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draw_heatmap.py
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from PIL import Image
import sys
import math
import numpy
import json
# set boundaries in query_padmapper
from query_padmapper import MAX_LAT, MAX_LON, MIN_LAT, MIN_LON
# change these to change how detailed the generated image is
# (1000x1000 is good, but very slow)
MAX_X=1000
MAX_Y=1000
DRAW_DOTS=True
# at what distance should we stop making predictions?
IGNORE_DIST=0.01
def pixel_to_ll(x,y):
delta_lat = MAX_LAT-MIN_LAT
delta_lon = MAX_LON-MIN_LON
# x is lon, y is lat
# 0,0 is MIN_LON, MAX_LAT
x_frac = float(x)/MAX_X
y_frac = float(y)/MAX_Y
lon = MIN_LON + x_frac*delta_lon
lat = MAX_LAT - y_frac*delta_lat
calc_x, calc_y = ll_to_pixel(lat, lon)
if abs(calc_x-x) > 1 or abs(calc_y-y) > 1:
print("Mismatch: %s, %s => %s %s" % (
x,y, calc_x, calc_y))
return lat, lon
def ll_to_pixel(lat,lon):
adj_lat = lat-MIN_LAT
adj_lon = lon-MIN_LON
delta_lat = MAX_LAT-MIN_LAT
delta_lon = MAX_LON-MIN_LON
# x is lon, y is lat
# 0,0 is MIN_LON, MAX_LAT
lon_frac = adj_lon/delta_lon
lat_frac = adj_lat/delta_lat
x = int(lon_frac*MAX_X)
y = int((1-lat_frac)*MAX_Y)
return x,y
def load_prices(fs):
raw_prices = []
seen = set()
for f in fs:
with open(f) as inf:
for line in inf:
if not line[0].isdigit():
continue
rent, bedrooms, apt_id, lon, lat = line.strip().split()
if apt_id in seen:
continue
else:
seen.add(apt_id)
rent, bedrooms = int(rent), int(bedrooms)
assert bedrooms >= 0
rooms = bedrooms + 1
assert bedrooms >= 0
if rent / (bedrooms + 1) < 150:
continue
raw_prices.append((bedrooms, rent, float(lat), float(lon)))
slope, y_intercept = linear_regression([(bedrooms, rent) for (bedrooms, rent, lat, lon) in raw_prices])
print("slope = %s" % slope)
print("y intercept = %s" % y_intercept)
# For each point, use the linear regression to convert to an estimate of
# what a 2br would cost instead.
def est_2br(rent, bedrooms):
estimated_rent_for_this_size = y_intercept + slope*bedrooms
proportion_of_expected_rent = rent / estimated_rent_for_this_size
return proportion_of_expected_rent * (y_intercept + slope*2)
prices = [(est_2br(rent, bedrooms), lat, lon, bedrooms) for (bedrooms, rent, lat, lon) in raw_prices]
return prices, slope, y_intercept
def linear_regression(pairs):
xs = [x for (x,y) in pairs]
ys = [y for (x,y) in pairs]
A = numpy.array([xs, numpy.ones(len(xs))])
w = numpy.linalg.lstsq(A.T,ys)[0]
return w[0], w[1]
def distance_squared(x1,y1,x2,y2):
return (x1-x2)*(x1-x2) + (y1-y2)*(y1-y2)
magma_data = [[0.001462, 0.000466, 0.013866],
[0.002258, 0.001295, 0.018331],
[0.003279, 0.002305, 0.023708],
[0.004512, 0.003490, 0.029965],
[0.005950, 0.004843, 0.037130],
[0.007588, 0.006356, 0.044973],
[0.009426, 0.008022, 0.052844],
[0.011465, 0.009828, 0.060750],
[0.013708, 0.011771, 0.068667],
[0.016156, 0.013840, 0.076603],
[0.018815, 0.016026, 0.084584],
[0.021692, 0.018320, 0.092610],
[0.024792, 0.020715, 0.100676],
[0.028123, 0.023201, 0.108787],
[0.031696, 0.025765, 0.116965],
[0.035520, 0.028397, 0.125209],
[0.039608, 0.031090, 0.133515],
[0.043830, 0.033830, 0.141886],
[0.048062, 0.036607, 0.150327],
[0.052320, 0.039407, 0.158841],
[0.056615, 0.042160, 0.167446],
[0.060949, 0.044794, 0.176129],
[0.065330, 0.047318, 0.184892],
[0.069764, 0.049726, 0.193735],
[0.074257, 0.052017, 0.202660],
[0.078815, 0.054184, 0.211667],
[0.083446, 0.056225, 0.220755],
[0.088155, 0.058133, 0.229922],
[0.092949, 0.059904, 0.239164],
[0.097833, 0.061531, 0.248477],
[0.102815, 0.063010, 0.257854],
[0.107899, 0.064335, 0.267289],
[0.113094, 0.065492, 0.276784],
[0.118405, 0.066479, 0.286321],
[0.123833, 0.067295, 0.295879],
[0.129380, 0.067935, 0.305443],
[0.135053, 0.068391, 0.315000],
[0.140858, 0.068654, 0.324538],
[0.146785, 0.068738, 0.334011],
[0.152839, 0.068637, 0.343404],
[0.159018, 0.068354, 0.352688],
[0.165308, 0.067911, 0.361816],
[0.171713, 0.067305, 0.370771],
[0.178212, 0.066576, 0.379497],
[0.184801, 0.065732, 0.387973],
[0.191460, 0.064818, 0.396152],
[0.198177, 0.063862, 0.404009],
[0.204935, 0.062907, 0.411514],
[0.211718, 0.061992, 0.418647],
[0.218512, 0.061158, 0.425392],
[0.225302, 0.060445, 0.431742],
[0.232077, 0.059889, 0.437695],
[0.238826, 0.059517, 0.443256],
[0.245543, 0.059352, 0.448436],
[0.252220, 0.059415, 0.453248],
[0.258857, 0.059706, 0.457710],
[0.265447, 0.060237, 0.461840],
[0.271994, 0.060994, 0.465660],
[0.278493, 0.061978, 0.469190],
[0.284951, 0.063168, 0.472451],
[0.291366, 0.064553, 0.475462],
[0.297740, 0.066117, 0.478243],
[0.304081, 0.067835, 0.480812],
[0.310382, 0.069702, 0.483186],
[0.316654, 0.071690, 0.485380],
[0.322899, 0.073782, 0.487408],
[0.329114, 0.075972, 0.489287],
[0.335308, 0.078236, 0.491024],
[0.341482, 0.080564, 0.492631],
[0.347636, 0.082946, 0.494121],
[0.353773, 0.085373, 0.495501],
[0.359898, 0.087831, 0.496778],
[0.366012, 0.090314, 0.497960],
[0.372116, 0.092816, 0.499053],
[0.378211, 0.095332, 0.500067],
[0.384299, 0.097855, 0.501002],
[0.390384, 0.100379, 0.501864],
[0.396467, 0.102902, 0.502658],
[0.402548, 0.105420, 0.503386],
[0.408629, 0.107930, 0.504052],
[0.414709, 0.110431, 0.504662],
[0.420791, 0.112920, 0.505215],
[0.426877, 0.115395, 0.505714],
[0.432967, 0.117855, 0.506160],
[0.439062, 0.120298, 0.506555],
[0.445163, 0.122724, 0.506901],
[0.451271, 0.125132, 0.507198],
[0.457386, 0.127522, 0.507448],
[0.463508, 0.129893, 0.507652],
[0.469640, 0.132245, 0.507809],
[0.475780, 0.134577, 0.507921],
[0.481929, 0.136891, 0.507989],
[0.488088, 0.139186, 0.508011],
[0.494258, 0.141462, 0.507988],
[0.500438, 0.143719, 0.507920],
[0.506629, 0.145958, 0.507806],
[0.512831, 0.148179, 0.507648],
[0.519045, 0.150383, 0.507443],
[0.525270, 0.152569, 0.507192],
[0.531507, 0.154739, 0.506895],
[0.537755, 0.156894, 0.506551],
[0.544015, 0.159033, 0.506159],
[0.550287, 0.161158, 0.505719],
[0.556571, 0.163269, 0.505230],
[0.562866, 0.165368, 0.504692],
[0.569172, 0.167454, 0.504105],
[0.575490, 0.169530, 0.503466],
[0.581819, 0.171596, 0.502777],
[0.588158, 0.173652, 0.502035],
[0.594508, 0.175701, 0.501241],
[0.600868, 0.177743, 0.500394],
[0.607238, 0.179779, 0.499492],
[0.613617, 0.181811, 0.498536],
[0.620005, 0.183840, 0.497524],
[0.626401, 0.185867, 0.496456],
[0.632805, 0.187893, 0.495332],
[0.639216, 0.189921, 0.494150],
[0.645633, 0.191952, 0.492910],
[0.652056, 0.193986, 0.491611],
[0.658483, 0.196027, 0.490253],
[0.664915, 0.198075, 0.488836],
[0.671349, 0.200133, 0.487358],
[0.677786, 0.202203, 0.485819],
[0.684224, 0.204286, 0.484219],
[0.690661, 0.206384, 0.482558],
[0.697098, 0.208501, 0.480835],
[0.703532, 0.210638, 0.479049],
[0.709962, 0.212797, 0.477201],
[0.716387, 0.214982, 0.475290],
[0.722805, 0.217194, 0.473316],
[0.729216, 0.219437, 0.471279],
[0.735616, 0.221713, 0.469180],
[0.742004, 0.224025, 0.467018],
[0.748378, 0.226377, 0.464794],
[0.754737, 0.228772, 0.462509],
[0.761077, 0.231214, 0.460162],
[0.767398, 0.233705, 0.457755],
[0.773695, 0.236249, 0.455289],
[0.779968, 0.238851, 0.452765],
[0.786212, 0.241514, 0.450184],
[0.792427, 0.244242, 0.447543],
[0.798608, 0.247040, 0.444848],
[0.804752, 0.249911, 0.442102],
[0.810855, 0.252861, 0.439305],
[0.816914, 0.255895, 0.436461],
[0.822926, 0.259016, 0.433573],
[0.828886, 0.262229, 0.430644],
[0.834791, 0.265540, 0.427671],
[0.840636, 0.268953, 0.424666],
[0.846416, 0.272473, 0.421631],
[0.852126, 0.276106, 0.418573],
[0.857763, 0.279857, 0.415496],
[0.863320, 0.283729, 0.412403],
[0.868793, 0.287728, 0.409303],
[0.874176, 0.291859, 0.406205],
[0.879464, 0.296125, 0.403118],
[0.884651, 0.300530, 0.400047],
[0.889731, 0.305079, 0.397002],
[0.894700, 0.309773, 0.393995],
[0.899552, 0.314616, 0.391037],
[0.904281, 0.319610, 0.388137],
[0.908884, 0.324755, 0.385308],
[0.913354, 0.330052, 0.382563],
[0.917689, 0.335500, 0.379915],
[0.921884, 0.341098, 0.377376],
[0.925937, 0.346844, 0.374959],
[0.929845, 0.352734, 0.372677],
[0.933606, 0.358764, 0.370541],
[0.937221, 0.364929, 0.368567],
[0.940687, 0.371224, 0.366762],
[0.944006, 0.377643, 0.365136],
[0.947180, 0.384178, 0.363701],
[0.950210, 0.390820, 0.362468],
[0.953099, 0.397563, 0.361438],
[0.955849, 0.404400, 0.360619],
[0.958464, 0.411324, 0.360014],
[0.960949, 0.418323, 0.359630],
[0.963310, 0.425390, 0.359469],
[0.965549, 0.432519, 0.359529],
[0.967671, 0.439703, 0.359810],
[0.969680, 0.446936, 0.360311],
[0.971582, 0.454210, 0.361030],
[0.973381, 0.461520, 0.361965],
[0.975082, 0.468861, 0.363111],
[0.976690, 0.476226, 0.364466],
[0.978210, 0.483612, 0.366025],
[0.979645, 0.491014, 0.367783],
[0.981000, 0.498428, 0.369734],
[0.982279, 0.505851, 0.371874],
[0.983485, 0.513280, 0.374198],
[0.984622, 0.520713, 0.376698],
[0.985693, 0.528148, 0.379371],
[0.986700, 0.535582, 0.382210],
[0.987646, 0.543015, 0.385210],
[0.988533, 0.550446, 0.388365],
[0.989363, 0.557873, 0.391671],
[0.990138, 0.565296, 0.395122],
[0.990871, 0.572706, 0.398714],
[0.991558, 0.580107, 0.402441],
[0.992196, 0.587502, 0.406299],
[0.992785, 0.594891, 0.410283],
[0.993326, 0.602275, 0.414390],
[0.993834, 0.609644, 0.418613],
[0.994309, 0.616999, 0.422950],
[0.994738, 0.624350, 0.427397],
[0.995122, 0.631696, 0.431951],
[0.995480, 0.639027, 0.436607],
[0.995810, 0.646344, 0.441361],
[0.996096, 0.653659, 0.446213],
[0.996341, 0.660969, 0.451160],
[0.996580, 0.668256, 0.456192],
[0.996775, 0.675541, 0.461314],
[0.996925, 0.682828, 0.466526],
[0.997077, 0.690088, 0.471811],
[0.997186, 0.697349, 0.477182],
[0.997254, 0.704611, 0.482635],
[0.997325, 0.711848, 0.488154],
[0.997351, 0.719089, 0.493755],
[0.997351, 0.726324, 0.499428],
[0.997341, 0.733545, 0.505167],
[0.997285, 0.740772, 0.510983],
[0.997228, 0.747981, 0.516859],
[0.997138, 0.755190, 0.522806],
[0.997019, 0.762398, 0.528821],
[0.996898, 0.769591, 0.534892],
[0.996727, 0.776795, 0.541039],
[0.996571, 0.783977, 0.547233],
[0.996369, 0.791167, 0.553499],
[0.996162, 0.798348, 0.559820],
[0.995932, 0.805527, 0.566202],
[0.995680, 0.812706, 0.572645],
[0.995424, 0.819875, 0.579140],
[0.995131, 0.827052, 0.585701],
[0.994851, 0.834213, 0.592307],
[0.994524, 0.841387, 0.598983],
[0.994222, 0.848540, 0.605696],
[0.993866, 0.855711, 0.612482],
[0.993545, 0.862859, 0.619299],
[0.993170, 0.870024, 0.626189],
[0.992831, 0.877168, 0.633109],
[0.992440, 0.884330, 0.640099],
[0.992089, 0.891470, 0.647116],
[0.991688, 0.898627, 0.654202],
[0.991332, 0.905763, 0.661309],
[0.990930, 0.912915, 0.668481],
[0.990570, 0.920049, 0.675675],
[0.990175, 0.927196, 0.682926],
[0.989815, 0.934329, 0.690198],
[0.989434, 0.941470, 0.697519],
[0.989077, 0.948604, 0.704863],
[0.988717, 0.955742, 0.712242],
[0.988367, 0.962878, 0.719649],
[0.988033, 0.970012, 0.727077],
[0.987691, 0.977154, 0.734536],
[0.987387, 0.984288, 0.742002],
[0.987053, 0.991438, 0.749504]]
# costs of 2br units
buckets = [
6000,
5000,
4500,
4000,
3800,
3600,
3400,
3200,
3000,
2800,
2600,
2400,
2200,
2100,
2000,
1900,
1800,
1700]
colors = []
n_colors = len(buckets) + 1
for i in range(n_colors):
r, g, b = magma_data[int((len(magma_data)-1) * (float(i)/n_colors))]
colors.append((int(r*256), int(g*256), int(b*256)))
def color(val, buckets):
if val is None:
return (255,255,255,0)
assert len(colors) - 1 == len(buckets)
for price, color in zip(buckets, colors):
if val > price:
return color
return colors[-1]
gaussian_variance = IGNORE_DIST/2
gaussian_a = 1 / (gaussian_variance * math.sqrt(2 * math.pi))
gaussian_negative_inverse_twice_variance_squared = -1 / (2 * gaussian_variance * gaussian_variance)
def gaussian(prices, lat, lon, ignore=None):
num = 0
dnm = 0
c = 0
for price, plat, plon, _ in prices:
if ignore:
ilat, ilon = ignore
if distance_squared(plat, plon, ilat, ilon) < 0.0001:
continue
weight = gaussian_a * math.exp(distance_squared(lat,lon,plat,plon) *
gaussian_negative_inverse_twice_variance_squared)
num += price * weight
dnm += weight
if weight > 2:
c += 1
# don't display any averages that don't take into account at least five data points with significant weight
if c < 5:
return None
return num/dnm
def start(fname):
print("loading data...")
priced_points, slope, y_intercept = load_prices([fname])
print("computing #bedroom adjustments...")
# compute what the error would be at each data point if we priced it without being able to take it into account
# do this on a per-bedroom basis, so that we can compute correction factors
bedroom_categories = list(sorted(set(bedrooms for _, _, _, bedrooms in priced_points)))
adjustments = {}
for bedroom_category in bedroom_categories:
print(" %sbr..." % (bedroom_category))
total_actual = 0
total_predicted = 0
for i, (price, plat, plon, bedroom) in enumerate(priced_points):
if bedroom != bedroom_category:
continue
x, y = ll_to_pixel(plat, plon)
predicted_price = gaussian(priced_points, plat, plon, ignore=(plat, plon))
if predicted_price:
total_actual += price
total_predicted += predicted_price
if total_predicted == 0:
# we might not make any predictions, if we don't have enough data
adjustment = 1.0
else:
adjustment = total_actual / total_predicted
adjustments[bedroom_category] = adjustment
print("pricing all the points...")
prices = {}
for x in range(MAX_X):
print(" %s/%s" % (x, MAX_X))
for y in range(MAX_Y):
lat, lon = pixel_to_ll(x,y)
prices[x,y] = gaussian(priced_points, lat, lon)
# color regions by price
I = Image.new('RGBA', (MAX_X, MAX_Y))
IM = I.load()
for x in range(MAX_X):
for y in range(MAX_Y):
IM[x,y] = color(prices[x,y], buckets)
if DRAW_DOTS:
for _, lat, lon, _ in priced_points:
x, y = ll_to_pixel(lat, lon)
if 0 <= x < MAX_X and 0 <= y < MAX_Y:
IM[x,y] = (0,0,0)
out_fname = fname + ".phantom." + str(MAX_X)
I.save(out_fname + ".png", "PNG")
with open(out_fname + ".metadata.json", "w") as outf:
outf.write(json.dumps({
"slope": slope,
"y_intercept": y_intercept,
"buckets": buckets,
"n": len(priced_points),
"adjustments": adjustments}))
if __name__ == "__main__":
if len(sys.argv) != 2:
print("usage: python draw_heatmap.py apts.txt")
else:
fname = sys.argv[1]
start(fname)