I am plotting a plot of Accuracy versus the var_smoothing curve of 4 different instances. My values are:
var_smoothing_values
>>
[1e-09, 1e-06, 0.001, 1]
gauss_accuracies
>>
[0.728, 0.8, 0.826, 0.832]
I have used the 2 subplots and on the second subplot, I am plotting this as:
f,ax = plt.subplots(1,2,figsize=(15,5))
ax[1].plot(var_smoothing_values,gauss_accuracies,marker='*',markersize=12)
ax[1].set_ylabel('Accuracy')
ax[1].set_xlabel('var_smoothing values')
ax[1].set_title('Accuracy vs var_smoothing | GaussianNB',size='large')
plt.show()
ax[1].set_xticks(var_smoothing_values) shows only 3 ticks.
How can I show only 4 ticks which corresponds to each of my var_smoothing_values??
You need to use the log scale on the x-axis since your x-values span acoss several orders of magnitude
ax[1].set_xscale('log')
ax[1].set_xticks(var_smoothing_values);
Related
I am trying to plot multiple different plots on a single matplotlib figure with in a for loop. At the moment it is all good in matlab as shown in the picture below and then am able to save the figure as a video frame. Here is a link of a sample video generated in matlab for 10 frames
In python, tried it as below
import matplotlib.pyplot as plt
for frame in range(FrameStart,FrameEnd):#loop1
# data generation code within a for loop for n frames from source video
array1 = np.zeros((200, 3800))
array2 = np.zeros((19,2))
array3 = np.zeros((60,60))
for i in range(len(array2)):#loop2
#generate data for arrays 1 to 3 from the frame data
#end loop2
plt.subplot(6,1,1)
plt.imshow(DataArray,cmap='gray')
plt.subplot(6, 1, 2)
plt.bar(data2D[:,0], data2D[:,1])
plt.subplot(2, 2, 3)
plt.contourf(mapData)
# for fourth plot, use array2[3] and array2[5], plot it as shown and keep the\is #plot without erasing for next frame
not sure how to do the 4th axes with line plots. This needs to be there (done using hold on for this axis in matlab) for the entire sequence of frames processing in the for loop while the other 3 axes needs to be erased and updated with new data for each frame in the movie. The contour plot needs to be square all the time with color bar on the side. At the end of each frame processing, once all the axes are updated, it needs to be saved as a frame of a movie. Again this is easily done in matlab, but not sure in python.
Any suggestions
thanks
I guess you need something like this format.
I have used comments # in code to answer your queries. Please check the snippet
import matplotlib.pyplot as plt
fig=plt.figure(figsize=(6,6))
ax1=fig.add_subplot(311) #3rows 1 column 1st plot
ax2=fig.add_subplot(312) #3rows 1 column 2nd plot
ax3=fig.add_subplot(325) #3rows 2 column 5th plot
ax4=fig.add_subplot(326) #3rows 2 column 6th plot
plt.show()
To turn off ticks you can use plt.axis('off'). I dont know how to interpolate your format so left it blank . You can adjust your figsize based on your requirements.
import numpy as np
from numpy import random
import matplotlib.pyplot as plt
fig=plt.figure(figsize=(6,6)) #First is width Second is height
ax1=fig.add_subplot(311)
ax2=fig.add_subplot(312)
ax3=fig.add_subplot(325)
ax4=fig.add_subplot(326)
#Bar Plot
langs = ['C', 'C++', 'Java', 'Python', 'PHP']
students = [23,17,35,29,12]
ax2.bar(langs,students)
#Contour Plot
xlist = np.linspace(-3.0, 3.0, 100)
ylist = np.linspace(-3.0, 3.0, 100)
X, Y = np.meshgrid(xlist, ylist)
Z = np.sqrt(X**2 + Y**2)
cp = ax3.contourf(X, Y, Z)
fig.colorbar(cp,ax=ax3) #Add a colorbar to a plot
#Multiple line plot
x = np.linspace(-1, 1, 50)
y1 = 2*x + 1
y2 = 2**x + 1
ax4.plot(x, y2)
ax4.plot(x, y1, color='red',linewidth=1.0)
plt.tight_layout() #Make sures plots dont overlap
plt.show()
I wish to create a sub plot that looks like the following picture,
it is supposed to contain 25 polar histograms, and I wish to add them to the plot one by one.
needs to be in python.
I already figured I need to use matplotlib but can't seem to figure it out completely.
thanks a lot!
You can create a grid of polar axes via projection='polar'.
hist creates a histogram, also when working with polar axes. Note that the x is in radians with a range of 2π. It works best when you give the bins explicitly as a linspace from 0 to 2π (or from -π to π, depending on the data). The third parameter of linspace should be one more than the number of bars that you'd want for the full circle.
About the exact parameters of axs[i][j].hist(x, bins=np.linspace(0, 2 * np.pi, np.random.randint(7, 30), endpoint=True), color='dodgerblue', ec='black'):
axs[i][j] draw on the jth subplot of the ith line
.hist create a histogram
x: the values that are put into bins
bins=: to enter the bins (either a fixed number between lowest and highest x or some explicit boundaries; default is 10 fixed boundaries)
np.random.randint(7, 30) a random whole number between 7 and 29
np.linspace(0, 2 * np.pi, n, endpoint=True) divide the range between 0 and 2π into n equal parts; endpoint=True makes boundaries at 0, at 2π and at n-2 positions in between; when endpoint=False there will be a boundary at 0, at n-1 positions in between but none at the end
color='dodgerblue': the color of the histogram bars will be blueish
ec='black': the edge color of the bars will be black
import numpy as np
import matplotlib.pyplot as plt
fig, axs = plt.subplots(5, 5, figsize=(8, 8),
subplot_kw=dict(projection='polar'))
for i in range(5):
for j in range(5):
x = np.random.uniform(0, 2 * np.pi, 50)
axs[i][j].hist(x, bins=np.linspace(0, 2 * np.pi, np.random.randint(7, 30)), color='dodgerblue', ec='black')
plt.tight_layout()
plt.show()
I would like to perform a personalized normalization on histograms on matplotlib. In particular I have two histograms and I would like to divide each of them by a given number (number of generated events).
I don't want to "normally" normalize it, because the "normal normalization" makes the area equal to 1. What I wish for is basically to divide the value of each bin by a given number N, so that if my histogram has 2 bins, one with 5 entries and one with 3, the resulting "normalized" (or "divided") histogram would have the first bin with 5/N entries and the second one with 3/N.
I searched far&wide and found nothing really helpful. Do you have any handy solution? This is my code, working with pandas:
num_bins = 128
list_1 = dataframe_1['E']
list_2 = dataframe_2['E']
fig, ax = plt.subplots()
ax.set_xlabel('Proton energy [MeV]')
ax.set_ylabel('Normalized frequency')
ax.set_title('Proton energy distribution')
n, bins, patches = ax.hist(list_1, num_bins, density=1, alpha=0.5, color='red', ec='red', label='label_1')
n, bins, patches = ax.hist(list_2, num_bins, density=1, alpha=0.5, color='blue', ec='blue', label='label_2')
plt.legend(loc='upper center', fontsize='x-large')
fig.savefig('NiceTitle.pdf')
plt.close('all')
I have two distributions within a hexbin plot, like the one shown:
One distributions has a max value of about 4000, while the other has a max value of about 2500. The plotting colours are therefore different.
I was thinking I could normalize it if I knew the max value of the hexbin plot. How do I know how many points are within the max hexbin other than looking at the colorbar? I am using matplotlib.pyplot.hexbin
You can get the min and max of the norm which is what is used to normalize the data for color picking.
hb = plt.hexbin(x, y)
print hb.norm.vmin, hb.norm.vmax
You could then go on to pass a norm with this information to the second plot. The problem with this is that the first plot must have more range than the second, otherwise the second plot will not all be colored.
Alternatively, and preferably, you can construct a norm which you pass to the hexbin function for both of your plots:
norm = plt.normalize(min_v, max_v)
hb1 = plt.hexbin(x1, y1, norm=norm)
hb2 = plt.hexbin(x2, y2, norm=norm)
HTH,
I'm plotting the degree of freedom against the square error,:
plt.plot([1,2,3,4], [0.5,0.6,0.9,0.85],'-')
It will produce
The problem is that ,the x ax is has 0.5 interval, and does not make sense in this context. Because there is simply no 1.5 degree of freedom.
How can I make the x axis into [1,2,3,4,], instead of [1, 1.5, 2, ...]?
Just add directly the positions and the strings you want to put in the x axis. Using your example:
import matplotlib.pyplot as plt
x = [1,2,3,4]
y = [0.5,0.6,0.9,0.85]
plt.plot(x,y,'-')
plt.xticks(list(range(1,max(x)+1)),[str(i) for i in range(1,max(x)+1)])
plt.grid()
plt.show()
, which results in:
You have to set the XTick 1 to 4, by 1 1:1:4 like below
plot([1,2,3,4], [0.5,0.6,0.9,0.85],'-');
set(gca,'XTick',1:1:4);
or
p = plot([1,2,3,4], [0.5,0.6,0.9,0.85],'-');
set(p,'XTick',1:1:4);