I've spent entirely too long researching how to get two subplots to share the same y-axis with a single colorbar shared between the two in Matplotlib.
What was happening was that when I called the colorbar() function in either subplot1 or subplot2, it would autoscale the plot such that the colorbar plus the plot would fit inside the 'subplot' bounding box, causing the two side-by-side plots to be two very different sizes.
To get around this, I tried to create a third subplot which I then hacked to render no plot with just a colorbar present.
The only problem is, now the heights and widths of the two plots are uneven, and I can't figure out how to make it look okay.
Here is my code:
from __future__ import division
import matplotlib.pyplot as plt
import numpy as np
from matplotlib import patches
from matplotlib.ticker import NullFormatter
# SIS Functions
TE = 1 # Einstein radius
g1 = lambda x,y: (TE/2) * (y**2-x**2)/((x**2+y**2)**(3/2))
g2 = lambda x,y: -1*TE*x*y / ((x**2+y**2)**(3/2))
kappa = lambda x,y: TE / (2*np.sqrt(x**2+y**2))
coords = np.linspace(-2,2,400)
X,Y = np.meshgrid(coords,coords)
g1out = g1(X,Y)
g2out = g2(X,Y)
kappaout = kappa(X,Y)
for i in range(len(coords)):
for j in range(len(coords)):
if np.sqrt(coords[i]**2+coords[j]**2) <= TE:
g1out[i][j]=0
g2out[i][j]=0
fig = plt.figure()
fig.subplots_adjust(wspace=0,hspace=0)
# subplot number 1
ax1 = fig.add_subplot(1,2,1,aspect='equal',xlim=[-2,2],ylim=[-2,2])
plt.title(r"$\gamma_{1}$",fontsize="18")
plt.xlabel(r"x ($\theta_{E}$)",fontsize="15")
plt.ylabel(r"y ($\theta_{E}$)",rotation='horizontal',fontsize="15")
plt.xticks([-2.0,-1.5,-1.0,-0.5,0,0.5,1.0,1.5])
plt.xticks([-2.0,-1.5,-1.0,-0.5,0,0.5,1.0,1.5])
plt.imshow(g1out,extent=(-2,2,-2,2))
plt.axhline(y=0,linewidth=2,color='k',linestyle="--")
plt.axvline(x=0,linewidth=2,color='k',linestyle="--")
e1 = patches.Ellipse((0,0),2,2,color='white')
ax1.add_patch(e1)
# subplot number 2
ax2 = fig.add_subplot(1,2,2,sharey=ax1,xlim=[-2,2],ylim=[-2,2])
plt.title(r"$\gamma_{2}$",fontsize="18")
plt.xlabel(r"x ($\theta_{E}$)",fontsize="15")
ax2.yaxis.set_major_formatter( NullFormatter() )
plt.axhline(y=0,linewidth=2,color='k',linestyle="--")
plt.axvline(x=0,linewidth=2,color='k',linestyle="--")
plt.imshow(g2out,extent=(-2,2,-2,2))
e2 = patches.Ellipse((0,0),2,2,color='white')
ax2.add_patch(e2)
# subplot for colorbar
ax3 = fig.add_subplot(1,1,1)
ax3.axis('off')
cbar = plt.colorbar(ax=ax2)
plt.show()
Just place the colorbar in its own axis and use subplots_adjust to make room for it.
As a quick example:
import numpy as np
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=2, ncols=2)
for ax in axes.flat:
im = ax.imshow(np.random.random((10,10)), vmin=0, vmax=1)
fig.subplots_adjust(right=0.8)
cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])
fig.colorbar(im, cax=cbar_ax)
plt.show()
Note that the color range will be set by the last image plotted (that gave rise to im) even if the range of values is set by vmin and vmax. If another plot has, for example, a higher max value, points with higher values than the max of im will show in uniform color.
You can simplify Joe Kington's code using the axparameter of figure.colorbar() with a list of axes.
From the documentation:
ax
None | parent axes object(s) from which space for a new colorbar axes will be stolen. If a list of axes is given they will all be resized to make room for the colorbar axes.
import numpy as np
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=2, ncols=2)
for ax in axes.flat:
im = ax.imshow(np.random.random((10,10)), vmin=0, vmax=1)
fig.colorbar(im, ax=axes.ravel().tolist())
plt.show()
This solution does not require manual tweaking of axes locations or colorbar size, works with multi-row and single-row layouts, and works with tight_layout(). It is adapted from a gallery example, using ImageGrid from matplotlib's AxesGrid Toolbox.
import numpy as np
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import ImageGrid
# Set up figure and image grid
fig = plt.figure(figsize=(9.75, 3))
grid = ImageGrid(fig, 111, # as in plt.subplot(111)
nrows_ncols=(1,3),
axes_pad=0.15,
share_all=True,
cbar_location="right",
cbar_mode="single",
cbar_size="7%",
cbar_pad=0.15,
)
# Add data to image grid
for ax in grid:
im = ax.imshow(np.random.random((10,10)), vmin=0, vmax=1)
# Colorbar
ax.cax.colorbar(im)
ax.cax.toggle_label(True)
#plt.tight_layout() # Works, but may still require rect paramater to keep colorbar labels visible
plt.show()
Using make_axes is even easier and gives a better result. It also provides possibilities to customise the positioning of the colorbar.
Also note the option of subplots to share x and y axes.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
fig, axes = plt.subplots(nrows=2, ncols=2, sharex=True, sharey=True)
for ax in axes.flat:
im = ax.imshow(np.random.random((10,10)), vmin=0, vmax=1)
cax,kw = mpl.colorbar.make_axes([ax for ax in axes.flat])
plt.colorbar(im, cax=cax, **kw)
plt.show()
As a beginner who stumbled across this thread, I'd like to add a python-for-dummies adaptation of abevieiramota's very neat answer (because I'm at the level that I had to look up 'ravel' to work out what their code was doing):
import numpy as np
import matplotlib.pyplot as plt
fig, ((ax1,ax2,ax3),(ax4,ax5,ax6)) = plt.subplots(2,3)
axlist = [ax1,ax2,ax3,ax4,ax5,ax6]
first = ax1.imshow(np.random.random((10,10)), vmin=0, vmax=1)
third = ax3.imshow(np.random.random((12,12)), vmin=0, vmax=1)
fig.colorbar(first, ax=axlist)
plt.show()
Much less pythonic, much easier for noobs like me to see what's actually happening here.
Shared colormap and colorbar
This is for the more complex case where the values are not just between 0 and 1; the cmap needs to be shared instead of just using the last one.
import numpy as np
from matplotlib.colors import Normalize
import matplotlib.pyplot as plt
import matplotlib.cm as cm
fig, axes = plt.subplots(nrows=2, ncols=2)
cmap=cm.get_cmap('viridis')
normalizer=Normalize(0,4)
im=cm.ScalarMappable(norm=normalizer)
for i,ax in enumerate(axes.flat):
ax.imshow(i+np.random.random((10,10)),cmap=cmap,norm=normalizer)
ax.set_title(str(i))
fig.colorbar(im, ax=axes.ravel().tolist())
plt.show()
As pointed out in other answers, the idea is usually to define an axes for the colorbar to reside in. There are various ways of doing so; one that hasn't been mentionned yet would be to directly specify the colorbar axes at subplot creation with plt.subplots(). The advantage is that the axes position does not need to be manually set and in all cases with automatic aspect the colorbar will be exactly the same height as the subplots. Even in many cases where images are used the result will be satisfying as shown below.
When using plt.subplots(), the use of gridspec_kw argument allows to make the colorbar axes much smaller than the other axes.
fig, (ax, ax2, cax) = plt.subplots(ncols=3,figsize=(5.5,3),
gridspec_kw={"width_ratios":[1,1, 0.05]})
Example:
import matplotlib.pyplot as plt
import numpy as np; np.random.seed(1)
fig, (ax, ax2, cax) = plt.subplots(ncols=3,figsize=(5.5,3),
gridspec_kw={"width_ratios":[1,1, 0.05]})
fig.subplots_adjust(wspace=0.3)
im = ax.imshow(np.random.rand(11,8), vmin=0, vmax=1)
im2 = ax2.imshow(np.random.rand(11,8), vmin=0, vmax=1)
ax.set_ylabel("y label")
fig.colorbar(im, cax=cax)
plt.show()
This works well, if the plots' aspect is autoscaled or the images are shrunk due to their aspect in the width direction (as in the above). If, however, the images are wider then high, the result would look as follows, which might be undesired.
A solution to fix the colorbar height to the subplot height would be to use mpl_toolkits.axes_grid1.inset_locator.InsetPosition to set the colorbar axes relative to the image subplot axes.
import matplotlib.pyplot as plt
import numpy as np; np.random.seed(1)
from mpl_toolkits.axes_grid1.inset_locator import InsetPosition
fig, (ax, ax2, cax) = plt.subplots(ncols=3,figsize=(7,3),
gridspec_kw={"width_ratios":[1,1, 0.05]})
fig.subplots_adjust(wspace=0.3)
im = ax.imshow(np.random.rand(11,16), vmin=0, vmax=1)
im2 = ax2.imshow(np.random.rand(11,16), vmin=0, vmax=1)
ax.set_ylabel("y label")
ip = InsetPosition(ax2, [1.05,0,0.05,1])
cax.set_axes_locator(ip)
fig.colorbar(im, cax=cax, ax=[ax,ax2])
plt.show()
New in matplotlib 3.4.0
Shared colorbars can now be implemented using subfigures:
New Figure.subfigures and Figure.add_subfigure allow ... localized figure artists (e.g., colorbars and suptitles) that only pertain to each subfigure.
The matplotlib gallery includes demos on how to plot subfigures.
Here is a minimal example with 2 subfigures, each with a shared colorbar:
fig = plt.figure(constrained_layout=True)
(subfig_l, subfig_r) = fig.subfigures(nrows=1, ncols=2)
axes_l = subfig_l.subplots(nrows=1, ncols=2, sharey=True)
for ax in axes_l:
im = ax.imshow(np.random.random((10, 10)), vmin=0, vmax=1)
# shared colorbar for left subfigure
subfig_l.colorbar(im, ax=axes_l, location='bottom')
axes_r = subfig_r.subplots(nrows=3, ncols=1, sharex=True)
for ax in axes_r:
mesh = ax.pcolormesh(np.random.randn(30, 30), vmin=-2.5, vmax=2.5)
# shared colorbar for right subfigure
subfig_r.colorbar(mesh, ax=axes_r)
The solution of using a list of axes by abevieiramota works very well until you use only one row of images, as pointed out in the comments. Using a reasonable aspect ratio for figsize helps, but is still far from perfect. For example:
import numpy as np
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=1, ncols=3, figsize=(9.75, 3))
for ax in axes.flat:
im = ax.imshow(np.random.random((10,10)), vmin=0, vmax=1)
fig.colorbar(im, ax=axes.ravel().tolist())
plt.show()
The colorbar function provides the shrink parameter which is a scaling factor for the size of the colorbar axes. It does require some manual trial and error. For example:
fig.colorbar(im, ax=axes.ravel().tolist(), shrink=0.75)
To add to #abevieiramota's excellent answer, you can get the euqivalent of tight_layout with constrained_layout. You will still get large horizontal gaps if you use imshow instead of pcolormesh because of the 1:1 aspect ratio imposed by imshow.
import numpy as np
import matplotlib.pyplot as plt
fig, axes = plt.subplots(nrows=2, ncols=2, constrained_layout=True)
for ax in axes.flat:
im = ax.pcolormesh(np.random.random((10,10)), vmin=0, vmax=1)
fig.colorbar(im, ax=axes.flat)
plt.show()
I noticed that almost every solution posted involved ax.imshow(im, ...) and did not normalize the colors displayed to the colorbar for the multiple subfigures. The im mappable is taken from the last instance, but what if the values of the multiple im-s are different? (I'm assuming these mappables are treated in the same way that the contour-sets and surface-sets are treated.) I have an example using a 3d surface plot below that creates two colorbars for a 2x2 subplot (one colorbar per one row). Although the question asks explicitly for a different arrangement, I think the example helps clarify some things. I haven't found a way to do this using plt.subplots(...) yet because of the 3D axes unfortunately.
If only I could position the colorbars in a better way... (There is probably a much better way to do this, but at least it should be not too difficult to follow.)
import matplotlib
from matplotlib import cm
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.mplot3d import Axes3D
cmap = 'plasma'
ncontours = 5
def get_data(row, col):
""" get X, Y, Z, and plot number of subplot
Z > 0 for top row, Z < 0 for bottom row """
if row == 0:
x = np.linspace(1, 10, 10, dtype=int)
X, Y = np.meshgrid(x, x)
Z = np.sqrt(X**2 + Y**2)
if col == 0:
pnum = 1
else:
pnum = 2
elif row == 1:
x = np.linspace(1, 10, 10, dtype=int)
X, Y = np.meshgrid(x, x)
Z = -np.sqrt(X**2 + Y**2)
if col == 0:
pnum = 3
else:
pnum = 4
print("\nPNUM: {}, Zmin = {}, Zmax = {}\n".format(pnum, np.min(Z), np.max(Z)))
return X, Y, Z, pnum
fig = plt.figure()
nrows, ncols = 2, 2
zz = []
axes = []
for row in range(nrows):
for col in range(ncols):
X, Y, Z, pnum = get_data(row, col)
ax = fig.add_subplot(nrows, ncols, pnum, projection='3d')
ax.set_title('row = {}, col = {}'.format(row, col))
fhandle = ax.plot_surface(X, Y, Z, cmap=cmap)
zz.append(Z)
axes.append(ax)
## get full range of Z data as flat list for top and bottom rows
zz_top = zz[0].reshape(-1).tolist() + zz[1].reshape(-1).tolist()
zz_btm = zz[2].reshape(-1).tolist() + zz[3].reshape(-1).tolist()
## get top and bottom axes
ax_top = [axes[0], axes[1]]
ax_btm = [axes[2], axes[3]]
## normalize colors to minimum and maximum values of dataset
norm_top = matplotlib.colors.Normalize(vmin=min(zz_top), vmax=max(zz_top))
norm_btm = matplotlib.colors.Normalize(vmin=min(zz_btm), vmax=max(zz_btm))
cmap = cm.get_cmap(cmap, ncontours) # number of colors on colorbar
mtop = cm.ScalarMappable(cmap=cmap, norm=norm_top)
mbtm = cm.ScalarMappable(cmap=cmap, norm=norm_btm)
for m in (mtop, mbtm):
m.set_array([])
# ## create cax to draw colorbar in
# cax_top = fig.add_axes([0.9, 0.55, 0.05, 0.4])
# cax_btm = fig.add_axes([0.9, 0.05, 0.05, 0.4])
cbar_top = fig.colorbar(mtop, ax=ax_top, orientation='vertical', shrink=0.75, pad=0.2) #, cax=cax_top)
cbar_top.set_ticks(np.linspace(min(zz_top), max(zz_top), ncontours))
cbar_btm = fig.colorbar(mbtm, ax=ax_btm, orientation='vertical', shrink=0.75, pad=0.2) #, cax=cax_btm)
cbar_btm.set_ticks(np.linspace(min(zz_btm), max(zz_btm), ncontours))
plt.show()
plt.close(fig)
## orientation of colorbar = 'horizontal' if done by column
This topic is well covered but I still would like to propose another approach in a slightly different philosophy.
It is a bit more complex to set-up but it allow (in my opinion) a bit more flexibility. For example, one can play with the respective ratios of each subplots / colorbar:
import matplotlib.pyplot as plt
import numpy as np
from matplotlib.gridspec import GridSpec
# Define number of rows and columns you want in your figure
nrow = 2
ncol = 3
# Make a new figure
fig = plt.figure(constrained_layout=True)
# Design your figure properties
widths = [3,4,5,1]
gs = GridSpec(nrow, ncol + 1, figure=fig, width_ratios=widths)
# Fill your figure with desired plots
axes = []
for i in range(nrow):
for j in range(ncol):
axes.append(fig.add_subplot(gs[i, j]))
im = axes[-1].pcolormesh(np.random.random((10,10)))
# Shared colorbar
axes.append(fig.add_subplot(gs[:, ncol]))
fig.colorbar(im, cax=axes[-1])
plt.show()
The answers above are great, but most of them use the fig.colobar() method applied to a fig object. This example shows how to use the plt.colobar() function, applied directly to pyplot:
def shared_colorbar_example():
fig, axs = plt.subplots(nrows=3, ncols=3)
for ax in axs.flat:
plt.sca(ax)
color = np.random.random((10))
plt.scatter(range(10), range(10), c=color, cmap='viridis', vmin=0, vmax=1)
plt.colorbar(ax=axs.ravel().tolist(), shrink=0.6)
plt.show()
shared_colorbar_example()
Since most answers above demonstrated usage on 2D matrices, I went with a simple scatter plot. The shrink keyword is optional and resizes the colorbar.
If vmin and vmax are not specified this approach will automatically analyze all of the subplots for the minimum and maximum value to be used on the colorbar. The above approaches when using fig.colorbar(im) scan only the image passed as argument for min and max values of the colorbar.
Result:
Looking at the matplotlib documentation, it seems the standard way to add an AxesSubplot to a Figure is to use Figure.add_subplot:
from matplotlib import pyplot
fig = pyplot.figure()
ax = fig.add_subplot(1,1,1)
ax.hist( some params .... )
I would like to be able to create AxesSubPlot-like objects independently of the figure, so I can use them in different figures. Something like
fig = pyplot.figure()
histoA = some_axes_subplot_maker.hist( some params ..... )
histoA = some_axes_subplot_maker.hist( some other params ..... )
# make one figure with both plots
fig.add_subaxes(histo1, 211)
fig.add_subaxes(histo1, 212)
fig2 = pyplot.figure()
# make a figure with the first plot only
fig2.add_subaxes(histo1, 111)
Is this possible in matplotlib and if so, how can I do this?
Update: I have not managed to decouple creation of Axes and Figures, but following examples in the answers below, can easily re-use previously created axes in new or olf Figure instances. This can be illustrated with a simple function:
def plot_axes(ax, fig=None, geometry=(1,1,1)):
if fig is None:
fig = plt.figure()
if ax.get_geometry() != geometry :
ax.change_geometry(*geometry)
ax = fig.axes.append(ax)
return fig
Typically, you just pass the axes instance to a function.
For example:
import matplotlib.pyplot as plt
import numpy as np
def main():
x = np.linspace(0, 6 * np.pi, 100)
fig1, (ax1, ax2) = plt.subplots(nrows=2)
plot(x, np.sin(x), ax1)
plot(x, np.random.random(100), ax2)
fig2 = plt.figure()
plot(x, np.cos(x))
plt.show()
def plot(x, y, ax=None):
if ax is None:
ax = plt.gca()
line, = ax.plot(x, y, 'go')
ax.set_ylabel('Yabba dabba do!')
return line
if __name__ == '__main__':
main()
To respond to your question, you could always do something like this:
def subplot(data, fig=None, index=111):
if fig is None:
fig = plt.figure()
ax = fig.add_subplot(index)
ax.plot(data)
Also, you can simply add an axes instance to another figure:
import matplotlib.pyplot as plt
fig1, ax = plt.subplots()
ax.plot(range(10))
fig2 = plt.figure()
fig2.axes.append(ax)
plt.show()
Resizing it to match other subplot "shapes" is also possible, but it's going to quickly become more trouble than it's worth. The approach of just passing around a figure or axes instance (or list of instances) is much simpler for complex cases, in my experience...
The following shows how to "move" an axes from one figure to another. This is the intended functionality of #JoeKington's last example, which in newer matplotlib versions is not working anymore, because axes cannot live in several figures at once.
You would first need to remove the axes from the first figure, then append it to the next figure and give it some position to live in.
import matplotlib.pyplot as plt
fig1, ax = plt.subplots()
ax.plot(range(10))
ax.remove()
fig2 = plt.figure()
ax.figure=fig2
fig2.axes.append(ax)
fig2.add_axes(ax)
dummy = fig2.add_subplot(111)
ax.set_position(dummy.get_position())
dummy.remove()
plt.close(fig1)
plt.show()
For line plots, you can deal with the Line2D objects themselves:
fig1 = pylab.figure()
ax1 = fig1.add_subplot(111)
lines = ax1.plot(scipy.randn(10))
fig2 = pylab.figure()
ax2 = fig2.add_subplot(111)
ax2.add_line(lines[0])
TL;DR based partly on Joe nice answer.
Opt.1: fig.add_subplot()
def fcn_return_plot():
return plt.plot(np.random.random((10,)))
n = 4
fig = plt.figure(figsize=(n*3,2))
#fig, ax = plt.subplots(1, n, sharey=True, figsize=(n*3,2)) # also works
for index in list(range(n)):
fig.add_subplot(1, n, index + 1)
fcn_return_plot()
plt.title(f"plot: {index}", fontsize=20)
Opt.2: pass ax[index] to a function that returns ax[index].plot()
def fcn_return_plot_input_ax(ax=None):
if ax is None:
ax = plt.gca()
return ax.plot(np.random.random((10,)))
n = 4
fig, ax = plt.subplots(1, n, sharey=True, figsize=(n*3,2))
for index in list(range(n)):
fcn_return_plot_input_ax(ax[index])
ax[index].set_title(f"plot: {index}", fontsize=20)
Outputs respect.
Note: Opt.1 plt.title() changed in opt.2 to ax[index].set_title(). Find more Matplotlib Gotchas in Van der Plas book.
To go deeper in the rabbit hole. Extending my previous answer, one could return a whole ax, and not ax.plot() only. E.g.
If dataframe had 100 tests of 20 types (here id):
dfA = pd.DataFrame(np.random.random((100,3)), columns = ['y1', 'y2', 'y3'])
dfB = pd.DataFrame(np.repeat(list(range(20)),5), columns = ['id'])
dfC = dfA.join(dfB)
And the plot function (this is the key of this whole answer):
def plot_feature_each_id(df, feature, id_range=[], ax=None, legend_bool=False):
feature = df[feature]
if not len(id_range): id_range=set(df['id'])
legend_arr = []
for k in id_range:
pass
mask = (df['id'] == k)
ax.plot(feature[mask])
legend_arr.append(f"id: {k}")
if legend_bool: ax.legend(legend_arr)
return ax
We can achieve:
feature_arr = dfC.drop('id',1).columns
id_range= np.random.randint(len(set(dfC.id)), size=(10,))
n = len(feature_arr)
fig, ax = plt.subplots(1, n, figsize=(n*6,4));
for i,k in enumerate(feature_arr):
plot_feature_each_id(dfC, k, np.sort(id_range), ax[i], legend_bool=(i+1==n))
ax[i].set_title(k, fontsize=20)
ax[i].set_xlabel("test nr. (id)", fontsize=20)
I'm new to programming and currently stuck on this: I create 4 different plots using a for loop and I want to assign each plot to a different subplot. Basically, I want 4 plots in a 2x2 grid so I can compare my data. Anyone knows how I can achieve this? My approach was to create a list of subplots and then assign every plot to a subplot using a nested plot:
import matplotlib.pyplot as plt
import numpy as np
def load_file(filename):
return np.loadtxt(filename, delimiter=',', usecols=(0, 1), unpack=True, skiprows=1)
fig = plt.figure()
ax1 = fig.add_subplot(221)
ax2 = fig.add_subplot(222)
ax3 = fig.add_subplot(223)
ax4 = fig.add_subplot(224)
ax_list=[ax1, ax2, ax3, ax4]
for i,filename in enumerate(file_list):
for p in ax_list:
x,y = load_file(filename)
plt.plot(x,y,
label=l, #I assign labels from a list beforehand, as well as colors
color=c,
linewidth=0.5,
ls='-',
)
p.plot()
The problem is, all plots are assigned to only one subplot and I don't know how to correct this. I'd appreciate any help!
I guess what you want is to show different data on all 4 plots, hence use a single loop. Make sure to use the axes plotting method, not plt.plot as the latter would always plot in the last subplot.
import matplotlib.pyplot as plt
import numpy as np
def load_file(filename):
return np.loadtxt(filename, delimiter=',', usecols=(0, 1), unpack=True, skiprows=1)
fig, ax_list = plt.subplots(2,2)
for i,(filename,ax) in enumerate(zip(file_list, ax_list.flatten())):
x,y = load_file(filename)
ax.plot(x,y, linewidth=0.5,ls='-' )
plt.show()
You don't need to loop over filenames and plots, only need to select the next plot in the list.
for i, filename in enumerate(file_list):
p = ax_list[i]:
x,y = load_file(filename)
p.plot(x, y,
label=l, #I assign labels from a list beforehand, as well as colors
color=c,
linewidth=0.5,
ls='-')
plt.plot()
You can also replace
fig = plt.figure()
ax1 = fig.add_subplot(221)
ax2 = fig.add_subplot(222)
ax3 = fig.add_subplot(223)
ax4 = fig.add_subplot(224)
ax_list=[ax1, ax2, ax3, ax4]
with just
fig, ax_list = plt.subplots(2, 2)
ax_list = ax_list.flatten()
To get a simple 2x2 grid.