with bz2.open("geo.pic.bz") as f:
return pickle.load(f)
import datetime, itertools
grid = open_cp.data.Grid(xsize=150, ysize=150, xoffset=0, yoffset=0)
grid = open_cp.geometry.mask_grid_by_intersection(load_geometry(), grid)
with scripted.Data(load_points,
load_geometry,
start=datetime.datetime(2007, 1, 1),
grid=grid) as state:
time_range = scripted.TimeRange(datetime.datetime(2007, 10, 1),
datetime.datetime(2008, 1, 1),
datetime.timedelta(days=1))
state.add_prediction(scripted.NaiveProvider, time_range)
for bw in range(50, 301, 10):
weight = open_cp.retrohotspot.Quartic(bw)
state.add_prediction(scripted.RetroHotspotProvider(weight), time_range)
for bw in range(50, 301, 10):
weight = open_cp.retrohotspot.Quartic(bw)
state.add_prediction(scripted.RetroHotspotCtsProvider(weight),
time_range)
for tb, sb in itertools.product([30, 50, 70, 90], [3, 4, 5, 6, 7]):
weight = open_cp.prohotspot.ClassicWeight(time_bandwidth=tb,
def load_geometry():
frame = gpd.read_file("SouthSide")
return frame.geometry[0]
def stringToDatetime(s):
return datetime.datetime(*(s.split("-")))
earliest_time = "2016-01-01"
start_time = "2016-10-01"
end_time = "2017-01-01"
# Perform the predictions; see `scripted_intro.md`
with scripted.Data(load_points,
load_geometry,
start=stringToDatetime(earliest_time)) as state:
time_range = scripted.TimeRange(stringToDatetime(start_time),
stringToDatetime(end_time),
datetime.timedelta(days=1))
state.add_prediction(scripted.NaiveProvider, time_range)
state.score(scripted.HitRateEvaluator)
state.score(scripted.HitCountEvaluator)
state.process(scripted.HitRateSave("ratesTest.csv"))
state.process(scripted.HitCountSave("countsTest.csv"))
