From d9b5bc1366cacb1519a5ceb3ac65c8a12f1aecdf Mon Sep 17 00:00:00 2001 From: AlzhraaIbrahim Date: Tue, 5 Mar 2024 12:17:12 +0100 Subject: [PATCH 01/15] update RamppEvenDetection to work on "ic" stride type --- .../event_detection/_rampp_event_detection.py | 241 ++++++++++++++++-- 1 file changed, 219 insertions(+), 22 deletions(-) diff --git a/gaitmap_mad/gaitmap_mad/event_detection/_rampp_event_detection.py b/gaitmap_mad/gaitmap_mad/event_detection/_rampp_event_detection.py index 0de53493..0e135072 100644 --- a/gaitmap_mad/gaitmap_mad/event_detection/_rampp_event_detection.py +++ b/gaitmap_mad/gaitmap_mad/event_detection/_rampp_event_detection.py @@ -7,7 +7,9 @@ from gaitmap._event_detection_common._event_detection_mixin import _detect_min_vel_gyr_energy, _EventDetectionMixin from gaitmap.base import BaseEventDetection -from gaitmap.data_transform import BaseFilter +from gaitmap.data_transform import BaseFilter, ButterworthFilter +from tpcp import cf +from typing_extensions import Literal class RamppEventDetection(_EventDetectionMixin, BaseEventDetection): @@ -30,6 +32,10 @@ class RamppEventDetection(_EventDetectionMixin, BaseEventDetection): The size of the sliding window for finding the minimum gyroscope energy in ms. memory An optional `joblib.Memory` object that can be provided to cache the detection of all events. + ic_lowpass_filter + An instance of a Filter-transform (e.g. :class:`~gaitmap.data_transform.ButterworthFilter`) that will be + applied to the gyr_ml data before the IC is detected. + While not enforced, this should be a lowpass filter to ensure that the results are as expected. enforce_consistency An optional bool that can be set to False if you wish to disable postprocessing (see Notes section for more information). @@ -38,6 +44,8 @@ class RamppEventDetection(_EventDetectionMixin, BaseEventDetection): By default, all events ("ic", "tc", "min_vel") are detected. If `min_vel` is not detected, the `min_vel_event_list_` output will not be available. If "ic" is not detected, the `pre_ic` will also not be available in the output. + stride_type + The stride list type that should be either "ic", or "segmented" Attributes ---------- @@ -150,21 +158,27 @@ class RamppEventDetection(_EventDetectionMixin, BaseEventDetection): biomedical engineering, 62(4), 1089-1097.. https://doi.org/10.1109/TBME.2014.2368211 """ - + ic_lowpass_filter: BaseFilter ic_search_region_ms: Tuple[float, float] min_vel_search_win_size_ms: float + stride_type: Literal["segmented", "ic"] def __init__( - self, - ic_search_region_ms: Tuple[float, float] = (80, 50), - min_vel_search_win_size_ms: float = 100, - memory: Optional[Memory] = None, - enforce_consistency: bool = True, - detect_only: Optional[Tuple[str, ...]] = None, + self, + ic_search_region_ms: Tuple[float, float] = (80, 50), + min_vel_search_win_size_ms: float = 100, + memory: Optional[Memory] = None, + enforce_consistency: bool = True, + detect_only: Optional[Tuple[str, ...]] = None, + ic_lowpass_filter: BaseFilter = cf(ButterworthFilter(order=2, cutoff_freq_hz=15)), + stride_type: Literal["segmented", "ic"] = "segmented", ): + self.ic_lowpass_filter = ic_lowpass_filter self.ic_search_region_ms = ic_search_region_ms self.min_vel_search_win_size_ms = min_vel_search_win_size_ms - super().__init__(memory=memory, enforce_consistency=enforce_consistency, detect_only=detect_only) + self.stride_type = stride_type + super().__init__(memory=memory, enforce_consistency=enforce_consistency, detect_only=detect_only, + stride_type=stride_type) def _select_all_event_detection_method(self) -> Callable: """Select the function to calculate the all events. @@ -186,19 +200,39 @@ def _get_detect_kwargs(self) -> Dict[str, Union[Tuple[int, int], int]]: "ic_search_region": ic_search_region, "min_vel_search_win_size": min_vel_search_win_size, "sampling_rate_hz": self.sampling_rate_hz, - "gyr_ic_lowpass_filter": None, + "gyr_ic_lowpass_filter": self.ic_lowpass_filter, } def _find_all_events( - gyr: pd.DataFrame, - acc: pd.DataFrame, - stride_list: pd.DataFrame, - events: Tuple[str, ...], - ic_search_region: Tuple[float, float], - min_vel_search_win_size: int, - sampling_rate_hz: float, - gyr_ic_lowpass_filter: Optional[BaseFilter], + gyr: pd.DataFrame, + acc: pd.DataFrame, + stride_list: pd.DataFrame, + events: Tuple[str, ...], + ic_search_region: Tuple[float, float], + min_vel_search_win_size: int, + sampling_rate_hz: float, + gyr_ic_lowpass_filter: Optional[BaseFilter], + stride_type: Literal["segmented", "ic"] +) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: + """Find events in provided data by checking the stride type and calling the relevant method.""" + if stride_type == 'ic': + return _find_all_events_for_ic_stride(gyr, acc, stride_list, events, ic_search_region, min_vel_search_win_size, + sampling_rate_hz, gyr_ic_lowpass_filter) + else: # the default stride type (segmented) + return _find_all_events_for_segmented_stride(gyr, acc, stride_list, events, ic_search_region, min_vel_search_win_size, + sampling_rate_hz, gyr_ic_lowpass_filter) + + +def _find_all_events_for_segmented_stride( + gyr: pd.DataFrame, + acc: pd.DataFrame, + stride_list: pd.DataFrame, + events: Tuple[str, ...], + ic_search_region: Tuple[float, float], + min_vel_search_win_size: int, + sampling_rate_hz: float, + gyr_ic_lowpass_filter: Optional[BaseFilter], ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: """Find events in provided data by looping over single strides.""" gyr_ml = gyr["gyr_ml"] @@ -225,9 +259,9 @@ def _find_all_events( gyr_ml_filtered_sec = gyr_ml_filtered[start:end] acc_sec = acc_pa[start:end] gyr_grad = np.gradient(gyr_ml[start:end]) - ic_events.append(start + _detect_ic(gyr_ml_filtered_sec, acc_sec, gyr_grad, ic_search_region)) + ic_events.append(start + _detect_ic_for_segmented_stride(gyr_ml_filtered_sec, acc_sec, gyr_grad, ic_search_region)) if "tc" in events: - tc_events.append(start + _detect_tc(gyr_ml[start:end])) + tc_events.append(start + _detect_tc_for_segmented_stride(gyr_ml[start:end])) if "min_vel" in events: min_vel_events.append(start + _detect_min_vel_gyr_energy(gyr[start:end], min_vel_search_win_size)) @@ -238,7 +272,142 @@ def _find_all_events( ) -def _detect_ic( +def _find_all_events_for_ic_stride( + gyr: pd.DataFrame, + acc: pd.DataFrame, + stride_list: pd.DataFrame, + events: Tuple[str, ...], + ic_search_region: Tuple[float, float], + min_vel_search_win_size: int, + sampling_rate_hz: float, + gyr_ic_lowpass_filter: Optional[BaseFilter], +) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: + """Find events in provided data by looping over single strides.""" + gyr_ml = gyr["gyr_ml"] + if "ic" in events: + if gyr_ic_lowpass_filter is not None: + gyr_ml_filtered = gyr_ic_lowpass_filter.filter( + gyr_ml, sampling_rate_hz=sampling_rate_hz + ).filtered_data_.to_numpy() + else: + gyr_ml_filtered = gyr_ml.to_numpy() + else: + gyr_ml_filtered = None + gyr = gyr.to_numpy() + acc_pa = -acc["acc_pa"].to_numpy() # have to invert acc data to work on rampp paper + ic_events = [] + tc_events = [] + min_vel_events = [] + _is_initial_stride = False + for index, stride in stride_list.iterrows(): + current_stride_start = stride["start"] + current_stride_end = stride["end"] + if "ic" in events: + if index == 0: + _is_initial_stride = True + # in the first stride, we search only the current stride + previous_stride_start = stride["start"] + previous_stride_end = stride["end"] + else: + previous_stride_start = stride_list.loc[index - 1]["start"] + previous_stride_end = stride_list.loc[index - 1]["end"] + # to get the ic event we use a search window between the peak of previous stride and part of the current + # stride get data of current stride + gyr_ml_filtered_sec_current_stride = gyr_ml_filtered[current_stride_start:current_stride_end] + acc_sec_current_stride = acc_pa[current_stride_start:current_stride_end] + # get data of previous stride + gyr_ml_filtered_sec_previous_stride = gyr_ml_filtered[previous_stride_start:previous_stride_end] + acc_sec_previous_stride = acc_pa[previous_stride_start:previous_stride_end] + + ic_events.append(current_stride_start + _detect_ic_for_ic_stride(gyr_ml_filtered_sec_previous_stride, + gyr_ml_filtered_sec_current_stride, + acc_sec_previous_stride, acc_sec_current_stride, + ic_search_region, _is_initial_stride)) + + _is_initial_stride = False + + if "tc" in events: + tc_events.append(current_stride_start + _detect_tc_for_ic_stride(gyr_ml_filtered[ + current_stride_start:current_stride_end])) # use the filtered signal here because for some patients there is a lot of noise around ic + if "min_vel" in events: + min_vel_events.append(current_stride_start + _detect_min_vel_gyr_energy(gyr[current_stride_start: + current_stride_end], + min_vel_search_win_size)) + + return ( + np.array(ic_events, dtype=float) if ic_events else None, + np.array(tc_events, dtype=float) if tc_events else None, + np.array(min_vel_events, dtype=float) if min_vel_events else None, + ) + + +def _detect_ic_for_ic_stride( + gyr_ml_previous_stride: np.ndarray, gyr_ml_current_stride: np.ndarray, acc_pa_inv_previous_stride: np.ndarray, + acc_pa_inv_current_stride: np.ndarray, ic_search_region: Tuple[float, float], is_initial_stride: bool +) -> float: + """Find the ic. + + Note, that this implementation expects the inverted signal of acc_pa compared to the normal bodyframe definition + in gaitmap. + This is because the algorithm was originally developed considering a different coordinate system. + To keep the logic identical to the original paper, we pass in the inverted signal axis (see parent function) + """ + # Determine rough search region + + # Determine rough search region as the region between the peak of the previous stride and 0.2 of the stance phase + # of the current stride + if is_initial_stride: + search_region = 0, int(0.2 * gyr_ml_current_stride.shape[0]) + else: + peak = _find_previous_stride_positive_peak(gyr_ml_previous_stride) + search_region = peak, int(gyr_ml_previous_stride.shape[0] + 0.1 * gyr_ml_current_stride.shape[0]) + if search_region[1] - search_region[0] < 0: + # The gyr argmax was not found + return np.nan + + # alternative: + # concatenate the two strides + gyr_ml_concatenated = np.concatenate((gyr_ml_previous_stride, gyr_ml_current_stride)) + gyr_ml_concatenated_grad = np.gradient(gyr_ml_concatenated) + + refined_search_region_start = int(search_region[0] + np.argmin(gyr_ml_concatenated_grad[slice(*search_region)])) + refined_search_region_end = int( + refined_search_region_start + np.argmax(gyr_ml_concatenated_grad[refined_search_region_start:search_region[1]]) + ) + + if refined_search_region_end - refined_search_region_start < 0: + return np.nan + + # Find heel strike candidate in search region based on gyr + if refined_search_region_start == refined_search_region_end: + heel_strike_candidate = refined_search_region_start + else: + heel_strike_candidate = refined_search_region_start + np.argmin( + gyr_ml_concatenated[refined_search_region_start:refined_search_region_end] + ) + + # Acc search window + acc_pa_inv_concatenated = np.concatenate((acc_pa_inv_previous_stride, acc_pa_inv_current_stride)) + acc_search_region_start = int(np.max(np.array([0, heel_strike_candidate - ic_search_region[0]]))) + acc_search_region_end = int(np.min(np.array([acc_pa_inv_concatenated.shape[0], + heel_strike_candidate + ic_search_region[1]]))) + if is_initial_stride: + return float(np.argmin(acc_pa_inv_current_stride[acc_search_region_start: + acc_search_region_end])) + else: + return float(acc_search_region_start + np.argmin(acc_pa_inv_concatenated[acc_search_region_start: + acc_search_region_end]) - + acc_pa_inv_previous_stride.shape[0]) + + +def _detect_tc_for_ic_stride(gyr_ml: np.ndarray) -> float: + try: + return _find_first_negative_before_peak(gyr_ml) + except IndexError: + return np.nan + + +def _detect_ic_for_segmented_stride( gyr_ml: np.ndarray, acc_pa_inv: np.ndarray, gyr_ml_grad: np.ndarray, ic_search_region: Tuple[float, float] ) -> float: """Find the ic. @@ -277,8 +446,36 @@ def _detect_ic( return float(acc_search_region_start + np.argmin(acc_pa_inv[acc_search_region_start:acc_search_region_end])) -def _detect_tc(gyr_ml: np.ndarray) -> float: +def _detect_tc_for_segmented_stride(gyr_ml: np.ndarray) -> float: try: return np.where(np.diff(np.signbit(gyr_ml)))[0][0] except IndexError: return np.nan + + +def _find_first_negative_before_peak(arr): + """To find the tc. + + find the peak of the signal and then search for the first negative value before the peak + """ + + peak_index = np.argmax(arr) # Find the index of the largest positive number (peak) + + # Find the index of the first negative number before the peak (where the sign of the signal change) + i = peak_index + for i in range(peak_index - 1, -1, -1): + if arr[i] < 0: + return i # Return the index of the smallest positive number before the peak + + return i + + +def _find_previous_stride_positive_peak(gyr_ml_previous_stride): + """To find the start of the search region for the ic. + + """ + try: + return np.argmax(gyr_ml_previous_stride) + except IndexError: + return np.nan + From ddb660f3f6b97efeb4e556cf1a2c843d3eefe620 Mon Sep 17 00:00:00 2001 From: AlzhraaIbrahim Date: Tue, 5 Mar 2024 12:29:46 +0100 Subject: [PATCH 02/15] update stride_list_conversion.py to convert "ic" stride into min_vel stride update _event_detection_mixin.py to check consistency of "ic" stride --- .../_event_detection_mixin.py | 12 +++++++----- .../event_detection/_herzer_event_detection.py | 8 +++++++- gaitmap/utils/stride_list_conversion.py | 17 +++++++++++------ 3 files changed, 25 insertions(+), 12 deletions(-) diff --git a/gaitmap/_event_detection_common/_event_detection_mixin.py b/gaitmap/_event_detection_common/_event_detection_mixin.py index 76dbbe9c..9971f8f6 100644 --- a/gaitmap/_event_detection_common/_event_detection_mixin.py +++ b/gaitmap/_event_detection_common/_event_detection_mixin.py @@ -7,6 +7,7 @@ from joblib import Memory from numpy.linalg import norm from typing_extensions import Self +from typing_extensions import Literal from gaitmap.utils._algo_helper import invert_result_dictionary, set_params_from_dict from gaitmap.utils._types import _Hashable @@ -38,16 +39,19 @@ class _EventDetectionMixin: data: SensorData sampling_rate_hz: float stride_list: pd.DataFrame + stride_type: Literal["segmented", "ic"] def __init__( self, memory: Optional[Memory] = None, enforce_consistency: bool = True, detect_only: Optional[Tuple[str, ...]] = None, + stride_type: Literal["segmented", "ic"] = "segmented", ): self.memory = memory self.enforce_consistency = enforce_consistency self.detect_only = detect_only + self.stride_type = stride_type def detect(self, data: SensorData, stride_list: StrideList, *, sampling_rate_hz: float) -> Self: """Find gait events in data within strides provided by stride_list. @@ -121,7 +125,7 @@ def _detect_single_dataset( # find events in all segments event_detection_func = self._select_all_event_detection_method() event_detection_func = memory.cache(event_detection_func) - ic, tc, min_vel = event_detection_func(gyr, acc, stride_list, events=events, **detect_kwargs) + ic, tc, min_vel = event_detection_func(gyr, acc, stride_list, events=events, stride_type=self.stride_type,**detect_kwargs) # build first dict / df based on segment start and end segmented_event_list = { @@ -132,13 +136,11 @@ def _detect_single_dataset( for event, event_list in zip(("ic", "tc", "min_vel"), (ic, tc, min_vel)): if event in events: segmented_event_list[event] = event_list - segmented_event_list = pd.DataFrame(segmented_event_list).set_index("s_id") - if self.enforce_consistency: # check for consistency, remove inconsistent strides segmented_event_list, _ = enforce_stride_list_consistency( - segmented_event_list, stride_type="segmented", check_stride_list=False + segmented_event_list, stride_type=self.stride_type, check_stride_list=False ) if "min_vel" not in events or self.enforce_consistency is False: @@ -148,7 +150,7 @@ def _detect_single_dataset( # convert to min_vel event list min_vel_event_list, _ = _segmented_stride_list_to_min_vel_single_sensor( - segmented_event_list, target_stride_type="min_vel" + segmented_event_list, source_stride_type=self.stride_type, target_stride_type="min_vel" ) output_order = [c for c in ["start", "end", "ic", "tc", "min_vel", "pre_ic"] if c in min_vel_event_list.columns] diff --git a/gaitmap/event_detection/_herzer_event_detection.py b/gaitmap/event_detection/_herzer_event_detection.py index f6365c21..7995a0d3 100644 --- a/gaitmap/event_detection/_herzer_event_detection.py +++ b/gaitmap/event_detection/_herzer_event_detection.py @@ -1,5 +1,6 @@ """An event detection algorithm optimized for stair ambulation developed by Liv Herzer in her Bachelor Thesis .""" from typing import Callable, Dict, Optional, Tuple, Union +from typing_extensions import Literal import numpy as np import pandas as pd @@ -187,6 +188,7 @@ class HerzerEventDetection(_EventDetectionMixin, BaseEventDetection): ic_lowpass_filter: BaseFilter memory: Optional[Memory] enforce_consistency: bool + stride_type: Literal["segmented"] def __init__( self, @@ -197,12 +199,15 @@ def __init__( memory: Optional[Memory] = None, enforce_consistency: bool = True, detect_only: Optional[Tuple[str, ...]] = None, + stride_type: Literal["segmented"] = "segmented", ): self.min_vel_search_win_size_ms = min_vel_search_win_size_ms self.mid_swing_peak_prominence = mid_swing_peak_prominence self.mid_swing_n_considered_peaks = mid_swing_n_considered_peaks self.ic_lowpass_filter = ic_lowpass_filter - super().__init__(memory=memory, enforce_consistency=enforce_consistency, detect_only=detect_only) + self.stride_type = stride_type + super().__init__(memory=memory, enforce_consistency=enforce_consistency, detect_only=detect_only, + stride_type=stride_type) def _get_detect_kwargs(self) -> Dict[str, int]: min_vel_search_win_size = int(self.min_vel_search_win_size_ms / 1000 * self.sampling_rate_hz) @@ -233,6 +238,7 @@ def _find_all_events( mid_swing_n_considered_peaks: int, ic_lowpass_filter: BaseFilter, sampling_rate_hz: float, + stride_type: Literal["segmented"] ) -> Tuple[Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray]]: """Find events in provided data by looping over single strides.""" gyr_ml = gyr["gyr_ml"].to_numpy() diff --git a/gaitmap/utils/stride_list_conversion.py b/gaitmap/utils/stride_list_conversion.py index af699f7a..6c945b5f 100644 --- a/gaitmap/utils/stride_list_conversion.py +++ b/gaitmap/utils/stride_list_conversion.py @@ -17,7 +17,8 @@ ) -def convert_segmented_stride_list(stride_list: StrideList, target_stride_type: Literal["min_vel", "ic"]) -> StrideList: +def convert_segmented_stride_list(stride_list: StrideList, target_stride_type: Literal["min_vel", "ic"], + source_stride_type: Literal["segmented", "ic"] = "segmented") -> StrideList: """Convert a segmented stride list with detected events into other types of stride lists. During the conversion some strides might be removed. @@ -29,6 +30,8 @@ def convert_segmented_stride_list(stride_list: StrideList, target_stride_type: L Stride list to be converted target_stride_type The stride list type that should be converted to + source_stride_type + The stride list type that should be converted from Returns ------- @@ -38,15 +41,18 @@ def convert_segmented_stride_list(stride_list: StrideList, target_stride_type: L """ stride_list_type = is_stride_list(stride_list, stride_type="segmented") if stride_list_type == "single": - return _segmented_stride_list_to_min_vel_single_sensor(stride_list, target_stride_type=target_stride_type)[0] + return _segmented_stride_list_to_min_vel_single_sensor(stride_list, target_stride_type=target_stride_type, + source_stride_type=source_stride_type)[0] return { - k: _segmented_stride_list_to_min_vel_single_sensor(v, target_stride_type=target_stride_type)[0] + k: _segmented_stride_list_to_min_vel_single_sensor(v, target_stride_type=target_stride_type, + source_stride_type=source_stride_type)[0] for k, v in stride_list.items() } def _segmented_stride_list_to_min_vel_single_sensor( - stride_list: SingleSensorStrideList, target_stride_type: Literal["min_vel", "ic"] + stride_list: SingleSensorStrideList, target_stride_type: Literal["min_vel", "ic"], + source_stride_type: Literal["segmented", "ic"] ) -> Tuple[SingleSensorStrideList, SingleSensorStrideList]: """Convert a segmented stride list with detected events into other types of stride lists. @@ -87,7 +93,7 @@ def _segmented_stride_list_to_min_vel_single_sensor( converted_stride_list["pre_ic"] = converted_stride_list["ic"] # ic of each stride is the ic in the subsequent segmented stride converted_stride_list["ic"] = converted_stride_list["ic"].shift(-1) - if "tc" in converted_stride_list.columns: + if "tc" in converted_stride_list.columns and source_stride_type == "segmented": #do not shift if source_stride_type is "ic" # tc of each stride is the tc in the subsequent segmented stride converted_stride_list["tc"] = converted_stride_list["tc"].shift(-1) @@ -149,7 +155,6 @@ def enforce_stride_list_consistency( if check_stride_list is True: is_single_sensor_stride_list(stride_list, stride_type=stride_type, raise_exception=True) order = SL_EVENT_ORDER[stride_type] - order = [c for c in order if c in stride_list.columns] if len(order) == 0: From 1167d3d52d51c37dcecbd6c29d28872817ac9ee3 Mon Sep 17 00:00:00 2001 From: AlzhraaIbrahim Date: Tue, 5 Mar 2024 13:08:36 +0100 Subject: [PATCH 03/15] fix linting --- .../_event_detection_mixin.py | 6 +-- .../_herzer_event_detection.py | 4 +- gaitmap/utils/stride_list_conversion.py | 17 ++++--- .../event_detection/_rampp_event_detection.py | 50 +++++++++---------- 4 files changed, 40 insertions(+), 37 deletions(-) diff --git a/gaitmap/_event_detection_common/_event_detection_mixin.py b/gaitmap/_event_detection_common/_event_detection_mixin.py index 9971f8f6..c33435ed 100644 --- a/gaitmap/_event_detection_common/_event_detection_mixin.py +++ b/gaitmap/_event_detection_common/_event_detection_mixin.py @@ -6,8 +6,7 @@ import pandas as pd from joblib import Memory from numpy.linalg import norm -from typing_extensions import Self -from typing_extensions import Literal +from typing_extensions import Literal, Self from gaitmap.utils._algo_helper import invert_result_dictionary, set_params_from_dict from gaitmap.utils._types import _Hashable @@ -125,7 +124,8 @@ def _detect_single_dataset( # find events in all segments event_detection_func = self._select_all_event_detection_method() event_detection_func = memory.cache(event_detection_func) - ic, tc, min_vel = event_detection_func(gyr, acc, stride_list, events=events, stride_type=self.stride_type,**detect_kwargs) + ic, tc, min_vel = event_detection_func(gyr, acc, stride_list, events=events, stride_type=self.stride_type, + **detect_kwargs) # build first dict / df based on segment start and end segmented_event_list = { diff --git a/gaitmap/event_detection/_herzer_event_detection.py b/gaitmap/event_detection/_herzer_event_detection.py index 7995a0d3..93e49107 100644 --- a/gaitmap/event_detection/_herzer_event_detection.py +++ b/gaitmap/event_detection/_herzer_event_detection.py @@ -1,12 +1,13 @@ """An event detection algorithm optimized for stair ambulation developed by Liv Herzer in her Bachelor Thesis .""" +import warnings from typing import Callable, Dict, Optional, Tuple, Union -from typing_extensions import Literal import numpy as np import pandas as pd from joblib import Memory from scipy import signal from tpcp import cf +from typing_extensions import Literal from gaitmap._event_detection_common._event_detection_mixin import _detect_min_vel_gyr_energy, _EventDetectionMixin from gaitmap.base import BaseEventDetection @@ -241,6 +242,7 @@ def _find_all_events( stride_type: Literal["segmented"] ) -> Tuple[Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray]]: """Find events in provided data by looping over single strides.""" + warnings.warn("This algorithm works on "+stride_type+" stride type, IC stride type is not supoorted") gyr_ml = gyr["gyr_ml"].to_numpy() gyr = gyr.to_numpy() # inverting acc, as this algorithm was developed assuming a flipped axis like the original Rampp algorithm diff --git a/gaitmap/utils/stride_list_conversion.py b/gaitmap/utils/stride_list_conversion.py index 6c945b5f..3daeec8e 100644 --- a/gaitmap/utils/stride_list_conversion.py +++ b/gaitmap/utils/stride_list_conversion.py @@ -51,7 +51,7 @@ def convert_segmented_stride_list(stride_list: StrideList, target_stride_type: L def _segmented_stride_list_to_min_vel_single_sensor( - stride_list: SingleSensorStrideList, target_stride_type: Literal["min_vel", "ic"], + stride_list: SingleSensorStrideList, target_stride_type: Literal["min_vel", "ic"], source_stride_type: Literal["segmented", "ic"] ) -> Tuple[SingleSensorStrideList, SingleSensorStrideList]: """Convert a segmented stride list with detected events into other types of stride lists. @@ -93,7 +93,8 @@ def _segmented_stride_list_to_min_vel_single_sensor( converted_stride_list["pre_ic"] = converted_stride_list["ic"] # ic of each stride is the ic in the subsequent segmented stride converted_stride_list["ic"] = converted_stride_list["ic"].shift(-1) - if "tc" in converted_stride_list.columns and source_stride_type == "segmented": #do not shift if source_stride_type is "ic" + if "tc" in converted_stride_list.columns and source_stride_type == "segmented": + # do not shift if source_stride_type is "ic" # tc of each stride is the tc in the subsequent segmented stride converted_stride_list["tc"] = converted_stride_list["tc"].shift(-1) @@ -117,9 +118,9 @@ def _segmented_stride_list_to_min_vel_single_sensor( def enforce_stride_list_consistency( - stride_list: SingleSensorStrideList, - stride_type=Literal["segmented", "min_vel", "ic"], - check_stride_list: bool = True, + stride_list: SingleSensorStrideList, + stride_type=Literal["segmented", "min_vel", "ic"], + check_stride_list: bool = True, ) -> Tuple[SingleSensorStrideList, SingleSensorStrideList]: """Exclude those strides where the gait events do not match the expected order or contain NaN. @@ -171,8 +172,8 @@ def enforce_stride_list_consistency( def intersect_stride_list( - stride_event_list: SingleSensorStrideList, - regions_of_interest: SingleSensorRegionsOfInterestList, + stride_event_list: SingleSensorStrideList, + regions_of_interest: SingleSensorRegionsOfInterestList, ) -> List[SingleSensorStrideList]: """Split the stride list into multiple stride lists based on the regions of interest. @@ -211,7 +212,7 @@ def intersect_stride_list( # find all strides that are fully contained in the roi partial_stride_list = stride_list.loc[ (stride_list["start"] >= roi["start"]) & (stride_list["end"] <= roi["end"]) - ] + ] partial_stride_list -= roi["start"] stride_lists.append(partial_stride_list) diff --git a/gaitmap_mad/gaitmap_mad/event_detection/_rampp_event_detection.py b/gaitmap_mad/gaitmap_mad/event_detection/_rampp_event_detection.py index 0e135072..2274c3f1 100644 --- a/gaitmap_mad/gaitmap_mad/event_detection/_rampp_event_detection.py +++ b/gaitmap_mad/gaitmap_mad/event_detection/_rampp_event_detection.py @@ -4,12 +4,12 @@ import numpy as np import pandas as pd from joblib import Memory +from tpcp import cf +from typing_extensions import Literal from gaitmap._event_detection_common._event_detection_mixin import _detect_min_vel_gyr_energy, _EventDetectionMixin from gaitmap.base import BaseEventDetection from gaitmap.data_transform import BaseFilter, ButterworthFilter -from tpcp import cf -from typing_extensions import Literal class RamppEventDetection(_EventDetectionMixin, BaseEventDetection): @@ -158,6 +158,7 @@ class RamppEventDetection(_EventDetectionMixin, BaseEventDetection): biomedical engineering, 62(4), 1089-1097.. https://doi.org/10.1109/TBME.2014.2368211 """ + ic_lowpass_filter: BaseFilter ic_search_region_ms: Tuple[float, float] min_vel_search_win_size_ms: float @@ -216,12 +217,12 @@ def _find_all_events( stride_type: Literal["segmented", "ic"] ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: """Find events in provided data by checking the stride type and calling the relevant method.""" - if stride_type == 'ic': + if stride_type == "ic": return _find_all_events_for_ic_stride(gyr, acc, stride_list, events, ic_search_region, min_vel_search_win_size, - sampling_rate_hz, gyr_ic_lowpass_filter) - else: # the default stride type (segmented) - return _find_all_events_for_segmented_stride(gyr, acc, stride_list, events, ic_search_region, min_vel_search_win_size, - sampling_rate_hz, gyr_ic_lowpass_filter) + sampling_rate_hz, gyr_ic_lowpass_filter) + # the default stride type (segmented) + return _find_all_events_for_segmented_stride(gyr, acc, stride_list, events, ic_search_region, + min_vel_search_win_size, sampling_rate_hz, gyr_ic_lowpass_filter) def _find_all_events_for_segmented_stride( @@ -259,7 +260,8 @@ def _find_all_events_for_segmented_stride( gyr_ml_filtered_sec = gyr_ml_filtered[start:end] acc_sec = acc_pa[start:end] gyr_grad = np.gradient(gyr_ml[start:end]) - ic_events.append(start + _detect_ic_for_segmented_stride(gyr_ml_filtered_sec, acc_sec, gyr_grad, ic_search_region)) + ic_events.append(start + _detect_ic_for_segmented_stride(gyr_ml_filtered_sec, acc_sec, gyr_grad, + ic_search_region)) if "tc" in events: tc_events.append(start + _detect_tc_for_segmented_stride(gyr_ml[start:end])) if "min_vel" in events: @@ -320,15 +322,17 @@ def _find_all_events_for_ic_stride( acc_sec_previous_stride = acc_pa[previous_stride_start:previous_stride_end] ic_events.append(current_stride_start + _detect_ic_for_ic_stride(gyr_ml_filtered_sec_previous_stride, - gyr_ml_filtered_sec_current_stride, - acc_sec_previous_stride, acc_sec_current_stride, - ic_search_region, _is_initial_stride)) + gyr_ml_filtered_sec_current_stride, + acc_sec_previous_stride, + acc_sec_current_stride, + ic_search_region, _is_initial_stride)) _is_initial_stride = False if "tc" in events: + # use the filtered signal here because for some patients there is a lot of noise around ic tc_events.append(current_stride_start + _detect_tc_for_ic_stride(gyr_ml_filtered[ - current_stride_start:current_stride_end])) # use the filtered signal here because for some patients there is a lot of noise around ic + current_stride_start:current_stride_end])) if "min_vel" in events: min_vel_events.append(current_stride_start + _detect_min_vel_gyr_energy(gyr[current_stride_start: current_stride_end], @@ -394,10 +398,10 @@ def _detect_ic_for_ic_stride( if is_initial_stride: return float(np.argmin(acc_pa_inv_current_stride[acc_search_region_start: acc_search_region_end])) - else: - return float(acc_search_region_start + np.argmin(acc_pa_inv_concatenated[acc_search_region_start: - acc_search_region_end]) - - acc_pa_inv_previous_stride.shape[0]) + + return float(acc_search_region_start + np.argmin(acc_pa_inv_concatenated[acc_search_region_start: + acc_search_region_end]) - + acc_pa_inv_previous_stride.shape[0]) def _detect_tc_for_ic_stride(gyr_ml: np.ndarray) -> float: @@ -408,7 +412,7 @@ def _detect_tc_for_ic_stride(gyr_ml: np.ndarray) -> float: def _detect_ic_for_segmented_stride( - gyr_ml: np.ndarray, acc_pa_inv: np.ndarray, gyr_ml_grad: np.ndarray, ic_search_region: Tuple[float, float] + gyr_ml: np.ndarray, acc_pa_inv: np.ndarray, gyr_ml_grad: np.ndarray, ic_search_region: Tuple[float, float] ) -> float: """Find the ic. @@ -428,7 +432,7 @@ def _detect_ic_for_segmented_stride( # refined_search_region_start, refined_search_region_end = search_region refined_search_region_start = int(search_region[0] + np.argmin(gyr_ml_grad[slice(*search_region)])) refined_search_region_end = int( - refined_search_region_start + np.argmax(gyr_ml_grad[refined_search_region_start : search_region[1]]) + refined_search_region_start + np.argmax(gyr_ml_grad[refined_search_region_start: search_region[1]]) ) if refined_search_region_end - refined_search_region_start <= 0: @@ -456,9 +460,8 @@ def _detect_tc_for_segmented_stride(gyr_ml: np.ndarray) -> float: def _find_first_negative_before_peak(arr): """To find the tc. - find the peak of the signal and then search for the first negative value before the peak - """ - + find the peak of the signal and then search for the first negative value before the peak + """ peak_index = np.argmax(arr) # Find the index of the largest positive number (peak) # Find the index of the first negative number before the peak (where the sign of the signal change) @@ -471,11 +474,8 @@ def _find_first_negative_before_peak(arr): def _find_previous_stride_positive_peak(gyr_ml_previous_stride): - """To find the start of the search region for the ic. - - """ + """To find the start of the search region for the ic.""" try: return np.argmax(gyr_ml_previous_stride) except IndexError: return np.nan - From 999452d52a0dba50912c63b209863df10517c5b8 Mon Sep 17 00:00:00 2001 From: AlzhraaIbrahim Date: Wed, 6 Mar 2024 10:40:43 +0100 Subject: [PATCH 04/15] fix naming --- .../_event_detection_mixin.py | 17 +- .../_herzer_event_detection.py | 21 ++- gaitmap/utils/stride_list_conversion.py | 41 +++-- .../event_detection/_rampp_event_detection.py | 172 +++++++++++------- 4 files changed, 151 insertions(+), 100 deletions(-) diff --git a/gaitmap/_event_detection_common/_event_detection_mixin.py b/gaitmap/_event_detection_common/_event_detection_mixin.py index c33435ed..eb113b5e 100644 --- a/gaitmap/_event_detection_common/_event_detection_mixin.py +++ b/gaitmap/_event_detection_common/_event_detection_mixin.py @@ -38,19 +38,19 @@ class _EventDetectionMixin: data: SensorData sampling_rate_hz: float stride_list: pd.DataFrame - stride_type: Literal["segmented", "ic"] + input_stride_type: Literal["segmented", "ic"] def __init__( self, memory: Optional[Memory] = None, enforce_consistency: bool = True, detect_only: Optional[Tuple[str, ...]] = None, - stride_type: Literal["segmented", "ic"] = "segmented", + input_stride_type: Literal["segmented", "ic"] = "segmented", ): self.memory = memory self.enforce_consistency = enforce_consistency self.detect_only = detect_only - self.stride_type = stride_type + self.input_stride_type = input_stride_type def detect(self, data: SensorData, stride_list: StrideList, *, sampling_rate_hz: float) -> Self: """Find gait events in data within strides provided by stride_list. @@ -124,8 +124,9 @@ def _detect_single_dataset( # find events in all segments event_detection_func = self._select_all_event_detection_method() event_detection_func = memory.cache(event_detection_func) - ic, tc, min_vel = event_detection_func(gyr, acc, stride_list, events=events, stride_type=self.stride_type, - **detect_kwargs) + ic, tc, min_vel = event_detection_func( + gyr, acc, stride_list, events=events, input_stride_type=self.input_stride_type, **detect_kwargs + ) # build first dict / df based on segment start and end segmented_event_list = { @@ -140,7 +141,7 @@ def _detect_single_dataset( if self.enforce_consistency: # check for consistency, remove inconsistent strides segmented_event_list, _ = enforce_stride_list_consistency( - segmented_event_list, stride_type=self.stride_type, check_stride_list=False + segmented_event_list, input_stride_type=self.input_stride_type, check_stride_list=False ) if "min_vel" not in events or self.enforce_consistency is False: @@ -150,7 +151,7 @@ def _detect_single_dataset( # convert to min_vel event list min_vel_event_list, _ = _segmented_stride_list_to_min_vel_single_sensor( - segmented_event_list, source_stride_type=self.stride_type, target_stride_type="min_vel" + segmented_event_list, source_stride_type=self.input_stride_type, target_stride_type="min_vel" ) output_order = [c for c in ["start", "end", "ic", "tc", "min_vel", "pre_ic"] if c in min_vel_event_list.columns] @@ -158,7 +159,7 @@ def _detect_single_dataset( # We enforce consistency again here, as a valid segmented stride list does not necessarily result in a valid # min_vel stride list min_vel_event_list, _ = enforce_stride_list_consistency( - min_vel_event_list[output_order], stride_type="min_vel", check_stride_list=False + min_vel_event_list[output_order], input_stride_type="min_vel", check_stride_list=False ) return {"min_vel_event_list": min_vel_event_list, "segmented_event_list": segmented_event_list} diff --git a/gaitmap/event_detection/_herzer_event_detection.py b/gaitmap/event_detection/_herzer_event_detection.py index 93e49107..7f20f477 100644 --- a/gaitmap/event_detection/_herzer_event_detection.py +++ b/gaitmap/event_detection/_herzer_event_detection.py @@ -1,5 +1,5 @@ """An event detection algorithm optimized for stair ambulation developed by Liv Herzer in her Bachelor Thesis .""" -import warnings + from typing import Callable, Dict, Optional, Tuple, Union import numpy as np @@ -189,7 +189,7 @@ class HerzerEventDetection(_EventDetectionMixin, BaseEventDetection): ic_lowpass_filter: BaseFilter memory: Optional[Memory] enforce_consistency: bool - stride_type: Literal["segmented"] + input_stride_type: Literal["segmented"] def __init__( self, @@ -200,15 +200,19 @@ def __init__( memory: Optional[Memory] = None, enforce_consistency: bool = True, detect_only: Optional[Tuple[str, ...]] = None, - stride_type: Literal["segmented"] = "segmented", + input_stride_type: Literal["segmented"] = "segmented", ): self.min_vel_search_win_size_ms = min_vel_search_win_size_ms self.mid_swing_peak_prominence = mid_swing_peak_prominence self.mid_swing_n_considered_peaks = mid_swing_n_considered_peaks self.ic_lowpass_filter = ic_lowpass_filter - self.stride_type = stride_type - super().__init__(memory=memory, enforce_consistency=enforce_consistency, detect_only=detect_only, - stride_type=stride_type) + self.input_stride_type = input_stride_type + super().__init__( + memory=memory, + enforce_consistency=enforce_consistency, + detect_only=detect_only, + input_stride_type=input_stride_type, + ) def _get_detect_kwargs(self) -> Dict[str, int]: min_vel_search_win_size = int(self.min_vel_search_win_size_ms / 1000 * self.sampling_rate_hz) @@ -239,10 +243,11 @@ def _find_all_events( mid_swing_n_considered_peaks: int, ic_lowpass_filter: BaseFilter, sampling_rate_hz: float, - stride_type: Literal["segmented"] + input_stride_type: Literal["segmented"], ) -> Tuple[Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray]]: """Find events in provided data by looping over single strides.""" - warnings.warn("This algorithm works on "+stride_type+" stride type, IC stride type is not supoorted") + if input_stride_type != "segmented": + raise NotImplementedError() gyr_ml = gyr["gyr_ml"].to_numpy() gyr = gyr.to_numpy() # inverting acc, as this algorithm was developed assuming a flipped axis like the original Rampp algorithm diff --git a/gaitmap/utils/stride_list_conversion.py b/gaitmap/utils/stride_list_conversion.py index 3daeec8e..0e4bc01c 100644 --- a/gaitmap/utils/stride_list_conversion.py +++ b/gaitmap/utils/stride_list_conversion.py @@ -1,4 +1,5 @@ """A couple of utils to convert stride lists into different formats.""" + from typing import List, Tuple import numpy as np @@ -17,8 +18,11 @@ ) -def convert_segmented_stride_list(stride_list: StrideList, target_stride_type: Literal["min_vel", "ic"], - source_stride_type: Literal["segmented", "ic"] = "segmented") -> StrideList: +def convert_segmented_stride_list( + stride_list: StrideList, + target_stride_type: Literal["min_vel", "ic"], + source_stride_type: Literal["segmented", "ic"] = "segmented", +) -> StrideList: """Convert a segmented stride list with detected events into other types of stride lists. During the conversion some strides might be removed. @@ -41,18 +45,21 @@ def convert_segmented_stride_list(stride_list: StrideList, target_stride_type: L """ stride_list_type = is_stride_list(stride_list, stride_type="segmented") if stride_list_type == "single": - return _segmented_stride_list_to_min_vel_single_sensor(stride_list, target_stride_type=target_stride_type, - source_stride_type=source_stride_type)[0] + return _segmented_stride_list_to_min_vel_single_sensor( + stride_list, target_stride_type=target_stride_type, source_stride_type=source_stride_type + )[0] return { - k: _segmented_stride_list_to_min_vel_single_sensor(v, target_stride_type=target_stride_type, - source_stride_type=source_stride_type)[0] + k: _segmented_stride_list_to_min_vel_single_sensor( + v, target_stride_type=target_stride_type, source_stride_type=source_stride_type + )[0] for k, v in stride_list.items() } def _segmented_stride_list_to_min_vel_single_sensor( - stride_list: SingleSensorStrideList, target_stride_type: Literal["min_vel", "ic"], - source_stride_type: Literal["segmented", "ic"] + stride_list: SingleSensorStrideList, + target_stride_type: Literal["min_vel", "ic"], + source_stride_type: Literal["segmented", "ic"], ) -> Tuple[SingleSensorStrideList, SingleSensorStrideList]: """Convert a segmented stride list with detected events into other types of stride lists. @@ -118,9 +125,9 @@ def _segmented_stride_list_to_min_vel_single_sensor( def enforce_stride_list_consistency( - stride_list: SingleSensorStrideList, - stride_type=Literal["segmented", "min_vel", "ic"], - check_stride_list: bool = True, + stride_list: SingleSensorStrideList, + input_stride_type=Literal["segmented", "min_vel", "ic"], + check_stride_list: bool = True, ) -> Tuple[SingleSensorStrideList, SingleSensorStrideList]: """Exclude those strides where the gait events do not match the expected order or contain NaN. @@ -136,7 +143,7 @@ def enforce_stride_list_consistency( ---------- stride_list A single sensor stride list in a Dataframe format - stride_type + input_stride_type Indicate which types of strides are expected to be in the stride list. This changes the expected columns and order of events. check_stride_list @@ -154,8 +161,8 @@ def enforce_stride_list_consistency( """ if check_stride_list is True: - is_single_sensor_stride_list(stride_list, stride_type=stride_type, raise_exception=True) - order = SL_EVENT_ORDER[stride_type] + is_single_sensor_stride_list(stride_list, stride_type=input_stride_type, raise_exception=True) + order = SL_EVENT_ORDER[input_stride_type] order = [c for c in order if c in stride_list.columns] if len(order) == 0: @@ -172,8 +179,8 @@ def enforce_stride_list_consistency( def intersect_stride_list( - stride_event_list: SingleSensorStrideList, - regions_of_interest: SingleSensorRegionsOfInterestList, + stride_event_list: SingleSensorStrideList, + regions_of_interest: SingleSensorRegionsOfInterestList, ) -> List[SingleSensorStrideList]: """Split the stride list into multiple stride lists based on the regions of interest. @@ -212,7 +219,7 @@ def intersect_stride_list( # find all strides that are fully contained in the roi partial_stride_list = stride_list.loc[ (stride_list["start"] >= roi["start"]) & (stride_list["end"] <= roi["end"]) - ] + ] partial_stride_list -= roi["start"] stride_lists.append(partial_stride_list) diff --git a/gaitmap_mad/gaitmap_mad/event_detection/_rampp_event_detection.py b/gaitmap_mad/gaitmap_mad/event_detection/_rampp_event_detection.py index 2274c3f1..60dfb2b8 100644 --- a/gaitmap_mad/gaitmap_mad/event_detection/_rampp_event_detection.py +++ b/gaitmap_mad/gaitmap_mad/event_detection/_rampp_event_detection.py @@ -1,4 +1,5 @@ """The event detection algorithm by Rampp et al. 2014.""" + from typing import Callable, Dict, Optional, Tuple, Union, cast import numpy as np @@ -44,7 +45,7 @@ class RamppEventDetection(_EventDetectionMixin, BaseEventDetection): By default, all events ("ic", "tc", "min_vel") are detected. If `min_vel` is not detected, the `min_vel_event_list_` output will not be available. If "ic" is not detected, the `pre_ic` will also not be available in the output. - stride_type + input_stride_type The stride list type that should be either "ic", or "segmented" Attributes @@ -162,24 +163,28 @@ class RamppEventDetection(_EventDetectionMixin, BaseEventDetection): ic_lowpass_filter: BaseFilter ic_search_region_ms: Tuple[float, float] min_vel_search_win_size_ms: float - stride_type: Literal["segmented", "ic"] + input_stride_type: Literal["segmented", "ic"] def __init__( - self, - ic_search_region_ms: Tuple[float, float] = (80, 50), - min_vel_search_win_size_ms: float = 100, - memory: Optional[Memory] = None, - enforce_consistency: bool = True, - detect_only: Optional[Tuple[str, ...]] = None, - ic_lowpass_filter: BaseFilter = cf(ButterworthFilter(order=2, cutoff_freq_hz=15)), - stride_type: Literal["segmented", "ic"] = "segmented", + self, + ic_search_region_ms: Tuple[float, float] = (80, 50), + min_vel_search_win_size_ms: float = 100, + memory: Optional[Memory] = None, + enforce_consistency: bool = True, + detect_only: Optional[Tuple[str, ...]] = None, + ic_lowpass_filter: BaseFilter = cf(ButterworthFilter(order=2, cutoff_freq_hz=15)), + input_stride_type: Literal["segmented", "ic"] = "segmented", ): self.ic_lowpass_filter = ic_lowpass_filter self.ic_search_region_ms = ic_search_region_ms self.min_vel_search_win_size_ms = min_vel_search_win_size_ms - self.stride_type = stride_type - super().__init__(memory=memory, enforce_consistency=enforce_consistency, detect_only=detect_only, - stride_type=stride_type) + self.input_stride_type = input_stride_type + super().__init__( + memory=memory, + enforce_consistency=enforce_consistency, + detect_only=detect_only, + input_stride_type=input_stride_type, + ) def _select_all_event_detection_method(self) -> Callable: """Select the function to calculate the all events. @@ -206,34 +211,50 @@ def _get_detect_kwargs(self) -> Dict[str, Union[Tuple[int, int], int]]: def _find_all_events( - gyr: pd.DataFrame, - acc: pd.DataFrame, - stride_list: pd.DataFrame, - events: Tuple[str, ...], - ic_search_region: Tuple[float, float], - min_vel_search_win_size: int, - sampling_rate_hz: float, - gyr_ic_lowpass_filter: Optional[BaseFilter], - stride_type: Literal["segmented", "ic"] + gyr: pd.DataFrame, + acc: pd.DataFrame, + stride_list: pd.DataFrame, + events: Tuple[str, ...], + ic_search_region: Tuple[float, float], + min_vel_search_win_size: int, + sampling_rate_hz: float, + gyr_ic_lowpass_filter: Optional[BaseFilter], + input_stride_type: Literal["segmented", "ic"], ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: """Find events in provided data by checking the stride type and calling the relevant method.""" - if stride_type == "ic": - return _find_all_events_for_ic_stride(gyr, acc, stride_list, events, ic_search_region, min_vel_search_win_size, - sampling_rate_hz, gyr_ic_lowpass_filter) + if input_stride_type == "ic": + return _find_all_events_for_ic_stride( + gyr, + acc, + stride_list, + events, + ic_search_region, + min_vel_search_win_size, + sampling_rate_hz, + gyr_ic_lowpass_filter, + ) # the default stride type (segmented) - return _find_all_events_for_segmented_stride(gyr, acc, stride_list, events, ic_search_region, - min_vel_search_win_size, sampling_rate_hz, gyr_ic_lowpass_filter) + return _find_all_events_for_segmented_stride( + gyr, + acc, + stride_list, + events, + ic_search_region, + min_vel_search_win_size, + sampling_rate_hz, + gyr_ic_lowpass_filter, + ) def _find_all_events_for_segmented_stride( - gyr: pd.DataFrame, - acc: pd.DataFrame, - stride_list: pd.DataFrame, - events: Tuple[str, ...], - ic_search_region: Tuple[float, float], - min_vel_search_win_size: int, - sampling_rate_hz: float, - gyr_ic_lowpass_filter: Optional[BaseFilter], + gyr: pd.DataFrame, + acc: pd.DataFrame, + stride_list: pd.DataFrame, + events: Tuple[str, ...], + ic_search_region: Tuple[float, float], + min_vel_search_win_size: int, + sampling_rate_hz: float, + gyr_ic_lowpass_filter: Optional[BaseFilter], ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: """Find events in provided data by looping over single strides.""" gyr_ml = gyr["gyr_ml"] @@ -260,8 +281,9 @@ def _find_all_events_for_segmented_stride( gyr_ml_filtered_sec = gyr_ml_filtered[start:end] acc_sec = acc_pa[start:end] gyr_grad = np.gradient(gyr_ml[start:end]) - ic_events.append(start + _detect_ic_for_segmented_stride(gyr_ml_filtered_sec, acc_sec, gyr_grad, - ic_search_region)) + ic_events.append( + start + _detect_ic_for_segmented_stride(gyr_ml_filtered_sec, acc_sec, gyr_grad, ic_search_region) + ) if "tc" in events: tc_events.append(start + _detect_tc_for_segmented_stride(gyr_ml[start:end])) if "min_vel" in events: @@ -275,14 +297,14 @@ def _find_all_events_for_segmented_stride( def _find_all_events_for_ic_stride( - gyr: pd.DataFrame, - acc: pd.DataFrame, - stride_list: pd.DataFrame, - events: Tuple[str, ...], - ic_search_region: Tuple[float, float], - min_vel_search_win_size: int, - sampling_rate_hz: float, - gyr_ic_lowpass_filter: Optional[BaseFilter], + gyr: pd.DataFrame, + acc: pd.DataFrame, + stride_list: pd.DataFrame, + events: Tuple[str, ...], + ic_search_region: Tuple[float, float], + min_vel_search_win_size: int, + sampling_rate_hz: float, + gyr_ic_lowpass_filter: Optional[BaseFilter], ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: """Find events in provided data by looping over single strides.""" gyr_ml = gyr["gyr_ml"] @@ -321,22 +343,31 @@ def _find_all_events_for_ic_stride( gyr_ml_filtered_sec_previous_stride = gyr_ml_filtered[previous_stride_start:previous_stride_end] acc_sec_previous_stride = acc_pa[previous_stride_start:previous_stride_end] - ic_events.append(current_stride_start + _detect_ic_for_ic_stride(gyr_ml_filtered_sec_previous_stride, - gyr_ml_filtered_sec_current_stride, - acc_sec_previous_stride, - acc_sec_current_stride, - ic_search_region, _is_initial_stride)) + ic_events.append( + current_stride_start + + _detect_ic_for_ic_stride( + gyr_ml_filtered_sec_previous_stride, + gyr_ml_filtered_sec_current_stride, + acc_sec_previous_stride, + acc_sec_current_stride, + ic_search_region, + _is_initial_stride, + ) + ) _is_initial_stride = False if "tc" in events: # use the filtered signal here because for some patients there is a lot of noise around ic - tc_events.append(current_stride_start + _detect_tc_for_ic_stride(gyr_ml_filtered[ - current_stride_start:current_stride_end])) + tc_events.append( + current_stride_start + + _detect_tc_for_ic_stride(gyr_ml_filtered[current_stride_start:current_stride_end]) + ) if "min_vel" in events: - min_vel_events.append(current_stride_start + _detect_min_vel_gyr_energy(gyr[current_stride_start: - current_stride_end], - min_vel_search_win_size)) + min_vel_events.append( + current_stride_start + + _detect_min_vel_gyr_energy(gyr[current_stride_start:current_stride_end], min_vel_search_win_size) + ) return ( np.array(ic_events, dtype=float) if ic_events else None, @@ -346,8 +377,12 @@ def _find_all_events_for_ic_stride( def _detect_ic_for_ic_stride( - gyr_ml_previous_stride: np.ndarray, gyr_ml_current_stride: np.ndarray, acc_pa_inv_previous_stride: np.ndarray, - acc_pa_inv_current_stride: np.ndarray, ic_search_region: Tuple[float, float], is_initial_stride: bool + gyr_ml_previous_stride: np.ndarray, + gyr_ml_current_stride: np.ndarray, + acc_pa_inv_previous_stride: np.ndarray, + acc_pa_inv_current_stride: np.ndarray, + ic_search_region: Tuple[float, float], + is_initial_stride: bool, ) -> float: """Find the ic. @@ -376,7 +411,8 @@ def _detect_ic_for_ic_stride( refined_search_region_start = int(search_region[0] + np.argmin(gyr_ml_concatenated_grad[slice(*search_region)])) refined_search_region_end = int( - refined_search_region_start + np.argmax(gyr_ml_concatenated_grad[refined_search_region_start:search_region[1]]) + refined_search_region_start + + np.argmax(gyr_ml_concatenated_grad[refined_search_region_start : search_region[1]]) ) if refined_search_region_end - refined_search_region_start < 0: @@ -393,15 +429,17 @@ def _detect_ic_for_ic_stride( # Acc search window acc_pa_inv_concatenated = np.concatenate((acc_pa_inv_previous_stride, acc_pa_inv_current_stride)) acc_search_region_start = int(np.max(np.array([0, heel_strike_candidate - ic_search_region[0]]))) - acc_search_region_end = int(np.min(np.array([acc_pa_inv_concatenated.shape[0], - heel_strike_candidate + ic_search_region[1]]))) + acc_search_region_end = int( + np.min(np.array([acc_pa_inv_concatenated.shape[0], heel_strike_candidate + ic_search_region[1]])) + ) if is_initial_stride: - return float(np.argmin(acc_pa_inv_current_stride[acc_search_region_start: - acc_search_region_end])) + return float(np.argmin(acc_pa_inv_current_stride[acc_search_region_start:acc_search_region_end])) - return float(acc_search_region_start + np.argmin(acc_pa_inv_concatenated[acc_search_region_start: - acc_search_region_end]) - - acc_pa_inv_previous_stride.shape[0]) + return float( + acc_search_region_start + + np.argmin(acc_pa_inv_concatenated[acc_search_region_start:acc_search_region_end]) + - acc_pa_inv_previous_stride.shape[0] + ) def _detect_tc_for_ic_stride(gyr_ml: np.ndarray) -> float: @@ -412,7 +450,7 @@ def _detect_tc_for_ic_stride(gyr_ml: np.ndarray) -> float: def _detect_ic_for_segmented_stride( - gyr_ml: np.ndarray, acc_pa_inv: np.ndarray, gyr_ml_grad: np.ndarray, ic_search_region: Tuple[float, float] + gyr_ml: np.ndarray, acc_pa_inv: np.ndarray, gyr_ml_grad: np.ndarray, ic_search_region: Tuple[float, float] ) -> float: """Find the ic. @@ -432,7 +470,7 @@ def _detect_ic_for_segmented_stride( # refined_search_region_start, refined_search_region_end = search_region refined_search_region_start = int(search_region[0] + np.argmin(gyr_ml_grad[slice(*search_region)])) refined_search_region_end = int( - refined_search_region_start + np.argmax(gyr_ml_grad[refined_search_region_start: search_region[1]]) + refined_search_region_start + np.argmax(gyr_ml_grad[refined_search_region_start : search_region[1]]) ) if refined_search_region_end - refined_search_region_start <= 0: From c460ae7aa7d27746b3b4a0e3a98db49e3c50ba3c Mon Sep 17 00:00:00 2001 From: AlzhraaIbrahim Date: Tue, 12 Mar 2024 06:26:00 +0100 Subject: [PATCH 05/15] fix documentation --- gaitmap/event_detection/_herzer_event_detection.py | 4 ++-- .../_filtered_rampp_event_detection.py | 8 +++++++- .../event_detection/_rampp_event_detection.py | 11 ++--------- tests/mixins/test_algorithm_mixin.py | 2 +- .../test_event_detection_rampp.py | 6 +++--- tests/test_utils/test_stride_list_conversion.py | 12 ++++++++---- 6 files changed, 23 insertions(+), 20 deletions(-) diff --git a/gaitmap/event_detection/_herzer_event_detection.py b/gaitmap/event_detection/_herzer_event_detection.py index 7f20f477..81b165d3 100644 --- a/gaitmap/event_detection/_herzer_event_detection.py +++ b/gaitmap/event_detection/_herzer_event_detection.py @@ -55,7 +55,8 @@ class HerzerEventDetection(_EventDetectionMixin, BaseEventDetection): By default, all events ("ic", "tc", "min_vel") are detected. If `min_vel` is not detected, the `min_vel_event_list_` output will not be available. If "ic" is not detected, the `pre_ic` will also not be available in the output. - + input_stride_type + The stride list type that should be either "ic", or "segmented". Attributes ---------- @@ -206,7 +207,6 @@ def __init__( self.mid_swing_peak_prominence = mid_swing_peak_prominence self.mid_swing_n_considered_peaks = mid_swing_n_considered_peaks self.ic_lowpass_filter = ic_lowpass_filter - self.input_stride_type = input_stride_type super().__init__( memory=memory, enforce_consistency=enforce_consistency, diff --git a/gaitmap_mad/gaitmap_mad/event_detection/_filtered_rampp_event_detection.py b/gaitmap_mad/gaitmap_mad/event_detection/_filtered_rampp_event_detection.py index 46acedd0..31315174 100644 --- a/gaitmap_mad/gaitmap_mad/event_detection/_filtered_rampp_event_detection.py +++ b/gaitmap_mad/gaitmap_mad/event_detection/_filtered_rampp_event_detection.py @@ -1,5 +1,6 @@ """The event detection algorithm by Rampp et al. 2014.""" -from typing import Dict, Optional, Tuple + +from typing import Dict, Optional, Tuple, Literal from joblib import Memory from tpcp import cf @@ -41,6 +42,8 @@ class FilteredRamppEventDetection(RamppEventDetection): By default, all events ("ic", "tc", "min_vel") are detected. If `min_vel` is not detected, the `min_vel_event_list_` output will not be available. If "ic" is not detected, the `pre_ic` will also not be available in the output. + input_stride_type + The stride_list_type that should be either "ic" or "segmented". Attributes ---------- @@ -96,14 +99,17 @@ def __init__( memory: Optional[Memory] = None, enforce_consistency: bool = True, detect_only: Optional[Tuple[str, ...]] = None, + input_stride_type: Literal["segmented", "ic"] = "segmented", ): self.ic_lowpass_filter = ic_lowpass_filter + self.input_stride_type = input_stride_type super().__init__( memory=memory, enforce_consistency=enforce_consistency, ic_search_region_ms=ic_search_region_ms, min_vel_search_win_size_ms=min_vel_search_win_size_ms, detect_only=detect_only, + input_stride_type=input_stride_type, ) def _get_detect_kwargs(self) -> Dict: diff --git a/gaitmap_mad/gaitmap_mad/event_detection/_rampp_event_detection.py b/gaitmap_mad/gaitmap_mad/event_detection/_rampp_event_detection.py index 60dfb2b8..4789a7cd 100644 --- a/gaitmap_mad/gaitmap_mad/event_detection/_rampp_event_detection.py +++ b/gaitmap_mad/gaitmap_mad/event_detection/_rampp_event_detection.py @@ -33,10 +33,6 @@ class RamppEventDetection(_EventDetectionMixin, BaseEventDetection): The size of the sliding window for finding the minimum gyroscope energy in ms. memory An optional `joblib.Memory` object that can be provided to cache the detection of all events. - ic_lowpass_filter - An instance of a Filter-transform (e.g. :class:`~gaitmap.data_transform.ButterworthFilter`) that will be - applied to the gyr_ml data before the IC is detected. - While not enforced, this should be a lowpass filter to ensure that the results are as expected. enforce_consistency An optional bool that can be set to False if you wish to disable postprocessing (see Notes section for more information). @@ -46,7 +42,7 @@ class RamppEventDetection(_EventDetectionMixin, BaseEventDetection): If `min_vel` is not detected, the `min_vel_event_list_` output will not be available. If "ic" is not detected, the `pre_ic` will also not be available in the output. input_stride_type - The stride list type that should be either "ic", or "segmented" + The stride list type that should be either "ic", or "segmented". Attributes ---------- @@ -160,7 +156,6 @@ class RamppEventDetection(_EventDetectionMixin, BaseEventDetection): """ - ic_lowpass_filter: BaseFilter ic_search_region_ms: Tuple[float, float] min_vel_search_win_size_ms: float input_stride_type: Literal["segmented", "ic"] @@ -172,10 +167,8 @@ def __init__( memory: Optional[Memory] = None, enforce_consistency: bool = True, detect_only: Optional[Tuple[str, ...]] = None, - ic_lowpass_filter: BaseFilter = cf(ButterworthFilter(order=2, cutoff_freq_hz=15)), input_stride_type: Literal["segmented", "ic"] = "segmented", ): - self.ic_lowpass_filter = ic_lowpass_filter self.ic_search_region_ms = ic_search_region_ms self.min_vel_search_win_size_ms = min_vel_search_win_size_ms self.input_stride_type = input_stride_type @@ -206,7 +199,7 @@ def _get_detect_kwargs(self) -> Dict[str, Union[Tuple[int, int], int]]: "ic_search_region": ic_search_region, "min_vel_search_win_size": min_vel_search_win_size, "sampling_rate_hz": self.sampling_rate_hz, - "gyr_ic_lowpass_filter": self.ic_lowpass_filter, + "gyr_ic_lowpass_filter": None, } diff --git a/tests/mixins/test_algorithm_mixin.py b/tests/mixins/test_algorithm_mixin.py index f01d1469..9954d02e 100644 --- a/tests/mixins/test_algorithm_mixin.py +++ b/tests/mixins/test_algorithm_mixin.py @@ -1,4 +1,5 @@ """A mixin for all common tests that should be run on all algorithm classes.""" + import inspect import joblib @@ -51,7 +52,6 @@ def test_all_attributes_documented(self, after_action_instance): documented_names = {p.name for p in docs["Attributes"]} actual_names = set(get_results(after_action_instance).keys()) - assert documented_names == actual_names def test_all_other_parameters_documented(self, after_action_instance): diff --git a/tests/test_event_detection/test_event_detection_rampp.py b/tests/test_event_detection/test_event_detection_rampp.py index 037c1b14..cee84654 100644 --- a/tests/test_event_detection/test_event_detection_rampp.py +++ b/tests/test_event_detection/test_event_detection_rampp.py @@ -13,7 +13,7 @@ from gaitmap.utils import coordinate_conversion, datatype_helper from gaitmap.utils.consts import BF_COLS from gaitmap.utils.exceptions import ValidationError -from gaitmap_mad.event_detection._rampp_event_detection import _detect_tc +from gaitmap_mad.event_detection._rampp_event_detection import _detect_tc_for_segmented_stride from tests.mixins.test_algorithm_mixin import TestAlgorithmMixin from tests.mixins.test_caching_mixin import TestCachingMixin @@ -223,11 +223,11 @@ def test_sign_change_for_detect_tc(self): """Test correct handling of signal that does or does not provide a change of the sign.""" # with sign change signal1 = np.concatenate([np.ones(10), np.ones(10) * -1]) - assert _detect_tc(signal1) == 9 + assert _detect_tc_for_segmented_stride(signal1) == 9 # without sign change signal2 = np.ones(10) - assert np.isnan(_detect_tc(signal2)) + assert np.isnan(_detect_tc_for_segmented_stride(signal2)) @pytest.mark.parametrize( "detect_only", diff --git a/tests/test_utils/test_stride_list_conversion.py b/tests/test_utils/test_stride_list_conversion.py index d36023d6..58d76efc 100644 --- a/tests/test_utils/test_stride_list_conversion.py +++ b/tests/test_utils/test_stride_list_conversion.py @@ -111,7 +111,9 @@ def _create_example_stride_list_with_pause(self): def test_simple_conversion(self, target): stride_list = self._create_example_stride_list_with_pause() - converted, dropped = _segmented_stride_list_to_min_vel_single_sensor(stride_list, target_stride_type=target) + converted, dropped = _segmented_stride_list_to_min_vel_single_sensor( + stride_list, source_stride_type="segmented", target_stride_type=target + ) # We do not test everything here, but just see if it passes the basic checks. assert np.all(converted["start"] == converted[target]) @@ -119,7 +121,7 @@ def test_simple_conversion(self, target): assert len(dropped) == 2 assert list(dropped.index) == [4, 9] # check consistency - _, tmp = enforce_stride_list_consistency(converted, stride_type=target) + _, tmp = enforce_stride_list_consistency(converted, input_stride_type=target) assert len(tmp) == 0 # Check that the length of all strides is still 1 assert np.all((converted["end"] - converted["start"]).round(2) == 1.0) @@ -129,7 +131,9 @@ def test_second_to_last_stride_is_break(self): stride_list = self._create_example_stride_list_with_pause() # Drop the second to last stride to create a pause stride_list = stride_list.drop(8) - converted, dropped = _segmented_stride_list_to_min_vel_single_sensor(stride_list, "min_vel") + converted, dropped = _segmented_stride_list_to_min_vel_single_sensor( + stride_list, source_stride_type="segmented", target_stride_type="min_vel" + ) # Check that the length of all strides is still 1 assert np.all((converted["end"] - converted["start"]).round(2) == 1.0) @@ -148,7 +152,7 @@ def test_simple_conversion_multiple(self, target): # We do not test everything here, but just see if it passes the basic checks. assert np.all(converted["start"] == converted[target]) - _, tmp = enforce_stride_list_consistency(converted, stride_type=target) + _, tmp = enforce_stride_list_consistency(converted, input_stride_type=target) assert len(tmp) == 0 assert_frame_equal(converted, converted_multiple) From d265ab7f909eb7efcb1e4126b7c1f4be690cab61 Mon Sep 17 00:00:00 2001 From: AlzhraaIbrahim Date: Tue, 12 Mar 2024 06:31:29 +0100 Subject: [PATCH 06/15] format & lint --- .../event_detection/_filtered_rampp_event_detection.py | 2 +- .../gaitmap_mad/event_detection/_rampp_event_detection.py | 3 +-- 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/gaitmap_mad/gaitmap_mad/event_detection/_filtered_rampp_event_detection.py b/gaitmap_mad/gaitmap_mad/event_detection/_filtered_rampp_event_detection.py index 31315174..689a6063 100644 --- a/gaitmap_mad/gaitmap_mad/event_detection/_filtered_rampp_event_detection.py +++ b/gaitmap_mad/gaitmap_mad/event_detection/_filtered_rampp_event_detection.py @@ -1,6 +1,6 @@ """The event detection algorithm by Rampp et al. 2014.""" -from typing import Dict, Optional, Tuple, Literal +from typing import Dict, Literal, Optional, Tuple from joblib import Memory from tpcp import cf diff --git a/gaitmap_mad/gaitmap_mad/event_detection/_rampp_event_detection.py b/gaitmap_mad/gaitmap_mad/event_detection/_rampp_event_detection.py index 4789a7cd..d4ee7b03 100644 --- a/gaitmap_mad/gaitmap_mad/event_detection/_rampp_event_detection.py +++ b/gaitmap_mad/gaitmap_mad/event_detection/_rampp_event_detection.py @@ -5,12 +5,11 @@ import numpy as np import pandas as pd from joblib import Memory -from tpcp import cf from typing_extensions import Literal from gaitmap._event_detection_common._event_detection_mixin import _detect_min_vel_gyr_energy, _EventDetectionMixin from gaitmap.base import BaseEventDetection -from gaitmap.data_transform import BaseFilter, ButterworthFilter +from gaitmap.data_transform import BaseFilter class RamppEventDetection(_EventDetectionMixin, BaseEventDetection): From a248739db3e86c9a2c5acbef5c167afe88e968be Mon Sep 17 00:00:00 2001 From: AlzhraaIbrahim Date: Tue, 9 Apr 2024 13:12:46 +0200 Subject: [PATCH 07/15] change detect_ic logic to search between [few samples before start, first part of stance] change detect_tc to return nan if no negative values were found --- .../_event_detection_mixin.py | 4 +- .../_herzer_event_detection.py | 5 +- gaitmap/utils/stride_list_conversion.py | 12 +- .../event_detection/_rampp_event_detection.py | 120 ++++++------------ .../test_utils/test_stride_list_conversion.py | 12 +- 5 files changed, 57 insertions(+), 96 deletions(-) diff --git a/gaitmap/_event_detection_common/_event_detection_mixin.py b/gaitmap/_event_detection_common/_event_detection_mixin.py index eb113b5e..14054f72 100644 --- a/gaitmap/_event_detection_common/_event_detection_mixin.py +++ b/gaitmap/_event_detection_common/_event_detection_mixin.py @@ -22,7 +22,7 @@ ) from gaitmap.utils.exceptions import ValidationError from gaitmap.utils.stride_list_conversion import ( - _segmented_stride_list_to_min_vel_single_sensor, + _stride_list_to_min_vel_single_sensor, enforce_stride_list_consistency, ) @@ -150,7 +150,7 @@ def _detect_single_dataset( return {"segmented_event_list": segmented_event_list} # convert to min_vel event list - min_vel_event_list, _ = _segmented_stride_list_to_min_vel_single_sensor( + min_vel_event_list, _ = _stride_list_to_min_vel_single_sensor( segmented_event_list, source_stride_type=self.input_stride_type, target_stride_type="min_vel" ) diff --git a/gaitmap/event_detection/_herzer_event_detection.py b/gaitmap/event_detection/_herzer_event_detection.py index 81b165d3..c5de5a71 100644 --- a/gaitmap/event_detection/_herzer_event_detection.py +++ b/gaitmap/event_detection/_herzer_event_detection.py @@ -134,6 +134,9 @@ class HerzerEventDetection(_EventDetectionMixin, BaseEventDetection): The window size can be adjusted via the `min_vel_search_win_size_ms` parameter. This approach is identical to [1]_. + The :func:`~gaitmap.event_detection.HerzerEventDetection.detect` method is implemented only for "segmented" stride + type + The :func:`~gaitmap.event_detection.HerzerEventDetection.detect` method provides a stride list `min_vel_event_list` with the gait events mentioned above and additionally `start` and `end` of each stride, which are aligned to the `min_vel` samples. @@ -247,7 +250,7 @@ def _find_all_events( ) -> Tuple[Optional[np.ndarray], Optional[np.ndarray], Optional[np.ndarray]]: """Find events in provided data by looping over single strides.""" if input_stride_type != "segmented": - raise NotImplementedError() + raise NotImplementedError("This method support only segmented stride type") gyr_ml = gyr["gyr_ml"].to_numpy() gyr = gyr.to_numpy() # inverting acc, as this algorithm was developed assuming a flipped axis like the original Rampp algorithm diff --git a/gaitmap/utils/stride_list_conversion.py b/gaitmap/utils/stride_list_conversion.py index 0e4bc01c..74f6f4c4 100644 --- a/gaitmap/utils/stride_list_conversion.py +++ b/gaitmap/utils/stride_list_conversion.py @@ -18,12 +18,12 @@ ) -def convert_segmented_stride_list( +def convert_stride_list( stride_list: StrideList, target_stride_type: Literal["min_vel", "ic"], source_stride_type: Literal["segmented", "ic"] = "segmented", ) -> StrideList: - """Convert a segmented stride list with detected events into other types of stride lists. + """Convert a stride list with detected events into other types of stride lists. During the conversion some strides might be removed. For more information about the different types of stride lists see the :ref:`stride list guide `. @@ -45,23 +45,23 @@ def convert_segmented_stride_list( """ stride_list_type = is_stride_list(stride_list, stride_type="segmented") if stride_list_type == "single": - return _segmented_stride_list_to_min_vel_single_sensor( + return _stride_list_to_min_vel_single_sensor( stride_list, target_stride_type=target_stride_type, source_stride_type=source_stride_type )[0] return { - k: _segmented_stride_list_to_min_vel_single_sensor( + k: _stride_list_to_min_vel_single_sensor( v, target_stride_type=target_stride_type, source_stride_type=source_stride_type )[0] for k, v in stride_list.items() } -def _segmented_stride_list_to_min_vel_single_sensor( +def _stride_list_to_min_vel_single_sensor( stride_list: SingleSensorStrideList, target_stride_type: Literal["min_vel", "ic"], source_stride_type: Literal["segmented", "ic"], ) -> Tuple[SingleSensorStrideList, SingleSensorStrideList]: - """Convert a segmented stride list with detected events into other types of stride lists. + """Convert a stride list with detected events into other types of stride lists. During the conversion some strides might be removed. For more information about the different types of stride lists see the :ref:`stride list guide `. diff --git a/gaitmap_mad/gaitmap_mad/event_detection/_rampp_event_detection.py b/gaitmap_mad/gaitmap_mad/event_detection/_rampp_event_detection.py index d4ee7b03..5f5fe1fd 100644 --- a/gaitmap_mad/gaitmap_mad/event_detection/_rampp_event_detection.py +++ b/gaitmap_mad/gaitmap_mad/event_detection/_rampp_event_detection.py @@ -23,7 +23,7 @@ class RamppEventDetection(_EventDetectionMixin, BaseEventDetection): ---------- ic_search_region_ms The region to look for the initial contact in the acc_pa signal in ms given an ic candidate. According to [1]_, - for the ic the algorithm first looks for a local minimum in the gyr_ml signal after the swing phase. The actual + for the ic the algorithm first looks for a local minimum in the gyr_ml signal. The actual ic is then determined in the acc_pa signal in the ic_search_region_ms around that gyr_ml minimum. ic_search_region_ms[0] describes the start and ic_search_region_ms[1] the end of the region to check around the gyr_ml minimum. The values of `ic_search_region_ms` must be greater or equal than the sample time @@ -51,7 +51,7 @@ class RamppEventDetection(_EventDetectionMixin, BaseEventDetection): Hence, the start sample of each stride corresponds to the min_vel sample of that stride and the end sample corresponds to the min_vel sample of the subsequent stride. Strides for which no valid events could be found are removed. - Additional strides might have been removed due to the conversion from segmented to min_vel strides. + Additional strides might have been removed due to the conversion from segmented or ic to min_vel strides. The 's_id' index is selected according to which segmented stride the pre-ic belongs to. segmented_event_list_ : A stride list or dictionary with such values @@ -99,7 +99,8 @@ class RamppEventDetection(_EventDetectionMixin, BaseEventDetection): initial contact (`ic`), originally called heel strike (HS) in the paper [1]_: At `ic` the foot decelerates rapidly when the foot hits the ground. For the detection of `ic` only the signal between the absolute maximum and the end of the first half of the - gyr_ml signal is considered. + gyr_ml signal is considered in the case of "segmented" input stride type whereas the window between + [few samples before stride start,.2 of stance phase] is considered in case of "ic" input stride type. Within this segment, `ic` is found by searching for the minimum between the point of the steepest negative slope and the point of the steepest positive slope in the following signal. After that the acc_pa signal is searched for a maximum in the area before and after the described minimum in @@ -125,7 +126,9 @@ class RamppEventDetection(_EventDetectionMixin, BaseEventDetection): the stride list. The :class:`~gaitmap.event_detection.RamppEventDetection` includes a consistency check that is enabled by default. - The gait events within one stride provided by the `stride_list` must occur in the expected order. + The gait events within one stride provided by the `stride_list` must occur in the expected order. For example, + in case of "segmented" input stride type, the expected order is ["tc", "ic", "min_vel"] whereas the expected order + for "ic" input stride type is ["ic", "min_vel", "tc"] Any stride where the gait events are detected in a different order or are not detected at all is dropped! For more infos on this see :func:`~gaitmap.utils.stride_list_conversion.enforce_stride_list_consistency`. If you wish to disable this consistency check, set `enforce_consistency` to False. @@ -133,13 +136,13 @@ class RamppEventDetection(_EventDetectionMixin, BaseEventDetection): all detected events for the exact stride list that was used as input. Note, that this list might contain NaN for some events. - Furthermore, during the conversion from the segmented stride list to the "min_vel" stride list, breaks in + Furthermore, during the conversion from the stride list to the "min_vel" stride list, breaks in continuous gait sequences ( with continuous subsequent strides according to the `stride_list`) are detected and the first (segmented) stride of each sequence is dropped. This is required due to the shift of stride borders between the `stride_list` and the `min_vel_event_list`. Thus, the first segmented stride of a continuous sequence only provides a pre_ic and a min_vel sample for the first stride in the `min_vel_event_list`. - Therefore, the `min_vel_event_list` list has one stride less per gait sequence than the `segmented_stride_list`. + Therefore, the `min_vel_event_list` list has one stride less per gait sequence than the `stride_list`. Further information regarding the coordinate system can be found :ref:`here` and regarding the different types of strides can be found :ref:`here`. @@ -315,51 +318,27 @@ def _find_all_events_for_ic_stride( tc_events = [] min_vel_events = [] _is_initial_stride = False - for index, stride in stride_list.iterrows(): - current_stride_start = stride["start"] - current_stride_end = stride["end"] + for _index, stride in stride_list.iterrows(): + start = stride["start"] + end = stride["end"] if "ic" in events: - if index == 0: - _is_initial_stride = True - # in the first stride, we search only the current stride - previous_stride_start = stride["start"] - previous_stride_end = stride["end"] - else: - previous_stride_start = stride_list.loc[index - 1]["start"] - previous_stride_end = stride_list.loc[index - 1]["end"] - # to get the ic event we use a search window between the peak of previous stride and part of the current - # stride get data of current stride - gyr_ml_filtered_sec_current_stride = gyr_ml_filtered[current_stride_start:current_stride_end] - acc_sec_current_stride = acc_pa[current_stride_start:current_stride_end] - # get data of previous stride - gyr_ml_filtered_sec_previous_stride = gyr_ml_filtered[previous_stride_start:previous_stride_end] - acc_sec_previous_stride = acc_pa[previous_stride_start:previous_stride_end] - + # to get the ic event we use a search window between the start of the stride and part of it ic_events.append( - current_stride_start - + _detect_ic_for_ic_stride( - gyr_ml_filtered_sec_previous_stride, - gyr_ml_filtered_sec_current_stride, - acc_sec_previous_stride, - acc_sec_current_stride, + _detect_ic_for_ic_stride( + start, + end, + gyr_ml_filtered, + acc_pa, + np.gradient(gyr_ml), ic_search_region, - _is_initial_stride, ) ) - _is_initial_stride = False - if "tc" in events: # use the filtered signal here because for some patients there is a lot of noise around ic - tc_events.append( - current_stride_start - + _detect_tc_for_ic_stride(gyr_ml_filtered[current_stride_start:current_stride_end]) - ) + tc_events.append(start + _detect_tc_for_ic_stride(gyr_ml_filtered[start:end])) if "min_vel" in events: - min_vel_events.append( - current_stride_start - + _detect_min_vel_gyr_energy(gyr[current_stride_start:current_stride_end], min_vel_search_win_size) - ) + min_vel_events.append(start + _detect_min_vel_gyr_energy(gyr[start:end], min_vel_search_win_size)) return ( np.array(ic_events, dtype=float) if ic_events else None, @@ -369,12 +348,12 @@ def _find_all_events_for_ic_stride( def _detect_ic_for_ic_stride( - gyr_ml_previous_stride: np.ndarray, - gyr_ml_current_stride: np.ndarray, - acc_pa_inv_previous_stride: np.ndarray, - acc_pa_inv_current_stride: np.ndarray, + start, + end, + gyr_ml: np.ndarray, + acc_pa_inv: np.ndarray, + gyr_ml_grad: np.ndarray, ic_search_region: Tuple[float, float], - is_initial_stride: bool, ) -> float: """Find the ic. @@ -385,26 +364,17 @@ def _detect_ic_for_ic_stride( """ # Determine rough search region - # Determine rough search region as the region between the peak of the previous stride and 0.2 of the stance phase + # Determine rough search region as the region between few samples before the start of the stride + # and 0.2 of the stance phase # of the current stride - if is_initial_stride: - search_region = 0, int(0.2 * gyr_ml_current_stride.shape[0]) - else: - peak = _find_previous_stride_positive_peak(gyr_ml_previous_stride) - search_region = peak, int(gyr_ml_previous_stride.shape[0] + 0.1 * gyr_ml_current_stride.shape[0]) + gyr_ml_sec = gyr_ml[start:end] + search_region = start - ic_search_region[0], start + int(0.2 * gyr_ml_sec.shape[0]) if search_region[1] - search_region[0] < 0: - # The gyr argmax was not found return np.nan - # alternative: - # concatenate the two strides - gyr_ml_concatenated = np.concatenate((gyr_ml_previous_stride, gyr_ml_current_stride)) - gyr_ml_concatenated_grad = np.gradient(gyr_ml_concatenated) - - refined_search_region_start = int(search_region[0] + np.argmin(gyr_ml_concatenated_grad[slice(*search_region)])) + refined_search_region_start = int(search_region[0] + np.argmin(gyr_ml_grad[slice(*search_region)])) refined_search_region_end = int( - refined_search_region_start - + np.argmax(gyr_ml_concatenated_grad[refined_search_region_start : search_region[1]]) + refined_search_region_start + np.argmax(gyr_ml_grad[refined_search_region_start : search_region[1]]) ) if refined_search_region_end - refined_search_region_start < 0: @@ -415,23 +385,14 @@ def _detect_ic_for_ic_stride( heel_strike_candidate = refined_search_region_start else: heel_strike_candidate = refined_search_region_start + np.argmin( - gyr_ml_concatenated[refined_search_region_start:refined_search_region_end] + gyr_ml[refined_search_region_start:refined_search_region_end] ) - # Acc search window - acc_pa_inv_concatenated = np.concatenate((acc_pa_inv_previous_stride, acc_pa_inv_current_stride)) acc_search_region_start = int(np.max(np.array([0, heel_strike_candidate - ic_search_region[0]]))) acc_search_region_end = int( - np.min(np.array([acc_pa_inv_concatenated.shape[0], heel_strike_candidate + ic_search_region[1]])) - ) - if is_initial_stride: - return float(np.argmin(acc_pa_inv_current_stride[acc_search_region_start:acc_search_region_end])) - - return float( - acc_search_region_start - + np.argmin(acc_pa_inv_concatenated[acc_search_region_start:acc_search_region_end]) - - acc_pa_inv_previous_stride.shape[0] + np.min(np.array([start + gyr_ml_sec.shape[0], heel_strike_candidate + ic_search_region[1]])) ) + return float(acc_search_region_start + np.argmin(acc_pa_inv[acc_search_region_start:acc_search_region_end])) def _detect_tc_for_ic_stride(gyr_ml: np.ndarray) -> float: @@ -493,14 +454,11 @@ def _find_first_negative_before_peak(arr): find the peak of the signal and then search for the first negative value before the peak """ peak_index = np.argmax(arr) # Find the index of the largest positive number (peak) + negative_index = np.where(arr[:peak_index] < 0)[0] + if len(negative_index) > 0: + return negative_index[-1] # Return the index of the last negative number before the peak - # Find the index of the first negative number before the peak (where the sign of the signal change) - i = peak_index - for i in range(peak_index - 1, -1, -1): - if arr[i] < 0: - return i # Return the index of the smallest positive number before the peak - - return i + return np.nan def _find_previous_stride_positive_peak(gyr_ml_previous_stride): diff --git a/tests/test_utils/test_stride_list_conversion.py b/tests/test_utils/test_stride_list_conversion.py index 58d76efc..a70aa461 100644 --- a/tests/test_utils/test_stride_list_conversion.py +++ b/tests/test_utils/test_stride_list_conversion.py @@ -6,8 +6,8 @@ from gaitmap.utils.consts import SL_EVENT_ORDER from gaitmap.utils.exceptions import ValidationError from gaitmap.utils.stride_list_conversion import ( - _segmented_stride_list_to_min_vel_single_sensor, - convert_segmented_stride_list, + _stride_list_to_min_vel_single_sensor, + convert_stride_list, enforce_stride_list_consistency, intersect_stride_list, ) @@ -111,7 +111,7 @@ def _create_example_stride_list_with_pause(self): def test_simple_conversion(self, target): stride_list = self._create_example_stride_list_with_pause() - converted, dropped = _segmented_stride_list_to_min_vel_single_sensor( + converted, dropped = _stride_list_to_min_vel_single_sensor( stride_list, source_stride_type="segmented", target_stride_type=target ) @@ -131,7 +131,7 @@ def test_second_to_last_stride_is_break(self): stride_list = self._create_example_stride_list_with_pause() # Drop the second to last stride to create a pause stride_list = stride_list.drop(8) - converted, dropped = _segmented_stride_list_to_min_vel_single_sensor( + converted, dropped = _stride_list_to_min_vel_single_sensor( stride_list, source_stride_type="segmented", target_stride_type="min_vel" ) @@ -144,10 +144,10 @@ def test_second_to_last_stride_is_break(self): @pytest.mark.parametrize("target", ("ic", "min_vel")) def test_simple_conversion_multiple(self, target): stride_list = self._create_example_stride_list_with_pause() - converted = convert_segmented_stride_list(stride_list, target_stride_type=target) + converted = convert_stride_list(stride_list, target_stride_type=target) stride_list = {"s1": stride_list} - converted_multiple = convert_segmented_stride_list(stride_list, target_stride_type=target) + converted_multiple = convert_stride_list(stride_list, target_stride_type=target) converted_multiple = converted_multiple["s1"] # We do not test everything here, but just see if it passes the basic checks. From debfe2dae650ea16c2fc716981c989fb381daa46 Mon Sep 17 00:00:00 2001 From: AlzhraaIbrahim Date: Fri, 12 Apr 2024 10:05:51 +0200 Subject: [PATCH 08/15] lint --- .../event_detection/_filtered_rampp_event_detection.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/gaitmap_mad/gaitmap_mad/event_detection/_filtered_rampp_event_detection.py b/gaitmap_mad/gaitmap_mad/event_detection/_filtered_rampp_event_detection.py index 689a6063..722ecb97 100644 --- a/gaitmap_mad/gaitmap_mad/event_detection/_filtered_rampp_event_detection.py +++ b/gaitmap_mad/gaitmap_mad/event_detection/_filtered_rampp_event_detection.py @@ -1,9 +1,10 @@ """The event detection algorithm by Rampp et al. 2014.""" -from typing import Dict, Literal, Optional, Tuple +from typing import Dict, Optional, Tuple from joblib import Memory from tpcp import cf +from typing_extensions import Literal from gaitmap.data_transform import BaseFilter, ButterworthFilter from gaitmap_mad.event_detection._rampp_event_detection import RamppEventDetection From d90bcc8e5caa74723e815c8001b7d06387c4201a Mon Sep 17 00:00:00 2001 From: AlzhraaIbrahim Date: Sat, 13 Apr 2024 00:01:56 +0200 Subject: [PATCH 09/15] add tests for event_detection, stride_list_conversion --- gaitmap/example_data.py | 31 +++ .../event_detection/_rampp_event_detection.py | 2 +- tests/conftest.py | 4 + .../test_event_detection_rampp.py | 231 +++++++++++++----- .../test_utils/test_stride_list_conversion.py | 69 +++++- 5 files changed, 271 insertions(+), 66 deletions(-) diff --git a/gaitmap/example_data.py b/gaitmap/example_data.py index 891f75ea..11fe81bc 100644 --- a/gaitmap/example_data.py +++ b/gaitmap/example_data.py @@ -69,6 +69,19 @@ def get_healthy_example_imu_data(): return data +def get_healthy_example_imu_data_ic_stride(): + """Get example IMU data from a healthy subject doing a 4x10 gait test. + + The sampling rate is 102.4 Hz + """ + test_data_path = _get_data("imu_sample_ic_stride.csv") + data = pd.read_csv(test_data_path, header=[0, 1], index_col=0) + + # Get index in seconds + data.index /= 102.4 + return data + + def get_ms_example_imu_data(): """Get example IMU data from a MS subject performing a longer uninterrupted walking sequence. @@ -116,6 +129,24 @@ def get_healthy_example_stride_borders(): return data +def get_healthy_example_stride_borders_ic_stride(): + """Get hand labeled stride borders for :func:`get_healthy_example_imu_data_ic_stride`. + + The stride borders are obtained from mocap where each stride starts with initial contact. + """ + test_data_path = _get_data("stride_borders_sample_ic_stride.csv") + data = pd.read_csv(test_data_path, header=0) + + # Convert to dict with sensor name as key. + # Sensor name here is derived from the foot. In the real pipeline that would be provided to the algo. + data["sensor"] = data["foot"] + "_sensor" + data = data.set_index("sensor") + data = data.groupby(level=0) + data = {k: v.reset_index(drop=True) for k, v in data} + + return data + + def get_healthy_example_mocap_data(): """Get 3D Mocap information of the foot synchronised with :func:`get_healthy_example_imu_data`. diff --git a/gaitmap_mad/gaitmap_mad/event_detection/_rampp_event_detection.py b/gaitmap_mad/gaitmap_mad/event_detection/_rampp_event_detection.py index 5f5fe1fd..68981fcb 100644 --- a/gaitmap_mad/gaitmap_mad/event_detection/_rampp_event_detection.py +++ b/gaitmap_mad/gaitmap_mad/event_detection/_rampp_event_detection.py @@ -303,7 +303,7 @@ def _find_all_events_for_ic_stride( ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]: """Find events in provided data by looping over single strides.""" gyr_ml = gyr["gyr_ml"] - if "ic" in events: + if "ic" in events or "tc" in events: if gyr_ic_lowpass_filter is not None: gyr_ml_filtered = gyr_ic_lowpass_filter.filter( gyr_ml, sampling_rate_hz=sampling_rate_hz diff --git a/tests/conftest.py b/tests/conftest.py index 2e423046..1d3a78db 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -11,10 +11,12 @@ from gaitmap.example_data import ( get_healthy_example_imu_data, + get_healthy_example_imu_data_ic_stride, get_healthy_example_mocap_data, get_healthy_example_orientation, get_healthy_example_position, get_healthy_example_stride_borders, + get_healthy_example_stride_borders_ic_stride, get_healthy_example_stride_events, get_ms_example_imu_data, ) @@ -47,8 +49,10 @@ def pytest_addoption(parser): healthy_example_imu_data = pytest.fixture()(get_healthy_example_imu_data) +healthy_example_imu_data_ic_stride = pytest.fixture()(get_healthy_example_imu_data_ic_stride) ms_example_imu_data = pytest.fixture()(get_ms_example_imu_data) healthy_example_stride_borders = pytest.fixture()(get_healthy_example_stride_borders) +healthy_example_stride_borders_ic_stride = pytest.fixture()(get_healthy_example_stride_borders_ic_stride) healthy_example_mocap_data = pytest.fixture()(get_healthy_example_mocap_data) healthy_example_stride_events = pytest.fixture()(get_healthy_example_stride_events) healthy_example_orientation = pytest.fixture()(get_healthy_example_orientation) diff --git a/tests/test_event_detection/test_event_detection_rampp.py b/tests/test_event_detection/test_event_detection_rampp.py index cee84654..cb9acccc 100644 --- a/tests/test_event_detection/test_event_detection_rampp.py +++ b/tests/test_event_detection/test_event_detection_rampp.py @@ -13,22 +13,29 @@ from gaitmap.utils import coordinate_conversion, datatype_helper from gaitmap.utils.consts import BF_COLS from gaitmap.utils.exceptions import ValidationError -from gaitmap_mad.event_detection._rampp_event_detection import _detect_tc_for_segmented_stride +from gaitmap_mad.event_detection._rampp_event_detection import _detect_tc_for_segmented_stride, _detect_tc_for_ic_stride from tests.mixins.test_algorithm_mixin import TestAlgorithmMixin from tests.mixins.test_caching_mixin import TestCachingMixin +common_arguments =pytest.mark.parametrize( + ("input_stride_type", "imu_data", "stride_borders", "sampling_rate"), + [ + ("segmented", "healthy_example_imu_data", "healthy_example_stride_borders", 204.8), + ("ic", "healthy_example_imu_data_ic_stride", "healthy_example_stride_borders_ic_stride", 102.4), + ], + ) class MetaTestConfig: algorithm_class = RamppEventDetection - @pytest.fixture() - def after_action_instance(self, healthy_example_imu_data, healthy_example_stride_borders) -> BaseType: - data_left = healthy_example_imu_data["left_sensor"] + @common_arguments + def after_action_instance(self, input_stride_type, imu_data, stride_borders, sampling_rate, request) -> BaseType: + data_left = request.getfixturevalue(imu_data)["left_sensor"] data_left.columns = BF_COLS # only use the first entry of the stride list - stride_list_left = healthy_example_stride_borders["left_sensor"].iloc[0:1] - ed = RamppEventDetection() - ed.detect(data_left, stride_list_left, sampling_rate_hz=204.8) + stride_list_left = request.getfixturevalue(stride_borders)["left_sensor"].iloc[0:1] + ed = RamppEventDetection(input_stride_type=input_stride_type) + ed.detect(data_left, stride_list_left, sampling_rate_hz=sampling_rate) return ed @@ -45,14 +52,15 @@ class TestEventDetectionRampp: algorithm_class = RamppEventDetection - def test_multi_sensor_input(self, healthy_example_imu_data, healthy_example_stride_borders, snapshot): + @common_arguments + def test_multi_sensor_input(self, input_stride_type, imu_data, stride_borders, sampling_rate, snapshot, request): """Dummy test to see if the algorithm is generally working on the example data.""" data = coordinate_conversion.convert_to_fbf( - healthy_example_imu_data, left=["left_sensor"], right=["right_sensor"] + request.getfixturevalue(imu_data), left=["left_sensor"], right=["right_sensor"] ) - ed = self.algorithm_class() - ed.detect(data, healthy_example_stride_borders, sampling_rate_hz=204.8) + ed = self.algorithm_class(input_stride_type=input_stride_type) + ed.detect(data, request.getfixturevalue(stride_borders), sampling_rate_hz=sampling_rate) snapshot.assert_match(ed.min_vel_event_list_["left_sensor"], "left", check_dtype=False) snapshot.assert_match(ed.min_vel_event_list_["right_sensor"], "right", check_dtype=False) @@ -60,7 +68,10 @@ def test_multi_sensor_input(self, healthy_example_imu_data, healthy_example_stri snapshot.assert_match(ed.segmented_event_list_["right_sensor"], "right_segmented", check_dtype=False) @pytest.mark.parametrize(("var1", "output"), ((True, 2), (False, 0))) - def test_postprocessing(self, healthy_example_imu_data, healthy_example_stride_borders, var1, output): + def test_postprocessing_segmented_stride( + self, healthy_example_imu_data, healthy_example_stride_borders, var1, output + ): + """Test postprocessing for "segmented" input_stride_type.""" data_left = healthy_example_imu_data["left_sensor"] data_left.columns = BF_COLS # only use the first entry of the stride list @@ -78,10 +89,33 @@ def mock_func(event_list, *args, **kwargs): assert mock.call_count == output + @pytest.mark.parametrize(("var1", "output"), ((True, 2), (False, 0))) + def test_postprocessing_ic_stride( + self, healthy_example_imu_data_ic_stride, healthy_example_stride_borders_ic_stride, var1, output + ): + """Test postprocessing for "ic" input_stride_type.""" + data_left = healthy_example_imu_data_ic_stride["left_sensor"] + data_left.columns = BF_COLS + # only use the first entry of the stride list + stride_list_left = healthy_example_stride_borders_ic_stride["left_sensor"].iloc[0:1] + + def mock_func(event_list, *args, **kwargs): + return event_list, None + + ed = self.algorithm_class(input_stride_type="ic", enforce_consistency=var1) + with patch( + "gaitmap._event_detection_common._event_detection_mixin.enforce_stride_list_consistency", + side_effect=mock_func, + ) as mock: + ed.detect(data_left, stride_list_left, sampling_rate_hz=102.4) + + assert mock.call_count == output + @pytest.mark.parametrize(("enforce_consistency", "output"), ((False, False), (True, True))) - def test_disable_min_vel_event_list( + def test_disable_min_vel_event_list_segmented_stride( self, healthy_example_imu_data, healthy_example_stride_borders, enforce_consistency, output ): + """Test for "segmented" input_stride_type.""" data_left = healthy_example_imu_data["left_sensor"] data_left.columns = BF_COLS # only use the first entry of the stride list @@ -92,53 +126,77 @@ def test_disable_min_vel_event_list( assert hasattr(ed, "min_vel_event_list_") == output - def test_multi_sensor_input_dict(self, healthy_example_imu_data, healthy_example_stride_borders): + @pytest.mark.parametrize(("enforce_consistency", "output"), ((False, False), (True, True))) + def test_disable_min_vel_event_list_ic_stride( + self, healthy_example_imu_data_ic_stride, healthy_example_stride_borders_ic_stride, enforce_consistency, output + ): + """Test for "ic" input_stride_type.""" + data_left = healthy_example_imu_data_ic_stride["left_sensor"] + data_left.columns = BF_COLS + # only use the first entry of the stride list + stride_list_left = healthy_example_stride_borders_ic_stride["left_sensor"].iloc[0:1] + + ed = self.algorithm_class(input_stride_type="ic", enforce_consistency=enforce_consistency) + ed.detect(data_left, stride_list_left, sampling_rate_hz=102.4) + + assert hasattr(ed, "min_vel_event_list_") == output + + @common_arguments + def test_multi_sensor_input_dict(self, input_stride_type, imu_data, stride_borders, sampling_rate, request): """Test to see if the algorithm is generally working on the example data when provided as dict.""" data = coordinate_conversion.convert_to_fbf( - healthy_example_imu_data, left=["left_sensor"], right=["right_sensor"] + request.getfixturevalue(imu_data), left=["left_sensor"], right=["right_sensor"] ) dict_keys = ["l", "r"] data_dict = {dict_keys[0]: data["left_sensor"], dict_keys[1]: data["right_sensor"]} stride_list_dict = { - dict_keys[0]: healthy_example_stride_borders["left_sensor"], - dict_keys[1]: healthy_example_stride_borders["right_sensor"], + dict_keys[0]: request.getfixturevalue(stride_borders)["left_sensor"], + dict_keys[1]: request.getfixturevalue(stride_borders)["right_sensor"], } - ed = self.algorithm_class() - ed.detect(data_dict, stride_list_dict, sampling_rate_hz=204.8) + ed = self.algorithm_class(input_stride_type=input_stride_type) + ed.detect(data_dict, stride_list_dict, sampling_rate_hz=sampling_rate) assert list(datatype_helper.get_multi_sensor_names(ed.min_vel_event_list_)) == dict_keys assert list(datatype_helper.get_multi_sensor_names(ed.segmented_event_list_)) == dict_keys - def test_equal_output_dict_df(self, healthy_example_imu_data, healthy_example_stride_borders): + @common_arguments + def test_equal_output_dict_df(self, input_stride_type, imu_data, stride_borders, sampling_rate, request): """Test if output is similar for input dicts or regular multisensor data sets.""" data = coordinate_conversion.convert_to_fbf( - healthy_example_imu_data, left=["left_sensor"], right=["right_sensor"] + request.getfixturevalue(imu_data), left=["left_sensor"], right=["right_sensor"] ) - ed_df = self.algorithm_class() - ed_df.detect(data, healthy_example_stride_borders, sampling_rate_hz=204.8) + ed_df = self.algorithm_class(input_stride_type=input_stride_type) + ed_df.detect(data, request.getfixturevalue(stride_borders), sampling_rate_hz=sampling_rate) dict_keys = ["l", "r"] data_dict = {dict_keys[0]: data["left_sensor"], dict_keys[1]: data["right_sensor"]} stride_list_dict = { - dict_keys[0]: healthy_example_stride_borders["left_sensor"], - dict_keys[1]: healthy_example_stride_borders["right_sensor"], + dict_keys[0]: request.getfixturevalue(stride_borders)["left_sensor"], + dict_keys[1]: request.getfixturevalue(stride_borders)["right_sensor"], } - ed_dict = self.algorithm_class() - ed_dict.detect(data_dict, stride_list_dict, sampling_rate_hz=204.8) + ed_dict = self.algorithm_class(input_stride_type=input_stride_type) + ed_dict.detect(data_dict, stride_list_dict, sampling_rate_hz=sampling_rate) assert_frame_equal(ed_df.min_vel_event_list_["left_sensor"], ed_dict.min_vel_event_list_["l"]) assert_frame_equal(ed_df.min_vel_event_list_["right_sensor"], ed_dict.min_vel_event_list_["r"]) - def test_valid_input_data(self, healthy_example_stride_borders): + @pytest.mark.parametrize( + ("input_stride_type", "stride_borders", "sampling_rate"), + [ + ("segmented", "healthy_example_stride_borders", 204.8), + ("ic", "healthy_example_stride_borders_ic_stride", 102.4), + ], + ) + def test_valid_input_data(self, input_stride_type, stride_borders, sampling_rate, request): """Test if error is raised correctly on invalid input data type.""" data = pd.DataFrame({"a": [0, 1, 2], "b": [3, 4, 5]}) - ed = self.algorithm_class() + ed = self.algorithm_class(input_stride_type=input_stride_type) with pytest.raises(ValidationError) as e: - ed.detect(data, healthy_example_stride_borders, sampling_rate_hz=204.8) + ed.detect(data, request.getfixturevalue(stride_borders), sampling_rate_hz=sampling_rate) assert "The passed object appears to be neither single- or multi-sensor data" in str(e) @@ -148,46 +206,52 @@ def test_min_vel_search_win_size_ms_dummy_data(self): with pytest.raises(ValueError, match=r"min_vel_search_win_size_ms is*"): _detect_min_vel_gyr_energy(dummy_gyr, dummy_gyr.size) - def test_valid_min_vel_search_win_size_ms(self, healthy_example_imu_data, healthy_example_stride_borders): + @common_arguments + def test_valid_min_vel_search_win_size_ms( + self, input_stride_type, imu_data, stride_borders, sampling_rate, request + ): """Test if error is raised correctly on too large min_vel_search_win_size_ms.""" - data_left = healthy_example_imu_data["left_sensor"] + data_left = request.getfixturevalue(imu_data)["left_sensor"] data_left = coordinate_conversion.convert_left_foot_to_fbf(data_left) - stride_list_left = healthy_example_stride_borders["left_sensor"] - ed = self.algorithm_class(min_vel_search_win_size_ms=5000) + stride_list_left = request.getfixturevalue(stride_borders)["left_sensor"] + ed = self.algorithm_class(input_stride_type=input_stride_type, min_vel_search_win_size_ms=5000) with pytest.raises(ValueError, match=r"min_vel_search_win_size_ms is *"): - ed.detect(data_left, stride_list_left, sampling_rate_hz=204.8) + ed.detect(data_left, stride_list_left, sampling_rate_hz=sampling_rate) - def test_valid_ic_search_region_ms(self, healthy_example_imu_data, healthy_example_stride_borders): + @common_arguments + def test_valid_ic_search_region_ms(self, input_stride_type, imu_data, stride_borders, sampling_rate, request): """Test if error is raised correctly on too small ic_search_region_ms.""" - data_left = healthy_example_imu_data["left_sensor"] + data_left = request.getfixturevalue(imu_data)["left_sensor"] data_left = coordinate_conversion.convert_left_foot_to_fbf(data_left) - stride_list_left = healthy_example_stride_borders["left_sensor"] - ed = self.algorithm_class(ic_search_region_ms=(1, 1)) + stride_list_left = request.getfixturevalue(stride_borders)["left_sensor"] + ed = self.algorithm_class(input_stride_type=input_stride_type, ic_search_region_ms=(1, 1)) with pytest.raises(ValueError): - ed.detect(data_left, stride_list_left, sampling_rate_hz=204.8) + ed.detect(data_left, stride_list_left, sampling_rate_hz=sampling_rate) - def test_input_stride_list_size_one(self, healthy_example_imu_data, healthy_example_stride_borders): + @common_arguments + def test_input_stride_list_size_one(self, input_stride_type, imu_data, stride_borders, sampling_rate, request): """Test if gait event detection also works with stride list of length 1.""" - data_left = healthy_example_imu_data["left_sensor"] + data_left = request.getfixturevalue(imu_data)["left_sensor"] data_left = coordinate_conversion.convert_left_foot_to_fbf(data_left) # only use the first entry of the stride list - stride_list_left = healthy_example_stride_borders["left_sensor"].iloc[0:1] - ed = self.algorithm_class() - ed.detect(data_left, stride_list_left, sampling_rate_hz=204.8) + stride_list_left = request.getfixturevalue(stride_borders)["left_sensor"].iloc[0:1] + ed = self.algorithm_class(input_stride_type=input_stride_type) + ed.detect(data_left, stride_list_left, sampling_rate_hz=sampling_rate) # per default min_vel_event_list_ has 6 columns assert_array_equal(np.array(ed.min_vel_event_list_.shape[1]), 6) # per default segmented_event_list_ has 5 columns assert_array_equal(np.array(ed.segmented_event_list_.shape[1]), 5) - def test_correct_s_id(self, healthy_example_imu_data, healthy_example_stride_borders): + @common_arguments + def test_correct_s_id(self, input_stride_type, imu_data, stride_borders, sampling_rate, request): """Test if the s_id from the stride list is correctly transferred to the output of event detection.""" - data_left = healthy_example_imu_data["left_sensor"] + data_left = request.getfixturevalue(imu_data)["left_sensor"] data_left = coordinate_conversion.convert_left_foot_to_fbf(data_left) - stride_list_left = healthy_example_stride_borders["left_sensor"] + stride_list_left = request.getfixturevalue(stride_borders)["left_sensor"] # switch s_ids in stride list to random numbers stride_list_left["s_id"] = random.sample(range(1000), stride_list_left["s_id"].size) - ed = self.algorithm_class() - ed.detect(data_left, stride_list_left, sampling_rate_hz=204.8) + ed = self.algorithm_class(input_stride_type=input_stride_type) + ed.detect(data_left, stride_list_left, sampling_rate_hz=sampling_rate) # Check that all of the old stride ids are still in the new one assert np.all(ed.min_vel_event_list_.index.isin(stride_list_left["s_id"])) @@ -201,26 +265,28 @@ def test_correct_s_id(self, healthy_example_imu_data, healthy_example_stride_bor assert np.all(combined["start_x"] == combined["start_y"]) assert np.all(combined["end_x"] == combined["end_y"]) - def test_single_data_multi_stride_list(self, healthy_example_imu_data, healthy_example_stride_borders): + @common_arguments + def test_single_data_multi_stride_list(self, input_stride_type, imu_data, stride_borders, sampling_rate, request): """Test correct error for combination of single sensor data set and multi sensor stride list.""" - data_left = healthy_example_imu_data["left_sensor"] + data_left = request.getfixturevalue(imu_data)["left_sensor"] data_left = coordinate_conversion.convert_left_foot_to_fbf(data_left) - stride_list_left = healthy_example_stride_borders - ed = self.algorithm_class() + stride_list_left = request.getfixturevalue(stride_borders) + ed = self.algorithm_class(input_stride_type=input_stride_type) with pytest.raises(ValidationError): - ed.detect(data_left, stride_list_left, sampling_rate_hz=204.8) + ed.detect(data_left, stride_list_left, sampling_rate_hz=sampling_rate) - def test_multi_data_single_stride_list(self, healthy_example_imu_data, healthy_example_stride_borders): + @common_arguments + def test_multi_data_single_stride_list(self, input_stride_type, imu_data, stride_borders, sampling_rate, request): """Test correct error for combination of multi sensor data set and single sensor stride list.""" - data_left = healthy_example_imu_data["left_sensor"] + data_left = request.getfixturevalue(imu_data)["left_sensor"] data_left = coordinate_conversion.convert_left_foot_to_fbf(data_left) - stride_list_left = healthy_example_stride_borders - ed = self.algorithm_class() + stride_list_left = request.getfixturevalue(stride_borders) + ed = self.algorithm_class(input_stride_type=input_stride_type) with pytest.raises(ValidationError): - ed.detect(data_left, stride_list_left, sampling_rate_hz=204.8) + ed.detect(data_left, stride_list_left, sampling_rate_hz=sampling_rate) - def test_sign_change_for_detect_tc(self): - """Test correct handling of signal that does or does not provide a change of the sign.""" + def test_sign_change_for_detect_tc_for_segmented_stride(self): + """Test correct handling of signal that does or does not provide a change of the sign in case of "segmented" input_stride_type.""" # with sign change signal1 = np.concatenate([np.ones(10), np.ones(10) * -1]) assert _detect_tc_for_segmented_stride(signal1) == 9 @@ -229,6 +295,16 @@ def test_sign_change_for_detect_tc(self): signal2 = np.ones(10) assert np.isnan(_detect_tc_for_segmented_stride(signal2)) + def test_sign_change_for_detect_tc_for_ic_stride(self): + """Test correct handling of signal that does or does not provide a change of the sign in case of "ic" input_stride_type.""" + # with sign change + signal1 = np.concatenate([np.ones(10) * -1, np.ones(10), np.ones(10) * -1, np.ones(10)]) + assert _detect_tc_for_ic_stride(signal1) == 9 + + # without sign change + signal2 = np.ones(10) + assert np.isnan(_detect_tc_for_ic_stride(signal2)) + @pytest.mark.parametrize( "detect_only", [ @@ -239,7 +315,7 @@ def test_sign_change_for_detect_tc(self): ("ic", "tc"), ], ) - def test_detect_only(self, detect_only, healthy_example_imu_data, healthy_example_stride_borders): + def test_detect_only_segmented_stride(self, detect_only, healthy_example_imu_data, healthy_example_stride_borders): """Test if only the specified events are detected.""" data_left = healthy_example_imu_data["left_sensor"] data_left = coordinate_conversion.convert_left_foot_to_fbf(data_left) @@ -255,3 +331,32 @@ def test_detect_only(self, detect_only, healthy_example_imu_data, healthy_exampl assert hasattr(ed, "min_vel_event_list_") is False assert set(ed.segmented_event_list_.columns) == {"start", "end", *detect_only} + + @pytest.mark.parametrize( + "detect_only", + [ + ("min_vel",), + ("ic",), + ("tc",), + ("min_vel", "ic"), + ("ic", "tc"), + ], + ) + def test_detect_only_ic_stride( + self, detect_only, healthy_example_imu_data_ic_stride, healthy_example_stride_borders_ic_stride + ): + """Test if only the specified events are detected.""" + data_left = healthy_example_imu_data_ic_stride["left_sensor"] + data_left = coordinate_conversion.convert_left_foot_to_fbf(data_left) + stride_list_left = healthy_example_stride_borders_ic_stride["left_sensor"] + ed = RamppEventDetection(input_stride_type="ic", detect_only=detect_only) + ed.detect(data_left, stride_list_left, sampling_rate_hz=102.4) + + expected_min_vel = ("pre_ic", *detect_only) if "ic" in detect_only else detect_only + + if "min_vel" in detect_only: + assert set(ed.min_vel_event_list_.columns) == {"start", "end", *expected_min_vel} + else: + assert hasattr(ed, "min_vel_event_list_") is False + + assert set(ed.segmented_event_list_.columns) == {"start", "end", *detect_only} diff --git a/tests/test_utils/test_stride_list_conversion.py b/tests/test_utils/test_stride_list_conversion.py index a70aa461..1e19437b 100644 --- a/tests/test_utils/test_stride_list_conversion.py +++ b/tests/test_utils/test_stride_list_conversion.py @@ -73,8 +73,11 @@ def test_nan_removal(self, stride_type): assert_frame_equal(event_list[event_list["s_id"].isin(nan_s_ids)], removed_strides) assert len(removed_strides) == len(nan_s_ids) - def test_check_stride_list(self): - stride_type = "segmented" + @pytest.mark.parametrize( + "stride_type", + ("segmented", "ic"), + ) + def test_check_stride_list(self, stride_type): # First use a stride list that works event_list = self._create_example_stride_list(stride_type) try: @@ -158,6 +161,68 @@ def test_simple_conversion_multiple(self, target): assert_frame_equal(converted, converted_multiple) +class TestConvertIcStrideList: + def _create_example_stride_list_with_pause(self): + events = SL_EVENT_ORDER["ic"] + event_dict = {} + event_dict["start"] = np.array(list(range(10))) + event_dict["s_id"] = event_dict["start"] + event_dict["end"] = np.array(list(range(1, 11))) + for i, e in enumerate(events): + event_dict[e] = event_dict["start"] + i * 1 / len(events) + stride_list = pd.DataFrame(event_dict) + stride_list = stride_list.drop(5) + return stride_list + + def test_simple_conversion(self): + stride_list = self._create_example_stride_list_with_pause() + + converted, dropped = _stride_list_to_min_vel_single_sensor( + stride_list, source_stride_type="ic", target_stride_type="min_vel" + ) + + # We do not test everything here, but just see if it passes the basic checks. + assert np.all(converted["start"] == converted["min_vel"]) + # We drop two strides, one at the end of both sequences + assert len(dropped) == 2 + assert list(dropped.index) == [4, 9] + # check consistency + _, tmp = enforce_stride_list_consistency(converted, input_stride_type="min_vel") + assert len(tmp) == 0 + # Check that the length of all strides is still 1 + assert np.all((converted["end"] - converted["start"]).round(2) == 1.0) + + def test_second_to_last_stride_is_break(self): + """Test an edge case where there is a break right before the last stride.""" + stride_list = self._create_example_stride_list_with_pause() + # Drop the second to last stride to create a pause + stride_list = stride_list.drop(8) + converted, dropped = _stride_list_to_min_vel_single_sensor( + stride_list, source_stride_type="ic", target_stride_type="min_vel" + ) + + # Check that the length of all strides is still 1 + assert np.all((converted["end"] - converted["start"]).round(2) == 1.0) + # We should have dropped 3 strides + assert len(dropped) == 3 + assert list(dropped.index) == [4, 7, 9] + + def test_simple_conversion_multiple(self): + stride_list = self._create_example_stride_list_with_pause() + converted = convert_stride_list(stride_list, source_stride_type="ic", target_stride_type="min_vel") + + stride_list = {"s1": stride_list} + converted_multiple = convert_stride_list(stride_list, source_stride_type="ic", target_stride_type="min_vel") + converted_multiple = converted_multiple["s1"] + + # We do not test everything here, but just see if it passes the basic checks. + assert np.all(converted["start"] == converted["min_vel"]) + _, tmp = enforce_stride_list_consistency(converted, input_stride_type="min_vel") + assert len(tmp) == 0 + + assert_frame_equal(converted, converted_multiple) + + class TestIntersectStrideList: def test_simple_with_overlap(self): From 6464b7e8c42519fbc3f8531b6f618882d1bb837d Mon Sep 17 00:00:00 2001 From: AlzhraaIbrahim Date: Sat, 13 Apr 2024 00:10:20 +0200 Subject: [PATCH 10/15] example_files --- example_data/imu_sample_ic_stride.csv | 4055 +++++++++++++++++ .../stride_borders_sample_ic_stride.csv | 15 + ..._stride_borders_ic_stride-102.4]_left.json | 41 + ...rders_ic_stride-102.4]_left_segmented.json | 60 + ...stride_borders_ic_stride-102.4]_right.json | 67 + ...ders_ic_stride-102.4]_right_segmented.json | 92 + ...hy_example_stride_borders-204.8]_left.json | 274 ++ ..._stride_borders-204.8]_left_segmented.json | 260 ++ ...y_example_stride_borders-204.8]_right.json | 301 ++ ...stride_borders-204.8]_right_segmented.json | 276 ++ ..._stride_borders_ic_stride-102.4]_left.json | 67 + ...rders_ic_stride-102.4]_left_segmented.json | 92 + ...stride_borders_ic_stride-102.4]_right.json | 67 + ...ders_ic_stride-102.4]_right_segmented.json | 92 + ...hy_example_stride_borders-204.8]_left.json | 274 ++ ..._stride_borders-204.8]_left_segmented.json | 260 ++ ...y_example_stride_borders-204.8]_right.json | 301 ++ ...stride_borders-204.8]_right_segmented.json | 276 ++ 18 files changed, 6870 insertions(+) create mode 100644 example_data/imu_sample_ic_stride.csv create mode 100644 example_data/stride_borders_sample_ic_stride.csv create mode 100644 tests/test_event_detection/snapshot/TestEventDetectionRampp.test_multi_sensor_input[ic-healthy_example_imu_data_ic_stride-healthy_example_stride_borders_ic_stride-102.4]_left.json create mode 100644 tests/test_event_detection/snapshot/TestEventDetectionRampp.test_multi_sensor_input[ic-healthy_example_imu_data_ic_stride-healthy_example_stride_borders_ic_stride-102.4]_left_segmented.json create mode 100644 tests/test_event_detection/snapshot/TestEventDetectionRampp.test_multi_sensor_input[ic-healthy_example_imu_data_ic_stride-healthy_example_stride_borders_ic_stride-102.4]_right.json create mode 100644 tests/test_event_detection/snapshot/TestEventDetectionRampp.test_multi_sensor_input[ic-healthy_example_imu_data_ic_stride-healthy_example_stride_borders_ic_stride-102.4]_right_segmented.json create mode 100644 tests/test_event_detection/snapshot/TestEventDetectionRampp.test_multi_sensor_input[segmented-healthy_example_imu_data-healthy_example_stride_borders-204.8]_left.json create mode 100644 tests/test_event_detection/snapshot/TestEventDetectionRampp.test_multi_sensor_input[segmented-healthy_example_imu_data-healthy_example_stride_borders-204.8]_left_segmented.json create mode 100644 tests/test_event_detection/snapshot/TestEventDetectionRampp.test_multi_sensor_input[segmented-healthy_example_imu_data-healthy_example_stride_borders-204.8]_right.json create mode 100644 tests/test_event_detection/snapshot/TestEventDetectionRampp.test_multi_sensor_input[segmented-healthy_example_imu_data-healthy_example_stride_borders-204.8]_right_segmented.json create mode 100644 tests/test_event_detection/snapshot/TestEventDetectionRamppFiltered.test_multi_sensor_input[ic-healthy_example_imu_data_ic_stride-healthy_example_stride_borders_ic_stride-102.4]_left.json create mode 100644 tests/test_event_detection/snapshot/TestEventDetectionRamppFiltered.test_multi_sensor_input[ic-healthy_example_imu_data_ic_stride-healthy_example_stride_borders_ic_stride-102.4]_left_segmented.json create mode 100644 tests/test_event_detection/snapshot/TestEventDetectionRamppFiltered.test_multi_sensor_input[ic-healthy_example_imu_data_ic_stride-healthy_example_stride_borders_ic_stride-102.4]_right.json create mode 100644 tests/test_event_detection/snapshot/TestEventDetectionRamppFiltered.test_multi_sensor_input[ic-healthy_example_imu_data_ic_stride-healthy_example_stride_borders_ic_stride-102.4]_right_segmented.json create mode 100644 tests/test_event_detection/snapshot/TestEventDetectionRamppFiltered.test_multi_sensor_input[segmented-healthy_example_imu_data-healthy_example_stride_borders-204.8]_left.json create mode 100644 tests/test_event_detection/snapshot/TestEventDetectionRamppFiltered.test_multi_sensor_input[segmented-healthy_example_imu_data-healthy_example_stride_borders-204.8]_left_segmented.json create mode 100644 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a/tests/test_event_detection/snapshot/TestEventDetectionRampp.test_multi_sensor_input[ic-healthy_example_imu_data_ic_stride-healthy_example_stride_borders_ic_stride-102.4]_left.json b/tests/test_event_detection/snapshot/TestEventDetectionRampp.test_multi_sensor_input[ic-healthy_example_imu_data_ic_stride-healthy_example_stride_borders_ic_stride-102.4]_left.json new file mode 100644 index 00000000..24d15276 --- /dev/null +++ b/tests/test_event_detection/snapshot/TestEventDetectionRampp.test_multi_sensor_input[ic-healthy_example_imu_data_ic_stride-healthy_example_stride_borders_ic_stride-102.4]_left.json @@ -0,0 +1,41 @@ +{ + "schema":{ + "fields":[ + { + "name":"s_id", + "type":"integer" + }, + { + "name":"start", + "type":"number" + }, + { + "name":"end", + "type":"number" + }, + { + "name":"ic", + "type":"number" + }, + { + "name":"tc", + "type":"number" + }, + { + "name":"min_vel", + "type":"number" + }, + { + "name":"pre_ic", + "type":"number" + } + ], + "primaryKey":[ + "s_id" + ], + "pandas_version":"1.4.0" + }, + "data":[ + + ] +} \ No newline at end of file diff --git a/tests/test_event_detection/snapshot/TestEventDetectionRampp.test_multi_sensor_input[ic-healthy_example_imu_data_ic_stride-healthy_example_stride_borders_ic_stride-102.4]_left_segmented.json b/tests/test_event_detection/snapshot/TestEventDetectionRampp.test_multi_sensor_input[ic-healthy_example_imu_data_ic_stride-healthy_example_stride_borders_ic_stride-102.4]_left_segmented.json new file mode 100644 index 00000000..d0fb4d43 --- /dev/null +++ b/tests/test_event_detection/snapshot/TestEventDetectionRampp.test_multi_sensor_input[ic-healthy_example_imu_data_ic_stride-healthy_example_stride_borders_ic_stride-102.4]_left_segmented.json @@ -0,0 +1,60 @@ +{ + "schema":{ + "fields":[ + { + "name":"s_id", + "type":"integer" + }, + { + "name":"start", + "type":"integer" + }, + { + "name":"end", + "type":"integer" + }, + { + "name":"ic", + "type":"number" + }, + { + "name":"tc", + "type":"number" + }, + { 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at end of file diff --git a/tests/test_event_detection/snapshot/TestEventDetectionRampp.test_multi_sensor_input[ic-healthy_example_imu_data_ic_stride-healthy_example_stride_borders_ic_stride-102.4]_right_segmented.json b/tests/test_event_detection/snapshot/TestEventDetectionRampp.test_multi_sensor_input[ic-healthy_example_imu_data_ic_stride-healthy_example_stride_borders_ic_stride-102.4]_right_segmented.json new file mode 100644 index 00000000..8c11e812 --- /dev/null +++ b/tests/test_event_detection/snapshot/TestEventDetectionRampp.test_multi_sensor_input[ic-healthy_example_imu_data_ic_stride-healthy_example_stride_borders_ic_stride-102.4]_right_segmented.json @@ -0,0 +1,92 @@ +{ + "schema":{ + "fields":[ + { + "name":"s_id", + "type":"integer" + }, + { + "name":"start", + "type":"integer" + }, + { + "name":"end", + "type":"integer" + }, + { + "name":"ic", + "type":"number" + }, + { + "name":"tc", + "type":"number" + }, + { + "name":"min_vel", + "type":"number" + } + ], + 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--git a/tests/test_event_detection/snapshot/TestEventDetectionRamppFiltered.test_multi_sensor_input[segmented-healthy_example_imu_data-healthy_example_stride_borders-204.8]_right.json b/tests/test_event_detection/snapshot/TestEventDetectionRamppFiltered.test_multi_sensor_input[segmented-healthy_example_imu_data-healthy_example_stride_borders-204.8]_right.json new file mode 100644 index 00000000..6f18e062 --- /dev/null +++ b/tests/test_event_detection/snapshot/TestEventDetectionRamppFiltered.test_multi_sensor_input[segmented-healthy_example_imu_data-healthy_example_stride_borders-204.8]_right.json @@ -0,0 +1,301 @@ +{ + "schema":{ + "fields":[ + { + "name":"s_id", + "type":"integer" + }, + { + "name":"start", + "type":"number" + }, + { + "name":"end", + "type":"number" + }, + { + "name":"ic", + "type":"number" + }, + { + "name":"tc", + "type":"number" + }, + { + "name":"min_vel", + "type":"number" + }, + { + "name":"pre_ic", + "type":"number" + } + ], + "primaryKey":[ + "s_id" + ], + 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b/tests/test_event_detection/snapshot/TestEventDetectionRamppFiltered.test_multi_sensor_input[segmented-healthy_example_imu_data-healthy_example_stride_borders-204.8]_right_segmented.json new file mode 100644 index 00000000..f5e26916 --- /dev/null +++ b/tests/test_event_detection/snapshot/TestEventDetectionRamppFiltered.test_multi_sensor_input[segmented-healthy_example_imu_data-healthy_example_stride_borders-204.8]_right_segmented.json @@ -0,0 +1,276 @@ +{ + "schema":{ + "fields":[ + { + "name":"s_id", + "type":"integer" + }, + { + "name":"start", + "type":"integer" + }, + { + "name":"end", + "type":"integer" + }, + { + "name":"ic", + "type":"number" + }, + { + "name":"tc", + "type":"number" + }, + { + "name":"min_vel", + "type":"number" + } + ], + "primaryKey":[ + "s_id" + ], + "pandas_version":"1.4.0" + }, + "data":[ + { + "s_id":28, + "start":475, + "end":691, + "ic":558.0, + "tc":479.0, + "min_vel":633.0 + }, + { + "s_id":29, + "start":691, + "end":913, + "ic":775.0, + "tc":694.0, 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"ic":5237.0, + "tc":5157.0, + "min_vel":5319.0 + }, + { + "s_id":50, + "start":5381, + "end":5601, + "ic":5460.0, + "tc":5384.0, + "min_vel":5506.0 + }, + { + "s_id":51, + "start":5601, + "end":5826, + "ic":5683.0, + "tc":5604.0, + "min_vel":5735.0 + }, + { + "s_id":52, + "start":5826, + "end":6051, + "ic":5908.0, + "tc":5829.0, + "min_vel":5954.0 + }, + { + "s_id":53, + "start":6051, + "end":6280, + "ic":6136.0, + "tc":6055.0, + "min_vel":6204.0 + }, + { + "s_id":54, + "start":6280, + "end":6511, + "ic":6366.0, + "tc":6284.0, + "min_vel":6453.0 + }, + { + "s_id":55, + "start":6511, + "end":6742, + "ic":6595.0, + "tc":6515.0, + "min_vel":6685.0 + }, + { + "s_id":56, + "start":6742, + "end":6966, + "ic":6827.0, + "tc":6746.0, + "min_vel":6916.0 + }, + { + "s_id":57, + "start":6966, + "end":7273, + "ic":7064.0, + "tc":6982.0, + "min_vel":7135.0 + } + ] +} \ No newline at end of file From 54ad8fd5bea7ca52d4ce9e7b363ac13560903c7e Mon Sep 17 00:00:00 2001 From: AlzhraaIbrahim Date: Mon, 15 Apr 2024 10:10:56 +0200 Subject: [PATCH 11/15] format fix tests --- .../test_event_detection_rampp.py | 29 ++++++++++--------- 1 file changed, 15 insertions(+), 14 deletions(-) diff --git a/tests/test_event_detection/test_event_detection_rampp.py b/tests/test_event_detection/test_event_detection_rampp.py index cb9acccc..25b76c71 100644 --- a/tests/test_event_detection/test_event_detection_rampp.py +++ b/tests/test_event_detection/test_event_detection_rampp.py @@ -13,29 +13,30 @@ from gaitmap.utils import coordinate_conversion, datatype_helper from gaitmap.utils.consts import BF_COLS from gaitmap.utils.exceptions import ValidationError -from gaitmap_mad.event_detection._rampp_event_detection import _detect_tc_for_segmented_stride, _detect_tc_for_ic_stride +from gaitmap_mad.event_detection._rampp_event_detection import _detect_tc_for_ic_stride, _detect_tc_for_segmented_stride from tests.mixins.test_algorithm_mixin import TestAlgorithmMixin from tests.mixins.test_caching_mixin import TestCachingMixin +common_arguments = pytest.mark.parametrize( + ("input_stride_type", "imu_data", "stride_borders", "sampling_rate"), + [ + ("segmented", "healthy_example_imu_data", "healthy_example_stride_borders", 204.8), + ("ic", "healthy_example_imu_data_ic_stride", "healthy_example_stride_borders_ic_stride", 102.4), + ], +) + -common_arguments =pytest.mark.parametrize( - ("input_stride_type", "imu_data", "stride_borders", "sampling_rate"), - [ - ("segmented", "healthy_example_imu_data", "healthy_example_stride_borders", 204.8), - ("ic", "healthy_example_imu_data_ic_stride", "healthy_example_stride_borders_ic_stride", 102.4), - ], - ) class MetaTestConfig: algorithm_class = RamppEventDetection - @common_arguments - def after_action_instance(self, input_stride_type, imu_data, stride_borders, sampling_rate, request) -> BaseType: - data_left = request.getfixturevalue(imu_data)["left_sensor"] + @pytest.fixture() + def after_action_instance(self, healthy_example_imu_data, healthy_example_stride_borders) -> BaseType: + data_left = healthy_example_imu_data["left_sensor"] data_left.columns = BF_COLS # only use the first entry of the stride list - stride_list_left = request.getfixturevalue(stride_borders)["left_sensor"].iloc[0:1] - ed = RamppEventDetection(input_stride_type=input_stride_type) - ed.detect(data_left, stride_list_left, sampling_rate_hz=sampling_rate) + stride_list_left = healthy_example_stride_borders["left_sensor"].iloc[0:1] + ed = RamppEventDetection() + ed.detect(data_left, stride_list_left, sampling_rate_hz=204.8) return ed From f5a3fdf94f10037ae0e5709dc8b336d9b9ccdb4f Mon Sep 17 00:00:00 2001 From: AlzhraaIbrahim <124270058+AlzhraaIbrahim@users.noreply.github.com> Date: Mon, 15 Apr 2024 10:45:50 +0200 Subject: [PATCH 12/15] Update _event_detection_mixin.py --- gaitmap/_event_detection_common/_event_detection_mixin.py | 1 - 1 file changed, 1 deletion(-) diff --git a/gaitmap/_event_detection_common/_event_detection_mixin.py b/gaitmap/_event_detection_common/_event_detection_mixin.py index c568a05c..08ff068c 100644 --- a/gaitmap/_event_detection_common/_event_detection_mixin.py +++ b/gaitmap/_event_detection_common/_event_detection_mixin.py @@ -47,7 +47,6 @@ def __init__( detect_only: Optional[Tuple[str, ...]] = None, input_stride_type: Literal["segmented", "ic"] = "segmented", ): - ) -> None: self.memory = memory self.enforce_consistency = enforce_consistency self.detect_only = detect_only From 3bff436195f66b07622d0020e0ddd844b6e83d53 Mon Sep 17 00:00:00 2001 From: AlzhraaIbrahim <124270058+AlzhraaIbrahim@users.noreply.github.com> Date: Mon, 15 Apr 2024 10:47:16 +0200 Subject: [PATCH 13/15] fix errors due to conflicts _herzer_event_detection.py --- gaitmap/event_detection/_herzer_event_detection.py | 1 - 1 file changed, 1 deletion(-) diff --git a/gaitmap/event_detection/_herzer_event_detection.py b/gaitmap/event_detection/_herzer_event_detection.py index 19b77a23..4c782c23 100644 --- a/gaitmap/event_detection/_herzer_event_detection.py +++ b/gaitmap/event_detection/_herzer_event_detection.py @@ -206,7 +206,6 @@ def __init__( detect_only: Optional[Tuple[str, ...]] = None, input_stride_type: Literal["segmented"] = "segmented", ): - ) -> None: self.min_vel_search_win_size_ms = min_vel_search_win_size_ms self.mid_swing_peak_prominence = mid_swing_peak_prominence self.mid_swing_n_considered_peaks = mid_swing_n_considered_peaks From 021a57dd344a3d3b9363b977c9cd51ce729b510f Mon Sep 17 00:00:00 2001 From: AlzhraaIbrahim <124270058+AlzhraaIbrahim@users.noreply.github.com> Date: Mon, 15 Apr 2024 10:48:33 +0200 Subject: [PATCH 14/15] fix errors resulted from conflict resolving --- .../event_detection/_filtered_rampp_event_detection.py | 1 - 1 file changed, 1 deletion(-) diff --git a/gaitmap_mad/gaitmap_mad/event_detection/_filtered_rampp_event_detection.py b/gaitmap_mad/gaitmap_mad/event_detection/_filtered_rampp_event_detection.py index 7f66a0f1..722ecb97 100644 --- a/gaitmap_mad/gaitmap_mad/event_detection/_filtered_rampp_event_detection.py +++ b/gaitmap_mad/gaitmap_mad/event_detection/_filtered_rampp_event_detection.py @@ -102,7 +102,6 @@ def __init__( detect_only: Optional[Tuple[str, ...]] = None, input_stride_type: Literal["segmented", "ic"] = "segmented", ): - ) -> None: self.ic_lowpass_filter = ic_lowpass_filter self.input_stride_type = input_stride_type super().__init__( From 3f25bd79bc00b8b83876d7f7e4d2509291b48191 Mon Sep 17 00:00:00 2001 From: AlzhraaIbrahim <124270058+AlzhraaIbrahim@users.noreply.github.com> Date: Mon, 15 Apr 2024 10:50:54 +0200 Subject: [PATCH 15/15] fix conflicts errors --- .../gaitmap_mad/event_detection/_rampp_event_detection.py | 1 - 1 file changed, 1 deletion(-) diff --git a/gaitmap_mad/gaitmap_mad/event_detection/_rampp_event_detection.py b/gaitmap_mad/gaitmap_mad/event_detection/_rampp_event_detection.py index 02863476..a0477879 100644 --- a/gaitmap_mad/gaitmap_mad/event_detection/_rampp_event_detection.py +++ b/gaitmap_mad/gaitmap_mad/event_detection/_rampp_event_detection.py @@ -171,7 +171,6 @@ def __init__( detect_only: Optional[Tuple[str, ...]] = None, input_stride_type: Literal["segmented", "ic"] = "segmented", ): - ) -> None: self.ic_search_region_ms = ic_search_region_ms self.min_vel_search_win_size_ms = min_vel_search_win_size_ms self.input_stride_type = input_stride_type