I'm trying to visualize a numpy array using imshow() since its similar to imagesc() in MATLAB.
import numpy as np
import matplotlib.pyplot as plt
plt.imshow(np.random.rand(8, 90), aspect='auto')
The resulting figure is as below with automatic selection of x-axis limits & y-axis limits. How could I set the parameters to change the x-axis & y-axis, which are of 2 different sizes of array of elements?
For Eg: x-axis = [100,], y-axis = [15,]
I tried using extent, however I need to provide [x-axis_min, x-axis_max, y-axis_min, y-axis_max]. But the problem is, I am having an array of elements as x-axis limits and an array of elements as y-axis limits. I don't know how to set these arrays as limits?
Thanks
If I understand your question, then this should do the trick?
img = np.random.rand(8, 90)
x = np.linspace(2.2e10, 2.475e10, 100)
y = np.linspace(-0.007, 0.007, 15)
plt.figure()
plt.imshow(img, aspect='auto', extent=[np.min(x), np.max(x), np.max(y), np.min(y)])
Related
I would like to have different font weights for each of my colorbar labels.
I have tried to let LaTeX format the labels in the following way:
import numpy as np
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
im = ax.imshow(np.random.rand(50, 50)/20)
cbar = ax.figure.colorbar(im, ticks=np.arange(0, 0.05, 0.01))
cbar.ax.set_yticklabels([r'{\fontsize{50pt}{3em}\selectfont{}{0}}',
r'{\fontsize{40pt}{3em}\selectfont{}{0.01}}',
r'{\fontsize{30pt}{3em}\selectfont{}{0.03}}',
r'{\fontsize{20pt}{3em}\selectfont{}{0.03}}',
r'{\fontsize{10pt}{3em}\selectfont{}{0.04}}',
r'{\fontsize{1pt}{3em}\selectfont{}{0.05}}', ])
but this only updates the text of the labels to the whole string (e.g., {\fontsize{50pt}{3em}\selectfont{}{0}}). The pyplot TeX demo works for me. Even if this solution would work it would not be ideal as I would probably need to specify everything manually.
Much more convenient would be something like in this question. There, I learned that the font size of single labels of the regular x and y axis can be updated by calling
label = axes.yaxis.get_major_ticks()[2].label
label.set_fontsize(size)
replacing set_fontsize by set_fontweight correctly updates the weight of the selected label.
Unfortunately I could not find the equivalent of axes.yaxis.get_major_ticks()[2].label for the colorbar.
Is it possible to change the font weight of individual labels of the colorbar directly? With directly I mean without using a workaround like plotting some new text above existing labels.
If this is not possible a solution plotting text above existing labels which automatically uses the position and content the previous labels and only adjusts the font weight would also be appreciated.
Thanks!
As pointed out by #ImportanceOfBingErnest , set_fontweight works for setting the weight of single colorbar labels too.
I had to try a couple of things to find which call would give me the text objects defining the colorbar labels. They are accessible in cbar.ax.get_yticklabels().
The code snippet below now properly changes the weight of the second colorbar label:
import numpy as np
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
im = ax.imshow(np.random.rand(50, 50)/20)
cbar = ax.figure.colorbar(im, ticks=np.arange(0, 0.05, 0.01))
cbar.ax.get_yticklabels()[1].set_fontweight(1000)
plt.show()
Output of code (not enough reputation for inline images)
I am using python-3.x and I would like to plot several boxplots in one figure, all the data from one numpy array where the shape of this array is (100, 301)
If I use the code below it will plot them all (I will have 301 boxplots in one figure which is too much)
fig, ax = plt.subplots()
ax.boxplot(my_data)
plt.show()
I don't want to plot all the data, I just want to plot 10, 15 or 20 (variable number) of the data by using for loop or any method that work best.
for example, I want to plot boxplots every 50 number of data that mean I will have around 6 boxplots from 301 in my figure, I tried to use for loop but no luck
Any advice would be much appreciated
You can just use indexing to plot every 50th data points using a variable step. To have separate box plots and avoid overlapping, you can specify the positions of individual box plot using the positions parameter. my_data[:, ::step] gives you the desired data to plot. Below is an example using some random data.
import numpy as np
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
my_data = np.random.randint(0, 20, (100, 301))
step = 50
posit = range(my_data[:, ::step].shape[1])
ax.boxplot(my_data[:, ::step], positions=posit)
plt.show()
So I have two lists one containing a bunch of years and the other one containing some integers, each list has 17 values.
when I make a simple bar chart
plt.bar(keys,values)
plt.show()
in the X axis of the graph it only contains some of the years in the keys list eg: the graph only has 2000,2002,2005,2007,2010,2012,2015. It has missed 2001,2003,2006,2008,2009 etc.
Is it because there is a maximum amount of keys allowed in the bar chart so it randomly took some keys?
If not how do i fix this?
There is a maximum amount of ticklabels on a matplotlib axes. This limit however lies well above 1000 and you would first run into severe lags when creating the figure.
The usual automatic ticking by matplotlib is that the axes are equipped with just as many labels as needed. I.e. if you plot 50 points on a plot, you would not want to have 50 labels as well. Further if you plot a point at 0.853164 you would not want to have such odd number being displayed as ticklabel on the axes.
Now, I cannot think of any reason matplotlib would produce the labels you report about, 2000,2002,2005,2007,2010,2012,2015, because the automatic locator for the ticks chooses equidistant points on the axes. For any help with this specific problem, a MCVE would be needed.
But in general there are two concepts from which you may choose.
Numerical axes
When plotting numbers, matplotlib will by default choose a linear axes and tick it automatically as described above.
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(2000, 2017)
y = np.random.randint(5,21, size=len(x))
plt.bar(x,y)
plt.show()
In this case an equidistant ticking of 2.5 units is chosen to have 7 nicely spaced ticks on the axes. If instead you would want to have every bar ticked, you could use a custom ticker.
E.g. a MultipleLocator with the interval set to 1,
import matplotlib.ticker as mticker
plt.gca().xaxis.set_major_locator(mticker.MultipleLocator(1))
plt.gca().tick_params(axis="x", rotation=90)
Or, a FixedLocator with the locations set to the x values of the bars,
import matplotlib.ticker as mticker
plt.gca().xaxis.set_major_locator(mticker.FixedLocator(x))
plt.gca().tick_params(axis="x", rotation=90)
Categorical axes
You may also decide that your xaxis shall be categorical. This means that every unique value gets its own tick and those ticks are equally spaced, independent of their value. This is easiest accomplished by converting the numbers to strings.
import matplotlib.pyplot as plt
import numpy as np
x = np.arange(2000, 2017)
y = np.random.randint(5,21, size=len(x))
cats = list(map(str, x))
plt.bar(cats,y)
plt.gca().tick_params(axis="x", rotation=90)
plt.show()
The result is visually the same as above, but this time, the number 2000 is not at location 2000, but at its index 0, 2001 is at 1 and so on.
You can show all the ticks in this way:
plt.xticks(np.arange(min(keys), max(keys)+1, 1.0), rotation=45)
Example:
keys = [2000, 2001,2002,2003,2004,2005,2006,2007,2008,2009,2010,2011,2012,2013,2014,2015,2016]
values = range(1,18)
import matplotlib.pyplot as plt
plt.bar(keys,values)
plt.xticks(np.arange(min(keys), max(keys)+1, 1.0), rotation=45)
plt.show()
I need help with setting the limits of y-axis on matplotlib. Here is the code that I tried, unsuccessfully.
import matplotlib.pyplot as plt
plt.figure(1, figsize = (8.5,11))
plt.suptitle('plot title')
ax = []
aPlot = plt.subplot(321, axisbg = 'w', title = "Year 1")
ax.append(aPlot)
plt.plot(paramValues,plotDataPrice[0], color = '#340B8C',
marker = 'o', ms = 5, mfc = '#EB1717')
plt.xticks(paramValues)
plt.ylabel('Average Price')
plt.xlabel('Mark-up')
plt.grid(True)
plt.ylim((25,250))
With the data I have for this plot, I get y-axis limits of 20 and 200. However, I want the limits 20 and 250.
Get current axis via plt.gca(), and then set its limits:
ax = plt.gca()
ax.set_xlim([xmin, xmax])
ax.set_ylim([ymin, ymax])
One thing you can do is to set your axis range by yourself by using matplotlib.pyplot.axis.
matplotlib.pyplot.axis
from matplotlib import pyplot as plt
plt.axis([0, 10, 0, 20])
0,10 is for x axis range.
0,20 is for y axis range.
or you can also use matplotlib.pyplot.xlim or matplotlib.pyplot.ylim
matplotlib.pyplot.ylim
plt.ylim(-2, 2)
plt.xlim(0,10)
Another workaround is to get the plot's axes and reassign changing only the y-values:
x1,x2,y1,y2 = plt.axis()
plt.axis((x1,x2,25,250))
You can instantiate an object from matplotlib.pyplot.axes and call the set_ylim() on it. It would be something like this:
import matplotlib.pyplot as plt
axes = plt.axes()
axes.set_ylim([0, 1])
Just for fine tuning. If you want to set only one of the boundaries of the axis and let the other boundary unchanged, you can choose one or more of the following statements
plt.xlim(right=xmax) #xmax is your value
plt.xlim(left=xmin) #xmin is your value
plt.ylim(top=ymax) #ymax is your value
plt.ylim(bottom=ymin) #ymin is your value
Take a look at the documentation for xlim and for ylim
This worked at least in matplotlib version 2.2.2:
plt.axis([None, None, 0, 100])
Probably this is a nice way to set up for example xmin and ymax only, etc.
To add to #Hima's answer, if you want to modify a current x or y limit you could use the following.
import numpy as np # you probably alredy do this so no extra overhead
fig, axes = plt.subplot()
axes.plot(data[:,0], data[:,1])
xlim = axes.get_xlim()
# example of how to zoomout by a factor of 0.1
factor = 0.1
new_xlim = (xlim[0] + xlim[1])/2 + np.array((-0.5, 0.5)) * (xlim[1] - xlim[0]) * (1 + factor)
axes.set_xlim(new_xlim)
I find this particularly useful when I want to zoom out or zoom in just a little from the default plot settings.
This should work. Your code works for me, like for Tamás and Manoj Govindan. It looks like you could try to update Matplotlib. If you can't update Matplotlib (for instance if you have insufficient administrative rights), maybe using a different backend with matplotlib.use() could help.
I have the results of a (H,ranges) = numpy.histogram2d() computation and I'm trying to plot it.
Given H I can easily put it into plt.imshow(H) to get the corresponding image. (see http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.imshow )
My problem is that the axis of the produced image are the "cell counting" of H and are completely unrelated to the values of ranges.
I know I can use the keyword extent (as pointed in: Change values on matplotlib imshow() graph axis ). But this solution does not work for me: my values on range are not growing linearly (actually they are going exponentially)
My question is: How can I put the value of range in plt.imshow()? Or at least, or can I manually set the label values of the plt.imshow resulting object?
Editing the extent is not a good solution.
You can just change the tick labels to something more appropriate for your data.
For example, here we'll set every 5th pixel to an exponential function:
import numpy as np
import matplotlib.pyplot as plt
im = np.random.rand(21,21)
fig,(ax1,ax2) = plt.subplots(1,2)
ax1.imshow(im)
ax2.imshow(im)
# Where we want the ticks, in pixel locations
ticks = np.linspace(0,20,5)
# What those pixel locations correspond to in data coordinates.
# Also set the float format here
ticklabels = ["{:6.2f}".format(i) for i in np.exp(ticks/5)]
ax2.set_xticks(ticks)
ax2.set_xticklabels(ticklabels)
ax2.set_yticks(ticks)
ax2.set_yticklabels(ticklabels)
plt.show()
Expanding a bit on #thomas answer
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.image as mi
im = np.random.rand(20, 20)
ticks = np.exp(np.linspace(0, 10, 20))
fig, ax = plt.subplots()
ax.pcolor(ticks, ticks, im, cmap='viridis')
ax.set_yscale('log')
ax.set_xscale('log')
ax.set_xlim([1, np.exp(10)])
ax.set_ylim([1, np.exp(10)])
By letting mpl take care of the non-linear mapping you can now accurately over-plot other artists. There is a performance hit for this (as pcolor is more expensive to draw than AxesImage), but getting accurate ticks is worth it.
imshow is for displaying images, so it does not support x and y bins.
You could either use pcolor instead,
H,xedges,yedges = np.histogram2d()
plt.pcolor(xedges,yedges,H)
or use plt.hist2d which directly plots your histogram.