Merge pull request #3 from dataforgoodfr/histogram-viz

Histogram viz

Dashboards/visualisation/dash_hist4_pxbar.py

@@ -0,0 +1,138 @@
+import pandas as pd
+import numpy as np
+import base64
+
+import sys
+from pathlib import Path
+
+import plotly.express as px
+
+from dash import Dash, dcc, html
+from dash.dependencies import Output, Input
+
+# Ajoute le dossier parent à sys.path
+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_lichen_ecology
+
+# image file path
+image_path = 'C:/Users/Galinette/Documents/GitHub/lichensgo/Dashboards/visualisation/xanthoria parietina.jpg'
+encoded_image = base64.b64encode(open(image_path, 'rb').read())
+
+# Initialize Dash app
+app = Dash(__name__)
+
+# Load datasets
+environment_df = get_environment_data()
+lichen_df = get_lichen_data()
+lichen_species_df = get_lichen_species_data()
+lichen_ecology_df = get_lichen_ecology()
+
+print("\n Lichen ecology info:")
+print(lichen_ecology_df.head())
+
+print("\n Taxon")
+print(lichen_ecology_df.loc[20,"taxon"])
+
+# group by species' type + add a column with the count occurence
+df_grouped=(
+    lichen_df
+    .groupby("species_id", as_index=False)
+    .agg(count_col=pd.NamedAgg(column="species_id", aggfunc="count"))
+)
+
+# concatenate dataframe "df_grouped" with the lichen species' names
+df_grouped_species=pd.concat([df_grouped, lichen_species_df.loc[:,"name"]], axis=1)
+
+# sort based on occurence 
+# (note): update_xaxes(categoryorder="total descending") does not work
+df_grouped_species=(
+    df_grouped_species
+    .sort_values(by="count_col", ascending=False, ignore_index=True)
+)
+
+# Define app layout 
+app.layout = html.Div([
+    html.H1("Select the Lichen's species"),
+    dcc.Dropdown(id="dropdown_Lichen",
+                 options=lichen_species_df["name"].unique(),
+                 value="Xanthoria parietina"
+                 ),
+    html.H2("Ecology information of the selected lichen:"),
+    html.H4(id="info_taxon"),
+    html.H4(id="info_pH"),
+    html.H4(id="info_aridity"),
+    html.Img(src='data:image/png;base64,{}'.format(encoded_image.decode()),
+            style={
+            'width': '10%',
+            'height': '10%',
+        }),
+    dcc.Graph(id="hist4")
+])
+
+# Define callback to update graph
+@app.callback(
+    Output("info_taxon", "children"),
+    Output("info_pH", "children"),
+    Output("info_aridity", "children"),
+    #Output("image", "src"),
+    Output("hist4", "figure"),
+    Input("dropdown_Lichen", "value")
+)
+def hist4_interactive(Lichen_selected):
+
+    # index in "df_grouped_species" corresponding to the selected species
+    idx=df_grouped_species["name"].loc[lambda x: x==Lichen_selected].index
+
+    # adjust the color based on the selected species
+    color_discrete_sequence=['#ec7c34']*len(df_grouped_species)
+    color_discrete_sequence[int(idx[0])]='#609cd4'
+
+    hist4=px.bar(
+        df_grouped_species, 
+        x="count_col", 
+        y="name",
+        orientation="h",
+        color="name",
+        color_discrete_sequence=color_discrete_sequence,
+        title="Espèces les plus observées par les observateurs Lichens GO"
+    )
+
+    # remove the legend
+    hist4.update(layout_showlegend=False)
+
+    # update the layout 
+    hist4.update_layout(
+        title_font=dict(color="grey",size=24),
+        title={"x": .5,"y": .95,"xanchor": "center"},
+        plot_bgcolor='white',
+        paper_bgcolor="white",
+        width=1100,
+        height=800, 
+    )
+
+    # update axes 
+    hist4.update_xaxes(
+        title="Count",
+        showline=True,
+        linecolor='black',
+        gridcolor="black",
+    )
+
+    hist4.update_yaxes(
+        title="",
+        showline=True,
+        linecolor='black',
+    )
+
+    info_taxon =f" Taxon : {lichen_ecology_df.loc[int(idx[0]),"taxon"]}"
+    info_pH =f" pH : {lichen_ecology_df.loc[int(idx[0]),"pH"]}"
+    info_aridity =f" Aridity : {lichen_ecology_df.loc[int(idx[0]),"aridity"]}"
+
+    return info_taxon, info_pH, info_aridity, hist4
+
+# Run the app
+if __name__ == "__main__":
+    app.run(debug=True,host="127.0.0.1",port=8050)
+    #app.run()

Dashboards/visualisation/hist3_pxbar.py

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+import pandas as pd
+import sys
+from pathlib import Path
+import plotly
+import plotly.express as px
+
+# Ajoute le dossier parent à sys.path
+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_tree_data, get_observation_data, get_table_data
+
+session = get_session()
+
+# 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()
+
+# Affichage des datasets
+# print("\nEnvironment Data")
+# print(environment_df.head())
+
+# print("\nLichen Data")
+# print(lichen_df.head())
+
+# print("\nLichen Species Data")
+# print(lichen_species_df.head())
+
+# print("\nObservation Data")
+# print(observation_df.head())
+
+print("\nTable Data")
+print(table_df.head())
+
+# print("\nTree Data")
+# print(tree_df.head())
+
+# Select one site based on latitude, longitude, date et user id
+# Filtering with query method
+user = 2
+date = "2022-01-20"
+lat = 45.822677
+long = 1.242670
+obs = 500
+
+site = observation_df.query("user_id == @user"
+                          and "date_obs == @date"
+                          and "localisation_lat == @lat"
+                          and "localisation_long == @long")
+
+# Tree data of the selected site
+site_tree = tree_df.query("observation_id == @obs")
+
+print("\nTree Data of the selected site")
+print(site_tree)
+
+# Lichen data of the selected site
+site_lichen = lichen_df.query("observation_id == @obs")
+
+print("\nLichen Data of the selected site")
+print(site_lichen)
+
+# Table data of the selected site
+lichen_species = site_lichen["id"].unique()
+
+# For each lichen observed per site, count the total number of quadrat
+quadrat = []
+
+for i in lichen_species:
+    site_lichen_table = table_df[table_df["lichen_id"] == i]
+
+    print("\nTable Data lichen")
+    print(site_lichen_table)
+
+    # Sum of the non-empty quadrat
+    sum_sq1 = site_lichen_table['sq1'].sum()
+    sum_sq2 = site_lichen_table['sq2'].sum()
+    sum_sq3 = site_lichen_table['sq3'].sum()
+    sum_sq4 = site_lichen_table['sq4'].sum()
+    sum_sq5 = site_lichen_table['sq5'].sum()
+
+    sum_quadrat = sum_sq1 + sum_sq2 + sum_sq3 + sum_sq4 + sum_sq5
+
+    # Letters to check
+    letters_to_check = ['N', 'E', 'S', 'O']
+
+    # Count based on the orientation
+    count_dict = {}
+    for letter in sum_quadrat:
+        if letter in count_dict:
+            count_dict[letter] += 1
+        else:
+            count_dict[letter] = 1
+
+    # Complete with 0 for any missing orientation
+    result_dict = []
+    for letter in letters_to_check:
+        if letter not in count_dict:
+            result_dict.append(0)
+        else:
+            result_dict.append(count_dict[letter])
+
+    # print("\nNon-empty quadrat")
+    # print(sum_quadrat)
+
+    # Append the results to the quadrat output
+    quadrat.append({"id": i,
+                    "sum_quadrat":len(sum_quadrat),
+                    "N":result_dict[0],
+                    "E":result_dict[1],
+                    "S":result_dict[2],
+                    "O":result_dict[3]})
+
+# Convert the quadrat list to a DataFrame
+quadrat_df = pd.DataFrame(quadrat)
+
+print("\nLichen Data of the selected site with orientation")
+print(quadrat_df)
+
+# Merge the DataFrame
+site_lichen_quadrat = pd.merge(site_lichen, quadrat_df)
+
+### Histogram 3 with Plotly express bar: ###
+# Espèces observées sur le site sélectionné
+
+# concatenate dataframe "site_lichen_quadrat" with the lichen species' names
+idx = site_lichen_quadrat["species_id"]
+df = lichen_species_df.loc[idx-1,"name"].reset_index(drop=True)
+
+site_lichen_quadrat_species=pd.concat([site_lichen_quadrat, df], axis=1)
+
+# sort based on occurence
+# (note): update_xaxes(categoryorder="total descending") does not work
+site_lichen_quadrat_species=(
+    site_lichen_quadrat_species
+    .sort_values(by="sum_quadrat", ignore_index=True)
+)
+
+print("\n Final table for histogram")
+print(site_lichen_quadrat_species)
+
+### Design bar plot ###
+
+hist3=px.bar(
+    site_lichen_quadrat_species,
+    x=["N", "E", "S", "O"],
+    y="name",
+    orientation="h",
+    # width=1500,
+    # height=800,
+    title="Espèces observées sur le site sélectionné"
+)
+
+# remove the legend
+#hist3.update(layout_showlegend=False)
+
+# update the title
+hist3.update_layout(
+    title_font=dict(color="grey",size=20),
+    title={"x": .5,"y": .9,"xanchor": "center"},
+)
+
+# update axes
+hist3.update_xaxes(title="Count",showgrid=False)
+hist3.update_yaxes(title="")
+
+hist3.show()