diff --git a/GG_MP_australiaweather/README.md b/GG_MP_australiaweather/README.md
new file mode 100644
index 000000000..df87fc008
--- /dev/null
+++ b/GG_MP_australiaweather/README.md
@@ -0,0 +1,13 @@
+Projet effectué par Martin Poulard et Grégoire Grégroire Gally. 
+
+Nous avons exploité la base de donnés du National Climatic Data Center (USA), elle rassemble des donnés météorologiques de miliers de station. Ces donnés sont classés par station sous forme de csv. Notre but va etre d'analyser.
+
+Les donnés journalières sont stockés initalement dans "daily reports", leru inventaire est fait par le fichier "stations". On va créer un dataframe afin de rassembler les mesures qui nous interessent. Pour cela il faut lires les fichier (c'est très long, c'est pour cela que nous avons mis en place un process pool), et ne garder que les informations utiles (le type de mesure: précipitations et laps de temps des mesures en annés).
+
+Une fois cela effectué ce dataframe est découpé par annés et sauvegardé dans un dossier afin de ne pas avoir à refaire l'opération à chaques fois.
+
+Le dossier géodata contient un fichier géojson. Celui ci rassemble les point nessécaires pour de faire le contour des différents états (sous forme de polygone) afin de pouvoir faire une carte avec une valeur par état. 
+
+Le fichier "allocations trades" contient les échanges d'eau ainsi que leurs prix. 
+
+Pour une raison de performance, toutes ces opérations sont faites au préalable et les dataframes sont enregistrés au format pickle dans les répertoire. 
\ No newline at end of file
diff --git a/GG_MP_australiaweather/australiaweather.py b/GG_MP_australiaweather/australiaweather.py
new file mode 100644
index 000000000..d5e81da63
--- /dev/null
+++ b/GG_MP_australiaweather/australiaweather.py
@@ -0,0 +1,261 @@
+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 os
+
+
+class australiaWeather():
+    START = 'Start'
+    STOP  = 'Stop'
+
+    def __init__(self, application = None):
+        os.getcwd()
+        df = pd.read_csv('GG_MP_australiaweather/data/stations_mesures.csv', sep=' ', names=['ID', 'lat','long','info','from','to'])
+        df = df.drop(columns=['lat','long','info'])
+        df = df.groupby('ID', as_index =False).agg({'from' : 'min', 'to' : 'max'})#.sort_values(by=['from','to'], ascending=True)
+        
+        self.stations_mesures = df
+        
+        self.number = len(df.index) - 150
+
+        self.data_inv = pd.read_pickle('GG_MP_australiaweather/data/data_inventory.pkl')
+        
+        self.data_colors = {'PRCP':'darkblue', 'DAPR':'red', 'DWPR':'green', 'MDPR':'brown', 'TMAX':'navy', 'TMIN':'crimson', 'DATX':'magenta', 'MDTX':'cyan', 'DATN':'purple', 'MDTN':'gray', 'TAVG':'coral'}
+        self.data = {'PRCP':'Precipitation', 'DAPR':'DAPR', 'DWPR':'DWPR', 'MDPR':'MDPR', 'TMAX':'Temperature maximum', 'TMIN':'Temperature minimum', 'DATX':'DATX', 'MDTX':'MDTX', 'DATN':'DATN', 'MDTN':'MDTN', 'TAVG':'Temperature moyenne'}
+
+        self.mesures_by_stations = pd.read_pickle('GG_MP_australiaweather/data/mesures_by_stations.csv')
+        self.price = pd.read_pickle('GG_MP_australiaweather/data/prices.pkl')
+        self.geo_json='GG_MP_australiaweather/data/geo_datas/states.geojson'
+        self.years = self.mesures_by_stations["date"].unique()
+        self.y_start = self.years.min()
+        self.y_end = self.years[:-2]
+        
+        self.main_layout = html.Div(children=[
+            html.H3(children='Précipitations et marchés de l\'eau en australie'),
+
+            html.Div([
+                    html.Div([ dcc.Graph(id='sls-main-graph'), ], style={'width':'90%', }),
+                ], style={
+                    'padding': '10px 50px', 
+                    'display':'flex',
+                    'justifyContent':'center'
+                }),
+            html.Div([
+                html.Div([
+                    html.Button(
+                            self.START,
+                            id='sls-button-start-stop', 
+                            style={'display':'inline-block'}
+                        ),
+                    html.Br(),
+                    dcc.Slider(
+                            id='sls-crossfilter-number-slider',
+                            min=0,
+                            max=self.number,
+                            step = 150,
+                            value= (self.number + self.number % 2) / 2,
+                            marks={str(number): str(number) for number in range(0, self.number, 1000)},
+                    ),
+                    html.Div('Déplacez la souris pour choisir le numéro de la station. Celà affichera également les 150 suivantes'),
+                ],style={'display':'inline-block', 'width':"90%"}),
+                dcc.Interval(            # fire a callback periodically
+                    id='sls-auto-stepper',
+                    interval=500,       # in milliseconds
+                    max_intervals = -1,  # start running
+                    n_intervals = 0
+                ),
+                ], style={
+                    'padding': '0px 50px', 
+                    'width':'100%'
+                }),
+                
+            html.Br(),
+            dcc.Markdown("""
+            
+Depuis 2008, le gouvernement australien a décidé d'ouvrir le marché de l'eau en transformant ce bien en produit financier.
+Nous avons décidé de nous interesser aux interaction entre ce marché et les précipitation.
+En effet, l'australie étant un immense pays, le prix de l'eau et les précipitation varient beaucoup selon la localisation. 
+Pour commencer, nous avons due determiner la fesabilité de cette étude : les donnés nessécaires etaient-elles disponibles ? Il se trouve que depuis toujours le stress hydrique est présent en australie ce qui à rendu les études météorologiques indispensable.  Les stations sont nombreuses cependant leur durée d'activité varie fortement. 
+Le graphique suivant nous montre la durée de vie des différentes stations.
+
+            """),
+           html.Br(),
+           html.Div([
+                    html.Div([ dcc.Graph(id='sws-main-graph'), ], style={'width':'80%', }),
+
+                    html.Div([
+                        html.Div('Type de donnée'),
+                        dcc.Checklist(
+                            id='sws-crossfilter-which-data',
+                            options=[{'label': self.data[i], 'value': i} for i in sorted(self.data_colors.keys())],
+                            value=sorted(self.data_colors.keys()),
+                        )
+                    ], style={'margin-left':'15px', 'width': '12em', 'float':'right'}),
+                ], style={
+                    'padding': '10px 50px', 
+                    'display':'flex',
+                    'justifyContent':'center'
+                }),            
+            
+        
+            html.Br(),
+            dcc.Markdown("""
+                * DAPR = Number of days included in the multiday precipiation 
+                * DWPR = Number of days with non-zero precipitation included in multiday precipitation total (MDPR)
+                * MDPR = Multiday precipitation total
+                * DATX = Number of days included in the multiday maximum temperature
+                * MDTX = Multiday maximum temperature
+                * DATN = Number of days included in the multiday minimum temperature
+                * MDTN = Multiday minimum temperature
+                
+Comme constaté précédement, le nombre de station est élevé. Cependant chacune n'effectue pas les mêmes mesures. Nous avons voulu vérifier que la mesure qui nous interesse (les précipitations) était assez représenté.
+Nous disposons donc de nombreuses mesures pour étudier les précipitations et les phénomènes météorologiques. Le nombre de stations ainsi que leurs activités sont montrés dans les graphiques suivants.
+            """),
+           html.Br(),
+            html.Div('Dans la carte suivante, chaque point représente une station de mesure, sa couleur représente le total de précipitation enregistré durant le mois.'), 
+            html.Div([dcc.Graph(figure=self.plotEvolutionMap()),], style={'width':'90%', }),
+            html.Div([dcc.Graph(figure=self.plotPrices()),], style={'width':'100%', }),
+
+            html.Br(),
+            
+            html.Br(),
+            dcc.Markdown("""
+On peut constater sur ces cartes que les disparités sont grandes : il pleut beaucoup au sud ouest et au nord est de ce pays. On peut constater que dans les zones peu pluvieuses le prix du ML d'eau est beacoup plus élevé. Ces cartes montrent donc que les précipitations garantissent un prix de l'eau bas. On peut cependant remettre en question la corrélation entre ces 2 cartes, en effet beaucoup d'autre éléments entrent en jeu dans ces variation. Ainsi, ce n'est pas forcement parce que les précipitations sont faibles que le prix de l'eau est élevé.
+Pour cette étude nous avons utilisé les bases de donnés suivantes :
+https://www1.ncdc.noaa.gov/ (National Climatic Data Center - USA) qui recense toutes les mesures de stations météorologiques à travers de monde. 
+http://www.bom.gov.au/ (Bureau of meteorologie - AUS) qui recense beaucoup de donnés liés aux problématiques climatique en australie. 
+
+(c) 2022 Martin Poulard
+(c) 2022 Grégoire Gally
+            """),
+        
+        ], style={
+                #'backgroundColor': 'rgb(240, 240, 240)',
+                 '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('sls-main-graph', 'figure'),
+            [ dash.dependencies.Input('sls-crossfilter-number-slider', 'value')])(self.dash_plotStationLifeSpan)
+        self.app.callback(
+            dash.dependencies.Output('sls-button-start-stop', 'children'),
+            dash.dependencies.Input('sls-button-start-stop', 'n_clicks'),
+            dash.dependencies.State('sls-button-start-stop', 'children'))(self.button_on_click)
+        self.app.callback(
+            dash.dependencies.Output('sls-auto-stepper', 'max_interval'),
+            [dash.dependencies.Input('sls-button-start-stop', 'children')])(self.run_movie)
+        self.app.callback(
+            dash.dependencies.Output('sls-crossfilter-number-slider', 'value'),
+            dash.dependencies.Input('sls-auto-stepper', 'n_intervals'),
+            [dash.dependencies.State('sls-crossfilter-number-slider', 'value'),
+             dash.dependencies.State('sls-button-start-stop', 'children')])(self.on_interval)
+        self.app.callback(
+            dash.dependencies.Output('sws-main-graph', 'figure'),
+            [ dash.dependencies.Input('sws-crossfilter-which-data', 'value')])(self.update_graph)
+        
+
+    def dash_plotStationLifeSpan(self, num_stations):
+        df = self.stations_mesures
+        if (num_stations + 150 >= len(df.index)):
+            num_stations = 0
+        df_plot = df.copy() if num_stations == None else df[num_stations:num_stations + 149].copy()
+        df_plot['timespan'] = df['to']-df['from']
+        df_plot = df_plot.sort_values(by='timespan', ascending=True)
+        fig = px.bar(df_plot, x='ID', y='timespan', base = 'from',  barmode = 'group',
+                     labels = {'ID':'Stations','timespan':'Years of existence'}, title = "Stations' lifespans",
+                     range_x=(0,df_plot.shape[0]),opacity=1) 
+        fig.update_traces(marker_color='green')
+        fig.update_xaxes(ticktext=[], tickvals=[])
+        fig.update_layout(yaxis_range=[min(df_plot['from'])*0.99, max(df_plot['to']*1.01)])
+        fig.update_xaxes(constrain='range')
+        fig.update_traces(hovertemplate ='<b>ID</b>: %{x}'+'<br>from : %{base}'+'<br>to : %{y}')
+
+        return fig
+
+    # start and stop the movie
+    def button_on_click(self, n_clicks, text):
+        if text == self.START:
+            return self.STOP
+        else:
+            return self.START
+
+    # this one is triggered by the previous one because we cannot have 2 outputs
+    # in the same callback
+    def run_movie(self, text):
+        if text == self.START:    # then it means we are stopped
+            return 0 
+        else:
+            return -1
+
+    # see if it should move the slider for simulating a movie
+    def on_interval(self, n_intervals, number, text):
+        if text == self.STOP:  # then we are running
+            if number + 150 >= self.number:
+                return 0
+            else:
+                return number + 150
+        else:
+            return number  # nothing changes
+
+    def update_graph(self, datatype):
+        column = ['year', 'number']
+        column = column + datatype
+        dfg = self.data_inv[column]
+        fig = px.bar(dfg, x = 'year', y = dfg.columns[2:], title = "Number of measure depending the year")
+        
+        return fig
+
+    def plotPrices(self):
+        '''
+        Plots the territories' water price evoluting by month
+        '''
+
+        fig = (px.choropleth(self.price,
+                    geojson=self.geo_json,
+                    locations=self.price['dest_state'],
+                    featureidkey="properties.STATE_NAME",
+                    color="price_per_ML",
+                    animation_frame='date_of_approval',
+                    projection="mercator",
+                    range_color=[0,500]))
+
+        fig.update_geos(fitbounds="locations", visible=True)
+        return fig
+
+    def plotEvolutionMap(self):
+        '''
+        Plots the map of all the stations and their precipitation evoluting by month
+        '''
+        fig = (px.scatter_mapbox(self.mesures_by_stations, lat='lat', lon='long',
+        hover_name="ID",
+        animation_group="ID",
+        animation_frame="date",
+        color='value',
+        range_color = [0,6000],
+        zoom=3, height=500))
+        fig.update_layout(mapbox_style="open-street-map")
+        fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
+        return fig
+    
+    
+    def run(self, debug=False, port=8050):
+        self.app.run_server(host="0.0.0.0", debug=debug, port=port)
+    
+        
+if __name__ == '__main__':
+    ws = WorldStats()
+    ws.run(port=8055)
\ No newline at end of file
diff --git a/GG_MP_australiaweather/data/data_inventory.pkl b/GG_MP_australiaweather/data/data_inventory.pkl
new file mode 100644
index 000000000..2845f0ec4
Binary files /dev/null and b/GG_MP_australiaweather/data/data_inventory.pkl differ
diff --git a/GG_MP_australiaweather/data/geo_datas/states.geojson b/GG_MP_australiaweather/data/geo_datas/states.geojson
new file mode 100644
index 000000000..7e3fe8ee8
--- /dev/null
+++ b/GG_MP_australiaweather/data/geo_datas/states.geojson
@@ -0,0 +1,117 @@
+{
+  "type": "FeatureCollection",
+  "features": [
+    {
+      "type": "Feature",
+      "id": 0,
+      "properties": {
+        "STATE_CODE": "1",
+        "STATE_NAME": "New South Wales"
+      },
+      "geometry": {
+        "type": "MultiPolygon",
+        "coordinates": [
+          [ [ [ 159.105420, -31.563994 ], [ 159.097750, -31.564275 ], [ 159.099634, -31.573372 ], [ 159.094217, -31.570970 ], [ 159.074599, -31.597285 ], [ 159.068740, -31.595697 ], [ 159.077238, -31.543533 ], [ 159.056995, -31.519772 ], [ 159.038463, -31.522425 ], [ 159.037960, -31.512156 ], [ 159.064657, -31.509666 ], [ 159.063790, -31.517379 ], [ 159.081178, -31.526607 ], [ 159.081913, -31.539767 ], [ 159.094678, -31.545036 ], [ 159.105420, -31.563994 ] ] ], [ [ [ 151.145550, -33.824079 ], [ 151.143844, -33.829174 ], [ 151.169325, -33.839309 ], [ 151.180206, -33.836165 ], [ 151.174277, -33.843006 ], [ 151.143088, -33.835565 ], [ 151.135902, -33.836469 ], [ 151.148783, -33.839386 ], [ 151.133837, -33.844837 ], [ 151.112863, -33.829724 ], [ 151.108625, -33.835555 ], [ 151.092828, -33.819609 ], [ 151.070345, -33.816445 ], [ 151.051336, -33.823662 ], [ 151.084007, -33.822080 ], [ 151.075192, -33.832683 ], [ 151.080873, -33.845750 ], [ 151.083228, -33.827897 ], [ 151.093817, -33.824443 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diff --git a/GG_MP_australiaweather/data/mesures_by_stations.csv b/GG_MP_australiaweather/data/mesures_by_stations.csv
new file mode 100644
index 000000000..b290ad963
Binary files /dev/null and b/GG_MP_australiaweather/data/mesures_by_stations.csv differ
diff --git a/GG_MP_australiaweather/data/prices.pkl b/GG_MP_australiaweather/data/prices.pkl
new file mode 100644
index 000000000..839bb2026
Binary files /dev/null and b/GG_MP_australiaweather/data/prices.pkl differ
diff --git a/GG_MP_australiaweather/data/stations_mesures.csv b/GG_MP_australiaweather/data/stations_mesures.csv
new file mode 100644
index 000000000..715478a1a
--- /dev/null
+++ b/GG_MP_australiaweather/data/stations_mesures.csv
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+ASN00099008 -40.1167 148.0167 DWPR 1946 1970
+ASN00099008 -40.1167 148.0167 MDPR 1946 1970
+ASN00099009 -39.8489 148.0167 PRCP 1915 2007
+ASN00099009 -39.8489 148.0167 DAPR 1921 2007
+ASN00099009 -39.8489 148.0167 DWPR 1921 2007
+ASN00099009 -39.8489 148.0167 MDPR 1921 2007
+ASN00099010 -40.0167 147.9167 PRCP 1915 1984
+ASN00099010 -40.0167 147.9167 DAPR 1916 1984
+ASN00099010 -40.0167 147.9167 DWPR 1916 1984
+ASN00099010 -40.0167 147.9167 MDPR 1916 1984
+ASN00099011 -39.9000 147.7833 PRCP 1922 1928
+ASN00099012 -40.2500 147.8000 PRCP 1881 1931
+ASN00099013 -40.0333 148.0500 PRCP 1956 1973
+ASN00099013 -40.0333 148.0500 DAPR 1966 1971
+ASN00099013 -40.0333 148.0500 DWPR 1966 1971
+ASN00099013 -40.0333 148.0500 MDPR 1966 1971
+ASN00099014 -40.0194 148.1825 PRCP 1961 2022
+ASN00099014 -40.0194 148.1825 DAPR 1963 2022
+ASN00099014 -40.0194 148.1825 DWPR 1963 2022
+ASN00099014 -40.0194 148.1825 MDPR 1963 2022
+ASN00099015 -40.1214 148.0167 TMAX 1964 1993
+ASN00099015 -40.1214 148.0167 TMIN 1964 1993
+ASN00099015 -40.1214 148.0167 PRCP 1962 2022
+ASN00099015 -40.1214 148.0167 DAPR 1969 2019
+ASN00099015 -40.1214 148.0167 DWPR 1969 2019
+ASN00099015 -40.1214 148.0167 MDPR 1969 2019
+ASN00099016 -40.0289 147.9306 PRCP 1997 2006
+ASN00099016 -40.0289 147.9306 DAPR 1997 2006
+ASN00099016 -40.0289 147.9306 DWPR 1997 2006
+ASN00099016 -40.0289 147.9306 MDPR 1997 2006
+ASN00099017 -40.2000 148.0500 PRCP 1968 1969
+ASN00099018 -40.0833 148.1500 PRCP 1968 1986
+ASN00099018 -40.0833 148.1500 DAPR 1969 1985
+ASN00099018 -40.0833 148.1500 DWPR 1969 1985
+ASN00099018 -40.0833 148.1500 MDPR 1969 1985
+ASN00099019 -39.8369 147.8358 PRCP 1968 2016
+ASN00099019 -39.8369 147.8358 DAPR 1970 2016
+ASN00099019 -39.8369 147.8358 DWPR 1970 2016
+ASN00099019 -39.8369 147.8358 MDPR 1970 2016
+ASN00099020 -40.1833 148.2000 PRCP 1968 1972
+ASN00099020 -40.1833 148.2000 DAPR 1970 1972
+ASN00099020 -40.1833 148.2000 DWPR 1970 1972
+ASN00099020 -40.1833 148.2000 MDPR 1970 1972
+ASN00099021 -40.1558 148.1853 PRCP 1995 2022
+ASN00099021 -40.1558 148.1853 DAPR 1995 2019
+ASN00099021 -40.1558 148.1853 DWPR 1995 2019
+ASN00099021 -40.1558 148.1853 MDPR 1995 2019
+ASN00099022 -40.2028 148.2372 TMAX 1992 1996
+ASN00099022 -40.2028 148.2372 TMIN 1992 1996
+ASN00099022 -40.2028 148.2372 PRCP 1992 1996
+ASN00099022 -40.2028 148.2372 DAPR 1993 1995
+ASN00099022 -40.2028 148.2372 DATN 1994 1995
+ASN00099022 -40.2028 148.2372 DATX 1994 1996
+ASN00099022 -40.2028 148.2372 DWPR 1993 1995
+ASN00099022 -40.2028 148.2372 MDPR 1993 1995
+ASN00099022 -40.2028 148.2372 MDTN 1994 1995
+ASN00099022 -40.2028 148.2372 MDTX 1994 1996
+ASN00099023 -40.1883 148.1178 PRCP 1997 2022
+ASN00099023 -40.1883 148.1178 DAPR 1999 2021
+ASN00099023 -40.1883 148.1178 DWPR 1999 2021
+ASN00099023 -40.1883 148.1178 MDPR 1999 2021
+ASN00099024 -40.5117 148.1239 PRCP 2000 2012
+ASN00099024 -40.5117 148.1239 DAPR 2000 2012
+ASN00099024 -40.5117 148.1239 DWPR 2000 2012
+ASN00099024 -40.5117 148.1239 MDPR 2000 2012
+ASN00099025 -40.2319 148.1900 PRCP 2002 2022
+ASN00099025 -40.2319 148.1900 DAPR 2002 2022
+ASN00099025 -40.2319 148.1900 DWPR 2002 2022
+ASN00099025 -40.2319 148.1900 MDPR 2002 2022
+ASN00099026 -40.0225 147.9192 PRCP 2006 2007
+ASN00099026 -40.0225 147.9192 DAPR 2006 2006
+ASN00099026 -40.0225 147.9192 DWPR 2006 2006
+ASN00099026 -40.0225 147.9192 MDPR 2006 2006
+ASN00109500 -34.1634 115.1279 PRCP 2003 2011
+ASN00109500 -34.1634 115.1279 DAPR 2003 2011
+ASN00109500 -34.1634 115.1279 DWPR 2003 2011
+ASN00109500 -34.1634 115.1279 MDPR 2003 2011
+ASN00109501 -32.9380 116.0240 PRCP 2004 2022
+ASN00109502 -34.2319 117.4839 PRCP 2004 2020
+ASN00109502 -34.2319 117.4839 DAPR 2004 2020
+ASN00109502 -34.2319 117.4839 DWPR 2004 2020
+ASN00109502 -34.2319 117.4839 MDPR 2004 2020
+ASN00109503 -34.1531 117.1272 PRCP 2003 2005
+ASN00109503 -34.1531 117.1272 DAPR 2004 2005
+ASN00109503 -34.1531 117.1272 DWPR 2004 2005
+ASN00109503 -34.1531 117.1272 MDPR 2004 2005
+ASN00109505 -33.5958 115.8186 PRCP 2005 2022
+ASN00109505 -33.5958 115.8186 DAPR 2005 2021
+ASN00109505 -33.5958 115.8186 DWPR 2005 2021
+ASN00109505 -33.5958 115.8186 MDPR 2005 2021
+ASN00109506 -33.5175 116.0894 PRCP 2005 2013
+ASN00109506 -33.5175 116.0894 DAPR 2005 2013
+ASN00109506 -33.5175 116.0894 DWPR 2005 2013
+ASN00109506 -33.5175 116.0894 MDPR 2005 2013
+ASN00109507 -33.4440 115.8536 PRCP 2005 2022
+ASN00109508 -33.7440 115.3432 PRCP 2006 2022
+ASN00109509 -33.7371 115.3545 PRCP 2006 2022
+ASN00109512 -34.7270 117.7830 PRCP 2007 2018
+ASN00123700 -34.8145 138.7742 PRCP 2007 2022
\ No newline at end of file
diff --git a/GG_MP_australiaweather/get_data.py b/GG_MP_australiaweather/get_data.py
new file mode 100644
index 000000000..e05d827cf
--- /dev/null
+++ b/GG_MP_australiaweather/get_data.py
@@ -0,0 +1,505 @@
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+from datetime import datetime
+import plotly.graph_objects as go
+import plotly.express as px
+import requests
+import gzip
+import os
+import pickle
+from datetime import datetime
+from multiprocessing import Pool, freeze_support
+
+
+
+def importStationDf(path='data/stations.csv'):
+	'''
+    Importe le dataset qui liste toutes les stations météos qui ont existé 
+    en australie 
+	'''
+	return pd.read_csv(path,na_values=['-999.9'])
+
+def downloadMissingFiles(station_df, path='data/daily_stations/'):
+    '''
+    Télécharge les relevés météos des stations dont le nom est présent dans
+    'station_df' si ils ne sont pas présent en local
+    '''
+    missing = 0
+    url = ' https://www1.ncdc.noaa.gov/pub/data/ghcn/daily/by_station/'
+    print('missing file : ')
+    for s in station_df['Station ID']: # Parcours le dataframe par ID
+        if not os.path.exists(path + s + '.csv'): #regarde si le fichier existe déja en local
+            missing += 1
+            r = requests.get(url + s + '.csv.gz', allow_redirects=True) #demande le fichier au site
+            f = gzip.decompress(r.content) #décompresse le fichier
+            open(path + s + '.csv' , 'wb').write(f) #sauvegarde le fichier sous forme de csv
+            print(s, ', ', end = '')
+    print('\n', missing , 'files were missing') 
+
+
+def downloadSingleFile(file_name, path='data/daily_stations/'):
+    '''
+    Télécharge un fichier si il n'existe pas en local
+    '''
+
+    url = ' https://www1.ncdc.noaa.gov/pub/data/ghcn/daily/by_station/'
+    if not os.path.exists(path + file_name + '.csv'):
+        r = requests.get(url + file_name + '.csv.gz', allow_redirects=True) #demande le fichier au site
+        f = gzip.decompress(r.content) #décompresse le fichier
+        open(path + file_name + '.csv' , 'wb').write(f) #sauvegarde le fichier sous forme de csv
+        print(file_name, " downloaded")
+
+
+def stationLifeSpan(path='data/stations_mesures.csv'):
+    df = pd.read_csv('data/stations_mesures.csv', sep=' ', names=['ID', 'lat','long','info','from','to'])
+    df = df.drop(columns=['lat','long','info'])
+    df = df.groupby('ID', as_index =False).agg({'from' : 'min', 'to' : 'max'})#.sort_values(by=['from','to'], ascending=True)
+    return df
+
+
+
+def dataInventory():
+    '''
+    Construit un dataframe qui fait l'inventaire du nombre mesures en fonction 
+    des stations météos et des années
+    '''
+    inventory = (pd.read_csv('data/stations_mesures.csv', sep = ' '
+                ,names = ['ID', 'lat', 'long', 'info', 'from', 'to']
+                ,usecols=['ID', 'lat', 'long', 'info', 'from', 'to']))
+
+    num_df = (pd.DataFrame({'year':list(range(
+                                        min(inventory['from']), 
+                                        max(inventory['to'])))})) #liste les annés entre les permières et les dernières mesures
+    num_df['number'] = 0 #initialise le nombre total de mesures
+    num_df[inventory['info'].unique()] = 0 #Crée un colone pour chaque type de mesure
+    for  index, r in inventory.iterrows():
+        num_df.loc[(num_df["year"] >= r['from']) & (num_df["year"] <= r['to']), r['info']] += 1
+        #^^ Sélectionne les annés tu df entre 'from' et 'to' puis incrépente la mesure 'info'
+        num_df.loc[(num_df["year"] >= r['from']) & (num_df["year"] <= r['to']), 'number'] += 1
+        #idem mais avec la colone number
+    return num_df
+
+def fromInventorySelect(start_year, end_year, info='PRCP'):
+    '''
+    Selectionne les stations qui ont enregristré des donnés du type séléctionné
+    entre 'start year et end_year'
+    '''
+    inventory = pd.read_csv('data/stations_mesures.csv', sep =' ',names = ['ID', 'lat', 'long', 'info', 'from', 'to'], usecols=['ID', 'lat', 'long', 'info', 'from', 'to'] )
+    inventory = inventory[inventory['info'] == 'PRCP']
+    x = inventory.loc[((inventory["from"] <= start_year) & (inventory["to"] >= end_year)),]
+    return x
+
+
+def saveByYear(range_df,start,end ,path = 'data/year_reports'):
+    '''
+    Sauvegarde par ans sur la période 'start', 'end' le df rangedf
+    range df est sous la forme :
+    date (=mois de la mesure) | ID (de la station) | latitude | longitude | valeur de la mesure 
+    '''
+    dates = pd.to_datetime(range_df['date'], format='%Y-%m') # convertie les dates en format datetime pour une comparaison future
+    range_df.reset_index(drop=True, inplace=True) # supprime l'index pour ne pas le sauvegarder dans le fichier
+
+    for y in range(start, end+1): #parcours les annés 
+
+        range_df.loc[dates.dt.year == y, ['date', 'ID', 'lat', 'long','value']].reset_index(drop=True).to_pickle(os.path.join(path,str(y)) + '.pkl')
+        #^^ selectionnes les entrés correpondant à l'année, puis les sauvegarde sous forme pkl
+
+'''
+'''
+
+def intervalNeeded(start, end, path = 'data/year_reports'):
+    '''
+    Determine les intervals non couvert par notre sauvegarde par année (cf saveByYear)
+    Par exemple si l'on demande l'interval 1900 - 2000 et que [1950,1951,1980,1981] sont présents dans la sauvegarde,
+    La fonctione renvera les intervals [(1900,1949),(1952,1979),(1982,2000)]
+    '''
+    existing = []
+    missing = []
+    for filename in os.listdir(path):
+        year = int(filename[0:4])
+        if year >= start and year <= end and filename[-4:] == '.pkl':
+            existing.append(year) #filtre les annés déja existantes
+                
+    i = start
+    while i < end :       
+        while i in existing:
+            i += 1
+        if not i < end:
+            break
+        y = i
+        while y not in existing and y < end:
+            y += 1
+        missing.append((i,y))
+        i = y
+        print(missing, existing)
+    return missing,existing
+
+
+def loadExisting(files,  path = 'data/year_reports/'):
+    '''
+    Loads the file, if the measures of the year have already been gathered and save localy
+    '''
+    dfs = []
+    print(files)
+    for f in files:
+        print('loading', f)
+        dfs.append(pd.read_pickle(path + str(f) + '.pkl')) #-------------------------------------change to load pickle
+    return dfs
+
+def csvToPickle(path='data/year_reports/'):
+    if os.path.isfile(path): # re
+        name, extension = os.path.splitext(path)
+        pd.read_csv(path).to_pickle(name + '.pkl')
+    if os.path.isdir(path):
+        for filename in os.listdir(path):
+            name, extension = os.path.splitext(filename)
+            pd.read_csv(os.path.join(path, filename)).reset_index(drop = False).to_pickle(os.path.join(path, name) +'.pkl')
+
+
+
+
+def readStationFile(ID, lat, lon, start_date, end_date):
+        '''
+        Construit un df depuis un fichier csv local, si celui ci n'est pas 
+        disponible, il est téléchargé. 
+        Le fichier csv a la forme :
+        'ID' | 'date' | 'info' | 'value' 
+
+        ID : l'id de la station 
+        lat : latitude de la station (utilisé sur pour afficher les donnés plus tard)
+        lon : longitude de la station (used for plotting later)
+        start_date, end_date : the lifetime of the station we are interessted in
+        '''
+        dateparse = lambda x: datetime.strptime(x, '%Y%m%d')
+
+        f = os.path.join('data','daily_stations', ID + '.csv')
+        if not os.path.isfile(f): # re
+            downloadSingleFile(ID)
+        
+        # Lit le fichier correspondant à la station
+        tmp = (pd.read_csv(f,usecols = ['ID','date','info','value'],
+                           date_parser = dateparse,parse_dates=['date'],
+                          names = ['ID','date','info','value']))
+        
+        #filtre les entrés qui correspondent à la période voulue 
+        #ainsi que l'information nécessaire (precipitations)
+        tmp = (tmp.loc[(tmp['info'] == 'PRCP') 
+                       & (tmp['date']>=start_date) 
+                       & (tmp['date']<=end_date)])
+
+        tmp.drop(['info'], inplace = True, axis =1) #nous ne gardons qu'un seul type d'info, préciser la nature de celle ci est inutile
+        
+        tmp['date'] = tmp['date'].dt.strftime('%Y-%m') #'arrondi la date au mois'
+        tmp['lat'] = lat #mets à jour les coordonés de la stations (ils ne sont pas présents dans le csv)
+        tmp['long'] = lon
+
+        '''
+        tmp = tmp.set_index('date')
+        tmp = (tmp.resample('M').mean())#.ffill(1).reindex(time_range))
+        tmp = tmp.reset_index('date')
+        '''
+        return tmp
+
+
+def creationDfProcessPool(stations_id_df, start_year, end_year,path = 'data/daily_stations'):
+    '''
+    Compute a final df from different station's files
+    '''
+    df = pd.DataFrame()
+    needed = stations_id_df.shape[0]
+    dateparse = lambda x: datetime.strptime(x, '%Y%m%d')
+
+    start_date = dateparse(str(start_year)+'0101').strftime('%Y-%m')
+    end_date = dateparse(str(end_year)+'1231').strftime('%Y-%m')
+
+        
+    #Construit la liste des paramètres pour les différents fichiers csv à lire afin de construire le dataset
+    files = []
+    for index, i in stations_id_df.iterrows():
+        files.append((i['ID'], i['lat'], i['long'], start_date, end_date))
+        
+    with Pool(5) as pool: #crée une pool de process
+        L = pool.starmap(func=readStationFile,iterable=files) #lit les différents fichiers et regroupe les dfs crées sous forme de liste
+    # ^^ Pour la masse de donnée énorme à trier et mettre au bon format c'est très efficace (facteur 10)
+    
+    #df = pd.concat(L) la concaténation est plus efficace si elle n'est faite qu'une fois
+    print(type(L[0]))
+    return L
+
+
+def createEvolutionDataFrameOpti(start, finish):
+    '''
+    Gathers the data from all differents station's files to create a df of measures
+    by year of all the stations which were activ on this year
+    '''
+    dfs = []
+    needed, existing = intervalNeeded(start, finish) #
+    for st,nd in needed:
+        s = fromInventorySelect(st, nd) #selectionne les stations qui ont des mesures sur cet interval
+        print(st, ' to ', nd, ' number of files needed', s.shape[0])
+        dfs.extend(creationDfProcessPool(s,st,nd)) #crée les dataframes pour les périodes non sauvegardés
+    
+    dfs.extend(loadExisting(existing))
+    df = pd.concat(dfs) #rassemble le tout
+    df = df.reset_index(drop=True)
+    df = (df.groupby(['date', 'ID', 'lat', 'long'], as_index = False )['value']
+            .sum() #somme toutes les mesures de précipitaions du même mois (les dates de mesures ont été arrondies au mois dans la fct readStationFile)
+            .sort_values(by='date'))
+
+    #saveByYear(df,start, finish) #sauvegarde les annés du df calculé afin de ne pas avoir à les recalculer la prochaine fois
+
+    return df
+
+
+def createPriceDf():
+    '''
+    Create the price df from the transaction files
+    '''
+    dateparse = lambda x: datetime.strptime(x, '%d-%m-%Y')
+    df = pd.read_csv('data/Allocation_Trades.csv', low_memory=False, date_parser = dateparse,parse_dates=['date_of_approval'])
+    df = df.drop(['qld_origin_trading_zone','qld_destination_trading_zone', 'regulated_unregulated', 'drainage_division' ,'murray_darling_basin_region'], axis = 1)
+    #df = df.loc[((df['net_price'] != 0) & (df['dest_state'] !='Queensland')) ]
+    df['date_of_approval'] = df['date_of_approval'].dt.strftime('%Y-%m')
+    df = df.groupby(['date_of_approval','dest_state'], as_index=False)['price_per_ML'].mean()
+    df = df.pivot(index='date_of_approval', columns='dest_state', values = 'price_per_ML').interpolate().stack().to_frame('price_per_ML').reset_index(drop=False)
+    #df = df.reset_index(drop = False)
+    return df
+
+def importStationDf(path='data/stations.csv'):
+    '''
+    Importe le dataset qui liste toutes les stations météos qui ont existé 
+    en australie 
+    '''
+    return pd.read_csv(path,na_values=['-999.9'])
+
+def csvToPickle(path='data/year_reports/'):
+    '''
+    lit et tranforme un fichier csv en format pickle
+    '''
+    if os.path.isfile(path): # re
+        name, extension = os.path.splitext(path)
+        pd.read_csv(path).to_pickle(name + '.pkl')
+    if os.path.isdir(path):
+        for filename in os.listdir(path):
+            name, extension = os.path.splitext(filename)
+            pd.read_csv(os.path.join(path, filename)).reset_index(drop = False).to_pickle(os.path.join(path, name) +'.pkl')
+
+
+
+
+
+def stationLifeSpan(path='data/stations_mesures.csv'):
+    """
+    Crée le dataframe contenant l'année de mise en service et d'arret des stations
+    """
+    df = pd.read_csv('data/stations_mesures.csv', sep=' ', names=['ID', 'lat','long','info','from','to'])
+    df = df.drop(columns=['lat','long','info'])
+    df = df.groupby('ID', as_index =False).agg({'from' : 'min', 'to' : 'max'})#.sort_values(by=['from','to'], ascending=True)
+    df.to_pickle('data/stations_lifespan.pkl')
+
+
+def dataInventory():
+    '''
+    Construit un dataframe qui fait l'inventaire du nombre mesures en fonction 
+    des stations météos et des années
+    '''
+    inventory = (pd.read_csv('data/stations_mesures.csv', sep = ' '
+                ,names = ['ID', 'lat', 'long', 'info', 'from', 'to']
+                ,usecols=['ID', 'lat', 'long', 'info', 'from', 'to']))
+
+    num_df = (pd.DataFrame({'year':list(range(
+                                        min(inventory['from']), 
+                                        max(inventory['to'])))})) #liste les annés entre les permières et les dernières mesures
+    num_df['number'] = 0 #initialise le nombre total de mesures
+    num_df[inventory['info'].unique()] = 0 #Crée un colone pour chaque type de mesure
+    for  index, r in inventory.iterrows():
+        num_df.loc[(num_df["year"] >= r['from']) & (num_df["year"] <= r['to']), r['info']] += 1
+        #^^ Sélectionne les annés tu df entre 'from' et 'to' puis incrépente la mesure 'info'
+        num_df.loc[(num_df["year"] >= r['from']) & (num_df["year"] <= r['to']), 'number'] += 1
+        #idem mais avec la colone number
+    num_df.to_pickle('data/data_inventory.pkl')
+
+
+def fromInventorySelect(start_year, end_year, info='PRCP'):
+    '''
+    Selectionne les stations qui ont enregristré des donnés du type séléctionné
+    entre 'start year et end_year'
+    '''
+    inventory = pd.read_csv('data/stations_mesures.csv', sep =' ',names = ['ID', 'lat', 'long', 'info', 'from', 'to'], usecols=['ID', 'lat', 'long', 'info', 'from', 'to'] )
+    inventory = inventory[inventory['info'] == 'PRCP']
+    x = inventory.loc[((inventory["from"] <= start_year) & (inventory["to"] >= end_year)),]
+    return x
+
+
+def saveByYear(range_df,start,end ,path = 'data/year_reports'):
+    '''
+    Sauvegarde par ans sur la période 'start', 'end' le df rangedf
+    range df est sous la forme :
+    date (=mois de la mesure) | ID (de la station) | latitude | longitude | valeur de la mesure 
+    '''
+    dates = pd.to_datetime(range_df['date'], format='%Y-%m') # convertie les dates en format datetime pour une comparaison future
+    range_df.reset_index(drop=True, inplace=True) # supprime l'index pour ne pas le sauvegarder dans le fichier
+
+    for y in range(start, end+1): #parcours les annés 
+
+        range_df.loc[dates.dt.year == y, ['date', 'ID', 'lat', 'long','value']].reset_index(drop=True).to_pickle(os.path.join(path,str(y)) + '.pkl')
+        #^^ selectionnes les entrés correpondant à l'année, puis les sauvegarde sous forme pkl
+
+def intervalNeeded(start, end, path = 'data/year_reports'):
+    '''
+    Determine les intervals non couvert par notre sauvegarde par année (cf saveByYear)
+    Par exemple si l'on demande l'interval 1900 - 2000 et que [1950,1951,1980,1981] sont présents dans la sauvegarde,
+    La fonctione renvera les intervals [(1900,1949),(1952,1979),(1982,2000)]
+    '''
+    existing = []
+    missing = []
+    for filename in os.listdir(path):
+        year = int(filename[0:4])
+        if year >= start and year <= end:
+            existing.append(year) #filtre les annés déja existantes
+                
+    i = start
+    while i < end :       
+        while i in existing:
+            i += 1
+        if not i < end:
+            break
+        y = i
+        while y not in existing and y < end:
+            y += 1
+        missing.append((i,y))
+        i = y
+    return missing,existing
+
+
+def loadExisting(files,  path = 'data/year_reports/'):
+    '''
+    Loads the file, if the measures of the year have already been gathered and save localy
+    '''
+    dfs = []
+    for f in files:
+        print('loading', f)
+        dfs.append(pd.read_pickle(path + str(f) + '.pkl')) #-------------------------------------change to load pickle
+    return dfs
+
+def readStationFile(ID, lat, lon, start_date, end_date):
+        '''
+        Construit un df depuis un fichier csv local, si celui ci n'est pas 
+        disponible, il est téléchargé. 
+        Le fichier csv a la forme :
+        'ID' | 'date' | 'info' | 'value' 
+        ID : l'id de la station 
+        lat : latitude de la station (utilisé sur pour afficher les donnés plus tard)
+        lon : longitude de la station (used for plotting later)
+        start_date, end_date : the lifetime of the station we are interessted in
+        '''
+        dateparse = lambda x: datetime.strptime(x, '%Y%m%d')
+
+        f = os.path.join('data','daily_stations', ID + '.csv')
+        if not os.path.isfile(f): # re
+            downloadSingleFile(ID)
+        
+        # Lit le fichier correspondant à la station
+        tmp = (pd.read_csv(f,usecols = ['ID','date','info','value'],
+                           date_parser = dateparse,parse_dates=['date'],
+                          names = ['ID','date','info','value']))
+        
+        #filtre les entrés qui correspondent à la période voulue 
+        #ainsi que l'information nécessaire (precipitations)
+        tmp = (tmp.loc[(tmp['info'] == 'PRCP') 
+                       & (tmp['date']>=start_date) 
+                       & (tmp['date']<=end_date)])
+
+        tmp.drop(['info'], inplace = True, axis =1) #nous ne gardons qu'un seul type d'info, préciser la nature de celle ci est inutile
+        
+        tmp['date'] = tmp['date'].dt.strftime('%Y-%m') #'arrondi la date au mois'
+        tmp['lat'] = lat #mets à jour les coordonés de la stations (ils ne sont pas présents dans le csv)
+        tmp['long'] = lon
+
+        '''
+        tmp = tmp.set_index('date')
+        tmp = (tmp.resample('M').mean())#.ffill(1).reindex(time_range))
+        tmp = tmp.reset_index('date')
+        '''
+        return tmp
+
+
+def creationDfProcessPool(stations_id_df, start_year, end_year,path = 'data/daily_stations'):
+    '''
+    Crée le pool de process pour pour lire et rassembler les donnés des différents fichiers de stations.
+    '''
+    df = pd.DataFrame()
+    needed = stations_id_df.shape[0]
+    dateparse = lambda x: datetime.strptime(x, '%Y%m%d')
+
+    start_date = dateparse(str(start_year)+'0101').strftime('%Y-%m')
+    end_date = dateparse(str(end_year)+'1231').strftime('%Y-%m')
+
+        
+    #Construit la liste des paramètres pour les différents fichiers csv à lire afin de construire le dataset
+    files = []
+    for index, i in stations_id_df.iterrows():
+        files.append((i['ID'], i['lat'], i['long'], start_date, end_date))
+        
+    with Pool(5) as pool: #crée une pool de process
+        L = pool.starmap(func=readStationFile,iterable=files) #lit les différents fichiers et regroupe les dfs crées sous forme de liste
+    # ^^ Pour la masse de donnée énorme à trier et mettre au bon format c'est très efficace (facteur 10)
+    
+    #df = pd.concat(L) la concaténation est plus efficace si elle n'est faite qu'une fois
+
+    return L
+
+
+def createEvolutionDataFrameOpti(start, finish):
+    '''
+    Crée le dataframe finale grace aux dataframes de toutes les stations étudiés.
+    '''
+    needed, existing = intervalNeeded(start, finish) #
+    dfs = []
+
+    for st,nd in needed:
+        s = fromInventorySelect(st, nd) #selectionne les stations qui ont des mesures sur cet interval
+        dfs.extend(creationDfToMapEvoOpti(s,st,nd)) #crée les dataframes pour les périodes non sauvegardés
+
+    dfs.extend(loadExisting(existing)) #Charge sous forme de df les années déja calculés
+    df = pd.concat(dfs) #rassemble le tout
+    df = df.reset_index(drop=True)
+    df = (df.groupby(['date', 'ID', 'lat', 'long'], as_index = False )['value']
+            .sum() #somme toutes les mesures de précipitaions du même mois (les dates de mesures ont été arrondies au mois dans la fct readStationFile)
+            .sort_values(by='date'))
+
+    #saveByYear(df,start, finish) #sauvegarde les annés du df calculé afin de ne pas avoir à les recalculer la prochaine fois
+    df.to_pickle('data/mesures_by_stations.csv')
+
+
+
+
+
+def createPriceDf():
+    '''
+    Create the price df from the transaction files
+    '''
+    dateparse = lambda x: datetime.strptime(x, '%d-%m-%Y')
+    df = pd.read_csv('data/Allocation_Trades.csv', low_memory=False, date_parser = dateparse,parse_dates=['date_of_approval'])
+    df = df.drop(['qld_origin_trading_zone','qld_destination_trading_zone', 'regulated_unregulated', 'drainage_division' ,'murray_darling_basin_region'], axis = 1)
+    #df = df.loc[((df['net_price'] != 0) & (df['dest_state'] !='Queensland')) ]
+    df['date_of_approval'] = df['date_of_approval'].dt.strftime('%Y-%m')
+    df = df.groupby(['date_of_approval','dest_state'], as_index=False)['price_per_ML'].mean() #effectue la moyenne sur le mois
+    
+    df = df.pivot(index='date_of_approval', columns='dest_state', values = 'price_per_ML').interpolate() #pivote le df pour mettre les sous indexes en colonnes pour pouvoir combles les donnés manquantes
+    
+    df = df.stack().to_frame('price_per_ML').reset_index(drop=False) # remets le dataframe dans sa configuration initiale et renomme la colone price_per_ML
+    #df = df.reset_index(drop = False)
+
+    df.to_pickle('data/prices.pkl')
+
+
+
+def main():
+    stationLifeSpan()
+    dataInventory()
+    createEvolutionDataFrameOpti(2008,2021)
+    createPriceDf()
+
+main()
\ No newline at end of file
diff --git a/delta.py b/delta.py
index deeea1d1d..adb3f619b 100644
--- a/delta.py
+++ b/delta.py
@@ -61,6 +61,7 @@
 from APAAL_criminalite_education import criminalite_education
 from ADHD_Movies import movies
 from ab_wg_apb_parcoursup import apb_parcoursup
+from GG_MP_australiaweather import australiaweather
 from ARLP_film_success_throughout_years_by_genre_1970_2020 import filmsuccess
 from AMEG_vaccination import AMEG_vaccination
 from PMPR_WineStats import dataAnalysis
@@ -130,6 +131,7 @@ def init():
     crim_edu = criminalite_education.Criminalite_Education(app)
     mvs = movies.MoviesStats(app)
     apb = apb_parcoursup.APB_PARCOURSUP(app)
+    prr = australiaweather.australiaWeather(app)
     filmsuc = filmsuccess.FilmSuccess(app)
     vac = AMEG_vaccination.Vaccinations(app)
     wine = dataAnalysis.WineStats(app)
@@ -205,6 +207,7 @@ def init():
                                   dcc.Link(html.Button("Criminalité et Education", style={"width": "100%"}), href="/criminalite-education"),
                                   dcc.Link(html.Button('Rentabilité des films', style={'width':"100%"}), href='/ADHD_Movies'),
                                   dcc.Link( html.Button("APB / Parcoursup", style={"width": "100%"}), href="/ab-wg_apb-parcoursup",),
+                                  dcc.Link(html.Button('Précipitation en australie', style={'width':"100%"}), href='/precipitation'),
                                   dcc.Link(html.Button('Succès des films par genre', style={'width':"100%"}), href='/filmsuccess'),
                                   dcc.Link(html.Button('Vaccination COVID-19', style={'width':'100%'}), href='/AMEG_vaccination'),
                                   dcc.Link(html.Button('Vins dans le monde', style={'width':"100%"}), href='/PMPR_WineStats'),
@@ -364,6 +367,8 @@ def display_page(pathname):
             return mvs.main_layout
         elif pathname == "/ab-wg_apb-parcoursup":
             return apb.main_layout
+        elif pathname == '/precipitation':
+            return prr.main_layout
         elif pathname == '/filmsuccess':
             return filmsuc.main_layout
         elif pathname == '/AMEG_vaccination':
diff --git "a/energies/Prix \303\251nergie.ipynb" "b/energies/Prix \303\251nergie.ipynb"
index 4b8b54fda..bcab6f8a9 100644
--- "a/energies/Prix \303\251nergie.ipynb"	
+++ "b/energies/Prix \303\251nergie.ipynb"	
@@ -1080,7 +1080,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.9.7"
   },
   "nbTranslate": {
    "displayLangs": [