I am trying to export my surface plot into a .png file. For some reason, the saving plot does not correspond to the 3D orientation of the plot showed in spyder. Here is my code:
import csv
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.ticker import LinearLocator
import matplotlib as mpl
import numpy as np
with open(r'path', 'r') as f:
voltpertime = list(csv.reader(f, delimiter=","))
voltpertime = np.array(voltpertime[0:], dtype=np.float)
Z= np.flipud(voltpertime)
fig, ax = plt.subplots(subplot_kw={"projection": "3d"})
# Make data.
X = np.arange(1, 36, 1)
Y = np.arange(-4, 8, 0.1)
X, Y = np.meshgrid(X, Y)
# Plot the surface.
norm = mpl.colors.Normalize(vmin=-0.5, vmax=7)
surf = ax.plot_surface(X, Y, Z, cmap=cm.jet, linewidth=1, antialiased=False, norm=norm)
# Customize the z axis.
ax.set_zlim(-3, 7)
ax.zaxis.set_major_locator(LinearLocator(4))
ax.zaxis.set_major_formatter('{x:.02f}')
plt.colorbar(surf, shrink=0.5, aspect=5, label='current (nA)', pad = 0.1)
plt.yticks((-4, -2, 0, 2, 4, 6, 8), ("8", "6", "4", "2", "0", "-2", "-4"))
# rotate the axes and update
for angle in range(160, 360):
ax.view_init(35, angle)
plt.draw()
plt.pause(.001)
fig.savefig(r'path',
transparent = True, bbox_inches= 'tight', dpi=600, edgecolor= None)
plt.show()
Here is the plot in spyder:
and here is the plot when I save it:
I want to export the plot exactly how it appears in spyder.
Any idea?
Thanks
Related
I am new to python programming. I was trying to make two subplots using matplotlib containing a line plot (panel-a) and 2-D color plot using imshow() (panel-b). I want the colorbar to be shown on the right side with same size as the color plot and it should not be within the subplot box limit.
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import numpy as np
import matplotlib.pyplot as plt
import matplotlib.dates as mdates
import datetime as dt
from mpl_toolkits.axes_grid1 import make_axes_locatable
# Panel (a)
x1 = np.linspace(2, -2, 5)
y1 = np.linspace(-2, 2, 5)
# Panel (b)
N = 10
arr = np.random.random((N, N))
x_lims = list(map(dt.datetime.fromtimestamp, [982376726, 982377321]))
x_lims = mdates.date2num(x_lims)
y_lims = [0, 40]
fig, ax = plt.subplots(2, 1, figsize=(14, 10))
ax[0].plot(x1, y1)
ax[0].set_ylim(-2, 2)
ax[0].set_xlim(2, -2)
ax[0].set_xticks([2, 1, 0, -1, -2])
ax[0].set_yticks([-2, -1, 0, 1, 2])
im = ax[1].imshow(arr, extent=[x_lims[0], x_lims[1], y_lims[0],
y_lims[1]],
aspect='auto')
divider = make_axes_locatable(ax[1])
cax = divider.append_axes("right", size="5%", pad=0.05)
plt.colorbar(im, cax=cax, label="diff. en. flux")
ax[1].xaxis_date()
date_format = mdates.DateFormatter('%H:%M:%S')
ax[1].xaxis.set_major_formatter(date_format)
I have a file 'mydata.tmp' which contains 3 colums like this:
3.81107 0.624698 0.000331622
3.86505 0.624698 0.000131237
3.91903 0.624698 5.15136e-05
3.97301 0.624698 1.93627e-05
1.32802 0.874721 1.59245
1.382 0.874721 1.542
1.43598 0.874721 1.572
1.48996 0.874721 4.27933
etc.
Then I want to make a heatmap color plot where the first two columns are coordinates, and the third column are the values of that coordinates.
Also, I would like to set the third column in log scale.
I have done this
import pandas as pd
import matplotlib.pyplot as plt
import scipy.interpolate
import numpy as np
import matplotlib.colors as colors
# import data
df = pd.read_csv('mydata.tmp', delim_whitespace=True,
comment='#',header=None,
names=['1','2','3'])
x = df['1']
y = df['2']
z = df['3']
spacing = 500
xi, yi = np.linspace(x.min(), x.max(), spacing), np.linspace(y.min(),
y.max(), spacing)
XI, YI = np.meshgrid(xi, yi)
rbf = scipy.interpolate.Rbf(x, y, z, function='linear')
ZI = rbf(XI, YI)
fig, ax = plt.subplots()
sc = ax.imshow(ZI, vmin=z.min(), vmax=z.max(), origin='lower',
extent=[x.min(), x.max(), y.min(),
y.max()], cmap="GnBu", norm=colors.LogNorm(vmin=ZI.min(),
vmax=ZI.max()))
fig.colorbar(sc, ax=ax, fraction=0.05, pad=0.01)
plt.show()
And I get this Image
which has all these empty pixels.
I am looking for something like this instead (I have done this other picture with GNUplot):
How can I do it?
You could use cmap.set_bad to define a color for the NaN values:
import numpy as np
import matplotlib.pyplot as plt
from scipy.interpolate import griddata
import matplotlib.colors as colors
from matplotlib import cm
import copy
# Some data
x = np.array([0, 1, 3, 0, 2, 4])
y = np.array([0, 0, 0, 1, 1, 1])
z = np.array([2, 2, 3, 2, 3, 4])
# Interpolation on a grid:
nrb_points = 101
xi = np.linspace(-.5, 4.5, nrb_points)
yi = np.linspace(-.5, 1.5, nrb_points)
XI, YI = np.meshgrid(xi, yi)
xy = np.vstack((x, y)).T
XY = (XI.ravel(), YI.ravel())
ZI = griddata(points, z, XY,
method='linear',
fill_value=np.nan) # Value used [for] points
# outside of the convex hull
# of the input points.
ZI = ZI.reshape(XI.shape)
# Color map:
cmap = copy.copy(cm.jet)
cmap.set_bad('grey', 1.)
# Graph:
plt.pcolormesh(xi, yi, ZI,
#norm=colors.LogNorm(),
cmap=cmap);
plt.colorbar(label='z');
plt.plot(x, y, 'ko');
plt.xlabel('x'); plt.ylabel('y');
the result is:
I would also use griddata instead of RBF method for the interpolation. Then, point outside the input data area (i.e. the convex hull) can be set to NaN.
I plan to create a figure in matplotlib, with a 3D surface on the left and its corresponding contour map on the right.
I used subplots but it only show the contour map (with blank space for the surface), and a separate figure for the surface.
Is it possible to create these plots in one figure side-by side?
EDIT: The code is as follows:
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d
import numpy as np
x = np.arange(-5, 5, 0.25)
y = np.arange(-5, 5, 0.25)
x, y = np.meshgrid(x, y)
r = np.sqrt(x**2 + y**2)
z = np.sin(r)
fig, (surf, cmap) = plt.subplots(1, 2)
fig = plt.figure()
surf = fig.gca(projection='3d')
surf.plot_surface(x,y,z)
cmap.contourf(x,y,z,25)
plt.show()
I guess it's hard to use plt.subplots() in order to create a grid of plots with different projections.
So the most straight forward solution is to create each subplot individually with plt.subplot.
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import axes3d
import numpy as np
x = np.arange(-5, 5, 0.25)
y = np.arange(-5, 5, 0.25)
x, y = np.meshgrid(x, y)
r = np.sqrt(x**2 + y**2)
z = np.sin(r)
ax = plt.subplot(121, projection='3d')
ax.plot_surface(x,y,z)
ax2 = plt.subplot(122)
ax2.contourf(x,y,z,25)
plt.show()
Of course one may also use the gridspec capabilities for more sophisticated grid structures.
I'm plotting a series of data points with x and y error but do NOT want the errorbars to be included in the legend (only the marker). Is there a way to do so?
Example:
import matplotlib.pyplot as plt
import numpy as np
subs=['one','two','three']
x=[1,2,3]
y=[1,2,3]
yerr=[2,3,1]
xerr=[0.5,1,1]
fig,(ax1)=plt.subplots(1,1)
for i in np.arange(len(x)):
ax1.errorbar(x[i],y[i],yerr=yerr[i],xerr=xerr[i],label=subs[i],ecolor='black',marker='o',ls='')
ax1.legend(loc='upper left', numpoints=1)
fig.savefig('test.pdf', bbox_inches=0)
You can modify the legend handler. See the legend guide of matplotlib.
Adapting your example, this could read:
import matplotlib.pyplot as plt
import numpy as np
subs=['one','two','three']
x=[1,2,3]
y=[1,2,3]
yerr=[2,3,1]
xerr=[0.5,1,1]
fig,(ax1)=plt.subplots(1,1)
for i in np.arange(len(x)):
ax1.errorbar(x[i],y[i],yerr=yerr[i],xerr=xerr[i],label=subs[i],ecolor='black',marker='o',ls='')
# get handles
handles, labels = ax1.get_legend_handles_labels()
# remove the errorbars
handles = [h[0] for h in handles]
# use them in the legend
ax1.legend(handles, labels, loc='upper left',numpoints=1)
plt.show()
This produces
Here is an ugly patch:
pp = []
colors = ['r', 'b', 'g']
for i, (y, yerr) in enumerate(zip(ys, yerrs)):
p = plt.plot(x, y, '-', color='%s' % colors[i])
pp.append(p[0])
plt.errorbar(x, y, yerr, color='%s' % colors[i])
plt.legend(pp, labels, numpoints=1)
Here is a figure for example:
The accepted solution works in simple cases but not in general. In particular, it did not work in my own more complex situation.
I found a more robust solution, which tests for ErrorbarContainer, which did work for me. It was proposed by Stuart W D Grieve and I copy it here for completeness
import matplotlib.pyplot as plt
from matplotlib import container
label = ['one', 'two', 'three']
color = ['red', 'blue', 'green']
x = [1, 2, 3]
y = [1, 2, 3]
yerr = [2, 3, 1]
xerr = [0.5, 1, 1]
fig, (ax1) = plt.subplots(1, 1)
for i in range(len(x)):
ax1.errorbar(x[i], y[i], yerr=yerr[i], xerr=xerr[i], label=label[i], color=color[i], ecolor='black', marker='o', ls='')
handles, labels = ax1.get_legend_handles_labels()
handles = [h[0] if isinstance(h, container.ErrorbarContainer) else h for h in handles]
ax1.legend(handles, labels)
plt.show()
It produces the following plot (on Matplotlib 3.1)
I works for me if I set the label argument as a None type.
plt.errorbar(x, y, yerr, label=None)
I am trying to plot some histogrammed data on a polar axis but it wont seem to work properly. An example is below, I use the custom projection found How to make the angles in a matplotlib polar plot go clockwise with 0° at the top? it works for a scatter plot so I think my problem is with the histogram function. This has been driving me nuts all day, does anyone know what I am doing wrong...........
import random
import numpy as np
import matplotlib.pyplot as plt
baz = np.zeros((20))
freq = np.zeros((20))
pwr = np.zeros((20))
for x in range(20):
baz[x] = random.randint(20,25)*10
freq[x] = random.randint(1,10)*10
pwr[x] = random.randint(-10,-1)*10
baz = baz*np.pi/180.
abins = np.linspace(0,2*np.pi,360) # 0 to 360 in steps of 360/N.
sbins = np.linspace(1, 100)
H, xedges, yedges = np.histogram2d(baz, freq, bins=(abins,sbins), weights=pwr)
plt.figure(figsize=(14,14))
plt.subplot(1, 1, 1, projection='northpolar')
#plt.scatter(baz, freq)
plt.pcolormesh(H)
plt.show()
Your code works if you explicitly pass a mgrid (with similar characteristics than your a bins and sbins) to the pcolormesh command.
Below is an example inspired by your code:
import matplotlib.pyplot as plt
import numpy as np
#Generate the data
size = 200
baz = 10*np.random.randint(20, 25, size)*np.pi/180.
freq = 10*np.random.randint(1, 10, size)
pwr = 10*np.random.randint(-10, -1, size)
abins = np.linspace(0, 2*np.pi, 360) # 0 to 360 in steps of 360/N.
sbins = np.linspace(1, 100, 50)
H, xedges, yedges = np.histogram2d(baz, freq, bins=(abins,sbins), weights=pwr)
#Grid to plot your data on using pcolormesh
theta, r = np.mgrid[0:2*np.pi:360j, 1:100:50j]
fig, ax = plt.subplots(figsize=(14,14), subplot_kw=dict(projection='northpolar'))
ax.pcolormesh(theta, r, H)
ax.set_yticklabels([]) #remove yticklabels
plt.show()