I have a (256,256) numpy array. Each element has a value of 1 or 2 or 3.
I would like to plot this array as an image by:`plt.plot(A)'
What I would like to do is assigning this colour scheme to every element;
1: red, 2:blue and 3: gray.
I have no idea how I can do that, Can someone please help me?
Thanks
You could use plt.imshow() and a ListedColormap. The example below uses yellow instead of gray to see the different colors a bit better ('lightgray' probably looks better than 'gray').
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
import numpy as np
from scipy.ndimage import gaussian_filter
fig = plt.figure(figsize=(18, 14))
A = (gaussian_filter(np.random.randn(256, 256), 20) * 200).astype(int) % 3 + 1
cmap = ListedColormap(['crimson', 'dodgerblue', 'yellow'])
plt.imshow(A, origin='lower', cmap=cmap)
cbar = plt.colorbar(ticks=np.linspace(1, 3, 7)[1::2])
cbar.set_ticklabels([1, 2, 3])
plt.show()
Related
I am trying to plot some data with the following code
from sklearn.datasets import make_blobs
import seaborn as sns
import numpy as np
X, y = make_blobs(n_samples=1000, n_features=2, centers=10, cluster_std=1.0, center_box=(-10.0, 10.0), shuffle=True, random_state=None)
palette = np.array(sns.color_palette("bright", 10)) #Chossing color
sns.scatterplot(X[:,0],X[:,1],legend='full',c=palette[y])
The color is beautiful, but the legend is missing.
When I check the documentation, I see:
How to draw the legend. If “brief”, numeric hue and size variables
....
So it seems I also need to include the hue argument.
But when I try the hue argument with the following codes, the following graph is created instead...
sns.scatterplot(X[:,0],X[:,1],legend='full',hue=y,c=palette[y])
The legend is showing, but the color is not what I want. After adding the hue argument, it seems it overwrite the palette argument. No matter what palette I choose, the color is still ugly as hell...
My question is:
How to show legend while maintaining the color that I want?
You would need to use the palette kwarg, and specify the hues with your y values.
from sklearn.datasets import make_blobs
import seaborn as sns
import matplotlib.pyplot as plt
X, y = make_blobs(n_samples=1000, n_features=2, centers=10, cluster_std=1.0,
center_box=(-10.0, 10.0), shuffle=True, random_state=None)
palette = sns.color_palette("bright", 10) #Choosing color
sns.scatterplot(X[:, 0], X[:, 1], palette=palette, hue=y, legend='full')
plt.show()
With a horizontal log-scaled color bar and logged labels along the bottom, is it possible to show the exponentiated (original) values along the top?
So in this example, there should be ticks and labels along the top of the color bar going from mat.min() = 0.058 to mat.max() = 13.396
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
mat = np.exp(np.random.randn(20, 20))
plt.matshow(mat)
norm = mpl.colors.Normalize(1, np.log(mat.max()))
plt.colorbar(plt.cm.ScalarMappable(norm=norm), orientation="horizontal")
plt.savefig("rand_mat.png", dpi=200)
Here is the best answer for your response. I've customized it based on that. Does this result match the intent of your question? The color bar and the size of the figure are not the same, so I adjusted them.
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
np.random.seed(20210404)
mat = np.exp(np.random.randn(20, 20))
norm = mpl.colors.Normalize(1, np.log(mat.max()))
fig, (ax, cax) = plt.subplots(nrows=2, gridspec_kw=dict(height_ratios=[15,1],hspace=0.5))
im = ax.matshow(mat)
cbar = plt.colorbar(plt.cm.ScalarMappable(norm=norm), orientation="horizontal", cax=cax)
cax2 = cax.twiny()
cbar.ax.xaxis.set_label_position("bottom")
iticks = np.arange(mat.min(), mat.max(), 2)
cax2.set_xticks(iticks)
ax_pos = ax.get_position()
cax_pos = cbar.ax.get_position()
new_size = [ax_pos.x0, cax_pos.y0, ax_pos.x1 - ax_pos.x0, cax_pos.y1 - cax_pos.y0]
cbar.ax.set_position(new_size)
plt.show()
At the risk of committing a faux pas, I'll answer my own question with the solution that best suits my needs:
cb.ax.secondary_xaxis("top", functions=(np.exp, np.log))
which gives
Full Code
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
mat = np.exp(np.random.randn(20, 20))
plt.matshow(mat)
norm = mpl.colors.Normalize(np.log(mat.min()), np.log(mat.max()))
cb = plt.colorbar(plt.cm.ScalarMappable(norm=norm), orientation="horizontal")
cb_ax_top = cb.ax.secondary_xaxis("top", functions=(np.exp, np.log))
cb_ax_top.set_xticks([0.1, 0.5, 1, 4, 10, 20])
import numpy as np
import matplotlib.pyplot as plt
% matplotlib inline
The following code shows the picture is black，Why not show white color?
a = np.ones(shape=(2, 2)) * 255
plt.imshow(a, cmap="gray")
plt.imshow() always normalizes by setting the minimum pixel to 0, and sclaing others between 0 and 1. If you have 1 pixel as 0, you could see what I mean.
If you wan't to avoid that, try:
plt.imshow(img, cmap='gray', vmin=0, vmax=255)
I want to create boxplot of data in comparing, my plot looks like
how can I add color like
You can color the box following this example. Beyond that, you will need to map your data in mind to color on the "rainbow" colormap with this module. Here is an example with random test data. I map colors with means in this example.
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import numpy as np
# Random test data
test_data = [np.random.normal(mean, 1, 100) for mean in range(50)]
fig, axes = plt.subplots(figsize=(12, 16))
# Horizontal box plot
bplot = axes.boxplot(test_data,
vert=False, # vertical box aligmnent
patch_artist=True) # fill with color
# Fill with colors
cmap = cm.ScalarMappable(cmap='rainbow')
test_mean = [np.mean(x) for x in test_data]
for patch, color in zip(bplot['boxes'], cmap.to_rgba(test_mean)):
patch.set_facecolor(color)
plt.show()
You can use the cmap property to actually be a function, accepting values between 0 and 1, and call it "normalising" your data. Using matplotlib example on boxplots:
import matplotlib.pyplot as plt
import numpy as np
# Random test data
np.random.seed(123)
all_data = [np.random.normal(0, 5, 100) for std in range(1, 21)]
fig, ax = plt.subplots(nrows=1, figsize=(9, 4))
# rectangular box plot
bplot = ax.boxplot(all_data, 0, '', 0, patch_artist=True)
cm = plt.cm.get_cmap('rainbow')
colors = [cm(val/len(all_data)) for val in range(len(all_data))]
for patch, color in zip(bplot['boxes'], colors):
patch.set_facecolor(color)
plt.show()
I'm trying to create a custom color bar for a matplotlib PolyCollection. Everything seems ok until I attempt to plot a masked array. The color bar no longer shows the correct colors even though the plot does. Is there a different procedure for plotting masked arrays?
I'm using matplotlib 1.4.0 and numpy 1.8.
Here's my plotting code:
import numpy
import matplotlib as mpl
import matplotlib.pyplot as plt
from matplotlib.collections import PolyCollection
vertices = numpy.load('vertices.npy')
array = numpy.load('array.npy')
# Take 2d slice out of 3D array
slice_ = array[:, :, 0:1].flatten(order='F')
fig, ax = plt.subplots()
poly = PolyCollection(vertices, array=slice_, edgecolors='black', linewidth=.25)
cm = mpl.colors.ListedColormap([(1.0, 0.0, 0.0), (.2, .5, .2)])
poly.set_cmap(cm)
bounds = [.1, .4, .6]
norm = mpl.colors.BoundaryNorm(bounds, cm.N)
fig.colorbar(poly, ax=ax, orientation='vertical', boundaries=bounds, norm=norm)
ax.add_collection(poly, autolim=True)
ax.autoscale_view()
plt.show()
Here's what the plot looks like:
However, when I plot a masked array with the following change before the slicing:
array = numpy.ma.array(array, mask=array > .5)
I get a color bar that now shows only a single color. Even though both colors are (correctly) still shown in the plot.
Is there some trick to keeping a colobar consistent when plotting a masked array? I know I can use cm.set_bad to change the color of masked values, but that's not quite what I'm looking for. I want the color bar to show up the same between these two plots since both colors and the color bar itself should remain unchanged.
Pass the BoundaryNorm to the PolyCollection, poly. Otherwise, poly.norm gets set to a matplotlib.colors.Normalize instance by default:
In [119]: poly.norm
Out[119]: <matplotlib.colors.Normalize at 0x7faac4dc8210>
I have not stepped through the source code sufficiently to explain exactly what is happening in the code you posted, but I speculate that the interaction of this Normalize instance and the BoundaryNorm make the range of values seen by the fig.colorbar different than what you expected.
In any case, if you pass norm=norm to PolyCollection, then the result looks correct:
import numpy
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.collections as mcoll
import matplotlib.colors as mcolors
numpy.random.seed(4)
N, M = 3, 3
vertices = numpy.random.random((N, M, 2))
array = numpy.random.random((1, N, 2))
# vertices = numpy.load('vertices.npy')
# array = numpy.load('array.npy')
array = numpy.ma.array(array, mask=array > .5)
# Take 2d slice out of 3D array
slice_ = array[:, :, 0:1].flatten(order='F')
fig, ax = plt.subplots()
bounds = [.1, .4, .6]
cm = mpl.colors.ListedColormap([(1.0, 0.0, 0.0), (.2, .5, .2)])
norm = mpl.colors.BoundaryNorm(bounds, cm.N)
poly = mcoll.PolyCollection(
vertices,
array=slice_,
edgecolors='black', linewidth=.25, norm=norm)
poly.set_cmap(cm)
fig.colorbar(poly, ax=ax, orientation='vertical')
ax.add_collection(poly, autolim=True)
ax.autoscale_view()
plt.show()