Proper automatic logarithmic axes when plot range less than a factor 10 (Matplotlib)-CodePudding

The

Using the suggestions from

I am aware that I could edit the tick labels by hand, but I am looking for a way to produce proper tick labels automatically for many figures, with different axis ranges.

The tick marks labels I am looking for are like the ones that Mathematica nicely generates in the same situation as follows. Is it possible to achieve something similar in Matplotlib?

To clarify my question, as a practical test, could one design a function that automatically produces proper tick labels when running my above procedure with either of the following three vectors x:

x = np.linspace(0.2, 8, 100) x = np.linspace(3, 60, 100) x = np.linspace(0.06, 0.5, 100)

NB: editing the tick labels by typing different values for each different case is not a useful solution, as my function has to run in a general procedure to be used with unknown x range.

CodePudding user response：

To reproduce something close to the mathematica's plot, you need to turn off the labeling of the minor ticks, i.e. the parts that are messed up.

Then you want to have a custom labeling of the major ticks. This can be done using

** First update:

For automatic spacing, I would recommend just employing the matplotlib original major_formatter that gives x-label only at points of which the log10 is an integer, i.e.:

import matplotlib.pyplot as plt from matplotlib import ticker import numpy as np fig, ax = plt.subplots() x = np.linspace(0.2, 8, 100) plt.semilogx(x, np.sin(x)) formatter = ticker.FuncFormatter(lambda y, _: f'{y:.3g}') ax.xaxis.set_major_formatter(formatter)

This seems to give ideal labeling spacings for every possible range of x for plotting purpose, irrespective of figure size, number of zeros, etc.

For your case of x = np.linspace(0.2, 8, 100), it gives

On the other extreme, if x = np.linspace(0.000002, 800, 100):

After such plot generation, one can always use FixedLocator if more xlabels (like 0,2, 0.5, 2, 5, 20, 50, ..) are needed/modified.

** Second update:

For the three cases that only span two orders of magnitude, one can automatically output the xticks (major ticks/the xlabels locations) with a xticksfinder function, without worrying too much about the number of zeros and figure size (which will lead to xlabels overlapping). As a general rule of thumb, one would want to specify {1, 2, 5} times 10^n, where n is the order of magnitude. Then the following xticksfinder function would be helpful

def order(num): return math.floor(math.log(num, 10)) def xticksfinder(x): x = x.tolist() xtickers = [] for i in range(order(x[0]), order(x[-1]) 1): xtickers.append(1*math.pow(10, i)) xtickers.append(2*math.pow(10, i)) xtickers.append(5*math.pow(10, i)) xticks = [x[0]] [item for item in xtickers if x[0] < item <= x[-1]] xticks = [int(k) if k >=1 else k for k in xticks] return xticks

Then you can call it in FixedLocator:

import matplotlib.pyplot as plt from matplotlib import ticker import numpy as np fig, ax = plt.subplots() x = np.linspace(0.2, 8, 100) plt.semilogx(x, np.sin(x)) formatter = ticker.FuncFormatter(lambda y, _: f'{y:.3g}') ax.xaxis.set_major_formatter(formatter) #ax.xaxis.set_minor_formatter(formatter) #comment this line out ax.xaxis.set_major_locator(ticker.FixedLocator(xticksfinder(x)))

For x = np.linspace(0.2, 8, 100), the output is:

For x = np.linspace(3, 60, 100), the output is:

For x = np.linspace(0.06, 0.5, 100), the output is: