# encoding: utf-8 # module contourpy._contourpy # from C:\Programs\Python\Python313\Lib\site-packages\contourpy\_contourpy.cp313-win_amd64.pyd # by generator 1.147 """ C++11 extension module wrapped using `pybind11`_. It should not be necessary to access classes and functions in this extension module directly. Instead, :func:`~contourpy.contour_generator` should be used to create :class:`~.ContourGenerator` objects, and the enums (:class:`~.FillType`, :class:`~.LineType` and :class:`~.ZInterp`) and :func:`.max_threads` function are all available in the :mod:`contourpy` module. """ # imports import pybind11_builtins as __pybind11_builtins # Variables with simple values NDEBUG = 1 __version__ = '1.3.1' # functions def max_threads(): # real signature unknown; restored from __doc__ """ max_threads() -> int Return the maximum number of threads, obtained from ``std::thread::hardware_concurrency()``. This is the number of threads used by a multithreaded ContourGenerator if the kwarg ``threads=0`` is passed to :func:`~.contour_generator`. """ return 0 # classes class ContourGenerator(__pybind11_builtins.pybind11_object): """ Abstract base class for contour generator classes, defining the interface that they all implement. """ def create_contour(self, level): # real signature unknown; restored from __doc__ """ create_contour(self: contourpy._contourpy.ContourGenerator, level: float) -> Sequence Synonym for :meth:`~.ContourGenerator.lines` to provide backward compatibility with Matplotlib. """ return () def create_filled_contour(self, lower_level, upper_level): # real signature unknown; restored from __doc__ """ create_filled_contour(self: contourpy._contourpy.ContourGenerator, lower_level: float, upper_level: float) -> tuple Synonym for :meth:`~.ContourGenerator.filled` to provide backward compatibility with Matplotlib. """ return () def filled(self, lower_level, upper_level): # real signature unknown; restored from __doc__ """ filled(self: contourpy._contourpy.ContourGenerator, lower_level: float, upper_level: float) -> tuple Calculate and return filled contours between two levels. Args: lower_level (float): Lower z-level of the filled contours, cannot be ``np.nan``. upper_level (float): Upper z-level of the filled contours, cannot be ``np.nan``. Return: Filled contour polygons as nested sequences of numpy arrays. The exact format is determined by the :attr:`~.ContourGenerator.fill_type` used by the ``ContourGenerator`` and the options are explained at :ref:`fill_type`. Raises a ``ValueError`` if ``lower_level >= upper_level`` or if ``lower_level`` or ``upper_level`` are ``np.nan``. To return filled contours below a ``level`` use ``filled(-np.inf, level)``. To return filled contours above a ``level`` use ``filled(level, np.inf)`` """ return () def lines(self, level): # real signature unknown; restored from __doc__ """ lines(self: contourpy._contourpy.ContourGenerator, level: float) -> Sequence Calculate and return contour lines at a particular level. Args: level (float): z-level to calculate contours at. Return: Contour lines (open line strips and closed line loops) as nested sequences of numpy arrays. The exact format is determined by the :attr:`~.ContourGenerator.line_type` used by the ``ContourGenerator`` and the options are explained at :ref:`line_type`. ``level`` may be ``np.nan``, ``np.inf`` or ``-np.inf``; they all return the same result which is an empty line set. """ return () def multi_filled(self, levels, numpy_float64=None): # real signature unknown; restored from __doc__ """ multi_filled(self: contourpy._contourpy.ContourGenerator, levels: numpy.ndarray[numpy.float64]) -> list Calculate and return filled contours between multiple pairs of adjacent levels. Args: levels (array-like of floats): z-levels to calculate filled contours between. There must be at least 2 levels, they cannot be NaN, and each level must be larger than the previous level. Return: List of filled contours, one per pair of levels. The length of the returned list is one less than ``len(levels)``. The format of returned contours is determined by the ``fill_type`` used by the ``ContourGenerator`` and the options are explained at :ref:`fill_type`. Raises a ``ValueError`` if ``levels`` are not increasing, or contain a NaN, or there are fewer than 2 ``levels``. Calling: .. code-block:: python ret = cont_gen.multi_filled(levels) is equivalent to: .. code-block:: python ret = [cont_gen.filled(lower, upper) for lower, upper in zip(levels[:-1], levels[1:])] .. versionadded:: 1.3.0 """ return [] def multi_lines(self, levels, numpy_float64=None): # real signature unknown; restored from __doc__ """ multi_lines(self: contourpy._contourpy.ContourGenerator, levels: numpy.ndarray[numpy.float64]) -> list Calculate and return contour lines at multiple levels. Args: levels (array-like of floats): z-levels to calculate contours at. Return: List of contour lines, one per level. The format of returned lines is determined by the ``line_type`` used by the ``ContourGenerator`` and the options are explained at :ref:`line_type`. Calling: .. code-block:: python ret = cont_gen.multi_lines(levels) is equivalent to: .. code-block:: python ret = [cont_gen.lines(level) for level in levels] .. versionadded:: 1.3.0 """ return [] def supports_corner_mask(self): # real signature unknown; restored from __doc__ """ supports_corner_mask() -> bool Return whether this algorithm supports ``corner_mask``. """ return False def supports_fill_type(self, fill_type): # real signature unknown; restored from __doc__ """ supports_fill_type(fill_type: contourpy._contourpy.FillType) -> bool Return whether this algorithm supports a particular :class:`~.FillType`. """ return False def supports_line_type(self, line_type): # real signature unknown; restored from __doc__ """ supports_line_type(line_type: contourpy._contourpy.LineType) -> bool Return whether this algorithm supports a particular :class:`~.LineType`. """ return False def supports_quad_as_tri(self): # real signature unknown; restored from __doc__ """ supports_quad_as_tri() -> bool Return whether this algorithm supports ``quad_as_tri``. """ return False def supports_threads(self): # real signature unknown; restored from __doc__ """ supports_threads() -> bool Return whether this algorithm supports the use of threads. """ return False def supports_z_interp(self): # real signature unknown; restored from __doc__ """ supports_z_interp() -> bool Return whether this algorithm supports ``z_interp`` values other than ``ZInterp.Linear`` which all support. """ return False def _pybind11_conduit_v1_(self, *args, **kwargs): # real signature unknown pass def __init__(self, *args, **kwargs): # real signature unknown pass chunk_count = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Return tuple of (y, x) chunk counts.""" chunk_size = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Return tuple of (y, x) chunk sizes.""" corner_mask = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Return whether ``corner_mask`` is set or not.""" fill_type = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Return the :class:`~.FillType`.""" line_type = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Return the :class:`~.LineType`.""" quad_as_tri = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Return whether ``quad_as_tri`` is set or not.""" thread_count = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Return the number of threads used.""" z_interp = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Return the ``ZInterp``.""" default_fill_type = None # (!) real value is '' default_line_type = None # (!) real value is '' class FillType(__pybind11_builtins.pybind11_object): """ Enum used for ``fill_type`` keyword argument in :func:`~.contour_generator`. This controls the format of filled contour data returned from :meth:`~.ContourGenerator.filled`. Members: OuterCode OuterOffset ChunkCombinedCode ChunkCombinedOffset ChunkCombinedCodeOffset ChunkCombinedOffsetOffset """ def _pybind11_conduit_v1_(self, *args, **kwargs): # real signature unknown pass def __eq__(self, other): # real signature unknown; restored from __doc__ """ __eq__(self: object, other: object) -> bool """ return False def __getstate__(self): # real signature unknown; restored from __doc__ """ __getstate__(self: object) -> int """ return 0 def __hash__(self): # real signature unknown; restored from __doc__ """ __hash__(self: object) -> int """ return 0 def __index__(self): # real signature unknown; restored from __doc__ """ __index__(self: contourpy._contourpy.FillType) -> int """ return 0 def __init__(self, value): # real signature unknown; restored from __doc__ """ __init__(self: contourpy._contourpy.FillType, value: int) -> None """ pass def __int__(self): # real signature unknown; restored from __doc__ """ __int__(self: contourpy._contourpy.FillType) -> int """ return 0 def __ne__(self, other): # real signature unknown; restored from __doc__ """ __ne__(self: object, other: object) -> bool """ return False def __repr__(self): # real signature unknown; restored from __doc__ """ __repr__(self: object) -> str """ return "" def __setstate__(self, state): # real signature unknown; restored from __doc__ """ __setstate__(self: contourpy._contourpy.FillType, state: int) -> None """ pass def __str__(self): # real signature unknown; restored from __doc__ """ __str__(self: object) -> str """ return "" name = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """name(self: object) -> str """ value = property(lambda self: object(), lambda self, v: None, lambda self: None) # default ChunkCombinedCode = None # (!) real value is '' ChunkCombinedCodeOffset = None # (!) forward: ChunkCombinedCodeOffset, real value is '' ChunkCombinedOffset = None # (!) real value is '' ChunkCombinedOffsetOffset = None # (!) forward: ChunkCombinedOffsetOffset, real value is '' OuterCode = None # (!) forward: OuterCode, real value is '' OuterOffset = None # (!) forward: OuterOffset, real value is '' __entries = { 'ChunkCombinedCode': ( None, # (!) real value is '' None, ), 'ChunkCombinedCodeOffset': ( None, # (!) forward: ChunkCombinedCodeOffset, real value is '' None, ), 'ChunkCombinedOffset': ( None, # (!) real value is '' None, ), 'ChunkCombinedOffsetOffset': ( None, # (!) forward: ChunkCombinedOffsetOffset, real value is '' None, ), 'OuterCode': ( None, # (!) forward: OuterCode, real value is '' None, ), 'OuterOffset': ( None, # (!) forward: OuterOffset, real value is '' None, ), } __members__ = { 'ChunkCombinedCode': None, # (!) real value is '' 'ChunkCombinedCodeOffset': None, # (!) forward: ChunkCombinedCodeOffset, real value is '' 'ChunkCombinedOffset': None, # (!) real value is '' 'ChunkCombinedOffsetOffset': None, # (!) forward: ChunkCombinedOffsetOffset, real value is '' 'OuterCode': None, # (!) forward: OuterCode, real value is '' 'OuterOffset': None, # (!) forward: OuterOffset, real value is '' } class LineType(__pybind11_builtins.pybind11_object): """ Enum used for ``line_type`` keyword argument in :func:`~.contour_generator`. This controls the format of contour line data returned from :meth:`~.ContourGenerator.lines`. ``LineType.ChunkCombinedNan`` added in version 1.2.0 Members: Separate SeparateCode ChunkCombinedCode ChunkCombinedOffset ChunkCombinedNan """ def _pybind11_conduit_v1_(self, *args, **kwargs): # real signature unknown pass def __eq__(self, other): # real signature unknown; restored from __doc__ """ __eq__(self: object, other: object) -> bool """ return False def __getstate__(self): # real signature unknown; restored from __doc__ """ __getstate__(self: object) -> int """ return 0 def __hash__(self): # real signature unknown; restored from __doc__ """ __hash__(self: object) -> int """ return 0 def __index__(self): # real signature unknown; restored from __doc__ """ __index__(self: contourpy._contourpy.LineType) -> int """ return 0 def __init__(self, value): # real signature unknown; restored from __doc__ """ __init__(self: contourpy._contourpy.LineType, value: int) -> None """ pass def __int__(self): # real signature unknown; restored from __doc__ """ __int__(self: contourpy._contourpy.LineType) -> int """ return 0 def __ne__(self, other): # real signature unknown; restored from __doc__ """ __ne__(self: object, other: object) -> bool """ return False def __repr__(self): # real signature unknown; restored from __doc__ """ __repr__(self: object) -> str """ return "" def __setstate__(self, state): # real signature unknown; restored from __doc__ """ __setstate__(self: contourpy._contourpy.LineType, state: int) -> None """ pass def __str__(self): # real signature unknown; restored from __doc__ """ __str__(self: object) -> str """ return "" name = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """name(self: object) -> str """ value = property(lambda self: object(), lambda self, v: None, lambda self: None) # default ChunkCombinedCode = None # (!) forward: ChunkCombinedCode, real value is '' ChunkCombinedNan = None # (!) forward: ChunkCombinedNan, real value is '' ChunkCombinedOffset = None # (!) forward: ChunkCombinedOffset, real value is '' Separate = None # (!) forward: Separate, real value is '' SeparateCode = None # (!) forward: SeparateCode, real value is '' __entries = { 'ChunkCombinedCode': ( None, # (!) forward: ChunkCombinedCode, real value is '' None, ), 'ChunkCombinedNan': ( None, # (!) forward: ChunkCombinedNan, real value is '' None, ), 'ChunkCombinedOffset': ( None, # (!) forward: ChunkCombinedOffset, real value is '' None, ), 'Separate': ( None, # (!) forward: Separate, real value is '' None, ), 'SeparateCode': ( None, # (!) forward: SeparateCode, real value is '' None, ), } __members__ = { 'ChunkCombinedCode': None, # (!) forward: ChunkCombinedCode, real value is '' 'ChunkCombinedNan': None, # (!) forward: ChunkCombinedNan, real value is '' 'ChunkCombinedOffset': None, # (!) forward: ChunkCombinedOffset, real value is '' 'Separate': None, # (!) forward: Separate, real value is '' 'SeparateCode': None, # (!) forward: SeparateCode, real value is '' } class Mpl2005ContourGenerator(ContourGenerator): """ ContourGenerator corresponding to ``name="mpl2005"``. This is the original 2005 Matplotlib algorithm. Does not support any of ``corner_mask``, ``quad_as_tri``, ``threads`` or ``z_interp``. Only supports ``line_type=LineType.SeparateCode`` and ``fill_type=FillType.OuterCode``. Only supports chunking for filled contours, not contour lines. .. warning:: This algorithm is in ``contourpy`` for historic comparison. No new features or bug fixes will be added to it, except for security-related bug fixes. """ def supports_fill_type(self, arg0): # real signature unknown; restored from __doc__ """ supports_fill_type(arg0: contourpy._contourpy.FillType) -> bool Return whether this algorithm supports a particular :class:`~.FillType`. """ return False def supports_line_type(self, arg0): # real signature unknown; restored from __doc__ """ supports_line_type(arg0: contourpy._contourpy.LineType) -> bool Return whether this algorithm supports a particular :class:`~.LineType`. """ return False def _pybind11_conduit_v1_(self, *args, **kwargs): # real signature unknown pass def __init__(self, x, numpy_float64=None, *args, **kwargs): # real signature unknown; NOTE: unreliably restored from __doc__ """ __init__(self: contourpy._contourpy.Mpl2005ContourGenerator, x: numpy.ndarray[numpy.float64], y: numpy.ndarray[numpy.float64], z: numpy.ndarray[numpy.float64], mask: numpy.ndarray[bool], *, x_chunk_size: int = 0, y_chunk_size: int = 0) -> None """ pass chunk_count = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Return tuple of (y, x) chunk counts.""" chunk_size = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Return tuple of (y, x) chunk sizes.""" fill_type = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Return the :class:`~.FillType`.""" line_type = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Return the :class:`~.LineType`.""" default_fill_type = None # (!) real value is '' default_line_type = None # (!) real value is '' class Mpl2014ContourGenerator(ContourGenerator): """ ContourGenerator corresponding to ``name="mpl2014"``. This is the 2014 Matplotlib algorithm, a replacement of the original 2005 algorithm that added ``corner_mask`` and made the code more maintainable. Only supports ``corner_mask``, does not support ``quad_as_tri``, ``threads`` or ``z_interp``. Only supports ``line_type=LineType.SeparateCode`` and ``fill_type=FillType.OuterCode``. .. warning:: This algorithm is in ``contourpy`` for historic comparison. No new features or bug fixes will be added to it, except for security-related bug fixes. """ def supports_corner_mask(self): # real signature unknown; restored from __doc__ """ supports_corner_mask() -> bool Return whether this algorithm supports ``corner_mask``. """ return False def supports_fill_type(self, arg0): # real signature unknown; restored from __doc__ """ supports_fill_type(arg0: contourpy._contourpy.FillType) -> bool Return whether this algorithm supports a particular :class:`~.FillType`. """ return False def supports_line_type(self, arg0): # real signature unknown; restored from __doc__ """ supports_line_type(arg0: contourpy._contourpy.LineType) -> bool Return whether this algorithm supports a particular :class:`~.LineType`. """ return False def _pybind11_conduit_v1_(self, *args, **kwargs): # real signature unknown pass def __init__(self, x, numpy_float64=None, *args, **kwargs): # real signature unknown; NOTE: unreliably restored from __doc__ """ __init__(self: contourpy._contourpy.Mpl2014ContourGenerator, x: numpy.ndarray[numpy.float64], y: numpy.ndarray[numpy.float64], z: numpy.ndarray[numpy.float64], mask: numpy.ndarray[bool], *, corner_mask: bool, x_chunk_size: int = 0, y_chunk_size: int = 0) -> None """ pass chunk_count = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Return tuple of (y, x) chunk counts.""" chunk_size = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Return tuple of (y, x) chunk sizes.""" corner_mask = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Return whether ``corner_mask`` is set or not.""" fill_type = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Return the :class:`~.FillType`.""" line_type = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Return the :class:`~.LineType`.""" default_fill_type = None # (!) real value is '' default_line_type = None # (!) real value is '' class SerialContourGenerator(ContourGenerator): """ ContourGenerator corresponding to ``name="serial"``, the default algorithm for ``contourpy``. Supports ``corner_mask``, ``quad_as_tri`` and ``z_interp`` but not ``threads``. Supports all options for ``line_type`` and ``fill_type``. """ def supports_corner_mask(self): # real signature unknown; restored from __doc__ """ supports_corner_mask() -> bool Return whether this algorithm supports ``corner_mask``. """ return False def supports_fill_type(self, arg0): # real signature unknown; restored from __doc__ """ supports_fill_type(arg0: contourpy._contourpy.FillType) -> bool Return whether this algorithm supports a particular :class:`~.FillType`. """ return False def supports_line_type(self, arg0): # real signature unknown; restored from __doc__ """ supports_line_type(arg0: contourpy._contourpy.LineType) -> bool Return whether this algorithm supports a particular :class:`~.LineType`. """ return False def supports_quad_as_tri(self): # real signature unknown; restored from __doc__ """ supports_quad_as_tri() -> bool Return whether this algorithm supports ``quad_as_tri``. """ return False def supports_z_interp(self): # real signature unknown; restored from __doc__ """ supports_z_interp() -> bool Return whether this algorithm supports ``z_interp`` values other than ``ZInterp.Linear`` which all support. """ return False def _pybind11_conduit_v1_(self, *args, **kwargs): # real signature unknown pass def _write_cache(self): # real signature unknown; restored from __doc__ """ _write_cache(self: contourpy._contourpy.SerialContourGenerator) -> None """ pass def __init__(self, x, numpy_float64=None, *args, **kwargs): # real signature unknown; NOTE: unreliably restored from __doc__ """ __init__(self: contourpy._contourpy.SerialContourGenerator, x: numpy.ndarray[numpy.float64], y: numpy.ndarray[numpy.float64], z: numpy.ndarray[numpy.float64], mask: numpy.ndarray[bool], *, corner_mask: bool, line_type: contourpy._contourpy.LineType, fill_type: contourpy._contourpy.FillType, quad_as_tri: bool, z_interp: contourpy._contourpy.ZInterp, x_chunk_size: int = 0, y_chunk_size: int = 0) -> None """ pass chunk_count = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Return tuple of (y, x) chunk counts.""" chunk_size = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Return tuple of (y, x) chunk sizes.""" corner_mask = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Return whether ``corner_mask`` is set or not.""" fill_type = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Return the :class:`~.FillType`.""" line_type = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Return the :class:`~.LineType`.""" quad_as_tri = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Return whether ``quad_as_tri`` is set or not.""" z_interp = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Return the ``ZInterp``.""" default_fill_type = None # (!) real value is '' default_line_type = None # (!) real value is '' class ThreadedContourGenerator(ContourGenerator): """ ContourGenerator corresponding to ``name="threaded"``, the multithreaded version of :class:`~.SerialContourGenerator`. Supports ``corner_mask``, ``quad_as_tri`` and ``z_interp`` and ``threads``. Supports all options for ``line_type`` and ``fill_type``. """ def supports_corner_mask(self): # real signature unknown; restored from __doc__ """ supports_corner_mask() -> bool Return whether this algorithm supports ``corner_mask``. """ return False def supports_fill_type(self, arg0): # real signature unknown; restored from __doc__ """ supports_fill_type(arg0: contourpy._contourpy.FillType) -> bool Return whether this algorithm supports a particular :class:`~.FillType`. """ return False def supports_line_type(self, arg0): # real signature unknown; restored from __doc__ """ supports_line_type(arg0: contourpy._contourpy.LineType) -> bool Return whether this algorithm supports a particular :class:`~.LineType`. """ return False def supports_quad_as_tri(self): # real signature unknown; restored from __doc__ """ supports_quad_as_tri() -> bool Return whether this algorithm supports ``quad_as_tri``. """ return False def supports_threads(self): # real signature unknown; restored from __doc__ """ supports_threads() -> bool Return whether this algorithm supports the use of threads. """ return False def supports_z_interp(self): # real signature unknown; restored from __doc__ """ supports_z_interp() -> bool Return whether this algorithm supports ``z_interp`` values other than ``ZInterp.Linear`` which all support. """ return False def _pybind11_conduit_v1_(self, *args, **kwargs): # real signature unknown pass def _write_cache(self): # real signature unknown; restored from __doc__ """ _write_cache(self: contourpy._contourpy.ThreadedContourGenerator) -> None """ pass def __init__(self, x, numpy_float64=None, *args, **kwargs): # real signature unknown; NOTE: unreliably restored from __doc__ """ __init__(self: contourpy._contourpy.ThreadedContourGenerator, x: numpy.ndarray[numpy.float64], y: numpy.ndarray[numpy.float64], z: numpy.ndarray[numpy.float64], mask: numpy.ndarray[bool], *, corner_mask: bool, line_type: contourpy._contourpy.LineType, fill_type: contourpy._contourpy.FillType, quad_as_tri: bool, z_interp: contourpy._contourpy.ZInterp, x_chunk_size: int = 0, y_chunk_size: int = 0, thread_count: int = 0) -> None """ pass chunk_count = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Return tuple of (y, x) chunk counts.""" chunk_size = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Return tuple of (y, x) chunk sizes.""" corner_mask = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Return whether ``corner_mask`` is set or not.""" fill_type = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Return the :class:`~.FillType`.""" line_type = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Return the :class:`~.LineType`.""" quad_as_tri = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Return whether ``quad_as_tri`` is set or not.""" thread_count = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Return the number of threads used.""" z_interp = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """Return the ``ZInterp``.""" default_fill_type = None # (!) real value is '' default_line_type = None # (!) real value is '' class ZInterp(__pybind11_builtins.pybind11_object): """ Enum used for ``z_interp`` keyword argument in :func:`~.contour_generator` This controls the interpolation used on ``z`` values to determine where contour lines intersect the edges of grid quads, and ``z`` values at quad centres. Members: Linear Log """ def _pybind11_conduit_v1_(self, *args, **kwargs): # real signature unknown pass def __eq__(self, other): # real signature unknown; restored from __doc__ """ __eq__(self: object, other: object) -> bool """ return False def __getstate__(self): # real signature unknown; restored from __doc__ """ __getstate__(self: object) -> int """ return 0 def __hash__(self): # real signature unknown; restored from __doc__ """ __hash__(self: object) -> int """ return 0 def __index__(self): # real signature unknown; restored from __doc__ """ __index__(self: contourpy._contourpy.ZInterp) -> int """ return 0 def __init__(self, value): # real signature unknown; restored from __doc__ """ __init__(self: contourpy._contourpy.ZInterp, value: int) -> None """ pass def __int__(self): # real signature unknown; restored from __doc__ """ __int__(self: contourpy._contourpy.ZInterp) -> int """ return 0 def __ne__(self, other): # real signature unknown; restored from __doc__ """ __ne__(self: object, other: object) -> bool """ return False def __repr__(self): # real signature unknown; restored from __doc__ """ __repr__(self: object) -> str """ return "" def __setstate__(self, state): # real signature unknown; restored from __doc__ """ __setstate__(self: contourpy._contourpy.ZInterp, state: int) -> None """ pass def __str__(self): # real signature unknown; restored from __doc__ """ __str__(self: object) -> str """ return "" name = property(lambda self: object(), lambda self, v: None, lambda self: None) # default """name(self: object) -> str """ value = property(lambda self: object(), lambda self, v: None, lambda self: None) # default Linear = None # (!) forward: Linear, real value is '' Log = None # (!) forward: Log, real value is '' __entries = { 'Linear': ( None, # (!) forward: Linear, real value is '' None, ), 'Log': ( None, # (!) forward: Log, real value is '' None, ), } __members__ = { 'Linear': None, # (!) forward: Linear, real value is '' 'Log': None, # (!) forward: Log, real value is '' } # variables with complex values ChunkCombinedCode = None # (!) real value is '' ChunkCombinedCodeOffset = None # (!) real value is '' ChunkCombinedNan = None # (!) real value is '' ChunkCombinedOffset = None # (!) real value is '' ChunkCombinedOffsetOffset = None # (!) real value is '' Linear = None # (!) real value is '' Log = None # (!) real value is '' OuterCode = None # (!) real value is '' OuterOffset = None # (!) real value is '' Separate = None # (!) real value is '' SeparateCode = None # (!) real value is '' __loader__ = None # (!) real value is '<_frozen_importlib_external.ExtensionFileLoader object at 0x000001FB4C69C050>' __spec__ = None # (!) real value is "ModuleSpec(name='contourpy._contourpy', loader=<_frozen_importlib_external.ExtensionFileLoader object at 0x000001FB4C69C050>, origin='C:\\\\Programs\\\\Python\\\\Python313\\\\Lib\\\\site-packages\\\\contourpy\\\\_contourpy.cp313-win_amd64.pyd')"