def makemap(dataloc):
"""
This function is not used in the web app. I need to move it to its own
file. It is used to generate the initial map of rides per day.
"""
# Generate a sub grid of latitudes and longitudes
latvec, longvec = loadutil.grid()
nlat = len(latvec)
nlong = len(longvec)
# get the mask
mask = loadutil.getmask(dataloc)
#import matplotlib.pyplot as plt
#extent = [longvec.min(), longvec.max(), latvec.min(), latvec.max()]
#plt.imshow(mask, extent=extent, origin='lower')
##plt.scatter(biglong, biglat)
#plt.show()
# load the data
loaddata = loadutil.load(dataloc)
popemp = loaddata[0]
mbta = loaddata[1]
station = loaddata[2]
zipscale = loaddata[3]
stationscale = loaddata[4]
subwayscale = loaddata[5]
stationfeatures = loaddata[6]
nrides = []
latlist = []
longlist = []
for i in range(nlat):
ilat = latvec[i]
for j in range(nlong):
ilong = longvec[j]
#print("we're actually doing something!")
iride = getride(ilat, ilong, popemp, mbta, station, zipscale,
stationscale, subwayscale, stationfeatures,
dataloc)
if iride > 10:
print(i, j, ilat, ilong, iride)
nrides.append(iride)
latlist.append(ilat)
longlist.append(ilong)
nrides = np.array(nrides)
nrides *= (1 - mask)
nrides = list(nrides)
ridedatadict = {
'nrides': nrides,
'latitude': latlist,
'longitude': longlist
}
ridedata = pd.DataFrame(ridedatadict)
ridedata.to_csv(dataloc + 'nridesmap.csv')
def makemap(dataloc):
"""
This function is not used in the web app. I need to move it to its own
file. It is used to generate the initial map of rides per day.
"""
# Generate a sub grid of latitudes and longitudes
latvec, longvec = loadutil.grid()
nlat = len(latvec)
nlong = len(longvec)
# get the mask
mask = loadutil.getmask(dataloc)
#import matplotlib.pyplot as plt
#extent = [longvec.min(), longvec.max(), latvec.min(), latvec.max()]
#plt.imshow(mask, extent=extent, origin='lower')
##plt.scatter(biglong, biglat)
#plt.show()
# load the data
loaddata = loadutil.load(dataloc)
popemp = loaddata[0]
mbta = loaddata[1]
station = loaddata[2]
zipscale = loaddata[3]
stationscale = loaddata[4]
subwayscale = loaddata[5]
stationfeatures = loaddata[6]
nrides = []
latlist = []
longlist = []
for i in range(nlat):
ilat = latvec[i]
for j in range(nlong):
ilong = longvec[j]
#print("we're actually doing something!")
iride = getride(ilat, ilong, popemp, mbta, station, zipscale,
stationscale, subwayscale, stationfeatures, dataloc)
if iride > 10:
print(i, j, ilat, ilong, iride)
nrides.append(iride)
latlist.append(ilat)
longlist.append(ilong)
nrides = np.array(nrides)
nrides *= (1 - mask)
nrides = list(nrides)
ridedatadict = {'nrides': nrides, 'latitude': latlist, 'longitude':
longlist}
ridedata = pd.DataFrame(ridedatadict)
ridedata.to_csv(dataloc + 'nridesmap.csv')
def remakemap(ilat, ilong, dataloc):
"""
Remake the map for a subgrid of 10x10 cells, since after adding a single
station the impact on the predicted daily rides should be localalized.
"""
# Generate a sub grid of latitudes and longitudes
latvec, longvec = loadutil.grid()
# Generate a sub grid of latitudes and longitudes
sublatvec, sublongvec = loadutil.subgrid(ilat, ilong, nsub=10)
# load the data
loaddata = loadutil.load(dataloc)
popemp = loaddata[0]
mbta = loaddata[1]
station = loaddata[2]
zipscale = loaddata[3]
stationscale = loaddata[4]
subwayscale = loaddata[5]
stationfeatures = loaddata[6]
# read in ride map from previous iteration
nrides = pd.read_csv(dataloc + 'nridesmap.csv')
# monitor how long this recomputing the subgrid takes
import time
currenttime = time.time()
ngrid = len(nrides)
for i in range(ngrid):
ilat = nrides['latitude'][i]
ilong = nrides['longitude'][i]
# skip this latitude if it isn't present in the subgrid
if ilat < sublatvec.min():
continue
if ilat > sublatvec.max():
continue
# skip this longitude if it isn't present in the subgrid
if ilong < sublongvec.min():
continue
if ilong > sublongvec.max():
continue
# compute predicted rides per day given the new station
iride = getride(ilat, ilong, popemp, mbta, station, zipscale,
stationscale, subwayscale, stationfeatures, dataloc)
# store the result in the nrides dataframe
nrides['nrides'][i] = iride
# make sure the Charles river is still masked out
mask = loadutil.getmask(dataloc)
nrides['nrides'] *= (1 - mask)
# how long did it take? should be 5-10 seconds
newtime = time.time()
runtime = newtime - currenttime
print("Took %d seconds to re-process the map." % runtime)
nrides.to_csv(dataloc + 'nridesmap.csv', index=False)
def remakemap(ilat, ilong, dataloc):
"""
Remake the map for a subgrid of 10x10 cells, since after adding a single
station the impact on the predicted daily rides should be localalized.
"""
# Generate a sub grid of latitudes and longitudes
latvec, longvec = loadutil.grid()
# Generate a sub grid of latitudes and longitudes
sublatvec, sublongvec = loadutil.subgrid(ilat, ilong, nsub=10)
# load the data
loaddata = loadutil.load(dataloc)
popemp = loaddata[0]
mbta = loaddata[1]
station = loaddata[2]
zipscale = loaddata[3]
stationscale = loaddata[4]
subwayscale = loaddata[5]
stationfeatures = loaddata[6]
# read in ride map from previous iteration
nrides = pd.read_csv(dataloc + 'nridesmap.csv')
# monitor how long this recomputing the subgrid takes
import time
currenttime = time.time()
ngrid = len(nrides)
for i in range(ngrid):
ilat = nrides['latitude'][i]
ilong = nrides['longitude'][i]
# skip this latitude if it isn't present in the subgrid
if ilat < sublatvec.min():
continue
if ilat > sublatvec.max():
continue
# skip this longitude if it isn't present in the subgrid
if ilong < sublongvec.min():
continue
if ilong > sublongvec.max():
continue
# compute predicted rides per day given the new station
iride = getride(ilat, ilong, popemp, mbta, station, zipscale,
stationscale, subwayscale, stationfeatures, dataloc)
# store the result in the nrides dataframe
nrides['nrides'][i] = iride
# make sure the Charles river is still masked out
mask = loadutil.getmask(dataloc)
nrides['nrides'] *= (1 - mask)
# how long did it take? should be 5-10 seconds
newtime = time.time()
runtime = newtime - currenttime
print("Took %d seconds to re-process the map." % runtime)
nrides.to_csv(dataloc + 'nridesmap.csv', index=False)
