{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 220,
   "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",
    "\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):\n",
    "    historic[\"period\"] = \"historique\"\n",
    "    forecast[\"period\"] = \"forecast\"\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,  # TODO: single dataframe\n",
    "    historic: 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_df = concatenate_historic_forecast(moderate, historic, cols_to_keep)\n",
    "    concatenated_df = concatenated_df.sort_values(by=x_axis)  # Ensure order\n",
    "\n",
    "    fig = go.Figure()\n",
    "    # colors = {\"historique\": \"blue\", \"forecast\": \"purple\"}\n",
    "\n",
    "    for condition_value in concatenated_df[\"period\"].unique():\n",
    "        segment = concatenated_df[concatenated_df[\"period\"] == condition_value]\n",
    "        fig.add_trace(go.Scatter(x=segment[x_axis], y=segment[column], \n",
    "                             mode='lines', \n",
    "                             name=f'Condition {condition_value}', \n",
    "                             line=dict(color='blue' if condition_value == 'historique' else 'purple')))\n",
    "\n",
    "    interpolation_df = concatenated_df[concatenated_df[x_axis]>2023]\n",
    "    fig.add_trace(go.Scatter(x=interpolation_df[x_axis], \n",
    "                            y=interpolation_df[column].interpolate(), \n",
    "                            mode='lines', \n",
    "                            name='', \n",
    "                            line=dict(color='blue'),showlegend=False),\n",
    "                            )\n",
    "\n",
    "    fig.update_layout(\n",
    "        title=f\"Historique et Forecast pour {column}\",\n",
    "        xaxis_title=\"Date\",\n",
    "        yaxis_title=column,\n",
    "    )\n",
    "\n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "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):\n",
    "    df[col_to_agg] = df.groupby('year')[col_to_agg].transform('mean')\n",
    "    return df\n",
    "\n",
    "def generate_plots(\n",
    "    moderate: pd.DataFrame,\n",
    "    historic: 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, x_axes[i], col))\n",
    "    return plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "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": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "root = \"/Users/akramsanad/Documents/hackathon/\"\n",
    "moderate = pd.read_csv(f\"{root}data/final_climate_data.csv\")\n",
    "moderate[\"time\"] = pd.to_datetime(moderate[\"time\"])\n",
    "moderate = moderate.sort_values('time')\n",
    "moderate['year'] = moderate[\"time\"].dt.year\n",
    "historic['year'] = historic[\"time\"].dt.year\n",
    "moderate[\"Precipitation (mm)\"] = moderate[\"Precipitation (mm)\"]*3600\n",
    "for col in cols_to_plot:\n",
    "    moderate = aggregate_yearly(moderate, col)\n",
    "    historic = aggregate_yearly(historic, col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "plots = generate_plots(moderate, historic, x_axes, cols_to_plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [
    {
     "data": {
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          "dtype": "f8"
         }
        }
       ],
       "layout": {
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
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         "mode": "lines",
         "name": "",
         "showlegend": false,
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          "dtype": "f8"
         }
        }
       ],
       "layout": {
        "template": {
         "data": {
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           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
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            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
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         "mode": "lines",
         "name": "",
         "showlegend": false,
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          "dtype": "f8"
         }
        }
       ],
       "layout": {
        "template": {
         "data": {
          "bar": [
           {
            "error_x": {
             "color": "#2a3f5f"
            },
            "error_y": {
             "color": "#2a3f5f"
            },
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "bar"
           }
          ],
          "barpolar": [
           {
            "marker": {
             "line": {
              "color": "#E5ECF6",
              "width": 0.5
             },
             "pattern": {
              "fillmode": "overlay",
              "size": 10,
              "solidity": 0.2
             }
            },
            "type": "barpolar"
           }
          ],
          "carpet": [
           {
            "aaxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "baxis": {
             "endlinecolor": "#2a3f5f",
             "gridcolor": "white",
             "linecolor": "white",
             "minorgridcolor": "white",
             "startlinecolor": "#2a3f5f"
            },
            "type": "carpet"
           }
          ],
          "choropleth": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "choropleth"
           }
          ],
          "contour": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
             [
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              "#0d0887"
             ],
             [
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              "#46039f"
             ],
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              "#7201a8"
             ],
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              "#9c179e"
             ],
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              "#bd3786"
             ],
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              "#d8576b"
             ],
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              "#ed7953"
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              "#fb9f3a"
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              "#fdca26"
             ],
             [
              1,
              "#f0f921"
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            ],
            "type": "contour"
           }
          ],
          "contourcarpet": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "type": "contourcarpet"
           }
          ],
          "heatmap": [
           {
            "colorbar": {
             "outlinewidth": 0,
             "ticks": ""
            },
            "colorscale": [
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              "#0d0887"
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        "xaxis": {
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          "text": "Date"
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        "yaxis": {
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   "source": [
    "for plot in plots:\n",
    "    plot.show() "
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Second part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = [\"Evaporation (including sublimation and transpiration) (mm)\"]"
   ]
  }
 ],
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