I am trying to create a number (>100) subplots for later analysis. A grid up to 5x5 seems to work fine, any larger than that and the y-axis begins to get very squashed and the whole thing is unreadable. I have tried various different things, like setting aspect etc, but to no avail.
Here is the output for a 5x50 grid:
squashed subplots
and here is my code:
from matplotlib.backends.backend_pdf import PdfPages
pp = PdfPages('./output.pdf')
num_investigate = len(investigate)
ncols = 5
nrows = 50#math.ceil(num_investigate/ncols)
fig, axs = plt.subplots(nrows=nrows, ncols=ncols, sharex=False, figsize=(15,15))
for ax, file in zip(axs.flat, investigate[:(ncols*nrows)]):
try:
df = get_df_from_csv(file)
df['perf'] = df['val'] / df['val'].ix[0] - 1
#ax.set_ylim(bottom=df['perf'].min(), top=df['perf'].max())
ax.set_aspect('auto')
df['perf'].plot(ax=ax, title=file)
except:
pass
plt.tight_layout()
pp.savefig()
pp.close()
I'm at a real loss of how to solve this after much research.
How do I ensure that the each subplot size is constant and the output goes to more than one pdf page?
thanks
PdfPages saves one matplotlib figure to one page. A second calls to the savefig command will lead to the creation of a second page. Hence, if you want a second page in the output pdf, you need to create a second figure.
E.g. you can produce the first figure with a 5x5 grid and put the first 25 plots in that figure, then save it. Then create the next figure, add the next 25 plots to it and save it again.
There is a multipage_pdf example on the matplotlib page.
Related
I have two imshow() problems that I suspect are closely related.
First, I can't figure out how to use set_data() to update an image I've created with imshow().
Second, I can't figure out why the colorbar I add to the imshow() plot after I'm done updating the plot doesn't match the colorbar I add to an imshow() plot of the same data that I create from scratch after I'm done taking data. The colorbar of the second plot appears to be correct.
Background.
I'm collecting measurement data in two nested loops, with each loop controlling one of the measurement conditions. I'm using pyplot.imshow() to plot my results, and I'm updating the imshow() plot every time I take data in the inner loop.
What I have works in terms of updating the imshow() plot but it seems to be getting increasingly slower as I add more loop iterations, so it's not scaling well. (The program I've included in with this post creates a plot that is eight rows high and six columns wide. A "real" plot might be 10x or 20x this size, in both dimensions.)
I think what I want to do is use the image's set_data() method but I can't figure out how. What I've tried either throws an error or doesn't appear to have any effect on the imshow() plot.
Once I'm done with the "take the data" loops, I add a colorbar to the imshow() plot I've been updating. However, the colorbar scale is obviously bogus.
In contrast, if I take create an entirely new imshow() plot, using the data I took in the loops, and then add a colorbar, the colorbar appears to be correct.
My hunch is the problem is with the vmin and vmax values associated with the imshow() plot I'm updating in the loops but I can't figure out how to fix it.
I've already looked at several related StackOverflow posts. For example:
update a figure made with imshow(), contour() and quiver()
Update matplotlib image in a function
How to update matplotlib's imshow() window interactively?
These have helped, in that they've pointed me to set_data() and given me solutions to some other
problems I had, but I still have the two problems I mentioned at the start.
Here's a simplified version of my code. Note that there are repeated zero values on the X and Y axes. This is on purpose.
I'm running Python 3.5.1, matplotlib 1.5.1, and numpy 1.10.4. (Yes, some of these are quite old. Corporate IT reasons.)
import numpy as np
import matplotlib.pyplot as plt
import random
import time
import warnings
warnings.filterwarnings("ignore", ".*GUI is implemented.*") # Filter out bogus matplotlib warning.
# Create the simulated data for plotting
v_max = 120
v_step_size = 40
h_max = 50
h_step_size = 25
scale = 8
v_points = np.arange(-1*abs(v_max), 0, abs(v_step_size))
v_points = np.append(v_points, [-0.0])
reversed_v_points = -1 * v_points[::-1] # Not just reverse order, but reversed sign
v_points = np.append(v_points, reversed_v_points)
h_points = np.arange(-1*abs(h_max), 0, abs(h_step_size))
h_points = np.append(h_points, [-0.0])
reversed_h_points = -1 * h_points[::-1] # Not just reverse order, but reversed sign
h_points = np.append(h_points, reversed_h_points)
h = 0 # Initialize
v = 0 # Initialize
plt.ion() # Turn on interactive mode.
fig, ax = plt.subplots() # So I have access to the figure and the axes of the plot.
# Initialize the data_points
data_points = np.zeros((v_points.size, h_points.size))
im = ax.imshow(data_points, cmap='hot', interpolation='nearest') # Specify the color map and interpolation
ax.set_title('Dummy title for initial plot')
# Set up the X-axis ticks and label them
ax.set_xticks(np.arange(len(h_points)))
ax.set_xticklabels(h_points)
ax.set_xlabel('Horizontal axis measurement values')
# Set up the Y-axis ticks and label them
ax.set_yticks(np.arange(len(v_points)))
ax.set_yticklabels(v_points)
ax.set_ylabel('Vertical axis measurement values')
plt.pause(0.0001) # In interactive mode, need a small delay to get the plot to appear
plt.show()
for v, v_value in enumerate(v_points):
for h, h_value in enumerate(h_points):
# Measurement goes here.
time.sleep(0.1) # Simulate the measurement delay.
measured_value = scale * random.uniform(0.0, 1.0) # Create simulated data
data_points[v][h] = measured_value # Update data_points with the simulated data
# Update the heat map with the latest point.
# - I *think* I want to use im.set_data() here, not ax.imshow(), but how?
ax.imshow(data_points, cmap='hot', interpolation='nearest') # Specify the color map and interpolation
plt.pause(0.0001) # In interactive mode, need a small delay to get the plot to appear
plt.draw()
# Create a colorbar
# - Except the colorbar here is wrong. It goes from -0.10 to +0.10 instead
# of matching the colorbar in the second imshow() plot, which goes from
# 0.0 to "scale". Why?
cbar = ax.figure.colorbar(im, ax=ax)
cbar.ax.set_ylabel('Default heatmap colorbar label')
plt.pause(0.0001) # In interactive mode, need a small delay to get the colorbar to appear
plt.show()
fig2, ax2 = plt.subplots() # So I have access to the figure and the axes of the plot.
im = ax2.imshow(data_points, cmap='hot', interpolation='nearest') # Specify the color map and interpolation
ax2.set_title('Dummy title for plot with pseudo-data')
# Set up the X-axis ticks and label them
ax2.set_xticks(np.arange(len(h_points)))
ax2.set_xticklabels(h_points)
ax2.set_xlabel('Horizontal axis measurement values')
# Set up the Y-axis ticks and label them
ax2.set_yticks(np.arange(len(v_points)))
ax2.set_yticklabels(v_points)
ax2.set_ylabel('Vertical axis measurement values')
# Create a colorbar
cbar = ax2.figure.colorbar(im, ax=ax2)
cbar.ax.set_ylabel('Default heatmap colorbar label')
plt.pause(0.0001) # In interactive mode, need a small delay to get the plot to appear
plt.show()
dummy = input("In interactive mode, press the Enter key when you're done with the plots.")
OK, I Googled some more. More importantly, I Googled smarter and figured out my own answer.
To update my plot inside my nested loops, I was using the command:
ax.imshow(data_points, cmap='hot', interpolation='nearest') # Specify the color map and interpolation
What I tried using to update my plot more efficiently was:
im.set_data(data_points)
What I should have used was:
im.set_data(data_points)
im.autoscale()
This updates the pixel scaling, which fixed both my "plot doesn't update" problem and my "colorbar has the wrong scale" problem.
I am using Python 3.5 with MatPlotLib package. My problem is as follows:
I generate, say 50 plots, which I save each to a PNG file. Then I generate 2 summarizing plots, which I want to both save and show on display. However, when I use the plt.show() command, it also shows all the previous 50 plots, which I don't want to display, just save them. How to suppress the show on these previous 50 plots and show only the last one?
Here is an example code:
import matplotlib.pyplot as plt
import numpy as np
for i in range(50):
fig = plt.figure()
plt.plot(np.arange(10),np.arange(10)) # just plot something
plt.savefig(f"plot_{i}.png")
# I want to save these plots but not show them
# summarizing plot
fig = plt.figure()
plt.plot(np.arange(100),np.arange(100))
plt.show() # now it shows this fig and the previous 50 figs, but I want only to show this one!
Close all after the loop:
plt.close("all") #this is the line to be added
fig = plt.figure()
plt.plot(np.arange(100),np.arange(100))
plt.show()
I'm trying to create a video of many figures, so I need the axis to remain steady across multiple, independent figures. However, the y-axis changes scale, so the framing of the axis keeps moving as the ticklabels change. I'm trying to manually tell matplotlib exactly what size the whole figure should be and tell it exactly the position of the axis within the figure, but it's not working properly.
Here's what a base figure looks like:
import matplotlib.pyplot as plt
fig=plt.figure(figsize=(8,4),facecolor=(0.5,0.5,0.5))
ax=fig.add_subplot()
ax.plot([5,10],[800,900])
plt.show()
Here is one way for how I'm trying to change it if I want the axis frame to start at left=0.5, bottom=0.5, width=0.2, and height=0.2. I've tried many different ways, and all have failed, so this is illustrative of what I'm trying to do:
fig=plt.figure(figsize=(8,4),facecolor=(0.5,0.5,0.5))
ax=fig.add_axes((0.5,0.5,0.2,0.2))
ax.plot([5,10],[800,900])
plt.show()
Now, I want it to look more like this so that the black box of the axis frame will be in the exact same position for every figure, and each figure will be the exact same size. That way, when I make it an animation, the black frame won't be jerking around. (Obviously, I wouldn't make the buffer that big in the real video.)
You need to use ax.set_position.
If your ax box initially occupies the full figure, you can create a new size relatively to the old one, for example:
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(8, 4), facecolor=(0.5, 0.5, 0.5))
ax = fig.add_subplot(111)
bbox = ax.get_position()
new_bbox = (bbox.x0+0.40, bbox.y0+0.40, bbox.width*0.5, bbox.height*0.5)
ax.set_position(new_bbox)
ax.plot([5, 10], [800, 900])
plt.show()
I'm trying to adjust a suptitle above a multi-panel figure and am having trouble figuring out how to adjust the figsize and subsequently position the suptitle.
The problem is that calling plt.suptitle("my title", y=...) to adjust the position of the suptitle also adjusts the figure dimensions. A few questions:
where does suptitle(..., y=1.1) actually put the title? As far as I can tell, the documentation for the y parameter of suptitle points to matplotlib.text.Text, but I don't know what figure coordinates mean when you have multiple subplots.
what is the effect on figure size when specifying y to suptitle?
how do I manually adjust figure size and spacing (subplots_adjust?) to add a figure title per panel and a suptitle for the entire figure, maintaining the size of each ax in the figure?
An example:
data = np.random.random(size=100)
f, a = plt.subplots(2, 2, figsize=(10, 5))
a[0,0].plot(data)
a[0,0].set_title("this is a really long title\n"*2)
a[0,1].plot(data)
a[1,1].plot(data)
plt.suptitle("a big long suptitle that runs into the title\n"*2, y=1.05);
Obviously I can tweak y each time I make a figure, but I need a solution that generally works without manual intervention. I've tried both constrained layout and tight layout; neither works reliably with figures of any complexity.
1. What do figure coordinates mean?
Figure coordinates go 0 to 1, where (0,0) is the lower left corner and (1,1) is the upper right corner. A coordinate of y=1.05 is hence slightly outside the figure.
2. what is the effect on figure size when specifying y to suptitle?
Specifying y to suptitle has no effect whatsoever on the figure size.
3a. How do I manually adjust figure size and spacing to add a figure title per panel and a suptitle for the entire figure?
First, one would not add an additional linebreak. I.e. if you want to have 2 lines, don't use 3 linebreaks (\n). Then one can adjust the subplot parameters as desired to leave space for the titles. E.g. fig.subplots_adjust(top=0.8) and use a y <= 1 for the title to be inside the figure.
import matplotlib.pyplot as plt
import numpy as np
data = np.random.random(size=100)
fig, axes = plt.subplots(2, 2, figsize=(10, 5))
fig.subplots_adjust(top=0.8)
axes[0,0].plot(data)
axes[0,0].set_title("\n".join(["this is a really long title"]*2))
axes[0,1].plot(data)
axes[1,1].plot(data)
fig.suptitle("\n".join(["a big long suptitle that runs into the title"]*2), y=0.98)
plt.show()
3b. ... while maintaining the size of each ax in the figure?
Maintaining the size of the axes and still have enough space for the titles is only possible by changing the overall figure size.
This could look as follows, where we define a function make_space_above which takes the array of axes as input, as well as the newly desired top margin in units of inches. So for example, you come to the conclusion that you need 1 inch of margin on top to host your titles:
import matplotlib.pyplot as plt
import numpy as np
data = np.random.random(size=100)
fig, axes = plt.subplots(2, 2, figsize=(10, 5), squeeze = False)
axes[0,0].plot(data)
axes[0,0].set_title("\n".join(["this is a really long title"]*2))
axes[0,1].plot(data)
axes[1,1].plot(data)
fig.suptitle("\n".join(["a big long suptitle that runs into the title"]*2), y=0.98)
def make_space_above(axes, topmargin=1):
""" increase figure size to make topmargin (in inches) space for
titles, without changing the axes sizes"""
fig = axes.flatten()[0].figure
s = fig.subplotpars
w, h = fig.get_size_inches()
figh = h - (1-s.top)*h + topmargin
fig.subplots_adjust(bottom=s.bottom*h/figh, top=1-topmargin/figh)
fig.set_figheight(figh)
make_space_above(axes, topmargin=1)
plt.show()
(left: without calling make_space_above; right: with call to make_space_above(axes, topmargin=1))
Short Answer
For those coming from Google for adjusting the title position on a scatter matrix, you can simply set the y parameter to a value slightly lower than 1:
plt.suptitle('My Title', y=0.92)
... or use constrained_layout:
import matplotlib.pyplot as plt
import numpy as np
data = np.random.random(size=100)
f, a = plt.subplots(2, 2, figsize=(10, 5), constrained_layout=True)
a[0,0].plot(data)
a[0,0].set_title("this is a really long title\n"*2)
a[0,1].plot(data)
a[1,1].plot(data)
plt.suptitle("a big long suptitle that runs into the title\n"*2);
A bit of a hacky solution, but if your plots only have 1 column, perhaps consider just add the main title to the title of the first plot, like so:
ax[0].set_title("Main Title\nFirst Plot")
I'm very new to Python, and I want to plot 13 different figures all in one plot. To do this nicely, I would like to plot the first 12 figures in a 6x2 grid (this works just fine), and then plot the 13th figure below it; either the same size as the other figures and centered, or larger than the rest so that its width is equal to twice the width of the other figures and all the edges are aligned. What would be the best way to specify axes of this kind using subplots? (So far, I've just used nrows=6, ncols=2, but I think something like that won't work with an odd number of figures to plot.) The code I have so far for plotting the first 12 plots looks like this (with simple test data):
fig, axes = plt.subplots(nrows=6, ncols=2, figsize=(45,10))
for ax in axes.flat:
ax.plot([1,2,3,4])
fig.subplots_adjust(right=0.5)
How can I add a 13th figure below the others?
You can use GridSpec (link to documentation) to generate flexible axes layout.
The following code creates the desired layout and puts all Axes objects in a list for easy access.
gs00 = matplotlib.gridspec.GridSpec(7, 2)
fig = plt.figure()
axs = []
for i in range(6):
for j in range(2):
ax = fig.add_subplot(gs00[i,j])
axs.append(ax)
ax = fig.add_subplot(gs00[6,:])
axs.append(ax)