ETAS_Ridgecrest_July_2019.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ETAS: Ridgecrest, 4ish July, 2019\n",
    "\n",
    "Time\n",
    "2019-07-04 17:33:49 (UTC)\n",
    "Location\n",
    "35.705°N 117.506°W\n",
    "Depth\n",
    "10.7 km"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "failed while loading urllib and/or urllib. maybe python 3.x?\n",
      "comcat not available. consider installing comcat for improved catalog operations; see https://github.com/usgs/libcomcat\n"
     ]
    }
   ],
   "source": [
    "import datetime as dtm\n",
    "import matplotlib.dates as mpd\n",
    "import pytz\n",
    "tzutc = pytz.timezone('UTC')\n",
    "\n",
    "#import operator\n",
    "import math\n",
    "import random\n",
    "import numpy\n",
    "import scipy\n",
    "import scipy.optimize as spo\n",
    "from scipy import interpolate\n",
    "import itertools\n",
    "import sys\n",
    "#import scipy.optimize as spo\n",
    "import os\n",
    "import operator\n",
    "#from PIL import Image as ipp\n",
    "import multiprocessing as mpp\n",
    "#\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from mpl_toolkits.mplot3d import Axes3D\n",
    "import json\n",
    "import pickle\n",
    "#\n",
    "\n",
    "import geopy.distance\n",
    "#from geopy.distance import vincenty\n",
    "#from geopy.distance import great_circle\n",
    "#\n",
    "#import shapely.geometry as sgp\n",
    "os.environ['PROJ_LIB'] = '{}/anaconda3/share/proj'.format(os.getenv('HOME'))\n",
    "#\n",
    "from mpl_toolkits.basemap import Basemap as Basemap\n",
    "from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas\n",
    "from geographiclib.geodesic import Geodesic as ggp\n",
    "#\n",
    "\n",
    "#import ANSStools as atp\n",
    "from yodiipy import ANSStools as atp\n",
    "#\n",
    "import contours2kml\n",
    "import globalETAS as gep\n",
    "\n",
    "#import global_etas_auto as ggep\n",
    "\n",
    "from eq_params import *\n",
    "#\n",
    "from nepal_figs import *\n",
    "import optimizers\n",
    "#\n",
    "import random\n",
    "import geopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "to_dt = dtm.datetime.now(pytz.timezone('UTC'))\n",
    "#to_dt = dtm.datetime(2019,7,5,0,0,0, tzinfo=pytz.timezone('UTC'))\n",
    "#\n",
    "Lr_factor = 10.\n",
    "# define these from the t_now in the actual etas object, in the event that we load it from pickle,\n",
    "#  rather than calc it here.\n",
    "#f_path = '/home/myoder/Dropbox/Research/etas/italy_2016_10/etas_{}'.format(to_dt)\n",
    "#f_root = 'etas_2016'\n",
    "#\n",
    "\n",
    "t0 = dtm.datetime.now(pytz.timezone('UTC'))\n",
    "t_ms = t0\n",
    "n_cpu=None\n",
    "#\n",
    "lat0 = 35.705\n",
    "lon0 = -117.506\n",
    "#\n",
    "#ll_sacramento = (lon0, lat0)\n",
    "\n",
    "#m0 = 7.8\n",
    "\n",
    "d_lat=2.\n",
    "d_lon=2.\n",
    "#\n",
    "lats = [lat0-d_lat, lat0+d_lat]\n",
    "lons = [lon0-d_lon, lon0+d_lon]\n",
    "#to_dt = t0-dtm.timedelta(hours=2)\n",
    "#to_dt = dtm.datetime.now(pytz.utc)\n",
    "#\n",
    "#etas = ggep.auto_etas(to_dt=to_dt, Lr_factor=Lr_factor, dt_0=5)\n",
    "#italy_prams = {'do_recarray': True, 'D_fract': 1.5,\n",
    "#                't_0':dtm.datetime(1990, 1, 1, 0, 0, tzinfo=pytz.timezone('UTC')),\n",
    "#                't_now':to_dt, \n",
    "#                'lats': [42.,43.5], 'p': 1.1, 'b1': 1.0, 'mc': 2.5, 'q': 1.5,\n",
    "#                'lons': [12.,15.], 'dmstar': 1.0, 'b2': 1.5, 'd_tau': 2.28,\n",
    "#                'incat': None, 'fit_factor': 2.0, 'd_lambda': 1.76}\n",
    "eq_prams = {'do_recarray': True, 'D_fract': 1.5,\n",
    "               't_0':dtm.datetime(1990, 1, 1, 0, 0, tzinfo=pytz.timezone('UTC')),\n",
    "               't_now':to_dt, 't_future':None ,\n",
    "               'lats': lats, 'p_cat': 1.1, 'b1': 1.0, 'mc': 3.0, 'q_cat': 1.5,\n",
    "               'p_etas':1.1, 'q_etas':1.5,\n",
    "               'lons': lons, 'dmstar': 1.0, 'b2': 1.5, 'd_tau': 2.28,\n",
    "               'incat': None, 'fit_factor': 2.0, 'd_lambda': 1.76, 'etas_range_padding':1.5,\n",
    "            'etas_range_factor':30.0, 'ab_ratio_expon':.25 }\n",
    "#eq_prams.update({'mc':3.0, 'd_lat':.25, 'd_lon':.25})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "etas_prams:  {'incat': None, 'lats': [32.0, 38.0], 'lons': [-117.0, -114.0], 'mc': 2.5, 'date_range': ['1990-1-1', None], 'D_fract': 1.5, 'd_lambda': 1.76, 'd_tau': 2.28, 'fit_factor': 1.5, 'p': 1.1, 'q': 1.5, 'dmstar': 1.0, 'b1': 1.0, 'b2': 1.5, 'do_recarray': False}\n",
      "results fetched.\n"
     ]
    }
   ],
   "source": [
    "# let's add a preliminary map...\n",
    "class Preliminary_Map(object):\n",
    "    def __init__(self, lats, lons):\n",
    "        pass\n",
    "#\n",
    "# comcat may not be working (overloaded, probably after the earthquake):\n",
    "# try anss:\n",
    "mycat = None\n",
    "#\n",
    "# we've added a new web-api based comcat catalog, and wrapped it around the old catfromANSS(), or we can use\n",
    "#. the new names.\n",
    "# mycat = atp.cat_from_comcat(lon=lons, lat=lats, minMag=2.5,\n",
    "# mycat = atp.catfromANSS(lon=lons, lat=lats, minMag=2.5,\n",
    "mycat = atp.cat_from_anss_comcat(lon=lons, lat=lats, minMag=eq_prams['mc'],\n",
    "                        dates0=[dtm.datetime(2005,1,1, tzinfo=pytz.timezone('UTC')), \n",
    "                                dtm.datetime.now(pytz.timezone('UTC'))],\n",
    "                            Nmax=None, fout=None, rec_array=True)\n",
    "#                        dates0=[dtm.datetime(2005,1,1, tzinfo=tzutc), None], Nmax=None, fout=None, rec_array=True)\n",
    "\n",
    "mycat = gep.make_ETAS_catalog_mpp(incat=mycat, n_cpu=n_cpu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "begin globalETAS.__init()__\n",
      "Overriding t_0 (etas catalog start date/time) for ETAS calculations. using catalog start, t_0 = t_now - catlen (3650.000000) = 2011-02-19 23:05:23.309772+00:00\n",
      "ETAS over etas_cat_range/xyz_range:  ([0, 2436], None)\n",
      "make_etas():\n",
      "etas_mpp worker xyz_range:  [0, 200]\n",
      "begin globalETAS.__init()__\n",
      "Overriding t_0 (etas catalog start date/time) for ETAS calculations. using catalog start, t_0 = t_now - catlen (3650.000000) = 2011-02-19 23:05:23.309772+00:00\n",
      "ETAS over etas_cat_range/xyz_range:  ([0, 2436], [0, 200])\n",
      "etas_mpp worker xyz_range:  [200, 400]\n",
      "begin globalETAS.__init()__\n",
      "Overriding t_0 (etas catalog start date/time) for ETAS calculations. using catalog start, t_0 = t_now - catlen (3650.000000) = 2011-02-19 23:05:23.309772+00:00\n",
      "ETAS over etas_cat_range/xyz_range:  ([0, 2436], [200, 400])\n",
      "etas_mpp worker xyz_range:  [400, 600]\n",
      "begin globalETAS.__init()__\n",
      "Overriding t_0 (etas catalog start date/time) for ETAS calculations. using catalog start, t_0 = t_now - catlen (3650.000000) = 2011-02-19 23:05:23.309772+00:00\n",
      "ETAS over etas_cat_range/xyz_range:  ([0, 2436], [400, 600])\n",
      "etas_mpp worker xyz_range:  [600, 800]\n",
      "begin globalETAS.__init()__\n",
      "Overriding t_0 (etas catalog start date/time) for ETAS calculations. using catalog start, t_0 = t_now - catlen (3650.000000) = 2011-02-19 23:05:23.309772+00:00\n",
      "ETAS over etas_cat_range/xyz_range:  ([0, 2436], [600, 800])\n",
      "etas_mpp worker xyz_range:  [800, 1000]\n",
      "begin globalETAS.__init()__\n",
      "Overriding t_0 (etas catalog start date/time) for ETAS calculations. using catalog start, t_0 = t_now - catlen (3650.000000) = 2011-02-19 23:05:23.309772+00:00\n",
      "ETAS over etas_cat_range/xyz_range:  ([0, 2436], [800, 1000])\n",
      "etas_mpp worker xyz_range:  [1000, 1200]\n",
      "begin globalETAS.__init()__\n",
      "Overriding t_0 (etas catalog start date/time) for ETAS calculations. using catalog start, t_0 = t_now - catlen (3650.000000) = 2011-02-19 23:05:23.309772+00:00\n",
      "ETAS over etas_cat_range/xyz_range:  ([0, 2436], [1000, 1200])\n",
      "etas_mpp worker xyz_range:  [1200, 1400]\n",
      "begin globalETAS.__init()__\n",
      "Overriding t_0 (etas catalog start date/time) for ETAS calculations. using catalog start, t_0 = t_now - catlen (3650.000000) = 2011-02-19 23:05:23.309772+00:00\n",
      "ETAS over etas_cat_range/xyz_range:  ([0, 2436], [1200, 1400])\n",
      "etas_mpp worker xyz_range:  [1400, 1600]\n",
      "begin globalETAS.__init()__\n",
      "Overriding t_0 (etas catalog start date/time) for ETAS calculations. using catalog start, t_0 = t_now - catlen (3650.000000) = 2011-02-19 23:05:23.309772+00:00\n",
      "ETAS over etas_cat_range/xyz_range:  ([0, 2436], [1400, 1600])\n",
      "begin make_etas_rtree()\n",
      "begin make_etas_rtree()\n",
      "** len(self.ETAS_array)[171001] = 200begin make_etas_rtree()\n",
      "** len(self.ETAS_array)[171004] = 200begin make_etas_rtree()\n",
      "\n",
      "Indices initiated. begin ETAS[171001] :: range: [0, 2436]** len(self.ETAS_array)[171009] = 200begin make_etas_rtree()\n",
      "begin make_etas_rtree()\n",
      "\n",
      "Indices initiated. begin ETAS[171004] :: range: [0, 2436]\n",
      "\n",
      "** len(self.ETAS_array)[171023] = 200\n",
      "begin make_etas_rtree()\n",
      "\n",
      "** len(self.ETAS_array)[171016] = 200** len(self.ETAS_array)[171034] = 200Indices initiated. begin ETAS[171023] :: range: [0, 2436]Indices initiated. begin ETAS[171009] :: range: [0, 2436]\n",
      "** len(self.ETAS_array)[171047] = 200\n",
      "\n",
      "begin make_etas_rtree()\n",
      "\n",
      "now gather sub-arrays...\n",
      "\n",
      "Indices initiated. begin ETAS[171016] :: range: [0, 2436]Indices initiated. begin ETAS[171034] :: range: [0, 2436]\n",
      "** len(self.ETAS_array)[171068] = 200Indices initiated. begin ETAS[171047] :: range: [0, 2436]\n",
      "\n",
      "\n",
      "Indices initiated. begin ETAS[171068] :: range: [0, 2436]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yoder/Dropbox/Research/globalETAS/globalETAS.py:1383: NumbaWarning: \u001b[1m\n",
      "Compilation is falling back to object mode WITH looplifting enabled because Function \"omori_rate\" failed type inference due to: \u001b[1m\u001b[1mnon-precise type pyobject\u001b[0m\n",
      "\u001b[0m\u001b[1mDuring: typing of argument at /home/yoder/Dropbox/Research/globalETAS/globalETAS.py (1401)\u001b[0m\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1401:\u001b[0m\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "     <source elided>\n",
      "\t\t#print(\"inputs: \", t, lon, lat, p, q)\n",
      "\u001b[1m\t\tt = (t or mpd.date2num(dtm.datetime.now(pytz.timezone('UTC'))))\n",
      "\u001b[0m  \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  @numba.jit\n",
      "/home/yoder/Dropbox/Research/globalETAS/globalETAS.py:1383: NumbaWarning: \u001b[1m\n",
      "Compilation is falling back to object mode WITH looplifting enabled because Function \"omori_rate\" failed type inference due to: \u001b[1m\u001b[1mnon-precise type pyobject\u001b[0m\n",
      "\u001b[0m\u001b[1mDuring: typing of argument at /home/yoder/Dropbox/Research/globalETAS/globalETAS.py (1401)\u001b[0m\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1401:\u001b[0m\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "     <source elided>\n",
      "\t\t#print(\"inputs: \", t, lon, lat, p, q)\n",
      "\u001b[1m\t\tt = (t or mpd.date2num(dtm.datetime.now(pytz.timezone('UTC'))))\n",
      "\u001b[0m  \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  @numba.jit\n",
      "/home/yoder/Dropbox/Research/globalETAS/globalETAS.py:1383: NumbaWarning: \u001b[1m\n",
      "Compilation is falling back to object mode WITH looplifting enabled because Function \"omori_rate\" failed type inference due to: \u001b[1m\u001b[1mnon-precise type pyobject\u001b[0m\n",
      "\u001b[0m\u001b[1mDuring: typing of argument at /home/yoder/Dropbox/Research/globalETAS/globalETAS.py (1401)\u001b[0m\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1401:\u001b[0m\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "     <source elided>\n",
      "\t\t#print(\"inputs: \", t, lon, lat, p, q)\n",
      "\u001b[1m\t\tt = (t or mpd.date2num(dtm.datetime.now(pytz.timezone('UTC'))))\n",
      "\u001b[0m  \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  @numba.jit\n",
      "/home/yoder/Dropbox/Research/globalETAS/globalETAS.py:1383: NumbaWarning: \u001b[1m\n",
      "Compilation is falling back to object mode WITH looplifting enabled because Function \"omori_rate\" failed type inference due to: \u001b[1m\u001b[1mnon-precise type pyobject\u001b[0m\n",
      "\u001b[0m\u001b[1mDuring: typing of argument at /home/yoder/Dropbox/Research/globalETAS/globalETAS.py (1401)\u001b[0m\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1401:\u001b[0m\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "     <source elided>\n",
      "\t\t#print(\"inputs: \", t, lon, lat, p, q)\n",
      "\u001b[1m\t\tt = (t or mpd.date2num(dtm.datetime.now(pytz.timezone('UTC'))))\n",
      "\u001b[0m  \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  @numba.jit\n",
      "/home/yoder/Dropbox/Research/globalETAS/globalETAS.py:1383: NumbaWarning: \u001b[1m\n",
      "Compilation is falling back to object mode WITH looplifting enabled because Function \"omori_rate\" failed type inference due to: \u001b[1m\u001b[1mnon-precise type pyobject\u001b[0m\n",
      "\u001b[0m\u001b[1mDuring: typing of argument at /home/yoder/Dropbox/Research/globalETAS/globalETAS.py (1401)\u001b[0m\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1401:\u001b[0m\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "     <source elided>\n",
      "\t\t#print(\"inputs: \", t, lon, lat, p, q)\n",
      "\u001b[1m\t\tt = (t or mpd.date2num(dtm.datetime.now(pytz.timezone('UTC'))))\n",
      "\u001b[0m  \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  @numba.jit\n",
      "/home/yoder/Dropbox/Research/globalETAS/globalETAS.py:1383: NumbaWarning: \u001b[1m\n",
      "Compilation is falling back to object mode WITH looplifting enabled because Function \"omori_rate\" failed type inference due to: \u001b[1m\u001b[1mnon-precise type pyobject\u001b[0m\n",
      "\u001b[0m\u001b[1mDuring: typing of argument at /home/yoder/Dropbox/Research/globalETAS/globalETAS.py (1401)\u001b[0m\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1401:\u001b[0m\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "     <source elided>\n",
      "\t\t#print(\"inputs: \", t, lon, lat, p, q)\n",
      "\u001b[1m\t\tt = (t or mpd.date2num(dtm.datetime.now(pytz.timezone('UTC'))))\n",
      "\u001b[0m  \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  @numba.jit\n",
      "/home/yoder/Dropbox/Research/globalETAS/globalETAS.py:1383: NumbaWarning: \u001b[1m\n",
      "Compilation is falling back to object mode WITH looplifting enabled because Function \"omori_rate\" failed type inference due to: \u001b[1m\u001b[1mnon-precise type pyobject\u001b[0m\n",
      "\u001b[0m\u001b[1mDuring: typing of argument at /home/yoder/Dropbox/Research/globalETAS/globalETAS.py (1401)\u001b[0m\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1401:\u001b[0m\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "     <source elided>\n",
      "\t\t#print(\"inputs: \", t, lon, lat, p, q)\n",
      "\u001b[1m\t\tt = (t or mpd.date2num(dtm.datetime.now(pytz.timezone('UTC'))))\n",
      "\u001b[0m  \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  @numba.jit\n",
      "/home/yoder/Dropbox/Research/globalETAS/globalETAS.py:1383: NumbaWarning: \u001b[1m\n",
      "Compilation is falling back to object mode WITH looplifting enabled because Function \"omori_rate\" failed type inference due to: \u001b[1m\u001b[1mnon-precise type pyobject\u001b[0m\n",
      "\u001b[0m\u001b[1mDuring: typing of argument at /home/yoder/Dropbox/Research/globalETAS/globalETAS.py (1401)\u001b[0m\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1401:\u001b[0m\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "     <source elided>\n",
      "\t\t#print(\"inputs: \", t, lon, lat, p, q)\n",
      "\u001b[1m\t\tt = (t or mpd.date2num(dtm.datetime.now(pytz.timezone('UTC'))))\n",
      "\u001b[0m  \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  @numba.jit\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:177: NumbaWarning: \u001b[1mFunction \"omori_rate\" was compiled in object mode without forceobj=True.\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1384:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaWarning(warn_msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:187: NumbaDeprecationWarning: \u001b[1m\n",
      "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1384:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:177: NumbaWarning: \u001b[1mFunction \"omori_rate\" was compiled in object mode without forceobj=True.\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1384:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaWarning(warn_msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:177: NumbaWarning: \u001b[1mFunction \"omori_rate\" was compiled in object mode without forceobj=True.\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1384:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaWarning(warn_msg,\n",
      "/home/yoder/Dropbox/Research/globalETAS/globalETAS.py:1375: NumbaWarning: \u001b[1m\n",
      "Compilation is falling back to object mode WITH looplifting enabled because Function \"spherical_dist\" failed type inference due to: \u001b[1m\u001b[1mnon-precise type pyobject\u001b[0m\n",
      "\u001b[0m\u001b[1mDuring: typing of argument at /home/yoder/Dropbox/Research/globalETAS/globalETAS.py (1377)\u001b[0m\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1377:\u001b[0m\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[1m\t\treturn spherical_dist(lon_lat_from=[self.lon, self.lat], lon_lat_to=to_lon_lat)\n",
      "\u001b[0m  \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  @numba.jit\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:187: NumbaDeprecationWarning: \u001b[1m\n",
      "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1384:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:177: NumbaWarning: \u001b[1mFunction \"omori_rate\" was compiled in object mode without forceobj=True.\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1384:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaWarning(warn_msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:187: NumbaDeprecationWarning: \u001b[1m\n",
      "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1384:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:187: NumbaDeprecationWarning: \u001b[1m\n",
      "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1384:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n",
      "/home/yoder/Dropbox/Research/globalETAS/globalETAS.py:1375: NumbaWarning: \u001b[1m\n",
      "Compilation is falling back to object mode WITH looplifting enabled because Function \"spherical_dist\" failed type inference due to: \u001b[1m\u001b[1mnon-precise type pyobject\u001b[0m\n",
      "\u001b[0m\u001b[1mDuring: typing of argument at /home/yoder/Dropbox/Research/globalETAS/globalETAS.py (1377)\u001b[0m\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1377:\u001b[0m\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[1m\t\treturn spherical_dist(lon_lat_from=[self.lon, self.lat], lon_lat_to=to_lon_lat)\n",
      "\u001b[0m  \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  @numba.jit\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:177: NumbaWarning: \u001b[1mFunction \"omori_rate\" was compiled in object mode without forceobj=True.\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1384:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaWarning(warn_msg,\n",
      "/home/yoder/Dropbox/Research/globalETAS/globalETAS.py:1375: NumbaWarning: \u001b[1m\n",
      "Compilation is falling back to object mode WITH looplifting enabled because Function \"spherical_dist\" failed type inference due to: \u001b[1m\u001b[1mnon-precise type pyobject\u001b[0m\n",
      "\u001b[0m\u001b[1mDuring: typing of argument at /home/yoder/Dropbox/Research/globalETAS/globalETAS.py (1377)\u001b[0m\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1377:\u001b[0m\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[1m\t\treturn spherical_dist(lon_lat_from=[self.lon, self.lat], lon_lat_to=to_lon_lat)\n",
      "\u001b[0m  \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  @numba.jit\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:187: NumbaDeprecationWarning: \u001b[1m\n",
      "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1384:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n",
      "/home/yoder/Dropbox/Research/globalETAS/globalETAS.py:1375: NumbaWarning: \u001b[1m\n",
      "Compilation is falling back to object mode WITH looplifting enabled because Function \"spherical_dist\" failed type inference due to: \u001b[1m\u001b[1mnon-precise type pyobject\u001b[0m\n",
      "\u001b[0m\u001b[1mDuring: typing of argument at /home/yoder/Dropbox/Research/globalETAS/globalETAS.py (1377)\u001b[0m\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1377:\u001b[0m\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[1m\t\treturn spherical_dist(lon_lat_from=[self.lon, self.lat], lon_lat_to=to_lon_lat)\n",
      "\u001b[0m  \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  @numba.jit\n",
      "/home/yoder/Dropbox/Research/globalETAS/globalETAS.py:1375: NumbaWarning: \u001b[1m\n",
      "Compilation is falling back to object mode WITH looplifting enabled because Function \"spherical_dist\" failed type inference due to: \u001b[1m\u001b[1mnon-precise type pyobject\u001b[0m\n",
      "\u001b[0m\u001b[1mDuring: typing of argument at /home/yoder/Dropbox/Research/globalETAS/globalETAS.py (1377)\u001b[0m\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1377:\u001b[0m\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[1m\t\treturn spherical_dist(lon_lat_from=[self.lon, self.lat], lon_lat_to=to_lon_lat)\n",
      "\u001b[0m  \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  @numba.jit\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:177: NumbaWarning: \u001b[1mFunction \"omori_rate\" was compiled in object mode without forceobj=True.\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1384:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaWarning(warn_msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:187: NumbaDeprecationWarning: \u001b[1m\n",
      "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1384:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n",
      "/home/yoder/Dropbox/Research/globalETAS/globalETAS.py:1375: NumbaWarning: \u001b[1m\n",
      "Compilation is falling back to object mode WITH looplifting enabled because Function \"spherical_dist\" failed type inference due to: \u001b[1m\u001b[1mnon-precise type pyobject\u001b[0m\n",
      "\u001b[0m\u001b[1mDuring: typing of argument at /home/yoder/Dropbox/Research/globalETAS/globalETAS.py (1377)\u001b[0m\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1377:\u001b[0m\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[1m\t\treturn spherical_dist(lon_lat_from=[self.lon, self.lat], lon_lat_to=to_lon_lat)\n",
      "\u001b[0m  \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  @numba.jit\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:177: NumbaWarning: \u001b[1mFunction \"spherical_dist\" was compiled in object mode without forceobj=True.\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1376:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaWarning(warn_msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:177: NumbaWarning: \u001b[1mFunction \"omori_rate\" was compiled in object mode without forceobj=True.\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1384:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaWarning(warn_msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:187: NumbaDeprecationWarning: \u001b[1m\n",
      "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1384:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:187: NumbaDeprecationWarning: \u001b[1m\n",
      "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1376:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:177: NumbaWarning: \u001b[1mFunction \"spherical_dist\" was compiled in object mode without forceobj=True.\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1376:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaWarning(warn_msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:177: NumbaWarning: \u001b[1mFunction \"spherical_dist\" was compiled in object mode without forceobj=True.\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1376:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaWarning(warn_msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:187: NumbaDeprecationWarning: \u001b[1m\n",
      "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1376:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:187: NumbaDeprecationWarning: \u001b[1m\n",
      "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1376:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:177: NumbaWarning: \u001b[1mFunction \"omori_rate\" was compiled in object mode without forceobj=True.\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1384:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaWarning(warn_msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:177: NumbaWarning: \u001b[1mFunction \"spherical_dist\" was compiled in object mode without forceobj=True.\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1376:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaWarning(warn_msg,\n",
      "/home/yoder/Dropbox/Research/globalETAS/globalETAS.py:1375: NumbaWarning: \u001b[1m\n",
      "Compilation is falling back to object mode WITH looplifting enabled because Function \"spherical_dist\" failed type inference due to: \u001b[1m\u001b[1mnon-precise type pyobject\u001b[0m\n",
      "\u001b[0m\u001b[1mDuring: typing of argument at /home/yoder/Dropbox/Research/globalETAS/globalETAS.py (1377)\u001b[0m\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1377:\u001b[0m\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[1m\t\treturn spherical_dist(lon_lat_from=[self.lon, self.lat], lon_lat_to=to_lon_lat)\n",
      "\u001b[0m  \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  @numba.jit\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:177: NumbaWarning: \u001b[1mFunction \"spherical_dist\" was compiled in object mode without forceobj=True.\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1376:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaWarning(warn_msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:187: NumbaDeprecationWarning: \u001b[1m\n",
      "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1376:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:187: NumbaDeprecationWarning: \u001b[1m\n",
      "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1384:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:187: NumbaDeprecationWarning: \u001b[1m\n",
      "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1376:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n",
      "/home/yoder/Dropbox/Research/globalETAS/globalETAS.py:1375: NumbaWarning: \u001b[1m\n",
      "Compilation is falling back to object mode WITH looplifting enabled because Function \"spherical_dist\" failed type inference due to: \u001b[1m\u001b[1mnon-precise type pyobject\u001b[0m\n",
      "\u001b[0m\u001b[1mDuring: typing of argument at /home/yoder/Dropbox/Research/globalETAS/globalETAS.py (1377)\u001b[0m\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1377:\u001b[0m\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[1m\t\treturn spherical_dist(lon_lat_from=[self.lon, self.lat], lon_lat_to=to_lon_lat)\n",
      "\u001b[0m  \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  @numba.jit\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/ir_utils.py:2031: NumbaPendingDeprecationWarning: \u001b[1m\n",
      "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'lon_lat_from' of function 'spherical_dist'.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 2427:\u001b[0m\n",
      "\u001b[1m@numba.jit\n",
      "\u001b[1mdef spherical_dist(lon_lat_from=[0., 0.], lon_lat_to=[0.,0.], Rearth = 6378.1):\n",
      "\u001b[0m\u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(NumbaPendingDeprecationWarning(msg, loc=loc))\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:177: NumbaWarning: \u001b[1mFunction \"spherical_dist\" was compiled in object mode without forceobj=True.\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1376:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaWarning(warn_msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:187: NumbaDeprecationWarning: \u001b[1m\n",
      "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1376:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/ir_utils.py:2031: NumbaPendingDeprecationWarning: \u001b[1m\n",
      "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'lon_lat_from' of function 'spherical_dist'.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 2427:\u001b[0m\n",
      "\u001b[1m@numba.jit\n",
      "\u001b[1mdef spherical_dist(lon_lat_from=[0., 0.], lon_lat_to=[0.,0.], Rearth = 6378.1):\n",
      "\u001b[0m\u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(NumbaPendingDeprecationWarning(msg, loc=loc))\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:177: NumbaWarning: \u001b[1mFunction \"spherical_dist\" was compiled in object mode without forceobj=True.\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1376:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaWarning(warn_msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/ir_utils.py:2031: NumbaPendingDeprecationWarning: \u001b[1m\n",
      "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'lon_lat_from' of function 'spherical_dist'.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 2427:\u001b[0m\n",
      "\u001b[1m@numba.jit\n",
      "\u001b[1mdef spherical_dist(lon_lat_from=[0., 0.], lon_lat_to=[0.,0.], Rearth = 6378.1):\n",
      "\u001b[0m\u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(NumbaPendingDeprecationWarning(msg, loc=loc))\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:187: NumbaDeprecationWarning: \u001b[1m\n",
      "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1376:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/ir_utils.py:2031: NumbaPendingDeprecationWarning: \u001b[1m\n",
      "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'lon_lat_from' of function 'spherical_dist'.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 2427:\u001b[0m\n",
      "\u001b[1m@numba.jit\n",
      "\u001b[1mdef spherical_dist(lon_lat_from=[0., 0.], lon_lat_to=[0.,0.], Rearth = 6378.1):\n",
      "\u001b[0m\u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(NumbaPendingDeprecationWarning(msg, loc=loc))\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:177: NumbaWarning: \u001b[1mFunction \"spherical_dist\" was compiled in object mode without forceobj=True.\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1376:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaWarning(warn_msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/ir_utils.py:2031: NumbaPendingDeprecationWarning: \u001b[1m\n",
      "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'lon_lat_from' of function 'spherical_dist'.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 2427:\u001b[0m\n",
      "\u001b[1m@numba.jit\n",
      "\u001b[1mdef spherical_dist(lon_lat_from=[0., 0.], lon_lat_to=[0.,0.], Rearth = 6378.1):\n",
      "\u001b[0m\u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(NumbaPendingDeprecationWarning(msg, loc=loc))\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:187: NumbaDeprecationWarning: \u001b[1m\n",
      "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1376:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/ir_utils.py:2031: NumbaPendingDeprecationWarning: \u001b[1m\n",
      "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'lon_lat_from' of function 'spherical_dist'.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 2427:\u001b[0m\n",
      "\u001b[1m@numba.jit\n",
      "\u001b[1mdef spherical_dist(lon_lat_from=[0., 0.], lon_lat_to=[0.,0.], Rearth = 6378.1):\n",
      "\u001b[0m\u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(NumbaPendingDeprecationWarning(msg, loc=loc))\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/ir_utils.py:2031: NumbaPendingDeprecationWarning: \u001b[1m\n",
      "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'lon_lat_from' of function 'spherical_dist'.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 2427:\u001b[0m\n",
      "\u001b[1m@numba.jit\n",
      "\u001b[1mdef spherical_dist(lon_lat_from=[0., 0.], lon_lat_to=[0.,0.], Rearth = 6378.1):\n",
      "\u001b[0m\u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(NumbaPendingDeprecationWarning(msg, loc=loc))\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/ir_utils.py:2031: NumbaPendingDeprecationWarning: \u001b[1m\n",
      "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'lon_lat_from' of function 'spherical_dist'.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 2427:\u001b[0m\n",
      "\u001b[1m@numba.jit\n",
      "\u001b[1mdef spherical_dist(lon_lat_from=[0., 0.], lon_lat_to=[0.,0.], Rearth = 6378.1):\n",
      "\u001b[0m\u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(NumbaPendingDeprecationWarning(msg, loc=loc))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[171001]: finished calculateing ETAS (rtree). wrap up in recarray and return.\n",
      "etas complete (from mpp_rtree run() loop); now pipe back([0, 2436])\n",
      "[171068]: finished calculateing ETAS (rtree). wrap up in recarray and return.[171004]: finished calculateing ETAS (rtree). wrap up in recarray and return.\n",
      "\n",
      "etas complete (from mpp_rtree run() loop); now pipe back([0, 2436])etas complete (from mpp_rtree run() loop); now pipe back([0, 2436])\n",
      "\n",
      "[171047]: finished calculateing ETAS (rtree). wrap up in recarray and return.\n",
      "etas complete (from mpp_rtree run() loop); now pipe back([0, 2436])\n",
      "[171009]: finished calculateing ETAS (rtree). wrap up in recarray and return.\n",
      "etas complete (from mpp_rtree run() loop); now pipe back([0, 2436])\n",
      "[171034]: finished calculateing ETAS (rtree). wrap up in recarray and return.\n",
      "etas complete (from mpp_rtree run() loop); now pipe back([0, 2436])\n",
      "[171016]: finished calculateing ETAS (rtree). wrap up in recarray and return.\n",
      "etas complete (from mpp_rtree run() loop); now pipe back([0, 2436])\n",
      "[171023]: finished calculateing ETAS (rtree). wrap up in recarray and return.\n",
      "etas complete (from mpp_rtree run() loop); now pipe back([0, 2436])\n",
      "ETAS complete.\n"
     ]
    }
   ],
   "source": [
    "#eq_prams['t_now'] = dtm.datetime(2019,7,6,18,0,0, tzinfo=pytz.timezone('UTC'))\n",
    "eq_prams['t_now'] = dtm.datetime.now(pytz.timezone('UTC'))\n",
    "#eq_prams['mc'] = 3.5\n",
    "# eq_prams['lats'] = [lat0 - 1., lat0 + 1.]\n",
    "# eq_prams['lons'] = [lon0 - 1., lon0 + 1.]\n",
    "#\n",
    "# eq_prams['lats'] = [lat0 - 2., lat0 + 2.]\n",
    "# eq_prams['lons'] = [lon0 - 2., lon0 + 2.]\n",
    "#\n",
    "n_cpu = 2*mpp.cpu_count()\n",
    "#n_cpu = 6\n",
    "#n_cpu=5\n",
    "etas = gep.ETAS_mpp(n_cpu=n_cpu, catalog=mycat, **eq_prams)\n",
    "#\n",
    "# we've run this; we can reload it from pickle:\n",
    "#with open('data/etas_201610.pkl', 'rb') as fin:\n",
    "#    etas = pickle.load(fin)\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "event_name = 'Ridgecrest_July_2019'\n",
    "#f_path = '/home/myoder/Dropbox/Research/etas/{}/etas_{}'.format(event_name, etas.t_now)\n",
    "#f_path = '{}/data_export/Research/etas/{}/etas_{}'.format(os.getenv('HOME'), event_name, etas.t_now)\n",
    "f_path = '{}/Dropbox/Research/etas/{}/etas_{}'.format(os.getenv('HOME'), event_name, etas.t_now)\n",
    "f_root = 'etas_{}_2019_07'.format(event_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-02-16 23:05:23.309772+00:00 18674.962075344585 ('2021-02-14T23:42:34.470000', 35.9725, -117.3201667, 3.6, 3.07, 18672.98789896, 1.0964782, 0.22709312, 3.33371144, 0.33113112, 0.01367537, 12.20129939, 1., 1.1, 1.5, 0.35740716, [1.19067941e-04, 1.85462408e-05], [[ 0.81673174, -0.57701757], [ 0.57701757,  0.81673174]], 23.)\n"
     ]
    }
   ],
   "source": [
    "print( etas.t_now, etas.t_forecast, etas.catalog[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yoder/Dropbox/Research/globalETAS/globalETAS.py:377: MatplotlibDeprecationWarning: The 'cmap' parameter to Colorbar has no effect because it is overridden by the mappable; it is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  plt.colorbar(etas_contours, cax=None, ax=ax, cmap=map_cmap)\n",
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ms:  ('2020-09-19T06:38:46.930000', 34.038, -118.08, 4.54, 16.87, 18524.27693206, 3.23593657, 2.30859015, 0.12004846, 0.97723722, 0.1708464, 1.08827692, 1., 1.1, 1.5, 1.1395534, [1., 1.], [[1., 0.], [0., 1.]], 1.) -118.08 34.038\n",
      "tt:  18659.962075344585 2005-01-04T08:49:24.820000 <class 'numpy.datetime64'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f2504035220>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fg=plt.figure(0, figsize=(12,10))\n",
    "ax=plt.gca()\n",
    "# lats_map= , lons_map=\n",
    "etas.make_etas_contour_map(n_contours=25, fignum=0, map_resolution='f', alpha=.3, ax=ax)\n",
    "#\n",
    "#mainshock = sorted(etas.catalog, key=lambda rw: rw['mag'])[-1]\n",
    "#print('mainshock: ', mainshock)\n",
    "# get mainshock. it's an m>6 event in the last week or so... this is subjective.\n",
    "# if we just look for the biggest event, we get the L'Aquila event, so we'll need to be more creative...\n",
    "# or just specify it.\n",
    "\n",
    "mainshock = etas.catalog[-1]\n",
    "for j,eq in enumerate(reversed(etas.catalog)):\n",
    "    #print('*** ', pytz.utc.localize(eq['event_date'].astype(dtm.datetime)))\n",
    "    if pytz.utc.localize(eq['event_date'].astype(dtm.datetime))<etas.t_now-dtm.timedelta(days=180): break\n",
    "    if eq['mag']>mainshock['mag']:\n",
    "        mainshock = eq\n",
    "        #\n",
    "    #\n",
    "#\n",
    "print('ms: ', mainshock, mainshock['lon'], mainshock['lat'])\n",
    "x,y = etas.cm(mainshock['lon'], mainshock['lat'])\n",
    "#\n",
    "#print('mm: ', max(etas.catalog['mag']))\n",
    "#\n",
    "# let's get everything m>6 in the last 6 months?\n",
    "m6s = [rw for rw in etas.catalog if rw['mag'] >= 6. \n",
    "       and pytz.utc.localize(rw['event_date'].astype(dtm.datetime))>to_dt-dtm.timedelta(days=180)]\n",
    "#\n",
    "# plot mainshock:\n",
    "dt = mainshock['event_date'].astype(dtm.datetime)\n",
    "dt=t0\n",
    "dt_str = '{}-{}-{} {}:{}:{}'.format(dt.year, dt.month, dt.day, dt.hour, dt.minute, dt.second)\n",
    "#etas.cm.plot([x], [y], latlon=False, marker='*', color='r', ms=16, zorder=11,\n",
    "#                   label='m={}, {}'.format(mainshock['mag'], dt_str))\n",
    "#etas.cm.plot([lon0], [lat0], latlon=False, marker='*', color='r', ms=16, zorder=11,\n",
    "#                   label='m={}, {}'.format(m0, dt_str))\n",
    "\n",
    "ax.set_title('ETAS: {}, {}\\n\\n'.format(event_name, etas.t_now), size=16)\n",
    "for j,m6 in enumerate(m6s):\n",
    "    clr = colors_[j%len(colors_)]\n",
    "    #\n",
    "    dt = m6['event_date'].astype(dtm.datetime)\n",
    "    dt_str = '{}-{}-{} {}:{}:{}'.format(dt.year, dt.month, dt.day, dt.hour, dt.minute, dt.second)\n",
    "    etas.cm.scatter(m6['lon'], m6['lat'], s=2*(m6['mag']+2.), edgecolors=clr, \n",
    "                          c='none', marker='o', zorder=11, label='m={}, {}'.format(m6['mag'], dt_str))\n",
    "\n",
    "#x,y = etas.cm(*ll_sacramento)\n",
    "#etas.cm.scatter([x],[y], marker='o', s=18, edgecolors='r', c='r',\n",
    "#                    label='Sacramento')\n",
    "t_cat = mpd.date2num(etas.t_now-dtm.timedelta(days=15))\n",
    "print('tt: ', t_cat, etas.catalog['event_date'][0], type(etas.catalog['event_date'][0]))\n",
    "k=0\n",
    "\n",
    "# for j,rw in enumerate(etas.catalog):\n",
    "#     if mpd.date2num(rw['event_date'].astype(dtm.datetime))<t_cat: continue\n",
    "#     k+=1\n",
    "#     clr = colors_[k%len(colors_)]\n",
    "#     #\n",
    "#     dt = rw['event_date'].astype(dtm.datetime)\n",
    "#     dt_str = '{}-{}-{} {}:{}:{}'.format(dt.year, dt.month, dt.day, dt.hour, dt.minute, dt.second)\n",
    "#     #etas.cm.scatter(rw['lon'],rw['lat'], s=3*(rw['mag']+12.), edgecolors=clr, \n",
    "#     #                      c='none', marker='o', zorder=11, label='m={}, {}'.format(rw['mag'], dt_str))\n",
    "#     etas.cm.plot(rw['lon'],rw['lat'], ms=2.*(rw['mag']+2.), color=clr, \n",
    "#                           marker='o', zorder=11, label='m={}, {}'.format(rw['mag'], dt_str), latlon=True)\n",
    "    \n",
    "    #\n",
    "plt.gca().legend()\n",
    "#print(etas.catalog[-10:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ms:  ('2020-09-19T06:38:46.930000', 34.038, -118.08, 4.54, 16.87, 18524.27693206, 3.23593657, 2.30859015, 0.12004846, 0.97723722, 0.1708464, 1.08827692, 1., 1.1, 1.5, 1.1395534, [1., 1.], [[1., 0.], [0., 1.]], 1.) -118.08 34.038\n",
      "tt:  18659.962075344585 2005-01-04T08:49:24.820000 <class 'numpy.datetime64'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f25062d3820>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fg2=plt.figure(0, figsize=(12,10))\n",
    "ax=plt.gca()\n",
    "etas.make_etas_contour_map(n_contours=25, fignum=0, map_resolution='f', alpha=.3, ax=ax)\n",
    "#\n",
    "#mainshock = sorted(etas.catalog, key=lambda rw: rw['mag'])[-1]\n",
    "#print('mainshock: ', mainshock)\n",
    "# get mainshock. it's an m>6 event in the last week or so... this is subjective.\n",
    "# if we just look for the biggest event, we get the L'Aquila event, so we'll need to be more creative...\n",
    "# or just specify it.\n",
    "\n",
    "mainshock = etas.catalog[-1]\n",
    "for j,eq in enumerate(reversed(etas.catalog)):\n",
    "    #print('*** ', pytz.utc.localize(eq['event_date'].astype(dtm.datetime)))\n",
    "    if pytz.utc.localize(eq['event_date'].astype(dtm.datetime))<etas.t_now-dtm.timedelta(days=180): break\n",
    "    if eq['mag']>mainshock['mag']:\n",
    "        mainshock = eq\n",
    "        #\n",
    "    #\n",
    "#\n",
    "print('ms: ', mainshock, mainshock['lon'], mainshock['lat'])\n",
    "x,y = etas.cm(mainshock['lon'], mainshock['lat'])\n",
    "#\n",
    "#print('mm: ', max(etas.catalog['mag']))\n",
    "#\n",
    "# let's get everything m>6 in the last 6 months?\n",
    "m6s = [rw for rw in etas.catalog if rw['mag'] >= 6. \n",
    "       and pytz.utc.localize(rw['event_date'].astype(dtm.datetime))>to_dt-dtm.timedelta(days=180)]\n",
    "#\n",
    "# plot mainshock:\n",
    "dt = mainshock['event_date'].astype(dtm.datetime)\n",
    "dt=t0\n",
    "dt_str = '{}-{}-{} {}:{}:{}'.format(dt.year, dt.month, dt.day, dt.hour, dt.minute, dt.second)\n",
    "#etas.cm.plot([x], [y], latlon=False, marker='*', color='r', ms=16, zorder=11,\n",
    "#                   label='m={}, {}'.format(mainshock['mag'], dt_str))\n",
    "#etas.cm.plot([lon0], [lat0], latlon=False, marker='*', color='r', ms=16, zorder=11,\n",
    "#                   label='m={}, {}'.format(m0, dt_str))\n",
    "\n",
    "ax.set_title('ETAS: {}, {}\\n\\n'.format(event_name, etas.t_now), size=16)\n",
    "# for j,m6 in enumerate(m6s):\n",
    "#     clr = colors_[j%len(colors_)]\n",
    "#     #\n",
    "#     dt = m6['event_date'].astype(dtm.datetime)\n",
    "#     dt_str = '{}-{}-{} {}:{}:{}'.format(dt.year, dt.month, dt.day, dt.hour, dt.minute, dt.second)\n",
    "#     etas.cm.scatter(m6['lon'], m6['lat'], s=2*(m6['mag']+2.), edgecolors=clr, \n",
    "#                           c='none', marker='o', zorder=11, label='m={}, {}'.format(m6['mag'], dt_str))\n",
    "\n",
    "# ix = (numpy.array([pytz.utc.localize(x.astype(dtm.datetime)) for x in etas.catalog['event_date']])<=etas.t_now and\n",
    "#       numpy.array([pytz.utc.localize(x.astype(dtm.datetime)) \n",
    "#                    for x in etas.catalog['event_date']])>=dtm.datetime(2019,7,4, tzinfo=pytz.timezone('UTC')))\n",
    "ix = numpy.array([x<=etas.t_now and x>dtm.datetime(2019,7,4, tzinfo=pytz.timezone('UTC'))\n",
    "      for x in [pytz.utc.localize(x.astype(dtm.datetime)) for x in etas.catalog['event_date']]])\n",
    "#\n",
    "etas.cm.plot(etas.catalog['lon'][ix], etas.catalog['lat'][ix], marker='.', ls='')\n",
    "#x,y = etas.cm(*ll_sacramento)\n",
    "#etas.cm.scatter([x],[y], marker='o', s=18, edgecolors='r', c='r',\n",
    "#                    label='Sacramento')\n",
    "t_cat = mpd.date2num(etas.t_now-dtm.timedelta(days=15))\n",
    "print('tt: ', t_cat, etas.catalog['event_date'][0], type(etas.catalog['event_date'][0]))\n",
    "k=0\n",
    "\n",
    "# for j,rw in enumerate(etas.catalog):\n",
    "#     if mpd.date2num(rw['event_date'].astype(dtm.datetime))<t_cat: continue\n",
    "#     k+=1\n",
    "#     clr = colors_[k%len(colors_)]\n",
    "#     #\n",
    "#     dt = rw['event_date'].astype(dtm.datetime)\n",
    "#     dt_str = '{}-{}-{} {}:{}:{}'.format(dt.year, dt.month, dt.day, dt.hour, dt.minute, dt.second)\n",
    "#     #etas.cm.scatter(rw['lon'],rw['lat'], s=3*(rw['mag']+12.), edgecolors=clr, \n",
    "#     #                      c='none', marker='o', zorder=11, label='m={}, {}'.format(rw['mag'], dt_str))\n",
    "#     etas.cm.plot(rw['lon'],rw['lat'], ms=2.*(rw['mag']+2.), color=clr, \n",
    "#                           marker='o', zorder=11, label='m={}, {}'.format(rw['mag'], dt_str), latlon=True)\n",
    "    \n",
    "    #\n",
    "ax.legend(loc=0)\n",
    "#print(etas.catalog[-10:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['2019-07-04T17:33:49.000000,6.4\\n', '2019-07-06T03:19:53.040000,7.1\\n', '2019-07-06T03:47:53.420000,5.5\\n', '2020-06-04T01:32:11.140000,5.53\\n', '2020-06-24T17:40:49.240000,5.8\\n']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(['{},{}\\n'.format(rw['event_date'], rw['mag'] ) for rw in etas.catalog if rw['mag'] >= 5.5])\n",
    "len(['{},{}\\n'.format(rw['event_date'], rw['mag'] ) for rw in etas.catalog if rw['mag'] >= 5.5\n",
    "     and rw['event_date'].astype(dtm.datetime)>dtm.datetime(2016,10,20)])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "kml bottom, top:  0 26\n"
     ]
    }
   ],
   "source": [
    "## hey, pickling works with the new ETAS objects (or at least it appears to...):\n",
    "# with open('data/etas_201610.pkl', 'wb') as fpkl:\n",
    "#     pickle.dump( etas, fpkl)    \n",
    "#\n",
    "#with open('data/etas_201610.pkl', 'rb') as fin:\n",
    "#    etas2 = pickle.load(fin)\n",
    "#\n",
    "#print(etas2.catalog[0:5])\n",
    "# TODO: we want the datetime part of the filename to come from the etas object itself, for purposes of\n",
    "# integrity. BUT, we want this script to be a bit more portable, so we should replace all the etas\n",
    "# references/object name to just 'etas'\n",
    "#\n",
    "etas.export_kml(os.path.join(f_path, '{}_{}.kml'.format(f_root, str(etas.t_now).replace(' ', '_'))))\n",
    "etas.export_xyz(os.path.join(f_path, '{}_{}.xyz'.format(f_root, str(etas.t_now).replace(' ', '_'))))\n",
    "fg.savefig(os.path.join(f_path, '{}_{}.png'.format(f_root, str(etas.t_now).replace(' ', '_'))))\n",
    "fg2.savefig(os.path.join(f_path, '{}_{}_with_equakes.png'.format(f_root, str(etas.t_now).replace(' ', '_'))))\n",
    "\n",
    "\n",
    "#\n",
    "# so this worked, once upon a time, but breaks maybe when the script does not run cleanly all the way through?\n",
    "with open (os.path.join(f_path, '{}_{}.pkl'.format(f_root, str(etas.t_now).replace(' ', '_'))), 'wb') as fpkl:\n",
    "   pickle.dump(etas, fpkl)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-11-f4b44531e29e>:3: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  ax1=plt.subplot('121')\n",
      "<ipython-input-11-f4b44531e29e>:4: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  ax2=plt.subplot('122')\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "plt.clf()\n",
    "ax1=plt.subplot('121')\n",
    "ax2=plt.subplot('122')\n",
    "ax2.grid()\n",
    "ax1.plot(etas.catalog['lon'], etas.catalog['lat'], ',')\n",
    "ax1.plot([mainshock['lon']], [mainshock['lat']], marker='*', color='r', ms=16, zorder=11)\n",
    "#\n",
    "ax2.plot([m for m in reversed(sorted(etas.catalog['mag']))], numpy.arange(1,len(etas.catalog)+1),\n",
    "         '.-', lw=2.)\n",
    "ax2.set_yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-12-6185e86ae60c>:7: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  ax1 = fg.add_subplot('211')\n",
      "<ipython-input-12-6185e86ae60c>:8: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  ax2 = fg.add_subplot('212', sharex=ax1)\n",
      "<ipython-input-12-6185e86ae60c>:34: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  ax1 = fg2.add_subplot('211')\n",
      "<ipython-input-12-6185e86ae60c>:35: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  ax2 = fg2.add_subplot('212', sharex=ax1)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.LineCollection at 0x7f24f77940a0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How 'bout some intervals and magnitudes:\n",
    "#\n",
    "# (we should probably build this plot int the class, or really, we should make a separate plotter class, that\n",
    "#  takes and ETAS object as an input.)\n",
    "#\n",
    "fg = plt.figure(figsize=(14,7))\n",
    "ax1 = fg.add_subplot('211')\n",
    "ax2 = fg.add_subplot('212', sharex=ax1)\n",
    "#\n",
    "ax1.grid()\n",
    "ax2.grid()\n",
    "#\n",
    "ax1.set_title('Intervals')\n",
    "ax1.set_ylabel('Mean Interval $\\Delta t / N$')\n",
    "ax2.set_title('Magnitudes')\n",
    "ax2.set_xlabel('time $t$')\n",
    "#\n",
    "n_int = 100\n",
    "X = etas.catalog['event_date_float']\n",
    "ix_m = etas.catalog['mag']>6.1\n",
    "ix_m5 = etas.catalog['mag']>5.0\n",
    "intervals = numpy.array(X[n_int:] - X[0:-n_int] )/float(n_int)\n",
    "#\n",
    "ax1.plot(X[n_int:], intervals, ls='-', lw=3., marker='', label='intervals')\n",
    "#\n",
    "ax1.vlines(X[ix_m], min(intervals), max(intervals), color='r', lw=2.5)\n",
    "ax1.vlines(X[ix_m5], min(intervals), .5*max(intervals), color='r',  lw=2.5)\n",
    "ax2.vlines(etas.catalog['event_date_float'], etas.catalog['mag'], min(etas.catalog['mag'])-2., color='b', lw=2.)\n",
    "#ax1.set_xlim(726000)\n",
    "#\n",
    "#\n",
    "#####\n",
    "fg2 = plt.figure(figsize=(14,7))\n",
    "ax1 = fg2.add_subplot('211')\n",
    "ax2 = fg2.add_subplot('212', sharex=ax1)\n",
    "#\n",
    "ax1.grid()\n",
    "ax2.grid()\n",
    "#\n",
    "ax1.set_title('Intervals')\n",
    "ax1.set_ylabel('Mean Interval $\\Delta t / N$')\n",
    "ax2.set_title('Magnitudes')\n",
    "#\n",
    "ax2.set_xlabel('Event Count $n$ (Natural time)')\n",
    "#\n",
    "ax1.plot(numpy.arange(1, len(intervals)+1), intervals, ls='-', lw=3., marker='', label='intervals')\n",
    "#\n",
    "ax1.vlines(numpy.arange(len(etas.catalog))[ix_m], min(intervals), max(intervals), color='r', lw=2.5)\n",
    "ax2.vlines(numpy.arange(len(etas.catalog)), etas.catalog['mag'], min(etas.catalog['mag'])-2., color='b', lw=2.)\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-13-2c83bbfe7335>:5: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  ax1 = fg2.add_subplot('211')\n",
      "<ipython-input-13-2c83bbfe7335>:6: MatplotlibDeprecationWarning: Passing non-integers as three-element position specification is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  ax2 = fg2.add_subplot('212', sharex=ax1)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x504 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# now, let's look at that last one up close:\n",
    "#\n",
    "#####\n",
    "fg2 = plt.figure(figsize=(14,7))\n",
    "ax1 = fg2.add_subplot('211')\n",
    "ax2 = fg2.add_subplot('212', sharex=ax1)\n",
    "#\n",
    "ax1.grid()\n",
    "ax2.grid()\n",
    "#\n",
    "ax1.set_title('Intervals')\n",
    "ax1.set_ylabel('Mean Interval $\\Delta t / N$')\n",
    "ax2.set_title('Magnitudes')\n",
    "#\n",
    "ax2.set_xlabel('Event Count $n$ (Natural time)')\n",
    "#\n",
    "#\n",
    "k0 = 3300\n",
    "ax1.plot(numpy.arange(1+k0, len(intervals)+1), intervals[k0:], ls='-', lw=3., marker='', label='intervals')\n",
    "#\n",
    "ax1.vlines(numpy.arange(len(etas.catalog))[ix_m], min(intervals), max(intervals), color='r', lw=2.5)\n",
    "ax2.vlines(numpy.arange(k0, len(etas.catalog)), etas.catalog['mag'][k0:], min(etas.catalog['mag'])-2., color='b', lw=2.)\n",
    "#\n",
    "#ax1.set_ylim(-.01, .41)\n",
    "ax1.set_yscale('log')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('event_date', 'lat', 'lon', 'mag', 'depth', 'event_date_float', 'L_r', 'r_0', 'chi', 'dt_r', 't_0', 'tau', 'dmstar', 'p', 'q', 'chi_norm', 'e_vals', 'e_vecs', 'N_eig_cat')\n"
     ]
    }
   ],
   "source": [
    "print(etas.catalog.dtype.names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "** mainshocks:  [('2019-07-04T17:33:49.000000', 35.7053333, -117.5038333, 6.4, 10.5, 18081.73181713, 27.54228703, 227.09312198, 0.00016708, 8.31763771, 25.27003406, 0.00911464, 1., 1.1, 1.5, 11.30220691, [0.00906216, 0.03066698], [[-0.72898579,  0.68452883], [-0.68452883, -0.72898579]], 1263.), ('2019-07-06T03:19:53.040000', 35.7695, -117.5993333, 7.1, 8., 18083.13880833, 61.65950019, 1277.03847158, 1.40581446e-05, 18.62087137, 165.68091867, 0.00150686, 1., 1.1, 1.5, 26.80175629, [0.09017107, 0.04433508], [[ 0.73375645,  0.6794126 ], [-0.6794126 ,  0.73375645]], 1593.)]\n"
     ]
    }
   ],
   "source": [
    "mainshocks = sorted(etas.catalog[etas.catalog['event_date']>dtm.datetime(2019,6,20)],\n",
    "                    key=lambda rw:rw['mag'])[-2:]\n",
    "#\n",
    "print('** mainshocks: ', mainshocks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "** ms2:  [('2019-07-04T17:33:49.000000', 35.7053333, -117.5038333, 6.4, 10.5, 18081.73181713, 27.54228703,  227.09312198, 1.67081361e-04,  8.31763771,  25.27003406, 0.00911464, 1., 1.1, 1.5, 11.30220691, [0.00906216, 0.03066698], [[-0.72898579,  0.68452883], [-0.68452883, -0.72898579]], 1263.)\n",
      " ('2019-07-06T03:19:53.040000', 35.7695   , -117.5993333, 7.1,  8. , 18083.13880833, 61.65950019, 1277.03847158, 1.40581446e-05, 18.62087137, 165.68091867, 0.00150686, 1., 1.1, 1.5, 26.80175629, [0.09017107, 0.04433508], [[ 0.73375645,  0.6794126 ], [-0.6794126 ,  0.73375645]], 1593.)]\n"
     ]
    }
   ],
   "source": [
    "ix = etas.catalog['event_date']>dtm.datetime(2019,6,20)\n",
    "\n",
    "ms2 = (etas.catalog[ix])[numpy.argsort( (etas.catalog[ix])['mag'])[-2:]]\n",
    "print('** ms2: ', ms2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "foreshock interval:  1.4069912037048198\n"
     ]
    }
   ],
   "source": [
    "m6, m7 = mainshocks\n",
    "print('foreshock interval: ', m7['event_date_float']- m6['event_date_float'])\n",
    "#\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "begin globalETAS.__init()__\n",
      "Overriding t_0 (etas catalog start date/time) for ETAS calculations. using catalog start, t_0 = t_now - catlen (3650.000000) = 2009-07-06 17:33:49+00:00\n",
      "ETAS over etas_cat_range/xyz_range:  ([0, 1187], None)\n",
      "make_etas():\n",
      "etas_mpp worker xyz_range:  [0, 200]\n",
      "begin globalETAS.__init()__\n",
      "Overriding t_0 (etas catalog start date/time) for ETAS calculations. using catalog start, t_0 = t_now - catlen (3650.000000) = 2009-07-06 17:33:49+00:00\n",
      "ETAS over etas_cat_range/xyz_range:  ([0, 1187], [0, 200])\n",
      "etas_mpp worker xyz_range:  [200, 400]\n",
      "begin globalETAS.__init()__\n",
      "Overriding t_0 (etas catalog start date/time) for ETAS calculations. using catalog start, t_0 = t_now - catlen (3650.000000) = 2009-07-06 17:33:49+00:00\n",
      "ETAS over etas_cat_range/xyz_range:  ([0, 1187], [200, 400])\n",
      "etas_mpp worker xyz_range:  [400, 600]\n",
      "begin globalETAS.__init()__\n",
      "Overriding t_0 (etas catalog start date/time) for ETAS calculations. using catalog start, t_0 = t_now - catlen (3650.000000) = 2009-07-06 17:33:49+00:00\n",
      "ETAS over etas_cat_range/xyz_range:  ([0, 1187], [400, 600])\n",
      "etas_mpp worker xyz_range:  [600, 800]\n",
      "begin globalETAS.__init()__\n",
      "Overriding t_0 (etas catalog start date/time) for ETAS calculations. using catalog start, t_0 = t_now - catlen (3650.000000) = 2009-07-06 17:33:49+00:00\n",
      "ETAS over etas_cat_range/xyz_range:  ([0, 1187], [600, 800])\n",
      "etas_mpp worker xyz_range:  [800, 1000]\n",
      "begin globalETAS.__init()__\n",
      "Overriding t_0 (etas catalog start date/time) for ETAS calculations. using catalog start, t_0 = t_now - catlen (3650.000000) = 2009-07-06 17:33:49+00:00\n",
      "ETAS over etas_cat_range/xyz_range:  ([0, 1187], [800, 1000])\n",
      "etas_mpp worker xyz_range:  [1000, 1200]\n",
      "begin globalETAS.__init()__\n",
      "Overriding t_0 (etas catalog start date/time) for ETAS calculations. using catalog start, t_0 = t_now - catlen (3650.000000) = 2009-07-06 17:33:49+00:00\n",
      "ETAS over etas_cat_range/xyz_range:  ([0, 1187], [1000, 1200])\n",
      "etas_mpp worker xyz_range:  [1200, 1400]\n",
      "begin globalETAS.__init()__\n",
      "Overriding t_0 (etas catalog start date/time) for ETAS calculations. using catalog start, t_0 = t_now - catlen (3650.000000) = 2009-07-06 17:33:49+00:00\n",
      "ETAS over etas_cat_range/xyz_range:  ([0, 1187], [1200, 1400])\n",
      "etas_mpp worker xyz_range:  [1400, 1600]\n",
      "begin globalETAS.__init()__\n",
      "Overriding t_0 (etas catalog start date/time) for ETAS calculations. using catalog start, t_0 = t_now - catlen (3650.000000) = 2009-07-06 17:33:49+00:00\n",
      "ETAS over etas_cat_range/xyz_range:  ([0, 1187], [1400, 1600])\n",
      "begin make_etas_rtree()begin make_etas_rtree()\n",
      "\n",
      "** len(self.ETAS_array)[171300] = 200begin make_etas_rtree()\n",
      "** len(self.ETAS_array)[171297] = 200\n",
      "\n",
      "begin make_etas_rtree()begin make_etas_rtree()\n",
      "\n",
      "Indices initiated. begin ETAS[171300] :: range: [0, 1187]** len(self.ETAS_array)[171314] = 200\n",
      "** len(self.ETAS_array)[171305] = 200begin make_etas_rtree()\n",
      "** len(self.ETAS_array)[171321] = 200begin make_etas_rtree()\n",
      "** len(self.ETAS_array)[171329] = 200now gather sub-arrays...\n",
      "Indices initiated. begin ETAS[171297] :: range: [0, 1187]\n",
      "\n",
      "\n",
      "Indices initiated. begin ETAS[171305] :: range: [0, 1187]\n",
      "\n",
      "\n",
      "** len(self.ETAS_array)[171328] = 200begin make_etas_rtree()\n",
      "Indices initiated. begin ETAS[171329] :: range: [0, 1187]\n",
      "Indices initiated. begin ETAS[171328] :: range: [0, 1187]Indices initiated. begin ETAS[171314] :: range: [0, 1187]Indices initiated. begin ETAS[171321] :: range: [0, 1187]\n",
      "** len(self.ETAS_array)[171336] = 200\n",
      "\n",
      "\n",
      "\n",
      "Indices initiated. begin ETAS[171336] :: range: [0, 1187]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yoder/Dropbox/Research/globalETAS/globalETAS.py:1383: NumbaWarning: \u001b[1m\n",
      "Compilation is falling back to object mode WITH looplifting enabled because Function \"omori_rate\" failed type inference due to: \u001b[1m\u001b[1mnon-precise type pyobject\u001b[0m\n",
      "\u001b[0m\u001b[1mDuring: typing of argument at /home/yoder/Dropbox/Research/globalETAS/globalETAS.py (1401)\u001b[0m\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1401:\u001b[0m\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "     <source elided>\n",
      "\t\t#print(\"inputs: \", t, lon, lat, p, q)\n",
      "\u001b[1m\t\tt = (t or mpd.date2num(dtm.datetime.now(pytz.timezone('UTC'))))\n",
      "\u001b[0m  \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  @numba.jit\n",
      "/home/yoder/Dropbox/Research/globalETAS/globalETAS.py:1383: NumbaWarning: \u001b[1m\n",
      "Compilation is falling back to object mode WITH looplifting enabled because Function \"omori_rate\" failed type inference due to: \u001b[1m\u001b[1mnon-precise type pyobject\u001b[0m\n",
      "\u001b[0m\u001b[1mDuring: typing of argument at /home/yoder/Dropbox/Research/globalETAS/globalETAS.py (1401)\u001b[0m\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1401:\u001b[0m\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "     <source elided>\n",
      "\t\t#print(\"inputs: \", t, lon, lat, p, q)\n",
      "\u001b[1m\t\tt = (t or mpd.date2num(dtm.datetime.now(pytz.timezone('UTC'))))\n",
      "\u001b[0m  \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  @numba.jit\n",
      "/home/yoder/Dropbox/Research/globalETAS/globalETAS.py:1383: NumbaWarning: \u001b[1m\n",
      "Compilation is falling back to object mode WITH looplifting enabled because Function \"omori_rate\" failed type inference due to: \u001b[1m\u001b[1mnon-precise type pyobject\u001b[0m\n",
      "\u001b[0m\u001b[1mDuring: typing of argument at /home/yoder/Dropbox/Research/globalETAS/globalETAS.py (1401)\u001b[0m\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1401:\u001b[0m\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "     <source elided>\n",
      "\t\t#print(\"inputs: \", t, lon, lat, p, q)\n",
      "\u001b[1m\t\tt = (t or mpd.date2num(dtm.datetime.now(pytz.timezone('UTC'))))\n",
      "\u001b[0m  \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  @numba.jit\n",
      "/home/yoder/Dropbox/Research/globalETAS/globalETAS.py:1383: NumbaWarning: \u001b[1m\n",
      "Compilation is falling back to object mode WITH looplifting enabled because Function \"omori_rate\" failed type inference due to: \u001b[1m\u001b[1mnon-precise type pyobject\u001b[0m\n",
      "\u001b[0m\u001b[1mDuring: typing of argument at /home/yoder/Dropbox/Research/globalETAS/globalETAS.py (1401)\u001b[0m\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1401:\u001b[0m\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "     <source elided>\n",
      "\t\t#print(\"inputs: \", t, lon, lat, p, q)\n",
      "\u001b[1m\t\tt = (t or mpd.date2num(dtm.datetime.now(pytz.timezone('UTC'))))\n",
      "\u001b[0m  \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  @numba.jit\n",
      "/home/yoder/Dropbox/Research/globalETAS/globalETAS.py:1383: NumbaWarning: \u001b[1m\n",
      "Compilation is falling back to object mode WITH looplifting enabled because Function \"omori_rate\" failed type inference due to: \u001b[1m\u001b[1mnon-precise type pyobject\u001b[0m\n",
      "\u001b[0m\u001b[1mDuring: typing of argument at /home/yoder/Dropbox/Research/globalETAS/globalETAS.py (1401)\u001b[0m\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1401:\u001b[0m\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "     <source elided>\n",
      "\t\t#print(\"inputs: \", t, lon, lat, p, q)\n",
      "\u001b[1m\t\tt = (t or mpd.date2num(dtm.datetime.now(pytz.timezone('UTC'))))\n",
      "\u001b[0m  \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  @numba.jit\n",
      "/home/yoder/Dropbox/Research/globalETAS/globalETAS.py:1383: NumbaWarning: \u001b[1m\n",
      "Compilation is falling back to object mode WITH looplifting enabled because Function \"omori_rate\" failed type inference due to: \u001b[1m\u001b[1mnon-precise type pyobject\u001b[0m\n",
      "\u001b[0m\u001b[1mDuring: typing of argument at /home/yoder/Dropbox/Research/globalETAS/globalETAS.py (1401)\u001b[0m\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1401:\u001b[0m\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "     <source elided>\n",
      "\t\t#print(\"inputs: \", t, lon, lat, p, q)\n",
      "\u001b[1m\t\tt = (t or mpd.date2num(dtm.datetime.now(pytz.timezone('UTC'))))\n",
      "\u001b[0m  \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  @numba.jit\n",
      "/home/yoder/Dropbox/Research/globalETAS/globalETAS.py:1383: NumbaWarning: \u001b[1m\n",
      "Compilation is falling back to object mode WITH looplifting enabled because Function \"omori_rate\" failed type inference due to: \u001b[1m\u001b[1mnon-precise type pyobject\u001b[0m\n",
      "\u001b[0m\u001b[1mDuring: typing of argument at /home/yoder/Dropbox/Research/globalETAS/globalETAS.py (1401)\u001b[0m\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1401:\u001b[0m\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "     <source elided>\n",
      "\t\t#print(\"inputs: \", t, lon, lat, p, q)\n",
      "\u001b[1m\t\tt = (t or mpd.date2num(dtm.datetime.now(pytz.timezone('UTC'))))\n",
      "\u001b[0m  \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  @numba.jit\n",
      "/home/yoder/Dropbox/Research/globalETAS/globalETAS.py:1383: NumbaWarning: \u001b[1m\n",
      "Compilation is falling back to object mode WITH looplifting enabled because Function \"omori_rate\" failed type inference due to: \u001b[1m\u001b[1mnon-precise type pyobject\u001b[0m\n",
      "\u001b[0m\u001b[1mDuring: typing of argument at /home/yoder/Dropbox/Research/globalETAS/globalETAS.py (1401)\u001b[0m\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1401:\u001b[0m\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "     <source elided>\n",
      "\t\t#print(\"inputs: \", t, lon, lat, p, q)\n",
      "\u001b[1m\t\tt = (t or mpd.date2num(dtm.datetime.now(pytz.timezone('UTC'))))\n",
      "\u001b[0m  \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  @numba.jit\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:177: NumbaWarning: \u001b[1mFunction \"omori_rate\" was compiled in object mode without forceobj=True.\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1384:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaWarning(warn_msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:187: NumbaDeprecationWarning: \u001b[1m\n",
      "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1384:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n",
      "/home/yoder/Dropbox/Research/globalETAS/globalETAS.py:1375: NumbaWarning: \u001b[1m\n",
      "Compilation is falling back to object mode WITH looplifting enabled because Function \"spherical_dist\" failed type inference due to: \u001b[1m\u001b[1mnon-precise type pyobject\u001b[0m\n",
      "\u001b[0m\u001b[1mDuring: typing of argument at /home/yoder/Dropbox/Research/globalETAS/globalETAS.py (1377)\u001b[0m\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1377:\u001b[0m\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[1m\t\treturn spherical_dist(lon_lat_from=[self.lon, self.lat], lon_lat_to=to_lon_lat)\n",
      "\u001b[0m  \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  @numba.jit\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:177: NumbaWarning: \u001b[1mFunction \"omori_rate\" was compiled in object mode without forceobj=True.\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1384:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaWarning(warn_msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:177: NumbaWarning: \u001b[1mFunction \"omori_rate\" was compiled in object mode without forceobj=True.\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1384:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaWarning(warn_msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:187: NumbaDeprecationWarning: \u001b[1m\n",
      "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1384:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:187: NumbaDeprecationWarning: \u001b[1m\n",
      "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1384:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yoder/Dropbox/Research/globalETAS/globalETAS.py:1375: NumbaWarning: \u001b[1m\n",
      "Compilation is falling back to object mode WITH looplifting enabled because Function \"spherical_dist\" failed type inference due to: \u001b[1m\u001b[1mnon-precise type pyobject\u001b[0m\n",
      "\u001b[0m\u001b[1mDuring: typing of argument at /home/yoder/Dropbox/Research/globalETAS/globalETAS.py (1377)\u001b[0m\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1377:\u001b[0m\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[1m\t\treturn spherical_dist(lon_lat_from=[self.lon, self.lat], lon_lat_to=to_lon_lat)\n",
      "\u001b[0m  \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  @numba.jit\n",
      "/home/yoder/Dropbox/Research/globalETAS/globalETAS.py:1375: NumbaWarning: \u001b[1m\n",
      "Compilation is falling back to object mode WITH looplifting enabled because Function \"spherical_dist\" failed type inference due to: \u001b[1m\u001b[1mnon-precise type pyobject\u001b[0m\n",
      "\u001b[0m\u001b[1mDuring: typing of argument at /home/yoder/Dropbox/Research/globalETAS/globalETAS.py (1377)\u001b[0m\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1377:\u001b[0m\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[1m\t\treturn spherical_dist(lon_lat_from=[self.lon, self.lat], lon_lat_to=to_lon_lat)\n",
      "\u001b[0m  \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  @numba.jit\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:177: NumbaWarning: \u001b[1mFunction \"omori_rate\" was compiled in object mode without forceobj=True.\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1384:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaWarning(warn_msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:187: NumbaDeprecationWarning: \u001b[1m\n",
      "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1384:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:177: NumbaWarning: \u001b[1mFunction \"omori_rate\" was compiled in object mode without forceobj=True.\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1384:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaWarning(warn_msg,\n",
      "/home/yoder/Dropbox/Research/globalETAS/globalETAS.py:1375: NumbaWarning: \u001b[1m\n",
      "Compilation is falling back to object mode WITH looplifting enabled because Function \"spherical_dist\" failed type inference due to: \u001b[1m\u001b[1mnon-precise type pyobject\u001b[0m\n",
      "\u001b[0m\u001b[1mDuring: typing of argument at /home/yoder/Dropbox/Research/globalETAS/globalETAS.py (1377)\u001b[0m\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1377:\u001b[0m\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[1m\t\treturn spherical_dist(lon_lat_from=[self.lon, self.lat], lon_lat_to=to_lon_lat)\n",
      "\u001b[0m  \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  @numba.jit\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:187: NumbaDeprecationWarning: \u001b[1m\n",
      "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1384:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n",
      "/home/yoder/Dropbox/Research/globalETAS/globalETAS.py:1375: NumbaWarning: \u001b[1m\n",
      "Compilation is falling back to object mode WITH looplifting enabled because Function \"spherical_dist\" failed type inference due to: \u001b[1m\u001b[1mnon-precise type pyobject\u001b[0m\n",
      "\u001b[0m\u001b[1mDuring: typing of argument at /home/yoder/Dropbox/Research/globalETAS/globalETAS.py (1377)\u001b[0m\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1377:\u001b[0m\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[1m\t\treturn spherical_dist(lon_lat_from=[self.lon, self.lat], lon_lat_to=to_lon_lat)\n",
      "\u001b[0m  \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  @numba.jit\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:177: NumbaWarning: \u001b[1mFunction \"spherical_dist\" was compiled in object mode without forceobj=True.\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1376:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaWarning(warn_msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:187: NumbaDeprecationWarning: \u001b[1m\n",
      "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1376:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:177: NumbaWarning: \u001b[1mFunction \"spherical_dist\" was compiled in object mode without forceobj=True.\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1376:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaWarning(warn_msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:187: NumbaDeprecationWarning: \u001b[1m\n",
      "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1376:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:177: NumbaWarning: \u001b[1mFunction \"omori_rate\" was compiled in object mode without forceobj=True.\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1384:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaWarning(warn_msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:177: NumbaWarning: \u001b[1mFunction \"omori_rate\" was compiled in object mode without forceobj=True.\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1384:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaWarning(warn_msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:187: NumbaDeprecationWarning: \u001b[1m\n",
      "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1384:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:177: NumbaWarning: \u001b[1mFunction \"spherical_dist\" was compiled in object mode without forceobj=True.\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1376:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaWarning(warn_msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:187: NumbaDeprecationWarning: \u001b[1m\n",
      "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1376:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:187: NumbaDeprecationWarning: \u001b[1m\n",
      "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1384:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n",
      "/home/yoder/Dropbox/Research/globalETAS/globalETAS.py:1375: NumbaWarning: \u001b[1m\n",
      "Compilation is falling back to object mode WITH looplifting enabled because Function \"spherical_dist\" failed type inference due to: \u001b[1m\u001b[1mnon-precise type pyobject\u001b[0m\n",
      "\u001b[0m\u001b[1mDuring: typing of argument at /home/yoder/Dropbox/Research/globalETAS/globalETAS.py (1377)\u001b[0m\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1377:\u001b[0m\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[1m\t\treturn spherical_dist(lon_lat_from=[self.lon, self.lat], lon_lat_to=to_lon_lat)\n",
      "\u001b[0m  \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  @numba.jit\n",
      "/home/yoder/Dropbox/Research/globalETAS/globalETAS.py:1375: NumbaWarning: \u001b[1m\n",
      "Compilation is falling back to object mode WITH looplifting enabled because Function \"spherical_dist\" failed type inference due to: \u001b[1m\u001b[1mnon-precise type pyobject\u001b[0m\n",
      "\u001b[0m\u001b[1mDuring: typing of argument at /home/yoder/Dropbox/Research/globalETAS/globalETAS.py (1377)\u001b[0m\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1377:\u001b[0m\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[1m\t\treturn spherical_dist(lon_lat_from=[self.lon, self.lat], lon_lat_to=to_lon_lat)\n",
      "\u001b[0m  \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  @numba.jit\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:177: NumbaWarning: \u001b[1mFunction \"spherical_dist\" was compiled in object mode without forceobj=True.\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1376:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaWarning(warn_msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:187: NumbaDeprecationWarning: \u001b[1m\n",
      "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1376:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:177: NumbaWarning: \u001b[1mFunction \"omori_rate\" was compiled in object mode without forceobj=True.\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1384:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaWarning(warn_msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:187: NumbaDeprecationWarning: \u001b[1m\n",
      "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1384:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n",
      "/home/yoder/Dropbox/Research/globalETAS/globalETAS.py:1375: NumbaWarning: \u001b[1m\n",
      "Compilation is falling back to object mode WITH looplifting enabled because Function \"spherical_dist\" failed type inference due to: \u001b[1m\u001b[1mnon-precise type pyobject\u001b[0m\n",
      "\u001b[0m\u001b[1mDuring: typing of argument at /home/yoder/Dropbox/Research/globalETAS/globalETAS.py (1377)\u001b[0m\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1377:\u001b[0m\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[1m\t\treturn spherical_dist(lon_lat_from=[self.lon, self.lat], lon_lat_to=to_lon_lat)\n",
      "\u001b[0m  \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  @numba.jit\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:177: NumbaWarning: \u001b[1mFunction \"spherical_dist\" was compiled in object mode without forceobj=True.\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1376:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaWarning(warn_msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/ir_utils.py:2031: NumbaPendingDeprecationWarning: \u001b[1m\n",
      "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'lon_lat_from' of function 'spherical_dist'.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 2427:\u001b[0m\n",
      "\u001b[1m@numba.jit\n",
      "\u001b[1mdef spherical_dist(lon_lat_from=[0., 0.], lon_lat_to=[0.,0.], Rearth = 6378.1):\n",
      "\u001b[0m\u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(NumbaPendingDeprecationWarning(msg, loc=loc))\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/ir_utils.py:2031: NumbaPendingDeprecationWarning: \u001b[1m\n",
      "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'lon_lat_from' of function 'spherical_dist'.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 2427:\u001b[0m\n",
      "\u001b[1m@numba.jit\n",
      "\u001b[1mdef spherical_dist(lon_lat_from=[0., 0.], lon_lat_to=[0.,0.], Rearth = 6378.1):\n",
      "\u001b[0m\u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(NumbaPendingDeprecationWarning(msg, loc=loc))\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:187: NumbaDeprecationWarning: \u001b[1m\n",
      "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1376:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/ir_utils.py:2031: NumbaPendingDeprecationWarning: \u001b[1m\n",
      "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'lon_lat_from' of function 'spherical_dist'.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 2427:\u001b[0m\n",
      "\u001b[1m@numba.jit\n",
      "\u001b[1mdef spherical_dist(lon_lat_from=[0., 0.], lon_lat_to=[0.,0.], Rearth = 6378.1):\n",
      "\u001b[0m\u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(NumbaPendingDeprecationWarning(msg, loc=loc))\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:177: NumbaWarning: \u001b[1mFunction \"spherical_dist\" was compiled in object mode without forceobj=True.\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1376:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaWarning(warn_msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:187: NumbaDeprecationWarning: \u001b[1m\n",
      "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1376:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:177: NumbaWarning: \u001b[1mFunction \"spherical_dist\" was compiled in object mode without forceobj=True.\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1376:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaWarning(warn_msg,\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/ir_utils.py:2031: NumbaPendingDeprecationWarning: \u001b[1m\n",
      "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'lon_lat_from' of function 'spherical_dist'.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 2427:\u001b[0m\n",
      "\u001b[1m@numba.jit\n",
      "\u001b[1mdef spherical_dist(lon_lat_from=[0., 0.], lon_lat_to=[0.,0.], Rearth = 6378.1):\n",
      "\u001b[0m\u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(NumbaPendingDeprecationWarning(msg, loc=loc))\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:187: NumbaDeprecationWarning: \u001b[1m\n",
      "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1376:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:177: NumbaWarning: \u001b[1mFunction \"spherical_dist\" was compiled in object mode without forceobj=True.\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1376:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaWarning(warn_msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:187: NumbaDeprecationWarning: \u001b[1m\n",
      "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1376:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef spherical_dist(self, to_lon_lat=[]):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/ir_utils.py:2031: NumbaPendingDeprecationWarning: \u001b[1m\n",
      "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'lon_lat_from' of function 'spherical_dist'.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 2427:\u001b[0m\n",
      "\u001b[1m@numba.jit\n",
      "\u001b[1mdef spherical_dist(lon_lat_from=[0., 0.], lon_lat_to=[0.,0.], Rearth = 6378.1):\n",
      "\u001b[0m\u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(NumbaPendingDeprecationWarning(msg, loc=loc))\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/ir_utils.py:2031: NumbaPendingDeprecationWarning: \u001b[1m\n",
      "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'lon_lat_from' of function 'spherical_dist'.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 2427:\u001b[0m\n",
      "\u001b[1m@numba.jit\n",
      "\u001b[1mdef spherical_dist(lon_lat_from=[0., 0.], lon_lat_to=[0.,0.], Rearth = 6378.1):\n",
      "\u001b[0m\u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(NumbaPendingDeprecationWarning(msg, loc=loc))\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/ir_utils.py:2031: NumbaPendingDeprecationWarning: \u001b[1m\n",
      "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'lon_lat_from' of function 'spherical_dist'.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 2427:\u001b[0m\n",
      "\u001b[1m@numba.jit\n",
      "\u001b[1mdef spherical_dist(lon_lat_from=[0., 0.], lon_lat_to=[0.,0.], Rearth = 6378.1):\n",
      "\u001b[0m\u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(NumbaPendingDeprecationWarning(msg, loc=loc))\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/ir_utils.py:2031: NumbaPendingDeprecationWarning: \u001b[1m\n",
      "Encountered the use of a type that is scheduled for deprecation: type 'reflected list' found for argument 'lon_lat_from' of function 'spherical_dist'.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-reflection-for-list-and-set-types\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 2427:\u001b[0m\n",
      "\u001b[1m@numba.jit\n",
      "\u001b[1mdef spherical_dist(lon_lat_from=[0., 0.], lon_lat_to=[0.,0.], Rearth = 6378.1):\n",
      "\u001b[0m\u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(NumbaPendingDeprecationWarning(msg, loc=loc))\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[171336]: finished calculateing ETAS (rtree). wrap up in recarray and return.\n",
      "etas complete (from mpp_rtree run() loop); now pipe back([0, 1187])\n",
      "[171329]: finished calculateing ETAS (rtree). wrap up in recarray and return.\n",
      "etas complete (from mpp_rtree run() loop); now pipe back([0, 1187])\n",
      "[171328]: finished calculateing ETAS (rtree). wrap up in recarray and return.\n",
      "etas complete (from mpp_rtree run() loop); now pipe back([0, 1187])\n",
      "[171297]: finished calculateing ETAS (rtree). wrap up in recarray and return.\n",
      "etas complete (from mpp_rtree run() loop); now pipe back([0, 1187])\n",
      "[171321]: finished calculateing ETAS (rtree). wrap up in recarray and return.\n",
      "etas complete (from mpp_rtree run() loop); now pipe back([0, 1187])\n",
      "[171300]: finished calculateing ETAS (rtree). wrap up in recarray and return.\n",
      "etas complete (from mpp_rtree run() loop); now pipe back([0, 1187])\n",
      "[171305]: finished calculateing ETAS (rtree). wrap up in recarray and return.\n",
      "etas complete (from mpp_rtree run() loop); now pipe back([0, 1187])\n",
      "[171314]: finished calculateing ETAS (rtree). wrap up in recarray and return.\n",
      "etas complete (from mpp_rtree run() loop); now pipe back([0, 1187])\n",
      "ETAS complete.\n"
     ]
    }
   ],
   "source": [
    "#\n",
    "cum_cat = mycat[mycat['event_date_float']<(mainshocks[1]['event_date_float']-1.25)]\n",
    "\n",
    "# try some cumulative experiments (we are developing another script for this elsewhere...):\n",
    "eq_prams2 = eq_prams.copy()\n",
    "eq_prams2['t_now']    = mpd.num2date(mainshocks[0]['event_date_float'])\n",
    "#eq_prams2['t_future'] = mpd.date2num(dtm.datetime.now(pytz.timezone('UTC'))) #mainshocks[1]['event_date_float']-.1\n",
    "eq_prams2['t_future'] = mainshocks[1]['event_date_float']-.1\n",
    "#\n",
    "#eq_prams2['cat_len'] = 30\n",
    "etas2 = gep.ETAS_mpp(n_cpu=n_cpu, catalog=cum_cat,\n",
    "                    **eq_prams2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yoder/Dropbox/Research/globalETAS/globalETAS.py:377: MatplotlibDeprecationWarning: The 'cmap' parameter to Colorbar has no effect because it is overridden by the mappable; it is deprecated since 3.3 and will be removed two minor releases later.\n",
      "  plt.colorbar(etas_contours, cax=None, ax=ax, cmap=map_cmap)\n",
      "No handles with labels found to put in legend.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ms:  ('2019-07-04T17:33:49.000000', 35.7053333, -117.5038333, 6.4, 10.5, 18081.73181713, 27.54228703, 227.09312198, 0.00016708, 8.31763771, 25.27003406, 0.00911464, 1., 1.1, 1.5, 11.30220691, [0.00906216, 0.03066698], [[-0.72898579,  0.68452883], [-0.68452883, -0.72898579]], 1263.) -117.5038333 35.7053333\n",
      "tt:  18066.73181712963 2005-01-04T08:49:24.820000 <class 'numpy.datetime64'>\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f24f726b730>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fg2=plt.figure(0, figsize=(12,10))\n",
    "ax=plt.gca()\n",
    "etas2.make_etas_contour_map(n_contours=25, fignum=0, map_resolution='f', alpha=.3, ax=ax)\n",
    "#\n",
    "#mainshock = sorted(etas.catalog, key=lambda rw: rw['mag'])[-1]\n",
    "#print('mainshock: ', mainshock)\n",
    "# get mainshock. it's an m>6 event in the last week or so... this is subjective.\n",
    "# if we just look for the biggest event, we get the L'Aquila event, so we'll need to be more creative...\n",
    "# or just specify it.\n",
    "\n",
    "mainshock = etas2.catalog[-1]\n",
    "for j,eq in enumerate(reversed(etas2.catalog)):\n",
    "    #print('*** ', pytz.utc.localize(eq['event_date'].astype(dtm.datetime)))\n",
    "    if pytz.utc.localize(eq['event_date'].astype(dtm.datetime))<etas2.t_now-dtm.timedelta(days=180): break\n",
    "    if eq['mag']>mainshock['mag']:\n",
    "        mainshock = eq\n",
    "        #\n",
    "    #\n",
    "#\n",
    "print('ms: ', mainshock, mainshock['lon'], mainshock['lat'])\n",
    "x,y = etas2.cm(mainshock['lon'], mainshock['lat'])\n",
    "#\n",
    "#print('mm: ', max(etas.catalog['mag']))\n",
    "#\n",
    "# let's get everything m>6 in the last 6 months?\n",
    "m6s = [rw for rw in etas2.catalog if rw['mag'] >= 6. \n",
    "       and pytz.utc.localize(rw['event_date'].astype(dtm.datetime))>to_dt-dtm.timedelta(days=180)]\n",
    "#\n",
    "# plot mainshock:\n",
    "dt = mainshock['event_date'].astype(dtm.datetime)\n",
    "dt=t0\n",
    "dt_str = '{}-{}-{} {}:{}:{}'.format(dt.year, dt.month, dt.day, dt.hour, dt.minute, dt.second)\n",
    "#etas.cm.plot([x], [y], latlon=False, marker='*', color='r', ms=16, zorder=11,\n",
    "#                   label='m={}, {}'.format(mainshock['mag'], dt_str))\n",
    "#etas.cm.plot([lon0], [lat0], latlon=False, marker='*', color='r', ms=16, zorder=11,\n",
    "#                   label='m={}, {}'.format(m0, dt_str))\n",
    "\n",
    "ax.set_title('ETAS: {},\\n {}-{}\\n\\n'.format(event_name, etas2.t_now, mpd.num2date(etas2.t_future)), size=16)\n",
    "# for j,m6 in enumerate(m6s):\n",
    "#     clr = colors_[j%len(colors_)]\n",
    "#     #\n",
    "#     dt = m6['event_date'].astype(dtm.datetime)\n",
    "#     dt_str = '{}-{}-{} {}:{}:{}'.format(dt.year, dt.month, dt.day, dt.hour, dt.minute, dt.second)\n",
    "#     etas.cm.scatter(m6['lon'], m6['lat'], s=2*(m6['mag']+2.), edgecolors=clr, \n",
    "#                           c='none', marker='o', zorder=11, label='m={}, {}'.format(m6['mag'], dt_str))\n",
    "\n",
    "# ix = (numpy.array([pytz.utc.localize(x.astype(dtm.datetime)) for x in etas.catalog['event_date']])<=etas.t_now and\n",
    "#       numpy.array([pytz.utc.localize(x.astype(dtm.datetime)) \n",
    "#                    for x in etas.catalog['event_date']])>=dtm.datetime(2019,7,4, tzinfo=pytz.timezone('UTC')))\n",
    "ix = numpy.array([x<=etas2.t_now and x>dtm.datetime(2019,7,4, tzinfo=pytz.timezone('UTC'))\n",
    "      for x in [pytz.utc.localize(x.astype(dtm.datetime)) for x in etas2.catalog['event_date']]])\n",
    "#\n",
    "etas2.cm.plot(etas2.catalog['lon'][ix], etas2.catalog['lat'][ix], marker='.', ls='')\n",
    "#x,y = etas.cm(*ll_sacramento)\n",
    "#etas.cm.scatter([x],[y], marker='o', s=18, edgecolors='r', c='r',\n",
    "#                    label='Sacramento')\n",
    "t_cat = mpd.date2num(etas2.t_now-dtm.timedelta(days=15))\n",
    "print('tt: ', t_cat, etas2.catalog['event_date'][0], type(etas2.catalog['event_date'][0]))\n",
    "\n",
    "#k=0\n",
    "\n",
    "# for j,rw in enumerate(etas.catalog):\n",
    "#     if mpd.date2num(rw['event_date'].astype(dtm.datetime))<t_cat: continue\n",
    "#     k+=1\n",
    "#     clr = colors_[k%len(colors_)]\n",
    "#     #\n",
    "#     dt = rw['event_date'].astype(dtm.datetime)\n",
    "#     dt_str = '{}-{}-{} {}:{}:{}'.format(dt.year, dt.month, dt.day, dt.hour, dt.minute, dt.second)\n",
    "#     #etas.cm.scatter(rw['lon'],rw['lat'], s=3*(rw['mag']+12.), edgecolors=clr, \n",
    "#     #                      c='none', marker='o', zorder=11, label='m={}, {}'.format(rw['mag'], dt_str))\n",
    "#     etas.cm.plot(rw['lon'],rw['lat'], ms=2.*(rw['mag']+2.), color=clr, \n",
    "#                           marker='o', zorder=11, label='m={}, {}'.format(rw['mag'], dt_str), latlon=True)\n",
    "    \n",
    "    #\n",
    "ax.legend(loc=0)\n",
    "#print(etas.catalog[-10:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([33.705, 33.805, 33.905, 34.005, 34.105, 34.205, 34.305, 34.405,\n",
       "       34.505, 34.605])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "etas2.latses[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/yoder/Dropbox/Research/globalETAS/globalETAS.py:1383: NumbaWarning: \u001b[1m\n",
      "Compilation is falling back to object mode WITH looplifting enabled because Function \"omori_rate\" failed type inference due to: \u001b[1m\u001b[1mnon-precise type pyobject\u001b[0m\n",
      "\u001b[0m\u001b[1mDuring: typing of argument at /home/yoder/Dropbox/Research/globalETAS/globalETAS.py (1401)\u001b[0m\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1401:\u001b[0m\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "     <source elided>\n",
      "\t\t#print(\"inputs: \", t, lon, lat, p, q)\n",
      "\u001b[1m\t\tt = (t or mpd.date2num(dtm.datetime.now(pytz.timezone('UTC'))))\n",
      "\u001b[0m  \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  @numba.jit\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:177: NumbaWarning: \u001b[1mFunction \"omori_rate\" was compiled in object mode without forceobj=True.\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1384:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaWarning(warn_msg,\n",
      "/home/yoder/anaconda3/lib/python3.8/site-packages/numba/core/object_mode_passes.py:187: NumbaDeprecationWarning: \u001b[1m\n",
      "Fall-back from the nopython compilation path to the object mode compilation path has been detected, this is deprecated behaviour.\n",
      "\n",
      "For more information visit https://numba.pydata.org/numba-doc/latest/reference/deprecation.html#deprecation-of-object-mode-fall-back-behaviour-when-using-jit\n",
      "\u001b[1m\n",
      "File \"globalETAS.py\", line 1384:\u001b[0m\n",
      "\u001b[1m\t@numba.jit\n",
      "\u001b[1m\tdef omori_rate(self, t=None, t_to=None, p=None):\n",
      "\u001b[0m \u001b[1m^\u001b[0m\u001b[0m\n",
      "\u001b[0m\n",
      "  warnings.warn(errors.NumbaDeprecationWarning(msg,\n"
     ]
    }
   ],
   "source": [
    "q_ms6 = gep.Earthquake(mainshocks[0])\n",
    "q_ms7 = gep.Earthquake(mainshocks[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('2019-07-04T21:02:55.820000', 35.69, -117.4831667, 3.47, 7.89, 18081.87703495, 0.94406088, 0.16478522, 5.27923339, 0.28510183, 0.00964434, 17.04404808, 1., 1.1, 1.5, 0.3044531, [5.29299953e-05, 6.72239224e-05], [[-0.96808003,  0.2506413 ], [-0.2506413 , -0.96808003]], 24.)\n",
      "Ns:  53.82493950839673 148 1258.9254117941662\n"
     ]
    }
   ],
   "source": [
    "print(etas2.catalog[-1])\n",
    "#\n",
    "# total number of events?\n",
    "# Note: this comes out low, but pushing order of magnitude...\n",
    "#\n",
    "N_est = numpy.sum(etas2.ETAS_array['z'])*etas2.d_lat*etas2.d_lon*numpy.cos(numpy.mean(etas2.latses)*deg2rad)*gep.deg2km**2.\n",
    "N_act = len(mycat[numpy.logical_and(mycat['event_date_float']>=etas2.t_forecast,\n",
    "                                    mycat['event_date_float']<=etas2.t_future)])\n",
    "#\n",
    "print('Ns: ', N_est, N_act, 10.**(q_ms7.mag - etas2.mc -1.))\n",
    "# fg = plt.figure(figsize=(10,8))\n",
    "# ax = plt.gca()\n",
    "# ax.grid()\n",
    "# #\n",
    "# ax.plot(etas2.ETAS_array['z'])\n",
    "# #ax.set_ylim(-.0001, .005 )\n",
    "# ax.set_yscale('log')\n",
    "#\n",
    "# fg = plt.figure(figsize=(8,6))\n",
    "# ax = plt.gca()\n",
    "# #\n",
    "# ax.plot(etas2.ETAS_array['z'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('2019-07-04T19:51:16.980000', 35.71     , -117.5561667, 3.35,  2.09, 18081.82727986, 0.82224265, 0.12255946,  8.06968489, 0.24831331, 0.00698671, 23.20452862, 1., 1.1, 1.5, 0.2625637 , [3.93735797e-05, 1.97696633e-05], [[ 0.73394111,  0.6792131 ], [-0.6792131 ,  0.73394111]],  22.)\n",
      " ('2019-07-04T19:56:00.500000', 35.6623333, -117.524    , 4.16,  1.49, 18081.83056134, 2.08929613, 0.904074  ,  0.46018028, 0.63095734, 0.0615563 ,  2.89116415, 1., 1.1, 1.5, 0.71312105, [3.29606554e-04, 1.44541845e-04], [[ 0.99467858, -0.10302683], [ 0.10302683,  0.99467858]], 113.)\n",
      " ('2019-07-04T19:56:25.890000', 35.6636667, -117.5225   , 3.27,  2.15, 18081.83085521, 0.74989421, 0.10060801, 10.70813208, 0.22646443, 0.00563559, 28.50401802, 1., 1.1, 1.5, 0.23789074, [2.20185969e-05, 3.31813545e-05], [[-0.99101317, -0.13376434], [ 0.13376434, -0.99101317]],  32.)\n",
      " ('2019-07-04T20:06:08.250000', 35.6991667, -117.4891667, 3.39, 10.27, 18081.83759549, 0.86099375, 0.13527078,  7.00532043, 0.26001596, 0.00777928, 20.93659654, 1., 1.1, 1.5, 0.27584382, [6.62644959e-05, 4.21870724e-05], [[ 0.99947755, -0.03232084], [ 0.03232084,  0.99947755]],  20.)\n",
      " ('2019-07-04T20:14:50.340000', 35.6991667, -117.5125   , 3.99,  5.75, 18081.84363819, 1.71790839, 0.59437581,  0.83945889, 0.51880004, 0.03898874,  4.47616497, 1., 1.1, 1.5, 0.57821829, [2.37083283e-04, 1.47021093e-04], [[ 0.98918361, -0.1466826 ], [ 0.1466826 ,  0.98918361]],  77.)\n",
      " ('2019-07-04T20:17:36.830000', 35.7      , -117.4836667, 3.09,  1.25, 18081.84556516, 0.6095369 , 0.06453204, 20.23682103, 0.1840772 , 0.00347487, 45.27996515, 1., 1.1, 1.5, 0.19052368, [6.67412798e-06, 2.73031329e-05], [[-0.72642991, -0.68724056], [ 0.68724056, -0.72642991]],  11.)\n",
      " ('2019-07-04T20:23:09.930000', 35.6408333, -117.5973333, 3.43, 10.39, 18081.84942049, 0.90157114, 0.14930045,  6.08134208, 0.27227013, 0.00866175, 18.89032445, 1., 1.1, 1.5, 0.28979563, [1.51612784e-04, 5.88782642e-06], [[ 0.99891245, -0.04662528], [ 0.04662528,  0.99891245]],   6.)\n",
      " ('2019-07-04T20:45:26.710000', 35.7028333, -117.4775   , 3.1 ,  1.31, 18081.86489248, 0.616595  , 0.06614388, 19.53372854, 0.18620871, 0.00356949, 44.13055784, 1., 1.1, 1.5, 0.1928884 , [5.55531098e-06, 3.48931563e-05], [[-0.84396484, -0.5363985 ], [ 0.5363985 , -0.84396484]],  11.)\n",
      " ('2019-07-04T20:55:25.070000', 35.7116667, -117.4808333, 3.4 ,  2.39, 18081.87181794, 0.87096359, 0.13864949,  6.76193299, 0.2630268 , 0.00799109, 20.40513241, 1., 1.1, 1.5, 0.2792675 , [3.78194790e-05, 2.23206381e-05], [[ 0.97938571, -0.20199907], [ 0.20199907,  0.97938571]],  14.)\n",
      " ('2019-07-04T21:02:55.820000', 35.69     , -117.4831667, 3.47,  7.89, 18081.87703495, 0.94406088, 0.16478522,  5.27923339, 0.28510183, 0.00964434, 17.04404808, 1., 1.1, 1.5, 0.3044531 , [5.29299953e-05, 6.72239224e-05], [[-0.96808003,  0.2506413 ], [-0.2506413 , -0.96808003]],  24.)]\n",
      "**  2009-07-06 17:33:49+00:00\n",
      "**  18081.73181712963 2019-07-04 17:33:49+00:00\n",
      "**  2019-07-04 17:33:49+00:00\n",
      "**  18083.038808333335 2019-07-06 00:55:53.040000+00:00\n"
     ]
    }
   ],
   "source": [
    "print(etas2.catalog[-10:])\n",
    "\n",
    "print('** ', etas2.t_0)\n",
    "print('** ', etas2.t_forecast, mpd.num2date(etas2.t_forecast))\n",
    "print('** ', etas2.t_now)\n",
    "print('** ', etas2.t_future, mpd.num2date(etas2.t_future))\n",
    "\n",
    "\n",
    "#print('** ', mainshocks)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "for rw in mainshocks:\n",
    "    print(rw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "q_ms6.orate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(etas2.t_forecast, q_ms6.event_date_float, q_ms7.event_date_float)\n",
    "print(etas2.t_future)\n",
    "print(etas2.t_future-etas2.t_forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(q_ms6.omori_rate(t=etas2.t_forecast, t_to=None) )\n",
    "print(q_ms6.omori_rate(t=q_ms6.event_date_float+.005, t_to=q_ms6.event_date_float+1.5))\n",
    "print('** ', q_ms6.omori_rate(t=etas2.t_forecast, t_to=etas2.t_future))\n",
    "#print(q_ms6.min)\n",
    "#print(etas2.mc, etas2.mc_etas)\n",
    "#\n",
    "#print('** ', 10**(6.4-2.5-1.))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(q_ms6.local_intensity(t=q_ms6.event_date_float+.005, t_to=q_ms6.event_date_float+1.5, lon=q_ms6.lon+.1, \n",
    "                            lat=q_ms6.lat+.1))\n",
    "#\n",
    "print(q_ms6.local_intensity(t=etas2.t_forecast+.1, t_to=etas2.t_future, lon=q_ms6.lon+.1, \n",
    "                            lat=q_ms6.lat+.1))\n",
    "#\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#cum_cat = mycat[mycat['event_date_float']<(mainshocks[0]['event_date_float']+1)]\n",
    "Ns = numpy.array([gep.Earthquake(rw).omori_rate(etas2.t_forecast, etas2.t_future+5) for rw in etas2.catalog])\n",
    "Ns2 = numpy.array([gep.Earthquake(rw).omori_rate(etas2.t_forecast, etas.t_forecast) for rw in etas.catalog])\n",
    "#\n",
    "print('Ns: ', Ns[-10:])\n",
    "#\n",
    "fg = plt.figure(figsize=(12,8))\n",
    "ax1 = plt.subplot('211')\n",
    "ax2 = plt.subplot('212')\n",
    "#\n",
    "#ax1.plot(etas2.catalog['event_date_float'], etas2.catalog['mag'])\n",
    "ax1.plot(etas2.catalog['mag'])\n",
    "#ax2.plot(etas2.catalog['event_date_float'], Ns)\n",
    "ax2.plot(range(len(Ns)), Ns, alpha=.5)\n",
    "ax2.plot(range(len(Ns2)), Ns2, alpha=.5)\n",
    "\n",
    "\n",
    "ax2.set_yscale('log')\n",
    "ax2.grid()\n",
    "#ax2.set_xlim((3300, None))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Ns = numpy.array([gep.Earthquake(rw).omori_rate(etas.t_forecast-10, None) for rw in etas.catalog])\n",
    "Ns2 = numpy.array([gep.Earthquake(rw).omori_rate(etas.t_forecast, None) for rw in etas.catalog])\n",
    "#\n",
    "print('Ns: ', Ns[-10:])\n",
    "#\n",
    "fg = plt.figure(figsize=(12,8))\n",
    "ax1 = plt.subplot('211')\n",
    "ax2 = plt.subplot('212', sharex=ax1)\n",
    "\n",
    "ax1.grid()\n",
    "ax2.grid()\n",
    "#\n",
    "ax1.set_ylabel('magnitude $m$')\n",
    "\n",
    "#\n",
    "#ax1.plot(etas2.catalog['event_date_float'], etas2.catalog['mag'])\n",
    "ax1.plot(etas.catalog['mag'])\n",
    "#ax2.plot(etas2.catalog['event_date_float'], Ns)\n",
    "ax2.plot(range(len(Ns)), Ns, alpha=.5)\n",
    "ax2.plot(range(len(Ns2)), Ns2, alpha=.5)\n",
    "\n",
    "\n",
    "ax2.set_yscale('log')\n",
    "ax2.grid()\n",
    "#ax2.set_xlim((3300, None))\n",
    "\n",
    "#fg = plt.figure(figsize=(12,4))\n",
    "#ax = plt.gca()\n",
    "ax = ax2.twinx()\n",
    "#\n",
    "#ax.set_yscale('log')\n",
    "#ax.plot([gep.Earthquake(rw, t_1=etas2.t_forecast, t_2=etas2.t_future).orate for rw in mycat])\n",
    "#ax.plot([gep.Earthquake(rw, t_1=etas2.t_forecast, t_2=etas2.t_future).t_1 - mainshocks[0]['event_date_float'] for rw in mycat])\n",
    "ax.plot([etas2.t_future - gep.Earthquake(rw, t_1=mainshocks[0]['event_date_float'],\n",
    "                        t_2=etas2.t_future).t_1 for rw in mycat], ls='--')\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's do one of those dist vs time plots:\n",
    "import numba\n",
    "@numba.jit\n",
    "def spherical_dist(lon_0=0., lat_0=0., lon_1=0., lat_1=0., Rearth = 6378.1):\n",
    "    #\n",
    "    #Rearth = 6378.1\t# km\n",
    "    #deg2rad=2.0*math.pi/360.\n",
    "    #\n",
    "    phif  = lat_1*deg2rad\n",
    "    lambf = lon_1*deg2rad\n",
    "    phis  = lat_0*deg2rad\n",
    "    lambs = lon_0*deg2rad\n",
    "    #\n",
    "    #\n",
    "    dphi = (phif - phis)\n",
    "    dlambda = (lambf - lambs)\n",
    "    #this one is supposed to be bulletproof:\n",
    "    #sighat3 = math.atan( math.sqrt((math.cos(phif)*math.sin(dlambda))**2.0 + (math.cos(phis)*math.sin(phif) - math.sin(phis)*math.cos(phif)*math.cos(dlambda))**2.0 ) / (math.sin(phis)*math.sin(phif) + math.cos(phis)*math.cos(phif)*math.cos(dlambda))  )\n",
    "    #R3 = Rearth * sighat3\n",
    "    #\n",
    "    #return R3\n",
    "    return Rearth*numpy.arctan( numpy.sqrt((numpy.cos(phif)*numpy.sin(dlambda))**2.0 + \n",
    "                             (numpy.cos(phis)*numpy.sin(phif) - numpy.sin(phis)*numpy.cos(phif)*numpy.cos(dlambda))**2.0 ) /\n",
    "                               (numpy.sin(phis)*numpy.sin(phif) + numpy.cos(phis)*numpy.cos(phif)*numpy.cos(dlambda))  )\n",
    "# \n",
    "#  gep.shperical_dist(lon_lat_from=[self.lon, self.lat], lon_lat_to=to_lon_lat)\n",
    "RT_6 = numpy.array([spherical_dist(mycat['lon'], mycat['lat'], q_ms6.lon, q_ms6.lat),\n",
    "                    mycat['event_date_float']-q_ms6.event_date_float]).T\n",
    "#\n",
    "RT_7 = numpy.array([spherical_dist(mycat['lon'], mycat['lat'], q_ms7.lon, q_ms7.lat),\n",
    "                    mycat['event_date_float']-q_ms7.event_date_float]).T\n",
    "\n",
    "\n",
    "\n",
    "ix = numpy.logical_and(numpy.logical_and(RT_6[:,1]>-1., RT_6[:,1]<26.), RT_6[:,0]<125.)\n",
    "fg = plt.figure(figsize=(14,8))\n",
    "ax = plt.gca()\n",
    "ax.grid()\n",
    "ax.set_xscale('log')\n",
    "ax.set_yscale('log')\n",
    "#\n",
    "ax.plot(RT_6[:,1][ix], RT_6[:,0][ix], ls='', marker='.')\n",
    "\n",
    "ix = numpy.logical_and(numpy.logical_and(RT_7[:,1]>-5., RT_7[:,1]<26.), RT_7[:,0]<125.)\n",
    "#ix = numpy.array([True for _ in RT_7])\n",
    "fg = plt.figure(figsize=(14,8))\n",
    "ax = plt.gca()\n",
    "ax.grid()\n",
    "ax.set_xscale('log')\n",
    "ax.set_yscale('log')\n",
    "#\n",
    "ax.plot(RT_7[:,1][ix], RT_7[:,0][ix], ls='', marker='.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(q_ms6.lon, q_ms6.lat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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