load_dwr_aem_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ebf42634-e6c7-424b-899f-8967e386af5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import libaarhusxyz\n",
    "def read_em_data_from_dat_xyz(em_data, name_gate_times='gate times', dat_type='dat'):\n",
    "    gate_times = np.array(em_data.info[name_gate_times])\n",
    "    df_em = em_data.flightlines\n",
    "    group_em = df_em.groupby('record')\n",
    "    records = list(group_em.groups.keys())\n",
    "    df_em_collar = group_em[['line_no','utmx','utmy','timestamp','elevation','alt','tilt']].mean()\n",
    "\n",
    "    dat_names = ['data_{:d}'.format(ii+1) for ii in range(gate_times.size)]\n",
    "    df_em[dat_names] = em_data.layer_data['data']\n",
    "    if dat_type == 'dat':    \n",
    "        dat_std_names = ['datastd_{:d}'.format(ii+1) for ii in range(gate_times.size)]\n",
    "        df_em[dat_std_names] = em_data.layer_data['datastd']    \n",
    "\n",
    "    em_data_nan = em_data.layer_data['data']\n",
    "    em_data_nan = em_data_nan.replace(9999, np.nan)\n",
    "    n_ch1_active = np.round((~np.isnan(em_data_nan.loc[df_em[df_em.segment==1].index]).values).sum(axis=1).mean())\n",
    "    n_ch2_active = np.round((~np.isnan(em_data_nan.loc[df_em[df_em.segment==2].index]).values).sum(axis=1).mean())\n",
    "    print (f\"Active # of channels: Ch1={n_ch1_active:.0f}, Ch2={n_ch2_active:.0f}\")\n",
    "\n",
    "    inds_lm = (~np.isnan(em_data_nan.loc[df_em[df_em.segment==1].index]).values).sum(axis=0) > 0.\n",
    "    inds_hm = (~np.isnan(em_data_nan.loc[df_em[df_em.segment==2].index]).values).sum(axis=0) > 0.\n",
    "\n",
    "    data_lm = []\n",
    "    data_hm = []\n",
    "    if dat_type == 'dat':\n",
    "        datastd_lm = []\n",
    "        datastd_hm = []\n",
    "    records_inv = []\n",
    "    for i_record in records:\n",
    "        df_tmp = group_em.get_group(i_record)\n",
    "        values = df_tmp[dat_names].values\n",
    "        if dat_type == 'dat':\n",
    "            values_std = df_tmp[dat_std_names].values\n",
    "        if df_tmp.shape[0] == 2:\n",
    "            i_lm = np.argwhere(df_tmp['segment'].values==1)[0][0]                \n",
    "            i_hm = np.argwhere(df_tmp['segment'].values==2)[0][0]\n",
    "            data_lm.append(values[i_lm,inds_lm])\n",
    "            data_hm.append(values[i_hm,inds_hm])\n",
    "            if dat_type == 'dat':\n",
    "                datastd_lm.append(values_std[i_lm,inds_lm])\n",
    "                datastd_hm.append(values_std[i_hm,inds_hm])        \n",
    "            records_inv.append(i_record)\n",
    "    data_lm = np.vstack(data_lm)\n",
    "    data_hm = np.vstack(data_hm)\n",
    "    if dat_type == 'dat':\n",
    "        datastd_lm = np.vstack(datastd_lm)\n",
    "        datastd_hm = np.vstack(datastd_hm)\n",
    "    records_inv = np.hstack(records_inv)\n",
    "    df_em_inv = df_em_collar.loc[records_inv]\n",
    "    times_lm_inv = gate_times[inds_lm]\n",
    "    times_hm_inv = gate_times[inds_hm]    \n",
    "\n",
    "    ch1_names = ['dbdt_ch1gt_{:d}'.format(ii+1) for ii in range(data_lm.shape[1])]\n",
    "    ch2_names = ['dbdt_ch2gt_{:d}'.format(ii+1) for ii in range(data_hm.shape[1])]\n",
    "    if dat_type == 'dat':    \n",
    "        std_ch1_names = ['dbdt_std_ch1gt_{:d}'.format(ii+1) for ii in range(data_lm.shape[1])]\n",
    "        std_ch2_names = ['dbdt_std_ch2gt_{:d}'.format(ii+1) for ii in range(data_hm.shape[1])]    \n",
    "\n",
    "    df_em_inv[ch1_names] = data_lm\n",
    "    df_em_inv[ch2_names] = data_hm\n",
    "    if dat_type == 'dat':    \n",
    "        df_em_inv[std_ch1_names] = datastd_lm\n",
    "        df_em_inv[std_ch2_names] = datastd_hm\n",
    "    \n",
    "    meta_data_dict = {}\n",
    "    meta_data_dict['ch1_names'] = ch1_names\n",
    "    meta_data_dict['ch2_names'] = ch2_names\n",
    "    if dat_type == 'dat':    \n",
    "        meta_data_dict['std_ch1_names'] = std_ch1_names\n",
    "        meta_data_dict['std_ch2_names'] = std_ch1_names\n",
    "    meta_data_dict['ch1_times'] = times_lm_inv\n",
    "    meta_data_dict['ch2_times'] = times_hm_inv\n",
    "    df_em_inv = df_em_inv.replace(9999, np.nan)\n",
    "    return df_em_inv, meta_data_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6b38acad-010b-41eb-80bb-c96a62a1599a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import dill"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7175cdc0-e5ab-4463-a434-407b69dc3cdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "work_dir = \"/Users/sgkang09/Dropbox/Stanford/Seogi-Jonny/targeted_inversion/em_data_cv/sa_4_2022_spring/\"\n",
    "fname = work_dir+\"WO4_SCIsmooth_MOD_dat.xyz\"\n",
    "aem_data = libaarhusxyz.XYZ(fname)\n",
    "fname = work_dir+\"WO4_SCIsmooth_MOD_syn.xyz\"\n",
    "aem_data_syn = libaarhusxyz.XYZ(fname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1b474042-4e14-4756-8f3c-467cd9ccfd9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "fname = work_dir+\"WO4_SCIsmooth_MOD_inv.xyz\"\n",
    "aem_model = libaarhusxyz.XYZ(fname)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c32959c4-6f00-48fc-b342-998ea572e360",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df_em_inv, meta_data_dict = read_em_data_from_dat_xyz(aem_data, name_gate_times='gate times (s)')\n",
    "# dill.dump(meta_data_dict, open('sa_4_meta_data.pik', 'wb'))\n",
    "# df_em_inv.to_parquet('sa_4_inv.parquet')\n",
    "# df_em_syn, _ = read_em_data_from_dat_xyz(aem_data_syn, name_gate_times='gate times (s)', dat_type='syn')\n",
    "# df_em_syn.to_parquet('sa_4_syn.parquet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "80f54545-338b-44c0-b9ff-f2e090d2577a",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_em_inv = pd.read_parquet(work_dir+\"sa_4_inv.parquet\")\n",
    "df_em_syn = pd.read_parquet(work_dir+\"sa_4_syn.parquet\")\n",
    "meta_data_dict = dill.load(open(work_dir+\"sa_4_meta_data.pik\", \"rb\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "ab585975-213b-4fd3-ba9c-963e833fcdba",
   "metadata": {},
   "outputs": [],
   "source": [
    "xy = aem_data.flightlines[['utmx', 'utmy']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a3e55cb2-36a8-4378-809c-a335a7ab2be2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fname = \"/Users/sgkang09/Dropbox/Stanford/Seogi-Jonny/targeted_inversion/em_data_cv/sa_5_2022_spring/SCI_smooth30L_MOD_syn.xyz\"\n",
    "# aem_data_5 = libaarhusxyz.XYZ(fname)\n",
    "# xy_5 = aem_data_5.flightlines[['utmx', 'utmy']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "52100cc1-40bd-4e72-aaa2-af06e11a772d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbf30482f80>]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "ch1_names = meta_data_dict['ch1_names']\n",
    "ch2_names = meta_data_dict['ch2_names']\n",
    "times_ch1 = meta_data_dict['ch1_times'] -1.99e-6\n",
    "times_ch2 = meta_data_dict['ch2_times']\n",
    "\n",
    "record_tmp = 1000\n",
    "plt.loglog(times_ch1, abs(df_em_inv.loc[record_tmp][ch1_names]), 'o')\n",
    "plt.loglog(times_ch2, abs(df_em_inv.loc[record_tmp][ch2_names]), 'o')\n",
    "plt.loglog(times_ch1, abs(df_em_syn.loc[record_tmp][ch1_names]), '-')\n",
    "plt.loglog(times_ch2, abs(df_em_syn.loc[record_tmp][ch2_names]), '-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "442a4acc-b6ad-4e3d-9cd1-497d6507f5be",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_rho_collar = aem_model.flightlines\n",
    "rho = aem_model.layer_data['rho_i'].loc[df_rho_collar[df_rho_collar['record']==record_tmp].index].values.flatten()\n",
    "thickness = aem_model.layer_data['thk'].loc[df_rho_collar[df_rho_collar['record']==record_tmp].index].values.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "4ff95b04-8c83-44bf-be1d-4cee421fb0ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "header [General] parsed\n",
      "header [Channel1] parsed\n",
      "header [Channel2] parsed\n",
      "-1.99e-06 0.0\n"
     ]
    }
   ],
   "source": [
    "system = libaarhusxyz.parse_gex(\"/Users/sgkang09/Dropbox/Stanford/Seogi-Jonny/targeted_inversion/em_data_cv/sa_4_2022_spring/20220406_20039_WO4_Ramboll_312_30Hz_SR2.gex\")\n",
    "area = system['General']['TxLoopArea']\n",
    "waveform_hm = system['General']['WaveformHMPoint']\n",
    "waveform_lm = system['General']['WaveformLMPoint']\n",
    "i_start_hm = int(system['Channel2']['RemoveInitialGates'])\n",
    "i_start_lm = int(system['Channel1']['RemoveInitialGates'])\n",
    "time_input_currents_hm = waveform_hm[:,0]\n",
    "input_currents_hm = waveform_hm[:,1]\n",
    "time_input_currents_lm = waveform_lm[:,0]\n",
    "input_currents_lm = waveform_lm[:,1]\n",
    "\n",
    "time_gates = system['General']['GateTime']\n",
    "# GateTimeShift=system['Channel1']['GateTimeShift']\n",
    "GateTimeShift = -1.99e-6\n",
    "MeaTimeDelay=system['Channel1']['MeaTimeDelay']\n",
    "NoGates=int(system['Channel1']['NoGates'])\n",
    "print (GateTimeShift, MeaTimeDelay)\n",
    "t0_lm = waveform_lm[:,0].max()\n",
    "times_lm = (time_gates[:NoGates,0] + GateTimeShift + MeaTimeDelay)[i_start_lm:]\n",
    "GateTimeShift=system['Channel2']['GateTimeShift']\n",
    "MeaTimeDelay=system['Channel2']['MeaTimeDelay']\n",
    "NoGates=int(system['Channel2']['NoGates'])\n",
    "t0_hm = waveform_hm[:,0].max()\n",
    "times_hm = (time_gates[:NoGates,0] + GateTimeShift + MeaTimeDelay)[i_start_hm:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "b8c3e32b-8b1f-44d5-8a83-5ac02af7010c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.2250e-06, 8.2300e-06, 1.0220e-05, 1.2730e-05, 1.6220e-05,\n",
       "       2.0720e-05, 2.6220e-05, 3.3230e-05, 4.2220e-05, 5.3720e-05,\n",
       "       6.8220e-05, 8.6220e-05, 1.0871e-04, 1.3671e-04, 1.7221e-04,\n",
       "       2.1771e-04, 2.7471e-04, 3.4671e-04, 4.3771e-04])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "times_lm[4:4+19]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e12df3b9-6926-48dd-b1a4-79be2f6ddcf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6.2250e-06, 8.2300e-06, 1.0220e-05, 1.2730e-05, 1.6220e-05,\n",
       "       2.0720e-05, 2.6220e-05, 3.3230e-05, 4.2220e-05, 5.3720e-05,\n",
       "       6.8220e-05, 8.6220e-05, 1.0871e-04, 1.3671e-04, 1.7221e-04,\n",
       "       2.1771e-04, 2.7471e-04, 3.4671e-04, 4.3771e-04])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "times_ch1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c65d401b-049e-4cc8-a422-7a3eabcb080a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.0004872, 0.0005227, 0.0005682, 0.0006252, 0.0006972, 0.0007882,\n",
       "       0.0009022, 0.001046 , 0.001228 , 0.001456 , 0.001744 , 0.002109 ,\n",
       "       0.002567 , 0.003145 , 0.003864 , 0.004744 , 0.005822 , 0.00714  ,\n",
       "       0.008754 , 0.01073  ])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "times_ch2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f36a5237-8b61-4cfa-a13a-545f0db018ee",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "034f6702-7a84-455f-b33e-913debace3cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# source_height = df_em_inv.loc[record_tmp]['alt']\n",
    "source_height = df_rho_collar[df_rho_collar['record']==record_tmp]['invalt'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "a1b5fb25-7b50-4cc7-8b9f-dae906423250",
   "metadata": {},
   "outputs": [],
   "source": [
    "xy_tmp = df_rho_collar[df_rho_collar['record']==record_tmp][['utmx', 'utmy']].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "da221757-ad2f-4a0c-ac03-b2e52eb04d65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([9.900e-01, 2.044e+05])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "system['General']['RxCoilLPFilter']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "7da0065d-95ec-41df-a880-0dd2d2b4da03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.e+00, 3.e+05])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "system['Channel1']['TiBLowPassFilter']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "5b87969f-8b30-40e2-90fa-cb8580a291f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.e+00, 3.e+05])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "system['Channel2']['TiBLowPassFilter']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e9ddca91-2c35-4072-96f9-b5e145fd0dc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from discretize import TensorMesh\n",
    "import numpy as np\n",
    "from simpeg import maps\n",
    "import simpeg.electromagnetics.time_domain as tdem\n",
    "from simpeg.electromagnetics.utils.em1d_utils import set_mesh_1d\n",
    "\n",
    "# Source properties\n",
    "source_location = np.array([0., 0., source_height])  \n",
    "source_orientation = \"z\"                      # \"x\", \"y\" or \"z\"\n",
    "source_current = 1.                           # maximum on-time current\n",
    "\n",
    "# Receiver properties\n",
    "receiver_location = np.array([13.25, 0., source_height+2.])\n",
    "receiver_orientation = \"z\"                    # \"x\", \"y\" or \"z\"\n",
    "# Receiver list\n",
    "# Define receivers at each location.\n",
    "receivers_lm_list = [\n",
    "    tdem.receivers.PointMagneticFluxTimeDerivative(\n",
    "        receiver_location, times_ch1, \"z\",\n",
    "        bw_cutoff_frequency=3e5,\n",
    "        bw_power=1,\n",
    "        lp_cutoff_frequency=2.044e+05,\n",
    "        lp_power=0.99,        \n",
    "    ),\n",
    "]  # Make a list containing all receivers even if just one\n",
    "receivers_hm_list = [\n",
    "    tdem.receivers.PointMagneticFluxTimeDerivative(\n",
    "        receiver_location, times_ch2, \"z\",\n",
    "        bw_cutoff_frequency=3e5,\n",
    "        bw_power=1,\n",
    "        lp_cutoff_frequency=2.044e+05,\n",
    "        lp_power=0.99,        \n",
    "    ),\n",
    "]  # Make a list containing all receivers even if just one\n",
    "\n",
    "waveform_lm = tdem.sources.PiecewiseLinearWaveform(\n",
    "        times=time_input_currents_lm, currents=input_currents_lm\n",
    ")\n",
    "waveform_hm = tdem.sources.PiecewiseLinearWaveform(\n",
    "        times=time_input_currents_hm, currents=input_currents_hm\n",
    ")\n",
    "# Must define the transmitter properties and associated receivers\n",
    "source_list = [\n",
    "    tdem.sources.MagDipole(\n",
    "        receivers_lm_list,\n",
    "        location=source_location,\n",
    "        waveform=waveform_lm,\n",
    "    ),\n",
    "    tdem.sources.MagDipole(\n",
    "        receivers_hm_list,\n",
    "        location=source_location,\n",
    "        waveform=waveform_hm,\n",
    "    ),    \n",
    "]\n",
    "\n",
    "survey = tdem.Survey(source_list)\n",
    "# Define the simulation\n",
    "simulation = tdem.Simulation1DLayered(\n",
    "    survey=survey, thicknesses=thickness, sigmaMap=maps.IdentityMap(nP=len(rho)),\n",
    ")\n",
    "# %%time\n",
    "dpred = simulation.dpred(1./rho)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "cdd237e6-4cad-48bf-bba1-ce1c2abf0af4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.57868164e-08,  8.43221246e-10, -1.18959831e-08, -1.24360341e-08,\n",
       "       -1.13641643e-08, -6.25072705e-09, -3.60095366e-09, -2.16579863e-09,\n",
       "       -1.33287098e-09, -8.31663153e-10, -5.23928878e-10, -3.31806133e-10,\n",
       "       -2.08634842e-10, -1.29593502e-10, -7.85262476e-11, -4.61253376e-11,\n",
       "       -2.66239490e-11, -1.50948916e-11, -8.46210183e-12, -2.75102758e-11,\n",
       "       -2.12690761e-11, -1.60089656e-11, -1.18429335e-11, -8.62254409e-12,\n",
       "       -6.20224059e-12, -4.43273960e-12, -3.14306496e-12, -2.20596126e-12,\n",
       "       -1.53353796e-12, -1.04921026e-12, -7.03850195e-13, -4.64159404e-13,\n",
       "       -3.00027617e-13, -1.91131736e-13, -1.20559606e-13, -7.49456840e-14,\n",
       "       -4.57328753e-14, -2.72406168e-14, -1.57766285e-14])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dpred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "02911c51-dcbb-43cf-bab5-ab467de49662",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ch1_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "81c25ee4-d3b9-4fd6-b6bf-6f43637bc714",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ch2_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "310ce538-b9d2-4fc5-9b3d-4a98b00f37a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(39,)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dpred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "d7b5b73b-7ad9-46ba-86eb-8488a80e4810",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbf1442d210>]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.loglog(times_ch1, abs(df_em_syn.loc[record_tmp][ch1_names]), 'o')\n",
    "plt.loglog(times_ch2, abs(df_em_syn.loc[record_tmp][ch2_names]), 'o')\n",
    "plt.loglog(times_ch1, abs(dpred[:len(ch1_names)]), '-')\n",
    "plt.loglog(times_ch2, abs(dpred[len(ch1_names):]), '-')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c77eaeb1-a33a-4041-b6aa-17206b9ab2a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fbf13aa50f0>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(xy[:,0], xy[:,1], ',')\n",
    "plt.plot(xy_tmp[:,0], xy_tmp[:,1], 'ro')\n"
   ]
  }
 ],
 "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}