I have two questions regarding usage of the contourf plotting function. I have been searching for answers but haven't found them.
In the contourf function, there is a variable named cmap. What is this used for and what is its meaning? And what is cmap=cm.jet mean?
When one puts x,y,z into contourf and then creates a colorbar, how do we get the minimum and maximum values by which to set the colorbar limits? I am doing it manually now, but is there no way to get the min and max directly from a contourf handle?
The cmap kwarg is the colormap that should be used to display the contour plot. If you do not specify one, the jet colormap (cm.jet) is used. You can change this to any other colormap that you want though (i.e. cm.gray). matplotlib has a large number of colormaps to choose from.
Here is a quick demo showing two contour plots with different colormaps selected.
import matplotlib.pyplot as plt
from matplotlib import cm
import numpy as np
data = np.random.rand(10,10)
plt.subplot(1,2,1)
con = plt.contourf(data, cmap=cm.jet)
plt.title('Jet')
plt.colorbar()
hax = plt.subplot(1,2,2)
con = plt.contourf(data, cmap=cm.gray)
plt.title('Gray')
plt.colorbar()
As far as getting the upper/lower bounds on the colorbar programmatically, you can do this by getting the clim value of the contourf plot object.
con = plt.contourf(data);
limits = con.get_clim()
(0.00, 1.05)
This returns a tuple containing the (lower, upper) bounds of the colorbar.
Related
I have a 2D array and it's contents will display correctly as an image when I simply use
img = plt.imshow(full2DArray)
but my problem is that the axes just naively show the number of rows and columns. For example if my 2D array is 53x53 then the axes will count 0-53 on the y-axis and 0-53 on the x-axis.
I need to show the exact same image but have the axes display a linear scale from -130 to +130 instead.
I have a similar answer to this question here but to explain for your case, we can take an array data = np.random.rand(53,53) filled with random values, and plot it with imshow. You simply need to adjust the extent=[<xmin>,<xmax>,<ymin>,<ymax>] parameter, so in the example code:
import numpy as np
import matplotlib.pyplot as plt
data = np.random.rand(53,53)
print(data.shape) # Displays (53,53)
plt.figure()
plt.xlabel("x")
plt.ylabel("y")
plt.imshow(data, origin='lower', aspect='auto',
extent = [-130,130,-130,130], cmap=plt.cm.jet)
plt.colorbar()
plt.show()
We get the following plot with your desired bounds:
If I understand it correctly, you need predifined axis, instead of pyplot infering these from the image.
Setting xlim before calling imshow will do the job.
plt.xlim([-130, 130])
Similarly, you can call ylim for the y axis.
I'm trying to create a histogram of a data column and plot it logarithmically (y-axis) and I'm not sure why the following code does not work:
import numpy as np
import matplotlib.pyplot as plt
data = np.loadtxt('foo.bar')
fig = plt.figure()
ax = fig.add_subplot(111)
plt.hist(data, bins=(23.0, 23.5,24.0,24.5,25.0,25.5,26.0,26.5,27.0,27.5,28.0))
ax.set_xlim(23.5, 28)
ax.set_ylim(0, 30)
ax.grid(True)
plt.yscale('log')
plt.show()
I've also tried instead of plt.yscale('log') adding Log=true in the plt.hist line and also I tried ax.set_yscale('log'), but nothing seems to work. I either get an empty plot, either the y-axis is indeed logarithmic (with the code as shown above), but there is no data plotted (no bins).
try
plt.yscale('log', nonposy='clip')
http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.yscale
The issue is with the bottom of bars being at y=0 and the default is to mask out in-valid points (log(0) -> undefined) when doing the log transformation (there was discussion of changing this, but I don't remember which way it went) so when it tries to draw the rectangles for you bar plot, the bottom edge is masked out -> no rectangles.
The hist constructor accepts the log parameter.
You can do this:
plt.hist(data, bins=bins, log=True)
np.logspace returns bins in [1-10], logarithmically spaced - in my case xx is a npvector >0 so the following code does the trick
logbins=np.max(xx)*(np.logspace(0, 1, num=1000) - 1)/9
hh,ee=np.histogram(xx, density=True, bins=logbins)
I have a user case that, let's say I have three series data: x,y,z.
I would like to make a scatter plot using (x,y) as coordinates and z as the color of scatter points, using cmap keyword of plt.scatter. However, I would like to highlight some specific point by using a different marker type and size than other points.
A minimum example is like below:
x,y,z = np.random.randn(3,10)
plt.scatter(x,y,c=z,cmap=matplotlib.cm.jet)
plt.colorbar()
If I want to use a different marker type for (x[5],y[5],z[5]), how could I do that?
The only way I can think of is to plot again for this point using plt.scatter([x[5],y[5]) but define the color by manually finding the colormap color corresponding to z[5]. However this is quite tedious. Is there a better way?
Each scatterplot has one single marker, you cannot by default use different markers in a single scatterplot. Hence, if you are happy to only change the markersize and leave the marker the same, you can supply an array of different sizes to the scatter's s argument.
import matplotlib.pyplot as plt
import numpy as np; np.random.seed(10)
x,y,z = np.random.randn(3,10)
sizes = [36]*len(x)
sizes[5] = 121
plt.scatter(x,y,c=z,s=sizes, cmap=plt.cm.jet)
plt.colorbar()
plt.show()
If you really need a different marker style, you can to plot a new scatter plot. You can then set the colorlimits of the second scatter to the ones from the first.
import matplotlib.pyplot as plt
import numpy as np; np.random.seed(10)
x,y,z = np.random.randn(3,10)
xs, ys, zs = [x[5]], [y[5]], [z[5]]
print xs, ys, zs
y[5] = np.nan
sc = plt.scatter(x,y,c=z,s=36, cmap=plt.cm.jet)
climx, climy = sc.get_clim()
plt.scatter(xs,ys,c=zs,s=121, marker="s", cmap=plt.cm.jet, vmin=climx, vmax=climy )
plt.colorbar()
plt.show()
Finally, a bit of a complicated solution to have several different markers in the same scatter plot would be given in this answer.
I'd like to make a matrix plot like the image below in matplotlib. I can make this a plot like this with this code:
m = numpy.random.rand(100,100)
matplotlib.pyplot.matshow(m)
How can I control the color scale, i.e. set the values corresponding to the "min" and "max" colors?
The matshow docs indicate that the options are mostly just passed to imshow (docs). Imshow takes arguments vmin and vmax that determine the min and max colors as you desire. Let's check out an example:
import numpy as np
import matplotlib.pyplot as plt
plt.ion()
A = np.arange(0,100).reshape(10,10)
plt.matshow(A) # defaults
plt.matshow(A, vmin=0, vmax=99) # same
plt.matshow(A, vmin=10, vmax=90) # top/bottom rows get min/max colors, respectively
Aside:
May I also recommend changing the colormap? eg. cmap='hot'. Though it is the default (why?), the 'jet' colormap is almost never the best choice.
x = np.random.randn(1000)
y = np.random.randn(1000)+5
plt.hist2d(x, y, bins=40, cmap='hot')
plt.colorbar()
I'm trying to create a histogram of a data column and plot it logarithmically (y-axis) and I'm not sure why the following code does not work:
import numpy as np
import matplotlib.pyplot as plt
data = np.loadtxt('foo.bar')
fig = plt.figure()
ax = fig.add_subplot(111)
plt.hist(data, bins=(23.0, 23.5,24.0,24.5,25.0,25.5,26.0,26.5,27.0,27.5,28.0))
ax.set_xlim(23.5, 28)
ax.set_ylim(0, 30)
ax.grid(True)
plt.yscale('log')
plt.show()
I've also tried instead of plt.yscale('log') adding Log=true in the plt.hist line and also I tried ax.set_yscale('log'), but nothing seems to work. I either get an empty plot, either the y-axis is indeed logarithmic (with the code as shown above), but there is no data plotted (no bins).
try
plt.yscale('log', nonposy='clip')
http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.yscale
The issue is with the bottom of bars being at y=0 and the default is to mask out in-valid points (log(0) -> undefined) when doing the log transformation (there was discussion of changing this, but I don't remember which way it went) so when it tries to draw the rectangles for you bar plot, the bottom edge is masked out -> no rectangles.
The hist constructor accepts the log parameter.
You can do this:
plt.hist(data, bins=bins, log=True)
np.logspace returns bins in [1-10], logarithmically spaced - in my case xx is a npvector >0 so the following code does the trick
logbins=np.max(xx)*(np.logspace(0, 1, num=1000) - 1)/9
hh,ee=np.histogram(xx, density=True, bins=logbins)