![]() ![]() Mark_inset(ax, axins, loc1=2, loc2=4, fc="none", ec="0.Veusz can be used as a Python module for plotting data. # connecting lines between the bbox and the inset axes area ![]() # draw a bbox of the region of the inset axes in the parent axes and The simplest way is to combine "zoomed_inset_axes" and "mark_inset", whose description andįrom mpl_toolkits.axes_grid1.inset_locator import zoomed_inset_axesįrom mpl_toolkits.axes_grid1.inset_locator import mark_insetįrom matplotlib.cbook import get_sample_dataį = get_sample_data("axes_grid/bivariate_normal.npy", asfileobj=False)Īx.imshow(Z2, extent=extent, interpolation="nearest",Īxins = zoomed_inset_axes(ax, 6, loc=1) # zoom = 6Īxins.imshow(Z2, extent=extent, interpolation="nearest", So, the converted code becomes ax2 = plt.axes(, axisbg='y')Īnd you could use it in your code like this: from numpy import *ĭata = loadtxt(os.getcwd()+txtfl, skiprows=1)Īx2 = plt.axes(, axisbg='y') Reading example code can builds experience, but there is no easy shortcut. Googling the names of functions along with "matplotlib" can help. It takes experience to know where all theseįunctions come from. in front of bare names like axes and setp, but sometimes theįunction come from numpy, and sometimes the call should come from an axes So how do we convert the matplotlib example to run with import matplotlib.pyplot as plt?ĭoing the conversion takes some experience with matplotlib. So you are doing it the right way, (though I would recommend using import numpy as np instead of from numpy import * too). Is the recommended way to use matplotlib when writing scripts, whileįrom pylab import * is for use in interactive sessions. The matplotlib FAQ says import matplotlib.pyplot as plt Whereas you use import matplotlib.pyplot as plt. Now, in theory you have enough information to apply this code to your problem.īut there is one more potential stumbling block: The matplotlib example uses So now we know that setp(a, xticks=, yticks=) removes the tick marks and labels from the axes a. Oh! now there are lots of tick marks and tick labels on the inset axes.įine. So what happens if we just comment out the whole line by placing a # at the beginning of the line: # setp(a, xticks=, yticks=) Obviously generates the text above the inset.įinally we come to setp(a, xticks=, yticks=). You'll want to call semilogx(data,data) here. That this line has something to do with the image inside the inset. If not, changing the number 400 to, say, 10, will produce an image with a muchĬhunkier histogram, so again by playing with the numbers you'll soon figure out ![]() ![]() You might recognize this as the matplotlib command for drawing a histogram, but On to the next line we have: n, bins, patches = hist(s, 400, normed=1) The numbers go from 0 to 1 and (0,0) is the located at the Location of the lower left corner of the inset, and (.2. If you play some more with the numbers you'll figure out that (.35. So the axes function controls the placement of the inset. You'll find the "Probability" inset moved to the left. N, bins, patches = hist(s, 400, normed=1)Ĭopy the example code into a new file, called, say, test.py. We know how to generate data and plot the main plot, so let's focus on the third stanza: a = axes(, axisbg='y') The third and fourth stanzas create the inset axes. The first stanza generates some data, the second stanza generates the main plot, Given the comments in the code, it appears the code is broken up into 4 main stanzas. So let's start with the code from the matplotlib example gallery. ![]()
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