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# This notebook assumes to be running from your FireCARES VM (eg. python manage.py shell_plus --notebook --no-browser)

import sys
import os
import time
import pandas as pd
pd.set_option('display.max_columns', 100)
pd.set_option('display.max_rows', 1000)
import numpy as np
sys.path.insert(0, os.path.realpath('..'))
import folium
import django
import sqlite3
django.setup()
from django.db import connections
from pretty import pprint
from IPython.display import display


    

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df = pd.read_csv('../predictions.2015.csv')


    

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# Those with empty predicted fires/fire spreads/deaths/ by structure hazard level, yet have a score

q = """
select department_id, level
from firestation_firedepartmentriskmodels
where dist_model_score is not null
    and (risk_model_fires is null or risk_model_fires_size1 is null or risk_model_fires_size2 is null or risk_model_deaths is null or risk_model_injuries is null)
order by department_id
"""

pd.read_sql_query(q, connections['default'])


    

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# Counts by region/size

df2 = df[df['fd_id'].isnull() == False].groupby(['region', 'fd_size']).count()
display(df2)
df2.to_csv('/tmp/outf.csv')


    

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# Fire spread percentages, but no original fire prediction...
df[df['lr_size_2'].notnull() & df['lr_fire'].isnull()]