UI.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "336254f7",
   "metadata": {},
   "source": [
    "### This notebook is created for users to be able to use the tool easily without running the backend code. However we request you to please check out the Functions.ipynb and Sample_Use.ipynb to get an idea on the total range of outputs. We also have an Accessing_and_Visualising.ipynb to access and visualise the downloaded data from this notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca7e2ba8",
   "metadata": {},
   "source": [
    "#### Currently the run-times are high since all the computation is done on the spot. However, we are working towards reducing these drastically and this will be updated soon . \n",
    "####  We have also made the back-end code available and users are free to fork the repo and make desired changes in the code for their own purposes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dfef4328",
   "metadata": {},
   "source": [
    "## Please ignore the code, just run the cells and select the values from the dropdowns generated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "56f9fc95",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import xarray as xr\n",
    "import plotly.graph_objects as go\n",
    "import ipywidgets as widgets\n",
    "from IPython.display import display , HTML , clear_output\n",
    "\n",
    "\n",
    "from Functions import shapefile_generator\n",
    "from Functions import graph_plotter_cropland \n",
    "from Functions import graph_plotter_marginal\n",
    "from Functions import graph_plotter_all\n",
    "from Functions import bokeh_plot , crop_show"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "36fb84e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re\n",
    "\n",
    "# Function to sanitize the key for NetCDF variable names\n",
    "def sanitize_key(key):\n",
    "    # Replace any non-alphanumeric characters with underscores\n",
    "    key = re.sub(r'[^a-zA-Z0-9_]', '_', key)\n",
    "    return key"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc183dba",
   "metadata": {},
   "source": [
    "## Select Geography (Country and Province)  \n",
    "\n",
    "### Simply run the cell , scroll to the drop down box, select your desired geography and press confirm.\n",
    "\n",
    "### Remark : Choose None in Province if you want for the whole country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fe6d486c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<b>Selected Country:</b> Nepal<br><b>Selected Province:</b> None"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Load data\n",
    "geography_df = pd.read_csv('dataset/Countries&Provinces.csv')\n",
    "\n",
    "# Get unique countries and provinces\n",
    "countries = geography_df['NAME_0'].unique()\n",
    "provinces = geography_df['NAME_1'].unique()\n",
    "\n",
    "# Create dropdown widgets\n",
    "country_dropdown = widgets.Dropdown(options=countries, description='Country:')\n",
    "province_dropdown = widgets.Dropdown(description='Province:')\n",
    "confirm_button = widgets.Button(description='Confirm Selection')\n",
    "\n",
    "# Global variables to store selected values\n",
    "selected_country = countries[0]\n",
    "selected_province = provinces[0]\n",
    "geography = None  # Initialize geography as None\n",
    "\n",
    "def update_provinces(change):\n",
    "    global selected_country\n",
    "    selected_country = change.new\n",
    "    filtered_provinces = geography_df[geography_df['NAME_0'] == selected_country]['NAME_1'].unique()\n",
    "\n",
    "    # Add an empty option to reselect or select a new province\n",
    "    province_dropdown.options = [None] + filtered_provinces.tolist()\n",
    "\n",
    "    # Set the value to the first province in the list, or 'Select Province' if the list is empty\n",
    "    province_dropdown.value = filtered_provinces[0] if filtered_provinces.any() else 'Select Province'\n",
    "\n",
    "# Attach the update_provinces function to the country dropdown's 'value' trait\n",
    "country_dropdown.observe(update_provinces, names='value')\n",
    "\n",
    "# Function to display selected values\n",
    "def on_confirm_button_click(button):\n",
    "    global selected_province, geography\n",
    "    clear_output(wait=True)  # Clear the output area before displaying new values\n",
    "\n",
    "    country_value = f\"<b>Selected Country:</b> {selected_country}\"\n",
    "\n",
    "    if province_dropdown.value and province_dropdown.value != 'Select Province':\n",
    "        selected_province = province_dropdown.value\n",
    "        province_value = f\"<b>Selected Province:</b> {selected_province}\"\n",
    "        \n",
    "        # Run the shapefile_generator code and assign the result to the global variable\n",
    "        geography = shapefile_generator(selected_country, selected_province)\n",
    "        \n",
    "        # Additional code if needed\n",
    "        \n",
    "        # Display a message to indicate success\n",
    "        print(\"Geography selected successfully!\")\n",
    "    else:\n",
    "        selected_province = None\n",
    "        province_value = \"<b>Selected Province:</b> None\"\n",
    "        geography = shapefile_generator(selected_country)\n",
    "\n",
    "    display(HTML(f\"{country_value}<br>{province_value}\"))\n",
    "\n",
    "# Attach the on_confirm_button_click function to the button's 'on_click' event\n",
    "confirm_button.on_click(on_confirm_button_click)\n",
    "\n",
    "# Style dropdown options with HTML formatting\n",
    "country_dropdown.style.description_width = 'initial'\n",
    "country_dropdown.style.description_color = 'black'\n",
    "country_dropdown.style.font_weight = 'bold'\n",
    "province_dropdown.style.description_width = 'initial'\n",
    "province_dropdown.style.description_color = 'black'\n",
    "province_dropdown.style.font_weight = 'bold'\n",
    "\n",
    "# Display the widgets\n",
    "display(widgets.HBox([country_dropdown, province_dropdown, confirm_button]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c30416f",
   "metadata": {},
   "source": [
    "### Simply run the cell , scroll to the dropdowns, select your desired climate model, irrigation conditions and Input Level and press confirm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ad0b62cf",
   "metadata": {},
   "outputs": [
    {
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       "<b>Selected Climate Model:</b> GFDL-ESM2M<br><b>Selected Water Supply Future:</b> Available water content of 200 mm/m (under irrigation conditions)<br><b>Selected Input Level:</b> High<br><b>Selected Water Supply in 2010:</b> Total"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     "output_type": "display_data"
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   "source": [
    "import ipywidgets as widgets\n",
    "from IPython.display import display, clear_output, HTML\n",
    "import plotly.graph_objects as go\n",
    "import numpy as np\n",
    "\n",
    "# Climate models options\n",
    "climate_models = ['GFDL-ESM2M', 'HadGEM2-ES', 'NorESM1-M', 'MIROC-ESM-CHEM', 'IPSL-CM5A-LR']\n",
    "\n",
    "# Water supply future options\n",
    "water_supply_future = ['Available water content of 200 mm/m (under irrigation conditions)',\n",
    "                       'Available water content of 200 mm/m (under rain-fed conditions)']\n",
    "\n",
    "# Input level options\n",
    "input_levels = ['High']\n",
    "\n",
    "# Global variables to store selected values\n",
    "selected_climate_model = 'GFDL-ESM2M'\n",
    "selected_water_supply = 'Available water content of 200 mm/m (under irrigation conditions)'\n",
    "selected_input_level = 'High'\n",
    "selected_water_supply_2010 = 'Total'  # New variable for the year 2010\n",
    "\n",
    "# Function to update selected climate model\n",
    "def update_climate_model(change):\n",
    "    global selected_climate_model\n",
    "    selected_climate_model = change.new\n",
    "\n",
    "# Function to update selected water supply future\n",
    "def update_water_supply(change):\n",
    "    global selected_water_supply\n",
    "    selected_water_supply = change.new\n",
    "\n",
    "# Function to update selected input level\n",
    "def update_input_level(change):\n",
    "    global selected_input_level\n",
    "    selected_input_level = change.new\n",
    "\n",
    "# Function to update selected water supply in 2010\n",
    "def update_water_supply_2010(change):\n",
    "    global selected_water_supply_2010\n",
    "    selected_water_supply_2010 = change.new\n",
    "\n",
    "# Function to display selected values\n",
    "def on_confirm_button_click(button):\n",
    "    clear_output(wait=True)  # Clear the output area before displaying new values\n",
    "\n",
    "    climate_model_value = f\"<b>Selected Climate Model:</b> {selected_climate_model}\"\n",
    "    water_supply_value = f\"<b>Selected Water Supply Future:</b> {selected_water_supply}\"\n",
    "    input_level_value = f\"<b>Selected Input Level:</b> {selected_input_level}\"\n",
    "    water_supply_2010_value = f\"<b>Selected Water Supply in 2010:</b> {selected_water_supply_2010}\"\n",
    "\n",
    "    display(HTML(f\"{climate_model_value}<br>{water_supply_value}<br>{input_level_value}<br>{water_supply_2010_value}\"))\n",
    "\n",
    "# Create dropdown widgets for climate models, water supply future, and input level\n",
    "climate_model_dropdown = widgets.Dropdown(options=climate_models, value=selected_climate_model, description='Climate Model:')\n",
    "water_supply_dropdown = widgets.Dropdown(options=water_supply_future, value=selected_water_supply, description='Water Supply Future:')\n",
    "input_level_dropdown = widgets.Dropdown(options=input_levels, value=selected_input_level, description='Input Level:')\n",
    "water_supply_2010_dropdown = widgets.Dropdown(options=['Total', 'Irrigated', 'Rainfed'], value=selected_water_supply_2010, description='Water Supply in 2010:')\n",
    "\n",
    "# Create a button for confirmation\n",
    "confirm_button = widgets.Button(description='Confirm Selection')\n",
    "\n",
    "# Attach the update functions to the dropdowns' 'value' trait\n",
    "climate_model_dropdown.observe(update_climate_model, names='value')\n",
    "water_supply_dropdown.observe(update_water_supply, names='value')\n",
    "input_level_dropdown.observe(update_input_level, names='value')\n",
    "water_supply_2010_dropdown.observe(update_water_supply_2010, names='value')\n",
    "\n",
    "# Attach the on_confirm_button_click function to the button's 'on_click' event\n",
    "confirm_button.on_click(on_confirm_button_click)\n",
    "\n",
    "# Style dropdown options with HTML formatting\n",
    "climate_model_dropdown.style.description_width = 'initial'\n",
    "climate_model_dropdown.style.description_color = 'black'\n",
    "climate_model_dropdown.style.font_weight = 'bold'\n",
    "water_supply_dropdown.style.description_width = 'initial'\n",
    "water_supply_dropdown.style.description_color = 'black'\n",
    "water_supply_dropdown.style.font_weight = 'bold'\n",
    "input_level_dropdown.style.description_width = 'initial'\n",
    "input_level_dropdown.style.description_color = 'black'\n",
    "input_level_dropdown.style.font_weight = 'bold'\n",
    "water_supply_2010_dropdown.style.description_width = 'initial'\n",
    "water_supply_2010_dropdown.style.description_color = 'black'\n",
    "water_supply_2010_dropdown.style.font_weight = 'bold'\n",
    "\n",
    "# Display the widgets in two rows\n",
    "display(widgets.HBox([climate_model_dropdown, water_supply_dropdown]))\n",
    "display(widgets.HBox([input_level_dropdown, water_supply_2010_dropdown, confirm_button]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d01c4fe",
   "metadata": {},
   "source": [
    "### Simply run the following cells and then click on Run. Wait for the code to run and the graphs to appear, then select the RCP and Time-period for whcih you want to download the data. Click Download and the xarrays generated will be downloaded as NetCDF4 files. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "235aa498",
   "metadata": {},
   "source": [
    "###  Cropland data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "571e72dd",
   "metadata": {},
   "outputs": [
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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "30a64d05bdde44ccbc20fca3b38adcfa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='RCP:', options=('RCP2.6', 'RCP4.5', 'RCP6.0', 'RCP8.5'), value='RCP2.6')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4dbdc727b9d345208caa480d89b8db20",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Time Period:', options=('2011-2040', '2041-2070', '2071-2100'), value='2011-2040')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "14bdb923d52a45a7a6f0e6fe40f5f78b",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Download NetCDF4', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ipywidgets as widgets\n",
    "from IPython.display import display, clear_output\n",
    "\n",
    "# Create a button to run the code\n",
    "run_button = widgets.Button(description='Run')\n",
    "\n",
    "# Create a button to download xarrays as NetCDF4\n",
    "download_nc_button = widgets.Button(description='Download NetCDF4')\n",
    "\n",
    "# Create dropdown widgets for RCP and time period\n",
    "rcp_dropdown = widgets.Dropdown(options=['RCP2.6', 'RCP4.5', 'RCP6.0', 'RCP8.5'], value='RCP2.6', description='RCP:')\n",
    "time_period_dropdown = widgets.Dropdown(options=['2011-2040', '2041-2070', '2071-2100'], value='2011-2040', description='Time Period:')\n",
    "\n",
    "# Function to run the code on button click\n",
    "def on_run_button_click(button):\n",
    "    clear_output(wait=True)  # Clear the output area before running the code\n",
    "    global fig_crop, xarrays_crop\n",
    "    fig_crop, xarrays_crop = graph_plotter_cropland(shapefile=geography, climate_model=selected_climate_model,\n",
    "                                          water_supply_future=selected_water_supply, input_level=selected_input_level)\n",
    "    \n",
    "    \n",
    "    # Display the dropdowns after the graph is loaded\n",
    "    display(rcp_dropdown)\n",
    "    display(time_period_dropdown)\n",
    "    \n",
    "    # Display the \"Download NetCDF4\" button after the graph is loaded\n",
    "    display(download_nc_button)\n",
    "\n",
    "# Attach the on_run_button_click function to the button's 'on_click' event\n",
    "run_button.on_click(on_run_button_click)\n",
    "\n",
    "# Function to download xarrays as NetCDF4 on button click\n",
    "def on_download_nc_button_click(button):\n",
    "    if xarrays_crop:\n",
    "        # Get the selected RCP and time period from the dropdowns\n",
    "        selected_rcp = rcp_dropdown.value\n",
    "        selected_time_period = time_period_dropdown.value\n",
    "\n",
    "        try:\n",
    "            # Access the selected xarray using the selected RCP and time period\n",
    "            selected_xarray = xarrays_crop[(selected_rcp, selected_time_period)]\n",
    "\n",
    "            # Save the selected xarray as a NetCDF4 file\n",
    "            selected_xarray.to_netcdf(f'{selected_country}_{selected_province}_{selected_rcp}_{selected_time_period}_cropland_data.nc')\n",
    "            print('NetCDF4 file downloaded successfully.')\n",
    "        except Exception as e:\n",
    "            print(f'Error creating dataset: {e}')\n",
    "    else:\n",
    "        print('No xarrays available. Please run the code first.')\n",
    "\n",
    "# Attach the on_download_nc_button_click function to the button's 'on_click' event\n",
    "download_nc_button.on_click(on_download_nc_button_click)\n",
    "\n",
    "# Display the \"Run\" button initially\n",
    "display(run_button)\n"
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    "### Marginal Land Data"
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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "134ecde890db43639bc6edc6c48cbee8",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='RCP:', options=('RCP2.6', 'RCP4.5', 'RCP6.0', 'RCP8.5'), value='RCP2.6')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "82a8093b65c543f8a4c40401b4dcca17",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Dropdown(description='Time Period:', options=('2011-2040', '2041-2070', '2071-2100'), value='2011-2040')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6ee21eda1aa142f68d9baf5a3daf7782",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Download NetCDF4', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ipywidgets as widgets\n",
    "from IPython.display import display, clear_output\n",
    "\n",
    "# Create a button to run the code\n",
    "run_button = widgets.Button(description='Run')\n",
    "\n",
    "# Create a button to download xarrays as NetCDF4\n",
    "download_nc_button = widgets.Button(description='Download NetCDF4')\n",
    "\n",
    "# Create dropdown widgets for RCP and time period\n",
    "rcp_dropdown = widgets.Dropdown(options=['RCP2.6', 'RCP4.5', 'RCP6.0', 'RCP8.5'], value='RCP2.6', description='RCP:')\n",
    "time_period_dropdown = widgets.Dropdown(options=['2011-2040', '2041-2070', '2071-2100'], value='2011-2040', description='Time Period:')\n",
    "\n",
    "# Function to run the code on button click\n",
    "def on_run_button_click(button):\n",
    "    clear_output(wait=True)  # Clear the output area before running the code\n",
    "    global fig_marg, xarrays_marg , final_potentials_marg\n",
    "    fig_marg, xarrays_marg , final_potentials_marg = graph_plotter_marginal(shapefile=geography, climate_model=selected_climate_model,\n",
    "                                          water_supply_future=selected_water_supply, input_level=selected_input_level)\n",
    "    \n",
    "    \n",
    "    # Display the dropdowns after the graph is loaded\n",
    "    display(rcp_dropdown)\n",
    "    display(time_period_dropdown)\n",
    "    \n",
    "    # Display the \"Download NetCDF4\" button after the graph is loaded\n",
    "    display(download_nc_button)\n",
    "\n",
    "# Attach the on_run_button_click function to the button's 'on_click' event\n",
    "run_button.on_click(on_run_button_click)\n",
    "\n",
    "# Function to download xarrays as NetCDF4 on button click\n",
    "def on_download_nc_button_click(button):\n",
    "    if xarrays_marg:\n",
    "        # Get the selected RCP and time period from the dropdowns\n",
    "        selected_rcp = rcp_dropdown.value\n",
    "        selected_time_period = time_period_dropdown.value\n",
    "\n",
    "        try:\n",
    "            # Access the selected xarray using the selected RCP and time period\n",
    "            selected_xarray = xarrays_marg[(selected_rcp, selected_time_period)]\n",
    "            selected_final_potentials = final_potentials_marg[(selected_rcp, selected_time_period)]\n",
    "            \n",
    "            # Drop the first dimension from selected_final_potentials and select the coords and dims\n",
    "            coords = {'latitude': selected_xarray['latitude'], 'longitude': selected_xarray['longitude']}\n",
    "            selected_final_potentials = selected_final_potentials[0]\n",
    "            \n",
    "           # Create a new xarray dataset with the same dimensions and coordinates\n",
    "            xarray_final_potentials= xr.DataArray(data=selected_final_potentials, dims=('y', 'x'), coords=coords , name='final_energy_potential')\n",
    "\n",
    "            # Save the selected xarray as a NetCDF4 file\n",
    "            selected_xarray.to_netcdf(f'{selected_country}_{selected_province}_{selected_rcp}_{selected_time_period}_marginal_land_data.nc')\n",
    "            \n",
    "            # Save the new xarray dataset as a NetCDF4 file\n",
    "            xarray_final_potentials.to_netcdf(f'{selected_country}_{selected_province}_{selected_rcp}_{selected_time_period}_marginal_land_final_energy_potential_data.nc', 'w')\n",
    "            \n",
    "        except Exception as e:\n",
    "            print(f'Error creating dataset: {e}')\n",
    "    else:\n",
    "        print('No xarrays available. Please run the code first.')\n",
    "\n",
    "# Attach the on_download_nc_button_click function to the button's 'on_click' event\n",
    "download_nc_button.on_click(on_download_nc_button_click)\n",
    "\n",
    "# Display the \"Run\" button initially\n",
    "display(run_button)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dcc8154f",
   "metadata": {},
   "source": [
    "### Cropland , Marginal and Total Land Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d3cb5754",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "38051c5d1e2e4320931ac1edfac9b9d7",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Button(description='Run All', style=ButtonStyle())"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import ipywidgets as widgets\n",
    "from IPython.display import display, clear_output\n",
    "import plotly.graph_objects as go\n",
    "import numpy as np\n",
    "import xarray as xr\n",
    "\n",
    "# Create a button to run the code\n",
    "run_button_all = widgets.Button(description='Run All')\n",
    "\n",
    "# Create a button to download xarrays as NetCDF4\n",
    "download_nc_button_all = widgets.Button(description='Download NetCDF4')\n",
    "\n",
    "# Create dropdown widgets for RCP and time period\n",
    "rcp_dropdown_all = widgets.Dropdown(options=['RCP2.6', 'RCP4.5', 'RCP6.0', 'RCP8.5'], value='RCP2.6', description='RCP:')\n",
    "time_period_dropdown_all = widgets.Dropdown(options=['2011-2040', '2041-2070', '2071-2100'], value='2011-2040', description='Time Period:')\n",
    "\n",
    "# Function to run the code on button click\n",
    "def on_run_button_all_click(button):\n",
    "    clear_output(wait=True)  # Clear the output area before running the code\n",
    "    global fig_crop_all, fig_marg_all, fig_total_all, xarrays_marg_all, xarrays_crop_all, final_potentials_marg_all, arrays_total_all\n",
    "    fig_crop_all, fig_marg_all, fig_total_all, xarrays_marg_all, xarrays_crop_all, final_potentials_marg_all, arrays_total_all = graph_plotter_all(\n",
    "        shapefile=geography, climate_model=selected_climate_model,\n",
    "        water_supply_future=selected_water_supply, input_level=selected_input_level, water_supply_2010=selected_water_supply_2010\n",
    "    )\n",
    "\n",
    "    # Display the dropdowns after the graphs are loaded\n",
    "    display(rcp_dropdown_all)\n",
    "    display(time_period_dropdown_all)\n",
    "\n",
    "    # Display the \"Download NetCDF4\" button after the graphs are loaded\n",
    "    display(download_nc_button_all)\n",
    "\n",
    "# Attach the on_run_button_all_click function to the button's 'on_click' event\n",
    "run_button_all.on_click(on_run_button_all_click)\n",
    "\n",
    "# Function to download xarrays as NetCDF4 on button click\n",
    "def on_download_nc_button_all_click(button):\n",
    "    if xarrays_crop_all and xarrays_marg_all and arrays_total_all: # and final_potentials_marg_all:\n",
    "        # Get the selected RCP and time period from the dropdowns\n",
    "        selected_rcp = rcp_dropdown_all.value\n",
    "        selected_time_period = time_period_dropdown_all.value\n",
    "\n",
    "        try:\n",
    "            # Choose the xarray based on the selected RCP and time period\n",
    "            selected_xarray_crop = xarrays_crop_all.get((selected_rcp, selected_time_period))\n",
    "            selected_xarray_marg = xarrays_marg_all.get((selected_rcp, selected_time_period))\n",
    "            selected_array_total = arrays_total_all.get((selected_rcp, selected_time_period))\n",
    "            selected_final_potentials_marg = final_potentials_marg_all.get(((selected_rcp, selected_time_period)))\n",
    "\n",
    "            if selected_xarray_crop is not None:\n",
    "                selected_xarray_crop.to_netcdf(f'{selected_country}_{selected_province}_{selected_rcp}_{selected_time_period}_cropland.nc')\n",
    "                print('NetCDF4 file for cropland downloaded successfully.')\n",
    "            if selected_xarray_marg is not None:\n",
    "                selected_xarray_marg.to_netcdf(f'{selected_country}_{selected_province}_{selected_rcp}_{selected_time_period}_marginal.nc')\n",
    "                print('NetCDF4 file for marginal land downloaded successfully.')\n",
    "                \n",
    "            if selected_final_potentials_marg is not None: \n",
    "                # Drop the first dimension from selected_final_potentials and select the coords and dims\n",
    "                coords = {'latitude': selected_xarray_crop['latitude'], 'longitude': selected_xarray_crop['longitude']}\n",
    "                selected_final_potentials_marg = selected_final_potentials_marg[0]\n",
    "            \n",
    "               # Create a new xarray dataset with the same dimensions and coordinates\n",
    "                xarray_final_potentials_marg= xr.DataArray(data=selected_final_potentials_marg, dims=('y', 'x'), coords=coords , name='final_energy_potential_marg')\n",
    "                xarray_final_potentials_marg.to_netcdf(f'{selected_country}_{selected_province}_{selected_rcp}_{selected_time_period}_marginal_land_final_energy_potential_data.nc', 'w')\n",
    "                \n",
    "            if selected_array_total is not None:\n",
    "                # Drop the first dimension from selected_final_potentials and select the coords and dims\n",
    "                coords = {'latitude': selected_xarray_crop['latitude'], 'longitude': selected_xarray_crop['longitude']}\n",
    "                selected_array_total = selected_array_total[0] if selected_array_total.ndim == 3 else selected_array_total\n",
    "            \n",
    "               # Create a new xarray dataset with the same dimensions and coordinates\n",
    "                xarray_total= xr.DataArray(data=selected_array_total, dims=('y', 'x'), coords=coords , name='total_energy_potential')\n",
    "                xarray_total.to_netcdf(f'{selected_country}_{selected_province}_{selected_rcp}_{selected_time_period}_marginal_land_final_energy_potential_data.nc', 'w')\n",
    "                print('NetCDF4 file for total land downloaded successfully.')\n",
    "                \n",
    "        except Exception as e:\n",
    "            print(f'Error creating dataset: {e}')\n",
    "    else:\n",
    "        print('No xarrays available. Please run the code first.')\n",
    "\n",
    "# Attach the on_download_nc_button_all_click function to the button's 'on_click' event\n",
    "download_nc_button_all.on_click(on_download_nc_button_all_click)\n",
    "\n",
    "# Display the \"Run All\" button initially\n",
    "display(run_button_all)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41702864",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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