Put this line after your plt.clim call,
cb.patch.set_facecolor((0.2, 0.2, 0.2, 1.0))
Changing colors of lines intersecting a box , Blend transparency with color in 2D images , Managing multiple figures in pyplot ,See Choosing Colormaps in Matplotlib for an in-depth discussion about colormaps, including colorblind-friendliness.
import numpy as np
import matplotlib.pyplot as plt
cmaps = [('Perceptually Uniform Sequential', [
'viridis', 'plasma', 'inferno', 'magma', 'cividis'
]),
('Sequential', [
'Greys', 'Purples', 'Blues', 'Greens', 'Oranges', 'Reds',
'YlOrBr', 'YlOrRd', 'OrRd', 'PuRd', 'RdPu', 'BuPu',
'GnBu', 'PuBu', 'YlGnBu', 'PuBuGn', 'BuGn', 'YlGn'
]),
('Sequential (2)', [
'binary', 'gist_yarg', 'gist_gray', 'gray', 'bone', 'pink',
'spring', 'summer', 'autumn', 'winter', 'cool', 'Wistia',
'hot', 'afmhot', 'gist_heat', 'copper'
]),
('Diverging', [
'PiYG', 'PRGn', 'BrBG', 'PuOr', 'RdGy', 'RdBu',
'RdYlBu', 'RdYlGn', 'Spectral', 'coolwarm', 'bwr', 'seismic'
]),
('Cyclic', ['twilight', 'twilight_shifted', 'hsv']),
('Qualitative', [
'Pastel1', 'Pastel2', 'Paired', 'Accent',
'Dark2', 'Set1', 'Set2', 'Set3',
'tab10', 'tab20', 'tab20b', 'tab20c'
]),
('Miscellaneous', [
'flag', 'prism', 'ocean', 'gist_earth', 'terrain', 'gist_stern',
'gnuplot', 'gnuplot2', 'CMRmap', 'cubehelix', 'brg',
'gist_rainbow', 'rainbow', 'jet', 'turbo', 'nipy_spectral',
'gist_ncar'
])
]
gradient = np.linspace(0, 1, 256)
gradient = np.vstack((gradient, gradient))
def plot_color_gradients(cmap_category, cmap_list):
# Create figure and adjust figure height to number of colormaps
nrows = len(cmap_list)
figh = 0.35 + 0.15 + (nrows + (nrows - 1) * 0.1) * 0.22
fig, axs = plt.subplots(nrows = nrows, figsize = (6.4, figh))
fig.subplots_adjust(top = 1 - .35 / figh, bottom = .15 / figh, left = 0.2, right = 0.99)
axs[0].set_title(cmap_category + ' colormaps', fontsize = 14)
for ax, cmap_name in zip(axs, cmap_list):
ax.imshow(gradient, aspect = 'auto', cmap = cmap_name)
ax.text(-.01, .5, cmap_name, va = 'center', ha = 'right', fontsize = 10,
transform = ax.transAxes)
# Turn off * all * ticks & spines, not just the ones with colormaps.
for ax in axs:
ax.set_axis_off()
for cmap_category, cmap_list in cmaps:
plot_color_gradients(cmap_category, cmap_list)
plt.show()Bokeh comes with five built-in themes to quickly change the appearance of one or more plots: caliber, dark_minimal, light_minimal, night_sky, and contrast.,Palettes are sequences of RGB(A) hex strings that define a colormap. The sequences you use for defining colormaps can be either lists or tuples. Once you have created a colormap, you can use it with the color attribute of many plot objects from bokeh.plotting.,Bokeh objects have several properties related to colors. Use those color properties to control the appearance of lines, fills, or text, for example.,For legends in the central layout area, such as those created automatically by bokeh.plotting, set location to one of the following values:
from bokeh.io
import curdoc
from bokeh.plotting
import figure, output_file, show
x = [1, 2, 3, 4, 5]
y = [6, 7, 6, 4, 5]
output_file("dark_minimal.html")
curdoc().theme = 'dark_minimal'
p = figure(title = 'dark_minimal', width = 300, height = 300)
p.line(x, y)
show(p)attrs:
Figure:
background_fill_color: '#2F2F2F'
border_fill_color: '#2F2F2F'
outline_line_color: '#444444'
Axis:
axis_line_color: !!null
Grid:
grid_line_dash: [6, 4]
grid_line_alpha: .3
Title:
text_color: "white"from bokeh.themes
import Theme
curdoc().theme = Theme(filename = "./theme.yml")>>> from bokeh.palettes
import Spectral6
>>>
Spectral6['#3288bd', '#99d594', '#e6f598', '#fee08b', '#fc8d59', '#d53e4f'] x < 0: 'red'
# values < low are clamped
0 >= x < 33: 'red'
33 >= x < 66: 'green'
66 >= x < 99: 'blue'
99 >= x: 'blue'
# values > high are clampedfrom bokeh.models
import ColorBar, ColumnDataSource
from bokeh.palettes
import Spectral6
from bokeh.plotting
import figure, output_file, show
from bokeh.transform
import linear_cmap
output_file("styling_linear_mappers.html", title = "styling_linear_mappers.py example")
x = [1, 2, 3, 4, 5, 7, 8, 9, 10]
y = [1, 2, 3, 4, 5, 7, 8, 9, 10]
#Use the field name of the column source
mapper = linear_cmap(field_name = 'y', palette = Spectral6, low = min(y), high = max(y))
source = ColumnDataSource(dict(x = x, y = y))
p = figure(width = 300, height = 300, title = "Linear Color Map Based on Y")
p.circle(x = 'x', y = 'y', line_color = mapper, color = mapper, fill_alpha = 1, size = 12, source = source)
color_bar = ColorBar(color_mapper = mapper['transform'], width = 8)
p.add_layout(color_bar, 'right')
show(p)You can change the color of bars in a barplot using color argument. RGB is a way of making colors. You have to to provide an amount of red, green, blue, and the transparency value to the color argument and it returns a color.,If you want to give different colors to each bar, just provide a list of color names to the color argument:,The edgecolor argument allows you to color the borders of barplots.,👋 This document is a work by Yan Holtz. Any feedback is highly encouraged. You can fill an issue on Github, drop me a message onTwitter, or send an email pasting yan.holtz.data with gmail.com.
# libraries
import numpy as np
import matplotlib.pyplot as plt
# create a dataset
height = [3, 12, 5, 18, 45]
bars = ('A', 'B', 'C', 'D', 'E')
x_pos = np.arange(len(bars))
# Create bars
plt.bar(x_pos, height, color = (0.2, 0.4, 0.6, 0.6))
# Create names on the x - axis
plt.xticks(x_pos, bars)
# Show graph
plt.show()# libraries
import numpy as np
import matplotlib.pyplot as plt
# create a dataset
height = [3, 12, 5, 18, 45]
bars = ('A', 'B', 'C', 'D', 'E')
x_pos = np.arange(len(bars))
# Create bars with different colors
plt.bar(x_pos, height, color = ['black', 'red', 'green', 'blue', 'cyan'])
# Create names on the x - axis
plt.xticks(x_pos, bars)
# Show graph
plt.show()# libraries
import numpy as np
import matplotlib.pyplot as plt
# create a dataset
height = [3, 12, 5, 18, 45]
bars = ('A', 'B', 'C', 'D', 'E')
x_pos = np.arange(len(bars))
# Create bars with blue edge color
plt.bar(x_pos, height, color = (0.1, 0.1, 0.1, 0.1), edgecolor = 'blue')
# Create names on the x - axis
plt.xticks(x_pos, bars)
# Show graph
plt.show()New resources are available to specify the opacity of classes of graphical elements: , Other concepts illustrated are the use of new resources cnFillPalette and vcLevelPalette to load desired colormaps, and the direct specification of color for the map backgrounds, rather than by giving colormap indices. , The first frame shows what the graphic would look like if you ran this script with NCL version 6.0.0 or earlier. You get a bluish color for the land, because this was the closest match to gray that NCL found in the workstation color map. , The second frame draws a partially opaque contour plot by setting cnFillOpacityF to 0.5. If you have NCL V6.3.0 or earlier, you will note that the labelbar colors do not reflect the same opacities.
res @gsLineColor = (/ 1., 0., 0., .5 /)The open source tool gdal_translate was used to convert an original image (in .png, .jpg, .gif, etc.), into a NetCDF file with the color-channels pre-separated:
gdal_translate - ot Int16 - of netCDF fuji_orig.jpg fuji.nc
As with example #9, the open source tool gdal_translate was used to convert the jpeg file to a NetCDF file:
gdal_translate - ot Int16 - of netCDF EarthMap_2500x1250.jpg EarthMap_2500x1250.nc
load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/gsn_code.ncl"
load "$NCARG_ROOT/lib/ncarg/nclscripts/csm/gsn_csm.ncl"
load "./fill_opacities_fix.ncl"