From ffca4300e09378ec35e649f8c469526b3321d39f Mon Sep 17 00:00:00 2001 From: "Karl N. Kappler" Date: Sat, 5 Oct 2024 15:02:24 -0700 Subject: [PATCH] minor fixes to new syntax --- notebooks/aurora/07_lemi_magdelena.ipynb | 2739 +++++++++++++++++++--- 1 file changed, 2357 insertions(+), 382 deletions(-) diff --git a/notebooks/aurora/07_lemi_magdelena.ipynb b/notebooks/aurora/07_lemi_magdelena.ipynb index 02bd78c..47bc042 100644 --- a/notebooks/aurora/07_lemi_magdelena.ipynb +++ b/notebooks/aurora/07_lemi_magdelena.ipynb @@ -25,7 +25,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "2022-10-16 17:46:39,705 [line 135] mth5.setup_logger - INFO: Logging file can be found /home/kkappler/software/irismt/mth5/logs/mth5_debug.log\n" + "/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" ] } ], @@ -43,8 +44,7 @@ "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 aurora.transfer_function.kernel_dataset import KernelDataset\n", - "from aurora.pipelines.run_summary import RunSummary\n", + "from mtpy.processing import KernelDataset, RunSummary\n", "\n", "warnings.filterwarnings('ignore')" ] @@ -75,8 +75,8 @@ } ], "source": [ - "here = Path(\".\")\n", - "data_dir = here.joinpath(\"../../data/time_series/lemi\")\n", + "here = Path(\".\").absolute()\n", + "data_dir = here.parent.parent.joinpath(\"data/time_series/lemi\")\n", "mth5_path = data_dir.joinpath(\"from_lemi.h5\")\n", "\n", "mth5_path.exists()" @@ -84,7 +84,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 3, "id": "4bda57f3-8533-4158-82cf-8512ce11d083", "metadata": {}, "outputs": [], @@ -94,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 4, "id": "137670a2-064e-4bcf-b743-02bff866ef9c", "metadata": {}, "outputs": [ @@ -102,7 +102,7 @@ "name": "stdout", "output_type": "stream", "text": [ - " Filename: ../../data/time_series/lemi/from_lemi.h5 \n", + " Filename: /home/kkappler/software/irismt/earthscope-mt-course/data/time_series/lemi/from_lemi.h5 \n", " Version: 0.2.0\n" ] } @@ -116,31 +116,17 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "195ed2ca-0f18-4a47-bd40-856ce13aa017", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2022-10-16 17:47:56,972 [line 241] mth5.tables.mth5_table.ChannelSummaryTable.add_row - ERROR: Data types are not equal:\n", - "Input dtypes:\n", - "[('survey', 'S30'), ('station', 'S30'), ('run', 'S20'), ('latitude', 'azimuth\n", " tilt\n", " units\n", + " has_data\n", " hdf5_reference\n", " run_hdf5_reference\n", " station_hdf5_reference\n", @@ -188,44 +175,929 @@ " \n", " \n", " 0\n", - " \n", - " \n", - " \n", + " iris_test\n", + " mt001\n", + " sr1_0001\n", + " 34.080655\n", + " -107.214079\n", + " 2202.8\n", + " 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<HDF5 object reference>\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", " \n", " \n", "\n", "" ], "text/plain": [ - " survey station run latitude longitude elevation component start end \\\n", - "0 0.0 0.0 0.0 NaT NaT \n", + " survey station run latitude longitude elevation \\\n", + "0 iris_test mt001 sr1_0001 34.080655 -107.214079 2202.8 \n", + "1 iris_test mt001 sr1_0001 34.080655 -107.214079 2202.8 \n", + "2 iris_test mt001 sr1_0001 34.080655 -107.214079 2202.8 \n", + "3 iris_test mt001 sr1_0001 34.080655 -107.214079 2202.8 \n", + "4 iris_test mt001 sr1_0001 34.080655 -107.214079 2202.8 \n", + "5 iris_test mt001 sr1_0001 34.080655 -107.214079 2202.8 \n", + "6 iris_test mt001 sr1_0001 34.080655 -107.214079 2202.8 \n", + "7 iris_test mt001 sr1_0002 34.080655 -107.214079 2202.8 \n", + "8 iris_test mt001 sr1_0002 34.080655 -107.214079 2202.8 \n", + "9 iris_test mt001 sr1_0002 34.080655 -107.214079 2202.8 \n", + "10 iris_test mt001 sr1_0002 34.080655 -107.214079 2202.8 \n", + "11 iris_test mt001 sr1_0002 34.080655 -107.214079 2202.8 \n", + "12 iris_test mt001 sr1_0002 34.080655 -107.214079 2202.8 \n", + "13 iris_test mt001 sr1_0002 34.080655 -107.214079 2202.8 \n", + "14 iris_test mt001 sr1_0003 34.080655 -107.214079 2202.8 \n", + "15 iris_test mt001 sr1_0003 34.080655 -107.214079 2202.8 \n", + "16 iris_test mt001 sr1_0003 34.080655 -107.214079 2202.8 \n", + "17 iris_test mt001 sr1_0003 34.080655 -107.214079 2202.8 \n", + "18 iris_test mt001 sr1_0003 34.080655 -107.214079 2202.8 \n", + "19 iris_test mt001 sr1_0003 34.080655 -107.214079 2202.8 \n", + "20 iris_test mt001 sr1_0003 34.080655 -107.214079 2202.8 \n", + "21 iris_test mt001 sr1_0004 34.080655 -107.214079 2202.8 \n", + "22 iris_test mt001 sr1_0004 34.080655 -107.214079 2202.8 \n", + "23 iris_test mt001 sr1_0004 34.080655 -107.214079 2202.8 \n", + "24 iris_test mt001 sr1_0004 34.080655 -107.214079 2202.8 \n", + "25 iris_test mt001 sr1_0004 34.080655 -107.214079 2202.8 \n", + "26 iris_test mt001 sr1_0004 34.080655 -107.214079 2202.8 \n", + "27 iris_test mt001 sr1_0004 34.080655 -107.214079 2202.8 \n", + "28 iris_test mt001 sr1_0005 34.080655 -107.214079 2202.8 \n", + "29 iris_test mt001 sr1_0005 34.080655 -107.214079 2202.8 \n", + "30 iris_test mt001 sr1_0005 34.080655 -107.214079 2202.8 \n", + "31 iris_test mt001 sr1_0005 34.080655 -107.214079 2202.8 \n", + "32 iris_test mt001 sr1_0005 34.080655 -107.214079 2202.8 \n", + "33 iris_test mt001 sr1_0005 34.080655 -107.214079 2202.8 \n", + "34 iris_test mt001 sr1_0005 34.080655 -107.214079 2202.8 \n", "\n", - " n_samples sample_rate measurement_type azimuth tilt units \\\n", - "0 0 0.0 0.0 0.0 \n", + " component start end \\\n", + "0 bx 2020-09-30 20:21:00+00:00 2020-09-30 20:28:15+00:00 \n", + "1 by 2020-09-30 20:21:00+00:00 2020-09-30 20:28:15+00:00 \n", + "2 bz 2020-09-30 20:21:00+00:00 2020-09-30 20:28:15+00:00 \n", + "3 e1 2020-09-30 20:21:00+00:00 2020-09-30 20:28:15+00:00 \n", + "4 e2 2020-09-30 20:21:00+00:00 2020-09-30 20:28:15+00:00 \n", + "5 temperature_e 2020-09-30 20:21:00+00:00 2020-09-30 20:28:15+00:00 \n", + "6 temperature_h 2020-09-30 20:21:00+00:00 2020-09-30 20:28:15+00:00 \n", + "7 bx 2020-09-30 20:29:00+00:00 2020-09-30 20:42:16+00:00 \n", + "8 by 2020-09-30 20:29:00+00:00 2020-09-30 20:42:16+00:00 \n", + "9 bz 2020-09-30 20:29:00+00:00 2020-09-30 20:42:16+00:00 \n", + "10 e1 2020-09-30 20:29:00+00:00 2020-09-30 20:42:16+00:00 \n", + "11 e2 2020-09-30 20:29:00+00:00 2020-09-30 20:42:16+00:00 \n", + "12 temperature_e 2020-09-30 20:29:00+00:00 2020-09-30 20:42:16+00:00 \n", + "13 temperature_h 2020-09-30 20:29:00+00:00 2020-09-30 20:42:16+00:00 \n", + "14 bx 2020-09-30 20:54:00+00:00 2020-09-30 21:11:01+00:00 \n", + "15 by 2020-09-30 20:54:00+00:00 2020-09-30 21:11:01+00:00 \n", + "16 bz 2020-09-30 20:54:00+00:00 2020-09-30 21:11:01+00:00 \n", + "17 e1 2020-09-30 20:54:00+00:00 2020-09-30 21:11:01+00:00 \n", + "18 e2 2020-09-30 20:54:00+00:00 2020-09-30 21:11:01+00:00 \n", + "19 temperature_e 2020-09-30 20:54:00+00:00 2020-09-30 21:11:01+00:00 \n", + "20 temperature_h 2020-09-30 20:54:00+00:00 2020-09-30 21:11:01+00:00 \n", + "21 bx 2020-09-30 21:12:00+00:00 2020-09-30 21:13:45+00:00 \n", + "22 by 2020-09-30 21:12:00+00:00 2020-09-30 21:13:45+00:00 \n", + "23 bz 2020-09-30 21:12:00+00:00 2020-09-30 21:13:45+00:00 \n", + "24 e1 2020-09-30 21:12:00+00:00 2020-09-30 21:13:45+00:00 \n", + "25 e2 2020-09-30 21:12:00+00:00 2020-09-30 21:13:45+00:00 \n", + "26 temperature_e 2020-09-30 21:12:00+00:00 2020-09-30 21:13:45+00:00 \n", + "27 temperature_h 2020-09-30 21:12:00+00:00 2020-09-30 21:13:45+00:00 \n", + "28 bx 2020-09-30 21:14:00+00:00 2020-10-07 17:05:46+00:00 \n", + "29 by 2020-09-30 21:14:00+00:00 2020-10-07 17:05:46+00:00 \n", + "30 bz 2020-09-30 21:14:00+00:00 2020-10-07 17:05:46+00:00 \n", + "31 e1 2020-09-30 21:14:00+00:00 2020-10-07 17:05:46+00:00 \n", + "32 e2 2020-09-30 21:14:00+00:00 2020-10-07 17:05:46+00:00 \n", + "33 temperature_e 2020-09-30 21:14:00+00:00 2020-10-07 17:05:46+00:00 \n", + "34 temperature_h 2020-09-30 21:14:00+00:00 2020-10-07 17:05:46+00:00 \n", "\n", - " hdf5_reference run_hdf5_reference \\\n", - "0 \n", + " n_samples sample_rate measurement_type azimuth tilt units has_data \\\n", + "0 436 1.0 magnetic 0.0 0.0 none True \n", + "1 436 1.0 magnetic 0.0 0.0 none True \n", + "2 436 1.0 magnetic 0.0 0.0 none True \n", + "3 436 1.0 electric 0.0 0.0 none True \n", + "4 436 1.0 electric 0.0 0.0 none True \n", + "5 436 1.0 auxiliary 0.0 0.0 none True \n", + "6 436 1.0 auxiliary 0.0 0.0 none True \n", + "7 797 1.0 magnetic 0.0 0.0 none True \n", + "8 797 1.0 magnetic 0.0 0.0 none True \n", + "9 797 1.0 magnetic 0.0 0.0 none True \n", + "10 797 1.0 electric 0.0 0.0 none True \n", + "11 797 1.0 electric 0.0 0.0 none True \n", + "12 797 1.0 auxiliary 0.0 0.0 none True \n", + "13 797 1.0 auxiliary 0.0 0.0 none True \n", + "14 1022 1.0 magnetic 0.0 0.0 none True \n", + "15 1022 1.0 magnetic 0.0 0.0 none True \n", + "16 1022 1.0 magnetic 0.0 0.0 none True \n", + "17 1022 1.0 electric 0.0 0.0 none True \n", + "18 1022 1.0 electric 0.0 0.0 none True \n", + "19 1022 1.0 auxiliary 0.0 0.0 none True \n", + "20 1022 1.0 auxiliary 0.0 0.0 none True \n", + "21 106 1.0 magnetic 0.0 0.0 none True \n", + "22 106 1.0 magnetic 0.0 0.0 none True \n", + "23 106 1.0 magnetic 0.0 0.0 none True \n", + "24 106 1.0 electric 0.0 0.0 none True \n", + "25 106 1.0 electric 0.0 0.0 none True \n", + "26 106 1.0 auxiliary 0.0 0.0 none True \n", + "27 106 1.0 auxiliary 0.0 0.0 none True \n", + "28 589907 1.0 magnetic 0.0 0.0 none True \n", + "29 589907 1.0 magnetic 0.0 0.0 none True \n", + "30 589907 1.0 magnetic 0.0 0.0 none True \n", + "31 589907 1.0 electric 0.0 0.0 none True \n", + "32 589907 1.0 electric 0.0 0.0 none True \n", + "33 589907 1.0 auxiliary 0.0 0.0 none True \n", + "34 589907 1.0 auxiliary 0.0 0.0 none True \n", "\n", - " station_hdf5_reference \n", - "0 " + " 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": 10, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -237,43 +1109,10 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "id": "e9e7074b-b102-4dc4-bb4a-434049bad9cf", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2022-10-16 17:47:31,880 [line 241] mth5.tables.mth5_table.ChannelSummaryTable.add_row - ERROR: Data types are not equal:\n", - "Input dtypes:\n", - "[('survey', 'S30'), ('station', 'S30'), ('run', 'S20'), ('latitude', '\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mmth5_object\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchannel_summary\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mclear_table\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mchannel_summary\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmth5_object\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchannel_summary\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msummarize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mchannel_summary_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmth5_object\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchannel_summary\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_dataframe\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/software/irismt/mth5/mth5/tables/channel_table.py\u001b[0m in \u001b[0;36msummarize\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 105\u001b[0m \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 106\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 107\u001b[0;31m \u001b[0mrecursive_get_channel_entry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparent\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[0;32m~/software/irismt/mth5/mth5/tables/channel_table.py\u001b[0m in \u001b[0;36mrecursive_get_channel_entry\u001b[0;34m(group)\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgroup\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0mrecursive_get_channel_entry\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnode\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 73\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgroup\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mh5py\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/software/irismt/mth5/mth5/tables/channel_table.py\u001b[0m in \u001b[0;36mrecursive_get_channel_entry\u001b[0;34m(group)\u001b[0m\n\u001b[1;32m 70\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgroup\u001b[0m\u001b[0;34m,\u001b[0m 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\u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mCHANNEL_DTYPE\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 101\u001b[0m )\n\u001b[0;32m--> 102\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_row\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mch_entry\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 104\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/software/irismt/mth5/mth5/tables/mth5_table.py\u001b[0m in \u001b[0;36madd_row\u001b[0;34m(self, row, index)\u001b[0m\n\u001b[1;32m 240\u001b[0m )\n\u001b[1;32m 241\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 242\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 243\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 244\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: Data types are not equal:\nInput dtypes:\n[('survey', 'S30'), ('station', 'S30'), ('run', 'S20'), ('latitude', 'azimuth\n", " tilt\n", " units\n", + " has_data\n", " hdf5_reference\n", " run_hdf5_reference\n", " station_hdf5_reference\n", " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " 0\n", + " iris_test\n", + " mt001\n", + " sr1_0001\n", + " 34.080655\n", + " -107.214079\n", + " 2202.8\n", + " bx\n", + " 2020-09-30 20:21:00+00:00\n", + " 2020-09-30 20:28:15+00:00\n", + " 436\n", + " 1.0\n", + " magnetic\n", + " 0.0\n", + " 0.0\n", + " none\n", + " True\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " \n", + " \n", + " 1\n", + " iris_test\n", + " mt001\n", + " sr1_0001\n", + " 34.080655\n", + " -107.214079\n", + " 2202.8\n", + " by\n", + " 2020-09-30 20:21:00+00:00\n", + " 2020-09-30 20:28:15+00:00\n", + " 436\n", + " 1.0\n", + " magnetic\n", + " 0.0\n", + " 0.0\n", + " none\n", + " True\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " \n", + " \n", + " 2\n", + " iris_test\n", + " mt001\n", + " sr1_0001\n", + " 34.080655\n", + " -107.214079\n", + " 2202.8\n", + " bz\n", + " 2020-09-30 20:21:00+00:00\n", + " 2020-09-30 20:28:15+00:00\n", + " 436\n", + " 1.0\n", + " magnetic\n", + " 0.0\n", + " 0.0\n", + " none\n", + " True\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " \n", + " \n", + " 3\n", + " iris_test\n", + " mt001\n", + " sr1_0001\n", + " 34.080655\n", + " -107.214079\n", + " 2202.8\n", + " e1\n", + " 2020-09-30 20:21:00+00:00\n", + " 2020-09-30 20:28:15+00:00\n", + " 436\n", + " 1.0\n", + " electric\n", + " 0.0\n", + " 0.0\n", + " none\n", + " True\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " \n", + " \n", + " 4\n", + " iris_test\n", + " mt001\n", + " sr1_0001\n", + " 34.080655\n", + " -107.214079\n", + " 2202.8\n", + " e2\n", + " 2020-09-30 20:21:00+00:00\n", + " 2020-09-30 20:28:15+00:00\n", + " 436\n", + " 1.0\n", + " electric\n", + " 0.0\n", + " 0.0\n", + " none\n", + " True\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " \n", + " \n", + " 5\n", + " iris_test\n", + " mt001\n", + " sr1_0001\n", + " 34.080655\n", + " -107.214079\n", + " 2202.8\n", + " temperature_e\n", + " 2020-09-30 20:21:00+00:00\n", + " 2020-09-30 20:28:15+00:00\n", + " 436\n", + " 1.0\n", + " auxiliary\n", + " 0.0\n", + " 0.0\n", + " none\n", + " True\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " \n", + " \n", + " 6\n", + " iris_test\n", + " mt001\n", + " sr1_0001\n", + " 34.080655\n", + " -107.214079\n", + " 2202.8\n", + " temperature_h\n", + " 2020-09-30 20:21:00+00:00\n", + " 2020-09-30 20:28:15+00:00\n", + " 436\n", + " 1.0\n", + " auxiliary\n", + " 0.0\n", + " 0.0\n", + " none\n", + " True\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " \n", + " \n", + " 7\n", + " iris_test\n", + " mt001\n", + " sr1_0002\n", + " 34.080655\n", + " -107.214079\n", + " 2202.8\n", + " bx\n", + " 2020-09-30 20:29:00+00:00\n", + " 2020-09-30 20:42:16+00:00\n", + " 797\n", + " 1.0\n", + " magnetic\n", + " 0.0\n", + " 0.0\n", + " none\n", + " True\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " \n", + " \n", + " 8\n", + " iris_test\n", + " mt001\n", + " sr1_0002\n", + " 34.080655\n", + " -107.214079\n", + " 2202.8\n", + " by\n", + " 2020-09-30 20:29:00+00:00\n", + " 2020-09-30 20:42:16+00:00\n", + " 797\n", + " 1.0\n", + " magnetic\n", + " 0.0\n", + " 0.0\n", + " none\n", + " True\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " \n", + " \n", + " 9\n", + " iris_test\n", + " mt001\n", + " sr1_0002\n", + " 34.080655\n", + " -107.214079\n", + " 2202.8\n", + " bz\n", + " 2020-09-30 20:29:00+00:00\n", + " 2020-09-30 20:42:16+00:00\n", + " 797\n", + " 1.0\n", + " magnetic\n", + " 0.0\n", + " 0.0\n", + " none\n", + " True\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " \n", + " \n", + " 10\n", + " iris_test\n", + " mt001\n", + " sr1_0002\n", + " 34.080655\n", + " -107.214079\n", + " 2202.8\n", + " e1\n", + " 2020-09-30 20:29:00+00:00\n", + " 2020-09-30 20:42:16+00:00\n", + " 797\n", + " 1.0\n", + " electric\n", + " 0.0\n", + " 0.0\n", + " none\n", + " True\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " \n", + " \n", + " 11\n", + " iris_test\n", + " mt001\n", + " sr1_0002\n", + " 34.080655\n", + " -107.214079\n", + " 2202.8\n", + " e2\n", + " 2020-09-30 20:29:00+00:00\n", + " 2020-09-30 20:42:16+00:00\n", + " 797\n", + " 1.0\n", + " electric\n", + " 0.0\n", + " 0.0\n", + " none\n", + " True\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " \n", " \n", - " 0\n", - " yellowstone\n", - " wb280\n", - " sr1024_0001\n", - " 44.147916\n", - " -111.049752\n", - " 1954.239\n", - " ex\n", - " 2017-07-01 02:19:59+00:00\n", - " 2017-07-03 20:19:42+00:00\n", - " 243284992\n", - " 1024.0\n", + " 12\n", + " iris_test\n", + " mt001\n", + " sr1_0002\n", + " 34.080655\n", + " -107.214079\n", + " 2202.8\n", + " temperature_e\n", + " 2020-09-30 20:29:00+00:00\n", + " 2020-09-30 20:42:16+00:00\n", + " 797\n", + " 1.0\n", + " auxiliary\n", + " 0.0\n", + " 0.0\n", + " none\n", + " True\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " \n", + " \n", + " 13\n", + " iris_test\n", + " mt001\n", + " sr1_0002\n", + " 34.080655\n", + " -107.214079\n", + " 2202.8\n", + " temperature_h\n", + " 2020-09-30 20:29:00+00:00\n", + " 2020-09-30 20:42:16+00:00\n", + " 797\n", + " 1.0\n", + " auxiliary\n", + " 0.0\n", + " 0.0\n", + " none\n", + " True\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " \n", + " \n", + " 14\n", + " iris_test\n", + " mt001\n", + " sr1_0003\n", + " 34.080655\n", + " -107.214079\n", + " 2202.8\n", + " bx\n", + " 2020-09-30 20:54:00+00:00\n", + " 2020-09-30 21:11:01+00:00\n", + " 1022\n", + " 1.0\n", + " magnetic\n", + " 0.0\n", + " 0.0\n", + " none\n", + " True\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " \n", + " \n", + " 15\n", + " iris_test\n", + " mt001\n", + " sr1_0003\n", + " 34.080655\n", + " -107.214079\n", + " 2202.8\n", + " by\n", + " 2020-09-30 20:54:00+00:00\n", + " 2020-09-30 21:11:01+00:00\n", + " 1022\n", + " 1.0\n", + " magnetic\n", + " 0.0\n", + " 0.0\n", + " none\n", + " True\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " \n", + " \n", + " 16\n", + " iris_test\n", + " mt001\n", + " sr1_0003\n", + " 34.080655\n", + " -107.214079\n", + " 2202.8\n", + " bz\n", + " 2020-09-30 20:54:00+00:00\n", + " 2020-09-30 21:11:01+00:00\n", + " 1022\n", + " 1.0\n", + " magnetic\n", + " 0.0\n", + " 0.0\n", + " none\n", + " True\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " \n", + " \n", + " 17\n", + " iris_test\n", + " mt001\n", + " sr1_0003\n", + " 34.080655\n", + " -107.214079\n", + " 2202.8\n", + " e1\n", + " 2020-09-30 20:54:00+00:00\n", + " 2020-09-30 21:11:01+00:00\n", + " 1022\n", + " 1.0\n", " electric\n", " 0.0\n", " 0.0\n", - " digital counts\n", + " none\n", + " True\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", " \n", " \n", - " 1\n", - " yellowstone\n", - " wb280\n", - " sr1024_0001\n", - " 44.147916\n", - " -111.049752\n", - " 1954.239\n", - " ey\n", - " 2017-07-01 02:19:59+00:00\n", - " 2017-07-03 20:19:42+00:00\n", - " 243284992\n", - " 1024.0\n", + " 18\n", + " iris_test\n", + " mt001\n", + " sr1_0003\n", + " 34.080655\n", + " -107.214079\n", + " 2202.8\n", + " e2\n", + " 2020-09-30 20:54:00+00:00\n", + " 2020-09-30 21:11:01+00:00\n", + " 1022\n", + " 1.0\n", " electric\n", - " 90.0\n", " 0.0\n", - " digital counts\n", + " 0.0\n", + " none\n", + " True\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", " \n", " \n", - " 2\n", - " yellowstone\n", - " wb280\n", - " sr1024_0001\n", - " 44.147916\n", - " -111.049752\n", - " 1954.239\n", - " hx\n", - " 2017-07-01 02:19:59+00:00\n", - " 2017-07-03 20:19:41.997070+00:00\n", - " 243284989\n", - " 1024.0\n", + " 19\n", + " iris_test\n", + " mt001\n", + " sr1_0003\n", + " 34.080655\n", + " -107.214079\n", + " 2202.8\n", + " temperature_e\n", + " 2020-09-30 20:54:00+00:00\n", + " 2020-09-30 21:11:01+00:00\n", + " 1022\n", + " 1.0\n", + " auxiliary\n", + " 0.0\n", + " 0.0\n", + " none\n", + " True\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " \n", + " \n", + " 20\n", + " iris_test\n", + " mt001\n", + " sr1_0003\n", + " 34.080655\n", + " -107.214079\n", + " 2202.8\n", + " temperature_h\n", + " 2020-09-30 20:54:00+00:00\n", + " 2020-09-30 21:11:01+00:00\n", + " 1022\n", + " 1.0\n", + " auxiliary\n", + " 0.0\n", + " 0.0\n", + " none\n", + " True\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " \n", + " \n", + " 21\n", + " iris_test\n", + " mt001\n", + " sr1_0004\n", + " 34.080655\n", + " -107.214079\n", + " 2202.8\n", + " bx\n", + " 2020-09-30 21:12:00+00:00\n", + " 2020-09-30 21:13:45+00:00\n", + " 106\n", + " 1.0\n", " magnetic\n", " 0.0\n", " 0.0\n", - " digital counts\n", + " none\n", + " True\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", " \n", " \n", - " 3\n", - " yellowstone\n", - " wb280\n", - " sr1024_0001\n", - " 44.147916\n", - " -111.049752\n", - " 1954.239\n", - " hy\n", - " 2017-07-01 02:19:59+00:00\n", - " 2017-07-03 20:19:41.999023+00:00\n", - " 243284991\n", - " 1024.0\n", + " 22\n", + " iris_test\n", + " mt001\n", + " sr1_0004\n", + " 34.080655\n", + " -107.214079\n", + " 2202.8\n", + " by\n", + " 2020-09-30 21:12:00+00:00\n", + " 2020-09-30 21:13:45+00:00\n", + " 106\n", + " 1.0\n", " magnetic\n", - " 90.0\n", " 0.0\n", - " digital counts\n", + " 0.0\n", + " none\n", + " True\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", " \n", " \n", - " 4\n", - " yellowstone\n", - " wb280\n", - " sr1024_0001\n", - " 44.147916\n", - " -111.049752\n", - " 1954.239\n", - " hz\n", - " 2017-07-01 02:19:59+00:00\n", - " 2017-07-03 20:19:42+00:00\n", - " 243284992\n", - " 1024.0\n", + " 23\n", + " iris_test\n", + " mt001\n", + " sr1_0004\n", + " 34.080655\n", + " -107.214079\n", + " 2202.8\n", + " bz\n", + " 2020-09-30 21:12:00+00:00\n", + " 2020-09-30 21:13:45+00:00\n", + " 106\n", + " 1.0\n", " magnetic\n", - " 90.0\n", " 0.0\n", - " digital counts\n", + " 0.0\n", + " none\n", + " True\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", " \n", " \n", - " 5\n", - " yellowstone\n", - " wb380\n", - " sr1024_0001\n", - " 44.291193\n", - " -110.614549\n", - " 2392.466\n", - " ex\n", - " 2017-07-02 03:01:00+00:00\n", - " 2017-07-05 16:24:42+00:00\n", - " 314800128\n", - " 1024.0\n", + " 24\n", + " iris_test\n", + " mt001\n", + " sr1_0004\n", + " 34.080655\n", + " -107.214079\n", + " 2202.8\n", + " e1\n", + " 2020-09-30 21:12:00+00:00\n", + " 2020-09-30 21:13:45+00:00\n", + " 106\n", + " 1.0\n", " electric\n", " 0.0\n", " 0.0\n", - " digital counts\n", + " none\n", + " True\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", " \n", " \n", - " 6\n", - " yellowstone\n", - " wb380\n", - " sr1024_0001\n", - " 44.291193\n", - " -110.614549\n", - " 2392.466\n", - " ey\n", - " 2017-07-02 03:01:00+00:00\n", - " 2017-07-05 16:24:42+00:00\n", - " 314800128\n", - " 1024.0\n", + " 25\n", + " iris_test\n", + " mt001\n", + " sr1_0004\n", + " 34.080655\n", + " -107.214079\n", + " 2202.8\n", + " e2\n", + " 2020-09-30 21:12:00+00:00\n", + " 2020-09-30 21:13:45+00:00\n", + " 106\n", + " 1.0\n", " electric\n", - " 90.0\n", " 0.0\n", - " digital counts\n", + " 0.0\n", + " none\n", + " True\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", " \n", " \n", - " 7\n", - " yellowstone\n", - " wb380\n", - " sr1024_0001\n", - " 44.291193\n", - " -110.614549\n", - " 2392.466\n", - " hx\n", - " 2017-07-02 03:01:00+00:00\n", - " 2017-07-05 16:24:42+00:00\n", - " 314800128\n", - " 1024.0\n", + " 26\n", + " iris_test\n", + " mt001\n", + " sr1_0004\n", + " 34.080655\n", + " -107.214079\n", + " 2202.8\n", + " temperature_e\n", + " 2020-09-30 21:12:00+00:00\n", + " 2020-09-30 21:13:45+00:00\n", + " 106\n", + " 1.0\n", + " auxiliary\n", + " 0.0\n", + " 0.0\n", + " none\n", + " True\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " \n", + " \n", + " 27\n", + " iris_test\n", + " mt001\n", + " sr1_0004\n", + " 34.080655\n", + " -107.214079\n", + " 2202.8\n", + " temperature_h\n", + " 2020-09-30 21:12:00+00:00\n", + " 2020-09-30 21:13:45+00:00\n", + " 106\n", + " 1.0\n", + " auxiliary\n", + " 0.0\n", + " 0.0\n", + " none\n", + " True\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " \n", + " \n", + " 28\n", + " iris_test\n", + " mt001\n", + " sr1_0005\n", + " 34.080655\n", + " -107.214079\n", + " 2202.8\n", + " bx\n", + " 2020-09-30 21:14:00+00:00\n", + " 2020-10-07 17:05:46+00:00\n", + " 589907\n", + " 1.0\n", " magnetic\n", " 0.0\n", " 0.0\n", - " digital counts\n", + " none\n", + " True\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", " \n", " \n", - " 8\n", - " yellowstone\n", - " wb380\n", - " sr1024_0001\n", - " 44.291193\n", - " -110.614549\n", - " 2392.466\n", - " hy\n", - " 2017-07-02 03:01:00+00:00\n", - " 2017-07-05 16:24:42+00:00\n", - " 314800128\n", - " 1024.0\n", + " 29\n", + " iris_test\n", + " mt001\n", + " sr1_0005\n", + " 34.080655\n", + " -107.214079\n", + " 2202.8\n", + " by\n", + " 2020-09-30 21:14:00+00:00\n", + " 2020-10-07 17:05:46+00:00\n", + " 589907\n", + " 1.0\n", " magnetic\n", - " 90.0\n", " 0.0\n", - " digital counts\n", + " 0.0\n", + " none\n", + " True\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", " \n", " \n", - " 9\n", - " yellowstone\n", - " wb380\n", - " sr1024_0001\n", - " 44.291193\n", - " -110.614549\n", - " 2392.466\n", - " hz\n", - " 2017-07-02 03:01:00+00:00\n", - " 2017-07-05 16:24:41+00:00\n", - " 314799104\n", - " 1024.0\n", + " 30\n", + " iris_test\n", + " mt001\n", + " sr1_0005\n", + " 34.080655\n", + " -107.214079\n", + " 2202.8\n", + " bz\n", + " 2020-09-30 21:14:00+00:00\n", + " 2020-10-07 17:05:46+00:00\n", + " 589907\n", + " 1.0\n", " magnetic\n", - " 90.0\n", " 0.0\n", - " digital counts\n", + " 0.0\n", + " none\n", + " True\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " \n", + " \n", + " 31\n", + " iris_test\n", + " mt001\n", + " sr1_0005\n", + " 34.080655\n", + " -107.214079\n", + " 2202.8\n", + " e1\n", + " 2020-09-30 21:14:00+00:00\n", + " 2020-10-07 17:05:46+00:00\n", + " 589907\n", + " 1.0\n", + " electric\n", + " 0.0\n", + " 0.0\n", + " none\n", + " True\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " \n", + " \n", + " 32\n", + " iris_test\n", + " mt001\n", + " sr1_0005\n", + " 34.080655\n", + " -107.214079\n", + " 2202.8\n", + " e2\n", + " 2020-09-30 21:14:00+00:00\n", + " 2020-10-07 17:05:46+00:00\n", + " 589907\n", + " 1.0\n", + " electric\n", + " 0.0\n", + " 0.0\n", + " none\n", + " True\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " \n", + " \n", + " 33\n", + " iris_test\n", + " mt001\n", + " sr1_0005\n", + " 34.080655\n", + " -107.214079\n", + " 2202.8\n", + " temperature_e\n", + " 2020-09-30 21:14:00+00:00\n", + " 2020-10-07 17:05:46+00:00\n", + " 589907\n", + " 1.0\n", + " auxiliary\n", + " 0.0\n", + " 0.0\n", + " none\n", + " True\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " \n", + " \n", + " 34\n", + " iris_test\n", + " mt001\n", + " sr1_0005\n", + " 34.080655\n", + " -107.214079\n", + " 2202.8\n", + " temperature_h\n", + " 2020-09-30 21:14:00+00:00\n", + " 2020-10-07 17:05:46+00:00\n", + " 589907\n", + " 1.0\n", + " auxiliary\n", + " 0.0\n", + " 0.0\n", + " none\n", + " True\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", @@ -543,56 +1943,156 @@ "" ], "text/plain": [ - " survey station run latitude longitude elevation \\\n", - "0 yellowstone wb280 sr1024_0001 44.147916 -111.049752 1954.239 \n", - "1 yellowstone wb280 sr1024_0001 44.147916 -111.049752 1954.239 \n", - "2 yellowstone wb280 sr1024_0001 44.147916 -111.049752 1954.239 \n", - "3 yellowstone wb280 sr1024_0001 44.147916 -111.049752 1954.239 \n", - "4 yellowstone wb280 sr1024_0001 44.147916 -111.049752 1954.239 \n", - "5 yellowstone wb380 sr1024_0001 44.291193 -110.614549 2392.466 \n", - "6 yellowstone wb380 sr1024_0001 44.291193 -110.614549 2392.466 \n", - "7 yellowstone wb380 sr1024_0001 44.291193 -110.614549 2392.466 \n", - "8 yellowstone wb380 sr1024_0001 44.291193 -110.614549 2392.466 \n", - "9 yellowstone wb380 sr1024_0001 44.291193 -110.614549 2392.466 \n", + " survey station run latitude longitude elevation \\\n", + "0 iris_test mt001 sr1_0001 34.080655 -107.214079 2202.8 \n", + "1 iris_test mt001 sr1_0001 34.080655 -107.214079 2202.8 \n", + "2 iris_test mt001 sr1_0001 34.080655 -107.214079 2202.8 \n", + "3 iris_test mt001 sr1_0001 34.080655 -107.214079 2202.8 \n", + "4 iris_test mt001 sr1_0001 34.080655 -107.214079 2202.8 \n", + "5 iris_test mt001 sr1_0001 34.080655 -107.214079 2202.8 \n", + "6 iris_test mt001 sr1_0001 34.080655 -107.214079 2202.8 \n", + "7 iris_test mt001 sr1_0002 34.080655 -107.214079 2202.8 \n", + "8 iris_test mt001 sr1_0002 34.080655 -107.214079 2202.8 \n", + "9 iris_test mt001 sr1_0002 34.080655 -107.214079 2202.8 \n", + "10 iris_test mt001 sr1_0002 34.080655 -107.214079 2202.8 \n", + "11 iris_test mt001 sr1_0002 34.080655 -107.214079 2202.8 \n", + "12 iris_test mt001 sr1_0002 34.080655 -107.214079 2202.8 \n", + "13 iris_test mt001 sr1_0002 34.080655 -107.214079 2202.8 \n", + "14 iris_test mt001 sr1_0003 34.080655 -107.214079 2202.8 \n", + "15 iris_test mt001 sr1_0003 34.080655 -107.214079 2202.8 \n", + "16 iris_test mt001 sr1_0003 34.080655 -107.214079 2202.8 \n", + "17 iris_test mt001 sr1_0003 34.080655 -107.214079 2202.8 \n", + "18 iris_test mt001 sr1_0003 34.080655 -107.214079 2202.8 \n", + "19 iris_test mt001 sr1_0003 34.080655 -107.214079 2202.8 \n", + "20 iris_test mt001 sr1_0003 34.080655 -107.214079 2202.8 \n", + "21 iris_test mt001 sr1_0004 34.080655 -107.214079 2202.8 \n", + "22 iris_test mt001 sr1_0004 34.080655 -107.214079 2202.8 \n", + "23 iris_test mt001 sr1_0004 34.080655 -107.214079 2202.8 \n", + "24 iris_test mt001 sr1_0004 34.080655 -107.214079 2202.8 \n", + "25 iris_test mt001 sr1_0004 34.080655 -107.214079 2202.8 \n", + "26 iris_test mt001 sr1_0004 34.080655 -107.214079 2202.8 \n", + "27 iris_test mt001 sr1_0004 34.080655 -107.214079 2202.8 \n", + "28 iris_test mt001 sr1_0005 34.080655 -107.214079 2202.8 \n", + "29 iris_test mt001 sr1_0005 34.080655 -107.214079 2202.8 \n", + "30 iris_test mt001 sr1_0005 34.080655 -107.214079 2202.8 \n", + "31 iris_test mt001 sr1_0005 34.080655 -107.214079 2202.8 \n", + "32 iris_test mt001 sr1_0005 34.080655 -107.214079 2202.8 \n", + "33 iris_test mt001 sr1_0005 34.080655 -107.214079 2202.8 \n", + "34 iris_test mt001 sr1_0005 34.080655 -107.214079 2202.8 \n", "\n", - " component start end \\\n", - "0 ex 2017-07-01 02:19:59+00:00 2017-07-03 20:19:42+00:00 \n", - "1 ey 2017-07-01 02:19:59+00:00 2017-07-03 20:19:42+00:00 \n", - "2 hx 2017-07-01 02:19:59+00:00 2017-07-03 20:19:41.997070+00:00 \n", - "3 hy 2017-07-01 02:19:59+00:00 2017-07-03 20:19:41.999023+00:00 \n", - "4 hz 2017-07-01 02:19:59+00:00 2017-07-03 20:19:42+00:00 \n", - "5 ex 2017-07-02 03:01:00+00:00 2017-07-05 16:24:42+00:00 \n", - "6 ey 2017-07-02 03:01:00+00:00 2017-07-05 16:24:42+00:00 \n", - "7 hx 2017-07-02 03:01:00+00:00 2017-07-05 16:24:42+00:00 \n", - "8 hy 2017-07-02 03:01:00+00:00 2017-07-05 16:24:42+00:00 \n", - "9 hz 2017-07-02 03:01:00+00:00 2017-07-05 16:24:41+00:00 \n", + " component start end \\\n", + "0 bx 2020-09-30 20:21:00+00:00 2020-09-30 20:28:15+00:00 \n", + "1 by 2020-09-30 20:21:00+00:00 2020-09-30 20:28:15+00:00 \n", + "2 bz 2020-09-30 20:21:00+00:00 2020-09-30 20:28:15+00:00 \n", + "3 e1 2020-09-30 20:21:00+00:00 2020-09-30 20:28:15+00:00 \n", + "4 e2 2020-09-30 20:21:00+00:00 2020-09-30 20:28:15+00:00 \n", + "5 temperature_e 2020-09-30 20:21:00+00:00 2020-09-30 20:28:15+00:00 \n", + "6 temperature_h 2020-09-30 20:21:00+00:00 2020-09-30 20:28:15+00:00 \n", + "7 bx 2020-09-30 20:29:00+00:00 2020-09-30 20:42:16+00:00 \n", + "8 by 2020-09-30 20:29:00+00:00 2020-09-30 20:42:16+00:00 \n", + "9 bz 2020-09-30 20:29:00+00:00 2020-09-30 20:42:16+00:00 \n", + "10 e1 2020-09-30 20:29:00+00:00 2020-09-30 20:42:16+00:00 \n", + "11 e2 2020-09-30 20:29:00+00:00 2020-09-30 20:42:16+00:00 \n", + "12 temperature_e 2020-09-30 20:29:00+00:00 2020-09-30 20:42:16+00:00 \n", + "13 temperature_h 2020-09-30 20:29:00+00:00 2020-09-30 20:42:16+00:00 \n", + "14 bx 2020-09-30 20:54:00+00:00 2020-09-30 21:11:01+00:00 \n", + "15 by 2020-09-30 20:54:00+00:00 2020-09-30 21:11:01+00:00 \n", + "16 bz 2020-09-30 20:54:00+00:00 2020-09-30 21:11:01+00:00 \n", + "17 e1 2020-09-30 20:54:00+00:00 2020-09-30 21:11:01+00:00 \n", + "18 e2 2020-09-30 20:54:00+00:00 2020-09-30 21:11:01+00:00 \n", + "19 temperature_e 2020-09-30 20:54:00+00:00 2020-09-30 21:11:01+00:00 \n", + "20 temperature_h 2020-09-30 20:54:00+00:00 2020-09-30 21:11:01+00:00 \n", + "21 bx 2020-09-30 21:12:00+00:00 2020-09-30 21:13:45+00:00 \n", + "22 by 2020-09-30 21:12:00+00:00 2020-09-30 21:13:45+00:00 \n", + "23 bz 2020-09-30 21:12:00+00:00 2020-09-30 21:13:45+00:00 \n", + "24 e1 2020-09-30 21:12:00+00:00 2020-09-30 21:13:45+00:00 \n", + "25 e2 2020-09-30 21:12:00+00:00 2020-09-30 21:13:45+00:00 \n", + "26 temperature_e 2020-09-30 21:12:00+00:00 2020-09-30 21:13:45+00:00 \n", + "27 temperature_h 2020-09-30 21:12:00+00:00 2020-09-30 21:13:45+00:00 \n", + "28 bx 2020-09-30 21:14:00+00:00 2020-10-07 17:05:46+00:00 \n", + "29 by 2020-09-30 21:14:00+00:00 2020-10-07 17:05:46+00:00 \n", + "30 bz 2020-09-30 21:14:00+00:00 2020-10-07 17:05:46+00:00 \n", + "31 e1 2020-09-30 21:14:00+00:00 2020-10-07 17:05:46+00:00 \n", + "32 e2 2020-09-30 21:14:00+00:00 2020-10-07 17:05:46+00:00 \n", + "33 temperature_e 2020-09-30 21:14:00+00:00 2020-10-07 17:05:46+00:00 \n", + "34 temperature_h 2020-09-30 21:14:00+00:00 2020-10-07 17:05:46+00:00 \n", "\n", - " n_samples sample_rate measurement_type azimuth tilt units \\\n", - "0 243284992 1024.0 electric 0.0 0.0 digital counts \n", - "1 243284992 1024.0 electric 90.0 0.0 digital counts \n", - "2 243284989 1024.0 magnetic 0.0 0.0 digital counts \n", - "3 243284991 1024.0 magnetic 90.0 0.0 digital counts \n", - "4 243284992 1024.0 magnetic 90.0 0.0 digital counts \n", - "5 314800128 1024.0 electric 0.0 0.0 digital counts \n", - "6 314800128 1024.0 electric 90.0 0.0 digital counts \n", - "7 314800128 1024.0 magnetic 0.0 0.0 digital counts \n", - "8 314800128 1024.0 magnetic 90.0 0.0 digital counts \n", - "9 314799104 1024.0 magnetic 90.0 0.0 digital counts \n", + " n_samples sample_rate measurement_type azimuth tilt units has_data \\\n", + "0 436 1.0 magnetic 0.0 0.0 none True \n", + "1 436 1.0 magnetic 0.0 0.0 none True \n", + "2 436 1.0 magnetic 0.0 0.0 none True \n", + "3 436 1.0 electric 0.0 0.0 none True \n", + "4 436 1.0 electric 0.0 0.0 none True \n", + "5 436 1.0 auxiliary 0.0 0.0 none True \n", + "6 436 1.0 auxiliary 0.0 0.0 none True \n", + "7 797 1.0 magnetic 0.0 0.0 none True \n", + "8 797 1.0 magnetic 0.0 0.0 none True \n", + "9 797 1.0 magnetic 0.0 0.0 none True \n", + "10 797 1.0 electric 0.0 0.0 none True \n", + "11 797 1.0 electric 0.0 0.0 none True \n", + "12 797 1.0 auxiliary 0.0 0.0 none True \n", + "13 797 1.0 auxiliary 0.0 0.0 none True \n", + "14 1022 1.0 magnetic 0.0 0.0 none True \n", + "15 1022 1.0 magnetic 0.0 0.0 none True \n", + "16 1022 1.0 magnetic 0.0 0.0 none True \n", + "17 1022 1.0 electric 0.0 0.0 none True \n", + "18 1022 1.0 electric 0.0 0.0 none True \n", + "19 1022 1.0 auxiliary 0.0 0.0 none True \n", + "20 1022 1.0 auxiliary 0.0 0.0 none True \n", + "21 106 1.0 magnetic 0.0 0.0 none True \n", + "22 106 1.0 magnetic 0.0 0.0 none True \n", + "23 106 1.0 magnetic 0.0 0.0 none True \n", + "24 106 1.0 electric 0.0 0.0 none True \n", + "25 106 1.0 electric 0.0 0.0 none True \n", + "26 106 1.0 auxiliary 0.0 0.0 none True \n", + "27 106 1.0 auxiliary 0.0 0.0 none True \n", + "28 589907 1.0 magnetic 0.0 0.0 none True \n", + "29 589907 1.0 magnetic 0.0 0.0 none True \n", + "30 589907 1.0 magnetic 0.0 0.0 none True \n", + "31 589907 1.0 electric 0.0 0.0 none True \n", + "32 589907 1.0 electric 0.0 0.0 none True \n", + "33 589907 1.0 auxiliary 0.0 0.0 none True \n", + "34 589907 1.0 auxiliary 0.0 0.0 none 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 " + " 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": 6, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -611,28 +2111,27 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 9, "id": "d43c8acf-fc00-4f5b-b1ca-a5cf811878e9", "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "2022-10-16 14:25:47,001 [line 753] mth5.mth5.MTH5.close_mth5 - INFO: Flushing and closing ../../data/time_series/zen/from_z3d.h5\n" + "\u001b[1m24:10:05T14:59:56 | INFO | line:777 |mth5.mth5 | close_mth5 | Flushing and closing /home/kkappler/software/irismt/earthscope-mt-course/data/time_series/lemi/from_lemi.h5\u001b[0m\n" ] } ], "source": [ "mth5_run_summary = RunSummary()\n", "mth5_run_summary.from_mth5s([mth5_path,])\n", - "run_summary = mth5_run_summary.clone()\n", - "run_summary.add_duration()" + "run_summary = mth5_run_summary.clone()\n" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "id": "024834f3-1345-4195-9674-812d026ec197", "metadata": {}, "outputs": [ @@ -657,71 +2156,156 @@ " \n", " \n", " \n", - " survey\n", - " station_id\n", - " run_id\n", - " start\n", + " channel_scale_factors\n", + " duration\n", " end\n", - " sample_rate\n", + " has_data\n", " input_channels\n", - " output_channels\n", - " channel_scale_factors\n", " mth5_path\n", - " duration\n", + " n_samples\n", + " output_channels\n", + " run\n", + " sample_rate\n", + " start\n", + " station\n", + " survey\n", + " run_hdf5_reference\n", + " station_hdf5_reference\n", " \n", " \n", " \n", " \n", " 0\n", - " yellowstone\n", - " wb280\n", - " sr1024_0001\n", - " 2017-07-01 02:19:59+00:00\n", - " 2017-07-03 20:19:42+00:00\n", - " 1024.0\n", - " [hx, hy]\n", - " [ex, ey, hz]\n", - " {'ex': 1.0, 'ey': 1.0, 'hx': 1.0, 'hy': 1.0, '...\n", - " ../../data/time_series/zen/from_z3d.h5\n", - " 237583.0\n", + " {'bx': 1.0, 'by': 1.0, 'bz': 1.0, 'e1': 1.0, '...\n", + " 435.0\n", + " 2020-09-30 20:28:15+00:00\n", + " True\n", + " [bx, by]\n", + " /home/kkappler/software/irismt/earthscope-mt-c...\n", + " 436\n", + " [bz, e1, e2]\n", + " sr1_0001\n", + " 1.0\n", + " 2020-09-30 20:21:00+00:00\n", + " mt001\n", + " iris_test\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", " \n", " \n", " 1\n", - " yellowstone\n", - " wb380\n", - " sr1024_0001\n", - " 2017-07-02 03:01:00+00:00\n", - " 2017-07-05 16:24:42+00:00\n", - " 1024.0\n", - " [hx, hy]\n", - " [ex, ey, hz]\n", - " {'ex': 1.0, 'ey': 1.0, 'hx': 1.0, 'hy': 1.0, '...\n", - " ../../data/time_series/zen/from_z3d.h5\n", - " 307422.0\n", + " {'bx': 1.0, 'by': 1.0, 'bz': 1.0, 'e1': 1.0, '...\n", + " 796.0\n", + " 2020-09-30 20:42:16+00:00\n", + " True\n", + " [bx, by]\n", + " /home/kkappler/software/irismt/earthscope-mt-c...\n", + " 797\n", + " [bz, e1, e2]\n", + " sr1_0002\n", + " 1.0\n", + " 2020-09-30 20:29:00+00:00\n", + " mt001\n", + " iris_test\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " \n", + " \n", + " 2\n", + " {'bx': 1.0, 'by': 1.0, 'bz': 1.0, 'e1': 1.0, '...\n", + " 1021.0\n", + " 2020-09-30 21:11:01+00:00\n", + " True\n", + " [bx, by]\n", + " /home/kkappler/software/irismt/earthscope-mt-c...\n", + " 1022\n", + " [bz, e1, e2]\n", + " sr1_0003\n", + " 1.0\n", + " 2020-09-30 20:54:00+00:00\n", + " mt001\n", + " iris_test\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " \n", + " \n", + " 3\n", + " {'bx': 1.0, 'by': 1.0, 'bz': 1.0, 'e1': 1.0, '...\n", + " 105.0\n", + " 2020-09-30 21:13:45+00:00\n", + " True\n", + " [bx, by]\n", + " /home/kkappler/software/irismt/earthscope-mt-c...\n", + " 106\n", + " [bz, e1, e2]\n", + " sr1_0004\n", + " 1.0\n", + " 2020-09-30 21:12:00+00:00\n", + " mt001\n", + " iris_test\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", + " \n", + " \n", + " 4\n", + " {'bx': 1.0, 'by': 1.0, 'bz': 1.0, 'e1': 1.0, '...\n", + " 589906.0\n", + " 2020-10-07 17:05:46+00:00\n", + " True\n", + " [bx, by]\n", + " /home/kkappler/software/irismt/earthscope-mt-c...\n", + " 589907\n", + " [bz, e1, e2]\n", + " sr1_0005\n", + " 1.0\n", + " 2020-09-30 21:14:00+00:00\n", + " mt001\n", + " iris_test\n", + " <HDF5 object reference>\n", + " <HDF5 object reference>\n", " \n", " \n", "\n", "" ], "text/plain": [ - " survey station_id run_id start \\\n", - "0 yellowstone wb280 sr1024_0001 2017-07-01 02:19:59+00:00 \n", - "1 yellowstone wb380 sr1024_0001 2017-07-02 03:01:00+00:00 \n", + " channel_scale_factors duration \\\n", + "0 {'bx': 1.0, 'by': 1.0, 'bz': 1.0, 'e1': 1.0, '... 435.0 \n", + "1 {'bx': 1.0, 'by': 1.0, 'bz': 1.0, 'e1': 1.0, '... 796.0 \n", + "2 {'bx': 1.0, 'by': 1.0, 'bz': 1.0, 'e1': 1.0, '... 1021.0 \n", + "3 {'bx': 1.0, 'by': 1.0, 'bz': 1.0, 'e1': 1.0, '... 105.0 \n", + "4 {'bx': 1.0, 'by': 1.0, 'bz': 1.0, 'e1': 1.0, '... 589906.0 \n", "\n", - " end sample_rate input_channels output_channels \\\n", - "0 2017-07-03 20:19:42+00:00 1024.0 [hx, hy] [ex, ey, hz] \n", - "1 2017-07-05 16:24:42+00:00 1024.0 [hx, hy] [ex, ey, hz] \n", + " end has_data input_channels \\\n", + "0 2020-09-30 20:28:15+00:00 True [bx, by] \n", + "1 2020-09-30 20:42:16+00:00 True [bx, by] \n", + "2 2020-09-30 21:11:01+00:00 True [bx, by] \n", + "3 2020-09-30 21:13:45+00:00 True [bx, by] \n", + "4 2020-10-07 17:05:46+00:00 True [bx, by] \n", "\n", - " channel_scale_factors \\\n", - "0 {'ex': 1.0, 'ey': 1.0, 'hx': 1.0, 'hy': 1.0, '... \n", - "1 {'ex': 1.0, 'ey': 1.0, 'hx': 1.0, 'hy': 1.0, '... \n", + " mth5_path n_samples \\\n", + "0 /home/kkappler/software/irismt/earthscope-mt-c... 436 \n", + "1 /home/kkappler/software/irismt/earthscope-mt-c... 797 \n", + "2 /home/kkappler/software/irismt/earthscope-mt-c... 1022 \n", + "3 /home/kkappler/software/irismt/earthscope-mt-c... 106 \n", + "4 /home/kkappler/software/irismt/earthscope-mt-c... 589907 \n", "\n", - " mth5_path duration \n", - "0 ../../data/time_series/zen/from_z3d.h5 237583.0 \n", - "1 ../../data/time_series/zen/from_z3d.h5 307422.0 " + " output_channels run sample_rate start station \\\n", + "0 [bz, e1, e2] sr1_0001 1.0 2020-09-30 20:21:00+00:00 mt001 \n", + "1 [bz, e1, e2] sr1_0002 1.0 2020-09-30 20:29:00+00:00 mt001 \n", + "2 [bz, e1, e2] sr1_0003 1.0 2020-09-30 20:54:00+00:00 mt001 \n", + "3 [bz, e1, e2] sr1_0004 1.0 2020-09-30 21:12:00+00:00 mt001 \n", + "4 [bz, e1, e2] sr1_0005 1.0 2020-09-30 21:14:00+00:00 mt001 \n", + "\n", + " survey run_hdf5_reference station_hdf5_reference \n", + "0 iris_test \n", + "1 iris_test \n", + "2 iris_test \n", + "3 iris_test \n", + "4 iris_test " ] }, - "execution_count": 8, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -732,7 +2316,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "id": "38c2f8c7-5b52-47c1-8591-eb73df673b9e", "metadata": {}, "outputs": [ @@ -758,44 +2342,80 @@ " \n", " \n", " survey\n", - " station_id\n", - " run_id\n", + " station\n", + " run\n", " start\n", " end\n", + " duration\n", " \n", " \n", " \n", " \n", " 0\n", - " yellowstone\n", - " wb280\n", - " sr1024_0001\n", - " 2017-07-01 02:19:59+00:00\n", - " 2017-07-03 20:19:42+00:00\n", + " iris_test\n", + " mt001\n", + " sr1_0001\n", + " 2020-09-30 20:21:00+00:00\n", + " 2020-09-30 20:28:15+00:00\n", + " 435.0\n", " \n", " \n", " 1\n", - " yellowstone\n", - " wb380\n", - " sr1024_0001\n", - " 2017-07-02 03:01:00+00:00\n", - " 2017-07-05 16:24:42+00:00\n", + " iris_test\n", + " mt001\n", + " sr1_0002\n", + " 2020-09-30 20:29:00+00:00\n", + " 2020-09-30 20:42:16+00:00\n", + " 796.0\n", + " \n", + " \n", + " 2\n", + " iris_test\n", + " mt001\n", + " sr1_0003\n", + " 2020-09-30 20:54:00+00:00\n", + " 2020-09-30 21:11:01+00:00\n", + " 1021.0\n", + " \n", + " \n", + " 3\n", + " iris_test\n", + " mt001\n", + " sr1_0004\n", + " 2020-09-30 21:12:00+00:00\n", + " 2020-09-30 21:13:45+00:00\n", + " 105.0\n", + " \n", + " \n", + " 4\n", + " iris_test\n", + " mt001\n", + " sr1_0005\n", + " 2020-09-30 21:14:00+00:00\n", + " 2020-10-07 17:05:46+00:00\n", + " 589906.0\n", " \n", " \n", "\n", "" ], "text/plain": [ - " survey station_id run_id start \\\n", - "0 yellowstone wb280 sr1024_0001 2017-07-01 02:19:59+00:00 \n", - "1 yellowstone wb380 sr1024_0001 2017-07-02 03:01:00+00:00 \n", + " survey station run start \\\n", + "0 iris_test mt001 sr1_0001 2020-09-30 20:21:00+00:00 \n", + "1 iris_test mt001 sr1_0002 2020-09-30 20:29:00+00:00 \n", + "2 iris_test mt001 sr1_0003 2020-09-30 20:54:00+00:00 \n", + "3 iris_test mt001 sr1_0004 2020-09-30 21:12:00+00:00 \n", + "4 iris_test mt001 sr1_0005 2020-09-30 21:14:00+00:00 \n", "\n", - " end \n", - "0 2017-07-03 20:19:42+00:00 \n", - "1 2017-07-05 16:24:42+00:00 " + " end duration \n", + "0 2020-09-30 20:28:15+00:00 435.0 \n", + "1 2020-09-30 20:42:16+00:00 796.0 \n", + "2 2020-09-30 21:11:01+00:00 1021.0 \n", + "3 2020-09-30 21:13:45+00:00 105.0 \n", + "4 2020-10-07 17:05:46+00:00 589906.0 " ] }, - "execution_count": 9, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -806,21 +2426,32 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "id": "3ad5a787-7006-42e4-ae4d-457163b5a2d1", "metadata": {}, "outputs": [], "source": [ - "local_station_id = \"wb280\"\n", - "remote_station_id = \"wb380\"" + "local_station_id = \"mt001\"\n", + "remote_station_id = None" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "id": "137d96f6-2219-4c82-9432-de3a13442c05", "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m24:10:05T15:00:02 | INFO | line:262 |mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column fc, adding and setting dtype to .\u001b[0m\n", + "\u001b[1m24:10:05T15:00:02 | INFO | line:262 |mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column remote, adding and setting dtype to .\u001b[0m\n", + "\u001b[1m24:10:05T15:00:02 | INFO | line:262 |mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column run_dataarray, adding and setting dtype to .\u001b[0m\n", + "\u001b[1m24:10:05T15:00:02 | INFO | line:262 |mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column stft, adding and setting dtype to .\u001b[0m\n", + "\u001b[1m24:10:05T15:00:02 | INFO | line:262 |mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column mth5_obj, adding and setting dtype to .\u001b[0m\n" + ] + }, { "data": { "text/html": [ @@ -843,8 +2474,8 @@ " \n", " \n", " survey\n", - " station_id\n", - " run_id\n", + " station\n", + " run\n", " start\n", " end\n", " duration\n", @@ -853,37 +2484,70 @@ " \n", " \n", " 0\n", - " yellowstone\n", - " wb280\n", - " sr1024_0001\n", - " 2017-07-02 03:01:00+00:00\n", - " 2017-07-03 20:19:42+00:00\n", - " 148722.0\n", + " iris_test\n", + " mt001\n", + " sr1_0001\n", + " 2020-09-30 20:21:00+00:00\n", + " 2020-09-30 20:28:15+00:00\n", + " 435.0\n", " \n", " \n", " 1\n", - " yellowstone\n", - " wb380\n", - " sr1024_0001\n", - " 2017-07-02 03:01:00+00:00\n", - " 2017-07-03 20:19:42+00:00\n", - " 148722.0\n", + " iris_test\n", + " mt001\n", + " sr1_0002\n", + " 2020-09-30 20:29:00+00:00\n", + " 2020-09-30 20:42:16+00:00\n", + " 796.0\n", + " \n", + " \n", + " 2\n", + " iris_test\n", + " mt001\n", + " sr1_0003\n", + " 2020-09-30 20:54:00+00:00\n", + " 2020-09-30 21:11:01+00:00\n", + " 1021.0\n", + " \n", + " \n", + " 3\n", + " iris_test\n", + " mt001\n", + " sr1_0004\n", + " 2020-09-30 21:12:00+00:00\n", + " 2020-09-30 21:13:45+00:00\n", + " 105.0\n", + " \n", + " \n", + " 4\n", + " iris_test\n", + " mt001\n", + " sr1_0005\n", + " 2020-09-30 21:14:00+00:00\n", + " 2020-10-07 17:05:46+00:00\n", + " 589906.0\n", " \n", " \n", "\n", "" ], "text/plain": [ - " survey station_id run_id start \\\n", - "0 yellowstone wb280 sr1024_0001 2017-07-02 03:01:00+00:00 \n", - "1 yellowstone wb380 sr1024_0001 2017-07-02 03:01:00+00:00 \n", + " survey station run start \\\n", + "0 iris_test mt001 sr1_0001 2020-09-30 20:21:00+00:00 \n", + "1 iris_test mt001 sr1_0002 2020-09-30 20:29:00+00:00 \n", + "2 iris_test mt001 sr1_0003 2020-09-30 20:54:00+00:00 \n", + "3 iris_test mt001 sr1_0004 2020-09-30 21:12:00+00:00 \n", + "4 iris_test mt001 sr1_0005 2020-09-30 21:14:00+00:00 \n", "\n", " end duration \n", - "0 2017-07-03 20:19:42+00:00 148722.0 \n", - "1 2017-07-03 20:19:42+00:00 148722.0 " + "0 2020-09-30 20:28:15+00:00 435.0 \n", + "1 2020-09-30 20:42:16+00:00 796.0 \n", + "2 2020-09-30 21:11:01+00:00 1021.0 \n", + "3 2020-09-30 21:13:45+00:00 105.0 \n", + "4 2020-10-07 17:05:46+00:00 589906.0 " ] }, - "execution_count": 11, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -904,7 +2568,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "id": "4e897158-087e-4942-aad7-54b221d2c50a", "metadata": {}, "outputs": [ @@ -912,20 +2576,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "Bands not defined; setting to EMTF BANDS_DEFAULT_FILE\n", - "/home/kkappler/software/irismt/aurora/aurora/config/emtf_band_setup/bs_test.cfg\n", - "OK\n" + "\u001b[1m24:10:05T15:00:05 | 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)" + "config = cc.create_from_kernel_dataset(kernel_dataset)\n", + "config.channel_nomenclature.keyword = \"LEMI12\"" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "id": "ff6efa3b-23b6-4dab-b5ca-8c959a6bb671", "metadata": {}, "outputs": [], @@ -936,30 +2599,342 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "id": "367b5849-d933-4191-8e26-68e4f4e23f77", "metadata": {}, "outputs": [ { - "name": "stderr", + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[31m\u001b[1m24:10:05T15:00:08 | 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[1m24:10:05T15:00:08 | 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[1m24:10:05T15:00:08 | 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[1m24:10:05T15:00:08 | 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[1m24:10:05T15:00:08 | 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[1m24:10:05T15:00:08 | 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[1m24:10:05T15:00:08 | 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[1m24:10:05T15:00:08 | 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[1m24:10:05T15:00:08 | 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[1m24:10:05T15:00:08 | 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[1m24:10:05T15:00:08 | 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[1m24:10:05T15:00:08 | INFO | line:277 |aurora.pipelines.transfer_function_kernel | show_processing_summary | Processing Summary Dataframe:\u001b[0m\n", + "\u001b[1m24:10:05T15:00:08 | 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", + "0 435.0 True 436 sr1_0001 mt001 iris_test False None None 0 1.0 1.000000 128.0 128 435.0 4.0\n", + "1 435.0 True 436 sr1_0001 mt001 iris_test False None None 1 4.0 0.250000 512.0 128 108.0 0.0\n", + "2 435.0 True 436 sr1_0001 mt001 iris_test False None None 2 4.0 0.062500 2048.0 128 27.0 0.0\n", + "3 435.0 True 436 sr1_0001 mt001 iris_test False None None 3 4.0 0.015625 8192.0 128 6.0 0.0\n", + "4 796.0 True 797 sr1_0002 mt001 iris_test False None None 0 1.0 1.000000 128.0 128 796.0 7.0\n", + "5 796.0 True 797 sr1_0002 mt001 iris_test False None None 1 4.0 0.250000 512.0 128 199.0 1.0\n", + "6 796.0 True 797 sr1_0002 mt001 iris_test False None None 2 4.0 0.062500 2048.0 128 49.0 0.0\n", + "7 796.0 True 797 sr1_0002 mt001 iris_test False None None 3 4.0 0.015625 8192.0 128 12.0 0.0\n", + "8 1021.0 True 1022 sr1_0003 mt001 iris_test False None None 0 1.0 1.000000 128.0 128 1021.0 10.0\n", + "9 1021.0 True 1022 sr1_0003 mt001 iris_test False None None 1 4.0 0.250000 512.0 128 255.0 2.0\n", + "10 1021.0 True 1022 sr1_0003 mt001 iris_test False None None 2 4.0 0.062500 2048.0 128 63.0 0.0\n", + "11 1021.0 True 1022 sr1_0003 mt001 iris_test False None None 3 4.0 0.015625 8192.0 128 15.0 0.0\n", + "12 105.0 True 106 sr1_0004 mt001 iris_test False None None 0 1.0 1.000000 128.0 128 105.0 0.0\n", + "13 105.0 True 106 sr1_0004 mt001 iris_test False None None 1 4.0 0.250000 512.0 128 26.0 0.0\n", + "14 105.0 True 106 sr1_0004 mt001 iris_test False None None 2 4.0 0.062500 2048.0 128 6.0 0.0\n", + "15 105.0 True 106 sr1_0004 mt001 iris_test False None None 3 4.0 0.015625 8192.0 128 1.0 0.0\n", + "16 589906.0 True 589907 sr1_0005 mt001 iris_test False None None 0 1.0 1.000000 128.0 128 589906.0 6144.0\n", + "17 589906.0 True 589907 sr1_0005 mt001 iris_test False None None 1 4.0 0.250000 512.0 128 147476.0 1535.0\n", + "18 589906.0 True 589907 sr1_0005 mt001 iris_test False None None 2 4.0 0.062500 2048.0 128 36869.0 383.0\n", + "19 589906.0 True 589907 sr1_0005 mt001 iris_test False None None 3 4.0 0.015625 8192.0 128 9217.0 95.0\u001b[0m\n", + "\u001b[1m24:10:05T15:00:08 | INFO | line:411 |aurora.pipelines.transfer_function_kernel | validate_processing | No RR station specified, switching RME_RR to RME\u001b[0m\n", + "\u001b[1m24:10:05T15:00:08 | INFO | line:411 |aurora.pipelines.transfer_function_kernel | validate_processing | No RR station specified, switching RME_RR to RME\u001b[0m\n", + "\u001b[1m24:10:05T15:00:08 | INFO | line:411 |aurora.pipelines.transfer_function_kernel | validate_processing | No RR station specified, switching RME_RR to RME\u001b[0m\n", + "\u001b[1m24:10:05T15:00:08 | INFO | line:411 |aurora.pipelines.transfer_function_kernel | validate_processing | No RR station specified, switching RME_RR to RME\u001b[0m\n", + "\u001b[1m24:10:05T15:00:08 | INFO | line:654 |aurora.pipelines.transfer_function_kernel | memory_check | Total memory: 62.74 GB\u001b[0m\n", + "\u001b[1m24:10:05T15:00:08 | INFO | line:658 |aurora.pipelines.transfer_function_kernel | memory_check | Total Bytes of Raw Data: 0.004 GB\u001b[0m\n", + "\u001b[1m24:10:05T15:00:08 | INFO | line:661 |aurora.pipelines.transfer_function_kernel | memory_check | Raw Data will use: 0.007 % of memory\u001b[0m\n", + "\u001b[1m24:10:05T15:00:08 | INFO | line:707 |aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: iris_test, station: mt001, run: sr1_0001-- Fourier coefficients will be computed\u001b[0m\n", + "\u001b[1m24:10:05T15:00:09 | INFO | line:777 |mth5.mth5 | close_mth5 | Flushing and closing /home/kkappler/software/irismt/earthscope-mt-course/data/time_series/lemi/from_lemi.h5\u001b[0m\n", + "\u001b[1m24:10:05T15:00:09 | INFO | line:707 |aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: iris_test, station: mt001, run: sr1_0002-- Fourier coefficients will be computed\u001b[0m\n", + "\u001b[1m24:10:05T15:00:09 | INFO | line:777 |mth5.mth5 | close_mth5 | Flushing and closing /home/kkappler/software/irismt/earthscope-mt-course/data/time_series/lemi/from_lemi.h5\u001b[0m\n", + "\u001b[1m24:10:05T15:00:09 | INFO | line:707 |aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: iris_test, station: mt001, run: sr1_0003-- Fourier coefficients will be computed\u001b[0m\n", + "\u001b[1m24:10:05T15:00:09 | INFO | line:777 |mth5.mth5 | close_mth5 | Flushing and closing /home/kkappler/software/irismt/earthscope-mt-course/data/time_series/lemi/from_lemi.h5\u001b[0m\n", + "\u001b[1m24:10:05T15:00:09 | INFO | line:707 |aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: iris_test, station: mt001, run: sr1_0004-- Fourier coefficients will be computed\u001b[0m\n", + "\u001b[1m24:10:05T15:00:09 | INFO | line:777 |mth5.mth5 | close_mth5 | Flushing and closing /home/kkappler/software/irismt/earthscope-mt-course/data/time_series/lemi/from_lemi.h5\u001b[0m\n", + "\u001b[1m24:10:05T15:00:09 | INFO | line:707 |aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: iris_test, station: mt001, run: sr1_0005-- Fourier coefficients will be computed\u001b[0m\n", + "\u001b[1m24:10:05T15:00:09 | INFO | line:777 |mth5.mth5 | close_mth5 | Flushing and closing /home/kkappler/software/irismt/earthscope-mt-course/data/time_series/lemi/from_lemi.h5\u001b[0m\n", + "\u001b[1m24:10:05T15:00:09 | INFO | line:248 |aurora.pipelines.transfer_function_kernel | check_if_fcs_already_exist | FC levels not present\u001b[0m\n", + "\u001b[1m24:10:05T15:00:09 | INFO | line:517 |aurora.pipelines.process_mth5 | process_mth5_legacy | Processing config indicates 4 decimation levels\u001b[0m\n", + "\u001b[1m24:10:05T15:00:09 | INFO | line:445 |aurora.pipelines.transfer_function_kernel | valid_decimations | After validation there are 4 valid decimation levels\u001b[0m\n", + "\u001b[1m24:10:05T15:00:12 | INFO | line:899 |mtpy.processing.kernel_dataset | initialize_dataframe_for_processing | Dataset dataframe initialized successfully\u001b[0m\n", + "\u001b[1m24:10:05T15:00:12 | INFO | line:143 |aurora.pipelines.transfer_function_kernel | update_dataset_df | Dataset Dataframe Updated for decimation level 0 Successfully\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel bx with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel by with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel bz with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel e1 with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel e2 with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel temperature_e with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel temperature_h with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[1m24:10:05T15:00:12 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel bx with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel by with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel bz with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel e1 with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel e2 with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel temperature_e with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel temperature_h with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[1m24:10:05T15:00:12 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel bx with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel by with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel bz with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel e1 with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel e2 with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel temperature_e with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel temperature_h with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:12 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[1m24:10:05T15:00:12 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:14 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel bx with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:14 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:14 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:14 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel by with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:14 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:14 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:14 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel bz with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:14 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:14 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:14 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel e1 with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:14 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:14 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:14 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel e2 with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:14 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:14 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:14 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel temperature_e with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:14 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:14 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:14 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel temperature_h with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:14 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:14 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[1m24:10:05T15:00:14 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m24:10:05T15:00:14 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 25.728968s (0.038867Hz)\u001b[0m\n", + "\u001b[1m24:10:05T15:00:14 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 19.929573s (0.050177Hz)\u001b[0m\n", + "\u001b[1m24:10:05T15:00:14 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 15.164131s (0.065945Hz)\u001b[0m\n", + "\u001b[1m24:10:05T15:00:14 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 11.746086s (0.085135Hz)\u001b[0m\n", + "\u001b[1m24:10:05T15:00:14 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 9.195791s (0.108745Hz)\u001b[0m\n", + "\u001b[1m24:10:05T15:00:15 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 7.362526s (0.135823Hz)\u001b[0m\n", + "\u001b[1m24:10:05T15:00:15 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 5.856115s (0.170762Hz)\u001b[0m\n", + "\u001b[1m24:10:05T15:00:15 | 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[1m24:10:05T15:00:16 | INFO | line:124 |aurora.pipelines.transfer_function_kernel | update_dataset_df | DECIMATION LEVEL 1\u001b[0m\n", + "\u001b[1m24:10:05T15:00:16 | INFO | line:143 |aurora.pipelines.transfer_function_kernel | update_dataset_df | Dataset Dataframe Updated for decimation level 1 Successfully\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:16 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel bx with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:16 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:16 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:16 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel by with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:16 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:16 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:16 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel bz with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:16 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:16 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:16 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel e1 with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:16 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:16 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:16 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel e2 with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:16 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:16 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:16 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel temperature_e with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:16 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:16 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:16 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel temperature_h with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:16 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:16 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[1m24:10:05T15:00:16 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:17 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel bx with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:17 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:17 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:17 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel by with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:17 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:17 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:17 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel bz with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:17 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:17 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:17 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel e1 with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:17 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:17 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:17 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel e2 with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:17 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:17 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:17 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel temperature_e with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:17 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:17 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:17 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel temperature_h with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:17 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:17 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[1m24:10:05T15:00:17 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m24:10:05T15:00:17 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 102.915872s (0.009717Hz)\u001b[0m\n", + "\u001b[1m24:10:05T15:00:17 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 85.631182s (0.011678Hz)\u001b[0m\n", + "\u001b[1m24:10:05T15:00:17 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 68.881694s (0.014518Hz)\u001b[0m\n", + "\u001b[1m24:10:05T15:00:17 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 54.195827s (0.018452Hz)\u001b[0m\n", + "\u001b[1m24:10:05T15:00:17 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 43.003958s (0.023254Hz)\u001b[0m\n", + "\u001b[1m24:10:05T15:00:17 | 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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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m24:10:05T15:00:18 | INFO | line:124 |aurora.pipelines.transfer_function_kernel | update_dataset_df | DECIMATION LEVEL 2\u001b[0m\n", + "\u001b[1m24:10:05T15:00:18 | INFO | line:143 |aurora.pipelines.transfer_function_kernel | update_dataset_df | Dataset Dataframe Updated for decimation level 2 Successfully\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:18 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel bx with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:18 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:18 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:18 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel by with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:18 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:18 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:18 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel bz with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:18 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:18 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:18 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel e1 with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:18 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:18 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:18 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel e2 with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:18 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:18 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:18 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel temperature_e with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:18 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:18 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:18 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel temperature_h with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:18 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:18 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[1m24:10:05T15:00:18 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m24:10:05T15:00:18 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 411.663489s (0.002429Hz)\u001b[0m\n", + "\u001b[1m24:10:05T15:00:18 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 342.524727s (0.002919Hz)\u001b[0m\n", + "\u001b[1m24:10:05T15:00:18 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 275.526776s (0.003629Hz)\u001b[0m\n", + "\u001b[1m24:10:05T15:00:18 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 216.783308s (0.004613Hz)\u001b[0m\n", + "\u001b[1m24:10:05T15:00:18 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 172.015831s (0.005813Hz)\u001b[0m\n", + "\u001b[1m24:10:05T15:00:18 | 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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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", "output_type": "stream", "text": [ - "2022-10-16 14:29:06,538 [line 2811] mth5.groups.master_station_run_channel.Magnetic.time_slice - ERROR: Requested slice is larger than data. Slice length = 152291328, data length = (243284989,) Check start and end times.\n" + "\u001b[1m24:10:05T15:00:19 | INFO | line:124 |aurora.pipelines.transfer_function_kernel | update_dataset_df | DECIMATION LEVEL 3\u001b[0m\n", + "\u001b[1m24:10:05T15:00:19 | INFO | line:143 |aurora.pipelines.transfer_function_kernel | update_dataset_df | Dataset Dataframe Updated for decimation level 3 Successfully\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:19 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel bx with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:19 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:19 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:19 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel by with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:19 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:19 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:19 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel bz with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:19 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:19 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:19 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel e1 with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:19 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:19 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:19 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel e2 with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:19 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:19 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:19 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel temperature_e with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:19 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:19 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:19 | WARNING | line:326 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | Channel temperature_h with empty filters list detected\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:19 | WARNING | line:340 |aurora.pipelines.time_series_helpers | calibrate_stft_obj | No filters to remove\u001b[0m\n", + "\u001b[33m\u001b[1m24:10:05T15:00:19 | WARNING | line:296 |mt_metadata.timeseries.filters.channel_response | complex_response | No filters associated with , returning 1\u001b[0m\n", + "\u001b[1m24:10:05T15:00:19 | INFO | line:354 |aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m24:10:05T15:00:19 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 1514.701336s (0.000660Hz)\u001b[0m\n", + "\u001b[1m24:10:05T15:00:19 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 1042.488956s (0.000959Hz)\u001b[0m\n", + "\u001b[1m24:10:05T15:00:19 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 723.371271s (0.001382Hz)\u001b[0m\n", + "\u001b[1m24:10:05T15:00:19 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 532.971560s (0.001876Hz)\u001b[0m\n", + "\u001b[1m24:10:05T15:00:19 | INFO | line:35 |aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 412.837995s (0.002422Hz)\u001b[0m\n" ] }, { - "ename": "ValueError", - "evalue": "Requested slice is larger than data. Slice length = 152291328, data length = (243284989,) Check start and end times.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "\u001b[0;32m/tmp/ipykernel_9002/2463673138.py\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0munits\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"MT\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mshow_plot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshow_plot\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mz_file_path\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 7\u001b[0m )\n", - "\u001b[0;32m~/software/irismt/aurora/aurora/pipelines/process_mth5.py\u001b[0m in \u001b[0;36mprocess_mth5\u001b[0;34m(config, tfk_dataset, units, show_plot, z_file_path, return_collection)\u001b[0m\n\u001b[1;32m 339\u001b[0m \u001b[0;31m# Assign additional columns to dataset_df, populate with mth5_objs and xr_ts\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 340\u001b[0m \u001b[0;31m# ANY MERGING OF RUNS IN TIME DOMAIN WOULD GO HERE\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 341\u001b[0;31m \u001b[0mtfk_dataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minitialize_dataframe_for_processing\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmth5_objs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 342\u001b[0m \u001b[0mdataset_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtfk_dataset\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 343\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/software/irismt/aurora/aurora/transfer_function/kernel_dataset.py\u001b[0m in \u001b[0;36minitialize_dataframe_for_processing\u001b[0;34m(self, mth5_objs)\u001b[0m\n\u001b[1;32m 313\u001b[0m )\n\u001b[1;32m 314\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"run_reference\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mat\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrun_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhdf5_group\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mref\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 315\u001b[0;31m \u001b[0mrun_ts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrun_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_runts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 316\u001b[0m \u001b[0mxr_ds\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrun_ts\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 317\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"run_dataarray\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mat\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxr_ds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"channel\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/software/irismt/mth5/mth5/groups/master_station_run_channel.py\u001b[0m in \u001b[0;36mto_runts\u001b[0;34m(self, start, end, n_samples)\u001b[0m\n\u001b[1;32m 1627\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1628\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mstart\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1629\u001b[0;31m \u001b[0mts_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mch_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime_slice\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mend\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mend\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_samples\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1630\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1631\u001b[0m \u001b[0mts_obj\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mch_obj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_channel_ts\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;32m~/software/irismt/mth5/mth5/groups/master_station_run_channel.py\u001b[0m in \u001b[0;36mtime_slice\u001b[0;34m(self, start, end, n_samples, return_type)\u001b[0m\n\u001b[1;32m 2810\u001b[0m )\n\u001b[1;32m 2811\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlogger\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0merror\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2812\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2813\u001b[0m \u001b[0;31m# create a regional reference that can be used, need +1 to be inclusive\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 2814\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", - "\u001b[0;31mValueError\u001b[0m: Requested slice is larger than data. Slice length = 152291328, data length = (243284989,) Check start and end times." + "data": { + "image/png": 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", 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