{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build Template Database\n",
    "\n",
    "In this notebook, we use the events detected in notebook 5, and further characterized in notebooks 6 ands 7, to build a database of template events. This step consists mostly in building data and metadata files but also allows the selection of events based on quality and the grouping of similar events. We want to avoid having too many similar templates in the database as it means useless, redundant computation in the template matching process (similar templates detect the same events)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from multiprocessing import cpu_count\n",
    "n_CPUs = max(cpu_count(), 24)\n",
    "os.environ[\"OMP_NUM_THREADS\"] = str(n_CPUs)\n",
    "\n",
    "import BPMF\n",
    "import h5py as h5\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from time import time as give_time\n",
    "from tqdm import tqdm\n",
    "\n",
    "from BPMF.data_reader_examples import data_reader_mseed\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%config InlineBackend.figure_formats = [\"svg\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# program parameters\n",
    "DB_FILENAME = \"reloc_bp.h5\"\n",
    "DB_PATH_T = \"template_db\"\n",
    "DATA_FOLDER = \"preprocessed_2_12\"\n",
    "NETWORK_FILENAME = \"network.csv\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# read network metadata\n",
    "net = BPMF.dataset.Network(NETWORK_FILENAME)\n",
    "net.read()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read the database of detected events\n",
    "\n",
    "We read the database of events detected with backprojection and, with them, create `BPMF.dataset.Template` instances."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HMAX_LOCATION_UNCERTAINTY_KM: All events with hmax_unc < HMAX_LOCATION_UNCERTAINTY_KM will be selected\n",
    "#                               as candidate template events\n",
    "HMAX_LOCATION_UNCERTAINTY_KM = 5.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Checking reloc_bp.h5...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████| 12/12 [00:03<00:00,  3.67it/s]\n"
     ]
    }
   ],
   "source": [
    "templates = []\n",
    "tid = 0\n",
    "\n",
    "with h5.File(os.path.join(BPMF.cfg.OUTPUT_PATH, DB_FILENAME), mode=\"r\") as f:\n",
    "    print(f\"Checking {DB_FILENAME}...\")\n",
    "    for key in tqdm(f.keys()):\n",
    "        event = BPMF.dataset.Event.read_from_file(\n",
    "            DB_FILENAME, db_path=BPMF.cfg.OUTPUT_PATH, gid=key\n",
    "        )\n",
    "        if \"NLLoc_reloc\" in event.aux_data:\n",
    "            if event.hmax_unc < HMAX_LOCATION_UNCERTAINTY_KM:\n",
    "                # if event was well relocated with NLLoc, use its location\n",
    "                event.set_moveouts_to_theoretical_times()\n",
    "        event.where = os.path.join(\n",
    "            BPMF.cfg.INPUT_PATH,\n",
    "            str(event.origin_time.year),\n",
    "            event.origin_time.strftime(\"%Y%m%d\"),\n",
    "        )\n",
    "        event.read_waveforms(\n",
    "            BPMF.cfg.TEMPLATE_LEN_SEC,\n",
    "            data_folder=DATA_FOLDER,\n",
    "            data_reader=data_reader_mseed,\n",
    "        )\n",
    "        template = BPMF.dataset.Template.init_from_event(event)\n",
    "        template.id = tid\n",
    "        template.set_aux_data({\"tid\": tid})\n",
    "        templates.append(template)\n",
    "        tid += 1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Remove redundant templates\n",
    "\n",
    "We first create an `BPMF.dataset.TemplateGroup` instance with the templates built in the previous cell. This class offers a number of methods to operate on groups of templates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "template_group = BPMF.dataset.TemplateGroup(templates, net)\n",
    "# this trick is necessary to make the class aware that the waveforms are already\n",
    "# attached to the Template instances (_update_attributes should never be called at\n",
    "# any other time of the workflow).\n",
    "template_group._update_attributes.append('read_waveforms')\n",
    "# normalize the waveforms\n",
    "template_group.normalize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# parameters controlling how the inter-template correlation matrix is computed\n",
    "DISTANCE_THRESHOLD_KM = 5. # in km, criterion on ellipsoid separation\n",
    "N_CLOSEST_STATIONS = 8 # > larger than total number of stations, i.e. use all stations\n",
    "MAX_LAG_SAMP = 5 # in samples, maximum time shift allowed when searching for similarity"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`TemplateGroup.compute_intertemplate_cc` computes the network-averaged correlation coefficient between templates. To account for possible location errors -- and, therefore, different alignments on the P- and S-wave arrivals -- we search for the maximum correlation within +/- `MAX_LAG_SAMP`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computing the similarity matrix...\n",
      "Computing the inter-template directional errors...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ebeauce/miniconda3/envs/hy7_py310/lib/python3.10/site-packages/BPMF/dataset.py:3452: RuntimeWarning: invalid value encountered in divide\n",
      "  unit_direction /= np.sqrt(np.sum(unit_direction**2, axis=1))[\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computed inter-template CCs in 0.86sec.\n"
     ]
    }
   ],
   "source": [
    "t1 = give_time()\n",
    "template_group.compute_intertemplate_cc(\n",
    "    distance_threshold=DISTANCE_THRESHOLD_KM,\n",
    "    n_stations=N_CLOSEST_STATIONS,\n",
    "    save_cc=False,\n",
    "    max_lag=MAX_LAG_SAMP,\n",
    "    compute_from_scratch=True,\n",
    ")\n",
    "t2 = give_time()\n",
    "print(f\"Computed inter-template CCs in {t2-t1:.2f}sec.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "          0         1         2         3         4         5         6   \\\n",
       "0   1.000000  0.510296  0.569837  0.448133  0.525567  0.548963  0.460508   \n",
       "1   0.510296  1.000000  0.425040  0.299094  0.391728  0.405746  0.347309   \n",
       "2   0.569837  0.425040  1.000000  0.379537  0.685845  0.516497  0.591343   \n",
       "3   0.448133  0.299094  0.379537  1.000000  0.334190  0.286154  0.348585   \n",
       "4   0.525567  0.391728  0.685845  0.334190  1.000000  0.461480  0.518231   \n",
       "5   0.548963  0.405746  0.516497  0.286154  0.461480  1.000000  0.440623   \n",
       "6   0.460508  0.347309  0.591343  0.348585  0.518231  0.440623  1.000000   \n",
       "7   0.431669  0.311757  0.385985  0.271180  0.356789  0.469559  0.399291   \n",
       "8   0.619462  0.513189  0.528158  0.436014  0.462075  0.451936  0.442941   \n",
       "9   0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "10  0.368910  0.338228  0.406006  0.247818  0.578226  0.424493  0.378051   \n",
       "11  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "\n",
       "          7         8         9         10        11  \n",
       "0   0.431669  0.619462  0.000000  0.368910  0.000000  \n",
       "1   0.311757  0.513189  0.000000  0.338228  0.000000  \n",
       "2   0.385985  0.528158  0.000000  0.406006  0.000000  \n",
       "3   0.271180  0.436014  0.000000  0.247818  0.000000  \n",
       "4   0.356789  0.462075  0.000000  0.578226  0.000000  \n",
       "5   0.469559  0.451936  0.000000  0.424493  0.000000  \n",
       "6   0.399291  0.442941  0.000000  0.378051  0.000000  \n",
       "7   1.000000  0.336727  0.000000  0.312243  0.000000  \n",
       "8   0.336727  1.000000  0.000000  0.350884  0.000000  \n",
       "9   0.000000  0.000000  1.000000  0.000000  0.137219  \n",
       "10  0.312243  0.350884  0.000000  1.000000  0.000000  \n",
       "11  0.000000  0.000000  0.137219  0.000000  1.000000  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "template_group.intertemplate_cc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# set the threshold to consider two template as redundant\n",
    "# this parameter is quite arbitrary and really depends on how much computation\n",
    "# time you are ready to spend in the matched-filter search\n",
    "# templates with very low correlation coefficients (~0.10) still detect\n",
    "# many events in common\n",
    "SIMILARITY_THRESHOLD = 0.50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Template 0 is similar to: Int64Index([0, 1, 2, 4, 5, 8], dtype='int64')\n",
      "Keeping only Template 2\n",
      "Template 6 is similar to: Int64Index([2, 4, 6], dtype='int64')\n",
      "Keeping only Template 2\n",
      "Template 10 is similar to: Int64Index([4, 10], dtype='int64')\n",
      "Keeping only Template 4\n"
     ]
    }
   ],
   "source": [
    "templates_filtered = []\n",
    "tids_processed = []\n",
    "for tid in template_group.tids:\n",
    "    if tid in tids_processed:\n",
    "        continue\n",
    "    tt = template_group.tindexes.loc[tid]\n",
    "    similar = template_group.intertemplate_cc.loc[tid] > SIMILARITY_THRESHOLD\n",
    "    n_similar = np.sum(similar)\n",
    "    if n_similar > 1:\n",
    "        similar_tids = template_group.intertemplate_cc[similar].index\n",
    "        print(f\"Template {tid} is similar to: {similar_tids}\")\n",
    "        tids_processed.extend(similar_tids.tolist())\n",
    "        best_tid = -10\n",
    "        best_unc = 10000000.0\n",
    "        for tid_sim in similar_tids:\n",
    "            tt_sim = template_group.tindexes.loc[tid_sim]\n",
    "            unc = np.sqrt(\n",
    "                template_group.templates[tt_sim].hmax_unc ** 2\n",
    "                + template_group.templates[tt_sim].vmax_unc ** 2\n",
    "            )\n",
    "            if unc < best_unc:\n",
    "                best_unc = unc\n",
    "                best_tid = tid_sim\n",
    "        print(f\"Keeping only Template {best_tid}\")\n",
    "        templates_filtered.append(\n",
    "            template_group.templates[\n",
    "                template_group.tindexes.loc[best_tid]\n",
    "            ]\n",
    "        )\n",
    "    elif n_similar == 0:\n",
    "        print(\n",
    "            f\"Problem with Template {tid}, it should at least be similar with\" \" itself\"\n",
    "        )\n",
    "    else:\n",
    "        tids_processed.append(tid)\n",
    "        templates_filtered.append(template_group.templates[tt])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the similar templates\n",
    "\n",
    "How good was our criterion to group similar templates together? Let's check the waveforms of the similar templates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1500x1500 with 24 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tid = 0\n",
    "fig = template_group.templates[template_group.tindexes.loc[tid]].plot(figsize=(15, 15))\n",
    "fig.suptitle(f\"Template {tid}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0.98, 'Template 1')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
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      "text/plain": [
       "<Figure size 1500x1500 with 24 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tid = 1\n",
    "fig = template_group.templates[template_group.tindexes.loc[tid]].plot(figsize=(15, 15))\n",
    "fig.suptitle(f\"Template {tid}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's plot the waveforms of all the templates similar to template 0 on SAUV.E ."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Velocity (AU)')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.signal import hilbert\n",
    "\n",
    "similar_tids = [0, 1, 3, 5, 9]\n",
    "fig = plt.figure(\"waveforms_similar_templates\", figsize=(15, 7))\n",
    "ax = fig.add_subplot(111)\n",
    "for tid in similar_tids:\n",
    "    tp = template_group.templates[template_group.tindexes.loc[tid]]\n",
    "    wav = tp.traces.select(station=\"SAUV\", component=\"E\")[0].data\n",
    "    norm = np.std(wav)\n",
    "    # align traces on max of envelope\n",
    "    time = np.arange(len(wav))\n",
    "    time -= np.abs(hilbert(wav)).argmax()\n",
    "    # time -= np.abs(wav).argmax()\n",
    "    if tid == 0:\n",
    "        ax.plot(time, wav/norm, color=\"k\", lw=1.5)\n",
    "    else:\n",
    "        ax.plot(time, wav/norm, color=\"dimgrey\", lw=0.75)\n",
    "ax.set_xlabel(\"Time to max (samples)\")\n",
    "ax.set_ylabel(\"Velocity (AU)\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The similarity of these templates would make them detect the same events and thus, spending time on redundant computation and writing redundant info in databases."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Save the non-redundant templates in a database"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_dir = os.path.join(BPMF.cfg.OUTPUT_PATH, DB_PATH_T)\n",
    "if not os.path.isdir(output_dir):\n",
    "    os.mkdir(output_dir)\n",
    "    \n",
    "for t, template in enumerate(templates_filtered):\n",
    "    tid = t\n",
    "    template.id = tid\n",
    "    template.set_aux_data({\"tid\": tid})\n",
    "    full_path = os.path.join(output_dir, f\"template{tid}.h5\")\n",
    "    template.write(\n",
    "        f\"template{tid}.h5\",\n",
    "        db_path=output_dir,\n",
    "        overwrite=True,\n",
    "    )"
   ]
  }
 ],
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