Plot 4d Data As Layered Heatmaps In Python

I would like to create layered heatmaps using (x,y,z) coordinates and a fourth dimension, color-based, to correlate to intensity. Each layer-related data sits in a text file with

Solution 1:

Say you have two txt files, namely data-z600.txt and data-z1200.txt, in the same folder as your python script, whose contents are exactly

data-z600.txt (yours)

XAYAZAGA20006001.2760006001.541200 06001.491800 06001.342400 06001.273000 06001.252006006001.286006006001.961200 6006001.121800 6006001.062400 6006001.063000 6006001.06

and data-z1200.txt (invented on purpose)

XAYAZAGA20001200 1.3160001200 21200 01200 1.631800 01200 1.362400 01200 1.313000 01200 1.352006001200 1.386006001200 1.361200 6001200 1.21800 6001200 1.12400 6001200 1.13000 6001200 1.11

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Let's import all the required libraries

# librariesfrom mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import scipy.interpolate as si
from matplotlib import cm
import pandas as pd
import numpy as np

and define grids_maker, a function that does the job of preparing data contained in a given file, here targeted via the filepath argument.

defgrids_maker(filepath):
    # Get the data
    df = pd.read_csv(filepath, sep=' ')

    # Make things more legible
    xy = df[['XA', 'YA']]
    x  = xy.XA
    y  = xy.YA
    z  = df.ZA
    g  = df.GA
    reso_x = reso_y = 50
    interp = 'cubic'# or 'nearest' or 'linear'# Convert the 4d-space's dimensions into grids
    grid_x, grid_y = np.mgrid[
        x.min():x.max():1j*reso_x,
        y.min():y.max():1j*reso_y
    ]

    grid_z = si.griddata(
        xy, z.values,
        (grid_x, grid_y),
        method=interp
    )

    grid_g = si.griddata(
        xy, g.values,
        (grid_x, grid_y),
        method=interp
    )

    return {
        'x' : grid_x,
        'y' : grid_y,
        'z' : grid_z,
        'g' : grid_g,
    }

Let's use grids_maker over our list of files and get the extrema of each file's 4th dimension.

# Let's retrieve all files' contents
fgrids = dict.fromkeys([
    'data-z600.txt',
    'data-z1200.txt'
])
g_mins = []
g_maxs = []

for fpath in fgrids.keys():
    fgrids[fpath] = grids = grids_maker(fpath)
    g_mins.append(grids['g'].min())
    g_maxs.append(grids['g'].max())

Let's create our (all-file unifying) color-scale

# Create the 4th color-rendered dimension
scam = plt.cm.ScalarMappable(
    norm=cm.colors.Normalize(min(g_mins), max(g_maxs)),
    cmap='jet'# see https://matplotlib.org/examples/color/colormaps_reference.html
)

... and finally make/show the plot

# Make the plot
fig = plt.figure()
ax  = fig.gca(projection='3d')
for grids in fgrids.values(): 
    scam.set_array([])   
    ax.plot_surface(
        grids['x'], grids['y'], grids['z'],
        facecolors  = scam.to_rgba(grids['g']),
        antialiased = True,
        rstride=1, cstride=1, alpha=None
    )
plt.show()