Travail finit sur la mortalité dans le monde

delta.py

@@ -6,6 +6,7 @@
 from nrj_energies import energies as nrj_lib
 from wfr_fertilite_revenus import main as wfr_lib
 from fdc_deces import deces as fdc_lib
+from mor_map import mortality as mor_lib
 
 # external_stylesheets = ['https://codepen.io/chriddyp/pen/bWLwgP.css']
 
@@ -14,6 +15,7 @@
 wfr = wfr_lib.WorldPopulationStats(app)
 nrj = nrj_lib.Energies(app)
 fdc = fdc_lib.Deces(app)
+mor = mor_lib.Mortality(app)
 
 main_layout = html.Div([
     html.Div(className = "row",
@@ -28,6 +30,8 @@
                               html.Br(),
                               dcc.Link(html.Button('Décès journaliers', style={'width':"100%"}), href='/fdc'),
                               html.Br(),
+                              dcc.Link(html.Button('Causes de décès', style={'width':"100%"}), href='/mor'),
+                              html.Br(),
                               html.Br(),
                               html.Br(),
                               html.Center(html.A('Code source', href='https://github.com/oricou/delta2')),
@@ -61,6 +65,8 @@ def display_page(pathname):
         return wfr.main_layout
     elif pathname == '/fdc':
         return fdc.main_layout
+    elif pathname == '/mor':
+        return mor.main_layout
     else:
         return home_page
 

mor_map/.jukit/.jukit_info.json

@@ -0,0 +1 @@
+{"terminal": "nvimterm"}

mor_map/assets/style.css

@@ -0,0 +1,16 @@
+html { background-color: papayawhip; }
+body {
+    min-height: 100vh;
+    max-width: 1600px;
+    background-color: papayawhip;
+    margin: 0 auto;
+    margin-top: 10px;
+}
+
+h1, h2, h3, h4, h5, h6 {
+    color: #222222;
+}
+
+.checkbox {
+  column-count: 2;
+}

mor_map/data/.jukit/.jukit_info.json

@@ -0,0 +1 @@
+{"terminal": "nvimterm"}

mor_map/mortality.py

@@ -0,0 +1,124 @@
+import sys
+import dash
+import flask
+from dash import dcc
+from dash import html
+import pandas as pd
+import numpy as np
+import plotly.graph_objs as go
+import plotly.express as px
+import dateutil as du
+import json
+
+class Mortality():
+
+
+    def __init__(self, application = None):
+        self.dir = 'mor_map/'
+        self.geo = json.load(open(self.dir + 'data/countries.geojson'))
+        self.df = pd.read_csv(self.dir + 'data/dataset.csv')
+        self.second = pd.read_csv(self.dir + 'data/second.csv')
+
+        self.main_layout = html.Div(children=[
+            html.H3(children='The different causes of death in the world'),
+            html.Div([dcc.Graph(figure=self.map(), id='map'), ], style={'width': '100%', }),
+
+            html.Br(),
+            html.Br(),
+
+            dcc.Markdown("""Here is the graph that represents the 9 most important causes of death in France""", id='Title'),
+            html.Div([dcc.Graph(figure=self.pie('France') , id='pie'), ], style={'width': '100%', }),
+
+            html.Div([dcc.Graph(figure=self.line('France',0) , id='line1'), ], style={'width': '100%', }),
+            html.Div([dcc.Graph(figure=self.line('France',1) , id='line2'), ], style={'width': '100%', }),
+            html.Div([dcc.Graph(figure=self.line('France',2) , id='line3'), ], style={'width': '100%', }),
+            html.Div([dcc.Graph(figure=self.line('France',3) , id='line4'), ], style={'width': '100%', }),
+            html.Div([dcc.Graph(figure=self.line('France',4) , id='line5'), ], style={'width': '100%', }),
+            html.Div([dcc.Graph(figure=self.line('France',5) , id='line6'), ], style={'width': '100%', }),
+            html.Div([dcc.Graph(figure=self.line('France',6) , id='line7'), ], style={'width': '100%', }),
+            html.Div([dcc.Graph(figure=self.line('France',7) , id='line8'), ], style={'width': '100%', }),
+            html.Div([dcc.Graph(figure=self.line('France',8) , id='line9'), ], style={'width': '100%', }),
+
+        ], style={
+            'backgroundColor': 'white',
+            'padding': '10px 50px 10px 50px',
+        }
+        )
+
+        if application:
+            self.app = application
+            # application should have its own layout and use self.main_layout as a page or in a component
+        else:
+            self.app = dash.Dash(__name__)
+            self.app.layout = self.main_layout
+
+        self.app.callback(
+            [dash.dependencies.Output('Title', 'children'), dash.dependencies.Output('pie', 'figure'), dash.dependencies.Output('line1', 'figure'), dash.dependencies.Output('line2', 'figure'), dash.dependencies.Output('line3', 'figure'), dash.dependencies.Output('line4', 'figure'), dash.dependencies.Output('line5', 'figure'), dash.dependencies.Output('line6', 'figure'), dash.dependencies.Output('line7', 'figure'), dash.dependencies.Output('line8', 'figure'), dash.dependencies.Output('line9', 'figure')],
+            dash.dependencies.Input('map', 'clickData')
+        )(self.onClick)
+
+    # Draw the map of the world
+    def map(self):
+        df = self.df
+        geojson = self.geo
+
+        fig = px.choropleth(df, locations="Code",
+                    color="Total", # lifeExp is a column of gapminder
+                    hover_name="Entity", # column to add to hover information
+                    color_continuous_scale=px.colors.sequential.Plasma)
+
+        fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
+
+        return fig
+
+
+    def onClick(self, clickData):
+        if clickData:
+            return "Here is the graph that represents the 9 most important causes of death in " + clickData['points'][0]['hovertext'], self.pie(clickData['points'][0]['hovertext']),self.line(clickData['points'][0]['hovertext'],0),self.line(clickData['points'][0]['hovertext'],1),self.line(clickData['points'][0]['hovertext'],2),self.line(clickData['points'][0]['hovertext'],3),self.line(clickData['points'][0]['hovertext'],4),self.line(clickData['points'][0]['hovertext'],5),self.line(clickData['points'][0]['hovertext'],6),self.line(clickData['points'][0]['hovertext'],7),self.line(clickData['points'][0]['hovertext'],8)
+
+
+        return 'Here is the graph that represents the 9 most important causes of death in France', self.pie('France'),self.line('France',0),self.line('France',1),self.line('France',2),self.line('France',3),self.line('France',4),self.line('France',5),self.line('France',6),self.line('France',7),self.line('France',8)
+
+    # Draw the bar 9 upper charts without Entity, Code and Total of the selected country
+
+    def pie(self, country):
+        df = self.df
+        df = df[df['Entity'] == country]
+        df = df.drop(['Entity', 'Code', 'Total'], axis=1)
+        df = df.transpose()
+        df = df.reset_index()
+        df.columns = ['Cause', 'Deaths']
+        df = df.sort_values('Deaths', ascending=False)
+        df = df.head(9)
+
+        fig = px.bar(df, x='Cause', y='Deaths', color='Cause', color_discrete_sequence=px.colors.qualitative.Dark24)
+        return fig
+
+    # Draw the evolution in time of a cause of death in the selected country
+    def line(self, country, n):
+        df = self.df
+        df = df[df['Entity'] == country]
+        df = df.drop(['Entity', 'Code', 'Total'], axis=1)
+        df = df.transpose()
+        df = df.reset_index()
+        df.columns = ['Cause', 'Deaths']
+        df = df.sort_values('Deaths', ascending=False)
+        df = df.head(9)
+
+        # get the first cause of death
+        cause = df['Cause'].iat[n]
+
+        df = self.second
+        df = df[df['Entity'] == country]
+        df = df.drop(['Entity', 'Code'], axis=1)
+
+        df.sort_values('Year', ascending=True, inplace=True)
+
+        fig = px.line(df, x='Year', y=cause, title='Evolution of '+cause+'in '+country)
+
+
+        return fig
+
+if __name__ == '__main__':
+    nrg = Mortality()
+    nrg.app.run_server(debug=True, port=8062)

mor_map/prepare_data.py

@@ -0,0 +1,68 @@
+import sys
+import pandas as pd
+import numpy as np
+import dateutil as du
+
+
+mois = {'janv': 1, 'févr': 2, 'mars': 3, 'avr': 4, 'mai': 5, 'juin': 6, 'juil': 7, 'août': 8, 'sept': 9, 'oct': 10,
+        'nov': 11, 'déc': 12}
+
+quoi = {"Prix d'une tonne de propane": [1000, 'Propane'], "Bouteille de butane de 13 kg": [13, 'Butane'],
+        "100 litres de FOD au tarif C1": [100, 'Fioul'], "Un litre d'essence ordinaire": [1, 'Essence'],
+        "Un litre de super carburant ARS": [1, 'Essence'], "Un litre de super sans plomb 95": [1, 'Essence'],
+        "Un litre de super sans plomb 98": [1, 'Essence'], "Un litre de gazole": [1, 'Gazole'],
+        "Un litre de GPLc": [1, 'GPL'], "1 kWh (contrat 3 kW)": [1, 'Electricité'],
+        "1 kWh (contrat 9 kW)": [1, 'Electricité'],
+        "Une tonne de granulés de bois en vrac": [1000 * 4.8, 'Electricité'],
+        "100 kWh PCI de bois en vrac": [100, 'Electricité']}
+
+densité = {'Essence': 0.75, 'Gazole': 0.85, 'Fioul': 0.85, 'GPL': 0.55}  # kg / l
+
+# https://fr.wikipedia.org/wiki/Pouvoir_calorifique
+calor = {'Essence': 47.3, 'Gazole': 44.8, 'Fioul': 42.6, 'Propane': 50.35, 'Butane': 49.51, 'GPL': 46, 'Bois': 15,
+         'Charbon': 20,
+         'Electricité': 3.6}  # en MJ / kg sauf électicité en MJ / kWh
+
+def _conv_date(d):
+    ma = d.split('-')  # coupe la chaine au - et ainsi ma[0] est le mois et ma[1] l'année
+    return du.parser.parse(
+        f"15-{mois[ma[0].lower()]}-{ma[1]}")  # parfois le mois a une majuscule d'où lower()
+
+def _make_dataframe_from_pegase(filename):
+    df = pd.read_csv(filename, sep=";", encoding="latin1", skiprows=[0, 1, 3], header=None)
+    df = df.set_index(0).T
+    df['date'] = df['Période'].apply(_conv_date)
+    df = df.set_index('date')
+    df.drop(columns=['Période'], inplace=True)
+    df = df.replace('-', np.nan).astype('float64')
+    return df
+
+bois = _make_dataframe_from_pegase("data/pegase_prix_bois_particulier.csv")
+petrole = _make_dataframe_from_pegase("data/pegase_prix_petrole_particulier.csv")
+
+bois.drop(columns=['Une tonne de granulés de bois en sacs', '100 kWh PCI de bois en sacs'], inplace=True)
+petrole.drop(columns=["Tarif d'une tonne de propane en citerne", "100 kWh PCI de propane en citerne",
+                      "100 kWh PCS de propane",
+                      "Un litre d'essence ordinaire",
+                      "100 kWh PCI de propane", "100 kWh PCI de FOD au tarif C1"], inplace=True)  # doublons
+
+electricite = pd.read_csv('data/prix_reglemente_electricite.csv', sep=';', decimal=',', parse_dates=['DATE_DEBUT'], infer_datetime_format=True)
+electricite = electricite.set_index(['P_SOUSCRITE',"DATE_DEBUT"])
+electricite.drop(columns=['DATE_FIN', 'PART_FIXE_HT', 'PART_FIXE_TTC', 'PART_VARIABLE_HT'], inplace=True)
+electricite.dropna(inplace=True)
+electricite = electricite.unstack().T.reset_index(0, drop=True)
+electricite = electricite.rename(columns={i: f"1 kWh (contrat {i:.0f} kW)" for i in electricite.columns})
+duration = pd.DatetimeIndex(electricite.index.values)
+new_index = pd.DatetimeIndex(
+    [f"{y}-{m:02d}-15" for y in range(duration[0].year, duration[-1].year + 1) for m in range(1, 13)])
+electricite = electricite.reindex(np.concatenate([electricite.index.values, new_index.values]))
+electricite.sort_index(inplace=True)
+electricite = electricite.fillna(method='ffill')
+electricite = electricite.loc[new_index]
+electricite = electricite.reindex(bois.index)
+electricite.drop(columns=["1 kWh (contrat 6 kW)", "1 kWh (contrat 12 kW)", "1 kWh (contrat 15 kW)"],
+                 inplace=True)
+
+energie = petrole.join(bois, how='outer').join(electricite, how='outer')
+#energie = pd.concat([petrole, bois, electricite])  # cela devrait faire comme ci-dessus mais j'ai 3 lignes par index...
+energie.to_pickle('data/energies.pkl')