build_compilation_v2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "4e370421-d2f7-4759-908a-b47e1c487b56",
   "metadata": {},
   "source": [
    "# build_compilation notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d54acb53-153e-4623-be27-a73a37001046",
   "metadata": {},
   "source": [
    "This notebook iterates through each paleomagnetic record (datasheet) of the vgp database, extracts data which meet user-specified criteria, and appends them to a new dataframe for later processing (to generate an APWP)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2daf731b-250b-4616-8cff-9e29f1d27413",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pmagpy import ipmag, pmag\n",
    "import scripts.auxiliar as aux\n",
    "from scripts.utils_compilation import split_datasheet, recalc_vgps, go_reverse, get_k, get_alpha95, get_ages\n",
    "\n",
    "import seaborn as sns\n",
    "\n",
    "pd.set_option('display.max_columns', None)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b00c203d-b834-475e-8c2e-5ea52c06ffff",
   "metadata": {},
   "source": [
    "Set the directory from which we will pull the datasheets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a7158b5f-cc72-44e6-a249-1955065044c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "current_path = os.getcwd()\n",
    "data_path_VGP = current_path + '/vgp_database'\n",
    "files_names = aux.get_files_in_directory(data_path_VGP)\n",
    "#csv_file_names = [os.path.splitext(os.path.basename(open(file,'r').name))[0] for file in files_names if file.endswith('.csv')] #consider just *csv files\n",
    "xlsx_file_names = [os.path.splitext(os.path.basename(open(file,'r').name))[0] for file in files_names if file.endswith('.xlsx')]\n",
    "paths = [file for file in files_names if file.endswith('.xlsx')]\n",
    "df_files = pd.DataFrame({'path': paths,  'name_xlsx': xlsx_file_names})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "778d9247-03ec-43ec-98b2-2ab283d7378a",
   "metadata": {},
   "source": [
    "### Split study- and site-level data\n",
    "Each datasheet contains both study-level poles and site-level vgps. We split and assign these to separate dataframes and cast types for their constituent series."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e5d1957-a9c2-4561-8cc5-5337a5725df1",
   "metadata": {},
   "source": [
    "### Generates an unfiltered dataset of VGPs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "bb497784-c8fa-4d94-9a24-2768c262a9db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "processing file 0\n",
      "processing file 1\n",
      "processing file 2\n",
      "processing file 3\n",
      "processing file 4\n",
      "processing file 5\n",
      "processing file 6\n",
      "processing file 7\n",
      "processing file 8\n",
      "processing file 9\n",
      "processing file 10\n",
      "processing file 11\n",
      "processing file 12\n",
      "processing file 13\n",
      "processing file 14\n",
      "processing file 15\n",
      "processing file 16\n",
      "processing file 17\n",
      "processing file 18\n",
      "processing file 19\n",
      "processing file 20\n",
      "processing file 21\n",
      "processing file 22\n",
      "processing file 23\n",
      "processing file 24\n",
      "processing file 25\n",
      "processing file 26\n",
      "processing file 27\n",
      "processing file 28\n",
      "processing file 29\n",
      "processing file 30\n",
      "processing file 31\n",
      "processing file 32\n",
      "processing file 33\n"
     ]
    }
   ],
   "source": [
    "for i in df_files.index:   # cycle over each file in database\n",
    "    print (f'processing file {i}')\n",
    "    \n",
    "    # import data and assign to dataframes\n",
    "    df_poles_temp, df_vgps_temp = split_datasheet(df_files, i)\n",
    "    \n",
    "    df_vgps_temp['rej_crit'] = [[int(float(j)) for j in str(i.rej_crit).split(';') if ~np.isnan(float(j))] for _,i in df_vgps_temp.iterrows()]\n",
    "    df_vgps_temp['Study'] = df_files.name_xlsx[i]\n",
    "\n",
    "    if not df_vgps_temp.empty:\n",
    "        \n",
    "        df_vgps_temp = recalc_vgps(df_vgps_temp)       \n",
    "        df_vgps_temp = go_reverse(df_vgps_temp)        \n",
    "        df_vgps_temp = get_alpha95(df_vgps_temp)        \n",
    "        df_vgps_temp = get_k(df_vgps_temp)\n",
    "        df_vgps_temp = get_ages(df_vgps_temp, round_ceil = False)\n",
    "        \n",
    "        df_vgps_temp['age_uncertainty'] = df_vgps_temp['max_age'] - df_vgps_temp['min_age']\n",
    "        \n",
    "        if i == 0 : df_vgp_unfiltered = pd.DataFrame(data=None, columns=df_vgps_temp.columns); df_poles_original = pd.DataFrame(data=None, columns=df_poles_temp.columns)\n",
    "        \n",
    "        # parse data\n",
    "        df_vgp_unfiltered = df_vgp_unfiltered.append(df_vgps_temp, ignore_index=True)\n",
    "        df_poles_original = df_poles_original.append(df_poles_temp, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "753545b5-6e0b-45aa-abbe-6de9b8f28a47",
   "metadata": {},
   "source": [
    "#### Split the dataset into two hyerarchical levels (mean of synchronous units and sites from \"synchronous\" units)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "115a22fa-b0f4-4b1a-b888-0a24c46e994f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Set a DataFrame for the means of the synchronous units\n",
    "# df_synch_unit_means = df_vgp_unfiltered[df_vgp_unfiltered['synch_unit'].str.contains(\"M\", na=False)]\n",
    "        \n",
    "# # Ignore the means of the synch_units and cast the synch_unit to int\n",
    "# df_vgp_unfiltered = df_vgp_unfiltered[~df_vgp_unfiltered['synch_unit'].str.contains(\"M\", na=False)]\n",
    "# df_vgp_unfiltered['synch_unit'] = df_vgp_unfiltered['synch_unit'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6f739449-1e8d-4e17-a5c6-ded114242946",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Set a DataFrame for the means of the synchronous units\n",
    "df_synch_unit_means = df_vgp_unfiltered[df_vgp_unfiltered['synch_unit'].str.contains(\"M\", na=False)]\n",
    "        \n",
    "# Ignore the means of the synch_units and cast the synch_unit to int\n",
    "# df_vgp_unfiltered = df_vgp_unfiltered[~df_vgp_unfiltered['synch_unit'].str.contains(\"M\", na=False)]\n",
    "# df_vgp_unfiltered['synch_unit'] = df_vgp_unfiltered['synch_unit'].astype(int)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9af93fac-32bf-4feb-8a8f-1e984209552a",
   "metadata": {},
   "source": [
    "#### Send rejection criterias to columns in the DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e0d8fe02-3111-4d02-aa1b-f173ded73e32",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Boolean type criterias\n",
    "df_vgp_unfiltered['author_selection'] = df_vgp_unfiltered.apply(lambda row: True if row['in_study_pole'] != 0 else False, axis=1)\n",
    "df_vgp_unfiltered['undemagnetized'] = [True if (1 in i.rej_crit) else False for _,i in df_vgp_unfiltered.iterrows()]\n",
    "df_vgp_unfiltered['sample_count'] = [True if (2 in i.rej_crit) else False for _,i in df_vgp_unfiltered.iterrows()]\n",
    "df_vgp_unfiltered['alpha_95'] = [True if (3 in i.rej_crit) else False for _,i in df_vgp_unfiltered.iterrows()]\n",
    "df_vgp_unfiltered['overprints'] = [True if (4 in i.rej_crit) else False for _,i in df_vgp_unfiltered.iterrows()]\n",
    "df_vgp_unfiltered['remagnetizations'] = [True if (5 in i.rej_crit) else False for _,i in df_vgp_unfiltered.iterrows()]\n",
    "df_vgp_unfiltered['uncertain_struct'] = [True if (6 in i.rej_crit) else False for _,i in df_vgp_unfiltered.iterrows()]\n",
    "df_vgp_unfiltered['rotated'] = [True if (7 in i.rej_crit) else False for _,i in df_vgp_unfiltered.iterrows()]\n",
    "df_vgp_unfiltered['shallowed'] = [True if (8 in i.rej_crit) else False for _,i in df_vgp_unfiltered.iterrows()]\n",
    "df_vgp_unfiltered['anomalous_dir'] = [True if (9 in i.rej_crit) else False for _,i in df_vgp_unfiltered.iterrows()]\n",
    "df_vgp_unfiltered['uncertain_age'] = [True if (10 in i.rej_crit) else False for _,i in df_vgp_unfiltered.iterrows()]\n",
    "df_vgp_unfiltered['distinct_age'] = [True if (11 in i.rej_crit) else False for _,i in df_vgp_unfiltered.iterrows()]\n",
    "df_vgp_unfiltered['sub-time_units'] = [True if (12 in i.rej_crit) else False for _,i in df_vgp_unfiltered.iterrows()]\n",
    "df_vgp_unfiltered['otherwise_rej'] = [True if (14 in i.rej_crit) else False for _,i in df_vgp_unfiltered.iterrows()]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7916d0c1-6b57-425b-8e86-ef1cd55b8cde",
   "metadata": {},
   "source": [
    "Lets see an overview of the most popular rejection criterias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "bfca8d2f-4e7c-4b8b-91d9-cbb1d2b8ce9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "keys = ['undemagnetized','sample_count','alpha_95','overprints','remagnetizations','uncertain_struct','rotated','shallowed',\n",
    "        'anomalous_dir','uncertain_age','distinct_age','sub-time_units','otherwise_rej']\n",
    "values = [df_vgp_unfiltered.groupby(['Study'])[i].sum().tolist() for i in keys]\n",
    "\n",
    "df_test = pd.DataFrame(columns = keys)\n",
    "for key, value in zip(keys, values):\n",
    "    df_test[key] = value\n",
    "        \n",
    "sns.set(rc={'figure.figsize':(11.7,8.27)})\n",
    "ax = sns.heatmap(df_test, \n",
    "                 cmap=\"YlGnBu\", yticklabels = df_vgp_unfiltered['Study'].unique(), annot = True, vmin = 0, vmax = 20)\n",
    "ax.tick_params(axis='x', rotation=70)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c691e5a0-6fd3-4489-aa61-278f543fd1e7",
   "metadata": {},
   "source": [
    "### Set data inclusion criteria\n",
    "Specify the inclusion criteria to be used in the data-selection. If author_selection is set=1, all other criteria will be ignored. Setting values other than 'None' for the remaining criteria allow them to be homogenized across studies."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "adaae54c-7133-4cee-ae90-bb8ba49c85b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "def discard_vgps_recursively(df_vgps, max_angle):\n",
    "    '''\n",
    "    takes a group of vgps and discards iteratively those entries that lie \"angle\" degrees away from the recomputed mean\n",
    "    '''\n",
    "    if len(df_vgps) < 4:\n",
    "        return df_vgps\n",
    "    \n",
    "    ppole = ipmag.fisher_mean(dec = df_vgps['vgp_lon_SH'].tolist(), inc = df_vgps['vgp_lat_SH'].tolist())       \n",
    "    df_vgps['dist_2_mean'] = df_vgps.apply(lambda row: pmag.angle([row.vgp_lon_SH, row.vgp_lat_SH], [ppole['dec'], ppole['inc']]), axis=1)    \n",
    "    max_angle_temp = df_vgps['dist_2_mean'].max()\n",
    "    if max_angle_temp < max_angle: return df_vgps\n",
    "\n",
    "    while max_angle_temp > max_angle:\n",
    "        if len(df_vgps) < 4: return df_vgps\n",
    "        df_vgps = df_vgps.loc[df_vgps['dist_2_mean'] < max_angle]\n",
    "        ppole = ipmag.fisher_mean(dec = df_vgps['vgp_lon_SH'].tolist(), inc = df_vgps['vgp_lat_SH'].tolist())       \n",
    "        df_vgps['dist_2_mean'] = df_vgps.apply(lambda row: pmag.angle([row.vgp_lon_SH, row.vgp_lat_SH], [ppole['dec'], ppole['inc']]), axis=1)    \n",
    "        max_angle_temp = df_vgps['dist_2_mean'].max()\n",
    "        if max_angle_temp < max_angle: return df_vgps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "171dd124-c0e7-4bde-9d38-e7ad5467dda5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def selection(df_unfiltered, incl_criteria):\n",
    "    '''\n",
    "    Pass an unfiltered DF of vgps and makes a subselection from a dictionary of criterias.\n",
    "    '''    \n",
    "           \n",
    "    df_unfiltered['keep'] = np.nan\n",
    "        \n",
    "    if incl_criteria['author_selection'] == True:        \n",
    "        df_unfiltered['keep'] = df_unfiltered.apply(lambda row: True if row.in_study_pole != 0 else row.keep, axis = 1)\n",
    "            \n",
    "    for i in incl_criteria:                       \n",
    "        if incl_criteria[i] == True:  # boolean cases          \n",
    "            df_unfiltered['keep'] = df_unfiltered.apply(lambda row: True if row[i] == True else row.keep, axis = 1)\n",
    "    \n",
    "    if incl_criteria['sample_count']: df_unfiltered['keep'] = df_unfiltered.apply(lambda row: True if row['n'] >= incl_criteria['sample_count'] else False, axis = 1)\n",
    "    if incl_criteria['alpha_95']: df_unfiltered['keep'] = df_unfiltered.apply(lambda row: True if row['alpha95'] <= incl_criteria['alpha_95'] else False, axis = 1)\n",
    "    if incl_criteria['uncertain_age']: df_unfiltered['keep'] = df_unfiltered.apply(lambda row: True if row['age_uncertainty'] <= incl_criteria['uncertain_age'] else False, axis = 1)\n",
    "    if incl_criteria['rock_type']: df_unfiltered['keep'] = df_unfiltered.apply(lambda row: True if row['rock_typ_1'] == incl_criteria['rock_type'] else False, axis = 1)\n",
    "    \n",
    "    # discard those discarded by other criterias\n",
    "    for i in incl_criteria:        \n",
    "        if incl_criteria[i] or i == 'rock_type': continue # boolean cases                    \n",
    "        df_unfiltered['keep'] = df_unfiltered.apply(lambda row: False if row[i] == True else row.keep, axis = 1)\n",
    "            \n",
    "    df_filtered = df_unfiltered.loc[df_unfiltered['keep'] == True]\n",
    "    del df_unfiltered['keep']\n",
    "    return df_filtered"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bbf2887-b4ff-4926-b463-5ece8140ba8c",
   "metadata": {},
   "source": [
    "### Set the including criterias. Note: default as None (not False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "064a4b1a-f1d8-4b8d-a2a8-24f1ccb6347f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# the basic dataset starts with the author selection\n",
    "incl_criteria = {\n",
    "    'author_selection': True,     # True or False; if 1, all other criteria will be ignored\n",
    "    'undemagnetized': None,    # None (defaults to author selection) or True  \n",
    "    'sample_count': None,      # None (defaults to author selection) or int: cutoff n (≥ x)\n",
    "    'alpha_95': None,          # None (defaults to author selection) or float: cutoff A95 (≤ x degrees)\n",
    "    'overprints': None,        # None (defaults to author selection) or True  \n",
    "    'remagnetizations': None,  # None (defaults to author selection) or True\n",
    "    'uncertain_struct': None,  # None (defaults to author selection) or True\n",
    "    'rotated': None,           # None (defaults to author selection) or True\n",
    "    'shallowed': None,         # None (defaults to author selection) or True [***can also implement cutoff f-value here if desired***]\n",
    "    'anomalous_dir': None,     # None (defaults to author selection) or float: cutoff distance (in degrees) between vgp and mean (≤ x degrees)\n",
    "    'uncertain_age': None,     # None (defaults to author selection) or float: cutoff age resolution (in Myr) between min and max (≤ x Myr)\n",
    "    'distinct_age': None,      # None (defaults to author selection) or True\n",
    "    'sub-time_units': True,    # None (defaults to author selection) or True (evaluates the dispersion of the supposed spot-readings)\n",
    "    'rock_type': None,         # None (defaults to author selection) or string: 'all' or 'igneous' or 'sedimentary'\n",
    "    'otherwise_rej': None     # None (defaults to author selection) or 'y'\n",
    "    }"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5131fe2c-d0b5-492e-b17b-a28020290bcc",
   "metadata": {},
   "source": [
    "#### Filtering and selection of entries\n",
    "First we select those entries based on booleand conditionals (e.g. include vgps that weren't demagnetized) and then we turn to select or discard those vgps based on nested conditionals (e.g. include or not anomalous directions xº apart from the mean, which implies first to compute a provitional PP, with also a potential recursive approach like Vandamme)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "d68fc77e-3108-482f-a139-05ec3c35aa36",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Absaroka_volcanics True\n",
      "Beaver_River_intrusions True\n",
      "Bighorn_Basin_sediments True\n",
      "Bishop_tuff True\n",
      "Bitterroot_Dome_intrusions True\n",
      "Central_Mexico_Plio-Pleistocene True\n",
      "Clear_Lake_volcanic_field True\n",
      "Coso_Range_volcanics True\n",
      "Dinan_Bay_lavas True\n",
      "Eastern_Alkaline_Province True\n",
      "Eastern_TMVB True\n",
      "Latir_volcanic_field True\n",
      "Long_Valley_Caldera_volcanics True\n",
      "Mariscal_Mtn_intrusions True\n",
      "Michoacan_Guanajuato_volcanic_field True\n",
      "Mogollon-Datil_volcanics True\n",
      "Monterey_intrusions True\n",
      "N_Montana_intrusions True\n",
      "Ramsay_Island_lavas True\n",
      "Rattlesnake_Hills_volcanics True\n",
      "Robinson_Antincline_intrusions True\n",
      "SW_USA_composite True\n",
      "San_Luis_Hills_volcanics True\n",
      "Sierra_de_Las_Cruces True\n",
      "Snake_River_Plain True\n",
      "Sonoma_volcanics True\n",
      "Stanislaus_Group True\n",
      "Stoddard_Mountain_laccolith True\n",
      "Tequila_volcanic_field True\n",
      "Valles_Caldera_volcanics True\n",
      "Western_Central_TMVB True\n"
     ]
    }
   ],
   "source": [
    "# Final DataFrame with the entries that follow all the specified criteria\n",
    "df_vgp_compilation = pd.DataFrame(data = None, columns = df_vgp_unfiltered.columns)\n",
    "df_pole_compilation = pd.DataFrame(data = None, columns = df_poles_original.columns)\n",
    "\n",
    "# Boolean conditionals\n",
    "df_selection = selection(df_vgp_unfiltered, incl_criteria)\n",
    "\n",
    "# We have to iterate through the different studies to evaluate nested conditionals\n",
    "for study, df_study in df_selection.groupby('Study'):\n",
    "    \n",
    "    # list wtih ALL the synch_units\n",
    "    synch_units = [i for i in df_study['synch_unit'].unique() if isinstance(i, (float, int))] \n",
    "    # means of the synch_units reported by authors (M entries)\n",
    "    synch_units_means = [int(i.replace('M', '')) for i in df_study['synch_unit'].unique() if isinstance(i, str)]\n",
    "    # entries with no reported mean by authors (but considered to belong to the same cooling unit)\n",
    "    synch_without_mean = list(set(synch_units) - set(synch_units_means)) \n",
    "\n",
    "    # dim a DF to fill-in with those entries that pass the criterias\n",
    "    df_temp = pd.DataFrame(data = None, columns = df_selection.columns)\n",
    "    \n",
    "    # Strip age distinct (and send to the final DF), then, continue working with the subselection (this is independet of whether or not the criteria was applied)\n",
    "    if (incl_criteria['distinct_age']) and (not df_study[df_study['distinct_age'] == True].empty): \n",
    "        df_vgp_compilation = df_vgp_compilation.append(df_study[df_study['distinct_age'] == True]) # send the vgps with distinct directly to the final compilation since there is no reason to discard them from it.\n",
    "        df_study = df_study.loc[~df_study['distinct_age'] == True]\n",
    "    \n",
    "    # Group by synchronous units to evaluate if they represent the same spot reading or not (the same spot reading should have high concentration parameter -- to be setted e.g. higher than 150)\n",
    "    for synch_unit, df_synch_unit in df_study.groupby('synch_unit'):\n",
    "                                         \n",
    "        if incl_criteria['sub-time_units']: \n",
    "            \n",
    "            # if the synch_unit equals zero, it is considered as a sopt-reading, and thus, it goes directly to the compilation      \n",
    "            if synch_unit == 0: df_temp = df_temp.append(df_synch_unit); continue\n",
    "            \n",
    "            # evaluate the concebtration of individual entries that were considered all together as spot reading and averaged accordingly (M)\n",
    "            if isinstance(synch_unit, str):\n",
    "                \n",
    "                df_synch_unit = df_study[df_study['synch_unit'] == float(synch_unit.replace('M', ''))] #subselect entries \n",
    "                df_synch_unit['in_study_pole'] = df_synch_unit_means.loc[(df_synch_unit_means['synch_unit'] == synch_unit) & \n",
    "                                                                         (df_synch_unit_means['Study'] == study)]['in_study_pole'].to_list()[0]\n",
    "\n",
    "                if df_synch_unit.shape[0] > 1: \n",
    "\n",
    "                    synch_unit_mean = ipmag.fisher_mean(dec = df_synch_unit['vgp_lon_SH'].tolist(), inc = df_synch_unit['vgp_lat_SH'].tolist())\n",
    "\n",
    "                    if synch_unit_mean['k'] < 150: # append all the entries otherwise consider as a single mean\n",
    "                        df_temp = df_temp.append(df_synch_unit)                                         \n",
    "                    else:\n",
    "                        df_temp = df_temp.append(df_synch_unit_means.loc[(df_synch_unit_means['synch_unit'] == synch_unit) & \n",
    "                                                                         (df_synch_unit_means['Study'] == study)])        \n",
    "            if synch_unit in synch_without_mean:                 \n",
    "                if int(synch_unit) == 0: continue\n",
    "                synch_unit_mean = ipmag.fisher_mean(dec = df_synch_unit['vgp_lon_SH'].tolist(), inc = df_synch_unit['vgp_lat_SH'].tolist())                        \n",
    "                site = ipmag.fisher_mean(dec = df_synch_unit['slon'].tolist(), inc = df_synch_unit['slat'].tolist()) \n",
    "\n",
    "                df_temp = df_temp.append({'Study': study, 'slat': site['inc'], 'slon': site['dec'],                                          \n",
    "                                          'vgp_lat_SH': synch_unit_mean['inc'], 'vgp_lon_SH': synch_unit_mean['dec'], 'k': synch_unit_mean['k'],'alpha95': synch_unit_mean['alpha95'],\n",
    "                                          'mean_age': df_synch_unit['mean_age'].mean(), 'min_age': df_synch_unit['min_age'].mean(), 'max_age': df_synch_unit['max_age'].mean(),\n",
    "                                          'synch_unit' : synch_unit},                   \n",
    "                                           ignore_index = True)            \n",
    "        \n",
    "        # if we not evaluate the entries within the cooling units, we take the M entries as face value (and we average the entries with no reported mean -i.e. M entry)                               \n",
    "        else:\n",
    "            # spot readings (0) goes straight to the final compilation\n",
    "            if synch_unit == 0: df_temp = df_temp.append(df_synch_unit); continue\n",
    "        \n",
    "            #print(df_temp[['Study','slat','synch_study']])\n",
    "            \n",
    "            \n",
    "            # reported means (M's) goes sraight to the final compilation\n",
    "            if isinstance(synch_unit, str):\n",
    "                #print('-'+str(synch_unit))\n",
    "                df_temp = df_temp.append(df_synch_unit)\n",
    "            \n",
    "            # if there are \n",
    "            if synch_unit in synch_without_mean: \n",
    "                if int(synch_unit) == 0: continue\n",
    "                synch_unit_mean = ipmag.fisher_mean(dec = df_synch_unit['vgp_lon_SH'].tolist(), inc = df_synch_unit['vgp_lat_SH'].tolist())                        \n",
    "                site = ipmag.fisher_mean(dec = df_synch_unit['slon'].tolist(), inc = df_synch_unit['slat'].tolist()) \n",
    "\n",
    "                df_temp = df_temp.append({'Study': study, 'slat': site['inc'], 'slon': site['dec'],                                          \n",
    "                                          'vgp_lat_SH': synch_unit_mean['inc'], 'vgp_lon_SH': synch_unit_mean['dec'], 'k': synch_unit_mean['k'],'alpha95': synch_unit_mean['alpha95'],\n",
    "                                          'mean_age': df_synch_unit['mean_age'].mean(), 'min_age': df_synch_unit['min_age'].mean(), 'max_age': df_synch_unit['max_age'].mean(),\n",
    "                                          'synch_unit' : synch_unit},                   \n",
    "                                           ignore_index = True)\n",
    "            \n",
    "    #print(study, df_temp[df_temp.index.duplicated()][['Study', 'name']])        \n",
    "    if incl_criteria['anomalous_dir']: \n",
    "        before = len(df_temp)\n",
    "        df_temp = discard_vgps_recursively(df_temp, incl_criteria['anomalous_dir'])\n",
    "        print('discarded : ', before - len(df_temp))\n",
    "    ppole = ipmag.fisher_mean(dec = df_temp['vgp_lon_SH'].tolist(), inc = df_temp['vgp_lat_SH'].tolist()) # final paleopole\n",
    "    mean_site = ipmag.fisher_mean(dec = df_temp['slon'].tolist(), inc = df_temp['slat'].tolist())\n",
    "    \n",
    "    df_vgp_compilation = df_vgp_compilation.append(df_temp.copy(deep=True), ignore_index = True)\n",
    "    \n",
    "    if len(ppole) == 0: continue \n",
    "    df_pole_compilation = df_pole_compilation.append({'name': study, \n",
    "                                                      'slat': mean_site['inc'], 'slon': mean_site['dec'],\n",
    "                                                      'Plat': ppole['inc'], 'Plon': ppole['dec'],\n",
    "                                                      'N': ppole['n'], 'K': ppole['k'], 'A95': ppole['alpha95'],\n",
    "                                                      'min_age': df_study.min_age.min(), 'max_age': df_study.max_age.max(), \n",
    "                                                      'mean_age': (df_study.max_age.max() + df_study.min_age.min()) / 2 }, \n",
    "                                                      ignore_index = True)\n",
    "    print(study, df_vgp_compilation.index.is_unique) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "cfac7a3d-605b-4b47-bda9-2da5486feab3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vgp_compilation.index.is_unique\n",
    "#df_vgp_compilation[df_vgp_compilation.index.duplicated()]\n",
    "#df_vgp_compilation.loc[0]\n",
    "#df_vgp_compilation[df_vgp_compilation['Study'] == 'Mistastin_Lake_impact']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8b1ca220-0842-42dc-906e-689216e4d751",
   "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>name</th>\n",
       "      <th>fm./loc.</th>\n",
       "      <th>slat</th>\n",
       "      <th>slon</th>\n",
       "      <th>n</th>\n",
       "      <th>dec</th>\n",
       "      <th>inc</th>\n",
       "      <th>k</th>\n",
       "      <th>alpha95</th>\n",
       "      <th>f_corr</th>\n",
       "      <th>VGP_lat</th>\n",
       "      <th>VGP_lon</th>\n",
       "      <th>K</th>\n",
       "      <th>A95</th>\n",
       "      <th>dp</th>\n",
       "      <th>dm</th>\n",
       "      <th>mean_age</th>\n",
       "      <th>min_age</th>\n",
       "      <th>2sig_min</th>\n",
       "      <th>max_age</th>\n",
       "      <th>2sig_max</th>\n",
       "      <th>uncer_dist</th>\n",
       "      <th>rock_typ_1</th>\n",
       "      <th>rock_typ_2</th>\n",
       "      <th>rock_typ_3</th>\n",
       "      <th>demag</th>\n",
       "      <th>struc_cont</th>\n",
       "      <th>rot_area</th>\n",
       "      <th>polarity</th>\n",
       "      <th>strat_group</th>\n",
       "      <th>ordering</th>\n",
       "      <th>synch_unit</th>\n",
       "      <th>in_study_pole</th>\n",
       "      <th>rej_crit</th>\n",
       "      <th>pmag_ref</th>\n",
       "      <th>age_ref</th>\n",
       "      <th>pmag_comments</th>\n",
       "      <th>age_comments</th>\n",
       "      <th>Study</th>\n",
       "      <th>VGP_lon_recalc</th>\n",
       "      <th>VGP_lat_recalc</th>\n",
       "      <th>dec_reverse</th>\n",
       "      <th>inc_reverse</th>\n",
       "      <th>vgp_lat_SH</th>\n",
       "      <th>vgp_lon_SH</th>\n",
       "      <th>age_uncertainty</th>\n",
       "      <th>Unnamed: 38</th>\n",
       "      <th>author_selection</th>\n",
       "      <th>undemagnetized</th>\n",
       "      <th>sample_count</th>\n",
       "      <th>alpha_95</th>\n",
       "      <th>overprints</th>\n",
       "      <th>remagnetizations</th>\n",
       "      <th>uncertain_struct</th>\n",
       "      <th>rotated</th>\n",
       "      <th>shallowed</th>\n",
       "      <th>anomalous_dir</th>\n",
       "      <th>uncertain_age</th>\n",
       "      <th>distinct_age</th>\n",
       "      <th>sub-time_units</th>\n",
       "      <th>otherwise_rej</th>\n",
       "      <th>keep</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Spears</td>\n",
       "      <td>34.16</td>\n",
       "      <td>-106.81</td>\n",
       "      <td>6.0</td>\n",
       "      <td>179.9</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>43.7</td>\n",
       "      <td>10.2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>76.0</td>\n",
       "      <td>73.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.5</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>uniform</td>\n",
       "      <td>igneous</td>\n",
       "      <td>volcanic</td>\n",
       "      <td>lava flow</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>[]</td>\n",
       "      <td>Diehl et al. (1988)</td>\n",
       "      <td>McIntosh et al. (1991)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Age determined from range of Spears ages given...</td>\n",
       "      <td>Mogollon-Datil_volcanics</td>\n",
       "      <td>253.5799</td>\n",
       "      <td>-76.075209</td>\n",
       "      <td>-36.4</td>\n",
       "      <td>179.9</td>\n",
       "      <td>-76.075209</td>\n",
       "      <td>253.5799</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Spears</td>\n",
       "      <td>34.16</td>\n",
       "      <td>-106.8</td>\n",
       "      <td>7.0</td>\n",
       "      <td>174.9</td>\n",
       "      <td>-32.0</td>\n",
       "      <td>275.4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>72.6</td>\n",
       "      <td>89.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.5</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>uniform</td>\n",
       "      <td>igneous</td>\n",
       "      <td>volcanic</td>\n",
       "      <td>lava flow</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>[]</td>\n",
       "      <td>Diehl et al. (1988)</td>\n",
       "      <td>McIntosh et al. (1991)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Age determined from range of Spears ages given...</td>\n",
       "      <td>Mogollon-Datil_volcanics</td>\n",
       "      <td>269.666296</td>\n",
       "      <td>-72.5818</td>\n",
       "      <td>-32.0</td>\n",
       "      <td>174.9</td>\n",
       "      <td>-72.5818</td>\n",
       "      <td>269.666296</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Spears</td>\n",
       "      <td>34.16</td>\n",
       "      <td>-106.8</td>\n",
       "      <td>8.0</td>\n",
       "      <td>181.9</td>\n",
       "      <td>-34.7</td>\n",
       "      <td>431.6</td>\n",
       "      <td>2.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>74.9</td>\n",
       "      <td>66.6</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.5</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>uniform</td>\n",
       "      <td>igneous</td>\n",
       "      <td>volcanic</td>\n",
       "      <td>ash flow</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>[]</td>\n",
       "      <td>Diehl et al. (1988)</td>\n",
       "      <td>McIntosh et al. (1991)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Age determined from range of Spears ages given...</td>\n",
       "      <td>Mogollon-Datil_volcanics</td>\n",
       "      <td>246.31823</td>\n",
       "      <td>-74.842206</td>\n",
       "      <td>-34.7</td>\n",
       "      <td>181.9</td>\n",
       "      <td>-74.842206</td>\n",
       "      <td>246.31823</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30.0</td>\n",
       "      <td>Spears</td>\n",
       "      <td>34.15</td>\n",
       "      <td>-106.82</td>\n",
       "      <td>7.0</td>\n",
       "      <td>183.7</td>\n",
       "      <td>-24.5</td>\n",
       "      <td>336.2</td>\n",
       "      <td>3.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68.4</td>\n",
       "      <td>63.4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.5</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>uniform</td>\n",
       "      <td>igneous</td>\n",
       "      <td>volcanic</td>\n",
       "      <td>lava flow</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>[]</td>\n",
       "      <td>Diehl et al. (1988)</td>\n",
       "      <td>McIntosh et al. (1991)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Age determined from range of Spears ages given...</td>\n",
       "      <td>Mogollon-Datil_volcanics</td>\n",
       "      <td>243.328708</td>\n",
       "      <td>-68.422861</td>\n",
       "      <td>-24.5</td>\n",
       "      <td>183.7</td>\n",
       "      <td>-68.422861</td>\n",
       "      <td>243.328708</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31.0</td>\n",
       "      <td>Spears</td>\n",
       "      <td>34.15</td>\n",
       "      <td>-106.82</td>\n",
       "      <td>6.0</td>\n",
       "      <td>178.4</td>\n",
       "      <td>-27.3</td>\n",
       "      <td>218.8</td>\n",
       "      <td>4.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>70.3</td>\n",
       "      <td>77.7</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34.5</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>uniform</td>\n",
       "      <td>igneous</td>\n",
       "      <td>volcanic</td>\n",
       "      <td>lava flow</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1</td>\n",
       "      <td>[]</td>\n",
       "      <td>Diehl et al. (1988)</td>\n",
       "      <td>McIntosh et al. (1991)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Age determined from range of Spears ages given...</td>\n",
       "      <td>Mogollon-Datil_volcanics</td>\n",
       "      <td>257.772902</td>\n",
       "      <td>-70.267462</td>\n",
       "      <td>-27.3</td>\n",
       "      <td>178.4</td>\n",
       "      <td>-70.267462</td>\n",
       "      <td>257.772902</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   name fm./loc.   slat    slon    n    dec   inc      k alpha95 f_corr  \\\n",
       "0   1.0   Spears  34.16 -106.81  6.0  179.9 -36.4   43.7    10.2    NaN   \n",
       "1   2.0   Spears  34.16  -106.8  7.0  174.9 -32.0  275.4     4.0    NaN   \n",
       "2   3.0   Spears  34.16  -106.8  8.0  181.9 -34.7  431.6     2.7    NaN   \n",
       "3  30.0   Spears  34.15 -106.82  7.0  183.7 -24.5  336.2     3.3    NaN   \n",
       "4  31.0   Spears  34.15 -106.82  6.0  178.4 -27.3  218.8     4.5    NaN   \n",
       "\n",
       "  VGP_lat VGP_lon    K  A95   dp   dm mean_age min_age 2sig_min max_age  \\\n",
       "0    76.0    73.7  NaN  NaN  NaN  NaN     34.5    32.0      NaN    37.0   \n",
       "1    72.6    89.7  NaN  NaN  NaN  NaN     34.5    32.0      NaN    37.0   \n",
       "2    74.9    66.6  NaN  NaN  NaN  NaN     34.5    32.0      NaN    37.0   \n",
       "3    68.4    63.4  NaN  NaN  NaN  NaN     34.5    32.0      NaN    37.0   \n",
       "4    70.3    77.7  NaN  NaN  NaN  NaN     34.5    32.0      NaN    37.0   \n",
       "\n",
       "  2sig_max uncer_dist rock_typ_1 rock_typ_2 rock_typ_3 demag struc_cont  \\\n",
       "0      NaN    uniform    igneous   volcanic  lava flow   NaN        NaN   \n",
       "1      NaN    uniform    igneous   volcanic  lava flow   NaN        NaN   \n",
       "2      NaN    uniform    igneous   volcanic   ash flow   NaN        NaN   \n",
       "3      NaN    uniform    igneous   volcanic  lava flow   NaN        NaN   \n",
       "4      NaN    uniform    igneous   volcanic  lava flow   NaN        NaN   \n",
       "\n",
       "  rot_area polarity strat_group ordering synch_unit in_study_pole rej_crit  \\\n",
       "0      NaN      NaN         0.0      NaN        0.0             1       []   \n",
       "1      NaN      NaN         0.0      NaN        0.0             1       []   \n",
       "2      NaN      NaN         0.0      NaN        0.0             1       []   \n",
       "3      NaN      NaN         0.0      NaN        0.0             1       []   \n",
       "4      NaN      NaN         0.0      NaN        0.0             1       []   \n",
       "\n",
       "              pmag_ref                 age_ref pmag_comments  \\\n",
       "0  Diehl et al. (1988)  McIntosh et al. (1991)           NaN   \n",
       "1  Diehl et al. (1988)  McIntosh et al. (1991)           NaN   \n",
       "2  Diehl et al. (1988)  McIntosh et al. (1991)           NaN   \n",
       "3  Diehl et al. (1988)  McIntosh et al. (1991)           NaN   \n",
       "4  Diehl et al. (1988)  McIntosh et al. (1991)           NaN   \n",
       "\n",
       "                                        age_comments  \\\n",
       "0  Age determined from range of Spears ages given...   \n",
       "1  Age determined from range of Spears ages given...   \n",
       "2  Age determined from range of Spears ages given...   \n",
       "3  Age determined from range of Spears ages given...   \n",
       "4  Age determined from range of Spears ages given...   \n",
       "\n",
       "                      Study VGP_lon_recalc VGP_lat_recalc dec_reverse  \\\n",
       "0  Mogollon-Datil_volcanics       253.5799     -76.075209       -36.4   \n",
       "1  Mogollon-Datil_volcanics     269.666296       -72.5818       -32.0   \n",
       "2  Mogollon-Datil_volcanics      246.31823     -74.842206       -34.7   \n",
       "3  Mogollon-Datil_volcanics     243.328708     -68.422861       -24.5   \n",
       "4  Mogollon-Datil_volcanics     257.772902     -70.267462       -27.3   \n",
       "\n",
       "  inc_reverse vgp_lat_SH  vgp_lon_SH age_uncertainty Unnamed: 38  \\\n",
       "0       179.9 -76.075209    253.5799             5.0         NaN   \n",
       "1       174.9   -72.5818  269.666296             5.0         NaN   \n",
       "2       181.9 -74.842206   246.31823             5.0         NaN   \n",
       "3       183.7 -68.422861  243.328708             5.0         NaN   \n",
       "4       178.4 -70.267462  257.772902             5.0         NaN   \n",
       "\n",
       "  author_selection undemagnetized sample_count alpha_95 overprints  \\\n",
       "0             True          False        False    False      False   \n",
       "1             True          False        False    False      False   \n",
       "2             True          False        False    False      False   \n",
       "3             True          False        False    False      False   \n",
       "4             True          False        False    False      False   \n",
       "\n",
       "  remagnetizations uncertain_struct rotated shallowed anomalous_dir  \\\n",
       "0            False            False   False     False         False   \n",
       "1            False            False   False     False         False   \n",
       "2            False            False   False     False         False   \n",
       "3            False            False   False     False         False   \n",
       "4            False            False   False     False         False   \n",
       "\n",
       "  uncertain_age distinct_age sub-time_units otherwise_rej  keep  \n",
       "0         False        False          False         False  True  \n",
       "1         False        False          False         False  True  \n",
       "2         False        False          False         False  True  \n",
       "3         False        False          False         False  True  \n",
       "4         False        False          False         False  True  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vgp_compilation[(df_vgp_compilation['Study'] == 'Absaroka_volcanics')]#[['name','synch_unit','in_study_pole','rej_crit']]\n",
    "df_vgp_compilation[(df_vgp_compilation['Study'] == 'Mogollon-Datil_volcanics')].head()#['synch_unit'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e4b4bc29-3e45-47e0-80b1-97fbfeca7f32",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Absaroka_volcanics [0 'M1' 'M2' 'M3' 4 'M4' 'M5' 'M6' 'M7' 'M8' 'M9' 'M10' 'M11' 'M12' 'M13']\n",
      "[0]\n",
      "\n",
      "Beaver_River_intrusions [0.0]\n",
      "[0.0]\n",
      "\n",
      "Bighorn_Basin_sediments [0.0]\n",
      "[0.0]\n",
      "\n",
      "Bishop_tuff ['M1']\n",
      "[]\n",
      "\n",
      "Bitterroot_Dome_intrusions [0.0 'M1' 'M2' 'M3' 'M4']\n",
      "[0.0]\n",
      "\n",
      "Central_Mexico_Plio-Pleistocene [0.0]\n",
      "[0.0]\n",
      "\n",
      "Clear_Lake_volcanic_field [0.0 1.0 'M1']\n",
      "[0.0]\n",
      "\n",
      "Coso_Range_volcanics [0.0]\n",
      "[0.0]\n",
      "\n",
      "Dinan_Bay_lavas [0.0]\n",
      "[0.0]\n",
      "\n",
      "Eastern_Alkaline_Province [0.0]\n",
      "[0.0]\n",
      "\n",
      "Eastern_TMVB [0.0]\n",
      "[0.0]\n",
      "\n",
      "Latir_volcanic_field [0.0 1.0 'M1']\n",
      "[0.0]\n",
      "\n",
      "Long_Valley_Caldera_volcanics [0.0]\n",
      "[0.0]\n",
      "\n",
      "Mariscal_Mtn_intrusions [0.0]\n",
      "[0.0]\n",
      "\n",
      "Michoacan_Guanajuato_volcanic_field [0.0]\n",
      "[0.0]\n",
      "\n",
      "Mistastin_Lake_impact [1.0]\n",
      "[1.0]\n",
      "\n",
      "Mogollon-Datil_volcanics [0.0 11.0 12.0 1.0 'M1' 'M2' 'M3' 'M4' 13.0 'M5' 'M6' 'M7' 'M8' 14.0 15.0\n",
      " 16.0 17.0 18.0 'M9' 'M10' 19.0 20.0 21.0 22.0 'M22' 23.0 'M23' 24.0 'M24'\n",
      " 25.0 'M25' 26.0 'M26' 27.0 'M27' 10.0 'M28' 'M19' 'M29' 'M18' 9.0 'M20'\n",
      " 'M13' 'M30' 'M31' 'M32' 'M12' 33.0 'M33' 6.0 34.0 'M34' 'M15' 35.0 'M35'\n",
      " 'M14' 'M16' 'M17' 'M36' 'M37' 'M38' 39.0 'M39']\n",
      "[0.0, 11.0, 21.0]\n",
      "\n",
      "Monterey_intrusions [0]\n",
      "[0]\n",
      "\n",
      "N_Montana_intrusions [0.0]\n",
      "[0.0]\n",
      "\n",
      "Ramsay_Island_lavas [0.0]\n",
      "[0.0]\n",
      "\n",
      "Rattlesnake_Hills_volcanics [0.0]\n",
      "[0.0]\n",
      "\n",
      "Robinson_Antincline_intrusions [0.0]\n",
      "[0.0]\n",
      "\n",
      "SW_USA_composite [0.0]\n",
      "[0.0]\n",
      "\n",
      "San_Luis_Hills_volcanics [0.0]\n",
      "[0.0]\n",
      "\n",
      "Sierra_de_Las_Cruces [0.0]\n",
      "[0.0]\n",
      "\n",
      "Snake_River_Plain [0.0]\n",
      "[0.0]\n",
      "\n",
      "Sonoma_volcanics [0.0]\n",
      "[0.0]\n",
      "\n",
      "Stanislaus_Group [0.0 'M1' 'M2' 3.0 'M3' 'M4']\n",
      "[0.0]\n",
      "\n",
      "Stoddard_Mountain_laccolith [0.0 'M1']\n",
      "[0.0]\n",
      "\n",
      "Tequila_volcanic_field [0.0]\n",
      "[0.0]\n",
      "\n",
      "Valles_Caldera_volcanics [0.0 'M1' 'M2']\n",
      "[0.0]\n",
      "\n",
      "Western_Central_TMVB [0.0]\n",
      "[0.0]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for study, df_study in df_vgp_unfiltered[df_vgp_unfiltered['sub-time_units'] == False].groupby('Study'):\n",
    "    print(study, df_study['synch_unit'].unique())\n",
    "    numbers = [i for i in df_study['synch_unit'].unique() if isinstance(i, (float, int))]\n",
    "    means = [int(i.replace('M', '')) for i in df_study['synch_unit'].unique() if isinstance(i, str)]\n",
    "    print(list(set(numbers) - set(means)))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ed872f69-0af1-4085-ac66-42103fb6fce6",
   "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>Study</th>\n",
       "      <th>name</th>\n",
       "      <th>synch_unit</th>\n",
       "      <th>in_study_pole</th>\n",
       "      <th>sub-time_units</th>\n",
       "      <th>remagnetizations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [Study, name, synch_unit, in_study_pole, sub-time_units, remagnetizations]\n",
       "Index: []"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vgp_compilation.groupby(['Study'])['sub-time_units'].sum().tolist()\n",
    "df_vgp_compilation[df_vgp_compilation['remagnetizations'] == True][['Study','name','synch_unit','in_study_pole','sub-time_units', 'remagnetizations']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "319053d6-a15e-445e-a6d4-09c0d6a7751d",
   "metadata": {},
   "source": [
    "#### In the following, we can visualize wich entries previously discarded by their authors are now included in the compilation.\n",
    "\n",
    "(note that the inclusion of a criterion can lead to the inclusion of entries discarded for other reasons)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "bfa1279a-2909-4df9-b5ea-643a59443c1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 842.4x595.44 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "keys = ['undemagnetized','sample_count','alpha_95','overprints','remagnetizations','uncertain_struct','rotated','shallowed',\n",
    "        'anomalous_dir','uncertain_age','distinct_age','sub-time_units','otherwise_rej']\n",
    "values = [df_vgp_compilation.groupby(['Study'])[i].sum().tolist() for i in keys]\n",
    "\n",
    "df_test = pd.DataFrame(columns = keys)\n",
    "for key, value in zip(keys, values):\n",
    "    df_test[key] = value\n",
    "        \n",
    "sns.set(rc={'figure.figsize':(11.7,8.27)})    \n",
    "ax = sns.heatmap(df_test, \n",
    "                 cmap=\"YlGnBu\", yticklabels = df_vgp_unfiltered['Study'].unique(), annot = True, vmin = 0, vmax = 20)\n",
    "ax.tick_params(axis='x', rotation=70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "6668a2e0-c59d-4172-80b6-a5d893455c9b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Study\n",
       "Absaroka_volcanics                         0\n",
       "Beaver_River_intrusions                    0\n",
       "Bighorn_Basin_sediments                    0\n",
       "Bishop_tuff                            False\n",
       "Bitterroot_Dome_intrusions                 0\n",
       "Central_Mexico_Plio-Pleistocene            0\n",
       "Clear_Lake_volcanic_field                  0\n",
       "Coso_Range_volcanics                       0\n",
       "Dinan_Bay_lavas                            0\n",
       "Eastern_Alkaline_Province                  0\n",
       "Eastern_TMVB                               0\n",
       "Latir_volcanic_field                       0\n",
       "Long_Valley_Caldera_volcanics              0\n",
       "Mariscal_Mtn_intrusions                    0\n",
       "Michoacan_Guanajuato_volcanic_field        0\n",
       "Mistastin_Lake_impact                      0\n",
       "Mogollon-Datil_volcanics                   0\n",
       "Monterey_intrusions                        0\n",
       "N_Montana_intrusions                       0\n",
       "Ramsay_Island_lavas                        0\n",
       "Rattlesnake_Hills_volcanics                0\n",
       "Robinson_Antincline_intrusions             0\n",
       "SW_USA_composite                           0\n",
       "San_Luis_Hills_volcanics                   0\n",
       "Sierra_de_Las_Cruces                       0\n",
       "Snake_River_Plain                          0\n",
       "Sonoma_volcanics                           0\n",
       "Stanislaus_Group                           0\n",
       "Stoddard_Mountain_laccolith                0\n",
       "Tequila_volcanic_field                     0\n",
       "Valles_Caldera_volcanics                   0\n",
       "Western_Central_TMVB                       0\n",
       "Name: undemagnetized, dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keys = ['undemagnetized','sample_count','alpha_95','overprints','remagnetizations','uncertain_struct','rotated','shallowed',\n",
    "        'anomalous_dir','uncertain_age','distinct_age','sub-time_units','otherwise_rej']\n",
    "values = [df_vgp_compilation.groupby(['Study'])[i].sum().tolist() for i in keys]\n",
    "\n",
    "df_test = pd.DataFrame(columns = keys)\n",
    "df_vgp_compilation.groupby(['Study'])['undemagnetized'].sum()\n",
    "\n",
    "# df_vgp_compilation[]\n",
    "# for key, value in zip(keys, values):\n",
    "#     df_test[key] = value\n",
    "#     print(value)\n",
    "        \n",
    "# sns.set(rc={'figure.figsize':(11.7,8.27)})    \n",
    "# ax = sns.heatmap(df_test, \n",
    "#                  cmap=\"YlGnBu\", yticklabels = df_vgp_unfiltered['Study'].unique(), annot = True, vmin = 0, vmax = 20)\n",
    "# ax.tick_params(axis='x', rotation=70)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ec67f27c-4ceb-44d2-9d05-053edeab29db",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_vgp_compilation[df_vgp_compilation['Study'] == 'Bishop_tuff']['undemagnetized'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0cf18ae8-c13b-4cc3-b772-736cf01de2e0",
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