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import dash
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from dash.dependencies import Input, Output
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import dash_core_components as dcc
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import dash_html_components as html
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import flask
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import pandas as pd
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import time
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import os
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import numpy as np
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import plotly
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import plotly.graph_objs as go
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import scipy.stats as scs
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server = flask.Flask('app')
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server.secret_key = os.environ.get('secret_key', 'secret')
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cocalc_project_id = os.environ['COCALC_PROJECT_ID']
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city = pd.DataFrame()
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boros = ["'Bronx'","'Staten Island'", "'Manhattan'", "'Queens'", "'Brooklyn'"]
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for boro in boros:
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    soql_url = ('https://data.cityofnewyork.us/resource/nwxe-4ae8.json?' +\
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            '$select=health,count(tree_id)' +\
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            '&$where=boroname='+boro +\
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            'AND NOT health=\'NaN\''
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            '&$group=health').replace(' ', '%20')
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    #print(soql_url)
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    soql_health = pd.read_json(soql_url)
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    soql_health['boroname'] = boro
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    city = city.append(soql_health)
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# With steward, Good Heatlth
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city2 = pd.DataFrame()
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boros = ["'Bronx'","'Staten Island'", "'Manhattan'", "'Queens'", "'Brooklyn'"]
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for boro in boros:
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    soql_url2 = ('https://data.cityofnewyork.us/resource/nwxe-4ae8.json?' +\
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        '$select=steward, count(health)' +\
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        '&$where=boroname='+boro +\
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        'AND NOT health=\'NaN\'' +\
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        'AND NOT steward=\'NaN\'' +\
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        'AND NOT steward=\'None\'' +\
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        'AND NOT health=\'Poor\'' +\
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        '&$group=steward').replace(' ', '%20')
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    soql_steward = pd.read_json(soql_url2)
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    soql_steward['boroname'] = boro
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    city2 = city2.append(soql_steward)
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city2 = city2.replace('3or4', 1)
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city2 = city2.replace('4orMore', 1)
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city2 = city2.replace('1or2', 1)
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city2 = city2.groupby('boroname').sum()
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city2 = city2.drop(columns = 'steward')
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city2
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# No Steward, Good Health
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city3 = pd.DataFrame()
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boros = ["'Bronx'","'Staten Island'", "'Manhattan'", "'Queens'", "'Brooklyn'"]
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for boro in boros:
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    soql_url2 = ('https://data.cityofnewyork.us/resource/nwxe-4ae8.json?' +\
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        '$select=steward, count(health)' +\
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        '&$where=boroname='+boro +\
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        'AND NOT health=\'NaN\'' +\
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        'AND steward=\'None\'' +\
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        'AND NOT steward=\'NaN\'' +\
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        'AND NOT health=\'Poor\'' +\
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        '&$group=steward').replace(' ', '%20')
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    soql_steward = pd.read_json(soql_url2)
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    soql_steward['boroname'] = boro
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    city3 = city3.append(soql_steward)
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city3 = city3.replace({'None': 0})
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city3 = city3.groupby('boroname').sum()
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city3 = city3.drop(columns = 'steward')
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city3
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# With steward, Bad Health
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city4 = pd.DataFrame()
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boros = ["'Bronx'","'Staten Island'", "'Manhattan'", "'Queens'", "'Brooklyn'"]
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for boro in boros:
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    soql_url2 = ('https://data.cityofnewyork.us/resource/nwxe-4ae8.json?' +\
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        '$select=steward, count(health)' +\
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        '&$where=boroname='+boro +\
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        'AND NOT health=\'NaN\'' +\
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        'AND NOT steward=\'NaN\'' +\
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        'AND NOT steward=\'None\'' +\
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        'AND health=\'Poor\'' +\
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        '&$group=steward').replace(' ', '%20')
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    soql_steward = pd.read_json(soql_url2)
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    soql_steward['boroname'] = boro
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    city4 = city4.append(soql_steward)
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city4 = city4.replace('3or4', 1)
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city4 = city4.replace('4orMore', 1)
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city4 = city4.replace('1or2', 1)
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city4 = city4.groupby('boroname').sum()
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city4 = city4.drop(columns = 'steward')
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city4
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# No Steward, Bad Health
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city5 = pd.DataFrame()
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boros = ["'Bronx'","'Staten Island'", "'Manhattan'", "'Queens'", "'Brooklyn'"]
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for boro in boros:
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    soql_url2 = ('https://data.cityofnewyork.us/resource/nwxe-4ae8.json?' +\
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        '$select=steward, count(health)' +\
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        '&$where=boroname='+boro +\
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        'AND NOT health=\'NaN\'' +\
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        'AND steward=\'None\'' +\
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        'AND NOT steward=\'NaN\'' +\
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        'AND health=\'Poor\'' +\
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        '&$group=steward').replace(' ', '%20')
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    soql_steward = pd.read_json(soql_url2)
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    soql_steward['boroname'] = boro
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    city5 = city5.append(soql_steward)
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city5 = city5.replace({'None': 0})
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city5 = city5.groupby('boroname').sum()
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city5 = city5.drop(columns = 'steward')
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city5
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# Make big df for analysis
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steward_good = city2
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steward_bad = city4
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no_good = city3
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no_bad = city5
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big = pd.DataFrame()
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steward_good
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big['steward good'] = steward_good['count_health']
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big['steward bad']  = steward_bad['count_health']
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big['no steward good'] = no_good['count_health']
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big['no steward bad']  = no_bad['count_health']
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big = big.transpose()
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# Statistics Test
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obs = [big[boro][0], big[boro][1] ]
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exp = [big[boro][2], big[boro][3] ]
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obs_total = np.sum(obs)
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exp_total = np.sum(exp)
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obs = obs/obs_total * 100
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exp = exp/exp_total * 100
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result = scs.chisquare(obs,exp)
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result = bool(result[1]<.05)
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if result == True:
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    conclusion = "We have sufficient evidence to suggest that stewardship effects tree health."
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else:
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    conclusion = "We have insufficient evidence to suggest that stewardship effects tree health."
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app = dash.Dash('app', server=server)
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app.scripts.config.serve_locally = False
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dcc._js_dist[0]['external_url'] = 'https://cdn.plot.ly/plotly-basic-latest.min.js'
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app.layout = html.Div([
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    html.H1('Tree Health by Boro'),
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    dcc.Dropdown(
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        id='my-dropdown',
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        options=[
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            {'label': 'Queens', 'value': "'Queens'"},
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            {'label': 'Brooklyn', 'value': "'Brooklyn'"},
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            {'label': 'Manhattan', 'value': "'Manhattan'"},
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            {'label': 'Staten Island', 'value': "'Staten Island'"},
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            {'label': 'Bronx', 'value': "'Bronx'"}
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        ],
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        value="'Queens'"
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    ),
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    dcc.Graph(id='my-graph'),
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    html.H1('Tree Health vs Stewardship'),
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    dcc.Dropdown(
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        id='my-dropdown2',
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        options=[
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            {'label': 'Queens', 'value': "'Queens'"},
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            {'label': 'Brooklyn', 'value': "'Brooklyn'"},
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            {'label': 'Manhattan', 'value': "'Manhattan'"},
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            {'label': 'Staten Island', 'value': "'Staten Island'"},
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            {'label': 'Bronx', 'value': "'Bronx'"}
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        ],
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        value="'Queens'"
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    ),
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    dcc.Graph(id='total-graph'),
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    html.H2("Conclustion: "),
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    html.H3(conclusion)
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], className="container")
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#interactive graph
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@app.callback(Output('my-graph', 'figure'),
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              [Input('my-dropdown', 'value')])
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def update_graph(selected_dropdown_value):
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    dff = city[city['boroname'] == selected_dropdown_value]
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    return {
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        'data': [{
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            'x': dff.health,
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            'y': dff.count_tree_id,
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            'type': 'bar'
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        }],
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        'layout': {
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            'margin': {
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                'l': 30,
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                'r': 20,
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                'b': 30,
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                't': 20
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            }
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        }
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    }
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# graph 2
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@app.callback(Output('total-graph', 'figure'),
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              [Input('my-dropdown2', 'value')])
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def update_graph2(selected_dropdown_value):
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    boro = selected_dropdown_value
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    trace1 = go.Bar(
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        x=["Good Health", "Bad Health"],
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        y= [big[boro][0], big[boro][1]],
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        name='With Steward'
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    )
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    trace2 = go.Bar(
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        x= ["Good Health", "Bad Health"],
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        y= [big[boro][2], big[boro][3]],
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        name='Without Steward'
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    )
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    return {
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        'data': [trace1, trace2],
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        'layout': {
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            'margin': {
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                'l': 30,
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                'r': 20,
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                'b': 30,
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                't': 20
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            }
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        }
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    }
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port = 9990
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pfx = "/{}/server/{}/".format(cocalc_project_id, port)
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app.config.requests_pathname_prefix = pfx
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if __name__ == '__main__':
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    print("browse to https://cocalc.com{}".format(pfx))
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    app.run_server(debug=True, port=port, host='0.0.0.0')
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