6_comparisons.ipynb

{
 "cells": [
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   "cell_type": "code",
   "execution_count": 1,
   "id": "d6036c64-5f6c-4cd9-840a-bf43c14ee39e",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "pd.set_option('display.max_columns', None)\n",
    "from pmagpy import ipmag, pmag\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_style(\"white\")\n",
    "from vgptools.auxiliar import (get_files_in_directory, spherical2cartesian, \n",
    "                               cartesian2spherical, GCD_cartesian, shape)                              \n",
    "from vgptools.utils_compilation import dfs_vgps_recomputed_poles\n",
    "from vgptools.utils_APWPs import (running_mean_APWP, running_mean_APWP_shape, RM_stats, get_pseudo_vgps,\n",
    "                                  running_mean_VGPs_bootstrapped, running_mean_bootstrapping_direction)\n",
    "\n",
    "from vgptools.utils_visualization import  RM_stats, plot_VGPs_and_APWP, plot_APWP_RM_ensemble, quantiles, PC\n",
    "np.random.seed(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "090d6cf3-317a-439b-9d69-5ee6aa0655b9",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Compiles a DataFrame for both, the VGPs and the recomputed poles by study"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "46a2b299-9fa6-4f1c-85aa-ca59b701edc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "current_path = os.getcwd()\n",
    "data_path_VGP = current_path + '/data/vgp_database'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9f974200-5004-48d3-9429-8b7374448f14",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_filtered_vgps, df_pole_compilation = dfs_vgps_recomputed_poles(data_path_VGP, by_study = True) # takes as many as poles as are described for one are\n",
    "df_filtered_vgps = pd.read_csv(current_path +'/data/df_vgps_filtered.csv')\n",
    "df_pole_compilation  = pd.read_csv(current_path +'/data/df_pole_compilation_by_study.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55ec846e-fc9e-4b81-8791-942dbe60a9fb",
   "metadata": {},
   "source": [
    "## Set the hyperparameters for further comparissons."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "f8a972ef-2ab5-4ac9-881e-58a0f6c533fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "min_age = 0\n",
    "max_age = 65\n",
    "window_length = 23\n",
    "time_step = 5\n",
    "# define a list to store all the models\n",
    "models = []"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cd7ede2-b7f1-4b12-959c-00d97148e910",
   "metadata": {},
   "source": [
    "### 1. Running means classic approach on poles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6b187aab-817f-4934-9115-c1bc70c1b688",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>age</th>\n",
       "      <th>N</th>\n",
       "      <th>n_studies</th>\n",
       "      <th>k</th>\n",
       "      <th>A95</th>\n",
       "      <th>csd</th>\n",
       "      <th>plon</th>\n",
       "      <th>plat</th>\n",
       "      <th>method</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>157.806367</td>\n",
       "      <td>3.053286</td>\n",
       "      <td>6.447966</td>\n",
       "      <td>-67.163587</td>\n",
       "      <td>-87.015353</td>\n",
       "      <td>RM_classic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>157.806367</td>\n",
       "      <td>3.053286</td>\n",
       "      <td>6.447966</td>\n",
       "      <td>-67.163587</td>\n",
       "      <td>-87.015353</td>\n",
       "      <td>RM_classic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>694.396487</td>\n",
       "      <td>3.489190</td>\n",
       "      <td>3.073840</td>\n",
       "      <td>-15.989200</td>\n",
       "      <td>-81.836813</td>\n",
       "      <td>RM_classic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>460.397603</td>\n",
       "      <td>2.816323</td>\n",
       "      <td>3.775013</td>\n",
       "      <td>-28.128989</td>\n",
       "      <td>-82.900850</td>\n",
       "      <td>RM_classic</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>40.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>254.364022</td>\n",
       "      <td>3.479776</td>\n",
       "      <td>5.078754</td>\n",
       "      <td>-28.898450</td>\n",
       "      <td>-83.239212</td>\n",
       "      <td>RM_classic</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age     N  n_studies           k       A95       csd       plon  \\\n",
       "0   0.0  15.0       15.0  157.806367  3.053286  6.447966 -67.163587   \n",
       "1  10.0  15.0       15.0  157.806367  3.053286  6.447966 -67.163587   \n",
       "2  20.0   4.0        4.0  694.396487  3.489190  3.073840 -15.989200   \n",
       "3  30.0   7.0        7.0  460.397603  2.816323  3.775013 -28.128989   \n",
       "4  40.0   8.0        8.0  254.364022  3.479776  5.078754 -28.898450   \n",
       "\n",
       "        plat      method  \n",
       "0 -87.015353  RM_classic  \n",
       "1 -87.015353  RM_classic  \n",
       "2 -81.836813  RM_classic  \n",
       "3 -82.900850  RM_classic  \n",
       "4 -83.239212  RM_classic  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RM_1_classic_by_study = running_mean_APWP (df_pole_compilation, \"Plon\", \"Plat\", \"mean_age\", window_length, time_step, max_age, min_age)\n",
    "RM_1_classic_by_study['method'] = \"RM_classic\"\n",
    "models.append(RM_1_classic_by_study)\n",
    "RM_1_classic_by_study.head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f784289e-7ba8-4189-aa18-15a6be863226",
   "metadata": {},
   "source": [
    "### 2. Running means ensemble from pseudo-VGPs approach (Vaes et al. 2022)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "566a1884-6483-46a5-a8c8-d0a25beb3719",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 28.7 s, sys: 179 ms, total: 28.9 s\n",
      "Wall time: 28.9 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "RM_2_ensemble_pseudoVGP = pd.DataFrame(columns=['run','n_studies','k','A95','csd'])\n",
    "\n",
    "for i in range(100):   \n",
    "    pseudo_vgps_temp = get_pseudo_vgps(df_pole_compilation)\n",
    "    RM_APWP_parametric_temp = pd.DataFrame()\n",
    "    RM_APWP_parametric_temp = running_mean_APWP_shape(pseudo_vgps_temp, 'Plon', 'Plat', 'mean_age', window_length, time_step, max_age, min_age)\n",
    "    RM_APWP_parametric_temp['run'] = i\n",
    "\n",
    "    RM_2_ensemble_pseudoVGP = RM_2_ensemble_pseudoVGP.append(RM_APWP_parametric_temp, ignore_index=True)\n",
    "RM_2_ensemble_pseudoVGP['plon'] = RM_2_ensemble_pseudoVGP.apply(lambda row: row.plon - 360 if row.plon > 180 else row.plon, axis =1)    \n",
    "RM_2_ensemble_pseudoVGP['method'] = \"RM_pseudoVGPs_ensemble\"\n",
    "models.append(RM_2_ensemble_pseudoVGP)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8af3059d-727f-4626-a6fe-90ba3b92fdce",
   "metadata": {},
   "source": [
    "Set quantiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "7deee0e8-3201-49ea-a68d-5a7dce7f3d92",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>run</th>\n",
       "      <th>n_studies</th>\n",
       "      <th>k</th>\n",
       "      <th>A95</th>\n",
       "      <th>csd</th>\n",
       "      <th>age</th>\n",
       "      <th>N</th>\n",
       "      <th>plon</th>\n",
       "      <th>plat</th>\n",
       "      <th>foliation</th>\n",
       "      <th>lineation</th>\n",
       "      <th>collinearity</th>\n",
       "      <th>coplanarity</th>\n",
       "      <th>method</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>21.299026</td>\n",
       "      <td>1.720005</td>\n",
       "      <td>17.551133</td>\n",
       "      <td>0.0</td>\n",
       "      <td>329.0</td>\n",
       "      <td>-39.688130</td>\n",
       "      <td>-86.466356</td>\n",
       "      <td>0.178871</td>\n",
       "      <td>0.553645</td>\n",
       "      <td>3.095223</td>\n",
       "      <td>0.732516</td>\n",
       "      <td>RM_pseudoVGPs_ensemble</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>20.197856</td>\n",
       "      <td>1.690451</td>\n",
       "      <td>18.02322</td>\n",
       "      <td>5.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>-40.648314</td>\n",
       "      <td>-86.477151</td>\n",
       "      <td>0.220881</td>\n",
       "      <td>0.545402</td>\n",
       "      <td>2.469207</td>\n",
       "      <td>0.766284</td>\n",
       "      <td>RM_pseudoVGPs_ensemble</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>18.513338</td>\n",
       "      <td>1.615115</td>\n",
       "      <td>18.825333</td>\n",
       "      <td>10.0</td>\n",
       "      <td>432.0</td>\n",
       "      <td>-27.970030</td>\n",
       "      <td>-85.431693</td>\n",
       "      <td>0.191487</td>\n",
       "      <td>0.557091</td>\n",
       "      <td>2.909282</td>\n",
       "      <td>0.748578</td>\n",
       "      <td>RM_pseudoVGPs_ensemble</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  run n_studies          k       A95        csd   age      N       plon  \\\n",
       "0   0      15.0  21.299026  1.720005  17.551133   0.0  329.0 -39.688130   \n",
       "1   0      16.0  20.197856  1.690451   18.02322   5.0  360.0 -40.648314   \n",
       "2   0      19.0  18.513338  1.615115  18.825333  10.0  432.0 -27.970030   \n",
       "\n",
       "        plat  foliation  lineation  collinearity  coplanarity  \\\n",
       "0 -86.466356   0.178871   0.553645      3.095223     0.732516   \n",
       "1 -86.477151   0.220881   0.545402      2.469207     0.766284   \n",
       "2 -85.431693   0.191487   0.557091      2.909282     0.748578   \n",
       "\n",
       "                   method  \n",
       "0  RM_pseudoVGPs_ensemble  \n",
       "1  RM_pseudoVGPs_ensemble  \n",
       "2  RM_pseudoVGPs_ensemble  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RM_2_quantiles_plat = quantiles(RM_2_ensemble_pseudoVGP,\"age\",\"plat\") # set quantiles of latitude groupedby age for visualization purposes\n",
    "RM_2_quantiles_plon = quantiles(RM_2_ensemble_pseudoVGP,\"age\",\"plon\") # set quantiles of longitude groupedby age for visualization purposes\n",
    "RM_2_ensemble_pseudoVGP.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ec55f67-eafa-444e-a421-433213cea533",
   "metadata": {},
   "source": [
    "## 3. Running means on VGPs. \n",
    "Three different approachs as follows:\n",
    "### 3.1. Running means on the computed dataset (one run and A95)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fabfa78d-bb26-4493-8ab3-0a4236927772",
   "metadata": {},
   "outputs": [],
   "source": [
    "RM_31_on_VGP = running_mean_APWP(df_filtered_vgps, 'vgp_lon_SH', 'vgp_lat_SH', 'mean_age', window_length, time_step, max_age, min_age)\n",
    "RM_31_on_VGP['method'] = \"RM_classic_VGPs\"\n",
    "models.append(RM_31_on_VGP)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "832761e4-bbd1-4694-9671-5252a28e0b78",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\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>age</th>\n",
       "      <th>N</th>\n",
       "      <th>n_studies</th>\n",
       "      <th>k</th>\n",
       "      <th>A95</th>\n",
       "      <th>csd</th>\n",
       "      <th>plon</th>\n",
       "      <th>plat</th>\n",
       "      <th>method</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>308.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>22.847346</td>\n",
       "      <td>1.713794</td>\n",
       "      <td>16.945998</td>\n",
       "      <td>-45.430085</td>\n",
       "      <td>-88.076649</td>\n",
       "      <td>RM_classic_VGPs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>22.741800</td>\n",
       "      <td>1.712371</td>\n",
       "      <td>16.985276</td>\n",
       "      <td>-45.145112</td>\n",
       "      <td>-87.982941</td>\n",
       "      <td>RM_classic_VGPs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.0</td>\n",
       "      <td>349.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>21.923213</td>\n",
       "      <td>1.644685</td>\n",
       "      <td>17.299475</td>\n",
       "      <td>-31.896117</td>\n",
       "      <td>-87.275556</td>\n",
       "      <td>RM_classic_VGPs</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age      N  n_studies          k       A95        csd       plon  \\\n",
       "0   0.0  308.0       15.0  22.847346  1.713794  16.945998 -45.430085   \n",
       "1   5.0  310.0       15.0  22.741800  1.712371  16.985276 -45.145112   \n",
       "2  10.0  349.0       17.0  21.923213  1.644685  17.299475 -31.896117   \n",
       "\n",
       "        plat           method  \n",
       "0 -88.076649  RM_classic_VGPs  \n",
       "1 -87.982941  RM_classic_VGPs  \n",
       "2 -87.275556  RM_classic_VGPs  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RM_31_on_VGP.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5dbc709-a407-4226-87c9-7ca3ff23b894",
   "metadata": {},
   "source": [
    "### 3.2. Running means on random samples (with replacement) from the original dataset to generate an ensemble of possible solution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0d0297d5-d2df-42e0-9ad3-8df5852fc441",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 14.4 s, sys: 113 ms, total: 14.5 s\n",
      "Wall time: 14.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "RM_32_boots_VGPs=running_mean_VGPs_bootstrapped(df_filtered_vgps, 'vgp_lon_SH', 'vgp_lat_SH', 'mean_age', window_length, time_step, max_age, min_age, n_bst = 100)\n",
    "RM_32_boots_VGPs['method']=\"RM_bootstrapped_VGPs_ensemble\"\n",
    "models.append(RM_32_boots_VGPs)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e5916bd8-6046-4f15-9928-0fa33fed1549",
   "metadata": {},
   "source": [
    "Set quantiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "89553138-cdc6-4b42-a2bd-14a6d565a42a",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>run</th>\n",
       "      <th>N</th>\n",
       "      <th>k</th>\n",
       "      <th>A95</th>\n",
       "      <th>csd</th>\n",
       "      <th>foliation</th>\n",
       "      <th>lineation</th>\n",
       "      <th>collinearity</th>\n",
       "      <th>coplanarity</th>\n",
       "      <th>age</th>\n",
       "      <th>n_studies</th>\n",
       "      <th>plon</th>\n",
       "      <th>plat</th>\n",
       "      <th>method</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>288.0</td>\n",
       "      <td>21.452094</td>\n",
       "      <td>1.832061</td>\n",
       "      <td>17.488404</td>\n",
       "      <td>0.291192</td>\n",
       "      <td>0.630837</td>\n",
       "      <td>2.166399</td>\n",
       "      <td>0.922029</td>\n",
       "      <td>0.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>-51.856171</td>\n",
       "      <td>-86.611353</td>\n",
       "      <td>RM_bootstrapped_VGPs_ensemble</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>293.0</td>\n",
       "      <td>20.977062</td>\n",
       "      <td>1.837748</td>\n",
       "      <td>17.68531</td>\n",
       "      <td>0.320265</td>\n",
       "      <td>0.584504</td>\n",
       "      <td>1.825064</td>\n",
       "      <td>0.904769</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>-50.815029</td>\n",
       "      <td>-86.215879</td>\n",
       "      <td>RM_bootstrapped_VGPs_ensemble</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>332.0</td>\n",
       "      <td>21.266985</td>\n",
       "      <td>1.713534</td>\n",
       "      <td>17.564349</td>\n",
       "      <td>0.262632</td>\n",
       "      <td>0.587776</td>\n",
       "      <td>2.238017</td>\n",
       "      <td>0.850408</td>\n",
       "      <td>10.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>-39.943320</td>\n",
       "      <td>-85.858235</td>\n",
       "      <td>RM_bootstrapped_VGPs_ensemble</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   run      N          k       A95        csd foliation lineation  \\\n",
       "0  0.0  288.0  21.452094  1.832061  17.488404  0.291192  0.630837   \n",
       "1  0.0  293.0  20.977062  1.837748   17.68531  0.320265  0.584504   \n",
       "2  0.0  332.0  21.266985  1.713534  17.564349  0.262632  0.587776   \n",
       "\n",
       "  collinearity coplanarity   age  n_studies       plon       plat  \\\n",
       "0     2.166399    0.922029   0.0       15.0 -51.856171 -86.611353   \n",
       "1     1.825064    0.904769   5.0       15.0 -50.815029 -86.215879   \n",
       "2     2.238017    0.850408  10.0       17.0 -39.943320 -85.858235   \n",
       "\n",
       "                          method  \n",
       "0  RM_bootstrapped_VGPs_ensemble  \n",
       "1  RM_bootstrapped_VGPs_ensemble  \n",
       "2  RM_bootstrapped_VGPs_ensemble  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RM_32_quantiles_plat = quantiles(RM_32_boots_VGPs,\"age\",\"plat\") # set quantiles of latitude groupedby age for visualization purposes\n",
    "RM_32_quantiles_plon = quantiles(RM_32_boots_VGPs,\"age\",\"plon\") # set quantiles of longitude groupedby age for visualization purposes\n",
    "RM_32_boots_VGPs.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f4410f95-eb87-4fa8-a5f4-25f3c4007811",
   "metadata": {},
   "source": [
    "### 3.3. Propation of directional error towards the VGP level. \n",
    "We take the original directions as a PDF to generate to generate a pseudo-Dataset that incorporates the uncertinty in the directional space and time. We aaply the running means on a number of $pseudo$-VGPs to generate an ensemble of possible paths"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "a2094478-8b8b-4674-8b43-1ea0df4c14bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 45.3 s, sys: 276 ms, total: 45.6 s\n",
      "Wall time: 45.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "RM_33_propagation=running_mean_bootstrapping_direction(df_filtered_vgps,'plon', 'plat', 'age', window_length, time_step, max_age, min_age, n_bst = 100)\n",
    "RM_33_propagation['method']=\"RM_DirErrorPropagated_ensemble\"\n",
    "models.append(RM_33_propagation)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a70f4ee3-7aa0-43ee-bb5f-2b2f8ab05a9a",
   "metadata": {},
   "source": [
    "Set quantiles"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "02a051ee-d698-4ad6-8c92-c4042c308e09",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>run</th>\n",
       "      <th>N</th>\n",
       "      <th>k</th>\n",
       "      <th>A95</th>\n",
       "      <th>csd</th>\n",
       "      <th>foliation</th>\n",
       "      <th>lineation</th>\n",
       "      <th>collinearity</th>\n",
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       "      <th>n_studies</th>\n",
       "      <th>plon</th>\n",
       "      <th>plat</th>\n",
       "      <th>method</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>291.0</td>\n",
       "      <td>16.429704</td>\n",
       "      <td>2.098274</td>\n",
       "      <td>19.983435</td>\n",
       "      <td>0.13353</td>\n",
       "      <td>0.645938</td>\n",
       "      <td>4.837398</td>\n",
       "      <td>0.779468</td>\n",
       "      <td>0.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>-5.836269</td>\n",
       "      <td>-88.653878</td>\n",
       "      <td>RM_DirErrorPropagated_ensemble</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>294.0</td>\n",
       "      <td>16.432633</td>\n",
       "      <td>2.087289</td>\n",
       "      <td>19.981654</td>\n",
       "      <td>0.119518</td>\n",
       "      <td>0.656571</td>\n",
       "      <td>5.493516</td>\n",
       "      <td>0.776089</td>\n",
       "      <td>5.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>-8.854118</td>\n",
       "      <td>-88.534319</td>\n",
       "      <td>RM_DirErrorPropagated_ensemble</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>330.0</td>\n",
       "      <td>16.429813</td>\n",
       "      <td>1.96979</td>\n",
       "      <td>19.983369</td>\n",
       "      <td>0.109447</td>\n",
       "      <td>0.667899</td>\n",
       "      <td>6.102473</td>\n",
       "      <td>0.777346</td>\n",
       "      <td>10.0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>-22.965424</td>\n",
       "      <td>-88.000721</td>\n",
       "      <td>RM_DirErrorPropagated_ensemble</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   run      N          k       A95        csd foliation lineation  \\\n",
       "0  0.0  291.0  16.429704  2.098274  19.983435   0.13353  0.645938   \n",
       "1  0.0  294.0  16.432633  2.087289  19.981654  0.119518  0.656571   \n",
       "2  0.0  330.0  16.429813   1.96979  19.983369  0.109447  0.667899   \n",
       "\n",
       "  collinearity coplanarity   age  n_studies       plon       plat  \\\n",
       "0     4.837398    0.779468   0.0       14.0  -5.836269 -88.653878   \n",
       "1     5.493516    0.776089   5.0       15.0  -8.854118 -88.534319   \n",
       "2     6.102473    0.777346  10.0       18.0 -22.965424 -88.000721   \n",
       "\n",
       "                           method  \n",
       "0  RM_DirErrorPropagated_ensemble  \n",
       "1  RM_DirErrorPropagated_ensemble  \n",
       "2  RM_DirErrorPropagated_ensemble  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RM_33_quantiles_plat = quantiles(RM_33_propagation,\"age\",\"plat\") # set quantiles of latitude groupedby age for visualization purposes\n",
    "RM_33_quantiles_plon = quantiles(RM_33_propagation,\"age\",\"plon\") # set quantiles of longitude groupedby age for visualization purposes\n",
    "RM_33_propagation.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9026773b-fb5b-42cd-a38d-4aa3a1c60c8b",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Get APW from Muller et al., 2016 and Torsvik and Cocks, 2017\n",
    "\n",
    "- Müller, R. D., Seton, M., Zahirovic, S., Williams, S. E., Matthews, K. J., Wright, N. M., ... & Cannon, J. (2016). Ocean basin evolution and global-scale plate reorganization events since Pangea breakup. Annual Review of Earth and Planetary Sciences, 44, 107-138. DOI:10.1146/annurev-earth-060115-012211\n",
    "- Torsvik, T. H., & Cocks, L. R. M. (2013). Gondwana from top to base in space and time. Gondwana Research, 24(3-4), 999-1030.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "2347700a-bbc0-424e-bff6-007a7af9030b",
   "metadata": {},
   "outputs": [],
   "source": [
    "Torsvik2017_path = np.loadtxt('model_APWP/code_output/NAM_0_60_torsvik_2017.txt', )\n",
    "Torsvik2017_path_df = pd.DataFrame(Torsvik2017_path, columns = ['age','plon', 'plat', 'Euler_angle', 'Euler_lon', 'Euler_lat'])\n",
    "Torsvik2017_path_df = Torsvik2017_path_df[Torsvik2017_path_df['age']>0]\n",
    "Torsvik2017_path_df['method'] = \"plate model\"\n",
    "models.append(Torsvik2017_path_df)\n",
    "\n",
    "Mueller2016_path = np.loadtxt('model_APWP/code_output/NAM_0_60_muller_2016.txt', )\n",
    "Mueller2016_path_df = pd.DataFrame(Mueller2016_path, columns = ['age','plon', 'plat', 'Euler_angle', 'Euler_lon', 'Euler_lat'])\n",
    "Mueller2016_path_df = Mueller2016_path_df[Mueller2016_path_df['age']>0]\n",
    "Mueller2016_path_df['method']=\"plate model\"\n",
    "models.append(Mueller2016_path_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5092c6a4-b0f1-422a-a472-f6b845ecc97d",
   "metadata": {},
   "source": [
    "Merge"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6f08583a-161f-4dfd-aab8-328fe1a2a1d0",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>run</th>\n",
       "      <th>n_studies</th>\n",
       "      <th>k</th>\n",
       "      <th>A95</th>\n",
       "      <th>csd</th>\n",
       "      <th>age</th>\n",
       "      <th>N</th>\n",
       "      <th>plon</th>\n",
       "      <th>plat</th>\n",
       "      <th>foliation</th>\n",
       "      <th>lineation</th>\n",
       "      <th>collinearity</th>\n",
       "      <th>coplanarity</th>\n",
       "      <th>method</th>\n",
       "      <th>Euler_angle</th>\n",
       "      <th>Euler_lon</th>\n",
       "      <th>Euler_lat</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>21.299026</td>\n",
       "      <td>1.720005</td>\n",
       "      <td>17.551133</td>\n",
       "      <td>0.0</td>\n",
       "      <td>329.0</td>\n",
       "      <td>-39.688130</td>\n",
       "      <td>-86.466356</td>\n",
       "      <td>0.178871</td>\n",
       "      <td>0.553645</td>\n",
       "      <td>3.095223</td>\n",
       "      <td>0.732516</td>\n",
       "      <td>RM_pseudoVGPs_ensemble</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>20.197856</td>\n",
       "      <td>1.690451</td>\n",
       "      <td>18.02322</td>\n",
       "      <td>5.0</td>\n",
       "      <td>360.0</td>\n",
       "      <td>-40.648314</td>\n",
       "      <td>-86.477151</td>\n",
       "      <td>0.220881</td>\n",
       "      <td>0.545402</td>\n",
       "      <td>2.469207</td>\n",
       "      <td>0.766284</td>\n",
       "      <td>RM_pseudoVGPs_ensemble</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>18.513338</td>\n",
       "      <td>1.615115</td>\n",
       "      <td>18.825333</td>\n",
       "      <td>10.0</td>\n",
       "      <td>432.0</td>\n",
       "      <td>-27.970030</td>\n",
       "      <td>-85.431693</td>\n",
       "      <td>0.191487</td>\n",
       "      <td>0.557091</td>\n",
       "      <td>2.909282</td>\n",
       "      <td>0.748578</td>\n",
       "      <td>RM_pseudoVGPs_ensemble</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>13.937001</td>\n",
       "      <td>2.219821</td>\n",
       "      <td>21.697033</td>\n",
       "      <td>15.0</td>\n",
       "      <td>310.0</td>\n",
       "      <td>-10.201933</td>\n",
       "      <td>-85.009971</td>\n",
       "      <td>0.065555</td>\n",
       "      <td>0.544506</td>\n",
       "      <td>8.306084</td>\n",
       "      <td>0.610061</td>\n",
       "      <td>RM_pseudoVGPs_ensemble</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>12.01779</td>\n",
       "      <td>2.42112</td>\n",
       "      <td>23.365372</td>\n",
       "      <td>20.0</td>\n",
       "      <td>306.0</td>\n",
       "      <td>-16.424040</td>\n",
       "      <td>-82.582295</td>\n",
       "      <td>0.019532</td>\n",
       "      <td>0.616828</td>\n",
       "      <td>31.581173</td>\n",
       "      <td>0.63636</td>\n",
       "      <td>RM_pseudoVGPs_ensemble</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  run n_studies          k       A95        csd   age      N       plon  \\\n",
       "0   0      15.0  21.299026  1.720005  17.551133   0.0  329.0 -39.688130   \n",
       "1   0      16.0  20.197856  1.690451   18.02322   5.0  360.0 -40.648314   \n",
       "2   0      19.0  18.513338  1.615115  18.825333  10.0  432.0 -27.970030   \n",
       "3   0      13.0  13.937001  2.219821  21.697033  15.0  310.0 -10.201933   \n",
       "4   0       9.0   12.01779   2.42112  23.365372  20.0  306.0 -16.424040   \n",
       "\n",
       "        plat foliation lineation collinearity coplanarity  \\\n",
       "0 -86.466356  0.178871  0.553645     3.095223    0.732516   \n",
       "1 -86.477151  0.220881  0.545402     2.469207    0.766284   \n",
       "2 -85.431693  0.191487  0.557091     2.909282    0.748578   \n",
       "3 -85.009971  0.065555  0.544506     8.306084    0.610061   \n",
       "4 -82.582295  0.019532  0.616828    31.581173     0.63636   \n",
       "\n",
       "                   method  Euler_angle  Euler_lon  Euler_lat  \n",
       "0  RM_pseudoVGPs_ensemble          NaN        NaN        NaN  \n",
       "1  RM_pseudoVGPs_ensemble          NaN        NaN        NaN  \n",
       "2  RM_pseudoVGPs_ensemble          NaN        NaN        NaN  \n",
       "3  RM_pseudoVGPs_ensemble          NaN        NaN        NaN  \n",
       "4  RM_pseudoVGPs_ensemble          NaN        NaN        NaN  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_all = pd.concat(models, axis=0)\n",
    "df_all.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0c64dd27-525e-45c4-8392-120b307956bf",
   "metadata": {},
   "source": [
    "# Comparisons plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "aa9540df-2e29-4a81-ba58-04a51cd07d88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f7d4858b130>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = \"comparisons\"\n",
    "\n",
    "df_method1=RM_2_ensemble_pseudoVGP\n",
    "df_method2=RM_33_propagation\n",
    "\n",
    "ensemble1_lat = quantiles(df_method1,\"age\",\"plat\") # set quantiles of latitude groupedby age for visualization purposes\n",
    "ensemble1_lon = quantiles(df_method1,\"age\",\"plon\") # set quantiles of longitude groupedby age for visualization purposes\n",
    "ensemble2_lat = quantiles(df_method2,\"age\",\"plat\") # set quantiles of latitude groupedby age for visualization purposes\n",
    "ensemble2_lon = quantiles(df_method2,\"age\",\"plon\") # set quantiles of longitude groupedby age for visualization purposes\n",
    "\n",
    "fig, axes = plt.subplots(2, 1, sharex=True, figsize=(15,6))\n",
    "fig.suptitle(title, fontsize= 18, fontweight ='bold')\n",
    "axes[0].set_title('Latitude (°N)', fontsize=12, fontweight ='bold')\n",
    "axes[1].set_title('Longitude (°E)', fontsize=12, fontweight ='bold')\n",
    "axes[0].set_ylabel(r'Latitude (°N)', fontweight ='bold')\n",
    "axes[1].set_ylabel(r'Longitude (°E)', fontweight ='bold')\n",
    "\n",
    "axes[0].set_ylim(-90,-70)\n",
    "axes[1].set_ylim(-100,60)\n",
    "# LATITUDE\n",
    "axes[0].fill_between(ensemble1_lat.X, ensemble1_lat.q16,ensemble1_lat.q84, color= \"#f98787\", alpha=.20)\n",
    "axes[0].scatter(PC(df_method1,\"age\",\"plat\",\"plon\").X, PC(df_method1,\"age\",\"plat\",\"plon\").PC()[1],color=\"#d12a2a\")\n",
    "axes[0].plot(PC(df_method1,\"age\",\"plat\",\"plon\").X, PC(df_method1,\"age\",\"plat\",\"plon\").PC()[1],color=\"#d12a2a\")\n",
    "\n",
    "axes[0].fill_between(ensemble2_lat.X, ensemble2_lat.q16,ensemble1_lat.q84, color= \"#0E7DC9\", alpha=.20)\n",
    "axes[0].scatter(PC(df_method2,\"age\",\"plat\",\"plon\").X, PC(df_method2,\"age\",\"plat\",\"plon\").PC()[1],color=\"#00497A\")\n",
    "axes[0].plot(PC(df_method2,\"age\",\"plat\",\"plon\").X, PC(df_method2,\"age\",\"plat\",\"plon\").PC()[1],color=\"#00497A\")\n",
    "\n",
    "axes[0].plot(RM_1_classic_by_study.age, RM_1_classic_by_study.plat, '-',color=\"black\") #CLASSIC\n",
    "axes[0].scatter(RM_1_classic_by_study.age, RM_1_classic_by_study.plat, color=\"black\") #CLASSIC\n",
    "\n",
    "\n",
    "# # LONGITUDE\n",
    "axes[1].fill_between(ensemble1_lon.X, ensemble1_lon.q16,ensemble1_lon.q84, color= \"#f9afaf\", alpha=.50,label=\"0.16-0.84 percentiles - Ensemble 1\")\n",
    "axes[1].plot(PC(df_method1,\"age\",\"plat\",\"plon\").X, PC(df_method1,\"age\",\"plat\",\"plon\").PC()[0], '-',color=\"#d12a2a\")\n",
    "axes[1].scatter(PC(df_method1,\"age\",\"plat\",\"plon\").X, PC(df_method1,\"age\",\"plat\",\"plon\").PC()[0],color=\"#d12a2a\")\n",
    "\n",
    "axes[1].fill_between(ensemble2_lon.X, ensemble2_lon.q16,ensemble2_lon.q84, color= \"#0E7DC9\", alpha=.20,label=\"0.16-0.84 percentiles - Ensemble 2\")\n",
    "axes[1].plot(PC(df_method2,\"age\",\"plat\",\"plon\").X, PC(df_method2,\"age\",\"plat\",\"plon\").PC()[0], '-',color=\"#00497A\")\n",
    "axes[1].scatter(PC(df_method2,\"age\",\"plat\",\"plon\").X, PC(df_method2,\"age\",\"plat\",\"plon\").PC()[0],color=\"#00497A\")\n",
    "axes[1].plot(RM_1_classic_by_study.age, RM_1_classic_by_study.plon, '-',color=\"black\", label = \"RM classic\") #CLASSIC\n",
    "axes[1].scatter(RM_1_classic_by_study.age, RM_1_classic_by_study.plon, color=\"black\") #CLASSIC\n",
    "\n",
    "plt.legend(loc=\"upper left\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e828244-5995-4398-af59-b46573afb660",
   "metadata": {},
   "source": [
    "# APWPs vs Plate Models Visualizations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "b7a9c710-2a81-4ce0-a9f4-527fab77efbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f7d5b22baf0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = \"Comparing against plate models (M2016 and T&C2017)\"\n",
    "\n",
    "fig, axes = plt.subplots(2, 1, sharex=True, figsize=(15,6))\n",
    "fig.suptitle(title, fontsize= 18, fontweight ='bold')\n",
    "axes[0].set_title('Latitude (°N)', fontsize=12, fontweight ='bold')\n",
    "axes[1].set_title('Longitude (°E)', fontsize=12, fontweight ='bold')\n",
    "axes[0].set_ylabel(r'Latitude (°N)', fontweight ='bold')\n",
    "axes[1].set_ylabel(r'Longitude (°E)', fontweight ='bold')\n",
    "# LATITUDE\n",
    "\n",
    "axes[0].plot(Mueller2016_path_df.age, Mueller2016_path_df.plat, '-',color=\"#d12a2a\")\n",
    "# axes[0].scatter(Mueller2016_path_df.age, Mueller2016_path_df.plat,s = 1, color=\"#d12a2a\")\n",
    "axes[0].plot(Torsvik2017_path_df.age, Torsvik2017_path_df.plat, '-',color=\"#00497A\") #CLASSIC\n",
    "# axes[0].scatter(Torsvik2017_path_df.age, Torsvik2017_path_df.plat, color=\"#00497A\") #CLASSIC\n",
    "axes[0].scatter(ensemble2_lat.X, ensemble2_lat.q50,color=\"black\")\n",
    "# LONGITUDE\n",
    "\n",
    "axes[1].plot(Mueller2016_path_df.age, Mueller2016_path_df.plon, '-',color=\"#d12a2a\", label = \"Muller2016\")\n",
    "# axes[1].scatter(Mueller2016_path_df.age, Mueller2016_path_df.plon,color=\"#d12a2a\")\n",
    "axes[1].plot(Torsvik2017_path_df.age, Torsvik2017_path_df.plon, '-',color=\"#00497A\", label = \"T&C2017\") #CLASSIC\n",
    "# axes[1].scatter(Torsvik2017_path_df.age, Torsvik2017_path_df.plon, color=\"#00497A\") #CLASSIC\n",
    "axes[1].scatter(ensemble2_lon.X, ensemble2_lon.q50,color=\"black\")\n",
    "\n",
    "# axes[1].set_ylim(-100, 50)\n",
    "plt.legend(loc=\"upper left\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "385570f0-9cb2-41b1-9994-760a72139207",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}