One way to do it is using spines: import math import numpy as np import matplotlib.pyplot as plt def sigmoid(x): a = [] for item in x: a.append(1/(1+math.exp(-item))) return a x = np.arange(-10., 10., 0.2) sig = sigmoid(x) fig = plt.figure() ax = fig.add_subplot(1, 1, 1) # Move left y-axis and bottom x-axis to centre, …
Pyplot has get_fignums method that returns a list of figure numbers. This should do what you want: import matplotlib.pyplot as plt import numpy as np x = np.arange(100) y = -x plt.figure() plt.plot(x) plt.figure() plt.plot(y) for i in plt.get_fignums(): plt.figure(i) plt.savefig(‘figure%d.png’ % i)
I think you’re confused on a few points about how matplotlib handles dates. You’re not actually plotting dates, at the moment. You’re plotting things on the x-axis with [0,1,2,…] and then manually labeling every point with a string representation of the date. Matplotlib will automatically position ticks. However, you’re over-riding matplotlib’s tick positioning functionality (Using …
If you are trying to display OpenCV image using matplotlib, use the code below. import numpy as np import cv2 import matplotlib.pyplot as plt %matplotlib inline # if you are running this code in Jupyter notebook # reads image ‘opencv-logo.png’ as grayscale img = cv2.imread(‘/path_to_image/opencv-logo.png’, 0) plt.imshow(img, cmap=’gray’)
The bins parameter tells you the number of bins that your data will be divided into. You can specify it as an integer or as a list of bin edges. For example, here we ask for 20 bins: import numpy as np import matplotlib.pyplot as plt x = np.random.randn(1000) plt.hist(x, bins=20) And here we ask …
Write between $$ to force matplotlib to interpret it. import matplotlib.pyplot as plt plt.plot(range(10), range(10), label = “Normal text $\it{Italics}$”) plt.legend() plt.show()
Simple, just plt.colorbar(): import numpy as np import matplotlib.pyplot as plt mat = np.random.random((10,10)) plt.imshow(mat, origin=”lower”, cmap=’gray’, interpolation=’nearest’) plt.colorbar() plt.show()
The pyplot.scatter() function was tailor made for this reason: import matplotlib.pyplot as plt im = plt.imread(image_name) implot = plt.imshow(im) # put a blue dot at (10, 20) plt.scatter([10], [20]) # put a red dot, size 40, at 2 locations: plt.scatter(x=[30, 40], y=[50, 60], c=”r”, s=40) plt.show() See the documentation for more info.
To display the multiple images use subplot() plt.figure() #subplot(r,c) provide the no. of rows and columns f, axarr = plt.subplots(4,1) # use the created array to output your multiple images. In this case I have stacked 4 images vertically axarr[0].imshow(v_slice[0]) axarr[1].imshow(v_slice[1]) axarr[2].imshow(v_slice[2]) axarr[3].imshow(v_slice[3])
This could work: import pylab fig = pylab.figure() figlegend = pylab.figure(figsize=(3,2)) ax = fig.add_subplot(111) lines = ax.plot(range(10), pylab.randn(10), range(10), pylab.randn(10)) figlegend.legend(lines, (‘one’, ‘two’), ‘center’) fig.show() figlegend.show() figlegend.savefig(‘legend.png’)