import streamlit as st
from app.utils.map_utils import plot_map
from app.utils.legends_utils import get_stat_unit
from app.pipelines.import_data import pipeline_data
from app.pipelines.import_config import pipeline_config
from app.pipelines.import_map import pipeline_map
from app.pipelines.import_scatter import pipeline_scatter
from app.utils.show_info import show_info_data, show_info_metric
st.set_page_config(layout="wide", page_title="Analyse interactive des précipitations en France (1959–2022)", page_icon="🌧️")
st.markdown("""
""", unsafe_allow_html=True)
st.markdown("""
""", unsafe_allow_html=True)
css = """
"""
st.markdown(css, unsafe_allow_html=True)
def show(
config_path: dict,
height: int=600
):
# Chargement des config
params_config = pipeline_config(config_path, type="stat")
config = params_config["config"]
stat_choice = params_config["stat_choice"]
season_choice = params_config["season_choice"]
stat_choice_key = params_config["stat_choice_key"]
scale_choice_key = params_config["scale_choice_key"]
min_year_choice = params_config["min_year_choice"]
max_year_choice = params_config["max_year_choice"]
season_choice_key = params_config["season_choice_key"]
missing_rate = params_config["missing_rate"]
quantile_choice = params_config["quantile_choice"]
scale_choice = params_config["scale_choice"]
show_relief = params_config["show_relief"]
show_stations = params_config["show_stations"]
# Préparation des paramètres pour pipeline_data
params_load = (
stat_choice_key,
scale_choice_key,
min_year_choice,
max_year_choice,
season_choice_key,
missing_rate,
quantile_choice,
scale_choice
)
# Obtention des données
result = pipeline_data(params_load, config, use_cache=True)
# Chargement des affichages graphiques
unit_label = get_stat_unit(stat_choice_key, scale_choice_key)
params_map = (
stat_choice_key,
result,
unit_label,
height
)
layer, scatter_layer, tooltip, view_state, html_legend = pipeline_map(params_map)
col1, col2, col3 = st.columns([1, 0.15, 1])
with col1:
scatter_layer = None if not show_stations else scatter_layer
deck = plot_map([layer, scatter_layer], view_state, tooltip, activate_relief=show_relief)
st.markdown(
f"""
{stat_choice} des précipitations de {min_year_choice} à {max_year_choice} ({season_choice.lower()})
""",
unsafe_allow_html=True
)
if deck:
st.pydeck_chart(deck, use_container_width=True, height=height)
with col2:
st.markdown(html_legend, unsafe_allow_html=True)
with col3:
params_scatter = (
result,
stat_choice_key,
scale_choice_key,
stat_choice,unit_label,
height
)
n_tot_mod, n_tot_obs, me, mae, rmse, r2, scatter = pipeline_scatter(params_scatter)
st.markdown(
"""
Données CP-RCM, 2.5 km, forçage ERA5, réanalyse ECMWF
""",
unsafe_allow_html=True
)
st.plotly_chart(scatter, use_container_width=True)
col0bis, col1bis, col2bis, col3bis, col4bis, col5bis, col6bis = st.columns(7)
show_info_data(col0bis, "CP-AROME map", result["modelised_show"].shape[0], n_tot_mod)
show_info_data(col1bis, "Stations", result["observed_show"].shape[0], n_tot_obs)
show_info_data(col2bis, "CP-AROME plot", result["modelised"].shape[0], n_tot_mod)
show_info_metric(col3bis, "ME", me)
show_info_metric(col4bis, "MAE", mae)
show_info_metric(col5bis, "RMSE", rmse)
show_info_metric(col6bis, "r²", r2)
if __name__ == "__main__":
config_path = "app/config/config.yaml"
st.markdown("""
Analyse interactive des précipitations en France — 1959 – 2022
""", unsafe_allow_html=True)
show(config_path)