Changing plot scale by a factor in matplotlib

I am creating a plot in python. Is there a way to re-scale the axis by a factor? The yscale and xscale commands only allow me to turn log scale off.

Edit:
For example. If I have a plot where the x scales goes from 1 nm to 50 nm, the x scale will range from 1×10^(-9) to 50×10^(-9) and I want it to change from 1 to 50. Thus, I want the plot function to divide the x values placed on the plot by 10^(-9)

Answers:

Thank you for visiting the Q&A section on Magenaut. Please note that all the answers may not help you solve the issue immediately. So please treat them as advisements. If you found the post helpful (or not), leave a comment & I’ll get back to you as soon as possible.

Method 1

As you have noticed, xscale and yscale does not support a simple linear re-scaling (unfortunately). As an alternative to Hooked’s answer, instead of messing with the data, you can trick the labels like so:

ticks = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x*scale))
ax.xaxis.set_major_formatter(ticks)

A complete example showing both x and y scaling:

import numpy as np
import pylab as plt
import matplotlib.ticker as ticker

# Generate data
x = np.linspace(0, 1e-9)
y = 1e3*np.sin(2*np.pi*x/1e-9) # one period, 1k amplitude

# setup figures
fig = plt.figure()
ax1 = fig.add_subplot(121)
ax2 = fig.add_subplot(122)
# plot two identical plots
ax1.plot(x, y)
ax2.plot(x, y)

# Change only ax2
scale_x = 1e-9
scale_y = 1e3
ticks_x = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x/scale_x))
ax2.xaxis.set_major_formatter(ticks_x)

ticks_y = ticker.FuncFormatter(lambda x, pos: '{0:g}'.format(x/scale_y))
ax2.yaxis.set_major_formatter(ticks_y)

ax1.set_xlabel("meters")
ax1.set_ylabel('volt')
ax2.set_xlabel("nanometers")
ax2.set_ylabel('kilovolt')

plt.show()

And finally I have the credits for a picture:

Left: ax1 no scaling, right: ax2 y axis scaled to kilo and x axis scaled to nano

Note that, if you have text.usetex: true as I have, you may want to enclose the labels in $, like so: '${0:g}$'.

Method 2

Instead of changing the ticks, why not change the units instead? Make a separate array X of x-values whose units are in nm. This way, when you plot the data it is already in the correct format! Just make sure you add a xlabel to indicate the units (which should always be done anyways).

from pylab import *

# Generate random test data in your range
N = 200
epsilon = 10**(-9.0)
X = epsilon*(50*random(N) + 1)
Y = random(N)

# X2 now has the "units" of nanometers by scaling X
X2 = (1/epsilon) * X

subplot(121)
scatter(X,Y)
xlim(epsilon,50*epsilon)
xlabel("meters")

subplot(122)
scatter(X2,Y)
xlim(1, 50)
xlabel("nanometers")

show()

enter image description here

Method 3

To set the range of the x-axis, you can use set_xlim(left, right), here are the docs

Update:

It looks like you want an identical plot, but only change the ‘tick values’, you can do that by getting the tick values and then just changing them to whatever you want. So for your need it would be like this:

ticks = your_plot.get_xticks()*10**9
your_plot.set_xticklabels(ticks)


All methods was sourced from stackoverflow.com or stackexchange.com, is licensed under cc by-sa 2.5, cc by-sa 3.0 and cc by-sa 4.0

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