sketch_processing_notebook.ipynb

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   "source": [
    "#### Processing strategies for urban seismic noise in Seattle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ab05caff",
   "metadata": {},
   "outputs": [],
   "source": [
    "# module imports\n",
    "import sys\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "d10d5c91",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add a function to get a stack for a station1.component1 -station2.component2 pair from NoisePy output files\n",
    "def get_stack_trace_from_noisepy(output_directory_of_stacks, sta1, cha1, sta2, cha2):\n",
    "    # something\n",
    "    tr = []\n",
    "    \n",
    "    # add sampling rate. tr could be an obspy trace, or just an array with a separate variable for sampling rate\n",
    "    return(tr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b64ee1a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add a function to get a stack for a station1.component1 -station2.component2 pair from Ruido output files\n",
    "def get_stack_trace_from_ruido(output_directory_of_stacks, sta1, cha1, sta2, cha2):\n",
    "    # something\n",
    "    tr = []\n",
    "    \n",
    "    # add sampling rate. tr could be an obspy trace, or just an array with a separate variable for sampling rate\n",
    "    return(tr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5b1eb6cc",
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   "outputs": [],
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    "# user input\n",
    "\n",
    "# The dictionaries could specify the different processing runs by a key.\n",
    "# This would also allow to identify noisepy outputs with further steps in ruido (using same key)\n",
    "\n",
    "# in each of the noisepy setups should be a directory with the noisepy main script prepared \n",
    "# to run by the user, with the processing specified as they wish\n",
    "noisepy_setups = {\"whiten_rma\": \"/path/to/directory1\",\n",
    "                   \"nowhiten\": \"/path/to/directory2\",\n",
    "                   \"nowhiten_and_postwhiten_in_ruido\": \"/path/to/directory2\",\n",
    "                   \"whiten_and_onebit\": \"/path/to/directory3\"}\n",
    "\n",
    "# for some of the noisepy output, we would then use further steps in ruido, like clustering and \n",
    "# cluster-specific stacking\n",
    "ruido_setups = {\"nowhiten\": \"/path/to/inputfile1\",\n",
    "                \"nowhiten_and_postwhiten_in_ruido\": \"/path/to/inputfile2\"}\n",
    "\n",
    "# using these directories, we could later also access the stacked output (both ruido and noisepy)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "a36d073d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for each key in the dictionary of noisepy: Run noisepy!\n",
    "# for (k, v) in noisepy....:\n",
    "#     os.system(run the command to invoke this noisepy setup...)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "86e68775",
   "metadata": {},
   "outputs": [],
   "source": [
    "# for the dictionary entries of ruido: Run ruido!\n",
    "# for (k, inputfile_x) in ruido_setups...:\n",
    "#    os.system(\"mpirun -np <cores> python /path/to/ruido_main.py /path/to/inputfile_x\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c559912e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# then, get the stack traces and compare.\n",
    "\n",
    "# plot different processing outcomes for a small number of station pairs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "34c338c5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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