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import math
import pandas as pd
import numpy as np
import folio
from utils import map_vals
from matplotlib import pyplot as plt
# NOTE
# This only needed to be ran once to generate the maps
# Maps are saved in the figures folder and loaded as html
service_data_pd = pd.read_csv("data/311-2016-2018.csv")
service_data_pd["Incident Zip"] = service_data_pd["Incident Zip"].astype("string")
service_data_pd["BBL"] = service_data_pd["BBL"].astype("string")
service_data_raw = pl.DataFrame(service_data_pd)
# service_data_raw = pl.read_csv("data/311-2016-2018.csv", null_values="", infer_schema_length=0)
# service_data_raw = service_data_raw.with_columns(
# pl.col("Latitude").cast(pl.Float64),
# pl.col("Longitude").cast(pl.Float64)
# )
# Clear some ram
del service_data_pd
gc.collect()
weather_data_raw = pd.read_csv("data/weather_NY_2010_2018Nov.csv")
def get_map_1():
fig, weather_map = map_vals(
weather_data_raw.loc[weather_data_raw["Year"] >= 2016],
cols=["Latitude", "Longitude"],
label_cols=["StationName"],
sample_size=1000,
color='red',
radius=3,
weight=4
)
fig, combined_map = map_vals(
service_data_raw,
cols=["Latitude", "Longitude"],
color="blue", submap=weather_map,
sample_size=1000,
weight=2,
radius=1
)
fig.save("figures/map1.html")
return fig
def get_map_2():
fig, service_map = map_vals(
service_data_raw,
cols=["Latitude", "Longitude"],
color="blue",
weight=2,
radius=1,
start_loc=[40.7128, -74.0060],
sample_size=1000,
zoom_start=10
)
fig, weather_map = map_vals(
weather_data_raw.loc[weather_data_raw["Year"] >= 2016],
cols=["Latitude", "Longitude"],
submap=service_map,
label_cols=["StationName"],
color='red',
radius=5,
weight=2,
sample_size=1000,
)
fig.save("figures/map2.html")
return fig
def get_bounded_map():
# Get prerecorded coords for the mins/max to maximize speed here
# In notebook this is recorded via code
lat_min = 40.49804421521046
lat_max = 40.91294056699566
long_min = -74.25521082506387
long_max = -73.70038354802529
fig = folium.Figure(height=500, width=750)
service_bounds_map = folium.Map(
location=[40.7128, -74.0060],
zoom_start=10,
tiles='cartodbpositron',
zoom_control=False,
scrollWheelZoom=False,
dragging=False
)
kw = {
"color": "#F1807E",
"line_cap": "round",
"fill": True,
"fill_color": "blue",
"weight": 3,
"popup": "Service Data Coverage Zone",
}
folium.Rectangle(
bounds=[[lat_min, long_min], [lat_max, long_max]],
line_join="round",
dash_array="5 5",
**kw,
).add_to(service_bounds_map)
fig.add_child(service_bounds_map)
fig.save("figures/bounded_map.html")
return fig
def get_final_map():
lat_min = 40.49804421521046
lat_max = 40.91294056699566
long_min = -74.25521082506387
long_max = -73.70038354802529
mincon_lat = weather_data_raw["Latitude"] >= lat_min
maxcon_lat = weather_data_raw["Latitude"] <= lat_max
mincon_long = weather_data_raw["Longitude"] >= long_min
maxcon_long = weather_data_raw["Longitude"] <= long_max
service_bounds_map = folium.Map(
location=[40.7128, -74.0060],
zoom_start=10,
tiles='cartodbpositron',
zoom_control=False,
scrollWheelZoom=False,
dragging=False
)
kw = {
"color": "#F1807E",
"line_cap": "round",
"fill": True,
"fill_color": "blue",
"weight": 3,
"popup": "Service Data Coverage Zone",
}
folium.Rectangle(
bounds=[[lat_min, long_min], [lat_max, long_max]],
line_join="round",
dash_array="5 5",
**kw,
).add_to(service_bounds_map)
wd_localized = weather_data_raw.loc[mincon_lat & maxcon_lat & mincon_long & maxcon_long]
fig, wd_local_map = map_vals(
wd_localized,
submap=service_bounds_map,
label_cols=["StationName"],
color='red',
radius=5,
weight=2,
sample_size=1000,
)
fig.save("figures/final_map.html")
return fig
def build_maps():
get_map_1()
get_map_2()
get_bounded_map()
get_final_map()
build_maps() |