I'm trying to make a somewhat interactive map plot with matplotlib utilizing a button added to the toolbar in Matplotlib's navigation Toolbar.
The objective:
The objective that I'm trying to achieve is to be able to change the axes projection on the fly without creating a new axes. There are many methods in the axes object that gets created by Matplotlib to change other aspects of the plot, but I want to be able to change projection from say,
PlateCarree to NorthPolarStereo and vice versa.
Some Source:
import os
import sys
import matplotlib.pyplot as plt
import matplotlib
import mpl_toolkits
import numpy as np
import cartopy
import cartopy.crs as ccrs
fig = plt.figure()
ax = plt.axes(projection=ccrs.NorthPolarStereo())
ax.stock_img()
ny_lon, ny_lat = -75, 43
delhi_lon, delhi_lat = 77.23, 28.61
plt.plot([ny_lon, delhi_lon], [ny_lat, delhi_lat],
color='blue', linewidth=2, marker='o',
transform=ccrs.Geodetic(),
)
plt.plot([ny_lon, delhi_lon], [ny_lat, delhi_lat],
color='gray', linestyle='--',
transform=ccrs.PlateCarree(),
)
ax.add_patch(matplotlib.patches.Polygon([[0,0],[20,0],[20,20],[0,20]],
fill = False,color='g',ls='--',
transform=ccrs.PlateCarree()))
ax.add_patch(matplotlib.patches.Circle([30,30],radius=10,color='g',ls='--',
transform=ccrs.PlateCarree()))
plt.text(ny_lon - 3, ny_lat - 12, 'New York',
horizontalalignment='right',
transform=ccrs.Geodetic())
plt.text(delhi_lon + 3, delhi_lat - 12, 'Delhi',
horizontalalignment='left',
transform=ccrs.Geodetic())
# ax.set_extent([-180,180,-90,90])
ax.set_global()
The problem:
As can be seen, the axes is created with a projection = ccrs.NorthPolarStereo().
The projection of the axes can be obtained by executing the following:
ax.projection
Then, I try setting the projection to ccrs.PlateCarree()
ax.projection = ccrs.PlateCaree()
This alone does not update the plot, however. I have noticed that in editing some of the properties you need to draw the canvas again with
ax.figure.canvas.draw()
however, this doesn't seem to have an effect. But if I do
ax.set_global()
to set the extent to the maximum, the axes updates and changes to the correct projection... however, the data on the plot does not get updated again. Yet calling
ax.projection
indicates that the projection is now a PlateCarree projection.
How can I update the children of the axes to reflect this new projection?
I have tried
ax.update(ax.properties())
as per the matplotlib dox, however, it throws an error.
Any Ideas?
Edit:
If it is not obvious... You will need to run this in an iPython console and run those extra commands while the figure is open in order to edit it. And it must be done this way in order to achieve what I want to do. I know I can just make a new axes with a new projection, however, the end goal of this project is to maintain this axes. This is for editing and different viewing purposes that my project requires. Also, I'm stuck using matplotlib and cartopy, so no new library recommendations for plotting, please.
In reading this question I notice that I use 'however' way too often.
Related
I am using a function which spits out a figure object of validation data. My script calculates a few model parameters that I would like to plot on top of this existing figure object. How can I do this? Whenever I try to plot my modeled data, it does so in a new window. Here's what my code looks like:
datafig = plotting_function(args) #Returning a figure object
datafig.show()
plt.plot([modeled_x],[modeled_y]) #Plotting in a new window
I've tried using plt.hold() / plt.hold(True) but this doesn't do anything. Any ideas?
Edit:
MCVE:
import matplotlib.pyplot as plt
def fig_create():
fig_1, ax_1 = plt.subplots()
ax_1.plot([0,1],[0,1])
fig_2, ax_2 = plt.subplots()
ax_2.plot([0,1],[0,5])
return fig_1, ax_1, fig_2, ax_2
figure_1, axes_1, figure_2, axes_2 = fig_create()
plt.close("all") # Spyder plots even without a plt.show(), so running the function generates figures. I'm closing them here.
figure_2.show()
plt.figure(2)
plt.plot([0,1],[0,10])
Result of the MCVE: https://i.imgur.com/FiCJX33.png
You need to specify which axis to plot on. plt.figure(2) will make a figure with a number of 2, regardless of whether an existing figure has that number or not! axes_2.plot(), however will plot whatever data you input directly onto axes_2 and whatever was there already. If it doesn't immediately show up you should add plt.draw() after the plot function.
Try not to mix plt, notation and ax notation as this will create confusion later on! If you are using fig and ax, stick with those!
You can specify which figure to plot to by calling plt.figure(my_figure_index) before any plt.plot (or any other plt plotting function) call.
For example:
plt.figure(10) # creates new figure if doesn't exist yet
plt.plot(...) # plots in figure 10
plt.figure(2) # creates new figure if doesn't exist yet
plt.plot(...) # plots in this figure 2
plt.figure(10) # figure already exists, just makes it the active one
plt.plot(...) # plots in figure 10 (in addition to already existing stuff)
I am trying to plot data to a figure and respective axis in matplotlib and as new work comes up, recall the figure with the additional plot on the axis:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
x=np.arange(0,20)
y=2*x
fig,ax=plt.subplots()
ax.scatter(x,x)
ax.scatter(x,y)
fig
Which works fine with matplotlib, if I however use seaborn's regplot:
fig2,ax2=plt.subplots()
sns.regplot(x,x,ax=ax2,fit_reg=False)
sns.regplot(x,y,ax=ax2,fit_reg=False)
fig2
fig2 generates the figure that I want but the regplot command generates an empty figure. Is there a way to suppress the regplot's empty output or have it display the updated ax2 without recalling fig2?
It seems you are using the jupyter notebook with the inline backend. In some circumstances regplot triggers the creation of a new figure even if the artists are being added to the previous one and this messes up the output. I don't know why this happens but I found a workaround that might help you, using plt.ioff to temporarily disable automatic display of figures.
plt.ioff()
fig, ax = plt.subplots()
sns.regplot(x, x, ax=ax)
fig
sns.regplot(x, 2 * x, ax=ax)
fig
You have to call plt.ioff before creating the figure for this to work. After that you have to explicitly display the figure. Then you can call plt.ion to restore the default behaviour.
regplot does not generate an empty figure. According to the documentation:
Understanding the difference between regplot() and lmplot() can be a
bit tricky. In fact, they are closely related, as lmplot() uses
regplot() internally and takes most of its parameters. However,
regplot() is an axes-level function, so it draws directly onto an axes
(either the currently active axes or the one provided by the ax
parameter), while lmplot() is a figure-level function and creates its
own figure, which is managed through a FacetGrid.
When I do the following:
fig2,ax2 = plt.subplots()
same_fig2 = sns.regplot(x,x,ax=ax2,fit_reg=False)
same_fig2.figure is fig2
>>> True
I will be making animations. In each frame I want to contain both a mayavi plot obtained with
mlab.pipeline.iso_surface(source, some other superfluous args)
and a matplotlib plot obtained using simply
pylab.plot(args)
I have scripts to do both separately, but have no idea how to go about combining them into one figure. I want the end product to be one script which contains the code from both the scripts that I currently have.
AFAIK, there is no direct way because the backends used are so different. It does not seem possible to add matplotlib axes to mayavi.figure or vice versa.
However, there is a "kind of a way" by using the the mlab.screenshot.
import mayavi.mlab as mlab
import matplotlib.pyplot as plt
# create and capture a mlab object
mlab.test_plot3d()
img = mlab.screenshot()
mlab.close()
# create a pyplot
fig = plt.figure()
ax1 = fig.add_subplot(121)
ax1.plot([0,1], [1,0], 'r')
# add the screen capture
ax2 = fig.add_subplot(122)
ax2.imshow(img)
ax2.set_axis_off()
This is not necessarily the nicest possible way of doing things, and you may bump into resolution problems, as well (check the size of the mayavi window). However, it gets the job done in most cases.
Adding to the answer by DrV which helped me a great deal, you can work with the mlab figure to set resolution before screenshot such as with batch plotting:
mfig = mlab.figure(size=(1024, 1024))
src = mlab.pipeline.scalar_field(field_3d_numpy_array)
mlab.pipeline.iso_surface(src)
iso_surface_plot = mlab.screenshot(figure=mfig, mode='rgba', antialiased=True)
mlab.clf(mfig)
mlab.close()
# Then later in a matplotlib fig:
plt.imshow(iso_surface_plot)
I'm trying to plot two sets of data in a bar graph with matplotlib, so I'm using two axes with the twinx() method. However, the second y-axis label gets cut off. I've tried a few different methods with no success (tight_layout(), setting the major_pads in rcParams, etc...). I feel like the solution is simple, but I haven't come across it yet.
Here's a MWE:
#!/usr/bin/env python
import numpy as np
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
matplotlib.rcParams.update({'font.size': 21})
ax = plt.gca()
plt.ylabel('Data1') #Left side
ax2 = ax.twinx()
for i in range(10):
if(i%2==0):
ax.bar(i,np.random.randint(10))
else:
ax2.bar(i,np.random.randint(1000),color='k')
plt.ylabel('Data2') #Right
side
plt.savefig("test.png")
I just figured it out: the trick is to use bbox_inches='tight' in savefig.
E.G. plt.savefig("test.png",bbox_inches='tight')
I encountered the same issue which plt.tight_layout() did not automatically solve.
Instead, I used the labelpad argument in ylabel/set_ylabel as such:
ax.set_ylabel('label here', rotation=270, color='k', labelpad=15)
I guess this was not implemented when you asked this question, but as it's the top result on google, hopefully it can help users of the current matplotlib version.
I'm editing my graphs step by step. Doing so, plt functions from matplotlib.pyplot apply instantly to my graphical output of pylab. That's great.
If I address axes of a subplot, it does not happen anymore.
Please find both alternatives in my minimal working example.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
f = plt.figure()
sp1 = f.add_subplot(1,1,1)
f.show()
# This works well
sp1.set_xlim([1,5])
# Now I plot the graph
df = pd.Series([0,5,9,10,15])
df.hist(bins=50, color="red", alpha=0.5, normed=True, ax=sp1)
# ... and try to change the ticks of the x-axis
sp1.set_xticks(np.arange(1, 15, 1))
# Unfortunately, it does not result in an instant change
# because my plot has already been drawn.
# If I wanted to use the code above,
# I would have to execute him before drawing the graph.
# Therefore, I have to use this function:
plt.xticks(np.arange(1, 15, 1))
I understand that there is a difference between matplotlib.pyplot and an axis instance. Did I miss anything or does it just work this way?
Most of pyplot functions (if not all) have a call to plt.draw_if_interactive() before returning. So if you do
plt.ion()
plt.plot([1,2,3])
plt.xlim([-1,4])
you obtain that the plot is updated as you go. If you have interactive off, it won't create or update the plot until you don't call plt.show().
But all pyplot functions are wrappers around corresponding (usually) Axes methods.
If you want to use the OO interface, and still draw stuff as you type, you can do something like this
plt.ion() # if you don't have this, you probably don't get anything until you don't call a blocking `plt.show`
fig, ax = plt.subplots() # create an empty plot
ax.plot([1,2,3]) # create the line
plt.draw() # draw it (you can also use `draw_if_interactive`)
ax.set_xlim([-1,4]) #set the limits
plt.draw() # updata the plot
You don't have to use the pyplot you don't want, just remember to draw
The plt.xticks() method calls a function draw_if_interactive() that comes from pylab_setup(), who is updating the graph. In order to do it using sp1.set_xticks(), just call the corresponding show() method:
sp1.figure.show()