def main():
print("Reading and analyzing file...")
spectrum, freqs = ReadAndAnalyze(inPath)
print(len(freqs))
print(len(spectrum[0]))
print("Scaling logarithmically (kinda)...")
for i, s in enumerate(spectrum):
spectrum[i] = list(map(loggish, s))
print("Generating linear scale...")
scale = scales.linear(domain=[spectrum.min(),
spectrum.max()],
codomain=[1, 10])
print("Normalizing spectrum data...")
datapoints = list(map(lambda x: list(map(scale, x)), spectrum))
print("Trimming spectrum data post-normalization...")
for i, x in enumerate(datapoints):
for j, v in enumerate(x):
if v > 9:
datapoints[i][j] = 9
print("Generating bars...")
bars = BarsND(datapoints, bar_width=1, bar_depth=1)
print("Rendering...")
code = bars.render(backend=OpenScadBackend)
print("Saving to file...")
with open(outPath, "w") as f:
f.write(code)
def main():
print "Reading and analyzing file..."
spectrum, freqs = ReadAndAnalyze(inPath)
print len(freqs)
print len(spectrum[0])
print "Scaling logarithmically (kinda)..."
for i, s in enumerate(spectrum):
spectrum[i] = map(loggish, s)
print "Generating linear scale..."
scale = scales.linear(domain=[spectrum.min(), spectrum.max()],
codomain=[1, 10])
print "Normalizing spectrum data..."
datapoints = map(lambda x: map(scale, x), spectrum)
print "Trimming spectrum data post-normalization..."
for i, x in enumerate(datapoints):
for j, v in enumerate(x):
if v > 9:
datapoints[i][j] = 9
print "Generating bars..."
bars = BarsND(datapoints,
bar_width=1,
bar_depth=1)
print "Rendering..."
code = bars.render(backend=OpenScadBackend)
print "Saving to file..."
with open(outPath, "w") as f:
f.write(code)
import csv
from itertools import chain
from tangible import scales
from tangible.shapes.bars import BarsND
from tangible.backends.openscad import OpenScadBackend
# Read data into list
datapoints = [list() for i in xrange(9)]
with open('analytics-full-13.csv', 'r') as datafile:
reader = csv.DictReader(datafile)
for row in reader:
date = row['Day']
month = int(date.split('/', 1)[0])
visits = int(row['Visits'])
datapoints[month - 1].append(visits)
# Normalize data
all_datapoints = list(chain.from_iterable(datapoints))
scale = scales.linear([min(all_datapoints), max(all_datapoints)], [10, 150])
datapoints = map(lambda x: map(scale, x), datapoints)
# Create shape
bars = BarsND(datapoints, bar_width=7, bar_depth=7, center_layers=False)
code = bars.render(backend=OpenScadBackend)
print(code)
