268 lines
11 KiB
Python
268 lines
11 KiB
Python
|
from __future__ import annotations
|
||
|
|
|
||
|
|
from typing import TYPE_CHECKING
|
||
|
|
|
||
|
|
import numpy as np
|
||
|
|
|
||
|
|
from contourpy._contourpy import (
|
||
|
|
ContourGenerator,
|
||
|
|
FillType,
|
||
|
|
LineType,
|
||
|
|
Mpl2005ContourGenerator,
|
||
|
|
Mpl2014ContourGenerator,
|
||
|
|
SerialContourGenerator,
|
||
|
|
ThreadedContourGenerator,
|
||
|
|
ZInterp,
|
||
|
|
max_threads,
|
||
|
|
)
|
||
|
|
from contourpy._version import __version__
|
||
|
|
from contourpy.chunk import calc_chunk_sizes
|
||
|
|
from contourpy.convert import convert_filled, convert_lines
|
||
|
|
from contourpy.dechunk import dechunk_filled, dechunk_lines
|
||
|
|
from contourpy.enum_util import as_fill_type, as_line_type, as_z_interp
|
||
|
|
|
||
|
|
if TYPE_CHECKING:
|
||
|
|
from typing import Any
|
||
|
|
|
||
|
|
from numpy.typing import ArrayLike
|
||
|
|
|
||
|
|
from ._contourpy import CoordinateArray, MaskArray
|
||
|
|
|
||
|
|
__all__ = [
|
||
|
|
"__version__",
|
||
|
|
"contour_generator",
|
||
|
|
"convert_filled",
|
||
|
|
"convert_lines",
|
||
|
|
"dechunk_filled",
|
||
|
|
"dechunk_lines",
|
||
|
|
"max_threads",
|
||
|
|
"FillType",
|
||
|
|
"LineType",
|
||
|
|
"ContourGenerator",
|
||
|
|
"Mpl2005ContourGenerator",
|
||
|
|
"Mpl2014ContourGenerator",
|
||
|
|
"SerialContourGenerator",
|
||
|
|
"ThreadedContourGenerator",
|
||
|
|
"ZInterp",
|
||
|
|
]
|
||
|
|
|
||
|
|
|
||
|
|
# Simple mapping of algorithm name to class name.
|
||
|
|
_class_lookup: dict[str, type[ContourGenerator]] = {
|
||
|
|
"mpl2005": Mpl2005ContourGenerator,
|
||
|
|
"mpl2014": Mpl2014ContourGenerator,
|
||
|
|
"serial": SerialContourGenerator,
|
||
|
|
"threaded": ThreadedContourGenerator,
|
||
|
|
}
|
||
|
|
|
||
|
|
|
||
|
|
def _remove_z_mask(
|
||
|
|
z: ArrayLike | np.ma.MaskedArray[Any, Any] | None,
|
||
|
|
) -> tuple[CoordinateArray, MaskArray | None]:
|
||
|
|
# Preserve mask if present.
|
||
|
|
z_array = np.ma.asarray(z, dtype=np.float64) # type: ignore[no-untyped-call]
|
||
|
|
z_masked = np.ma.masked_invalid(z_array, copy=False) # type: ignore[no-untyped-call]
|
||
|
|
|
||
|
|
if np.ma.is_masked(z_masked): # type: ignore[no-untyped-call]
|
||
|
|
mask = np.ma.getmask(z_masked) # type: ignore[no-untyped-call]
|
||
|
|
else:
|
||
|
|
mask = None
|
||
|
|
|
||
|
|
return np.ma.getdata(z_masked), mask # type: ignore[no-untyped-call]
|
||
|
|
|
||
|
|
|
||
|
|
def contour_generator(
|
||
|
|
x: ArrayLike | None = None,
|
||
|
|
y: ArrayLike | None = None,
|
||
|
|
z: ArrayLike | np.ma.MaskedArray[Any, Any] | None = None,
|
||
|
|
*,
|
||
|
|
name: str = "serial",
|
||
|
|
corner_mask: bool | None = None,
|
||
|
|
line_type: LineType | str | None = None,
|
||
|
|
fill_type: FillType | str | None = None,
|
||
|
|
chunk_size: int | tuple[int, int] | None = None,
|
||
|
|
chunk_count: int | tuple[int, int] | None = None,
|
||
|
|
total_chunk_count: int | None = None,
|
||
|
|
quad_as_tri: bool = False,
|
||
|
|
z_interp: ZInterp | str | None = ZInterp.Linear,
|
||
|
|
thread_count: int = 0,
|
||
|
|
) -> ContourGenerator:
|
||
|
|
"""Create and return a :class:`~contourpy._contourpy.ContourGenerator` object.
|
||
|
|
|
||
|
|
The class and properties of the returned :class:`~contourpy._contourpy.ContourGenerator` are
|
||
|
|
determined by the function arguments, with sensible defaults.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
x (array-like of shape (ny, nx) or (nx,), optional): The x-coordinates of the ``z`` values.
|
||
|
|
May be 2D with the same shape as ``z.shape``, or 1D with length ``nx = z.shape[1]``.
|
||
|
|
If not specified are assumed to be ``np.arange(nx)``. Must be ordered monotonically.
|
||
|
|
y (array-like of shape (ny, nx) or (ny,), optional): The y-coordinates of the ``z`` values.
|
||
|
|
May be 2D with the same shape as ``z.shape``, or 1D with length ``ny = z.shape[0]``.
|
||
|
|
If not specified are assumed to be ``np.arange(ny)``. Must be ordered monotonically.
|
||
|
|
z (array-like of shape (ny, nx), may be a masked array): The 2D gridded values to calculate
|
||
|
|
the contours of. May be a masked array, and any invalid values (``np.inf`` or
|
||
|
|
``np.nan``) will also be masked out.
|
||
|
|
name (str): Algorithm name, one of ``"serial"``, ``"threaded"``, ``"mpl2005"`` or
|
||
|
|
``"mpl2014"``, default ``"serial"``.
|
||
|
|
corner_mask (bool, optional): Enable/disable corner masking, which only has an effect if
|
||
|
|
``z`` is a masked array. If ``False``, any quad touching a masked point is masked out.
|
||
|
|
If ``True``, only the triangular corners of quads nearest these points are always masked
|
||
|
|
out, other triangular corners comprising three unmasked points are contoured as usual.
|
||
|
|
If not specified, uses the default provided by the algorithm ``name``.
|
||
|
|
line_type (LineType or str, optional): The format of contour line data returned from calls
|
||
|
|
to :meth:`~contourpy.ContourGenerator.lines`, specified either as a
|
||
|
|
:class:`~contourpy.LineType` or its string equivalent such as ``"SeparateCode"``.
|
||
|
|
If not specified, uses the default provided by the algorithm ``name``.
|
||
|
|
fill_type (FillType or str, optional): The format of filled contour data returned from calls
|
||
|
|
to :meth:`~contourpy.ContourGenerator.filled`, specified either as a
|
||
|
|
:class:`~contourpy.FillType` or its string equivalent such as ``"OuterOffset"``.
|
||
|
|
If not specified, uses the default provided by the algorithm ``name``.
|
||
|
|
chunk_size (int or tuple(int, int), optional): Chunk size in (y, x) directions, or the same
|
||
|
|
size in both directions if only one value is specified.
|
||
|
|
chunk_count (int or tuple(int, int), optional): Chunk count in (y, x) directions, or the
|
||
|
|
same count in both directions if only one value is specified.
|
||
|
|
total_chunk_count (int, optional): Total number of chunks.
|
||
|
|
quad_as_tri (bool): Enable/disable treating quads as 4 triangles, default ``False``.
|
||
|
|
If ``False``, a contour line within a quad is a straight line between points on two of
|
||
|
|
its edges. If ``True``, each full quad is divided into 4 triangles using a virtual point
|
||
|
|
at the centre (mean x, y of the corner points) and a contour line is piecewise linear
|
||
|
|
within those triangles. Corner-masked triangles are not affected by this setting, only
|
||
|
|
full unmasked quads.
|
||
|
|
z_interp (ZInterp or str, optional): How to interpolate ``z`` values when determining where
|
||
|
|
contour lines intersect the edges of quads and the ``z`` values of the central points of
|
||
|
|
quads, specified either as a :class:`~contourpy.ZInterp` or its string equivalent such
|
||
|
|
as ``"Log"``. Default is ``ZInterp.Linear``.
|
||
|
|
thread_count (int): Number of threads to use for contour calculation, default 0. Threads can
|
||
|
|
only be used with an algorithm ``name`` that supports threads (currently only
|
||
|
|
``name="threaded"``) and there must be at least the same number of chunks as threads.
|
||
|
|
If ``thread_count=0`` and ``name="threaded"`` then it uses the maximum number of threads
|
||
|
|
as determined by the C++11 call ``std::thread::hardware_concurrency()``. If ``name`` is
|
||
|
|
something other than ``"threaded"`` then the ``thread_count`` will be set to ``1``.
|
||
|
|
|
||
|
|
Return:
|
||
|
|
:class:`~contourpy._contourpy.ContourGenerator`.
|
||
|
|
|
||
|
|
Note:
|
||
|
|
A maximum of one of ``chunk_size``, ``chunk_count`` and ``total_chunk_count`` may be
|
||
|
|
specified.
|
||
|
|
|
||
|
|
Warning:
|
||
|
|
The ``name="mpl2005"`` algorithm does not implement chunking for contour lines.
|
||
|
|
"""
|
||
|
|
x = np.asarray(x, dtype=np.float64)
|
||
|
|
y = np.asarray(y, dtype=np.float64)
|
||
|
|
z, mask = _remove_z_mask(z)
|
||
|
|
|
||
|
|
# Check arguments: z.
|
||
|
|
if z.ndim != 2:
|
||
|
|
raise TypeError(f"Input z must be 2D, not {z.ndim}D")
|
||
|
|
|
||
|
|
if z.shape[0] < 2 or z.shape[1] < 2:
|
||
|
|
raise TypeError(f"Input z must be at least a (2, 2) shaped array, but has shape {z.shape}")
|
||
|
|
|
||
|
|
ny, nx = z.shape
|
||
|
|
|
||
|
|
# Check arguments: x and y.
|
||
|
|
if x.ndim != y.ndim:
|
||
|
|
raise TypeError(f"Number of dimensions of x ({x.ndim}) and y ({y.ndim}) do not match")
|
||
|
|
|
||
|
|
if x.ndim == 0:
|
||
|
|
x = np.arange(nx, dtype=np.float64)
|
||
|
|
y = np.arange(ny, dtype=np.float64)
|
||
|
|
x, y = np.meshgrid(x, y)
|
||
|
|
elif x.ndim == 1:
|
||
|
|
if len(x) != nx:
|
||
|
|
raise TypeError(f"Length of x ({len(x)}) must match number of columns in z ({nx})")
|
||
|
|
if len(y) != ny:
|
||
|
|
raise TypeError(f"Length of y ({len(y)}) must match number of rows in z ({ny})")
|
||
|
|
x, y = np.meshgrid(x, y)
|
||
|
|
elif x.ndim == 2:
|
||
|
|
if x.shape != z.shape:
|
||
|
|
raise TypeError(f"Shapes of x {x.shape} and z {z.shape} do not match")
|
||
|
|
if y.shape != z.shape:
|
||
|
|
raise TypeError(f"Shapes of y {y.shape} and z {z.shape} do not match")
|
||
|
|
else:
|
||
|
|
raise TypeError(f"Inputs x and y must be None, 1D or 2D, not {x.ndim}D")
|
||
|
|
|
||
|
|
# Check mask shape just in case.
|
||
|
|
if mask is not None and mask.shape != z.shape:
|
||
|
|
raise ValueError("If mask is set it must be a 2D array with the same shape as z")
|
||
|
|
|
||
|
|
# Check arguments: name.
|
||
|
|
if name not in _class_lookup:
|
||
|
|
raise ValueError(f"Unrecognised contour generator name: {name}")
|
||
|
|
|
||
|
|
# Check arguments: chunk_size, chunk_count and total_chunk_count.
|
||
|
|
y_chunk_size, x_chunk_size = calc_chunk_sizes(
|
||
|
|
chunk_size, chunk_count, total_chunk_count, ny, nx)
|
||
|
|
|
||
|
|
cls = _class_lookup[name]
|
||
|
|
|
||
|
|
# Check arguments: corner_mask.
|
||
|
|
if corner_mask is None:
|
||
|
|
# Set it to default, which is True if the algorithm supports it.
|
||
|
|
corner_mask = cls.supports_corner_mask()
|
||
|
|
elif corner_mask and not cls.supports_corner_mask():
|
||
|
|
raise ValueError(f"{name} contour generator does not support corner_mask=True")
|
||
|
|
|
||
|
|
# Check arguments: line_type.
|
||
|
|
if line_type is None:
|
||
|
|
line_type = cls.default_line_type
|
||
|
|
else:
|
||
|
|
line_type = as_line_type(line_type)
|
||
|
|
|
||
|
|
if not cls.supports_line_type(line_type):
|
||
|
|
raise ValueError(f"{name} contour generator does not support line_type {line_type}")
|
||
|
|
|
||
|
|
# Check arguments: fill_type.
|
||
|
|
if fill_type is None:
|
||
|
|
fill_type = cls.default_fill_type
|
||
|
|
else:
|
||
|
|
fill_type = as_fill_type(fill_type)
|
||
|
|
|
||
|
|
if not cls.supports_fill_type(fill_type):
|
||
|
|
raise ValueError(f"{name} contour generator does not support fill_type {fill_type}")
|
||
|
|
|
||
|
|
# Check arguments: quad_as_tri.
|
||
|
|
if quad_as_tri and not cls.supports_quad_as_tri():
|
||
|
|
raise ValueError(f"{name} contour generator does not support quad_as_tri=True")
|
||
|
|
|
||
|
|
# Check arguments: z_interp.
|
||
|
|
if z_interp is None:
|
||
|
|
z_interp = ZInterp.Linear
|
||
|
|
else:
|
||
|
|
z_interp = as_z_interp(z_interp)
|
||
|
|
|
||
|
|
if z_interp != ZInterp.Linear and not cls.supports_z_interp():
|
||
|
|
raise ValueError(f"{name} contour generator does not support z_interp {z_interp}")
|
||
|
|
|
||
|
|
# Check arguments: thread_count.
|
||
|
|
if thread_count not in (0, 1) and not cls.supports_threads():
|
||
|
|
raise ValueError(f"{name} contour generator does not support thread_count {thread_count}")
|
||
|
|
|
||
|
|
# Prepare args and kwargs for contour generator constructor.
|
||
|
|
args = [x, y, z, mask]
|
||
|
|
kwargs: dict[str, int | bool | LineType | FillType | ZInterp] = {
|
||
|
|
"x_chunk_size": x_chunk_size,
|
||
|
|
"y_chunk_size": y_chunk_size,
|
||
|
|
}
|
||
|
|
|
||
|
|
if name not in ("mpl2005", "mpl2014"):
|
||
|
|
kwargs["line_type"] = line_type
|
||
|
|
kwargs["fill_type"] = fill_type
|
||
|
|
|
||
|
|
if cls.supports_corner_mask():
|
||
|
|
kwargs["corner_mask"] = corner_mask
|
||
|
|
|
||
|
|
if cls.supports_quad_as_tri():
|
||
|
|
kwargs["quad_as_tri"] = quad_as_tri
|
||
|
|
|
||
|
|
if cls.supports_z_interp():
|
||
|
|
kwargs["z_interp"] = z_interp
|
||
|
|
|
||
|
|
if cls.supports_threads():
|
||
|
|
kwargs["thread_count"] = thread_count
|
||
|
|
|
||
|
|
# Create contour generator.
|
||
|
|
return cls(*args, **kwargs)
|