Improve Charts

pyproject.toml

@@ -64,17 +64,19 @@ extra-dependencies = [
   "nbsphinx",
   "taipy",
   "streamlit",
+
 ]
 [tool.hatch.envs.docs.scripts]
 build = "sphinx-build -b html doc/ doc/_build"
 
 [tool.hatch.envs.serve]
 extra-dependencies = [
   "streamlit",
+  "streamlit-analytics2",
 ]
 
 [tool.hatch.envs.serve.scripts]
-prod = "streamlit run --server.port 8502 --server.baseUrlPath energy_forecast  src/energy_forecast/dashboard/dashboard_streamlit.py"
+prod = "streamlit run --server.port 8502 --server.baseUrlPath energy_forecast  src/energy_forecast/dashboard/Accueil.py"
 
 [tool.coverage.run]
 source_pkgs = ["energy_forecast", "tests"]

src/energy_forecast/dashboard/Accueil.py

@@ -0,0 +1,21 @@
+import streamlit as st
+
+if __name__ == "__main__":
+    st.set_page_config(
+        page_title="Prévision ENR",
+        page_icon="🌞",
+    )
+
+    st.markdown("""# Bienvenue dans Energy Forecast! 🌞
+Cette application est une démonstration d'un tableau de bord de prévision énergétique simple.
+
+Il est divisé en trois sections:
+
+- <a href="weather" target = "_self">Prévision météo</a>
+- <a href="power_generation" target = "_self">Prévision énergétique</a>
+- <a href="consumption_prediction" target = "_self">Prévision de consommation</a>
+- <a href="prediction_tempo" target = "_self">Prédiction Tempo</a>
+"""
+    , unsafe_allow_html=True)
+
+    st.sidebar.success("Sélectionnez une démo ci-dessus.")

src/energy_forecast/dashboard/pages/1_weather.py → src/energy_forecast/dashboard/pages/1_Météo.py

@@ -3,6 +3,7 @@
 import pandas as pd
 import streamlit as st
 import altair as alt
+alt.renderers.set_embed_options(time_format_locale="fr-FR", format_locale="fr-FR")
 
 
 @memory.cache
@@ -91,23 +92,27 @@ def compute_data_wind(date: str)->pd.DataFrame:
     return data
 
 if __name__ == "__main__":
+
     st.title("Prévision meteo")
+    st.markdown("Cette page affiche les prévisions météorologiques pour les prochains jours."
+                " Les données sont obtenues à partir de [Météo France](https://www.meteofrance.com/).")
     date = pd.Timestamp.now().strftime("%Y-%m-%d")
     wind_data = compute_data_wind(date)
 
+
     wind_barplot = alt.Chart(wind_data).mark_bar().encode(
-        y="wind_speed",
-        x="time"
-    )
+        y=alt.Y("wind_speed:Q").title("Vitesse du vent (m/s)"),
+        x=alt.X("time:T").title("Date")
+    ).properties(title="Vitesse moyenne du vent en France.")
     st.altair_chart(wind_barplot,
                     use_container_width=True
                 )
 
     sun_data = compute_data_sun(date)
     sun_barplot = alt.Chart(sun_data).mark_bar().encode(
-        y="sun_flux",
-        x="time"
-    )
+        y=alt.Y("sun_flux:Q").title("Flux solaire (W/m^2)"),
+        x=alt.X("time:T").title("Date")
+    ).properties(title="Flux solaire moyen en France.")
     st.altair_chart(sun_barplot,
                     use_container_width=True
                 )

src/energy_forecast/dashboard/pages/2_power_generation.py → src/energy_forecast/dashboard/pages/2_Production.py

@@ -9,6 +9,8 @@
 import pandas as pd
 import streamlit as st
 import altair as alt
+alt.renderers.set_embed_options(time_format_locale="fr-FR", format_locale="fr-FR")
+
 
 @memory.cache
 def compute_data_pv_power(date: str)->pd.DataFrame:
@@ -110,6 +112,7 @@ def compute_energy(date:str):
     energy =  r.read_file()
 
     energy = energy[["Eolien", "Solaire"]]
+    energy.rename(columns={"Eolien": "Eolien Production", "Solaire": "PV Production"}, inplace=True)
     offset_days = 5
     history_start = pd.Timestamp(date) - pd.Timedelta(f"{offset_days} days")
     energy = energy[history_start:]
@@ -132,29 +135,34 @@ def compute_energy(date:str):
     energy = energy[energy["time"].dt.minute == 0]
 
     st.title("Prévision de production ENR")
+    st.markdown("Cette page affiche les prévisions de production d'énergie renouvelable pour la date sélectionnée."
+                " Les données sont obtenues à partir de [RTE](https://www.rte-france.com/eco2mix/la-production-delectricite-en-temps-reel).")
+
+
+    energy_long = energy.melt(id_vars="time", var_name="source", value_name="power")
+    eolen_long = energy_long[energy_long["source"].str.contains("Eolien")]
+    pv_long = energy_long[energy_long["source"].str.contains("PV")]
+
+    eolen_title = alt.Title("Production Eolien",
+                            subtitle="Production historique et prévisionnelle")
 
-    wind_history = alt.Chart(energy).mark_line().encode(
-        y="Eolien",
-        x="time"
-    ).interactive()
-    wind_prediction = alt.Chart(energy).mark_line(color="red").encode(
-        y="Eolien Prediction",
-        x="time"
-    ).interactive()
-    wind_barplot = wind_prediction + wind_history
-    st.altair_chart(wind_barplot,
+    eolen_chart = alt.Chart(eolen_long, title=eolen_title).mark_line().encode(
+        x=alt.X("time:T", title="Date"),
+        y=alt.Y("power:Q", title="Production (MW)"),
+        color=alt.Color("source:N", title="Type de production"),
+    )
+
+    st.altair_chart(eolen_chart,
                     use_container_width=True
                 )
 
-    sun_history = alt.Chart(energy).mark_line().encode(
-        y="Solaire",
-        x="time"
-    )
-    sun_prediction = alt.Chart(energy).mark_line(color="red").encode(
-        y="PV Prediction",
-        x="time"
+    sun_title = alt.Title("Production Solaire",
+                          subtitle="Production historique et prévisionnelle")
+    sun_chart = alt.Chart(pv_long, title=sun_title).mark_line().encode(
+        x=alt.X("time:T", title="Date"),
+        y=alt.Y("power:Q", title="Production (MW)"),
+        color=alt.Color("source:N", title="Type de production"),
     )
-    sun_barplot = sun_prediction + sun_history
-    st.altair_chart(sun_barplot,
+    st.altair_chart(sun_chart,
                     use_container_width=True
                 )

src/energy_forecast/dashboard/pages/3_Consomation.py

@@ -0,0 +1,55 @@
+"""Create a Streamlit page to display the consumption forecast.
+
+The Consumption forecast is obtained from the :class:`energy_forecast.consumption_forecast.PredictionForecastAPI` class.
+It uses RTE predictions.
+"""
+
+from energy_forecast.consumption_forecast import PredictionForecastAPI
+from energy_forecast import ROOT_DIR
+import pandas as pd
+import streamlit as st
+import altair as alt
+alt.renderers.set_embed_options(time_format_locale="fr-FR", format_locale="fr-FR")
+
+@st.cache
+def get_weekly_forecast(secret: str)->pd.DataFrame:
+    """Get the weekly forecast for the total electricity consumption.
+
+    Parameters
+    ----------
+    secret : str
+        The secret key to access the RTE API.
+
+    Returns
+    -------
+    pd.DataFrame
+        The weekly forecast for the total electricity consumption.
+        The columns are ["time", "predicted_consumption"]
+    """
+    consumption_forecast_api = PredictionForecastAPI(secret=secret)
+    consumption_forecast = consumption_forecast_api.get_weekly_forecast(
+        start_date=pd.Timestamp.now().date()
+    ).reset_index()
+    return consumption_forecast
+
+if __name__ == "__main__":
+    env_file = ROOT_DIR / ".env"
+    with env_file.open("r") as f:
+        secret = f.readline().strip().split("=", 1)[1]
+    consumption_forecast = get_weekly_forecast(secret)
+
+
+    st.title("Prévision de consomation electrique total")
+    st.markdown("Prévision de consomation electrique total pour la semaine prochaine"
+                " en utilisant les données de RTE.")
+
+    title = alt.Title("Consomation electrique total prévue", anchor="start",
+                      subtitle="France métropolitaine")
+
+    consumption_chart = alt.Chart(consumption_forecast, title=title).mark_line().encode(
+        y=alt.Y("predicted_consumption:Q", title="Consomation electrique (MW)"),
+        x=alt.X("time:T", title="Date")
+    )
+    st.altair_chart(consumption_chart,
+                    use_container_width=True
+                )

src/energy_forecast/dashboard/pages/4_prediction_tempo.py → src/energy_forecast/dashboard/pages/4_Jours_Tempos.py

@@ -12,6 +12,7 @@
 
     for offset, col in zip(range(3), [col1, col2, col3]):
         text = f"Prévision pour le {today + pd.Timedelta(days=offset)}"
-        text += "\n\n <h1 style='color: white;'> Jour Bleu </h1>"
+        text += "\n\n <h1 style='color: white;'> Jour Bleu </h1> \n\n"
         # an html blue square the size of the column with the text and H1 in white
-        col.markdown(f"<div style='background-color: blue ; padding: 10px ; border-radius: 10px ; color: white ; text-align: center'>{text}</div>", unsafe_allow_html=True)
+        col.markdown(f"<div style='background-color: blue ; padding: 10px ; border-radius: 10px ; color: white ; text-align: center'>{text}</div> \n", unsafe_allow_html=True)
+        col.markdown("\n")