Gauge viz

Dashboards/demo_streamlit.py

@@ -7,29 +7,74 @@
 # Source : https://discuss.streamlit.io/t/develop-a-dashboard-app-with-streamlit-using-plotly/37148/4
 # run with : streamlit run Dashboards/demo_streamlit.py
 
+# Finalement > prendre les données issue de la vue
 lichen_ecology = df.get_lichen_ecology()
+lichen_frequency = df.get_lichen_frequency()
 
-# Debug data
-# st.dataframe(df)
+# Calcul somme des fréquences
+def calc_frequences(df):
+    df_agg = df.groupby("main_lichenspecies").agg({
+        "id": "first",
+        "frequency": "sum"
+    }).reset_index()
 
-# Calculer les proportions des espèces selon leur tolérance à l'anthropisation
-artificialisation_proportions = lichen_ecology['poleotolerance'].value_counts(normalize=True) * 100
+    return df_agg
+
+calc_freq = calc_frequences(lichen_frequency)
+calc_freq = calc_freq[["main_lichenspecies", "frequency"]]
+
+def deg_artif(id_site: int, species_name : str):
+    # Calcul filtrable
+    freq = lichen_frequency[lichen_frequency["id"] == id_site]["frequency"].values[0]
+    freq_g = calc_freq[calc_freq["main_lichenspecies"] == species_name]["frequency"].values[0] 
+
+    return round(freq / freq_g * 100, 2)
+
+
+# Sélection du site 
+id_site = st.selectbox(
+    "Sur quel site voulez-vous ?",
+    lichen_frequency["id"],
+    index=None,
+    placeholder="site n°",
+)
+
+# Sélection des espèces 
+species_name = st.selectbox(
+    "Sur quel espèce voulez-vous ?",
+    calc_freq["main_lichenspecies"],
+    index=None,
+    placeholder="Je sélectionne l'espèce...",
+)
+
+# Affichage des éléments
+if id_site and species_name != None:
+    pass
+else:
+    id_site = 460
+    species_name = "Physcia aipolia/stellaris"
+# le calcul
+artificialisation_proportions = deg_artif(id_site, species_name)
 
 # # Dataviz charts
 fig1 = go.Figure(go.Indicator(
     domain = {'x': [0, 1], 'y': [0, 1]},
-    value = artificialisation_proportions["intermediate"],
+    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': "#E3D7FF"},
-                 {'range': [25, 50], 'color': "#AFA2FF"},
-                 {'range': [50, 75], 'color': "#7A89C2"},
-                 {'range': [75, 100], 'color': "#72788D"}
+                #  {'range': [0, 25], 'color': "#E3D7FF"},
+                #  {'range': [25, 50], 'color': "#AFA2FF"},
+                #  {'range': [50, 75], 'color': "#7A89C2"},
+                #  {'range': [75, 100], 'color': "#72788D"}
+                 {'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["intermediate"]}
+             'threshold' : {'line': {'color': "#000000", 'width': 4}, 'thickness': 0.75, 'value': artificialisation_proportions}
              }))
 
 x_values = [10, 8, 6, 5, 4, 3, 2, 1]
@@ -60,61 +105,16 @@
 hand_length = np.sqrt(2) / 4
 hand_angle = np.pi * (1 - (max(min_value, min(max_value, current_value)) - min_value) / (max_value - min_value))
 
-## Version 3 non retenue du code python
-# fig3 = go.Figure(
-#     data=[
-#         go.Pie(
-#             values=[0.5] + (np.ones(n_quadrants) / 2 / n_quadrants).tolist(),
-#             rotation=90,
-#             hole=0.5,
-#             marker_colors=quadrant_colors,
-#             textinfo="text",
-#             hoverinfo="skip",
-#         ),
-#     ],
-#     layout=go.Layout(
-#         showlegend=False,
-#         margin=dict(b=0,t=10,l=10,r=10),
-#         width=450,
-#         height=450,
-#         paper_bgcolor=plot_bgcolor,
-#         annotations=[
-#             go.layout.Annotation(
-#                 text=f"<b>Degrés d'artificialisation:</b><br>{current_value} %",
-#                 x=0.5, xanchor="center", xref="paper",
-#                 y=0.25, yanchor="bottom", yref="paper",
-#                 showarrow=False,
-#             )
-#         ],
-#         shapes=[
-#             go.layout.Shape(
-#                 type="circle",
-#                 x0=0.48, x1=0.52,
-#                 y0=0.48, y1=0.52,
-#                 fillcolor="#333",
-#                 line_color="#333",
-#             ),
-#             go.layout.Shape(
-#                 type="line",
-#                 x0=0.5, x1=0.5 + hand_length * np.cos(hand_angle),
-#                 y0=0.5, y1=0.5 + hand_length * np.sin(hand_angle),
-#                 line=dict(color="#333", width=4)
-#             )
-#         ]
-#     )
-# )
-
 # Display streamlit
 st.title("Dataviz POC")
-tab1, tab2, tab3= st.tabs(["Gauge", "Histogram", "df debug"])
+tab1, tab2, tab3= st.tabs(["Gauge", "Histogram", "df Fréquences"])
 with tab1:
-    st.write("**Mode de calcul**`df['poleotolerance'].value_counts(normalize=True) * 100`")
+    st.write(f"Degrés d'artificialisation sur le site n°**{id_site}** pour l'espèce **{species_name}**")
     st.plotly_chart(fig1)
 
 with tab2:
     st.plotly_chart(fig2)
 
 with tab3:
-    st.write("Debug du dataset")
-    # st.dataframe(df)
-
+    st.write("les données de fréquences")
+    st.write(calc_freq)

Dashboards/my_data/datasets.py

@@ -1,6 +1,7 @@
 import pandas as pd
+from sqlalchemy import text
 from my_data.db_connect import get_session
-from my_data.model import Tree, TreeSpecies, Observation, Lichen, LichenSpecies, Environment, Table, LichenEcology
+from my_data.model import Tree, TreeSpecies, Observation, Lichen, LichenSpecies, Environment, Table, LichenEcology, LichenFrequency
 
 session = get_session()
 
@@ -100,7 +101,6 @@ def get_tree_species():
         })
     return pd.DataFrame(tree_species_data)
 
-# Nouvelles données
 def get_lichen_ecology():
     data = session.query(LichenEcology).all()
     lichen_ecology_data = []
@@ -111,6 +111,17 @@ def get_lichen_ecology():
             "pH": lichen_ecology.pH,
             "aridity": lichen_ecology.aridity,
             "eutrophication": lichen_ecology.eutrophication,
-            "poleotolerance" : lichen_ecology.poleotolerance
+            "poleotolerance": lichen_ecology.poleotolerance
         })
-    return pd.DataFrame(lichen_ecology_data)
+    return pd.DataFrame(lichen_ecology_data)
+
+def get_lichen_frequency():
+    data = session.query(LichenFrequency).all()
+    lichen_frequency_data = []
+    for lichen_frequency in data:
+        lichen_frequency_data.append({
+            "id": lichen_frequency.id_site,
+            "main_lichenspecies": lichen_frequency.main_lichenspecies,
+            "frequency": lichen_frequency.frequency
+        })
+    return pd.DataFrame(lichen_frequency_data)

Dashboards/my_data/model.py

@@ -1,5 +1,6 @@
-from sqlalchemy import Column, BigInteger, Integer, String, ForeignKey, ARRAY, Boolean, Date, Text, Float
+from sqlalchemy import Column, BigInteger, Integer, String, ForeignKey, ARRAY, Boolean, Date, Text, Float, text
 from sqlalchemy.orm import declarative_base, relationship
+from my_data.db_connect import engine
 
 Base = declarative_base()
 
@@ -97,4 +98,13 @@ class LichenEcology(Base):
     aridity = Column(String(255))
     eutrophication = Column(String(255))
     poleotolerance = Column(String(255))
-
+
+# Table pour les fréquences
+class LichenFrequency(Base):
+    __tablename__ = 'lichen_frequency'
+    __table_args__ = {'autoload_with': engine} 
+
+    id = Column(BigInteger, primary_key=True, autoincrement=True)
+    id_site = Column(BigInteger)
+    main_lichenspecies = Column(String(255))
+    frequency = Column(BigInteger)

Dashboards/my_data/sql_stuff.sql

@@ -0,0 +1,58 @@
+-- Listing des modifications faites sur la base de données
+-- POC requête pour afficher les fréquences 17/07/2024
+SELECT 
+    o.id AS id_site, 
+    ls.name AS main_lichenspecies, 
+    COUNT(l.id) AS frequency 
+FROM 
+    main_observation o 
+    JOIN main_tree t ON o.id = t.observation_id 
+    JOIN main_lichen l ON o.id = l.observation_id 
+    JOIN main_lichenspecies ls ON l.species_id = ls.id 
+
+GROUP BY 
+    o.id, ls.name 
+
+ORDER BY o.id, frequency DESC;
+
+-- Pour la commandline PSQL
+SELECT o.id AS id_site, ls.name AS main_lichenspecies, COUNT(l.id) AS frequency FROM main_observation o JOIN main_tree t ON o.id = t.observation_id JOIN main_lichen l ON o.id = l.observation_id JOIN main_lichenspecies ls ON l.species_id = ls.id GROUP BY o.id, ls.name ORDER BY o.id, frequency DESC;
+
+-- Créer une table view récupérable dans le modèle intégrée dans la base de données le 17/07/2024
+CREATE VIEW lichen_frequency AS
+SELECT 
+    o.id AS id_site, 
+    ls.name AS main_lichenspecies, 
+    COUNT(l.id) AS frequency 
+FROM 
+    main_observation o 
+    JOIN main_tree t ON o.id = t.observation_id 
+    JOIN main_lichen l ON o.id = l.observation_id 
+    JOIN main_lichenspecies ls ON l.species_id = ls.id 
+GROUP BY 
+    o.id, ls.name 
+ORDER BY 
+    o.id, frequency DESC;
+
+-- Pour la commandline PSQL
+CREATE VIEW lichen_frequency AS SELECT o.id AS id_site, ls.name AS main_lichenspecies, COUNT(l.id) AS frequency FROM main_observation o JOIN main_tree t ON o.id = t.observation_id JOIN main_lichen l ON o.id = l.observation_id JOIN main_lichenspecies ls ON l.species_id = ls.id GROUP BY o.id, ls.name ORDER BY o.id, frequency DESC;
+
+
+-- Update SQL command
+CREATE VIEW lichen_frequency AS
+SELECT 
+    ROW_NUMBER() OVER (ORDER BY o.id, ls.name) AS id,
+    o.id AS id_site, 
+    ls.name AS main_lichenspecies, 
+    COUNT(l.id) AS frequency 
+FROM 
+    main_observation o 
+    JOIN main_tree t ON o.id = t.observation_id 
+    JOIN main_lichen l ON o.id = l.observation_id 
+    JOIN main_lichenspecies ls ON l.species_id = ls.id 
+GROUP BY 
+    o.id, ls.name 
+ORDER BY 
+    o.id, frequency DESC;
+
+CREATE VIEW lichen_frequency AS SELECT ROW_NUMBER() OVER (ORDER BY o.id, ls.name) AS id, o.id AS id_site, ls.name AS main_lichenspecies, COUNT(l.id) AS frequency FROM main_observation o JOIN main_tree t ON o.id = t.observation_id JOIN main_lichen l ON o.id = l.observation_id JOIN main_lichenspecies ls ON l.species_id = ls.id GROUP BY o.id, ls.name ORDER BY o.id, frequency DESC;

Dashboards/tests/test.py

@@ -6,7 +6,7 @@
 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
+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, get_lichen_frequency
 
 session = get_session()
 
@@ -19,28 +19,31 @@
 tree_df = get_tree_data()
 tree_species_df = get_tree_species()
 lichen_ecology_df = get_lichen_ecology()
+lichen_frequency_df = get_lichen_frequency()
 
 # Affichage des datasets > test dataset
-print("\nEnvironment Data")
-print(environment_df.head())
+# print("\nEnvironment Data")
+# print(environment_df.head())
 
-print("\nLichen Data")
-print(lichen_df.head())
+# print("\nLichen Data")
+# print(lichen_df.head())
 
-print("\nLichen Species Data")
-print(lichen_species_df.head())
+# print("\nLichen Species Data")
+# print(lichen_species_df.head())
 
-print("\nObservation Data")
-print(observation_df.head())
+# print("\nObservation Data")
+# print(observation_df.head())
 
-print("\nTable Data")
-print(table_df.head())
+# print("\nTable Data")
+# print(table_df.head())
 
-print("\nTree Data")
-print(tree_df.head())
+# print("\nTree Data")
+# print(tree_df.head())
 
-print("\nTree Species Data")
-print(tree_species_df.head())
+# print("\nTree Species Data")
+# print(tree_species_df.head())
 
-print("\nLichen Ecology Data")
-print(lichen_ecology_df)
+# print("\nLichen Ecology Data")
+# print(lichen_ecology_df)
+
+# Correction de l'appel de méthode