Normally if you plot two different figures using the default settings in pyplot, they will be exactly the same size, and if saved can be neatly aligned in PowerPoint or the like. I'd like to generate one figure, however, which has a legend outside of the figure. The script I'm using is shown below.
import numpy as np
import matplotlib.pyplot as plt
x=np.linspace(0,1,201)
y1=x**2
y2=np.sin(x)
fig1=plt.figure(1)
plt.plot(x,y1,label='y1')
handles1,labels1=plt.gca().get_legend_handles_labels()
lgd1=plt.gca().legend(handles1,labels1,bbox_to_anchor=(1.27,1),borderaxespad=0.)
fig2=plt.figure(2)
plt.plot(x,y2)
fig1.savefig('fig1',bbox_extra_artists=(lgd1,),bbox_inches='tight')
fig2.savefig('fig2')
plt.show()
The problem is that in PowerPoint, I can no longer align the two figures left and have their axes aligned. Due to the use of the 'extra artists' and 'bbox_inches=tight' arguments for the first figure, the width of its margins becomes different from the second figure.
Is there any way to 'transfer' the clip box from the first figure to the second figure, such that they can be aligned by 'align left' in PowerPoint?
I think an easier way to achieve what you want is to just construct one figure with two subplots, and let matplotlib align everything for you.
Do you think doing something like this is a good idea?
import matplotlib.pyplot as plt
import numpy as np
x=np.linspace(0,1,201)
y1=x**2
y2=np.sin(x)
fig = plt.figure()
a = fig.add_subplot(211)
a.plot(x,y1, label='y1')
lgd1 = a.legend(bbox_to_anchor = (1.27,1), borderaxespad=0.)
a = fig.add_subplot(212)
a.plot(x,y2)
fig.savefig('fig',bbox_extra_artists=(lgd1,),bbox_inches='tight')
Related
I'm experimenting with seaborn and have a question about specifying axes properties. In my code below, I've taken two approaches to creating a heatmap of a matrix and placing the results on two sets of axes in a figure.
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
A=np.random.randn(4,4)
labels=['a','b','c','d']
fig, ax = plt.subplots(2)
sns.heatmap(ax =ax[0], data = A)
ax[0].set_xticks(range(len(labels)))
ax[0].set_xticklabels(labels,fontsize=10,rotation=45)
ax[0].set_yticks(range(len(labels)))
ax[0].set_yticklabels(labels,fontsize=10,rotation=45)
ax[1].set_xticks(range(len(labels)))
ax[1].set_xticklabels(labels,fontsize=10,rotation=45)
ax[1].set_yticks(range(len(labels)))
ax[1].set_yticklabels(labels,fontsize=10,rotation=45)
sns.heatmap(ax =ax[1], data = A,xticklabels=labels, yticklabels=labels)
plt.show()
The resulting figure looks like this:
Normally, I would always take the first approach of creating the heatmap and then specifying axis properties. However, when creating an animation (to be embedded on a tkinter canvas), which is what I'm ultimately interested in doing, I found such an ordering in my update function leads to "flickering" of axis labels. The second approach will eliminate this effect, and it also centers the tickmarks within squares along the axes.
However, the second approach does not rotate the y-axis tickmark labels as desired. Is there a simple fix to this?
I'm not sure this is what you're looking for. It looks like you create your figure after you change the yticklabels. so the figure is overwriting your yticklabels.
Below would fix your issue.
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
A=np.random.randn(4,4)
labels=['a','b','c','d']
fig, ax = plt.subplots(2)
sns.heatmap(ax =ax[0], data = A)
ax[0].set_xticks(range(len(labels)))
ax[0].set_xticklabels(labels,fontsize=10,rotation=45)
ax[0].set_yticks(range(len(labels)))
ax[0].set_yticklabels(labels,fontsize=10,rotation=45)
ax[1].set_xticks(range(len(labels)))
ax[1].set_xticklabels(labels,fontsize=10,rotation=45)
ax[1].set_yticks(range(len(labels)))
sns.heatmap(ax =ax[1], data = A,xticklabels=labels, yticklabels=labels)
ax[1].set_yticklabels(labels,fontsize=10,rotation=45)
plt.show()
I noticed a 'strange' behaviour when running the following code:
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import (MultipleLocator, AutoMinorLocator)
freqs = np.logspace(2,4)
freqs_ext = np.logspace(2, 10)
fig, ax = plt.subplots(1,2)
ax [0].plot(freqs , freqs**2)
#ax[0].xaxis.set_minor_locator(AutoMinorLocator(5))
ax[0].grid(which='both')
#ax[0].minorticks_on()
ax[0].set_xscale( 'log')
ax[1].plot(freqs_ext,freqs_ext**2)
#ax[l].xaxis.set_minor_locator(AutoMinorLocator(5))
ax[1].grid(which='both')
#ax[1].minorticks on()
ax[1].set_xscale('log')
The output is the following:
I have tried more variants than I care to report, (some are commented out in the code above), but I cannot get matplotlib to draw minor gridlines for the plot on the right side, as it does for the one on the left.
I think I have understood that the "problem" lies in where the ticks are located for the second plot, which has a much larger span. They are every two decades and I believe this might be the source of the minor grid lines not displaying.
I have played with xaxis.set_xticks and obtained ticks every decade, but still cannot get this to correctly produce the gridlines.
It is probably something stupid but I can't see it.
NOTE : I know that matplotlib doesn't turn the minor ticks on by default, and in this case this action is "triggered" by changing the scale to log (that's why axis.grid(which='both') actually only acts on the x axis)
OK, I have found this answer:
Matplotlib: strange double-decade axis ticks in log plot
which actually shows how the issue is a design choice for matplotlib starting with v2. Answer was given in 2017 so, not the newest issue :)
The following code correctly plots the minor grids as wanted:
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import LogLocator
freqs = np.logspace(2,4)
freqs_ext = np.logspace(2, 10)
fig, ax = plt.subplots(1,2)
ax[0].plot(freqs , freqs**2)
ax[0].grid(which='both')
ax[0].set_xscale( 'log')
ax[1].plot(freqs_ext,freqs_ext**2)
ax[1].set_xscale('log')
ax[1].xaxis.set_major_locator(LogLocator(numticks=15))
ax[1].xaxis.set_minor_locator(LogLocator(numticks=15,subs=np.arange(2,10)))
ax[1].grid(which='both')
I have a figure which contains a labelled colourbar below the x axis of the main plot. When I attempt to save this using plt.savefig(), the very bottom of the subscript character in the label is cropped from the saved image, like this, despite using bbox_inches="tight". However, if I simply save the figure manually in the pop-up window, the subscript character is not cropped, like this.
Although the latter image could be manually cropped, or cropped using additional lines in the code, I would be grateful for any advice on how to resolve this issue without the need for this additional work.
I have tried to add a line break to the colourbar label like so:
label="$U/U_{"+(u"\u221e")+"}$\n"
But this simply adds white space below the label; the bottom of the subscript character is still cropped.
I have also tried to add the line:
cb.set_label(label,labelpad=5)
But this simply offsets the label from the bottom of the colourbar; no additional padding is provided below the label to fully display the subscript character.
The code is below:
import numpy
import random
import matplotlib as mpl
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import matplotlib.colors as mcolors
import matplotlib.colorbar as cbar
from matplotlib import cm
##########################################################
# Centre colourmap so 0=white
class MidpointNormalize(mpl.colors.Normalize):
def __init__(self,vmin=None,vmax=None,midpoint=None,clip=False):
self.midpoint=midpoint
mpl.colors.Normalize.__init__(self,vmin,vmax,clip)
def __call__(self,value,clip=None):
x,y=[self.vmin,self.midpoint,self.vmax],[0,0.5,1]
return numpy.ma.masked_array(numpy.interp(value,x,y),numpy.isnan(value))
##########################################################
# Set min and max values
xymin=0
xymax=10
valmin=-5
valmax=5
val=numpy.zeros((xymax,xymax),dtype=float)
# Configure plot
fig,ax=plt.subplots()
ax.set_xlim([xymin,xymax])
ax.set_ylim([xymin,xymax])
# Configure colour bar
colours=plt.cm.RdBu(numpy.linspace(0,1,256))
colourmap=mcolors.LinearSegmentedColormap.from_list('colourmap',colours)
normalisecolors=mpl.colors.Normalize(vmin=valmin,vmax=valmax)
scalecolors=cm.ScalarMappable(norm=normalisecolors,cmap=colourmap)
label="$U/U_{"+(u"\u221e")+"}$"
for ix in range(xymin,xymax):
for iy in range(xymin,xymax):
xlow=ix*+1 # Calculate vertices of patch
xhigh=(ix*1)+1
ylow=iy*1
yhigh=(iy*1)+1
val[ix][iy]=random.randint(valmin,valmax) # Patch value
rgbacolor=scalecolors.to_rgba(val[ix][iy]) # Calculate RGBA colour for value
ax.add_patch(patches.Polygon([(xlow,ylow),(xlow,yhigh),(xhigh,yhigh),(xhigh,ylow)],fill=True,facecolor=rgbacolor)) # Add value as polygon patch
cax,_=cbar.make_axes(ax,orientation="horizontal")
cb=cbar.ColorbarBase(cax,cmap=colourmap,norm=MidpointNormalize(midpoint=0,vmin=valmin,vmax=valmax),orientation="horizontal",label=label)
plt.savefig("C:/Users/Christopher/Desktop/test.png",dpi=1200,bbox_inches="tight")
plt.clf
plt.close()
I'm afraid I don't really have a good answer for you. This appears to be related to this bug https://github.com/matplotlib/matplotlib/issues/15313
The good news is that it is being worked on, the bad news is that there is no fix as of yet.
Two points to consider anyway (based on reading the thread on github):
the higher the dpi, the worst it is. So you may want to save at a lower dpi (300 works fine for me)
the problem is not present on the pdf backend, so you could save your plot in pdf (and eventually convert to png if needed)
BTW (this is unrelated to the bug in question): I'm confused by the complexity of your code. It seems to me the following code produces the same output:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import TwoSlopeNorm
N=10
valmin=-5
valmax=5
valmid=0
val=np.random.randint(low=valmin, high=valmax, size=(N,N))
cmap = 'RdBu'
norm = TwoSlopeNorm(vcenter=valmid, vmin=valmin, vmax=valmax)
label="$U/U_{"+(u"\u221e")+"}$"
# Configure plot
fig, ax=plt.subplots()
im = ax.imshow(val, cmap=cmap, norm=norm, aspect='auto', origin='lower')
cbar = fig.colorbar(im, orientation='horizontal', label=label)
fig.savefig('./test-1200.png',dpi=1200,bbox_inches="tight") # subscript is cut
fig.savefig('./test-300.png',dpi=300,bbox_inches="tight") # subscript is not cut
fig.savefig('./test-pdf.pdf',dpi=1200,bbox_inches="tight") # subscript is not cut
1200 dpi:
300 dpi:
pdf:
I am trying to find an efficient way to draw an arbitrary number of images in a series inside a figure in Python using matplotlib.
This is what I want to achieve:
Each image will have a different width but the same height. Of course, we can use subplot but the problem with subplot images must have the same width plus the issue of spacing between different columns.
If I need to use another library/package to achieve that that is totally fine for me.
Notes:
The borders around each image should not exist but it is just for illustration.
I want to consider the figure as an area where I can plot images in any position and add text in a specific location.
You can use gridspec and specifying left, right, bottom, top, width_ratios and height_ratios to generate subplots in a very flexible way. Pls see doc for details.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
nrows=2
ncols=2
fig = plt.figure(figsize=(7.2, 7.2))
gs = fig.add_gridspec(nrows=nrows, ncols=ncols, left=0.1, right=0.6, bottom=0.55, top=0.95, width_ratios=[1, 2], height_ratios=[1, 1])
axes = [fig.add_subplot(gs[row, col]) for row in range(nrows) for col in range(ncols)]
I have been given a data for which I need to find a histogram. So I used pandas hist() function and plot it using matplotlib. The code runs on a remote server so I cannot directly see it and hence I save the image. Here is what the image looks like
Here is my code below
import matplotlib.pyplot as plt
df_hist = pd.DataFrame(np.array(raw_data)).hist(bins=5) // raw_data is the data supplied to me
plt.savefig('/path/to/file.png')
plt.close()
As you can see the x axis labels are overlapping. So I used this function plt.tight_layout() like so
import matplotlib.pyplot as plt
df_hist = pd.DataFrame(np.array(raw_data)).hist(bins=5)
plt.tight_layout()
plt.savefig('/path/to/file.png')
plt.close()
There is some improvement now
But still the labels are too close. Is there a way to ensure the labels do not touch each other and there is fair spacing between them? Also I want to resize the image to make it smaller.
I checked the documentation here https://matplotlib.org/api/_as_gen/matplotlib.pyplot.savefig.html but not sure which parameter to use for savefig.
Since raw_data is not already a pandas dataframe there's no need to turn it into one to do the plotting. Instead you can plot directly with matplotlib.
There are many different ways to achieve what you'd like. I'll start by setting up some data which looks similar to yours:
import matplotlib.pyplot as plt
import numpy as np
from scipy.stats import gamma
raw_data = gamma.rvs(a=1, scale=1e6, size=100)
If we go ahead and use matplotlib to create the histogram we may find the xticks too close together:
fig, ax = plt.subplots(1, 1, figsize=[5, 3])
ax.hist(raw_data, bins=5)
fig.tight_layout()
The xticks are hard to read with all the zeros, regardless of spacing. So, one thing you may wish to do would be to use scientific formatting. This makes the x-axis much easier to interpret:
ax.ticklabel_format(style='sci', axis='x', scilimits=(0,0))
Another option, without using scientific formatting would be to rotate the ticks (as mentioned in the comments):
ax.tick_params(axis='x', rotation=45)
fig.tight_layout()
Finally, you also mentioned altering the size of the image. Note that this is best done when the figure is initialised. You can set the size of the figure with the figsize argument. The following would create a figure 5" wide and 3" in height:
fig, ax = plt.subplots(1, 1, figsize=[5, 3])
I think the two best fixes were mentioned by Pam in the comments.
You can rotate the labels with
plt.xticks(rotation=45
For more information, look here: Rotate axis text in python matplotlib
The real problem is too many zeros that don't provide any extra info. Numpy arrays are pretty easy to work with, so pd.DataFrame(np.array(raw_data)/1000).hist(bins=5) should get rid of three zeros off of both axes. Then just add a 'kilo' in the axes labels.
To change the size of the graph use rcParams.
from matplotlib import rcParams
rcParams['figure.figsize'] = 7, 5.75 #the numbers are the dimensions