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stringlengths 2
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⌀ | name
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| docstring
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⌀ | code
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20,971 | argufy.parser | __get_description | Get argument description from docstring. | @staticmethod
def __get_description(
name: str, docstring: 'Docstring'
) -> Optional['DocstringParam']:
"""Get argument description from docstring."""
return next((d for d in docstring.params if d.arg_name == name), None)
| (name: str, docstring: 'Docstring') -> Optional[ForwardRef('DocstringParam')] | [
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20,972 | argufy.parser | __get_keyword_args | Get keyward arguments from docstring. | @staticmethod
def __get_keyword_args(
signature: 'Signature', docstring: 'Docstring'
) -> List[str]:
"""Get keyward arguments from docstring."""
return [
x.arg_name
for x in docstring.params
if x.arg_name not in list(signature.parameters)
]
| (signature: 'Signature', docstring: 'Docstring') -> List[str] | [
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20,973 | argufy.parser | __get_parent_module | Get name of module importing this module. | @staticmethod
def __get_parent_module() -> Optional[ModuleType]:
"""Get name of module importing this module."""
stack = inspect.stack()
# TODO: need way to better identify parent module
stack_frame = stack[2]
result = inspect.getmodule(stack_frame[0]) or None
return result
| () -> Optional[module] | [
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] |
20,974 | argufy.parser | __set_main_arguments | Separate and set main arguments from builder function.
Paramters
---------
ns: Namespace
Argparse namespace object for a command.
Returns
-------
Namespace:
Argparse namespace object with command arguments.
| def __set_main_arguments(self, ns: 'Namespace') -> 'Namespace':
"""Separate and set main arguments from builder function.
Paramters
---------
ns: Namespace
Argparse namespace object for a command.
Returns
-------
Namespace:
Argparse namespace object with command arguments.
"""
# pass main arguments to builder function
if self.main_args_builder:
builder_mod = sys.modules[self.main_args_builder['module']]
builder = getattr(builder_mod, self.main_args_builder['function'])
builder_signature = inspect.signature(builder)
builder_args = {}
for param in builder_signature.parameters:
if param in vars(ns):
builder_args[param] = vars(ns).pop(param)
builder_mod.__dict__[self.main_args_builder['instance']] = builder(
**builder_args
)
return ns
| (self, ns: 'Namespace') -> 'Namespace' | [
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] |
20,975 | argufy.parser | __set_module_arguments | Separate and set module arguments from functions.
Paramters
---------
fn: Callable
Function used to seperate module arguments from function.
ns: Namespace
Argparse namespace object for a command.
Returns
-------
Namespace:
Argparse namespace object with command arguments.
| def __set_module_arguments(
self, fn: Callable[[F], F], ns: 'Namespace'
) -> 'Namespace':
"""Separate and set module arguments from functions.
Paramters
---------
fn: Callable
Function used to seperate module arguments from function.
ns: Namespace
Argparse namespace object for a command.
Returns
-------
Namespace:
Argparse namespace object with command arguments.
"""
# XXX: only works on subcommands that use 'mod'
if 'mod' in ns:
mod = vars(ns).pop('mod')
else:
mod = None
# separate namespace from other variables
signature = inspect.signature(fn)
docstring = docparse(fn.__doc__) if fn.__doc__ else None
# inspect non-signature keyword args
keywords = (
self.__get_keyword_args(signature, docstring)
if docstring
else list(signature.parameters)
)
args = [
{k: vars(ns).pop(k)}
for k in list(vars(ns).keys()).copy()
if not signature.parameters.get(k) and k not in keywords
]
log.debug("arguments %s, %s", args, keywords)
# set module variables
if mod and self.use_module_args:
for arg in args:
for k, v in arg.items():
mod.__dict__[k] = v
return ns
| (self, fn: Callable[[~F], ~F], ns: 'Namespace') -> 'Namespace' | [
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20,976 | argufy.parser | __init__ | Initialize parser.
Parameters
----------
prog: str
The name of the program
usage: str
The string describing the program usage
description: str
Text to display before the argument help
epilog: str
Text to display after the argument help
parents: list
A list of ArgumentParser objects whose arguments should also
be included
formatter_class: Object
A class for customizing the help output
prefix_chars: char
The set of characters that prefix optional arguments
fromfile_prefix_chars: None
The set of characters that prefix files from which additional
arguments should be read
argument_default: None
The global default value for arguments
conflict_handler: Object
The strategy for resolving conflicting optionals
add_help: str
Add a -h/--help option to the parser
allow_abbrev: bool
Allows long options to be abbreviated if the abbreviation is
unambiguous
| def __init__(self, **kwargs: Any) -> None:
"""Initialize parser.
Parameters
----------
prog: str
The name of the program
usage: str
The string describing the program usage
description: str
Text to display before the argument help
epilog: str
Text to display after the argument help
parents: list
A list of ArgumentParser objects whose arguments should also
be included
formatter_class: Object
A class for customizing the help output
prefix_chars: char
The set of characters that prefix optional arguments
fromfile_prefix_chars: None
The set of characters that prefix files from which additional
arguments should be read
argument_default: None
The global default value for arguments
conflict_handler: Object
The strategy for resolving conflicting optionals
add_help: str
Add a -h/--help option to the parser
allow_abbrev: bool
Allows long options to be abbreviated if the abbreviation is
unambiguous
"""
# TODO: handle environment variables
module = self.__get_parent_module()
if module and module.__doc__:
docstring = docparse(module.__doc__)
if not kwargs.get('description'):
kwargs['description'] = docstring.short_description
if 'prog' not in kwargs:
kwargs['prog'] = module.__name__.split('.')[0]
if 'version' in kwargs:
self.prog_version = kwargs.pop('version')
# if 'prefix' in kwargs:
# self.prefix = kwargs.pop('prefix')
# else:
# self.prefix = kwargs['prog'].upper()
# log.debug(str(self.prefix))
if 'log_level' in kwargs:
log.setLevel(getattr(logging, kwargs.pop('log_level').upper()))
if 'log_handler' in kwargs:
log_handler = kwargs.pop('log_handler')
log.addHandler(logging.StreamHandler(log_handler))
self.use_module_args = kwargs.pop('use_module_args', False)
self.main_args_builder = kwargs.pop('main_args_builder', None)
self.command_type = kwargs.pop('command_type', None)
self.command_scheme = kwargs.pop('command_scheme', None)
if 'formatter_class' not in kwargs:
self.formatter_class = ArgufyHelpFormatter
super().__init__(**kwargs)
# NOTE: cannot move to formatter
self._positionals.title = ArgufyHelpFormatter.font(
self._positionals.title or 'arguments'
)
self._optionals.title = ArgufyHelpFormatter.font(
self._optionals.title or 'flags'
)
# XXX version lookup infinite loop when absent
if hasattr(self, 'prog_version'):
self.add_argument(
'--version',
action='version',
version=f"%(prog)s {self.prog_version}",
help='display application version',
)
| (self, **kwargs: Any) -> NoneType | [
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20,983 | argufy.parser | _get_excludes | Combine class excludes with instance. | @staticmethod
def _get_excludes(exclude_prefixes: Tuple[str, ...] = tuple()) -> tuple:
"""Combine class excludes with instance."""
if exclude_prefixes != ():
return tuple(exclude_prefixes) + Parser.exclude_prefixes
return Parser.exclude_prefixes
| (exclude_prefixes: Tuple[str, ...] = ()) -> tuple | [
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] |
21,006 | argparse | add_argument |
add_argument(dest, ..., name=value, ...)
add_argument(option_string, option_string, ..., name=value, ...)
| def add_argument(self, *args, **kwargs):
"""
add_argument(dest, ..., name=value, ...)
add_argument(option_string, option_string, ..., name=value, ...)
"""
# if no positional args are supplied or only one is supplied and
# it doesn't look like an option string, parse a positional
# argument
chars = self.prefix_chars
if not args or len(args) == 1 and args[0][0] not in chars:
if args and 'dest' in kwargs:
raise ValueError('dest supplied twice for positional argument')
kwargs = self._get_positional_kwargs(*args, **kwargs)
# otherwise, we're adding an optional argument
else:
kwargs = self._get_optional_kwargs(*args, **kwargs)
# if no default was supplied, use the parser-level default
if 'default' not in kwargs:
dest = kwargs['dest']
if dest in self._defaults:
kwargs['default'] = self._defaults[dest]
elif self.argument_default is not None:
kwargs['default'] = self.argument_default
# create the action object, and add it to the parser
action_class = self._pop_action_class(kwargs)
if not callable(action_class):
raise ValueError('unknown action "%s"' % (action_class,))
action = action_class(**kwargs)
# raise an error if the action type is not callable
type_func = self._registry_get('type', action.type, action.type)
if not callable(type_func):
raise ValueError('%r is not callable' % (type_func,))
if type_func is FileType:
raise ValueError('%r is a FileType class object, instance of it'
' must be passed' % (type_func,))
# raise an error if the metavar does not match the type
if hasattr(self, "_get_formatter"):
try:
self._get_formatter()._format_args(action, None)
except TypeError:
raise ValueError("length of metavar tuple does not match nargs")
return self._add_action(action)
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] |
21,008 | argufy.parser | add_arguments | Add arguments to parser/subparser.
Parameters
----------
obj: Any
Verious module, function, or arguments that can be inspected.
parser: ArgumentParser, optional
Parser/Subparser that arguments will be added.
Returns
-------
self:
Return object itself to allow chaining functions.
| def add_arguments(
self, obj: Any, parser: Optional[ArgumentParser] = None
) -> 'Parser':
"""Add arguments to parser/subparser.
Parameters
----------
obj: Any
Verious module, function, or arguments that can be inspected.
parser: ArgumentParser, optional
Parser/Subparser that arguments will be added.
Returns
-------
self:
Return object itself to allow chaining functions.
"""
if not parser:
parser = self
# prep object for inspection
docstring = docparse(obj.__doc__)
signature = inspect.signature(obj)
# populate subcommand with keyword arguments
for arg in signature.parameters:
param = signature.parameters[arg]
description = self.__get_description(arg, docstring)
log.debug("param: %s, %s", param, param.kind)
if not param.kind == Parameter.VAR_KEYWORD:
log.debug("param annotation: %s", param.annotation)
argument = self.__clean_args(Argument(description, param))
name = argument.pop('name')
# print(name, argument)
parser.add_argument(*name, **argument)
# populate options
# log.debug("params %s", params)
if docstring:
for arg in self.__get_keyword_args(signature, docstring):
description = self.__get_description(arg, docstring)
arguments = self.__clean_args(Argument(docstring=description))
parser.add_argument(f"--{arg.replace('_', '-')}", **arguments)
# log.debug("arguments %s", arguments)
# TODO for any docstring not collected parse here (args, kwargs)
# log.debug('docstring params', docstring.params)
return self
| (self, obj: Any, parser: Optional[argparse.ArgumentParser] = None) -> argufy.parser.Parser | [
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] |
21,009 | argufy.parser | add_commands | Add commands.
Parameters
----------
module: ModuleType,
Module used to import functions for CLI commands.
parser: ArgumentParser, optional
Parser used to append subparsers to create subcommands.
exclude_prefixes: tuple,
Methods from a module that should be excluded.
command_type: str, optional
Choose format type of commands to be created.
Returns
-------
self:
Return object itself to allow chaining functions.
| def add_commands( # pylint: disable=too-many-locals,too-many-branches
self,
module: ModuleType,
parser: Optional[ArgumentParser] = None,
exclude_prefixes: tuple = tuple(),
command_type: Optional[str] = None,
) -> 'Parser':
"""Add commands.
Parameters
----------
module: ModuleType,
Module used to import functions for CLI commands.
parser: ArgumentParser, optional
Parser used to append subparsers to create subcommands.
exclude_prefixes: tuple,
Methods from a module that should be excluded.
command_type: str, optional
Choose format type of commands to be created.
Returns
-------
self:
Return object itself to allow chaining functions.
"""
# use self or an existing parser
if not parser:
parser = self
parser.formatter_class = ArgufyHelpFormatter
module_name = module.__name__.split('.')[-1]
docstring = docparse(module.__doc__) if module.__doc__ else None
excludes = Parser._get_excludes(exclude_prefixes)
# use exsiting subparser or create a new one
if not any(isinstance(x, SubParsersAction) for x in parser._actions):
# TODO: use metavar for hidden commands
parser.add_subparsers(dest=module_name, parser_class=Parser)
# check if command exists
command = next(
(x for x in parser._actions if isinstance(x, SubParsersAction)),
None,
)
# set command name scheme
if command_type is None:
command_type = self.command_type
# create subcommand for command
if command_type == 'subcommand':
if command:
msg = docstring.short_description if docstring else None
subcommand = command.add_parser(
module_name.replace('_', '-'),
description=msg,
formatter_class=self.formatter_class,
help=msg,
)
subcommand.set_defaults(mod=module)
# append subcommand to exsiting command or create a new one
return self.add_commands(
module=module,
parser=subcommand,
exclude_prefixes=Parser._get_excludes(exclude_prefixes),
command_type='command',
)
# TODO: separate into method
# pylint: disable-next=too-many-nested-blocks
for name, value in inspect.getmembers(module):
# TODO: Possible singledispatch candidate
if not name.startswith(excludes):
# skip classes for now
if inspect.isclass(value):
# TODO: check if dataclass instance
# TODO: check if class instance
continue # pragma: no cover
# create commands from functions
if inspect.isfunction(value):
# TODO: Turn parameter-less function into switch
# merge builder function maing_args into parser
if (
self.main_args_builder
and name == self.main_args_builder['function']
):
self.add_arguments(value, parser)
# create commands from functions
elif (
module.__name__ == value.__module__
and not name.startswith(', '.join(excludes))
or (
self.main_args_builder
and name == self.main_args_builder['function']
)
):
# create command from function
if command:
# control command name format
if self.command_scheme == 'chain':
cmd_name = f"{module_name}.{name}"
else:
cmd_name = name
msg = (
docparse(value.__doc__).short_description
if value.__doc__
else None
)
cmd = command.add_parser(
cmd_name.replace('_', '-'),
description=msg,
formatter_class=self.formatter_class,
help=msg,
)
cmd.set_defaults(mod=module, fn=value)
# add arguments from function
# log.debug("command %s %s %s", name, value, cmd)
self.add_arguments(value, cmd)
# create arguments from module varibles
elif (
self.use_module_args
and not isinstance(value, ModuleType)
and not hasattr(typing, name)
and (
self.main_args_builder
and name != self.main_args_builder['instance']
)
):
# TODO: Reconcile inspect parameters with dict
# TODO: use argparse.SUPPRESS for hidden arguments
arguments = self.__clean_args(
Argument(
self.__get_description(name, docstring)
if docstring
else None,
self.__generate_parameter(name, module),
)
)
name = arguments.pop('name')
parser.add_argument(*name, **arguments)
return self
| (self, module: module, parser: Optional[argparse.ArgumentParser] = None, exclude_prefixes: tuple = (), command_type: Optional[str] = None) -> argufy.parser.Parser | [
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] |
21,013 | argufy.parser | dispatch | Call command with arguments.
Paramters
---------
args: Sequence[str]
Command line arguments passed to the parser.
ns: Optional[Namespace]
Argparse namespace object for a command.
Returns
-------
Optional[Callable[[F], F]]:
Call function with arguments.
| def dispatch(
self,
args: Sequence[str] = sys.argv[1:],
ns: Optional['Namespace'] = None,
) -> Optional[Callable[[F], F]]:
"""Call command with arguments.
Paramters
---------
args: Sequence[str]
Command line arguments passed to the parser.
ns: Optional[Namespace]
Argparse namespace object for a command.
Returns
-------
Optional[Callable[[F], F]]:
Call function with arguments.
"""
# parse variables
arguments, namespace = self.retrieve(args, ns)
log.debug("dispatch: %s, %s", arguments, namespace)
main_ns_result = self.__set_main_arguments(namespace)
# call function with variables
if 'fn' in namespace:
ns_vars = vars(namespace)
fn = ns_vars.pop('fn')
self.__set_module_arguments(fn, main_ns_result)
# XXX: only takes standard types
# attempt to plug parameters using inspect
splat = None
signature = inspect.signature(fn)
for arg in signature.parameters:
param = signature.parameters[arg]
if str(param).startswith('*') and not str(param).startswith(
'**'
):
splat = ns_vars.pop(arg)
# XXX: only works with splat and kwargs
if splat:
fn(*splat, **ns_vars)
else:
fn(**ns_vars)
return self.dispatch(arguments) if arguments != [] else None
| (self, args: Sequence[str] = ['--package', 'argufy', '--s3_bucket', '/data'], ns: Optional[ForwardRef('Namespace')] = None) -> Optional[Callable[[~F], ~F]] | [
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] |
21,026 | argufy.parser | retrieve | Retrieve parsed values from CLI input.
Paramters
---------
args: Sequence[str]
Command line arguments passed to the parser.
ns: Optional[Namespace]
Argparse namespace object for a command.
Returns
-------
List[str]:
Argparse remaining unparse arguments.
Namespace:
Argparse namespace object with command arguments.
| def retrieve(
self,
args: Sequence[str] = sys.argv[1:],
ns: Optional['Namespace'] = None,
) -> Tuple[List[str], 'Namespace']:
"""Retrieve parsed values from CLI input.
Paramters
---------
args: Sequence[str]
Command line arguments passed to the parser.
ns: Optional[Namespace]
Argparse namespace object for a command.
Returns
-------
List[str]:
Argparse remaining unparse arguments.
Namespace:
Argparse namespace object with command arguments.
"""
# TODO: handle invalid argument
# show help when no arguments provided
if args == []:
args = ['--help'] # pragma: no cover
main_ns, main_args = self.parse_known_args(args, ns)
if main_args == [] and 'fn' in vars(main_ns):
return main_args, main_ns
# default to help message for subcommand
if 'mod' in vars(main_ns):
mod_args = []
mod_args.append(vars(main_ns)['mod'].__name__.split('.')[-1])
mod_args.append('--help')
self.parse_args(mod_args)
return main_args, main_ns
| (self, args: Sequence[str] = ['--package', 'argufy', '--s3_bucket', '/data'], ns: Optional[ForwardRef('Namespace')] = None) -> Tuple[List[str], ForwardRef('Namespace')] | [
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] |
21,032 | tfields.core | Container |
Store lists of tfields objects. Save mechanisms are provided
Examples:
>>> import numpy as np
>>> import tfields
>>> sphere = tfields.Mesh3D.grid(
... (1, 1, 1),
... (-np.pi, np.pi, 3),
... (-np.pi / 2, np.pi / 2, 3),
... coord_sys='spherical')
>>> sphere2 = sphere.copy() * 3
>>> c = tfields.Container([sphere, sphere2])
>>> c.save("~/tmp/spheres.npz")
>>> c1 = tfields.Container.load("~/tmp/spheres.npz")
| class Container(AbstractFields):
"""
Store lists of tfields objects. Save mechanisms are provided
Examples:
>>> import numpy as np
>>> import tfields
>>> sphere = tfields.Mesh3D.grid(
... (1, 1, 1),
... (-np.pi, np.pi, 3),
... (-np.pi / 2, np.pi / 2, 3),
... coord_sys='spherical')
>>> sphere2 = sphere.copy() * 3
>>> c = tfields.Container([sphere, sphere2])
>>> c.save("~/tmp/spheres.npz")
>>> c1 = tfields.Container.load("~/tmp/spheres.npz")
"""
def __init__(self, *items, labels=None):
if len(items) == 1 and issubclass(type(items[0]), list):
# Container([a, b, ...]) - includes copy constructor
items = items[0]
if labels is None and issubclass(type(items), Container):
labels = items.labels
super().__init__(items)
self.labels = labels
def __setstate__(self, state):
self.__dict__ = state
def copy(self):
return deepcopy(self)
@property
def items(self):
"""
items of the container as a list
"""
return list(self)
@items.setter
def items(self, items):
del self[:]
for item in items:
self.append(item)
def _kwargs(self):
return {"labels": self.labels}
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21,033 | tfields.core | __getitem__ | null | def __getitem__(self, index):
if isinstance(index, str):
for field in self:
if hasattr(field, "name") and field.name == index:
return field
return super().__getitem__(index)
| (self, index) | [
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21,034 | tfields.core | __init__ | null | def __init__(self, *items, labels=None):
if len(items) == 1 and issubclass(type(items[0]), list):
# Container([a, b, ...]) - includes copy constructor
items = items[0]
if labels is None and issubclass(type(items), Container):
labels = items.labels
super().__init__(items)
self.labels = labels
| (self, *items, labels=None) | [
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21,035 | tfields.core | __setitem__ | null | def __setitem__(self, index, value):
if isinstance(index, str):
if not hasattr(value, "name"):
raise TypeError(
f"Value type {type(value)} does not support the 'name' field"
)
# We could set value.name = index but that would be very confusing since we do not copy
assert (
value.name == index
), "We do not dynamically want to change the name of a tensor"
for i, field in enumerate(self):
if hasattr(field, "name") and field.name == index:
index = i
break
else:
self.append(value)
return
super().__setitem__(index, value)
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21,036 | tfields.core | __setstate__ | null | def __setstate__(self, state):
self.__dict__ = state
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21,037 | tfields.core | _args | null | def _args(self):
return super()._args() + tuple(self)
| (self) | [
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21,038 | tfields.core | _as_dict |
Get an object represenation in a dict format. This is necessary e.g. for saving the full
file uniquely in the npz format
Args:
recurse: dict of {attribute: callable(iterable) -> dict}
Returns:
dict: object packed as nested dictionary
| def _as_dict(self, recurse: typing.Dict[str, typing.Callable] = None) -> dict:
"""
Get an object represenation in a dict format. This is necessary e.g. for saving the full
file uniquely in the npz format
Args:
recurse: dict of {attribute: callable(iterable) -> dict}
Returns:
dict: object packed as nested dictionary
"""
content = {}
# type
content["type"] = type(self).__name__
# args and kwargs
for base_attr, iterable in [
("args", ((str(i), arg) for i, arg in enumerate(self._args()))),
("kwargs", self._kwargs().items()),
]:
for attr, value in iterable:
attr = base_attr + _HIERARCHY_SEPARATOR + attr
if (recurse is not None and attr in recurse) or hasattr(
value, "_as_dict"
):
if recurse is not None and attr in recurse:
part_dict = recurse[attr](value)
else:
part_dict = value._as_dict() # pylint: disable=protected-access
for part_attr, part_value in part_dict.items():
content[attr + _HIERARCHY_SEPARATOR + part_attr] = part_value
else:
content[attr] = value
return content
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21,039 | tfields.core | _kwargs | null | def _kwargs(self):
return {"labels": self.labels}
| (self) | [
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21,040 | rna.polymorphism | _save_dill | null | def _save_dill(self, path):
import dill # pylint:disable = import-outside-toplevel
with open(path, "wb") as out_file:
dill.dump(self, out_file)
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21,041 | tfields.core | _save_npz |
Args:
path (open file or str/unicode): destination to save file to.
Examples:
Build some dummies:
>>> import tfields
>>> from tempfile import NamedTemporaryFile
>>> out_file = NamedTemporaryFile(suffix='.npz')
>>> p = tfields.Points3D([[1., 2., 3.], [4., 5., 6.], [1, 2, -6]],
... name='my_points')
>>> scalars = tfields.Tensors([0, 1, 2], name=42)
>>> vectors = tfields.Tensors([[0, 0, 0], [0, 0, 1], [0, -1, 0]])
>>> maps = [tfields.TensorFields([[0, 1, 2], [0, 1, 2]], [42, 21]),
... tfields.TensorFields([[1], [2]], [-42, -21])]
>>> m = tfields.TensorMaps(vectors, scalars,
... maps=maps)
Simply give the file name to save
>>> p.save(out_file.name)
>>> _ = out_file.seek(0) # this is only necessary in the test
>>> p1 = tfields.Points3D.load(out_file.name)
>>> assert p.equal(p1)
>>> assert p.coord_sys == p1.coord_sys
The fully nested structure of a TensorMaps object is reconstructed
>>> out_file_maps = NamedTemporaryFile(suffix='.npz')
>>> m.save(out_file_maps.name)
>>> _ = out_file_maps.seek(0)
>>> m1 = tfields.TensorMaps.load(out_file_maps.name,
... allow_pickle=True)
>>> assert m.equal(m1)
>>> assert m.maps[3].dtype == m1.maps[3].dtype
Names are preserved
>>> assert p.name == 'my_points'
>>> m.names
[42]
| def _save_npz(self, path):
"""
Args:
path (open file or str/unicode): destination to save file to.
Examples:
Build some dummies:
>>> import tfields
>>> from tempfile import NamedTemporaryFile
>>> out_file = NamedTemporaryFile(suffix='.npz')
>>> p = tfields.Points3D([[1., 2., 3.], [4., 5., 6.], [1, 2, -6]],
... name='my_points')
>>> scalars = tfields.Tensors([0, 1, 2], name=42)
>>> vectors = tfields.Tensors([[0, 0, 0], [0, 0, 1], [0, -1, 0]])
>>> maps = [tfields.TensorFields([[0, 1, 2], [0, 1, 2]], [42, 21]),
... tfields.TensorFields([[1], [2]], [-42, -21])]
>>> m = tfields.TensorMaps(vectors, scalars,
... maps=maps)
Simply give the file name to save
>>> p.save(out_file.name)
>>> _ = out_file.seek(0) # this is only necessary in the test
>>> p1 = tfields.Points3D.load(out_file.name)
>>> assert p.equal(p1)
>>> assert p.coord_sys == p1.coord_sys
The fully nested structure of a TensorMaps object is reconstructed
>>> out_file_maps = NamedTemporaryFile(suffix='.npz')
>>> m.save(out_file_maps.name)
>>> _ = out_file_maps.seek(0)
>>> m1 = tfields.TensorMaps.load(out_file_maps.name,
... allow_pickle=True)
>>> assert m.equal(m1)
>>> assert m.maps[3].dtype == m1.maps[3].dtype
Names are preserved
>>> assert p.name == 'my_points'
>>> m.names
[42]
"""
content_dict = self._as_dict()
content_dict["tfields_version"] = tfields.__version__
np.savez(path, **content_dict)
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21,042 | rna.polymorphism | _save_pickle | null | def _save_pickle(self, path):
import pickle # pylint:disable = import-outside-toplevel
with open(path, "wb") as out_file:
pickle.dump(self, out_file)
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] |
21,044 | rna.polymorphism | reload |
Update the object from path. This requires the inheritor of this polymorphism
to provide the 'def __update__(self, other):' method
| def reload(self, path):
"""
Update the object from path. This requires the inheritor of this polymorphism
to provide the 'def __update__(self, other):' method
"""
if not hasattr(self, "__update__"):
raise NotImplementedError("__update__ required")
other = type(self).load(path)
self.___update__(other)
| (self, path) | [
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21,045 | rna.polymorphism | save |
Saving by redirecting to the correct save method depending on path.
Implement _save_{extension}(self, path, **kwargs) for saving to your extension of choice
Args:
path (str | buffer)
*args: joined with path
create_parents: create the parent dir(s) if required
**kwargs:
extension (str | List[str]): explicit extension required if path is buffer,
if list of extensions save as all extension formats.
... remaining:forwarded to extension specific method
| def save(
self,
path: typing.Union[str, io.IOBase],
*args: [str],
create_parents: bool = True,
extension: typing.Optional[typing.Union[str, typing.List[str]]] = None,
**kwargs
) -> None:
"""
Saving by redirecting to the correct save method depending on path.
Implement _save_{extension}(self, path, **kwargs) for saving to your extension of choice
Args:
path (str | buffer)
*args: joined with path
create_parents: create the parent dir(s) if required
**kwargs:
extension (str | List[str]): explicit extension required if path is buffer,
if list of extensions save as all extension formats.
... remaining:forwarded to extension specific method
"""
# get the extension
if isinstance(extension, list):
for ext in extension:
LOGGER.info("Saving to path '%s' with extension %s.", path, ext)
self.save(
path, *args, create_parents=create_parents, extension=ext, **kwargs
)
return
if isinstance(path, (str, pathlib.Path)):
path = rna.path.resolve(path, *args)
if extension is None:
extension = rna.path.extension(path)
if not path.endswith(extension):
path += "." + extension
if not extension:
raise ValueError(
"Path {path} requires extension for auto rooting.".format(**locals())
)
# get the save method
if (
self.EXPLICIT_SAVE_METHODS is not None
and extension
in self.EXPLICIT_SAVE_METHODS # pylint:disable=unsupported-membership-test
):
# pylint:disable=unsubscriptable-object
method_name = self.EXPLICIT_SAVE_METHODS[extension]
else:
method_name = "_save_" + extension
try:
save_method = getattr(self, method_name)
except AttributeError as err:
raise NotImplementedError(
"Save method {method_name} for extension: {extension} required.".format(
**locals()
)
) from err
if create_parents:
rna.path.mkdir(path)
log = logging.getLogger(__name__)
log.info("Saving to path '%s' with method %s.", path, save_method)
save_method(path, **kwargs)
| (self, path: Union[str, io.IOBase], *args: [<class 'str'>], create_parents: bool = True, extension: Union[str, List[str], NoneType] = None, **kwargs) -> NoneType | [
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] |
21,046 | tfields.core | Maps |
Container for TensorFields sorted by dimension, i.e indexing by dimension
Args:
*args (
List(TensorFields):
| List(Tuple(int, TensorFields)):
| TensorFields:
| Tuple(Tensors, *Fields)):
TODO: document
)
**kwargs: forwarded to SortedDict
TODO: further documentation
| class Maps(sortedcontainers.SortedDict, AbstractObject):
"""
Container for TensorFields sorted by dimension, i.e indexing by dimension
Args:
*args (
List(TensorFields):
| List(Tuple(int, TensorFields)):
| TensorFields:
| Tuple(Tensors, *Fields)):
TODO: document
)
**kwargs: forwarded to SortedDict
TODO: further documentation
"""
def __init__(self, *args, **kwargs):
if args and args[0] is None:
# None key passed e.g. by copy. We do not change keys here.
args = args[1:]
if len(args) == 1 and issubclass(type(args[0]), (list, dict)):
new_args = []
if issubclass(type(args[0]), list):
# Maps([...])
iterator = args[0]
elif issubclass(type(args[0]), dict):
# Maps({}), Maps(Maps(...)) - includes Maps i.e. copy
iterator = args[0].items()
for entry in iterator:
dimension = None
if issubclass(type(entry), tuple):
if np.issubdtype(type(entry[0]), np.integer):
# Maps([(key, value), ...]), Maps({key: value, ...})
maps = self.to_map(entry[1], copy=True)
dimension = entry[0]
else:
# Maps([(tensors, field1, field2), ...])
maps = self.to_map(*entry, copy=True)
else:
# Maps([mp, mp, ...])
maps = self.to_map(entry, copy=True)
if dimension is None:
dimension = dim(maps)
new_args.append((dimension, maps))
args = (new_args,)
super().__init__(*args, **kwargs)
@staticmethod
def to_map(map_, *fields, copy=False, **kwargs):
"""
Args:
map_ (TensorFields)
*fields (Tensors)
copy (bool)
**kwargs: passed to TensorFields constructor
"""
if not copy:
if isinstance(map_, TensorFields) and not fields:
if not np.issubdtype(map_.dtype, np.integer):
map_ = map_.astype(int)
else:
copy = True
if copy: # not else, because in case of wrong map_ type we initialize
kwargs.setdefault("dtype", int)
map_ = TensorFields(map_, *fields, **kwargs)
return map_
def __setitem__(self, dimension, map_):
map_ = self.to_map(map_)
super().__setitem__(dimension, map_)
def _args(self):
return super()._args() + (list(self.items()),)
def _as_dict(self, recurse: typing.Dict[str, typing.Callable] = None) -> dict:
if recurse is None:
recurse = {}
def recurse_args_0(
iterable: typing.List[typing.Tuple[int, typing.Any]]
) -> dict:
# iterable is list of tuple
part_dict = {"type": "list"}
for i, (dim, tensor) in enumerate(iterable):
content = tensor._as_dict()
tuple_key = _HIERARCHY_SEPARATOR.join(["args", str(i), ""])
part_dict[tuple_key + "type"] = "tuple"
args_key = tuple_key + _HIERARCHY_SEPARATOR.join(["args", ""])
part_dict[
args_key + _HIERARCHY_SEPARATOR.join(["0", "args", "0"])
] = dim
part_dict[args_key + _HIERARCHY_SEPARATOR.join(["0", "type"])] = "int"
for key, value in content.items():
part_dict[args_key + _HIERARCHY_SEPARATOR.join(["1", key])] = value
return part_dict
attr = "args" + _HIERARCHY_SEPARATOR + str(0)
recurse[attr] = recurse_args_0
return super()._as_dict(recurse=recurse)
def equal(self, other, **kwargs):
"""
Test equality with other object.
Args:
**kwargs: passed to each item on equality check
"""
if not self.keys() == other.keys():
return False
# pylint:disable=consider-using-dict-items
for dimension in self.keys():
if not self[dimension].equal(other[dimension], **kwargs):
return False
return True
| (*args, **kwargs) | [
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21,047 | sortedcontainers.sorteddict | copy | Return a shallow copy of the sorted dict.
Runtime complexity: `O(n)`
:return: new sorted dict
| def copy(self):
"""Return a shallow copy of the sorted dict.
Runtime complexity: `O(n)`
:return: new sorted dict
"""
return self.__class__(self._key, self.items())
| (self) | [
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21,048 | sortedcontainers.sorteddict | __delitem__ | Remove item from sorted dict identified by `key`.
``sd.__delitem__(key)`` <==> ``del sd[key]``
Runtime complexity: `O(log(n))` -- approximate.
>>> sd = SortedDict({'a': 1, 'b': 2, 'c': 3})
>>> del sd['b']
>>> sd
SortedDict({'a': 1, 'c': 3})
>>> del sd['z']
Traceback (most recent call last):
...
KeyError: 'z'
:param key: `key` for item lookup
:raises KeyError: if key not found
| def __delitem__(self, key):
"""Remove item from sorted dict identified by `key`.
``sd.__delitem__(key)`` <==> ``del sd[key]``
Runtime complexity: `O(log(n))` -- approximate.
>>> sd = SortedDict({'a': 1, 'b': 2, 'c': 3})
>>> del sd['b']
>>> sd
SortedDict({'a': 1, 'c': 3})
>>> del sd['z']
Traceback (most recent call last):
...
KeyError: 'z'
:param key: `key` for item lookup
:raises KeyError: if key not found
"""
dict.__delitem__(self, key)
self._list_remove(key)
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21,049 | tfields.core | __init__ | null | def __init__(self, *args, **kwargs):
if args and args[0] is None:
# None key passed e.g. by copy. We do not change keys here.
args = args[1:]
if len(args) == 1 and issubclass(type(args[0]), (list, dict)):
new_args = []
if issubclass(type(args[0]), list):
# Maps([...])
iterator = args[0]
elif issubclass(type(args[0]), dict):
# Maps({}), Maps(Maps(...)) - includes Maps i.e. copy
iterator = args[0].items()
for entry in iterator:
dimension = None
if issubclass(type(entry), tuple):
if np.issubdtype(type(entry[0]), np.integer):
# Maps([(key, value), ...]), Maps({key: value, ...})
maps = self.to_map(entry[1], copy=True)
dimension = entry[0]
else:
# Maps([(tensors, field1, field2), ...])
maps = self.to_map(*entry, copy=True)
else:
# Maps([mp, mp, ...])
maps = self.to_map(entry, copy=True)
if dimension is None:
dimension = dim(maps)
new_args.append((dimension, maps))
args = (new_args,)
super().__init__(*args, **kwargs)
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21,050 | sortedcontainers.sorteddict | __ior__ | null | def __ior__(self, other):
self._update(other)
return self
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21,051 | sortedcontainers.sorteddict | __iter__ | Return an iterator over the keys of the sorted dict.
``sd.__iter__()`` <==> ``iter(sd)``
Iterating the sorted dict while adding or deleting items may raise a
:exc:`RuntimeError` or fail to iterate over all keys.
| def __iter__(self):
"""Return an iterator over the keys of the sorted dict.
``sd.__iter__()`` <==> ``iter(sd)``
Iterating the sorted dict while adding or deleting items may raise a
:exc:`RuntimeError` or fail to iterate over all keys.
"""
return self._list_iter()
| (self) | [
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21,052 | sortedcontainers.sorteddict | __or__ | null | def __or__(self, other):
if not isinstance(other, Mapping):
return NotImplemented
items = chain(self.items(), other.items())
return self.__class__(self._key, items)
| (self, other) | [
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21,053 | sortedcontainers.sorteddict | __reduce__ | Support for pickle.
The tricks played with caching references in
:func:`SortedDict.__init__` confuse pickle so customize the reducer.
| def __reduce__(self):
"""Support for pickle.
The tricks played with caching references in
:func:`SortedDict.__init__` confuse pickle so customize the reducer.
"""
items = dict.copy(self)
return (type(self), (self._key, items))
| (self) | [
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21,054 | sortedcontainers.sorteddict | __repr__ | Return string representation of sorted dict.
``sd.__repr__()`` <==> ``repr(sd)``
:return: string representation
| """Sorted Dict
==============
:doc:`Sorted Containers<index>` is an Apache2 licensed Python sorted
collections library, written in pure-Python, and fast as C-extensions. The
:doc:`introduction<introduction>` is the best way to get started.
Sorted dict implementations:
.. currentmodule:: sortedcontainers
* :class:`SortedDict`
* :class:`SortedKeysView`
* :class:`SortedItemsView`
* :class:`SortedValuesView`
"""
import sys
import warnings
from itertools import chain
from .sortedlist import SortedList, recursive_repr
from .sortedset import SortedSet
###############################################################################
# BEGIN Python 2/3 Shims
###############################################################################
try:
from collections.abc import (
ItemsView, KeysView, Mapping, ValuesView, Sequence
)
except ImportError:
from collections import ItemsView, KeysView, Mapping, ValuesView, Sequence
###############################################################################
# END Python 2/3 Shims
###############################################################################
class SortedDict(dict):
"""Sorted dict is a sorted mutable mapping.
Sorted dict keys are maintained in sorted order. The design of sorted dict
is simple: sorted dict inherits from dict to store items and maintains a
sorted list of keys.
Sorted dict keys must be hashable and comparable. The hash and total
ordering of keys must not change while they are stored in the sorted dict.
Mutable mapping methods:
* :func:`SortedDict.__getitem__` (inherited from dict)
* :func:`SortedDict.__setitem__`
* :func:`SortedDict.__delitem__`
* :func:`SortedDict.__iter__`
* :func:`SortedDict.__len__` (inherited from dict)
Methods for adding items:
* :func:`SortedDict.setdefault`
* :func:`SortedDict.update`
Methods for removing items:
* :func:`SortedDict.clear`
* :func:`SortedDict.pop`
* :func:`SortedDict.popitem`
Methods for looking up items:
* :func:`SortedDict.__contains__` (inherited from dict)
* :func:`SortedDict.get` (inherited from dict)
* :func:`SortedDict.peekitem`
Methods for views:
* :func:`SortedDict.keys`
* :func:`SortedDict.items`
* :func:`SortedDict.values`
Methods for miscellany:
* :func:`SortedDict.copy`
* :func:`SortedDict.fromkeys`
* :func:`SortedDict.__reversed__`
* :func:`SortedDict.__eq__` (inherited from dict)
* :func:`SortedDict.__ne__` (inherited from dict)
* :func:`SortedDict.__repr__`
* :func:`SortedDict._check`
Sorted list methods available (applies to keys):
* :func:`SortedList.bisect_left`
* :func:`SortedList.bisect_right`
* :func:`SortedList.count`
* :func:`SortedList.index`
* :func:`SortedList.irange`
* :func:`SortedList.islice`
* :func:`SortedList._reset`
Additional sorted list methods available, if key-function used:
* :func:`SortedKeyList.bisect_key_left`
* :func:`SortedKeyList.bisect_key_right`
* :func:`SortedKeyList.irange_key`
Sorted dicts may only be compared for equality and inequality.
"""
def __init__(self, *args, **kwargs):
"""Initialize sorted dict instance.
Optional key-function argument defines a callable that, like the `key`
argument to the built-in `sorted` function, extracts a comparison key
from each dictionary key. If no function is specified, the default
compares the dictionary keys directly. The key-function argument must
be provided as a positional argument and must come before all other
arguments.
Optional iterable argument provides an initial sequence of pairs to
initialize the sorted dict. Each pair in the sequence defines the key
and corresponding value. If a key is seen more than once, the last
value associated with it is stored in the new sorted dict.
Optional mapping argument provides an initial mapping of items to
initialize the sorted dict.
If keyword arguments are given, the keywords themselves, with their
associated values, are added as items to the dictionary. If a key is
specified both in the positional argument and as a keyword argument,
the value associated with the keyword is stored in the
sorted dict.
Sorted dict keys must be hashable, per the requirement for Python's
dictionaries. Keys (or the result of the key-function) must also be
comparable, per the requirement for sorted lists.
>>> d = {'alpha': 1, 'beta': 2}
>>> SortedDict([('alpha', 1), ('beta', 2)]) == d
True
>>> SortedDict({'alpha': 1, 'beta': 2}) == d
True
>>> SortedDict(alpha=1, beta=2) == d
True
"""
if args and (args[0] is None or callable(args[0])):
_key = self._key = args[0]
args = args[1:]
else:
_key = self._key = None
self._list = SortedList(key=_key)
# Reaching through ``self._list`` repeatedly adds unnecessary overhead
# so cache references to sorted list methods.
_list = self._list
self._list_add = _list.add
self._list_clear = _list.clear
self._list_iter = _list.__iter__
self._list_reversed = _list.__reversed__
self._list_pop = _list.pop
self._list_remove = _list.remove
self._list_update = _list.update
# Expose some sorted list methods publicly.
self.bisect_left = _list.bisect_left
self.bisect = _list.bisect_right
self.bisect_right = _list.bisect_right
self.index = _list.index
self.irange = _list.irange
self.islice = _list.islice
self._reset = _list._reset
if _key is not None:
self.bisect_key_left = _list.bisect_key_left
self.bisect_key_right = _list.bisect_key_right
self.bisect_key = _list.bisect_key
self.irange_key = _list.irange_key
self._update(*args, **kwargs)
@property
def key(self):
"""Function used to extract comparison key from keys.
Sorted dict compares keys directly when the key function is none.
"""
return self._key
@property
def iloc(self):
"""Cached reference of sorted keys view.
Deprecated in version 2 of Sorted Containers. Use
:func:`SortedDict.keys` instead.
"""
# pylint: disable=attribute-defined-outside-init
try:
return self._iloc
except AttributeError:
warnings.warn(
'sorted_dict.iloc is deprecated.'
' Use SortedDict.keys() instead.',
DeprecationWarning,
stacklevel=2,
)
_iloc = self._iloc = SortedKeysView(self)
return _iloc
def clear(self):
"""Remove all items from sorted dict.
Runtime complexity: `O(n)`
"""
dict.clear(self)
self._list_clear()
def __delitem__(self, key):
"""Remove item from sorted dict identified by `key`.
``sd.__delitem__(key)`` <==> ``del sd[key]``
Runtime complexity: `O(log(n))` -- approximate.
>>> sd = SortedDict({'a': 1, 'b': 2, 'c': 3})
>>> del sd['b']
>>> sd
SortedDict({'a': 1, 'c': 3})
>>> del sd['z']
Traceback (most recent call last):
...
KeyError: 'z'
:param key: `key` for item lookup
:raises KeyError: if key not found
"""
dict.__delitem__(self, key)
self._list_remove(key)
def __iter__(self):
"""Return an iterator over the keys of the sorted dict.
``sd.__iter__()`` <==> ``iter(sd)``
Iterating the sorted dict while adding or deleting items may raise a
:exc:`RuntimeError` or fail to iterate over all keys.
"""
return self._list_iter()
def __reversed__(self):
"""Return a reverse iterator over the keys of the sorted dict.
``sd.__reversed__()`` <==> ` | (self) | [
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21,055 | sortedcontainers.sorteddict | __reversed__ | Return a reverse iterator over the keys of the sorted dict.
``sd.__reversed__()`` <==> ``reversed(sd)``
Iterating the sorted dict while adding or deleting items may raise a
:exc:`RuntimeError` or fail to iterate over all keys.
| def __reversed__(self):
"""Return a reverse iterator over the keys of the sorted dict.
``sd.__reversed__()`` <==> ``reversed(sd)``
Iterating the sorted dict while adding or deleting items may raise a
:exc:`RuntimeError` or fail to iterate over all keys.
"""
return self._list_reversed()
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21,056 | sortedcontainers.sorteddict | __ror__ | null | def __ror__(self, other):
if not isinstance(other, Mapping):
return NotImplemented
items = chain(other.items(), self.items())
return self.__class__(self._key, items)
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21,057 | tfields.core | __setitem__ | null | def __setitem__(self, dimension, map_):
map_ = self.to_map(map_)
super().__setitem__(dimension, map_)
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21,058 | tfields.core | _args | null | def _args(self):
return super()._args() + (list(self.items()),)
| (self) | [
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21,059 | tfields.core | _as_dict | null | def _as_dict(self, recurse: typing.Dict[str, typing.Callable] = None) -> dict:
if recurse is None:
recurse = {}
def recurse_args_0(
iterable: typing.List[typing.Tuple[int, typing.Any]]
) -> dict:
# iterable is list of tuple
part_dict = {"type": "list"}
for i, (dim, tensor) in enumerate(iterable):
content = tensor._as_dict()
tuple_key = _HIERARCHY_SEPARATOR.join(["args", str(i), ""])
part_dict[tuple_key + "type"] = "tuple"
args_key = tuple_key + _HIERARCHY_SEPARATOR.join(["args", ""])
part_dict[
args_key + _HIERARCHY_SEPARATOR.join(["0", "args", "0"])
] = dim
part_dict[args_key + _HIERARCHY_SEPARATOR.join(["0", "type"])] = "int"
for key, value in content.items():
part_dict[args_key + _HIERARCHY_SEPARATOR.join(["1", key])] = value
return part_dict
attr = "args" + _HIERARCHY_SEPARATOR + str(0)
recurse[attr] = recurse_args_0
return super()._as_dict(recurse=recurse)
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21,060 | sortedcontainers.sorteddict | _check | Check invariants of sorted dict.
Runtime complexity: `O(n)`
| def _check(self):
"""Check invariants of sorted dict.
Runtime complexity: `O(n)`
"""
_list = self._list
_list._check()
assert len(self) == len(_list)
assert all(key in self for key in _list)
| (self) | [
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21,061 | tfields.core | _kwargs |
Used for allowing the polymorphic signature Class(obj) as a copy/casting constructor
| def _kwargs(self) -> dict: # pylint: disable=no-self-use
"""
Used for allowing the polymorphic signature Class(obj) as a copy/casting constructor
"""
return dict()
| (self) -> dict | [
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21,065 | sortedcontainers.sorteddict | __setitem__ | Store item in sorted dict with `key` and corresponding `value`.
``sd.__setitem__(key, value)`` <==> ``sd[key] = value``
Runtime complexity: `O(log(n))` -- approximate.
>>> sd = SortedDict()
>>> sd['c'] = 3
>>> sd['a'] = 1
>>> sd['b'] = 2
>>> sd
SortedDict({'a': 1, 'b': 2, 'c': 3})
:param key: key for item
:param value: value for item
| def __setitem__(self, key, value):
"""Store item in sorted dict with `key` and corresponding `value`.
``sd.__setitem__(key, value)`` <==> ``sd[key] = value``
Runtime complexity: `O(log(n))` -- approximate.
>>> sd = SortedDict()
>>> sd['c'] = 3
>>> sd['a'] = 1
>>> sd['b'] = 2
>>> sd
SortedDict({'a': 1, 'b': 2, 'c': 3})
:param key: key for item
:param value: value for item
"""
if key not in self:
self._list_add(key)
dict.__setitem__(self, key, value)
| (self, key, value) | [
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] |
21,066 | sortedcontainers.sorteddict | update | Update sorted dict with items from `args` and `kwargs`.
Overwrites existing items.
Optional arguments `args` and `kwargs` may be a mapping, an iterable of
pairs or keyword arguments. See :func:`SortedDict.__init__` for
details.
:param args: mapping or iterable of pairs
:param kwargs: keyword arguments mapping
| def update(self, *args, **kwargs):
"""Update sorted dict with items from `args` and `kwargs`.
Overwrites existing items.
Optional arguments `args` and `kwargs` may be a mapping, an iterable of
pairs or keyword arguments. See :func:`SortedDict.__init__` for
details.
:param args: mapping or iterable of pairs
:param kwargs: keyword arguments mapping
"""
if not self:
dict.update(self, *args, **kwargs)
self._list_update(dict.__iter__(self))
return
if not kwargs and len(args) == 1 and isinstance(args[0], dict):
pairs = args[0]
else:
pairs = dict(*args, **kwargs)
if (10 * len(pairs)) > len(self):
dict.update(self, pairs)
self._list_clear()
self._list_update(dict.__iter__(self))
else:
for key in pairs:
self._setitem(key, pairs[key])
| (self, *args, **kwargs) | [
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21,067 | sortedcontainers.sorteddict | clear | Remove all items from sorted dict.
Runtime complexity: `O(n)`
| def clear(self):
"""Remove all items from sorted dict.
Runtime complexity: `O(n)`
"""
dict.clear(self)
self._list_clear()
| (self) | [
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21,069 | tfields.core | equal |
Test equality with other object.
Args:
**kwargs: passed to each item on equality check
| def equal(self, other, **kwargs):
"""
Test equality with other object.
Args:
**kwargs: passed to each item on equality check
"""
if not self.keys() == other.keys():
return False
# pylint:disable=consider-using-dict-items
for dimension in self.keys():
if not self[dimension].equal(other[dimension], **kwargs):
return False
return True
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21,070 | sortedcontainers.sorteddict | items | Return new sorted items view of the sorted dict's items.
See :class:`SortedItemsView` for details.
:return: new sorted items view
| def items(self):
"""Return new sorted items view of the sorted dict's items.
See :class:`SortedItemsView` for details.
:return: new sorted items view
"""
return SortedItemsView(self)
| (self) | [
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] |
21,071 | sortedcontainers.sorteddict | keys | Return new sorted keys view of the sorted dict's keys.
See :class:`SortedKeysView` for details.
:return: new sorted keys view
| def keys(self):
"""Return new sorted keys view of the sorted dict's keys.
See :class:`SortedKeysView` for details.
:return: new sorted keys view
"""
return SortedKeysView(self)
| (self) | [
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21,072 | sortedcontainers.sorteddict | peekitem | Return ``(key, value)`` pair at `index` in sorted dict.
Optional argument `index` defaults to -1, the last item in the sorted
dict. Specify ``index=0`` for the first item in the sorted dict.
Unlike :func:`SortedDict.popitem`, the sorted dict is not modified.
If the `index` is out of range, raises :exc:`IndexError`.
Runtime complexity: `O(log(n))`
>>> sd = SortedDict({'a': 1, 'b': 2, 'c': 3})
>>> sd.peekitem()
('c', 3)
>>> sd.peekitem(0)
('a', 1)
>>> sd.peekitem(100)
Traceback (most recent call last):
...
IndexError: list index out of range
:param int index: index of item (default -1)
:return: key and value pair
:raises IndexError: if `index` out of range
| def peekitem(self, index=-1):
"""Return ``(key, value)`` pair at `index` in sorted dict.
Optional argument `index` defaults to -1, the last item in the sorted
dict. Specify ``index=0`` for the first item in the sorted dict.
Unlike :func:`SortedDict.popitem`, the sorted dict is not modified.
If the `index` is out of range, raises :exc:`IndexError`.
Runtime complexity: `O(log(n))`
>>> sd = SortedDict({'a': 1, 'b': 2, 'c': 3})
>>> sd.peekitem()
('c', 3)
>>> sd.peekitem(0)
('a', 1)
>>> sd.peekitem(100)
Traceback (most recent call last):
...
IndexError: list index out of range
:param int index: index of item (default -1)
:return: key and value pair
:raises IndexError: if `index` out of range
"""
key = self._list[index]
return key, self[key]
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21,073 | sortedcontainers.sorteddict | pop | Remove and return value for item identified by `key`.
If the `key` is not found then return `default` if given. If `default`
is not given then raise :exc:`KeyError`.
Runtime complexity: `O(log(n))` -- approximate.
>>> sd = SortedDict({'a': 1, 'b': 2, 'c': 3})
>>> sd.pop('c')
3
>>> sd.pop('z', 26)
26
>>> sd.pop('y')
Traceback (most recent call last):
...
KeyError: 'y'
:param key: `key` for item
:param default: `default` value if key not found (optional)
:return: value for item
:raises KeyError: if `key` not found and `default` not given
| def pop(self, key, default=__not_given):
"""Remove and return value for item identified by `key`.
If the `key` is not found then return `default` if given. If `default`
is not given then raise :exc:`KeyError`.
Runtime complexity: `O(log(n))` -- approximate.
>>> sd = SortedDict({'a': 1, 'b': 2, 'c': 3})
>>> sd.pop('c')
3
>>> sd.pop('z', 26)
26
>>> sd.pop('y')
Traceback (most recent call last):
...
KeyError: 'y'
:param key: `key` for item
:param default: `default` value if key not found (optional)
:return: value for item
:raises KeyError: if `key` not found and `default` not given
"""
if key in self:
self._list_remove(key)
return dict.pop(self, key)
else:
if default is self.__not_given:
raise KeyError(key)
return default
| (self, key, default=<not-given>) | [
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21,074 | sortedcontainers.sorteddict | popitem | Remove and return ``(key, value)`` pair at `index` from sorted dict.
Optional argument `index` defaults to -1, the last item in the sorted
dict. Specify ``index=0`` for the first item in the sorted dict.
If the sorted dict is empty, raises :exc:`KeyError`.
If the `index` is out of range, raises :exc:`IndexError`.
Runtime complexity: `O(log(n))`
>>> sd = SortedDict({'a': 1, 'b': 2, 'c': 3})
>>> sd.popitem()
('c', 3)
>>> sd.popitem(0)
('a', 1)
>>> sd.popitem(100)
Traceback (most recent call last):
...
IndexError: list index out of range
:param int index: `index` of item (default -1)
:return: key and value pair
:raises KeyError: if sorted dict is empty
:raises IndexError: if `index` out of range
| def popitem(self, index=-1):
"""Remove and return ``(key, value)`` pair at `index` from sorted dict.
Optional argument `index` defaults to -1, the last item in the sorted
dict. Specify ``index=0`` for the first item in the sorted dict.
If the sorted dict is empty, raises :exc:`KeyError`.
If the `index` is out of range, raises :exc:`IndexError`.
Runtime complexity: `O(log(n))`
>>> sd = SortedDict({'a': 1, 'b': 2, 'c': 3})
>>> sd.popitem()
('c', 3)
>>> sd.popitem(0)
('a', 1)
>>> sd.popitem(100)
Traceback (most recent call last):
...
IndexError: list index out of range
:param int index: `index` of item (default -1)
:return: key and value pair
:raises KeyError: if sorted dict is empty
:raises IndexError: if `index` out of range
"""
if not self:
raise KeyError('popitem(): dictionary is empty')
key = self._list_pop(index)
value = dict.pop(self, key)
return (key, value)
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21,077 | sortedcontainers.sorteddict | setdefault | Return value for item identified by `key` in sorted dict.
If `key` is in the sorted dict then return its value. If `key` is not
in the sorted dict then insert `key` with value `default` and return
`default`.
Optional argument `default` defaults to none.
Runtime complexity: `O(log(n))` -- approximate.
>>> sd = SortedDict()
>>> sd.setdefault('a', 1)
1
>>> sd.setdefault('a', 10)
1
>>> sd
SortedDict({'a': 1})
:param key: key for item
:param default: value for item (default None)
:return: value for item identified by `key`
| def setdefault(self, key, default=None):
"""Return value for item identified by `key` in sorted dict.
If `key` is in the sorted dict then return its value. If `key` is not
in the sorted dict then insert `key` with value `default` and return
`default`.
Optional argument `default` defaults to none.
Runtime complexity: `O(log(n))` -- approximate.
>>> sd = SortedDict()
>>> sd.setdefault('a', 1)
1
>>> sd.setdefault('a', 10)
1
>>> sd
SortedDict({'a': 1})
:param key: key for item
:param default: value for item (default None)
:return: value for item identified by `key`
"""
if key in self:
return self[key]
dict.__setitem__(self, key, default)
self._list_add(key)
return default
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21,078 | tfields.core | to_map |
Args:
map_ (TensorFields)
*fields (Tensors)
copy (bool)
**kwargs: passed to TensorFields constructor
| @staticmethod
def to_map(map_, *fields, copy=False, **kwargs):
"""
Args:
map_ (TensorFields)
*fields (Tensors)
copy (bool)
**kwargs: passed to TensorFields constructor
"""
if not copy:
if isinstance(map_, TensorFields) and not fields:
if not np.issubdtype(map_.dtype, np.integer):
map_ = map_.astype(int)
else:
copy = True
if copy: # not else, because in case of wrong map_ type we initialize
kwargs.setdefault("dtype", int)
map_ = TensorFields(map_, *fields, **kwargs)
return map_
| (map_, *fields, copy=False, **kwargs) | [
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21,080 | sortedcontainers.sorteddict | values | Return new sorted values view of the sorted dict's values.
See :class:`SortedValuesView` for details.
:return: new sorted values view
| def values(self):
"""Return new sorted values view of the sorted dict's values.
See :class:`SortedValuesView` for details.
:return: new sorted values view
"""
return SortedValuesView(self)
| (self) | [
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21,081 | tfields.mesh_3d | Mesh3D |
Points3D child used as vertices combined with faces to build a geometrical mesh of triangles
Examples:
>>> import tfields
>>> import numpy as np
>>> m = tfields.Mesh3D([[1,2,3], [3,3,3], [0,0,0], [5,6,7]], faces=[[0, 1, 2], [1, 2, 3]])
>>> m.equal([[1, 2, 3],
... [3, 3, 3],
... [0, 0, 0],
... [5, 6, 7]])
True
>>> np.array_equal(m.faces, [[0, 1, 2], [1, 2, 3]])
True
conversion to points only
>>> tfields.Points3D(m).equal([[1, 2, 3],
... [3, 3, 3],
... [0, 0, 0],
... [5, 6, 7]])
True
Empty instances
>>> m = tfields.Mesh3D([]);
going from Mesh3D to Triangles3D instance is easy and will be cached.
>>> m = tfields.Mesh3D([[1,0,0], [0,1,0], [0,0,0]], faces=[[0, 1, 2]]);
>>> assert m.triangles().equal(tfields.Triangles3D([[ 1., 0., 0.],
... [ 0., 1., 0.],
... [ 0., 0., 0.]]))
a list of scalars is assigned to each face
>>> mScalar = tfields.Mesh3D([[1,0,0], [0,1,0], [0,0,0]],
... faces=([[0, 1, 2]], [.5]));
>>> np.array_equal(mScalar.faces.fields, [[ 0.5]])
True
adding together two meshes:
>>> m2 = tfields.Mesh3D([[1,0,0],[2,0,0],[0,3,0]],
... faces=([[0,1,2]], [.7]))
>>> msum = tfields.Mesh3D.merged(mScalar, m2)
>>> msum.equal([[ 1., 0., 0.],
... [ 0., 1., 0.],
... [ 0., 0., 0.],
... [ 1., 0., 0.],
... [ 2., 0., 0.],
... [ 0., 3., 0.]])
True
>>> assert np.array_equal(msum.faces, [[0, 1, 2], [3, 4, 5]])
Saving and reading
>>> from tempfile import NamedTemporaryFile
>>> outFile = NamedTemporaryFile(suffix='.npz')
>>> m.save(outFile.name)
>>> _ = outFile.seek(0)
>>> m1 = tfields.Mesh3D.load(outFile.name, allow_pickle=True)
>>> bool(np.all(m == m1))
True
>>> assert np.array_equal(m1.faces, np.array([[0, 1, 2]]))
| class Mesh3D(tfields.TensorMaps):
# pylint: disable=R0904
"""
Points3D child used as vertices combined with faces to build a geometrical mesh of triangles
Examples:
>>> import tfields
>>> import numpy as np
>>> m = tfields.Mesh3D([[1,2,3], [3,3,3], [0,0,0], [5,6,7]], faces=[[0, 1, 2], [1, 2, 3]])
>>> m.equal([[1, 2, 3],
... [3, 3, 3],
... [0, 0, 0],
... [5, 6, 7]])
True
>>> np.array_equal(m.faces, [[0, 1, 2], [1, 2, 3]])
True
conversion to points only
>>> tfields.Points3D(m).equal([[1, 2, 3],
... [3, 3, 3],
... [0, 0, 0],
... [5, 6, 7]])
True
Empty instances
>>> m = tfields.Mesh3D([]);
going from Mesh3D to Triangles3D instance is easy and will be cached.
>>> m = tfields.Mesh3D([[1,0,0], [0,1,0], [0,0,0]], faces=[[0, 1, 2]]);
>>> assert m.triangles().equal(tfields.Triangles3D([[ 1., 0., 0.],
... [ 0., 1., 0.],
... [ 0., 0., 0.]]))
a list of scalars is assigned to each face
>>> mScalar = tfields.Mesh3D([[1,0,0], [0,1,0], [0,0,0]],
... faces=([[0, 1, 2]], [.5]));
>>> np.array_equal(mScalar.faces.fields, [[ 0.5]])
True
adding together two meshes:
>>> m2 = tfields.Mesh3D([[1,0,0],[2,0,0],[0,3,0]],
... faces=([[0,1,2]], [.7]))
>>> msum = tfields.Mesh3D.merged(mScalar, m2)
>>> msum.equal([[ 1., 0., 0.],
... [ 0., 1., 0.],
... [ 0., 0., 0.],
... [ 1., 0., 0.],
... [ 2., 0., 0.],
... [ 0., 3., 0.]])
True
>>> assert np.array_equal(msum.faces, [[0, 1, 2], [3, 4, 5]])
Saving and reading
>>> from tempfile import NamedTemporaryFile
>>> outFile = NamedTemporaryFile(suffix='.npz')
>>> m.save(outFile.name)
>>> _ = outFile.seek(0)
>>> m1 = tfields.Mesh3D.load(outFile.name, allow_pickle=True)
>>> bool(np.all(m == m1))
True
>>> assert np.array_equal(m1.faces, np.array([[0, 1, 2]]))
"""
def __new__(cls, tensors, *fields, **kwargs):
kwargs["dim"] = 3
if "maps" in kwargs and "faces" in kwargs:
raise ValueError("Conflicting options maps and faces")
faces = kwargs.pop("faces", None)
maps = kwargs.pop("maps", None)
if faces is not None:
if len(faces) == 0:
# faces = []
faces = np.empty((0, 3))
maps = [faces]
if maps is not None:
kwargs["maps"] = maps
obj = super(Mesh3D, cls).__new__(cls, tensors, *fields, **kwargs)
if len(obj.maps) > 1:
raise ValueError("Mesh3D only allows one map")
if obj.maps and (len(obj.maps) > 1 or obj.maps.keys()[0] != 3):
raise ValueError("Face dimension should be 3")
return obj
def _save_obj(self, path, **kwargs):
"""
Save obj as wavefront/.obj file
"""
obj = kwargs.pop("object", None)
group = kwargs.pop("group", None)
cmap = kwargs.pop("cmap", "viridis")
map_index = kwargs.pop("map_index", None)
path = path.replace(".obj", "")
directory, name = os.path.split(path)
if map_index is not None:
scalars = self.maps[3].fields[map_index]
min_scalar = scalars[~np.isnan(scalars)].min()
max_scalar = scalars[~np.isnan(scalars)].max()
vmin = kwargs.pop("vmin", min_scalar)
vmax = kwargs.pop("vmax", max_scalar)
if vmin == vmax:
if vmin == 0.0:
vmax = 1.0
else:
vmin = 0.0
import matplotlib.colors as colors
import matplotlib.pyplot as plt
norm = colors.Normalize(vmin, vmax)
color_map = plt.get_cmap(cmap)
else:
# switch for not coloring the triangles and thus not producing the
# materials
norm = None
if len(kwargs) != 0:
raise ValueError("Unused arguments.")
if norm is not None:
mat_name = name + "_frame_{0}.mat".format(map_index)
scalars[np.isnan(scalars)] = min_scalar - 1
sorted_scalars = scalars[scalars.argsort()]
sorted_scalars[sorted_scalars == min_scalar - 1] = np.nan
sorted_faces = self.faces[scalars.argsort()]
scalar_set = np.unique(sorted_scalars)
scalar_set[scalar_set == min_scalar - 1] = np.nan
mat_path = os.path.join(directory, mat_name)
with open(mat_path, "w") as mf:
for s in scalar_set:
if np.isnan(s):
mf.write("newmtl nan")
mf.write("Kd 0 0 0\n\n")
else:
mf.write("newmtl mtl_{0}\n".format(s))
mf.write(
"Kd {c[0]} {c[1]} {c[2]}\n\n".format(c=color_map(norm(s)))
)
else:
sorted_faces = self.faces
# writing of the obj file
with open(path + ".obj", "w") as f:
f.write("# File saved with tfields Mesh3D._save_obj method\n\n")
if norm is not None:
f.write("mtllib ./{0}\n\n".format(mat_name))
if obj is not None:
f.write("o {0}\n".format(obj))
if group is not None:
f.write("g {0}\n".format(group))
for vertex in self:
f.write("v {v[0]} {v[1]} {v[2]}\n".format(v=vertex))
last_scalar = None
for i, face in enumerate(sorted_faces + 1):
if norm is not None:
if not last_scalar == sorted_scalars[i]:
last_scalar = sorted_scalars[i]
f.write("usemtl mtl_{0}\n".format(last_scalar))
f.write("f {f[0]} {f[1]} {f[2]}\n".format(f=face))
@classmethod
def _load_obj(cls, path, *group_names):
"""
Factory method
Given a path to a obj/wavefront file, construct the object
"""
import csv
log = logging.getLogger()
with open(path, mode="r") as f:
reader = csv.reader(f, delimiter=" ")
groups = []
group = None
vertex_no = 1
for line in reader:
if not line:
continue
if line[0] == "#":
continue
if line[0] == "g":
if group:
groups.append(group)
group = dict(name=line[1], vertices={}, faces=[])
elif line[0] == "v":
if not group:
log.debug("No group found. Setting default 'Group'")
group = dict(name="Group", vertices={}, faces=[])
vertex = list(map(float, line[1:4]))
group["vertices"][vertex_no] = vertex
vertex_no += 1
elif line[0] == "f":
face = []
for v in line[1:]:
w = v.split("/")
face.append(int(w[0]))
group["faces"].append(face)
else:
groups.append(group)
vertices = []
for g in groups[:]:
vertices.extend(g["vertices"].values())
if len(group_names) != 0:
groups = [g for g in groups if g["name"] in group_names]
faces = []
for g in groups:
faces.extend(g["faces"])
faces = np.add(np.array(faces), -1).tolist()
"""
Building the class from retrieved vertices and faces
"""
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21,082 | tfields.core | __array_finalize__ | null | def __array_finalize__(self, obj):
if obj is None:
return
for attr in self._iter_slots():
setattr(self, attr, getattr(obj, attr, None))
| (self, obj) | [
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21,083 | tfields.core | __array_wrap__ | null | def __array_wrap__(self, out_arr, context=None): # pylint: disable=arguments-differ
return np.ndarray.__array_wrap__( # pylint: disable=too-many-function-args
self, out_arr, context
)
| (self, out_arr, context=None) | [
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21,084 | tfields.core | __getitem__ |
In addition to the usual, also slice fields
Examples:
>>> import tfields
>>> import numpy as np
>>> vectors = tfields.Tensors([[0, 0, 0], [0, 0, 1], [0, -1, 0],
... [1, 1, 1], [-1, -1, -1]])
>>> maps=[tfields.TensorFields([[0, 1, 2], [0, 1, 3], [2, 3, 4]],
... [[1, 2], [3, 4], [5, 6]]),
... tfields.TensorFields([[0], [1], [2], [3], [4]])]
>>> mesh = tfields.TensorMaps(vectors,
... [42, 21, 10.5, 1, 1],
... [1, 2, 3, 3, 3],
... maps=maps)
Slicing
>>> sliced = mesh[2:]
>>> assert isinstance(sliced, tfields.TensorMaps)
>>> assert isinstance(sliced.fields[0], tfields.Tensors)
>>> assert isinstance(sliced.maps[3], tfields.TensorFields)
>>> assert sliced.fields[0].equal([10.5, 1, 1])
>>> assert sliced.maps[3].equal([[0, 1, 2]])
>>> assert sliced.maps[3].fields[0].equal([[5, 6]])
Picking
>>> picked = mesh[1]
>>> assert np.array_equal(picked, [0, 0, 1])
>>> assert np.array_equal(picked.maps[3], np.empty((0, 3)))
>>> assert np.array_equal(picked.maps[1], [[0]])
Masking
>>> masked = mesh[np.array([True, False, True, True, True])]
>>> assert masked.equal([[0, 0, 0], [0, -1, 0],
... [1, 1, 1], [-1, -1, -1]])
>>> assert masked.fields[0].equal([42, 10.5, 1, 1])
>>> assert masked.fields[1].equal([1, 3, 3, 3])
>>> assert masked.maps[3].equal([[1, 2, 3]])
>>> assert masked.maps[1].equal([[0], [1], [2], [3]])
Iteration
>>> _ = [vertex for vertex in mesh]
| def __getitem__(self, index):
"""
In addition to the usual, also slice fields
Examples:
>>> import tfields
>>> import numpy as np
>>> vectors = tfields.Tensors([[0, 0, 0], [0, 0, 1], [0, -1, 0],
... [1, 1, 1], [-1, -1, -1]])
>>> maps=[tfields.TensorFields([[0, 1, 2], [0, 1, 3], [2, 3, 4]],
... [[1, 2], [3, 4], [5, 6]]),
... tfields.TensorFields([[0], [1], [2], [3], [4]])]
>>> mesh = tfields.TensorMaps(vectors,
... [42, 21, 10.5, 1, 1],
... [1, 2, 3, 3, 3],
... maps=maps)
Slicing
>>> sliced = mesh[2:]
>>> assert isinstance(sliced, tfields.TensorMaps)
>>> assert isinstance(sliced.fields[0], tfields.Tensors)
>>> assert isinstance(sliced.maps[3], tfields.TensorFields)
>>> assert sliced.fields[0].equal([10.5, 1, 1])
>>> assert sliced.maps[3].equal([[0, 1, 2]])
>>> assert sliced.maps[3].fields[0].equal([[5, 6]])
Picking
>>> picked = mesh[1]
>>> assert np.array_equal(picked, [0, 0, 1])
>>> assert np.array_equal(picked.maps[3], np.empty((0, 3)))
>>> assert np.array_equal(picked.maps[1], [[0]])
Masking
>>> masked = mesh[np.array([True, False, True, True, True])]
>>> assert masked.equal([[0, 0, 0], [0, -1, 0],
... [1, 1, 1], [-1, -1, -1]])
>>> assert masked.fields[0].equal([42, 10.5, 1, 1])
>>> assert masked.fields[1].equal([1, 3, 3, 3])
>>> assert masked.maps[3].equal([[1, 2, 3]])
>>> assert masked.maps[1].equal([[0], [1], [2], [3]])
Iteration
>>> _ = [vertex for vertex in mesh]
"""
item = super(TensorMaps, self).__getitem__(index)
if issubclass(type(item), TensorMaps): # pylint: disable=too-many-nested-blocks
if isinstance(index, tuple):
index = index[0]
if item.maps:
item.maps = Maps(item.maps)
indices = np.arange(len(self))
keep_indices = indices[index]
if isinstance(keep_indices, (int, np.integer)):
keep_indices = [keep_indices]
delete_indices = set(indices).difference(set(keep_indices))
# correct all maps that contain deleted indices
for map_dim in self.maps:
# build mask, where the map should be deleted
map_delete_mask = np.full(
(len(self.maps[map_dim]),), False, dtype=bool
)
for i, map_ in enumerate( # pylint: disable=invalid-name
self.maps[map_dim]
):
for node_index in map_:
if node_index in delete_indices:
map_delete_mask[i] = True
break
map_mask = ~map_delete_mask
# build the correction counters
move_up_counter = np.zeros(self.maps[map_dim].shape, dtype=int)
for delete_index in delete_indices:
move_up_counter[self.maps[map_dim] > delete_index] -= 1
item.maps[map_dim] = (self.maps[map_dim] + move_up_counter)[
map_mask
]
return item
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21,085 | tfields.core | __iter__ |
Forwarding iterations to the bulk array. Otherwise __getitem__ would
kick in and slow down imensely.
Examples:
>>> import tfields
>>> vectors = tfields.Tensors([[0, 0, 0], [0, 0, 1], [0, -1, 0]])
>>> scalar_field = tfields.TensorFields(
... vectors, [42, 21, 10.5], [1, 2, 3])
>>> [(point.rank, point.dim) for point in scalar_field]
[(0, 1), (0, 1), (0, 1)]
| def __iter__(self):
"""
Forwarding iterations to the bulk array. Otherwise __getitem__ would
kick in and slow down imensely.
Examples:
>>> import tfields
>>> vectors = tfields.Tensors([[0, 0, 0], [0, 0, 1], [0, -1, 0]])
>>> scalar_field = tfields.TensorFields(
... vectors, [42, 21, 10.5], [1, 2, 3])
>>> [(point.rank, point.dim) for point in scalar_field]
[(0, 1), (0, 1), (0, 1)]
"""
for index in range(len(self)):
yield super(Tensors, self).__getitem__(index).view(Tensors)
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21,086 | tfields.mesh_3d | __new__ | null | def __new__(cls, tensors, *fields, **kwargs):
kwargs["dim"] = 3
if "maps" in kwargs and "faces" in kwargs:
raise ValueError("Conflicting options maps and faces")
faces = kwargs.pop("faces", None)
maps = kwargs.pop("maps", None)
if faces is not None:
if len(faces) == 0:
# faces = []
faces = np.empty((0, 3))
maps = [faces]
if maps is not None:
kwargs["maps"] = maps
obj = super(Mesh3D, cls).__new__(cls, tensors, *fields, **kwargs)
if len(obj.maps) > 1:
raise ValueError("Mesh3D only allows one map")
if obj.maps and (len(obj.maps) > 1 or obj.maps.keys()[0] != 3):
raise ValueError("Face dimension should be 3")
return obj
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21,087 | tfields.core | __reduce__ |
important for pickling (see `here <https://stackoverflow.com/questions/26598109/preserve-custom-attributes-when-pickling-subclass-of-numpy-array>`_)
Examples:
>>> from tempfile import NamedTemporaryFile
>>> import pickle
>>> import tfields
Build a dummy scalar field
>>> scalars = tfields.Tensors([0, 1, 2])
>>> vectors = tfields.Tensors([[0, 0, 0], [0, 0, 1], [0, -1, 0]])
>>> scalar_field = tfields.TensorFields(
... vectors,
... scalars,
... coord_sys='cylinder')
Save it and restore it
>>> out_file = NamedTemporaryFile(suffix='.pickle')
>>> pickle.dump(scalar_field,
... out_file)
>>> _ = out_file.seek(0)
>>> sf = pickle.load(out_file)
>>> sf.coord_sys == 'cylinder'
True
>>> sf.fields[0][2] == 2.
True
| def __reduce__(self):
"""
important for pickling (see `here <https://stackoverflow.com/questions/\
26598109/preserve-custom-attributes-when-pickling-subclass-of-numpy-array>`_)
Examples:
>>> from tempfile import NamedTemporaryFile
>>> import pickle
>>> import tfields
Build a dummy scalar field
>>> scalars = tfields.Tensors([0, 1, 2])
>>> vectors = tfields.Tensors([[0, 0, 0], [0, 0, 1], [0, -1, 0]])
>>> scalar_field = tfields.TensorFields(
... vectors,
... scalars,
... coord_sys='cylinder')
Save it and restore it
>>> out_file = NamedTemporaryFile(suffix='.pickle')
>>> pickle.dump(scalar_field,
... out_file)
>>> _ = out_file.seek(0)
>>> sf = pickle.load(out_file)
>>> sf.coord_sys == 'cylinder'
True
>>> sf.fields[0][2] == 2.
True
"""
# Get the parent's __reduce__ tuple
pickled_state = super(AbstractNdarray, self).__reduce__()
# Create our own tuple to pass to __setstate__
new_state = pickled_state[2] + tuple(
getattr(self, slot) for slot in self._iter_slots()
)
# Return a tuple that replaces the parent's __setstate__
# tuple with our own
return (pickled_state[0], pickled_state[1], new_state)
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21,088 | tfields.core | __setattr__ | null | def __setattr__(self, name, value):
if name in self.__slots__:
index = self.__slots__.index(name)
try:
setter = self.__slot_setters__[index]
except IndexError:
setter = None
if isinstance(setter, str):
setter = getattr(self, setter)
if setter is not None:
value = setter(value)
super(AbstractNdarray, self).__setattr__(name, value)
| (self, name, value) | [
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] |
21,089 | tfields.core | __setitem__ |
In addition to the usual, also slice fields
Examples:
>>> import tfields
>>> import numpy as np
>>> original = tfields.TensorFields(
... [[0, 0, 0], [0, 0, 1], [0, -1, 0]],
... [42, 21, 10.5], [1, 2, 3])
>>> obj = tfields.TensorFields(
... [[0, 0, 0], [0, 0, np.nan],
... [0, -1, 0]], [42, 22, 10.5], [1, -1, 3])
>>> slice_obj = obj.copy()
>>> assert not obj.equal(original)
>>> obj[1] = original[1]
>>> assert obj[:2].equal(original[:2])
>>> assert not slice_obj.equal(original)
>>> slice_obj[:] = original[:]
>>> assert slice_obj.equal(original)
| def __setitem__(self, index, item):
"""
In addition to the usual, also slice fields
Examples:
>>> import tfields
>>> import numpy as np
>>> original = tfields.TensorFields(
... [[0, 0, 0], [0, 0, 1], [0, -1, 0]],
... [42, 21, 10.5], [1, 2, 3])
>>> obj = tfields.TensorFields(
... [[0, 0, 0], [0, 0, np.nan],
... [0, -1, 0]], [42, 22, 10.5], [1, -1, 3])
>>> slice_obj = obj.copy()
>>> assert not obj.equal(original)
>>> obj[1] = original[1]
>>> assert obj[:2].equal(original[:2])
>>> assert not slice_obj.equal(original)
>>> slice_obj[:] = original[:]
>>> assert slice_obj.equal(original)
"""
super(TensorFields, self).__setitem__(index, item)
if issubclass(type(item), TensorFields):
if isinstance(index, slice):
for i, field in enumerate(item.fields):
self.fields[i].__setitem__(index, field)
elif isinstance(index, tuple):
for i, field in enumerate(item.fields):
self.fields[i].__setitem__(index[0], field)
else:
for i, field in enumerate(item.fields):
self.fields[i].__setitem__(index, field)
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] |
21,090 | tfields.core | __setstate__ |
Counterpart to __reduce__. Important for unpickling.
| def __setstate__(self, state):
"""
Counterpart to __reduce__. Important for unpickling.
"""
# Call the parent's __setstate__ with the other tuple elements.
super(AbstractNdarray, self).__setstate__(state[0 : -len(self._iter_slots())])
# set the __slot__ attributes
valid_slot_attrs = list(self._iter_slots())
# attributes that have been added later have not been pickled with the full information
# and thus need to be excluded from the __setstate__ need to be in the same order as they
# have been added to __slots__
added_slot_attrs = ["name"]
n_np = 5 # number of numpy array states
n_old = len(valid_slot_attrs) - len(state[n_np:])
if n_old > 0:
for latest_index in range(n_old):
new_slot = added_slot_attrs[-latest_index]
warnings.warn(
"Slots with names '{new_slot}' appears to have "
"been added after the creation of the reduced "
"state. No corresponding state found in "
"__setstate__.".format(**locals())
)
valid_slot_attrs.pop(valid_slot_attrs.index(new_slot))
setattr(self, new_slot, None)
for slot_index, slot in enumerate(valid_slot_attrs):
state_index = n_np + slot_index
setattr(self, slot, state[state_index])
| (self, state) | [
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21,091 | tfields.core | _args | null | def _args(self) -> tuple:
return super()._args() + tuple(self.fields)
| (self) -> tuple | [
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] |
21,093 | tfields.mesh_3d | _cut_sympy |
Partition the mesh with the cuts given and return the template
| def _cut_sympy(self, expression, at_intersection="remove", _in_recursion=False):
"""
Partition the mesh with the cuts given and return the template
"""
eps = 0.000000001
# direct return if self is empty
if len(self) == 0:
return self.copy(), self.copy()
inst = self.copy()
"""
add the indices of the vertices and maps to the fields. They will be
removed afterwards
"""
if not _in_recursion:
inst.fields.append(tfields.Tensors(np.arange(len(inst))))
for mp in inst.maps.values():
mp.fields.append(tfields.Tensors(np.arange(len(mp))))
# mask for points that do not fulfill the cut expression
mask = inst.evalf(expression)
# remove the points
if not any(~mask):
# no vertex is valid
inst = inst[mask]
elif all(~mask):
# all vertices are valid
inst = inst[mask]
elif at_intersection == "keep":
expression_parts = tfields.lib.symbolics.split_expression(expression)
if len(expression_parts) > 1:
new_mesh = inst.copy()
for expr_part in expression_parts:
inst, _ = inst._cut_sympy(
expr_part, at_intersection=at_intersection, _in_recursion=True
)
elif len(expression_parts) == 1:
face_delete_indices = set([])
for i, face in enumerate(inst.maps[3]):
"""
vertices_rejected is a mask for each face that is True, where
a Point is on the rejected side of the plane
"""
vertices_rejected = [~mask[f] for f in face]
if all(vertices_rejected):
# delete face
face_delete_indices.add(i)
mask = np.full(len(inst.maps[3]), True, dtype=bool)
for face_idx in range(len(inst.maps[3])):
if face_idx in face_delete_indices:
mask[face_idx] = False
inst.maps[3] = inst.maps[3][mask]
else:
raise ValueError("Sympy expression is not splitable.")
inst = inst.cleaned()
elif at_intersection == "split" or at_intersection == "split_rough":
"""
add vertices and faces that are at the border of the cuts
"""
expression_parts = tfields.lib.symbolics.split_expression(expression)
if len(expression_parts) > 1:
new_mesh = inst.copy()
if at_intersection == "split_rough":
"""
the following is, to speed up the process. Problem is, that
triangles can exist, where all points lie outside the cut,
but part of the area
still overlaps with the cut.
These are at the intersection line between two cuts.
"""
face_inters_mask = np.full((inst.faces.shape[0]), False, dtype=bool)
for i, face in enumerate(inst.faces):
vertices_rejected = [-mask[f] for f in face]
face_on_edge = any(vertices_rejected) and not all(
vertices_rejected
)
if face_on_edge:
face_inters_mask[i] = True
new_mesh.remove_faces(-face_inters_mask)
for expr_part in expression_parts:
inst, _ = inst._cut_sympy(
expr_part, at_intersection="split", _in_recursion=True
)
elif len(expression_parts) == 1:
# build plane from cut expression
plane_sympy = tfields.lib.symbolics.to_plane(expression)
norm_sympy = np.array(plane_sympy.normal_vector).astype(float)
d = -norm_sympy.dot(np.array(plane_sympy.p1).astype(float))
plane = {"normal": norm_sympy, "d": d}
# initialize empty containers
norm_vectors = inst.triangles().norms()
new_vertices = np.empty((0, 3))
new_faces = np.empty((0, 3))
new_fields = [
tfields.Tensors(
np.empty((0,) + field.shape[1:]), coord_sys=field.coord_sys
)
for field in inst.fields
]
new_map_fields = [[] for field in inst.maps[3].fields]
new_norm_vectors = []
# copy TODO?
vertices = np.array(inst)
faces = np.array(inst.maps[3])
fields = [np.array(field) for field in inst.fields]
faces_fields = [np.array(field) for field in inst.maps[3].fields]
face_delete_indices = set([]) # indexing not intersected faces
for i, face in enumerate(inst.maps[3]):
"""
vertices_rejected is a mask for each face that is True
where a point is on the rejected side of the plane
"""
vertices_rejected = [~mask[f] for f in face]
if any(vertices_rejected):
# delete face
face_delete_indices.add(i)
if any(vertices_rejected) and not all(vertices_rejected):
# face on edge
n_true = vertices_rejected.count(True)
lonely_bool = True if n_true == 1 else False
triangle_points = [vertices[f] for f in face]
"""
Add the intersection points and faces
"""
intersection = _intersect(
triangle_points, plane, vertices_rejected
)
last_idx = len(vertices)
for tri_list in intersection:
new_face = []
for item in tri_list:
if isinstance(item, int):
# reference to old vertex
new_face.append(face[item])
elif isinstance(item, complex):
# reference to new vertex that has been
# concatenated already
new_face.append(last_idx + int(item.imag))
else:
# new vertex
new_face.append(len(vertices))
vertices = np.append(
vertices, [[float(x) for x in item]], axis=0
)
fields = [
np.append(
field,
np.full((1,) + field.shape[1:], np.nan),
axis=0,
)
for field in fields
]
faces = np.append(faces, [new_face], axis=0)
faces_fields = [
np.append(field, [field[i]], axis=0)
for field in faces_fields
]
faces_fields[-1][-1] = i
face_map = tfields.TensorFields(
faces, *faces_fields, dtype=int, coord_sys=inst.maps[3].coord_sys
)
inst = tfields.Mesh3D(
vertices, *fields, maps=[face_map], coord_sys=inst.coord_sys
)
mask = np.full(len(inst.maps[3]), True, dtype=bool)
for face_idx in range(len(inst.maps[3])):
if face_idx in face_delete_indices:
mask[face_idx] = False
inst.maps[3] = inst.maps[3][mask]
else:
raise ValueError("Sympy expression is not splitable.")
inst = inst.cleaned()
elif at_intersection == "remove":
inst = inst[mask]
else:
raise AttributeError(
"No at_intersection method called {at_intersection} "
"implemented".format(**locals())
)
if _in_recursion:
template = None
else:
templ | (self, expression, at_intersection='remove', _in_recursion=False) | [
0.05564875528216362,
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21,094 | tfields.mesh_3d | _cut_template |
Args:
template (tfields.Mesh3D)
Examples:
>>> import tfields
>>> import numpy as np
Build mesh
>>> mmap = tfields.TensorFields([[0, 1, 2], [0, 3, 4]],
... [[42, 21], [-42, -21]])
>>> m = tfields.Mesh3D([[0]*3, [1]*3, [2]*3, [3]*3, [4]*3],
... [0.0, 0.1, 0.2, 0.3, 0.4],
... [0.0, -0.1, -0.2, -0.3, -0.4],
... maps=[mmap])
Build template
>>> tmap = tfields.TensorFields([[0, 3, 4], [0, 1, 2]],
... [1, 0])
>>> t = tfields.Mesh3D([[0]*3, [-1]*3, [-2]*3, [-3]*3, [-4]*3],
... [1, 0, 3, 2, 4],
... maps=[tmap])
Use template as instruction to make a fast cut
>>> res = m._cut_template(t)
>>> assert np.array_equal(res.fields,
... [[0.1, 0.0, 0.3, 0.2, 0.4],
... [-0.1, 0.0, -0.3, -0.2, -0.4]])
>>> assert np.array_equal(res.maps[3].fields[0],
... [[-42, -21], [42, 21]])
| def _cut_template(self, template):
"""
Args:
template (tfields.Mesh3D)
Examples:
>>> import tfields
>>> import numpy as np
Build mesh
>>> mmap = tfields.TensorFields([[0, 1, 2], [0, 3, 4]],
... [[42, 21], [-42, -21]])
>>> m = tfields.Mesh3D([[0]*3, [1]*3, [2]*3, [3]*3, [4]*3],
... [0.0, 0.1, 0.2, 0.3, 0.4],
... [0.0, -0.1, -0.2, -0.3, -0.4],
... maps=[mmap])
Build template
>>> tmap = tfields.TensorFields([[0, 3, 4], [0, 1, 2]],
... [1, 0])
>>> t = tfields.Mesh3D([[0]*3, [-1]*3, [-2]*3, [-3]*3, [-4]*3],
... [1, 0, 3, 2, 4],
... maps=[tmap])
Use template as instruction to make a fast cut
>>> res = m._cut_template(t)
>>> assert np.array_equal(res.fields,
... [[0.1, 0.0, 0.3, 0.2, 0.4],
... [-0.1, 0.0, -0.3, -0.2, -0.4]])
>>> assert np.array_equal(res.maps[3].fields[0],
... [[-42, -21], [42, 21]])
"""
# Possible Extension (small todo): check: len(field(s)) == len(self/maps)
# Redirect fields
fields = []
if template.fields:
template_field = np.array(template.fields[0])
if len(self) > 0:
"""
if new vertices have been created in the template, it is
in principle unclear what fields we have to refer to.
Thus in creating the template, we gave np.nan.
To make it fast, we replace nan with 0 as a dummy and correct
the field entries afterwards with np.nan.
"""
nan_mask = np.isnan(template_field)
template_field[nan_mask] = 0 # dummy reference to index 0.
template_field = template_field.astype(int)
for field in self.fields:
projected_field = field[template_field]
projected_field[nan_mask] = np.nan # correction for nan
fields.append(projected_field)
# Redirect maps and their fields
maps = []
for mp, template_mp in zip(self.maps.values(), template.maps.values()):
mp_fields = []
for field in mp.fields:
if len(template_mp) == 0 and len(template_mp.fields) == 0:
mp_fields.append(field[0:0]) # np.empty
else:
mp_fields.append(field[template_mp.fields[0].astype(int)])
new_mp = tfields.TensorFields(tfields.Tensors(template_mp), *mp_fields)
maps.append(new_mp)
inst = tfields.Mesh3D(tfields.Tensors(template), *fields, maps=maps)
return inst
| (self, template) | [
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21,095 | tfields.core | _kwargs | null | def _kwargs(self) -> dict:
content = super()._kwargs()
content.pop("fields") # instantiated via _args
return content
| (self) -> dict | [
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] |
21,098 | tfields.mesh_3d | _save_obj |
Save obj as wavefront/.obj file
| def _save_obj(self, path, **kwargs):
"""
Save obj as wavefront/.obj file
"""
obj = kwargs.pop("object", None)
group = kwargs.pop("group", None)
cmap = kwargs.pop("cmap", "viridis")
map_index = kwargs.pop("map_index", None)
path = path.replace(".obj", "")
directory, name = os.path.split(path)
if map_index is not None:
scalars = self.maps[3].fields[map_index]
min_scalar = scalars[~np.isnan(scalars)].min()
max_scalar = scalars[~np.isnan(scalars)].max()
vmin = kwargs.pop("vmin", min_scalar)
vmax = kwargs.pop("vmax", max_scalar)
if vmin == vmax:
if vmin == 0.0:
vmax = 1.0
else:
vmin = 0.0
import matplotlib.colors as colors
import matplotlib.pyplot as plt
norm = colors.Normalize(vmin, vmax)
color_map = plt.get_cmap(cmap)
else:
# switch for not coloring the triangles and thus not producing the
# materials
norm = None
if len(kwargs) != 0:
raise ValueError("Unused arguments.")
if norm is not None:
mat_name = name + "_frame_{0}.mat".format(map_index)
scalars[np.isnan(scalars)] = min_scalar - 1
sorted_scalars = scalars[scalars.argsort()]
sorted_scalars[sorted_scalars == min_scalar - 1] = np.nan
sorted_faces = self.faces[scalars.argsort()]
scalar_set = np.unique(sorted_scalars)
scalar_set[scalar_set == min_scalar - 1] = np.nan
mat_path = os.path.join(directory, mat_name)
with open(mat_path, "w") as mf:
for s in scalar_set:
if np.isnan(s):
mf.write("newmtl nan")
mf.write("Kd 0 0 0\n\n")
else:
mf.write("newmtl mtl_{0}\n".format(s))
mf.write(
"Kd {c[0]} {c[1]} {c[2]}\n\n".format(c=color_map(norm(s)))
)
else:
sorted_faces = self.faces
# writing of the obj file
with open(path + ".obj", "w") as f:
f.write("# File saved with tfields Mesh3D._save_obj method\n\n")
if norm is not None:
f.write("mtllib ./{0}\n\n".format(mat_name))
if obj is not None:
f.write("o {0}\n".format(obj))
if group is not None:
f.write("g {0}\n".format(group))
for vertex in self:
f.write("v {v[0]} {v[1]} {v[2]}\n".format(v=vertex))
last_scalar = None
for i, face in enumerate(sorted_faces + 1):
if norm is not None:
if not last_scalar == sorted_scalars[i]:
last_scalar = sorted_scalars[i]
f.write("usemtl mtl_{0}\n".format(last_scalar))
f.write("f {f[0]} {f[1]} {f[2]}\n".format(f=face))
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21,100 | tfields.mesh_3d | _save_stl |
Saves the mesh in stl format
| def _save_stl(self, path, **kwargs):
"""
Saves the mesh in stl format
"""
self.triangles()._save_stl(path, **kwargs)
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21,101 | tfields.core | _save_txt |
Save as text file.
Args:
**kwargs passed to np.savetxt.
| def _save_txt(self, path, **kwargs):
"""
Save as text file.
Args:
**kwargs passed to np.savetxt.
"""
header = kwargs.get("header", [])
if isinstance(header, dict):
header = [
f"{key}: {type(value).__name__} = {value}"
for key, value in header.items()
]
if isinstance(header, list):
# statictyping like attribute saving
header = [
f"{key}: {type(getattr(self, key)).__name__} = {getattr(self, key)}"
for key in self._iter_slots()
] + header
kwargs["header"] = "\n".join(header)
np.savetxt(path, self, **kwargs)
| (self, path, **kwargs) | [
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21,102 | tfields.core | _weights |
Expansion of Tensors._weights with integer inputs
Args:
weights (np.ndarray | int | None):
if weights is int: use field at index <weights>
else: see Tensors._weights
| def _weights(self, weights, rigid=True):
"""
Expansion of Tensors._weights with integer inputs
Args:
weights (np.ndarray | int | None):
if weights is int: use field at index <weights>
else: see Tensors._weights
"""
if isinstance(weights, int):
weights = self.fields[weights]
return super(TensorFields, self)._weights(weights, rigid=rigid)
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21,103 | tfields.mesh_3d | centroids | null | def centroids(self):
return self.triangles().centroids()
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] |
21,104 | tfields.core | cleaned |
Args:
stale (bool): remove stale vertices
duplicates (bool): replace duplicate vertices by originals
Examples:
>>> import numpy as np
>>> import tfields
>>> mp1 = tfields.TensorFields([[0, 1, 2], [3, 4, 5]],
... *zip([1,2,3,4,5], [6,7,8,9,0]))
>>> mp2 = tfields.TensorFields([[0], [3]])
>>> tm = tfields.TensorMaps([[0,0,0], [1,1,1], [2,2,2], [0,0,0],
... [3,3,3], [4,4,4], [5,6,7]],
... maps=[mp1, mp2])
>>> c = tm.cleaned()
>>> assert c.equal([[0., 0., 0.],
... [1., 1., 1.],
... [2., 2., 2.],
... [3., 3., 3.],
... [4., 4., 4.]])
>>> assert np.array_equal(c.maps[3], [[0, 1, 2], [0, 3, 4]])
>>> assert np.array_equal(c.maps[1], [[0], [0]])
Returns:
copy of self without stale vertices and duplicat points (depending
on arguments)
| def cleaned(self, stale=True, duplicates=True):
"""
Args:
stale (bool): remove stale vertices
duplicates (bool): replace duplicate vertices by originals
Examples:
>>> import numpy as np
>>> import tfields
>>> mp1 = tfields.TensorFields([[0, 1, 2], [3, 4, 5]],
... *zip([1,2,3,4,5], [6,7,8,9,0]))
>>> mp2 = tfields.TensorFields([[0], [3]])
>>> tm = tfields.TensorMaps([[0,0,0], [1,1,1], [2,2,2], [0,0,0],
... [3,3,3], [4,4,4], [5,6,7]],
... maps=[mp1, mp2])
>>> c = tm.cleaned()
>>> assert c.equal([[0., 0., 0.],
... [1., 1., 1.],
... [2., 2., 2.],
... [3., 3., 3.],
... [4., 4., 4.]])
>>> assert np.array_equal(c.maps[3], [[0, 1, 2], [0, 3, 4]])
>>> assert np.array_equal(c.maps[1], [[0], [0]])
Returns:
copy of self without stale vertices and duplicat points (depending
on arguments)
"""
if not stale and not duplicates:
inst = self.copy()
if stale:
# remove stale vertices i.e. those that are not referred by any
# map
remove_mask = self.stale()
inst = self.removed(remove_mask)
if duplicates: # pylint: disable=too-many-nested-blocks
# remove duplicates in order to not have any artificial separations
if not stale:
# we have not yet made a copy but want to work on inst
inst = self.copy()
remove_mask = np.full(inst.shape[0], False, dtype=bool)
duplicates = tfields.lib.util.duplicates(inst, axis=0)
tensor_indices = np.arange(inst.shape[0])
duplicates_mask = duplicates != tensor_indices
if duplicates_mask.any():
# redirect maps. Note: work on inst.maps instead of
# self.maps in case stale vertices where removed
keys = tensor_indices[duplicates_mask]
values = duplicates[duplicates_mask]
for map_dim in inst.maps:
tfields.lib.sets.remap(
inst.maps[map_dim], keys, values, inplace=True
)
# mark duplicates for removal
remove_mask[keys] = True
if remove_mask.any():
# prevent another copy
inst = inst.removed(remove_mask)
return inst
| (self, stale=True, duplicates=True) | [
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] |
21,105 | tfields.core | closest |
Args:
other (Tensors): closest points to what? -> other
**kwargs: forwarded to scipy.spatial.cKDTree.query
Returns:
array shape(len(self)): Indices of other points that are closest to
own points
Examples:
>>> import tfields
>>> m = tfields.Tensors([[1,0,0], [0,1,0], [1,1,0], [0,0,1],
... [1,0,1]])
>>> p = tfields.Tensors([[1.1,1,0], [0,0.1,1], [1,0,1.1]])
>>> p.closest(m)
array([2, 3, 4])
| def closest(self, other, **kwargs):
"""
Args:
other (Tensors): closest points to what? -> other
**kwargs: forwarded to scipy.spatial.cKDTree.query
Returns:
array shape(len(self)): Indices of other points that are closest to
own points
Examples:
>>> import tfields
>>> m = tfields.Tensors([[1,0,0], [0,1,0], [1,1,0], [0,0,1],
... [1,0,1]])
>>> p = tfields.Tensors([[1.1,1,0], [0,0.1,1], [1,0,1.1]])
>>> p.closest(m)
array([2, 3, 4])
"""
with other.tmp_transform(self.coord_sys):
# balanced_tree option gives huge speedup!
kd_tree = scipy.spatial.cKDTree( # noqa: E501 pylint: disable=no-member
other, 1000, balanced_tree=False
)
res = kd_tree.query(self, **kwargs)
array = res[1]
return array
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] |
21,106 | tfields.core | contains |
Inspired by a speed argument @
stackoverflow.com/questions/14766194/testing-whether-a-numpy-array-contains-a-given-row
Examples:
>>> import tfields
>>> p = tfields.Tensors([[1,2,3], [4,5,6], [6,7,8]])
>>> p.contains([4,5,6])
True
| def contains(self, other):
"""
Inspired by a speed argument @
stackoverflow.com/questions/14766194/testing-whether-a-numpy-array-contains-a-given-row
Examples:
>>> import tfields
>>> p = tfields.Tensors([[1,2,3], [4,5,6], [6,7,8]])
>>> p.contains([4,5,6])
True
"""
return any(self.equal(other, return_bool=False).all(1))
| (self, other) | [
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21,107 | tfields.core | copy |
The standard ndarray copy does not copy slots. Correct for this.
Examples:
>>> import tfields
>>> m = tfields.TensorMaps(
... [[1,2,3], [3,3,3], [0,0,0], [5,6,7]],
... [[1], [3], [0], [5]],
... maps=[
... ([[0, 1, 2], [1, 2, 3]], [21, 42]),
... [[1]],
... [[0, 1, 2, 3]]
... ])
>>> mc = m.copy()
>>> mc.equal(m)
True
>>> mc is m
False
>>> mc.fields is m.fields
False
>>> mc.fields[0] is m.fields[0]
False
>>> mc.maps[3].fields[0] is m.maps[3].fields[0]
False
| def copy(self, **kwargs): # pylint: disable=arguments-differ
"""
The standard ndarray copy does not copy slots. Correct for this.
Examples:
>>> import tfields
>>> m = tfields.TensorMaps(
... [[1,2,3], [3,3,3], [0,0,0], [5,6,7]],
... [[1], [3], [0], [5]],
... maps=[
... ([[0, 1, 2], [1, 2, 3]], [21, 42]),
... [[1]],
... [[0, 1, 2, 3]]
... ])
>>> mc = m.copy()
>>> mc.equal(m)
True
>>> mc is m
False
>>> mc.fields is m.fields
False
>>> mc.fields[0] is m.fields[0]
False
>>> mc.maps[3].fields[0] is m.maps[3].fields[0]
False
"""
if kwargs:
raise NotImplementedError(
"Copying with arguments {kwargs} not yet supported"
)
# works with __reduce__ / __setstate__
return deepcopy(self)
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21,108 | tfields.core | cov_eig |
Calculate the covariance eigenvectors with lenghts of eigenvalues
Args:
weights (np.array | int | None): index to scalars to weight with
| def cov_eig(self, weights=None):
"""
Calculate the covariance eigenvectors with lenghts of eigenvalues
Args:
weights (np.array | int | None): index to scalars to weight with
"""
# weights = self.getNormedWeightedAreas(weights=weights)
weights = self._weights(weights)
cov = np.cov(self.T, ddof=0, aweights=weights)
# calculate eigenvalues and eigenvectors of covariance
evalfs, evecs = np.linalg.eigh(cov)
idx = evalfs.argsort()[::-1]
evalfs = evalfs[idx]
evecs = evecs[:, idx]
res = np.concatenate((evecs, evalfs.reshape(1, 3)))
return res.T.reshape(
12,
)
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] |
21,109 | tfields.mesh_3d | cut |
cut method for Mesh3D.
Args:
expression (sympy logical expression | Mesh3D):
sympy locical expression: Sympy expression that defines planes
in 3D
Mesh3D: A mesh3D will be interpreted as a template, i.e. a
fast instruction of how to cut the triangles.
It is the second part of the tuple, returned by a previous
cut with a sympy locial expression with 'return_template=True'.
We use the vertices and maps of the Mesh as the skelleton of
the returned mesh. The fields are mapped according to
indices in the template.maps[i].fields.
coord_sys (coordinate system to cut in):
at_intersection (str): instruction on what to do, when a cut will intersect a triangle.
Options: 'remove' (Default) - remove the faces that are on the edge
'keep' - keep the faces that are on the edge
'split' - Create new triangles that make up the old one.
return_template (bool): If True: return the template
to redo the same cut fast
Examples:
define the cut
>>> import numpy as np
>>> import tfields
>>> from sympy.abc import x,y,z
>>> cut_expr = x > 1.5
>>> m = tfields.Mesh3D.grid((0, 3, 4),
... (0, 3, 4),
... (0, 0, 1))
>>> m.fields.append(tfields.Tensors(np.linspace(0, len(m) - 1,
... len(m))))
>>> m.maps[3].fields.append(
... tfields.Tensors(np.linspace(0,
... len(m.maps[3]) - 1,
... len(m.maps[3]))))
>>> mNew = m.cut(cut_expr)
>>> len(mNew)
8
>>> mNew.nfaces()
6
>>> float(mNew[:, 0].min())
2.0
Cutting with the 'keep' option will leave triangles on the edge
untouched:
>>> m_keep = m.cut(cut_expr, at_intersection='keep')
>>> float(m_keep[:, 0].min())
1.0
>>> m_keep.nfaces()
12
Cutting with the 'split' option will create new triangles on the edge:
>>> m_split = m.cut(cut_expr, at_intersection='split')
>>> float(m_split[:, 0].min())
1.5
>>> len(m_split)
15
>>> m_split.nfaces()
15
Cut with 'return_template=True' will return the exact same mesh but
additionally an instruction to conduct the exact same cut fast (template)
>>> m_split_2, template = m.cut(cut_expr, at_intersection='split',
... return_template=True)
>>> m_split_template = m.cut(template)
>>> assert m_split.equal(m_split_2, equal_nan=True)
>>> assert m_split.equal(m_split_template, equal_nan=True)
>>> assert len(template.fields) == 1
>>> assert len(m_split.fields) == 1
>>> assert len(m_split_template.fields) == 1
>>> assert m_split.fields[0].equal(
... list(range(8, 16)) + [np.nan] * 7, equal_nan=True)
>>> assert m_split_template.fields[0].equal(
... list(range(8, 16)) + [np.nan] * 7, equal_nan=True)
This seems irrelevant at first but consider, the map field or the
tensor field changes:
>>> m_altered_fields = m.copy()
>>> m_altered_fields[0] += 42
>>> assert not m_split.equal(m_altered_fields.cut(template))
>>> assert tfields.Tensors(m_split).equal(
... m_altered_fields.cut(template))
>>> assert tfields.Tensors(m_split.maps[3]).equal(
... m_altered_fields.cut(template).maps[3])
The cut expression may be a sympy.BooleanFunction:
>>> cut_expr_bool_fun = (x > 1.5) & (y < 1.5) & (y >0.2) & (z > -0.5)
>>> m_split_bool = m.cut(cut_expr_bool_fun,
... at_intersection='split')
Returns:
copy of cut mesh
* optional: template
| def cut(self, *args, **kwargs):
"""
cut method for Mesh3D.
Args:
expression (sympy logical expression | Mesh3D):
sympy locical expression: Sympy expression that defines planes
in 3D
Mesh3D: A mesh3D will be interpreted as a template, i.e. a
fast instruction of how to cut the triangles.
It is the second part of the tuple, returned by a previous
cut with a sympy locial expression with 'return_template=True'.
We use the vertices and maps of the Mesh as the skelleton of
the returned mesh. The fields are mapped according to
indices in the template.maps[i].fields.
coord_sys (coordinate system to cut in):
at_intersection (str): instruction on what to do, when a cut will intersect a triangle.
Options: 'remove' (Default) - remove the faces that are on the edge
'keep' - keep the faces that are on the edge
'split' - Create new triangles that make up the old one.
return_template (bool): If True: return the template
to redo the same cut fast
Examples:
define the cut
>>> import numpy as np
>>> import tfields
>>> from sympy.abc import x,y,z
>>> cut_expr = x > 1.5
>>> m = tfields.Mesh3D.grid((0, 3, 4),
... (0, 3, 4),
... (0, 0, 1))
>>> m.fields.append(tfields.Tensors(np.linspace(0, len(m) - 1,
... len(m))))
>>> m.maps[3].fields.append(
... tfields.Tensors(np.linspace(0,
... len(m.maps[3]) - 1,
... len(m.maps[3]))))
>>> mNew = m.cut(cut_expr)
>>> len(mNew)
8
>>> mNew.nfaces()
6
>>> float(mNew[:, 0].min())
2.0
Cutting with the 'keep' option will leave triangles on the edge
untouched:
>>> m_keep = m.cut(cut_expr, at_intersection='keep')
>>> float(m_keep[:, 0].min())
1.0
>>> m_keep.nfaces()
12
Cutting with the 'split' option will create new triangles on the edge:
>>> m_split = m.cut(cut_expr, at_intersection='split')
>>> float(m_split[:, 0].min())
1.5
>>> len(m_split)
15
>>> m_split.nfaces()
15
Cut with 'return_template=True' will return the exact same mesh but
additionally an instruction to conduct the exact same cut fast (template)
>>> m_split_2, template = m.cut(cut_expr, at_intersection='split',
... return_template=True)
>>> m_split_template = m.cut(template)
>>> assert m_split.equal(m_split_2, equal_nan=True)
>>> assert m_split.equal(m_split_template, equal_nan=True)
>>> assert len(template.fields) == 1
>>> assert len(m_split.fields) == 1
>>> assert len(m_split_template.fields) == 1
>>> assert m_split.fields[0].equal(
... list(range(8, 16)) + [np.nan] * 7, equal_nan=True)
>>> assert m_split_template.fields[0].equal(
... list(range(8, 16)) + [np.nan] * 7, equal_nan=True)
This seems irrelevant at first but consider, the map field or the
tensor field changes:
>>> m_altered_fields = m.copy()
>>> m_altered_fields[0] += 42
>>> assert not m_split.equal(m_altered_fields.cut(template))
>>> assert tfields.Tensors(m_split).equal(
... m_altered_fields.cut(template))
>>> assert tfields.Tensors(m_split.maps[3]).equal(
... m_altered_fields.cut(template).maps[3])
The cut expression may be a sympy.BooleanFunction:
>>> cut_expr_bool_fun = (x > 1.5) & (y < 1.5) & (y >0.2) & (z > -0.5)
>>> m_split_bool = m.cut(cut_expr_bool_fun,
... at_intersection='split')
Returns:
copy of cut mesh
* optional: template
"""
return super().cut(*args, **kwargs)
| (self, *args, **kwargs) | [
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21,110 | tfields.core | disjoint_map |
Find the disjoint sets of map = self.maps[map_dim]
As an example, this method is interesting for splitting a mesh
consisting of seperate parts
Args:
map_dim (int): reference to map position
used like: self.maps[map_dim]
Returns:
Tuple(int, List(List(int))): map description(tuple): see self.parts
Examples:
>>> import tfields
>>> a = tfields.TensorMaps(
... [[0, 0, 0], [1, 0, 0], [1, 1, 0], [0, 1, 0]],
... maps=[[[0, 1, 2], [0, 2, 3]]])
>>> b = a.copy()
>>> b[:, 0] += 2
>>> m = tfields.TensorMaps.merged(a, b)
>>> mp_description = m.disjoint_map(3)
>>> parts = m.parts(mp_description)
>>> aa, ba = parts
>>> assert aa.maps[3].equal(ba.maps[3])
>>> assert aa.equal(a)
>>> assert ba.equal(b)
| def disjoint_map(self, map_dim):
"""
Find the disjoint sets of map = self.maps[map_dim]
As an example, this method is interesting for splitting a mesh
consisting of seperate parts
Args:
map_dim (int): reference to map position
used like: self.maps[map_dim]
Returns:
Tuple(int, List(List(int))): map description(tuple): see self.parts
Examples:
>>> import tfields
>>> a = tfields.TensorMaps(
... [[0, 0, 0], [1, 0, 0], [1, 1, 0], [0, 1, 0]],
... maps=[[[0, 1, 2], [0, 2, 3]]])
>>> b = a.copy()
>>> b[:, 0] += 2
>>> m = tfields.TensorMaps.merged(a, b)
>>> mp_description = m.disjoint_map(3)
>>> parts = m.parts(mp_description)
>>> aa, ba = parts
>>> assert aa.maps[3].equal(ba.maps[3])
>>> assert aa.equal(a)
>>> assert ba.equal(b)
"""
maps_list = tfields.lib.sets.disjoint_group_indices(self.maps[map_dim])
return (map_dim, maps_list)
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21,111 | tfields.mesh_3d | disjoint_parts |
Returns:
disjoint_parts(List(cls)), templates(List(cls))
>>> import tfields
>>> a = tfields.Mesh3D(
... [[0, 0, 0], [1, 0, 0], [1, 1, 0], [0, 1, 0]],
... maps=[[[0, 1, 2], [0, 2, 3]]])
>>> b = a.copy()
>>> b[:, 0] += 2
>>> m = tfields.Mesh3D.merged(a, b)
>>> parts = m.disjoint_parts()
>>> aa, ba = parts
>>> assert aa.maps[3].equal(ba.maps[3])
>>> assert aa.equal(a)
>>> assert ba.equal(b)
| def disjoint_parts(self, return_template=False):
"""
Returns:
disjoint_parts(List(cls)), templates(List(cls))
>>> import tfields
>>> a = tfields.Mesh3D(
... [[0, 0, 0], [1, 0, 0], [1, 1, 0], [0, 1, 0]],
... maps=[[[0, 1, 2], [0, 2, 3]]])
>>> b = a.copy()
>>> b[:, 0] += 2
>>> m = tfields.Mesh3D.merged(a, b)
>>> parts = m.disjoint_parts()
>>> aa, ba = parts
>>> assert aa.maps[3].equal(ba.maps[3])
>>> assert aa.equal(a)
>>> assert ba.equal(b)
"""
mp_description = self.disjoint_map(3)
parts = self.parts(mp_description)
if not return_template:
return parts
else:
templates = []
for i, part in enumerate(parts):
template = part.copy()
template.maps[3].fields = [tfields.Tensors(mp_description[1][i])]
templates.append(template)
return parts, templates
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] |
21,112 | tfields.core | distances |
Args:
other(Iterable)
**kwargs:
... is forwarded to scipy.spatial.distance.cdist
Examples:
>>> import tfields
>>> p = tfields.Tensors.grid((0, 2, 3j),
... (0, 2, 3j),
... (0, 0, 1j))
>>> p[4,2] = 1
>>> p.distances(p)[0,0]
0.0
>>> p.distances(p)[5,1]
1.4142135623730951
>>> p.distances([[0,1,2]])[-1][0] == 3
True
| def distances(self, other, **kwargs):
"""
Args:
other(Iterable)
**kwargs:
... is forwarded to scipy.spatial.distance.cdist
Examples:
>>> import tfields
>>> p = tfields.Tensors.grid((0, 2, 3j),
... (0, 2, 3j),
... (0, 0, 1j))
>>> p[4,2] = 1
>>> p.distances(p)[0,0]
0.0
>>> p.distances(p)[5,1]
1.4142135623730951
>>> p.distances([[0,1,2]])[-1][0] == 3
True
"""
if issubclass(type(other), Tensors) and self.coord_sys != other.coord_sys:
other = other.copy()
other.transform(self.coord_sys)
return scipy.spatial.distance.cdist(self, other, **kwargs)
| (self, other, **kwargs) | [
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21,113 | tfields.core | dot |
Computes the n-d dot product between self and other defined as in
`mathematica <https://reference.wolfram.com/legacy/v5/Built-inFunctions/
AdvancedDocumentation/LinearAlgebra/2.7.html>`_
by summing over the last dimension.
When self and b are both one-dimensional vectors, this is just the "usual" dot product;
when self and b are 2D matrices, this is matrix multiplication.
Note:
* This is not the same as the numpy.dot function.
Examples:
>>> import tfields
>>> import numpy as np
Scalar product by transposed dot product
>>> a = tfields.Tensors([[4, 0, 4]])
>>> b = tfields.Tensors([[10, 0, 0.5]])
>>> c = a.t.dot(b)
>>> assert c.equal([42])
>>> assert c.equal(np.dot(a[0], b[0]))
>>> assert c.rank == 0
To get the angle between a and b you now just need
>>> angle = np.arccos(c)
Matrix vector multiplication
>>> a = tfields.Tensors([[[1, 20, 0], [2, 18, 1], [1, 5, 10]]])
>>> b = tfields.Tensors([[1, 2, 3]])
>>> c = a.dot(b)
>>> assert c.equal([[41,41,41]])
TODO: generalize dot product to inner
# Matrix matrix multiplication can not be done like this. It requires
# >>> a = tfields.Tensors([[[1, 8], [2, 4]]])
# >>> b = tfields.Tensors([[[1, 2], [1/2, 1/4]]])
# >>> c = a.dot(b)
# >>> c
# >>> assert c.equal([[[5, 4], [4, 5]]])
TODO: handle types, fields and maps (which fields etc to choose for the output?)
| def dot(self, b, out=None):
# pylint:disable=line-too-long
"""
Computes the n-d dot product between self and other defined as in
`mathematica <https://reference.wolfram.com/legacy/v5/Built-inFunctions/
AdvancedDocumentation/LinearAlgebra/2.7.html>`_
by summing over the last dimension.
When self and b are both one-dimensional vectors, this is just the "usual" dot product;
when self and b are 2D matrices, this is matrix multiplication.
Note:
* This is not the same as the numpy.dot function.
Examples:
>>> import tfields
>>> import numpy as np
Scalar product by transposed dot product
>>> a = tfields.Tensors([[4, 0, 4]])
>>> b = tfields.Tensors([[10, 0, 0.5]])
>>> c = a.t.dot(b)
>>> assert c.equal([42])
>>> assert c.equal(np.dot(a[0], b[0]))
>>> assert c.rank == 0
To get the angle between a and b you now just need
>>> angle = np.arccos(c)
Matrix vector multiplication
>>> a = tfields.Tensors([[[1, 20, 0], [2, 18, 1], [1, 5, 10]]])
>>> b = tfields.Tensors([[1, 2, 3]])
>>> c = a.dot(b)
>>> assert c.equal([[41,41,41]])
TODO: generalize dot product to inner
# Matrix matrix multiplication can not be done like this. It requires
# >>> a = tfields.Tensors([[[1, 8], [2, 4]]])
# >>> b = tfields.Tensors([[[1, 2], [1/2, 1/4]]])
# >>> c = a.dot(b)
# >>> c
# >>> assert c.equal([[[5, 4], [4, 5]]])
TODO: handle types, fields and maps (which fields etc to choose for the output?)
"""
if out is not None:
raise NotImplementedError("performance feature 'out' not yet implemented")
return Tensors(np.einsum("t...i,t...i->t...", self, b))
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21,114 | tfields.core | epsilon_neighbourhood |
Returns:
indices for those sets of points that lie within epsilon around the
other
Examples:
Create mesh grid with one extra point that will have 8 neighbours
within epsilon
>>> import tfields
>>> p = tfields.Tensors.grid((0, 1, 2j),
... (0, 1, 2j),
... (0, 1, 2j))
>>> p = tfields.Tensors.merged(p, [[0.5, 0.5, 0.5]])
>>> [len(en) for en in p.epsilon_neighbourhood(0.9)]
[2, 2, 2, 2, 2, 2, 2, 2, 9]
| def epsilon_neighbourhood(self, epsilon):
"""
Returns:
indices for those sets of points that lie within epsilon around the
other
Examples:
Create mesh grid with one extra point that will have 8 neighbours
within epsilon
>>> import tfields
>>> p = tfields.Tensors.grid((0, 1, 2j),
... (0, 1, 2j),
... (0, 1, 2j))
>>> p = tfields.Tensors.merged(p, [[0.5, 0.5, 0.5]])
>>> [len(en) for en in p.epsilon_neighbourhood(0.9)]
[2, 2, 2, 2, 2, 2, 2, 2, 9]
"""
indices = np.arange(self.shape[0])
dists = self.distances(self) # this takes long
dists_in_epsilon = dists <= epsilon
indices = [indices[die] for die in dists_in_epsilon] # this takes long
return indices
| (self, epsilon) | [
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21,115 | tfields.core | equal |
Test, whether the instance has the same content as other.
Args:
other (iterable)
optional:
see TensorFields.equal
Examples:
>>> import tfields
>>> maps = [tfields.TensorFields([[1]], [42])]
>>> tm = tfields.TensorMaps(maps[0], maps=maps)
# >>> assert tm.equal(tm)
>>> cp = tm.copy()
# >>> assert tm.equal(cp)
>>> cp.maps[1].fields[0] = -42
>>> assert tm.maps[1].fields[0] == 42
>>> assert not tm.equal(cp)
| def equal(self, other, **kwargs):
"""
Test, whether the instance has the same content as other.
Args:
other (iterable)
optional:
see TensorFields.equal
Examples:
>>> import tfields
>>> maps = [tfields.TensorFields([[1]], [42])]
>>> tm = tfields.TensorMaps(maps[0], maps=maps)
# >>> assert tm.equal(tm)
>>> cp = tm.copy()
# >>> assert tm.equal(cp)
>>> cp.maps[1].fields[0] = -42
>>> assert tm.maps[1].fields[0] == 42
>>> assert not tm.equal(cp)
"""
if not issubclass(type(other), Tensors):
return super(TensorMaps, self).equal(other, **kwargs)
with other.tmp_transform(self.coord_sys):
mask = super(TensorMaps, self).equal(other, **kwargs)
if issubclass(type(other), TensorMaps):
mask &= self.maps.equal(other.maps, **kwargs)
return mask
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21,116 | tfields.core | evalf |
Args:
expression (sympy logical expression)
coord_sys (str): coord_sys to evalfuate the expression in.
Returns:
np.ndarray: mask of dtype bool with lenght of number of points in
self. This array is True, where expression evalfuates True.
Examples:
>>> import tfields
>>> import numpy as np
>>> import sympy
>>> x, y, z = sympy.symbols('x y z')
>>> p = tfields.Tensors([[1., 2., 3.], [4., 5., 6.], [1, 2, -6],
... [-5, -5, -5], [1,0,-1], [0,1,-1]])
>>> np.array_equal(p.evalf(x > 0),
... [True, True, True, False, True, False])
True
>>> np.array_equal(p.evalf(x >= 0),
... [True, True, True, False, True, True])
True
And combination
>>> np.array_equal(p.evalf((x > 0) & (y < 3)),
... [True, False, True, False, True, False])
True
Or combination
>>> np.array_equal(p.evalf((x > 0) | (y > 3)),
... [True, True, True, False, True, False])
True
| def evalf(self, expression=None, coord_sys=None):
"""
Args:
expression (sympy logical expression)
coord_sys (str): coord_sys to evalfuate the expression in.
Returns:
np.ndarray: mask of dtype bool with lenght of number of points in
self. This array is True, where expression evalfuates True.
Examples:
>>> import tfields
>>> import numpy as np
>>> import sympy
>>> x, y, z = sympy.symbols('x y z')
>>> p = tfields.Tensors([[1., 2., 3.], [4., 5., 6.], [1, 2, -6],
... [-5, -5, -5], [1,0,-1], [0,1,-1]])
>>> np.array_equal(p.evalf(x > 0),
... [True, True, True, False, True, False])
True
>>> np.array_equal(p.evalf(x >= 0),
... [True, True, True, False, True, True])
True
And combination
>>> np.array_equal(p.evalf((x > 0) & (y < 3)),
... [True, False, True, False, True, False])
True
Or combination
>>> np.array_equal(p.evalf((x > 0) | (y > 3)),
... [True, True, True, False, True, False])
True
"""
coords = sympy.symbols("x y z")
with self.tmp_transform(coord_sys or self.coord_sys):
mask = tfields.evalf(np.array(self), expression, coords=coords)
return mask
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21,117 | tfields.mesh_3d | in_faces |
Check whether points lie within triangles with Barycentric Technique
see Triangles3D.in_triangles. If multiple requests are done on huge meshes,
this can be hugely optimized by requesting the property
self.tree or setting it to self.tree = <saved tree> before calling in_faces.
| def in_faces(self, points, delta, **kwargs):
"""
Check whether points lie within triangles with Barycentric Technique
see Triangles3D.in_triangles. If multiple requests are done on huge meshes,
this can be hugely optimized by requesting the property
self.tree or setting it to self.tree = <saved tree> before calling in_faces.
"""
key = "mesh_tree"
if hasattr(self, "_cache") and key in self._cache:
log = logging.getLogger()
log.info("Using cached decision tree to speed up point - face mapping.")
indices = self.tree.in_faces(points, delta, **kwargs)
else:
indices = self.triangles().in_triangles(points, delta, **kwargs)
return indices
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21,118 | tfields.core | index |
Args:
tensor
Returns:
int: index of tensor occuring
| def index(self, tensor, **kwargs):
"""
Args:
tensor
Returns:
int: index of tensor occuring
"""
indices = self.indices(tensor, **kwargs)
if not indices:
return None
if len(indices) == 1:
return indices[0]
raise ValueError("Multiple occurences of value {}".format(tensor))
| (self, tensor, **kwargs) | [
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21,119 | tfields.core | indices |
Returns:
list of int: indices of tensor occuring
Examples:
Rank 1 Tensors
>>> import tfields
>>> p = tfields.Tensors([[1,2,3], [4,5,6], [6,7,8], [4,5,6],
... [4.1, 5, 6]])
>>> p.indices([4,5,6])
array([1, 3])
>>> p.indices([4,5,6.1], rtol=1e-5, atol=1e-1)
array([1, 3, 4])
Rank 0 Tensors
>>> p = tfields.Tensors([2, 3, 6, 3.01])
>>> p.indices(3)
array([1])
>>> p.indices(3, rtol=1e-5, atol=1e-1)
array([1, 3])
| def indices(self, tensor, rtol=None, atol=None):
"""
Returns:
list of int: indices of tensor occuring
Examples:
Rank 1 Tensors
>>> import tfields
>>> p = tfields.Tensors([[1,2,3], [4,5,6], [6,7,8], [4,5,6],
... [4.1, 5, 6]])
>>> p.indices([4,5,6])
array([1, 3])
>>> p.indices([4,5,6.1], rtol=1e-5, atol=1e-1)
array([1, 3, 4])
Rank 0 Tensors
>>> p = tfields.Tensors([2, 3, 6, 3.01])
>>> p.indices(3)
array([1])
>>> p.indices(3, rtol=1e-5, atol=1e-1)
array([1, 3])
"""
self_array, other_array = np.asarray(self), np.asarray(tensor)
if rtol is None and atol is None:
equal_method = np.equal
else:
equal_method = lambda a, b: np.isclose(a, b, rtol=rtol, atol=atol) # NOQA
# inspired by
# https://stackoverflow.com/questions/19228295/find-ordered-vector-in-numpy-array
if self.rank == 0:
indices = np.where(equal_method((self_array - other_array), 0))[0]
elif self.rank == 1:
indices = np.where(
np.all(equal_method((self_array - other_array), 0), axis=1)
)[0]
else:
raise NotImplementedError()
return indices
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] |
21,120 | tfields.core | keep |
Return copy of self with vertices where keep_condition is True
Copy because self is immutable
Examples:
>>> import numpy as np
>>> import tfields
>>> m = tfields.TensorMaps(
... [[0,0,0], [1,1,1], [2,2,2], [0,0,0],
... [3,3,3], [4,4,4], [5,5,5]],
... maps=[tfields.TensorFields([[0, 1, 2], [0, 1, 3],
... [3, 4, 5], [3, 4, 1],
... [3, 4, 6]],
... [1, 3, 5, 7, 9],
... [2, 4, 6, 8, 0])])
>>> c = m.removed([True, True, True, False, False, False, False])
>>> c.equal([[0, 0, 0],
... [3, 3, 3],
... [4, 4, 4],
... [5, 5, 5]])
True
>>> assert c.maps[3].equal(np.array([[0, 1, 2], [0, 1, 3]]))
>>> assert c.maps[3].fields[0].equal([5, 9])
>>> assert c.maps[3].fields[1].equal([6, 0])
| def keep(self, keep_condition):
"""
Return copy of self with vertices where keep_condition is True
Copy because self is immutable
Examples:
>>> import numpy as np
>>> import tfields
>>> m = tfields.TensorMaps(
... [[0,0,0], [1,1,1], [2,2,2], [0,0,0],
... [3,3,3], [4,4,4], [5,5,5]],
... maps=[tfields.TensorFields([[0, 1, 2], [0, 1, 3],
... [3, 4, 5], [3, 4, 1],
... [3, 4, 6]],
... [1, 3, 5, 7, 9],
... [2, 4, 6, 8, 0])])
>>> c = m.removed([True, True, True, False, False, False, False])
>>> c.equal([[0, 0, 0],
... [3, 3, 3],
... [4, 4, 4],
... [5, 5, 5]])
True
>>> assert c.maps[3].equal(np.array([[0, 1, 2], [0, 1, 3]]))
>>> assert c.maps[3].fields[0].equal([5, 9])
>>> assert c.maps[3].fields[1].equal([6, 0])
"""
keep_condition = np.array(keep_condition)
return self[keep_condition]
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21,121 | tfields.core | main_axes |
Returns:
Main Axes eigen-vectors
| def main_axes(self, weights=None):
"""
Returns:
Main Axes eigen-vectors
"""
# weights = self.getNormedWeightedAreas(weights=weights)
weights = self._weights(weights)
mean = np.array(self).mean(axis=0)
relative_coords = self - mean
cov = np.cov(relative_coords.T, ddof=0, aweights=weights)
# calculate eigenvalues and eigenvectors of covariance
evalfs, evecs = np.linalg.eigh(cov)
return (evecs * evalfs.T).T
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21,122 | tfields.core | min_dists |
Args:
other(array | None): if None: closest distance to self
**kwargs:
memory_saving (bool): for very large array comparisons
default False
... rest is forwarded to scipy.spatial.distance.cdist
Returns:
np.array: minimal distances of self to other
Examples:
>>> import tfields
>>> import numpy as np
>>> p = tfields.Tensors.grid((0, 2, 3),
... (0, 2, 3),
... (0, 0, 1))
>>> p[4,2] = 1
>>> dMin = p.min_dists()
>>> expected = [1] * 9
>>> expected[4] = np.sqrt(2)
>>> np.array_equal(dMin, expected)
True
>>> dMin2 = p.min_dists(memory_saving=True)
>>> bool((dMin2 == dMin).all())
True
| def min_dists(self, other=None, **kwargs):
"""
Args:
other(array | None): if None: closest distance to self
**kwargs:
memory_saving (bool): for very large array comparisons
default False
... rest is forwarded to scipy.spatial.distance.cdist
Returns:
np.array: minimal distances of self to other
Examples:
>>> import tfields
>>> import numpy as np
>>> p = tfields.Tensors.grid((0, 2, 3),
... (0, 2, 3),
... (0, 0, 1))
>>> p[4,2] = 1
>>> dMin = p.min_dists()
>>> expected = [1] * 9
>>> expected[4] = np.sqrt(2)
>>> np.array_equal(dMin, expected)
True
>>> dMin2 = p.min_dists(memory_saving=True)
>>> bool((dMin2 == dMin).all())
True
"""
memory_saving = kwargs.pop("memory_saving", False)
if other is None:
other = self
else:
raise NotImplementedError(
"Should be easy but make shure not to remove diagonal"
)
try:
if memory_saving:
raise MemoryError()
dists = self.distances(other, **kwargs)
return dists[dists > 0].reshape(dists.shape[0], -1).min(axis=1)
except MemoryError:
min_dists = np.empty(self.shape[0])
for i, point in enumerate(np.array(other)):
dists = self.distances([point], **kwargs)
min_dists[i] = dists[dists > 0].reshape(-1).min()
return min_dists
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] |
21,123 | tfields.core | mirror |
Reflect/Mirror the entries meeting <condition> at <coordinate> = 0
Args:
coordinate (int): coordinate index
Examples:
>>> import tfields
>>> p = tfields.Tensors([[1., 2., 3.], [4., 5., 6.], [1, 2, -6]])
>>> p.mirror(1)
>>> assert p.equal([[1, -2, 3], [4, -5, 6], [1, -2, -6]])
multiple coordinates can be mirrored at the same time
i.e. a point mirrorion would be
>>> p = tfields.Tensors([[1., 2., 3.], [4., 5., 6.], [1, 2, -6]])
>>> p.mirror([0,2])
>>> assert p.equal([[-1, 2, -3], [-4, 5, -6], [-1, 2., 6.]])
You can give a condition as mask or as str.
The mirroring will only be applied to the points meeting the
condition.
>>> import sympy
>>> x, y, z = sympy.symbols('x y z')
>>> p.mirror([0, 2], y > 3)
>>> p.equal([[-1, 2, -3], [4, 5, 6], [-1, 2, 6]])
True
| def mirror(self, coordinate, condition=None):
"""
Reflect/Mirror the entries meeting <condition> at <coordinate> = 0
Args:
coordinate (int): coordinate index
Examples:
>>> import tfields
>>> p = tfields.Tensors([[1., 2., 3.], [4., 5., 6.], [1, 2, -6]])
>>> p.mirror(1)
>>> assert p.equal([[1, -2, 3], [4, -5, 6], [1, -2, -6]])
multiple coordinates can be mirrored at the same time
i.e. a point mirrorion would be
>>> p = tfields.Tensors([[1., 2., 3.], [4., 5., 6.], [1, 2, -6]])
>>> p.mirror([0,2])
>>> assert p.equal([[-1, 2, -3], [-4, 5, -6], [-1, 2., 6.]])
You can give a condition as mask or as str.
The mirroring will only be applied to the points meeting the
condition.
>>> import sympy
>>> x, y, z = sympy.symbols('x y z')
>>> p.mirror([0, 2], y > 3)
>>> p.equal([[-1, 2, -3], [4, 5, 6], [-1, 2, 6]])
True
"""
if condition is None:
condition = np.array([True for i in range(len(self))])
elif isinstance(condition, sympy.Basic):
condition = self.evalf(condition)
if isinstance(coordinate, (list, tuple)):
for coord in coordinate:
self.mirror(coord, condition=condition)
elif isinstance(coordinate, int):
self[:, coordinate][condition] *= -1
else:
raise TypeError()
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21,124 | tfields.core | moment |
Returns:
Moments of the distribution.
Args:
moment (int): n-th moment
Examples:
>>> import tfields
Skalars
>>> t = tfields.Tensors(range(1, 6))
>>> assert t.moment(1) == 0
>>> assert t.moment(1, weights=[-2, -1, 20, 1, 2]) == 0.5
>>> assert t.moment(2, weights=[0.25, 1, 17.5, 1, 0.25]) == 0.2
Vectors
>>> t = tfields.Tensors(list(zip(range(1, 6), range(1, 6))))
>>> assert tfields.Tensors([0.5, 0.5]).equal(
... t.moment(1, weights=[-2, -1, 20, 1, 2]))
>>> assert tfields.Tensors([1. , 0.5]).equal(
... t.moment(1, weights=list(zip([-2, -1, 10, 1, 2],
... [-2, -1, 20, 1, 2]))))
| def moment(self, moment, weights=None):
"""
Returns:
Moments of the distribution.
Args:
moment (int): n-th moment
Examples:
>>> import tfields
Skalars
>>> t = tfields.Tensors(range(1, 6))
>>> assert t.moment(1) == 0
>>> assert t.moment(1, weights=[-2, -1, 20, 1, 2]) == 0.5
>>> assert t.moment(2, weights=[0.25, 1, 17.5, 1, 0.25]) == 0.2
Vectors
>>> t = tfields.Tensors(list(zip(range(1, 6), range(1, 6))))
>>> assert tfields.Tensors([0.5, 0.5]).equal(
... t.moment(1, weights=[-2, -1, 20, 1, 2]))
>>> assert tfields.Tensors([1. , 0.5]).equal(
... t.moment(1, weights=list(zip([-2, -1, 10, 1, 2],
... [-2, -1, 20, 1, 2]))))
"""
array = tfields.lib.stats.moment(self, moment, weights=weights)
if self.rank == 0: # scalar
array = [array]
return Tensors(array, coord_sys=self.coord_sys)
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21,125 | tfields.mesh_3d | nfaces | null | def nfaces(self):
return self.faces.shape[0]
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21,126 | tfields.core | norm |
Calculate the norm up to rank 2
Args:
See numpy.linal.norm except redefinition in axis
axis: by default omitting first axis
Examples:
>>> import tfields
>>> a = tfields.Tensors([[1, 0, 0]])
>>> assert a.norm().equal([1])
| def norm(self, ord=None, axis=None, keepdims=False):
"""
Calculate the norm up to rank 2
Args:
See numpy.linal.norm except redefinition in axis
axis: by default omitting first axis
Examples:
>>> import tfields
>>> a = tfields.Tensors([[1, 0, 0]])
>>> assert a.norm().equal([1])
"""
if axis is None:
axis = tuple(range(self.ndim)[1:])
return Tensors(np.linalg.norm(self, ord=ord, axis=axis, keepdims=keepdims))
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] |
21,127 | tfields.core | normalized |
Return the self / norm(self)
Args:
forwarded to :meth:norm
Examples:
>>> import tfields
>>> a = tfields.Tensors([[1, 4, 3]])
>>> assert not a.norm().equal([1])
>>> a = a.normalized()
>>> assert a.norm().equal([1])
>>> a = tfields.Tensors([[1, 0, 0],
... [0, 2, 0],
... [0, 0, 3]])
>>> assert a.norm().equal([1, 2, 3])
>>> a = a.normalized()
>>> assert a.equal([
... [1, 0, 0],
... [0, 1, 0],
... [0, 0, 1],
... ])
>>> assert a.norm().equal([1, 1, 1])
| def normalized(self, *args, **kwargs):
"""
Return the self / norm(self)
Args:
forwarded to :meth:norm
Examples:
>>> import tfields
>>> a = tfields.Tensors([[1, 4, 3]])
>>> assert not a.norm().equal([1])
>>> a = a.normalized()
>>> assert a.norm().equal([1])
>>> a = tfields.Tensors([[1, 0, 0],
... [0, 2, 0],
... [0, 0, 3]])
>>> assert a.norm().equal([1, 2, 3])
>>> a = a.normalized()
>>> assert a.equal([
... [1, 0, 0],
... [0, 1, 0],
... [0, 0, 1],
... ])
>>> assert a.norm().equal([1, 1, 1])
"""
# return np.divide(self.T, self.norm(*args, **kwargs)).T
return np.divide(self, self.norm(*args, **kwargs)[:, None])
| (self, *args, **kwargs) | [
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] |
21,128 | tfields.core | parts |
Args:
*map_descriptions (Tuple(int, List(List(int)))): tuples of
map_dim (int): reference to map position
used like: self.maps[map_dim]
map_indices_list (List(List(int))): each int refers
to index in a map.
Returns:
List(cls): One TensorMaps or TensorMaps subclass per
map_description
| def parts(self, *map_descriptions):
"""
Args:
*map_descriptions (Tuple(int, List(List(int)))): tuples of
map_dim (int): reference to map position
used like: self.maps[map_dim]
map_indices_list (List(List(int))): each int refers
to index in a map.
Returns:
List(cls): One TensorMaps or TensorMaps subclass per
map_description
"""
parts = []
for map_description in map_descriptions:
map_dim, map_indices_list = map_description
for map_indices in map_indices_list:
obj = self.copy()
map_indices = set(map_indices) # for speed up
map_delete_mask = np.array(
[i not in map_indices for i in range(len(self.maps[map_dim]))]
)
obj.maps[map_dim] = obj.maps[map_dim][~map_delete_mask]
obj = obj.cleaned(duplicates=False)
parts.append(obj)
return parts
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] |
21,129 | tfields.core | paths |
Find the minimal amount of graphs building the original graph with
maximum of two links per node i.e.
"o-----o o-----o"
" \ / \ /"
"" \ / \ /""
"o--o--o o--o 8--o"
| |
| = | + +
o o o
/ \ / \
/ \ / \
o o o o
where 8 is a duplicated node (one has two links and one has only one.)
Examples:
>>> import tfields
Ascii figure above:
>>> a = tfields.TensorMaps([[1, 0], [3, 0], [2, 2], [0, 4], [2, 4],
... [4, 4], [1, 6], [3, 6], [2, 2]],
... maps=[[[0, 2], [2, 4], [3, 4], [5, 4],
... [1, 8], [6, 4], [6, 7], [7, 4]]])
>>> paths = a.paths(2)
>>> assert paths[0].equal([[ 1., 0.],
... [ 2., 2.],
... [ 2., 4.],
... [ 0., 4.]])
>>> assert paths[0].maps[4].equal([[ 0., 1., 2., 3.]])
>>> assert paths[1].equal([[ 4., 4.],
... [ 2., 4.],
... [ 1., 6.],
... [ 3., 6.],
... [ 2., 4.]])
>>> assert paths[2].equal([[ 3., 0.],
... [ 2., 2.]])
Note:
The Longest path problem is a NP-hard problem.
| def paths(
self, map_dim
): # noqa: E501 pylint: disable=too-many-locals,too-many-branches,too-many-statements
"""
Find the minimal amount of graphs building the original graph with
maximum of two links per node i.e.
"o-----o o-----o"
" \\ / \\ /"
"" \\ / \\ /""
"o--o--o o--o 8--o"
| |
| = | + +
o o o
/ \\ / \\
/ \\ / \\
o o o o
where 8 is a duplicated node (one has two links and one has only one.)
Examples:
>>> import tfields
Ascii figure above:
>>> a = tfields.TensorMaps([[1, 0], [3, 0], [2, 2], [0, 4], [2, 4],
... [4, 4], [1, 6], [3, 6], [2, 2]],
... maps=[[[0, 2], [2, 4], [3, 4], [5, 4],
... [1, 8], [6, 4], [6, 7], [7, 4]]])
>>> paths = a.paths(2)
>>> assert paths[0].equal([[ 1., 0.],
... [ 2., 2.],
... [ 2., 4.],
... [ 0., 4.]])
>>> assert paths[0].maps[4].equal([[ 0., 1., 2., 3.]])
>>> assert paths[1].equal([[ 4., 4.],
... [ 2., 4.],
... [ 1., 6.],
... [ 3., 6.],
... [ 2., 4.]])
>>> assert paths[2].equal([[ 3., 0.],
... [ 2., 2.]])
Note:
The Longest path problem is a NP-hard problem.
"""
obj = self.cleaned()
flat_map = np.array(obj.maps[map_dim].flat)
values, counts = np.unique(flat_map, return_counts=True)
counts = dict(zip(values, counts))
# last is a helper
last = np.full(max(flat_map) + 1, -3, dtype=int)
duplicat_indices = []
d_index = len(obj)
for i, val in enumerate(flat_map.copy()):
if counts[val] > 2:
# The first two occurences are uncritical
if last[val] < -1:
last[val] += 1
continue
# Now we talk about nodes with more than two edges
if last[val] == -1:
# append a point and re-link
duplicat_indices.append(val)
flat_map[i] = d_index
last[val] = d_index
d_index += 1
else:
# last occurence of val was a duplicate, so we use the same
# value again.
flat_map[i] = last[val]
last[val] = -1
if duplicat_indices:
duplicates = obj[duplicat_indices]
obj = type(obj).merged(obj, duplicates)
obj.maps = [flat_map.reshape(-1, *obj.maps[map_dim].shape[1:])]
paths = obj.parts(obj.disjoint_map(map_dim))
# remove duplicate map entries and sort
sorted_paths = []
for path in paths:
# find start index
values, counts = np.unique(path.maps[map_dim].flat, return_counts=True)
first_node = None
for value, count in zip(values, counts):
if count == 1:
first_node = value
break
edges = [list(edge) for edge in path.maps[map_dim]]
if first_node is None:
first_node = 0 # edges[0][0]
path = path[list(range(len(path))) + [0]]
found_first_node = False
for edge in edges:
if first_node in edge:
if found_first_node:
edge[edge.index(first_node)] = len(path) - 1
break
found_first_node = True
# follow the edges until you hit the end
chain = [first_node]
visited = set()
n_edges = len(edges)
node = first_node
while len(visited) < n_edges:
for i, edge in enumerate(edges):
if i in visited:
continue
if node not in edge:
continue
# found edge
visited.add(i)
if edge.index(node) != 0:
edge = list(reversed(edge))
chain.extend(edge[1:])
node = edge[-1]
path = path[chain]
path_map = Maps.to_map([sorted(chain)])
path.maps[dim(path_map)] = path_map
sorted_paths.append(path)
paths = sorted_paths
return paths
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21,130 | tfields.mesh_3d | planes | null | def planes(self):
return self._planes
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21,131 | tfields.core | plot |
Generic plotting method of TensorMaps.
Args:
*args:
Depending on Positional arguments passed to the underlying
:func:`rna.plotting.plot_tensor_map` function for arbitrary .
dim (int): dimension of the plot representation (axes).
map (int): index of the map to plot (default is 3).
edgecolor (color): color of the edges (dim = 3)
| def plot(self, *args, **kwargs): # pragma: no cover
"""
Generic plotting method of TensorMaps.
Args:
*args:
Depending on Positional arguments passed to the underlying
:func:`rna.plotting.plot_tensor_map` function for arbitrary .
dim (int): dimension of the plot representation (axes).
map (int): index of the map to plot (default is 3).
edgecolor (color): color of the edges (dim = 3)
"""
scalars_demanded = (
"color" not in kwargs
and "facecolors" not in kwargs
and any(v in kwargs for v in ["vmin", "vmax", "cmap"])
)
map_ = self.maps[kwargs.pop("map", 3)]
map_index = kwargs.pop("map_index", None if not scalars_demanded else 0)
if map_index is not None:
if not len(map_) == 0:
kwargs["color"] = map_.fields[map_index]
if map_.dim == 3:
# TODO: Mesh plotting only for Mesh3D().
# Overload this function for Mesh3D() specifically.
return rna.plotting.plot_mesh(self, *args, map_, **kwargs)
return rna.plotting.plot_tensor_map(self, *args, map_, **kwargs)
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