I am currently attempting to use passed axis object created in function, e.g.:
def drawfig_1():
import matplotlib.pyplot as plt
# Create a figure with one axis (ax1)
fig, ax1 = plt.subplots(figsize=(4,2))
# Plot some data
ax1.plot(range(10))
# Return axis object
return ax1
My question is, how can I use the returned axis object, ax1, in another figure? For example, I would like to use it in this manner:
# Setup plots for analysis
fig2 = plt.figure(figsize=(12, 8))
# Set up 2 axes, one for a pixel map, the other for an image
ax_map = plt.subplot2grid((3, 3), (0, 0), rowspan=3)
ax_image = plt.subplot2grid((3, 3), (0, 1), colspan=2, rowspan=3)
# Plot the image
ax_psf.imshow(image, vmin=0.00000001, vmax=0.000001, cmap=cm.gray)
# Plot the map
???? <----- #I don't know how to display my passed axis here...
I've tried statements such as:
ax_map.axes = ax1
and although my script does not crash, my axis comes up empty. Any help would be appreciated!
You are trying to make a plot first and then put that plot as a subplot in another plot (defined by subplot2grid). Unfortunately, that is not possible. Also see this post: How do I include a matplotlib Figure object as subplot?.
You would have to make the subplot first and pass the axis of the subplot to your drawfig_1() function to plot it. Of course, drawfig_1() will need to be modified. e.g:
def drawfig_1(ax1):
ax1.plot(range(10))
return ax1
# Setup plots for analysis
fig2 = plt.figure(figsize=(12, 8))
# Set up 2 axes, one for a pixel map, the other for an image
ax_map = plt.subplot2grid((3, 3), (0, 0), rowspan=3)
ax_image = plt.subplot2grid((3, 3), (0, 1), colspan=2, rowspan=3)
# Plot the image
ax_image.imshow(image, vmin=0.00000001, vmax=0.000001, cmap=cm.gray)
# Plot the map:
drawfig_1(ax_map)
Related
I am using the Subplot2Grid functionality within Matplotlib to combine two figures with different orientations, 4 bar plots (full width) and then 3 scatter plots splitting the full width into 3 columns, plus an extra space for a legend. The different sections have axes that need to align so I have used sharex = ax1 and sharey = ax1 within Subplot2Grid to implement this successfully.
However, I now cannot seem to control the axis labels the same as I would just using regular subplots function, having the x-axis tick labels showing only on the final bar plot and the y-axis tick labels showing only on the left-most scatter plot.
Plotting using Subplot2Grid, extra axis labels showing
I have tried the ax.set_xticklabels('') to try and switch them off, but the sharex/sharey seems to override them? I have also put the ax.set_xticklabels('') at the end of the code (after they are defined in ax4) and it switches them all off, not just the axis the one that is called (ax1, ax2 or ax3)
Relevant parts of the code are below:
# figure setup
fig = plt.figure()
fig.set_figheight(15)
fig.set_figwidth(9)
ax1 = plt.subplot2grid(shape=(6, 3), loc=(0, 0), colspan=3)
ax2 = plt.subplot2grid(shape=(6, 3), loc=(1, 0), colspan=3,sharex=ax1)
ax3 = plt.subplot2grid(shape=(6, 3), loc=(2, 0), colspan=3,sharex=ax1)
ax4 = plt.subplot2grid(shape=(6, 3), loc=(3, 0), colspan=3,sharex=ax1)
ax5 = plt.subplot2grid(shape=(6, 3), loc=(5, 0))
ax6 = plt.subplot2grid(shape=(6, 3), loc=(5, 1),sharex=ax5,sharey=ax5)
ax7 = plt.subplot2grid(shape=(6, 3), loc=(5, 2),sharex=ax5,sharey=ax5)
# plotting bars here
# first bar plot
ax1.set_title('Inundation area')
ax1.set_xticklabels('')
ylbl0 = 'Inundation area \n' + r'$(km^2)$'
ax1.set_ylabel(ylbl0)
# repeat for ax2 & ax3
# last bar plot
ax4.set_title(r'$\Delta$ Shear Stress')
ax4.set_xticks(np.arange(len(df_bars)))
ax4.set_xticklabels(df_bars['Reach Number'])
ax4.invert_xaxis()
ax4.axhline(y=0,c='k',lw = 0.5)
ax4.set_xlabel('Reach number')
ax4.set_ylabel('% change \n (2019-2020)')
Same occurs when using sharey for the 3 scatter plots and ax.set_yticklabels('')
ax1.tick_params(labelbottom=False)
does what you want.
This example works for me:
fig = plt.figure()
fig.set_figheight(15)
fig.set_figwidth(9)
ax1 = plt.subplot2grid(shape=(2, 1), loc=(0, 0), colspan=3)
ax1.plot(np.random.rand(10))
ax2 = plt.subplot2grid(shape=(2, 1), loc=(1, 0), colspan=3,sharex=ax1)
ax2.plot(np.random.rand(10))
ax1.tick_params(labelbottom=False)
plt.show()
I'm trying to create a figure with a number of non-uniform subplots. I would like to be able to create the plots using an iterable index so that I do not have to create each plot individually.
I can create a series of uniform subplots using fig, ax = plt.subplots(5) where I can plot to the various axes using ax[i].
fig, ax = plt.subplots(5)
Going forward I can plot to each plot using ax[i] using ax[0].plt etc.
However I would like to be able to create a series of plots that looks like:
gridsize = (10,3)
fig = plt.figure(figsize=(5,3))
ax0 = plt.subplot2grid(gridsize, (0, 0), colspan=3, rowspan=1)
for i in range(1,5):
ax1 = plt.subplot2grid(gridsize, (i, 0), colspan=2, rowspan=1)
ax2 = plt.subplot2grid(gridsize, (i, 2), colspan=2, rowspan=1)
where I can call each plot using ax[i] as above.
Does anyone have any ideas? Thanks.
You may append the axes to a list from which to index the respective item or over which to iterate.
import numpy as np
import matplotlib.pyplot as plt
gridsize = (10,3)
fig = plt.figure(figsize=(5,3))
ax0 = plt.subplot2grid(gridsize, (0, 0), colspan=3, rowspan=1)
ax = [ax0]
for i in range(1,5):
ax.append(plt.subplot2grid(gridsize, (i, 0), colspan=2, rowspan=1))
ax.append(plt.subplot2grid(gridsize, (i, 2), colspan=2, rowspan=1))
## Now plot to those axes:
for i in range(2*4+1):
ax[i].plot(np.arange(14),np.random.randn(14))
for axi in ax:
axi.plot(np.arange(14),np.random.randn(14))
plt.show()
I am trying to add subplots of differing sizes to a particular matplotlib figure, and am unsure of how to do so. In the case of there only being one figure, the "subplot2grid" can be utilized as follows:
import matplotlib.pyplot as plt
fig = plt.figure()
ax1 = plt.subplot2grid((2, 2), (0, 0), colspan=2)
ax1 = plt.subplot2grid((2, 2), (1, 1))
plt.show()
The above code creates a figure, and adds two subplots to that figure, each with different dimensions. Now, my issue arises in the case of having multiple figures -- I cannot find the appropriate way to add subplots to a particular figure using "subplot2grid." Using the more simple "add_subplot" method, one can add subplots to a particular figure, as seen in the below code:
import matplotlib.pyplot as plt
fig1 = plt.figure()
fig2 = plt.figure()
ax1 = fig1.add_subplot(2, 2, 1)
ax2 = fig1.add_subplot(2, 2, 4)
plt.show()
I am looking for the analogous method for adding subplots of different sizes (preferably using some sort of grid manager, e.g. "subplot2grid") to a particular figure. I have reservations about using the plt."x" style because it operates on the last figure that was created -- my code will have several figures, all of which I will need to have subplots of different sizes.
Thanks in advance,
Curtis M.
In the future (probably the upcoming release?), subplot2grid
will take a fig argument
subplot2grid(shape, loc, rowspan=1, colspan=1, fig=None, **kwargs)
such that the following would be possible:
import matplotlib.pyplot as plt
fig1=plt.figure()
fig2=plt.figure()
ax1 = plt.subplot2grid((2, 2), (0, 0), colspan=2, fig=fig1)
ax2 = plt.subplot2grid((2, 2), (1, 1), fig=fig1)
plt.show()
As of now (version 2.0.2) this is not yet possible. Alternatively, you can manually define the underlying GridSpec
import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec
fig1=plt.figure()
fig2=plt.figure()
spec1 = GridSpec(2, 2).new_subplotspec((0,0), colspan=2)
ax1 = fig1.add_subplot(spec1)
spec2 = GridSpec(2, 2).new_subplotspec((1,1))
ax2 = fig1.add_subplot(spec2)
plt.show()
Or you can simply set the current figure, such that plt.subplot2grid will work on that exact figure (as shown in this question)
import matplotlib.pyplot as plt
fig1=plt.figure(1)
fig2=plt.figure(2)
# ... some other stuff
plt.figure(1) # set current figure to fig1
ax1 = plt.subplot2grid((2, 2), (0, 0), colspan=2)
ax2 = plt.subplot2grid((2, 2), (1, 1))
plt.show()
I am new to python and having some difficulties with plotting using pyplot. My goal is to plot a grid of plots in-line (%pylab inline) in Juypter Notebook.
I programmed a function plot_CV which plots cross-validation erorr over the degree of polynomial of some x where across plots the degree of penalization (lambda) is supposed to vary. Ultimately there are 10 elements in lambda and they are controlled by the first argument in plot_CV. So
fig = plt.figure()
ax1 = fig.add_subplot(1,1,1)
ax1 = plot_CV(1,CV_ve=CV_ve)
Gives
Now I think I have to use add_subplot to create a grid of plots as in
fig = plt.figure()
ax1 = fig.add_subplot(2,2,1)
ax1 = plot_CV(1,CV_ve=CV_ve)
ax2 = fig.add_subplot(2,2,2)
ax2 = plot_CV(2,CV_ve=CV_ve)
ax3 = fig.add_subplot(2,2,3)
ax3 = plot_CV(3,CV_ve=CV_ve)
ax4 = fig.add_subplot(2,2,4)
ax4 = plot_CV(4,CV_ve=CV_ve)
plt.show()
If I continue this, however, then the plots get smaller and smaller and start to overlap on the x and y labels. Here a picture with a 3 by 3 plot.
Is there a way to space the plots evenly, so that they do not overlap and make better use of the horizontal and vertical in-line space in Jupyter Notebook? To illustrate this point here a screenshot from jupyter:
Final note: I still need to add a title or annotation with the current level of lambda used in plot_CV.
Edit: Using the tight layout as suggested, gives:
Edit 2: Using the fig.set_figheight and fig.set_figwidth I could finally use the full length and heigth available.
The first suggestion to your problem would be taking a look at the "Tight Layout guide" for matplotlib.
They have an example that looks visually very similar to your situation. As well they have examples and suggestions for taking into consideration axis labels and plot titles.
Furthermore you can control the overall figure size by using Figure from the matplotlib.figure class.
Figure(figsize = (x,y))
figsize: x,y (inches)
EDIT:
Here is an example that I pulled from the matplotlib website and added in the:
fig.set_figheight(15)
fig.set_figwidth(15)
example:
import matplotlib.pyplot as plt
plt.rcParams['savefig.facecolor'] = "0.8"
def example_plot(ax, fontsize=12):
ax.plot([1, 2])
ax.locator_params(nbins=3)
ax.set_xlabel('x-label', fontsize=fontsize)
ax.set_ylabel('y-label', fontsize=fontsize)
ax.set_title('Title', fontsize=fontsize)
plt.close('all')
fig = plt.figure()
fig.set_figheight(15)
fig.set_figwidth(15)
ax1 = plt.subplot2grid((3, 3), (0, 0))
ax2 = plt.subplot2grid((3, 3), (0, 1), colspan=2)
ax3 = plt.subplot2grid((3, 3), (1, 0), colspan=2, rowspan=2)
ax4 = plt.subplot2grid((3, 3), (1, 2), rowspan=2)
example_plot(ax1)
example_plot(ax2)
example_plot(ax3)
example_plot(ax4)
plt.tight_layout()
You can achieve padding of your subplots by using tight_layout this way:
plt.tight_layout(pad=0.4, w_pad=0.5, h_pad=1.0)
That way you can keep your subplots from crowding each other even further.
Have a good one!
In this figure, in the 1st plot, the grid divides the plot in "windows" and each window is divided in subwindows (made with let's say 5 data).
Then the slope of each subwindow is calculated and saved.
Next I divide the polar plane in 16 quadrants and calculate which quadrant correspond to each slope. So, I get something like this:
1,1,1,1,1,1,1,1,1,1,1,2,2,2,3,4
4,1,-1,-2,...
In the dataset above, each number is the quadrant that represents the slope of a subwindow and each row represents a window (The histograms are calculated with this dataset).
What I'm looking for is that the figure at the top, the 2nd plot shows the histogram of each window under its corresponding window.
All I could get is this from the matplotlib page but none of those examples are close to what I need because I need the histograms next to each other without blocking each other.
Sometimes, depending on the parameters used, it could be more than 800 histograms in the same plot.
Here's an example of how you can display multiple plots side-by-side below a larger one using Gridspec:
import numpy as np
import matplotlib.pyplot as plt
# generate some data
x = np.arange(0, 10, 0.2)
y = np.sin(x)
# plot it
fig = plt.figure(figsize=(8, 6))
ax0 = plt.subplot2grid((2, 4), (0, 0), colspan=4)
ax0.plot(x, y)
ax1 = plt.subplot2grid((2, 4), (1, 0))
ax1.hist(y)
ax2 = plt.subplot2grid((2, 4), (1, 1))
ax2.hist(y)
ax2.set_yticklabels([])
ax2.set_yticks([])
ax3 = plt.subplot2grid((2, 4), (1, 2))
ax3.hist(y)
ax3.set_yticklabels([])
ax3.set_yticks([])
ax4 = plt.subplot2grid((2, 4), (1, 3))
ax4.hist(y)
ax4.set_yticklabels([])
ax4.set_yticks([])
plt.subplots_adjust(wspace=0) # no space left between hists in 2nd row
Results in: