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colours = {
 'Alkali-feldpar syenite': (244, 60, 108),
 'Alkali-feldspar granite': (255, 209, 220),
 'Alkali-feldspar rhyolite': (254, 220, 126),
 'Alkali-feldspar trachyte': (254, 183, 134),
 'Alkalic intrusive rock': (255, 111, 145),
 'Alkalic volcanic rock': (194, 65, 0),
 'Alkaline basalt': (169, 101, 55),
 'Alluvial fan': (255, 255, 183),
 'Alluvial terrace': (250, 238, 122),

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"""
Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
BSD License
"""
import numpy as np

# data I/O
data = open('input.txt', 'r').read() # should be simple plain text file
chars = list(set(data))
data_size, vocab_size = len(data), len(chars)

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import numpy as np
import matplotlib.pyplot as plt
from matplotlib.patches import Rectangle
import requests
from io import StringIO
from welly import Well

# Fetch LAS file.
url = "https://dropbox.com/s/n52qezp5byap4mi/WellA.las?raw=1"
r = requests.get(url)

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mpl2qgis

See script below.

Make sure the Python file is executable. Then:

$ ./mpl2qgis.py viridis bone

This writes a file colourmaps.xml. Result:

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Ripping data from pseudocolour images

Because viridis, like all good colourmaps, is perceptually linear, it's easy to get the data from it: just use a greyscale version of the image. But you can rip the data from any pseudocolour image if you know (or can guess) the colourmap.

In the rip-data.py example, here's the approach:

1. Read the image and transform the values to the range 0-1.
2. Guess the colourmap, in this case it's viridis. Matplotlib conveniently gives us the RGB triples that make up a colourmap.

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import numpy as np

arr = np.random.randint(0, 256, (200, 200), dtype=np.uint8)

def func(arr1d):
    kernel = np.ones(3) / 3
    return np.convolve(arr1d, kernel, mode='same')

first_pass = np.apply_along_axis(func, axis=0, arr=arr)
final_result = np.apply_along_axis(func, axis=1, arr=first_pass)

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import numpy as np
import scipy.signal as ss


def to_volume(points, max_mb=10):
    """
    Convert N x 3 array of points in a point cloud to a 3D image
    or 'volume'. The degree of upscaling is controlled by ``max_mb``
    which is the target size of the 3D image in memory.
    

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import io
import requests
import numpy as np
from PIL import Image
import matplotlib.pyplot as plt
from matplotlib.colors import LinearSegmentedColormap as LSC
from scipy.interpolate import Rbf

class HolidayCard():
    """A holiday card class."""