main.py

"""
CSC110 Final Project - Predicting the Flooding Susceptibility of the Vancouver region

This is the main module, it will:
    - Load the necessary files from the datasets
    - Perform computations on the data
    - Produce an interactive output

===================================================
Instructions:
-ensure that all of the required modules from requirements.txt
are successfully installed
-download vancouver_surface_elevation.asc, pacificocean_sea_level.csv
RUN THIS FILE TO SEE DASH VISUALIZATION
-click the link in the console output to view the visualizations
===================================================

This file is Copyright (c) 2020 Lorena Buciu, Rafee Rahman, Kevin Yang, Ricky Yi
"""
import dash
import dash_core_components as dcc
import dash_html_components as html
import pandas as pd
from dash.dependencies import Input, Output
from data_cleaning import read_csv_data, group_means, means_to_csv, data_to_datetime_csv
from models import display_graph, display_annual_mean, display_map, predicted_sea_level
from canada_dsm import run_file


# Create the datasets & Call computing functions
dataset = read_csv_data("pacificocean_sea_level.csv")
# calculate the annual means
condensed = group_means(dataset)
# CREATE data_predictions.csv
means_to_csv(condensed)

data_to_datetime_csv(dataset)

# initialize below sea level file
df = pd.DataFrame(list())
df.to_csv('below_sea_level.csv')

external_stylesheets = [
    'https://codepen.io/chriddyp/pen/bWLwgP.css',
    {
        'href': 'https://stackpath.bootstrapcdn.com/bootstrap/4.1.3/css/bootstrap.min.css',
        'rel': 'stylesheet',
        'integrity': 'sha384-MCw98/SFnGE8fJT3GXwEOngsV7Zt27NXFoaoApmYm81iuXoPkFOJwJ8ERdknLPMO',
        'crossorigin': 'anonymous'
    }
]

# OUTPUT
app = dash.Dash(__name__,
                external_stylesheets=external_stylesheets)
colors = {
    'background': '#000000',
    'text': '#7FDBFF'
}

app.layout = html.Div([
    html.H1(children="Flooding Susceptibility in Vancouver Coastal Regions",
            style={
                'color': colors['text'],
                'fontSize': 28,
                'paddingTop': 20,
                'paddingBottom': 20,
            }),
    html.Div(
        className="row",
        children=[
            html.Div(
                className="six columns",
                children=[
                    html.Div([
                        dcc.Graph(id='map_plot'),
                        html.Div([
                            dcc.Slider(
                                id='year_slider',
                                min=2020,
                                max=2300,
                                step=1,
                                marks={year: str(year) for year in range(2020, 2320, 20)}, value=2020,
                            )], style={'margin-top': '15px'}),
                        dcc.Slider(
                            id='sea_level_slider',
                            min=0,
                            max=100,
                            step=0.001,
                            marks={num: str(num) + 'm' for num in range(0, 110, 10)}, value=0.071
                        ),
                    ])
                ]
            ),
            html.Div(
                className="six columns",
                children=html.Div([
                    dcc.Graph(
                        id='right-top-graph',
                        figure=display_graph(dataset),
                        # style={'padding-right': 40}
                    ),
                    dcc.Graph(
                        id='sarimax model',
                        figure=predicted_sea_level('Sarimax_Model_Data.csv'),
                        # style={'padding-right': 40}
                    ),
                    dcc.Graph(
                        id='right-bottom-graph',
                        figure=display_annual_mean('data_predictions.csv'),
                        # style={'padding-right': 40}
                    ),

                ], style={"maxHeight": "800px", "overflow": "scroll"})
            )
        ]
    )
], style={'backgroundColor': colors['background'],
          'padding-left': 50, 'padding-right': 50},)


@app.callback(Output('map_plot', 'figure'),
              Input('year_slider', 'value'),
              Input('sea_level_slider', 'value'))
def update_map(year: int, sea_level: float) -> any:
    """
    Updates the map based on the value of the most recently used slider
    """
    ctx = dash.callback_context

    # run on start-up or if year_slider is used
    if ctx.triggered[0]['prop_id'] == '.' or ctx.triggered[0]['prop_id'] == 'year_slider.value':

        # get the predicted mean sea level at a given year in mm and convert to m
        predictions = pd.read_csv('data_predictions.csv')
        row_id = predictions.index[predictions['year'] == year].tolist()
        val = predictions.loc[row_id[0]]['mean_sea_level']
        sea_level = val / 1000

        # run_file() function will call other computation functions in canada_dsm.py
        run_file('elevation_data.asc', sea_level)

    # run if sea_level_slider is used
    else:
        run_file('elevation_data.asc', sea_level)

    return display_map()


if __name__ == '__main__':
    app.run_server(debug=True, use_reloader=False)