.
a = array([[0., 1.], [1., 1.], [2., 1.], [3., 1.]])
m, n = a.shape
u, s, vh = linalg.svd(a)
b = dot(transpose(u[:, n:]), a)
assert_array_almost_equal(b, np.zeros((2, 2)))
def test_norm_vector_badarg(self):
# Regression for #786: Froebenius norm for vectors raises
# TypeError.
assert_raises(ValueError, linalg.norm, array([1., 2., 3.]), 'fro')
def test_lapack_endian(self):
# For bug #1482
a = array([[5.7998084, -2.1825367],
[-2.1825367, 9.85910595]], dtype='>f8')
b = array(a, dtype='<f8')
ap = linalg.cholesky(a)
bp = linalg.cholesky(b)
assert_array_equal(ap, bp)
def test_large_svd_32bit(self):
# See gh-4442, 64bit would require very large/slow matrices.
x = np.eye(1000, 66)
np.linalg.svd(x)
def test_svd_no_uv(self):
# gh-4733
for shape in (3, 4), (4, 4), (4, 3):
for t in float, complex:
a = np.ones(shape, dtype=t)
w = linalg.svd(a, compute_uv=False)
c = np.count_nonzero(np.absolute(w) > 0.5)
assert_equal(c, 1)
assert_equal(np.linalg.matrix_rank(a), 1)
assert_array_less(1, np.linalg.norm(a, ord=2))
def test_norm_object_array(self):
# gh-7575
testvector = np.array([np.array([0, 1]), 0, 0], dtype=object)
norm = linalg.norm(testvector)
assert_array_equal(norm, [0, 1])
assert_(norm.dtype == np.dtype('float64'))
norm = linalg.norm(testvector, ord=1)
assert_array_equal(norm, [0, 1])
assert_(norm.dtype != np.dtype('float64'))
norm = linalg.norm(testvector, ord=2)
assert_array_equal(norm, [0, 1])
assert_(norm.dtype == np.dtype('float64'))
assert_raises(ValueError, linalg.norm, testvector, ord='fro')
assert_raises(ValueError, linalg.norm, testvector, ord='nuc')
assert_raises(ValueError, linalg.norm, testvector, ord=np.inf)
assert_raises(ValueError, linalg.norm, testvector, ord=-np.inf)
with warnings.catch_warnings():
warnings.simplefilter("error", DeprecationWarning)
assert_raises((AttributeError, DeprecationWarning),
linalg.norm, testvector, ord=0)
assert_raises(ValueError, linalg.norm, testvector, ord=-1)
assert_raises(ValueError, linalg.norm, testvector, ord=-2)
testmatrix = np.array([[np.array([0, 1]), 0, 0],
[0, 0, 0]], dtype=object)
norm = linalg.norm(testmatrix)
assert_array_equal(norm, [0, 1])
assert_(norm.dtype == np.dtype('float64'))
norm = linalg.norm(testmatrix, ord='fro')
assert_array_equal(norm, [0, 1])
assert_(norm.dtype == np.dtype('float64'))
assert_raises(TypeError, linalg.norm, testmatrix, ord='nuc')
assert_raises(ValueError, linalg.norm, testmatrix, ord=np.inf)
assert_raises(ValueError, linalg.norm, testmatrix, ord=-np.inf)
assert_raises(ValueError, linalg.norm, testmatrix, ord=0)
assert_raises(ValueError, linalg.norm, testmatrix, ord=1)
assert_raises(ValueError, linalg.norm, testmatrix, ord=-1)
assert_raises(TypeError, linalg.norm, testmatrix, ord=2)
assert_raises(TypeError, linalg.norm, testmatrix, ord=-2)
assert_raises(ValueError, linalg.norm, testmatrix, ord=3)
def test_lstsq_complex_larger_rhs(self):
# gh-9891
size = 20
n_rhs = 70
G = np.random.randn(size, size) + 1j * np.random.randn(size, size)
u = np.random.randn(size, n_rhs) + 1j * np.random.randn(size, n_rhs)
b = G.dot(u)
# This should work without segmentation fault.
u_lstsq, res, rank, sv = linalg.lstsq(G, b, rcond=None)
# check results just in case
assert_array_almost_equal(u_lstsq, u)
if __name__ == '__main__':
run_module_suite()
| 5,741 | 36.045161 | 79 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/linalg/tests/test_deprecations.py
|
"""Test deprecation and future warnings.
"""
from __future__ import division, absolute_import, print_function
import numpy as np
from numpy.testing import assert_warns, run_module_suite
def test_qr_mode_full_future_warning():
"""Check mode='full' FutureWarning.
In numpy 1.8 the mode options 'full' and 'economic' in linalg.qr were
deprecated. The release date will probably be sometime in the summer
of 2013.
"""
a = np.eye(2)
assert_warns(DeprecationWarning, np.linalg.qr, a, mode='full')
assert_warns(DeprecationWarning, np.linalg.qr, a, mode='f')
assert_warns(DeprecationWarning, np.linalg.qr, a, mode='economic')
assert_warns(DeprecationWarning, np.linalg.qr, a, mode='e')
if __name__ == "__main__":
run_module_suite()
| 776 | 27.777778 | 73 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/linalg/tests/test_build.py
|
from __future__ import division, absolute_import, print_function
from subprocess import PIPE, Popen
import sys
import re
from numpy.linalg import lapack_lite
from numpy.testing import run_module_suite, assert_, dec
class FindDependenciesLdd(object):
def __init__(self):
self.cmd = ['ldd']
try:
p = Popen(self.cmd, stdout=PIPE, stderr=PIPE)
stdout, stderr = p.communicate()
except OSError:
raise RuntimeError("command %s cannot be run" % self.cmd)
def get_dependencies(self, lfile):
p = Popen(self.cmd + [lfile], stdout=PIPE, stderr=PIPE)
stdout, stderr = p.communicate()
if not (p.returncode == 0):
raise RuntimeError("failed dependencies check for %s" % lfile)
return stdout
def grep_dependencies(self, lfile, deps):
stdout = self.get_dependencies(lfile)
rdeps = dict([(dep, re.compile(dep)) for dep in deps])
founds = []
for l in stdout.splitlines():
for k, v in rdeps.items():
if v.search(l):
founds.append(k)
return founds
class TestF77Mismatch(object):
@dec.skipif(not(sys.platform[:5] == 'linux'),
"Skipping fortran compiler mismatch on non Linux platform")
def test_lapack(self):
f = FindDependenciesLdd()
deps = f.grep_dependencies(lapack_lite.__file__,
[b'libg2c', b'libgfortran'])
assert_(len(deps) <= 1,
"""Both g77 and gfortran runtimes linked in lapack_lite ! This is likely to
cause random crashes and wrong results. See numpy INSTALL.txt for more
information.""")
if __name__ == "__main__":
run_module_suite()
| 1,756 | 29.293103 | 100 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/linalg/tests/test_linalg.py
|
""" Test functions for linalg module
"""
from __future__ import division, absolute_import, print_function
import os
import sys
import itertools
import traceback
import textwrap
import subprocess
import numpy as np
from numpy import array, single, double, csingle, cdouble, dot, identity
from numpy import multiply, atleast_2d, inf, asarray, matrix
from numpy import linalg
from numpy.linalg import matrix_power, norm, matrix_rank, multi_dot, LinAlgError
from numpy.linalg.linalg import _multi_dot_matrix_chain_order
from numpy.testing import (
assert_, assert_equal, assert_raises, assert_array_equal,
assert_almost_equal, assert_allclose, run_module_suite,
dec, SkipTest, suppress_warnings
)
def ifthen(a, b):
return not a or b
def imply(a, b):
return not a or b
old_assert_almost_equal = assert_almost_equal
def assert_almost_equal(a, b, single_decimal=6, double_decimal=12, **kw):
if asarray(a).dtype.type in (single, csingle):
decimal = single_decimal
else:
decimal = double_decimal
old_assert_almost_equal(a, b, decimal=decimal, **kw)
def get_real_dtype(dtype):
return {single: single, double: double,
csingle: single, cdouble: double}[dtype]
def get_complex_dtype(dtype):
return {single: csingle, double: cdouble,
csingle: csingle, cdouble: cdouble}[dtype]
def get_rtol(dtype):
# Choose a safe rtol
if dtype in (single, csingle):
return 1e-5
else:
return 1e-11
# used to categorize tests
all_tags = {
'square', 'nonsquare', 'hermitian', # mutually exclusive
'generalized', 'size-0', 'strided' # optional additions
}
class LinalgCase(object):
def __init__(self, name, a, b, tags=set()):
"""
A bundle of arguments to be passed to a test case, with an identifying
name, the operands a and b, and a set of tags to filter the tests
"""
assert_(isinstance(name, str))
self.name = name
self.a = a
self.b = b
self.tags = frozenset(tags) # prevent shared tags
def check(self, do):
"""
Run the function `do` on this test case, expanding arguments
"""
do(self.a, self.b, tags=self.tags)
def __repr__(self):
return "<LinalgCase: %s>" % (self.name,)
def apply_tag(tag, cases):
"""
Add the given tag (a string) to each of the cases (a list of LinalgCase
objects)
"""
assert tag in all_tags, "Invalid tag"
for case in cases:
case.tags = case.tags | {tag}
return cases
#
# Base test cases
#
np.random.seed(1234)
CASES = []
# square test cases
CASES += apply_tag('square', [
LinalgCase("single",
array([[1., 2.], [3., 4.]], dtype=single),
array([2., 1.], dtype=single)),
LinalgCase("double",
array([[1., 2.], [3., 4.]], dtype=double),
array([2., 1.], dtype=double)),
LinalgCase("double_2",
array([[1., 2.], [3., 4.]], dtype=double),
array([[2., 1., 4.], [3., 4., 6.]], dtype=double)),
LinalgCase("csingle",
array([[1. + 2j, 2 + 3j], [3 + 4j, 4 + 5j]], dtype=csingle),
array([2. + 1j, 1. + 2j], dtype=csingle)),
LinalgCase("cdouble",
array([[1. + 2j, 2 + 3j], [3 + 4j, 4 + 5j]], dtype=cdouble),
array([2. + 1j, 1. + 2j], dtype=cdouble)),
LinalgCase("cdouble_2",
array([[1. + 2j, 2 + 3j], [3 + 4j, 4 + 5j]], dtype=cdouble),
array([[2. + 1j, 1. + 2j, 1 + 3j], [1 - 2j, 1 - 3j, 1 - 6j]], dtype=cdouble)),
LinalgCase("0x0",
np.empty((0, 0), dtype=double),
np.empty((0,), dtype=double),
tags={'size-0'}),
LinalgCase("0x0_matrix",
np.empty((0, 0), dtype=double).view(np.matrix),
np.empty((0, 1), dtype=double).view(np.matrix),
tags={'size-0'}),
LinalgCase("8x8",
np.random.rand(8, 8),
np.random.rand(8)),
LinalgCase("1x1",
np.random.rand(1, 1),
np.random.rand(1)),
LinalgCase("nonarray",
[[1, 2], [3, 4]],
[2, 1]),
LinalgCase("matrix_b_only",
array([[1., 2.], [3., 4.]]),
matrix([2., 1.]).T),
LinalgCase("matrix_a_and_b",
matrix([[1., 2.], [3., 4.]]),
matrix([2., 1.]).T),
])
# non-square test-cases
CASES += apply_tag('nonsquare', [
LinalgCase("single_nsq_1",
array([[1., 2., 3.], [3., 4., 6.]], dtype=single),
array([2., 1.], dtype=single)),
LinalgCase("single_nsq_2",
array([[1., 2.], [3., 4.], [5., 6.]], dtype=single),
array([2., 1., 3.], dtype=single)),
LinalgCase("double_nsq_1",
array([[1., 2., 3.], [3., 4., 6.]], dtype=double),
array([2., 1.], dtype=double)),
LinalgCase("double_nsq_2",
array([[1., 2.], [3., 4.], [5., 6.]], dtype=double),
array([2., 1., 3.], dtype=double)),
LinalgCase("csingle_nsq_1",
array(
[[1. + 1j, 2. + 2j, 3. - 3j], [3. - 5j, 4. + 9j, 6. + 2j]], dtype=csingle),
array([2. + 1j, 1. + 2j], dtype=csingle)),
LinalgCase("csingle_nsq_2",
array(
[[1. + 1j, 2. + 2j], [3. - 3j, 4. - 9j], [5. - 4j, 6. + 8j]], dtype=csingle),
array([2. + 1j, 1. + 2j, 3. - 3j], dtype=csingle)),
LinalgCase("cdouble_nsq_1",
array(
[[1. + 1j, 2. + 2j, 3. - 3j], [3. - 5j, 4. + 9j, 6. + 2j]], dtype=cdouble),
array([2. + 1j, 1. + 2j], dtype=cdouble)),
LinalgCase("cdouble_nsq_2",
array(
[[1. + 1j, 2. + 2j], [3. - 3j, 4. - 9j], [5. - 4j, 6. + 8j]], dtype=cdouble),
array([2. + 1j, 1. + 2j, 3. - 3j], dtype=cdouble)),
LinalgCase("cdouble_nsq_1_2",
array(
[[1. + 1j, 2. + 2j, 3. - 3j], [3. - 5j, 4. + 9j, 6. + 2j]], dtype=cdouble),
array([[2. + 1j, 1. + 2j], [1 - 1j, 2 - 2j]], dtype=cdouble)),
LinalgCase("cdouble_nsq_2_2",
array(
[[1. + 1j, 2. + 2j], [3. - 3j, 4. - 9j], [5. - 4j, 6. + 8j]], dtype=cdouble),
array([[2. + 1j, 1. + 2j], [1 - 1j, 2 - 2j], [1 - 1j, 2 - 2j]], dtype=cdouble)),
LinalgCase("8x11",
np.random.rand(8, 11),
np.random.rand(8)),
LinalgCase("1x5",
np.random.rand(1, 5),
np.random.rand(1)),
LinalgCase("5x1",
np.random.rand(5, 1),
np.random.rand(5)),
LinalgCase("0x4",
np.random.rand(0, 4),
np.random.rand(0),
tags={'size-0'}),
LinalgCase("4x0",
np.random.rand(4, 0),
np.random.rand(4),
tags={'size-0'}),
])
# hermitian test-cases
CASES += apply_tag('hermitian', [
LinalgCase("hsingle",
array([[1., 2.], [2., 1.]], dtype=single),
None),
LinalgCase("hdouble",
array([[1., 2.], [2., 1.]], dtype=double),
None),
LinalgCase("hcsingle",
array([[1., 2 + 3j], [2 - 3j, 1]], dtype=csingle),
None),
LinalgCase("hcdouble",
array([[1., 2 + 3j], [2 - 3j, 1]], dtype=cdouble),
None),
LinalgCase("hempty",
np.empty((0, 0), dtype=double),
None,
tags={'size-0'}),
LinalgCase("hnonarray",
[[1, 2], [2, 1]],
None),
LinalgCase("matrix_b_only",
array([[1., 2.], [2., 1.]]),
None),
LinalgCase("hmatrix_a_and_b",
matrix([[1., 2.], [2., 1.]]),
None),
LinalgCase("hmatrix_1x1",
np.random.rand(1, 1),
None),
])
#
# Gufunc test cases
#
def _make_generalized_cases():
new_cases = []
for case in CASES:
if not isinstance(case.a, np.ndarray):
continue
a = np.array([case.a, 2 * case.a, 3 * case.a])
if case.b is None:
b = None
else:
b = np.array([case.b, 7 * case.b, 6 * case.b])
new_case = LinalgCase(case.name + "_tile3", a, b,
tags=case.tags | {'generalized'})
new_cases.append(new_case)
a = np.array([case.a] * 2 * 3).reshape((3, 2) + case.a.shape)
if case.b is None:
b = None
else:
b = np.array([case.b] * 2 * 3).reshape((3, 2) + case.b.shape)
new_case = LinalgCase(case.name + "_tile213", a, b,
tags=case.tags | {'generalized'})
new_cases.append(new_case)
return new_cases
CASES += _make_generalized_cases()
#
# Generate stride combination variations of the above
#
def _stride_comb_iter(x):
"""
Generate cartesian product of strides for all axes
"""
if not isinstance(x, np.ndarray):
yield x, "nop"
return
stride_set = [(1,)] * x.ndim
stride_set[-1] = (1, 3, -4)
if x.ndim > 1:
stride_set[-2] = (1, 3, -4)
if x.ndim > 2:
stride_set[-3] = (1, -4)
for repeats in itertools.product(*tuple(stride_set)):
new_shape = [abs(a * b) for a, b in zip(x.shape, repeats)]
slices = tuple([slice(None, None, repeat) for repeat in repeats])
# new array with different strides, but same data
xi = np.empty(new_shape, dtype=x.dtype)
xi.view(np.uint32).fill(0xdeadbeef)
xi = xi[slices]
xi[...] = x
xi = xi.view(x.__class__)
assert_(np.all(xi == x))
yield xi, "stride_" + "_".join(["%+d" % j for j in repeats])
# generate also zero strides if possible
if x.ndim >= 1 and x.shape[-1] == 1:
s = list(x.strides)
s[-1] = 0
xi = np.lib.stride_tricks.as_strided(x, strides=s)
yield xi, "stride_xxx_0"
if x.ndim >= 2 and x.shape[-2] == 1:
s = list(x.strides)
s[-2] = 0
xi = np.lib.stride_tricks.as_strided(x, strides=s)
yield xi, "stride_xxx_0_x"
if x.ndim >= 2 and x.shape[:-2] == (1, 1):
s = list(x.strides)
s[-1] = 0
s[-2] = 0
xi = np.lib.stride_tricks.as_strided(x, strides=s)
yield xi, "stride_xxx_0_0"
def _make_strided_cases():
new_cases = []
for case in CASES:
for a, a_label in _stride_comb_iter(case.a):
for b, b_label in _stride_comb_iter(case.b):
new_case = LinalgCase(case.name + "_" + a_label + "_" + b_label, a, b,
tags=case.tags | {'strided'})
new_cases.append(new_case)
return new_cases
CASES += _make_strided_cases()
#
# Test different routines against the above cases
#
def _check_cases(func, require=set(), exclude=set()):
"""
Run func on each of the cases with all of the tags in require, and none
of the tags in exclude
"""
for case in CASES:
# filter by require and exclude
if case.tags & require != require:
continue
if case.tags & exclude:
continue
try:
case.check(func)
except Exception:
msg = "In test case: %r\n\n" % case
msg += traceback.format_exc()
raise AssertionError(msg)
class LinalgSquareTestCase(object):
def test_sq_cases(self):
_check_cases(self.do, require={'square'}, exclude={'generalized', 'size-0'})
def test_empty_sq_cases(self):
_check_cases(self.do, require={'square', 'size-0'}, exclude={'generalized'})
class LinalgNonsquareTestCase(object):
def test_nonsq_cases(self):
_check_cases(self.do, require={'nonsquare'}, exclude={'generalized', 'size-0'})
def test_empty_nonsq_cases(self):
_check_cases(self.do, require={'nonsquare', 'size-0'}, exclude={'generalized'})
class HermitianTestCase(object):
def test_herm_cases(self):
_check_cases(self.do, require={'hermitian'}, exclude={'generalized', 'size-0'})
def test_empty_herm_cases(self):
_check_cases(self.do, require={'hermitian', 'size-0'}, exclude={'generalized'})
class LinalgGeneralizedSquareTestCase(object):
@dec.slow
def test_generalized_sq_cases(self):
_check_cases(self.do, require={'generalized', 'square'}, exclude={'size-0'})
@dec.slow
def test_generalized_empty_sq_cases(self):
_check_cases(self.do, require={'generalized', 'square', 'size-0'})
class LinalgGeneralizedNonsquareTestCase(object):
@dec.slow
def test_generalized_nonsq_cases(self):
_check_cases(self.do, require={'generalized', 'nonsquare'}, exclude={'size-0'})
@dec.slow
def test_generalized_empty_nonsq_cases(self):
_check_cases(self.do, require={'generalized', 'nonsquare', 'size-0'})
class HermitianGeneralizedTestCase(object):
@dec.slow
def test_generalized_herm_cases(self):
_check_cases(self.do,
require={'generalized', 'hermitian'},
exclude={'size-0'})
@dec.slow
def test_generalized_empty_herm_cases(self):
_check_cases(self.do,
require={'generalized', 'hermitian', 'size-0'},
exclude={'none'})
def dot_generalized(a, b):
a = asarray(a)
if a.ndim >= 3:
if a.ndim == b.ndim:
# matrix x matrix
new_shape = a.shape[:-1] + b.shape[-1:]
elif a.ndim == b.ndim + 1:
# matrix x vector
new_shape = a.shape[:-1]
else:
raise ValueError("Not implemented...")
r = np.empty(new_shape, dtype=np.common_type(a, b))
for c in itertools.product(*map(range, a.shape[:-2])):
r[c] = dot(a[c], b[c])
return r
else:
return dot(a, b)
def identity_like_generalized(a):
a = asarray(a)
if a.ndim >= 3:
r = np.empty(a.shape, dtype=a.dtype)
for c in itertools.product(*map(range, a.shape[:-2])):
r[c] = identity(a.shape[-2])
return r
else:
return identity(a.shape[0])
class TestSolve(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase):
def do(self, a, b, tags):
x = linalg.solve(a, b)
assert_almost_equal(b, dot_generalized(a, x))
assert_(imply(isinstance(b, matrix), isinstance(x, matrix)))
def test_types(self):
def check(dtype):
x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
assert_equal(linalg.solve(x, x).dtype, dtype)
for dtype in [single, double, csingle, cdouble]:
yield check, dtype
def test_0_size(self):
class ArraySubclass(np.ndarray):
pass
# Test system of 0x0 matrices
a = np.arange(8).reshape(2, 2, 2)
b = np.arange(6).reshape(1, 2, 3).view(ArraySubclass)
expected = linalg.solve(a, b)[:, 0:0, :]
result = linalg.solve(a[:, 0:0, 0:0], b[:, 0:0, :])
assert_array_equal(result, expected)
assert_(isinstance(result, ArraySubclass))
# Test errors for non-square and only b's dimension being 0
assert_raises(linalg.LinAlgError, linalg.solve, a[:, 0:0, 0:1], b)
assert_raises(ValueError, linalg.solve, a, b[:, 0:0, :])
# Test broadcasting error
b = np.arange(6).reshape(1, 3, 2) # broadcasting error
assert_raises(ValueError, linalg.solve, a, b)
assert_raises(ValueError, linalg.solve, a[0:0], b[0:0])
# Test zero "single equations" with 0x0 matrices.
b = np.arange(2).reshape(1, 2).view(ArraySubclass)
expected = linalg.solve(a, b)[:, 0:0]
result = linalg.solve(a[:, 0:0, 0:0], b[:, 0:0])
assert_array_equal(result, expected)
assert_(isinstance(result, ArraySubclass))
b = np.arange(3).reshape(1, 3)
assert_raises(ValueError, linalg.solve, a, b)
assert_raises(ValueError, linalg.solve, a[0:0], b[0:0])
assert_raises(ValueError, linalg.solve, a[:, 0:0, 0:0], b)
def test_0_size_k(self):
# test zero multiple equation (K=0) case.
class ArraySubclass(np.ndarray):
pass
a = np.arange(4).reshape(1, 2, 2)
b = np.arange(6).reshape(3, 2, 1).view(ArraySubclass)
expected = linalg.solve(a, b)[:, :, 0:0]
result = linalg.solve(a, b[:, :, 0:0])
assert_array_equal(result, expected)
assert_(isinstance(result, ArraySubclass))
# test both zero.
expected = linalg.solve(a, b)[:, 0:0, 0:0]
result = linalg.solve(a[:, 0:0, 0:0], b[:, 0:0, 0:0])
assert_array_equal(result, expected)
assert_(isinstance(result, ArraySubclass))
class TestInv(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase):
def do(self, a, b, tags):
a_inv = linalg.inv(a)
assert_almost_equal(dot_generalized(a, a_inv),
identity_like_generalized(a))
assert_(imply(isinstance(a, matrix), isinstance(a_inv, matrix)))
def test_types(self):
def check(dtype):
x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
assert_equal(linalg.inv(x).dtype, dtype)
for dtype in [single, double, csingle, cdouble]:
yield check, dtype
def test_0_size(self):
# Check that all kinds of 0-sized arrays work
class ArraySubclass(np.ndarray):
pass
a = np.zeros((0, 1, 1), dtype=np.int_).view(ArraySubclass)
res = linalg.inv(a)
assert_(res.dtype.type is np.float64)
assert_equal(a.shape, res.shape)
assert_(isinstance(res, ArraySubclass))
a = np.zeros((0, 0), dtype=np.complex64).view(ArraySubclass)
res = linalg.inv(a)
assert_(res.dtype.type is np.complex64)
assert_equal(a.shape, res.shape)
assert_(isinstance(res, ArraySubclass))
class TestEigvals(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase):
def do(self, a, b, tags):
ev = linalg.eigvals(a)
evalues, evectors = linalg.eig(a)
assert_almost_equal(ev, evalues)
def test_types(self):
def check(dtype):
x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
assert_equal(linalg.eigvals(x).dtype, dtype)
x = np.array([[1, 0.5], [-1, 1]], dtype=dtype)
assert_equal(linalg.eigvals(x).dtype, get_complex_dtype(dtype))
for dtype in [single, double, csingle, cdouble]:
yield check, dtype
def test_0_size(self):
# Check that all kinds of 0-sized arrays work
class ArraySubclass(np.ndarray):
pass
a = np.zeros((0, 1, 1), dtype=np.int_).view(ArraySubclass)
res = linalg.eigvals(a)
assert_(res.dtype.type is np.float64)
assert_equal((0, 1), res.shape)
# This is just for documentation, it might make sense to change:
assert_(isinstance(res, np.ndarray))
a = np.zeros((0, 0), dtype=np.complex64).view(ArraySubclass)
res = linalg.eigvals(a)
assert_(res.dtype.type is np.complex64)
assert_equal((0,), res.shape)
# This is just for documentation, it might make sense to change:
assert_(isinstance(res, np.ndarray))
class TestEig(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase):
def do(self, a, b, tags):
evalues, evectors = linalg.eig(a)
assert_allclose(dot_generalized(a, evectors),
np.asarray(evectors) * np.asarray(evalues)[..., None, :],
rtol=get_rtol(evalues.dtype))
assert_(imply(isinstance(a, matrix), isinstance(evectors, matrix)))
def test_types(self):
def check(dtype):
x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
w, v = np.linalg.eig(x)
assert_equal(w.dtype, dtype)
assert_equal(v.dtype, dtype)
x = np.array([[1, 0.5], [-1, 1]], dtype=dtype)
w, v = np.linalg.eig(x)
assert_equal(w.dtype, get_complex_dtype(dtype))
assert_equal(v.dtype, get_complex_dtype(dtype))
for dtype in [single, double, csingle, cdouble]:
yield check, dtype
def test_0_size(self):
# Check that all kinds of 0-sized arrays work
class ArraySubclass(np.ndarray):
pass
a = np.zeros((0, 1, 1), dtype=np.int_).view(ArraySubclass)
res, res_v = linalg.eig(a)
assert_(res_v.dtype.type is np.float64)
assert_(res.dtype.type is np.float64)
assert_equal(a.shape, res_v.shape)
assert_equal((0, 1), res.shape)
# This is just for documentation, it might make sense to change:
assert_(isinstance(a, np.ndarray))
a = np.zeros((0, 0), dtype=np.complex64).view(ArraySubclass)
res, res_v = linalg.eig(a)
assert_(res_v.dtype.type is np.complex64)
assert_(res.dtype.type is np.complex64)
assert_equal(a.shape, res_v.shape)
assert_equal((0,), res.shape)
# This is just for documentation, it might make sense to change:
assert_(isinstance(a, np.ndarray))
class TestSVD(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase):
def do(self, a, b, tags):
if 'size-0' in tags:
assert_raises(LinAlgError, linalg.svd, a, 0)
return
u, s, vt = linalg.svd(a, 0)
assert_allclose(a, dot_generalized(np.asarray(u) * np.asarray(s)[..., None, :],
np.asarray(vt)),
rtol=get_rtol(u.dtype))
assert_(imply(isinstance(a, matrix), isinstance(u, matrix)))
assert_(imply(isinstance(a, matrix), isinstance(vt, matrix)))
def test_types(self):
def check(dtype):
x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
u, s, vh = linalg.svd(x)
assert_equal(u.dtype, dtype)
assert_equal(s.dtype, get_real_dtype(dtype))
assert_equal(vh.dtype, dtype)
s = linalg.svd(x, compute_uv=False)
assert_equal(s.dtype, get_real_dtype(dtype))
for dtype in [single, double, csingle, cdouble]:
yield check, dtype
def test_0_size(self):
# These raise errors currently
# (which does not mean that it may not make sense)
a = np.zeros((0, 0), dtype=np.complex64)
assert_raises(linalg.LinAlgError, linalg.svd, a)
a = np.zeros((0, 1), dtype=np.complex64)
assert_raises(linalg.LinAlgError, linalg.svd, a)
a = np.zeros((1, 0), dtype=np.complex64)
assert_raises(linalg.LinAlgError, linalg.svd, a)
class TestCondSVD(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase):
def do(self, a, b, tags):
c = asarray(a) # a might be a matrix
if 'size-0' in tags:
assert_raises(LinAlgError, linalg.svd, c, compute_uv=False)
return
s = linalg.svd(c, compute_uv=False)
assert_almost_equal(
s[..., 0] / s[..., -1], linalg.cond(a),
single_decimal=5, double_decimal=11)
def test_stacked_arrays_explicitly(self):
A = np.array([[1., 2., 1.], [0, -2., 0], [6., 2., 3.]])
assert_equal(linalg.cond(A), linalg.cond(A[None, ...])[0])
class TestCond2(LinalgSquareTestCase):
def do(self, a, b, tags):
c = asarray(a) # a might be a matrix
if 'size-0' in tags:
assert_raises(LinAlgError, linalg.svd, c, compute_uv=False)
return
s = linalg.svd(c, compute_uv=False)
assert_almost_equal(
s[..., 0] / s[..., -1], linalg.cond(a, 2),
single_decimal=5, double_decimal=11)
def test_stacked_arrays_explicitly(self):
A = np.array([[1., 2., 1.], [0, -2., 0], [6., 2., 3.]])
assert_equal(linalg.cond(A, 2), linalg.cond(A[None, ...], 2)[0])
class TestCondInf(object):
def test(self):
A = array([[1., 0, 0], [0, -2., 0], [0, 0, 3.]])
assert_almost_equal(linalg.cond(A, inf), 3.)
class TestPinv(LinalgSquareTestCase,
LinalgNonsquareTestCase,
LinalgGeneralizedSquareTestCase,
LinalgGeneralizedNonsquareTestCase):
def do(self, a, b, tags):
a_ginv = linalg.pinv(a)
# `a @ a_ginv == I` does not hold if a is singular
dot = dot_generalized
assert_almost_equal(dot(dot(a, a_ginv), a), a, single_decimal=5, double_decimal=11)
assert_(imply(isinstance(a, matrix), isinstance(a_ginv, matrix)))
class TestDet(LinalgSquareTestCase, LinalgGeneralizedSquareTestCase):
def do(self, a, b, tags):
d = linalg.det(a)
(s, ld) = linalg.slogdet(a)
if asarray(a).dtype.type in (single, double):
ad = asarray(a).astype(double)
else:
ad = asarray(a).astype(cdouble)
ev = linalg.eigvals(ad)
assert_almost_equal(d, multiply.reduce(ev, axis=-1))
assert_almost_equal(s * np.exp(ld), multiply.reduce(ev, axis=-1))
s = np.atleast_1d(s)
ld = np.atleast_1d(ld)
m = (s != 0)
assert_almost_equal(np.abs(s[m]), 1)
assert_equal(ld[~m], -inf)
def test_zero(self):
assert_equal(linalg.det([[0.0]]), 0.0)
assert_equal(type(linalg.det([[0.0]])), double)
assert_equal(linalg.det([[0.0j]]), 0.0)
assert_equal(type(linalg.det([[0.0j]])), cdouble)
assert_equal(linalg.slogdet([[0.0]]), (0.0, -inf))
assert_equal(type(linalg.slogdet([[0.0]])[0]), double)
assert_equal(type(linalg.slogdet([[0.0]])[1]), double)
assert_equal(linalg.slogdet([[0.0j]]), (0.0j, -inf))
assert_equal(type(linalg.slogdet([[0.0j]])[0]), cdouble)
assert_equal(type(linalg.slogdet([[0.0j]])[1]), double)
def test_types(self):
def check(dtype):
x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
assert_equal(np.linalg.det(x).dtype, dtype)
ph, s = np.linalg.slogdet(x)
assert_equal(s.dtype, get_real_dtype(dtype))
assert_equal(ph.dtype, dtype)
for dtype in [single, double, csingle, cdouble]:
yield check, dtype
def test_0_size(self):
a = np.zeros((0, 0), dtype=np.complex64)
res = linalg.det(a)
assert_equal(res, 1.)
assert_(res.dtype.type is np.complex64)
res = linalg.slogdet(a)
assert_equal(res, (1, 0))
assert_(res[0].dtype.type is np.complex64)
assert_(res[1].dtype.type is np.float32)
a = np.zeros((0, 0), dtype=np.float64)
res = linalg.det(a)
assert_equal(res, 1.)
assert_(res.dtype.type is np.float64)
res = linalg.slogdet(a)
assert_equal(res, (1, 0))
assert_(res[0].dtype.type is np.float64)
assert_(res[1].dtype.type is np.float64)
class TestLstsq(LinalgSquareTestCase, LinalgNonsquareTestCase):
def do(self, a, b, tags):
if 'size-0' in tags:
assert_raises(LinAlgError, linalg.lstsq, a, b)
return
arr = np.asarray(a)
m, n = arr.shape
u, s, vt = linalg.svd(a, 0)
x, residuals, rank, sv = linalg.lstsq(a, b, rcond=-1)
if m <= n:
assert_almost_equal(b, dot(a, x))
assert_equal(rank, m)
else:
assert_equal(rank, n)
assert_almost_equal(sv, sv.__array_wrap__(s))
if rank == n and m > n:
expect_resids = (
np.asarray(abs(np.dot(a, x) - b)) ** 2).sum(axis=0)
expect_resids = np.asarray(expect_resids)
if np.asarray(b).ndim == 1:
expect_resids.shape = (1,)
assert_equal(residuals.shape, expect_resids.shape)
else:
expect_resids = np.array([]).view(type(x))
assert_almost_equal(residuals, expect_resids)
assert_(np.issubdtype(residuals.dtype, np.floating))
assert_(imply(isinstance(b, matrix), isinstance(x, matrix)))
assert_(imply(isinstance(b, matrix), isinstance(residuals, matrix)))
def test_future_rcond(self):
a = np.array([[0., 1., 0., 1., 2., 0.],
[0., 2., 0., 0., 1., 0.],
[1., 0., 1., 0., 0., 4.],
[0., 0., 0., 2., 3., 0.]]).T
b = np.array([1, 0, 0, 0, 0, 0])
with suppress_warnings() as sup:
w = sup.record(FutureWarning, "`rcond` parameter will change")
x, residuals, rank, s = linalg.lstsq(a, b)
assert_(rank == 4)
x, residuals, rank, s = linalg.lstsq(a, b, rcond=-1)
assert_(rank == 4)
x, residuals, rank, s = linalg.lstsq(a, b, rcond=None)
assert_(rank == 3)
# Warning should be raised exactly once (first command)
assert_(len(w) == 1)
class TestMatrixPower(object):
R90 = array([[0, 1], [-1, 0]])
Arb22 = array([[4, -7], [-2, 10]])
noninv = array([[1, 0], [0, 0]])
arbfloat = array([[0.1, 3.2], [1.2, 0.7]])
large = identity(10)
t = large[1, :].copy()
large[1, :] = large[0,:]
large[0, :] = t
def test_large_power(self):
assert_equal(
matrix_power(self.R90, 2 ** 100 + 2 ** 10 + 2 ** 5 + 1), self.R90)
def test_large_power_trailing_zero(self):
assert_equal(
matrix_power(self.R90, 2 ** 100 + 2 ** 10 + 2 ** 5), identity(2))
def testip_zero(self):
def tz(M):
mz = matrix_power(M, 0)
assert_equal(mz, identity(M.shape[0]))
assert_equal(mz.dtype, M.dtype)
for M in [self.Arb22, self.arbfloat, self.large]:
yield tz, M
def testip_one(self):
def tz(M):
mz = matrix_power(M, 1)
assert_equal(mz, M)
assert_equal(mz.dtype, M.dtype)
for M in [self.Arb22, self.arbfloat, self.large]:
yield tz, M
def testip_two(self):
def tz(M):
mz = matrix_power(M, 2)
assert_equal(mz, dot(M, M))
assert_equal(mz.dtype, M.dtype)
for M in [self.Arb22, self.arbfloat, self.large]:
yield tz, M
def testip_invert(self):
def tz(M):
mz = matrix_power(M, -1)
assert_almost_equal(identity(M.shape[0]), dot(mz, M))
for M in [self.R90, self.Arb22, self.arbfloat, self.large]:
yield tz, M
def test_invert_noninvertible(self):
import numpy.linalg
assert_raises(numpy.linalg.linalg.LinAlgError,
lambda: matrix_power(self.noninv, -1))
class TestBoolPower(object):
def test_square(self):
A = array([[True, False], [True, True]])
assert_equal(matrix_power(A, 2), A)
class TestEigvalsh(HermitianTestCase, HermitianGeneralizedTestCase):
def do(self, a, b, tags):
# note that eigenvalue arrays returned by eig must be sorted since
# their order isn't guaranteed.
ev = linalg.eigvalsh(a, 'L')
evalues, evectors = linalg.eig(a)
evalues.sort(axis=-1)
assert_allclose(ev, evalues, rtol=get_rtol(ev.dtype))
ev2 = linalg.eigvalsh(a, 'U')
assert_allclose(ev2, evalues, rtol=get_rtol(ev.dtype))
def test_types(self):
def check(dtype):
x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
w = np.linalg.eigvalsh(x)
assert_equal(w.dtype, get_real_dtype(dtype))
for dtype in [single, double, csingle, cdouble]:
yield check, dtype
def test_invalid(self):
x = np.array([[1, 0.5], [0.5, 1]], dtype=np.float32)
assert_raises(ValueError, np.linalg.eigvalsh, x, UPLO="lrong")
assert_raises(ValueError, np.linalg.eigvalsh, x, "lower")
assert_raises(ValueError, np.linalg.eigvalsh, x, "upper")
def test_UPLO(self):
Klo = np.array([[0, 0], [1, 0]], dtype=np.double)
Kup = np.array([[0, 1], [0, 0]], dtype=np.double)
tgt = np.array([-1, 1], dtype=np.double)
rtol = get_rtol(np.double)
# Check default is 'L'
w = np.linalg.eigvalsh(Klo)
assert_allclose(w, tgt, rtol=rtol)
# Check 'L'
w = np.linalg.eigvalsh(Klo, UPLO='L')
assert_allclose(w, tgt, rtol=rtol)
# Check 'l'
w = np.linalg.eigvalsh(Klo, UPLO='l')
assert_allclose(w, tgt, rtol=rtol)
# Check 'U'
w = np.linalg.eigvalsh(Kup, UPLO='U')
assert_allclose(w, tgt, rtol=rtol)
# Check 'u'
w = np.linalg.eigvalsh(Kup, UPLO='u')
assert_allclose(w, tgt, rtol=rtol)
def test_0_size(self):
# Check that all kinds of 0-sized arrays work
class ArraySubclass(np.ndarray):
pass
a = np.zeros((0, 1, 1), dtype=np.int_).view(ArraySubclass)
res = linalg.eigvalsh(a)
assert_(res.dtype.type is np.float64)
assert_equal((0, 1), res.shape)
# This is just for documentation, it might make sense to change:
assert_(isinstance(res, np.ndarray))
a = np.zeros((0, 0), dtype=np.complex64).view(ArraySubclass)
res = linalg.eigvalsh(a)
assert_(res.dtype.type is np.float32)
assert_equal((0,), res.shape)
# This is just for documentation, it might make sense to change:
assert_(isinstance(res, np.ndarray))
class TestEigh(HermitianTestCase, HermitianGeneralizedTestCase):
def do(self, a, b, tags):
# note that eigenvalue arrays returned by eig must be sorted since
# their order isn't guaranteed.
ev, evc = linalg.eigh(a)
evalues, evectors = linalg.eig(a)
evalues.sort(axis=-1)
assert_almost_equal(ev, evalues)
assert_allclose(dot_generalized(a, evc),
np.asarray(ev)[..., None, :] * np.asarray(evc),
rtol=get_rtol(ev.dtype))
ev2, evc2 = linalg.eigh(a, 'U')
assert_almost_equal(ev2, evalues)
assert_allclose(dot_generalized(a, evc2),
np.asarray(ev2)[..., None, :] * np.asarray(evc2),
rtol=get_rtol(ev.dtype), err_msg=repr(a))
def test_types(self):
def check(dtype):
x = np.array([[1, 0.5], [0.5, 1]], dtype=dtype)
w, v = np.linalg.eigh(x)
assert_equal(w.dtype, get_real_dtype(dtype))
assert_equal(v.dtype, dtype)
for dtype in [single, double, csingle, cdouble]:
yield check, dtype
def test_invalid(self):
x = np.array([[1, 0.5], [0.5, 1]], dtype=np.float32)
assert_raises(ValueError, np.linalg.eigh, x, UPLO="lrong")
assert_raises(ValueError, np.linalg.eigh, x, "lower")
assert_raises(ValueError, np.linalg.eigh, x, "upper")
def test_UPLO(self):
Klo = np.array([[0, 0], [1, 0]], dtype=np.double)
Kup = np.array([[0, 1], [0, 0]], dtype=np.double)
tgt = np.array([-1, 1], dtype=np.double)
rtol = get_rtol(np.double)
# Check default is 'L'
w, v = np.linalg.eigh(Klo)
assert_allclose(w, tgt, rtol=rtol)
# Check 'L'
w, v = np.linalg.eigh(Klo, UPLO='L')
assert_allclose(w, tgt, rtol=rtol)
# Check 'l'
w, v = np.linalg.eigh(Klo, UPLO='l')
assert_allclose(w, tgt, rtol=rtol)
# Check 'U'
w, v = np.linalg.eigh(Kup, UPLO='U')
assert_allclose(w, tgt, rtol=rtol)
# Check 'u'
w, v = np.linalg.eigh(Kup, UPLO='u')
assert_allclose(w, tgt, rtol=rtol)
def test_0_size(self):
# Check that all kinds of 0-sized arrays work
class ArraySubclass(np.ndarray):
pass
a = np.zeros((0, 1, 1), dtype=np.int_).view(ArraySubclass)
res, res_v = linalg.eigh(a)
assert_(res_v.dtype.type is np.float64)
assert_(res.dtype.type is np.float64)
assert_equal(a.shape, res_v.shape)
assert_equal((0, 1), res.shape)
# This is just for documentation, it might make sense to change:
assert_(isinstance(a, np.ndarray))
a = np.zeros((0, 0), dtype=np.complex64).view(ArraySubclass)
res, res_v = linalg.eigh(a)
assert_(res_v.dtype.type is np.complex64)
assert_(res.dtype.type is np.float32)
assert_equal(a.shape, res_v.shape)
assert_equal((0,), res.shape)
# This is just for documentation, it might make sense to change:
assert_(isinstance(a, np.ndarray))
class _TestNorm(object):
dt = None
dec = None
def test_empty(self):
assert_equal(norm([]), 0.0)
assert_equal(norm(array([], dtype=self.dt)), 0.0)
assert_equal(norm(atleast_2d(array([], dtype=self.dt))), 0.0)
def test_vector_return_type(self):
a = np.array([1, 0, 1])
exact_types = np.typecodes['AllInteger']
inexact_types = np.typecodes['AllFloat']
all_types = exact_types + inexact_types
for each_inexact_types in all_types:
at = a.astype(each_inexact_types)
an = norm(at, -np.inf)
assert_(issubclass(an.dtype.type, np.floating))
assert_almost_equal(an, 0.0)
with suppress_warnings() as sup:
sup.filter(RuntimeWarning, "divide by zero encountered")
an = norm(at, -1)
assert_(issubclass(an.dtype.type, np.floating))
assert_almost_equal(an, 0.0)
an = norm(at, 0)
assert_(issubclass(an.dtype.type, np.floating))
assert_almost_equal(an, 2)
an = norm(at, 1)
assert_(issubclass(an.dtype.type, np.floating))
assert_almost_equal(an, 2.0)
an = norm(at, 2)
assert_(issubclass(an.dtype.type, np.floating))
assert_almost_equal(an, an.dtype.type(2.0)**an.dtype.type(1.0/2.0))
an = norm(at, 4)
assert_(issubclass(an.dtype.type, np.floating))
assert_almost_equal(an, an.dtype.type(2.0)**an.dtype.type(1.0/4.0))
an = norm(at, np.inf)
assert_(issubclass(an.dtype.type, np.floating))
assert_almost_equal(an, 1.0)
def test_matrix_return_type(self):
a = np.array([[1, 0, 1], [0, 1, 1]])
exact_types = np.typecodes['AllInteger']
# float32, complex64, float64, complex128 types are the only types
# allowed by `linalg`, which performs the matrix operations used
# within `norm`.
inexact_types = 'fdFD'
all_types = exact_types + inexact_types
for each_inexact_types in all_types:
at = a.astype(each_inexact_types)
an = norm(at, -np.inf)
assert_(issubclass(an.dtype.type, np.floating))
assert_almost_equal(an, 2.0)
with suppress_warnings() as sup:
sup.filter(RuntimeWarning, "divide by zero encountered")
an = norm(at, -1)
assert_(issubclass(an.dtype.type, np.floating))
assert_almost_equal(an, 1.0)
an = norm(at, 1)
assert_(issubclass(an.dtype.type, np.floating))
assert_almost_equal(an, 2.0)
an = norm(at, 2)
assert_(issubclass(an.dtype.type, np.floating))
assert_almost_equal(an, 3.0**(1.0/2.0))
an = norm(at, -2)
assert_(issubclass(an.dtype.type, np.floating))
assert_almost_equal(an, 1.0)
an = norm(at, np.inf)
assert_(issubclass(an.dtype.type, np.floating))
assert_almost_equal(an, 2.0)
an = norm(at, 'fro')
assert_(issubclass(an.dtype.type, np.floating))
assert_almost_equal(an, 2.0)
an = norm(at, 'nuc')
assert_(issubclass(an.dtype.type, np.floating))
# Lower bar needed to support low precision floats.
# They end up being off by 1 in the 7th place.
old_assert_almost_equal(an, 2.7320508075688772, decimal=6)
def test_vector(self):
a = [1, 2, 3, 4]
b = [-1, -2, -3, -4]
c = [-1, 2, -3, 4]
def _test(v):
np.testing.assert_almost_equal(norm(v), 30 ** 0.5,
decimal=self.dec)
np.testing.assert_almost_equal(norm(v, inf), 4.0,
decimal=self.dec)
np.testing.assert_almost_equal(norm(v, -inf), 1.0,
decimal=self.dec)
np.testing.assert_almost_equal(norm(v, 1), 10.0,
decimal=self.dec)
np.testing.assert_almost_equal(norm(v, -1), 12.0 / 25,
decimal=self.dec)
np.testing.assert_almost_equal(norm(v, 2), 30 ** 0.5,
decimal=self.dec)
np.testing.assert_almost_equal(norm(v, -2), ((205. / 144) ** -0.5),
decimal=self.dec)
np.testing.assert_almost_equal(norm(v, 0), 4,
decimal=self.dec)
for v in (a, b, c,):
_test(v)
for v in (array(a, dtype=self.dt), array(b, dtype=self.dt),
array(c, dtype=self.dt)):
_test(v)
def test_matrix_2x2(self):
A = matrix([[1, 3], [5, 7]], dtype=self.dt)
assert_almost_equal(norm(A), 84 ** 0.5)
assert_almost_equal(norm(A, 'fro'), 84 ** 0.5)
assert_almost_equal(norm(A, 'nuc'), 10.0)
assert_almost_equal(norm(A, inf), 12.0)
assert_almost_equal(norm(A, -inf), 4.0)
assert_almost_equal(norm(A, 1), 10.0)
assert_almost_equal(norm(A, -1), 6.0)
assert_almost_equal(norm(A, 2), 9.1231056256176615)
assert_almost_equal(norm(A, -2), 0.87689437438234041)
assert_raises(ValueError, norm, A, 'nofro')
assert_raises(ValueError, norm, A, -3)
assert_raises(ValueError, norm, A, 0)
def test_matrix_3x3(self):
# This test has been added because the 2x2 example
# happened to have equal nuclear norm and induced 1-norm.
# The 1/10 scaling factor accommodates the absolute tolerance
# used in assert_almost_equal.
A = (1 / 10) * \
np.array([[1, 2, 3], [6, 0, 5], [3, 2, 1]], dtype=self.dt)
assert_almost_equal(norm(A), (1 / 10) * 89 ** 0.5)
assert_almost_equal(norm(A, 'fro'), (1 / 10) * 89 ** 0.5)
assert_almost_equal(norm(A, 'nuc'), 1.3366836911774836)
assert_almost_equal(norm(A, inf), 1.1)
assert_almost_equal(norm(A, -inf), 0.6)
assert_almost_equal(norm(A, 1), 1.0)
assert_almost_equal(norm(A, -1), 0.4)
assert_almost_equal(norm(A, 2), 0.88722940323461277)
assert_almost_equal(norm(A, -2), 0.19456584790481812)
def test_axis(self):
# Vector norms.
# Compare the use of `axis` with computing the norm of each row
# or column separately.
A = array([[1, 2, 3], [4, 5, 6]], dtype=self.dt)
for order in [None, -1, 0, 1, 2, 3, np.Inf, -np.Inf]:
expected0 = [norm(A[:, k], ord=order) for k in range(A.shape[1])]
assert_almost_equal(norm(A, ord=order, axis=0), expected0)
expected1 = [norm(A[k, :], ord=order) for k in range(A.shape[0])]
assert_almost_equal(norm(A, ord=order, axis=1), expected1)
# Matrix norms.
B = np.arange(1, 25, dtype=self.dt).reshape(2, 3, 4)
nd = B.ndim
for order in [None, -2, 2, -1, 1, np.Inf, -np.Inf, 'fro']:
for axis in itertools.combinations(range(-nd, nd), 2):
row_axis, col_axis = axis
if row_axis < 0:
row_axis += nd
if col_axis < 0:
col_axis += nd
if row_axis == col_axis:
assert_raises(ValueError, norm, B, ord=order, axis=axis)
else:
n = norm(B, ord=order, axis=axis)
# The logic using k_index only works for nd = 3.
# This has to be changed if nd is increased.
k_index = nd - (row_axis + col_axis)
if row_axis < col_axis:
expected = [norm(B[:].take(k, axis=k_index), ord=order)
for k in range(B.shape[k_index])]
else:
expected = [norm(B[:].take(k, axis=k_index).T, ord=order)
for k in range(B.shape[k_index])]
assert_almost_equal(n, expected)
def test_keepdims(self):
A = np.arange(1, 25, dtype=self.dt).reshape(2, 3, 4)
allclose_err = 'order {0}, axis = {1}'
shape_err = 'Shape mismatch found {0}, expected {1}, order={2}, axis={3}'
# check the order=None, axis=None case
expected = norm(A, ord=None, axis=None)
found = norm(A, ord=None, axis=None, keepdims=True)
assert_allclose(np.squeeze(found), expected,
err_msg=allclose_err.format(None, None))
expected_shape = (1, 1, 1)
assert_(found.shape == expected_shape,
shape_err.format(found.shape, expected_shape, None, None))
# Vector norms.
for order in [None, -1, 0, 1, 2, 3, np.Inf, -np.Inf]:
for k in range(A.ndim):
expected = norm(A, ord=order, axis=k)
found = norm(A, ord=order, axis=k, keepdims=True)
assert_allclose(np.squeeze(found), expected,
err_msg=allclose_err.format(order, k))
expected_shape = list(A.shape)
expected_shape[k] = 1
expected_shape = tuple(expected_shape)
assert_(found.shape == expected_shape,
shape_err.format(found.shape, expected_shape, order, k))
# Matrix norms.
for order in [None, -2, 2, -1, 1, np.Inf, -np.Inf, 'fro', 'nuc']:
for k in itertools.permutations(range(A.ndim), 2):
expected = norm(A, ord=order, axis=k)
found = norm(A, ord=order, axis=k, keepdims=True)
assert_allclose(np.squeeze(found), expected,
err_msg=allclose_err.format(order, k))
expected_shape = list(A.shape)
expected_shape[k[0]] = 1
expected_shape[k[1]] = 1
expected_shape = tuple(expected_shape)
assert_(found.shape == expected_shape,
shape_err.format(found.shape, expected_shape, order, k))
def test_bad_args(self):
# Check that bad arguments raise the appropriate exceptions.
A = array([[1, 2, 3], [4, 5, 6]], dtype=self.dt)
B = np.arange(1, 25, dtype=self.dt).reshape(2, 3, 4)
# Using `axis=<integer>` or passing in a 1-D array implies vector
# norms are being computed, so also using `ord='fro'`
# or `ord='nuc'` raises a ValueError.
assert_raises(ValueError, norm, A, 'fro', 0)
assert_raises(ValueError, norm, A, 'nuc', 0)
assert_raises(ValueError, norm, [3, 4], 'fro', None)
assert_raises(ValueError, norm, [3, 4], 'nuc', None)
# Similarly, norm should raise an exception when ord is any finite
# number other than 1, 2, -1 or -2 when computing matrix norms.
for order in [0, 3]:
assert_raises(ValueError, norm, A, order, None)
assert_raises(ValueError, norm, A, order, (0, 1))
assert_raises(ValueError, norm, B, order, (1, 2))
# Invalid axis
assert_raises(np.AxisError, norm, B, None, 3)
assert_raises(np.AxisError, norm, B, None, (2, 3))
assert_raises(ValueError, norm, B, None, (0, 1, 2))
class TestNorm_NonSystematic(object):
def test_longdouble_norm(self):
# Non-regression test: p-norm of longdouble would previously raise
# UnboundLocalError.
x = np.arange(10, dtype=np.longdouble)
old_assert_almost_equal(norm(x, ord=3), 12.65, decimal=2)
def test_intmin(self):
# Non-regression test: p-norm of signed integer would previously do
# float cast and abs in the wrong order.
x = np.array([-2 ** 31], dtype=np.int32)
old_assert_almost_equal(norm(x, ord=3), 2 ** 31, decimal=5)
def test_complex_high_ord(self):
# gh-4156
d = np.empty((2,), dtype=np.clongdouble)
d[0] = 6 + 7j
d[1] = -6 + 7j
res = 11.615898132184
old_assert_almost_equal(np.linalg.norm(d, ord=3), res, decimal=10)
d = d.astype(np.complex128)
old_assert_almost_equal(np.linalg.norm(d, ord=3), res, decimal=9)
d = d.astype(np.complex64)
old_assert_almost_equal(np.linalg.norm(d, ord=3), res, decimal=5)
class TestNormDouble(_TestNorm):
dt = np.double
dec = 12
class TestNormSingle(_TestNorm):
dt = np.float32
dec = 6
class TestNormInt64(_TestNorm):
dt = np.int64
dec = 12
class TestMatrixRank(object):
def test_matrix_rank(self):
# Full rank matrix
yield assert_equal, 4, matrix_rank(np.eye(4))
# rank deficient matrix
I = np.eye(4)
I[-1, -1] = 0.
yield assert_equal, matrix_rank(I), 3
# All zeros - zero rank
yield assert_equal, matrix_rank(np.zeros((4, 4))), 0
# 1 dimension - rank 1 unless all 0
yield assert_equal, matrix_rank([1, 0, 0, 0]), 1
yield assert_equal, matrix_rank(np.zeros((4,))), 0
# accepts array-like
yield assert_equal, matrix_rank([1]), 1
# greater than 2 dimensions treated as stacked matrices
ms = np.array([I, np.eye(4), np.zeros((4,4))])
yield assert_equal, matrix_rank(ms), np.array([3, 4, 0])
# works on scalar
yield assert_equal, matrix_rank(1), 1
def test_symmetric_rank(self):
yield assert_equal, 4, matrix_rank(np.eye(4), hermitian=True)
yield assert_equal, 1, matrix_rank(np.ones((4, 4)), hermitian=True)
yield assert_equal, 0, matrix_rank(np.zeros((4, 4)), hermitian=True)
# rank deficient matrix
I = np.eye(4)
I[-1, -1] = 0.
yield assert_equal, 3, matrix_rank(I, hermitian=True)
# manually supplied tolerance
I[-1, -1] = 1e-8
yield assert_equal, 4, matrix_rank(I, hermitian=True, tol=0.99e-8)
yield assert_equal, 3, matrix_rank(I, hermitian=True, tol=1.01e-8)
def test_reduced_rank():
# Test matrices with reduced rank
rng = np.random.RandomState(20120714)
for i in range(100):
# Make a rank deficient matrix
X = rng.normal(size=(40, 10))
X[:, 0] = X[:, 1] + X[:, 2]
# Assert that matrix_rank detected deficiency
assert_equal(matrix_rank(X), 9)
X[:, 3] = X[:, 4] + X[:, 5]
assert_equal(matrix_rank(X), 8)
class TestQR(object):
def check_qr(self, a):
# This test expects the argument `a` to be an ndarray or
# a subclass of an ndarray of inexact type.
a_type = type(a)
a_dtype = a.dtype
m, n = a.shape
k = min(m, n)
# mode == 'complete'
q, r = linalg.qr(a, mode='complete')
assert_(q.dtype == a_dtype)
assert_(r.dtype == a_dtype)
assert_(isinstance(q, a_type))
assert_(isinstance(r, a_type))
assert_(q.shape == (m, m))
assert_(r.shape == (m, n))
assert_almost_equal(dot(q, r), a)
assert_almost_equal(dot(q.T.conj(), q), np.eye(m))
assert_almost_equal(np.triu(r), r)
# mode == 'reduced'
q1, r1 = linalg.qr(a, mode='reduced')
assert_(q1.dtype == a_dtype)
assert_(r1.dtype == a_dtype)
assert_(isinstance(q1, a_type))
assert_(isinstance(r1, a_type))
assert_(q1.shape == (m, k))
assert_(r1.shape == (k, n))
assert_almost_equal(dot(q1, r1), a)
assert_almost_equal(dot(q1.T.conj(), q1), np.eye(k))
assert_almost_equal(np.triu(r1), r1)
# mode == 'r'
r2 = linalg.qr(a, mode='r')
assert_(r2.dtype == a_dtype)
assert_(isinstance(r2, a_type))
assert_almost_equal(r2, r1)
def test_qr_empty(self):
a = np.zeros((0, 2))
assert_raises(linalg.LinAlgError, linalg.qr, a)
def test_mode_raw(self):
# The factorization is not unique and varies between libraries,
# so it is not possible to check against known values. Functional
# testing is a possibility, but awaits the exposure of more
# of the functions in lapack_lite. Consequently, this test is
# very limited in scope. Note that the results are in FORTRAN
# order, hence the h arrays are transposed.
a = array([[1, 2], [3, 4], [5, 6]], dtype=np.double)
# Test double
h, tau = linalg.qr(a, mode='raw')
assert_(h.dtype == np.double)
assert_(tau.dtype == np.double)
assert_(h.shape == (2, 3))
assert_(tau.shape == (2,))
h, tau = linalg.qr(a.T, mode='raw')
assert_(h.dtype == np.double)
assert_(tau.dtype == np.double)
assert_(h.shape == (3, 2))
assert_(tau.shape == (2,))
def test_mode_all_but_economic(self):
a = array([[1, 2], [3, 4]])
b = array([[1, 2], [3, 4], [5, 6]])
for dt in "fd":
m1 = a.astype(dt)
m2 = b.astype(dt)
self.check_qr(m1)
self.check_qr(m2)
self.check_qr(m2.T)
self.check_qr(matrix(m1))
for dt in "fd":
m1 = 1 + 1j * a.astype(dt)
m2 = 1 + 1j * b.astype(dt)
self.check_qr(m1)
self.check_qr(m2)
self.check_qr(m2.T)
self.check_qr(matrix(m1))
def test_0_size(self):
# There may be good ways to do (some of this) reasonably:
a = np.zeros((0, 0))
assert_raises(linalg.LinAlgError, linalg.qr, a)
a = np.zeros((0, 1))
assert_raises(linalg.LinAlgError, linalg.qr, a)
a = np.zeros((1, 0))
assert_raises(linalg.LinAlgError, linalg.qr, a)
class TestCholesky(object):
# TODO: are there no other tests for cholesky?
def test_basic_property(self):
# Check A = L L^H
shapes = [(1, 1), (2, 2), (3, 3), (50, 50), (3, 10, 10)]
dtypes = (np.float32, np.float64, np.complex64, np.complex128)
for shape, dtype in itertools.product(shapes, dtypes):
np.random.seed(1)
a = np.random.randn(*shape)
if np.issubdtype(dtype, np.complexfloating):
a = a + 1j*np.random.randn(*shape)
t = list(range(len(shape)))
t[-2:] = -1, -2
a = np.matmul(a.transpose(t).conj(), a)
a = np.asarray(a, dtype=dtype)
c = np.linalg.cholesky(a)
b = np.matmul(c, c.transpose(t).conj())
assert_allclose(b, a,
err_msg="{} {}\n{}\n{}".format(shape, dtype, a, c),
atol=500 * a.shape[0] * np.finfo(dtype).eps)
def test_0_size(self):
class ArraySubclass(np.ndarray):
pass
a = np.zeros((0, 1, 1), dtype=np.int_).view(ArraySubclass)
res = linalg.cholesky(a)
assert_equal(a.shape, res.shape)
assert_(res.dtype.type is np.float64)
# for documentation purpose:
assert_(isinstance(res, np.ndarray))
a = np.zeros((1, 0, 0), dtype=np.complex64).view(ArraySubclass)
res = linalg.cholesky(a)
assert_equal(a.shape, res.shape)
assert_(res.dtype.type is np.complex64)
assert_(isinstance(res, np.ndarray))
def test_byteorder_check():
# Byte order check should pass for native order
if sys.byteorder == 'little':
native = '<'
else:
native = '>'
for dtt in (np.float32, np.float64):
arr = np.eye(4, dtype=dtt)
n_arr = arr.newbyteorder(native)
sw_arr = arr.newbyteorder('S').byteswap()
assert_equal(arr.dtype.byteorder, '=')
for routine in (linalg.inv, linalg.det, linalg.pinv):
# Normal call
res = routine(arr)
# Native but not '='
assert_array_equal(res, routine(n_arr))
# Swapped
assert_array_equal(res, routine(sw_arr))
def test_generalized_raise_multiloop():
# It should raise an error even if the error doesn't occur in the
# last iteration of the ufunc inner loop
invertible = np.array([[1, 2], [3, 4]])
non_invertible = np.array([[1, 1], [1, 1]])
x = np.zeros([4, 4, 2, 2])[1::2]
x[...] = invertible
x[0, 0] = non_invertible
assert_raises(np.linalg.LinAlgError, np.linalg.inv, x)
def test_xerbla_override():
# Check that our xerbla has been successfully linked in. If it is not,
# the default xerbla routine is called, which prints a message to stdout
# and may, or may not, abort the process depending on the LAPACK package.
XERBLA_OK = 255
try:
pid = os.fork()
except (OSError, AttributeError):
# fork failed, or not running on POSIX
raise SkipTest("Not POSIX or fork failed.")
if pid == 0:
# child; close i/o file handles
os.close(1)
os.close(0)
# Avoid producing core files.
import resource
resource.setrlimit(resource.RLIMIT_CORE, (0, 0))
# These calls may abort.
try:
np.linalg.lapack_lite.xerbla()
except ValueError:
pass
except Exception:
os._exit(os.EX_CONFIG)
try:
a = np.array([[1.]])
np.linalg.lapack_lite.dorgqr(
1, 1, 1, a,
0, # <- invalid value
a, a, 0, 0)
except ValueError as e:
if "DORGQR parameter number 5" in str(e):
# success, reuse error code to mark success as
# FORTRAN STOP returns as success.
os._exit(XERBLA_OK)
# Did not abort, but our xerbla was not linked in.
os._exit(os.EX_CONFIG)
else:
# parent
pid, status = os.wait()
if os.WEXITSTATUS(status) != XERBLA_OK:
raise SkipTest('Numpy xerbla not linked in.')
def test_sdot_bug_8577():
# Regression test that loading certain other libraries does not
# result to wrong results in float32 linear algebra.
#
# There's a bug gh-8577 on OSX that can trigger this, and perhaps
# there are also other situations in which it occurs.
#
# Do the check in a separate process.
bad_libs = ['PyQt5.QtWidgets', 'IPython']
template = textwrap.dedent("""
import sys
{before}
try:
import {bad_lib}
except ImportError:
sys.exit(0)
{after}
x = np.ones(2, dtype=np.float32)
sys.exit(0 if np.allclose(x.dot(x), 2.0) else 1)
""")
for bad_lib in bad_libs:
code = template.format(before="import numpy as np", after="",
bad_lib=bad_lib)
subprocess.check_call([sys.executable, "-c", code])
# Swapped import order
code = template.format(after="import numpy as np", before="",
bad_lib=bad_lib)
subprocess.check_call([sys.executable, "-c", code])
class TestMultiDot(object):
def test_basic_function_with_three_arguments(self):
# multi_dot with three arguments uses a fast hand coded algorithm to
# determine the optimal order. Therefore test it separately.
A = np.random.random((6, 2))
B = np.random.random((2, 6))
C = np.random.random((6, 2))
assert_almost_equal(multi_dot([A, B, C]), A.dot(B).dot(C))
assert_almost_equal(multi_dot([A, B, C]), np.dot(A, np.dot(B, C)))
def test_basic_function_with_dynamic_programing_optimization(self):
# multi_dot with four or more arguments uses the dynamic programing
# optimization and therefore deserve a separate
A = np.random.random((6, 2))
B = np.random.random((2, 6))
C = np.random.random((6, 2))
D = np.random.random((2, 1))
assert_almost_equal(multi_dot([A, B, C, D]), A.dot(B).dot(C).dot(D))
def test_vector_as_first_argument(self):
# The first argument can be 1-D
A1d = np.random.random(2) # 1-D
B = np.random.random((2, 6))
C = np.random.random((6, 2))
D = np.random.random((2, 2))
# the result should be 1-D
assert_equal(multi_dot([A1d, B, C, D]).shape, (2,))
def test_vector_as_last_argument(self):
# The last argument can be 1-D
A = np.random.random((6, 2))
B = np.random.random((2, 6))
C = np.random.random((6, 2))
D1d = np.random.random(2) # 1-D
# the result should be 1-D
assert_equal(multi_dot([A, B, C, D1d]).shape, (6,))
def test_vector_as_first_and_last_argument(self):
# The first and last arguments can be 1-D
A1d = np.random.random(2) # 1-D
B = np.random.random((2, 6))
C = np.random.random((6, 2))
D1d = np.random.random(2) # 1-D
# the result should be a scalar
assert_equal(multi_dot([A1d, B, C, D1d]).shape, ())
def test_dynamic_programming_logic(self):
# Test for the dynamic programming part
# This test is directly taken from Cormen page 376.
arrays = [np.random.random((30, 35)),
np.random.random((35, 15)),
np.random.random((15, 5)),
np.random.random((5, 10)),
np.random.random((10, 20)),
np.random.random((20, 25))]
m_expected = np.array([[0., 15750., 7875., 9375., 11875., 15125.],
[0., 0., 2625., 4375., 7125., 10500.],
[0., 0., 0., 750., 2500., 5375.],
[0., 0., 0., 0., 1000., 3500.],
[0., 0., 0., 0., 0., 5000.],
[0., 0., 0., 0., 0., 0.]])
s_expected = np.array([[0, 1, 1, 3, 3, 3],
[0, 0, 2, 3, 3, 3],
[0, 0, 0, 3, 3, 3],
[0, 0, 0, 0, 4, 5],
[0, 0, 0, 0, 0, 5],
[0, 0, 0, 0, 0, 0]], dtype=int)
s_expected -= 1 # Cormen uses 1-based index, python does not.
s, m = _multi_dot_matrix_chain_order(arrays, return_costs=True)
# Only the upper triangular part (without the diagonal) is interesting.
assert_almost_equal(np.triu(s[:-1, 1:]),
np.triu(s_expected[:-1, 1:]))
assert_almost_equal(np.triu(m), np.triu(m_expected))
def test_too_few_input_arrays(self):
assert_raises(ValueError, multi_dot, [])
assert_raises(ValueError, multi_dot, [np.random.random((3, 3))])
if __name__ == "__main__":
run_module_suite()
| 63,459 | 35.097838 | 96 |
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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/linalg/tests/__init__.py
| 0 | 0 | 0 |
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cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/polynomial/legendre.py
|
"""
Legendre Series (:mod: `numpy.polynomial.legendre`)
===================================================
.. currentmodule:: numpy.polynomial.polynomial
This module provides a number of objects (mostly functions) useful for
dealing with Legendre series, including a `Legendre` class that
encapsulates the usual arithmetic operations. (General information
on how this module represents and works with such polynomials is in the
docstring for its "parent" sub-package, `numpy.polynomial`).
Constants
---------
.. autosummary::
:toctree: generated/
legdomain Legendre series default domain, [-1,1].
legzero Legendre series that evaluates identically to 0.
legone Legendre series that evaluates identically to 1.
legx Legendre series for the identity map, ``f(x) = x``.
Arithmetic
----------
.. autosummary::
:toctree: generated/
legmulx multiply a Legendre series in P_i(x) by x.
legadd add two Legendre series.
legsub subtract one Legendre series from another.
legmul multiply two Legendre series.
legdiv divide one Legendre series by another.
legpow raise a Legendre series to an positive integer power
legval evaluate a Legendre series at given points.
legval2d evaluate a 2D Legendre series at given points.
legval3d evaluate a 3D Legendre series at given points.
leggrid2d evaluate a 2D Legendre series on a Cartesian product.
leggrid3d evaluate a 3D Legendre series on a Cartesian product.
Calculus
--------
.. autosummary::
:toctree: generated/
legder differentiate a Legendre series.
legint integrate a Legendre series.
Misc Functions
--------------
.. autosummary::
:toctree: generated/
legfromroots create a Legendre series with specified roots.
legroots find the roots of a Legendre series.
legvander Vandermonde-like matrix for Legendre polynomials.
legvander2d Vandermonde-like matrix for 2D power series.
legvander3d Vandermonde-like matrix for 3D power series.
leggauss Gauss-Legendre quadrature, points and weights.
legweight Legendre weight function.
legcompanion symmetrized companion matrix in Legendre form.
legfit least-squares fit returning a Legendre series.
legtrim trim leading coefficients from a Legendre series.
legline Legendre series representing given straight line.
leg2poly convert a Legendre series to a polynomial.
poly2leg convert a polynomial to a Legendre series.
Classes
-------
Legendre A Legendre series class.
See also
--------
numpy.polynomial.polynomial
numpy.polynomial.chebyshev
numpy.polynomial.laguerre
numpy.polynomial.hermite
numpy.polynomial.hermite_e
"""
from __future__ import division, absolute_import, print_function
import warnings
import numpy as np
import numpy.linalg as la
from numpy.core.multiarray import normalize_axis_index
from . import polyutils as pu
from ._polybase import ABCPolyBase
__all__ = [
'legzero', 'legone', 'legx', 'legdomain', 'legline', 'legadd',
'legsub', 'legmulx', 'legmul', 'legdiv', 'legpow', 'legval', 'legder',
'legint', 'leg2poly', 'poly2leg', 'legfromroots', 'legvander',
'legfit', 'legtrim', 'legroots', 'Legendre', 'legval2d', 'legval3d',
'leggrid2d', 'leggrid3d', 'legvander2d', 'legvander3d', 'legcompanion',
'leggauss', 'legweight']
legtrim = pu.trimcoef
def poly2leg(pol):
"""
Convert a polynomial to a Legendre series.
Convert an array representing the coefficients of a polynomial (relative
to the "standard" basis) ordered from lowest degree to highest, to an
array of the coefficients of the equivalent Legendre series, ordered
from lowest to highest degree.
Parameters
----------
pol : array_like
1-D array containing the polynomial coefficients
Returns
-------
c : ndarray
1-D array containing the coefficients of the equivalent Legendre
series.
See Also
--------
leg2poly
Notes
-----
The easy way to do conversions between polynomial basis sets
is to use the convert method of a class instance.
Examples
--------
>>> from numpy import polynomial as P
>>> p = P.Polynomial(np.arange(4))
>>> p
Polynomial([ 0., 1., 2., 3.], domain=[-1, 1], window=[-1, 1])
>>> c = P.Legendre(P.legendre.poly2leg(p.coef))
>>> c
Legendre([ 1. , 3.25, 1. , 0.75], domain=[-1, 1], window=[-1, 1])
"""
[pol] = pu.as_series([pol])
deg = len(pol) - 1
res = 0
for i in range(deg, -1, -1):
res = legadd(legmulx(res), pol[i])
return res
def leg2poly(c):
"""
Convert a Legendre series to a polynomial.
Convert an array representing the coefficients of a Legendre series,
ordered from lowest degree to highest, to an array of the coefficients
of the equivalent polynomial (relative to the "standard" basis) ordered
from lowest to highest degree.
Parameters
----------
c : array_like
1-D array containing the Legendre series coefficients, ordered
from lowest order term to highest.
Returns
-------
pol : ndarray
1-D array containing the coefficients of the equivalent polynomial
(relative to the "standard" basis) ordered from lowest order term
to highest.
See Also
--------
poly2leg
Notes
-----
The easy way to do conversions between polynomial basis sets
is to use the convert method of a class instance.
Examples
--------
>>> c = P.Legendre(range(4))
>>> c
Legendre([ 0., 1., 2., 3.], [-1., 1.])
>>> p = c.convert(kind=P.Polynomial)
>>> p
Polynomial([-1. , -3.5, 3. , 7.5], [-1., 1.])
>>> P.leg2poly(range(4))
array([-1. , -3.5, 3. , 7.5])
"""
from .polynomial import polyadd, polysub, polymulx
[c] = pu.as_series([c])
n = len(c)
if n < 3:
return c
else:
c0 = c[-2]
c1 = c[-1]
# i is the current degree of c1
for i in range(n - 1, 1, -1):
tmp = c0
c0 = polysub(c[i - 2], (c1*(i - 1))/i)
c1 = polyadd(tmp, (polymulx(c1)*(2*i - 1))/i)
return polyadd(c0, polymulx(c1))
#
# These are constant arrays are of integer type so as to be compatible
# with the widest range of other types, such as Decimal.
#
# Legendre
legdomain = np.array([-1, 1])
# Legendre coefficients representing zero.
legzero = np.array([0])
# Legendre coefficients representing one.
legone = np.array([1])
# Legendre coefficients representing the identity x.
legx = np.array([0, 1])
def legline(off, scl):
"""
Legendre series whose graph is a straight line.
Parameters
----------
off, scl : scalars
The specified line is given by ``off + scl*x``.
Returns
-------
y : ndarray
This module's representation of the Legendre series for
``off + scl*x``.
See Also
--------
polyline, chebline
Examples
--------
>>> import numpy.polynomial.legendre as L
>>> L.legline(3,2)
array([3, 2])
>>> L.legval(-3, L.legline(3,2)) # should be -3
-3.0
"""
if scl != 0:
return np.array([off, scl])
else:
return np.array([off])
def legfromroots(roots):
"""
Generate a Legendre series with given roots.
The function returns the coefficients of the polynomial
.. math:: p(x) = (x - r_0) * (x - r_1) * ... * (x - r_n),
in Legendre form, where the `r_n` are the roots specified in `roots`.
If a zero has multiplicity n, then it must appear in `roots` n times.
For instance, if 2 is a root of multiplicity three and 3 is a root of
multiplicity 2, then `roots` looks something like [2, 2, 2, 3, 3]. The
roots can appear in any order.
If the returned coefficients are `c`, then
.. math:: p(x) = c_0 + c_1 * L_1(x) + ... + c_n * L_n(x)
The coefficient of the last term is not generally 1 for monic
polynomials in Legendre form.
Parameters
----------
roots : array_like
Sequence containing the roots.
Returns
-------
out : ndarray
1-D array of coefficients. If all roots are real then `out` is a
real array, if some of the roots are complex, then `out` is complex
even if all the coefficients in the result are real (see Examples
below).
See Also
--------
polyfromroots, chebfromroots, lagfromroots, hermfromroots,
hermefromroots.
Examples
--------
>>> import numpy.polynomial.legendre as L
>>> L.legfromroots((-1,0,1)) # x^3 - x relative to the standard basis
array([ 0. , -0.4, 0. , 0.4])
>>> j = complex(0,1)
>>> L.legfromroots((-j,j)) # x^2 + 1 relative to the standard basis
array([ 1.33333333+0.j, 0.00000000+0.j, 0.66666667+0.j])
"""
if len(roots) == 0:
return np.ones(1)
else:
[roots] = pu.as_series([roots], trim=False)
roots.sort()
p = [legline(-r, 1) for r in roots]
n = len(p)
while n > 1:
m, r = divmod(n, 2)
tmp = [legmul(p[i], p[i+m]) for i in range(m)]
if r:
tmp[0] = legmul(tmp[0], p[-1])
p = tmp
n = m
return p[0]
def legadd(c1, c2):
"""
Add one Legendre series to another.
Returns the sum of two Legendre series `c1` + `c2`. The arguments
are sequences of coefficients ordered from lowest order term to
highest, i.e., [1,2,3] represents the series ``P_0 + 2*P_1 + 3*P_2``.
Parameters
----------
c1, c2 : array_like
1-D arrays of Legendre series coefficients ordered from low to
high.
Returns
-------
out : ndarray
Array representing the Legendre series of their sum.
See Also
--------
legsub, legmul, legdiv, legpow
Notes
-----
Unlike multiplication, division, etc., the sum of two Legendre series
is a Legendre series (without having to "reproject" the result onto
the basis set) so addition, just like that of "standard" polynomials,
is simply "component-wise."
Examples
--------
>>> from numpy.polynomial import legendre as L
>>> c1 = (1,2,3)
>>> c2 = (3,2,1)
>>> L.legadd(c1,c2)
array([ 4., 4., 4.])
"""
# c1, c2 are trimmed copies
[c1, c2] = pu.as_series([c1, c2])
if len(c1) > len(c2):
c1[:c2.size] += c2
ret = c1
else:
c2[:c1.size] += c1
ret = c2
return pu.trimseq(ret)
def legsub(c1, c2):
"""
Subtract one Legendre series from another.
Returns the difference of two Legendre series `c1` - `c2`. The
sequences of coefficients are from lowest order term to highest, i.e.,
[1,2,3] represents the series ``P_0 + 2*P_1 + 3*P_2``.
Parameters
----------
c1, c2 : array_like
1-D arrays of Legendre series coefficients ordered from low to
high.
Returns
-------
out : ndarray
Of Legendre series coefficients representing their difference.
See Also
--------
legadd, legmul, legdiv, legpow
Notes
-----
Unlike multiplication, division, etc., the difference of two Legendre
series is a Legendre series (without having to "reproject" the result
onto the basis set) so subtraction, just like that of "standard"
polynomials, is simply "component-wise."
Examples
--------
>>> from numpy.polynomial import legendre as L
>>> c1 = (1,2,3)
>>> c2 = (3,2,1)
>>> L.legsub(c1,c2)
array([-2., 0., 2.])
>>> L.legsub(c2,c1) # -C.legsub(c1,c2)
array([ 2., 0., -2.])
"""
# c1, c2 are trimmed copies
[c1, c2] = pu.as_series([c1, c2])
if len(c1) > len(c2):
c1[:c2.size] -= c2
ret = c1
else:
c2 = -c2
c2[:c1.size] += c1
ret = c2
return pu.trimseq(ret)
def legmulx(c):
"""Multiply a Legendre series by x.
Multiply the Legendre series `c` by x, where x is the independent
variable.
Parameters
----------
c : array_like
1-D array of Legendre series coefficients ordered from low to
high.
Returns
-------
out : ndarray
Array representing the result of the multiplication.
Notes
-----
The multiplication uses the recursion relationship for Legendre
polynomials in the form
.. math::
xP_i(x) = ((i + 1)*P_{i + 1}(x) + i*P_{i - 1}(x))/(2i + 1)
"""
# c is a trimmed copy
[c] = pu.as_series([c])
# The zero series needs special treatment
if len(c) == 1 and c[0] == 0:
return c
prd = np.empty(len(c) + 1, dtype=c.dtype)
prd[0] = c[0]*0
prd[1] = c[0]
for i in range(1, len(c)):
j = i + 1
k = i - 1
s = i + j
prd[j] = (c[i]*j)/s
prd[k] += (c[i]*i)/s
return prd
def legmul(c1, c2):
"""
Multiply one Legendre series by another.
Returns the product of two Legendre series `c1` * `c2`. The arguments
are sequences of coefficients, from lowest order "term" to highest,
e.g., [1,2,3] represents the series ``P_0 + 2*P_1 + 3*P_2``.
Parameters
----------
c1, c2 : array_like
1-D arrays of Legendre series coefficients ordered from low to
high.
Returns
-------
out : ndarray
Of Legendre series coefficients representing their product.
See Also
--------
legadd, legsub, legdiv, legpow
Notes
-----
In general, the (polynomial) product of two C-series results in terms
that are not in the Legendre polynomial basis set. Thus, to express
the product as a Legendre series, it is necessary to "reproject" the
product onto said basis set, which may produce "unintuitive" (but
correct) results; see Examples section below.
Examples
--------
>>> from numpy.polynomial import legendre as L
>>> c1 = (1,2,3)
>>> c2 = (3,2)
>>> P.legmul(c1,c2) # multiplication requires "reprojection"
array([ 4.33333333, 10.4 , 11.66666667, 3.6 ])
"""
# s1, s2 are trimmed copies
[c1, c2] = pu.as_series([c1, c2])
if len(c1) > len(c2):
c = c2
xs = c1
else:
c = c1
xs = c2
if len(c) == 1:
c0 = c[0]*xs
c1 = 0
elif len(c) == 2:
c0 = c[0]*xs
c1 = c[1]*xs
else:
nd = len(c)
c0 = c[-2]*xs
c1 = c[-1]*xs
for i in range(3, len(c) + 1):
tmp = c0
nd = nd - 1
c0 = legsub(c[-i]*xs, (c1*(nd - 1))/nd)
c1 = legadd(tmp, (legmulx(c1)*(2*nd - 1))/nd)
return legadd(c0, legmulx(c1))
def legdiv(c1, c2):
"""
Divide one Legendre series by another.
Returns the quotient-with-remainder of two Legendre series
`c1` / `c2`. The arguments are sequences of coefficients from lowest
order "term" to highest, e.g., [1,2,3] represents the series
``P_0 + 2*P_1 + 3*P_2``.
Parameters
----------
c1, c2 : array_like
1-D arrays of Legendre series coefficients ordered from low to
high.
Returns
-------
quo, rem : ndarrays
Of Legendre series coefficients representing the quotient and
remainder.
See Also
--------
legadd, legsub, legmul, legpow
Notes
-----
In general, the (polynomial) division of one Legendre series by another
results in quotient and remainder terms that are not in the Legendre
polynomial basis set. Thus, to express these results as a Legendre
series, it is necessary to "reproject" the results onto the Legendre
basis set, which may produce "unintuitive" (but correct) results; see
Examples section below.
Examples
--------
>>> from numpy.polynomial import legendre as L
>>> c1 = (1,2,3)
>>> c2 = (3,2,1)
>>> L.legdiv(c1,c2) # quotient "intuitive," remainder not
(array([ 3.]), array([-8., -4.]))
>>> c2 = (0,1,2,3)
>>> L.legdiv(c2,c1) # neither "intuitive"
(array([-0.07407407, 1.66666667]), array([-1.03703704, -2.51851852]))
"""
# c1, c2 are trimmed copies
[c1, c2] = pu.as_series([c1, c2])
if c2[-1] == 0:
raise ZeroDivisionError()
lc1 = len(c1)
lc2 = len(c2)
if lc1 < lc2:
return c1[:1]*0, c1
elif lc2 == 1:
return c1/c2[-1], c1[:1]*0
else:
quo = np.empty(lc1 - lc2 + 1, dtype=c1.dtype)
rem = c1
for i in range(lc1 - lc2, - 1, -1):
p = legmul([0]*i + [1], c2)
q = rem[-1]/p[-1]
rem = rem[:-1] - q*p[:-1]
quo[i] = q
return quo, pu.trimseq(rem)
def legpow(c, pow, maxpower=16):
"""Raise a Legendre series to a power.
Returns the Legendre series `c` raised to the power `pow`. The
argument `c` is a sequence of coefficients ordered from low to high.
i.e., [1,2,3] is the series ``P_0 + 2*P_1 + 3*P_2.``
Parameters
----------
c : array_like
1-D array of Legendre series coefficients ordered from low to
high.
pow : integer
Power to which the series will be raised
maxpower : integer, optional
Maximum power allowed. This is mainly to limit growth of the series
to unmanageable size. Default is 16
Returns
-------
coef : ndarray
Legendre series of power.
See Also
--------
legadd, legsub, legmul, legdiv
Examples
--------
"""
# c is a trimmed copy
[c] = pu.as_series([c])
power = int(pow)
if power != pow or power < 0:
raise ValueError("Power must be a non-negative integer.")
elif maxpower is not None and power > maxpower:
raise ValueError("Power is too large")
elif power == 0:
return np.array([1], dtype=c.dtype)
elif power == 1:
return c
else:
# This can be made more efficient by using powers of two
# in the usual way.
prd = c
for i in range(2, power + 1):
prd = legmul(prd, c)
return prd
def legder(c, m=1, scl=1, axis=0):
"""
Differentiate a Legendre series.
Returns the Legendre series coefficients `c` differentiated `m` times
along `axis`. At each iteration the result is multiplied by `scl` (the
scaling factor is for use in a linear change of variable). The argument
`c` is an array of coefficients from low to high degree along each
axis, e.g., [1,2,3] represents the series ``1*L_0 + 2*L_1 + 3*L_2``
while [[1,2],[1,2]] represents ``1*L_0(x)*L_0(y) + 1*L_1(x)*L_0(y) +
2*L_0(x)*L_1(y) + 2*L_1(x)*L_1(y)`` if axis=0 is ``x`` and axis=1 is
``y``.
Parameters
----------
c : array_like
Array of Legendre series coefficients. If c is multidimensional the
different axis correspond to different variables with the degree in
each axis given by the corresponding index.
m : int, optional
Number of derivatives taken, must be non-negative. (Default: 1)
scl : scalar, optional
Each differentiation is multiplied by `scl`. The end result is
multiplication by ``scl**m``. This is for use in a linear change of
variable. (Default: 1)
axis : int, optional
Axis over which the derivative is taken. (Default: 0).
.. versionadded:: 1.7.0
Returns
-------
der : ndarray
Legendre series of the derivative.
See Also
--------
legint
Notes
-----
In general, the result of differentiating a Legendre series does not
resemble the same operation on a power series. Thus the result of this
function may be "unintuitive," albeit correct; see Examples section
below.
Examples
--------
>>> from numpy.polynomial import legendre as L
>>> c = (1,2,3,4)
>>> L.legder(c)
array([ 6., 9., 20.])
>>> L.legder(c, 3)
array([ 60.])
>>> L.legder(c, scl=-1)
array([ -6., -9., -20.])
>>> L.legder(c, 2,-1)
array([ 9., 60.])
"""
c = np.array(c, ndmin=1, copy=1)
if c.dtype.char in '?bBhHiIlLqQpP':
c = c.astype(np.double)
cnt, iaxis = [int(t) for t in [m, axis]]
if cnt != m:
raise ValueError("The order of derivation must be integer")
if cnt < 0:
raise ValueError("The order of derivation must be non-negative")
if iaxis != axis:
raise ValueError("The axis must be integer")
iaxis = normalize_axis_index(iaxis, c.ndim)
if cnt == 0:
return c
c = np.moveaxis(c, iaxis, 0)
n = len(c)
if cnt >= n:
c = c[:1]*0
else:
for i in range(cnt):
n = n - 1
c *= scl
der = np.empty((n,) + c.shape[1:], dtype=c.dtype)
for j in range(n, 2, -1):
der[j - 1] = (2*j - 1)*c[j]
c[j - 2] += c[j]
if n > 1:
der[1] = 3*c[2]
der[0] = c[1]
c = der
c = np.moveaxis(c, 0, iaxis)
return c
def legint(c, m=1, k=[], lbnd=0, scl=1, axis=0):
"""
Integrate a Legendre series.
Returns the Legendre series coefficients `c` integrated `m` times from
`lbnd` along `axis`. At each iteration the resulting series is
**multiplied** by `scl` and an integration constant, `k`, is added.
The scaling factor is for use in a linear change of variable. ("Buyer
beware": note that, depending on what one is doing, one may want `scl`
to be the reciprocal of what one might expect; for more information,
see the Notes section below.) The argument `c` is an array of
coefficients from low to high degree along each axis, e.g., [1,2,3]
represents the series ``L_0 + 2*L_1 + 3*L_2`` while [[1,2],[1,2]]
represents ``1*L_0(x)*L_0(y) + 1*L_1(x)*L_0(y) + 2*L_0(x)*L_1(y) +
2*L_1(x)*L_1(y)`` if axis=0 is ``x`` and axis=1 is ``y``.
Parameters
----------
c : array_like
Array of Legendre series coefficients. If c is multidimensional the
different axis correspond to different variables with the degree in
each axis given by the corresponding index.
m : int, optional
Order of integration, must be positive. (Default: 1)
k : {[], list, scalar}, optional
Integration constant(s). The value of the first integral at
``lbnd`` is the first value in the list, the value of the second
integral at ``lbnd`` is the second value, etc. If ``k == []`` (the
default), all constants are set to zero. If ``m == 1``, a single
scalar can be given instead of a list.
lbnd : scalar, optional
The lower bound of the integral. (Default: 0)
scl : scalar, optional
Following each integration the result is *multiplied* by `scl`
before the integration constant is added. (Default: 1)
axis : int, optional
Axis over which the integral is taken. (Default: 0).
.. versionadded:: 1.7.0
Returns
-------
S : ndarray
Legendre series coefficient array of the integral.
Raises
------
ValueError
If ``m < 0``, ``len(k) > m``, ``np.ndim(lbnd) != 0``, or
``np.ndim(scl) != 0``.
See Also
--------
legder
Notes
-----
Note that the result of each integration is *multiplied* by `scl`.
Why is this important to note? Say one is making a linear change of
variable :math:`u = ax + b` in an integral relative to `x`. Then
:math:`dx = du/a`, so one will need to set `scl` equal to
:math:`1/a` - perhaps not what one would have first thought.
Also note that, in general, the result of integrating a C-series needs
to be "reprojected" onto the C-series basis set. Thus, typically,
the result of this function is "unintuitive," albeit correct; see
Examples section below.
Examples
--------
>>> from numpy.polynomial import legendre as L
>>> c = (1,2,3)
>>> L.legint(c)
array([ 0.33333333, 0.4 , 0.66666667, 0.6 ])
>>> L.legint(c, 3)
array([ 1.66666667e-02, -1.78571429e-02, 4.76190476e-02,
-1.73472348e-18, 1.90476190e-02, 9.52380952e-03])
>>> L.legint(c, k=3)
array([ 3.33333333, 0.4 , 0.66666667, 0.6 ])
>>> L.legint(c, lbnd=-2)
array([ 7.33333333, 0.4 , 0.66666667, 0.6 ])
>>> L.legint(c, scl=2)
array([ 0.66666667, 0.8 , 1.33333333, 1.2 ])
"""
c = np.array(c, ndmin=1, copy=1)
if c.dtype.char in '?bBhHiIlLqQpP':
c = c.astype(np.double)
if not np.iterable(k):
k = [k]
cnt, iaxis = [int(t) for t in [m, axis]]
if cnt != m:
raise ValueError("The order of integration must be integer")
if cnt < 0:
raise ValueError("The order of integration must be non-negative")
if len(k) > cnt:
raise ValueError("Too many integration constants")
if np.ndim(lbnd) != 0:
raise ValueError("lbnd must be a scalar.")
if np.ndim(scl) != 0:
raise ValueError("scl must be a scalar.")
if iaxis != axis:
raise ValueError("The axis must be integer")
iaxis = normalize_axis_index(iaxis, c.ndim)
if cnt == 0:
return c
c = np.moveaxis(c, iaxis, 0)
k = list(k) + [0]*(cnt - len(k))
for i in range(cnt):
n = len(c)
c *= scl
if n == 1 and np.all(c[0] == 0):
c[0] += k[i]
else:
tmp = np.empty((n + 1,) + c.shape[1:], dtype=c.dtype)
tmp[0] = c[0]*0
tmp[1] = c[0]
if n > 1:
tmp[2] = c[1]/3
for j in range(2, n):
t = c[j]/(2*j + 1)
tmp[j + 1] = t
tmp[j - 1] -= t
tmp[0] += k[i] - legval(lbnd, tmp)
c = tmp
c = np.moveaxis(c, 0, iaxis)
return c
def legval(x, c, tensor=True):
"""
Evaluate a Legendre series at points x.
If `c` is of length `n + 1`, this function returns the value:
.. math:: p(x) = c_0 * L_0(x) + c_1 * L_1(x) + ... + c_n * L_n(x)
The parameter `x` is converted to an array only if it is a tuple or a
list, otherwise it is treated as a scalar. In either case, either `x`
or its elements must support multiplication and addition both with
themselves and with the elements of `c`.
If `c` is a 1-D array, then `p(x)` will have the same shape as `x`. If
`c` is multidimensional, then the shape of the result depends on the
value of `tensor`. If `tensor` is true the shape will be c.shape[1:] +
x.shape. If `tensor` is false the shape will be c.shape[1:]. Note that
scalars have shape (,).
Trailing zeros in the coefficients will be used in the evaluation, so
they should be avoided if efficiency is a concern.
Parameters
----------
x : array_like, compatible object
If `x` is a list or tuple, it is converted to an ndarray, otherwise
it is left unchanged and treated as a scalar. In either case, `x`
or its elements must support addition and multiplication with
with themselves and with the elements of `c`.
c : array_like
Array of coefficients ordered so that the coefficients for terms of
degree n are contained in c[n]. If `c` is multidimensional the
remaining indices enumerate multiple polynomials. In the two
dimensional case the coefficients may be thought of as stored in
the columns of `c`.
tensor : boolean, optional
If True, the shape of the coefficient array is extended with ones
on the right, one for each dimension of `x`. Scalars have dimension 0
for this action. The result is that every column of coefficients in
`c` is evaluated for every element of `x`. If False, `x` is broadcast
over the columns of `c` for the evaluation. This keyword is useful
when `c` is multidimensional. The default value is True.
.. versionadded:: 1.7.0
Returns
-------
values : ndarray, algebra_like
The shape of the return value is described above.
See Also
--------
legval2d, leggrid2d, legval3d, leggrid3d
Notes
-----
The evaluation uses Clenshaw recursion, aka synthetic division.
Examples
--------
"""
c = np.array(c, ndmin=1, copy=0)
if c.dtype.char in '?bBhHiIlLqQpP':
c = c.astype(np.double)
if isinstance(x, (tuple, list)):
x = np.asarray(x)
if isinstance(x, np.ndarray) and tensor:
c = c.reshape(c.shape + (1,)*x.ndim)
if len(c) == 1:
c0 = c[0]
c1 = 0
elif len(c) == 2:
c0 = c[0]
c1 = c[1]
else:
nd = len(c)
c0 = c[-2]
c1 = c[-1]
for i in range(3, len(c) + 1):
tmp = c0
nd = nd - 1
c0 = c[-i] - (c1*(nd - 1))/nd
c1 = tmp + (c1*x*(2*nd - 1))/nd
return c0 + c1*x
def legval2d(x, y, c):
"""
Evaluate a 2-D Legendre series at points (x, y).
This function returns the values:
.. math:: p(x,y) = \\sum_{i,j} c_{i,j} * L_i(x) * L_j(y)
The parameters `x` and `y` are converted to arrays only if they are
tuples or a lists, otherwise they are treated as a scalars and they
must have the same shape after conversion. In either case, either `x`
and `y` or their elements must support multiplication and addition both
with themselves and with the elements of `c`.
If `c` is a 1-D array a one is implicitly appended to its shape to make
it 2-D. The shape of the result will be c.shape[2:] + x.shape.
Parameters
----------
x, y : array_like, compatible objects
The two dimensional series is evaluated at the points `(x, y)`,
where `x` and `y` must have the same shape. If `x` or `y` is a list
or tuple, it is first converted to an ndarray, otherwise it is left
unchanged and if it isn't an ndarray it is treated as a scalar.
c : array_like
Array of coefficients ordered so that the coefficient of the term
of multi-degree i,j is contained in ``c[i,j]``. If `c` has
dimension greater than two the remaining indices enumerate multiple
sets of coefficients.
Returns
-------
values : ndarray, compatible object
The values of the two dimensional Legendre series at points formed
from pairs of corresponding values from `x` and `y`.
See Also
--------
legval, leggrid2d, legval3d, leggrid3d
Notes
-----
.. versionadded:: 1.7.0
"""
try:
x, y = np.array((x, y), copy=0)
except Exception:
raise ValueError('x, y are incompatible')
c = legval(x, c)
c = legval(y, c, tensor=False)
return c
def leggrid2d(x, y, c):
"""
Evaluate a 2-D Legendre series on the Cartesian product of x and y.
This function returns the values:
.. math:: p(a,b) = \\sum_{i,j} c_{i,j} * L_i(a) * L_j(b)
where the points `(a, b)` consist of all pairs formed by taking
`a` from `x` and `b` from `y`. The resulting points form a grid with
`x` in the first dimension and `y` in the second.
The parameters `x` and `y` are converted to arrays only if they are
tuples or a lists, otherwise they are treated as a scalars. In either
case, either `x` and `y` or their elements must support multiplication
and addition both with themselves and with the elements of `c`.
If `c` has fewer than two dimensions, ones are implicitly appended to
its shape to make it 2-D. The shape of the result will be c.shape[2:] +
x.shape + y.shape.
Parameters
----------
x, y : array_like, compatible objects
The two dimensional series is evaluated at the points in the
Cartesian product of `x` and `y`. If `x` or `y` is a list or
tuple, it is first converted to an ndarray, otherwise it is left
unchanged and, if it isn't an ndarray, it is treated as a scalar.
c : array_like
Array of coefficients ordered so that the coefficient of the term of
multi-degree i,j is contained in `c[i,j]`. If `c` has dimension
greater than two the remaining indices enumerate multiple sets of
coefficients.
Returns
-------
values : ndarray, compatible object
The values of the two dimensional Chebyshev series at points in the
Cartesian product of `x` and `y`.
See Also
--------
legval, legval2d, legval3d, leggrid3d
Notes
-----
.. versionadded:: 1.7.0
"""
c = legval(x, c)
c = legval(y, c)
return c
def legval3d(x, y, z, c):
"""
Evaluate a 3-D Legendre series at points (x, y, z).
This function returns the values:
.. math:: p(x,y,z) = \\sum_{i,j,k} c_{i,j,k} * L_i(x) * L_j(y) * L_k(z)
The parameters `x`, `y`, and `z` are converted to arrays only if
they are tuples or a lists, otherwise they are treated as a scalars and
they must have the same shape after conversion. In either case, either
`x`, `y`, and `z` or their elements must support multiplication and
addition both with themselves and with the elements of `c`.
If `c` has fewer than 3 dimensions, ones are implicitly appended to its
shape to make it 3-D. The shape of the result will be c.shape[3:] +
x.shape.
Parameters
----------
x, y, z : array_like, compatible object
The three dimensional series is evaluated at the points
`(x, y, z)`, where `x`, `y`, and `z` must have the same shape. If
any of `x`, `y`, or `z` is a list or tuple, it is first converted
to an ndarray, otherwise it is left unchanged and if it isn't an
ndarray it is treated as a scalar.
c : array_like
Array of coefficients ordered so that the coefficient of the term of
multi-degree i,j,k is contained in ``c[i,j,k]``. If `c` has dimension
greater than 3 the remaining indices enumerate multiple sets of
coefficients.
Returns
-------
values : ndarray, compatible object
The values of the multidimensional polynomial on points formed with
triples of corresponding values from `x`, `y`, and `z`.
See Also
--------
legval, legval2d, leggrid2d, leggrid3d
Notes
-----
.. versionadded:: 1.7.0
"""
try:
x, y, z = np.array((x, y, z), copy=0)
except Exception:
raise ValueError('x, y, z are incompatible')
c = legval(x, c)
c = legval(y, c, tensor=False)
c = legval(z, c, tensor=False)
return c
def leggrid3d(x, y, z, c):
"""
Evaluate a 3-D Legendre series on the Cartesian product of x, y, and z.
This function returns the values:
.. math:: p(a,b,c) = \\sum_{i,j,k} c_{i,j,k} * L_i(a) * L_j(b) * L_k(c)
where the points `(a, b, c)` consist of all triples formed by taking
`a` from `x`, `b` from `y`, and `c` from `z`. The resulting points form
a grid with `x` in the first dimension, `y` in the second, and `z` in
the third.
The parameters `x`, `y`, and `z` are converted to arrays only if they
are tuples or a lists, otherwise they are treated as a scalars. In
either case, either `x`, `y`, and `z` or their elements must support
multiplication and addition both with themselves and with the elements
of `c`.
If `c` has fewer than three dimensions, ones are implicitly appended to
its shape to make it 3-D. The shape of the result will be c.shape[3:] +
x.shape + y.shape + z.shape.
Parameters
----------
x, y, z : array_like, compatible objects
The three dimensional series is evaluated at the points in the
Cartesian product of `x`, `y`, and `z`. If `x`,`y`, or `z` is a
list or tuple, it is first converted to an ndarray, otherwise it is
left unchanged and, if it isn't an ndarray, it is treated as a
scalar.
c : array_like
Array of coefficients ordered so that the coefficients for terms of
degree i,j are contained in ``c[i,j]``. If `c` has dimension
greater than two the remaining indices enumerate multiple sets of
coefficients.
Returns
-------
values : ndarray, compatible object
The values of the two dimensional polynomial at points in the Cartesian
product of `x` and `y`.
See Also
--------
legval, legval2d, leggrid2d, legval3d
Notes
-----
.. versionadded:: 1.7.0
"""
c = legval(x, c)
c = legval(y, c)
c = legval(z, c)
return c
def legvander(x, deg):
"""Pseudo-Vandermonde matrix of given degree.
Returns the pseudo-Vandermonde matrix of degree `deg` and sample points
`x`. The pseudo-Vandermonde matrix is defined by
.. math:: V[..., i] = L_i(x)
where `0 <= i <= deg`. The leading indices of `V` index the elements of
`x` and the last index is the degree of the Legendre polynomial.
If `c` is a 1-D array of coefficients of length `n + 1` and `V` is the
array ``V = legvander(x, n)``, then ``np.dot(V, c)`` and
``legval(x, c)`` are the same up to roundoff. This equivalence is
useful both for least squares fitting and for the evaluation of a large
number of Legendre series of the same degree and sample points.
Parameters
----------
x : array_like
Array of points. The dtype is converted to float64 or complex128
depending on whether any of the elements are complex. If `x` is
scalar it is converted to a 1-D array.
deg : int
Degree of the resulting matrix.
Returns
-------
vander : ndarray
The pseudo-Vandermonde matrix. The shape of the returned matrix is
``x.shape + (deg + 1,)``, where The last index is the degree of the
corresponding Legendre polynomial. The dtype will be the same as
the converted `x`.
"""
ideg = int(deg)
if ideg != deg:
raise ValueError("deg must be integer")
if ideg < 0:
raise ValueError("deg must be non-negative")
x = np.array(x, copy=0, ndmin=1) + 0.0
dims = (ideg + 1,) + x.shape
dtyp = x.dtype
v = np.empty(dims, dtype=dtyp)
# Use forward recursion to generate the entries. This is not as accurate
# as reverse recursion in this application but it is more efficient.
v[0] = x*0 + 1
if ideg > 0:
v[1] = x
for i in range(2, ideg + 1):
v[i] = (v[i-1]*x*(2*i - 1) - v[i-2]*(i - 1))/i
return np.moveaxis(v, 0, -1)
def legvander2d(x, y, deg):
"""Pseudo-Vandermonde matrix of given degrees.
Returns the pseudo-Vandermonde matrix of degrees `deg` and sample
points `(x, y)`. The pseudo-Vandermonde matrix is defined by
.. math:: V[..., (deg[1] + 1)*i + j] = L_i(x) * L_j(y),
where `0 <= i <= deg[0]` and `0 <= j <= deg[1]`. The leading indices of
`V` index the points `(x, y)` and the last index encodes the degrees of
the Legendre polynomials.
If ``V = legvander2d(x, y, [xdeg, ydeg])``, then the columns of `V`
correspond to the elements of a 2-D coefficient array `c` of shape
(xdeg + 1, ydeg + 1) in the order
.. math:: c_{00}, c_{01}, c_{02} ... , c_{10}, c_{11}, c_{12} ...
and ``np.dot(V, c.flat)`` and ``legval2d(x, y, c)`` will be the same
up to roundoff. This equivalence is useful both for least squares
fitting and for the evaluation of a large number of 2-D Legendre
series of the same degrees and sample points.
Parameters
----------
x, y : array_like
Arrays of point coordinates, all of the same shape. The dtypes
will be converted to either float64 or complex128 depending on
whether any of the elements are complex. Scalars are converted to
1-D arrays.
deg : list of ints
List of maximum degrees of the form [x_deg, y_deg].
Returns
-------
vander2d : ndarray
The shape of the returned matrix is ``x.shape + (order,)``, where
:math:`order = (deg[0]+1)*(deg([1]+1)`. The dtype will be the same
as the converted `x` and `y`.
See Also
--------
legvander, legvander3d. legval2d, legval3d
Notes
-----
.. versionadded:: 1.7.0
"""
ideg = [int(d) for d in deg]
is_valid = [id == d and id >= 0 for id, d in zip(ideg, deg)]
if is_valid != [1, 1]:
raise ValueError("degrees must be non-negative integers")
degx, degy = ideg
x, y = np.array((x, y), copy=0) + 0.0
vx = legvander(x, degx)
vy = legvander(y, degy)
v = vx[..., None]*vy[..., None,:]
return v.reshape(v.shape[:-2] + (-1,))
def legvander3d(x, y, z, deg):
"""Pseudo-Vandermonde matrix of given degrees.
Returns the pseudo-Vandermonde matrix of degrees `deg` and sample
points `(x, y, z)`. If `l, m, n` are the given degrees in `x, y, z`,
then The pseudo-Vandermonde matrix is defined by
.. math:: V[..., (m+1)(n+1)i + (n+1)j + k] = L_i(x)*L_j(y)*L_k(z),
where `0 <= i <= l`, `0 <= j <= m`, and `0 <= j <= n`. The leading
indices of `V` index the points `(x, y, z)` and the last index encodes
the degrees of the Legendre polynomials.
If ``V = legvander3d(x, y, z, [xdeg, ydeg, zdeg])``, then the columns
of `V` correspond to the elements of a 3-D coefficient array `c` of
shape (xdeg + 1, ydeg + 1, zdeg + 1) in the order
.. math:: c_{000}, c_{001}, c_{002},... , c_{010}, c_{011}, c_{012},...
and ``np.dot(V, c.flat)`` and ``legval3d(x, y, z, c)`` will be the
same up to roundoff. This equivalence is useful both for least squares
fitting and for the evaluation of a large number of 3-D Legendre
series of the same degrees and sample points.
Parameters
----------
x, y, z : array_like
Arrays of point coordinates, all of the same shape. The dtypes will
be converted to either float64 or complex128 depending on whether
any of the elements are complex. Scalars are converted to 1-D
arrays.
deg : list of ints
List of maximum degrees of the form [x_deg, y_deg, z_deg].
Returns
-------
vander3d : ndarray
The shape of the returned matrix is ``x.shape + (order,)``, where
:math:`order = (deg[0]+1)*(deg([1]+1)*(deg[2]+1)`. The dtype will
be the same as the converted `x`, `y`, and `z`.
See Also
--------
legvander, legvander3d. legval2d, legval3d
Notes
-----
.. versionadded:: 1.7.0
"""
ideg = [int(d) for d in deg]
is_valid = [id == d and id >= 0 for id, d in zip(ideg, deg)]
if is_valid != [1, 1, 1]:
raise ValueError("degrees must be non-negative integers")
degx, degy, degz = ideg
x, y, z = np.array((x, y, z), copy=0) + 0.0
vx = legvander(x, degx)
vy = legvander(y, degy)
vz = legvander(z, degz)
v = vx[..., None, None]*vy[..., None,:, None]*vz[..., None, None,:]
return v.reshape(v.shape[:-3] + (-1,))
def legfit(x, y, deg, rcond=None, full=False, w=None):
"""
Least squares fit of Legendre series to data.
Return the coefficients of a Legendre series of degree `deg` that is the
least squares fit to the data values `y` given at points `x`. If `y` is
1-D the returned coefficients will also be 1-D. If `y` is 2-D multiple
fits are done, one for each column of `y`, and the resulting
coefficients are stored in the corresponding columns of a 2-D return.
The fitted polynomial(s) are in the form
.. math:: p(x) = c_0 + c_1 * L_1(x) + ... + c_n * L_n(x),
where `n` is `deg`.
Parameters
----------
x : array_like, shape (M,)
x-coordinates of the M sample points ``(x[i], y[i])``.
y : array_like, shape (M,) or (M, K)
y-coordinates of the sample points. Several data sets of sample
points sharing the same x-coordinates can be fitted at once by
passing in a 2D-array that contains one dataset per column.
deg : int or 1-D array_like
Degree(s) of the fitting polynomials. If `deg` is a single integer
all terms up to and including the `deg`'th term are included in the
fit. For NumPy versions >= 1.11.0 a list of integers specifying the
degrees of the terms to include may be used instead.
rcond : float, optional
Relative condition number of the fit. Singular values smaller than
this relative to the largest singular value will be ignored. The
default value is len(x)*eps, where eps is the relative precision of
the float type, about 2e-16 in most cases.
full : bool, optional
Switch determining nature of return value. When it is False (the
default) just the coefficients are returned, when True diagnostic
information from the singular value decomposition is also returned.
w : array_like, shape (`M`,), optional
Weights. If not None, the contribution of each point
``(x[i],y[i])`` to the fit is weighted by `w[i]`. Ideally the
weights are chosen so that the errors of the products ``w[i]*y[i]``
all have the same variance. The default value is None.
.. versionadded:: 1.5.0
Returns
-------
coef : ndarray, shape (M,) or (M, K)
Legendre coefficients ordered from low to high. If `y` was
2-D, the coefficients for the data in column k of `y` are in
column `k`. If `deg` is specified as a list, coefficients for
terms not included in the fit are set equal to zero in the
returned `coef`.
[residuals, rank, singular_values, rcond] : list
These values are only returned if `full` = True
resid -- sum of squared residuals of the least squares fit
rank -- the numerical rank of the scaled Vandermonde matrix
sv -- singular values of the scaled Vandermonde matrix
rcond -- value of `rcond`.
For more details, see `linalg.lstsq`.
Warns
-----
RankWarning
The rank of the coefficient matrix in the least-squares fit is
deficient. The warning is only raised if `full` = False. The
warnings can be turned off by
>>> import warnings
>>> warnings.simplefilter('ignore', RankWarning)
See Also
--------
chebfit, polyfit, lagfit, hermfit, hermefit
legval : Evaluates a Legendre series.
legvander : Vandermonde matrix of Legendre series.
legweight : Legendre weight function (= 1).
linalg.lstsq : Computes a least-squares fit from the matrix.
scipy.interpolate.UnivariateSpline : Computes spline fits.
Notes
-----
The solution is the coefficients of the Legendre series `p` that
minimizes the sum of the weighted squared errors
.. math:: E = \\sum_j w_j^2 * |y_j - p(x_j)|^2,
where :math:`w_j` are the weights. This problem is solved by setting up
as the (typically) overdetermined matrix equation
.. math:: V(x) * c = w * y,
where `V` is the weighted pseudo Vandermonde matrix of `x`, `c` are the
coefficients to be solved for, `w` are the weights, and `y` are the
observed values. This equation is then solved using the singular value
decomposition of `V`.
If some of the singular values of `V` are so small that they are
neglected, then a `RankWarning` will be issued. This means that the
coefficient values may be poorly determined. Using a lower order fit
will usually get rid of the warning. The `rcond` parameter can also be
set to a value smaller than its default, but the resulting fit may be
spurious and have large contributions from roundoff error.
Fits using Legendre series are usually better conditioned than fits
using power series, but much can depend on the distribution of the
sample points and the smoothness of the data. If the quality of the fit
is inadequate splines may be a good alternative.
References
----------
.. [1] Wikipedia, "Curve fitting",
http://en.wikipedia.org/wiki/Curve_fitting
Examples
--------
"""
x = np.asarray(x) + 0.0
y = np.asarray(y) + 0.0
deg = np.asarray(deg)
# check arguments.
if deg.ndim > 1 or deg.dtype.kind not in 'iu' or deg.size == 0:
raise TypeError("deg must be an int or non-empty 1-D array of int")
if deg.min() < 0:
raise ValueError("expected deg >= 0")
if x.ndim != 1:
raise TypeError("expected 1D vector for x")
if x.size == 0:
raise TypeError("expected non-empty vector for x")
if y.ndim < 1 or y.ndim > 2:
raise TypeError("expected 1D or 2D array for y")
if len(x) != len(y):
raise TypeError("expected x and y to have same length")
if deg.ndim == 0:
lmax = deg
order = lmax + 1
van = legvander(x, lmax)
else:
deg = np.sort(deg)
lmax = deg[-1]
order = len(deg)
van = legvander(x, lmax)[:, deg]
# set up the least squares matrices in transposed form
lhs = van.T
rhs = y.T
if w is not None:
w = np.asarray(w) + 0.0
if w.ndim != 1:
raise TypeError("expected 1D vector for w")
if len(x) != len(w):
raise TypeError("expected x and w to have same length")
# apply weights. Don't use inplace operations as they
# can cause problems with NA.
lhs = lhs * w
rhs = rhs * w
# set rcond
if rcond is None:
rcond = len(x)*np.finfo(x.dtype).eps
# Determine the norms of the design matrix columns.
if issubclass(lhs.dtype.type, np.complexfloating):
scl = np.sqrt((np.square(lhs.real) + np.square(lhs.imag)).sum(1))
else:
scl = np.sqrt(np.square(lhs).sum(1))
scl[scl == 0] = 1
# Solve the least squares problem.
c, resids, rank, s = la.lstsq(lhs.T/scl, rhs.T, rcond)
c = (c.T/scl).T
# Expand c to include non-fitted coefficients which are set to zero
if deg.ndim > 0:
if c.ndim == 2:
cc = np.zeros((lmax+1, c.shape[1]), dtype=c.dtype)
else:
cc = np.zeros(lmax+1, dtype=c.dtype)
cc[deg] = c
c = cc
# warn on rank reduction
if rank != order and not full:
msg = "The fit may be poorly conditioned"
warnings.warn(msg, pu.RankWarning, stacklevel=2)
if full:
return c, [resids, rank, s, rcond]
else:
return c
def legcompanion(c):
"""Return the scaled companion matrix of c.
The basis polynomials are scaled so that the companion matrix is
symmetric when `c` is an Legendre basis polynomial. This provides
better eigenvalue estimates than the unscaled case and for basis
polynomials the eigenvalues are guaranteed to be real if
`numpy.linalg.eigvalsh` is used to obtain them.
Parameters
----------
c : array_like
1-D array of Legendre series coefficients ordered from low to high
degree.
Returns
-------
mat : ndarray
Scaled companion matrix of dimensions (deg, deg).
Notes
-----
.. versionadded:: 1.7.0
"""
# c is a trimmed copy
[c] = pu.as_series([c])
if len(c) < 2:
raise ValueError('Series must have maximum degree of at least 1.')
if len(c) == 2:
return np.array([[-c[0]/c[1]]])
n = len(c) - 1
mat = np.zeros((n, n), dtype=c.dtype)
scl = 1./np.sqrt(2*np.arange(n) + 1)
top = mat.reshape(-1)[1::n+1]
bot = mat.reshape(-1)[n::n+1]
top[...] = np.arange(1, n)*scl[:n-1]*scl[1:n]
bot[...] = top
mat[:, -1] -= (c[:-1]/c[-1])*(scl/scl[-1])*(n/(2*n - 1))
return mat
def legroots(c):
"""
Compute the roots of a Legendre series.
Return the roots (a.k.a. "zeros") of the polynomial
.. math:: p(x) = \\sum_i c[i] * L_i(x).
Parameters
----------
c : 1-D array_like
1-D array of coefficients.
Returns
-------
out : ndarray
Array of the roots of the series. If all the roots are real,
then `out` is also real, otherwise it is complex.
See Also
--------
polyroots, chebroots, lagroots, hermroots, hermeroots
Notes
-----
The root estimates are obtained as the eigenvalues of the companion
matrix, Roots far from the origin of the complex plane may have large
errors due to the numerical instability of the series for such values.
Roots with multiplicity greater than 1 will also show larger errors as
the value of the series near such points is relatively insensitive to
errors in the roots. Isolated roots near the origin can be improved by
a few iterations of Newton's method.
The Legendre series basis polynomials aren't powers of ``x`` so the
results of this function may seem unintuitive.
Examples
--------
>>> import numpy.polynomial.legendre as leg
>>> leg.legroots((1, 2, 3, 4)) # 4L_3 + 3L_2 + 2L_1 + 1L_0, all real roots
array([-0.85099543, -0.11407192, 0.51506735])
"""
# c is a trimmed copy
[c] = pu.as_series([c])
if len(c) < 2:
return np.array([], dtype=c.dtype)
if len(c) == 2:
return np.array([-c[0]/c[1]])
m = legcompanion(c)
r = la.eigvals(m)
r.sort()
return r
def leggauss(deg):
"""
Gauss-Legendre quadrature.
Computes the sample points and weights for Gauss-Legendre quadrature.
These sample points and weights will correctly integrate polynomials of
degree :math:`2*deg - 1` or less over the interval :math:`[-1, 1]` with
the weight function :math:`f(x) = 1`.
Parameters
----------
deg : int
Number of sample points and weights. It must be >= 1.
Returns
-------
x : ndarray
1-D ndarray containing the sample points.
y : ndarray
1-D ndarray containing the weights.
Notes
-----
.. versionadded:: 1.7.0
The results have only been tested up to degree 100, higher degrees may
be problematic. The weights are determined by using the fact that
.. math:: w_k = c / (L'_n(x_k) * L_{n-1}(x_k))
where :math:`c` is a constant independent of :math:`k` and :math:`x_k`
is the k'th root of :math:`L_n`, and then scaling the results to get
the right value when integrating 1.
"""
ideg = int(deg)
if ideg != deg or ideg < 1:
raise ValueError("deg must be a non-negative integer")
# first approximation of roots. We use the fact that the companion
# matrix is symmetric in this case in order to obtain better zeros.
c = np.array([0]*deg + [1])
m = legcompanion(c)
x = la.eigvalsh(m)
# improve roots by one application of Newton
dy = legval(x, c)
df = legval(x, legder(c))
x -= dy/df
# compute the weights. We scale the factor to avoid possible numerical
# overflow.
fm = legval(x, c[1:])
fm /= np.abs(fm).max()
df /= np.abs(df).max()
w = 1/(fm * df)
# for Legendre we can also symmetrize
w = (w + w[::-1])/2
x = (x - x[::-1])/2
# scale w to get the right value
w *= 2. / w.sum()
return x, w
def legweight(x):
"""
Weight function of the Legendre polynomials.
The weight function is :math:`1` and the interval of integration is
:math:`[-1, 1]`. The Legendre polynomials are orthogonal, but not
normalized, with respect to this weight function.
Parameters
----------
x : array_like
Values at which the weight function will be computed.
Returns
-------
w : ndarray
The weight function at `x`.
Notes
-----
.. versionadded:: 1.7.0
"""
w = x*0.0 + 1.0
return w
#
# Legendre series class
#
class Legendre(ABCPolyBase):
"""A Legendre series class.
The Legendre class provides the standard Python numerical methods
'+', '-', '*', '//', '%', 'divmod', '**', and '()' as well as the
attributes and methods listed in the `ABCPolyBase` documentation.
Parameters
----------
coef : array_like
Legendre coefficients in order of increasing degree, i.e.,
``(1, 2, 3)`` gives ``1*P_0(x) + 2*P_1(x) + 3*P_2(x)``.
domain : (2,) array_like, optional
Domain to use. The interval ``[domain[0], domain[1]]`` is mapped
to the interval ``[window[0], window[1]]`` by shifting and scaling.
The default value is [-1, 1].
window : (2,) array_like, optional
Window, see `domain` for its use. The default value is [-1, 1].
.. versionadded:: 1.6.0
"""
# Virtual Functions
_add = staticmethod(legadd)
_sub = staticmethod(legsub)
_mul = staticmethod(legmul)
_div = staticmethod(legdiv)
_pow = staticmethod(legpow)
_val = staticmethod(legval)
_int = staticmethod(legint)
_der = staticmethod(legder)
_fit = staticmethod(legfit)
_line = staticmethod(legline)
_roots = staticmethod(legroots)
_fromroots = staticmethod(legfromroots)
# Virtual properties
nickname = 'leg'
domain = np.array(legdomain)
window = np.array(legdomain)
| 57,404 | 30.300436 | 79 |
py
|
cba-pipeline-public
|
cba-pipeline-public-master/containernet/ndn-containers/ndn_headless-player/bandits/venv/lib/python3.6/site-packages/numpy/polynomial/chebyshev.py
|
"""
Objects for dealing with Chebyshev series.
This module provides a number of objects (mostly functions) useful for
dealing with Chebyshev series, including a `Chebyshev` class that
encapsulates the usual arithmetic operations. (General information
on how this module represents and works with such polynomials is in the
docstring for its "parent" sub-package, `numpy.polynomial`).
Constants
---------
- `chebdomain` -- Chebyshev series default domain, [-1,1].
- `chebzero` -- (Coefficients of the) Chebyshev series that evaluates
identically to 0.
- `chebone` -- (Coefficients of the) Chebyshev series that evaluates
identically to 1.
- `chebx` -- (Coefficients of the) Chebyshev series for the identity map,
``f(x) = x``.
Arithmetic
----------
- `chebadd` -- add two Chebyshev series.
- `chebsub` -- subtract one Chebyshev series from another.
- `chebmul` -- multiply two Chebyshev series.
- `chebdiv` -- divide one Chebyshev series by another.
- `chebpow` -- raise a Chebyshev series to an positive integer power
- `chebval` -- evaluate a Chebyshev series at given points.
- `chebval2d` -- evaluate a 2D Chebyshev series at given points.
- `chebval3d` -- evaluate a 3D Chebyshev series at given points.
- `chebgrid2d` -- evaluate a 2D Chebyshev series on a Cartesian product.
- `chebgrid3d` -- evaluate a 3D Chebyshev series on a Cartesian product.
Calculus
--------
- `chebder` -- differentiate a Chebyshev series.
- `chebint` -- integrate a Chebyshev series.
Misc Functions
--------------
- `chebfromroots` -- create a Chebyshev series with specified roots.
- `chebroots` -- find the roots of a Chebyshev series.
- `chebvander` -- Vandermonde-like matrix for Chebyshev polynomials.
- `chebvander2d` -- Vandermonde-like matrix for 2D power series.
- `chebvander3d` -- Vandermonde-like matrix for 3D power series.
- `chebgauss` -- Gauss-Chebyshev quadrature, points and weights.
- `chebweight` -- Chebyshev weight function.
- `chebcompanion` -- symmetrized companion matrix in Chebyshev form.
- `chebfit` -- least-squares fit returning a Chebyshev series.
- `chebpts1` -- Chebyshev points of the first kind.
- `chebpts2` -- Chebyshev points of the second kind.
- `chebtrim` -- trim leading coefficients from a Chebyshev series.
- `chebline` -- Chebyshev series representing given straight line.
- `cheb2poly` -- convert a Chebyshev series to a polynomial.
- `poly2cheb` -- convert a polynomial to a Chebyshev series.
- `chebinterpolate` -- interpolate a function at the Chebyshev points.
Classes
-------
- `Chebyshev` -- A Chebyshev series class.
See also
--------
`numpy.polynomial`
Notes
-----
The implementations of multiplication, division, integration, and
differentiation use the algebraic identities [1]_:
.. math ::
T_n(x) = \\frac{z^n + z^{-n}}{2} \\\\
z\\frac{dx}{dz} = \\frac{z - z^{-1}}{2}.
where
.. math :: x = \\frac{z + z^{-1}}{2}.
These identities allow a Chebyshev series to be expressed as a finite,
symmetric Laurent series. In this module, this sort of Laurent series
is referred to as a "z-series."
References
----------
.. [1] A. T. Benjamin, et al., "Combinatorial Trigonometry with Chebyshev
Polynomials," *Journal of Statistical Planning and Inference 14*, 2008
(preprint: http://www.math.hmc.edu/~benjamin/papers/CombTrig.pdf, pg. 4)
"""
from __future__ import division, absolute_import, print_function
import numbers
import warnings
import numpy as np
import numpy.linalg as la
from numpy.core.multiarray import normalize_axis_index
from . import polyutils as pu
from ._polybase import ABCPolyBase
__all__ = [
'chebzero', 'chebone', 'chebx', 'chebdomain', 'chebline', 'chebadd',
'chebsub', 'chebmulx', 'chebmul', 'chebdiv', 'chebpow', 'chebval',
'chebder', 'chebint', 'cheb2poly', 'poly2cheb', 'chebfromroots',
'chebvander', 'chebfit', 'chebtrim', 'chebroots', 'chebpts1',
'chebpts2', 'Chebyshev', 'chebval2d', 'chebval3d', 'chebgrid2d',
'chebgrid3d', 'chebvander2d', 'chebvander3d', 'chebcompanion',
'chebgauss', 'chebweight', 'chebinterpolate']
chebtrim = pu.trimcoef
#
# A collection of functions for manipulating z-series. These are private
# functions and do minimal error checking.
#
def _cseries_to_zseries(c):
"""Covert Chebyshev series to z-series.
Covert a Chebyshev series to the equivalent z-series. The result is
never an empty array. The dtype of the return is the same as that of
the input. No checks are run on the arguments as this routine is for
internal use.
Parameters
----------
c : 1-D ndarray
Chebyshev coefficients, ordered from low to high
Returns
-------
zs : 1-D ndarray
Odd length symmetric z-series, ordered from low to high.
"""
n = c.size
zs = np.zeros(2*n-1, dtype=c.dtype)
zs[n-1:] = c/2
return zs + zs[::-1]
def _zseries_to_cseries(zs):
"""Covert z-series to a Chebyshev series.
Covert a z series to the equivalent Chebyshev series. The result is
never an empty array. The dtype of the return is the same as that of
the input. No checks are run on the arguments as this routine is for
internal use.
Parameters
----------
zs : 1-D ndarray
Odd length symmetric z-series, ordered from low to high.
Returns
-------
c : 1-D ndarray
Chebyshev coefficients, ordered from low to high.
"""
n = (zs.size + 1)//2
c = zs[n-1:].copy()
c[1:n] *= 2
return c
def _zseries_mul(z1, z2):
"""Multiply two z-series.
Multiply two z-series to produce a z-series.
Parameters
----------
z1, z2 : 1-D ndarray
The arrays must be 1-D but this is not checked.
Returns
-------
product : 1-D ndarray
The product z-series.
Notes
-----
This is simply convolution. If symmetric/anti-symmetric z-series are
denoted by S/A then the following rules apply:
S*S, A*A -> S
S*A, A*S -> A
"""
return np.convolve(z1, z2)
def _zseries_div(z1, z2):
"""Divide the first z-series by the second.
Divide `z1` by `z2` and return the quotient and remainder as z-series.
Warning: this implementation only applies when both z1 and z2 have the
same symmetry, which is sufficient for present purposes.
Parameters
----------
z1, z2 : 1-D ndarray
The arrays must be 1-D and have the same symmetry, but this is not
checked.
Returns
-------
(quotient, remainder) : 1-D ndarrays
Quotient and remainder as z-series.
Notes
-----
This is not the same as polynomial division on account of the desired form
of the remainder. If symmetric/anti-symmetric z-series are denoted by S/A
then the following rules apply:
S/S -> S,S
A/A -> S,A
The restriction to types of the same symmetry could be fixed but seems like
unneeded generality. There is no natural form for the remainder in the case
where there is no symmetry.
"""
z1 = z1.copy()
z2 = z2.copy()
len1 = len(z1)
len2 = len(z2)
if len2 == 1:
z1 /= z2
return z1, z1[:1]*0
elif len1 < len2:
return z1[:1]*0, z1
else:
dlen = len1 - len2
scl = z2[0]
z2 /= scl
quo = np.empty(dlen + 1, dtype=z1.dtype)
i = 0
j = dlen
while i < j:
r = z1[i]
quo[i] = z1[i]
quo[dlen - i] = r
tmp = r*z2
z1[i:i+len2] -= tmp
z1[j:j+len2] -= tmp
i += 1
j -= 1
r = z1[i]
quo[i] = r
tmp = r*z2
z1[i:i+len2] -= tmp
quo /= scl
rem = z1[i+1:i-1+len2].copy()
return quo, rem
def _zseries_der(zs):
"""Differentiate a z-series.
The derivative is with respect to x, not z. This is achieved using the
chain rule and the value of dx/dz given in the module notes.
Parameters
----------
zs : z-series
The z-series to differentiate.
Returns
-------
derivative : z-series
The derivative
Notes
-----
The zseries for x (ns) has been multiplied by two in order to avoid
using floats that are incompatible with Decimal and likely other
specialized scalar types. This scaling has been compensated by
multiplying the value of zs by two also so that the two cancels in the
division.
"""
n = len(zs)//2
ns = np.array([-1, 0, 1], dtype=zs.dtype)
zs *= np.arange(-n, n+1)*2
d, r = _zseries_div(zs, ns)
return d
def _zseries_int(zs):
"""Integrate a z-series.
The integral is with respect to x, not z. This is achieved by a change
of variable using dx/dz given in the module notes.
Parameters
----------
zs : z-series
The z-series to integrate
Returns
-------
integral : z-series
The indefinite integral
Notes
-----
The zseries for x (ns) has been multiplied by two in order to avoid
using floats that are incompatible with Decimal and likely other
specialized scalar types. This scaling has been compensated by
dividing the resulting zs by two.
"""
n = 1 + len(zs)//2
ns = np.array([-1, 0, 1], dtype=zs.dtype)
zs = _zseries_mul(zs, ns)
div = np.arange(-n, n+1)*2
zs[:n] /= div[:n]
zs[n+1:] /= div[n+1:]
zs[n] = 0
return zs
#
# Chebyshev series functions
#
def poly2cheb(pol):
"""
Convert a polynomial to a Chebyshev series.
Convert an array representing the coefficients of a polynomial (relative
to the "standard" basis) ordered from lowest degree to highest, to an
array of the coefficients of the equivalent Chebyshev series, ordered
from lowest to highest degree.
Parameters
----------
pol : array_like
1-D array containing the polynomial coefficients
Returns
-------
c : ndarray
1-D array containing the coefficients of the equivalent Chebyshev
series.
See Also
--------
cheb2poly
Notes
-----
The easy way to do conversions between polynomial basis sets
is to use the convert method of a class instance.
Examples
--------
>>> from numpy import polynomial as P
>>> p = P.Polynomial(range(4))
>>> p
Polynomial([ 0., 1., 2., 3.], domain=[-1, 1], window=[-1, 1])
>>> c = p.convert(kind=P.Chebyshev)
>>> c
Chebyshev([ 1. , 3.25, 1. , 0.75], domain=[-1, 1], window=[-1, 1])
>>> P.poly2cheb(range(4))
array([ 1. , 3.25, 1. , 0.75])
"""
[pol] = pu.as_series([pol])
deg = len(pol) - 1
res = 0
for i in range(deg, -1, -1):
res = chebadd(chebmulx(res), pol[i])
return res
def cheb2poly(c):
"""
Convert a Chebyshev series to a polynomial.
Convert an array representing the coefficients of a Chebyshev series,
ordered from lowest degree to highest, to an array of the coefficients
of the equivalent polynomial (relative to the "standard" basis) ordered
from lowest to highest degree.
Parameters
----------
c : array_like
1-D array containing the Chebyshev series coefficients, ordered
from lowest order term to highest.
Returns
-------
pol : ndarray
1-D array containing the coefficients of the equivalent polynomial
(relative to the "standard" basis) ordered from lowest order term
to highest.
See Also
--------
poly2cheb
Notes
-----
The easy way to do conversions between polynomial basis sets
is to use the convert method of a class instance.
Examples
--------
>>> from numpy import polynomial as P
>>> c = P.Chebyshev(range(4))
>>> c
Chebyshev([ 0., 1., 2., 3.], [-1., 1.])
>>> p = c.convert(kind=P.Polynomial)
>>> p
Polynomial([ -2., -8., 4., 12.], [-1., 1.])
>>> P.cheb2poly(range(4))
array([ -2., -8., 4., 12.])
"""
from .polynomial import polyadd, polysub, polymulx
[c] = pu.as_series([c])
n = len(c)
if n < 3:
return c
else:
c0 = c[-2]
c1 = c[-1]
# i is the current degree of c1
for i in range(n - 1, 1, -1):
tmp = c0
c0 = polysub(c[i - 2], c1)
c1 = polyadd(tmp, polymulx(c1)*2)
return polyadd(c0, polymulx(c1))
#
# These are constant arrays are of integer type so as to be compatible
# with the widest range of other types, such as Decimal.
#
# Chebyshev default domain.
chebdomain = np.array([-1, 1])
# Chebyshev coefficients representing zero.
chebzero = np.array([0])
# Chebyshev coefficients representing one.
chebone = np.array([1])
# Chebyshev coefficients representing the identity x.
chebx = np.array([0, 1])
def chebline(off, scl):
"""
Chebyshev series whose graph is a straight line.
Parameters
----------
off, scl : scalars
The specified line is given by ``off + scl*x``.
Returns
-------
y : ndarray
This module's representation of the Chebyshev series for
``off + scl*x``.
See Also
--------
polyline
Examples
--------
>>> import numpy.polynomial.chebyshev as C
>>> C.chebline(3,2)
array([3, 2])
>>> C.chebval(-3, C.chebline(3,2)) # should be -3
-3.0
"""
if scl != 0:
return np.array([off, scl])
else:
return np.array([off])
def chebfromroots(roots):
"""
Generate a Chebyshev series with given roots.
The function returns the coefficients of the polynomial
.. math:: p(x) = (x - r_0) * (x - r_1) * ... * (x - r_n),
in Chebyshev form, where the `r_n` are the roots specified in `roots`.
If a zero has multiplicity n, then it must appear in `roots` n times.
For instance, if 2 is a root of multiplicity three and 3 is a root of
multiplicity 2, then `roots` looks something like [2, 2, 2, 3, 3]. The
roots can appear in any order.
If the returned coefficients are `c`, then
.. math:: p(x) = c_0 + c_1 * T_1(x) + ... + c_n * T_n(x)
The coefficient of the last term is not generally 1 for monic
polynomials in Chebyshev form.
Parameters
----------
roots : array_like
Sequence containing the roots.
Returns
-------
out : ndarray
1-D array of coefficients. If all roots are real then `out` is a
real array, if some of the roots are complex, then `out` is complex
even if all the coefficients in the result are real (see Examples
below).
See Also
--------
polyfromroots, legfromroots, lagfromroots, hermfromroots,
hermefromroots.
Examples
--------
>>> import numpy.polynomial.chebyshev as C
>>> C.chebfromroots((-1,0,1)) # x^3 - x relative to the standard basis
array([ 0. , -0.25, 0. , 0.25])
>>> j = complex(0,1)
>>> C.chebfromroots((-j,j)) # x^2 + 1 relative to the standard basis
array([ 1.5+0.j, 0.0+0.j, 0.5+0.j])
"""
if len(roots) == 0:
return np.ones(1)
else:
[roots] = pu.as_series([roots], trim=False)
roots.sort()
p = [chebline(-r, 1) for r in roots]
n = len(p)
while n > 1:
m, r = divmod(n, 2)
tmp = [chebmul(p[i], p[i+m]) for i in range(m)]
if r:
tmp[0] = chebmul(tmp[0], p[-1])
p = tmp
n = m
return p[0]
def chebadd(c1, c2):
"""
Add one Chebyshev series to another.
Returns the sum of two Chebyshev series `c1` + `c2`. The arguments
are sequences of coefficients ordered from lowest order term to
highest, i.e., [1,2,3] represents the series ``T_0 + 2*T_1 + 3*T_2``.
Parameters
----------
c1, c2 : array_like
1-D arrays of Chebyshev series coefficients ordered from low to
high.
Returns
-------
out : ndarray
Array representing the Chebyshev series of their sum.
See Also
--------
chebsub, chebmul, chebdiv, chebpow
Notes
-----
Unlike multiplication, division, etc., the sum of two Chebyshev series
is a Chebyshev series (without having to "reproject" the result onto
the basis set) so addition, just like that of "standard" polynomials,
is simply "component-wise."
Examples
--------
>>> from numpy.polynomial import chebyshev as C
>>> c1 = (1,2,3)
>>> c2 = (3,2,1)
>>> C.chebadd(c1,c2)
array([ 4., 4., 4.])
"""
# c1, c2 are trimmed copies
[c1, c2] = pu.as_series([c1, c2])
if len(c1) > len(c2):
c1[:c2.size] += c2
ret = c1
else:
c2[:c1.size] += c1
ret = c2
return pu.trimseq(ret)
def chebsub(c1, c2):
"""
Subtract one Chebyshev series from another.
Returns the difference of two Chebyshev series `c1` - `c2`. The
sequences of coefficients are from lowest order term to highest, i.e.,
[1,2,3] represents the series ``T_0 + 2*T_1 + 3*T_2``.
Parameters
----------
c1, c2 : array_like
1-D arrays of Chebyshev series coefficients ordered from low to
high.
Returns
-------
out : ndarray
Of Chebyshev series coefficients representing their difference.
See Also
--------
chebadd, chebmul, chebdiv, chebpow
Notes
-----
Unlike multiplication, division, etc., the difference of two Chebyshev
series is a Chebyshev series (without having to "reproject" the result
onto the basis set) so subtraction, just like that of "standard"
polynomials, is simply "component-wise."
Examples
--------
>>> from numpy.polynomial import chebyshev as C
>>> c1 = (1,2,3)
>>> c2 = (3,2,1)
>>> C.chebsub(c1,c2)
array([-2., 0., 2.])
>>> C.chebsub(c2,c1) # -C.chebsub(c1,c2)
array([ 2., 0., -2.])
"""
# c1, c2 are trimmed copies
[c1, c2] = pu.as_series([c1, c2])
if len(c1) > len(c2):
c1[:c2.size] -= c2
ret = c1
else:
c2 = -c2
c2[:c1.size] += c1
ret = c2
return pu.trimseq(ret)
def chebmulx(c):
"""Multiply a Chebyshev series by x.
Multiply the polynomial `c` by x, where x is the independent
variable.
Parameters
----------
c : array_like
1-D array of Chebyshev series coefficients ordered from low to
high.
Returns
-------
out : ndarray
Array representing the result of the multiplication.
Notes
-----
.. versionadded:: 1.5.0
"""
# c is a trimmed copy
[c] = pu.as_series([c])
# The zero series needs special treatment
if len(c) == 1 and c[0] == 0:
return c
prd = np.empty(len(c) + 1, dtype=c.dtype)
prd[0] = c[0]*0
prd[1] = c[0]
if len(c) > 1:
tmp = c[1:]/2
prd[2:] = tmp
prd[0:-2] += tmp
return prd
def chebmul(c1, c2):
"""
Multiply one Chebyshev series by another.
Returns the product of two Chebyshev series `c1` * `c2`. The arguments
are sequences of coefficients, from lowest order "term" to highest,
e.g., [1,2,3] represents the series ``T_0 + 2*T_1 + 3*T_2``.
Parameters
----------
c1, c2 : array_like
1-D arrays of Chebyshev series coefficients ordered from low to
high.
Returns
-------
out : ndarray
Of Chebyshev series coefficients representing their product.
See Also
--------
chebadd, chebsub, chebdiv, chebpow
Notes
-----
In general, the (polynomial) product of two C-series results in terms
that are not in the Chebyshev polynomial basis set. Thus, to express
the product as a C-series, it is typically necessary to "reproject"
the product onto said basis set, which typically produces
"unintuitive live" (but correct) results; see Examples section below.
Examples
--------
>>> from numpy.polynomial import chebyshev as C
>>> c1 = (1,2,3)
>>> c2 = (3,2,1)
>>> C.chebmul(c1,c2) # multiplication requires "reprojection"
array([ 6.5, 12. , 12. , 4. , 1.5])
"""
# c1, c2 are trimmed copies
[c1, c2] = pu.as_series([c1, c2])
z1 = _cseries_to_zseries(c1)
z2 = _cseries_to_zseries(c2)
prd = _zseries_mul(z1, z2)
ret = _zseries_to_cseries(prd)
return pu.trimseq(ret)
def chebdiv(c1, c2):
"""
Divide one Chebyshev series by another.
Returns the quotient-with-remainder of two Chebyshev series
`c1` / `c2`. The arguments are sequences of coefficients from lowest
order "term" to highest, e.g., [1,2,3] represents the series
``T_0 + 2*T_1 + 3*T_2``.
Parameters
----------
c1, c2 : array_like
1-D arrays of Chebyshev series coefficients ordered from low to
high.
Returns
-------
[quo, rem] : ndarrays
Of Chebyshev series coefficients representing the quotient and
remainder.
See Also
--------
chebadd, chebsub, chebmul, chebpow
Notes
-----
In general, the (polynomial) division of one C-series by another
results in quotient and remainder terms that are not in the Chebyshev
polynomial basis set. Thus, to express these results as C-series, it
is typically necessary to "reproject" the results onto said basis
set, which typically produces "unintuitive" (but correct) results;
see Examples section below.
Examples
--------
>>> from numpy.polynomial import chebyshev as C
>>> c1 = (1,2,3)
>>> c2 = (3,2,1)
>>> C.chebdiv(c1,c2) # quotient "intuitive," remainder not
(array([ 3.]), array([-8., -4.]))
>>> c2 = (0,1,2,3)
>>> C.chebdiv(c2,c1) # neither "intuitive"
(array([ 0., 2.]), array([-2., -4.]))
"""
# c1, c2 are trimmed copies
[c1, c2] = pu.as_series([c1, c2])
if c2[-1] == 0:
raise ZeroDivisionError()
lc1 = len(c1)
lc2 = len(c2)
if lc1 < lc2:
return c1[:1]*0, c1
elif lc2 == 1:
return c1/c2[-1], c1[:1]*0
else:
z1 = _cseries_to_zseries(c1)
z2 = _cseries_to_zseries(c2)
quo, rem = _zseries_div(z1, z2)
quo = pu.trimseq(_zseries_to_cseries(quo))
rem = pu.trimseq(_zseries_to_cseries(rem))
return quo, rem
def chebpow(c, pow, maxpower=16):
"""Raise a Chebyshev series to a power.
Returns the Chebyshev series `c` raised to the power `pow`. The
argument `c` is a sequence of coefficients ordered from low to high.
i.e., [1,2,3] is the series ``T_0 + 2*T_1 + 3*T_2.``
Parameters
----------
c : array_like
1-D array of Chebyshev series coefficients ordered from low to
high.
pow : integer
Power to which the series will be raised
maxpower : integer, optional
Maximum power allowed. This is mainly to limit growth of the series
to unmanageable size. Default is 16
Returns
-------
coef : ndarray
Chebyshev series of power.
See Also
--------
chebadd, chebsub, chebmul, chebdiv
Examples
--------
"""
# c is a trimmed copy
[c] = pu.as_series([c])
power = int(pow)
if power != pow or power < 0:
raise ValueError("Power must be a non-negative integer.")
elif maxpower is not None and power > maxpower:
raise ValueError("Power is too large")
elif power == 0:
return np.array([1], dtype=c.dtype)
elif power == 1:
return c
else:
# This can be made more efficient by using powers of two
# in the usual way.
zs = _cseries_to_zseries(c)
prd = zs
for i in range(2, power + 1):
prd = np.convolve(prd, zs)
return _zseries_to_cseries(prd)
def chebder(c, m=1, scl=1, axis=0):
"""
Differentiate a Chebyshev series.
Returns the Chebyshev series coefficients `c` differentiated `m` times
along `axis`. At each iteration the result is multiplied by `scl` (the
scaling factor is for use in a linear change of variable). The argument
`c` is an array of coefficients from low to high degree along each
axis, e.g., [1,2,3] represents the series ``1*T_0 + 2*T_1 + 3*T_2``
while [[1,2],[1,2]] represents ``1*T_0(x)*T_0(y) + 1*T_1(x)*T_0(y) +
2*T_0(x)*T_1(y) + 2*T_1(x)*T_1(y)`` if axis=0 is ``x`` and axis=1 is
``y``.
Parameters
----------
c : array_like
Array of Chebyshev series coefficients. If c is multidimensional
the different axis correspond to different variables with the
degree in each axis given by the corresponding index.
m : int, optional
Number of derivatives taken, must be non-negative. (Default: 1)
scl : scalar, optional
Each differentiation is multiplied by `scl`. The end result is
multiplication by ``scl**m``. This is for use in a linear change of
variable. (Default: 1)
axis : int, optional
Axis over which the derivative is taken. (Default: 0).
.. versionadded:: 1.7.0
Returns
-------
der : ndarray
Chebyshev series of the derivative.
See Also
--------
chebint
Notes
-----
In general, the result of differentiating a C-series needs to be
"reprojected" onto the C-series basis set. Thus, typically, the
result of this function is "unintuitive," albeit correct; see Examples
section below.
Examples
--------
>>> from numpy.polynomial import chebyshev as C
>>> c = (1,2,3,4)
>>> C.chebder(c)
array([ 14., 12., 24.])
>>> C.chebder(c,3)
array([ 96.])
>>> C.chebder(c,scl=-1)
array([-14., -12., -24.])
>>> C.chebder(c,2,-1)
array([ 12., 96.])
"""
c = np.array(c, ndmin=1, copy=1)
if c.dtype.char in '?bBhHiIlLqQpP':
c = c.astype(np.double)
cnt, iaxis = [int(t) for t in [m, axis]]
if cnt != m:
raise ValueError("The order of derivation must be integer")
if cnt < 0:
raise ValueError("The order of derivation must be non-negative")
if iaxis != axis:
raise ValueError("The axis must be integer")
iaxis = normalize_axis_index(iaxis, c.ndim)
if cnt == 0:
return c
c = np.moveaxis(c, iaxis, 0)
n = len(c)
if cnt >= n:
c = c[:1]*0
else:
for i in range(cnt):
n = n - 1
c *= scl
der = np.empty((n,) + c.shape[1:], dtype=c.dtype)
for j in range(n, 2, -1):
der[j - 1] = (2*j)*c[j]
c[j - 2] += (j*c[j])/(j - 2)
if n > 1:
der[1] = 4*c[2]
der[0] = c[1]
c = der
c = np.moveaxis(c, 0, iaxis)
return c
def chebint(c, m=1, k=[], lbnd=0, scl=1, axis=0):
"""
Integrate a Chebyshev series.
Returns the Chebyshev series coefficients `c` integrated `m` times from
`lbnd` along `axis`. At each iteration the resulting series is
**multiplied** by `scl` and an integration constant, `k`, is added.
The scaling factor is for use in a linear change of variable. ("Buyer
beware": note that, depending on what one is doing, one may want `scl`
to be the reciprocal of what one might expect; for more information,
see the Notes section below.) The argument `c` is an array of
coefficients from low to high degree along each axis, e.g., [1,2,3]
represents the series ``T_0 + 2*T_1 + 3*T_2`` while [[1,2],[1,2]]
represents ``1*T_0(x)*T_0(y) + 1*T_1(x)*T_0(y) + 2*T_0(x)*T_1(y) +
2*T_1(x)*T_1(y)`` if axis=0 is ``x`` and axis=1 is ``y``.
Parameters
----------
c : array_like
Array of Chebyshev series coefficients. If c is multidimensional
the different axis correspond to different variables with the
degree in each axis given by the corresponding index.
m : int, optional
Order of integration, must be positive. (Default: 1)
k : {[], list, scalar}, optional
Integration constant(s). The value of the first integral at zero
is the first value in the list, the value of the second integral
at zero is the second value, etc. If ``k == []`` (the default),
all constants are set to zero. If ``m == 1``, a single scalar can
be given instead of a list.
lbnd : scalar, optional
The lower bound of the integral. (Default: 0)
scl : scalar, optional
Following each integration the result is *multiplied* by `scl`
before the integration constant is added. (Default: 1)
axis : int, optional
Axis over which the integral is taken. (Default: 0).
.. versionadded:: 1.7.0
Returns
-------
S : ndarray
C-series coefficients of the integral.
Raises
------
ValueError
If ``m < 1``, ``len(k) > m``, ``np.ndim(lbnd) != 0``, or
``np.ndim(scl) != 0``.
See Also
--------
chebder
Notes
-----
Note that the result of each integration is *multiplied* by `scl`.
Why is this important to note? Say one is making a linear change of
variable :math:`u = ax + b` in an integral relative to `x`. Then
:math:`dx = du/a`, so one will need to set `scl` equal to
:math:`1/a`- perhaps not what one would have first thought.
Also note that, in general, the result of integrating a C-series needs
to be "reprojected" onto the C-series basis set. Thus, typically,
the result of this function is "unintuitive," albeit correct; see
Examples section below.
Examples
--------
>>> from numpy.polynomial import chebyshev as C
>>> c = (1,2,3)
>>> C.chebint(c)
array([ 0.5, -0.5, 0.5, 0.5])
>>> C.chebint(c,3)
array([ 0.03125 , -0.1875 , 0.04166667, -0.05208333, 0.01041667,
0.00625 ])
>>> C.chebint(c, k=3)
array([ 3.5, -0.5, 0.5, 0.5])
>>> C.chebint(c,lbnd=-2)
array([ 8.5, -0.5, 0.5, 0.5])
>>> C.chebint(c,scl=-2)
array([-1., 1., -1., -1.])
"""
c = np.array(c, ndmin=1, copy=1)
if c.dtype.char in '?bBhHiIlLqQpP':
c = c.astype(np.double)
if not np.iterable(k):
k = [k]
cnt, iaxis = [int(t) for t in [m, axis]]
if cnt != m:
raise ValueError("The order of integration must be integer")
if cnt < 0:
raise ValueError("The order of integration must be non-negative")
if len(k) > cnt:
raise ValueError("Too many integration constants")
if np.ndim(lbnd) != 0:
raise ValueError("lbnd must be a scalar.")
if np.ndim(scl) != 0:
raise ValueError("scl must be a scalar.")
if iaxis != axis:
raise ValueError("The axis must be integer")
iaxis = normalize_axis_index(iaxis, c.ndim)
if cnt == 0:
return c
c = np.moveaxis(c, iaxis, 0)
k = list(k) + [0]*(cnt - len(k))
for i in range(cnt):
n = len(c)
c *= scl
if n == 1 and np.all(c[0] == 0):
c[0] += k[i]
else:
tmp = np.empty((n + 1,) + c.shape[1:], dtype=c.dtype)
tmp[0] = c[0]*0
tmp[1] = c[0]
if n > 1:
tmp[2] = c[1]/4
for j in range(2, n):
t = c[j]/(2*j + 1)
tmp[j + 1] = c[j]/(2*(j + 1))
tmp[j - 1] -= c[j]/(2*(j - 1))
tmp[0] += k[i] - chebval(lbnd, tmp)
c = tmp
c = np.moveaxis(c, 0, iaxis)
return c
def chebval(x, c, tensor=True):
"""
Evaluate a Chebyshev series at points x.
If `c` is of length `n + 1`, this function returns the value:
.. math:: p(x) = c_0 * T_0(x) + c_1 * T_1(x) + ... + c_n * T_n(x)
The parameter `x` is converted to an array only if it is a tuple or a
list, otherwise it is treated as a scalar. In either case, either `x`
or its elements must support multiplication and addition both with
themselves and with the elements of `c`.
If `c` is a 1-D array, then `p(x)` will have the same shape as `x`. If
`c` is multidimensional, then the shape of the result depends on the
value of `tensor`. If `tensor` is true the shape will be c.shape[1:] +
x.shape. If `tensor` is false the shape will be c.shape[1:]. Note that
scalars have shape (,).
Trailing zeros in the coefficients will be used in the evaluation, so
they should be avoided if efficiency is a concern.
Parameters
----------
x : array_like, compatible object
If `x` is a list or tuple, it is converted to an ndarray, otherwise
it is left unchanged and treated as a scalar. In either case, `x`
or its elements must support addition and multiplication with
with themselves and with the elements of `c`.
c : array_like
Array of coefficients ordered so that the coefficients for terms of
degree n are contained in c[n]. If `c` is multidimensional the
remaining indices enumerate multiple polynomials. In the two
dimensional case the coefficients may be thought of as stored in
the columns of `c`.
tensor : boolean, optional
If True, the shape of the coefficient array is extended with ones
on the right, one for each dimension of `x`. Scalars have dimension 0
for this action. The result is that every column of coefficients in
`c` is evaluated for every element of `x`. If False, `x` is broadcast
over the columns of `c` for the evaluation. This keyword is useful
when `c` is multidimensional. The default value is True.
.. versionadded:: 1.7.0
Returns
-------
values : ndarray, algebra_like
The shape of the return value is described above.
See Also
--------
chebval2d, chebgrid2d, chebval3d, chebgrid3d
Notes
-----
The evaluation uses Clenshaw recursion, aka synthetic division.
Examples
--------
"""
c = np.array(c, ndmin=1, copy=1)
if c.dtype.char in '?bBhHiIlLqQpP':
c = c.astype(np.double)
if isinstance(x, (tuple, list)):
x = np.asarray(x)
if isinstance(x, np.ndarray) and tensor:
c = c.reshape(c.shape + (1,)*x.ndim)
if len(c) == 1:
c0 = c[0]
c1 = 0
elif len(c) == 2:
c0 = c[0]
c1 = c[1]
else:
x2 = 2*x
c0 = c[-2]
c1 = c[-1]
for i in range(3, len(c) + 1):
tmp = c0
c0 = c[-i] - c1
c1 = tmp + c1*x2
return c0 + c1*x
def chebval2d(x, y, c):
"""
Evaluate a 2-D Chebyshev series at points (x, y).
This function returns the values:
.. math:: p(x,y) = \\sum_{i,j} c_{i,j} * T_i(x) * T_j(y)
The parameters `x` and `y` are converted to arrays only if they are
tuples or a lists, otherwise they are treated as a scalars and they
must have the same shape after conversion. In either case, either `x`
and `y` or their elements must support multiplication and addition both
with themselves and with the elements of `c`.
If `c` is a 1-D array a one is implicitly appended to its shape to make
it 2-D. The shape of the result will be c.shape[2:] + x.shape.
Parameters
----------
x, y : array_like, compatible objects
The two dimensional series is evaluated at the points `(x, y)`,
where `x` and `y` must have the same shape. If `x` or `y` is a list
or tuple, it is first converted to an ndarray, otherwise it is left
unchanged and if it isn't an ndarray it is treated as a scalar.
c : array_like
Array of coefficients ordered so that the coefficient of the term
of multi-degree i,j is contained in ``c[i,j]``. If `c` has
dimension greater than 2 the remaining indices enumerate multiple
sets of coefficients.
Returns
-------
values : ndarray, compatible object
The values of the two dimensional Chebyshev series at points formed
from pairs of corresponding values from `x` and `y`.
See Also
--------
chebval, chebgrid2d, chebval3d, chebgrid3d
Notes
-----
.. versionadded:: 1.7.0
"""
try:
x, y = np.array((x, y), copy=0)
except Exception:
raise ValueError('x, y are incompatible')
c = chebval(x, c)
c = chebval(y, c, tensor=False)
return c
def chebgrid2d(x, y, c):
"""
Evaluate a 2-D Chebyshev series on the Cartesian product of x and y.
This function returns the values:
.. math:: p(a,b) = \\sum_{i,j} c_{i,j} * T_i(a) * T_j(b),
where the points `(a, b)` consist of all pairs formed by taking
`a` from `x` and `b` from `y`. The resulting points form a grid with
`x` in the first dimension and `y` in the second.
The parameters `x` and `y` are converted to arrays only if they are
tuples or a lists, otherwise they are treated as a scalars. In either
case, either `x` and `y` or their elements must support multiplication
and addition both with themselves and with the elements of `c`.
If `c` has fewer than two dimensions, ones are implicitly appended to
its shape to make it 2-D. The shape of the result will be c.shape[2:] +
x.shape + y.shape.
Parameters
----------
x, y : array_like, compatible objects
The two dimensional series is evaluated at the points in the
Cartesian product of `x` and `y`. If `x` or `y` is a list or
tuple, it is first converted to an ndarray, otherwise it is left
unchanged and, if it isn't an ndarray, it is treated as a scalar.
c : array_like
Array of coefficients ordered so that the coefficient of the term of
multi-degree i,j is contained in `c[i,j]`. If `c` has dimension
greater than two the remaining indices enumerate multiple sets of
coefficients.
Returns
-------
values : ndarray, compatible object
The values of the two dimensional Chebyshev series at points in the
Cartesian product of `x` and `y`.
See Also
--------
chebval, chebval2d, chebval3d, chebgrid3d
Notes
-----
.. versionadded:: 1.7.0
"""
c = chebval(x, c)
c = chebval(y, c)
return c
def chebval3d(x, y, z, c):
"""
Evaluate a 3-D Chebyshev series at points (x, y, z).
This function returns the values:
.. math:: p(x,y,z) = \\sum_{i,j,k} c_{i,j,k} * T_i(x) * T_j(y) * T_k(z)
The parameters `x`, `y`, and `z` are converted to arrays only if
they are tuples or a lists, otherwise they are treated as a scalars and
they must have the same shape after conversion. In either case, either
`x`, `y`, and `z` or their elements must support multiplication and
addition both with themselves and with the elements of `c`.
If `c` has fewer than 3 dimensions, ones are implicitly appended to its
shape to make it 3-D. The shape of the result will be c.shape[3:] +
x.shape.
Parameters
----------
x, y, z : array_like, compatible object
The three dimensional series is evaluated at the points
`(x, y, z)`, where `x`, `y`, and `z` must have the same shape. If
any of `x`, `y`, or `z` is a list or tuple, it is first converted
to an ndarray, otherwise it is left unchanged and if it isn't an
ndarray it is treated as a scalar.
c : array_like
Array of coefficients ordered so that the coefficient of the term of
multi-degree i,j,k is contained in ``c[i,j,k]``. If `c` has dimension
greater than 3 the remaining indices enumerate multiple sets of
coefficients.
Returns
-------
values : ndarray, compatible object
The values of the multidimensional polynomial on points formed with
triples of corresponding values from `x`, `y`, and `z`.
See Also
--------
chebval, chebval2d, chebgrid2d, chebgrid3d
Notes
-----
.. versionadded:: 1.7.0
"""
try:
x, y, z = np.array((x, y, z), copy=0)
except Exception:
raise ValueError('x, y, z are incompatible')
c = chebval(x, c)
c = chebval(y, c, tensor=False)
c = chebval(z, c, tensor=False)
return c
def chebgrid3d(x, y, z, c):
"""
Evaluate a 3-D Chebyshev series on the Cartesian product of x, y, and z.
This function returns the values:
.. math:: p(a,b,c) = \\sum_{i,j,k} c_{i,j,k} * T_i(a) * T_j(b) * T_k(c)
where the points `(a, b, c)` consist of all triples formed by taking
`a` from `x`, `b` from `y`, and `c` from `z`. The resulting points form
a grid with `x` in the first dimension, `y` in the second, and `z` in
the third.
The parameters `x`, `y`, and `z` are converted to arrays only if they
are tuples or a lists, otherwise they are treated as a scalars. In
either case, either `x`, `y`, and `z` or their elements must support
multiplication and addition both with themselves and with the elements
of `c`.
If `c` has fewer than three dimensions, ones are implicitly appended to
its shape to make it 3-D. The shape of the result will be c.shape[3:] +
x.shape + y.shape + z.shape.
Parameters
----------
x, y, z : array_like, compatible objects
The three dimensional series is evaluated at the points in the
Cartesian product of `x`, `y`, and `z`. If `x`,`y`, or `z` is a
list or tuple, it is first converted to an ndarray, otherwise it is
left unchanged and, if it isn't an ndarray, it is treated as a
scalar.
c : array_like
Array of coefficients ordered so that the coefficients for terms of
degree i,j are contained in ``c[i,j]``. If `c` has dimension
greater than two the remaining indices enumerate multiple sets of
coefficients.
Returns
-------
values : ndarray, compatible object
The values of the two dimensional polynomial at points in the Cartesian
product of `x` and `y`.
See Also
--------
chebval, chebval2d, chebgrid2d, chebval3d
Notes
-----
.. versionadded:: 1.7.0
"""
c = chebval(x, c)
c = chebval(y, c)
c = chebval(z, c)
return c
def chebvander(x, deg):
"""Pseudo-Vandermonde matrix of given degree.
Returns the pseudo-Vandermonde matrix of degree `deg` and sample points
`x`. The pseudo-Vandermonde matrix is defined by
.. math:: V[..., i] = T_i(x),
where `0 <= i <= deg`. The leading indices of `V` index the elements of
`x` and the last index is the degree of the Chebyshev polynomial.
If `c` is a 1-D array of coefficients of length `n + 1` and `V` is the
matrix ``V = chebvander(x, n)``, then ``np.dot(V, c)`` and
``chebval(x, c)`` are the same up to roundoff. This equivalence is
useful both for least squares fitting and for the evaluation of a large
number of Chebyshev series of the same degree and sample points.
Parameters
----------
x : array_like
Array of points. The dtype is converted to float64 or complex128
depending on whether any of the elements are complex. If `x` is
scalar it is converted to a 1-D array.
deg : int
Degree of the resulting matrix.
Returns
-------
vander : ndarray
The pseudo Vandermonde matrix. The shape of the returned matrix is
``x.shape + (deg + 1,)``, where The last index is the degree of the
corresponding Chebyshev polynomial. The dtype will be the same as
the converted `x`.
"""
ideg = int(deg)
if ideg != deg:
raise ValueError("deg must be integer")
if ideg < 0:
raise ValueError("deg must be non-negative")
x = np.array(x, copy=0, ndmin=1) + 0.0
dims = (ideg + 1,) + x.shape
dtyp = x.dtype
v = np.empty(dims, dtype=dtyp)
# Use forward recursion to generate the entries.
v[0] = x*0 + 1
if ideg > 0:
x2 = 2*x
v[1] = x
for i in range(2, ideg + 1):
v[i] = v[i-1]*x2 - v[i-2]
return np.moveaxis(v, 0, -1)
def chebvander2d(x, y, deg):
"""Pseudo-Vandermonde matrix of given degrees.
Returns the pseudo-Vandermonde matrix of degrees `deg` and sample
points `(x, y)`. The pseudo-Vandermonde matrix is defined by
.. math:: V[..., (deg[1] + 1)*i + j] = T_i(x) * T_j(y),
where `0 <= i <= deg[0]` and `0 <= j <= deg[1]`. The leading indices of
`V` index the points `(x, y)` and the last index encodes the degrees of
the Chebyshev polynomials.
If ``V = chebvander2d(x, y, [xdeg, ydeg])``, then the columns of `V`
correspond to the elements of a 2-D coefficient array `c` of shape
(xdeg + 1, ydeg + 1) in the order
.. math:: c_{00}, c_{01}, c_{02} ... , c_{10}, c_{11}, c_{12} ...
and ``np.dot(V, c.flat)`` and ``chebval2d(x, y, c)`` will be the same
up to roundoff. This equivalence is useful both for least squares
fitting and for the evaluation of a large number of 2-D Chebyshev
series of the same degrees and sample points.
Parameters
----------
x, y : array_like
Arrays of point coordinates, all of the same shape. The dtypes
will be converted to either float64 or complex128 depending on
whether any of the elements are complex. Scalars are converted to
1-D arrays.
deg : list of ints
List of maximum degrees of the form [x_deg, y_deg].
Returns
-------
vander2d : ndarray
The shape of the returned matrix is ``x.shape + (order,)``, where
:math:`order = (deg[0]+1)*(deg([1]+1)`. The dtype will be the same
as the converted `x` and `y`.
See Also
--------
chebvander, chebvander3d. chebval2d, chebval3d
Notes
-----
.. versionadded:: 1.7.0
"""
ideg = [int(d) for d in deg]
is_valid = [id == d and id >= 0 for id, d in zip(ideg, deg)]
if is_valid != [1, 1]:
raise ValueError("degrees must be non-negative integers")
degx, degy = ideg
x, y = np.array((x, y), copy=0) + 0.0
vx = chebvander(x, degx)
vy = chebvander(y, degy)
v = vx[..., None]*vy[..., None,:]
return v.reshape(v.shape[:-2] + (-1,))
def chebvander3d(x, y, z, deg):
"""Pseudo-Vandermonde matrix of given degrees.
Returns the pseudo-Vandermonde matrix of degrees `deg` and sample
points `(x, y, z)`. If `l, m, n` are the given degrees in `x, y, z`,
then The pseudo-Vandermonde matrix is defined by
.. math:: V[..., (m+1)(n+1)i + (n+1)j + k] = T_i(x)*T_j(y)*T_k(z),
where `0 <= i <= l`, `0 <= j <= m`, and `0 <= j <= n`. The leading
indices of `V` index the points `(x, y, z)` and the last index encodes
the degrees of the Chebyshev polynomials.
If ``V = chebvander3d(x, y, z, [xdeg, ydeg, zdeg])``, then the columns
of `V` correspond to the elements of a 3-D coefficient array `c` of
shape (xdeg + 1, ydeg + 1, zdeg + 1) in the order
.. math:: c_{000}, c_{001}, c_{002},... , c_{010}, c_{011}, c_{012},...
and ``np.dot(V, c.flat)`` and ``chebval3d(x, y, z, c)`` will be the
same up to roundoff. This equivalence is useful both for least squares
fitting and for the evaluation of a large number of 3-D Chebyshev
series of the same degrees and sample points.
Parameters
----------
x, y, z : array_like
Arrays of point coordinates, all of the same shape. The dtypes will
be converted to either float64 or complex128 depending on whether
any of the elements are complex. Scalars are converted to 1-D
arrays.
deg : list of ints
List of maximum degrees of the form [x_deg, y_deg, z_deg].
Returns
-------
vander3d : ndarray
The shape of the returned matrix is ``x.shape + (order,)``, where
:math:`order = (deg[0]+1)*(deg([1]+1)*(deg[2]+1)`. The dtype will
be the same as the converted `x`, `y`, and `z`.
See Also
--------
chebvander, chebvander3d. chebval2d, chebval3d
Notes
-----
.. versionadded:: 1.7.0
"""
ideg = [int(d) for d in deg]
is_valid = [id == d and id >= 0 for id, d in zip(ideg, deg)]
if is_valid != [1, 1, 1]:
raise ValueError("degrees must be non-negative integers")
degx, degy, degz = ideg
x, y, z = np.array((x, y, z), copy=0) + 0.0
vx = chebvander(x, degx)
vy = chebvander(y, degy)
vz = chebvander(z, degz)
v = vx[..., None, None]*vy[..., None,:, None]*vz[..., None, None,:]
return v.reshape(v.shape[:-3] + (-1,))
def chebfit(x, y, deg, rcond=None, full=False, w=None):
"""
Least squares fit of Chebyshev series to data.
Return the coefficients of a Chebyshev series of degree `deg` that is the
least squares fit to the data values `y` given at points `x`. If `y` is
1-D the returned coefficients will also be 1-D. If `y` is 2-D multiple
fits are done, one for each column of `y`, and the resulting
coefficients are stored in the corresponding columns of a 2-D return.
The fitted polynomial(s) are in the form
.. math:: p(x) = c_0 + c_1 * T_1(x) + ... + c_n * T_n(x),
where `n` is `deg`.
Parameters
----------
x : array_like, shape (M,)
x-coordinates of the M sample points ``(x[i], y[i])``.
y : array_like, shape (M,) or (M, K)
y-coordinates of the sample points. Several data sets of sample
points sharing the same x-coordinates can be fitted at once by
passing in a 2D-array that contains one dataset per column.
deg : int or 1-D array_like
Degree(s) of the fitting polynomials. If `deg` is a single integer,
all terms up to and including the `deg`'th term are included in the
fit. For NumPy versions >= 1.11.0 a list of integers specifying the
degrees of the terms to include may be used instead.
rcond : float, optional
Relative condition number of the fit. Singular values smaller than
this relative to the largest singular value will be ignored. The
default value is len(x)*eps, where eps is the relative precision of
the float type, about 2e-16 in most cases.
full : bool, optional
Switch determining nature of return value. When it is False (the
default) just the coefficients are returned, when True diagnostic
information from the singular value decomposition is also returned.
w : array_like, shape (`M`,), optional
Weights. If not None, the contribution of each point
``(x[i],y[i])`` to the fit is weighted by `w[i]`. Ideally the
weights are chosen so that the errors of the products ``w[i]*y[i]``
all have the same variance. The default value is None.
.. versionadded:: 1.5.0
Returns
-------
coef : ndarray, shape (M,) or (M, K)
Chebyshev coefficients ordered from low to high. If `y` was 2-D,
the coefficients for the data in column k of `y` are in column
`k`.
[residuals, rank, singular_values, rcond] : list
These values are only returned if `full` = True
resid -- sum of squared residuals of the least squares fit
rank -- the numerical rank of the scaled Vandermonde matrix
sv -- singular values of the scaled Vandermonde matrix
rcond -- value of `rcond`.
For more details, see `linalg.lstsq`.
Warns
-----
RankWarning
The rank of the coefficient matrix in the least-squares fit is
deficient. The warning is only raised if `full` = False. The
warnings can be turned off by
>>> import warnings
>>> warnings.simplefilter('ignore', RankWarning)
See Also
--------
polyfit, legfit, lagfit, hermfit, hermefit
chebval : Evaluates a Chebyshev series.
chebvander : Vandermonde matrix of Chebyshev series.
chebweight : Chebyshev weight function.
linalg.lstsq : Computes a least-squares fit from the matrix.
scipy.interpolate.UnivariateSpline : Computes spline fits.
Notes
-----
The solution is the coefficients of the Chebyshev series `p` that
minimizes the sum of the weighted squared errors
.. math:: E = \\sum_j w_j^2 * |y_j - p(x_j)|^2,
where :math:`w_j` are the weights. This problem is solved by setting up
as the (typically) overdetermined matrix equation
.. math:: V(x) * c = w * y,
where `V` is the weighted pseudo Vandermonde matrix of `x`, `c` are the
coefficients to be solved for, `w` are the weights, and `y` are the
observed values. This equation is then solved using the singular value
decomposition of `V`.
If some of the singular values of `V` are so small that they are
neglected, then a `RankWarning` will be issued. This means that the
coefficient values may be poorly determined. Using a lower order fit
will usually get rid of the warning. The `rcond` parameter can also be
set to a value smaller than its default, but the resulting fit may be
spurious and have large contributions from roundoff error.
Fits using Chebyshev series are usually better conditioned than fits
using power series, but much can depend on the distribution of the
sample points and the smoothness of the data. If the quality of the fit
is inadequate splines may be a good alternative.
References
----------
.. [1] Wikipedia, "Curve fitting",
http://en.wikipedia.org/wiki/Curve_fitting
Examples
--------
"""
x = np.asarray(x) + 0.0
y = np.asarray(y) + 0.0
deg = np.asarray(deg)
# check arguments.
if deg.ndim > 1 or deg.dtype.kind not in 'iu' or deg.size == 0:
raise TypeError("deg must be an int or non-empty 1-D array of int")
if deg.min() < 0:
raise ValueError("expected deg >= 0")
if x.ndim != 1:
raise TypeError("expected 1D vector for x")
if x.size == 0:
raise TypeError("expected non-empty vector for x")
if y.ndim < 1 or y.ndim > 2:
raise TypeError("expected 1D or 2D array for y")
if len(x) != len(y):
raise TypeError("expected x and y to have same length")
if deg.ndim == 0:
lmax = deg
order = lmax + 1
van = chebvander(x, lmax)
else:
deg = np.sort(deg)
lmax = deg[-1]
order = len(deg)
van = chebvander(x, lmax)[:, deg]
# set up the least squares matrices in transposed form
lhs = van.T
rhs = y.T
if w is not None:
w = np.asarray(w) + 0.0
if w.ndim != 1:
raise TypeError("expected 1D vector for w")
if len(x) != len(w):
raise TypeError("expected x and w to have same length")
# apply weights. Don't use inplace operations as they
# can cause problems with NA.
lhs = lhs * w
rhs = rhs * w
# set rcond
if rcond is None:
rcond = len(x)*np.finfo(x.dtype).eps
# Determine the norms of the design matrix columns.
if issubclass(lhs.dtype.type, np.complexfloating):
scl = np.sqrt((np.square(lhs.real) + np.square(lhs.imag)).sum(1))
else:
scl = np.sqrt(np.square(lhs).sum(1))
scl[scl == 0] = 1
# Solve the least squares problem.
c, resids, rank, s = la.lstsq(lhs.T/scl, rhs.T, rcond)
c = (c.T/scl).T
# Expand c to include non-fitted coefficients which are set to zero
if deg.ndim > 0:
if c.ndim == 2:
cc = np.zeros((lmax + 1, c.shape[1]), dtype=c.dtype)
else:
cc = np.zeros(lmax + 1, dtype=c.dtype)
cc[deg] = c
c = cc
# warn on rank reduction
if rank != order and not full:
msg = "The fit may be poorly conditioned"
warnings.warn(msg, pu.RankWarning, stacklevel=2)
if full:
return c, [resids, rank, s, rcond]
else:
return c
def chebcompanion(c):
"""Return the scaled companion matrix of c.
The basis polynomials are scaled so that the companion matrix is
symmetric when `c` is a Chebyshev basis polynomial. This provides
better eigenvalue estimates than the unscaled case and for basis
polynomials the eigenvalues are guaranteed to be real if
`numpy.linalg.eigvalsh` is used to obtain them.
Parameters
----------
c : array_like
1-D array of Chebyshev series coefficients ordered from low to high
degree.
Returns
-------
mat : ndarray
Scaled companion matrix of dimensions (deg, deg).
Notes
-----
.. versionadded:: 1.7.0
"""
# c is a trimmed copy
[c] = pu.as_series([c])
if len(c) < 2:
raise ValueError('Series must have maximum degree of at least 1.')
if len(c) == 2:
return np.array([[-c[0]/c[1]]])
n = len(c) - 1
mat = np.zeros((n, n), dtype=c.dtype)
scl = np.array([1.] + [np.sqrt(.5)]*(n-1))
top = mat.reshape(-1)[1::n+1]
bot = mat.reshape(-1)[n::n+1]
top[0] = np.sqrt(.5)
top[1:] = 1/2
bot[...] = top
mat[:, -1] -= (c[:-1]/c[-1])*(scl/scl[-1])*.5
return mat
def chebroots(c):
"""
Compute the roots of a Chebyshev series.
Return the roots (a.k.a. "zeros") of the polynomial
.. math:: p(x) = \\sum_i c[i] * T_i(x).
Parameters
----------
c : 1-D array_like
1-D array of coefficients.
Returns
-------
out : ndarray
Array of the roots of the series. If all the roots are real,
then `out` is also real, otherwise it is complex.
See Also
--------
polyroots, legroots, lagroots, hermroots, hermeroots
Notes
-----
The root estimates are obtained as the eigenvalues of the companion
matrix, Roots far from the origin of the complex plane may have large
errors due to the numerical instability of the series for such
values. Roots with multiplicity greater than 1 will also show larger
errors as the value of the series near such points is relatively
insensitive to errors in the roots. Isolated roots near the origin can
be improved by a few iterations of Newton's method.
The Chebyshev series basis polynomials aren't powers of `x` so the
results of this function may seem unintuitive.
Examples
--------
>>> import numpy.polynomial.chebyshev as cheb
>>> cheb.chebroots((-1, 1,-1, 1)) # T3 - T2 + T1 - T0 has real roots
array([ -5.00000000e-01, 2.60860684e-17, 1.00000000e+00])
"""
# c is a trimmed copy
[c] = pu.as_series([c])
if len(c) < 2:
return np.array([], dtype=c.dtype)
if len(c) == 2:
return np.array([-c[0]/c[1]])
m = chebcompanion(c)
r = la.eigvals(m)
r.sort()
return r
def chebinterpolate(func, deg, args=()):
"""Interpolate a function at the Chebyshev points of the first kind.
Returns the Chebyshev series that interpolates `func` at the Chebyshev
points of the first kind in the interval [-1, 1]. The interpolating
series tends to a minmax approximation to `func` with increasing `deg`
if the function is continuous in the interval.
.. versionadded:: 1.14.0
Parameters
----------
func : function
The function to be approximated. It must be a function of a single
variable of the form ``f(x, a, b, c...)``, where ``a, b, c...`` are
extra arguments passed in the `args` parameter.
deg : int
Degree of the interpolating polynomial
args : tuple, optional
Extra arguments to be used in the function call. Default is no extra
arguments.
Returns
-------
coef : ndarray, shape (deg + 1,)
Chebyshev coefficients of the interpolating series ordered from low to
high.
Examples
--------
>>> import numpy.polynomial.chebyshev as C
>>> C.chebfromfunction(lambda x: np.tanh(x) + 0.5, 8)
array([ 5.00000000e-01, 8.11675684e-01, -9.86864911e-17,
-5.42457905e-02, -2.71387850e-16, 4.51658839e-03,
2.46716228e-17, -3.79694221e-04, -3.26899002e-16])
Notes
-----
The Chebyshev polynomials used in the interpolation are orthogonal when
sampled at the Chebyshev points of the first kind. If it is desired to
constrain some of the coefficients they can simply be set to the desired
value after the interpolation, no new interpolation or fit is needed. This
is especially useful if it is known apriori that some of coefficients are
zero. For instance, if the function is even then the coefficients of the
terms of odd degree in the result can be set to zero.
"""
deg = np.asarray(deg)
# check arguments.
if deg.ndim > 0 or deg.dtype.kind not in 'iu' or deg.size == 0:
raise TypeError("deg must be an int")
if deg < 0:
raise ValueError("expected deg >= 0")
order = deg + 1
xcheb = chebpts1(order)
yfunc = func(xcheb, *args)
m = chebvander(xcheb, deg)
c = np.dot(m.T, yfunc)
c[0] /= order
c[1:] /= 0.5*order
return c
def chebgauss(deg):
"""
Gauss-Chebyshev quadrature.
Computes the sample points and weights for Gauss-Chebyshev quadrature.
These sample points and weights will correctly integrate polynomials of
degree :math:`2*deg - 1` or less over the interval :math:`[-1, 1]` with
the weight function :math:`f(x) = 1/\\sqrt{1 - x^2}`.
Parameters
----------
deg : int
Number of sample points and weights. It must be >= 1.
Returns
-------
x : ndarray
1-D ndarray containing the sample points.
y : ndarray
1-D ndarray containing the weights.
Notes
-----
.. versionadded:: 1.7.0
The results have only been tested up to degree 100, higher degrees may
be problematic. For Gauss-Chebyshev there are closed form solutions for
the sample points and weights. If n = `deg`, then
.. math:: x_i = \\cos(\\pi (2 i - 1) / (2 n))
.. math:: w_i = \\pi / n
"""
ideg = int(deg)
if ideg != deg or ideg < 1:
raise ValueError("deg must be a non-negative integer")
x = np.cos(np.pi * np.arange(1, 2*ideg, 2) / (2.0*ideg))
w = np.ones(ideg)*(np.pi/ideg)
return x, w
def chebweight(x):
"""
The weight function of the Chebyshev polynomials.
The weight function is :math:`1/\\sqrt{1 - x^2}` and the interval of
integration is :math:`[-1, 1]`. The Chebyshev polynomials are
orthogonal, but not normalized, with respect to this weight function.
Parameters
----------
x : array_like
Values at which the weight function will be computed.
Returns
-------
w : ndarray
The weight function at `x`.
Notes
-----
.. versionadded:: 1.7.0
"""
w = 1./(np.sqrt(1. + x) * np.sqrt(1. - x))
return w
def chebpts1(npts):
"""
Chebyshev points of the first kind.
The Chebyshev points of the first kind are the points ``cos(x)``,
where ``x = [pi*(k + .5)/npts for k in range(npts)]``.
Parameters
----------
npts : int
Number of sample points desired.
Returns
-------
pts : ndarray
The Chebyshev points of the first kind.
See Also
--------
chebpts2
Notes
-----
.. versionadded:: 1.5.0
"""
_npts = int(npts)
if _npts != npts:
raise ValueError("npts must be integer")
if _npts < 1:
raise ValueError("npts must be >= 1")
x = np.linspace(-np.pi, 0, _npts, endpoint=False) + np.pi/(2*_npts)
return np.cos(x)
def chebpts2(npts):
"""
Chebyshev points of the second kind.
The Chebyshev points of the second kind are the points ``cos(x)``,
where ``x = [pi*k/(npts - 1) for k in range(npts)]``.
Parameters
----------
npts : int
Number of sample points desired.
Returns
-------
pts : ndarray
The Chebyshev points of the second kind.
Notes
-----
.. versionadded:: 1.5.0
"""
_npts = int(npts)
if _npts != npts:
raise ValueError("npts must be integer")
if _npts < 2:
raise ValueError("npts must be >= 2")
x = np.linspace(-np.pi, 0, _npts)
return np.cos(x)
#
# Chebyshev series class
#
class Chebyshev(ABCPolyBase):
"""A Chebyshev series class.
The Chebyshev class provides the standard Python numerical methods
'+', '-', '*', '//', '%', 'divmod', '**', and '()' as well as the
methods listed below.
Parameters
----------
coef : array_like
Chebyshev coefficients in order of increasing degree, i.e.,
``(1, 2, 3)`` gives ``1*T_0(x) + 2*T_1(x) + 3*T_2(x)``.
domain : (2,) array_like, optional
Domain to use. The interval ``[domain[0], domain[1]]`` is mapped
to the interval ``[window[0], window[1]]`` by shifting and scaling.
The default value is [-1, 1].
window : (2,) array_like, optional
Window, see `domain` for its use. The default value is [-1, 1].
.. versionadded:: 1.6.0
"""
# Virtual Functions
_add = staticmethod(chebadd)
_sub = staticmethod(chebsub)
_mul = staticmethod(chebmul)
_div = staticmethod(chebdiv)
_pow = staticmethod(chebpow)
_val = staticmethod(chebval)
_int = staticmethod(chebint)
_der = staticmethod(chebder)
_fit = staticmethod(chebfit)
_line = staticmethod(chebline)
_roots = staticmethod(chebroots)
_fromroots = staticmethod(chebfromroots)
@classmethod
def interpolate(cls, func, deg, domain=None, args=()):
"""Interpolate a function at the Chebyshev points of the first kind.
Returns the series that interpolates `func` at the Chebyshev points of
the first kind scaled and shifted to the `domain`. The resulting series
tends to a minmax approximation of `func` when the function is
continuous in the domain.
.. versionadded:: 1.14.0
Parameters
----------
func : function
The function to be interpolated. It must be a function of a single
variable of the form ``f(x, a, b, c...)``, where ``a, b, c...`` are
extra arguments passed in the `args` parameter.
deg : int
Degree of the interpolating polynomial.
domain : {None, [beg, end]}, optional
Domain over which `func` is interpolated. The default is None, in
which case the domain is [-1, 1].
args : tuple, optional
Extra arguments to be used in the function call. Default is no
extra arguments.
Returns
-------
polynomial : Chebyshev instance
Interpolating Chebyshev instance.
Notes
-----
See `numpy.polynomial.chebfromfunction` for more details.
"""
if domain is None:
domain = cls.domain
xfunc = lambda x: func(pu.mapdomain(x, cls.window, domain), *args)
coef = chebinterpolate(xfunc, deg)
return cls(coef, domain=domain)
# Virtual properties
nickname = 'cheb'
domain = np.array(chebdomain)
window = np.array(chebdomain)
| 66,969 | 29.565952 | 79 |
py
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