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 "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=[0, 20000])
print "Generating ring-shaped pieces..."
rIndexes = range(len(freqs)-1)
unitAngle = 360.0/len(spectrum[0]) # depends on total time & sample rate
angles = numpy.arange(0, 360, unitAngle)
print len(spectrum[0])
print len(angles)
print len(freqs)
rings = []
for rIndex in rIndexes:
rInner = freqs[rIndex] + 8000
rOuter = freqs[rIndex+1] + 8000
print "Generating ring for region: %s Hz - %s Hz" % (rInner, rOuter)
print "-- Normalizing spectrum data with linear scale..."
data = map(scale, spectrum[:][rIndex])
ringSectors = []
for angleIndex, angle in enumerate(angles):
sector = ast.CircleSector(rOuter, unitAngle)
if rInner:
killer = ast.CircleSector(rInner, unitAngle)
sector = ast.Difference([sector, killer])
sector = ast.Rotate(unitAngle*angleIndex, (0,0,1), sector)
sector = ast.LinearExtrusion(data[angleIndex], sector)
ringSectors.append(sector)
ring = ast.Union(ringSectors)
rings.append(ring)
print "Generating unified geometry..."
union = ast.Union(rings)
print "Rendering..."
with open(outPath, "w") as f:
code = OpenScadBackend(union).generate()
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)
def test_linear_inverted_codomain(param, clamp, expected):
"""Test the linear scale with inverted codomain."""
domain = (1, 3)
codomain = (20, 10)
scale = scales.linear(domain, codomain, clamp)
assert scale(param) == expected
def test_linear(param, clamp, expected):
"""Test the linear scale."""
domain = (2, 4)
codomain = (10, 20)
scale = scales.linear(domain, codomain, clamp)
assert scale(param) == expected
# -*- coding: utf-8 -*-
from __future__ import print_function, division, absolute_import, unicode_literals
import csv
from tangible import scales
from tangible.shapes.vertical import SquareTower1D
from tangible.backends.openscad import OpenScadBackend
# Read data into list
datapoints = []
with open('analytics-sep-13.csv', 'r') as datafile:
reader = csv.DictReader(datafile)
for row in reader:
visits = int(row['Visits'])
datapoints.append(visits)
# Normalize data
scale = scales.linear([min(datapoints), max(datapoints)], [10, 50])
datapoints = [scale(p) for p in datapoints]
# Create shape
tower = SquareTower1D(datapoints, layer_height=10)
code = tower.render(backend=OpenScadBackend)
print(code)
# -*- coding: utf-8 -*-
from __future__ import print_function, division, absolute_import, unicode_literals
import csv
from tangible import scales
from tangible.shapes.bars import Bars1D
from tangible.backends.openscad import OpenScadBackend
# Read data into list
datapoints = []
with open('analytics-sep-13.csv', 'r') as datafile:
reader = csv.DictReader(datafile)
for row in reader:
visits = int(row['Visits'])
datapoints.append(visits)
# Normalize data
scale = scales.linear([min(datapoints), max(datapoints)], [10, 80])
datapoints = map(scale, datapoints)
# Create shape
bars1d = Bars1D(datapoints, bar_width=10, bar_depth=10)
code = bars1d.render(backend=OpenScadBackend)
print(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)
def test_linear_inverted_codomain(param, clamp, expected):
"""Test the linear scale with inverted codomain."""
domain = (1, 3)
codomain = (20, 10)
scale = scales.linear(domain, codomain, clamp)
assert scale(param) == expected
def test_linear(param, clamp, expected):
"""Test the linear scale."""
domain = (2, 4)
codomain = (10, 20)
scale = scales.linear(domain, codomain, clamp)
assert scale(param) == expected
