QuPath_to_LMD.ipynb

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   "source": [
    "# One Jupyter notebook. From GeoJSON to .xml "
   ]
  },
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   "source": [
    "## Imports"
   ]
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   "source": [
    "import geopandas\n",
    "import pandas\n",
    "import numpy\n",
    "import datetime\n",
    "datetime = datetime.datetime.today().strftime(\"%Y%m%d\")\n",
    "import tifffile\n",
    "import shapely\n",
    "# import streamlit as st\n",
    "# from python_functions import dataframe_to_xml_v2\n",
    "from lmd.lib import SegmentationLoader\n",
    "from lmd.lib import Collection, Shape\n",
    "from lmd import tools\n",
    "from PIL import Image\n",
    "from pathlib import Path\n",
    "import ast\n",
    "import string"
   ]
  },
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   "id": "36985444-7a8a-4bf8-9da8-57c786abcfdb",
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   },
   "source": [
    "## From GeoJSON to Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ef43c7fe-4e7f-4595-9d99-7aa268099a36",
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   "source": [
    "#user defined variables:\n",
    "path_to_file = '/Users/jnimoca/Jose_BI/Repositories/Qupath_to_LMD/example_input/test_input.geojson'\n",
    "path_to_save_files = '/Users/jnimoca/Jose_BI/Repositories/Qupath_to_LMD/example_output/'\n",
    "#the naming of these should match exactly the names in the geoJSON file\n",
    "calibration_points = ['calib12','calib13','calib20']"
   ]
  },
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   "source": [
    "### Samples and wells"
   ]
  },
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   "source": [
    "# if you want to pass a custom well loadout use the following dictionary:  \n",
    "# The dictionary keys will have to be the class name used in qupath, it has to be the exact names.  \n",
    "# please choose wells with a 2-row 2-column margin. not rows (A, B, N, O) not columns (1,2,23,24)  "
   ]
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   "source": [
    "samples_and_wells = { \n",
    " '187_HBC_mid_3': 'C3',\n",
    " '187_HBC_nVEC_1': 'C4',\n",
    " '187_HBC_nVEC_2': 'C5',\n",
    " '187_HBC_mid_2': 'C6',\n",
    " '187_HBC_nTroph_3': 'C7',\n",
    " '187_HBC_nTroph_1': 'C8',\n",
    " '187_HBC_mid_1': 'C9',\n",
    " '187_HBC_nVEC_3': 'C10',\n",
    " '187_HBC_nTroph_2': 'C11',\n",
    " '108_STB_nVEC_1': 'F3',\n",
    " '108_STB_nVEC_2': 'F4',\n",
    " '108_STB_nVEC_3': 'F5',\n",
    " '108_STB_gen_1': 'F6',\n",
    " '108_STB_gen_2': 'F7',\n",
    " '108_STB_gen_3': 'F8',\n",
    " '108_CTB_1_1': 'F9',\n",
    " '108_CTB_2_1': 'F10',\n",
    " '108_CTB_3_1': 'F11',\n",
    " '108_HBC_nTroph_1': 'G3',\n",
    " '108_HBC_nTroph_2': 'G4',\n",
    " '108_HBC_nTroph_3': 'G5',\n",
    " '108_HBC_nVEC_1': 'G6',\n",
    " '108_HBC_nVEC_2': 'G7',\n",
    " '108_HBC_nVEC_3': 'G8',\n",
    " '108_HBC_mid_1': 'G9',\n",
    " '108_HBC_mid_2': 'G10',\n",
    " '108_HBC_mid_3': 'G11',\n",
    " '108_CCT_1': 'H3',\n",
    " '108_CCT_2': 'H4',\n",
    " '108_CCT_3': 'H5',\n",
    " '108_VEC_1': 'H6',\n",
    " '108_VEC_2': 'H7',\n",
    " '108_VEC_3': 'H8',\n",
    " '108_Str_1': 'H9',\n",
    " '108_Str_2': 'H10',\n",
    " '108_Str_3': 'H11',\n",
    " '187_STB_nVEC_1': 'D3',\n",
    " '187_STB_nVEC_2': 'D4',\n",
    " '187_STB_nVEC_3': 'D5',\n",
    " '187_STB_gen_1': 'D6',\n",
    " '187_STB_gen_2': 'D7',\n",
    " '187_STB_gen_3': 'D8',\n",
    " '187_CTB_1': 'D9',\n",
    " '187_CTB_2': 'D10',\n",
    " '187_CTB_3': 'D11',\n",
    " '187_CCT_1': 'E3',\n",
    " '187_CCT_2': 'E4',\n",
    " '187_CCT_3': 'E5',\n",
    " '187_VEC_1': 'E6',\n",
    " '187_VEC_2': 'E7',\n",
    " '187_VEC_3': 'E8',\n",
    " '187_str_1': 'E9',\n",
    " '187_str_2': 'E10',\n",
    " '187_str_3': 'E11'\n",
    " }"
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   "source": [
    "## Code"
   ]
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   "source": [
    "### process dataframe"
   ]
  },
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   "execution_count": 8,
   "metadata": {},
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   "source": [
    "def load_and_QC_geojson_file(geojson_path: str, list_of_calibpoint_names: list = ['calib1','calib2','calib3']):\n",
    "\n",
    "   #load geojson file\n",
    "   df = geopandas.read_file(geojson_path)\n",
    "\n",
    "   #save calib points in a list\n",
    "   caliblist = []\n",
    "   for point_name in list_of_calibpoint_names:\n",
    "      if point_name in df['name'].unique():\n",
    "            caliblist.append(df.loc[df['name'] == point_name, 'geometry'].values[0])\n",
    "      # else:\n",
    "      #       st.write('Your given name is not present in the file', \n",
    "      #       f'These are the calib points you passed: {list_of_calibpoint_names}',\n",
    "      #       f\"These are the calib points found in the geojson you gave me: {df['name'].unique()}\")\n",
    "   #create coordenate list\n",
    "   listarray = []\n",
    "   for point in caliblist:\n",
    "      listarray.append([point.x, point.y])\n",
    "   calib_np_array = numpy.array(listarray)\n",
    "\n",
    "   #now that calibration points are saved, remove them from the dataframe\n",
    "   df = df[df['name'].isin(list_of_calibpoint_names) == False]\n",
    "\n",
    "   #check and remove empty classifications \n",
    "   if df['classification'].isna().sum() !=0 :\n",
    "      # st.write(f\"you have {df['classification'].isna().sum()} NaNs in your classification column\",\n",
    "      #       \"these are unclassified objects from Qupath, they will be ignored\") \n",
    "      df = df[df['classification'].notna()]\n",
    "\n",
    "   #check for MultiPolygon objects\n",
    "   if 'MultiPolygon' in df.geometry.geom_type.value_counts().keys():\n",
    "      # st.write('MultiPolygon objects present:',\n",
    "      # #print out the classification name of the MultiPolygon objects\n",
    "      # f\"{df[df.geometry.geom_type == 'MultiPolygon']['classification']}\", \n",
    "      # 'these are not supported, please convert them to polygons in Qupath',\n",
    "      # 'the script will continue but these objects will be ignored')\n",
    "      #remove MultiPolygon objects\n",
    "      df = df[df.geometry.geom_type != 'MultiPolygon']\n",
    "\n",
    "   # reformat shape coordenate list\n",
    "   df['coords'] = numpy.nan\n",
    "   df['coords'] = df['coords'].astype('object')\n",
    "   # simplify to reduce number of points\n",
    "   df['simple'] = df.geometry.simplify(1)\n",
    "   # df['coords'] = df['simple'].apply(lambda geom: numpy.array(list(geom.exterior.coords)))\n",
    "   df['coords'] = df['simple'].apply(lambda geom: numpy.array(geom.exterior.coords))\n",
    "   # for i in df.index:\n",
    "   #    geom=df.at[i, 'simple']\n",
    "   #    tmp = list(geom.exterior.coords)\n",
    "   #    tmp_lol = [list(i) for i in tmp]\n",
    "   #    df.at[i,'coords'] = tmp_lol\n",
    "\n",
    "   #extract classification name into a new column\n",
    "   df['Name'] = numpy.nan\n",
    "   for i in df.index:\n",
    "      tmp = df.classification[i].get('name')\n",
    "      df.at[i,'Name'] = tmp\n",
    "\n",
    "   # st.write('The file loading is complete')\n",
    "\n",
    "   #save dataframe as csv\n",
    "   df.to_csv(f\"./{datetime}_QCed_geojson.csv\", index=False)\n",
    "   #save numpy array as csv\n",
    "   numpy.savetxt(f\"./{datetime}_calib_points.csv\", calib_np_array, delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "expected an indented block (2432633645.py, line 34)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  Cell \u001b[0;32mIn[9], line 34\u001b[0;36m\u001b[0m\n\u001b[0;31m    # st.write('The samples and wells scheme QC is done!')\u001b[0m\n\u001b[0m                                                          ^\u001b[0m\n\u001b[0;31mIndentationError\u001b[0m\u001b[0;31m:\u001b[0m expected an indented block\n"
     ]
    }
   ],
   "source": [
    "def load_and_QC_SamplesandWells(samples_and_wells_input, df_csv):\n",
    "\n",
    "   df = pandas.read_csv(df_csv)\n",
    "\n",
    "   # parse common human copy paste formats\n",
    "   # remove newlines\n",
    "   samples_and_wells_processed = samples_and_wells_input.replace(\"\\n\", \"\")\n",
    "   # remove spaces\n",
    "   samples_and_wells_processed = samples_and_wells_processed.replace(\" \", \"\")\n",
    "   #parse into python dictionary\n",
    "   samples_and_wells = ast.literal_eval(samples_and_wells_processed)\n",
    "\n",
    "   #create list of acceptable wells, default is using a space in between columns\n",
    "   list_of_acceptable_wells =[]\n",
    "   for row in list(string.ascii_uppercase[2:14]):\n",
    "      for column in range(2,22):\n",
    "         list_of_acceptable_wells.append(str(row) + str(column))\n",
    "\n",
    "   #check for improper wells\n",
    "   # for well in samples_and_wells.values():\n",
    "   #    if well not in list_of_acceptable_wells:\n",
    "            # st.write(f'Your well {well} is not in the list of acceptable wells, please correct it',\n",
    "            # 'the LMD is not able to collect into this well, the script will stop here')\n",
    "            # st.stop()\n",
    "\n",
    "   #check that names in df are all present in the samples and wells\n",
    "   # for name in df.Name.unique():\n",
    "      # if name not in samples_and_wells.keys():\n",
    "            # st.write(f'Your name {name} is not in the list of samples_and_wells, please correct either',\n",
    "            # 'please change the class name in Qupath or add it to the samples_and_wells dictionary',\n",
    "            # 'and then rerun the web app')\n",
    "            # st.stop()\n",
    "\n",
    "   # st.write('The samples and wells scheme QC is done!')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "calibration_point_1 = \"calib12\"\n",
    "calibration_point_2 = \"calib13\"\n",
    "calibration_point_3 = \"calib20\"\n",
    "list_of_calibpoint_names = [calibration_point_1, calibration_point_2, calibration_point_3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_collection(df_csv, calib_np_array_csv, samples_and_wells_input ):\n",
    "    df = pandas.read_csv(df_csv)\n",
    "    calib_np_array = numpy.loadtxt(calib_np_array_csv, delimiter=\",\")\n",
    "\n",
    "    # samples_and_wells_processed = samples_and_wells_input.replace(\"\\n\", \"\")\n",
    "    # samples_and_wells_processed = samples_and_wells_processed.replace(\" \", \"\")\n",
    "    # samples_and_wells = ast.literal_eval(samples_and_wells_processed)\n",
    "    samples_and_wells = samples_and_wells_input\n",
    "\n",
    "    #create the collection of py-lmd-env package\n",
    "    #uses caliblist passed on the function, order matters\n",
    "    #orientation vector is for QuPath coordenate system\n",
    "    the_collection = Collection(calibration_points = calib_np_array)\n",
    "    the_collection.orientation_transform = numpy.array([[1,0 ], [0,-1]])\n",
    "    for i in df.index:\n",
    "        the_collection.new_shape(df.at[i,'coords'], well = samples_and_wells[df.at[i, \"Name\"]])\n",
    "\n",
    "    the_collection.plot(save_name= \"./TheCollection.png\")\n",
    "    # st.image(\"./TheCollection.png\", caption='Your Contours', use_column_width=True)\n",
    "    # st.write(the_collection.stats())\n",
    "    the_collection.save(f\"./{datetime}_LMD_ready_contours.xml\")\n",
    "\n",
    "\n",
    "    #create and export dataframe with sample placement in 384 well plate\n",
    "    rows_A_P= [i for i in string.ascii_uppercase[:16]]\n",
    "    columns_1_24 = [str(i) for i in range(1,25)]\n",
    "    df_wp384 = pd.DataFrame('',columns=columns_1_24, index=rows_A_P)\n",
    "    #fill in the dataframe with samples and wells\n",
    "    for i in samples_and_wells:\n",
    "        location = samples_and_wells[i]\n",
    "        df_wp384.at[location[0],location[1:]] = i\n",
    "    #save dataframe as csv\n",
    "    df_wp384.to_csv(f\"./{datetime}_384_wellplate.csv\", index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'load_and_QC_geojson_file' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m/Users/jnimoca/Jose_BI/Repositories/Qupath_to_LMD_v2/QuPath_to_LMD.ipynb Cell 15\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/jnimoca/Jose_BI/Repositories/Qupath_to_LMD_v2/QuPath_to_LMD.ipynb#X21sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m load_and_QC_geojson_file(geojson_path\u001b[39m=\u001b[39m\u001b[39m'\u001b[39m\u001b[39m/Users/jnimoca/Jose_BI/Repositories/Qupath_to_LMD_v2/example_input/test_input.geojson\u001b[39m\u001b[39m'\u001b[39m, list_of_calibpoint_names\u001b[39m=\u001b[39mlist_of_calibpoint_names)\n",
      "\u001b[0;31mNameError\u001b[0m: name 'load_and_QC_geojson_file' is not defined"
     ]
    }
   ],
   "source": [
    "load_and_QC_geojson_file(geojson_path='/Users/jnimoca/Jose_BI/Repositories/Qupath_to_LMD_v2/example_input/test_input.geojson', list_of_calibpoint_names=list_of_calibpoint_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "calib_np_array = numpy.loadtxt(f\"./{datetime}_calib_points.csv\", delimiter=\",\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 2)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calib_np_array.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pandas.read_csv(\"/Users/jnimoca/Jose_BI/Repositories/Qupath_to_LMD_v2/20231204_QCed_geojson.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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       "      <th>4</th>\n",
       "      <td>64dc48fa-7583-4300-b1d3-a9c35c3ea010</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_HBC_mid_2', 'color': [0, 128, 128]}</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>187_HBC_mid_2</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>2105</th>\n",
       "      <td>8f21e230-2922-4738-be72-2291e3c4b53a</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_STB_nVEC_2', 'color': [0, 128, 1...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((9088 7678, 9082 7681, 9081.66 7681.6...</td>\n",
       "      <td>[[9088. 7678.]\\n [9079. 7684.]\\n [9075. 7692.]...</td>\n",
       "      <td>POLYGON ((9088 7678, 9079 7684, 9075 7692, 907...</td>\n",
       "      <td>187_STB_nVEC_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2106</th>\n",
       "      <td>d91a7a6f-2d60-43c7-bda1-52f224847ed3</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_HBC_mid_3', 'color': [0, 128, 128]}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((8393 8306, 8389 8308, 8388 8309, 838...</td>\n",
       "      <td>[[8393. 8306.]\\n [8386. 8311.]\\n [8383. 8318.]...</td>\n",
       "      <td>POLYGON ((8393 8306, 8386 8311, 8383 8318, 838...</td>\n",
       "      <td>187_HBC_mid_3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2107</th>\n",
       "      <td>82d18f39-add1-4c1e-b8ce-571de4c4cad0</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_HBC_mid_2', 'color': [0, 128, 128]}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((8238 8447, 8236 8448, 8235 8448, 823...</td>\n",
       "      <td>[[8250. 8448.]\\n [8235. 8448.]\\n [8229. 8454.]...</td>\n",
       "      <td>POLYGON ((8250 8448, 8235 8448, 8229 8454, 822...</td>\n",
       "      <td>187_HBC_mid_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2108</th>\n",
       "      <td>7675f39f-58ce-41ea-8078-c58217d634ff</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_HBC_nTroph_2', 'color': [0, 128,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((9173 10250, 9172 10251, 9170 10252, ...</td>\n",
       "      <td>[[ 9173. 10250.]\\n [ 9168. 10255.]\\n [ 9168. 1...</td>\n",
       "      <td>POLYGON ((9173 10250, 9168 10255, 9168 10263, ...</td>\n",
       "      <td>187_HBC_nTroph_2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2109</th>\n",
       "      <td>c2f88c50-3fa6-4e43-84d0-df609336908c</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_CTB_3', 'color': [0, 128, 128]}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((9153 10289, 9152 10290, 9150 10291, ...</td>\n",
       "      <td>[[ 9153. 10289.]\\n [ 9148. 10294.]\\n [ 9148. 1...</td>\n",
       "      <td>POLYGON ((9153 10289, 9148 10294, 9148 10303, ...</td>\n",
       "      <td>187_CTB_3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2110 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        id  objectType  \\\n",
       "0     38af0704-dec2-48e9-99cc-d6262ec92722  annotation   \n",
       "1     8ac78eff-262b-4942-9580-2badf20e22b5  annotation   \n",
       "2     4586ad21-65e0-48e9-b882-328bdc081c44  annotation   \n",
       "3     33822c54-ae0a-4ad4-8ad9-d0eea9ad91c6  annotation   \n",
       "4     64dc48fa-7583-4300-b1d3-a9c35c3ea010  annotation   \n",
       "...                                    ...         ...   \n",
       "2105  8f21e230-2922-4738-be72-2291e3c4b53a  annotation   \n",
       "2106  d91a7a6f-2d60-43c7-bda1-52f224847ed3  annotation   \n",
       "2107  82d18f39-add1-4c1e-b8ce-571de4c4cad0  annotation   \n",
       "2108  7675f39f-58ce-41ea-8078-c58217d634ff  annotation   \n",
       "2109  c2f88c50-3fa6-4e43-84d0-df609336908c  annotation   \n",
       "\n",
       "                                         classification  name  \\\n",
       "0     {'name': '187_HBC_mid_1', 'color': [0, 128, 128]}   NaN   \n",
       "1     {'name': '187_HBC_nVEC_2', 'color': [0, 128, 1...   NaN   \n",
       "2     {'name': '187_HBC_mid_1', 'color': [0, 128, 128]}   NaN   \n",
       "3     {'name': '187_HBC_mid_1', 'color': [0, 128, 128]}   NaN   \n",
       "4     {'name': '187_HBC_mid_2', 'color': [0, 128, 128]}   NaN   \n",
       "...                                                 ...   ...   \n",
       "2105  {'name': '187_STB_nVEC_2', 'color': [0, 128, 1...   NaN   \n",
       "2106  {'name': '187_HBC_mid_3', 'color': [0, 128, 128]}   NaN   \n",
       "2107  {'name': '187_HBC_mid_2', 'color': [0, 128, 128]}   NaN   \n",
       "2108  {'name': '187_HBC_nTroph_2', 'color': [0, 128,...   NaN   \n",
       "2109      {'name': '187_CTB_3', 'color': [0, 128, 128]}   NaN   \n",
       "\n",
       "                                               geometry  \\\n",
       "0     POLYGON ((7925 12328, 7924 12329, 7919 12329, ...   \n",
       "1     POLYGON ((8011 12393, 8010 12394, 8008 12394, ...   \n",
       "2     POLYGON ((8047 12321, 8046 12322, 8044 12322, ...   \n",
       "3     POLYGON ((8084 12325, 8083 12326, 8082 12326, ...   \n",
       "4     POLYGON ((7917 12268, 7916 12269, 7914 12269, ...   \n",
       "...                                                 ...   \n",
       "2105  POLYGON ((9088 7678, 9082 7681, 9081.66 7681.6...   \n",
       "2106  POLYGON ((8393 8306, 8389 8308, 8388 8309, 838...   \n",
       "2107  POLYGON ((8238 8447, 8236 8448, 8235 8448, 823...   \n",
       "2108  POLYGON ((9173 10250, 9172 10251, 9170 10252, ...   \n",
       "2109  POLYGON ((9153 10289, 9152 10290, 9150 10291, ...   \n",
       "\n",
       "                                                 coords  \\\n",
       "0     [[ 7931. 12328.]\\n [ 7919. 12329.]\\n [ 7913. 1...   \n",
       "1     [[ 8011. 12393.]\\n [ 8003. 12398.]\\n [ 8001. 1...   \n",
       "2     [[ 8047. 12321.]\\n [ 8040. 12324.]\\n [ 8032. 1...   \n",
       "3     [[ 8084. 12325.]\\n [ 8074. 12329.]\\n [ 8070. 1...   \n",
       "4     [[ 7917. 12268.]\\n [ 7913. 12270.]\\n [ 7902. 1...   \n",
       "...                                                 ...   \n",
       "2105  [[9088. 7678.]\\n [9079. 7684.]\\n [9075. 7692.]...   \n",
       "2106  [[8393. 8306.]\\n [8386. 8311.]\\n [8383. 8318.]...   \n",
       "2107  [[8250. 8448.]\\n [8235. 8448.]\\n [8229. 8454.]...   \n",
       "2108  [[ 9173. 10250.]\\n [ 9168. 10255.]\\n [ 9168. 1...   \n",
       "2109  [[ 9153. 10289.]\\n [ 9148. 10294.]\\n [ 9148. 1...   \n",
       "\n",
       "                                                 simple              Name  \n",
       "0     POLYGON ((7931 12328, 7919 12329, 7913 12333, ...     187_HBC_mid_1  \n",
       "1     POLYGON ((8011 12393, 8003 12398, 8001 12402, ...    187_HBC_nVEC_2  \n",
       "2     POLYGON ((8047 12321, 8040 12324, 8032 12331, ...     187_HBC_mid_1  \n",
       "3     POLYGON ((8084 12325, 8074 12329, 8070 12333, ...     187_HBC_mid_1  \n",
       "4     POLYGON ((7917 12268, 7913 12270, 7902 12271, ...     187_HBC_mid_2  \n",
       "...                                                 ...               ...  \n",
       "2105  POLYGON ((9088 7678, 9079 7684, 9075 7692, 907...    187_STB_nVEC_2  \n",
       "2106  POLYGON ((8393 8306, 8386 8311, 8383 8318, 838...     187_HBC_mid_3  \n",
       "2107  POLYGON ((8250 8448, 8235 8448, 8229 8454, 822...     187_HBC_mid_2  \n",
       "2108  POLYGON ((9173 10250, 9168 10255, 9168 10263, ...  187_HBC_nTroph_2  \n",
       "2109  POLYGON ((9153 10289, 9148 10294, 9148 10303, ...         187_CTB_3  \n",
       "\n",
       "[2110 rows x 8 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def replace_coords_simple(df):\n",
    "    df['lol_simple'] = np.nan\n",
    "    df['lol_simple'] = df['lol_simple'].astype('object')\n",
    "    df['simple'] = df.geometry.simplify(1)\n",
    "    \n",
    "    for i in df.index:\n",
    "        #get geometry object for row i\n",
    "        geom=df.at[i, 'simple']\n",
    "        #list the coordenate points as tuples\n",
    "        tmp = list(geom.exterior.coords)\n",
    "        #transform list of tuples to list of lists\n",
    "        tmp_lol = [list(i) for i in tmp]\n",
    "        #add list of list to lol column\n",
    "        df.at[i,'lol_simple'] = tmp_lol\n",
    "    return(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def replace_coords(df):\n",
    "    df['lol'] = np.nan\n",
    "    df['lol'] = df['lol'].astype('object')\n",
    "    \n",
    "    for i in df.index:\n",
    "        #get geometry object for row i\n",
    "        geom=df.at[i, 'geometry']\n",
    "        #list the coordenate points as tuples\n",
    "        tmp = list(geom.exterior.coords)\n",
    "        #transform list of tuples to list of lists\n",
    "        tmp_lol = [list(i) for i in tmp]\n",
    "        #add list of list to lol column\n",
    "        df.at[i,'lol'] = tmp_lol\n",
    "    return(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = geopandas.read_file(\"/Users/jnimoca/Jose_BI/Repositories/Qupath_to_LMD_v2/example_input/test_input.geojson\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>objectType</th>\n",
       "      <th>classification</th>\n",
       "      <th>name</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>38af0704-dec2-48e9-99cc-d6262ec92722</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_HBC_mid_1', 'color': [0, 128, 128]}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((7925.00000 12328.00000, 7924.00000 1...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8ac78eff-262b-4942-9580-2badf20e22b5</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_HBC_nVEC_2', 'color': [0, 128, 1...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((8011.00000 12393.00000, 8010.00000 1...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4586ad21-65e0-48e9-b882-328bdc081c44</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_HBC_mid_1', 'color': [0, 128, 128]}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((8047.00000 12321.00000, 8046.00000 1...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33822c54-ae0a-4ad4-8ad9-d0eea9ad91c6</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_HBC_mid_1', 'color': [0, 128, 128]}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((8084.00000 12325.00000, 8083.00000 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64dc48fa-7583-4300-b1d3-a9c35c3ea010</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_HBC_mid_2', 'color': [0, 128, 128]}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((7917.00000 12268.00000, 7916.00000 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>2111</th>\n",
       "      <td>7675f39f-58ce-41ea-8078-c58217d634ff</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_HBC_nTroph_2', 'color': [0, 128,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((9173.00000 10250.00000, 9172.00000 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2112</th>\n",
       "      <td>c2f88c50-3fa6-4e43-84d0-df609336908c</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_CTB_3', 'color': [0, 128, 128]}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((9153.00000 10289.00000, 9152.00000 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2113</th>\n",
       "      <td>b8daa84d-2e25-40c2-b3fd-bbbdc059bb5f</td>\n",
       "      <td>annotation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>calib12</td>\n",
       "      <td>POINT (11343.79000 6900.13000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2114</th>\n",
       "      <td>6feb2a83-5054-4c9b-876e-98f354012ae3</td>\n",
       "      <td>annotation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>calib13</td>\n",
       "      <td>POINT (9635.17000 14764.84000)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2115</th>\n",
       "      <td>fdbe94ba-45c2-421a-9972-9c8de130a315</td>\n",
       "      <td>annotation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>calib20</td>\n",
       "      <td>POINT (642.12000 14103.67000)</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>2116 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        id  objectType  \\\n",
       "0     38af0704-dec2-48e9-99cc-d6262ec92722  annotation   \n",
       "1     8ac78eff-262b-4942-9580-2badf20e22b5  annotation   \n",
       "2     4586ad21-65e0-48e9-b882-328bdc081c44  annotation   \n",
       "3     33822c54-ae0a-4ad4-8ad9-d0eea9ad91c6  annotation   \n",
       "4     64dc48fa-7583-4300-b1d3-a9c35c3ea010  annotation   \n",
       "...                                    ...         ...   \n",
       "2111  7675f39f-58ce-41ea-8078-c58217d634ff  annotation   \n",
       "2112  c2f88c50-3fa6-4e43-84d0-df609336908c  annotation   \n",
       "2113  b8daa84d-2e25-40c2-b3fd-bbbdc059bb5f  annotation   \n",
       "2114  6feb2a83-5054-4c9b-876e-98f354012ae3  annotation   \n",
       "2115  fdbe94ba-45c2-421a-9972-9c8de130a315  annotation   \n",
       "\n",
       "                                         classification     name  \\\n",
       "0     {'name': '187_HBC_mid_1', 'color': [0, 128, 128]}      NaN   \n",
       "1     {'name': '187_HBC_nVEC_2', 'color': [0, 128, 1...      NaN   \n",
       "2     {'name': '187_HBC_mid_1', 'color': [0, 128, 128]}      NaN   \n",
       "3     {'name': '187_HBC_mid_1', 'color': [0, 128, 128]}      NaN   \n",
       "4     {'name': '187_HBC_mid_2', 'color': [0, 128, 128]}      NaN   \n",
       "...                                                 ...      ...   \n",
       "2111  {'name': '187_HBC_nTroph_2', 'color': [0, 128,...      NaN   \n",
       "2112      {'name': '187_CTB_3', 'color': [0, 128, 128]}      NaN   \n",
       "2113                                                NaN  calib12   \n",
       "2114                                                NaN  calib13   \n",
       "2115                                                NaN  calib20   \n",
       "\n",
       "                                               geometry  \n",
       "0     POLYGON ((7925.00000 12328.00000, 7924.00000 1...  \n",
       "1     POLYGON ((8011.00000 12393.00000, 8010.00000 1...  \n",
       "2     POLYGON ((8047.00000 12321.00000, 8046.00000 1...  \n",
       "3     POLYGON ((8084.00000 12325.00000, 8083.00000 1...  \n",
       "4     POLYGON ((7917.00000 12268.00000, 7916.00000 1...  \n",
       "...                                                 ...  \n",
       "2111  POLYGON ((9173.00000 10250.00000, 9172.00000 1...  \n",
       "2112  POLYGON ((9153.00000 10289.00000, 9152.00000 1...  \n",
       "2113                     POINT (11343.79000 6900.13000)  \n",
       "2114                     POINT (9635.17000 14764.84000)  \n",
       "2115                      POINT (642.12000 14103.67000)  \n",
       "\n",
       "[2116 rows x 5 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#save calib points in a list\n",
    "caliblist = []\n",
    "for point_name in list_of_calibpoint_names:\n",
    "    if point_name in df['name'].unique():\n",
    "        caliblist.append(df.loc[df['name'] == point_name, 'geometry'].values[0])\n",
    "    else:\n",
    "        st.write('Your given name is not present in the file', \n",
    "        f'These are the calib points you passed: {list_of_calibpoint_names}',\n",
    "        f\"These are the calib points found in the geojson you gave me: {df['name'].unique()}\")\n",
    "#create coordenate list\n",
    "listarray = []\n",
    "for point in caliblist:\n",
    "    listarray.append([point.x, point.y])\n",
    "calib_np_array = numpy.array(listarray)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[11343.79,  6900.13],\n",
       "       [ 9635.17, 14764.84],\n",
       "       [  642.12, 14103.67]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "calib_np_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['name'].isin(list_of_calibpoint_names) == False]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       POLYGON ((7925.00000 12328.00000, 7924.00000 1...\n",
       "1       POLYGON ((8011.00000 12393.00000, 8010.00000 1...\n",
       "2       POLYGON ((8047.00000 12321.00000, 8046.00000 1...\n",
       "3       POLYGON ((8084.00000 12325.00000, 8083.00000 1...\n",
       "4       POLYGON ((7917.00000 12268.00000, 7916.00000 1...\n",
       "                              ...                        \n",
       "2108    POLYGON ((9088.00000 7678.00000, 9082.00000 76...\n",
       "2109    POLYGON ((8393.00000 8306.00000, 8389.00000 83...\n",
       "2110    POLYGON ((8238.00000 8447.00000, 8236.00000 84...\n",
       "2111    POLYGON ((9173.00000 10250.00000, 9172.00000 1...\n",
       "2112    POLYGON ((9153.00000 10289.00000, 9152.00000 1...\n",
       "Name: geometry, Length: 2113, dtype: geometry"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.geometry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "#check and remove empty classifications \n",
    "if df['classification'].isna().sum() !=0 :\n",
    "    # st.write(f\"you have {df['classification'].isna().sum()} NaNs in your classification column\",\n",
    "    #     \"these are unclassified objects from Qupath, they will be ignored\") \n",
    "    df = df[df['classification'].notna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>objectType</th>\n",
       "      <th>classification</th>\n",
       "      <th>name</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>38af0704-dec2-48e9-99cc-d6262ec92722</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_HBC_mid_1', 'color': [0, 128, 128]}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((7925.00000 12328.00000, 7924.00000 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8ac78eff-262b-4942-9580-2badf20e22b5</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_HBC_nVEC_2', 'color': [0, 128, 1...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((8011.00000 12393.00000, 8010.00000 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4586ad21-65e0-48e9-b882-328bdc081c44</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_HBC_mid_1', 'color': [0, 128, 128]}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((8047.00000 12321.00000, 8046.00000 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33822c54-ae0a-4ad4-8ad9-d0eea9ad91c6</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_HBC_mid_1', 'color': [0, 128, 128]}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((8084.00000 12325.00000, 8083.00000 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64dc48fa-7583-4300-b1d3-a9c35c3ea010</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_HBC_mid_2', 'color': [0, 128, 128]}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((7917.00000 12268.00000, 7916.00000 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2108</th>\n",
       "      <td>8f21e230-2922-4738-be72-2291e3c4b53a</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_STB_nVEC_2', 'color': [0, 128, 1...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((9088.00000 7678.00000, 9082.00000 76...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2109</th>\n",
       "      <td>d91a7a6f-2d60-43c7-bda1-52f224847ed3</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_HBC_mid_3', 'color': [0, 128, 128]}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((8393.00000 8306.00000, 8389.00000 83...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2110</th>\n",
       "      <td>82d18f39-add1-4c1e-b8ce-571de4c4cad0</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_HBC_mid_2', 'color': [0, 128, 128]}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((8238.00000 8447.00000, 8236.00000 84...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2111</th>\n",
       "      <td>7675f39f-58ce-41ea-8078-c58217d634ff</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_HBC_nTroph_2', 'color': [0, 128,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((9173.00000 10250.00000, 9172.00000 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2112</th>\n",
       "      <td>c2f88c50-3fa6-4e43-84d0-df609336908c</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_CTB_3', 'color': [0, 128, 128]}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((9153.00000 10289.00000, 9152.00000 1...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2110 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        id  objectType  \\\n",
       "0     38af0704-dec2-48e9-99cc-d6262ec92722  annotation   \n",
       "1     8ac78eff-262b-4942-9580-2badf20e22b5  annotation   \n",
       "2     4586ad21-65e0-48e9-b882-328bdc081c44  annotation   \n",
       "3     33822c54-ae0a-4ad4-8ad9-d0eea9ad91c6  annotation   \n",
       "4     64dc48fa-7583-4300-b1d3-a9c35c3ea010  annotation   \n",
       "...                                    ...         ...   \n",
       "2108  8f21e230-2922-4738-be72-2291e3c4b53a  annotation   \n",
       "2109  d91a7a6f-2d60-43c7-bda1-52f224847ed3  annotation   \n",
       "2110  82d18f39-add1-4c1e-b8ce-571de4c4cad0  annotation   \n",
       "2111  7675f39f-58ce-41ea-8078-c58217d634ff  annotation   \n",
       "2112  c2f88c50-3fa6-4e43-84d0-df609336908c  annotation   \n",
       "\n",
       "                                         classification name  \\\n",
       "0     {'name': '187_HBC_mid_1', 'color': [0, 128, 128]}  NaN   \n",
       "1     {'name': '187_HBC_nVEC_2', 'color': [0, 128, 1...  NaN   \n",
       "2     {'name': '187_HBC_mid_1', 'color': [0, 128, 128]}  NaN   \n",
       "3     {'name': '187_HBC_mid_1', 'color': [0, 128, 128]}  NaN   \n",
       "4     {'name': '187_HBC_mid_2', 'color': [0, 128, 128]}  NaN   \n",
       "...                                                 ...  ...   \n",
       "2108  {'name': '187_STB_nVEC_2', 'color': [0, 128, 1...  NaN   \n",
       "2109  {'name': '187_HBC_mid_3', 'color': [0, 128, 128]}  NaN   \n",
       "2110  {'name': '187_HBC_mid_2', 'color': [0, 128, 128]}  NaN   \n",
       "2111  {'name': '187_HBC_nTroph_2', 'color': [0, 128,...  NaN   \n",
       "2112      {'name': '187_CTB_3', 'color': [0, 128, 128]}  NaN   \n",
       "\n",
       "                                               geometry  \n",
       "0     POLYGON ((7925.00000 12328.00000, 7924.00000 1...  \n",
       "1     POLYGON ((8011.00000 12393.00000, 8010.00000 1...  \n",
       "2     POLYGON ((8047.00000 12321.00000, 8046.00000 1...  \n",
       "3     POLYGON ((8084.00000 12325.00000, 8083.00000 1...  \n",
       "4     POLYGON ((7917.00000 12268.00000, 7916.00000 1...  \n",
       "...                                                 ...  \n",
       "2108  POLYGON ((9088.00000 7678.00000, 9082.00000 76...  \n",
       "2109  POLYGON ((8393.00000 8306.00000, 8389.00000 83...  \n",
       "2110  POLYGON ((8238.00000 8447.00000, 8236.00000 84...  \n",
       "2111  POLYGON ((9173.00000 10250.00000, 9172.00000 1...  \n",
       "2112  POLYGON ((9153.00000 10289.00000, 9152.00000 1...  \n",
       "\n",
       "[2110 rows x 5 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "#check for MultiPolygon objects\n",
    "if 'MultiPolygon' in df.geometry.geom_type.value_counts().keys():\n",
    "    st.write('MultiPolygon objects present:',\n",
    "    #print out the classification name of the MultiPolygon objects\n",
    "    f\"{df[df.geometry.geom_type == 'MultiPolygon']['classification']}\", \n",
    "    'these are not supported, please convert them to polygons in Qupath',\n",
    "    'the script will continue but these objects will be ignored')\n",
    "    #remove MultiPolygon objects\n",
    "    df = df[df.geometry.geom_type != 'MultiPolygon']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>objectType</th>\n",
       "      <th>classification</th>\n",
       "      <th>name</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>38af0704-dec2-48e9-99cc-d6262ec92722</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_HBC_mid_1', 'color': [0, 128, 128]}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((7925.00000 12328.00000, 7924.00000 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8ac78eff-262b-4942-9580-2badf20e22b5</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_HBC_nVEC_2', 'color': [0, 128, 1...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((8011.00000 12393.00000, 8010.00000 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4586ad21-65e0-48e9-b882-328bdc081c44</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_HBC_mid_1', 'color': [0, 128, 128]}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((8047.00000 12321.00000, 8046.00000 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33822c54-ae0a-4ad4-8ad9-d0eea9ad91c6</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_HBC_mid_1', 'color': [0, 128, 128]}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((8084.00000 12325.00000, 8083.00000 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64dc48fa-7583-4300-b1d3-a9c35c3ea010</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_HBC_mid_2', 'color': [0, 128, 128]}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((7917.00000 12268.00000, 7916.00000 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2108</th>\n",
       "      <td>8f21e230-2922-4738-be72-2291e3c4b53a</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_STB_nVEC_2', 'color': [0, 128, 1...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((9088.00000 7678.00000, 9082.00000 76...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2109</th>\n",
       "      <td>d91a7a6f-2d60-43c7-bda1-52f224847ed3</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_HBC_mid_3', 'color': [0, 128, 128]}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((8393.00000 8306.00000, 8389.00000 83...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2110</th>\n",
       "      <td>82d18f39-add1-4c1e-b8ce-571de4c4cad0</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_HBC_mid_2', 'color': [0, 128, 128]}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((8238.00000 8447.00000, 8236.00000 84...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2111</th>\n",
       "      <td>7675f39f-58ce-41ea-8078-c58217d634ff</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_HBC_nTroph_2', 'color': [0, 128,...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((9173.00000 10250.00000, 9172.00000 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2112</th>\n",
       "      <td>c2f88c50-3fa6-4e43-84d0-df609336908c</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_CTB_3', 'color': [0, 128, 128]}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((9153.00000 10289.00000, 9152.00000 1...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2110 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        id  objectType  \\\n",
       "0     38af0704-dec2-48e9-99cc-d6262ec92722  annotation   \n",
       "1     8ac78eff-262b-4942-9580-2badf20e22b5  annotation   \n",
       "2     4586ad21-65e0-48e9-b882-328bdc081c44  annotation   \n",
       "3     33822c54-ae0a-4ad4-8ad9-d0eea9ad91c6  annotation   \n",
       "4     64dc48fa-7583-4300-b1d3-a9c35c3ea010  annotation   \n",
       "...                                    ...         ...   \n",
       "2108  8f21e230-2922-4738-be72-2291e3c4b53a  annotation   \n",
       "2109  d91a7a6f-2d60-43c7-bda1-52f224847ed3  annotation   \n",
       "2110  82d18f39-add1-4c1e-b8ce-571de4c4cad0  annotation   \n",
       "2111  7675f39f-58ce-41ea-8078-c58217d634ff  annotation   \n",
       "2112  c2f88c50-3fa6-4e43-84d0-df609336908c  annotation   \n",
       "\n",
       "                                         classification name  \\\n",
       "0     {'name': '187_HBC_mid_1', 'color': [0, 128, 128]}  NaN   \n",
       "1     {'name': '187_HBC_nVEC_2', 'color': [0, 128, 1...  NaN   \n",
       "2     {'name': '187_HBC_mid_1', 'color': [0, 128, 128]}  NaN   \n",
       "3     {'name': '187_HBC_mid_1', 'color': [0, 128, 128]}  NaN   \n",
       "4     {'name': '187_HBC_mid_2', 'color': [0, 128, 128]}  NaN   \n",
       "...                                                 ...  ...   \n",
       "2108  {'name': '187_STB_nVEC_2', 'color': [0, 128, 1...  NaN   \n",
       "2109  {'name': '187_HBC_mid_3', 'color': [0, 128, 128]}  NaN   \n",
       "2110  {'name': '187_HBC_mid_2', 'color': [0, 128, 128]}  NaN   \n",
       "2111  {'name': '187_HBC_nTroph_2', 'color': [0, 128,...  NaN   \n",
       "2112      {'name': '187_CTB_3', 'color': [0, 128, 128]}  NaN   \n",
       "\n",
       "                                               geometry  \n",
       "0     POLYGON ((7925.00000 12328.00000, 7924.00000 1...  \n",
       "1     POLYGON ((8011.00000 12393.00000, 8010.00000 1...  \n",
       "2     POLYGON ((8047.00000 12321.00000, 8046.00000 1...  \n",
       "3     POLYGON ((8084.00000 12325.00000, 8083.00000 1...  \n",
       "4     POLYGON ((7917.00000 12268.00000, 7916.00000 1...  \n",
       "...                                                 ...  \n",
       "2108  POLYGON ((9088.00000 7678.00000, 9082.00000 76...  \n",
       "2109  POLYGON ((8393.00000 8306.00000, 8389.00000 83...  \n",
       "2110  POLYGON ((8238.00000 8447.00000, 8236.00000 84...  \n",
       "2111  POLYGON ((9173.00000 10250.00000, 9172.00000 1...  \n",
       "2112  POLYGON ((9153.00000 10289.00000, 9152.00000 1...  \n",
       "\n",
       "[2110 rows x 5 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reformat shape coordenate list\n",
    "df['coords'] = numpy.nan\n",
    "df['coords'] = df['coords'].astype('object')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>annotation</td>\n",
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       "...                                    ...         ...   \n",
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       "\n",
       "                                         classification name  \\\n",
       "0     {'name': '187_HBC_mid_1', 'color': [0, 128, 128]}  NaN   \n",
       "1     {'name': '187_HBC_nVEC_2', 'color': [0, 128, 1...  NaN   \n",
       "2     {'name': '187_HBC_mid_1', 'color': [0, 128, 128]}  NaN   \n",
       "3     {'name': '187_HBC_mid_1', 'color': [0, 128, 128]}  NaN   \n",
       "4     {'name': '187_HBC_mid_2', 'color': [0, 128, 128]}  NaN   \n",
       "...                                                 ...  ...   \n",
       "2108  {'name': '187_STB_nVEC_2', 'color': [0, 128, 1...  NaN   \n",
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       "2110  {'name': '187_HBC_mid_2', 'color': [0, 128, 128]}  NaN   \n",
       "2111  {'name': '187_HBC_nTroph_2', 'color': [0, 128,...  NaN   \n",
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       "\n",
       "                                               geometry coords  \n",
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       "1     POLYGON ((8011.00000 12393.00000, 8010.00000 1...    NaN  \n",
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       "4     POLYGON ((7917.00000 12268.00000, 7916.00000 1...    NaN  \n",
       "...                                                 ...    ...  \n",
       "2108  POLYGON ((9088.00000 7678.00000, 9082.00000 76...    NaN  \n",
       "2109  POLYGON ((8393.00000 8306.00000, 8389.00000 83...    NaN  \n",
       "2110  POLYGON ((8238.00000 8447.00000, 8236.00000 84...    NaN  \n",
       "2111  POLYGON ((9173.00000 10250.00000, 9172.00000 1...    NaN  \n",
       "2112  POLYGON ((9153.00000 10289.00000, 9152.00000 1...    NaN  \n",
       "\n",
       "[2110 rows x 6 columns]"
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     },
     "execution_count": 41,
     "metadata": {},
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    }
   ],
   "source": [
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   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['simple'] = df.geometry.simplify(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
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       "      <td>8f21e230-2922-4738-be72-2291e3c4b53a</td>\n",
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       "      <td>{'name': '187_STB_nVEC_2', 'color': [0, 128, 1...</td>\n",
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       "                                        id  objectType  \\\n",
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       "2112  c2f88c50-3fa6-4e43-84d0-df609336908c  annotation   \n",
       "\n",
       "                                         classification name  \\\n",
       "0     {'name': '187_HBC_mid_1', 'color': [0, 128, 128]}  NaN   \n",
       "1     {'name': '187_HBC_nVEC_2', 'color': [0, 128, 1...  NaN   \n",
       "2     {'name': '187_HBC_mid_1', 'color': [0, 128, 128]}  NaN   \n",
       "3     {'name': '187_HBC_mid_1', 'color': [0, 128, 128]}  NaN   \n",
       "4     {'name': '187_HBC_mid_2', 'color': [0, 128, 128]}  NaN   \n",
       "...                                                 ...  ...   \n",
       "2108  {'name': '187_STB_nVEC_2', 'color': [0, 128, 1...  NaN   \n",
       "2109  {'name': '187_HBC_mid_3', 'color': [0, 128, 128]}  NaN   \n",
       "2110  {'name': '187_HBC_mid_2', 'color': [0, 128, 128]}  NaN   \n",
       "2111  {'name': '187_HBC_nTroph_2', 'color': [0, 128,...  NaN   \n",
       "2112      {'name': '187_CTB_3', 'color': [0, 128, 128]}  NaN   \n",
       "\n",
       "                                               geometry coords  \\\n",
       "0     POLYGON ((7925.00000 12328.00000, 7924.00000 1...    NaN   \n",
       "1     POLYGON ((8011.00000 12393.00000, 8010.00000 1...    NaN   \n",
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       "...                                                 ...    ...   \n",
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       "2112  POLYGON ((9153.00000 10289.00000, 9152.00000 1...    NaN   \n",
       "\n",
       "                                                 simple  \n",
       "0     POLYGON ((7931.00000 12328.00000, 7919.00000 1...  \n",
       "1     POLYGON ((8011.00000 12393.00000, 8003.00000 1...  \n",
       "2     POLYGON ((8047.00000 12321.00000, 8040.00000 1...  \n",
       "3     POLYGON ((8084.00000 12325.00000, 8074.00000 1...  \n",
       "4     POLYGON ((7917.00000 12268.00000, 7913.00000 1...  \n",
       "...                                                 ...  \n",
       "2108  POLYGON ((9088.00000 7678.00000, 9079.00000 76...  \n",
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       "2110  POLYGON ((8250.00000 8448.00000, 8235.00000 84...  \n",
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       "2112  POLYGON ((9153.00000 10289.00000, 9148.00000 1...  \n",
       "\n",
       "[2110 rows x 7 columns]"
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     },
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  {
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   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['coords'] = df['simple'].apply(lambda geom: numpy.array(list(geom.exterior.coords)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
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       "      <th>id</th>\n",
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       "      <th>classification</th>\n",
       "      <th>name</th>\n",
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       "      <th>simple</th>\n",
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       "      <th>0</th>\n",
       "      <td>38af0704-dec2-48e9-99cc-d6262ec92722</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_HBC_mid_1', 'color': [0, 128, 128]}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((7925.00000 12328.00000, 7924.00000 1...</td>\n",
       "      <td>[[7931.0, 12328.0], [7919.0, 12329.0], [7913.0...</td>\n",
       "      <td>POLYGON ((7931.00000 12328.00000, 7919.00000 1...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8ac78eff-262b-4942-9580-2badf20e22b5</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_HBC_nVEC_2', 'color': [0, 128, 1...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((8011.00000 12393.00000, 8010.00000 1...</td>\n",
       "      <td>[[8011.0, 12393.0], [8003.0, 12398.0], [8001.0...</td>\n",
       "      <td>POLYGON ((8011.00000 12393.00000, 8003.00000 1...</td>\n",
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       "      <td>4586ad21-65e0-48e9-b882-328bdc081c44</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_HBC_mid_1', 'color': [0, 128, 128]}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((8047.00000 12321.00000, 8046.00000 1...</td>\n",
       "      <td>[[8047.0, 12321.0], [8040.0, 12324.0], [8032.0...</td>\n",
       "      <td>POLYGON ((8047.00000 12321.00000, 8040.00000 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33822c54-ae0a-4ad4-8ad9-d0eea9ad91c6</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_HBC_mid_1', 'color': [0, 128, 128]}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((8084.00000 12325.00000, 8083.00000 1...</td>\n",
       "      <td>[[8084.0, 12325.0], [8074.0, 12329.0], [8070.0...</td>\n",
       "      <td>POLYGON ((8084.00000 12325.00000, 8074.00000 1...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64dc48fa-7583-4300-b1d3-a9c35c3ea010</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_HBC_mid_2', 'color': [0, 128, 128]}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((7917.00000 12268.00000, 7916.00000 1...</td>\n",
       "      <td>[[7917.0, 12268.0], [7913.0, 12270.0], [7902.0...</td>\n",
       "      <td>POLYGON ((7917.00000 12268.00000, 7913.00000 1...</td>\n",
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       "                                     id  objectType  \\\n",
       "0  38af0704-dec2-48e9-99cc-d6262ec92722  annotation   \n",
       "1  8ac78eff-262b-4942-9580-2badf20e22b5  annotation   \n",
       "2  4586ad21-65e0-48e9-b882-328bdc081c44  annotation   \n",
       "3  33822c54-ae0a-4ad4-8ad9-d0eea9ad91c6  annotation   \n",
       "4  64dc48fa-7583-4300-b1d3-a9c35c3ea010  annotation   \n",
       "\n",
       "                                      classification name  \\\n",
       "0  {'name': '187_HBC_mid_1', 'color': [0, 128, 128]}  NaN   \n",
       "1  {'name': '187_HBC_nVEC_2', 'color': [0, 128, 1...  NaN   \n",
       "2  {'name': '187_HBC_mid_1', 'color': [0, 128, 128]}  NaN   \n",
       "3  {'name': '187_HBC_mid_1', 'color': [0, 128, 128]}  NaN   \n",
       "4  {'name': '187_HBC_mid_2', 'color': [0, 128, 128]}  NaN   \n",
       "\n",
       "                                            geometry  \\\n",
       "0  POLYGON ((7925.00000 12328.00000, 7924.00000 1...   \n",
       "1  POLYGON ((8011.00000 12393.00000, 8010.00000 1...   \n",
       "2  POLYGON ((8047.00000 12321.00000, 8046.00000 1...   \n",
       "3  POLYGON ((8084.00000 12325.00000, 8083.00000 1...   \n",
       "4  POLYGON ((7917.00000 12268.00000, 7916.00000 1...   \n",
       "\n",
       "                                              coords  \\\n",
       "0  [[7931.0, 12328.0], [7919.0, 12329.0], [7913.0...   \n",
       "1  [[8011.0, 12393.0], [8003.0, 12398.0], [8001.0...   \n",
       "2  [[8047.0, 12321.0], [8040.0, 12324.0], [8032.0...   \n",
       "3  [[8084.0, 12325.0], [8074.0, 12329.0], [8070.0...   \n",
       "4  [[7917.0, 12268.0], [7913.0, 12270.0], [7902.0...   \n",
       "\n",
       "                                              simple  \n",
       "0  POLYGON ((7931.00000 12328.00000, 7919.00000 1...  \n",
       "1  POLYGON ((8011.00000 12393.00000, 8003.00000 1...  \n",
       "2  POLYGON ((8047.00000 12321.00000, 8040.00000 1...  \n",
       "3  POLYGON ((8084.00000 12325.00000, 8074.00000 1...  \n",
       "4  POLYGON ((7917.00000 12268.00000, 7913.00000 1...  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/x7/grkjlk8s223dy6234rnz1885mxz2_6/T/ipykernel_45886/1993851206.py:4: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value '187_HBC_mid_1' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n",
      "  df.at[i,'Name'] = tmp\n"
     ]
    }
   ],
   "source": [
    "df['Name'] = numpy.nan\n",
    "for i in df.index:\n",
    "    tmp = df.classification[i].get('name')\n",
    "    df.at[i,'Name'] = tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
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       "      <td>38af0704-dec2-48e9-99cc-d6262ec92722</td>\n",
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       "      <td>{'name': '187_HBC_mid_1', 'color': [0, 128, 128]}</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
       "      <td>8ac78eff-262b-4942-9580-2badf20e22b5</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_HBC_nVEC_2', 'color': [0, 128, 1...</td>\n",
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       "      <td>POLYGON ((8011.00000 12393.00000, 8010.00000 1...</td>\n",
       "      <td>[[8011.0, 12393.0], [8003.0, 12398.0], [8001.0...</td>\n",
       "      <td>POLYGON ((8011.00000 12393.00000, 8003.00000 1...</td>\n",
       "      <td>187_HBC_nVEC_2</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4586ad21-65e0-48e9-b882-328bdc081c44</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_HBC_mid_1', 'color': [0, 128, 128]}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((8047.00000 12321.00000, 8046.00000 1...</td>\n",
       "      <td>[[8047.0, 12321.0], [8040.0, 12324.0], [8032.0...</td>\n",
       "      <td>POLYGON ((8047.00000 12321.00000, 8040.00000 1...</td>\n",
       "      <td>187_HBC_mid_1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>33822c54-ae0a-4ad4-8ad9-d0eea9ad91c6</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_HBC_mid_1', 'color': [0, 128, 128]}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((8084.00000 12325.00000, 8083.00000 1...</td>\n",
       "      <td>[[8084.0, 12325.0], [8074.0, 12329.0], [8070.0...</td>\n",
       "      <td>POLYGON ((8084.00000 12325.00000, 8074.00000 1...</td>\n",
       "      <td>187_HBC_mid_1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>64dc48fa-7583-4300-b1d3-a9c35c3ea010</td>\n",
       "      <td>annotation</td>\n",
       "      <td>{'name': '187_HBC_mid_2', 'color': [0, 128, 128]}</td>\n",
       "      <td>NaN</td>\n",
       "      <td>POLYGON ((7917.00000 12268.00000, 7916.00000 1...</td>\n",
       "      <td>[[7917.0, 12268.0], [7913.0, 12270.0], [7902.0...</td>\n",
       "      <td>POLYGON ((7917.00000 12268.00000, 7913.00000 1...</td>\n",
       "      <td>187_HBC_mid_2</td>\n",
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      "text/plain": [
       "                                     id  objectType  \\\n",
       "0  38af0704-dec2-48e9-99cc-d6262ec92722  annotation   \n",
       "1  8ac78eff-262b-4942-9580-2badf20e22b5  annotation   \n",
       "2  4586ad21-65e0-48e9-b882-328bdc081c44  annotation   \n",
       "3  33822c54-ae0a-4ad4-8ad9-d0eea9ad91c6  annotation   \n",
       "4  64dc48fa-7583-4300-b1d3-a9c35c3ea010  annotation   \n",
       "\n",
       "                                      classification name  \\\n",
       "0  {'name': '187_HBC_mid_1', 'color': [0, 128, 128]}  NaN   \n",
       "1  {'name': '187_HBC_nVEC_2', 'color': [0, 128, 1...  NaN   \n",
       "2  {'name': '187_HBC_mid_1', 'color': [0, 128, 128]}  NaN   \n",
       "3  {'name': '187_HBC_mid_1', 'color': [0, 128, 128]}  NaN   \n",
       "4  {'name': '187_HBC_mid_2', 'color': [0, 128, 128]}  NaN   \n",
       "\n",
       "                                            geometry  \\\n",
       "0  POLYGON ((7925.00000 12328.00000, 7924.00000 1...   \n",
       "1  POLYGON ((8011.00000 12393.00000, 8010.00000 1...   \n",
       "2  POLYGON ((8047.00000 12321.00000, 8046.00000 1...   \n",
       "3  POLYGON ((8084.00000 12325.00000, 8083.00000 1...   \n",
       "4  POLYGON ((7917.00000 12268.00000, 7916.00000 1...   \n",
       "\n",
       "                                              coords  \\\n",
       "0  [[7931.0, 12328.0], [7919.0, 12329.0], [7913.0...   \n",
       "1  [[8011.0, 12393.0], [8003.0, 12398.0], [8001.0...   \n",
       "2  [[8047.0, 12321.0], [8040.0, 12324.0], [8032.0...   \n",
       "3  [[8084.0, 12325.0], [8074.0, 12329.0], [8070.0...   \n",
       "4  [[7917.0, 12268.0], [7913.0, 12270.0], [7902.0...   \n",
       "\n",
       "                                              simple            Name  \n",
       "0  POLYGON ((7931.00000 12328.00000, 7919.00000 1...   187_HBC_mid_1  \n",
       "1  POLYGON ((8011.00000 12393.00000, 8003.00000 1...  187_HBC_nVEC_2  \n",
       "2  POLYGON ((8047.00000 12321.00000, 8040.00000 1...   187_HBC_mid_1  \n",
       "3  POLYGON ((8084.00000 12325.00000, 8074.00000 1...   187_HBC_mid_1  \n",
       "4  POLYGON ((7917.00000 12268.00000, 7913.00000 1...   187_HBC_mid_2  "
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "#save dataframe as csv\n",
    "df.to_csv(f\"./QCed_geojson.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pandas.read_csv(\"./QCed_geojson.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = geopandas.read_file(\"inputs_that_dont_work/MultiPolygon.geojson\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
       "      <td>c1bb1604-39e8-49fe-8f09-aaed31bb6a79</td>\n",
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       "      <td>Spreading_CC_13.30</td>\n",
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       "      <td>POLYGON ((17815.00000 11775.00000, 17747.00000...</td>\n",
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       "      <th>1</th>\n",
       "      <td>3b8e1d73-a409-45f9-b328-5200042ef921</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>POINT (22355.58000 3377.24000)</td>\n",
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       "      <th>2</th>\n",
       "      <td>cec8fe9d-2bd1-480e-8b99-b54e9f8279db</td>\n",
       "      <td>annotation</td>\n",
       "      <td>Tumorbed_bottomLeft_3.1</td>\n",
       "      <td>{'name': 'I8', 'color': [239, 240, 121]}</td>\n",
       "      <td>POLYGON ((17512.00000 9776.00000, 17361.00000 ...</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>90714e2e-2758-4366-97bd-8c6706b52ea9</td>\n",
       "      <td>annotation</td>\n",
       "      <td>Spreading_CC_6.1</td>\n",
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       "      <td>POLYGON ((21906.00000 11603.00000, 21802.00000...</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>812d5b88-e88a-4b1e-90e1-eb079dcc67a4</td>\n",
       "      <td>annotation</td>\n",
       "      <td>Tumorbed_TopRight_1.2</td>\n",
       "      <td>{'name': 'H18', 'color': [222, 219, 71]}</td>\n",
       "      <td>POLYGON ((21276.00000 6040.00000, 21127.00000 ...</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>7764f722-fb51-4e64-a600-c63cce6d8ae7</td>\n",
       "      <td>annotation</td>\n",
       "      <td>Tumorbed_TopRight_2.2</td>\n",
       "      <td>{'name': 'H20', 'color': [227, 43, 198]}</td>\n",
       "      <td>POLYGON ((21509.00000 4746.00000, 21415.00000 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>ba7dffb6-deb5-4482-93e3-5717eda6048b</td>\n",
       "      <td>annotation</td>\n",
       "      <td>Spreading_CC_13.44</td>\n",
       "      <td>{'name': 'C18', 'color': [161, 115, 81]}</td>\n",
       "      <td>POLYGON ((17542.00000 12975.00000, 17501.00000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>a7f02a86-2ab6-454d-a74b-74ee0554ee6d</td>\n",
       "      <td>annotation</td>\n",
       "      <td>StromaTumorBed_1.3</td>\n",
       "      <td>{'name': 'G2', 'color': [235, 132, 89]}</td>\n",
       "      <td>POLYGON ((18229.00000 5411.00000, 18209.00000 ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>21a0c0b1-d5bc-438b-9ec3-4c9bf20d77a9</td>\n",
       "      <td>annotation</td>\n",
       "      <td>Desmo_OutTumor_3.1</td>\n",
       "      <td>{'name': 'B12', 'color': [20, 5, 135]}</td>\n",
       "      <td>POLYGON ((23143.00000 11369.00000, 22950.00000...</td>\n",
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       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>3800bfe8-d56c-49ca-b2a0-206d49fb74d5</td>\n",
       "      <td>annotation</td>\n",
       "      <td>Spreading_CC_13.9</td>\n",
       "      <td>{'name': 'C18', 'color': [161, 115, 81]}</td>\n",
       "      <td>POLYGON ((21160.00000 9791.00000, 21118.00000 ...</td>\n",
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       "                                       id  objectType  \\\n",
       "0    c1bb1604-39e8-49fe-8f09-aaed31bb6a79  annotation   \n",
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       "4    812d5b88-e88a-4b1e-90e1-eb079dcc67a4  annotation   \n",
       "..                                    ...         ...   \n",
       "149  7764f722-fb51-4e64-a600-c63cce6d8ae7  annotation   \n",
       "150  ba7dffb6-deb5-4482-93e3-5717eda6048b  annotation   \n",
       "151  a7f02a86-2ab6-454d-a74b-74ee0554ee6d  annotation   \n",
       "152  21a0c0b1-d5bc-438b-9ec3-4c9bf20d77a9  annotation   \n",
       "153  3800bfe8-d56c-49ca-b2a0-206d49fb74d5  annotation   \n",
       "\n",
       "                        name                            classification  \\\n",
       "0         Spreading_CC_13.30  {'name': 'C18', 'color': [161, 115, 81]}   \n",
       "1                     Calib1                                       NaN   \n",
       "2    Tumorbed_bottomLeft_3.1  {'name': 'I8', 'color': [239, 240, 121]}   \n",
       "3           Spreading_CC_6.1  {'name': 'D10', 'color': [134, 181, 75]}   \n",
       "4      Tumorbed_TopRight_1.2  {'name': 'H18', 'color': [222, 219, 71]}   \n",
       "..                       ...                                       ...   \n",
       "149    Tumorbed_TopRight_2.2  {'name': 'H20', 'color': [227, 43, 198]}   \n",
       "150       Spreading_CC_13.44  {'name': 'C18', 'color': [161, 115, 81]}   \n",
       "151       StromaTumorBed_1.3   {'name': 'G2', 'color': [235, 132, 89]}   \n",
       "152       Desmo_OutTumor_3.1    {'name': 'B12', 'color': [20, 5, 135]}   \n",
       "153        Spreading_CC_13.9  {'name': 'C18', 'color': [161, 115, 81]}   \n",
       "\n",
       "                                              geometry  \n",
       "0    POLYGON ((17815.00000 11775.00000, 17747.00000...  \n",
       "1                       POINT (22355.58000 3377.24000)  \n",
       "2    POLYGON ((17512.00000 9776.00000, 17361.00000 ...  \n",
       "3    POLYGON ((21906.00000 11603.00000, 21802.00000...  \n",
       "4    POLYGON ((21276.00000 6040.00000, 21127.00000 ...  \n",
       "..                                                 ...  \n",
       "149  POLYGON ((21509.00000 4746.00000, 21415.00000 ...  \n",
       "150  POLYGON ((17542.00000 12975.00000, 17501.00000...  \n",
       "151  POLYGON ((18229.00000 5411.00000, 18209.00000 ...  \n",
       "152  POLYGON ((23143.00000 11369.00000, 22950.00000...  \n",
       "153  POLYGON ((21160.00000 9791.00000, 21118.00000 ...  \n",
       "\n",
       "[154 rows x 5 columns]"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[df['classification'].notna()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/mambaforge/base/envs/py-lmd-env/lib/python3.9/site-packages/geopandas/geodataframe.py:1543: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  super().__setitem__(key, value)\n",
      "/var/folders/x7/grkjlk8s223dy6234rnz1885mxz2_6/T/ipykernel_45886/1993851206.py:4: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value 'C18' has dtype incompatible with float64, please explicitly cast to a compatible dtype first.\n",
      "  df.at[i,'Name'] = tmp\n"
     ]
    }
   ],
   "source": [
    "df['Name'] = numpy.nan\n",
    "for i in df.index:\n",
    "    tmp = df.classification[i].get('name')\n",
    "    df.at[i,'Name'] = tmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df[df.geometry.geom_type == 'MultiPolygon']['Name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/homebrew/Caskroom/mambaforge/base/envs/py-lmd-env/lib/python3.9/site-packages/geopandas/geodataframe.py:1543: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  super().__setitem__(key, value)\n",
      "/opt/homebrew/Caskroom/mambaforge/base/envs/py-lmd-env/lib/python3.9/site-packages/geopandas/geodataframe.py:1543: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  super().__setitem__(key, value)\n"
     ]
    }
   ],
   "source": [
    "# df[df.geometry.geom_type == 'MultiPolygon']\n",
    "# x = df[['name','classification']]\n",
    "\n",
    "df['classification_name'] = df['classification'].apply(lambda x: x.get('name'))\n",
    "df['annotation_name'] = df['name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "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>id</th>\n",
       "      <th>objectType</th>\n",
       "      <th>name</th>\n",
       "      <th>classification</th>\n",
       "      <th>geometry</th>\n",
       "      <th>Name</th>\n",
       "      <th>classification_name</th>\n",
       "      <th>annotation_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>c1bb1604-39e8-49fe-8f09-aaed31bb6a79</td>\n",
       "      <td>annotation</td>\n",
       "      <td>Spreading_CC_13.30</td>\n",
       "      <td>{'name': 'C18', 'color': [161, 115, 81]}</td>\n",
       "      <td>POLYGON ((17815.00000 11775.00000, 17747.00000...</td>\n",
       "      <td>C18</td>\n",
       "      <td>C18</td>\n",
       "      <td>Spreading_CC_13.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>cec8fe9d-2bd1-480e-8b99-b54e9f8279db</td>\n",
       "      <td>annotation</td>\n",
       "      <td>Tumorbed_bottomLeft_3.1</td>\n",
       "      <td>{'name': 'I8', 'color': [239, 240, 121]}</td>\n",
       "      <td>POLYGON ((17512.00000 9776.00000, 17361.00000 ...</td>\n",
       "      <td>I8</td>\n",
       "      <td>I8</td>\n",
       "      <td>Tumorbed_bottomLeft_3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>90714e2e-2758-4366-97bd-8c6706b52ea9</td>\n",
       "      <td>annotation</td>\n",
       "      <td>Spreading_CC_6.1</td>\n",
       "      <td>{'name': 'D10', 'color': [134, 181, 75]}</td>\n",
       "      <td>POLYGON ((21906.00000 11603.00000, 21802.00000...</td>\n",
       "      <td>D10</td>\n",
       "      <td>D10</td>\n",
       "      <td>Spreading_CC_6.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>812d5b88-e88a-4b1e-90e1-eb079dcc67a4</td>\n",
       "      <td>annotation</td>\n",
       "      <td>Tumorbed_TopRight_1.2</td>\n",
       "      <td>{'name': 'H18', 'color': [222, 219, 71]}</td>\n",
       "      <td>POLYGON ((21276.00000 6040.00000, 21127.00000 ...</td>\n",
       "      <td>H18</td>\n",
       "      <td>H18</td>\n",
       "      <td>Tumorbed_TopRight_1.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4d9ed5d8-77d6-4415-94ed-3ce5235e5934</td>\n",
       "      <td>annotation</td>\n",
       "      <td>Spreading_CC_13.37</td>\n",
       "      <td>{'name': 'C18', 'color': [161, 115, 81]}</td>\n",
       "      <td>POLYGON ((17929.00000 12155.00000, 17921.00000...</td>\n",
       "      <td>C18</td>\n",
       "      <td>C18</td>\n",
       "      <td>Spreading_CC_13.37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>7764f722-fb51-4e64-a600-c63cce6d8ae7</td>\n",
       "      <td>annotation</td>\n",
       "      <td>Tumorbed_TopRight_2.2</td>\n",
       "      <td>{'name': 'H20', 'color': [227, 43, 198]}</td>\n",
       "      <td>POLYGON ((21509.00000 4746.00000, 21415.00000 ...</td>\n",
       "      <td>H20</td>\n",
       "      <td>H20</td>\n",
       "      <td>Tumorbed_TopRight_2.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>ba7dffb6-deb5-4482-93e3-5717eda6048b</td>\n",
       "      <td>annotation</td>\n",
       "      <td>Spreading_CC_13.44</td>\n",
       "      <td>{'name': 'C18', 'color': [161, 115, 81]}</td>\n",
       "      <td>POLYGON ((17542.00000 12975.00000, 17501.00000...</td>\n",
       "      <td>C18</td>\n",
       "      <td>C18</td>\n",
       "      <td>Spreading_CC_13.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>a7f02a86-2ab6-454d-a74b-74ee0554ee6d</td>\n",
       "      <td>annotation</td>\n",
       "      <td>StromaTumorBed_1.3</td>\n",
       "      <td>{'name': 'G2', 'color': [235, 132, 89]}</td>\n",
       "      <td>POLYGON ((18229.00000 5411.00000, 18209.00000 ...</td>\n",
       "      <td>G2</td>\n",
       "      <td>G2</td>\n",
       "      <td>StromaTumorBed_1.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>21a0c0b1-d5bc-438b-9ec3-4c9bf20d77a9</td>\n",
       "      <td>annotation</td>\n",
       "      <td>Desmo_OutTumor_3.1</td>\n",
       "      <td>{'name': 'B12', 'color': [20, 5, 135]}</td>\n",
       "      <td>POLYGON ((23143.00000 11369.00000, 22950.00000...</td>\n",
       "      <td>B12</td>\n",
       "      <td>B12</td>\n",
       "      <td>Desmo_OutTumor_3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>153</th>\n",
       "      <td>3800bfe8-d56c-49ca-b2a0-206d49fb74d5</td>\n",
       "      <td>annotation</td>\n",
       "      <td>Spreading_CC_13.9</td>\n",
       "      <td>{'name': 'C18', 'color': [161, 115, 81]}</td>\n",
       "      <td>POLYGON ((21160.00000 9791.00000, 21118.00000 ...</td>\n",
       "      <td>C18</td>\n",
       "      <td>C18</td>\n",
       "      <td>Spreading_CC_13.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>151 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       id  objectType  \\\n",
       "0    c1bb1604-39e8-49fe-8f09-aaed31bb6a79  annotation   \n",
       "2    cec8fe9d-2bd1-480e-8b99-b54e9f8279db  annotation   \n",
       "3    90714e2e-2758-4366-97bd-8c6706b52ea9  annotation   \n",
       "4    812d5b88-e88a-4b1e-90e1-eb079dcc67a4  annotation   \n",
       "5    4d9ed5d8-77d6-4415-94ed-3ce5235e5934  annotation   \n",
       "..                                    ...         ...   \n",
       "149  7764f722-fb51-4e64-a600-c63cce6d8ae7  annotation   \n",
       "150  ba7dffb6-deb5-4482-93e3-5717eda6048b  annotation   \n",
       "151  a7f02a86-2ab6-454d-a74b-74ee0554ee6d  annotation   \n",
       "152  21a0c0b1-d5bc-438b-9ec3-4c9bf20d77a9  annotation   \n",
       "153  3800bfe8-d56c-49ca-b2a0-206d49fb74d5  annotation   \n",
       "\n",
       "                        name                            classification  \\\n",
       "0         Spreading_CC_13.30  {'name': 'C18', 'color': [161, 115, 81]}   \n",
       "2    Tumorbed_bottomLeft_3.1  {'name': 'I8', 'color': [239, 240, 121]}   \n",
       "3           Spreading_CC_6.1  {'name': 'D10', 'color': [134, 181, 75]}   \n",
       "4      Tumorbed_TopRight_1.2  {'name': 'H18', 'color': [222, 219, 71]}   \n",
       "5         Spreading_CC_13.37  {'name': 'C18', 'color': [161, 115, 81]}   \n",
       "..                       ...                                       ...   \n",
       "149    Tumorbed_TopRight_2.2  {'name': 'H20', 'color': [227, 43, 198]}   \n",
       "150       Spreading_CC_13.44  {'name': 'C18', 'color': [161, 115, 81]}   \n",
       "151       StromaTumorBed_1.3   {'name': 'G2', 'color': [235, 132, 89]}   \n",
       "152       Desmo_OutTumor_3.1    {'name': 'B12', 'color': [20, 5, 135]}   \n",
       "153        Spreading_CC_13.9  {'name': 'C18', 'color': [161, 115, 81]}   \n",
       "\n",
       "                                              geometry Name  \\\n",
       "0    POLYGON ((17815.00000 11775.00000, 17747.00000...  C18   \n",
       "2    POLYGON ((17512.00000 9776.00000, 17361.00000 ...   I8   \n",
       "3    POLYGON ((21906.00000 11603.00000, 21802.00000...  D10   \n",
       "4    POLYGON ((21276.00000 6040.00000, 21127.00000 ...  H18   \n",
       "5    POLYGON ((17929.00000 12155.00000, 17921.00000...  C18   \n",
       "..                                                 ...  ...   \n",
       "149  POLYGON ((21509.00000 4746.00000, 21415.00000 ...  H20   \n",
       "150  POLYGON ((17542.00000 12975.00000, 17501.00000...  C18   \n",
       "151  POLYGON ((18229.00000 5411.00000, 18209.00000 ...   G2   \n",
       "152  POLYGON ((23143.00000 11369.00000, 22950.00000...  B12   \n",
       "153  POLYGON ((21160.00000 9791.00000, 21118.00000 ...  C18   \n",
       "\n",
       "    classification_name          annotation_name  \n",
       "0                   C18       Spreading_CC_13.30  \n",
       "2                    I8  Tumorbed_bottomLeft_3.1  \n",
       "3                   D10         Spreading_CC_6.1  \n",
       "4                   H18    Tumorbed_TopRight_1.2  \n",
       "5                   C18       Spreading_CC_13.37  \n",
       "..                  ...                      ...  \n",
       "149                 H20    Tumorbed_TopRight_2.2  \n",
       "150                 C18       Spreading_CC_13.44  \n",
       "151                  G2       StromaTumorBed_1.3  \n",
       "152                 B12       Desmo_OutTumor_3.1  \n",
       "153                 C18        Spreading_CC_13.9  \n",
       "\n",
       "[151 rows x 8 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1      Calib1\n",
       "76     Calib2\n",
       "133    Calib3\n",
       "Name: name, dtype: object"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.geometry.geom_type == 'Point']['name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "13    C20\n",
       "Name: Name, dtype: object"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['lol'] = np.nan\n",
    "    df['lol'] = df['lol'].astype('object')\n",
    "    \n",
    "    for i in df.index:\n",
    "        #get geometry object for row i\n",
    "        geom=df.at[i, 'geometry']\n",
    "        #list the coordenate points as tuples\n",
    "        tmp = list(geom.exterior.coords)\n",
    "        #transform list of tuples to list of lists\n",
    "        tmp_lol = [list(i) for i in tmp]\n",
    "        #add list of list to lol column\n",
    "        df.at[i,'lol'] = tmp_lol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'POLYGON ((7931 12328, 7919 12329, 7913 12333, 7912 12344, 7914 12348, 7920 12353, 7924 12355, 7929 12355, 7940 12348, 7942 12344, 7942 12338, 7935 12330, 7931 12328))'"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.simple[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'str' object has no attribute 'exterior'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m/Users/jnimoca/Jose_BI/Repositories/Qupath_to_LMD_v2/QuPath_to_LMD.ipynb Cell 21\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/jnimoca/Jose_BI/Repositories/Qupath_to_LMD_v2/QuPath_to_LMD.ipynb#X61sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m x, y \u001b[39m=\u001b[39m df\u001b[39m.\u001b[39;49msimple[\u001b[39m0\u001b[39;49m]\u001b[39m.\u001b[39;49mexterior\u001b[39m.\u001b[39mxy\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'str' object has no attribute 'exterior'"
     ]
    }
   ],
   "source": [
    "x, y = df.simple[0].exterior.xy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'POLYGON ((7925 12328, 7924 12329, 7919 12329, 7918 12330, 7917 12330, 7916 12331, 7915 12331, 7915 12332, 7914 12332, 7914 12333, 7913 12333, 7913 12334, 7912 12335, 7912 12336, 7912 12337, 7912 12338, 7912 12339, 7912 12340, 7912 12341, 7912 12342, 7912 12343, 7912 12344, 7913 12345, 7913 12346, 7914 12347, 7914 12348, 7915 12348, 7915 12349, 7916 12349, 7916 12350, 7917 12350, 7918 12351, 7919 12351, 7919 12352, 7920 12352, 7920 12353, 7921 12353, 7922 12354, 7923 12354, 7924 12355, 7929 12355, 7930 12354, 7931 12354, 7932 12353, 7933 12353, 7934 12352, 7935 12352, 7936 12351, 7937 12351, 7937 12350, 7938 12350, 7938 12349, 7939 12349, 7939 12348, 7940 12348, 7940 12347, 7941 12346, 7941 12345, 7942 12344, 7942 12343, 7942 12342, 7942 12341, 7942 12340, 7942 12339, 7942 12338, 7941 12337, 7941 12336, 7940 12336, 7940 12335, 7939 12335, 7939 12334, 7938 12334, 7938 12333, 7937 12333, 7937 12332, 7936 12332, 7936 12331, 7935 12331, 7935 12330, 7934 12330, 7933 12329, 7932 12329, 7931 12328, 7925 12328))'"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.geometry[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1'"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.simple[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'str' object has no attribute 'get'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m/Users/jnimoca/Jose_BI/Repositories/Qupath_to_LMD_v2/QuPath_to_LMD.ipynb Cell 21\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/jnimoca/Jose_BI/Repositories/Qupath_to_LMD_v2/QuPath_to_LMD.ipynb#X60sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m df[\u001b[39m'\u001b[39m\u001b[39msimple\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m df[\u001b[39m'\u001b[39;49m\u001b[39msimple\u001b[39;49m\u001b[39m'\u001b[39;49m]\u001b[39m.\u001b[39;49mapply(shapely\u001b[39m.\u001b[39;49mgeometry\u001b[39m.\u001b[39;49mshape)\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/mambaforge/base/envs/py-lmd-env/lib/python3.9/site-packages/pandas/core/series.py:4753\u001b[0m, in \u001b[0;36mSeries.apply\u001b[0;34m(self, func, convert_dtype, args, by_row, **kwargs)\u001b[0m\n\u001b[1;32m   4625\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mapply\u001b[39m(\n\u001b[1;32m   4626\u001b[0m     \u001b[39mself\u001b[39m,\n\u001b[1;32m   4627\u001b[0m     func: AggFuncType,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   4632\u001b[0m     \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs,\n\u001b[1;32m   4633\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m DataFrame \u001b[39m|\u001b[39m Series:\n\u001b[1;32m   4634\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   4635\u001b[0m \u001b[39m    Invoke function on values of Series.\u001b[39;00m\n\u001b[1;32m   4636\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   4751\u001b[0m \u001b[39m    dtype: float64\u001b[39;00m\n\u001b[1;32m   4752\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 4753\u001b[0m     \u001b[39mreturn\u001b[39;00m SeriesApply(\n\u001b[1;32m   4754\u001b[0m         \u001b[39mself\u001b[39;49m,\n\u001b[1;32m   4755\u001b[0m         func,\n\u001b[1;32m   4756\u001b[0m         convert_dtype\u001b[39m=\u001b[39;49mconvert_dtype,\n\u001b[1;32m   4757\u001b[0m         by_row\u001b[39m=\u001b[39;49mby_row,\n\u001b[1;32m   4758\u001b[0m         args\u001b[39m=\u001b[39;49margs,\n\u001b[1;32m   4759\u001b[0m         kwargs\u001b[39m=\u001b[39;49mkwargs,\n\u001b[1;32m   4760\u001b[0m     )\u001b[39m.\u001b[39;49mapply()\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/mambaforge/base/envs/py-lmd-env/lib/python3.9/site-packages/pandas/core/apply.py:1207\u001b[0m, in \u001b[0;36mSeriesApply.apply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1204\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mapply_compat()\n\u001b[1;32m   1206\u001b[0m \u001b[39m# self.func is Callable\u001b[39;00m\n\u001b[0;32m-> 1207\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mapply_standard()\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/mambaforge/base/envs/py-lmd-env/lib/python3.9/site-packages/pandas/core/apply.py:1287\u001b[0m, in \u001b[0;36mSeriesApply.apply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1281\u001b[0m \u001b[39m# row-wise access\u001b[39;00m\n\u001b[1;32m   1282\u001b[0m \u001b[39m# apply doesn't have a `na_action` keyword and for backward compat reasons\u001b[39;00m\n\u001b[1;32m   1283\u001b[0m \u001b[39m# we need to give `na_action=\"ignore\"` for categorical data.\u001b[39;00m\n\u001b[1;32m   1284\u001b[0m \u001b[39m# TODO: remove the `na_action=\"ignore\"` when that default has been changed in\u001b[39;00m\n\u001b[1;32m   1285\u001b[0m \u001b[39m#  Categorical (GH51645).\u001b[39;00m\n\u001b[1;32m   1286\u001b[0m action \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mignore\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(obj\u001b[39m.\u001b[39mdtype, CategoricalDtype) \u001b[39melse\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m-> 1287\u001b[0m mapped \u001b[39m=\u001b[39m obj\u001b[39m.\u001b[39;49m_map_values(\n\u001b[1;32m   1288\u001b[0m     mapper\u001b[39m=\u001b[39;49mcurried, na_action\u001b[39m=\u001b[39;49maction, convert\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconvert_dtype\n\u001b[1;32m   1289\u001b[0m )\n\u001b[1;32m   1291\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(mapped) \u001b[39mand\u001b[39;00m \u001b[39misinstance\u001b[39m(mapped[\u001b[39m0\u001b[39m], ABCSeries):\n\u001b[1;32m   1292\u001b[0m     \u001b[39m# GH#43986 Need to do list(mapped) in order to get treated as nested\u001b[39;00m\n\u001b[1;32m   1293\u001b[0m     \u001b[39m#  See also GH#25959 regarding EA support\u001b[39;00m\n\u001b[1;32m   1294\u001b[0m     \u001b[39mreturn\u001b[39;00m obj\u001b[39m.\u001b[39m_constructor_expanddim(\u001b[39mlist\u001b[39m(mapped), index\u001b[39m=\u001b[39mobj\u001b[39m.\u001b[39mindex)\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/mambaforge/base/envs/py-lmd-env/lib/python3.9/site-packages/pandas/core/base.py:921\u001b[0m, in \u001b[0;36mIndexOpsMixin._map_values\u001b[0;34m(self, mapper, na_action, convert)\u001b[0m\n\u001b[1;32m    918\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(arr, ExtensionArray):\n\u001b[1;32m    919\u001b[0m     \u001b[39mreturn\u001b[39;00m arr\u001b[39m.\u001b[39mmap(mapper, na_action\u001b[39m=\u001b[39mna_action)\n\u001b[0;32m--> 921\u001b[0m \u001b[39mreturn\u001b[39;00m algorithms\u001b[39m.\u001b[39;49mmap_array(arr, mapper, na_action\u001b[39m=\u001b[39;49mna_action, convert\u001b[39m=\u001b[39;49mconvert)\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/mambaforge/base/envs/py-lmd-env/lib/python3.9/site-packages/pandas/core/algorithms.py:1814\u001b[0m, in \u001b[0;36mmap_array\u001b[0;34m(arr, mapper, na_action, convert)\u001b[0m\n\u001b[1;32m   1812\u001b[0m values \u001b[39m=\u001b[39m arr\u001b[39m.\u001b[39mastype(\u001b[39mobject\u001b[39m, copy\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\n\u001b[1;32m   1813\u001b[0m \u001b[39mif\u001b[39;00m na_action \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m-> 1814\u001b[0m     \u001b[39mreturn\u001b[39;00m lib\u001b[39m.\u001b[39;49mmap_infer(values, mapper, convert\u001b[39m=\u001b[39;49mconvert)\n\u001b[1;32m   1815\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m   1816\u001b[0m     \u001b[39mreturn\u001b[39;00m lib\u001b[39m.\u001b[39mmap_infer_mask(\n\u001b[1;32m   1817\u001b[0m         values, mapper, mask\u001b[39m=\u001b[39misna(values)\u001b[39m.\u001b[39mview(np\u001b[39m.\u001b[39muint8), convert\u001b[39m=\u001b[39mconvert\n\u001b[1;32m   1818\u001b[0m     )\n",
      "File \u001b[0;32mlib.pyx:2917\u001b[0m, in \u001b[0;36mpandas._libs.lib.map_infer\u001b[0;34m()\u001b[0m\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/mambaforge/base/envs/py-lmd-env/lib/python3.9/site-packages/shapely/geometry/geo.py:92\u001b[0m, in \u001b[0;36mshape\u001b[0;34m(context)\u001b[0m\n\u001b[1;32m     90\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m     91\u001b[0m     ob \u001b[39m=\u001b[39m context\n\u001b[0;32m---> 92\u001b[0m geom_type \u001b[39m=\u001b[39m ob\u001b[39m.\u001b[39;49mget(\u001b[39m\"\u001b[39m\u001b[39mtype\u001b[39m\u001b[39m\"\u001b[39m)\u001b[39m.\u001b[39mlower()\n\u001b[1;32m     93\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39m\"\u001b[39m\u001b[39mcoordinates\u001b[39m\u001b[39m\"\u001b[39m \u001b[39min\u001b[39;00m ob \u001b[39mand\u001b[39;00m _is_coordinates_empty(ob[\u001b[39m\"\u001b[39m\u001b[39mcoordinates\u001b[39m\u001b[39m\"\u001b[39m]):\n\u001b[1;32m     94\u001b[0m     \u001b[39mreturn\u001b[39;00m _empty_shape_for_no_coordinates(geom_type)\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'str' object has no attribute 'get'"
     ]
    }
   ],
   "source": [
    "df['simple'] = df['simple'].apply(shapely.geometry.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'str' object has no attribute 'exterior'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m/Users/jnimoca/Jose_BI/Repositories/Qupath_to_LMD_v2/QuPath_to_LMD.ipynb Cell 20\u001b[0m line \u001b[0;36m2\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/jnimoca/Jose_BI/Repositories/Qupath_to_LMD_v2/QuPath_to_LMD.ipynb#X55sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39m#coords should be a list of coordernates from the simple column a geojson object in the dataframe\u001b[39;00m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/jnimoca/Jose_BI/Repositories/Qupath_to_LMD_v2/QuPath_to_LMD.ipynb#X55sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m df[\u001b[39m'\u001b[39m\u001b[39mcoords\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m df[\u001b[39m'\u001b[39;49m\u001b[39msimple\u001b[39;49m\u001b[39m'\u001b[39;49m]\u001b[39m.\u001b[39;49mapply(\u001b[39mlambda\u001b[39;49;00m geom: numpy\u001b[39m.\u001b[39;49marray(geom\u001b[39m.\u001b[39;49mexterior\u001b[39m.\u001b[39;49mcoords))\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/mambaforge/base/envs/py-lmd-env/lib/python3.9/site-packages/pandas/core/series.py:4753\u001b[0m, in \u001b[0;36mSeries.apply\u001b[0;34m(self, func, convert_dtype, args, by_row, **kwargs)\u001b[0m\n\u001b[1;32m   4625\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mapply\u001b[39m(\n\u001b[1;32m   4626\u001b[0m     \u001b[39mself\u001b[39m,\n\u001b[1;32m   4627\u001b[0m     func: AggFuncType,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   4632\u001b[0m     \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs,\n\u001b[1;32m   4633\u001b[0m ) \u001b[39m-\u001b[39m\u001b[39m>\u001b[39m DataFrame \u001b[39m|\u001b[39m Series:\n\u001b[1;32m   4634\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"\u001b[39;00m\n\u001b[1;32m   4635\u001b[0m \u001b[39m    Invoke function on values of Series.\u001b[39;00m\n\u001b[1;32m   4636\u001b[0m \n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   4751\u001b[0m \u001b[39m    dtype: float64\u001b[39;00m\n\u001b[1;32m   4752\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m-> 4753\u001b[0m     \u001b[39mreturn\u001b[39;00m SeriesApply(\n\u001b[1;32m   4754\u001b[0m         \u001b[39mself\u001b[39;49m,\n\u001b[1;32m   4755\u001b[0m         func,\n\u001b[1;32m   4756\u001b[0m         convert_dtype\u001b[39m=\u001b[39;49mconvert_dtype,\n\u001b[1;32m   4757\u001b[0m         by_row\u001b[39m=\u001b[39;49mby_row,\n\u001b[1;32m   4758\u001b[0m         args\u001b[39m=\u001b[39;49margs,\n\u001b[1;32m   4759\u001b[0m         kwargs\u001b[39m=\u001b[39;49mkwargs,\n\u001b[1;32m   4760\u001b[0m     )\u001b[39m.\u001b[39;49mapply()\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/mambaforge/base/envs/py-lmd-env/lib/python3.9/site-packages/pandas/core/apply.py:1207\u001b[0m, in \u001b[0;36mSeriesApply.apply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1204\u001b[0m     \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mapply_compat()\n\u001b[1;32m   1206\u001b[0m \u001b[39m# self.func is Callable\u001b[39;00m\n\u001b[0;32m-> 1207\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mapply_standard()\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/mambaforge/base/envs/py-lmd-env/lib/python3.9/site-packages/pandas/core/apply.py:1287\u001b[0m, in \u001b[0;36mSeriesApply.apply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1281\u001b[0m \u001b[39m# row-wise access\u001b[39;00m\n\u001b[1;32m   1282\u001b[0m \u001b[39m# apply doesn't have a `na_action` keyword and for backward compat reasons\u001b[39;00m\n\u001b[1;32m   1283\u001b[0m \u001b[39m# we need to give `na_action=\"ignore\"` for categorical data.\u001b[39;00m\n\u001b[1;32m   1284\u001b[0m \u001b[39m# TODO: remove the `na_action=\"ignore\"` when that default has been changed in\u001b[39;00m\n\u001b[1;32m   1285\u001b[0m \u001b[39m#  Categorical (GH51645).\u001b[39;00m\n\u001b[1;32m   1286\u001b[0m action \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mignore\u001b[39m\u001b[39m\"\u001b[39m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(obj\u001b[39m.\u001b[39mdtype, CategoricalDtype) \u001b[39melse\u001b[39;00m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m-> 1287\u001b[0m mapped \u001b[39m=\u001b[39m obj\u001b[39m.\u001b[39;49m_map_values(\n\u001b[1;32m   1288\u001b[0m     mapper\u001b[39m=\u001b[39;49mcurried, na_action\u001b[39m=\u001b[39;49maction, convert\u001b[39m=\u001b[39;49m\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mconvert_dtype\n\u001b[1;32m   1289\u001b[0m )\n\u001b[1;32m   1291\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(mapped) \u001b[39mand\u001b[39;00m \u001b[39misinstance\u001b[39m(mapped[\u001b[39m0\u001b[39m], ABCSeries):\n\u001b[1;32m   1292\u001b[0m     \u001b[39m# GH#43986 Need to do list(mapped) in order to get treated as nested\u001b[39;00m\n\u001b[1;32m   1293\u001b[0m     \u001b[39m#  See also GH#25959 regarding EA support\u001b[39;00m\n\u001b[1;32m   1294\u001b[0m     \u001b[39mreturn\u001b[39;00m obj\u001b[39m.\u001b[39m_constructor_expanddim(\u001b[39mlist\u001b[39m(mapped), index\u001b[39m=\u001b[39mobj\u001b[39m.\u001b[39mindex)\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/mambaforge/base/envs/py-lmd-env/lib/python3.9/site-packages/pandas/core/base.py:921\u001b[0m, in \u001b[0;36mIndexOpsMixin._map_values\u001b[0;34m(self, mapper, na_action, convert)\u001b[0m\n\u001b[1;32m    918\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39misinstance\u001b[39m(arr, ExtensionArray):\n\u001b[1;32m    919\u001b[0m     \u001b[39mreturn\u001b[39;00m arr\u001b[39m.\u001b[39mmap(mapper, na_action\u001b[39m=\u001b[39mna_action)\n\u001b[0;32m--> 921\u001b[0m \u001b[39mreturn\u001b[39;00m algorithms\u001b[39m.\u001b[39;49mmap_array(arr, mapper, na_action\u001b[39m=\u001b[39;49mna_action, convert\u001b[39m=\u001b[39;49mconvert)\n",
      "File \u001b[0;32m/opt/homebrew/Caskroom/mambaforge/base/envs/py-lmd-env/lib/python3.9/site-packages/pandas/core/algorithms.py:1814\u001b[0m, in \u001b[0;36mmap_array\u001b[0;34m(arr, mapper, na_action, convert)\u001b[0m\n\u001b[1;32m   1812\u001b[0m values \u001b[39m=\u001b[39m arr\u001b[39m.\u001b[39mastype(\u001b[39mobject\u001b[39m, copy\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\n\u001b[1;32m   1813\u001b[0m \u001b[39mif\u001b[39;00m na_action \u001b[39mis\u001b[39;00m \u001b[39mNone\u001b[39;00m:\n\u001b[0;32m-> 1814\u001b[0m     \u001b[39mreturn\u001b[39;00m lib\u001b[39m.\u001b[39;49mmap_infer(values, mapper, convert\u001b[39m=\u001b[39;49mconvert)\n\u001b[1;32m   1815\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[1;32m   1816\u001b[0m     \u001b[39mreturn\u001b[39;00m lib\u001b[39m.\u001b[39mmap_infer_mask(\n\u001b[1;32m   1817\u001b[0m         values, mapper, mask\u001b[39m=\u001b[39misna(values)\u001b[39m.\u001b[39mview(np\u001b[39m.\u001b[39muint8), convert\u001b[39m=\u001b[39mconvert\n\u001b[1;32m   1818\u001b[0m     )\n",
      "File \u001b[0;32mlib.pyx:2917\u001b[0m, in \u001b[0;36mpandas._libs.lib.map_infer\u001b[0;34m()\u001b[0m\n",
      "\u001b[1;32m/Users/jnimoca/Jose_BI/Repositories/Qupath_to_LMD_v2/QuPath_to_LMD.ipynb Cell 20\u001b[0m line \u001b[0;36m2\n\u001b[1;32m      <a href='vscode-notebook-cell:/Users/jnimoca/Jose_BI/Repositories/Qupath_to_LMD_v2/QuPath_to_LMD.ipynb#X55sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m \u001b[39m#coords should be a list of coordernates from the simple column a geojson object in the dataframe\u001b[39;00m\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/jnimoca/Jose_BI/Repositories/Qupath_to_LMD_v2/QuPath_to_LMD.ipynb#X55sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m df[\u001b[39m'\u001b[39m\u001b[39mcoords\u001b[39m\u001b[39m'\u001b[39m] \u001b[39m=\u001b[39m df[\u001b[39m'\u001b[39m\u001b[39msimple\u001b[39m\u001b[39m'\u001b[39m]\u001b[39m.\u001b[39mapply(\u001b[39mlambda\u001b[39;00m geom: numpy\u001b[39m.\u001b[39marray(geom\u001b[39m.\u001b[39;49mexterior\u001b[39m.\u001b[39mcoords))\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'str' object has no attribute 'exterior'"
     ]
    }
   ],
   "source": [
    "#coords should be a list of coordernates from the simple column a geojson object in the dataframe\n",
    "df['coords'] = df['simple'].apply(lambda geom: numpy.array(geom.exterior.coords))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'[[ 7931. 12328.]\\n [ 7919. 12329.]\\n [ 7913. 12333.]\\n [ 7912. 12344.]\\n [ 7914. 12348.]\\n [ 7920. 12353.]\\n [ 7924. 12355.]\\n [ 7929. 12355.]\\n [ 7940. 12348.]\\n [ 7942. 12344.]\\n [ 7942. 12338.]\\n [ 7935. 12330.]\\n [ 7931. 12328.]]'"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.coords[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in df.index:\n",
    "    geom=df.at[i, 'simple']\n",
    "    tmp = list(geom.exterior.coords)\n",
    "    tmp_lol = [list(i) for i in tmp]\n",
    "    df.at[i,'coords'] = tmp_lol"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       POLYGON ((7931 12328, 7919 12329, 7913 12333, ...\n",
       "1       POLYGON ((8011 12393, 8003 12398, 8001 12402, ...\n",
       "2       POLYGON ((8047 12321, 8040 12324, 8032 12331, ...\n",
       "3       POLYGON ((8084 12325, 8074 12329, 8070 12333, ...\n",
       "4       POLYGON ((7917 12268, 7913 12270, 7902 12271, ...\n",
       "                              ...                        \n",
       "2105    POLYGON ((9088 7678, 9079 7684, 9075 7692, 907...\n",
       "2106    POLYGON ((8393 8306, 8386 8311, 8383 8318, 838...\n",
       "2107    POLYGON ((8250 8448, 8235 8448, 8229 8454, 822...\n",
       "2108    POLYGON ((9173 10250, 9168 10255, 9168 10263, ...\n",
       "2109    POLYGON ((9153 10289, 9148 10294, 9148 10303, ...\n",
       "Name: simple, Length: 2110, dtype: object"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.simple"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "geom = df.at[10, 'simple']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'POLYGON ((8079 12046, 8073 12048, 8065 12056, 8063 12062, 8064 12072, 8071 12079, 8074 12080, 8093 12079, 8102 12072, 8104 12066, 8103 12061, 8090 12050, 8079 12046))'"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geom"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'str' object has no attribute 'coords'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m/Users/jnimoca/Jose_BI/Repositories/Qupath_to_LMD_v2/QuPath_to_LMD.ipynb Cell 25\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/jnimoca/Jose_BI/Repositories/Qupath_to_LMD_v2/QuPath_to_LMD.ipynb#X54sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m geom\u001b[39m.\u001b[39;49mcoords\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'str' object has no attribute 'coords'"
     ]
    }
   ],
   "source": [
    "geom.coords"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'str' object has no attribute 'exterior'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m/Users/jnimoca/Jose_BI/Repositories/Qupath_to_LMD_v2/QuPath_to_LMD.ipynb Cell 25\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/jnimoca/Jose_BI/Repositories/Qupath_to_LMD_v2/QuPath_to_LMD.ipynb#X52sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m tmp \u001b[39m=\u001b[39m \u001b[39mlist\u001b[39m(geom\u001b[39m.\u001b[39;49mexterior\u001b[39m.\u001b[39mcoords)\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'str' object has no attribute 'exterior'"
     ]
    }
   ],
   "source": [
    "tmp = list(geom.exterior.coords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = df.at[0,'coords']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'str' object has no attribute 'dtype'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m/Users/jnimoca/Jose_BI/Repositories/Qupath_to_LMD_v2/QuPath_to_LMD.ipynb Cell 22\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/jnimoca/Jose_BI/Repositories/Qupath_to_LMD_v2/QuPath_to_LMD.ipynb#X42sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m x\u001b[39m.\u001b[39;49mdtype\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'str' object has no attribute 'dtype'"
     ]
    }
   ],
   "source": [
    "x.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "please provide a numpy array of shape (N, 2)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m/Users/jnimoca/Jose_BI/Repositories/Qupath_to_LMD_v2/QuPath_to_LMD.ipynb Cell 17\u001b[0m line \u001b[0;36m1\n\u001b[0;32m----> <a href='vscode-notebook-cell:/Users/jnimoca/Jose_BI/Repositories/Qupath_to_LMD_v2/QuPath_to_LMD.ipynb#X34sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m create_collection(df_csv\u001b[39m=\u001b[39;49m\u001b[39mf\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39m./\u001b[39;49m\u001b[39m{\u001b[39;49;00mdatetime\u001b[39m}\u001b[39;49;00m\u001b[39m_QCed_geojson.csv\u001b[39;49m\u001b[39m\"\u001b[39;49m, calib_np_array_csv\u001b[39m=\u001b[39;49m\u001b[39mf\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39m./\u001b[39;49m\u001b[39m{\u001b[39;49;00mdatetime\u001b[39m}\u001b[39;49;00m\u001b[39m_calib_points.csv\u001b[39;49m\u001b[39m\"\u001b[39;49m, samples_and_wells_input\u001b[39m=\u001b[39;49msamples_and_wells)\n",
      "\u001b[1;32m/Users/jnimoca/Jose_BI/Repositories/Qupath_to_LMD_v2/QuPath_to_LMD.ipynb Cell 17\u001b[0m line \u001b[0;36m1\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/jnimoca/Jose_BI/Repositories/Qupath_to_LMD_v2/QuPath_to_LMD.ipynb#X34sZmlsZQ%3D%3D?line=12'>13</a>\u001b[0m the_collection\u001b[39m.\u001b[39morientation_transform \u001b[39m=\u001b[39m numpy\u001b[39m.\u001b[39marray([[\u001b[39m1\u001b[39m,\u001b[39m0\u001b[39m ], [\u001b[39m0\u001b[39m,\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m]])\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/jnimoca/Jose_BI/Repositories/Qupath_to_LMD_v2/QuPath_to_LMD.ipynb#X34sZmlsZQ%3D%3D?line=13'>14</a>\u001b[0m \u001b[39mfor\u001b[39;00m i \u001b[39min\u001b[39;00m df\u001b[39m.\u001b[39mindex:\n\u001b[0;32m---> <a href='vscode-notebook-cell:/Users/jnimoca/Jose_BI/Repositories/Qupath_to_LMD_v2/QuPath_to_LMD.ipynb#X34sZmlsZQ%3D%3D?line=14'>15</a>\u001b[0m     the_collection\u001b[39m.\u001b[39;49mnew_shape(df\u001b[39m.\u001b[39;49mat[i,\u001b[39m'\u001b[39;49m\u001b[39mcoords\u001b[39;49m\u001b[39m'\u001b[39;49m], well \u001b[39m=\u001b[39;49m samples_and_wells[df\u001b[39m.\u001b[39;49mat[i, \u001b[39m\"\u001b[39;49m\u001b[39mName\u001b[39;49m\u001b[39m\"\u001b[39;49m]])\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/jnimoca/Jose_BI/Repositories/Qupath_to_LMD_v2/QuPath_to_LMD.ipynb#X34sZmlsZQ%3D%3D?line=16'>17</a>\u001b[0m the_collection\u001b[39m.\u001b[39mplot(save_name\u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m./TheCollection.png\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/jnimoca/Jose_BI/Repositories/Qupath_to_LMD_v2/QuPath_to_LMD.ipynb#X34sZmlsZQ%3D%3D?line=17'>18</a>\u001b[0m \u001b[39m# st.image(\"./TheCollection.png\", caption='Your Contours', use_column_width=True)\u001b[39;00m\n\u001b[1;32m     <a href='vscode-notebook-cell:/Users/jnimoca/Jose_BI/Repositories/Qupath_to_LMD_v2/QuPath_to_LMD.ipynb#X34sZmlsZQ%3D%3D?line=18'>19</a>\u001b[0m \u001b[39m# st.write(the_collection.stats())\u001b[39;00m\n",
      "File \u001b[0;32m~/Jose_BI/Repositories/py-lmd/src/lmd/lib.py:211\u001b[0m, in \u001b[0;36mCollection.new_shape\u001b[0;34m(self, points, well, name)\u001b[0m\n\u001b[1;32m    197\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mnew_shape\u001b[39m(\u001b[39mself\u001b[39m, points: np\u001b[39m.\u001b[39mndarray, \n\u001b[1;32m    198\u001b[0m              well: Optional[\u001b[39mstr\u001b[39m] \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m, \n\u001b[1;32m    199\u001b[0m              name: Optional[\u001b[39mstr\u001b[39m] \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m):\n\u001b[1;32m    201\u001b[0m \u001b[39m    \u001b[39m\u001b[39m\"\"\"Directly create a new Shape in the current collection.\u001b[39;00m\n\u001b[1;32m    202\u001b[0m \n\u001b[1;32m    203\u001b[0m \u001b[39m    Args:\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    208\u001b[0m \u001b[39m        name: Name of the shape.\u001b[39;00m\n\u001b[1;32m    209\u001b[0m \u001b[39m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 211\u001b[0m     to_add \u001b[39m=\u001b[39m Shape(points, well\u001b[39m=\u001b[39;49mwell, name\u001b[39m=\u001b[39;49mname, orientation_transform \u001b[39m=\u001b[39;49m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49morientation_transform)\n\u001b[1;32m    212\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39madd_shape(to_add)\n",
      "File \u001b[0;32m~/Jose_BI/Repositories/py-lmd/src/lmd/lib.py:385\u001b[0m, in \u001b[0;36mShape.__init__\u001b[0;34m(self, points, well, name, orientation_transform)\u001b[0m\n\u001b[1;32m    381\u001b[0m point_shapes \u001b[39m=\u001b[39m points\u001b[39m.\u001b[39mshape\n\u001b[1;32m    382\u001b[0m \u001b[39mif\u001b[39;00m ((\u001b[39mlen\u001b[39m(point_shapes) \u001b[39m!=\u001b[39m \u001b[39m2\u001b[39m) \u001b[39mor\u001b[39;00m \n\u001b[1;32m    383\u001b[0m     (point_shapes[\u001b[39m1\u001b[39m] \u001b[39m!=\u001b[39m \u001b[39m2\u001b[39m) \u001b[39mor\u001b[39;00m\n\u001b[1;32m    384\u001b[0m     (point_shapes[\u001b[39m0\u001b[39m] \u001b[39m==\u001b[39m \u001b[39m0\u001b[39m)):\n\u001b[0;32m--> 385\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\u001b[39m'\u001b[39m\u001b[39mplease provide a numpy array of shape (N, 2)\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[1;32m    387\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpoints: np\u001b[39m.\u001b[39mndarray \u001b[39m=\u001b[39m points\n\u001b[1;32m    389\u001b[0m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mname: Optional[\u001b[39mstr\u001b[39m] \u001b[39m=\u001b[39m name\n",
      "\u001b[0;31mValueError\u001b[0m: please provide a numpy array of shape (N, 2)"
     ]
    }
   ],
   "source": [
    "create_collection(df_csv=f\"./{datetime}_QCed_geojson.csv\", calib_np_array_csv=f\"./{datetime}_calib_points.csv\", samples_and_wells_input=samples_and_wells)"
   ]
  },
  {
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   "cell_type": "markdown",
   "id": "39a634b4-5386-452f-86e7-1a1af6ebafc9",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Processing from GeoJSON to Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1 load and QC geojson file\n",
    "def load_and_QC_geojson_file(geojson_path: str, list_of_calibpoint_names: list = ['calib1','calib2','calib3']):\n",
    "\n",
    "    #load geojson file\n",
    "    df = geopandas.read_file(path_to_file)\n",
    "\n",
    "    #save calib points in a list\n",
    "    caliblist = []\n",
    "    for point_name in list_of_calibpoint_names:\n",
    "        if point_name in df['name'].unique():\n",
    "            caliblist.append(df.loc[df['name'] == point_name, 'geometry'].values[0])\n",
    "        else:\n",
    "            st.write('Your given name is not present in the file', \n",
    "            f'These are the calib points you passed: {list_of_calibpoint_names}',\n",
    "            f\"These are the calib points found in the geojson you gave me: {df['name'].unique()}\")\n",
    "    #create coordenate list\n",
    "    listarray = []\n",
    "    for point in pointlist:\n",
    "        listarray.append([point.x, point.y])\n",
    "    calib_np_array = np.array(listarray)\n",
    "\n",
    "    #now that calibration points are saved, remove them from the dataframe\n",
    "    df = df[df['name'].isin(list_of_calibpoint_names) == False]\n",
    "\n",
    "    #check and remove empty classifications \n",
    "    if df['classification'].isna().sum() !=0 :\n",
    "        st.write(f\"you have {df['classification'].isna().sum()} NaNs in your classification column\",\n",
    "                \"these are unclassified objects from Qupath, they will be ignored\") \n",
    "        df = df[df['classification'].notna()]\n",
    "\n",
    "    #check for MultiPolygon objects\n",
    "    if 'MultiPolygon' in df.geometry.geom_type.value_counts().keys():\n",
    "        st.write('MultiPolygon objects present:',\n",
    "        #print out the classification name of the MultiPolygon objects\n",
    "        f\"{df[df.geometry.geom_type == 'MultiPolygon']['classification']}\", \n",
    "        'these are not supported, please convert them to polygons in Qupath',\n",
    "        'the script will continue but these objects will be ignored')\n",
    "        #remove MultiPolygon objects\n",
    "        df = df[df.geometry.geom_type != 'MultiPolygon']\n",
    "\n",
    "    # reformat shape coordenate list\n",
    "    df['coords'] = np.nan\n",
    "    df['coords'] = df['coords'].astype('object')\n",
    "    # simplify to reduce number of points\n",
    "    df['simple'] = df.geometry.simplify(1)\n",
    "    for i in df.index:\n",
    "        geom=df.at[i, 'simple']\n",
    "        tmp = list(geom.exterior.coords)\n",
    "        tmp_lol = [list(i) for i in tmp]\n",
    "        df.at[i,'coords'] = tmp_lol\n",
    "\n",
    "    #extract classification name into a new column\n",
    "    df['Name'] = np.nan\n",
    "    for i in df.index:\n",
    "        tmp = df.classification[i].get('name')\n",
    "        df.at[i,'Name'] = tmp\n",
    "\n",
    "    st.write('The file loading is complete')\n",
    "\n",
    "    return(df,calib_np_array)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 2 parse and check samples and wells\n",
    "def load_and_QC_SamplesandWells(samples_and_wells_input, df):\n",
    "\n",
    "    # parse common human copy paste formats\n",
    "    # remove newlines\n",
    "    samples_and_wells_processed = samples_and_wells_input.replace(\"\\n\", \"\")\n",
    "    # remove spaces\n",
    "    samples_and_wells_processed = samples_and_wells_processed.replace(\" \", \"\")\n",
    "    #parse into python dictionary\n",
    "    samples_and_wells = ast.literal_eval(samples_and_wells_processed)\n",
    "\n",
    "    #create list of acceptable wells, default is using a space in between columns\n",
    "    list_of_acceptable_wells =[]\n",
    "    for row in list(string.ascii_uppercase[2:14]):\n",
    "        for column in range(2,22):\n",
    "            list_of_acceptable_wells.append(str(row) + str(column))\n",
    "\n",
    "    #check for improper wells\n",
    "    for well in samples_and_wells.values():\n",
    "        if well not in list_of_acceptable_wells:\n",
    "            st.write(f'Your well {well} is not in the list of acceptable wells, please correct it',\n",
    "            'the LMD is not able to collect into this well, the script will stop here')\n",
    "            st.stop()\n",
    "\n",
    "    #check that names in df are all present in the samples and wells\n",
    "    for name in df.Name.unique():\n",
    "        if name not in samples_and_wells.keys():\n",
    "            st.write(f'Your name {name} is not in the list of samples_and_wells, please correct either',\n",
    "            'please change the class name in Qupath or add it to the samples_and_wells dictionary',\n",
    "            'and then rerun the web app')\n",
    "            st.stop()\n",
    "    \n",
    "    return samples_and_wells"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 3 create collection\n",
    "def create_collection(df = df, calib_np_array = calib_np_array, samples_and_wells = samples_and_wells ):\n",
    "    #create the collection of py-lmd-env package\n",
    "    #uses caliblist passed on the function, order matters\n",
    "    #orientation vector is for QuPath coordenate system\n",
    "    the_collection = Collection(calibration_points = calib_np_array)\n",
    "    the_collection.orientation_transform = np.array([[1,0 ], [0,-1]])\n",
    "    for i in df.index:\n",
    "        the_collection.new_shape(df.at[i,'coords'], well = samples_and_wells[df.at[i, \"Name\"]])\n",
    "\n",
    "    the_collection.plot(save_name= \"./TheCollection.png\")\n",
    "    st.image(\"./TheCollection.png\", caption='Your Contours', use_column_width=True)\n",
    "    st.write(the_collection.stats())\n",
    "    \n",
    "    the_collection.save(f\"./{datetime}_LMD_ready_contours.xml\")\n",
    "    st.download_button(\"Download file\", Path(f\"./{datetime}_LMD_ready_contours.xml\").read_text(), f\"./{datetime}_LMD_ready_contours.xml\")\n",
    "    \n",
    "    #create and export dataframe with sample placement in 384 well plate\n",
    "    rows_A_P= [i for i in string.ascii_uppercase[:16]]\n",
    "    columns_1_24 = [str(i) for i in range(1,25)]\n",
    "    df_wp384 = pd.DataFrame('',columns=columns_1_24, index=rows_A_P)\n",
    "    #fill in the dataframe with samples and wells\n",
    "    for i in samples_and_wells:\n",
    "        location = samples_and_wells[i]\n",
    "        df_wp384.at[location[0],location[1:]] = i\n",
    "\n",
    "    if st.button('Download 384 well plate layout'):\n",
    "        df_wp384.to_csv(f\"./{datetime}_384_wellplate.csv\", index=True)\n",
    "        st.write('Your 384 well plate layout has been downloaded')\n"
   ]
  }
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