Related
This question already has answers here:
How to plot in multiple subplots
(12 answers)
Closed 1 year ago.
I have 864 plots to plot.
On running a loop, I can do plt.show() in the for loop, and print 864 plots, but that's difficult to view.
Is there a way I can print 4 plots per row? (That would be 216 x 4).
And how can I save them at the same time?
Edit: Example:
import matplotlib.pyplot as plt
i = 0
for i in range(100):
plt.scatter(x[i],x[i])
plt.scatter(y[i],y[i])
plt.title('Vector: {}/100'.format(i+1))
plt.show()
where x & y are list of list of cosine vectors.
The easiest option that comes to my mind is using subplot:
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(4,216))
axs = fig.axes
gs = fig.add_gridspec(ncols=4, nrows=216)
axs = gs.subplots()
There we are creating a figure with 4x216 plots. Probably you will need to adjust the figsize to your desired dimentions.
To plot something you just need to acces the axis using its index. For example:
x = [1, 2, 3]
y = [[1, 2], [3, 4], [5, 6]]
axs[0,0].plot(x, y)
To save it you can use fig.savefig("plot.png"). That creates a huge image. My suggestion is to create a pdf and store 4x6 plots per page. Here is an example of how to do it:
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
pp = PdfPages('plot.pdf')
for i in range(36):
fig = plt.figure(figsize=(16,24))
axs = fig.axes
gs = fig.add_gridspec(ncols=4, nrows=6)
axs = gs.subplots()
pp.savefig()
pp.close()
This process takes some time since it has to render a lot of images. Plotting a line made of 1000 random points (previously calculated) in each figure takes 37s. Here is the code of the test:
import time
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
import numpy as np
start = time.time()
pp = PdfPages('plot.pdf')
xx = np.random.randint(0,100, 1000)
yy = np.random.randint(0,100, 1000)
for i in range(36):
print(i)
fig = plt.figure(figsize=(16,24))
axs = fig.axes
gs = fig.add_gridspec(ncols=4, nrows=6)
axs = gs.subplots()
for x in range(4):
for y in range(6):
axs[y,x].plot(xx, yy)
pp.savefig()
pp.close()
print(time.time() - start)
Commented inline
# 10 plots
for j in range(10):
# close existing plots if any
plt.close('all')
plt.figure(figsize=(10,5))
for i in range(4):
# Plot with 1 row and 4 columns and current plot being drawn is (i+1)
plt.subplot(1,4,i+1)
x = np.random.randint(0,100, 100)
y = np.random.randint(0,100, 100)
plt.scatter(x,y)
plt.scatter(y,x)
# Finally save the plot
plt.savefig(f"plot_{j}.png")
Adjust figsize based on length of your x and y axis.
I have some troubles understanding how the matplotlib subplots allow for sharing axis between them. I saw some exemples but i could not modify one to fit my use case..; Here i replaced my data by uniforms so the plots wont be interesting but whatever...
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib import cm
d = 4
n1 = 100000
n2 = 100
background_data = np.random.uniform(size=(n1,d))
foreground_data = np.random.uniform(size=(n2,d))
fig = plt.figure()
for i in np.arange(d):
for j in np.arange(d):
if i != j:
ax = fig.add_subplot(d,d,1+i*d+j)
ax = plt.hist2d(background_data[:, i], background_data[:, j],
bins=3*n2,
cmap=cm.get_cmap('Greys'),
norm=mpl.colors.LogNorm())
ax = plt.plot(foreground_data[:,i],foreground_data[:,j],'o',markersize=0.2)
Q : How can i share the x and y axes for all plots ?
By far the easiest option is to use sharex and sharey arguments of plt.subplots.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
d = 4
n1 = 100000
n2 = 100
background_data = np.random.uniform(size=(n1,d))
foreground_data = np.random.uniform(size=(n2,d))
fig, axs = plt.subplots(d,d, sharex=True, sharey=True)
for i in np.arange(d):
for j in np.arange(d):
if i != j:
ax = axs[j,i]
ax.hist2d(background_data[:, i], background_data[:, j],
bins=3*n2,
cmap=plt.get_cmap('Greys'),
norm=mpl.colors.LogNorm())
ax.plot(foreground_data[:,i],foreground_data[:,j],'o',markersize=2)
else:
axs[j,i].remove()
fig.savefig("sharedaxes.png")
plt.show()
I have a sequence of line plots for two variables (x,y) for a number of different values of a variable z. I would normally add the line plots with legends like this:
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
# suppose mydata is a list of tuples containing (xs, ys, z)
# where xs and ys are lists of x's and y's and z is a number.
legns = []
for(xs,ys,z) in mydata:
pl = ax.plot(xs,ys,color = (z,0,0))
legns.append("z = %f"%(z))
ax.legends(legns)
plt.show()
But I have too many graphs and the legends will cover the graph. I'd rather have a colorbar indicating the value of z corresponding to the color. I can't find anything like that in the galery and all my attempts do deal with the colorbar failed. Apparently I must create a collection of plots before trying to add a colorbar.
Is there an easy way to do this? Thanks.
EDIT (clarification):
I wanted to do something like this:
import matplotlib.pyplot as plt
import matplotlib.cm as cm
fig = plt.figure()
ax = fig.add_subplot(111)
mycmap = cm.hot
# suppose mydata is a list of tuples containing (xs, ys, z)
# where xs and ys are lists of x's and y's and z is a number between 0 and 1
plots = []
for(xs,ys,z) in mydata:
pl = ax.plot(xs,ys,color = mycmap(z))
plots.append(pl)
fig.colorbar(plots)
plt.show()
But this won't work according to the Matplotlib reference because a list of plots is not a "mappable", whatever this means.
I've created an alternative plot function using LineCollection:
def myplot(ax,xs,ys,zs, cmap):
plot = lc([zip(x,y) for (x,y) in zip(xs,ys)], cmap = cmap)
plot.set_array(array(zs))
x0,x1 = amin(xs),amax(xs)
y0,y1 = amin(ys),amax(ys)
ax.add_collection(plot)
ax.set_xlim(x0,x1)
ax.set_ylim(y0,y1)
return plot
xs and ys are lists of lists of x and y coordinates and zs is a list of the different conditions to colorize each line. It feels a bit like a cludge though... I thought that there would be a more neat way to do this. I like the flexibility of the plt.plot() function.
(I know this is an old question but...) Colorbars require a matplotlib.cm.ScalarMappable, plt.plot produces lines which are not scalar mappable, therefore, in order to make a colorbar, we are going to need to make a scalar mappable.
Ok. So the constructor of a ScalarMappable takes a cmap and a norm instance. (norms scale data to the range 0-1, cmaps you have already worked with and take a number between 0-1 and returns a color). So in your case:
import matplotlib.pyplot as plt
sm = plt.cm.ScalarMappable(cmap=my_cmap, norm=plt.normalize(min=0, max=1))
plt.colorbar(sm)
Because your data is in the range 0-1 already, you can simplify the sm creation to:
sm = plt.cm.ScalarMappable(cmap=my_cmap)
EDIT: For matplotlib v1.2 or greater the code becomes:
import matplotlib.pyplot as plt
sm = plt.cm.ScalarMappable(cmap=my_cmap, norm=plt.normalize(vmin=0, vmax=1))
# fake up the array of the scalar mappable. Urgh...
sm._A = []
plt.colorbar(sm)
EDIT: For matplotlib v1.3 or greater the code becomes:
import matplotlib.pyplot as plt
sm = plt.cm.ScalarMappable(cmap=my_cmap, norm=plt.Normalize(vmin=0, vmax=1))
# fake up the array of the scalar mappable. Urgh...
sm._A = []
plt.colorbar(sm)
EDIT: For matplotlib v3.1 or greater simplifies to:
import matplotlib.pyplot as plt
sm = plt.cm.ScalarMappable(cmap=my_cmap, norm=plt.Normalize(vmin=0, vmax=1))
plt.colorbar(sm)
Here's one way to do it while still using plt.plot(). Basically, you make a throw-away plot and get the colorbar from there.
import matplotlib as mpl
import matplotlib.pyplot as plt
min, max = (-40, 30)
step = 10
# Setting up a colormap that's a simple transtion
mymap = mpl.colors.LinearSegmentedColormap.from_list('mycolors',['blue','red'])
# Using contourf to provide my colorbar info, then clearing the figure
Z = [[0,0],[0,0]]
levels = range(min,max+step,step)
CS3 = plt.contourf(Z, levels, cmap=mymap)
plt.clf()
# Plotting what I actually want
X=[[1,2],[1,2],[1,2],[1,2]]
Y=[[1,2],[1,3],[1,4],[1,5]]
Z=[-40,-20,0,30]
for x,y,z in zip(X,Y,Z):
# setting rgb color based on z normalized to my range
r = (float(z)-min)/(max-min)
g = 0
b = 1-r
plt.plot(x,y,color=(r,g,b))
plt.colorbar(CS3) # using the colorbar info I got from contourf
plt.show()
It's a little wasteful, but convenient. It's also not very wasteful if you make multiple plots as you can call plt.colorbar() without regenerating the info for it.
Here is a slightly simplied example inspired by the top answer given by Boris and Hooked (Thanks for the great idea!):
1. Discrete colorbar
Discrete colorbar is more involved, because colormap generated by mpl.cm.get_cmap() is not a mappable image needed as a colorbar() argument. A dummie mappable needs to generated as shown below:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
n_lines = 5
x = np.linspace(0, 10, 100)
y = np.sin(x[:, None] + np.pi * np.linspace(0, 1, n_lines))
c = np.arange(1, n_lines + 1)
cmap = mpl.cm.get_cmap('jet', n_lines)
fig, ax = plt.subplots(dpi=100)
# Make dummie mappable
dummie_cax = ax.scatter(c, c, c=c, cmap=cmap)
# Clear axis
ax.cla()
for i, yi in enumerate(y.T):
ax.plot(x, yi, c=cmap(i))
fig.colorbar(dummie_cax, ticks=c)
plt.show();
This will produce a plot with a discrete colorbar:
2. Continuous colorbar
Continuous colorbar is less involved, as mpl.cm.ScalarMappable() allows us to obtain an "image" for colorbar().
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
n_lines = 5
x = np.linspace(0, 10, 100)
y = np.sin(x[:, None] + np.pi * np.linspace(0, 1, n_lines))
c = np.arange(1, n_lines + 1)
norm = mpl.colors.Normalize(vmin=c.min(), vmax=c.max())
cmap = mpl.cm.ScalarMappable(norm=norm, cmap=mpl.cm.jet)
cmap.set_array([])
fig, ax = plt.subplots(dpi=100)
for i, yi in enumerate(y.T):
ax.plot(x, yi, c=cmap.to_rgba(i + 1))
fig.colorbar(cmap, ticks=c)
plt.show();
This will produce a plot with a continuous colorbar:
[Side note] In this example, I personally don't know why cmap.set_array([]) is necessary (otherwise we'd get error messages). If someone understand the principles under the hood, please comment :)
As other answers here do try to use dummy plots, which is not really good style, here is a generic code for a
Discrete colorbar
A discrete colorbar is produced in the same way a continuous colorbar is created, just with a different Normalization. In this case a BoundaryNorm should be used.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors
n_lines = 5
x = np.linspace(0, 10, 100)
y = np.sin(x[:, None] + np.pi * np.linspace(0, 1, n_lines))
c = np.arange(1., n_lines + 1)
cmap = plt.get_cmap("jet", len(c))
norm = matplotlib.colors.BoundaryNorm(np.arange(len(c)+1)+0.5,len(c))
sm = plt.cm.ScalarMappable(norm=norm, cmap=cmap)
sm.set_array([]) # this line may be ommitted for matplotlib >= 3.1
fig, ax = plt.subplots(dpi=100)
for i, yi in enumerate(y.T):
ax.plot(x, yi, c=cmap(i))
fig.colorbar(sm, ticks=c)
plt.show()
I'm trying to generate a grid of squares with a small offset between each and thought I would use my superior matplotlib skills. Unfortunately the output only shows one square, so I suspect that the matplotlib isn't getting the message to plot all the patch objects I am initializing, but despite much trawling of stackoverflow I cannot fix this issue.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as patches
import matplotlib.collections as coll
wid = 1
hei = 1
nrows = 5
ncols = 7
inbetween = 0.1
xx = np.arange(0, ncols, (wid+inbetween))
yy = np.arange(0, nrows, (hei+inbetween))
fig = plt.figure()
ax = plt.subplot(111, aspect='equal')
pat = []
for xi in xx:
for yi in yy:
sq = patches.Rectangle((xi, yi), wid, hei, fill=True)
ax.add_patch(sq)
pc = coll.PatchCollection(pat)
ax.add_collection(pc)
plt.axis('off')
plt.show()
plt.savefig('test.png', dpi=90)
Any ideas as to what is going wrong?
There are two issues here. First, you only see (part of) one patch here, because the axes go from 0 to 1 and not up to ncols and nrows.
This can be counteracted by relimiting the axes
ax.relim()
ax.autoscale_view()
or by setting the limis manually, e.g. ax.axis([0,ncols+1,0,nrows+1]).
Second, the PatchCollection is not doing anything, because an empty list (pat=[]) is added to it. Either delete the two lines
pc = coll.PatchCollection(pat)
ax.add_collection(pc)
or replace ax.add_patch(sq) by pat.append(sq). Note that relim() does not work when using the PatchCollection and manually relimitting the axes would be required.
I have a sequence of line plots for two variables (x,y) for a number of different values of a variable z. I would normally add the line plots with legends like this:
import matplotlib.pyplot as plt
fig = plt.figure()
ax = fig.add_subplot(111)
# suppose mydata is a list of tuples containing (xs, ys, z)
# where xs and ys are lists of x's and y's and z is a number.
legns = []
for(xs,ys,z) in mydata:
pl = ax.plot(xs,ys,color = (z,0,0))
legns.append("z = %f"%(z))
ax.legends(legns)
plt.show()
But I have too many graphs and the legends will cover the graph. I'd rather have a colorbar indicating the value of z corresponding to the color. I can't find anything like that in the galery and all my attempts do deal with the colorbar failed. Apparently I must create a collection of plots before trying to add a colorbar.
Is there an easy way to do this? Thanks.
EDIT (clarification):
I wanted to do something like this:
import matplotlib.pyplot as plt
import matplotlib.cm as cm
fig = plt.figure()
ax = fig.add_subplot(111)
mycmap = cm.hot
# suppose mydata is a list of tuples containing (xs, ys, z)
# where xs and ys are lists of x's and y's and z is a number between 0 and 1
plots = []
for(xs,ys,z) in mydata:
pl = ax.plot(xs,ys,color = mycmap(z))
plots.append(pl)
fig.colorbar(plots)
plt.show()
But this won't work according to the Matplotlib reference because a list of plots is not a "mappable", whatever this means.
I've created an alternative plot function using LineCollection:
def myplot(ax,xs,ys,zs, cmap):
plot = lc([zip(x,y) for (x,y) in zip(xs,ys)], cmap = cmap)
plot.set_array(array(zs))
x0,x1 = amin(xs),amax(xs)
y0,y1 = amin(ys),amax(ys)
ax.add_collection(plot)
ax.set_xlim(x0,x1)
ax.set_ylim(y0,y1)
return plot
xs and ys are lists of lists of x and y coordinates and zs is a list of the different conditions to colorize each line. It feels a bit like a cludge though... I thought that there would be a more neat way to do this. I like the flexibility of the plt.plot() function.
(I know this is an old question but...) Colorbars require a matplotlib.cm.ScalarMappable, plt.plot produces lines which are not scalar mappable, therefore, in order to make a colorbar, we are going to need to make a scalar mappable.
Ok. So the constructor of a ScalarMappable takes a cmap and a norm instance. (norms scale data to the range 0-1, cmaps you have already worked with and take a number between 0-1 and returns a color). So in your case:
import matplotlib.pyplot as plt
sm = plt.cm.ScalarMappable(cmap=my_cmap, norm=plt.normalize(min=0, max=1))
plt.colorbar(sm)
Because your data is in the range 0-1 already, you can simplify the sm creation to:
sm = plt.cm.ScalarMappable(cmap=my_cmap)
EDIT: For matplotlib v1.2 or greater the code becomes:
import matplotlib.pyplot as plt
sm = plt.cm.ScalarMappable(cmap=my_cmap, norm=plt.normalize(vmin=0, vmax=1))
# fake up the array of the scalar mappable. Urgh...
sm._A = []
plt.colorbar(sm)
EDIT: For matplotlib v1.3 or greater the code becomes:
import matplotlib.pyplot as plt
sm = plt.cm.ScalarMappable(cmap=my_cmap, norm=plt.Normalize(vmin=0, vmax=1))
# fake up the array of the scalar mappable. Urgh...
sm._A = []
plt.colorbar(sm)
EDIT: For matplotlib v3.1 or greater simplifies to:
import matplotlib.pyplot as plt
sm = plt.cm.ScalarMappable(cmap=my_cmap, norm=plt.Normalize(vmin=0, vmax=1))
plt.colorbar(sm)
Here's one way to do it while still using plt.plot(). Basically, you make a throw-away plot and get the colorbar from there.
import matplotlib as mpl
import matplotlib.pyplot as plt
min, max = (-40, 30)
step = 10
# Setting up a colormap that's a simple transtion
mymap = mpl.colors.LinearSegmentedColormap.from_list('mycolors',['blue','red'])
# Using contourf to provide my colorbar info, then clearing the figure
Z = [[0,0],[0,0]]
levels = range(min,max+step,step)
CS3 = plt.contourf(Z, levels, cmap=mymap)
plt.clf()
# Plotting what I actually want
X=[[1,2],[1,2],[1,2],[1,2]]
Y=[[1,2],[1,3],[1,4],[1,5]]
Z=[-40,-20,0,30]
for x,y,z in zip(X,Y,Z):
# setting rgb color based on z normalized to my range
r = (float(z)-min)/(max-min)
g = 0
b = 1-r
plt.plot(x,y,color=(r,g,b))
plt.colorbar(CS3) # using the colorbar info I got from contourf
plt.show()
It's a little wasteful, but convenient. It's also not very wasteful if you make multiple plots as you can call plt.colorbar() without regenerating the info for it.
Here is a slightly simplied example inspired by the top answer given by Boris and Hooked (Thanks for the great idea!):
1. Discrete colorbar
Discrete colorbar is more involved, because colormap generated by mpl.cm.get_cmap() is not a mappable image needed as a colorbar() argument. A dummie mappable needs to generated as shown below:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
n_lines = 5
x = np.linspace(0, 10, 100)
y = np.sin(x[:, None] + np.pi * np.linspace(0, 1, n_lines))
c = np.arange(1, n_lines + 1)
cmap = mpl.cm.get_cmap('jet', n_lines)
fig, ax = plt.subplots(dpi=100)
# Make dummie mappable
dummie_cax = ax.scatter(c, c, c=c, cmap=cmap)
# Clear axis
ax.cla()
for i, yi in enumerate(y.T):
ax.plot(x, yi, c=cmap(i))
fig.colorbar(dummie_cax, ticks=c)
plt.show();
This will produce a plot with a discrete colorbar:
2. Continuous colorbar
Continuous colorbar is less involved, as mpl.cm.ScalarMappable() allows us to obtain an "image" for colorbar().
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
n_lines = 5
x = np.linspace(0, 10, 100)
y = np.sin(x[:, None] + np.pi * np.linspace(0, 1, n_lines))
c = np.arange(1, n_lines + 1)
norm = mpl.colors.Normalize(vmin=c.min(), vmax=c.max())
cmap = mpl.cm.ScalarMappable(norm=norm, cmap=mpl.cm.jet)
cmap.set_array([])
fig, ax = plt.subplots(dpi=100)
for i, yi in enumerate(y.T):
ax.plot(x, yi, c=cmap.to_rgba(i + 1))
fig.colorbar(cmap, ticks=c)
plt.show();
This will produce a plot with a continuous colorbar:
[Side note] In this example, I personally don't know why cmap.set_array([]) is necessary (otherwise we'd get error messages). If someone understand the principles under the hood, please comment :)
As other answers here do try to use dummy plots, which is not really good style, here is a generic code for a
Discrete colorbar
A discrete colorbar is produced in the same way a continuous colorbar is created, just with a different Normalization. In this case a BoundaryNorm should be used.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors
n_lines = 5
x = np.linspace(0, 10, 100)
y = np.sin(x[:, None] + np.pi * np.linspace(0, 1, n_lines))
c = np.arange(1., n_lines + 1)
cmap = plt.get_cmap("jet", len(c))
norm = matplotlib.colors.BoundaryNorm(np.arange(len(c)+1)+0.5,len(c))
sm = plt.cm.ScalarMappable(norm=norm, cmap=cmap)
sm.set_array([]) # this line may be ommitted for matplotlib >= 3.1
fig, ax = plt.subplots(dpi=100)
for i, yi in enumerate(y.T):
ax.plot(x, yi, c=cmap(i))
fig.colorbar(sm, ticks=c)
plt.show()