Figure8.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from obspy import read_events\n",
    "from obspy import read_inventory\n",
    "from obspy.clients.fdsn import Client\n",
    "client_wm = Client(\"IRIS\")\n",
    "from obspy.taup import TauPyModel\n",
    "from obspy import Stream, Trace\n",
    "import matplotlib.pyplot as plt\n",
    "from mpl_toolkits.basemap import Basemap\n",
    "from obspy import UTCDateTime\n",
    "import matplotlib.pyplot as plt\n",
    "from obspy.geodetics import locations2degrees,degrees2kilometers\n",
    "from scipy import signal\n",
    "plt.rc('font', size=10) \n",
    "%matplotlib widget\n",
    "import datetime\n",
    "from obspy.signal.trigger import recursive_sta_lta,plot_trigger,coincidence_trigger\n",
    "import pandas as pd\n",
    "from scipy.stats import linregress"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "inv0 = client_wm.get_stations(\n",
    "    channel=\"BH*\", starttime=UTCDateTime(2020,9,1,0,0,0),endtime=UTCDateTime(2020,10,1,0,0,0),\n",
    "    network=\"AK\", sta=\"SCRK,J25K,K27K\",\n",
    "    level=\"response\")\n",
    "the_station=inv0[0][2]\n",
    "evid_dum='https://earthquake.usgs.gov/fdsnws/event/1/query?&starttime=2020-09-01&endtime=2020-9-10&latitude=63.98&longitude=-143.99&maxradius=4&minmagnitude=0'\n",
    "cat1 = read_events(evid_dum)\n",
    "evid_dum='https://earthquake.usgs.gov/fdsnws/event/1/query?&starttime=2020-09-01&endtime=2020-9-10&latitude=63.98&longitude=-143.99&minradius=4&maxradius=8&minmagnitude=1'\n",
    "cat2 = read_events(evid_dum)\n",
    "evid_dum='https://earthquake.usgs.gov/fdsnws/event/1/query?&starttime=2020-09-01&endtime=2020-9-10&latitude=63.98&longitude=-143.99&minradius=8&maxradius=20&minmagnitude=2'\n",
    "cat3 = read_events(evid_dum)\n",
    "evid_dum='https://earthquake.usgs.gov/fdsnws/event/1/query?&starttime=2020-09-01&endtime=2020-9-10&latitude=63.98&longitude=-143.99&minradius=20&maxradius=100&minmagnitude=3'\n",
    "cat4 = read_events(evid_dum)\n",
    "evid_dum='https://earthquake.usgs.gov/fdsnws/event/1/query?&starttime=2020-09-01&endtime=2020-9-10&latitude=63.98&longitude=-143.99&minradius=100&maxradius=180&minmagnitude=4'\n",
    "cat5 = read_events(evid_dum)\n",
    "cat_all=cat1+cat2+cat3+cat4+cat5\n",
    "channel = \"BHZ\"\n",
    "start_time=UTCDateTime(2020,9,1,0,0,0)\n",
    "end_time=UTCDateTime(2020,9,10,0,0,0)\n",
    "#st_2day = client_wm.get_waveforms(\"AK\", \"SCRK,K27K,J25K\", \"*\", \"BHZ\", start_time, end_time)\n",
    "st_10day = client_wm.get_waveforms(\"AK\", \"SCRK\", \"*\", \"BHZ\", start_time, end_time)\n",
    "EQ_data=pd.read_csv(\"detections/SCRK_outputs/X_prediction_results.csv\")\n",
    "eq_arv_t=EQ_data['event_start_time']\n",
    "eq_arv_s_t=EQ_data['s_arrival_time']\n",
    "st_10day2=st_10day.copy()\n",
    "st_10day2.merge(method=0,fill_value=0)\n",
    "st_10day2.detrend()\n",
    "st_10day2.filter('bandpass',freqmin=0.8, freqmax=25.0)\n",
    "trace=st_10day2[0]\n",
    "model = TauPyModel(model=\"prem\")\n",
    "df = trace.stats.sampling_rate\n",
    "npts = trace.stats.npts\n",
    "t = np.arange(npts, dtype=np.float32) / df\n",
    "cft = recursive_sta_lta(trace.data, int(5 * df), int(10 * df))\n",
    "eqt_array=[]\n",
    "for eqtm in eq_arv_t:\n",
    "    dumt=UTCDateTime(eqtm)\n",
    "    #ax.vlines(dumt-trace.stats.starttime, ii,jj, color='y',linestyles='dashdot', lw=3)\n",
    "    eqt_array.append(dumt-trace.stats.starttime)\n",
    "    picker_time_a=[]\n",
    "for event in cat_all:\n",
    "    disD=locations2degrees(the_station.latitude,the_station.longitude,event.origins[0].latitude,event.origins[0].longitude)\n",
    "    if event.origins[0].depth<0:\n",
    "        event.origins[0].depth=0\n",
    "    arrivals = model.get_travel_times(source_depth_in_km=event.origins[0].depth/1000,\n",
    "                                  distance_in_degree=disD)    \n",
    "    picker_time_a.append(event.origins[0].time+arrivals[0].time-trace.stats.starttime)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "max_search_range_b=10\n",
    "max_search_range_a=30\n",
    "cft_thr=1.35\n",
    "\n",
    "sel_cat_list=[]\n",
    "\n",
    "lij=-1\n",
    "for event in cat_all:\n",
    "    lij=lij+1\n",
    "    disD=locations2degrees(the_station.latitude,the_station.longitude,event.origins[0].latitude,event.origins[0].longitude)\n",
    "    picker_time=picker_time_a[lij]\n",
    "    t0=int(picker_time/trace.stats.delta)\n",
    "    tarray=range(int(t0-max_search_range_b*trace.stats.sampling_rate),int(t0+max_search_range_a*trace.stats.sampling_rate))\n",
    "    try:\n",
    "        if np.max(cft[tarray])>cft_thr:\n",
    "            sel_cat_list.append(event)\n",
    "            # if flag==0:\n",
    "            #     cat_all_sel=event\n",
    "            #     flag=1\n",
    "            # else:\n",
    "            #     cat_all_sel=cat_all_sel+event\n",
    "    except Exception:\n",
    "        continue\n",
    "    \n",
    "for i,event in enumerate(sel_cat_list):\n",
    "    if i==0:\n",
    "        dum=cat_all[0:1]\n",
    "        dum.clear()\n",
    "        all_sel_event=dum+event\n",
    "    else:\n",
    "        all_sel_event=all_sel_event+event\n",
    "lij=-1\n",
    "eqt_max_time=25\n",
    "for i,picker_time in enumerate(picker_time_a):\n",
    "    if np.abs(np.array(eqt_array)-picker_time).min()<eqt_max_time:\n",
    "        lij=lij+1\n",
    "        if lij==0:\n",
    "            dum=cat_all[0:1]\n",
    "            dum.clear()\n",
    "            eqt_sel_event=dum+cat_all[i]\n",
    "        else:\n",
    "            eqt_sel_event=eqt_sel_event+cat_all[i]\n",
    "eqtpall_sel_event=all_sel_event+eqt_sel_event\n",
    "event_in_list=[]\n",
    "for event in eqtpall_sel_event:\n",
    "    disD=locations2degrees(the_station.latitude,the_station.longitude,event.origins[0].latitude,event.origins[0].longitude)\n",
    "    event_list=[event.origins[0].longitude,event.origins[0].latitude,event.origins[0].depth,event.magnitudes[0].mag,disD,event.event_type,event.origins[0].time]\n",
    "    event_in_list.append(event_list)\n",
    "eqtpall_sel_event_pd=pd.DataFrame(event_in_list,columns=['longitude','latitude','depth','magnitude','disD','event_type','time'])\n",
    "bh_catalog=eqtpall_sel_event_pd.drop_duplicates(subset=['longitude','latitude','magnitude'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bh_catalog"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def threshold_trigger(th,record_time,trace):\n",
    "    record_number=record_time*trace.stats.sampling_rate\n",
    "    abs_st_data=np.abs(trace.data)\n",
    "    ind_th=[]\n",
    "    recording=False\n",
    "    for i,abs_st in enumerate(abs_st_data):\n",
    "        if abs_st>th and recording is False:\n",
    "            ind_th.append(i)\n",
    "            recording=True\n",
    "        if ind_th!=[] and (i-ind_th[-1])>record_number:\n",
    "            recording=False\n",
    "    return len(ind_th),ind_th\n",
    "\n",
    "def cal_bhcat_t(bh_catalog):\n",
    "    cat_arl_t=[]\n",
    "    cat_arl_i=[]\n",
    "    for i in bh_catalog.index:\n",
    "        arrivals = model.get_travel_times(source_depth_in_km=bh_catalog.loc[i,'depth']/1000,\n",
    "                                    distance_in_degree=bh_catalog.loc[i,'disD'])    \n",
    "        picker_time=bh_catalog.loc[i,'time']+arrivals[0].time-trace.stats.starttime\n",
    "        cat_arl_t.append(picker_time)\n",
    "        cat_arl_i.append(i)\n",
    "    return cat_arl_t,cat_arl_i\n",
    "\n",
    "\n",
    "def event_associate(pre_time,after_time,cat_arl_t,t,ind_th,cat_arl_i):\n",
    "    n_recorded=0\n",
    "    index_triggered=[]\n",
    "    index_cat=[]\n",
    "    for i in ind_th:\n",
    "        for j,cat_t in enumerate(cat_arl_t):\n",
    "            if cat_t>t[i]-pre_time and cat_t<t[i]+after_time:\n",
    "                n_recorded=n_recorded+1\n",
    "                index_triggered.append(cat_arl_i[j])\n",
    "                index_cat.append(j)\n",
    "                break\n",
    "    return n_recorded,index_triggered,index_cat\n",
    "def threshold_trigger_w_avg(th_fold,record_time,trace,window_len):\n",
    "    record_number=record_time*trace.stats.sampling_rate\n",
    "    abs_st_data=np.abs(trace.data)\n",
    "    ind_th=[]\n",
    "    recording=False\n",
    "    rtt=0\n",
    "    cm_sum=0\n",
    "    ltavg=np.sum(abs_st_data[0:window_len])/window_len\n",
    "    for i,abs_st in enumerate(abs_st_data):\n",
    "        rtt=rtt+1\n",
    "        if rtt>window_len:\n",
    "            rtt=0\n",
    "            ltavg=cm_sum/window_len\n",
    "            #print(ltavg)\n",
    "            cm_sum=abs_st\n",
    "        cm_sum=cm_sum+abs_st\n",
    "        if abs_st>th_fold*ltavg and recording is False:\n",
    "            ind_th.append(i)\n",
    "            recording=True\n",
    "        if ind_th!=[] and (i-ind_th[-1])>record_number:\n",
    "            recording=False\n",
    "    return len(ind_th),ind_th\n",
    "def cal_tauc(tr,sat_t,end_t):\n",
    "    df = tr.stats.sampling_rate\n",
    "    npts = tr.stats.npts\n",
    "    t = np.arange(npts, dtype=np.float32) / df\n",
    "    ind_range=(t<end_t) * (t>sat_t)\n",
    "    r=np.sum(np.diff(tr[ind_range])**2/tr.stats.delta)/np.sum(tr[ind_range]**2)\n",
    "    return 2*np.pi/np.sqrt(r)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "bh_catalog_nd=bh_catalog.reset_index()\n",
    "#trace=st_10day2[0]\n",
    "event_array=[]\n",
    "event_arlt_array=[]\n",
    "pre_min=0.5\n",
    "after_min=5\n",
    "for i in range(len(bh_catalog_nd)):\n",
    "\n",
    "    event=bh_catalog_nd.loc[i]\n",
    "    arrivals = model.get_travel_times(source_depth_in_km=event.depth/1000,distance_in_degree=event.disD)  \n",
    "    arl_time=event.time+arrivals[0].time\n",
    "    #arl_time=event_arlt_array[i]\n",
    "    trace_slc=trace.slice(arl_time-pre_min*60,arl_time+after_min*60)\n",
    "    event_array.append(trace_slc)\n",
    "    event_arlt_array.append(arl_time)\n",
    "pre_time=80\n",
    "after_time=10\n",
    "th_fold=10\n",
    "th_value=100\n",
    "window_len=50*60*1\n",
    "rest_time=30*60\n",
    "event_sel_array=[]\n",
    "event_arlt_sel_array=[]\n",
    "ind_slt=[]\n",
    "for i in range(len(bh_catalog_nd)):\n",
    "#for i in range(1):\n",
    "    event=bh_catalog_nd.loc[i]\n",
    "    tr=event_array[i]\n",
    "    df = tr.stats.sampling_rate\n",
    "    npts = tr.stats.npts\n",
    "    t = np.arange(npts, dtype=np.float32) / df\n",
    "    arl_time=event_arlt_array[i]\n",
    "    #n,ind_th=threshold_trigger_w_avg(th_fold,rest_time,tr,window_len)\n",
    "    n,ind_th=threshold_trigger(th_value,rest_time,tr)\n",
    "    for k in ind_th:\n",
    "        if arl_time-tr.stats.starttime>t[k]-pre_time and arl_time-tr.stats.starttime<t[k]+after_time:\n",
    "            event_sel_array.append(tr)\n",
    "            event_arlt_sel_array.append(arl_time)\n",
    "            ind_slt.append(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for detected events\n",
    "sat_t=20\n",
    "end_t=45\n",
    "tauc_over_events=[]\n",
    "mag_array=np.array(bh_catalog_nd.loc[ind_slt]['magnitude'])\n",
    "for i,trc in enumerate(event_sel_array):\n",
    "    #event=bh_cat_sort_mag_sel.loc[i]\n",
    "    tauc_over_events.append(cal_tauc(trc,sat_t,end_t))\n",
    "A = np.vstack([mag_array, np.ones(len(mag_array))]).T\n",
    "logtauc=np.log(tauc_over_events)\n",
    "m, c = np.linalg.lstsq(A, logtauc, rcond=None)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "len(ind_slt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for all events\n",
    "sat_t=20\n",
    "end_t=45\n",
    "tauc_over_events=[]\n",
    "mag_array=np.array(bh_catalog_nd['magnitude'])\n",
    "for i,trc in enumerate(event_array):\n",
    "    #event=bh_cat_sort_mag_sel.loc[i]\n",
    "    tauc_over_events.append(cal_tauc(trc,sat_t,end_t))\n",
    "A = np.vstack([mag_array, np.ones(len(mag_array))]).T\n",
    "logtauc=np.log(tauc_over_events)\n",
    "m, c = np.linalg.lstsq(A, logtauc, rcond=None)[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y=m*mag_array+c\n",
    "msft=np.sqrt(np.sum((y-mag_array)**2))\n",
    "convar=np.sum((mag_array-mag_array.mean())*(logtauc-np.array(logtauc).mean()))/mag_array.size\n",
    "plt.figure(figsize=(8,6))\n",
    "plt.scatter(mag_array,logtauc,label='Original tauc')\n",
    "plt.plot(mag_array,y,'b-',label='fitted tauc')\n",
    "plt.xlabel('magnitude (mw)')\n",
    "plt.ylabel('log(tauc)')\n",
    "#plt.title(\"misfit= %f, m= %f, cov= %f\" %(msft,m,convar))\n",
    "corr_mat=np.corrcoef(mag_array,logtauc)\n",
    "plt.title('correlation coefficient = %4.3f' %(corr_mat[0][1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def moving_average(x, w):\n",
    "    return np.convolve(x, np.ones(w), 'valid') / w\n",
    "def cal_spec_from_trc(trc,wid):\n",
    "    dt=trc.stats.delta\n",
    "    N=trc.stats.npts\n",
    "    T=N*dt\n",
    "    dw=2*np.pi/T\n",
    "    w=np.array(range(int(N/2)))*dw\n",
    "    spec_amp=np.abs(np.fft.fft(trc.data)[:int(N/2)])\n",
    "    spec_smth=moving_average(spec_amp,wid)\n",
    "    #max_ind=spec_amp.argmax()\n",
    "    max_ind=spec_smth.argmax()\n",
    "    if wid==1:\n",
    "        w_plot=w\n",
    "    else:\n",
    "        w_plot=w[:-wid+1]\n",
    "    return spec_smth, w_plot[max_ind],w_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tauc_test_peakf=[]\n",
    "win_len=1200\n",
    "peak_f_a=[]\n",
    "for i,trc in enumerate(event_sel_array):\n",
    "    #event=bh_cat_sort_mag_sel.loc[i]\n",
    "    tauc_test_peakf.append(cal_tauc(trc,sat_t,end_t))\n",
    "    spec_amp,peak_w,w_plot=cal_spec_from_trc(trc,win_len)\n",
    "    peak_f_a.append(peak_w/(2*np.pi))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "tauc_test_peakf=[]\n",
    "peak_f_a=[]\n",
    "win_len=100\n",
    "for i,trc in enumerate(event_sel_array):\n",
    "    #event=bh_cat_sort_mag_sel.loc[i]\n",
    "    #tauc_test_peakf.append(cal_tauc(trc,sat_t,end_t))\n",
    "    spec_amp,peak_w,w_plot=cal_spec_from_trc(trc,win_len)\n",
    "    peak_f_a.append(peak_w/(2*np.pi))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "peak_inv=1/np.array(peak_f_a)\n",
    "x=mag_array\n",
    "A = np.vstack([x, np.ones(len(x))]).T\n",
    "#logtaucf=np.log(tauc_test_peakf)\n",
    "#logtaucf=mag_array\n",
    "m, c = np.linalg.lstsq(A, peak_inv, rcond=None)[0]\n",
    "y=m*x+c\n",
    "plt.scatter(x,peak_inv)\n",
    "plt.plot(x,y,'b-')\n",
    "plt.ylabel('1/(peak frequency) (s)')\n",
    "plt.xlabel('magnitude')\n",
    "#plt.title('mag vs 1/f (freq peak from 25s segment)')\n",
    "corr_mat=np.corrcoef(x,peak_inv)\n",
    "plt.title('correlation coefficient = %4.3f' %(corr_mat[0][1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "st_10day_unfilter=st_10day.copy()\n",
    "st_10day_unfilter_1=st_10day[0:3].merge(method=0,fill_value=0).detrend()\n",
    "st_10day_unfilter_2=st_10day[3:5].merge(method=0,fill_value=0).detrend()\n",
    "st_10day_unfilter_3=st_10day[5:8].merge(method=0,fill_value=0).detrend()\n",
    "st_10day_unfilter=st_10day_unfilter_1+st_10day_unfilter_2+st_10day_unfilter_3\n",
    "st_10day_unfilter[2].data=np.append(st_10day_unfilter[2].data,0)\n",
    "# get unfilterd data slice\n",
    "trace=st_10day_unfilter[2]\n",
    "df = trace.stats.sampling_rate\n",
    "npts = trace.stats.npts\n",
    "t = np.arange(npts, dtype=np.float32) / df\n",
    "T=npts/df\n",
    "dw=2*np.pi/T\n",
    "event_array_unflt=[]\n",
    "event_arlt_array_unflt=[]\n",
    "pre_min=0.3\n",
    "after_min=2\n",
    "cat_bh_uft=bh_catalog_nd.loc[ind_slt].reset_index()\n",
    "for i in range(len(cat_bh_uft)):\n",
    "    event=cat_bh_uft.loc[i]\n",
    "    arrivals = model.get_travel_times(source_depth_in_km=event.depth/1000,distance_in_degree=event.disD)  \n",
    "    arl_time=event.time+arrivals[0].time\n",
    "    trace_slc=trace.slice(arl_time-pre_min*60,arl_time+after_min*60)\n",
    "    event_array_unflt.append(trace_slc)\n",
    "    event_arlt_array_unflt.append(arl_time)\n",
    "event_arlt_array_unflt_h0p1=[]\n",
    "for i in range(len(cat_bh_uft)):\n",
    "    flt_trc=event_array_unflt[i].detrend().filter('bandpass', freqmin=0.1,freqmax=25)\n",
    "    event_arlt_array_unflt_h0p1.append(flt_trc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "win_len=80\n",
    "f_divide=1\n",
    "f_highcut=5\n",
    "ratio_freq=[]\n",
    "for i in range(len(cat_bh_uft)):\n",
    "    trc=event_arlt_array_unflt_h0p1[i]\n",
    "    dt=trc.stats.delta\n",
    "    N=trc.stats.npts\n",
    "    T=N*dt\n",
    "    dw=2*np.pi/T\n",
    "    w=np.array(range(int(N/2)))*dw\n",
    "    spec_plot,peak_w,w_plot=cal_spec_from_trc(trc,win_len)\n",
    "    w_sel_a=(w_plot>f_divide*2*np.pi)*(w_plot<f_highcut*2*np.pi)\n",
    "    w_sel_an=w_plot<=f_divide*2*np.pi\n",
    "    ratio_freq.append(spec_plot[w_sel_an].sum()/spec_plot[w_sel_a].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(8,6))\n",
    "x=mag_array\n",
    "log_ratio=np.log(ratio_freq)\n",
    "A = np.vstack([x, np.ones(len(x))]).T\n",
    "#logtaucf=np.log(tauc_test_peakf)\n",
    "#logtaucf=mag_array\n",
    "m, c = np.linalg.lstsq(A, log_ratio, rcond=None)[0]\n",
    "y=m*x+c\n",
    "plt.plot(x,y,'b-')\n",
    "plt.scatter(x,log_ratio)\n",
    "plt.xlabel('magnitude (mw)')\n",
    "plt.ylabel('log(low/high freq content)')\n",
    "corr_mat=np.corrcoef(x,log_ratio)\n",
    "plt.title('correlation coefficient = %4.3f' %(corr_mat[0][1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "testtrcn=5\n",
    "test_trc=event_arlt_array_unflt_h0p1[testtrcn].copy()\n",
    "test_trc.detrend().plot()\n",
    "test_trc=event_arlt_array_unflt_h0p1[testtrcn].copy()\n",
    "test_trc.detrend().filter('bandpass',freqmin=0.1, freqmax=24).plot()\n",
    "test_trc=event_arlt_array_unflt_h0p1[testtrcn].copy()\n",
    "test_trc.detrend().filter('bandpass',freqmin=2, freqmax=8).plot()\n",
    "test_trc=event_arlt_array_unflt_h0p1[testtrcn].copy()\n",
    "test_trc.detrend().filter('bandpass',freqmin=0.8, freqmax=2).plot()\n",
    "test_trc=event_arlt_array_unflt_h0p1[testtrcn].copy()\n",
    "test_trc.detrend().filter('bandpass',freqmin=0.8, freqmax=2).plot()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_trc=event_arlt_array_unflt_h0p1[testtrcn].copy()\n",
    "test_trc.detrend().filter('bandpass',freqmin=2, freqmax=4).plot()\n",
    "test_trc=event_arlt_array_unflt_h0p1[testtrcn].copy()\n",
    "test_trc.detrend().filter('bandpass',freqmin=2, freqmax=20).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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