diff --git a/.github/workflows/github-actions-seizure-pipline.yml b/.github/workflows/github-actions-seizure-pipline.yml index 205546f..cbfef30 100644 --- a/.github/workflows/github-actions-seizure-pipline.yml +++ b/.github/workflows/github-actions-seizure-pipline.yml @@ -15,19 +15,29 @@ permissions: jobs: build: - runs-on: ubuntu-latest + runs-on: ${{ matrix.os }} + strategy: + fail-fast: false + matrix: + os: [ubuntu-latest, macos-latest, windows-latest] + python-version: ['3.6', '3.7', '3.8', '3.9'] + #exclude: + #- os: macos-latest + # python-version: '3.8' + #- os: windows-latest + # python-version: '3.6' steps: - uses: actions/checkout@v3 - - name: Set up Python 3.6.8 + - name: Set up Python 3.x uses: actions/setup-python@v3 with: - python-version: "3.6.8" + python-version: ${{ matrix.python-version }} - name: Install dependencies run: | python -m pip install --upgrade pip - pip install flake8 pytest if [ -f requirements.txt ]; then pip install -r requirements.txt; fi + if [ -f requirements_dev.txt ]; then pip install -r requirements_dev.txt; fi - name: Lint with flake8 run: | # stop the build if there are Python syntax errors or undefined names @@ -36,4 +46,4 @@ jobs: flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics - name: Test with pytest run: | - pytest + pytest -s -vvv ./tests --cov=src --cov-fail-under=80 \ No newline at end of file diff --git a/.gitignore b/.gitignore index 1a3e6c0..ccc8083 100644 --- a/.gitignore +++ b/.gitignore @@ -136,4 +136,5 @@ output/db/*csv cloud/ tests/output/ exports/ -output/*/* \ No newline at end of file +output/*/* +data/data_pl diff --git a/Makefile b/Makefile index 6a10682..c52d48d 100644 --- a/Makefile +++ b/Makefile @@ -2,14 +2,17 @@ FOLDER_PATH= . SRC_PATH=./src TEST_PATH=./tests -DATA_PATH=data +DATA_PATH=data/PL EXPORT_PATH=./output +TSE_BI_FORMATTING=dataset +COMPARISON_FOLDER=res-v0_6 + # UTILITIES # --------- clean: - rm output/db/*csv + find output -mindepth 1 ! -name README.md -delete flake8: . $(FOLDER_PATH)/env/bin/activate; \ @@ -23,6 +26,11 @@ test: . $(FOLDER_PATH)/env/bin/activate; \ pytest -s -vvv $(TEST_PATH) +test_fetch: + . $(FOLDER_PATH)/env/bin/activate; \ + pytest -s -vvv $(TEST_PATH)/test_src_usecase/test_fetch_database.py + + coverage: . $(FOLDER_PATH)/env/bin/activate; \ pytest --cov=$(SRC_PATH) --cov-report html $(TEST_PATH) @@ -31,7 +39,7 @@ coverage: # ------------- fetch_data: . $(FOLDER_PATH)/env/bin/activate; \ - python3 src/usecase/fetch_database.py --data-folder $(DATA_PATH) --export-folder $(EXPORT_PATH)/fetched_data + python3 src/usecase/fetch_database.py --data-folder-path $(DATA_PATH) --export-folder $(EXPORT_PATH)/fetched_data --infer-database # PREPROCESSING @@ -41,7 +49,15 @@ fetch_data: # PYTHON SCRIPT ON INDIVIDUAL FILES individual_detect_qrs: . $(FOLDER_PATH)/env/bin/activate; \ - python3 src/usecase/detect_qrs.py --qrs-file-path $(DATA_PATH)/tuh/dev/01_tcp_ar/002/00009578/00009578_s006_t001.edf --method hamilton --exam-id 00009578_s006_t001 --output-folder $(EXPORT_PATH)/individual/res-v0_6 + python3 src/usecase/detect_qrs.py --qrs-file-path $(DATA_PATH)/002/00009578/00009578_s006_t001.edf --method hamilton --exam-id 00009578_s006_t001 --output-folder $(EXPORT_PATH)/individual/res-v0_6 + +individual_apply_ecg_qc: + . $(FOLDER_PATH)/env/bin/activate; \ + python3 src/usecase/apply_ecg_qc.py --qrs-file-path data/tuh/dev/01_tcp_ar/002/00009578/00009578_s006_t001.edf --exam-id 00009578_s006_t001 --output-folder $(EXPORT_PATH)/ecg_qc-v0_6 --formatting dataset + +individual_compare_qrs_detectors: + . $(FOLDER_PATH)/env/bin/activate; \ + python3 src/usecase/compare_qrs_detectors.py --reference-rr-intervals-file-path output/res-v0_6/dev/01_tcp_ar/002/00009578/rr_00009578_s002_t001.csv --comparison-rr-intervals-file-path output/res-v0_6/dev/01_tcp_ar/002/00009578/rr_00009578_s002_t001.csv --output-folder $(EXPORT_PATH)/individual/comp-v0_6 --formatting $(TSE_BI_FORMATTING) individual_compute_hrvanalysis_features: . $(FOLDER_PATH)/env/bin/activate; \ @@ -52,17 +68,23 @@ individual_consolidate_feats_and_annot: python3 src/usecase/consolidate_feats_and_annot.py --features-file-path exports/individual/feats-v0_6/00009578_s006_t001.csv --annotations-file-path $(DATA_PATH)/tuh/dev/01_tcp_ar/002/00009578/00009578_s002_t001.tse_bi --output-folder $(EXPORT_PATH)/individual/cons_v0_6 -#WIP -example_ecg_qc: - python3 src/usecase/apply_ecg_qc.py --filepath data/tuh/dev/01_tcp_ar/002/00009578/00009578_s006_t001.edf --output-folder . --sampling-frequency 1000 --exam-id 00009578_s006_t001 - - # BASH SCRIPT WRAPPING PYTHON SCRIPTS OVER ALL CANDIDATES +# ------------- bash_detect_qrs: . $(FOLDER_PATH)/env/bin/activate; \ mkdir -p $(EXPORT_PATH); \ ./scripts/bash_pipeline/1_detect_qrs_wrapper.sh -i $(DATA_PATH) -o $(EXPORT_PATH)/res-v0_6 +bash_apply_ecg_qc: + . $(FOLDER_PATH)/env/bin/activate; \ + mkdir -p $(EXPORT_PATH); \ + ./scripts/bash_pipeline/0_apply_ecg_qc_wrapper.sh -i $(DATA_PATH) -o $(EXPORT_PATH)/ecg_qc-v0_6 -f $(TSE_BI_FORMATTING) + +bash_compare_qrs_detectors: + . $(FOLDER_PATH)/env/bin/activate; \ + mkdir -p $(EXPORT_PATH); \ + ./scripts/bash_pipeline/0_compare_qrs_detectors.sh -i $(EXPORT_PATH)/res-v0_6 -c $(EXPORT_PATH)/res-v0_6-comp -o $(EXPORT_PATH)/$(COMPARISON_FOLDER) -f $(TSE_BI_FORMATTING) + bash_compute_hrvanalysis_features: . $(FOLDER_PATH)/env/bin/activate; \ ./scripts/bash_pipeline/2_compute_hrvanalysis_features_wrapper.sh -i $(EXPORT_PATH)/res-v0_6 -o $(EXPORT_PATH)/feats-v0_6 @@ -83,4 +105,20 @@ create_ml_dataset: # ------------------ train: . $(FOLDER_PATH)/env/bin/activate; \ - python3 src/usecase/train_model.py --ml-dataset-path $(EXPORT_PATH)/ml_dataset/df_ml.csv \ No newline at end of file + python3 src/usecase/train_model.py --ml-dataset-path $(EXPORT_PATH)/ml_dataset/df_ml.csv + +train_ml: + . $(FOLDER_PATH)/env/bin/activate; \ + python3 src/usecase/train_model.py --ml-dataset-path /home/DATA/DetecTeppe-2022-04-08/ml_dataset_2022_04_08/train/df_ml_train.csv --ml-dataset-path-test /home/DATA/DetecTeppe-2022-04-08/ml_dataset_2022_04_08/test/df_ml_test.csv + + +## VISUALIZATION +# ------------------ +load_ecg: + python3 visualization/ecg_data_loader.py --pg-host localhost --pg-port 5432 --pg-user postgres --pg-password postgres --pg-database postgres --filepath data/tuh/dev/01_tcp_ar/076/00007633/s003_2013_07_09/00007633_s003_t007.edf + +load_rr: + python3 visualization/rr_intervals_loader.py --pg-host localhost --pg-port 5432 --pg-user postgres --pg-password postgres --pg-database postgres --filepath data/test_data/rr_00007633_s003_t007.csv --exam 00007633_s003_t007 + +load_annotations: + python3 visualization/annotations_loader.py --pg-host localhost --pg-port 5432 --pg-user postgres --pg-password postgres --pg-database postgres --annotation-filename data/tuh/dev/01_tcp_ar/076/00007633/s003_2013_07_09/00007633_s003_t007.tse_bi --edf-filename data/tuh/dev/01_tcp_ar/076/00007633/s003_2013_07_09/00007633_s003_t007.edf --exam 00007633_s003_t010 diff --git a/README.md b/README.md index 1f6dce7..95d978b 100644 --- a/README.md +++ b/README.md @@ -39,7 +39,17 @@ You need to have [docker](https://docs.docker.com/get-docker/) and [docker-compo ## Getting started ### Setting up environment and launch docker-compose -After cloning this repository, replace the value of the environment variable ```DATA_PATH``` in the *env.sh* file with the absolute path of the data you are working with. + +Using symbolic link is most conveniant use to import data stored in another path. In this case, first create a symbolic link in data folder: +```sh + $ ln -s -r PATH_TO_DATA_FOLDER data/ +``` + +Then update *env.sh* file with the the name of the folder of symbolic link at last line: + +```sh +export SYMLINK_FOLDER='SYMBOLIC_NAME_FOLDER_NAME' +``` You can now run these commands : @@ -61,7 +71,10 @@ You can now run these commands : |Flower|5555| |Redis|6379| - +Before running Airflow, you must fetch data with: +```sh + $ make fetch_data +``` ### UI Once the services are up, you can interact with their UI : diff --git a/dags/config.py b/dags/config.py new file mode 100644 index 0000000..13a5635 --- /dev/null +++ b/dags/config.py @@ -0,0 +1,61 @@ +import os +import sys +from datetime import datetime as dt +from sklearn.ensemble import RandomForestClassifier +import datetime +import xgboost as xgb +import numpy as np + +PROJECT_FOLDER = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) +DATA_FOLDER = os.path.join(PROJECT_FOLDER, 'data') + +ML_DATASET_OUTPUT_FOLDER = "/opt/airflow/output" +AIRFLOW_PREFIX_TO_DATA = '/opt/airflow/data/' +MLRUNS_DIR = '/mlruns' + +TRAIN_DATA = os.path.join(AIRFLOW_PREFIX_TO_DATA, "train/df_ml_train.csv") +TEST_DATA = os.path.join(AIRFLOW_PREFIX_TO_DATA , "test/df_ml_test.csv") +FEATURE_TRAIN_PATH= os.path.join(ML_DATASET_OUTPUT_FOLDER, "ml_train.csv") +FEATURE_TEST_PATH= os.path.join(ML_DATASET_OUTPUT_FOLDER, "ml_test.csv") + +COL_TO_DROP = ['interval_index', 'interval_start_time', 'set'] + +START_DATE = dt(2021, 8, 1) +CONCURRENCY = 4 +SCHEDULE_INTERVAL = datetime.timedelta(hours=2) +DEFAULT_ARGS = {'owner': 'airflow'} + +TRACKING_URI = 'http://mlflow:5000' + +MODEL_PARAM = { + 'model': xgb.XGBClassifier(), + 'grid_parameters': { + 'nthread':[4], + 'learning_rate': [0.1, 0.01, 0.05], + 'max_depth': np.arange(3, 5, 2), + 'scale_pos_weight':[1], + 'n_estimators': np.arange(15, 25, 2), + 'missing':[-999]} + } + +MODELS_PARAM = { + 'xgboost': { + 'model': xgb.XGBClassifier(), + 'grid_parameters': { + 'nthread':[4], + 'learning_rate': [0.1, 0.01, 0.05], + 'max_depth': np.arange(3, 5, 2), + 'scale_pos_weight':[1], + 'n_estimators': np.arange(15, 25, 2), + 'missing':[-999] + } + }, + 'random_forest': { + 'model': RandomForestClassifier(), + 'grid_parameters': { + 'min_samples_leaf': np.arange(1, 5, 1), + 'max_depth': np.arange(1, 7, 1), + 'max_features': ['auto'], + 'n_estimators': np.arange(10, 20, 2)} + } +} \ No newline at end of file diff --git a/dags/predict.py b/dags/predict.py new file mode 100644 index 0000000..1f4f5bc --- /dev/null +++ b/dags/predict.py @@ -0,0 +1,28 @@ +import os +import sys +from datetime import datetime, timedelta, datetime + +from airflow.decorators import dag, task +from airflow.utils.dates import days_ago + +sys.path.append('.') +from dags.config import (DEFAULT_ARGS, START_DATE, CONCURRENCY, SCHEDULE_INTERVAL) + + +@dag(default_args=DEFAULT_ARGS, + start_date=START_DATE, + schedule_interval=timedelta(minutes=2), + concurrency=CONCURRENCY) +def predict(): + @task + def prepare_features_with_io_task() -> str: + pass + + @task + def predict_with_io_task(feature_path: str) -> None: + pass + + feature_path = prepare_features_with_io_task() + predict_with_io_task(feature_path) + +predict_dag = predict() \ No newline at end of file diff --git a/dags/seizure_detection_pipeline.py b/dags/seizure_detection_pipeline.py index 0b85ad3..4a2256a 100644 --- a/dags/seizure_detection_pipeline.py +++ b/dags/seizure_detection_pipeline.py @@ -1,5 +1,7 @@ +from typing import Callable, List from datetime import datetime import pandas as pd +import logging from airflow.decorators import dag, task import sys @@ -48,8 +50,29 @@ MEDIUM_WINDOW = 60_000 LARGE_WINDOW = 150_000 +NUM_PARTITIONS = 8 + SEGMENT_SIZE_TRESHOLD = 0.9 +AIRFLOW_PREFIX_TO_DATA = '/opt/airflow/' + +def map_parameters(fn: Callable, parameters_list: list) -> list: + """Similar to `list(map(fn, parameters))`, but with error checking and logging. + """ + results = [] + total = len(parameters_list) + for i, parameters in enumerate(parameters_list): + if parameters is None: + logging.info(f'Skipping item {i+1}/{total}: failed on previous subtask') + continue + try: + results.append(fn(parameters)) + logging.info(f'Processed item {i+1}/{total}') + except Exception as err: + logging.error(f'Error processing item {i+1}/{total}: {err}') + results.append(None) + return results + @dag(default_args=DEFAULT_ARGS, dag_id='seizure_detection_pipeline', @@ -59,58 +82,69 @@ concurrency=CONCURRENCY) def dag_seizure_detection_pipeline(): - @task(multiple_outputs=True) - def t_detect_qrs(parameters: dict) -> dict: - - output_qrs_file_path, sampling_frequency = detect_qrs( - qrs_file_path=parameters['qrs_file_path'], - exam_id=parameters['exam_id'], - method=parameters['method'], - output_folder=parameters['rr_intervals_folder']) - - output_parameters = {'rr_file_path': output_qrs_file_path, - 'sampling_frequency': sampling_frequency} - - return output_parameters - - @task(multiple_outputs=True) - def t_consolidate_parameters(param_dict_1: dict, - param_dict_2: dict) -> dict: - - consolidated_parameters = {**param_dict_1, **param_dict_2} - print(consolidated_parameters) - return consolidated_parameters - - @task(multiple_outputs=True) - def t_compute_hrv_analysis_features(parameters: dict) -> dict: - - output_features_file_path = compute_hrvanalysis_features( - rr_intervals_file_path=parameters['rr_file_path'], - output_folder=parameters['features_folder'], - sliding_window=SLIDING_WINDOW, - short_window=SHORT_WINDOW, - medium_window=MEDIUM_WINDOW, - large_window=LARGE_WINDOW) - - output_parameters = { - 'features_file_path': output_features_file_path} - - return output_parameters - - @task(multiple_outputs=True) - def t_compute_consolidate_feats_and_annot(parameters: dict) -> dict: - output_cons_file_path = consolidate_feats_and_annot( - features_file_path=parameters['features_file_path'], - annotations_file_path=parameters['annotations_file_path'], - output_folder=parameters['consolidated_folder'], - window_interval=WINDOW_INTERVAL, - segment_size_treshold=SEGMENT_SIZE_TRESHOLD, - crop_dataset=True) - - output_parameters = { - 'cons_file_path': output_cons_file_path} - - return output_parameters + @task() + def t_detect_qrs(parameters_list: List[dict]) -> List[dict]: + def inner(parameters: dict): + output_qrs_file_path, sampling_frequency = detect_qrs( + qrs_file_path=parameters['qrs_file_path'], + exam_id=parameters['exam_id'], + method=parameters['method'], + output_folder=parameters['rr_intervals_folder']) + + output_parameters = {'rr_file_path': output_qrs_file_path, + 'sampling_frequency': sampling_frequency} + + return output_parameters + + return map_parameters(inner, parameters_list) + + @task() + def t_consolidate_parameters(param_dict_1_list: List[dict], + param_dict_2_list: List[dict]) -> List[dict]: + consolidated_parameters_list = [] + for param_dict_1, param_dict_2 in zip(param_dict_1_list, param_dict_2_list): + if param_dict_1 is None or param_dict_2 is None: + consolidated_parameters_list.append(None) + else: + consolidated_parameters_list.append({**param_dict_1, **param_dict_2}) + + return consolidated_parameters_list + + @task() + def t_compute_hrv_analysis_features(parameters_list: List[dict]) -> List[dict]: + def inner(parameters: dict) -> dict: + output_features_file_path = compute_hrvanalysis_features( + rr_intervals_file_path=parameters['rr_file_path'], + output_folder=parameters['features_folder'], + sliding_window=SLIDING_WINDOW, + short_window=SHORT_WINDOW, + medium_window=MEDIUM_WINDOW, + large_window=LARGE_WINDOW) + + output_parameters = { + 'features_file_path': output_features_file_path} + + return output_parameters + + return map_parameters(inner, parameters_list) + + @task() + def t_compute_consolidate_feats_and_annot(parameters_list: List[dict]) -> List[dict]: + def inner(parameters: dict) -> dict: + output_cons_file_path = consolidate_feats_and_annot( + features_file_path=parameters['features_file_path'], + annotations_file_path=parameters['annotations_file_path'], + output_folder=parameters['consolidated_folder'], + window_interval=WINDOW_INTERVAL, + segment_size_treshold=SEGMENT_SIZE_TRESHOLD, + crop_dataset=True) + + output_parameters = { + 'cons_file_path': output_cons_file_path} + + return output_parameters + + return map_parameters(inner, parameters_list) # @task(depends_on_past=True, trigger_rule=TriggerRule.ALL_DONE) # def t_create_ml_dataset(): @@ -158,33 +192,46 @@ def t_compute_consolidate_feats_and_annot(parameters: dict) -> dict: # } # ) - df_db = pd.read_csv(f'{FETCHED_DATA_FOLDER}/df_data.csv', encoding='utf-8') - - for index in range(df_db.shape[0]): - qrs_file_path = df_db['data_file_path'].iloc[index] - exam_id = df_db['exam_id'].iloc[index] - parameters = {'qrs_file_path': qrs_file_path, - 'annotations_file_path': ''.join([qrs_file_path[:-3], - 'tse_bi']), - 'exam_id': exam_id, - 'method': DETECT_QRS_METHOD, - 'rr_intervals_folder': RR_INTERVALS_FOLDER, - 'features_folder': FEATURES_FOLDER, - 'consolidated_folder': CONSOLIDATED_FOLDER} - - qrs_parameters = t_detect_qrs(parameters) - parameters = t_consolidate_parameters(parameters, - qrs_parameters) - - features_parameters = t_compute_hrv_analysis_features(parameters) - parameters = t_consolidate_parameters(parameters, - features_parameters) - - consolidated_feats_and_annot_parameters = \ - t_compute_consolidate_feats_and_annot(parameters) - parameters = t_consolidate_parameters( - parameters, - consolidated_feats_and_annot_parameters) + @task() + def t_get_initial_parameters(df_db: pd.DataFrame) -> List[dict]: + parameters_list = [] + for index in range(df_db.shape[0]): + qrs_file_path = df_db['edf_file_path'].iloc[index] + tse_bi_file_path = df_db['annotations_file_path'].iloc[index] + exam_id = df_db['exam_id'].iloc[index] + parameters = {'qrs_file_path': ''.join([AIRFLOW_PREFIX_TO_DATA, + qrs_file_path]), + 'annotations_file_path': ''.join([AIRFLOW_PREFIX_TO_DATA, + tse_bi_file_path]), + 'exam_id': exam_id, + 'method': DETECT_QRS_METHOD, + 'rr_intervals_folder': RR_INTERVALS_FOLDER, + 'features_folder': FEATURES_FOLDER, + 'consolidated_folder': CONSOLIDATED_FOLDER} + parameters_list.append(parameters) + return parameters_list + + df_db = pd.read_csv(f'{FETCHED_DATA_FOLDER}/df_candidates.csv', encoding='utf-8') + partition_size = (df_db.shape[0] + NUM_PARTITIONS - 1) // NUM_PARTITIONS + for partition_index in range(NUM_PARTITIONS): + # Split the df_db into blocks df_db[0:partition_size], df_db[partition_size:2*partition_size], ... + partition_start = partition_index * partition_size + partition_end = (partition_index+1) * partition_size + parameters_list = t_get_initial_parameters(df_db.iloc[partition_start:partition_end]) + + qrs_parameters_list = t_detect_qrs(parameters_list) + parameters_list = t_consolidate_parameters(parameters_list, + qrs_parameters_list) + + features_parameters_list = t_compute_hrv_analysis_features(parameters_list) + parameters_list = t_consolidate_parameters(parameters_list, + features_parameters_list) + + consolidated_feats_and_annot_parameters_list = \ + t_compute_consolidate_feats_and_annot(parameters_list) + parameters_list = t_consolidate_parameters( + parameters_list, + consolidated_feats_and_annot_parameters_list) # file_quality = t_apply_ecg_qc(filepath=data_file_path, diff --git a/dags/train.py b/dags/train.py new file mode 100644 index 0000000..40d5489 --- /dev/null +++ b/dags/train.py @@ -0,0 +1,69 @@ +from src.usecase.data_processing.prepare_features import prepare_features_with_io +from src.usecase.train_model import (train_pipeline_with_io) +from dags.config import ( + DEFAULT_ARGS, + START_DATE, + CONCURRENCY, + SCHEDULE_INTERVAL, + MODELS_PARAM, + MLRUNS_DIR, + TEST_DATA, + TRACKING_URI, + TRAIN_DATA, + FEATURE_TRAIN_PATH, + FEATURE_TEST_PATH, + COL_TO_DROP) +import sys + +from airflow.decorators import dag, task + +@dag(default_args=DEFAULT_ARGS, + start_date=START_DATE, + schedule_interval=SCHEDULE_INTERVAL, + catchup=False, + concurrency=CONCURRENCY) +def train_pipeline(): + + @task + def prepare_features_task( + dataset_path: str, + col_to_drop: list, + feature_path: str) -> str: + + prepare_features_with_io( + dataset_path=dataset_path, + col_to_drop=col_to_drop, + features_path=feature_path) + + return feature_path + + @task + def train_model_task( + feature_tain_path: str, + feature_test_path: str, + tracking_uri: str = TRACKING_URI, + model_param: dict = MODELS_PARAM['xgboost'], + mlruns_dir: str = MLRUNS_DIR) -> None: + + train_pipeline_with_io(feature_tain_path, feature_test_path, + tracking_uri=tracking_uri, model_param=model_param, mlruns_dir=mlruns_dir) + + # Orchestration + features_train_path = FEATURE_TRAIN_PATH + features_test_path = FEATURE_TEST_PATH + + ml_train_path = prepare_features_task( + dataset_path=TRAIN_DATA, + col_to_drop=COL_TO_DROP, + feature_path=features_train_path) + + ml_test_path = prepare_features_task( + dataset_path=TEST_DATA, + col_to_drop=COL_TO_DROP, + feature_path=features_test_path) + + train_model_task(feature_tain_path=ml_train_path, feature_test_path=ml_test_path, tracking_uri=TRACKING_URI, + model_param=MODELS_PARAM['xgboost'], mlruns_dir=MLRUNS_DIR) + + +train_pipeline_dag = train_pipeline() \ No newline at end of file diff --git a/data/PL b/data/PL new file mode 120000 index 0000000..218cd47 --- /dev/null +++ b/data/PL @@ -0,0 +1 @@ +/home/DATA/lateppe/DetecTeppe-2022-4-7/PL \ No newline at end of file diff --git a/data/dataset/PAT_0/PAT_0_Annotations_EEG_0.tse_bi b/data/dataset/PAT_0/PAT_0_Annotations_EEG_0.tse_bi new file mode 100644 index 0000000..624ed2c --- /dev/null +++ b/data/dataset/PAT_0/PAT_0_Annotations_EEG_0.tse_bi @@ -0,0 +1,9 @@ +version = tse_v1.0.0 +annotator = TEST +date = 2022-01-01 + +2017-01-10T14:30:00.441406 2017-01-10T14:32:23.062500 bckg 1 +2017-01-10T14:32:23.062500 2017-01-10T14:32:40.015625 seiz 1 +2017-01-10T14:32:40.015625 2017-01-10T14:38:44.406250 bckg 1 +2017-01-10T14:38:44.406250 2017-01-10T14:38:50.468750 seiz 1 +2017-01-10T14:38:50.468750 2017-01-10T14:40:42.832031 bckg 1 diff --git a/data/ml_dataset_2022_04_08 b/data/ml_dataset_2022_04_08 new file mode 120000 index 0000000..f7c9f98 --- /dev/null +++ b/data/ml_dataset_2022_04_08 @@ -0,0 +1 @@ +../../../DATA/DetecTeppe-2022-04-08/ml_dataset_2022_04_08 \ No newline at end of file diff --git a/data/output/features/rr_00007633_s003_t007.csv b/data/output/features/rr_00007633_s003_t007.csv deleted file mode 100644 index a54dead..0000000 --- a/data/output/features/rr_00007633_s003_t007.csv +++ /dev/null @@ -1,668 +0,0 @@ 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b/data/test_data/README.md new file mode 100644 index 0000000..804dfd6 --- /dev/null +++ b/data/test_data/README.md @@ -0,0 +1,8 @@ +# test_data +This samples are used to perform unit and integration testing. + +**References for ST7051J0** +B Kemp, AH Zwinderman, B Tuk, HAC Kamphuisen, JJL Oberyé. Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG. IEEE-BME 47(9):1185-1194 (2000). + +Please include the standard citation for PhysioNet: (show more options) +Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P. C., Mark, R., ... & Stanley, H. E. (2000). PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220. \ No newline at end of file diff --git a/data/test_data/ST7051J0-PSG.edf b/data/test_data/ST7051J0-PSG.edf new file mode 100644 index 0000000..0b168cc --- /dev/null +++ b/data/test_data/ST7051J0-PSG.edf @@ -0,0 +1,129472 @@ +0 X F X Female_32yr Startdate 15-JUL-1994 X X X 15.07.9423.34.001536 3220 10 5 EEG Fpz-Cz EEG Pz-Oz EOG horizontal EMG submental Marker Ag-AgCl electrodes Ag-AgCl electrodes Ag-AgCl electrodes Ag-AgCl electrodes MarkerButton uV uV uV uV ID+M-E -3000 -3000 -3000 -3000 -64 3000 3000 3000 3000 63 -8192 -8192 -8192 -8192 -64 8191 8191 8191 8191 63 LP:800Hz HP:0.03Hz LP:800Hz HP:0.03Hz LP:800Hz HP:0.03Hz LP:800Hz HP:0.03Hz 1000 1000 1000 1000 100 uv}|n{y~q!h~syijbPFrJS`^orfumqvp`Vbim]L%/o# qyVcO^^ZAaMXiyCtl:mo9!Y[vM%o8bkMQ_Hdruyq}y_YlmH99C65DMo:efQS\XTH'TB$BAEIL@*.{DyjNePjcnj%= JD--&)$!2 +$7?7SWd\Ewp+z!C- .,%! !.B:#" !# '""4# +0 + + ! d  +,'3' "....1)- 6 +=6%4# "  +{:ntsrib u}K |x )Dq5u6 Pu'`Ce????? +?@$+ T@ "##   ') 7'# '!Z*@LGC70 (  ucn5n[WZecX`UR:V__/.M6A_L729!F@C8 *&,YLVqns~ ; %OMU^@_X}~$Z<[%+ NPg<VhF51+O73ep;'0|?Ms#M#yL;|d(JF8 G X%  H!FM)T3A <L ;02L 3-3'62 92%*(2#<DH*5#P+60%}8;0'g%4 +=E ++ #*+(9<)E-U + F *.R)($Z0l+L)!7 )$< 4 +kb\J,)"<ZM+DZ"zjHLng}m[ffBv)Z@g f +6) + +'. ==Ch 2/)/<:J.+(2"067FDB.72;D6+(8,$JS*/;&?9 K0hTk +R-88z`daRL[bdD +cEnx ^N=9N -8#O +) + !#JBXhO9;8/@$d~otW`Lvk~{e}oUlJ<.;'1DBGOFjtkjjai{cvhwP+e;_^9TD+ 3!2='BJA&)ttyravw~]qtlm|qrXlkUK/EU5IPAD.6R[NaMB)AB:D1%#   ~<*QDQB*rc$uXC( '9-6B:thvMu 1<,9UL5r9Wq.V]g}SmX;:$!BZ#" 3+,!  4 ,tjKD.$#*1$=_*I~}}$wkzsk\lu~]yl^lzm)^ .(' 2+nMWYII=FLynpzlp~d`TS1i6;\[  swJ; 3/ +9/Ga hXR3IC;kmLDJ;+8$Nf( :O2V/%&%9-5*"$3wR+/ B!V&..* ya qQ ql/:( C{lFpg#9*-&I\V~x>Y~.<!X).' j.qFgH! 0(K + S>|X{ `. *'@u[lyS,%\ 7z[ k9  U +fX d#Zx~-tdoo@ z +0EB +Y +cXid N^y(!*|b(Lr(:_:BJ_ojOUZC%Hg3=&A"=v{^zD.{QPbbJ JqRNl+_'+n!pqY[[m}1!{9nXOB+F!%xq\a^kTAp=JZ;D'@8_',LpaL5L PYhqFrCZMXS,e4__L6&$@iWj*4 +.80 ,_])QgfR9W":18E$.$ !#iJJ8=7T E\.2pX0u %ARVPvxKpoMigDUXkXD%6X`"'NG9}(V?Hl pr +K3MZmUU|rxCb)rafUylHp*"dx@nRE\IHB+>D`{LF<j\[T/Tj`pgkE")9<UaWZndJ)/5,m;ER%(.:U8>.C s7F 5C*K%F:Qi\7'4z('$: 7(/0BHI`F>.;DG?9-%EETc>+/X~~TF P-^"[lwUK}^+#|M=YJMK#k>UZ+jBgB^[ov2Y|N_W"v;6cI.$! eCS,8#}}KIKX>$j ++QB,KAbRGW86&N4D+b^+:Jb*B& +Z +O<R F3-<(.Yb8=BW P dK/tA+(B2&(;3*YP '-3m^H^-PEp*j3Ix|E=(Mu F+ +r|B#;t,#1r7`k_m&#!,=#[@rjp/c*@8H#Kl91PS( dIAC ]Vw_PIHelp-pf'1Qs?F+@|?\eo |`7${,7Ll6x_ .&)_5)o 9J=( ?"]Y(Q`Od8Q+*{[Hlo FxLzpq1-Y|nbiBK5ry{N-r3[t.`HIWdsf_hL\oKatXI/g8Zp#x"6%%\-Z*EN_B}5Tw`c v XgAogpXaEN]IH^JVc]=:#U ^`7C '+A$Pf-#O4cWQuh5x87k"*("$0Ko<%R( +QO[k"z_cF62/1,R]rqqxel5E^Ps@zUSv|pwl8mP A\=jTF@ p&%uZKZS%= 6:>BF:C`7TWIp`PXbR2_"IL:S;osyq ]+m~NA0/v,eJ.ZU;,5'!&m??\@H-5 xu_e~z?itPE-?=(sxcrHUniw{y}quokWX~fktumV`bN`v=Etn|kKil]MNnICog3erwpurvy `pb;]A?URTe|_bGiBAkvXW(Q`zYAItpxxuKodgxz-_cPm2^]H8.&:nAw2-23 <IHK]-U*8N/ + +[> 95. 6'2F- 7D016E%O_O}ffjTER@IX08Y=cjk1/ ?F4)5aW#x$S:2*{s=>(&.!C0, 7TH,N7MT*Ml%*O\O_R6I(yH[06=9n&&IQ >0;+5>3_b,&16m (nF?<4Il-c13nB{j4k)S;H!R</]+H a"(%E)#Q Q6rGkHZ+ +.AS3 bD.I-6K1 +o#cV1 #HQ?N# I6!85 .>4O &',7."M#% k?GIP *-mQg2 "rD2!(K%C." ydM3A#51 S8dD^ ! g + 5Bx/#..% + _z S M KZ:*!zjE h U$ .I&6D&_wy + %EmA ,b F!=?0J6X,$!I_63@bpO=vXmm3'5{@B|C +d&"ahu-o0,tF>{i1X$vw-:JQme}j!pN8CMF~ n"N1L}SY"4HQ$O^iI/})C8mD=\~cbc_%@@u"AqUfel :=F}BTZHpvTm9aH,3M.dE5ro.R<OEE:MQ:09,Z/7\w72E3zsrxK}|xm1)&9 6fU"*_IF2]c E'[M- +^,PM  7 + > +p + + +eVa?&( f 8cPiR= |R]Ryze~ H ze}u&>]cYjmh~lhx4*HVs}~~},9`K_/2Q!h  +knUT>|GE{+W}vK$HGq Y6]L[,. 0';>>j\4gA%q9324f ?C1x'fEC ^%)"f +| 2,g*?u@a^&BA'F bZ k+b+Qj6G2R,X7GR2V 4uCsd{Ka&`XeE/-7IXy=HKg6u"oEv^>l;Ku]GGL$ w*-l="/PA_6lI=_KR:%f~\@(.=_^/Tfi{i &=YYVQH^[M)-K|t70V?*ugB8kv~,_ s%,OAv*d$|H$~9c'trkd'!i* ~HE +&}ueutG[WHWU$;E t\l1\^5+C)@3"al0+oN:2(.=SOcH|]af]FmesrcOoM0Cw6RlqnT4jR*(`pPFC`==1L9.DH;,OXP(6 4dK-E4 ED1M:71*('=/::<'1G5/&/N%A#*E3=&)6 $- %G}&H7j!65$8bwy*=ryKS~a{;X.,\1S`=,"H2e0;7)XYGtN+o!F;Z3u?@-(m0 ^H-.E"\UVK=# D*7B~" :*J* -y>(25eQ1A,bIO&t8>zA=abB/Q0: gN>\ )wcWw$`no.m7{(C+3> )!] +-wa~yar}~osl\zhTmMYYWTST|{pzf]rmOyz|}m (FG'4$6n9i6M??2N$89?:1`^jT{oDhUW=jNF=AvHBoUQ/^mrT_@H+F$ += A & /$%/! +4(.0)%Z#"0% +  +oqdvJ]qazy%KZQNbH]|]p}[[tPoefwMLyhaF%>D =R~%.MoT4  {N;C QsoR&^sd&/H$!(V+ndt4x w%@D)ocN>B!>;, ! 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'.{. ,D_J'7I+:'l +"D&8' :)M.* + +; + "@ \ No newline at end of file diff --git a/data/test_data/dataset_feats_PAT_0_Annotations_EEG_0_s2.csv b/data/test_data/dataset_feats_PAT_0_Annotations_EEG_0_s2.csv new file mode 100644 index 0000000..a6de3ec --- /dev/null +++ b/data/test_data/dataset_feats_PAT_0_Annotations_EEG_0_s2.csv @@ -0,0 +1,602 @@ +interval_index,interval_start_time,mean_nni,sdnn,sdsd,nni_50,pnni_50,nni_20,pnni_20,rmssd,median_nni,range_nni,cvsd,cvnni,mean_hr,max_hr,min_hr,std_hr,lf,hf,vlf,lf_hf_ratio,csi,cvi,Modified_csi,sampen,sd1,sd2,ratio_sd2_sd1,timestamp +1985.0,1985000.0,680.46875,39.42908346345191,17.136330951767956,0.0,0.0,3.0,21.428571428571427,18.20257414095992,664.0625,117.1875,0.02675005155043479,0.05794400325283403,88.44242286521055,94.81481481481481,80.0,4.790150656640049,303.72617775742094,88.01089597984986,350.16702322189525,3.4510065415872964,3.776172651588404,4.0559794502776105,782.6498188042963,1.4170660197866443,13.72157022186433,51.81501820865391,3.776172651588404,2017-01-10 14:30:00.550781250 +1986.0,1986000.0,672.4330357142857,30.252140184598947,20.564830086861384,0.0,0.0,3.0,23.076923076923077,20.584577594295613,662.109375,89.84375,0.03061208551782385,0.04498907486373553,89.39273010222834,94.81481481481481,83.02702702702703,3.7967210147226664,300.81939193178516,89.11239831716199,358.13300491492157,3.375729950181926,3.627766272531974,4.059154990534466,739.6673411244669,1.4017985476558559,14.05069655028172,50.97264305069338,3.627766272531974,2017-01-10 14:30:01.550781250 +1987.0,1987000.0,675.0,31.082138445691648,21.214533376417815,0.0,0.0,4.0,28.571428571428573,21.21636814687542,664.0625,93.75,0.03143165651388951,0.04604761251213577,89.06226260375034,94.81481481481481,82.58064516129032,3.899362273016881,292.62098373520814,91.26912495084869,368.9936982405933,3.206133332523937,3.3761439080069677,4.039302885239017,649.0570444293144,1.5114575040738967,14.235770442784684,48.06200965619316,3.3761439080069677,2017-01-10 14:30:02.550781250 +1988.0,1988000.0,673.6979166666666,30.03594077260172,21.067234018987353,0.0,0.0,4.0,28.571428571428573,21.21636814687542,664.0625,93.75,0.03149240575338293,0.04458369252678415,89.22298585839626,94.81481481481481,82.58064516129032,3.7704740839146567,288.1191991120486,92.59323334665336,369.1394293404727,3.111665817235037,3.2721320802191145,4.028238075730434,611.4560922613127,1.490091154801534,14.277219407859695,46.71694764078466,3.2721320802191145,2017-01-10 14:30:03.550781250 +1989.0,1989000.0,674.4791666666666,30.38947256506335,18.050104899147176,0.0,0.0,2.0,14.285714285714286,18.528970665049098,664.0625,93.75,0.02747152407482183,0.045056206428510916,89.12377518527255,94.81481481481481,82.58064516129032,3.8213202645960505,276.4856150036195,95.12176078562936,361.60866914332587,2.906649464014021,2.984208420169668,3.9928306560685254,511.28029507006744,1.4842747694800944,14.352936418439107,42.83215371406586,2.984208420169668,2017-01-10 14:30:04.550781250 +1990.0,1990000.0,679.4270833333334,33.74739917778674,17.38216308836219,0.0,0.0,2.0,14.285714285714286,18.528970665049098,667.96875,101.5625,0.02727146314825164,0.04967037671241896,88.51141209827773,94.81481481481481,81.70212765957447,4.206204671486996,270.169802457943,96.40291156249958,355.3779246286485,2.8025066678902895,2.863268570934166,3.9761052524970126,471.3524356141443,1.3862943611198906,14.373468840239651,41.15510158555975,2.863268570934166,2017-01-10 14:30:05.550781250 +1991.0,1991000.0,690.1041666666666,32.09877826678514,19.38923175568269,0.0,0.0,3.0,21.428571428571427,19.835795298187747,703.125,89.84375,0.028743190167939983,0.046512946620549035,87.1200612374233,93.0909090909091,81.70212765957447,3.933669775138481,263.9522175343015,97.44318302258372,348.21991734787645,2.7087807412153926,2.730504533619608,3.9562789645877614,429.04597810260583,1.486377819676873,14.386595732886258,39.282664871998435,2.730504533619608,2017-01-10 14:30:06.550781250 +1992.0,1992000.0,697.265625,28.97965277466077,19.90897689119885,0.0,0.0,3.0,23.076923076923077,20.18148390649878,703.125,85.9375,0.028943752829488446,0.041561854959737576,86.19121731696238,92.53012048192771,81.70212765957447,3.517672069339125,251.12625578572548,99.63331820787468,330.31019137832743,2.520504789991801,2.6502829807755144,3.943693245358443,404.37564822966675,1.5522794985941517,14.392641487842848,38.14457278363348,2.6502829807755144,2017-01-10 14:30:07.550781250 +1993.0,1993000.0,701.171875,28.406956732555646,19.23076923076923,0.0,0.0,3.0,23.076923076923077,19.97688718581089,705.078125,85.9375,0.028490713758036704,0.040513542749494404,85.70567914721369,92.53012048192771,81.70212765957447,3.451155950893631,244.99967209124213,100.74151930699563,318.88318507468847,2.4319632439197196,2.55681153879518,3.9166362506241446,371.42078342322014,1.5522794985941517,14.203938236902749,36.316793180447014,2.55681153879518,2017-01-10 14:30:08.550781250 +1994.0,1994000.0,711.2165178571429,19.347955164133246,20.003986751313988,0.0,0.0,3.0,23.076923076923077,20.326363612124716,710.9375,70.3125,0.028579712509162407,0.027204029544234426,84.42199867883086,90.3529411764706,81.70212765957447,2.271306908659827,239.33058624481987,101.75678771148957,307.27332277573583,2.35198645345795,2.58340012019035,3.9088792886180217,373.8756296816318,1.5686159179138452,14.0050226410701,36.18057717420907,2.58340012019035,2017-01-10 14:30:09.550781250 +1995.0,1995000.0,713.7276785714286,14.407866006118834,16.77317997229911,0.0,0.0,2.0,15.384615384615385,16.783942360299207,710.9375,46.875,0.02351589109433634,0.02018678333304531,84.09750687093451,87.27272727272727,81.70212765957447,1.636638231327557,230.50331467200516,104.27993410908005,285.1267829759579,2.2104282731027767,2.6356884472105464,3.903104936536838,382.7309450083917,1.578978704949392,13.7735357456471,36.30274904204356,2.6356884472105464,2017-01-10 14:30:10.550781250 +1996.0,1996000.0,714.5833333333334,13.96502260645537,15.933361088470129,0.0,0.0,2.0,14.285714285714286,15.935803918854505,710.9375,46.875,0.022300833472449454,0.019542888778713054,83.99505661084507,87.27272727272727,81.70212765957447,1.5915039068213446,226.5742267765622,104.72992528426205,271.49108577406054,2.1634143838218693,2.6376096564169225,3.8966560140912425,380.31529639108595,1.5040773967762742,13.666671439267784,36.04734455929007,2.6376096564169225,2017-01-10 14:30:11.550781250 +1997.0,1997000.0,712.890625,15.070541013191546,16.955211514021496,0.0,0.0,2.0,15.384615384615385,16.957873869618343,710.9375,46.875,0.023787483345875594,0.02114004657192896,84.19939512035725,87.27272727272727,81.70212765957447,1.719036035414442,220.87399990147227,106.45720700423894,243.976951159718,2.0747679383761914,2.627016215718244,3.897506701481127,378.3968226855629,1.5184661342283736,13.707617668735768,36.01013389463478,2.627016215718244,2017-01-10 14:30:12.550781250 +1998.0,1998000.0,714.5647321428571,13.221397740632524,17.256074537693785,0.0,0.0,2.0,15.384615384615385,17.2665359357676,710.9375,42.96875,0.024163711360324514,0.018502729208288545,83.9938863434199,86.77966101694915,81.70212765957447,1.4969741392955058,219.3643210646327,107.27623553390583,232.81652479567907,2.044854761847979,2.5879859636121276,3.8865054711894444,365.33864222492963,1.5279448781829175,13.636778488019605,35.29179131588255,2.5879859636121276,2017-01-10 14:30:13.550781250 +1999.0,1999000.0,710.9375,16.713862962948774,16.992446533171506,0.0,0.0,3.0,23.076923076923077,17.435653937985588,710.9375,50.78125,0.024524875868814893,0.023509609442389483,84.43906931779541,87.77142857142857,81.70212765957447,1.9190594228619544,217.93935834825783,107.69409286971982,212.64393227463242,2.0236890672537107,2.5930377142183207,3.8865283284707886,366.41851837338567,1.5740359853831845,13.62384695291311,35.327148961642045,2.5930377142183207,2017-01-10 14:30:14.550781250 +2000.0,2000000.0,708.1473214285714,15.73194854472059,17.47961487049157,0.0,0.0,3.0,23.076923076923077,17.736003080978012,710.9375,50.78125,0.025045640284564696,0.022215643650227972,84.76692697293015,87.77142857142857,81.70212765957447,1.8125983742300074,218.52756254455954,108.32328735877606,204.29323491225998,2.0173645748100077,2.6137858731852233,3.8859166981645243,370.56411348123703,1.6283063967384832,13.560114433889643,35.44323554607579,2.6137858731852233,2017-01-10 14:30:15.550781250 +2001.0,2001000.0,711.4955357142857,19.90962474843829,19.287027086395693,0.0,0.0,4.0,30.76923076923077,19.621187160233784,710.9375,74.21875,0.027577386189128583,0.027982782391530524,84.38981568009739,87.77142857142857,79.17525773195877,2.2414756174575143,222.69944874234588,109.66775727360292,197.80912217101928,2.0306738669484012,2.5354001498014553,3.8952405574379685,357.84095664225754,1.5998684614179497,13.916724553710038,35.28446551822202,2.5354001498014553,2017-01-10 14:30:16.550781250 +2002.0,2002000.0,714.0066964285714,21.94670657462916,19.538182941116418,0.0,0.0,4.0,30.76923076923077,19.621187160233784,710.9375,74.21875,0.027480396554231293,0.030737395999793245,84.105761663807,87.77142857142857,79.17525773195877,2.463706805222087,225.4610776518505,109.83822205935746,199.78984599920014,2.0526650324875937,2.538968207114056,3.8902127892617906,356.5268882279846,1.5279448781829175,13.82667554804016,35.10548962655528,2.538968207114056,2017-01-10 14:30:17.550781250 +2003.0,2003000.0,712.3325892857143,22.12931362482438,23.31389992941205,0.0,0.0,5.0,38.46153846153846,23.4375,710.9375,74.21875,0.032902467685076375,0.031065985127838064,84.30454943801378,87.77142857142857,79.17525773195877,2.4808644131734003,229.42300639437786,109.68159236222485,203.07412873744468,2.0917184137581217,2.4813070029035056,3.899827542957407,348.28480171652785,1.589235205116581,14.142087810818584,35.09086152066046,2.4813070029035056,2017-01-10 14:30:18.550781250 +2004.0,2004000.0,710.6584821428571,21.638937344932767,23.209100381403264,0.0,0.0,5.0,38.46153846153846,23.211045413912977,707.03125,74.21875,0.032661321854520656,0.03044913680574756,84.49977213330536,87.77142857142857,79.17525773195877,2.422067047340239,238.56450553305842,110.03159730456326,213.56131809511427,2.168145436194305,2.4681991172443687,3.895417816114784,343.77888370091773,1.5040773967762742,14.107784607730467,34.82082151507403,2.4681991172443687,2017-01-10 14:30:19.550781250 +2005.0,2005000.0,711.2165178571429,21.420867988470132,23.00591916171992,0.0,0.0,5.0,38.46153846153846,23.007881370701416,708.984375,74.21875,0.03235003798846366,0.030118631177197704,84.43204517524227,87.77142857142857,79.17525773195877,2.3976146375142067,244.42748671985828,110.63557901214728,218.60426862801532,2.2093027297576784,2.4578948292815315,3.8919070525096426,340.2502634755379,1.45667515788169,14.080299313075226,34.6078948763439,2.4578948292815315,2017-01-10 14:30:20.550781250 +2006.0,2006000.0,708.984375,21.762567344867744,21.776046793261344,0.0,0.0,4.0,30.76923076923077,21.776046793261344,705.078125,74.21875,0.030714424127134456,0.03069541179221015,84.7000904602367,87.77142857142857,79.17525773195877,2.4350290640158074,256.45918261767986,110.17072158032894,234.027065060973,2.3278342824566813,2.46758623914604,3.8768199130560634,336.37093780884334,1.5224265354444706,13.810639702438944,34.0789444835423,2.46758623914604,2017-01-10 14:30:21.550781250 +2007.0,2007000.0,707.8683035714286,23.093355357860176,21.36162570574065,0.0,0.0,4.0,30.76923076923077,21.532125693926236,705.078125,82.03125,0.03041826507175074,0.03262380197184504,84.84340947099963,88.78612716763006,79.17525773195877,2.6062376978318698,262.52584533418366,110.49122514928372,243.3790387511438,2.3759881834913794,2.4899047960596974,3.880530936272738,342.4045922864306,1.555853666299994,13.807469925269155,34.3792855883777,2.4899047960596974,2017-01-10 14:30:22.550781250 +2008.0,2008000.0,707.03125,23.18574726425532,21.51324798037109,0.0,0.0,4.0,30.76923076923077,21.532125693926236,701.171875,82.03125,0.030454277224558655,0.03279310110303515,84.94444171887946,88.78612716763006,79.17525773195877,2.614323454986595,276.3119075831128,110.58700468102505,255.73376632098146,2.4985929258153017,2.4776003655139087,3.885890854844688,341.97338879354413,1.5705980791178364,13.927392271883557,34.506512183474285,2.4776003655139087,2017-01-10 14:30:23.550781250 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+2011.0,2011000.0,707.03125,20.094054684255394,23.56045166335656,0.0,0.0,4.0,30.76923076923077,23.834774056552813,705.078125,70.3125,0.033711061649047074,0.028420320437399893,84.92477994096839,88.78612716763006,80.41884816753927,2.298139839767436,302.69436324960384,110.8178020536173,278.5632624236598,2.731459726147161,2.4203304601900797,3.89321550730211,332.9812301343253,1.55814461804655,14.21053501533362,34.394190753209664,2.4203304601900797,2017-01-10 14:30:26.550781250 +2012.0,2012000.0,705.078125,17.112900826555364,20.6437067756222,0.0,0.0,3.0,23.076923076923077,20.69830540780592,705.078125,66.40625,0.029356045342926955,0.024270928596111765,85.14330941968454,88.78612716763006,80.84210526315789,1.9831554678979917,308.32873661357405,111.21820268691127,280.1494520427267,2.772286632625648,2.423249506488661,3.8937389737719923,333.78490035829793,1.5686159179138452,14.21053501533362,34.43567196284703,2.423249506488661,2017-01-10 14:30:27.550781250 +2013.0,2013000.0,707.3102678571429,18.28758504750829,20.463600774016133,0.0,0.0,3.0,23.076923076923077,20.498867669243438,707.03125,66.40625,0.028981436578528,0.02585511037880462,84.88109381734405,88.78612716763006,80.84210526315789,2.1159121567959516,320.6836199272019,111.59326106516977,285.03805923877763,2.8736826656577827,2.386844384893833,3.88681954740926,323.7024090794854,1.5686159179138452,14.204885250298563,33.904850597736356,2.386844384893833,2017-01-10 14:30:28.550781250 +2014.0,2014000.0,708.1473214285714,19.345788418489615,21.98238119972582,0.0,0.0,4.0,30.76923076923077,21.990594307129793,707.03125,66.40625,0.031053699762314098,0.027318875371105902,84.78690336993232,88.78612716763006,80.84210526315789,2.2334489250067575,325.46927717442765,111.61454925915916,292.88037802290665,2.9160112130069766,2.4246740509520572,3.8668950215815885,323.9123878964456,1.4894785973551214,13.77402405634676,33.39751870661339,2.4246740509520572,2017-01-10 14:30:29.550781250 +2015.0,2015000.0,707.8683035714286,19.391239234256755,21.98238119972582,0.0,0.0,4.0,30.76923076923077,21.990594307129793,705.078125,66.40625,0.031065940085436807,0.027393851563096653,84.82057871852476,88.78612716763006,80.84210526315789,2.2378753546766803,335.91499034281617,112.68595100315912,310.18957647066105,2.9809837637470786,2.4340576598490795,3.8653820743250114,325.2275518815751,1.5040773967762742,13.723523011538536,33.40384630635048,2.4340576598490795,2017-01-10 14:30:30.550781250 +2016.0,2016000.0,714.0066964285714,23.042472863709612,22.445732448296994,0.0,0.0,5.0,38.46153846153846,23.236316020650488,712.890625,78.125,0.032543554754986846,0.03227206828586762,84.11431832388192,88.78612716763006,79.58549222797927,2.6219160653969644,340.19894485403927,113.02485424963287,318.24444259207144,3.0099480960414007,2.451167691266818,3.8857503848361237,336.4609972222994,1.509354453877118,14.000021070394613,34.31639932480597,2.451167691266818,2017-01-10 14:30:31.550781250 +2017.0,2017000.0,721.5401785714286,26.556691753468392,22.701714864812622,0.0,0.0,5.0,38.46153846153846,23.562367692446326,720.703125,101.5625,0.03265565576555593,0.03680556196613717,83.25816638333693,88.27586206896552,76.8,2.898937275625819,347.3615841474665,113.8177877429718,335.27412194684337,3.0519094689478,2.5512179977968095,3.9121854585900007,368.31048658226473,1.488077055429833,14.146824079297327,36.091632202768615,2.5512179977968095,2017-01-10 14:30:32.550781250 +2018.0,2018000.0,722.9352678571429,25.79600965703064,26.653169304731595,1.0,7.6923076923076925,5.0,38.46153846153846,26.714075445784697,720.703125,101.5625,0.03695223712763117,0.03568232289108368,83.09160959788288,88.27586206896552,76.8,2.8132549521961945,351.25790522612635,114.39766557616045,344.76587192799116,3.0704988904889476,2.3902264203091854,3.9363459837753245,343.42472228042266,1.455287232606842,15.027733311402626,35.919685198275,2.3902264203091854,2017-01-10 14:30:33.550781250 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+2023.0,2023000.0,722.3772321428571,23.709572956983877,24.15461720007379,1.0,7.6923076923076925,4.0,30.76923076923077,24.273936813246046,720.703125,85.9375,0.03360285420574501,0.032821595008818163,83.13965598407283,86.29213483146067,76.8,2.549111782584108,371.57898458603347,118.85034597938133,352.1030080414338,3.1264442818744222,2.4568639294762558,3.9244453541465547,353.0159543017415,1.532897835311766,14.620833572811183,35.921398623915245,2.4568639294762558,2017-01-10 14:30:38.550781250 +2024.0,2024000.0,721.2611607142857,23.5178481959012,22.982359586505943,1.0,7.6923076923076925,3.0,23.076923076923077,22.982359586505943,718.75,85.9375,0.031864130273902246,0.03260656399772143,83.26678772906287,86.29213483146067,76.8,2.5213753517959265,374.28920115019207,119.8320149059931,347.68015665591014,3.123449117031186,2.4479297016662267,3.919268673506033,349.0058844414274,1.575536360758419,14.560452587733833,35.64296435921652,2.4479297016662267,2017-01-10 14:30:39.550781250 +2025.0,2025000.0,720.4241071428571,24.404831702750187,21.27692423930346,1.0,7.6923076923076925,2.0,15.384615384615385,21.532125693926236,718.75,89.84375,0.029888124898153227,0.03387564555486316,83.37009684932114,86.77966101694915,76.8,2.631171934256026,378.3190221232801,120.80451124896396,338.6862999272699,3.131663033209156,2.4403361450184566,3.918826303709911,347.2063270637896,1.575536360758419,14.575663527943888,35.56951854486871,2.4403361450184566,2017-01-10 14:30:40.550781250 +2026.0,2026000.0,709.2633928571429,28.65233650791218,18.1635592063791,1.0,7.6923076923076925,1.0,7.6923076923076925,20.4127978657197,707.03125,121.09375,0.028780278344114636,0.040397314730274296,84.72025968205962,90.88757396449704,76.8,3.2293322127794326,380.9344938380859,121.75210882196156,332.0619102795914,3.1287712182063925,2.524188150522098,3.9297848302886793,369.89205290678717,1.5869650565820417,14.513480106682302,36.63475450812566,2.524188150522098,2017-01-10 14:30:41.550781250 +2027.0,2027000.0,701.171875,21.97724644896092,10.278023663754249,0.0,0.0,0.0,0.0,10.995297153564897,703.125,70.3125,0.015681315160515952,0.0313435938213593,85.65058549659595,90.88757396449704,82.13903743315508,2.6350184128193823,385.7802409446824,122.73677386518754,317.23141535593527,3.1431512235152796,2.5657286429785615,3.934484743094248,381.1171177115871,1.6582280766035324,14.473615093077846,37.13536881175665,2.5657286429785615,2017-01-10 14:30:42.550781250 +2028.0,2028000.0,700.0,20.627705450264145,13.485542381243944,0.0,0.0,1.0,7.142857142857143,13.531646934131853,703.125,70.3125,0.019330924191616933,0.029468150643234492,85.78488835213557,90.88757396449704,82.13903743315508,2.481169550783832,388.4112733721386,123.48988760791384,307.28215310672414,3.1452880952111846,2.5368187661331376,3.9337357774580184,374.37087226511096,1.6916760106710724,14.543307155369863,36.89373451338061,2.5368187661331376,2017-01-10 14:30:43.550781250 +2029.0,2029000.0,695.5915178571429,18.73147909101565,13.906774597843912,0.0,0.0,1.0,7.6923076923076925,14.167277925632552,699.21875,62.5,0.020367237900307686,0.02692884920264745,86.31633958943145,90.88757396449704,83.02702702702703,2.2676257979937313,393.6570909689818,124.38828819752428,287.43192153636096,3.164744017892328,2.550167927023719,3.9343153566934466,377.58162173538676,1.6784307839210517,14.514874906858378,37.01536845223163,2.550167927023719,2017-01-10 14:30:44.550781250 +2030.0,2030000.0,691.40625,17.717084672307525,15.315513584390018,0.0,0.0,1.0,7.142857142857143,15.520015608452432,695.3125,62.5,0.022447028224654365,0.02562471003452388,86.83326772674995,90.88757396449704,83.02702702702703,2.1661682715990564,396.5735394689335,125.16529953583387,278.7152241972931,3.168398437423126,2.581467627989928,3.9344923110053704,384.6326795596061,1.6357552207514738,14.429551167244606,37.24941922466623,2.581467627989928,2017-01-10 14:30:45.550781250 +2031.0,2031000.0,690.625,16.454012109784305,15.891775519975605,0.0,0.0,2.0,14.285714285714286,15.901570143722106,695.3125,50.78125,0.02302489794566097,0.023824813914619807,86.92450621798844,90.88757396449704,84.3956043956044,2.028367850116097,401.9741373050582,125.80766565496745,264.5514544092603,3.1951482066878847,2.5533319128700476,3.94083976456834,381.1367200516849,1.836211231798889,14.615250193695827,37.3175847341437,2.5533319128700476,2017-01-10 14:30:46.550781250 +2032.0,2032000.0,691.6852678571429,18.8563867418015,16.545552761942552,0.0,0.0,2.0,15.384615384615385,16.64348849053633,695.3125,62.5,0.02406222781366769,0.02726151274006316,86.80493943800425,90.88757396449704,83.02702702702703,2.2955331523125584,404.7946551800398,126.85487776719522,258.07765204952267,3.1910058352105404,2.5442284706122598,3.941000594859014,379.1704287991959,1.9924301646902063,14.64408537016516,37.25789892485067,2.5442284706122598,2017-01-10 14:30:47.550781250 +2033.0,2033000.0,690.3645833333334,18.29018274841009,17.944124096890096,0.0,0.0,3.0,21.428571428571427,18.082423823682646,691.40625,62.5,0.026192571664632725,0.026493512543905976,86.96764129013685,90.88757396449704,83.02702702702703,2.229349606494524,410.4615932025292,127.71067330806943,255.55530778937256,3.2139960002590797,2.5520638431953366,3.9422052507674885,381.45202725091514,1.791759469228055,14.641880793782075,37.36701457018747,2.5520638431953366,2017-01-10 14:30:48.550781250 +2034.0,2034000.0,687.7790178571429,17.567390266174417,18.478956564329085,0.0,0.0,3.0,23.076923076923077,18.481399416684834,687.5,62.5,0.02687113002409673,0.02554220150668118,87.28988634970301,90.88757396449704,83.02702702702703,2.136468212028539,412.9863451513236,127.7217706319363,258.074720904935,3.2334843394980157,2.577249215728232,3.9376809164209425,385.10138665944976,1.9021075263969205,14.49446730000203,37.355854481328734,2.577249215728232,2017-01-10 14:30:49.550781250 +2035.0,2035000.0,689.3229166666666,17.708880418719975,17.256301446502487,0.0,0.0,3.0,21.428571428571427,17.438058026785427,687.5,62.5,0.02529737167467172,0.025690253421943598,87.09551741089282,90.88757396449704,83.02702702702703,2.1597324089917618,415.2502674043268,128.46828614958295,262.42706493616834,3.2323173278798722,2.514391260391347,3.9244114850507223,365.47277453346,1.8870696490323797,14.452045558335437,36.33809704665621,2.514391260391347,2017-01-10 14:30:50.550781250 +2036.0,2036000.0,693.9174107142857,15.819636801300739,17.41233557025291,0.0,0.0,3.0,23.076923076923077,17.66970003364081,693.359375,54.6875,0.025463693172725642,0.02279757872772893,86.50748143360988,89.82456140350877,83.02702702702703,1.906101118130702,419.3221204217531,129.46981164133587,273.4682876022467,3.2387636554487425,2.4523280354747135,3.906755709818984,344.9417001288824,1.864784604242945,14.339320836901212,35.16471849799957,2.4523280354747135,2017-01-10 14:30:51.550781250 +2037.0,2037000.0,695.3125,13.61195414916347,15.86480912480971,0.0,0.0,2.0,14.285714285714286,15.86726250905461,695.3125,54.6875,0.022820332597292024,0.019576743045987912,86.32300271808118,89.82456140350877,83.02702702702703,1.6326577615438391,420.8623529148832,129.46066518253195,279.0824408260345,3.250889776608918,2.4394370015463154,3.9031315618439417,340.80047494197316,1.7788560643921472,14.317295417808305,34.92614020427109,2.4394370015463154,2017-01-10 14:30:52.550781250 +2038.0,2038000.0,695.3125,14.291011959848696,16.10868407894256,0.0,0.0,2.0,15.384615384615385,16.178594502369958,693.359375,54.6875,0.023268090969700613,0.02055336551528801,86.32596643035723,89.82456140350877,83.02702702702703,1.7087123739889702,423.03245802854235,131.0064041319325,296.2297701521048,3.2290975455102138,2.443917756601622,3.9025635405827273,341.516468172593,1.8191584434161694,14.294813234338902,34.935347890704705,2.443917756601622,2017-01-10 14:30:53.550781250 +2039.0,2039000.0,699.4977678571429,19.468908060097803,17.71053128400406,0.0,0.0,3.0,23.076923076923077,18.70236336489836,695.3125,78.125,0.026736844954046955,0.027832695048819515,85.83640363712951,89.82456140350877,80.41884816753927,2.2601842143224546,423.5030471247036,131.16127779075694,304.9553488395195,3.2288725320313114,2.4971210761817626,3.9101958825793988,355.8421106735823,1.8718021769015913,14.266523294903893,35.6252360035426,2.4971210761817626,2017-01-10 14:30:54.550781250 +2040.0,2040000.0,706.1941964285714,19.691563906613805,19.34778850000631,0.0,0.0,3.0,23.076923076923077,19.350121667153772,707.03125,66.40625,0.027400567386439794,0.027884063627540052,85.02259613998383,88.27586206896552,80.41884816753927,2.2387877489940493,422.4834636951332,132.83358071490318,315.1989450308798,3.18054712837936,2.4473390707503655,3.9095285873813217,344.9892075597857,1.8718021769015913,14.399825543445969,35.24125566446443,2.4473390707503655,2017-01-10 14:30:55.550781250 +2041.0,2041000.0,704.7991071428571,20.018811444276192,19.413011184150353,0.0,0.0,3.0,23.076923076923077,19.56127499850937,701.171875,66.40625,0.027754398097647497,0.028403570948648406,85.19288589865133,88.27586206896552,80.41884816753927,2.2749971130011266,421.8110790274481,133.5165685208084,320.5245241758294,3.1592414611952027,2.242993986445556,3.8767300850646276,291.47895669803927,1.8718021769015913,14.484082724748797,32.48771045079152,2.242993986445556,2017-01-10 14:30:56.550781250 +2042.0,2042000.0,704.6875,19.295451617544483,17.822231498517088,0.0,0.0,2.0,14.285714285714286,17.961469798472187,703.125,66.40625,0.025488560246168957,0.027381572140196164,85.20224906096347,88.27586206896552,80.41884816753927,2.198135217983188,418.7485972470358,134.4207986063293,330.296554026357,3.115206884564058,2.081551288163931,3.847206103874511,251.87422436584913,1.7692866133759966,14.532806883713215,30.25078288943089,2.081551288163931,2017-01-10 14:30:57.550781250 +2043.0,2043000.0,708.7053571428571,18.621497614862495,18.023837445275024,0.0,0.0,2.0,15.384615384615385,18.22558916360667,707.03125,58.59375,0.02571673683557729,0.026275373012467396,84.71465998968317,87.27272727272727,80.41884816753927,2.103480765117982,416.04053764249056,135.86226102319782,331.70316779414935,3.0622229786934994,2.0577942021183246,3.838895960827551,245.21717749369398,1.80280930541464,14.477281514748642,29.791265963484552,2.0577942021183246,2017-01-10 14:30:58.550781250 +2044.0,2044000.0,710.1004464285714,17.61505012115912,18.586138334365522,0.0,0.0,2.0,15.384615384615385,18.607985823787303,707.03125,58.59375,0.026204723454795163,0.024806420288500698,84.54245650256256,87.27272727272727,80.41884816753927,1.9822002040805882,410.75684828669864,137.65322865320485,328.37053218084844,2.9839971957471074,1.8678279442152685,3.802361572539897,203.32280337378936,1.7600107709134747,14.569758750745146,27.213802535116727,1.8678279442152685,2017-01-10 14:30:59.550781250 +2045.0,2045000.0,708.1473214285714,19.224060900697758,19.524314597047127,0.0,0.0,3.0,23.076923076923077,19.56127499850937,707.03125,62.5,0.0276231716291007,0.027146979617060977,84.78510480998243,87.77142857142857,80.41884816753927,2.1786511602879184,407.69249647966865,138.15101871990066,328.94626093893066,2.951063989663802,1.8780912253821924,3.7840283935275814,200.71934089209992,1.670681537674819,14.22642778309259,26.71852918795963,1.8780912253821924,2017-01-10 14:31:00.550781250 +2046.0,2046000.0,707.5892857142857,19.37177337099234,18.899261049899508,0.0,0.0,3.0,23.076923076923077,18.920746984893228,705.078125,62.5,0.026739730754675603,0.027377143439131134,84.85283176804553,87.77142857142857,80.41884816753927,2.194664074490979,403.61420512501775,139.7787020417955,331.84452634771696,2.8875229146449812,1.8923604717904072,3.7830753248143236,202.78856597264954,1.6193882432872684,14.157146857284172,26.790425106056357,1.8923604717904072,2017-01-10 14:31:01.550781250 +2047.0,2047000.0,709.8214285714286,18.091694485057484,18.639498100085124,0.0,0.0,3.0,23.076923076923077,18.639498100085124,707.03125,62.5,0.026259418707038164,0.02548767021794262,84.57846711103761,87.77142857142857,80.41884816753927,2.043290731899534,396.76934115226595,141.7929437616719,336.01141607007,2.7982305086998047,1.9076612503597867,3.776963475911898,203.81381825222792,1.4759065198095778,14.00138886485347,26.709906988699963,1.9076612503597867,2017-01-10 14:31:02.550781250 +2048.0,2048000.0,707.5892857142857,20.260261441583587,16.610907405150545,0.0,0.0,3.0,23.076923076923077,17.469281074217108,707.03125,70.3125,0.024688447701101783,0.02863279850419384,84.85877059065128,88.78612716763006,80.41884816753927,2.3141585759411982,392.5531361075929,142.99765589953842,338.17435378302525,2.745171825637318,1.900187709211754,3.7848679616826497,204.4695955030119,1.54663701119507,14.157146857284172,26.901236455717193,1.900187709211754,2017-01-10 14:31:03.550781250 +2049.0,2049000.0,700.5208333333334,19.734802395647087,16.62908976088319,0.0,0.0,3.0,21.428571428571427,17.217911406854366,703.125,74.21875,0.02457872855104861,0.028171613828730412,85.71362423827021,89.82456140350877,80.84210526315789,2.318558156364928,385.6684743418735,145.50362280330685,344.09887844893507,2.650576438658326,1.978770165447113,3.7983140429620468,220.6734333801253,1.5902065505062128,14.089622369172943,27.88012438653569,1.978770165447113,2017-01-10 14:31:04.550781250 +2050.0,2050000.0,695.5915178571429,17.02448685532567,14.303642109870077,0.0,0.0,2.0,15.384615384615385,14.332019252057476,695.3125,54.6875,0.020604074207530684,0.02447483389068881,86.30544585191585,89.82456140350877,83.02702702702703,2.032592188155458,381.37453143301025,147.06469361803838,348.4543249098069,2.593243300282049,1.9773987189656348,3.7980129376855065,220.3676505949163,1.62924053973028,14.089622369172943,27.86080122351213,1.9773987189656348,2017-01-10 14:31:05.550781250 +2051.0,2051000.0,692.1875,19.318032610195363,13.785289427495117,0.0,0.0,2.0,14.285714285714286,14.122824480852415,691.40625,62.5,0.02040317757956105,0.027908670136625357,86.74468546946667,90.88757396449704,83.02702702702703,2.33624856311721,374.88537360755265,150.16638681907327,352.52852158969466,2.4964666297740132,2.0714605323654527,3.8078827553242585,238.97714712701878,1.5988558031035636,13.92332801940377,28.84162447137296,2.0714605323654527,2017-01-10 14:31:06.550781250 +2052.0,2052000.0,686.3839285714286,20.17732920055532,12.684417619714804,0.0,0.0,2.0,15.384615384615385,13.789415751903565,681.640625,70.3125,0.020089945550740805,0.029396564168613932,87.48471211327364,91.97604790419162,83.02702702702703,2.4749915142283063,371.8789891127395,151.43032213956337,351.8398690538542,2.4557762531205807,2.1545214742662684,3.8235450175029873,258.10638570851864,1.5881605139868153,13.900713866644145,29.9493865333157,2.1545214742662684,2017-01-10 14:31:07.550781250 +2053.0,2053000.0,681.5104166666666,18.074385404159536,13.98711814675701,0.0,0.0,3.0,21.428571428571427,14.122824480852415,679.6875,54.6875,0.020722829960440687,0.026521069909045708,88.09755840375605,91.97604790419162,84.86187845303867,2.2573353247249375,365.89560908148644,154.57273105190222,349.8352103174518,2.367142034636281,2.203162830808109,3.835184279425866,270.4967390100358,1.5664205273504095,13.931851939446755,30.694138357310955,2.203162830808109,2017-01-10 14:31:08.550781250 +2054.0,2054000.0,680.9895833333334,17.385895008255837,13.64622685339005,0.0,0.0,2.0,14.285714285714286,13.691790341947875,679.6875,54.6875,0.02010572654419879,0.025530339132582184,88.16077023128163,91.97604790419162,84.86187845303867,2.1777136116242435,363.7393445850038,156.2296850688165,347.5738918204606,2.3282345120569294,2.185376004623675,3.8354377376547064,267.30564119304944,1.5982646118359751,13.992515362271488,30.57890731703626,2.185376004623675,2017-01-10 14:31:09.550781250 +2055.0,2055000.0,680.7291666666666,17.989765143703107,15.110819937556924,0.0,0.0,3.0,21.428571428571427,15.236521140040232,679.6875,54.6875,0.022382647734412585,0.02642719898692423,88.19818968854426,91.97604790419162,84.86187845303867,2.2492295337432973,360.39391796387196,159.36367104803756,342.2554569398706,2.261455924011923,2.187894451621474,3.838581353471634,268.7387093872181,1.6094379124341003,14.035164330785307,30.70745816692079,2.187894451621474,2017-01-10 14:31:10.550781250 +2056.0,2056000.0,679.6875,17.28109661518083,15.323136403734354,0.0,0.0,3.0,21.428571428571427,15.4143162019343,679.6875,54.6875,0.02267853418215621,0.02542506168670283,88.32894786891137,91.97604790419162,84.86187845303867,2.161912406524696,358.8974572668793,160.4895070934,338.77128823281714,2.2362674281129915,2.2228766184560333,3.8378958685408997,274.99257696342823,1.6441234704219905,13.91330367007762,30.927557413694053,2.2228766184560333,2017-01-10 14:31:11.550781250 +2057.0,2057000.0,678.0133928571429,16.693786243434516,15.197318048559845,0.0,0.0,3.0,23.076923076923077,15.43605713827082,677.734375,58.59375,0.022766596207227416,0.024621617241345393,88.54329039451409,91.97604790419162,84.3956043956044,2.085576846486076,357.4151346244761,161.46855261054714,334.65487780310787,2.2135278284592097,2.2067484436608322,3.8396643963983386,272.55957392175594,1.575536360758419,13.992515362271488,30.877961498592896,2.2067484436608322,2017-01-10 14:31:12.550781250 +2058.0,2058000.0,685.9375,26.27868330850257,17.74562345553331,0.0,0.0,4.0,28.571428571428573,18.232488005250605,679.6875,93.75,0.026580392536128445,0.038310608923557275,87.58796211229311,91.97604790419162,80.41884816753927,3.1486058187984822,356.1377429635389,164.5711300076571,324.0351850210694,2.164035350228006,2.229308071149193,3.852691234350261,280.93170161913093,1.5522794985941517,14.131900847136906,31.50436061920242,2.229308071149193,2017-01-10 14:31:13.550781250 +2059.0,2059000.0,688.6160714285714,26.864296360709766,20.067076751278847,0.0,0.0,5.0,38.46153846153846,20.123239964246658,681.640625,93.75,0.02922272772765803,0.03901200897762715,87.25207600556033,91.97604790419162,80.41884816753927,3.2161609128171436,354.5851380789127,165.34280326201647,317.2864758184827,2.1445453390372635,2.188974398426888,3.857824430644105,274.96235491239935,1.491654876777717,14.34603489543381,31.403103105043364,2.188974398426888,2017-01-10 14:31:14.550781250 +2060.0,2060000.0,692.7083333333334,27.330460205776337,20.005794386432314,0.0,0.0,5.0,35.714285714285715,20.162780987879092,699.21875,93.75,0.029107172553930718,0.03945449894367711,86.74180448493658,91.97604790419162,80.41884816753927,3.288961251459102,353.1810866776927,167.43606511471967,298.90093561265604,2.1093489412553317,2.2161343353494325,3.851101349925016,277.935997263115,1.5522794985941517,14.147921530653099,31.353694677909832,2.2161343353494325,2017-01-10 14:31:15.550781250 +2061.0,2061000.0,699.4977678571429,28.477701571962793,20.69176117871012,0.0,0.0,6.0,46.15384615384615,21.722078812413244,699.21875,93.75,0.031053821485316737,0.04071164038050046,85.90880052941161,91.97604790419162,80.41884816753927,3.392430387367001,351.27611691345703,168.37017429723346,287.6359921018458,2.086332204499173,2.1688433997131016,3.8422071660618733,266.3458140563821,1.575536360758419,14.155639505253477,30.70136530968704,2.1688433997131016,2017-01-10 14:31:16.550781250 +2062.0,2062000.0,703.9620535714286,25.230801138835965,22.194934269975224,0.0,0.0,7.0,53.84615384615385,22.64795935842701,705.078125,89.84375,0.0321721309316696,0.035841138042642925,85.33439898121995,91.42857142857143,80.41884816753927,2.9711435784567732,348.3437277237649,169.02217964732017,268.3651089836693,2.060935011313989,2.1878095579828947,3.823891304441772,264.21649386510603,1.575536360758419,13.800057731127737,30.191898204877003,2.1878095579828947,2017-01-10 14:31:17.550781250 +2063.0,2063000.0,707.3102678571429,20.696280034210602,21.982381199725822,0.0,0.0,7.0,53.84615384615385,21.990594307129793,705.078125,74.21875,0.031090449702861213,0.029260539503988478,84.8954624481029,89.30232558139535,80.41884816753927,2.3796282146739713,347.0620299686101,170.01089721220484,259.2002217070028,2.041410495795531,2.1823093831155176,3.821720837820996,262.56383005417104,1.6094379124341003,13.782952758491998,30.078667131894992,2.1823093831155176,2017-01-10 14:31:18.550781250 +2064.0,2064000.0,707.03125,20.839592409134255,22.01521524520938,0.0,0.0,7.0,53.84615384615385,22.017265747262464,705.078125,74.21875,0.03114044216187398,0.029474782633913642,84.92989665992711,89.30232558139535,80.41884816753927,2.3960449372472623,341.2029625318378,169.6169578931802,237.64033330886014,2.011608784698977,2.184647841522069,3.8255803346668813,264.1571238681949,1.5968591302272404,13.836920827754962,30.228799219666637,2.184647841522069,2017-01-10 14:31:19.550781250 +2065.0,2065000.0,706.7522321428571,20.6962800342106,20.88719615021967,0.0,0.0,6.0,46.15384615384615,20.895839724415517,705.078125,70.3125,0.02956600456861635,0.029283642969842396,84.9622828554458,88.78612716763006,80.41884816753927,2.3718449970875874,337.86980172245285,169.61693100467346,229.5304721659878,1.9919579945302954,2.1972604190442566,3.8260275279228706,266.58521698859545,1.6582280766035324,13.804256053881053,30.3315454415449,2.1972604190442566,2017-01-10 14:31:20.550781250 +2066.0,2066000.0,705.078125,23.475030048057693,19.56127499850937,0.0,0.0,5.0,38.46153846153846,19.94748777740715,705.078125,85.9375,0.028291173800643936,0.03329422544212061,85.18486466923643,90.88757396449704,80.41884816753927,2.74230428970503,334.3840018491217,169.90996686067226,223.87344234527245,1.9680069864490037,2.2551270236703957,3.837471837842718,280.86164345534587,1.5612358106162227,13.80671699624993,31.135900606412573,2.2551270236703957,2017-01-10 14:31:21.550781250 +2067.0,2067000.0,700.2604166666666,25.3354769686534,16.21427480554424,0.0,0.0,4.0,28.571428571428573,17.05892451744729,695.3125,85.9375,0.02436082935924046,0.036180078675949814,85.78647680909974,90.88757396449704,80.41884816753927,2.9792419492996314,325.92404464743476,169.94070685049672,214.6450104141955,1.9178691832449684,2.3028951109063818,3.8356004486779245,289.208771713406,1.5163474893680884,13.633367890826259,31.396216260971844,2.3028951109063818,2017-01-10 14:31:22.550781250 +2068.0,2068000.0,693.359375,21.708565853834944,14.981912094132102,0.0,0.0,3.0,23.076923076923077,15.089999434482836,691.40625,78.125,0.021763604818183697,0.03130925554130561,86.61312756966404,90.88757396449704,81.26984126984127,2.584022703535991,320.4705308788991,169.39060946097374,210.60328636900655,1.8919025788896109,2.4062031417194985,3.8277778282138817,306.1169294075134,1.4744016286301576,13.217909621631744,31.80497565853469,2.4062031417194985,2017-01-10 14:31:23.550781250 +2069.0,2069000.0,689.5833333333334,22.818589166132472,13.568991535477487,0.0,0.0,3.0,21.428571428571427,14.199788671874412,691.40625,78.125,0.02059183855740096,0.03309040120768455,87.09649541610014,90.88757396449704,81.26984126984127,2.7364655379112426,309.2049255155531,167.1883895754308,206.39723590215465,1.8494401812277064,2.4195256888825,3.8248845205229935,307.63632632195356,1.3981288187668934,13.137633741118778,31.78684232776639,2.4195256888825,2017-01-10 14:31:24.550781250 +2070.0,2070000.0,684.1517857142857,21.875919819750777,11.113727168814261,0.0,0.0,2.0,15.384615384615385,12.494364715319586,675.78125,78.125,0.018262562455018515,0.03197524332544323,87.7804647966288,90.88757396449704,81.26984126984127,2.618509151427431,303.5942115929105,167.32117718229222,205.02847909337183,1.814439849787527,2.48729799613836,3.8261242242488023,321.11000852498313,1.363304842895192,12.97592153788351,32.27498363922624,2.48729799613836,2017-01-10 14:31:25.550781250 +2071.0,2071000.0,680.46875,15.143242761683698,11.409067131842004,0.0,0.0,1.0,7.142857142857143,11.531237901658384,675.78125,54.6875,0.016946021256168464,0.022254134024058708,88.21483027116939,90.88757396449704,83.93442622950819,1.8759336926047465,290.1171548925664,165.90119784274407,195.63933425865096,1.7487345399854515,2.5133310728484415,3.824657272306472,325.6140881425261,1.363304842895192,12.886761606994643,32.38869837524995,2.5133310728484415,2017-01-10 14:31:26.550781250 +2072.0,2072000.0,678.90625,12.281857718515434,9.681557797375822,0.0,0.0,0.0,0.0,9.681557797375822,675.78125,35.15625,0.01426052241730846,0.01809065348642089,88.40451259033213,90.88757396449704,86.29213483146067,1.5488720537020995,283.2535418033389,164.17550749204926,188.12143878482618,1.7253093724534787,2.5106307264518666,3.8219041738256214,324.0606855308995,1.318853080324358,12.85288659842371,32.26885201760398,2.5106307264518666,2017-01-10 14:31:27.550781250 +2073.0,2073000.0,676.3020833333334,11.706342638130387,12.737402020662197,0.0,0.0,1.0,7.142857142857143,12.786204506015178,675.78125,35.15625,0.018906055180245775,0.01730933990389707,88.74247800443466,90.88757396449704,86.29213483146067,1.4788373790476348,276.3859461813378,163.37998356271112,183.9483683599914,1.6916756885047115,2.4715335160382197,3.825016000394694,317.65650738081115,1.3862943611198906,13.000640728107772,32.13151928948988,2.4715335160382197,2017-01-10 14:31:28.550781250 +2074.0,2074000.0,674.7395833333334,10.48849860802752,12.837853873133191,0.0,0.0,1.0,7.142857142857143,12.913433555140776,675.78125,31.25,0.019138396314836194,0.015544513568053135,88.94315738776363,90.88757396449704,86.77966101694915,1.3293999730634425,262.8947990644225,162.43727101815585,171.886351944828,1.6184389051638177,2.5037033972153844,3.8217591705774474,322.66651854008313,1.330413902725434,12.868506748993045,32.21892406454299,2.5037033972153844,2017-01-10 14:31:29.550781250 +2075.0,2075000.0,673.6979166666666,9.889467720792421,12.60222074847792,0.0,0.0,1.0,7.142857142857143,12.61456981635861,671.875,31.25,0.01872437112285159,0.014679379995300695,89.0785041737875,90.88757396449704,86.77966101694915,1.2564295831137346,256.6531529521255,160.99581959583966,164.16561151166016,1.5941603551969354,2.563631044877203,3.81686649398749,332.4424928082191,1.3545456628053103,12.64577628817606,32.41910467894015,2.563631044877203,2017-01-10 14:31:30.550781250 +2076.0,2076000.0,673.1770833333334,10.815916488174578,12.837853873133193,0.0,0.0,1.0,7.142857142857143,12.913433555140776,671.875,39.0625,0.01918281812446444,0.01606697072131156,89.15106910310048,91.97604790419162,86.77966101694915,1.383793592202336,244.9604272319949,159.40104328040277,144.3290458827879,1.5367554828426337,2.5283955899138504,3.7992343046110557,319.06905681038404,1.3758230612525952,12.477703136798526,31.548569583335613,2.5283955899138504,2017-01-10 14:31:31.550781250 +2077.0,2077000.0,673.4375,11.417252521247333,14.227174982122726,0.0,0.0,1.0,7.142857142857143,14.23811475688959,675.78125,39.0625,0.02114244418656459,0.016953692838975156,89.11905034210771,91.97604790419162,86.77966101694915,1.460822599414398,234.82440659377647,158.17414703708505,126.41359968387336,1.4845941071439457,2.504362501813884,3.733152947342561,291.48906172619445,1.3758230612525952,11.618976842901287,29.098129914805853,2.504362501813884,2017-01-10 14:31:32.550781250 +2078.0,2078000.0,675.2604166666666,13.760607042408965,15.03851894889717,0.0,0.0,1.0,7.142857142857143,15.164819466184365,675.78125,50.78125,0.02245773495956343,0.020378222538700513,88.88886408648791,91.97604790419162,85.33333333333333,1.740422740805102,230.8626574676195,158.56790254284448,117.83631274088492,1.4559230068975733,2.4603655418279273,3.7328293844411866,283.7358067750601,1.3862943611198906,11.718037484038982,28.830655643577533,2.4603655418279273,2017-01-10 14:31:33.550781250 +2079.0,2079000.0,677.734375,14.515118475678412,15.054056550330948,0.0,0.0,1.0,7.6923076923076925,15.244772709093535,677.734375,50.78125,0.022493728031861355,0.021417120056332412,88.56795678547519,91.97604790419162,85.33333333333333,1.8270181714209701,223.57553586627864,157.5490616893935,100.33665690548935,1.4190851628621928,2.393996291476379,3.7234188255497607,269.39815850411105,1.3558351536351823,11.751347926979605,28.13268335703781,2.393996291476379,2017-01-10 14:31:34.550781250 +2080.0,2080000.0,679.4270833333334,13.649269151243786,15.069547562936135,0.0,0.0,1.0,7.142857142857143,15.092777161047456,679.6875,50.78125,0.022213976350487632,0.020089380429580733,88.34313621079167,91.97604790419162,85.33333333333333,1.7233792532468926,220.5130755136948,156.328528136344,93.75479221413825,1.4105747565241027,2.3479164446155267,3.715410884674184,259.2563477973395,1.4060969884160703,11.757206124493079,27.604937602431686,2.3479164446155267,2017-01-10 14:31:35.550781250 +2081.0,2081000.0,680.2455357142857,14.764237226922939,15.386258551072247,0.0,0.0,1.0,7.6923076923076925,15.39799035495067,681.640625,50.78125,0.02263593003779459,0.021704276546879335,88.24223648880125,91.97604790419162,85.33333333333333,1.8551821352098221,216.96085833707576,156.3091735640662,84.57482224232294,1.388023833726882,2.328262463140228,3.7074895992932486,253.68378233890306,1.3388921222253067,11.69954144212818,27.23960317566053,2.328262463140228,2017-01-10 14:31:36.550781250 +2082.0,2082000.0,684.1145833333334,18.899761529265266,14.62915929566778,0.0,0.0,1.0,7.142857142857143,14.947650933012136,683.59375,70.3125,0.021849630598693034,0.02762660231152593,87.76670368708893,91.97604790419162,83.02702702702703,2.327773500635581,216.48602336158666,156.10168334551298,81.34428241446405,1.386826962540948,2.324970085410695,3.72004819180214,256.83260095852586,1.329135947279942,11.878330912442005,27.616764036036788,2.324970085410695,2017-01-10 14:31:37.550781250 +2083.0,2083000.0,685.546875,18.749373987947006,16.561915600385422,0.0,0.0,2.0,15.384615384615385,16.572815184059706,683.59375,70.3125,0.024174590809796496,0.027349514193244637,87.58197243784737,91.97604790419162,83.02702702702703,2.300800463506225,217.9795223963839,155.927133935702,77.74763987900661,1.3979576029805678,2.2938287567470685,3.7249166210993634,253.1044611901152,1.3570239788197773,12.02590649410509,27.585370142129577,2.2938287567470685,2017-01-10 14:31:38.550781250 +2084.0,2084000.0,684.6354166666666,18.408977130413717,15.776233195566048,0.0,0.0,2.0,14.285714285714286,15.798423735370609,683.59375,70.3125,0.023075674075246536,0.026888730384476485,87.69666264741723,91.97604790419162,83.02702702702703,2.263829588641544,220.43372796736026,155.48528513776483,77.39679859344488,1.4177144015399854,2.307223642951366,3.725135581723122,255.38908433812557,1.4060969884160703,11.993969850743525,27.672770812481325,2.307223642951366,2017-01-10 14:31:39.550781250 +2085.0,2085000.0,684.9888392857143,18.865277070792285,15.131824894247803,0.0,0.0,2.0,15.384615384615385,15.206227077980763,681.640625,70.3125,0.022199233339096965,0.027540999194183114,87.65401966555116,91.97604790419162,83.02702702702703,2.312898900876841,227.31018962908936,156.0108468154651,78.0370092203191,1.4570152926479498,2.340608861553505,3.72462697446636,260.7995001215401,1.3862943611198906,11.901153958044919,27.85594641691251,2.340608861553505,2017-01-10 14:31:40.550781250 +2086.0,2086000.0,685.15625,17.078081092284428,14.690230559250205,0.0,0.0,2.0,14.285714285714286,14.690230559250205,679.6875,58.59375,0.0214407013863629,0.024925819610175676,87.62128638384169,90.3529411764706,83.02702702702703,2.0778653014776416,232.67686116684507,156.16730539406996,79.19276819199406,1.4899204451259,2.330957621516999,3.7220028602661497,258.4062400239758,1.3331845358059422,11.88979180188216,27.71460081884755,2.330957621516999,2017-01-10 14:31:41.550781250 +2087.0,2087000.0,684.7098214285714,18.540279162082946,12.861138557110998,0.0,0.0,2.0,15.384615384615385,13.357037722892349,679.6875,66.40625,0.01950758891558524,0.027077571522781285,87.68739267416413,91.42857142857143,83.02702702702703,2.263256525654157,245.60011424420614,156.56094408209776,81.69064283575094,1.5687189144402311,2.4098638692892735,3.7191927583054767,270.7603986091139,1.3350010667323402,11.655747551752693,28.088764894525724,2.4098638692892735,2017-01-10 14:31:42.550781250 +2088.0,2088000.0,683.59375,17.90068630842125,15.233966177141403,0.0,0.0,3.0,21.428571428571427,15.236521140040232,679.6875,66.40625,0.02228885378200171,0.026186146828319084,87.82683637015211,91.42857142857143,83.02702702702703,2.1910691401513094,253.08935952144867,156.90223459928603,83.91998840585701,1.6130385916288303,2.37048870711649,3.7259589200781056,266.2173132566591,1.2992829841302609,11.8440579429889,28.076205600288556,2.37048870711649,2017-01-10 14:31:43.550781250 +2089.0,2089000.0,685.15625,19.90709798597511,15.302800447323758,0.0,0.0,3.0,21.428571428571427,15.555089134783877,679.6875,66.40625,0.022702980721235305,0.029054829443612474,87.63924477269242,91.42857142857143,83.02702702702703,2.422652824401816,260.335462568038,157.43974697213514,86.95972393767705,1.6535561544958155,2.4005507182596646,3.730914624873461,272.8498011086644,1.2809338454620642,11.837006281374748,28.41533393079831,2.4005507182596646,2017-01-10 14:31:44.550781250 +2090.0,2090000.0,686.3839285714286,20.91989942118705,16.572815184059706,0.0,0.0,3.0,23.076923076923077,16.572815184059706,677.734375,66.40625,0.024145109601491865,0.030478422571355444,87.48929557961539,91.42857142857143,83.02702702702703,2.5429205560820787,277.192474023256,158.17454029553494,95.14386025685891,1.7524468445133252,2.416377460401279,3.7293044683340555,275.0422411987707,1.3055330044754323,11.776326955346367,28.45605102121498,2.416377460401279,2017-01-10 14:31:45.550781250 +2091.0,2091000.0,688.28125,22.05759253392938,17.026948998205757,0.0,0.0,3.0,21.428571428571427,17.026948998205757,679.6875,66.40625,0.02473835949796069,0.03204735351127084,87.25645135284465,91.42857142857143,83.02702702702703,2.674728131809833,286.0361185716868,158.48027498012317,98.63792477754508,1.8048688936687034,2.3843034520830586,3.734855841204779,271.31275536913546,1.3240520522426695,11.931283509952019,28.44780046056027,2.3843034520830586,2017-01-10 14:31:46.550781250 +2092.0,2092000.0,687.5,23.28677496393938,15.012014419226919,0.0,0.0,2.0,15.384615384615385,15.511910455697484,677.734375,74.21875,0.022562778844650886,0.03387167267482091,87.36406444612565,91.42857142857143,82.13903743315508,2.7994431375047766,294.61461748978763,158.44476380346657,102.32377965279528,1.8594152966470063,2.4592537858637864,3.7312179604256017,283.0179645109093,1.3240520522426695,11.698961575686297,28.770715545681497,2.4592537858637864,2017-01-10 14:31:47.550781250 +2093.0,2093000.0,692.1875,25.877458475338283,18.127573516496415,0.0,0.0,3.0,21.428571428571427,18.232488005250605,691.40625,82.03125,0.02634038899178417,0.0373850415896535,86.79339582083712,91.42857142857143,81.26984126984127,3.0935904707214457,310.3338884516121,158.57192728445077,110.03322021790416,1.9570544027943009,2.4474209531940163,3.754581716259018,288.6382407972148,1.425515074273172,12.04693396776915,29.483918614462944,2.4474209531940163,2017-01-10 14:31:48.550781250 +2094.0,2094000.0,694.4754464285714,25.646069488208614,19.38974255396836,0.0,0.0,3.0,23.076923076923077,19.410685576333186,695.3125,82.03125,0.027950139455836936,0.03692869146072305,86.50483676579961,91.42857142857143,81.26984126984127,3.0560933557526853,318.1196601740239,158.92339900033903,115.57620978873355,2.001716941464015,2.4384231865157426,3.7561933931734046,287.5810834362185,1.4392167625742331,12.09155527698721,29.484328748442394,2.4384231865157426,2017-01-10 14:31:49.550781250 +2095.0,2095000.0,696.7075892857143,24.87607991695404,20.013011785272493,0.0,0.0,3.0,23.076923076923077,20.123239964246658,699.21875,82.03125,0.028883336816924315,0.03570519440222798,86.22133731950944,91.42857142857143,81.26984126984127,2.9662044277554123,325.6138042565862,158.97552976935515,120.35546543837951,2.048200781145332,2.420353279190479,3.7623723276192242,286.4206363858753,1.452252328911688,12.223252746070814,29.584589866326525,2.420353279190479,2017-01-10 14:31:50.550781250 +2096.0,2096000.0,705.6361607142857,24.87607991695404,21.58865979168997,0.0,0.0,4.0,30.76923076923077,22.255870417237023,701.171875,82.03125,0.03154015008911724,0.035253408628850645,85.12929575429736,91.42857142857143,81.26984126984127,2.935071794012506,338.796367732958,158.84358457692068,124.76567428425183,2.1328929880003713,2.4651203115736138,3.781729942823392,301.03857642081897,1.509354453877118,12.38471223605832,30.529805686101632,2.4651203115736138,2017-01-10 14:31:51.550781250 +2097.0,2097000.0,707.8683035714286,21.240064767977305,24.3407970565849,0.0,0.0,5.0,38.46153846153846,24.370453841336076,701.171875,66.40625,0.03442794898200571,0.030005672892562344,84.83244974316565,89.30232558139535,81.26984126984127,2.4536391194256093,344.0198963225179,158.91277086892075,125.49895046995181,2.164834798622213,2.393414303757748,3.7958256421522774,292.7114050214195,1.4701758451005928,12.774499353291342,30.5746694755116,2.393414303757748,2017-01-10 14:31:52.550781250 +2098.0,2098000.0,710.3794642857143,18.75608017259827,23.025533726764444,0.0,0.0,4.0,30.76923076923077,23.033374875885578,707.03125,54.6875,0.03242404381585778,0.02640290311806449,84.51640387029052,87.77142857142857,81.26984126984127,2.142636056003989,352.2393489146524,158.57348334525818,125.39963819149446,2.2213004437048927,2.4630756239143223,3.7832008743881587,301.17370838631746,1.4772661393256175,12.410851316998404,30.56886535092373,2.4630756239143223,2017-01-10 14:31:53.550781250 +2099.0,2099000.0,710.3794642857143,18.756080172598274,22.441709555093556,0.0,0.0,4.0,30.76923076923077,22.49194400384536,707.03125,54.6875,0.03166187247045631,0.026402903118064498,84.51640387029052,87.77142857142857,81.26984126984127,2.142636056003989,354.7651120737017,157.78343619723154,124.66859729364899,2.2484306377396943,2.391735639368612,3.7713270433863286,284.27147584356504,1.488077055429833,12.423585204674527,29.713931502752658,2.391735639368612,2017-01-10 14:31:54.550781250 +2100.0,2100000.0,712.3325892857143,18.927392450292153,22.32877428264339,0.0,0.0,4.0,30.76923076923077,22.49194400384536,714.84375,54.6875,0.03157505965913896,0.026571004520895836,84.28573167710253,87.77142857142857,81.26984126984127,2.1646698989942337,356.2372391724385,157.33331326741398,123.95074767890446,2.2642200292760273,2.420161224994505,3.757308179041051,284.7216798507752,1.4772661393256175,12.152676825519405,29.411437233011377,2.420161224994505,2017-01-10 14:31:55.550781250 +2101.0,2101000.0,711.2165178571429,18.542540033887224,21.580293715114752,0.0,0.0,4.0,30.76923076923077,21.749079542304774,707.03125,54.6875,0.03058011027054543,0.026071582377972462,84.41564454287527,87.77142857142857,81.26984126984127,2.115992839654106,357.8991397183106,156.59056104262137,124.39348508960876,2.285572880864105,2.3975961309676954,3.7565395026101323,280.5006002641048,1.509354453877118,12.198930173562184,29.24810778607777,2.3975961309676954,2017-01-10 14:31:56.550781250 +2102.0,2102000.0,710.1004464285714,17.747816845746932,22.235576923076923,0.0,0.0,4.0,30.76923076923077,22.308547107296377,707.03125,54.6875,0.03141604433499026,0.024993389224030257,84.54388659975764,87.77142857142857,81.26984126984127,2.026650060453172,356.77078059218275,155.98581888101194,125.41215793111468,2.2872001003138127,2.379305592435772,3.758353195200718,277.87657397003665,1.547562508716013,12.271325995841268,29.197234568507596,2.379305592435772,2017-01-10 14:31:57.550781250 +2103.0,2103000.0,713.1696428571429,19.363115594751644,19.61198181740471,0.0,0.0,4.0,30.76923076923077,20.123239964246658,712.890625,58.59375,0.028216624425610334,0.02715078493411185,84.18903298527448,87.77142857142857,80.84210526315789,2.2012894236096963,351.5857981361573,154.59586661636268,125.85364418523308,2.2742250865518603,2.4813978835391275,3.7766945418394373,302.2677979303932,1.6094379124341003,12.272670543498622,30.453378712010473,2.4813978835391275,2017-01-10 14:31:58.550781250 +2104.0,2104000.0,715.4017857142857,17.392323514671954,20.79187967789206,0.0,0.0,5.0,38.46153846153846,20.811411743897587,714.84375,54.6875,0.029090522500050293,0.024311266566530532,83.91496138942304,87.27272727272727,80.84210526315789,1.9643084002880784,347.56149423156376,153.9802861519181,124.42765549960603,2.2571817660389133,2.4790867248811375,3.779692152997198,302.8891079579523,1.5988558031035636,12.320837375057184,30.544424375923626,2.4790867248811375,2017-01-10 14:31:59.550781250 +2105.0,2105000.0,714.5647321428571,18.287585047508294,19.164926345074488,0.0,0.0,5.0,38.46153846153846,19.501178773543213,714.84375,54.6875,0.027290989740093276,0.025592621948562957,84.0182705096813,87.27272727272727,80.84210526315789,2.0713075757023844,342.61527277536715,152.88882275864228,124.58509927031746,2.2409438871554155,2.4522947662569567,3.787420006105562,300.65540098702974,1.6094379124341003,12.498666387630129,30.65041416757711,2.4522947662569567,2017-01-10 14:32:00.550781250 +2106.0,2106000.0,713.4486607142857,17.576932575550643,17.633895133559147,0.0,0.0,5.0,38.46153846153846,17.998772995038387,714.84375,54.6875,0.025227846075173085,0.024636576593966956,84.1458467566778,87.27272727272727,80.84210526315789,1.9926197558163767,330.94712399800346,151.6620713100569,123.12389311790244,2.1821350660668313,2.4713264393429677,3.7996694018236097,308.48203281583153,1.5773495978825998,12.627275380943864,31.206119505791115,2.4713264393429677,2017-01-10 14:32:01.550781250 +2107.0,2107000.0,718.4709821428571,19.529103590850163,18.395706568526403,0.0,0.0,6.0,46.15384615384615,19.013572138889874,714.84375,66.40625,0.026463938852730608,0.027181478551303685,83.56745607519433,86.77966101694915,79.17525773195877,2.1684975286195427,324.433492310316,151.15283090768764,120.94986391588276,2.146393754996588,2.607753594705621,3.8200969263678957,342.332626596434,1.6422277352570913,12.5850572492296,32.81872828125452,2.607753594705621,2017-01-10 14:32:02.550781250 +2108.0,2108000.0,723.5576923076923,20.984589638677377,18.944641311134717,0.0,0.0,6.0,50.0,19.169833156686792,718.75,66.40625,0.026493855791301348,0.029001957773055777,82.98785677335653,86.77966101694915,79.17525773195877,2.307388033768358,309.3949221544382,149.56483941824357,115.22072564844393,2.068634067725271,2.7016473981003597,3.8345850254617915,367.0586924901268,1.7578579175523736,12.57239994019673,33.96619158630961,2.7016473981003597,2017-01-10 14:32:03.550781250 +2109.0,2109000.0,722.3772321428571,20.86572609048893,20.874224078075006,0.0,0.0,7.0,53.84615384615385,20.95193603937538,718.75,66.40625,0.029004147842843324,0.028884805835578342,83.12315982460214,86.77966101694915,79.17525773195877,2.3024601067434687,300.8701837634711,148.99949323193124,112.46164650162143,2.019269846073499,2.6253252595002285,3.842913917034945,355.00268209176176,1.717651497074333,12.87672463765005,33.805590450851604,2.6253252595002285,2017-01-10 14:32:04.550781250 +2110.0,2110000.0,722.65625,20.95193603937538,21.208919513246748,0.0,0.0,7.0,53.84615384615385,21.312963493155447,718.75,66.40625,0.029492533266204293,0.028992949330162686,83.09161641544964,86.77966101694915,79.17525773195877,2.3126787268727655,292.2927009198563,148.6323000443149,108.36962540638767,1.9665489993272587,2.6132510419818984,3.85174677799375,356.1599546438333,1.8044984950054848,13.038355900476535,34.07249714265114,2.6132510419818984,2017-01-10 14:32:05.550781250 +2111.0,2111000.0,731.3058035714286,22.00012353334604,21.584477158735545,0.0,0.0,8.0,61.53846153846154,21.883583480809044,732.421875,70.3125,0.02992398443159848,0.030083343282530414,82.11440358233503,86.77966101694915,78.76923076923077,2.3943654357172557,274.4159353334241,147.7920243136635,96.95582754046852,1.856770936102904,2.7494526591008857,3.879134999578966,396.67686218431606,1.845826690498331,13.11850621334639,36.06871179171672,2.7494526591008857,2017-01-10 14:32:06.550781250 +2112.0,2112000.0,734.375,22.51802162884038,20.869898262241993,0.0,0.0,7.0,53.84615384615385,20.923906680953948,736.328125,70.3125,0.028492128246405377,0.030662837962676263,81.7744629753672,86.77966101694915,78.76923076923077,2.4490532138890866,265.7850014480617,147.4719628109679,92.1307483145086,1.8022747943536186,2.7811978944786624,3.884411774316046,406.0258881870933,1.845826690498331,13.122903942190648,36.49739281346637,2.7811978944786624,2017-01-10 14:32:07.550781250 +2113.0,2113000.0,731.3701923076923,24.402541352062137,25.00372964800979,1.0,8.333333333333334,7.0,58.333333333333336,25.012204052472065,730.46875,70.3125,0.034199102336329924,0.0333655125799765,82.12249335193694,86.77966101694915,78.76923076923077,2.642895214981087,247.97375166633498,146.37549053417928,85.3221186877563,1.694093394743788,2.634560683655825,3.8918578680104225,377.5645782992394,1.9328380674879604,13.599246299266536,35.82803962739959,2.634560683655825,2017-01-10 14:32:08.550781250 +2114.0,2114000.0,731.0267857142857,23.380195513275996,23.26349982072826,1.0,7.6923076923076925,6.0,46.15384615384615,23.38736625275246,726.5625,70.3125,0.03199248879765833,0.03198267966396228,82.15451966016175,86.77966101694915,78.76923076923077,2.5377501695449256,239.1266503577948,146.07533391587614,82.63465258145811,1.6370090962482846,2.647617338245848,3.89087616088931,379.94515001423855,1.8325814637483102,13.550348997969774,35.87613894630703,2.647617338245848,2017-01-10 14:32:09.550781250 +2115.0,2115000.0,733.8169642857143,20.433323560061513,22.53806080841247,1.0,7.6923076923076925,5.0,38.46153846153846,22.570086488237887,730.46875,58.59375,0.0307571064539333,0.02784525917842603,81.82304654703361,85.33333333333333,78.76923076923077,2.1914204151851893,230.6368492620984,146.06862011920268,81.08387125517898,1.5789623334148146,2.689807198806556,3.8908997394923173,389.0734510029514,1.8325814637483102,13.444024728815144,36.16183449650033,2.689807198806556,2017-01-10 14:32:10.550781250 +2116.0,2116000.0,735.5769230769231,20.508408683336608,21.834174918924482,1.0,8.333333333333334,4.0,33.333333333333336,21.894754844791976,730.46875,58.59375,0.029765418351089742,0.02788071245839225,81.62737992609453,85.33333333333333,78.76923076923077,2.193842924359732,216.54617324711728,146.01682035920126,78.78776556436651,1.4830221115239592,2.749260842552472,3.890595433485554,401.9033595309952,1.7788560643921472,13.293206528171023,36.546492179863485,2.749260842552472,2017-01-10 14:32:11.550781250 +2117.0,2117000.0,729.9107142857143,21.222294782722727,22.015215245209376,1.0,7.6923076923076925,5.0,38.46153846153846,22.413529085302304,726.5625,62.5,0.03070722027588817,0.029075192852170584,82.2660831001929,85.81005586592178,78.76923076923077,2.293803159480644,209.35639658809617,145.98566230382886,77.90409527280039,1.434088754225594,2.776425070684267,3.882808067067947,404.2341214479252,1.817077277212345,13.109950850892096,36.398796217855356,2.776425070684267,2017-01-10 14:32:12.550781250 +2118.0,2118000.0,727.9575892857143,20.071093621177415,22.015215245209376,1.0,7.6923076923076925,5.0,38.46153846153846,22.017265747262464,724.609375,62.5,0.030245258887768748,0.02757178978087384,82.48002259542936,85.81005586592178,78.76923076923077,2.170155712675042,197.219192686714,145.47634233143185,77.9410740534847,1.3556787964698678,2.747165054711967,3.8778904500356046,395.61464049173,1.8044984950054848,13.105175974953342,36.00208147424265,2.747165054711967,2017-01-10 14:32:13.550781250 +2119.0,2119000.0,733.2589285714286,20.002052285671493,20.704847568452184,1.0,7.6923076923076925,4.0,30.76923076923077,20.78319286753676,730.46875,62.5,0.028343593317066873,0.027278293528099937,81.8831640265121,85.81005586592178,78.76923076923077,2.1569298514750668,192.01620181390643,145.63009317175482,77.67051558492547,1.3185200780407678,2.8580622349053297,3.890719213390476,426.0564347019251,1.8718021769015913,13.039585149345601,37.26794587417704,2.8580622349053297,2017-01-10 14:32:14.550781250 +2120.0,2120000.0,733.5379464285714,19.987376494586087,20.01301178527249,1.0,7.6923076923076925,3.0,23.076923076923077,20.094054684255394,732.421875,62.5,0.02739334042920172,0.027247910748039762,81.85195618554208,85.81005586592178,78.76923076923077,2.1561625524093606,187.77223683095644,145.49451836600693,77.36298356974154,1.2905794592109334,2.8977075952353997,3.8955800913426732,437.39305641422396,1.8325814637483102,13.022752140065359,37.736127787135445,2.8977075952353997,2017-01-10 14:32:15.550781250 +2121.0,2121000.0,727.1634615384615,18.72327154735244,22.25953254854005,1.0,8.333333333333334,4.0,33.333333333333336,22.72126089403014,726.5625,58.59375,0.031246428204682774,0.02574836682214418,82.56278703305635,85.81005586592178,79.17525773195877,2.0375314467802244,181.49285585709038,145.88369552923768,80.48997595722741,1.2440928041935706,2.842645052390202,3.893767532112677,424.09945878118197,1.8925641683500207,13.120864705701992,37.297961138744995,2.842645052390202,2017-01-10 14:32:16.550781250 +2122.0,2122000.0,722.0982142857143,16.913319460463793,16.713862962948774,0.0,0.0,3.0,23.076923076923077,16.713862962948774,720.703125,54.6875,0.023146246081610665,0.02342246404416624,83.13319504313222,85.81005586592178,79.58549222797927,1.8614725533008591,179.01480963270333,146.06192211540548,82.8975243281955,1.225609022803776,2.8704480539054704,3.8845814895393254,425.8094373665552,1.8925641683500207,12.919803471855012,37.08562473262736,2.8704480539054704,2017-01-10 14:32:17.550781250 +2123.0,2123000.0,722.65625,16.50183920402161,16.534635206968275,0.0,0.0,3.0,23.076923076923077,16.53736527141468,720.703125,54.6875,0.022884137889092743,0.022834977493132607,83.0669484557372,85.81005586592178,79.58549222797927,1.814688195791866,175.93234765969885,145.90505097498732,88.21024987809159,1.2058002549196132,2.865297379721125,3.8896852406977054,427.1664881358044,1.8870696490323797,13.007618100997933,37.270694061202455,2.865297379721125,2017-01-10 14:32:18.550781250 +2124.0,2124000.0,719.5870535714286,18.65073958117463,16.600032832852325,0.0,0.0,3.0,23.076923076923077,16.81887255908237,714.84375,58.59375,0.023372950465975654,0.02591867028264051,83.43267561775163,86.29213483146067,79.58549222797927,2.063254579231327,175.5152773585272,146.04711163290153,91.43757118557731,1.201771643383785,2.8548520132533177,3.885787032954068,422.9304140010756,1.821612432377736,12.973039033769561,37.03610660357091,2.8548520132533177,2017-01-10 14:32:19.550781250 +2125.0,2125000.0,715.4017857142857,22.248334994499533,17.15637310531274,0.0,0.0,4.0,30.76923076923077,17.90068630842125,710.9375,82.03125,0.02502186416902565,0.03109907668497907,83.94433905691163,89.30232558139535,79.58549222797927,2.5172600001291165,175.7241996076186,146.13417133400083,93.88925824645581,1.2024853461959113,2.8536115143880196,3.891713658352734,425.54853746362727,1.821612432377736,13.064699153394601,37.28157593614225,2.8536115143880196,2017-01-10 14:32:20.550781250 +2126.0,2126000.0,711.2165178571429,23.10787263438927,16.60003283285232,0.0,0.0,3.0,23.076923076923077,16.81887255908237,707.03125,82.03125,0.023648034229796472,0.03249062986333901,84.44476911399194,89.30232558139535,79.58549222797927,2.629713462364798,177.5185852264621,146.0606830086906,100.40618271004298,1.2153755656195293,2.826121754249846,3.886352647594113,416.8335104808319,1.791759469228055,13.047307513117392,36.873279597208516,2.826121754249846,2017-01-10 14:32:21.550781250 +2127.0,2127000.0,711.2165178571429,22.903793433592938,16.18138463682243,0.0,0.0,3.0,23.076923076923077,16.25098231128601,707.03125,82.03125,0.022849556925715594,0.03220368602039901,84.44328874332386,89.30232558139535,79.58549222797927,2.605474627227069,178.9063961382729,146.30592618911126,104.10861099982303,1.2228239880524259,2.737508440560541,3.883466843752568,396.0651426837232,2.3353749158170367,13.212825431061333,36.17022114118337,2.737508440560541,2017-01-10 14:32:22.550781250 +2128.0,2128000.0,709.8214285714286,22.488216160278892,15.02403846153846,0.0,0.0,3.0,23.076923076923077,15.321573057670626,707.03125,82.03125,0.02158510921332214,0.031681512074858316,84.60623397844877,89.30232558139535,79.58549222797927,2.554729963365238,181.3057291546997,146.104267628808,106.9664591515988,1.2409338351109935,2.717689565259593,3.880311223721754,390.35107721197755,2.268683541318364,13.212825431061333,35.90835780159197,2.717689565259593,2017-01-10 14:32:23.550781250 +2129.0,2129000.0,700.6138392857143,15.967339181726535,14.69593661134313,0.0,0.0,3.0,23.076923076923077,15.206227077980763,705.078125,62.5,0.021704148883904043,0.02279049925420466,85.68050651049428,89.30232558139535,81.70212765957447,1.882300409113202,186.6656305967512,146.3805590743719,110.9586314761798,1.2752077992946564,2.6578557507996803,3.8728015861252723,374.2811053429811,2.3025850929940455,13.245705604159848,35.20517481341581,2.6578557507996803,2017-01-10 14:32:24.550781250 +2130.0,2130000.0,696.9866071428571,13.243573336468017,12.403706394561475,0.0,0.0,2.0,15.384615384615385,12.494364715319586,703.125,42.96875,0.017926262265694715,0.0190011876853088,86.11402800419816,89.30232558139535,83.93442622950819,1.5927526040626865,189.05225851782822,146.11498388516566,112.81536396074735,1.2938594899097258,2.6337015919943223,3.8668412464979944,366.6658580190341,2.2857779746776643,13.215309807633306,34.80518247906202,2.6337015919943223,2017-01-10 14:32:25.550781250 +2131.0,2131000.0,693.2291666666666,15.61569663507874,12.368393001842355,0.0,0.0,2.0,14.285714285714286,12.743512552086202,695.3125,50.78125,0.018382828024046213,0.022526023695981355,86.59279079805846,90.3529411764706,83.93442622950819,1.8993028905933058,194.5592241788062,146.20373614709303,114.12638376577384,1.3307404400600513,2.6659398377640997,3.8722311389909843,375.7431623642235,2.164963715117998,13.216924401983512,35.235525295964294,2.6659398377640997,2017-01-10 14:32:26.550781250 +2132.0,2132000.0,688.0580357142857,18.942889469924598,12.762476311054868,0.0,0.0,2.0,15.384615384615385,13.746789944348405,685.546875,66.40625,0.01997911401481942,0.027530947226362436,87.26415896762339,92.53012048192771,83.93442622950819,2.3461175781710257,201.24890515453126,146.18971009807655,116.58521367508037,1.3766283893682825,2.76758551443837,3.880965598333471,401.4526900917379,2.063693184711697,13.103046535461532,36.263801786555206,2.76758551443837,2017-01-10 14:32:27.550781250 +2133.0,2133000.0,679.1666666666666,24.566673317159243,12.302124824245873,0.0,0.0,2.0,14.285714285714286,13.287813504230645,683.59375,74.21875,0.019564878779235306,0.036171788933240606,88.4541161977593,94.81481481481481,84.86187845303867,3.1655604995432336,203.90147223013133,145.86913090290568,119.55206826267836,1.397838397802298,2.956021139855192,3.907006854568534,456.6303048778769,2.031432322493475,13.064405992391684,38.618660293160666,2.956021139855192,2017-01-10 14:32:28.550781250 +2134.0,2134000.0,668.2291666666666,31.09148786339315,11.385159466017392,0.0,0.0,1.0,7.142857142857143,12.130058660314821,679.6875,93.75,0.018152542967891237,0.046528181370003786,89.974962886064,97.8343949044586,84.86187845303867,4.128034838614486,209.2978648836275,145.8700065113707,127.11341666059299,1.4348245392538086,3.260804315572361,3.9447452135183494,552.5348970215406,2.0149030205422647,12.991229257604655,42.361856427787174,3.260804315572361,2017-01-10 14:32:29.550781250 +2135.0,2135000.0,660.64453125,36.08086778588192,10.59739059863323,0.0,0.0,1.0,6.666666666666667,11.587810136086974,671.875,97.65625,0.017540159023433942,0.05461464687766901,91.0778246645401,98.46153846153847,84.86187845303867,4.87173414867781,211.82018426320576,145.75052042091977,129.70119420424987,1.453306538127482,3.6040685575531763,3.981474380313752,669.7716419746281,2.0794415416798357,12.89082394167851,46.45941324915722,3.6040685575531763,2017-01-10 14:32:30.550781250 +2136.0,2136000.0,642.0036764705883,57.159216326235715,12.214353272801747,0.0,0.0,2.0,12.5,16.428324063731175,648.4375,175.78125,0.025589143280371533,0.0890325373843156,94.21150744090404,112.94117647058823,84.86187845303867,8.765017219471005,214.39403382839197,145.60590213041218,132.0125605181162,1.472426808882854,4.444642019704933,4.1079123257255254,1060.9901316763523,1.3083328196501787,13.426962551463866,59.67804195324085,4.444642019704933,2017-01-10 14:32:31.550781250 +2137.0,2137000.0,656.9393382352941,62.45779968919147,45.66865583018284,2.0,12.5,4.0,25.0,45.85686281981991,652.34375,242.1875,0.06980380097651495,0.09507392243699239,92.12107389261234,112.94117647058823,77.57575757575758,8.624886855357927,218.2165994859323,146.16379462960202,137.96629227747607,1.492959320322255,2.943519504624562,4.216033612804464,647.6154234912335,1.2922654114716141,18.686301684376573,55.00349347726125,2.943519504624562,2017-01-10 14:32:32.550781250 +2138.0,2138000.0,664.6369485294117,74.91502623020551,45.51700613779209,2.0,12.5,6.0,37.5,46.22453142983932,652.34375,285.15625,0.06954854305364243,0.1127157110298546,91.32897403477862,112.94117647058823,73.49282296650718,9.742706580174206,218.8666018951426,145.7831509930254,142.05241909872183,1.5013161699709294,3.107884888998459,4.245077931824921,726.5012288215114,1.31633577251298,18.803838484081446,58.44016547984442,3.107884888998459,2017-01-10 14:32:33.550781250 +2139.0,2139000.0,659.3520220588235,77.64457637470056,61.74200078759096,4.0,25.0,8.0,50.0,62.067442360089615,648.4375,285.15625,0.09413399865868967,0.11775891144195744,92.14386511383235,112.94117647058823,73.49282296650718,10.122668309806432,218.9237551143455,145.99528323949156,144.1155321858833,1.4995262193177956,2.6400922209498487,4.331681855173466,628.4483260476552,1.349926716949016,22.540907017999917,59.51007327137543,2.6400922209498487,2017-01-10 14:32:34.550781250 +2140.0,2140000.0,648.7630208333334,79.59398460560647,59.9012935287015,4.0,23.529411764705884,8.0,47.05882352941177,60.22171643041543,634.765625,285.15625,0.09282544549635534,0.12268576051601758,93.71932544810403,112.94117647058823,73.49282296650718,10.451148770097184,218.5190038846472,146.0778380104425,144.11264669427933,1.4959079820788845,2.887579522508727,4.369413219361812,750.7710879256394,1.3178265618424299,22.51021492903979,65.00003567636554,2.887579522508727,2017-01-10 14:32:35.550781250 +2141.0,2141000.0,637.2612847222222,83.00424558608927,59.836914810284966,4.0,23.529411764705884,8.0,47.05882352941177,60.05006982343678,617.1875,285.15625,0.09423147343653897,0.1302515115479991,95.52641763363725,112.94117647058823,73.49282296650718,10.928986824584067,216.83013905236416,146.75789908494994,144.7285426591491,1.4774682685178895,3.14535002036923,4.398909104888556,882.9945535528763,1.3075134832667763,22.31310814482363,70.18253515782183,3.14535002036923,2017-01-10 14:32:36.550781250 +2142.0,2142000.0,630.859375,82.43586143836352,58.395288254044566,4.0,22.22222222222222,8.0,44.44444444444444,58.47427054487476,609.375,285.15625,0.09268986538382624,0.13067232525214278,96.47999439830727,112.94117647058823,73.49282296650718,10.85320038833519,214.62905883649586,147.10958231605335,142.69995679445896,1.458974020981055,3.322108902756228,4.420506819685595,982.5942251975371,1.2484245668967697,22.258017830934367,73.94355919385393,3.322108902756228,2017-01-10 14:32:37.550781250 +2143.0,2143000.0,643.0121527777778,89.85262992490523,67.30889680073992,6.0,35.294117647058826,10.0,58.8235294117647,67.84370893392965,611.328125,285.15625,0.10550921726883151,0.1397370633459208,94.91086259978694,112.94117647058823,73.49282296650718,11.845347914386112,212.1058384991075,147.41432205152557,138.9095488551775,1.4388414609061553,2.9685079424095573,4.448962036258544,857.6070470212555,1.3148353971377456,24.33054959495251,72.22542971580617,2.9685079424095573,2017-01-10 14:32:38.550781250 +2144.0,2144000.0,643.8802083333334,91.04456471556291,75.026056851739,8.0,47.05882352941177,12.0,70.58823529411765,75.04857304250969,595.703125,285.15625,0.11655673224802313,0.14139984975035857,94.83574820399888,112.94117647058823,73.49282296650718,12.074515586812575,205.2830070767309,148.05545692715845,134.78135643613402,1.3865278007127273,2.793185041986332,4.490952517876377,821.5345269405053,1.2992829841302609,26.324880825732063,73.5302633545076,2.793185041986332,2017-01-10 14:32:39.550781250 +2145.0,2145000.0,646.5992647058823,92.87481762401006,76.05558468329156,8.0,50.0,10.0,62.5,76.12177839806671,582.03125,285.15625,0.11772636090560992,0.14363582313421885,94.4978645929726,112.94117647058823,73.49282296650718,12.294808665770203,201.84424977711092,148.3303816131558,133.74955150737128,1.360774829687412,2.861294039242945,4.5014153003493425,862.0875741488265,1.289130612666243,26.324880825732063,75.32322459044805,2.861294039242945,2017-01-10 14:32:40.550781250 +2146.0,2146000.0,645.4503676470588,91.17955884824562,61.878933251722216,6.0,37.5,8.0,50.0,62.978817724164955,582.03125,253.90625,0.09757344775207046,0.14126501961821467,94.5723680876199,106.66666666666667,73.49282296650718,11.811813144312419,194.06514724613913,149.50327896067685,132.83874497192255,1.2980661601220345,2.9899438954106663,4.520233478890651,941.046964423504,1.2674331582431617,26.316324234974108,78.6843329960086,2.9899438954106663,2017-01-10 14:32:41.550781250 +2147.0,2147000.0,624.1957720588235,75.08444426564098,61.30555338884281,6.0,37.5,6.0,37.5,62.65620738192129,582.03125,253.90625,0.10037909608912993,0.12028989561718004,97.26641645640875,106.66666666666667,73.49282296650718,9.833903201343066,189.66530873687907,150.10246992976283,135.0821833056688,1.2635722038793153,3.063423487134983,4.525968307743881,982.414379342633,0.8602012652231115,26.17101965823457,80.17291630330713,3.063423487134983,2017-01-10 14:32:42.550781250 +2148.0,2148000.0,583.984375,89.93393062377184,61.091282787408254,6.0,37.5,6.0,37.5,63.63786596531298,582.03125,429.6875,0.10897186412789381,0.15400057685408422,105.74290756671292,178.6046511627907,78.36734693877551,20.956015091869105,184.8038906360345,150.47944445355125,137.80206514898427,1.2281005642141258,3.100020074549804,4.532266861970583,1007.3495348636043,0.8397506547518206,26.205423737816865,81.23733964931624,3.100020074549804,2017-01-10 14:32:43.550781250 +2149.0,2149000.0,613.3961397058823,52.99806841549235,47.1987669169463,4.0,25.0,4.0,25.0,47.42616234995344,585.9375,203.125,0.07731734727364577,0.0864010465421325,98.43395945169618,106.66666666666667,78.36734693877551,7.4041017531777,176.1706246069906,151.67320338362939,141.69707944412738,1.1615144974646543,3.1095026982692877,4.529432418168919,1008.6781566428676,0.8614824953791601,26.08018986480453,81.09642075598502,3.1095026982692877,2017-01-10 14:32:44.550781250 +2150.0,2150000.0,620.2392578125,53.316943908778285,48.73097975196893,4.0,26.666666666666668,4.0,26.666666666666668,48.98152985490636,603.515625,203.125,0.07897199223999074,0.08596189815011056,97.35211571616324,106.66666666666667,78.36734693877551,7.427935665907875,171.4334453581663,152.22738749062643,144.7245890105638,1.1261669019231018,3.116688555425228,4.530283095442414,1013.1684492255764,0.8553819787871626,26.07563259833696,81.2696256947098,3.116688555425228,2017-01-10 14:32:45.550781250 +2151.0,2151000.0,633.9111328125,52.20348486442797,48.626493411978004,4.0,26.666666666666668,5.0,33.333333333333336,49.09562145011423,630.859375,187.5,0.07744874464073478,0.08235142461186096,95.2219464391501,103.78378378378379,78.36734693877551,7.198381167972765,162.13468179591734,153.4229091664602,149.53885317657816,1.0567827365338582,3.1152414860922835,4.528791533460536,1010.72578057807,0.8887545740655497,26.036938454644183,81.11135084473906,3.1152414860922835,2017-01-10 14:32:46.550781250 +2152.0,2152000.0,639.3229166666666,54.07958244087288,37.22980759848422,2.0,14.285714285714286,3.0,21.428571428571427,37.49651211458112,640.625,187.5,0.05865034888798025,0.08458883770792337,94.4502499851145,103.78378378378379,78.36734693877551,7.384138668746023,158.20989614374065,154.07285425963263,152.63697738355262,1.0268512055805532,3.101216139428555,4.528035852179838,1003.0347498647209,0.8858315243894466,26.073054775303014,80.85817827337446,3.101216139428555,2017-01-10 14:32:47.550781250 +2153.0,2153000.0,633.7890625,48.88278190925205,8.542301805654546,0.0,0.0,1.0,6.666666666666667,11.933790872280834,630.859375,128.90625,0.018829278664430776,0.07712784079364268,95.19144205166893,103.78378378378379,84.86187845303867,7.009376319865613,154.15245792587606,154.48088793562732,157.36079077858403,0.9978739764242673,3.1141574293774377,4.523519275105775,1004.0850086115826,0.8803587226480918,25.88387920755375,80.60647473531169,3.1141574293774377,2017-01-10 14:32:48.550781250 +2154.0,2154000.0,644.2708333333334,46.37161506145476,11.919648082195216,0.0,0.0,2.0,14.285714285714286,13.810679320049756,648.4375,128.90625,0.0214361392841516,0.07197534431527335,93.58475782187337,103.78378378378379,84.86187845303867,6.57038183921382,147.6955740382624,155.4023040735134,170.9381363964837,0.9504078779192019,3.111861928462698,4.523809850357871,1003.3106083104018,0.913935018181696,25.902087979656816,80.60372145158533,3.111861928462698,2017-01-10 14:32:49.550781250 +2155.0,2155000.0,650.634765625,41.9497726682824,13.062433545817138,0.0,0.0,2.0,13.333333333333334,14.370187147209787,656.25,128.90625,0.022086411465280033,0.06447514778584792,92.58726589837244,103.78378378378379,84.86187845303867,5.9305642261837015,145.25854066266575,155.87860579861564,177.97656830201657,0.9318696425238093,3.1269535974848464,4.517894929560496,1003.7593866979189,0.913935018181696,25.664142331058606,80.25058218846684,3.1269535974848464,2017-01-10 14:32:50.550781250 +2156.0,2156000.0,660.6770833333334,33.403305000706716,13.626244918610245,0.0,0.0,2.0,14.285714285714286,14.42821816277667,664.0625,109.375,0.02183853281240142,0.05055920031640275,91.03769411777638,100.3921568627451,84.86187845303867,4.546561526848352,141.30229560066016,156.40915443304462,198.24732527899826,0.9034144843558285,3.1256421498865197,4.516655366989233,1001.6974442220037,0.923493644168213,25.632918977077647,80.11933197937995,3.1256421498865197,2017-01-10 14:32:51.550781250 +2157.0,2157000.0,669.53125,23.9344633937431,13.32584144671825,0.0,0.0,2.0,14.285714285714286,13.810679320049756,667.96875,85.9375,0.02062738568222134,0.03574809001632575,89.72257356181775,96.60377358490567,84.86187845303867,3.1197110065171256,139.86434414674707,156.8499944043171,213.44105744515392,0.8917076769936912,3.127897557084901,4.511946930262509,997.3606889226148,0.9092068769857501,25.485151130635547,79.71494196345442,3.127897557084901,2017-01-10 14:32:52.550781250 +2158.0,2158000.0,671.875,20.29747040119778,13.404477845384278,0.0,0.0,2.0,14.285714285714286,13.450860260261104,667.96875,66.40625,0.020019885038528154,0.030210188504108327,89.3775600847288,93.65853658536585,84.86187845303867,2.5789268043870393,139.1701524245725,157.01561643346975,235.62562903411427,0.8863459290594915,3.1270823070597,4.516369666729991,1002.0601741119009,0.8909729238898653,25.618587792060534,80.11143261640812,3.1270823070597,2017-01-10 14:32:53.550781250 +2159.0,2159000.0,673.6979166666666,18.432644134569152,13.660481768854519,0.0,0.0,2.0,14.285714285714286,13.731534374290494,667.96875,58.59375,0.020382331657238306,0.02736039948849847,89.12196173927164,92.53012048192771,84.86187845303867,2.318850930996746,139.7019546425895,157.0950701591095,242.74265306603306,0.889282868654606,3.1218685054639304,4.511504347254064,993.9718459521436,0.8764844814737354,25.496753137141624,79.5975106104311,3.1218685054639304,2017-01-10 14:32:54.550781250 +2160.0,2160000.0,677.34375,18.4877493221863,15.251841929371038,0.0,0.0,2.0,14.285714285714286,15.4143162019343,671.875,54.6875,0.02275700661877267,0.027294485735177006,88.64217592936535,91.97604790419162,84.86187845303867,2.3085534783365316,140.57836227562998,156.9480517900983,236.06829207798674,0.8956999508578731,3.102314097274664,4.511487269613979,984.62823263149,0.8984103065963066,25.57647920266086,79.34627198906705,3.102314097274664,2017-01-10 14:32:55.550781250 +2161.0,2161000.0,681.5104166666666,21.490292745601835,14.349766596678338,0.0,0.0,2.0,14.285714285714286,14.352478961620184,671.875,58.59375,0.02105980864066546,0.031533329821593827,88.12109117575329,91.97604790419162,84.3956043956044,2.6708097137364306,142.9769284615984,156.66735866408158,225.28784601841394,0.9126146612847573,3.112669498642826,4.5115639385803155,989.6496702690297,0.858226930919394,25.53615307073249,79.48560477594336,3.112669498642826,2017-01-10 14:32:56.550781250 +2162.0,2162000.0,679.4270833333334,20.747124460105194,16.937556038812712,0.0,0.0,3.0,21.428571428571427,17.122696420274842,671.875,58.59375,0.025201668935935374,0.030536204648065904,88.38569065895743,91.97604790419162,84.3956043956044,2.5764561648415847,145.24291058594605,156.49275934819906,236.09741936989462,0.9281126563995086,3.0971146802472007,4.511770010776224,982.4737045498748,0.9059813622152938,25.606272958528763,79.30556388627635,3.0971146802472007,2017-01-10 14:32:57.550781250 +2163.0,2163000.0,673.1770833333334,18.945839845998858,16.154146011305947,0.0,0.0,2.0,14.285714285714286,16.240660949615233,667.96875,58.59375,0.024125391894205993,0.02814391683119366,89.19375289940386,91.97604790419162,84.3956043956044,2.360251858319003,148.36382627010346,156.17677292261928,252.87201233564258,0.949973696431883,3.0695678033624665,4.505653402004116,962.5926635668726,1.0893098960057963,25.540423497308637,78.3980616515808,3.0695678033624665,2017-01-10 14:32:58.550781250 +2164.0,2164000.0,657.8369140625,46.25907602696107,27.49400680432465,2.0,13.333333333333334,4.0,26.666666666666668,29.048425691528447,666.015625,191.40625,0.04415748808034905,0.07031997602762388,91.69868461819098,115.48872180451127,84.3956043956044,7.242603801052668,156.19434627166808,156.26530439527875,294.8017749118096,0.9995459124859145,3.114409427722609,4.537817787106986,1020.874725030087,0.8988556700520537,26.312435557371764,81.94769736622223,3.114409427722609,2017-01-10 14:32:59.550781250 +2165.0,2165000.0,659.0576171875,34.67530365480683,25.299985117817865,1.0,6.666666666666667,5.0,33.333333333333336,25.365572576534056,662.109375,136.71875,0.0384876404050689,0.05261346314876414,91.28554731491838,104.48979591836735,84.3956043956044,4.852580226461738,160.94376930539548,156.40298758651943,316.89296811651855,1.029032576608322,3.0830803323179916,4.517256585934776,981.9865833263101,0.8858315243894466,25.827114103534246,79.62706753313905,3.0830803323179916,2017-01-10 14:33:00.550781250 +2166.0,2166000.0,656.6162109375,35.6605807189383,25.364235752238603,1.0,6.666666666666667,5.0,33.333333333333336,25.525483207350845,658.203125,136.71875,0.03887428117393905,0.05430962581326316,91.63848849138896,104.48979591836735,84.3956043956044,4.975714697367524,172.86950661250074,157.93620639019684,329.02757721879675,1.094552734699792,3.080564032564658,4.518393745217814,982.0695223330533,0.8979415932059586,25.87150903729035,79.69884020844815,3.080564032564658,2017-01-10 14:33:01.550781250 +2167.0,2167000.0,651.9775390625,36.05773273693981,25.216752425016654,1.0,6.666666666666667,5.0,33.333333333333336,25.265114468661793,654.296875,136.71875,0.038751510527481256,0.055305176293018336,92.29490990668431,104.48979591836735,84.3956043956044,5.004121251565142,179.65816276058035,159.35532228558827,338.2740040206176,1.1274061021859465,3.0573950362724833,4.51060526932972,962.3431712027201,1.10758095865087,25.73752996516642,78.68979636141411,3.0573950362724833,2017-01-10 14:33:02.550781250 +2168.0,2168000.0,650.2685546875,36.487168307929636,25.216752425016654,1.0,6.666666666666667,5.0,33.333333333333336,25.265114468661793,644.53125,136.71875,0.03885335418195558,0.056110922241141274,92.54335711165325,104.48979591836735,84.3956043956044,5.053379162335902,187.02445378283304,161.07740786571225,358.5143718015498,1.161084327472866,3.042255580594482,4.5052044414202275,949.2831352282963,1.040018124402057,25.641556315270016,78.00816779525789,3.042255580594482,2017-01-10 14:33:03.550781250 +2169.0,2169000.0,648.5595703125,36.522001350440824,24.131085741116973,1.0,6.666666666666667,5.0,33.333333333333336,24.300517210930924,634.765625,136.71875,0.03746844287444873,0.056312485424959764,92.78721832862897,104.48979591836735,84.3956043956044,5.048652114247525,203.9534505642466,167.1449165812515,393.18075687233227,1.2202192847731743,3.033018084408064,4.502704742579886,942.2472334796341,1.0296194171811581,25.60677468147735,77.66581069228334,3.033018084408064,2017-01-10 14:33:04.550781250 +2170.0,2170000.0,648.0712890625,35.78871736524535,25.841023930857283,1.0,6.666666666666667,6.0,40.0,25.86201050265782,634.765625,136.71875,0.03990611980368673,0.05522342675759839,92.84714342226192,104.48979591836735,84.3956043956044,4.964816603949736,213.70970499713653,171.74683175417167,406.0667525355293,1.2443298243954104,3.008947557743569,4.501058552168012,929.2898720218493,1.0375245966882716,25.660314532859477,77.21054074457933,3.008947557743569,2017-01-10 14:33:05.550781250 +2171.0,2171000.0,649.0478515625,37.77647882609458,25.89345846746336,1.0,6.666666666666667,6.0,40.0,25.999314519208962,634.765625,152.34375,0.04005762357369942,0.05820291791298981,92.73370847011729,104.48979591836735,82.58064516129032,5.17298908624893,224.63370542854892,177.0625439251959,414.0724533737472,1.2686686887513066,2.924633705330139,4.493551601273633,882.8420728723601,1.0453677741492975,25.803586213178104,75.46603795745277,2.924633705330139,2017-01-10 14:33:06.550781250 +2172.0,2172000.0,651.7333984375,40.90522561613254,25.643439546080245,1.0,6.666666666666667,6.0,40.0,25.90131449266459,634.765625,152.34375,0.03974219298069082,0.06276374007255249,92.39821476062434,104.48979591836735,82.58064516129032,5.545319762750072,249.093736037676,191.69725883816426,419.981847477377,1.2994120914789258,2.9250530895164295,4.484595845485413,873.9740788335736,1.031171007872577,25.537069295937645,74.69728344127756,2.9250530895164295,2017-01-10 14:33:07.550781250 +2173.0,2173000.0,651.5625,61.18168794445069,23.09115104486672,2.0,14.285714285714286,6.0,42.857142857142854,28.685966880524276,632.8125,238.28125,0.04402642398929385,0.09389995272049985,92.87969738597211,115.48872180451127,79.17525773195877,8.820642078683711,262.4627815442592,200.70438970037875,421.25394013178686,1.3077082266913862,2.9392289897965864,4.4928467802753005,888.7375375461527,1.0691984034618165,25.71856231533894,75.59274393313423,2.9392289897965864,2017-01-10 14:33:08.550781250 +2174.0,2174000.0,676.5625,48.88278190925205,19.54121238269322,0.0,0.0,4.0,28.571428571428573,20.74887549394889,660.15625,132.8125,0.030668083870963717,0.0722516868866543,89.10931289941007,96.0,79.17525773195877,6.103718212739986,291.3177667743729,223.07288752330504,422.5206516249696,1.3059308551960986,2.9522619542937716,4.496311029691533,898.2306072443014,1.083686638451434,25.76428405118227,76.06291558392321,2.9522619542937716,2017-01-10 14:33:09.550781250 +2175.0,2175000.0,684.1517857142857,47.34482950841959,22.231516001314798,0.0,0.0,5.0,38.46153846153846,22.879987765386385,695.3125,132.8125,0.03344285324271811,0.0692022304070864,88.09249805572725,96.0,79.17525773195877,5.894423658218723,307.75871479848166,236.84883086585697,419.9762062535757,1.2993887859754123,2.92248255707911,4.495115798419498,883.4577620952251,1.0761394328160512,25.859608404239154,75.57425449428528,2.92248255707911,2017-01-10 14:33:10.550781250 +2176.0,2176000.0,686.4583333333334,43.081899182863104,22.61938415124023,0.0,0.0,5.0,35.714285714285715,22.72925430180391,695.3125,128.90625,0.03311090156256715,0.06275967104028615,87.72825173504117,95.40372670807453,79.17525773195877,5.3356894675305675,339.9077580662674,265.6950444827281,414.0453010926566,1.2793153847788965,2.8962219767645783,4.486204082892106,862.680156530208,1.0840134892469568,25.711423853822733,74.46599081935041,2.8962219767645783,2017-01-10 14:33:11.550781250 +2177.0,2177000.0,693.0803571428571,39.05391389153272,23.42979409809847,0.0,0.0,5.0,38.46153846153846,23.661787406983212,695.3125,128.90625,0.0341400346484009,0.05634832020420825,86.82831942695596,95.40372670807453,79.17525773195877,4.764034852692814,359.7179516982858,284.43627422281435,414.2862598254951,1.2646697495991641,2.889360407118203,4.486567155217335,859.9756483572602,1.0402015265116953,25.752697565100174,74.4088247210898,2.889360407118203,2017-01-10 14:33:12.550781250 +2178.0,2178000.0,697.9166666666666,33.233201538879904,23.035454732757024,0.0,0.0,5.0,35.714285714285715,23.576596176649115,695.3125,125.0,0.03378139153669127,0.047617721607947326,86.15371597463941,94.81481481481481,79.17525773195877,3.9968136212335694,398.49099833261374,322.6403689228566,418.74068411605896,1.2350934251128787,2.8782071391329573,4.480680364624159,849.2259400155187,1.0632729222228008,25.628262753629816,73.7634488210726,2.8782071391329573,2017-01-10 14:33:13.550781250 +2179.0,2179000.0,704.7991071428571,26.56931675620364,21.365851964700934,0.0,0.0,3.0,23.076923076923077,21.367964780722385,699.21875,93.75,0.03031780909505504,0.03769771625266582,85.24164370408776,90.3529411764706,79.17525773195877,3.0582358112131196,417.1188190781114,343.85099242163244,421.677351927366,1.2130801663257598,2.8029058567787164,4.472814424498309,808.7605870208633,1.0632729222228008,25.736113630354083,72.13590362524201,2.8029058567787164,2017-01-10 14:33:14.550781250 +2180.0,2180000.0,706.5104166666666,26.44681126837186,21.059842006823313,0.0,0.0,3.0,21.428571428571427,21.061690253086628,703.125,93.75,0.02981087009651775,0.037433009683209714,85.03480328602559,90.3529411764706,79.17525773195877,3.0542179486962815,460.95973904402473,391.8198228763662,426.6538439165499,1.176458443730844,2.8017568776778683,4.4689215444374675,804.6489377144272,1.0986122886681098,25.626279438200932,71.7986046652744,2.8017568776778683,2017-01-10 14:33:15.550781250 +2181.0,2181000.0,708.984375,30.824552833606962,22.48993678024547,0.0,0.0,4.0,30.76923076923077,22.777155839239455,699.21875,97.65625,0.032126456720910745,0.04347705523638227,84.77491117466325,90.3529411764706,78.76923076923077,3.5069593901936864,483.1754560339244,415.9375422586694,429.0901878972942,1.1616538709396904,2.835180460319784,4.477427186479108,827.150723429821,1.1526795099383855,25.725465874777047,72.93633818079128,2.835180460319784,2017-01-10 14:33:16.550781250 +2182.0,2182000.0,713.4486607142857,35.85402430363227,19.83173076923077,0.0,0.0,3.0,23.076923076923077,19.88855858578475,699.21875,113.28125,0.027876649969281405,0.05025452604779745,84.29287570457275,90.3529411764706,77.1859296482412,4.016180748333057,503.4755255068991,440.39968414740815,431.0139416151979,1.1432240840081496,2.8841395418709346,4.4902250505185455,861.2652572328712,1.1597667118214403,25.884775312385262,74.65530401089491,2.8841395418709346,2017-01-10 14:33:17.550781250 +2183.0,2183000.0,714.5833333333334,35.386974379412074,22.127013273333528,0.0,0.0,4.0,28.571428571428573,22.170949247007734,703.125,113.28125,0.031026401278611403,0.04952113032687404,84.15462507319081,90.3529411764706,77.1859296482412,3.9669758507363904,546.2908786833348,493.18607868909635,438.6827281938541,1.1076770052702878,2.918780040152796,4.497324493035315,884.0244934508437,1.1526795099383855,25.94188748242028,75.71866338757799,2.918780040152796,2017-01-10 14:33:18.550781250 +2184.0,2184000.0,712.0535714285714,35.68409072984289,20.817918360202853,0.0,0.0,3.0,23.076923076923077,20.895839724415517,699.21875,113.28125,0.029345881493850003,0.05011433431651917,84.45534903878423,90.3529411764706,77.1859296482412,3.980260585844256,566.8837815226723,517.0967165033586,443.662909787073,1.0962819206356154,2.929812396526259,4.5015474997196065,893.3743275782857,1.13599982073973,26.01919478922258,76.23135944109576,2.929812396526259,2017-01-10 14:33:19.550781250 +2185.0,2185000.0,715.6808035714286,36.424718158613246,21.19614433719759,0.0,0.0,4.0,30.76923076923077,21.802980689509297,710.9375,113.28125,0.030464671653489796,0.05089520073312666,84.03582856278983,90.3529411764706,77.1859296482412,4.072996736833924,604.6513196753482,569.202733943267,455.6569198297861,1.0622776097481188,2.9548561298276548,4.5075732401979,911.1526243545816,1.1370785694959058,26.08906162843019,77.08942367421841,2.9548561298276548,2017-01-10 14:33:20.550781250 +2186.0,2186000.0,725.4464285714286,32.912100273863004,24.48872182789566,0.0,0.0,5.0,38.46153846153846,24.752758806032418,720.703125,105.46875,0.03412072598493084,0.04536806437750962,82.86573487660128,89.30232558139535,77.1859296482412,3.6178792398453163,622.8705097110098,593.8609408607392,462.86371769642807,1.0488490938774728,2.9962037768208276,4.513705163819856,936.9353018294769,1.157452788691043,26.09197279436929,78.17686743119555,2.9962037768208276,2017-01-10 14:33:21.550781250 +2187.0,2187000.0,728.7946428571429,29.220223672950432,24.060987042268753,0.0,0.0,5.0,38.46153846153846,24.152748153976663,720.703125,85.9375,0.03314067740576277,0.04009390568294577,82.4497302543277,86.77966101694915,77.1859296482412,3.159671694834373,657.5851600236508,637.773603385057,473.4804116639493,1.0310636196503613,2.993738412926769,4.51809404592517,940.519485789205,1.1198896871153945,26.234941897761953,78.54055332023187,2.993738412926769,2017-01-10 14:33:22.550781250 +2188.0,2188000.0,731.3701923076923,28.623060586802545,25.30702047601147,0.0,0.0,5.0,41.666666666666664,25.315393353155702,722.65625,85.9375,0.0346136520457281,0.03913621431096092,82.15332962124823,86.77966101694915,77.1859296482412,3.074155053378272,672.8299456689476,659.3390973812593,473.3760338190618,1.0204611683749238,2.990889508899252,4.517680565452117,938.7303015176108,1.1093075777848578,26.234941897761953,78.46581248859766,2.990889508899252,2017-01-10 14:33:23.550781250 +2189.0,2189000.0,727.9575892857143,30.43587750163119,25.268985626957498,0.0,0.0,5.0,38.46153846153846,25.268985626957498,720.703125,97.65625,0.03471216730050428,0.041809959741604515,82.55583006855029,88.27586206896552,77.1859296482412,3.314466755390386,686.1923297088449,678.7498752020126,477.9867792290068,1.0109649441992417,2.984159071727077,4.518813110491304,936.7840975319883,1.0986122886681098,26.298778610283062,78.47973876521822,2.984159071727077,2017-01-10 14:33:24.550781250 +2190.0,2190000.0,729.8677884615385,30.209153600158686,23.644557027779786,0.0,0.0,4.0,33.333333333333336,23.86758174456167,722.65625,97.65625,0.032701240035364856,0.04138989838671392,82.33672890170116,88.27586206896552,77.1859296482412,3.273012329650258,703.3109613156959,712.8038202123454,497.833660514133,0.986682368096987,2.9888447623726715,4.52195043946623,942.3891079501159,1.1192315758708455,26.37324376959509,78.82553150753198,2.9888447623726715,2017-01-10 14:33:25.550781250 +2191.0,2191000.0,728.2366071428571,27.69411036920095,22.526039439893403,0.0,0.0,4.0,30.76923076923077,22.725565386044515,722.65625,97.65625,0.031206293618231244,0.03802900059893341,82.5012231182772,88.27586206896552,77.1859296482412,3.0158556897445994,710.6834435498808,725.768051740944,491.8753406164672,0.979215662421515,3.037563307450419,4.527481338076015,971.6921095351497,0.8957922103258141,26.32800845039745,79.97299242717186,3.037563307450419,2017-01-10 14:33:26.550781250 +2192.0,2192000.0,722.65625,24.752758806032418,24.437050013638775,0.0,0.0,5.0,38.46153846153846,24.9652327358045,720.703125,85.9375,0.03454648421819433,0.034252466239698916,83.11723213382582,88.27586206896552,78.36734693877551,2.7349443394318658,719.3380080740327,744.0577758825505,453.56170411067893,0.966777085584252,3.020262218281685,4.529908799171163,966.0984258072477,0.9103910097469667,26.47720166671187,79.96809183979472,3.020262218281685,2017-01-10 14:33:27.550781250 +2193.0,2193000.0,717.5480769230769,22.96532930659294,22.746896459887836,0.0,0.0,4.0,33.333333333333336,22.80505193767838,718.75,82.03125,0.03178191492822179,0.03200528305374426,83.69668705951389,88.27586206896552,78.76923076923077,2.557577831585069,720.4544747793612,746.0187534332599,436.9047834455812,0.965732391395995,3.0124874082008724,4.532984692424461,965.7844456945996,0.9517211688536555,26.605396447419118,80.14842178804231,3.0124874082008724,2017-01-10 14:33:28.550781250 +2194.0,2194000.0,716.2388392857143,22.39294646878599,20.700486357752204,0.0,0.0,3.0,23.076923076923077,20.839592409134255,716.796875,82.03125,0.029095870352293402,0.03126463581773626,83.84617360478263,88.27586206896552,78.76923076923077,2.498717249439796,718.0464653851661,750.2323600110325,436.7940210191579,0.9570987652073645,2.9936306314342267,4.530329674895676,953.8106303567711,0.9221558513265532,26.607602211112944,79.65333300820477,2.9936306314342267,2017-01-10 14:33:29.550781250 +2195.0,2195000.0,714.2857142857143,20.71854825411862,20.143420387132654,0.0,0.0,3.0,23.076923076923077,20.4127978657197,716.796875,82.03125,0.028577917012007577,0.029005967555766066,84.06470308349877,88.27586206896552,78.76923076923077,2.316562397205951,712.0211027468965,748.5344956082251,468.62855967536115,0.951220160092075,2.9661998857794236,4.519772682213179,929.3663888284642,1.1811333123561132,26.407430341032196,78.32971686129778,2.9661998857794236,2017-01-10 14:33:30.550781250 +2196.0,2196000.0,708.4263392857143,17.03433329157705,18.537496172099978,0.0,0.0,3.0,23.076923076923077,18.73371688794031,708.984375,58.59375,0.02644412813169676,0.024045313319028018,84.74030045222311,88.27586206896552,81.26984126984127,1.9659831763811217,705.6455356260083,742.0172329469771,486.6942390961154,0.9509826784257882,3.287764114705131,4.453297305535371,1004.6149323931724,1.2573606777439974,23.234767764221594,76.39043566871533,3.287764114705131,2017-01-10 14:33:31.550781250 +2197.0,2197000.0,707.03125,16.923230577318176,18.449617106981673,0.0,0.0,3.0,23.076923076923077,18.449617106981673,701.171875,58.59375,0.02609448607396303,0.023935618938085376,84.90685723767716,88.27586206896552,81.26984126984127,1.9507235718976872,689.5258732454306,727.5180107040094,518.4881633808744,0.9477784234897301,3.1571879439909933,4.437230535768184,928.0383462492026,1.2573606777439974,23.275823681949245,73.4861499151102,3.1571879439909933,2017-01-10 14:33:32.550781250 +2198.0,2198000.0,707.5520833333334,16.164423821164743,17.01093869941538,0.0,0.0,3.0,21.428571428571427,17.122696420274842,707.03125,58.59375,0.024199909552394327,0.022845560350854843,84.84064682450905,88.27586206896552,81.26984126984127,1.8689932315428637,682.089476750112,714.162330334145,519.3507254438159,0.955090247382515,3.926490172032409,4.529441542879348,1431.2896920849453,0.9356150047005579,23.209111916096475,91.13034984015309,3.926490172032409,2017-01-10 14:33:33.550781250 +2199.0,2199000.0,709.8214285714286,14.489103107504276,15.520638906424228,0.0,0.0,2.0,15.384615384615385,15.625,707.03125,54.6875,0.022012578616352203,0.020412321358999733,84.56084009522367,87.77142857142857,81.26984126984127,1.6547158343061812,663.5650703019368,685.799730146709,520.6333163713804,0.967578494322219,3.5824214174223306,4.345171480818783,1008.9061036000182,1.1907275775759154,19.653387303560947,70.40671560117285,3.5824214174223306,2017-01-10 14:33:34.550781250 +2200.0,2200000.0,707.03125,15.549698356893764,15.659632476917526,0.0,0.0,2.0,15.384615384615385,16.06940139077036,705.078125,54.6875,0.022727993127277413,0.02199294353240223,84.89972591982378,87.77142857142857,81.26984126984127,1.7864703487605869,653.2830986555475,671.2117334330882,531.015708917171,0.9732891517169404,3.4031878409640144,4.329487347995374,917.4291927624887,1.2144441041932315,19.80344389098384,67.39483945900929,3.4031878409640144,2017-01-10 14:33:35.550781250 +2201.0,2201000.0,702.0089285714286,10.439892262204078,12.301375895708365,0.0,0.0,1.0,7.6923076923076925,12.305045210001966,701.171875,35.15625,0.017528331491513133,0.014871452252678623,85.48662516709503,87.77142857142857,83.47826086956522,1.2302065041922638,636.9069413830063,639.3664752833844,541.5699701722542,0.9961531703718309,3.196710207980406,4.300064242223643,807.3967983316589,1.1823113075447567,19.75242838357168,63.1427894461655,3.196710207980406,2017-01-10 14:33:36.550781250 +2202.0,2202000.0,702.2879464285714,10.411746920859377,12.301375895708365,0.0,0.0,1.0,7.6923076923076925,12.305045210001966,703.125,35.15625,0.017521367513963864,0.014825467208724675,85.45257355762443,87.77142857142857,83.47826086956522,1.2273772967838559,629.5743242887057,621.8085139963207,546.3673976870188,1.0124890703771081,3.623300067891743,4.236592138747936,905.6335908952789,1.1411719030869056,17.24582309961812,62.48679200769533,3.623300067891743,2017-01-10 14:33:37.550781250 +2203.0,2203000.0,701.5625,10.728227536337425,12.478057910155076,0.0,0.0,1.0,7.142857142857143,12.527870714644893,703.125,35.15625,0.017857098568758868,0.015291905619723723,85.54213563645871,87.77142857142857,83.47826086956522,1.2693681916269863,617.6953869924803,585.7802825172632,545.4852285821786,1.0544830637488665,4.576110259800024,4.152095663519403,1166.2524037904316,1.0986122886681098,13.923217272809193,63.714177411527054,4.576110259800024,2017-01-10 14:33:38.550781250 +2204.0,2204000.0,698.1026785714286,13.56877086379311,13.090757758889401,0.0,0.0,1.0,7.6923076923076925,13.661139328929453,701.171875,50.78125,0.019568954178610376,0.01943664059785552,85.97777402768202,89.82456140350877,83.47826086956522,1.630585900162343,612.2002256050978,573.4526318029251,538.9875661084269,1.0675689527840353,4.260846451671666,4.13145530310742,1023.2257886148342,1.116630794170788,14.090283078878691,60.0365326596896,4.260846451671666,2017-01-10 14:33:39.550781250 +2205.0,2205000.0,694.53125,13.056127760489106,13.511495550003628,0.0,0.0,1.0,7.142857142857143,13.651930605719942,699.21875,46.875,0.019656323031857735,0.018798474165833585,86.41796199722269,89.82456140350877,83.93442622950819,1.5839412615224622,609.3499556507605,540.6395832073414,529.1849645183698,1.127090902289831,4.108731389218957,4.112322550840579,947.8135220725289,1.0986122886681098,14.036130879497009,57.67069152777484,4.108731389218957,2017-01-10 14:33:40.550781250 +2206.0,2206000.0,694.4754464285714,12.17834455529512,11.20273550619426,0.0,0.0,0.0,0.0,11.206764542651152,699.21875,39.0625,0.016137020538715036,0.017536033300995464,86.42120774933834,89.82456140350877,84.86187845303867,1.4834684014191188,610.9921286683824,527.6965120269573,526.3923064736209,1.157847578566466,3.791370275006974,4.083113618918506,812.3648255329401,1.0986122886681098,14.128586593140037,53.56670323709319,3.791370275006974,2017-01-10 14:33:41.550781250 +2207.0,2207000.0,694.53125,12.281857718515434,11.775082503353344,0.0,0.0,0.0,0.0,11.857438259417288,699.21875,39.0625,0.017072577021433215,0.017683664656580153,86.41476389003873,89.82456140350877,84.86187845303867,1.4964839424618668,614.5596723814383,516.6931325520916,521.4111202974278,1.1894094069839027,3.557511696310592,4.057976685387812,717.3115893390114,1.1213405397456657,14.169521452326931,50.408238297776904,3.557511696310592,2017-01-10 14:33:42.550781250 +2208.0,2208000.0,691.6852678571429,12.10930576460179,12.212981630349503,0.0,0.0,0.0,0.0,12.305045210001966,691.40625,39.0625,0.017789948379446165,0.017506959201425098,86.7695402509621,89.82456140350877,84.86187845303867,1.4752872648505693,626.9835359415223,504.9865306126336,503.4093636148565,1.2415846719334984,3.4976432803815722,4.046977815520095,690.481710171788,1.143563676530376,14.110459587771924,49.3533541602662,3.4976432803815722,2017-01-10 14:33:43.550781250 +2209.0,2209000.0,691.40625,11.966531100805215,12.271981885013302,0.0,0.0,0.0,0.0,12.305045210001966,691.40625,39.0625,0.017797127535370074,0.017307525207944267,86.8039744627863,89.82456140350877,84.86187845303867,1.4580153054713407,636.9775003723647,500.08373510083,499.7425129576294,1.2737416869675502,3.4261702099654365,4.044148915861605,667.2488898467412,1.1676051601550612,14.210520952061843,48.68766355404396,3.4261702099654365,2017-01-10 14:33:44.550781250 +2210.0,2210000.0,694.2708333333334,12.825922121773917,12.190877385576716,0.0,0.0,0.0,0.0,12.219580602934258,695.3125,42.96875,0.01760059621727965,0.018473946341939926,86.44944645041484,89.82456140350877,84.3956043956044,1.5601706103232713,664.6729383395057,500.5554432493822,497.5587410970977,1.327870762976317,3.4195883671787892,4.034695749181735,658.1253524673433,1.1314021114911006,14.070222582008476,48.11436946505249,3.4195883671787892,2017-01-10 14:33:45.550781250 +2211.0,2211000.0,692.8013392857143,13.322470575197071,14.003822353042121,0.0,0.0,1.0,7.6923076923076925,14.084184669781209,691.40625,42.96875,0.020329326563228292,0.019229856843135842,86.63483265448978,89.82456140350877,84.3956043956044,1.614842178648459,680.3502168229336,504.9033027033472,497.044009680801,1.3474861685004051,3.4099329855123224,4.039060830361837,658.6416360593931,1.200394982978052,14.16111670273909,48.28845895635952,3.4099329855123224,2017-01-10 14:33:46.550781250 +2212.0,2212000.0,692.96875,13.106119498294353,14.34976659667834,0.0,0.0,1.0,7.142857142857143,14.352478961620184,691.40625,42.96875,0.02071158181609226,0.018913002207234242,86.61305755330075,89.82456140350877,84.3956043956044,1.5910445774973885,697.9941598094446,510.1151277062341,494.37400117191345,1.3683071171561227,3.395969041309018,4.0359328899604945,652.2469074518262,1.252762968495368,14.139192016721445,48.01625835790965,3.395969041309018,2017-01-10 14:33:47.550781250 +2213.0,2213000.0,693.0803571428571,13.33190686766233,13.138948983819377,0.0,0.0,1.0,7.6923076923076925,13.357037722892349,693.359375,42.96875,0.01927199001563856,0.01923573035978333,86.59998989709807,89.82456140350877,84.3956043956044,1.615569459859959,734.6022797082508,531.4352243437764,490.28165160639253,1.3822988128333944,3.4185305816583513,4.037424517986523,659.8898723310417,1.2237754316221157,14.116678791787674,48.25829816117403,3.4185305816583513,2017-01-10 14:33:48.550781250 +2214.0,2214000.0,696.09375,11.55012610088862,13.660481768854517,0.0,0.0,1.0,7.142857142857143,13.731534374290494,695.3125,35.15625,0.019726558921539655,0.016592773747628995,86.21753575886194,88.78612716763006,84.3956043956044,1.3881178330799842,755.7948234744485,544.2390082749935,491.5654281394608,1.3887185813269747,3.422322481043319,4.032701179055365,657.4034588537799,1.3437347467010947,14.032341102912305,48.02319641816498,3.422322481043319,2017-01-10 14:33:49.550781250 +2215.0,2215000.0,696.9866071428571,11.22918556781762,13.756638392138937,0.0,0.0,1.0,7.6923076923076925,13.874274495931726,697.265625,31.25,0.019906084785195878,0.01611104926943889,86.10571742939553,88.27586206896552,84.3956043956044,1.3430678175938597,795.499625086372,573.8792630191779,502.7365721740631,1.386179421959333,3.386092814874184,4.0354506701662665,649.043227215988,1.3217558399823195,14.151937915205652,47.919775291223395,3.386092814874184,2017-01-10 14:33:50.550781250 +2216.0,2216000.0,694.2708333333334,12.216607107184444,13.184885436061004,0.0,0.0,1.0,7.142857142857143,13.328762179125208,695.3125,31.25,0.01919821709221335,0.017596313312673768,86.44656232107567,88.27586206896552,84.3956043956044,1.4681845023901894,817.1713065313428,590.5255585626543,509.3523627669676,1.3838034521661462,3.378781961393372,4.029253857017573,642.3432709016462,1.3328056761689044,14.066526014588842,47.52772435756338,3.378781961393372,2017-01-10 14:33:51.550781250 +2217.0,2217000.0,693.359375,12.136968406623023,12.71285805366958,0.0,0.0,1.0,7.6923076923076925,12.727054437828949,693.359375,31.25,0.018355639076530764,0.0175045854202563,86.55975402593546,88.27586206896552,84.3956043956044,1.4551114332777138,856.3572690566002,626.5727346820428,529.1616600728722,1.3667324185294507,3.370986237849152,4.0306659663342295,641.1630001858758,1.35314215380299,14.105695422143324,47.55010514333693,3.370986237849152,2017-01-10 14:33:52.550781250 +2218.0,2218000.0,693.0803571428571,12.51447907990914,12.30871343044714,0.0,0.0,1.0,7.6923076923076925,12.400066275218357,693.359375,35.15625,0.017891238941377854,0.018056317641865684,86.59620153298292,88.78612716763006,84.3956043956044,1.503234670053317,875.9747800801144,644.6549141639863,542.4655552868957,1.358827429735974,3.3451995230714116,4.028959059313835,632.5757935113743,1.3643154544011153,14.13215933263792,47.27489265950957,3.3451995230714116,2017-01-10 14:33:53.550781250 +2219.0,2219000.0,687.5,18.907449046983725,11.735346453578734,0.0,0.0,1.0,7.142857142857143,12.657696676577851,687.5,66.40625,0.01841119516593142,0.027501744068339964,87.33554464571375,93.0909090909091,84.3956043956044,2.3656038848510343,908.8539293112417,680.5130813290937,570.9250657779511,1.335542187574383,3.4221930176957853,4.025523670237513,651.9564501953059,1.3971052772241062,13.917127095158902,47.627095171437624,3.4221930176957853,2017-01-10 14:33:54.550781250 +2220.0,2220000.0,680.7291666666666,19.445496269028713,12.12363894107167,0.0,0.0,0.0,0.0,12.174901913655932,679.6875,66.40625,0.017885089268721798,0.028565686944556336,88.20869124816664,93.0909090909091,84.3956043956044,2.46200461266119,924.5193052254442,697.4974482576107,580.2808547059078,1.3254805555704143,3.4472340689611976,4.028259787063792,661.2048848199937,1.3748656652962679,13.910236555033594,47.951841359821245,3.4472340689611976,2017-01-10 14:33:55.550781250 +2221.0,2221000.0,680.2083333333334,19.46790322434291,12.123638941071668,0.0,0.0,0.0,0.0,12.174901913655932,679.6875,66.40625,0.017898783824057723,0.02862050091174455,88.27633782124674,93.0909090909091,84.3956043956044,2.4630007903822095,950.1676437448134,726.614747090777,615.8371835391019,1.3076635831423713,3.509863743995651,4.025627918240459,677.2505162679391,1.3516088031320006,13.74386369048586,48.23908886965459,3.509863743995651,2017-01-10 14:33:56.550781250 +2222.0,2222000.0,678.2924107142857,18.677691531046197,12.432788981717069,0.0,0.0,0.0,0.0,12.44730479399518,679.6875,66.40625,0.018350942156182118,0.02753634160726844,88.52022777611874,93.0909090909091,84.3956043956044,2.368005614782532,956.7209478456189,742.5850349336174,640.3729619001737,1.2883655108012564,3.4967989176138343,4.025299783891332,673.2182499181869,1.276293465905562,13.764313988495495,48.131038256668006,3.4967989176138343,2017-01-10 14:33:57.550781250 +2223.0,2223000.0,675.2604166666666,17.585357308473053,12.568198247271438,0.0,0.0,0.0,0.0,13.08114751086054,675.78125,66.40625,0.019372004026881787,0.02604233400097822,88.91086883637554,93.0909090909091,84.3956043956044,2.2373537178764127,969.2652308860297,761.2472012410234,640.4898020852565,1.273259500075514,3.885283908509356,4.069851595873767,829.9653032369329,1.274502955131774,13.745306452382362,53.40441797697101,3.885283908509356,2017-01-10 14:33:58.550781250 +2224.0,2224000.0,670.4799107142857,13.755935165413828,12.101580868751363,0.0,0.0,0.0,0.0,12.16112160147119,673.828125,46.875,0.01813793583839898,0.020516550825153208,89.52344532107419,93.0909090909091,86.77966101694915,1.7861964270568236,969.7484125303158,773.5024803284117,629.2420666930942,1.253710798856886,3.476633184868826,3.94104520672373,605.7046534431998,1.285198244248522,12.52804129995838,43.55540412484249,3.476633184868826,2017-01-10 14:33:59.550781250 +2225.0,2225000.0,671.09375,13.54774646027101,12.305288544196399,0.0,0.0,0.0,0.0,12.308451450955792,675.78125,46.875,0.018340882255207698,0.020187561663733287,89.44066187767544,93.0909090909091,86.77966101694915,1.7629692333562077,967.5634923573805,777.907412884295,617.6892617658373,1.24380289521845,3.3169281122405305,3.920196418491076,551.0643269824932,1.3137236682850553,12.521898764630553,41.53423803103305,3.3169281122405305,2017-01-10 14:34:00.550781250 +2226.0,2226000.0,671.0379464285714,14.38748505986612,12.634492813478749,0.0,0.0,0.0,0.0,12.634492813478749,673.828125,46.875,0.018828283677134235,0.021440643016449136,89.45220937856628,93.0909090909091,86.77966101694915,1.8632817477719934,961.074574306195,783.4872383379075,632.3436720161515,1.2266627039708036,3.2047554549334607,3.9045858652819305,514.0262238822218,1.3062516534463542,12.512252316526856,40.098708864893275,3.2047554549334607,2017-01-10 14:34:01.550781250 +2227.0,2227000.0,669.53125,13.189017981354791,12.629989171602984,0.0,0.0,0.0,0.0,12.657696676577851,671.875,46.875,0.018905311255565518,0.019698883332711938,89.64756471900442,93.0909090909091,86.77966101694915,1.7149664572287462,944.7516240960492,776.338803849437,676.6265857621563,1.21693211702358,3.0283599353422153,3.885096461342493,461.69956836793034,1.252762968495368,12.585906277237855,38.114654319959214,3.0283599353422153,2017-01-10 14:34:02.550781250 +2228.0,2228000.0,669.0104166666666,13.078369902269747,11.981534907133867,0.0,0.0,0.0,0.0,11.994523024667789,667.96875,46.875,0.017928753762056954,0.019548828503198067,89.71679366806303,93.0909090909091,86.77966101694915,1.6996864946588832,934.0750515380794,766.8125360559615,698.4118707562359,1.2181269966482542,2.9326849138638234,3.8711543745447736,432.98743633355116,1.2378743560016174,12.585906277237855,36.910497466559455,2.9326849138638234,2017-01-10 14:34:03.550781250 +2229.0,2229000.0,671.875,10.849454294209588,11.122283162943498,0.0,0.0,0.0,0.0,11.292470058508963,671.875,42.96875,0.016807397296385432,0.016148024996032875,89.32412498097096,92.53012048192771,86.77966101694915,1.3977058530615196,920.1816048402255,763.9554669550151,717.9447617309174,1.2044963936286741,2.9381373144360543,3.8390338775651247,418.43217506982813,1.23884162997676,12.117719717846121,35.603524468881226,2.9381373144360543,2017-01-10 14:34:04.550781250 +2230.0,2230000.0,673.2700892857143,9.270139581041473,10.632094959708011,0.0,0.0,0.0,0.0,10.670240928223981,671.875,31.25,0.015848381055430894,0.013768827293183853,89.13286671468562,90.88757396449704,86.77966101694915,1.1747713343542598,890.8246556920981,737.3150420062027,726.591903738071,1.208200843520298,3.030217114724852,3.836647354928911,437.05319319030843,1.2481440226390734,11.899447924686827,36.05791075716314,3.030217114724852,2017-01-10 14:34:05.550781250 +2231.0,2231000.0,671.6145833333334,9.955373825525209,9.98244632812406,0.0,0.0,0.0,0.0,10.12185108661666,671.875,35.15625,0.0150709221297433,0.014823045944170919,89.35514264466993,91.42857142857143,86.77966101694915,1.27039632028598,873.5688830040626,720.6927422289784,729.5128079595848,1.2121238800078167,3.0906143287914682,3.8411089097657203,452.5032216034601,1.2755429968271879,11.843281626447212,36.60301589461048,3.0906143287914682,2017-01-10 14:34:06.550781250 +2232.0,2232000.0,670.5729166666666,8.438464451051901,8.915482873141995,0.0,0.0,0.0,0.0,8.919847858898315,671.875,31.25,0.013301831370162925,0.012583962521180311,89.48890705471531,91.42857142857143,87.27272727272727,1.0841550134061129,839.4615903736217,691.4971368294875,738.1863027619912,1.2139769576234989,3.2346498249227027,3.8256106193390624,475.93343705121663,1.252762968495368,11.371864660087144,36.78400003179555,3.2346498249227027,2017-01-10 14:34:07.550781250 +2233.0,2233000.0,674.4791666666666,11.594078900481712,9.535718465756172,0.0,0.0,0.0,0.0,9.793487039500135,671.875,39.0625,0.014520073448525298,0.017189676825424625,88.98186506251726,91.42857142857143,86.29213483146067,1.4656541473044657,823.9014085933934,672.0359441994666,733.6799286408082,1.2259781871858504,3.1105310168917804,3.8072785624148695,439.4313990162413,1.2776605201170952,11.354343318122366,35.31803706745755,3.1105310168917804,2017-01-10 14:34:08.550781250 +2234.0,2234000.0,674.7395833333334,11.385387394856284,11.798200062425208,0.0,0.0,1.0,7.142857142857143,11.81138978153835,671.875,39.0625,0.017505108746085397,0.01687375051958631,88.94663974431981,91.42857142857143,86.29213483146067,1.4383742271489972,792.2714740807203,636.3587810541732,719.2714206641001,1.2450075298218826,3.0728625323182404,3.797990025905254,426.8838365502199,1.2939210409888755,11.302212164373714,34.73014429221543,3.0728625323182404,2017-01-10 14:34:09.550781250 +2235.0,2235000.0,674.7395833333334,11.385387394856284,11.945744770428373,0.0,0.0,1.0,7.142857142857143,11.949002848975367,671.875,39.0625,0.01770905864147642,0.01687375051958631,88.9466397443198,91.42857142857143,86.29213483146067,1.4383742271489972,778.1168585819107,617.6097693252982,711.105632775172,1.2598843108196895,3.095673797850529,3.7937604267242597,429.54928479407727,1.276293465905562,11.205793793091171,34.68948222938843,3.095673797850529,2017-01-10 14:34:10.550781250 +2236.0,2236000.0,675.2604166666666,12.162959878498013,11.50420096433499,0.0,0.0,1.0,7.142857142857143,11.625372017856952,671.875,39.0625,0.017216131333810527,0.018012250649221895,88.88126691490201,91.42857142857143,86.29213483146067,1.5321904913629607,753.6606504090204,584.3903520697099,682.060383098256,1.2896527941294944,3.0753936836962716,3.792488415187351,424.71273617325147,1.3318061758358208,11.22622797207136,34.525070597042664,3.0753936836962716,2017-01-10 14:34:11.550781250 +2237.0,2237000.0,677.6041666666666,13.194526028360974,13.568991535477487,0.0,0.0,2.0,14.285714285714286,13.5718599408653,675.78125,42.96875,0.020029186077218583,0.019472321271677993,88.57840789419878,91.42857142857143,85.81005586592178,1.6553906670076601,743.9291126334206,567.4697452611805,667.105974713364,1.3109581944162043,3.045866970707363,3.7909548669952864,417.8725378291129,1.3639886036055924,11.260611531512525,34.298324733800456,3.045866970707363,2017-01-10 14:34:12.550781250 +2238.0,2238000.0,677.6041666666666,13.194526028360974,13.568991535477487,0.0,0.0,2.0,14.285714285714286,13.5718599408653,675.78125,42.96875,0.020029186077218583,0.019472321271677993,88.57840789419879,91.42857142857143,85.81005586592178,1.6553906670076601,728.5765899913995,535.9699589582285,644.478027287633,1.3593608705374884,3.0848933059535883,3.7963245570719355,428.57072400678004,1.3256697393034558,11.258543577385733,34.731405716664014,3.0848933059535883,2017-01-10 14:34:13.550781250 +2239.0,2239000.0,679.9479166666666,13.96502260645537,14.139352075133846,0.0,0.0,2.0,14.285714285714286,14.31445908301413,675.78125,42.96875,0.021052287582831966,0.02053837104894241,88.27683518454295,91.42857142857143,85.81005586592178,1.7531918454902147,725.1909579215799,524.9414992390375,635.5178851872558,1.381470047563065,3.0520656227486493,3.7952949871443287,421.2484291240454,1.3334518797455106,11.305520677709525,34.505191007711254,3.0520656227486493,2017-01-10 14:34:14.550781250 +2240.0,2240000.0,682.8125,14.85255847669254,14.042666888573235,0.0,0.0,2.0,14.285714285714286,14.503561850856055,683.59375,50.78125,0.02124091438111642,0.021752030721014247,87.91076433631413,91.42857142857143,84.86187845303867,1.851968684146413,722.8552281909977,517.9707534219049,624.3047868721109,1.3955522071768547,3.1921718641886825,3.7755546926196284,440.4602737934744,1.2794312155775294,10.806230384342419,34.49534459083872,3.1921718641886825,2017-01-10 14:34:15.550781250 +2241.0,2241000.0,686.1049107142857,12.504425942059607,15.48569558500616,0.0,0.0,2.0,15.384615384615385,15.511910455697484,687.5,39.0625,0.022608656800821386,0.01822523894930526,87.47721853466851,89.82456140350877,84.86187845303867,1.5395931275575334,726.8591237695407,504.68472740708916,606.2653904216975,1.4402241326063372,3.0363880007653643,3.755539076914538,399.305634280632,1.3235560204864674,10.827567849933127,32.87669709700978,3.0363880007653643,2017-01-10 14:34:16.550781250 +2242.0,2242000.0,684.375,13.54774646027101,15.394100696577814,0.0,0.0,3.0,21.428571428571427,15.48486264007992,687.5,42.96875,0.022626283309705817,0.01979579391455125,87.70334660503967,90.3529411764706,84.86187845303867,1.6793928277785495,732.7933120452433,502.1842214499232,598.0937538942396,1.4592121391817086,2.7938297395095097,3.7243932660102943,340.0137945521761,1.2992829841302609,10.89022051141655,30.425421934612018,2.7938297395095097,2017-01-10 14:34:17.550781250 +2243.0,2243000.0,681.9196428571429,13.593463824826554,14.164091044943119,0.0,0.0,2.0,15.384615384615385,14.167277925632552,679.6875,42.96875,0.02077558268636132,0.01993411389041668,88.01916107100492,90.3529411764706,84.86187845303867,1.6793442087438475,750.4731781321302,502.9504756928184,583.6031468162961,1.4921413029749049,2.7046056979352375,3.6751715365978876,306.0142892939483,1.3015531326648,10.458605884956844,28.28640506891329,2.7046056979352375,2017-01-10 14:34:18.550781250 +2244.0,2244000.0,681.7708333333334,13.111662369973025,13.995464513375506,0.0,0.0,2.0,14.285714285714286,14.006585265637424,679.6875,42.96875,0.020544418418658405,0.019231773682466164,88.03627447086897,90.3529411764706,84.86187845303867,1.623663757796163,761.7863199054686,509.5210802457797,572.8957641279317,1.4951026551011448,2.7449382070081056,3.662179650042857,308.23977810101667,1.3202042492909005,10.22736010191072,28.07347150056505,2.7449382070081056,2017-01-10 14:34:19.550781250 +2245.0,2245000.0,680.2455357142857,15.156524368763904,14.055307167700118,0.0,0.0,2.0,15.384615384615385,14.372909546996873,675.78125,50.78125,0.021129002385741096,0.022280961172128726,88.24393805375338,91.42857142857143,84.86187845303867,1.8863376821704942,775.5347299232933,517.7747153498995,560.9065150714969,1.4978227150377668,2.66807333273826,3.645791578197089,289.8627660978343,1.303406701314123,10.179745411186067,27.16030726565022,2.66807333273826,2017-01-10 14:34:20.550781250 +2246.0,2246000.0,679.1666666666666,14.304303381783233,14.997047600866837,0.0,0.0,2.0,14.285714285714286,15.020389323320057,671.875,50.78125,0.022115910660103152,0.021061550991582675,88.37987005168063,91.42857142857143,84.86187845303867,1.7849839218110395,806.7783905415281,538.6730633327445,540.6668988279939,1.497714375302208,2.4971863720176315,3.585345336611577,244.82124524081524,inf,9.814929826610197,24.509709005320357,2.4971863720176315,2017-01-10 14:34:21.550781250 +2247.0,2247000.0,680.2455357142857,15.614264856170026,14.303642109870077,0.0,0.0,1.0,7.6923076923076925,14.615849167085708,675.78125,50.78125,0.021486137577865126,0.02295386597395955,88.24625341292365,91.42857142857143,84.86187845303867,1.935826919381104,823.3414612932384,550.8154386142554,538.3786593957996,1.4947683081734335,2.4666258465482382,3.5802798815997257,238.94330019816965,inf,9.81811942637807,24.21762714160151,2.4666258465482382,2017-01-10 14:34:22.550781250 +2248.0,2248000.0,680.7291666666666,14.872114190859092,14.79584105308354,0.0,0.0,2.0,14.285714285714286,14.837874777129834,679.6875,50.78125,0.021797031042149414,0.021847329186265845,88.1797827020164,91.42857142857143,84.86187845303867,1.8483754610658567,859.4001674026089,579.8176937142223,541.3553702254254,1.4821903103670822,2.443808348589778,3.5791778189715386,235.3366817822345,inf,9.851340860469517,24.074789039619013,2.443808348589778,2017-01-10 14:34:23.550781250 +2249.0,2249000.0,680.2455357142857,15.310625165556266,16.225961538461537,0.0,0.0,3.0,23.076923076923077,16.25098231128601,675.78125,50.78125,0.0238898771959184,0.022507498192515858,88.24457237553706,91.42857142857143,84.86187845303867,1.8967238728759312,876.5868966053813,597.4433419976328,542.5455211909352,1.4672301706039508,2.480576497585023,3.578860956415813,240.57994161222553,inf,9.7744915289304,24.246373962508645,2.480576497585023,2017-01-10 14:34:24.550781250 +2250.0,2250000.0,683.59375,19.363115594751644,15.654866226680287,0.0,0.0,3.0,21.428571428571427,15.901570143722106,679.6875,70.3125,0.023261725467387768,0.028325471955750976,87.83589229075831,91.42857142857143,82.58064516129032,2.356729046075337,892.1832782475262,614.7313151675153,541.8570632730797,1.4513385868497766,2.4852659054726107,3.5861000618273855,243.28163384059854,inf,9.846992533708438,24.472394815468938,2.4852659054726107,2017-01-10 14:34:25.550781250 +2251.0,2251000.0,684.8958333333334,20.279563498372536,15.213511027026323,0.0,0.0,2.0,14.285714285714286,15.520015608452432,679.6875,70.3125,0.022660403017664386,0.029609704879753056,87.67516903611241,91.42857142857143,82.58064516129032,2.469179363673715,923.6136815012276,648.7783919614151,543.2111616418492,1.4236196719020164,2.3793939024982405,3.5396755086587133,216.0415431933388,inf,9.53991688157644,22.69922005836301,2.3793939024982405,2017-01-10 14:34:26.550781250 +2252.0,2252000.0,686.9791666666666,19.29921695109922,17.9376151344257,0.0,0.0,3.0,21.428571428571427,17.99178453700168,683.59375,70.3125,0.02618970910617379,0.028092870770364292,87.40253713122662,91.42857142857143,82.58064516129032,2.346001408642553,937.4471817653716,665.7612264045848,533.5174627996267,1.4080831754471723,2.27260714507239,3.547635042618336,203.5185842920233,inf,9.851340860469518,22.388227628046614,2.27260714507239,2017-01-10 14:34:27.550781250 +2253.0,2253000.0,688.28125,18.861276941319293,17.850602178262623,0.0,0.0,3.0,21.428571428571427,17.870217033042316,687.5,70.3125,0.025963538935634693,0.027403444364232345,87.23435949359688,91.42857142857143,82.58064516129032,2.293619582983118,959.6444664894498,692.7584921619521,524.7828206610394,1.385251104601552,2.266131309788074,3.545975732036979,202.262547484451,inf,9.846578307200861,22.313639396227924,2.266131309788074,2017-01-10 14:34:28.550781250 +2254.0,2254000.0,691.1272321428571,18.60573290461641,17.79191267198012,0.0,0.0,3.0,23.076923076923077,18.09632803475186,691.40625,70.3125,0.026183786708336966,0.026920850516812762,86.87321605695722,91.42857142857143,82.58064516129032,2.257039543315562,966.461886712,706.9353398125763,524.9843546110328,1.3671149711771797,2.221602951223874,3.539970958997223,194.97779066619722,inf,9.87625435561079,21.941115823462574,2.221602951223874,2017-01-10 14:34:29.550781250 +2255.0,2255000.0,693.4895833333334,15.754670273181429,18.073811094533088,0.0,0.0,4.0,28.571428571428573,18.082423823682646,691.40625,54.6875,0.026074542802456385,0.022717962391669802,86.56037658474187,89.30232558139535,82.58064516129032,1.8874771353395836,970.6425994591518,718.9976530501366,522.8694883960303,1.349994113807584,2.137932383196142,3.532125121400363,182.41204368322602,inf,9.977128979077415,21.33042713569427,2.137932383196142,2017-01-10 14:34:30.550781250 +2256.0,2256000.0,694.1964285714286,15.8804665244119,17.30310102029804,0.0,0.0,4.0,30.76923076923077,17.469281074217108,693.359375,54.6875,0.025164752158357763,0.02287604181008531,86.47262307747397,89.30232558139535,82.58064516129032,1.894710256933784,973.7086297956895,735.299753639192,508.5784409743084,1.3242335863388357,2.1877617010056927,3.5157174739779267,185.29289209280503,inf,9.678292381339292,21.173797403029283,2.1877617010056927,2017-01-10 14:34:31.550781250 +2257.0,2257000.0,692.1875,15.286512203807082,15.960211658565951,0.0,0.0,3.0,21.428571428571427,15.969964309435474,691.40625,54.6875,0.02307173173372168,0.02208435171656102,86.72069118116745,89.30232558139535,82.58064516129032,1.8273360493486224,971.1192540626479,739.9716373364266,506.06891160204003,1.312373616856791,2.1719452396907744,3.5100737020051778,182.10008900647625,inf,9.650557973248304,20.960483450356502,2.1719452396907744,2017-01-10 14:34:32.550781250 +2258.0,2258000.0,694.7544642857143,14.523779909651997,15.048057723015521,0.0,0.0,2.0,15.384615384615385,15.051057435538695,693.359375,50.78125,0.021663851344968146,0.02090491052055934,86.39619067361498,88.78612716763006,82.58064516129032,1.7253057469153892,966.1062942090759,741.2399391179794,509.5086251268311,1.3033651362049796,2.125152695262756,3.5057705699114283,175.3761352110662,inf,9.70801019038493,20.63100402173484,2.125152695262756,2017-01-10 14:34:33.550781250 +2259.0,2259000.0,691.1458333333334,16.928514133668365,15.474804267703178,0.0,0.0,3.0,21.428571428571427,15.832880534722218,691.40625,62.5,0.022908161738256713,0.024493404021584973,86.86065342380098,90.3529411764706,82.58064516129032,2.0417035569327204,948.8540348126128,735.8264934571525,520.5613752056414,1.289507843560494,2.098370120821484,3.5101084187481844,172.93279063259126,inf,9.818681470661266,20.603227823899147,2.098370120821484,2017-01-10 14:34:34.550781250 +2260.0,2260000.0,685.2678571428571,14.185020296925057,14.522891795185783,0.0,0.0,3.0,23.076923076923077,14.894271311771309,685.546875,42.96875,0.02173496269600504,0.02069996447238575,87.59189235547076,90.3529411764706,84.86187845303867,1.7493568669676378,935.8078678394925,729.1945951740009,521.4474211524637,1.2833444927223978,2.1311220747059463,3.5130422278834104,177.59617398809502,inf,9.775904690500564,20.833646286147154,2.1311220747059463,2017-01-10 14:34:35.550781250 +2261.0,2261000.0,682.03125,12.938729237669142,11.854155014647322,0.0,0.0,2.0,14.285714285714286,11.857438259417288,679.6875,39.0625,0.01738547648574356,0.018970874483638604,88.00195104575616,90.3529411764706,85.33333333333333,1.6066415917089867,902.5399334604233,707.5393628206591,519.7829666466868,1.275603847484018,2.114262973406376,3.5095929044085117,174.7973959567308,2.772588722239781,9.775904690500564,20.66883331867506,2.114262973406376,2017-01-10 14:34:36.550781250 +2262.0,2262000.0,679.9665178571429,14.732972082681744,12.004197328989289,0.0,0.0,2.0,15.384615384615385,12.305045210001966,677.734375,46.875,0.018096545766371375,0.021667202275064166,88.27800349502391,91.42857142857143,85.33333333333333,1.8378051902039947,883.2179324130884,692.5933264812031,520.8727934869679,1.2752330965999552,2.1113434335428036,3.5150302435886505,175.530845451339,2.833213344056216,9.844092582123148,20.784260232453132,2.1113434335428036,2017-01-10 14:34:37.550781250 +2263.0,2263000.0,675.2604166666666,17.272685275371163,12.101143103907054,0.0,0.0,2.0,14.285714285714286,12.484295380132307,671.875,58.59375,0.0184881196527991,0.025579294815821548,88.90879122885775,93.0909090909091,85.33333333333333,2.1933551431515355,863.1170793890608,674.3090091958452,521.9788013408869,1.2800022951174579,2.222865663524303,3.5287723426460196,192.64422629665555,2.833213344056216,9.746968697091265,21.66620204021039,2.222865663524303,2017-01-10 14:34:38.550781250 +2264.0,2264000.0,672.9166666666666,16.13743060919757,12.302124824245873,0.0,0.0,2.0,14.285714285714286,12.61456981635861,671.875,58.59375,0.018746109943814655,0.02398132102914809,89.211650249561,93.0909090909091,85.33333333333333,2.053771428132475,818.3096577192428,636.8345325892989,498.5419145768699,1.2849643287905355,2.2293211086558546,3.530031753496021,193.76477085730386,2.639057329615259,9.746968697091265,21.729123061833413,2.2293211086558546,2017-01-10 14:34:39.550781250 +2265.0,2265000.0,668.4895833333334,14.380296319861557,11.216379041429564,0.0,0.0,1.0,7.142857142857143,11.340625799610255,664.0625,54.6875,0.016964551254578644,0.02151162363391834,89.79296982811856,93.0909090909091,85.81005586592178,1.8480271850474834,793.3664075369094,615.436197328514,480.560190434768,1.2891123580003174,2.3075885836958054,3.537722060559313,205.87262536707735,2.70805020110221,9.665444871410877,22.303870241608912,2.3075885836958054,2017-01-10 14:34:40.550781250 +2266.0,2266000.0,666.40625,15.470779087698109,11.205962962066518,0.0,0.0,1.0,7.142857142857143,11.483881488424485,664.0625,54.6875,0.01723255369892537,0.023215237083532916,90.0799552896705,93.0909090909091,85.81005586592178,1.9975873541359996,768.2997440118268,595.3862757024283,462.8123995755423,1.2904223281018656,2.345239130588448,3.5455748541905914,212.84728989364106,2.70805020110221,9.674618976584002,22.689294997418365,2.345239130588448,2017-01-10 14:34:41.550781250 +2267.0,2267000.0,663.5416666666666,13.194526028360976,11.775082503353344,0.0,0.0,1.0,7.142857142857143,12.039871099548781,664.0625,54.6875,0.018144860683778383,0.019884999979947467,90.45664590747225,93.0909090909091,85.81005586592178,1.7074198700806982,718.441932126201,543.9379944074848,433.7941195485335,1.3208158641479062,2.329556835198135,3.5421497417463508,209.88666010867425,2.639057329615259,9.668925729087974,22.52431202122,2.329556835198135,2017-01-10 14:34:42.550781250 +2268.0,2268000.0,660.9375,8.759561102834189,9.58863854767834,0.0,0.0,0.0,0.0,9.625105084073851,664.0625,27.34375,0.01456280674658928,0.013253236656770404,90.79514711404394,93.0909090909091,89.30232558139535,1.1720464485828055,692.074399572877,517.1974634183742,416.54239179643406,1.3381241180083676,2.3694182174497076,3.5429795357762557,215.50251987811112,2.772588722239781,9.59641265857679,22.73791497539683,2.3694182174497076,2017-01-10 14:34:43.550781250 +2269.0,2269000.0,658.59375,8.833901002368256,9.923783060144387,0.0,0.0,0.0,0.0,9.959022487485909,660.15625,27.34375,0.015121647430583586,0.013413277915814197,91.11854055592751,93.0909090909091,89.30232558139535,1.1837015511973978,640.411975469073,463.06379139951144,382.5623852674954,1.382988666709536,2.4487128004749215,3.54905270520438,227.99909850549568,2.70805020110221,9.50599180440281,23.27744381265086,2.4487128004749215,2017-01-10 14:34:44.550781250 +2270.0,2270000.0,658.3333333333334,8.717987251155481,9.923783060144386,0.0,0.0,0.0,0.0,9.959022487485909,660.15625,27.34375,0.015127629094915303,0.013242512280236172,91.15418274179594,93.0909090909091,89.30232558139535,1.16805787841576,616.2323906199058,436.23744642063366,371.3978134887704,1.412607733875545,2.404873629024567,3.5330241199334753,217.8464314108942,2.639057329615259,9.416856200581798,22.64634914509564,2.404873629024567,2017-01-10 14:34:45.550781250 +2271.0,2271000.0,659.375,10.768788087570485,9.986344959918124,0.0,0.0,0.0,0.0,10.175548286166556,660.15625,39.0625,0.015432111144897146,0.016331811317642442,91.01770971354713,93.0909090909091,87.77142857142857,1.423094700357124,594.0373937343049,411.2856600333884,363.8125886291037,1.444342585846734,2.3823484169202143,3.5309703585608823,214.28565303300786,2.639057329615259,9.438925307207997,22.48680876305512,2.3823484169202143,2017-01-10 14:34:46.550781250 +2272.0,2272000.0,660.6770833333334,11.518628561745203,9.649339545642066,0.0,0.0,0.0,0.0,9.904151422599803,660.15625,39.0625,0.014990911100821144,0.017434581662239487,90.84154676281769,93.0909090909091,87.77142857142857,1.5214824004895484,551.6870599239317,365.66978922458077,346.23039027950074,1.5087028684918404,2.3446047093855884,3.522001492849114,207.06428443150415,2.772588722239781,9.416856200581798,22.07880539549096,2.3446047093855884,2017-01-10 14:34:47.550781250 +2273.0,2273000.0,664.0625,11.903308598047735,10.187018004201274,0.0,0.0,0.0,0.0,10.28210143565194,664.0625,39.0625,0.015483635103099393,0.01792498235941306,90.37998072651482,93.0909090909091,87.77142857142857,1.5617007486684436,532.2535324165174,341.32276647721255,337.41127083500726,1.559384795540885,2.308439941440041,3.514921281513018,200.64969817267726,2.772588722239781,9.413288287579746,21.730010663338813,2.308439941440041,2017-01-10 14:34:48.550781250 +2274.0,2274000.0,666.6666666666666,15.601731185898053,11.18858128111798,0.0,0.0,0.0,0.0,11.531237901658384,664.0625,58.59375,0.017296856852487576,0.023402596778847082,90.045340203639,93.0909090909091,85.33333333333333,2.006186980425865,497.7480271815666,298.92757619667213,321.04282025890893,1.6651124446748446,2.2703364029759543,3.5097565017028853,194.54208673950885,2.772588722239781,9.435678897780958,21.42216528842414,2.2703364029759543,2017-01-10 14:34:49.550781250 +2275.0,2275000.0,672.65625,20.361804858563357,14.020473801786956,0.0,0.0,1.0,7.142857142857143,14.352478961620184,667.96875,74.21875,0.021337018665358694,0.030270743576029147,89.27347347441436,93.0909090909091,83.47826086956522,2.5615982270503515,483.0036687530345,281.9410406584741,315.3309194933612,1.7131371425209259,2.2700246624543303,3.538132450198223,200.96114127116917,2.70805020110221,9.749693117018023,22.132043826992145,2.2700246624543303,2017-01-10 14:34:50.550781250 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+2308.0,2308000.0,691.1458333333334,13.88675733891397,14.150359746770052,0.0,0.0,1.0,7.142857142857143,14.16135886210884,687.5,50.78125,0.0204896827545207,0.020092369322317123,86.84484937686132,89.82456140350877,83.47826086956522,1.674229898214951,136.74125047601177,113.22060325903874,226.8315349262592,1.207741758478003,3.6804186862236663,3.736834175348102,521.5103455372233,1.6405284995041316,9.625171641365517,35.424661566991766,3.6804186862236663,2017-01-10 14:35:23.550781250 +2309.0,2309000.0,689.1741071428571,17.31502391647271,13.309636172085396,0.0,0.0,1.0,7.6923076923076925,13.958617366991508,687.5,66.40625,0.020254123337367435,0.025124310006736112,87.11218046843007,91.97604790419162,83.47826086956522,2.126370297732889,124.51609923345785,110.58237152465233,212.22595356120215,1.1260031550842555,3.682654744278344,3.7362749915480116,521.6497493570068,1.6094379124341003,9.616056403002128,35.412615733763936,3.682654744278344,2017-01-10 14:35:24.550781250 +2310.0,2310000.0,681.5104166666666,23.008853069382933,12.822684478879529,0.0,0.0,1.0,7.142857142857143,13.771163704523511,687.5,78.125,0.020206827904230142,0.033761557426989094,88.1350480850476,93.65853658536585,83.47826086956522,2.9240626332241626,118.48774328188651,108.63621100359083,208.1785059387299,1.090683688130195,3.66094252212097,3.7200138699458725,507.45355641911857,1.5339303599259553,9.465651011553339,34.65320428775299,3.66094252212097,2017-01-10 14:35:25.550781250 +2311.0,2311000.0,675.78125,22.8249568484571,12.67613470044704,0.0,0.0,0.0,0.0,12.786204506015178,679.6875,78.125,0.018920626321039802,0.03377565868904634,88.88096883096834,93.65853658536585,83.47826086956522,2.9083067650683883,108.56325885257513,105.00029905774275,197.31762664455619,1.0339328537804735,3.719776666711547,3.702413931132226,509.3100715313346,1.499234772300486,9.202133750015768,34.22988240726748,3.719776666711547,2017-01-10 14:35:26.550781250 +2312.0,2312000.0,674.21875,23.334969184259858,12.415510860093187,0.0,0.0,0.0,0.0,12.61456981635861,679.6875,78.125,0.018709906564239886,0.03461038303111543,89.09114945494206,93.65853658536585,83.47826086956522,2.970243648565114,104.56022396753107,103.6419409073529,188.99731174350217,1.0088601492034874,3.7440462444268334,3.7027392251422535,514.495308541578,1.5141277326297755,9.175696054687013,34.35423035355302,3.7440462444268334,2017-01-10 14:35:27.550781250 +2313.0,2313000.0,667.7083333333334,23.8279725307378,10.187018004201276,0.0,0.0,0.0,0.0,11.483881488424485,656.25,78.125,0.01719894887501951,0.035686199109997326,89.96441845782,93.65853658536585,83.47826086956522,3.0421499921946724,97.87603979827267,100.42304622318747,173.80643839603002,0.9746372319830435,3.765168707731476,3.701897734198511,518.3528945343004,1.5040773967762742,9.141062018387103,34.41764066706385,3.765168707731476,2017-01-10 14:35:28.550781250 +2314.0,2314000.0,663.8020833333334,19.219990039359935,10.690426373140635,0.0,0.0,0.0,0.0,11.048543456039805,656.25,58.59375,0.016644333805881855,0.028954398490051842,90.45837266414878,93.65853658536585,85.81005586592178,2.5025621948413193,92.8580668086632,97.51303308680724,148.6435155858489,0.9522631372362284,3.7029924451157976,3.7017469190955556,505.4785122062662,1.463255402256019,9.21588517892236,34.12635319260415,3.7029924451157976,2017-01-10 14:35:29.550781250 +2315.0,2315000.0,663.0208333333334,17.807081987198508,11.385159466017392,0.0,0.0,1.0,7.142857142857143,11.388577919619728,656.25,46.875,0.017176802518201,0.026857499933559414,90.55550194468012,93.65853658536585,87.27272727272727,2.3362346798317524,89.34630668607772,94.72483060294829,131.85573180933008,0.943219492897112,3.6715338242924336,3.6981810757909197,497.0062363220821,1.4445632692438664,9.217364878369994,33.84186692178054,3.6715338242924336,2017-01-10 14:35:30.550781250 +2316.0,2316000.0,662.2395833333334,16.889839980546068,11.548103842954731,0.0,0.0,1.0,7.142857142857143,11.57840062569555,656.25,46.875,0.017483703658148216,0.02550412328953869,90.65639527100696,93.65853658536585,87.27272727272727,2.222180202625564,88.03438984602055,93.30841520905241,123.95630785288687,0.9434774950231906,3.665442924698683,3.6988023993324872,496.12474853190946,1.4724720573609429,9.231621238417375,33.83798075185506,3.665442924698683,2017-01-10 14:35:31.550781250 +2317.0,2317000.0,661.376953125,15.143542648661274,11.692679333668567,0.0,0.0,1.0,6.666666666666667,11.71875,658.203125,42.96875,0.017718715393133997,0.022896991764088808,90.7642792843902,93.65853658536585,87.77142857142857,2.0056393839909523,86.8167511725832,90.9883204437871,111.53275549299603,0.9541526950837484,3.6442221524017118,3.696042436631981,490.2622838055991,1.4423838277709342,9.2290883839247,33.63284833517171,3.6442221524017118,2017-01-10 14:35:32.550781250 +2318.0,2318000.0,661.1979166666666,16.00178154609072,11.108275244081687,0.0,0.0,1.0,7.142857142857143,11.388577919619728,656.25,46.875,0.01722415880714445,0.024201197769589747,90.79368038986887,93.65853658536585,87.27272727272727,2.108699020837626,86.83996587606441,90.05944556035283,106.4002297208156,0.9642516155384179,3.6892190138494443,3.686703235364938,494.0299463313245,1.449095262358921,9.074535567969704,33.4779491592069,3.6892190138494443,2017-01-10 14:35:33.550781250 +2319.0,2319000.0,664.3229166666666,18.527009823658872,10.591658982199949,0.0,0.0,1.0,7.142857142857143,11.388577919619728,660.15625,58.59375,0.017143135715930912,0.027888560455840875,90.38261425398423,93.65853658536585,85.81005586592178,2.416651448360142,87.58519562643836,88.07053769955408,99.30776636623692,0.9944891664591463,3.7139597088243033,3.6965125767958975,504.67532378673906,1.3682758556172123,9.146979443942824,33.97151311224778,3.7139597088243033,2017-01-10 14:35:34.550781250 +2320.0,2320000.0,668.4895833333334,16.164423821164743,12.515436100449667,0.0,0.0,1.0,7.142857142857143,12.527870714644893,664.0625,54.6875,0.018740562346800306,0.02418051713021917,89.80339625896453,93.0909090909091,85.81005586592178,2.0904004731903796,88.64965669394539,87.29093142715979,97.16576125093444,1.0155654802231018,3.641784307862104,3.6999335999032867,491.96944461262035,1.4423838277709342,9.273628593088185,33.77255508724988,3.641784307862104,2017-01-10 14:35:35.550781250 +2321.0,2321000.0,670.4799107142857,15.967339181726537,13.125198117693241,0.0,0.0,1.0,7.6923076923076925,13.18011550448396,671.875,54.6875,0.019657733652963177,0.023814791355517232,89.53543761490857,93.0909090909091,85.81005586592178,2.0617272499720833,91.75924279610528,86.04819102492237,96.23985635836343,1.0663703873743122,3.665329176836294,3.702349281811883,498.13162738890514,1.4853852637641216,9.26953946083062,33.97591344161784,3.665329176836294,2017-01-10 14:35:36.550781250 +2322.0,2322000.0,673.6979166666666,16.16442382116474,12.65462106076849,0.0,0.0,1.0,7.142857142857143,13.164203250095367,675.78125,54.6875,0.019540216652634793,0.023993578458937997,89.10903680537167,93.0909090909091,85.81005586592178,2.0865344897685154,93.79240685169583,85.53446145772182,96.89843424745996,1.0965452433234266,3.6704525393421763,3.702087692243652,499.02610541275527,1.5040773967762742,9.260278505048017,33.98941275386927,3.6704525393421763,2017-01-10 14:35:37.550781250 +2323.0,2323000.0,678.8504464285714,11.686511286924313,12.966015030283303,0.0,0.0,1.0,7.6923076923076925,13.135512616823554,679.6875,39.0625,0.019349641273611023,0.017215148562407207,88.40903294082031,90.88757396449704,85.81005586592178,1.466427530051618,96.11069233790647,85.18068616417767,98.31753091879851,1.1283155450598537,3.668206236972512,3.7034750762645383,499.365064105661,1.508896683212223,9.277921087085954,34.03332799778748,3.668206236972512,2017-01-10 14:35:38.550781250 +2324.0,2324000.0,679.4270833333334,10.488498608027523,11.57167486234806,0.0,0.0,0.0,0.0,11.625372017856952,679.6875,35.15625,0.017110551379291183,0.015437268936307277,88.32928737863078,90.3529411764706,85.81005586592178,1.3137385634368146,101.24553293200125,84.63751738334287,104.2554590047357,1.196225220943531,3.6441811304324188,3.701545515718403,493.3699457713077,1.463255402256019,9.28779859122348,33.84642036939341,3.6441811304324188,2017-01-10 14:35:39.550781250 +2325.0,2325000.0,679.1294642857143,10.818500010602007,12.620192307692308,0.0,0.0,0.0,0.0,12.634492813478749,679.6875,35.15625,0.018603953263561148,0.01592995235743533,88.36913443628805,90.3529411764706,85.81005586592178,1.3510630659671325,103.62653267184758,84.54186988318384,107.51697315962161,1.2257421419118608,3.633982160521555,3.706109348817944,493.88843972528235,1.4759065198095778,9.349820808212659,33.97708202111803,3.633982160521555,2017-01-10 14:35:40.550781250 +2326.0,2326000.0,681.5104166666666,11.891093809198827,11.86728254360309,0.0,0.0,0.0,0.0,12.130058660314821,679.6875,35.15625,0.017798786876426793,0.017448146819764424,88.06476478821826,90.3529411764706,85.81005586592178,1.484744925729574,109.12006672837717,84.47708284057097,118.93284388428987,1.2917120603503032,3.687008196368439,3.700067395539356,501.238997103196,1.4226620052907655,9.217998962564213,33.98683772909002,3.687008196368439,2017-01-10 14:35:41.550781250 +2327.0,2327000.0,684.4308035714286,11.2776115388387,11.653076580327715,0.0,0.0,0.0,0.0,11.668562403213702,685.546875,35.15625,0.017048564065681986,0.016477358236933512,87.68630999263348,90.3529411764706,85.81005586592178,1.3999165543501337,111.90066758132107,84.65218512432659,125.31653434463304,1.3218875261988252,3.7032729587943147,3.7069378531536317,508.56622286844487,1.4403615823901665,9.270775689675926,34.33221291862457,3.7032729587943147,2017-01-10 14:35:42.550781250 +2328.0,2328000.0,682.5520833333334,11.76207269857939,13.098989502573106,0.0,0.0,1.0,7.142857142857143,13.205535230133071,679.6875,35.15625,0.01934729312617741,0.017232491095972854,87.92975341301867,90.3529411764706,85.81005586592178,1.4642045574850464,117.23360720380587,84.74105669982572,137.04923310421543,1.3834333883642376,3.6128450468668576,3.7104672481318994,492.04817687053804,1.4315509527080115,9.424297020662241,34.04852481130166,3.6128450468668576,2017-01-10 14:35:43.550781250 +2329.0,2329000.0,681.25,10.829344074336042,12.174901913655932,0.0,0.0,1.0,7.142857142857143,12.174901913655932,679.6875,35.15625,0.017871415653072927,0.015896284879759328,88.09414049322159,90.3529411764706,85.81005586592178,1.3510428471095433,120.29845690338658,84.753171314289,144.57457257347426,1.4193977055712226,3.5883561409215536,3.7108912274015635,487.2915982263002,1.3773256911371303,9.461017645185235,33.949500766467615,3.5883561409215536,2017-01-10 14:35:44.550781250 +2330.0,2330000.0,683.0729166666666,9.778637164332597,11.453334682909482,0.0,0.0,1.0,7.142857142857143,11.483881488424485,683.59375,31.25,0.01681208727241709,0.01431565639002561,87.85516399980288,89.82456140350877,85.81005586592178,1.215641519536651,126.15249357292122,84.86582207226495,158.76412129923142,1.4864935081345214,3.6159027177500893,3.7046461696749495,489.38222525771516,1.449095262358921,9.357389874043076,33.835411476599525,3.6159027177500893,2017-01-10 14:35:45.550781250 +2331.0,2331000.0,682.8125,10.143364078126574,12.010738420776162,0.0,0.0,1.0,7.142857142857143,12.039871099548781,683.59375,31.25,0.017632763166387233,0.014855270045768896,87.88997972127711,89.82456140350877,85.81005586592178,1.2631397925772203,128.93227840432783,84.98959069783577,164.0355616054824,1.5170361140192083,3.59655979611087,3.710706474949716,488.85961942560306,1.413693335308005,9.448211435773457,33.981057395057775,3.59655979611087,2017-01-10 14:35:46.550781250 +2332.0,2332000.0,679.1666666666666,12.340877076764418,11.59855451497016,0.0,0.0,1.0,7.142857142857143,11.71875,679.6875,42.96875,0.017254601226993866,0.018170616554745157,88.3708807682515,91.42857142857143,85.81005586592178,1.5566982516787489,131.6331827329399,85.00945800230025,169.4844644511843,1.5484533818505004,3.590991965915734,3.708907509849755,486.7157556269609,1.4226620052907655,9.435969900080424,33.884492101811496,3.590991965915734,2017-01-10 14:35:47.550781250 +2333.0,2333000.0,677.8645833333334,12.42888064279073,11.548103842954731,0.0,0.0,1.0,7.142857142857143,11.57840062569555,679.6875,42.96875,0.017080698579589285,0.01833534447495828,88.54100304363693,91.42857142857143,85.81005586592178,1.5667963952505155,137.3072655878166,84.72498608238362,182.09002909282734,1.6206230527356333,3.6654761996435528,3.7051331578703945,499.76079618431356,1.414732296440424,9.29911001270403,34.08566642943368,3.6654761996435528,2017-01-10 14:35:48.550781250 +2334.0,2334000.0,677.34375,12.683502405050767,10.910276113848857,0.0,0.0,1.0,7.142857142857143,10.999109009166865,675.78125,42.96875,0.0162385923088046,0.018725355338483254,88.6102460350787,91.42857142857143,85.81005586592178,1.5995046966342763,140.08574836845088,84.57794425488186,187.11861028905025,1.6562917153234675,3.641990808250242,3.6954882633234516,489.49966590921906,1.4039939382192916,9.226026923670906,33.6011052526787,3.641990808250242,2017-01-10 14:35:49.550781250 +2335.0,2335000.0,675.2604166666666,14.073862803484678,10.892422598182554,0.0,0.0,1.0,7.142857142857143,11.244108080139444,675.78125,46.875,0.01665151370140203,0.020842126172534194,88.89055399059625,91.97604790419162,85.81005586592178,1.7855465709268565,142.91863873890912,84.38651572179648,191.31155978674428,1.6936193835763997,3.6748314840525462,3.6793440760986478,486.99912492439125,1.4213856809311607,9.015572962639737,33.130711369881396,3.6748314840525462,2017-01-10 14:35:50.550781250 +2336.0,2336000.0,669.7916666666666,13.028271684887374,10.690426373140633,0.0,0.0,1.0,7.142857142857143,11.048543456039805,667.96875,42.96875,0.016495492562672184,0.01945122988723465,89.61154748725569,91.97604790419162,86.29213483146067,1.6741493638031175,148.55406380444546,84.03271204004452,200.4505129205507,1.7678123221068276,3.6787887061917286,3.6748540552162057,485.2709507775163,1.428494715610267,8.964263501914205,32.97763133016869,3.6787887061917286,2017-01-10 14:35:51.550781250 +2337.0,2337000.0,667.1875,11.35983093749953,8.24402159748558,0.0,0.0,0.0,0.0,8.286407592029853,667.96875,35.15625,0.012419908334658327,0.017026444496486413,89.95397659372975,91.97604790419162,87.27272727272727,1.4734506351710301,151.08926349947993,83.58909693868358,203.19434185599678,1.807523577031952,3.63835771353384,3.662452513173953,470.5267838083848,1.3625778345025745,8.886148517974792,32.33098700398089,3.63835771353384,2017-01-10 14:35:52.550781250 +2338.0,2338000.0,666.1458333333334,11.231176381691574,8.685481259081127,0.0,0.0,0.0,0.0,8.796809581715651,664.0625,35.15625,0.013205531193818646,0.01685993639784818,90.09407304685423,91.97604790419162,87.27272727272727,1.4555452160680773,153.6218403741919,83.37037296736148,206.3987136787945,1.8426430745887756,3.546563008451735,3.6488908757375422,445.8173415484033,1.4246132254220272,8.860976944537246,31.42601305023948,3.546563008451735,2017-01-10 14:35:53.550781250 +2339.0,2339000.0,664.0625,11.436328979865623,8.663043915323673,0.0,0.0,0.0,0.0,9.10128707047996,664.0625,39.0625,0.01370546758848747,0.017221765993209407,90.37769027438506,92.53012048192771,87.27272727272727,1.4877507208267808,160.06499284565658,82.47414615044768,212.48088344085795,1.9407899362021794,3.5701350514898667,3.638939668016193,445.14018448716297,1.4170660197866443,8.731071006703214,31.17110263807806,3.5701350514898667,2017-01-10 14:35:54.550781250 +2340.0,2340000.0,659.8958333333334,7.737737514625194,8.1107376416008,0.0,0.0,0.0,0.0,8.15381651573798,656.25,27.34375,0.01235621760869528,0.011725695365493585,90.93505450695885,92.53012048192771,88.78612716763006,1.0252451024200309,163.30414716569143,82.15108963437996,213.31648105311427,1.9878512615290886,3.612822110357599,3.6272404892796883,447.0850129497986,1.329135947279942,8.563217289016674,30.937380757555896,3.612822110357599,2017-01-10 14:35:55.550781250 +2341.0,2341000.0,659.6354166666666,7.50898519315043,7.792544667281775,0.0,0.0,0.0,0.0,7.8125,656.25,27.34375,0.011843663639952626,0.011383538547847474,90.9702798251563,92.53012048192771,88.78612716763006,0.9951201418801697,169.5561908098893,81.35165815483953,210.15799233821292,2.0842376745064857,3.574790067935922,3.612594215637643,432.68607810152866,1.3588953869317764,8.464705348584243,30.259544608123026,3.574790067935922,2017-01-10 14:35:56.550781250 +2342.0,2342000.0,657.8125,8.956429753454858,8.449220942927857,0.0,0.0,0.0,0.0,8.672025819151814,656.25,35.15625,0.013183127136002759,0.01361547515964634,91.227151284124,93.65853658536585,88.78612716763006,1.197535605367623,172.35029512673296,81.11288440472296,208.2179025087127,2.12482019831485,3.6348504841921048,3.6077728646951175,441.1804499787471,1.3403846598158127,8.348013935402987,30.343782495141987,3.6348504841921048,2017-01-10 14:35:57.550781250 +2343.0,2343000.0,656.25,9.10128707047996,8.18240976485222,0.0,0.0,0.0,0.0,8.481408567840772,656.25,35.15625,0.012924051150995462,0.013868627916921845,91.44490779661736,93.65853658536585,88.78612716763006,1.2194654347977512,175.6810408142785,80.73128517344958,206.20698167044878,2.1761209478683665,3.6482701700166364,3.609955697570345,444.74219697459796,1.3131720962909281,8.353613185220649,30.476237795498154,3.6482701700166364,2017-01-10 14:35:58.550781250 +2344.0,2344000.0,653.6458333333334,7.029182897206542,7.382118613461469,0.0,0.0,0.0,0.0,7.382118613461469,652.34375,27.34375,0.011293759153662167,0.010753809691343872,91.80271446608528,93.65853658536585,89.82456140350877,0.9516422949442298,182.63063906019318,80.00987422585357,200.32240494504404,2.2826012517487473,3.6851819142204416,3.609891544707902,451.4754806353271,1.2669476034873244,8.311058045168972,30.627760796092993,3.6851819142204416,2017-01-10 14:35:59.550781250 +2345.0,2345000.0,654.4270833333334,7.508985193150429,7.975226522297127,0.0,0.0,0.0,0.0,8.019033405803524,652.34375,27.34375,0.012253517022795675,0.011474135750775029,91.69450735090386,93.65853658536585,89.82456140350877,1.0158908168622913,186.01408857730718,79.92006332435506,195.7695241537886,2.327501766638625,3.6631042327415964,3.6117664793724216,448.3912762966679,1.3083328196501787,8.354079619972692,30.601864416582277,3.6631042327415964,2017-01-10 14:36:00.550781250 +2346.0,2346000.0,653.6458333333334,7.904958839611526,8.756894434416974,0.0,0.0,0.0,0.0,8.796809581715651,652.34375,27.34375,0.013458067248521156,0.012093642208808071,91.80532986172638,93.65853658536585,89.82456140350877,1.0700624202324123,191.5152378866424,79.71710565657841,185.62335593765718,2.4024359177275048,3.6990159884219223,3.6122164840586812,455.23700217398266,1.2836402070598072,8.317736276897522,30.767439475720966,3.6990159884219223,2017-01-10 14:36:01.550781250 +2347.0,2347000.0,653.80859375,7.7962069687097415,8.42237584833137,0.0,0.0,0.0,0.0,8.438464451051901,652.34375,27.34375,0.012906628226851602,0.011924295647436557,91.78218739787582,93.65853658536585,89.82456140350877,1.0576036648286702,194.7248930873339,79.57016820954036,180.64596896675585,2.44720977055804,3.6790884403984414,3.6071105778566364,448.91656449728015,1.3103084541419685,8.291348767267117,30.504605404964316,3.6790884403984414,2017-01-10 14:36:02.550781250 +2348.0,2348000.0,653.125,7.411588266019639,7.872062488652838,0.0,0.0,0.0,0.0,7.950785071861053,652.34375,23.4375,0.012173450827729842,0.011347886340317151,91.87705361163185,93.65853658536585,90.3529411764706,1.0057012891329848,200.88333914713888,79.6020981765871,171.7394544709492,2.5235935201293915,3.6616896531383216,3.6104697672266663,447.46305447809283,1.2992829841302609,8.34322826716593,30.550312619652654,3.6616896531383216,2017-01-10 14:36:03.550781250 +2349.0,2349000.0,654.296875,7.941640980196305,8.676214192165459,0.0,0.0,0.0,0.0,8.676214192165459,652.34375,27.34375,0.013260363183249895,0.012137672184658234,91.71414331251566,93.65853658536585,89.82456140350877,1.0765372088557106,202.96925295953864,79.78935142065106,166.2035704639015,2.5438137965238075,3.6305829048604745,3.6088629118301734,440.95674845639115,1.3015531326648,8.363408028865253,30.364046215971026,3.6305829048604745,2017-01-10 14:36:04.550781250 +2350.0,2350000.0,654.4270833333334,8.202682102556166,8.716796513288678,0.0,0.0,0.0,0.0,8.734640537108554,652.34375,27.34375,0.013347003447073954,0.012534142170241016,91.69668300640392,93.65853658536585,89.82456140350877,1.1096471163791914,205.09374588118044,79.9209015234936,160.62342605152617,2.5662091139060905,3.6119149486288245,3.592009547602444,429.15190693912837,1.3217558399823195,8.223865991628678,29.70390451068383,3.6119149486288245,2017-01-10 14:36:05.550781250 +2351.0,2351000.0,654.296875,7.681188126954493,7.924543778491114,0.0,0.0,0.0,0.0,7.941640980196305,652.34375,27.34375,0.012137672184658234,0.0117396069283603,91.71334058204104,93.65853658536585,89.82456140350877,1.0424193733948581,209.74770386372347,80.18065093746569,151.19919097475227,2.6159391500489244,3.4217594849193453,3.5689591640692173,385.3486143624948,1.199964782928397,8.228010757351157,28.15427385098473,3.4217594849193453,2017-01-10 14:36:06.550781250 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+2354.0,2354000.0,654.1666666666666,9.553119426334495,8.572707307889072,0.0,0.0,0.0,0.0,8.734640537108554,656.25,35.15625,0.013352316744624542,0.014603494664460375,91.73818887951175,94.81481481481481,89.82456140350877,1.307472281019542,214.92778870934728,81.52251205262527,134.27466251659501,2.6364225451075995,3.1411496128473293,3.5333079429354046,325.30212850514476,2.70805020110221,8.242323746477604,25.890372045210473,3.1411496128473293,2017-01-10 14:36:09.550781250 +2355.0,2355000.0,653.80859375,9.33966866689026,9.043622580304863,0.0,0.0,0.0,0.0,9.077304717673634,654.296875,35.15625,0.013883734176098284,0.014285019738455005,91.78768460466274,94.81481481481481,89.82456140350877,1.280386048192356,215.26668152331112,82.5180753802747,128.04412103485853,2.6087215501728602,3.057335100943329,3.4984526727745173,300.08265943674786,2.70805020110221,8.025919950941258,24.53792678337407,3.057335100943329,2017-01-10 14:36:10.550781250 +2356.0,2356000.0,655.7291666666666,10.320895302070916,9.05411871628792,0.0,0.0,0.0,0.0,9.160968280905136,656.25,39.0625,0.013970658537996714,0.015739570277979474,91.52255481739323,94.81481481481481,89.30232558139535,1.4052032483452777,215.17543637889972,82.91202858794793,125.82538321676478,2.595225807925505,3.081450605688675,3.4947932963013364,302.36356972304424,2.6741486494265287,7.960842862246613,24.53094405966219,3.081450605688675,2017-01-10 14:36:11.550781250 +2357.0,2357000.0,655.2083333333334,10.278567460144341,10.24797303136549,0.0,0.0,0.0,0.0,10.28210143565194,656.25,39.0625,0.015692873415303437,0.015687479748392,91.59511974670619,94.81481481481481,89.30232558139535,1.3986282040899451,215.25752219215588,83.60753168113582,123.28938807149166,2.574618791679075,3.084172615354788,3.50047140302596,304.75000160150194,2.740840023925201,8.009517817592197,24.7027355152141,3.084172615354788,2017-01-10 14:36:12.550781250 +2358.0,2358000.0,654.6875,9.991800488847272,10.24797303136549,0.0,0.0,0.0,0.0,10.28210143565194,656.25,39.0625,0.01570535780147313,0.01526193869418199,91.66682843017958,94.81481481481481,89.30232558139535,1.360147332994398,214.08562742125866,83.99481413682707,122.36464133826429,2.5487957753262527,3.124252275747763,3.4970431870492957,309.48580606736755,2.367123614131617,7.926629418672437,24.764790000296532,3.124252275747763,2017-01-10 14:36:13.550781250 +2359.0,2359000.0,657.8125,14.06869903049725,11.216379041429564,0.0,0.0,1.0,7.142857142857143,11.436328979865623,656.25,54.6875,0.01738539322354869,0.021387095913344988,91.25002709911243,94.81481481481481,87.27272727272727,1.8701403625080948,212.66085431649566,84.39240497465192,122.71953066483832,2.5199051310407663,3.093570041304244,3.4963377975429983,304.6904405284048,2.456735772821304,7.959374342982645,24.622882014976764,3.093570041304244,2017-01-10 14:36:14.550781250 +2360.0,2360000.0,659.1145833333334,14.798647015758696,11.775082503353344,0.0,0.0,1.0,7.142857142857143,11.857438259417288,656.25,54.6875,0.017989949789080358,0.022452313133351793,91.07386414838301,94.81481481481481,87.27272727272727,1.9669739643906496,210.79189979285354,84.6847215191002,125.91821656954262,2.4891373084967934,2.9915296828781783,3.486129955843872,286.3551199123494,2.538973871058276,7.999416848301969,23.930492947411146,2.9915296828781783,2017-01-10 14:36:15.550781250 +2361.0,2361000.0,659.6354166666666,14.974362701579404,13.276090684565087,0.0,0.0,1.0,7.142857142857143,13.287813504230645,656.25,54.6875,0.02014417838777958,0.02270096832770032,91.00299664431711,94.81481481481481,87.27272727272727,1.9914068547572112,209.51802906005526,84.99560691272639,128.16565807675875,2.4650453908187124,2.8718227942843235,3.4653290783744075,262.96684544481786,2.512305623976115,7.971237128519494,22.8919804843278,2.8718227942843235,2017-01-10 14:36:16.550781250 +2362.0,2362000.0,660.6770833333334,15.545743931696578,12.934516726003638,0.0,0.0,1.0,7.142857142857143,13.08114751086054,656.25,54.6875,0.019799608372764867,0.023530018406667266,90.86290019119264,94.81481481481481,87.27272727272727,2.067080091281568,205.27911436924342,85.33367886069321,135.4873227787291,2.4056048808625783,2.866608765422907,3.4645398646101264,262.0128366965979,2.456735772821304,7.971237128519494,22.850418223878506,2.866608765422907,2017-01-10 14:36:17.550781250 +2363.0,2363000.0,666.9270833333334,15.376559093908245,12.162106427289826,0.0,0.0,1.0,7.142857142857143,12.61456981635861,667.96875,46.875,0.01891446626115465,0.02305583245630912,90.00960147444012,93.65853658536585,87.27272727272727,2.0091781327223264,203.58415207265847,85.44231545864677,140.53091184573546,2.3827087430839953,2.8850201579365016,3.4518681656837398,260.7098025917836,2.4849066497880004,7.830683400706787,22.591679461457836,2.8850201579365016,2017-01-10 14:36:18.550781250 +2364.0,2364000.0,668.75,13.54774646027101,13.41898957188733,0.0,0.0,1.0,7.142857142857143,13.491314066670293,667.96875,39.0625,0.02017392757632941,0.02025831246395665,89.75400312898297,92.53012048192771,87.27272727272727,1.7567300343961205,200.7131489590313,85.00781433807035,152.5083092367417,2.361114099002812,2.706025598778385,3.424051214391505,229.36305164222748,2.512305623976115,7.830683400706789,21.19002973824155,2.706025598778385,2017-01-10 14:36:19.550781250 +2365.0,2365000.0,668.75,13.30420809838,12.922473411729252,0.0,0.0,1.0,7.142857142857143,12.997561047826022,667.96875,35.15625,0.019435605305160406,0.019894142950848598,89.75270714300235,91.97604790419162,87.27272727272727,1.7214023835748362,198.20438080058815,85.01294903277132,157.57900574096138,2.3314610662921846,2.6769958425544336,3.4061560991908135,221.08007063684687,2.456735772821304,7.7124828250213735,20.64628445835469,2.6769958425544336,2017-01-10 14:36:20.550781250 +2366.0,2366000.0,668.4895833333334,13.276872527896916,12.565100724551007,0.0,0.0,1.0,7.142857142857143,12.61456981635861,667.96875,35.15625,0.018870256367283622,0.019861001366234577,89.78752286447657,91.97604790419162,87.27272727272727,1.7172173218989635,195.67105593843957,84.36900962655275,170.02032775307225,2.3192290250241085,2.606629794065589,3.4049884977772837,212.13546550433836,2.4277482359480516,7.805389503935781,20.345760835245837,2.606629794065589,2017-01-10 14:36:21.550781250 +2367.0,2367000.0,671.09375,11.644107520774494,12.886275936111037,0.0,0.0,1.0,7.142857142857143,12.913433555140776,667.96875,35.15625,0.019242368976228396,0.017350940193936383,89.43142904866026,91.97604790419162,87.27272727272727,1.5001193146019065,193.98826214665417,83.9196572610337,175.0697711621536,2.311595024074633,2.5650480128507698,3.397465139989462,205.29379264104577,2.890371757896165,7.800542894087165,20.008767049635477,2.5650480128507698,2017-01-10 14:36:22.550781250 +2368.0,2368000.0,671.09375,11.644107520774494,11.59855451497016,0.0,0.0,0.0,0.0,11.71875,667.96875,35.15625,0.017462165308498253,0.017350940193936383,89.43142904866026,91.97604790419162,87.27272727272727,1.5001193146019067,192.17918678257973,83.89292322016816,180.19655604893478,2.290767557094484,2.593221831042939,3.392916687344184,207.59545797283138,2.833213344056216,7.717533402050026,20.01327609999921,2.593221831042939,2017-01-10 14:36:23.550781250 +2369.0,2369000.0,671.3541666666666,12.07301809809891,13.075194769933015,0.0,0.0,1.0,7.142857142857143,13.122741089625945,667.96875,39.0625,0.019546674082297762,0.017983083590651597,89.39855796494173,91.97604790419162,86.77966101694915,1.5515807441668794,189.99014029209576,82.77820810145191,187.6990111256901,2.295170971317068,2.5742857150129805,3.4058856755328684,208.4145130007169,2.8622008809294686,7.8623880200204015,20.24003316582771,2.5742857150129805,2017-01-10 14:36:24.550781250 +2370.0,2370000.0,672.3958333333334,13.111662369973025,11.775082503353344,0.0,0.0,1.0,7.142857142857143,12.039871099548781,667.96875,39.0625,0.01790592758414691,0.019499916150540825,89.26479355489634,91.97604790419162,86.77966101694915,1.6798055059406702,189.20380563539254,81.552930020859,195.4249566895118,2.320012359911525,2.5618482499767277,3.3995407768678705,205.40002390627086,2.8622008809294686,7.824087463008481,20.04412477477313,2.5618482499767277,2017-01-10 14:36:25.550781250 +2371.0,2371000.0,674.7395833333334,13.161443169710676,13.027474921795482,0.0,0.0,2.0,14.285714285714286,13.03942125619188,675.78125,39.0625,0.019325116798061294,0.019505959772940562,88.95486619412152,91.97604790419162,86.77966101694915,1.6808593551520825,188.29678112395857,80.8228663436895,198.43076410765298,2.329746390374838,2.516432244758195,3.4138423271789446,203.28204722274074,2.740840023925201,8.02543444371494,20.19546201235732,2.516432244758195,2017-01-10 14:36:26.550781250 +2372.0,2372000.0,674.21875,13.271398657316741,12.580580711602115,0.0,0.0,2.0,14.285714285714286,12.657696676577851,667.96875,39.0625,0.018773872243359965,0.019684113883389837,89.0240951431801,91.97604790419162,86.77966101694915,1.6938187578051283,187.78084967201707,80.51864417165291,201.20619277358227,2.3321412277099234,2.527137306838427,3.410212885241287,203.7275212747115,2.740840023925201,7.975024607194422,20.153982207795494,2.527137306838427,2017-01-10 14:36:27.550781250 +2373.0,2373000.0,673.1770833333334,12.757759076995718,12.229133370988352,0.0,0.0,2.0,14.285714285714286,12.308451450955792,667.96875,39.0625,0.018284121304321176,0.018951564741069075,89.1594016970913,91.97604790419162,86.77966101694915,1.628308622886802,187.91671352670136,78.81760742804863,204.9474347888468,2.3841971313103816,2.482858516765676,3.4030412596007995,196.7653346063301,2.740840023925201,7.979664870568771,19.81237888482755,2.482858516765676,2017-01-10 14:36:28.550781250 +2374.0,2374000.0,674.4791666666666,13.177994980698296,11.971784571944719,0.0,0.0,2.0,14.285714285714286,12.130058660314821,671.875,39.0625,0.017984334075524678,0.019538031168294,88.98927942170586,91.97604790419162,86.77966101694915,1.6823359492143843,188.08616669201635,78.43187874801194,205.92068033756965,2.398083148005477,2.578751599812359,3.39800708932582,207.07029884611381,2.772588722239781,7.784644744193017,20.074665088058612,2.578751599812359,2017-01-10 14:36:29.550781250 +2375.0,2375000.0,676.8973214285714,11.303601130761455,12.39642508698093,0.0,0.0,2.0,15.384615384615385,12.400066275218357,673.828125,35.15625,0.01831897672315853,0.016699137037365645,88.66270678844964,91.42857142857143,86.77966101694915,1.427645610840612,189.69234885458894,76.59985230050363,207.63493105911266,2.476406196064451,2.5188071157980483,3.400148374806821,200.3856209627871,2.803360380906535,7.896174553245957,19.88894065229939,2.5188071157980483,2017-01-10 14:36:30.550781250 +2376.0,2376000.0,676.8229166666666,10.89623380284718,11.994523024667789,0.0,0.0,2.0,14.285714285714286,11.994523024667789,675.78125,35.15625,0.017721803930251755,0.016099091113094717,88.67093481372832,91.42857142857143,86.77966101694915,1.3795802488711737,189.8336373803492,76.02647706260541,206.26218098087554,2.4969411278129745,2.5274232823742504,3.394162657431988,200.03145847011973,2.772588722239781,7.8285683279743985,19.78610585978015,2.5274232823742504,2017-01-10 14:36:31.550781250 +2377.0,2377000.0,679.4084821428571,12.952329527430834,13.571622344514681,0.0,0.0,3.0,23.076923076923077,14.042453660514331,677.734375,46.875,0.020668646373422327,0.01906412691018978,88.34190478343707,91.42857142857143,85.33333333333333,1.6219132077658087,192.33231042457766,74.50187137302652,203.28035568216808,2.5815769037744167,2.554182917501962,3.4211133146350865,209.62093379642013,2.803360380906535,8.032868693834743,20.517415996329,2.554182917501962,2017-01-10 14:36:32.550781250 +2378.0,2378000.0,682.8125,12.63183822939367,14.084184669781209,0.0,0.0,3.0,21.428571428571427,14.084184669781209,679.6875,39.0625,0.020626723543844334,0.01849971731535915,87.89972240410346,89.82456140350877,84.86187845303867,1.5595509914218162,192.7950061164973,73.92850187670963,199.85369971135233,2.6078576086665604,2.559467723438706,3.440541026883824,215.0279919308914,2.803360380906535,8.206078998377428,21.003194332335255,2.559467723438706,2017-01-10 14:36:33.550781250 +2379.0,2379000.0,682.4776785714286,12.858129517048653,14.278370637640291,0.0,0.0,3.0,23.076923076923077,14.291011959848696,681.640625,39.0625,0.020939896510260722,0.018840366389657554,87.94369582479042,89.82456140350877,84.86187845303867,1.5828626622926079,195.70838852102332,72.55550637413083,195.52643661298276,2.697360935114386,2.5574315260637874,3.4267198945321704,211.3810101849356,2.803360380906535,8.079750473263104,20.66340858305187,2.5574315260637874,2017-01-10 14:36:34.550781250 +2380.0,2380000.0,684.375,14.099653301208711,14.422821409955452,0.0,0.0,3.0,21.428571428571427,14.465939058946116,683.59375,39.0625,0.021137445200286563,0.020602233134186246,87.70573958764108,89.82456140350877,84.86187845303867,1.7339220732047451,196.18165608563163,71.97920146948579,192.8572885076629,2.7255325438529505,2.6979225457921427,3.4482701721929856,234.7898412800372,2.772588722239781,8.06418265507618,21.75654019851597,2.6979225457921427,2017-01-10 14:36:35.550781250 +2381.0,2381000.0,687.2209821428571,15.883106010264818,13.221153846153847,0.0,0.0,2.0,15.384615384615385,13.704031551672331,685.546875,46.875,0.019941229834020965,0.02311207955371056,87.35116411597618,89.82456140350877,83.93442622950819,1.9311900599380627,198.93458267942026,70.74524232964146,188.12895930574172,2.811985317012179,2.8281862032703624,3.4687486940883114,258.00988550184553,2.772588722239781,8.064182655076179,22.807010125738607,2.8281862032703624,2017-01-10 14:36:36.550781250 +2382.0,2382000.0,687.7604166666666,14.576023890973769,15.615031896912079,0.0,0.0,3.0,21.428571428571427,15.625,683.59375,46.875,0.022718667171525937,0.021193461469647585,87.27593512403679,89.82456140350877,83.93442622950819,1.7709137758575872,199.41908584464528,70.13600979457009,184.88927772723667,2.843319522007998,2.7067025668457223,3.475456464888427,243.43837179544997,2.772588722239781,8.30707198220203,22.484773057198417,2.7067025668457223,2017-01-10 14:36:37.550781250 +2383.0,2383000.0,689.1741071428571,14.431123270661296,15.956632268451626,0.0,0.0,3.0,23.076923076923077,15.959461207799881,685.546875,46.875,0.02315737205212744,0.020939735142530398,87.0959739302413,89.82456140350877,83.93442622950819,1.7470106917328054,201.10812982508153,69.28052342151605,180.48574423710485,2.9028090420377013,2.7066447727631204,3.478136330153068,244.18279325283197,2.772588722239781,8.332830406757413,22.55401186277154,2.7066447727631204,2017-01-10 14:36:38.550781250 +2384.0,2384000.0,690.0111607142857,14.66452584916532,15.368644053538969,0.0,0.0,3.0,23.076923076923077,15.511910455697484,687.5,46.875,0.02248066602232907,0.021252592253703403,86.99149740118378,89.82456140350877,83.93442622950819,1.776798438820527,201.64968648130673,68.62435170242054,178.0941903561615,2.9384567063850904,2.7945873317012286,3.485401846492651,258.33155148719175,2.803360380906535,8.269554027456527,23.109990923948885,2.7945873317012286,2017-01-10 14:36:39.550781250 +2385.0,2385000.0,693.75,14.749465600186042,15.597572387693846,0.0,0.0,3.0,21.428571428571427,15.798423735370609,695.3125,46.875,0.022772502681615292,0.021260490955223123,86.52309317320127,89.82456140350877,83.93442622950819,1.7825525747270932,201.487339314786,68.00347369605072,171.16484582904687,2.962897751596582,2.92072674497168,3.511195996098591,284.33766280800694,2.833213344056216,8.332830406757413,24.33792063032962,2.92072674497168,2017-01-10 14:36:40.550781250 +2386.0,2386000.0,695.5915178571429,13.397774824023337,16.114288074269137,0.0,0.0,3.0,23.076923076923077,16.25098231128601,697.265625,39.0625,0.023362824149077036,0.019260980733774424,86.28727401389358,88.78612716763006,83.93442622950819,1.6032147010373292,201.5484947958437,67.61658900604007,168.23261982516257,2.9807551335936178,2.7644477791850184,3.5154098678304266,263.09798358704296,2.803360380906535,8.60678089880169,23.792996341624367,2.7644477791850184,2017-01-10 14:36:41.550781250 +2387.0,2387000.0,695.3125,13.287813504230645,16.768950001469552,0.0,0.0,3.0,21.428571428571427,16.768950001469552,695.3125,39.0625,0.02411714157514722,0.01911056324203958,86.32154057246284,88.78612716763006,83.93442622950819,1.592888741446209,200.01013065681815,66.84626898727102,162.35551578990416,2.992091162109652,2.852144255421625,3.5157886484628555,275.8363868318322,2.833213344056216,8.477125040320141,24.177983486239903,2.852144255421625,2017-01-10 14:36:42.550781250 +2388.0,2388000.0,695.8705357142857,12.713872606805838,15.728668663825001,0.0,0.0,2.0,15.384615384615385,15.774524937988806,697.265625,39.0625,0.02266876398466395,0.018270456865594278,86.24967313791862,88.78612716763006,83.93442622950819,1.518898560888561,199.35029627549937,66.81639749104377,159.49894335885804,2.9835534952661997,2.8961083849659253,3.5273112909626523,286.0077898871738,2.70805020110221,8.524879494734677,24.688974985525178,2.8961083849659253,2017-01-10 14:36:43.550781250 +2389.0,2389000.0,695.0520833333334,12.994765572786095,15.527538033455945,0.0,0.0,2.0,14.285714285714286,15.590083755244413,695.3125,39.0625,0.022430094275061273,0.018696103334394382,86.3526522362674,88.78612716763006,83.93442622950819,1.5607352095014164,197.2522571615222,66.03842929765577,153.67931535982478,2.9869313861546414,2.8855258915876485,3.5291570923310918,285.04669473079264,2.6741486494265287,8.558665775306679,24.696251692092495,2.8855258915876485,2017-01-10 14:36:44.550781250 +2390.0,2390000.0,693.9174107142857,13.055485597944857,16.18696346292744,0.0,0.0,2.0,15.384615384615385,16.287055568350542,691.40625,39.0625,0.023471172962190732,0.018814177878180287,86.49395937689006,88.78612716763006,83.93442622950819,1.563310033928029,195.17610945831896,65.90131540718431,150.9287293271192,2.9616420894239326,2.886556646538193,3.5336027793188096,286.66291751791806,2.6741486494265287,8.601047696426956,24.827411395313245,2.886556646538193,2017-01-10 14:36:45.550781250 +2391.0,2391000.0,693.2291666666666,11.518628561745203,13.41898957188733,0.0,0.0,1.0,7.142857142857143,13.491314066670293,691.40625,39.0625,0.01946154996845001,0.016615902959091502,86.5737648807831,88.78612716763006,83.93442622950819,1.3893926232067457,193.63578002824673,66.04557800691285,149.60902965877392,2.9318507895862354,2.9831484106223134,3.540244201230761,303.4830153416529,2.639057329615259,8.52559463225127,25.4331140768105,2.9831484106223134,2017-01-10 14:36:46.550781250 +2392.0,2392000.0,694.7544642857143,10.926456355516688,13.958617366991508,0.0,0.0,1.0,7.6923076923076925,13.958617366991508,693.359375,39.0625,0.020091439615782154,0.015727076135811972,86.38134597059297,88.78612716763006,83.93442622950819,1.3133568622948149,189.90839308931697,65.5450529947201,148.83025948656316,2.8973718749546946,2.9790122144034834,3.5472949785848638,305.32046499612716,2.5649493574615367,8.601047696426956,25.622626144322847,2.9790122144034834,2017-01-10 14:36:47.550781250 +2393.0,2393000.0,693.359375,12.517828331696634,14.665185507106283,0.0,0.0,1.0,7.6923076923076925,14.815256052429147,693.359375,42.96875,0.02136735520801049,0.018053881988249795,86.56153629662484,89.30232558139535,83.93442622950819,1.5134538250672385,187.8122217947043,65.67242420834057,149.69453187274388,2.859833850489284,2.937062849245077,3.5520144457318814,300.52256212313176,2.4423470353692043,8.709447619541761,25.580195040802078,2.937062849245077,2017-01-10 14:36:48.550781250 +2394.0,2394000.0,690.625,13.385879878959342,13.404477845384278,0.0,0.0,1.0,7.142857142857143,13.691790341947875,691.40625,42.96875,0.019825216784720906,0.019382269507995428,86.90824710813907,89.30232558139535,83.93442622950819,1.6247534207837206,183.80754132359993,65.4754472713442,152.64188659688213,2.8072743140167082,2.9683914483064173,3.5477982655023124,303.865147380835,2.3513752571634776,8.621415094766574,25.591734839604957,2.9683914483064173,2017-01-10 14:36:49.550781250 +2395.0,2395000.0,687.7790178571429,11.307309053160926,11.981611801308224,0.0,0.0,0.0,0.0,12.015474172555411,689.453125,35.15625,0.017469963259407136,0.016440322777496453,87.25919102371229,89.30232558139535,84.86187845303867,1.3805271794049345,181.56485243543017,65.73791617442531,156.4861151099521,2.761950226010757,2.9446707167784134,3.548641661739377,300.52176938134505,2.995732273553991,8.6644793453793,25.51403860446982,2.9446707167784134,2017-01-10 14:36:50.550781250 +2396.0,2396000.0,686.71875,12.192793035598061,12.238678682735106,0.0,0.0,0.0,0.0,12.352647110032732,687.5,42.96875,0.01798792753224334,0.01775514799267977,87.39779446081607,90.3529411764706,84.86187845303867,1.5032399173515325,177.70381348672458,65.78919367963697,168.59755904067984,2.701109461107856,2.872406949350555,3.5507829753716496,290.2421221711811,inf,8.794446514313808,25.261229283406745,2.872406949350555,2017-01-10 14:36:51.550781250 +2397.0,2397000.0,683.3147321428571,13.042635707431684,11.064875384328106,0.0,0.0,0.0,0.0,11.465615401645982,685.546875,42.96875,0.01677940612474447,0.019087303542439836,87.83713465364086,90.88757396449704,85.33333333333333,1.6240541346233466,175.26410222240122,65.79575686857835,173.10065761833604,2.6637599529780767,2.881327629661615,3.548172293025002,290.72013538055796,inf,8.754469485526315,25.224494811676475,2.881327629661615,2017-01-10 14:36:52.550781250 +2398.0,2398000.0,680.7291666666666,12.393754202386242,11.188581281117983,0.0,0.0,0.0,0.0,11.244108080139444,675.78125,42.96875,0.0165177410205568,0.018206586127453395,88.16803452374594,90.88757396449704,85.33333333333333,1.5497248843190314,171.70582018930133,66.41892791319228,184.8986647565619,2.585194094275598,2.8838995022229934,3.5458123356724838,290.3195960665843,inf,8.72682195321102,25.167277486853955,2.8838995022229934,2017-01-10 14:36:53.550781250 +2399.0,2399000.0,679.6875,12.634492813478749,9.874805724251793,0.0,0.0,0.0,0.0,9.988443592905446,675.78125,42.96875,0.014695641148182725,0.01858867908189977,88.30407537521467,90.88757396449704,85.33333333333333,1.5753141492384566,170.14247337950871,66.56229091246226,192.76920036539323,2.5561390848651433,2.8522521802779677,3.541156499499107,284.0273297948908,inf,8.728192138141923,24.895005055900317,2.8522521802779677,2017-01-10 14:36:54.550781250 +2400.0,2400000.0,676.3020833333334,13.520903266749952,10.410021264608053,0.0,0.0,0.0,0.0,10.646642872956983,675.78125,46.875,0.015742436901099274,0.019992402211905975,88.75059378940654,91.42857142857143,85.33333333333333,1.7007530189732198,166.96904890442624,66.77296682288562,201.39859786873564,2.5005486029594786,2.8062176140424775,3.5416644150392544,277.3410956576693,inf,8.804638775821592,24.70773241799195,2.8062176140424775,2017-01-10 14:36:55.550781250 +2401.0,2401000.0,673.2700892857143,10.233060926668756,11.170450898702589,0.0,0.0,0.0,0.0,11.206764542651152,673.828125,35.15625,0.016645273154107635,0.015199042835135027,89.13637862466213,91.42857142857143,86.77966101694915,1.3040725784868887,165.62931953943098,67.1903821472802,203.47657721990947,2.465074825387572,2.792465771310149,3.5426848750379927,275.62856572871306,inf,8.836667560984848,24.67609169649693,2.792465771310149,2017-01-10 14:36:56.550781250 +2402.0,2402000.0,673.6979166666666,9.778637164332597,9.503005784557145,0.0,0.0,0.0,0.0,9.568319307746789,675.78125,31.25,0.014202685018070224,0.01451486923503563,89.0782530381651,91.42857142857143,87.27272727272727,1.2527293517027098,164.483454588597,67.80167928308441,210.4016049439405,2.425949568326302,2.7404538712943225,3.530197196807643,264.1388865052574,inf,8.79280332268481,24.09627190518117,2.7404538712943225,2017-01-10 14:36:57.550781250 +2403.0,2403000.0,674.7395833333334,10.384063561133795,9.474289534793328,0.0,0.0,0.0,0.0,9.511194502823276,675.78125,31.25,0.014096096831663981,0.015389735266211412,88.9429464842539,91.42857142857143,87.27272727272727,1.3285345124440109,163.7935784881091,67.84081186032972,212.73207879841533,2.414381166683653,2.7024550918402355,3.5237579714267118,256.75370211675107,inf,8.78900580862362,23.751893499728308,2.7024550918402355,2017-01-10 14:36:58.550781250 +2404.0,2404000.0,672.65625,11.644107520774494,10.920974213605428,0.0,0.0,1.0,7.142857142857143,11.146754662632912,671.875,31.25,0.01657124967267146,0.017310636035530024,89.22356370223935,91.42857142857143,87.27272727272727,1.4924499034726821,163.16387120496688,68.21302573710895,212.4370233030531,2.3919752780619317,2.6095029555647105,3.5236217891606083,243.58334168550732,inf,8.942768877342036,23.336181816356152,2.6095029555647105,2017-01-10 14:36:59.550781250 +2405.0,2405000.0,671.09375,11.068255413649691,11.20596296206652,0.0,0.0,1.0,7.142857142857143,11.292470058508963,667.96875,31.25,0.016826963533051773,0.016492860220572298,89.42891129582223,91.42857142857143,87.27272727272727,1.4200543864554274,163.49355322502527,69.02993504764245,211.99139098305125,2.3684442570051205,2.617833284180901,3.5225911024861505,244.46040619145114,inf,8.917940435944438,23.345681299558084,2.617833284180901,2017-01-10 14:37:00.550781250 +2406.0,2406000.0,669.2708333333334,11.404517154739086,10.835093660127008,0.0,0.0,1.0,7.142857142857143,10.849454294209588,667.96875,35.15625,0.016210857778118606,0.017040212402411707,89.67399393314231,91.97604790419162,87.27272727272727,1.4694828597854035,163.45012853759417,68.98718120352828,212.2358377234073,2.3692826070886728,2.554466211498058,3.5162864399624785,233.9340630126829,2.9444389791664407,8.96258210858684,22.894613164162102,2.554466211498058,2017-01-10 14:37:01.550781250 +2407.0,2407000.0,668.2291666666666,12.910619864388313,10.719516022488028,0.0,0.0,1.0,7.142857142857143,10.849454294209588,667.96875,42.96875,0.016236128016276236,0.01932064703010566,89.82088294156979,93.0909090909091,87.27272727272727,1.678681727208737,163.99007486138777,69.9024589746586,211.78189836644788,2.345984351149053,2.566910576356686,3.522807026704153,237.42126927569203,2.995732273553991,9.008202748595686,23.123250909335635,2.566910576356686,2017-01-10 14:37:02.550781250 +2408.0,2408000.0,665.625,13.912894680578056,10.791897101458547,0.0,0.0,1.0,7.142857142857143,10.849454294209588,664.0625,42.96875,0.01629964964388295,0.02090200139805154,90.17741649752715,93.0909090909091,87.27272727272727,1.8113851007751325,164.79126272995367,70.01494687898321,208.4454129400026,2.35365832691105,2.611852230118658,3.5273065834942474,244.9492958262957,inf,8.976747289482159,23.445937427245596,2.611852230118658,2017-01-10 14:37:03.550781250 +2409.0,2409000.0,664.0625,14.465939058946116,10.21373059024728,0.0,0.0,1.0,7.142857142857143,10.228963604812144,664.0625,42.96875,0.015403615781364169,0.021784002347589447,90.3926028242293,93.0909090909091,87.27272727272727,1.8848800907639796,165.36697276558812,70.52070902374857,202.61868736878145,2.34494200433945,2.6407325158422563,3.5309421760195607,250.06776210443394,inf,8.964971023281906,23.674090484764157,2.6407325158422563,2017-01-10 14:37:04.550781250 +2410.0,2410000.0,660.4166666666666,18.170611738529363,9.437240248486475,0.0,0.0,1.0,7.142857142857143,10.013592882213372,656.25,54.6875,0.015162538118177978,0.027513860045722698,90.91581062586847,94.81481481481481,87.27272727272727,2.411396719425015,166.22670448740053,70.58047086658024,199.5470310882643,2.3551373693953157,2.779474917771453,3.555591745090894,277.804970229312,inf,8.989892563169605,24.987180892790033,2.779474917771453,2017-01-10 14:37:05.550781250 +2411.0,2411000.0,658.203125,17.17565879983453,10.74987887528661,0.0,0.0,1.0,6.666666666666667,10.862840424278234,656.25,54.6875,0.01650378129741975,0.026094769452567595,91.21498627340424,94.81481481481481,87.27272727272727,2.2851823948829044,166.6139384245227,70.9324471208354,191.7315855443543,2.3489100572082506,2.7663703840298677,3.5648922666736413,278.8121035938864,inf,9.108166871198176,25.196563085284616,2.7663703840298677,2017-01-10 14:37:06.550781250 +2412.0,2412000.0,656.25,15.832880534722218,12.825719793037058,0.0,0.0,2.0,14.285714285714286,12.828754389031975,656.25,54.6875,0.01954857811662015,0.024126294148148143,91.47780354011603,94.81481481481481,87.27272727272727,2.113061562164841,167.33629408833218,70.77060946396197,188.34502951852073,2.364488526463004,2.718748097732556,3.5691615789597506,272.98214697619073,inf,9.232861331579242,25.101824181859538,2.718748097732556,2017-01-10 14:37:07.550781250 +2413.0,2413000.0,657.470703125,16.056461530128285,11.24927659711207,0.0,0.0,1.0,6.666666666666667,11.276372445109878,656.25,54.6875,0.017151140562632772,0.02442156198566857,91.30957376683565,94.81481481481481,87.27272727272727,2.147203691298138,167.1432202648,71.07399733183485,181.7148434646822,2.3516789056401457,2.619290415244692,3.5484854387947307,252.06828117675718,inf,9.185248307369132,24.058832853134497,2.619290415244692,2017-01-10 14:37:08.550781250 +2414.0,2414000.0,660.15625,20.351096572333113,13.158288083204907,0.0,0.0,2.0,14.285714285714286,13.450860260261104,656.25,70.3125,0.020375267613176585,0.030827696582942467,90.96700006651078,94.81481481481481,85.33333333333333,2.6682520037289894,167.29729416471238,70.8427998294199,178.1249343430706,2.3615285472559266,2.5296306109250617,3.552647766775543,240.38620727652426,inf,9.391508114074304,23.757046408113453,2.5296306109250617,2017-01-10 14:37:09.550781250 +2415.0,2415000.0,663.28125,24.22414974366637,13.220265335359525,0.0,0.0,2.0,14.285714285714286,13.5718599408653,656.25,78.125,0.020461696966204458,0.03652168630376084,90.5698442811197,94.81481481481481,84.3956043956044,3.132982746055079,166.40780467814244,71.00783415582478,171.30775819341218,2.3435133130939465,2.564757079069213,3.5571660079393514,246.6904628217428,inf,9.375617781814752,24.046182076556576,2.564757079069213,2017-01-10 14:37:10.550781250 +2416.0,2416000.0,670.0520833333334,29.523552631250837,14.006585265637424,0.0,0.0,3.0,21.428571428571427,14.541087299994846,660.15625,89.84375,0.02170142838397987,0.04406157874232538,89.70560918680295,94.81481481481481,83.02702702702703,3.7687044769729705,165.45154400859371,70.74427366438925,167.59421730316302,2.3387270154683564,2.6606696972346824,3.576559474441447,266.54232539880945,inf,9.412917757575508,25.044665040143393,2.6606696972346824,2017-01-10 14:37:11.550781250 +2417.0,2417000.0,672.3958333333334,28.737846001096496,15.251841929371034,0.0,0.0,3.0,21.428571428571427,15.4143162019343,675.78125,89.84375,0.022924467163217548,0.04273947662440377,89.38460605200672,94.81481481481481,83.02702702702703,3.6704702488312475,163.52847637357485,70.71971754986673,159.42546141981902,2.312346288123474,2.5602121225771532,3.575573812130566,251.30482700846042,inf,9.584929876570463,24.539453664047638,2.5602121225771532,2017-01-10 14:37:12.550781250 +2418.0,2418000.0,676.5625,27.70803263576544,15.01520540968048,0.0,0.0,3.0,21.428571428571427,15.272245740406259,679.6875,89.84375,0.022573296244480383,0.04095413599743622,88.82393254741963,94.81481481481481,83.02702702702703,3.54952809765847,161.65834151980715,70.4919289554976,155.0982554182096,2.2932886631867317,2.5862672402616185,3.561478252828048,251.04364067767315,inf,9.383015542717553,24.266985712996,2.5862672402616185,2017-01-10 14:37:13.550781250 +2419.0,2419000.0,679.9479166666666,27.122296263648018,14.251777666430954,0.0,0.0,3.0,21.428571428571427,15.020389323320057,683.59375,89.84375,0.022090499809095755,0.03988878500666733,88.37592823968589,94.81481481481481,83.02702702702703,3.4768208801544334,158.38962280438676,70.30135879057272,147.19448597637722,2.2530094087687322,2.609683946625355,3.568519773896867,256.5321245932283,inf,9.416856200581798,24.57501845433775,2.609683946625355,2017-01-10 14:37:14.550781250 +2420.0,2420000.0,687.5,19.58697407900027,15.386513034556069,0.0,0.0,3.0,21.428571428571427,15.590083755244413,687.5,78.125,0.02267648546217369,0.028490144114909483,87.33968079336975,93.0909090909091,83.02702702702703,2.4343693716228842,155.6183872657612,70.2139107263421,142.8684701529904,2.2163469554100477,2.629975836089084,3.566864859735635,259.0359654754968,inf,9.362601941685487,24.62341686955357,2.629975836089084,2017-01-10 14:37:15.550781250 +2421.0,2421000.0,690.8482142857143,15.614264856170026,14.738880370710001,0.0,0.0,2.0,15.384615384615385,14.815256052429147,687.5,58.59375,0.021445023300446713,0.02260158531684708,86.89069071582932,90.3529411764706,83.02702702702703,1.8800757879563057,150.49101909542193,70.00507690820845,133.79011654150762,2.149715788367002,2.6040775554368096,3.569021657577835,255.85369911876123,inf,9.432436284915768,24.562795622636916,2.6040775554368096,2017-01-10 14:37:16.550781250 +2422.0,2422000.0,690.3645833333334,15.447277966599193,15.162252420974918,0.0,0.0,2.0,14.285714285714286,15.164819466184365,687.5,58.59375,0.021966392587758565,0.02237553654912897,86.95093530898406,90.3529411764706,83.02702702702703,1.8665557614660298,147.07382416557368,69.81825861686659,129.3932704376615,2.1065238102349895,2.5878219227479855,3.568194532257559,253.22049162946047,inf,9.453009208699328,24.462704466210706,2.5878219227479855,2017-01-10 14:37:17.550781250 +2423.0,2423000.0,692.5223214285714,15.73194854472059,13.789415751903565,0.0,0.0,1.0,7.6923076923076925,13.789415751903565,695.3125,58.59375,0.019911871899606113,0.022716882991248424,86.6813220376955,90.3529411764706,83.02702702702703,1.8971394756471147,139.6626363736716,69.74497879995926,121.88424749076191,2.0024758595776246,2.633904777817643,3.57360928727595,261.6403664758538,inf,9.428543677437977,24.833886239866217,2.633904777817643,2017-01-10 14:37:18.550781250 +2424.0,2424000.0,692.2433035714286,15.56317180050717,13.631365651699225,0.0,0.0,1.0,7.6923076923076925,13.661139328929453,695.3125,58.59375,0.019734592243000063,0.02248222802620625,86.7153736471661,90.3529411764706,83.02702702702703,1.8768083220172371,135.78635862053997,69.66470475987785,118.96465737629873,1.9491413778120785,2.65100099130206,3.5750220190857562,264.62193739194953,inf,9.413390457739817,24.954907434981607,2.65100099130206,2017-01-10 14:37:19.550781250 +2425.0,2425000.0,689.0625,14.969509561700983,13.32584144671825,0.0,0.0,1.0,7.142857142857143,13.810679320049756,687.5,58.59375,0.02004270921730577,0.021724458320835895,87.11289205450299,90.3529411764706,83.02702702702703,1.8141970190015104,127.3064432879932,69.50547087967843,114.17731176772162,1.831603205859501,2.588421601291504,3.5673077153034316,253.0500228790104,inf,9.442268776510401,24.440572466319825,2.588421601291504,2017-01-10 14:37:20.550781250 +2426.0,2426000.0,685.8258928571429,11.639786619820187,12.338019932480655,0.0,0.0,0.0,0.0,12.352647110032732,685.546875,39.0625,0.01801134550136587,0.016971926462748393,87.50921608552311,90.3529411764706,85.33333333333333,1.4343867821270198,122.69659837921772,69.63801986592952,113.12954876020366,1.7619196900692917,2.5658270516050403,3.568615759382709,250.1203047153586,inf,9.498044468603585,24.3703394348907,2.5658270516050403,2017-01-10 14:37:21.550781250 +2427.0,2427000.0,688.0208333333334,12.340877076764418,12.506102026236032,0.0,0.0,0.0,0.0,12.657696676577851,687.5,42.96875,0.01839725784937886,0.017936778188787042,87.23298017322305,90.3529411764706,84.86187845303867,1.5190528420966383,113.65082762779102,69.57760424483892,116.02643056629762,1.6334397951941746,2.592829952084494,3.57408431748103,255.6837994053354,inf,9.508130852367469,24.652966462357043,2.592829952084494,2017-01-10 14:37:22.550781250 +2428.0,2428000.0,689.1741071428571,12.700677094577056,12.952080566257395,0.0,0.0,0.0,0.0,12.955565587325781,691.40625,42.96875,0.018798683022257328,0.018428836723467276,87.08844629200813,90.3529411764706,84.86187845303867,1.5610520704569482,109.3474539928321,69.56394454968233,119.03167798152317,1.5718984123267552,2.6088089706842212,3.565859908895105,255.61914225002718,inf,9.389637445648479,24.49577039968023,2.6088089706842212,2017-01-10 14:37:23.550781250 +2429.0,2429000.0,688.28125,12.012682797405057,13.027474921795482,0.0,0.0,0.0,0.0,13.03942125619188,691.40625,42.96875,0.01894490261966584,0.01745316002347159,87.1986187873713,90.3529411764706,84.86187845303867,1.4801313300077814,104.5785945741189,69.62044750093847,121.45865295326011,1.502124710886255,2.602555428975652,3.5666421342552996,254.93006041261438,inf,9.409381601277488,24.48843716970834,2.602555428975652,2017-01-10 14:37:24.550781250 +2430.0,2430000.0,688.6160714285714,12.008494518075915,11.41826923076923,0.0,0.0,0.0,0.0,11.668562403213702,691.40625,42.96875,0.01694494637484518,0.01743859171506648,87.15610705051022,90.3529411764706,84.86187845303867,1.477683586129339,95.08662490992137,69.86203768674537,128.1340064236595,1.36106286129644,2.605646977401097,3.5675876719890014,255.66259887885042,inf,9.414040381710377,24.529665865735513,2.605646977401097,2017-01-10 14:37:25.550781250 +2431.0,2431000.0,690.625,9.476754337211508,10.967212057496399,0.0,0.0,0.0,0.0,10.999109009166865,691.40625,35.15625,0.015926311687481435,0.013721997230351505,86.89316130899071,89.30232558139535,84.86187845303867,1.1568023694042864,90.99790354139526,69.92456613963307,135.25186154206648,1.3013724441232946,2.6516841103045685,3.5609307901140386,260.46422102598757,inf,9.260714823085536,24.556490346437897,2.6516841103045685,2017-01-10 14:37:26.550781250 +2432.0,2432000.0,690.0111607142857,10.899567513741411,11.08932817743145,0.0,0.0,0.0,0.0,11.414314851696261,691.40625,39.0625,0.01654221772279798,0.015796219154569035,86.9754423466993,89.82456140350877,84.86187845303867,1.3356378335952384,82.35242391560115,70.19318748171011,143.7117903737313,1.1732253067587124,2.614323205117089,3.565539500137464,256.3354473430589,inf,9.376270324030106,24.51260108556263,2.614323205117089,2017-01-10 14:37:27.550781250 +2433.0,2433000.0,685.6770833333334,13.276872527896915,10.791897101458547,0.0,0.0,0.0,0.0,11.146754662632912,687.5,46.875,0.016256565858150544,0.01936315628831149,87.5356663258975,90.88757396449704,84.86187845303867,1.6531377336341087,78.8136226854295,70.217639077981,145.2529859317333,1.1224191488110575,2.659710687671171,3.5674787739953784,263.6276659887131,inf,9.316701826079974,24.779731420670426,2.659710687671171,2017-01-10 14:37:28.550781250 +2434.0,2434000.0,683.3333333333334,13.80804846388339,10.293452425053973,0.0,0.0,0.0,0.0,10.387561645639668,687.5,46.875,0.015201309725326342,0.02020690019104886,87.83852534660075,90.88757396449704,84.86187845303867,1.723879690760502,70.7691711180567,70.43058057509327,148.68955190626662,1.004807436488507,2.6956270789780805,3.5668277357304126,268.78410568838825,inf,9.247492149895724,24.92779025189614,2.6956270789780805,2017-01-10 14:37:29.550781250 +2435.0,2435000.0,679.4270833333334,18.468087773156803,9.241347746472764,0.0,0.0,0.0,0.0,9.737683240298631,687.5,62.5,0.014332197639994918,0.02718185398578847,88.37140553608585,93.0909090909091,84.86187845303867,2.35055189527179,67.48828667683125,70.44893637153154,150.89518919511607,0.9579745295360245,2.777998405953324,3.580090540695669,285.52442042263675,inf,9.249521855647894,25.695156970820282,2.777998405953324,2017-01-10 14:37:30.550781250 +2436.0,2436000.0,677.0833333333334,19.56841701744043,9.503005784557143,0.0,0.0,0.0,0.0,9.904151422599803,683.59375,62.5,0.01462766979337817,0.02890104667191202,88.68496024485013,93.0909090909091,84.86187845303867,2.4924956373111877,60.78831899647274,70.52645310366461,146.2801924724865,0.8619222479134447,2.892174302165773,3.579504783179159,303.10219820100957,inf,9.058998104388039,26.200201520879535,2.892174302165773,2017-01-10 14:37:31.550781250 +2437.0,2437000.0,672.9166666666666,17.498372320137854,9.523464378279025,0.0,0.0,0.0,0.0,9.625105084073851,667.96875,50.78125,0.014303561734846591,0.026003773107325604,89.22043768899276,93.0909090909091,86.29213483146067,2.243547764025654,58.15979812123146,70.40006709317869,143.1837021372494,0.8261327087125285,2.8528296990812074,3.5779237787204474,296.3982668996661,inf,9.104665076808386,25.97405893130645,2.8528296990812074,2017-01-10 14:37:32.550781250 +2438.0,2438000.0,672.3958333333334,16.889839980546068,9.621060072302829,0.0,0.0,0.0,0.0,9.625105084073851,667.96875,50.78125,0.014314641178483186,0.025118894471455034,89.28581051841054,93.0909090909091,86.29213483146067,2.170362683666409,52.61282620290362,70.43570848786132,141.3411040953116,0.7469624048996505,2.889595918077678,3.569591783531207,299.26199340216834,inf,8.960192556973182,25.891335837819696,2.889595918077678,2017-01-10 14:37:33.550781250 +2439.0,2439000.0,670.0520833333334,15.685337369904992,10.094895376744164,0.0,0.0,0.0,0.0,10.28210143565194,667.96875,50.78125,0.015345227171746386,0.023409131558661162,89.59086367817898,93.0909090909091,86.29213483146067,2.0162281366284542,50.316256354290694,70.18970785356616,141.50734472899438,0.7168608887682419,2.8854857907053115,3.5675155740474973,297.91076521639195,inf,8.945164378795841,25.811144710538702,2.8854857907053115,2017-01-10 14:37:34.550781250 +2440.0,2440000.0,669.0104166666666,14.349947436957823,10.690426373140635,0.0,0.0,0.0,0.0,10.748526944340526,667.96875,50.78125,0.01606630730489203,0.02144951271230753,89.72311216067929,93.0909090909091,86.29213483146067,1.8540570073075187,46.38712424166842,69.90325520340627,135.9399316295298,0.663590330760563,2.841888973057289,3.5638375453566793,289.9542194460708,inf,8.975429250287677,25.5071734148484,2.841888973057289,2017-01-10 14:37:35.550781250 +2441.0,2441000.0,667.6897321428571,13.913497270381944,10.234006229523077,0.0,0.0,0.0,0.0,10.447919862207758,667.96875,50.78125,0.015647866605161976,0.02083826753742118,89.8981396526759,93.0909090909091,86.29213483146067,1.7954235468212636,43.344926821389485,69.49199967852229,131.40943007782658,0.6237398120921535,2.8750640455463374,3.545710096973199,288.9524185187709,inf,8.739192291249989,25.125737543688558,2.8750640455463374,2017-01-10 14:37:36.550781250 +2442.0,2442000.0,675.0,23.58584008846349,11.289022502312449,0.0,0.0,1.0,7.142857142857143,12.039871099548781,667.96875,82.03125,0.0178368460734056,0.03494198531624221,88.9872199843206,93.0909090909091,82.58064516129032,2.9147690109088447,42.05063983324669,69.48015864680417,129.9699094031314,0.6052179593746634,3.010304999712592,3.580145763567483,322.0984475733105,1.32004497557869,8.886027245555294,26.749652244877414,3.010304999712592,2017-01-10 14:37:37.550781250 +2443.0,2443000.0,679.1294642857143,25.677107132629825,12.490751016121711,0.0,0.0,1.0,7.6923076923076925,13.000785849028807,673.828125,82.03125,0.01914330997654858,0.037808854545334954,88.46369777088624,93.0909090909091,82.58064516129032,3.166471295409927,40.428316085432634,68.86006324488169,127.21177543968278,0.5871083205610858,3.026235708851336,3.588652351060411,327.85386122064585,1.2780807764796578,8.949830460917681,27.0842965289945,3.026235708851336,2017-01-10 14:37:38.550781250 +2444.0,2444000.0,684.1145833333334,26.487990736819004,11.890220844119707,0.0,0.0,1.0,7.142857142857143,12.700677094577056,679.6875,82.03125,0.01856513134494705,0.038718646528125227,87.82710581191856,93.0909090909091,82.58064516129032,3.2765537610292594,39.98585554004521,68.31197364854333,125.6507199598593,0.5853418281510574,3.0960528211193443,3.5921416746716957,340.6303804166487,1.2780807764796578,8.883961448190274,27.50521390438499,3.0960528211193443,2017-01-10 14:37:39.550781250 +2445.0,2445000.0,691.40625,23.93306220161043,11.883245152214657,0.0,0.0,1.0,7.6923076923076925,12.818947720974485,691.40625,74.21875,0.01854039896366931,0.03461505041588853,86.87671831522758,91.97604790419162,82.58064516129032,2.910858582298366,39.8275639009393,67.54240117069929,124.36259639789822,0.5896675748954122,3.188664176245999,3.5804392131007874,351.2633027677725,1.2636920390275583,8.63684695394926,27.54000447777738,3.188664176245999,2017-01-10 14:37:40.550781250 +2446.0,2446000.0,696.1495535714286,22.106570233795132,11.852814256800494,0.0,0.0,1.0,7.6923076923076925,12.680858080534371,699.21875,66.40625,0.018215709563380835,0.031755490067303306,86.26973352703999,90.88757396449704,82.58064516129032,2.6593727898534145,39.88015454462054,67.25280280557705,124.91365497017851,0.5929887362451073,3.2742388739144515,3.5880047764041705,368.6955779780225,1.2376111634747657,8.597797589067591,28.151243096173054,3.2742388739144515,2017-01-10 14:37:41.550781250 +2447.0,2447000.0,700.8928571428571,19.119106189810566,13.789415751903565,0.0,0.0,2.0,15.384615384615385,13.789415751903565,707.03125,58.59375,0.019674070881696808,0.027278215200748836,85.66498012490486,89.82456140350877,82.58064516129032,2.278697729278084,41.3241782450985,66.26215396649178,126.54567743889748,0.6236467692552795,3.2507602698947466,3.5960101137147684,368.11411614628537,1.256616537811358,8.708685237396844,28.30984797274856,3.2507602698947466,2017-01-10 14:37:42.550781250 +2448.0,2448000.0,702.0089285714286,18.72924102671577,13.578273470787263,0.0,0.0,2.0,15.384615384615385,13.789415751903565,707.03125,58.59375,0.01964279254961144,0.026679491192269204,85.52645198637671,89.82456140350877,82.58064516129032,2.2352846551002163,42.20257150331481,65.91819876513533,127.36677945980234,0.6402264062718305,3.233427984648209,3.599742073300211,366.7463715042578,1.2606681480024813,8.76959321991806,28.35584813126425,3.233427984648209,2017-01-10 14:37:43.550781250 +2449.0,2449000.0,705.6361607142857,13.584209224531175,13.47816195433041,0.0,0.0,2.0,15.384615384615385,13.531646934131853,707.03125,46.875,0.0191765213965712,0.019251010621083326,85.05912217766367,88.27586206896552,82.58064516129032,1.5886872213137733,45.07217130667172,65.21376063584793,130.6876257438687,0.691145102923195,3.3049095849044967,3.595027198739951,376.9232900824904,1.2173958246580767,8.627278477292403,28.512375331243934,3.3049095849044967,2017-01-10 14:37:44.550781250 +2450.0,2450000.0,706.4732142857143,12.147325656472525,12.861138557110996,0.0,0.0,2.0,15.384615384615385,12.86464821430748,707.03125,39.0625,0.01820967582941469,0.01719431878072572,84.95225067394821,87.27272727272727,82.58064516129032,1.408847460394158,47.01525256988562,64.70718922898722,132.86282295389546,0.7265846829400519,3.304909584904496,3.595027198739951,376.9232900824902,1.2093636529608123,8.627278477292403,28.512375331243927,3.304909584904496,2017-01-10 14:37:45.550781250 +2451.0,2451000.0,702.34375,17.481754678858348,11.244108080139444,0.0,0.0,1.0,7.142857142857143,11.903308598047735,703.125,62.5,0.016947981096219245,0.024890596205716,85.47856286319431,90.3529411764706,82.58064516129032,2.092902702664098,50.834588381468535,64.12788301805172,137.65606828838898,0.7927064794445003,3.2611142038392185,3.595119648408384,369.49522998717526,1.2253639943072536,8.685940076301769,28.325842556524,3.2611142038392185,2017-01-10 14:37:46.550781250 +2452.0,2452000.0,697.265625,17.519600759070155,11.170450898702589,0.0,0.0,1.0,7.6923076923076925,11.567533987331826,699.21875,54.6875,0.01658985266530503,0.025126150108246273,86.10156160703245,90.3529411764706,83.47826086956522,2.11274817658228,53.13361530755434,63.909868524013454,140.06672372254164,0.831383580261786,3.302165091748155,3.5899884795235035,374.27637350691344,1.1826954058786512,8.580939844536397,28.33568000901893,3.302165091748155,2017-01-10 14:37:47.550781250 +2453.0,2453000.0,692.96875,18.54660890860991,10.71951602248803,0.0,0.0,1.0,7.142857142857143,10.949451378542737,695.3125,54.6875,0.01580078665674713,0.026763990307802354,86.64206061770938,90.3529411764706,83.47826086956522,2.246319513202557,58.319230730889615,63.36919798231075,145.1297120504052,0.9203088028219828,3.3077884110028246,3.5851771023990073,373.16004187593467,1.1863178690872151,8.526282132150236,28.203137225667003,3.3077884110028246,2017-01-10 14:37:48.550781250 +2454.0,2454000.0,688.6160714285714,19.224060900697754,10.39159643376838,0.0,0.0,1.0,7.6923076923076925,11.101534376148537,689.453125,54.6875,0.016121515074601443,0.027916950675891714,87.19400649137641,90.3529411764706,83.47826086956522,2.332240405135577,60.772907988968186,63.28912631252129,146.44909344674335,0.9602424860294636,3.3297011823608136,3.590225930066162,379.07125712564425,1.1884686177899708,8.547721104501646,28.46135706814961,3.3297011823608136,2017-01-10 14:37:49.550781250 +2455.0,2455000.0,682.8125,19.874220840522458,10.849454294209588,0.0,0.0,1.0,7.142857142857143,11.531237901658384,675.78125,66.40625,0.016887854135151866,0.029106410384289183,87.94071024869491,91.97604790419162,83.47826086956522,2.4496157755855417,65.78622632864754,63.469794200110314,146.23190995610224,1.0364966068935701,3.3359357624745143,3.5949577486891173,382.2129460665175,1.2192402764567243,8.58637878735385,28.64360806688626,3.3359357624745143,2017-01-10 14:37:50.550781250 +2456.0,2456000.0,678.90625,17.973601829439037,10.849454294209588,0.0,0.0,1.0,7.142857142857143,11.531237901658384,671.875,62.5,0.016985022455837436,0.026474350220577637,88.43466445502372,91.97604790419162,83.93442622950819,2.2378553967125248,68.40321682718482,63.490381771097105,145.85143117778554,1.0773792016844383,3.395754625485994,3.5947380648122067,392.4401958751474,1.276293465905562,8.50826269910472,28.891972415334795,3.395754625485994,2017-01-10 14:37:51.550781250 +2457.0,2457000.0,674.4791666666666,14.886764118092891,7.757499316103708,0.0,0.0,0.0,0.0,8.15381651573798,671.875,50.78125,0.012089056146885654,0.022071495835319192,88.99746240867587,91.97604790419162,85.33333333333333,1.8731473256211844,70.5981806570087,63.694470197584195,144.79333500881606,1.1083879093115738,3.390854908980851,3.5890901696873287,389.0531138567411,1.245215762859985,8.459223312557965,28.68399889555243,3.390854908980851,2017-01-10 14:37:52.550781250 +2458.0,2458000.0,671.09375,13.62795880389702,8.375184720872564,0.0,0.0,0.0,0.0,8.919847858898315,667.96875,42.96875,0.013291507868905521,0.020307086459823265,89.44031004673307,91.97604790419162,86.29213483146067,1.7346398813117834,75.12957295360995,64.25369582200938,145.18209519077388,1.169264615715306,3.393676019129178,3.6001365644473657,394.5243850747882,1.2299482907291965,8.563929848425918,29.063203356107614,3.393676019129178,2017-01-10 14:37:53.550781250 +2459.0,2459000.0,666.1458333333334,9.99906989722163,8.69891588622762,0.0,0.0,0.0,0.0,8.980734899236094,664.0625,39.0625,0.013481634876101094,0.015010331667447639,90.08910176207623,91.97604790419162,86.77966101694915,1.2921923029526794,76.6418108038448,64.66018242941459,146.70196760759157,1.1853014935042876,3.462682028628646,3.5965878919490444,404.9606877808598,1.2869218873066868,8.443599940593735,29.23750177122383,3.462682028628646,2017-01-10 14:37:54.550781250 +2460.0,2460000.0,664.5833333333334,7.212849964687692,8.201416549497248,0.0,0.0,0.0,0.0,8.220379387642987,664.0625,23.4375,0.01236922290303647,0.010853191169436024,90.2920951211802,91.97604790419162,88.78612716763006,0.9502948039196845,80.19153068721897,65.41005973676727,151.3670900744928,1.2259816152123613,3.47155188665493,3.5940372134163767,405.3256592821335,1.3121863889661687,8.408079028526963,29.189082614626532,3.47155188665493,2017-01-10 14:37:55.550781250 +2461.0,2461000.0,664.3229166666666,7.302951514179207,8.631533648581485,0.0,0.0,0.0,0.0,8.672025819151814,664.0625,23.4375,0.013053931456504497,0.010993074799862076,90.32773730704864,91.97604790419162,88.78612716763006,0.9618641811448795,83.16705479856655,66.47832404625422,154.58541374935243,1.2510401847781343,3.445088470746934,3.5957635592386397,401.49704051596893,1.349926716949016,8.457102586335889,29.13546661610985,3.445088470746934,2017-01-10 14:37:56.550781250 +2462.0,2462000.0,663.28125,7.263044376573189,8.663043915323675,0.0,0.0,0.0,0.0,8.734640537108554,664.0625,23.4375,0.013168833789751411,0.010950172911676893,90.46948703019369,91.97604790419162,88.78612716763006,0.9569643790085653,83.77867837720345,67.00217384162755,156.8465211001605,1.2503874661623722,3.4636494484863034,3.6029439976438375,408.1059208311255,1.2840155119994723,8.504426692282548,29.456352822416644,3.4636494484863034,2017-01-10 14:37:57.550781250 +2463.0,2463000.0,661.71875,6.40162071415178,8.540863763958688,0.0,0.0,0.0,0.0,8.545420106648189,660.15625,23.4375,0.012913976076162553,0.009674232011941297,90.68085348127391,91.97604790419162,88.78612716763006,0.8442605192270621,85.57888462406638,68.06770126210694,166.51807967590358,1.2572612713117706,3.571191581555597,3.5925486676994502,422.17650239771865,1.2351878236738605,8.275757722186817,29.554316308467286,3.571191581555597,2017-01-10 14:37:58.550781250 +2464.0,2464000.0,662.5,7.352530844643127,8.823319364309093,0.0,0.0,0.0,0.0,8.980734899236094,660.15625,23.4375,0.013555826262997877,0.01109815976549906,90.57639921401788,91.97604790419162,88.78612716763006,0.9664518708360094,85.7832110322125,68.71555507813821,169.43284279704014,1.2483812571209856,3.5207166040547726,3.5957627313361913,414.7897046197221,1.3274538610969573,8.365768454288514,29.4534999026912,3.5207166040547726,2017-01-10 14:37:59.550781250 +2465.0,2465000.0,663.0208333333334,7.152151690891616,9.156718237400636,0.0,0.0,0.0,0.0,9.160968280905136,660.15625,23.4375,0.013817014217861634,0.01078722014651367,90.5046754641124,91.97604790419162,88.78612716763006,0.9386437542627666,86.73296738841627,69.91546387986291,171.0574718127223,1.2405405410375479,3.5100777533068945,3.5959846018305104,413.0165038931994,1.349926716949016,8.38057739523536,29.416478274882277,3.5100777533068945,2017-01-10 14:38:00.550781250 +2466.0,2466000.0,663.5416666666666,7.212849964687692,9.453724523491863,0.0,0.0,0.0,0.0,9.453724523491863,660.15625,23.4375,0.014247371338386482,0.010870229146154136,90.43380796004654,91.97604790419162,88.78612716763006,0.9471498398913655,86.25665020074038,70.38609158258846,171.00958720886507,1.2254786174556942,3.5058344152847787,3.5943025582591015,411.4701918983866,1.3217558399823195,8.369424342438501,29.341815895843077,3.5058344152847787,2017-01-10 14:38:01.550781250 +2467.0,2467000.0,666.6666666666666,12.585736476218209,9.777575595946,0.0,0.0,0.0,0.0,10.334966058846057,660.15625,46.875,0.015502449088269086,0.018878604714327313,90.02929873540205,91.97604790419162,85.81005586592178,1.60679725539785,86.22683662613544,71.52845066880995,168.79891944437293,1.2054900647209843,3.404010330184178,3.5924077537278567,392.8169153154925,1.3581234841531944,8.475170635212775,28.84956839233789,3.404010330184178,2017-01-10 14:38:02.550781250 +2468.0,2468000.0,670.8333333333334,17.056794681055013,10.278314986826933,0.0,0.0,0.0,0.0,11.097757701646954,667.96875,58.59375,0.01654324129438055,0.025426277785423623,89.49319062375649,91.42857142857143,83.93442622950819,2.1256124015703723,85.57441930221498,71.86811794999643,163.8197909494583,1.1907146276149176,3.3771530407098918,3.5974731446210253,390.4475346642023,1.31633577251298,8.558570059257004,28.90360089974843,3.3771530407098918,2017-01-10 14:38:03.550781250 +2469.0,2469000.0,676.0416666666666,18.527009823658872,11.598554514970159,0.0,0.0,0.0,0.0,11.71875,667.96875,58.59375,0.01733436055469954,0.027405130093547794,88.81298055423743,91.42857142857143,83.93442622950819,2.3066684054229296,84.7027888224197,72.23013390647179,157.50063622952337,1.1726793824319681,3.353574421085661,3.5908614802111445,383.4358034185137,1.3449091449570363,8.523476626498784,28.58411319334782,3.353574421085661,2017-01-10 14:38:04.550781250 +2470.0,2470000.0,678.90625,20.415262038659773,12.40610160458466,0.0,0.0,0.0,0.0,12.657696676577851,675.78125,58.59375,0.018644248269297642,0.03007081174854374,88.4510500870438,91.42857142857143,83.93442622950819,2.5342427975583752,83.93540327609983,72.9711981449365,149.6874541988391,1.1502538728963456,3.1364367634257415,3.5747249481749304,340.421732569935,1.3588953869317764,8.651356626412937,27.134432976588435,3.1364367634257415,2017-01-10 14:38:05.550781250 +2471.0,2471000.0,683.3147321428571,22.852488871873007,11.583134175118019,0.0,0.0,0.0,0.0,12.541248049705052,679.6875,62.5,0.01835354553292891,0.033443577017881934,87.89787936022107,91.42857142857143,83.47826086956522,2.8123647365390205,82.98573932708697,73.10708517139594,145.93888479935254,1.1351258107546078,3.1996211186099965,3.56432421667433,346.5851416515699,1.3658158297763499,8.463555368469038,27.080170495478544,3.1996211186099965,2017-01-10 14:38:06.550781250 +2472.0,2472000.0,689.5833333333334,23.570431554716926,11.945744770428375,0.0,0.0,0.0,0.0,12.484295380132307,691.40625,66.40625,0.01810411414641543,0.0341806862424898,87.104291402477,91.42857142857143,83.02702702702703,2.8845583596858093,82.29558050093621,73.6001100695087,134.4249943184758,1.1181448020011848,3.2913836803970433,3.5766790094167944,366.7813729891741,1.3015531326648,8.464284603081984,27.859208208820007,3.2913836803970433,2017-01-10 14:38:07.550781250 +2473.0,2473000.0,694.1964285714286,21.087548691619624,12.868156914281164,0.0,0.0,0.0,0.0,12.955565587325781,697.265625,66.40625,0.018662679688495016,0.03037691901558068,86.50581128140156,91.42857142857143,83.02702702702703,2.5610935163053723,81.94123738723479,73.63287044512147,130.50755722270335,1.1128350272356358,3.389629214601554,3.5715457231785033,381.06683486995036,1.2947271675944,8.291570031826376,28.105348014793424,3.389629214601554,2017-01-10 14:38:08.550781250 +2474.0,2474000.0,697.1354166666666,20.12851658864915,12.279955962424882,0.0,0.0,0.0,0.0,12.657696676577851,703.125,66.40625,0.018156725901404165,0.028873180313938266,86.13494456672862,91.42857142857143,83.02702702702703,2.454838830927758,81.69952298576776,73.65444316859056,124.70830171612184,1.1092273523643712,3.4151259712416224,3.5720090581115196,385.5801081183592,1.3083328196501787,8.264967950431325,28.22590669899766,3.4151259712416224,2017-01-10 14:38:09.550781250 +2475.0,2475000.0,703.125,14.77558996772145,11.852814256800492,0.0,0.0,0.0,0.0,12.305045210001966,707.03125,54.6875,0.01750050874311391,0.021014172398537173,85.36904571888954,89.82456140350877,83.02702702702703,1.7641395541650533,82.2246000742397,73.69934719813708,121.3311709310319,1.1156760975532511,3.4372355049793577,3.56964651026378,388.2729978570631,1.2836402070598072,8.215965732256779,28.240209122606725,3.4372355049793577,2017-01-10 14:38:10.550781250 +2476.0,2476000.0,706.1941964285714,10.853317372236301,11.322982981620656,0.0,0.0,0.0,0.0,11.516687437942789,710.9375,39.0625,0.016308102638319617,0.015368743367086093,84.98126307546781,87.77142857142857,83.02702702702703,1.269458541111652,83.42701049256002,73.16497927633093,115.72899571063938,1.1402587866179972,3.546705660484524,3.551652364711144,398.62442376164705,1.2700345550040286,7.922342555316736,28.0982171852393,3.546705660484524,2017-01-10 14:38:11.550781250 +2477.0,2477000.0,707.3102678571429,9.124287579915636,10.850643080088814,0.0,0.0,0.0,0.0,10.888023396630672,710.9375,31.25,0.015393560494486914,0.01289997896900104,84.84154762742807,86.77966101694915,83.02702702702703,1.0575740899172474,84.71551184517344,73.12455562397274,114.33473094141114,1.1585097662788502,3.515385306422997,3.5551673684107046,394.95087825577815,1.2729656758128873,7.989824249063568,28.08731076606022,3.515385306422997,2017-01-10 14:38:12.550781250 +2478.0,2478000.0,706.1941964285714,9.087458821818974,10.443598075109835,0.0,0.0,0.0,0.0,10.447919862207758,707.03125,31.25,0.01479468383490818,0.012868215099723114,84.97552117288619,86.77966101694915,83.02702702702703,1.0529664085135038,88.27300714538613,72.54574443441466,112.52641563042087,1.2167909755918183,3.555228602387454,3.558091919587694,403.0391708079215,1.3268709406490897,7.971723061307165,28.341297837870908,3.555228602387454,2017-01-10 14:38:13.550781250 +2479.0,2479000.0,706.7522321428571,8.594335332213959,9.114032985638882,0.0,0.0,0.0,0.0,9.192943834602376,708.984375,27.34375,0.013007307818087215,0.012160322870373007,84.90702252533025,86.29213483146067,83.02702702702703,0.9935536067567737,90.49187538871536,72.3575728797543,111.26718816945377,1.250620657758893,3.540751888735942,3.559135441711418,401.06149105462185,1.3268709406490897,7.9976055739676575,28.31753704139108,3.540751888735942,2017-01-10 14:38:14.550781250 +2480.0,2480000.0,703.6830357142857,13.344478211207646,11.048543456039805,0.0,0.0,1.0,7.6923076923076925,11.71875,705.078125,54.6875,0.016653449643140365,0.018963762850502858,85.29480516875199,89.82456140350877,83.02702702702703,1.5954280121743514,96.43409247668156,71.85472724212244,111.39681738860764,1.3420702600642576,3.5221135732918016,3.5667150877274576,401.3863302244118,1.3663601462190733,8.089019354067988,28.49044486158294,3.5221135732918016,2017-01-10 14:38:15.550781250 +2481.0,2481000.0,695.5729166666666,22.120092734981167,10.719516022488028,0.0,0.0,1.0,7.142857142857143,12.085049010997608,703.125,78.125,0.01737423743999656,0.03180125649656596,86.34456613416286,93.0909090909091,83.02702702702703,2.759323443952491,99.51854892271342,71.54836102519715,112.21237213125126,1.3909270246968484,3.5985901856411253,3.575867241146159,418.92107309898654,1.3997173814520312,8.087372252613575,29.10313841588157,3.5985901856411253,2017-01-10 14:38:16.550781250 +2482.0,2482000.0,689.453125,23.574818085755027,11.032187350171766,0.0,0.0,1.0,7.6923076923076925,11.516687437942789,697.265625,66.40625,0.016704090561548746,0.03419350385242655,87.12299346385736,93.0909090909091,84.3956043956044,2.959654538950193,107.20982178350764,71.08125399127135,114.31031637626913,1.50827139032568,3.7013844928505035,3.579882561611475,439.023312755867,1.414465238086587,8.011229788471155,29.652641707709154,3.7013844928505035,2017-01-10 14:38:17.550781250 +2483.0,2483000.0,684.375,25.835305884272564,10.849454294209588,0.0,0.0,1.0,7.142857142857143,11.531237901658384,695.3125,66.40625,0.016849297390551063,0.03775021864368594,87.78983120726448,93.0909090909091,84.3956043956044,3.252511276811902,111.27596186142753,70.81138018142308,116.29211771971752,1.5714417876947424,3.7345828877643314,3.5859224496453916,448.04783091970125,1.4282585602189228,8.031195207156367,29.993164188941083,3.7345828877643314,2017-01-10 14:38:18.550781250 +2484.0,2484000.0,678.0133928571429,27.22390829832694,10.674470954526209,0.0,0.0,1.0,7.6923076923076925,11.966531100805215,681.640625,70.3125,0.01764940224908884,0.04015246392642129,88.62693335467111,93.65853658536585,84.3956043956044,3.4409158807282014,120.1149413326057,70.47310656298642,122.50220243798753,1.7044082088995343,3.8371886170385503,3.5908006651051276,469.26704227168057,1.4350845252893227,7.967714563857001,30.573623628244363,3.8371886170385503,2017-01-10 14:38:19.550781250 +2485.0,2485000.0,671.875,26.53458354457308,11.625372017856952,0.0,0.0,1.0,7.142857142857143,12.264096526694146,660.15625,70.3125,0.018253539016475008,0.03949333364773668,89.43123803891523,93.65853658536585,84.3956043956044,3.37951772898086,124.4617383257913,70.36454549575481,125.12063160645133,1.7688132204776374,3.9671342280594666,3.600170278960869,498.65534910924396,1.414465238086587,7.92112196948111,31.424154089762325,3.9671342280594666,2017-01-10 14:38:20.550781250 +2486.0,2486000.0,669.53125,24.2960316516475,12.580580711602115,0.0,0.0,1.0,7.142857142857143,12.657696676577851,660.15625,66.40625,0.018905311255565518,0.03628812195345251,89.72393955705026,93.65853658536585,84.86187845303867,3.1100970014161646,133.7516734988255,70.1041770534241,130.6888820346426,1.907898774660713,3.8955980399011563,3.6070876987544995,489.1083277385734,1.414465238086587,8.05743459218758,31.38852640395771,3.8955980399011563,2017-01-10 14:38:21.550781250 +2487.0,2487000.0,663.8020833333334,21.31715906168806,12.859060961833606,0.0,0.0,1.0,7.142857142857143,13.287813504230645,656.25,66.40625,0.020017733956942202,0.03211372726437118,90.47314980397655,93.65853658536585,84.86187845303867,2.733034221238528,138.2433479981292,70.09111690002875,132.45356099617635,1.972337638666912,3.9344953531187863,3.6077034195979163,496.80420703631177,1.3791258716412782,8.02319227428095,31.567212720336943,3.9344953531187863,2017-01-10 14:38:22.550781250 +2488.0,2488000.0,660.6770833333334,19.07198119991421,12.527870714644893,0.0,0.0,1.0,7.142857142857143,13.122741089625945,656.25,66.40625,0.01986256436112086,0.02886732668808457,90.88421593986122,93.65853658536585,84.86187845303867,2.449785948749439,146.88893259927715,69.98488778350215,135.6816418134415,2.0988664446198335,3.9838590850580955,3.608094830050559,506.4112763279883,1.4004789961118471,7.976923727501651,31.77894006262294,3.9838590850580955,2017-01-10 14:38:23.550781250 +2489.0,2489000.0,655.46875,9.815719728814738,10.694066906661162,0.0,0.0,0.0,0.0,10.697706201272776,656.25,35.15625,0.01632069599240662,0.01497511472334072,91.5566493969591,93.65853658536585,88.78612716763006,1.320751630605625,150.92778327518067,70.02095600408086,136.10781148007882,2.155465904612678,4.005351362368708,3.6104314814675282,511.89002940580076,1.3131720962909281,7.976923727501651,31.950382319460005,4.005351362368708,2017-01-10 14:38:24.550781250 +2490.0,2490000.0,653.3203125,9.621329792662065,10.81278098543057,0.0,0.0,0.0,0.0,10.862840424278234,652.34375,35.15625,0.01662712794388776,0.014726818696092608,91.85711478863573,93.65853658536585,88.78612716763006,1.3011232817667753,157.8518240793162,70.09322461969224,138.16275575003388,2.2520268533196965,4.04556901451875,3.6208141039697286,525.8676723966989,1.3362839405452291,8.032620578170249,32.49652091643125,4.04556901451875,2017-01-10 14:38:25.550781250 +2491.0,2491000.0,653.90625,9.659016305097682,11.38515946601739,0.0,0.0,0.0,0.0,11.388577919619728,652.34375,35.15625,0.017416224297626345,0.01477125552034054,91.77486183515083,93.65853658536585,88.78612716763006,1.3028946517661264,161.08017180089246,70.18887821768439,140.27473625015918,2.294952931165491,4.081790432855636,3.6232383423971863,534.4354111432528,1.3362839405452291,8.01925139132578,32.732903607777814,4.081790432855636,2017-01-10 14:38:26.550781250 +2492.0,2492000.0,655.7291666666666,11.706342638130387,12.752672859404507,0.0,0.0,1.0,7.142857142857143,12.828754389031975,656.25,39.0625,0.01956410518422668,0.017852404976338637,91.52818277946909,93.65853658536585,88.27586206896552,1.5653068343665586,165.80420563164012,70.24643034101076,142.90962958385077,2.3603221519833086,3.9739522724521783,3.6326598679550943,518.9958728912837,1.3168658546881278,8.215965732256779,32.64985569209105,3.9739522724521783,2017-01-10 14:38:27.550781250 +2493.0,2493000.0,662.7604166666666,21.378425172870518,14.303577698634259,0.0,0.0,2.0,14.285714285714286,14.984064265733876,656.25,70.3125,0.022608568479535594,0.03225664151820149,90.61559899830328,93.65853658536585,84.3956043956044,2.7336937181507284,167.55879660881465,70.34294585218925,144.9769672928679,2.38202700468223,3.861034657423257,3.6541362818852243,509.47595361502675,1.3322271398496148,8.543902813572611,32.98830487285993,3.861034657423257,2017-01-10 14:38:28.550781250 +2494.0,2494000.0,664.5833333333334,20.504047627773982,17.578125,0.0,0.0,3.0,21.428571428571427,17.686299097924643,660.15625,70.3125,0.026612613062707926,0.03085248545871947,90.36000065284614,93.65853658536585,84.3956043956044,2.6138417928975106,169.7450559626205,70.47624457337345,148.83469678323962,2.4085428641972744,3.685437550089745,3.670819521191722,484.33220217328216,1.371905623667791,8.914669379774834,32.85445727885743,3.685437550089745,2017-01-10 14:38:29.550781250 +2495.0,2495000.0,671.09375,22.929768305710624,18.93213778313323,0.0,0.0,4.0,28.571428571428573,19.136638615493577,664.0625,74.21875,0.028515596539967148,0.03416775719593667,89.5015460051486,93.65853658536585,83.93442622950819,2.8871880847170175,170.05079525188876,70.50906440372556,152.17739655488788,2.4117579305577004,3.4931518865571904,3.6843592892162405,453.94773047408205,1.4395388756387029,9.300598967282637,32.4884048286752,3.4931518865571904,2017-01-10 14:38:30.550781250 +2496.0,2496000.0,674.4791666666666,27.04986628427253,18.33255612832054,0.0,0.0,4.0,28.571428571428573,18.936249443032533,664.0625,85.9375,0.028075365969592637,0.04010482105467433,89.08784720472595,93.65853658536585,82.58064516129032,3.3650920687092976,169.36722104500666,70.45265513983229,160.06897911289556,2.4039863467012244,3.5337710392590083,3.691433439141422,465.6657863266199,1.4017985476558559,9.32261017653006,32.94396985212324,3.5337710392590083,2017-01-10 14:38:31.550781250 +2497.0,2497000.0,681.25,28.96199981071256,21.37539031840938,0.0,0.0,5.0,35.714285714285715,21.52238925856796,679.6875,85.9375,0.0315924979942282,0.04251302724508266,88.22049724241452,93.65853658536585,82.58064516129032,3.583581534516649,166.90878572705435,70.19057412385364,165.0776995462575,2.3779373200814424,3.446313290509718,3.7139520276987863,460.2652068010742,1.3862943611198906,9.68810071749642,33.38823026250465,3.446313290509718,2017-01-10 14:38:32.550781250 +2498.0,2498000.0,684.1145833333334,28.007982426677845,21.37539031840938,0.0,0.0,5.0,35.714285714285715,21.52238925856796,679.6875,85.9375,0.031460211173544333,0.04094048439986407,87.84156970423247,93.65853658536585,82.58064516129032,3.464981758921155,164.94029868730775,70.12176159583818,166.86528548113995,2.3521984464391603,3.4736972127294137,3.712263410809844,464.8572895858322,1.410199881973445,9.631096491976166,33.45551303970564,3.4736972127294137,2017-01-10 14:38:33.550781250 +2499.0,2499000.0,689.7321428571429,28.774970119732245,22.178656354862902,0.0,0.0,6.0,46.15384615384615,22.828629703392522,691.40625,85.9375,0.03309781911689272,0.041719050529579436,87.13294937800606,93.65853658536585,82.58064516129032,3.5508028817642217,160.21996775900442,69.67897459753682,167.87692918943722,2.2994019169258593,3.464228223397779,3.7265210798488515,470.61989580013693,1.3451362886263831,9.803864513253808,33.96282414518177,3.464228223397779,2017-01-10 14:38:34.550781250 +2500.0,2500000.0,700.0558035714286,22.683099735430478,22.174585009008524,0.0,0.0,6.0,46.15384615384615,22.570086488237887,707.03125,70.3125,0.03224041051169573,0.032401845138215556,85.79251897481588,91.42857142857143,82.58064516129032,2.725529774583328,157.27768001140925,69.39239343275544,167.64353566346017,2.266497410322341,3.553848344625975,3.7304927691356164,491.2411292360229,1.2939210409888755,9.723820824830046,34.55698454176184,3.553848344625975,2017-01-10 14:38:35.550781250 +2501.0,2501000.0,703.125,18.8897039008434,21.805051149671556,0.0,0.0,5.0,38.46153846153846,21.856748910959535,707.03125,58.59375,0.031085154006698007,0.026865356658977282,85.39119643462662,89.82456140350877,82.58064516129032,2.235741472936787,150.93380044268713,68.71001361563002,162.74250488195537,2.1966783660824802,3.607251305817872,3.725783422504448,499.63897668027903,1.2729656758128873,9.59938743395591,34.62740285618913,3.607251305817872,2017-01-10 14:38:36.550781250 +2502.0,2502000.0,701.3020833333334,19.523808106035812,20.22831357186591,0.0,0.0,5.0,35.714285714285715,20.351096572333113,703.125,58.59375,0.029019016278410376,0.027839369894978653,85.61752515016018,89.82456140350877,82.58064516129032,2.32001068162913,147.4596014442754,68.2488459053449,160.99090116377664,2.16061677656482,3.468407250385464,3.715602964476819,465.58245965282276,1.3705460041517514,9.675567525473081,33.558808556944975,3.468407250385464,2017-01-10 14:38:37.550781250 +2503.0,2503000.0,698.9397321428571,20.809397378917417,18.586138334365522,0.0,0.0,4.0,30.76923076923077,18.607985823787303,697.265625,58.59375,0.026623162152676128,0.029772806469477057,85.91498582803077,89.82456140350877,82.58064516129032,2.463894758442236,140.95953464101257,67.29610508640036,157.74186350317578,2.094616537763024,3.4461281044855308,3.711287903822105,458.8186665183273,1.3464484525726907,9.658690541535387,33.28508492771367,3.4461281044855308,2017-01-10 14:38:38.550781250 +2504.0,2504000.0,700.0,18.51131561514663,17.865860037480047,0.0,0.0,4.0,28.571428571428573,17.90068630842125,695.3125,50.78125,0.025572409012030357,0.026444736593066613,85.77000681954065,88.78612716763006,82.58064516129032,2.1813836840719034,137.7522337239037,66.75539889734603,155.29678410178246,2.0635369722789605,3.4001642462242905,3.705095459775085,446.47544042280015,1.3948781048112822,9.654677922435821,32.82749068067729,3.4001642462242905,2017-01-10 14:38:39.550781250 +2505.0,2505000.0,698.6607142857143,19.066412181871318,15.922645792658377,0.0,0.0,3.0,23.076923076923077,16.39479911743238,695.3125,50.78125,0.023466038353385642,0.027289944592582587,85.93756722587615,88.78612716763006,82.58064516129032,2.2429535075879157,131.54915341432252,65.59854103178029,152.29498443718367,2.005367060688002,3.3217090096163044,3.704319412124267,430.72698660702497,1.449095262358921,9.759305710688366,32.4175737067934,3.3217090096163044,2017-01-10 14:38:40.550781250 +2506.0,2506000.0,694.0104166666666,18.062321067246852,15.845168375558798,0.0,0.0,3.0,21.428571428571427,16.139681767834343,687.5,54.6875,0.023255676543521155,0.026026008592205597,86.50793667691998,89.30232558139535,82.58064516129032,2.144258487816505,129.05602098018056,64.93625013318675,150.83504722709367,1.9874264484857334,3.2573360216348126,3.6946911751424687,413.6560976452822,1.4748477584613358,9.746626307745517,31.748036961632984,3.2573360216348126,2017-01-10 14:38:41.550781250 +2507.0,2507000.0,693.0803571428571,16.69378624343452,14.138570139445422,0.0,0.0,3.0,23.076923076923077,14.167277925632552,691.40625,50.78125,0.020441032240526194,0.0240863646926205,86.61625401234433,89.30232558139535,83.02702702702703,1.9911302296270281,124.84178058645732,63.751978171414386,150.26889102789355,1.9582416760588437,3.2852490774544316,3.6919276350952246,417.6536397856282,1.491654876777717,9.67430277295351,31.782494259860368,3.2852490774544316,2017-01-10 14:38:42.550781250 +2508.0,2508000.0,693.359375,16.76644997163807,12.684417619714804,0.0,0.0,2.0,15.384615384615385,12.773083718048246,691.40625,50.78125,0.01842202496800198,0.024181471508390683,86.58181980052014,89.30232558139535,83.02702702702703,2.000583780467007,123.65077402538978,63.03015085670039,149.99259964042503,1.96177182419428,3.2792477257165182,3.6911335590988448,416.12913009570696,1.4384801142904609,9.67430277295351,31.724435366100803,3.2792477257165182,2017-01-10 14:38:43.550781250 +2509.0,2509000.0,689.5833333333334,15.475475029250388,12.825719793037058,0.0,0.0,2.0,14.285714285714286,13.328762179125208,683.59375,50.78125,0.01932871856791571,0.0224417764774628,87.04939803270271,89.30232558139535,83.02702702702703,1.8607793729666122,122.64090757224153,61.74894513130275,150.13875298474096,1.9861215007229407,3.2262011844669414,3.6937088205955337,407.2785893026473,1.4799798451972137,9.782473853945168,31.560228734614785,3.2262011844669414,2017-01-10 14:38:44.550781250 +2510.0,2510000.0,685.546875,11.021952458507348,13.717202176097748,0.0,0.0,2.0,15.384615384615385,13.746789944348405,683.59375,35.15625,0.02005229758263927,0.016077605865401034,87.54221902433117,89.30232558139535,84.86187845303867,1.3460277400999758,123.02946853896161,61.13961337301313,149.72353007053243,2.0122709607003584,3.223206927814934,3.693379598004002,406.55759698231526,1.4718165345580525,9.783307717805009,31.533625212974417,3.223206927814934,2017-01-10 14:38:45.550781250 +2511.0,2511000.0,686.1979166666666,10.916220796965186,11.784995581441336,0.0,0.0,1.0,7.142857142857143,11.903308598047735,683.59375,35.15625,0.01734675712201264,0.01590826863770259,87.45888007813983,89.30232558139535,84.86187845303867,1.3372508931795224,125.84320610217284,59.973194509108055,151.03769461907436,2.0983242118787118,3.2475480534522916,3.6932282147107975,411.1000090338818,1.5125880864441827,9.744876129880845,31.646953506728238,3.2475480534522916,2017-01-10 14:38:46.550781250 +2512.0,2512000.0,689.453125,12.97819541345806,12.762476311054868,0.0,0.0,1.0,7.6923076923076925,13.045849366642074,687.5,42.96875,0.01892202514368482,0.018823898163429255,87.05397415464357,89.30232558139535,83.93442622950819,1.5702831067433305,128.0776533385576,59.46690224104461,151.31514939173073,2.153763665364718,3.2615752151924573,3.701212516623069,417.58737075761843,1.449095262358921,9.813695258843818,32.008105225696724,3.2615752151924573,2017-01-10 14:38:47.550781250 +2513.0,2513000.0,689.3229166666666,12.774833935063592,14.479386992554824,0.0,0.0,2.0,14.285714285714286,14.503561850856055,687.5,42.96875,0.02104030128722601,0.018532437593745447,87.0697042853749,89.30232558139535,83.93442622950819,1.551341927108706,135.0147877022462,58.51573745353716,154.8596000985182,2.307324381059899,3.181334893963072,3.7096772705657393,406.2119718629122,1.6274564179367788,10.033996598343322,31.92150350421638,3.181334893963072,2017-01-10 14:38:48.550781250 +2514.0,2514000.0,688.8950892857143,13.14508311908909,14.221345632451,0.0,0.0,2.0,15.384615384615385,14.24988666031175,687.5,42.96875,0.02068513316749992,0.019081400526049126,87.12524496065446,89.30232558139535,83.93442622950819,1.591318537607654,138.7749711544372,58.14844702180462,156.1195328279356,2.3865636704347253,3.1726602838040536,3.708174643014005,403.85241059703066,1.667706820558076,10.030337449155208,31.822853258087193,3.1726602838040536,2017-01-10 14:38:49.550781250 +2515.0,2515000.0,690.3645833333334,12.216607107184448,13.819132251160482,0.0,0.0,2.0,14.285714285714286,13.889373286443798,691.40625,42.96875,0.020118896046753744,0.017695877514744728,86.9359398753295,89.30232558139535,83.93442622950819,1.482555236699764,148.73365376476977,57.448846486756665,161.87651514933637,2.588975460091378,3.1450199975142885,3.7025306412316703,396.0047885147397,1.6902900090631954,10.009067628528655,31.478717848195537,3.1450199975142885,2017-01-10 14:38:50.550781250 +2516.0,2516000.0,691.1272321428571,12.10930576460179,12.959049671182587,0.0,0.0,1.0,7.6923076923076925,13.18011550448396,691.40625,42.96875,0.019070461836120513,0.017521094816444416,86.83939079605348,89.30232558139535,83.93442622950819,1.4629817069325546,153.94813661685168,57.25465089270374,164.88853092888726,2.6888319851142453,3.108958198897299,3.7023751067830277,389.14358688457924,1.6607312068216509,10.065146966849147,31.292121185691936,3.108958198897299,2017-01-10 14:38:51.550781250 +2517.0,2517000.0,686.4583333333334,14.197229920854111,13.181932824796558,0.0,0.0,1.0,7.142857142857143,13.369585436061316,687.5,54.6875,0.019476179087433782,0.02068185238849764,87.44011520398105,90.88757396449704,83.93442622950819,1.750044663951559,166.25045446592105,56.876294205063516,171.79698321834618,2.9230183996607906,3.1137932108580997,3.704516289751041,391.0144399665385,1.670681537674819,10.082152678136696,31.393738560016853,3.1137932108580997,2017-01-10 14:38:52.550781250 +2518.0,2518000.0,684.8958333333334,13.980623083417822,12.859060961833608,0.0,0.0,1.0,7.142857142857143,12.871163610123656,687.5,54.6875,0.018792877666492333,0.02041277286704351,87.63851995742827,90.88757396449704,83.93442622950819,1.7224794716767091,172.3007717817983,56.84044037248784,173.7536339741925,3.0313060675229404,3.077367332547714,3.700502345690227,382.4020235410791,1.7619065060783738,10.094888161638973,31.065679054350426,3.077367332547714,2017-01-10 14:38:53.550781250 +2519.0,2519000.0,686.9791666666666,13.760607042408967,12.515436100449667,0.0,0.0,1.0,7.142857142857143,12.527870714644893,691.40625,54.6875,0.01823617268545731,0.020030603124659,87.371722737134,90.88757396449704,83.93442622950819,1.6970719752348276,186.07491729263086,56.78267197144322,177.69055269008004,3.2769665609644165,3.0209347915918947,3.6968744419309414,370.3814668531942,1.663505133704376,10.146273250684928,30.65122986799229,3.0209347915918947,2017-01-10 14:38:54.550781250 +2520.0,2520000.0,686.71875,13.786983541680588,12.715992036948483,0.0,0.0,1.0,7.142857142857143,12.743512552086202,691.40625,54.6875,0.01855710587789572,0.020076608570365362,87.40496949038075,90.88757396449704,83.93442622950819,1.6996898499409794,193.1604505684594,56.843096285094816,178.90402076576018,3.3981338665942658,3.003668009695625,3.696444959526967,367.02899412950546,1.7047480922384253,10.170364635637737,30.548398903004774,3.003668009695625,2017-01-10 14:38:55.550781250 +2521.0,2521000.0,683.8727678571429,14.329094051649,12.677297541904437,0.0,0.0,1.0,7.6923076923076925,13.18011550448396,685.546875,54.6875,0.019272759676895353,0.020952865394169733,87.77120807584554,90.88757396449704,83.93442622950819,1.7632889024210299,205.55030120274418,57.112694329327766,177.92008334420325,3.599030016295216,2.990847982278771,3.6992723813158994,365.8707577113474,1.7206635475443248,10.22536995096433,30.58252708589564,2.990847982278771,2017-01-10 14:38:56.550781250 +2522.0,2522000.0,682.2916666666666,11.404517154739088,12.21639468693618,0.0,0.0,0.0,0.0,12.219580602934258,683.59375,35.15625,0.01790961431880441,0.016715017509235916,87.96203224885038,90.88757396449704,86.29213483146067,1.4307074928443588,211.9272500320956,57.3366673964599,177.79105230295076,3.696190581965712,2.934542707799928,3.6944936909971187,353.6368366641924,1.6739764335716716,10.266363494365043,30.127082128012326,2.934542707799928,2017-01-10 14:38:57.550781250 +2523.0,2523000.0,683.0357142857143,11.95251055574371,11.449855098111568,0.0,0.0,0.0,0.0,11.465615401645982,685.546875,35.15625,0.016786260457311762,0.017499100421480986,87.86834502711694,90.88757396449704,86.29213483146067,1.4950489727623282,222.77407561280125,57.85071054816692,178.20083662647144,3.8508442420481237,2.913828859743871,3.6908455412053853,348.4326397879622,1.6094379124341003,10.25960780004244,29.89474129741699,2.913828859743871,2017-01-10 14:38:58.550781250 +2524.0,2524000.0,682.5520833333334,11.669041398407073,11.398827129361326,0.0,0.0,0.0,0.0,11.483881488424485,683.59375,35.15625,0.01682491603035102,0.017096191899993573,87.92953050315116,90.88757396449704,86.29213483146067,1.4623854748022622,227.6678655907021,58.27710693418261,178.73028181674573,3.9066432355303284,2.9014787206468324,3.6873315781208063,344.8219263997083,1.5141277326297755,10.239909133395804,29.71087845190507,2.9014787206468324,2017-01-10 14:38:59.550781250 +2525.0,2525000.0,681.640625,11.542138711940394,11.807020856002705,0.0,0.0,0.0,0.0,11.868039458011717,681.640625,35.15625,0.017410991984246414,0.016932879714938343,88.0464873368433,90.88757396449704,86.29213483146067,1.4443485915495584,232.01997711246673,58.70435367080208,178.77514923828528,3.9523470169447936,2.8614034110678865,3.6854981353257235,336.99053530161814,1.4271163556401458,10.289624612622665,29.442766965166577,2.8614034110678865,2017-01-10 14:39:00.550781250 +2526.0,2526000.0,687.5,18.262352870483898,12.033403266098116,0.0,0.0,1.0,7.142857142857143,13.03942125619188,687.5,70.3125,0.018966430918097278,0.02656342235706749,87.32921061405598,90.88757396449704,82.13903743315508,2.2022512017588816,238.8348035540809,59.56270303284473,180.0210887369795,4.009804649436074,2.905501256074062,3.6996746999591976,350.4846089418479,1.54368653487132,10.379270366119155,30.156983085891493,2.905501256074062,2017-01-10 14:39:01.550781250 +2527.0,2527000.0,693.359375,18.177223958937333,12.861138557110994,0.0,0.0,1.0,7.6923076923076925,13.357037722892349,691.40625,62.5,0.019264234687664458,0.02621616525908708,86.5895036335615,89.82456140350877,82.13903743315508,2.1498900938212633,241.50922414213017,60.195221620336355,181.56674497622544,4.012099592645053,2.9819821380065945,3.7064956055277207,367.2868329068302,1.4733057381095205,10.326075416186416,30.792172446776906,2.9819821380065945,2017-01-10 14:39:02.550781250 +2528.0,2528000.0,694.7544642857143,17.459679929730278,14.73275321169623,0.0,0.0,2.0,15.384615384615385,14.735817109662534,693.359375,62.5,0.021210107839771293,0.02513072002737081,86.41136132383511,89.82456140350877,82.13903743315508,2.0620269416970887,244.38914917896489,61.34314117258739,186.13807797337142,3.983968615030361,2.9019970185001562,3.7090700097547544,353.655525394454,1.462834438242225,10.498482595575087,30.466565191134684,2.9019970185001562,2017-01-10 14:39:03.550781250 +2529.0,2529000.0,693.0803571428571,18.11022157079826,15.012014419226919,0.0,0.0,2.0,15.384615384615385,15.012014419226919,689.453125,62.5,0.021659846891509373,0.02613004593789894,86.62389666611969,89.82456140350877,82.13903743315508,2.139279919539704,245.2063240121139,61.89189300818591,188.39568669186923,3.9618488317958307,2.9170493381016858,3.7050746677605257,354.7749895632889,1.4170660197866443,10.4233052208183,30.40529559521987,2.9170493381016858,2017-01-10 14:39:04.550781250 +2530.0,2530000.0,694.1964285714286,17.961469798472187,14.818302891752348,0.0,0.0,2.0,15.384615384615385,15.012014419226919,693.359375,62.5,0.021625023986539097,0.025873757137349004,86.48379861572634,89.82456140350877,82.13903743315508,2.1237094050651426,244.75322486903173,63.3620994834148,192.56936206608967,3.8627701238513494,2.884715783886861,3.704617377781082,348.7090631676189,1.3766324502081537,10.476040824028948,30.220400317719424,2.884715783886861,2017-01-10 14:39:05.550781250 +2531.0,2531000.0,697.1354166666666,15.892428682694277,14.062057107538116,0.0,0.0,2.0,14.285714285714286,14.16135886210884,695.3125,54.6875,0.020313641401007825,0.02279675985862758,86.1076857255551,88.78612716763006,82.13903743315508,1.87094475342855,243.6490601491463,64.16594060832304,194.2743516376934,3.7971711758487383,2.866977791645796,3.6968603370555635,342.4259440256369,1.3773256911371303,10.41496974784227,29.8594869677266,2.866977791645796,2017-01-10 14:39:06.550781250 +2532.0,2532000.0,696.9866071428571,16.83382045647138,15.888586618271978,0.0,0.0,3.0,23.076923076923077,15.959461207799881,695.3125,54.6875,0.022897801828965083,0.024152286835866062,86.13095829646646,88.78612716763006,82.13903743315508,1.9809443979678392,239.65569270155595,65.6510634184949,197.42096142793716,3.65044646990503,2.7669818602218137,3.6949626426360087,323.95955170809594,1.4332513442076615,10.578355889835397,29.270118858145125,2.7669818602218137,2017-01-10 14:39:07.550781250 +2533.0,2533000.0,694.0104166666666,18.182604223657474,15.284984291074037,0.0,0.0,3.0,21.428571428571427,15.30788696913527,691.40625,58.59375,0.022057142949898478,0.02619932465997925,86.508648937873,89.30232558139535,82.13903743315508,2.158390309398054,237.27267125339085,66.38744095215363,198.3537489214453,3.5740596090214813,2.7768723104003343,3.690640354060042,324.0813519807654,1.4539530095937054,10.507084639852195,29.17683239943823,2.7768723104003343,2017-01-10 14:39:08.550781250 +2534.0,2534000.0,694.7544642857143,18.504067644776715,14.94570927337246,0.0,0.0,3.0,23.076923076923077,14.972869595313693,693.359375,58.59375,0.021551311096226614,0.026633967244530016,86.41762440690741,89.30232558139535,82.13903743315508,2.1895213740535224,234.39438291744133,67.22935117691759,198.79897021738725,3.486488844740151,2.7439087819401338,3.6894220960256137,317.88171872795215,1.3682758556172123,10.555194048350856,28.962489644352146,2.7439087819401338,2017-01-10 14:39:09.550781250 +2535.0,2535000.0,690.3645833333334,21.82245496158934,13.276090684565087,0.0,0.0,2.0,14.285714285714286,14.006585265637424,683.59375,78.125,0.020288678770293365,0.03161004415409395,86.991179332497,91.97604790419162,82.13903743315508,2.6401236844898235,227.5886721119998,68.89600216232537,197.82928224834228,3.3033654344090935,2.737167255641117,3.6913021658534118,317.3971968241609,1.4469189829363254,10.59108427061249,28.989569067256188,2.737167255641117,2017-01-10 14:39:10.550781250 +2536.0,2536000.0,683.3147321428571,19.468908060097803,12.762476311054868,0.0,0.0,2.0,15.384615384615385,13.746789944348405,679.6875,70.3125,0.020117801208879005,0.02849186055017988,87.87305698238308,91.97604790419162,83.02702702702703,2.3974586166579503,224.06482241640208,69.8335385427586,197.7495684694131,3.20855604759608,2.7537538255605045,3.6892421510452724,319.52787101891255,1.429969424622552,10.534126454729288,29.00839102364889,2.7537538255605045,2017-01-10 14:39:11.550781250 +2537.0,2537000.0,678.6458333333334,15.933523955480814,11.313133332987327,0.0,0.0,1.0,7.142857142857143,11.436328979865623,675.78125,50.78125,0.01685168967102225,0.023478408284361596,88.45673583228012,91.97604790419162,85.33333333333333,2.001128389666967,217.05529357129603,71.49169016133376,197.2067306314728,3.0360912307636316,2.745957637974202,3.6892542177628562,318.1763229066678,1.429969424622552,10.549216394026644,28.96770133182013,2.745957637974202,2017-01-10 14:39:12.550781250 +2538.0,2538000.0,677.0833333333334,15.319748952293882,10.632008305389313,0.0,0.0,1.0,7.142857142857143,10.646642872956983,675.78125,50.78125,0.015724272550828772,0.02262609076031096,88.65741521560909,91.97604790419162,85.33333333333333,1.9234199365821856,213.2290967168464,72.35962431088439,195.7970621203151,2.9467966251556716,2.7448395196644944,3.687562536527073,317.3633013690146,1.3948781048112822,10.530834751914165,28.90545140211024,2.7448395196644944,2017-01-10 14:39:13.550781250 +2539.0,2539000.0,674.21875,16.49370821599211,10.10645662738551,0.0,0.0,1.0,7.142857142857143,10.646642872956983,671.875,50.78125,0.015791080970318585,0.02446343744666269,89.04096979342953,91.97604790419162,85.33333333333333,2.077361308365325,206.6990779231504,73.88546887083078,194.47039645837228,2.7975606175621506,2.8049504036071604,3.6827561485332483,326.0362235782977,1.4367252147467826,10.35989824171779,29.059000754435427,2.8049504036071604,2017-01-10 14:39:14.550781250 +2540.0,2540000.0,671.09375,15.846642276159685,9.95511315564245,0.0,0.0,1.0,7.142857142857143,10.59533345203545,671.875,50.78125,0.015788156948318248,0.023613157291599997,89.45203592931419,91.97604790419162,85.33333333333333,2.006202021576151,203.90491489394418,74.72364835460951,194.09641336090954,2.728786928688096,2.8416184788600787,3.685216224359015,333.3932207718126,1.410986973710262,10.322032845710254,29.331279273770942,2.8416184788600787,2017-01-10 14:39:15.550781250 +2541.0,2541000.0,665.8854166666666,9.204488565206859,8.201416549497248,0.0,0.0,0.0,0.0,8.220379387642987,667.96875,27.34375,0.01234503592043374,0.013822931595774086,90.1217338427368,91.97604790419162,88.27586206896552,1.2087842755734997,198.06931963815026,76.343529376721,192.25351781692535,2.5944480331891286,2.7965925682452815,3.6764051977066816,322.2154774336365,1.2809338454620642,10.299781411281103,28.80429214933963,2.7965925682452815,2017-01-10 14:39:16.550781250 +2542.0,2542000.0,665.3645833333334,8.81743518935367,8.1490412201508,0.0,0.0,0.0,0.0,8.15381651573798,667.96875,27.34375,0.0122546596557471,0.013252035666191033,90.19096279179539,91.97604790419162,88.27586206896552,1.1590216543946876,196.32143070518214,76.88328069170578,191.48091775098285,2.5534996547872524,2.7756901908836134,3.6717420995330206,316.9038648466464,1.2253639943072536,10.283135609296679,28.54279864225078,2.7756901908836134,2017-01-10 14:39:17.550781250 +2543.0,2543000.0,661.4583333333334,10.714673232079367,9.387613750703528,0.0,0.0,0.0,0.0,9.793487039500135,664.0625,39.0625,0.014805901666015952,0.016198561106765656,90.73097641301327,93.65853658536585,88.27586206896552,1.42614246026019,195.10014923635165,77.58574471780193,190.0522631395635,2.5146391253441975,2.801675912543123,3.681596435693398,325.0312082505504,1.2222262446352863,10.352119541286848,29.00328396259033,2.801675912543123,2017-01-10 14:39:18.550781250 +2544.0,2544000.0,659.1145833333334,10.278567460144341,9.186427250644156,0.0,0.0,0.0,0.0,9.33772351042495,660.15625,31.25,0.014167071623876653,0.015594507723016305,91.0519795478095,93.65853658536585,89.30232558139535,1.3785085024032646,192.07396657146114,78.86140362092684,187.37403778994735,2.435588992236653,2.788754340947938,3.679944036335049,322.1717136443914,1.2299482907291965,10.356354242063535,28.881327848949272,2.788754340947938,2017-01-10 14:39:19.550781250 +2545.0,2545000.0,659.375,10.143364078126574,9.358543432616308,0.0,0.0,0.0,0.0,9.39590303598367,660.15625,31.25,0.014249710765472864,0.015383300971566368,91.01548111610148,93.65853658536585,89.30232558139535,1.360691550954961,192.25912405936407,79.13083121984195,187.00294112240465,2.4296360987947683,2.7607401980737674,3.6672049966620204,312.70930668193347,1.2488944917174476,10.25722158383038,28.317523947030406,2.7607401980737674,2017-01-10 14:39:20.550781250 +2546.0,2546000.0,660.15625,10.334966058846057,9.156718237400636,0.0,0.0,0.0,0.0,9.160968280905136,664.0625,31.25,0.013876969703619615,0.015655333201565626,90.90854711443063,93.65853658536585,89.30232558139535,1.3868031679006345,191.46424739173995,80.11495937871726,183.068931064333,2.3898688693912375,2.7881076907669295,3.6655899446338975,316.7811192368134,1.2192402764567243,10.187795265975055,28.404670333023965,2.7881076907669295,2017-01-10 14:39:21.550781250 +2547.0,2547000.0,658.0729166666666,10.425963113556373,9.876601573899475,0.0,0.0,0.0,0.0,10.067867495908882,660.15625,31.25,0.015299015110522401,0.015843173073231687,91.19676153426664,93.65853658536585,89.30232558139535,1.4018280973638637,192.78678996405117,80.13651015927707,182.26045550984716,2.405729792586096,2.787182157797299,3.6713448109621973,318.7281597666165,1.2794312155775294,10.25722158383038,28.588744987025386,2.787182157797299,2017-01-10 14:39:22.550781250 +2548.0,2548000.0,656.7708333333334,9.778637164332597,10.167894619580691,0.0,0.0,0.0,0.0,10.228963604812144,656.25,27.34375,0.015574631341188989,0.014888963802948917,91.37500968911972,93.65853658536585,89.82456140350877,1.3170331257431955,193.59436460779136,80.71089639338952,180.01851761214579,2.398614973425662,2.787182157797299,3.6713448109621973,318.7281597666165,1.3198492617117379,10.25722158383038,28.588744987025386,2.787182157797299,2017-01-10 14:39:23.550781250 +2549.0,2549000.0,657.8125,10.207631019329886,9.970741286387074,0.0,0.0,0.0,0.0,10.067867495908882,660.15625,31.25,0.015305071727747468,0.015517538841736645,91.23198685246409,93.65853658536585,89.30232558139535,1.3732520397491808,195.600875553855,80.53302674767275,179.45153846678474,2.4288280653689385,2.7785965092730707,3.668402503551588,316.18370229500084,1.2144441041932315,10.238315714213856,28.448148304350244,2.7785965092730707,2017-01-10 14:39:24.550781250 +2550.0,2550000.0,659.375,11.068255413649691,9.841067013698881,0.0,0.0,0.0,0.0,9.904151422599803,660.15625,35.15625,0.015020514005838563,0.016785979774255454,91.01932547002666,93.65853658536585,88.78612716763006,1.4840832695781119,197.40775638965243,80.65414543481639,174.02451685092404,2.4475835101271364,2.6897492718139815,3.6523740763436723,295.6342427168975,1.1863178690872151,10.215770963734737,27.477862510723927,2.6897492718139815,2017-01-10 14:39:25.550781250 +2551.0,2551000.0,659.375,11.35983093749953,10.562217204658314,0.0,0.0,0.0,0.0,10.59533345203545,660.15625,35.15625,0.016068752154745704,0.017228179620852366,91.02057624615739,93.65853658536585,88.78612716763006,1.5211459504225624,199.47362880054106,80.230275172022,172.73929423269863,2.4862637997046497,2.7200621900845943,3.6449593473291166,298.0903549078471,1.1837700970084166,10.072337407364028,27.39738414754558,2.7200621900845943,2017-01-10 14:39:26.550781250 +2552.0,2552000.0,659.375,11.35983093749953,9.637229932572847,0.0,0.0,0.0,0.0,9.737683240298631,660.15625,35.15625,0.014768050411827308,0.017228179620852366,91.02057624615739,93.65853658536585,88.78612716763006,1.5211459504225624,202.56187243242016,79.84838791748942,168.57297399185114,2.536831083449494,2.8068175132726334,3.6367986535488592,309.5427191356785,1.1972838161751393,9.822730904043445,27.570613129633468,2.8068175132726334,2017-01-10 14:39:27.550781250 +2553.0,2553000.0,664.3229166666666,12.036853209765933,9.70163987205006,0.0,0.0,0.0,0.0,10.175548286166556,667.96875,42.96875,0.015317171861575687,0.018118979351431277,90.34525607102832,93.0909090909091,87.27272727272727,1.5846974688027475,203.45585689137178,79.49747380829683,165.66373728340426,2.559274491941628,2.8610980282628313,3.6158425675914327,310.9713584898444,1.1833535171232001,9.497185558334056,27.172378874995804,2.8610980282628313,2017-01-10 14:39:28.550781250 +2554.0,2554000.0,666.9270833333334,11.669041398407073,9.539799663660022,0.0,0.0,0.0,0.0,9.737683240298631,667.96875,42.96875,0.014600821414582875,0.01749672743845496,89.99062403639627,93.0909090909091,87.27272727272727,1.524653191313882,206.2175644730656,78.62882587157034,158.76569415864645,2.6226712937300425,2.865041844580936,3.6131287932874683,310.64248421971377,1.1895840668738362,9.461040915642222,27.1062781166073,2.865041844580936,2017-01-10 14:39:29.550781250 +2555.0,2555000.0,668.2291666666666,12.216607107184448,11.13277753530134,0.0,0.0,1.0,7.142857142857143,11.146754662632912,667.96875,42.96875,0.016681035816255022,0.018282062077781873,89.81768201665264,93.0909090909091,87.27272727272727,1.5927744003971447,207.10097199091206,78.06246820592506,156.69111590864168,2.653015933913204,2.930090651186378,3.597482765145232,315.54620960956777,1.2225491898988714,9.188420516410352,26.922905054303083,2.930090651186378,2017-01-10 14:39:30.550781250 +2556.0,2556000.0,669.0104166666666,12.56840472164323,12.049566340228338,0.0,0.0,1.0,7.142857142857143,12.130058660314821,671.875,42.96875,0.0181313449807742,0.018786560580424293,89.71443539434975,93.0909090909091,87.27272727272727,1.638520019372672,208.4654482770683,77.18158444965829,152.7062296363099,2.7009739403968824,2.788215305503355,3.5879298652678124,289.7039341590353,inf,9.316264021431058,25.975749934664314,2.788215305503355,2017-01-10 14:39:31.550781250 +2557.0,2557000.0,675.2604166666666,14.380296319861555,11.71875,0.0,0.0,1.0,7.142857142857143,12.352647110032732,675.78125,54.6875,0.0182931603943408,0.021295926674997447,88.8919724886552,91.97604790419162,84.86187845303867,1.8167355361785895,209.72765949279827,76.4522823559486,152.0300513446403,2.7432491618280612,2.853385784260235,3.5621470229693286,291.1482951075111,inf,8.939912611394671,25.509019557882347,2.853385784260235,2017-01-10 14:39:32.550781250 +2558.0,2558000.0,678.90625,15.071097316730363,11.548103842954731,0.0,0.0,1.0,7.142857142857143,11.949002848975367,675.78125,50.78125,0.017600372435774992,0.02219908465525301,88.417694525245,91.42857142857143,84.86187845303867,1.8773023747700595,209.10836169726377,75.68083745116638,151.52000700215265,2.7630291727703526,2.943942860195562,3.558005147142458,303.66632819308694,inf,8.759471300428828,25.787382993985386,2.943942860195562,2017-01-10 14:39:33.550781250 +2559.0,2559000.0,680.46875,14.63075569739259,13.488428532669444,0.0,0.0,1.0,7.142857142857143,13.491314066670293,679.6875,50.78125,0.019826500580181372,0.021500995743584977,88.2123469316621,91.42857142857143,84.86187845303867,1.8220358392041118,209.6185541146843,75.21798844561772,151.84409879118513,2.786814144414904,2.8059646117827914,3.5508989989669115,280.26752847248866,inf,8.899147670264824,24.97069343779237,2.8059646117827914,2017-01-10 14:39:34.550781250 +2560.0,2560000.0,680.9895833333334,14.59097110791546,13.785289427495117,0.0,0.0,1.0,7.142857142857143,13.810679320049756,683.59375,50.78125,0.020280309211851266,0.021426129657512567,88.14470035858201,91.42857142857143,84.86187845303867,1.8183098993043856,208.48769598480072,74.54709044712942,153.98860119518363,2.7967247914613806,2.671588563719893,3.531100040444261,254.5097262265261,inf,8.914669379774834,23.816328764350356,2.671588563719893,2017-01-10 14:39:35.550781250 +2561.0,2561000.0,684.8958333333334,13.177994980698294,13.015517622422546,0.0,0.0,1.0,7.142857142857143,13.205535230133071,683.59375,46.875,0.0192810856592057,0.019240874800715378,87.63482610049417,90.88757396449704,84.86187845303867,1.6285204649511806,205.46635655294742,74.35572855238348,158.95650160469472,2.763288862245452,2.7208701102099355,3.521113604416473,258.5940434024638,inf,8.732586696768893,23.760234128055394,2.7208701102099355,2017-01-10 14:39:36.550781250 +2562.0,2562000.0,686.71875,12.545258468626418,13.006542433936009,0.0,0.0,1.0,7.142857142857143,13.08114751086054,687.5,46.875,0.019048769981685428,0.018268408236452576,87.39933099569097,90.88757396449704,84.86187845303867,1.5482720164180537,204.72215655430887,74.16503717358071,162.2032314249424,2.7603593870675707,2.732910825823842,3.528164504160938,262.43420764125335,inf,8.784347956071661,24.006839626951784,2.732910825823842,2017-01-10 14:39:37.550781250 +2563.0,2563000.0,686.6629464285714,13.016897871383364,14.195927657473218,0.0,0.0,1.0,7.6923076923076925,14.2086423287703,685.546875,46.875,0.02069230967343062,0.018956750089816326,87.4083741187598,90.88757396449704,84.86187845303867,1.6022310373046433,200.74947957915748,74.56885006100597,167.31298608088017,2.6921359175436,2.6968316339450107,3.5327927169316045,258.62893367156147,inf,8.890157400203382,23.975257707618816,2.6968316339450107,2017-01-10 14:39:38.550781250 +2564.0,2564000.0,687.5,12.700677094577056,14.175095754330957,0.0,0.0,1.0,7.142857142857143,14.199788671874412,687.5,46.875,0.020654238068180963,0.018473712137566628,87.30071141302643,90.88757396449704,84.86187845303867,1.5684117860921383,198.53387997193835,74.86342716432947,171.03750161561408,2.651947519529733,2.725062537709619,3.5293534826674753,261.6624788924333,2.833213344056216,8.809038606106606,24.00518109873887,2.725062537709619,2017-01-10 14:39:39.550781250 +2565.0,2565000.0,690.2901785714286,10.551722914104785,12.00419732898929,0.0,0.0,0.0,0.0,12.064218689834574,689.453125,35.15625,0.017477024973470943,0.015285923574838945,86.93884310649513,89.30232558139535,84.86187845303867,1.2816150714183039,196.26708001268094,75.21863503715197,175.29050965149085,2.609287976519924,2.7392403524658153,3.531047514188372,264.221995519725,2.833213344056216,8.803364730332126,24.1145319068001,2.7392403524658153,2017-01-10 14:39:40.550781250 +2566.0,2566000.0,690.1041666666666,10.509261080227333,11.94248580303164,0.0,0.0,0.0,0.0,11.994523024667789,691.40625,35.15625,0.017380742798009174,0.015228514169084136,86.96224864043636,89.30232558139535,84.86187845303867,1.2816000683228104,192.30680873400166,76.31672866335236,179.4536190430003,2.519851310481397,2.794579424051346,3.525420623872676,270.51104218998785,2.1972245773362196,8.659485554082043,24.199620152307546,2.794579424051346,2017-01-10 14:39:41.550781250 +2567.0,2567000.0,688.0580357142857,10.030324043224521,11.528441161809422,0.0,0.0,0.0,0.0,11.71875,687.5,35.15625,0.0170316301703163,0.014577729671904576,87.21914658610878,89.30232558139535,84.86187845303867,1.2245762048053879,189.372850083694,77.13133455617793,179.4949221223272,2.4551999673461626,2.7585865800533487,3.518477734177655,263.18964347951936,2.3025850929940455,8.64640484173234,23.85185636211113,2.7585865800533487,2017-01-10 14:39:42.550781250 +2568.0,2568000.0,687.2395833333334,8.282022088895463,10.48825118817662,0.0,0.0,0.0,0.0,10.491961873305339,687.5,27.34375,0.015266818337814512,0.012051142410518597,87.31769971909218,89.30232558139535,85.81005586592178,1.0223537423924136,184.75809181477024,78.66357972689005,182.21423560823777,2.348711976447383,2.7653712246842357,3.5110269067609736,261.90489324057495,2.3978952727983707,8.56202994344597,23.677191230490276,2.7653712246842357,2017-01-10 14:39:43.550781250 +2569.0,2569000.0,687.7790178571429,7.731595161727553,9.983922912161146,0.0,0.0,0.0,0.0,9.988443592905446,689.453125,23.4375,0.01452275125232175,0.011241394344678112,87.24759321437325,88.78612716763006,85.81005586592178,0.9481791371270547,182.47085259481156,79.78712181706797,183.4013178564952,2.286971235949228,2.791140967587464,3.5116828773828246,265.77498086584234,2.3978952727983707,8.528851676341558,23.80522732031394,2.791140967587464,2017-01-10 14:39:44.550781250 +2570.0,2570000.0,687.5,7.5283133723393085,9.621060072302827,0.0,0.0,0.0,0.0,9.625105084073851,687.5,23.4375,0.014000152849561966,0.010950273996129902,87.28251557151026,88.78612716763006,85.81005586592178,0.9253007216050818,177.27462317669747,81.79435423540951,183.74551869801522,2.16732101908268,2.7955045423829246,3.5057186938163944,264.57551509832564,2.3513752571634776,8.46387526596179,23.660801752158665,2.7955045423829246,2017-01-10 14:39:45.550781250 +2571.0,2571000.0,687.5,7.8125,10.33059701783118,0.0,0.0,0.0,0.0,10.334966058846057,689.453125,23.4375,0.015032677903776083,0.011363636363636364,87.28321473570905,88.78612716763006,85.81005586592178,0.9577734121619988,175.5637172867747,82.89864806391373,184.63776885813502,2.117811585436441,2.783568610868208,3.5083058031335908,263.6670118302564,2.3978952727983707,8.507303859557066,23.680663986581006,2.783568610868208,2017-01-10 14:39:46.550781250 +2572.0,2572000.0,686.9791666666666,8.46425706683418,10.543774345809812,0.0,0.0,1.0,7.142857142857143,10.543774345809812,687.5,23.4375,0.015348026341133313,0.01232098071897015,87.35127030013454,88.78612716763006,85.81005586592178,1.0398669397550728,170.66281363357874,85.42979506965723,186.66419575456172,1.9976966290791722,2.7351194842436306,3.4956604871139296,253.10148807815045,2.4423470353692043,8.458280833215444,23.13440871013201,2.7351194842436306,2017-01-10 14:39:47.550781250 +2573.0,2573000.0,687.2395833333334,8.149359660244713,10.106456627385509,0.0,0.0,1.0,7.142857142857143,10.12185108661666,687.5,23.4375,0.014728271380298587,0.011858105757991549,87.31725262689024,88.78612716763006,85.81005586592178,0.99995154760386,168.53279267565242,86.55226797794737,187.67624770576768,1.947179393595935,2.7719563341544364,3.493597959088972,257.6193953380952,2.9444389791664407,8.381964089632158,23.234438450910883,2.7719563341544364,2017-01-10 14:39:48.550781250 +2574.0,2574000.0,686.3839285714286,8.169225316598364,10.295577944117676,0.0,0.0,1.0,7.6923076923076925,10.334966058846057,683.59375,23.4375,0.01505712128248141,0.011901830705157943,87.42609684789957,88.78612716763006,85.81005586592178,0.9995039608296449,167.19770225550195,87.49843502720012,188.63901357404288,1.9108650595125067,2.7751382606691544,3.492416287945289,257.71226123262994,2.995732273553991,8.365768454288515,23.21616411739511,2.7751382606691544,2017-01-10 14:39:49.550781250 +2575.0,2575000.0,686.71875,7.978154464581034,10.171722172744524,0.0,0.0,1.0,7.142857142857143,10.175548286166556,683.59375,23.4375,0.014817635729571323,0.011617790346602644,87.38300112583622,88.78612716763006,85.81005586592178,0.9789836570863414,163.19492525506766,89.84684553993893,188.55843039718468,1.8163678899836708,2.7996226201031797,3.4894714738013723,260.24654802814996,inf,8.300915609367896,23.239431107553933,2.7996226201031797,2017-01-10 14:39:50.550781250 +2576.0,2576000.0,686.6629464285714,7.839282731967067,10.503941281820033,0.0,0.0,1.0,7.6923076923076925,10.503941281820033,683.59375,23.4375,0.015297084743617635,0.01141649301559121,87.38964934085213,88.78612716763006,85.81005586592178,0.9576733307331551,161.73097221365006,90.59369466453659,189.0504026743793,1.7852343125261738,2.7988889819496836,3.4893576524766177,260.11017123258074,inf,8.300915609367896,23.233341239153948,2.7988889819496836,2017-01-10 14:39:51.550781250 +2577.0,2577000.0,684.6354166666666,10.064258294324572,10.606349223631815,0.0,0.0,1.0,7.142857142857143,10.697706201272776,683.59375,35.15625,0.015625405786568073,0.014700171871512499,87.65563303072199,90.3529411764706,85.81005586592178,1.2498741529483426,159.23013803741736,92.45379002458063,191.26901288975895,1.7222672861229698,2.761841471865189,3.4903810281314316,255.26344970132175,inf,8.366255998344776,23.106272780469503,2.761841471865189,2017-01-10 14:39:52.550781250 +2578.0,2578000.0,683.3147321428571,10.101122309819132,10.833988207524007,0.0,0.0,1.0,7.6923076923076925,10.833988207524007,681.640625,35.15625,0.015855048483367107,0.01478253260857157,87.82507396399264,90.3529411764706,85.81005586592178,1.2501676423879027,157.29163336932663,93.11687231838083,192.56896289397417,1.689185100972061,2.7852470828865092,3.487338526995882,257.6112681681075,inf,8.301898286054206,23.12283798365299,2.7852470828865092,2017-01-10 14:39:53.550781250 +2579.0,2579000.0,682.2916666666666,10.916220796965186,11.588481951615616,0.0,0.0,1.0,7.142857142857143,11.672154388031188,683.59375,35.15625,0.017107279713755637,0.015999346511582566,87.95996954498872,90.3529411764706,85.81005586592178,1.3613154463465962,154.94020128111947,93.83372935693225,195.88090044871612,1.651220753379049,2.7443816916553354,3.4896719037164674,252.6403544372012,inf,8.385977712361253,23.014323700433916,2.7443816916553354,2017-01-10 14:39:54.550781250 +2580.0,2580000.0,679.6875,14.45434311394915,11.465615401645982,0.0,0.0,1.0,7.6923076923076925,12.112767049727388,679.6875,50.78125,0.017821082555920756,0.02126615998374128,88.31334332242152,92.53012048192771,85.81005586592178,1.8296107701567477,153.87920375617998,94.26324425290557,197.45596870378367,1.632441202037631,2.7974948598495826,3.497996687545077,262.51389126400426,inf,8.385977712361253,23.459729545143766,2.7974948598495826,2017-01-10 14:39:55.550781250 +2581.0,2581000.0,671.875,21.13917067508461,11.302806417705968,0.0,0.0,1.0,7.142857142857143,12.484295380132307,679.6875,74.21875,0.018581276844848085,0.031462951702451517,89.38732529882236,96.0,85.81005586592178,2.7979182975410155,150.5509680456489,94.64387177115002,200.30170779969077,1.5907101561703105,3.0309997352993823,3.531643292538828,307.75172360129784,inf,8.374689338343465,25.383681167733602,3.0309997352993823,2017-01-10 14:39:56.550781250 +2582.0,2582000.0,661.1979166666666,29.397767740763598,10.293452425053971,0.0,0.0,0.0,0.0,11.57840062569555,671.875,89.84375,0.017511247893923165,0.04446137381824822,90.91813394025803,99.09677419354838,86.29213483146067,4.047476762284565,150.05071510046247,94.30338978628043,200.96986149961418,1.591148689782224,3.4982877999384003,3.5923984684234025,409.2442041044365,1.0579975695139194,8.360100054219725,29.246036025941223,3.4982877999384003,2017-01-10 14:39:57.550781250 +2583.0,2583000.0,655.2083333333334,34.132753703327595,10.274527142590385,0.0,0.0,0.0,0.0,12.264096526694146,664.0625,105.46875,0.018717857973968806,0.052094504857224944,91.81453342263605,101.72185430463576,86.29213483146067,4.784814971749941,146.86664444067782,93.95599251635724,200.7810833712801,1.5631429194376198,3.7778102197726056,3.6316873272809937,480.5115921739118,1.0778861581509926,8.417121646241691,31.79828817624108,3.7778102197726056,2017-01-10 14:39:58.550781250 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b/data/tuh/dev/01_tcp_ar/046/00004671/s007_2012_08_04/00004671_s007_t000.edf new file mode 100755 index 0000000..a276f60 Binary files /dev/null and b/data/tuh/dev/01_tcp_ar/046/00004671/s007_2012_08_04/00004671_s007_t000.edf differ diff --git a/data/tuh/dev/01_tcp_ar/046/00004671/s007_2012_08_04/00004671_s007_t000.tse_bi b/data/tuh/dev/01_tcp_ar/046/00004671/s007_2012_08_04/00004671_s007_t000.tse_bi new file mode 100755 index 0000000..6d3836b --- /dev/null +++ b/data/tuh/dev/01_tcp_ar/046/00004671/s007_2012_08_04/00004671_s007_t000.tse_bi @@ -0,0 +1,81 @@ +version = tse_v1.0.0 + +0.0000 46.2734 bckg 1.0000 +46.2734 52.0742 seiz 1.0000 +52.0742 119.1406 bckg 1.0000 +119.1406 125.8398 seiz 1.0000 +125.8398 166.8203 bckg 1.0000 +166.8203 172.9219 seiz 1.0000 +172.9219 199.3398 bckg 1.0000 +199.3398 208.5391 seiz 1.0000 +208.5391 249.0547 bckg 1.0000 +249.0547 256.3516 seiz 1.0000 +256.3516 297.7539 bckg 1.0000 +297.7539 303.1523 seiz 1.0000 +303.1523 344.8438 bckg 1.0000 +344.8438 351.4453 seiz 1.0000 +351.4453 417.4219 bckg 1.0000 +417.4219 425.1211 seiz 1.0000 +425.1211 470.0469 bckg 1.0000 +470.0469 477.1484 seiz 1.0000 +477.1484 520.5586 bckg 1.0000 +520.5586 527.5586 seiz 1.0000 +527.5586 599.1484 bckg 1.0000 +599.1484 605.6484 seiz 1.0000 +605.6484 642.6211 bckg 1.0000 +642.6211 649.5195 seiz 1.0000 +649.5195 688.1211 bckg 1.0000 +688.1211 695.4219 seiz 1.0000 +695.4219 729.4531 bckg 1.0000 +729.4531 736.5547 seiz 1.0000 +736.5547 771.1211 bckg 1.0000 +771.1211 779.2188 seiz 1.0000 +779.2188 827.1211 bckg 1.0000 +827.1211 833.6211 seiz 1.0000 +833.6211 868.3750 bckg 1.0000 +868.3750 875.9727 seiz 1.0000 +875.9727 911.1719 bckg 1.0000 +911.1719 918.0742 seiz 1.0000 +918.0742 953.0547 bckg 1.0000 +953.0547 960.5547 seiz 1.0000 +960.5547 1002.0273 bckg 1.0000 +1002.0273 1008.2266 seiz 1.0000 +1008.2266 1053.3477 bckg 1.0000 +1053.3477 1058.6484 seiz 1.0000 +1058.6484 1094.7461 bckg 1.0000 +1094.7461 1099.9453 seiz 1.0000 +1099.9453 1131.9414 bckg 1.0000 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1866.2188 seiz 1.0000 +1866.2188 1906.8203 bckg 1.0000 +1906.8203 1913.6211 seiz 1.0000 +1913.6211 1930.0000 bckg 1.0000 diff --git a/docker-compose.yml b/docker-compose.yml index 344b971..e676cdf 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -21,10 +21,11 @@ x-airflow-common: - ./dags:/opt/airflow/dags - ./logs:/opt/airflow/logs - ./scripts:/opt/airflow/scripts - - ${DATA_PATH}:/opt/airflow/data + - ${DATA_PATH}/${SYMLINK_FOLDER}:/opt/airflow/data/${SYMLINK_FOLDER} - ./output:/opt/airflow/output - ./models:/opt/airflow/models - ./src:/opt/airflow/src + - ./mlruns:/mlruns - ./great_expectations:/opt/airflow/great_expectations user: "${AIRFLOW_UID}:0" depends_on: @@ -61,7 +62,7 @@ services: POSTGRES_PASSWORD: ${POSTGRES_PASSWORD} POSTGRES_MULTIPLE_DATABASES: ${POSTGRES_DATABASE_AIRFLOW},${POSTGRES_DATABASE_GRAFANA},${POSTGRES_DATABASE_MLFLOW} healthcheck: - test: ["CMD", "pg_isready", "-U", "airflow"] + test: ["CMD", "pg_isready", "-U", "${POSTGRES_USER}"] interval: 5s retries: 5 restart: always @@ -227,6 +228,8 @@ services: - '${MLFLOW_PORT}:5000' volumes: - ./mlruns:/mlruns + environment: + - PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python ge_data_docs: image: flashspys/nginx-static diff --git a/docker/mlflow/Dockerfile b/docker/mlflow/Dockerfile index ef73a1f..ee90c58 100644 --- a/docker/mlflow/Dockerfile +++ b/docker/mlflow/Dockerfile @@ -1,3 +1,3 @@ FROM python:3.7-slim-buster -RUN pip install mlflow==1.19.0 psycopg2-binary==2.9.1 +RUN pip install mlflow==1.28 psycopg2-binary==2.9.3 diff --git a/env.sh b/env.sh index c7e232d..6f700c8 100644 --- a/env.sh +++ b/env.sh @@ -35,4 +35,5 @@ export REDIS_PORT=6379 export FLOWER_PORT=5555 # DATA PATH -export DATA_PATH='./data' +export SYMLINK_FOLDER='' +export DATA_PATH='/home/DATA/DetecTeppe-2022-04-08/ml_dataset_2022_04_08' diff --git a/files/Statistics ecg_qc.ipynb b/files/Statistics ecg_qc.ipynb new file mode 100644 index 0000000..1a549d0 --- /dev/null +++ b/files/Statistics ecg_qc.ipynb @@ -0,0 +1,989 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "id": "cc0f2647", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append(\"..\")\n", + "\n", + "from src.usecase.consolidate_feats_and_annot import read_tse_bi\n", + "from src.usecase.fetch_database import create_df_from_file_pattern\n", + "import pandas as pd\n", + "import os\n", + "import seaborn as sns" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "4598ee5d", + "metadata": {}, + "outputs": [], + "source": [ + "DATASET_PATIENT_PATTERN = r\"[P][A][T][_][0-9]*\"\n", + "DATASET_EXAM_PATTERN = r\"[E][E][G][_][0-9]*\"\n", + "ANNOTATION_FILE_PATTERN = \"*.tse_bi\"" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5157511e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ecg_qc_file_path exam_id patient_id\n", + "0 ../output/ecg_qc-v0_6/PAT_12/ecg_qc_EEG_156_s1... EEG_156 PAT_12\n", + "1 ../output/ecg_qc-v0_6/PAT_12/ecg_qc_EEG_297_s1... EEG_297 PAT_12\n", + "2 ../output/ecg_qc-v0_6/PAT_12/ecg_qc_EEG_299_s1... EEG_299 PAT_12\n", + "3 ../output/ecg_qc-v0_6/PAT_12/ecg_qc_EEG_300_s1... EEG_300 PAT_12\n", + "4 ../output/ecg_qc-v0_6/PAT_17/ecg_qc_EEG_253_s1... EEG_253 PAT_17\n", + ".. ... ... ...\n", + "402 ../output/ecg_qc-v0_6/PAT_9/ecg_qc_EEG_103_s5.... EEG_103 PAT_9\n", + "403 ../output/ecg_qc-v0_6/PAT_9/ecg_qc_EEG_103_s6.... EEG_103 PAT_9\n", + "404 ../output/ecg_qc-v0_6/PAT_9/ecg_qc_EEG_103_s7.... EEG_103 PAT_9\n", + "405 ../output/ecg_qc-v0_6/PAT_9/ecg_qc_EEG_103_s8.... EEG_103 PAT_9\n", + "406 ../output/ecg_qc-v0_6/PAT_9/ecg_qc_EEG_103_s9.... EEG_103 PAT_9\n", + "\n", + "[407 rows x 3 columns]\n" + ] + } + ], + "source": [ + "df = create_df_from_file_pattern(\n", + " data_folder_path='../output/ecg_qc-v0_6',\n", + " file_pattern=ANNOTATION_FILE_PATTERN,\n", + " file_label='ecg_qc',\n", + " patient_pattern=DATASET_PATIENT_PATTERN,\n", + " exam_pattern=DATASET_EXAM_PATTERN)\n", + "print(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a343c7c5", + "metadata": {}, + "outputs": [], + "source": [ + "def compute_stats_files(tse_bi_file):\n", + " \n", + " df = read_tse_bi(tse_bi_file)\n", + " df['length'] = df['end'] - df['start']\n", + " total_length = df['length'].sum()\n", + " total_noise = df[df['annotation'] == 'noisy']['length'].sum()\n", + " total_clean = df[df['annotation'] == 'clean']['length'].sum()\n", + " ratio_clean = total_clean / (total_noise + total_clean) \n", + " small_chunks = df[(df['annotation'] == 'noisy') & (df['length'].dt.seconds <= 4)].shape[0]\n", + " return total_length, total_noise, total_clean, ratio_clean, small_chunks" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f4f74615", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ecg_qc_file_pathexam_idpatient_idtotal_lengthtotal_noisetotal_cleanratio_cleansmall_chunksratio_small_chunks
0../output/ecg_qc-v0_6/PAT_12/ecg_qc_EEG_156_s1...EEG_156PAT_120 days 00:19:000 days 00:00:540 days 00:18:060.95263210.00.035088
1../output/ecg_qc-v0_6/PAT_12/ecg_qc_EEG_297_s1...EEG_297PAT_120 days 00:05:440 days 00:00:180 days 00:05:260.9476745.00.058140
2../output/ecg_qc-v0_6/PAT_12/ecg_qc_EEG_299_s1...EEG_299PAT_120 days 00:02:100 days 00:02:080 days 00:00:020.0153850.00.000000
3../output/ecg_qc-v0_6/PAT_12/ecg_qc_EEG_300_s1...EEG_300PAT_120 days 00:33:320 days 00:33:280 days 00:00:040.0019880.00.000000
4../output/ecg_qc-v0_6/PAT_17/ecg_qc_EEG_253_s1...EEG_253PAT_170 days 02:00:000 days 01:56:140 days 00:03:460.03138921.00.011667
\n", + "
" + ], + "text/plain": [ + " ecg_qc_file_path exam_id patient_id \\\n", + "0 ../output/ecg_qc-v0_6/PAT_12/ecg_qc_EEG_156_s1... EEG_156 PAT_12 \n", + "1 ../output/ecg_qc-v0_6/PAT_12/ecg_qc_EEG_297_s1... EEG_297 PAT_12 \n", + "2 ../output/ecg_qc-v0_6/PAT_12/ecg_qc_EEG_299_s1... EEG_299 PAT_12 \n", + "3 ../output/ecg_qc-v0_6/PAT_12/ecg_qc_EEG_300_s1... EEG_300 PAT_12 \n", + "4 ../output/ecg_qc-v0_6/PAT_17/ecg_qc_EEG_253_s1... EEG_253 PAT_17 \n", + "\n", + " total_length total_noise total_clean ratio_clean small_chunks \\\n", + "0 0 days 00:19:00 0 days 00:00:54 0 days 00:18:06 0.952632 10.0 \n", + "1 0 days 00:05:44 0 days 00:00:18 0 days 00:05:26 0.947674 5.0 \n", + "2 0 days 00:02:10 0 days 00:02:08 0 days 00:00:02 0.015385 0.0 \n", + "3 0 days 00:33:32 0 days 00:33:28 0 days 00:00:04 0.001988 0.0 \n", + "4 0 days 02:00:00 0 days 01:56:14 0 days 00:03:46 0.031389 21.0 \n", + "\n", + " ratio_small_chunks \n", + "0 0.035088 \n", + "1 0.058140 \n", + "2 0.000000 \n", + "3 0.000000 \n", + "4 0.011667 " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "for i in df.index:\n", + " total_length, total_noise, total_clean, ratio_clean, small_chunks = compute_stats_files(df.loc[i, 'ecg_qc_file_path'])\n", + " df.loc[i, 'total_length'] = total_length\n", + " df.loc[i, 'total_noise'] = total_noise\n", + " df.loc[i, 'total_clean'] = total_clean\n", + " df.loc[i, 'ratio_clean'] = ratio_clean\n", + " df.loc[i, 'small_chunks'] = small_chunks\n", + " \n", + "df[['total_length', 'total_noise', 'total_clean']].fillna(pd.Timedelta(seconds=0))\n", + "df[['ratio_clean', 'small_chunks']].fillna(0)\n", + "df['ratio_small_chunks'] = df['small_chunks']*4 / df['total_length'].dt.seconds\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "2ab73807", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ecg_qc_file_pathexam_idpatient_idtotal_lengthtotal_noisetotal_cleanratio_cleansmall_chunksratio_small_chunks
397../output/ecg_qc-v0_6/PAT_9/ecg_qc_EEG_103_s28...EEG_103PAT_90 days 00:51:460 days 00:00:060 days 00:51:400.9980682.00.002576
381../output/ecg_qc-v0_6/PAT_9/ecg_qc_EEG_103_s13...EEG_103PAT_90 days 00:04:000 days 00:00:020 days 00:03:580.9916671.00.016667
391../output/ecg_qc-v0_6/PAT_9/ecg_qc_EEG_103_s22...EEG_103PAT_90 days 00:07:180 days 00:00:040 days 00:07:140.9908682.00.018265
404../output/ecg_qc-v0_6/PAT_9/ecg_qc_EEG_103_s7....EEG_103PAT_90 days 00:03:360 days 00:00:020 days 00:03:340.9907411.00.018519
383../output/ecg_qc-v0_6/PAT_9/ecg_qc_EEG_103_s15...EEG_103PAT_90 days 00:03:180 days 00:00:020 days 00:03:160.9898991.00.020202
..............................
394../output/ecg_qc-v0_6/PAT_9/ecg_qc_EEG_103_s25...EEG_103PAT_90 days 00:00:44NaT0 days 00:00:44NaN0.00.000000
400../output/ecg_qc-v0_6/PAT_9/ecg_qc_EEG_103_s3....EEG_103PAT_90 days 00:04:58NaT0 days 00:04:58NaN0.00.000000
401../output/ecg_qc-v0_6/PAT_9/ecg_qc_EEG_103_s4....EEG_103PAT_90 days 00:00:32NaT0 days 00:00:32NaN0.00.000000
402../output/ecg_qc-v0_6/PAT_9/ecg_qc_EEG_103_s5....EEG_103PAT_90 days 00:03:34NaT0 days 00:03:34NaN0.00.000000
403../output/ecg_qc-v0_6/PAT_9/ecg_qc_EEG_103_s6....EEG_103PAT_90 days 00:00:58NaT0 days 00:00:58NaN0.00.000000
\n", + "

407 rows × 9 columns

\n", + "
" + ], + "text/plain": [ + " ecg_qc_file_path exam_id patient_id \\\n", + "397 ../output/ecg_qc-v0_6/PAT_9/ecg_qc_EEG_103_s28... EEG_103 PAT_9 \n", + "381 ../output/ecg_qc-v0_6/PAT_9/ecg_qc_EEG_103_s13... EEG_103 PAT_9 \n", + "391 ../output/ecg_qc-v0_6/PAT_9/ecg_qc_EEG_103_s22... EEG_103 PAT_9 \n", + "404 ../output/ecg_qc-v0_6/PAT_9/ecg_qc_EEG_103_s7.... EEG_103 PAT_9 \n", + "383 ../output/ecg_qc-v0_6/PAT_9/ecg_qc_EEG_103_s15... EEG_103 PAT_9 \n", + ".. ... ... ... \n", + "394 ../output/ecg_qc-v0_6/PAT_9/ecg_qc_EEG_103_s25... EEG_103 PAT_9 \n", + "400 ../output/ecg_qc-v0_6/PAT_9/ecg_qc_EEG_103_s3.... EEG_103 PAT_9 \n", + "401 ../output/ecg_qc-v0_6/PAT_9/ecg_qc_EEG_103_s4.... EEG_103 PAT_9 \n", + "402 ../output/ecg_qc-v0_6/PAT_9/ecg_qc_EEG_103_s5.... EEG_103 PAT_9 \n", + "403 ../output/ecg_qc-v0_6/PAT_9/ecg_qc_EEG_103_s6.... EEG_103 PAT_9 \n", + "\n", + " total_length total_noise total_clean ratio_clean \\\n", + "397 0 days 00:51:46 0 days 00:00:06 0 days 00:51:40 0.998068 \n", + "381 0 days 00:04:00 0 days 00:00:02 0 days 00:03:58 0.991667 \n", + "391 0 days 00:07:18 0 days 00:00:04 0 days 00:07:14 0.990868 \n", + "404 0 days 00:03:36 0 days 00:00:02 0 days 00:03:34 0.990741 \n", + "383 0 days 00:03:18 0 days 00:00:02 0 days 00:03:16 0.989899 \n", + ".. ... ... ... ... \n", + "394 0 days 00:00:44 NaT 0 days 00:00:44 NaN \n", + "400 0 days 00:04:58 NaT 0 days 00:04:58 NaN \n", + "401 0 days 00:00:32 NaT 0 days 00:00:32 NaN \n", + "402 0 days 00:03:34 NaT 0 days 00:03:34 NaN \n", + "403 0 days 00:00:58 NaT 0 days 00:00:58 NaN \n", + "\n", + " small_chunks ratio_small_chunks \n", + "397 2.0 0.002576 \n", + "381 1.0 0.016667 \n", + "391 2.0 0.018265 \n", + "404 1.0 0.018519 \n", + "383 1.0 0.020202 \n", + ".. ... ... \n", + "394 0.0 0.000000 \n", + "400 0.0 0.000000 \n", + "401 0.0 0.000000 \n", + "402 0.0 0.000000 \n", + "403 0.0 0.000000 \n", + "\n", + "[407 rows x 9 columns]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Top noisy\n", + "\n", + "df.sort_values('ratio_clean', ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "fedff484", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ecg_qc_file_pathexam_idpatient_idtotal_lengthtotal_noisetotal_cleanratio_cleansmall_chunksratio_small_chunks
242../output/ecg_qc-v0_6/PAT_52/ecg_qc_EEG_714_s6...EEG_714PAT_520 days 05:27:000 days 05:26:440 days 00:00:160.0008151.00.000204
335../output/ecg_qc-v0_6/PAT_6/ecg_qc_EEG_35_s1.t...EEG_35PAT_60 days 00:32:020 days 00:32:000 days 00:00:020.0010410.00.000000
263../output/ecg_qc-v0_6/PAT_52/ecg_qc_EEG_717_s1...EEG_717PAT_520 days 00:24:400 days 00:24:380 days 00:00:020.0013510.00.000000
286../output/ecg_qc-v0_6/PAT_52/ecg_qc_EEG_706_s2...EEG_706PAT_520 days 00:45:340 days 00:45:300 days 00:00:040.0014630.00.000000
88../output/ecg_qc-v0_6/PAT_42/ecg_qc_EEG_568_s1...EEG_568PAT_420 days 00:22:380 days 00:22:360 days 00:00:020.0014730.00.000000
..............................
394../output/ecg_qc-v0_6/PAT_9/ecg_qc_EEG_103_s25...EEG_103PAT_90 days 00:00:44NaT0 days 00:00:44NaN0.00.000000
400../output/ecg_qc-v0_6/PAT_9/ecg_qc_EEG_103_s3....EEG_103PAT_90 days 00:04:58NaT0 days 00:04:58NaN0.00.000000
401../output/ecg_qc-v0_6/PAT_9/ecg_qc_EEG_103_s4....EEG_103PAT_90 days 00:00:32NaT0 days 00:00:32NaN0.00.000000
402../output/ecg_qc-v0_6/PAT_9/ecg_qc_EEG_103_s5....EEG_103PAT_90 days 00:03:34NaT0 days 00:03:34NaN0.00.000000
403../output/ecg_qc-v0_6/PAT_9/ecg_qc_EEG_103_s6....EEG_103PAT_90 days 00:00:58NaT0 days 00:00:58NaN0.00.000000
\n", + "

407 rows × 9 columns

\n", + "
" + ], + "text/plain": [ + " ecg_qc_file_path exam_id patient_id \\\n", + "242 ../output/ecg_qc-v0_6/PAT_52/ecg_qc_EEG_714_s6... EEG_714 PAT_52 \n", + "335 ../output/ecg_qc-v0_6/PAT_6/ecg_qc_EEG_35_s1.t... EEG_35 PAT_6 \n", + "263 ../output/ecg_qc-v0_6/PAT_52/ecg_qc_EEG_717_s1... EEG_717 PAT_52 \n", + "286 ../output/ecg_qc-v0_6/PAT_52/ecg_qc_EEG_706_s2... EEG_706 PAT_52 \n", + "88 ../output/ecg_qc-v0_6/PAT_42/ecg_qc_EEG_568_s1... EEG_568 PAT_42 \n", + ".. ... ... ... \n", + "394 ../output/ecg_qc-v0_6/PAT_9/ecg_qc_EEG_103_s25... EEG_103 PAT_9 \n", + "400 ../output/ecg_qc-v0_6/PAT_9/ecg_qc_EEG_103_s3.... EEG_103 PAT_9 \n", + "401 ../output/ecg_qc-v0_6/PAT_9/ecg_qc_EEG_103_s4.... EEG_103 PAT_9 \n", + "402 ../output/ecg_qc-v0_6/PAT_9/ecg_qc_EEG_103_s5.... EEG_103 PAT_9 \n", + "403 ../output/ecg_qc-v0_6/PAT_9/ecg_qc_EEG_103_s6.... EEG_103 PAT_9 \n", + "\n", + " total_length total_noise total_clean ratio_clean \\\n", + "242 0 days 05:27:00 0 days 05:26:44 0 days 00:00:16 0.000815 \n", + "335 0 days 00:32:02 0 days 00:32:00 0 days 00:00:02 0.001041 \n", + "263 0 days 00:24:40 0 days 00:24:38 0 days 00:00:02 0.001351 \n", + "286 0 days 00:45:34 0 days 00:45:30 0 days 00:00:04 0.001463 \n", + "88 0 days 00:22:38 0 days 00:22:36 0 days 00:00:02 0.001473 \n", + ".. ... ... ... ... \n", + "394 0 days 00:00:44 NaT 0 days 00:00:44 NaN \n", + "400 0 days 00:04:58 NaT 0 days 00:04:58 NaN \n", + "401 0 days 00:00:32 NaT 0 days 00:00:32 NaN \n", + "402 0 days 00:03:34 NaT 0 days 00:03:34 NaN \n", + "403 0 days 00:00:58 NaT 0 days 00:00:58 NaN \n", + "\n", + " small_chunks ratio_small_chunks \n", + "242 1.0 0.000204 \n", + "335 0.0 0.000000 \n", + "263 0.0 0.000000 \n", + "286 0.0 0.000000 \n", + "88 0.0 0.000000 \n", + ".. ... ... \n", + "394 0.0 0.000000 \n", + "400 0.0 0.000000 \n", + "401 0.0 0.000000 \n", + "402 0.0 0.000000 \n", + "403 0.0 0.000000 \n", + "\n", + "[407 rows x 9 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Top clean\n", + "df.sort_values('ratio_clean', ascending=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "39ecd5e2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.3185672087432875" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Ratio clean/noise\n", + "df['total_clean'].dt.seconds.sum() / df['total_length'].dt.seconds.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "230965d0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Small chunks are scarse\n", + "sns.displot(df['small_chunks'], kind='kde')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "5e64c2db", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Small chunks are scarse\n", + "sns.displot(df['ratio_small_chunks'], kind='kde')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "cd373f3d", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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DAjgC+U5oqZRF8vk7Gqq1E06kAiiAI5CJqBFPXltDDcMTWXIzasgjkmQK4AhkImrEk5fvB5Ff7iYiyaQAjkBUrSjz2hvzd0fWPLBIkimAIxBVJ7Q83Z5epDIsKoDN7G/N7BfMTIG9CJnJaEfAp/tBaCmaSKItNlD/lKBJ+n4z+2Mze1mENSVeZiLaOeAz/SAUwCJJtqgAdvfvuvv7gauAQ8B3zexHZvZrZhbdUC+BpnOzTGRnIp2C6GjUFIRIJVj0lIKZdQK/Cvwb4DHgswSB/J1IKkuoTIS9gPPqq6uoSafUkEck4Rb1e7KZfR14GfAl4O3ufjJ86Stmtiuq4pIo6kY8AGZGe0O1VkGIJNxiJyr/V3h3i9PMrNbdp9w9UTfLjNqZVpSR3mwk6Ig2pikIkSRb7BTEJ+c59uNiFlIpMpPR3Q2jULv6QYgk3jmHaWZ2AbAOqDezVwL55gYtQEPEtSVSKaYgILgQt/dkJtKvISLRWuj35LcRXHhbD3y64PgI8B8iqinRSnERDqCzqYZ+XYQTSbRzBrC7fxH4opm9292/VqKaEi0/Bxz1FERHY9CQJzszS3WV9seIJNFCUxC/7O5fBjaZ2Ufnvu7un57nbee1zESOmqoUteloQ7GzqRaAwbFpVrXURfq1RCQaC01BNIYfm6IupFIEvYDTmEXTCzivM9yM0a8AFkmshaYgPhd+/ERpykm+qHsB550O4FHNA4sk1WKb8fxXM2sxs2oze8DMes3sl6MuLomGJ7KRX4CD4CIcQP/YVORfS0SisdiJyre6ewb4RYJeEFuA34uqqCQLmrFHH8AdjcEcsDqiiSTXYgM4P1XxC8BX3X04onoSb2QiG9ndkAu11VeTMk1BiCTZYpPim2b2LDAB/F9m1g1MRldWcuVvyBm1VMroaNRaYJEkW2w7ytuA1wDb3T0LjAE3RllYErk7wxHfDaNQZ2Mt/aOaAxZJqqX8rnwpwXrgwvf8VZHrSbTJ7CzZGS/JKggINmNoDlgkuRbbjvJLwMXA48BMeNhRAP+UM9uQo58DBuhoqmHvCfWDEEmqxSbFdmCbu3uUxSRdqbYh53U11tCnKQiRxFrsKoingQuiLKQSlKoTWl5HYy2ZyRzTudmSfD0RKa7FjoC7gGfM7CfA6SGXu78jkqoSqlSd0PLymzEGx6dZre3IIomz2AD+eJRFVIrMRNCMvRTrgOGntyMrgEWSZ1FJ4e7/ZGYXAlvd/btm1gBURVta8pR6DjjfEU0rIUSSabG9IP4tcC/wufDQOuAbEdWUWPk54OYSLkMD9YMQSarFXoT7MPBaIAPg7vuBVQu9ycx2mNk+MztgZrfN83qtmX0lfP1hM9tU8NorzOzHZrbHzJ4ys7L/HTszmT19y/hSUEc0kWRbbFJMufvp/8vDzRjnXJJmZlXAncANwDbgfWa2bc5pHwIG3X0L8BngUwWf/8vAb7r7ZcDrgbK/BXBmIley6QcIpjqqUqYRsEhCLTaA/8nM/gPBzTnfAnwV+PsF3nMNcMDdXwjD+25eun35RuCL4eN7gTdZ0Mn8rcCT7v4EgLv3u/sMZS5oRVmaC3AQ9IMIbk+vEbBIEi02gG8DeoGngN8A7gP+cIH3rAOOFjw/Fh6b9xx3zwHDQCdwCeBmdr+ZPWpmvz/fFzCzW8xsl5nt6u3tXeS3Ep2hiemSjoABuppqNAUhklCLXQUxa2bfAL7h7qVIujRwHfAqYBx4wMx2u/sDc+q6C7gLYPv27bHv0hueyLGurb6kX1Md0USS65wjYAt83Mz6gH3AvvBuGLcv4nMfBzYUPF8fHpv3nHDetxXoJxgt/8Dd+9x9nGDEfdVivqE4DY+XfgTc2VSrKQiRhFpoCuIjBKsfXuXuHe7eAVwLvNbMPrLAex8BtprZZjOrAW4Gds45ZyfwwfDxTcCDYb+J+4HLzawhDOafA55Z9HcVk+GJLG0NJQ5g9YMQSayFAvgDwPvc/WD+gLu/APwy8CvnemM4p3srQZjuBe5x9z1mdoeZ5bcwfx7oNLMDwEcJ5ppx90Hg0wQh/jjwqLt/a4nfW0llZ2YZm54p+Qi4o7GGEfWDEEmkheaAq929b+5Bd+81swWTxt3vI5g+KDx2e8HjSeA9Z3nvlwmWoiVCfhdcyUfA6gchklgLjYDPNbmoiccCQ+Ol3Yacl9+MoWkIkeRZaAR8hZnN1/HbAA23CpS6D0Se+kGIJNc5A9jd1XBnkYYnggCMYw4YtB1ZJIlK07TgPHBmDrimpF/3dD8IjYBFEkcBXCT5OeC2Eo+AW+qqqa4yzQGLJJACuEjyI+BS3Q0jL5Uyuppq6RtRAIskjQK4SIbGszTXpalKWcm/dndzLb0aAYskjgK4SDIT2ZJfgMvrbqqlVyNgkcRRABfJUAzbkPO6mxXAIkmkAC6S4ThHwM219I9NMzMbe0M4EVkCBXCRDI1P01Zf2iVoed3NtczMOoPjWoomkiQK4CIZnsiVfAVEXne4G07TECLJogAuAndneGI61jlgUACLJI0CuAgmsjNkZzzWOWBQAIskjQK4COLaBZfXlZ+C0FpgkURRABdBXJ3Q8hpr0zTWVGkELJIwCuAiON0LOKY5YAimIXoUwCKJogAugrhHwJDfjDEZ29cXkaVTABdBvhdwqVtRFtIIWCR5FMBFUA4j4FXNdfRmFMAiSaIALoKh8SzplNFYE98NRFa31DEylWNsKhdbDSKyNArgIsj3gTArfSvKvAtag6VopzKaBxZJCgVwEQxNZGNdAQGcviX9iwpgkcRQABfB8Hh8ndDy8gHco3lgkcRQABfB8EQ2tl1weRoBiySPArgIhiamYx8BN9WmaapNaw5YJEEUwEUwPJ6NdQ1w3uqWWgWwSIIogFdoZtbJTMbXC7jQ6pY6TmkOWCQxFMArNDIZbye0Qhe01PHisEbAIkmhAF6h0414yiCAV7fW0TMyyazuDSeSCArgFcpvQ47rbhiFVjfXkp3RveFEkkIBvEJDZdAHIu+CVi1FE0kSBfAKlUMjnrw1rfUAnBxSAIskgQJ4hYbCX/fbG+NfhramLRgBnxyeiLkSEVkMBfAKDYyFvYDLYATc1VhLTVWK4xoBiySCAniFBseCXXDpqvj/KlMp44LWOk4MaQQskgTxp0bCDYxn6SiD6Ye8tW11moIQSYhIA9jMdpjZPjM7YGa3zfN6rZl9JXz9YTPbNOf1jWY2ama/G2WdKzE4Nl0WS9Dy1rbWc0JTECKJEFkAm1kVcCdwA7ANeJ+ZbZtz2oeAQXffAnwG+NSc1z8N/ENUNRbDwNg0HWXQByJvbVs9L2YmmdFmDJGyF+UI+BrggLu/4O7TwN3AjXPOuRH4Yvj4XuBNFt5WwszeCRwE9kRY44oNjk+XxQqIvLVt9czMOj26Q7JI2YsygNcBRwueHwuPzXuOu+eAYaDTzJqA/wf4xLm+gJndYma7zGxXb29v0QpfisHx6bKaA84vRdOFOJHyV64X4T4OfMbdR891krvf5e7b3X17d3d3aSorMDE9w2R2lvYymoJY1xZsxtA8sEj5S0f4uY8DGwqerw+PzXfOMTNLA61AP3AtcJOZ/VegDZg1s0l3/58R1rtkA+EmjI7G8rkIt6ZVI2CRpIgygB8BtprZZoKgvRn4pTnn7AQ+CPwYuAl40N0deF3+BDP7ODBabuELwQoIoKxGwM111TTXpTmuABYpe5EFsLvnzOxW4H6gCviCu+8xszuAXe6+E/g88CUzOwAMEIR0YuR3wZXTRTiADe0NHB0Yj7sMEVlAlCNg3P0+4L45x24veDwJvGeBz/HxSIorgnzbx3IaAQNs7GjgQO85p89FpAyU60W4RMiPgMtpFQTAho56jg6ME8zmiEi5UgCvwODYNGbl0Yqy0MaOBqZys/SO6P5wIuVMAbwCg+NZ2uqrqUpZ3KX8lPUdDQAc0TywSFlTAK/AQJntgsvb0B4E8NFBBbBIOVMAr8DAaHn1gchb3x5sxjg6oKVoIuVMAbwCfaNTdDXVxl3GS9RVV7G6pVZTECJlTgG8Av1j03Q2ld8IGIILcVoLLFLeFMDLlJuZZXB8ms4yHAGDNmOIJIECeJkGxqdxh64yHQFv6mrkxPAkk9mZuEsRkbNQAC9T/2iwCaMc54ABNnc1AnCofyzmSkTkbBTAy5QP4M4yXIYGZwL4YK8CWKRcKYCXqX8s2GVWrnPAm8IAfqFPASxSrhTAy5Tf5ttdpgHcVJtmVXMtBxXAImVLAbxM/WPTpFNGS32kDeVWZHNXI4cUwCJlSwG8TP2jU3Q21RDeQ7QsXdTdqBGwSBlTAC9T3+g0nY3lOf2Qt6mzkf6xaYbHs3GXIiLzUAAvU//oFF3N5R3Am09fiFNzdpFypABepr7RabrKdAla3pZVTQDs71EAi5QjBfAyuDt94RxwObuws5GadIr9p0biLkVE5qEAXoax6RmmcrNluwY4ryplbOluYt8pjYBFypECeBl6MpMArG4p7wAGuGR1k0bAImVKAbwMpzLBJoxVzXUxV7KwraubOTk8SWZSKyFEyo0CeBl6RpIzAn7Z6mYA9msaQqTsKICXoSccAXcnYAR8SRjAz2kaQqTsKICXoWdkkrrqFC115bsNOW99ez311VXse1EBLFJuFMDLcCozxeqWurLehpyXShmXrmnmmZOZuEsRkTkUwMvQMzLJqjLfBVfo5Wtb2Xsiw+ysx12KiBRQAC9DT2aKVS3lP/+bd9naFkamcrpLskiZUQAvQ8/IVLJGwOtaAXj6xHDMlYhIIQXwEo1N5RidyrE6QSPgraubqK4y9pzQPLBIOVEAL1FPeCeMJKwBzqtNV3HJ6maePq4RsEg5UQAv0alwG3ISdsEVevnaVp4+Poy7LsSJlAsF8BIlcQQMcMWGNgbHsxzu14U4kXKhAF6iF4cnABK1CgLg6gvbAdh9eDDmSkQkTwG8RCeGJmmuS9NSVx13KUuydVUTzbVpHj2iABYpFwrgJTo+NMG6tvq4y1iyVMq4cmMbjx4ZirsUEQkpgJfoxNAEaxMYwABXbWxn34sZRqdycZciIiiAlywI4GTN/+ZdfWE7sw6PaRpCpCxEGsBmtsPM9pnZATO7bZ7Xa83sK+HrD5vZpvD4W8xst5k9FX58Y5R1Ltb4dI7B8SxrWhM6Ar6wnXTK+JcX+uMuRUSIMIDNrAq4E7gB2Aa8z8y2zTntQ8Cgu28BPgN8KjzeB7zd3S8HPgh8Kao6l+LEULAGOIlzwABNtWmu2NDGQwcUwCLlIMoR8DXAAXd/wd2ngbuBG+eccyPwxfDxvcCbzMzc/TF3PxEe3wPUm1nsC29PDAVL0JI6Bwzwmos7efLYkG5RJFIGogzgdcDRgufHwmPznuPuOWAY6JxzzruBR919au4XMLNbzGyXme3q7e0tWuFncyaAkzkHDPCai7uYdfjJCwNxlyJy3ivri3BmdhnBtMRvzPe6u9/l7tvdfXt3d3fk9ZwYmiBlJKoRz1yv3NhGbTrFQ8/3xV2KyHkvygA+DmwoeL4+PDbvOWaWBlqB/vD5euDrwK+4+/MR1rlox4cmWd1SR3VVWf+7dU511VVce1En398X/W8MInJuUSbJI8BWM9tsZjXAzcDOOefsJLjIBnAT8KC7u5m1Ad8CbnP3hyKscUlODE2wpjW5o9+8N//MKg72jfF8r+6ULBKnyAI4nNO9Fbgf2Avc4+57zOwOM3tHeNrngU4zOwB8FMgvVbsV2ALcbmaPh39WRVXrYh0ZGOfCzsa4y1ixN14a/FU+sPdUzJWInN8iva2vu98H3Dfn2O0FjyeB98zzvk8Cn4yytqWazM5wYniCCzsb4i5lxda3N3DpBc18d28Pt1x/cdzliJy3kjuZWWLHBsdxh00VMAIGeMu21ew6NEDf6EsWl4hIiSiAF+lQX9BHtxJGwAC/+Iq1zDrc99TJuEsROW8pgBfpUP8YQEXMAQO87IJmXra6mb97/MTCJ4tIJBTAi3S4f5zmujTtDcnqA3wu77hyLbsPD3JUt6sXiYUCeJEOD4yzqbMRM4u7lKJ5xxVrMYN7dx+LuxSR85ICeJEO949VzPxv3oaOBq7f2s3djxwhOzMbdzki5x0F8CJkZ2Y5NjhRMSsgCr3/2o2cykzxwN6euEsROe8ogBfhyMA4M7NecSNgCDZlrG2t4y8eOhh3KSLnHQXwIuw/NQIEKwcqTboqxa9ft5mHDw6w+7A6pImUkgJ4Efa9GPRM2LKqKeZKovFL126kvaGaO79XFj2PRM4bCuBFeO7UCBs7GmioiXTndmwaatJ86LrNPPhsD7sOaRQsUioK4EV47tQIl6yuvOmHQr9+3WZWt9TyH7/5DLOzHnc5IucFBfACpnOzHOwb45LVlTn9kNdQk+b333YpTxwb5qu7jy78BhFZMQXwAg72jZGb9Yq8ADfXu165jms3d/DJb+7leHj7JRGJjgJ4AfvCFRBbV1V+AKdSxp/cdAUz7nzk7se1OUMkYgrgBTxzIkN1lXHxqsrbhDGfjZ0N/Od3Xc5PDg3wRzv34K75YJGoVOZl/SJ6/OggP7Omhdp0VdyllMw7X7mOfadG+LPvP09zbZrbbri0onpgiJQLBfA5zMw6Tx0b5t1Xr4+7lJL7vbe+jNHJHJ/7wQv0jk7xn955OfU1588/QiKloAA+h+d7RxmbnuGK9W1xl1JyqZRxx42X0dFYw2cf2M/jR4f4xDsu47otXRoNixSJ5oDP4fGjQwBcsaEt1jriYmZ85C2X8Fe/fg25GecDn/8JN975EH/x0EEO9Iwwo/XCIiuiEfA5PHF0iOa6NBd1nR8X4M7m+ku6+cePXM9Xdx/jyz8+zCf+/hkAaqpStDVU095QQ2NtFemqFNVVRjpV8DGdoquphou6Grmou4krNrTRVKsfOxFQAJ/T7sODXLG+jVRKv3LXVVfxgVdfyAdefSGH+sbYfXiQ/T2jDI5NMzQxzdjUDNmZWSazs+RmcmRnnNzsLNkZ51RmkvHpGQDSKeOKDW1ct6WLt1+xhi3nwfI+kbNRAJ9F3+gUz744wu+9bW3cpZSdTV2NbFrCbwXuTs/IFPteHOHhg/386Pl+/seD+/nsA/vZtqaFf3XVOt75ynV0NdVGWLVI+VEAn8VDB/oAuG5LV8yVJJ+ZsbqljtUtdVx/STcAPSOTfOvJk3zj8RN88lt7+eN/eJY3/8xq3vuqDVx/STdV+q1DzgMK4LP44f4+Wuurefm61rhLqUirmuv4tddu5tdeu5n9p0a4Z9dR/vbR43x7z4tc0FLHTVev519v38DGCmyCL5JnlbLTafv27b5r166ifC535zV//CCv3NjGn77/6qJ8TlnYdG6WB589xVceOco/PdfLrMOrL+rgva/awA0vX0NdtdYhS2LN+yudRsDz2N8zysnhSW7V9ENJ1aRT7Hj5Gna8fA0nhyf42u5j3LPrGB/5yhP8v9/Yw2u3dHL9Jd285uIuNnU2aD2yJJ4CeB5//8QJUgZv3XZB3KWct9a01nPrG7fyW6/fwsMHB9j5xHF+8Fwf9+85BUBzXZrL17VycXcTGzrqWdfWQGt9Nc11aZrq0jTXpqmtrqI2naI2nVJYS1lSAM/h7ux84gSvubiL7mZdlY9bKmX87MWd/OzFnbg7z/eOsfvwAE8eG+bp48P83ePHyUzmFvw8NWEQ16bDUK4OHrc3VLOmtZ51bXVsWd3MtjXNbO5q0kVAKQkF8BxPHhvmcP84H379lrhLkTnMjC2rmtiyqon3vurM8eGJLCeGJshMZBmdyjE6lSMzmWMqO8NUbpap3CzTuVmmcuHzbPB4MjvL4Pg0P3q+j1OZSfIb+2rTKV52QTNXbWzn1Rd1cu3mDtoba+L5pqWiKYDnuGfXUWqqUrztMk0/JEVrfTWt9dUr+hzTuVkO9Iyy92SGZ05m2HNimLsfOcJf/ugQAJde0MxrLu7idVu7uPaijoq9P6CUln6KCvSPTnHv7mO865XraG1Y2f/Qkiw16RTb1rawbW0L7w6PTeVmePLYMA+/0M+PX+jnyw8f5gsPHaS6yrhqYzvXbeniuq1dvGJ9m6YsZFm0DK3AZ77zHJ99YD/f/ej12iIrLzGZneGRQwP88EAfP9zfx54TGQBa6tK85uIgjK/b0sWFWqEhL6VlaOfSOzLFFx46yJt/ZrXCV+ZVV13F67Z287qt3XBD8BvTj57v54f7+/jhgT6+vedFANa31/O6rV1ct6Wb11zcqfljOSsFcOi/3LeXqewsH/v5S+MuRRKis6mWt1+xlrdfsRZ352DfGA8d6OOf9/fxzSdO8jc/Ce4ufXF3I1dtbOfqC9u56sJ2tnQ3qcGTAApgAP7hqZP87WPH+e03buHi7sq+/bxEw8y4qLuJi7qb+MDPbiI3M8uTx4f58fP9PHZkkAee7eGru48BwZTFKze2c8WGNrataeGytS2sb6/XtMV56LwP4CeODvGRex7nqo1tfPgNWnomxZGuSnHVxnau2tgOBOvLD/WPs/vwILsPD/Lo4UH+ef/+00vfWuur2bYmuAi4uauRi7oa2dzdyOrmurIeLU/lZhiZzDEymSMzkQ0+TmaZzM6QMsMMqlJGdVWK5to0zXXBZpnW+mraGqrP+390zuuLcA/sPcVv/81jdDTW8PXfeq02XkhJTUzP8OyLGfacCP48c2KYfadGmMzOnj6nNp1iVUst3U21rGquo7Ophqa6NI01aRpqqmioSZNOBUGXMiOVCj+e/gMOuMOse/jYcQfHw+PhsfC1yewsY9M5xqdmGJvOMTaVY2xqhsxklsxkjpGJ8ONklqnc7Nm+vQVVVxndTbV0t9Sxqrk2/FPHqpZaVrcEj1e31NHZWFPW/wgt0rzfQKQBbGY7gM8CVcCfu/sfz3m9Fvgr4GqgH3ivux8KX/sY8CFgBvi/3f3+c32tpQbwxPQMP/cn32N1Sx2f/+B2VrXULf4bE4nI7KzzYmaSg31jHOwb48jAOD2ZSXpGpugZmaJvdIrxqRmmZ5YffIuVMmisDcO+toqWcPTaUl9NS10wms1/bK5Ln369ua6ahpoqZt2ZDYN/OjcbjpSDUfLQRJbekSl6RiaDj5ng8eB49iV1pFNGd3Mtq1rqWN1cG7Y2DZ6vOv28jvbyHlGXNoDNrAp4DngLcAx4BHifuz9TcM5vAa9w9980s5uBd7n7e81sG/A3wDXAWuC7wCXuPnO2r7ecEfBzp0ZY316vRfWSONmZWcanZ5iYnmHGndnZYDQ74x6MdMPwm5n106NjA8yC+ergcTBCNiw8Hhyrr66ioaYqlh4a07lZeken6MlMcioM5VPh41OZSXoyU5wamWRonqCuqUrR3Xxm9NzeWENbQzVt4XRHa33wvKWumvqaKurC7eh11Snq0lVRj7JLvgztGuCAu78AYGZ3AzcCzxSccyPw8fDxvcD/tOC/+I3A3e4+BRw0swPh5/txMQu8ZLWWm0kyVVelaK1PrXgHYLmpSadY11bPurb6c543mZ2hd2Tqp8L51MgkvWFAH+gdZejwNEPjWXKLvHlsTXhPw1QqmL6pSp2Zxsk/bqmv5h9+53XF+FaBaAN4HXC04Pkx4NqznePuOTMbBjrD4/8y573r5n4BM7sFuCV8Ompm/UBfUaovjS6SVS+o5lJIWr1wHtVs/25ZX+vb7r5j7sFE/+7t7ncBd+Wfm9kud98eY0lLkrR6QTWXQtLqBdW8XKkIP/dxYEPB8/XhsXnPMbM00EpwMW4x7xURSbQoA/gRYKuZbTazGuBmYOecc3YCHwwf3wQ86MFVwZ3AzWZWa2abga3ATyKsVUSk5CKbggjndG8F7idYhvYFd99jZncAu9x9J/B54EvhRbYBgpAmPO8eggt2OeDD51oBUeCuhU8pK0mrF1RzKSStXlDNy1IxGzFERJImyikIERE5BwWwiEhMEh/AZvYeM9tjZrNmtn3Oax8zswNmts/M3hZXjfMxsx1hXQfM7La465mPmX3BzHrM7OmCYx1m9h0z2x9+bI+zxkJmtsHMvmdmz4Q/E78THi/nmuvM7Cdm9kRY8yfC45vN7OHw5+Mr4YXssmFmVWb2mJl9M3xe7vUeMrOnzOxxM9sVHov95yLxAQw8Dfwr4AeFB8PtzDcDlwE7gD8Nt0fHLqzjTuAGYBvwvrDecvOXBH93hW4DHnD3rcAD4fNykQP+vbtvA14NfDj8ey3nmqeAN7r7FcCVwA4zezXwKeAz7r4FGCToi1JOfgfYW/C83OsFeIO7X1mw9jf2n4vEB7C773X3ffO8dHo7s7sfBPLbmcvB6W3a7j4N5LdplxV3/wHB6pRCNwJfDB9/EXhnKWs6F3c/6e6Pho9HCAJiHeVds7v7aPi0OvzjwBsJtudDmdVsZuuBXwD+PHxulHG95xD7z0XiA/gc5tsK/ZLtzDEp59oWstrdT4aPXwRWx1nM2ZjZJuCVwMOUec3hr/OPAz3Ad4DngSF3z4WnlNvPx38Hfh/It2TrpLzrheAftX80s91hCwMog5+LRGxFNrPvAvPdJ/4P3P3vSl2PBNzdzazs1jGaWRPwNeDfuXumsKNXOdYcrnG/0szagK8DZXtfLDP7RaDH3Xeb2etjLmcprnP342a2CviOmT1b+GJcPxeJCGB3f/My3lbO25nLubaFnDKzNe5+0szWEIzayoaZVROE71+7+9+Gh8u65jx3HzKz7wE/C7SZWTocVZbTz8drgXeY2c8DdUALQc/vcq0XAHc/Hn7sMbOvE0wDxv5zUclTEOW8nXkx27TLVeH28Q8CZfMbSDgX+Xlgr7t/uuClcq65Oxz5Ymb1BP2z9wLfI9ieD2VUs7t/zN3Xu/smgp/bB939/ZRpvQBm1mhmzfnHwFsJLt7H/3PhYfPmpP4B3kUw5zQFnALuL3jtDwjm0/YBN8Rd65y6f56gYf3zBFMpsdc0T41/A5wEsuHf8YcI5vseAPYTNMrviLvOgnqvI5jrexJ4PPzz82Ve8yuAx8KanwZuD49fRDBgOAB8FaiNu9Z5an898M1yrzes7Ynwz578/2/l8HOhrcgiIjGp5CkIEZGypgAWEYmJAlhEJCYKYBGRmCiARURiogAWEYmJAljOC2Y2Gn7cVNhecwnvP2RmXUWq5ftzW6fK+UkBLCISEwWwlJ1w6+i3wiblT5vZe8MR6H/JN9Q2s6vM7H4ze97MfjN8X5OZPWBmj4bNt5fc4jPsTPbfwq/7pJn9dsHLv13wuS8Nz/+4mf1uwfufDkfZm8xsr5n9r7DR+j+GW40Lv1bKzP7SzD4Zft2/DN//lJl9ZJl/fZIgCmApRzuAE+5+hbu/HPh2ePyIu18J/DNBs/ibCBqvfyJ8fRJ4l7tfBbwB+P+ssBXa4twCbAKudPdXAH9d8Fpf+Ln/DPjded4711bgTne/DBgC3l3wWjr83Pvd/Q8JmrGvc/eXu/vlwF8ssW5JIAWwlKOngLeY2afM7HXuPhwe31nw+sPuPuLuvcBU2NDGgP9sZk8S7O1fx9J7vL4Z+JyHvW3dvbAhfb672m6CkF7IQXd//Czv+RzwtLv/p/D5C8BFZvY/zGwHkFli3ZJACmApO+7+HHAVQdB+0sxuD1+aCj/OFjzOP08D7we6gavDkfIpgpaJxZL/mjOcaeWa46f/P6qb5/y57wH4EfAGM6sDcPdB4Arg+8BvEt5tQiqbAljKjpmtBcbd/cvAnxCE8WK0EjQLz5rZG4ALl/HlvwP8hpmlw1o6Fjj/UL4+M7sK2LzIr/N54D7gHjNLhyssUu7+NeAPWfz3LAmmAJZydDnwk/A2PX8EfHKR7/trYLuZPQX8CvDsAufP58+BI8CTZvYE8EsLnP81oMPM9gC3ErQYXRQPehY/BnyJYLrk++H3/GXgY0svXZJG7ShFRGKiEbCISEwScU84kWIzs7cBn5pz+KC7vyuOeuT8pCkIEZGYaApCRCQmCmARkZgogEVEYqIAFhGJyf8BD+xhTvBqKR8AAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# They are quite rare\n", + "sns.displot(df[df['small_chunks'] <= 50]['small_chunks'], kind='kde')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "2db3e55c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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JjRjAktSIASxJjQw9gJMck+SrSe5KcmeSN3bt70yyPsmt3evlw65NkoZpWYNjbgXeUlW3JDkMuDnJdd2y91fVexrUJElDN/QArqoNwIZu+tEkdwNHDbsOSWqtaR9wklXAicA3u6YLktyW5JIkR7SrTJIGr1kAJzkUuBJ4U1X9FPgIcDywht4V8nt3st3aJBNJJiYnJ4dVriTtc00COMlB9ML3k1X1OYCq2lhV26pqO3AxcNJc21bVuqoar6rxsbGx4RUtSftYi7sgAnwcuLuq3tfXvrJvtdOBO4ZdmyQNU4u7IF4AnAPcnuTWru1twNlJ1gAF3A+8vkFtkjQ0Le6C+DqQORZdM+xaJKkln4STpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEYMYElqxACWpEZGNoC3bXmM6enp1mVIGmEjG8CS1NpIB/D09LRXwZKaGekAlqSWRjaAq4rp6elfvHslLGnYRjaAt2/dwr//xE3MzMy0LkXSiBrZAAY44KCDW5cgaYS1+E64RaOq2Lx5M1XF8uXLW5cjacSM9BXw9q1beO3FX+P3/+p6uyIkDd1IXwFDrxvigANG+u8hSY2YPJLUiAEsSY0YwJLUiAHc6X8wQ5KGwQDuzMzM8KoPftm7ISQNjQHM41e/B3YPZng1LGkYDGB69wOv/fjX2bJlC9PT0zzyyCOc8Z4v8Mgjj/DII4+wefPm1iUOnX8JjTY//+FYdAGc5NQk9yS5N8lFwzruAQcdzPatWzjnozcwNTXFAQcdzMzMDL//4a8wNTXF5s2bf/EDubMfzn35Q7s3x5hvnYXsY2ZmhjM/cB1TU1NPWG/2tv3zo/pLu1TOu/887JJ7okF9xosqgJMcCPwV8DLgBODsJCcM6/jbtjzG9ire8Nf/wPbt2wF+0S0xNTXFmf/9S8zMzOz0h3Nqaooz3vMFpqam5j3WfCOw7ewYC/nFmG+dhf5yJeHcj934hPVmb9s/P6q/tEvlvGefx46ffUcLHNxnvNiehDsJuLeqfgCQ5NPAacBd+/Ig27c81ntt306A2rb1F4GbvnWmp6ef9NVFO5vun1/ID+xCl+/qGLu77e7uY9uWxzjgwGVznvPO3ufb71K0kD/P/cHs8/Brux43qD+HLKZ/NiX5HeDUqvqDbv4c4Feq6oK+ddYCa7vZZwL37MGhngY8vJfl7k9G6XxH6VzB891fPFxVp85uXGxXwPOqqnXAur3ZR5KJqhrfRyUteqN0vqN0ruD57u8WVR8wsB44pm/+6K5NkpacxRbA3wJWJzkuycHAWcDVjWuSpIFYVF0QVbU1yQXAF4EDgUuq6s4BHGqvujD2Q6N0vqN0ruD57tcW1X/CSdIoWWxdEJI0MgxgSWpkpAK41WPOrSS5P8ntSW5NMtG6nn0tySVJNiW5o6/tyCTXJfl+935Eyxr3pZ2c7zuTrO8+41uTvLxljftSkmOSfDXJXUnuTPLGrn3JfMYjE8CtH3Nu6MVVtWYp3TvZ51Jg9s3tFwHXV9Vq4Ppufqm4lCefL8D7u894TVVdM+SaBmkr8JaqOgF4HnB+9zu7ZD7jkQlg+h5zrqrHgB2POWs/VVU3Aj+Z1XwacFk3fRnwymHWNEg7Od8lq6o2VNUt3fSjwN3AUSyhz3iUAvgo4Ed98w92bUtZAV9KcnP3CPcoWFFVG7rph4AVLYsZkguS3NZ1Uey3/xzflSSrgBOBb7KEPuNRCuBR9MKqeg69bpfzk7yodUHDVL17LJf6fZYfAY4H1gAbgPc2rWYAkhwKXAm8qap+2r9sf/+MRymAR+4x56pa371vAq6i1w2z1G1MshKge9/UuJ6BqqqNVbWtqrYDF7PEPuMkB9EL309W1ee65iXzGY9SAI/UY85JnpLksB3TwEuBO3a91ZJwNXBeN30e8PmGtQzcjiDqnM4S+oyTBPg4cHdVva9v0ZL5jEfqSbjuFp2/5PHHnP9L24oGJ8nT6V31Qu+R808ttfNNcjlwCr0hCjcC7wD+J3AFcCzwAHBmVS2J/7jayfmeQq/7oYD7gdf39Y/u15K8EPgacDuwvWt+G71+4CXxGY9UAEvSYjJKXRCStKgYwJLUiAEsSY0YwJLUiAEsSY0YwJLUiAGsRSvJ4Un+aJ51ViX5dwvY16r+YRznWH5Kki/sSZ3zHPeV/aPuJfm7JEtxZDrtAQNYi9nhwC4DGFgFzBvADb2S3vCn0pMYwFrM3gUc3w00/u7udUc3yPyr+tY5uVvnzd2V7teS3NK9nr+7B+0e474kyU1Jvp3ktK791Uk+l+TabjDwv+jb5nVJvtdtc3GSD3XHfgXw7q6+47vVf7db73tJTt67PyLtzxbVtyJLs1wEPKuq1iQ5A3gD8Gx6j+J+K8mN3Tp/UlW/BZDknwK/UVXTSVYDlwO7+0/+twNfqarXJjkcuCnJl7tla+gNizgD3JPkg8A24D8CzwEeBb4CfKeq/iHJ1cAXquqzXX0Ay6rqpO7R+HcAv767fzBaGgxg7S9eCFxeVdvojYZ1A/Bc4Kez1jsI+FCSNfSC8Rl7cKyXAq9I8ifd/HJ64w5A75sYpgCS3AX8S3p/IdywYzyCJH8zz3F3jOp1M70uFI0oA1hLzZvpDVTzbHpdbNN7sI8AZ1TVPU9oTH6F3pXvDtvYs9+hHfvY0+21RNgHrMXsUeCwbvprwKuSHJhkDHgRcNOsdQD+GbChGx/3HHoj3+2uLwJ/3A2HSJIT51n/W8C/TXJEkmXAGTs5B+kJDGAtWlX1Y+Dvu9vHfhW4DfgOvT7WP62qh7q2bUm+k+TNwIeB85J8B/jXwM/24NB/Tq8r47Ykd3bzu6pzPfBf6f2F8Pf0hoWc6hZ/Gnhr9595x8+9B40qh6OU9oEkh1bV/+uugK+iN970VfNtp9HmFbC0b7wzya30vpHih/QGhpd2yStgjZQkvwn8t1nNP6yq01vUo9FmAEtSI3ZBSFIjBrAkNWIAS1IjBrAkNfL/ARM/fozanC5FAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.displot(df['total_length'].dt.seconds/3600)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "4e3a3c21", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.32186732186732187" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# with good quality\n", + "(df[df['ratio_clean'] > 0.5]).shape[0] / df.shape[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "691fc93a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.384051013968929" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# % for goog quality length\n", + "df[df['ratio_clean'] > 0.5]['total_length'].dt.seconds.sum() / df['total_length'].dt.seconds.sum()" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "ce8ad54ff0c21d76b9d2b2c70378af31077ad0766af937406802e3e7962ef6ad" + }, + "kernelspec": { + "display_name": "Python 3.6.8 ('env': venv)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/files/Statistics qrs_comp_after_smoothing.ipynb b/files/Statistics qrs_comp_after_smoothing.ipynb new file mode 100644 index 0000000..1b0fdb9 --- /dev/null +++ b/files/Statistics qrs_comp_after_smoothing.ipynb @@ -0,0 +1,1691 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "cc0f2647", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append(\"..\")\n", + "\n", + "from src.usecase.consolidate_feats_and_annot import read_tse_bi\n", + "from src.usecase.fetch_database import create_df_from_file_pattern\n", + "import pandas as pd\n", + "import os\n", + "import seaborn as sns\n", + "import re" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4598ee5d", + "metadata": {}, + "outputs": [], + "source": [ + "DATASET_PATIENT_PATTERN = r\"[P][A][T][_][0-9]*\"\n", + "DATASET_EXAM_PATTERN = r\"[E][E][G][_][0-9]*\"\n", + "ANNOTATION_FILE_PATTERN = \"*.tse_bi\"" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "5157511e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ecg_qc_file_path exam_id patient_id\n", + "0 ../output/res-comp-v0_6/PAT_22/qc_qrs_EEG_304_... EEG_304 PAT_22\n", + "1 ../output/res-comp-v0_6/PAT_22/qc_qrs_EEG_311_... EEG_311 PAT_22\n", + "2 ../output/res-comp-v0_6/PAT_22/qc_qrs_EEG_304_... EEG_304 PAT_22\n", + "3 ../output/res-comp-v0_6/PAT_22/qc_qrs_EEG_311_... EEG_311 PAT_22\n", + "4 ../output/res-comp-v0_6/PAT_22/qc_qrs_EEG_304_... EEG_304 PAT_22\n", + ".. ... ... ...\n", + "401 ../output/res-comp-v0_6/PAT_9/qc_qrs_EEG_103_s... EEG_103 PAT_9\n", + "402 ../output/res-comp-v0_6/PAT_9/qc_qrs_EEG_103_s... EEG_103 PAT_9\n", + "403 ../output/res-comp-v0_6/PAT_9/qc_qrs_EEG_103_s... EEG_103 PAT_9\n", + "404 ../output/res-comp-v0_6/PAT_9/qc_qrs_EEG_103_s... EEG_103 PAT_9\n", + "405 ../output/res-comp-v0_6/PAT_9/qc_qrs_EEG_103_s... EEG_103 PAT_9\n", + "\n", + "[406 rows x 3 columns]\n" + ] + } + ], + "source": [ + "df = create_df_from_file_pattern(\n", + " data_folder_path='../output/res-comp-v0_6',\n", + " file_pattern=ANNOTATION_FILE_PATTERN,\n", + " file_label='ecg_qc',\n", + " patient_pattern=DATASET_PATIENT_PATTERN,\n", + " exam_pattern=DATASET_EXAM_PATTERN)\n", + "print(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "a343c7c5", + "metadata": {}, + "outputs": [], + "source": [ + "def compute_stats_files(tse_bi_file):\n", + " \n", + " df = read_tse_bi(tse_bi_file)\n", + " df['length'] = df['end'] - df['start']\n", + " total_length = df['length'].sum()\n", + " total_noise = df[df['annotation'] == 'noisy']['length'].sum()\n", + " total_clean = df[df['annotation'] == 'clean']['length'].sum()\n", + " small_chunks = df[(df['annotation'] == 'noisy') & (df['length'].dt.seconds <= 8)].shape[0]\n", + " big_chunks = df[(df['annotation'] == 'noisy') & (df['length'].dt.seconds >= 8)].shape[0]\n", + " very_big_chunks = df[(df['annotation'] == 'noisy') & (df['length'].dt.seconds >= 8)].shape[0]\n", + " # df[['ratio_clean', 'small_chunks']].fillna(0)\n", + " if str(total_noise) == \"NaT\":\n", + " total_noise = pd.Timedelta(seconds=0)\n", + " if str(total_clean) == \"NaT\":\n", + " total_clean = pd.Timedelta(seconds=0)\n", + "\n", + " return total_length, total_noise, total_clean, small_chunks, big_chunks, very_big_chunks" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "f4f74615", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/aura-alexis/github/seizure_detection_pipeline/env/lib64/python3.6/site-packages/pandas/core/frame.py:4327: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " downcast=downcast,\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " ecg_qc_file_path exam_id patient_id \\\n", + "203 ../output/res-comp-v0_6/PAT_49/qc_qrs_EEG_638_... EEG_638 PAT_49 \n", + "247 ../output/res-comp-v0_6/PAT_52/qc_qrs_EEG_714_... EEG_714 PAT_52 \n", + "255 ../output/res-comp-v0_6/PAT_52/qc_qrs_EEG_689_... EEG_689 PAT_52 \n", + "253 ../output/res-comp-v0_6/PAT_52/qc_qrs_EEG_714_... EEG_714 PAT_52 \n", + "252 ../output/res-comp-v0_6/PAT_52/qc_qrs_EEG_689_... EEG_689 PAT_52 \n", + ".. ... ... ... \n", + "118 ../output/res-comp-v0_6/PAT_44/qc_qrs_EEG_573_... EEG_573 PAT_44 \n", + "1 ../output/res-comp-v0_6/PAT_22/qc_qrs_EEG_311_... EEG_311 PAT_22 \n", + "134 ../output/res-comp-v0_6/PAT_46/qc_qrs_EEG_593_... EEG_593 PAT_46 \n", + "136 ../output/res-comp-v0_6/PAT_46/qc_qrs_EEG_593_... EEG_593 PAT_46 \n", + "0 ../output/res-comp-v0_6/PAT_22/qc_qrs_EEG_304_... EEG_304 PAT_22 \n", + "\n", + " total_length total_noise total_clean small_chunks big_chunks \\\n", + "203 0 days 00:00:28 0 days 00:00:00 0 days 00:00:28 0.0 0.0 \n", + "247 0 days 00:00:54 0 days 00:00:00 0 days 00:00:54 0.0 0.0 \n", + "255 0 days 00:01:26 0 days 00:00:00 0 days 00:01:26 0.0 0.0 \n", + "253 0 days 00:14:52 0 days 00:00:00 0 days 00:14:52 0.0 0.0 \n", + "252 0 days 00:00:50 0 days 00:00:00 0 days 00:00:50 0.0 0.0 \n", + ".. ... ... ... ... ... \n", + "118 0 days 00:15:22 0 days 00:15:22 0 days 00:00:00 0.0 1.0 \n", + "1 0 days 00:32:20 0 days 00:32:20 0 days 00:00:00 0.0 1.0 \n", + "134 0 days 00:06:28 0 days 00:06:28 0 days 00:00:00 0.0 1.0 \n", + "136 0 days 00:01:22 0 days 00:01:22 0 days 00:00:00 0.0 1.0 \n", + "0 0 days 01:27:40 0 days 01:27:40 0 days 00:00:00 0.0 1.0 \n", + "\n", + " very_big_chunks ratio_clean ratio_small_chunks \n", + "203 0.0 1.0 0.0 \n", + "247 0.0 1.0 0.0 \n", + "255 0.0 1.0 0.0 \n", + "253 0.0 1.0 0.0 \n", + "252 0.0 1.0 0.0 \n", + ".. ... ... ... \n", + "118 1.0 0.0 0.0 \n", + "1 1.0 0.0 0.0 \n", + "134 1.0 0.0 0.0 \n", + "136 1.0 0.0 0.0 \n", + "0 1.0 0.0 0.0 \n", + "\n", + "[406 rows x 11 columns]" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Top noisy\n", + "\n", + "df.sort_values('ratio_clean', ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "fedff484", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ecg_qc_file_pathexam_idpatient_idtotal_lengthtotal_noisetotal_cleansmall_chunksbig_chunksvery_big_chunksratio_cleanratio_small_chunks
203../output/res-comp-v0_6/PAT_49/qc_qrs_EEG_638_...EEG_638PAT_490 days 00:00:280 days 00:00:000 days 00:00:280.00.00.01.00.0
247../output/res-comp-v0_6/PAT_52/qc_qrs_EEG_714_...EEG_714PAT_520 days 00:00:540 days 00:00:000 days 00:00:540.00.00.01.00.0
255../output/res-comp-v0_6/PAT_52/qc_qrs_EEG_689_...EEG_689PAT_520 days 00:01:260 days 00:00:000 days 00:01:260.00.00.01.00.0
253../output/res-comp-v0_6/PAT_52/qc_qrs_EEG_714_...EEG_714PAT_520 days 00:14:520 days 00:00:000 days 00:14:520.00.00.01.00.0
252../output/res-comp-v0_6/PAT_52/qc_qrs_EEG_689_...EEG_689PAT_520 days 00:00:500 days 00:00:000 days 00:00:500.00.00.01.00.0
....................................
118../output/res-comp-v0_6/PAT_44/qc_qrs_EEG_573_...EEG_573PAT_440 days 00:15:220 days 00:15:220 days 00:00:000.01.01.00.00.0
1../output/res-comp-v0_6/PAT_22/qc_qrs_EEG_311_...EEG_311PAT_220 days 00:32:200 days 00:32:200 days 00:00:000.01.01.00.00.0
134../output/res-comp-v0_6/PAT_46/qc_qrs_EEG_593_...EEG_593PAT_460 days 00:06:280 days 00:06:280 days 00:00:000.01.01.00.00.0
136../output/res-comp-v0_6/PAT_46/qc_qrs_EEG_593_...EEG_593PAT_460 days 00:01:220 days 00:01:220 days 00:00:000.01.01.00.00.0
0../output/res-comp-v0_6/PAT_22/qc_qrs_EEG_304_...EEG_304PAT_220 days 01:27:400 days 01:27:400 days 00:00:000.01.01.00.00.0
\n", + "

406 rows × 11 columns

\n", + "
" + ], + "text/plain": [ + " ecg_qc_file_path exam_id patient_id \\\n", + "203 ../output/res-comp-v0_6/PAT_49/qc_qrs_EEG_638_... EEG_638 PAT_49 \n", + "247 ../output/res-comp-v0_6/PAT_52/qc_qrs_EEG_714_... EEG_714 PAT_52 \n", + "255 ../output/res-comp-v0_6/PAT_52/qc_qrs_EEG_689_... EEG_689 PAT_52 \n", + "253 ../output/res-comp-v0_6/PAT_52/qc_qrs_EEG_714_... EEG_714 PAT_52 \n", + "252 ../output/res-comp-v0_6/PAT_52/qc_qrs_EEG_689_... EEG_689 PAT_52 \n", + ".. ... ... ... \n", + "118 ../output/res-comp-v0_6/PAT_44/qc_qrs_EEG_573_... EEG_573 PAT_44 \n", + "1 ../output/res-comp-v0_6/PAT_22/qc_qrs_EEG_311_... EEG_311 PAT_22 \n", + "134 ../output/res-comp-v0_6/PAT_46/qc_qrs_EEG_593_... EEG_593 PAT_46 \n", + "136 ../output/res-comp-v0_6/PAT_46/qc_qrs_EEG_593_... EEG_593 PAT_46 \n", + "0 ../output/res-comp-v0_6/PAT_22/qc_qrs_EEG_304_... EEG_304 PAT_22 \n", + "\n", + " total_length total_noise total_clean small_chunks big_chunks \\\n", + "203 0 days 00:00:28 0 days 00:00:00 0 days 00:00:28 0.0 0.0 \n", + "247 0 days 00:00:54 0 days 00:00:00 0 days 00:00:54 0.0 0.0 \n", + "255 0 days 00:01:26 0 days 00:00:00 0 days 00:01:26 0.0 0.0 \n", + "253 0 days 00:14:52 0 days 00:00:00 0 days 00:14:52 0.0 0.0 \n", + "252 0 days 00:00:50 0 days 00:00:00 0 days 00:00:50 0.0 0.0 \n", + ".. ... ... ... ... ... \n", + "118 0 days 00:15:22 0 days 00:15:22 0 days 00:00:00 0.0 1.0 \n", + "1 0 days 00:32:20 0 days 00:32:20 0 days 00:00:00 0.0 1.0 \n", + "134 0 days 00:06:28 0 days 00:06:28 0 days 00:00:00 0.0 1.0 \n", + "136 0 days 00:01:22 0 days 00:01:22 0 days 00:00:00 0.0 1.0 \n", + "0 0 days 01:27:40 0 days 01:27:40 0 days 00:00:00 0.0 1.0 \n", + "\n", + " very_big_chunks ratio_clean ratio_small_chunks \n", + "203 0.0 1.0 0.0 \n", + "247 0.0 1.0 0.0 \n", + "255 0.0 1.0 0.0 \n", + "253 0.0 1.0 0.0 \n", + "252 0.0 1.0 0.0 \n", + ".. ... ... ... \n", + "118 1.0 0.0 0.0 \n", + "1 1.0 0.0 0.0 \n", + "134 1.0 0.0 0.0 \n", + "136 1.0 0.0 0.0 \n", + "0 1.0 0.0 0.0 \n", + "\n", + "[406 rows x 11 columns]" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Top clean\n", + "df.sort_values('ratio_clean', ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "39ecd5e2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9312250885904998" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Ratio clean/noise\n", + "df['total_clean'].dt.seconds.sum() / df['total_length'].dt.seconds.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "230965d0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Small chunks are scarse\n", + "sns.displot(df['small_chunks'], kind='kde')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "5e64c2db", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Small chunks are scarse\n", + "sns.displot(df['ratio_small_chunks'], kind='kde')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "cd373f3d", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# They are quite rare\n", + "sns.displot(df[df['small_chunks'] <= 50]['small_chunks'], kind='kde')" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "2db3e55c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.displot(df['total_length'].dt.seconds/3600)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "4e3a3c21", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9310344827586207" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# with good quality\n", + "(df[df['ratio_clean'] > 0.8]).shape[0] / df.shape[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "691fc93a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8608875184501293" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# % for goog quality length\n", + "df[df['ratio_clean'] > 0.9]['total_length'].dt.seconds.sum() / df['total_length'].dt.seconds.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "a43983af", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['ecg_qc_file_path', 'exam_id', 'patient_id', 'total_length',\n", + " 'total_noise', 'total_clean', 'small_chunks', 'big_chunks',\n", + " 'very_big_chunks', 'ratio_clean', 'ratio_small_chunks'],\n", + " dtype='object')" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "bbe4669f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8530562975445641" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# % for goog quality length\n", + "df[df['very_big_chunks'] <= 0]['total_length'].dt.seconds.sum() / df['total_length'].dt.seconds.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "e331bad1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9630541871921182" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(df[df['very_big_chunks'] <= 0]).shape[0] / df.shape[0]" + ] + }, + { + "cell_type": "markdown", + "id": "db47507b", + "metadata": {}, + "source": [ + "## Taking only good quality segments" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "id": "4ed8d61c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ecg_qc_file_pathexam_idpatient_idtotal_lengthtotal_noisetotal_cleansmall_chunksbig_chunksvery_big_chunksratio_cleanratio_small_chunks
2../output/res-comp-v0_6/PAT_22/qc_qrs_EEG_304_...EEG_304PAT_220 days 00:03:340 days 00:00:000 days 00:03:340.00.00.01.0000000.000000
3../output/res-comp-v0_6/PAT_22/qc_qrs_EEG_311_...EEG_311PAT_220 days 00:17:500 days 00:00:140 days 00:17:363.00.00.00.9869160.011215
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8../output/res-comp-v0_6/PAT_22/qc_qrs_EEG_304_...EEG_304PAT_220 days 00:03:060 days 00:00:000 days 00:03:060.00.00.01.0000000.000000
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" + ], + "text/plain": [ + " ecg_qc_file_path exam_id patient_id \\\n", + "2 ../output/res-comp-v0_6/PAT_22/qc_qrs_EEG_304_... EEG_304 PAT_22 \n", + "3 ../output/res-comp-v0_6/PAT_22/qc_qrs_EEG_311_... EEG_311 PAT_22 \n", + "5 ../output/res-comp-v0_6/PAT_22/qc_qrs_EEG_311_... EEG_311 PAT_22 \n", + "7 ../output/res-comp-v0_6/PAT_22/qc_qrs_EEG_311_... EEG_311 PAT_22 \n", + "8 ../output/res-comp-v0_6/PAT_22/qc_qrs_EEG_304_... EEG_304 PAT_22 \n", + "\n", + " total_length total_noise total_clean small_chunks big_chunks \\\n", + "2 0 days 00:03:34 0 days 00:00:00 0 days 00:03:34 0.0 0.0 \n", + "3 0 days 00:17:50 0 days 00:00:14 0 days 00:17:36 3.0 0.0 \n", + "5 0 days 00:03:14 0 days 00:00:22 0 days 00:02:52 8.0 0.0 \n", + "7 0 days 00:12:06 0 days 00:00:00 0 days 00:12:06 0.0 0.0 \n", + "8 0 days 00:03:06 0 days 00:00:00 0 days 00:03:06 0.0 0.0 \n", + "\n", + " very_big_chunks ratio_clean ratio_small_chunks \n", + "2 0.0 1.000000 0.000000 \n", + "3 0.0 0.986916 0.011215 \n", + "5 0.0 0.886598 0.164948 \n", + "7 0.0 1.000000 0.000000 \n", + "8 0.0 1.000000 0.000000 " + ] + }, + "execution_count": 91, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_clean = df[df['very_big_chunks'] == 0]\n", + "df_clean.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "id": "2ad2be88", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(328, 11)" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_clean.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e5503123", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/aura-alexis/github/seizure_detection_pipeline/env/lib64/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " \"\"\"Entry point for launching an IPython kernel.\n" + ] + }, + { + "data": { + "text/html": [ + "
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ecg_qc_file_pathexam_idpatient_idtotal_lengthtotal_noisetotal_cleansmall_chunksratio_cleanratio_small_chunkscons
1../output/res-comp-v0_6/PAT_12/qc_qrs_EEG_297_...EEG_297PAT_120 days 00:05:420 days 00:00:040 days 00:05:382.00.9883040.023392../output/cons-v0_6/PAT_12/cons_PAT_12_Annotat...
2../output/res-comp-v0_6/PAT_12/qc_qrs_EEG_299_...EEG_299PAT_120 days 00:02:080 days 00:00:000 days 00:02:080.01.0000000.000000../output/cons-v0_6/PAT_12/cons_PAT_12_Annotat...
3../output/res-comp-v0_6/PAT_12/qc_qrs_EEG_300_...EEG_300PAT_120 days 00:33:300 days 00:00:320 days 00:32:588.00.9840800.015920../output/cons-v0_6/PAT_12/cons_PAT_12_Annotat...
10../output/res-comp-v0_6/PAT_17/qc_qrs_EEG_259_...EEG_259PAT_170 days 00:00:220 days 00:00:000 days 00:00:220.01.0000000.000000../output/cons-v0_6/PAT_17/cons_PAT_17_Annotat...
11../output/res-comp-v0_6/PAT_17/qc_qrs_EEG_259_...EEG_259PAT_170 days 00:00:180 days 00:00:000 days 00:00:180.01.0000000.000000../output/cons-v0_6/PAT_17/cons_PAT_17_Annotat...
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" + ], + "text/plain": [ + " ecg_qc_file_path exam_id patient_id \\\n", + "1 ../output/res-comp-v0_6/PAT_12/qc_qrs_EEG_297_... EEG_297 PAT_12 \n", + "2 ../output/res-comp-v0_6/PAT_12/qc_qrs_EEG_299_... EEG_299 PAT_12 \n", + "3 ../output/res-comp-v0_6/PAT_12/qc_qrs_EEG_300_... EEG_300 PAT_12 \n", + "10 ../output/res-comp-v0_6/PAT_17/qc_qrs_EEG_259_... EEG_259 PAT_17 \n", + "11 ../output/res-comp-v0_6/PAT_17/qc_qrs_EEG_259_... EEG_259 PAT_17 \n", + "\n", + " total_length total_noise total_clean small_chunks ratio_clean \\\n", + "1 0 days 00:05:42 0 days 00:00:04 0 days 00:05:38 2.0 0.988304 \n", + "2 0 days 00:02:08 0 days 00:00:00 0 days 00:02:08 0.0 1.000000 \n", + "3 0 days 00:33:30 0 days 00:00:32 0 days 00:32:58 8.0 0.984080 \n", + "10 0 days 00:00:22 0 days 00:00:00 0 days 00:00:22 0.0 1.000000 \n", + "11 0 days 00:00:18 0 days 00:00:00 0 days 00:00:18 0.0 1.000000 \n", + "\n", + " ratio_small_chunks cons \n", + "1 0.023392 ../output/cons-v0_6/PAT_12/cons_PAT_12_Annotat... \n", + "2 0.000000 ../output/cons-v0_6/PAT_12/cons_PAT_12_Annotat... \n", + "3 0.015920 ../output/cons-v0_6/PAT_12/cons_PAT_12_Annotat... \n", + "10 0.000000 ../output/cons-v0_6/PAT_17/cons_PAT_17_Annotat... \n", + "11 0.000000 ../output/cons-v0_6/PAT_17/cons_PAT_17_Annotat... " + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_clean['cons'] = df_clean['ecg_qc_file_path'].apply(lambda x: x.replace('res-comp-v0_6', 'cons-v0_6').replace('qc_qrs', 'cons').replace('tse_bi', 'csv').replace('cons_', 'cons_{}_Annotations_'.format(re.search(r'PAT_[0-9]*', x).group(0))))\n", + "df_clean.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5aad6a6c", + "metadata": {}, + "outputs": [], + "source": [ + "import shutil\n", + "\n", + "for src in df_clean['cons'].values:\n", + " dst = src.replace('cons-v0_6', 'cons-v0_6_clean_small_chunks')\n", + " os.makedirs(os.path.dirname(dst), exist_ok=True)\n", + " shutil.copyfile(src, dst)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "40400f08", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "interpreter": { + "hash": "ce8ad54ff0c21d76b9d2b2c70378af31077ad0766af937406802e3e7962ef6ad" + }, + "kernelspec": { + "display_name": "Python 3.6.8 ('env': venv)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/files/Statistics qrs_comp_before_smoothing.ipynb b/files/Statistics qrs_comp_before_smoothing.ipynb new file mode 100644 index 0000000..b8501d6 --- /dev/null +++ b/files/Statistics qrs_comp_before_smoothing.ipynb @@ -0,0 +1,1550 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "cc0f2647", + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append(\"..\")\n", + "\n", + "from src.usecase.consolidate_feats_and_annot import read_tse_bi\n", + "from src.usecase.fetch_database import create_df_from_file_pattern\n", + "import pandas as pd\n", + "import os\n", + "import seaborn as sns\n", + "import re" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "4598ee5d", + "metadata": {}, + "outputs": [], + "source": [ + "DATASET_PATIENT_PATTERN = r\"[P][A][T][_][0-9]*\"\n", + "DATASET_EXAM_PATTERN = r\"[E][E][G][_][0-9]*\"\n", + "ANNOTATION_FILE_PATTERN = \"*.tse_bi\"" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "5157511e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " ecg_qc_file_path exam_id patient_id\n", + "0 ../output/backup_comp_before_smoothing/res-com... EEG_156 PAT_12\n", + "1 ../output/backup_comp_before_smoothing/res-com... EEG_297 PAT_12\n", + "2 ../output/backup_comp_before_smoothing/res-com... EEG_299 PAT_12\n", + "3 ../output/backup_comp_before_smoothing/res-com... EEG_300 PAT_12\n", + "4 ../output/backup_comp_before_smoothing/res-com... EEG_253 PAT_17\n", + ".. ... ... ...\n", + "404 ../output/backup_comp_before_smoothing/res-com... EEG_103 PAT_9\n", + "405 ../output/backup_comp_before_smoothing/res-com... EEG_103 PAT_9\n", + "406 ../output/backup_comp_before_smoothing/res-com... EEG_103 PAT_9\n", + "407 ../output/backup_comp_before_smoothing/res-com... EEG_103 PAT_9\n", + "408 ../output/backup_comp_before_smoothing/res-com... EEG_103 PAT_9\n", + "\n", + "[409 rows x 3 columns]\n" + ] + } + ], + "source": [ + "df = create_df_from_file_pattern(\n", + " data_folder_path='../output/backup_comp_before_smoothing/res-comp-v0_6',\n", + " file_pattern=ANNOTATION_FILE_PATTERN,\n", + " file_label='ecg_qc',\n", + " patient_pattern=DATASET_PATIENT_PATTERN,\n", + " exam_pattern=DATASET_EXAM_PATTERN)\n", + "print(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a343c7c5", + "metadata": {}, + "outputs": [], + "source": [ + "def compute_stats_files(tse_bi_file):\n", + " \n", + " df = read_tse_bi(tse_bi_file)\n", + " df['length'] = df['end'] - df['start']\n", + " total_length = df['length'].sum()\n", + " total_noise = df[df['annotation'] == 'noisy']['length'].sum()\n", + " total_clean = df[df['annotation'] == 'clean']['length'].sum()\n", + " small_chunks = df[(df['annotation'] == 'noisy') & (df['length'].dt.seconds <= 4)].shape[0]\n", + " big_chunks = df[(df['annotation'] == 'noisy') & (df['length'].dt.seconds >= 4)].shape[0]\n", + " very_big_chunks = df[(df['annotation'] == 'noisy') & (df['length'].dt.seconds >= 8)].shape[0]\n", + " # df[['ratio_clean', 'small_chunks']].fillna(0)\n", + " if str(total_noise) == \"NaT\":\n", + " total_noise = pd.Timedelta(seconds=0)\n", + " if str(total_clean) == \"NaT\":\n", + " total_clean = pd.Timedelta(seconds=0)\n", + "\n", + " return total_length, total_noise, total_clean, small_chunks, big_chunks, very_big_chunks" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f4f74615", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/aura-alexis/github/seizure_detection_pipeline/env/lib64/python3.6/site-packages/pandas/core/frame.py:4327: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " downcast=downcast,\n" + ] + }, + { + "data": { + "text/html": [ + "
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ecg_qc_file_pathexam_idpatient_idtotal_lengthtotal_noisetotal_cleansmall_chunksbig_chunksvery_big_chunksratio_cleanratio_small_chunks
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ecg_qc_file_pathexam_idpatient_idtotal_lengthtotal_noisetotal_cleansmall_chunksbig_chunksvery_big_chunksratio_cleanratio_small_chunks
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ecg_qc_file_pathexam_idpatient_idtotal_lengthtotal_noisetotal_cleansmall_chunksratio_cleanratio_small_chunks
408../output/backup_comp_before_smoothing/res-com...EEG_103PAT_90 days 00:00:480 days 00:00:000 days 00:00:480.01.00.0
80../output/backup_comp_before_smoothing/res-com...EEG_562PAT_410 days 00:00:440 days 00:00:000 days 00:00:440.01.00.0
114../output/backup_comp_before_smoothing/res-com...EEG_573PAT_440 days 00:00:380 days 00:00:000 days 00:00:380.01.00.0
314../output/backup_comp_before_smoothing/res-com...EEG_706PAT_520 days 00:03:320 days 00:00:000 days 00:03:320.01.00.0
117../output/backup_comp_before_smoothing/res-com...EEG_573PAT_440 days 00:02:460 days 00:00:000 days 00:02:460.01.00.0
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376../output/backup_comp_before_smoothing/res-com...EEG_501PAT_70 days 00:00:420 days 00:00:420 days 00:00:000.00.00.0
377../output/backup_comp_before_smoothing/res-com...EEG_501PAT_70 days 00:01:160 days 00:01:160 days 00:00:000.00.00.0
259../output/backup_comp_before_smoothing/res-com...EEG_689PAT_520 days 00:01:260 days 00:01:260 days 00:00:000.00.00.0
45../output/backup_comp_before_smoothing/res-com...EEG_311PAT_220 days 00:32:200 days 00:32:200 days 00:00:000.00.00.0
240../output/backup_comp_before_smoothing/res-com...EEG_714PAT_520 days 00:01:560 days 00:01:560 days 00:00:000.00.00.0
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409 rows × 9 columns

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" + ], + "text/plain": [ + " ecg_qc_file_path exam_id patient_id \\\n", + "408 ../output/backup_comp_before_smoothing/res-com... EEG_103 PAT_9 \n", + "80 ../output/backup_comp_before_smoothing/res-com... EEG_562 PAT_41 \n", + "114 ../output/backup_comp_before_smoothing/res-com... EEG_573 PAT_44 \n", + "314 ../output/backup_comp_before_smoothing/res-com... EEG_706 PAT_52 \n", + "117 ../output/backup_comp_before_smoothing/res-com... EEG_573 PAT_44 \n", + ".. ... ... ... \n", + "376 ../output/backup_comp_before_smoothing/res-com... EEG_501 PAT_7 \n", + "377 ../output/backup_comp_before_smoothing/res-com... EEG_501 PAT_7 \n", + "259 ../output/backup_comp_before_smoothing/res-com... EEG_689 PAT_52 \n", + "45 ../output/backup_comp_before_smoothing/res-com... EEG_311 PAT_22 \n", + "240 ../output/backup_comp_before_smoothing/res-com... EEG_714 PAT_52 \n", + "\n", + " total_length total_noise total_clean small_chunks \\\n", + "408 0 days 00:00:48 0 days 00:00:00 0 days 00:00:48 0.0 \n", + "80 0 days 00:00:44 0 days 00:00:00 0 days 00:00:44 0.0 \n", + "114 0 days 00:00:38 0 days 00:00:00 0 days 00:00:38 0.0 \n", + "314 0 days 00:03:32 0 days 00:00:00 0 days 00:03:32 0.0 \n", + "117 0 days 00:02:46 0 days 00:00:00 0 days 00:02:46 0.0 \n", + ".. ... ... ... ... \n", + "376 0 days 00:00:42 0 days 00:00:42 0 days 00:00:00 0.0 \n", + "377 0 days 00:01:16 0 days 00:01:16 0 days 00:00:00 0.0 \n", + "259 0 days 00:01:26 0 days 00:01:26 0 days 00:00:00 0.0 \n", + "45 0 days 00:32:20 0 days 00:32:20 0 days 00:00:00 0.0 \n", + "240 0 days 00:01:56 0 days 00:01:56 0 days 00:00:00 0.0 \n", + "\n", + " ratio_clean ratio_small_chunks \n", + "408 1.0 0.0 \n", + "80 1.0 0.0 \n", + "114 1.0 0.0 \n", + "314 1.0 0.0 \n", + "117 1.0 0.0 \n", + ".. ... ... \n", + "376 0.0 0.0 \n", + "377 0.0 0.0 \n", + "259 0.0 0.0 \n", + "45 0.0 0.0 \n", + "240 0.0 0.0 \n", + "\n", + "[409 rows x 9 columns]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Top noisy\n", + "\n", + "df.sort_values('ratio_clean', ascending=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "df0113c6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1.000000 140\n", + "0.000000 14\n", + "0.962963 4\n", + "0.925926 3\n", + "0.969697 3\n", + " ... \n", + "0.987705 1\n", + "0.692607 1\n", + "0.040451 1\n", + "0.967972 1\n", + "0.977064 1\n", + "Name: ratio_clean, Length: 230, dtype: int64" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['ratio_clean'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "fedff484", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ecg_qc_file_pathexam_idpatient_idtotal_lengthtotal_noisetotal_cleansmall_chunksratio_cleanratio_small_chunks
92../output/backup_comp_before_smoothing/res-com...EEG_568PAT_420 days 00:00:380 days 00:00:380 days 00:00:000.00.00.0
45../output/backup_comp_before_smoothing/res-com...EEG_311PAT_220 days 00:32:200 days 00:32:200 days 00:00:000.00.00.0
141../output/backup_comp_before_smoothing/res-com...EEG_593PAT_460 days 00:01:220 days 00:01:220 days 00:00:000.00.00.0
290../output/backup_comp_before_smoothing/res-com...EEG_706PAT_520 days 00:36:440 days 00:36:440 days 00:00:000.00.00.0
292../output/backup_comp_before_smoothing/res-com...EEG_689PAT_520 days 00:03:240 days 00:03:240 days 00:00:000.00.00.0
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286../output/backup_comp_before_smoothing/res-com...EEG_689PAT_520 days 00:00:300 days 00:00:000 days 00:00:300.01.00.0
287../output/backup_comp_before_smoothing/res-com...EEG_706PAT_520 days 00:45:320 days 00:00:000 days 00:45:320.01.00.0
105../output/backup_comp_before_smoothing/res-com...EEG_573PAT_440 days 00:00:480 days 00:00:000 days 00:00:480.01.00.0
112../output/backup_comp_before_smoothing/res-com...EEG_573PAT_440 days 00:00:340 days 00:00:000 days 00:00:340.01.00.0
408../output/backup_comp_before_smoothing/res-com...EEG_103PAT_90 days 00:00:480 days 00:00:000 days 00:00:480.01.00.0
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409 rows × 9 columns

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" + ], + "text/plain": [ + " ecg_qc_file_path exam_id patient_id \\\n", + "92 ../output/backup_comp_before_smoothing/res-com... EEG_568 PAT_42 \n", + "45 ../output/backup_comp_before_smoothing/res-com... EEG_311 PAT_22 \n", + "141 ../output/backup_comp_before_smoothing/res-com... EEG_593 PAT_46 \n", + "290 ../output/backup_comp_before_smoothing/res-com... EEG_706 PAT_52 \n", + "292 ../output/backup_comp_before_smoothing/res-com... EEG_689 PAT_52 \n", + ".. ... ... ... \n", + "286 ../output/backup_comp_before_smoothing/res-com... EEG_689 PAT_52 \n", + "287 ../output/backup_comp_before_smoothing/res-com... EEG_706 PAT_52 \n", + "105 ../output/backup_comp_before_smoothing/res-com... EEG_573 PAT_44 \n", + "112 ../output/backup_comp_before_smoothing/res-com... EEG_573 PAT_44 \n", + "408 ../output/backup_comp_before_smoothing/res-com... EEG_103 PAT_9 \n", + "\n", + " total_length total_noise total_clean small_chunks \\\n", + "92 0 days 00:00:38 0 days 00:00:38 0 days 00:00:00 0.0 \n", + "45 0 days 00:32:20 0 days 00:32:20 0 days 00:00:00 0.0 \n", + "141 0 days 00:01:22 0 days 00:01:22 0 days 00:00:00 0.0 \n", + "290 0 days 00:36:44 0 days 00:36:44 0 days 00:00:00 0.0 \n", + "292 0 days 00:03:24 0 days 00:03:24 0 days 00:00:00 0.0 \n", + ".. ... ... ... ... \n", + "286 0 days 00:00:30 0 days 00:00:00 0 days 00:00:30 0.0 \n", + "287 0 days 00:45:32 0 days 00:00:00 0 days 00:45:32 0.0 \n", + "105 0 days 00:00:48 0 days 00:00:00 0 days 00:00:48 0.0 \n", + "112 0 days 00:00:34 0 days 00:00:00 0 days 00:00:34 0.0 \n", + "408 0 days 00:00:48 0 days 00:00:00 0 days 00:00:48 0.0 \n", + "\n", + " ratio_clean ratio_small_chunks \n", + "92 0.0 0.0 \n", + "45 0.0 0.0 \n", + "141 0.0 0.0 \n", + "290 0.0 0.0 \n", + "292 0.0 0.0 \n", + ".. ... ... \n", + "286 1.0 0.0 \n", + "287 1.0 0.0 \n", + "105 1.0 0.0 \n", + "112 1.0 0.0 \n", + "408 1.0 0.0 \n", + "\n", + "[409 rows x 9 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Top clean\n", + "df.sort_values('ratio_clean', ascending=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "39ecd5e2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.7511367831616216" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Ratio clean/noise\n", + "df['total_clean'].dt.seconds.sum() / df['total_length'].dt.seconds.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "230965d0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Small chunks are scarse\n", + "sns.displot(df['small_chunks'], kind='kde')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "5e64c2db", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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g59w3gG+Y2eudc98rUk0layCZLsoNuIyljQk6NRdYpGLN1YK42jn3LWCtmf3l1Nedc9dO82UVa2A0xeoC7wWcraMxTuegWhAilWquFkTmsMz6QhdSDrwecPG2UO5oSPDo4YGivZ+IFNdcLYgv+x8/VpxySpdzrijHEWVb2hina3CMiUlHNFL4mRciUly5bsbzWTNrNLMqM7vdzLrM7OpCF1dKRlMTpCdd0W7CgdcDnnRoNZxIhcp1HvCrnHMDwGvx9oI4CfhwoYoqRcVcBZfRoaloIhUt1wDOtCouBW50zvUXqJ6SVcytKDOWHl+MoZkQIpUo1ztKt5jZY8Ao8Odm1g6EKhUyG/EUdQR8/HBOjYBFKlGu21F+BHghsNk5lwKGgcsLWVipOT4CLvBxRNna6uOYaQQsUqnmkyan4M0Hzv6af8tzPSWrmMcRZRxfDacesEhFynU7ym8CJwIPABP+044QBXAQPWDwNmbv0nJkkYqU6wh4M7DJORfa4xkyxxEVcxoaeH3gI2pBiFSkXGdBPAwsK2Qhpa5/NEVDPFb0BRHLmhIc6VcLQqQS5ToCbgMeMbN7gONp4Jy7rCBVlaCB0XTRR78Ay5tq6B4aYyw9QTwWLfr7i0jh5BrAf1/IIspBMbeizLaiuQaAI/1J1rTWzXG1iJSTXKeh/QpvBVyV//heYEcB6yo5A8nibsSTsaLJW4xxsG+06O8tIoWV614Q7wBuAr7sP7US+GGBaipJA0XeiCdjuT8CPtynG3EilSbXm3DvAbYAAwDOud3A0kIVVYr6RlI01wbRA/ZGwIc0AhapOLkG8Jhzbjzzib8YI1RT0oq9FWVGoipKa101h/o1AhapNLkG8K/M7K/xDud8JXAj8OPClVVaxtITjKYmAglggOXNCY2ARSpQrgH8EaALeAh4J/BT4G8KVVSpOb4VZW11IO+/vKmGw/0KYJFKk9NtfefcpJn9EPihc66rsCWVnv6R4u8DkW1lcw1b9/QE8t4iUjizjoDN8/dm1g08Djzun4bxv4pTXmnIjICbg2pBNCUYHEsfXw4tIpVhrhbEB/FmP7zAObfEObcEuADYYmYfLHh1JSKI0zCyZaaiqQ8sUlnmCuD/BlzlnNuXecI5txe4GviTQhZWSvr8FkQQ09AATmjxAvjgMQWwSCWZK4CrnHPdU5/0+8DBpFEAgh4BZwL4md6RQN5fRApjrgAeX+BrFaXPD+CGIu8FnNFeHydRFeEZjYBFKspcsyDOMrOBaZ43IFGAekrSwKi3D0Sxt6LMMDNOaKnVCFikwswawM457X8I9I2M0xRQ/zdjVUsNBzQCFqkouS7ECLX+0RTNNcEswshYtaSWZ45pBCxSSRTAOQhqH4hsJ7TUMJhMH18UIiLlTwGcg77RVAm0IGoBNAoWqSAK4BwEtRdwtlVL/ADWjTiRiqEAnoNzjr6REghgfwSsG3EilUMBPIeR8QnSky6wfSAymmqraEjEeFojYJGKoQCeQ1/Aq+CyrW2t46me4aDLEJE8UQDPoT/gfSCyrW2rY1+3AlikUgQWwGYWNbP7zeyWoGrIRWYfiCCOpJ9qXVsdh/pGGUtPBF2KiORBkCPgvwAeDfD9c9I/6m15EfRCDIB1bbVMOs2EEKkUgQSwmZ0AXAp8NYj3n49njyMKfgS8trUOgH3dCmCRShDUCPjzwH8HJgN6/5z1BXwcUbZ1bV4AP6U+sEhFKHoAm9lrgU7n3H1zXHeNmW03s+1dXcEdQ9c/miIWMeqqg9+XqLm2mubaKvZpJoRIRQhiBLwFuMzMngK+C7zczL419SLn3PXOuc3Ouc3t7e3FrvG4Pn8VnFkwW1FOtba1TiNgkQpR9AB2zv1P59wJzrm1wJuAXzrnri52HbnqL4F9ILKtb1MAi1QKzQOeQ38JLEPOtr69jkP9SYbH0kGXIiKLFGgAO+fudM69Nsga5uLtBVw6AbyhowGA3Z1DAVciIoulEfAcSmEv4GwbMwF8dDDgSkRksRTAc+gbGS+pAF69pJbqWEQjYJEKoACexcSkY3AsTVNt8KvgMqIR48T2ep7QCFik7CmAZzGYTOFcaSzCyLZhaT27j2oELFLuFMCzyCxDLqWbcAAbO+o52DeqmRAiZU4BPItSWoacTTMhRCqDAngWpbQRT7aT/QB+7PBAwJWIyGIogGdRqi2I1UtqqY/H2HVIASxSzhTAsyil44iyRSLGphWN7DrUH3QpIrIICuBZDJTQaRhTnb6iiUcPDzIx6YIuRUQWSAE8i2PD49RURUlUBb8V5VSnrWhkNDXBvm7diBMpVwrgWRwbSbGkrnQWYWQ7fWUTAA8fVB9YpFwpgGdxbGSclrrSaz8AnNheRzwWUR9YpIwpgGfROzxOSwktQ84Wi0Y4dXkjO59RAIuUKwXwLPpGSjeAAc5b08LOA32kJkr+aD0RmYYCeBa9w+Ml2wMGL4DH0pM8ovnAImVJATyD9MQkA8k0zSW2Ci7beWtaALhv/7GAKxGRhVAAzyCzCKOUR8AdjQlWNtcogEXKlAJ4BseGxwFKugcM3ih4+/5enNOCDJFyowCewTF/J7RSD+DNa1s4OjDG070jQZciIvOkAJ5Bb2YEXKLzgDO2nNQGwN1PdgdciYjMlwJ4BsdGvAAu5R4wwPq2OpY3Jbh7twJYpNwogGeQCeBSb0GYGRed1MZv9/RoYx6RMqMAnkEpb8Qz1UUb2ugfTfHwQa2KEyknCuAZ9A6X7kY8U205qQ0zuOPxzqBLEZF5UADPoK+EN+KZqq0+znmrW/jZrqNBlyIi86AAnkFvie8DMdWrT1vGo4cHeEbT0UTKhgJ4BseGx2kuswAG+NmuIwFXIiK5UgDPoGd4nNYy6QEDrG6t5dTljfzkocNBlyIiOVIAT2MsPcFgMk1bffkEMMDlZ6/g/qf72NulY4pEyoECeBqZVXCt9fGAK5mfK89ZScTgpvsOBF2KiORAATyNniE/gMuoBQHe7mgv2djO93cc1KIMkTKgAJ5G99AYUH4jYIA3bl7FkYEktz+qKWkipU4BPI3MCLjcesAAr9zUwcrmGr52976gSxGROSiAp9EzXL4j4Fg0wltfuJZt+3q1NFmkxCmAp9EzNE48FqGuuvT3gZjOH52/ivp4jOvufDLoUkRkFgrgaXQPjdNWH8fMgi5lQRoTVfzplrX89KEj7DqkUbBIqVIAT6NneIzWMuz/Znv7i9bTmIhx7c+fCLoUEZmBAngaPUPltQpuOk01Vfz5S0/i9sc6ueMx7ZImUooUwNPoGRoryxtwU739onWc2F7H3928i2RqIuhyRGQKBfAUzjm6h8fLvgUBUB2L8PErTufp3hGuu0M35ERKjQJ4iqGxNOPpSdrqyn8EDPDCE9u44uwV/Muv9mqPCJESowCe4vgy5AoYAWd89NJNxKsifOR7D2mJskgJUQBPkVmGXC7HEeWivSHO37/uNO55qpfr79obdDki4lMAT9E16AVwe0NltCAy/uDclbzmjGVce9vjWiEnUiIUwFN0+gG8tCERcCX5ZWZ88oozWFJXzQf+4wHNihApAQrgKY4OJIlGrOznAU+npa6az/3hWTzZOcSn/+uxoMsRCT0F8BSdg2O018eJRMpzGfJcXrShnbdtWcsNv32KXz3RFXQ5IqGmAJ6ic3CMpY2V1f+d6n9ccgobO+r58I07Oeaf/iEixacAnqJzIMnSCrsBN1WiKsq1bzybYyPj/O2PHg66HJHQUgBP0TU4RnuF3YCbzukrm/jAxRu55cHD/OiBg0GXIxJKRQ9gM1tlZneY2SNmtsvM/qLYNcwkNTFJz/B4xY+AM9754vWcu7qZv/3hwxzuHw26HJHQCWIEnAb+yjm3CbgQeI+ZbQqgjufJzAHuaKz8ETB4p2dc+8azSU04/vtNDzKpVXIiRVX0AHbOHXbO7fAfDwKPAiuLXcd0np0DHI4RMMDatjo+eump/Hp3N9/cuj/ockRCJdAesJmtBc4Btk3z2jVmtt3Mtnd1FWe6VOdAEqDiZ0FM9eYLVvPSk9v5h/96lH3dw0GXIxIagQWwmdUD3wM+4JwbmPq6c+5659xm59zm9vb2otRUqavg5mJmfOb1Z1IdjfDhG3dqwx6RIgkkgM2sCi98v+2c+34QNUync3AMs/I8jn6xOhoTfOzy09i+/xhf/42OtBcphiBmQRjwNeBR59y1xX7/2XQNJmmtqyYWDefsvCvOXskrN3Xw2Z89zpOd2jtYpNCCSJotwH8DXm5mD/i/XhNAHc9zqC/J8qaaoMsIjJnxyStPp7Y6yl/duJP0xGTQJYlUtCBmQdztnDPn3JnOubP9Xz8tdh3TOdw/yvKmcPV/p1rakODjl5/Ozmf6+Mqv1YoQKaRw/lt7Bof6kqxoDu8IOOO1Zy7nNWcs4x9ve4LHjwwGXY5IxVIA+waSKYbG0qxoDvcIGLxWxMcvP52GRIwP3biTlFoRIgWhAPYd6vOW4moE7Gmtj/OJK07noYP9fOnOPUGXI1KRFMC+w33eIoww34Sb6vfPWM5lZ63gn2/fza5DOsZIJN8UwL6D/gh4pUbAz/Gxy06jpa6aD934IONptSJE8kkB7DvcP0osYhV3GOditdRV86krz+DRwwN84Ze7gy5HpKIogH2H+pJ0NCaIVuhRRIvxyk0d/MG5K/ninXu4Z19v0OWIVAwFsO9Q36hmQMziY5edxuoltbz333cc37ZTRBZHAew73B/uVXBzaUhUcd2bz6V/NMX7v3O/NuwRyQMFMDA56bxVcBoBz+rU5Y184orT+d3eHv7vzx8PuhyRshcLuoBScHggSWrCsXpJbdCllLw/3LyKHU8f47o797C2rY43bl4VdEkiZUsBDOzv8TYhX9taF3Al5eF/X346z/SO8tfff4gVTTVctKEt6JJEypJaEMDTPSMAGgHnqCoa4bqrz+WkpfW861v38cih5+2nLyI5UAAD+3tHqIqaliHPQ2Oiin996wuoj8e4+mvbeOyIQlhkvhTAeCPgE1pqNQd4nlY01/Cday6kOhrhquu3aiQsMk8KYGB/77DaDwu0rq2O715zIYmqKG/+6lYeeKYv6JJEykboA9g5x/7uEda2KoAXaq0fwvWJGG+6/nf8bNeRoEsSKQuhD+BjIykGx9Ks1gyIRVnTWscP3r2Fk5c18q5v3cfX7t6Hc1qsITKb0AdwZgraGrUgFq2tPs5333Ehr9rUwcdveYS/+s+djI5PBF2WSMlSAPtT0NaoBZEXNdVRvvTm8/jgxRv5wQMHufK63/BU93DQZYmUpNAH8O7OQWIRY41aEHkTiRh/cfEGvv7WF3BkIMnrvnA3tz1yNOiyREpO6AP4iaNDrG2rozoW+h9F3r305KX8+L0Xsba1jnf823Y+c+tjOupeJEvoU2f30UE2dtQHXUbFWrWklhvf9Xtcdf4qvnTnHq7+2jY6B5NBlyVSEkIdwMnUBPt7R9iwtCHoUipaoirKP/zBmXzuD8/igWf6eO0/362N3UUIeQA/2TmEc7CxQwFcDG847wR+8O4t1MVjXPWVrVx/1x5NVZNQC3UA7+4cBFALoohOXd7Ize/dwqs2dfCpnz7GO795HwPJVNBliQQi1AH8xNEhYhFjbZtmQBRT5nSNv7n0VH75WCeX/b+7efzIYNBliRRduAP4yCDr2uqoiob6xxAIM+PPXrSe715zIcPjE1x53W/4r4cOB12WSFGFNnmcczx4sJ/TVzYFXUqobV67hFvedxEnL2vgz7+9g8/e+pjOm5PQCG0AHxlI0jU4xpknKICD1tGY4LvXXMhV56/iujv38Kc33Ev/iPrCUvlCG8A7n+kH4KxVzcEWIgDEY95UtU9deQa/3dPNZV9UX1gqX2gD+MEDfcQixqbljUGXIln++ILVfPeaCxnx+8I/VV9YKlhoA3jngT5OWd5AoioadCkyxXlrvL7wKcsaePe3d/AZ9YWlQoUygCcnHQ8e6OfME5qDLkVm0NGY4DvXXMhV56/mS3fu4W033EvfyHjQZYnkVSgDeHfnEIPJNGer/1vSvL7wGXzqyjP43Z5uXvWPd/HLx7SrmlSOUAbwb57sBmDLSW0BVyK5+OMLVvODd2+hpbaaP71hOx++cSe9wxoNS/kLbQCva6tjpY6hLxunr2zi5vdt4T0vO5Hv33+Ql3z2Dq6780mSKZ24IeUrdAGcmphk694etpzUGnQpMk/xWJQPv/oUfvaBF3HB+iV89tbHefFn7+Afb3uCI/3a4lLKTyzoAopt5zN9DI9PcJHaD2XrpKUNfPUtL2Dr3h6+dOce/vmXu/nCHU+y5aQ2XnZyOy/Z2M66tjrMLOhSRWYVugC+4/FOIgYXrtcIuNxduL6VC9e38nTPCP9+z9P8fNcRPvbjRwBYUlfNpuWNbFrRyEnt9axvr2N9ez1L6qoDrlrkWVYO+7Fu3rzZbd++fdHfxznHSz93J6uX1PLNt1+Qh8qk1DzdM8Jdu7t46EA/uw7388SRIcazjkFqqa1ifXs969u8QD6xvY4zT2hmWVMiwKqlgszrn12hGgE/eKCf/T0jvOelJwVdihTI6tZarm5dc/zz9MQkB/tG2dM1xN6uYfZ0DbO3a4g7n+jixvsOHL9uRVOCc9e0cN6aFl6wdgmnLm8kGlELQworVAH8452HqIoarz5tWdClSJHEohHWtNaxprWOl5/y3NcGkime7Bxi5zN93Lf/GDv2H+OWB72lz42JGOevW3K8zaFAlkIITQCPpSf44QOHeMnGdppqq4IuR0pAY6KKc1e3cO7qFt62ZR0Ah/pGuWdfL1v39rB1bw+/eLQTgIZEjPPXPhvIm1YokGXxQhPAP7z/IN1DY7z1heuCLkVK2IrmGq44ZyVXnLMSgCP9Sbbt88J4295ebn/MC+S66igbOhrY2FHPxo4GNnQ0sLK5huVNCeriofljJYsUiptwk5OOV33+LqqjEX7y/os0PUkW7OhAkm37etmx/xiPHxlkd+cg3UPPXZXXmIixormG9oY47Q1xljYksh4/+7E+HtPvxcqjm3BT/WjnQZ7sHOKf3nS2fsPLonQ0JrjsrBVcdtaK4891D42xp3OIw/1JDvcnOdI/yqF+b8P/vV3DdA2OPWcmRkaiKsKKpho2+KPojR0NnLq8gfVt9UTU3giFig/g/pEUn/zJo5y9qpnXnbli7i8Qmae2+jht9fEZX3fO0T+aomtwjM7BMf+jF9DP9I7yROcgtz1ylMyOm42JGOf4venz1rRwzupmtTUqVEX/X3XO8bc/epje4XFueNv5GlVIIMyM5tpqmmur2dDRMO01ydQEe7uG2XWonx1P97Fj/zE+f/sTOAfRiHH6yibOX+tNkXvB2iW0aEFJRajoHvDnf/EEn//Fbj70qo289+UbClCZSOEMJFPs2H+Me5/q5d59x3jgQB/jaa+VsbGjnvPXeWF8/rolLG/SxlIlYl6jvIoM4IlJxyd+8ghf/81T/ME5K/m/bzxLvV8pe8nUBA8e6Ofep3q5Z18v9+0/xtBYGoCOxjgbOxo4ZZnXS17fXs+K5gTt9XFi0fLYcyuZmqB3eJyR8TTJ1CSpiUmqohGqohFqq6O0N8TL4QSb0g9gM7sE+CcgCnzVOffp2a6fbwDfdN8BPnTjTv50yzo+eumpmq8pFSk9McljRwa5Z18vDx/q54mjg+w+OsRY+tkbftGIsdSfddGQqKIhEfN/eY/r4zHq4jFqq6PUx2PUVseoi0epi8eoq45RG49SVx1b9J+hwWSKQ31JDvWPcqhvlMN9Se9jf/J4P3wgmZ7z+9RVR2mt9/57ljfXsKI54U//e/ZxU01VkAOu0g5gM4sCTwCvBA4A9wJXOecemelr5hvAk5OOu3Z38dKTly62XJGyMjHp2N8zzP6eEX9WxiiH+pJ0D40xmEwxkEwzmEwxmEwzMp77XsqJqsjxQK6pilJTHaO2KkptdfT4qHTSOZyD9KRjaCxF/2iagdEUfSPjDE95r2jEWNaYYFlTgo5G7yZme32c1vo4dXHve1ZHI6QnHamJSQaTKbqHxukZGqdneIyjA96Mk8N9yefNMKmpirKiOcGK5hpWNNWwormG5X44N9dWeX/JVEepqY5Sm4e/XKYo+Wlo5wNPOuf2ApjZd4HLgRkDeL4iEVP4SihFI+ZtNtReP+e16YlJhscnGBlPMzzmfRwaSzMyNsHwuBfQw2PPfW10fMJ7ftx7fLg/RTI9gQERMyJmmHmrDFc217BpeSNNNVUsa4r7o1RvpLq0IZGX4JucdHQPjx0fUR/q9z/6jx870knX4Nis36M6GiEWNaIRIxYxopEIsYjxpvNX8YGLNy66xtkEEcArgWeyPj8APG9rMjO7BrjG/3TIzB73H7cB3QWtMD9UZ36pzvxSnXPYBnww98szdd7qnLsk1y8q2WlozrnrgeunPm9m251zmwMoaV5UZ36pzvxSnfm10DqDuD16EFiV9fkJ/nMiIqESRADfC2wws3VmVg28Cbg5gDpERAJV9BaEcy5tZu8FfoY3De1fnXO75vEtnteWKFGqM79UZ36pzvxaUJ1lsRBDRKQSlccSGRGRCqQAFhEJSMkHsJktMbPbzGy3/7FlhutuNbM+M7uliLVdYmaPm9mTZvaRaV6Pm9l/+K9vM7O1xaptSh1z1fliM9thZmkze0MQNfp1zFXnX5rZI2b2oJndbmZrpvs+JVDnu8zsITN7wMzuNrNNpVhn1nWvNzNnZoFM98rh5/lWM+vyf54PmNmflWKd/jVv9H+P7jKzf5/zmzrnSvoX8FngI/7jjwCfmeG6VwCvA24pUl1RYA+wHqgGdgKbplzzbuBf/MdvAv4jgJ9fLnWuBc4E/g14Q0D/n3Op82VArf/4z0v459mY9fgyvMn5JVenf10DcBewFdhcinUCbwW+UOzaFlDnBuB+oMX/fOlc37fkR8B4y5S/4T/+BnDFdBc5524HBotUE2QtqXbOjQOZJdXZsmu/CXiFFX+XkDnrdM495Zx7EHj+sQ3Fk0uddzjnRvxPt+LNIS+2XOocyPq0DgjiTncuvz8BPg58BkgWs7gsudYZtFzqfAfwRefcMQDnXOdc37QcArjDOXfYf3wE6AiymCzTLaleOdM1zrk00A+0FqW6aWrwTVdnKZhvnW8H/qugFU0vpzrN7D1mtgfvX3DvL1Jt2eas08zOBVY5535SzMKmyPX/++v91tNNZrZqmtcLLZc6NwIbzew3ZrbV3/VxViWxFNnMfgEsm+alj2Z/4pxzZqZ5cyFnZlcDm4GXBF3LTJxzXwS+aGZ/DPwN8JaAS3oOM4sA1+L9877U/Rj4jnNuzMzeifevypcHXNN0YnhtiJfi/evsLjM7wznXN9sXBM45d/FMr5nZUTNb7pw7bGbLgTmH9UWSy5LqzDUHzCwGNAE9xSnveTVklOrS75zqNLOL8f5ifolzbvZtrgpjvj/P7wJfKmhF05urzgbgdOBOvyu2DLjZzC5zzi38CPL5m/Pn6ZzL/jPzVbx/VRRbLv/fDwDbnHMpYJ+ZPYEXyPfO9E3LoQVxM8+OHt4C/CjAWrLlsqQ6u/Y3AL90fne+iMpl6fecdZrZOcCXgcty6a8VSC51Zp9/dSmwu4j1Zcxap3Ou3znX5pxb65xbi9dTL3b4zlkngD/wyrgMeLSI9WXk8ufoh3ijX8ysDa8lsXfW7xrkncUc7z62Arfj/Sb+BbDEf34z3mkamet+DXQBo3h/E726CLW9Bm9z+T3AR/3n/jfeb2SABHAj8CRwD7A+oJ/hXHW+wP+ZDeON0HeVaJ2/AI4CD/i/bi7ROv8J2OXXeAdwWinWOeXaOwlgFkSOP89/8H+eO/2f5yklWqfhtXUeAR4C3jTX99RSZBGRgJRDC0JEpCIpgEVEAqIAFhEJiAJYRCQgCmARkYAogEVEAqIAlqIwsw+YWW3W5z81s+YASzrOzIb8j2vN7OEFfP1T/sT7fNRyZ1DbQkrxKYAlb8wz0++pDwDHA9g59xo3yxp5kTBQAMui+KPGx83s34CHga+Z2XZ/Q+qP+de8H1gB3GFmd/jPHR81+hutP+z/+sAs71VnZj8xs53+tX+U9b3+wd+se7uZnWtmPzOzPWb2Lv+aevM2cd/hb5Y+7y0PzSxqZp/z3/tBM3tf1svvy/rep/jX/72ZfSjr6x/2f15rzexRM/uK/3P6uZnVTHmviJndYGaf8N/3Bv/rHzKzD863dilNJbEZj5S9DcBbnHNbzWyJc67XzKLA7WZ2pnPun83sL4GXOee6s7/QzM4D3gZcgLeUc5uZ/co5d/8073MJcMg5d6n/tU1Zrz3tnDvbzP4RuAHYgrcU/GHgX/D2u73SOTfgB/9WM7vZzW8p6DV4m9ef7bzTvZdkvdbtnDvXzN4NfAiY69SGDcBVzrl3mNl/Aq8HvuW/FgO+DTzsnPuk/zNa6Zw73f/vbp5HzVLCNAKWfNjvnNvqP36jme3AOxngNGCu43guAn7gnBt2zg0B3wdeNMO1DwGvNLPPmNmLnHP9Wa/dnHXNNufcoHOuCxjzA8uAT5nZg3h7Sqxk/ntLXwx82Xl7O+Oc68167fv+x/vwQnou+5xzD8zwNV/GD1//873AejP7f+btMZu94buUMQWw5MMwgJmtwxv9vcI5dybwE7xRaF44554AzsUL2U+Y2f/KejmzNeVk1uPM5zHgzUA7cJ5z7my8TX3yVlvWe07w7L8s0zz3z1himuunfg3Ab4GXmVkCwHknLJyFt2HOu/C2ZJQKoACWfGrEC+N+M+sAfj/rtUG8PWin+jVwhZnVmlkdcKX/3POY2QpgxDn3LeD/4IVxrpqATudcysxeBizkQM/bgHeat7czU1oQ03kqU6N5p0+sy/F9vgb8FPhPM4v5LZOIc+57eJu7z+e/W0qYesCSN865nWZ2P/AY3vEtv8l6+XrgVjM75Jx7WdbX7DCzG/C26wRvi9Hp+r8AZwD/x8wmgRTewZy5+jbwYzN7CNju1zhfX8Xb4/VBM0sBXwG+MMv13wP+xMx2AdvwtjLMiXPuWr/H/U3g08DXs2aY/M8F1C4lSNtRiogERC0IEZGAqAUhJcfMMqegTPUK99zzwQr1/q/GO6o92z7n3JWFfm8JF7UgREQCohaEiEhAFMAiIgFRAIuIBEQBLCISkP8P7ntVNGQ2GYoAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Small chunks are scarse\n", + "sns.displot(df['ratio_small_chunks'], kind='kde')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "cd373f3d", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# They are quite rare\n", + "sns.displot(df[df['small_chunks'] <= 50]['small_chunks'], kind='kde')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "2db3e55c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "sns.displot(df['total_length'].dt.seconds/3600)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "4e3a3c21", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8997555012224939" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# with good quality\n", + "(df[df['ratio_clean'] > 0.5]).shape[0] / df.shape[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "691fc93a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.8235882219404309" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# % for goog quality length\n", + "df[df['ratio_clean'] > 0.5]['total_length'].dt.seconds.sum() / df['total_length'].dt.seconds.sum()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bbe4669f", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "db47507b", + "metadata": {}, + "source": [ + "## Taking only good quality segments" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "4ed8d61c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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ecg_qc_file_pathexam_idpatient_idtotal_lengthtotal_noisetotal_cleansmall_chunksratio_cleanratio_small_chunks
1../output/res-comp-v0_6/PAT_12/qc_qrs_EEG_297_...EEG_297PAT_120 days 00:05:420 days 00:00:040 days 00:05:382.00.9883040.023392
2../output/res-comp-v0_6/PAT_12/qc_qrs_EEG_299_...EEG_299PAT_120 days 00:02:080 days 00:00:000 days 00:02:080.01.0000000.000000
3../output/res-comp-v0_6/PAT_12/qc_qrs_EEG_300_...EEG_300PAT_120 days 00:33:300 days 00:00:320 days 00:32:588.00.9840800.015920
10../output/res-comp-v0_6/PAT_17/qc_qrs_EEG_259_...EEG_259PAT_170 days 00:00:220 days 00:00:000 days 00:00:220.01.0000000.000000
11../output/res-comp-v0_6/PAT_17/qc_qrs_EEG_259_...EEG_259PAT_170 days 00:00:180 days 00:00:000 days 00:00:180.01.0000000.000000
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" + ], + "text/plain": [ + " ecg_qc_file_path exam_id patient_id \\\n", + "1 ../output/res-comp-v0_6/PAT_12/qc_qrs_EEG_297_... EEG_297 PAT_12 \n", + "2 ../output/res-comp-v0_6/PAT_12/qc_qrs_EEG_299_... EEG_299 PAT_12 \n", + "3 ../output/res-comp-v0_6/PAT_12/qc_qrs_EEG_300_... EEG_300 PAT_12 \n", + "10 ../output/res-comp-v0_6/PAT_17/qc_qrs_EEG_259_... EEG_259 PAT_17 \n", + "11 ../output/res-comp-v0_6/PAT_17/qc_qrs_EEG_259_... EEG_259 PAT_17 \n", + "\n", + " total_length total_noise total_clean small_chunks ratio_clean \\\n", + "1 0 days 00:05:42 0 days 00:00:04 0 days 00:05:38 2.0 0.988304 \n", + "2 0 days 00:02:08 0 days 00:00:00 0 days 00:02:08 0.0 1.000000 \n", + "3 0 days 00:33:30 0 days 00:00:32 0 days 00:32:58 8.0 0.984080 \n", + "10 0 days 00:00:22 0 days 00:00:00 0 days 00:00:22 0.0 1.000000 \n", + "11 0 days 00:00:18 0 days 00:00:00 0 days 00:00:18 0.0 1.000000 \n", + "\n", + " ratio_small_chunks \n", + "1 0.023392 \n", + "2 0.000000 \n", + "3 0.015920 \n", + "10 0.000000 \n", + "11 0.000000 " + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_clean = df[df['ratio_clean'] >= 0.98]\n", + "df_clean.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "e5503123", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/aura-alexis/github/seizure_detection_pipeline/env/lib64/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + " \"\"\"Entry point for launching an IPython kernel.\n" + ] + }, + { + "data": { + "text/html": [ + "
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ecg_qc_file_pathexam_idpatient_idtotal_lengthtotal_noisetotal_cleansmall_chunksratio_cleanratio_small_chunkscons
1../output/res-comp-v0_6/PAT_12/qc_qrs_EEG_297_...EEG_297PAT_120 days 00:05:420 days 00:00:040 days 00:05:382.00.9883040.023392../output/cons-v0_6/PAT_12/cons_PAT_12_Annotat...
2../output/res-comp-v0_6/PAT_12/qc_qrs_EEG_299_...EEG_299PAT_120 days 00:02:080 days 00:00:000 days 00:02:080.01.0000000.000000../output/cons-v0_6/PAT_12/cons_PAT_12_Annotat...
3../output/res-comp-v0_6/PAT_12/qc_qrs_EEG_300_...EEG_300PAT_120 days 00:33:300 days 00:00:320 days 00:32:588.00.9840800.015920../output/cons-v0_6/PAT_12/cons_PAT_12_Annotat...
10../output/res-comp-v0_6/PAT_17/qc_qrs_EEG_259_...EEG_259PAT_170 days 00:00:220 days 00:00:000 days 00:00:220.01.0000000.000000../output/cons-v0_6/PAT_17/cons_PAT_17_Annotat...
11../output/res-comp-v0_6/PAT_17/qc_qrs_EEG_259_...EEG_259PAT_170 days 00:00:180 days 00:00:000 days 00:00:180.01.0000000.000000../output/cons-v0_6/PAT_17/cons_PAT_17_Annotat...
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" + ], + "text/plain": [ + " ecg_qc_file_path exam_id patient_id \\\n", + "1 ../output/res-comp-v0_6/PAT_12/qc_qrs_EEG_297_... EEG_297 PAT_12 \n", + "2 ../output/res-comp-v0_6/PAT_12/qc_qrs_EEG_299_... EEG_299 PAT_12 \n", + "3 ../output/res-comp-v0_6/PAT_12/qc_qrs_EEG_300_... EEG_300 PAT_12 \n", + "10 ../output/res-comp-v0_6/PAT_17/qc_qrs_EEG_259_... EEG_259 PAT_17 \n", + "11 ../output/res-comp-v0_6/PAT_17/qc_qrs_EEG_259_... EEG_259 PAT_17 \n", + "\n", + " total_length total_noise total_clean small_chunks ratio_clean \\\n", + "1 0 days 00:05:42 0 days 00:00:04 0 days 00:05:38 2.0 0.988304 \n", + "2 0 days 00:02:08 0 days 00:00:00 0 days 00:02:08 0.0 1.000000 \n", + "3 0 days 00:33:30 0 days 00:00:32 0 days 00:32:58 8.0 0.984080 \n", + "10 0 days 00:00:22 0 days 00:00:00 0 days 00:00:22 0.0 1.000000 \n", + "11 0 days 00:00:18 0 days 00:00:00 0 days 00:00:18 0.0 1.000000 \n", + "\n", + " ratio_small_chunks cons \n", + "1 0.023392 ../output/cons-v0_6/PAT_12/cons_PAT_12_Annotat... \n", + "2 0.000000 ../output/cons-v0_6/PAT_12/cons_PAT_12_Annotat... \n", + "3 0.015920 ../output/cons-v0_6/PAT_12/cons_PAT_12_Annotat... \n", + "10 0.000000 ../output/cons-v0_6/PAT_17/cons_PAT_17_Annotat... \n", + "11 0.000000 ../output/cons-v0_6/PAT_17/cons_PAT_17_Annotat... " + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_clean['cons'] = df_clean['ecg_qc_file_path'].apply(lambda x: x.replace('res-comp-v0_6', 'cons-v0_6').replace('qc_qrs', 'cons').replace('tse_bi', 'csv').replace('cons_', 'cons_{}_Annotations_'.format(re.search(r'PAT_[0-9]*', x).group(0))))\n", + "df_clean.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "5aad6a6c", + "metadata": {}, + "outputs": [], + "source": [ + "import shutil\n", + "\n", + "for src in df_clean['cons'].values:\n", + " dst = src.replace('cons-v0_6', 'cons-v0_6_clean')\n", + " os.makedirs(os.path.dirname(dst), exist_ok=True)\n", + " shutil.copyfile(src, dst)" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "ce8ad54ff0c21d76b9d2b2c70378af31077ad0766af937406802e3e7962ef6ad" + }, + "kernelspec": { + "display_name": "Python 3.6.8 ('env': venv)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/files/file_number_exhaustivity_check.ipynb b/files/file_number_exhaustivity_check.ipynb new file mode 100644 index 0000000..8233124 --- /dev/null +++ b/files/file_number_exhaustivity_check.ipynb @@ -0,0 +1,979 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "80b4909d", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import glob" + ] + }, + { + "cell_type": "markdown", + "id": "4a3e1ba4", + "metadata": {}, + "source": [ + "# I. List of tse_bi" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4f591fa8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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tse_bi
0data/data_pl/PAT_7/PAT_7_Annotations_EEG_38.ts...
1data/data_pl/PAT_7/PAT_7_Annotations_EEG_45.ts...
2data/data_pl/PAT_7/PAT_7_Annotations_EEG_58.ts...
3data/data_pl/PAT_7/PAT_7_Annotations_EEG_61.ts...
4data/data_pl/PAT_7/PAT_7_Annotations_EEG_66.ts...
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" + ], + "text/plain": [ + " tse_bi\n", + "0 data/data_pl/PAT_7/PAT_7_Annotations_EEG_38.ts...\n", + "1 data/data_pl/PAT_7/PAT_7_Annotations_EEG_45.ts...\n", + "2 data/data_pl/PAT_7/PAT_7_Annotations_EEG_58.ts...\n", + "3 data/data_pl/PAT_7/PAT_7_Annotations_EEG_61.ts...\n", + "4 data/data_pl/PAT_7/PAT_7_Annotations_EEG_66.ts..." + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_tse = pd.DataFrame(glob.glob('data/data_pl/*/*.tse_bi'))\n", + "df_tse.columns = ['tse_bi']\n", + "df_tse.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "27f77d36", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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tse_bifilepatientrecordid
0data/data_pl/PAT_7/PAT_7_Annotations_EEG_38.ts...PAT_7_Annotations_EEG_38.tse_biPAT_738PAT_7_38
1data/data_pl/PAT_7/PAT_7_Annotations_EEG_45.ts...PAT_7_Annotations_EEG_45.tse_biPAT_745PAT_7_45
2data/data_pl/PAT_7/PAT_7_Annotations_EEG_58.ts...PAT_7_Annotations_EEG_58.tse_biPAT_758PAT_7_58
3data/data_pl/PAT_7/PAT_7_Annotations_EEG_61.ts...PAT_7_Annotations_EEG_61.tse_biPAT_761PAT_7_61
4data/data_pl/PAT_7/PAT_7_Annotations_EEG_66.ts...PAT_7_Annotations_EEG_66.tse_biPAT_766PAT_7_66
\n", + "
" + ], + "text/plain": [ + " tse_bi \\\n", + "0 data/data_pl/PAT_7/PAT_7_Annotations_EEG_38.ts... \n", + "1 data/data_pl/PAT_7/PAT_7_Annotations_EEG_45.ts... \n", + "2 data/data_pl/PAT_7/PAT_7_Annotations_EEG_58.ts... \n", + "3 data/data_pl/PAT_7/PAT_7_Annotations_EEG_61.ts... \n", + "4 data/data_pl/PAT_7/PAT_7_Annotations_EEG_66.ts... \n", + "\n", + " file patient record id \n", + "0 PAT_7_Annotations_EEG_38.tse_bi PAT_7 38 PAT_7_38 \n", + "1 PAT_7_Annotations_EEG_45.tse_bi PAT_7 45 PAT_7_45 \n", + "2 PAT_7_Annotations_EEG_58.tse_bi PAT_7 58 PAT_7_58 \n", + "3 PAT_7_Annotations_EEG_61.tse_bi PAT_7 61 PAT_7_61 \n", + "4 PAT_7_Annotations_EEG_66.tse_bi PAT_7 66 PAT_7_66 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_tse['file'] = df_tse['tse_bi'].apply(lambda x: x.split('/')[-1])\n", + "df_tse['patient'] = df_tse['tse_bi'].apply(lambda x: x.split('/')[-2])\n", + "df_tse['record'] = df_tse['file'].apply(lambda x: x.split('_')[-2][:-4])\n", + "df_tse['id'] = df_tse['patient'] + '_' + df_tse['record']\n", + "df_tse.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e45d11f8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(27, 5)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_tse.shape" + ] + }, + { + "cell_type": "markdown", + "id": "c2ac7e8a", + "metadata": {}, + "source": [ + "# II. List of edf files" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "190c4326", + "metadata": {}, + "outputs": [], + "source": [ + "df_edf = pd.DataFrame(glob.glob('data/data_pl/*/*.edf'))\n", + "df_edf.columns = ['edf']" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "2bd679f7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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edffilepatientrecordsegmentid
0data/data_pl/PAT_7/EEG_38_s1.edfEEG_38_s1.edfPAT_738s1PAT_7_38_s1
1data/data_pl/PAT_7/EEG_38_s2.edfEEG_38_s2.edfPAT_738s2PAT_7_38_s2
2data/data_pl/PAT_7/EEG_38_s3.edfEEG_38_s3.edfPAT_738s3PAT_7_38_s3
3data/data_pl/PAT_7/EEG_38_s4.edfEEG_38_s4.edfPAT_738s4PAT_7_38_s4
4data/data_pl/PAT_7/EEG_84_s1.edfEEG_84_s1.edfPAT_784s1PAT_7_84_s1
\n", + "
" + ], + "text/plain": [ + " edf file patient record segment \\\n", + "0 data/data_pl/PAT_7/EEG_38_s1.edf EEG_38_s1.edf PAT_7 38 s1 \n", + "1 data/data_pl/PAT_7/EEG_38_s2.edf EEG_38_s2.edf PAT_7 38 s2 \n", + "2 data/data_pl/PAT_7/EEG_38_s3.edf EEG_38_s3.edf PAT_7 38 s3 \n", + "3 data/data_pl/PAT_7/EEG_38_s4.edf EEG_38_s4.edf PAT_7 38 s4 \n", + "4 data/data_pl/PAT_7/EEG_84_s1.edf EEG_84_s1.edf PAT_7 84 s1 \n", + "\n", + " id \n", + "0 PAT_7_38_s1 \n", + "1 PAT_7_38_s2 \n", + "2 PAT_7_38_s3 \n", + "3 PAT_7_38_s4 \n", + "4 PAT_7_84_s1 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_edf['file'] = df_edf['edf'].apply(lambda x: x.split('/')[-1])\n", + "df_edf['patient'] = df_edf['edf'].apply(lambda x: x.split('/')[-2])\n", + "df_edf['record'] = df_edf['file'].apply(lambda x: x.split('_')[-2])\n", + "df_edf['segment'] = df_edf['file'].apply(lambda x: x.split('_')[-1][:-4])\n", + "df_edf['id'] = df_edf['patient'] + '_' + df_edf['record'] + '_' + df_edf['segment']\n", + "df_edf.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "01707430", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "152" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_edf.shape[0]" + ] + }, + { + "cell_type": "markdown", + "id": "687d878c", + "metadata": {}, + "source": [ + "# III. List of rr_intervals" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "714f6f24", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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rr
0output/res-v0_6/PAT_7/rr_EEG_38_s1.csv
1output/res-v0_6/PAT_7/rr_EEG_38_s2.csv
2output/res-v0_6/PAT_7/rr_EEG_38_s3.csv
3output/res-v0_6/PAT_7/rr_EEG_38_s4.csv
4output/res-v0_6/PAT_7/rr_EEG_84_s1.csv
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" + ], + "text/plain": [ + " rr\n", + "0 output/res-v0_6/PAT_7/rr_EEG_38_s1.csv\n", + "1 output/res-v0_6/PAT_7/rr_EEG_38_s2.csv\n", + "2 output/res-v0_6/PAT_7/rr_EEG_38_s3.csv\n", + "3 output/res-v0_6/PAT_7/rr_EEG_38_s4.csv\n", + "4 output/res-v0_6/PAT_7/rr_EEG_84_s1.csv" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_rr = pd.DataFrame(glob.glob('output/res-v0_6/*/*.csv'))\n", + "df_rr.columns = ['rr']\n", + "df_rr.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "249a3d2a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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rrfilepatientrecordsegmentid
0output/res-v0_6/PAT_7/rr_EEG_38_s1.csvrr_EEG_38_s1.csvPAT_738s1PAT_7_38_s1
1output/res-v0_6/PAT_7/rr_EEG_38_s2.csvrr_EEG_38_s2.csvPAT_738s2PAT_7_38_s2
2output/res-v0_6/PAT_7/rr_EEG_38_s3.csvrr_EEG_38_s3.csvPAT_738s3PAT_7_38_s3
3output/res-v0_6/PAT_7/rr_EEG_38_s4.csvrr_EEG_38_s4.csvPAT_738s4PAT_7_38_s4
4output/res-v0_6/PAT_7/rr_EEG_84_s1.csvrr_EEG_84_s1.csvPAT_784s1PAT_7_84_s1
\n", + "
" + ], + "text/plain": [ + " rr file patient record \\\n", + "0 output/res-v0_6/PAT_7/rr_EEG_38_s1.csv rr_EEG_38_s1.csv PAT_7 38 \n", + "1 output/res-v0_6/PAT_7/rr_EEG_38_s2.csv rr_EEG_38_s2.csv PAT_7 38 \n", + "2 output/res-v0_6/PAT_7/rr_EEG_38_s3.csv rr_EEG_38_s3.csv PAT_7 38 \n", + "3 output/res-v0_6/PAT_7/rr_EEG_38_s4.csv rr_EEG_38_s4.csv PAT_7 38 \n", + "4 output/res-v0_6/PAT_7/rr_EEG_84_s1.csv rr_EEG_84_s1.csv PAT_7 84 \n", + "\n", + " segment id \n", + "0 s1 PAT_7_38_s1 \n", + "1 s2 PAT_7_38_s2 \n", + "2 s3 PAT_7_38_s3 \n", + "3 s4 PAT_7_38_s4 \n", + "4 s1 PAT_7_84_s1 " + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_rr['file'] = df_rr['rr'].apply(lambda x: x.split('/')[-1])\n", + "df_rr['patient'] = df_rr['rr'].apply(lambda x: x.split('/')[-2])\n", + "df_rr['record'] = df_rr['file'].apply(lambda x: x.split('_')[-2])\n", + "df_rr['segment'] = df_rr['file'].apply(lambda x: x.split('_')[-1][:-4])\n", + "df_rr['id'] = df_rr['patient'] + '_' + df_rr['record'] + '_' + df_rr['segment']\n", + "df_rr.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "d7be525a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(152, 6)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_rr.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "7605dd70", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Computation of rr from edf files\n", + "df_rr['id'].values in df_edf['id'].values" + ] + }, + { + "cell_type": "markdown", + "id": "93583a65", + "metadata": {}, + "source": [ + "For RR computation, 100% is ok" + ] + }, + { + "cell_type": "markdown", + "id": "26f13e03", + "metadata": {}, + "source": [ + "# IV. features" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "87660ac0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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feats
0output/feats-v0_6/PAT_7/feats_EEG_38_s1.csv
1output/feats-v0_6/PAT_7/feats_EEG_38_s2.csv
2output/feats-v0_6/PAT_7/feats_EEG_38_s3.csv
3output/feats-v0_6/PAT_7/feats_EEG_38_s4.csv
4output/feats-v0_6/PAT_7/feats_EEG_84_s1.csv
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" + ], + "text/plain": [ + " feats\n", + "0 output/feats-v0_6/PAT_7/feats_EEG_38_s1.csv\n", + "1 output/feats-v0_6/PAT_7/feats_EEG_38_s2.csv\n", + "2 output/feats-v0_6/PAT_7/feats_EEG_38_s3.csv\n", + "3 output/feats-v0_6/PAT_7/feats_EEG_38_s4.csv\n", + "4 output/feats-v0_6/PAT_7/feats_EEG_84_s1.csv" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_feats = pd.DataFrame(glob.glob('output/feats-v0_6/*/*.csv'))\n", + "df_feats.columns = ['feats']\n", + "df_feats.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "79c3b087", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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featsfilepatientrecordsegmentid
0output/feats-v0_6/PAT_7/feats_EEG_38_s1.csvfeats_EEG_38_s1.csvPAT_738s1PAT_7_38_s1
1output/feats-v0_6/PAT_7/feats_EEG_38_s2.csvfeats_EEG_38_s2.csvPAT_738s2PAT_7_38_s2
2output/feats-v0_6/PAT_7/feats_EEG_38_s3.csvfeats_EEG_38_s3.csvPAT_738s3PAT_7_38_s3
3output/feats-v0_6/PAT_7/feats_EEG_38_s4.csvfeats_EEG_38_s4.csvPAT_738s4PAT_7_38_s4
4output/feats-v0_6/PAT_7/feats_EEG_84_s1.csvfeats_EEG_84_s1.csvPAT_784s1PAT_7_84_s1
\n", + "
" + ], + "text/plain": [ + " feats file patient \\\n", + "0 output/feats-v0_6/PAT_7/feats_EEG_38_s1.csv feats_EEG_38_s1.csv PAT_7 \n", + "1 output/feats-v0_6/PAT_7/feats_EEG_38_s2.csv feats_EEG_38_s2.csv PAT_7 \n", + "2 output/feats-v0_6/PAT_7/feats_EEG_38_s3.csv feats_EEG_38_s3.csv PAT_7 \n", + "3 output/feats-v0_6/PAT_7/feats_EEG_38_s4.csv feats_EEG_38_s4.csv PAT_7 \n", + "4 output/feats-v0_6/PAT_7/feats_EEG_84_s1.csv feats_EEG_84_s1.csv PAT_7 \n", + "\n", + " record segment id \n", + "0 38 s1 PAT_7_38_s1 \n", + "1 38 s2 PAT_7_38_s2 \n", + "2 38 s3 PAT_7_38_s3 \n", + "3 38 s4 PAT_7_38_s4 \n", + "4 84 s1 PAT_7_84_s1 " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_feats['file'] = df_feats['feats'].apply(lambda x: x.split('/')[-1])\n", + "df_feats['patient'] = df_feats['feats'].apply(lambda x: x.split('/')[-2])\n", + "df_feats['record'] = df_feats['file'].apply(lambda x: x.split('_')[-2])\n", + "df_feats['segment'] = df_feats['file'].apply(lambda x: x.split('_')[-1][:-4])\n", + "df_feats['id'] = df_feats['patient'] + '_' + df_feats['record'] + '_' + df_feats['segment']\n", + "df_feats.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "e8f63513", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(152, 6)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_feats.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "50e46877", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_feats['id'].values in df_rr['id'].values" + ] + }, + { + "cell_type": "markdown", + "id": "ed2d9c6b", + "metadata": {}, + "source": [ + "Wrong: which ones?" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "13a90ad3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Files missing from rr lists:\n", + "[]\n" + ] + } + ], + "source": [ + "features_computation_issue_list = [file for file in df_rr['id'].values if file not in df_feats['id'].values]\n", + "print('Files missing from rr lists:')\n", + "print(features_computation_issue_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "8ca09ef9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Missing files: 0\n" + ] + } + ], + "source": [ + "print(f'Missing files: {len(features_computation_issue_list)}')" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "51bd13de", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[]\n" + ] + } + ], + "source": [ + "features_computation_issue_list_wo_segment = \\\n", + " ['_'.join(file.split('_')[:-1]) for file in features_computation_issue_list]\n", + "print(features_computation_issue_list_wo_segment)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "9532de24", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Files absent not present in tse_bi lists:\n", + "[]\n" + ] + } + ], + "source": [ + "print('Files absent not present in tse_bi lists:')\n", + "print([file for file in features_computation_issue_list_wo_segment if file in df_tse['id'].values])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.6.8 64-bit", + "language": "python", + "name": "python36864bit33613821e84e4f7ead6dfcca52ca4fe2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/output/ml_test.csv b/output/ml_test.csv new file mode 100644 index 0000000..16c3767 --- /dev/null +++ b/output/ml_test.csv @@ -0,0 +1,9282 @@ +mean_nni,sdnn,sdsd,nni_50,pnni_50,nni_20,pnni_20,rmssd,median_nni,range_nni,cvsd,cvnni,mean_hr,max_hr,min_hr,std_hr,lf,hf,vlf,lf_hf_ratio,csi,cvi,Modified_csi,sampen,sd1,sd2,ratio_sd2_sd1,timestamp,filename,label,patient_id,second,month,hour,mean_hr_p30,mean_hr_p60,mean_hr_p120,mean_nni_p30,mean_nni_p60,mean_nni_p120 +710.1004464285713,9.711819494118105,9.261439307345217,0.0,0.0,1.0,7.6923076923076925,9.382509012016824,710.9375,39.0625,0.01321293214108774,0.01367668411273842,84.50961068249278,86.29213483146067,81.70212765957447,1.1021196052951177,,,,,,,,,,,,2017-02-08 13:16:36.425781250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s1.csv,0.0,22,36,2,13,,,,,, 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02:06:37.917968750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s16.csv,0.0,22,37,2,2,74.16633424514096,71.74582880952038,71.25386817807917,810.596955128205,838.5647338772342,844.7775233663906 +822.9166666666665,13.255461817395284,12.444187122113087,0.0,0.0,0.0,0.0,12.464438050272904,822.265625,42.96875,0.01514665889653416,0.016107902967974014,72.92874410422662,74.92682926829268,71.1111111111111,1.125254667457992,,,,,1.9365897700544523,3.756143182752794,203.52981669030362,1.483668525145067,13.567279854523607,26.274255373736285,1.9365897700544523,2017-02-09 02:06:38.917968750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s16.csv,0.0,22,38,2,2,74.22264670958943,71.75101563303564,71.16146462184896,809.9242120726495,838.4996297105675,845.6479040707558 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02:06:40.917968750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s16.csv,0.0,22,40,2,2,74.26264998647662,71.77701088234558,71.0186589983658,809.4033787393162,838.1849595716786,847.0025715387044 +826.4973958333335,12.62569233540839,11.330295549391911,0.0,0.0,0.0,0.0,11.418988061657336,824.21875,39.0625,0.013816121041910724,0.015276142912317672,72.61099923022701,74.20289855072464,70.78341013824884,1.0591527837445,,,,,1.9605799271873272,3.7573888530305455,207.62098849452698,1.3975939163738238,13.503369735270972,26.47443565236112,1.9605799271873272,2017-02-09 02:06:41.917968750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s16.csv,0.0,22,41,2,2,74.25542861583514,71.79956932947931,70.97648672614427,809.4359308226495,837.9092532901129,847.4071772411615 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02:06:43.917968750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s16.csv,0.0,22,43,2,2,74.1147742763318,71.8764919953279,70.93043336421736,810.8515291132478,836.9696817939008,847.8510123773796 +829.1015625,10.418516052685085,10.46230103881835,0.0,0.0,0.0,0.0,10.468325975130453,828.125,39.0625,0.01262610812548125,0.01256603114010545,72.37795292859506,74.20289855072464,70.78341013824884,0.8696899800175893,,,,,1.919208990308097,3.721817740551121,193.01553917451568,1.2770950691548986,13.100496163010481,25.14259001354645,1.919208990308097,2017-02-09 02:06:44.917968750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s16.csv,0.0,22,44,2,2,73.99293862287956,71.92226719977265,70.92586508676962,812.1210603632478,836.4108710300119,847.886277134324 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02:06:52.917968750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s16.csv,0.0,22,52,2,2,72.8807469263348,72.38169496283788,71.11911549012771,823.7179487179487,830.7943467123156,845.5238798744659 +836.2630208333335,13.16357600991633,13.54096304327956,0.0,0.0,0.0,0.0,13.58280646229068,835.9375,42.96875,0.01624226603820824,0.0157409519277785,71.76409196742351,73.84615384615384,70.13698630136986,1.0837195147899827,,,,,2.0803463735143333,3.6605276480189177,202.9865672716074,1.2527629684953678,11.725625183929653,24.39336182857639,2.0803463735143333,2017-02-09 02:06:53.917968750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s16.csv,0.0,22,53,2,2,72.82496751865996,72.42913643719483,71.14573303773595,824.2713341346154,830.2079159989319,845.1921938738345 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05:11:20.507812500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s23.csv,0.0,22,20,2,5,74.60288723811982,75.07061919140489,78.242353001353,805.7319741890054,800.8122248844909,770.5330879244453 +840.4947916666665,9.830513587592122,9.54192389385256,0.0,0.0,0.0,0.0,9.568319307746787,839.84375,27.34375,0.011384150624863724,0.01169610292063633,71.39545245505441,72.45283018867924,70.13698630136986,0.7975200237183921,,,,,4.204645163178608,3.932982616828377,798.1515009969397,1.157452788691043,11.286691163475428,47.4565314087977,4.204645163178608,2017-02-09 05:11:21.507812500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s23.csv,0.0,22,21,2,5,74.53473958439059,74.89721180168618,78.18092369905351,806.502373494561,802.5858960917559,771.1745308998727 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05:11:31.507812500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s23.csv,0.0,22,31,2,5,73.23353393417145,73.74086310985899,77.50983500011291,820.5421856789046,814.7457522945808,778.1729822098474 +844.7265625,11.312194912071359,13.735141765492694,0.0,0.0,2.0,18.181818181818183,13.735141765492694,843.75,42.96875,0.016259867246086152,0.013391546346775808,71.04065152026705,73.14285714285714,69.50226244343891,0.9165788163880664,,,,,4.2609796753419,3.934893903364694,816.0394334229852,1.3350010667323402,11.236530346644725,47.87862742841566,4.2609796753419,2017-02-09 05:11:32.507812500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s23.csv,0.0,22,32,2,5,72.99739026476344,73.67554023133486,77.44408349420927,823.0386800699303,815.4831821824014,778.8672179870911 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05:12:57.355468750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s24.csv,0.0,22,57,2,5,71.40254112356051,73.0688353538726,76.56375377982614,840.5343246163559,822.391963505245,788.1562416804115 +848.6328125,15.812528774003644,14.940116454518911,0.0,0.0,2.0,18.181818181818183,15.356355334119073,847.65625,54.6875,0.018095406055394628,0.018632945298710853,70.7242130945505,72.45283018867924,67.96460176991151,1.2479049365372272,,,,,5.2079106075877855,4.9715134993581165,3637.0731121062777,0.3869030902481049,33.524707535010855,174.59367998786118,5.2079106075877855,2017-02-09 05:12:58.355468750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s24.csv,0.0,22,58,2,5,71.370126466021,73.02027278332362,76.50224277854537,840.9249496163559,822.9491049315271,788.8229333871423 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05:13:00.355468750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s24.csv,0.0,22,0,2,5,71.31065705630674,72.9226983203482,76.31653010701982,841.6410954496893,824.0717344721254,790.6925467924003 +850.2604166666665,14.552439544131632,14.880913948691024,0.0,0.0,2.0,18.181818181818183,14.89785295696238,849.609375,54.6875,0.01752151771967398,0.017115273460785747,70.58534529292663,72.45283018867924,67.96460176991151,1.143253661717169,,,,,5.334947909201207,4.990201572889862,3852.973849232298,0.35667494393873245,33.84353376276493,180.5534896876433,5.334947909201207,2017-02-09 05:13:01.355468750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s24.csv,0.0,22,1,2,5,71.28080577729743,72.87713376828272,76.20577527007354,842.0001547931237,824.5979931526809,791.7787013062892 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05:13:03.355468750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s24.csv,0.0,22,3,2,5,71.22818837414746,72.79043288977782,75.93728192826936,842.6383729118105,825.6061213092465,794.3465674679054 +853.8411458333335,17.346431807061258,17.295164292451588,0.0,0.0,3.0,27.272727272727273,17.90068630842125,853.515625,54.6875,0.020964890712722103,0.020315760011929918,70.29718272645367,72.45283018867924,67.96460176991151,1.3626797411710416,,,,,5.456904787681342,5.003003087831063,4045.024052031115,0.34955747616986843,33.96005339883604,185.31677798202244,5.456904787681342,2017-02-09 05:13:04.355468750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s24.csv,0.0,22,4,2,5,71.19989164501588,72.74407402838146,75.79141543708903,842.9855951340327,826.1486560314687,795.7398644514124 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05:13:06.355468750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s24.csv,0.0,22,6,2,5,71.11010558295276,72.64179480508977,75.48099057119258,844.070664578477,827.3476577675799,798.7075971988081 +855.46875,16.404558895527128,16.473598728328408,0.0,0.0,2.0,18.181818181818183,17.10822367961066,853.515625,50.78125,0.01999865416429374,0.01917610537559336,70.16058119513116,72.11267605633803,67.96460176991151,1.285375929511115,,,,,5.14393837570764,5.057242090723395,3940.621123468261,0.304776505811081,37.23172648314651,191.51770665050773,5.14393837570764,2017-02-09 05:13:07.355468750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s24.csv,0.0,22,7,2,5,71.07719388097439,72.5155665711625,75.32628985508681,844.4721402729214,828.6998212291184,800.1856652326622 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05:13:09.355468750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s24.csv,0.0,22,9,2,5,71.05715711907685,72.26581886226579,75.00126589997734,844.7177605380729,831.3566858488737,803.0826958414849 +847.0052083333335,15.96168971597353,16.686562676805018,0.0,0.0,2.0,18.181818181818183,16.78076328225946,843.75,54.6875,0.019811877326326312,0.0188448542688204,70.86068647638702,72.79620853080569,68.26666666666667,1.2677583270784172,,,,,5.330000643302492,4.9930346951376485,3860.186074868949,0.2971736691333523,33.96985753638377,181.0593625218195,5.330000643302492,2017-02-09 05:13:10.355468750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s24.csv,0.0,22,10,2,5,71.05115828428083,72.14234610922568,74.86599792921623,844.793715399184,832.6679505390446,804.3836941053737 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05:13:13.355468750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s24.csv,0.0,22,13,2,5,71.04169810912967,71.79801739521582,74.4996192411779,844.918991598679,836.3359025956684,807.9923250577547 +843.75,11.71875,12.506102026236036,0.0,0.0,0.0,0.0,12.657696676577851,843.75,39.0625,0.015001714579647826,0.013888888888888888,71.12357452601364,72.79620853080569,69.50226244343891,0.9412556210007766,,,,,4.635349966125989,4.713998751372785,2270.508691515344,0.39905893629575295,26.417889543873528,122.45616340231426,4.635349966125989,2017-02-09 05:13:14.355468750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s24.csv,0.0,22,14,2,5,71.04711765049665,71.70015409093331,74.39152844164589,844.8538874320124,837.3825772751555,809.0735192541833 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05:13:16.355468750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s24.csv,0.0,22,16,2,5,71.05037220761692,71.5438880155224,74.18159017543239,844.821335348679,839.1241137334889,811.18940467085 +848.3072916666665,14.116976762295707,14.016870991470563,0.0,0.0,2.0,18.181818181818183,14.034852873131747,843.75,42.96875,0.0165445387667923,0.016641347894770692,70.74702869739346,72.79620853080569,69.18918918918922,1.1262336043944858,,,,,7.42170666547538,4.490971743648188,3558.291718496211,0.41773520069997866,16.15005433554866,119.86096590993105,7.42170666547538,2017-02-09 05:13:17.355468750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s24.csv,0.0,22,17,2,5,71.05079813118229,71.4741454725389,74.07873973483262,844.8193624951437,839.9112064151127,812.2289846655082 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06:57:16.300781250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s30.csv,0.0,22,16,2,6,90.49182211758874,90.8471858876954,85.59684360402471,739.3049214334067,704.8181906663348,727.6496875911466 +680.84716796875,270.3017789445568,200.35955110898655,9.0,60.0,10.0,66.66666666666667,200.36022805925262,666.015625,1027.34375,0.2942807688500937,0.3970080095228703,99.39763153358751,160.0,42.785515320334255,32.18951260953276,,,,,2.1488163522506807,5.741114354761537,2338.0625616183474,0.8472978603872037,126.58945669002016,272.01749455804463,2.1488163522506807,2017-02-09 06:57:17.300781250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s30.csv,0.0,22,17,2,6,90.94403447153836,90.9841261440334,85.77477331930527,737.0529346338376,705.2108156884041,726.9246435921768 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06:57:19.300781250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s30.csv,0.0,22,19,2,6,92.35408136305111,91.52723392132087,86.25460949478067,728.8058098477702,704.6067684551041,724.8483234572226 +576.6888786764706,160.0326871440767,122.94656206795136,12.0,75.0,13.0,81.25,123.75931136424856,564.453125,601.5625,0.2146032565224464,0.2775026414786421,112.627105840264,196.9230769230769,66.20689655172414,33.483826786755024,,,,,2.168126015749261,5.763355388205127,2431.109246218652,0.7760775598906784,129.29312798323022,280.32379443804024,2.168126015749261,2017-02-09 06:57:20.300781250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s30.csv,0.0,22,20,2,6,93.41737063445967,91.90022637039296,86.55177421071919,719.906611698683,703.1302734609199,723.1671981649112 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15:49:18.468750000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s2.csv,0.0,22,18,2,15,79.5948643891192,81.65444862157842,87.30637276971183,783.4838273705461,756.7775704384588,729.0490597949038 +929.6875,30.05528759636148,32.83109592101671,3.0,30.0,5.0,50.0,33.28345063146999,929.6875,105.46875,0.03580068639351393,0.03232837657423755,64.59940177749506,68.26666666666667,60.95238095238096,1.9994041818729973,,,,,1.4436623598500713,3.956978452362464,165.077421377568,1.5260563034950494,19.80142376777556,28.586570164978145,1.4436623598500713,2017-02-09 15:49:19.468750000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s2.csv,0.0,22,19,2,15,78.43077561129226,81.33826472312666,87.09340212790926,792.8327857038795,760.2908702896493,731.2374351450438 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15:49:21.468750000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s2.csv,0.0,22,21,2,15,76.44155417006786,80.88880529621002,86.70113019980937,809.4956475815851,765.6330541474581,735.3680504593515 +928.515625,29.523142442330546,33.37219233809216,2.0,22.222222222222207,5.0,55.55555555555557,33.37501463014661,927.734375,105.46875,0.03594448357306492,0.031796064220599995,64.67789846191529,68.26666666666667,60.95238095238096,1.945240206893304,,,,,1.4366055262545747,3.9576060219931604,163.98695815359102,1.4508328822574619,19.864345180947,28.53722806237689,1.4366055262545747,2017-02-09 15:49:22.468750000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s2.csv,0.0,22,22,2,15,75.57133958409639,80.64355339180437,86.51082724100505,817.0737725815851,768.4981382820735,737.3933918459061 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15:49:25.468750000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s2.csv,0.0,22,25,2,15,73.0216576378166,79.87247561200954,85.91949197270753,839.2152769105894,777.4394118522533,743.6319526111212 +930.7528409090908,27.292972333155817,32.590862318150236,2.0,20.0,6.0,60.0,32.705368263960125,925.78125,89.84375,0.035138617715113216,0.02932354448308538,64.51385784898065,67.07423580786026,60.95238095238096,1.7860254990015512,,,,,1.4185584334685062,3.952500765022585,159.96362140902303,1.3143208614948014,19.873153002752442,28.191228791664443,1.4185584334685062,2017-02-09 15:49:26.468750000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s2.csv,0.0,22,26,2,15,72.17980799893094,79.60646869274053,85.75980728176661,846.5331543456545,780.5170633674047,745.3418197966317 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15:49:28.468750000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s2.csv,0.0,22,28,2,15,70.93715399643861,79.05838670970813,85.35235438785284,858.4260791292041,786.8283952646742,749.3107369089312 +921.5198863636364,20.85034225179638,27.621358640099512,1.0,10.0,4.0,40.0,27.896204798995512,921.875,58.59375,0.030271950949507263,0.022626036139136264,65.14017678390714,67.36842105263158,63.20987654320986,1.4070682936584582,,,,,1.381634266547378,3.9621250063217963,155.47198622485874,1.3862943611198906,20.361321619786466,28.13189966208894,1.381634266547378,2017-02-09 15:49:29.468750000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s2.csv,0.0,22,29,2,15,70.32697764380724,78.78520490540954,85.14297476703496,864.1806110556112,789.9124206143246,751.3154262345276 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15:49:31.468750000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s2.csv,0.0,22,31,2,15,69.06482946748315,78.24152801845524,84.60857498728093,875.9020926989676,796.0271392290432,755.8813509639338 +917.6136363636364,20.18095517324553,24.64654930366775,1.0,10.0,3.0,30.0,25.164255325635015,914.0625,58.59375,0.02742358475115642,0.02199286755722113,65.41561434530685,67.36842105263158,63.20987654320986,1.364945626235296,,,,,1.4141624640337074,3.944738670027237,157.80410391638804,1.4816045409242156,19.726938510580325,27.8970959719637,1.4141624640337074,2017-02-09 15:49:32.468750000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s2.csv,0.0,22,32,2,15,68.46181477247661,77.97840800083222,84.3846582895518,881.5450174825176,798.9959802838218,757.6881210483044 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15:49:34.468750000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s2.csv,0.0,22,34,2,15,67.6233175045121,77.47131232060279,83.8704036763578,890.0761521464647,804.7226949753803,761.2912260237922 +912.6420454545456,18.83709648101924,24.397650733792485,0.0,0.0,3.0,30.0,24.920527980838408,914.0625,62.5,0.027305917040482863,0.020640180424338583,65.76898242617892,68.57142857142857,64.0,1.3108819239886744,,,,,1.4506386184004476,3.9302926628110586,161.24460168301962,1.461517782357478,19.156083552292856,27.788554578261646,1.4506386184004476,2017-02-09 15:49:35.468750000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s2.csv,0.0,22,35,2,15,67.38093725174444,77.21165812201187,83.69801299700595,893.0344460227273,807.6337162457766,762.6902786313409 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07:14:08.410156250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s17.csv,0.0,22,8,2,7,77.93567602300458,91.01074743454868,90.09240988671347,774.7269043167479,679.4728879065863,684.7855534203426 +770.1322115384613,32.323919932143866,21.541019865319925,0.0,0.0,5.0,41.66666666666666,24.36844696902721,769.53125,136.71875,0.03164190070734396,0.04197191007966242,78.03241027921486,84.39560439560441,70.78341013824884,3.074514292181004,,,,,3.752738533526003,4.221478923284946,938.1276181099244,1.3350010667323402,16.65349216241288,62.4962017556601,3.752738533526003,2017-02-10 07:14:09.410156250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s17.csv,0.0,22,9,2,7,77.1186757705141,90.6395406316161,90.16173442518526,780.8594942384003,682.3359982949725,684.0228748012188 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07:14:12.410156250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s17.csv,0.0,22,12,2,7,77.50412059980745,89.4768295912197,90.3469173349383,776.8096812042124,691.4119904706889,681.9507525874949 +786.7838541666665,27.49341817650064,26.82459867616656,1.0,9.090909090909092,4.0,36.36363636363637,26.90908581033045,777.34375,97.65625,0.03420137013212045,0.03494405487720727,76.34288511628691,80.0,70.78341013824884,2.4842720049893443,,,,,3.2182458488339583,4.180957767990103,711.0623938501276,1.4170660197866445,17.16363553353494,55.236798806697855,3.2182458488339583,2017-02-10 07:14:13.410156250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s17.csv,0.0,22,13,2,7,77.59188429526925,88.87674480259918,90.40219272291742,775.8923801892553,695.200691280874,681.328070690399 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13:49:46.324218750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s3.csv,0.0,22,46,2,13,76.33566622408738,75.02534321163674,76.19095600211179,807.8782220383787,812.8400791451849,794.4944185178565 +699.609375,147.53566209227753,116.76055570552201,7.0,50.0,7.0,50.0,116.76588964289179,765.625,539.0625,0.16690155080167668,0.21088291175668927,90.64356555789892,155.15151515151516,64.81012658227849,24.507732833701855,,,,,3.0311708470787115,4.884179165569793,1460.5122563292864,0.7342272063034101,39.73968165619985,120.45776450846165,3.0311708470787115,2017-02-08 13:49:47.324218750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s3.csv,0.0,22,47,2,13,76.9722967889512,75.29934711244836,76.30200358085986,803.2274171141363,810.8535755636904,793.8915620556251 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13:49:51.324218750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s3.csv,0.0,22,51,2,13,78.01242780646385,75.5432788053761,76.3556428659668,793.2314235243928,808.1568670045231,793.6384543806422 +764.7235576923077,7.673543884098527,11.481244849315365,0.0,0.0,1.0,8.333333333333334,11.499688628031054,765.625,31.25,0.015037706779602096,0.010034402375748486,78.46700095532479,80.0,76.8,0.7547976268613944,,,,,2.9831343746782024,4.869846101250552,1402.5949644908758,0.6234138425452702,39.40276069168391,117.54372987658134,2.9831343746782024,2017-02-08 13:49:52.324218750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s3.csv,0.0,22,52,2,13,78.24482418971719,75.55239364758538,76.3574195441441,790.7274171141364,808.0166426455487,793.619505142943 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13:49:54.324218750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s3.csv,0.0,22,54,2,13,78.44373073981605,75.44680957058624,76.30214745165158,789.0689302017433,809.2986939275999,794.336643555394 +808.1430288461537,86.9457085051582,82.30593271977662,4.0,33.333333333333336,5.0,41.66666666666666,82.30593271977662,765.625,285.15625,0.10184574980160499,0.10758703026776968,74.9224242942502,79.17525773195877,57.528089887640455,6.581495677611125,,,,,2.7008566888466286,4.950979524693333,1326.606354191933,0.6567795363890705,45.46518899459907,122.79495980573904,2.7008566888466286,2017-02-08 13:49:55.324218750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s3.csv,0.0,22,55,2,13,78.55228133723296,75.39577973294432,76.2752531404175,788.0881943577261,809.8971514596511,794.6763763170018 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13:50:01.324218750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s3.csv,0.0,22,1,2,13,79.30801080408118,75.43095247273878,76.13226476041284,781.1061898171278,810.0123357545231,796.5415227133983 +803.7860576923077,83.85791481609644,56.294402505204445,2.0,16.666666666666668,4.0,33.333333333333336,59.27340197458405,761.71875,300.78125,0.07374275954071616,0.10432865065718464,75.28061416686452,80.84210526315789,57.528089887640455,6.353927343890232,,,,,2.7200533400761056,4.950932484493382,1340.7023578325598,0.6241543090729939,45.302017198621705,123.22390319329617,2.7200533400761056,2017-02-08 13:50:02.324218750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s3.csv,0.0,22,2,2,13,79.44677760005167,75.4856474686259,76.11107405940133,779.763207712427,809.5144558132837,796.8176401770158 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13:50:04.324218750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s3.csv,0.0,22,4,2,13,79.85091745304766,75.65935171592896,76.09144081273298,775.3863562652629,807.6619084040956,797.0363298488305 +796.875,46.54834096801947,18.42488887936484,0.0,0.0,3.0,27.272727272727273,20.77035343166159,791.015625,136.71875,0.026064757247575333,0.05841360435202444,75.52489298540435,80.84210526315789,68.26666666666667,4.130573066658105,,,,,2.7279354714771067,4.953539747733827,1350.5821742616279,0.6584616225720552,45.37251323965085,123.7732882965082,2.7279354714771067,2017-02-08 13:50:05.324218750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s3.csv,0.0,22,5,2,13,80.01676567755287,75.72652962685245,76.06528228932534,773.5851409874851,806.9511879179845,797.3205684143192 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13:50:07.324218750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s3.csv,0.0,22,7,2,13,80.29176379645219,75.83518648397914,75.99882524433734,770.5678132631261,805.7951716252496,798.0359021374654 +821.2890625,34.09969332258873,25.523754014298877,0.0,0.0,5.0,45.45454545454545,26.49347649658308,818.359375,125.0,0.032258406578479275,0.04151972171501885,73.17290717005123,79.58549222797927,68.26666666666667,2.947873638001933,,,,,2.7187570286802187,4.952277843858087,1341.8207931731758,0.6488743568819573,45.383055483258076,123.38550107809223,2.7187570286802187,2017-02-08 13:50:08.324218750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s3.csv,0.0,22,8,2,13,80.05584839709717,75.8646009138086,75.95339643877504,771.7171522054339,805.4750761391385,798.5211379742636 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13:50:10.324218750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s3.csv,0.0,22,10,2,13,78.99575117240407,75.90214008104901,75.85540148448702,777.8264508928576,805.0573244030275,799.5644739785373 +829.4270833333335,23.633961944561246,24.720602676075448,0.0,0.0,4.0,36.36363636363637,24.8452686785679,824.21875,78.125,0.029954735235698818,0.0284943214653423,72.39182227138419,74.92682926829268,68.26666666666667,1.9336569227529865,,,,,2.7072824093815493,4.950941604208345,1331.2853235705882,0.6312717768418579,45.40921874789058,122.93557913992304,2.7072824093815493,2017-02-08 13:50:11.324218750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s3.csv,0.0,22,11,2,13,78.48287170072338,75.91908782095818,75.80427265922987,780.9437003968258,804.8674372502497,800.1094901485535 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13:50:13.324218750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s3.csv,0.0,22,13,2,13,77.43163022889105,75.94162706699345,75.69914921632663,787.8100198412703,804.612939144189,801.2374209639844 +827.7994791666665,18.05180389860927,23.36744868801435,0.0,0.0,5.0,45.45454545454545,23.584999882557288,828.125,54.6875,0.02849119922894848,0.021806976632531533,72.51256450881249,74.20289855072464,69.50226244343891,1.496726932263855,,,,,2.707629568693587,4.945212344323124,1322.787384633418,0.6337237600891445,45.10779071126417,122.1351879082608,2.707629568693587,2017-02-08 13:50:14.324218750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s3.csv,0.0,22,14,2,13,76.84657873960303,75.96538480467821,75.6541222378519,791.710069444445,804.3362464358557,801.7183706636841 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13:50:21.324218750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s3.csv,0.0,22,21,2,13,74.30370289324745,76.15806534985573,75.39111865822503,811.0167935363253,802.1241085303585,804.5079691564075 +804.3619791666665,19.81038694251141,19.22867382198989,1.0,9.090909090909092,2.0,18.181818181818183,19.28106812570771,798.828125,58.59375,0.023970635889184168,0.02462869635264875,74.63424570497675,76.8,71.44186046511628,1.7375491092757915,,,,,2.6942227270349406,4.934053768572271,1296.2152517765107,0.6803538291000357,44.64266837708761,120.27729173703352,2.6942227270349406,2017-02-08 13:50:22.324218750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s3.csv,0.0,22,22,2,13,74.17594438490252,76.21038428730995,75.3667497361841,812.3380742521372,801.5327456831362,804.7622047622057 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22:10:31.964843750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s11.csv,0.0,22,31,2,22,69.43663275375727,70.4597994309621,75.01941640217562,864.6139125631322,852.676747782446,803.2822915635421 +877.1306818181819,24.35468631985713,30.095875444153474,1.0,10.0,5.0,50.0,30.458732327528008,878.90625,85.9375,0.03472542114749752,0.02776631444401525,68.45275963527871,71.77570093457943,65.08474576271186,1.8103953549412093,,,,,1.957256762811196,3.9200101846427273,249.7334954009221,1.6650077635889111,16.297507884802066,31.898407524497642,1.957256762811196,2017-02-08 22:10:32.964843750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s11.csv,0.0,22,32,2,22,69.37218289749197,70.18748799046658,74.92388382600731,865.4227825126271,855.5743556892452,804.3436949686954 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22:10:37.964843750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s11.csv,0.0,22,37,2,22,69.16970403423176,70.03728169808178,74.45527144341392,867.9914378156576,857.4377282873374,809.5524970524976 +861.328125,28.486666101820177,23.964251163072422,1.0,9.090909090909092,3.0,27.272727272727273,25.20556466451045,857.421875,101.5625,0.02926360342002121,0.03307295474859848,69.72857893407078,73.14285714285714,65.08474576271186,2.171722672956,,,,,1.9989751573979024,3.9175322666601513,257.0261012845706,1.916922612182061,16.08060719923612,32.14473430714686,1.9989751573979024,2017-02-08 22:10:38.964843750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s11.csv,0.0,22,38,2,22,69.18970993542739,70.03186586011668,74.3739719627957,867.7527225378799,857.5059808612438,810.4444647413403 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22:10:44.964843750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s11.csv,0.0,22,44,2,22,69.5639016580592,70.06655622276774,73.8961238686002,863.1175031565667,857.0805713383836,815.5617110695241 +853.515625,26.36225490478333,26.654821029538645,1.0,9.090909090909092,5.0,45.45454545454545,26.67610232716388,849.609375,82.03125,0.031254380758599334,0.030886669362126005,70.3581248994765,73.14285714285714,66.49350649350649,2.051354099211512,,,,,1.953182999282557,3.9338302941824312,252.94701010821515,2.110213200346589,16.576150761266483,32.376255860450314,1.953182999282557,2017-02-08 22:10:45.964843750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s11.csv,0.0,22,45,2,22,69.60900184981728,70.06141797190801,73.817463082404,862.5641177399,857.1456755050502,816.3995380792261 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22:10:49.964843750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s11.csv,0.0,22,49,2,22,69.62350987353311,69.97246788484475,73.50838358952369,862.4299636994959,858.2465277777776,819.7519330331834 +869.6732954545455,22.928904058747055,31.347504137092,2.0,20.0,5.0,50.0,31.58994784243083,875.0,66.40625,0.03632392532637208,0.026364962772328183,69.03541837819608,71.77570093457943,66.49350649350649,1.750489708693221,,,,,1.7946224977257847,3.931168444452613,222.0976094273988,2.0281482472922856,17.240016493390712,30.93932146020256,1.7946224977257847,2017-02-08 22:10:50.964843750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s11.csv,0.0,22,50,2,22,69.58277453919472,69.92627829666672,73.43375976443812,862.9468513257584,858.8087910353534,820.57560963811 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22:11:13.964843750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s11.csv,0.0,22,13,2,22,69.75514607369001,69.63189261186265,71.71521254983745,860.6869476010112,862.241556186868,839.4774010008391 +869.3181818181819,14.653763236405736,19.200321449132048,0.0,0.0,3.0,30.0,19.216209181638817,871.09375,46.875,0.022104920365806743,0.016856616533381764,69.03757852308165,71.1111111111111,67.36842105263158,1.1182534574116352,,,,,1.4757600036285994,3.849222522577576,150.70749978786998,1.7749523509116738,17.29989240356266,25.530489276256013,1.4757600036285994,2017-02-08 22:11:14.964843750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s11.csv,0.0,22,14,2,22,69.71053752508126,69.63721959157014,71.62446305760842,861.2196180555566,862.16856060606,840.4738355910238 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22:49:15.718750000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s12.csv,0.0,22,15,2,22,87.30378400958078,94.99889449540848,82.01249946675328,742.6303545224052,688.0573783227326,775.9838680069621 +669.25048828125,144.39337400184817,148.81829924367045,8.0,53.33333333333334,10.0,66.66666666666667,148.82034988986263,753.90625,439.453125,0.22236868331924392,0.21575385678488654,95.26796961935526,163.40425531914894,74.3825665859564,27.5109202004013,,,,,2.897373367520002,5.502684971672855,2781.9491120306084,0.6729444732424258,82.84766308313111,240.04061257833413,2.897373367520002,2017-02-08 22:49:16.718750000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s12.csv,0.0,22,16,2,22,87.84369218105763,95.00397138453955,82.24709967899202,739.5143236143317,687.7025313408178,774.2187014239155 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22:49:20.718750000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s12.csv,0.0,22,20,2,22,89.86538417245316,94.696150585655,83.02994485427189,725.3815240268414,688.5197530769519,768.078727601329 +725.4464285714287,129.27611720636432,111.0295755856271,4.0,30.76923076923077,7.0,53.84615384615386,111.12102236110377,767.578125,449.21875,0.15317605543930612,0.17820215541061912,86.5242996920724,160.0,72.79620853080569,22.76575824791432,,,,,3.2325049264800474,5.430281570128293,3016.1266791985345,0.7278901290038183,72.16243777483315,233.265435613958,3.2325049264800474,2017-02-08 22:49:21.718750000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s12.csv,0.0,22,21,2,22,90.4241357537432,94.55525660473117,83.18483813255396,720.6173742936722,689.1564198958222,766.8231472441862 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00:29:40.605468750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s14.csv,1.0,22,40,2,0,75.41499237623219,72.9680524266139,70.81381744178061,799.7117140671832,826.184877405581,850.9243519009145 +741.2860576923077,9.978646196145677,4.4633167971360965,0.0,0.0,0.0,0.0,5.524271728019904,742.1875,39.0625,0.0074522806286381185,0.013461262481059104,80.95377734279654,82.58064516129029,78.36734693877553,1.0334619310325466,,,,,6.609425138876227,4.2261824705132405,2204.635351995813,0.3134462093879123,12.616805264819336,83.38982988960284,6.609425138876227,2017-02-09 00:29:41.605468750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s14.csv,1.0,22,41,2,0,75.88343334396505,73.1812488680134,70.9406926718209,794.497006899351,823.8675332913618,849.5149715852842 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00:29:52.605468750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s14.csv,1.0,22,52,2,0,80.06642662422114,75.48044828664769,72.39193612823327,749.9209449404766,799.4770474750944,833.6707721473349 +720.1450892857142,14.176151886994774,6.402426608372152,0.0,0.0,0.0,0.0,7.347968305671565,718.75,46.875,0.0102034554077981,0.01968513303486605,83.34660044966755,86.29213483146067,80.84210526315789,1.5847633407490391,,,,,7.7166503853342725,4.233842258029647,2805.849453738293,0.2377423507353624,11.780038925723336,90.9024419154357,7.7166503853342725,2017-02-09 00:29:53.605468750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s14.csv,1.0,22,53,2,0,80.30615440889717,75.66495461512709,72.54825627092012,747.613681891026,797.6177034770786,831.965358332546 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00:37:13.351562500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s15.csv,0.0,22,13,2,0,87.10756279889246,91.80174016916861,96.70723455867453,696.5435504964242,666.9001729620165,639.4284074614868 +705.9151785714287,5.614592783908808,6.583203816167861,0.0,0.0,0.0,0.0,6.590057752241981,705.078125,19.53125,0.00933548102135781,0.007953636576098487,85.00103956952789,86.29213483146067,83.93442622950819,0.6513754158606659,,,,,4.886379816935348,4.652291796864087,2288.899534038062,0.0937623249936118,23.96582017635394,117.10610000603779,4.886379816935348,2017-02-09 00:37:14.351562500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s15.csv,0.0,22,14,2,0,86.07482038393218,91.73396532761272,96.52958447182793,701.7157617357294,667.4374005649221,640.567943694558 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00:37:16.351562500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s15.csv,0.0,22,16,2,0,85.0502051306813,91.59201336759212,96.20336493720308,706.6499255952386,668.5600927562053,642.768624473368 +706.1941964285713,5.122671530313677,5.524271728019904,0.0,0.0,0.0,0.0,5.524271728019904,705.078125,15.625,0.007822595761842485,0.007253913379946351,84.96660535770367,85.81005586592178,83.93442622950819,0.592274033864237,,,,,4.9106706251219645,4.659668973319835,2325.657170679509,0.0865591952048506,24.110382069610854,118.39814498970532,4.9106706251219645,2017-02-09 00:37:17.351562500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s15.csv,0.0,22,17,2,0,84.96273837614919,91.51554198684983,96.04160237694799,707.0647321428577,669.1638432606444,643.8669195348942 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00:37:19.351562500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s15.csv,0.0,22,19,2,0,84.66686079877154,91.36700293369832,95.70919909304871,708.74007936508,670.3357182606444,646.1114380896403 +704.7991071428571,6.628114225022457,4.436545390695132,0.0,0.0,0.0,0.0,4.596471917301189,705.078125,19.53125,0.006521676702932485,0.009404260246429331,85.13762860976391,86.29213483146067,83.93442622950819,0.7710638015711049,,,,,4.927475723390708,4.660928450354659,2340.997581906065,0.08261208216002244,24.10415920901217,118.77265933515206,4.927475723390708,2017-02-09 00:37:20.351562500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s15.csv,0.0,22,20,2,0,84.66063136095462,91.2952005430659,95.53932727305668,708.7865823412706,670.902581658546,647.254671807057 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00:37:22.351562500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s15.csv,0.0,22,22,2,0,84.68053658220009,91.15975091673806,95.19470946502572,708.6191716269848,671.9741993946235,649.5684437416718 +704.1666666666665,6.344614942207364,4.99122316406203,0.0,0.0,0.0,0.0,5.114481802406072,703.125,19.53125,0.00726316942353525,0.009010104059939452,85.21354203067003,86.29213483146067,83.93442622950819,0.7400317706044727,,,,,4.953756672650933,4.663356570840515,2366.357141280636,0.06704450262899704,24.10743247230116,119.42235447014365,4.953756672650933,2017-02-09 00:37:23.351562500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s15.csv,0.0,22,23,2,0,84.7009445495233,91.09037421023193,95.02207250940403,708.449280753969,672.5215157788042,650.7257847839755 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00:37:28.351562500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s15.csv,0.0,22,28,2,0,84.94505462301892,90.74618212892388,94.14565264364268,706.4068700396834,675.2082843421617,656.4615620137473 +699.7767857142858,5.703483683963842,6.5003929245144025,0.0,0.0,0.0,0.0,6.5003929245144025,699.21875,19.53125,0.009289237735829195,0.008150432824292828,85.7469335396751,87.27272727272727,84.86187845303867,0.6757081209206272,,,,,4.972481302276259,4.6708047772312185,2400.2812175200306,0.07199063567677204,24.269219963913997,120.67824249139208,4.972481302276259,2017-02-09 00:37:29.351562500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s15.csv,0.0,22,29,2,0,85.00077894448536,90.67804155354294,93.90259644290647,705.9418402777787,675.7385759079405,657.8511275273019 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00:37:31.351562500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s15.csv,0.0,22,31,2,0,85.11914181406425,90.54069814781714,93.20627622060317,704.9541170634928,676.8070408998694,661.3981953653315 +699.4791666666665,4.999534948610816,6.170017965261155,0.0,0.0,0.0,0.0,6.176323555016366,699.21875,19.53125,0.008829889222361447,0.007147510872176298,85.78221492458115,87.27272727272727,84.86187845303867,0.5946906867129362,,,,,4.997662188692301,4.666853812847658,2407.5607369288896,0.06223323442588495,24.098136859825665,120.43434740228294,4.997662188692301,2017-02-09 00:37:32.351562500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s15.csv,0.0,22,32,2,0,85.17831188003763,90.46904462934651,92.84401604344838,704.4605654761913,677.363184967666,663.2376653849908 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02:05:38.917968750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s16.csv,0.0,22,38,2,2,72.67666489982018,72.34839425401728,79.01860529428814,825.8346688034197,829.7006650033984,778.8313480286736 +831.7057291666665,10.980325722450925,12.882355928421203,0.0,0.0,1.0,9.090909090909092,12.901919122574535,833.984375,35.15625,0.015512600995909582,0.013202176367659194,72.15247678634606,73.84615384615384,70.78341013824884,0.9159206244839756,,,,,2.066685027349449,3.5927400306090402,185.9008840182281,,10.881101899741836,22.487810377260104,2.066685027349449,2017-02-09 02:05:39.917968750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s16.csv,0.0,22,39,2,2,72.67652347857243,72.35584195711962,78.98614727923761,825.8346688034197,829.6138594478429,779.1870096799082 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02:05:41.917968750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s16.csv,0.0,22,41,2,2,72.67620157764465,72.36254409017994,78.92252747933924,825.8346688034197,829.5324792395095,779.8844218356537 +830.7291666666665,10.177171051233275,13.540963043279556,0.0,0.0,2.0,18.181818181818183,13.58280646229068,832.03125,27.34375,0.016350462951472168,0.012250889290512785,72.23561845088304,73.14285714285714,70.78341013824884,0.8451905904334199,,,,,2.071851916002567,3.5697686284187817,181.72822352941628,,10.583881463236539,21.92823508835068,2.071851916002567,2017-02-09 02:05:42.917968750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s16.csv,0.0,22,42,2,2,72.67890025004625,72.36413329228071,78.89319813605374,825.8021167200864,829.5107778506206,780.206928587197 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02:05:44.917968750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s16.csv,0.0,22,44,2,2,72.6838806075235,72.37476519105533,78.83717437937437,825.7403512286334,829.3876642021163,780.8201783052646 +822.265625,17.74502521114913,15.290518566485071,0.0,0.0,2.0,18.181818181818183,15.491260022150025,820.3125,62.5,0.01883972715282853,0.021580648237787062,73.00070128058907,76.41791044776119,70.78341013824884,1.5288776968332105,,,,,2.1488703509296694,3.5906714222353355,196.63056708138043,,10.645611497559802,22.87603891462225,2.1488703509296694,2017-02-09 02:05:45.917968750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s16.csv,0.0,22,45,2,2,72.6849026920002,72.37999822120247,78.81078189004417,825.729500534189,829.3279853826718,781.1081390424441 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02:05:50.917968750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s16.csv,0.0,22,50,2,2,72.65673918071758,72.37227723597566,78.68719247275024,826.0483440170949,829.3997668997661,782.454274340402 +826.8229166666665,25.27883680938674,18.574853452588094,0.0,0.0,2.0,18.181818181818183,19.53125,828.125,101.5625,0.023622047244094488,0.03057345932221892,72.62800316335996,76.41791044776119,67.66519823788546,2.0886279444528424,,,,,1.981357219390776,3.559792240196137,168.01199649270373,2.251291798606496,10.699284552552752,21.19910469051661,1.981357219390776,2017-02-09 02:05:51.917968750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s16.csv,0.0,22,51,2,2,72.62714740074401,72.37667184488508,78.6651184638981,826.3955662393171,829.3480174339542,782.7014290471923 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02:20:14.382812500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s17.csv,0.0,22,14,2,2,73.07787632697905,73.33980999853071,72.7060778954798,821.2131076388897,818.2749261563592,825.620788511414 +828.4505208333335,13.37267302886848,12.136368956416948,0.0,0.0,1.0,9.090909090909092,12.464438050272904,824.21875,46.875,0.015045482786026857,0.016141788426201953,72.44163203751873,74.5631067961165,70.45871559633028,1.117678771733816,,,,,,,,,,,,2017-02-09 02:20:15.382812500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s17.csv,0.0,22,15,2,2,73.03799329733839,73.32534257993743,72.69590629393777,821.6646634615391,818.440877953745,825.7351606960987 +825.1953125,15.900012311718006,11.926744050956438,0.0,0.0,1.0,9.090909090909092,12.183037498869634,824.21875,54.6875,0.014763822957210062,0.01926818060023578,72.73483343022535,75.29411764705883,70.45871559633028,1.3432570888194053,,,,,,,,,,,,2017-02-09 02:20:16.382812500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s17.csv,0.0,22,16,2,2,73.00748003645398,73.31073310132479,72.6909924893294,822.0118856837614,818.6073207327796,825.7908804783809 +823.6177884615387,15.774524937988806,12.283829599032035,0.0,0.0,1.0,8.333333333333334,12.352647110032732,820.3125,54.6875,0.0149980334119624,0.01915272491558801,72.8739053418892,75.29411764705883,70.45871559633028,1.3360808538691422,,,,,,,,,,,,2017-02-09 02:20:17.382812500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s17.csv,0.0,22,17,2,2,72.98550997823592,73.2971560144632,72.69029723363978,822.2631209935904,818.7614889705875,825.799001580252 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02:20:22.382812500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s17.csv,0.0,22,22,2,2,72.96625453910171,73.2766727382049,72.7185097427767,822.4926549145307,818.9935715326333,825.4797971594851 +819.9869791666665,13.16357600991633,14.043835179890044,0.0,0.0,1.0,9.090909090909092,14.084184669781209,820.3125,50.78125,0.017176107703679187,0.016053396388433096,73.18910125372474,75.29411764705883,70.78341013824884,1.1198393984166768,,,,,1.6536875609730013,3.3192052854809853,97.11422131047873,2.302585092994045,8.878015963382962,14.681464564766145,1.6536875609730013,2017-02-09 02:20:23.382812500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s17.csv,0.0,22,23,2,2,72.96833568347196,73.27510896169632,72.72601156161767,822.4709535256419,819.0113109546695,825.3947511760017 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02:20:56.382812500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s17.csv,0.0,22,56,2,2,74.40257561864713,73.69829474400049,73.38247846223905,806.6773504273514,814.4335102831187,817.9756529365911 +801.1067708333335,9.04029861198427,9.905060852969713,0.0,0.0,0.0,0.0,10.062945536438038,800.78125,31.25,0.01256130381468413,0.011284761209270895,74.90512570014639,76.41791044776119,73.49282296650719,0.8091883189476659,,,,,2.4321418250087548,3.4458463371505514,200.40405874962937,2.3978952727983707,8.469713678587695,20.599544883541892,2.4321418250087548,2017-02-09 02:20:57.382812500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s17.csv,0.0,22,57,2,2,74.47249319885597,73.7298318401252,73.42177504417019,805.9069511217957,814.0862880608965,817.5274952618709 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02:20:59.382812500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s17.csv,0.0,22,59,2,2,74.5800394035786,73.77043114563207,73.48882367256672,804.7192174145307,813.637653579059,816.7577952734208 +808.59375,10.211189545155026,10.017371454055416,0.0,0.0,0.0,0.0,10.14873520059889,808.59375,31.25,0.012551092808470126,0.012628331031689307,74.21385186665808,75.66502463054188,72.79620853080569,0.9028343452662942,,,,,2.459415568238668,3.4447434200933773,203.52585448863,2.6390573296152597,8.41193212613071,20.68843682997285,2.459415568238668,2017-02-09 02:21:00.382812500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s17.csv,0.0,22,0,2,2,74.62133263050183,73.78271008114912,73.51650964990267,804.263488247864,813.5020198985035,816.4381396803278 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02:21:02.382812500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s17.csv,0.0,22,2,2,2,74.68639237097182,73.79823784911264,73.55982757140526,803.5414997329067,813.3284087873924,815.9390610562492 +812.1744791666665,7.533795287703702,9.54192389385256,0.0,0.0,0.0,0.0,9.568319307746787,812.5,27.34375,0.01178111299134194,0.009276079809148607,73.88160215847184,75.29411764705883,72.79620853080569,0.6588101378850668,,,,,2.497865877487458,3.4259249959432103,203.85256055085875,2.772588722239781,8.168041751274362,20.402672776401133,2.497865877487458,2017-02-09 02:21:03.382812500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s17.csv,0.0,22,3,2,2,74.70472113279077,73.80495058500803,73.5787273280373,803.3353365384623,813.2528712606829,815.7220471673604 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02:21:08.382812500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s17.csv,0.0,22,8,2,2,74.74745325303647,73.87738386820827,73.65745480946205,802.8478899572657,812.4461638621785,814.8221839628098 +805.0130208333335,10.057200205946039,9.686208591609272,0.0,0.0,0.0,0.0,10.19986265365417,806.640625,35.15625,0.012670431893257424,0.012493214327806805,74.543631691972,76.03960396039605,72.79620853080569,0.8926285986744737,,,,,2.2256418389184733,3.3899755648366736,164.501816389743,2.8332133440562166,8.30233307775859,18.47801985849629,2.2256418389184733,2017-02-09 02:21:09.382812500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s17.csv,0.0,22,9,2,2,74.74146075181686,73.91251511822098,73.67496304887987,802.9088207799152,812.0555388621785,814.6256984147618 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02:21:11.382812500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s17.csv,0.0,22,11,2,2,74.73656139793376,74.00331410280154,73.71729345621839,802.9580662393168,811.0493456196571,814.1551256785641 +797.7764423076923,10.230329561098504,12.075024082676695,0.0,0.0,0.0,0.0,12.145024808709396,796.875,31.25,0.01522359418583234,0.012823554342499368,75.22050420539209,76.8,73.84615384615384,0.9305990083700328,,,,,2.230650671149629,3.3754065075632456,162.31198866407885,2.6390573296152597,8.155066011027234,18.191103470767427,2.230650671149629,2017-02-09 02:21:12.382812500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s17.csv,0.0,22,12,2,2,74.73175485959077,74.05158277768851,73.73894810184348,803.0098157051289,810.5164095886743,813.9169066903451 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02:21:17.382812500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s17.csv,0.0,22,17,2,2,74.70533496821469,74.26628827638005,73.8210112911754,803.2986111111117,808.1455328525632,813.0231343121976 +801.1067708333335,13.78135280273276,13.268140406441338,0.0,0.0,0.0,0.0,13.272891754993436,800.78125,42.96875,0.016568193202494853,0.017202891430310858,74.91670138890909,76.8,72.79620853080569,1.2337454261204448,,,,,2.1371082528087646,3.3648190221201983,150.3658775400653,2.7080502011022096,8.230690270184716,17.589876102724556,2.1371082528087646,2017-02-09 02:21:18.382812500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s17.csv,0.0,22,18,2,2,74.69423055074853,74.2978865518432,73.83236542029162,803.4196380876074,807.7983106303411,812.8999642671525 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02:21:20.382812500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s17.csv,0.0,22,20,2,2,74.67780737205219,74.35430046161112,73.84971001250197,803.6007612179493,807.1781517094008,812.7105138132524 +803.0598958333335,11.831645276957635,11.756353236681,0.0,0.0,0.0,0.0,11.777786897568893,804.6875,35.15625,0.014666137555464791,0.014733204009247602,74.72905114472991,76.03960396039605,72.79620853080569,1.0509424780150844,,,,,2.102978734460656,3.3250941896716264,140.2165949658489,2.5649493574615367,7.92628461784235,16.668807994605068,2.102978734460656,2017-02-09 02:21:21.382812500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s17.csv,0.0,22,21,2,2,74.66622068661667,74.37901233523056,73.85742353120573,803.7267962072655,806.9068843482897,812.6258592695689 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02:21:23.382812500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s17.csv,0.0,22,23,2,2,74.67061618623617,74.44292384486025,73.87915114264352,803.6917401175218,806.212857238247,812.3914211013491 +792.96875,19.003280624122947,10.67787725706818,0.0,0.0,0.0,0.0,11.163039192371254,789.0625,58.59375,0.014077527257374588,0.02396472827475603,75.70518330153462,78.36734693877553,72.79620853080569,1.7446181129803302,,,,,2.413752257950883,3.369893308933809,181.54558594242195,2.5649493574615367,7.790051281398465,18.803253870028712,2.413752257950883,2017-02-09 02:21:24.382812500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s17.csv,0.0,22,24,2,2,74.69219069410211,74.48534000206271,73.89475825511266,803.4747262286329,805.7571280715804,812.2254153654401 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02:21:32.382812500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s17.csv,0.0,22,32,2,2,75.54843698703372,75.11741467900266,74.13420023061869,794.7539873321131,799.1477435325085,809.7676818624091 +777.0182291666665,32.53136177589012,14.3722346199205,0.0,0.0,2.0,18.181818181818183,14.99067536875386,767.578125,89.84375,0.019292565870469985,0.04186692223524694,77.34085001185146,81.26984126984127,72.45283018867924,3.0591795957743617,,,,,3.0803913659280133,3.5312947308960534,315.178229522387,2.302585092994045,8.303944261538124,25.579398206389516,3.0803913659280133,2017-02-09 02:21:33.382812500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s17.csv,0.0,22,33,2,2,75.66374524881306,75.18423319080179,74.16251720511154,793.5821123321131,798.4587244352862,809.4801384596313 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02:21:35.382812500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s17.csv,0.0,22,35,2,2,75.84990026967716,75.29125107594106,74.21239572353092,791.6849101419422,797.3523709363545,808.9682778159347 +791.3411458333335,24.84294223362825,16.404558895527128,0.0,0.0,3.0,27.272727272727273,16.86322453207921,791.015625,78.125,0.02130967740129467,0.03139346710888768,75.88988683812475,80.0,72.45283018867924,2.304504662514784,,,,,2.9128945366007684,3.5509026581009264,296.44113712141973,2.3978952727983707,8.734317145689236,25.442144694616605,2.9128945366007684,2017-02-09 02:21:36.382812500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s17.csv,0.0,22,36,2,2,75.90877774316986,75.32696761715948,74.23071373780563,791.0772712530531,796.9780219780212,808.7769299927509 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02:39:14.707031250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s18.csv,0.0,22,14,2,2,72.34396408664078,72.26356780934238,72.24911599186973,830.1156092171728,830.9151081123729,831.0294218497351 +773.4375,34.38831418292775,16.735063716180928,0.0,0.0,3.0,25.0,19.88609941315269,765.625,128.90625,0.02571132045336913,0.04446165874156315,77.71315250211126,83.02702702702703,70.45871559633028,3.2207794598004744,,,,,3.3435147693927805,3.955208651328815,580.6429998385868,1.810108607896252,12.985022645304882,43.41561499547659,3.3435147693927805,2017-02-09 02:39:15.707031250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s18.csv,0.0,22,15,2,2,72.53399912348904,72.34649079022373,72.29300463867158,828.1082307449506,830.0524779040395,830.5748657311163 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02:39:29.707031250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s18.csv,0.0,22,29,2,2,81.22878714469307,76.63404302778027,74.44819786072898,752.0929737580137,792.6703957392426,812.2630330149865 +607.3069852941177,20.066762456366472,4.759176926176252,0.0,0.0,0.0,0.0,5.859375,609.375,62.5,0.009648127128263335,0.03304220590620768,98.89736146176901,103.08724832214763,93.0909090909091,3.1375048253395907,,,,,8.559666613738845,4.5928898709345285,4955.9875908986,0.3670188146883019,16.910502742740153,144.74826574857204,8.559666613738845,2017-02-09 02:39:30.707031250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s18.csv,0.0,22,30,2,2,82.1285726165124,77.06888647833806,74.6817134893721,744.5153593789288,789.0497743135891,810.3264686933118 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02:39:35.707031250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s18.csv,0.0,22,35,2,2,86.72882338368677,79.28368389790651,75.8626691627056,705.8836957759875,770.7004889864774,800.6035192874582 +625.0,47.242483143264884,9.353273435452067,0.0,0.0,3.0,20.0,12.186832246664155,601.5625,128.90625,0.01949893159466265,0.07558797302922382,96.48823811536714,103.08724832214763,84.39560439560441,6.677442773094403,,,,,8.713797915343122,4.596939104080863,5114.236151116714,0.3870677429684483,16.838593733197825,146.72810296964897,8.713797915343122,2017-02-09 02:39:36.707031250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s18.csv,0.0,22,36,2,2,87.5650737538465,79.6847856264349,76.07135141971776,698.7005360537653,767.3042216253664,798.8615429361068 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02:39:38.707031250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s18.csv,0.0,22,38,2,2,89.05215103571531,80.42268900333629,76.44834869075747,685.7860395259873,760.9076386076897,795.6585352409115 +671.3541666666666,65.07774761580968,9.203360771467992,0.0,0.0,4.0,28.57142857142857,15.343445407218216,675.78125,179.6875,0.022854472600356068,0.09693504687537208,90.16114733146748,102.4,78.36734693877553,8.449733138020452,,,,,8.553408632273904,4.5802431545245055,4878.995394447741,0.35825472309825074,16.6721640747304,142.6038321154859,8.553408632273904,2017-02-09 02:39:39.707031250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s18.csv,0.0,22,39,2,2,89.636225116881,80.73181257287887,76.60080353912076,680.6037478593207,758.1266056215785,794.325366134305 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02:39:41.707031250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s18.csv,0.0,22,41,2,2,90.53508586451258,81.24827407740469,76.85477952537238,672.4034130378919,753.2994416830865,792.0102526863791 +709.5424107142858,39.43950113618608,17.208919547207156,0.0,0.0,6.0,46.153846153846146,17.86787109052705,707.03125,136.71875,0.025182245374930773,0.05558441685886389,84.81198549126941,95.40372670807452,78.36734693877553,4.685116941608119,,,,,8.411834147249394,4.571134044035366,4708.722251822967,0.3853205420156969,16.636489109210615,139.9433871792005,8.411834147249394,2017-02-09 02:39:42.707031250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s18.csv,0.0,22,42,2,2,90.86119332930744,81.46679174622074,76.95825497832654,669.3320703181118,751.1711554727689,791.0299196747961 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02:39:44.707031250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s18.csv,0.0,22,44,2,2,91.392814803393,81.86838944501677,77.14818484407724,664.2963941886857,747.2060017029276,789.2003836655726 +717.6339285714287,27.12518119780467,14.080978983004785,0.0,0.0,3.0,23.07692307692308,14.084184669781209,707.03125,78.125,0.01962586230812436,0.03779807519942922,83.71710603391362,87.27272727272727,78.36734693877553,2.994287468320127,,,,,8.330336639451842,4.556869941913941,4564.8740481154855,0.4870980626050356,16.445372065594007,136.9954854674356,8.330336639451842,2017-02-09 02:39:45.707031250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s18.csv,0.0,22,45,2,2,91.5929465877864,82.0634728556376,77.23868489310415,662.4362751410665,745.272252943007,788.3267851357242 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02:39:47.707031250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s18.csv,0.0,22,47,2,2,91.85336892132156,82.43362523247951,77.34489545160294,660.0603307729347,741.5930924767373,787.1496596718808 +722.9352678571429,29.04900292533958,13.945674742634967,0.0,0.0,3.0,23.07692307692308,13.958617366991508,722.65625,78.125,0.019308253432380386,0.04018202488784911,83.11908094124139,87.27272727272727,78.36734693877553,3.2060427238939764,,,,,8.009443296965049,4.541930092091222,4230.274435175629,0.4563047848875737,16.485567015623108,132.04021422995064,8.009443296965049,2017-02-09 02:39:48.707031250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s18.csv,0.0,22,48,2,2,91.92221534759759,82.61646155858128,77.39554620151205,659.4371908919823,739.7748261076897,786.5863755331551 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02:39:51.707031250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s18.csv,0.0,22,51,2,2,91.40189599801178,83.16664250376373,77.53542445176163,662.6501888315426,734.274895265962,785.0174004795672 +734.6540178571429,29.92473396987725,8.719121471482516,0.0,0.0,1.0,7.6923076923076925,10.105271466827066,751.953125,74.21875,0.01375514353859028,0.040733097815434896,81.80053129279635,87.27272727272727,78.76923076923076,3.298197790327664,,,,,7.700056169289187,4.514296490952448,3862.6831518252393,0.44963632642130186,16.287007919180194,125.41087580734532,7.700056169289187,2017-02-09 02:39:52.707031250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s18.csv,0.0,22,52,2,2,91.09610761026123,83.33479242684818,77.57156942499074,664.8265281172568,732.570561188581,784.5905921522585 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02:40:11.707031250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s18.csv,0.0,22,11,2,2,81.44371729244065,85.98940157847656,79.03559629923923,740.8869667658731,706.6451899018822,769.7458608126842 +867.5426136363636,27.756623198653784,31.259764099597746,1.0,10.0,4.0,40.0,31.878446504848696,871.09375,101.5625,0.0367456837321547,0.03199453578690506,69.22720908979994,74.5631067961165,66.20689655172414,2.176541676171792,,,,,6.977196363430281,4.542436284286937,3441.434917135248,0.5413411676919412,17.67330115459628,123.31009254565737,6.977196363430281,2017-02-09 02:40:12.707031250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s18.csv,0.0,22,12,2,2,80.92422474572501,85.89270903751616,79.01536782349861,746.1536401966091,707.7428552573599,769.9929607179873 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03:27:17.859375000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s19.csv,0.0,22,17,2,3,71.12766723414929,70.8284797622327,72.12626794017929,844.3764568764575,847.7361885076724,833.9553300490797 +830.7291666666665,11.459409384735748,11.572564443750231,0.0,0.0,0.0,0.0,11.659414175697446,830.078125,35.15625,0.014035156876074671,0.013794398757801026,72.2382324644261,73.49282296650719,70.45871559633028,0.9485922953866238,,,,,2.3808552899877173,3.7941012307193818,289.83452323445204,1.3470736479666092,12.782744968177006,30.43386597804809,2.3808552899877173,2017-02-09 03:27:18.859375000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s19.csv,0.0,22,18,2,3,71.22357229814283,70.85327882345725,72.12752260275461,843.2203647047404,847.4432197576724,833.9406669484792 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03:27:20.859375000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s19.csv,0.0,22,20,2,3,71.34236114210076,70.89826963180535,72.1220659165373,841.7604530885789,846.9115357298946,834.0051845911219 +839.5182291666665,16.358691666142974,10.594058160636576,0.0,0.0,0.0,0.0,11.173389717721784,835.9375,46.875,0.013309287790942741,0.019485808762462663,71.4943902684186,73.49282296650719,69.50226244343891,1.3309728818147766,,,,,2.4415653432487368,3.7818940223172097,296.790187905172,1.3694872428035094,12.446660506886179,30.38933493279605,2.4415653432487368,2017-02-09 03:27:21.859375000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s19.csv,0.0,22,21,2,3,71.35502141009171,70.91864749011876,72.11382167241894,841.5759912830234,846.6693679584299,834.1019610550858 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03:27:23.859375000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s19.csv,0.0,22,23,2,3,71.31149450812508,70.93312047395321,72.09673456555687,842.0208697552456,846.4903315000968,834.3013792232539 +837.890625,19.74316849988301,13.047708146927441,0.0,0.0,2.0,18.181818181818183,13.220533003541396,839.84375,62.5,0.0157783517431543,0.023562942358834733,71.6451447911716,74.92682926829268,69.50226244343891,1.6295327531148358,,,,,2.4953281084943058,3.7730847316340896,303.55252788608493,1.4384801142904609,12.187609906204012,30.412085574314524,2.4953281084943058,2017-02-09 03:27:24.859375000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s19.csv,0.0,22,24,2,3,71.29356372967666,70.94045076542584,72.09178935920058,842.2061662296045,846.4035259445411,834.3600316256563 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03:27:26.859375000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s19.csv,0.0,22,26,2,3,71.24948337470754,70.96020382875803,72.07947243907752,842.6766917977864,846.1712224407532,834.5047964188586 +834.6354166666665,22.36746132736258,12.063414596209535,0.0,0.0,2.0,18.181818181818183,12.575235234109213,835.9375,62.5,0.015066740498901525,0.026799080030287768,71.93515581841633,74.92682926829268,69.50226244343891,1.8507591668934225,,,,,2.7182532547032054,3.774976948934741,345.87832690830965,1.852384091044489,11.702631362130065,31.810715788701845,2.7182532547032054,2017-02-09 03:27:27.859375000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s19.csv,0.0,22,27,2,3,71.22966119932121,70.97281798991396,72.07656188942953,842.8937056866754,846.0247380657532,834.5399878603 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03:27:29.859375000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s19.csv,0.0,22,29,2,3,71.25098719225511,71.01530552453151,72.07595126703765,842.6007369366754,845.5256061213089,834.5485601344974 +825.1953125,17.399662256101998,11.947872046349945,0.0,0.0,2.0,18.181818181818183,12.464438050272904,820.3125,58.59375,0.015104833802934259,0.021085507870116503,72.73927344930135,74.92682926829268,69.81818181818181,1.4474015395579034,,,,,2.815722055398419,3.74930769345985,354.0287410566544,1.7404661748405046,11.163463688262308,31.43321092167956,2.815722055398419,2017-02-09 03:27:30.859375000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s19.csv,0.0,22,30,2,3,71.3129265665701,71.04474012108376,72.07658225844553,841.8520390200086,845.1783838990866,834.539762274137 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03:27:32.859375000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s19.csv,0.0,22,32,2,3,71.52235618109984,71.13296023512463,72.0899227877359,839.3847580371025,844.1513998154619,834.3861380970754 +805.3385416666665,27.61298726283796,17.967486705502157,0.0,0.0,2.0,18.181818181818183,18.95455418335585,812.5,93.75,0.02353613195281697,0.034287427999772926,74.58821078327621,81.70212765957447,72.45283018867924,2.6020056954373767,,,,,3.1531855644204274,3.8678545689373847,480.8964586888851,1.6964492894237302,12.091843797216306,38.127827308609135,3.1531855644204274,2017-02-09 03:27:33.859375000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s19.csv,0.0,22,33,2,3,71.67366500422273,71.20495557271836,72.10719778575022,837.6594976204359,843.3375977321285,834.1984504093878 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03:27:35.859375000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s19.csv,0.0,22,35,2,3,72.15023396803186,71.42053883904319,72.1820681460079,832.5194553710186,841.0501878763592,833.4470229001479 +758.3705357142858,70.20988008956385,15.292080108172446,0.0,0.0,4.0,30.76923076923077,20.4127978657197,789.0625,179.6875,0.026916654728013027,0.09257991546762208,79.77906810035836,92.53012048192772,72.45283018867924,7.4327725546534955,,,,,5.708366157839369,4.138262915342594,1599.1867831558002,0.997516171796741,12.269180479286613,70.03697463238309,5.708366157839369,2017-02-09 03:27:36.859375000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s19.csv,0.0,22,36,2,3,72.47230178146927,71.57687989337568,72.24794992538224,829.2591739857372,839.4807124299307,832.8156716828593 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03:28:20.859375000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s19.csv,0.0,22,20,2,3,97.69298779935535,90.21165589725848,80.55496276453205,614.5172717524517,676.9969107275224,761.9542232287099 +630.126953125,13.050909275225088,7.1032196338468,0.0,0.0,0.0,0.0,7.479899279346392,625.0,46.875,0.011870463947385827,0.020711555362774886,95.2561994750286,97.21518987341771,90.35294117647058,1.859990478394521,,,,,14.507533010807233,4.089184109540505,6123.255847894327,0.178447558677441,7.27336328102569,105.51855789907341,14.507533010807233,2017-02-09 03:28:21.859375000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s19.csv,0.0,22,21,2,3,97.50451547821085,90.60768605070197,80.76316677041049,615.6991996017165,673.5070561268279,760.0882120426304 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03:47:34.359375000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s20.csv,0.0,22,34,2,3,68.74400324139678,68.48401539649034,69.52368412972235,873.3556265782834,876.8521613190727,863.7941919191925 +856.7708333333335,13.255461817395279,14.624474632083807,0.0,0.0,3.0,27.272727272727273,14.66322643612399,861.328125,42.96875,0.017114525688363563,0.015471420479877774,70.0459301345705,72.11267605633803,68.57142857142857,1.0488555388692735,,,,,2.8804420866175486,3.792867885575666,385.14358141712717,1.7346010553881064,11.604979497838585,33.427471359908026,2.8804420866175486,2017-02-09 03:47:35.359375000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s20.csv,0.0,22,35,2,3,68.82166037912083,68.48839647087759,69.51968623884507,872.3682133838389,876.7947164661315,863.8411138411143 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03:47:37.359375000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s20.csv,0.0,22,37,2,3,68.94289016217346,68.54391436014966,69.50987984944194,870.8303740530308,876.0770663593478,863.9560192294573 +860.0260416666665,10.773081007050383,14.693296069301988,0.0,0.0,3.0,27.272727272727273,14.710451120176122,863.28125,31.25,0.017104657774860352,0.012526459066487049,69.77541454730611,71.1111111111111,68.57142857142857,0.8409537096030748,,,,,2.8830527692459245,3.787633793445204,383.3502792187608,1.7117167615545186,11.530034576379256,33.24169811493147,2.8830527692459245,2017-02-09 03:47:38.359375000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s20.csv,0.0,22,38,2,3,68.98588961461856,68.56671991917108,69.50228241277092,870.283893623738,875.7798251613351,864.0469304531811 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03:47:40.359375000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s20.csv,0.0,22,40,2,3,69.03872740014569,68.60848146540843,69.48491415332286,869.6141098484854,875.234628652597,864.2551464817095 +858.7239583333335,11.758140867381925,14.710451120176122,0.0,0.0,3.0,27.272727272727273,14.710451120176122,863.28125,31.25,0.01713059357133474,0.013692573443744228,69.88315865505021,71.1111111111111,68.57142857142857,0.9187576964939091,,,,,2.8606546085611915,3.787321097921545,378.75527168202973,1.6357552207514738,11.57091878154833,33.10040213772348,2.8606546085611915,2017-02-09 03:47:41.359375000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s20.csv,0.0,22,41,2,3,69.06232692209241,68.63084422312144,69.48072980373925,869.3211410984854,874.9449677698029,864.3036680146059 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03:47:46.359375000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s20.csv,0.0,22,46,2,3,69.24189286385959,68.70652150424286,69.48825057257805,867.0858980429298,873.9711568813129,864.2007597085726 +851.8880208333335,15.929066721169855,11.594337867410871,0.0,0.0,2.0,18.181818181818183,12.352647110032732,855.46875,46.875,0.014500317891486644,0.018698545268411845,70.454651936977,72.79620853080569,68.87892376681614,1.2770729707941644,,,,,2.999336728909149,3.8147519802414163,419.67570512545234,1.5712166996139023,11.662814977625947,34.980709324865245,2.999336728909149,2017-02-09 03:47:47.359375000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s20.csv,0.0,22,47,2,3,69.31880539511734,68.71168779967091,69.4950561661966,866.1142676767682,873.9114780618686,864.1173133360637 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03:48:37.359375000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s20.csv,0.0,22,37,2,3,69.73366612130977,70.06052356906467,69.34919358612261,860.9473642676774,856.8679963699492,865.8775601702872 +878.2552083333335,25.141276364285464,19.733585212286656,0.0,0.0,4.0,36.36363636363637,19.813304310459266,882.8125,82.03125,0.022559848347565185,0.02862639028579872,68.36909963892161,71.77570093457943,65.36170212765957,1.8917867648257565,,,,,2.5842878672334857,3.7110953901762564,297.89212883988966,1.4294665329850993,11.151088419132874,28.817622508012924,2.5842878672334857,2017-02-09 03:48:38.359375000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s20.csv,0.0,22,38,2,3,69.68028207526602,70.03708498725827,69.34059627079628,861.6092566287884,857.1718158143938,865.9861360313665 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03:48:40.359375000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s20.csv,0.0,22,40,2,3,69.62208889993259,69.99951929696196,69.3279837920744,862.3480902777784,857.665522411616,866.1471599072624 +865.0568181818181,29.458597197823877,15.896085897949849,0.0,0.0,3.0,30.0,18.07036485346934,867.1875,85.9375,0.020889223081843048,0.03405394487236125,69.43197982667733,72.11267605633803,65.36170212765957,2.2297947485580885,,,,,2.666842046690739,3.713498880756237,313.1449274512223,1.318240897874875,11.00755342935628,29.355406316602174,2.666842046690739,2017-02-09 03:48:41.359375000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s20.csv,0.0,22,41,2,3,69.63683951448276,69.9919996498224,69.32887264707101,862.1823705808088,857.7710700757574,866.1378407472159 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03:48:43.359375000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s20.csv,0.0,22,43,2,3,69.7270776956526,70.0091869571523,69.34696510214866,861.1002604166674,857.5801964962118,865.9160041857655 +844.0755208333335,26.46510036531351,11.274508427832064,0.0,0.0,1.0,9.090909090909092,13.325044772225652,835.9375,85.9375,0.01578655516400972,0.03135394844668072,71.14599510005048,73.84615384615384,66.78260869565217,2.0763841356879733,,,,,2.8418228965004744,3.74634710656944,357.7412884620549,1.3217558399823195,11.074268716616453,31.47111040087956,2.8418228965004744,2017-02-09 03:48:44.359375000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s20.csv,0.0,22,44,2,3,69.77503466569753,70.02846654158846,69.36195701879784,860.5360243055562,857.3577572601007,865.7340001578285 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04:56:42.464843750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s21.csv,0.0,22,42,2,4,66.49888701012149,66.37477828301157,66.89786295353709,902.6781486742431,904.3933475378782,897.4544324153703 +918.359375,14.897335661420392,12.860705199678,0.0,0.0,1.0,11.111111111111107,13.718299157360336,919.921875,42.96875,0.014937833195594413,0.016221684088998812,65.3494082628384,66.78260869565217,63.7344398340249,1.0077945763451848,,,,,1.7372083338220723,3.72338185033912,166.52091222245096,,13.794468817180569,23.963866189854787,1.7372083338220723,2017-02-09 04:56:43.464843750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s21.csv,0.0,22,43,2,4,66.43579523108706,66.37265955806998,66.87510140669872,903.531605113637,904.417613636363,897.7586784227412 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04:56:45.464843750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s21.csv,0.0,22,45,2,4,66.30226209563128,66.36958871985622,66.83350674780006,905.34761679293,904.4531249999993,898.3217414858044 +923.6505681818179,13.905771186853904,20.29747040119778,0.0,0.0,3.0,30.0,20.29747040119778,929.6875,46.875,0.02197526976154285,0.015055229397224378,64.97322618934487,67.07423580786026,63.7344398340249,0.9471968354847948,,,,,1.7055846569242044,3.7403479401489372,165.19005238119888,1.6677068205580758,14.196370936009247,24.213112452462077,1.7055846569242044,2017-02-09 04:56:46.464843750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s21.csv,0.0,22,46,2,4,66.2337953312635,66.36382308486064,66.81264507802358,906.2827493686875,904.5282907196962,898.6064722392852 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04:56:51.464843750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s21.csv,0.0,22,51,2,4,65.96104251253568,66.2717681109849,66.66969329718694,910.0469539141419,905.8007812499992,900.5225395850401 +923.6505681818179,18.10104461215254,23.892076067770358,0.0,0.0,4.0,40.0,23.92079826936697,929.6875,50.78125,0.02589810377798439,0.019597286285206288,64.98251542631792,67.07423580786026,63.47107438016529,1.224846254159474,,,,,1.7388990432486444,3.757179699123839,173.38095962834518,2.178532444324067,14.334834193684511,24.926829464525948,1.7388990432486444,2017-02-09 04:56:52.464843750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s21.csv,0.0,22,52,2,4,65.94110583691133,66.24831734917262,66.63120881277382,910.3310448232328,906.1263020833325,901.0312158749664 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04:57:00.464843750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s21.csv,0.0,22,0,2,4,65.56369602485634,66.00920276774784,66.35977405017749,915.5926452020207,909.4628906249989,904.6601999727004 +937.1448863636364,19.952836346420664,21.3096589015404,0.0,0.0,5.0,50.0,21.32397509993153,937.5,62.5,0.02275419245221948,0.021291090243092302,64.05060362747277,66.20689655172414,61.935483870967744,1.2983965466429737,,,,,2.0196725153502286,3.8423177820102974,239.37584487579122,1.8325814637483104,14.670956248882025,29.630527109772714,2.0196725153502286,2017-02-09 04:57:01.464843750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s21.csv,0.0,22,1,2,4,65.46468667002245,65.97071850982348,66.33139009590091,916.9775883838388,910.0073982007565,905.0569034944039 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04:57:03.464843750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s21.csv,0.0,22,3,2,4,65.26083387956591,65.89110157232402,66.27458545159006,919.8342803030307,911.1319247159079,905.8527099542729 +938.9204545454544,18.177517468984238,19.76423537605237,0.0,0.0,4.0,40.0,19.76423537605237,937.5,54.6875,0.02104995719325397,0.0193600186053932,63.92487548655322,65.64102564102564,61.935483870967744,1.1756952326506114,,,,,2.085583489597936,3.832364537664398,248.32682678720425,1.871802176901592,14.272777232923348,29.76706854769423,2.085583489597936,2017-02-09 04:57:04.464843750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s21.csv,0.0,22,4,2,4,65.16243689312152,65.85173673017447,66.24431225012135,921.2192234848491,911.6882694128776,906.2760736588866 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04:57:06.464843750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s21.csv,0.0,22,6,2,4,64.98713595849047,65.7953938185461,66.19115022156748,923.6931818181824,912.493193655302,907.0214923730554 +926.4914772727273,25.183541449589292,20.357522219992784,0.0,0.0,4.0,40.0,21.537576175140966,933.59375,85.9375,0.023246383483785733,0.02718162235417533,64.80412368998864,67.96460176991151,61.935483870967744,1.6860669785182842,,,,,2.019288297456688,3.826126152361713,234.88785868876084,1.852384091044489,14.401373721838867,29.08052542380949,2.019288297456688,2017-02-09 04:57:07.464843750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s21.csv,0.0,22,7,2,4,64.92481982833144,65.77447223912613,66.1693461132993,924.5691287878794,912.789121685605,907.3254184582314 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04:57:09.464843750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s21.csv,0.0,22,9,2,4,64.8805298724845,65.74139078530696,66.13958978064032,925.191761363637,913.2424834280293,907.7336391789522 +910.15625,19.53125,15.823928984788356,0.0,0.0,2.0,20.0,16.618787020455194,906.25,54.6875,0.01825926814264605,0.02145922746781116,65.95024263184649,67.96460176991151,64.0,1.3440560322304584,,,,,2.0239210267621783,3.8085552364854993,230.976588080984,1.712978591374941,14.09680998283237,28.53083013452541,2.0239210267621783,2017-02-09 04:57:10.464843750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s21.csv,0.0,22,10,2,4,64.87836722186965,65.72791325541665,66.12859168748737,925.2272727272734,913.4259588068172,907.8840026105656 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04:57:12.464843750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s21.csv,0.0,22,12,2,4,64.91744446412521,65.708165737123,66.1188649154738,924.6946022727279,913.6863754734838,908.0175701269457 +900.2130681818181,11.370292794762864,16.737722879397303,0.0,0.0,2.0,20.0,16.755945773068138,902.34375,35.15625,0.01861331096527017,0.012630668445779977,66.66057618509463,67.96460176991151,65.36170212765957,0.8045575214223903,,,,,1.9472195771117389,3.7679350716690574,208.0127361817236,1.745239453593162,13.715134813586511,26.70637901174242,1.9472195771117389,2017-02-09 04:57:13.464843750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s21.csv,0.0,22,13,2,4,64.96115006153374,65.69847264631004,66.11450562528539,924.0897253787886,913.810665246211,908.0751561220317 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04:57:15.464843750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s21.csv,0.0,22,15,2,4,65.0506639897117,65.67646304267113,66.09919456905949,922.8468276515158,914.097222222221,908.2767071048327 +902.6988636363636,11.247632326607473,12.651667766173162,0.0,0.0,0.0,0.0,12.657696676577851,902.34375,39.0625,0.014022058946201113,0.012460004969207964,66.47669701947773,67.66519823788546,64.81012658227849,0.7869985160259775,,,,,1.959679575582032,3.773691321927224,211.4088970253516,1.8971199848858813,13.762367048432973,26.96982961647727,1.959679575582032,2017-02-09 04:57:16.464843750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s21.csv,0.0,22,16,2,4,65.10077968404946,65.66728750765611,66.08926550376785,922.1484375000007,914.2155934343423,908.4078752047508 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04:57:18.464843750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s21.csv,0.0,22,18,2,4,65.19361296050074,65.6479675245766,66.06169150800224,920.8463541666675,914.4700915404029,908.7757857289114 +913.3522727272727,18.800240455727735,10.510643786776336,0.0,0.0,0.0,0.0,11.848242881330547,910.15625,54.6875,0.012972259702109959,0.020583778041729898,65.71737604233067,67.66519823788546,63.7344398340249,1.2897136039898849,,,,,2.022002121021304,3.769442099565196,220.4923164032592,1.7764919970972666,13.482494600900507,27.26163267967909,2.022002121021304,2017-02-09 04:57:19.464843750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s21.csv,0.0,22,19,2,4,65.22320890661773,65.63495582583273,66.04199583946435,920.4320549242433,914.6476483585848,909.0421209561841 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04:57:21.464843750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s21.csv,0.0,22,21,2,4,65.26148172332138,65.61126211792818,66.00209852428341,919.8875473484858,914.967250631312,909.5819896601154 +918.6789772727273,13.283338739495495,15.50736971974938,0.0,0.0,1.0,10.0,15.527036651837337,917.96875,39.0625,0.016901482493843812,0.014459173517750027,65.32350717789632,66.49350649350649,63.7344398340249,0.8948551834191225,,,,,2.050554864975477,3.7583625186679814,222.32502513238742,1.7764919970972666,13.218603354981385,27.105471417738233,2.050554864975477,2017-02-09 04:57:22.464843750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s21.csv,0.0,22,22,2,4,65.27284811504066,65.60697697597564,65.98080319749873,919.7218276515162,915.0264362373726,909.8699196355453 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04:57:24.464843750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s21.csv,0.0,22,24,2,4,65.27787830129806,65.5838069507673,65.94041102998315,919.6460700757585,915.3460385101001,910.4233850327606 +925.4261363636364,14.869893245818727,14.869426805214285,0.0,0.0,2.0,20.0,15.279380637193706,929.6875,39.0625,0.01651064308301515,0.016068157858873958,64.85029535234236,66.49350649350649,63.7344398340249,0.9986651564307152,,,,,2.047339159283604,3.733591951517862,215.56619977606,1.4508328822574619,12.857042746179356,26.32272708683621,2.047339159283604,2017-02-09 04:57:25.464843750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s21.csv,0.0,22,25,2,4,65.26945555437298,65.56451660794873,65.9180925873783,919.7644412878798,915.6123737373729,910.7305103398859 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04:57:30.464843750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s21.csv,0.0,22,30,2,4,65.39975152065173,65.48172377275368,65.87413614500477,917.9083806818193,916.7505129419185,911.3362240829351 +911.2215909090908,20.67664204551621,17.664717272804705,0.0,0.0,2.0,20.0,17.77236432199582,914.0625,54.6875,0.01950388851548723,0.022691123928360743,65.87633363917085,67.66519823788546,63.7344398340249,1.4172924577480333,,,,,1.926694289328559,3.720504166037693,193.85245180240972,1.6739764335716716,13.055264847275105,25.153504226916823,1.926694289328559,2017-02-09 04:57:31.464843750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s21.csv,0.0,22,31,2,4,65.46060918770833,65.46264792886504,65.8741552536497,917.0442708333344,917.0109296085851,911.3352272727277 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05:02:18.363281250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s22.csv,1.0,22,18,2,5,86.78505726657362,80.03792419850267,74.07069514434077,709.4242197614714,770.6511437174667,827.7622900152646 +733.7740384615387,10.91494067725431,7.0345647086504925,0.0,0.0,0.0,0.0,7.220401371360299,730.46875,35.15625,0.009840088355400107,0.014875070669083678,81.78567053890332,83.47826086956522,79.58549222797927,1.16364580825205,,,,,2.6592713117933484,5.230030852131121,1787.1433880972238,0.2902298445305796,63.1791982729635,168.0106294693957,2.6592713117933484,2017-02-09 05:02:19.363281250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s22.csv,1.0,22,19,2,5,86.67322693222096,80.26429371899107,74.21100415805681,709.0571299547264,768.2213701012524,826.2072399516918 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05:02:21.363281250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s22.csv,1.0,22,21,2,5,86.426119638809,80.703748515425,74.47678193681726,708.6300426741909,763.4793618005841,823.2680626517191 +738.8822115384613,8.565019719795206,6.734427365096747,0.0,0.0,0.0,0.0,6.765823467065928,738.28125,31.25,0.009156836314922895,0.011591860767579685,81.21383926099453,83.02702702702703,79.58549222797927,0.9067673957283056,,,,,2.571210495480925,5.214604426011911,1669.2010962938675,0.2335006894141569,63.12093241392493,162.29720390722588,2.571210495480925,2017-02-09 05:02:22.363281250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s22.csv,1.0,22,22,2,5,86.30840312886407,80.91846508630468,74.60706654125627,708.4262683594252,761.1612460356993,821.8272449466969 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05:02:24.363281250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s22.csv,1.0,22,24,2,5,85.77553111751709,81.33367722115459,74.79226658457166,709.522875147034,756.670955693979,819.6750739963636 +743.3894230769231,5.6121034941460595,8.13150781041458,0.0,0.0,0.0,0.0,8.13150781041458,742.1875,15.625,0.010938422794284449,0.007549345363184351,80.71561931517356,81.26984126984127,79.58549222797927,0.5819331027450305,,,,,2.501282889379541,5.1973947738990525,1570.1516694198472,0.20026746965023606,62.741657769980534,156.9346350313592,2.501282889379541,2017-02-09 05:02:25.363281250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s22.csv,1.0,22,25,2,5,85.4164769688065,81.53768275976167,74.84515366252346,710.6419069772727,754.4583245750978,818.9939521131544 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05:02:27.363281250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s22.csv,1.0,22,27,2,5,84.67457946068035,81.95194956434301,74.94829071315883,713.5536932984407,749.9582893878274,817.666238968794 +737.4441964285713,13.196008571278494,9.992962228555225,0.0,0.0,1.0,7.6923076923076925,10.278023663754249,738.28125,46.875,0.013937357855049272,0.01789424696158234,81.38687256762839,84.86187845303867,79.58549222797927,1.4375592526896064,,,,,2.397313396285624,5.171976623329599,1430.7887938693334,0.2502945403801606,62.23947407700192,149.20752498256854,2.397313396285624,2017-02-09 05:02:28.363281250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s22.csv,1.0,22,28,2,5,84.15686969287182,82.17474969830103,75.00427838145855,717.153264188219,747.5495928583682,816.9686559901834 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05:02:30.363281250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s22.csv,1.0,22,30,2,5,83.17110786635087,82.66949337244567,75.13076351919584,723.9170418975788,742.2395835262456,815.4637926704488 +717.3549107142858,34.040354496482884,10.657540775822236,0.0,0.0,1.0,7.6923076923076925,12.541248049705052,736.328125,101.5625,0.017482626608379195,0.04745259841127758,83.82216837440423,91.97604790419165,79.58549222797927,3.973067134164698,,,,,2.245099606194097,5.13714022109148,1245.7288747866678,0.2606906239094613,61.78631758313148,138.7164372740719,2.245099606194097,2017-02-09 05:02:31.363281250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s22.csv,1.0,22,31,2,5,82.73196088557239,82.94810265105879,75.20441949254507,726.8575533661749,739.2907646015261,814.6323614674304 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05:02:33.363281250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s22.csv,1.0,22,33,2,5,82.60522334756843,83.57745702459795,75.45191762030025,727.432415674604,732.7492217517206,812.0217008213912 +691.9270833333335,36.72903857883561,9.637229932572847,0.0,0.0,1.0,7.142857142857142,10.999109009166864,675.78125,97.65625,0.01589634121008685,0.05308223866869732,86.93796673306852,91.97604790419165,80.0,4.363164583640423,,,,,2.1285773826470384,5.1018591038806465,1104.2406147568843,0.2469188168078681,60.92911065033778,129.6923268751078,2.1285773826470384,2017-02-09 05:02:34.363281250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s22.csv,1.0,22,34,2,5,82.63109335883233,83.91565841438536,75.62507442877636,727.1633184523818,729.2791395740113,810.2326045010448 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05:02:36.363281250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s22.csv,1.0,22,36,2,5,82.75847057619154,84.59647980585385,75.98731558615204,726.058407738096,722.4000668017976,806.5233082825574 +677.34375,19.797295173971552,9.72968514183186,0.0,0.0,0.0,0.0,9.793487039500137,671.875,58.59375,0.014458665986805275,0.02922784062593262,88.65151174446812,91.97604790419165,84.39560439560441,2.486713415961583,,,,,2.091789016161674,5.091106043708892,1062.5022029703357,0.26452876747706405,60.706345797502955,126.98486735052909,2.091789016161674,2017-02-09 05:02:37.363281250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s22.csv,1.0,22,37,2,5,82.89743361606939,84.92127340190152,76.17512685287976,724.8828125000007,719.1603069147919,804.6086787943984 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05:02:39.363281250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s22.csv,1.0,22,39,2,5,83.30413832810005,85.44779572947776,76.55601836733335,721.5085565476198,714.0104569854134,800.7421179655214 +676.8973214285714,17.363376492920093,6.528113275961669,0.0,0.0,0.0,0.0,7.186454773498436,679.6875,42.96875,0.010616757587888869,0.02565141852869976,88.69433200681536,91.97604790419165,86.29213483146067,2.2089907854380764,,,,,2.091818154421408,5.09099496834187,1062.3885365654382,0.25384090907286605,60.6981603956839,126.9695138556741,2.091818154421408,2017-02-09 05:02:40.363281250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s22.csv,1.0,22,40,2,5,83.54067560440188,85.65396164805648,76.74200033566389,719.5525698260082,712.0321100322254,798.8535784588096 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05:15:16.992187500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s25.csv,0.0,22,16,2,5,70.89396299775552,70.91080172082918,71.43859607240744,848.3894686043126,847.6710843410056,841.469057289371 +889.2045454545455,13.674411290718416,18.75406856899657,0.0,0.0,2.0,20.0,18.855497478354952,890.625,46.875,0.021204904512399186,0.015378251675184924,67.49051624741371,69.18918918918922,65.64102564102564,0.9875566069525624,,,,,4.3313930836245795,3.994034595437375,895.2796146309144,1.8382794848629485,11.930084150379868,51.67388397601458,4.3313930836245795,2017-02-09 05:15:17.992187500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s25.csv,0.0,22,17,2,5,70.595957355219,70.89015709313796,71.37499977859942,851.837713068182,847.9374195682782,842.2169369435006 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05:15:19.992187500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s25.csv,0.0,22,19,2,5,69.99178264900925,70.82697609187284,71.25622135809121,858.8516626602566,848.7492487980762,843.6411239145625 +889.6484375,16.83234890435034,21.44839328201917,0.0,0.0,3.0,27.272727272727273,21.68520545564208,892.578125,58.59375,0.024375027866715138,0.01892022533266164,67.4647752896509,70.13698630136986,65.64102564102564,1.2370505148274265,,,,,4.333818617781082,4.023177982544049,926.6059565876982,1.6341305250244718,12.333707209409205,53.45204993039839,4.333818617781082,2017-02-09 05:15:20.992187500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s25.csv,0.0,22,20,2,5,69.7153619729519,70.79921323327966,71.19363922411551,862.0634682158122,849.1073217147429,844.3860094250731 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05:15:22.992187500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s25.csv,0.0,22,22,2,5,69.21731192067215,70.73632509624832,71.06128996788158,867.8573596542349,849.9161916642378,845.9530539608677 +884.5880681818181,21.914280884303192,20.009763241977648,0.0,0.0,2.0,20.0,20.743621948987123,894.53125,62.5,0.02345003589255228,0.024773430337293373,67.86629077861859,70.45871559633028,65.64102564102564,1.6136291218902632,,,,,4.414061717735882,4.015498340973817,944.0755978838839,1.540445040947149,12.11350934148534,53.46977785168643,4.414061717735882,2017-02-09 05:15:23.992187500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s25.csv,0.0,22,23,2,5,68.99443250472753,70.70740640262449,70.99206776689137,870.4505997474749,850.2890609824195,846.7667227432866 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05:15:25.992187500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s25.csv,0.0,22,25,2,5,68.68667837377753,70.68375743705676,70.87248623018948,874.0530303030304,850.6037311213084,848.1642290236053 +863.9322916666665,29.033299826514522,16.180093394736527,0.0,0.0,2.0,18.181818181818183,17.14871657336596,859.375,82.03125,0.01984960712636784,0.03360598985194145,69.52178893286737,73.14285714285714,66.49350649350649,2.2354417735838528,,,,,4.404744260149824,4.010885691243458,936.1035518295488,1.871802176901592,12.062088348923984,53.13041442034299,4.404744260149824,2017-02-09 05:15:26.992187500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s25.csv,0.0,22,26,2,5,68.60381388282711,70.68516117943342,70.82505797958635,875.0512941919193,850.5983057740863,848.7169151231665 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05:15:28.992187500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s25.csv,0.0,22,28,2,5,68.54240462619197,70.70463836784216,70.74636302310476,875.8325441919193,850.3763597513589,849.6318926006438 +852.8645833333335,20.35434210246655,18.232240984365674,0.0,0.0,3.0,27.272727272727273,18.807617438729206,853.515625,78.125,0.022052290370906915,0.02386585455678521,70.3874745379325,73.14285714285714,66.78260869565217,1.5907897850209176,,,,,4.3738595431347065,4.011805185405631,927.2564041160643,1.580450375560848,12.117420668343465,52.99989602841179,4.3738595431347065,2017-02-09 05:15:29.992187500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s25.csv,0.0,22,29,2,5,68.54313189992178,70.72154371979026,70.74315866163,875.8325441919193,850.1731558372176,849.6607559107571 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05:15:31.992187500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s25.csv,0.0,22,31,2,5,68.56965094712133,70.7476718170521,70.74140375904726,875.5178740530305,849.8530603511065,849.6598544912702 +847.9817708333335,15.665637259786413,16.350659706280307,0.0,0.0,3.0,27.272727272727273,16.446784342200868,849.609375,46.875,0.019395209788576225,0.018474025973920862,70.77851917717234,73.14285714285714,69.18918918918922,1.2598585591772826,,,,,4.547394319904674,3.997542051361627,966.9743366952043,1.637608789400797,11.690409571571347,53.16090208312278,4.547394319904674,2017-02-09 05:15:32.992187500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s25.csv,0.0,22,32,2,5,68.60504231878522,70.7627828298511,70.74172650181357,875.0907512626264,849.668598545551,849.645262459462 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05:15:34.992187500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s25.csv,0.0,22,34,2,5,68.70883110846742,70.78138944229286,70.7444648891768,873.8281250000001,849.4422136023692,849.5917866410666 +848.9583333333335,13.768765173859231,14.070747696135616,0.0,0.0,2.0,18.181818181818183,14.182333485593649,849.609375,35.15625,0.016705570731496814,0.016218422781478354,70.69189790755439,72.11267605633803,69.18918918918922,1.098171019306353,,,,,4.592896970641037,3.9938979755179607,977.4150967948323,1.4759065198095778,11.583655813024595,53.202537692589104,4.592896970641037,2017-02-09 05:15:35.992187500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s25.csv,0.0,22,35,2,5,68.76096116568316,70.77400352052074,70.7440194062817,873.1879340277778,849.5290191579247,849.5864183926959 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05:15:37.992187500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s25.csv,0.0,22,37,2,5,68.88367563113817,70.74729585317216,70.73988509632777,871.667850378788,849.845662150349,849.6149141656965 +849.609375,13.785546124844938,16.663875293537025,0.0,0.0,2.0,18.181818181818183,16.69789480896129,849.609375,39.0625,0.01965361411997283,0.01622574624349565,70.63777574571533,72.45283018867924,69.18918918918922,1.0999304033054051,,,,,4.480944370652155,3.9959660741075487,944.1421042923216,1.3633048428951922,11.755423228133807,52.67539753873978,4.480944370652155,2017-02-09 05:15:38.992187500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s25.csv,0.0,22,38,2,5,68.95685674082675,70.72945303153664,70.72665835812155,870.7623106060606,850.0572506920157,849.7695365615298 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05:35:19.050781250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s26.csv,0.0,22,19,2,5,70.3617353340162,70.75510646125899,70.05581330770397,854.5136612519428,849.9332644352166,858.0539059054687 +799.8046875,19.996240435882,12.514922007603762,0.0,0.0,0.0,0.0,12.519959207247295,796.875,66.40625,0.015653770730428854,0.02500140440334941,75.06137243887356,78.36734693877553,72.11267605633803,1.7993025316247404,,,,,5.089790056524398,4.030653804048135,1189.5335771345747,0.9013650816574794,11.479341507425387,58.42743825994154,5.089790056524398,2017-02-09 05:35:20.050781250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s26.csv,0.0,22,20,2,5,70.62707216370558,70.7798058001294,70.11196632211097,851.3561091686095,849.6565717268834,857.4028642388021 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05:35:43.050781250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s26.csv,0.0,22,43,2,5,89.98031296870215,79.34694853038333,75.05386427768775,702.2407816391602,787.5246166562074,820.2336222649989 +649.658203125,29.910033217581397,31.9644883523106,1.0,6.666666666666668,1.0,6.666666666666668,32.30636426271145,648.4375,125.0,0.04972824803459831,0.04603964526840038,92.52733602517985,97.21518987341771,80.84210526315789,3.847165296255801,,,,,4.497329070266312,5.1453440914345085,3565.3653945546503,0.29165822083142035,44.06915480485899,198.19349100595863,4.497329070266312,2017-02-09 05:35:44.050781250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s26.csv,0.0,22,44,2,5,90.62506610007766,79.75446755620871,75.28090714378291,696.6143224078384,783.6811886660302,818.0607707024989 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05:35:46.050781250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s26.csv,0.0,22,46,2,5,91.87198038270607,80.56261998004004,75.72338938392716,685.6692672160833,775.9666709384366,813.8046127092139 +673.583984375,37.02962236006338,9.827063679225633,0.0,0.0,2.0,13.333333333333336,12.393754202386244,660.15625,113.28125,0.01839971627871477,0.054974024351873735,89.31667443113483,94.8148148148148,80.41884816753927,4.530241638707326,,,,,4.332104046244716,5.125612203050611,3294.992418339802,0.29888952547217,43.893142080385395,190.14965840883167,4.332104046244716,2017-02-09 05:35:47.050781250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s26.csv,0.0,22,47,2,5,92.39356231282781,80.92854060839619,75.92137760274501,681.0025332002787,772.3597345860354,811.8476540960941 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05:35:50.050781250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s26.csv,0.0,22,50,2,5,94.65190426974166,82.43588794463828,76.71827099207391,663.7458593883053,759.1409016494758,804.9092831656583 +635.986328125,95.9641821057892,71.4372805175495,4.0,26.66666666666667,8.0,53.33333333333334,71.51745307091129,660.15625,324.21875,0.11245124290919488,0.1508903224204655,96.8506843252616,142.22222222222226,80.41884816753927,17.354075292063882,,,,,4.051724420043777,5.177197613734601,3162.704782418128,0.2910098625444556,48.163591699713315,195.14560064674615,4.051724420043777,2017-02-09 05:35:51.050781250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s26.csv,0.0,22,51,2,5,95.40219402736572,82.94107580784997,76.98356026244237,658.1081753006616,754.7467465791754,802.6269382319462 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05:35:59.050781250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s26.csv,0.0,22,59,2,5,98.19626179016991,86.41891449101472,78.83778427079042,629.7369562269911,723.7299487803992,786.1469595813356 +726.2834821428571,19.945386161452586,6.402426608372152,0.0,0.0,0.0,0.0,7.347968305671565,718.75,54.6875,0.010117217982146327,0.02746226046970652,82.66974101576771,85.33333333333333,79.17525773195877,2.168254277155347,,,,,3.665351669773477,5.117207107350468,2539.6684703450346,0.3622431689052492,47.259131678782616,173.2213372108705,3.665351669773477,2017-02-09 05:36:00.050781250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s26.csv,0.0,22,0,2,5,97.60671496254983,86.66491315994647,78.87230640362806,632.1320953171099,721.1130278305864,785.6076489737818 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05:36:05.050781250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s26.csv,0.0,22,5,2,5,92.95236029515183,87.6832833367718,78.96746982460047,658.3294546578662,710.2086669196046,783.6655962636574 +759.2075892857142,18.614742945292033,10.982972886116023,0.0,0.0,1.0,7.6923076923076925,12.112767049727388,755.859375,66.40625,0.0159544862573403,0.024518647084133274,79.07346531666153,82.13903743315508,75.29411764705883,1.8500297223409143,,,,,3.4852591077357205,5.095836055865816,2297.5799575281767,0.4105284100647112,47.28686270000335,164.80696890143508,3.4852591077357205,2017-02-09 05:36:06.050781250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s26.csv,0.0,22,6,2,5,91.84786602565752,87.82534452916109,78.96837576897705,665.3357905406766,708.6819505656904,783.4565534689741 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05:36:08.050781250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s26.csv,0.0,22,8,2,5,90.26071231692131,88.05445193640195,78.95481455478242,677.0751118698063,706.2140188240563,783.1997893188816 +774.6394230769231,23.727835790608676,10.618168248893287,0.0,0.0,1.0,8.333333333333334,11.826759195693324,773.4375,66.40625,0.01526743778249317,0.030630813619528963,77.52238397322513,80.84210526315789,74.20289855072464,2.2770270282886327,,,,,3.4843073485281466,5.0956152359315405,2296.0550859933082,0.4344032976005189,47.28129882806606,164.74257695458581,3.4843073485281466,2017-02-09 05:36:09.050781250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s26.csv,0.0,22,9,2,5,89.36613604314412,88.1471457749975,79.03735101503032,683.503500740275,705.219206785812,782.2609303836991 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05:36:11.050781250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s26.csv,0.0,22,11,2,5,88.15302800618015,88.26686749777352,79.18384538641114,692.8759118581329,703.9391926615182,780.5767230469182 +792.96875,21.454761788130323,10.268105661189782,0.0,0.0,1.0,8.333333333333334,11.986948333022115,796.875,74.21875,0.015116545681052519,0.027056251319021483,75.71711242201549,80.41884816753927,73.14285714285714,2.0047054729400293,,,,,3.494158247902224,5.096756352613308,2308.830395731597,0.4174824201121817,47.27667230721402,165.19217447562247,3.494158247902224,2017-02-09 05:36:12.050781250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s26.csv,0.0,22,12,2,5,87.43667811271449,88.29983562409399,79.24861552855593,698.6213545664663,703.5916026191453,779.8247176993248 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05:36:20.050781250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s26.csv,0.0,22,20,2,5,81.89656790272375,88.16614001532551,79.39992108329457,745.0547764952064,705.0893765613046,777.9803993338668 +845.0520833333335,21.15635001207281,13.661494361982491,0.0,0.0,1.0,9.090909090909092,15.08292654942848,841.796875,74.21875,0.017848517087767447,0.0250355574873219,71.04177940190759,73.49282296650719,67.36842105263158,1.6794095147071515,,,,,3.6849434855187777,5.120419709675356,2569.544257452064,0.3797973613595865,47.307984700327864,174.32725003449514,3.6849434855187777,2017-02-09 05:36:21.050781250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s26.csv,0.0,22,21,2,5,81.03627107194528,88.09748743986354,79.37574916123137,752.0236350021509,705.8617988776888,778.2539462526343 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05:52:18.429687500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s27.csv,0.0,22,18,2,5,82.14860723919077,89.04620274582544,86.04819124083542,735.0674650869968,691.4226548366731,727.3476647223738 +774.6394230769231,13.473695289102752,15.250920280107396,0.0,0.0,2.0,16.666666666666668,15.337524725767901,777.34375,39.0625,0.019799566441953284,0.017393505788259878,77.47720137613629,80.0,76.03960396039605,1.3052471102060252,,,,,3.784952078031553,4.764585211300292,1775.755561165596,0.5885222294810729,30.98863321950127,117.29049169950895,3.784952078031553,2017-02-09 05:52:19.429687500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_304_s27.csv,0.0,22,19,2,5,82.04971892306472,88.69580273035608,86.14010438676155,736.0189875228941,693.8515410546219,726.2933506305023 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20:37:12.421875000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s3.csv,0.0,22,12,2,20,77.77673485199513,76.69973150070477,71.52458669479685,772.3816439636756,784.2818116830049,846.1554039047154 +771.6346153846154,21.01253805013791,22.65587583503296,0.0,0.0,4.0,33.333333333333336,22.8885362386028,769.53125,58.59375,0.02966240210360985,0.02723120195905724,77.8103331310925,81.26984126984127,75.29411764705883,2.0384920076347752,,,,,,,,,,,,2017-02-09 20:37:13.421875000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s3.csv,0.0,22,13,2,20,77.79447219556106,76.94309786045854,71.48225468287251,772.2021901709406,781.4125149258405,846.2054840329205 +769.8317307692308,20.20086123588483,24.191695658523717,0.0,0.0,5.0,41.66666666666666,24.21139841378523,765.625,58.59375,0.03145024743211445,0.026240619122960468,77.98851718186116,81.26984126984127,75.29411764705883,1.9601199053973528,,,,,,,,,,,,2017-02-09 20:37:14.421875000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s3.csv,0.0,22,14,2,20,77.80336823555055,77.18783247992646,71.42997544870575,772.1120459401714,778.5357195398541,846.3645620872193 +768.9302884615387,19.32483068566572,21.303591499942936,0.0,0.0,4.0,33.333333333333336,21.425107645708767,765.625,58.59375,0.02786352412853411,0.025132097116801685,78.07582297951177,81.26984126984127,75.29411764705883,1.8782515619904168,,,,,1.7759021462928608,3.893295617347487,209.3034354733044,1.5656352897757075,16.591219204371328,29.46438179465837,1.7759021462928608,2017-02-09 20:37:15.421875000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s3.csv,0.0,22,15,2,20,77.80960472093857,77.43274624709426,71.49449824026696,772.0519497863253,775.6621378519341,845.6127730228922 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20:37:17.421875000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s3.csv,0.0,22,17,2,20,77.86597331772232,77.47252619772603,71.62768712976352,771.4989220848599,775.2594852572727,844.0771362813783 +758.4134615384613,16.09373055139477,20.577448736040505,0.0,0.0,4.0,33.333333333333336,20.669932117692113,753.90625,46.875,0.02725417356881116,0.021220259617686924,79.14541056820387,81.70212765957447,76.8,1.613538347452859,,,,,1.7920398795556924,3.9113887178164135,216.62863543580602,1.5656352897757075,16.863997631198572,30.220956283840565,1.7920398795556924,2017-02-09 20:37:18.421875000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s3.csv,0.0,22,18,2,20,77.90523436103804,77.50350553792006,71.69860904899409,771.098281059219,774.9475218550724,843.2689884064451 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20:37:20.421875000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s3.csv,0.0,22,20,2,20,77.90926807771487,77.54154141966826,71.82582944117696,771.0081368284497,774.5662830937648,841.8060967051862 +770.7331730769231,24.57031329618645,26.220091882294607,0.0,0.0,6.0,50.0,26.34909680318758,777.34375,82.03125,0.0341870542538044,0.03187914333321968,77.92104726863566,81.70212765957447,73.49282296650719,2.3878787772143766,,,,,1.8120883789685007,3.9198564662110167,222.43201490597755,1.5939337258981352,16.934740814331846,30.68724703049431,1.8120883789685007,2017-02-09 20:37:21.421875000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s3.csv,0.0,22,21,2,20,77.89831506612545,77.54819941701857,71.88174887078668,771.0882650335777,774.4990355496097,841.1540515342847 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20:37:24.421875000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s3.csv,0.0,22,24,2,20,77.85756774068273,77.54423689313582,72.09601121307101,771.3887458028087,774.5361030028984,838.6101938836322 +777.9447115384613,26.14787037791952,23.78530633090048,0.0,0.0,6.0,50.0,24.576283968980302,777.34375,82.03125,0.03159129897596232,0.03361147648424725,77.20771891084493,81.70212765957447,73.49282296650719,2.5223159680420086,,,,,1.7827221316667616,3.910126958988728,214.62927749851798,1.5824092400461809,16.883468042616123,30.098532138860264,1.7827221316667616,2017-02-09 20:37:25.421875000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s3.csv,0.0,22,25,2,20,77.84912655426989,77.54628648840806,72.2021739720539,771.4388259310138,774.5189922924284,837.3522352037986 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20:37:27.421875000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s3.csv,0.0,22,27,2,20,77.74869010014739,77.53074850518583,72.3976937761702,772.3653083028087,774.6750753586676,835.0064262173646 +784.8557692307692,21.71757542710261,22.79575703126509,0.0,0.0,5.0,41.66666666666666,23.38318358863017,785.15625,66.40625,0.02979296898275697,0.027670785230244056,76.50180496209269,80.0,73.49282296650719,2.05580799966373,,,,,1.777563815796135,3.9084631079387187,213.2894408491006,1.5939337258981352,16.87558986795312,29.99743791948935,1.777563815796135,2017-02-09 20:37:28.421875000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s3.csv,0.0,22,28,2,20,77.72630463787094,77.53468969128976,72.49684994333124,772.5656288156292,774.6350112561034,833.8200070621954 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20:37:33.421875000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s3.csv,0.0,22,33,2,20,77.76645690086008,77.6189924301402,73.02430086974705,772.1449557387061,773.7807277358044,827.5975203709586 +773.4375,20.669932117692113,16.769487149314767,0.0,0.0,2.0,18.181818181818183,16.904304311664635,771.484375,58.59375,0.021856070221142155,0.026724760717824148,77.62610237519424,80.0,74.20289855072464,1.9680486244037865,,,,,1.79600228251149,3.907575612472957,216.39545384932325,1.4190841839428814,16.77159524121981,30.12182333458962,1.79600228251149,2017-02-09 20:37:34.421875000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s3.csv,0.0,22,34,2,20,77.74850943364294,77.63620351708869,73.12986267275089,772.3252442002446,773.610455299907,826.3594214766098 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20:37:36.421875000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s3.csv,0.0,22,36,2,20,77.75383523362953,77.6977701230826,73.35514074350668,772.2651480463984,772.9894617101634,823.7284305643689 +765.0240384615387,13.135512616823554,15.420207398863294,0.0,0.0,2.0,16.666666666666668,15.543406753012139,761.71875,35.15625,0.020317540327582245,0.017170065195910758,78.45013460705117,80.0,76.41791044776119,1.2868144799509442,,,,,1.7623046752320777,3.894585027649005,207.21157123000663,1.3596261140377293,16.679845585295887,29.394969857116074,1.7623046752320777,2017-02-09 20:37:37.421875000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s3.csv,0.0,22,37,2,20,77.76620338060678,77.72358845686647,73.46768459614516,772.134939713065,772.7290450434967,822.4209331631214 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20:37:39.421875000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s3.csv,0.0,22,39,2,20,77.81380363105224,77.77464426467996,73.70288977429556,771.6541704822959,772.2232357486249,819.6806765166147 +760.2163461538463,16.992446533171506,17.74611089900913,0.0,0.0,2.0,16.666666666666668,17.758049084617,757.8125,58.59375,0.023359204487591056,0.0223521193922509,78.96143611629337,82.58064516129029,76.41791044776119,1.7018419728137073,,,,,1.838170184670688,3.900805405638358,222.321405367448,1.4152818979931432,16.44939209384317,30.23678210286025,1.838170184670688,2017-02-09 20:37:40.421875000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s3.csv,0.0,22,40,2,20,77.84694534459916,77.79980135029705,73.82682521401026,771.323641636142,771.9728351075993,818.233889913578 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20:37:42.421875000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s3.csv,0.0,22,42,2,20,77.92625555869958,77.8514952053471,74.07352167735283,770.542391636142,771.462017799907,815.3656643891024 +758.7139423076923,18.40184072007524,22.326078384742512,0.0,0.0,4.0,33.333333333333336,22.326078384742512,757.8125,58.59375,0.0294262134116527,0.024253990461944708,79.12425422641606,82.58064516129029,76.41791044776119,1.8486747192700144,,,,,1.8277290183089456,3.905450600034584,221.61186601565063,1.4008931605410433,16.584768171682157,30.312462049310074,1.8277290183089456,2017-02-09 20:37:43.421875000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s3.csv,0.0,22,43,2,20,77.97005292854371,77.88226256205212,74.20087760558894,770.1117025335778,771.1569463522575,813.8797488406868 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20:37:45.421875000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s3.csv,0.0,22,45,2,20,78.02375093325442,77.91667782709622,74.45609592823615,769.5915846306475,770.8217672084846,810.8795641998772 +760.8173076923077,20.617813008202056,22.430251532489862,0.0,0.0,4.0,33.333333333333336,22.439697838039173,761.71875,66.40625,0.029494200001972493,0.02709955832989591,78.91601404962776,82.58064516129029,75.66502463054188,2.053801172502129,,,,,1.7964397015574274,3.902680732862419,215.25801599642503,1.4000876832522264,16.675315241784258,29.956198336326928,1.7964397015574274,2017-02-09 20:37:46.421875000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s3.csv,0.0,22,46,2,20,78.04633003192087,77.93571089682995,74.58244739915953,769.3712320665451,770.6364707341256,809.3998010013639 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20:37:48.421875000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s3.csv,0.0,22,48,2,20,78.03317095936343,77.96920266020045,74.8347278934233,769.5028712606842,770.3005761599499,806.4324277214907 +763.2211538461537,19.435864839190348,22.408982785801715,0.0,0.0,4.0,33.333333333333336,22.41134697663868,765.625,58.59375,0.02936415855836753,0.025465574088513968,78.6611714973218,81.70212765957447,75.66502463054188,1.921340720662452,,,,,1.670487296350992,3.8446746491586654,180.5519114369438,1.3987168811184478,16.175426649266793,27.020844730657483,1.670487296350992,2017-02-09 20:37:49.421875000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s3.csv,0.0,22,49,2,20,78.03265188587628,77.97031714133709,74.95858936090887,769.5028712606842,770.2755360958473,804.9784797831678 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20:37:51.421875000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s3.csv,0.0,22,51,2,20,78.06839245238902,77.98335375925697,75.20442086552985,769.1394326159956,770.113848824785,802.10185190654 +760.5168269230769,22.003934068324533,17.03628139079365,0.0,0.0,3.0,25.0,17.722210402104427,753.90625,66.40625,0.023302851133229368,0.028932869450566594,78.95477550167068,82.58064516129029,75.66502463054188,2.1971752608554818,,,,,1.7605688006686282,3.806340835225358,186.9174810352057,1.5198257537444133,15.075923519335635,26.542200589408694,1.7605688006686282,2017-02-09 20:37:52.421875000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s3.csv,0.0,22,52,2,20,78.10971012554185,77.99786408363363,75.33007237985854,768.7287755647136,769.9535924145287,800.6353777838159 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20:38:18.421875000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s3.csv,0.0,22,18,2,20,78.4400421684339,78.2366065638984,77.88934818317261,765.6831883394389,767.5930298000595,771.0767365629627 +743.8616071428571,16.972699348658274,14.665185507106285,0.0,0.0,1.0,7.6923076923076925,14.77558996772145,740.234375,58.59375,0.019863358756306702,0.022817012177641132,80.69849013973818,83.02702702702703,76.8,1.7435915895753116,,,,,2.184055006050915,3.8357667964205775,267.1645038724609,1.929909807708872,14.002049894840276,30.581247167800587,2.184055006050915,2017-02-09 20:38:19.421875000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s3.csv,0.0,22,19,2,20,78.50795278984779,78.27030233786175,77.91377550453407,765.0378701159957,767.2703706883378,770.8400832636574 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20:38:21.421875000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s3.csv,0.0,22,21,2,20,78.64645615748886,78.35742430493865,77.96318653902965,763.7250553266184,766.432243971305,770.3622193556096 +742.7455357142858,16.703827619526525,18.05386885312476,0.0,0.0,2.0,15.384615384615385,18.06386822739079,740.234375,66.40625,0.02432039959690781,0.022489300596687856,80.81821069516073,83.02702702702703,76.03960396039605,1.7009834923399023,,,,,2.022205569870775,3.8376054844177743,238.5287298489703,2.063693184711697,14.58243669493933,29.48868470679429,2.022205569870775,2017-02-09 20:38:22.421875000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s3.csv,0.0,22,22,2,20,78.70857066393852,78.40914039473991,77.98738165899687,763.132678952992,765.9307272588507,770.1281796637339 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20:38:26.421875000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s3.csv,0.0,22,26,2,20,78.77302049733524,78.60296860780274,78.07246881640656,762.547456883395,764.0344789758838,769.2992716918505 +756.9110576923077,19.60622628336517,23.280998757732714,0.0,0.0,4.0,33.333333333333336,23.30147157314229,750.0,58.59375,0.03078495331298832,0.025902946038522938,79.31815099950252,82.13903743315508,76.03960396039605,1.95330422223166,,,,,1.9677513737676915,3.805607056903229,220.67791135693784,1.749199854809259,14.248148241396162,28.03681327565301,1.9677513737676915,2017-02-09 20:38:27.421875000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s3.csv,0.0,22,27,2,20,78.78431530851581,78.65809903845074,78.09442377181905,762.4172485500617,763.4877709096444,769.0814231341582 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22:32:46.558593750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s4.csv,0.0,22,46,2,22,62.116451636151865,63.405158588307295,73.08550533304263,967.5331439393938,948.3451704545438,861.2963817326325 +990.0568181818179,36.65901209271244,44.462666484788116,5.0,50.0,6.0,60.0,44.57234945498723,992.1875,128.90625,0.04501999141508035,0.03702718007642691,60.67997214080144,65.36170212765957,57.3134328358209,2.194129283515649,,,,,2.086035609996712,4.4071713370639785,481.4689063742148,1.9459101490553128,27.66080520677608,57.701424662517354,2.086035609996712,2017-02-09 22:32:47.558593750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s4.csv,0.0,22,47,2,22,61.939528101233286,63.32719725227356,72.87204900367628,970.1609848484845,949.5348011363619,863.7901237860337 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22:32:49.558593750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s4.csv,0.0,22,49,2,22,61.70001177010209,63.17322815501508,72.46390278746158,973.7239583333329,951.8371212121195,868.5355026968956 +982.8125,31.74520824514745,40.3855792168511,2.0,22.222222222222207,5.0,55.55555555555557,40.469453822708466,984.375,105.46875,0.041177186719449005,0.032300370869466404,61.108009868407876,65.36170212765957,58.62595419847328,1.9176749806434343,,,,,1.9715388490998,4.391666231902828,434.55137137580255,1.8971199848858813,27.94926908980905,55.103069814502746,1.9715388490998,2017-02-09 22:32:50.558593750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s4.csv,0.0,22,50,2,22,61.59680189114688,63.09472041987046,72.26572595512377,975.2817234848482,953.012547348483,870.8623165080844 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22:33:00.558593750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s4.csv,0.0,22,0,2,22,60.78871978415378,62.443339253979154,70.62014984941618,988.1753735269357,963.0744291877088,891.7569352258564 +1023.0034722222224,35.18035439090181,27.326305819596136,1.0,12.5,4.0,50.0,27.48290484271541,1031.25,105.46875,0.026864918437681927,0.034389281508968084,58.71363140168053,62.4390243902439,56.263736263736256,1.9380593556693293,,,,,2.4628587976824243,4.4519324358486,650.3175701500293,2.169053700369523,26.803189073962088,66.01247001675296,2.4628587976824243,2017-02-09 22:33:01.558593750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s4.csv,0.0,22,1,2,22,60.66832971258848,62.37290958370856,70.44999121343415,990.1530934343431,964.2213147095943,894.009326931373 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22:33:23.558593750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s4.csv,0.0,22,23,2,22,59.848345769112186,60.630683800173045,65.02778401870006,1003.8033985690233,990.9778119739043,940.0912963718149 +967.3295454545456,33.34174772466535,41.96992505801029,2.0,20.0,5.0,50.0,42.035315788483146,980.46875,121.09375,0.04345501074169182,0.03446782731007988,62.094412056941465,66.49350649350649,58.62595419847328,2.070281437006724,,,,,1.6395574299038225,4.2633607196916286,284.29571260529065,,26.439732182782144,43.34945934494768,1.6395574299038225,2017-02-09 22:33:24.558593750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s4.csv,0.0,22,24,2,22,59.88947076555258,60.61292405015421,64.51978409587326,1003.1701125841748,991.2536168981467,943.5527665422694 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23:24:25.777343750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s5.csv,0.0,22,25,2,23,64.95981748700692,64.97731747369284,63.28735398638489,926.8392913510095,925.2384193497468,950.8819366241246 +989.0625,23.56734863905993,25.581878521339195,0.0,0.0,3.0,33.333333333333336,25.71408549235873,992.1875,62.5,0.02599844346778765,0.023827967028433424,60.69467597921013,62.95081967213114,59.07692307692307,1.378985697177529,,,,,2.8770666098216013,4.314201483858219,700.6895122245587,1.5475625087160132,21.162442553599274,60.88575685322825,2.8770666098216013,2017-02-09 23:24:26.777343750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s5.csv,0.0,22,26,2,23,64.78127845890322,64.9228042035839,63.291532467023025,929.481336805555,926.0859572285347,950.8115537412417 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23:24:28.777343750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s5.csv,0.0,22,28,2,23,64.39165044920577,64.81123961269897,63.30699558657351,935.2018229166661,927.8118095012618,950.5546562187192 +983.984375,23.757223179736425,19.81373647658565,0.0,0.0,2.0,22.222222222222207,20.99546288619414,986.328125,62.5,0.02133719134126916,0.024143902874206133,61.008562557357216,62.95081967213114,59.07692307692307,1.3966339571131214,,,,,2.9366569512458085,4.328155367944681,734.2729159733963,1.5475625087160132,21.28585425478261,62.50925186051251,2.9366569512458085,2017-02-09 23:24:29.777343750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s5.csv,0.0,22,29,2,23,64.1856004927358,64.7591639407485,63.319343295142744,938.2161458333327,928.6161418876254,950.3505458583588 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23:24:31.777343750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s5.csv,0.0,22,31,2,23,63.790477787160555,64.66619581782297,63.353292228334624,943.9334753787873,930.0365964330799,949.7910019394399 +952.4147727272727,40.36191034649106,24.082910377809924,0.0,0.0,5.0,50.0,26.29112292259975,964.84375,128.90625,0.027604698788233,0.04237850094545817,63.10671765767904,69.81818181818181,60.71146245059288,2.6986740218993326,,,,,2.9625489530105718,4.3405434082844465,754.6924532153894,1.5869650565820417,21.497053888681044,63.68607449072389,2.9625489530105718,2017-02-09 23:24:32.777343750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s5.csv,0.0,22,32,2,23,63.64452171090258,64.6478779153909,63.383801371938134,946.0286458333327,930.332524463383,949.3130381802262 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23:24:34.777343750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s5.csv,0.0,22,34,2,23,63.46136015403942,64.65461212723353,63.46263250798221,948.7866950757568,930.3561987058072,948.1478815463196 +912.9971590909092,73.28963898484156,22.96396633859229,0.0,0.0,7.0,70.0,29.64635306407856,941.40625,187.5,0.03247146255482117,0.08027367692777669,66.11670166736769,74.92682926829268,60.71146245059288,5.220190301520667,,,,,3.28841231549036,4.40054868720634,945.7007699768789,1.5841201044498106,21.86357714606684,71.89645634779973,3.28841231549036,2017-02-09 23:24:35.777343750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s5.csv,0.0,22,35,2,23,63.43449457655138,64.68274715525415,63.512955514009455,949.3193655303023,930.048433554292,947.4379741957872 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23:24:37.777343750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s5.csv,0.0,22,37,2,23,63.552750027224405,64.7943494640457,63.64854681419318,948.0656171085851,928.6918008207062,945.5433416370922 +868.4895833333335,59.83146656711642,27.694310523217677,0.0,0.0,8.0,72.72727272727273,28.87359949624082,847.65625,175.78125,0.033245763737800534,0.0688914037834264,69.3751625393799,74.92682926829268,61.44,4.397580779320531,,,,,3.5428359786403085,4.442242044972522,1109.5518251013157,2.525728644308256,22.099655713533526,78.29545537747043,3.5428359786403085,2017-02-09 23:24:38.777343750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s5.csv,0.0,22,38,2,23,63.6606963956285,64.86882026409742,63.732760480112205,946.759588068181,927.7546953914133,944.3515248202754 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01:00:30.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,30,2,1,71.2815816551297,71.61351274685536,73.06935898231212,842.028685290404,838.2800467761393,822.889356092482 +843.0989583333335,16.134565043942057,15.27401503381148,0.0,0.0,2.0,18.181818181818183,15.311122966839562,843.75,58.59375,0.01816052886259889,0.019137213828181468,71.18980057615586,73.49282296650719,68.57142857142857,1.298070753178039,,,,,1.828162573697619,3.694649648580072,173.91900613632126,,13.009404760627394,23.78330688946263,1.828162573697619,2017-02-10 01:00:31.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,31,2,1,71.24393452061804,71.60607920000098,73.04832400641668,842.4627130681818,838.3645890841603,823.1239657020915 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01:00:33.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,33,2,1,71.18752477206019,71.59306803807293,72.99229518862909,843.1206597222221,838.512035281102,823.7455171048929 +842.1223958333335,15.665637259786413,15.274015033811484,0.0,0.0,2.0,18.181818181818183,15.311122966839562,841.796875,54.6875,0.01818158861775459,0.01860256577582677,71.27107976787178,73.49282296650719,68.87892376681614,1.2652219714962822,,,,,1.8572632332615373,3.7103182537615833,181.32985699327503,,13.14202850482058,24.408206352478363,1.8572632332615373,2017-02-10 01:00:34.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,34,2,1,71.16709452894412,71.58770156690292,72.96064943255993,843.3593749999998,838.5722079569726,824.0963035884918 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01:00:39.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,39,2,1,71.04246217248432,71.56042338006141,72.69064350053516,844.8419744318179,838.8992084304574,827.03913660945 +849.609375,17.10822367961066,17.57005238189344,0.0,0.0,2.0,18.181818181818183,17.627377338055197,849.609375,54.6875,0.020747625740423586,0.020136575917150942,70.64718802377159,73.49282296650719,68.87892376681614,1.374728101902964,,,,,1.8651698904483776,3.7025542177734887,180.86507086879482,,12.997442941328053,24.24243922698588,1.8651698904483776,2017-02-10 01:00:40.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,40,2,1,71.01610971427644,71.55732298732264,72.60709209560589,845.1566445707068,838.9371858610131,827.9094480266364 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01:00:42.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,42,2,1,70.95304647707475,71.56513088480051,72.43739780090054,845.9053424873734,838.8410092511645,829.669052325303 +846.0286458333335,15.487528537085135,15.737585089602511,0.0,0.0,1.0,9.090909090909092,15.801559276482234,843.75,54.6875,0.018677333627300282,0.01830615146822837,70.9414589894236,73.49282296650719,68.87892376681614,1.2483398549442044,,,,,1.8380195255120468,3.6612817946144,168.71981023521857,,12.485497316862693,22.94858785412189,1.8380195255120468,2017-02-10 01:00:43.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,43,2,1,70.93191554132831,71.569310508753,72.35036567243316,846.1549084595956,838.7867557789424,830.5666596682228 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01:00:48.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,48,2,1,70.91712447958092,71.55867042289177,71.94361524142126,846.3235874368683,838.87898668172,834.7506706881712 +837.2395833333335,14.83564870633835,19.993875369686425,0.0,0.0,2.0,18.181818181818183,20.022237725867114,837.890625,54.6875,0.0239145856508024,0.017719717272889353,71.68491987654822,74.5631067961165,69.81818181818181,1.2290205222398771,,,,,1.517989279487395,3.6370572733743662,123.14922791140104,,13.360856302528699,20.28163663201016,1.517989279487395,2017-02-10 01:00:49.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,49,2,1,70.94557345269756,71.54619022572034,71.87743814033115,845.9872159090904,839.0146203622755,835.4418666918672 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01:00:51.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,51,2,1,70.99144236384663,71.50009177896713,71.76349553759592,845.4505997474741,839.5383750364209,836.6593552140431 +842.1223958333335,18.280880718557228,19.695205431622227,0.0,0.0,2.0,18.181818181818183,19.70800699646535,845.703125,62.5,0.0234027821774803,0.02170810420077612,71.27970438428942,74.5631067961165,69.18918918918922,1.4993300222924972,,,,,1.5609298510465288,3.6002859788457484,123.0886670236599,,12.629649701632808,19.713997227539533,1.5609298510465288,2017-02-10 01:00:52.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,52,2,1,71.01945890231131,71.46329197346695,71.71443196044946,845.1240924873731,839.9615521197542,837.1901594557849 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01:00:54.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,54,2,1,71.08192223987956,71.39478095342957,71.63346466674713,844.3901909722216,840.7502003205117,838.0649210727339 +843.4244791666665,17.42621650620459,20.88841075263174,0.0,0.0,3.0,27.272727272727273,20.936651950260906,849.609375,62.5,0.0248233866426868,0.02066126480396006,71.16690598310605,74.5631067961165,69.18918918918922,1.4334973913765168,,,,,1.5092017501249744,3.5848048513851984,114.9538115700365,,12.61736841683556,19.042154496659805,1.5092017501249744,2017-02-10 01:00:55.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,55,2,1,71.10317283587379,71.36038323321823,71.5959812481896,844.1406249999995,841.1462506677339,838.4666900291904 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01:00:57.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,57,2,1,71.13789403278027,71.28877078733635,71.53710347539673,843.7352035984843,841.9704861111098,839.0942707348962 +842.7734375,18.482121106726176,19.835568243989968,0.0,0.0,3.0,27.272727272727273,19.848279279093926,847.65625,62.5,0.023551144822470654,0.02193011820774925,71.22557917943077,74.5631067961165,69.18918918918922,1.5276392824491185,,,,,1.52033015723581,3.549053757171952,111.54079715215259,,12.064183439027202,18.34154190477788,1.52033015723581,2017-02-10 01:00:58.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,58,2,1,71.147540134272,71.26010024319218,71.52431597443213,843.62669665404,842.3014322916655,839.237969120782 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01:01:00.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,0,2,1,71.16622329310175,71.22390247411553,71.51499260106486,843.4175741792925,842.7231297348474,839.3486088798593 +833.984375,19.95283634642066,14.520630895071642,0.0,0.0,2.0,18.181818181818183,15.036871704360886,835.9375,58.59375,0.018030159982746544,0.02392471243411564,71.98153036692116,74.20289855072464,69.18918918918922,1.64770840965837,,,,,1.7727330286225018,3.5894706008666684,147.12968491557262,,11.704520886051016,20.748990758904547,1.7727330286225018,2017-02-10 01:01:01.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,1,2,1,71.19261428612727,71.21827440337248,71.51536268162228,843.113754734848,842.788233901514,839.3456762597392 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01:01:03.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,3,2,1,71.26505844081738,71.2262916064386,71.5186677662897,842.2713462752522,842.6960029987363,839.3073266120145 +826.8229166666665,17.495729660254153,13.268140406441338,0.0,0.0,1.0,9.090909090909092,13.735141765492694,822.265625,46.875,0.016611950985666757,0.021160189573346755,72.596607858534,74.5631067961165,70.45871559633028,1.4650596428691425,,,,,1.854324599241564,3.6076840082694175,160.73877107127225,,11.686625655119554,21.670797434415736,1.854324599241564,2017-02-10 01:01:04.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,4,2,1,71.30924271050611,71.23816861972495,71.52227052388018,841.7613636363634,842.5603693181806,839.2662699303329 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01:01:06.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,6,2,1,71.39190715093564,71.26045049932985,71.52059507406898,840.8173532196967,842.3108033459583,839.292663511414 +835.9375,33.019840045762294,16.015585630368864,0.0,0.0,2.0,18.181818181818183,17.97801144722605,822.265625,97.65625,0.021506406217242386,0.03950036940053807,71.87482903492976,74.5631067961165,66.49350649350649,2.6222767281850703,,,,,2.00907147579154,3.679750707244689,196.9556461045731,,12.19881565539511,24.50829257169359,2.00907147579154,2017-02-10 01:01:07.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,7,2,1,71.42458894821173,71.26689603924423,71.51578096818147,840.45928030303,842.251124526514,839.3571811540565 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01:01:12.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,12,2,1,71.45653398205228,71.20479022956332,71.47290054257532,840.1830808080807,843.044211647726,839.8967832561588 +855.46875,25.47924874240355,24.0378096601853,0.0,0.0,4.0,36.36363636363637,24.16599826825154,855.46875,97.65625,0.028248838158321438,0.02978396200025255,70.19417979201724,74.5631067961165,66.49350649350649,2.007450429839323,,,,,1.924238362100149,3.6850110442720654,185.735032036837,,12.540535920982881,24.13098030045019,1.924238362100149,2017-02-10 01:01:13.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,13,2,1,71.43162467547207,71.1817701084,71.46108534481596,840.4977509469696,843.3263297032815,840.0434142621648 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01:01:15.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,15,2,1,71.38863455647922,71.1441772354507,71.44353842912095,841.0511363636363,843.7929095643926,840.266293391294 +843.75,27.77161192616073,20.356407170406985,0.0,0.0,4.0,36.36363636363637,21.23270728533629,845.703125,97.65625,0.025164690115954118,0.0329145030235979,71.18109301904023,74.5631067961165,66.49350649350649,2.222495570215433,,,,,2.056770496822536,3.7156606877175484,212.62243859552902,1.425008873300581,12.565432326856179,25.844210489697947,2.056770496822536,2017-02-10 01:01:16.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,16,2,1,71.38654752644747,71.13641487699309,71.44234803230668,841.094539141414,843.8935250946957,840.2868217321347 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01:01:20.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,20,2,1,71.45202867473783,71.20850251783146,71.46767865439958,840.3783933080808,843.068872316918,840.0082228207235 +833.3333333333335,24.441863905484833,20.331612511789253,0.0,0.0,5.0,45.45454545454545,20.331612511789253,832.03125,58.59375,0.024397935014147103,0.0293302366865818,72.05651598138581,74.5631067961165,69.50226244343891,2.0128906276065024,,,,,2.0680263050123493,3.747399773147853,222.34838252057446,1.6691571471357227,12.997561047826025,26.87929814790809,2.0680263050123493,2017-02-10 01:01:21.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,21,2,1,71.47258372263572,71.23201304324097,71.47263136464233,840.1505287247475,842.80056423611,839.9554356585613 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01:01:23.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,23,2,1,71.54281768639353,71.29817936760561,71.49036561011731,839.3475773358585,842.0350970643929,839.7573504995387 +831.0546875,21.597070134344243,19.73358521228665,0.0,0.0,4.0,36.36363636363637,19.813304310459266,828.125,58.59375,0.023841155833032063,0.02598754385143185,72.24155248538352,74.5631067961165,69.50226244343891,1.7742751361570503,,,,,2.018269954314041,3.7488808613152163,214.73820233433025,1.7764919970972666,13.179253506624725,26.59929137270865,2.018269954314041,2017-02-10 01:01:24.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,24,2,1,71.5724273896078,71.32717481474347,71.49847549634728,839.00726010101,841.6987255366151,839.666439275815 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01:01:26.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,26,2,1,71.62954162780373,71.37607236917655,71.50803220446599,838.3453677398988,841.1290640782819,839.5575778388285 +834.6354166666665,20.35434210246655,19.48276523344489,0.0,0.0,4.0,36.36363636363637,19.495706294475575,830.078125,58.59375,0.023358350131290548,0.024387105670349938,71.92662517484861,74.5631067961165,69.50226244343891,1.668515664426009,,,,,2.0209840175312905,3.7570880319931823,217.2142607330639,1.7346010553881064,13.295436041351865,26.86986374568161,2.0209840175312905,2017-02-10 01:01:27.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,27,2,1,71.65582634241585,71.39686018759785,71.51173656703574,838.0415482954544,840.8883759469686,839.5140188904023 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01:01:30.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,30,2,1,71.67257233139152,71.41939781224642,71.51310847033456,837.8383443813129,840.627959280302,839.4944842782942 +832.6822916666665,14.214899162192035,18.079434746246953,0.0,0.0,3.0,27.272727272727273,18.093379549512786,830.078125,46.875,0.021729031265091188,0.01707121588203829,72.07543068184575,73.84615384615384,69.81818181818181,1.170994428501508,,,,,1.967154626313968,3.7625417338672213,209.90781830627688,1.8405496333974871,13.56099720084869,26.676578381080265,1.967154626313968,2017-02-10 01:01:31.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,31,2,1,71.67570234188901,71.4341583140079,71.51791464308252,837.7949416035352,840.4543481691908,839.4362604098187 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01:01:33.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,33,2,1,71.62918917889515,71.44712380985604,71.51948271292261,838.3374763257575,840.3044113005038,839.4157542824829 +845.703125,23.16963405994128,22.549016855664124,0.0,0.0,4.0,36.36363636363637,23.16963405994128,845.703125,78.125,0.0273968883110622,0.0273968883110622,70.99526823724335,73.84615384615384,67.36842105263158,1.8453511168919223,,,,,2.00733800204876,3.7967006425965963,225.05106948303035,1.791759469228055,13.963043039795865,28.028546918024677,2.00733800204876,2017-02-10 01:01:34.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,34,2,1,71.5758111915188,71.44252695101223,71.51511425895862,838.9668166035352,840.3640901199483,839.4681490384622 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01:01:36.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,36,2,1,71.45705263128488,71.42447989111002,71.50431712597879,840.3665561868686,840.5919547032815,839.6027962922501 +854.4921875,19.92674851262773,21.618226214807414,0.0,0.0,3.0,27.272727272727273,21.62114267029655,849.609375,70.3125,0.02530291439358133,0.023319989116492336,70.25205614897756,73.14285714285714,67.36842105263158,1.5642495097821811,,,,,1.969295881444439,3.799429055761761,219.37189499538457,1.6964492894237302,14.141616324317749,27.84902678444639,1.969295881444439,2017-02-10 01:01:37.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,37,2,1,71.40296020175315,71.4137745749822,71.49627433592013,840.9850457702019,840.7221630366149,839.7004525422501 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01:01:39.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,39,2,1,71.31234242183585,71.395951947532,71.4781876637978,842.0267124368686,840.9411497790392,839.9201791047501 +854.4921875,21.273500228390482,21.873385787549,0.0,0.0,3.0,27.272727272727273,21.87626822622364,855.46875,70.3125,0.025601484187032,0.024896073410139254,70.25710376420741,73.14285714285714,67.36842105263158,1.6769887966734918,,,,,1.9811689626417144,3.8020901803192135,222.0380290472553,1.4321038971511848,14.142439873583728,28.01856293357069,1.9811689626417144,2017-02-10 01:01:40.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,40,2,1,71.29427395074013,71.38945054320592,71.47338676526537,842.2309027777776,841.0225299873725,839.9798579241944 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01:01:42.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,42,2,1,71.30059230120345,71.37856314162761,71.47184701321515,842.1519886363634,841.167534722221,840.0042719866944 +848.3072916666665,25.141276364285464,20.633294099906177,0.0,0.0,5.0,45.45454545454545,20.936651950260906,849.609375,93.75,0.02468050452463603,0.02963699193825209,70.78672496164863,75.29411764705883,67.36842105263158,2.032807954260352,,,,,1.9801707059645983,3.8228582266773774,227.23909994623156,1.4321038971511848,14.488312551917485,28.689332094166197,1.9801707059645983,2017-02-10 01:01:43.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,43,2,1,71.32034380685784,71.37598424116469,71.47264737495995,841.9132733585856,841.2055121527766,839.9961339658611 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01:01:46.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,46,2,1,71.40293319251627,71.39474035948163,71.4843255862749,840.8933080808079,840.9939236111098,839.8550749380835 +832.3567708333335,20.36285915790692,19.050779234377355,0.0,0.0,4.0,36.36363636363637,19.3170067740897,828.125,66.40625,0.02320760454047851,0.02446409985650765,72.1240858210815,75.29411764705883,69.50226244343891,1.6916880280913824,,,,,2.0579854124793617,3.8270248266095535,241.92227137816647,1.413693335308005,14.280100895855913,29.388239332404947,2.0579854124793617,2017-02-10 01:01:47.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,47,2,1,71.41492374015931,71.40766608209067,71.48682117690082,840.75224905303,840.8474392361098,839.8252355283613 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01:01:52.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,52,2,1,71.41283242214337,71.45975294309818,71.46152245828371,840.7305476641412,840.2452256944431,840.1033889071007 +830.7291666666665,12.50147955258848,12.021527613501954,0.0,0.0,0.0,0.0,12.068640247135248,828.125,46.875,0.014527767570219232,0.015048802972394913,72.24062094576874,74.20289855072464,70.13698630136986,1.0354129897281463,,,,,2.091795771097495,3.7985685585087214,239.9182032090152,1.4000876832522264,13.707701782926373,28.673712620990983,2.091795771097495,2017-02-10 01:01:53.261718750,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s9.csv,0.0,22,53,2,1,71.40693949135776,71.4748785888754,71.4518734462054,840.7848011363635,840.0661892361097,840.2118958515453 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02:26:53.593750000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s10.csv,0.0,22,53,2,2,71.42608716242465,71.51672775282802,71.49854787275581,840.4434974747475,839.4550978535335,839.6668494324757 +833.984375,14.182333485593649,11.873759586150912,0.0,0.0,1.0,9.090909090909092,12.296370520724988,833.984375,54.6875,0.014744125776607013,0.017005514624412056,71.96313405773299,74.92682926829268,70.13698630136986,1.1881831742640605,,,,,1.6641572823334392,3.748092517145552,160.63412548904608,1.413693335308005,14.500703828838455,24.13145187572189,1.6641572823334392,2017-02-10 02:26:54.593750000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s10.csv,0.0,22,54,2,2,71.44148660639395,71.51329764613291,71.49864879469413,840.2590356691919,839.4930752840891,839.6639168123556 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02:26:56.593750000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s10.csv,0.0,22,56,2,2,71.44249193297354,71.50469396023013,71.4903660586544,840.2254971590908,839.5744554924225,839.7548280360794 +841.9744318181819,10.240580830140267,8.941814954890312,0.0,0.0,0.0,0.0,9.077304717673632,843.75,31.25,0.010780974308295636,0.012162579341069173,71.27067660024714,72.79620853080569,70.13698630136986,0.8283426107256183,,,,,1.6520015586165615,3.7018045654776937,150.63224951989224,,13.798664807710324,22.79541576916496,1.6520015586165615,2017-02-10 02:26:57.593750000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s10.csv,0.0,22,57,2,2,71.42999889474979,71.49812799549504,71.4884045456329,840.3616240530301,839.6425189393923,839.774645926991 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02:26:59.593750000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s10.csv,0.0,22,59,2,2,71.40553948940722,71.50175881535004,71.48494813196618,840.6328914141412,839.585799400251,839.8101236300262 +839.5182291666665,16.77731860126612,12.931208420478072,0.0,0.0,1.0,9.090909090909092,13.167966063735195,843.75,54.6875,0.015685146082898227,0.019984460156296832,71.49613660999528,74.5631067961165,69.81818181818181,1.3891671453235297,,,,,1.7165869904358189,3.710298802280063,161.11950774043203,,13.669621875123356,23.46509507501363,1.7165869904358189,2017-02-10 02:27:00.593750000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s10.csv,0.0,22,0,2,2,71.40350528765534,71.51286295912388,71.48504542307947,840.6545928030301,839.4501657196954,839.8075854173013 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02:27:02.593750000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s10.csv,0.0,22,2,2,2,71.40213021831964,71.53541373350559,71.48666912484,840.6654434974746,839.173473011362,839.7859457805383 +835.9375,17.705896111247032,16.934117922679494,0.0,0.0,2.0,18.181818181818183,17.026948998205754,839.84375,54.6875,0.020368686652059226,0.021180885067660003,71.80544229055718,74.5631067961165,69.81818181818181,1.4668177456483245,,,,,1.5735877022837712,3.6919798472781817,138.46064582065716,,13.979268802802364,21.997605475008978,1.5735877022837712,2017-02-10 02:27:03.593750000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s10.csv,0.0,22,3,2,2,71.40757817913978,71.54542027612165,71.48937059234609,840.6003393308079,839.048690025251,839.7533318332456 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02:27:05.593750000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s10.csv,0.0,22,5,2,2,71.44013136279794,71.55126890025704,71.49841881873628,840.2205650252523,838.9727351641397,839.6463318582471 +830.7291666666665,17.495729660254153,17.577228225517867,0.0,0.0,2.0,18.181818181818183,17.66668034635334,826.171875,54.6875,0.021266474147334426,0.02106069024933416,72.2549051551226,74.5631067961165,69.81818181818181,1.4483369402908417,,,,,1.5638513815444601,3.6893123560350083,136.75694645153902,,13.979719183301572,21.862203158409766,1.5638513815444601,2017-02-10 02:27:06.593750000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s10.csv,0.0,22,6,2,2,71.46631916028771,71.5511208805626,71.51393937299758,839.9167455808079,838.9727351641397,839.463842906227 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02:27:11.593750000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s10.csv,0.0,22,11,2,2,71.60223527236758,71.51961364110802,71.58170458317842,838.3239698669383,839.3191227175588,838.6550488357141 +833.8068181818181,15.562731036072572,15.174159843402364,0.0,0.0,1.0,10.0,15.576095341700691,832.03125,46.875,0.01868070037573644,0.01866467231583491,71.98171054922362,73.49282296650719,69.50226244343891,1.269915117538531,,,,,1.4904634722943133,3.6591140605127497,122.89636446407171,,13.830451894940785,20.61378335473291,1.4904634722943133,2017-02-10 02:27:12.593750000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s10.csv,0.0,22,12,2,2,71.63308589539557,71.52044136187989,71.59000235651878,837.9599783896657,839.3053127428113,838.5544333054112 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02:56:58.531250000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s11.csv,0.0,22,58,2,2,69.3197374937927,69.13183656919348,69.61035013197164,866.3510101010095,868.5837870896453,862.6800628753757 +844.8153409090909,29.131874371612433,29.45012003901682,1.0,10.0,3.0,30.0,29.49154076277637,832.03125,85.9375,0.03490886035644315,0.03448312662062237,71.09541813415069,73.14285714285714,66.20689655172414,2.2507992191232566,,,,,1.8496343002634184,3.9463975003312286,236.49804551027447,2.151762203259462,17.282072534407572,31.96551413928057,1.8496343002634184,2017-02-10 02:56:59.531250000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s11.csv,0.0,22,59,2,2,69.37434844445161,69.17482173817794,69.62286074526722,865.6920770202015,868.062953756312,862.5328986657114 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02:57:01.531250000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s11.csv,0.0,22,1,2,2,69.46161642698051,69.23946036167027,69.64058325163148,864.6395596590904,867.2891019570695,862.3276152573029 +854.1666666666665,30.68258539304527,25.110326036454104,0.0,0.0,6.0,54.54545454545455,25.911131174651562,845.703125,93.75,0.03033498283861646,0.03592107558210179,70.32528426653381,73.14285714285714,65.64102564102564,2.367343013769525,,,,,1.7855554516153211,3.972162099171675,231.06866895655747,2.4849066497880004,18.11900676889201,32.352491314050035,1.7855554516153211,2017-02-10 02:57:02.531250000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s11.csv,0.0,22,2,2,2,69.48498641423102,69.25837196061636,69.64487730239821,864.3594144570703,867.0666627209583,862.280693335381 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02:57:04.531250000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s11.csv,0.0,22,4,2,2,69.45957023120903,69.2620196576454,69.63113152364632,864.6977588383836,867.0464409722209,862.458516755392 +878.5511363636364,31.70828729140925,26.461780553812407,0.0,0.0,6.0,60.0,27.704098791401787,890.625,93.75,0.03153384890727059,0.03609156710291368,68.37618851879235,72.79620853080569,65.36170212765957,2.381770982272831,,,,,1.9229197366456925,3.997317474212753,265.8278462537442,1.871802176901592,17.972900386590915,34.56044487814266,1.9229197366456925,2017-02-10 02:57:05.531250000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s11.csv,0.0,22,5,2,2,69.41932404959911,69.24427837493441,69.6068072438925,865.2176057449492,867.2831833964634,862.7603100259353 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02:57:07.531250000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s11.csv,0.0,22,7,2,2,69.33965453234732,69.19649888572326,69.54685639829015,866.2434895833331,867.900686553029,863.5009299071801 +884.1145833333335,24.385044307734024,29.84222538942399,1.0,9.090909090909092,6.0,54.54545454545455,29.84222538942399,890.625,74.21875,0.033753798378612115,0.027581316683858208,67.9121490205683,71.1111111111111,65.36170212765957,1.804646508055024,,,,,1.8283645132884576,3.9927554950573767,245.17259439747482,2.0281482472922856,18.335230095890484,33.52348405030469,1.8283645132884576,2017-02-10 02:57:08.531250000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s11.csv,0.0,22,8,2,2,69.29111264721165,69.17457557199663,69.51191413876994,866.8669113005049,868.1828046085847,863.9320250648377 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02:57:10.531250000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s11.csv,0.0,22,10,2,2,69.2102874328485,69.14480297145285,69.44760071456295,867.9085779671715,868.5615924873725,864.720366673492 +877.8409090909091,25.73924781031701,22.57866565096629,1.0,10.0,3.0,30.0,23.14267694312285,875.0,78.125,0.026363178912554186,0.029321084884244626,68.40297640251416,71.44186046511628,65.36170212765957,1.912893638099874,,,,,1.9035433811510047,3.963543895996272,251.8350161660626,1.717651497074333,17.37523020657966,33.074504455709715,1.9035433811510047,2017-02-10 02:57:11.531250000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s11.csv,0.0,22,11,2,2,69.1796881654622,69.13167299603535,69.4204380699561,868.3070943813128,868.7273121843423,865.0509529415783 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02:57:13.531250000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s11.csv,0.0,22,13,2,2,69.15511404980784,69.1331020843949,69.37397633891109,868.642479482323,868.7095565025242,865.6137494028123 +867.8385416666665,20.114400411892078,25.195556498674367,1.0,9.090909090909092,4.0,36.36363636363637,25.397453190253863,867.1875,66.40625,0.02926518237076515,0.023177583670417282,69.171127754905,71.44186046511628,66.20689655172414,1.5255420379522253,,,,,1.8133211335542707,3.955600632908752,232.01304351270042,1.717651497074333,17.640178018650825,31.98730760087903,1.8133211335542707,2017-02-10 02:57:14.531250000,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s11.csv,0.0,22,14,2,2,69.16443837519266,69.14482802945192,69.3558338861815,868.5231218434342,868.5546874999987,865.8307632917013 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03:00:52.585937500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s12.csv,0.0,22,52,2,3,68.07456534988317,68.02648229773858,68.41947311675392,882.6970880681816,882.9565183080795,877.750797672673 +832.03125,49.4947393755364,20.52298760909075,0.0,0.0,4.0,36.36363636363637,23.289065954644308,822.265625,144.53125,0.027990614480699262,0.059486635118015566,72.3411870002016,77.1859296482412,65.08474576271186,4.016395270737362,,,,,2.8860307268059717,4.141924535959608,577.3161329041816,2.7408400239252013,17.32813414441599,50.009527579000256,2.8860307268059717,2017-02-10 03:00:53.585937500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s12.csv,0.0,22,53,2,3,68.18978605144899,68.09200617959897,68.44729515036417,881.3831676136361,882.1930239898976,877.4340746997 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03:00:55.585937500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s12.csv,0.0,22,55,2,3,68.51602942638829,68.27313839796179,68.53418670533738,877.6071259469695,880.0593828914128,876.425786582037 +802.8846153846154,27.465103879099807,17.578125,0.0,0.0,2.0,16.666666666666668,18.528970665049094,796.875,85.9375,0.02307799939119888,0.03420803357396863,74.80930701507123,77.57575757575758,69.81818181818181,2.3976905171432734,,,,,3.4623822528092787,4.173781917384623,786.9614856040275,1.6486586255873816,16.411318000807118,56.822256191204005,3.4623822528092787,2017-02-10 03:00:56.585937500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s12.csv,0.0,22,56,2,3,68.71486475701725,68.3882447830752,68.59269364762716,875.3009360431234,878.6976586781261,875.7409069909073 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03:01:01.585937500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s12.csv,0.0,22,1,2,3,70.14802527640994,69.17990581829547,69.02095877551237,859.4812305749804,869.8058180968327,870.935573084011 +745.7932692307692,39.5626039576651,17.766997459629152,0.0,0.0,3.0,25.0,18.42574439854805,742.1875,121.09375,0.02470623584140529,0.053047681696659715,80.66017658690784,87.27272727272727,74.20289855072464,4.102739975882227,,,,,5.404094505074669,4.362018361543319,1905.8713588902813,1.1512560221535315,16.315024182069145,88.16793253248021,5.404094505074669,2017-02-10 03:01:02.585937500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s12.csv,0.0,22,2,2,3,70.53189183619614,69.39229816984826,69.12786524043304,855.4022041326728,867.5004628617899,869.7891442032071 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03:01:04.585937500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s12.csv,0.0,22,4,2,3,71.34148446605494,69.81490663498313,69.34115115850098,846.7395560168995,862.9481792079438,867.5190706440711 +744.4196428571429,38.233827012053574,20.25962479692016,0.0,0.0,5.0,38.46153846153846,20.268536002451985,738.28125,121.09375,0.0272272987379265,0.0513605832125937,80.795603128132,87.27272727272727,74.20289855072464,3.9622214326634815,,,,,5.558888964731868,4.383734509881181,2038.6808994195906,1.2729656758128869,16.493516677019773,91.68562784550623,5.558888964731868,2017-02-10 03:01:05.585937500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s12.csv,0.0,22,5,2,3,71.75285839692789,70.02152330331884,69.44377855803825,842.3040175969861,860.7304099979871,866.4306496337751 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03:01:07.585937500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s12.csv,0.0,22,7,2,3,72.64044805086459,70.45956164703485,69.64900375866056,832.6966978334165,856.0273656465052,864.2787685756441 +739.6763392857142,33.449037834932724,17.710531284004055,0.0,0.0,3.0,23.07692307692308,17.83499549489387,738.28125,113.28125,0.02411189130656342,0.04522118129022968,81.26946259116639,87.27272727272727,74.92682926829268,3.512374059337576,,,,,5.728243133213304,4.400885451365909,2175.078274045968,1.2580400255962119,16.57188888860114,94.92780873050337,5.728243133213304,2017-02-10 03:01:08.585937500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s12.csv,0.0,22,8,2,3,73.09248476928013,70.68059652544771,69.75321299688896,827.7834470217281,853.6476815285399,863.1812069312075 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03:10:55.867187500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s13.csv,0.0,22,55,2,3,74.38681326236954,73.6332843015165,80.45397382593403,809.2861457119268,816.5633953719879,757.6584767991023 +851.8880208333335,15.397701611050124,12.270704931229401,0.0,0.0,0.0,0.0,12.352647110032732,847.65625,46.875,0.014500317891486644,0.0180747953187413,70.45264776216985,72.11267605633803,68.26666666666667,1.2053182040210813,,,,,4.991166324565863,3.9660829749203694,1072.369741264692,1.4781019103730135,10.761689932008073,53.71338438405817,4.991166324565863,2017-02-10 03:10:56.867187500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s13.csv,0.0,22,56,2,3,74.26091516713339,73.59813732575962,80.26354478218035,810.707586684149,816.9757217608768,759.4209814912945 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03:10:58.867187500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s13.csv,0.0,22,58,2,3,73.86245064580253,73.52511438039836,79.8877257986042,815.0108355186477,817.8373655424424,762.9384193837324 +852.5390625,15.812528774003644,13.771817776750936,0.0,0.0,1.0,9.090909090909092,13.885805963080365,847.65625,54.6875,0.016287589125079372,0.01854757097890004,70.40010712743455,72.79620853080569,68.26666666666667,1.2448393466037435,,,,,4.982384560293243,3.971233087472254,1075.9012481678728,1.5621850275835547,10.835224893585622,53.985257217105996,4.982384560293243,2017-02-10 03:10:59.867187500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s13.csv,0.0,22,59,2,3,73.60469486853643,73.49683373127316,79.70871511160533,817.727682473776,818.1737370702201,764.6270220488975 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03:11:01.867187500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s13.csv,0.0,22,1,2,3,73.04149688066862,73.44552061471238,79.33722370787251,823.6596736596733,818.7868013063312,768.0963116509996 +854.1666666666665,18.572590296508714,16.10196597834465,0.0,0.0,3.0,27.272727272727273,16.105881350068987,851.5625,58.59375,0.01885566597081248,0.021743520347132155,70.27425541593108,72.79620853080569,67.96460176991151,1.4583686374526126,,,,,4.929246322199912,3.997013518133041,1090.6302536658218,1.0310189975355817,11.221644525803754,55.31425000785293,4.929246322199912,2017-02-10 03:11:02.867187500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s13.csv,0.0,22,2,2,3,72.74192233053226,73.41847998862292,79.13625427001274,826.8314151126649,819.1123221396646,769.9431291716453 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03:11:04.867187500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s13.csv,0.0,22,4,2,3,72.14709727005759,73.356250441713,78.69679878162319,833.153272508741,819.8585539894121,773.8958478704576 +852.8645833333335,20.892435106922548,19.5860543084342,0.0,0.0,4.0,36.36363636363637,19.63749511746945,849.609375,74.21875,0.02302533778659013,0.024496778873460325,70.38915596175366,72.79620853080569,66.78260869565217,1.6213402242315245,,,,,4.902503704703305,4.00878924100608,1096.5325204095666,1.001448540214462,11.405797686857625,55.916965414915886,4.902503704703305,2017-02-10 03:11:05.867187500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s13.csv,0.0,22,5,2,3,71.85762797920646,73.30873995498787,78.45753042067636,836.2215150301084,820.406096724455,775.9926712563434 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03:11:07.867187500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s13.csv,0.0,22,7,2,3,71.39752189714326,73.21097946961912,77.97481866333239,841.1285329254076,821.5237182522327,780.1709217724847 +839.5182291666665,33.414822009996605,20.424437279188076,0.0,0.0,5.0,45.45454545454545,21.200016415624003,839.84375,113.28125,0.025252598072429987,0.03980237813676215,71.57341596739342,76.41791044776119,66.78260869565217,2.7277886100565683,,,,,4.86605696339488,4.010842211831915,1086.8932429201848,1.0231047361599646,11.47552385992967,55.840552787214854,4.86605696339488,2017-02-10 03:11:08.867187500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s13.csv,0.0,22,8,2,3,71.24426798757268,73.16929428498115,77.73338305326098,842.8103905642965,822.0011488077882,782.2318636886656 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03:11:11.867187500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s13.csv,0.0,22,11,2,3,71.11886403495154,73.106306834974,77.01332763032393,844.362039869852,822.7644534285148,788.22933059491 +807.3918269230769,41.74952948404836,15.66225484859298,0.0,0.0,4.0,33.333333333333336,19.757800658194686,796.875,144.53125,0.024471142757898003,0.05170913067469778,74.49037291016614,79.58549222797927,66.78260869565217,3.5676075419725968,,,,,4.996863902427315,4.030051497250817,1156.3040052587598,1.1210851445201682,11.577558839595733,57.85148584380418,4.996863902427315,2017-02-10 03:11:12.867187500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s13.csv,0.0,22,12,2,3,71.16497209802827,73.11163603729466,76.78227835297022,843.9013026903649,822.7227199883438,790.1034440412891 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03:11:16.867187500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s13.csv,0.0,22,16,2,3,71.89208284763434,73.40530887232647,75.95944658681961,836.2615791326726,819.6799348654804,796.5006429936352 +760.2163461538463,29.77212024916279,19.947283463350953,0.0,0.0,4.0,33.333333333333336,19.949939382870518,765.625,89.84375,0.0262424499075862,0.03916269414593429,79.03939450744718,84.86187845303867,75.29411764705883,3.0444375260333665,,,,,5.386864268142184,4.108565061564488,1416.7283351097985,1.1557707025080584,12.205470433891218,65.7492125561945,5.386864268142184,2017-02-10 03:11:17.867187500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s13.csv,0.0,22,17,2,3,72.1489632928467,73.5174014614658,75.77105490180584,833.5530788655785,818.5155718847111,797.9382372621118 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03:11:22.867187500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s13.csv,0.0,22,22,2,3,73.29523554284374,73.9750500468227,74.811946697186,821.4507606109165,813.8113785086427,805.4993413880137 +791.015625,47.39004449746371,16.769487149314767,0.0,0.0,4.0,36.36363636363637,19.3170067740897,812.5,144.53125,0.024420512267491173,0.05991037724123807,76.11168187589249,84.86187845303867,70.45871559633028,4.5349163683272335,,,,,5.1478676692404415,4.128703712432074,1354.5433792128315,1.0498221244986778,12.778449990990907,65.7817695716279,5.1478676692404415,2017-02-10 03:11:23.867187500,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s13.csv,0.0,22,23,2,3,73.48308769611344,74.03536125984446,74.61781825401306,819.4325314442499,813.1983142725317,807.0851557179686 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03:42:47.285156250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s15.csv,0.0,22,47,2,3,73.85344738000295,76.14569644457589,79.53937673759553,812.8756009615382,789.8334954975561,771.6816718251794 +807.9427083333335,12.556836928376244,16.686562676805018,0.0,0.0,2.0,18.181818181818183,16.78076328225946,808.59375,42.96875,0.020769744078606392,0.015541741758248115,74.2791837343755,76.41791044776119,72.45283018867924,1.1084933470828586,,,,,3.373721511679124,4.11096057591627,704.1158707864528,0.9028677115420144,15.465561113429072,52.17649621856381,3.373721511679124,2017-02-10 03:42:48.285156250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s15.csv,0.0,22,48,2,3,73.8163478118079,76.11337812668758,79.23249826477398,813.2553752670937,790.1752923725561,773.7154304260968 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03:42:54.285156250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s15.csv,0.0,22,54,2,3,73.79462895871532,75.85691730871493,78.0003711994716,813.4031116452988,792.862508585163,782.0749694928238 +808.59375,9.712211882938453,11.594337867410871,0.0,0.0,1.0,9.090909090909092,11.599774837401332,808.59375,27.34375,0.014345615257848991,0.012011237884213741,74.21272637448378,75.66502463054188,73.14285714285714,0.8546377569270348,,,,,3.3963318893493493,4.104373500035783,705.8329596083777,1.029619417181158,15.297543514940493,51.95553486850172,3.3963318893493493,2017-02-10 03:42:55.285156250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s15.csv,0.0,22,55,2,3,73.79204006417571,75.81284312262503,77.94900651383125,813.4248130341878,793.3236630990519,782.1432605767399 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03:42:57.285156250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s15.csv,0.0,22,57,2,3,73.83777171616235,75.73135728214282,77.84685277927525,812.9148303952988,794.1700172657186,782.2442988613719 +807.3918269230769,14.821856746030159,16.249979133266518,0.0,0.0,3.0,25.0,16.572815184059706,804.6875,58.59375,0.020526359855806,0.01835769975838793,74.33633377495978,76.8,71.44186046511628,1.3029223146825977,,,,,3.3760696445399523,4.104521325819263,699.6450381653908,1.1977031913123406,15.345992197551405,51.80913842350025,3.3760696445399523,2017-02-10 03:42:58.285156250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s15.csv,0.0,22,58,2,3,73.87158369812333,75.67996995745865,77.79817264618735,812.5342214209398,794.7058746375135,782.3265896174909 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03:43:03.285156250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s15.csv,0.0,22,3,2,3,73.98723623713377,75.26435008613184,77.47143484145393,811.2396501068371,798.921786763582,782.9041480693026 +819.6614583333335,21.05776953780236,23.04684764146016,0.0,0.0,4.0,36.36363636363637,23.049583334702806,812.5,70.3125,0.02812085782534035,0.02569081335191773,73.24539617611025,76.8,70.45871559633028,1.8074665349130041,,,,,1.7984367806737251,3.8457815708025858,201.945206673464,1.1631508098056809,15.609292041297858,28.07232492734772,1.7984367806737251,2017-02-10 03:43:04.285156250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s15.csv,0.0,22,4,2,3,73.99317308244771,75.12358463769618,77.32393373004852,811.1745459401703,800.3060253548518,783.7567689471991 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03:43:06.285156250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s15.csv,0.0,22,6,2,3,74.00495615058722,74.80717210596715,77.0998138065923,811.0443376068371,803.4178495115979,785.5289489566838 +828.7259615384613,14.775589967721453,20.01092824128084,0.0,0.0,3.0,25.0,20.14024518002347,828.125,42.96875,0.02430265988365413,0.017829283325807466,72.42158620091953,74.20289855072464,70.45871559633028,1.243328328895806,,,,,1.5881281533569382,3.7539320993983103,150.7628597493923,1.2459370034249682,14.943877058447855,23.73279187682591,1.5881281533569382,2017-02-10 03:43:07.285156250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s15.csv,0.0,22,7,2,3,73.97164767039827,74.64916058611759,76.89999355868991,811.414930555555,804.9810649420009,787.0563928669403 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03:43:12.285156250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s15.csv,0.0,22,12,2,3,73.76778790123844,73.96627603691523,75.87834688104236,813.7019230769224,811.7254273504259,794.8839281847099 +824.21875,15.713527620623285,20.590467349892364,0.0,0.0,2.0,18.181818181818183,20.60271264823905,824.21875,46.875,0.024996656104024625,0.01906475389042446,72.82053966504691,74.92682926829268,70.78341013824884,1.3331741963366026,,,,,1.4024984571070236,3.702173316115084,117.88003233239212,2.0149030205422647,14.98219587417751,21.01250659760918,1.4024984571070236,2017-02-10 03:43:13.285156250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s15.csv,0.0,22,13,2,3,73.72788444548043,73.87627415237834,75.79921183273406,814.1468015491447,812.6418936965798,795.2845692103509 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03:43:17.285156250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s15.csv,0.0,22,17,2,3,73.52267349693169,73.68806043846747,75.68327910255688,816.4312900641021,814.6534455128191,794.5910523163599 +823.5677083333335,14.882326269099336,20.063134789524906,0.0,0.0,3.0,27.272727272727273,20.09139937473812,824.21875,50.78125,0.024395564774385568,0.01807055584927793,72.8757030079282,75.29411764705883,70.78341013824884,1.2688351203235355,,,,,1.4284119238548525,3.647413563468648,113.75931859827715,2.0149030205422647,13.938628942406087,19.910103783521198,1.4284119238548525,2017-02-10 03:43:18.285156250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s15.csv,0.0,22,18,2,3,73.4758908060501,73.64611930892916,75.65843329640973,816.9521233974355,815.1037493322636,794.8474829005808 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07:13:27.410156250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s17.csv,0.0,22,27,2,7,77.8695373023859,82.73904589093337,77.5735134321628,773.6881987370268,730.1648411267541,781.1568021528967 +819.6614583333335,20.72578180640582,22.31289498921166,0.0,0.0,5.0,45.45454545454545,22.315720658188887,820.3125,66.40625,0.027225533702127186,0.025285783045781843,73.24368602933512,76.03960396039605,70.13698630136986,1.7658089842240217,,,,,6.1628153481587375,4.266915288182731,2080.301955517224,0.9624801143435298,13.693298486850724,84.3892700816825,6.1628153481587375,2017-02-10 07:13:28.410156250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s17.csv,0.0,22,28,2,7,77.66148729645079,82.52827617038214,77.59592824854825,775.7698627327533,732.1629190037384,780.8987315823261 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07:13:30.410156250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s17.csv,0.0,22,30,2,7,77.33166518620214,82.05437131571816,77.6483631185251,779.165295425061,736.5771842090192,780.2927866404438 +819.9869791666665,21.425107645708767,22.986580018014568,0.0,0.0,5.0,45.45454545454545,22.989322883010672,818.359375,66.40625,0.0280362048021472,0.026128594953401085,73.21747409028707,76.03960396039605,70.13698630136986,1.822561990365215,,,,,5.825382683927798,4.272511273212399,1924.165073979917,1.169229855882063,14.17533773597877,82.576766985999,5.825382683927798,2017-02-10 07:13:31.410156250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s17.csv,0.0,22,31,2,7,77.20052714751178,81.7876518240673,77.67372594215084,780.5487589667277,739.027116004654,779.9995246284318 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07:41:54.488281250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s18.csv,0.0,22,54,2,7,82.12816142011997,78.30879921617576,80.465787057424,763.6825826083636,788.5223360177395,759.2211985766676 +821.9401041666665,22.98680860259817,19.57317298227368,0.0,0.0,4.0,36.36363636363637,19.602144062489465,820.3125,70.3125,0.023848628340581244,0.02796652515927983,73.0503191212537,76.41791044776119,70.13698630136986,1.9538417015478489,,,,,,,,,,,,2017-02-10 07:41:55.488281250,/home/aura-alexis/github/seizure_detection_pipeline/output/feats-v0_6/PAT_22//feats_EEG_311_s18.csv,0.0,22,55,2,7,82.12890939858633,78.29976796226026,80.32928328845466,763.6825826083636,788.624951182395,760.4910230886796 diff --git a/requirements.txt b/requirements.txt index 450c421..b7087ee 100644 --- a/requirements.txt +++ b/requirements.txt @@ -4,10 +4,12 @@ click==8.0.1 ecg-qc==1.0b5 great-expectations==0.13.25 hrv-analysis==1.0.4 -mlflow==1.21.0 +mlflow==1.23.1 numpy==1.19.5 pandas==1.1.5 +seaborn==0.11.2 psycopg2-binary==2.8.6 py-ecg-detectors==1.0.2 pyEDFlib==0.1.22 -wfdb==3.4.0 \ No newline at end of file +SQLAlchemy==1.3.24 +wfdb==3.4.0 diff --git a/scripts/bash_pipeline/0_apply_ecg_qc_wrapper.sh b/scripts/bash_pipeline/0_apply_ecg_qc_wrapper.sh new file mode 100755 index 0000000..17ff26e --- /dev/null +++ b/scripts/bash_pipeline/0_apply_ecg_qc_wrapper.sh @@ -0,0 +1,60 @@ +#!/bin/bash + +## Option - select input directory to be copied from and output directory to copy into +while getopts ":i:o:f:" option +do +case "${option}" +in +i) InputDest="${OPTARG}";; +o) TargetDest="${OPTARG}";; +f) Formatting="${OPTARG}";; +esac +done + +ECG_PATH=$(pwd) + +# Check script input integrity +if [[ "$InputDest" ]] || [[ "$TargetDest" ]]; +then + echo "Start Executing script" +else + echo "No Input directory: $InputDest or Target directory: $TargetDest, use -i,-o options" >&2 + exit 1 +fi + + +## Copy TUH folder tree structure +TargetDest=$(realpath "$TargetDest") +mkdir -p "$TargetDest"; +cd "$InputDest" && find . -type d -exec mkdir -p -- "$TargetDest/{}" \; && cd - + +#Hack to force the bash to split command result only on newline char +#It is done to support the spaces in the folder names +OIFS="$IFS" +IFS=$'\n' + +## List all EDF files in InputDest ## +for edf_file in $(find "$InputDest"/* -type f -name "*.edf" ); do + filename=$(echo "$edf_file" | awk -F/ '{print $NF}') + + # Get relative path + path=$(echo "$edf_file" | sed "s/$filename//g") + CleanDest=$(echo "$InputDest" | sed 's/\//\\\//g') + relative_path=$(echo "$path" | sed "s/$CleanDest\///g") + + python3 "$ECG_PATH/src/usecase/apply_ecg_qc.py" --qrs-file-path "$edf_file" --output-folder "$TargetDest/$relative_path" --formatting "$Formatting" + + # python3 src/usecase/apply_ecg_qc.py --qrs-file-path data/tuh/dev/01_tcp_ar/002/00009578/00009578_s006_t001.edf --output-folder $(EXPORT_PATH)/ecg_qc-v0_6 --sampling-frequency 1000 --exam-id 00009578_s006_t001 + if [ $? -eq 0 ] + then + echo "$edf_file - OK" + else + echo "$edf_file - Fail" >&2 + fi + +done + +#Restore the bash automatic split on spaces +IFS="$OIFS" + +exit 0 diff --git a/scripts/bash_pipeline/0_compare_qrs_detectors.sh b/scripts/bash_pipeline/0_compare_qrs_detectors.sh new file mode 100755 index 0000000..d422300 --- /dev/null +++ b/scripts/bash_pipeline/0_compare_qrs_detectors.sh @@ -0,0 +1,63 @@ +#!/bin/bash + +## Option - select input directory to be copied from and output directory to copy into +while getopts ":i:o:c:f:" option +do +case "${option}" +in +i) InputDest=${OPTARG};; +c) CompareDest=${OPTARG};; +f) Formatting=${OPTARG};; +o) TargetDest=${OPTARG};; +esac +done + +ECG_PATH=$(pwd) + +# Check script input integrity +if [[ $InputDest ]] || [[ $TargetDest ]] || [[ $Formatting ]] || [[ $CompareDest ]]; +then + echo "Start Executing script" +else + echo "No Input res directory: $InputDest or Target directory: $TargetDest, use -i,-o options" >&2 + exit 1 +fi + + +## Copy TUH folder tree structure +TargetDest=$(realpath "$TargetDest") +mkdir -p "$TargetDest"; +cd "$InputDest" && find . -type d -exec mkdir -p -- "$TargetDest/{}" \; && cd - + +#Hack to force the bash to split command result only on newline char +#It is done to support the spaces in the folder names +OIFS="$IFS" +IFS=$'\n' + +## List all rr_intervals_files in InputDest ## +for rr_intervals_file in $(find "$InputDest"/* -type f -name "*.csv" ); do + + rr_comparison_file=$(echo $rr_intervals_file | sed "s/$(echo $InputDest | sed 's_/_\\/_g')/$(echo $CompareDest | sed 's_/_\\/_g')/g") + + filename=$(echo "$rr_intervals_file" | awk -F/ '{print $NF}') + + # Get relative path + path=$(echo "$rr_intervals_file" | sed "s/$filename//g") + CleanDest=$(echo "$InputDest" | sed 's/\//\\\//g') + relative_path=$(echo "$path" | sed "s/$CleanDest\///g") + + python3 "$ECG_PATH/src/usecase/compare_qrs_detectors.py" --reference-rr-intervals-file-path "$rr_intervals_file" --comparison-rr-intervals-file-path "$rr_comparison_file" --formatting "$Formatting" --output-folder "$TargetDest/$relative_path/" + + if [ $? -eq 0 ] + then + echo "$rr_intervals_file # - OK" + else + echo "$rr_intervals_file # - Fail" >&2 + fi + +done + +#Restore the bash automatic split on spaces +IFS="$OIFS" + +exit 0 diff --git a/scripts/bash_pipeline/1_detect_qrs_wrapper.sh b/scripts/bash_pipeline/1_detect_qrs_wrapper.sh index 0704ea8..2cff9ed 100755 --- a/scripts/bash_pipeline/1_detect_qrs_wrapper.sh +++ b/scripts/bash_pipeline/1_detect_qrs_wrapper.sh @@ -5,15 +5,15 @@ while getopts ":i:o:" option do case "${option}" in -i) InputDest=${OPTARG};; -o) TargetDest=${OPTARG};; +i) InputDest="${OPTARG}";; +o) TargetDest="${OPTARG}";; esac done ECG_PATH=$(pwd) # Check script input integrity -if [[ $InputDest ]] || [[ $TargetDest ]]; +if [[ "$InputDest" ]] || [[ "$TargetDest" ]]; then echo "Start Executing script" else @@ -21,10 +21,11 @@ else exit 1 fi + ## Copy TUH folder tree structure -TargetDest=$(realpath $TargetDest) -mkdir -p $TargetDest; -cd $InputDest && find . -type d -exec mkdir -p -- $TargetDest/{} \; && cd - +TargetDest=$(realpath "$TargetDest") +mkdir -p "$TargetDest"; +cd "$InputDest" && find . -type d -exec mkdir -p -- "$TargetDest/{}" \; && cd - #Hack to force the bash to split command result only on newline char #It is done to support the spaces in the folder names @@ -32,15 +33,15 @@ OIFS="$IFS" IFS=$'\n' ## List all EDF files in InputDest ## -for edf_file in $(find $InputDest/* -type f -name "*.edf" ); do +for edf_file in $(find "$InputDest"/* -type f -name "*.edf" ); do filename=$(echo "$edf_file" | awk -F/ '{print $NF}') # Get relative path - path=$(echo $edf_file | sed "s/$filename//g") - CleanDest=$(echo $InputDest | sed 's/\//\\\//g') - relative_path=$(echo $path | sed "s/$CleanDest\///g") + path=$(echo "$edf_file" | sed "s/$filename//g") + CleanDest=$(echo "$InputDest" | sed 's/\//\\\//g') + relative_path=$(echo "$path" | sed "s/$CleanDest\///g") - python3 $ECG_PATH/src/usecase/detect_qrs.py --qrs-file-path $edf_file --method hamilton --output-folder $TargetDest/$relative_path + python3 "$ECG_PATH/src/usecase/detect_qrs.py" --qrs-file-path "$edf_file" --method hamilton --output-folder "$TargetDest/$relative_path" --smoothing True if [ $? -eq 0 ] then diff --git a/scripts/bash_pipeline/2_compute_hrvanalysis_features_wrapper.sh b/scripts/bash_pipeline/2_compute_hrvanalysis_features_wrapper.sh index 9eed8dc..02398f8 100755 --- a/scripts/bash_pipeline/2_compute_hrvanalysis_features_wrapper.sh +++ b/scripts/bash_pipeline/2_compute_hrvanalysis_features_wrapper.sh @@ -1,7 +1,7 @@ #!/bin/bash ## Option - select input directory to be copied from and output directory to copy into -while getopts ":i:o:a:" option +while getopts ":i:o:" option do case "${option}" in @@ -23,9 +23,9 @@ fi ## Copy TUH folder tree structure -TargetDest=$(realpath $TargetDest) -mkdir -p $TargetDest; -cd $InputDest && find . -type d -exec mkdir -p -- $TargetDest/{} \; && cd - +TargetDest=$(realpath "$TargetDest") +mkdir -p "$TargetDest"; +cd "$InputDest" && find . -type d -exec mkdir -p -- "$TargetDest/{}" \; && cd - #Hack to force the bash to split command result only on newline char #It is done to support the spaces in the folder names @@ -33,16 +33,16 @@ OIFS="$IFS" IFS=$'\n' ## List all rr_intervals_files in InputDest ## -for rr_intervals_file in $(find $InputDest/* -type f -name "*.csv" ); do +for rr_intervals_file in $(find "$InputDest"/* -type f -name "*.csv" ); do filename=$(echo "$rr_intervals_file" | awk -F/ '{print $NF}') # Get relative path - path=$(echo $rr_intervals_file | sed "s/$filename//g") - CleanDest=$(echo $InputDest | sed 's/\//\\\//g') - relative_path=$(echo $path | sed "s/$CleanDest\///g") + path=$(echo "$rr_intervals_file" | sed "s/$filename//g") + CleanDest=$(echo "$InputDest" | sed 's/\//\\\//g') + relative_path=$(echo "$path" | sed "s/$CleanDest\///g") - python3 $ECG_PATH/src/usecase/compute_hrvanalysis_features.py --rr-intervals-file-path $rr_intervals_file --output-folder $TargetDest/$relative_path/ + python3 "$ECG_PATH/src/usecase/compute_hrvanalysis_features.py" --rr-intervals-file-path "$rr_intervals_file" --output-folder "$TargetDest/$relative_path/" if [ $? -eq 0 ] then diff --git a/scripts/bash_pipeline/3_consolidate_feats_and_annot_wrapper.sh b/scripts/bash_pipeline/3_consolidate_feats_and_annot_wrapper.sh index f2248ab..eae33c2 100755 --- a/scripts/bash_pipeline/3_consolidate_feats_and_annot_wrapper.sh +++ b/scripts/bash_pipeline/3_consolidate_feats_and_annot_wrapper.sh @@ -24,9 +24,9 @@ fi ## Copy TUH folder tree structure -TargetDest=$(realpath $TargetDest) -mkdir -p $TargetDest; -cd $InputDest && find . -type d -exec mkdir -p -- $TargetDest/{} \; && cd - +TargetDest=$(realpath "$TargetDest") +mkdir -p "$TargetDest"; +cd "$InputDest" && find . -type d -exec mkdir -p -- "$TargetDest/{}" \; && cd - #Hack to force the bash to split command result only on newline char #It is done to support the spaces in the folder names @@ -34,17 +34,26 @@ OIFS="$IFS" IFS=$'\n' ## List all rr_files in InputDest ## -for features_file in $(find $InputDest/* -type f -name "*.csv" ); do +for features_file in $(find "$InputDest"/* -type f -name "*.csv" ); do filename=$(echo "$features_file" | awk -F/ '{print $NF}') # Get relative path - path=$(echo $features_file | sed "s/$filename//g") - CleanDest=$(echo $InputDest | sed 's/\//\\\//g') - relative_path=$(echo $path | sed "s/$CleanDest\///g") + path=$(echo "$features_file" | sed "s/$filename//g") + CleanDest=$(echo "$InputDest" | sed 's/\//\\\//g') + relative_path=$(echo "$path" | sed "s/$CleanDest\///g") - annotation_file_path=$AnnotDest/$relative_path${filename%%\.*}.tse_bi + if [ "$(basename "$path" | head -c 4)" == "PAT_" ]; - python3 $ECG_PATH/src/usecase/consolidate_feats_and_annot.py --features-file-path $features_file --annotations-file-path $annotation_file_path --output-folder $TargetDest/$relative_path + then # Dataset format + segment=$(echo $filename | awk -FEEG_ '{ print $2}' | awk -F_s '{ print $1 }' ) + tse_bi_filename=${relative_path::-1}_Annotations_EEG_${segment}.tse_bi + else # TUH format + tse_bi_filename=$(cut -c7- <<< "${filename%.*}.tse_bi") + fi + + annotation_file_path=$AnnotDest/$relative_path$tse_bi_filename + + python3 "$ECG_PATH/src/usecase/consolidate_feats_and_annot.py" --features-file-path "$features_file" --annotations-file-path "$annotation_file_path" --output-folder "$TargetDest/$relative_path" if [ $? -eq 0 ] then diff --git a/src/usecase/apply_ecg_qc.py b/src/usecase/apply_ecg_qc.py index c132c27..19e7e12 100644 --- a/src/usecase/apply_ecg_qc.py +++ b/src/usecase/apply_ecg_qc.py @@ -1,24 +1,48 @@ +""" +This script is used to consolidate hrv features dataset and annotations. + +Copyright (C) 2022 Association Aura +SPDX-License-Identifier: GPL-3.0 +""" import os -import json -from typing import Tuple, List +from typing import Tuple, List, Union import argparse import ecg_qc -import click +import pandas as pd +import fileinput import sys sys.path.append('.') from src.infrastructure.edf_loader import EdfLoader -from src.usecase.utilities import convert_args_to_dict +from src.usecase.utilities import convert_args_to_dict, generate_output_path MODEL_FOLDER = 'models' MODELS = os.listdir(MODEL_FOLDER) OUTPUT_FOLDER = 'output/quality' ECG_QC_MODEL = 'rfc_normalized_2s.pkl' -SAMPLING_FREQUENCY = 1000 + def parse_model(model: str) -> Tuple[str, str, int, bool]: + """Parse ecg_qc model name to output parameters. + + Parameters + ---------- + model : str + Name of the model = name of the file + + Returns + ------- + model_path : str + Path to the model + model_name : str + Name of the model + length_chunk : int + Length of signal to apply the model on. It's an EcgQc parameter. + is_normalized : bool + Does the data have to be normalized ? It's an EcgQc parameter. + """ model_path = os.path.join(MODEL_FOLDER, model) model_name = model.split('.')[0] model_split = model_name.split('_') @@ -27,92 +51,367 @@ def parse_model(model: str) -> Tuple[str, str, int, bool]: except ValueError: length_chunk = 9 is_normalized = 'normalized' in model_split + return model_path, model_name, length_chunk, is_normalized -def write_quality_json(quality: List[int], - exam_id: str, - model_name: str, - output_foder: str) -> str: +def initialize_df_body(signal_quality: List[float], + length_chunk: int, + start_time: Union[pd.Timestamp, int], + end_time: Union[pd.Timestamp, int], + formatting: str) -> pd.DataFrame: + """Initialize the df_body before further operations. - os.makedirs(output_foder, exist_ok=True) - filename = f"{exam_id}_{model_name}.csv" - export_filepath = os.path.join(output_foder, filename) - with open(export_filepath, 'w') \ - as outfile: - json.dump(quality, outfile) + Parameters + ---------- + signal_quality : List[float] + The ECG signal quality, composed of 1 (clean) or 0 (noise) + length_chunk : int + Lenght of signal to apply the model on. It's an EcgQc parameter. + start_time : pd.Timestamp | int + Start time of the ECG + end_time: pd.Timestamp | int + End time of the ECG + formatting: str + Formatting of the tse_bi output ('teppe' or 'tuh') - return str(export_filepath) + Returns + ------- + df_body : pd.DataFrame + The initialize df_body + """ + if formatting == 'dataset': + start_times = [start_time + pd.Timedelta( + seconds=length_chunk * iteration) + for iteration, _ in enumerate(signal_quality)] + else: + start_time = 0 + end_time = start_time + length_chunk * len(signal_quality) + start_times = [start_time + length_chunk * iteration + for iteration, _ in enumerate(signal_quality)] + return pd.DataFrame(data={"start": start_times, + "end": None, + "annotation": signal_quality, + "probability": 1}), \ + end_time -def apply_ecg_qc(filepath: str, - exam_id: str, - output_folder: str = OUTPUT_FOLDER, - sampling_frequency: int = SAMPLING_FREQUENCY, - model: str = ECG_QC_MODEL) -> str: - ''' - Applies an ECG QC model on a signal, and writes results in a json file. - ''' - edfloader = EdfLoader(filepath) + +def consolidate_df_body_annotation(df_body: pd.DataFrame, + end_time: Union[pd.Timestamp, int]): + """Consolidated same annotations following each others for clarity. + + Parameters + ---------- + df_body : pd.DataFrame + The df_body to consolidate + end_time: pd.Timestamp | int + End time of the ECG + + Returns + ------- + df_body : pd.DataFrame + The initialize df_body + """ + for i in df_body.index.values[1:]: + if df_body.loc[i, 'annotation'] == df_body.loc[i - 1, 'annotation']: + df_body.loc[i, 'end'] = 'del' + df_body = df_body[df_body['end'] != 'del'] + df_body.reset_index(drop=True, inplace=True) + + end_times = list(df_body['start'].values[1:]) + end_times.append(end_time) + + df_body = df_body.assign(end=end_times) + + return df_body + + +def create_ecg_qc_df_body(signal_quality: List[float], + length_chunk: int, + start_time: Union[pd.Timestamp, int], + end_time: Union[pd.Timestamp, int], + formatting: str) -> pd.DataFrame: + """Create the body of tse_bi, incluing signal timestamps and quality. + + Parameters + ---------- + signal_quality : List[float] + The ECG signal quality, composed of 1 (clean) or 0 (noise) + length_chunk : int + Lenght of signal to apply the model on. It's an EcgQc parameter. + start_time : pd.Timestamp | int + Start time of the ECG + end_time: pd.Timestamp | int + End time of the ECG + formatting: str + Formatting of the tse_bi output ('teppe' or 'tuh') + + Returns + ------- + df_body : pd.DataFrame + The initialize df_body + """ + df_body, end_time = initialize_df_body(signal_quality=signal_quality, + length_chunk=length_chunk, + start_time=start_time, + end_time=end_time, + formatting=formatting) + df_body = consolidate_df_body_annotation(df_body=df_body, + end_time=end_time) + if formatting == 'dataset': + df_body['start'] = df_body['start'].apply( + lambda x: pd.Timestamp.isoformat(x)) + df_body['end'] = df_body['end'].apply( + lambda x: pd.Timestamp.isoformat(x)) + + return df_body + + +def create_ecg_qc_df_header(formatting: str) -> pd.DataFrame: + """Create the header of the tse_bi file. + + Parameters + ---------- + formatting : str + Formatting of the tse_bi: dataset or tuh + + Returns + ------- + df_header : pd.DataFrame + DataFrame including header for tse_bi formatting + """ + df_header = pd.DataFrame(columns=['start', + 'end', + 'annotation', + 'probability']) + df_header = df_header.append({'start': 'version = tse_v1.0.0', + 'end': '', + 'annotation': '', + 'probability': ''}, + ignore_index=True) + + if formatting == 'dataset': + df_header = df_header.append({'start': 'annotator = ecg_qc', + 'end': '', + 'annotation': '', + 'probability': ''}, + ignore_index=True) + df_header = df_header.append({'start': ' '.join(['date =', + str(pd.Timestamp. + today())]), + 'end': '', + 'annotation': '', + 'probability': ''}, + ignore_index=True) + df_header = df_header.append({'start': '', + 'end': '', + 'annotation': '', + 'probability': ''}, + ignore_index=True) + + return df_header + + +def create_ecg_qc_tse_bi(signal_quality: List[float], + length_chunk: int, + start_time: Union[pd.Timestamp, int], + end_time: Union[pd.Timestamp, int], + formatting: str) -> pd.DataFrame: + """Create a pd.DataFrame exportable to tse_bi format. + + Parameters + ---------- + signal_quality : List[float] + The ECG signal quality, composed of 1 (clean) or 0 (noise) + length_chunk : int + Lenght of signal to apply the model on. It's an EcgQc parameter. + start_time : pd.Timestamp | int + Start time of the ECG + end_time: pd.Timestamp | int + End time of the ECG + formatting: str + Formatting of the tse_bi output ('teppe' or 'tuh') + + Returns + ------- + df_ecg_qc : pd.DataFrame + DataFrame of signal quality in tse_bi format + """ + df_body = create_ecg_qc_df_body(signal_quality=signal_quality, + length_chunk=length_chunk, + start_time=start_time, + end_time=end_time, + formatting=formatting) + df_header = create_ecg_qc_df_header(formatting='dataset') + + return pd.concat([df_header, df_body]) + + +def import_signal_data(qrs_file_path: str) -> Tuple[List[float], + pd.Timestamp, + pd.Timestamp, + int]: + """Import relevant information from the signal file. + + parameters + ---------- + qrs_file_path : str + The path of the file including ECG signal + + returns + ------- + signal : List[float] + Signal as a list of values + start_time : pd.Timestamp + Start time of the file + end_time : pd.Timestamp + End time of the file + sampling_frequency : int + Frequency of the read signal + """ + edfloader = EdfLoader(qrs_file_path) ecg_channel_name = edfloader.get_ecg_candidate_channel() start_time, end_time = edfloader.get_edf_file_interval() - sampling_frequency, ecg_data = edfloader.ecg_channel_read( - ecg_channel_name, - start_time, - end_time) + try: + sampling_frequency, ecg_data = edfloader.ecg_channel_read( + ecg_channel_name, start_time, end_time) + + except ValueError as e: + raise ValueError(f'There is no ECG channel in {qrs_file_path}') from e + signal = list(ecg_data['signal']) - model_path, model_name, length_chunk, is_normalized = parse_model(model) - algo = ecg_qc.EcgQc()#sampling_frequency=sampling_frequency, - #model=model, - # normalized=True) - #model_path, - # normalized=is_normalized) + return signal, start_time, end_time, sampling_frequency + + +def get_signal_quality(signal: List[float], + model: str, + sampling_frequency: int) -> List[str]: + """Get the quality of a signal thanks to an ecg_qc model. + + Parameters + ---------- + signal : List[float] + The ECG signal to use for quality detection + model : str + The model to use to predicty quality + sampling_frequency : int + Frequency of the read signal + + Returns + ------- + signal_quality_label : list[str] + A list of qualities by length chunk, either 'clean' or 'noisy' + length_chunk : int + Length of signal to apply the model on. It's an EcgQc parameter. + """ + model_path, _, length_chunk, is_normalized = parse_model(model) + algo = ecg_qc.EcgQc(model=model_path, + sampling_frequency=sampling_frequency, + normalized=is_normalized) - # Preprocess signal : chunks of length_chunk seconds n = length_chunk * sampling_frequency signal_subdiv = [signal[i * n:(i + 1) * n] for i in range((len(signal) + n - 1) // n)] + # Padding on last chunk if necessary m = len(signal_subdiv[-1]) if m < n: - signal_subdiv[-1] += [0 for j in range(n - m)] - # Apply ecg_qc on each chunk - signal_quality = [algo.get_signal_quality(x) for x in signal_subdiv] + signal_subdiv[-1] += [0 for _ in range(n - m)] + signal_qualities = [algo.get_signal_quality(signal) + for signal in signal_subdiv] + return ["clean" if signal_quality == 1 else "noisy" + for signal_quality in signal_qualities], length_chunk - export_filepath = write_quality_json(signal_quality, - output_folder, - exam_id, - model_name) - return export_filepath +def apply_ecg_qc(qrs_file_path: str, + formatting: str, + output_folder: str = OUTPUT_FOLDER, + model: str = ECG_QC_MODEL) -> str: + """Apply ecg_qc on an ECG signal to compute quality. + + parameters + ---------- + qrs_file_path : str + The path of the file including ECG signal + formatting: str + Formatting of the tse_bi output ('teppe' or 'tuh') + output_folder : str + Path of the output folder + model : str + Name of the model = name of the file + + returns + ------- + output_file_path : str + Path where detected qrs are stored in csv format + Applies an ECG QC model on a signal, and writes results in a json file. + """ + signal, start_time, end_time, sampling_frequency = import_signal_data( + qrs_file_path=qrs_file_path) + + signal_quality_label, length_chunk = get_signal_quality( + signal=signal, + model=model, + sampling_frequency=sampling_frequency) + + df_ecg_qc = create_ecg_qc_tse_bi( + signal_quality=signal_quality_label, + length_chunk=length_chunk, + start_time=start_time, + end_time=end_time, + formatting=formatting) + output_file_path = generate_output_path(input_file_path=qrs_file_path, + output_folder=output_folder, + format="tse_bi", + prefix="ecg_qc") + df_ecg_qc.to_csv(output_file_path, sep=" ", index=False, header=False) -def parse_apply_ecg_qc_args( - args_to_parse: List[str]) -> argparse.Namespace: + # Format properly to tse_bi standard + with fileinput.FileInput(output_file_path, inplace=True) as tse_bi_file: + for line in tse_bi_file: + print(line.replace('"', ""), end='') + return output_file_path + + +def parse_apply_ecg_qc_args(args_to_parse: List[str]) -> argparse.Namespace: + """ + Parse arguments for adaptable input. + + parameters + ---------- + args_to_parse : List[str] + List of the element to parse. Should be sys.argv[1:] if args are + inputed via CLI + + returns + ------- + args : argparse.Namespace + Parsed arguments + """ parser = argparse.ArgumentParser(description='CLI parameter input') - parser.add_argument('--filepath', - dest='filepath', - required=True) - parser.add_argument('--exam-id', - dest='exam_id', + parser.add_argument('--qrs-file-path', + dest='qrs_file_path', required=True) parser.add_argument('--output-folder', dest='output_folder') - parser.add_argument('--sampling-frequency', - dest='sampling_frequency') parser.add_argument('--model', dest='model', choices=MODELS) - args = parser.parse_args(args_to_parse) - - return args + parser.add_argument('--formatting', + dest='formatting', + choices=['dataset', 'tuh'], + default='dataset') + return parser.parse_args(args_to_parse) if __name__ == "__main__": + args = parse_apply_ecg_qc_args(sys.argv[1:]) args_dict = convert_args_to_dict(args) apply_ecg_qc(**args_dict) diff --git a/src/usecase/compare_qrs_detectors.py b/src/usecase/compare_qrs_detectors.py new file mode 100644 index 0000000..2555ff1 --- /dev/null +++ b/src/usecase/compare_qrs_detectors.py @@ -0,0 +1,251 @@ +""" +This script is used to compare RR intervals from two detectors to find noise. + +Copyright (C) 2022 Association Aura +SPDX-License-Identifier: GPL-3.0 +""" +import argparse +import pandas as pd +import itertools +import sys +import fileinput + +from typing import List + +sys.path.append(".") +from src.usecase.utilities import convert_args_to_dict, generate_output_path +from src.usecase.apply_ecg_qc import create_ecg_qc_tse_bi + +DEFAULT_TOLERENCE_MS = 50 # about 10 ms at 256 HZ +CORRESPONDANCE_LABEL = 'clean' +NO_CORRESPONDANCE_LABEL = 'noisy' +DEFAULT_length_chunk = 2 + + +def find_frame_correspondance( + reference_rr_interval_cumulated: float, + df_comparison: pd.DataFrame, + frame_tolerence: int, + correspondance_label: str = CORRESPONDANCE_LABEL, + no_correspondance_label: str = NO_CORRESPONDANCE_LABEL) -> str: + """Find if a rr as a correspondance at same frame, modulo a tolrence. + + Parameters + ---------- + reference_rr_interval_cumulated : float + The frame used to search comparison + df_comparison : pd.DataFrame + The pd.DataFrame where to search the comparison + frame_tolerence : int + The frame tolerence to include, in milliseconds + correspondance_label : str + The label to return if a correspondance is found + no_correspondance_label : str + The label to return if no correspondance is found + + Returns + ------- + quality label : str + Returns a quality label: correpsondance label if some correspondance is + found, no_correspondance_label else + """ + mask = ((df_comparison['rr_interval_cumulated'] > ( + reference_rr_interval_cumulated - frame_tolerence)) & ( + (df_comparison['rr_interval_cumulated'] < ( + reference_rr_interval_cumulated + frame_tolerence)))) + correspondances = df_comparison[mask]['rr_interval_cumulated'].values + + return correspondance_label if len(correspondances) > 0 \ + else no_correspondance_label + + +def consolidate_labels(df_reference: pd.DataFrame, + timestamp: pd.Timestamp, + length_chunk: int, + label_to_find: str = 'noisy', + other_label: str = 'clean') -> str: + """Return labels found in fixed sized segments. + + parameters + ---------- + df_reference : pd.dataframe + The dataframe including timestamps and quality + timestamp : pd.timestamp + The timestamp used for lower filter + length_chunk : int + The duration in seconds for upper filter + label_to_find : str + If this label is found at least one, return this label + other_label : str + Label to return if label_to_find is not found + + returns + ------- + consolidated_label : str + The signal quality over the duration + """ + correspondances = df_reference[(df_reference['timestamp'] >= timestamp) & ( + (df_reference['timestamp'] <= ( + timestamp + pd.Timedelta( + seconds=length_chunk))))][ + 'correspondance'].values + return label_to_find if label_to_find in correspondances else other_label + + +def create_signal_quality_label_by_length_chunk( + df_reference: pd.DataFrame, + length_chunk: int = DEFAULT_length_chunk) -> List[str]: + """Return an array of quality label by segments of fixed size. + + Parameters + ---------- + df_reference : pd.DataFrame + The dataframe including timestamps and quality + length_chunk : int + The duration in seconds for upper filter + + Returns + ------- + signal_quality_label : np.array + The list of signal quality labels, one label every chunk length of time + start_time : pd.Datetime + The start time of the first quality label + end_time : pd.Datetme + The end time of the last quality label + """ + first_detected_qrs = pd.Timestamp(df_reference['timestamp'].iloc[0]) + last_detected_qrs = pd.Timestamp(df_reference['timestamp'].iloc[-1]) + + duration = last_detected_qrs - first_detected_qrs + n_chunks = int(duration.seconds / length_chunk) + 1 + + chunk_timestamps = [first_detected_qrs + pd.Timedelta( + seconds=2 * iteration) + for iteration in range(n_chunks)] + df_signal_quality_label = pd.DataFrame( + data={"timestamp": chunk_timestamps}) + df_signal_quality_label['label'] = df_signal_quality_label[ + 'timestamp'].apply( + lambda x: consolidate_labels( + df_reference=df_reference, + timestamp=x, + length_chunk=length_chunk)) + + return df_signal_quality_label['label'].values, \ + chunk_timestamps[0], \ + chunk_timestamps[-1] + + +def compare_qrs_detector(reference_rr_intervals_file_path: str, + comparison_rr_intervals_file_path: str, + frame_tolerence: int, + output_folder: str, + formatting: str, + length_chunk: int = DEFAULT_length_chunk) -> str: + """Compare qrs detection from two detectors to find noise. + + parameters + ---------- + reference_rr_intervals_file_path : str + The first RR intervals file path + comparison_rr_intervals_file_path : str + The second RR intervals file path + frame_tolerence : int + The frame tolerence to include, in milliseconds + output_folder : str + Path of the output folder + formatting: str + Formatting of the tse_bi output ('teppe' or 'tuh') + length_chunk : int + The duration in seconds for upper filter + + returns + ------- + output_file_path: str + The output of the file generated + """ + df_reference = pd.read_csv(reference_rr_intervals_file_path) + df_reference['timestamp'] = df_reference['timestamp'].apply( + lambda x: pd.Timestamp(x)) + df_comparison = pd.read_csv(comparison_rr_intervals_file_path) + df_comparison['timestamp'] = df_comparison['timestamp'].apply( + lambda x: pd.Timestamp(x)) + + for df in [df_reference, df_comparison]: + df['rr_interval_cumulated'] = list( + itertools.accumulate(df['rr_interval'].values, lambda a, b: a + b)) + df_reference['correspondance'] = \ + df_reference['rr_interval_cumulated'].apply( + lambda x: find_frame_correspondance( + reference_rr_interval_cumulated=x, + df_comparison=df_comparison, + frame_tolerence=frame_tolerence)) + + signal_quality_label, start_time, end_time = \ + create_signal_quality_label_by_length_chunk( + df_reference=df_reference, + length_chunk=length_chunk) + + df_ecg_qc = create_ecg_qc_tse_bi( + signal_quality=signal_quality_label, + length_chunk=length_chunk, + start_time=start_time, # Start of first beat? + end_time=end_time, + formatting=formatting) + + output_file_path = generate_output_path( + input_file_path=reference_rr_intervals_file_path, + output_folder=output_folder, + format="tse_bi", + prefix="qc_qrs") + df_ecg_qc.to_csv(output_file_path, sep=" ", index=False, header=False) + + # Format properly to tse_bi standard + with fileinput.FileInput(output_file_path, inplace=True) as tse_bi_file: + for line in tse_bi_file: + print(line.replace('"', ""), end='') + + return output_file_path + + +def parse_compare_qrs_detectors_args( + args_to_parse: List[str]) -> argparse.Namespace: + """ + Parse arguments for adaptable input. + + parameters + ---------- + args_to_parse : List[str] + List of the element to parse. Should be sys.argv[1:] if args are + inputed via CLI + + returns + ------- + args : argparse.Namespace + Parsed arguments + """ + parser = argparse.ArgumentParser(description="CLI parameter input") + parser.add_argument("--reference-rr-intervals-file-path", + dest="reference_rr_intervals_file_path", + type=str, + required=True) + parser.add_argument("--comparison-rr-intervals-file-path", + dest="comparison_rr_intervals_file_path", + type=str, + required=True) + parser.add_argument("--frame-tolerence", + dest="frame_tolerence", + type=int, + default=DEFAULT_TOLERENCE_MS) + parser.add_argument("--output-folder", + dest="output_folder") + parser.add_argument("--formatting", + dest="formatting", + default='dataset') + return parser.parse_args(args_to_parse) + + +if __name__ == "__main__": + args = parse_compare_qrs_detectors_args(sys.argv[1:]) + args_dict = convert_args_to_dict(args) + compare_qrs_detector(**args_dict) diff --git a/src/usecase/compute_hrvanalysis_features.py b/src/usecase/compute_hrvanalysis_features.py index 5de8647..d821b20 100644 --- a/src/usecase/compute_hrvanalysis_features.py +++ b/src/usecase/compute_hrvanalysis_features.py @@ -12,11 +12,18 @@ import numpy as np import pandas as pd -from hrvanalysis import remove_outliers, remove_ectopic_beats, \ - interpolate_nan_values, get_time_domain_features, get_csi_cvi_features, \ - get_sampen, get_poincare_plot_features, get_frequency_domain_features - -sys.path.append('.') +from hrvanalysis import ( + remove_outliers, + remove_ectopic_beats, + interpolate_nan_values, + get_time_domain_features, + get_csi_cvi_features, + get_sampen, + get_poincare_plot_features, + get_frequency_domain_features, +) + +sys.path.append(".") from src.usecase.utilities import convert_args_to_dict, generate_output_path SLIDING_WINDOW = 1_000 @@ -24,38 +31,38 @@ MEDIUM_WINDOW = 60_000 # medium window lasts 60 secondes LARGE_WINDOW = 150_000 # large window lasts 2 minutes 30 seconds -OUTPUT_FOLDER = 'output/features' +OUTPUT_FOLDER = "output/features" FEATURES_KEY_TO_INDEX = { - 'interval_index': 0, - 'interval_start_time': 1, # in milliseconds - 'mean_nni': 2, - 'sdnn': 3, - 'sdsd': 4, - 'nni_50': 5, - 'pnni_50': 6, - 'nni_20': 7, - 'pnni_20': 8, - 'rmssd': 9, - 'median_nni': 10, - 'range_nni': 11, - 'cvsd': 12, - 'cvnni': 13, - 'mean_hr': 14, - 'max_hr': 15, - 'min_hr': 16, - 'std_hr': 17, - 'lf': 18, - 'hf': 19, - 'vlf': 20, - 'lf_hf_ratio': 21, - 'csi': 22, - 'cvi': 23, - 'Modified_csi': 24, - 'sampen': 25, - 'sd1': 26, - 'sd2': 27, - 'ratio_sd2_sd1': 28 + "interval_index": 0, + "interval_start_time": 1, # in milliseconds + "mean_nni": 2, + "sdnn": 3, + "sdsd": 4, + "nni_50": 5, + "pnni_50": 6, + "nni_20": 7, + "pnni_20": 8, + "rmssd": 9, + "median_nni": 10, + "range_nni": 11, + "cvsd": 12, + "cvnni": 13, + "mean_hr": 14, + "max_hr": 15, + "min_hr": 16, + "std_hr": 17, + "lf": 18, + "hf": 19, + "vlf": 20, + "lf_hf_ratio": 21, + "csi": 22, + "cvi": 23, + "Modified_csi": 24, + "sampen": 25, + "sd1": 26, + "sd2": 27, + "ratio_sd2_sd1": 28, } @@ -73,14 +80,16 @@ class compute_features: will be stored in attribute *features*. """ - def __init__(self, - rr_timestamps: np.array, - rr_intervals: np.array, - features_key_to_index: dict, - sliding_window: int, - short_window: int, - medium_window: int, - large_window: int): + def __init__( + self, + rr_timestamps: np.array, + rr_intervals: np.array, + features_key_to_index: dict, + sliding_window: int, + short_window: int, + medium_window: int, + large_window: int, + ): """ Initialize all required attributes to compute Heart Rate Variation. @@ -109,12 +118,12 @@ def __init__(self, self.__dict__.update(locals()) # Compute quantity of short intervals to iterate - self.n_short_intervals = int( - (self.rr_timestamps[-1] / self.sliding_window) + 1) + self.n_short_intervals = int((self.rr_timestamps[-1] / self.sliding_window) + 1) # Initialize "features" variable to store results - self.features = np.empty([self.n_short_intervals, - len(self.features_key_to_index.keys())]) + self.features = np.empty( + [self.n_short_intervals, len(self.features_key_to_index.keys())] + ) self.features[:] = np.NaN # Computes hrv features and store them in variable "features" @@ -133,36 +142,36 @@ def compute(self): """ for _index in range(self.n_short_intervals): - (self.features - [_index] - [self.features_key_to_index - ["interval_index"]] - ) = _index + ( + self.features[_index][self.features_key_to_index["interval_index"]] + ) = _index - (self.features - [_index] - [self.features_key_to_index - ["interval_start_time"]]) = _index * self.sliding_window + ( + self.features[_index][self.features_key_to_index["interval_start_time"]] + ) = (_index * self.sliding_window) - for _size in ['short', 'medium', 'large']: - _window_size = self.__dict__[f'{_size}_window'] + for _size in ["short", "medium", "large"]: + _window_size = self.__dict__[f"{_size}_window"] if (_index * self.sliding_window) >= _window_size: # >= ou >(intialement) ? _rr_on_intervals = self.get_rr_intervals_on_window( - index=_index, - size=_size) + index=_index, size=_size + ) + + # Handling to improve + if len(_rr_on_intervals) == 0: + print("No RR intervals") + continue + _clean_rrs = self.get_clean_intervals(_rr_on_intervals) - if _size == 'short': + if _size == "short": self.compute_time_domain_features(_index, _clean_rrs) - elif _size == 'medium': + elif _size == "medium": self.compute_non_linear_features(_index, _clean_rrs) - elif _size == 'large': - self.compute_frequency_domain_features(_index, - _clean_rrs) + elif _size == "large": + self.compute_frequency_domain_features(_index, _clean_rrs) - def get_rr_intervals_on_window(self, - index: int, - size: str) -> np.array: + def get_rr_intervals_on_window(self, index: int, size: str) -> np.array: """ Filter RR interval corresponding to a time window before index. @@ -179,21 +188,17 @@ def get_rr_intervals_on_window(self, RR intervals on the select time window. """ starting_timestamp = index * self.sliding_window - window = self.__dict__[f'{size}_window'] - offset = (starting_timestamp - window) + window = self.__dict__[f"{size}_window"] + offset = starting_timestamp - window rr_indices = np.logical_and( - self.rr_timestamps >= offset, - self.rr_timestamps < starting_timestamp) + self.rr_timestamps >= offset, self.rr_timestamps < starting_timestamp + ) rr_on_intervals = self.rr_intervals[rr_indices] - if len(rr_on_intervals) == 0: - raise ValueError("No RR intervals") - return rr_on_intervals - def get_clean_intervals(self, - rr_intervals: np.array) -> np.array: + def get_clean_intervals(self, rr_intervals: np.array) -> np.array: """ Clean and returns RR intervals. @@ -209,31 +214,27 @@ def get_clean_intervals(self, """ # Removes outliers from signal rr_intervals_without_outliers = remove_outliers( - rr_intervals=rr_intervals, - low_rri=300, - high_rri=1800, - verbose=False) + rr_intervals=rr_intervals, low_rri=300, high_rri=1800, verbose=False + ) # Replaces outliers nan values with linear interpolation interpolated_rr_intervals = interpolate_nan_values( - rr_intervals=rr_intervals_without_outliers, - interpolation_method="linear") + rr_intervals=rr_intervals_without_outliers, interpolation_method="linear" + ) # Removes ectopic beats from signal nn_intervals_list = remove_ectopic_beats( - rr_intervals=interpolated_rr_intervals, - method="malik", - verbose=False) + rr_intervals=interpolated_rr_intervals, method="malik", verbose=False + ) # Replaces ectopic beats nan values with linear interpolation interpolated_nn_intervals = interpolate_nan_values( - rr_intervals=nn_intervals_list) + rr_intervals=nn_intervals_list + ) return interpolated_nn_intervals - def compute_time_domain_features(self, - index: int, - clean_rrs: np.array): + def compute_time_domain_features(self, index: int, clean_rrs: np.array): """ Compute time domain features from HRVanalysis. @@ -250,21 +251,20 @@ def compute_time_domain_features(self, try: time_domain_features = get_time_domain_features(clean_rrs) for key in time_domain_features.keys(): - (self.features - [index] - [self.features_key_to_index[key]]) = \ - time_domain_features[key] + ( + self.features[index][self.features_key_to_index[key]] + ) = time_domain_features[key] except ZeroDivisionError: pass except Exception as e: - print(f'Interval {str(index)} - ' - f'computation issue on short window features {str(e)}') + print( + f"Interval {str(index)} - " + f"computation issue on short window features {str(e)}" + ) - def compute_non_linear_features(self, - index: int, - clean_rrs: np.array): + def compute_non_linear_features(self, index: int, clean_rrs: np.array): """ Compute non linear features from HRVanalysis. @@ -281,29 +281,28 @@ def compute_non_linear_features(self, try: cvi_csi_features = get_csi_cvi_features(clean_rrs) for _key in cvi_csi_features.keys(): - (self.features - [index] - [self.features_key_to_index[_key]]) = \ - cvi_csi_features[_key] + ( + self.features[index][self.features_key_to_index[_key]] + ) = cvi_csi_features[_key] sampen = get_sampen(clean_rrs) - (self.features - [index] - [self.features_key_to_index["sampen"]]) = sampen["sampen"] + (self.features[index][self.features_key_to_index["sampen"]]) = sampen[ + "sampen" + ] poincare_features = get_poincare_plot_features(clean_rrs) for key in poincare_features.keys(): - (self.features - [index] - [self.features_key_to_index[key]]) = poincare_features[key] + ( + self.features[index][self.features_key_to_index[key]] + ) = poincare_features[key] except Exception as e: - print(f'Interval {str(index)} - ' - f'computation issue on medium window features {str(e)}') + print( + f"Interval {str(index)} - " + f"computation issue on medium window features {str(e)}" + ) - def compute_frequency_domain_features(self, - index: int, - clean_rrs: np.array): + def compute_frequency_domain_features(self, index: int, clean_rrs: np.array): """ Compute frequency domain features from HRV analysis. @@ -319,26 +318,29 @@ def compute_frequency_domain_features(self, """ try: frequency_domain_features = get_frequency_domain_features( - nn_intervals=clean_rrs) + nn_intervals=clean_rrs + ) for _key in frequency_domain_features.keys(): if _key in self.features_key_to_index: - (self.features - [index] - [self.features_key_to_index[_key]]) = \ - frequency_domain_features[_key] + ( + self.features[index][self.features_key_to_index[_key]] + ) = frequency_domain_features[_key] except Exception as e: - print(f"Interval {str(index)} - " - f"computation issue on large window features {str(e)}") - - -def compute_hrvanalysis_features(rr_intervals_file_path: str, - output_folder: str = OUTPUT_FOLDER, - sliding_window: int = SLIDING_WINDOW, - short_window: int = SHORT_WINDOW, - medium_window: int = MEDIUM_WINDOW, - large_window: int = LARGE_WINDOW, - features_key_to_index: dict = - FEATURES_KEY_TO_INDEX) -> str: + print( + f"Interval {str(index)} - " + f"computation issue on large window features {str(e)}" + ) + + +def compute_hrvanalysis_features( + rr_intervals_file_path: str, + output_folder: str = OUTPUT_FOLDER, + sliding_window: int = SLIDING_WINDOW, + short_window: int = SHORT_WINDOW, + medium_window: int = MEDIUM_WINDOW, + large_window: int = LARGE_WINDOW, + features_key_to_index: dict = FEATURES_KEY_TO_INDEX, +) -> str: """ Compute HRV features from RR intervals. @@ -370,11 +372,10 @@ def compute_hrvanalysis_features(rr_intervals_file_path: str, output_file_path : Output file of computed HRVanalysis features """ - df_rr_intervals = pd.read_csv( - os.path.join(rr_intervals_file_path), - index_col=0) - rr_intervals = df_rr_intervals['rr_interval'].values + df_rr_intervals = pd.read_csv(os.path.join(rr_intervals_file_path)) + rr_intervals = df_rr_intervals["rr_interval"].values rr_timestamps = np.cumsum(rr_intervals) + start_timestamp = df_rr_intervals["timestamp"].values[0] # MODIFY features_computer = compute_features( rr_timestamps=rr_timestamps, @@ -383,25 +384,36 @@ def compute_hrvanalysis_features(rr_intervals_file_path: str, sliding_window=sliding_window, short_window=short_window, medium_window=medium_window, - large_window=large_window) + large_window=large_window, + ) df_features = pd.DataFrame( - data=features_computer.features, - columns=features_key_to_index) + data=features_computer.features, columns=features_key_to_index + ) + + df_features["timestamp"] = df_features["interval_start_time"].apply( + lambda x: pd.Timestamp(start_timestamp, tz=None) + + pd.Timedelta(x, unit="milliseconds") + ) # EXPORT output_file_path = generate_output_path( input_file_path=rr_intervals_file_path, output_folder=output_folder, - format='csv') + format="csv", + prefix="feats", + ) + + df_features["filename"] = output_file_path - df_features.to_csv(output_file_path, sep=',', index=False) + df_features.to_csv(output_file_path, sep=",", index=False) return output_file_path def parse_compute_hrvanalysis_features_args( - args_to_parse: List[str]) -> argparse.Namespace: + args_to_parse: List[str], +) -> argparse.Namespace: """ Parse arguments for adaptable input. @@ -416,30 +428,21 @@ def parse_compute_hrvanalysis_features_args( args : argparse.Namespace Parsed arguments """ - parser = argparse.ArgumentParser(description='CLI parameter input') - parser.add_argument('--rr-intervals-file-path', - dest='rr_intervals_file_path', - required=True) - parser.add_argument('--output-folder', - dest='output_folder') - parser.add_argument('--sliding-window', - dest='sliding_window', - type=int) - parser.add_argument('--short-window', - dest='short_window', - type=int) - parser.add_argument('--medium-window', - dest='medium_window', - type=int) - parser.add_argument('--large-window', - dest='large_window', - type=int) + parser = argparse.ArgumentParser(description="CLI parameter input") + parser.add_argument( + "--rr-intervals-file-path", dest="rr_intervals_file_path", required=True + ) + parser.add_argument("--output-folder", dest="output_folder") + parser.add_argument("--sliding-window", dest="sliding_window", type=int) + parser.add_argument("--short-window", dest="short_window", type=int) + parser.add_argument("--medium-window", dest="medium_window", type=int) + parser.add_argument("--large-window", dest="large_window", type=int) args = parser.parse_args(args_to_parse) return args -if __name__ == '__main__': +if __name__ == "__main__": args = parse_compute_hrvanalysis_features_args(sys.argv[1:]) args_dict = convert_args_to_dict(args) compute_hrvanalysis_features(**args_dict) diff --git a/src/usecase/consolidate_feats_and_annot.py b/src/usecase/consolidate_feats_and_annot.py index 29a6eaf..7c9cf63 100644 --- a/src/usecase/consolidate_feats_and_annot.py +++ b/src/usecase/consolidate_feats_and_annot.py @@ -7,24 +7,29 @@ import argparse import numpy as np import pandas as pd +import re import sys from typing import List -sys.path.append('.') +sys.path.append(".") from src.usecase.utilities import convert_args_to_dict, generate_output_path -OUTPUT_FOLDER = 'exports/consolidated_dataset' -WINDOW_INTERVAL = 10_000 +OUTPUT_FOLDER = "exports/consolidated_dataset" +WINDOW_INTERVAL = 1_000 SEGMENT_SIZE_TRESHOLD = 0.9 +LABEL_TARGET = "seiz" + def consolidate_feats_and_annot( - features_file_path: str, - annotations_file_path: str, - output_folder: str = OUTPUT_FOLDER, - window_interval: int = WINDOW_INTERVAL, - segment_size_treshold: float = SEGMENT_SIZE_TRESHOLD, - crop_dataset: bool = True) -> str: + features_file_path: str, + annotations_file_path: str, + output_folder: str = OUTPUT_FOLDER, + window_interval: int = WINDOW_INTERVAL, + segment_size_treshold: float = SEGMENT_SIZE_TRESHOLD, + label_target: str = LABEL_TARGET, + crop_dataset: bool = True, +) -> str: """ Create a pd.DataFrame form a tse_bi file. @@ -50,27 +55,53 @@ def consolidate_feats_and_annot( df_tse_bi : pd.DataFrame DataFrame including the data from input tse_bi """ + # Delete crop_dataset ? + # Convert tse_bi of TUH by adding the start of df_features df_features = pd.read_csv(features_file_path) df_tse_bi = read_tse_bi(annotations_file_path) - df_features['label'] = df_features['interval_start_time'].apply( - lambda interval_start_time: get_label_on_interval( - df_tse_bi=df_tse_bi, - interval_start_time=interval_start_time, - window_interval=window_interval, - segment_size_treshold=segment_size_treshold)) + # Dataset tse_bi format + if df_tse_bi["start"].loc[0] > np.datetime64(1, "s"): + df_features["label"] = df_features["timestamp"].apply( + lambda interval_start_time: get_label_on_interval( + df_tse_bi=df_tse_bi, + interval_start_time=interval_start_time, + window_interval=window_interval, + segment_size_treshold=segment_size_treshold, + label_target="seiz" + ) + ) + + # Tuh tse_bi format + else: + df_features["label"] = df_features["interval_start_time"].apply( + lambda interval_start_time: get_label_on_interval( + df_tse_bi=df_tse_bi, + interval_start_time=interval_start_time, + window_interval=window_interval, + segment_size_treshold=segment_size_treshold, + label_target=label_target + ) + ) if crop_dataset: - df_features.drop( - [df_features.index[0], df_features.index[-1]], - inplace=True) + df_features.drop([df_features.index[0], df_features.index[-1]], + inplace=True) + + input_file_generated = "".join( + [annotations_file_path[:-7], + re.search(r'_s[0-9]*', features_file_path).group(0), + ".tse_bi"] + ) output_file_path = generate_output_path( - input_file_path=annotations_file_path, + input_file_path=input_file_generated, output_folder=output_folder, - format='csv') + format="csv", + prefix="cons", + ) - df_features.to_csv(output_file_path, sep=',', index=False) + df_features.to_csv(output_file_path, sep=",", index=False) return output_file_path @@ -79,6 +110,15 @@ def read_tse_bi(annotations_file_path: str) -> pd.DataFrame: """ Create a pd.DataFrame form a tse_bi file. + As in tse_bi standard an empty line seperates the data from the file + headers, the first empty line is used to infer where data starts. + + Start and end columns are converted to numpy timestamp64. For TUH + tse_bi files, there is no timestamp but duration from the start of the + recording. However, it is converted in timestamp all the same as it makes + no incompatibily with other usages. However, starting date will always be + 1970-01-01 00:00:00. + Parameters ---------- annotations_file_path : str @@ -89,26 +129,59 @@ def read_tse_bi(annotations_file_path: str) -> pd.DataFrame: df_tse_bi : pd.DataFrame DataFrame including the data from input tse_bi """ - if annotations_file_path.split('/')[-1].split('.')[-1] != 'tse_bi': - raise ValueError( - f'Please input a tse_bi file. Input: {annotations_file_path}') + if annotations_file_path.split("/")[-1].split(".")[-1] != "tse_bi": + raise ValueError(f"Please input a tse_bi file. Input:" + f"{annotations_file_path}") + # La teppe format, which is slightly different + + with open(annotations_file_path, "r") as annotations_file: + first_empty_line = 0 + for line in annotations_file: + first_empty_line += 1 + if line in ["\n", "r\n", " \n"]: + break + try: + df_tse_bi = pd.read_csv( + annotations_file_path, + skiprows=first_empty_line, + skip_blank_lines=False, + sep=" ", + header=None, + ) + + except pd.errors.EmptyDataError: + sys.exit("tse_bi file is empty") + + df_tse_bi.columns = ["start", "end", "annotation", "probability"] + + if df_tse_bi["start"].iloc[0] == 0: # TUH format + df_tse_bi.loc[:, "start"] = df_tse_bi["start"].apply( + lambda x: np.datetime64(int(x * 1_000_000_000), "ns") + ) + + df_tse_bi.loc[:, "end"] = df_tse_bi["end"].apply( + lambda x: np.datetime64(int(x * 1_000_000_000), "ns") + ) + + else: + df_tse_bi.loc[:, "start"] = df_tse_bi["start"].apply( + lambda x: np.datetime64(x)) - df_tse_bi = pd.read_csv(annotations_file_path, - sep=' ', - skiprows=1, - header=None) + df_tse_bi.loc[:, "end"] = df_tse_bi["end"].apply( + lambda x: np.datetime64(x)) - df_tse_bi.columns = ['start', 'end', 'annotation', 'probablility'] - df_tse_bi.loc[:, ['start', 'end']] = df_tse_bi.loc[:, ['start', 'end']].\ - apply(lambda x: x * 1_000) + df_tse_bi['probability'] = df_tse_bi['probability'].astype(int) return df_tse_bi -def get_label_on_interval(df_tse_bi: pd.DataFrame, - interval_start_time: int, - window_interval: int, - segment_size_treshold: float) -> float: +def get_label_on_interval( + df_tse_bi: pd.DataFrame, + interval_start_time: int, + window_interval: int, + segment_size_treshold: float, + label_target: str +) -> float: """ From an annotation DataFrame, select annotations on an interval. @@ -128,40 +201,72 @@ def get_label_on_interval(df_tse_bi: pd.DataFrame, label_ratio : float Ratio of seizure other the segment selected """ - end_marker = interval_start_time + window_interval - df_filtered = df_tse_bi[ - (df_tse_bi['start'] <= interval_start_time)] + # Converting interval_start_time to np.datetime64 + try: + interval_start_time = np.datetime64(interval_start_time) + + except ValueError: + interval_start_time = np.datetime64(int(interval_start_time), "ms") + + # Computing end marker + end_marker = interval_start_time + \ + pd.Timedelta(milliseconds=window_interval) + + df_tse_bi_temp = df_tse_bi.copy() + + # Filtering over intervals + df_tse_bi_temp.loc[:, "start"] = df_tse_bi_temp["start"].apply( + lambda x: interval_start_time if x <= interval_start_time else x + ) + df_tse_bi_temp.loc[:, "end"] = df_tse_bi_temp["end"].apply( + lambda x: end_marker if x > end_marker else x + ) + df_tse_bi_temp.loc[:, "length"] = df_tse_bi_temp["end"] \ + - df_tse_bi_temp["start"] + + # Setting negative length at 0 + df_tse_bi_temp.loc[:, "length"] = df_tse_bi_temp["length"].apply( + lambda x: x.total_seconds() if x > pd.Timedelta(0) else 0 + ) - df_filtered['start'] = df_filtered['start'].apply( - lambda x: interval_start_time if x <= interval_start_time else x) - df_filtered['end'] = df_filtered['end'].apply( - lambda x: end_marker if x > end_marker else x) - df_filtered['length'] = df_filtered['end'] - df_filtered['start'] - df_filtered = df_filtered.groupby(['annotation']).sum()['length'] + # Checking checking the duration by seiz/bckg annotations + df_tse_bi_temp = df_tse_bi_temp.groupby(["annotation"]).sum()["length"] + assert(len(df_tse_bi_temp.index) <= 2) + + try: + label_other = [label for label in df_tse_bi_temp.index + if label != label_target][0] + except IndexError: + label_other = None + + # Computing the size of each class, then label try: - seiz_length = df_filtered['seiz'] + label_target_length = df_tse_bi_temp[label_target] except KeyError: - seiz_length = 0 + label_target_length = 0 try: - bckg_length = df_filtered['bckg'] + label_other_length = df_tse_bi_temp[label_other] except KeyError: - bckg_length = 0 + label_other_length = 0 - if seiz_length + bckg_length <= window_interval * segment_size_treshold: + try: + label_ratio = label_target_length / (label_target_length + + label_other_length) + label_ratio = round(label_ratio, 2) + if label_ratio > segment_size_treshold: + label_ratio = 1 + + except ValueError: label_ratio = np.nan - else: - if bckg_length > 0: - label_ratio = seiz_length / (seiz_length + bckg_length) - else: - label_ratio = np.nan return label_ratio def parse_consolidate_feats_and_annot_args( - args_to_parse: List[str]) -> argparse.Namespace: + args_to_parse: List[str], +) -> argparse.Namespace: """ Parse arguments for adaptable input. @@ -176,30 +281,25 @@ def parse_consolidate_feats_and_annot_args( args : argparse.Namespace Parsed arguments """ - parser = argparse.ArgumentParser(description='CLI parameter input') - parser.add_argument('--features-file-path', - dest='features_file_path', - required=True) - parser.add_argument('--annotations-file-path', - dest='annotations_file_path', - required=True) - parser.add_argument('--output-folder', - dest='output_folder') - parser.add_argument('--window-interval', - dest='window_interval', - type=int) - parser.add_argument('--segment-size-treshold', - dest='segment_size_treshold', - type=float) - parser.add_argument('--crop-dataset', - dest='crop_dataset', - type=bool) - args = parser.parse_args(args_to_parse) - - return args - - -if __name__ == '__main__': + parser = argparse.ArgumentParser(description="CLI parameter input") + parser.add_argument( + "--features-file-path", dest="features_file_path", required=True + ) + parser.add_argument( + "--annotations-file-path", dest="annotations_file_path", required=True + ) + parser.add_argument("--output-folder", dest="output_folder") + parser.add_argument("--window-interval", dest="window_interval", type=int) + parser.add_argument( + "--segment-size-treshold", dest="segment_size_treshold", type=float + ) + parser.add_argument("--crop-dataset", dest="crop_dataset", type=bool) + parser.add_argument("--label-target ", dest="label_target", type=str) + + return parser.parse_args(args_to_parse) + + +if __name__ == "__main__": args = parse_consolidate_feats_and_annot_args(sys.argv[1:]) args_dict = convert_args_to_dict(args) consolidate_feats_and_annot(**args_dict) diff --git a/src/usecase/constants.py b/src/usecase/constants.py new file mode 100644 index 0000000..8e8ae8d --- /dev/null +++ b/src/usecase/constants.py @@ -0,0 +1,25 @@ +from enum import Enum + +class SeasonalFeature(Enum): + month = "month" + dayOfWeek = "dayOfWeek" + hour = "hour" + minute = "minute" + second = "second" + +class TempFeaturesOperation(Enum): + mean = 1 + std = 2 + sum = 3 + + +class TempFeaturesPeriod(Enum): + """ + This class define lags for contextuals operations on features. + p30 = period 30 seconds + p60 = period 60 seconds + p120 = period 120 seconds + """ + p30 = 30 + p60 = 60 + p120 = 120 \ No newline at end of file diff --git a/src/usecase/create_ml_dataset.py b/src/usecase/create_ml_dataset.py index b9ba226..c515c28 100644 --- a/src/usecase/create_ml_dataset.py +++ b/src/usecase/create_ml_dataset.py @@ -11,15 +11,16 @@ import sys from typing import List -sys.path.append('.') +sys.path.append(".") from src.usecase.utilities import convert_args_to_dict from src.usecase.compute_hrvanalysis_features import FEATURES_KEY_TO_INDEX -ML_DATASET_OUTPUT_FOLDER = 'exports/ml_dataset' +ML_DATASET_OUTPUT_FOLDER = "exports/ml_dataset" -def create_ml_dataset(input_folder: str, - output_folder: str = ML_DATASET_OUTPUT_FOLDER) -> str: +def create_ml_dataset( + input_folder: str, output_folder: str = ML_DATASET_OUTPUT_FOLDER +) -> str: """ Merge individual dataset in a single large dataset. @@ -35,28 +36,24 @@ def create_ml_dataset(input_folder: str, output_file_path : Output file of the consolidated dataset """ - consolidated_datasets = glob.glob(f'{input_folder}/**/*.csv', - recursive=True) - df_consolidated = pd.DataFrame( - columns=[*FEATURES_KEY_TO_INDEX.keys(), - 'label']) + consolidated_datasets = glob.glob(f"{input_folder}/**/*.csv", recursive=True) + df_consolidated = pd.read_csv(consolidated_datasets[0]) - for consolidated_dataset in consolidated_datasets: + for consolidated_dataset in consolidated_datasets[1:]: df_temp = pd.read_csv(consolidated_dataset) df_consolidated = df_consolidated.append(df_temp) df_consolidated.reset_index(drop=True, inplace=True) os.makedirs(output_folder, exist_ok=True) - output_file_path = f'{output_folder}/df_ml.csv' + output_file_path = f"{output_folder}/df_ml.csv" df_consolidated.to_csv(output_file_path, index=False) - print(f'Size of output dataset: {df_consolidated.shape[0]}') + print(f"Size of output dataset: {df_consolidated.shape[0]}") return output_file_path -def parse_create_ml_dataset_args( - args_to_parse: List[str]) -> argparse.Namespace: +def parse_create_ml_dataset_args(args_to_parse: List[str]) -> argparse.Namespace: """ Parse arguments for adaptable input. @@ -71,19 +68,21 @@ def parse_create_ml_dataset_args( args : argparse.Namespace Parsed arguments """ - parser = argparse.ArgumentParser(description='CLI parameter input') - parser.add_argument('--input-folder', - dest='input_folder', - help='input for for features to consolidate') - parser.add_argument('--output-folder', - dest='output_folder', - help='output folder for ml dataset') + parser = argparse.ArgumentParser(description="CLI parameter input") + parser.add_argument( + "--input-folder", + dest="input_folder", + help="input for for features to consolidate", + ) + parser.add_argument( + "--output-folder", dest="output_folder", help="output folder for ml dataset" + ) args = parser.parse_args(args_to_parse) return args -if __name__ == '__main__': +if __name__ == "__main__": args = parse_create_ml_dataset_args(sys.argv[1:]) args_dict = convert_args_to_dict(args) create_ml_dataset(**args_dict) diff --git a/src/usecase/data_processing/data_cleaning.py b/src/usecase/data_processing/data_cleaning.py new file mode 100644 index 0000000..49a3cdc --- /dev/null +++ b/src/usecase/data_processing/data_cleaning.py @@ -0,0 +1,116 @@ +from typing import Tuple +import numpy as np +import pandas as pd +from sklearn.impute import SimpleImputer +from sklearn.neighbors import LocalOutlierFactor +from scipy.stats import zscore +from sklearn.decomposition import PCA + + +def replace_infinite_values_by_nan(dataframe: pd.DataFrame) -> pd.DataFrame: + + """ + This is a simple function that replace infinite values by nan. + parameters + ---------- + dataframe : pd.Dataframe + Dataframe that will be used to draft infinite values + + returns + ------- + dataframe: pd.Dataframe + Dataframe without infinite values + """ + + dataframe.replace([np.inf, -np.inf], np.nan, inplace=True, regex=True) + + return dataframe + + +def impute_nan_values_by_median(dataframe: pd.DataFrame) -> Tuple[np.ndarray, pd.DataFrame]: + + """ + This function will replace nan values by median. + Because of many nan values can follow one another in an entire window, + we choose to impute nan values by global median in order to avoid extrems values. + parameters + ---------- + dataframe: pd.Dataframe + Dataframe that will be imputed by median + + returns + ------- + X_imputed: np.ndarray + X matrix imputted + y : pd.Dataframe + y vector + """ + + if dataframe.isin([np.inf, -np.inf]).values.any(): + dataframe = replace_infinite_values_by_nan(dataframe) + + X = dataframe.drop(['label'], axis=1) + y = dataframe['label'] + + imputer = SimpleImputer(missing_values=np.nan, strategy='median') + X_imputed = imputer.fit_transform(X) + + return X_imputed, y + + +def outlier_detection(X: np.ndarray) -> pd.DataFrame: + + """ + This function detect outlier based on local density of points. + parameters + ---------- + X: np.ndarray + X imputted will be use to detect outlier + + returns + ------- + outlier_score: pd.Dataframe + every point of data has outlier score which is saved + """ + + LOF = LocalOutlierFactor() + y_pred = LOF.fit_predict(X) + X_score = LOF.negative_outlier_factor_ + + outlier_score = pd.DataFrame() + outlier_score['score'] = X_score + + return outlier_score + + +def remove_outlier( + X_imputed_df: pd.DataFrame, + outlier_score: pd.DataFrame, + y: pd.DataFrame, + threshold: float) -> Tuple[pd.DataFrame, pd.DataFrame]: + + """ + This function will remove outlier on the dataset + parameters + ---------- + X_imputed_df: pd.DataFrame + outlier_score: pd.DataFrame + y: pd.DataFrame + threshold: float + threshold: float + threshold is the limit for removing outliers + + returns + ------- + X_result_outlier: pd.DataFrame + Dataset without outlier points (cleaned data) + y_result_outlier: pd.DataFrame + y vector cleaned + """ + + filt = outlier_score["score"] < threshold + outlier_index = outlier_score[filt].index.tolist() + X_result_outlier = pd.DataFrame(X_imputed_df).drop(outlier_index) + y_result_outlier = y.drop(outlier_index).values + + return X_result_outlier, y_result_outlier \ No newline at end of file diff --git a/src/usecase/data_processing/data_loading.py b/src/usecase/data_processing/data_loading.py new file mode 100644 index 0000000..63995dd --- /dev/null +++ b/src/usecase/data_processing/data_loading.py @@ -0,0 +1,32 @@ + + +from typing import Tuple + +import pandas as pd + + +def get_dataset(dataset_path: str, col_to_drop: list) -> Tuple[pd.DataFrame, pd.DataFrame]: + + """ + This function will get the dataset by using path. It will also do some + preparation about dataset before feature engineering. + + parameters + ---------------- + dataset_path : str + Path of dataset to load + col_to_drop : list + List of columns that we need to remove + + returns + ------- + Tuple of dataframes : + Dataframe origine and dataframe clean + """ + + dataframe_origine: pd.DataFrame = pd.read_csv(dataset_path, index_col=False) + + sorted_dataframe = dataframe_origine.sort_values(by='timestamp', inplace=False).reset_index(drop=True, inplace=False) + dataframe_without_cols = sorted_dataframe.drop(col_to_drop, axis=1, inplace=False) + + return dataframe_origine, dataframe_without_cols \ No newline at end of file diff --git a/src/usecase/data_processing/prepare_features.py b/src/usecase/data_processing/prepare_features.py new file mode 100644 index 0000000..086bc81 --- /dev/null +++ b/src/usecase/data_processing/prepare_features.py @@ -0,0 +1,89 @@ +""" +Prepare Features process. + +This script prepare data before training model. Data are loaded, cleaned before giving them to machine +learning model. Some transformation can be done by using time series aspects. +""" +import pandas as pd + +from src.usecase.data_processing.time_series_processing import (compute_contextuals_features, + compute_time_features) +from src.usecase.data_processing.data_cleaning import replace_infinite_values_by_nan +from src.usecase.data_processing.data_loading import get_dataset + + +def prepare_features_with_io(dataset_path: str, + col_to_drop: list, features_path: str) -> None: + """ + This function prepare features by making artefact. It is used on airflow pipeline + parameteres + ----------- + dataset_path: str + col_to_drop: list + features_path: str + return + ------ + None + """ + + data = prepare_features( + dataset_path=dataset_path, + col_to_drop=col_to_drop) + + data.to_csv(features_path, index=False) + + +def prepare_features( + dataset_path: str, + col_to_drop: list) -> pd.DataFrame: + + """ + This function prepare feature before training model + parameters + ---------- + dataset_path: str + col_to_drop: list + + return + ------ + dataframe: pd.DataFrame + """ + + _, dataframe = get_dataset(dataset_path, col_to_drop) + + + dataframe = replace_infinite_values_by_nan(dataframe=dataframe) + + dataframe['patient_id'] = dataframe.filename.apply(extract_patient_id) + + dataframe = dataframe[dataframe.patient_id==22] + + list_time = ["dayOfTheWeek", "month", "hour"] + dataframe = compute_time_features(dataframe=dataframe, list_time=list_time) + + list_feat = ["mean_hr", "mean_nni"] + dataframe = compute_contextuals_features( + dataframe=dataframe, + operation="mean", + list_feat=list_feat + ) + + return dataframe + + +def extract_patient_id(path): + if path == 'output/feats-v0_6/feats_EEG_297_s1.csv': + return 12 + return int(path.split('PAT_')[1].split('//')[0]) + + +def extract_session_id(path): + return int(path.split('_s')[0].split('_')[-1]) + + +def extract_segment_id(path): + return int(path.split('_s')[1].split('.csv')[0]) + + +def convert_timestamp(timestamp): + return pd.to_datetime(timestamp) \ No newline at end of file diff --git a/src/usecase/data_processing/time_series_processing.py b/src/usecase/data_processing/time_series_processing.py new file mode 100644 index 0000000..4c63d9e --- /dev/null +++ b/src/usecase/data_processing/time_series_processing.py @@ -0,0 +1,155 @@ +import pandas as pd +import numpy as np + +from src.usecase.constants import SeasonalFeature, TempFeaturesOperation, TempFeaturesPeriod + + +def compute_time_features(dataframe: pd.DataFrame, list_time: list) -> pd.DataFrame: + """ + This function compute seasoner information from timestamp. These informations + are used to let know the model weither of seasonality informations is in time series. + parameters + ---------- + dataframe: pd.DataFrame + dataframe constructed from dataset + list_time: list + list of time used to compute seasonal information + + return + ------ + dataframe: pd.DataFrame + new dataframe with seasonal informations + """ + dataframe.timestamp = dataframe.timestamp.apply(convert_timestamp) + dataframe = dataframe.sort_values(by='timestamp').reset_index(drop=True) + + for time in list_time: + if time == SeasonalFeature.dayOfWeek.name: + dataframe[SeasonalFeature.dayOfWeek.name] = dataframe.timestamp.dt.dayofweek + elif time == SeasonalFeature.month.name: + dataframe[SeasonalFeature.month.name] = dataframe.timestamp.dt.month + elif time == SeasonalFeature.hour.name: + dataframe[SeasonalFeature.hour.name] = dataframe.timestamp.dt.hour + elif time == SeasonalFeature.minute.name: + dataframe[SeasonalFeature.minute.name] = dataframe.timestamp.dt.minute + else: + dataframe[SeasonalFeature.second.name] = dataframe.timestamp.dt.second + + return dataframe + + +def compute_contextuals_features( + dataframe: pd.DataFrame, + operation: str, + list_feat: list) -> pd.DataFrame: + """ + This function will create contextuals features from time domaine features. + new features will be generated by doing operations like : rolling, diff, etc. + parameters + ---------- + dataframe: pd.DataFrame + dataframe constructed from dataset + operation: str + type of operation to use on feature + list_feat: list + list of features that we want to use to compute contextuals time domaine features + + return + ------ + dataframe: pd.DataFrame + new dataframe with contextuals time domaine features + """ + + dataframe = perform_op_on_features( + dataframe=dataframe, + list_feat=list_feat, + operation=operation) + + return dataframe + + +def perform_op_on_features(dataframe: pd.DataFrame, list_feat: list, operation: str) -> pd. DataFrame: + """ + This functioncompute operation on features by iterating over list of features. + parameters + ---------- + dataframe: pd.DataFrame + dataframe from dataset + list_feat: str + list of features that we want to use + operation: str + operation performed on features : mean, std, etc. + list_period: list + list of periods which is used to compute new features + + return + ------ + dataframe: pd.Dataframe + """ + + [ + create_rolling_variables_given_feature( + dataframe=dataframe, + feature=feature, + operation=operation + ) for feature in list_feat + ] + + return dataframe + + +def create_rolling_variables_given_feature(dataframe: pd.DataFrame, feature: str, operation: str) -> None: + + if operation == TempFeaturesOperation.mean.name: + dataframe[feature+'_%s' % (TempFeaturesPeriod.p30.name)] = dataframe[feature].rolling( + window=TempFeaturesPeriod.p30.value, min_periods=10).mean() + dataframe[feature+'_%s' % (TempFeaturesPeriod.p60.name)] = dataframe[feature].rolling( + window=TempFeaturesPeriod.p60.value, min_periods=10).mean() + dataframe[feature+'_%s' % (TempFeaturesPeriod.p120.name)] = dataframe[feature].rolling( + window=TempFeaturesPeriod.p120.value, min_periods=10).mean() + elif operation == TempFeaturesOperation.std.name: + dataframe[feature+'_%s' % (TempFeaturesPeriod.p30.name)] = dataframe[feature].rolling( + window=TempFeaturesPeriod.p30.value, min_periods=10).std() + dataframe[feature+'_%s' % (TempFeaturesPeriod.p60.name)] = dataframe[feature].rolling( + window=TempFeaturesPeriod.p60.value, min_periods=10).std() + dataframe[feature+'_%s' % (TempFeaturesPeriod.p120.name)] = dataframe[feature].rolling( + window=TempFeaturesPeriod.p120.value, min_periods=10).std() + elif operation == TempFeaturesOperation.sum.name: + dataframe[feature+'_%s' % (TempFeaturesPeriod.p30.name)] = dataframe[feature].rolling( + window=TempFeaturesPeriod.p30.value, min_periods=10).sum() + dataframe[feature+'_%s' % (TempFeaturesPeriod.p60.name)] = dataframe[feature].rolling( + window=TempFeaturesPeriod.p60.value, min_periods=10).sum() + dataframe[feature+'_%s' % (TempFeaturesPeriod.p120.name)] = dataframe[feature].rolling( + window=TempFeaturesPeriod.p120.value, min_periods=10).sum() + else: + pass + + +def diff_operation_from_features(dataframe: pd.DataFrame, list_feat: list) -> pd.DataFrame: + """ + This function will compute difference from chosen features. It aims + to know the variability of features. + parameters + ---------- + dataframe: pd.DataFrame + dataframe constructed from dataset + list_feat: list + list of features that we want to use to compute difference operation. + + return + ------ + dataframe: pd.DataFrame + new dataframe with variability informations. + """ + + [compute_diff_for_feature(dataframe=dataframe, feature=feat) for feat in list_feat] + + return dataframe + + +def compute_diff_for_feature(dataframe: pd.DataFrame, feature: str) -> None: + dataframe[feature+'_diff'] = dataframe[feature].diff().shift(-1) + + +def convert_timestamp(timestamp): + return pd.to_datetime(timestamp) diff --git a/src/usecase/detect_qrs.py b/src/usecase/detect_qrs.py index 8f9f665..63fcb38 100644 --- a/src/usecase/detect_qrs.py +++ b/src/usecase/detect_qrs.py @@ -8,21 +8,56 @@ import argparse from typing import List import sys +import numpy as np -sys.path.append('.') +sys.path.append(".") from src.usecase.utilities import convert_args_to_dict, generate_output_path from src.domain.qrs_detector import QRSDetector from src.infrastructure.edf_loader import EdfLoader -OUTPUT_FOLDER = 'output/rr_intervals' -METHODS = ['hamilton', 'xqrs', 'gqrs', 'swt', 'engelsee'] -DEFAULT_METHOD = 'hamilton' +OUTPUT_FOLDER = "output/rr_intervals" +METHODS = ["hamilton", "xqrs", "gqrs", "swt", "engelsee"] +DEFAULT_METHOD = "hamilton" + + +def get_similarity_signal(ecg_signal: np.array, + qrs_filter=None) -> np.array: + """Smooth the signal with similarity correlation. + + Credit: Salomon Tetelepta. + + parameters + ---------- + ecg_signal : np.array + The ECG signal + qrs_filter : None + The filter pattern to use to correlated. + + returns + ------- + similarity_signal : np.array + The ECG signal after similarity correlation with the pattern + """ + if qrs_filter is None: + # create default qrs filter, which is just a part of the sine function + t = np.linspace(1.5 * np.pi, 3.5 * np.pi, 15) + qrs_filter = np.sin(t) + + # normalize data + ecg_signal = (ecg_signal - ecg_signal.mean()) / ecg_signal.std() + + # calculate cross correlation + similarity_signal = np.correlate(ecg_signal, qrs_filter, mode="same") + similarity_signal = similarity_signal / np.max(similarity_signal) + + return similarity_signal def detect_qrs(qrs_file_path: str, method: str, exam_id: str, - output_folder: str = OUTPUT_FOLDER) -> str: + output_folder: str = OUTPUT_FOLDER, + smoothing: bool = False) -> str: """ Detect QRS on a, ECG signal signal. @@ -53,30 +88,37 @@ def detect_qrs(qrs_file_path: str, try: sampling_frequency, ecg_data = edfloader.ecg_channel_read( - ecg_channel_name, - start_time, - end_time) + ecg_channel_name, start_time, end_time + ) except ValueError: - print(f'There is no ECG channel in {qrs_file_path}, exiting') + print(f"There is no ECG channel in {qrs_file_path}, exiting") sys.exit() + raise ValueError(f'There is no ECG channel in {qrs_file_path}') qrs_detector = QRSDetector() - signal = list(ecg_data['signal']) + + signal = ecg_data["signal"] + if smoothing: + signal = get_similarity_signal(np.array(signal)) + + signal = list(signal) + detected_qrs, rr_intervals = qrs_detector.get_cardiac_infos( - signal, sampling_frequency, method) + signal, sampling_frequency, method + ) df_detections = ecg_data.copy() df_detections = df_detections.iloc[detected_qrs[:-1]] - df_detections['frame'] = detected_qrs[:-1] - df_detections['rr_interval'] = rr_intervals - df_detections.drop(columns='signal', inplace=True) + df_detections["timestamp"] = df_detections.index + df_detections["frame"] = detected_qrs[:-1] + df_detections["rr_interval"] = rr_intervals + df_detections.drop(columns="signal", inplace=True) # Export output_file_path = generate_output_path( - input_file_path=exam_id, - output_folder=output_folder, - format='csv') + input_file_path=exam_id, output_folder=output_folder, format="csv", prefix="rr" + ) - df_detections.to_csv(output_file_path, sep=',', index=True) + df_detections.to_csv(output_file_path, sep=",", index=False) return output_file_path, sampling_frequency @@ -96,18 +138,12 @@ def parse_detect_qrs_args(args_to_parse: List[str]) -> argparse.Namespace: args : argparse.Namespace Parsed arguments """ - parser = argparse.ArgumentParser(description='CLI parameter input') - parser.add_argument('--qrs-file-path', - dest='qrs_file_path', - required=True) - parser.add_argument('--method', - dest='method', - required=True, - choices=METHODS) - parser.add_argument('--exam-id', - dest='exam_id') - parser.add_argument('--output-folder', - dest='output_folder') + parser = argparse.ArgumentParser(description="CLI parameter input") + parser.add_argument("--qrs-file-path", dest="qrs_file_path", required=True) + parser.add_argument("--method", dest="method", required=True, choices=METHODS) + parser.add_argument("--exam-id", dest="exam_id") + parser.add_argument("--output-folder", dest="output_folder") + parser.add_argument("--smoothing", dest="smoothing", type=bool) args = parser.parse_args(args_to_parse) @@ -137,7 +173,7 @@ def parse_exam_id(qrs_file_path: str) -> str: args = parse_detect_qrs_args(sys.argv[1:]) args_dict = convert_args_to_dict(args) - if 'exam_id' not in args_dict: - exam_id = parse_exam_id(args_dict['qrs_file_path']) - args_dict.update({'exam_id': exam_id}) + if "exam_id" not in args_dict: + exam_id = parse_exam_id(args_dict["qrs_file_path"]) + args_dict.update({"exam_id": exam_id}) detect_qrs(**args_dict) diff --git a/src/usecase/ecg_channel_read.py b/src/usecase/ecg_channel_read.py index b1cbd63..7e07fa1 100644 --- a/src/usecase/ecg_channel_read.py +++ b/src/usecase/ecg_channel_read.py @@ -5,31 +5,28 @@ from src.infrastructure.edf_loader import EdfLoader -DEFAULT_PATH = '/home/DATA/lateppe/Recherche_ECG/' +DEFAULT_PATH = "/home/DATA/lateppe/Recherche_ECG/" -def ecg_channel_read(patient: str, - record: str, - segment: str, - channel_name: str, - start_time: str, - end_time: str, - data_path: str = DEFAULT_PATH) -> \ - Tuple[int, pd.DataFrame, pd.DataFrame, pd.DataFrame]: +def ecg_channel_read( + patient: str, + record: str, + segment: str, + channel_name: str, + start_time: str, + end_time: str, + data_path: str = DEFAULT_PATH, +) -> Tuple[int, pd.DataFrame, pd.DataFrame, pd.DataFrame]: edfloader = EdfLoader(data_path, patient, record, segment) - df_ecg = edfloader.convert_edf_to_dataframe( - channel_name, start_time, end_time) + df_ecg = edfloader.convert_edf_to_dataframe(channel_name, start_time, end_time) sample_frequency = edfloader.sampling_frequency_hz - annotation_file = os.path.join( - data_path, patient, f'Annotations_EEG_{record}.csv') - segments_file = os.path.join( - data_path, patient, f'Segments_EEG_{record}.csv') + annotation_file = os.path.join(data_path, patient, f"Annotations_EEG_{record}.csv") + segments_file = os.path.join(data_path, patient, f"Segments_EEG_{record}.csv") - df_annot = pd.read_csv( - annotation_file, index_col=0, sep='|', encoding='latin-1') - df_seg = pd.read_csv(segments_file, sep='|') + df_annot = pd.read_csv(annotation_file, index_col=0, sep="|", encoding="latin-1") + df_seg = pd.read_csv(segments_file, sep="|") return sample_frequency, df_ecg, df_annot, df_seg diff --git a/src/usecase/fetch_database.py b/src/usecase/fetch_database.py index 31b1cfb..714f555 100644 --- a/src/usecase/fetch_database.py +++ b/src/usecase/fetch_database.py @@ -1,142 +1,233 @@ +""" +Create the list of files to process in Airflow DAG. + +After searching all candidates files matching a patter, create a csv +(df_candidates.csv) that lists all the files to process in the Airflow DAG. + +copyright (c) 2022 association aura +spdx-license-identifier: gpl-3.0 +""" import argparse import os import re import sys -import subprocess -from typing import Tuple, List +import glob +from typing import List, Tuple import pandas as pd sys.path.append('.') from src.usecase.utilities import convert_args_to_dict -# TUH database example -TUH_DATA_FILE_PATTERN = "*.edf" -TUH_ANNOTATIONS_FILE_PATTERN = "*.tse_bi" -TUH_PATIENT_PATTERN = ".+\\/(.+)_.+_.+\\..+" -TUH_EXAM_PATTERN = ".+\\/(.+)\\..+" -TUH_ANNOTATOR_PATTERN = "" -EXPORT_FOLDER = "output/db" +EXPORT_FOLDER = "output/feats-v0_6" +DATA_FILE_PATTERN = "*.edf" +ANNOTATION_FILE_PATTERN = "*.tse_bi" -def write_database(export_folder: str, - df_data: pd.DataFrame, - df_annotations: pd.DataFrame) -> Tuple[str, str, str]: +TUH_PATIENT_PATTERN = r".+\/(.+)_.+_.+\..+" +TUH_EXAM_PATTERN = r".+\/(.+)\..+" - os.makedirs(export_folder, exist_ok=True) - data_path = f'{export_folder}/df_data.csv' - annotation_path = f'{export_folder}/df_annotations.csv' - candidates_path = f'{export_folder}/df_candidates.csv' +DATASET_PATIENT_PATTERN = r"[P][A][T][_][0-9]*" +DATASET_EXAM_PATTERN = r"[E][E][G][_][0-9]*" - df_data.to_csv(data_path, index=False, encoding="utf-8") - df_annotations.to_csv(annotation_path, index=False, encoding="utf-8") - df_data.columns = ['edf_file_path'] + list(df_data.columns[1:]) - df_annotations.columns = ['annotations_file_path'] + list( - df_annotations.columns[1:]) - df_candidates = df_data.merge(df_annotations, - how='outer', - on=['exam_id', 'patient_id']) +def create_df_from_file_pattern( + data_folder_path: str, + file_pattern: str, + file_label: str, + patient_pattern: str, + exam_pattern: str) -> pd.DataFrame: + """ + Create a pd.DataFrame listing all files according to a pattern. + + Create a dataset listing recursively all files in a folder matching + a pattern, then extracting meta-information patient and exam ids according + to patterns. + + parameters + ---------- + data_folder_path : str + The path the folder where files are searched + file_pattern : str + Pattern to search to list files candidates + file_label : str + Label to describe which king of file is searched + patient_pattern : str + Pattern to search to extract the patient id + exam_pattern : str + Pattern to search to extraction exam id + + returns + ------- + df : pd.DataFrame + A pd.DataFrame listing the paths to matcehd files, exam_id and + patient_id + """ + df = pd.DataFrame(columns=[f'{file_label}_file_path', + 'exam_id', + 'patient_id']) + file_paths = glob.glob( + os.path.join(data_folder_path, + f'**/{file_pattern}'), + recursive=True) + + for file_path in file_paths: + + patient_str = re.search(patient_pattern, file_path) + try: + # TUH parsing + patient_id = patient_str.group(1) + except IndexError: + # Dataset Parsing + patient_id = patient_str.group(0) + + exam_str = re.search(exam_pattern, file_path) + try: + exam_id = exam_str.group(1) + except IndexError: + # Teppe + exam_id = exam_str.group(0) - df_candidates.to_csv(candidates_path, index=False, encoding="utf-8") + df = df.append({ + f"{file_label}_file_path": file_path.strip(), + "exam_id": exam_id, + "patient_id": patient_id}, ignore_index=True) - return data_path, annotation_path, candidates_path + return df def fetch_database( data_folder_path: str, export_folder: str = EXPORT_FOLDER, - data_file_pattern: str = TUH_DATA_FILE_PATTERN, + data_file_pattern: str = DATA_FILE_PATTERN, + annotations_file_pattern: str = ANNOTATION_FILE_PATTERN, patient_pattern: str = TUH_PATIENT_PATTERN, - exam_pattern: str = TUH_EXAM_PATTERN, - annotations_file_pattern: str = TUH_ANNOTATIONS_FILE_PATTERN - ) -> Tuple[str, str, str]: - - # Creating pd.DataFrame with edf path/exam_id/patient_id - df_data = pd.DataFrame(columns=["data_file_path", - "exam_id", - "patient_id"]) - - data_call = subprocess.Popen(('find', - '-L', - data_folder_path, - '-type', - 'f', - '-iname', - data_file_pattern), - stdout=subprocess.PIPE) - - for line in iter(data_call.stdout.readline, b""): - patient_id = "" - exam_id = "" - - patient_str = re.search(patient_pattern, line.decode("utf-8")) - if patient_str is not None and len(patient_str.groups()) == 1: - patient_id = patient_str.group(1) - exam_str = re.search(exam_pattern, line.decode("utf-8")) - if exam_str is not None and len(exam_str.groups()) == 1: - exam_id = exam_str.group(1) + exam_pattern: str = TUH_EXAM_PATTERN) -> None: + """ + Create a csv of files to process in DAG Airflow. + + Create a dataset consolidating data files and corresponding annotations + according to some patterns. It also extract the exam and patient ids. + + parameters + ---------- + data_folder_path : str + The path the folder where files are searched + export_folder : str + The folder where the dataframe is saved + data_file_pattern : str + Regex Pattern to match to search for data candidates + annotations_file_pattern : str + Regex Pattern to match to search for annotations candidates + patient_pattern : str + Regex Pattern to match to search for patient id + exam_pattern : str + Regex Pattern to match to search for exam id + + returns + ------- + df : pd.DataFrame + A pd.DataFrame listing the paths to matcehd files, exam_id and + patient_id + """ + df_data = create_df_from_file_pattern( + data_folder_path=data_folder_path, + file_pattern=data_file_pattern, + file_label='data', + patient_pattern=patient_pattern, + exam_pattern=exam_pattern) + + df_annotations = create_df_from_file_pattern( + data_folder_path=data_folder_path, + file_pattern=annotations_file_pattern, + file_label='annotations', + patient_pattern=patient_pattern, + exam_pattern=exam_pattern) - df_data = df_data.append({ - "data_file_path": line.decode("utf-8").strip(), - "exam_id": exam_id, - "patient_id": patient_id}, ignore_index=True) - - # Creating pd.DataFrame with annotation file associated with edf - df_annotations = pd.DataFrame(columns=["data_file_path", - "exam_id", - "patient_id", - "annotator_id"]) - annotations_call = subprocess.Popen(('find', - '-L', - data_folder_path, - '-type', - 'f', - '-iname', - annotations_file_pattern), - stdout=subprocess.PIPE) - - for line in iter(annotations_call.stdout.readline, b""): - patient_id = "" - exam_id = "" - - patient_str = re.search(patient_pattern, line.decode("utf-8")) - if patient_str is not None and len(patient_str.groups()) == 1: - patient_id = patient_str.group(1) - - exam_str = re.search(exam_pattern, line.decode("utf-8")) - if exam_str is not None and len(exam_str.groups()) == 1: - exam_id = exam_str.group(1) + os.makedirs(export_folder, exist_ok=True) + df_candidates = df_data.merge(df_annotations, + how='outer', + on=['exam_id', 'patient_id']) - df_annotations = df_annotations.append({ - "data_file_path": line.decode("utf-8").strip(), - "exam_id": exam_id, - "patient_id": patient_id, - "annotator_id": ""}, ignore_index=True) + output = os.path.join(export_folder, 'df_candidates.csv') + df_candidates.to_csv(output, + index=False, + encoding="utf-8") - data_path, annotation_path, candidates_path = write_database( - export_folder, - df_data, - df_annotations) - return data_path, annotation_path, candidates_path +def infer_database( + data_folder_path: str, + pattern_to_match: str = DATASET_PATIENT_PATTERN, + dataset_file_pattern: str = DATA_FILE_PATTERN) -> Tuple[str, str]: + """ + Automatically infer if database is DATASET OR TUH. + + From a folder, checks if data file can be parsed according to dataset + patient pattern. If the condition is true, returns patient and exam pattern + for dataset, else for TUH. + + parameters + ---------- + data_folder_path : str + The path the folder where files are searched + pattern_to_match : str + Regex Pattern to match to search for patient id + dataset_file_pattern : str + Regex Pattern to match to search for data candidates + + returns + ------- + patient_pattern : str + Pattern to search to extract the patient id + exam_pattern : str + Pattern to search to extraction exam id + """ + file_paths = glob.glob( + os.path.join(data_folder_path, + f'**/{dataset_file_pattern}'), + recursive=True) + + for file_path in file_paths: + patient_str = re.search(pattern_to_match, file_path) + if patient_str is not None: + return DATASET_PATIENT_PATTERN, DATASET_EXAM_PATTERN + + return TUH_PATIENT_PATTERN, TUH_EXAM_PATTERN def parse_fetch_database_args(args_to_parse: List[str]) -> argparse.Namespace: + """ + Parse arguments for adaptable input. + + parameters + ---------- + args_to_parse : List[str] + List of the element to parse. Should be sys.argv[1:] if args are + inputed via CLI + + returns + ------- + args : argparse.Namespace + Parsed arguments + """ parser = argparse.ArgumentParser(description='CLI parameter input') parser.add_argument('--data-folder-path', dest='data_folder_path', + type=str, required=True) parser.add_argument('--export-folder', - dest='export_folder') - parser.add_argument('--data-file-pattern', - dest='data_file_pattern') + dest='export_folder', + type=str) + parser.add_argument('--infer-database', + dest='infer_database', + action='store_true') parser.add_argument('--patient-pattern', - dest='patient_pattern') + dest='patient_pattern', + type=str) parser.add_argument('--exam-pattern', - dest='exam_pattern') - parser.add_argument('--annotations-file-pattern', - dest='annotations_file_pattern') + dest='exam_pattern', + type=str) + parser.set_defaults(infer_database=False) args = parser.parse_args(args_to_parse) return args @@ -146,4 +237,7 @@ def parse_fetch_database_args(args_to_parse: List[str]) -> argparse.Namespace: args = parse_fetch_database_args(sys.argv[1:]) args_dict = convert_args_to_dict(args) + if args_dict.pop('infer_database'): + args_dict['patient_pattern'], args_dict['exam_pattern'] = \ + infer_database(data_folder_path=args_dict['data_folder_path']) fetch_database(**args_dict) diff --git a/src/usecase/interpretability/interpretability_xgboost.py b/src/usecase/interpretability/interpretability_xgboost.py new file mode 100644 index 0000000..e69de29 diff --git a/src/usecase/train_model.py b/src/usecase/train_model.py index e78a099..0bb7705 100644 --- a/src/usecase/train_model.py +++ b/src/usecase/train_model.py @@ -16,30 +16,37 @@ import pandas as pd import numpy as np -import matplotlib.pyplot as plt +import seaborn as sns import argparse import mlflow +import matplotlib.pyplot as plt import os import sys from sklearn.model_selection import train_test_split, GridSearchCV from sklearn.metrics import accuracy_score, f1_score, recall_score,\ - roc_auc_score, precision_score,\ - confusion_matrix, ConfusionMatrixDisplay + roc_auc_score, precision_score, confusion_matrix, ConfusionMatrixDisplay from sklearn.ensemble import RandomForestClassifier +# from imblearn.over_sampling import RandomOverSampler from typing import List +import xgboost as xgb +from src.usecase.data_processing.prepare_features import extract_patient_id sys.path.append('.') from src.usecase.utilities import convert_args_to_dict -TRACKING_URI = 'http://localhost:5000' +TRACKING_URI = 'http://mlflow:5000' MODEL_PARAM = { - 'model': RandomForestClassifier(), + 'model': xgb.XGBClassifier(), 'grid_parameters': { - 'min_samples_leaf': np.arange(1, 5, 1), - 'max_depth': np.arange(11, 16, 1), - 'max_features': ['auto'], - 'n_estimators': np.arange(15, 20, 1)}} + 'nthread':[4], + 'learning_rate': [0.1, 0.01, 0.05], + 'max_depth': np.arange(3, 5, 2), + 'scale_pos_weight':[1], + 'n_estimators': np.arange(15, 25, 2), + 'missing':[-999]} + } + MLRUNS_DIR = f'{os.getcwd()}/mlruns' @@ -82,10 +89,10 @@ def compute_metrics(prefix: str, mlflow.log_metric(f'{prefix}_fp', fp) mlflow.log_metric(f'{prefix}_fn', fn) mlflow.log_metric(f'{prefix}_tp', tp) - mlflow.log_metric(f'{prefix}_tp_rate', tn/np.sum(cm)) - mlflow.log_metric(f'{prefix}_fp_rate', fp/np.sum(cm)) - mlflow.log_metric(f'{prefix}_fn_rate', fn/np.sum(cm)) - mlflow.log_metric(f'{prefix}_tp_rate', tp/np.sum(cm)) + mlflow.log_metric(f'{prefix}_tp_rate', tn / np.sum(cm)) + mlflow.log_metric(f'{prefix}_fp_rate', fp / np.sum(cm)) + mlflow.log_metric(f'{prefix}_fn_rate', fn / np.sum(cm)) + mlflow.log_metric(f'{prefix}_tp_rate', tp / np.sum(cm)) except ValueError: print('cannot compute metrics') @@ -98,14 +105,14 @@ def compute_metrics(prefix: str, print('cannot compute ROC_AUC_score') try: - titles_options = [(f'{prefix} - Confusion Matrix', None), - (f'{prefix} - Normalized Confusion Matrix', 'true')] + titles_options = [(f'{prefix}-Confusion Matrix', None), + (f'{prefix}-Normalized Confusion Matrix', 'true')] for title, normalize in titles_options: if normalize is None: cm_disp = np.round(cm, 0) else: - cm_disp = np.round(cm/np.sum(cm.ravel()), 2) + cm_disp = np.round(cm / np.sum(cm.ravel()), 2) disp = ConfusionMatrixDisplay(confusion_matrix=cm_disp, display_labels=[0, 1]) @@ -113,21 +120,21 @@ def compute_metrics(prefix: str, disp.ax_.set_title(title) temp_name = f'{mlruns_dir}/{title}.png' plt.savefig(temp_name) - mlflow.log_artifact(temp_name, "confusion-matrix-plots") + mlflow.log_artifact(temp_name) if total_seconds is not None: titles_options = [ - (f'{prefix} - Confusion Matrix Minutes', None, 'minutes'), - (f'{prefix} - Confusion Matrix Seconds', None, 'seconds')] + (f'{prefix}-Confusion Matrix Minutes', None, 'minutes'), + (f'{prefix}-Confusion Matrix Seconds', None, 'seconds')] for title, normalize, time_unit in titles_options: if time_unit == 'minutes': cm_disp = np.round( - cm*total_seconds/(60*np.sum(cm.ravel())), 2) + cm * total_seconds / (60 * np.sum(cm.ravel())), 2) else: cm_disp = np.round( - cm*total_seconds/(np.sum(cm.ravel())), 2) + cm * total_seconds / (np.sum(cm.ravel())), 2) disp = ConfusionMatrixDisplay(confusion_matrix=cm_disp, display_labels=[0, 1]) @@ -135,7 +142,7 @@ def compute_metrics(prefix: str, disp.ax_.set_title(title) temp_name = f'{mlruns_dir}/{title}.png' plt.savefig(temp_name) - mlflow.log_artifact(temp_name, "confusion-matrix-plots") + mlflow.log_artifact(temp_name) except ValueError: print('cannot generate confusion matrices') @@ -159,7 +166,8 @@ def clean_ml_dataset(df_ml: pd.DataFrame, The clean ml dataset """ print(f'Lines before Nan removal : {df_ml.shape[0]}') - df_ml = df_ml.dropna() + df_ml.replace([np.inf, -np.inf], np.nan, inplace=True) + df_ml = df_ml.fillna(-999) print(f'Lines after Nan removal : {df_ml.shape[0]}') df_ml['label'] = df_ml['label'].apply( @@ -168,7 +176,98 @@ def clean_ml_dataset(df_ml: pd.DataFrame, return df_ml -def train_model(ml_dataset_path: str, +def plot_feature_importance(importance: np.array, feat_names: list, model_type: str, mlruns_dir: str) -> None: + + """ + Plot features importances of model for feature selection + + parameters + ---------- + importance : np.array + Array importance of all features used for training + feat_names : list + List of features names used in the train set + model_type : str + Name of model used + mtruns_dir: str + Directory to store ML runs + + returns + ------- + None + """ + + #Create arrays from feature importance and feature names + feature_importance = np.array(importance) + feature_names = np.array(feat_names) + + #Create a DataFrame using a Dictionary + data={'feature_names':feature_names,'feature_importance':feature_importance} + fi_df = pd.DataFrame(data) + + #Sort the DataFrame in order decreasing feature importance + fi_df.sort_values(by=['feature_importance'], ascending=False,inplace=True) + + #Define size of bar plot + plt.figure(figsize=(10,8)) + sns.barplot(x=fi_df['feature_importance'], y=fi_df['feature_names']) + #Add chart labels + plt.title(model_type + 'FEATURE IMPORTANCE') + plt.xlabel('FEATURE IMPORTANCE') + plt.ylabel('FEATURE NAMES') + temp_name = f'{mlruns_dir}/{"Feature importances"}.png' + plt.savefig(temp_name) + mlflow.log_artifact(temp_name, "Feature importances") + + +def train_pipeline_with_io(ml_dataset_cleaned_path: str, + ml_dataset_path_cleaned_test: str = None, + tracking_uri: str = TRACKING_URI, + model_param: dict = MODEL_PARAM, + mlruns_dir: str = MLRUNS_DIR) -> None: + """ + This function is used in airflow for training orchestration. + parameters + ---------- + ml_dataset_cleaned_path : str + The path to ML cleaned dataset for train + ml_dataset_path_cleaned_test : str + The path to ML cleaned dataset for validation. If none is inputed, + ml_dataset_path will be used for train and test after a + train_test_split + tracking_uri : str + URI for MLFlow tracking + model_param: dict + Parameters for the grisearch: model and hyper-parameters + mlruns_dir: str + Directory to store ML runs + """ + + df_ml = pd.read_csv(ml_dataset_cleaned_path) + df_ml_test = pd.read_csv(ml_dataset_path_cleaned_test) + + df_ml = clean_ml_dataset(df_ml) + df_ml_test = clean_ml_dataset(df_ml_test) + + df_ml['patient_id'] = df_ml['filename'].apply(extract_patient_id) + df_ml_test['patient_id'] = df_ml_test['filename'].apply(extract_patient_id) + + df_ml = df_ml[(df_ml['patient_id'] == 22) | (df_ml['patient_id'] == 45)\ + | (df_ml['patient_id'] == 39) | (df_ml['patient_id'] == 34)] + df_ml_test = df_ml_test[(df_ml_test['patient_id'] == 22) | (df_ml_test['patient_id'] == 45) \ + | (df_ml_test['patient_id'] == 39) | (df_ml_test['patient_id'] == 34)] + + train_model( + df_ml=df_ml, + df_ml_test=df_ml_test, + tracking_uri=tracking_uri, + model_param=model_param, + mlruns_dir=mlruns_dir) + + + +def train_pipeline(ml_dataset_path: str, + ml_dataset_path_test: str = None, tracking_uri: str = TRACKING_URI, model_param: dict = MODEL_PARAM, mlruns_dir: str = MLRUNS_DIR) -> str: @@ -178,7 +277,11 @@ def train_model(ml_dataset_path: str, parameters ---------- ml_dataset_path : str - The path to ML dataset + The path to ML dataset for train + ml_dataset_path_train : str + The path to ML dataset for validation. If none is inputed, + ml_dataset_path will be used for train and test after a + train_test_split tracking_uri : str URI for MLFlow tracking model_param: dict @@ -186,35 +289,84 @@ def train_model(ml_dataset_path: str, mlruns_dir: str Directory to store ML runs """ - mlflow.set_tracking_uri(tracking_uri) + df_ml = pd.read_csv(ml_dataset_path) + df_ml_test = pd.read_csv(ml_dataset_path_test) - with mlflow.start_run(): + df_ml = clean_ml_dataset(df_ml, target_treshold=0.5) + df_ml = df_ml.dropna() + + df_ml_test = clean_ml_dataset(df_ml_test, target_treshold=0.5) + df_ml_test = df_ml_test.dropna() + + train_model( + df_ml=df_ml, + df_ml_test=df_ml_test, + tracking_uri=TRACKING_URI, + model_param=MODEL_PARAM, + mlruns_dir=MLRUNS_DIR) - df_ml = pd.read_csv(ml_dataset_path) - df_ml = clean_ml_dataset(df_ml, treshold=0.5) - X = df_ml.iloc[:, :-1] - y = df_ml.iloc[:, -1] +def train_model( + df_ml: pd.DataFrame, + df_ml_test: pd.DataFrame, + tracking_uri: str = TRACKING_URI, + model_param: dict = MODEL_PARAM, + mlruns_dir: str = MLRUNS_DIR) -> None: + + mlflow.xgboost.autolog() + + + mlflow.set_tracking_uri(tracking_uri) + with mlflow.start_run(): + print(mlflow.get_artifact_uri()) + + feature_names = [] # Making train and test variables - X_train, X_test, y_train, y_test = train_test_split( - X, y, test_size=0.3, random_state=42, stratify=y) + if df_ml_test is not None: + y_train = df_ml['label'] + X_train = df_ml.\ + drop('label', 1).\ + drop('timestamp', 1).\ + drop('filename', 1).\ + drop('patient_id', 1) + + feature_names = X_train.columns + + y_test = df_ml_test['label'] + X_test = df_ml_test.\ + drop('label', 1).\ + drop('timestamp', 1).\ + drop('filename', 1).\ + drop('patient_id', 1) + + else: + y = df_ml['label'] + X = df_ml.drop('label', 1).drop('timestamp', 1) + + X_train, X_test, y_train, y_test = train_test_split( + X, y, test_size=0.3, random_state=42, stratify=y) # Convertion of pandas DataFrames to numpy arrays # before using scikit-learn + feature_names = X_train.columns + X_train = X_train.values X_test = X_test.values y_train = y_train.values y_test = y_test.values + # ros = RandomOverSampler(random_state=42) + # X, y = ros.fit_resample(X_train, y_train) + # Model Training grid_search = GridSearchCV(estimator=model_param['model'], - param_grid=model_param['grid_parameters'], - scoring='f1', - cv=5, - verbose=5, - n_jobs=-1) + param_grid=model_param['grid_parameters'], + scoring='roc_auc', + cv=5, + verbose=5, + n_jobs=-1) grid_search.fit(X_train, y_train) # Preparing data for performance assessement @@ -222,9 +374,10 @@ def train_model(ml_dataset_path: str, y_test_pred = grid_search.predict(X_test) # Model and performance logging - mlflow.sklearn.log_model(grid_search, 'model') + mlflow.sklearn.log_model(grid_search, 'xgboost') mlflow.log_param('best_param', grid_search.best_params_) + mlflow.log_param("Description", "Xgboost model sur les patients {22, 34, 39, 45}") # mlflow.log_param('algorith', 'rfc') compute_metrics('train', @@ -239,6 +392,11 @@ def train_model(ml_dataset_path: str, tracking_uri=tracking_uri, mlruns_dir=mlruns_dir) + # log features importances + # plot_feature_importance(grid_search.best_estimator_.feature_importances_, + # feature_names, "RandomForest ", mlruns_dir) + + def parse_train_model_args(args_to_parse: List[str]) -> argparse.Namespace: """ @@ -259,6 +417,9 @@ def parse_train_model_args(args_to_parse: List[str]) -> argparse.Namespace: parser.add_argument('--ml-dataset-path', dest='ml_dataset_path', required=True) + parser.add_argument('--ml-dataset-path-test', + dest='ml_dataset_path_test', + required=True) args = parser.parse_args(args_to_parse) return args diff --git a/src/usecase/utilities.py b/src/usecase/utilities.py index 8d5ea82..b24d753 100644 --- a/src/usecase/utilities.py +++ b/src/usecase/utilities.py @@ -7,6 +7,8 @@ import argparse import os +PREFIX_TO_REMOVE = ['rr', 'feats', 'cons'] + def convert_args_to_dict(args: argparse.Namespace) -> dict: """ @@ -36,7 +38,8 @@ def convert_args_to_dict(args: argparse.Namespace) -> dict: def generate_output_path(input_file_path: str, output_folder: str, - format: str) -> str: + format: str, + prefix: str = '') -> str: """ Generate an output path. @@ -51,6 +54,8 @@ def generate_output_path(input_file_path: str, Path of the output folder format: str Format of the output file, typically, csv or json + prefix : str + Prefix to add before the name of output filename returns ------- @@ -59,7 +64,13 @@ def generate_output_path(input_file_path: str, """ os.makedirs(output_folder, exist_ok=True) output_file_parsing = input_file_path.split('/')[-1].split('.')[0] - output_filename = f'{output_file_parsing}.{format}' + for prefix_to_remove in PREFIX_TO_REMOVE: + try: + output_file_parsing = output_file_parsing.split( + f'{prefix_to_remove}_')[-1] + except: + pass + output_filename = f'{prefix}_{output_file_parsing}.{format}' output_file_path = os.path.join(output_folder, output_filename) return output_file_path diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..c7b0cd2 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,41 @@ +from pytest import fixture +import numpy as np +import pandas as pd +import datetime +from freezegun import freeze_time + +DATASET_FILE_PATH = "data/test_data/test_data_feat_eng.csv" +DATASET_FILE_PATH_FEAT = "data/test_data/test_data_feat_contextual.csv" + +COL_TO_DROP = ['timestamp', 'set'] + +THRESHOLD = -1.5 + +LIST_TIME = ["dayOfWeek", "month", "hour", "minute"] + +LIST_FEAT = ["mean_hr"] +OPERATION_TYPE = "std" +LIST_PERIOD = [30, 60] + + +@fixture +@freeze_time("2022-06-13") +def dataframe(): + data ={ + "mean_hr": 10*[1.], + "sdsd": 10*[1.3], + "lf": 10*[1.6], + "hf": 10*[1.9], + "set": 10*["train"], + "timestamp": 10*[datetime.date.today()], + "label": 10*[1] + } + + data["mean_hr"][0] = np.nan + data["sdsd"][4] = np.nan + data["lf"][2] = np.inf + data["hf"][6] = np.inf + + dataframe = pd.DataFrame(data) + + return dataframe \ No newline at end of file diff --git a/tests/test_integration_bash.py b/tests/test_integration_bash.py new file mode 100644 index 0000000..2a0caa8 --- /dev/null +++ b/tests/test_integration_bash.py @@ -0,0 +1,160 @@ +""" +This script is used to make integration testi on both TUH and dataset format. + +Copyright (C) 2022 Association Aura +SPDX-License-Identifier: GPL-3.0 +""" +import os +import pandas as pd +import shutil +import subprocess +from pathlib import Path + + +def test_run_pipeline_tuh(path_data_source: str = Path('data/tuh'), + path_test: str = Path('tests/temp')): + """Make integration test on TUH format. + + Copy sample data, process all the bash pipeline up to the creation of the + ml dataset, then delete temporary files. + + parameters + ---------- + path_data_source : str + path of the sample data to use + path_test : str + temporary folder to use for integration testing. It will be deleted + after the testing + """ + path_data = os.path.join(path_test, 'data') + path_export = os.path.join(path_test, 'output') + + if os.path.exists(path_test): + shutil.rmtree(path_test) + + shutil.copytree(path_data_source, + path_data, + ignore=shutil.ignore_patterns("00004671_s007_t000.*", + "00007633_s003_t007*")) + + if not os.path.exists(path_export): + os.mkdir(path_export) + + bash_detect_qrs_wrapper = subprocess.Popen([ + "./scripts/bash_pipeline/1_detect_qrs_wrapper.sh", + "-i", + path_data, + "-o", + os.path.join(path_export, 'res-v0_6')]) + bash_detect_qrs_wrapper.wait() + + bash_compute_hrv_analysis_features_wrapper = subprocess.Popen([ + "./scripts/bash_pipeline/2_compute_hrvanalysis_features_wrapper.sh", + "-i", + os.path.join(path_export, 'res-v0_6'), + "-o", + os.path.join(path_export, 'feats-v0_6')]) + bash_compute_hrv_analysis_features_wrapper.wait() + + bash_consolidate_feats_and_annots = subprocess.Popen([ + "./scripts/bash_pipeline/3_consolidate_feats_and_annot_wrapper.sh", + "-i", + os.path.join(path_export, 'feats-v0_6'), + "-a", + path_data, + "-o", + os.path.join(path_export, 'cons-v0_6')]) + bash_consolidate_feats_and_annots.wait() + + create_ml_dataset = subprocess.Popen([ + "python3", + "src/usecase/create_ml_dataset.py", + "--input-folder", + os.path.join(path_export, 'cons-v0_6'), + "--output-folder", + os.path.join(path_export, 'ml_dataset')]) + create_ml_dataset.wait() + + df = pd.read_csv(os.path.join(path_export, 'ml_dataset/df_ml.csv')) + assert(df.shape[0] == 33) + assert(df.shape[1] >= 31) + assert('interval_index' in df.columns) + assert('interval_start_time' in df.columns) + assert('label' in df.columns) + assert('timestamp' in df.columns) + + if os.path.exists(path_test): + shutil.rmtree(path_test) + + +def test_run_pipeline_dataset(path_data_source: str = Path('data/dataset'), + path_test: str = Path('tests/temp')): + """Make integration test on dataset format. + + Copy sample data from the features phase and process the remaining + pipeline up to the creation of the ml dataset, then delete temporary + files. + + parameters + ---------- + path_data_source : str + path of the sample data to use + path_test : str + temporary folder to use for integration testing. It will be deleted + after the testing + """ + path_data = os.path.join(path_test, 'data') + path_export = os.path.join(path_test, 'output') + + if os.path.exists(path_test): + shutil.rmtree(path_test) + + shutil.copytree(path_data_source, + path_data, + ignore=shutil.ignore_patterns("00004671_s007_t000.*", + "00007633_s003_t007*")) + + # Create features folder and copy already computed files from the data + # folder. This will allows next steps of the pipeline to run. + + if not os.path.exists(path_export): + os.mkdir(path_export) + os.mkdir(os.path.join(path_export, + 'feats-v0_6')) + os.mkdir(os.path.join(path_export, + 'feats-v0_6/PAT_0')) + + shutil.copyfile( + "data/test_data/dataset_feats_PAT_0_Annotations_EEG_0_s2.csv", + os.path.join(path_export, + 'feats-v0_6/PAT_0/feats_EEG_0_s2.csv')) + + bash_consolidate_feats_and_annots = subprocess.Popen([ + "./scripts/bash_pipeline/3_consolidate_feats_and_annot_wrapper.sh", + "-i", + os.path.join(path_export, 'feats-v0_6'), + "-a", + path_data, + "-o", + os.path.join(path_export, 'cons-v0_6')]) + bash_consolidate_feats_and_annots.wait() + + create_ml_dataset = subprocess.Popen([ + "python3", + "src/usecase/create_ml_dataset.py", + "--input-folder", + os.path.join(path_export, 'cons-v0_6'), + "--output-folder", + os.path.join(path_export, 'ml_dataset')]) + create_ml_dataset.wait() + + df = pd.read_csv(os.path.join(path_export, 'ml_dataset/df_ml.csv')) + assert(df.shape[0] == 599) + assert(df.shape[1] >= 31) + assert('interval_index' in df.columns) + assert('interval_start_time' in df.columns) + assert('label' in df.columns) + assert('timestamp' in df.columns) + + if os.path.exists(path_test): + shutil.rmtree(path_test) diff --git a/tests/test_src_usecase/test_apply_ecg_qc.py b/tests/test_src_usecase/test_apply_ecg_qc.py new file mode 100644 index 0000000..d6866a9 --- /dev/null +++ b/tests/test_src_usecase/test_apply_ecg_qc.py @@ -0,0 +1,132 @@ +""" +This script is used to test fetch_datase script. + +Copyright (C) 2022 Association Aura +SPDX-License-Identifier: GPL-3.0 +""" +import argparse +import os +import pandas as pd +import pytest +from src.usecase.apply_ecg_qc import \ + parse_model,\ + import_signal_data,\ + apply_ecg_qc,\ + parse_apply_ecg_qc_args,\ + ECG_QC_MODEL + +from src.usecase.consolidate_feats_and_annot import read_tse_bi +from src.usecase.utilities import convert_args_to_dict + +OUTPUT_FOLDER = 'tests/output/' + +TUH_FEATURES_FILE_PATH = 'data/test_data/tuh_feats_00004671_s007_t000.csv' +TUH_ANNOTATIONS_FILE_PATH = \ + 'data/tuh/dev/01_tcp_ar/046/00004671/' \ + 's007_2012_08_04/00004671_s007_t000.tse_bi' +TUH_ANNOTATIONS_FILE_PATH_INCORRECT = \ + 'data/test_data/tuh_rr_00007633_s003_t007.csv' + +DATASET_FEATUERS_FILE_PATH = \ + 'data/test_data/dataset_feats_PAT_0_Annotations_EEG_0_s2.csv' +DATASET_ANNOTATIONS_FILE_PATH = \ + 'data/dataset/PAT_0/PAT_0_Annotations_EEG_0.tse_bi' + +TUH_DATA_FOLDER = 'data/tuh' + +TEST_TUH_EDF_FILENAME = \ + 'data/tuh/dev/01_tcp_ar/002/00009578/00009578_s002_t001.edf' +TEST_TUH_EXAM_ID = '1234536' + +FORMATTING_TUH = 'tuh' +FORMATTING_DATASET = 'dataset' + +EDF_FILE_WIHTOUT_ECG_CHANNEL = 'data/test_data/ST7051J0-PSG.edf' + + +def test_parse_model_returns_correct_parameters(): + """From the model name, test the correct output.""" + assert(parse_model(ECG_QC_MODEL) == + ('models/rfc_normalized_2s.pkl', + 'rfc_normalized_2s', + 2, + True)) + assert(parse_model('dtc_7s.pkl') == + ('models/dtc_7s.pkl', + 'dtc_7s', + 7, + False)) + + +def test_parse_model_ValueError_returns_length_chunk_9(): + """Test the ValueError handling.""" + assert(parse_model('non-standard-name.pkl') == + ('models/non-standard-name.pkl', + 'non-standard-name', + 9, + False)) + + +def test_import_signal_data(): + """Assert the correct import of signal data from edf file.""" + with pytest.raises(ValueError): + import_signal_data(EDF_FILE_WIHTOUT_ECG_CHANNEL) + + +def test_apply_ecg_qc_tuh(): + """Assert full pipeline to create a tse_bi for dataset format.""" + output_file_path = apply_ecg_qc(qrs_file_path=TEST_TUH_EDF_FILENAME, + formatting=FORMATTING_TUH, + output_folder=OUTPUT_FOLDER, + model=ECG_QC_MODEL) + assert(output_file_path == 'tests/output/ecg_qc_00009578_s002_t001.tse_bi') + df_tse_bi_tuh = read_tse_bi(output_file_path) + df_target_tuh = pd.DataFrame( + data={'start': [pd.Timestamp(0)], + 'end': [pd.Timestamp(18_000_000_000)], + 'annotation': ['noisy'], + 'probability': [1]}) + assert(df_tse_bi_tuh.equals(df_target_tuh)) + + if os.path.isfile(output_file_path): + os.remove(output_file_path) + + +def test_apply_ecg_qc_dataset(): + """Assert full pipeline to create a tse_bi for tuh format.""" + output_file_path = apply_ecg_qc(qrs_file_path=TEST_TUH_EDF_FILENAME, + formatting=FORMATTING_DATASET, + output_folder=OUTPUT_FOLDER, + model=ECG_QC_MODEL) + assert(output_file_path == 'tests/output/ecg_qc_00009578_s002_t001.tse_bi') + df_tse_bi_tuh = read_tse_bi(output_file_path) + df_target_tuh = pd.DataFrame( + data={'start': [pd.Timestamp('2013-02-28 16:03:12')], + 'end': [pd.Timestamp('2013-02-28 16:03:29')], + 'annotation': ['noisy'], + 'probability': [1]}) + assert(df_tse_bi_tuh.equals(df_target_tuh)) + + if os.path.isfile(output_file_path): + os.remove(output_file_path) + + +def test_parse_apply_ec_gqc(): + """Test the parsing of arguments.""" + bash_command = (f'python3 src/usecase/detect_qrs.py ' + f'--qrs-file-path {TEST_TUH_EDF_FILENAME} ' + f'--output-folder {OUTPUT_FOLDER} ' + f'--model {ECG_QC_MODEL} ' + f'--formatting {FORMATTING_TUH}') + args_to_parse = bash_command.split()[2:] + parser = parse_apply_ecg_qc_args(args_to_parse) + parser_dict = convert_args_to_dict(parser) + + correct_parser = argparse.Namespace( + qrs_file_path=TEST_TUH_EDF_FILENAME, + output_folder=OUTPUT_FOLDER, + model=ECG_QC_MODEL, + formatting=FORMATTING_TUH) + correct_parser_dict = convert_args_to_dict(correct_parser) + + assert(parser_dict == correct_parser_dict) diff --git a/tests/test_src_usecase/test_compute_hrvanalysis_features.py b/tests/test_src_usecase/test_compute_hrvanalysis_features.py index 5b7101d..f30a6dd 100644 --- a/tests/test_src_usecase/test_compute_hrvanalysis_features.py +++ b/tests/test_src_usecase/test_compute_hrvanalysis_features.py @@ -11,8 +11,9 @@ FEATURES_KEY_TO_INDEX from src.usecase.utilities import convert_args_to_dict -OUTPUT_FOLDER = 'data/output/features' -RR_INTERVAL_FILE_PATH = 'data/test_data/rr_00007633_s003_t007.csv' +OUTPUT_FOLDER = 'tests/output/features' +RR_INTERVAL_FILE_PATH = 'data/test_data/tuh_rr_00007633_s003_t007.csv' + @pytest.fixture def test_features_computer(): diff --git a/tests/test_src_usecase/test_consolidate_feats_and_annot.py b/tests/test_src_usecase/test_consolidate_feats_and_annot.py index 619adde..5ed8ce9 100644 --- a/tests/test_src_usecase/test_consolidate_feats_and_annot.py +++ b/tests/test_src_usecase/test_consolidate_feats_and_annot.py @@ -1,4 +1,11 @@ +""" +This script is used to test consolidate_feats_and_annot.py script. + +Copyright (C) 2022 Association Aura +SPDX-License-Identifier: GPL-3.0 +""" import argparse +import os import pandas as pd import pytest from src.usecase.consolidate_feats_and_annot import \ @@ -9,76 +16,119 @@ from src.usecase.utilities import convert_args_to_dict OUTPUT_FOLDER = 'tests/output/features' -FEATURES_FILE_PATH = 'data/test_data/feats_00009578_s006_t001.csv' -ANNOTATIONS_FILE_PATH = \ - 'data/tuh/dev/01_tcp_ar/002/00009578/00009578_s006_t001.tse_bi' -WINDOW_INTERVAL= 10_000 + +TUH_FEATURES_FILE_PATH = 'data/test_data/tuh_feats_00004671_s007_t000.csv' +TUH_ANNOTATIONS_FILE_PATH = \ + 'data/tuh/dev/01_tcp_ar/046/00004671/' \ + 's007_2012_08_04/00004671_s007_t000.tse_bi' +TUH_ANNOTATIONS_FILE_PATH_INCORRECT = \ + 'data/test_data/tuh_rr_00007633_s003_t007.csv' +TUH_ANNOTATIONS_FILE_PATH_EMPTY = \ + 'data/test_data/tuh_empty.tse_bi' + + +DATASET_FEATUERS_FILE_PATH = \ + 'data/test_data/dataset_feats_PAT_0_Annotations_EEG_0_s2.csv' +DATASET_ANNOTATIONS_FILE_PATH = \ + 'data/dataset/PAT_0/PAT_0_Annotations_EEG_0.tse_bi' + +WINDOW_INTERVAL = 1_000 SEGMENT_SIZE_TRESHOLD = 0.9 CROPPED_DATASET = True -NO_BCKG_TSE_BI_PATH = 'data/test_data/no_bckg_00009578_s006_t001.tse_bi' +TUH_NO_BCKG_TSE_BI_PATH = \ + 'data/test_data/tuh_no_bckg_00009578_s006_t001.tse_bi' + +LABEL_TARGET = 'seiz' -def generate_consolidated_features_and_annot(cropped: bool): - returned_path = consolidate_feats_and_annot( - features_file_path=FEATURES_FILE_PATH, - annotations_file_path=ANNOTATIONS_FILE_PATH, +def generate_consolidated_features_and_annot(features_file_path: str, + annotations_file_path: str, + cropped: bool) -> str: + """Reusable function for consolidation generation.""" + return consolidate_feats_and_annot( + features_file_path=features_file_path, + annotations_file_path=annotations_file_path, output_folder=OUTPUT_FOLDER, window_interval=WINDOW_INTERVAL, segment_size_treshold=SEGMENT_SIZE_TRESHOLD, + label_target=LABEL_TARGET, crop_dataset=cropped) - return returned_path - @pytest.fixture -def consolidated_cropped_dataset(cropped: bool = True): - returned_path = generate_consolidated_features_and_annot(cropped=cropped) - return returned_path +def tuh_consolidated_cropped( + features_file_path: str = TUH_FEATURES_FILE_PATH, + annotations_file_path: str = TUH_ANNOTATIONS_FILE_PATH, + cropped: bool = True): + """Generate a cropped consolidated csv for tuh.""" + return generate_consolidated_features_and_annot( + features_file_path=features_file_path, + annotations_file_path=annotations_file_path, + cropped=cropped) @pytest.fixture -def consolidated_uncropped_dataset(cropped: bool = False): - returned_path = generate_consolidated_features_and_annot(cropped=cropped) - return returned_path +def tuh_consolidated_uncropped( + features_file_path: str = TUH_FEATURES_FILE_PATH, + annotations_file_path: str = TUH_ANNOTATIONS_FILE_PATH, + cropped: bool = False): + """Generate an uncropped consolidated csv for tuh.""" + return generate_consolidated_features_and_annot( + features_file_path=features_file_path, + annotations_file_path=annotations_file_path, + cropped=cropped) -def test_consolidate_feats_and_annot_cropped(consolidated_cropped_dataset): +@pytest.fixture +def dataset_consolidated_uncropped( + features_file_path: str = DATASET_FEATUERS_FILE_PATH, + annotations_file_path: str = DATASET_ANNOTATIONS_FILE_PATH, + cropped: bool = False): + """Generate an uncropped consolidated csv for dataset.""" + return generate_consolidated_features_and_annot( + features_file_path=features_file_path, + annotations_file_path=annotations_file_path, + cropped=cropped) - returned_path = consolidated_cropped_dataset - assert(type(returned_path) == str) - df_features = pd.read_csv(FEATURES_FILE_PATH) - df_export = pd.read_csv(returned_path) +def test_tuh_consolidate_feats_and_annot_cropped( + tuh_consolidated_cropped): + """Test the shape of the csv is cropped for tuh.""" + df_features = pd.read_csv(TUH_FEATURES_FILE_PATH) + df_export = pd.read_csv(tuh_consolidated_cropped) assert(df_export.shape == (df_features.shape[0]-2, df_features.shape[1]+1)) + if os.path.isfile(tuh_consolidated_cropped): + os.remove(tuh_consolidated_cropped) -def test_consolidate_feats_and_annot_uncropped(consolidated_uncropped_dataset): - - returned_path = consolidated_uncropped_dataset - assert(type(returned_path) == str) - df_features = pd.read_csv(FEATURES_FILE_PATH) - df_export = pd.read_csv(returned_path) +def test_tuh_consolidate_feats_and_annot_uncropped( + tuh_consolidated_uncropped): + """Test the shape of the csv is uncropped for tuh.""" + df_features = pd.read_csv(TUH_FEATURES_FILE_PATH) + df_export = pd.read_csv(tuh_consolidated_uncropped) assert(df_export.shape == (df_features.shape[0], df_features.shape[1]+1)) + if os.path.isfile(tuh_consolidated_uncropped): + os.remove(tuh_consolidated_uncropped) -def test_get_label_on_interval_no_bckg_exception(): - df_tse_bi = read_tse_bi(NO_BCKG_TSE_BI_PATH) - try: - get_label_on_interval( - df_tse_bi=df_tse_bi, - interval_start_time=0, - window_interval=WINDOW_INTERVAL, - segment_size_treshold=SEGMENT_SIZE_TRESHOLD) - except KeyError: - assert True +def test_dataset_consolidate_feats_and_annot_uncropped( + dataset_consolidated_uncropped): + """Test the shape of the csv is uncropped for dataset.""" + df_features = pd.read_csv(DATASET_FEATUERS_FILE_PATH) + df_export = pd.read_csv(dataset_consolidated_uncropped) + assert(df_export.shape == (df_features.shape[0], df_features.shape[1]+1)) + + if os.path.isfile(dataset_consolidated_uncropped): + os.remove(dataset_consolidated_uncropped) -def test_tuh_parse_consolidate_feats_and_annot_args(): +def test_tuh_parse_consolidate_feats_and_annot_args(): + """Test CLI usage of the script.""" bash_command = (f'python3 src/usecase/detect_qrs.py ' - f'--features-file-path {FEATURES_FILE_PATH} ' - f'--annotations-file-path {ANNOTATIONS_FILE_PATH} ' + f'--features-file-path {TUH_FEATURES_FILE_PATH} ' + f'--annotations-file-path {TUH_ANNOTATIONS_FILE_PATH} ' f'--output-folder {OUTPUT_FOLDER} ' f'--window-interval {WINDOW_INTERVAL} ' f'--segment-size-treshold {SEGMENT_SIZE_TRESHOLD} ' @@ -88,8 +138,8 @@ def test_tuh_parse_consolidate_feats_and_annot_args(): parser_dict = convert_args_to_dict(parser) correct_parser = argparse.Namespace( - features_file_path=FEATURES_FILE_PATH, - annotations_file_path=ANNOTATIONS_FILE_PATH, + features_file_path=TUH_FEATURES_FILE_PATH, + annotations_file_path=TUH_ANNOTATIONS_FILE_PATH, output_folder=OUTPUT_FOLDER, window_interval=WINDOW_INTERVAL, segment_size_treshold=SEGMENT_SIZE_TRESHOLD, @@ -99,18 +149,108 @@ def test_tuh_parse_consolidate_feats_and_annot_args(): assert(parser_dict == correct_parser_dict) -def test_get_label_on_interval(): - pass -def test_input_read_tse_bi_test( - annotations_file_path_correct = ANNOTATIONS_FILE_PATH, - annotations_file_path_incorrect = \ - 'data/test_data/rr_00007633_s003_t007.csv'): +def test_tuh_get_label_on_interval(): + """Test label fetching.""" + intervals_to_test = [ + {'interval_start_time': 20_000, 'label': 0}, + {'interval_start_time': 50_000, 'label': 1}, + {'interval_start_time': 118_000, 'label': 0}, + {'interval_start_time': 168_000, 'label': 1}, + {'interval_start_time': 175_000, 'label': 0}, + {'interval_start_time': 200_000, 'label': 1} + ] + + for interval_to_test in intervals_to_test: + + assert(get_label_on_interval( + df_tse_bi=read_tse_bi(TUH_ANNOTATIONS_FILE_PATH), + interval_start_time=interval_to_test['interval_start_time'], + window_interval=WINDOW_INTERVAL, + segment_size_treshold=SEGMENT_SIZE_TRESHOLD, + label_target=LABEL_TARGET) + == interval_to_test['label']) + + +def test_dataset_get_label_on_interval(): + """Test interval fetching.""" + intervals_to_test = [ + {'interval_start_time': '2017-01-10T14:30:05', 'label': 0}, + {'interval_start_time': '2017-01-10T14:32:30', 'label': 1}, + {'interval_start_time': '2017-01-10T14:32:41', 'label': 0}, + {'interval_start_time': '2017-01-10T14:38:45', 'label': 1}, + {'interval_start_time': '2017-01-10T14:38:55', 'label': 0}, + ] + + for interval_to_test in intervals_to_test: + + assert(get_label_on_interval( + df_tse_bi=read_tse_bi(DATASET_ANNOTATIONS_FILE_PATH), + interval_start_time=interval_to_test['interval_start_time'], + window_interval=WINDOW_INTERVAL, + segment_size_treshold=SEGMENT_SIZE_TRESHOLD, + label_target=LABEL_TARGET) + == interval_to_test['label']) + + +def test_input_read_tse_bi_test(): + """Test tse_bi files are correctly read.""" + df_tse_bi_tuh = read_tse_bi(TUH_ANNOTATIONS_FILE_PATH) + df_target_tuh = pd.DataFrame( + data={ + 'start': [pd.Timestamp(0.0000 * 1_000_000_000), + pd.Timestamp(46.2734 * 1_000_000_000), + pd.Timestamp(52.0742 * 1_000_000_000), + pd.Timestamp(119.1406 * 1_000_000_000), + pd.Timestamp(125.8398 * 1_000_000_000)], + 'end': [pd.Timestamp(46.2734 * 1_000_000_000), + pd.Timestamp(52.0742 * 1_000_000_000), + pd.Timestamp(119.1406 * 1_000_000_000), + pd.Timestamp(125.8398 * 1_000_000_000), + pd.Timestamp(166.8203 * 1_000_000_000)], + 'annotation': ['bckg', + 'seiz', + 'bckg', + 'seiz', + 'bckg'], + 'probability': [1, + 1, + 1, + 1, + 1]}) + + assert(df_tse_bi_tuh.iloc[:5].equals(df_target_tuh)) + + df_tse_bi_dataset = read_tse_bi(DATASET_ANNOTATIONS_FILE_PATH) + df_target_dataset = pd.DataFrame( + data={ + 'start': [pd.Timestamp('2017-01-10T14:30:00.441406'), + pd.Timestamp('2017-01-10T14:32:23.062500'), + pd.Timestamp('2017-01-10T14:32:40.015625'), + pd.Timestamp('2017-01-10T14:38:44.406250'), + pd.Timestamp('2017-01-10T14:38:50.468750')], + 'end': [pd.Timestamp('2017-01-10T14:32:23.062500'), + pd.Timestamp('2017-01-10T14:32:40.015625'), + pd.Timestamp('2017-01-10T14:38:44.406250'), + pd.Timestamp('2017-01-10T14:38:50.468750'), + pd.Timestamp('2017-01-10T14:40:42.832031')], + 'annotation': ['bckg', + 'seiz', + 'bckg', + 'seiz', + 'bckg'], + 'probability': [1, + 1, + 1, + 1, + 1]}) + assert(df_tse_bi_dataset.equals(df_target_dataset)) - read_tse_bi(annotations_file_path_correct) - assert True + # Change for better + with pytest.raises(ValueError): + read_tse_bi(TUH_ANNOTATIONS_FILE_PATH_INCORRECT) - try: - read_tse_bi(annotations_file_path_incorrect) - except ValueError: - assert True \ No newline at end of file + with pytest.raises(SystemExit) as e: + read_tse_bi(TUH_ANNOTATIONS_FILE_PATH_EMPTY) + assert e.type == SystemExit + assert e.value.code == 'tse_bi file is empty' diff --git a/tests/test_src_usecase/test_data_processing/test_data_cleaning.py b/tests/test_src_usecase/test_data_processing/test_data_cleaning.py new file mode 100644 index 0000000..2d223ff --- /dev/null +++ b/tests/test_src_usecase/test_data_processing/test_data_cleaning.py @@ -0,0 +1,127 @@ +import pandas as pd +import pytest +import sys +from pandas.testing import assert_frame_equal + +import numpy as np + +sys.path.append('.') +from src.usecase.data_processing.data_loading import get_dataset +from src.usecase.data_processing.data_cleaning import ( + impute_nan_values_by_median, + outlier_detection, remove_outlier, + replace_infinite_values_by_nan) +from tests.conftest import (COL_TO_DROP, DATASET_FILE_PATH, THRESHOLD) + + +def test_replace_infinite_values_by_nan_when_data_has_infinite_values_check_no_exist_infinite_values(dataframe): + # Given + expected_response = False + + # When + dataframe = replace_infinite_values_by_nan(dataframe) + actual_response = dataframe.isin([np.inf, -np.inf]).values.any() + + # Then + assert actual_response == expected_response + + +def test_replace_infinite_values_by_nan_when_data_hasnt_infinite_values_check_any_operation_done(dataframe): + # Given + expected_response = False + dataframe_without_nan = dataframe.replace([np.inf, -np.inf], np.nan) + + # When + returned_dataframe = replace_infinite_values_by_nan(dataframe_without_nan) + actual_response = dataframe_without_nan.isin([np.inf, -np.inf]).values.any() + + # Then + assert actual_response == expected_response + assert_frame_equal(dataframe_without_nan, returned_dataframe) + + +def test_impute_values_by_median_when_inf_values_exist_check_imputation_done(dataframe): + # Given + expected_response = False + col_to_drop = COL_TO_DROP + dataframe_wihtout_col = dataframe.drop(col_to_drop, 1) + + # When + X_imputed, y = impute_nan_values_by_median(dataframe_wihtout_col) + actual_response = pd.DataFrame(X_imputed).isna().values.any() + + # Then + assert actual_response == expected_response + assert dataframe.shape[0] == y.shape[0] + + +def test_impute_values_by_median_when_inf_values_not_exist_check_imputation_done(dataframe): + # Given + expected_response = False + col_to_drop = COL_TO_DROP + dataframe_wihtout_col = dataframe.drop(col_to_drop, 1) + dataframe_without_infinite = replace_infinite_values_by_nan( + dataframe_wihtout_col) + + # When + X_imputed, y = impute_nan_values_by_median(dataframe_without_infinite) + actual_response = pd.DataFrame(X_imputed).isna().values.any() + + # Then + assert actual_response == expected_response + assert dataframe_without_infinite.shape[0] == y.shape[0] + + +def test_outlier_detection_when_dataframe_is_imputed_check_score_column_created(dataframe): + # Given + expected_response = False + col_to_drop = COL_TO_DROP + dataframe_wihtout_col = dataframe.drop(col_to_drop, 1) + X_imputed, y = impute_nan_values_by_median(dataframe_wihtout_col) + dataframe_imputed = pd.DataFrame(X_imputed) + + # When + outlier_score = outlier_detection(X_imputed) + actual_response = dataframe_imputed.isna().values.any() + + # Then + assert 'score' in outlier_score + assert actual_response == expected_response + + +def test_outlier_detection_when_dataframe_is_not_imputed_check_raise_exception(): + # Given + dataset_path = DATASET_FILE_PATH + col_to_drop = COL_TO_DROP + _, dataframe = get_dataset(dataset_path, col_to_drop) + + # When + X = dataframe.drop(['label'], axis=1) + y = dataframe['label'] + + # Then + with pytest.raises(Exception) as excep: + outlier_score = outlier_detection(X) + + +def test_remove_outlier_given_imputed_dataframe_without_outlier_check_shape_are_identic(): + # Given + dataset_path = DATASET_FILE_PATH + col_to_drop = COL_TO_DROP + threshold = THRESHOLD + _, dataframe = get_dataset(dataset_path, col_to_drop) + X_imputed, y = impute_nan_values_by_median(dataframe) + dataframe_imputed = pd.DataFrame(X_imputed) + outlier_score = outlier_detection(X_imputed) + + # When + X_result_outlier, y_result_outlier = remove_outlier( + dataframe_imputed, + outlier_score, + y, + threshold + ) + + # Then + assert X_result_outlier.shape[0] == dataframe_imputed.shape[0] + assert y_result_outlier.shape[0] == y.shape[0] \ No newline at end of file diff --git a/tests/test_src_usecase/test_data_processing/test_data_loading.py b/tests/test_src_usecase/test_data_processing/test_data_loading.py new file mode 100644 index 0000000..8aa7aae --- /dev/null +++ b/tests/test_src_usecase/test_data_processing/test_data_loading.py @@ -0,0 +1,42 @@ +import pytest +import sys +from pandas.testing import assert_frame_equal +import pandas as pd + +sys.path.append('.') +from tests.conftest import DATASET_FILE_PATH, DATASET_FILE_PATH_FEAT, COL_TO_DROP +from src.usecase.data_processing.data_loading import get_dataset + + +def test_get_dataset_given_path_col_to_drop_return_dataframe_without_col_to_drop(dataframe): + # Given + dataset_path = DATASET_FILE_PATH + col_to_drop = COL_TO_DROP + expected_reponse = False + expected_dataframe = dataframe.drop(col_to_drop, axis=1) + + # When + df_init, df_cleaned = get_dataset(dataset_path, col_to_drop) + + #Then + assert (df_init.empty == expected_reponse and df_cleaned.empty == expected_reponse) + assert_frame_equal(df_cleaned, expected_dataframe) + assert 'timestamp' not in df_cleaned.columns + assert 'set' not in df_cleaned.columns + + +def test_get_dataset_given_path_col_to_drop_return_df_origin_and_df_without_col_to_drop(dataframe): + # Given + dataset_path = DATASET_FILE_PATH + col_to_drop = COL_TO_DROP + expected_dataframe = dataframe + expected_dataframe['timestamp'] = pd.to_datetime(expected_dataframe['timestamp'], format="%Y-%m-%d") + + # When + df_init, df_cleaned = get_dataset(dataset_path, col_to_drop) + + #Then + assert 'timestamp' in df_init.columns + assert 'set' in df_init.columns + assert 'timestamp' not in df_cleaned.columns + assert 'set' not in df_cleaned.columns diff --git a/tests/test_src_usecase/test_data_processing/test_prepare_features.py b/tests/test_src_usecase/test_data_processing/test_prepare_features.py new file mode 100644 index 0000000..32899d6 --- /dev/null +++ b/tests/test_src_usecase/test_data_processing/test_prepare_features.py @@ -0,0 +1,25 @@ +import sys +import pandas as pd + +sys.path.append('.') +from tests.conftest import DATASET_FILE_PATH_FEAT +from src.usecase.data_processing.prepare_features import prepare_features_with_io + + +def test_prepare_features_with_io_check_output_dataframe_is_created(): + # Given + expected_response = False + dataset_path = DATASET_FILE_PATH_FEAT, + col_to_drop= ['interval_index', 'interval_start_time', 'filename'] + features_path = "data/test_data/ml.csv" + + # When + prepare_features_with_io( + dataset_path=dataset_path, + col_to_drop=col_to_drop, + features_path=features_path) + + actual_response = pd.read_csv(features_path) + + # Then + assert actual_response.empty == expected_response \ No newline at end of file diff --git a/tests/test_src_usecase/test_data_processing/test_time_series_processing.py b/tests/test_src_usecase/test_data_processing/test_time_series_processing.py new file mode 100644 index 0000000..f28ba64 --- /dev/null +++ b/tests/test_src_usecase/test_data_processing/test_time_series_processing.py @@ -0,0 +1,151 @@ +import pytest +import sys +from pandas.testing import assert_frame_equal +from src.usecase.constants import TempFeaturesOperation + +sys.path.append('.') +from src.usecase.data_processing.time_series_processing import (compute_time_features, create_rolling_variables_given_feature, + diff_operation_from_features, compute_diff_for_feature, + perform_op_on_features) +from src.usecase.data_processing.data_loading import get_dataset +from tests.conftest import DATASET_FILE_PATH_FEAT, LIST_TIME, LIST_FEAT, OPERATION_TYPE, LIST_PERIOD + + +def test_compute_time_features_given_list_time_check_seasonals_features_are_created(): + # Given + dataset_path = DATASET_FILE_PATH_FEAT + col_to_drop = [] + list_time = LIST_TIME + _, dataframe_without_cols = get_dataset(dataset_path, col_to_drop) + + # When + dataframe_with_seasonal_informations = compute_time_features( + dataframe=dataframe_without_cols, + list_time=list_time + ) + + # Then + assert "dayOfWeek" in dataframe_with_seasonal_informations.columns + assert "month" in dataframe_with_seasonal_informations.columns + assert "hour" in dataframe_with_seasonal_informations.columns + assert "minute" in dataframe_with_seasonal_informations.columns + + +def test_compute_time_feature_not_given_list_time_check_seasonals_features_not_created(): + # Given + dataset_path = DATASET_FILE_PATH_FEAT + col_to_drop = [] + list_time = [] + _, dataframe_without_cols = get_dataset(dataset_path, col_to_drop) + + # When + dataframe_with_seasonal_informations = compute_time_features( + dataframe=dataframe_without_cols, + list_time=list_time + ) + + # Then + assert "dayOfWeek" not in dataframe_with_seasonal_informations.columns + assert "month" not in dataframe_with_seasonal_informations.columns + assert "hour" not in dataframe_with_seasonal_informations.columns + assert "minute" not in dataframe_with_seasonal_informations.columns + + +def test_perform_operations_on_features_given_list_features_and_list_period_check_periods_features_are_created(): + # Given + dataset_path = DATASET_FILE_PATH_FEAT + col_to_drop = [] + list_feat = LIST_FEAT + operation = OPERATION_TYPE + list_period = LIST_PERIOD + _, dataframe_without_cols = get_dataset(dataset_path, col_to_drop) + + # When + df_with_operation_result = perform_op_on_features( + dataframe=dataframe_without_cols.copy(), + list_feat=list_feat, + operation=operation + ) + + # Then + assert "mean_hr_p30" in df_with_operation_result.keys() + assert "mean_nni_p30" not in dataframe_without_cols.keys() + + +def test_diff_operation_from_features_given_list_feat_check_diff_feature_created(): + # Given + dataset_path = DATASET_FILE_PATH_FEAT + col_to_drop = [] + list_feat = LIST_FEAT + _, dataframe_without_cols = get_dataset(dataset_path, col_to_drop) + + # When + df_with_diff_features = diff_operation_from_features( + dataframe=dataframe_without_cols, + list_feat=list_feat) + + # Then + assert "mean_hr_diff" in df_with_diff_features.columns + assert "mean_nni_diff" not in df_with_diff_features.columns + + +def test_diff_operation_from_features_not_given_list_feat_check_dataframes_are_identic(): + # Given + dataset_path = DATASET_FILE_PATH_FEAT + col_to_drop = [] + list_feat = [] + _, dataframe_with_cols = get_dataset(dataset_path, col_to_drop) + + # When + df_with_diff_features = diff_operation_from_features( + dataframe=dataframe_with_cols, + list_feat=list_feat) + + # Then + assert_frame_equal(dataframe_with_cols, df_with_diff_features) + + +def test_create_rolling_variables_given_feature_and_operation_check_all_periods_created(): + # Given + dataset_path = DATASET_FILE_PATH_FEAT + col_to_drop = [] + feature_name = "mean_hr" + operation = TempFeaturesOperation.mean.name + _, df_with_rolling_variables = get_dataset(dataset_path, col_to_drop) + + # When + create_rolling_variables_given_feature(dataframe=df_with_rolling_variables, + feature=feature_name, operation=operation) + + # Then + assert "mean_hr_p30" in df_with_rolling_variables.columns + assert "mean_hr_p60" in df_with_rolling_variables.columns + assert "mean_hr_p120" in df_with_rolling_variables.columns + + +def test_compute_diff_for_feature_given_featuer_check_feature_diff_created(): + # Given + dataset_path = DATASET_FILE_PATH_FEAT + col_to_drop = [] + feature_name = "mean_hr" + _, df_with_diff_variables = get_dataset(dataset_path, col_to_drop) + + # When + compute_diff_for_feature(dataframe=df_with_diff_variables, feature=feature_name) + + # Then + assert "mean_hr_diff" in df_with_diff_variables.columns + + +def test_perform_op_on_features_given_two_features_check_feature_period_are_created_for_both_features(): + # Given + dataset_path = DATASET_FILE_PATH_FEAT + col_to_drop = [] + list_features = ["mean_hr", "mean_nni"] + _, df_with_period_variables = get_dataset(dataset_path, col_to_drop) + # When + perform_op_on_features(dataframe=df_with_period_variables, list_feat=list_features, operation=TempFeaturesOperation.mean.name) + + # Then + assert "mean_hr_p30" in df_with_period_variables.columns + assert "mean_nni_p30" in df_with_period_variables.columns \ No newline at end of file diff --git a/tests/test_src_usecase/test_detect_qrs.py b/tests/test_src_usecase/test_detect_qrs.py index 35c6ea3..a2a81ce 100644 --- a/tests/test_src_usecase/test_detect_qrs.py +++ b/tests/test_src_usecase/test_detect_qrs.py @@ -23,6 +23,7 @@ def test_tuh_detect_qrs(): assert (export_filename == ''.join( [OUTPUT_FOLDER, '/', + 'rr_', TEST_TUH_EXAM_ID, '.csv'])) diff --git a/tests/test_src_usecase/test_ecg_channel_read.py b/tests/test_src_usecase/test_ecg_channel_read.py new file mode 100644 index 0000000..a48461a --- /dev/null +++ b/tests/test_src_usecase/test_ecg_channel_read.py @@ -0,0 +1,30 @@ +import pytest +import os +import sys + +from src.infrastructure.edf_loader import EdfLoader +from src.usecase.ecg_channel_read import ecg_channel_read + +DEFAULT_PATH = '/home/DATA/lateppe/Recherche_ECG/' +FILE_PATH = '/home/aura-sakhite/seizure_detection_pipeline/data/PL/PAT_12/EEG_156_s1.edf' +RECORD = 156 +PATIENT = 'PAT_12' +SEGMENT ='Segments_EEG_{RECORD}.csv' + +def test_ecg_channel_read__if_all_inputs_are_given_return_true(): + + #Given + expected_result=True + #edfloader = EdfLoader(FILE_PATH) + #ecg_channel_name = edfloader.get_ecg_candidate_channel() + #start_time, end_time = edfloader.get_edf_file_interval() + + #sampling_frequency, ecg_data = ecg_channel_read( + # PATIENT, RECORD, SEGMENT, ecg_channel_name, start_time, end_time + #) + + #When + #result= (sampling_frequency!=0 and not ecg_data.empty) + + #Then + assert True \ No newline at end of file diff --git a/tests/test_src_usecase/test_fetch_database.py b/tests/test_src_usecase/test_fetch_database.py new file mode 100644 index 0000000..1711bb0 --- /dev/null +++ b/tests/test_src_usecase/test_fetch_database.py @@ -0,0 +1,247 @@ +""" +This script is used to test fetch_datase script. + +Copyright (C) 2022 Association Aura +SPDX-License-Identifier: GPL-3.0 +""" +import argparse +import pandas as pd +import pytest +from src.usecase.fetch_database import \ + ANNOTATION_FILE_PATTERN,\ + parse_fetch_database_args,\ + infer_database,\ + create_df_from_file_pattern,\ + fetch_database,\ + TUH_EXAM_PATTERN,\ + TUH_PATIENT_PATTERN,\ + DATASET_EXAM_PATTERN,\ + DATASET_PATIENT_PATTERN,\ + DATA_FILE_PATTERN + +from pathlib import Path +import shutil +import os +from src.usecase.utilities import convert_args_to_dict + +OUTPUT_FOLDER = 'tests/output/features' + +# TUH_FEATURES_FILE_PATH = 'data/test_data/tuh_feats_00009578_s006_t001.csv' +TUH_FEATURES_FILE_PATH = 'data/test_data/tuh_feats_00004671_s007_t000.csv' +TUH_ANNOTATIONS_FILE_PATH = \ + 'data/tuh/dev/01_tcp_ar/046/00004671/' \ + 's007_2012_08_04/00004671_s007_t000.tse_bi' +# 'data/tuh/dev/01_tcp_ar/002/00009578/00009578_s006_t001.tse_bi' +TUH_ANNOTATIONS_FILE_PATH_INCORRECT = \ + 'data/test_data/tuh_rr_00007633_s003_t007.csv' + +DATASET_FEATUERS_FILE_PATH = \ + 'data/test_data/dataset_feats_PAT_0_Annotations_EEG_0_s2.csv' +DATASET_ANNOTATIONS_FILE_PATH = \ + 'data/dataset/PAT_0/PAT_0_Annotations_EEG_0.tse_bi' + +TUH_DATA_FOLDER = 'data/tuh' +EXPORT_FOLDER = 'tests/output' + +DATASET_DATA_SOURCE = Path('data/dataset') +PATH_TEST_TEMP = Path('tests/temp') + + +@pytest.fixture +def create_mock_dataset_sample(path_data_source: Path = DATASET_DATA_SOURCE, + path_test: Path = PATH_TEST_TEMP): + """Initialize mock dataset tree structure sample.""" + path_data = os.path.join(path_test, 'dataset') + + if os.path.exists(path_test): + shutil.rmtree(path_test) + shutil.copytree(path_data_source, + path_data) + os.mknod(os.path.join(path_data, + 'PAT_0', + 'EEG_0_s1.edf')) + + yield + + if os.path.exists(path_test): + shutil.rmtree(path_test) + + +def test_parse_fetch_database_args_with_infer_database(): + """Test the fetch_database parsing with inferance of database.""" + # With inference + bash_command = (f'python3 src/usecase/fetch_database.py ' + f'--data-folder-path {TUH_DATA_FOLDER} ' + f'--export-folder {EXPORT_FOLDER} ' + f'--infer-database ' + f'--patient-pattern {TUH_PATIENT_PATTERN} ' + f'--exam-pattern {TUH_EXAM_PATTERN}') + + args_to_parse = bash_command.split()[2:] + parser = parse_fetch_database_args(args_to_parse) + parser_dict = convert_args_to_dict(parser) + + correct_parser = argparse.Namespace( + data_folder_path=TUH_DATA_FOLDER, + export_folder=EXPORT_FOLDER, + infer_database=True, + patient_pattern=TUH_PATIENT_PATTERN, + exam_pattern=TUH_EXAM_PATTERN) + correct_parser_dict = convert_args_to_dict(correct_parser) + + assert(parser_dict == correct_parser_dict) + + +def test_parse_fetch_database_args_without_infer_database(): + """Test the fetch_database parsing without inferance of database.""" + bash_command = (f'python3 src/usecase/fetch_database.py ' + f'--data-folder-path {TUH_DATA_FOLDER} ' + f'--export-folder {EXPORT_FOLDER} ' + f'--patient-pattern {TUH_PATIENT_PATTERN} ' + f'--exam-pattern {TUH_EXAM_PATTERN}') + + args_to_parse = bash_command.split()[2:] + parser = parse_fetch_database_args(args_to_parse) + parser_dict = convert_args_to_dict(parser) + + correct_parser = argparse.Namespace( + data_folder_path=TUH_DATA_FOLDER, + export_folder=EXPORT_FOLDER, + infer_database=False, + patient_pattern=TUH_PATIENT_PATTERN, + exam_pattern=TUH_EXAM_PATTERN) + correct_parser_dict = convert_args_to_dict(correct_parser) + + assert(parser_dict == correct_parser_dict) + + +def test_infer_database_tuh(): + """Infer database is tuh.""" + assert(infer_database( + data_folder_path=TUH_DATA_FOLDER, + pattern_to_match=DATASET_PATIENT_PATTERN, + dataset_file_pattern=DATA_FILE_PATTERN) == ( + TUH_PATIENT_PATTERN, TUH_EXAM_PATTERN)) + + +def test_infer_database_dataset(create_mock_dataset_sample): + """Infer database is dataset.""" + assert(infer_database( + data_folder_path=PATH_TEST_TEMP, + pattern_to_match=DATASET_PATIENT_PATTERN, + dataset_file_pattern=DATA_FILE_PATTERN) == ( + DATASET_PATIENT_PATTERN, DATASET_EXAM_PATTERN)) + + +def test_dataset_create_df_from_file_pattern(create_mock_dataset_sample): + """Test function create_df_from_file_pattern with dataset.""" + df = create_df_from_file_pattern( + data_folder_path=PATH_TEST_TEMP, + file_pattern=DATA_FILE_PATTERN, + file_label='data', + patient_pattern=DATASET_PATIENT_PATTERN, + exam_pattern=DATASET_EXAM_PATTERN) + df_target = pd.DataFrame( + data={'data_file_path': ['tests/temp/dataset/PAT_0/EEG_0_s1.edf'], + 'exam_id': ['EEG_0'], + 'patient_id': ['PAT_0']}) + assert(df.shape == (1, 3)) + assert(df.equals(df_target)) + + +def test_tuh_create_df_from_file_pattern(): + """Test function create_df_from_file_pattern with TUH.""" + df = create_df_from_file_pattern( + data_folder_path=TUH_DATA_FOLDER, + file_pattern=DATA_FILE_PATTERN, + file_label='data', + patient_pattern=TUH_PATIENT_PATTERN, + exam_pattern=TUH_EXAM_PATTERN) + + df_target = pd.DataFrame( + data={'data_file_path': ['data/tuh/dev/01_tcp_ar/002/00009578/' + '00009578_s002_t001.edf', + 'data/tuh/dev/01_tcp_ar/002/00009578/' + '00009578_s006_t001.edf', + 'data/tuh/dev/01_tcp_ar/076/00007633/' + 's003_2013_07_09/00007633_s003_t007.edf', + 'data/tuh/dev/01_tcp_ar/046/00004671/' + 's007_2012_08_04/00004671_s007_t000.edf'], + 'exam_id': ['00009578_s002_t001', + '00009578_s006_t001', + '00007633_s003_t007', + '00004671_s007_t000'], + 'patient_id': ['00009578', + '00009578', + '00007633', + '00004671']}) + df.sort_values(by='data_file_path', inplace=True) + df.reset_index(drop=True, inplace=True) + + df_target.sort_values(by='data_file_path', inplace=True) + df_target.reset_index(drop=True, inplace=True) + + assert(df.equals(df_target)) + + +def test_dataset_fetch_database(create_mock_dataset_sample): + """Test function fech_database with dataset.""" + fetch_database(data_folder_path=PATH_TEST_TEMP, + export_folder=PATH_TEST_TEMP, + data_file_pattern=DATA_FILE_PATTERN, + annotations_file_pattern=ANNOTATION_FILE_PATTERN, + patient_pattern=DATASET_PATIENT_PATTERN, + exam_pattern=DATASET_EXAM_PATTERN) + df = pd.read_csv(os.path.join(PATH_TEST_TEMP, 'df_candidates.csv')) + df_target = pd.DataFrame( + data={'data_file_path': ['tests/temp/dataset/PAT_0/EEG_0_s1.edf'], + 'exam_id': ['EEG_0'], + 'patient_id': ['PAT_0'], + 'annotations_file_path': [ + 'tests/temp/dataset/PAT_0/PAT_0_Annotations_EEG_0.tse_bi']}) + print(df.head()) + print(df_target.head()) + assert(df.equals(df_target)) + + +def test_tuh_fetch_database(): + """Test function fech_database with TUH.""" + fetch_database(data_folder_path=TUH_DATA_FOLDER, + export_folder=EXPORT_FOLDER, + data_file_pattern=DATA_FILE_PATTERN, + annotations_file_pattern=ANNOTATION_FILE_PATTERN, + patient_pattern=TUH_PATIENT_PATTERN, + exam_pattern=TUH_EXAM_PATTERN) + df = pd.read_csv(os.path.join(EXPORT_FOLDER, 'df_candidates.csv')) + df_target = pd.DataFrame( + data={'data_file_path': ['data/tuh/dev/01_tcp_ar/002/00009578/' + '00009578_s002_t001.edf', + 'data/tuh/dev/01_tcp_ar/002/00009578/' + '00009578_s006_t001.edf', + 'data/tuh/dev/01_tcp_ar/076/00007633/' + 's003_2013_07_09/00007633_s003_t007.edf', + 'data/tuh/dev/01_tcp_ar/046/00004671/' + 's007_2012_08_04/00004671_s007_t000.edf'], + 'exam_id': ['00009578_s002_t001', + '00009578_s006_t001', + '00007633_s003_t007', + '00004671_s007_t000'], + 'patient_id': [9578, + 9578, + 7633, + 4671], + 'annotations_file_path': [ + 'data/tuh/dev/01_tcp_ar/002/00009578/' + '00009578_s002_t001.tse_bi', + 'data/tuh/dev/01_tcp_ar/002/00009578/' + '00009578_s006_t001.tse_bi', + 'data/tuh/dev/01_tcp_ar/076/00007633/' + 's003_2013_07_09/00007633_s003_t007.tse_bi', + 'data/tuh/dev/01_tcp_ar/046/00004671/' + 's007_2012_08_04/00004671_s007_t000.tse_bi']}) + df.sort_values(by='data_file_path', inplace=True) + df.reset_index(drop=True, inplace=True) + + df_target.sort_values(by='data_file_path', inplace=True) + df_target.reset_index(drop=True, inplace=True) + assert(df.equals(df_target)) diff --git a/visualization/README.md b/visualization/README.md new file mode 100644 index 0000000..b8d1cbe --- /dev/null +++ b/visualization/README.md @@ -0,0 +1,67 @@ +# Getting Started +v 1.0 + +## Set up the Postgre database + +1/ Install Postgre + +```bash +foo@bar:sudo -u postgres createuser +foo@bar: sudo -u postgres createdb +sudo -u postgres psql +psql$ alter user with encrypted password ''; +psql$ grant all privileges on database to ; + +``` + +In the demo case: + * dbname = postgres + * username = postgres + * password = postgres + + + ### Upload the ECG data + + ```bash + python3 ecg_loader.py --pg-host "localhost" --pg-port 5432 --pg-user --pg-password --pg-database --filepath "00007633_s003_t007.edf" --exam "00007633_s003_t007" + ``` + +### Upload the RR-intervals + +```bash +python3 rr_intervals_loader.py --pg-host "localhost" --pg-port 5432 --pg-user --pg-password --pg-database --filepath "rr_00007633_s003_t007.csv" --exam "00007633_s003_t007" +``` + +### Upload the annotations + +```bash +python3 annotations_loader.py --pg-host "localhost" --pg-port 5432 --pg-user --pg-password --pg-database --annotation-filename "00007633_s003_t007.tse_bi" --edf-filename "00007633_s003_t007.edf" --exam "00007633_s003_t007" +``` + +## 2/ Working with Docker + +### a) Starting at least postgres container: + +```bash + $ source setup_env.sh + $ docker-compose build postgres + $ docker-compose up postgres -d +``` + +### b) Using Makefile to test + +```bash +Make load_ecg +Make load_rr +Make load_annotations +``` + +## Set up the Grafana visualisation + +## Possible improvements + +* common functions to handle database creation +* common functions to load data +* better creation of database schema +* better parsing +* Taking into consideration environement variable to simplify credentials handling diff --git a/visualization/annotations_loader.py b/visualization/annotations_loader.py new file mode 100644 index 0000000..d03cd2b --- /dev/null +++ b/visualization/annotations_loader.py @@ -0,0 +1,139 @@ +import pandas as pd +import psycopg2 +from psycopg2 import Error +from psycopg2.extensions import connection, ISOLATION_LEVEL_AUTOCOMMIT +import argparse +import sys +from typing import List +from pyedflib import highlevel +from pyedflib import edfreader +import datetime + +## example CLI -> python3 physio_data_loader.py --pg-host "localhost" --pg-port 5432 --pg-user "lecoued" --pg-password "DemoAICE" --pg-database "demo_aice" --filepath "rr_00007633_s003_t007.csv" --exam "00007633_s003_t007" +PG_HOST = "localhost" +PG_PORT = 5432 +PG_USER = "postgres" +PG_PASSWD = "postgres" +PG_DATABASE = "postgres" +PG_TABLE = "annotations" + +TSE_BI_FILE = "00007633_s003_t007.tse_bi" +EXAM = "00007633_s003_t007" + + +def read_tse_bi(filename: str) -> pd.DataFrame: + if filename.split('/')[-1].split('.')[-1] != 'tse_bi': + raise ValueError( + f'Please input a tse_bi file. Input: {filename}') + + df_tse_bi = pd.read_csv(filename, + sep=' ', + skiprows=1, + header=None) + + df_tse_bi.columns = ['start', 'end', 'annotation', 'probablility'] + df_tse_bi.loc[:, ['start', 'end']] = df_tse_bi.loc[:, ['start', 'end']].\ + apply(lambda x: x * 1_000) + + return df_tse_bi + +def get_connection_to_db(host: str = PG_HOST, port: int = PG_PORT, + database: str = PG_DATABASE, user: str = PG_USER, + password: str = PG_PASSWD) -> connection: + try: + conn = psycopg2.connect(host=host, port=port, + database=database, user=user, + password=password) + except (Exception, Error): + print(f"Database {database} does not exist. Creating one...") + conn = psycopg2.connect(host=host, port=port, user=user, + password=password) + conn.set_isolation_level(ISOLATION_LEVEL_AUTOCOMMIT) + cursor = conn.cursor() + cursor.execute(f"CREATE DATABASE {database};") + print(f"Database {database} has been created.") + cursor.close() + conn.close() + conn = psycopg2.connect(host=host, port=port, + database=database, user=user, + password=password) + finally: + conn.set_isolation_level(ISOLATION_LEVEL_AUTOCOMMIT) + print("Database is OK") + return conn + + +def write_annotations_to_db(tsebi_file: str, edf_file:str, pg_host: str, pg_port: int, pg_database: str, pg_user: str, pg_password: str) -> None: + conn = get_connection_to_db(pg_host, pg_port, pg_database, pg_user, pg_password) + cursor = conn.cursor() + # Check if the table already exists + cursor.execute(f"SELECT COUNT(*) FROM information_schema.tables WHERE \ + table_name='{PG_TABLE}';") + if not cursor.fetchone()[0]: + print(f"Table {PG_TABLE} does not exist. Creating one...") + cursor.execute(f"CREATE TABLE {PG_TABLE} \ + (id SERIAL PRIMARY KEY, \ + start_time TIMESTAMP, \ + end_time TIMESTAMP, \ + tag VARCHAR(50), \ + exam VARCHAR(100) \ + );") + print(f"Table {PG_TABLE} has been created") + + edf_headers = highlevel.read_edf_header(edf_file) + start_date = edf_headers['startdate'] + print(start_date) + df_tse=read_tse_bi(tsebi_file) + df_tse["start"] = df_tse["start"].apply(lambda x: (start_date + datetime.timedelta(milliseconds=x))) + df_tse["end"] = df_tse["end"].apply(lambda x: start_date + datetime.timedelta(milliseconds=x)) + + for _, row in df_tse.iterrows(): + print(row) + cursor.execute(f"INSERT INTO {PG_TABLE} VALUES \ + (DEFAULT, \ + '{row['start']}', \ + '{row['end']}', \ + '{row['annotation']}', \ + '{EXAM}' \ + );") + + print("Annotations values added") + cursor.close() + conn.close() + + +def parse_load_annotations_args(args_to_parse: List[str]) -> argparse.Namespace: + parser = argparse.ArgumentParser(description='CLI parameter input') + parser.add_argument('--pg-host', + dest='pg_host', + required=True) + parser.add_argument('--pg-port', + dest='pg_port', + required=True) + parser.add_argument('--pg-user', + dest='pg_user', + required=True) + parser.add_argument('--pg-password', + dest='pg_password', + required=True) + parser.add_argument('--pg-database', + dest='pg_database', + required=True) + parser.add_argument('--annotation-filename', + dest='annotation_filename', + required=True) + parser.add_argument('--edf-filename', + dest='edf_filename', + required=True) + parser.add_argument('--exam', + dest='exam', + required=True) + args = parser.parse_args(args_to_parse) + + return args + + +if __name__ == "__main__": + args = parse_load_annotations_args(sys.argv[1:]) + d = vars(args) + write_annotations_to_db(d["annotation_filename"], d["edf_filename"], d["pg_host"], d["pg_port"], d["pg_database"], d["pg_user"], d["pg_password"]) diff --git a/visualization/ecg_data_loader.py b/visualization/ecg_data_loader.py new file mode 100644 index 0000000..9250259 --- /dev/null +++ b/visualization/ecg_data_loader.py @@ -0,0 +1,155 @@ +import pandas as pd +import psycopg2 +import argparse +from psycopg2 import Error +from psycopg2.extensions import connection, ISOLATION_LEVEL_AUTOCOMMIT +from pyedflib import highlevel +import sys +from pyedflib import edfreader +from sqlalchemy import create_engine +from typing import List + +PG_HOST = "localhost" +PG_PORT = 5432 +PG_USER = "postgres" +PG_PASSWD = "postgres" +PG_DATABASE = "postgres" +PG_TABLE = "ecg" + +edf_file = "00007633_s003_t007.edf" +EXAM = "00007633_s003_t007" + +def get_connection_to_db(host: str = PG_HOST, port: int = PG_PORT, + database: str = PG_DATABASE, user: str = PG_USER, + password: str = PG_PASSWD) -> connection: + try: + conn = psycopg2.connect(host=host, port=port, + database=database, user=user, + password=password) + except (Exception, Error): + print(f"Database {database} does not exist. Creating one...") + conn = psycopg2.connect(host=host, port=port, user=user, + password=password) + conn.set_isolation_level(ISOLATION_LEVEL_AUTOCOMMIT) + cursor = conn.cursor() + cursor.execute(f"CREATE DATABASE {database};") + print(f"Database {database} has been created.") + cursor.close() + conn.close() + conn = psycopg2.connect(host=host, port=port, + database=database, user=user, + password=password) + finally: + conn.set_isolation_level(ISOLATION_LEVEL_AUTOCOMMIT) + print("Database is OK") + return conn + + +def write_ecg_to_db(edf_file: str, + pg_host: str = PG_HOST, + pg_port: int = PG_PORT, + pg_database: str = PG_DATABASE, + pg_user: str = PG_USER, + pg_password: str = PG_PASSWD) -> None: + + conn = get_connection_to_db(pg_host, pg_port, pg_database, pg_user, pg_password) + cursor = conn.cursor() + # Check if the table already exists + cursor.execute(f"SELECT COUNT(*) FROM information_schema.tables WHERE \ + table_name='{PG_TABLE}';") + if not cursor.fetchone()[0]: + print(f"Table {PG_TABLE} does not exist. Creating one...") + cursor.execute(f"CREATE TABLE {PG_TABLE} \ + (index INT, \ + ecg_signal REAL, \ + time TIMESTAMP, \ + patient VARCHAR(100), \ + PRIMARY KEY(index, patient) \ + );") + print(f"Table {PG_TABLE} has been created") + + df = convert_edf_to_dataframe(edf_file=edf_file) + + engine = create_engine(f'postgresql://{pg_user}:{pg_password}@{pg_host}:{pg_port}/{pg_database}') + + try: + df.to_sql(name=PG_TABLE, + con=engine, + if_exists='append', + chunksize=10_000) + print("ECG values added") + + except Exception as e: + # occur most of the time if segment is already loaded + print(e) + + finally: + cursor.close() + conn.close() + +def convert_edf_to_dataframe (edf_file: str, + channel_name: str = 'EEG FP1-REF')-> pd.DataFrame: + + # From its path, load an edf file for a selected channel and + # adapt it in DataFrame + + headers = highlevel.read_edf_header(edf_file) + channels = headers['channels'] + + startdate = pd.to_datetime( + headers['startdate']) + + # InfluxDB API + + fs = headers[ + 'SignalHeaders'][ + channels.index(channel_name)]['sample_rate'] + + with edfreader.EdfReader(edf_file) as f: + signals = f.readSignal(channels.index(channel_name)) + # to modify? + freq_ns = int(1/fs*1_000_000_000) + + df = pd.DataFrame(signals, + columns=['ecg_signal'], + index=pd.date_range(startdate, + periods=len(signals), + freq=f'{freq_ns}ns' + )) + df['time'] = df.index + df.reset_index(drop=True, inplace=True) + df['patient'] = edf_file + + return df + + +def parse_load_physio_args(args_to_parse: List[str]) -> argparse.Namespace: + parser = argparse.ArgumentParser(description='CLI parameter input') + parser.add_argument('--pg-host', + dest='pg_host', + required=True) + parser.add_argument('--pg-port', + dest='pg_port', + required=True) + parser.add_argument('--pg-user', + dest='pg_user', + required=True) + parser.add_argument('--pg-password', + dest='pg_password', + required=True) + parser.add_argument('--pg-database', + dest='pg_database', + required=True) + parser.add_argument('--filepath', + dest='filepath', + required=True) + args = parser.parse_args(args_to_parse) + + return args + + + +if __name__ == '__main__': + args = parse_load_physio_args(sys.argv[1:]) + d = vars(args) + write_ecg_to_db(d["filepath"], d["pg_host"], d["pg_port"], d["pg_database"], d["pg_user"], d["pg_password"]) diff --git a/visualization/grafana_dashboard_DemoAICE-1637344463172.json b/visualization/grafana_dashboard_DemoAICE-1637344463172.json new file mode 100644 index 0000000..11660a5 --- /dev/null +++ b/visualization/grafana_dashboard_DemoAICE-1637344463172.json @@ -0,0 +1,231 @@ +{ + "__inputs": [ + { + "name": "DS_POSTGRESQL", + "label": "PostgreSQL", + "description": "", + "type": "datasource", + "pluginId": "postgres", + "pluginName": "PostgreSQL" + } + ], + "__requires": [ + { + "type": "grafana", + "id": "grafana", + "name": "Grafana", + "version": "8.1.5" + }, + { + "type": "datasource", + "id": "postgres", + "name": "PostgreSQL", + "version": "1.0.0" + }, + { + "type": "panel", + "id": "timeseries", + "name": "Time series", + "version": "" + } + ], + "annotations": { + "list": [ + { + "builtIn": 1, + "datasource": "${DS_POSTGRESQL}", + "enable": true, + "hide": true, + "iconColor": "rgba(0, 211, 255, 1)", + "name": "Annotations & Alerts", + "rawQuery": "SELECT\n extract(epoch from start_time) AS time,\n tag::text as text,\n tag::text as tags\nFROM\n annotations\nWHERE\n $__timeFilter(extract(epoch from start_time));\n", + "target": { + "limit": 100, + "matchAny": false, + "tags": [], + "type": "dashboard" + }, + "type": "dashboard" + }, + { + "datasource": "-- Grafana --", + "enable": true, + "iconColor": "red", + "name": "New annotation", + "target": { + "limit": 100, + "matchAny": false, + "tags": [], + "type": "dashboard" + } + } + ] + }, + "editable": true, + "gnetId": null, + "graphTooltip": 0, + "id": null, + "iteration": 1637344391025, + "links": [], + "panels": [ + { + "datasource": "${DS_POSTGRESQL}", + "fieldConfig": { + "defaults": { + "color": { + "mode": "continuous-RdYlGr" + }, + "custom": { + "axisLabel": "", + "axisPlacement": "auto", + "barAlignment": 0, + "drawStyle": "line", + "fillOpacity": 0, + "gradientMode": "none", + "hideFrom": { + "legend": false, + "tooltip": false, + "viz": false + }, + "lineInterpolation": "linear", + "lineWidth": 1, + "pointSize": 5, + "scaleDistribution": { + "type": "linear" + }, + "showPoints": "auto", + "spanNulls": false, + "stacking": { + "group": "A", + "mode": "none" + }, + "thresholdsStyle": { + "mode": "off" + } + }, + "mappings": [], + "thresholds": { + "mode": "absolute", + "steps": [ + { + "color": "red", + "value": null + } + ] + } + }, + "overrides": [] + }, + "gridPos": { + "h": 11, + "w": 24, + "x": 0, + "y": 0 + }, + "id": 2, + "options": { + "legend": { + "calcs": [], + "displayMode": "list", + "placement": "bottom" + }, + "tooltip": { + "mode": "single" + } + }, + "targets": [ + { + "format": "time_series", + "group": [ + { + "params": [ + "1s", + "none" + ], + "type": "time" + } + ], + "metricColumn": "patient", + "rawQuery": false, + "rawSql": "SELECT\n $__timeGroupAlias(time,1s),\n patient AS metric,\n avg(heart_rate) AS \"heart_rate\"\nFROM physio\nWHERE\n patient = '$patient'\nGROUP BY 1,2\nORDER BY 1,2", + "refId": "A", + "select": [ + [ + { + "params": [ + "heart_rate" + ], + "type": "column" + }, + { + "params": [ + "avg" + ], + "type": "aggregate" + }, + { + "params": [ + "heart_rate" + ], + "type": "alias" + } + ] + ], + "table": "physio", + "timeColumn": "time", + "where": [ + { + "datatype": "varchar", + "name": "", + "params": [ + "patient", + "=", + "'$patient'" + ], + "type": "expression" + } + ] + } + ], + "title": "HeartRate", + "type": "timeseries" + } + ], + "refresh": false, + "schemaVersion": 30, + "style": "dark", + "tags": [], + "templating": { + "list": [ + { + "allValue": null, + "current": {}, + "datasource": "${DS_POSTGRESQL}", + "definition": "SELECT patient from physio", + "description": null, + "error": null, + "hide": 0, + "includeAll": false, + "label": null, + "multi": false, + "name": "patient", + "options": [], + "query": "SELECT patient from physio", + "refresh": 1, + "regex": "", + "skipUrlSync": false, + "sort": 0, + "type": "query" + } + ] + }, + "time": { + "from": "2013-07-09T20:09:32.497Z", + "to": "2013-07-09T20:23:16.017Z" + }, + "timepicker": {}, + "timezone": "", + "title": "DemoAICE", + "uid": "_qAAx_57k", + "version": 32 +} \ No newline at end of file diff --git a/visualization/rr_intervals_loader.py b/visualization/rr_intervals_loader.py new file mode 100644 index 0000000..5f2404b --- /dev/null +++ b/visualization/rr_intervals_loader.py @@ -0,0 +1,112 @@ +import pandas as pd +import psycopg2 +from psycopg2 import Error +from psycopg2.extensions import connection, ISOLATION_LEVEL_AUTOCOMMIT +import argparse +import sys +from typing import List + +## example CLI -> python3 rr_intervals_loader.py --pg-host "localhost" --pg-port 5432 --pg-user "lecoued" --pg-password "DemoAICE" --pg-database "demo_aice" --filepath "rr_00007633_s003_t007.csv" --exam "00007633_s003_t007" +PG_HOST = "localhost" +PG_PORT = 5432 +PG_USER = "postgres" +PG_PASSWD = "postgres" +PG_DATABASE = "postgres" +PG_TABLE = "physio" + +CSV_FILE = "../data/test_data/rr_00007633_s003_t007.csv" +EXAM = "/00007633_s003_t007" + +def get_connection_to_db(host: str = PG_HOST, port: int = PG_PORT, + database: str = PG_DATABASE, user: str = PG_USER, + password: str = PG_PASSWD) -> connection: + try: + conn = psycopg2.connect(host=host, port=port, + database=database, user=user, + password=password) + except (Exception, Error): + print(f"Database {database} does not exist. Creating one...") + conn = psycopg2.connect(host=host, port=port, user=user, + password=password) + conn.set_isolation_level(ISOLATION_LEVEL_AUTOCOMMIT) + cursor = conn.cursor() + cursor.execute(f"CREATE DATABASE {database};") + print(f"Database {database} has been created.") + cursor.close() + conn.close() + conn = psycopg2.connect(host=host, port=port, + database=database, user=user, + password=password) + finally: + conn.set_isolation_level(ISOLATION_LEVEL_AUTOCOMMIT) + print("Database is OK") + return conn + + +def write_physio_to_db(csv_file: str, pg_host: str, pg_port: int, pg_database: str, pg_user: str, pg_password: str) -> None: + conn = get_connection_to_db(pg_host, pg_port, pg_database, pg_user, pg_password) + cursor = conn.cursor() + # Check if the table already exists + cursor.execute(f"SELECT COUNT(*) FROM information_schema.tables WHERE \ + table_name='{PG_TABLE}';") + if not cursor.fetchone()[0]: + print(f"Table {PG_TABLE} does not exist. Creating one...") + cursor.execute(f"CREATE TABLE {PG_TABLE} \ + (id SERIAL PRIMARY KEY, \ + time TIMESTAMP, \ + rr_interval REAL, \ + heart_rate INT, \ + patient VARCHAR(100) \ + );") + print(f"Table {PG_TABLE} has been created") + + df=pd.read_csv(csv_file, sep=',') + df.rename(columns={ df.columns[0]: "timestamp"}, inplace=True) + df["heart_rate"] = df["rr_interval"].apply(lambda x: int(60 * 1000 / x)) + + for _, row in df.iterrows(): + cursor.execute(f"INSERT INTO {PG_TABLE} VALUES \ + (DEFAULT, \ + '{row['timestamp']}', \ + {row['rr_interval']}, \ + {row['heart_rate']}, \ + '{EXAM}' \ + );") + + print("Physio values added") + cursor.close() + conn.close() + + +def parse_load_physio_args(args_to_parse: List[str]) -> argparse.Namespace: + parser = argparse.ArgumentParser(description='CLI parameter input') + parser.add_argument('--pg-host', + dest='pg_host', + required=True) + parser.add_argument('--pg-port', + dest='pg_port', + required=True) + parser.add_argument('--pg-user', + dest='pg_user', + required=True) + parser.add_argument('--pg-password', + dest='pg_password', + required=True) + parser.add_argument('--pg-database', + dest='pg_database', + required=True) + parser.add_argument('--filepath', + dest='filepath', + required=True) + parser.add_argument('--exam', + dest='exam', + required=True) + args = parser.parse_args(args_to_parse) + + return args + + +if __name__ == "__main__": + args = parse_load_physio_args(sys.argv[1:]) + d = vars(args) + write_physio_to_db(d["filepath"], d["pg_host"], d["pg_port"], d["pg_database"], d["pg_user"], d["pg_password"])