{ "cells": [ { "cell_type": "markdown", "id": "e5130f04", "metadata": {}, "source": [ "# Project Initialization\n", "\n", "In this first notebook, we initialize the project by creating the directory tree where inputs and outputs will be stored. Path variables are environment variables that are defined within the `parameters.cfg` file, which is located in your working directory. We also define a number of other environment variables that will be used at later stages of the BPMF workflow." ] }, { "cell_type": "code", "execution_count": 12, "id": "c69780b4", "metadata": {}, "outputs": [], "source": [ "import os\n", "import pandas as pd\n", "\n", "import BPMF" ] }, { "cell_type": "code", "execution_count": 13, "id": "798af93d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "You are using BPMF v2.0.0.alpha1\n" ] } ], "source": [ "print(f\"You are using BPMF v{BPMF.__version__}\")" ] }, { "cell_type": "code", "execution_count": 2, "id": "f317b11f", "metadata": {}, "outputs": [], "source": [ "config = {}" ] }, { "cell_type": "markdown", "id": "81efc24d", "metadata": {}, "source": [ "## Define the path variables\n", "\n", "NB: path variables can either be relative or absolute paths" ] }, { "cell_type": "code", "execution_count": 3, "id": "cebfd0e4", "metadata": {}, "outputs": [], "source": [ "# INPUT_PATH: path to root directory where all input data will be stored\n", "config[\"INPUT_PATH\"] = \"../BPMF_data\"\n", "# OUTPUT_PATH: path to root directory where all outputs produced by BPMF will be stored\n", "config[\"OUTPUT_PATH\"] = \"../BPMF_outputs\"\n", "# NETWORK_PATH: path to directory where the network metadata file will be stored\n", "config[\"NETWORK_PATH\"] = \"../network\"\n", "# MOVEOUTS_PATH: path to directory where travel times will be stored\n", "config[\"MOVEOUTS_PATH\"] = \"../moveouts\"\n", "# NLLOC_INPUT_PATH: path to directory where NLLoc input files will be stored\n", "config[\"NLLOC_INPUT_PATH\"] = os.path.join(config[\"INPUT_PATH\"], \"NLLoc_inputs\")\n", "# NLLOC_OUTPUT_PATH: path to directory where NLLoc output files will be stored\n", "config[\"NLLOC_OUTPUT_PATH\"] = os.path.join(config[\"OUTPUT_PATH\"], \"NLLoc_outputs\")" ] }, { "cell_type": "markdown", "id": "4d28db41", "metadata": {}, "source": [ "## Create folder tree" ] }, { "cell_type": "code", "execution_count": 4, "id": "c63068a4", "metadata": {}, "outputs": [], "source": [ "for var in [\"INPUT_PATH\", \"OUTPUT_PATH\", \"NETWORK_PATH\", \"MOVEOUTS_PATH\", \"NLLOC_INPUT_PATH\", \"NLLOC_OUTPUT_PATH\"]:\n", " if not os.path.isdir(config[var]):\n", " os.makedirs(config[var])" ] }, { "cell_type": "markdown", "id": "697ee07a", "metadata": {}, "source": [ "## Define the preprocessing parameters" ] }, { "cell_type": "code", "execution_count": 5, "id": "9f934341", "metadata": {}, "outputs": [], "source": [ "# SAMPLING_RATE_HZ: target sampling rate of the resampled data\n", "config[\"SAMPLING_RATE_HZ\"] = 25.\n", "# MIN_FREQ_HZ: minimum frequency, in Hertz, of the bandpass filter\n", "config[\"MIN_FREQ_HZ\"] = 2.\n", "# MAX_FREQ_HZ: maximum frequency, in Hertz, of the bandpass filter\n", "config[\"MAX_FREQ_HZ\"] = 12.\n", "# DATA_BUFFER_SEC: duration, in seconds, of the buffer data at the start and end of each day\n", "config[\"DATA_BUFFER_SEC\"] = 500." ] }, { "cell_type": "markdown", "id": "d6b17b7e", "metadata": {}, "source": [ "## Define the backprojection parameters" ] }, { "cell_type": "code", "execution_count": 6, "id": "1885b512", "metadata": {}, "outputs": [], "source": [ "# N_DEV_BP_THRESHOLD: number of deviations (e.g. rms or mad) above central tendency (e.g. mean or median) for\n", "# backprojection detection threshold (not used in this tutorial)\n", "config[\"N_DEV_BP_THRESHOLD\"] = 15." ] }, { "cell_type": "markdown", "id": "4fea4c84", "metadata": {}, "source": [ "## Define the template matching parameters" ] }, { "cell_type": "code", "execution_count": 7, "id": "437628ac", "metadata": {}, "outputs": [], "source": [ "# TEMPLATE_LEN_SEC: template length, in seconds\n", "config[\"TEMPLATE_LEN_SEC\"] = 8.\n", "# MATCHED_FILTER_STEP_SAMP: step size, in samples, between two correlation coefficient measurements\n", "config[\"MATCHED_FILTER_STEP_SAMP\"] = 1\n", "# N_DEV_MF_THRESHOLD: number of deviations (e.g. rms or mad) above central tendency (e.g. mean or median) for\n", "# matched filter detection threshold\n", "config[\"N_DEV_MF_THRESHOLD\"] = 8." ] }, { "cell_type": "markdown", "id": "ce7f9a78", "metadata": {}, "source": [ "## Miscelleanous" ] }, { "cell_type": "code", "execution_count": 8, "id": "768be8d0", "metadata": {}, "outputs": [], "source": [ "# NLLOC_BASENAME: basename of NLLoc files\n", "config[\"NLLOC_BASENAME\"] = \"NAF\"\n", "# BUFFER_EXTRACTED_EVENTS_SEC: duration taken before origin time when reading an event's data\n", "config[\"BUFFER_EXTRACTED_EVENTS_SEC\"] = 20." ] }, { "cell_type": "markdown", "id": "21631c62", "metadata": {}, "source": [ "## Write the `parameters.csv` file in current working directory" ] }, { "cell_type": "code", "execution_count": 9, "id": "9135bb37", "metadata": {}, "outputs": [], "source": [ "# convert config dictionary to pandas.DataFrame for pretty print\n", "config_pd = pd.DataFrame(data=[], index=[], columns=[\"parameter_value\"])\n", "config_pd.index.name = \"parameter_name\"\n", "for key in config:\n", " config_pd.loc[key] = config[key]" ] }, { "cell_type": "code", "execution_count": 10, "id": "4f56173e", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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parameter_value
parameter_name
INPUT_PATH../BPMF_data
OUTPUT_PATH../BPMF_outputs
NETWORK_PATH../network
MOVEOUTS_PATH../moveouts
NLLOC_INPUT_PATH../BPMF_data/NLLoc_inputs
NLLOC_OUTPUT_PATH../BPMF_outputs/NLLoc_outputs
SAMPLING_RATE_HZ25.0
MIN_FREQ_HZ2.0
MAX_FREQ_HZ12.0
DATA_BUFFER_SEC500.0
N_DEV_BP_THRESHOLD15.0
TEMPLATE_LEN_SEC8.0
MATCHED_FILTER_STEP_SAMP1
N_DEV_MF_THRESHOLD8.0
NLLOC_BASENAMENAF
BUFFER_EXTRACTED_EVENTS_SEC20.0
\n", "
" ], "text/plain": [ " parameter_value\n", "parameter_name \n", "INPUT_PATH ../BPMF_data\n", "OUTPUT_PATH ../BPMF_outputs\n", "NETWORK_PATH ../network\n", "MOVEOUTS_PATH ../moveouts\n", "NLLOC_INPUT_PATH ../BPMF_data/NLLoc_inputs\n", "NLLOC_OUTPUT_PATH ../BPMF_outputs/NLLoc_outputs\n", "SAMPLING_RATE_HZ 25.0\n", "MIN_FREQ_HZ 2.0\n", "MAX_FREQ_HZ 12.0\n", "DATA_BUFFER_SEC 500.0\n", "N_DEV_BP_THRESHOLD 15.0\n", "TEMPLATE_LEN_SEC 8.0\n", "MATCHED_FILTER_STEP_SAMP 1\n", "N_DEV_MF_THRESHOLD 8.0\n", "NLLOC_BASENAME NAF\n", "BUFFER_EXTRACTED_EVENTS_SEC 20.0" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "config_pd" ] }, { "cell_type": "code", "execution_count": 11, "id": "e4ceae43", "metadata": {}, "outputs": [], "source": [ "with open(\"BPMF_parameters.cfg\", \"w\") as fparams:\n", " for key in config:\n", " fparams.write(f\"{key} = {config[key]}\\n\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3.10.4 ('hy7_py310')", "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": "3.1.-1" }, "vscode": { "interpreter": { "hash": "221f0e5b1b98151b07a79bf3b6d0c1d306576197d2c4531763770570a29e708e" } } }, "nbformat": 4, "nbformat_minor": 5 }