I am making a polar scatter plot for a college project with matplotlib and I can't find out how to add a label to the radial axis. Here is my code ( I left out the data because it was read out of a csv)
import matplotlib.pyplot as plt
ax = plt.subplot(111, polar=True)
ax.set_rmax(1)
c = plt.scatter(theta, radii)
ax.set_title("Spread of Abell Cluster Supernova Events as a Function of Fractional Radius", va='bottom')
ax.legend(['Supernova'])
plt.show()
(My plot looks like this. I can't seem to find any straight forward method to do it. Has anyone dealt with this before and have any suggestions?
I don't know of a built in way to do it, but you could use ax.text to make your own. You can get the position of the radial tick labels using ax.get_rlabel_position(), and the mid point of the radial axis using ax.get_rmax()/2.
For example, here's your code (with some random data):
import matplotlib.pyplot as plt
import numpy as np
theta=np.random.rand(40)*np.pi*2.
radii=np.random.rand(40)
ax = plt.subplot(111, polar=True)
ax.set_rmax(1)
c = plt.scatter(theta, radii)
ax.set_title("Spread of Abell Cluster Supernova Events as a Function of Fractional Radius", va='bottom')
ax.legend(['Supernova'])
label_position=ax.get_rlabel_position()
ax.text(np.radians(label_position+10),ax.get_rmax()/2.,'My label',
rotation=label_position,ha='center',va='center')
plt.show()
And here's the output:
I'd be interested to see if there's a more elegant solution, but hopefully this helps you.
from pylab import *
N = 150
r = 2*rand(N)
theta = 2*pi*rand(N)
area = 200*r**2*rand(N)
colors = theta
ax = subplot(111, polar=True)
c = scatter(theta, r, c=colors, s=area, cmap=cm.hsv)
c.set_alpha(0.75)
ax.set_ylabel('Radius', rotation=45, size=11)
show()
A slightly different method from #tom. This uses directly the plt.legend option.
Example:
import matplotlib.pyplot as plt
import numpy as np
theta=np.random.rand(40)*np.pi*2.
radii=np.random.rand(40)
ax = plt.subplot(111, polar=True)
ax.set_rmax(1)
c = plt.scatter(theta, radii,label='Supernova')
ax.set_title("Spread of Abell Cluster Supernova Events as a Function of Fractional Radius", va='bottom')
ax.legend(loc='lower right', scatterpoints=1)
plt.show()
You can change lower right to upper right or even to best to leave the alignment of the legend to matplotlib.
Related
I am trying to create a scatter plot with the positions of particles from an sph simulation and I want to color the based on the density of each particle. I have created my colorbar for my colormap and I tried to do that but it only works if i set vmax to be 10**-4 of the larger value of my densities. If i try to go vmin=np.amin(density) and vmax=np.amax(density) I get only one color, representing the lowest value of my cmap. I attach the code so that you can see exactly what I did.
# Importing modules and libraries
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.colors import ListedColormap, LinearSegmentedColormap
import os
import matplotlib.ticker as ticker
# I load my files
path=os.path.abspath('BOXSINK.column.tmp')
file= np.loadtxt(path,skiprows=4)
position=file[::,0:3]
velocity=file[::,3:6]
density=file[::,8]
x=position[::,0]
y=position[::,1]
z=position[::,2]
# This is a function to format the ticks of the colorbar to scientific
def fmt(x, pos):
a, b = '{:.1e}'.format(x).split('e')
b = int(b)
return r'${} \times 10^{{{}}}$'.format(a, b)
# constract colorbar
n_lines=density[density!=0].size
cmap = mpl.cm.get_cmap('Wistia', n_lines)
c = density[density!=0]
norm = mpl.colors.Normalize(vmin=c.min(), vmax=c.max())
cmap = mpl.cm.ScalarMappable(norm=norm, cmap=mpl.cm.Wistia)
cmap.set_array([])
# plot
fig=plt.figure()
ax=fig.add_axes([0,0,1,1])
c=(density)
print(np.amax(density))
psm = ax.scatter(x, y, s=0.05, alpha=0.6, c=c,cmap='Wistia', vmin=0, vmax=6e-21)
ax.scatter(x[density==0],y[density==0], s=1, alpha=1, c='Black')
l=fig.colorbar(cmap, format=ticker.FuncFormatter(fmt), label='density [g cm-3]')
l.set_label('density [g cm-3]',labelpad=15, weight='bold')
ax.set_xlabel('x [pc]', weight='bold')
ax.set_ylabel('y [pc]', weight='bold')
fig.set_size_inches(8, 6)
plt.show()
First (almost correct) plot
The issue is that 6e-21 is close to the median of my values, with my maximum density value being 3.5e-16. But if I set vmax=np.amax(density) I get this plot
Plot with the values ranging for min and max of the density array
I would settle for the first plot but I need it to be publication ready and thus, accurate and I am feeling like I am not sampling the densities correctly with it.
I am reading the following discussion:
setting axis scale in matplotlib contour plot
From the discussion above, to get arbitrary ratio, we could use
plt.figure(figsize=(8,2))
# ...
plt.tight_layout()
However, this setting is for figure not for contourf.
I used the above codes in my codes
import matplotlib.pyplot as plt
from matplotlib import cm
import numpy as np
import pandas as pd
import math
rm = pd.read_excel("test_3d.xlsx", header = None)
# find min values of noise
rec = np.shape(rm)
# grid
X = np.arange(1,rec[1]+1,1)
Y = np.arange(1,rec[0]+1,1)
x , y = np.meshgrid(X,Y)
# plots
plt.clf()
con = plt.contourf(x,y,rm, cmap=cm.jet)
plt.figure(figsize=(8,2))
plt.tight_layout()
plt.title('2457MHz')
plt.show()
The result I got is
The ratio of bottom plot is what I want; however, I use plt.figure(figsize=(8,2)), which is not for contourf. Therefore, I did not get the correct result.
Is there any way that I can plot arbitrary ratio for contourf?
Instead of setting the figsize, use Axes.set_aspect to change the aspect ratio of the contour plot's Axes:
fig, ax = plt.subplots()
ax.contourf(x, y, rm, cmap='viridis')
ax.set_aspect(0.25)
If you prefer to stick with the plt syntax, access the Axes using plt.gca:
plt.contourf(x, y, rm, cmap='viridis')
plt.gca().set_aspect(0.25)
What I want to achieve with Python 3.6 is something like this :
Obviously made in paint and missing some ticks on the xAxis. Is something like this possible? Essentially, can I control exactly where to plot a histogram (and with what orientation)?
I specifically want them to be on the same axes just like the figure above and not on separate axes or subplots.
fig = plt.figure()
ax2Handler = fig.gca()
ax2Handler.scatter(np.array(np.arange(0,len(xData),1)), xData)
ax2Handler.hist(xData,bins=60,orientation='horizontal',normed=True)
This and other approaches (of inverting the axes) gave me no results. xData is loaded from a panda dataframe.
# This also doesn't work as intended
fig = plt.figure()
axHistHandler = fig.gca()
axScatterHandler = fig.gca()
axHistHandler.invert_xaxis()
axHistHandler.hist(xData,orientation='horizontal')
axScatterHandler.scatter(np.array(np.arange(0,len(xData),1)), xData)
A. using two axes
There is simply no reason not to use two different axes. The plot from the question can easily be reproduced with two different axes:
import numpy as np
import matplotlib.pyplot as plt
plt.style.use("ggplot")
xData = np.random.rand(1000)
fig,(ax,ax2)= plt.subplots(ncols=2, sharey=True)
fig.subplots_adjust(wspace=0)
ax2.scatter(np.linspace(0,1,len(xData)), xData, s=9)
ax.hist(xData,bins=60,orientation='horizontal',normed=True)
ax.invert_xaxis()
ax.spines['right'].set_visible(False)
ax2.spines['left'].set_visible(False)
ax2.tick_params(axis="y", left=0)
plt.show()
B. using a single axes
Just for the sake of answering the question: In order to plot both in the same axes, one can shift the bars by their length towards the left, effectively giving a mirrored histogram.
import numpy as np
import matplotlib.pyplot as plt
plt.style.use("ggplot")
xData = np.random.rand(1000)
fig,ax= plt.subplots(ncols=1)
fig.subplots_adjust(wspace=0)
ax.scatter(np.linspace(0,1,len(xData)), xData, s=9)
xlim1 = ax.get_xlim()
_,__,bars = ax.hist(xData,bins=60,orientation='horizontal',normed=True)
for bar in bars:
bar.set_x(-bar.get_width())
xlim2 = ax.get_xlim()
ax.set_xlim(-xlim2[1],xlim1[1])
plt.show()
You might be interested in seaborn jointplots:
# Import and fake data
import seaborn as sns
import numpy as np
import matplotlib.pyplot as plt
data = np.random.randn(2,1000)
# actual plot
jg = sns.jointplot(data[0], data[1], marginal_kws={"bins":100})
jg.ax_marg_x.set_visible(False) # remove the top axis
plt.subplots_adjust(top=1.15) # fill the empty space
produces this:
See more examples of bivariate distribution representations, available in Seaborn.
I try to make colorful scatter plot using third variable to define color. It is simple to use the following code:
plt.scatter(mH, mA, s=1, c=mHc)
plt.colorbar()
plt.show()
But I do not have many choices to modify the frame of the plot. I am trying the following code to make colorful scatter plot, at the same time I try to optimize the frame of the plot:
import numpy as np
import math
from matplotlib import rcParams
import matplotlib.pyplot as plt
from matplotlib.ticker import AutoMinorLocator
fig, ax = plt.subplots()
cax = ax.scatter(mH,mA,s=0.5,c=mHc) ### mH, mA, mHC are the dataset
fig.colorbar(cax)
minor_locator1 = AutoMinorLocator(6)
minor_locator2 = AutoMinorLocator(6)
ax.xaxis.set_minor_locator(minor_locator1)
ax.yaxis.set_minor_locator(minor_locator2)
ax.tick_params('both', length=10, width=2, which='major')
ax.tick_params('both', length=5, width=2, which='minor')
ax.set_xlabel(r'$m_H$')
ax.set_ylabel(r'$m_A$')
ax.set_xticks([300,600,900,1200,1500])
ax.set_yticks([300,600,900,1200,1500])
plt.savefig('mH_mA.png',bbox_inches='tight')
plt.show()
But the plot I got is black-white. It looks like the problem lies in the marker size argument, but I do not have much idea how to correct it. I want to have smaller marker size. Anyone can offer me some idea to approach this issue. Thanks.
size=0.5 is extremely small - probably all you are seeing is the marker outlines. I would suggest you increase the size a bit, and perhaps pass edgecolors="none" to turn off the marker edge stroke:
import numpy as np
from matplotlib import pyplot as plt
n = 10000
x, y = np.random.randn(2, n)
z = -(x**2 + y**2)**0.5
fig, ax = plt.subplots(1, 1)
ax.scatter(x, y, s=5, c=z, cmap="jet", edgecolors="none")
You might also want to experiment with making the points semi-transparent using the alpha= parameter:
ax.scatter(x, y, s=20, c=z, alpha=0.1, cmap="jet", edgecolors="none")
It can be difficult to get scatter plots to look nice when you have such a massive number of overlapping points. I would be tempted to plot your data as a 2D histogram or contour plot instead, or perhaps even a combination of a scatter plot and a contour plot:
density, xe, ye = np.histogram2d(x, y, bins=20, normed=True)
ax.hold(True)
ax.scatter(x, y, s=5, c=z, cmap="jet", edgecolors="none")
ax.contour(0.5*(xe[:-1] + xe[1:]), 0.5*(ye[:-1] + ye[1:]), density,
colors='k')
I have questions related to creating a simple lineplot in Python with mplot3D where the area under the plot is filled. I am using Python 2.7.5 on RedHatEnterprise 7.2, matplotlib 1.2.0 and numpy 1.7.2.
Using the code below, I am able to generate a line plot. This is displayed as expected with the beginning / end of the plot set by the limits of the imported data set.
I am then trying to fill the area between the line plot and -0.1 using the answer given by Bart from Plotting a series of 2D plots projected in 3D in a perspectival way. This works, however, the filled area is continued beyond the limits of the data set. This is also the case when running the example from the link.
This screen shot shows the plot generated with filled area extending beyond the set axis limits.
How do I achieve that the filled area is only the range of the data set or the axis limits whichever is smaller?
How do I add a legend for those plots onto the figure?
Code as follows:
from numpy import *
import matplotlib.pylab as plt
from mpl_toolkits.mplot3d import Axes3D
x,y = genfromtxt("data.dat",unpack=True)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.add_collection3d(plt.fill_between(x,y,-0.1, color='orange', alpha=0.3,label="filled plot"),1, zdir='y')
ax.plot(x,y,1,zdir="y",label="line plot")
ax.legend()
ax.set_xlim3d(852.353,852.359)
ax.set_zlim3d(-0.1,5)
ax.set_ylim3d(0,2)
ax.get_xaxis().get_major_formatter().set_useOffset(False)
plt.show()
I don't know how to put fill_between working the way you want it to, but I can provide an alternative using a 3D polygon:
from numpy import *
import matplotlib.pylab as plt
from mpl_toolkits.mplot3d import Axes3D
from mpl_toolkits.mplot3d.art3d import Poly3DCollection # New import
#x,y = genfromtxt("data.dat",unpack=True)
# Generated some random data
w = 3
x,y = np.arange(100), np.random.randint(0,100+w,100)
y = np.array([y[i-w:i+w].mean() for i in range(3,100+w)])
z = np.zeros(x.shape)
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
#ax.add_collection3d(plt.fill_between(x,y,-0.1, color='orange', alpha=0.3,label="filled plot"),1, zdir='y')
verts = [(x[i],z[i],y[i]) for i in range(len(x))] + [(x.max(),0,0),(x.min(),0,0)]
ax.add_collection3d(Poly3DCollection([verts],color='orange')) # Add a polygon instead of fill_between
ax.plot(x,z,y,label="line plot")
ax.legend()
ax.set_ylim(-1,1)
plt.show()
The code above generates some random data. Builds vertices from it and plots a polygon with those vertices. This will give you the plot you wish (but does not use fill_between). The result is: