How can I make a 2-d color plot of the domain of a function across two dimensions? Something like this:
def f(x):
return x[0]**2+6*x[1]**2+x[0]*x[1]+np.sin(x[0])+3*x[0]
x = np.arange(-5,5,0.1)
y = np.arange(-5,5,0.1)
plt.contours(x,y,f([x,y])
Change your last line to
plt.contour(f(np.meshgrid(x,y)))
That will evaluate f across a meshed grid of x and y and plot contours of that function. The tutorial on producing contour plots in matplotlib is here. In general, the tutorials there are pretty good and you can often find what you want.
If you want the axes labelled with the ranges in your x and y ranges - you need
plt.contour(x,y,f(np.meshgrid(x,y)))
You could instead do plt.pcolormesh(f(np.meshgrid(x,y))) if you prefer a 'heatmap' style to a contour plot.
For fun, I expanded the range and amplified the sin component in your function and produced a contour map and a heatmap (see output)
import matplotlib.pyplot as plt
import numpy as np
def f(x):
return x[0]**2+6*x[1]**2+x[0]*x[1]+150*np.sin(x[0])+3*x[0]
x = np.arange(-50,50,0.1)
y = np.arange(-50,50,0.1)
plt.contour(x,y,f(np.meshgrid(x,y)))
plt.show()
Contour output
pcolormesh output
Related
I don't know where to start, as I think it is a new approach for me. Using matplotlib with python, I would like to plot a set of fuzzy numbers (for instance a set of triangular or bell curve fuzzy numbers) as in the picture below:
You can plot the curves recurrently. My try at reproducing your example (including the superposition of labels 1 and 6):
import matplotlib.pyplot as plt
import numpy as np
# creating the figure and axis
fig, ax = plt.subplots(1,1,constrained_layout=True)
# generic gaussian
y = np.linspace(-1,1,100)
x = np.exp(-5*y**2)
center_x = (0,2,4,1,3,0,5)
center_y = (6,2,3,4,5,6,7)
# loop for all the values
for i in range(len(center_x)):
x_c, y_c = center_x[i], center_y[i]
# plotting the several bells, relocated to (x_c, y_c)
ax.plot(x + x_c,y + y_c,
color='red',linewidth=2.0)
ax.plot(x_c,y_c,
'o',color='blue',markersize=3)
# adding label
ax.annotate(
str(i+1),
(x_c - 0.1,y_c), # slight shift in x
horizontalalignment='right',
verticalalignment='center',
color='blue',
)
ax.grid()
Every call to ax.plot() is adding points or curves (to be more precise, Artists) to the same axis. The same for ax.annotate() to create the labels.
I seem to have got stuck at a relatively simple problem but couldn't fix it after searching for last hour and after lot of experimenting.
I have two numpy arrays x and y and I am using seaborn's jointplot to plot them:
sns.jointplot(x, y)
Now I want to label the xaxis and yaxis as "X-axis label" and "Y-axis label" respectively. If I use plt.xlabel, the labels goes to the marginal distribution. How can I make them appear on the joint axes?
sns.jointplot returns a JointGrid object, which gives you access to the matplotlib axes and you can then manipulate from there.
import seaborn as sns
import numpy as np
# example data
X = np.random.randn(1000,)
Y = 0.2 * np.random.randn(1000) + 0.5
h = sns.jointplot(X, Y)
# JointGrid has a convenience function
h.set_axis_labels('x', 'y', fontsize=16)
# or set labels via the axes objects
h.ax_joint.set_xlabel('new x label', fontweight='bold')
# also possible to manipulate the histogram plots this way, e.g.
h.ax_marg_y.grid('on') # with ugly consequences...
# labels appear outside of plot area, so auto-adjust
h.figure.tight_layout()
(The problem with your attempt is that functions such as plt.xlabel("text") operate on the current axis, which is not the central one in sns.jointplot; but the object-oriented interface is more specific as to what it will operate on).
Note that the last command uses the figure attribute of the JointGrid. The initial version of this answer used the simpler - but not object-oriented - approach via the matplotlib.pyplot interface.
To use the pyplot interface:
import matplotlib.pyplot as plt
plt.tight_layout()
Alternatively, you can specify the axes labels in a pandas DataFrame in the call to jointplot.
import pandas as pd
import seaborn as sns
x = ...
y = ...
data = pd.DataFrame({
'X-axis label': x,
'Y-axis label': y,
})
sns.jointplot(x='X-axis label', y='Y-axis label', data=data)
when I change the viewpoint of plot_surface in matplolib in (90,0), the zaxis is not clear, and it's useless in this plot. I want to generate the image same as matlab as below
matlab surf with view (0,90),with contour lines
I used matplotlib function imshow can generate matrix image, but there are no lines (similar in contourplot lines)in the image. how can i generate the image with plot_surface in python with a viewpoint of (90,0),bu without zaxis?
matplotlib with plot_surface,view(90,0),withou lines and zaxis
You can use contourf from matplotlib:
import matplotlib.pyplot as plt
import numpy as np
X, Y = np.meshgrid(range(100), range(100))
Z = X**2+Y**2
plt.contourf(X, Y, Z, [i for i in np.linspace(Z.min(),Z.max(),30)])
plt.show()
, which results in:
I'm currently writing line and contour plotting functions for my PyQuante quantum chemistry package using matplotlib. I have some great functions that evaluate basis sets along a (npts,3) array of points, e.g.
from somewhere import basisset, line
bfs = basisset(h2) # Generate a basis set
points = line((0,0,-5),(0,0,5)) # Create a line in 3d space
bfmesh = bfs.mesh(points)
for i in range(bfmesh.shape[1]):
plot(bfmesh[:,i])
This is fast because it evaluates all of the basis functions at once, and I got some great help from stackoverflow here and here to make them extra-nice.
I would now like to update this to do contour plotting as well. The slow way I've done this in the past is to create two one-d vectors using linspace(), mesh these into a 2D grid using meshgrid(), and then iterating over all xyz points and evaluating each one:
f = np.empty((50,50),dtype=float)
xvals = np.linspace(0,10)
yvals = np.linspace(0,20)
z = 0
for x in xvals:
for y in yvals:
f = bf(x,y,z)
X,Y = np.meshgrid(xvals,yvals)
contourplot(X,Y,f)
(this isn't real code -- may have done something dumb)
What I would like to do is to generate the mesh in more or less the same way I do in the contour plot example, "unravel" it to a (npts,3) list of points, evaluate the basis functions using my new fast routines, then "re-ravel" it back to X,Y matrices for plotting with contourplot.
The problem is that I don't have anything that I can simply call .ravel() on: I either have 1d meshes of xvals and yvals, the 2D versions X,Y, and the single z value.
Can anyone think of a nice, pythonic way to do this?
If you can express f as a function of X and Y, you could avoid the Python for-loops this way:
import matplotlib.pyplot as plt
import numpy as np
def bf(x, y):
return np.sin(np.sqrt(x**2+y**2))
xvals = np.linspace(0,10)
yvals = np.linspace(0,20)
X, Y = np.meshgrid(xvals,yvals)
f = bf(X,Y)
plt.contour(X,Y,f)
plt.show()
yields
I would like to plot a curve in the (x,y) plane, where the color of the curve depends on a value of another variable T. x is a 1D numpy array, y is a 1D numpy array.
T=np.linspace(0,1,np.size(x))**2
fig = plt.figure()
ax = fig.add_subplot(111)
ax.plot(x,y)
I want the line to change from blue to red (using RdBu colormap) depending on the value of T (one value of T exists for every (x,y) pair).
I found this, but I don't know how to warp it to my simple example. How would I use the linecollection for my example? http://matplotlib.org/examples/pylab_examples/multicolored_line.html
Thanks.
One idea could be to set the color using color=(R,G,B) then split your plot into n segments and continuously vary either one of the R, G or B (or a combinations)
import pylab as plt
import numpy as np
# Make some data
n=1000
x=np.linspace(0,100,n)
y=np.sin(x)
# Your coloring array
T=np.linspace(0,1,np.size(x))**2
fig = plt.figure()
ax = fig.add_subplot(111)
# Segment plot and color depending on T
s = 10 # Segment length
for i in range(0,n-s,s):
ax.plot(x[i:i+s+1],y[i:i+s+1],color=(0.0,0.5,T[i]))
Hope this is helpful