{ "cells": [ { "cell_type": "markdown", "id": "4831226b-6033-4b15-a120-d2ef9b81f686", "metadata": { "tags": [] }, "source": [ "# Process CAS04 with Remote Reference\n", "\n", "\n", "This notebook is a companion to the 2024 JOSS manuscript.\n", "\n", "This notebook is shows the workflow for getting data from Earthscope for a few example stations and generating transfer functions using aurora. The data download step is based on condensed version of a tutorial in the mth5 documentation which can be found at: https://github.com/kujaku11/mth5/tree/master/docs/examples/notebooks. \n", "\n" ] }, { "cell_type": "code", "execution_count": 1, "id": "31595e4a-9a71-451a-a811-91e1126cdc99", "metadata": {}, "outputs": [], "source": [ "# %matplotlib notebook \n", "# %matplotlib widget\n" ] }, { "cell_type": "code", "execution_count": null, "id": "95ae061a-dc05-471b-a88c-4aaaef4ddc50", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/kkappler/software/irismt/mtpy-v2/mtpy/modeling/simpeg/recipes/inversion_2d.py:39: UserWarning: Pardiso not installed see https://github.com/simpeg/pydiso/blob/main/README.md.\n", " warnings.warn(\n" ] } ], "source": [ "#Imports\n", "\n", "import pandas as pd\n", "import pathlib\n", "import os\n", "#from aurora.sandbox.mth5_channel_summary_helpers import channel_summary_to_make_mth5\n", "#from aurora.config import BANDS_DEFAULT_FILE\n", "from aurora.config.config_creator import ConfigCreator\n", "from aurora.pipelines.process_mth5 import process_mth5\n", "from mth5.mth5 import MTH5\n", "from mth5.clients.make_mth5 import FDSN\n", "from mth5.utils.helpers import initialize_mth5\n", "from mth5.processing import RunSummary, KernelDataset\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "d5c3fc25-fb66-4d80-8e76-c9a23f2054c2", "metadata": {}, "outputs": [], "source": [ "import logging, sys\n", "logging.disable(sys.maxsize)\n", "import warnings\n", "warnings.filterwarnings('ignore')" ] }, { "cell_type": "markdown", "id": "33e2b452-94f7-4f9e-9cf5-36765541fc2e", "metadata": {}, "source": [ "# Make MTH5 from IRIS Data Managment Center v0.2.0 \n", "\n", "This example demonstrates how to build an MTH5 from data archived at IRIS, it could work with any MT data stored at an FDSN data center (probably).\n", "\n", "We will use the `mth5.clients.FDSN` class to build the file. There is also second way using the more generic `mth5.clients.MakeMTH5` class, which will be highlighted below. \n", "\n", "**Note:** this example assumes that data availability (Network, Station, Channel, Start, End) are all previously known. If you do not know the data that you want to download use [IRIS tools](https://ds.iris.edu/ds/nodes/dmc/tools/##) to get data availability. " ] }, { "cell_type": "markdown", "id": "c3177306-86bc-43ae-aec3-11a71f75325d", "metadata": {}, "source": [ "## Initialize a MakeMTH5 object\n", "\n", "Here, we are setting the MTH5 file version to 0.2.0 so that we can have multiple surveys in a single file. Also, setting the client to \"IRIS\". Here, we are using `obspy.clients` tools for the request. Here are the available [FDSN clients](https://docs.obspy.org/packages/obspy.clients.fdsn.html). \n", "\n", "**Note:** Only the \"IRIS\" client has been tested." ] }, { "cell_type": "code", "execution_count": 4, "id": "46655a42-0bcf-4c86-a972-cb58f0d77158", "metadata": {}, "outputs": [], "source": [ "fdsn_object = FDSN(mth5_version='0.2.0')\n", "fdsn_object.client = \"IRIS\"" ] }, { "cell_type": "markdown", "id": "3b1c9eb9-b60a-409e-98e7-003bc76c2f77", "metadata": {}, "source": [ "## Make the data inquiry as a DataFrame\n", "\n", "There are a few ways to make the inquiry to request data. \n", "\n", "1. Make a DataFrame by hand. Here we will make a list of entries and then create a DataFrame with the proper column names\n", "2. You can create a CSV file with a row for each entry. There are some formatting that you need to be aware of. That is the column names and making sure that date-times are YYYY-MM-DDThh:mm:ss\n", "\n", "\n", "| Column Name | Description |\n", "| ------------------- | --------------------------------------------------------------------------------------------------------------|\n", "| **network** | [FDSN Network code (2 letters)](http://www.fdsn.org/networks/) |\n", "| **station** | [FDSN Station code (usually 5 characters)](https://ds.iris.edu/ds/nodes/dmc/data/formats/seed-channel-naming/)|\n", "| **location** | [FDSN Location code (typically not used for MT)](http://docs.fdsn.org/projects/source-identifiers/en/v1.0/location-codes.html) |\n", "| **channel** | [FDSN Channel code (3 characters)](http://docs.fdsn.org/projects/source-identifiers/en/v1.0/channel-codes.html)|\n", "| **start** | Start time (YYYY-MM-DDThh:mm:ss) UTC |\n", "| **end** | End time (YYYY-MM-DDThh:mm:ss) UTC |\n", "\n", "In the example below, the stage is set to use two stations: CAS04 and NVR08. Commented out is an example of how to add a third station, REV06" ] }, { "cell_type": "code", "execution_count": 5, "id": "1888e0a6-ddf2-428b-a851-b1a9b0f5a0da", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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networkstationlocationchannelstartend
08PCAS04LFE2020-06-02T19:00:002020-07-13T19:00:00
18PCAS04LFN2020-06-02T19:00:002020-07-13T19:00:00
28PCAS04LFZ2020-06-02T19:00:002020-07-13T19:00:00
38PCAS04LQE2020-06-02T19:00:002020-07-13T19:00:00
48PCAS04LQN2020-06-02T19:00:002020-07-13T19:00:00
58PNVR08LFE2020-06-02T19:00:002020-07-13T19:00:00
68PNVR08LFN2020-06-02T19:00:002020-07-13T19:00:00
78PNVR08LFZ2020-06-02T19:00:002020-07-13T19:00:00
88PNVR08LQE2020-06-02T19:00:002020-07-13T19:00:00
98PNVR08LQN2020-06-02T19:00:002020-07-13T19:00:00
\n", "
" ], "text/plain": [ " network station location channel start end\n", "0 8P CAS04 LFE 2020-06-02T19:00:00 2020-07-13T19:00:00\n", "1 8P CAS04 LFN 2020-06-02T19:00:00 2020-07-13T19:00:00\n", "2 8P CAS04 LFZ 2020-06-02T19:00:00 2020-07-13T19:00:00\n", "3 8P CAS04 LQE 2020-06-02T19:00:00 2020-07-13T19:00:00\n", "4 8P CAS04 LQN 2020-06-02T19:00:00 2020-07-13T19:00:00\n", "5 8P NVR08 LFE 2020-06-02T19:00:00 2020-07-13T19:00:00\n", "6 8P NVR08 LFN 2020-06-02T19:00:00 2020-07-13T19:00:00\n", "7 8P NVR08 LFZ 2020-06-02T19:00:00 2020-07-13T19:00:00\n", "8 8P NVR08 LQE 2020-06-02T19:00:00 2020-07-13T19:00:00\n", "9 8P NVR08 LQN 2020-06-02T19:00:00 2020-07-13T19:00:00" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "channels = [\"LFE\", \"LFN\", \"LFZ\", \"LQE\", \"LQN\"]\n", "CAS04 = [\"8P\", \"CAS04\", '2020-06-02T19:00:00', '2020-07-13T19:00:00'] \n", "NVR08 = [\"8P\", \"NVR08\", '2020-06-02T19:00:00', '2020-07-13T19:00:00']\n", "# REV06 = [\"8P\", \"REV06\", '2020-06-02T19:00:00', '2020-07-13T19:00:00']\n", "\n", "stations = [CAS04, NVR08,]\n", "# stations.append(REV06)\n", "\n", "request_list = []\n", "for entry in stations:\n", " for channel in channels:\n", " request_list.append(\n", " [entry[0], entry[1], \"\", channel, entry[2], entry[3]]\n", " )\n", "\n", "# Turn list into dataframe\n", "request_df = pd.DataFrame(request_list, columns=fdsn_object.request_columns) \n", "request_df" ] }, { "cell_type": "code", "execution_count": 6, "id": "496678c6-18b2-41cb-a0b5-5ebf51bab0eb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m24:09:03T20:08:45 | INFO | line:679 |mth5.mth5 | _initialize_file | Initialized MTH5 0.2.0 file /home/kkappler/software/irismt/aurora/docs/tutorials/8P_CAS04_NVR08.h5 in mode w\u001b[0m\n", "\u001b[1m24:09:03T20:09:03 | INFO | line:133 |mt_metadata.timeseries.filters.obspy_stages | create_filter_from_stage | Converting PoleZerosResponseStage electric_si_units to a CoefficientFilter.\u001b[0m\n", "\u001b[1m24:09:03T20:09:03 | INFO | line:133 |mt_metadata.timeseries.filters.obspy_stages | create_filter_from_stage | Converting PoleZerosResponseStage electric_dipole_92.000 to a CoefficientFilter.\u001b[0m\n", "\u001b[1m24:09:03T20:09:03 | INFO | line:133 |mt_metadata.timeseries.filters.obspy_stages | create_filter_from_stage | Converting PoleZerosResponseStage electric_si_units to a CoefficientFilter.\u001b[0m\n", "\u001b[1m24:09:03T20:09:03 | INFO | line:133 |mt_metadata.timeseries.filters.obspy_stages | create_filter_from_stage | Converting PoleZerosResponseStage electric_dipole_92.000 to a CoefficientFilter.\u001b[0m\n", "\u001b[1m24:09:03T20:09:03 | INFO | line:133 |mt_metadata.timeseries.filters.obspy_stages | create_filter_from_stage | Converting PoleZerosResponseStage electric_si_units to a CoefficientFilter.\u001b[0m\n", "\u001b[1m24:09:03T20:09:03 | INFO | line:133 |mt_metadata.timeseries.filters.obspy_stages | create_filter_from_stage | Converting PoleZerosResponseStage electric_dipole_92.000 to a CoefficientFilter.\u001b[0m\n", "\u001b[1m24:09:03T20:09:03 | INFO | line:133 |mt_metadata.timeseries.filters.obspy_stages | create_filter_from_stage | Converting PoleZerosResponseStage electric_si_units to a CoefficientFilter.\u001b[0m\n", "\u001b[1m24:09:03T20:09:03 | INFO | line:133 |mt_metadata.timeseries.filters.obspy_stages | create_filter_from_stage | Converting PoleZerosResponseStage electric_dipole_92.000 to a CoefficientFilter.\u001b[0m\n", "\u001b[1m24:09:03T20:09:03 | INFO | line:133 |mt_metadata.timeseries.filters.obspy_stages | create_filter_from_stage | Converting PoleZerosResponseStage electric_si_units to a CoefficientFilter.\u001b[0m\n", "\u001b[1m24:09:03T20:09:03 | INFO | line:133 |mt_metadata.timeseries.filters.obspy_stages | create_filter_from_stage | Converting PoleZerosResponseStage electric_dipole_92.000 to a CoefficientFilter.\u001b[0m\n", "\u001b[1m24:09:03T20:09:03 | INFO | line:133 |mt_metadata.timeseries.filters.obspy_stages | create_filter_from_stage | Converting PoleZerosResponseStage electric_si_units to a CoefficientFilter.\u001b[0m\n", "\u001b[1m24:09:03T20:09:03 | INFO | line:133 |mt_metadata.timeseries.filters.obspy_stages | create_filter_from_stage | Converting PoleZerosResponseStage electric_dipole_94.000 to a CoefficientFilter.\u001b[0m\n", "\u001b[1m24:09:03T20:09:03 | INFO | line:133 |mt_metadata.timeseries.filters.obspy_stages | create_filter_from_stage | Converting PoleZerosResponseStage electric_si_units to a CoefficientFilter.\u001b[0m\n", "\u001b[1m24:09:03T20:09:03 | INFO | line:133 |mt_metadata.timeseries.filters.obspy_stages | create_filter_from_stage | Converting PoleZerosResponseStage electric_dipole_94.000 to a CoefficientFilter.\u001b[0m\n", "\u001b[1m24:09:03T20:09:05 | INFO | line:331 |mth5.groups.base | _add_group | RunGroup a already exists, returning existing group.\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:05 | WARNING | line:645 |mth5.timeseries.run_ts | validate_metadata | start time of dataset 2020-06-02T19:00:00+00:00 does not match metadata start 2020-06-02T18:41:43+00:00 updating metatdata value to 2020-06-02T19:00:00+00:00\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:06 | WARNING | line:677 |mth5.groups.run | from_runts | Channel run.id sr1_001 != group run.id a. Setting to ch.run_metadata.id to a\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:06 | WARNING | line:677 |mth5.groups.run | from_runts | Channel run.id sr1_001 != group run.id a. Setting to ch.run_metadata.id to a\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:06 | WARNING | line:677 |mth5.groups.run | from_runts | Channel run.id sr1_001 != group run.id a. Setting to ch.run_metadata.id to a\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:06 | WARNING | line:677 |mth5.groups.run | from_runts | Channel run.id sr1_001 != group run.id a. Setting to ch.run_metadata.id to a\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:06 | WARNING | line:677 |mth5.groups.run | from_runts | Channel run.id sr1_001 != group run.id a. Setting to ch.run_metadata.id to a\u001b[0m\n", "\u001b[1m24:09:03T20:09:06 | INFO | line:331 |mth5.groups.base | _add_group | RunGroup b already exists, returning existing group.\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:07 | WARNING | line:677 |mth5.groups.run | from_runts | Channel run.id sr1_001 != group run.id b. Setting to ch.run_metadata.id to b\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:07 | WARNING | line:677 |mth5.groups.run | from_runts | Channel run.id sr1_001 != group run.id b. Setting to ch.run_metadata.id to b\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:08 | WARNING | line:677 |mth5.groups.run | from_runts | Channel run.id sr1_001 != group run.id b. Setting to ch.run_metadata.id to b\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:08 | WARNING | line:677 |mth5.groups.run | from_runts | Channel run.id sr1_001 != group run.id b. Setting to ch.run_metadata.id to b\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:08 | WARNING | line:677 |mth5.groups.run | from_runts | Channel run.id sr1_001 != group run.id b. Setting to ch.run_metadata.id to b\u001b[0m\n", "\u001b[1m24:09:03T20:09:08 | INFO | line:331 |mth5.groups.base | _add_group | RunGroup c already exists, returning existing group.\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:09 | WARNING | line:677 |mth5.groups.run | from_runts | Channel run.id sr1_001 != group run.id c. Setting to ch.run_metadata.id to c\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:09 | WARNING | line:677 |mth5.groups.run | from_runts | Channel run.id sr1_001 != group run.id c. Setting to ch.run_metadata.id to c\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:10 | WARNING | line:677 |mth5.groups.run | from_runts | Channel run.id sr1_001 != group run.id c. Setting to ch.run_metadata.id to c\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:10 | WARNING | line:677 |mth5.groups.run | from_runts | Channel run.id sr1_001 != group run.id c. Setting to ch.run_metadata.id to c\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:10 | WARNING | line:677 |mth5.groups.run | from_runts | Channel run.id sr1_001 != group run.id c. Setting to ch.run_metadata.id to c\u001b[0m\n", "\u001b[1m24:09:03T20:09:10 | INFO | line:331 |mth5.groups.base | _add_group | RunGroup d already exists, returning existing group.\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:11 | WARNING | line:658 |mth5.timeseries.run_ts | validate_metadata | end time of dataset 2020-07-13T19:00:00+00:00 does not match metadata end 2020-07-13T21:46:12+00:00 updating metatdata value to 2020-07-13T19:00:00+00:00\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:11 | WARNING | line:677 |mth5.groups.run | from_runts | Channel run.id sr1_001 != group run.id d. Setting to ch.run_metadata.id to d\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:11 | WARNING | line:677 |mth5.groups.run | from_runts | Channel run.id sr1_001 != group run.id d. Setting to ch.run_metadata.id to d\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:11 | WARNING | line:677 |mth5.groups.run | from_runts | Channel run.id sr1_001 != group run.id d. Setting to ch.run_metadata.id to d\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:12 | WARNING | line:677 |mth5.groups.run | from_runts | Channel run.id sr1_001 != group run.id d. Setting to ch.run_metadata.id to d\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:12 | WARNING | line:677 |mth5.groups.run | from_runts | Channel run.id sr1_001 != group run.id d. Setting to ch.run_metadata.id to d\u001b[0m\n", "\u001b[1m24:09:03T20:09:12 | INFO | line:331 |mth5.groups.base | _add_group | RunGroup a already exists, returning existing group.\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:12 | WARNING | line:677 |mth5.groups.run | from_runts | Channel run.id sr1_001 != group run.id a. Setting to ch.run_metadata.id to a\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:12 | WARNING | line:677 |mth5.groups.run | from_runts | Channel run.id sr1_001 != group run.id a. Setting to ch.run_metadata.id to a\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:13 | WARNING | line:677 |mth5.groups.run | from_runts | Channel run.id sr1_001 != group run.id a. Setting to ch.run_metadata.id to a\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:13 | WARNING | line:677 |mth5.groups.run | from_runts | Channel run.id sr1_001 != group run.id a. Setting to ch.run_metadata.id to a\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:13 | WARNING | line:677 |mth5.groups.run | from_runts | Channel run.id sr1_001 != group run.id a. Setting to ch.run_metadata.id to a\u001b[0m\n", "\u001b[1m24:09:03T20:09:13 | INFO | line:331 |mth5.groups.base | _add_group | RunGroup b already exists, returning existing group.\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:14 | WARNING | line:677 |mth5.groups.run | from_runts | Channel run.id sr1_001 != group run.id b. Setting to ch.run_metadata.id to b\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:14 | WARNING | line:677 |mth5.groups.run | from_runts | Channel run.id sr1_001 != group run.id b. Setting to ch.run_metadata.id to b\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:14 | WARNING | line:677 |mth5.groups.run | from_runts | Channel run.id sr1_001 != group run.id b. Setting to ch.run_metadata.id to b\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:14 | WARNING | line:677 |mth5.groups.run | from_runts | Channel run.id sr1_001 != group run.id b. Setting to ch.run_metadata.id to b\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:15 | WARNING | line:677 |mth5.groups.run | from_runts | Channel run.id sr1_001 != group run.id b. Setting to ch.run_metadata.id to b\u001b[0m\n", "\u001b[1m24:09:03T20:09:15 | INFO | line:331 |mth5.groups.base | _add_group | RunGroup c already exists, returning existing group.\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:15 | WARNING | line:677 |mth5.groups.run | from_runts | Channel run.id sr1_001 != group run.id c. Setting to ch.run_metadata.id to c\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:16 | WARNING | line:677 |mth5.groups.run | from_runts | Channel run.id sr1_001 != group run.id c. Setting to ch.run_metadata.id to c\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:16 | WARNING | line:677 |mth5.groups.run | from_runts | Channel run.id sr1_001 != group run.id c. Setting to ch.run_metadata.id to c\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:16 | WARNING | line:677 |mth5.groups.run | from_runts | Channel run.id sr1_001 != group run.id c. Setting to ch.run_metadata.id to c\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:16 | WARNING | line:677 |mth5.groups.run | from_runts | Channel run.id sr1_001 != group run.id c. Setting to ch.run_metadata.id to c\u001b[0m\n", "\u001b[1m24:09:03T20:09:16 | INFO | line:771 |mth5.mth5 | close_mth5 | Flushing and closing /home/kkappler/software/irismt/aurora/docs/tutorials/8P_CAS04_NVR08.h5\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:16 | WARNING | line:330 |mth5.mth5 | filename | MTH5 file is not open or has not been created yet. Returning default name\u001b[0m\n", "Created /home/kkappler/software/irismt/aurora/docs/tutorials/8P_CAS04_NVR08.h5\n", "CPU times: user 14.5 s, sys: 349 ms, total: 14.8 s\n", "Wall time: 31.9 s\n" ] } ], "source": [ "%%time\n", "\n", "mth5_filename = fdsn_object.make_mth5_from_fdsn_client(request_df)\n", "\n", "print(f\"Created {mth5_filename}\")" ] }, { "cell_type": "code", "execution_count": 7, "id": "7c69ae65-db2c-4fd8-ab2b-2a44ff9085a0", "metadata": {}, "outputs": [], "source": [ "mth5_path = pathlib.Path(\"8P_CAS04_NVR08.h5\")" ] }, { "cell_type": "code", "execution_count": 8, "id": "8c07f52e-7e2b-4589-9632-9213d8d7050b", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/html": [ "
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0CONUS SouthCAS04a37.633351-121.468382335.261765ex2020-06-02 19:00:00+00:002020-06-02 22:07:46+00:00112671.0electric13.20.0digital countsTrue<HDF5 object reference><HDF5 object reference><HDF5 object reference>
1CONUS SouthCAS04a37.633351-121.468382335.261765ey2020-06-02 19:00:00+00:002020-06-02 22:07:46+00:00112671.0electric103.20.0digital countsTrue<HDF5 object reference><HDF5 object reference><HDF5 object reference>
2CONUS SouthCAS04a37.633351-121.468382335.261765hx2020-06-02 19:00:00+00:002020-06-02 22:07:46+00:00112671.0magnetic13.20.0digital countsTrue<HDF5 object reference><HDF5 object reference><HDF5 object reference>
3CONUS SouthCAS04a37.633351-121.468382335.261765hy2020-06-02 19:00:00+00:002020-06-02 22:07:46+00:00112671.0magnetic103.20.0digital countsTrue<HDF5 object reference><HDF5 object reference><HDF5 object reference>
4CONUS SouthCAS04a37.633351-121.468382335.261765hz2020-06-02 19:00:00+00:002020-06-02 22:07:46+00:00112671.0magnetic0.090.0digital countsTrue<HDF5 object reference><HDF5 object reference><HDF5 object reference>
5CONUS SouthCAS04b37.633351-121.468382335.261765ex2020-06-02 22:24:55+00:002020-06-12 17:52:23+00:008476491.0electric13.20.0digital countsTrue<HDF5 object reference><HDF5 object reference><HDF5 object reference>
6CONUS SouthCAS04b37.633351-121.468382335.261765ey2020-06-02 22:24:55+00:002020-06-12 17:52:23+00:008476491.0electric103.20.0digital countsTrue<HDF5 object reference><HDF5 object reference><HDF5 object reference>
7CONUS SouthCAS04b37.633351-121.468382335.261765hx2020-06-02 22:24:55+00:002020-06-12 17:52:23+00:008476491.0magnetic13.20.0digital countsTrue<HDF5 object reference><HDF5 object reference><HDF5 object reference>
8CONUS SouthCAS04b37.633351-121.468382335.261765hy2020-06-02 22:24:55+00:002020-06-12 17:52:23+00:008476491.0magnetic103.20.0digital countsTrue<HDF5 object reference><HDF5 object reference><HDF5 object reference>
9CONUS SouthCAS04b37.633351-121.468382335.261765hz2020-06-02 22:24:55+00:002020-06-12 17:52:23+00:008476491.0magnetic0.090.0digital countsTrue<HDF5 object reference><HDF5 object reference><HDF5 object reference>
10CONUS SouthCAS04c37.633351-121.468382335.261765ex2020-06-12 18:32:17+00:002020-07-01 17:32:59+00:0016380431.0electric13.20.0digital countsTrue<HDF5 object reference><HDF5 object reference><HDF5 object reference>
11CONUS SouthCAS04c37.633351-121.468382335.261765ey2020-06-12 18:32:17+00:002020-07-01 17:32:59+00:0016380431.0electric103.20.0digital countsTrue<HDF5 object reference><HDF5 object reference><HDF5 object reference>
12CONUS SouthCAS04c37.633351-121.468382335.261765hx2020-06-12 18:32:17+00:002020-07-01 17:32:59+00:0016380431.0magnetic13.20.0digital countsTrue<HDF5 object reference><HDF5 object reference><HDF5 object reference>
13CONUS SouthCAS04c37.633351-121.468382335.261765hy2020-06-12 18:32:17+00:002020-07-01 17:32:59+00:0016380431.0magnetic103.20.0digital countsTrue<HDF5 object reference><HDF5 object reference><HDF5 object reference>
14CONUS SouthCAS04c37.633351-121.468382335.261765hz2020-06-12 18:32:17+00:002020-07-01 17:32:59+00:0016380431.0magnetic0.090.0digital countsTrue<HDF5 object reference><HDF5 object reference><HDF5 object reference>
15CONUS SouthCAS04d37.633351-121.468382335.261765ex2020-07-01 19:36:55+00:002020-07-13 19:00:00+00:0010345861.0electric13.20.0digital countsTrue<HDF5 object reference><HDF5 object reference><HDF5 object reference>
16CONUS SouthCAS04d37.633351-121.468382335.261765ey2020-07-01 19:36:55+00:002020-07-13 19:00:00+00:0010345861.0electric103.20.0digital countsTrue<HDF5 object reference><HDF5 object reference><HDF5 object reference>
17CONUS SouthCAS04d37.633351-121.468382335.261765hx2020-07-01 19:36:55+00:002020-07-13 19:00:00+00:0010345861.0magnetic13.20.0digital countsTrue<HDF5 object reference><HDF5 object reference><HDF5 object reference>
18CONUS SouthCAS04d37.633351-121.468382335.261765hy2020-07-01 19:36:55+00:002020-07-13 19:00:00+00:0010345861.0magnetic103.20.0digital countsTrue<HDF5 object reference><HDF5 object reference><HDF5 object reference>
19CONUS SouthCAS04d37.633351-121.468382335.261765hz2020-07-01 19:36:55+00:002020-07-13 19:00:00+00:0010345861.0magnetic0.090.0digital countsTrue<HDF5 object reference><HDF5 object reference><HDF5 object reference>
20CONUS SouthNVR08a38.326630-118.0823821377.902271ex2020-06-03 19:10:11+00:002020-06-03 19:57:51+00:0028611.0electric12.60.0digital countsTrue<HDF5 object reference><HDF5 object reference><HDF5 object reference>
21CONUS SouthNVR08a38.326630-118.0823821377.902271ey2020-06-03 19:10:11+00:002020-06-03 19:57:51+00:0028611.0electric102.60.0digital countsTrue<HDF5 object reference><HDF5 object reference><HDF5 object reference>
22CONUS SouthNVR08a38.326630-118.0823821377.902271hx2020-06-03 19:10:11+00:002020-06-03 19:57:51+00:0028611.0magnetic12.60.0digital countsTrue<HDF5 object reference><HDF5 object reference><HDF5 object reference>
23CONUS SouthNVR08a38.326630-118.0823821377.902271hy2020-06-03 19:10:11+00:002020-06-03 19:57:51+00:0028611.0magnetic102.60.0digital countsTrue<HDF5 object reference><HDF5 object reference><HDF5 object reference>
24CONUS SouthNVR08a38.326630-118.0823821377.902271hz2020-06-03 19:10:11+00:002020-06-03 19:57:51+00:0028611.0magnetic0.090.0digital countsTrue<HDF5 object reference><HDF5 object reference><HDF5 object reference>
25CONUS SouthNVR08b38.326630-118.0823821377.902271ex2020-06-03 20:14:13+00:002020-06-14 16:56:02+00:009385101.0electric12.60.0digital countsTrue<HDF5 object reference><HDF5 object reference><HDF5 object reference>
26CONUS SouthNVR08b38.326630-118.0823821377.902271ey2020-06-03 20:14:13+00:002020-06-14 16:56:02+00:009385101.0electric102.60.0digital countsTrue<HDF5 object reference><HDF5 object reference><HDF5 object reference>
27CONUS SouthNVR08b38.326630-118.0823821377.902271hx2020-06-03 20:14:13+00:002020-06-14 16:56:02+00:009385101.0magnetic12.60.0digital countsTrue<HDF5 object reference><HDF5 object reference><HDF5 object reference>
28CONUS SouthNVR08b38.326630-118.0823821377.902271hy2020-06-03 20:14:13+00:002020-06-14 16:56:02+00:009385101.0magnetic102.60.0digital countsTrue<HDF5 object reference><HDF5 object reference><HDF5 object reference>
29CONUS SouthNVR08b38.326630-118.0823821377.902271hz2020-06-03 20:14:13+00:002020-06-14 16:56:02+00:009385101.0magnetic0.090.0digital countsTrue<HDF5 object reference><HDF5 object reference><HDF5 object reference>
30CONUS SouthNVR08c38.326630-118.0823821377.902271ex2020-06-14 18:00:44+00:002020-06-24 15:55:46+00:008565031.0electric12.60.0digital countsTrue<HDF5 object reference><HDF5 object reference><HDF5 object reference>
31CONUS SouthNVR08c38.326630-118.0823821377.902271ey2020-06-14 18:00:44+00:002020-06-24 15:55:46+00:008565031.0electric102.60.0digital countsTrue<HDF5 object reference><HDF5 object reference><HDF5 object reference>
32CONUS SouthNVR08c38.326630-118.0823821377.902271hx2020-06-14 18:00:44+00:002020-06-24 15:55:46+00:008565031.0magnetic12.60.0digital countsTrue<HDF5 object reference><HDF5 object reference><HDF5 object reference>
33CONUS SouthNVR08c38.326630-118.0823821377.902271hy2020-06-14 18:00:44+00:002020-06-24 15:55:46+00:008565031.0magnetic102.60.0digital countsTrue<HDF5 object reference><HDF5 object reference><HDF5 object reference>
34CONUS SouthNVR08c38.326630-118.0823821377.902271hz2020-06-14 18:00:44+00:002020-06-24 15:55:46+00:008565031.0magnetic0.090.0digital countsTrue<HDF5 object reference><HDF5 object reference><HDF5 object reference>
\n", "
" ], "text/plain": [ " survey station run latitude longitude elevation component \\\n", "0 CONUS South CAS04 a 37.633351 -121.468382 335.261765 ex \n", "1 CONUS South CAS04 a 37.633351 -121.468382 335.261765 ey \n", "2 CONUS South CAS04 a 37.633351 -121.468382 335.261765 hx \n", "3 CONUS South CAS04 a 37.633351 -121.468382 335.261765 hy \n", "4 CONUS South CAS04 a 37.633351 -121.468382 335.261765 hz \n", "5 CONUS South CAS04 b 37.633351 -121.468382 335.261765 ex \n", "6 CONUS South CAS04 b 37.633351 -121.468382 335.261765 ey \n", "7 CONUS South CAS04 b 37.633351 -121.468382 335.261765 hx \n", "8 CONUS South CAS04 b 37.633351 -121.468382 335.261765 hy \n", "9 CONUS South CAS04 b 37.633351 -121.468382 335.261765 hz \n", "10 CONUS South CAS04 c 37.633351 -121.468382 335.261765 ex \n", "11 CONUS South CAS04 c 37.633351 -121.468382 335.261765 ey \n", "12 CONUS South CAS04 c 37.633351 -121.468382 335.261765 hx \n", "13 CONUS South CAS04 c 37.633351 -121.468382 335.261765 hy \n", "14 CONUS South CAS04 c 37.633351 -121.468382 335.261765 hz \n", "15 CONUS South CAS04 d 37.633351 -121.468382 335.261765 ex \n", "16 CONUS South CAS04 d 37.633351 -121.468382 335.261765 ey \n", "17 CONUS South CAS04 d 37.633351 -121.468382 335.261765 hx \n", "18 CONUS South CAS04 d 37.633351 -121.468382 335.261765 hy \n", "19 CONUS South CAS04 d 37.633351 -121.468382 335.261765 hz \n", "20 CONUS South NVR08 a 38.326630 -118.082382 1377.902271 ex \n", "21 CONUS South NVR08 a 38.326630 -118.082382 1377.902271 ey \n", "22 CONUS South NVR08 a 38.326630 -118.082382 1377.902271 hx \n", "23 CONUS South NVR08 a 38.326630 -118.082382 1377.902271 hy \n", "24 CONUS South NVR08 a 38.326630 -118.082382 1377.902271 hz \n", "25 CONUS South NVR08 b 38.326630 -118.082382 1377.902271 ex \n", "26 CONUS South NVR08 b 38.326630 -118.082382 1377.902271 ey \n", "27 CONUS South NVR08 b 38.326630 -118.082382 1377.902271 hx \n", "28 CONUS South NVR08 b 38.326630 -118.082382 1377.902271 hy \n", "29 CONUS South NVR08 b 38.326630 -118.082382 1377.902271 hz \n", "30 CONUS South NVR08 c 38.326630 -118.082382 1377.902271 ex \n", "31 CONUS South NVR08 c 38.326630 -118.082382 1377.902271 ey \n", "32 CONUS South NVR08 c 38.326630 -118.082382 1377.902271 hx \n", "33 CONUS South NVR08 c 38.326630 -118.082382 1377.902271 hy \n", "34 CONUS South NVR08 c 38.326630 -118.082382 1377.902271 hz \n", "\n", " start end n_samples \\\n", "0 2020-06-02 19:00:00+00:00 2020-06-02 22:07:46+00:00 11267 \n", "1 2020-06-02 19:00:00+00:00 2020-06-02 22:07:46+00:00 11267 \n", "2 2020-06-02 19:00:00+00:00 2020-06-02 22:07:46+00:00 11267 \n", "3 2020-06-02 19:00:00+00:00 2020-06-02 22:07:46+00:00 11267 \n", "4 2020-06-02 19:00:00+00:00 2020-06-02 22:07:46+00:00 11267 \n", "5 2020-06-02 22:24:55+00:00 2020-06-12 17:52:23+00:00 847649 \n", "6 2020-06-02 22:24:55+00:00 2020-06-12 17:52:23+00:00 847649 \n", "7 2020-06-02 22:24:55+00:00 2020-06-12 17:52:23+00:00 847649 \n", "8 2020-06-02 22:24:55+00:00 2020-06-12 17:52:23+00:00 847649 \n", "9 2020-06-02 22:24:55+00:00 2020-06-12 17:52:23+00:00 847649 \n", "10 2020-06-12 18:32:17+00:00 2020-07-01 17:32:59+00:00 1638043 \n", "11 2020-06-12 18:32:17+00:00 2020-07-01 17:32:59+00:00 1638043 \n", "12 2020-06-12 18:32:17+00:00 2020-07-01 17:32:59+00:00 1638043 \n", "13 2020-06-12 18:32:17+00:00 2020-07-01 17:32:59+00:00 1638043 \n", "14 2020-06-12 18:32:17+00:00 2020-07-01 17:32:59+00:00 1638043 \n", "15 2020-07-01 19:36:55+00:00 2020-07-13 19:00:00+00:00 1034586 \n", "16 2020-07-01 19:36:55+00:00 2020-07-13 19:00:00+00:00 1034586 \n", "17 2020-07-01 19:36:55+00:00 2020-07-13 19:00:00+00:00 1034586 \n", "18 2020-07-01 19:36:55+00:00 2020-07-13 19:00:00+00:00 1034586 \n", "19 2020-07-01 19:36:55+00:00 2020-07-13 19:00:00+00:00 1034586 \n", "20 2020-06-03 19:10:11+00:00 2020-06-03 19:57:51+00:00 2861 \n", "21 2020-06-03 19:10:11+00:00 2020-06-03 19:57:51+00:00 2861 \n", "22 2020-06-03 19:10:11+00:00 2020-06-03 19:57:51+00:00 2861 \n", "23 2020-06-03 19:10:11+00:00 2020-06-03 19:57:51+00:00 2861 \n", "24 2020-06-03 19:10:11+00:00 2020-06-03 19:57:51+00:00 2861 \n", "25 2020-06-03 20:14:13+00:00 2020-06-14 16:56:02+00:00 938510 \n", "26 2020-06-03 20:14:13+00:00 2020-06-14 16:56:02+00:00 938510 \n", "27 2020-06-03 20:14:13+00:00 2020-06-14 16:56:02+00:00 938510 \n", "28 2020-06-03 20:14:13+00:00 2020-06-14 16:56:02+00:00 938510 \n", "29 2020-06-03 20:14:13+00:00 2020-06-14 16:56:02+00:00 938510 \n", "30 2020-06-14 18:00:44+00:00 2020-06-24 15:55:46+00:00 856503 \n", "31 2020-06-14 18:00:44+00:00 2020-06-24 15:55:46+00:00 856503 \n", "32 2020-06-14 18:00:44+00:00 2020-06-24 15:55:46+00:00 856503 \n", "33 2020-06-14 18:00:44+00:00 2020-06-24 15:55:46+00:00 856503 \n", "34 2020-06-14 18:00:44+00:00 2020-06-24 15:55:46+00:00 856503 \n", "\n", " sample_rate measurement_type azimuth tilt units has_data \\\n", "0 1.0 electric 13.2 0.0 digital counts True \n", "1 1.0 electric 103.2 0.0 digital counts True \n", "2 1.0 magnetic 13.2 0.0 digital counts True \n", "3 1.0 magnetic 103.2 0.0 digital counts True \n", "4 1.0 magnetic 0.0 90.0 digital counts True \n", "5 1.0 electric 13.2 0.0 digital counts True \n", "6 1.0 electric 103.2 0.0 digital counts True \n", "7 1.0 magnetic 13.2 0.0 digital counts True \n", "8 1.0 magnetic 103.2 0.0 digital counts True \n", "9 1.0 magnetic 0.0 90.0 digital counts True \n", "10 1.0 electric 13.2 0.0 digital counts True \n", "11 1.0 electric 103.2 0.0 digital counts True \n", "12 1.0 magnetic 13.2 0.0 digital counts True \n", "13 1.0 magnetic 103.2 0.0 digital counts True \n", "14 1.0 magnetic 0.0 90.0 digital counts True \n", "15 1.0 electric 13.2 0.0 digital counts True \n", "16 1.0 electric 103.2 0.0 digital counts True \n", "17 1.0 magnetic 13.2 0.0 digital counts True \n", "18 1.0 magnetic 103.2 0.0 digital counts True \n", "19 1.0 magnetic 0.0 90.0 digital counts True \n", "20 1.0 electric 12.6 0.0 digital counts True \n", "21 1.0 electric 102.6 0.0 digital counts True \n", "22 1.0 magnetic 12.6 0.0 digital counts True \n", "23 1.0 magnetic 102.6 0.0 digital counts True \n", "24 1.0 magnetic 0.0 90.0 digital counts True \n", "25 1.0 electric 12.6 0.0 digital counts True \n", "26 1.0 electric 102.6 0.0 digital counts True \n", "27 1.0 magnetic 12.6 0.0 digital counts True \n", "28 1.0 magnetic 102.6 0.0 digital counts True \n", "29 1.0 magnetic 0.0 90.0 digital counts True \n", "30 1.0 electric 12.6 0.0 digital counts True \n", "31 1.0 electric 102.6 0.0 digital counts True \n", "32 1.0 magnetic 12.6 0.0 digital counts True \n", "33 1.0 magnetic 102.6 0.0 digital counts True \n", "34 1.0 magnetic 0.0 90.0 digital counts True \n", "\n", " hdf5_reference run_hdf5_reference station_hdf5_reference \n", "0 \n", "1 \n", "2 \n", "3 \n", "4 \n", "5 \n", "6 \n", "7 \n", "8 \n", "9 \n", "10 \n", "11 \n", "12 \n", "13 \n", "14 \n", "15 \n", "16 \n", "17 \n", "18 \n", "19 \n", "20 \n", "21 \n", "22 \n", "23 \n", "24 \n", "25 \n", "26 \n", "27 \n", "28 \n", "29 \n", "30 \n", "31 \n", "32 \n", "33 \n", "34 " ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "m = initialize_mth5(mth5_path)\n", "m.channel_summary.summarize()\n", "df = m.channel_summary.to_dataframe()\n", "df" ] }, { "cell_type": "markdown", "id": "33ced6db-834d-4be1-9858-42fe12fd86fe", "metadata": {}, "source": [ "## Fix Survey Name\n", "\n", "- The survey name is extracted from the metadata provided by Earthscope\n", "- The value of the survey name was changed from \"CONUS South\" to \"CONUS SoCal\", and the notebook was updated to reflect this, however, as of June 30, 2024, the name seems to have changed back to \"CONUS South\".\n", "- To avoid problems wiht these change of nomencalature we extract the survey name as a variable" ] }, { "cell_type": "code", "execution_count": 9, "id": "757817bc-9c4b-4208-adfd-af8e8ffb3439", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'CONUS South'" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "survey_id = df[\"survey\"].unique()[0]\n", "survey_id" ] }, { "cell_type": "code", "execution_count": 10, "id": "c859de21-1c56-4393-b971-c732d2cb7735", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['CAS04', 'NVR08'], dtype=object)" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.station.unique()" ] }, { "cell_type": "code", "execution_count": 11, "id": "8a6c8a47-b91d-41e1-ae8d-a5f98d8aeb7b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m24:09:03T20:09:17 | INFO | line:771 |mth5.mth5 | close_mth5 | Flushing and closing 8P_CAS04_NVR08.h5\u001b[0m\n" ] }, { "data": { "text/html": [ "
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channel_scale_factorsdurationendhas_datainput_channelsmth5_pathn_samplesoutput_channelsrunsample_ratestartstationsurveyrun_hdf5_referencestation_hdf5_reference
0{'ex': 1.0, 'ey': 1.0, 'hx': 1.0, 'hy': 1.0, '...11266.02020-06-02 22:07:46+00:00True[hx, hy]8P_CAS04_NVR08.h511267[ex, ey, hz]a1.02020-06-02 19:00:00+00:00CAS04CONUS South<HDF5 object reference><HDF5 object reference>
1{'ex': 1.0, 'ey': 1.0, 'hx': 1.0, 'hy': 1.0, '...847648.02020-06-12 17:52:23+00:00True[hx, hy]8P_CAS04_NVR08.h5847649[ex, ey, hz]b1.02020-06-02 22:24:55+00:00CAS04CONUS South<HDF5 object reference><HDF5 object reference>
2{'ex': 1.0, 'ey': 1.0, 'hx': 1.0, 'hy': 1.0, '...1638042.02020-07-01 17:32:59+00:00True[hx, hy]8P_CAS04_NVR08.h51638043[ex, ey, hz]c1.02020-06-12 18:32:17+00:00CAS04CONUS South<HDF5 object reference><HDF5 object reference>
3{'ex': 1.0, 'ey': 1.0, 'hx': 1.0, 'hy': 1.0, '...1034585.02020-07-13 19:00:00+00:00True[hx, hy]8P_CAS04_NVR08.h51034586[ex, ey, hz]d1.02020-07-01 19:36:55+00:00CAS04CONUS South<HDF5 object reference><HDF5 object reference>
4{'ex': 1.0, 'ey': 1.0, 'hx': 1.0, 'hy': 1.0, '...2860.02020-06-03 19:57:51+00:00True[hx, hy]8P_CAS04_NVR08.h52861[ex, ey, hz]a1.02020-06-03 19:10:11+00:00NVR08CONUS South<HDF5 object reference><HDF5 object reference>
5{'ex': 1.0, 'ey': 1.0, 'hx': 1.0, 'hy': 1.0, '...938509.02020-06-14 16:56:02+00:00True[hx, hy]8P_CAS04_NVR08.h5938510[ex, ey, hz]b1.02020-06-03 20:14:13+00:00NVR08CONUS South<HDF5 object reference><HDF5 object reference>
6{'ex': 1.0, 'ey': 1.0, 'hx': 1.0, 'hy': 1.0, '...856502.02020-06-24 15:55:46+00:00True[hx, hy]8P_CAS04_NVR08.h5856503[ex, ey, hz]c1.02020-06-14 18:00:44+00:00NVR08CONUS South<HDF5 object reference><HDF5 object reference>
\n", "
" ], "text/plain": [ " channel_scale_factors duration \\\n", "0 {'ex': 1.0, 'ey': 1.0, 'hx': 1.0, 'hy': 1.0, '... 11266.0 \n", "1 {'ex': 1.0, 'ey': 1.0, 'hx': 1.0, 'hy': 1.0, '... 847648.0 \n", "2 {'ex': 1.0, 'ey': 1.0, 'hx': 1.0, 'hy': 1.0, '... 1638042.0 \n", "3 {'ex': 1.0, 'ey': 1.0, 'hx': 1.0, 'hy': 1.0, '... 1034585.0 \n", "4 {'ex': 1.0, 'ey': 1.0, 'hx': 1.0, 'hy': 1.0, '... 2860.0 \n", "5 {'ex': 1.0, 'ey': 1.0, 'hx': 1.0, 'hy': 1.0, '... 938509.0 \n", "6 {'ex': 1.0, 'ey': 1.0, 'hx': 1.0, 'hy': 1.0, '... 856502.0 \n", "\n", " end has_data input_channels mth5_path \\\n", "0 2020-06-02 22:07:46+00:00 True [hx, hy] 8P_CAS04_NVR08.h5 \n", "1 2020-06-12 17:52:23+00:00 True [hx, hy] 8P_CAS04_NVR08.h5 \n", "2 2020-07-01 17:32:59+00:00 True [hx, hy] 8P_CAS04_NVR08.h5 \n", "3 2020-07-13 19:00:00+00:00 True [hx, hy] 8P_CAS04_NVR08.h5 \n", "4 2020-06-03 19:57:51+00:00 True [hx, hy] 8P_CAS04_NVR08.h5 \n", "5 2020-06-14 16:56:02+00:00 True [hx, hy] 8P_CAS04_NVR08.h5 \n", "6 2020-06-24 15:55:46+00:00 True [hx, hy] 8P_CAS04_NVR08.h5 \n", "\n", " n_samples output_channels run sample_rate start \\\n", "0 11267 [ex, ey, hz] a 1.0 2020-06-02 19:00:00+00:00 \n", "1 847649 [ex, ey, hz] b 1.0 2020-06-02 22:24:55+00:00 \n", "2 1638043 [ex, ey, hz] c 1.0 2020-06-12 18:32:17+00:00 \n", "3 1034586 [ex, ey, hz] d 1.0 2020-07-01 19:36:55+00:00 \n", "4 2861 [ex, ey, hz] a 1.0 2020-06-03 19:10:11+00:00 \n", "5 938510 [ex, ey, hz] b 1.0 2020-06-03 20:14:13+00:00 \n", "6 856503 [ex, ey, hz] c 1.0 2020-06-14 18:00:44+00:00 \n", "\n", " station survey run_hdf5_reference station_hdf5_reference \n", "0 CAS04 CONUS South \n", "1 CAS04 CONUS South \n", "2 CAS04 CONUS South \n", "3 CAS04 CONUS South \n", "4 NVR08 CONUS South \n", "5 NVR08 CONUS South \n", "6 NVR08 CONUS South " ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mth5_run_summary = RunSummary()\n", "mth5_run_summary.from_mth5s([mth5_path,])\n", "run_summary = mth5_run_summary.clone()\n", "run_summary.df" ] }, { "cell_type": "code", "execution_count": 12, "id": "774d7973-267f-4fc8-a440-a36b7e92fe4c", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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surveystationrunstartend
0CONUS SouthCAS04a2020-06-02 19:00:00+00:002020-06-02 22:07:46+00:00
1CONUS SouthCAS04b2020-06-02 22:24:55+00:002020-06-12 17:52:23+00:00
2CONUS SouthCAS04c2020-06-12 18:32:17+00:002020-07-01 17:32:59+00:00
3CONUS SouthCAS04d2020-07-01 19:36:55+00:002020-07-13 19:00:00+00:00
4CONUS SouthNVR08a2020-06-03 19:10:11+00:002020-06-03 19:57:51+00:00
5CONUS SouthNVR08b2020-06-03 20:14:13+00:002020-06-14 16:56:02+00:00
6CONUS SouthNVR08c2020-06-14 18:00:44+00:002020-06-24 15:55:46+00:00
\n", "
" ], "text/plain": [ " survey station run start end\n", "0 CONUS South CAS04 a 2020-06-02 19:00:00+00:00 2020-06-02 22:07:46+00:00\n", "1 CONUS South CAS04 b 2020-06-02 22:24:55+00:00 2020-06-12 17:52:23+00:00\n", "2 CONUS South CAS04 c 2020-06-12 18:32:17+00:00 2020-07-01 17:32:59+00:00\n", "3 CONUS South CAS04 d 2020-07-01 19:36:55+00:00 2020-07-13 19:00:00+00:00\n", "4 CONUS South NVR08 a 2020-06-03 19:10:11+00:00 2020-06-03 19:57:51+00:00\n", "5 CONUS South NVR08 b 2020-06-03 20:14:13+00:00 2020-06-14 16:56:02+00:00\n", "6 CONUS South NVR08 c 2020-06-14 18:00:44+00:00 2020-06-24 15:55:46+00:00" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "coverage_short_list_columns = [\"survey\", 'station', 'run', 'start', 'end', ]\n", "run_summary.df[coverage_short_list_columns]" ] }, { "cell_type": "code", "execution_count": 13, "id": "03b8add3-46d5-4f71-a527-3dfb3a284fec", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m24:09:03T20:09:17 | INFO | line:250 |mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column fc, adding and setting dtype to .\u001b[0m\n", "\u001b[1m24:09:03T20:09:17 | INFO | line:250 |mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column remote, adding and setting dtype to .\u001b[0m\n", "\u001b[1m24:09:03T20:09:17 | INFO | line:250 |mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column run_dataarray, adding and setting dtype to .\u001b[0m\n", "\u001b[1m24:09:03T20:09:17 | INFO | line:250 |mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column stft, adding and setting dtype to .\u001b[0m\n", "\u001b[1m24:09:03T20:09:17 | INFO | line:250 |mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column mth5_obj, adding and setting dtype to .\u001b[0m\n" ] }, { "data": { "text/html": [ "
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surveystationrunstartendduration
0CONUS SouthCAS04b2020-06-03 19:10:11+00:002020-06-03 19:57:51+00:002860.0
1CONUS SouthNVR08a2020-06-03 19:10:11+00:002020-06-03 19:57:51+00:002860.0
2CONUS SouthCAS04b2020-06-03 20:14:13+00:002020-06-12 17:52:23+00:00769090.0
3CONUS SouthNVR08b2020-06-03 20:14:13+00:002020-06-12 17:52:23+00:00769090.0
4CONUS SouthCAS04c2020-06-12 18:32:17+00:002020-06-14 16:56:02+00:00167025.0
5CONUS SouthNVR08b2020-06-12 18:32:17+00:002020-06-14 16:56:02+00:00167025.0
6CONUS SouthCAS04c2020-06-14 18:00:44+00:002020-06-24 15:55:46+00:00856502.0
7CONUS SouthNVR08c2020-06-14 18:00:44+00:002020-06-24 15:55:46+00:00856502.0
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" ], "text/plain": [ " survey station run start \\\n", "0 CONUS South CAS04 b 2020-06-03 19:10:11+00:00 \n", "1 CONUS South NVR08 a 2020-06-03 19:10:11+00:00 \n", "2 CONUS South CAS04 b 2020-06-03 20:14:13+00:00 \n", "3 CONUS South NVR08 b 2020-06-03 20:14:13+00:00 \n", "4 CONUS South CAS04 c 2020-06-12 18:32:17+00:00 \n", "5 CONUS South NVR08 b 2020-06-12 18:32:17+00:00 \n", "6 CONUS South CAS04 c 2020-06-14 18:00:44+00:00 \n", "7 CONUS South NVR08 c 2020-06-14 18:00:44+00:00 \n", "\n", " end duration \n", "0 2020-06-03 19:57:51+00:00 2860.0 \n", "1 2020-06-03 19:57:51+00:00 2860.0 \n", "2 2020-06-12 17:52:23+00:00 769090.0 \n", "3 2020-06-12 17:52:23+00:00 769090.0 \n", "4 2020-06-14 16:56:02+00:00 167025.0 \n", "5 2020-06-14 16:56:02+00:00 167025.0 \n", "6 2020-06-24 15:55:46+00:00 856502.0 \n", "7 2020-06-24 15:55:46+00:00 856502.0 " ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "kernel_dataset = KernelDataset()\n", "station_id = \"CAS04\"\n", "remote_reference_id = \"NVR08\"\n", "kernel_dataset.from_run_summary(run_summary, station_id, remote_reference_id)\n", "kernel_dataset.mini_summary" ] }, { "cell_type": "code", "execution_count": 14, "id": "c2e4c7a9-94a8-4a23-948b-35d78b65b629", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m24:09:03T20:09:17 | INFO | line:250 |mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column fc, adding and setting dtype to .\u001b[0m\n", "\u001b[1m24:09:03T20:09:17 | INFO | line:250 |mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column remote, adding and setting dtype to .\u001b[0m\n", "\u001b[1m24:09:03T20:09:17 | INFO | line:250 |mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column run_dataarray, adding and setting dtype to .\u001b[0m\n", "\u001b[1m24:09:03T20:09:17 | INFO | line:250 |mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column stft, adding and setting dtype to .\u001b[0m\n", "\u001b[1m24:09:03T20:09:17 | INFO | line:250 |mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column mth5_obj, adding and setting dtype to .\u001b[0m\n" ] }, { "data": { "text/html": [ "
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surveystationrunstartend
0CONUS SouthCAS04b2020-06-03 20:14:13+00:002020-06-12 17:52:23+00:00
1CONUS SouthNVR08b2020-06-03 20:14:13+00:002020-06-12 17:52:23+00:00
2CONUS SouthCAS04c2020-06-14 18:00:44+00:002020-06-24 15:55:46+00:00
3CONUS SouthNVR08c2020-06-14 18:00:44+00:002020-06-24 15:55:46+00:00
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" ], "text/plain": [ " survey station run start end\n", "0 CONUS South CAS04 b 2020-06-03 20:14:13+00:00 2020-06-12 17:52:23+00:00\n", "1 CONUS South NVR08 b 2020-06-03 20:14:13+00:00 2020-06-12 17:52:23+00:00\n", "2 CONUS South CAS04 c 2020-06-14 18:00:44+00:00 2020-06-24 15:55:46+00:00\n", "3 CONUS South NVR08 c 2020-06-14 18:00:44+00:00 2020-06-24 15:55:46+00:00" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "kernel_dataset = KernelDataset()\n", "kernel_dataset.from_run_summary(run_summary, station_id, remote_reference_id)\n", "cutoff_duration_in_seconds = 180000\n", "kernel_dataset.drop_runs_shorter_than(cutoff_duration_in_seconds)\n", "kernel_dataset.df[coverage_short_list_columns]" ] }, { "cell_type": "code", "execution_count": 15, "id": "10a169bf-41c1-4146-bfd1-e5b1b842ddd5", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m24:09:03T20:09:17 | INFO | line:108 |aurora.config.config_creator | determine_band_specification_style | Bands not defined; setting to EMTF BANDS_DEFAULT_FILE\u001b[0m\n" ] } ], "source": [ "cc = ConfigCreator()\n", "config = cc.create_from_kernel_dataset(kernel_dataset,) \n", "# emtf_band_file=BANDS_DEFAULT_FILE,)" ] }, { "cell_type": "code", "execution_count": 16, "id": "ec03e63c-ec46-4f7c-8f38-e627eed884ff", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "{\n", " \"processing\": {\n", " \"band_setup_file\": \"/home/kkappler/software/irismt/aurora/aurora/config/emtf_band_setup/bs_test.cfg\",\n", " \"band_specification_style\": \"EMTF\",\n", " \"channel_nomenclature.ex\": \"ex\",\n", " \"channel_nomenclature.ey\": \"ey\",\n", " \"channel_nomenclature.hx\": \"hx\",\n", " \"channel_nomenclature.hy\": \"hy\",\n", " \"channel_nomenclature.hz\": \"hz\",\n", " \"decimations\": [\n", " {\n", " \"decimation_level\": {\n", " \"anti_alias_filter\": \"default\",\n", " \"bands\": [\n", " {\n", " \"band\": {\n", " \"center_averaging_type\": 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\"input_channels\": [\n", " \"hx\",\n", " \"hy\"\n", " ],\n", " \"method\": \"fft\",\n", " \"min_num_stft_windows\": 2,\n", " \"output_channels\": [\n", " \"ex\",\n", " \"ey\",\n", " \"hz\"\n", " ],\n", " \"pre_fft_detrend_type\": \"linear\",\n", " \"prewhitening_type\": \"first difference\",\n", " \"recoloring\": true,\n", " \"reference_channels\": [\n", " \"hx\",\n", " \"hy\"\n", " ],\n", " \"regression.max_iterations\": 10,\n", " \"regression.max_redescending_iterations\": 2,\n", " \"regression.minimum_cycles\": 10,\n", " \"regression.r0\": 1.5,\n", " \"regression.tolerance\": 0.005,\n", " \"regression.u0\": 2.8,\n", " \"regression.verbosity\": 0,\n", " \"save_fcs\": false,\n", " \"window.clock_zero_type\": \"ignore\",\n", " \"window.num_samples\": 128,\n", " \"window.overlap\": 32,\n", " \"window.type\": \"boxcar\"\n", " }\n", " }\n", " ],\n", " \"id\": \"CAS04-rr_NVR08_sr1\",\n", " \"stations.local.id\": \"CAS04\",\n", " \"stations.local.mth5_path\": \"8P_CAS04_NVR08.h5\",\n", " \"stations.local.remote\": false,\n", " \"stations.local.runs\": [\n", " {\n", " \"run\": {\n", " \"id\": \"b\",\n", " \"input_channels\": [\n", " {\n", " \"channel\": {\n", " \"id\": \"hx\",\n", " \"scale_factor\": 1.0\n", " }\n", " },\n", " {\n", " \"channel\": {\n", " \"id\": \"hy\",\n", " \"scale_factor\": 1.0\n", " }\n", " }\n", " ],\n", " \"output_channels\": [\n", " {\n", " \"channel\": {\n", " \"id\": \"ex\",\n", " \"scale_factor\": 1.0\n", " }\n", " },\n", " {\n", " \"channel\": {\n", " \"id\": \"ey\",\n", " \"scale_factor\": 1.0\n", " }\n", " },\n", " {\n", " \"channel\": {\n", " \"id\": \"hz\",\n", " \"scale_factor\": 1.0\n", " }\n", " }\n", " ],\n", " \"sample_rate\": 1.0,\n", " \"time_periods\": [\n", " {\n", " \"time_period\": {\n", " \"end\": \"2020-06-12T17:52:23+00:00\",\n", " \"start\": \"2020-06-03T20:14:13+00:00\"\n", " }\n", " }\n", " ]\n", " }\n", " },\n", " {\n", " \"run\": {\n", " \"id\": \"c\",\n", " \"input_channels\": [\n", " {\n", " \"channel\": {\n", " \"id\": \"hx\",\n", " \"scale_factor\": 1.0\n", " }\n", " },\n", " {\n", " \"channel\": {\n", " \"id\": \"hy\",\n", " \"scale_factor\": 1.0\n", " }\n", " }\n", " ],\n", " \"output_channels\": [\n", " {\n", " \"channel\": {\n", " \"id\": \"ex\",\n", " \"scale_factor\": 1.0\n", " }\n", " },\n", " {\n", " \"channel\": {\n", " \"id\": \"ey\",\n", " \"scale_factor\": 1.0\n", " }\n", " },\n", " {\n", " \"channel\": {\n", " \"id\": \"hz\",\n", " \"scale_factor\": 1.0\n", " }\n", " }\n", " ],\n", " \"sample_rate\": 1.0,\n", " \"time_periods\": [\n", " {\n", " \"time_period\": {\n", " \"end\": \"2020-06-24T15:55:46+00:00\",\n", " \"start\": \"2020-06-14T18:00:44+00:00\"\n", " }\n", " }\n", " ]\n", " }\n", " }\n", " ],\n", " \"stations.remote\": [\n", " {\n", " \"station\": {\n", " \"id\": \"NVR08\",\n", " \"mth5_path\": \"8P_CAS04_NVR08.h5\",\n", " \"remote\": true,\n", " \"runs\": [\n", " {\n", " \"run\": {\n", " \"id\": \"b\",\n", " \"input_channels\": [\n", " {\n", " \"channel\": {\n", " \"id\": \"hx\",\n", " \"scale_factor\": 1.0\n", " }\n", " },\n", " {\n", " \"channel\": {\n", " \"id\": \"hy\",\n", " \"scale_factor\": 1.0\n", " }\n", " }\n", " ],\n", " \"output_channels\": [\n", " {\n", " \"channel\": {\n", " \"id\": \"ex\",\n", " \"scale_factor\": 1.0\n", " }\n", " },\n", " {\n", " \"channel\": {\n", " \"id\": \"ey\",\n", " \"scale_factor\": 1.0\n", " }\n", " },\n", " {\n", " \"channel\": {\n", " \"id\": \"hz\",\n", " \"scale_factor\": 1.0\n", " }\n", " }\n", " ],\n", " \"sample_rate\": 1.0,\n", " \"time_periods\": [\n", " {\n", " \"time_period\": {\n", " \"end\": \"2020-06-12T17:52:23+00:00\",\n", " \"start\": \"2020-06-03T20:14:13+00:00\"\n", " }\n", " }\n", " ]\n", " }\n", " },\n", " {\n", " \"run\": {\n", " \"id\": \"c\",\n", " \"input_channels\": [\n", " {\n", " \"channel\": {\n", " \"id\": \"hx\",\n", " \"scale_factor\": 1.0\n", " }\n", " },\n", " {\n", " \"channel\": {\n", " \"id\": \"hy\",\n", " \"scale_factor\": 1.0\n", " }\n", " }\n", " ],\n", " \"output_channels\": [\n", " {\n", " \"channel\": {\n", " \"id\": \"ex\",\n", " \"scale_factor\": 1.0\n", " }\n", " },\n", " {\n", " \"channel\": {\n", " \"id\": \"ey\",\n", " \"scale_factor\": 1.0\n", " }\n", " },\n", " {\n", " \"channel\": {\n", " \"id\": \"hz\",\n", " \"scale_factor\": 1.0\n", " }\n", " }\n", " ],\n", " \"sample_rate\": 1.0,\n", " \"time_periods\": [\n", " {\n", " \"time_period\": {\n", " \"end\": \"2020-06-24T15:55:46+00:00\",\n", " \"start\": \"2020-06-14T18:00:44+00:00\"\n", " }\n", " }\n", " ]\n", " }\n", " }\n", " ]\n", " }\n", " }\n", " ]\n", " }\n", "}" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "config" ] }, { "cell_type": "code", "execution_count": 17, "id": "31276eea-60b1-4c11-b6f0-92fd1198c63d", "metadata": {}, "outputs": [], "source": [ "for dec_level in config.decimations:\n", " dec_level.stft.window.type = \"hamming\"" ] }, { "cell_type": "code", "execution_count": 18, "id": "586d7a82-da55-47b6-ad81-93f13e7fa4c9", "metadata": {}, "outputs": [], "source": [ "tf_file_base = f\"{station_id}_RR{remote_reference_id}\"" ] }, { "cell_type": "code", "execution_count": 19, "id": "3ba3daaa-5338-4f5f-ac1f-c1c23bfb8422", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m24:09:03T20:09:17 | INFO | line:277 |aurora.pipelines.transfer_function_kernel | show_processing_summary | Processing Summary Dataframe:\u001b[0m\n", "\u001b[1m24:09:03T20:09:17 | INFO | line:278 |aurora.pipelines.transfer_function_kernel | show_processing_summary | \n", " duration has_data n_samples run station survey run_hdf5_reference station_hdf5_reference fc remote stft mth5_obj dec_level dec_factor sample_rate window_duration num_samples_window num_samples num_stft_windows\n", "0 769090.0 True 847649 b CAS04 CONUS South False False None None 0 1.0 1.000000 128.0 128 769090.0 8011.0\n", "1 769090.0 True 847649 b CAS04 CONUS South False False None None 1 4.0 0.250000 512.0 128 192272.0 2002.0\n", "2 769090.0 True 847649 b CAS04 CONUS South False False None None 2 4.0 0.062500 2048.0 128 48068.0 500.0\n", "3 769090.0 True 847649 b CAS04 CONUS South False False None None 3 4.0 0.015625 8192.0 128 12017.0 124.0\n", "4 856502.0 True 1638043 c CAS04 CONUS South False False None None 0 1.0 1.000000 128.0 128 856502.0 8921.0\n", "5 856502.0 True 1638043 c CAS04 CONUS South False False None None 1 4.0 0.250000 512.0 128 214125.0 2230.0\n", "6 856502.0 True 1638043 c CAS04 CONUS South False False None None 2 4.0 0.062500 2048.0 128 53531.0 557.0\n", "7 856502.0 True 1638043 c CAS04 CONUS South False False None None 3 4.0 0.015625 8192.0 128 13382.0 139.0\n", "8 769090.0 True 938510 b NVR08 CONUS South False True None None 0 1.0 1.000000 128.0 128 769090.0 8011.0\n", "9 769090.0 True 938510 b NVR08 CONUS South False True None None 1 4.0 0.250000 512.0 128 192272.0 2002.0\n", "10 769090.0 True 938510 b NVR08 CONUS South False True None None 2 4.0 0.062500 2048.0 128 48068.0 500.0\n", "11 769090.0 True 938510 b NVR08 CONUS South False True None None 3 4.0 0.015625 8192.0 128 12017.0 124.0\n", "12 856502.0 True 856503 c NVR08 CONUS South False True None None 0 1.0 1.000000 128.0 128 856502.0 8921.0\n", "13 856502.0 True 856503 c NVR08 CONUS South False True None None 1 4.0 0.250000 512.0 128 214125.0 2230.0\n", "14 856502.0 True 856503 c NVR08 CONUS South False True None None 2 4.0 0.062500 2048.0 128 53531.0 557.0\n", "15 856502.0 True 856503 c NVR08 CONUS South False True None None 3 4.0 0.015625 8192.0 128 13382.0 139.0\u001b[0m\n", "\u001b[1m24:09:03T20:09:17 | INFO | line:654 |aurora.pipelines.transfer_function_kernel | memory_check | Total memory: 62.74 GB\u001b[0m\n", "\u001b[1m24:09:03T20:09:17 | INFO | line:658 |aurora.pipelines.transfer_function_kernel | memory_check | Total Bytes of Raw Data: 0.024 GB\u001b[0m\n", "\u001b[1m24:09:03T20:09:17 | INFO | line:661 |aurora.pipelines.transfer_function_kernel | memory_check | Raw Data will use: 0.039 % of memory\u001b[0m\n", "\u001b[1m24:09:03T20:09:17 | INFO | line:517 |aurora.pipelines.process_mth5 | process_mth5_legacy | Processing config indicates 4 decimation levels\u001b[0m\n", "\u001b[1m24:09:03T20:09:17 | INFO | line:445 |aurora.pipelines.transfer_function_kernel | valid_decimations | After validation there are 4 valid decimation levels\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:18 | WARNING | line:645 |mth5.timeseries.run_ts | validate_metadata | start time of dataset 2020-06-03T20:14:13+00:00 does not match metadata start 2020-06-02T22:24:55+00:00 updating metatdata value to 2020-06-03T20:14:13+00:00\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:19 | WARNING | line:658 |mth5.timeseries.run_ts | validate_metadata | end time of dataset 2020-06-12T17:52:23+00:00 does not match metadata end 2020-06-14T16:56:02+00:00 updating metatdata value to 2020-06-12T17:52:23+00:00\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:20 | WARNING | line:645 |mth5.timeseries.run_ts | validate_metadata | start time of dataset 2020-06-14T18:00:44+00:00 does not match metadata start 2020-06-12T18:32:17+00:00 updating metatdata value to 2020-06-14T18:00:44+00:00\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:09:20 | WARNING | line:658 |mth5.timeseries.run_ts | validate_metadata | end time of dataset 2020-06-24T15:55:46+00:00 does not match metadata end 2020-07-01T17:32:59+00:00 updating metatdata value to 2020-06-24T15:55:46+00:00\u001b[0m\n", "\u001b[1m24:09:03T20:09:22 | INFO | line:889 |mtpy.processing.kernel_dataset | initialize_dataframe_for_processing | Dataset dataframe initialized successfully\u001b[0m\n", "\u001b[1m24:09:03T20:09:22 | INFO | line:143 |aurora.pipelines.transfer_function_kernel | update_dataset_df | Dataset Dataframe Updated for decimation level 0 Successfully\u001b[0m\n", "\u001b[1m24:09:03T20:09:23 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:09:24 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:09:26 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:09:27 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:09:27 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 25.728968s (0.038867Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:09:27 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 19.929573s (0.050177Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:09:27 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 15.164131s (0.065945Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:09:28 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 11.746086s (0.085135Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:09:28 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 9.195791s (0.108745Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:09:29 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 7.362526s (0.135823Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:09:29 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 5.856115s (0.170762Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:09:29 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 4.682492s (0.213562Hz)\u001b[0m\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m24:09:03T20:09:31 | INFO | line:124 |aurora.pipelines.transfer_function_kernel | update_dataset_df | DECIMATION LEVEL 1\u001b[0m\n", "\u001b[1m24:09:03T20:09:31 | INFO | line:143 |aurora.pipelines.transfer_function_kernel | update_dataset_df | Dataset Dataframe Updated for decimation level 1 Successfully\u001b[0m\n", "\u001b[1m24:09:03T20:09:32 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:09:32 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:09:33 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:09:33 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:09:33 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 102.915872s (0.009717Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:09:33 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 85.631182s (0.011678Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:09:34 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 68.881694s (0.014518Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:09:34 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 54.195827s (0.018452Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:09:34 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 43.003958s (0.023254Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:09:34 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 33.310722s (0.030020Hz)\u001b[0m\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m24:09:03T20:09:35 | INFO | line:124 |aurora.pipelines.transfer_function_kernel | update_dataset_df | DECIMATION LEVEL 2\u001b[0m\n", "\u001b[1m24:09:03T20:09:35 | INFO | line:143 |aurora.pipelines.transfer_function_kernel | update_dataset_df | Dataset Dataframe Updated for decimation level 2 Successfully\u001b[0m\n", "\u001b[1m24:09:03T20:09:35 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:09:36 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:09:36 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:09:37 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:09:37 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 411.663489s (0.002429Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:09:37 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 342.524727s (0.002919Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:09:37 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 275.526776s (0.003629Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:09:37 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 216.783308s (0.004613Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:09:37 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 172.015831s (0.005813Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:09:37 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 133.242890s (0.007505Hz)\u001b[0m\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m24:09:03T20:09:38 | INFO | line:124 |aurora.pipelines.transfer_function_kernel | update_dataset_df | DECIMATION LEVEL 3\u001b[0m\n", "\u001b[1m24:09:03T20:09:38 | INFO | line:143 |aurora.pipelines.transfer_function_kernel | update_dataset_df | Dataset Dataframe Updated for decimation level 3 Successfully\u001b[0m\n", "\u001b[1m24:09:03T20:09:38 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:09:38 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:09:39 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:09:39 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:09:39 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 1514.701336s (0.000660Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:09:39 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 1042.488956s (0.000959Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:09:39 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 723.371271s (0.001382Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:09:39 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 532.971560s (0.001876Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:09:39 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 412.837995s (0.002422Hz)\u001b[0m\n" ] }, { "data": { 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m24:09:03T20:09:40 | INFO | line:771 |mth5.mth5 | close_mth5 | Flushing and closing 8P_CAS04_NVR08.h5\u001b[0m\n", "\u001b[1m24:09:03T20:09:40 | INFO | line:771 |mth5.mth5 | close_mth5 | Flushing and closing 8P_CAS04_NVR08.h5\u001b[0m\n" ] } ], "source": [ "show_plot = True\n", "z_file_path = pathlib.Path(f\"{tf_file_base}.zrr\")\n", "tf_cls = process_mth5(config,\n", " kernel_dataset,\n", " units=\"MT\",\n", " show_plot=show_plot,\n", " z_file_path=z_file_path,\n", " )" ] }, { "cell_type": "code", "execution_count": 20, "id": "2ee6e117-c7e1-40ba-9981-5f2a189e404a", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "MT( station='CAS04', latitude=37.63, longitude=-121.47, elevation=335.26 )" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "tf_cls.write(fn=f\"{tf_file_base}.xml\", file_type=\"emtfxml\")\n", "tf_cls.write(fn=f\"{tf_file_base}.edi\", file_type=\"edi\")\n", "tf_cls.write(fn=f\"{tf_file_base}.zrr\", file_type=\"zrr\")" ] }, { "cell_type": "code", "execution_count": 21, "id": "763704e0-ceed-43be-ad70-82e7709d7758", "metadata": {}, "outputs": [], "source": [ "archived_z_file = pathlib.Path(f\"CAS04bcd_REV06.zrr\")" ] }, { "cell_type": "code", "execution_count": 22, "id": "e711cde6-6e35-4335-a1ef-e022f6af7839", "metadata": {}, "outputs": [], "source": [ "from aurora.transfer_function.plot.comparison_plots import compare_two_z_files\n", "z_file_path = \"CAS04_RRNVR08.zrr\"" ] }, { "cell_type": "markdown", "id": "500c63da-86c7-42bc-948f-561473982c2f", "metadata": {}, "source": [ "# To compare with the archived file, we need to set the coordinate system to geographic\n", "\n", "The TF will be output with a header like this:\n", "\n", "```\n", "TRANSFER FUNCTIONS IN MEASUREMENT COORDINATES\n", "********* WITH FULL ERROR COVARIANCE ********\n", "Aurora Robust Remote Reference\n", "station: CAS04\n", "coordinate 37.633 -121.468 declination 13.17\n", "number of channels 5 number of frequencies 25\n", " orientations and tilts of each channel\n", " 1 13.20 0.00 CAS04 Hx\n", " 2 103.20 0.00 CAS04 Hy\n", " 3 0.00 90.00 CAS04 Hz\n", " 4 13.20 0.00 CAS04 Ex\n", " 5 103.20 0.00 CAS04 Ey\n", "```\n", "\n", "To remove the rotation, we can use a variety of tools, but another way is just to overwrite the orientations:\n", "\n", "```\n", "TRANSFER FUNCTIONS IN MEASUREMENT COORDINATES\n", "********* WITH FULL ERROR COVARIANCE ********\n", " Aurora Robust Remote Reference\n", "station: CAS04\n", "coordinate 37.633 -121.468 declination 13.17\n", "number of channels 5 number of frequencies 25\n", " orientations and tilts of each channel\n", " 1 0.00 0.00 CAS04 Hx\n", " 2 90.00 0.00 CAS04 Hy\n", " 3 0.00 90.00 CAS04 Hz\n", " 4 0.00 0.00 CAS04 Ex\n", " 5 90.00 0.00 CAS04 Ey\n", "```\n", "\n", "This is why we set angle1=13.2 degrees in the comparison plotter." ] }, { "cell_type": "code", "execution_count": 23, "id": "f5901d39-cacc-4c3f-9a1b-fd2fb33458e9", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CAS04_RRNVR08.zrr\n", "CAS04bcd_REV06.zrr\n", "CAS04_RRNVR08\n" ] } ], "source": [ "print(z_file_path)\n", "print(archived_z_file)\n", "print(tf_file_base)" ] }, { "cell_type": "code", "execution_count": 24, "id": "e3a85530-c001-45b3-a550-1f57548deb1d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m24:09:03T20:09:41 | INFO | line:86 |aurora.transfer_function.plot.comparison_plots | compare_two_z_files | Sacling TF scale_factor1: 1\u001b[0m\n" ] }, { "data": { "image/png": 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "compare_two_z_files(\n", " z_file_path,\n", " archived_z_file,\n", " angle1=+13.2,\n", " label1=\"aurora\",\n", " label2=\"emtf\",\n", " scale_factor1=1,\n", " out_file=f\"{tf_file_base}compare.png\",\n", " markersize=3,\n", " rho_ylims=[1e0, 1e3],\n", " xlims=[0.99, 2000],\n", " rho_ax_label_size=12,\n", " phi_ax_label_size=12\n", ")" ] }, { "cell_type": "code", "execution_count": null, "id": "dca59e0a-69cf-453c-8c8b-461750c25deb", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "id": "5fe72445-8acd-4fb0-8df6-6cce87b068f5", "metadata": {}, "source": [ "# Part II: Logic to save FCs\n", "\n", "Storage of FCs was intended to be an option to provide to users on the fly, by setting the decimation_level\n", "part of the processing config to `dec_level.save_fcs = True` and `dec_level.save_fcs_type = \"h5\"`.\n", "\n", "This works in some cases but not in general. Details are in aurora issue #319 https://github.com/simpeg/aurora/issues/319. \n", "\n", "The proposed solution is to generate FCs per station by processing as a single station.\n", "\n", "We start with the Run Summary table:" ] }, { "cell_type": "code", "execution_count": 25, "id": "729d27e8-61c3-4946-817b-fbee4217eb0d", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m24:09:03T20:09:42 | INFO | line:771 |mth5.mth5 | close_mth5 | Flushing and closing 8P_CAS04_NVR08.h5\u001b[0m\n" ] }, { "data": { "text/html": [ "
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6{'ex': 1.0, 'ey': 1.0, 'hx': 1.0, 'hy': 1.0, '...856502.02020-06-24 15:55:46+00:00True[hx, hy]8P_CAS04_NVR08.h5856503[ex, ey, hz]c1.02020-06-14 18:00:44+00:00NVR08CONUS South<HDF5 object reference><HDF5 object reference>
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" ], "text/plain": [ " channel_scale_factors duration \\\n", "0 {'ex': 1.0, 'ey': 1.0, 'hx': 1.0, 'hy': 1.0, '... 11266.0 \n", "1 {'ex': 1.0, 'ey': 1.0, 'hx': 1.0, 'hy': 1.0, '... 847648.0 \n", "2 {'ex': 1.0, 'ey': 1.0, 'hx': 1.0, 'hy': 1.0, '... 1638042.0 \n", "3 {'ex': 1.0, 'ey': 1.0, 'hx': 1.0, 'hy': 1.0, '... 1034585.0 \n", "4 {'ex': 1.0, 'ey': 1.0, 'hx': 1.0, 'hy': 1.0, '... 2860.0 \n", "5 {'ex': 1.0, 'ey': 1.0, 'hx': 1.0, 'hy': 1.0, '... 938509.0 \n", "6 {'ex': 1.0, 'ey': 1.0, 'hx': 1.0, 'hy': 1.0, '... 856502.0 \n", "\n", " end has_data input_channels mth5_path \\\n", "0 2020-06-02 22:07:46+00:00 True [hx, hy] 8P_CAS04_NVR08.h5 \n", "1 2020-06-12 17:52:23+00:00 True [hx, hy] 8P_CAS04_NVR08.h5 \n", "2 2020-07-01 17:32:59+00:00 True [hx, hy] 8P_CAS04_NVR08.h5 \n", "3 2020-07-13 19:00:00+00:00 True [hx, hy] 8P_CAS04_NVR08.h5 \n", "4 2020-06-03 19:57:51+00:00 True [hx, hy] 8P_CAS04_NVR08.h5 \n", "5 2020-06-14 16:56:02+00:00 True [hx, hy] 8P_CAS04_NVR08.h5 \n", "6 2020-06-24 15:55:46+00:00 True [hx, hy] 8P_CAS04_NVR08.h5 \n", "\n", " n_samples output_channels run sample_rate start \\\n", "0 11267 [ex, ey, hz] a 1.0 2020-06-02 19:00:00+00:00 \n", "1 847649 [ex, ey, hz] b 1.0 2020-06-02 22:24:55+00:00 \n", "2 1638043 [ex, ey, hz] c 1.0 2020-06-12 18:32:17+00:00 \n", "3 1034586 [ex, ey, hz] d 1.0 2020-07-01 19:36:55+00:00 \n", "4 2861 [ex, ey, hz] a 1.0 2020-06-03 19:10:11+00:00 \n", "5 938510 [ex, ey, hz] b 1.0 2020-06-03 20:14:13+00:00 \n", "6 856503 [ex, ey, hz] c 1.0 2020-06-14 18:00:44+00:00 \n", "\n", " station survey run_hdf5_reference station_hdf5_reference \n", "0 CAS04 CONUS South \n", "1 CAS04 CONUS South \n", "2 CAS04 CONUS South \n", "3 CAS04 CONUS South \n", "4 NVR08 CONUS South \n", "5 NVR08 CONUS South \n", "6 NVR08 CONUS South " ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mth5_run_summary = RunSummary()\n", "mth5_run_summary.from_mth5s([mth5_path,])\n", "run_summary = mth5_run_summary.clone()\n", "run_summary.df" ] }, { "cell_type": "markdown", "id": "2f4d6ad0-e6a4-428f-a4c4-840ac448ac72", "metadata": {}, "source": [ "### But this time, process stations individually (e.g. CAS04 as below)" ] }, { "cell_type": "code", "execution_count": 26, "id": "dae34d63-e84a-4825-9535-a5e8eac48392", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m24:09:03T20:09:42 | INFO | line:250 |mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column fc, adding and setting dtype to .\u001b[0m\n", "\u001b[1m24:09:03T20:09:42 | INFO | line:250 |mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column remote, adding and setting dtype to .\u001b[0m\n", "\u001b[1m24:09:03T20:09:42 | INFO | line:250 |mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column run_dataarray, adding and setting dtype to .\u001b[0m\n", "\u001b[1m24:09:03T20:09:42 | INFO | line:250 |mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column stft, adding and setting dtype to .\u001b[0m\n", "\u001b[1m24:09:03T20:09:42 | INFO | line:250 |mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column mth5_obj, adding and setting dtype to .\u001b[0m\n" ] }, { "data": { "text/html": [ "
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surveystationrunstartendduration
0CONUS SouthCAS04a2020-06-02 19:00:00+00:002020-06-02 22:07:46+00:0011266.0
1CONUS SouthCAS04b2020-06-02 22:24:55+00:002020-06-12 17:52:23+00:00847648.0
2CONUS SouthCAS04c2020-06-12 18:32:17+00:002020-07-01 17:32:59+00:001638042.0
3CONUS SouthCAS04d2020-07-01 19:36:55+00:002020-07-13 19:00:00+00:001034585.0
\n", "
" ], "text/plain": [ " survey station run start \\\n", "0 CONUS South CAS04 a 2020-06-02 19:00:00+00:00 \n", "1 CONUS South CAS04 b 2020-06-02 22:24:55+00:00 \n", "2 CONUS South CAS04 c 2020-06-12 18:32:17+00:00 \n", "3 CONUS South CAS04 d 2020-07-01 19:36:55+00:00 \n", "\n", " end duration \n", "0 2020-06-02 22:07:46+00:00 11266.0 \n", "1 2020-06-12 17:52:23+00:00 847648.0 \n", "2 2020-07-01 17:32:59+00:00 1638042.0 \n", "3 2020-07-13 19:00:00+00:00 1034585.0 " ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "kernel_dataset = KernelDataset()\n", "station_id = \"CAS04\"\n", "remote_reference_id = None\n", "kernel_dataset.from_run_summary(run_summary, station_id, remote_reference_id)\n", "kernel_dataset.mini_summary" ] }, { "cell_type": "markdown", "id": "f9c5c2fc-04d7-4a35-916d-3532b51249b2", "metadata": {}, "source": [ "Before adding the FCs, take a look at the file stats:" ] }, { "cell_type": "code", "execution_count": 27, "id": "4ab4bbd5-ec58-4f69-8eff-1e10918f7098", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "file_info: \n", " os.stat_result(st_mode=33204, st_ino=89922093, st_dev=66306, st_nlink=1, st_uid=1001, st_gid=1001, st_size=107289751, st_atime=1725419382, st_mtime=1725419382, st_ctime=1725419382)\n", "file_size_before_fc_addition 107289751\n" ] } ], "source": [ "file_info = os.stat(mth5_path)\n", "print(f\"file_info: \\n {file_info}\")\n", "\n", "file_size_before_fc_addition = file_info.st_size\n", "print(f\"file_size_before_fc_addition {file_size_before_fc_addition}\")" ] }, { "cell_type": "code", "execution_count": 28, "id": "499693a7-e57b-4244-9e13-5da2f7fed74c", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m24:09:03T20:09:42 | INFO | line:108 |aurora.config.config_creator | determine_band_specification_style | Bands not defined; setting to EMTF BANDS_DEFAULT_FILE\u001b[0m\n" ] } ], "source": [ "cc = ConfigCreator()\n", "config = cc.create_from_kernel_dataset(kernel_dataset,) \n", "for dec_level in config.decimations:\n", " dec_level.stft.window.type = \"hamming\"\n", "# dec_level.stft.window.overlap = int(dec_level.stft.window.num_samples/4)\n", " dec_level.save_fcs = True\n", " dec_level.save_fcs_type = \"h5\"" ] }, { "cell_type": "code", "execution_count": 29, "id": "74c00db4-68b7-4964-9395-48fe508d079f", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "{\n", " \"processing\": {\n", " \"band_setup_file\": \"/home/kkappler/software/irismt/aurora/aurora/config/emtf_band_setup/bs_test.cfg\",\n", " \"band_specification_style\": \"EMTF\",\n", " \"channel_nomenclature.ex\": \"ex\",\n", " \"channel_nomenclature.ey\": \"ey\",\n", " \"channel_nomenclature.hx\": \"hx\",\n", " \"channel_nomenclature.hy\": \"hy\",\n", " \"channel_nomenclature.hz\": \"hz\",\n", " \"decimations\": [\n", " {\n", " \"decimation_level\": {\n", " \"anti_alias_filter\": \"default\",\n", " \"bands\": [\n", " {\n", " \"band\": {\n", " \"center_averaging_type\": \"geometric\",\n", " \"closed\": \"left\",\n", " \"decimation_level\": 0,\n", " \"frequency_max\": 0.23828125,\n", " \"frequency_min\": 0.19140625,\n", " \"index_max\": 30,\n", " \"index_min\": 25\n", " }\n", " },\n", " {\n", " \"band\": {\n", " \"center_averaging_type\": \"geometric\",\n", " \"closed\": \"left\",\n", " \"decimation_level\": 0,\n", " \"frequency_max\": 0.19140625,\n", " \"frequency_min\": 0.15234375,\n", " \"index_max\": 24,\n", " \"index_min\": 20\n", " }\n", " },\n", " {\n", " \"band\": {\n", " \"center_averaging_type\": \"geometric\",\n", " \"closed\": \"left\",\n", " \"decimation_level\": 0,\n", " \"frequency_max\": 0.15234375,\n", " \"frequency_min\": 0.12109375,\n", " \"index_max\": 19,\n", " \"index_min\": 16\n", " }\n", " },\n", " {\n", " \"band\": {\n", " \"center_averaging_type\": \"geometric\",\n", " \"closed\": \"left\",\n", " \"decimation_level\": 0,\n", " \"frequency_max\": 0.12109375,\n", " \"frequency_min\": 0.09765625,\n", " \"index_max\": 15,\n", " \"index_min\": 13\n", " }\n", " },\n", " {\n", " \"band\": {\n", " \"center_averaging_type\": \"geometric\",\n", " \"closed\": \"left\",\n", " \"decimation_level\": 0,\n", " \"frequency_max\": 0.09765625,\n", " \"frequency_min\": 0.07421875,\n", " \"index_max\": 12,\n", " \"index_min\": 10\n", " }\n", " },\n", " {\n", " \"band\": {\n", " \"center_averaging_type\": \"geometric\",\n", " \"closed\": \"left\",\n", " \"decimation_level\": 0,\n", " \"frequency_max\": 0.07421875,\n", " \"frequency_min\": 0.05859375,\n", " \"index_max\": 9,\n", " \"index_min\": 8\n", " }\n", " },\n", " {\n", " \"band\": {\n", " \"center_averaging_type\": \"geometric\",\n", " \"closed\": \"left\",\n", " \"decimation_level\": 0,\n", " \"frequency_max\": 0.05859375,\n", " \"frequency_min\": 0.04296875,\n", " \"index_max\": 7,\n", " \"index_min\": 6\n", " }\n", " },\n", " {\n", " \"band\": {\n", " \"center_averaging_type\": \"geometric\",\n", " \"closed\": \"left\",\n", " \"decimation_level\": 0,\n", " \"frequency_max\": 0.04296875,\n", " \"frequency_min\": 0.03515625,\n", " \"index_max\": 5,\n", " \"index_min\": 5\n", " }\n", " }\n", " ],\n", " \"decimation.factor\": 1.0,\n", " \"decimation.level\": 0,\n", " \"decimation.method\": \"default\",\n", " \"decimation.sample_rate\": 1.0,\n", " \"estimator.engine\": \"RME_RR\",\n", " \"estimator.estimate_per_channel\": true,\n", " \"extra_pre_fft_detrend_type\": \"linear\",\n", " \"input_channels\": [\n", " \"hx\",\n", " \"hy\"\n", " ],\n", " \"method\": \"fft\",\n", " \"min_num_stft_windows\": 2,\n", " \"output_channels\": [\n", " \"ex\",\n", " \"ey\",\n", " \"hz\"\n", " ],\n", " \"pre_fft_detrend_type\": \"linear\",\n", " \"prewhitening_type\": \"first difference\",\n", " \"recoloring\": true,\n", " \"reference_channels\": [\n", " \"hx\",\n", " \"hy\"\n", " ],\n", " \"regression.max_iterations\": 10,\n", " \"regression.max_redescending_iterations\": 2,\n", " \"regression.minimum_cycles\": 10,\n", " \"regression.r0\": 1.5,\n", " \"regression.tolerance\": 0.005,\n", " \"regression.u0\": 2.8,\n", " \"regression.verbosity\": 0,\n", " \"save_fcs\": true,\n", " \"save_fcs_type\": \"h5\",\n", " \"window.clock_zero_type\": \"ignore\",\n", " \"window.num_samples\": 128,\n", " \"window.overlap\": 32,\n", " \"window.type\": \"hamming\"\n", " }\n", " },\n", " {\n", " \"decimation_level\": {\n", " \"anti_alias_filter\": \"default\",\n", " \"bands\": [\n", " {\n", " \"band\": {\n", " \"center_averaging_type\": \"geometric\",\n", " \"closed\": \"left\",\n", " \"decimation_level\": 1,\n", " \"frequency_max\": 0.0341796875,\n", " \"frequency_min\": 0.0263671875,\n", " \"index_max\": 17,\n", " \"index_min\": 14\n", " }\n", " },\n", " {\n", " \"band\": {\n", " \"center_averaging_type\": \"geometric\",\n", " \"closed\": \"left\",\n", " \"decimation_level\": 1,\n", " \"frequency_max\": 0.0263671875,\n", " \"frequency_min\": 0.0205078125,\n", " \"index_max\": 13,\n", " \"index_min\": 11\n", " }\n", " },\n", " {\n", " \"band\": {\n", " \"center_averaging_type\": \"geometric\",\n", " \"closed\": \"left\",\n", " \"decimation_level\": 1,\n", " \"frequency_max\": 0.0205078125,\n", " \"frequency_min\": 0.0166015625,\n", " \"index_max\": 10,\n", " \"index_min\": 9\n", " }\n", " },\n", " {\n", " \"band\": {\n", " \"center_averaging_type\": \"geometric\",\n", " \"closed\": \"left\",\n", " \"decimation_level\": 1,\n", " \"frequency_max\": 0.0166015625,\n", " \"frequency_min\": 0.0126953125,\n", " \"index_max\": 8,\n", " \"index_min\": 7\n", " }\n", " },\n", " {\n", " \"band\": {\n", " \"center_averaging_type\": \"geometric\",\n", " \"closed\": \"left\",\n", " \"decimation_level\": 1,\n", " \"frequency_max\": 0.0126953125,\n", " \"frequency_min\": 0.0107421875,\n", " \"index_max\": 6,\n", " \"index_min\": 6\n", " }\n", " },\n", " {\n", " \"band\": {\n", " \"center_averaging_type\": \"geometric\",\n", " \"closed\": \"left\",\n", " \"decimation_level\": 1,\n", " \"frequency_max\": 0.0107421875,\n", " \"frequency_min\": 0.0087890625,\n", " \"index_max\": 5,\n", " \"index_min\": 5\n", " }\n", " }\n", " ],\n", " \"decimation.factor\": 4.0,\n", " \"decimation.level\": 1,\n", " \"decimation.method\": \"default\",\n", " \"decimation.sample_rate\": 0.25,\n", " \"estimator.engine\": \"RME_RR\",\n", " \"estimator.estimate_per_channel\": true,\n", " \"extra_pre_fft_detrend_type\": \"linear\",\n", " \"input_channels\": [\n", " \"hx\",\n", " \"hy\"\n", " ],\n", " \"method\": \"fft\",\n", " \"min_num_stft_windows\": 2,\n", " \"output_channels\": [\n", " \"ex\",\n", " \"ey\",\n", " \"hz\"\n", " ],\n", " \"pre_fft_detrend_type\": \"linear\",\n", " \"prewhitening_type\": \"first difference\",\n", " \"recoloring\": true,\n", " \"reference_channels\": [\n", " \"hx\",\n", " \"hy\"\n", " ],\n", " \"regression.max_iterations\": 10,\n", " \"regression.max_redescending_iterations\": 2,\n", " \"regression.minimum_cycles\": 10,\n", " \"regression.r0\": 1.5,\n", " \"regression.tolerance\": 0.005,\n", " \"regression.u0\": 2.8,\n", " \"regression.verbosity\": 0,\n", " \"save_fcs\": true,\n", " \"save_fcs_type\": \"h5\",\n", " \"window.clock_zero_type\": \"ignore\",\n", " \"window.num_samples\": 128,\n", " \"window.overlap\": 32,\n", " \"window.type\": \"hamming\"\n", " }\n", " },\n", " {\n", " \"decimation_level\": {\n", " \"anti_alias_filter\": \"default\",\n", " \"bands\": [\n", " {\n", " \"band\": {\n", " \"center_averaging_type\": \"geometric\",\n", " \"closed\": \"left\",\n", " \"decimation_level\": 2,\n", " \"frequency_max\": 0.008544921875,\n", " \"frequency_min\": 0.006591796875,\n", " \"index_max\": 17,\n", " \"index_min\": 14\n", " }\n", " },\n", " {\n", " \"band\": {\n", " \"center_averaging_type\": \"geometric\",\n", " \"closed\": \"left\",\n", " \"decimation_level\": 2,\n", " \"frequency_max\": 0.006591796875,\n", " \"frequency_min\": 0.005126953125,\n", " \"index_max\": 13,\n", " \"index_min\": 11\n", " }\n", " },\n", " {\n", " \"band\": {\n", " \"center_averaging_type\": \"geometric\",\n", " \"closed\": \"left\",\n", " \"decimation_level\": 2,\n", " \"frequency_max\": 0.005126953125,\n", " \"frequency_min\": 0.004150390625,\n", " \"index_max\": 10,\n", " \"index_min\": 9\n", " }\n", " },\n", " {\n", " \"band\": {\n", " \"center_averaging_type\": \"geometric\",\n", " \"closed\": \"left\",\n", " \"decimation_level\": 2,\n", " \"frequency_max\": 0.004150390625,\n", " \"frequency_min\": 0.003173828125,\n", " \"index_max\": 8,\n", " \"index_min\": 7\n", " }\n", " },\n", " {\n", " \"band\": {\n", " \"center_averaging_type\": \"geometric\",\n", " \"closed\": \"left\",\n", " \"decimation_level\": 2,\n", " \"frequency_max\": 0.003173828125,\n", " \"frequency_min\": 0.002685546875,\n", " \"index_max\": 6,\n", " \"index_min\": 6\n", " }\n", " },\n", " {\n", " \"band\": {\n", " \"center_averaging_type\": \"geometric\",\n", " \"closed\": \"left\",\n", " \"decimation_level\": 2,\n", " \"frequency_max\": 0.002685546875,\n", " \"frequency_min\": 0.002197265625,\n", " \"index_max\": 5,\n", " \"index_min\": 5\n", " }\n", " }\n", " ],\n", " \"decimation.factor\": 4.0,\n", " \"decimation.level\": 2,\n", " \"decimation.method\": \"default\",\n", " \"decimation.sample_rate\": 0.0625,\n", " \"estimator.engine\": \"RME_RR\",\n", " \"estimator.estimate_per_channel\": true,\n", " \"extra_pre_fft_detrend_type\": \"linear\",\n", " \"input_channels\": [\n", " \"hx\",\n", " \"hy\"\n", " ],\n", " \"method\": \"fft\",\n", " \"min_num_stft_windows\": 2,\n", " \"output_channels\": [\n", " \"ex\",\n", " \"ey\",\n", " \"hz\"\n", " ],\n", " \"pre_fft_detrend_type\": \"linear\",\n", " \"prewhitening_type\": \"first difference\",\n", " \"recoloring\": true,\n", " \"reference_channels\": [\n", " \"hx\",\n", " \"hy\"\n", " ],\n", " \"regression.max_iterations\": 10,\n", " \"regression.max_redescending_iterations\": 2,\n", " \"regression.minimum_cycles\": 10,\n", " \"regression.r0\": 1.5,\n", " \"regression.tolerance\": 0.005,\n", " \"regression.u0\": 2.8,\n", " \"regression.verbosity\": 0,\n", " \"save_fcs\": true,\n", " \"save_fcs_type\": \"h5\",\n", " \"window.clock_zero_type\": \"ignore\",\n", " \"window.num_samples\": 128,\n", " \"window.overlap\": 32,\n", " \"window.type\": \"hamming\"\n", " }\n", " },\n", " {\n", " \"decimation_level\": {\n", " \"anti_alias_filter\": \"default\",\n", " \"bands\": [\n", " {\n", " \"band\": {\n", " \"center_averaging_type\": \"geometric\",\n", " \"closed\": \"left\",\n", " \"decimation_level\": 3,\n", " \"frequency_max\": 0.00274658203125,\n", " \"frequency_min\": 0.00213623046875,\n", " \"index_max\": 22,\n", " \"index_min\": 18\n", " }\n", " },\n", " {\n", " \"band\": {\n", " \"center_averaging_type\": \"geometric\",\n", " \"closed\": \"left\",\n", " \"decimation_level\": 3,\n", " \"frequency_max\": 0.00213623046875,\n", " \"frequency_min\": 0.00164794921875,\n", " \"index_max\": 17,\n", " \"index_min\": 14\n", " }\n", " },\n", " {\n", " \"band\": {\n", " \"center_averaging_type\": \"geometric\",\n", " \"closed\": \"left\",\n", " \"decimation_level\": 3,\n", " \"frequency_max\": 0.00164794921875,\n", " \"frequency_min\": 0.00115966796875,\n", " \"index_max\": 13,\n", " \"index_min\": 10\n", " }\n", " },\n", " {\n", " \"band\": {\n", " \"center_averaging_type\": \"geometric\",\n", " \"closed\": \"left\",\n", " \"decimation_level\": 3,\n", " \"frequency_max\": 0.00115966796875,\n", " \"frequency_min\": 0.00079345703125,\n", " \"index_max\": 9,\n", " \"index_min\": 7\n", " }\n", " },\n", " {\n", " \"band\": {\n", " \"center_averaging_type\": \"geometric\",\n", " \"closed\": \"left\",\n", " \"decimation_level\": 3,\n", " \"frequency_max\": 0.00079345703125,\n", " \"frequency_min\": 0.00054931640625,\n", " \"index_max\": 6,\n", " \"index_min\": 5\n", " }\n", " }\n", " ],\n", " \"decimation.factor\": 4.0,\n", " \"decimation.level\": 3,\n", " \"decimation.method\": \"default\",\n", " \"decimation.sample_rate\": 0.015625,\n", " \"estimator.engine\": \"RME_RR\",\n", " \"estimator.estimate_per_channel\": true,\n", " \"extra_pre_fft_detrend_type\": \"linear\",\n", " \"input_channels\": [\n", " \"hx\",\n", " \"hy\"\n", " ],\n", " \"method\": \"fft\",\n", " \"min_num_stft_windows\": 2,\n", " \"output_channels\": [\n", " \"ex\",\n", " \"ey\",\n", " \"hz\"\n", " ],\n", " \"pre_fft_detrend_type\": \"linear\",\n", " \"prewhitening_type\": \"first difference\",\n", " \"recoloring\": true,\n", " \"reference_channels\": [\n", " \"hx\",\n", " \"hy\"\n", " ],\n", " \"regression.max_iterations\": 10,\n", " \"regression.max_redescending_iterations\": 2,\n", " \"regression.minimum_cycles\": 10,\n", " \"regression.r0\": 1.5,\n", " \"regression.tolerance\": 0.005,\n", " \"regression.u0\": 2.8,\n", " \"regression.verbosity\": 0,\n", " \"save_fcs\": true,\n", " \"save_fcs_type\": \"h5\",\n", " \"window.clock_zero_type\": \"ignore\",\n", " \"window.num_samples\": 128,\n", " \"window.overlap\": 32,\n", " \"window.type\": \"hamming\"\n", " }\n", " }\n", " ],\n", " \"id\": \"CAS04_sr1\",\n", " \"stations.local.id\": \"CAS04\",\n", " \"stations.local.mth5_path\": \"8P_CAS04_NVR08.h5\",\n", " \"stations.local.remote\": false,\n", " \"stations.local.runs\": [\n", " {\n", " \"run\": {\n", " \"id\": \"a\",\n", " \"input_channels\": [\n", " {\n", " \"channel\": {\n", " \"id\": \"hx\",\n", " \"scale_factor\": 1.0\n", " }\n", " },\n", " {\n", " \"channel\": {\n", " \"id\": \"hy\",\n", " \"scale_factor\": 1.0\n", " }\n", " }\n", " ],\n", " \"output_channels\": [\n", " {\n", " \"channel\": {\n", " \"id\": \"ex\",\n", " \"scale_factor\": 1.0\n", " }\n", " },\n", " {\n", " \"channel\": {\n", " \"id\": \"ey\",\n", " \"scale_factor\": 1.0\n", " }\n", " },\n", " {\n", " \"channel\": {\n", " \"id\": \"hz\",\n", " \"scale_factor\": 1.0\n", " }\n", " }\n", " ],\n", " \"sample_rate\": 1.0,\n", " \"time_periods\": [\n", " {\n", " \"time_period\": {\n", " \"end\": \"2020-06-02T22:07:46+00:00\",\n", " \"start\": \"2020-06-02T19:00:00+00:00\"\n", " }\n", " }\n", " ]\n", " }\n", " },\n", " {\n", " \"run\": {\n", " \"id\": \"b\",\n", " \"input_channels\": [\n", " {\n", " \"channel\": {\n", " \"id\": \"hx\",\n", " \"scale_factor\": 1.0\n", " }\n", " },\n", " {\n", " \"channel\": {\n", " \"id\": \"hy\",\n", " \"scale_factor\": 1.0\n", " }\n", " }\n", " ],\n", " \"output_channels\": [\n", " {\n", " \"channel\": {\n", " \"id\": \"ex\",\n", " \"scale_factor\": 1.0\n", " }\n", " },\n", " {\n", " \"channel\": {\n", " \"id\": \"ey\",\n", " \"scale_factor\": 1.0\n", " }\n", " },\n", " {\n", " \"channel\": {\n", " \"id\": \"hz\",\n", " \"scale_factor\": 1.0\n", " }\n", " }\n", " ],\n", " \"sample_rate\": 1.0,\n", " \"time_periods\": [\n", " {\n", " \"time_period\": {\n", " \"end\": \"2020-06-12T17:52:23+00:00\",\n", " \"start\": \"2020-06-02T22:24:55+00:00\"\n", " }\n", " }\n", " ]\n", " }\n", " },\n", " {\n", " \"run\": {\n", " \"id\": \"c\",\n", " \"input_channels\": [\n", " {\n", " \"channel\": {\n", " \"id\": \"hx\",\n", " \"scale_factor\": 1.0\n", " }\n", " },\n", " {\n", " \"channel\": {\n", " \"id\": \"hy\",\n", " \"scale_factor\": 1.0\n", " }\n", " }\n", " ],\n", " \"output_channels\": [\n", " {\n", " \"channel\": {\n", " \"id\": \"ex\",\n", " \"scale_factor\": 1.0\n", " }\n", " },\n", " {\n", " \"channel\": {\n", " \"id\": \"ey\",\n", " \"scale_factor\": 1.0\n", " }\n", " },\n", " {\n", " \"channel\": {\n", " \"id\": \"hz\",\n", " \"scale_factor\": 1.0\n", " }\n", " }\n", " ],\n", " \"sample_rate\": 1.0,\n", " \"time_periods\": [\n", " {\n", " \"time_period\": {\n", " \"end\": \"2020-07-01T17:32:59+00:00\",\n", " \"start\": \"2020-06-12T18:32:17+00:00\"\n", " }\n", " }\n", " ]\n", " }\n", " },\n", " {\n", " \"run\": {\n", " \"id\": \"d\",\n", " \"input_channels\": [\n", " {\n", " \"channel\": {\n", " \"id\": \"hx\",\n", " \"scale_factor\": 1.0\n", " }\n", " },\n", " {\n", " \"channel\": {\n", " \"id\": \"hy\",\n", " \"scale_factor\": 1.0\n", " }\n", " }\n", " ],\n", " \"output_channels\": [\n", " {\n", " \"channel\": {\n", " \"id\": \"ex\",\n", " \"scale_factor\": 1.0\n", " }\n", " },\n", " {\n", " \"channel\": {\n", " \"id\": \"ey\",\n", " \"scale_factor\": 1.0\n", " }\n", " },\n", " {\n", " \"channel\": {\n", " \"id\": \"hz\",\n", " \"scale_factor\": 1.0\n", " }\n", " }\n", " ],\n", " \"sample_rate\": 1.0,\n", " \"time_periods\": [\n", " {\n", " \"time_period\": {\n", " \"end\": \"2020-07-13T19:00:00+00:00\",\n", " \"start\": \"2020-07-01T19:36:55+00:00\"\n", " }\n", " }\n", " ]\n", " }\n", " }\n", " ],\n", " \"stations.remote\": []\n", " }\n", "}" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "config" ] }, { "cell_type": "code", "execution_count": 30, "id": "117661a7-9918-4dca-9cc5-b142fa906417", "metadata": {}, "outputs": [], "source": [ "tf_file_base = f\"{station_id}_SS\"" ] }, { "cell_type": "code", "execution_count": 31, "id": "ef23917a-6db4-4c11-896d-2457f36c0b24", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m24:09:03T20:09:42 | INFO | line:277 |aurora.pipelines.transfer_function_kernel | show_processing_summary | Processing Summary Dataframe:\u001b[0m\n", "\u001b[1m24:09:03T20:09:42 | INFO | line:278 |aurora.pipelines.transfer_function_kernel | show_processing_summary | \n", " duration has_data n_samples run station survey run_hdf5_reference station_hdf5_reference fc remote stft mth5_obj dec_level dec_factor sample_rate window_duration num_samples_window num_samples num_stft_windows\n", "0 11266.0 True 11267 a CAS04 CONUS South False False None None 0 1.0 1.000000 128.0 128 11266.0 117.0\n", "1 11266.0 True 11267 a CAS04 CONUS South False False None None 1 4.0 0.250000 512.0 128 2816.0 29.0\n", "2 11266.0 True 11267 a CAS04 CONUS South False False None None 2 4.0 0.062500 2048.0 128 704.0 7.0\n", "3 11266.0 True 11267 a CAS04 CONUS South False False None None 3 4.0 0.015625 8192.0 128 176.0 1.0\n", "4 847648.0 True 847649 b CAS04 CONUS South False False None None 0 1.0 1.000000 128.0 128 847648.0 8829.0\n", "5 847648.0 True 847649 b CAS04 CONUS South False False None None 1 4.0 0.250000 512.0 128 211912.0 2207.0\n", "6 847648.0 True 847649 b CAS04 CONUS South False False None None 2 4.0 0.062500 2048.0 128 52978.0 551.0\n", "7 847648.0 True 847649 b CAS04 CONUS South False False None None 3 4.0 0.015625 8192.0 128 13244.0 137.0\n", "8 1638042.0 True 1638043 c CAS04 CONUS South False False None None 0 1.0 1.000000 128.0 128 1638042.0 17062.0\n", "9 1638042.0 True 1638043 c CAS04 CONUS South False False None None 1 4.0 0.250000 512.0 128 409510.0 4265.0\n", "10 1638042.0 True 1638043 c CAS04 CONUS South False False None None 2 4.0 0.062500 2048.0 128 102377.0 1066.0\n", "11 1638042.0 True 1638043 c CAS04 CONUS South False False None None 3 4.0 0.015625 8192.0 128 25594.0 266.0\n", "12 1034585.0 True 1034586 d CAS04 CONUS South False False None None 0 1.0 1.000000 128.0 128 1034585.0 10776.0\n", "13 1034585.0 True 1034586 d CAS04 CONUS South False False None None 1 4.0 0.250000 512.0 128 258646.0 2693.0\n", "14 1034585.0 True 1034586 d CAS04 CONUS South False False None None 2 4.0 0.062500 2048.0 128 64661.0 673.0\n", "15 1034585.0 True 1034586 d CAS04 CONUS South False False None None 3 4.0 0.015625 8192.0 128 16165.0 168.0\u001b[0m\n", "\u001b[1m24:09:03T20:09:42 | INFO | line:411 |aurora.pipelines.transfer_function_kernel | validate_processing | No RR station specified, switching RME_RR to RME\u001b[0m\n", "\u001b[1m24:09:03T20:09:42 | INFO | line:411 |aurora.pipelines.transfer_function_kernel | validate_processing | No RR station specified, switching RME_RR to RME\u001b[0m\n", "\u001b[1m24:09:03T20:09:42 | INFO | line:411 |aurora.pipelines.transfer_function_kernel | validate_processing | No RR station specified, switching RME_RR to RME\u001b[0m\n", "\u001b[1m24:09:03T20:09:42 | INFO | line:411 |aurora.pipelines.transfer_function_kernel | validate_processing | No RR station specified, switching RME_RR to RME\u001b[0m\n", "\u001b[1m24:09:03T20:09:42 | INFO | line:654 |aurora.pipelines.transfer_function_kernel | memory_check | Total memory: 62.74 GB\u001b[0m\n", "\u001b[1m24:09:03T20:09:42 | INFO | line:658 |aurora.pipelines.transfer_function_kernel | memory_check | Total Bytes of Raw Data: 0.026 GB\u001b[0m\n", "\u001b[1m24:09:03T20:09:42 | INFO | line:661 |aurora.pipelines.transfer_function_kernel | memory_check | Raw Data will use: 0.042 % of memory\u001b[0m\n", "\u001b[1m24:09:03T20:09:42 | INFO | line:517 |aurora.pipelines.process_mth5 | process_mth5_legacy | Processing config indicates 4 decimation levels\u001b[0m\n", "\u001b[1m24:09:03T20:09:42 | INFO | line:445 |aurora.pipelines.transfer_function_kernel | valid_decimations | After validation there are 4 valid decimation levels\u001b[0m\n", "\u001b[1m24:09:03T20:09:48 | INFO | line:889 |mtpy.processing.kernel_dataset | initialize_dataframe_for_processing | Dataset dataframe initialized successfully\u001b[0m\n", "\u001b[1m24:09:03T20:09:48 | INFO | line:143 |aurora.pipelines.transfer_function_kernel | update_dataset_df | Dataset Dataframe Updated for decimation level 0 Successfully\u001b[0m\n", "\u001b[1m24:09:03T20:09:48 | INFO | line:364 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Saving FC level\u001b[0m\n", "\u001b[1m24:09:03T20:09:50 | INFO | line:364 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Saving FC level\u001b[0m\n", "\u001b[1m24:09:03T20:09:51 | INFO | line:364 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Saving FC level\u001b[0m\n", "\u001b[1m24:09:03T20:09:53 | INFO | line:364 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Saving FC level\u001b[0m\n", "\u001b[1m24:09:03T20:09:53 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 25.728968s (0.038867Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:09:53 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 19.929573s (0.050177Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:09:54 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 15.164131s (0.065945Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:09:54 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 11.746086s (0.085135Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:09:55 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 9.195791s (0.108745Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:09:55 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 7.362526s (0.135823Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:09:56 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 5.856115s (0.170762Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:09:58 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 4.682492s (0.213562Hz)\u001b[0m\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m24:09:03T20:09:59 | INFO | line:124 |aurora.pipelines.transfer_function_kernel | update_dataset_df | DECIMATION LEVEL 1\u001b[0m\n", "\u001b[1m24:09:03T20:10:00 | INFO | line:143 |aurora.pipelines.transfer_function_kernel | update_dataset_df | Dataset Dataframe Updated for decimation level 1 Successfully\u001b[0m\n", "\u001b[1m24:09:03T20:10:00 | INFO | line:364 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Saving FC level\u001b[0m\n", "\u001b[1m24:09:03T20:10:01 | INFO | line:364 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Saving FC level\u001b[0m\n", "\u001b[1m24:09:03T20:10:02 | INFO | line:364 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Saving FC level\u001b[0m\n", "\u001b[1m24:09:03T20:10:03 | INFO | line:364 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Saving FC level\u001b[0m\n", "\u001b[1m24:09:03T20:10:03 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 102.915872s (0.009717Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:10:03 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 85.631182s (0.011678Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:10:04 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 68.881694s (0.014518Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:10:04 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 54.195827s (0.018452Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:10:04 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 43.003958s (0.023254Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:10:04 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 33.310722s (0.030020Hz)\u001b[0m\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m24:09:03T20:10:05 | INFO | line:124 |aurora.pipelines.transfer_function_kernel | update_dataset_df | DECIMATION LEVEL 2\u001b[0m\n", "\u001b[1m24:09:03T20:10:05 | INFO | line:143 |aurora.pipelines.transfer_function_kernel | update_dataset_df | Dataset Dataframe Updated for decimation level 2 Successfully\u001b[0m\n", "\u001b[1m24:09:03T20:10:05 | INFO | line:364 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Saving FC level\u001b[0m\n", "\u001b[1m24:09:03T20:10:06 | INFO | line:364 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Saving FC level\u001b[0m\n", "\u001b[1m24:09:03T20:10:06 | INFO | line:364 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Saving FC level\u001b[0m\n", "\u001b[1m24:09:03T20:10:07 | INFO | line:364 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Saving FC level\u001b[0m\n", "\u001b[1m24:09:03T20:10:07 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 411.663489s (0.002429Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:10:07 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 342.524727s (0.002919Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:10:07 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 275.526776s (0.003629Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:10:07 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 216.783308s (0.004613Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:10:08 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 172.015831s (0.005813Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:10:08 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 133.242890s (0.007505Hz)\u001b[0m\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m24:09:03T20:10:08 | INFO | line:124 |aurora.pipelines.transfer_function_kernel | update_dataset_df | DECIMATION LEVEL 3\u001b[0m\n", "\u001b[1m24:09:03T20:10:08 | INFO | line:143 |aurora.pipelines.transfer_function_kernel | update_dataset_df | Dataset Dataframe Updated for decimation level 3 Successfully\u001b[0m\n", "\u001b[1m24:09:03T20:10:09 | INFO | line:364 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Saving FC level\u001b[0m\n", "\u001b[1m24:09:03T20:10:09 | INFO | line:364 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Saving FC level\u001b[0m\n", "\u001b[1m24:09:03T20:10:10 | INFO | line:364 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Saving FC level\u001b[0m\n", "\u001b[1m24:09:03T20:10:10 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 1514.701336s (0.000660Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:10:10 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 1042.488956s (0.000959Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:10:10 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 723.371271s (0.001382Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:10:10 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 532.971560s (0.001876Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:10:10 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 412.837995s (0.002422Hz)\u001b[0m\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m24:09:03T20:10:11 | INFO | line:771 |mth5.mth5 | close_mth5 | Flushing and closing 8P_CAS04_NVR08.h5\u001b[0m\n" ] } ], "source": [ "show_plot = True\n", "z_file_path = pathlib.Path(f\"{tf_file_base}.zrr\")\n", "tf_cls = process_mth5(config,\n", " kernel_dataset,\n", " units=\"MT\",\n", " show_plot=show_plot,\n", " z_file_path=z_file_path,\n", " )" ] }, { "cell_type": "code", "execution_count": 32, "id": "1850608a-c590-4830-96ef-8aca2b6af74e", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "file_info: \n", " os.stat_result(st_mode=33204, st_ino=89922093, st_dev=66306, st_nlink=1, st_uid=1001, st_gid=1001, st_size=358591655, st_atime=1725419411, st_mtime=1725419411, st_ctime=1725419411)\n", "file_size_before_fc_addition 107289751\n", "file_size_after_fc_addition 358591655\n" ] } ], "source": [ "file_info = os.stat(mth5_path)\n", "print(f\"file_info: \\n {file_info}\")\n", "\n", "file_size_after_fc_addition = file_info.st_size\n", "print(f\"file_size_before_fc_addition {file_size_before_fc_addition}\")\n", "print(f\"file_size_after_fc_addition {file_size_after_fc_addition}\")" ] }, { "cell_type": "markdown", "id": "4ef36ce2-af8e-4b08-8aa8-6f846b6e20c1", "metadata": {}, "source": [ "# Now that the FCs are saved we can access them:|\n", "- These plats are intended to be put in spectrogram class" ] }, { "cell_type": "code", "execution_count": 33, "id": "f1724874-6cea-4e57-b0da-efe5c06f7822", "metadata": {}, "outputs": [], "source": [ "# Choose what specific FCs we want:\n", "# survey_id = \"CONUS SoCal\" # declared directly from dataframe to avoid spurious name changes in archived metadata\n", "station_id = \"CAS04\"\n", "run_id = \"b\"\n", "decimation_level_id = \"0\"" ] }, { "cell_type": "code", "execution_count": 34, "id": "1d55fe89-8e04-44a2-981f-0dbec4fb018d", "metadata": {}, "outputs": [], "source": [ "m = initialize_mth5(mth5_path)" ] }, { "cell_type": "code", "execution_count": 35, "id": "92d4609f-36dc-485a-bd42-323b1090c5c2", "metadata": {}, "outputs": [], "source": [ "survey_group = m.get_survey(survey_id)" ] }, { "cell_type": "code", "execution_count": 36, "id": "b73e4690-382c-4f47-bdc7-79233a49a5b1", "metadata": {}, "outputs": [], "source": [ "station_obj = survey_group.stations_group.get_station(station_id)" ] }, { "cell_type": "code", "execution_count": 37, "id": "5a945256-e717-4727-af7f-c0c852533af7", "metadata": {}, "outputs": [], "source": [ "fc_group = station_obj.fourier_coefficients_group.get_fc_group(run_id)\n", "fc_decimation_level = fc_group.get_decimation_level(decimation_level_id)\n", "stft_obj = fc_decimation_level.to_xarray()" ] }, { "cell_type": "code", "execution_count": 38, "id": "aa2f4b06-2d10-4d78-adc7-27cbaf282e3f", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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       "  * frequency  (frequency) float64 512B 0.0 0.007812 0.01562 ... 0.4844 0.4922\n",
       "Data variables:\n",
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       "    ey         (time, frequency) complex128 9MB (nan+nanj) ... (1.14205368827...\n",
       "    hx         (time, frequency) complex128 9MB 0j ... (-7.255455721291723e-1...\n",
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" ], "text/plain": [ " Size: 45MB\n", "Dimensions: (time: 8829, frequency: 64)\n", "Coordinates:\n", " * time (time) datetime64[ns] 71kB 2020-06-02T22:24:55 ... 2020-06-12T...\n", " * frequency (frequency) float64 512B 0.0 0.007812 0.01562 ... 0.4844 0.4922\n", "Data variables:\n", " ex (time, frequency) complex128 9MB (nan+nanj) ... (6.43984651804...\n", " ey (time, frequency) complex128 9MB (nan+nanj) ... (1.14205368827...\n", " hx (time, frequency) complex128 9MB 0j ... (-7.255455721291723e-1...\n", " hy (time, frequency) complex128 9MB 0j ... (-2.6411456422455584e-...\n", " hz (time, frequency) complex128 9MB 0j ... (2.8711476749705306e-1..." ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" } ], "source": [ "stft_obj" ] }, { "cell_type": "code", "execution_count": 39, "id": "ff5edafc-18c9-4ac6-8a73-d3478aac7f53", "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 40, "id": "a2d79ebb-3f30-4cb7-93a8-3cadd953ea62", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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<xarray.DataArray 'ex' (time: 8829, frequency: 63)> Size: 9MB\n",
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       "Attributes:\n",
       "    component:                     ex\n",
       "    frequency_max:                 0.4921875\n",
       "    frequency_min:                 0.0\n",
       "    hdf5_reference:                <HDF5 object reference>\n",
       "    mth5_type:                     FCChannel\n",
       "    sample_rate_decimation_level:  1.0\n",
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       "    units:                         counts
" ], "text/plain": [ " Size: 9MB\n", "array([[-2.78081633e-10-1.02555611e-09j, 2.31781444e-10+1.03764950e-09j,\n", " -1.41550822e-10-5.30183803e-10j, ...,\n", " -2.33846288e-13+3.78092145e-13j, -2.47517622e-13+2.97032949e-13j,\n", " -1.74394227e-13+5.33374852e-14j],\n", " [-8.61482145e-10+8.01328799e-10j, 7.58095286e-10-6.14537638e-10j,\n", " -4.36876512e-10+4.48085068e-10j, ...,\n", " -1.02114148e-13+1.87080731e-13j, -1.65973397e-13+1.33987254e-13j,\n", " -5.96086160e-14+4.73218577e-14j],\n", " [-6.04310100e-10+2.78710599e-10j, 2.87240419e-10-4.11793024e-10j,\n", " 1.95180812e-10+5.92839940e-10j, ...,\n", " -7.23711253e-13+1.98678662e-13j, -2.22044210e-14-3.39406903e-14j,\n", " 7.41191439e-14+2.94245970e-13j],\n", " ...,\n", " [-4.13217703e-11+1.09955015e-10j, -6.43407588e-11+3.08625349e-10j,\n", " 7.63238077e-11-1.46336863e-10j, ...,\n", " 1.15193186e-14-1.81437181e-13j, -1.21823774e-13+1.25575543e-13j,\n", " 2.68503600e-14+6.82965584e-15j],\n", " [-4.32857850e-10-3.61859337e-10j, 4.70934913e-10-1.20136627e-10j,\n", " -1.31343395e-11+1.25999723e-10j, ...,\n", " -3.00292308e-13-2.17749579e-13j, 1.64397728e-13-4.27592123e-14j,\n", " -2.80084815e-14+1.08537469e-13j],\n", " [ 8.24399932e-11-4.68078003e-10j, -1.81195763e-10-8.34900678e-11j,\n", " 1.11277791e-10-5.20690939e-11j, ...,\n", " 2.01639036e-13+5.84642545e-14j, -1.51389614e-13+4.86360942e-14j,\n", " 6.43984652e-14-3.82770840e-14j]])\n", "Coordinates:\n", " * time (time) datetime64[ns] 71kB 2020-06-02T22:24:55 ... 2020-06-12T...\n", " * frequency (frequency) float64 504B 0.007812 0.01562 ... 0.4844 0.4922\n", "Attributes:\n", " component: ex\n", " frequency_max: 0.4921875\n", " frequency_min: 0.0\n", " hdf5_reference: \n", " mth5_type: FCChannel\n", " sample_rate_decimation_level: 1.0\n", " sample_rate_window_step: 96.0\n", " time_period.end: 2020-06-12T17:49:43+00:00\n", " time_period.start: 2020-06-02T22:24:55+00:00\n", " units: counts" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ex = stft_obj.ex\n", "ex = ex.dropna(dim=\"frequency\")\n", "ex" ] }, { "cell_type": "code", "execution_count": 41, "id": "90473a26-579b-4ea9-98b1-c89a3994b05f", "metadata": {}, "outputs": [], "source": [ "ex = np.abs(ex)" ] }, { "cell_type": "code", "execution_count": 42, "id": "a2fb4c9e-1f74-40b0-9778-5f35e304010b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array(['2020-06-02T22:24:55.000000000', '2020-06-02T22:26:31.000000000',\n", " '2020-06-02T22:28:07.000000000', ...,\n", " '2020-06-12T17:46:31.000000000', '2020-06-12T17:48:07.000000000',\n", " '2020-06-12T17:49:43.000000000'], dtype='datetime64[ns]')" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "ex.time.data" ] }, { "cell_type": "markdown", "id": "28798f8d-38df-43b7-a424-44127042b3c6", "metadata": {}, "source": [ "Plotting spectrograms with dates:\n", "\n", "The cell below was adapted from:\n", "\n", "https://stackoverflow.com/questions/23139595/dates-in-the-xaxis-for-a-matplotlib-plot-with-imshow" ] }, { "cell_type": "code", "execution_count": 43, "id": "8a699e1a-0880-4f5e-85b3-5672eed2c2e9", "metadata": { "tags": [] }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "import matplotlib.dates as mdates\n", "\n", "import datetime as dt\n", "\n", "x_lims = [ex.time.data[0], ex.time.data[-1]]\n", "\n", "# You can then convert these datetime.datetime objects to the correct\n", "# format for matplotlib to work with.\n", "x_lims = mdates.date2num(x_lims)\n", "\n", "# Set y-limits.\n", "y_lims = [ex.frequency.data[0], ex.frequency.data[-1]]\n", "\n", "fig, ax = plt.subplots()\n", "\n", "# Using ax.imshow we set two keyword arguments. The first is extent.\n", "# We give extent the values from x_lims and y_lims above.\n", "# We also set the aspect to \"auto\" which should set the plot up nicely.\n", "ax.imshow(np.log10(ex.T), extent = [x_lims[0], x_lims[1], y_lims[0], y_lims[1]], \n", " aspect='auto', origin='lower' )\n", "\n", "# # We tell Matplotlib that the x-axis is filled with datetime data, \n", "# # this converts it from a float (which is the output of date2num) \n", "# # into a nice datetime string.\n", "ax.xaxis_date()\n", "\n", "# # We can use a DateFormatter to choose how this datetime string will look.\n", "# # I have chosen HH:MM:SS though you could add DD/MM/YY if you had data\n", "# # over different days.\n", "date_format = mdates.DateFormatter('%Y-%m-%d')# %H:%M:%S')\n", "\n", "ax.xaxis.set_major_formatter(date_format)\n", "\n", "# # This simply sets the x-axis data to diagonal so it fits better.\n", "fig.autofmt_xdate()\n", "ax.set_ylabel(\"Frequency (Hz)\")\n", "ax.set_xlabel(\"Time\")\n", "ax.set_title(f\"log_{10} Amplitude Spectrogram for {station_id}, run {run_id}\")\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "eedbbc32-b08b-4be0-a26c-18efdd72f475", "metadata": {}, "source": [ "# Absolute Minimal Example\n", "\n", "- This is the code from Figure 3 in the JOSS manuscript intended to show that the processing can be run in 8 lines including saving results to `edi` file format." ] }, { "cell_type": "code", "execution_count": null, "id": "52f879f8-3743-4966-8452-3369c942d703", "metadata": {}, "outputs": [], "source": [ "from aurora.config.config_creator import ConfigCreator\n", "from aurora.pipelines.process_mth5 import process_mth5\n", "from mth5.processing import KernelDataset, RunSummary" ] }, { "cell_type": "code", "execution_count": 45, "id": "7aaf67a8-2bd3-4637-8f3b-fc58d3254a97", "metadata": { "tags": [] }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[1m24:09:03T20:10:12 | INFO | line:771 |mth5.mth5 | close_mth5 | Flushing and closing 8P_CAS04_NVR08.h5\u001b[0m\n", "\u001b[1m24:09:03T20:10:12 | INFO | line:250 |mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column fc, adding and setting dtype to .\u001b[0m\n", "\u001b[1m24:09:03T20:10:12 | INFO | line:250 |mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column remote, adding and setting dtype to .\u001b[0m\n", "\u001b[1m24:09:03T20:10:12 | INFO | line:250 |mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column run_dataarray, adding and setting dtype to .\u001b[0m\n", "\u001b[1m24:09:03T20:10:12 | INFO | line:250 |mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column stft, adding and setting dtype to .\u001b[0m\n", "\u001b[1m24:09:03T20:10:12 | INFO | line:250 |mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column mth5_obj, adding and setting dtype to .\u001b[0m\n", "\u001b[1m24:09:03T20:10:12 | INFO | line:108 |aurora.config.config_creator | determine_band_specification_style | Bands not defined; setting to EMTF BANDS_DEFAULT_FILE\u001b[0m\n", "\u001b[31m\u001b[1m24:09:03T20:10:12 | ERROR | line:50 |aurora.time_series.window_helpers | available_number_of_windows_in_array | Window is longer than the time series -- no complete windows can be returned\u001b[0m\n", "\u001b[31m\u001b[1m24:09:03T20:10:12 | ERROR | line:50 |aurora.time_series.window_helpers | available_number_of_windows_in_array | Window is longer than the time series -- no complete windows can be returned\u001b[0m\n", "\u001b[1m24:09:03T20:10:12 | INFO | line:277 |aurora.pipelines.transfer_function_kernel | show_processing_summary | Processing Summary Dataframe:\u001b[0m\n", "\u001b[1m24:09:03T20:10:12 | INFO | line:278 |aurora.pipelines.transfer_function_kernel | show_processing_summary | \n", " duration has_data n_samples run station survey run_hdf5_reference station_hdf5_reference fc remote stft mth5_obj dec_level dec_factor sample_rate window_duration num_samples_window num_samples num_stft_windows\n", "0 2860.0 True 847649 b CAS04 CONUS South False False None None 0 1.0 1.000000 128.0 128 2860.0 29.0\n", "1 2860.0 True 847649 b CAS04 CONUS South False False None None 1 4.0 0.250000 512.0 128 715.0 7.0\n", "2 2860.0 True 847649 b CAS04 CONUS South False False None None 2 4.0 0.062500 2048.0 128 178.0 1.0\n", "3 2860.0 True 847649 b CAS04 CONUS South False False None None 3 4.0 0.015625 8192.0 128 44.0 0.0\n", "4 769090.0 True 847649 b CAS04 CONUS South False False None None 0 1.0 1.000000 128.0 128 769090.0 8011.0\n", "5 769090.0 True 847649 b CAS04 CONUS South False False None None 1 4.0 0.250000 512.0 128 192272.0 2002.0\n", "6 769090.0 True 847649 b CAS04 CONUS South False False None None 2 4.0 0.062500 2048.0 128 48068.0 500.0\n", "7 769090.0 True 847649 b CAS04 CONUS South False False None None 3 4.0 0.015625 8192.0 128 12017.0 124.0\n", "8 167025.0 True 1638043 c CAS04 CONUS South False False None None 0 1.0 1.000000 128.0 128 167025.0 1739.0\n", "9 167025.0 True 1638043 c CAS04 CONUS South False False None None 1 4.0 0.250000 512.0 128 41756.0 434.0\n", "10 167025.0 True 1638043 c CAS04 CONUS South False False None None 2 4.0 0.062500 2048.0 128 10439.0 108.0\n", "11 167025.0 True 1638043 c CAS04 CONUS South False False None None 3 4.0 0.015625 8192.0 128 2609.0 26.0\n", "12 856502.0 True 1638043 c CAS04 CONUS South False False None None 0 1.0 1.000000 128.0 128 856502.0 8921.0\n", "13 856502.0 True 1638043 c CAS04 CONUS South False False None None 1 4.0 0.250000 512.0 128 214125.0 2230.0\n", "14 856502.0 True 1638043 c CAS04 CONUS South False False None None 2 4.0 0.062500 2048.0 128 53531.0 557.0\n", "15 856502.0 True 1638043 c CAS04 CONUS South False False None None 3 4.0 0.015625 8192.0 128 13382.0 139.0\n", "16 2860.0 True 2861 a NVR08 CONUS South False True None None 0 1.0 1.000000 128.0 128 2860.0 29.0\n", "17 2860.0 True 2861 a NVR08 CONUS South False True None None 1 4.0 0.250000 512.0 128 715.0 7.0\n", "18 2860.0 True 2861 a NVR08 CONUS South False True None None 2 4.0 0.062500 2048.0 128 178.0 1.0\n", "19 2860.0 True 2861 a NVR08 CONUS South False True None None 3 4.0 0.015625 8192.0 128 44.0 0.0\n", "20 769090.0 True 938510 b NVR08 CONUS South False True None None 0 1.0 1.000000 128.0 128 769090.0 8011.0\n", "21 769090.0 True 938510 b NVR08 CONUS South False True None None 1 4.0 0.250000 512.0 128 192272.0 2002.0\n", "22 769090.0 True 938510 b NVR08 CONUS South False True None None 2 4.0 0.062500 2048.0 128 48068.0 500.0\n", "23 769090.0 True 938510 b NVR08 CONUS South False True None None 3 4.0 0.015625 8192.0 128 12017.0 124.0\n", "24 167025.0 True 938510 b NVR08 CONUS South False True None None 0 1.0 1.000000 128.0 128 167025.0 1739.0\n", "25 167025.0 True 938510 b NVR08 CONUS South False True None None 1 4.0 0.250000 512.0 128 41756.0 434.0\n", "26 167025.0 True 938510 b NVR08 CONUS South False True None None 2 4.0 0.062500 2048.0 128 10439.0 108.0\n", "27 167025.0 True 938510 b NVR08 CONUS South False True None None 3 4.0 0.015625 8192.0 128 2609.0 26.0\n", "28 856502.0 True 856503 c NVR08 CONUS South False True None None 0 1.0 1.000000 128.0 128 856502.0 8921.0\n", "29 856502.0 True 856503 c NVR08 CONUS South False True None None 1 4.0 0.250000 512.0 128 214125.0 2230.0\n", "30 856502.0 True 856503 c NVR08 CONUS South False True None None 2 4.0 0.062500 2048.0 128 53531.0 557.0\n", "31 856502.0 True 856503 c NVR08 CONUS South False True None None 3 4.0 0.015625 8192.0 128 13382.0 139.0\u001b[0m\n", "\u001b[1m24:09:03T20:10:12 | INFO | line:654 |aurora.pipelines.transfer_function_kernel | memory_check | Total memory: 62.74 GB\u001b[0m\n", "\u001b[1m24:09:03T20:10:12 | INFO | line:658 |aurora.pipelines.transfer_function_kernel | memory_check | Total Bytes of Raw Data: 0.027 GB\u001b[0m\n", "\u001b[1m24:09:03T20:10:12 | INFO | line:661 |aurora.pipelines.transfer_function_kernel | memory_check | Raw Data will use: 0.043 % of memory\u001b[0m\n", "\u001b[1m24:09:03T20:10:12 | INFO | line:517 |aurora.pipelines.process_mth5 | process_mth5_legacy | Processing config indicates 4 decimation levels\u001b[0m\n", "\u001b[1m24:09:03T20:10:12 | INFO | line:445 |aurora.pipelines.transfer_function_kernel | valid_decimations | After validation there are 4 valid decimation levels\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:10:13 | WARNING | line:645 |mth5.timeseries.run_ts | validate_metadata | start time of dataset 2020-06-03T19:10:11+00:00 does not match metadata start 2020-06-02T22:24:55+00:00 updating metatdata value to 2020-06-03T19:10:11+00:00\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:10:13 | WARNING | line:658 |mth5.timeseries.run_ts | validate_metadata | end time of dataset 2020-06-03T19:57:51+00:00 does not match metadata end 2020-06-12T17:52:23+00:00 updating metatdata value to 2020-06-03T19:57:51+00:00\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:10:14 | WARNING | line:645 |mth5.timeseries.run_ts | validate_metadata | start time of dataset 2020-06-03T20:14:13+00:00 does not match metadata start 2020-06-02T22:24:55+00:00 updating metatdata value to 2020-06-03T20:14:13+00:00\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:10:16 | WARNING | line:658 |mth5.timeseries.run_ts | validate_metadata | end time of dataset 2020-06-12T17:52:23+00:00 does not match metadata end 2020-06-14T16:56:02+00:00 updating metatdata value to 2020-06-12T17:52:23+00:00\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:10:16 | WARNING | line:658 |mth5.timeseries.run_ts | validate_metadata | end time of dataset 2020-06-14T16:56:02+00:00 does not match metadata end 2020-07-01T17:32:59+00:00 updating metatdata value to 2020-06-14T16:56:02+00:00\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:10:17 | WARNING | line:645 |mth5.timeseries.run_ts | validate_metadata | start time of dataset 2020-06-12T18:32:17+00:00 does not match metadata start 2020-06-03T20:14:13+00:00 updating metatdata value to 2020-06-12T18:32:17+00:00\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:10:18 | WARNING | line:645 |mth5.timeseries.run_ts | validate_metadata | start time of dataset 2020-06-14T18:00:44+00:00 does not match metadata start 2020-06-12T18:32:17+00:00 updating metatdata value to 2020-06-14T18:00:44+00:00\u001b[0m\n", "\u001b[33m\u001b[1m24:09:03T20:10:18 | WARNING | line:658 |mth5.timeseries.run_ts | validate_metadata | end time of dataset 2020-06-24T15:55:46+00:00 does not match metadata end 2020-07-01T17:32:59+00:00 updating metatdata value to 2020-06-24T15:55:46+00:00\u001b[0m\n", "\u001b[1m24:09:03T20:10:20 | INFO | line:889 |mtpy.processing.kernel_dataset | initialize_dataframe_for_processing | Dataset dataframe initialized successfully\u001b[0m\n", "\u001b[1m24:09:03T20:10:20 | INFO | line:143 |aurora.pipelines.transfer_function_kernel | update_dataset_df | Dataset Dataframe Updated for decimation level 0 Successfully\u001b[0m\n", "\u001b[1m24:09:03T20:10:20 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:10:20 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:10:21 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:10:23 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:10:23 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:10:24 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:10:25 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:10:26 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:10:26 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 25.728968s (0.038867Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:10:26 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 19.929573s (0.050177Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:10:27 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 15.164131s (0.065945Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:10:27 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 11.746086s (0.085135Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:10:28 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 9.195791s (0.108745Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:10:28 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 7.362526s (0.135823Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:10:29 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 5.856115s (0.170762Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:10:29 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 4.682492s (0.213562Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:10:30 | INFO | line:124 |aurora.pipelines.transfer_function_kernel | update_dataset_df | DECIMATION LEVEL 1\u001b[0m\n", "\u001b[1m24:09:03T20:10:31 | INFO | line:143 |aurora.pipelines.transfer_function_kernel | update_dataset_df | Dataset Dataframe Updated for decimation level 1 Successfully\u001b[0m\n", "\u001b[1m24:09:03T20:10:31 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:10:31 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:10:32 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:10:32 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:10:33 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:10:33 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:10:34 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:10:34 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:10:34 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 102.915872s (0.009717Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:10:34 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 85.631182s (0.011678Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:10:35 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 68.881694s (0.014518Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:10:35 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 54.195827s (0.018452Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:10:35 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 43.003958s (0.023254Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:10:35 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 33.310722s (0.030020Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:10:35 | INFO | line:124 |aurora.pipelines.transfer_function_kernel | update_dataset_df | DECIMATION LEVEL 2\u001b[0m\n", "\u001b[1m24:09:03T20:10:36 | INFO | line:143 |aurora.pipelines.transfer_function_kernel | update_dataset_df | Dataset Dataframe Updated for decimation level 2 Successfully\u001b[0m\n", "\u001b[1m24:09:03T20:10:36 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:10:36 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:10:37 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:10:37 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:10:37 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:10:38 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:10:38 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 411.663489s (0.002429Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:10:38 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 342.524727s (0.002919Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:10:38 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 275.526776s (0.003629Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:10:38 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 216.783308s (0.004613Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:10:38 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 172.015831s (0.005813Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:10:38 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 133.242890s (0.007505Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:10:38 | INFO | line:124 |aurora.pipelines.transfer_function_kernel | update_dataset_df | DECIMATION LEVEL 3\u001b[0m\n", "\u001b[1m24:09:03T20:10:39 | INFO | line:143 |aurora.pipelines.transfer_function_kernel | update_dataset_df | Dataset Dataframe Updated for decimation level 3 Successfully\u001b[0m\n", "\u001b[1m24:09:03T20:10:39 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:10:39 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:10:39 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:10:40 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:10:40 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:10:40 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", "\u001b[1m24:09:03T20:10:40 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 1514.701336s (0.000660Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:10:40 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 1042.488956s (0.000959Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:10:40 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 723.371271s (0.001382Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:10:41 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 532.971560s (0.001876Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:10:41 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 412.837995s (0.002422Hz)\u001b[0m\n", "\u001b[1m24:09:03T20:10:41 | INFO | line:771 |mth5.mth5 | close_mth5 | Flushing and closing 8P_CAS04_NVR08.h5\u001b[0m\n", "\u001b[1m24:09:03T20:10:41 | INFO | line:771 |mth5.mth5 | close_mth5 | Flushing and closing 8P_CAS04_NVR08.h5\u001b[0m\n" ] }, { "data": { "text/plain": [ "Station: CAS04\n", "--------------------------------------------------\n", "\tSurvey: CONUS South\n", "\tProject: USMTArray\n", "\tAcquired by: None\n", "\tAcquired date: 2020-06-02\n", "\tLatitude: 37.633\n", "\tLongitude: -121.468\n", "\tElevation: 335.262\n", "\tImpedance: True\n", "\tTipper: True\n", "\tNumber of periods: 25\n", "\t\tPeriod Range: 4.68249E+00 -- 1.51470E+03 s\n", "\t\tFrequency Range 6.60196E-04 -- 2.13561E-01 s" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "run_summary = RunSummary()\n", "run_summary.from_mth5s([\"8P_CAS04_NVR08.h5\",])\n", "kernel_dataset = KernelDataset()\n", "kernel_dataset.from_run_summary(run_summary, \"CAS04\", \"NVR08\")\n", "cc = ConfigCreator()\n", "config = cc.create_from_kernel_dataset(kernel_dataset) \n", "tf = process_mth5(config, kernel_dataset)\n", "tf.write(fn=\"CAS04_rrNVR08.edi\", file_type=\"edi\")" ] } ], "metadata": { "kernelspec": { "display_name": "aurora-test", "language": "python", "name": "aurora-test" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.19" } }, "nbformat": 4, "nbformat_minor": 5 }