Using matplotlib in Python I drew a 3D graph. When I rotate the graph I noticed that the axes labels swap automatically which does not look interesting or helping to me. In fact it disturbs my focusing on the purpose of rotation which is to explore visually the presented data.
Q: How to disable auto-swapping axes labels while rotating in matplotlib?
I grabbed some ideas from SO, examined many and finally developed the following solution. It simply works.
from __future__ import division
import scipy as sp
import mpl_toolkits.mplot3d as a3d
import pylab as pl
nan = sp.nan
def axesoff():
box = [[-1,1,1,-1,-1,1,1,-1,-1,-1,nan,1,1,nan,1,1,nan,-1,-1],
[-1,-1,-1,-1,1,1,1,1,-1,-1,nan,-1,1,nan,1,-1,nan,1,1],
[-1,-1,1,1,1,1,-1,-1,-1,1,nan,-1,-1,nan,1,1,nan,-1,1]]
ax3.plot(*box,color='k')
for axis in (ax3.w_xaxis,ax3.w_yaxis,ax3.w_zaxis):
for obj in axis.get_ticklines(): obj.set_visible(False)
axis.set_ticklabels('')
axis.line.set_visible(False)
axis.pane.set_visible(False)
ax3.grid(False)
ax3.axis('equal')
#------here we go
x,y,z = sp.random.uniform(low=-1,high=1,size=(3,1000))
c = (x+1)+(y+1)+(z+1)
s = c*10
ax3 = a3d.Axes3D(pl.figure())
ax3.scatter(x,y,z,lw=0,s=s,c=c,alpha=0.5)
axesoff()
pl.show()
Related
Although I am providing a excerpt of the code I am using, but this piece contains the problem I am facing. I am trying to plot density of the particles over the disc and hence polar plot seems natural to use. So I have used following piece of code to read a density file which contains the density with rows and column representing radius and angular direction.
#! /usr/bin/env python
import numpy as np
import matplotlib.pyplot as plt
from os.path import exists
from os import sys
import matplotlib as mpl
from matplotlib import rc
NUMBINS=100
rmax=20.0
dR2=rmax*rmax/NUMBINS
density = np.random.random((NUMBINS, NUMBINS))
r = np.sqrt(np.arange(0,rmax*rmax,dR2) )[:NUMBINS]
theta = np.linspace(0,2*np.pi,NUMBINS)
mpl.rcParams['legend.fontsize'] = 10
mpl.rcParams['pcolor.shading'] ='nearest'
fig = plt.figure(figsize=(5, 5))
ax1 = plt.subplot(111,projection="polar")
rad, th = np.meshgrid(r,theta)
ax1.set_yticks(np.arange(0,rmax,3))
ax1.pcolormesh(th,rad,density,cmap='Blues')
#ax1.set_ylim([rad[0,0], rad[0,NUMBINS-1]])
plt.tight_layout()
plt.show()
which gives me the following plot :
As you can see that the radius starts from 0 to rmax, removing the commented line
ax1.set_ylim([rad[0,0], rad[0,NUMBINS-1]])
shall not have any effect on the plot but it shifts the center of the plot :
I don't understand why setting ymin=0 creates this white space in the center?
Turns out that it is a problem with version of matplotlib. I tried a different version and the plot works as expected. Apologies for not trying it earlier.
I want to create a smooth cylinder using matplotlib/pyplot. I've adapted a tutorial online and produced the following minimal example:
from numpy import meshgrid,linspace,pi,sin,cos,shape
from matplotlib import pyplot
import matplotlib.tri as mtri
from mpl_toolkits.mplot3d import Axes3D
u,v = meshgrid(linspace(0,10,10),linspace(0,2*pi,20))
u = u.flatten()
v = v.flatten()
x = u
z = sin(v)
y = cos(v)
tri = mtri.Triangulation(u, v)
fig = pyplot.figure()
ax = fig.add_axes([0,0,1,1],projection='3d')
ax.plot_trisurf(x,y,z,triangles=tri.triangles,linewidth=0)
pyplot.show()
which produces a cylinder. I set linewidth=0 to remove the wireframe, however, there is now the "ghost" of the wireframe because the triangulation has (presumably) been spaced assuming the wireframe is there to fill in the gaps. This looks to be specific to plot_trisurf, because there are other 3d plotting examples (e.g., using plot_surface) which set linewidth=0 without these gaps showing up.
Doing an mtri.Triangulation?, it seems like it might not be possible to "perfectly" fill in the gaps, since it states
>Notes
> -----
> For a Triangulation to be valid it must not have duplicate points,
> triangles formed from colinear points, or overlapping triangles.
One partial solution is to just color the wireframe the same shade of blue, but after I've fixed this problem I also want to add a light source/shading on the surface, which would put me back at square one.
Is there a way to make this work? Or can someone suggest a different approach? Thanks for any help.
ax.plot_trisurf(x,y,z,triangles=tri.triangles,linewidth=0, antialiased=False)
I have difficulties in plotting e.g. polygons across the boundaries of a map generated using matplotlib-basemap. In the example below, the map boundary is specified by the dateline. I try to plot a triangle across the dateline by specifying the coordinates of vertices of a triangle. This works fine when all coordinates are within the map, but if they go across the map boundary, basemap performs strange extrapolation, as it seems not to know how to draw the rectangles in the right way.
Right way would mean in my sense that the triangle is drawn until the map boundary and would then continue at the other side of the map.
Below is a minimum code example and a figure illustrating the general problem.
Any ideas how to solve this problem in a general way are highly welcome.
from mpl_toolkits.basemap import Basemap
import matplotlib.pylab as plt
import numpy as np
import matplotlib.path as mpath
import matplotlib.patches as mpatches
import matplotlib as mpl
from matplotlib.collections import PatchCollection
![plt.close('all')
Path = mpath.Path
fig=plt.figure(); ax=fig.add_subplot(121); ax1=fig.add_subplot(122)
def do_plot(ax,lons,lats,title):
patches=\[\]
m = Basemap(projection='robin', resolution='c',lon_0=0.,ax=ax) #todo: how to make it properly work for other projections ???
m.drawmapboundary(fill_color='grey')
m.drawcoastlines()
#--- generate first sample with no problem
x,y=m(lons,lats)
verts = np.asarray(\[x,y\]).T
codes = \[Path.MOVETO,Path.LINETO,Path.LINETO\]
patches.append(mpatches.PathPatch(mpath.Path(verts, codes,closed=True)))
#--- generate collection
cmap = plt.cm.get_cmap('jet', 50); norm = mpl.colors.Normalize(vmin=None, vmax=None) #colorbar mapping
collection = PatchCollection(patches, cmap=cmap,norm=norm, alpha=1.,match_original=False) #construct library of all objects
colors = np.asarray(np.random.random(len(patches)))
collection.set_array(np.array(colors)) #assign data values here
#--- do actual plotting
im=m.ax.add_collection(collection)
ax.set_title(title)
do_plot(ax,\[-10.,0.,20.\],\[30.,50.,20.\],'This works')
do_plot(ax1,\[170,180,-175\],\[30.,50.,20.\],'... and here is the boundary problem')
plt.show()][1]
You cannot get around this problem with Basemap in a simple way. In your line x,y=m(lons,lats) you have transformed the points to map coordinates, and drawing the polygon just draws between those projected points.
You might try using Cartopy, which can do this. This example may help.
I am trying to create a simple stereographic sun path diagram similar to these:
http://wiki.naturalfrequency.com/wiki/Sun-Path_Diagram
I am able to rotate a polar plot and set the scale to 90. How do I go about reversing the y-axis?
Currently the axis goes from 0>90, how do I reverse the axis to 90>0 to represent the azimuth?
I have tried:
ax.invert_yaxis()
ax.yaxis_inverted()
Further, how would I go about creating a stereographic projection as opposed to a equidistant?
My code:
import matplotlib.pylab as plt
testFig = plt.figure(1, figsize=(8,8))
rect = [0.1,0.1,0.8,0.8]
testAx = testFig.add_axes(rect,polar=True)
testAx.invert_yaxis()
testAx.set_theta_zero_location('N')
testAx.set_theta_direction(-1)
Azi = [90,180,270]
Alt= [0,42,0]
testAx.plot(Azi,Alt)
plt.show()
Currently my code doesn't seem to even plot the lines correctly, do I need need to convert the angle or degrees into something else?
Any help is greatly appreciated.
I finally had time to play around with matplotlib. After much searching, the correct way as Joe Kington points out is to subclass the Axes. I found a much quicker way utilising the excellent basemap module.
Below is some code I have adapted for stackoverflow. The sun altitude and azimuth were calculated with Pysolar with a set of timeseries stamps created in pandas.
import matplotlib.pylab as plt
from mpl_toolkits.basemap import Basemap
import numpy as np
winterAzi = datafomPySolarAzi
winterAlt = datafromPySolarAlt
# create instance of basemap, note we want a south polar projection to 90 = E
myMap = Basemap(projection='spstere',boundinglat=0,lon_0=180,resolution='l',round=True,suppress_ticks=True)
# set the grid up
gridX,gridY = 10.0,15.0
parallelGrid = np.arange(-90.0,90.0,gridX)
meridianGrid = np.arange(-180.0,180.0,gridY)
# draw parallel and meridian grid, not labels are off. We have to manually create these.
myMap.drawparallels(parallelGrid,labels=[False,False,False,False])
myMap.drawmeridians(meridianGrid,labels=[False,False,False,False],labelstyle='+/-',fmt='%i')
# we have to send our values through basemap to convert coordinates, note -winterAlt
winterX,winterY = myMap(winterAzi,-winterAlt)
# plot azimuth labels, with a North label.
ax = plt.gca()
ax.text(0.5,1.025,'N',transform=ax.transAxes,horizontalalignment='center',verticalalignment='bottom',size=25)
for para in np.arange(gridY,360,gridY):
x= (1.1*0.5*np.sin(np.deg2rad(para)))+0.5
y= (1.1*0.5*np.cos(np.deg2rad(para)))+0.5
ax.text(x,y,u'%i\N{DEGREE SIGN}'%para,transform=ax.transAxes,horizontalalignment='center',verticalalignment='center')
# plot the winter values
myMap.plot(winterX,winterY ,'bo')
Note that currently I am only plotting points, you will have to make sure that line points have a point at alt 0 at sunrise/sunset.
I want to draw some lines and circles on the screen using of matplotlib. I do not need the X axis and Y axis. Is this possible? How can I do it?
You can hide the axes with axes.get_xaxis().set_visible(False) or by using axis('off').
Example:
from pylab import *
gca().get_xaxis().set_visible(False) # Removes x-axis from current figure
gca().get_yaxis().set_visible(False) # Removes y-axis from current figure
a = arange(10)
b = sin(a)
plot(a, b)
show() # Plot has no x and y axes
If you don't want axes, and are happy to work in the range 0-1:
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
fig = plt.figure()
fig.patches.append(mpatches.Circle([0.5, 0.5], 0.25, transform=fig.transFigure))
fig.show()
There are a couple of benefits to using #Dhara's solution. The primary being you can use a data coordinate system which automatically scales to your data, but if you just want to draw a couple of shapes, my solution works pretty well.
Some useful documentation if you go down the route I have explained:
http://matplotlib.sourceforge.net/api/artist_api.html#matplotlib.patches.Circle
http://matplotlib.sourceforge.net/api/artist_api.html#matplotlib.lines.Line2D
http://matplotlib.sourceforge.net/api/artist_api.html#matplotlib.patches.Rectangle