I'm trying to show a contour plot using matplotlib from a complex array. The array is a 2x2 complex matrix, generated by the (C like) method:
for i in max_y:
for j in max_x:
pos_x = pos_x + step
z = complex(pos_x,pos_y)
c_arr[i][j] = complex_function(z)
pos_y = pos_y + step
I would like to plot this c_arr (real part) using contourplot, but so far the only thing that I can get from contour is
TypeError: Input z must be a 2D array.
The c_arr.real is a 2D array, and doesn't matter if I make a grid with x, y, or pos_x, or pos_y, the result is always the same. The docs from matplotlib tells me how to use it, but not the datatypes necessary to use it, so I feel left in the dark.
EDIT: Thanks for the answer. My problem now is that I have to get the complex values from a function in this form:
def f(z):
return np.sum(np.arange(n)*np.sqrt(z-1)**np.arange(n))
where the sum must be added up. How can this be accomplished using the meshgrid form that contour needs? Thanks again.
matplotlib.pyplot.contour() allows complex-valued input arrays. It extracts real values from the array implicitly:
#!/usr/bin/env python
import numpy as np
from matplotlib import pyplot as plt
# generate data
x = np.r_[0:100:30j]
y = np.r_[0:1:20j]
X, Y = np.meshgrid(x, y)
Z = X*np.exp(1j*Y) # some arbitrary complex data
# plot it
def plotit(z, title):
plt.figure()
cs = plt.contour(X,Y,z) # contour() accepts complex values
plt.clabel(cs, inline=1, fontsize=10) # add labels to contours
plt.title(title)
plt.savefig(title+'.png')
plotit(Z, 'real')
plotit(Z.real, 'explicit real')
plotit(Z.imag, 'imagenary')
plt.show()
real
explicit real
imagenary
Related
I am trying to plot (magnetic) field lines. I came to know stream function is the tool used to plot field lines. However, an error comes up telling ValueError: 'x' values must be equally spaced.
Please note that the code uses Fourier basis along x so it is equally spaced along x and uses Chebyshev basis along y and hence it is non-uniform grid along y. I do not understand why the code says "x must be uniformly spaced" when it's uniform along x.
The other thing I want to know is, how to plot field lines for a non-uniform grid?
Also is there any other method to plot field lines apart from streamplot?
Code that I am running is attached below for reference:
import numpy as np
import h5py
import matplotlib.pyplot as plt
f = h5py.File('./B_mm/Re_10k/Bx_v24/Bx_v24_s1.h5', 'r')
y = f['/scales/y/1.0'][:]
x = f['/scales/x/1.0'][:]
Bxtotal = f['/tasks/Bxtotal'][:]
Bytotal = f['/tasks/Bytotal'][:]
t = f['scales']['sim_time'][:]
print(np.shape(y))
print('y = ', y)
print(np.shape(x))
print('x= ', x)
X, Y = np.meshgrid(y,x)
print(np.shape(X))
print(np.shape(Y))
Bx = Bxtotal[len(Bxtotal)-1, :, :]
By = Bytotal[len(Bytotal)-1, :, :]
print(np.shape(Bx))
print(np.shape(By))
plt.figure(figsize=(4,4),facecolor="w")
plt.streamplot(X, Y, Bx, By, color='k', density=1.3, minlength=0.9, arrowstyle='-')
plt.show()
And this is my error message;
I would like to plot in 3D with Pandas / MatplotLib (Wireframe or other, I do not care) but in a specific way..
I'm using RFID sensors and I'm trying to record the signal I receive at different distance + different angles. And I want to see the correlation between the rising of the distance and the angle.
So that's why I want to plot in 3D :
X Axis -> the Distance, Y Axis -> the Angle, Z Axis -> the signal received which means a float
My CSV file from where I generate my DataFrame is organized like this a double entry table :
Distance;0;23;45;90;120;180
0;-53.145;-53.08;-53.1;-53.035;-53.035;-53.035
5;-53.145;-53.145;-53.05;-53.145;-53.145;-53.145
15;-53.145;-53.145;-53.145;-53.145;-53.145;-53.145
25;-53.145;-52.145;-53.145;-53.002;-53.145;-53.145
40;-53.145;-53.002;-51.145;-53.145;-54.255;-53.145
60;-53.145;-53.145;-53.145;-53.145;-53.145;-53.145
80;-53.145;-53.145;-53.145;-53.145;-60;-53.145
100;-53.145;-52;-53.145;-54;-53.145;-53.145
120;-53.145;-53.145;-53.145;-53.145;-53.002;-53.145
140;-51.754;-53.145;-51.845;-53.145;-53.145;-53.145
160;-53.145;-53.145;-49;-53.145;-53.145;-53.145
180;-53.145;-53.145;-53.145;-53.145;-53.145;-53.002
200;-53.145;-53.145;-53.145;-53.145;-53.145;-53.145
On the first label row we've different angles : 0°, 23°, 45°, ...
And the index of the DataFrame is the distance : 0 cm, 15 cm...
And the matrix inside represents the signal, so, values of Z Axis...
But I do not know how to generate a 3D Scatter, WireFrame... because in every tutorial I see people that use specific columns as axis.
Indeed, in my CSV file on the first row I've the label of all columns
Distance;0 ;23 ;45 ;90 ;120;180
And I do not know how to generate a 3D plot with a double entry table.
Do you know how to do it ? Or, to generate my CSV file in a better way to see the same result at the end !
I would be grateful if you would help me about this !
Thank you !
maybe contour is enough
b = np.array([0,5,15,25,40,60,80,100,120,140,160,180,200])
a = np.array([0,23,45,90,120,180])
x, y = np.meshgrid(a, b)
z = np.random.randint(-50,-40, (x.shape))
scm = plt.contourf(x, y, z, cmap='inferno')
plt.colorbar(scm)
plt.xticks(a)
plt.yticks(b)
plt.xlabel('Distance')
plt.ylabel('Angle')
plt.show()
displays
You can get a contour plot with something like this (but for the data shown it is not very interesting since all the values are constant at -45):
df = pd.read_csv(sep=';')
df = df.set_index('Distance')
x = df.index
y = df.columns.astype(int)
z = df.values
X,Y = np.meshgrid(x,y)
Z = z.T
plt.contourf(X,Y,Z,cmap='jet')
plt.colorbar()
plt.show()
Welcome to stackoverflow, your question can be split into several steps:
Step 1 - read the data
I have stored your data in a file called data.txt.
I don't know Pandas very well but this can also be handled with the nice simple function of Numpy called loadtxt. Your data is a bit problematic because of the text 'Distance' value in the first column and first row. But don't panic we load the file as a matrix of strings:
raw_data = np.loadtxt('data.txt', delimiter=';', dtype=np.string_)
Step 2 - transform the raw data
To extract the wanted data from the raw data we can do the following:
angle = raw_data[0 , 1:].astype(float)
distance = raw_data[1:, 0 ].astype(float)
data = raw_data[1:, 1:].astype(float)
With indexing the raw data we select the data that we want and with astype we change the string values to numbers.
Intermediate step - making the data a bit fancier
Your data was a bit boring, only the value -45, i took the liberty to make it a bit fancier:
data = (50 + angle[np.newaxis,:]) / (10 + np.sqrt(distance[:,np.newaxis]))
Step 4 - make a wireframe plot
The example at matplotlib.org looks easy enough:
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.plot_wireframe(X, Y, Z)
plt.show()
But the trick is to get the X, Y, Z parameters right...
Step 3 - make the X and Y data
The Z data is simply our data values:
Z = data
The X and Y should also be 2D array's such that plot_wireframe can find the x and y for each value of Z in the 2D arrays X an Y at the same array locations. There is a Numpy function to create these 2D array's:
X, Y = np.meshgrid(angle, distance)
Step 5 - fancing it up a bit
ax.set_xticks(angle)
ax.set_yticks(distance[::2])
ax.set_xlabel('angle')
ax.set_ylabel('distance')
Putting it together
All steps together in the right order:
# necessary includes...
from mpl_toolkits.mplot3d import axes3d
import matplotlib.pyplot as plt
import numpy as np
raw_data = np.loadtxt('data.txt', delimiter=';', dtype=np.string_)
angle = raw_data[0 , 1:].astype(float)
distance = raw_data[1:, 0 ].astype(float)
data = raw_data[1:, 1:].astype(float)
# make the example data a bit more interesting...
data = (50 + angle[np.newaxis,:]) / (10 + np.sqrt(distance[:,np.newaxis]))
# setting up the plot
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
# the trickey part creating the data that plot_wireframe wants
Z = data
X, Y = np.meshgrid(angle, distance)
ax.plot_wireframe(X, Y, Z)
# fancing it up a bit
ax.set_xticks(angle)
ax.set_yticks(distance[::2])
ax.set_xlabel('angle')
ax.set_ylabel('distance')
# and showing the plot ...
plt.show()
Is there a way to extract the data from an array, which corresponds to a line of a contourplot in python? I.e. I have the following code:
n = 100
x, y = np.mgrid[0:1:n*1j, 0:1:n*1j]
plt.contour(x,y,values)
where values is a 2d array with data (I stored the data in a file but it seems not to be possible to upload it here). The picture below shows the corresponding contourplot. My question is, if it is possible to get exactly the data from values, which corresponds e.g. to the left contourline in the plot?
Worth noting here, since this post was the top hit when I had the same question, that this can be done with scikit-image much more simply than with matplotlib. I'd encourage you to check out skimage.measure.find_contours. A snippet of their example:
from skimage import measure
x, y = np.ogrid[-np.pi:np.pi:100j, -np.pi:np.pi:100j]
r = np.sin(np.exp((np.sin(x)**3 + np.cos(y)**2)))
contours = measure.find_contours(r, 0.8)
which can then be plotted/manipulated as you need. I like this more because you don't have to get into the deep weeds of matplotlib.
plt.contour returns a QuadContourSet. From that, we can access the individual lines using:
cs.collections[0].get_paths()
This returns all the individual paths. To access the actual x, y locations, we need to look at the vertices attribute of each path. The first contour drawn should be accessible using:
X, Y = cs.collections[0].get_paths()[0].vertices.T
See the example below to see how to access any of the given lines. In the example I only access the first one:
import matplotlib.pyplot as plt
import numpy as np
n = 100
x, y = np.mgrid[0:1:n*1j, 0:1:n*1j]
values = x**0.5 * y**0.5
fig1, ax1 = plt.subplots(1)
cs = plt.contour(x, y, values)
lines = []
for line in cs.collections[0].get_paths():
lines.append(line.vertices)
fig1.savefig('contours1.png')
fig2, ax2 = plt.subplots(1)
ax2.plot(lines[0][:, 0], lines[0][:, 1])
fig2.savefig('contours2.png')
contours1.png:
contours2.png:
plt.contour returns a QuadContourSet which holds the data you're after.
See Get coordinates from the contour in matplotlib? (which this question is probably a duplicate of...)
I'm using scipy.stats.binned_statistic_2d and then plotting the output. When I use stat="count", I have no problems. When I use stat="mean" (or np.max() for that matter), I end up with negative values in each bin (as identified by the color bar), which should not be the case because I have constructed zvals such that it is always greater than zero. Does anyone know why this is the case? I've included the minimal code I use to generate the plots. I also get an invalid value RunTime warning, which makes me think that something strange is going on in binned_statistic_2d. The following code should just copy and run.
From the documentation:
'count' : compute the count of points within each bin. This is
identical to an unweighted histogram. `values` array is not
referenced.
which leads me to believe that there might be something going on in binned_statistic_2d and how it handles z-values.
import numbers as _numbers
import numpy as _np
import scipy as _scipy
import matplotlib as _mpl
import types as _types
import scipy.stats
from matplotlib import pyplot as _plt
norm_args = (0, 3, int(1e5)) # loc, scale, size
x = _np.random.random(norm_args[-1]) # xvals can be log scaled.
y = _np.random.normal(*norm_args) #_np.random.random(norm_args[-1]) #
z = _np.abs(_np.random.normal(1e2, *norm_args[1:]))
nbins = 1e2
kwargs = {}
stat = _np.max
fig, ax = _plt.subplots()
binned_stats = _scipy.stats.binned_statistic_2d(x, y, z, stat,
nbins)
H, xedges, yedges, binnumber = binned_stats
Hplot = H
if isinstance(stat, str):
cbar_title = stat.title()
elif isinstance(stat, _types.FunctionType):
cbar_title = stat.__name__.title()
XX, YY = _np.meshgrid(xedges, yedges)
Image = ax.pcolormesh(XX, YY, Hplot.T) #norm=norm,
ax.autoscale(tight=True)
grid_kargs = {'orientation': 'vertical'}
cax, kw = _mpl.colorbar.make_axes_gridspec(ax, **grid_kargs)
cbar = fig.colorbar(Image, cax=cax)
cbar.set_label(cbar_title)
Here's the runtime warning:
/Users/balterma/Library/Enthought/Canopy_64bit/User/lib/python2.7/sitepackages/matplotlib/colors.py:584: RuntimeWarning: invalid value encountered in less cbook._putmask(xa, xa < 0.0, -1)
Image with mean:
Image with max:
Image with count:
Turns out the problem was interfacing with plt.pcolormesh. I had to convert the output array from binned_statistic_2d to a masked array that masked the NaNs.
Here's the question that gave me the answer:
pcolormesh with missing values?
I have got this code to generate a surface plot. But it gives a zero division error. I am not able to figure out what is wrong. Thank you.
import pylab, csv
import numpy
from mayavi.mlab import *
def getData(fileName):
try:
data = csv.reader(open(fileName,'rb'))
except:
print 'File not found'
else:
data = [[float(row[0]), float(row[1]),float(row[2])] for row in data]
x = [row[0] for row in data]
y = [row[1] for row in data]
z = [row[2] for row in data]
return (x, y, z)
def plotData(fileName):
xVals, yVals, zVals = getData(fileName)
xVals = pylab.array(xVals)
yVals = pylab.array(yVals)
zVals = (pylab.array(zVals)*10**3)
x, y = numpy.mgrid[-0.5:0.5:0.001, -0.5:0.5:0.001]
s = surf(x, y, zVals)
return s
plotData('data')
If I have understood the code correctly, there is a problem with zVals in mayavi.mlab.surf.
According to the documentation of the function, s is the elevation matrix, a 2D array, where indices along the first array axis represent x locations, and indices along the second array axis represent y locations. Your file reader seems to return a 1D vector instead of an array.
However, this may not be the most difficult problem. Your file seems to contain triplets of x, y, and z coordinates. You can use mayavi.mlab.surf only if your x and y coordinates in the file form a regular square grid. If this is the case, then you just have to recover that grid and form nice 2D arrays of all three parts. If the points are in the file in a known order, it is easy, otherwise it is rather tricky.
Maybe you would want to start with mayavi.mlab.points3d(xVals, yVals, zVals). That will give you an overall impression of your data. (Or if already know more about your data, you might give us a hint by editing your question and adding more information!)
Just to give you an idea of probably slightly pythonic style of writing this, your code is rewritten (and surf replaced) in the following:
import mayavi.mlab as ml
import numpy
def plot_data(filename):
data = numpy.loadtxt(filename)
xvals = data[:,0]
yvals = data[:,1]
zvals = data[:,2] * 1000.
return ml.points3d(x, y, z)
plot_data('data')
(Essential changes: the use of numpy.loadtxt, get rid of pylab namespace here, no import *, no CamelCase variable or function names. For more information, see PEP 8.)
If you only need to see the shape of the surface, and the data in the file is ordered row-by-row and with the same number of data points in each row (i.e. fixed number of columns), then you may use:
import mayavi.mlab as ml
import numpy
importt matplotlib.pyplot as plt
# whatever you have as the number of points per row
columns = 13
data = numpy.loadtxt(filename)
# draw the data points into a XY plane to check that they really for a rectangular grid:
plt.plot(data[:,0], data[:,1])
# draw the surface
zvals = data[:,2].reshape(-1,columns)
ml.surf(zvals, warp_scale='auto')
As you can see, this code allows you to check that your values really are in the right kind of grid. It does not check that they are in the correct order, but at least you can see they form a nice grid. Also, you have to input the number of columns manually. The keyword warp_scale takes care of the surface scaling so that it should look reasonable.