Ok, this is my first time asking a question on here, so please be patient with me ;-)
I'm trying to create a series of subplots (with two y-axes each) in a figure using matplotlib and then saving that figure. I'm using GridSpec to create a grid for the subplots and realised that they're overlapping a little, which I don't want. So I'm trying to use tight_layout() to sort this out, which according to the matplotlib documentation should work just fine. Simplifying things a bit, my code looks something like this:
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
fig = plt.figure(num=None, facecolor='w', edgecolor='k')
grid = gridspec.GridSpec(2, numRows)
# numRows comes from the number of subplots required
# then I loop over all the data files I'm importing and create a subplot with two y-axes each time
ax1 = fig.add_subplot(grid[column, row])
# now I do all sorts of stuff with ax1...
ax2 = ax1.twinx()
# again doing some stuff here
After the loop for data processing is done and I have created all the subplots, I eventually end with
fig.tight_layout()
fig.savefig(str(location))
As far as I can work out, this should work, however when calling tight_layout(), I get a ValueError from the function self.subplotpars: left cannot be >= right. My question is: How do I figure out what's causing this error and how do I fix it?
I've had this error before, and I have a solution that worked for me. I'm not sure if it will work for you though. In matplotlib, the command
plt.fig.subplots_adjust()
can be used to sort of stretch the plot. The left and bottom stretch more the smaller the number gets, while the top and right stretch more the greater the number is. So if left is greater than or equal to the right, or bottom is greater than or equal to the top, than the graph would kind of flip over. I adjusted my command to look like this:
fig = plt.figure()
fig.subplots_adjust(bottom = 0)
fig.subplots_adjust(top = 1)
fig.subplots_adjust(right = 1)
fig.subplots_adjust(left = 0)
Then you can fill in your own numbers to adjust this, as long as you keep the left and bottom smaller. I hope this fixes your problem.
Related
I am looping through a bunch of CSV files containing various measurements.
Each file might be from one of 4 different data sources.
In each file, I merge the data into monthly datasets, that I then plot in a 3x4 grid. After this plot has been saved, the loop moves on and does the same to the next file.
This part I got figured out, however I would like to add a visual clue to the plots, as to what data it is. As far as I understand it (and tried it)
plt.subplot(4,3,1)
plt.hist(Jan_Data,facecolor='Red')
plt.ylabel('value count')
plt.title('January')
does work, however this way, I would have to add the facecolor='Red' by hand to every 12 subplots. Looping through the plots wont work for this situation, since I want the ylabel only for the leftmost plots, and xlabels for the bottom row.
Setting facecolor at the beginning in
fig = plt.figure(figsize=(20,15),facecolor='Red')
does not work, since it only changes the background color of the 20 by 15 figure now, which subsequently gets ignored when I save it to a PNG, since it only gets set for screen output.
So is there just a simple setthecolorofallbars='Red' command for plt.hist(… or plt.savefig(… I am missing, or should I just copy n' paste it to all twelve months?
You can use mpl.rc("axes", color_cycle="red") to set the default color cycle for all your axes.
In this little toy example, I use the with mpl.rc_context block to limit the effects of mpl.rc to just the block. This way you don't spoil the default parameters for your whole session.
import matplotlib as mpl
import matplotlib.pylab as plt
import numpy as np
np.random.seed(42)
# create some toy data
n, m = 2, 2
data = []
for i in range(n*m):
data.append(np.random.rand(30))
# and do the plotting
with mpl.rc_context():
mpl.rc("axes", color_cycle="red")
fig, axes = plt.subplots(n, m, figsize=(8,8))
for ax, d in zip(axes.flat, data):
ax.hist(d)
The problem with the x- and y-labels (when you use loops) can be solved by using plt.subplots as you can access every axis seperately.
import matplotlib.pyplot as plt
import numpy.random
# creating figure with 4 plots
fig,ax = plt.subplots(2,2)
# some data
data = numpy.random.randn(4,1000)
# some titles
title = ['Jan','Feb','Mar','April']
xlabel = ['xlabel1','xlabel2']
ylabel = ['ylabel1','ylabel2']
for i in range(ax.size):
a = ax[i/2,i%2]
a.hist(data[i],facecolor='r',bins=50)
a.set_title(title[i])
# write the ylabels on all axis on the left hand side
for j in range(ax.shape[0]):
ax[j,0].set_ylabel(ylabel[j])
# write the xlabels an all axis on the bottom
for j in range(ax.shape[1]):
ax[-1,j].set_xlabel(xlabels[j])
fig.tight_layout()
All features (like titles) which are not constant can be put into arrays and placed at the appropriate axis.
I have found that I can not get axes autoscale to work on the 1st axes after creating a second axes using twinx. Is this expected?
import numpy as np
import matplotlib.pyplot as plt
x = np.arange(0, 10, 0.1)
y1 = 0.05 * x**2
y2 = -1 *y1
fig, axL = plt.subplots() # Make Left Axes
axR = axL.twinx() # Make Left Axes
axL.plot(x, y1, 'g-') # Plot on Left
axL.grid()
axL.autoscale(enable=True, axis=u'both', tight=False)
plt.show()
# Do some stuff then later plot on axR
When I run the above code it autoscales in the y-direction correctly on the left axes (0 to 5) but changes the X-Axis scale to +/- 0.06 instead of the correct 0 to 10. However, once axR is no longer blank and something is plotted on axR it behaves as I would expect.
This is only an example as I first came across this issue in more complicated PyQT4 GUI that allows the user to create multiple subplots & left/right combinations. Since the user is the one manually controlling the plot creation order it is possible for the above situation to present itself.
Is there a way for autoscale to work with a blank twinx right axes. Or is the Xlimit just going to have to be manually set?
FYI, I am using Python 3.4 as part of Anaconda v2.0.1 with Matplotlib v1.3.1
Thanks.
This is merely a workaround than a proper solution or explanation.
Simply add an invisible point in the right axes so it is not completely empty:
axR.plot(0, 0, visible=False)
You have to make sure though, that the invisible point lies within the ranges of the data that you plot in axL. E.g.:
axR.plot(np.mean(x),np.mean(y1),visible=False)
As for an explanation (I'm guessing):
axR.dataLim is [-np.inf, np.inf] initially. The union of axR.dataLim and axL.dataLim still gives [-np.inf, np.inf] which is then collapsed to [0,0].
EDIT: This was fixed recently (here). Upgrading to matplotlib v1.4.* should solve the problem.
I want a simple x,y plot created with matplotlib stretched physically in x-direction.
The intention is to get a result were it is easier for me to detect features in the signal.
So I don't want to change any scales or values or limits. Just change the distance between two gridpoint in my output file...
I want to do that on four subplots which should have the same size afterwards.
Thanks in advance... I tried for hours now and I think one of you could probably help me...
David Zwicker already solved my problem in this special case, thanks a lot for that, but in general... If I plot 2 subplots like in this code:
fig = plt.figure()
ax1 = fig.add_subplot(1,2,1)
plot(u_av,z)
ax2 = fig.add_subplot(1,2,2)
plot(pgrd_av,z)
clf()
and want to stretch only one of them. What can I do?
You can change the figure size by using plt.figure(figsize=(20,5)). See the documentation of the figure command.
I know, this is a bit out of the context. But if someone is looking for a solution while using pandas plot which internally uses matplotlib. Here is the solution.
df.plot('col_x', 'col_y', title='stretched_plot', figsize=(20, 1))
You can directly add axes to the canvas at an arbitrary position with plt.axes(). For instance:
ax1 = plt.axes([0, 0, 3, 0.5])
ax2 = plt.axes([0, 0.6, 1, 1])
You can do this:
x = 1.5 # or your needed amount
plt.plot(x_array * x, y_array)
Your line or graph will move to the right depending on your x value
I am building a bar chart using matplotlib using the code below. When my first or last column of data is 0, my first column is wedged against the Y-axis.
An example of this. Note that the first column is ON the x=0 point.
If I have data in this column, I get a huge padding between the Y-Axis and the first column as seen here. Note the additional bar, now at X=0. This effect is repeated if I have data in my last column as well.
My code is as follows:
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.ticker import MultipleLocator
binVals = [0,5531608,6475325,1311915,223000,609638,291151,449434,1398731,2516755,3035532,2976924,2695079,1822865,1347155,304911,3562,157,5,0,0,0,0,0,0,0,0]
binTot = sum(binVals)
binNorm = []
for v in range(len(binVals)):
binNorm.append(float(binVals[v])/binTot)
fig = plt.figure(figsize=(6,4))
ax1 = fig.add_subplot(1,1,1)
ax1.bar(range(len(binNorm)),binNorm,align='center', label='Values')
plt.legend(loc=1)
plt.title("Demo Histogram")
plt.xlabel("Value")
plt.xticks(range(len(binLabels)),binLabels,rotation='vertical')
plt.grid(b=True, which='major', color='grey', linestyle='--', alpha=0.35)
ax1.xaxis.grid(False)
plt.ylabel("% of Count")
plt.subplots_adjust(bottom=0.15)
plt.tight_layout()
plt.show()
How can I set a constant margin between the Y-axis and my first/last bar?
Additionally, I realize it's labeled "Demo Histogram", that is a because I missed it when correcting problems discussed here.
I can't run the code snippet you gave, and even with some modification I couldn't replicate the big space. Aside from that, if you need to enforce a border to matplotlib, you ca do somthing like this:
ax.set_xlim( min(your_data) - 10, None )
The first term tells the axis to put the border at 10 units of distance from the minimum of your data, the None parameter teels it to keep the present value.
to put it into contest:
from collections import Counter
from pylab import *
data = randint(20,size=1000)
res = Counter(data)
vals = arange(20)
ax = gca()
ax.bar(vals-0.4, [ res[i] for i in vals ], width=0.8)
ax.set_xlim( min(data)-1, None )
show()
searching around stackoverflow I just learned a new trick: you can call
ax.margins( margin_you_desire )
to let automatically let matplotlib put that amount of space around your plot. It can also be configured differently between x and y.
In your case the best solution would be something like
ax.margins(0.01, None)
The little catch is that the unit is in axes unit, referred to the size of you plot, so a margin of 1 will put space around your plot at both sizes big as your present plot
The problem is align='center'. Remove it.
I would like to draw a standard 2D line graph with pylot, but force the axes' values to be between 0 and 600 on the x, and 10k and 20k on the y. Let me go with an example...
import pylab as p
p.title(save_file)
p.axis([0.0,600.0,1000000.0,2000000.0])
#define keys and items elsewhere..
p.plot(keys,items)
p.savefig(save_file, dpi=100)
However, the axes still adjust to the size of the data. I'm interpreting the effect of p.axis to be setting what the max and min could be, not enforcing them to be the max or min. The same happens when I try to use p.xlim() etc.
Any thoughts?
Thanks.
Calling p.plot after setting the limits is why it is rescaling. You are correct in that turning autoscaling off will get the right answer, but so will calling xlim() or ylim() after your plot command.
I use this quite a lot to invert the x axis, I work in astronomy and we use a magnitude system which is backwards (ie. brighter stars have a smaller magnitude) so I usually swap the limits with
lims = xlim()
xlim([lims[1], lims[0]])
To answer my own question, the trick is to turn auto scaling off...
p.axis([0.0,600.0, 10000.0,20000.0])
ax = p.gca()
ax.set_autoscale_on(False)
I tried all of those above answers, and I then summarized a pipeline of how to draw the fixed-axes image. It applied both to show function and savefig function.
before you plot:
fig = pylab.figure()
ax = fig.gca()
ax.set_autoscale_on(False)
This is to request an ax which is subplot(1,1,1).
During the plot:
ax.plot('You plot argument') # Put inside your argument, like ax.plot(x,y,label='test')
ax.axis('The list of range') # Put in side your range [xmin,xmax,ymin,ymax], like ax.axis([-5,5,-5,200])
After the plot:
To show the image :
fig.show()
To save the figure :
fig.savefig('the name of your figure')
I find out that put axis at the front of the code won't work even though I have set autoscale_on to False.
I used this code to create a series of animation. And below is the example of combing multiple fixed axes images into an animation.
Try putting the call to axis after all plotting commands.