thesis/eds/.ipynb_checkpoints/EDS-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f1de3e2e-cc89-4b12-a2fb-42c1270755ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy as sc\n",
    "\n",
    "import os\n",
    "import glob\n",
    "from pathlib import Path\n",
    "import warnings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "38262129-8cd6-447a-b0b5-554ab370f153",
   "metadata": {},
   "outputs": [],
   "source": [
    "def list_files_pathlib(\n",
    "    directory_path, \n",
    "    verbose=None,\n",
    "):\n",
    "    \"\"\"\n",
    "    Lists files using the modern pathlib module and returns them as a list.\n",
    "    \n",
    "    Args:\n",
    "        directory_path (str): The path to the directory to inspect.\n",
    "        verbose (bool): If True, prints detailed output to the console. \n",
    "                        Defaults to False.\n",
    "\n",
    "    Returns:\n",
    "        list: A list of file names (str) found in the directory. \n",
    "              Returns an empty list if the directory is not found or is empty.\n",
    "    \"\"\"\n",
    "\n",
    "    import os\n",
    "    import glob\n",
    "    from pathlib import Path\n",
    "    import warnings\n",
    "    \n",
    "    if verbose is None:\n",
    "        verbose = False\n",
    "    elif verbose is True:\n",
    "        print(f\"--- Listing contents in '{directory_path}' using pathlib ---\")\n",
    "    \n",
    "    try:\n",
    "\n",
    "        # Step 1 - Check if the directory_path is valid\n",
    "        # Note - this code is almost too bulletproof? I love it :D\n",
    "        path_obj = Path(directory_path)\n",
    "        if not path_obj.exists():\n",
    "            warnings.warn(f\"Directory not found at '{directory_path}'\", UserWarning)\n",
    "            return []  # Return empty list on error\n",
    "        if not path_obj.is_dir():\n",
    "            warnings.warn(f\"The path '{directory_path}' is not a directory.\", UserWarning)\n",
    "            return []  # Return empty list on error\n",
    "        entries = sorted(list(path_obj.iterdir()))\n",
    "        if not entries:\n",
    "            warnings.warn(f\"The directory at '{directory_path}' is empty.\", UserWarning)\n",
    "            return []  # Return empty list on error\n",
    "        \n",
    "\n",
    "        # Step 2 - Collect all the files in the directory\n",
    "        files_list = []\n",
    "        entries = sorted(list(path_obj.iterdir()))\n",
    "        for entry in entries:\n",
    "            if entry.is_file():\n",
    "                files_list.append(str(entry))\n",
    "            if verbose:\n",
    "                entry_type = \"dir\" if entry.is_dir() else \"file\"\n",
    "                print(f\"- {entry.name:<20} ({entry_type})\")        \n",
    "        return files_list\n",
    "\n",
    "    except Exception as e:\n",
    "        warnings.warn(f\"An unexpected error occurred: {e}\", RuntimeWarning)\n",
    "        return [] # Return empty list on unexpected exception\n",
    "    finally:\n",
    "        if verbose:\n",
    "            print(\"-\" * 50)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "76cae071-2cee-48e5-9465-e7c392ed135d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['HIPed_Stellite1/EDS_Point_Carbide1.txt',\n",
       " 'HIPed_Stellite1/EDS_Point_Carbide2.txt',\n",
       " 'HIPed_Stellite1/EDS_Point_Carbide3.txt',\n",
       " 'HIPed_Stellite1/EDS_Point_SolidSolution1.txt',\n",
       " 'HIPed_Stellite1/EDS_Point_SolidSolution2.txt',\n",
       " 'HIPed_Stellite1/EDS_Point_SolidSolution3.txt']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filenames = list_files_pathlib(\"HIPed_Stellite1\")\n",
    "filenames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b9825c82-42b0-47b2-afca-81466478f841",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>cps/eV</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>keV</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>-0.20</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.19</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.18</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.17</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>-0.16</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20.23</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20.24</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20.25</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20.26</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20.27</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2048 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        cps/eV\n",
       "keV           \n",
       "-0.20      0.0\n",
       "-0.19      0.0\n",
       "-0.18      0.0\n",
       "-0.17      0.0\n",
       "-0.16      0.0\n",
       "...        ...\n",
       " 20.23     0.0\n",
       " 20.24     0.0\n",
       " 20.25     0.0\n",
       " 20.26     0.0\n",
       " 20.27     0.0\n",
       "\n",
       "[2048 rows x 1 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def import_commented_file_to_df(file_path):\n",
    "    \"\"\"\n",
    "    Imports a two-column, comma-separated file into a pandas DataFrame,\n",
    "    ignoring any lines that start with a '#' character.\n",
    "\n",
    "    Args:\n",
    "        file_path (str or file-like object): The full path to the data file \n",
    "                                             or a file-like object.\n",
    "        column_names (list of str): A list of names to assign to the columns.\n",
    "                                    The number of names should match the number\n",
    "                                    of columns in the data.\n",
    "\n",
    "    Returns:\n",
    "        pandas.DataFrame: A DataFrame containing the imported data. \n",
    "                          Returns an empty DataFrame if an error occurs \n",
    "                          (e.g., file not found).\n",
    "    \"\"\"\n",
    "\n",
    "    import pandas as pd\n",
    "\n",
    "    try:\n",
    "\n",
    "        df = pd.read_csv(\n",
    "            file_path,\n",
    "            header=None,\n",
    "            names=[\"keV\", \"cps/eV\"],\n",
    "            sep=',',\n",
    "            comment='#'\n",
    "        )\n",
    "\n",
    "        df.set_index(\"keV\", inplace=True)\n",
    "        \n",
    "        return df\n",
    "    except FileNotFoundError:\n",
    "        print(f\"Error: The file was not found at '{file_path}'\")\n",
    "        return pd.DataFrame() # Return an empty DataFrame on error\n",
    "    except Exception as e:\n",
    "        print(f\"An unexpected error occurred: {e}\")\n",
    "        return pd.DataFrame() # Return an empty DataFrame on error\n",
    "\n",
    "\n",
    "import_commented_file_to_df(filenames[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d5cb1098-cd15-491b-bad3-cbd915859179",
   "metadata": {},
   "outputs": [],
   "source": [
    "def import_eds_emsa(file_path: str | Path) -> tuple[pd.DataFrame | None, dict | None]:\n",
    "    \"\"\"\n",
    "    Parses an EMSA/MAS spectral data file robustly, handling common format errors\n",
    "    and structured multi-line metadata (e.g., ##OXINSTLABEL).\n",
    "\n",
    "    This function extracts:\n",
    "    1. Repeatable '##OXINSTLABEL' annotation lines into a 'ANNOTATIONS' dictionary.\n",
    "    2. Standard metadata from '#' lines into a dictionary.\n",
    "    3. Spectral data into a pandas DataFrame.\n",
    "\n",
    "    Args:\n",
    "        file_path (str or Path): The path to the EMSA file.\n",
    "\n",
    "    Returns:\n",
    "        tuple[pd.DataFrame | None, dict | None]: A tuple containing:\n",
    "            - A DataFrame of the spectral data.\n",
    "            - A dictionary of the header metadata, including annotations.\n",
    "        Returns (None, None) if a critical error occurs.\n",
    "    \"\"\"\n",
    "\n",
    "    import pandas as pd\n",
    "    import io\n",
    "    import warnings\n",
    "    from pathlib import Path\n",
    "    \n",
    "    metadata = {'ANNOTATIONS': {}}\n",
    "    data_lines = []\n",
    "    \n",
    "    try:\n",
    "        file_path = Path(file_path)\n",
    "        with file_path.open('r', encoding='utf-8', errors='ignore') as f:\n",
    "            \n",
    "            for i, line in enumerate(f, 1):\n",
    "                line = line.strip()\n",
    "                if not line: continue\n",
    "\n",
    "                # --- Handle element annotation ---\n",
    "                if line.startswith('##OXINSTLABEL'): \n",
    "                    \n",
    "                    parts = line.split(':', 1)\n",
    "                    if len(parts) != 2: continue # Skip malformed annotation lines\n",
    "                    \n",
    "                    # value_parts: e.g., ['27', '6.930', 'Co']\n",
    "                    value_parts = [p.strip() for p in parts[1].split(',')]\n",
    "                    \n",
    "                    if len(value_parts) == 3:\n",
    "                        try:\n",
    "                            atomic_num = int(value_parts[0])\n",
    "                            energy_kev = float(value_parts[1])\n",
    "                            element = value_parts[2]\n",
    "\n",
    "                            # Ensure the element entry exists in the ANNOTATIONS dict\n",
    "                            if element not in metadata['ANNOTATIONS']:\n",
    "                                metadata['ANNOTATIONS'][element] = []\n",
    "                            \n",
    "                            # Append the structured annotation\n",
    "                            metadata['ANNOTATIONS'][element].append(energy_kev)\n",
    "                            \n",
    "                        except (ValueError, IndexError) as e:\n",
    "                            warnings.warn(f\"Could not parse annotation on line {i}: '{line}'. Reason: {e}\", UserWarning)\n",
    "\n",
    "                # --- Handle standard single-line metadata ---\n",
    "                elif line.startswith('#'):\n",
    "                    \n",
    "                    parts = line[1:].split(':', 1)\n",
    "                    if len(parts) == 2:\n",
    "                        key = parts[0].strip().upper()\n",
    "                        value = parts[1].strip()\n",
    "                        metadata[key] = value\n",
    "\n",
    "                # --- Collect potential data lines ---        \n",
    "                else:\n",
    "                    data_lines.append(line)\n",
    "\n",
    "        # --- Sort the collected annotation lists numerically ---\n",
    "        for element in metadata['ANNOTATIONS']: metadata['ANNOTATIONS'][element].sort()        \n",
    "        \n",
    "        # --- Data Parsing and Validation ---\n",
    "        if not data_lines:\n",
    "            warnings.warn(f\"No data lines found in '{file_path.name}'.\", UserWarning)\n",
    "            return pd.DataFrame(), metadata\n",
    "\n",
    "        df = pd.read_csv(\n",
    "            io.StringIO('\\n'.join(data_lines)),\n",
    "            header=None,\n",
    "            sep=r'\\s*,\\s*',\n",
    "            engine='python',\n",
    "            names=['keV', 'counts'],\n",
    "            comment='#'\n",
    "        ).dropna()\n",
    "\n",
    "        df['keV'] = pd.to_numeric(df['keV'], errors='coerce')\n",
    "        df['counts'] = pd.to_numeric(df['counts'], errors='coerce')\n",
    "\n",
    "        original_rows = len(df)\n",
    "        df.dropna(inplace=True)\n",
    "        if len(df) < original_rows:\n",
    "            warnings.warn(f\"Removed {original_rows - len(df)} non-numeric data rows.\", UserWarning)\n",
    "\n",
    "        # Please warn if the datalines are missing data for whatever reason.\n",
    "        if 'NPOINTS' in metadata:\n",
    "            try:\n",
    "                try:\n",
    "                    npoints_meta = int(metadata['NPOINTS'].strip())\n",
    "                except ValueError:                \n",
    "                    npoints_meta = int(float(metadata['NPOINTS'].strip()))\n",
    "\n",
    "                if npoints_meta != len(df):\n",
    "                    warnings.warn(f\"Header #NPOINTS ({npoints_meta}) does not match actual data points ({len(df)}).\", UserWarning)\n",
    "            except ValueError:\n",
    "                warnings.warn(f\"#NPOINTS value '{metadata['NPOINTS']}' is not a valid integer.\", UserWarning)\n",
    "\n",
    "        df.set_index('keV', inplace=True)\n",
    "        return df, metadata\n",
    "\n",
    "    except FileNotFoundError:\n",
    "        warnings.warn(f\"Error: The file was not found at '{file_path}'\", UserWarning)\n",
    "        return None, None\n",
    "    except Exception as e:\n",
    "        warnings.warn(f\"An unexpected error occurred: {e}\", RuntimeWarning)\n",
    "        return None, None\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8b609f36-55a6-419c-8f18-162e53b1e3df",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        counts\n",
      "keV           \n",
      "-0.20      0.0\n",
      "-0.19      0.0\n",
      "-0.18      0.0\n",
      "-0.17      0.0\n",
      "-0.16      0.0\n",
      "...        ...\n",
      " 20.23     0.0\n",
      " 20.24     0.0\n",
      " 20.25     0.0\n",
      " 20.26     0.0\n",
      " 20.27     0.0\n",
      "\n",
      "[2048 rows x 1 columns]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'ANNOTATIONS': {'Co': [0.678, 0.694, 0.776, 6.93, 7.65],\n",
       "  'W': [0.21,\n",
       "   0.222,\n",
       "   1.379,\n",
       "   1.775,\n",
       "   2.035,\n",
       "   7.388,\n",
       "   8.398,\n",
       "   8.724,\n",
       "   9.673,\n",
       "   9.962,\n",
       "   11.286,\n",
       "   11.608],\n",
       "  'Cr': [0.5, 0.573, 5.415, 5.947],\n",
       "  'C': [0.277]},\n",
       " 'FORMAT': 'EMSA/MAS Spectral Data File',\n",
       " 'VERSION': '1.0',\n",
       " 'TITLE': 'Spectrum 1',\n",
       " 'DATE': '21-JUL-2025',\n",
       " 'TIME': '12:52',\n",
       " 'OWNER': '',\n",
       " 'NPOINTS': '2048.0',\n",
       " 'NCOLUMNS': '1.0',\n",
       " 'XUNITS': 'keV',\n",
       " 'YUNITS': 'counts',\n",
       " 'DATATYPE': 'XY',\n",
       " 'XPERCHAN': '0.01',\n",
       " 'OFFSET': '-0.2',\n",
       " 'SIGNALTYPE': 'EDS',\n",
       " 'CHOFFSET': '20.0',\n",
       " 'LIVETIME': '30.0',\n",
       " 'REALTIME': '30.062997818',\n",
       " 'BEAMKV': '20.0',\n",
       " 'PROBECUR': '0.0',\n",
       " 'MAGCAM': '5000.0',\n",
       " 'XTILTSTGE': '0.0',\n",
       " 'AZIMANGLE': '0.0',\n",
       " 'ELEVANGLE': '35.0',\n",
       " 'XPOSITION MM': '-3.9563',\n",
       " 'YPOSITION MM': '1.9538',\n",
       " 'ZPOSITION MM': '14.8410',\n",
       " '#OXINSTPT': '2',\n",
       " '#OXINSTSTROB': '76.71',\n",
       " '#OXINSTELEMS': '27,74,24,6',\n",
       " 'SPECTRUM': 'Spectral Data Starts Here',\n",
       " 'ENDOFDATA': ''}"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eds_df, eds_metadata = import_eds_emsa(filenames[0])\n",
    "print(eds_df)\n",
    "eds_metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "03c73d3d-e4af-46cc-9844-b6392a0007be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='keV'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eds_df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "5ef92a88-f39e-48ba-bbca-c6364a0a9b36",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_eds_spectrum(\n",
    "    df: pd.DataFrame, \n",
    "    metadata: dict, \n",
    "    fig=None, \n",
    "    ax=None, \n",
    "    label=None,\n",
    "    color=None,\n",
    "):\n",
    "    \"\"\"\n",
    "    Plots the EDS spectrum and annotates elemental peaks on a given or new axis.\n",
    "\n",
    "    Args:\n",
    "        df (pd.DataFrame): DataFrame containing the spectral data with 'keV' as the index\n",
    "                           and 'counts' as a column.\n",
    "        metadata (dict): Dictionary containing metadata, including 'ANNOTATIONS'\n",
    "                         for elemental peaks.\n",
    "        fig (matplotlib.figure.Figure, optional): The figure to plot on. If None, a new\n",
    "                                                   figure is created. Defaults to None.\n",
    "        ax (matplotlib.axes.Axes, optional): The axes to plot on. If None, a new\n",
    "                                             axes object is created. Defaults to None.\n",
    "    \n",
    "    Returns:\n",
    "        tuple: A tuple containing the figure and axes objects used for plotting.\n",
    "    \"\"\"\n",
    "\n",
    "    import pandas as pd\n",
    "    import matplotlib.pyplot as plt\n",
    "    import warnings\n",
    "    from pathlib import Path\n",
    "    import io\n",
    "    \n",
    "    if df is None or df.empty:\n",
    "        print(\"DataFrame is empty or None. Cannot plot.\")\n",
    "        return\n",
    "\n",
    "    # If no axes are provided, create a new figure and axes\n",
    "    if ax is None or fig is None:\n",
    "        plt.style.use('seaborn-v0_8-whitegrid')\n",
    "        fig, ax = plt.subplots(figsize=(12, 7))\n",
    "        show_plot = True\n",
    "    else:\n",
    "        show_plot = False # Don't call plt.show() if using existing axes\n",
    "\n",
    "    if label is None: \n",
    "        label = \"EDS Spectrum\"\n",
    "\n",
    "    if color is None: \n",
    "        color = \"royalblue\"\n",
    "    \n",
    "\n",
    "    # Plot the primary spectrum data\n",
    "    ax.plot(\n",
    "        df.index, df['counts'], \n",
    "        label=label, color=color, linewidth=1,\n",
    "    )\n",
    "\n",
    "    # --- Labeling and Formatting ---\n",
    "    ax.set_xlabel('Energy (keV)', fontsize=12)\n",
    "    ax.set_ylabel('Counts', fontsize=12)\n",
    "    ax.tick_params(axis='both', which='major', labelsize=10)\n",
    "    \n",
    "    # Set a lower limit for y-axis to avoid cutting off baseline\n",
    "    ax.set_ylim(bottom=0, top=90)\n",
    "    ax.set_xlim(left=0, right=12)\n",
    "\n",
    "    # Create a legend, avoiding duplicate labels\n",
    "    handles, labels = ax.get_legend_handles_labels()\n",
    "    by_label = dict(zip(labels, handles))\n",
    "    ax.legend(by_label.values(), by_label.keys(), fontsize=10)\n",
    "\n",
    "    if show_plot:\n",
    "        plt.tight_layout()\n",
    "        plt.show()\n",
    "\n",
    "    return fig, ax\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "3f9428e6-2da2-456f-8564-f9cadde7c4f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1800x1200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import warnings\n",
    "from pathlib import Path\n",
    "import io\n",
    "\n",
    "plt.style.use('seaborn-v0_8-whitegrid')\n",
    "fig, ax = plt.subplots(figsize=(6, 4), dpi=300)\n",
    "\n",
    "# --- Read and Plot the data ---\n",
    "\n",
    "eds_dfs = [None for filename in filenames]\n",
    "eds_metadatas = [None for filename in filenames]\n",
    "\n",
    "for i, filename in enumerate(filenames[:3]):\n",
    "    eds_df, eds_metadata = import_eds_emsa(filename)\n",
    "    if eds_df is not None:\n",
    "        eds_dfs[i] = eds_df\n",
    "        eds_metadatas[i] = eds_metadata\n",
    "        plot_eds_spectrum(eds_df, eds_metadata, fig=fig, ax=ax, label=\"Carbide\", color=\"royalblue\")\n",
    "\n",
    "for i, filename in enumerate(filenames[3:]):\n",
    "    eds_df, eds_metadata = import_eds_emsa(filename)\n",
    "    if eds_df is not None:\n",
    "        eds_dfs[i+3] = eds_df\n",
    "        eds_metadatas[i+3] = eds_metadata\n",
    "        plot_eds_spectrum(eds_df, eds_metadata, fig=fig, ax=ax, label=\"Solid Solution\", color=\"firebrick\")\n",
    "\n",
    "# --- Annotate Elemental Peaks ---\n",
    "#for df, metadata in zip(eds_dfs, eds_metadatas):\n",
    "#    if 'ANNOTATIONS' in eds_metadata and eds_metadata['ANNOTATIONS']:           \n",
    "\n",
    "# Find unique elements\n",
    "annotations = [meta['ANNOTATIONS'] for meta in eds_metadatas]\n",
    "nested_list = [list(annotation.keys()) for annotation in annotations]\n",
    "unique_elements = list(set(item for sublist in nested_list for item in sublist))\n",
    "\n",
    "\n",
    "    \n",
    "maximum_profile = np.max([np.interp(np.arange(0,20,0.05), eds_df.index, eds_df['counts']) for eds_df in eds_dfs], axis=0)\n",
    "\n",
    "lines = {}\n",
    "\n",
    "lines[\"C\"] = [\n",
    "    (r'K$\\alpha_1$', 0.277, 10)                            ##OXINSTLABEL: 6, 0.277, C\n",
    "]\n",
    "\n",
    "\n",
    "\n",
    "##OXINSTLABEL: 24, 0.500, Cr\n",
    "lines[\"Cr\"] = [\n",
    "    (r'K$\\alpha_1$, K$\\alpha_2$', 5.41472, 63),            ##OXINSTLABEL: 24, 5.415, Cr\n",
    "    (None, 5.405509, 63), \n",
    "    (r'K$\\beta_1$',  5.94671, 14),                         ##OXINSTLABEL: 24, 5.947, Cr\n",
    "    (r'L$\\alpha_1$, L$\\alpha_2$, L$\\beta_1$', 0.5728, 20), ##OXINSTLABEL: 24, 0.573, Cr\n",
    "    (None, 0.5728, 20), \n",
    "    (None, 0.5828, 20), \n",
    "]\n",
    "\n",
    "\n",
    "\n",
    "##OXINSTLABEL: 27, 0.694, Co\n",
    "##OXINSTLABEL: 27, 0.678, Co\n",
    "lines[\"Co\"] = [\n",
    "    (r'K$\\alpha_1$, $\\alpha_2$', 6.93032, 25),              ##OXINSTLABEL: 27, 6.930, Co\n",
    "    (None, 6.91530, 25),     \n",
    "    (r'K$\\beta_1$',  7.64943, 10),                          ##OXINSTLABEL: 27, 7.650, Co    \n",
    "    (r'L$\\alpha_1$, L$\\alpha_2$, L$\\beta_1$',  0.7762, 25), ##OXINSTLABEL: 27, 0.776, Co\n",
    "    (None,  0.7762, 25),     \n",
    "    (None,  0.7914, 25), \n",
    "]\n",
    "\n",
    "\n",
    "\n",
    "##OXINSTLABEL: 74, 8.724, W\n",
    "\n",
    "##OXINSTLABEL: 74, 11.608, W\n",
    "##OXINSTLABEL: 74, 7.388, W\n",
    "\n",
    "##OXINSTLABEL: 74, 2.035, W\n",
    "##OXINSTLABEL: 74, 1.379, W\n",
    "##OXINSTLABEL: 74, 0.222, W\n",
    "##OXINSTLABEL: 74, 0.210, W\n",
    "lines[\"W\"] = [\n",
    "    (r'K$\\alpha_1$', 59.31824, 20), \n",
    "    (r'K$\\alpha_2$', 57.9817, 20), \n",
    "    (r'K$\\beta_1$',  67.2443, 20),         \n",
    "    (r'L$\\alpha_1$, L$\\alpha_2$', 8.3976, 10), ##OXINSTLABEL: 74, 8.398, W\n",
    "    (None, 8.3352, 10), \n",
    "    (r'L$\\beta_1$',  9.67235, 5),              ##OXINSTLABEL: 74, 9.673, W\n",
    "    (r'L$\\beta_2$',  9.9615, 5),               ##OXINSTLABEL: 74, 9.962, W\n",
    "    (r'L$\\gamma_1$',  11.2859, 5),             ##OXINSTLABEL: 74, 11.286, W\n",
    "    (r'M$\\alpha_1$',  1.7754, 41),             ##OXINSTLABEL: 74, 1.775, W\n",
    "] \n",
    "\n",
    "# Iterate through your 'lines' dictionary to plot each element's lines\n",
    "for element, spectral_lines in lines.items():\n",
    "    for line_name, energy, y_height in spectral_lines:\n",
    "        # Only plot lines that are within the energy range of your spectrum\n",
    "        if 0 <= energy < 15:\n",
    "                        \n",
    "            # Plot the vertical line from the x-axis to the spectrum\n",
    "            ax.plot(\n",
    "                [energy, energy], \n",
    "                [0, y_height], \n",
    "                linestyle='--', color='black', linewidth=0.5)\n",
    "\n",
    "            # Plot the triangle marker at the top of the line\n",
    "            ax.plot(\n",
    "                energy, \n",
    "                y_height, \n",
    "                marker='^', markersize=4, color='red', markeredgecolor='black')\n",
    "\n",
    "            if line_name is not None:\n",
    "                # Add the text label for the element and line type\n",
    "                ax.text(energy, \n",
    "                    y_height+1.5, \n",
    "                    f'{element} {line_name}', rotation=90,\n",
    "                    verticalalignment='bottom', horizontalalignment='center', fontsize=8)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4312541b-6f43-4977-ab24-ac496effdd49",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "24023520-d1ae-4c33-ae0b-d66fbfdb585e",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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