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interpolate.py
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interpolate.py
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import sys
import argparse
import requests
import json
import numpy as np
from math import floor, ceil
import scipy.interpolate
import pykrige
# data format
# ['timestamp', 'ID', 'age', 'pm_0', 'pm_1', 'pm_2', 'pm_3', 'pm_4', 'pm_5', 'pm_6', 'conf', 'Type', 'Label', 'Lat', 'Lon', 'isOwner', 'Flags', 'CH']
def aqiFromPM(pm):
if pm < 0:
raise ValueError('pm must be > 0: '+str(pm))
# wildfires have provent this to be wrong
# if pm > 1200:
# raise ValueError('pm must be < 1200: '+str(pm))
# Good 0 - 50 0.0 - 15.0 0.0 – 12.0
# Moderate 51 - 100 >15.0 - 40 12.1 – 35.4
# Unhealthy for Sensitive Groups 101 – 150 >40 – 65 35.5 – 55.4
# Unhealthy 151 – 200 > 65 – 150 55.5 – 150.4
# Very Unhealthy 201 – 300 > 150 – 250 150.5 – 250.4
# Hazardous 301 – 400 > 250 – 350 250.5 – 350.4
# Hazardous 401 – 500 > 350 – 500 350.5 – 500
if pm > 350.5:
return calculateAQI(pm, 500, 401, 500, 350.5)
elif pm > 250.5:
return calculateAQI(pm, 400, 301, 350.4, 250.5)
elif pm > 150.5:
return calculateAQI(pm, 300, 201, 250.4, 150.5)
elif pm > 55.5:
return calculateAQI(pm, 200, 151, 150.4, 55.5)
elif pm > 35.5:
return calculateAQI(pm, 150, 101, 55.4, 35.5)
elif pm > 12.1:
return calculateAQI(pm, 100, 51, 35.4, 12.1)
else:
return calculateAQI(pm, 50, 0, 12, 0)
def calculateAQI(Cp, Ih, Il, BPh, BPl):
a = Ih - Il
b = BPh - BPl
c = Cp - BPl
return round((a/b) * c + Il)
class AQIInterpolator():
def __init__(self,box,mesh_size=100,resolution=None):
self.box = box
self.values = {}
self.mesh_size = mesh_size
self.resolution = resolution
# TODO: only for northern / western hemisphere?
self.lat_size = abs(box[0]-box[2])
self.lon_size = abs(box[1]-box[3])
# calculate the resolution from the mesh size
if self.resolution is None:
# compute the resolution based on the largest dimension of the box
max = self.lat_size if self.lat_size > self.lon_size else self.lon_size
self.resolution = max / self.mesh_size
self.lat_grid_size = ceil( self.lat_size / self.resolution )
self.lon_grid_size = ceil( self.lon_size / self.resolution )
def add(self,lat,lon,aqi):
if lat > self.box[0] or lat < self.box[2] or lon < self.box[1] or lon > self.box[3]:
return False
lat_pos = int(floor(abs((self.box[0] - lat) / self.resolution)))
lon_pos = int(floor(abs((self.box[1] - lon) / self.resolution)))
pos = (lat_pos,lon_pos)
if pos in self.values:
N, current_aqi = self.values[pos]
factor = float(N)/float(N+1)
current_aqi = list(map(lambda v : int(round(v[0]*factor + v[1]/float(N+1))), zip(current_aqi, aqi) ))
self.values[pos] = (N+1,current_aqi)
else:
self.values[pos] = (1,aqi)
return True
def aqi_estimator(self,index=2,method='nearest'):
points = list(self.values.keys())
values = [self.values[p][1][index] for p in points]
def f(x,y):
return scipy.interpolate.griddata(points,values,(x,y), method=method, fill_value=0)
return f
def generate_grid(self,index=2,method='nearest'):
if method.startswith('krige-'):
return self.generate_krige_grid(index=index,method=method[6:])
f = self.aqi_estimator(index=index,method=method)
grid = np.fromfunction(f,(self.lat_grid_size,self.lon_grid_size),dtype=int)
return grid
def generate_krige_grid(self,index=2,method='linear'):
x = [p[0] for p in self.values.keys()]
y = [p[1] for p in self.values.keys()]
z = [self.values[(x[pos],y[pos])][1][index] for pos in range(len(x))]
krige = pykrige.ok.OrdinaryKriging(x,y,z,variogram_model=method)
mesh_x = [float(pos) for pos in range(self.lat_grid_size)]
mesh_y = [float(pos) for pos in range(self.lon_grid_size)]
grid, sigmasq = krige.execute('grid',mesh_x,mesh_y)
return grid.data.T
def plot_grid(grid,colormap=None):
import matplotlib.pyplot as plt
plt.imshow(grid,cmap=plt.get_cmap(colormap) if colormap is not None else None)
plt.show()
def loader(box,urls,mesh_size=100,resolution=None,verbose=False):
interpolator = AQIInterpolator(box,mesh_size=mesh_size,resolution=resolution)
for url in urls:
resp = requests.get(url)
if resp.status_code==200:
data = resp.json()
headers = data[0]
count = 0
for row in data[1:]:
if row[2]<30 and row[11]==0:
aqi = list(map(lambda v : aqiFromPM(float(v)) if v is not None else 0,row[3:7]))
if row[13] is None or row[14] is None:
if verbose:
print('Ignoring: '+(','.join(map(str,[row[1],row[11],row[12],row[13],row[14]]))))
continue
if interpolator.add(float(row[13]),float(row[14]),aqi):
count += 1
if verbose:
print('Count: '+str(count))
else:
raise ValueError('Cannot load {}, status {}'.format(url,str(resp.status_code)))
return interpolator
_box_tests = {
# Bay Area: 38.41646632263371,-124.02669995117195,36.98663820370443,-120.12930004882817
'bayarea' : [38.41646632263371,-124.02669995117195,36.98663820370443,-120.12930004882817],
# San Francisco: 37.80888750820881,-122.57097888976305,37.719593811785046,-122.32739139586647
'sf' : [37.80888750820881,-122.57097888976305,37.719593811785046,-122.32739139586647]
}
if __name__ == '__main__':
argparser = argparse.ArgumentParser(description='interpolate-aq')
argparser.add_argument('--verbose',help='Verbose output',action='store_true',default=False)
argparser.add_argument('--size',help='The grid mesh size (integer)',type=int,default=100)
argparser.add_argument('--resolution',help='The grid resolution (float)',type=float)
argparser.add_argument('--index',help='The pm measurement to use',type=int,default=2)
argparser.add_argument('--method',help='The interpolation method',choices=['linear','cubic','nearest','krige-linear', 'krige-power', 'krige-gaussian', 'krige-spherical', 'krige-exponential', 'krige-hole-effect'],default='linear')
argparser.add_argument('--bounding-box',help='The bounding box (nwlat,nwlon,selat,selon)',default='37.80888750820881,-122.57097888976305,37.719593811785046,-122.32739139586647')
argparser.add_argument('urls',help='The urls',nargs='+')
args = argparser.parse_args()
box = list(map(float,args.bounding_box.split(',')))
if args.verbose:
print('Bounding box: '+(','.join(map(str,box))))
interpolator = loader(box,args.urls,mesh_size=args.size,resolution=args.resolution)
grid = interpolator.generate_grid(method=args.method,index=args.index)
for lat_pos in range(len(grid)):
for lon_pos in range(len(grid[lat_pos])):
sys.stdout.write(' ')
sys.stdout.write(str(round(grid[lat_pos][lon_pos])))
sys.stdout.write('\n')