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[英]Matplotlib + Cartopy: How to use contourf with custom colormap
[英]How to create a custom diverging colormap in matplotlib?
我最近只是尝试创建一个颜色图来满足我的要求。 这是我尝试构建您需要的颜色图。 我知道这并不完美。 但它向您展示了如何开始。
import matplotlib
import matplotlib.cm as cm
from matplotlib.colors import Normalize
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
# create sample data set
# both will be: 0 - 1
x = np.random.rand(400)
y = np.random.rand(400)
# for realistic use
# set extreme values -900, +900 (approx.)
rval = 900
z = ((x+y)-1)*rval
# set up fig/ax for plotting
fig, ax = plt.subplots(figsize=(5, 5))
# option: set background color
ax.set_facecolor('silver')
# the colormap to create
low2hiColor = None
# create listedColormap
bottom = cm.get_cmap('Blues', 256)
top = cm.get_cmap('Reds_r', 256)
mycolormap = np.vstack((bottom(np.linspace(0.25, 1, 64)),
np.array([
[0.03137255, 0.08823529, 0.41960784, 1.],
[0.02137255, 0.04823529, 0.21960784, 1.],
[0.01137255, 0.02823529, 0.11960784, 1.],
[0.00037255, 0.00823529, 0.00960784, 1.],
#[0.00000255, 0.00000529, 0.00060784, 1.],
])
))
mycolormap = np.vstack((mycolormap,
np.array([
#[0.00060784, 0.00000529, 0.00000255, 1.],
[0.00960784, 0.00823529, 0.00037255, 1.],
[0.11960784, 0.02823529, 0.01137255, 1.],
[0.21960784, 0.04823529, 0.02137255, 1.],
[0.41960784, 0.08823529, 0.03137255, 1.],
])
))
mycolormap = np.vstack((mycolormap,
top(np.linspace(0, 0.75, 64)),
))
low2hiColor = ListedColormap(mycolormap, name='low2hiColor')
# colorbar is created separately using pre-determined `cmap`
minz = -900 #min(z)
maxz = 900 #max(z)
norm_low2hiColor = matplotlib.colors.Normalize(minz, maxz)
# plot dataset as filled contour
norm1 = matplotlib.colors.Normalize(minz, maxz)
cntr1 = ax.tricontourf(x, y, z, levels=64, cmap=low2hiColor, norm=norm1)
gridlines = ax.grid(b=True) # this plot grid
cbar= plt.colorbar( cntr1 )
plt.title("Light-Dark Blue Black Dark-Light Red")
plt.show()
示例图:
我想通过简单地组合现有的颜色图来创建不同的颜色图。
这是代码:
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib import colors
from typing import List, Tuple
def get_hex_col(cmap) -> List[str]:
"""Return list of hex colors for cmap"""
return [colors.rgb2hex(cmap(i)) for i in range(cmap.N)]
def get_cmap_list(
cmap_name: str, length_n: int) -> [str]:
"""Create a classified colormap of length N
"""
cmap = plt.cm.get_cmap(cmap_name, length_n)
cmap_list = get_hex_col(cmap)
return cmap_list
def get_diverging_colormap(
cmap_diverging:Tuple[str,str], color_count: int = k_classes):
"""Create a diverging colormap from two existing with k classes"""
div_cmaps: List[List[str]] = []
for cmap_name in cmap_diverging:
cmap_list = get_cmap_list(
cmap_name, length_n=color_count)
div_cmaps.append(cmap_list)
div_cmaps[1] = list(reversed(div_cmaps[1]))
cmap_nodata_list = div_cmaps[1] + div_cmaps[0]
return colors.ListedColormap(cmap_nodata_list)
# apply
cmaps_diverging: Tuple[str] = ("OrRd", "Purples")
cmap = get_diverging_colormap(cmaps_diverging)
# visualize
def display_hex_colors(hex_colors: List[str]):
"""Visualize a list of hex colors using pandas"""
df = pd.DataFrame(hex_colors).T
df.columns = hex_colors
df.iloc[0,0:len(hex_colors)] = ""
display(df.style.apply(lambda x: apply_formatting(x, hex_colors)))
display_hex_colors(cmap.colors)
我制作了一个简单的工具来帮助创建颜色图并生成所需的代码:
https://eltos.github.io/gradient/#4C71FF-0025B3-000000-C7030D-FC4A53
-->以及您从下载按钮获得的代码:
#!/usr/bin/env python from matplotlib.colors import LinearSegmentedColormap my_gradient = LinearSegmentedColormap.from_list('my_gradient', ( # Edit this gradient at https://eltos.github.io/gradient/#4C71FF-0025B3-000000-C7030D-FC4A53 (0.000, (0.298, 0.443, 1.000)), (0.250, (0.000, 0.145, 0.702)), (0.500, (0.000, 0.000, 0.000)), (0.750, (0.780, 0.012, 0.051)), (1.000, (0.988, 0.290, 0.325)))) if __name__ == '__main__': import numpy as np from matplotlib import pyplot as plt plt.imshow([np.arange(1000)], aspect="auto", cmap=my_gradient) plt.show()
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