Quick hack to explore creating Joy Division style album cover from LIDAR data

bath-unchained.py

#Bath Unchained
#
#based on original: http://matplotlib.org/examples/animation/unchained.html
#
#pass the script a path to a ASC file, e.g after unpacking the EA data I ran
#
#python bath-unchained.py LIDAR-DSM-1M-ST76/st7465_DSM_1m.asc
#
#output: https://twitter.com/ldodds/status/661279776292929536
import sys
import numpy as np

import matplotlib.pyplot as plt
import matplotlib.animation as animation
from osgeo import gdal

# Create new Figure with black background
fig = plt.figure(figsize=(8, 8), facecolor='black')

# Add a subplot with no frame
ax = plt.subplot(111, frameon=False)

# Read LIDAR data
file = sys.argv[1]
layer = gdal.Open(file)
gt = layer.GetGeoTransform()
bands = layer.RasterCount

data = []

for i in range(500, 1000, 8):
	lidar_values = layer.GetRasterBand(1).ReadAsArray(i, 500, 1, 500)
	flattened = np.array( [item for sublist in lidar_values for item in sublist] )
	flattened[flattened < 0] = 0
	data.append( flattened )

#normalise
data = np.array(data)
#this normalises to zero
#data = data / data.max(axis=0)
#this looks better for st7465_DSM_1m.asc
data = data * 0.05

X = np.linspace(-1, 1, data.shape[-1])
G = 1.5 * np.exp(-4 * X * X)

#plot lines
for i in range(len(data)):
    # Small reduction of the X extents to get a cheap perspective effect
    xscale = 1 - i / 200.
    # Same for linewidth (thicker strokes on bottom)
    lw = 1.5 - i / 100.0
    #print data[i]
    line, = ax.plot(xscale * X, i+G+data[i], color="w", lw=lw)

# Set y limit (or first line is cropped because of thickness)
ax.set_ylim(-1, 70)

# No ticks
ax.set_xticks([])
ax.set_yticks([])

# 2 part titles to get different font weights
ax.text(0.5, 1.0, "BATH LIDAR ", transform=ax.transAxes,
        ha="right", va="bottom", color="w",
        family="sans-serif", fontweight="light", fontsize=16)
ax.text(0.5, 1.0, "UNCHAINED", transform=ax.transAxes,
        ha="left", va="bottom", color="w",
        family="sans-serif", fontweight="bold", fontsize=16)

plt.draw()
plt.savefig('bath-unchained')
plt.show()