আমি জানি যে এই গেমটি দেরীতে হয়েছে তবে আমি কেবল এই প্রক্রিয়াটি পেরিয়েছিলাম এবং এমন একটি সমাধান নিয়ে এসেছি যা সম্ভবত সাবক্লাসিংয়ের চেয়ে কম দৃust়তর স্বাভাবিক, তবে আরও সহজ। আমি ভেবেছিলাম উত্তরসূরীদের জন্য এটি এখানে ভাগ করে নেওয়া ভাল।
কাজ
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import AxesGrid
def shiftedColorMap(cmap, start=0, midpoint=0.5, stop=1.0, name='shiftedcmap'):
'''
Function to offset the "center" of a colormap. Useful for
data with a negative min and positive max and you want the
middle of the colormap's dynamic range to be at zero.
Input
-----
cmap : The matplotlib colormap to be altered
start : Offset from lowest point in the colormap's range.
Defaults to 0.0 (no lower offset). Should be between
0.0 and `midpoint`.
midpoint : The new center of the colormap. Defaults to
0.5 (no shift). Should be between 0.0 and 1.0. In
general, this should be 1 - vmax / (vmax + abs(vmin))
For example if your data range from -15.0 to +5.0 and
you want the center of the colormap at 0.0, `midpoint`
should be set to 1 - 5/(5 + 15)) or 0.75
stop : Offset from highest point in the colormap's range.
Defaults to 1.0 (no upper offset). Should be between
`midpoint` and 1.0.
'''
cdict = {
'red': [],
'green': [],
'blue': [],
'alpha': []
}
reg_index = np.linspace(start, stop, 257)
shift_index = np.hstack([
np.linspace(0.0, midpoint, 128, endpoint=False),
np.linspace(midpoint, 1.0, 129, endpoint=True)
])
for ri, si in zip(reg_index, shift_index):
r, g, b, a = cmap(ri)
cdict['red'].append((si, r, r))
cdict['green'].append((si, g, g))
cdict['blue'].append((si, b, b))
cdict['alpha'].append((si, a, a))
newcmap = matplotlib.colors.LinearSegmentedColormap(name, cdict)
plt.register_cmap(cmap=newcmap)
return newcmap
একটি উদাহরণ
biased_data = np.random.random_integers(low=-15, high=5, size=(37,37))
orig_cmap = matplotlib.cm.coolwarm
shifted_cmap = shiftedColorMap(orig_cmap, midpoint=0.75, name='shifted')
shrunk_cmap = shiftedColorMap(orig_cmap, start=0.15, midpoint=0.75, stop=0.85, name='shrunk')
fig = plt.figure(figsize=(6,6))
grid = AxesGrid(fig, 111, nrows_ncols=(2, 2), axes_pad=0.5,
label_mode="1", share_all=True,
cbar_location="right", cbar_mode="each",
cbar_size="7%", cbar_pad="2%")
im0 = grid[0].imshow(biased_data, interpolation="none", cmap=orig_cmap)
grid.cbar_axes[0].colorbar(im0)
grid[0].set_title('Default behavior (hard to see bias)', fontsize=8)
im1 = grid[1].imshow(biased_data, interpolation="none", cmap=orig_cmap, vmax=15, vmin=-15)
grid.cbar_axes[1].colorbar(im1)
grid[1].set_title('Centered zero manually,\nbut lost upper end of dynamic range', fontsize=8)
im2 = grid[2].imshow(biased_data, interpolation="none", cmap=shifted_cmap)
grid.cbar_axes[2].colorbar(im2)
grid[2].set_title('Recentered cmap with function', fontsize=8)
im3 = grid[3].imshow(biased_data, interpolation="none", cmap=shrunk_cmap)
grid.cbar_axes[3].colorbar(im3)
grid[3].set_title('Recentered cmap with function\nand shrunk range', fontsize=8)
for ax in grid:
ax.set_yticks([])
ax.set_xticks([])
উদাহরণের ফলাফল:
