gauge_chart merge

Dashboards/demo_dash.py

@@ -1,153 +1,40 @@
 from dash import Dash, html, dcc, Output, Input
 import plotly.express as px
-import plotly.graph_objects as go
 import pandas as pd
-from pathlib import Path
-import sys
-chemin_dossier_parent = Path(__file__).parent.parent
-sys.path.append(str(chemin_dossier_parent))
-from my_data.db_connect import get_session
-from my_data.datasets import get_environment_data, get_lichen_data, get_lichen_species_data, get_observation_data, get_table_data, get_tree_data, get_tree_species, get_lichen_ecology
+
+# Source : https://discuss.streamlit.io/t/develop-a-dashboard-app-with-streamlit-using-plotly/37148/4 
+# Dash version
+# run with : python Dashboard/demo_dash.py
+
+# Load data function
+def load_data():
+    return pd.read_csv("https://people.sc.fsu.edu/~jburkardt/data/csv/airtravel.csv")
 
 # Initialize Dash app
 app = Dash(__name__)
 
 # Load data
-lichen_df = get_lichen_data()
-lichen_species_df = get_lichen_species_data()
-observation_df = get_observation_data()
-table_df = get_table_data()
-tree_species_df = get_tree_species()
-ecology_df = get_lichen_ecology()
-
-# Fonction pour calculer la fréquence des valeurs E, N, O, S
-def calculate_frequency(column):
-    return column.apply(lambda x: sum(1 for char in x if char in ['E', 'N', 'O', 'S']))
-
-# Calculer la fréquence
-table_df['freq'] = (
-    table_df['sq1'].apply(lambda x: len(x) if pd.notnull(x).any() else 0)  +
-    table_df['sq2'].apply(lambda x: len(x) if pd.notnull(x).any() else 0) +
-    table_df['sq3'].apply(lambda x: len(x) if pd.notnull(x).any() else 0) +
-    table_df['sq4'].apply(lambda x: len(x) if pd.notnull(x).any() else 0) +
-    table_df['sq5'].apply(lambda x: len(x) if pd.notnull(x).any() else 0)
-)
-
-# Joindre table avec lichen et observation
-merged_df = table_df.merge(lichen_df, left_on='lichen_id', right_on='id', suffixes=('', '_l'))
-merged_df = merged_df.merge(lichen_species_df, left_on='species_id', right_on='id', suffixes=('', '_ls'))
-merged_df = merged_df.merge(observation_df, left_on='observation_id', right_on='id', suffixes=('', '_o'))
-
-# Grouper par 'species' et 'observation_id' et additionner les fréquences
-grouped_df = merged_df.groupby(['name', 'observation_id'])['freq'].sum().reset_index()
-
-# Regrouper les deux tables afficher les données écologiques
-grouped_df = grouped_df.merge(ecology_df, left_on='name', right_on='cleaned_taxon', suffixes=('', '_e'))
-
-# ajustement des noms finaux
-grouped_df = grouped_df[['observation_id', 'name', 'freq','pH','eutrophication', 'poleotolerance']]
-grouped_df = grouped_df.rename(
-    columns={
-        'observation_id': 'id', 
-        'name': 'lichen', 
-        'freq': 'freq',
-        'pH': 'ph',
-        'eutrophication': 'eutrophication', 
-        'poleotolerance': 'poleotolerance'
-        })
-
-# Calcul du degrés d'artificialisation
-def deg_artif(my_input: int):
-    global_freq = grouped_df[grouped_df['id']== my_input]['freq'].sum()
-    base_freq = grouped_df[(grouped_df['id'] == my_input) & (grouped_df['poleotolerance'] == 'resistant')]['freq'].sum()
-
-    return round((base_freq / global_freq) * 100, 2)
-
-# Calcul de la pollution acidé 
-def pollution_acide(my_input: int):
-    global_freq = grouped_df[grouped_df['id']== my_input]['freq'].sum()
-    acid_freq = grouped_df[(grouped_df['id'] == my_input) & (grouped_df['ph'] == 'acidophilous')]['freq'].sum()
-
-    return round((acid_freq / global_freq) * 100, 2)
-
-def pollution_azote(my_input: int):
-    global_freq = grouped_df[grouped_df['id']== my_input]['freq'].sum()
-    azote_freq = grouped_df[(grouped_df['id'] == my_input) & (grouped_df['eutrophication'] == 'eutrophic')]['freq'].sum()
-
-    return round((azote_freq / global_freq) * 100, 2)
+df = load_data()
 
 # Define app layout with a hidden input
 app.layout = html.Div([
-    html.H1("Gauge bar Dash", style={'textAlign': 'center'}),
-    dcc.Dropdown(grouped_df["id"].unique(), 419, id='dropdown-selection', style={'width': '50%', 'margin': 'auto'}),
-    html.Div([
-        dcc.Graph(id='graph-content1', style={'flex': '1', 'margin': '10px'}),
-        dcc.Graph(id='graph-content2', style={'flex': '1', 'margin': '10px'}),
-        dcc.Graph(id='graph-content3', style={'flex': '1', 'margin': '10px'})
-    ], style={'display': 'flex', 'justify-content': 'space-around', 'width': '100%'})
+    html.H1("Air Travel Time Series Plot", style={'textAlign': 'center'}),
+    html.Div("This chart shows the number of air passengers traveled in each month from 1949 to 1960", style={'textAlign': 'center'}),
+    dcc.Graph(id='air-travel-graph'),
+    html.Div(id='dummy-div', style={'display': 'none'})  # Hidden div acting as a placeholder
 ])
 
+
 # Define callback to update graph
 @app.callback(
-    [
-        Output('graph-content1', 'figure'),
-        Output('graph-content2', 'figure'),
-        Output('graph-content3', 'figure')
-    ],
-    Input('dropdown-selection', 'value')
+    Output('air-travel-graph', 'figure'),
+    Input('dummy-div', 'children')  # Use the hidden div as input
 )
-
 def update_graph(value):
-    fig1 = go.Figure(go.Indicator(
-        domain = {'x': [0, 1], 'y': [0, 1]},
-        value = deg_artif(value),
-        mode = "gauge+number",
-        title = {'text': "Degré d'artificialisation"},
-        gauge = {'axis': {'range': [0, 100], 'dtick': 25},
-                'bar': {'color': "#000000"},
-                'steps' : [
-                    {'range': [0, 25], 'color': "green"},
-                    {'range': [25, 50], 'color': "yellow"},
-                    {'range': [50, 75], 'color': "orange"},
-                    {'range': [75, 100], 'color': "red"}
-                    ],
-                'threshold' : {'line': {'color': "#000000", 'width': 4}, 'thickness': 0.75, 'value': deg_artif(value)}
-                }))
-
-    fig2 = go.Figure(go.Indicator(
-        domain = {'x': [0, 1], 'y': [0, 1]},
-        value = pollution_acide(value),
-        mode = "gauge+number",
-        title = {'text': "Degré d'artificialisation"},
-        gauge = {'axis': {'range': [0, 100], 'dtick': 25},
-                'bar': {'color': "#000000"},
-                'steps' : [
-                    {'range': [0, 25], 'color': "green"},
-                    {'range': [25, 50], 'color': "yellow"},
-                    {'range': [50, 75], 'color': "orange"},
-                    {'range': [75, 100], 'color': "red"}
-                    ],
-                'threshold' : {'line': {'color': "#000000", 'width': 4}, 'thickness': 0.75, 'value': pollution_acide(value)}
-                }))
-
-    fig3 = go.Figure(go.Indicator(
-        domain = {'x': [0, 1], 'y': [0, 1]},
-        value = pollution_azote(value),
-        mode = "gauge+number",
-        title = {'text': "Degré d'artificialisation"},
-        gauge = {'axis': {'range': [0, 100], 'dtick': 25},
-                'bar': {'color': "#000000"},
-                'steps' : [
-                    {'range': [0, 25], 'color': "green"},
-                    {'range': [25, 50], 'color': "yellow"},
-                    {'range': [50, 75], 'color': "orange"},
-                    {'range': [75, 100], 'color': "red"}
-                    ],
-                'threshold' : {'line': {'color': "#000000", 'width': 4}, 'thickness': 0.75, 'value': pollution_azote(value)}
-                }))
-    return fig1, fig2, fig3
+    fig = px.line(df, x="Month", y=df.columns[1:], title="Air Passenger Travel")
+    return fig
 
 
 # Run the app
 if __name__ == '__main__':
-    app.run_server(debug=True)
+    app.run_server(debug=True)

Dashboards/demo_streamlit.py

@@ -1,134 +1,20 @@
 import streamlit as st
 import pandas as pd
-import my_data.datasets as df
-import plotly.graph_objects as go
-import numpy as np
-
-from pathlib import Path
-import sys
-chemin_dossier_parent = Path(__file__).parent.parent
-sys.path.append(str(chemin_dossier_parent))
-from my_data.db_connect import get_session
-from my_data.datasets import get_environment_data, get_lichen_data, get_lichen_species_data, get_observation_data, get_table_data, get_tree_data, get_tree_species, get_lichen_ecology
+import plotly.express as px
 
 # Source : https://discuss.streamlit.io/t/develop-a-dashboard-app-with-streamlit-using-plotly/37148/4
-# run with : streamlit run Dashboards/demo_streamlit.py
-
-# Récupération des datasets
-environment_df = get_environment_data()
-lichen_df = get_lichen_data()
-lichen_species_df = get_lichen_species_data()
-observation_df = get_observation_data()
-table_df = get_table_data()
-tree_df = get_tree_data()
-tree_species_df = get_tree_species()
-ecology_df = get_lichen_ecology()
-
-# Fonction pour calculer la fréquence des valeurs E, N, O, S
-def calculate_frequency(column):
-    return column.apply(lambda x: sum(1 for char in x if char in ['E', 'N', 'O', 'S']))
-
-# Calculer la fréquence
-table_df['freq'] = (
-    table_df['sq1'].apply(lambda x: len(x) if pd.notnull(x).any() else 0)  +
-    table_df['sq2'].apply(lambda x: len(x) if pd.notnull(x).any() else 0) +
-    table_df['sq3'].apply(lambda x: len(x) if pd.notnull(x).any() else 0) +
-    table_df['sq4'].apply(lambda x: len(x) if pd.notnull(x).any() else 0) +
-    table_df['sq5'].apply(lambda x: len(x) if pd.notnull(x).any() else 0)
-)
-
-# Joindre table avec lichen et observation
-merged_df = table_df.merge(lichen_df, left_on='lichen_id', right_on='id', suffixes=('', '_l'))
-merged_df = merged_df.merge(lichen_species_df, left_on='species_id', right_on='id', suffixes=('', '_ls'))
-merged_df = merged_df.merge(observation_df, left_on='observation_id', right_on='id', suffixes=('', '_o'))
-
-# Grouper par 'species' et 'observation_id' et additionner les fréquences
-grouped_df = merged_df.groupby(['name', 'observation_id'])['freq'].sum().reset_index()
-
-# Regrouper les deux tables afficher les données écologiques
-grouped_df = grouped_df.merge(ecology_df, left_on='name', right_on='cleaned_taxon', suffixes=('', '_e'))
-
-# ajustement des noms finaux
-grouped_df = grouped_df[['observation_id', 'name', 'freq','pH','eutrophication', 'poleotolerance']]
-grouped_df = grouped_df.rename(
-    columns={
-        'observation_id': 'id', 
-        'name': 'lichen', 
-        'freq': 'freq',
-        'pH': 'ph',
-        'eutrophication': 'eutrophication', 
-        'poleotolerance': 'poleotolerance'
-        })
-
-# Calcul du degrés d'artificialisation
-def deg_artif(my_input: int):
-    global_freq = grouped_df[grouped_df['id']== my_input]['freq'].sum()
-    base_freq = grouped_df[(grouped_df['id'] == my_input) & (grouped_df['poleotolerance'] == 'resistant')]['freq'].sum()
-
-    return round((base_freq / global_freq) * 100, 2)
-
-# Calcul de la pollution acidé 
-def pollution_acide(my_input: int):
-    global_freq = grouped_df[grouped_df['id']== my_input]['freq'].sum()
-    acid_freq = grouped_df[(grouped_df['id'] == my_input) & (grouped_df['ph'] == 'acidophilous')]['freq'].sum()
-
-    return round((acid_freq / global_freq) * 100, 2)
-
-def pollution_azote(my_input: int):
-    global_freq = grouped_df[grouped_df['id']== my_input]['freq'].sum()
-    azote_freq = grouped_df[(grouped_df['id'] == my_input) & (grouped_df['eutrophication'] == 'eutrophic')]['freq'].sum()
-
-    return round((azote_freq / global_freq) * 100, 2)
+# run with : streamlit run Dashboard/demo_streamlit.py
 
-# Sélection du site 
-id_site = st.selectbox(
-    "Sur quel site voulez-vous ?",
-    grouped_df["id"].unique(),
-    index=None,
-    placeholder="site n°",
-)
+def load_data():
+    return pd.read_csv("https://people.sc.fsu.edu/~jburkardt/data/csv/airtravel.csv")
 
-# Sélection metrique
-metrics = st.selectbox(
-    "Quelle métrique voulez-vous ?",
-    ("Degré d'artificialisation", "Pollution acide", "Pollution azote"),
-    index=None,
-    placeholder="Je sélectionne la métrique...",
-)
+# Load data
+df = load_data()
 
-# Affichage des éléments
-if id_site:
-    if metrics == "Degré d'artificialisation":
-        artificialisation_proportions = deg_artif(id_site)
-    elif metrics == "Pollution acide":
-        artificialisation_proportions = pollution_acide(id_site)
-    elif metrics == "Pollution azote":
-        artificialisation_proportions = pollution_azote(id_site)
+# Display some information
+st.title("Air Travel Time Series Plot")
+st.write("This chart shows the number of air passenger traveled in each month from 1949 to 1960")
 
-# Dataviz charts
-if id_site and metrics:
-    ##########################
-    # Affichage du graphique #
-    ##########################
-    st.write("# Gauge bar")
-    st.write(f"Site : {id_site}")
-    st.write(f"Lichen : {metrics}")
-    fig1 = go.Figure(go.Indicator(
-        domain = {'x': [0, 1], 'y': [0, 1]},
-        value = artificialisation_proportions,
-        mode = "gauge+number",
-        title = {'text': "Degré d'artificialisation"},
-        gauge = {'axis': {'range': [0, 100], 'dtick': 25},
-                'bar': {'color': "#000000"},
-                'steps' : [
-                    {'range': [0, 25], 'color': "green"},
-                    {'range': [25, 50], 'color': "yellow"},
-                    {'range': [50, 75], 'color': "orange"},
-                    {'range': [75, 100], 'color': "red"}
-                    ],
-                'threshold' : {'line': {'color': "#000000", 'width': 4}, 'thickness': 0.75, 'value': artificialisation_proportions}
-                }))
-    # Afficher les donnée dans streamlit
-    st.plotly_chart(fig1)
-else:
-    st.write("Veuillez sélectionner un site et une métrique")
+# Plotly figure
+fig = px.line(df, x="Month", y=df.columns[1:], title="Air Passenger Travel")
+st.plotly_chart(fig)