From b7ec3d3e9c1ee2c4fb87bf8100ba431b60dc1d28 Mon Sep 17 00:00:00 2001 From: "Karl N. Kappler" Date: Sat, 5 Oct 2024 14:47:34 -0700 Subject: [PATCH] Update syntax - notebook runs on patches --- notebooks/mth5/06_make_mth5_from_lemi.ipynb | 843 +++++++++++++------- 1 file changed, 560 insertions(+), 283 deletions(-) diff --git a/notebooks/mth5/06_make_mth5_from_lemi.ipynb b/notebooks/mth5/06_make_mth5_from_lemi.ipynb index b0c7555..13102aa 100644 --- a/notebooks/mth5/06_make_mth5_from_lemi.ipynb +++ b/notebooks/mth5/06_make_mth5_from_lemi.ipynb @@ -33,15 +33,7 @@ "execution_count": 1, "id": "1208c257-8064-4ed3-a7db-eebe14b248d2", "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "2022-09-27 15:49:17,897 [line 135] mth5.setup_logger - INFO: Logging file can be found C:\\Users\\jpeacock\\OneDrive - DOI\\Documents\\GitHub\\mth5\\logs\\mth5_debug.log\n" - ] - } - ], + "outputs": [], "source": [ "from pathlib import Path\n", "from mth5.mth5 import MTH5\n", @@ -83,9 +75,18 @@ "execution_count": 2, "id": "a60deaec-473c-4d1c-a0cb-38314512e2f2", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "/home/kkappler/software/irismt/earthscope-mt-course/data/time_series/lemi\n" + ] + } + ], "source": [ - "lemi_path = Path().cwd().parent.parent.joinpath(\"data\", \"time_series\", \"lemi\")" + "lemi_path = Path().cwd().parent.parent.joinpath(\"data\", \"time_series\", \"lemi\")\n", + "print(lemi_path)" ] }, { @@ -95,7 +96,7 @@ "metadata": {}, "outputs": [], "source": [ - "zc = LEMICollection(lemi_path)\n", + "zc = LEMICollection(lemi_path, file_ext=\"TXT\")\n", "\n", "# input some high level metadata\n", "zc.station_id = \"mt001\"\n", @@ -178,6 +179,11 @@ " file_size\n", " n_samples\n", " sequence_number\n", + " dipole\n", + " coil_number\n", + " latitude\n", + " longitude\n", + " elevation\n", " instrument_id\n", " calibration_fn\n", " \n", @@ -192,11 +198,16 @@ " 2020-09-30 20:28:15+00:00\n", " 1\n", " temperature_e,temperature_h,e1,e2,bx,by,bz\n", - " C:\\Users\\jpeacock\\OneDrive - DOI\\Documents\\Git...\n", + " /home/kkappler/software/irismt/earthscope-mt-c...\n", " 1.0\n", " 66272\n", " 436\n", " 0\n", + " None\n", + " None\n", + " None\n", + " None\n", + " None\n", " LEMI424\n", " None\n", " \n", @@ -208,17 +219,20 @@ " survey station run start \\\n", "0 iris_test mt001 sr1_0001 2020-09-30 20:21:00+00:00 \n", "\n", - " end channel_id \\\n", - "0 2020-09-30 20:28:15+00:00 1 \n", + " end channel_id \\\n", + "0 2020-09-30 20:28:15+00:00 1 \n", "\n", " component \\\n", "0 temperature_e,temperature_h,e1,e2,bx,by,bz \n", "\n", " fn sample_rate file_size \\\n", - "0 C:\\Users\\jpeacock\\OneDrive - DOI\\Documents\\Git... 1.0 66272 \n", + "0 /home/kkappler/software/irismt/earthscope-mt-c... 1.0 66272 \n", "\n", - " n_samples sequence_number instrument_id calibration_fn \n", - "0 436 0 LEMI424 None " + " n_samples sequence_number dipole coil_number latitude longitude elevation \\\n", + "0 436 0 None None None None None \n", + "\n", + " instrument_id calibration_fn \n", + "0 LEMI424 None " ] }, "metadata": {}, @@ -257,6 +271,11 @@ " file_size\n", " n_samples\n", " sequence_number\n", + " dipole\n", + " coil_number\n", + " latitude\n", + " longitude\n", + " elevation\n", " instrument_id\n", " calibration_fn\n", " \n", @@ -271,11 +290,16 @@ " 2020-09-30 20:42:16+00:00\n", " 1\n", " temperature_e,temperature_h,e1,e2,bx,by,bz\n", - " C:\\Users\\jpeacock\\OneDrive - DOI\\Documents\\Git...\n", + " /home/kkappler/software/irismt/earthscope-mt-c...\n", " 1.0\n", " 121144\n", " 797\n", " 0\n", + " None\n", + " None\n", + " None\n", + " None\n", + " None\n", " LEMI424\n", " None\n", " \n", @@ -287,17 +311,20 @@ " survey station run start \\\n", "1 iris_test mt001 sr1_0002 2020-09-30 20:29:00+00:00 \n", "\n", - " end channel_id \\\n", - "1 2020-09-30 20:42:16+00:00 1 \n", + " end channel_id \\\n", + "1 2020-09-30 20:42:16+00:00 1 \n", "\n", " component \\\n", "1 temperature_e,temperature_h,e1,e2,bx,by,bz \n", "\n", " fn sample_rate file_size \\\n", - "1 C:\\Users\\jpeacock\\OneDrive - DOI\\Documents\\Git... 1.0 121144 \n", + "1 /home/kkappler/software/irismt/earthscope-mt-c... 1.0 121144 \n", + "\n", + " n_samples sequence_number dipole coil_number latitude longitude elevation \\\n", + "1 797 0 None None None None None \n", "\n", - " n_samples sequence_number instrument_id calibration_fn \n", - "1 797 0 LEMI424 None " + " instrument_id calibration_fn \n", + "1 LEMI424 None " ] }, "metadata": {}, @@ -336,6 +363,11 @@ " file_size\n", " n_samples\n", " sequence_number\n", + " dipole\n", + " coil_number\n", + " latitude\n", + " longitude\n", + " elevation\n", " instrument_id\n", " calibration_fn\n", " \n", @@ -350,11 +382,16 @@ " 2020-09-30 21:11:01+00:00\n", " 1\n", " temperature_e,temperature_h,e1,e2,bx,by,bz\n", - " C:\\Users\\jpeacock\\OneDrive - DOI\\Documents\\Git...\n", + " /home/kkappler/software/irismt/earthscope-mt-c...\n", " 1.0\n", " 155344\n", " 1022\n", " 0\n", + " None\n", + " None\n", + " None\n", + " None\n", + " None\n", " LEMI424\n", " None\n", " \n", @@ -366,17 +403,20 @@ " survey station run start \\\n", "2 iris_test mt001 sr1_0003 2020-09-30 20:54:00+00:00 \n", "\n", - " end channel_id \\\n", - "2 2020-09-30 21:11:01+00:00 1 \n", + " end channel_id \\\n", + "2 2020-09-30 21:11:01+00:00 1 \n", "\n", " component \\\n", "2 temperature_e,temperature_h,e1,e2,bx,by,bz \n", "\n", " fn sample_rate file_size \\\n", - "2 C:\\Users\\jpeacock\\OneDrive - DOI\\Documents\\Git... 1.0 155344 \n", + "2 /home/kkappler/software/irismt/earthscope-mt-c... 1.0 155344 \n", + "\n", + " n_samples sequence_number dipole coil_number latitude longitude elevation \\\n", + "2 1022 0 None None None None None \n", "\n", - " n_samples sequence_number instrument_id calibration_fn \n", - "2 1022 0 LEMI424 None " + " instrument_id calibration_fn \n", + "2 LEMI424 None " ] }, "metadata": {}, @@ -415,6 +455,11 @@ " file_size\n", " n_samples\n", " sequence_number\n", + " dipole\n", + " coil_number\n", + " latitude\n", + " longitude\n", + " elevation\n", " instrument_id\n", " calibration_fn\n", " \n", @@ -429,11 +474,16 @@ " 2020-09-30 21:13:45+00:00\n", " 1\n", " temperature_e,temperature_h,e1,e2,bx,by,bz\n", - " C:\\Users\\jpeacock\\OneDrive - DOI\\Documents\\Git...\n", + " /home/kkappler/software/irismt/earthscope-mt-c...\n", " 1.0\n", " 16112\n", " 106\n", " 0\n", + " None\n", + " None\n", + " None\n", + " None\n", + " None\n", " LEMI424\n", " None\n", " \n", @@ -445,17 +495,20 @@ " survey station run start \\\n", "3 iris_test mt001 sr1_0004 2020-09-30 21:12:00+00:00 \n", "\n", - " end channel_id \\\n", - "3 2020-09-30 21:13:45+00:00 1 \n", + " end channel_id \\\n", + "3 2020-09-30 21:13:45+00:00 1 \n", "\n", " component \\\n", "3 temperature_e,temperature_h,e1,e2,bx,by,bz \n", "\n", " fn sample_rate file_size \\\n", - "3 C:\\Users\\jpeacock\\OneDrive - DOI\\Documents\\Git... 1.0 16112 \n", + "3 /home/kkappler/software/irismt/earthscope-mt-c... 1.0 16112 \n", + "\n", + " n_samples sequence_number dipole coil_number latitude longitude elevation \\\n", + "3 106 0 None None None None None \n", "\n", - " n_samples sequence_number instrument_id calibration_fn \n", - "3 106 0 LEMI424 None " + " instrument_id calibration_fn \n", + "3 LEMI424 None " ] }, "metadata": {}, @@ -494,6 +547,11 @@ " file_size\n", " n_samples\n", " sequence_number\n", + " dipole\n", + " coil_number\n", + " latitude\n", + " longitude\n", + " elevation\n", " instrument_id\n", " calibration_fn\n", " \n", @@ -508,11 +566,16 @@ " 2020-09-30 23:59:59+00:00\n", " 1\n", " temperature_e,temperature_h,e1,e2,bx,by,bz\n", - " C:\\Users\\jpeacock\\OneDrive - DOI\\Documents\\Git...\n", + " /home/kkappler/software/irismt/earthscope-mt-c...\n", " 1.0\n", " 1513920\n", " 9960\n", " 0\n", + " None\n", + " None\n", + " None\n", + " None\n", + " None\n", " LEMI424\n", " None\n", " \n", @@ -525,11 +588,16 @@ " 2020-10-01 23:59:59+00:00\n", " 1\n", " temperature_e,temperature_h,e1,e2,bx,by,bz\n", - " C:\\Users\\jpeacock\\OneDrive - DOI\\Documents\\Git...\n", + " /home/kkappler/software/irismt/earthscope-mt-c...\n", " 1.0\n", " 13132800\n", " 86400\n", " 0\n", + " None\n", + " None\n", + " None\n", + " None\n", + " None\n", " LEMI424\n", " None\n", " \n", @@ -542,11 +610,16 @@ " 2020-10-02 23:59:59+00:00\n", " 1\n", " temperature_e,temperature_h,e1,e2,bx,by,bz\n", - " C:\\Users\\jpeacock\\OneDrive - DOI\\Documents\\Git...\n", + " /home/kkappler/software/irismt/earthscope-mt-c...\n", " 1.0\n", " 13132800\n", " 86400\n", " 0\n", + " None\n", + " None\n", + " None\n", + " None\n", + " None\n", " LEMI424\n", " None\n", " \n", @@ -559,11 +632,16 @@ " 2020-10-03 23:59:59+00:00\n", " 1\n", " temperature_e,temperature_h,e1,e2,bx,by,bz\n", - " C:\\Users\\jpeacock\\OneDrive - DOI\\Documents\\Git...\n", + " /home/kkappler/software/irismt/earthscope-mt-c...\n", " 1.0\n", " 13132800\n", " 86400\n", " 0\n", + " None\n", + " None\n", + " None\n", + " None\n", + " None\n", " LEMI424\n", " None\n", " \n", @@ -576,11 +654,16 @@ " 2020-10-04 23:59:59+00:00\n", " 1\n", " temperature_e,temperature_h,e1,e2,bx,by,bz\n", - " C:\\Users\\jpeacock\\OneDrive - DOI\\Documents\\Git...\n", + " /home/kkappler/software/irismt/earthscope-mt-c...\n", " 1.0\n", " 13132800\n", " 86400\n", " 0\n", + " None\n", + " None\n", + " None\n", + " None\n", + " None\n", " LEMI424\n", " None\n", " \n", @@ -593,11 +676,16 @@ " 2020-10-05 23:59:59+00:00\n", " 1\n", " temperature_e,temperature_h,e1,e2,bx,by,bz\n", - " C:\\Users\\jpeacock\\OneDrive - DOI\\Documents\\Git...\n", + " /home/kkappler/software/irismt/earthscope-mt-c...\n", " 1.0\n", " 13132801\n", " 86400\n", " 0\n", + " None\n", + " None\n", + " None\n", + " None\n", + " None\n", " LEMI424\n", " None\n", " \n", @@ -610,11 +698,16 @@ " 2020-10-06 23:59:59+00:00\n", " 1\n", " temperature_e,temperature_h,e1,e2,bx,by,bz\n", - " C:\\Users\\jpeacock\\OneDrive - DOI\\Documents\\Git...\n", + " /home/kkappler/software/irismt/earthscope-mt-c...\n", " 1.0\n", " 13132800\n", " 86400\n", " 0\n", + " None\n", + " None\n", + " None\n", + " None\n", + " None\n", " LEMI424\n", " None\n", " \n", @@ -627,11 +720,16 @@ " 2020-10-07 14:19:46+00:00\n", " 1\n", " temperature_e,temperature_h,e1,e2,bx,by,bz\n", - " C:\\Users\\jpeacock\\OneDrive - DOI\\Documents\\Git...\n", + " /home/kkappler/software/irismt/earthscope-mt-c...\n", " 1.0\n", " 7841224\n", " 51587\n", " 0\n", + " None\n", + " None\n", + " None\n", + " None\n", + " None\n", " LEMI424\n", " None\n", " \n", @@ -650,15 +748,15 @@ "10 iris_test mt001 sr1_0005 2020-10-06 00:00:00+00:00 \n", "11 iris_test mt001 sr1_0005 2020-10-07 00:00:00+00:00 \n", "\n", - " end channel_id \\\n", - "4 2020-09-30 23:59:59+00:00 1 \n", - "5 2020-10-01 23:59:59+00:00 1 \n", - "6 2020-10-02 23:59:59+00:00 1 \n", - "7 2020-10-03 23:59:59+00:00 1 \n", - "8 2020-10-04 23:59:59+00:00 1 \n", - "9 2020-10-05 23:59:59+00:00 1 \n", - "10 2020-10-06 23:59:59+00:00 1 \n", - "11 2020-10-07 14:19:46+00:00 1 \n", + " end channel_id \\\n", + "4 2020-09-30 23:59:59+00:00 1 \n", + "5 2020-10-01 23:59:59+00:00 1 \n", + "6 2020-10-02 23:59:59+00:00 1 \n", + "7 2020-10-03 23:59:59+00:00 1 \n", + "8 2020-10-04 23:59:59+00:00 1 \n", + "9 2020-10-05 23:59:59+00:00 1 \n", + "10 2020-10-06 23:59:59+00:00 1 \n", + "11 2020-10-07 14:19:46+00:00 1 \n", "\n", " component \\\n", "4 temperature_e,temperature_h,e1,e2,bx,by,bz \n", @@ -671,24 +769,34 @@ "11 temperature_e,temperature_h,e1,e2,bx,by,bz \n", "\n", " fn sample_rate file_size \\\n", - "4 C:\\Users\\jpeacock\\OneDrive - DOI\\Documents\\Git... 1.0 1513920 \n", - "5 C:\\Users\\jpeacock\\OneDrive - DOI\\Documents\\Git... 1.0 13132800 \n", - "6 C:\\Users\\jpeacock\\OneDrive - DOI\\Documents\\Git... 1.0 13132800 \n", - "7 C:\\Users\\jpeacock\\OneDrive - DOI\\Documents\\Git... 1.0 13132800 \n", - "8 C:\\Users\\jpeacock\\OneDrive - DOI\\Documents\\Git... 1.0 13132800 \n", - "9 C:\\Users\\jpeacock\\OneDrive - DOI\\Documents\\Git... 1.0 13132801 \n", - "10 C:\\Users\\jpeacock\\OneDrive - DOI\\Documents\\Git... 1.0 13132800 \n", - "11 C:\\Users\\jpeacock\\OneDrive - DOI\\Documents\\Git... 1.0 7841224 \n", + "4 /home/kkappler/software/irismt/earthscope-mt-c... 1.0 1513920 \n", + "5 /home/kkappler/software/irismt/earthscope-mt-c... 1.0 13132800 \n", + "6 /home/kkappler/software/irismt/earthscope-mt-c... 1.0 13132800 \n", + "7 /home/kkappler/software/irismt/earthscope-mt-c... 1.0 13132800 \n", + "8 /home/kkappler/software/irismt/earthscope-mt-c... 1.0 13132800 \n", + "9 /home/kkappler/software/irismt/earthscope-mt-c... 1.0 13132801 \n", + "10 /home/kkappler/software/irismt/earthscope-mt-c... 1.0 13132800 \n", + "11 /home/kkappler/software/irismt/earthscope-mt-c... 1.0 7841224 \n", + "\n", + " n_samples sequence_number dipole coil_number latitude longitude elevation \\\n", + "4 9960 0 None None None None None \n", + "5 86400 0 None None None None None \n", + "6 86400 0 None None None None None \n", + "7 86400 0 None None None None None \n", + "8 86400 0 None None None None None \n", + "9 86400 0 None None None None None \n", + "10 86400 0 None None None None None \n", + "11 51587 0 None None None None None \n", "\n", - " n_samples sequence_number instrument_id calibration_fn \n", - "4 9960 0 LEMI424 None \n", - "5 86400 0 LEMI424 None \n", - "6 86400 0 LEMI424 None \n", - "7 86400 0 LEMI424 None \n", - "8 86400 0 LEMI424 None \n", - "9 86400 0 LEMI424 None \n", - "10 86400 0 LEMI424 None \n", - "11 51587 0 LEMI424 None " + " instrument_id calibration_fn \n", + "4 LEMI424 None \n", + "5 LEMI424 None \n", + "6 LEMI424 None \n", + "7 LEMI424 None \n", + "8 LEMI424 None \n", + "9 LEMI424 None \n", + "10 LEMI424 None \n", + "11 LEMI424 None " ] }, "metadata": {}, @@ -722,11 +830,135 @@ "metadata": {}, "outputs": [ { - "name": "stderr", - "output_type": "stream", - "text": [ - "2022-09-27 15:49:19,492 [line 672] mth5.mth5.MTH5._initialize_file - INFO: Initialized MTH5 0.2.0 file C:\\Users\\jpeacock\\OneDrive - DOI\\Documents\\GitHub\\mt_examples\\data\\time_series\\lemi\\from_lemi.h5 in mode a\n" - ] + "data": { + "text/plain": [ + "/:\n", + "====================\n", + " |- Group: Experiment\n", + " --------------------\n", + " |- Group: Reports\n", + " -----------------\n", + " |- Group: Standards\n", + " -------------------\n", + " --> Dataset: summary\n", + " ......................\n", + " |- Group: Surveys\n", + " -----------------\n", + " |- Group: iris_test\n", + " -------------------\n", + " |- Group: Filters\n", + " -----------------\n", + " |- Group: coefficient\n", + " ---------------------\n", + " |- Group: fap\n", + " -------------\n", + " |- Group: fir\n", + " -------------\n", + " |- Group: time_delay\n", + " --------------------\n", + " |- Group: zpk\n", + " -------------\n", + " |- Group: Reports\n", + " -----------------\n", + " |- Group: Standards\n", + " -------------------\n", + " --> Dataset: summary\n", + " ......................\n", + " |- Group: Stations\n", + " ------------------\n", + " |- Group: mt001\n", + " ---------------\n", + " |- Group: Transfer_Functions\n", + " ----------------------------\n", + " |- Group: sr1_0001\n", + " ------------------\n", + " --> Dataset: bx\n", + " .................\n", + " --> Dataset: by\n", + " .................\n", + " --> Dataset: bz\n", + " .................\n", + " --> Dataset: e1\n", + " .................\n", + " --> Dataset: e2\n", + " .................\n", + " --> Dataset: temperature_e\n", + " ............................\n", + " --> Dataset: temperature_h\n", + " ............................\n", + " |- Group: sr1_0002\n", + " ------------------\n", + " --> Dataset: bx\n", + " .................\n", + " --> Dataset: by\n", + " .................\n", + " --> Dataset: bz\n", + " .................\n", + " --> Dataset: e1\n", + " .................\n", + " --> Dataset: e2\n", + " .................\n", + " --> Dataset: temperature_e\n", + " ............................\n", + " --> Dataset: temperature_h\n", + " ............................\n", + " |- Group: sr1_0003\n", + " ------------------\n", + " --> Dataset: bx\n", + " .................\n", + " --> Dataset: by\n", + " .................\n", + " --> Dataset: bz\n", + " .................\n", + " --> Dataset: e1\n", + " .................\n", + " --> Dataset: e2\n", + " .................\n", + " --> Dataset: temperature_e\n", + " ............................\n", + " --> Dataset: temperature_h\n", + " ............................\n", + " |- Group: sr1_0004\n", + " ------------------\n", + " --> Dataset: bx\n", + " .................\n", + " --> Dataset: by\n", + " .................\n", + " --> Dataset: bz\n", + " .................\n", + " --> Dataset: e1\n", + " .................\n", + " --> Dataset: e2\n", + " .................\n", + " --> Dataset: temperature_e\n", + " ............................\n", + " --> Dataset: temperature_h\n", + " ............................\n", + " |- Group: sr1_0005\n", + " ------------------\n", + " --> Dataset: bx\n", + " .................\n", + " --> Dataset: by\n", + " .................\n", + " --> Dataset: bz\n", + " .................\n", + " --> Dataset: e1\n", + " .................\n", + " --> Dataset: e2\n", + " .................\n", + " --> Dataset: temperature_e\n", + " ............................\n", + " --> Dataset: temperature_h\n", + " ............................\n", + " --> Dataset: channel_summary\n", + " ..............................\n", + " --> Dataset: tf_summary\n", + " ........................." + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ @@ -739,7 +971,15 @@ "execution_count": 7, "id": "2957f307-ee67-4c61-90a3-cfdbc6e961f9", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m2024-10-05T14:46:28.768755-0700 | INFO | mth5.groups.survey | add_survey | survey iris_test already exists, returning existing group.\u001b[0m\n" + ] + } + ], "source": [ "survey_group = m.add_survey(zc.survey_id)" ] @@ -754,7 +994,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Wall time: 24.1 s\n" + "\u001b[1m2024-10-05T14:46:28.781543-0700 | INFO | mth5.groups.base | _add_group | StationGroup mt001 already exists, returning existing group.\u001b[0m\n", + "\u001b[1m2024-10-05T14:46:28.798611-0700 | INFO | mth5.groups.base | _add_group | RunGroup sr1_0001 already exists, returning existing group.\u001b[0m\n", + "\u001b[1m2024-10-05T14:46:30.484353-0700 | INFO | mth5.groups.base | _add_group | RunGroup sr1_0002 already exists, returning existing group.\u001b[0m\n", + "\u001b[1m2024-10-05T14:46:32.256260-0700 | INFO | mth5.groups.base | _add_group | RunGroup sr1_0003 already exists, returning existing group.\u001b[0m\n", + "\u001b[1m2024-10-05T14:46:34.052138-0700 | INFO | mth5.groups.base | _add_group | RunGroup sr1_0004 already exists, returning existing group.\u001b[0m\n", + "\u001b[1m2024-10-05T14:46:35.730032-0700 | INFO | mth5.groups.base | _add_group | RunGroup sr1_0005 already exists, returning existing group.\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-05T14:46:39.375274-0700 | WARNING | mth5.timeseries.run_ts | validate_metadata | end time of dataset 2020-10-07T17:05:46+00:00 does not match metadata end 2020-10-07T14:19:46+00:00 updating metatdata value to 2020-10-07T17:05:46+00:00\u001b[0m\n", + "CPU times: user 12.3 s, sys: 201 ms, total: 12.5 s\n", + "Wall time: 12.5 s\n" ] } ], @@ -769,10 +1017,9 @@ " run_group.from_runts(run_ts)\n", " station_group.metadata.update(run_ts.station_metadata)\n", " station_group.write_metadata()\n", - " station_group.validate_station_metadata()\n", - "\n", + " \n", "# update survey metadata from input station\n", - "survey_group.update_survey_metadata()" + "survey_group.update_metadata()" ] }, { @@ -981,6 +1228,7 @@ " azimuth\n", " tilt\n", " units\n", + " has_data\n", " hdf5_reference\n", " run_hdf5_reference\n", " station_hdf5_reference\n", @@ -989,7 +1237,7 @@ " \n", " \n", " 0\n", - " iris_test\n", + " none\n", " mt001\n", " sr1_0001\n", " 34.080655\n", @@ -997,20 +1245,21 @@ " 2202.8\n", " bx\n", " 2020-09-30 20:21:00+00:00\n", - " 2020-09-30 20:28:16+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", + " none\n", " mt001\n", " sr1_0001\n", " 34.080655\n", @@ -1018,20 +1267,21 @@ " 2202.8\n", " by\n", " 2020-09-30 20:21:00+00:00\n", - " 2020-09-30 20:28:16+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", + " none\n", " mt001\n", " sr1_0001\n", " 34.080655\n", @@ -1039,20 +1289,21 @@ " 2202.8\n", " bz\n", " 2020-09-30 20:21:00+00:00\n", - " 2020-09-30 20:28:16+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", + " none\n", " mt001\n", " sr1_0001\n", " 34.080655\n", @@ -1060,20 +1311,21 @@ " 2202.8\n", " e1\n", " 2020-09-30 20:21:00+00:00\n", - " 2020-09-30 20:28:16+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", + " none\n", " mt001\n", " sr1_0001\n", " 34.080655\n", @@ -1081,20 +1333,21 @@ " 2202.8\n", " e2\n", " 2020-09-30 20:21:00+00:00\n", - " 2020-09-30 20:28:16+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", + " none\n", " mt001\n", " sr1_0001\n", " 34.080655\n", @@ -1102,20 +1355,21 @@ " 2202.8\n", " temperature_e\n", " 2020-09-30 20:21:00+00:00\n", - " 2020-09-30 20:28:16+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", + " none\n", " mt001\n", " sr1_0001\n", " 34.080655\n", @@ -1123,20 +1377,21 @@ " 2202.8\n", " temperature_h\n", " 2020-09-30 20:21:00+00:00\n", - " 2020-09-30 20:28:16+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", + " none\n", " mt001\n", " sr1_0002\n", " 34.080655\n", @@ -1144,20 +1399,21 @@ " 2202.8\n", " bx\n", " 2020-09-30 20:29:00+00:00\n", - " 2020-09-30 20:42:17+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", + " none\n", " mt001\n", " sr1_0002\n", " 34.080655\n", @@ -1165,20 +1421,21 @@ " 2202.8\n", " by\n", " 2020-09-30 20:29:00+00:00\n", - " 2020-09-30 20:42:17+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", + " none\n", " mt001\n", " sr1_0002\n", " 34.080655\n", @@ -1186,20 +1443,21 @@ " 2202.8\n", " bz\n", " 2020-09-30 20:29:00+00:00\n", - " 2020-09-30 20:42:17+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", + " none\n", " mt001\n", " sr1_0002\n", " 34.080655\n", @@ -1207,20 +1465,21 @@ " 2202.8\n", " e1\n", " 2020-09-30 20:29:00+00:00\n", - " 2020-09-30 20:42:17+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", + " none\n", " mt001\n", " sr1_0002\n", " 34.080655\n", @@ -1228,20 +1487,21 @@ " 2202.8\n", " e2\n", " 2020-09-30 20:29:00+00:00\n", - " 2020-09-30 20:42:17+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", " 12\n", - " iris_test\n", + " none\n", " mt001\n", " sr1_0002\n", " 34.080655\n", @@ -1249,20 +1509,21 @@ " 2202.8\n", " temperature_e\n", " 2020-09-30 20:29:00+00:00\n", - " 2020-09-30 20:42:17+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", + " none\n", " mt001\n", " sr1_0002\n", " 34.080655\n", @@ -1270,20 +1531,21 @@ " 2202.8\n", " temperature_h\n", " 2020-09-30 20:29:00+00:00\n", - " 2020-09-30 20:42:17+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", + " none\n", " mt001\n", " sr1_0003\n", " 34.080655\n", @@ -1291,20 +1553,21 @@ " 2202.8\n", " bx\n", " 2020-09-30 20:54:00+00:00\n", - " 2020-09-30 21:11:02+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", + " none\n", " mt001\n", " sr1_0003\n", " 34.080655\n", @@ -1312,20 +1575,21 @@ " 2202.8\n", " by\n", " 2020-09-30 20:54:00+00:00\n", - " 2020-09-30 21:11:02+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", + " none\n", " mt001\n", " sr1_0003\n", " 34.080655\n", @@ -1333,20 +1597,21 @@ " 2202.8\n", " bz\n", " 2020-09-30 20:54:00+00:00\n", - " 2020-09-30 21:11:02+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", + " none\n", " mt001\n", " sr1_0003\n", " 34.080655\n", @@ -1354,20 +1619,21 @@ " 2202.8\n", " e1\n", " 2020-09-30 20:54:00+00:00\n", - " 2020-09-30 21:11:02+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", " none\n", + " True\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", " \n", " \n", " 18\n", - " iris_test\n", + " none\n", " mt001\n", " sr1_0003\n", " 34.080655\n", @@ -1375,20 +1641,21 @@ " 2202.8\n", " e2\n", " 2020-09-30 20:54:00+00:00\n", - " 2020-09-30 21:11:02+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", " none\n", + " True\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", " \n", " \n", " 19\n", - " iris_test\n", + " none\n", " mt001\n", " sr1_0003\n", " 34.080655\n", @@ -1396,20 +1663,21 @@ " 2202.8\n", " temperature_e\n", " 2020-09-30 20:54:00+00:00\n", - " 2020-09-30 21:11:02+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", + " none\n", " mt001\n", " sr1_0003\n", " 34.080655\n", @@ -1417,20 +1685,21 @@ " 2202.8\n", " temperature_h\n", " 2020-09-30 20:54:00+00:00\n", - " 2020-09-30 21:11:02+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", + " none\n", " mt001\n", " sr1_0004\n", " 34.080655\n", @@ -1438,20 +1707,21 @@ " 2202.8\n", " bx\n", " 2020-09-30 21:12:00+00:00\n", - " 2020-09-30 21:13:46+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", " none\n", + " True\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", " \n", " \n", " 22\n", - " iris_test\n", + " none\n", " mt001\n", " sr1_0004\n", " 34.080655\n", @@ -1459,20 +1729,21 @@ " 2202.8\n", " by\n", " 2020-09-30 21:12:00+00:00\n", - " 2020-09-30 21:13:46+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", " none\n", + " True\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", " \n", " \n", " 23\n", - " iris_test\n", + " none\n", " mt001\n", " sr1_0004\n", " 34.080655\n", @@ -1480,20 +1751,21 @@ " 2202.8\n", " bz\n", " 2020-09-30 21:12:00+00:00\n", - " 2020-09-30 21:13:46+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", " none\n", + " True\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", " \n", " \n", " 24\n", - " iris_test\n", + " none\n", " mt001\n", " sr1_0004\n", " 34.080655\n", @@ -1501,20 +1773,21 @@ " 2202.8\n", " e1\n", " 2020-09-30 21:12:00+00:00\n", - " 2020-09-30 21:13:46+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", " none\n", + " True\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", " \n", " \n", " 25\n", - " iris_test\n", + " none\n", " mt001\n", " sr1_0004\n", " 34.080655\n", @@ -1522,20 +1795,21 @@ " 2202.8\n", " e2\n", " 2020-09-30 21:12:00+00:00\n", - " 2020-09-30 21:13:46+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", " none\n", + " True\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", " \n", " \n", " 26\n", - " iris_test\n", + " none\n", " mt001\n", " sr1_0004\n", " 34.080655\n", @@ -1543,20 +1817,21 @@ " 2202.8\n", " temperature_e\n", " 2020-09-30 21:12:00+00:00\n", - " 2020-09-30 21:13:46+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", + " none\n", " mt001\n", " sr1_0004\n", " 34.080655\n", @@ -1564,20 +1839,21 @@ " 2202.8\n", " temperature_h\n", " 2020-09-30 21:12:00+00:00\n", - " 2020-09-30 21:13:46+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", + " none\n", " mt001\n", " sr1_0005\n", " 34.080655\n", @@ -1585,20 +1861,21 @@ " 2202.8\n", " bx\n", " 2020-09-30 21:14:00+00:00\n", - " 2020-10-07 17:05:47+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", " none\n", + " True\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", " \n", " \n", " 29\n", - " iris_test\n", + " none\n", " mt001\n", " sr1_0005\n", " 34.080655\n", @@ -1606,20 +1883,21 @@ " 2202.8\n", " by\n", " 2020-09-30 21:14:00+00:00\n", - " 2020-10-07 17:05:47+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", " none\n", + " True\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", " \n", " \n", " 30\n", - " iris_test\n", + " none\n", " mt001\n", " sr1_0005\n", " 34.080655\n", @@ -1627,20 +1905,21 @@ " 2202.8\n", " bz\n", " 2020-09-30 21:14:00+00:00\n", - " 2020-10-07 17:05:47+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", " none\n", + " True\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", " <HDF5 object reference>\n", " \n", " \n", " 31\n", - " iris_test\n", + " none\n", " mt001\n", " sr1_0005\n", " 34.080655\n", @@ -1648,20 +1927,21 @@ " 2202.8\n", " e1\n", " 2020-09-30 21:14:00+00:00\n", - " 2020-10-07 17:05:47+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", + " none\n", " mt001\n", " sr1_0005\n", " 34.080655\n", @@ -1669,20 +1949,21 @@ " 2202.8\n", " e2\n", " 2020-09-30 21:14:00+00:00\n", - " 2020-10-07 17:05:47+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", + " none\n", " mt001\n", " sr1_0005\n", " 34.080655\n", @@ -1690,20 +1971,21 @@ " 2202.8\n", " temperature_e\n", " 2020-09-30 21:14:00+00:00\n", - " 2020-10-07 17:05:47+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", + " none\n", " mt001\n", " sr1_0005\n", " 34.080655\n", @@ -1711,13 +1993,14 @@ " 2202.8\n", " temperature_h\n", " 2020-09-30 21:14:00+00:00\n", - " 2020-10-07 17:05:47+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", @@ -1727,116 +2010,116 @@ "" ], "text/plain": [ - " 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", + " survey station run latitude longitude elevation component \\\n", + "0 none mt001 sr1_0001 34.080655 -107.214079 2202.8 bx \n", + "1 none mt001 sr1_0001 34.080655 -107.214079 2202.8 by \n", + "2 none mt001 sr1_0001 34.080655 -107.214079 2202.8 bz \n", + "3 none mt001 sr1_0001 34.080655 -107.214079 2202.8 e1 \n", + "4 none mt001 sr1_0001 34.080655 -107.214079 2202.8 e2 \n", + "5 none mt001 sr1_0001 34.080655 -107.214079 2202.8 temperature_e \n", + "6 none mt001 sr1_0001 34.080655 -107.214079 2202.8 temperature_h \n", + "7 none mt001 sr1_0002 34.080655 -107.214079 2202.8 bx \n", + "8 none mt001 sr1_0002 34.080655 -107.214079 2202.8 by \n", + "9 none mt001 sr1_0002 34.080655 -107.214079 2202.8 bz \n", + "10 none mt001 sr1_0002 34.080655 -107.214079 2202.8 e1 \n", + "11 none mt001 sr1_0002 34.080655 -107.214079 2202.8 e2 \n", + "12 none mt001 sr1_0002 34.080655 -107.214079 2202.8 temperature_e \n", + "13 none mt001 sr1_0002 34.080655 -107.214079 2202.8 temperature_h \n", + "14 none mt001 sr1_0003 34.080655 -107.214079 2202.8 bx \n", + "15 none mt001 sr1_0003 34.080655 -107.214079 2202.8 by \n", + "16 none mt001 sr1_0003 34.080655 -107.214079 2202.8 bz \n", + "17 none mt001 sr1_0003 34.080655 -107.214079 2202.8 e1 \n", + "18 none mt001 sr1_0003 34.080655 -107.214079 2202.8 e2 \n", + "19 none mt001 sr1_0003 34.080655 -107.214079 2202.8 temperature_e \n", + "20 none mt001 sr1_0003 34.080655 -107.214079 2202.8 temperature_h \n", + "21 none mt001 sr1_0004 34.080655 -107.214079 2202.8 bx \n", + "22 none mt001 sr1_0004 34.080655 -107.214079 2202.8 by \n", + "23 none mt001 sr1_0004 34.080655 -107.214079 2202.8 bz \n", + "24 none mt001 sr1_0004 34.080655 -107.214079 2202.8 e1 \n", + "25 none mt001 sr1_0004 34.080655 -107.214079 2202.8 e2 \n", + "26 none mt001 sr1_0004 34.080655 -107.214079 2202.8 temperature_e \n", + "27 none mt001 sr1_0004 34.080655 -107.214079 2202.8 temperature_h \n", + "28 none mt001 sr1_0005 34.080655 -107.214079 2202.8 bx \n", + "29 none mt001 sr1_0005 34.080655 -107.214079 2202.8 by \n", + "30 none mt001 sr1_0005 34.080655 -107.214079 2202.8 bz \n", + "31 none mt001 sr1_0005 34.080655 -107.214079 2202.8 e1 \n", + "32 none mt001 sr1_0005 34.080655 -107.214079 2202.8 e2 \n", + "33 none mt001 sr1_0005 34.080655 -107.214079 2202.8 temperature_e \n", + "34 none mt001 sr1_0005 34.080655 -107.214079 2202.8 temperature_h \n", "\n", - " component start end \\\n", - "0 bx 2020-09-30 20:21:00+00:00 2020-09-30 20:28:16+00:00 \n", - "1 by 2020-09-30 20:21:00+00:00 2020-09-30 20:28:16+00:00 \n", - "2 bz 2020-09-30 20:21:00+00:00 2020-09-30 20:28:16+00:00 \n", - "3 e1 2020-09-30 20:21:00+00:00 2020-09-30 20:28:16+00:00 \n", - "4 e2 2020-09-30 20:21:00+00:00 2020-09-30 20:28:16+00:00 \n", - "5 temperature_e 2020-09-30 20:21:00+00:00 2020-09-30 20:28:16+00:00 \n", - "6 temperature_h 2020-09-30 20:21:00+00:00 2020-09-30 20:28:16+00:00 \n", - "7 bx 2020-09-30 20:29:00+00:00 2020-09-30 20:42:17+00:00 \n", - "8 by 2020-09-30 20:29:00+00:00 2020-09-30 20:42:17+00:00 \n", - "9 bz 2020-09-30 20:29:00+00:00 2020-09-30 20:42:17+00:00 \n", - "10 e1 2020-09-30 20:29:00+00:00 2020-09-30 20:42:17+00:00 \n", - "11 e2 2020-09-30 20:29:00+00:00 2020-09-30 20:42:17+00:00 \n", - "12 temperature_e 2020-09-30 20:29:00+00:00 2020-09-30 20:42:17+00:00 \n", - "13 temperature_h 2020-09-30 20:29:00+00:00 2020-09-30 20:42:17+00:00 \n", - "14 bx 2020-09-30 20:54:00+00:00 2020-09-30 21:11:02+00:00 \n", - "15 by 2020-09-30 20:54:00+00:00 2020-09-30 21:11:02+00:00 \n", - "16 bz 2020-09-30 20:54:00+00:00 2020-09-30 21:11:02+00:00 \n", - "17 e1 2020-09-30 20:54:00+00:00 2020-09-30 21:11:02+00:00 \n", - "18 e2 2020-09-30 20:54:00+00:00 2020-09-30 21:11:02+00:00 \n", - "19 temperature_e 2020-09-30 20:54:00+00:00 2020-09-30 21:11:02+00:00 \n", - "20 temperature_h 2020-09-30 20:54:00+00:00 2020-09-30 21:11:02+00:00 \n", - "21 bx 2020-09-30 21:12:00+00:00 2020-09-30 21:13:46+00:00 \n", - "22 by 2020-09-30 21:12:00+00:00 2020-09-30 21:13:46+00:00 \n", - "23 bz 2020-09-30 21:12:00+00:00 2020-09-30 21:13:46+00:00 \n", - "24 e1 2020-09-30 21:12:00+00:00 2020-09-30 21:13:46+00:00 \n", - "25 e2 2020-09-30 21:12:00+00:00 2020-09-30 21:13:46+00:00 \n", - "26 temperature_e 2020-09-30 21:12:00+00:00 2020-09-30 21:13:46+00:00 \n", - "27 temperature_h 2020-09-30 21:12:00+00:00 2020-09-30 21:13:46+00:00 \n", - "28 bx 2020-09-30 21:14:00+00:00 2020-10-07 17:05:47+00:00 \n", - "29 by 2020-09-30 21:14:00+00:00 2020-10-07 17:05:47+00:00 \n", - "30 bz 2020-09-30 21:14:00+00:00 2020-10-07 17:05:47+00:00 \n", - "31 e1 2020-09-30 21:14:00+00:00 2020-10-07 17:05:47+00:00 \n", - "32 e2 2020-09-30 21:14:00+00:00 2020-10-07 17:05:47+00:00 \n", - "33 temperature_e 2020-09-30 21:14:00+00:00 2020-10-07 17:05:47+00:00 \n", - "34 temperature_h 2020-09-30 21:14:00+00:00 2020-10-07 17:05:47+00:00 \n", + " start end n_samples \\\n", + "0 2020-09-30 20:21:00+00:00 2020-09-30 20:28:15+00:00 436 \n", + "1 2020-09-30 20:21:00+00:00 2020-09-30 20:28:15+00:00 436 \n", + "2 2020-09-30 20:21:00+00:00 2020-09-30 20:28:15+00:00 436 \n", + "3 2020-09-30 20:21:00+00:00 2020-09-30 20:28:15+00:00 436 \n", + "4 2020-09-30 20:21:00+00:00 2020-09-30 20:28:15+00:00 436 \n", + "5 2020-09-30 20:21:00+00:00 2020-09-30 20:28:15+00:00 436 \n", + "6 2020-09-30 20:21:00+00:00 2020-09-30 20:28:15+00:00 436 \n", + "7 2020-09-30 20:29:00+00:00 2020-09-30 20:42:16+00:00 797 \n", + "8 2020-09-30 20:29:00+00:00 2020-09-30 20:42:16+00:00 797 \n", + "9 2020-09-30 20:29:00+00:00 2020-09-30 20:42:16+00:00 797 \n", + "10 2020-09-30 20:29:00+00:00 2020-09-30 20:42:16+00:00 797 \n", + "11 2020-09-30 20:29:00+00:00 2020-09-30 20:42:16+00:00 797 \n", + "12 2020-09-30 20:29:00+00:00 2020-09-30 20:42:16+00:00 797 \n", + "13 2020-09-30 20:29:00+00:00 2020-09-30 20:42:16+00:00 797 \n", + "14 2020-09-30 20:54:00+00:00 2020-09-30 21:11:01+00:00 1022 \n", + "15 2020-09-30 20:54:00+00:00 2020-09-30 21:11:01+00:00 1022 \n", + "16 2020-09-30 20:54:00+00:00 2020-09-30 21:11:01+00:00 1022 \n", + "17 2020-09-30 20:54:00+00:00 2020-09-30 21:11:01+00:00 1022 \n", + "18 2020-09-30 20:54:00+00:00 2020-09-30 21:11:01+00:00 1022 \n", + "19 2020-09-30 20:54:00+00:00 2020-09-30 21:11:01+00:00 1022 \n", + "20 2020-09-30 20:54:00+00:00 2020-09-30 21:11:01+00:00 1022 \n", + "21 2020-09-30 21:12:00+00:00 2020-09-30 21:13:45+00:00 106 \n", + "22 2020-09-30 21:12:00+00:00 2020-09-30 21:13:45+00:00 106 \n", + "23 2020-09-30 21:12:00+00:00 2020-09-30 21:13:45+00:00 106 \n", + "24 2020-09-30 21:12:00+00:00 2020-09-30 21:13:45+00:00 106 \n", + "25 2020-09-30 21:12:00+00:00 2020-09-30 21:13:45+00:00 106 \n", + "26 2020-09-30 21:12:00+00:00 2020-09-30 21:13:45+00:00 106 \n", + "27 2020-09-30 21:12:00+00:00 2020-09-30 21:13:45+00:00 106 \n", + "28 2020-09-30 21:14:00+00:00 2020-10-07 17:05:46+00:00 589907 \n", + "29 2020-09-30 21:14:00+00:00 2020-10-07 17:05:46+00:00 589907 \n", + "30 2020-09-30 21:14:00+00:00 2020-10-07 17:05:46+00:00 589907 \n", + "31 2020-09-30 21:14:00+00:00 2020-10-07 17:05:46+00:00 589907 \n", + "32 2020-09-30 21:14:00+00:00 2020-10-07 17:05:46+00:00 589907 \n", + "33 2020-09-30 21:14:00+00:00 2020-10-07 17:05:46+00:00 589907 \n", + "34 2020-09-30 21:14:00+00:00 2020-10-07 17:05:46+00:00 589907 \n", "\n", - " n_samples sample_rate measurement_type azimuth tilt units \\\n", - "0 436 1.0 magnetic 0.0 0.0 none \n", - "1 436 1.0 magnetic 0.0 0.0 none \n", - "2 436 1.0 magnetic 0.0 0.0 none \n", - "3 436 1.0 electric 0.0 0.0 none \n", - "4 436 1.0 electric 0.0 0.0 none \n", - "5 436 1.0 auxiliary 0.0 0.0 none \n", - "6 436 1.0 auxiliary 0.0 0.0 none \n", - "7 797 1.0 magnetic 0.0 0.0 none \n", - "8 797 1.0 magnetic 0.0 0.0 none \n", - "9 797 1.0 magnetic 0.0 0.0 none \n", - "10 797 1.0 electric 0.0 0.0 none \n", - "11 797 1.0 electric 0.0 0.0 none \n", - "12 797 1.0 auxiliary 0.0 0.0 none \n", - "13 797 1.0 auxiliary 0.0 0.0 none \n", - "14 1022 1.0 magnetic 0.0 0.0 none \n", - "15 1022 1.0 magnetic 0.0 0.0 none \n", - "16 1022 1.0 magnetic 0.0 0.0 none \n", - "17 1022 1.0 electric 0.0 0.0 none \n", - "18 1022 1.0 electric 0.0 0.0 none \n", - "19 1022 1.0 auxiliary 0.0 0.0 none \n", - "20 1022 1.0 auxiliary 0.0 0.0 none \n", - "21 106 1.0 magnetic 0.0 0.0 none \n", - "22 106 1.0 magnetic 0.0 0.0 none \n", - "23 106 1.0 magnetic 0.0 0.0 none \n", - "24 106 1.0 electric 0.0 0.0 none \n", - "25 106 1.0 electric 0.0 0.0 none \n", - "26 106 1.0 auxiliary 0.0 0.0 none \n", - "27 106 1.0 auxiliary 0.0 0.0 none \n", - "28 589907 1.0 magnetic 0.0 0.0 none \n", - "29 589907 1.0 magnetic 0.0 0.0 none \n", - "30 589907 1.0 magnetic 0.0 0.0 none \n", - "31 589907 1.0 electric 0.0 0.0 none \n", - "32 589907 1.0 electric 0.0 0.0 none \n", - "33 589907 1.0 auxiliary 0.0 0.0 none \n", - "34 589907 1.0 auxiliary 0.0 0.0 none \n", + " sample_rate measurement_type azimuth tilt units has_data \\\n", + "0 1.0 magnetic 0.0 0.0 none True \n", + "1 1.0 magnetic 0.0 0.0 none True \n", + "2 1.0 magnetic 0.0 0.0 none True \n", + "3 1.0 electric 0.0 0.0 none True \n", + "4 1.0 electric 0.0 0.0 none True \n", + "5 1.0 auxiliary 0.0 0.0 none True \n", + "6 1.0 auxiliary 0.0 0.0 none True \n", + "7 1.0 magnetic 0.0 0.0 none True \n", + "8 1.0 magnetic 0.0 0.0 none True \n", + "9 1.0 magnetic 0.0 0.0 none True \n", + "10 1.0 electric 0.0 0.0 none True \n", + "11 1.0 electric 0.0 0.0 none True \n", + "12 1.0 auxiliary 0.0 0.0 none True \n", + "13 1.0 auxiliary 0.0 0.0 none True \n", + "14 1.0 magnetic 0.0 0.0 none True \n", + "15 1.0 magnetic 0.0 0.0 none True \n", + "16 1.0 magnetic 0.0 0.0 none True \n", + "17 1.0 electric 0.0 0.0 none True \n", + "18 1.0 electric 0.0 0.0 none True \n", + "19 1.0 auxiliary 0.0 0.0 none True \n", + "20 1.0 auxiliary 0.0 0.0 none True \n", + "21 1.0 magnetic 0.0 0.0 none True \n", + "22 1.0 magnetic 0.0 0.0 none True \n", + "23 1.0 magnetic 0.0 0.0 none True \n", + "24 1.0 electric 0.0 0.0 none True \n", + "25 1.0 electric 0.0 0.0 none True \n", + "26 1.0 auxiliary 0.0 0.0 none True \n", + "27 1.0 auxiliary 0.0 0.0 none True \n", + "28 1.0 magnetic 0.0 0.0 none True \n", + "29 1.0 magnetic 0.0 0.0 none True \n", + "30 1.0 magnetic 0.0 0.0 none True \n", + "31 1.0 electric 0.0 0.0 none True \n", + "32 1.0 electric 0.0 0.0 none True \n", + "33 1.0 auxiliary 0.0 0.0 none True \n", + "34 1.0 auxiliary 0.0 0.0 none True \n", "\n", " hdf5_reference run_hdf5_reference station_hdf5_reference \n", "0 \n", @@ -1903,7 +2186,16 @@ "execution_count": 11, "id": "e7d1161b-2825-438e-8ee6-756164d30f19", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[33m\u001b[1m2024-10-05T14:46:42.342332-0700 | WARNING | mth5.timeseries.run_ts | validate_metadata | start time of dataset 2020-10-01T00:00:00+00:00 does not match metadata start 2020-09-30T21:14:00+00:00 updating metatdata value to 2020-10-01T00:00:00+00:00\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-05T14:46:42.343259-0700 | WARNING | mth5.timeseries.run_ts | validate_metadata | end time of dataset 2020-10-04T23:59:59+00:00 does not match metadata end 2020-10-07T17:05:46+00:00 updating metatdata value to 2020-10-04T23:59:59+00:00\u001b[0m\n" + ] + } + ], "source": [ "run_group = m.get_run(\"mt001\", \"sr1_0005\", survey=\"iris_test\")\n", "run_ts_object = run_group.to_runts(start=\"2020-10-01T00:00:00\", n_samples=86400*4)" @@ -1923,7 +2215,7 @@ "\tchannels_recorded_auxiliary = ['temperature_e', 'temperature_h']\n", "\tchannels_recorded_electric = ['e1', 'e2']\n", "\tchannels_recorded_magnetic = ['bx', 'by', 'bz']\n", - "\tdata_logger.firmware.author = none\n", + "\tdata_logger.firmware.author = None\n", "\tdata_logger.firmware.name = None\n", "\tdata_logger.firmware.version = None\n", "\tdata_logger.id = None\n", @@ -1940,7 +2232,7 @@ "\tid = sr1_0005\n", "\tmth5_type = Run\n", "\tsample_rate = 1.0\n", - "\ttime_period.end = 2020-10-07T17:05:47+00:00\n", + "\ttime_period.end = 2020-10-07T17:05:46+00:00\n", "\ttime_period.start = 2020-09-30T21:14:00+00:00\n" ] } @@ -1957,24 +2249,9 @@ "outputs": [ { "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "efde7a2db7d844d8a621cc7f576ceaa4", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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", 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