From b32a9d96bd94ca62e5321eca21e629fc0684e7bc Mon Sep 17 00:00:00 2001 From: Carlos Garcia Jurado Suarez Date: Mon, 29 Jan 2024 11:33:01 -0800 Subject: [PATCH 1/2] chore: Precommit check for notebook outputs --- .pre-commit-config.yaml | 11 + script/write_speed/plot.ipynb | 54 +- .../attenuation_singlestation.ipynb | 20 +- tutorials/noisepy_datastore.ipynb | 150 +-- tutorials/noisepy_pnwstore_tutorial.ipynb | 3 +- tutorials/noisepy_scedc_tutorial.ipynb | 1020 ++++++++--------- .../old/cross_correlation_from_sac.ipynb | 72 +- .../download_toASDF_cross_correlation.ipynb | 76 +- tutorials/plot_stacks.ipynb | 210 ++-- tutorials/run_mpi_scedc.ipynb | 56 +- 10 files changed, 685 insertions(+), 987 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 28abbbeb..5abcbfce 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -15,6 +15,17 @@ repos: exclude: \.ipy(n|nb)$ args: ["--autofix", "--indent=2", "--no-sort-keys"] + # Clear output from jupyter notebooks so that only the input cells are committed. + - repo: local + hooks: + - id: jupyter-nb-clear-output + name: Clear output from Jupyter notebooks + description: Clear output from Jupyter notebooks. + files: \.ipynb$ + stages: [commit] + language: system + entry: jupyter nbconvert --clear-output + - repo: https://github.com/PyCQA/isort rev: 5.12.0 hooks: diff --git a/script/write_speed/plot.ipynb b/script/write_speed/plot.ipynb index f14c093a..243e8862 100644 --- a/script/write_speed/plot.ipynb +++ b/script/write_speed/plot.ipynb @@ -2,39 +2,9 @@ "cells": [ { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[autoreload of write failed: Traceback (most recent call last):\n", - " File \"/Users/carlosg/repos/NoisePy/.env/lib/python3.10/site-packages/IPython/extensions/autoreload.py\", line 276, in check\n", - " superreload(m, reload, self.old_objects)\n", - " File \"/Users/carlosg/repos/NoisePy/.env/lib/python3.10/site-packages/IPython/extensions/autoreload.py\", line 500, in superreload\n", - " update_generic(old_obj, new_obj)\n", - " File \"/Users/carlosg/repos/NoisePy/.env/lib/python3.10/site-packages/IPython/extensions/autoreload.py\", line 397, in update_generic\n", - " update(a, b)\n", - " File \"/Users/carlosg/repos/NoisePy/.env/lib/python3.10/site-packages/IPython/extensions/autoreload.py\", line 349, in update_class\n", - " if update_generic(old_obj, new_obj):\n", - " File \"/Users/carlosg/repos/NoisePy/.env/lib/python3.10/site-packages/IPython/extensions/autoreload.py\", line 397, in update_generic\n", - " update(a, b)\n", - " File \"/Users/carlosg/repos/NoisePy/.env/lib/python3.10/site-packages/IPython/extensions/autoreload.py\", line 309, in update_function\n", - " setattr(old, name, getattr(new, name))\n", - "ValueError: __init__() requires a code object with 1 free vars, not 0\n", - "]\n" - ] - } - ], + "outputs": [], "source": [ "# plot a series of write speed data from text files on S3\n", "%load_ext autoreload\n", @@ -55,20 +25,9 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "df = pd.DataFrame()\n", "for w in writer_types:\n", @@ -129,9 +88,8 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" - }, - "orig_nbformat": 4 + "version": "3.10.13" + } }, "nbformat": 4, "nbformat_minor": 2 diff --git a/tutorials/monitoring/attenuation_singlestation.ipynb b/tutorials/monitoring/attenuation_singlestation.ipynb index 695f06c6..35f8b3ba 100644 --- a/tutorials/monitoring/attenuation_singlestation.ipynb +++ b/tutorials/monitoring/attenuation_singlestation.ipynb @@ -122,9 +122,7 @@ "cell_type": "code", "execution_count": null, "id": "1a3ebcc4", - "metadata": { - "scrolled": true - }, + "metadata": {}, "outputs": [], "source": [ "# Ready to read in h5 file\n", @@ -195,9 +193,7 @@ "cell_type": "code", "execution_count": null, "id": "50ace094", - "metadata": { - "scrolled": true - }, + "metadata": {}, "outputs": [], "source": [ "freq = [0.5, 1, 2,4] # targeted frequency band for waveform monitoring\n", @@ -264,9 +260,7 @@ "cell_type": "code", "execution_count": null, "id": "d00a24e6", - "metadata": { - "scrolled": true - }, + "metadata": {}, "outputs": [], "source": [ "# get the mean-squared value on each componet and also the average waveform\n", @@ -472,9 +466,7 @@ "cell_type": "code", "execution_count": null, "id": "c9d6f840", - "metadata": { - "scrolled": true - }, + "metadata": {}, "outputs": [], "source": [ "# getting the sum of squared residuals (SSR) between Eobs and Esyn \n", @@ -539,9 +531,7 @@ "cell_type": "code", "execution_count": null, "id": "beda26dc", - "metadata": { - "scrolled": false - }, + "metadata": {}, "outputs": [], "source": [ "# getting the optimal value from the SSR\n", diff --git a/tutorials/noisepy_datastore.ipynb b/tutorials/noisepy_datastore.ipynb index fee44dbf..14b59f69 100644 --- a/tutorials/noisepy_datastore.ipynb +++ b/tutorials/noisepy_datastore.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -37,27 +37,11 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": { "id": "vceZgD83PnNc" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/marinedenolle/opt/miniconda3/envs/noisepy/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", - " from .autonotebook import tqdm as notebook_tqdm\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Using NoisePy version 0.9.72.dev12\n" - ] - } - ], + "outputs": [], "source": [ "from noisepy.seis import __version__ # noisepy core functions\n", "from noisepy.seis.scedc_s3store import SCEDCS3DataStore, channel_filter # Object to query SCEDC data from on S3\n", @@ -72,19 +56,11 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "id": "yojR0Z3ALm6K" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2022-01-02T00:00:00 - 2022-01-04T00:00:00\n" - ] - } - ], + "outputs": [], "source": [ "# timeframe for analysis\n", "start = datetime(2022, 1, 2)\n", @@ -123,22 +99,11 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": { "id": "jq2DKIS9Rl2H" }, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# SCEDC S3 bucket common URL characters for that day.\n", "S3_DATA = \"s3://scedc-pds/continuous_waveforms/\"\n", @@ -162,17 +127,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['_abc_impl', '_ensure_channels_loaded', '_load_channels', '_parse_channel', '_parse_timespan', 'ch_filter', 'channel_catalog', 'channels', 'date_range', 'file_re', 'fs', 'get_channels', 'get_inventory', 'get_timespans', 'path', 'paths', 'read_data']\n" - ] - } - ], + "outputs": [], "source": [ "method_list = [method for method in dir(raw_store) if method.startswith('__') is False]\n", "print(method_list)" @@ -187,17 +144,9 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[2022-01-02T00:00:00+0000 - 2022-01-03T00:00:00+0000, 2022-01-03T00:00:00+0000 - 2022-01-04T00:00:00+0000]\n" - ] - } - ], + "outputs": [], "source": [ "span = raw_store.get_timespans()\n", "print(span)" @@ -214,40 +163,9 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2023-09-09 16:15:29,206 4376315264 INFO utils.log_raw(): TIMING: 1.4821 secs. for Loading 4035 files from s3://scedc-pds/continuous_waveforms/2022/2022_002/\n", - "2023-09-09 16:15:29,245 4376315264 INFO utils.log_raw(): TIMING: 0.0388 secs. for Init: 1 timespans and 9 channels\n", - "2023-09-09 16:15:29,349 11307921408 INFO channelcatalog._get_inventory_from_file(): Reading StationXML file s3://scedc-pds/FDSNstationXML/CI/CI_SBC.xml\n", - "2023-09-09 16:15:29,493 11341574144 INFO channelcatalog._get_inventory_from_file(): Reading StationXML file s3://scedc-pds/FDSNstationXML/CI/CI_DEV.xml\n", - "2023-09-09 16:15:29,506 11324747776 INFO channelcatalog._get_inventory_from_file(): Reading StationXML file s3://scedc-pds/FDSNstationXML/CI/CI_RIO.xml\n", - "2023-09-09 16:15:40,119 4376315264 INFO scedc_s3store.get_channels(): Getting 9 channels for 2022-01-02T00:00:00+0000 - 2022-01-03T00:00:00+0000: [CI.DEV.BHE, CI.DEV.BHN, CI.DEV.BHZ, CI.RIO.BHE, CI.RIO.BHN, CI.RIO.BHZ, CI.SBC.BHE, CI.SBC.BHN, CI.SBC.BHZ]\n" - ] - }, - { - "data": { - "text/plain": [ - "[CI.DEV.BHE,\n", - " CI.DEV.BHN,\n", - " CI.DEV.BHZ,\n", - " CI.RIO.BHE,\n", - " CI.RIO.BHN,\n", - " CI.RIO.BHZ,\n", - " CI.SBC.BHE,\n", - " CI.SBC.BHN,\n", - " CI.SBC.BHZ]" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "channels = raw_store.get_channels(span[0])\n", "channels" @@ -265,21 +183,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "1 Trace(s) in Stream:\n", - "CI.DEV..BHZ | 2022-01-02T00:00:00.019539Z - 2022-01-02T23:59:59.994539Z | 40.0 Hz, 3456000 samples" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "d = raw_store.read_data(span[0], channels[2])\n", "d.stream" @@ -287,31 +193,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "d.stream.plot()" ] diff --git a/tutorials/noisepy_pnwstore_tutorial.ipynb b/tutorials/noisepy_pnwstore_tutorial.ipynb index 1dd0693e..c1ffed42 100644 --- a/tutorials/noisepy_pnwstore_tutorial.ipynb +++ b/tutorials/noisepy_pnwstore_tutorial.ipynb @@ -279,8 +279,7 @@ "cell_type": "code", "execution_count": null, "metadata": { - "id": "pWcrfWO8W1tH", - "scrolled": true + "id": "pWcrfWO8W1tH" }, "outputs": [], "source": [ diff --git a/tutorials/noisepy_scedc_tutorial.ipynb b/tutorials/noisepy_scedc_tutorial.ipynb index d03e4e5c..687f14d9 100644 --- a/tutorials/noisepy_scedc_tutorial.ipynb +++ b/tutorials/noisepy_scedc_tutorial.ipynb @@ -1,512 +1,512 @@ { - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "PIA2IaqUOeOA" - }, - "source": [ - "# NoisePy SCEDC Tutorial\n", - "\n", - "Noisepy is a python software package to process ambient seismic noise cross correlations. This tutorial aims to introduce the use of noisepy for a toy problem on the SCEDC data. It can be ran locally or on the cloud.\n", - "\n", - "\n", - "The data is stored on AWS S3 as the SCEDC Data Set: https://scedc.caltech.edu/data/getstarted-pds.html\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "First, we install the noisepy-seis package" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# Uncomment and run this line if the environment doesn't have noisepy already installed:\n", - "# ! pip install noisepy-seis " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "__Warning__: NoisePy uses ```obspy``` as a core Python module to manipulate seismic data. If you use Google Colab, restart the runtime now for proper installation of ```obspy``` on Colab." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "WtDb2_y3Oreg" - }, - "source": [ - "## Import necessary modules\n", - "\n", - "Then we import the basic modules" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "vceZgD83PnNc" - }, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "from noisepy.seis import cross_correlate, stack_cross_correlations, __version__ # noisepy core functions\n", - "from noisepy.seis.asdfstore import ASDFCCStore, ASDFStackStore # Object to store ASDF data within noisepy\n", - "from noisepy.seis.scedc_s3store import SCEDCS3DataStore, channel_filter # Object to query SCEDC data from on S3\n", - "from noisepy.seis.datatypes import CCMethod, ConfigParameters, FreqNorm, RmResp, StackMethod, TimeNorm # Main configuration object\n", - "from noisepy.seis.channelcatalog import XMLStationChannelCatalog # Required stationXML handling object\n", - "import os\n", - "import shutil\n", - "from datetime import datetime, timezone\n", - "from datetimerange import DateTimeRange\n", - "\n", - "\n", - "from noisepy.seis.plotting_modules import plot_all_moveout\n", - "\n", - "print(f\"Using NoisePy version {__version__}\")\n", - "\n", - "path = \"./scedc_data\" \n", - "\n", - "os.makedirs(path, exist_ok=True)\n", - "cc_data_path = os.path.join(path, \"CCF\")\n", - "stack_data_path = os.path.join(path, \"STACK\")\n", - "S3_STORAGE_OPTIONS = {\"s3\": {\"anon\": True}}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "pntYzIYGNTn8" - }, - "source": [ - "We will work with a single day worth of data on SCEDC. The continuous data is organized with a single day and channel per miniseed (https://scedc.caltech.edu/data/cloud.html). For this example, you can choose any year since 2002. We will just cross correlate a single day." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "yojR0Z3ALm6K" - }, - "outputs": [], - "source": [ - "# SCEDC S3 bucket common URL characters for that day.\n", - "S3_DATA = \"s3://scedc-pds/continuous_waveforms/\"\n", - "# timeframe for analysis\n", - "start = datetime(2002, 1, 1, tzinfo=timezone.utc)\n", - "end = datetime(2002, 1, 3, tzinfo=timezone.utc)\n", - "timerange = DateTimeRange(start, end)\n", - "print(timerange)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "E1CC-BljNzus" - }, - "source": [ - "The station information, including the instrumental response, is stored as stationXML in the following bucket" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "mhfgrMPALsYS" - }, - "outputs": [], - "source": [ - "S3_STATION_XML = \"s3://scedc-pds/FDSNstationXML/CI/\" # S3 storage of stationXML\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "ssaL5fa5IhI7" - }, - "source": [ - "## Ambient Noise Project Configuration\n", - "\n", - "We prepare the configuration of the workflow by declaring and storing parameters into the ``ConfigParameters()`` object and/or editing the ``config.yml`` file.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "dIjBD7riIfdJ" - }, - "outputs": [], - "source": [ - "# Initialize ambient noise workflow configuration\n", - "config = ConfigParameters() # default config parameters which can be customized\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Tsp7RfC8IwE-" - }, - "source": [ - "Customize the job parameters below:" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "ByEiHRjmIAPB" - }, - "outputs": [], - "source": [ - "config.start_date = start\n", - "config.end_date = end\n", - "config.acorr_only = False # only perform auto-correlation or not\n", - "config.xcorr_only = True # only perform cross-correlation or not\n", - "\n", - "config.inc_hours = 24 # INC_HOURS is used in hours (integer) as the \n", - " #chunk of time that the paralelliztion will work.\n", - " # data will be loaded in memory, so reduce memory with smaller \n", - " # inc_hours if there are over 400+ stations.\n", - " # At regional scale for SCEDC, we can afford 20Hz data and inc_hour \n", - " # being a day of data.\n", - "\n", - " \n", - "# pre-processing parameters\n", - "config.samp_freq= 20 # (int) Sampling rate in Hz of desired processing (it can be different than the data sampling rate)\n", - "config.cc_len= 3600 # (float) basic unit of data length for fft (sec)\n", - "config.step= 1800.0 # (float) overlapping between each cc_len (sec)\n", - "\n", - "config.ncomp = 3 # 1 or 3 component data (needed to decide whether do rotation)\n", - "\n", - "config.stationxml= False # station.XML file used to remove instrument response for SAC/miniseed data\n", - " # If True, the stationXML file is assumed to be provided.\n", - "config.rm_resp= RmResp.INV # select 'no' to not remove response and use 'inv' if you use the stationXML,'spectrum',\n", - "\n", - "\n", - "############## NOISE PRE-PROCESSING ##################\n", - "config.freqmin,config.freqmax = 0.05,2.0 # broad band filtering of the data before cross correlation\n", - "config.max_over_std = 10 # threshold to remove window of bad signals: set it to 10*9 if prefer not to remove them\n", - "\n", - "################### SPECTRAL NORMALIZATION ############\n", - "config.freq_norm= FreqNorm.RMA # choose between \"rma\" for a soft whitening or \"no\" for no whitening. Pure whitening is not implemented correctly at this point.\n", - "config.smoothspect_N = 10 # moving window length to smooth spectrum amplitude (points)\n", - " # here, choose smoothspect_N for the case of a strict whitening (e.g., phase_only)\n", - "\n", - "\n", - "#################### TEMPORAL NORMALIZATION ##########\n", - "config.time_norm = TimeNorm.ONE_BIT # 'no' for no normalization, or 'rma', 'one_bit' for normalization in time domain,\n", - "config.smooth_N= 10 # moving window length for time domain normalization if selected (points)\n", - "\n", - "\n", - "############ cross correlation ##############\n", - "\n", - "config.cc_method= CCMethod.XCORR # 'xcorr' for pure cross correlation OR 'deconv' for deconvolution;\n", - " # FOR \"COHERENCY\" PLEASE set freq_norm to \"rma\", time_norm to \"no\" and cc_method to \"xcorr\"\n", - "\n", - "# OUTPUTS:\n", - "config.substack = True # True = smaller stacks within the time chunk. False: it will stack over inc_hours\n", - "config.substack_len = config.cc_len # how long to stack over (for monitoring purpose): need to be multiples of cc_len\n", - " # if substack=True, substack_len=2*cc_len, then you pre-stack every 2 correlation windows.\n", - " # for instance: substack=True, substack_len=cc_len means that you keep ALL of the correlations\n", - " # if substack=False, the cross correlation will be stacked over the inc_hour window\n", - "\n", - "### For monitoring applications ####\n", - "## we recommend substacking ever 2-4 cross correlations and storing the substacks\n", - "# e.g. \n", - "# config.substack = True \n", - "# config.substack_len = 4* config.cc_len\n", - "\n", - "config.maxlag= 200 # lags of cross-correlation to save (sec)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# For this tutorial make sure the previous run is empty\n", - "#os.system(f\"rm -rf {cc_data_path}\")\n", - "if os.path.exists(cc_data_path):\n", - " shutil.rmtree(cc_data_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "Wwv1QCQhP_0Y" - }, - "source": [ - "## Step 1: Cross-correlation\n", - "\n", - "In this instance, we read directly the data from the scedc bucket into the cross correlation code. We are not attempting to recreate a data store. Therefore we go straight to step 1 of the cross correlations." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We first declare the data and cross correlation stores" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "jq2DKIS9Rl2H" - }, - "outputs": [], - "source": [ - "#stations = \"RPV,STS,LTP,LGB,WLT,CPP,PDU,CLT,SVD,BBR\".split(\",\") # filter to these stations\n", - "stations = \"RPV,SVD,BBR\".split(\",\") # filter to these stations\n", - "# stations = \"DGR,DEV,DLA,DNR,FMP,HLL,LGU,LLS,MLS,PDU,PDR,RIN,RIO,RVR,SMS,BBR,CHN,MWC,RIO,BBS,RPV,ADO,DEV\".split(\",\") # filter to these stations\n", - "\n", - "# There are 2 ways to load stations: You can either pass a list of stations or load the stations from a text file.\n", - "# TODO : will be removed with issue #270\n", - "config.load_stations(stations)\n", - "\n", - "# For loading it from a text file, write the path of the file in stations_file field of config instance as below\n", - "# config.stations_file = os.path.join(os.path.dirname(__file__), \"path/my_stations.txt\")\n", - "\n", - "# TODO : will be removed with issue #270\n", - "# config.load_stations()\n", - "\n", - "catalog = XMLStationChannelCatalog(S3_STATION_XML, storage_options=S3_STORAGE_OPTIONS) # Station catalog\n", - "raw_store = SCEDCS3DataStore(S3_DATA, catalog, channel_filter(config.stations, \"BH\"), timerange, storage_options=S3_STORAGE_OPTIONS) # Store for reading raw data from S3 bucket\n", - "cc_store = ASDFCCStore(cc_data_path) # Store for writing CC data" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "get the time range of the data in the data store inventory" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "span = raw_store.get_timespans()\n", - "print(span)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Get the channels available during a given time spane" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "channel_list=raw_store.get_channels(span[1])\n", - "print(channel_list)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "5qsPGkNp9Msx" - }, - "source": [ - "## Perform the cross correlation\n", - "The data will be pulled from SCEDC, cross correlated, and stored locally if this notebook is ran locally.\n", - "If you are re-calculating, we recommend to clear the old ``cc_store``." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "49MnDXYp9Msx" - }, - "outputs": [], - "source": [ - "cross_correlate(raw_store, config, cc_store)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "GMeH9BslQSSJ" - }, - "source": [ - "The cross correlations are saved as a single file for each channel pair and each increment of inc_hours. We now will stack all the cross correlations over all time chunk and look at all station pairs results.\n", - "\n", - "## Step 2: Stack the cross correlation\n", - "\n", - "We now create the stack stores. Because this tutorial runs locally, we will use an ASDF stack store to output the data. ASDF is a data container in HDF5 that is used in full waveform modeling and inversion. H5 behaves well locally. \n", - "\n", - "Each station pair will have 1 H5 file with all components of the cross correlations. While this produces **many** H5 files, it has come down to the noisepy team's favorite option: \n", - "1. the thread-safe installation of HDF5 is not trivial\n", - "2. the choice of grouping station pairs within a single file is not flexible to a broad audience of users." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "cd32ntmAVx-z" - }, - "outputs": [], - "source": [ - "# open a new cc store in read-only mode since we will be doing parallel access for stacking\n", - "cc_store = ASDFCCStore(cc_data_path, mode=\"r\")\n", - "stack_store = ASDFStackStore(stack_data_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Configure the stacking\n", - "\n", - "There are various methods for optimal stacking. We refern to Yang et al (2022) for a discussion and comparison of the methods:\n", - "\n", - "Yang X, Bryan J, Okubo K, Jiang C, Clements T, Denolle MA. Optimal stacking of noise cross-correlation functions. Geophysical Journal International. 2023 Mar;232(3):1600-18. https://doi.org/10.1093/gji/ggac410\n", - "\n", - "Users have the choice to implement *linear*, phase weighted stacks *pws* (Schimmel et al, 1997), *robust* stacking (Yang et al, 2022), *acf* autocovariance filter (Nakata et al, 2019), *nroot* stacking. Users may choose the stacking method of their choice by entering the strings in ``config.stack_method``.\n", - "\n", - "If chosen *all*, the current code only ouputs *linear*, *pws*, *robust* since *nroot* is less common and *acf* is computationally expensive.\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "config.stack_method = StackMethod.LINEAR" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "method_list = [method for method in dir(StackMethod) if not method.startswith(\"__\")]\n", - "print(method_list)\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cc_store.get_station_pairs()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "stack_cross_correlations(cc_store, stack_store, config)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "jQ-ey7uX9Msx" - }, - "source": [ - "## QC_1 of the cross correlations for Imaging\n", - "We now explore the quality of the cross correlations. We plot the moveout of the cross correlations, filtered in some frequency band." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "cc_store.get_station_pairs()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pairs = stack_store.get_station_pairs()\n", - "print(f\"Found {len(pairs)} station pairs\")\n", - "sta_stacks = stack_store.read_bulk(None, pairs) # no timestamp used in ASDFStackStore" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "QKSeQpk7WKlW" - }, - "outputs": [], - "source": [ - "plot_all_moveout(sta_stacks, 'Allstack_linear', 0.1, 0.2, 'ZZ', 1)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "@webio": { - "lastCommId": null, - "lastKernelId": null - }, - "colab": { - "provenance": [] - }, - "gpuClass": "standard", - "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": "3.10.13" - } - }, - "nbformat": 4, - "nbformat_minor": 0 + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "PIA2IaqUOeOA" + }, + "source": [ + "# NoisePy SCEDC Tutorial\n", + "\n", + "Noisepy is a python software package to process ambient seismic noise cross correlations. This tutorial aims to introduce the use of noisepy for a toy problem on the SCEDC data. It can be ran locally or on the cloud.\n", + "\n", + "\n", + "The data is stored on AWS S3 as the SCEDC Data Set: https://scedc.caltech.edu/data/getstarted-pds.html\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, we install the noisepy-seis package" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Uncomment and run this line if the environment doesn't have noisepy already installed:\n", + "# ! pip install noisepy-seis " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__Warning__: NoisePy uses ```obspy``` as a core Python module to manipulate seismic data. If you use Google Colab, restart the runtime now for proper installation of ```obspy``` on Colab." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "WtDb2_y3Oreg" + }, + "source": [ + "## Import necessary modules\n", + "\n", + "Then we import the basic modules" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "vceZgD83PnNc" + }, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "from noisepy.seis import cross_correlate, stack_cross_correlations, __version__ # noisepy core functions\n", + "from noisepy.seis.asdfstore import ASDFCCStore, ASDFStackStore # Object to store ASDF data within noisepy\n", + "from noisepy.seis.scedc_s3store import SCEDCS3DataStore, channel_filter # Object to query SCEDC data from on S3\n", + "from noisepy.seis.datatypes import CCMethod, ConfigParameters, FreqNorm, RmResp, StackMethod, TimeNorm # Main configuration object\n", + "from noisepy.seis.channelcatalog import XMLStationChannelCatalog # Required stationXML handling object\n", + "import os\n", + "import shutil\n", + "from datetime import datetime, timezone\n", + "from datetimerange import DateTimeRange\n", + "\n", + "\n", + "from noisepy.seis.plotting_modules import plot_all_moveout\n", + "\n", + "print(f\"Using NoisePy version {__version__}\")\n", + "\n", + "path = \"./scedc_data\" \n", + "\n", + "os.makedirs(path, exist_ok=True)\n", + "cc_data_path = os.path.join(path, \"CCF\")\n", + "stack_data_path = os.path.join(path, \"STACK\")\n", + "S3_STORAGE_OPTIONS = {\"s3\": {\"anon\": True}}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "pntYzIYGNTn8" + }, + "source": [ + "We will work with a single day worth of data on SCEDC. The continuous data is organized with a single day and channel per miniseed (https://scedc.caltech.edu/data/cloud.html). For this example, you can choose any year since 2002. We will just cross correlate a single day." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "yojR0Z3ALm6K" + }, + "outputs": [], + "source": [ + "# SCEDC S3 bucket common URL characters for that day.\n", + "S3_DATA = \"s3://scedc-pds/continuous_waveforms/\"\n", + "# timeframe for analysis\n", + "start = datetime(2002, 1, 1, tzinfo=timezone.utc)\n", + "end = datetime(2002, 1, 3, tzinfo=timezone.utc)\n", + "timerange = DateTimeRange(start, end)\n", + "print(timerange)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "E1CC-BljNzus" + }, + "source": [ + "The station information, including the instrumental response, is stored as stationXML in the following bucket" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "mhfgrMPALsYS" + }, + "outputs": [], + "source": [ + "S3_STATION_XML = \"s3://scedc-pds/FDSNstationXML/CI/\" # S3 storage of stationXML\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ssaL5fa5IhI7" + }, + "source": [ + "## Ambient Noise Project Configuration\n", + "\n", + "We prepare the configuration of the workflow by declaring and storing parameters into the ``ConfigParameters()`` object and/or editing the ``config.yml`` file.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "dIjBD7riIfdJ" + }, + "outputs": [], + "source": [ + "# Initialize ambient noise workflow configuration\n", + "config = ConfigParameters() # default config parameters which can be customized\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Tsp7RfC8IwE-" + }, + "source": [ + "Customize the job parameters below:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "ByEiHRjmIAPB" + }, + "outputs": [], + "source": [ + "config.start_date = start\n", + "config.end_date = end\n", + "config.acorr_only = False # only perform auto-correlation or not\n", + "config.xcorr_only = True # only perform cross-correlation or not\n", + "\n", + "config.inc_hours = 24 # INC_HOURS is used in hours (integer) as the \n", + " #chunk of time that the paralelliztion will work.\n", + " # data will be loaded in memory, so reduce memory with smaller \n", + " # inc_hours if there are over 400+ stations.\n", + " # At regional scale for SCEDC, we can afford 20Hz data and inc_hour \n", + " # being a day of data.\n", + "\n", + " \n", + "# pre-processing parameters\n", + "config.samp_freq= 20 # (int) Sampling rate in Hz of desired processing (it can be different than the data sampling rate)\n", + "config.cc_len= 3600 # (float) basic unit of data length for fft (sec)\n", + "config.step= 1800.0 # (float) overlapping between each cc_len (sec)\n", + "\n", + "config.ncomp = 3 # 1 or 3 component data (needed to decide whether do rotation)\n", + "\n", + "config.stationxml= False # station.XML file used to remove instrument response for SAC/miniseed data\n", + " # If True, the stationXML file is assumed to be provided.\n", + "config.rm_resp= RmResp.INV # select 'no' to not remove response and use 'inv' if you use the stationXML,'spectrum',\n", + "\n", + "\n", + "############## NOISE PRE-PROCESSING ##################\n", + "config.freqmin,config.freqmax = 0.05,2.0 # broad band filtering of the data before cross correlation\n", + "config.max_over_std = 10 # threshold to remove window of bad signals: set it to 10*9 if prefer not to remove them\n", + "\n", + "################### SPECTRAL NORMALIZATION ############\n", + "config.freq_norm= FreqNorm.RMA # choose between \"rma\" for a soft whitening or \"no\" for no whitening. Pure whitening is not implemented correctly at this point.\n", + "config.smoothspect_N = 10 # moving window length to smooth spectrum amplitude (points)\n", + " # here, choose smoothspect_N for the case of a strict whitening (e.g., phase_only)\n", + "\n", + "\n", + "#################### TEMPORAL NORMALIZATION ##########\n", + "config.time_norm = TimeNorm.ONE_BIT # 'no' for no normalization, or 'rma', 'one_bit' for normalization in time domain,\n", + "config.smooth_N= 10 # moving window length for time domain normalization if selected (points)\n", + "\n", + "\n", + "############ cross correlation ##############\n", + "\n", + "config.cc_method= CCMethod.XCORR # 'xcorr' for pure cross correlation OR 'deconv' for deconvolution;\n", + " # FOR \"COHERENCY\" PLEASE set freq_norm to \"rma\", time_norm to \"no\" and cc_method to \"xcorr\"\n", + "\n", + "# OUTPUTS:\n", + "config.substack = True # True = smaller stacks within the time chunk. False: it will stack over inc_hours\n", + "config.substack_len = config.cc_len # how long to stack over (for monitoring purpose): need to be multiples of cc_len\n", + " # if substack=True, substack_len=2*cc_len, then you pre-stack every 2 correlation windows.\n", + " # for instance: substack=True, substack_len=cc_len means that you keep ALL of the correlations\n", + " # if substack=False, the cross correlation will be stacked over the inc_hour window\n", + "\n", + "### For monitoring applications ####\n", + "## we recommend substacking ever 2-4 cross correlations and storing the substacks\n", + "# e.g. \n", + "# config.substack = True \n", + "# config.substack_len = 4* config.cc_len\n", + "\n", + "config.maxlag= 200 # lags of cross-correlation to save (sec)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# For this tutorial make sure the previous run is empty\n", + "#os.system(f\"rm -rf {cc_data_path}\")\n", + "if os.path.exists(cc_data_path):\n", + " shutil.rmtree(cc_data_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Wwv1QCQhP_0Y" + }, + "source": [ + "## Step 1: Cross-correlation\n", + "\n", + "In this instance, we read directly the data from the scedc bucket into the cross correlation code. We are not attempting to recreate a data store. Therefore we go straight to step 1 of the cross correlations." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We first declare the data and cross correlation stores" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "jq2DKIS9Rl2H" + }, + "outputs": [], + "source": [ + "#stations = \"RPV,STS,LTP,LGB,WLT,CPP,PDU,CLT,SVD,BBR\".split(\",\") # filter to these stations\n", + "stations = \"RPV,SVD,BBR\".split(\",\") # filter to these stations\n", + "# stations = \"DGR,DEV,DLA,DNR,FMP,HLL,LGU,LLS,MLS,PDU,PDR,RIN,RIO,RVR,SMS,BBR,CHN,MWC,RIO,BBS,RPV,ADO,DEV\".split(\",\") # filter to these stations\n", + "\n", + "# There are 2 ways to load stations: You can either pass a list of stations or load the stations from a text file.\n", + "# TODO : will be removed with issue #270\n", + "config.load_stations(stations)\n", + "\n", + "# For loading it from a text file, write the path of the file in stations_file field of config instance as below\n", + "# config.stations_file = os.path.join(os.path.dirname(__file__), \"path/my_stations.txt\")\n", + "\n", + "# TODO : will be removed with issue #270\n", + "# config.load_stations()\n", + "\n", + "catalog = XMLStationChannelCatalog(S3_STATION_XML, storage_options=S3_STORAGE_OPTIONS) # Station catalog\n", + "raw_store = SCEDCS3DataStore(S3_DATA, catalog, channel_filter(config.stations, \"BH\"), timerange, storage_options=S3_STORAGE_OPTIONS) # Store for reading raw data from S3 bucket\n", + "cc_store = ASDFCCStore(cc_data_path) # Store for writing CC data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "get the time range of the data in the data store inventory" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "span = raw_store.get_timespans()\n", + "print(span)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Get the channels available during a given time spane" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "channel_list=raw_store.get_channels(span[1])\n", + "print(channel_list)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5qsPGkNp9Msx" + }, + "source": [ + "## Perform the cross correlation\n", + "The data will be pulled from SCEDC, cross correlated, and stored locally if this notebook is ran locally.\n", + "If you are re-calculating, we recommend to clear the old ``cc_store``." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "49MnDXYp9Msx" + }, + "outputs": [], + "source": [ + "cross_correlate(raw_store, config, cc_store)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "GMeH9BslQSSJ" + }, + "source": [ + "The cross correlations are saved as a single file for each channel pair and each increment of inc_hours. We now will stack all the cross correlations over all time chunk and look at all station pairs results.\n", + "\n", + "## Step 2: Stack the cross correlation\n", + "\n", + "We now create the stack stores. Because this tutorial runs locally, we will use an ASDF stack store to output the data. ASDF is a data container in HDF5 that is used in full waveform modeling and inversion. H5 behaves well locally. \n", + "\n", + "Each station pair will have 1 H5 file with all components of the cross correlations. While this produces **many** H5 files, it has come down to the noisepy team's favorite option: \n", + "1. the thread-safe installation of HDF5 is not trivial\n", + "2. the choice of grouping station pairs within a single file is not flexible to a broad audience of users." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "cd32ntmAVx-z" + }, + "outputs": [], + "source": [ + "# open a new cc store in read-only mode since we will be doing parallel access for stacking\n", + "cc_store = ASDFCCStore(cc_data_path, mode=\"r\")\n", + "stack_store = ASDFStackStore(stack_data_path)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Configure the stacking\n", + "\n", + "There are various methods for optimal stacking. We refern to Yang et al (2022) for a discussion and comparison of the methods:\n", + "\n", + "Yang X, Bryan J, Okubo K, Jiang C, Clements T, Denolle MA. Optimal stacking of noise cross-correlation functions. Geophysical Journal International. 2023 Mar;232(3):1600-18. https://doi.org/10.1093/gji/ggac410\n", + "\n", + "Users have the choice to implement *linear*, phase weighted stacks *pws* (Schimmel et al, 1997), *robust* stacking (Yang et al, 2022), *acf* autocovariance filter (Nakata et al, 2019), *nroot* stacking. Users may choose the stacking method of their choice by entering the strings in ``config.stack_method``.\n", + "\n", + "If chosen *all*, the current code only ouputs *linear*, *pws*, *robust* since *nroot* is less common and *acf* is computationally expensive.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "config.stack_method = StackMethod.LINEAR" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "method_list = [method for method in dir(StackMethod) if not method.startswith(\"__\")]\n", + "print(method_list)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cc_store.get_station_pairs()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "stack_cross_correlations(cc_store, stack_store, config)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jQ-ey7uX9Msx" + }, + "source": [ + "## QC_1 of the cross correlations for Imaging\n", + "We now explore the quality of the cross correlations. We plot the moveout of the cross correlations, filtered in some frequency band." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "cc_store.get_station_pairs()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pairs = stack_store.get_station_pairs()\n", + "print(f\"Found {len(pairs)} station pairs\")\n", + "sta_stacks = stack_store.read_bulk(None, pairs) # no timestamp used in ASDFStackStore" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "QKSeQpk7WKlW" + }, + "outputs": [], + "source": [ + "plot_all_moveout(sta_stacks, 'Allstack_linear', 0.1, 0.2, 'ZZ', 1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "@webio": { + "lastCommId": null, + "lastKernelId": null + }, + "colab": { + "provenance": [] + }, + "gpuClass": "standard", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 0 } diff --git a/tutorials/old/cross_correlation_from_sac.ipynb b/tutorials/old/cross_correlation_from_sac.ipynb index 3b49db3e..75c6d234 100644 --- a/tutorials/old/cross_correlation_from_sac.ipynb +++ b/tutorials/old/cross_correlation_from_sac.ipynb @@ -58,7 +58,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -87,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -133,22 +133,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "ename": "AttributeError", - "evalue": "'dict' object has no attribute 'inc_hours'", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[6], line 7\u001b[0m\n\u001b[1;32m 4\u001b[0m slat \u001b[39m=\u001b[39m tr_source[\u001b[39m0\u001b[39m]\u001b[39m.\u001b[39mstats\u001b[39m.\u001b[39msac[\u001b[39m'\u001b[39m\u001b[39mstla\u001b[39m\u001b[39m'\u001b[39m]\n\u001b[1;32m 6\u001b[0m \u001b[39m# cut source traces into small segments and make statistics\u001b[39;00m\n\u001b[0;32m----> 7\u001b[0m trace_stdS,dataS_t,dataS \u001b[39m=\u001b[39m noise_module\u001b[39m.\u001b[39;49mcut_trace_make_stat(fc_para,tr_source)\n\u001b[1;32m 8\u001b[0m \u001b[39m# do fft to freq-domain\u001b[39;00m\n\u001b[1;32m 9\u001b[0m source_white \u001b[39m=\u001b[39m noise_module\u001b[39m.\u001b[39mnoise_processing(fc_para,dataS)\n", - "File \u001b[0;32m~/GitHub/NoisePy/src/noisepy/seis/noise_module.py:493\u001b[0m, in \u001b[0;36mcut_trace_make_stat\u001b[0;34m(fc_para, ch_data)\u001b[0m\n\u001b[1;32m 490\u001b[0m dataS \u001b[39m=\u001b[39m []\n\u001b[1;32m 492\u001b[0m \u001b[39m# useful parameters for trace sliding\u001b[39;00m\n\u001b[0;32m--> 493\u001b[0m nseg \u001b[39m=\u001b[39m \u001b[39mint\u001b[39m(np\u001b[39m.\u001b[39mfloor((fc_para\u001b[39m.\u001b[39;49minc_hours \u001b[39m/\u001b[39m \u001b[39m24\u001b[39m \u001b[39m*\u001b[39m \u001b[39m86400\u001b[39m \u001b[39m-\u001b[39m fc_para\u001b[39m.\u001b[39mcc_len) \u001b[39m/\u001b[39m fc_para\u001b[39m.\u001b[39mstep))\n\u001b[1;32m 494\u001b[0m sps \u001b[39m=\u001b[39m \u001b[39mint\u001b[39m(ch_data\u001b[39m.\u001b[39msampling_rate)\n\u001b[1;32m 495\u001b[0m starttime \u001b[39m=\u001b[39m ch_data\u001b[39m.\u001b[39mstart_timestamp\n", - "\u001b[0;31mAttributeError\u001b[0m: 'dict' object has no attribute 'inc_hours'" - ] - } - ], + "outputs": [], "source": [ "# read source and some meta info\n", "tr_source = obspy.read(sfiles[0])\n", @@ -209,28 +196,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[-0.00122514 0.00055406 0.00109561 ..., -0.00040456 -0.00018136\n", - " 0.00081051] 4001\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# find the segments of good data for both source and receiver\n", "bb=np.intersect1d(sou_ind,rec_ind)\n", @@ -293,32 +259,8 @@ { "cell_type": "code", "execution_count": null, - "metadata": { - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['E.AYHM_E.ENZM'] ['UU']\n", - "[-0.00122514 0.00055406 0.00109561 ..., -0.00040456 -0.00018136\n", - " 0.00081051] {'azi': 185.50987, 'baz': 5.5054541, 'cc_method': 'xcorr', 'comp': 'UU', 'dist': 7.1563296, 'dt': 0.050000000000000003, 'latR': 35.60844, 'latS': 35.672642, 'lonR': 139.70786, 'lonS': 139.71544, 'maxlag': 100, 'ngood': 156, 'substack': False, 'time': 1292457600.0}\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "metadata": {}, + "outputs": [], "source": [ "with pyasdf.ASDFDataSet(cc_h5,mode='r') as ds:\n", " data_type = ds.auxiliary_data.list()\n", diff --git a/tutorials/old/download_toASDF_cross_correlation.ipynb b/tutorials/old/download_toASDF_cross_correlation.ipynb index b25e256c..6486fa08 100644 --- a/tutorials/old/download_toASDF_cross_correlation.ipynb +++ b/tutorials/old/download_toASDF_cross_correlation.ipynb @@ -56,7 +56,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -88,7 +88,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -132,18 +132,9 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "working on station K62A\n", - "working on station K63A\n" - ] - } - ], + "outputs": [], "source": [ "# write into ASDF file: using start and end time as file name\n", "ff=os.path.join('./',s1+'T'+s2+'.h5')\n", @@ -192,7 +183,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -237,17 +228,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "source of TA.K62A and receiver TA.K63A\n" - ] - } - ], + "outputs": [], "source": [ "# read source and some meta info\n", "with pyasdf.ASDFDataSet(sfile[0],mode='r') as ds:\n", @@ -291,7 +274,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -332,22 +315,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# find the segments of good data for both source and receiver\n", "bb=np.intersect1d(sou_ind,rec_ind)\n", @@ -376,7 +346,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -408,29 +378,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "['TA.K62A_TA.K63A'] ['ZZ']\n" - ] - }, - { - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "with pyasdf.ASDFDataSet(cc_h5,mode='r') as ds:\n", " data_type = ds.auxiliary_data.list()\n", diff --git a/tutorials/plot_stacks.ipynb b/tutorials/plot_stacks.ipynb index e1a6f736..ac8bc629 100644 --- a/tutorials/plot_stacks.ipynb +++ b/tutorials/plot_stacks.ipynb @@ -1,108 +1,108 @@ { - "cells": [ - { - "attachments": {}, - "cell_type": "markdown", - "metadata": { - "id": "PIA2IaqUOeOA" - }, - "source": [ - "# NoisePy Plotting Stacks" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "vceZgD83PnNc" - }, - "outputs": [], - "source": [ - "%load_ext autoreload\n", - "%autoreload 2\n", - "from noisepy.seis import __version__ # noisepy core functions\n", - "from noisepy.seis.plotting_modules import plot_all_moveout\n", - "from noisepy.seis.numpystore import NumpyStackStore\n", - "import random\n", - "print(f\"Using NoisePy version {__version__}\")\n", - "\n", - "\n", - "stack_data_path = \"s3://scoped-noise/scedc_CI_2022_stack/\"\n", - "\n", - "S3_STORAGE_OPTIONS = {\"s3\": {\"anon\": False}}\n", - "stack_store = NumpyStackStore(stack_data_path, storage_options=S3_STORAGE_OPTIONS)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "pairs = stack_store.get_station_pairs()\n", - "print(f\"Found {len(pairs)} station pairs\")\n", - "# Get the first timespan available for the first pair\n", - "ts = stack_store.get_timespans(*pairs[0])[0]\n", - "print(f\"Timespan: {ts}\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# load 10% of the data to plot\n", - "sample = random.sample(pairs, int(len(pairs)*.1))\n", - "print(len(sample))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "sta_stacks = stack_store.read_bulk(ts, sample)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "plot_all_moveout(sta_stacks, 'Allstack_linear', 0.1, 0.2, 'ZZ', 1)" - ] - } - ], - "metadata": { - "@webio": { - "lastCommId": null, - "lastKernelId": null - }, - "colab": { - "provenance": [] - }, - "gpuClass": "standard", - "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": "3.10.13" - } + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "id": "PIA2IaqUOeOA" + }, + "source": [ + "# NoisePy Plotting Stacks" + ] }, - "nbformat": 4, - "nbformat_minor": 0 + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "vceZgD83PnNc" + }, + "outputs": [], + "source": [ + "%load_ext autoreload\n", + "%autoreload 2\n", + "from noisepy.seis import __version__ # noisepy core functions\n", + "from noisepy.seis.plotting_modules import plot_all_moveout\n", + "from noisepy.seis.numpystore import NumpyStackStore\n", + "import random\n", + "print(f\"Using NoisePy version {__version__}\")\n", + "\n", + "\n", + "stack_data_path = \"s3://scoped-noise/scedc_CI_2022_stack/\"\n", + "\n", + "S3_STORAGE_OPTIONS = {\"s3\": {\"anon\": False}}\n", + "stack_store = NumpyStackStore(stack_data_path, storage_options=S3_STORAGE_OPTIONS)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "pairs = stack_store.get_station_pairs()\n", + "print(f\"Found {len(pairs)} station pairs\")\n", + "# Get the first timespan available for the first pair\n", + "ts = stack_store.get_timespans(*pairs[0])[0]\n", + "print(f\"Timespan: {ts}\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# load 10% of the data to plot\n", + "sample = random.sample(pairs, int(len(pairs)*.1))\n", + "print(len(sample))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "sta_stacks = stack_store.read_bulk(ts, sample)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plot_all_moveout(sta_stacks, 'Allstack_linear', 0.1, 0.2, 'ZZ', 1)" + ] + } + ], + "metadata": { + "@webio": { + "lastCommId": null, + "lastKernelId": null + }, + "colab": { + "provenance": [] + }, + "gpuClass": "standard", + "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": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 0 } diff --git a/tutorials/run_mpi_scedc.ipynb b/tutorials/run_mpi_scedc.ipynb index c837d5c7..f69efa5e 100644 --- a/tutorials/run_mpi_scedc.ipynb +++ b/tutorials/run_mpi_scedc.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -36,7 +36,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -52,17 +52,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "ACP,ADO,AGM,AGO,ALP,APL,ARV,ASP,AVC,AVM,BAC,BAI,BAK,BAR,BBR,BBS,BC3,BCW,BEL,BFS,BHP,BLA2,BLC,BLY,BOM,BOR,BRE,BTP,BUE,CAC,CAR,CBC,CBC,CBC,CBC,CCA,CCC,CDD,CDM,CFD,CFS,CFT,CGO,CHF,CHI,CHK,CHN,CHR,CIA,CJM,CJV2,CKP,CLC,CLI2,CLO,CLT,COA,COK2,CPO,CPT2,CRF,CRF,CRG,CRN,CRR,CSH,CSL,CSL,CTC,CTW,CVW,CWC,CWP,CYP,CZN,DAN,DAW,DEC,DEV,DGR,DJJ,DJJB,DLA,DNR,DPP,DRE,DSC,DTC,DTC,DTP,DZA,EDW2,ELS2,EML,EMS,EOC,ERR,ERR,ESI2,ESI2,FDR,FHO,FIG,FMO,FMP,FON,FOX2,FRK,FRM,FUL,FUR,FUR,GATR,GCC,GFF,GFS,GLA,GMA,GMR,GOR,GOU,GR2,GRA,GSA,GSC,GVR,HAR,HAY,HDH,HDH,HEC,HIW,HLL,HLN,HMT2,HOL,HYS,IDO,IDQ,IDY,IKP,IMP,IPT,IRG5,IRM,ISA,IVY,JEM,JNH2,JPLS,JRC2,JTH,JVA,KIK,KML,KYV,LAF,LAT,LBW1,LBW2,LCG,LCP,LDF,LDR,LEO,LFP,LGB,LJR,LKH,LLS,LMH,LMR2,LMS,LMY,LOC,LPC,LRL,LRR2,LTP,LUC2,LUG,LUS,LVO,LVY,LVY,LYP,MAG,MCT,MES,MGE,MIK,MIKB,MIS,MLAC,MLS,MMC,MMC,MNO,MOP,MOR,MPI,MPM,MPP,MRS,MSC,MSJ,MTA,MTG,MTP,MUR,MWC,NBS,NCH,NEE2,NEN,NJQ,NOT,NPN,NSS2,NWH,NWH,OAT,OCP,OGC,OLI,OLP,OSI,PALA,PASC,PDE,PDM,PDR,PDU,PDW,PDW,PER,PGA,PHL,PLM,PLS,PMD,POB2,POR,PSD,PSR,PTD,PUT,QAD,QLC,QUG,RAG,RCR,RCT,RCU,RCU,RFR,RHC2,RHR,RIN,RINB,RIO,RKMO,RMM,RPV,RRX,RSB,RSI,RSS,RUN,RUS,RVR,RXH,SAL,SAN,SBB2,SBC,SBI,SBPX,SCI2,SCZ2,SDD,SDG,SDR,SES,SGL,SHO,SHO,SHU,SIL,SIL,SLA,SLB,SLH,SLM,SLR,SLV,SMF2,SMI,SMM,SMR,SMT,SMV,SMW,SNCC,SNO,SNR,SOC,SPF,SPG2,SQC,SRA,SRI,SRN,SRT,SSS,SSS,STC,STG,STS,SVD,SWP,SWP,SWS,SYN,SYP,SYP,TA2,TEH,TEJ,TER2,TFT,THC,THM,TIN,TJR,TOR,TOW2,TPO,TUQ,USB,USC,VCP,VCS,VDJ,VES,VLO,VLY,VOG,VTV,WAS2,WBM,WBP,WBS,WCS2,WES,WGR,WHF,WLH2,WLS2,WLT,WMD,WMF,WNM,WNS,WOR,WRC2,WRV2,WSS,WTT2,WVP2,WWC,WWF,YEG2,YUC,YUH2\n" - ] - } - ], + "outputs": [], "source": [ "sta=\",\".join(list(pd.read_csv(\"full_socal.csv\")[\"station\"]))\n", "print(sta)" @@ -70,18 +62,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2002-01-02T00:00:00 - 2002-01-04T00:00:00\n", - "2002_01_02\n" - ] - } - ], + "outputs": [], "source": [ "\n", "\n", @@ -97,17 +80,9 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "mpiexec -n 3 noisepy cross_correlate --raw_data_path s3://scedc-pds/continuous_waveforms/ --xml_path s3://scedc-pds/FDSNstationXML/CI/ --ccf_path ~/s3tmp/data/CCF --freq_norm rma --stations \"SBC,RIO,DEV\" --start 2002_01_02 --end 2002_01_04\n" - ] - } - ], + "outputs": [], "source": [ "print((f'mpiexec -n {nproc} noisepy cross_correlate \\\n", "--raw_data_path s3://scedc-pds/continuous_waveforms/ \\\n", @@ -118,20 +93,9 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "256" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "os.system(f'mpiexec -n {nproc} noisepy cross_correlate \\\n", "--raw_data_path s3://scedc-pds/continuous_waveforms/ \\\n", From 65ba350d1ffc1f71db5a6a630637d5b989099b6c Mon Sep 17 00:00:00 2001 From: Carlos Garcia Jurado Suarez Date: Mon, 29 Jan 2024 12:06:07 -0800 Subject: [PATCH 2/2] add jupyter install to the precommit workflow --- .github/workflows/precommit.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/precommit.yaml b/.github/workflows/precommit.yaml index a2da8703..de0a0833 100644 --- a/.github/workflows/precommit.yaml +++ b/.github/workflows/precommit.yaml @@ -15,7 +15,7 @@ jobs: uses: actions/setup-python@v4.5.0 with: python-version: '3.10' - - name: Install pre-commit - run: pip install pre-commit==3.3.3 + - name: Install pre-commit and Jupyter + run: pip install pre-commit==3.3.3 jupyter==1.0.0 - name: Run pre-commit checks run: pre-commit run --all-files --show-diff-on-failure