"""
Custom grid and ticklines.

This example demonstrates how to use GridHelperCurveLinear to define
custom grids and ticklines by applying a transformation on the grid.
As showcase on the plot, a 5x5 matrix is displayed on the axes.
"""

import numpy as np
import matplotlib.pyplot as plt

from mpl_toolkits.axes_grid.grid_helper_curvelinear import \
    GridHelperCurveLinear
from mpl_toolkits.axes_grid.axislines import Subplot

import mpl_toolkits.axes_grid.angle_helper as angle_helper


def curvelinear_test1(fig):
    """
    grid for custom transform.
    """

    def tr(x, y):
        sgn = np.sign(x)
        x, y = np.abs(np.asarray(x)), np.asarray(y)
        return sgn*x**.5, y

    def inv_tr(x, y):
        sgn = np.sign(x)
        x, y = np.asarray(x), np.asarray(y)
        return sgn*x**2, y

    extreme_finder = angle_helper.ExtremeFinderCycle(20, 20,
                                                     lon_cycle=None,
                                                     lat_cycle=None,
                                                     # (0, np.inf),
                                                     lon_minmax=None,
                                                     lat_minmax=None,
                                                     )

    grid_helper = GridHelperCurveLinear((tr, inv_tr),
                                        extreme_finder=extreme_finder)

    ax1 = Subplot(fig, 111, grid_helper=grid_helper)
    # ax1 will have a ticks and gridlines defined by the given
    # transform (+ transData of the Axes). Note that the transform of
    # the Axes itself (i.e., transData) is not affected by the given
    # transform.

    fig.add_subplot(ax1)

    ax1.imshow(np.arange(25).reshape(5, 5),
               vmax=50, cmap=plt.cm.gray_r,
               interpolation="nearest",
               origin="lower")

    # tick density
    grid_helper.grid_finder.grid_locator1._nbins = 6
    grid_helper.grid_finder.grid_locator2._nbins = 6


if 1:
    fig = plt.figure(1, figsize=(7, 4))
    fig.clf()

    curvelinear_test1(fig)
    plt.show() 

　

Traceback (most recent call last):
  File "M:\_____\demo_curvelinear_grid2.py", line 12, in 
    from mpl_toolkits.axes_grid.grid_helper_curvelinear import \
ModuleNotFoundError: No module named 'mpl_toolkits.axes_grid' 

Traceback (most recent call last):
  File "E:\______\demo_curvelinear_grid2.py", line 12, in 
    from mpl_toolkits.axes_grid.grid_helper_curvelinear import \
ModuleNotFoundError: No module named 'mpl_toolkits.axes_grid' 

"""
======================
Demo CurveLinear Grid2
======================

Custom grid and ticklines.

This example demonstrates how to use GridHelperCurveLinear to define
custom grids and ticklines by applying a transformation on the grid.
As showcase on the plot, a 5x5 matrix is displayed on the axes.
"""

import matplotlib.pyplot as plt
import numpy as np

from mpl_toolkits.axisartist.axislines import Axes
from mpl_toolkits.axisartist.grid_finder import (ExtremeFinderSimple,
                                                 MaxNLocator)
from mpl_toolkits.axisartist.grid_helper_curvelinear import \
    GridHelperCurveLinear


def curvelinear_test1(fig):
    """Grid for custom transform."""

    def tr(x, y):
        return np.sign(x)*abs(x)**.5, y

    def inv_tr(x, y):
        return np.sign(x)*x**2, y

    grid_helper = GridHelperCurveLinear(
        (tr, inv_tr),
        extreme_finder=ExtremeFinderSimple(20, 20),
        # better tick density
        grid_locator1=MaxNLocator(nbins=6), grid_locator2=MaxNLocator(nbins=6))

    ax1 = fig.add_subplot(axes_class=Axes, grid_helper=grid_helper)
    # ax1 will have a ticks and gridlines defined by the given
    # transform (+ transData of the Axes). Note that the transform of the Axes
    # itself (i.e., transData) is not affected by the given transform.

    ax1.imshow(np.arange(25).reshape(5, 5),
               vmax=50, cmap=plt.cm.gray_r, origin="lower")


if __name__ == "__main__":
    fig = plt.figure(figsize=(7, 4))
    curvelinear_test1(fig)
    plt.show() 

　Python 3.11.6 (matplotlib 3.7.1) 及び Python 3.12.0 (matplotlib 3.8.1) 共に、正常に、実行できる。

　

• 2023/11/13 Ver=1.04 Python 3.12.0 (matplotlib 3.8.1)で確認
• 2023/11/13 Ver=1.04 Python 3.11.6 (matplotlib 3.7.1)で確認
• 2023/03/28 Ver=1.03 Python 3.11.2 で確認
• 2020/10/30 Ver=1.01 Python 3.7.8 で確認
• 2018/11/24 Ver=1.01 初版リリース

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