120 lines
3.2 KiB
Plaintext
120 lines
3.2 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n",
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"sys.path.append(\"../../utils/\")\n",
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"\n",
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"from src_rec_file_helper import read_src_rec_file\n",
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"\n",
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"events_true = read_src_rec_file(\"./src_rec_test_out.dat\")\n",
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"events_calc = read_src_rec_file(\"./src_rec_test_out_out.dat\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# check objective function value\n",
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"v_obj = 0.0\n",
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"for i_ev in range(len(events_true)):\n",
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" for i_rec in range(len(events_true[i_ev].rec_list)):\n",
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" v_obj += abs(events_true[i_ev].rec_list[i_rec].arr_time - events_calc[i_ev].rec_list[i_rec].arr_time)**2/2.0\n",
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"\n",
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"print(\"v_obj: \", v_obj)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# calculate summation of difference of arrival times at each receiver\n",
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"list_diff_v_obj = []\n",
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"for i_ev in range(len(events_true)):\n",
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" for i_rec in range(len(events_true[i_ev].rec_list)):\n",
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" #tmp_v_obj = abs(events_true[i_ev].rec_list[i_rec].arr_time - events_calc[i_ev].rec_list[i_rec].arr_time)**2/2.0\n",
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" tmp_v_obj = events_true[i_ev].rec_list[i_rec].arr_time - events_calc[i_ev].rec_list[i_rec].arr_time\n",
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"\n",
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" if (i_ev == 0):\n",
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" list_diff_v_obj.append(tmp_v_obj)\n",
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" else:\n",
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" list_diff_v_obj[i_rec] += tmp_v_obj\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# plot list_diff_v_obj on map\n",
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"\n",
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"# colormap by list_diff_v_obj\n",
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"cmap = plt.get_cmap('viridis')\n",
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"norm = plt.Normalize(vmin=min(list_diff_v_obj), vmax=max(list_diff_v_obj))\n",
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"\n",
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"print(\"min: \", min(list_diff_v_obj))\n",
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"print(\"max: \", max(list_diff_v_obj))\n",
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"\n",
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"list_lon = []\n",
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"list_lat = []\n",
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"\n",
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"for i_rec in range(len(events_true[0].rec_list)):\n",
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" list_lon.append(events_true[0].rec_list[i_rec].lon)\n",
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" list_lat.append(events_true[0].rec_list[i_rec].lat)\n",
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"\n",
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"plt.scatter(list_lon, list_lat, c=list_diff_v_obj, cmap=cmap, norm=norm)\n",
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"\n",
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"# color bar\n",
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"plt.colorbar()\n",
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"\n",
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"# tight \n",
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"plt.tight_layout()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3.9.1 64-bit ('3.9.1')",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.1"
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},
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"vscode": {
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"interpreter": {
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"hash": "02f83e1f4cd9619657a6845405e2dd67c4de23753ba48bca5dce2ebf57b3813a"
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}
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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