diff --git a/notebooks/mtpy/12_aurora_processing.ipynb b/notebooks/mtpy/12_aurora_processing.ipynb index f184f98..91d7a57 100644 --- a/notebooks/mtpy/12_aurora_processing.ipynb +++ b/notebooks/mtpy/12_aurora_processing.ipynb @@ -54,8 +54,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1m2024-10-10T13:04:44.128710-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", - "\u001b[1m2024-10-10T13:04:44.570037-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\remote\\remote_02.h5\u001b[0m\n" + "\u001b[1m2024-10-10T14:04:49.524821-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", + "\u001b[1m2024-10-10T14:04:49.981028-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\remote\\remote_02.h5\u001b[0m\n" ] } ], @@ -419,11 +419,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1m2024-10-10T13:04:44.645072-0700 | INFO | mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column fc, adding and setting dtype to .\u001b[0m\n", - "\u001b[1m2024-10-10T13:04:44.646088-0700 | INFO | mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column remote, adding and setting dtype to .\u001b[0m\n", - "\u001b[1m2024-10-10T13:04:44.647074-0700 | INFO | mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column run_dataarray, adding and setting dtype to .\u001b[0m\n", - "\u001b[1m2024-10-10T13:04:44.648073-0700 | INFO | mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column stft, adding and setting dtype to .\u001b[0m\n", - "\u001b[1m2024-10-10T13:04:44.649076-0700 | INFO | mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column mth5_obj, adding and setting dtype to .\u001b[0m\n" + "\u001b[1m2024-10-10T14:04:50.033301-0700 | INFO | mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column fc, adding and setting dtype to .\u001b[0m\n", + "\u001b[1m2024-10-10T14:04:50.033301-0700 | INFO | mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column remote, adding and setting dtype to .\u001b[0m\n", + "\u001b[1m2024-10-10T14:04:50.042487-0700 | INFO | mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column run_dataarray, adding and setting dtype to .\u001b[0m\n", + "\u001b[1m2024-10-10T14:04:50.044503-0700 | INFO | mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column stft, adding and setting dtype to .\u001b[0m\n", + "\u001b[1m2024-10-10T14:04:50.045495-0700 | INFO | mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column mth5_obj, adding and setting dtype to .\u001b[0m\n" ] } ], @@ -472,11 +472,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1m2024-10-10T13:04:44.716560-0700 | INFO | mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column fc, adding and setting dtype to .\u001b[0m\n", - "\u001b[1m2024-10-10T13:04:44.716560-0700 | INFO | mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column remote, adding and setting dtype to .\u001b[0m\n", - "\u001b[1m2024-10-10T13:04:44.716560-0700 | INFO | mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column run_dataarray, adding and setting dtype to .\u001b[0m\n", - "\u001b[1m2024-10-10T13:04:44.716560-0700 | INFO | mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column stft, adding and setting dtype to .\u001b[0m\n", - "\u001b[1m2024-10-10T13:04:44.716560-0700 | INFO | mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column mth5_obj, adding and setting dtype to .\u001b[0m\n" + "\u001b[1m2024-10-10T14:04:50.127927-0700 | INFO | mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column fc, adding and setting dtype to .\u001b[0m\n", + "\u001b[1m2024-10-10T14:04:50.127927-0700 | INFO | mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column remote, adding and setting dtype to .\u001b[0m\n", + "\u001b[1m2024-10-10T14:04:50.127927-0700 | INFO | mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column run_dataarray, adding and setting dtype to .\u001b[0m\n", + "\u001b[1m2024-10-10T14:04:50.127927-0700 | INFO | mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column stft, adding and setting dtype to .\u001b[0m\n", + "\u001b[1m2024-10-10T14:04:50.142500-0700 | INFO | mtpy.processing.kernel_dataset | _add_columns | KernelDataset DataFrame needs column mth5_obj, adding and setting dtype to .\u001b[0m\n" ] } ], @@ -544,8 +544,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "\u001b[1m2024-10-10T13:04:44.858025-0700 | INFO | aurora.pipelines.transfer_function_kernel | show_processing_summary | Processing Summary Dataframe:\u001b[0m\n", - "\u001b[1m2024-10-10T13:04:44.873694-0700 | INFO | aurora.pipelines.transfer_function_kernel | show_processing_summary | \n", + "\u001b[1m2024-10-10T14:04:50.259307-0700 | INFO | aurora.pipelines.transfer_function_kernel | show_processing_summary | Processing Summary Dataframe:\u001b[0m\n", + "\u001b[1m2024-10-10T14:04:50.266907-0700 | INFO | 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 69521.993333 True 26352000 sr150_0001 1003 MIST True None None 0 1.0 150.000000 0.853333 128 10428298.0 162941.0\n", "1 69521.993333 True 26352000 sr150_0001 1003 MIST True None None 1 4.0 37.500000 3.413333 128 2607074.0 40734.0\n", @@ -563,208 +563,208 @@ "13 69521.993333 True 10428300 sr150_0001 9043 MIST False None None 5 4.0 0.146484 873.813333 128 10183.0 158.0\n", "14 69521.993333 True 10428300 sr150_0001 9043 MIST False None None 6 4.0 0.036621 3495.253333 128 2545.0 38.0\n", "15 69521.993333 True 10428300 sr150_0001 9043 MIST False None None 7 4.0 0.009155 13981.013333 128 636.0 8.0\u001b[0m\n", - "\u001b[1m2024-10-10T13:04:44.873694-0700 | INFO | aurora.pipelines.transfer_function_kernel | memory_check | Total memory: 31.83 GB\u001b[0m\n", - "\u001b[1m2024-10-10T13:04:44.873694-0700 | INFO | aurora.pipelines.transfer_function_kernel | memory_check | Total Bytes of Raw Data: 0.155 GB\u001b[0m\n", - "\u001b[1m2024-10-10T13:04:44.873694-0700 | INFO | aurora.pipelines.transfer_function_kernel | memory_check | Raw Data will use: 0.488 % of memory\u001b[0m\n", - "\u001b[1m2024-10-10T13:04:44.904915-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 1003, run: sr150_0001-- Fourier coefficients will be computed\u001b[0m\n", - "\u001b[1m2024-10-10T13:04:44.904915-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\remote\\remote_02.h5\u001b[0m\n", - "\u001b[1m2024-10-10T13:04:44.983505-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr150_0001-- Fourier coefficients will be computed\u001b[0m\n", - "\u001b[1m2024-10-10T13:04:44.983505-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", - "\u001b[1m2024-10-10T13:04:44.983505-0700 | INFO | aurora.pipelines.transfer_function_kernel | check_if_fcs_already_exist | FC levels not present\u001b[0m\n", - "\u001b[1m2024-10-10T13:04:44.983505-0700 | INFO | aurora.pipelines.process_mth5 | process_mth5_legacy | Processing config indicates 8 decimation levels\u001b[0m\n", - "\u001b[1m2024-10-10T13:04:44.983505-0700 | INFO | aurora.pipelines.transfer_function_kernel | valid_decimations | After validation there are 8 valid decimation levels\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:04:47.557703-0700 | WARNING | mth5.timeseries.run_ts | validate_metadata | start time of dataset 2024-03-28T14:45:58+00:00 does not match metadata start 2024-03-27T13:51:05+00:00 updating metatdata value to 2024-03-28T14:45:58+00:00\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:04:47.557703-0700 | WARNING | mth5.timeseries.run_ts | validate_metadata | end time of dataset 2024-03-29T10:04:39.993333333+00:00 does not match metadata end 2024-03-29T14:39:04.993333333+00:00 updating metatdata value to 2024-03-29T10:04:39.993333333+00:00\u001b[0m\n", - "\u001b[1m2024-10-10T13:04:47.683725-0700 | INFO | mtpy.processing.kernel_dataset | initialize_dataframe_for_processing | Dataset dataframe initialized successfully\u001b[0m\n", - "\u001b[1m2024-10-10T13:04:47.699326-0700 | INFO | aurora.pipelines.transfer_function_kernel | update_dataset_df | Dataset Dataframe Updated for decimation level 0 Successfully\u001b[0m\n", - "\u001b[1m2024-10-10T13:04:59.504339-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:04.399589-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:04.621312-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.078307s (12.770212Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:07.786062-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.061305s (16.311810Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:11.270564-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.049084s (20.373444Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:15.363737-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.039041s (25.614250Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:20.372573-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.031217s (32.034227Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:26.491910-0700 | INFO | aurora.pipelines.transfer_function_kernel | update_dataset_df | DECIMATION LEVEL 1\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:27.843789-0700 | INFO | aurora.pipelines.transfer_function_kernel | update_dataset_df | Dataset Dataframe Updated for decimation level 1 Successfully\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:30.837436-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:32.277877-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:32.473661-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.361306s (2.767741Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:33.129384-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.286693s (3.488051Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:33.976157-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.222071s (4.503055Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:35.483825-0700 | INFO | aurora.pipelines.transfer_function_kernel | update_dataset_df | DECIMATION LEVEL 2\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:35.860859-0700 | INFO | aurora.pipelines.transfer_function_kernel | update_dataset_df | Dataset Dataframe Updated for decimation level 2 Successfully\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:36.849331-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:37.288554-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:37.335435-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 2.744423s (0.364375Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:37.539446-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 2.125821s (0.470406Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:37.773360-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 1.617507s (0.618235Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:38.039992-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 1.252916s (0.798138Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:38.324253-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.980884s (1.019488Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:38.574945-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.785336s (1.273340Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:38.968177-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.624652s (1.600891Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:39.327421-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.499466s (2.002139Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:40.208208-0700 | INFO | aurora.pipelines.transfer_function_kernel | update_dataset_df | DECIMATION LEVEL 3\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:40.324970-0700 | INFO | aurora.pipelines.transfer_function_kernel | update_dataset_df | Dataset Dataframe Updated for decimation level 3 Successfully\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:40.804102-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:41.007201-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:41.022829-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 10.977693s (0.091094Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:41.169886-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 9.133993s (0.109481Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:41.320436-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 7.347381s (0.136103Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:41.493152-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 5.780888s (0.172984Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:41.670494-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 4.587089s (0.218003Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:41.854283-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 3.553144s (0.281441Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:42.450335-0700 | INFO | aurora.pipelines.transfer_function_kernel | update_dataset_df | DECIMATION LEVEL 4\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:42.482110-0700 | INFO | aurora.pipelines.transfer_function_kernel | update_dataset_df | Dataset Dataframe Updated for decimation level 4 Successfully\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:42.874342-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:43.096746-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:43.112370-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 43.910772s (0.022773Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:43.253407-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 36.535971s (0.027370Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:43.394967-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 29.389523s (0.034026Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:43.517698-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 23.123553s (0.043246Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:43.643136-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 18.348355s (0.054501Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:43.774113-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 14.212575s (0.070360Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:44.272211-0700 | INFO | aurora.pipelines.transfer_function_kernel | update_dataset_df | DECIMATION LEVEL 5\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:44.288259-0700 | INFO | aurora.pipelines.transfer_function_kernel | update_dataset_df | Dataset Dataframe Updated for decimation level 5 Successfully\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:44.600404-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:44.741385-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:44.757010-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 175.643089s (0.005693Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:44.882811-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 146.143884s (0.006843Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:45.008353-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 117.558091s (0.008506Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:45.117848-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 92.494212s (0.010811Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:45.243216-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 73.393421s (0.013625Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:45.368618-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 56.850300s (0.017590Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:45.824438-0700 | INFO | aurora.pipelines.transfer_function_kernel | update_dataset_df | DECIMATION LEVEL 6\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:45.840079-0700 | INFO | aurora.pipelines.transfer_function_kernel | update_dataset_df | Dataset Dataframe Updated for decimation level 6 Successfully\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:46.136064-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:46.280129-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:46.280129-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 796.642169s (0.001255Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:46.405664-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 511.196476s (0.001956Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:46.515534-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 339.089381s (0.002949Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:46.625403-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 243.378203s (0.004109Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:46.955526-0700 | INFO | aurora.pipelines.transfer_function_kernel | update_dataset_df | DECIMATION LEVEL 7\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:46.971151-0700 | INFO | aurora.pipelines.transfer_function_kernel | update_dataset_df | Dataset Dataframe Updated for decimation level 7 Successfully\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:47.236709-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:47.377833-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:47.381840-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 16143.883623s (0.000062Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:47.487996-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 7219.764240s (0.000139Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:47.613500-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 4726.445162s (0.000212Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:47.727684-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 3186.568678s (0.000314Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:47.834400-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 2044.785902s (0.000489Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:47.944276-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 1305.155635s (0.000766Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:48.648993-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:48.648993-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\remote\\remote_02.h5\u001b[0m\n", - "\u001b[31m\u001b[1m2024-10-10T13:05:48.711959-0700 | ERROR | 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[1m2024-10-10T13:05:48.711959-0700 | ERROR | 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[1m2024-10-10T13:05:48.711959-0700 | ERROR | 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[1m2024-10-10T13:05:48.711959-0700 | ERROR | 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[1m2024-10-10T13:05:48.711959-0700 | ERROR | 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[1m2024-10-10T13:05:48.711959-0700 | ERROR | 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[1m2024-10-10T13:05:48.727590-0700 | ERROR | 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[1m2024-10-10T13:05:48.727590-0700 | ERROR | 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[1m2024-10-10T13:05:48.727590-0700 | ERROR | 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[1m2024-10-10T13:05:48.727590-0700 | ERROR | 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[1m2024-10-10T13:05:48.727590-0700 | ERROR | 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[1m2024-10-10T13:05:48.727590-0700 | ERROR | 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[1m2024-10-10T13:05:48.743178-0700 | ERROR | 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[1m2024-10-10T13:05:48.743178-0700 | ERROR | 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[1m2024-10-10T13:05:48.743178-0700 | ERROR | 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[1m2024-10-10T13:05:48.743178-0700 | ERROR | 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[1m2024-10-10T13:05:48.758806-0700 | ERROR | 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[1m2024-10-10T13:05:48.758806-0700 | ERROR | 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[1m2024-10-10T13:05:48.758806-0700 | ERROR | 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[1m2024-10-10T13:05:48.758806-0700 | ERROR | 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[1m2024-10-10T13:05:48.758806-0700 | ERROR | 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[1m2024-10-10T13:05:48.758806-0700 | ERROR | 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[1m2024-10-10T13:05:48.775056-0700 | ERROR | 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[1m2024-10-10T13:05:48.775738-0700 | ERROR | 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[1m2024-10-10T13:05:48.775738-0700 | ERROR | 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[1m2024-10-10T13:05:48.788263-0700 | ERROR | 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[1m2024-10-10T13:05:48.790302-0700 | ERROR | 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[1m2024-10-10T13:05:48.791269-0700 | ERROR | 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[1m2024-10-10T13:05:48.792270-0700 | ERROR | 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[1m2024-10-10T13:05:48.793268-0700 | ERROR | 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[1m2024-10-10T13:05:48.804298-0700 | ERROR | 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[1m2024-10-10T13:05:48.804298-0700 | ERROR | 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[1m2024-10-10T13:05:48.805268-0700 | ERROR | 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[1m2024-10-10T13:05:48.806269-0700 | ERROR | 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[1m2024-10-10T13:05:48.807272-0700 | ERROR | 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[1m2024-10-10T13:05:48.818295-0700 | ERROR | 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[1m2024-10-10T13:05:48.819268-0700 | ERROR | 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[1m2024-10-10T13:05:48.820269-0700 | ERROR | 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[1m2024-10-10T13:05:48.821269-0700 | ERROR | 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[1m2024-10-10T13:05:48.822346-0700 | ERROR | 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[1m2024-10-10T13:05:48.822346-0700 | ERROR | 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[1m2024-10-10T13:05:48.822346-0700 | ERROR | 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[1m2024-10-10T13:05:48.822346-0700 | ERROR | 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[1m2024-10-10T13:05:48.822346-0700 | ERROR | 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[1m2024-10-10T13:05:48.822346-0700 | ERROR | 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[1m2024-10-10T13:05:48.837974-0700 | ERROR | 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[1m2024-10-10T13:05:48.837974-0700 | ERROR | 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[1m2024-10-10T13:05:48.837974-0700 | ERROR | 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[1m2024-10-10T13:05:48.837974-0700 | ERROR | 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[1m2024-10-10T13:05:48.837974-0700 | ERROR | 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[1m2024-10-10T13:05:48.853602-0700 | ERROR | 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[1m2024-10-10T13:05:48.853602-0700 | ERROR | 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[1m2024-10-10T13:05:48.853602-0700 | ERROR | 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[1m2024-10-10T13:05:48.853602-0700 | ERROR | 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[1m2024-10-10T13:05:48.853602-0700 | ERROR | 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[1m2024-10-10T13:05:48.869226-0700 | ERROR | 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[1m2024-10-10T13:05:48.869226-0700 | ERROR | 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[1m2024-10-10T13:05:48.869226-0700 | ERROR | 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[1m2024-10-10T13:05:48.869226-0700 | ERROR | 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[1m2024-10-10T13:05:48.869226-0700 | ERROR | 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[1m2024-10-10T13:05:48.885306-0700 | ERROR | 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[1m2024-10-10T13:05:48.885306-0700 | ERROR | 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[1m2024-10-10T13:05:48.885306-0700 | ERROR | 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[1m2024-10-10T13:05:48.885306-0700 | ERROR | 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[1m2024-10-10T13:05:48.885306-0700 | ERROR | 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[1m2024-10-10T13:05:48.900969-0700 | ERROR | 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[1m2024-10-10T13:05:48.900969-0700 | ERROR | 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[1m2024-10-10T13:05:48.900969-0700 | ERROR | 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[1m2024-10-10T13:05:48.900969-0700 | ERROR | 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[1m2024-10-10T13:05:48.900969-0700 | ERROR | 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[1m2024-10-10T13:05:48.900969-0700 | ERROR | 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[1m2024-10-10T13:05:48.900969-0700 | ERROR | 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[1m2024-10-10T13:05:48.916565-0700 | ERROR | 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[1m2024-10-10T13:05:48.916565-0700 | ERROR | 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[1m2024-10-10T13:05:48.916565-0700 | ERROR | 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[1m2024-10-10T13:05:48.916565-0700 | ERROR | 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[1m2024-10-10T13:05:48.916565-0700 | ERROR | 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[1m2024-10-10T13:05:48.916565-0700 | ERROR | 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[1m2024-10-10T13:05:48.916565-0700 | ERROR | 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[1m2024-10-10T13:05:48.932189-0700 | ERROR | 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[1m2024-10-10T13:05:48.932189-0700 | ERROR | 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[1m2024-10-10T13:05:48.932189-0700 | ERROR | 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[1m2024-10-10T13:05:48.932189-0700 | ERROR | 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[1m2024-10-10T13:05:48.932189-0700 | ERROR | 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[1m2024-10-10T13:05:48.932189-0700 | ERROR | 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[1m2024-10-10T13:05:48.947815-0700 | ERROR | 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[1m2024-10-10T13:05:48.947815-0700 | ERROR | 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[1m2024-10-10T13:05:48.963442-0700 | ERROR | 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[1m2024-10-10T13:05:48.963442-0700 | ERROR | 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[1m2024-10-10T13:05:48.963442-0700 | ERROR | 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[1m2024-10-10T13:05:48.979070-0700 | ERROR | 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[1m2024-10-10T13:05:48.979070-0700 | ERROR | 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[1m2024-10-10T13:05:48.979070-0700 | ERROR | 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[1m2024-10-10T13:05:48.979070-0700 | ERROR | 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[1m2024-10-10T13:05:48.979070-0700 | ERROR | 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[1m2024-10-10T13:05:48.995120-0700 | ERROR | 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[1m2024-10-10T13:05:48.995120-0700 | ERROR | 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[1m2024-10-10T13:05:48.995120-0700 | ERROR | 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[1m2024-10-10T13:05:48.995120-0700 | ERROR | 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[1m2024-10-10T13:05:48.995120-0700 | ERROR | 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[1m2024-10-10T13:05:48.995120-0700 | ERROR | 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[1m2024-10-10T13:05:49.010786-0700 | ERROR | 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[1m2024-10-10T13:05:49.010786-0700 | ERROR | 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[1m2024-10-10T13:05:49.010786-0700 | ERROR | 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[1m2024-10-10T13:05:49.010786-0700 | ERROR | 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[1m2024-10-10T13:05:49.010786-0700 | ERROR | 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[1m2024-10-10T13:05:49.010786-0700 | ERROR | 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[1m2024-10-10T13:05:49.010786-0700 | ERROR | 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[1m2024-10-10T13:05:49.026384-0700 | ERROR | 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[1m2024-10-10T13:05:49.026384-0700 | ERROR | 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[1m2024-10-10T13:05:49.026384-0700 | INFO | aurora.pipelines.transfer_function_kernel | show_processing_summary | Processing Summary Dataframe:\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:49.057661-0700 | INFO | aurora.pipelines.transfer_function_kernel | show_processing_summary | \n", + "\u001b[1m2024-10-10T14:04:50.271906-0700 | INFO | aurora.pipelines.transfer_function_kernel | memory_check | Total memory: 31.83 GB\u001b[0m\n", + "\u001b[1m2024-10-10T14:04:50.272906-0700 | INFO | aurora.pipelines.transfer_function_kernel | memory_check | Total Bytes of Raw Data: 0.155 GB\u001b[0m\n", + "\u001b[1m2024-10-10T14:04:50.273875-0700 | INFO | aurora.pipelines.transfer_function_kernel | memory_check | Raw Data will use: 0.488 % of memory\u001b[0m\n", + "\u001b[1m2024-10-10T14:04:50.324721-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 1003, run: sr150_0001-- Fourier coefficients will be computed\u001b[0m\n", + "\u001b[1m2024-10-10T14:04:50.325723-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\remote\\remote_02.h5\u001b[0m\n", + "\u001b[1m2024-10-10T14:04:50.393013-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr150_0001-- Fourier coefficients will be computed\u001b[0m\n", + "\u001b[1m2024-10-10T14:04:50.393013-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", + "\u001b[1m2024-10-10T14:04:50.393013-0700 | INFO | aurora.pipelines.transfer_function_kernel | check_if_fcs_already_exist | FC levels not present\u001b[0m\n", + "\u001b[1m2024-10-10T14:04:50.393013-0700 | INFO | aurora.pipelines.process_mth5 | process_mth5_legacy | Processing config indicates 8 decimation levels\u001b[0m\n", + "\u001b[1m2024-10-10T14:04:50.393013-0700 | INFO | aurora.pipelines.transfer_function_kernel | valid_decimations | After validation there are 8 valid decimation levels\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:04:53.025424-0700 | WARNING | mth5.timeseries.run_ts | validate_metadata | start time of dataset 2024-03-28T14:45:58+00:00 does not match metadata start 2024-03-27T13:51:05+00:00 updating metatdata value to 2024-03-28T14:45:58+00:00\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:04:53.025424-0700 | WARNING | mth5.timeseries.run_ts | validate_metadata | end time of dataset 2024-03-29T10:04:39.993333333+00:00 does not match metadata end 2024-03-29T14:39:04.993333333+00:00 updating metatdata value to 2024-03-29T10:04:39.993333333+00:00\u001b[0m\n", + "\u001b[1m2024-10-10T14:04:53.166419-0700 | INFO | mtpy.processing.kernel_dataset | initialize_dataframe_for_processing | Dataset dataframe initialized successfully\u001b[0m\n", + "\u001b[1m2024-10-10T14:04:53.166419-0700 | INFO | aurora.pipelines.transfer_function_kernel | update_dataset_df | Dataset Dataframe Updated for decimation level 0 Successfully\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:05.294821-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:10.113824-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:10.326918-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.078307s (12.770212Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:13.212011-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.061305s (16.311810Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:16.098058-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.049084s (20.373444Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:19.851015-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.039041s (25.614250Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:24.805160-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.031217s (32.034227Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:30.943890-0700 | INFO | aurora.pipelines.transfer_function_kernel | update_dataset_df | DECIMATION LEVEL 1\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:32.294543-0700 | INFO | aurora.pipelines.transfer_function_kernel | update_dataset_df | Dataset Dataframe Updated for decimation level 1 Successfully\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:35.380823-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:36.680291-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:36.865550-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.361306s (2.767741Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:37.515655-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.286693s (3.488051Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:38.366621-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.222071s (4.503055Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:39.951073-0700 | INFO | aurora.pipelines.transfer_function_kernel | update_dataset_df | DECIMATION LEVEL 2\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:40.351798-0700 | INFO | aurora.pipelines.transfer_function_kernel | update_dataset_df | Dataset Dataframe Updated for decimation level 2 Successfully\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:41.335629-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:41.802553-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:41.869390-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 2.744423s (0.364375Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:42.069514-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 2.125821s (0.470406Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:42.336234-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 1.617507s (0.618235Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:42.603297-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 1.252916s (0.798138Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:42.936736-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.980884s (1.019488Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:43.299916-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.785336s (1.273340Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:43.620706-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.624652s (1.600891Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:44.021111-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.499466s (2.002139Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:44.938632-0700 | INFO | aurora.pipelines.transfer_function_kernel | update_dataset_df | DECIMATION LEVEL 3\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:45.055424-0700 | INFO | aurora.pipelines.transfer_function_kernel | update_dataset_df | Dataset Dataframe Updated for decimation level 3 Successfully\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:45.539070-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:45.773197-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:45.805818-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 10.977693s (0.091094Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:45.955954-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 9.133993s (0.109481Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:46.122722-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 7.347381s (0.136103Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:46.306939-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 5.780888s (0.172984Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:46.497568-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 4.587089s (0.218003Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:46.691189-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 3.553144s (0.281441Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:47.224545-0700 | INFO | aurora.pipelines.transfer_function_kernel | update_dataset_df | DECIMATION LEVEL 4\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:47.272241-0700 | INFO | aurora.pipelines.transfer_function_kernel | update_dataset_df | Dataset Dataframe Updated for decimation level 4 Successfully\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:47.640760-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:47.841487-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:47.858970-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 43.910772s (0.022773Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:47.974948-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 36.535971s (0.027370Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:48.107967-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 29.389523s (0.034026Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:48.224642-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 23.123553s (0.043246Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:48.358027-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 18.348355s (0.054501Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:48.491503-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 14.212575s (0.070360Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:49.025277-0700 | INFO | aurora.pipelines.transfer_function_kernel | update_dataset_df | DECIMATION LEVEL 5\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:49.042127-0700 | INFO | aurora.pipelines.transfer_function_kernel | update_dataset_df | Dataset Dataframe Updated for decimation level 5 Successfully\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:49.375499-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:49.560362-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:49.575589-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 175.643089s (0.005693Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:49.692710-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 146.143884s (0.006843Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:49.842598-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 117.558091s (0.008506Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:49.959403-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 92.494212s (0.010811Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:50.076276-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 73.393421s (0.013625Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:50.208727-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 56.850300s (0.017590Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:50.643290-0700 | INFO | aurora.pipelines.transfer_function_kernel | update_dataset_df | DECIMATION LEVEL 6\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:50.677966-0700 | INFO | aurora.pipelines.transfer_function_kernel | update_dataset_df | Dataset Dataframe Updated for decimation level 6 Successfully\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:50.943532-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:51.093578-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:51.093578-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 796.642169s (0.001255Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:51.230574-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 511.196476s (0.001956Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:51.344095-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 339.089381s (0.002949Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:51.460635-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 243.378203s (0.004109Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:51.811109-0700 | INFO | aurora.pipelines.transfer_function_kernel | update_dataset_df | DECIMATION LEVEL 7\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:51.827710-0700 | INFO | aurora.pipelines.transfer_function_kernel | update_dataset_df | Dataset Dataframe Updated for decimation level 7 Successfully\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:52.127795-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:52.279499-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:52.291477-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 16143.883623s (0.000062Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:52.395281-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 7219.764240s (0.000139Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:52.529039-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 4726.445162s (0.000212Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:52.645140-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 3186.568678s (0.000314Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:52.778426-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 2044.785902s (0.000489Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:52.896014-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 1305.155635s (0.000766Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:53.629047-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:53.629047-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\remote\\remote_02.h5\u001b[0m\n", + "\u001b[31m\u001b[1m2024-10-10T14:05:53.679218-0700 | ERROR | 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[1m2024-10-10T14:05:53.679218-0700 | ERROR | 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[1m2024-10-10T14:05:53.679218-0700 | ERROR | 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[1m2024-10-10T14:05:53.679218-0700 | ERROR | 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[1m2024-10-10T14:05:53.694747-0700 | ERROR | 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[1m2024-10-10T14:05:53.696649-0700 | ERROR | 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[1m2024-10-10T14:05:53.696649-0700 | ERROR | 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[1m2024-10-10T14:05:53.696649-0700 | ERROR | 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[1m2024-10-10T14:05:53.696649-0700 | ERROR | 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[1m2024-10-10T14:05:53.696649-0700 | ERROR | 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[1m2024-10-10T14:05:53.712441-0700 | ERROR | 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[1m2024-10-10T14:05:53.712441-0700 | ERROR | 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[1m2024-10-10T14:05:53.712441-0700 | ERROR | 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[1m2024-10-10T14:05:53.712441-0700 | ERROR | 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[1m2024-10-10T14:05:53.712441-0700 | ERROR | 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[1m2024-10-10T14:05:53.728978-0700 | ERROR | 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[1m2024-10-10T14:05:53.728978-0700 | ERROR | 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[1m2024-10-10T14:05:53.728978-0700 | ERROR | 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[1m2024-10-10T14:05:53.728978-0700 | ERROR | 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[1m2024-10-10T14:05:53.728978-0700 | ERROR | 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[1m2024-10-10T14:05:53.746392-0700 | ERROR | 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[1m2024-10-10T14:05:53.746392-0700 | ERROR | 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[1m2024-10-10T14:05:53.746392-0700 | ERROR | 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[1m2024-10-10T14:05:53.746392-0700 | ERROR | 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[1m2024-10-10T14:05:53.746392-0700 | ERROR | 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[1m2024-10-10T14:05:53.761907-0700 | ERROR | 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[1m2024-10-10T14:05:53.763348-0700 | ERROR | 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[1m2024-10-10T14:05:53.763348-0700 | ERROR | 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[1m2024-10-10T14:05:53.763348-0700 | ERROR | 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[1m2024-10-10T14:05:53.763348-0700 | ERROR | 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[1m2024-10-10T14:05:53.763348-0700 | ERROR | 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[1m2024-10-10T14:05:53.763348-0700 | ERROR | 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[1m2024-10-10T14:05:53.763348-0700 | ERROR | 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[1m2024-10-10T14:05:53.779107-0700 | ERROR | 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[1m2024-10-10T14:05:53.779107-0700 | ERROR | 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[1m2024-10-10T14:05:53.779107-0700 | ERROR | 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[1m2024-10-10T14:05:53.779107-0700 | ERROR | 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[1m2024-10-10T14:05:53.779107-0700 | ERROR | 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[1m2024-10-10T14:05:53.779107-0700 | ERROR | 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[1m2024-10-10T14:05:53.795762-0700 | ERROR | 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[1m2024-10-10T14:05:53.795762-0700 | ERROR | 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[1m2024-10-10T14:05:53.795762-0700 | ERROR | 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[1m2024-10-10T14:05:53.795762-0700 | ERROR | 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[1m2024-10-10T14:05:53.795762-0700 | ERROR | 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[1m2024-10-10T14:05:53.795762-0700 | ERROR | 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[1m2024-10-10T14:05:53.813079-0700 | ERROR | 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[1m2024-10-10T14:05:53.813079-0700 | ERROR | 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[1m2024-10-10T14:05:53.813079-0700 | ERROR | 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[1m2024-10-10T14:05:53.813079-0700 | ERROR | 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[1m2024-10-10T14:05:53.813079-0700 | ERROR | 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[1m2024-10-10T14:05:53.829184-0700 | ERROR | 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[1m2024-10-10T14:05:53.829184-0700 | ERROR | 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[1m2024-10-10T14:05:53.829184-0700 | ERROR | 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[1m2024-10-10T14:05:53.829184-0700 | ERROR | 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[1m2024-10-10T14:05:53.829184-0700 | ERROR | 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[1m2024-10-10T14:05:53.846840-0700 | ERROR | 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[1m2024-10-10T14:05:53.846840-0700 | ERROR | 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[1m2024-10-10T14:05:53.846840-0700 | ERROR | 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[1m2024-10-10T14:05:53.846840-0700 | ERROR | 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[1m2024-10-10T14:05:53.846840-0700 | ERROR | 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[1m2024-10-10T14:05:53.846840-0700 | ERROR | 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[1m2024-10-10T14:05:53.861883-0700 | ERROR | 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[1m2024-10-10T14:05:53.863938-0700 | ERROR | 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[1m2024-10-10T14:05:53.863938-0700 | ERROR | 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[1m2024-10-10T14:05:53.863938-0700 | ERROR | 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[1m2024-10-10T14:05:53.863938-0700 | ERROR | 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[1m2024-10-10T14:05:53.863938-0700 | ERROR | 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[1m2024-10-10T14:05:53.863938-0700 | ERROR | 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[1m2024-10-10T14:05:53.878714-0700 | ERROR | 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[1m2024-10-10T14:05:53.880724-0700 | ERROR | 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[1m2024-10-10T14:05:53.889754-0700 | ERROR | 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[1m2024-10-10T14:05:53.890755-0700 | ERROR | 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[1m2024-10-10T14:05:53.891725-0700 | ERROR | 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[1m2024-10-10T14:05:53.892727-0700 | ERROR | 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[1m2024-10-10T14:05:53.895725-0700 | ERROR | 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[1m2024-10-10T14:05:53.910721-0700 | ERROR | 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[1m2024-10-10T14:05:53.912786-0700 | ERROR | 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[1m2024-10-10T14:05:53.912786-0700 | ERROR | 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[1m2024-10-10T14:05:53.912786-0700 | ERROR | 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[1m2024-10-10T14:05:53.912786-0700 | ERROR | 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[1m2024-10-10T14:05:53.928448-0700 | ERROR | 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[1m2024-10-10T14:05:53.929989-0700 | ERROR | 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[1m2024-10-10T14:05:53.929989-0700 | ERROR | 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[1m2024-10-10T14:05:53.929989-0700 | ERROR | 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[1m2024-10-10T14:05:53.929989-0700 | ERROR | 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[1m2024-10-10T14:05:53.945987-0700 | ERROR | 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[1m2024-10-10T14:05:53.945987-0700 | ERROR | 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[1m2024-10-10T14:05:53.945987-0700 | ERROR | 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[1m2024-10-10T14:05:53.945987-0700 | ERROR | 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[1m2024-10-10T14:05:53.945987-0700 | ERROR | 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[1m2024-10-10T14:05:53.945987-0700 | ERROR | 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[1m2024-10-10T14:05:53.945987-0700 | ERROR | 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[1m2024-10-10T14:05:53.945987-0700 | ERROR | 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[1m2024-10-10T14:05:53.962663-0700 | ERROR | 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[1m2024-10-10T14:05:53.962663-0700 | ERROR | 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[1m2024-10-10T14:05:53.962663-0700 | ERROR | 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[1m2024-10-10T14:05:53.962663-0700 | ERROR | 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[1m2024-10-10T14:05:53.962663-0700 | ERROR | 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[1m2024-10-10T14:05:53.962663-0700 | ERROR | 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[1m2024-10-10T14:05:53.978823-0700 | ERROR | 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[1m2024-10-10T14:05:53.979252-0700 | ERROR | 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[1m2024-10-10T14:05:53.979252-0700 | ERROR | 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[1m2024-10-10T14:05:53.979252-0700 | ERROR | 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[1m2024-10-10T14:05:53.979252-0700 | ERROR | 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[1m2024-10-10T14:05:53.979252-0700 | ERROR | 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[1m2024-10-10T14:05:53.995897-0700 | ERROR | 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[1m2024-10-10T14:05:53.995897-0700 | ERROR | 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[1m2024-10-10T14:05:53.995897-0700 | ERROR | 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[1m2024-10-10T14:05:53.995897-0700 | ERROR | 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[1m2024-10-10T14:05:53.995897-0700 | ERROR | 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[1m2024-10-10T14:05:54.012170-0700 | INFO | aurora.pipelines.transfer_function_kernel | show_processing_summary | Processing Summary Dataframe:\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:54.029189-0700 | INFO | 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 0.999958 True 24000 sr24k_0001 9043 MIST False None None 0 1.0 24000.000000 0.042667 1024 23998.0 30.0\n", "1 0.999958 True 24000 sr24k_0001 9043 MIST False None None 1 4.0 6000.000000 0.170667 1024 5999.0 7.0\n", @@ -942,355 +942,459 @@ "173 0.999958 True 24000 sr24k_0022 9043 MIST False None None 5 4.0 23.437500 43.690667 1024 23.0 0.0\n", "174 0.999958 True 24000 sr24k_0022 9043 MIST False None None 6 4.0 5.859375 174.762667 1024 5.0 0.0\n", "175 0.999958 True 24000 sr24k_0022 9043 MIST False None None 7 4.0 1.464844 699.050667 1024 1.0 0.0\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:49.057661-0700 | INFO | aurora.pipelines.transfer_function_kernel | memory_check | Total memory: 31.83 GB\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:49.057661-0700 | INFO | aurora.pipelines.transfer_function_kernel | memory_check | Total Bytes of Raw Data: 0.004 GB\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:49.057661-0700 | INFO | aurora.pipelines.transfer_function_kernel | memory_check | Raw Data will use: 0.012 % of memory\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:49.120626-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0001-- Fourier coefficients will be computed\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:49.120626-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:49.185131-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0002-- Fourier coefficients will be computed\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:49.185131-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:49.246085-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0003-- Fourier coefficients will be computed\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:49.246085-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:49.308412-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0004-- Fourier coefficients will be computed\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:49.309382-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:49.355087-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0005-- Fourier coefficients will be computed\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:49.355087-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:49.418088-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0006-- Fourier coefficients will be computed\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:49.418088-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:49.486128-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0007-- Fourier coefficients will be computed\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:49.486128-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:49.543585-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0008-- Fourier coefficients will be computed\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:49.543585-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:49.590820-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0009-- Fourier coefficients will be computed\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:49.606486-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:49.653360-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0010-- Fourier coefficients will be computed\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:49.653360-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:49.716323-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0011-- Fourier coefficients will be computed\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:49.716323-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:49.778821-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0012-- Fourier coefficients will be computed\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:49.778821-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:49.842470-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0013-- Fourier coefficients will be computed\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:49.843438-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:49.903812-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0014-- Fourier coefficients will be computed\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:49.903812-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:49.966313-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0015-- Fourier coefficients will be computed\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:49.966313-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:50.029216-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0016-- Fourier coefficients will be computed\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:50.029216-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:50.092110-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0017-- Fourier coefficients will be computed\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:50.092110-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:50.170277-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0018-- Fourier coefficients will be computed\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:50.170277-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:50.217596-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0019-- Fourier coefficients will be computed\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:50.217596-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:50.288078-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0020-- Fourier coefficients will be computed\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:50.288078-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:50.359406-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0021-- Fourier coefficients will be computed\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:50.359406-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:50.422219-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0022-- Fourier coefficients will be computed\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:50.422219-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:50.422219-0700 | INFO | aurora.pipelines.transfer_function_kernel | check_if_fcs_already_exist | FC levels not present\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:50.422219-0700 | INFO | aurora.pipelines.process_mth5 | process_mth5_legacy | Processing config indicates 8 decimation levels\u001b[0m\n", - "\u001b[1m2024-10-10T13:05:50.422219-0700 | INFO | aurora.pipelines.transfer_function_kernel | valid_decimations | After validation there are 2 valid decimation levels\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:01.658852-0700 | INFO | mtpy.processing.kernel_dataset | initialize_dataframe_for_processing | Dataset dataframe initialized successfully\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:01.659852-0700 | INFO | aurora.pipelines.transfer_function_kernel | update_dataset_df | Dataset Dataframe Updated for decimation level 0 Successfully\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:01.846996-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:01.892184-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:01.927921-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:01.928920-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:01.968329-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:01.970287-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:02.030668-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:02.031669-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:02.034747-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:02.229593-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:02.274007-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:02.321414-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:02.323548-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:02.345538-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:02.361168-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:02.392455-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:02.392455-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:02.407464-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:02.625381-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:02.661814-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:02.703792-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:02.704752-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:02.740106-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:02.742108-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:02.777059-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:02.778062-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:02.780066-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:02.961043-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:03.002473-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:03.036943-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:03.037910-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:03.072204-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:03.074175-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:03.107104-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:03.108106-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:03.110149-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:03.286870-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:03.325034-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:03.359908-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:03.360908-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:03.396836-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:03.397836-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:03.429885-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:03.430882-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:03.432851-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:03.608166-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:03.643171-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:03.669717-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:03.669717-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:03.708087-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:03.708087-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:03.736360-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:03.736360-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:03.752801-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:03.930033-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:03.970208-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:04.003212-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:04.008252-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:04.041266-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:04.043270-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:04.092470-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:04.094477-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:04.095476-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:04.279860-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:04.320473-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:04.353232-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:04.353232-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:04.395565-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:04.397548-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:04.430929-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:04.431895-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:04.432896-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:04.599781-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:04.632138-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:04.674058-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:04.676059-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:04.708808-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:04.708808-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:04.752457-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:04.753851-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:04.753851-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:04.937218-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:04.971045-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:05.009292-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:05.009292-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:05.037838-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:05.037838-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:05.071976-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:05.086171-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:05.088079-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:05.254398-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:05.309853-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:05.353969-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:05.354740-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:05.387774-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:05.387774-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:05.421235-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:05.421235-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:05.421235-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:05.599559-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:05.621757-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:05.654752-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:05.654752-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:05.687999-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:05.702289-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:05.735613-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:05.736622-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:05.738336-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:05.936306-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:05.970572-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:06.010654-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:06.010654-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:06.052175-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:06.053437-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:06.086574-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:06.086574-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:06.086574-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:06.272397-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:06.303447-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:06.335130-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:06.335130-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:06.373662-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:06.375630-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:06.420721-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:06.421721-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:06.423723-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:06.597824-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:06.631879-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:06.665145-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:06.667112-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:06.701808-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:06.703617-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:06.739642-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:06.741608-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:06.742609-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:06.941012-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:06.980783-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:07.017898-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:07.019801-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:07.056937-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:07.057969-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:07.091765-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:07.092775-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:07.094775-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:07.288838-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:07.331010-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:07.371838-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:07.388096-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:07.529370-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:07.529370-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:07.586575-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:07.589457-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:07.591475-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:07.888640-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:07.935915-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:07.967165-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:07.967165-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:08.014443-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:08.014443-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:08.045706-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:08.061331-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:08.061331-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:08.249682-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:08.280937-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:08.328259-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:08.328259-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:08.359527-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:08.359527-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:08.414372-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:08.416370-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:08.418370-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:08.750797-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:08.891829-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:08.948562-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:08.950566-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:08.986915-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:08.986915-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:09.018554-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:09.018554-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:09.018554-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:09.190947-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:09.238222-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:09.269478-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:09.269478-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:09.300728-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:09.300728-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:09.332475-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:09.332475-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:09.348066-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:09.537130-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:09.575324-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:09.617657-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:09.619653-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:09.654031-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:09.656079-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:09.691277-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:09.693306-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:09.694276-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:09.721073-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.000489s (2043.233880Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:09.898789-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.000383s (2609.889545Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:10.086565-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.000307s (3259.750997Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:10.399045-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.000244s (4098.279936Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:10.623111-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.000195s (5125.476350Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:11.203863-0700 | INFO | aurora.pipelines.transfer_function_kernel | update_dataset_df | DECIMATION LEVEL 1\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:11.494731-0700 | INFO | aurora.pipelines.transfer_function_kernel | update_dataset_df | Dataset Dataframe Updated for decimation level 1 Successfully\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:11.802117-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:12.118479-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:12.455170-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:12.754156-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:13.036850-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:13.335231-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:13.621521-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:13.922376-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:14.232181-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:14.544566-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:14.844959-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:15.158123-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:15.473884-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:15.772065-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:16.126499-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:16.442665-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:16.762082-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:17.079573-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:17.413912-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:17.757775-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:18.042123-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:18.344973-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:18.366948-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.002258s (442.838523Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:18.474805-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.001792s (558.088167Hz)\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:18.614539-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.001388s (720.488726Hz)\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:18.943423-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:18.944423-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:18.945425-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:18.946607-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:18.946607-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:18.947607-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:18.948607-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:18.949610-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:18.950606-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:18.950606-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:18.951607-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:18.952618-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:18.953607-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:18.956608-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:18.957607-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:18.958608-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:18.959610-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:18.960609-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:18.961606-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:18.962607-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:18.963273-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:18.963273-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:18.963273-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:18.963273-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:18.963273-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:18.963273-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:18.963273-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:18.963273-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:18.963273-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:18.963273-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:18.963273-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:18.963273-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:18.963273-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:18.963273-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:18.963273-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", - "\u001b[33m\u001b[1m2024-10-10T13:06:18.978288-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:19.329488-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", - "\u001b[1m2024-10-10T13:06:20.477607-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n" + "\u001b[1m2024-10-10T14:05:54.046680-0700 | INFO | aurora.pipelines.transfer_function_kernel | memory_check | Total memory: 31.83 GB\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:54.046680-0700 | INFO | aurora.pipelines.transfer_function_kernel | memory_check | Total Bytes of Raw Data: 0.004 GB\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:54.046680-0700 | INFO | aurora.pipelines.transfer_function_kernel | memory_check | Raw Data will use: 0.012 % of memory\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:54.096502-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0001-- Fourier coefficients will be computed\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:54.096502-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:54.162753-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0002-- Fourier coefficients will be computed\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:54.162753-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:54.229645-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0003-- Fourier coefficients will be computed\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:54.229645-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:54.279536-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0004-- Fourier coefficients will be computed\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:54.279536-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:54.346322-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0005-- Fourier coefficients will be computed\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:54.346322-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:54.411629-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0006-- Fourier coefficients will be computed\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:54.412626-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:54.463059-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0007-- Fourier coefficients will be computed\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:54.463059-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:54.530284-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0008-- Fourier coefficients will be computed\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:54.530284-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:54.580278-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0009-- Fourier coefficients will be computed\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:54.580278-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:54.646592-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0010-- Fourier coefficients will be computed\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:54.646592-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:54.697350-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0011-- Fourier coefficients will be computed\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:54.697350-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:54.763457-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0012-- Fourier coefficients will be computed\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:54.763457-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:54.846690-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0013-- Fourier coefficients will be computed\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:54.846690-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:54.913391-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0014-- Fourier coefficients will be computed\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:54.913391-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:54.976923-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0015-- Fourier coefficients will be computed\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:54.977920-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:55.030118-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0016-- Fourier coefficients will be computed\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:55.030118-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:55.096786-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0017-- Fourier coefficients will be computed\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:55.096786-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:55.146907-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0018-- Fourier coefficients will be computed\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:55.146907-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:55.213831-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0019-- Fourier coefficients will be computed\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:55.213831-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:55.278897-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0020-- Fourier coefficients will be computed\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:55.280490-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:55.330958-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0021-- Fourier coefficients will be computed\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:55.330958-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:55.396115-0700 | INFO | aurora.pipelines.transfer_function_kernel | mth5_has_fcs | Fourier coefficients not detected for survey: MIST, station: 9043, run: sr24k_0022-- Fourier coefficients will be computed\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:55.397153-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:55.397153-0700 | INFO | aurora.pipelines.transfer_function_kernel | check_if_fcs_already_exist | FC levels not present\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:55.397153-0700 | INFO | aurora.pipelines.process_mth5 | process_mth5_legacy | Processing config indicates 8 decimation levels\u001b[0m\n", + "\u001b[1m2024-10-10T14:05:55.397153-0700 | INFO | aurora.pipelines.transfer_function_kernel | valid_decimations | After validation there are 2 valid decimation levels\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:06.616047-0700 | INFO | mtpy.processing.kernel_dataset | initialize_dataframe_for_processing | Dataset dataframe initialized successfully\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:06.617056-0700 | INFO | aurora.pipelines.transfer_function_kernel | update_dataset_df | Dataset Dataframe Updated for decimation level 0 Successfully\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:06.773471-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:06.806641-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:06.840046-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:06.840046-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:06.873525-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:06.888582-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:06.923386-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:06.923386-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:06.923386-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:07.090160-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:07.124130-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:07.162941-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:07.164942-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:07.191201-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:07.191201-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:07.223542-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:07.223542-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:07.223542-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:07.390601-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:07.424963-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:07.458103-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:07.458103-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:07.491318-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:07.491318-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:07.524497-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:07.524497-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:07.524497-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:07.690637-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:07.724262-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:07.758198-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:07.758198-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:07.790828-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:07.790828-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:07.824268-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:07.824268-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:07.839801-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:08.007694-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:08.041458-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:08.073929-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:08.074646-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:08.106065-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:08.108444-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:08.140972-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:08.141737-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:08.141737-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:08.308344-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:08.341913-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:08.377198-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:08.378199-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:08.407879-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:08.407879-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:08.458474-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:08.458474-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:08.458474-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:08.608621-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:08.658812-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:08.691384-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:08.693036-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:08.725846-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:08.727806-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:08.758200-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:08.758200-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:08.758200-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:08.925671-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:08.958311-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:08.991764-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:08.991764-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:09.025797-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:09.025797-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:09.058970-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:09.058970-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:09.073983-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:09.242832-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:09.278856-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:09.308754-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:09.308754-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:09.342243-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:09.342243-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:09.376604-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:09.376604-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:09.376604-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:09.542156-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:09.576836-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:09.609133-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:09.609133-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:09.642482-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:09.642482-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:09.677211-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:09.677211-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:09.677211-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:09.842831-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:09.877482-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:09.909279-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:09.909279-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:09.943496-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:09.943496-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:09.977609-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:09.977609-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:09.977609-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:10.142817-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:10.177851-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:10.226093-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:10.226093-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:10.259461-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:10.259461-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:10.293918-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:10.293918-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:10.293918-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:10.459697-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:10.492901-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:10.527435-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:10.527435-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:10.577252-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:10.578261-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:10.609858-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:10.609858-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:10.609858-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:10.778575-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:10.810605-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:10.847857-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:10.848894-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:10.878501-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:10.878501-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:10.910240-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:10.910240-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:10.910240-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:11.078909-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:11.110784-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:11.143996-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:11.143996-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:11.179124-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:11.179124-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:11.210226-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:11.225886-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:11.227770-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:11.399189-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:11.432979-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:11.460492-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:11.460492-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:11.493984-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:11.493984-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:11.534118-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:11.535119-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:11.537086-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:11.693974-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:11.737258-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:11.760936-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:11.760936-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:11.794155-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:11.794155-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:11.827353-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:11.827353-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:11.842984-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:12.010467-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:12.044528-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:12.080158-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:12.080158-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:12.111008-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:12.111008-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:12.144438-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:12.144438-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:12.159949-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:12.327997-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:12.362071-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:12.394649-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:12.394649-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:12.430175-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:12.430175-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:12.470203-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:12.472235-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:12.473208-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:12.628328-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:12.661674-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:12.695970-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:12.711601-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:12.744937-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:12.744937-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:12.781708-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:12.781708-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:12.781708-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:12.966318-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:12.996165-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:13.029886-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:13.045213-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:13.082017-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:13.082017-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:13.112626-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:13.112626-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:13.112626-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:13.295291-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:13.328667-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:13.361999-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:13.361999-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:13.395440-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:13.395440-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:13.429286-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:13.429286-0700 | WARNING | mt_metadata.timeseries.filters.frequency_response_table_filter | complex_response | Extrapolating frequencies larger (11976.5625 Hz) than table frequencies (10240.0 Hz).\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:13.429286-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:13.467309-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.000489s (2043.233880Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:13.627772-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.000383s (2609.889545Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:13.782649-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.000307s (3259.750997Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:14.035112-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.000244s (4098.279936Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:14.232104-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.000195s (5125.476350Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:14.762154-0700 | INFO | aurora.pipelines.transfer_function_kernel | update_dataset_df | DECIMATION LEVEL 1\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:15.029111-0700 | INFO | aurora.pipelines.transfer_function_kernel | update_dataset_df | Dataset Dataframe Updated for decimation level 1 Successfully\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:15.313877-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:15.614837-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:15.897684-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:16.181820-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:16.464830-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:16.764988-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:17.065165-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:17.365403-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:17.665690-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:17.985864-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:18.286677-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:18.586931-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:18.881819-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:19.167172-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:19.467329-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:19.750688-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:20.050948-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:20.351927-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:20.634718-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:20.932253-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:21.218600-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:21.503114-0700 | INFO | aurora.pipelines.process_mth5 | save_fourier_coefficients | Skip saving FCs. dec_level_config.save_fc = False\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:21.536568-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.002258s (442.838523Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:21.652345-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.001792s (558.088167Hz)\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:21.785162-0700 | INFO | aurora.time_series.frequency_band_helpers | get_band_for_tf_estimate | Processing band 0.001388s (720.488726Hz)\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:22.219554-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:22.219554-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:22.219554-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:22.219554-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:22.219554-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:22.219554-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:22.219554-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:22.219554-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:22.219554-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:22.219554-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:22.219554-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:22.219554-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:22.219554-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:22.219554-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:22.219554-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:22.235568-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:22.235969-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:22.235969-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:22.235969-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:22.235969-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:22.235969-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:22.235969-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:22.235969-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:22.235969-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:22.235969-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:22.235969-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:22.235969-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:22.235969-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:22.250979-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:22.252826-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:22.252826-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:22.252826-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:22.252826-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:22.252826-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:22.252826-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", + "\u001b[33m\u001b[1m2024-10-10T14:06:22.252826-0700 | WARNING | aurora.pipelines.transfer_function_kernel | make_decimation_dict_for_tf | Possibly invalid decimation level\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:22.553211-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n", + "\u001b[1m2024-10-10T14:06:23.971058-0700 | INFO | mth5.mth5 | close_mth5 | Flushing and closing c:\\Users\\jpeacock\\OneDrive - DOI\\mt\\mt_short_course\\data\\phx\\9043\\phx_9043.h5\u001b[0m\n" ] + } + ], + "source": [ + "\n", + "tf_processed = ap.process(processing_dict=processing_dict)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Processed Data\n", + "\n", + "Now the data have processed and we can have a look at the results. \n", + "\n", + "First have a look at what the out put is. It is a dictionary with keys as the sample rate and values as more dictionaries with information on if the sample rate was properly processed and if it was the transfer function as an `mtpy.MT` object.\n", + "\n", + "Notice that there are 3 entries in the dictionary with one for `\"combined\"` which is the combined transfer function of the various sample rates you processed." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{150: {'processed': True,\n", + " 'tf': TF( survey='MIST', station='9043', latitude=33.05, longitude=-115.98, elevation=-47.76 )},\n", + " 24000: {'processed': True,\n", + " 'tf': TF( survey='MIST', station='9043', latitude=33.05, longitude=-115.98, elevation=-47.76 )},\n", + " 'combined': {'processed': True,\n", + " 'tf': TF( survey='MIST', station='9043', latitude=33.05, longitude=-115.98, elevation=-47.76 )}}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf_processed" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot The Combined Response\n", + "\n", + "Now lets plot the response. Including all 4 components and the induction vectors." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" }, { - "ename": "KeyError", - "evalue": "\"not all values found in index 'input'\"", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[8], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m tf_processed \u001b[38;5;241m=\u001b[39m \u001b[43map\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mprocess\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprocessing_dict\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mprocessing_dict\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32m~\\OneDrive - DOI\\Documents\\GitHub\\mtpy-v2\\mtpy\\processing\\aurora\\process_aurora.py:359\u001b[0m, in \u001b[0;36mAuroraProcessing.process\u001b[1;34m(self, sample_rates, processing_dict, merge, save_to_mth5)\u001b[0m\n\u001b[0;32m 355\u001b[0m processed[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcombined\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mprocessed\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;28;01mTrue\u001b[39;00m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtf\u001b[39m\u001b[38;5;124m\"\u001b[39m: combined_tf}\n\u001b[0;32m 357\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m save_to_mth5:\n\u001b[0;32m 358\u001b[0m \u001b[38;5;66;03m### add tf to local MTH5\u001b[39;00m\n\u001b[1;32m--> 359\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_add_tf_to_local_mth5\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprocessed\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 361\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m processed\n", - "File \u001b[1;32m~\\OneDrive - DOI\\Documents\\GitHub\\mtpy-v2\\mtpy\\processing\\aurora\\process_aurora.py:438\u001b[0m, in \u001b[0;36mAuroraProcessing._add_tf_to_local_mth5\u001b[1;34m(self, tf_dict)\u001b[0m\n\u001b[0;32m 436\u001b[0m m\u001b[38;5;241m.\u001b[39mopen_mth5(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlocal_mth5_path)\n\u001b[0;32m 437\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m p_dict \u001b[38;5;129;01min\u001b[39;00m tf_dict\u001b[38;5;241m.\u001b[39mvalues():\n\u001b[1;32m--> 438\u001b[0m \u001b[43mm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madd_transfer_function\u001b[49m\u001b[43m(\u001b[49m\u001b[43mp_dict\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32m~\\OneDrive - DOI\\Documents\\GitHub\\mth5\\mth5\\mth5.py:1608\u001b[0m, in \u001b[0;36mMTH5.add_transfer_function\u001b[1;34m(self, tf_object, update_metadata)\u001b[0m\n\u001b[0;32m 1600\u001b[0m ch_dataset \u001b[38;5;241m=\u001b[39m run_group\u001b[38;5;241m.\u001b[39madd_channel(\n\u001b[0;32m 1601\u001b[0m ch\u001b[38;5;241m.\u001b[39mcomponent,\n\u001b[0;32m 1602\u001b[0m ch\u001b[38;5;241m.\u001b[39mtype,\n\u001b[0;32m 1603\u001b[0m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m 1604\u001b[0m channel_metadata\u001b[38;5;241m=\u001b[39mch,\n\u001b[0;32m 1605\u001b[0m )\n\u001b[0;32m 1606\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m 1607\u001b[0m tf_group \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m-> 1608\u001b[0m \u001b[43mstation_group\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtransfer_functions_group\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madd_transfer_function\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1609\u001b[0m \u001b[43m \u001b[49m\u001b[43mtf_object\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtf_id\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtf_object\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtf_object\u001b[49m\n\u001b[0;32m 1610\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1611\u001b[0m )\n\u001b[0;32m 1612\u001b[0m \u001b[38;5;66;03m# need to update time_period from TF here\u001b[39;00m\n\u001b[0;32m 1613\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m (\u001b[38;5;167;01mOSError\u001b[39;00m, \u001b[38;5;167;01mRuntimeError\u001b[39;00m, \u001b[38;5;167;01mValueError\u001b[39;00m):\n", - "File \u001b[1;32m~\\OneDrive - DOI\\Documents\\GitHub\\mth5\\mth5\\groups\\transfer_function.py:152\u001b[0m, in \u001b[0;36mTransferFunctionsGroup.add_transfer_function\u001b[1;34m(self, name, tf_object)\u001b[0m\n\u001b[0;32m 146\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_update_time_period_from_tf(tf_object)\n\u001b[0;32m 147\u001b[0m tf_group \u001b[38;5;241m=\u001b[39m TransferFunctionGroup(\n\u001b[0;32m 148\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhdf5_group\u001b[38;5;241m.\u001b[39mcreate_group(name),\n\u001b[0;32m 149\u001b[0m group_metadata\u001b[38;5;241m=\u001b[39mtf_object\u001b[38;5;241m.\u001b[39mstation_metadata\u001b[38;5;241m.\u001b[39mtransfer_function,\n\u001b[0;32m 150\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset_options,\n\u001b[0;32m 151\u001b[0m )\n\u001b[1;32m--> 152\u001b[0m \u001b[43mtf_group\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfrom_tf_object\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtf_object\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mupdate_metadata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[0;32m 154\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 155\u001b[0m tf_group \u001b[38;5;241m=\u001b[39m TransferFunctionGroup(\n\u001b[0;32m 156\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhdf5_group\u001b[38;5;241m.\u001b[39mcreate_group(name), \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset_options\n\u001b[0;32m 157\u001b[0m )\n", - "File \u001b[1;32m~\\OneDrive - DOI\\Documents\\GitHub\\mth5\\mth5\\groups\\transfer_function.py:590\u001b[0m, in \u001b[0;36mTransferFunctionGroup.from_tf_object\u001b[1;34m(self, tf_obj, update_metadata)\u001b[0m\n\u001b[0;32m 588\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m estimate_name \u001b[38;5;129;01min\u001b[39;00m accepted_estimates:\n\u001b[0;32m 589\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 590\u001b[0m estimate \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mgetattr\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mtf_obj\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mestimate_name\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 591\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m estimate \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 592\u001b[0m _ \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39madd_statistical_estimate(estimate_name, estimate)\n", - "File \u001b[1;32m~\\OneDrive - DOI\\Documents\\GitHub\\mt_metadata\\mt_metadata\\transfer_functions\\core.py:1245\u001b[0m, in \u001b[0;36mTF.inverse_signal_power\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1243\u001b[0m \u001b[38;5;129m@property\u001b[39m\n\u001b[0;32m 1244\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21minverse_signal_power\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[1;32m-> 1245\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mhas_inverse_signal_power\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[0;32m 1246\u001b[0m ds \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdataset\u001b[38;5;241m.\u001b[39minverse_signal_power\u001b[38;5;241m.\u001b[39mloc[\n\u001b[0;32m 1247\u001b[0m \u001b[38;5;28mdict\u001b[39m(\n\u001b[0;32m 1248\u001b[0m \u001b[38;5;28minput\u001b[39m\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ch_input_dict[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124misp\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[0;32m 1249\u001b[0m output\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_ch_output_dict[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124misp\u001b[39m\u001b[38;5;124m\"\u001b[39m],\n\u001b[0;32m 1250\u001b[0m )\n\u001b[0;32m 1251\u001b[0m ]\n\u001b[0;32m 1252\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key, mkey \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_dataset_attr_dict\u001b[38;5;241m.\u001b[39mitems():\n", - "File \u001b[1;32m~\\OneDrive - DOI\\Documents\\GitHub\\mt_metadata\\mt_metadata\\transfer_functions\\core.py:1232\u001b[0m, in \u001b[0;36mTF.has_inverse_signal_power\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 1221\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mhas_inverse_signal_power\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m 1222\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 1223\u001b[0m \u001b[38;5;124;03m Check to see if the transfer function is not 0 and has\u001b[39;00m\n\u001b[0;32m 1224\u001b[0m \u001b[38;5;124;03m transfer function components\u001b[39;00m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1228\u001b[0m \n\u001b[0;32m 1229\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m 1231\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m np\u001b[38;5;241m.\u001b[39mall(\n\u001b[1;32m-> 1232\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_transfer_function\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minverse_signal_power\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mloc\u001b[49m\u001b[43m[\u001b[49m\n\u001b[0;32m 1233\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mdict\u001b[39;49m\u001b[43m(\u001b[49m\n\u001b[0;32m 1234\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_ch_input_dict\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43misp\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1235\u001b[0m \u001b[43m \u001b[49m\u001b[43moutput\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_ch_output_dict\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43misp\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m 1236\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 1237\u001b[0m \u001b[43m \u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mdata\n\u001b[0;32m 1238\u001b[0m \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[0;32m 1239\u001b[0m ):\n\u001b[0;32m 1240\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mFalse\u001b[39;00m\n\u001b[0;32m 1241\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\jpeacock\\Anaconda3\\envs\\py39\\lib\\site-packages\\xarray\\core\\dataarray.py:231\u001b[0m, in \u001b[0;36m_LocIndexer.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 229\u001b[0m labels \u001b[38;5;241m=\u001b[39m indexing\u001b[38;5;241m.\u001b[39mexpanded_indexer(key, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata_array\u001b[38;5;241m.\u001b[39mndim)\n\u001b[0;32m 230\u001b[0m key \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m(\u001b[38;5;28mzip\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata_array\u001b[38;5;241m.\u001b[39mdims, labels))\n\u001b[1;32m--> 231\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata_array\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[1;32mc:\\Users\\jpeacock\\Anaconda3\\envs\\py39\\lib\\site-packages\\xarray\\core\\dataarray.py:1590\u001b[0m, in \u001b[0;36mDataArray.sel\u001b[1;34m(self, indexers, method, tolerance, drop, **indexers_kwargs)\u001b[0m\n\u001b[0;32m 1480\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21msel\u001b[39m(\n\u001b[0;32m 1481\u001b[0m \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m 1482\u001b[0m indexers: Mapping[Any, Any] \u001b[38;5;241m|\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1486\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mindexers_kwargs: Any,\n\u001b[0;32m 1487\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Self:\n\u001b[0;32m 1488\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Return a new DataArray whose data is given by selecting index\u001b[39;00m\n\u001b[0;32m 1489\u001b[0m \u001b[38;5;124;03m labels along the specified dimension(s).\u001b[39;00m\n\u001b[0;32m 1490\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 1588\u001b[0m \u001b[38;5;124;03m Dimensions without coordinates: points\u001b[39;00m\n\u001b[0;32m 1589\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[1;32m-> 1590\u001b[0m ds \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_to_temp_dataset()\u001b[38;5;241m.\u001b[39msel(\n\u001b[0;32m 1591\u001b[0m indexers\u001b[38;5;241m=\u001b[39mindexers,\n\u001b[0;32m 1592\u001b[0m drop\u001b[38;5;241m=\u001b[39mdrop,\n\u001b[0;32m 1593\u001b[0m method\u001b[38;5;241m=\u001b[39mmethod,\n\u001b[0;32m 1594\u001b[0m tolerance\u001b[38;5;241m=\u001b[39mtolerance,\n\u001b[0;32m 1595\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mindexers_kwargs,\n\u001b[0;32m 1596\u001b[0m )\n\u001b[0;32m 1597\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_from_temp_dataset(ds)\n", - "File \u001b[1;32mc:\\Users\\jpeacock\\Anaconda3\\envs\\py39\\lib\\site-packages\\xarray\\core\\dataset.py:3047\u001b[0m, in \u001b[0;36mDataset.sel\u001b[1;34m(self, indexers, method, tolerance, drop, **indexers_kwargs)\u001b[0m\n\u001b[0;32m 2986\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Returns a new dataset with each array indexed by tick labels\u001b[39;00m\n\u001b[0;32m 2987\u001b[0m \u001b[38;5;124;03malong the specified dimension(s).\u001b[39;00m\n\u001b[0;32m 2988\u001b[0m \n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 3044\u001b[0m \u001b[38;5;124;03mDataArray.sel\u001b[39;00m\n\u001b[0;32m 3045\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m 3046\u001b[0m indexers \u001b[38;5;241m=\u001b[39m either_dict_or_kwargs(indexers, indexers_kwargs, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124msel\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m-> 3047\u001b[0m query_results \u001b[38;5;241m=\u001b[39m \u001b[43mmap_index_queries\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m 3048\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mindexers\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindexers\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmethod\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmethod\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtolerance\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtolerance\u001b[49m\n\u001b[0;32m 3049\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m 3051\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m drop:\n\u001b[0;32m 3052\u001b[0m no_scalar_variables \u001b[38;5;241m=\u001b[39m {}\n", - "File \u001b[1;32mc:\\Users\\jpeacock\\Anaconda3\\envs\\py39\\lib\\site-packages\\xarray\\core\\indexing.py:193\u001b[0m, in \u001b[0;36mmap_index_queries\u001b[1;34m(obj, indexers, method, tolerance, **indexers_kwargs)\u001b[0m\n\u001b[0;32m 191\u001b[0m results\u001b[38;5;241m.\u001b[39mappend(IndexSelResult(labels))\n\u001b[0;32m 192\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 193\u001b[0m results\u001b[38;5;241m.\u001b[39mappend(index\u001b[38;5;241m.\u001b[39msel(labels, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39moptions))\n\u001b[0;32m 195\u001b[0m merged \u001b[38;5;241m=\u001b[39m merge_sel_results(results)\n\u001b[0;32m 197\u001b[0m \u001b[38;5;66;03m# drop dimension coordinates found in dimension indexers\u001b[39;00m\n\u001b[0;32m 198\u001b[0m \u001b[38;5;66;03m# (also drop multi-index if any)\u001b[39;00m\n\u001b[0;32m 199\u001b[0m \u001b[38;5;66;03m# (.sel() already ensures alignment)\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\jpeacock\\Anaconda3\\envs\\py39\\lib\\site-packages\\xarray\\core\\indexes.py:784\u001b[0m, in \u001b[0;36mPandasIndex.sel\u001b[1;34m(self, labels, method, tolerance)\u001b[0m\n\u001b[0;32m 782\u001b[0m indexer \u001b[38;5;241m=\u001b[39m get_indexer_nd(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mindex, label_array, method, tolerance)\n\u001b[0;32m 783\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m np\u001b[38;5;241m.\u001b[39many(indexer \u001b[38;5;241m<\u001b[39m \u001b[38;5;241m0\u001b[39m):\n\u001b[1;32m--> 784\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnot all values found in index \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mcoord_name\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m 786\u001b[0m \u001b[38;5;66;03m# attach dimension names and/or coordinates to positional indexer\u001b[39;00m\n\u001b[0;32m 787\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(label, Variable):\n", - "\u001b[1;31mKeyError\u001b[0m: \"not all values found in index 'input'\"" - ] + "data": { + "text/plain": [ + "Plotting PlotMTResponse" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ + "tf_processed[\"combined\"][\"tf\"].plot_mt_response(plot_num=2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Uh-oh\n", "\n", - "tf_processed = ap.process(processing_dict=processing_dict)" + "Looks like there was a flipped channel. Which one do you think it was?\n", + "\n", + "We can flip the channel in the transfer function by using `flip_phase`." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "tf_processed[\"combined\"][\"tf\"].flip_phase(zyx=True, zyy=True, tzy=True, inplace=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "Plotting PlotMTResponse" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tf_processed[\"combined\"][\"tf\"].plot_mt_response(plot_num=2)" ] }, {