ETAS_YellowStone.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (Not-) ETAS: Yellowstone 2017, General Purpose\n",
    "\n",
    "#### Aftershock Scalint Type Estimated Seismicity Field\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
    "#\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",
    "\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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# event was some time on the 24th or maybe late the 23rd. this, plus defaults, should find the event:\n",
    "#to_dt = dtm.datetime(2016,8,25, tzinfo=pytz.timezone('UTC'))\n",
    "to_dt = dtm.datetime.now(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",
    "to_dt\n",
    "#\n",
    "# sacramento:\n",
    "lat0 = 44.4280\n",
    "lon0 = -110.4885\n",
    "#ll_sacramento = (lon0, lat0)\n",
    "\n",
    "#m0 = 7.8\n",
    "\n",
    "d_lat=6.\n",
    "d_lon=6.\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",
    "\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':None, \n",
    "#                'lats': lats, 'p_cat': 1.1, 'b1': 1.0, 'mc': 1., 'q_cat': 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",
    "\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, 'cat_len':3650,\n",
    "               'lats': lats, 'p': 1.1, 'b1': 1.0, 'mc': 1.5, 'q': 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,\n",
    "            'etas_range_factor':30, 'etas_range_padding':1.5, 'ab_ratio_expon':.5 }\n",
    "\n",
    "eq_prams.update({'mc':2.0, 'd_lat':.25, 'd_lon':.25})\n",
    "# 't_now':dtm.datetime(2017,6,16, tzinfo=pytz.timezone('UTC'))\n",
    "#  'cat_len':365*10.,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# let's add a preliminary map...\n",
    "class Preliminary_Map(object):\n",
    "    def __init__(self, lats, lons):\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "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": [
    "n_cpu = 2\n",
    "incat = None\n",
    "incat = gep.make_ETAS_catalog_mpp(atp.ANSS_Comcat_catalog(min_lon=lons[0], max_lon=lons[1], min_lat=lats[0], max_lat=lats[1],\n",
    "                                m_c = eq_prams['mc'], from_date=t0-dtm.timedelta(days=4000),\n",
    "                                to_date=to_dt, Nmax=None).as_recarray(), n_cpu=n_cpu )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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 22:23:56.733473+00:00\n",
      "ETAS over etas_cat_range/xyz_range:  ([0, 8095], None)\n",
      "make_etas():\n",
      "etas_mpp worker xyz_range:  [0, 1152]\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 22:23:56.733473+00:00\n",
      "ETAS over etas_cat_range/xyz_range:  ([0, 8095], [0, 1152])\n",
      "etas_mpp worker xyz_range:  [1152, 2304]\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 22:23:56.733473+00:00\n",
      "ETAS over etas_cat_range/xyz_range:  ([0, 8095], [1152, 2304])\n",
      "begin make_etas_rtree()\n",
      "** len(self.ETAS_array)[168745] = 1152\n",
      "begin make_etas_rtree()\n",
      "** len(self.ETAS_array)[168748] = 1152\n",
      "Indices initiated. begin ETAS[168745] :: range: [0, 8095]\n",
      "Indices initiated. begin ETAS[168748] :: range: [0, 8095]\n",
      "now gather sub-arrays...\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/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: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/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 2426:\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 2426:\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": [
      "[168748]: finished calculateing ETAS (rtree). wrap up in recarray and return.\n",
      "etas complete (from mpp_rtree run() loop); now pipe back([0, 8095])\n",
      "[168745]: finished calculateing ETAS (rtree). wrap up in recarray and return.\n",
      "etas complete (from mpp_rtree run() loop); now pipe back([0, 8095])\n",
      "ETAS complete.\n"
     ]
    }
   ],
   "source": [
    "#n_cpu = 2*mpp.cpu_count()\n",
    "\n",
    "#n_cpu=5\n",
    "etas = gep.ETAS_mpp(n_cpu=n_cpu, catalog=incat, **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",
    "#\n",
    "\n",
    "event_name = 'YellowStone2017'\n",
    "f_path = '{}/Dropbox/Research/etas/{}/etas_{}'.format(os.environ['HOME'], event_name,etas.t_now)\n",
    "f_root = 'etas_{}'.format(event_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#print(etas.t0, etas.t_now, etas.t_forecast, etas.catalog[-1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "'vertices' must be 2D with shape (M, 2). Your input has shape (0,).",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-ac7429da9dcc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mfg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgca\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0metas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_etas_contour_map\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_contours\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m25\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfignum\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmap_resolution\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'f'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/Dropbox/Research/globalETAS/globalETAS.py\u001b[0m in \u001b[0;36mmake_etas_contour_map\u001b[0;34m(self, n_contours, fignum, fig_size, contour_fig_file, contour_kml_file, kml_contours_bottom, kml_contours_top, alpha, alpha_kml, refresh_etas, map_resolution, map_projection, map_cmap, lat_interval, lon_interval, lats_map, lons_map, ax, do_colorbar, do_states, do_rivers, lake_color, Z)\u001b[0m\n\u001b[1;32m    357\u001b[0m                 \u001b[0;31m# mm.draw_map(d_lat_range=10., d_lon_range=20., fignum=0)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    358\u001b[0m                 \u001b[0;31m#cm = self.draw_map(fignum=fignum, fig_size=fig_size, map_resolution=map_resolution, map_projection=map_projection)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 359\u001b[0;31m                 \u001b[0mcm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_map\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfignum\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfignum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfig_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfig_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmap_resolution\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmap_resolution\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmap_projection\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmap_projection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md_lon_range\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlon_interval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md_lat_range\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlat_interval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlons_map\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlons_map\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlats_map\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlats_map\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdo_states\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdo_states\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdo_rivers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdo_rivers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlake_color\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlake_color\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    360\u001b[0m                 \u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    361\u001b[0m                 \u001b[0mfg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgcf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Dropbox/Research/globalETAS/globalETAS.py\u001b[0m in \u001b[0;36mdraw_map\u001b[0;34m(self, fignum, fig_size, map_resolution, map_projection, d_lon_range, d_lat_range, lats_map, lons_map, ax, do_states, do_rivers, lake_color, lat_label_indices, lon_label_indices)\u001b[0m\n\u001b[1;32m    330\u001b[0m                 \u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    331\u001b[0m                 \u001b[0;31m#cm.drawlsmask(land_color='0.8', ocean_color='b', resolution=map_resolution)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 332\u001b[0;31m                 \u001b[0mcm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrawcoastlines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'gray'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzorder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    333\u001b[0m                 \u001b[0mcm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrawcountries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'black'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzorder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    334\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mdo_states\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrawstates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'black'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzorder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/mpl_toolkits/basemap/__init__.py\u001b[0m in \u001b[0;36mdrawcoastlines\u001b[0;34m(self, linewidth, linestyle, color, antialiased, ax, zorder)\u001b[0m\n\u001b[1;32m   1853\u001b[0m         \u001b[0;31m# get current axes instance (if none specified).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1854\u001b[0m         \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_ax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1855\u001b[0;31m         \u001b[0mcoastlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLineCollection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoastsegs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mantialiaseds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mantialiased\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1856\u001b[0m         \u001b[0mcoastlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_color\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1857\u001b[0m         \u001b[0mcoastlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_linestyle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlinestyle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, segments, linewidths, colors, antialiaseds, linestyles, offsets, transOffset, norm, cmap, pickradius, zorder, facecolors, **kwargs)\u001b[0m\n\u001b[1;32m   1391\u001b[0m             **kwargs)\n\u001b[1;32m   1392\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1393\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_segments\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msegments\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1394\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1395\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mset_segments\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msegments\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36mset_segments\u001b[0;34m(self, segments)\u001b[0m\n\u001b[1;32m   1406\u001b[0m             \u001b[0m_segments\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_offsets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_segments\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1407\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1408\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_paths\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mmpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_seg\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_seg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_segments\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1409\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstale\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1410\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   1406\u001b[0m             \u001b[0m_segments\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_offsets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_segments\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1407\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1408\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_paths\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mmpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_seg\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_seg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_segments\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1409\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstale\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1410\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/matplotlib/path.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, vertices, codes, _interpolation_steps, closed, readonly)\u001b[0m\n\u001b[1;32m    130\u001b[0m         \"\"\"\n\u001b[1;32m    131\u001b[0m         \u001b[0mvertices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_to_unmasked_float_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvertices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 132\u001b[0;31m         \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvertices\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvertices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    133\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcodes\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36m_check_shape\u001b[0;34m(_shape, **kwargs)\u001b[0m\n\u001b[1;32m   2301\u001b[0m                                     for n in target_shape))\n\u001b[1;32m   2302\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2303\u001b[0;31m             raise ValueError(\n\u001b[0m\u001b[1;32m   2304\u001b[0m                 \u001b[0;34mf\"{k!r} must be {len(target_shape)}D \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2305\u001b[0m                 \u001b[0;34mf\"with shape ({text_shape}). \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: 'vertices' must be 2D with shape (M, 2). Your input has shape (0,)."
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fg=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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "'vertices' must be 2D with shape (M, 2). Your input has shape (0,).",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-7-ac26318a61f4>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mfg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgca\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0metas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_etas_contour_map\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_contours\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m25\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfignum\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmap_resolution\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'f'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m.3\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;31m#mainshock = sorted(etas.catalog, key=lambda rw: rw['mag'])[-1]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Dropbox/Research/globalETAS/globalETAS.py\u001b[0m in \u001b[0;36mmake_etas_contour_map\u001b[0;34m(self, n_contours, fignum, fig_size, contour_fig_file, contour_kml_file, kml_contours_bottom, kml_contours_top, alpha, alpha_kml, refresh_etas, map_resolution, map_projection, map_cmap, lat_interval, lon_interval, lats_map, lons_map, ax, do_colorbar, do_states, do_rivers, lake_color, Z)\u001b[0m\n\u001b[1;32m    357\u001b[0m                 \u001b[0;31m# mm.draw_map(d_lat_range=10., d_lon_range=20., fignum=0)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    358\u001b[0m                 \u001b[0;31m#cm = self.draw_map(fignum=fignum, fig_size=fig_size, map_resolution=map_resolution, map_projection=map_projection)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 359\u001b[0;31m                 \u001b[0mcm\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_map\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfignum\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfignum\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfig_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfig_size\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmap_resolution\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmap_resolution\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmap_projection\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmap_projection\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md_lon_range\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlon_interval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0md_lat_range\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlat_interval\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlons_map\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlons_map\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlats_map\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlats_map\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdo_states\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdo_states\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdo_rivers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdo_rivers\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlake_color\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlake_color\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    360\u001b[0m                 \u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    361\u001b[0m                 \u001b[0mfg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgcf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/Dropbox/Research/globalETAS/globalETAS.py\u001b[0m in \u001b[0;36mdraw_map\u001b[0;34m(self, fignum, fig_size, map_resolution, map_projection, d_lon_range, d_lat_range, lats_map, lons_map, ax, do_states, do_rivers, lake_color, lat_label_indices, lon_label_indices)\u001b[0m\n\u001b[1;32m    330\u001b[0m                 \u001b[0;31m#\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    331\u001b[0m                 \u001b[0;31m#cm.drawlsmask(land_color='0.8', ocean_color='b', resolution=map_resolution)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 332\u001b[0;31m                 \u001b[0mcm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrawcoastlines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'gray'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzorder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    333\u001b[0m                 \u001b[0mcm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrawcountries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'black'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzorder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    334\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0mdo_states\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mcm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdrawstates\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'black'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mzorder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/mpl_toolkits/basemap/__init__.py\u001b[0m in \u001b[0;36mdrawcoastlines\u001b[0;34m(self, linewidth, linestyle, color, antialiased, ax, zorder)\u001b[0m\n\u001b[1;32m   1853\u001b[0m         \u001b[0;31m# get current axes instance (if none specified).\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1854\u001b[0m         \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_ax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1855\u001b[0;31m         \u001b[0mcoastlines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLineCollection\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcoastsegs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mantialiaseds\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mantialiased\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1856\u001b[0m         \u001b[0mcoastlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_color\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1857\u001b[0m         \u001b[0mcoastlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_linestyle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlinestyle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, segments, linewidths, colors, antialiaseds, linestyles, offsets, transOffset, norm, cmap, pickradius, zorder, facecolors, **kwargs)\u001b[0m\n\u001b[1;32m   1391\u001b[0m             **kwargs)\n\u001b[1;32m   1392\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1393\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_segments\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msegments\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1394\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1395\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mset_segments\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msegments\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36mset_segments\u001b[0;34m(self, segments)\u001b[0m\n\u001b[1;32m   1406\u001b[0m             \u001b[0m_segments\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_offsets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_segments\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1407\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1408\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_paths\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mmpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_seg\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_seg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_segments\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1409\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstale\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1410\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/matplotlib/collections.py\u001b[0m in \u001b[0;36m<listcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m   1406\u001b[0m             \u001b[0m_segments\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_offsets\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_segments\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1407\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1408\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_paths\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mmpath\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPath\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_seg\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_seg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0m_segments\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1409\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstale\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1410\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/matplotlib/path.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, vertices, codes, _interpolation_steps, closed, readonly)\u001b[0m\n\u001b[1;32m    130\u001b[0m         \"\"\"\n\u001b[1;32m    131\u001b[0m         \u001b[0mvertices\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_to_unmasked_float_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvertices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 132\u001b[0;31m         \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_shape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvertices\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mvertices\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    133\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    134\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcodes\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/matplotlib/cbook/__init__.py\u001b[0m in \u001b[0;36m_check_shape\u001b[0;34m(_shape, **kwargs)\u001b[0m\n\u001b[1;32m   2301\u001b[0m                                     for n in target_shape))\n\u001b[1;32m   2302\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2303\u001b[0;31m             raise ValueError(\n\u001b[0m\u001b[1;32m   2304\u001b[0m                 \u001b[0;34mf\"{k!r} must be {len(target_shape)}D \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2305\u001b[0m                 \u001b[0;34mf\"with shape ({text_shape}). \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: 'vertices' must be 2D with shape (M, 2). Your input has shape (0,)."
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fg=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'] >= 5.5 \n",
    "#       and pytz.utc.localize(rw['event_date'].astype(dtm.datetime))>to_dt-dtm.timedelta(days=180)]\n",
    "m6s = [rw for rw in etas.catalog if rw['mag'] >= 3.5 \n",
    "       and pytz.utc.localize(rw['event_date'].astype(dtm.datetime))>to_dt-dtm.timedelta(days=5)]\n",
    "\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=3*(m6['mag']+12.), 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",
    "for j,rw in enumerate(etas.catalog):\n",
    "    if mpd.date2num(rw['event_date'].astype(dtm.datetime))<t_cat: continue\n",
    "    if rw['mag']<5.: 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=1.*(rw['mag']+2.), color=clr, \n",
    "                          marker='o', zorder=11, label='m={}, {}'.format(rw['mag'], dt_str), latlon=True)\n",
    "    \n",
    "    #\n",
    "etas.cm.plot(etas.catalog['lon'], etas.catalog['lat'], ',', color='b', alpha=.2, zorder=4)\n",
    "\n",
    "plt.gca().legend()\n",
    "#print(etas.catalog[-10:])"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {
    "scrolled": false
   },
   "source": [
    "fg=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",
    "#\n",
    "# let's get everything m>6 in the last 6 months?\n",
    "m6s = [rw for rw in etas.catalog if rw['mag'] >= 5.5 \n",
    "       and pytz.utc.localize(rw['event_date'].astype(dtm.datetime))>to_dt-dtm.timedelta(days=120)]\n",
    "#\n",
    "# plot mainshock:\n",
    "dt = mainshock['event_date'].astype(dtm.datetime)\n",
    "etas.cm.plot([x], [y], latlon=False, marker='*', color='r', ms=16, zorder=11,\n",
    "                   label='m={}, {}'.format(mainshock['mag'], dt_str))\n",
    "ax.set_title('ETAS: Italy, {}\\n\\n'.format(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=3*(m6['mag']+12.), edgecolors=clr, \n",
    "                          c='none', marker='o', zorder=11, label='m={}, {}'.format(m6['mag'], dt_str))\n",
    "    #\n",
    "plt.gca().legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "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": null,
   "metadata": {},
   "outputs": [],
   "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",
    "#\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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "plt.clf()\n",
    "ax1=plt.subplot('121')\n",
    "ax2=plt.subplot('122')\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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#print(etas.mc, etas.mc_etas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plot a cross-section of ETAS. let's get smart about this and do a PCA on the xyz field...\n",
    "#\n",
    "#plt.figure()\n",
    "##my_xy = [[x,y,z] for x,y in zip(etas.catalog['lon'], etas['lat']) if x>=42.25 and x<=42.75]\n",
    "#my_xyz = [[x,y,z] for x,y,z in etas.ETAS_array if y>=42.25 and y<=42.75]\n",
    "#plt.figure(figsize=(8,6))\n",
    "#plt.plot([x for x,y,z in my_xyz ], [z for x,y,z in my_xyz], '.')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mycat = atp.catfromANSS(lon=lons, lat=lats, minMag=2.5,\n",
    "                        dates0=[dtm.datetime(2005,1,1, tzinfo=tzutc), None], Nmax=None, fout=None, rec_array=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(mycat.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### Temporal rate assessment\n",
    "- How well does this predict the rate of earthquakes?\n",
    "- Since we have not yet merged JMW's Omori integration update, we can only work from the instantaneous rate.\n",
    "- Compute this by averaging over the entire map and compare to a simple count."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mc=2.5\n",
    "deg2km = gep.deg2km\n",
    "deg2rad = gep.deg2rad\n",
    "total_rate = numpy.sum([z*etas.d_lat*etas.d_lon*numpy.cos(y*deg2rad)*deg2km**2 for x,y,z in etas.ETAS_array])\n",
    "\n",
    "total_rate2 = numpy.sum([z*numpy.cos(y*deg2rad) \n",
    "            for x,y,z in etas.ETAS_array])*etas.d_lon*etas.d_lat*(deg2km**2)*(10**(etas.mc_etas - mc))*.1#10**(etas.mc_etas - mc)\n",
    "#tr3 = etas.total_rate(mc=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(total_rate*3600.*24, etas.mc_etas)\n",
    "print(total_rate2*3600.*24, etas.mc_etas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "rate_empirical = 0\n",
    "n_days = 2\n",
    "dt0 = etas.catalog['event_date'][-1].astype(dtm.datetime)-dtm.timedelta(days=n_days)\n",
    "for rw in reversed(etas.catalog['event_date']):\n",
    "    if rw.astype(dtm.datetime)<dt0: break\n",
    "    rate_empirical += 1\n",
    "    #\n",
    "#\n",
    "rate_empirical/=n_days\n",
    "print('empirical rate: {}'.format(rate_empirical))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(etas.catalog['event_date'][-1], type(etas.catalog['event_date'][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(len(etas.catalog))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}