{
"cells": [
{
"cell_type": "code",
"execution_count": 344,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd # stress hydrique and rendement, besoin en eau\n",
"import plotly.graph_objects as go\n",
"from typing import List\n",
"import plotly.express as px\n",
"import os\n",
"from forecast import get_forecast_datasets, get_forecast_data\n",
"\n",
"cols_to_keep = [\n",
" \"Precipitation (mm)\",\n",
" \"Near Surface Air Temperature (°C)\",\n",
" \"Surface Downwelling Shortwave Radiation (W/m²)\",\n",
" \"year\",\n",
" \"period\",\n",
"]\n",
"\n",
"\n",
"def concatenate_historic_forecast(historic, forecast, cols_to_keep, value_period_col = \"forecast scénario modéré\"):\n",
" historic[\"period\"] = \"historique\"\n",
" forecast[\"period\"] = value_period_col\n",
" historic = historic[cols_to_keep]\n",
" forecast = forecast[cols_to_keep]\n",
" full_data = pd.concat([historic, forecast])\n",
" return full_data\n",
"\n",
"\n",
"def visualize_climate(\n",
" moderate: pd.DataFrame,\n",
" historic: pd.DataFrame,\n",
" pessimist: pd.DataFrame,\n",
" x_axis=\"year\",\n",
" column: str = \"Precipitation (mm)\",\n",
" cols_to_keep: List[str] = [\n",
" \"Precipitation (mm)\",\n",
" \"Near Surface Air Temperature (°C)\",\n",
" \"Surface Downwelling Shortwave Radiation (W/m²)\",\n",
" \"year\",\n",
" \"period\",\n",
" ],\n",
"):\n",
" concatenated_moderate = concatenate_historic_forecast(historic, moderate, cols_to_keep)\n",
" concatenated_moderate = concatenated_moderate.sort_values(by=x_axis) # Ensure order\n",
"\n",
" fig = go.Figure()\n",
"\n",
" if column == \"Precipitation (mm)\":\n",
" for condition_value in concatenated_moderate[\"period\"].unique():\n",
" segment = concatenated_moderate[concatenated_moderate[\"period\"] == condition_value]\n",
" avg_precipitation = segment.groupby(x_axis)[column].mean().reset_index() \n",
" \n",
" fig.add_trace(\n",
" go.Bar(\n",
" x=avg_precipitation[x_axis], \n",
" y=avg_precipitation[column], \n",
" name=f\"{condition_value}\",\n",
" marker=dict(color=\"blue\" if condition_value == \"historique\" else \"purple\"),\n",
" )\n",
" )\n",
" \n",
" concatenated_pessimist = concatenate_historic_forecast(historic, pessimist, cols_to_keep, \"forecast scénario pessimiste\")\n",
" concatenated_pessimist = concatenated_pessimist.sort_values(by=x_axis)\n",
" concatenated_pessimist = concatenated_pessimist[concatenated_pessimist[\"period\"]!=\"historique\"]\n",
" for condition_value in concatenated_pessimist[\"period\"].unique():\n",
" segment = concatenated_pessimist[concatenated_pessimist[\"period\"] == condition_value]\n",
" avg_precipitation = segment.groupby(x_axis)[column].mean().reset_index() \n",
" \n",
" fig.add_trace(\n",
" go.Bar(\n",
" x=avg_precipitation[x_axis], \n",
" y=avg_precipitation[column], \n",
" name=f\"{condition_value}\",\n",
" marker=dict(color=\"orange\" if condition_value != \"historique\" else \"blue\"),\n",
" )\n",
" )\n",
" \n",
" # Update layout for bar chart\n",
" fig.update_layout(\n",
" title=f\"Moyenne de {column} par année\",\n",
" xaxis_title=\"Année\", # Set the x-axis title to Year\n",
" yaxis_title=\"Précipitation (mm)\", # Set the y-axis title to Precipitation\n",
" barmode='group' # Group bars for different conditions\n",
" )\n",
" \n",
" else:\n",
" # For other columns, continue with the line plot as before\n",
" for condition_value in concatenated_moderate[\"period\"].unique():\n",
" segment = concatenated_moderate[concatenated_moderate[\"period\"] == condition_value]\n",
" if condition_value == \"historique\":\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=segment[x_axis], # Years on x-axis\n",
" y=segment[column], # Precipitation values on y-axis\n",
" mode=\"lines\",\n",
" name=f\"{condition_value}\",\n",
" line=dict(color=\"blue\" if condition_value == \"historique\" else \"purple\"),\n",
" showlegend=False\n",
" )\n",
" )\n",
" else:\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=segment[x_axis], # Years on x-axis\n",
" y=segment[column], # Precipitation values on y-axis\n",
" mode=\"lines\",\n",
" name=f\"{condition_value}\",\n",
" line=dict(color=\"blue\" if condition_value == \"historique\" else \"purple\"),\n",
" )\n",
" )\n",
" \n",
" # Continue with pessimistic data as in the original function...\n",
" concatenated_pessimist = concatenate_historic_forecast(historic, pessimist, cols_to_keep, \"forecast scénario pessimiste\")\n",
" concatenated_pessimist = concatenated_pessimist.sort_values(by=x_axis)\n",
" for condition_value in concatenated_pessimist[\"period\"].unique():\n",
" segment = concatenated_pessimist[concatenated_pessimist[\"period\"] == condition_value]\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=segment[x_axis], # Years on x-axis\n",
" y=segment[column], # Precipitation values on y-axis\n",
" mode=\"lines\",\n",
" name=f\"{condition_value}\",\n",
" line=dict(color=\"blue\" if condition_value == \"historique\" else \"orange\"),\n",
" )\n",
" )\n",
" \n",
" # Interpolation for the pessimistic scenario...\n",
" interpolation_pessimist = concatenated_pessimist[concatenated_pessimist[x_axis] > 2023]\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=interpolation_pessimist[x_axis],\n",
" y=interpolation_pessimist[column].interpolate(),\n",
" mode=\"lines\",\n",
" name = \"forecast scénario pessimiste\",\n",
" line=dict(color=\"orange\"),\n",
" showlegend=False\n",
" \n",
" ),\n",
" )\n",
" interpolation_moderate = concatenated_moderate[concatenated_moderate[x_axis] > 2023]\n",
" fig.add_trace(\n",
" go.Scatter(\n",
" x=interpolation_moderate[x_axis],\n",
" y=interpolation_moderate[column].interpolate(),\n",
" mode=\"lines\",\n",
" name = \"forecast scénario modéré\",\n",
" line=dict(color=\"purple\"),\n",
" showlegend=False\n",
" \n",
" ),\n",
" )\n",
" fig.update_layout(\n",
" title=f\"Historique et Forecast pour {column}\",\n",
" xaxis_title=\"Year\", # Set the x-axis title to Year\n",
" yaxis_title=column, # Set the y-axis title to Precipitation\n",
" )\n",
"\n",
" return fig\n"
]
},
{
"cell_type": "code",
"execution_count": 345,
"metadata": {},
"outputs": [],
"source": [
"cols_to_plot = [\n",
" \"Precipitation (mm)\",\n",
" \"Near Surface Air Temperature (°C)\",\n",
" \"Surface Downwelling Shortwave Radiation (W/m²)\",\n",
"]\n",
"x_axes = [\"year\"] * len(cols_to_plot)\n",
"\n",
"\n",
"def aggregate_yearly(df, col_to_agg, operation = \"mean\"):\n",
" df[col_to_agg] = df.groupby(\"year\")[col_to_agg].transform(operation)\n",
" return df\n",
"\n",
"\n",
"def generate_plots(\n",
" moderate: pd.DataFrame,\n",
" historic: pd.DataFrame,\n",
" pessimist: pd.DataFrame,\n",
" x_axes: List[str],\n",
" cols_to_plot: List[str],\n",
"):\n",
" plots = []\n",
" for i, col in enumerate(cols_to_plot):\n",
" plots.append(visualize_climate(moderate, historic, pessimist, x_axes[i], col))\n",
" return plots"
]
},
{
"cell_type": "code",
"execution_count": 346,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Coordinates 46.99472427368164°N 4.967532157897949°E\n",
"Elevation 201.0 m asl\n",
"Timezone None None\n",
"Timezone difference to GMT+0 0 s\n"
]
}
],
"source": [
"from data_pipelines.historical_weather_data import (\n",
" download_historical_weather_data,\n",
" aggregate_hourly_weather_data,\n",
")\n",
"\n",
"latitude = 47\n",
"longitude = 5\n",
"start_year = 2000\n",
"end_year = 2025\n",
"df = download_historical_weather_data(latitude, longitude, start_year, end_year)\n",
"historic = aggregate_hourly_weather_data(df)\n",
"historic= historic.reset_index()\n",
"historic = historic.rename(\n",
" columns={\n",
" \"precipitation\": \"Precipitation (mm)\",\n",
" \"air_temperature_mean\": \"Near Surface Air Temperature (°C)\",\n",
" \"irradiance\": \"Surface Downwelling Shortwave Radiation (W/m²)\",\n",
" 'index': 'time'\n",
" }\n",
")\n",
"historic[\"time\"] = pd.to_datetime(historic[\"time\"])\n",
"historic = historic.sort_values('time')\n",
"historic = historic[historic[\"time\"]<\"2025-01-01\"]\n"
]
},
{
"cell_type": "code",
"execution_count": 347,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing surface_downwelling_shortwave_radiation (Surface Downwelling Shortwave Radiation, W/m², rsds)...\n",
"Time values: ['2025-01-16T12:00:00.000000000' '2025-02-15T00:00:00.000000000'\n",
" '2025-03-16T12:00:00.000000000' '2025-04-16T00:00:00.000000000'\n",
" '2025-05-16T12:00:00.000000000']\n",
"Data values: [ 56.527912 115.35918 165.2781 198.94925 268.55258 ]\n",
"Processing moisture_in_upper_portion_of_soil_column (Moisture in Upper Portion of Soil Column, kg m-2, mrsos)...\n",
"Time values: ['2025-01-16T12:00:00.000000000' '2025-02-15T00:00:00.000000000'\n",
" '2025-03-16T12:00:00.000000000' '2025-04-16T00:00:00.000000000'\n",
" '2025-05-16T12:00:00.000000000']\n",
"Data values: [37.17831 35.047794 31.540066 30.021376 25.469845]\n",
"Processing precipitation (Precipitation, kg m-2 s-1, pr)...\n",
"Time values: ['2025-01-16T12:00:00.000000000' '2025-02-15T00:00:00.000000000'\n",
" '2025-03-16T12:00:00.000000000' '2025-04-16T00:00:00.000000000'\n",
" '2025-05-16T12:00:00.000000000']\n",
"Data values: [2.4469095e-05 1.8132760e-05 2.3262006e-05 3.2253953e-05 1.9975125e-05]\n",
"Processing near_surface_relative_humidity (Relative Humidity, %, hurs)...\n",
"Time values: ['2025-01-16T12:00:00.000000000' '2025-02-15T00:00:00.000000000'\n",
" '2025-03-16T12:00:00.000000000' '2025-04-16T00:00:00.000000000'\n",
" '2025-05-16T12:00:00.000000000']\n",
"Data values: [84.20817 70.5419 74.01712 75.56472 67.9183 ]\n",
"Processing evaporation_including_sublimation_and_transpiration (Evaporation (including sublimation and transpiration), kg m-2 s-1, evspsbl)...\n",
"Time values: ['2025-01-16T12:00:00.000000000' '2025-02-15T00:00:00.000000000'\n",
" '2025-03-16T12:00:00.000000000' '2025-04-16T00:00:00.000000000'\n",
" '2025-05-16T12:00:00.000000000']\n",
"Data values: [4.5341171e-06 9.0692965e-06 2.1621307e-05 2.3066174e-05 3.7059166e-05]\n",
"Processing total_runoff (Total Runoff, kg m-2 s-1, mrro)...\n",
"Time values: ['2025-01-16T12:00:00.000000000' '2025-02-15T00:00:00.000000000'\n",
" '2025-03-16T12:00:00.000000000' '2025-04-16T00:00:00.000000000'\n",
" '2025-05-16T12:00:00.000000000']\n",
"Data values: [2.2649770e-05 1.8449240e-05 1.2937878e-05 1.0781837e-05 5.3132662e-06]\n",
"Processing daily_minimum_near_surface_air_temperature (Daily Minimum Near Surface Air Temperature, °C, tasmin)...\n",
"Time values: ['2025-01-16T12:00:00.000000000' '2025-02-15T00:00:00.000000000'\n",
" '2025-03-16T12:00:00.000000000' '2025-04-16T00:00:00.000000000'\n",
" '2025-05-16T12:00:00.000000000']\n",
"Data values: [-1.3704834 -1.5664673 3.8347473 1.8583679 9.399872 ]\n",
"Processing daily_maximum_near_surface_air_temperature (Daily Maximum Near Surface Air Temperature, °C, tasmax)...\n",
"Time values: ['2025-01-16T12:00:00.000000000' '2025-02-15T00:00:00.000000000'\n",
" '2025-03-16T12:00:00.000000000' '2025-04-16T00:00:00.000000000'\n",
" '2025-05-16T12:00:00.000000000']\n",
"Data values: [ 4.6723633 7.5112 13.662933 11.345947 21.358429 ]\n",
"Processing near_surface_wind_speed (Near Surface Wind Speed, m/s, sfcWind)...\n",
"Time values: ['2025-01-16T12:00:00.000000000' '2025-02-15T00:00:00.000000000'\n",
" '2025-03-16T12:00:00.000000000' '2025-04-16T00:00:00.000000000'\n",
" '2025-05-16T12:00:00.000000000']\n",
"Data values: [3.1137166 2.898267 3.3749728 2.816573 2.3026316]\n",
"Processing near_surface_air_temperature (Near Surface Air Temperature, °C, tas)...\n",
"Time values: ['2025-01-16T12:00:00.000000000' '2025-02-15T00:00:00.000000000'\n",
" '2025-03-16T12:00:00.000000000' '2025-04-16T00:00:00.000000000'\n",
" '2025-05-16T12:00:00.000000000']\n",
"Data values: [ 1.478241 2.4737244 8.627563 6.8439636 15.954193 ]\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Processing surface_downwelling_shortwave_radiation (Surface Downwelling Shortwave Radiation, W/m², rsds)...\n",
"Time values: ['2025-01-16T12:00:00.000000000' '2025-02-15T00:00:00.000000000'\n",
" '2025-03-16T12:00:00.000000000' '2025-04-16T00:00:00.000000000'\n",
" '2025-05-16T12:00:00.000000000']\n",
"Data values: [ 56.673943 95.93133 164.22237 179.87556 234.53908 ]\n",
"Processing moisture_in_upper_portion_of_soil_column (Moisture in Upper Portion of Soil Column, kg m-2, mrsos)...\n",
"Time values: ['2025-01-16T12:00:00.000000000' '2025-02-15T00:00:00.000000000'\n",
" '2025-03-16T12:00:00.000000000' '2025-04-16T00:00:00.000000000'\n",
" '2025-05-16T12:00:00.000000000']\n",
"Data values: [36.030197 37.549957 34.82986 33.80241 32.310314]\n",
"Processing precipitation (Precipitation, kg m-2 s-1, pr)...\n",
"Time values: ['2025-01-16T12:00:00.000000000' '2025-02-15T00:00:00.000000000'\n",
" '2025-03-16T12:00:00.000000000' '2025-04-16T00:00:00.000000000'\n",
" '2025-05-16T12:00:00.000000000']\n",
"Data values: [4.1012718e-05 4.2356012e-05 2.2875112e-05 5.8805726e-05 3.8959399e-05]\n",
"Processing near_surface_relative_humidity (Relative Humidity, %, hurs)...\n",
"Time values: ['2025-01-16T12:00:00.000000000' '2025-02-15T00:00:00.000000000'\n",
" '2025-03-16T12:00:00.000000000' '2025-04-16T00:00:00.000000000'\n",
" '2025-05-16T12:00:00.000000000']\n",
"Data values: [85.743546 73.03426 74.506615 81.08522 80.1974 ]\n",
"Processing evaporation_including_sublimation_and_transpiration (Evaporation (including sublimation and transpiration), kg m-2 s-1, evspsbl)...\n",
"Time values: ['2025-01-16T12:00:00.000000000' '2025-02-15T00:00:00.000000000'\n",
" '2025-03-16T12:00:00.000000000' '2025-04-16T00:00:00.000000000'\n",
" '2025-05-16T12:00:00.000000000']\n",
"Data values: [5.0246440e-06 6.1679884e-06 1.5823740e-05 2.6137883e-05 3.4693843e-05]\n",
"Processing total_runoff (Total Runoff, kg m-2 s-1, mrro)...\n",
"Time values: ['2025-01-16T12:00:00.000000000' '2025-02-15T00:00:00.000000000'\n",
" '2025-03-16T12:00:00.000000000' '2025-04-16T00:00:00.000000000'\n",
" '2025-05-16T12:00:00.000000000']\n",
"Data values: [2.6853075e-05 2.7253527e-05 2.2040354e-05 2.6314607e-05 1.9199733e-05]\n",
"Processing daily_minimum_near_surface_air_temperature (Daily Minimum Near Surface Air Temperature, °C, tasmin)...\n",
"Time values: ['2025-01-16T12:00:00.000000000' '2025-02-15T00:00:00.000000000'\n",
" '2025-03-16T12:00:00.000000000' '2025-04-16T00:00:00.000000000'\n",
" '2025-05-16T12:00:00.000000000']\n",
"Data values: [-0.52978516 -4.0076294 -1.0578613 4.1943054 5.8277283 ]\n",
"Processing daily_maximum_near_surface_air_temperature (Daily Maximum Near Surface Air Temperature, °C, tasmax)...\n",
"Time values: ['2025-01-16T12:00:00.000000000' '2025-02-15T00:00:00.000000000'\n",
" '2025-03-16T12:00:00.000000000' '2025-04-16T00:00:00.000000000'\n",
" '2025-05-16T12:00:00.000000000']\n",
"Data values: [ 5.2298584 3.2523804 8.524567 13.032715 14.992493 ]\n",
"Processing near_surface_wind_speed (Near Surface Wind Speed, m/s, sfcWind)...\n",
"Time values: ['2025-01-16T12:00:00.000000000' '2025-02-15T00:00:00.000000000'\n",
" '2025-03-16T12:00:00.000000000' '2025-04-16T00:00:00.000000000'\n",
" '2025-05-16T12:00:00.000000000']\n",
"Data values: [3.0686219 3.5536988 3.0945094 3.3852775 2.7981563]\n",
"Processing near_surface_air_temperature (Near Surface Air Temperature, °C, tas)...\n",
"Time values: ['2025-01-16T12:00:00.000000000' '2025-02-15T00:00:00.000000000'\n",
" '2025-03-16T12:00:00.000000000' '2025-04-16T00:00:00.000000000'\n",
" '2025-05-16T12:00:00.000000000']\n",
"Data values: [ 2.1499023 -0.4963684 3.7083435 8.798035 10.812775 ]\n"
]
}
],
"source": [
"folder_to_parse_moderate = \"climate_data_moderate/\"\n",
"climate_sub_folder_moderate = [\n",
" os.path.join(folder_to_parse_moderate, e)\n",
" for e in os.listdir(folder_to_parse_moderate)\n",
" if os.path.isdir(os.path.join(folder_to_parse_moderate, e))\n",
"]\n",
"climate_sub_files_moderate = [\n",
" os.path.join(e, i)\n",
" for e in climate_sub_folder_moderate\n",
" for i in os.listdir(e)\n",
" if i.endswith(\".nc\")\n",
"]\n",
"datasets_moderate = get_forecast_datasets(climate_sub_files_moderate)\n",
"moderate = get_forecast_data(datasets_moderate, latitude, longitude)\n",
"\n",
"folder_to_parse_pessimist = \"climate_data_pessimist/\"\n",
"climate_sub_folder_pessimist = [\n",
" os.path.join(folder_to_parse_pessimist, e)\n",
" for e in os.listdir(folder_to_parse_pessimist)\n",
" if os.path.isdir(os.path.join(folder_to_parse_pessimist, e))\n",
"]\n",
"climate_sub_files_pessimist = [\n",
" os.path.join(e, i)\n",
" for e in climate_sub_folder_pessimist\n",
" for i in os.listdir(e)\n",
" if i.endswith(\".nc\")\n",
"]\n",
"datasets_pessimist = get_forecast_datasets(climate_sub_files_pessimist)\n",
"pessimist = get_forecast_data(datasets_pessimist, latitude, longitude)"
]
},
{
"cell_type": "code",
"execution_count": 348,
"metadata": {},
"outputs": [],
"source": [
"moderate = moderate.rename(columns={\"Precipitation (kg m-2 s-1)\": \"Precipitation (mm)\"})#TODO : do yearly precipitations\n",
"moderate[\"time\"] = pd.to_datetime(moderate[\"time\"])\n",
"moderate = moderate.sort_values('time')\n",
"moderate['year'] = moderate[\"time\"].dt.year\n",
"moderate[\"Precipitation (mm)\"] = moderate[\"Precipitation (mm)\"]*31536000\n",
"pessimist = pessimist.rename(columns={\"Precipitation (kg m-2 s-1)\": \"Precipitation (mm)\"})\n",
"pessimist[\"time\"] = pd.to_datetime(pessimist[\"time\"])\n",
"pessimist = pessimist.sort_values('time')\n",
"pessimist['year'] = pessimist[\"time\"].dt.year\n",
"pessimist[\"Precipitation (mm)\"] = pessimist[\"Precipitation (mm)\"]*31536000\n",
"pessimist[\"period\"] = \"forecast scénario pessimiste\"\n",
"historic['year'] = historic[\"time\"].dt.year\n",
"historic[\"Precipitation (mm)\"] = historic[\"Precipitation (mm)\"]*8760.0\n",
"for col in cols_to_plot:\n",
" moderate = aggregate_yearly(moderate, col)\n",
" historic = aggregate_yearly(historic, col)\n",
" pessimist = aggregate_yearly(pessimist, col)"
]
},
{
"cell_type": "code",
"execution_count": 349,
"metadata": {},
"outputs": [],
"source": [
"plots = generate_plots(moderate, historic, pessimist, x_axes, cols_to_plot)"
]
},
{
"cell_type": "code",
"execution_count": 350,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
"config": {
"plotlyServerURL": "https://plot.ly"
},
"data": [
{
"marker": {
"color": "blue"
},
"name": "historique",
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