CoLab_convert_clean4_clean.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a6edddd0-bc2b-462b-abb0-89949e290146",
   "metadata": {},
   "source": [
    "[![image](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Philliec459/Science-and-Technology-Society-Use-of-NASA-STELLA-Q2-Spectrometer/blob/main/CoLab_convert_clean4_clean.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10ac69d9-1160-4970-9de4-4580d179c224",
   "metadata": {},
   "source": [
    "# Conversion Code to write STELLA data and Plots to Excel in easy to read formats.\n",
    "---\n",
    "\n",
    "\n",
    "## **Format STELLA-Q2 data for Timestamp and Channel irradiance data**\n",
    "---\n",
    "\n",
    "\n",
    "\n",
    "# **Are you using Google CoLab?**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "88abecad-4826-460c-941c-2ee6b3c6ff4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "using_colab = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a251716a-8000-4457-a4b2-38d7f72e5b59",
   "metadata": {},
   "outputs": [],
   "source": [
    "if using_colab:\n",
    "\n",
    "    !wget 'https://github.com/Philliec459/Science-and-Technology-Society-Use-of-NASA-STELLA-Q2-Spectrometer/raw/main/data_white_grass_shade_whiteshade.xlsx'\n",
    "    !wget 'https://github.com/Philliec459/Science-and-Technology-Society-Use-of-NASA-STELLA-Q2-Spectrometer/raw/main/data_white_FullSun.xlsx'\n",
    "    !wget 'https://github.com/Philliec459/Science-and-Technology-Society-Use-of-NASA-STELLA-Q2-Spectrometer/raw/main/Backyard_colors.png'\n",
    "    \n",
    "    \n",
    "    #https://colab.research.google.com/github/Philliec459/Science-and-Technology-Society-Use-of-NASA-STELLA-Q2-Spectrometer/blob/main/STELLA_brief_ver2_backyard_grass_shoreline.ipynb\n",
    "    \n",
    "    #https://github.com/Philliec459/Science-and-Technology-Society-Use-of-NASA-STELLA-Q2-Spectrometer/raw/main/STELLA_brief_ver2_backyard_grass_shoreline.ipynb    \n",
    "    #https://github.com/Philliec459/Science-and-Technology-Society-Use-of-NASA-STELLA-Q2-Spectrometer/raw/main/data_white_grass_shade_whiteshade.xlsx\n",
    "    #https://github.com/Philliec459/Science-and-Technology-Society-Use-of-NASA-STELLA-Q2-Spectrometer/raw/main/data_white_FullSun.xlsx\n",
    "\n",
    "\n",
    "    !pip install xlsxwriter\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4d22f4cf-82c3-4a3b-9484-efa6d4bce681",
   "metadata": {},
   "source": [
    "## Read in STELLA-Q2 that has been converted to an Excel file with a White-Card Excel file too:\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "badb9e7d-6291-45a3-ad2a-6242cd9703d0",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import xlsxwriter\n",
    "\n",
    "\n",
    "# Relative Path for STELLA Raw and White-Card Excel files\n",
    "data_csv_xlsx_path    = r'./data_white_grass_shade_whiteshade.xlsx'\n",
    "white_card_xlsx_path  = r'./data_white_FullSun.xlsx'\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8b35c14-f74b-46b5-a424-45060748018c",
   "metadata": {},
   "source": [
    "## 1) Write Raw and White Card Irradiance data to **1_filtered_data.xlsx** with easy to read columns:\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2e95af42-28d7-46fb-ab1e-23d864d2b7bd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Read the Excel file\n",
    "df1 = pd.read_excel(data_csv_xlsx_path,index_col=False)\n",
    "df2 = pd.read_excel(white_card_xlsx_path,index_col=False, nrows = 1)\n",
    "#df1 = pd.read_excel('data_Nick_HomeDepot.xlsx')\n",
    "#df2 = pd.read_excel('data_Nick_HomeDepot_white.xlsx')\n",
    "\n",
    "\n",
    "# Remove leading/trailing whitespaces in column names\n",
    "df1.columns = df1.columns.str.strip()\n",
    "df2.columns = df2.columns.str.strip()\n",
    "\n",
    "# Find the column containing 'timestamp' in its name\n",
    "timestamp_column_1 = [col for col in df1.columns if 'timestamp' in col.lower()][0]\n",
    "timestamp_column_2 = [col for col in df2.columns if 'timestamp' in col.lower()][0]\n",
    "\n",
    "# Extract wavelength values from column names and add 'nm'\n",
    "new_column_headings_1 = [col.split('_')[1] + 'nm' if 'nm' not in col.split('_')[1] else col.split('_')[1] for col in df1.columns if 'irradiance_' in col and '_irradiance_uW_per_cm_squared' in col]\n",
    "new_column_headings_2 = [col.split('_')[1] + 'nm' if 'nm' not in col.split('_')[1] else col.split('_')[1] for col in df2.columns if 'irradiance_' in col and '_irradiance_uW_per_cm_squared' in col]\n",
    "\n",
    "# Filter the columns based on your criteria\n",
    "filtered_columns_1 = ['Test', 'batch', timestamp_column_1] + [col for col in df1.columns if 'irradiance_' in col and '_irradiance_uW_per_cm_squared' in col]\n",
    "filtered_columns_2 = ['Test', 'batch', timestamp_column_2] + [col for col in df2.columns if 'irradiance_' in col and '_irradiance_uW_per_cm_squared' in col]\n",
    "\n",
    "# Subset the DataFrame with the filtered columns\n",
    "filtered_data_1 = df1[filtered_columns_1]\n",
    "filtered_data_2 = df2[filtered_columns_2]\n",
    "\n",
    "# Rename the columns\n",
    "filtered_data_1.columns = ['Test', 'batch', timestamp_column_1] + new_column_headings_1\n",
    "filtered_data_2.columns = ['Test', 'batch', timestamp_column_2] + new_column_headings_2\n",
    "\n",
    "# Write the filtered data to a new Excel file\n",
    "with pd.ExcelWriter('1_filtered_data.xlsx') as writer:\n",
    "    # Write the filtered data to the 'Raw' tab\n",
    "    df1.to_excel(writer, sheet_name='Original Data', index=False)\n",
    "\n",
    "    # Write the filtered data to the 'Raw' tab\n",
    "    filtered_data_1.to_excel(writer, sheet_name='Raw Filtered', index=False)\n",
    "\n",
    "    # Write the filtered data to the 'White' tab\n",
    "    filtered_data_2.to_excel(writer, sheet_name='White Card Filtered', index=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "084567a6-c543-48ec-9968-2cbcdf464a64",
   "metadata": {},
   "source": [
    "## 2) Write Raw, White Card Irradiance and White Card Corrected data to **2_white_card_corrected.xlsx**:\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3e612810-a04c-49f4-888c-bb09eeb480bd",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Read the Excel files\n",
    "df_raw = pd.read_excel('1_filtered_data.xlsx', sheet_name='Raw Filtered')\n",
    "df_white = pd.read_excel('1_filtered_data.xlsx', sheet_name='White Card Filtered')\n",
    "\n",
    "# Divide each irradiance channel in the Raw tab by the first irradiance value in the White Card tab\n",
    "df_raw_corrected = df_raw.copy()\n",
    "for col in df_raw.columns:\n",
    "    if 'nm' in col:\n",
    "        wavelength = col.split('nm')[0]\n",
    "        df_raw_corrected[col] = df_raw[col] / df_white[wavelength + 'nm'].iloc[0]\n",
    "\n",
    "# Create a new Excel file\n",
    "with pd.ExcelWriter('2_white_card_corrected.xlsx', engine='xlsxwriter') as writer:\n",
    "    # Write the filtered data to the 'Raw' tab\n",
    "    df1.to_excel(writer, sheet_name='Original Data', index=False)\n",
    "    \n",
    "    # Add the Raw data\n",
    "    df_raw.to_excel(writer, sheet_name='Raw Filtered', index=False)\n",
    "    \n",
    "    # Add the White Card data\n",
    "    df_white.to_excel(writer, sheet_name='White Card Filtered', index=False)\n",
    "    \n",
    "    # Add the corrected Raw data\n",
    "    df_raw_corrected.to_excel(writer, sheet_name='White Card Corrected', index=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0287b2f-4028-4074-ac12-e75247523500",
   "metadata": {},
   "source": [
    "## 3) Write Raw and White Card Irradiance data from filtered_data.xlsx to create Time-Series plots for **3_Raw_filtered_data_with_aggregated_line_time_series_plots.xlsx** Excel File:\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "4b42f73b-900d-4e91-9d67-e73cafdb186e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Read data from both tabs\n",
    "df_raw = pd.read_excel('1_filtered_data.xlsx', sheet_name='Raw Filtered')\n",
    "df_white = pd.read_excel('1_filtered_data.xlsx', sheet_name='White Card Filtered')\n",
    "\n",
    "# Create a new Excel file\n",
    "with pd.ExcelWriter('3_Raw_filtered_data_with_aggregated_line_time_series_plots.xlsx', engine='xlsxwriter') as writer:\n",
    "    # Write the Raw data to the Excel file\n",
    "    df_raw.to_excel(writer, sheet_name='Raw', index=False)\n",
    "    \n",
    "    # Write the White Card data to the Excel file\n",
    "    df_white.to_excel(writer, sheet_name='White Card', index=False)\n",
    "    \n",
    "    # Add line plot for Raw data\n",
    "    workbook = writer.book\n",
    "    worksheet_raw = writer.sheets['Raw']\n",
    "    worksheet_white = writer.sheets['White Card']\n",
    "    \n",
    "    # Aggregate data for Raw tab\n",
    "    timestamps_raw = df_raw['timestamp_iso8601']\n",
    "    wavelengths_raw = [col for col in df_raw.columns if col.endswith('nm')]\n",
    "    values_raw = df_raw[wavelengths_raw].astype(float)\n",
    "    \n",
    "    # Add line plot for Raw data\n",
    "    chart_raw = workbook.add_chart({'type': 'line'})\n",
    "    for col_idx, col_name in enumerate(wavelengths_raw):\n",
    "        chart_raw.add_series({\n",
    "            'name': f'{col_name}',\n",
    "            'categories': ['Raw', 1, 0, len(timestamps_raw), 0],\n",
    "            'values': ['Raw', 1, col_idx + 1, len(timestamps_raw), col_idx + 1],\n",
    "        })\n",
    "    chart_raw.set_title({'name': 'Raw Time-Series Data'})\n",
    "    chart_raw.set_x_axis({'name': 'Timestamp'})\n",
    "    chart_raw.set_y_axis({'name': 'Wavelength'})\n",
    "    worksheet_raw.insert_chart('E2', chart_raw)\n",
    "    \n",
    "    # Aggregate data for White Card tab\n",
    "    timestamps_white = df_white['timestamp_iso8601']\n",
    "    wavelengths_white = [col for col in df_white.columns if col.endswith('nm')]\n",
    "    values_white = df_white[wavelengths_white].astype(float)\n",
    "    \n",
    "    # Add line plot for White Card data\n",
    "    chart_white = workbook.add_chart({'type': 'line'})\n",
    "    for col_idx, col_name in enumerate(wavelengths_white):\n",
    "        chart_white.add_series({\n",
    "            'name': f'{col_name}',\n",
    "            'categories': ['White Card', 1, 0, len(timestamps_white), 0],\n",
    "            'values': ['White Card', 1, col_idx + 1, len(timestamps_white), col_idx + 1],\n",
    "        })\n",
    "    chart_white.set_title({'name': 'White Card Time-Series Data'})\n",
    "    chart_white.set_x_axis({'name': 'Timestamp'})\n",
    "    chart_white.set_y_axis({'name': 'Wavelength', 'min': min(values_white.min()), 'max': max(values_white.max())})\n",
    "    worksheet_white.insert_chart('E2', chart_white)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19a20fbb-6893-4c72-af6d-af6e99003d8b",
   "metadata": {},
   "source": [
    "## 4) Wavelength plots for Raw and White Card data written to **4_Raw_wavelength_plots.xlsx** Excel File:\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "18cc5200-a0ce-4fe7-a474-370efc400755",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Read the Excel files\n",
    "df_raw = pd.read_excel('1_filtered_data.xlsx', sheet_name='Raw Filtered')\n",
    "df_white = pd.read_excel('1_filtered_data.xlsx', sheet_name='White Card Filtered')\n",
    "\n",
    "# Create a new Excel file\n",
    "with pd.ExcelWriter('4_Raw_wavelength_plots.xlsx', engine='xlsxwriter') as writer:\n",
    "    # Add the Raw data\n",
    "    df_raw.to_excel(writer, sheet_name='Raw', index=False)\n",
    "    workbook = writer.book\n",
    "    worksheet_raw = writer.sheets['Raw']\n",
    "\n",
    "    # Add a new worksheet for the Raw data plots\n",
    "    chart_worksheet_raw = workbook.add_worksheet('Raw Plots')\n",
    "\n",
    "    # Extract wavelengths and time stamps for Raw data\n",
    "    wavelengths_raw = [col.split('nm')[0] for col in df_raw.columns if 'nm' in col]\n",
    "    timestamps_raw = df_raw['timestamp_iso8601']\n",
    "\n",
    "    # Write the wavelengths to the worksheet\n",
    "    chart_worksheet_raw.write_row('A1', ['Timestamp'] + wavelengths_raw)\n",
    "\n",
    "    # Write the timestamps and irradiance values to the worksheet\n",
    "    for i, ts in enumerate(timestamps_raw, start=1):\n",
    "        chart_worksheet_raw.write(i, 0, ts)\n",
    "        chart_worksheet_raw.write_row(i, 1, df_raw.iloc[i - 1][[col + 'nm' for col in wavelengths_raw]])\n",
    "\n",
    "    # Create a line chart for all rows in Raw data\n",
    "    chart_raw = workbook.add_chart({'type': 'line'})\n",
    "    for i in range(len(timestamps_raw)):\n",
    "        chart_raw.add_series({\n",
    "            'name': f'Row {i + 1}',\n",
    "            'categories': ['Raw Plots', 1, 0, len(timestamps_raw), 0],\n",
    "            'values': ['Raw Plots', 1 + i, 1, 1 + i, len(wavelengths_raw)],\n",
    "        })\n",
    "    chart_raw.set_x_axis({'name': 'Timestamp'})\n",
    "    chart_raw.set_y_axis({'name': 'Irradiance'})\n",
    "    chart_raw.set_title({'name': 'Raw Data Wavelenght Plot'})\n",
    "    chart_worksheet_raw.insert_chart('E1', chart_raw)\n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    # Add the White Card data\n",
    "    df_white.to_excel(writer, sheet_name='White Card', index=False)\n",
    "    worksheet_white = writer.sheets['White Card']\n",
    "\n",
    "    # Add a new worksheet for the White Card data plots\n",
    "    chart_worksheet_white = workbook.add_worksheet('White Card Plots')\n",
    "\n",
    "    # Extract wavelengths and time stamps for White Card data\n",
    "    wavelengths_white = [col.split('nm')[0] for col in df_white.columns if 'nm' in col]\n",
    "    timestamps_white = df_white['timestamp_iso8601']\n",
    "\n",
    "    # Write the wavelengths to the worksheet\n",
    "    chart_worksheet_white.write_row('A1', ['Timestamp'] + wavelengths_white)\n",
    "\n",
    "    # Write the timestamps and irradiance values to the worksheet\n",
    "    for i, ts in enumerate(timestamps_white, start=1):\n",
    "        chart_worksheet_white.write(i, 0, ts)\n",
    "        chart_worksheet_white.write_row(i, 1, df_white.iloc[i - 1][[col + 'nm' for col in wavelengths_white]])\n",
    "\n",
    "    # Create a line chart for all rows in White Card data\n",
    "    chart_white = workbook.add_chart({'type': 'line'})\n",
    "    for i in range(len(timestamps_white)):\n",
    "        chart_white.add_series({\n",
    "            'name': f'Row {i + 1}',\n",
    "            'categories': ['White Card Plots', 1, 0, len(timestamps_white), 0],\n",
    "            'values': ['White Card Plots', 1 + i, 1, 1 + i, len(wavelengths_white)],\n",
    "        })\n",
    "    chart_white.set_x_axis({'name': 'Timestamp'})\n",
    "    chart_white.set_y_axis({'name': 'Irradiance'})\n",
    "    chart_white.set_title({'name': 'Raw White Card Data Wavelength Plot'})\n",
    "    chart_worksheet_white.insert_chart('E1', chart_white)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b808987a-694f-41a6-89b0-3d600e6449ef",
   "metadata": {},
   "source": [
    "## 5) Wavelength plots for Raw and White Card Corrected Data written to **5_wavelength_plots_White_Card_Corr_NDVI.xlsx** Excel File with NDVI time-series plot with Test as x-axis:\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1d6d1196-3602-4117-94a9-a32cb1114f40",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Read the Excel files\n",
    "df_raw = pd.read_excel('2_white_card_corrected.xlsx', sheet_name='Raw Filtered')\n",
    "df_white = pd.read_excel('2_white_card_corrected.xlsx', sheet_name='White Card Corrected')\n",
    "#df_white_corr = pd.read_excel('2_white_card_corrected.xlsx', sheet_name='White Card Corrected')\n",
    "\n",
    "# Calculate NDVI\n",
    "#ndvi_values = (near IR          -       Red        ) / (near IR                  + Red       )\n",
    "ndvi_values = (df_white['860nm'] - df_white['645nm']) / (df_white['860nm'] + df_white['645nm'])\n",
    "\n",
    "# Create a new Excel file\n",
    "with pd.ExcelWriter('5_wavelength_plots_White_Card_Corrected_NDVI.xlsx', engine='xlsxwriter') as writer:\n",
    "\n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "    # Add the Raw data\n",
    "    df_raw.to_excel(writer, sheet_name='Raw', index=False)\n",
    "    workbook = writer.book\n",
    "    worksheet_raw = writer.sheets['Raw']\n",
    "\n",
    "    # Add a new worksheet for the Raw data plots\n",
    "    chart_worksheet_raw = workbook.add_worksheet('Raw Plots')\n",
    "\n",
    "    # Extract wavelengths and time stamps for Raw data\n",
    "    wavelengths_raw = [col.split('nm')[0] for col in df_raw.columns if 'nm' in col]\n",
    "    timestamps_raw = df_raw['timestamp_iso8601']\n",
    "\n",
    "    # Write the wavelengths to the worksheet\n",
    "    chart_worksheet_raw.write_row('A1', ['Timestamp'] + wavelengths_raw)\n",
    "\n",
    "    # Write the timestamps and irradiance values to the worksheet\n",
    "    for i, ts in enumerate(timestamps_raw, start=1):\n",
    "        chart_worksheet_raw.write(i, 0, ts)\n",
    "        chart_worksheet_raw.write_row(i, 1, df_raw.iloc[i - 1][[col + 'nm' for col in wavelengths_raw]])\n",
    "\n",
    "    # Create a line chart for all rows in Raw data\n",
    "    chart_raw = workbook.add_chart({'type': 'line'})\n",
    "    for i in range(len(timestamps_raw)):\n",
    "        chart_raw.add_series({\n",
    "            'name': f'Row {i + 1}',\n",
    "            'categories': ['Raw Plots', 1, 0, len(timestamps_raw), 0],\n",
    "            'values': ['Raw Plots', 1 + i, 1, 1 + i, len(wavelengths_raw)],\n",
    "        })\n",
    "    chart_raw.set_x_axis({'name': 'Timestamp'})\n",
    "    chart_raw.set_y_axis({'name': 'Irradiance'})\n",
    "    chart_raw.set_title({'name': 'Raw Data Wavelenght Plot'})\n",
    "    chart_worksheet_raw.insert_chart('E1', chart_raw)\n",
    "\n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "   # Add the White Card data\n",
    "    df_white.to_excel(writer, sheet_name='White Card Corrected', index=False)\n",
    "    worksheet_white = writer.sheets['White Card Corrected']\n",
    "\n",
    "    # Add a new worksheet for the White Card data plots\n",
    "    chart_worksheet_white = workbook.add_worksheet('White Card Corr Plots')\n",
    "\n",
    "    # Extract wavelengths and time stamps for White Card data\n",
    "    wavelengths_white = [col.split('nm')[0] for col in df_white.columns if 'nm' in col]\n",
    "    timestamps_white = df_white['timestamp_iso8601']\n",
    "\n",
    "    # Write the wavelengths to the worksheet\n",
    "    chart_worksheet_white.write_row('A1', ['Timestamp'] + wavelengths_white)\n",
    "\n",
    "    # Write the timestamps and irradiance values to the worksheet\n",
    "    for i, ts in enumerate(timestamps_white, start=1):\n",
    "        chart_worksheet_white.write(i, 0, ts)\n",
    "        chart_worksheet_white.write_row(i, 1, df_white.iloc[i - 1][[col + 'nm' for col in wavelengths_white]])\n",
    "\n",
    "    # Create a line chart for all rows in White Card data\n",
    "    chart_white = workbook.add_chart({'type': 'line'})\n",
    "    for i in range(len(timestamps_white)):\n",
    "        chart_white.add_series({\n",
    "            'name': f'Row {i + 1}',\n",
    "            'categories': ['White Card Corr Plots', 1, 0, len(timestamps_white), 0],\n",
    "            'values': ['White Card Corr Plots', 1 + i, 1, 1 + i, len(wavelengths_white)],\n",
    "        })\n",
    "    chart_white.set_x_axis({'name': 'Timestamp'})\n",
    "    chart_white.set_y_axis({'name': 'Irradiance'})\n",
    "    chart_white.set_title({'name': 'White Card Corrected Wavelength Plot'})\n",
    "    chart_worksheet_white.insert_chart('E1', chart_white)\n",
    "\n",
    "\n",
    "\n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    \n",
    "    # Add NDVI data to a new worksheet\n",
    "    ndvi_sheet_name = 'NDVI'\n",
    "    ndvi_df = pd.DataFrame({'Test': df_white['Test'], 'NDVI': ndvi_values})\n",
    "    ndvi_df.to_excel(writer, sheet_name=ndvi_sheet_name, index=False)\n",
    "\n",
    "    # Create a new workbook object\n",
    "    workbook = writer.book\n",
    "    \n",
    "    # Create a new worksheet for the NDVI plot\n",
    "    ndvi_chart_worksheet = workbook.add_worksheet('NDVI Plot')\n",
    "\n",
    "    # Write the Test and NDVI values to the worksheet\n",
    "    ndvi_chart_worksheet.write_row('A1', ['Test', 'NDVI'])\n",
    "    ndvi_chart_worksheet.write_column('A2', ndvi_df['Test'])\n",
    "    ndvi_chart_worksheet.write_column('B2', ndvi_df['NDVI'])\n",
    "\n",
    "    # Create a line chart for NDVI\n",
    "    ndvi_chart = workbook.add_chart({'type': 'line'})\n",
    "    #ndvi_chart = workbook.add_chart({'type': 'line', 'size': {'width': 1800, 'height': 1600}})\n",
    "\n",
    "    ndvi_chart.add_series({\n",
    "        'categories': [ndvi_sheet_name, 1, 0, len(ndvi_values), 0],\n",
    "        'values': [ndvi_sheet_name, 1, 1, len(ndvi_values), 1],\n",
    "        'name': 'NDVI',\n",
    "        'line': {'color': 'green', 'dash_type': 'dash'},\n",
    "        'marker': {'type': 'circle', 'size': 8, 'fill': {'color': 'green'}, 'border': {'color': 'green'}}\n",
    "       })\n",
    "    ndvi_chart.set_x_axis({'name': 'Test'})\n",
    "    \n",
    "    #ndvi_chart.set_y_axis({'name': 'NDVI'})\n",
    "    ndvi_chart.set_y_axis({'name': 'NDVI', 'min': 0, 'max': 1})  # Set the y-axis limits\n",
    "\n",
    "    ndvi_chart.set_title({'name': 'NDVI Time Series Plot'})\n",
    "    \n",
    "    # Set the dimensions of the chart\n",
    "    ndvi_chart.set_size({'width': 1200, 'height': 600})\n",
    "    \n",
    "    ndvi_chart_worksheet.insert_chart('C2', ndvi_chart)\n",
    "    \n",
    "    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5edf636-eedb-4a78-ad24-a976bec196e6",
   "metadata": {},
   "source": [
    "## End of Code\n",
    "---\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68129e95-2a35-4890-9eff-a7ad8fb57779",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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