I see two ways to solve this: The first way is to add some jitter to the x/y coordinates of the plot features. This has the advantage that you can easily modify a plot, but you have to draw the axes yourself if you want to have them xkcdyfied (see @Rody Oldenhuis’ solution). The second … Read more
As far as I see, the problem is (as you already wrote), that MeanResidualLife takes a long time to compute, even for a single evaluation. Now, the FindMinimum or similar functions try to find a minimum to the function. Finding a minimum requires either to set the first derivative of the function zero and solve … Read more
You should use the OO interface to matplotlib, rather than the state machine interface. Almost all of the plt.* function are thin wrappers that basically do gca().*. plt.subplot returns an axes object. Once you have a reference to the axes object you can plot directly to it, change its limits, etc. import matplotlib.pyplot as plt … Read more
Here is an even simpler solution using base graphics and alpha-blending (which does not work on all graphics devices): set.seed(42) p1 <- hist(rnorm(500,4)) # centered at 4 p2 <- hist(rnorm(500,6)) # centered at 6 plot( p1, col=rgb(0,0,1,1/4), xlim=c(0,10)) # first histogram plot( p2, col=rgb(1,0,0,1/4), xlim=c(0,10), add=T) # second The key is that the colours are … Read more
Use matplotlib‘s calls that won’t block: Using draw(): from matplotlib.pyplot import plot, draw, show plot([1,2,3]) draw() print(‘continue computation’) # at the end call show to ensure window won’t close. show() Using interactive mode: from matplotlib.pyplot import plot, ion, show ion() # enables interactive mode plot([1,2,3]) # result shows immediatelly (implicit draw()) print(‘continue computation’) # at … Read more
Here you have a working example: import random import numpy from matplotlib import pyplot x = [random.gauss(3,1) for _ in range(400)] y = [random.gauss(4,2) for _ in range(400)] bins = numpy.linspace(-10, 10, 100) pyplot.hist(x, bins, alpha=0.5, label=”x”) pyplot.hist(y, bins, alpha=0.5, label=”y”) pyplot.legend(loc=”upper right”) pyplot.show()
There are two closely-related questions, and an answer for each. 1. An image will be generated in future in my script, how do I save it to disk? To save a plot, you need to do the following: Open a device, using png(), bmp(), pdf() or similar Plot your model Close the device using dev.off() … Read more
Another way is to use the subplots function and pass the width ratio with gridspec_kw matplotlib Tutorial: Customizing Figure Layouts Using GridSpec and Other Functions matplotlib.gridspec.GridSpec has available gridspect_kw options import numpy as np import matplotlib.pyplot as plt # generate some data x = np.arange(0, 10, 0.2) y = np.sin(x) # plot it f, (a0, … Read more
plt.subplots() is a function that returns a tuple containing a figure and axes object(s). Thus when using fig, ax = plt.subplots() you unpack this tuple into the variables fig and ax. Having fig is useful if you want to change figure-level attributes or save the figure as an image file later (e.g. with fig.savefig(‘yourfilename.png’)). You … Read more
Basically you have two options scale_x_continuous(limits = c(-5000, 5000)) or coord_cartesian(xlim = c(-5000, 5000)) Where the first removes all data points outside the given range and the second only adjusts the visible area. In most cases you would not see the difference, but if you fit anything to the data it would probably change the … Read more