From 7f8c02c7996c74ad1efad7881d7929d38121ae9c Mon Sep 17 00:00:00 2001 From: Peter Kok Date: Thu, 29 Aug 2024 13:31:11 +0200 Subject: [PATCH 01/14] Add and enable pytype --- .github/workflows/build-and-test.yml | 6 +- README.md | 25 ++- poetry.lock | 259 ++++++++++++++++++++++++++- pyproject.toml | 3 + pytype.cfg | 12 ++ 5 files changed, 294 insertions(+), 11 deletions(-) create mode 100644 pytype.cfg diff --git a/.github/workflows/build-and-test.yml b/.github/workflows/build-and-test.yml index 2c2a2a1..3f128b4 100644 --- a/.github/workflows/build-and-test.yml +++ b/.github/workflows/build-and-test.yml @@ -54,9 +54,9 @@ jobs: - name: Test with pytest run: | poetry run pytest - # - name: Type check - # run: | - # poetry run pytype . + - name: Type check + run: | + poetry run pytype . # Build the package - name: Build the package diff --git a/README.md b/README.md index d4c5186..1cf71b0 100644 --- a/README.md +++ b/README.md @@ -2,23 +2,36 @@ Digital Biomarkers for Parkinson's Disease Toolbox -## Implementation progress - -See [here](https://docs.google.com/spreadsheets/d/19AZZ44cOJeHonGaSpAPh0eMXW2ix5JHz9L9zYTNnH6g/edit?usp=sharing). +A package ([documentation](https://biomarkersparkinson.github.io/paradigma/)) to process wearable sensor data for Parkinson's disease. ## Installation -The package requires Python 3.9 or higher. It can be installed using [Poetry](https://python-poetry.org/docs/#installation): + +The package is available in PyPi and requires [Python 3.9](https://www.python.org/downloads/) or higher. It can be installed using: ```bash -$ poetry install +pip install paradigma ``` ## Usage -- TODO +See our [extended documentation](https://biomarkersparkinson.github.io/paradigma/). + ## Development +### Installation +The package requires Python 3.9 or higher. Use [Poetry](https://python-poetry.org/docs/#installation) to set up the environment and install the dependencies: + +```bash +poetry install +``` + +### Testing + +```bash +poetry run pytest +``` + ### Building documentation ```bash diff --git a/poetry.lock b/poetry.lock index a4d0b7c..fec8daf 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.6.1 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. 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[tool.poetry.group.testing.dependencies] ipykernel = "^6.27.1" diff --git a/pytype.cfg b/pytype.cfg new file mode 100644 index 0000000..41538f8 --- /dev/null +++ b/pytype.cfg @@ -0,0 +1,12 @@ +[pytype] +# Path to source files +inputs = . +# Path to exclude from analysis +exclude = + **/node_modules + **/venv + **/.venv + **/build + **/dist +# Python version (matches the project's runtime environment) +python_version = 3.9 From 2b254983680798c30f279195a762d26384d942c1 Mon Sep 17 00:00:00 2001 From: Peter Kok Date: Thu, 29 Aug 2024 15:58:57 +0200 Subject: [PATCH 02/14] Fix types --- src/paradigma/feature_extraction.py | 28 ++++++++++++++-------------- src/paradigma/gait_analysis.py | 12 +++++++----- src/paradigma/heart_rate_util.py | 4 ++-- src/paradigma/imu_preprocessing.py | 20 +++++++++++--------- src/paradigma/ppg_preprocessing.py | 4 ++-- src/paradigma/util.py | 8 ++++---- src/paradigma/windowing.py | 10 ++++++---- 7 files changed, 46 insertions(+), 40 deletions(-) diff --git a/src/paradigma/feature_extraction.py b/src/paradigma/feature_extraction.py index 9fc7a1c..3f97e7e 100644 --- a/src/paradigma/feature_extraction.py +++ b/src/paradigma/feature_extraction.py @@ -125,8 +125,8 @@ def signal_to_ffts( def compute_power_in_bandwidth( sensor_col: list, - fmin: int, - fmax: int, + fmin: float, + fmax: float, sampling_frequency: int = 100, window_type: str = 'hann', ) -> float: @@ -140,9 +140,9 @@ def compute_power_in_bandwidth( sensor_col: list The sensor column to be transformed (e.g. x-axis of accelerometer). This corresponds to a single window, which is a single row of the dataframe, and contains values of individual timestamps composing the window. - fmin: int + fmin: float The lower bound of the frequency band - fmax: int + fmax: float The upper bound of the frequency band sampling_frequency: int The sampling frequency of the signal (default: 100) @@ -162,10 +162,10 @@ def compute_power_in_bandwidth( def compute_perc_power( sensor_col: list, - fmin_band: int, - fmax_band: int, - fmin_total: int = 0, - fmax_total: int = 100, + fmin_band: float, + fmax_band: float, + fmin_total: float = 0, + fmax_total: float = 100, sampling_frequency: int = 100, window_type: str = 'hann' ) -> float: @@ -177,13 +177,13 @@ def compute_perc_power( ---------- sensor_col: list The sensor column to be transformed (e.g. x-axis of accelerometer). This corresponds to a single window, which is a single row of the dataframe - fmin_band: int + fmin_band: float The lower bound of the frequency band - fmax_band: int + fmax_band: float The upper bound of the frequency band - fmin_total: int + fmin_total: float The lower bound of the frequency spectrum (default: 0) - fmax_total: int + fmax_total: float The upper bound of the frequency spectrum (default: 100) sampling_frequency: int The sampling frequency of the signal (default: 100) @@ -217,8 +217,8 @@ def compute_perc_power( def get_dominant_frequency( signal_ffts: list, signal_freqs: list, - fmin: int, - fmax: int + fmin: float, + fmax: float ) -> float: """Note: signal_ffts and signal_freqs are single cells (which corresponds to a single window) of signal_ffts and signal_freqs, as it is used with apply function. diff --git a/src/paradigma/gait_analysis.py b/src/paradigma/gait_analysis.py index 0bae20a..2d81629 100644 --- a/src/paradigma/gait_analysis.py +++ b/src/paradigma/gait_analysis.py @@ -1,6 +1,8 @@ import os import numpy as np import pandas as pd +from pathlib import Path +from typing import Union import tsdf @@ -16,7 +18,7 @@ from paradigma.util import get_end_iso8601, write_data, read_metadata -def extract_gait_features(input_path: str, output_path: str, config: GaitFeatureExtractionConfig) -> None: +def extract_gait_features(input_path: Union[str, Path], output_path: Union[str, Path], config: GaitFeatureExtractionConfig) -> None: # load data metadata_time, metadata_samples = read_metadata(input_path, config.meta_filename, config.time_filename, config.values_filename) df = tsdf.load_dataframe_from_binaries([metadata_time, metadata_samples], tsdf.constants.ConcatenationType.columns) @@ -57,7 +59,7 @@ def extract_gait_features(input_path: str, output_path: str, config: GaitFeature write_data(metadata_time, metadata_samples, output_path, 'gait_meta.json', df_windowed) -def detect_gait(input_path: str, output_path: str, path_to_classifier_input: str, config: GaitDetectionConfig) -> None: +def detect_gait(input_path: Union[str, Path], output_path: Union[str, Path], path_to_classifier_input: Union[str, Path], config: GaitDetectionConfig) -> None: # Load the data metadata_time, metadata_samples = read_metadata(input_path, config.meta_filename, config.time_filename, config.values_filename) @@ -98,7 +100,7 @@ def detect_gait(input_path: str, output_path: str, path_to_classifier_input: str write_data(metadata_time, metadata_samples, output_path, 'gait_meta.json', df) -def extract_arm_swing_features(input_path: str, output_path: str, config: ArmSwingFeatureExtractionConfig) -> None: +def extract_arm_swing_features(input_path: Union[str, Path], output_path: Union[str, Path], config: ArmSwingFeatureExtractionConfig) -> None: # load accelerometer and gyroscope data l_dfs = [] for sensor in ['accelerometer', 'gyroscope']: @@ -281,7 +283,7 @@ def extract_arm_swing_features(input_path: str, output_path: str, config: ArmSwi write_data(metadata_time, metadata_samples, output_path, 'arm_swing_meta.json', df_windowed) -def detect_arm_swing(input_path: str, output_path: str, path_to_classifier_input: str, config: ArmSwingDetectionConfig) -> None: +def detect_arm_swing(input_path: Union[str, Path], output_path: Union[str, Path], path_to_classifier_input: Union[str, Path], config: ArmSwingDetectionConfig) -> None: # Load the data metadata_time, metadata_samples = read_metadata(input_path, config.meta_filename, config.time_filename, config.values_filename) df = tsdf.load_dataframe_from_binaries([metadata_time, metadata_samples], tsdf.constants.ConcatenationType.columns) @@ -323,7 +325,7 @@ def detect_arm_swing(input_path: str, output_path: str, path_to_classifier_input write_data(metadata_time, metadata_samples, output_path, 'arm_swing_meta.json', df) -def quantify_arm_swing(path_to_feature_input: str, path_to_prediction_input: str, output_path: str, config: ArmSwingQuantificationConfig) -> None: +def quantify_arm_swing(path_to_feature_input: Union[str, Path], path_to_prediction_input: Union[str, Path], output_path: Union[str, Path], config: ArmSwingQuantificationConfig) -> None: # Load the features & predictions metadata_time, metadata_samples = read_metadata(path_to_feature_input, config.meta_filename, config.time_filename, config.values_filename) df_features = tsdf.load_dataframe_from_binaries([metadata_time, metadata_samples], tsdf.constants.ConcatenationType.columns) diff --git a/src/paradigma/heart_rate_util.py b/src/paradigma/heart_rate_util.py index 6be31ab..bfc3c69 100644 --- a/src/paradigma/heart_rate_util.py +++ b/src/paradigma/heart_rate_util.py @@ -1,4 +1,4 @@ -from typing import List, Tuple +from typing import List, Tuple, Union import pickle import pandas as pd import numpy as np @@ -72,7 +72,7 @@ def extract_ppg_features(arr_ppg: np.ndarray, sampling_frequency: int) -> np.nda # print(features_df) -def peakdet(v: np.ndarray, delta, x: np.ndarray=None) -> Tuple[List[Tuple[int, float]], List[Tuple[int, float]]]: +def peakdet(v: np.ndarray, delta, x: Union[np.ndarray, None]=None) -> Tuple[List[Tuple[int, float]], List[Tuple[int, float]]]: """ Detect peaks in a vector. diff --git a/src/paradigma/imu_preprocessing.py b/src/paradigma/imu_preprocessing.py index a1c2293..0eccc53 100644 --- a/src/paradigma/imu_preprocessing.py +++ b/src/paradigma/imu_preprocessing.py @@ -1,3 +1,4 @@ +from pathlib import Path from typing import List, Union import numpy as np import pandas as pd @@ -10,10 +11,11 @@ from paradigma.preprocessing_config import IMUPreprocessingConfig -def preprocess_imu_data(input_path: str, output_path: str, config: IMUPreprocessingConfig) -> None: +def preprocess_imu_data(input_path: Union[str, Path], output_path: Union[str, Path], config: IMUPreprocessingConfig) -> None: # Load data - metadata_time, metadata_samples = read_metadata(input_path, config.meta_filename, config.time_filename, config.values_filename) + metadata_time, metadata_samples = read_metadata(str(input_path), str(config.meta_filename), + str(config.time_filename), str(config.values_filename)) df = tsdf.load_dataframe_from_binaries([metadata_time, metadata_samples], tsdf.constants.ConcatenationType.columns) # Rename columns @@ -71,10 +73,10 @@ def preprocess_imu_data(input_path: str, output_path: str, config: IMUPreprocess write_data(metadata_time, metadata_samples, output_path, f'{sensor}_meta.json', df_sensor) def transform_time_array( - time_array: np.ndarray, + time_array: pd.Series, scale_factor: float, - input_unit_type: TimeUnit, - output_unit_type: TimeUnit, + input_unit_type: str, + output_unit_type: str, start_time: float = 0.0, ) -> np.ndarray: """ @@ -127,11 +129,11 @@ def transform_time_array( def resample_data( df: pd.DataFrame, - time_column : DataColumns, - time_unit_type: TimeUnit, - unscaled_column_names: list, + time_column : str, + time_unit_type: str, + unscaled_column_names: List[str], resampling_frequency: int, - scale_factors: list = [], + scale_factors: List[float] = [], start_time: float = 0.0, ) -> pd.DataFrame: """ diff --git a/src/paradigma/ppg_preprocessing.py b/src/paradigma/ppg_preprocessing.py index c9e8162..3a282d6 100644 --- a/src/paradigma/ppg_preprocessing.py +++ b/src/paradigma/ppg_preprocessing.py @@ -3,7 +3,7 @@ import numpy as np import pandas as pd from pathlib import Path -from typing import List +from typing import List, Union from datetime import datetime, timedelta import tsdf @@ -31,7 +31,7 @@ def scan_and_sync_segments(input_path_ppg, input_path_imu): return metadatas_ppg, metadatas_imu -def preprocess_ppg_data(tsdf_meta_ppg: tsdf.TSDFMetadata, tsdf_meta_imu: tsdf.TSDFMetadata, output_path: str, ppg_config: PPGPreprocessingConfig, imu_config: IMUPreprocessingConfig): +def preprocess_ppg_data(tsdf_meta_ppg: tsdf.TSDFMetadata, tsdf_meta_imu: tsdf.TSDFMetadata, output_path: Union[str, Path], ppg_config: PPGPreprocessingConfig, imu_config: IMUPreprocessingConfig): # Load PPG data metadata_time_ppg = tsdf_meta_ppg[ppg_config.time_filename] diff --git a/src/paradigma/util.py b/src/paradigma/util.py index b57ab18..9c19c0e 100644 --- a/src/paradigma/util.py +++ b/src/paradigma/util.py @@ -26,13 +26,13 @@ def write_data(metadata_time: TSDFMetadata, metadata_samples: TSDFMetadata, # Make sure the iso8601 format is correctly set #TODO: this should be properly validated in the tsdf library instead - start_date = parser.parse(metadata_time.__getattribute__('start_iso8601')) + start_date = parser.parse(metadata_time.start_iso8601) metadata_time.start_iso8601 = format_datetime_to_iso8601(start_date) - end_date = parser.parse(metadata_time.__getattribute__('end_iso8601')) + end_date = parser.parse(metadata_time.end_iso8601) metadata_time.end_iso8601 = format_datetime_to_iso8601(end_date) - start_date = parser.parse(metadata_samples.__getattribute__('start_iso8601')) + start_date = parser.parse(metadata_samples.start_iso8601) metadata_samples.start_iso8601 = format_datetime_to_iso8601(start_date) - end_date = parser.parse(metadata_samples.__getattribute__('end_iso8601')) + end_date = parser.parse(metadata_samples.end_iso8601) metadata_samples.end_iso8601 = format_datetime_to_iso8601(end_date) # TODO: improve the way the metadata is stored at a different location diff --git a/src/paradigma/windowing.py b/src/paradigma/windowing.py index 519f01d..2dba847 100644 --- a/src/paradigma/windowing.py +++ b/src/paradigma/windowing.py @@ -2,6 +2,8 @@ import numpy as np import math +from typing import Union, List + def create_window( df: pd.DataFrame, @@ -57,11 +59,11 @@ def tabulate_windows( df: pd.DataFrame, time_column_name: str, data_point_level_cols: list, - window_length_s: int = 6, - window_step_size_s: int = 1, + window_length_s: Union[int, float] = 6, + window_step_size_s: Union[int, float] = 1, sampling_frequency: int = 100, - segment_nr_colname: str = None, - segment_nr: int = None, + segment_nr_colname: Union[str, None] = None, + segment_nr: Union[int, None] = None, ) -> pd.DataFrame: """Compiles multiple windows into a single dataframe From 634ffd63e378dc86d2b604d5da4a86acd4e49b7e Mon Sep 17 00:00:00 2001 From: Peter Kok Date: Thu, 29 Aug 2024 16:43:39 +0200 Subject: [PATCH 03/14] Fix types in docstrings --- src/paradigma/imu_preprocessing.py | 6 +++--- src/paradigma/windowing.py | 4 ++-- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/src/paradigma/imu_preprocessing.py b/src/paradigma/imu_preprocessing.py index 0eccc53..1258343 100644 --- a/src/paradigma/imu_preprocessing.py +++ b/src/paradigma/imu_preprocessing.py @@ -84,13 +84,13 @@ def transform_time_array( Parameters ---------- - time_array : np.ndarray + time_array : pd.Series The time array in milliseconds to transform. scale_factor : float The scale factor to apply to the time array. - input_unit_type : TimeUnit + input_unit_type : str The time unit type of the input time array. Raw PPP data was in `TimeUnit.difference_ms`. - output_unit_type : TimeUnit + output_unit_type : str The time unit type of the output time array. The processing is often done in `TimeUnit.relative_ms`. start_time : float, optional The start time of the time array in UNIX milliseconds (default is 0.0) diff --git a/src/paradigma/windowing.py b/src/paradigma/windowing.py index 2dba847..150df90 100644 --- a/src/paradigma/windowing.py +++ b/src/paradigma/windowing.py @@ -75,9 +75,9 @@ def tabulate_windows( The name of the time column data_point_level_cols: list The names of the columns that are to be kept as individual datapoints in a list instead of aggregates - window_length_s: int, optional + window_length_s: int | float, optional The number of seconds a window constitutes (default: 6) - window_step_size_s: int, optional + window_step_size_s: int | float, optional The number of seconds between the end of the previous and the start of the next window (default: 1) sampling_frequency: int, optional The sampling frequency of the data (default: 100) From 3d76b1869b58f779ec31c5d167fa8ea79a3d0743 Mon Sep 17 00:00:00 2001 From: Peter Kok Date: Thu, 29 Aug 2024 21:07:02 +0200 Subject: [PATCH 04/14] Use actual Enum for constants and types --- src/paradigma/constants.py | 8 ++++--- src/paradigma/gait_analysis.py | 6 +++--- src/paradigma/gait_analysis_config.py | 30 +++++++++++++++------------ src/paradigma/heart_rate_analysis.py | 6 +++--- src/paradigma/imu_preprocessing.py | 23 ++++++++++---------- src/paradigma/ppg_preprocessing.py | 16 +++++++------- src/paradigma/preprocessing_config.py | 14 ++++++------- 7 files changed, 55 insertions(+), 48 deletions(-) diff --git a/src/paradigma/constants.py b/src/paradigma/constants.py index b312cd0..ebe7c25 100644 --- a/src/paradigma/constants.py +++ b/src/paradigma/constants.py @@ -1,4 +1,6 @@ -class DataColumns: +from enum import Enum + +class DataColumns(Enum): """ Enum for the data channels in tsdf. """ @@ -11,7 +13,7 @@ class DataColumns: PPG = "green" TIME = "time" -class DataUnits: +class DataUnits(Enum): """ Enum for the data channel unit types in tsdf. """ @@ -21,7 +23,7 @@ class DataUnits: """ The rotation is in degrees per second. """ -class TimeUnit: +class TimeUnit(Enum): """ Enum for the `time` channel unit types in tsdf. """ diff --git a/src/paradigma/gait_analysis.py b/src/paradigma/gait_analysis.py index 2d81629..01a6cee 100644 --- a/src/paradigma/gait_analysis.py +++ b/src/paradigma/gait_analysis.py @@ -122,9 +122,9 @@ def extract_arm_swing_features(input_path: Union[str, Path], output_path: Union[ # perform principal component analysis on the gyroscope signals to obtain the angular velocity in the # direction of the swing of the arm df[config.velocity_colname] = pca_transform_gyroscope( - df=df, - y_gyro_colname=DataColumns.GYROSCOPE_Y, - z_gyro_colname=DataColumns.GYROSCOPE_Z, + df=df, + y_gyro_colname=DataColumns.GYROSCOPE_Y.value, + z_gyro_colname=DataColumns.GYROSCOPE_Z.value, pred_gait_colname=config.pred_gait_colname ) diff --git a/src/paradigma/gait_analysis_config.py b/src/paradigma/gait_analysis_config.py index f9061e4..1bf2517 100644 --- a/src/paradigma/gait_analysis_config.py +++ b/src/paradigma/gait_analysis_config.py @@ -30,10 +30,10 @@ def __init__(self) -> None: self.time_colname = 'time' - self.l_accelerometer_cols: List[str] = [ - DataColumns.ACCELEROMETER_X, - DataColumns.ACCELEROMETER_Y, - DataColumns.ACCELEROMETER_Z + self.l_accelerometer_cols: List[str] = [ # strings, not DataColumns, as we'll edit them + "accelerometer_x", + "accelerometer_y", + "accelerometer_z", ] self.l_gravity_cols: List[str] = [f'grav_{x}' for x in self.l_accelerometer_cols] @@ -95,7 +95,7 @@ def __init__(self) -> None: self.time_filename = 'gait_time.bin' self.values_filename = 'gait_values.bin' - self.l_accel_cols = [DataColumns.ACCELEROMETER_X, DataColumns.ACCELEROMETER_Y, DataColumns.ACCELEROMETER_Z] + self.l_accel_cols = [DataColumns.ACCELEROMETER_X.value, DataColumns.ACCELEROMETER_Y.value, DataColumns.ACCELEROMETER_Z.value] self.time_colname = 'time' @@ -140,15 +140,15 @@ def initialize_column_names(self, time_colname='time', pred_gait_colname='pred_g self.segment_nr_colname = segment_nr_colname self.l_accelerometer_cols: List[str] = [ - DataColumns.ACCELEROMETER_X, - DataColumns.ACCELEROMETER_Y, - DataColumns.ACCELEROMETER_Z + DataColumns.ACCELEROMETER_X.value, + DataColumns.ACCELEROMETER_Y.value, + DataColumns.ACCELEROMETER_Z.value ] self.l_gyroscope_cols: List[str] = [ - DataColumns.GYROSCOPE_X, - DataColumns.GYROSCOPE_Y, - DataColumns.GYROSCOPE_Z + DataColumns.GYROSCOPE_X.value, + DataColumns.GYROSCOPE_Y.value, + DataColumns.GYROSCOPE_Z.value ] self.l_gravity_cols: List[str] = [f'grav_{x}' for x in self.l_accelerometer_cols] @@ -225,8 +225,12 @@ def __init__(self) -> None: self.time_filename = 'arm_swing_time.bin' self.values_filename = 'arm_swing_values.bin' - self.l_accel_cols = [DataColumns.ACCELEROMETER_X, DataColumns.ACCELEROMETER_Y, DataColumns.ACCELEROMETER_Z] - self.l_gyro_cols = [DataColumns.GYROSCOPE_X, DataColumns.GYROSCOPE_Y, DataColumns.GYROSCOPE_Z] + self.l_accel_cols = [DataColumns.ACCELEROMETER_X.value, + DataColumns.ACCELEROMETER_Y.value, + DataColumns.ACCELEROMETER_Z.value] + self.l_gyro_cols = [DataColumns.GYROSCOPE_X.value, + DataColumns.GYROSCOPE_Y.value, + DataColumns.GYROSCOPE_Z.value] class ArmSwingQuantificationConfig: diff --git a/src/paradigma/heart_rate_analysis.py b/src/paradigma/heart_rate_analysis.py index 18b866f..d259607 100644 --- a/src/paradigma/heart_rate_analysis.py +++ b/src/paradigma/heart_rate_analysis.py @@ -16,12 +16,12 @@ def extract_signal_quality_features(input_path: str, classifier_path: str, outpu # load data metadata_time_ppg, metadata_samples_ppg = read_metadata(input_path, "PPG_meta.json", "PPG_time.bin", "PPG_samples.bin") df_ppg = tsdf.load_dataframe_from_binaries([metadata_time_ppg, metadata_samples_ppg], tsdf.constants.ConcatenationType.columns) - arr_ppg = df_ppg[DataColumns.PPG].to_numpy() - relative_time_ppg = df_ppg[DataColumns.TIME].to_numpy() + arr_ppg = df_ppg[DataColumns.PPG.value].to_numpy() + relative_time_ppg = df_ppg[DataColumns.TIME.value].to_numpy() metadata_time_acc, metadata_samples_acc = read_metadata(input_path, "accelerometer_meta.json", "accelerometer_time.bin", "accelerometer_samples.bin") df_acc = tsdf.load_dataframe_from_binaries([metadata_time_acc, metadata_samples_acc], tsdf.constants.ConcatenationType.columns) - arr_acc = df_acc[[DataColumns.ACCELEROMETER_X, DataColumns.ACCELEROMETER_Y, DataColumns.ACCELEROMETER_Z]].to_numpy() + arr_acc = df_acc[[DataColumns.ACCELEROMETER_X.value, DataColumns.ACCELEROMETER_Y.value, DataColumns.ACCELEROMETER_Z.value]].to_numpy() sampling_frequency_ppg = config.sampling_frequency_ppg sampling_frequency_imu = config.sampling_frequency_imu diff --git a/src/paradigma/imu_preprocessing.py b/src/paradigma/imu_preprocessing.py index 1258343..b734405 100644 --- a/src/paradigma/imu_preprocessing.py +++ b/src/paradigma/imu_preprocessing.py @@ -24,7 +24,7 @@ def preprocess_imu_data(input_path: Union[str, Path], output_path: Union[str, Pa # convert to relative seconds from delta milliseconds df[config.time_colname] = transform_time_array( - time_array=df[config.time_colname], + time_array=df[config.time_colname.value], scale_factor=1000, input_unit_type = TimeUnit.difference_ms, output_unit_type = TimeUnit.relative_ms) @@ -39,7 +39,7 @@ def preprocess_imu_data(input_path: Union[str, Path], output_path: Union[str, Pa resampling_frequency=config.sampling_frequency) if config.side_watch == 'left': - df[DataColumns.ACCELEROMETER_X] *= -1 + df[DataColumns.ACCELEROMETER_X.value] *= -1 for col in config.d_channels_accelerometer.keys(): @@ -61,7 +61,7 @@ def preprocess_imu_data(input_path: Union[str, Path], output_path: Union[str, Pa # Store data for sensor, units in zip(['accelerometer', 'gyroscope'], ['g', config.rotation_units]): - df_sensor = df[[config.time_colname] + [x for x in df.columns if sensor in x]] + df_sensor = df[[config.time_colname.value] + [x for x in df.columns if sensor in x]] metadata_samples.channels = [x for x in df.columns if sensor in x] metadata_samples.units = list(np.repeat(units, len(metadata_samples.channels))) @@ -75,8 +75,8 @@ def preprocess_imu_data(input_path: Union[str, Path], output_path: Union[str, Pa def transform_time_array( time_array: pd.Series, scale_factor: float, - input_unit_type: str, - output_unit_type: str, + input_unit_type: TimeUnit, + output_unit_type: TimeUnit, start_time: float = 0.0, ) -> np.ndarray: """ @@ -88,9 +88,9 @@ def transform_time_array( The time array in milliseconds to transform. scale_factor : float The scale factor to apply to the time array. - input_unit_type : str + input_unit_type : TimeUnit The time unit type of the input time array. Raw PPP data was in `TimeUnit.difference_ms`. - output_unit_type : str + output_unit_type : TimeUnit The time unit type of the output time array. The processing is often done in `TimeUnit.relative_ms`. start_time : float, optional The start time of the time array in UNIX milliseconds (default is 0.0) @@ -129,8 +129,8 @@ def transform_time_array( def resample_data( df: pd.DataFrame, - time_column : str, - time_unit_type: str, + time_column : DataColumns, + time_unit_type: TimeUnit, unscaled_column_names: List[str], resampling_frequency: int, scale_factors: List[float] = [], @@ -177,7 +177,7 @@ def resample_data( t_resampled = np.arange(start_time, time_abs_array[-1], 1 / resampling_frequency) # create dataframe - df = pd.DataFrame(t_resampled, columns=[time_column]) + df = pd.DataFrame(t_resampled, columns=[time_column.value]) # interpolate IMU - maybe a separate method? for j, sensor_col in enumerate(unscaled_column_names): @@ -185,7 +185,8 @@ def resample_data( raise ValueError("time_abs_array is not strictly increasing") cs = CubicSpline(time_abs_array, scaled_values.T[j]) - df[sensor_col] = cs(df[time_column]) + #TODO: isn't sensor_col of type DataColumns? + df[sensor_col] = cs(df[time_column.value]) return df diff --git a/src/paradigma/ppg_preprocessing.py b/src/paradigma/ppg_preprocessing.py index 3a282d6..0d887da 100644 --- a/src/paradigma/ppg_preprocessing.py +++ b/src/paradigma/ppg_preprocessing.py @@ -50,16 +50,16 @@ def preprocess_ppg_data(tsdf_meta_ppg: tsdf.TSDFMetadata, tsdf_meta_imu: tsdf.TS # Transform the time arrays to absolute milliseconds start_time_ppg = parse_iso8601_to_datetime(metadata_time_ppg.start_iso8601).timestamp() - df_imu[DataColumns.TIME] = paradigma.imu_preprocessing.transform_time_array( - time_array=df_imu[DataColumns.TIME], + df_imu[DataColumns.TIME.value] = paradigma.imu_preprocessing.transform_time_array( + time_array=df_imu[DataColumns.TIME.value], scale_factor=1000, input_unit_type = paradigma.constants.TimeUnit.difference_ms, output_unit_type = paradigma.constants.TimeUnit.absolute_ms, start_time = start_time_ppg) start_time_imu = parse_iso8601_to_datetime(metadata_time_imu.start_iso8601).timestamp() - df_ppg[DataColumns.TIME] = paradigma.imu_preprocessing.transform_time_array( - time_array=df_ppg[DataColumns.TIME], + df_ppg[DataColumns.TIME.value] = paradigma.imu_preprocessing.transform_time_array( + time_array=df_ppg[DataColumns.TIME.value], scale_factor=1000, input_unit_type = paradigma.constants.TimeUnit.difference_ms, output_unit_type = paradigma.constants.TimeUnit.absolute_ms, @@ -122,16 +122,16 @@ def preprocess_ppg_data(tsdf_meta_ppg: tsdf.TSDFMetadata, tsdf_meta_imu: tsdf.TS df_ppg_proc = df_ppg_proc.drop(columns=[col]) df_ppg_proc = df_ppg_proc.rename(columns={f'filt_{col}': col}) - df_imu_proc[DataColumns.TIME] = paradigma.imu_preprocessing.transform_time_array( - time_array=df_imu_proc[DataColumns.TIME], + df_imu_proc[DataColumns.TIME.value] = paradigma.imu_preprocessing.transform_time_array( + time_array=df_imu_proc[DataColumns.TIME.value], scale_factor=1, input_unit_type=paradigma.constants.TimeUnit.absolute_ms, output_unit_type=paradigma.constants.TimeUnit.relative_ms, start_time=start_time_ppg, ) - df_ppg_proc[DataColumns.TIME] = paradigma.imu_preprocessing.transform_time_array( - time_array=df_ppg_proc[DataColumns.TIME], + df_ppg_proc[DataColumns.TIME.value] = paradigma.imu_preprocessing.transform_time_array( + time_array=df_ppg_proc[DataColumns.TIME.value], scale_factor=1, input_unit_type=paradigma.constants.TimeUnit.absolute_ms, output_unit_type=paradigma.constants.TimeUnit.relative_ms, diff --git a/src/paradigma/preprocessing_config.py b/src/paradigma/preprocessing_config.py index 86d2abf..16c6ee5 100644 --- a/src/paradigma/preprocessing_config.py +++ b/src/paradigma/preprocessing_config.py @@ -37,14 +37,14 @@ def __init__(self) -> None: self.rotation_units = 'deg/s' self.d_channels_accelerometer = { - DataColumns.ACCELEROMETER_X: self.acceleration_units, - DataColumns.ACCELEROMETER_Y: self.acceleration_units, - DataColumns.ACCELEROMETER_Z: self.acceleration_units, + DataColumns.ACCELEROMETER_X.value: self.acceleration_units, + DataColumns.ACCELEROMETER_Y.value: self.acceleration_units, + DataColumns.ACCELEROMETER_Z.value: self.acceleration_units, } self.d_channels_gyroscope = { - DataColumns.GYROSCOPE_X: self.rotation_units, - DataColumns.GYROSCOPE_Y: self.rotation_units, - DataColumns.GYROSCOPE_Z: self.rotation_units, + DataColumns.GYROSCOPE_X.value: self.rotation_units, + DataColumns.GYROSCOPE_Y.value: self.rotation_units, + DataColumns.GYROSCOPE_Z.value: self.rotation_units, } self.d_channels_imu = {**self.d_channels_accelerometer, **self.d_channels_gyroscope} @@ -58,7 +58,7 @@ def __init__(self) -> None: self.time_filename = 'PPG_time.bin' self.d_channels_ppg = { - DataColumns.PPG: 'none' + DataColumns.PPG.value: 'none' } self.sampling_frequency = 30 From b2603c80bca2336b06d8316c36bef9acfacb5130 Mon Sep 17 00:00:00 2001 From: Vedran Kasalica Date: Mon, 2 Sep 2024 09:43:07 +0200 Subject: [PATCH 05/14] Add the initial badges --- README.md | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/README.md b/README.md index 1cf71b0..27d098d 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,13 @@ # paradigma +| Badges | | +|:----:|----| +| **Packages and Releases** | [![Latest release](https://img.shields.io/github/release/biomarkersparkinson/paradigma.svg)](https://github.com/biomarkersparkinson/paradigma/releases/latest) [![PyPI](https://img.shields.io/pypi/v/paradigma.svg)](https://pypi.python.org/pypi/paradigma/) [![Static Badge](https://img.shields.io/badge/RSD-paradigma-lib)](https://research-software-directory.org/software/paradigma) | +| **Build Status** | [![](https://img.shields.io/badge/python-3.9+-blue.svg)](https://www.python.org/downloads/) [![Build and test](https://github.com/biomarkersParkinson/paradigma/actions/workflows/build-and-test.yml/badge.svg)](https://github.com/biomarkersParkinson/paradigma/actions/workflows/build-and-test.yml) [![pages-build-deployment](https://github.com/biomarkersParkinson/paradigma/actions/workflows/pages/pages-build-deployment/badge.svg)](https://github.com/biomarkersParkinson/paradigma/actions/workflows/pages/pages-build-deployment) | +| **License** | [![GitHub license](https://img.shields.io/github/license/biomarkersParkinson/paradigma)](https://github.com/biomarkersparkinson/paradigma/blob/main/LICENSE) | + + + Digital Biomarkers for Parkinson's Disease Toolbox A package ([documentation](https://biomarkersparkinson.github.io/paradigma/)) to process wearable sensor data for Parkinson's disease. From ef836e3ba6b24474c8e92e55e943c6793c647c44 Mon Sep 17 00:00:00 2001 From: Vedran Kasalica Date: Mon, 2 Sep 2024 11:38:35 +0200 Subject: [PATCH 06/14] Add the citation file --- CITATION.cff | 50 ++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 50 insertions(+) create mode 100644 CITATION.cff diff --git a/CITATION.cff b/CITATION.cff new file mode 100644 index 0000000..1f427d9 --- /dev/null +++ b/CITATION.cff @@ -0,0 +1,50 @@ +# This CITATION.cff file was generated with cffinit. +# Visit https://bit.ly/cffinit to generate yours today! + +cff-version: 1.2.0 +title: paradigma +abstract: 'Digital Biomarkers for Parkinson's Disease Toolbox' +message: >- + If you use this software, please cite it using the + metadata from this file. +type: software +authors: + - given-names: Peter + family-names: Kok + email: p.kok@esciencecenter.nl + affiliation: Netherlands eScience Center + orcid: 'https://orcid.org/0000-0002-6630-7326' + - given-names: Vedran + family-names: Kasalica + email: v.kasalica@esciencecenter.nl + affiliation: Netherlands eScience Center + orcid: 'https://orcid.org/0000-0002-0097-1056' + - given-names: Luc J.W. + family-names: Evers + email: luc.evers@radboudumc.nl + affiliation: + - Radboud University, Institute for Computing and Information Sciences, Department of Data Science, Nijmegen, Netherlands + - Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson and Movement Disorders, Nijmegen, Netherlands + orcid: 'https://orcid.org/0000-0002-8241-5087' + corresponding: false + - given-names: Erik + family-names: Post + email: erik.post@radboudumc.nl + affiliation: + - Radboud University, Institute for Computing and Information Sciences, Department of Data Science, Nijmegen, Netherlands + - Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology, Center of Expertise for Parkinson and Movement Disorders, Nijmegen, Netherlands + orcid: 'https://orcid.org/0000-0002-1937-9976' + - given-names: Kars + family-names: Veldkamp + email: Kars.Veldkamp@radboudumc.nl + affiliation: Radboud University Medical Center, Nijmegen, Netherlands + - given-names: Nienke + family-names: Timmermans + email: nienke.timmermans@radboudumc.nl + affiliation: Radboud University Medical Center, Nijmegen, Netherlands +identifiers: + - type: doi + value: 10.5281/zenodo.7867900 +repository-code: 'https://github.com/biomarkersParkinson/tsdf' +url: 'https://biomarkersparkinson.github.io/tsdf/' +license: Apache-2.0 From d50495117b91efe0547eb52873377786280518dc Mon Sep 17 00:00:00 2001 From: Vedran Kasalica Date: Mon, 2 Sep 2024 15:33:37 +0200 Subject: [PATCH 07/14] Update authors in citation.cff --- CITATION.cff | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/CITATION.cff b/CITATION.cff index 1f427d9..5c2dfa3 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -42,6 +42,10 @@ authors: family-names: Timmermans email: nienke.timmermans@radboudumc.nl affiliation: Radboud University Medical Center, Nijmegen, Netherlands + - given-names: Diogo + family-names: Soriano + email: diogo.soriano@ufabc.edu.br + affiliation: Center for Engineering, Modeling and Applied Social Sciences (CECS), Federal University of ABC (UFABC), Brazil identifiers: - type: doi value: 10.5281/zenodo.7867900 From 226ea905a07d3f2d28770a985b79f78f1cfbaa46 Mon Sep 17 00:00:00 2001 From: Vedran Kasalica Date: Mon, 2 Sep 2024 17:32:24 +0200 Subject: [PATCH 08/14] Update naming --- src/paradigma/constants.py | 6 +++--- src/paradigma/gait_analysis_config.py | 12 +++++------ src/paradigma/imu_preprocessing.py | 30 +++++++++++++-------------- src/paradigma/ppg_preprocessing.py | 24 ++++++++++----------- 4 files changed, 36 insertions(+), 36 deletions(-) diff --git a/src/paradigma/constants.py b/src/paradigma/constants.py index ebe7c25..a7a1c57 100644 --- a/src/paradigma/constants.py +++ b/src/paradigma/constants.py @@ -27,11 +27,11 @@ class TimeUnit(Enum): """ Enum for the `time` channel unit types in tsdf. """ - relative_ms = "relative_ms" + RELATIVE_MS = "relative_ms" """ The time is relative to the start time in milliseconds. """ - absolute_ms = "absolute_ms" + ABSOLUTE_MS = "absolute_ms" """ The time is absolute in milliseconds. """ - difference_ms = "difference_ms" + DIFFERENCE_MS = "difference_ms" """ The time is the difference between consecutive samples in milliseconds. """ UNIX_TICKS_MS: int = 1000 diff --git a/src/paradigma/gait_analysis_config.py b/src/paradigma/gait_analysis_config.py index 1bf2517..d4d88d0 100644 --- a/src/paradigma/gait_analysis_config.py +++ b/src/paradigma/gait_analysis_config.py @@ -16,8 +16,8 @@ def __init__(self) -> None: self.window_step_size_s: int = 1 # cepstral coefficients - self.cc_low_frequency = 0 - self.cc_high_frequency = 25 + self.cc_low_frequency: int = 0 + self.cc_high_frequency: int = 25 self.n_dct_filters_cc: int = 20 self.n_coefficients_cc: int = 12 @@ -140,14 +140,14 @@ def initialize_column_names(self, time_colname='time', pred_gait_colname='pred_g self.segment_nr_colname = segment_nr_colname self.l_accelerometer_cols: List[str] = [ - DataColumns.ACCELEROMETER_X.value, - DataColumns.ACCELEROMETER_Y.value, + DataColumns.ACCELEROMETER_X.value, + DataColumns.ACCELEROMETER_Y.value, DataColumns.ACCELEROMETER_Z.value ] self.l_gyroscope_cols: List[str] = [ - DataColumns.GYROSCOPE_X.value, - DataColumns.GYROSCOPE_Y.value, + DataColumns.GYROSCOPE_X.value, + DataColumns.GYROSCOPE_Y.value, DataColumns.GYROSCOPE_Z.value ] diff --git a/src/paradigma/imu_preprocessing.py b/src/paradigma/imu_preprocessing.py index b734405..e48f218 100644 --- a/src/paradigma/imu_preprocessing.py +++ b/src/paradigma/imu_preprocessing.py @@ -26,14 +26,14 @@ def preprocess_imu_data(input_path: Union[str, Path], output_path: Union[str, Pa df[config.time_colname] = transform_time_array( time_array=df[config.time_colname.value], scale_factor=1000, - input_unit_type = TimeUnit.difference_ms, - output_unit_type = TimeUnit.relative_ms) + input_unit_type = TimeUnit.DIFFERENCE_MS, + output_unit_type = TimeUnit.RELATIVE_MS) df = resample_data( df=df, time_column=config.time_colname, - time_unit_type=TimeUnit.relative_ms, + time_unit_type=TimeUnit.RELATIVE_MS, unscaled_column_names = list(config.d_channels_imu.keys()), scale_factors=metadata_samples.scale_factors, resampling_frequency=config.sampling_frequency) @@ -89,9 +89,9 @@ def transform_time_array( scale_factor : float The scale factor to apply to the time array. input_unit_type : TimeUnit - The time unit type of the input time array. Raw PPP data was in `TimeUnit.difference_ms`. + The time unit type of the input time array. Raw PPP data was in `TimeUnit.DIFFERENCE_MS`. output_unit_type : TimeUnit - The time unit type of the output time array. The processing is often done in `TimeUnit.relative_ms`. + The time unit type of the output time array. The processing is often done in `TimeUnit.RELATIVE_MS`. start_time : float, optional The start time of the time array in UNIX milliseconds (default is 0.0) @@ -100,28 +100,28 @@ def transform_time_array( time_array The transformed time array in milliseconds, with the specified time unit type. """ - # Scale time array and transform to relative time (`TimeUnit.relative_ms`) - if input_unit_type == TimeUnit.difference_ms: + # Scale time array and transform to relative time (`TimeUnit.RELATIVE_MS`) + if input_unit_type == TimeUnit.DIFFERENCE_MS: # Convert a series of differences into cumulative sum to reconstruct original time series. time_array = np.cumsum(np.double(time_array)) / scale_factor - elif input_unit_type == TimeUnit.absolute_ms: + elif input_unit_type == TimeUnit.ABSOLUTE_MS: # Set the start time if not provided. if np.isclose(start_time, 0.0, rtol=1e-09, atol=1e-09): start_time = time_array[0] # Convert absolute time stamps into a time series relative to start_time. time_array = (time_array - start_time) / scale_factor - elif input_unit_type == TimeUnit.relative_ms: + elif input_unit_type == TimeUnit.RELATIVE_MS: # Scale the relative time series as per the scale_factor. time_array = time_array / scale_factor - # Transform the time array from `TimeUnit.relative_ms` to the specified time unit type - if output_unit_type == TimeUnit.absolute_ms: + # Transform the time array from `TimeUnit.RELATIVE_MS` to the specified time unit type + if output_unit_type == TimeUnit.ABSOLUTE_MS: # Converts time array to absolute time by adding the start time to each element. time_array = time_array + start_time - elif output_unit_type == TimeUnit.difference_ms: + elif output_unit_type == TimeUnit.DIFFERENCE_MS: # Creates a new array starting with 0, followed by the differences between consecutive elements. time_array = np.diff(np.insert(time_array, 0, start_time)) - elif output_unit_type == TimeUnit.relative_ms: + elif output_unit_type == TimeUnit.RELATIVE_MS: # The array is already in relative format, do nothing. pass return time_array @@ -146,7 +146,7 @@ def resample_data( time_column : str The name of the time column. time_unit_type : TimeUnit - The time unit type of the time array. The method currently works only for `TimeUnit.relative_ms`. + The time unit type of the time array. The method currently works only for `TimeUnit.RELATIVE_MS`. unscaled_column_names : list The names of the columns to resample. resampling_frequency : int @@ -162,7 +162,7 @@ def resample_data( The resampled data. """ # We need a start_time if the time is in absolute time format - if time_unit_type == TimeUnit.absolute_ms and start_time == 0.0: + if time_unit_type == TimeUnit.ABSOLUTE_MS and start_time == 0.0: raise ValueError("start_time is required for absolute time format") # get time and values diff --git a/src/paradigma/ppg_preprocessing.py b/src/paradigma/ppg_preprocessing.py index 0d887da..3e73852 100644 --- a/src/paradigma/ppg_preprocessing.py +++ b/src/paradigma/ppg_preprocessing.py @@ -53,16 +53,16 @@ def preprocess_ppg_data(tsdf_meta_ppg: tsdf.TSDFMetadata, tsdf_meta_imu: tsdf.TS df_imu[DataColumns.TIME.value] = paradigma.imu_preprocessing.transform_time_array( time_array=df_imu[DataColumns.TIME.value], scale_factor=1000, - input_unit_type = paradigma.constants.TimeUnit.difference_ms, - output_unit_type = paradigma.constants.TimeUnit.absolute_ms, + input_unit_type = TimeUnit.DIFFERENCE_MS, + output_unit_type = TimeUnit.ABSOLUTE_MS, start_time = start_time_ppg) start_time_imu = parse_iso8601_to_datetime(metadata_time_imu.start_iso8601).timestamp() df_ppg[DataColumns.TIME.value] = paradigma.imu_preprocessing.transform_time_array( time_array=df_ppg[DataColumns.TIME.value], scale_factor=1000, - input_unit_type = paradigma.constants.TimeUnit.difference_ms, - output_unit_type = paradigma.constants.TimeUnit.absolute_ms, + input_unit_type = TimeUnit.DIFFERENCE_MS, + output_unit_type = TimeUnit.ABSOLUTE_MS, start_time = start_time_imu) # Extract overlapping segments @@ -74,7 +74,7 @@ def preprocess_ppg_data(tsdf_meta_ppg: tsdf.TSDFMetadata, tsdf_meta_imu: tsdf.TS df_imu_proc = paradigma.imu_preprocessing.resample_data( df=df_imu_overlapping, time_column=DataColumns.TIME, - time_unit_type=paradigma.constants.TimeUnit.absolute_ms, + time_unit_type=TimeUnit.ABSOLUTE_MS, unscaled_column_names = list(imu_config.d_channels_accelerometer.keys()), resampling_frequency=imu_config.sampling_frequency, scale_factors=metadata_samples_imu.scale_factors[0:3], @@ -84,7 +84,7 @@ def preprocess_ppg_data(tsdf_meta_ppg: tsdf.TSDFMetadata, tsdf_meta_imu: tsdf.TS df_ppg_proc = paradigma.imu_preprocessing.resample_data( df=df_ppg_overlapping, time_column=DataColumns.TIME, - time_unit_type=paradigma.constants.TimeUnit.absolute_ms, + time_unit_type=TimeUnit.ABSOLUTE_MS, unscaled_column_names = list(ppg_config.d_channels_ppg.keys()), scale_factors=metadata_samples_imu.scale_factors, resampling_frequency=ppg_config.sampling_frequency, @@ -125,16 +125,16 @@ def preprocess_ppg_data(tsdf_meta_ppg: tsdf.TSDFMetadata, tsdf_meta_imu: tsdf.TS df_imu_proc[DataColumns.TIME.value] = paradigma.imu_preprocessing.transform_time_array( time_array=df_imu_proc[DataColumns.TIME.value], scale_factor=1, - input_unit_type=paradigma.constants.TimeUnit.absolute_ms, - output_unit_type=paradigma.constants.TimeUnit.relative_ms, + input_unit_type=TimeUnit.ABSOLUTE_MS, + output_unit_type=TimeUnit.RELATIVE_MS, start_time=start_time_ppg, ) df_ppg_proc[DataColumns.TIME.value] = paradigma.imu_preprocessing.transform_time_array( time_array=df_ppg_proc[DataColumns.TIME.value], scale_factor=1, - input_unit_type=paradigma.constants.TimeUnit.absolute_ms, - output_unit_type=paradigma.constants.TimeUnit.relative_ms, + input_unit_type=TimeUnit.ABSOLUTE_MS, + output_unit_type=TimeUnit.RELATIVE_MS, start_time=start_time_imu, ) @@ -142,14 +142,14 @@ def preprocess_ppg_data(tsdf_meta_ppg: tsdf.TSDFMetadata, tsdf_meta_imu: tsdf.TS metadata_samples_imu.channels = list(imu_config.d_channels_accelerometer.keys()) metadata_samples_imu.units = list(imu_config.d_channels_accelerometer.values()) metadata_samples_imu.file_name = 'accelerometer_samples.bin' - metadata_time_imu.units = [TimeUnit.absolute_ms] + metadata_time_imu.units = [TimeUnit.ABSOLUTE_MS] metadata_time_imu.file_name = 'accelerometer_time.bin' write_data(metadata_time_imu, metadata_samples_imu, output_path, 'accelerometer_meta.json', df_imu_proc) metadata_samples_ppg.channels = list(ppg_config.d_channels_ppg.keys()) metadata_samples_ppg.units = list(ppg_config.d_channels_ppg.values()) metadata_samples_ppg.file_name = 'PPG_samples.bin' - metadata_time_ppg.units = [TimeUnit.absolute_ms] + metadata_time_ppg.units = [TimeUnit.ABSOLUTE_MS] metadata_time_ppg.file_name = 'PPG_time.bin' write_data(metadata_time_ppg, metadata_samples_ppg, output_path, 'PPG_meta.json', df_ppg_proc) From ac392346b260f9a320417a8d9e3ce3b18c93b56d Mon Sep 17 00:00:00 2001 From: Vedran Kasalica Date: Thu, 5 Sep 2024 10:35:01 +0200 Subject: [PATCH 09/14] Remove Enums and fix typing issues --- src/paradigma/constants.py | 23 ++- src/paradigma/feature_extraction.py | 31 ++- src/paradigma/gait_analysis.py | 4 +- src/paradigma/gait_analysis_config.py | 281 ++++++++++++++------------ src/paradigma/heart_rate_analysis.py | 6 +- src/paradigma/imu_preprocessing.py | 26 +-- src/paradigma/ppg_preprocessing.py | 16 +- src/paradigma/preprocessing_config.py | 14 +- 8 files changed, 235 insertions(+), 166 deletions(-) diff --git a/src/paradigma/constants.py b/src/paradigma/constants.py index a7a1c57..e61042a 100644 --- a/src/paradigma/constants.py +++ b/src/paradigma/constants.py @@ -1,6 +1,8 @@ -from enum import Enum +from dataclasses import dataclass -class DataColumns(Enum): + +@dataclass(frozen=True) +class DataColumns(): """ Enum for the data channels in tsdf. """ @@ -13,7 +15,18 @@ class DataColumns(Enum): PPG = "green" TIME = "time" -class DataUnits(Enum): + # The following are used in gait analysis + GRAV_ACCELEROMETER_X = "grav_accelerometer_x" + GRAV_ACCELEROMETER_Y = "grav_accelerometer_y" + GRAV_ACCELEROMETER_Z = "grav_accelerometer_z" + PRED_GAIT = "pred_gait" + ANGLE = "angle" + ANGLE_SMOOTH = "angle_smooth" + VELOCITY = "velocity" + SEGMENT_NR = "segment_nr" + +@dataclass(frozen=True) +class DataUnits(): """ Enum for the data channel unit types in tsdf. """ @@ -21,9 +34,9 @@ class DataUnits(Enum): """ The acceleration is in m/s^2. """ ROTATION = "deg/s" """ The rotation is in degrees per second. """ - -class TimeUnit(Enum): +@dataclass(frozen=True) +class TimeUnit(): """ Enum for the `time` channel unit types in tsdf. """ diff --git a/src/paradigma/feature_extraction.py b/src/paradigma/feature_extraction.py index 3f97e7e..39e672d 100644 --- a/src/paradigma/feature_extraction.py +++ b/src/paradigma/feature_extraction.py @@ -1,3 +1,4 @@ +from typing import List import numpy as np import pandas as pd from sklearn.decomposition import PCA @@ -6,6 +7,9 @@ from scipy.integrate import cumulative_trapezoid from scipy.signal import find_peaks +from paradigma.constants import DataColumns +from paradigma.gait_analysis_config import GaitFeatureExtractionConfig, ArmSwingFeatureExtractionConfig + def generate_statistics( sensor_col: pd.Series, @@ -39,7 +43,7 @@ def generate_statistics( def generate_std_norm( df: pd.DataFrame, - cols: list, + cols: List[str], ) -> pd.Series: """Generate the standard deviation of the norm of the accelerometer axes. @@ -47,7 +51,7 @@ def generate_std_norm( ---------- df: pd.DataFrame The dataframe containing the accelerometer axes - cols: list + cols: List[str] The names of the columns containing the accelerometer axes Returns @@ -602,7 +606,28 @@ def extract_peak_angular_velocity( return -def extract_temporal_domain_features(config, df_windowed, l_gravity_stats=['mean', 'std']): +def extract_temporal_domain_features(config:GaitFeatureExtractionConfig | ArmSwingFeatureExtractionConfig, df_windowed:pd.DataFrame, l_gravity_stats=['mean', 'std']) -> pd.DataFrame: + """ + Compute temporal domain features for the accelerometer signal. The features are added to the dataframe. Therefore the original dataframe is modified, and the modified dataframe is returned. + + Parameters + ---------- + + config: GaitFeatureExtractionConfig + The configuration object containing the parameters for the feature extraction + + df_windowed: pd.DataFrame + The dataframe containing the windowed accelerometer signal + + l_gravity_stats: list, optional + The statistics to be computed for the gravity component of the accelerometer signal (default: ['mean', 'std']) + + Returns + ------- + pd.DataFrame + The dataframe with the added temporal domain features. + """ + # compute the mean and standard deviation of the gravity component of the acceleration signal for each axis for col in config.l_gravity_cols: for stat in l_gravity_stats: diff --git a/src/paradigma/gait_analysis.py b/src/paradigma/gait_analysis.py index 01a6cee..016d0dc 100644 --- a/src/paradigma/gait_analysis.py +++ b/src/paradigma/gait_analysis.py @@ -123,8 +123,8 @@ def extract_arm_swing_features(input_path: Union[str, Path], output_path: Union[ # direction of the swing of the arm df[config.velocity_colname] = pca_transform_gyroscope( df=df, - y_gyro_colname=DataColumns.GYROSCOPE_Y.value, - z_gyro_colname=DataColumns.GYROSCOPE_Z.value, + y_gyro_colname=DataColumns.GYROSCOPE_Y, + z_gyro_colname=DataColumns.GYROSCOPE_Z, pred_gait_colname=config.pred_gait_colname ) diff --git a/src/paradigma/gait_analysis_config.py b/src/paradigma/gait_analysis_config.py index d4d88d0..86fe56c 100644 --- a/src/paradigma/gait_analysis_config.py +++ b/src/paradigma/gait_analysis_config.py @@ -6,12 +6,12 @@ class GaitFeatureExtractionConfig: def __init__(self) -> None: - self.set_sensor('accelerometer') + self.set_sensor("accelerometer") self.set_sampling_frequency(100) - self.window_type: str = 'hann' + self.window_type: str = "hann" self.verbose: int = 0 - + self.window_length_s: int = 6 self.window_step_size_s: int = 1 @@ -20,65 +20,76 @@ def __init__(self) -> None: self.cc_high_frequency: int = 25 self.n_dct_filters_cc: int = 20 self.n_coefficients_cc: int = 12 - + self.d_frequency_bandwidths: Dict[str, List[float]] = { - 'power_below_gait': [0.3, 0.7], - 'power_gait': [0.7, 3.5], - 'power_tremor': [3.5, 8], - 'power_above_tremor': [8, self.sampling_frequency] + "power_below_gait": [0.3, 0.7], + "power_gait": [0.7, 3.5], + "power_tremor": [3.5, 8], + "power_above_tremor": [8, self.sampling_frequency], } - self.time_colname = 'time' - - self.l_accelerometer_cols: List[str] = [ # strings, not DataColumns, as we'll edit them - "accelerometer_x", - "accelerometer_y", - "accelerometer_z", + self.time_colname = "time" + + self.l_accelerometer_cols: List[str] = ( + [ # strings, not DataColumns, as we'll edit them + "accelerometer_x", + "accelerometer_y", + "accelerometer_z", + ] + ) + + self.l_gravity_cols: List[str] = [ + f"grav_{x}" for x in self.l_accelerometer_cols + ] + self.l_window_level_cols: List[str] = [ + "id", + "window_nr", + "window_start", + "window_end", ] + self.l_data_point_level_cols: List[str] = ( + self.l_accelerometer_cols + self.l_gravity_cols + ) - self.l_gravity_cols: List[str] = [f'grav_{x}' for x in self.l_accelerometer_cols] - self.l_window_level_cols: List[str] = ['id', 'window_nr', 'window_start', 'window_end'] - self.l_data_point_level_cols: List[str] = self.l_accelerometer_cols + self.l_gravity_cols - # TODO: generate this dictionary using object attributes (self.X) and parameters (e.g., n_dct_filters for cc) self.d_channels_values: Dict[str, str] = { - f'grav_{self.sensor}_x_mean': 'g', - f'grav_{self.sensor}_y_mean': 'g', - f'grav_{self.sensor}_z_mean': 'g', - f'grav_{self.sensor}_x_std': 'g', - f'grav_{self.sensor}_y_std': 'g', - f'grav_{self.sensor}_z_std': 'g', - f'{self.sensor}_x_power_below_gait': 'g^2/Hz', - f'{self.sensor}_y_power_below_gait': 'g^2/Hz', - f'{self.sensor}_z_power_below_gait': 'g^2/Hz', - f'{self.sensor}_x_power_gait': 'g^2/Hz', - f'{self.sensor}_y_power_gait': 'g^2/Hz', - f'{self.sensor}_z_power_gait': 'g^2/Hz', - f'{self.sensor}_x_power_tremor': 'g^2/Hz', - f'{self.sensor}_y_power_tremor': 'g^2/Hz', - f'{self.sensor}_z_power_tremor': 'g^2/Hz', - f'{self.sensor}_x_power_above_tremor': 'g^2/Hz', - f'{self.sensor}_y_power_above_tremor': 'g^2/Hz', - f'{self.sensor}_z_power_above_tremor': 'g^2/Hz', - f'{self.sensor}_x_dominant_frequency': 'Hz', - f'{self.sensor}_y_dominant_frequency': 'Hz', - f'{self.sensor}_z_dominant_frequency': 'Hz', - f'std_norm_acc': 'g', + f"grav_{self.sensor}_x_mean": "g", + f"grav_{self.sensor}_y_mean": "g", + f"grav_{self.sensor}_z_mean": "g", + f"grav_{self.sensor}_x_std": "g", + f"grav_{self.sensor}_y_std": "g", + f"grav_{self.sensor}_z_std": "g", + f"{self.sensor}_x_power_below_gait": "g^2/Hz", + f"{self.sensor}_y_power_below_gait": "g^2/Hz", + f"{self.sensor}_z_power_below_gait": "g^2/Hz", + f"{self.sensor}_x_power_gait": "g^2/Hz", + f"{self.sensor}_y_power_gait": "g^2/Hz", + f"{self.sensor}_z_power_gait": "g^2/Hz", + f"{self.sensor}_x_power_tremor": "g^2/Hz", + f"{self.sensor}_y_power_tremor": "g^2/Hz", + f"{self.sensor}_z_power_tremor": "g^2/Hz", + f"{self.sensor}_x_power_above_tremor": "g^2/Hz", + f"{self.sensor}_y_power_above_tremor": "g^2/Hz", + f"{self.sensor}_z_power_above_tremor": "g^2/Hz", + f"{self.sensor}_x_dominant_frequency": "Hz", + f"{self.sensor}_y_dominant_frequency": "Hz", + f"{self.sensor}_z_dominant_frequency": "Hz", + f"std_norm_acc": "g", } - for cc_coef in range(1, self.n_coefficients_cc+1): - self.d_channels_values[f'cc_{cc_coef}_{self.sensor}'] = 'g' + for cc_coef in range(1, self.n_coefficients_cc + 1): + self.d_channels_values[f"cc_{cc_coef}_{self.sensor}"] = "g" # TODO: move to higher level config class (duplicate in armswing feature extraction) def set_sensor(self, sensor: str) -> None: - """ Sets the sensor and derived filenames """ + """Sets the sensor and derived filenames""" self.sensor: str = sensor - self.meta_filename: str = f'{self.sensor}_meta.json' - self.values_filename: str = f'{self.sensor}_samples.bin' - self.time_filename: str = f'{self.sensor}_time.bin' + self.meta_filename: str = f"{self.sensor}_meta.json" + self.values_filename: str = f"{self.sensor}_samples.bin" + self.time_filename: str = f"{self.sensor}_time.bin" def set_sampling_frequency(self, sampling_frequency: int) -> None: - """ Sets the sampling frequency and derived variables """ + """Sets the sampling frequency and derived variables""" self.sampling_frequency: int = sampling_frequency self.spectrum_low_frequency: int = 0 # Hz self.spectrum_high_frequency: int = int(self.sampling_frequency / 2) # Hz @@ -88,16 +99,20 @@ def set_sampling_frequency(self, sampling_frequency: int) -> None: class GaitDetectionConfig: def __init__(self) -> None: - self.classifier_file_name = 'gd_classifier.pkl' - self.thresholds_file_name = 'gd_threshold.txt' + self.classifier_file_name = "gd_classifier.pkl" + self.thresholds_file_name = "gd_threshold.txt" - self.meta_filename = 'gait_meta.json' - self.time_filename = 'gait_time.bin' - self.values_filename = 'gait_values.bin' + self.meta_filename = "gait_meta.json" + self.time_filename = "gait_time.bin" + self.values_filename = "gait_values.bin" - self.l_accel_cols = [DataColumns.ACCELEROMETER_X.value, DataColumns.ACCELEROMETER_Y.value, DataColumns.ACCELEROMETER_Z.value] + self.l_accel_cols = [ + DataColumns.ACCELEROMETER_X, + DataColumns.ACCELEROMETER_Y, + DataColumns.ACCELEROMETER_Z, + ] - self.time_colname = 'time' + self.time_colname = "time" class ArmSwingFeatureExtractionConfig: @@ -117,10 +132,10 @@ def initialize_sampling_frequency_fields(self, sampling_frequency: int) -> None: self.spectrum_high_frequency = int(sampling_frequency / 2) self.d_frequency_bandwidths = { - 'power_below_gait': [0.3, 0.7], - 'power_gait': [0.7, 3.5], - 'power_tremor': [3.5, 8], - 'power_above_tremor': [8, sampling_frequency] + "power_below_gait": [0.3, 0.7], + "power_gait": [0.7, 3.5], + "power_tremor": [3.5, 8], + "power_above_tremor": [8, sampling_frequency], } # cepstral coefficients @@ -129,9 +144,15 @@ def initialize_sampling_frequency_fields(self, sampling_frequency: int) -> None: self.n_dct_filters_cc: int = 20 self.n_coefficients_cc: int = 12 - def initialize_column_names(self, time_colname='time', pred_gait_colname='pred_gait', - angle_smooth_colname='angle_smooth', angle_colname='angle', - velocity_colname='velocity', segment_nr_colname='segment_nr') -> None: + def initialize_column_names( + self, + time_colname=DataColumns.TIME, + pred_gait_colname=DataColumns.PRED_GAIT, + angle_smooth_colname: str = DataColumns.ANGLE_SMOOTH, + angle_colname=DataColumns.ANGLE, + velocity_colname=DataColumns.VELOCITY, + segment_nr_colname=DataColumns.SEGMENT_NR, + ) -> None: self.time_colname = time_colname self.pred_gait_colname = pred_gait_colname self.angle_smooth_colname = angle_smooth_colname @@ -140,30 +161,37 @@ def initialize_column_names(self, time_colname='time', pred_gait_colname='pred_g self.segment_nr_colname = segment_nr_colname self.l_accelerometer_cols: List[str] = [ - DataColumns.ACCELEROMETER_X.value, - DataColumns.ACCELEROMETER_Y.value, - DataColumns.ACCELEROMETER_Z.value + DataColumns.ACCELEROMETER_X, + DataColumns.ACCELEROMETER_Y, + DataColumns.ACCELEROMETER_Z, ] self.l_gyroscope_cols: List[str] = [ - DataColumns.GYROSCOPE_X.value, - DataColumns.GYROSCOPE_Y.value, - DataColumns.GYROSCOPE_Z.value + DataColumns.GYROSCOPE_X, + DataColumns.GYROSCOPE_Y, + DataColumns.GYROSCOPE_Z, ] - - self.l_gravity_cols: List[str] = [f'grav_{x}' for x in self.l_accelerometer_cols] - self.l_data_point_level_cols = self.l_accelerometer_cols + self.l_gyroscope_cols + self.l_gravity_cols + [ - angle_smooth_colname, velocity_colname + self.l_gravity_cols: List[str] = [ + DataColumns.GRAV_ACCELEROMETER_X, + DataColumns.GRAV_ACCELEROMETER_Y, + DataColumns.GRAV_ACCELEROMETER_Z, ] + self.l_data_point_level_cols: List[str] = ( + self.l_accelerometer_cols + + self.l_gyroscope_cols + + self.l_gravity_cols + + [angle_smooth_colname, velocity_colname] + ) + def __init__(self) -> None: # general - self.sensor = 'IMU' - self.units = 'degrees' + self.sensor = "IMU" + self.units = "degrees" # windowing - self.window_type = 'hann' + self.window_type = "hann" self.initialize_window_length_fields(3) self.initialize_sampling_frequency_fields(100) @@ -171,76 +199,79 @@ def __init__(self) -> None: self.initialize_column_names() self.d_channels_values = { - 'angle_perc_power': 'proportion', - 'range_of_motion': 'deg', - 'forward_peak_ang_vel_mean': 'deg/s', - 'forward_peak_ang_vel_std': 'deg/s', - 'backward_peak_ang_vel_mean': 'deg/s', - 'backward_peak_ang_vel_std': 'deg/s', - 'std_norm_acc': 'g', - 'grav_accelerometer_x_mean': 'g', - 'grav_accelerometer_x_std': 'g', - 'grav_accelerometer_y_mean': 'g', - 'grav_accelerometer_y_std': 'g', - 'grav_accelerometer_z_mean': 'g', - 'grav_accelerometer_z_std': 'g', - 'accelerometer_x_power_below_gait': 'X', - 'accelerometer_x_power_gait': 'X', - 'accelerometer_x_power_tremor': 'X', - 'accelerometer_x_power_above_tremor': 'X', - 'accelerometer_x_dominant_frequency': 'Hz', - 'accelerometer_y_power_below_gait': 'X', - 'accelerometer_y_power_gait': 'X', - 'accelerometer_y_power_tremor': 'X', - 'accelerometer_y_power_above_tremor': 'X', - 'accelerometer_y_dominant_frequency': 'Hz', - 'accelerometer_z_power_below_gait': 'X', - 'accelerometer_z_power_gait': 'X', - 'accelerometer_z_power_tremor': 'X', - 'accelerometer_z_power_above_tremor': 'X', - 'accelerometer_z_dominant_frequency': 'Hz', - 'angle_dominant_frequency': 'Hz', + "angle_perc_power": "proportion", + "range_of_motion": "deg", + "forward_peak_ang_vel_mean": "deg/s", + "forward_peak_ang_vel_std": "deg/s", + "backward_peak_ang_vel_mean": "deg/s", + "backward_peak_ang_vel_std": "deg/s", + "std_norm_acc": "g", + "grav_accelerometer_x_mean": "g", + "grav_accelerometer_x_std": "g", + "grav_accelerometer_y_mean": "g", + "grav_accelerometer_y_std": "g", + "grav_accelerometer_z_mean": "g", + "grav_accelerometer_z_std": "g", + "accelerometer_x_power_below_gait": "X", + "accelerometer_x_power_gait": "X", + "accelerometer_x_power_tremor": "X", + "accelerometer_x_power_above_tremor": "X", + "accelerometer_x_dominant_frequency": "Hz", + "accelerometer_y_power_below_gait": "X", + "accelerometer_y_power_gait": "X", + "accelerometer_y_power_tremor": "X", + "accelerometer_y_power_above_tremor": "X", + "accelerometer_y_dominant_frequency": "Hz", + "accelerometer_z_power_below_gait": "X", + "accelerometer_z_power_gait": "X", + "accelerometer_z_power_tremor": "X", + "accelerometer_z_power_above_tremor": "X", + "accelerometer_z_dominant_frequency": "Hz", + "angle_dominant_frequency": "Hz", } - for sensor in ['accelerometer', 'gyroscope']: - for cc_coef in range(1, self.n_coefficients_cc+1): - self.d_channels_values[f'cc_{cc_coef}_{sensor}'] = 'g' - + for sensor in ["accelerometer", "gyroscope"]: + for cc_coef in range(1, self.n_coefficients_cc + 1): + self.d_channels_values[f"cc_{cc_coef}_{sensor}"] = "g" # TODO: move to higher level config class (duplicate in gait feature extraction) def set_sensor(self, sensor: str) -> None: - """ Sets the sensor and derived filenames """ + """Sets the sensor and derived filenames""" self.sensor: str = sensor - self.meta_filename: str = f'{self.sensor}_meta.json' - self.values_filename: str = f'{self.sensor}_samples.bin' - self.time_filename: str = f'{self.sensor}_time.bin' + self.meta_filename: str = f"{self.sensor}_meta.json" + self.values_filename: str = f"{self.sensor}_samples.bin" + self.time_filename: str = f"{self.sensor}_time.bin" class ArmSwingDetectionConfig: def __init__(self) -> None: - self.classifier_file_name = 'asd_classifier.pkl' + self.classifier_file_name = "asd_classifier.pkl" - self.meta_filename = 'arm_swing_meta.json' - self.time_filename = 'arm_swing_time.bin' - self.values_filename = 'arm_swing_values.bin' + self.meta_filename = "arm_swing_meta.json" + self.time_filename = "arm_swing_time.bin" + self.values_filename = "arm_swing_values.bin" - self.l_accel_cols = [DataColumns.ACCELEROMETER_X.value, - DataColumns.ACCELEROMETER_Y.value, - DataColumns.ACCELEROMETER_Z.value] - self.l_gyro_cols = [DataColumns.GYROSCOPE_X.value, - DataColumns.GYROSCOPE_Y.value, - DataColumns.GYROSCOPE_Z.value] + self.l_accel_cols: List[str] = [ + DataColumns.ACCELEROMETER_X, + DataColumns.ACCELEROMETER_Y, + DataColumns.ACCELEROMETER_Z, + ] + self.l_gyro_cols: List[str] = [ + DataColumns.GYROSCOPE_X, + DataColumns.GYROSCOPE_Y, + DataColumns.GYROSCOPE_Z, + ] class ArmSwingQuantificationConfig: def __init__(self) -> None: - self.meta_filename = 'arm_swing_meta.json' - self.time_filename = 'arm_swing_time.bin' - self.values_filename = 'arm_swing_values.bin' + self.meta_filename = "arm_swing_meta.json" + self.time_filename = "arm_swing_time.bin" + self.values_filename = "arm_swing_values.bin" - self.pred_arm_swing_colname = 'pred_arm_swing' + self.pred_arm_swing_colname = "pred_arm_swing" self.window_length_s = 3 self.window_step_size = 0.75 diff --git a/src/paradigma/heart_rate_analysis.py b/src/paradigma/heart_rate_analysis.py index d259607..18b866f 100644 --- a/src/paradigma/heart_rate_analysis.py +++ b/src/paradigma/heart_rate_analysis.py @@ -16,12 +16,12 @@ def extract_signal_quality_features(input_path: str, classifier_path: str, outpu # load data metadata_time_ppg, metadata_samples_ppg = read_metadata(input_path, "PPG_meta.json", "PPG_time.bin", "PPG_samples.bin") df_ppg = tsdf.load_dataframe_from_binaries([metadata_time_ppg, metadata_samples_ppg], tsdf.constants.ConcatenationType.columns) - arr_ppg = df_ppg[DataColumns.PPG.value].to_numpy() - relative_time_ppg = df_ppg[DataColumns.TIME.value].to_numpy() + arr_ppg = df_ppg[DataColumns.PPG].to_numpy() + relative_time_ppg = df_ppg[DataColumns.TIME].to_numpy() metadata_time_acc, metadata_samples_acc = read_metadata(input_path, "accelerometer_meta.json", "accelerometer_time.bin", "accelerometer_samples.bin") df_acc = tsdf.load_dataframe_from_binaries([metadata_time_acc, metadata_samples_acc], tsdf.constants.ConcatenationType.columns) - arr_acc = df_acc[[DataColumns.ACCELEROMETER_X.value, DataColumns.ACCELEROMETER_Y.value, DataColumns.ACCELEROMETER_Z.value]].to_numpy() + arr_acc = df_acc[[DataColumns.ACCELEROMETER_X, DataColumns.ACCELEROMETER_Y, DataColumns.ACCELEROMETER_Z]].to_numpy() sampling_frequency_ppg = config.sampling_frequency_ppg sampling_frequency_imu = config.sampling_frequency_imu diff --git a/src/paradigma/imu_preprocessing.py b/src/paradigma/imu_preprocessing.py index e48f218..812da15 100644 --- a/src/paradigma/imu_preprocessing.py +++ b/src/paradigma/imu_preprocessing.py @@ -24,7 +24,7 @@ def preprocess_imu_data(input_path: Union[str, Path], output_path: Union[str, Pa # convert to relative seconds from delta milliseconds df[config.time_colname] = transform_time_array( - time_array=df[config.time_colname.value], + time_array=df[config.time_colname], scale_factor=1000, input_unit_type = TimeUnit.DIFFERENCE_MS, output_unit_type = TimeUnit.RELATIVE_MS) @@ -39,7 +39,7 @@ def preprocess_imu_data(input_path: Union[str, Path], output_path: Union[str, Pa resampling_frequency=config.sampling_frequency) if config.side_watch == 'left': - df[DataColumns.ACCELEROMETER_X.value] *= -1 + df[DataColumns.ACCELEROMETER_X] *= -1 for col in config.d_channels_accelerometer.keys(): @@ -61,7 +61,7 @@ def preprocess_imu_data(input_path: Union[str, Path], output_path: Union[str, Pa # Store data for sensor, units in zip(['accelerometer', 'gyroscope'], ['g', config.rotation_units]): - df_sensor = df[[config.time_colname.value] + [x for x in df.columns if sensor in x]] + df_sensor = df[[config.time_colname] + [x for x in df.columns if sensor in x]] metadata_samples.channels = [x for x in df.columns if sensor in x] metadata_samples.units = list(np.repeat(units, len(metadata_samples.channels))) @@ -75,8 +75,8 @@ def preprocess_imu_data(input_path: Union[str, Path], output_path: Union[str, Pa def transform_time_array( time_array: pd.Series, scale_factor: float, - input_unit_type: TimeUnit, - output_unit_type: TimeUnit, + input_unit_type: str, + output_unit_type: str, start_time: float = 0.0, ) -> np.ndarray: """ @@ -88,9 +88,9 @@ def transform_time_array( The time array in milliseconds to transform. scale_factor : float The scale factor to apply to the time array. - input_unit_type : TimeUnit + input_unit_type : str The time unit type of the input time array. Raw PPP data was in `TimeUnit.DIFFERENCE_MS`. - output_unit_type : TimeUnit + output_unit_type : str The time unit type of the output time array. The processing is often done in `TimeUnit.RELATIVE_MS`. start_time : float, optional The start time of the time array in UNIX milliseconds (default is 0.0) @@ -129,8 +129,8 @@ def transform_time_array( def resample_data( df: pd.DataFrame, - time_column : DataColumns, - time_unit_type: TimeUnit, + time_column : str, + time_unit_type: str, unscaled_column_names: List[str], resampling_frequency: int, scale_factors: List[float] = [], @@ -145,9 +145,9 @@ def resample_data( The data to resample. time_column : str The name of the time column. - time_unit_type : TimeUnit + time_unit_type : str The time unit type of the time array. The method currently works only for `TimeUnit.RELATIVE_MS`. - unscaled_column_names : list + unscaled_column_names : List[str] The names of the columns to resample. resampling_frequency : int The frequency to resample the data to. @@ -177,7 +177,7 @@ def resample_data( t_resampled = np.arange(start_time, time_abs_array[-1], 1 / resampling_frequency) # create dataframe - df = pd.DataFrame(t_resampled, columns=[time_column.value]) + df = pd.DataFrame(t_resampled, columns=[time_column]) # interpolate IMU - maybe a separate method? for j, sensor_col in enumerate(unscaled_column_names): @@ -186,7 +186,7 @@ def resample_data( cs = CubicSpline(time_abs_array, scaled_values.T[j]) #TODO: isn't sensor_col of type DataColumns? - df[sensor_col] = cs(df[time_column.value]) + df[sensor_col] = cs(df[time_column]) return df diff --git a/src/paradigma/ppg_preprocessing.py b/src/paradigma/ppg_preprocessing.py index 3e73852..84f8231 100644 --- a/src/paradigma/ppg_preprocessing.py +++ b/src/paradigma/ppg_preprocessing.py @@ -50,16 +50,16 @@ def preprocess_ppg_data(tsdf_meta_ppg: tsdf.TSDFMetadata, tsdf_meta_imu: tsdf.TS # Transform the time arrays to absolute milliseconds start_time_ppg = parse_iso8601_to_datetime(metadata_time_ppg.start_iso8601).timestamp() - df_imu[DataColumns.TIME.value] = paradigma.imu_preprocessing.transform_time_array( - time_array=df_imu[DataColumns.TIME.value], + df_imu[DataColumns.TIME] = paradigma.imu_preprocessing.transform_time_array( + time_array=df_imu[DataColumns.TIME], scale_factor=1000, input_unit_type = TimeUnit.DIFFERENCE_MS, output_unit_type = TimeUnit.ABSOLUTE_MS, start_time = start_time_ppg) start_time_imu = parse_iso8601_to_datetime(metadata_time_imu.start_iso8601).timestamp() - df_ppg[DataColumns.TIME.value] = paradigma.imu_preprocessing.transform_time_array( - time_array=df_ppg[DataColumns.TIME.value], + df_ppg[DataColumns.TIME] = paradigma.imu_preprocessing.transform_time_array( + time_array=df_ppg[DataColumns.TIME], scale_factor=1000, input_unit_type = TimeUnit.DIFFERENCE_MS, output_unit_type = TimeUnit.ABSOLUTE_MS, @@ -122,16 +122,16 @@ def preprocess_ppg_data(tsdf_meta_ppg: tsdf.TSDFMetadata, tsdf_meta_imu: tsdf.TS df_ppg_proc = df_ppg_proc.drop(columns=[col]) df_ppg_proc = df_ppg_proc.rename(columns={f'filt_{col}': col}) - df_imu_proc[DataColumns.TIME.value] = paradigma.imu_preprocessing.transform_time_array( - time_array=df_imu_proc[DataColumns.TIME.value], + df_imu_proc[DataColumns.TIME] = paradigma.imu_preprocessing.transform_time_array( + time_array=df_imu_proc[DataColumns.TIME], scale_factor=1, input_unit_type=TimeUnit.ABSOLUTE_MS, output_unit_type=TimeUnit.RELATIVE_MS, start_time=start_time_ppg, ) - df_ppg_proc[DataColumns.TIME.value] = paradigma.imu_preprocessing.transform_time_array( - time_array=df_ppg_proc[DataColumns.TIME.value], + df_ppg_proc[DataColumns.TIME] = paradigma.imu_preprocessing.transform_time_array( + time_array=df_ppg_proc[DataColumns.TIME], scale_factor=1, input_unit_type=TimeUnit.ABSOLUTE_MS, output_unit_type=TimeUnit.RELATIVE_MS, diff --git a/src/paradigma/preprocessing_config.py b/src/paradigma/preprocessing_config.py index 16c6ee5..86d2abf 100644 --- a/src/paradigma/preprocessing_config.py +++ b/src/paradigma/preprocessing_config.py @@ -37,14 +37,14 @@ def __init__(self) -> None: self.rotation_units = 'deg/s' self.d_channels_accelerometer = { - DataColumns.ACCELEROMETER_X.value: self.acceleration_units, - DataColumns.ACCELEROMETER_Y.value: self.acceleration_units, - DataColumns.ACCELEROMETER_Z.value: self.acceleration_units, + DataColumns.ACCELEROMETER_X: self.acceleration_units, + DataColumns.ACCELEROMETER_Y: self.acceleration_units, + DataColumns.ACCELEROMETER_Z: self.acceleration_units, } self.d_channels_gyroscope = { - DataColumns.GYROSCOPE_X.value: self.rotation_units, - DataColumns.GYROSCOPE_Y.value: self.rotation_units, - DataColumns.GYROSCOPE_Z.value: self.rotation_units, + DataColumns.GYROSCOPE_X: self.rotation_units, + DataColumns.GYROSCOPE_Y: self.rotation_units, + DataColumns.GYROSCOPE_Z: self.rotation_units, } self.d_channels_imu = {**self.d_channels_accelerometer, **self.d_channels_gyroscope} @@ -58,7 +58,7 @@ def __init__(self) -> None: self.time_filename = 'PPG_time.bin' self.d_channels_ppg = { - DataColumns.PPG.value: 'none' + DataColumns.PPG: 'none' } self.sampling_frequency = 30 From d44b3afc6694e9fb8343d390d04e05b4a03456ec Mon Sep 17 00:00:00 2001 From: Vedran Kasalica Date: Thu, 5 Sep 2024 11:51:31 +0200 Subject: [PATCH 10/14] Refactored Gait configuration files and improved constraints --- src/paradigma/constants.py | 60 ++++--- src/paradigma/feature_extraction.py | 4 +- src/paradigma/gait_analysis.py | 24 +-- src/paradigma/gait_analysis_config.py | 245 ++++++++++++-------------- 4 files changed, 166 insertions(+), 167 deletions(-) diff --git a/src/paradigma/constants.py b/src/paradigma/constants.py index e61042a..dadd6a6 100644 --- a/src/paradigma/constants.py +++ b/src/paradigma/constants.py @@ -4,47 +4,59 @@ @dataclass(frozen=True) class DataColumns(): """ - Enum for the data channels in tsdf. + Class containing the data channels in `tsdf`. """ - ACCELEROMETER_X = "accelerometer_x" - ACCELEROMETER_Y = "accelerometer_y" - ACCELEROMETER_Z = "accelerometer_z" - GYROSCOPE_X = "gyroscope_x" - GYROSCOPE_Y = "gyroscope_y" - GYROSCOPE_Z = "gyroscope_z" - PPG = "green" - TIME = "time" + ACCELEROMETER_X : str = "accelerometer_x" + ACCELEROMETER_Y : str = "accelerometer_y" + ACCELEROMETER_Z : str = "accelerometer_z" + GYROSCOPE_X : str = "gyroscope_x" + GYROSCOPE_Y : str = "gyroscope_y" + GYROSCOPE_Z : str = "gyroscope_z" + PPG : str = "green" + TIME : str = "time" # The following are used in gait analysis - GRAV_ACCELEROMETER_X = "grav_accelerometer_x" - GRAV_ACCELEROMETER_Y = "grav_accelerometer_y" - GRAV_ACCELEROMETER_Z = "grav_accelerometer_z" - PRED_GAIT = "pred_gait" - ANGLE = "angle" - ANGLE_SMOOTH = "angle_smooth" - VELOCITY = "velocity" - SEGMENT_NR = "segment_nr" + GRAV_ACCELEROMETER_X : str = "grav_accelerometer_x" + GRAV_ACCELEROMETER_Y : str = "grav_accelerometer_y" + GRAV_ACCELEROMETER_Z : str = "grav_accelerometer_z" + PRED_GAIT : str = "pred_gait" + PRED_ARM_SWING : str = "pred_arm_swing" + ANGLE : str = "angle" + ANGLE_SMOOTH : str = "angle_smooth" + VELOCITY : str = "velocity" + SEGMENT_NR : str = "segment_nr" @dataclass(frozen=True) class DataUnits(): """ - Enum for the data channel unit types in tsdf. + Class containing the data channel unit types in `tsdf`. """ - ACCELERATION = "m/s^2" + ACCELERATION: str = "m/s^2" """ The acceleration is in m/s^2. """ - ROTATION = "deg/s" + + ROTATION: str = "deg/s" """ The rotation is in degrees per second. """ + + GRAVITY: str = "g" + """ The acceleration due to gravity is in g. """ + + POWER_SPECTRAL_DENSITY: str = "g^2/Hz" + """ The power spectral density is in g^2/Hz. """ + + FREQUENCY: str = "Hz" + """ The frequency is in Hz. """ + @dataclass(frozen=True) class TimeUnit(): """ - Enum for the `time` channel unit types in tsdf. + Class containing the `time` channel unit types in `tsdf`. """ - RELATIVE_MS = "relative_ms" + RELATIVE_MS : str = "relative_ms" """ The time is relative to the start time in milliseconds. """ - ABSOLUTE_MS = "absolute_ms" + ABSOLUTE_MS : str = "absolute_ms" """ The time is absolute in milliseconds. """ - DIFFERENCE_MS = "difference_ms" + DIFFERENCE_MS : str = "difference_ms" """ The time is the difference between consecutive samples in milliseconds. """ UNIX_TICKS_MS: int = 1000 diff --git a/src/paradigma/feature_extraction.py b/src/paradigma/feature_extraction.py index 39e672d..6040f03 100644 --- a/src/paradigma/feature_extraction.py +++ b/src/paradigma/feature_extraction.py @@ -8,7 +8,7 @@ from scipy.signal import find_peaks from paradigma.constants import DataColumns -from paradigma.gait_analysis_config import GaitFeatureExtractionConfig, ArmSwingFeatureExtractionConfig +from paradigma.gait_analysis_config import IMUConfig def generate_statistics( @@ -606,7 +606,7 @@ def extract_peak_angular_velocity( return -def extract_temporal_domain_features(config:GaitFeatureExtractionConfig | ArmSwingFeatureExtractionConfig, df_windowed:pd.DataFrame, l_gravity_stats=['mean', 'std']) -> pd.DataFrame: +def extract_temporal_domain_features(config: IMUConfig, df_windowed:pd.DataFrame, l_gravity_stats=['mean', 'std']) -> pd.DataFrame: """ Compute temporal domain features for the accelerometer signal. The features are added to the dataframe. Therefore the original dataframe is modified, and the modified dataframe is returned. diff --git a/src/paradigma/gait_analysis.py b/src/paradigma/gait_analysis.py index 016d0dc..6864126 100644 --- a/src/paradigma/gait_analysis.py +++ b/src/paradigma/gait_analysis.py @@ -71,12 +71,12 @@ def detect_gait(input_path: Union[str, Path], output_path: Union[str, Path], pat threshold = float(f.read()) # Prepare the data - clf.feature_names_in_ = [f'{x}_power_below_gait' for x in config.l_accel_cols] + \ - [f'{x}_power_gait' for x in config.l_accel_cols] + \ - [f'{x}_power_tremor' for x in config.l_accel_cols] + \ - [f'{x}_power_above_tremor' for x in config.l_accel_cols] + \ - ['std_norm_acc'] + [f'cc_{i}_accelerometer' for i in range(1, 13)] + [f'grav_{x}_{y}' for x in config.l_accel_cols for y in ['mean', 'std']] + \ - [f'{x}_dominant_frequency' for x in config.l_accel_cols] + clf.feature_names_in_ = [f'{x}_power_below_gait' for x in config.l_accelerometer_cols] + \ + [f'{x}_power_gait' for x in config.l_accelerometer_cols] + \ + [f'{x}_power_tremor' for x in config.l_accelerometer_cols] + \ + [f'{x}_power_above_tremor' for x in config.l_accelerometer_cols] + \ + ['std_norm_acc'] + [f'cc_{i}_accelerometer' for i in range(1, 13)] + [f'grav_{x}_{y}' for x in config.l_accelerometer_cols for y in ['mean', 'std']] + \ + [f'{x}_dominant_frequency' for x in config.l_accelerometer_cols] X = df.loc[:, clf.feature_names_in_] # Make prediction @@ -292,15 +292,15 @@ def detect_arm_swing(input_path: Union[str, Path], output_path: Union[str, Path] clf = pd.read_pickle(os.path.join(path_to_classifier_input, config.classifier_file_name)) # Prepare the data - clf.feature_names_in_ = ['std_norm_acc'] + [f'{x}_power_below_gait' for x in config.l_accel_cols] + \ - [f'{x}_power_gait' for x in config.l_accel_cols] + \ - [f'{x}_power_tremor' for x in config.l_accel_cols] + \ - [f'{x}_power_above_tremor' for x in config.l_accel_cols] + \ + clf.feature_names_in_ = ['std_norm_acc'] + [f'{x}_power_below_gait' for x in config.l_accelerometer_cols] + \ + [f'{x}_power_gait' for x in config.l_accelerometer_cols] + \ + [f'{x}_power_tremor' for x in config.l_accelerometer_cols] + \ + [f'{x}_power_above_tremor' for x in config.l_accelerometer_cols] + \ [f'cc_{i}_accelerometer' for i in range(1, 13)] + [f'cc_{i}_gyroscope' for i in range(1, 13)] + \ - [f'grav_{x}_mean' for x in config.l_accel_cols] + [f'grav_{x}_std' for x in config.l_accel_cols] + \ + [f'grav_{x}_mean' for x in config.l_accelerometer_cols] + [f'grav_{x}_std' for x in config.l_accelerometer_cols] + \ ['range_of_motion', 'forward_peak_ang_vel_mean', 'backward_peak_ang_vel_mean', 'forward_peak_ang_vel_std', 'backward_peak_ang_vel_std', 'angle_perc_power', 'angle_dominant_frequency'] + \ - [f'{x}_dominant_frequency' for x in config.l_accel_cols] + [f'{x}_dominant_frequency' for x in config.l_accelerometer_cols] X = df.loc[:, clf.feature_names_in_] diff --git a/src/paradigma/gait_analysis_config.py b/src/paradigma/gait_analysis_config.py index 86fe56c..b073969 100644 --- a/src/paradigma/gait_analysis_config.py +++ b/src/paradigma/gait_analysis_config.py @@ -1,11 +1,68 @@ from typing import Dict, List -from paradigma.constants import DataColumns +from paradigma.constants import DataColumns, DataUnits -class GaitFeatureExtractionConfig: + +class IMUConfig: + """ + Base class for Gait feature extraction and Gait detection configurations, based on the IMU data (accelerometer, gyroscope). + """ + def __init__(self): + + self.time_colname = DataColumns.TIME + + self.l_accelerometer_cols: List[str] = [ + DataColumns.ACCELEROMETER_X, + DataColumns.ACCELEROMETER_Y, + DataColumns.ACCELEROMETER_Z, + ] + self.l_gyroscope_cols: List[str] = [ + DataColumns.GYROSCOPE_X, + DataColumns.GYROSCOPE_Y, + DataColumns.GYROSCOPE_Z, + ] + + self.l_gravity_cols: List[str] = [ + DataColumns.GRAV_ACCELEROMETER_X, + DataColumns.GRAV_ACCELEROMETER_Y, + DataColumns.GRAV_ACCELEROMETER_Z, + ] + + def set_sensor(self, sensor: str) -> None: + """Sets the sensor and derived filenames""" + self.sensor: str = sensor + self.set_filenames(sensor) + + def set_filenames(self, prefix: str) -> None: + """Sets the filenames based on the prefix, + + Parameters + ---------- + prefix : str + The prefix for the filenames. + """ + self.meta_filename = f"{prefix}_meta.json" + self.time_filename = f"{prefix}_time.bin" + self.values_filename = f"{prefix}_samples.bin" + + def set_filenames_values(self, prefix: str) -> None: + """Sets the filenames based on the prefix, + + Parameters + ---------- + prefix : str + The prefix for the filenames. + """ + self.meta_filename = f"{prefix}_meta.json" + self.time_filename = f"{prefix}_time.bin" + self.values_filename = f"{prefix}_values.bin" + + +class GaitFeatureExtractionConfig (IMUConfig): def __init__(self) -> None: + super().__init__() self.set_sensor("accelerometer") self.set_sampling_frequency(100) @@ -28,19 +85,7 @@ def __init__(self) -> None: "power_above_tremor": [8, self.sampling_frequency], } - self.time_colname = "time" - - self.l_accelerometer_cols: List[str] = ( - [ # strings, not DataColumns, as we'll edit them - "accelerometer_x", - "accelerometer_y", - "accelerometer_z", - ] - ) - self.l_gravity_cols: List[str] = [ - f"grav_{x}" for x in self.l_accelerometer_cols - ] self.l_window_level_cols: List[str] = [ "id", "window_nr", @@ -53,41 +98,33 @@ def __init__(self) -> None: # TODO: generate this dictionary using object attributes (self.X) and parameters (e.g., n_dct_filters for cc) self.d_channels_values: Dict[str, str] = { - f"grav_{self.sensor}_x_mean": "g", - f"grav_{self.sensor}_y_mean": "g", - f"grav_{self.sensor}_z_mean": "g", - f"grav_{self.sensor}_x_std": "g", - f"grav_{self.sensor}_y_std": "g", - f"grav_{self.sensor}_z_std": "g", - f"{self.sensor}_x_power_below_gait": "g^2/Hz", - f"{self.sensor}_y_power_below_gait": "g^2/Hz", - f"{self.sensor}_z_power_below_gait": "g^2/Hz", - f"{self.sensor}_x_power_gait": "g^2/Hz", - f"{self.sensor}_y_power_gait": "g^2/Hz", - f"{self.sensor}_z_power_gait": "g^2/Hz", - f"{self.sensor}_x_power_tremor": "g^2/Hz", - f"{self.sensor}_y_power_tremor": "g^2/Hz", - f"{self.sensor}_z_power_tremor": "g^2/Hz", - f"{self.sensor}_x_power_above_tremor": "g^2/Hz", - f"{self.sensor}_y_power_above_tremor": "g^2/Hz", - f"{self.sensor}_z_power_above_tremor": "g^2/Hz", - f"{self.sensor}_x_dominant_frequency": "Hz", - f"{self.sensor}_y_dominant_frequency": "Hz", - f"{self.sensor}_z_dominant_frequency": "Hz", - f"std_norm_acc": "g", + f"grav_{self.sensor}_x_mean": DataUnits.GRAVITY, + f"grav_{self.sensor}_y_mean": DataUnits.GRAVITY, + f"grav_{self.sensor}_z_mean": DataUnits.GRAVITY, + f"grav_{self.sensor}_x_std": DataUnits.GRAVITY, + f"grav_{self.sensor}_y_std": DataUnits.GRAVITY, + f"grav_{self.sensor}_z_std": DataUnits.GRAVITY, + f"{self.sensor}_x_power_below_gait": DataUnits.POWER_SPECTRAL_DENSITY, + f"{self.sensor}_y_power_below_gait": DataUnits.POWER_SPECTRAL_DENSITY, + f"{self.sensor}_z_power_below_gait": DataUnits.POWER_SPECTRAL_DENSITY, + f"{self.sensor}_x_power_gait": DataUnits.POWER_SPECTRAL_DENSITY, + f"{self.sensor}_y_power_gait": DataUnits.POWER_SPECTRAL_DENSITY, + f"{self.sensor}_z_power_gait": DataUnits.POWER_SPECTRAL_DENSITY, + f"{self.sensor}_x_power_tremor": DataUnits.POWER_SPECTRAL_DENSITY, + f"{self.sensor}_y_power_tremor": DataUnits.POWER_SPECTRAL_DENSITY, + f"{self.sensor}_z_power_tremor": DataUnits.POWER_SPECTRAL_DENSITY, + f"{self.sensor}_x_power_above_tremor": DataUnits.POWER_SPECTRAL_DENSITY, + f"{self.sensor}_y_power_above_tremor": DataUnits.POWER_SPECTRAL_DENSITY, + f"{self.sensor}_z_power_above_tremor": DataUnits.POWER_SPECTRAL_DENSITY, + f"{self.sensor}_x_dominant_frequency": DataUnits.FREQUENCY, + f"{self.sensor}_y_dominant_frequency": DataUnits.FREQUENCY, + f"{self.sensor}_z_dominant_frequency": DataUnits.FREQUENCY, + "std_norm_acc": DataUnits.GRAVITY, } for cc_coef in range(1, self.n_coefficients_cc + 1): self.d_channels_values[f"cc_{cc_coef}_{self.sensor}"] = "g" - # TODO: move to higher level config class (duplicate in armswing feature extraction) - def set_sensor(self, sensor: str) -> None: - """Sets the sensor and derived filenames""" - self.sensor: str = sensor - self.meta_filename: str = f"{self.sensor}_meta.json" - self.values_filename: str = f"{self.sensor}_samples.bin" - self.time_filename: str = f"{self.sensor}_time.bin" - def set_sampling_frequency(self, sampling_frequency: int) -> None: """Sets the sampling frequency and derived variables""" self.sampling_frequency: int = sampling_frequency @@ -96,26 +133,18 @@ def set_sampling_frequency(self, sampling_frequency: int) -> None: self.filter_length: int = self.spectrum_high_frequency - 1 -class GaitDetectionConfig: +class GaitDetectionConfig(IMUConfig): def __init__(self) -> None: + super().__init__() self.classifier_file_name = "gd_classifier.pkl" self.thresholds_file_name = "gd_threshold.txt" - self.meta_filename = "gait_meta.json" - self.time_filename = "gait_time.bin" - self.values_filename = "gait_values.bin" - - self.l_accel_cols = [ - DataColumns.ACCELEROMETER_X, - DataColumns.ACCELEROMETER_Y, - DataColumns.ACCELEROMETER_Z, - ] + self.set_filenames_values("gait") - self.time_colname = "time" -class ArmSwingFeatureExtractionConfig: +class ArmSwingFeatureExtractionConfig(IMUConfig): def initialize_window_length_fields(self, window_length_s: int) -> None: self.window_length_s = window_length_s @@ -145,47 +174,25 @@ def initialize_sampling_frequency_fields(self, sampling_frequency: int) -> None: self.n_coefficients_cc: int = 12 def initialize_column_names( - self, - time_colname=DataColumns.TIME, - pred_gait_colname=DataColumns.PRED_GAIT, - angle_smooth_colname: str = DataColumns.ANGLE_SMOOTH, - angle_colname=DataColumns.ANGLE, - velocity_colname=DataColumns.VELOCITY, - segment_nr_colname=DataColumns.SEGMENT_NR, + self ) -> None: - self.time_colname = time_colname - self.pred_gait_colname = pred_gait_colname - self.angle_smooth_colname = angle_smooth_colname - self.angle_colname = angle_colname - self.velocity_colname = velocity_colname - self.segment_nr_colname = segment_nr_colname - - self.l_accelerometer_cols: List[str] = [ - DataColumns.ACCELEROMETER_X, - DataColumns.ACCELEROMETER_Y, - DataColumns.ACCELEROMETER_Z, - ] - self.l_gyroscope_cols: List[str] = [ - DataColumns.GYROSCOPE_X, - DataColumns.GYROSCOPE_Y, - DataColumns.GYROSCOPE_Z, - ] + self.pred_gait_colname=DataColumns.PRED_GAIT + self.angle_smooth_colname: str = DataColumns.ANGLE_SMOOTH + self.angle_colname=DataColumns.ANGLE + self.velocity_colname=DataColumns.VELOCITY + self.segment_nr_colname=DataColumns.SEGMENT_NR - self.l_gravity_cols: List[str] = [ - DataColumns.GRAV_ACCELEROMETER_X, - DataColumns.GRAV_ACCELEROMETER_Y, - DataColumns.GRAV_ACCELEROMETER_Z, - ] self.l_data_point_level_cols: List[str] = ( self.l_accelerometer_cols + self.l_gyroscope_cols + self.l_gravity_cols - + [angle_smooth_colname, velocity_colname] + + [self.angle_smooth_colname, self.velocity_colname] ) def __init__(self) -> None: + super().__init__() # general self.sensor = "IMU" self.units = "degrees" @@ -201,77 +208,57 @@ def __init__(self) -> None: self.d_channels_values = { "angle_perc_power": "proportion", "range_of_motion": "deg", - "forward_peak_ang_vel_mean": "deg/s", - "forward_peak_ang_vel_std": "deg/s", - "backward_peak_ang_vel_mean": "deg/s", - "backward_peak_ang_vel_std": "deg/s", - "std_norm_acc": "g", - "grav_accelerometer_x_mean": "g", - "grav_accelerometer_x_std": "g", - "grav_accelerometer_y_mean": "g", - "grav_accelerometer_y_std": "g", - "grav_accelerometer_z_mean": "g", - "grav_accelerometer_z_std": "g", + "forward_peak_ang_vel_mean": DataUnits.ROTATION, + "forward_peak_ang_vel_std": DataUnits.ROTATION, + "backward_peak_ang_vel_mean": DataUnits.ROTATION, + "backward_peak_ang_vel_std": DataUnits.ROTATION, + "std_norm_acc": DataUnits.GRAVITY, + "grav_accelerometer_x_mean": DataUnits.GRAVITY, + "grav_accelerometer_x_std": DataUnits.GRAVITY, + "grav_accelerometer_y_mean": DataUnits.GRAVITY, + "grav_accelerometer_y_std": DataUnits.GRAVITY, + "grav_accelerometer_z_mean": DataUnits.GRAVITY, + "grav_accelerometer_z_std": DataUnits.GRAVITY, "accelerometer_x_power_below_gait": "X", "accelerometer_x_power_gait": "X", "accelerometer_x_power_tremor": "X", "accelerometer_x_power_above_tremor": "X", - "accelerometer_x_dominant_frequency": "Hz", + "accelerometer_x_dominant_frequency": DataUnits.FREQUENCY, "accelerometer_y_power_below_gait": "X", "accelerometer_y_power_gait": "X", "accelerometer_y_power_tremor": "X", "accelerometer_y_power_above_tremor": "X", - "accelerometer_y_dominant_frequency": "Hz", + "accelerometer_y_dominant_frequency": DataUnits.FREQUENCY, "accelerometer_z_power_below_gait": "X", "accelerometer_z_power_gait": "X", "accelerometer_z_power_tremor": "X", "accelerometer_z_power_above_tremor": "X", - "accelerometer_z_dominant_frequency": "Hz", - "angle_dominant_frequency": "Hz", + "accelerometer_z_dominant_frequency": DataUnits.FREQUENCY, + "angle_dominant_frequency": DataUnits.FREQUENCY, } for sensor in ["accelerometer", "gyroscope"]: for cc_coef in range(1, self.n_coefficients_cc + 1): - self.d_channels_values[f"cc_{cc_coef}_{sensor}"] = "g" - - # TODO: move to higher level config class (duplicate in gait feature extraction) - def set_sensor(self, sensor: str) -> None: - """Sets the sensor and derived filenames""" - self.sensor: str = sensor - self.meta_filename: str = f"{self.sensor}_meta.json" - self.values_filename: str = f"{self.sensor}_samples.bin" - self.time_filename: str = f"{self.sensor}_time.bin" + self.d_channels_values[f"cc_{cc_coef}_{sensor}"] = DataUnits.GRAVITY -class ArmSwingDetectionConfig: +class ArmSwingDetectionConfig(IMUConfig): def __init__(self) -> None: + super().__init__() self.classifier_file_name = "asd_classifier.pkl" - self.meta_filename = "arm_swing_meta.json" - self.time_filename = "arm_swing_time.bin" - self.values_filename = "arm_swing_values.bin" + self.set_filenames_values("arm_swing") - self.l_accel_cols: List[str] = [ - DataColumns.ACCELEROMETER_X, - DataColumns.ACCELEROMETER_Y, - DataColumns.ACCELEROMETER_Z, - ] - self.l_gyro_cols: List[str] = [ - DataColumns.GYROSCOPE_X, - DataColumns.GYROSCOPE_Y, - DataColumns.GYROSCOPE_Z, - ] -class ArmSwingQuantificationConfig: +class ArmSwingQuantificationConfig(IMUConfig): def __init__(self) -> None: - self.meta_filename = "arm_swing_meta.json" - self.time_filename = "arm_swing_time.bin" - self.values_filename = "arm_swing_values.bin" + super().__init__() + self.set_filenames_values("arm_swing") - self.pred_arm_swing_colname = "pred_arm_swing" + self.pred_arm_swing_colname = DataColumns.PRED_ARM_SWING self.window_length_s = 3 self.window_step_size = 0.75 From 6ee825d1d679b570b26816d4a44b059c35275cb6 Mon Sep 17 00:00:00 2001 From: Vedran Kasalica Date: Tue, 10 Sep 2024 15:49:19 +0200 Subject: [PATCH 11/14] Bump python to 3.10 --- .github/workflows/build-and-test.yml | 2 +- .readthedocs.yml | 2 +- README.md | 6 +++--- pyproject.toml | 2 +- pytype.cfg | 2 +- 5 files changed, 7 insertions(+), 7 deletions(-) diff --git a/.github/workflows/build-and-test.yml b/.github/workflows/build-and-test.yml index 3f128b4..f4461ee 100644 --- a/.github/workflows/build-and-test.yml +++ b/.github/workflows/build-and-test.yml @@ -20,7 +20,7 @@ jobs: runs-on: ubuntu-latest strategy: matrix: - python-version: ['3.9'] + python-version: ['3.10'] outputs: version: ${{ steps.get_version.outputs.version }} diff --git a/.readthedocs.yml b/.readthedocs.yml index 758d263..6ef7bdf 100644 --- a/.readthedocs.yml +++ b/.readthedocs.yml @@ -7,7 +7,7 @@ build: # Configuration python: - version: 3.9 + version: 3.10 install: - requirements: docs/requirements.txt - method: pip diff --git a/README.md b/README.md index 27d098d..01b854b 100644 --- a/README.md +++ b/README.md @@ -3,7 +3,7 @@ | Badges | | |:----:|----| | **Packages and Releases** | [![Latest release](https://img.shields.io/github/release/biomarkersparkinson/paradigma.svg)](https://github.com/biomarkersparkinson/paradigma/releases/latest) [![PyPI](https://img.shields.io/pypi/v/paradigma.svg)](https://pypi.python.org/pypi/paradigma/) [![Static Badge](https://img.shields.io/badge/RSD-paradigma-lib)](https://research-software-directory.org/software/paradigma) | -| **Build Status** | [![](https://img.shields.io/badge/python-3.9+-blue.svg)](https://www.python.org/downloads/) [![Build and test](https://github.com/biomarkersParkinson/paradigma/actions/workflows/build-and-test.yml/badge.svg)](https://github.com/biomarkersParkinson/paradigma/actions/workflows/build-and-test.yml) [![pages-build-deployment](https://github.com/biomarkersParkinson/paradigma/actions/workflows/pages/pages-build-deployment/badge.svg)](https://github.com/biomarkersParkinson/paradigma/actions/workflows/pages/pages-build-deployment) | +| **Build Status** | [![](https://img.shields.io/badge/python-3.10+-blue.svg)](https://www.python.org/downloads/) [![Build and test](https://github.com/biomarkersParkinson/paradigma/actions/workflows/build-and-test.yml/badge.svg)](https://github.com/biomarkersParkinson/paradigma/actions/workflows/build-and-test.yml) [![pages-build-deployment](https://github.com/biomarkersParkinson/paradigma/actions/workflows/pages/pages-build-deployment/badge.svg)](https://github.com/biomarkersParkinson/paradigma/actions/workflows/pages/pages-build-deployment) | | **License** | [![GitHub license](https://img.shields.io/github/license/biomarkersParkinson/paradigma)](https://github.com/biomarkersparkinson/paradigma/blob/main/LICENSE) | @@ -14,7 +14,7 @@ A package ([documentation](https://biomarkersparkinson.github.io/paradigma/)) to ## Installation -The package is available in PyPi and requires [Python 3.9](https://www.python.org/downloads/) or higher. It can be installed using: +The package is available in PyPi and requires [Python 3.10](https://www.python.org/downloads/) or higher. It can be installed using: ```bash pip install paradigma @@ -28,7 +28,7 @@ See our [extended documentation](https://biomarkersparkinson.github.io/paradigma ## Development ### Installation -The package requires Python 3.9 or higher. Use [Poetry](https://python-poetry.org/docs/#installation) to set up the environment and install the dependencies: +The package requires Python 3.10 or higher. Use [Poetry](https://python-poetry.org/docs/#installation) to set up the environment and install the dependencies: ```bash poetry install diff --git a/pyproject.toml b/pyproject.toml index 3fab4dc..67bf278 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -13,7 +13,7 @@ license = "Apache License 2.0" readme = "README.md" [tool.poetry.dependencies] -python = "^3.9" +python = "^3.10" pandas = "^2.1.4" scikit-learn = "^1.3.2" tsdf = "^0.5.2" diff --git a/pytype.cfg b/pytype.cfg index 41538f8..fe1d292 100644 --- a/pytype.cfg +++ b/pytype.cfg @@ -9,4 +9,4 @@ exclude = **/build **/dist # Python version (matches the project's runtime environment) -python_version = 3.9 +python_version = 3.10 From f275d85904f6d7c2244e780588f878be14903701 Mon Sep 17 00:00:00 2001 From: Vedran Kasalica Date: Tue, 10 Sep 2024 15:53:05 +0200 Subject: [PATCH 12/14] Improve the usage of constants in config files --- src/paradigma/constants.py | 3 +++ src/paradigma/preprocessing_config.py | 25 +++++++++---------------- 2 files changed, 12 insertions(+), 16 deletions(-) diff --git a/src/paradigma/constants.py b/src/paradigma/constants.py index dadd6a6..8640214 100644 --- a/src/paradigma/constants.py +++ b/src/paradigma/constants.py @@ -45,6 +45,9 @@ class DataUnits(): FREQUENCY: str = "Hz" """ The frequency is in Hz. """ + + NONE: str = "none" + """ The data channel has no unit. """ @dataclass(frozen=True) diff --git a/src/paradigma/preprocessing_config.py b/src/paradigma/preprocessing_config.py index 86d2abf..14f3fd8 100644 --- a/src/paradigma/preprocessing_config.py +++ b/src/paradigma/preprocessing_config.py @@ -1,5 +1,5 @@ -from paradigma.constants import DataColumns - +from paradigma.constants import DataColumns, DataUnits +from paradigma.gait_analysis_config import IMUConfig class BasePreprocessingConfig: @@ -9,8 +9,8 @@ def __init__(self) -> None: self.values_filename = '' self.time_filename = '' - self.acceleration_units = 'm/s^2' - self.rotation_units = 'deg/s' + self.acceleration_units = DataUnits.ACCELERATION + self.rotation_units = DataUnits.ROTATION self.time_colname = DataColumns.TIME @@ -23,18 +23,14 @@ def __init__(self) -> None: self.upper_cutoff_frequency = 3.5 self.filter_order = 4 - class IMUPreprocessingConfig(BasePreprocessingConfig): def __init__(self) -> None: super().__init__() - self.meta_filename = 'IMU_meta.json' - self.values_filename = 'IMU_samples.bin' - self.time_filename = 'IMU_time.bin' - - self.acceleration_units = 'm/s^2' - self.rotation_units = 'deg/s' + IMUConfig.set_filenames(self, 'IMU') + self.acceleration_units = DataUnits.ACCELERATION + self.rotation_units = DataUnits.ROTATION self.d_channels_accelerometer = { DataColumns.ACCELEROMETER_X: self.acceleration_units, @@ -53,12 +49,9 @@ class PPGPreprocessingConfig(BasePreprocessingConfig): def __init__(self) -> None: super().__init__() - self.meta_filename = 'PPG_meta.json' - self.values_filename = 'PPG_samples.bin' - self.time_filename = 'PPG_time.bin' - + IMUConfig.set_filenames(self, 'PPG') self.d_channels_ppg = { - DataColumns.PPG: 'none' + DataColumns.PPG: DataUnits.NONE } self.sampling_frequency = 30 From 48f321f89770526a2025599b64f0272de8dfa7ec Mon Sep 17 00:00:00 2001 From: Vedran Kasalica Date: Tue, 10 Sep 2024 15:54:59 +0200 Subject: [PATCH 13/14] Update `paradigma` version to `v0.3` --- poetry.lock | 1943 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"Peter Kok ", "Vedran Kasalica ", From 1ef5d420a8c9d065c61365b6608daa4c4773444c Mon Sep 17 00:00:00 2001 From: Vedran Kasalica Date: Tue, 10 Sep 2024 17:17:17 +0200 Subject: [PATCH 14/14] Fix typing error --- src/paradigma/preprocessing_config.py | 16 ++++++++++++++-- 1 file changed, 14 insertions(+), 2 deletions(-) diff --git a/src/paradigma/preprocessing_config.py b/src/paradigma/preprocessing_config.py index 14f3fd8..ecc7936 100644 --- a/src/paradigma/preprocessing_config.py +++ b/src/paradigma/preprocessing_config.py @@ -23,12 +23,24 @@ def __init__(self) -> None: self.upper_cutoff_frequency = 3.5 self.filter_order = 4 + def set_filenames(self, prefix: str) -> None: + """Sets the filenames based on the prefix. This method is duplicated from `gaits_analysis_config.py`. + + Parameters + ---------- + prefix : str + The prefix for the filenames. + """ + self.meta_filename = f"{prefix}_meta.json" + self.time_filename = f"{prefix}_time.bin" + self.values_filename = f"{prefix}_samples.bin" + class IMUPreprocessingConfig(BasePreprocessingConfig): def __init__(self) -> None: super().__init__() - IMUConfig.set_filenames(self, 'IMU') + self.set_filenames('IMU') self.acceleration_units = DataUnits.ACCELERATION self.rotation_units = DataUnits.ROTATION @@ -49,7 +61,7 @@ class PPGPreprocessingConfig(BasePreprocessingConfig): def __init__(self) -> None: super().__init__() - IMUConfig.set_filenames(self, 'PPG') + self.set_filenames('PPG') self.d_channels_ppg = { DataColumns.PPG: DataUnits.NONE }