I'm having some trouble with color maps. Basically, what I would like to produce is similar to the image below.
On the bottom subplot I would like to be able to plot the relevant colour, but spanning the entire background of the subplot.i.e it would just look like a colourmap over the entire plot, with no lines or points plotted. It should still correspond to the colours shown in the scatter plot.
Is it possible to do this? what I would ideally like to do is put this background under the top subplot. ( the y scales are in diferent units)
Thanks for and help.
code for bottom scatter subplot:
x = np.arange(len(wind))
y = wind
t = y
plt.scatter(x, y, c=t)
where wind is a 1D array
You can use imshow to display your wind array. It needs to be reshaped to a 2D array, but the 'height' dimensions can be length 1. Setting the extent to the dimensions of the top axes makes it align with it.
wind = np.random.randn(100) + np.random.randn(100).cumsum() * 0.5
x = np.arange(len(wind))
y = wind
t = y
fig, ax = plt.subplots(2,1,figsize=(10,6))
ax[0].plot(x,y)
ax[1].plot(x, 100- y * 10, lw=2, c='black')
ymin, ymax = ax[1].get_ybound()
xmin, xmax = ax[1].get_xbound()
im = ax[1].imshow(y.reshape(1, y.size), extent=[xmin,xmax,ymin,ymax], interpolation='none', alpha=.5, cmap=plt.cm.RdYlGn_r)
ax[1].set_aspect(ax[0].get_aspect())
cax = fig.add_axes([.95,0.3,0.01,0.4])
cb = plt.colorbar(im, cax=cax)
cb.set_label('Y parameter [-]')
If you want to use it as a 'background' you should first plot whatever you want. Then grab the extent of the bottom plot and set it as an extent to imshow. You can also provide any colormap you want to imshow by using cmap=.
Related
I have data for few points along the polar coordinates. The data is aligned as shown below
Data
I need help in plotting a 2D polar plot as shown below. I cant understand how do I reshape the values needed to plot contour plot.
enter image description here
Borrowed this code to modify and use for my test case :
theta = np.radians(azimuths)
zeniths = np.array(zeniths)
values = np.array(values)
values = values.reshape((len(azimuths), len(zeniths)))
r, theta = np.meshgrid(zeniths, np.radians(azimuths))
print(r,theta)
print(r.shape)
print(theta.shape)
fig, ax = subplots(subplot_kw=dict(projection='polar'))
ax.set_theta_zero_location("N")
ax.set_theta_direction(-1)
autumn()
cax = ax.contourf(theta, r, values, 30)
autumn()
cb = fig.colorbar(cax)
return fig, ax, cax
I'd like to create a continuous colormap with matplotlib on a 3D Surface Plot, where the color depends on the z-value of the surface. But with the "normal" plt functions the colormap fills the space between the gridpoints with the same color like shown in the picture. So there is no continuous change in color, rather there are just some colored surfaces stitched together:
x = range(0,126)
y = range(0,3)
#z is my data from the experiment
# make a grid of the x/y plane
X,Y= np.meshgrid(x,y)
# get the colormap for the graph
cmap=plt.get_cmap("RdBu")
# cmap = clr.LinearColormap.from_list('custom blue', ['#244162','#DCE6F1'], N=256)
#plot the corresponding z-value at every knot of the grid
surface = ax.plot_surface(X,Y,z, cmap = cmap, antialiased=True, edgecolor='gray' , linewidth=0.2)
m = cm.ScalarMappable(cmap=surface.cmap,norm=surface.norm)
m.set_array(z)
plt.colorbar(m)
ax.set_yticks(y)
ax.set_xticks(x[::25])
plt.show()
which looks like this:
3D-surface Plot
Do I need to interpolate the surface in between with more gridpoints, or is there a more elegant way? I'm a little lost in the documentation and syntax
Thanks in advance,
masterblibla
Using matplotlib, I am attempting to display the histograms of 2 sets of data simultaneously on the side walls of a 3D plot, using this Matlab code and plot from wikipedia as my guide: https://commons.wikimedia.org/wiki/File:MultivariateNormal.png
I am able to plot my raw data on the base plane and have created and plotted my Gaussian fits on the side walls using the 'zdir' kwarg.
This example is able to leverage the 'zdir' kwarg to force where the curves are plotted,
Matplotlib 2d Plot on Faces of 3d Plot
but the matplotlib documentation confirms my AttributeErrors: Unknown property zdir; hist and hist2d don't support this argument.
This example seems to be plotting bars manually on the figure
plotting 3d histogram/barplot in python matplotlib as a way around the problem.
I've tried both .hist and .hist2d with and without zdir=''.
# data is a 2D np.array defined elsewhere
# define plot limits
X = np.linspace(0, np.amax(data), 100)
Y = np.linspace(0, np.amax(data), 100)
# initialize data into x and y sets
x_data = data[:, 0]
y_data = data[:, 1]
# fit a gaussian to both sets
x_mean, x_std = norm.fit(x_data)
x_gauss = norm.pdf(X, x_mean, x_std)
y_mean, y_std = norm.fit(y_data)
y_gauss = norm.pdf(Y, y_mean, y_std)
# initialize plot
figure = plt.figure()
ax = figure.add_subplot(111, projection='3d')
# label axes
ax.set_xlabel('Delta X (um)')
ax.set_ylabel('Delta Y (um)')
ax.set_zlabel('P (X,Y)')
# plot data on base plane
ax.scatter3D(x_data, y_data, zdir='z', zs=0.0, c='k', marker='.')
# plot histograms on walls
ax.hist((x_data, x_gauss), bins=30) #these 2 lines
ax.hist((y_data, y_gauss), bins=30) #are where I'm looking for help
# plot gaussians on walls
ax.plot3D(X, x_gauss, zdir='y', zs=np.amax(data), c='b')
ax.plot3D(Y, y_gauss, zdir='x', zs=np.amax(data), c='g')
# show plot
plt.show()
Is there a direct match in matplotlib for the method Matlab that draws histograms on a specific plane of a 3D plot? Thank you for your help! I am very new to plotting and welcome any other idiomatic or depreciated changes you can see. I always like to see how other coders think.
I am trying to get the radial (or y-axis) labels on a polar plot to go on top of the lines that are plotted on the graph. Right now they are underneath the lines and are covered up.
Here is a simplified version of the code for just one city and one line:
fig, ax = plt.subplots(figsize=(10,6) , nrows=1, ncols=1,subplot_kw=dict(projection='polar'))
rmax = 15
rticks = np.arange(9,rmax,1.5)
rticklabel = np.arange(18,rmax*2,3).astype(int)
theta = np.arange(0,6.3, 0.17) #plots a circle
r = np.ones(len(theta))*(21/2)
ax.plot(theta, r,c='r', linestyle='-',linewidth = 4,zorder=1)
ax.set_rmax(rmax)
ax.set_rticks(rticks) # less radial ticks
ax.set_xticklabels([])
ax.set_rlabel_position(285) # get radial labels away from plotted line
ax.grid(True)
ax.set_facecolor('white')
ax.yaxis.grid(color='silver', linestyle=':',linewidth = 1.5,zorder=10)
ax.set_yticklabels(rticklabel,fontsize=12,zorder=10) #this zorder does nothing
I have already tried this:
plt.rcParams["axes.axisbelow"] = False
This brings the text to the front as I wish, however, it also brings the grid lines. I would like those to stay behind the colored lines.
I have also tried changing the zorder of the yaxis grid, but that does not work.
Most solutions for this are not for the polar axis. Any suggestions?
Unfortunately it seems that the zorder of the grid and labes is tied to that of the axes: https://matplotlib.org/api/_as_gen/matplotlib.axes.Axes.grid.html
One possible solution even if not elegant is to draw the gridlines yourself
fig, ax = plt.subplots(figsize=(10,6) , nrows=1, ncols=1,subplot_kw=dict(projection='polar'))
rmax = 15
rticks = np.arange(9,rmax,1.5)
rticklabel = np.arange(18,rmax*2,3).astype(int)
theta = np.arange(0,6.3, 0.17) #plots a circle
r = np.ones(len(theta))*(21/2)
ax.plot(theta, r,c='r', linestyle='-',linewidth = 4,zorder=2)
ax.set_rticks(rticks) # less radial ticks
ax.set_xticklabels([])
ax.set_rlabel_position(285) # get radial labels away from plotted line
ax.xaxis.grid(True)
ax.yaxis.grid(False)
ax.set_facecolor('white')
ax.set_yticklabels(rticklabel,fontsize=12,zorder=10) #this zorder does nothing
ax.yaxis.set_zorder(10)
#ax.yaxis.grid(color='silver', linestyle=':',linewidth = 1.5,zorder=10)
x = np.arange(0,2*np.pi,0.05)
y = np.outer( np.ones(x.shape), rticks)
ax.plot( x,y, zorder=1, color='silver', linestyle=':')
ax.set_ylim(0,rmax)
I try to plot two polar plots in one figure. See code below:
fig = super(PlotWindPowerDensity, self).get_figure()
rect = [0.1, 0.1, 0.8, 0.8]
ax = WindSpeedDirectionAxes(fig, rect)
self.values_dict = collections.OrderedDict(sorted(self.values_dict.items()))
values = self.values_dict.items()
di, wpd = zip(*values)
wpd = np.array(wpd).astype(np.double)
wpdmask = np.isfinite(wpd)
theta = self.radar_factory(int(len(wpd)))
# spider plot
ax.plot(theta[wpdmask], wpd[wpdmask], color = 'b', alpha = 0.5)
ax.fill(theta[wpdmask], wpd[wpdmask], facecolor = 'b', alpha = 0.5)
# bar plot
ax.plot_bar(table=self.table, sectors=self.sectors, speedbins=self.wpdbins, option='wind_power_density', colorfn=get_sequential_colors)
fig.add_axes(ax)
return fig
The length of the bar is the data base (how many sampling points for this sector). The colors of the bars show the frequency of certain value bins (eg. 2.5-5 m/s) in the correspondent sector (blue: low, red: high). The blue spider plot shows the mean value for each sector.
In the shown figure, the values of each plot are similar, but this is rare. I need to assign the second plot to another axis and show this axis in another direction.
EDIT:
After the nice answer of Joe, i get the result of the figure.
That's almost everything i wanted to achieve. But there are some points i wasn't able to figure out.
The plot is made for dynamicly changing data bases. Therefore i need a dynamic way to get the same location of the circles. Till now I solve it with:
start, end = ax2.get_ylim()
ax2.yaxis.set_ticks(np.arange(0, end, end / len(ax.yaxis.get_ticklocs())))
means: for second axis i alter the ticks in order to fit the ticklocs to the one's of first axis.
In most cases i get some decimal places, but i don't want that, because it corrupts the clearness of the plot. Is there a way to solve this problem more smartly?
The ytics (the radial one's) range from 0 to the next-to-last circle. How can i achieve that the values range from the first circle to the very last (the border)? The same like for the first axis.
So, as I understand it, you want to display data with very different magnitudes on the same polar plot. Basically you're asking how to do something similar to twinx for polar axes.
As an example to illustrate the problem, it would be nice to display the green series on the plot below at a different scale than the blue series, while keeping them on the same polar axes for easy comparison.:
import numpy as np
import matplotlib.pyplot as plt
numpoints = 30
theta = np.linspace(0, 2*np.pi, numpoints)
r1 = np.random.random(numpoints)
r2 = 5 * np.random.random(numpoints)
params = dict(projection='polar', theta_direction=-1, theta_offset=np.pi/2)
fig, ax = plt.subplots(subplot_kw=params)
ax.fill_between(theta, r2, color='blue', alpha=0.5)
ax.fill_between(theta, r1, color='green', alpha=0.5)
plt.show()
However, ax.twinx() doesn't work for polar plots.
It is possible to work around this, but it's not very straight-forward. Here's an example:
import numpy as np
import matplotlib.pyplot as plt
def main():
numpoints = 30
theta = np.linspace(0, 2*np.pi, numpoints)
r1 = np.random.random(numpoints)
r2 = 5 * np.random.random(numpoints)
params = dict(projection='polar', theta_direction=-1, theta_offset=np.pi/2)
fig, ax = plt.subplots(subplot_kw=params)
ax2 = polar_twin(ax)
ax.fill_between(theta, r2, color='blue', alpha=0.5)
ax2.fill_between(theta, r1, color='green', alpha=0.5)
plt.show()
def polar_twin(ax):
ax2 = ax.figure.add_axes(ax.get_position(), projection='polar',
label='twin', frameon=False,
theta_direction=ax.get_theta_direction(),
theta_offset=ax.get_theta_offset())
ax2.xaxis.set_visible(False)
# There should be a method for this, but there isn't... Pull request?
ax2._r_label_position._t = (22.5 + 180, 0.0)
ax2._r_label_position.invalidate()
# Ensure that original axes tick labels are on top of plots in twinned axes
for label in ax.get_yticklabels():
ax.figure.texts.append(label)
return ax2
main()
That does what we want, but it looks fairly bad at first. One improvement would be to the tick labels to correspond to what we're plotting:
plt.setp(ax2.get_yticklabels(), color='darkgreen')
plt.setp(ax.get_yticklabels(), color='darkblue')
However, we still have the double-grids, which are rather confusing. One easy way around this is to manually set the r-limits (and/or r-ticks) such that the grids will fall on top of each other. Alternately, you could write a custom locator to do this automatically. Let's stick with the simple approach here:
ax.set_rlim([0, 5])
ax2.set_rlim([0, 1])
Caveat: Because shared axes don't work for polar plots, the implmentation I have above will have problems with anything that changes the position of the original axes. For example, adding a colorbar to the figure will cause all sorts of problems. It's possible to work around this, but I've left that part out. If you need it, let me know, and I'll add an example.
At any rate, here's the full, stand-alone code to generate the final figure:
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(1977)
def main():
numpoints = 30
theta = np.linspace(0, 2*np.pi, numpoints)
r1 = np.random.random(numpoints)
r2 = 5 * np.random.random(numpoints)
params = dict(projection='polar', theta_direction=-1, theta_offset=np.pi/2)
fig, ax = plt.subplots(subplot_kw=params)
ax2 = polar_twin(ax)
ax.fill_between(theta, r2, color='blue', alpha=0.5)
ax2.fill_between(theta, r1, color='green', alpha=0.5)
plt.setp(ax2.get_yticklabels(), color='darkgreen')
plt.setp(ax.get_yticklabels(), color='darkblue')
ax.set_ylim([0, 5])
ax2.set_ylim([0, 1])
plt.show()
def polar_twin(ax):
ax2 = ax.figure.add_axes(ax.get_position(), projection='polar',
label='twin', frameon=False,
theta_direction=ax.get_theta_direction(),
theta_offset=ax.get_theta_offset())
ax2.xaxis.set_visible(False)
# There should be a method for this, but there isn't... Pull request?
ax2._r_label_position._t = (22.5 + 180, 0.0)
ax2._r_label_position.invalidate()
# Bit of a hack to ensure that the original axes tick labels are on top of
# whatever is plotted in the twinned axes. Tick labels will be drawn twice.
for label in ax.get_yticklabels():
ax.figure.texts.append(label)
return ax2
if __name__ == '__main__':
main()
Just to add onto #JoeKington 's (great) answer, I found that the "hack to ensure that the original axes tick labels are on top of whatever is plotted in the twinned axes" didn't work for me so as an alternative I've used:
from matplotlib.ticker import MaxNLocator
#Match the tick point locations by setting the same number of ticks in the
# 2nd axis as the first
ax2.yaxis.set_major_locator(MaxNLocator(nbins=len(ax1.get_yticks())))
#Set the last tick as the plot limit
ax2.set_ylim(0, ax2.get_yticks()[-1])
#Remove the tick label at zero
ax2.yaxis.get_major_ticks()[0].label1.set_visible(False)