A good way to show the concentration of the data points in a plot is using a scatter plot with non-unit transparency. As a result, the areas with more concentration would appear darker.
# this is synthetic example
N = 10000 # a very very large number
x = np.random.normal(0, 1, N)
y = np.random.normal(0, 1, N)
plt.scatter(x, y, marker='.', alpha=0.1) # an area full of dots, darker wherever the number of dots is more
which gives something like this:
Imagine the case we want to emphasize on the outliers. So the situation is almost reversed: A plot in which the less-concentrated areas are bolder. (There might be a trick to apply for my simple example, but imagine a general case where a distribution of points are not known prior, or it's difficult to define a rule for transparency/weight on color.)
I was thinking if there's anything handy same as alpha that is designed for this job specifically. Although other ideas for emphasizing on outliers are also welcomed.
UPDATE: This is what happens when more then one data point is scattered on the same area:
I'm looking for something like the picture below, the more data point, the less transparent the marker.
To answer the question: You can calculate the density of points, normalize it and encode it in the alpha channel of a colormap.
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap
# this is synthetic example
N = 10000 # a very very large number
x = np.random.normal(0, 1, N)
y = np.random.normal(0, 1, N)
fig, (ax,ax2) = plt.subplots(ncols=2, figsize=(8,5))
ax.scatter(x, y, marker='.', alpha=0.1)
values = np.vstack([x,y])
kernel = stats.gaussian_kde(values)
weights = kernel(values)
weights = weights/weights.max()
cols = plt.cm.Blues([0.8, 0.5])
cols[:,3] = [1., 0.005]
cmap = LinearSegmentedColormap.from_list("", cols)
ax2.scatter(x, y, c=weights, s = 1, marker='.', cmap=cmap)
plt.show()
Left is the original image, right is the image where higher density points have a lower alpha.
Note, however, that this is undesireable, because high density transparent points are undistinguishable from low density. I.e. in the right image it really looks as though you have a hole in the middle of your distribution.
Clearly, a solution with a colormap which does not contain the color of the background is a lot less confusing to the reader.
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
# this is synthetic example
N = 10000 # a very very large number
x = np.random.normal(0, 1, N)
y = np.random.normal(0, 1, N)
fig, ax = plt.subplots(figsize=(5,5))
values = np.vstack([x,y])
kernel = stats.gaussian_kde(values)
weights = kernel(values)
weights = weights/weights.max()
ax.scatter(x, y, c = weights, s=9, edgecolor="none", marker='.', cmap="magma")
plt.show()
Here, low density points are still emphazised by darker color, but at the same time it's clear to the viewer that the highest density lies in the middle.
As far as I know, there is no "direct" solution to this quite interesting problem. As a workaround, I propose this solution:
N = 10000 # a very very large number
x = np.random.normal(0, 1, N)
y = np.random.normal(0, 1, N)
fig = plt.figure() # create figure directly to be able to extract the bg color
ax = fig.gca()
ax.scatter(x, y, marker='.') # plot all markers without alpha
bgcolor = ax.get_facecolor() # extract current background color
# plot with alpha, "overwriting" dense points
ax.scatter(x, y, marker='.', color=bgcolor, alpha=0.2)
This will plot all points without transparency and then plot all points again with some transparency, "overwriting" those points with the highest density the most. Setting the alpha value to other higher values will put more emphasis to outliers and vice versa.
Of course the color of the second scatter plot needs to be adjusted to your background color. In my example this is done by extracting the background color and setting it as the new scatter plot's color.
This solution is independent of the kind of distribution. It only depends on the density of the points. However it produces twice the amount of points, thus may take slightly longer to render.
Reproducing the edit in the question, my solution is showing exactly the desired behavior. The leftmost point is a single point and is the darkest, the rightmost is consisting of three points and is the lightest color.
x = [0, 1, 1, 2, 2, 2]
y = [0, 0, 0, 0, 0, 0]
fig = plt.figure() # create figure directly to be able to extract the bg color
ax = fig.gca()
ax.scatter(x, y, marker='.', s=10000) # plot all markers without alpha
bgcolor = ax.get_facecolor() # extract current background color
# plot with alpha, "overwriting" dense points
ax.scatter(x, y, marker='.', color=bgcolor, alpha=0.2, s=10000)
Assuming that the distributions are centered around a specific point (e.g. (0,0) in this case), I would use this:
import numpy as np
import matplotlib.pyplot as plt
N = 500
# 0 mean, 0.2 std
x = np.random.normal(0,0.2,N)
y = np.random.normal(0,0.2,N)
# calculate the distance to (0, 0).
color = np.sqrt((x-0)**2 + (y-0)**2)
plt.scatter(x , y, c=color, cmap='plasma', alpha=0.7)
plt.show()
Results:
I don't know if it helps you, because it's not exactly you asked for, but you can simply color points, which values are bigger than some threshold. For example:
import matplotlib.pyplot as plt
num = 100
threshold = 80
x = np.linspace(0, 100, num=num)
y = np.random.normal(size=num)*45
fig = plt.figure()
ax = fig.add_subplot(1, 1, 1)
ax.scatter(x[np.abs(y) < threshold], y[np.abs(y) < threshold], color="#00FFAA")
ax.scatter(x[np.abs(y) >= threshold], y[np.abs(y) >= threshold], color="#AA00FF")
plt.show()
Related
I'm very new to Python, and I was trying to use this problem as a learning exercise, but I can't get anywhere with it.
What I want to do is to show that for two random variables that come uniformly distributed within a 200ns window, the probability of them arriving within 7ns of each other is ~5%:
X, Y ~ U[0, 200]
Z = X - Y
P(|Z| < 7) = ?
I wanted to know the most analytical way of doing this, because I thought Python might have some useful libraries to help, and because if I wanted to do a stochastic simulation I would do it in C++ ROOT which would take me far less time!
The way that I've done it is below, but it's different from what I've calculated analytically. Can anyone suggest a better/more accurate way of solving the same problem?
Thanks a lot!
from scipy.stats import uniform, expon
import matplotlib.pyplot as plt
import numpy as np
fig, ax = plt.subplots(1, 1)
a, b = 0, 200
size = 1000000
# Genrating uniform distribution
uniform_distribution = uniform(loc=a, scale=b)
x = uniform_distribution.rvs(size=size)
y = uniform_distribution.rvs(size=size)
z=x-y
ax.hist(z)
zsmall=[z for i in z if abs(i)<7]
n=len(zsmall)
print("probability = ",n/size)
Edit: added some code to improve the figure.
Your code is fine, and the results do agree with the analytically derived value. To see this more readily, I have modified your code slightly, scaling the domains of X and Y down to [0, 1] and computing P(|Z| < 7/200), so that this is still equivalent to your original question.
from scipy.stats import uniform
import matplotlib.pyplot as plt
a, b = 0, 1
size = 1000000
# generate uniformly distributed x and y
uniform_distribution = uniform(loc=a, scale=b)
x = uniform_distribution.rvs(size=size)
y = uniform_distribution.rvs(size=size)
z = x - y
# set up figure
fig, ax = plt.subplots(figsize = [16, 8])
ax.set_aspect('equal')
ax.set_xlim([-1, 1])
ax.set_ylim([0, 1])
ax.set_xticks([-1, 0, 1])
ax.set_xticklabels([-1, 0, 1], size=20)
ax.set_yticks([0, 1])
ax.set_yticklabels([0, 1], size=20)
# plot histogram with y-axis scaled to show density,
# increased bin number for better resolution
ax.hist(z, density=True, bins=200, alpha=0.5)
# plot lines around the area we want to estimate
plt.axvline(-7/200, color='black', linestyle='--')
ax.annotate('x = -7/200', xy=(-7/200, 0.4), xytext=(-0.05, 0.4), fontsize=16, ha='right')
plt.axvline( 7/200, color='black', linestyle='--')
ax.annotate('x = 7/200', xy=(7/200, 0.2), xytext=(0.05, 0.2), fontsize=16)
# plot theoretical probability density function
ax.plot([-1, 0], [0, 1], color='gray', linestyle=':')
ax.plot([ 0, 1], [1, 0], color='gray', linestyle=':')
zsmall = [1 for i in z if abs(i) < 7/200]
n = len(zsmall)
print("probability =", n/size)
probability = 0.06857
As you can see, this approaches the theoretically expected triangular distribution (gray dotted lines) pretty closely already. For comparison, we can calculate the theoretical probability, which is the area between the dashed lines and below the dotted lines. We can compute this as the area of the whole rectangle between the dashed lines minus the area of the square consisting of the two small triangles above the dotted lines:
2*(7/200) - (7/200)**2
= 0.068775
So the theoretical value does agree with your simulation result.
I am trying to make use the polar plot projection to make a radar chart. I would like to know how to put only one grid line in bold (while the others should remain standard).
For my specific case, I would like to highlight the gridline associated to the ytick "0".
from matplotlib import pyplot as plt
import pandas as pd
import numpy as np
#Variables
sespi = pd.read_csv("country_progress.csv")
labels = sespi.country
progress = sespi.progress
angles=np.linspace(0, 2*np.pi, len(labels), endpoint=False)
#Concatenation to close the plots
progress=np.concatenate((progress,[progress[0]]))
angles=np.concatenate((angles,[angles[0]]))
#Polar plot
fig=plt.figure()
ax = fig.add_subplot(111, polar=True)
ax.plot(angles, progress, '.--', linewidth=1, c="g")
#ax.fill(angles, progress, alpha=0.25)
ax.set_thetagrids(angles * 180/np.pi, labels)
ax.set_yticklabels([-200,-150,-100,-50,0,50,100,150,200])
#ax.set_title()
ax.grid(True)
plt.show()
The gridlines of a plot are Line2D objects. Therefore you can't make it bold. What you can do (as shown, in part, in the other answer) is to increase the linewidth and change the colour but rather than plot a new line you can do this to the specified gridline.
You first need to find the index of the y tick labels which you want to change:
y_tick_labels = [-100,-10,0,10]
ind = y_tick_labels.index(0) # find index of value 0
You can then get a list of the gridlines using gridlines = ax.yaxis.get_gridlines(). Then use the index you found previously on this list to change the properties of the correct gridline.
Using the example from the gallery as a basis, a full example is shown below:
r = np.arange(0, 2, 0.01)
theta = 2 * np.pi * r
ax = plt.subplot(111, projection='polar')
ax.set_rmax(2)
ax.set_rticks([0.5, 1, 1.5, 2]) # less radial ticks
ax.set_rlabel_position(-22.5) # get radial labels away from plotted line
ax.grid(True)
y_tick_labels = [-100, -10, 0, 10]
ax.set_yticklabels(y_tick_labels)
ind = y_tick_labels.index(0) # find index of value 0
gridlines = ax.yaxis.get_gridlines()
gridlines[ind].set_color("k")
gridlines[ind].set_linewidth(2.5)
plt.show()
Which gives:
It is just a trick, but I guess you could just plot a circle and change its linewidth and color to whatever could be bold for you.
For example:
import matplotlib.pyplot as plt
import numpy as np
Yline = 0
Npoints = 300
angles = np.linspace(0,360,Npoints)*np.pi/180
line = 0*angles + Yline
ax = plt.subplot(111, projection='polar')
plt.plot(angles, line, color = 'k', linewidth = 3)
plt.ylim([-1,1])
plt.grid(True)
plt.show()
In this piece of code, I plot a line using plt.plot between any point of the two vectors angles and line. The former is actually all the angles between 0 and 2*np.pi. The latter is constant, and equal to the 'height' you want to plot that line Yline.
I suggest you try to decrease and increase Npoints while having a look to the documentaion of np.linspace() in order to understand your problem with the roundness of the circle.
This question already has answers here:
Shifted colorbar matplotlib
(1 answer)
Defining the midpoint of a colormap in matplotlib
(10 answers)
Closed 5 years ago.
For my current project I need a heat map. The heat map needs a scalable color palette, because the values are interesting only in a small range. That means, even if I have values from 0 to 1, interesting is only the part between 0.6 and 0.9; so I would like to scale the heat map colors accordingly, plus show the scale next to the chart.
In Matplotlib I had no way of setting the mid point of a color palette except for overloading the original class, like shown here in the matplotlib guide.
This is exactly what I need, but without the disadvantages of the unclean data structure in Matplotlib.
So I tried Bokeh.
In five minutes I achieved more than with Matplotlib in an hour, however, I got stuck when I wanted to show the color scale next to the heatmap and when I wanted to change the scale of the color palette.
So, here are my questions:
How can I scale the color palette in Bokeh or Matplotlib?
Is there a way to display the annotated color bar next to the heatmap?
import pandas
scores_df = pd.DataFrame(myScores, index=c_range, columns=gamma_range)
import bkcharts
from bokeh.palettes import Inferno256
hm = bkcharts.HeatMap(scores_df, palette=Inferno256)
# here: how to insert a color bar?
# here: how to correctly scale the inferno256 palette?
hm.ylabel = "C"
hm.xlabel = "gamma"
bkcharts.output_file('heatmap.html')
Following Aarons tips, i now implemented it as follows:
import matplotlib.pyplot as plt
import matplotlib.colors as colors
from bokeh.palettes import Inferno256
def print_scores(scores, gamma_range, C_range):
# load a color map
# find other colormaps here
# https://docs.bokeh.org/en/latest/docs/reference/palettes.html
cmap = colors.ListedColormap(Inferno256, len(Inferno256))
fig, ax = plt.subplots(1, 1, figsize=(6, 5))
# adjust lower, midlle and upper bound of the colormap
cmin = np.percentile(scores, 10)
cmid = np.percentile(scores, 75)
cmax = np.percentile(scores, 99)
bounds = np.append(np.linspace(cmin, cmid), np.linspace(cmid, cmax))
norm = colors.BoundaryNorm(boundaries=bounds, ncolors=len(Inferno256))
pcm = ax.pcolormesh(np.log10(gamma_range),
np.log10(C_range),
scores,
norm=norm,
cmap=cmap)
fig.colorbar(pcm, ax=ax, extend='both', orientation='vertical')
plt.show()
ImportanceOfBeingErnest correctly pointed out that my first comment wasn't entirely clear (or accurately worded)..
Most plotting functions in mpl have a kwarg: norm= this denotes a class (subclass of mpl.colors.Normalize) that will map your array of data to the values [0 - 1] for the purpose of mapping to the colormap, but not actually impact the numerical values of the data. There are several built in subclasses, and you can also create your own. For this application, I would probably utilize BoundaryNorm. This class maps N-1 evenly spaced colors to the space between N discreet boundaries.
I have modified the example slightly to better fit your application:
#adaptation of https://matplotlib.org/users/colormapnorms.html#discrete-bounds
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as colors
from matplotlib.mlab import bivariate_normal
#example data
N = 100
X, Y = np.mgrid[-3:3:complex(0, N), -2:2:complex(0, N)]
Z1 = (bivariate_normal(X, Y, 1., 1., 1.0, 1.0))**2 \
- 0.4 * (bivariate_normal(X, Y, 1.0, 1.0, -1.0, 0.0))**2
Z1 = Z1/0.03
'''
BoundaryNorm: For this one you provide the boundaries for your colors,
and the Norm puts the first color in between the first pair, the
second color between the second pair, etc.
'''
fig, ax = plt.subplots(3, 1, figsize=(8, 8))
ax = ax.flatten()
# even bounds gives a contour-like effect
bounds = np.linspace(-1, 1)
norm = colors.BoundaryNorm(boundaries=bounds, ncolors=256)
pcm = ax[0].pcolormesh(X, Y, Z1,
norm=norm,
cmap='RdBu_r')
fig.colorbar(pcm, ax=ax[0], extend='both', orientation='vertical')
# clipped bounds emphasize particular region of data:
bounds = np.linspace(-.2, .5)
norm = colors.BoundaryNorm(boundaries=bounds, ncolors=256)
pcm = ax[1].pcolormesh(X, Y, Z1, norm=norm, cmap='RdBu_r')
fig.colorbar(pcm, ax=ax[1], extend='both', orientation='vertical')
# now if we want 0 to be white still, we must have 0 in the middle of our array
bounds = np.append(np.linspace(-.2, 0), np.linspace(0, .5))
norm = colors.BoundaryNorm(boundaries=bounds, ncolors=256)
pcm = ax[2].pcolormesh(X, Y, Z1, norm=norm, cmap='RdBu_r')
fig.colorbar(pcm, ax=ax[2], extend='both', orientation='vertical')
fig.show()
I have quite a messy plot and so, in order to tidy it up, I want to make those points with larger error bars less significant by reducing their alpha value. Preferably, I'd like to map a continuous scale of alpha values (like a colourmap) to each point and its errorbar according to their errorbar size - I'm not too sure what the best/efficient way to go about doing this would be.
You can certainly set the alpha of the errorbars, but I think you need to plot each one separately because Matplotlib won't set the opacity (or color) of the vertical lines and caplines to a sequence (as far as I know).
If you want the markers to have their opacity matching the errorbars, its probably easier to build a sequence of colors based on some normalization:
import numpy as np
import matplotlib.pyplot as plt
n = 20
x = np.linspace(1, 10, n)
y = x - x**2
minerr = 2
yerr = abs(np.random.randn(n) * 15) + minerr
maxerr = max(yerr)
err_range = maxerr - minerr
alphas = [1 - (err-minerr)/(err_range) for err in yerr]
colors = np.asarray([(1,0,0, alpha) for alpha in alphas])
plt.scatter(x,y, c=colors, edgecolors=colors)
for pos, ypt, err, color in zip(x, y, yerr, colors):
plotline, caplines, (barlinecols,) = plt.errorbar(pos, ypt, err, lw=2, color=color, capsize=5, capthick=2)
plt.xlim(0,11)
plt.show()
However, you might want to think about whether the effect you create might misrepresent your data (i.e. make it look more accurate than it is by emphasizing only the points with small error bars).
You can set a colour as the third variable on a scatter graph (see this answer). To change alpha, you could change only the fourth value (transparency) in color based on the scaled range. As a minimal example,
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(0, 20, 100)
y = np.sin(x)
z = x + 20 * y
scaled_z = (z - z.min()) / z.ptp()
colors = [[0., 0., 0., i] for i in scaled_z]
plt.scatter(x, y, marker='x', edgecolors=colors, s=150, linewidths=4)
plt.show()
Which looks like
I am trying to make a discrete colorbar for a scatterplot in matplotlib
I have my x, y data and for each point an integer tag value which I want to be represented with a unique colour, e.g.
plt.scatter(x, y, c=tag)
typically tag will be an integer ranging from 0-20, but the exact range may change
so far I have just used the default settings, e.g.
plt.colorbar()
which gives a continuous range of colours. Ideally i would like a set of n discrete colours (n=20 in this example). Even better would be to get a tag value of 0 to produce a gray colour and 1-20 be colourful.
I have found some 'cookbook' scripts but they are very complicated and I cannot think they are the right way to solve a seemingly simple problem
You can create a custom discrete colorbar quite easily by using a BoundaryNorm as normalizer for your scatter. The quirky bit (in my method) is making 0 showup as grey.
For images i often use the cmap.set_bad() and convert my data to a numpy masked array. That would be much easier to make 0 grey, but i couldnt get this to work with the scatter or the custom cmap.
As an alternative you can make your own cmap from scratch, or read-out an existing one and override just some specific entries.
import numpy as np
import matplotlib as mpl
import matplotlib.pylab as plt
fig, ax = plt.subplots(1, 1, figsize=(6, 6)) # setup the plot
x = np.random.rand(20) # define the data
y = np.random.rand(20) # define the data
tag = np.random.randint(0, 20, 20)
tag[10:12] = 0 # make sure there are some 0 values to show up as grey
cmap = plt.cm.jet # define the colormap
# extract all colors from the .jet map
cmaplist = [cmap(i) for i in range(cmap.N)]
# force the first color entry to be grey
cmaplist[0] = (.5, .5, .5, 1.0)
# create the new map
cmap = mpl.colors.LinearSegmentedColormap.from_list(
'Custom cmap', cmaplist, cmap.N)
# define the bins and normalize
bounds = np.linspace(0, 20, 21)
norm = mpl.colors.BoundaryNorm(bounds, cmap.N)
# make the scatter
scat = ax.scatter(x, y, c=tag, s=np.random.randint(100, 500, 20),
cmap=cmap, norm=norm)
# create a second axes for the colorbar
ax2 = fig.add_axes([0.95, 0.1, 0.03, 0.8])
cb = plt.colorbar.ColorbarBase(ax2, cmap=cmap, norm=norm,
spacing='proportional', ticks=bounds, boundaries=bounds, format='%1i')
ax.set_title('Well defined discrete colors')
ax2.set_ylabel('Very custom cbar [-]', size=12)
I personally think that with 20 different colors its a bit hard to read the specific value, but thats up to you of course.
You could follow this example below or the newly added example in the documentation
#!/usr/bin/env python
"""
Use a pcolor or imshow with a custom colormap to make a contour plot.
Since this example was initially written, a proper contour routine was
added to matplotlib - see contour_demo.py and
http://matplotlib.sf.net/matplotlib.pylab.html#-contour.
"""
from pylab import *
delta = 0.01
x = arange(-3.0, 3.0, delta)
y = arange(-3.0, 3.0, delta)
X,Y = meshgrid(x, y)
Z1 = bivariate_normal(X, Y, 1.0, 1.0, 0.0, 0.0)
Z2 = bivariate_normal(X, Y, 1.5, 0.5, 1, 1)
Z = Z2 - Z1 # difference of Gaussians
cmap = cm.get_cmap('PiYG', 11) # 11 discrete colors
im = imshow(Z, cmap=cmap, interpolation='bilinear',
vmax=abs(Z).max(), vmin=-abs(Z).max())
axis('off')
colorbar()
show()
which produces the following image:
The above answers are good, except they don't have proper tick placement on the colorbar. I like having the ticks in the middle of the color so that the number -> color mapping is more clear. You can solve this problem by changing the limits of the matshow call:
import matplotlib.pyplot as plt
import numpy as np
def discrete_matshow(data):
# get discrete colormap
cmap = plt.get_cmap('RdBu', np.max(data) - np.min(data) + 1)
# set limits .5 outside true range
mat = plt.matshow(data, cmap=cmap, vmin=np.min(data) - 0.5,
vmax=np.max(data) + 0.5)
# tell the colorbar to tick at integers
cax = plt.colorbar(mat, ticks=np.arange(np.min(data), np.max(data) + 1))
# generate data
a = np.random.randint(1, 9, size=(10, 10))
discrete_matshow(a)
To set a values above or below the range of the colormap, you'll want to use the set_over and set_under methods of the colormap. If you want to flag a particular value, mask it (i.e. create a masked array), and use the set_bad method. (Have a look at the documentation for the base colormap class: http://matplotlib.org/api/colors_api.html#matplotlib.colors.Colormap )
It sounds like you want something like this:
import matplotlib.pyplot as plt
import numpy as np
# Generate some data
x, y, z = np.random.random((3, 30))
z = z * 20 + 0.1
# Set some values in z to 0...
z[:5] = 0
cmap = plt.get_cmap('jet', 20)
cmap.set_under('gray')
fig, ax = plt.subplots()
cax = ax.scatter(x, y, c=z, s=100, cmap=cmap, vmin=0.1, vmax=z.max())
fig.colorbar(cax, extend='min')
plt.show()
This topic is well covered already but I wanted to add something more specific : I wanted to be sure that a certain value would be mapped to that color (not to any color).
It is not complicated but as it took me some time, it might help others not lossing as much time as I did :)
import matplotlib
from matplotlib.colors import ListedColormap
# Let's design a dummy land use field
A = np.reshape([7,2,13,7,2,2], (2,3))
vals = np.unique(A)
# Let's also design our color mapping: 1s should be plotted in blue, 2s in red, etc...
col_dict={1:"blue",
2:"red",
13:"orange",
7:"green"}
# We create a colormar from our list of colors
cm = ListedColormap([col_dict[x] for x in col_dict.keys()])
# Let's also define the description of each category : 1 (blue) is Sea; 2 (red) is burnt, etc... Order should be respected here ! Or using another dict maybe could help.
labels = np.array(["Sea","City","Sand","Forest"])
len_lab = len(labels)
# prepare normalizer
## Prepare bins for the normalizer
norm_bins = np.sort([*col_dict.keys()]) + 0.5
norm_bins = np.insert(norm_bins, 0, np.min(norm_bins) - 1.0)
print(norm_bins)
## Make normalizer and formatter
norm = matplotlib.colors.BoundaryNorm(norm_bins, len_lab, clip=True)
fmt = matplotlib.ticker.FuncFormatter(lambda x, pos: labels[norm(x)])
# Plot our figure
fig,ax = plt.subplots()
im = ax.imshow(A, cmap=cm, norm=norm)
diff = norm_bins[1:] - norm_bins[:-1]
tickz = norm_bins[:-1] + diff / 2
cb = fig.colorbar(im, format=fmt, ticks=tickz)
fig.savefig("example_landuse.png")
plt.show()
I have been investigating these ideas and here is my five cents worth. It avoids calling BoundaryNorm as well as specifying norm as an argument to scatter and colorbar. However I have found no way of eliminating the rather long-winded call to matplotlib.colors.LinearSegmentedColormap.from_list.
Some background is that matplotlib provides so-called qualitative colormaps, intended to use with discrete data. Set1, e.g., has 9 easily distinguishable colors, and tab20 could be used for 20 colors. With these maps it could be natural to use their first n colors to color scatter plots with n categories, as the following example does. The example also produces a colorbar with n discrete colors approprately labelled.
import matplotlib, numpy as np, matplotlib.pyplot as plt
n = 5
from_list = matplotlib.colors.LinearSegmentedColormap.from_list
cm = from_list(None, plt.cm.Set1(range(0,n)), n)
x = np.arange(99)
y = x % 11
z = x % n
plt.scatter(x, y, c=z, cmap=cm)
plt.clim(-0.5, n-0.5)
cb = plt.colorbar(ticks=range(0,n), label='Group')
cb.ax.tick_params(length=0)
which produces the image below. The n in the call to Set1 specifies
the first n colors of that colormap, and the last n in the call to from_list
specifies to construct a map with n colors (the default being 256). In order to set cm as the default colormap with plt.set_cmap, I found it to be necessary to give it a name and register it, viz:
cm = from_list('Set15', plt.cm.Set1(range(0,n)), n)
plt.cm.register_cmap(None, cm)
plt.set_cmap(cm)
...
plt.scatter(x, y, c=z)
I think you'd want to look at colors.ListedColormap to generate your colormap, or if you just need a static colormap I've been working on an app that might help.