/
colormaps.py
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/
colormaps.py
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"""Module for commonly used colormaps and palettes for visualizing Earth Engine data.
"""
# *******************************************************************************#
# This module contains extra features of the geemap package. #
# The geemap community will maintain the extra features. #
# *******************************************************************************#
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
from box import Box
_palette_dict = {
"ndvi": [
"FFFFFF",
"CE7E45",
"DF923D",
"F1B555",
"FCD163",
"99B718",
"74A901",
"66A000",
"529400",
"3E8601",
"207401",
"056201",
"004C00",
"023B01",
"012E01",
"011D01",
"011301",
],
"ndwi": [
"#ece7f2",
"#d0d1e6",
"#a6bddb",
"#74a9cf",
"#3690c0",
"#0570b0",
"#045a8d",
"#023858",
],
"dem": ["006633", "E5FFCC", "662A00", "D8D8D8", "F5F5F5"],
"dw": [
"#419BDF",
"#397D49",
"#88B053",
"#7A87C6",
"#E49635",
"#DFC35A",
"#C4281B",
"#A59B8F",
"#B39FE1",
],
"esri_lulc": [
"#1A5BAB",
"#358221",
"#000000",
"#87D19E",
"#FFDB5C",
"#000000",
"#ED022A",
"#EDE9E4",
"#F2FAFF",
"#C8C8C8",
"#C6AD8D",
],
}
def get_palette(cmap_name=None, n_class=None, hashtag=False):
"""Get a palette from a matplotlib colormap. See the list of colormaps at https://matplotlib.org/stable/tutorials/colors/colormaps.html.
Args:
cmap_name (str, optional): The name of the matplotlib colormap. Defaults to None.
n_class (int, optional): The number of colors. Defaults to None.
hashtag (bool, optional): Whether to return a list of hex colors. Defaults to False.
Returns:
list: A list of hex colors.
"""
if cmap_name in ["ndvi", "ndwi", "dem", "dw", "esri_lulc"]:
colors = _palette_dict[cmap_name]
else:
cmap = plt.cm.get_cmap(cmap_name, n_class)
colors = [mpl.colors.rgb2hex(cmap(i))[1:] for i in range(cmap.N)]
if hashtag:
colors = ["#" + i for i in colors]
return colors
def get_colorbar(
colors,
vmin=0,
vmax=1,
width=6.0,
height=0.4,
orientation="horizontal",
discrete=False,
return_fig=False,
):
"""Creates a colorbar based on custom colors.
Args:
colors (list): A list of hex colors.
vmin (float, optional): The minimum value range. Defaults to 0.
vmax (float, optional): The maximum value range. Defaults to 1.0.
width (float, optional): The width of the colormap. Defaults to 6.0.
height (float, optional): The height of the colormap. Defaults to 0.4.
orientation (str, optional): The orientation of the colormap. Defaults to "horizontal".
discrete (bool, optional): Whether to create a discrete colormap.
return_fig (bool, optional): Whether to return the figure. Defaults to False.
"""
hexcodes = [i if i[0] == "#" else "#" + i for i in colors]
fig, ax = plt.subplots(figsize=(width, height))
if discrete:
cmap = mpl.colors.ListedColormap(hexcodes)
vals = np.linspace(vmin, vmax, cmap.N + 1)
norm = mpl.colors.BoundaryNorm(vals, cmap.N)
else:
cmap = mpl.colors.LinearSegmentedColormap.from_list("custom", hexcodes, N=256)
norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
mpl.colorbar.ColorbarBase(ax, norm=norm, cmap=cmap, orientation=orientation)
if return_fig:
return fig
else:
plt.show()
def list_colormaps(add_extra=False, lowercase=False):
"""List all available colormaps. See a complete lost of colormaps at https://matplotlib.org/stable/tutorials/colors/colormaps.html.
Returns:
list: The list of colormap names.
"""
result = plt.colormaps()
if add_extra:
result += ["dem", "ndvi", "ndwi"]
if lowercase:
result = [i.lower() for i in result]
result.sort()
return result
def plot_colormap(
cmap,
width=8.0,
height=0.4,
orientation="horizontal",
vmin=0,
vmax=1.0,
axis_off=True,
show_name=False,
font_size=12,
return_fig=False,
):
"""Plot a matplotlib colormap.
Args:
cmap (str): The name of the colormap.
width (float, optional): The width of the colormap. Defaults to 8.0.
height (float, optional): The height of the colormap. Defaults to 0.4.
orientation (str, optional): The orientation of the colormap. Defaults to "horizontal".
vmin (float, optional): The minimum value range. Defaults to 0.
vmax (float, optional): The maximum value range. Defaults to 1.0.
axis_off (bool, optional): Whether to turn axis off. Defaults to True.
show_name (bool, optional): Whether to show the colormap name. Defaults to False.
font_size (int, optional): Font size of the text. Defaults to 12.
return_fig (bool, optional): Whether to return the figure. Defaults to False.
"""
fig, ax = plt.subplots(figsize=(width, height))
col_map = plt.get_cmap(cmap)
norm = mpl.colors.Normalize(vmin=vmin, vmax=vmax)
mpl.colorbar.ColorbarBase(ax, norm=norm, cmap=col_map, orientation=orientation)
if axis_off:
ax.set_axis_off()
if show_name:
pos = list(ax.get_position().bounds)
x_text = pos[0] - 0.01
y_text = pos[1] + pos[3] / 2.0
fig.text(x_text, y_text, cmap, va="center", ha="right", fontsize=font_size)
if return_fig:
return fig
else:
plt.show()
def plot_colormaps(width=8.0, height=0.4):
"""Plot all available colormaps.
Args:
width (float, optional): Width of the colormap. Defaults to 8.0.
height (float, optional): Height of the colormap. Defaults to 0.4.
"""
cmap_list = list_colormaps()
nrows = len(cmap_list)
fig, axes = plt.subplots(nrows=nrows, figsize=(width, height * nrows))
fig.subplots_adjust(top=0.95, bottom=0.01, left=0.2, right=0.99)
gradient = np.linspace(0, 1, 256)
gradient = np.vstack((gradient, gradient))
for ax, name in zip(axes, cmap_list):
ax.imshow(gradient, aspect="auto", cmap=plt.get_cmap(name))
ax.set_axis_off()
pos = list(ax.get_position().bounds)
x_text = pos[0] - 0.01
y_text = pos[1] + pos[3] / 2.0
fig.text(x_text, y_text, name, va="center", ha="right", fontsize=12)
# Turn off *all* ticks & spines, not just the ones with colormaps.
for ax in axes:
ax.set_axis_off()
plt.show()
for index, cmap_name in enumerate(list_colormaps()):
if index < len(list_colormaps()):
color_dict = {}
color_dict["default"] = get_palette(cmap_name)
for i in range(3, 13):
name = "n" + str(i).zfill(2)
colors = get_palette(cmap_name, i)
color_dict[name] = colors
_palette_dict[cmap_name] = color_dict
palettes = Box(_palette_dict, frozen_box=True)