visualize_cells_xml_.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import xml.etree.ElementTree as ET\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cell_classification.xml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Image_Filename</th>\n",
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       "        Image_Filename Type MarkerX MarkerY MarkerZ\n",
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       "\n",
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     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Parse the XML file\n",
    "tree = ET.parse('/Users/grant/Desktop/work/Denman_Lab/Cal_Light/cellfinder/cellfinder_output/G21/G21_cellfinder_output_021423/points/cell_classification.xml')\n",
    "\n",
    "# Get the root element\n",
    "root = tree.getroot()\n",
    "\n",
    "# Get the Image_Filename\n",
    "image_filename = root.find('Image_Properties/Image_Filename').text\n",
    "\n",
    "# Create a list of dictionaries to store the data\n",
    "data = []\n",
    "for marker_type in root.findall('Marker_Data/Marker_Type'):\n",
    "    type_id = marker_type.find('Type').text\n",
    "    for marker in marker_type.findall('Marker'):\n",
    "        row = {'Image_Filename': image_filename,\n",
    "               'Type': type_id,\n",
    "               'MarkerX': marker.find('MarkerX').text,\n",
    "               'MarkerY': marker.find('MarkerY').text,\n",
    "               'MarkerZ': marker.find('MarkerZ').text}\n",
    "        data.append(row)\n",
    "\n",
    "# Create a pandas DataFrame from the data\n",
    "df = pd.DataFrame(data, columns=['Image_Filename', 'Type', 'MarkerX', 'MarkerY', 'MarkerZ'])\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cells.xml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Image_Filename</th>\n",
       "      <th>Type</th>\n",
       "      <th>MarkerX</th>\n",
       "      <th>MarkerY</th>\n",
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Parse the XML file\n",
    "tree = ET.parse('/Users/grant/Desktop/work/Denman_Lab/Cal_Light/cellfinder/cellfinder_output/G21/G21_cellfinder_output_021423/points/cells.xml')\n",
    "\n",
    "# Get the root element\n",
    "root = tree.getroot()\n",
    "\n",
    "# Get the Image_Filename\n",
    "image_filename = root.find('Image_Properties/Image_Filename').text\n",
    "\n",
    "# Create a list of dictionaries to store the data\n",
    "data = []\n",
    "for marker_type in root.findall('Marker_Data/Marker_Type'):\n",
    "    type_id = marker_type.find('Type').text\n",
    "    for marker in marker_type.findall('Marker'):\n",
    "        row = {'Image_Filename': image_filename,\n",
    "               'Type': type_id,\n",
    "               'MarkerX': marker.find('MarkerX').text,\n",
    "               'MarkerY': marker.find('MarkerY').text,\n",
    "               'MarkerZ': marker.find('MarkerZ').text}\n",
    "        data.append(row)\n",
    "\n",
    "# Create a pandas DataFrame from the data\n",
    "df = pd.DataFrame(data, columns=['Image_Filename', 'Type', 'MarkerX', 'MarkerY', 'MarkerZ'])\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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