I want to create a plot that looks like the image below. There are two unique plots in the figure. img1 was generated using plt.imshow(), while img2 was generated using plt.plot(). The code I used to generate each of the plots is provided below
plt.clf()
plt.imshow(my_matrix)
plt.savefig("mymatrix.png")
plt.clf()
plt.plot(x,y,'o-')
plt.savefig("myplot.png")
The matrix used in img1 is 64x64. The same range for img2's x-axis (x=range(64)). Ideally, the x-axes of the two img2's align with the axes of img1.
It is important to note that the final image is reminiscent of seaborn's jointplot(), but the marginal subplots (img2) in the image below do not show distribution plots.
You can use the make_axes_locatable functionality of the mpl_toolkits.axes_grid1 to create shared axes along both directions of the central imshow plot.
Here is an example:
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import numpy as np; np.random.seed(0)
Z = np.random.poisson(lam=6, size=(64,64))
x = np.mean(Z, axis=0)
y = np.mean(Z, axis=1)
fig, ax = plt.subplots()
ax.imshow(Z)
# create new axes on the right and on the top of the current axes.
divider = make_axes_locatable(ax)
axtop = divider.append_axes("top", size=1.2, pad=0.3, sharex=ax)
axright = divider.append_axes("right", size=1.2, pad=0.4, sharey=ax)
#plot to the new axes
axtop.plot(np.arange(len(x)), x, marker="o", ms=1, mfc="k", mec="k")
axright.plot(y, np.arange(len(y)), marker="o", ms=1, mfc="k", mec="k")
#adjust margins
axright.margins(y=0)
axtop.margins(x=0)
plt.tight_layout()
plt.show()
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I want to make a 3x2 subplot image in python. With the images in third row I have added a colorbar. But it the image size gets small as compared to the top rows. Is there anyway to fix the image size the same as of top two rows while having a colorbar in the third row?
Here's my python code
#Imports
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import matplotlib.image as image
import matplotlib.colors
from matplotlib.colors import ListedColormap
#data
bird = image.imread('Desktop/bird.jpeg')
fig, (ax1, ax2, ax3) = plt.subplots(3,2,figsize=(5,5))
ax1[0].imshow(bird)
ax1[0].set_ylabel('Row 1', size=8)
ax1[0].set_yticks([]) #display no ticks
ax1[0].set_xticks([])
ax1[1].imshow(bird)
ax1[1].set_yticks([])
ax1[1].set_xticks([])
ax2[0].imshow(bird)
ax2[0].set_yticks([])
ax2[0].set_xticks([])
ax2[0].set_ylabel('Row 2', size=8)
ax2[1].imshow(bird)
ax2[1].set_yticks([])
ax2[1].set_xticks([])
#Generating Color Map
cmap = matplotlib.colors.LinearSegmentedColormap.from_list("", ["Red","Green","Blue"])
# Right Image
bird_3 = ax3[1].imshow(bird, cmap = cmap)
ax3[1].set_yticks([])
ax3[1].set_xticks([])
cbar_int = fig.colorbar(bird_3,orientation='horizontal', ax=ax3[1])
cbar_int.set_label('CBar', size=8, rotation=0)
cbar_int.ax.tick_params(labelsize=8)
bird_3.set_clim(vmin=-1, vmax=1)
# Left Image
bird_4 = ax3[0].imshow(bird, cmap = cmap)
ax3[0].set_yticks([])
ax3[0].set_xticks([])
ax3[0].set_ylabel('Row 3', size=8)
cbar_int = fig.colorbar(bird_4,orientation='horizontal', ax=ax3[0])
cbar_int.set_label('CBar', size=8, rotation=0)
cbar_int.ax.tick_params(labelsize=8)
bird_3.set_clim(vmin=-1, vmax=1)
plt.show()
The following results I get with it. You see row 3 images are small compared to row 1 and 2.
Matplotlib steals space from the host axes. However, you can specify more than one axes to steal space from. So above you can easily do:
import matplotlib.pyplot as plt
import numpy as np
import matplotlib as mpl
fig, axs = plt.subplots(3, 2)
for ax in axs.flat:
pc = ax.imshow(np.random.randn(20,40))
fig.colorbar(pc, ax=axs[:, 1], orientation='horizontal')
fig.colorbar(pc, ax=axs[:, 0], orientation='horizontal')
plt.show()
and space is stolen from all three axes in each column.
You can also specify constrained_layout=True for slightly better layout.
Note that with imshow the axes have a fixed aspect ratio, so there is always going to be issues with white space.
I'm trying to plot a colorbar next to my density plot with marginal axes.
It does plot the colorbar, but unfortunately not on the side.
That's what a tried so far:
sns.jointplot(x,y, data=df3, kind="kde", color="skyblue", legend=True, cbar=True,
xlim=[-10,40], ylim=[900,1040])
It looks like this:
I also tried this:
from matplotlib import pyplot as plt
import seaborn as sns
import numpy as np
kdeplot = sns.jointplot(x=tumg, y=pumg, kind="kde")
plt.subplots_adjust(left=0.2, right=0.8, top=0.8, bottom=0.2)
cbar_ax = kdeplot.fig.add_axes([.85, .25, .05, .4])
plt.colorbar(cax=cbar_ax)
plt.show()
But with the second option I'm getting a runtime error:
No mappable was found to use for colorbar creation.
First define a mappable such as an image (with imshow) or a contour set (with contourf).
Does anyone have an idea how to solve the problem?
There only seems to be information for a colorbar when effectively creating the colorbar.
So, an idea is to combine both approaches: add a colorbar via kdeplot, and then move it to the desired location. This will leave the main joint plot with insufficient width, so its width also should be adapted:
from matplotlib import pyplot as plt
import seaborn as sns
import numpy as np
# create some dummy data: gaussian multivariate with 10 centers with each 1000 points
tumg = np.random.normal(np.tile(np.random.uniform(10, 20, 10), 1000), 2)
pumg = np.random.normal(np.tile(np.random.uniform(10, 20, 10), 1000), 2)
kdeplot = sns.jointplot(x=tumg, y=pumg, kind="kde", cbar=True)
plt.subplots_adjust(left=0.1, right=0.8, top=0.9, bottom=0.1)
# get the current positions of the joint ax and the ax for the marginal x
pos_joint_ax = kdeplot.ax_joint.get_position()
pos_marg_x_ax = kdeplot.ax_marg_x.get_position()
# reposition the joint ax so it has the same width as the marginal x ax
kdeplot.ax_joint.set_position([pos_joint_ax.x0, pos_joint_ax.y0, pos_marg_x_ax.width, pos_joint_ax.height])
# reposition the colorbar using new x positions and y positions of the joint ax
kdeplot.fig.axes[-1].set_position([.83, pos_joint_ax.y0, .07, pos_joint_ax.height])
plt.show()
I have two numpy multi dimmensional matrices that have five features each like this
array1 = array([ 1. , 0.97572023, 0.97671645, 0.99772446,
0.99326534, 0.94841498]....)
array2 = array([ 0.97572023, 1. , 0.99343976, 0.9844228 ,
0.9880037 , 0.96203135]....)
I want to plot these multidimesional matrices as colormaps and label each feature on the graph..Whats the best way to plot multidimensional array.
from matplotlib import pyplot as plt
from matplotlib import cm as cm
fig = plt.figure()
ax1 = fig.add_subplot(111)
cmap = cm.get_cmap('jet', 30)
cax = ax1.imshow(df, interpolation="nearest", cmap=cmap)
ax1.grid(True)
plt.title('Abalone Feature Correlation')
labels=['feat1','feat2','feat3','feat4','feat5']
ax1.set_xticklabels(labels,fontsize=6)
ax1.set_yticklabels(labels,fontsize=6)
# Add colorbar, make sure to specify tick locations to match desired ticklabels
fig.colorbar(cax, ticks=[0.1,0.2,0.3,0.4,0.5,0.6,.75,.8,.85,.90,.95,1])
plt.show()
I am using this function but the features are not displayed properly..The labels and the points are not displayed correctly.Any help?
Instructing matplotlib to use specific ticks for the imshow plot ensures that labels appear in the right places,
import numpy as np
from matplotlib import pyplot as plt
from matplotlib import cm as cm
# Generate some data for the sake of example
array = np.random.uniform(0, 1, (5, 5))
fig = plt.figure()
ax1 = fig.add_subplot(111)
cmap = cm.get_cmap('jet', 30)
cax = ax1.imshow(array, interpolation="nearest", cmap=cmap)
ax1.grid(True)
plt.title('Abalone Feature Correlation')
labels=['feat1', 'feat2', 'feat3', 'feat4', 'feat5']
# Explicitly set ticks for the plot
ax1.set_xticks(np.arange(len(labels)))
ax1.set_yticks(np.arange(len(labels)))
ax1.set_xticklabels(labels,fontsize=6)
ax1.set_yticklabels(labels,fontsize=6)
# Add colorbar, make sure to specify tick locations to match desired ticklabels
fig.colorbar(cax, ticks=[0.1,0.2,0.3,0.4,0.5,0.6,.75,.8,.85,.90,.95,1])
plt.show()
I want to plot an image and colorbar with its associated histogram below. The two axes of the image and the histogram must have the same width.
Furthermore, the colorbar should be the same height as the image.
The part that is (and should not) be complicated is to superpose a plot of a cumulative histogram with the percentage of each bin in respect to the size of the data.
For the moment, I obtained something like this:
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
data = np.random.normal(0,2,size=(100,100))
fig = plt.figure()
ax = fig.add_subplot(2,1,1)
im = ax.imshow(data,cmap="bone")
divider = make_axes_locatable(ax)
ax1 = divider.append_axes("right", size="5%", pad=0.05)
plt.colorbar(im,cax=ax1)
ax2 = divider.append_axes("bottom",size="100%",pad = 0.3)
n,bins,patches = ax2.hist(data.flatten(),bins=20)
ax3 = ax2.twinx()
ax3.plot(bins[:-1],np.cumsum(n*100/np.size(data)),lw=2)
plt.show()
Everything is going smoothly until I try to use twinx on ax2 (in order to plot my cumulative distribution on ax3 with a different y-scale). The resulting axis, instead of being with ax2, is wrapping all the axes of the figure.
I don't understand what is wrong and how I can fix this.
This is a hard one. The problem is that the axes_grid1 toolkit is designed to position the axes at the time of drawing. Apparently it draws the twin axis first and only after that relocates the axes according to the divider.
What makes things worse is that you want to have an axes with equal aspect ratio bound to an axes with unequal aspect, which makes it impossible to use AxisGrid.
While any two-fold combination of equal+unequal or equal+twin or unequal+twin would work in one way or the other, all three are just too much.
So the solution is probably to start from scratch, just putting the axes to the canvas and only at the very end reposition/resize them. This can be done using an event listener connected to a function which gets the position of the axes with equal aspect and resizes the other two axes accordingly.
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
data = np.random.normal(0,2,size=(100,100))
fig = plt.figure()
ax = fig.add_subplot(211)
ax2 = fig.add_subplot(212)
im = ax.imshow(data,cmap="bone")
n,bins,patches = ax2.hist(data.flatten(),bins=20)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.05)
plt.colorbar(im, cax=cax)
ax3 = ax2.twinx()
ax3.plot(bins[:-1],np.cumsum(n*100/np.size(data)),lw=2, c=plt.cm.bone(0.4))
def resize(event):
axpos = ax.get_position()
axpos2 = ax2.get_position()
newpos = [axpos.x0, axpos2.y0, axpos.width, axpos2.height]
ax2.set_position(newpos)
ax3.set_position(newpos)
cid = fig.canvas.mpl_connect('draw_event', resize)
cid2 = fig.canvas.mpl_connect('resize_event', resize)
#if you want to save the figure, trigger the event manually
save=False
if save:
fig.canvas.draw()
resize()
plt.savefig(__file__+".png")
plt.show()
I have a matplotlib plot with a colorbar attached. I want to position the colorbar so that it is horizontal, and underneath my plot.
I have almost done this via the following:
plt.colorbar(orientation="horizontal",fraction=0.07,anchor=(1.0,0.0))
But the colorbar is still overlapping with the plot slightly (and the labels of the x axis). I want to move the colorbar further down, but I can't figure out how to do it.
using padding pad
In order to move the colorbar relative to the subplot, one may use the pad argument to fig.colorbar.
import matplotlib.pyplot as plt
import numpy as np; np.random.seed(1)
fig, ax = plt.subplots(figsize=(4,4))
im = ax.imshow(np.random.rand(11,16))
ax.set_xlabel("x label")
fig.colorbar(im, orientation="horizontal", pad=0.2)
plt.show()
using an axes divider
One can use an instance of make_axes_locatable to divide the axes and create a new axes which is perfectly aligned to the image plot. Again, the pad argument would allow to set the space between the two axes.
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
import numpy as np; np.random.seed(1)
fig, ax = plt.subplots(figsize=(4,4))
im = ax.imshow(np.random.rand(11,16))
ax.set_xlabel("x label")
divider = make_axes_locatable(ax)
cax = divider.new_vertical(size="5%", pad=0.7, pack_start=True)
fig.add_axes(cax)
fig.colorbar(im, cax=cax, orientation="horizontal")
plt.show()
using subplots
One can directly create two rows of subplots, one for the image and one for the colorbar. Then, setting the height_ratios as gridspec_kw={"height_ratios":[1, 0.05]} in the figure creation, makes one of the subplots much smaller in height than the other and this small subplot can host the colorbar.
import matplotlib.pyplot as plt
import numpy as np; np.random.seed(1)
fig, (ax, cax) = plt.subplots(nrows=2,figsize=(4,4),
gridspec_kw={"height_ratios":[1, 0.05]})
im = ax.imshow(np.random.rand(11,16))
ax.set_xlabel("x label")
fig.colorbar(im, cax=cax, orientation="horizontal")
plt.show()
Edit: Updated for matplotlib version >= 3.
Three great ways to do this have already been shared in this answer.
The matplotlib documentation advises to use inset_locator. This would work as follows:
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
import numpy as np
rng = np.random.default_rng(1)
fig, ax = plt.subplots(figsize=(4,4))
im = ax.imshow(rng.random((11, 16)))
ax.set_xlabel("x label")
axins = inset_axes(ax,
width="100%",
height="5%",
loc='lower center',
borderpad=-5
)
fig.colorbar(im, cax=axins, orientation="horizontal")