Creating subplots with equal axis scale, Python, matplotlib

Question

I am plotting seismological data and am creating a figure featuring 16 subplots of different depth slices. Each subplot displays the lat/lon of the epicenter and the color is scaled to its magnitude. I am trying to do two things:

1. Adjust the scale of all plots to equal the x and y min and max for the area selected. This will allow easy comparison across the plots. (so all plots would range from xmin to xmax etc)

2. adjust the magnitude colors so they also represent the scale (ie colors represent all available points not just the points on that specific sub plot)

I have seen this accomplished a number of ways but am struggling to apply them to the loop in my code. The data I am using is here: Data.

I posted my code and what the current output looks like below.

import matplotlib.pyplot as plt
import pandas as pd

eq_df = pd.read_csv(eq_csv)
eq_data = eq_df[['LON', 'LAT', 'DEPTH', 'MAG']]
nbound = max(eq_data.LAT)
sbound = min(eq_data.LAT)
ebound = max(eq_data.LON)
wbound = min(eq_data.LON)

xlimit = (wbound, ebound)
ylimit = (sbound, nbound)

magmin = min(eq_data.MAG)
magmax = max(eq_data.MAG)

for n in list(range(1,17)):
    km = eq_data[(eq_data.DEPTH > n - 1) & (eq_data.DEPTH <= n)]
    plt.subplot(4, 4, n)
    plt.scatter(km["LON"], km['LAT'], s = 10, c = km['MAG'], vmin = magmin, vmax = magmax) #added vmin/vmax to scale my magnitude data
    plt.ylim(sbound, nbound) # set y limits of plot
    plt.xlim(wbound, ebound) # set x limits of plot
    plt.tick_params(axis='both', which='major', labelsize= 6)
    plt.subplots_adjust(hspace = 1)
    plt.gca().set_title('Depth = ' + str(n - 1) +'km to ' + str(n) + 'km', size = 8) #set title of subplots
    plt.suptitle('Magnitude of Events at Different Depth Slices, 1950 to Today')
plt.show()

ETA: new code to resolve my issue

Answer 1

In response to this comment on the other answer, here is a demonstration of the use of sharex=True and sharey=True for this use case:

import matplotlib.pyplot as plt
import numpy as np

# Supply the limits since random data will be plotted
wbound = -0.1
ebound = 1.1
sbound = -0.1
nbound = 1.1

fig, axs = plt.subplots(nrows=4, ncols=4, figsize=(16,12), sharex=True, sharey=True)
plt.xlim(wbound, ebound)
plt.ylim(sbound, nbound)
for n, ax in enumerate(axs.flatten()):
    ax.scatter(np.random.random(20), np.random.random(20), 
               c = np.random.random(20), marker = '.')
    ticks = [n % 4 == 0, n > 12]
    ax.tick_params(left=ticks[0], bottom=ticks[1])
    ax.set_title('Depth = ' + str(n - 1) +'km to ' + str(n) + 'km', size = 12)

plt.suptitle('Magnitude of Events at Different Depth Slices, 1950 to Today', y = 0.95)
plt.subplots_adjust(wspace=0.05)
plt.show()

Explanation of a couple things:

1. I have reduced the horizontal spacing between subplots with subplots_adjust(wspace=0.05)
2. plt.suptitle does not need to be (and should not be) in the loop.
3. ticks = [n % 4 == 0, n > 12] creates a pair of bools for each axis which is then used to control which tick marks are drawn.
4. Left and bottom tick marks are controlled for each axis with ax.tick_params(left=ticks[0], bottom=ticks[1])
5. plt.xlim() and plt.ylim() need only be called once, before the loop