# Import libraries we need

import csv

import open_cp.data

import geopandas as gpd

import pyproj

import dateutil.parser

import open_cp.scripted as scripted

import datetime

# Load the input data; see `scripted_intro.md`

def row_to_datetime(row):

datetime_string = row[1]

return dateutil.parser.parse(datetime_string)

proj = pyproj.Proj({"init" : "epsg:2790"})

def row_to_coords(row):

x = float(row[5])

y = float(row[4])

return proj(x, y)

def load_points():

with open("example.csv") as file:

reader = csv.reader(file)

header = next(reader)

# Assume the header is correct

times = []

xcoords = []

ycoords = []

for row in reader:

times.append(row_to_datetime(row))

x, y = row_to_coords(row)

xcoords.append(x)

ycoords.append(y)

# Maybe `times` is not sorted.

times, xcoords, ycoords = open_cp.data.order_by_time(times, xcoords, ycoords)

return open_cp.data.TimedPoints.from_coords(times, xcoords, ycoords)

def load_geometry():

frame = gpd.read_file("SouthSide")

return frame.geometry[0]

# Perform the predictions; see `scripted_intro.md`

with scripted.Data(load_points, load_geometry,

start=datetime.datetime(2016,1,1)) as state:

time_range = scripted.TimeRange(datetime.datetime(2016,10,1),

datetime.datetime(2017,1,1), datetime.timedelta(days=1))

state.add_prediction(scripted.NaiveProvider, time_range)

state.score(scripted.HitRateEvaluator)

state.score(scripted.HitCountEvaluator)

state.process(scripted.HitRateSave("rates.csv"))

state.process(scripted.HitCountSave("counts.csv"))