I need to draw subplots of a figure through loop iterations; each iteration calls a function defined in another module (=another py file), which draws a pair of subplots. Here is what I tried -- and alas does not work:
1) Before the loop, create a figure with the adequate number of rows, and 2 columns:
import matplotlib.pyplot as plt
fig, axarr = plt.subplots(nber_rows,2)
2) Inside the loop, at iteration number iter_nber, call on the function drawing each subplot:
fig, axarr = module.graph_function(fig,axarr,iter_nber,some_parameters, some_data)
3) The function in question is basically like this; each iteration creates a pair of subplots on the same row:
def graph_function(fig,axarr,iter_nber,some_parameters, some_data):
axarr[iter_nber,1].plot(--some plotting 1--)
axarr[iter_nber,2].plot(--some plotting 2--)
return fig,axarr
This does not work. I end up with an empty figure at the end of the loop.
I have tried various combinations of the above, like leaving only axarr in the function's return argument, to no avail. Obviously I do not understand the logic of this figure and its subplots.
Any suggestions much appreciated.
The code you've posted seems largely correct. Other than the indexing, as #hitzg mentioned, nothing you're doing looks terribly out of the ordinary.
However, it doesn't make much sense to return the figure and axes array from your plotting function. (If you need access to the figure object, you can always get it through ax.figure.) It won't change anything to pass them in and return them, though.
Here's a quick example of the type of thing it sounds like you're trying to do. Maybe it helps clear some confusion?
import numpy as np
import matplotlib.pyplot as plt
def main():
nrows = 3
fig, axes = plt.subplots(nrows, 2)
for row in axes:
x = np.random.normal(0, 1, 100).cumsum()
y = np.random.normal(0, 0.5, 100).cumsum()
plot(row, x, y)
plt.show()
def plot(axrow, x, y):
axrow[0].plot(x, color='red')
axrow[1].plot(y, color='green')
main()
Related
I have a function I want to use several times to plot data in the same Figure/Axis.
The function toto() in the following code works fine. I use plt.figure(num='Single plot') which check, whenever it is called, if a figure with id 'Single plot' as been already created. If yes, the same figure is reused (the figure instance is global I think) :
import matplotlib.pyplot as plt
import numpy as np
def toto(x, y):
plt.figure('Single plot')
plt.plot(x, y)
x = np.linspace(0, 2)
toto(x, x)
toto(x, x**2)
plt.show()
Now, I want to use subplot()instead of figure() because it will be more useful for me. This function would be :
def toto2(x, y):
fig, axes = plt.subplots(num='Single plot 2')
axes.plot(x, y)
But the result is not as expected at all : y ticks are overprinted and the linear line is not plotted (or as been overprinted).
I'd like to understand the rationale behind this and what to modify so that it works as expected.
Every time you execute plt.subplots, you create a new figure and a new set of axes. If you want to reuse the figure, you should create the fig and axes objects outside of your function, and pass them as argument to your function.
import matplotlib.pyplot as plt
import numpy as np
def toto3(fig, axes, x, y):
axes.plot(x, y)
x = np.linspace(0, 2)
fig, axes = plt.subplots(num='Single plot 2')
toto3(fig, axes, x, x)
toto3(fig, axes, x, x**2)
plt.show()
(in this case, you could even simplify your code to only pass axes to your function, but the idea is applicable to both the fig and the axes objects)
Is there a way to animate a graph in matplotlib without resorting to the built in animation functions? I find them extremely awkward to use and feel it would be much simpler to just plot a point, wipe the graph, then plot the next point.
I envision something such as:
def f():
# do stuff here
return x, y, t
where each t would be a different frame.
I mean, I've tried stuff like using plt.clf(), plt.close() etc. but nothing seems to work.
It is sure possible to animate without FuncAnimation. The purpose of "the enivisioned function", however, is not really clear. In an animation, the time is the independent variable, i.e. for each time step you produce some new data to plot or similar. Therefore the function would take t as an input and give some data back.
import matplotlib.pyplot as plt
import numpy as np
def f(t):
x=np.random.rand(1)
y=np.random.rand(1)
return x,y
fig, ax = plt.subplots()
ax.set_xlim(0,1)
ax.set_ylim(0,1)
for t in range(100):
x,y = f(t)
# optionally clear axes and reset limits
#plt.gca().cla()
#ax.set_xlim(0,1)
#ax.set_ylim(0,1)
ax.plot(x, y, marker="s")
ax.set_title(str(t))
fig.canvas.draw()
plt.pause(0.1)
plt.show()
Also, it is not clear why you would want to avoid FuncAnimation. The same animation as above can be produced with FuncAnimation as follows:
import matplotlib.pyplot as plt
import matplotlib.animation
import numpy as np
def f(t):
x=np.random.rand(1)
y=np.random.rand(1)
return x,y
fig, ax = plt.subplots()
ax.set_xlim(0,1)
ax.set_ylim(0,1)
def update(t):
x,y = f(t)
# optionally clear axes and reset limits
#plt.gca().cla()
#ax.set_xlim(0,1)
#ax.set_ylim(0,1)
ax.plot(x, y, marker="s")
ax.set_title(str(t))
ani = matplotlib.animation.FuncAnimation(fig, update, frames=100)
plt.show()
There is not much changed, you have the same number of lines, nothing really awkward to see here.
Plus you have all the benefits from FuncAnimation when the animation gets more complex, when you want to repeat the animation, when you want to use blitting, or when you want to export it to a file.
it is not clear why you would want to avoid FuncAnimation.
For very simple tests, where you want to check a situation deep inside a loop, it is not easy to set up an animation function.
For instance, I wanted to visualize what happens with this strange sort algorithm: https://arxiv.org/pdf/2110.01111.pdf. To my opinion, the simplest way to do it is:
import numpy as np
import matplotlib.pyplot as plt
def sort(table):
n = len(table)
for i in range (n):
for j in range (n):
if table[i] < table[j]:
tmp = table[i]
table[i] = table[j]
table[j] = tmp
plt.plot(table, 'ro')
plt.title(f"i {i} j {j}")
plt.pause(0.001)
plt.clf() # clear figure
return table
n = 50
table = np.random.randint(1,101,n)
sort(table)
```python
I agree that FuncAnimation is awkward to use (not pythonic at all). Actually I believe this function doesn't make too much sense. What is the advantage to have it?
Yes, it introduces an implicit loop that you do not have to write yourself. But the reader cannot fully control this loop and -unless he knows the syntax of the function in advance- he cannot even understand it. Personally I avoid FuncAnimation for reasons of clarity and versatility. Here's a minimal pseudocode example to do that:
fig=plt.figure("animation")
M=zeros((sizeX,sizeY)) # initialize the data (your image)
im=plt.imshow(M) # make an initial plot
########### RUN THE "ANIMATION" ###########################
while {some condition}:
M=yourfunction() # updates your image
im.set_array(M) # prepare the new image
fig.canvas.draw() # draw the image
plt.pause(0.1) # slow down the "animation"
Very simple and you can see what is happening in your code.
I've been having an issue with saving matplotlib graphs as images.
The images are saving differently from what shows up when I call the .show() method on the graph.
An example is here:
http://s1.postimg.org/lbyei5cfz/blue5.png
I'm not sure what else to do. I've spent the past hours trying to figure out what's causing it, but I can't figure it out.
Here is my code in it's entirety.
import matplotlib.pyplot as plt
import random
turn = 1 #for the x values
class Graph():
def __init__(self, name, color):
self.currentValue = 5 #for the y values
self.x = [turn]
self.y = [self.currentValue]
self.name = name
self.color = color
def update(self):
if random.randint(0,1): #just to show if the graph's value goes up or down
self.currentValue += random.randint(0,10)
self.y.append(self.currentValue)
else:
self.currentValue -= random.randint(0,10)
self.y.append(self.currentValue)
self.x.append(turn)
def plot(self):
lines = plt.plot(self.x,self.y)
plt.setp(lines, 'color',self.color)
plt.savefig(self.name + str(turn))
#plt.show() will have a different result from plt.savefig(args)
graphs = [Graph("red",'r'),Graph("blue",'b'),Graph("green",'g')]
for i in range(5):
for i in graphs:
i.update() #changes the x and y value
i.plot() #saves the picture of the graph
turn += 1
Sorry if this is a stupid mistake I'm making, I just find it peculiar how plt.show() and plt.savefig are different.
Thanks for the help.
As stated correctly by David, plt.show() resets current figure. plt.savefig(), however, does not, so you need to reset it explicitly. plt.clf() or plt.figure() are two functions that can do it dor you. Just insert the call right after plt.savefig:
plt.savefig(self.name + str(turn))
plt.clf()
If you want to save the figure after displaying it, you'll need to hold on to the figure instance. The reason that plt.savefig doesn't work after calling show is that the current figure has been reset.
pyplot keeps track of which figures, axes, etc are "current" (i.e. have not yet been displayed with show) behind-the-scenes. gcf and gca get the current figure and current axes instances, respectively. plt.savefig (and essentially any other pyplot method) just does plt.gcf().savefig(...). In other words, get the current figure instance and call its savefig method. Similarly plt.plot basically does plt.gca().plot(...).
After show is called, the list of "current" figures and axes is empty.
In general, you're better off directly using the figure and axes instances to plot/save/show/etc, rather than using plt.plot, etc, to implicitly get the current figure/axes and plot on it. There's nothing wrong with using pyplot for everything (especially interactively), but it makes it easier to shoot yourself in the foot.
Use pyplot for plt.show() and to generate a figure and an axes object(s), but then use the figure or axes methods directly. (e.g. ax.plot(x, y) instead of plt.plot(x, y), etc) The main advantage of this is that it's explicit. You know what objects you're plotting on, and don't have to reason about what the pyplot state-machine does (though it's not that hard to understand the state-machine interface, either).
As an example of the "recommended" way of doing things, do something like:
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-1, 1, 100)
y = x**2
fig, ax = plt.subplots()
ax.plot(x, y)
fig.savefig('fig1.pdf')
plt.show()
fig.savefig('fig2.pdf')
If you'd rather use the pyplot interface for everything, then just grab the figure instance before you call show. For example:
import numpy as np
import matplotlib.pyplot as plt
x = np.linspace(-1, 1, 100)
y = x**2
plt.plot(x, y)
fig = plt.gcf()
fig.savefig('fig1.pdf')
plt.show()
fig.savefig('fig2.pdf')
source
I have a while function that generates two lists of numbers and at the end I plot them using matplotlib.pyplot.
I'm doing
while True:
#....
plt.plot(list1)
plt.plot(list2)
plt.show()
But in order to see the progression I have to close the plot window.
Is there a way to refresh it with the new data every x seconds?
The most robust way to do what you want is to use matplotlib.animation. Here's an example of animating two lines, one representing sine and one representing cosine.
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.animation as animation
fig, ax = plt.subplots()
sin_l, = ax.plot(np.sin(0))
cos_l, = ax.plot(np.cos(0))
ax.set_ylim(-1, 1)
ax.set_xlim(0, 5)
dx = 0.1
def update(i):
# i is a counter for each frame.
# We'll increment x by dx each frame.
x = np.arange(0, i) * dx
sin_l.set_data(x, np.sin(x))
cos_l.set_data(x, np.cos(x))
return sin_l, cos_l
ani = animation.FuncAnimation(fig, update, frames=51, interval=50)
plt.show()
For your particular example, you would get rid of the while True and put the logic inside that while loop in the update function. Then, you just have to make sure to do set_data instead of making a whole new plt.plot call.
More details can be found in this nice blog post, the animation API, or the animation examples.
I think what you're looking for is the "animation" feature.
Here is an example
This example is a second one.
So now I can get unique files, hurrah! but it seems the second file is plotting both the first and the second plot, the third is plotting all three, fourth is plotting all four, etc. here is the new code:
for j in range(2):
dhulist=pyfits.open('test.fits')
row=5
colum=j
ax=[]
val=[]
for i in range(1600,3040):
val.append((dhulist[0].data[i,row,colum]))
ax.append(((((dhulist[0].header['CRPIX3'] -i)*(dhulist[0].header['CDELT3']))+5000)/1000))
plt.plot(ax,val)
#plt.show()
plt.savefig("5_{0}.png".format(j))
the plot function of matplotlib updates the current figure, or creates a new figure if there is no current figure. Here is an example that does a good job of explicitly tracking the figure object from creation to closing.
import numpy as np
from matplotlib import pyplot as plt
x = np.linspace(0, 10)
for j in range(3):
y = x ** j
f = plt.figure()
plt.plot(x, y, figure=f)
f.savefig("test_{}.png".format(j))
plt.close(f)
Notice that every operation that involves a figure, opening, plotting, saving, and closing explicitly references the figure object. IMHO, that's a very nice coding style and very helpful if you ever need to work with multiple figures at once. Matplotlib also lets you work with the "current figure" implicitly, which is fine if you're doing something simple. That would look more like this:
import numpy as np
from matplotlib import pyplot as plt
x = np.linspace(0, 10)
for j in range(3):
y = x ** j
plt.figure()
plt.plot(x, y)
plt.savefig("test2_{}.png".format(j))
plt.close()