I think matplotlib is poorly documented and too object-oriented to be immediately usable. Trying to be too many things at once. Besides, the default values and default behaviour is kooky sometimes.
For instance, histogram bar widths: they are different for different datasets, and you cannot compare two distributions if that's the case. You have to resort to hacks like this:
So, what makes an ugly plot look decent? First of all, ticks:
Some parameters, redefining matplotlib's defaults (these plots are for printouts, so font sizes are big):
I would not claim the plots shown are publication-quality (just look at slightly differing histogram widths). But they look way better than default plots one would get with matplotlib.
For instance, histogram bar widths: they are different for different datasets, and you cannot compare two distributions if that's the case. You have to resort to hacks like this:
hist, bins = np.histogram(data, bins = 10)
width=1*(bins[1]-bins[0])
And I think it's a bloody hack, calling histogram method from some other module in order to be able to call matplotlib's histogram plotting routine.
Nevertheless. It's flexible and, since I already use Python to pull my data from databases, I decided to give it a try when I had to prepare some plots for a review poster. So, what makes an ugly plot look decent? First of all, ticks:
minor_locator = plt.MultipleLocator(plotTitles.yMinorTicks)
Ymajor_locator = plt.MultipleLocator(plotTitles.yMajorTicks)
major_locator = plt.MultipleLocator(plotTitles.xMajorTicks)
Xminor_locator = plt.MultipleLocator(plotTitles.xMinorTicks)
ax.xaxis.set_major_locator(major_locator)
ax.xaxis.set_minor_locator(Xminor_locator)
ax.yaxis.set_major_locator(Ymajor_locator)
ax.yaxis.set_minor_locator(minor_locator)
They have to be set separately for each axis, I pass them as parameters from a wrapper class.
Then, hatched bars. Some parameters, redefining matplotlib's defaults (these plots are for printouts, so font sizes are big):
params = {'backend': 'ps',
'axes.labelsize': 10,
'text.fontsize': 10,
'legend.fontsize': 10,
'xtick.labelsize': 8,
'ytick.labelsize': 8,
'text.usetex': True,
'font': 'serif',
'font.size': 16,
'ylabel.fontsize': 20}
If you'd like to have different symbols (markers) in a scatterplot, this is useful:
markers = itertools.cycle('.^*')
p1 = ax.plot(gd.data[0], gd.data[1], linestyle='none', marker=markers.next(), markersize=8, color=gd.colour, mec=gd.colour, alpha = 1)
But the double symbols in the legend, why is that? No sense.I would not claim the plots shown are publication-quality (just look at slightly differing histogram widths). But they look way better than default plots one would get with matplotlib.
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