from __future__ import annotations
import io
from typing import TYPE_CHECKING, Any, cast
import matplotlib.collections as mcollections
import matplotlib.pyplot as plt
import numpy as np
from contourpy import FillType, LineType
from contourpy.util.mpl_util import filled_to_mpl_paths, lines_to_mpl_paths, mpl_codes_to_offsets
from contourpy.util.renderer import Renderer
if TYPE_CHECKING:
from matplotlib.axes import Axes
from matplotlib.figure import Figure
from numpy.typing import ArrayLike
import contourpy._contourpy as cpy
class MplRenderer(Renderer):
_axes: Axes
_fig: Figure
_want_tight: bool
"""Utility renderer using Matplotlib to render a grid of plots over the same (x, y) range.
Args:
nrows (int, optional): Number of rows of plots, default ``1``.
ncols (int, optional): Number of columns of plots, default ``1``.
figsize (tuple(float, float), optional): Figure size in inches, default ``(9, 9)``.
show_frame (bool, optional): Whether to show frame and axes ticks, default ``True``.
backend (str, optional): Matplotlib backend to use or ``None`` for default backend.
Default ``None``.
gridspec_kw (dict, optional): Gridspec keyword arguments to pass to ``plt.subplots``,
default None.
"""
def __init__(
self,
nrows: int = 1,
ncols: int = 1,
figsize: tuple[float, float] = (9, 9),
show_frame: bool = True,
backend: str | None = None,
gridspec_kw: dict[str, Any] | None = None,
) -> None:
if backend is not None:
import matplotlib
matplotlib.use(backend)
kwargs = dict(figsize=figsize, squeeze=False, sharex=True, sharey=True)
if gridspec_kw is not None:
kwargs["gridspec_kw"] = gridspec_kw
else:
kwargs["subplot_kw"] = dict(aspect="equal")
self._fig, axes = plt.subplots(nrows, ncols, **kwargs)
self._axes = axes.flatten()
if not show_frame:
for ax in self._axes:
ax.axis("off")
self._want_tight = True
def __del__(self) -> None:
if hasattr(self, "_fig"):
plt.close(self._fig)
def _autoscale(self) -> None:
# Using axes._need_autoscale attribute if need to autoscale before rendering after adding
# lines/filled. Only want to autoscale once per axes regardless of how many lines/filled
# added.
for ax in self._axes:
if getattr(ax, "_need_autoscale", False):
ax.autoscale_view(tight=True)
ax._need_autoscale = False
if self._want_tight and len(self._axes) > 1:
self._fig.tight_layout()
def _get_ax(self, ax: Axes | int) -> Axes:
if isinstance(ax, int):
ax = self._axes[ax]
return ax
def filled(
self,
filled: cpy.FillReturn,
fill_type: FillType,
ax: Axes | int = 0,
color: str = "C0",
alpha: float = 0.7,
) -> None:
"""Plot filled contours on a single Axes.
Args:
filled (sequence of arrays): Filled contour data as returned by
:func:`~contourpy.ContourGenerator.filled`.
fill_type (FillType): Type of ``filled`` data, as returned by
:attr:`~contourpy.ContourGenerator.fill_type`.
ax (int or Maplotlib Axes, optional): Which axes to plot on, default ``0``.
color (str, optional): Color to plot with. May be a string color or the letter ``"C"``
followed by an integer in the range ``"C0"`` to ``"C9"`` to use a color from the
``tab10`` colormap. Default ``"C0"``.
alpha (float, optional): Opacity to plot with, default ``0.7``.
"""
ax = self._get_ax(ax)
paths = filled_to_mpl_paths(filled, fill_type)
collection = mcollections.PathCollection(
paths, facecolors=color, edgecolors="none", lw=0, alpha=alpha)
ax.add_collection(collection)
ax._need_autoscale = True
def grid(
self,
x: ArrayLike,
y: ArrayLike,
ax: Axes | int = 0,
color: str = "black",
alpha: float = 0.1,
point_color: str | None = None,
quad_as_tri_alpha: float = 0,
) -> None:
"""Plot quad grid lines on a single Axes.
Args:
x (array-like of shape (ny, nx) or (nx,)): The x-coordinates of the grid points.
y (array-like of shape (ny, nx) or (ny,)): The y-coordinates of the grid points.
ax (int or Matplotlib Axes, optional): Which Axes to plot on, default ``0``.
color (str, optional): Color to plot grid lines, default ``"black"``.
alpha (float, optional): Opacity to plot lines with, default ``0.1``.
point_color (str, optional): Color to plot grid points or ``None`` if grid points
should not be plotted, default ``None``.
quad_as_tri_alpha (float, optional): Opacity to plot ``quad_as_tri`` grid, default 0.
Colors may be a string color or the letter ``"C"`` followed by an integer in the range
``"C0"`` to ``"C9"`` to use a color from the ``tab10`` colormap.
Warning:
``quad_as_tri_alpha > 0`` plots all quads as though they are unmasked.
"""
ax = self._get_ax(ax)
x, y = self._grid_as_2d(x, y)
kwargs = dict(color=color, alpha=alpha)
ax.plot(x, y, x.T, y.T, **kwargs)
if quad_as_tri_alpha > 0:
# Assumes no quad mask.
xmid = 0.25*(x[:-1, :-1] + x[1:, :-1] + x[:-1, 1:] + x[1:, 1:])
ymid = 0.25*(y[:-1, :-1] + y[1:, :-1] + y[:-1, 1:] + y[1:, 1:])
kwargs["alpha"] = quad_as_tri_alpha
ax.plot(
np.stack((x[:-1, :-1], xmid, x[1:, 1:])).reshape((3, -1)),
np.stack((y[:-1, :-1], ymid, y[1:, 1:])).reshape((3, -1)),
np.stack((x[1:, :-1], xmid, x[:-1, 1:])).reshape((3, -1)),
np.stack((y[1:, :-1], ymid, y[:-1, 1:])).reshape((3, -1)),
**kwargs)
if point_color is not None:
ax.plot(x, y, color=point_color, alpha=alpha, marker="o", lw=0)
ax._need_autoscale = True
def lines(
self,
lines: cpy.LineReturn,
line_type: LineType,
ax: Axes | int = 0,
color: str = "C0",
alpha: float = 1.0,
linewidth: float = 1,
) -> None:
"""Plot contour lines on a single Axes.
Args:
lines (sequence of arrays): Contour line data as returned by
:func:`~contourpy.ContourGenerator.lines`.
line_type (LineType): Type of ``lines`` data, as returned by
:attr:`~contourpy.ContourGenerator.line_type`.
ax (int or Matplotlib Axes, optional): Which Axes to plot on, default ``0``.
color (str, optional): Color to plot lines. May be a string color or the letter ``"C"``
followed by an integer in the range ``"C0"`` to ``"C9"`` to use a color from the
``tab10`` colormap. Default ``"C0"``.
alpha (float, optional): Opacity to plot lines with, default ``1.0``.
linewidth (float, optional): Width of lines, default ``1``.
"""
ax = self._get_ax(ax)
paths = lines_to_mpl_paths(lines, line_type)
collection = mcollections.PathCollection(
paths, facecolors="none", edgecolors=color, lw=linewidth, alpha=alpha)
ax.add_collection(collection)
ax._need_autoscale = True
def mask(
self,
x: ArrayLike,
y: ArrayLike,
z: ArrayLike | np.ma.MaskedArray[Any, Any],
ax: Axes | int = 0,
color: str = "black",
) -> None:
"""Plot masked out grid points as circles on a single Axes.
Args:
x (array-like of shape (ny, nx) or (nx,)): The x-coordinates of the grid points.
y (array-like of shape (ny, nx) or (ny,)): The y-coordinates of the grid points.
z (masked array of shape (ny, nx): z-values.
ax (int or Matplotlib Axes, optional): Which Axes to plot on, default ``0``.
color (str, optional): Circle color, default ``"black"``.
"""
mask = np.ma.getmask(z) # type: ignore[no-untyped-call]
if mask is np.ma.nomask:
return
ax = self._get_ax(ax)
x, y = self._grid_as_2d(x, y)
ax.plot(x[mask], y[mask], "o", c=color)
def save(self, filename: str, transparent: bool = False) -> None:
"""Save plots to SVG or PNG file.
Args:
filename (str): Filename to save to.
transparent (bool, optional): Whether background should be transparent, default
``False``.
"""
self._autoscale()
self._fig.savefig(filename, transparent=transparent)
def save_to_buffer(self) -> io.BytesIO:
"""Save plots to an ``io.BytesIO`` buffer.
Return:
BytesIO: PNG image buffer.
"""
self._autoscale()
buf = io.BytesIO()
self._fig.savefig(buf, format="png")
buf.seek(0)
return buf
def show(self) -> None:
"""Show plots in an interactive window, in the usual Matplotlib manner.
"""
self._autoscale()
plt.show()
def title(self, title: str, ax: Axes | int = 0, color: str | None = None) -> None:
"""Set the title of a single Axes.
Args:
title (str): Title text.
ax (int or Matplotlib Axes, optional): Which Axes to set the title of, default ``0``.
color (str, optional): Color to set title. May be a string color or the letter ``"C"``
followed by an integer in the range ``"C0"`` to ``"C9"`` to use a color from the
``tab10`` colormap. Default is ``None`` which uses Matplotlib's default title color
that depends on the stylesheet in use.
"""
if color:
self._get_ax(ax).set_title(title, color=color)
else:
self._get_ax(ax).set_title(title)
def z_values(
self,
x: ArrayLike,
y: ArrayLike,
z: ArrayLike,
ax: Axes | int = 0,
color: str = "green",
fmt: str = ".1f",
quad_as_tri: bool = False,
) -> None:
"""Show ``z`` values on a single Axes.
Args:
x (array-like of shape (ny, nx) or (nx,)): The x-coordinates of the grid points.
y (array-like of shape (ny, nx) or (ny,)): The y-coordinates of the grid points.
z (array-like of shape (ny, nx): z-values.
ax (int or Matplotlib Axes, optional): Which Axes to plot on, default ``0``.
color (str, optional): Color of added text. May be a string color or the letter ``"C"``
followed by an integer in the range ``"C0"`` to ``"C9"`` to use a color from the
``tab10`` colormap. Default ``"green"``.
fmt (str, optional): Format to display z-values, default ``".1f"``.
quad_as_tri (bool, optional): Whether to show z-values at the ``quad_as_tri`` centers
of quads.
Warning:
``quad_as_tri=True`` shows z-values for all quads, even if masked.
"""
ax = self._get_ax(ax)
x, y = self._grid_as_2d(x, y)
z = np.asarray(z)
ny, nx = z.shape
for j in range(ny):
for i in range(nx):
ax.text(x[j, i], y[j, i], f"{z[j, i]:{fmt}}", ha="center", va="center",
color=color, clip_on=True)
if quad_as_tri:
for j in range(ny-1):
for i in range(nx-1):
xx = np.mean(x[j:j+2, i:i+2])
yy = np.mean(y[j:j+2, i:i+2])
zz = np.mean(z[j:j+2, i:i+2])
ax.text(xx, yy, f"{zz:{fmt}}", ha="center", va="center", color=color,
clip_on=True)
class MplTestRenderer(MplRenderer):
"""Test renderer implemented using Matplotlib.
No whitespace around plots and no spines/ticks displayed.
Uses Agg backend, so can only save to file/buffer, cannot call ``show()``.
"""
def __init__(
self,
nrows: int = 1,
ncols: int = 1,
figsize: tuple[float, float] = (9, 9),
) -> None:
gridspec = {
"left": 0.01,
"right": 0.99,
"top": 0.99,
"bottom": 0.01,
"wspace": 0.01,
"hspace": 0.01,
}
super().__init__(
nrows, ncols, figsize, show_frame=True, backend="Agg", gridspec_kw=gridspec,
)
for ax in self._axes:
ax.set_xmargin(0.0)
ax.set_ymargin(0.0)
ax.set_xticks([])
ax.set_yticks([])
self._want_tight = False
class MplDebugRenderer(MplRenderer):
"""Debug renderer implemented using Matplotlib.
Extends ``MplRenderer`` to add extra information to help in debugging such as markers, arrows,
text, etc.
"""
def __init__(
self,
nrows: int = 1,
ncols: int = 1,
figsize: tuple[float, float] = (9, 9),
show_frame: bool = True,
) -> None:
super().__init__(nrows, ncols, figsize, show_frame)
def _arrow(
self,
ax: Axes,
line_start: cpy.CoordinateArray,
line_end: cpy.CoordinateArray,
color: str,
alpha: float,
arrow_size: float,
) -> None:
mid = 0.5*(line_start + line_end)
along = line_end - line_start
along /= np.sqrt(np.dot(along, along)) # Unit vector.
right = np.asarray((along[1], -along[0]))
arrow = np.stack((
mid - (along*0.5 - right)*arrow_size,
mid + along*0.5*arrow_size,
mid - (along*0.5 + right)*arrow_size,
))
ax.plot(arrow[:, 0], arrow[:, 1], "-", c=color, alpha=alpha)
def _filled_to_lists_of_points_and_offsets(
self,
filled: cpy.FillReturn,
fill_type: FillType,
) -> tuple[list[cpy.PointArray], list[cpy.OffsetArray]]:
if fill_type == FillType.OuterCode:
if TYPE_CHECKING:
filled = cast(cpy.FillReturn_OuterCode, filled)
all_points = filled[0]
all_offsets = [mpl_codes_to_offsets(codes) for codes in filled[1]]
elif fill_type == FillType.ChunkCombinedCode:
if TYPE_CHECKING:
filled = cast(cpy.FillReturn_ChunkCombinedCode, filled)
all_points = [points for points in filled[0] if points is not None]
all_offsets = [mpl_codes_to_offsets(codes) for codes in filled[1] if codes is not None]
elif fill_type == FillType.OuterOffset:
if TYPE_CHECKING:
filled = cast(cpy.FillReturn_OuterOffset, filled)
all_points = filled[0]
all_offsets = filled[1]
elif fill_type == FillType.ChunkCombinedOffset:
if TYPE_CHECKING:
filled = cast(cpy.FillReturn_ChunkCombinedOffset, filled)
all_points = [points for points in filled[0] if points is not None]
all_offsets = [offsets for offsets in filled[1] if offsets is not None]
elif fill_type == FillType.ChunkCombinedCodeOffset:
if TYPE_CHECKING:
filled = cast(cpy.FillReturn_ChunkCombinedCodeOffset, filled)
all_points = []
all_offsets = []
for points, codes, outer_offsets in zip(*filled):
if points is None:
continue
if TYPE_CHECKING:
assert codes is not None and outer_offsets is not None
all_points += np.split(points, outer_offsets[1:-1])
all_codes = np.split(codes, outer_offsets[1:-1])
all_offsets += [mpl_codes_to_offsets(codes) for codes in all_codes]
elif fill_type == FillType.ChunkCombinedOffsetOffset:
if TYPE_CHECKING:
filled = cast(cpy.FillReturn_ChunkCombinedOffsetOffset, filled)
all_points = []
all_offsets = []
for points, offsets, outer_offsets in zip(*filled):
if points is None:
continue
if TYPE_CHECKING:
assert offsets is not None and outer_offsets is not None
for i in range(len(outer_offsets)-1):
offs = offsets[outer_offsets[i]:outer_offsets[i+1]+1]
all_points.append(points[offs[0]:offs[-1]])
all_offsets.append(offs - offs[0])
else:
raise RuntimeError(f"Rendering FillType {fill_type} not implemented")
return all_points, all_offsets
def _lines_to_list_of_points(
self, lines: cpy.LineReturn, line_type: LineType,
) -> list[cpy.PointArray]:
if line_type == LineType.Separate:
if TYPE_CHECKING:
lines = cast(cpy.LineReturn_Separate, lines)
all_lines = lines
elif line_type == LineType.SeparateCode:
if TYPE_CHECKING:
lines = cast(cpy.LineReturn_SeparateCode, lines)
all_lines = lines[0]
elif line_type == LineType.ChunkCombinedCode:
if TYPE_CHECKING:
lines = cast(cpy.LineReturn_ChunkCombinedCode, lines)
all_lines = []
for points, codes in zip(*lines):
if points is not None:
if TYPE_CHECKING:
assert codes is not None
offsets = mpl_codes_to_offsets(codes)
for i in range(len(offsets)-1):
all_lines.append(points[offsets[i]:offsets[i+1]])
elif line_type == LineType.ChunkCombinedOffset:
if TYPE_CHECKING:
lines = cast(cpy.LineReturn_ChunkCombinedOffset, lines)
all_lines = []
for points, all_offsets in zip(*lines):
if points is not None:
if TYPE_CHECKING:
assert all_offsets is not None
for i in range(len(all_offsets)-1):
all_lines.append(points[all_offsets[i]:all_offsets[i+1]])
else:
raise RuntimeError(f"Rendering LineType {line_type} not implemented")
return all_lines
def filled(
self,
filled: cpy.FillReturn,
fill_type: FillType,
ax: Axes | int = 0,
color: str = "C1",
alpha: float = 0.7,
line_color: str = "C0",
line_alpha: float = 0.7,
point_color: str = "C0",
start_point_color: str = "red",
arrow_size: float = 0.1,
) -> None:
super().filled(filled, fill_type, ax, color, alpha)
if line_color is None and point_color is None:
return
ax = self._get_ax(ax)
all_points, all_offsets = self._filled_to_lists_of_points_and_offsets(filled, fill_type)
# Lines.
if line_color is not None:
for points, offsets in zip(all_points, all_offsets):
for start, end in zip(offsets[:-1], offsets[1:]):
xys = points[start:end]
ax.plot(xys[:, 0], xys[:, 1], c=line_color, alpha=line_alpha)
if arrow_size > 0.0:
n = len(xys)
for i in range(n-1):
self._arrow(ax, xys[i], xys[i+1], line_color, line_alpha, arrow_size)
# Points.
if point_color is not None:
for points, offsets in zip(all_points, all_offsets):
mask = np.ones(offsets[-1], dtype=bool)
mask[offsets[1:]-1] = False # Exclude end points.
if start_point_color is not None:
start_indices = offsets[:-1]
mask[start_indices] = False # Exclude start points.
ax.plot(
points[:, 0][mask], points[:, 1][mask], "o", c=point_color, alpha=line_alpha)
if start_point_color is not None:
ax.plot(points[:, 0][start_indices], points[:, 1][start_indices], "o",
c=start_point_color, alpha=line_alpha)
def lines(
self,
lines: cpy.LineReturn,
line_type: LineType,
ax: Axes | int = 0,
color: str = "C0",
alpha: float = 1.0,
linewidth: float = 1,
point_color: str = "C0",
start_point_color: str = "red",
arrow_size: float = 0.1,
) -> None:
super().lines(lines, line_type, ax, color, alpha, linewidth)
if arrow_size == 0.0 and point_color is None:
return
ax = self._get_ax(ax)
all_lines = self._lines_to_list_of_points(lines, line_type)
if arrow_size > 0.0:
for line in all_lines:
for i in range(len(line)-1):
self._arrow(ax, line[i], line[i+1], color, alpha, arrow_size)
if point_color is not None:
for line in all_lines:
start_index = 0
end_index = len(line)
if start_point_color is not None:
ax.plot(line[0, 0], line[0, 1], "o", c=start_point_color, alpha=alpha)
start_index = 1
if line[0][0] == line[-1][0] and line[0][1] == line[-1][1]:
end_index -= 1
ax.plot(line[start_index:end_index, 0], line[start_index:end_index, 1], "o",
c=color, alpha=alpha)
def point_numbers(
self,
x: ArrayLike,
y: ArrayLike,
z: ArrayLike,
ax: Axes | int = 0,
color: str = "red",
) -> None:
ax = self._get_ax(ax)
x, y = self._grid_as_2d(x, y)
z = np.asarray(z)
ny, nx = z.shape
for j in range(ny):
for i in range(nx):
quad = i + j*nx
ax.text(x[j, i], y[j, i], str(quad), ha="right", va="top", color=color,
clip_on=True)
def quad_numbers(
self,
x: ArrayLike,
y: ArrayLike,
z: ArrayLike,
ax: Axes | int = 0,
color: str = "blue",
) -> None:
ax = self._get_ax(ax)
x, y = self._grid_as_2d(x, y)
z = np.asarray(z)
ny, nx = z.shape
for j in range(1, ny):
for i in range(1, nx):
quad = i + j*nx
xmid = x[j-1:j+1, i-1:i+1].mean()
ymid = y[j-1:j+1, i-1:i+1].mean()
ax.text(xmid, ymid, str(quad), ha="center", va="center", color=color, clip_on=True)
def z_levels(
self,
x: ArrayLike,
y: ArrayLike,
z: ArrayLike,
lower_level: float,
upper_level: float | None = None,
ax: Axes | int = 0,
color: str = "green",
) -> None:
ax = self._get_ax(ax)
x, y = self._grid_as_2d(x, y)
z = np.asarray(z)
ny, nx = z.shape
for j in range(ny):
for i in range(nx):
zz = z[j, i]
if upper_level is not None and zz > upper_level:
z_level = 2
elif zz > lower_level:
z_level = 1
else:
z_level = 0
ax.text(x[j, i], y[j, i], z_level, ha="left", va="bottom", color=color,
clip_on=True)