Develop Reference

Colorbars for 2D plots using Numpy, Matplotlib and os (rendering probelm)?

I have multiple .plx files that contain two column of numbers formatted as strings (1.plx , 2.plx...)
I managed to modify a code to load the data, convert it to floats, and plot it with the appropriate colorbar, but there are two issues I couldn't solve:
The color of the lines does not update
The lines rendering looks wrong (probably due to duplicates)
I want to try to avoid that rendering problem by plotting a numpy matrix, so I want to :
Load the data
store it in a numpy matrix (outside the loop so that I can do other data processing stuff)
create a 2D plot with the colorbar
Here is my attempt and the result:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import os
IdVg = [IdVg for IdVg in os.listdir() if IdVg.endswith(".plx")]
n_lines = 20
steps = np.linspace(0.1, 50, 20)
norm = mpl.colors.Normalize(vmin=steps.min(), vmax=steps.max())
cmap = mpl.cm.ScalarMappable(norm=norm, cmap=mpl.cm.BuPu)
cmap.set_array([])
for i in IdVg:
x, y = np.loadtxt(i, delimiter=' ', unpack=True, skiprows= 1, dtype=str)
x = x.astype(np.float64)
y = y.astype(np.float64)
for z, ai in enumerate(steps.T): # Problem here, I want to store x, y values in a 40XN matrix
# (x1, y1, x2, y2...x20, y20) and find a way to plot them
# using Matplotlib and numpy
plt.plot(x, y, c=cmap.to_rgba(z+1))
plt.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))
plt.xlabel('$V_{GS}$ (V)', fontsize=14)
plt.ylabel('$I_{DS}$ (A)', fontsize=14)
plt.tick_params(axis='both', labelsize='12')
plt.grid(True, which="both", ls="-")
plt.colorbar(cmap, ticks=steps)
plt.show()
Thanks !

Since you didn't provide data, I'm going to generate my own. I assume you want to obtain the following result:
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
import os
n_lines = 20
steps = np.linspace(0.1, 50, 20)
norm = mpl.colors.Normalize(vmin=steps.min(), vmax=steps.max())
norm_steps = norm(steps)
cmap = mpl.cm.BuPu
plt.figure()
x = np.linspace(0, np.pi / 2)
for i in range(n_lines):
y = i / n_lines * np.sin(x)
plt.plot(x, y, c=cmap(norm_steps[i]))
plt.ticklabel_format(style='sci', axis='y', scilimits=(0, 0))
plt.xlabel('$V_{GS}$ (V)', fontsize=14)
plt.ylabel('$I_{DS}$ (A)', fontsize=14)
plt.tick_params(axis='both', labelsize='12')
plt.grid(True, which="both", ls="-")
plt.colorbar(mpl.cm.ScalarMappable(norm=norm, cmap=mpl.cm.BuPu), ticks=steps)
plt.show()
Obviously, you would have to change the colormap to something more readable in the lower values!

Plotting a two variable function with two parameters in python

I am trying to plot a 2 variable function with additional parameters which can be changed. Below is the function-
f(x,y) = (x - a*y)/(b+y)
I want to plot it in 3d and would like to see the change in the plot by changing the values of a and b, i.e. when a=1 and b=0, etc.
I can plot it for specific a and b cases, below is the code that works for a=1 and b=0. Is there any way where I don't need to map separately for different cases of a and b?
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
from matplotlib import cm
import numpy as np
x = np.linspace(30,7000,10000)
y = np.linspace(1,11000, 10000)
def delCAD(x,y):
return (x-y)/(y) # the function when a=1 and b=0
fig = plt.figure(figsize=(12,8))
ax = Axes3D(fig)
ax = fig.gca(projection = "3d")
surf = ax.plot_trisurf(x, y, delCAD(x,y), cmap = cm.coolwarm)
fig.colorbar(surf, shrink=0.5, aspect=5)
plt.show()

I generally use IPython or Jupyter for that sort of thing — maybe that's an option for you? For example, using ipywidgets.interact():
import matplotlib.pyplot as plt
import numpy as np
from ipywidgets import interact
x = np.linspace(1, 20, 50)
y = np.linspace(1, 20, 50)
y, x = np.meshgrid(y, x)
def delCAD(x, y, a=1, b=0):
return (x - a * y) / (b + y)
#interact(a=(1, 10), b=(0, 10))
def plot(a, b):
fig, ax = plt.subplots(subplot_kw={"projection": "3d"}, figsize=(12, 6))
surf = ax.plot_trisurf(x.flat, y.flat, delCAD(x, y, a, b).flat, cmap='coolwarm')
fig.colorbar(surf, ax=ax, shrink=0.5, aspect=5)
ax.view_init(elev=30, azim=160)
plt.show()
return
Produces this:
As well as the interact wrapper, I introduced the meshgrid line to compute all the locations in the grid, and I changed some of your parameters a bit so you can see more going on in the function. I hope you can unpick the various pieces to fit your needs.

Anti-aliased Fonts in Animation

I'm trying to create an animation with two subplots--one 3D and another 2D. I can't seem to figure out if there is a way to get better font rendering from the 2D axes however. I tried playing around with various settings with font_manager, and even changing the frame_format to raw, but I've had no success. Does anyone have any ideas how to fix this? I get the same results with mpeg4.
The strange thing is that the 3D figure seems to render the font properly.
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.animation as animation
from mpl_toolkits.mplot3d import Axes3D
w, h = matplotlib.figure.figaspect(.5)
fig = plt.figure(figsize=(w,h))
ax3d = fig.add_subplot(121, projection='3d')
ax2d = fig.add_subplot(122)
ax3d.set_xlim(-3, 3)
ax3d.set_ylim(-3, 3)
ax3d.azim = -90
ax3d.elev = 0
ax3d.set_title('Car on Parking Ramp')
ax2d.set_xlim(-20,20)
ax2d.set_ylim(-20,20)
ax2d.set_ylabel('y')
ax2d.set_xlabel('x')
ax2d.set_title('Intersection with z=0')
''' Helix '''
K = 3 ## Angular velocity
H = 2*np.pi ## Height
t = np.linspace(0, H, 100, endpoint=True)
x = np.cos(K*t)
y = np.sin(K*t)
z = H - t
ax3d.plot(x, y, z, color='k')
''' z = 0 Plane '''
xx, yy = np.meshgrid([-20,20], [-20,20])
ax3d.plot_surface(xx, yy, 0, alpha=0.3, facecolor='b', rstride=1, cstride=1, shade=True)
ax3d.set_axis_off()
''' Tangent Line Data '''
xdata = np.array([ np.cos(K*t), np.cos(K*t) - K*(H - t)*np.sin(K*t) ])
ydata = np.array([ np.sin(K*t), np.sin(K*t) + K*(H - t)*np.cos(K*t) ])
''' Graph Lines '''
proj, = ax2d.plot([],[])
tangent, = ax3d.plot([], [], [], color='b')
def update_graph(n, tangent, proj, xdata, ydata):
tangent.set_data(xdata[:,n],
ydata[:,n])
tangent.set_3d_properties([H - t[n], 0])
proj.set_xdata(xdata[1,:n])
proj.set_ydata(ydata[1,:n])
ani = animation.FuncAnimation(fig, update_graph, len(t),
fargs=(tangent, proj, xdata, ydata), interval=75, blit=True)
ani.save('im.gif', writer='imagemagick', fps=10)
#ani.save('im.mp4', extra_args=['-vcodec', 'libx264'])

For people who face the same issue, it's indeed related to matplotlib backend.
Using different backend might help. In my case, the
%matplotlib nbagg
solved it (thanks to the linked question: Pixelated fonts when plot is saved as jpeg) .

convert a scatter plot into a contour plot in matplotllib [duplicate]

I'd like to make a scatter plot where each point is colored by the spatial density of nearby points.
I've come across a very similar question, which shows an example of this using R:
R Scatter Plot: symbol color represents number of overlapping points
What's the best way to accomplish something similar in python using matplotlib?

In addition to hist2d or hexbin as #askewchan suggested, you can use the same method that the accepted answer in the question you linked to uses.
If you want to do that:
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import gaussian_kde
# Generate fake data
x = np.random.normal(size=1000)
y = x * 3 + np.random.normal(size=1000)
# Calculate the point density
xy = np.vstack([x,y])
z = gaussian_kde(xy)(xy)
fig, ax = plt.subplots()
ax.scatter(x, y, c=z, s=100)
plt.show()
If you'd like the points to be plotted in order of density so that the densest points are always on top (similar to the linked example), just sort them by the z-values. I'm also going to use a smaller marker size here as it looks a bit better:
import numpy as np
import matplotlib.pyplot as plt
from scipy.stats import gaussian_kde
# Generate fake data
x = np.random.normal(size=1000)
y = x * 3 + np.random.normal(size=1000)
# Calculate the point density
xy = np.vstack([x,y])
z = gaussian_kde(xy)(xy)
# Sort the points by density, so that the densest points are plotted last
idx = z.argsort()
x, y, z = x[idx], y[idx], z[idx]
fig, ax = plt.subplots()
ax.scatter(x, y, c=z, s=50)
plt.show()

Plotting >100k data points?
The accepted answer, using gaussian_kde() will take a lot of time. On my machine, 100k rows took about 11 minutes. Here I will add two alternative methods (mpl-scatter-density and datashader) and compare the given answers with same dataset.
In the following, I used a test data set of 100k rows:
import matplotlib.pyplot as plt
import numpy as np
# Fake data for testing
x = np.random.normal(size=100000)
y = x * 3 + np.random.normal(size=100000)
Output & computation time comparison
Below is a comparison of different methods.
1: mpl-scatter-density
Installation
pip install mpl-scatter-density
Example code
import mpl_scatter_density # adds projection='scatter_density'
from matplotlib.colors import LinearSegmentedColormap
# "Viridis-like" colormap with white background
white_viridis = LinearSegmentedColormap.from_list('white_viridis', [
(0, '#ffffff'),
(1e-20, '#440053'),
(0.2, '#404388'),
(0.4, '#2a788e'),
(0.6, '#21a784'),
(0.8, '#78d151'),
(1, '#fde624'),
], N=256)
def using_mpl_scatter_density(fig, x, y):
ax = fig.add_subplot(1, 1, 1, projection='scatter_density')
density = ax.scatter_density(x, y, cmap=white_viridis)
fig.colorbar(density, label='Number of points per pixel')
fig = plt.figure()
using_mpl_scatter_density(fig, x, y)
plt.show()
Drawing this took 0.05 seconds:
And the zoom-in looks quite nice:
2: datashader
Datashader is an interesting project. It has added support for matplotlib in datashader 0.12.
Installation
pip install datashader
Code (source & parameterer listing for dsshow):
import datashader as ds
from datashader.mpl_ext import dsshow
import pandas as pd
def using_datashader(ax, x, y):
df = pd.DataFrame(dict(x=x, y=y))
dsartist = dsshow(
df,
ds.Point("x", "y"),
ds.count(),
vmin=0,
vmax=35,
norm="linear",
aspect="auto",
ax=ax,
)
plt.colorbar(dsartist)
fig, ax = plt.subplots()
using_datashader(ax, x, y)
plt.show()
It took 0.83 s to draw this:
There is also possibility to colorize by third variable. The third parameter for dsshow controls the coloring. See more examples here and the source for dsshow here.
3: scatter_with_gaussian_kde
def scatter_with_gaussian_kde(ax, x, y):
# https://stackoverflow.com/a/20107592/3015186
# Answer by Joel Kington
xy = np.vstack([x, y])
z = gaussian_kde(xy)(xy)
ax.scatter(x, y, c=z, s=100, edgecolor='')
It took 11 minutes to draw this:
4: using_hist2d
import matplotlib.pyplot as plt
def using_hist2d(ax, x, y, bins=(50, 50)):
# https://stackoverflow.com/a/20105673/3015186
# Answer by askewchan
ax.hist2d(x, y, bins, cmap=plt.cm.jet)
It took 0.021 s to draw this bins=(50,50):
It took 0.173 s to draw this bins=(1000,1000):
Cons: The zoomed-in data does not look as good as in with mpl-scatter-density or datashader. Also you have to determine the number of bins yourself.
5: density_scatter
The code is as in the answer by Guillaume.
It took 0.073 s to draw this with bins=(50,50):
It took 0.368 s to draw this with bins=(1000,1000):

Also, if the number of point makes KDE calculation too slow, color can be interpolated in np.histogram2d [Update in response to comments: If you wish to show the colorbar, use plt.scatter() instead of ax.scatter() followed by plt.colorbar()]:
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.colors import Normalize
from scipy.interpolate import interpn
def density_scatter( x , y, ax = None, sort = True, bins = 20, **kwargs ) :
"""
Scatter plot colored by 2d histogram
"""
if ax is None :
fig , ax = plt.subplots()
data , x_e, y_e = np.histogram2d( x, y, bins = bins, density = True )
z = interpn( ( 0.5*(x_e[1:] + x_e[:-1]) , 0.5*(y_e[1:]+y_e[:-1]) ) , data , np.vstack([x,y]).T , method = "splinef2d", bounds_error = False)
#To be sure to plot all data
z[np.where(np.isnan(z))] = 0.0
# Sort the points by density, so that the densest points are plotted last
if sort :
idx = z.argsort()
x, y, z = x[idx], y[idx], z[idx]
ax.scatter( x, y, c=z, **kwargs )
norm = Normalize(vmin = np.min(z), vmax = np.max(z))
cbar = fig.colorbar(cm.ScalarMappable(norm = norm), ax=ax)
cbar.ax.set_ylabel('Density')
return ax
if "__main__" == __name__ :
x = np.random.normal(size=100000)
y = x * 3 + np.random.normal(size=100000)
density_scatter( x, y, bins = [30,30] )

You could make a histogram:
import numpy as np
import matplotlib.pyplot as plt
# fake data:
a = np.random.normal(size=1000)
b = a*3 + np.random.normal(size=1000)
plt.hist2d(a, b, (50, 50), cmap=plt.cm.jet)
plt.colorbar()

Python matplotlib 3d surface plot

I am trying to convert an equation for a surface plot written for mathematica (image and script below) to a python script using matplotlib. There are only a handful of examples of surface plots on the web.
Help would be appreciated for my non functioning one of many attempts
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = np.linspace(2,-2)
y = np.linspace(2,-2)
z = np.linspace(2,-2)
surfx = -1 * (pow(y, 10) + pow(z, 10) - 100)
surfy = -1 * (pow(x, 10) + pow(z, 10) - 100)
surfz = -1 * (pow(x, 10) + pow(y, 10) - 100)
ax.plot_surface(surfx,surfy,surfz, rstride=4, cstride=4, color='b')
plt.show()

I don't think that matplotlib has an equivalent function at this point. If you are not restricted to using matplotlib, you might want to take a look at mayavi and its contour3d()function.
The following code produces a similar plot to your example using mayavi. I am not sure if it's possible to add the wireframe outline however.
import numpy as np
from mayavi import mlab
x, y, z = np.ogrid[-2:2:25j, -2:2:25j, -2:2:25j]
s = np.power(x, 10) + np.power(y, 10) + np.power(z, 10) - 100
mlab.figure(bgcolor=(1,1,1))
mlab.contour3d(s, contours=[2], color=(.5,.5,.5), transparent=True, opacity=.5)
ax = mlab.axes(nb_labels=5, ranges=(-2,2,-2,2,-2,2))
ax.axes.property.color = (0,0,0)
ax.axes.axis_title_text_property.color = (0,0,0)
ax.axes.axis_label_text_property.color = (0,0,0)
ax.axes.label_format='%.0f'
mlab.show()