From 360242b673800ce54bf9a00d796928b5bddc51cc Mon Sep 17 00:00:00 2001 From: Erikpostt Date: Tue, 22 Oct 2024 13:49:46 +0200 Subject: [PATCH 1/8] Standardizing angle and velocity column names --- src/paradigma/gait/feature_extraction.py | 66 ++++++++++++------------ 1 file changed, 33 insertions(+), 33 deletions(-) diff --git a/src/paradigma/gait/feature_extraction.py b/src/paradigma/gait/feature_extraction.py index 55ad864..8ba1dc3 100644 --- a/src/paradigma/gait/feature_extraction.py +++ b/src/paradigma/gait/feature_extraction.py @@ -444,75 +444,75 @@ def extract_angle_extremes( The extracted angle extremes (peaks) """ # determine peaks - df['angle_maxima'] = df.apply(lambda x: find_peaks(x[angle_colname], distance=sampling_frequency * 0.6 / x[dominant_frequency_colname], prominence=2)[0], axis=1) - df['angle_minima'] = df.apply(lambda x: find_peaks([-x for x in x[angle_colname]], distance=sampling_frequency * 0.6 / x[dominant_frequency_colname], prominence=2)[0], axis=1) + df[f'{angle_colname}_maxima'] = df.apply(lambda x: find_peaks(x[angle_colname], distance=sampling_frequency * 0.6 / x[dominant_frequency_colname], prominence=2)[0], axis=1) + df[f'{angle_colname}_minima'] = df.apply(lambda x: find_peaks([-x for x in x[angle_colname]], distance=sampling_frequency * 0.6 / x[dominant_frequency_colname], prominence=2)[0], axis=1) - df['angle_new_minima'] = df['angle_minima'].copy() - df['angle_new_maxima'] = df['angle_maxima'].copy() + df[f'{angle_colname}_new_minima'] = df[f'{angle_colname}_minima'].copy() + df[f'{angle_colname}_new_maxima'] = df[f'{angle_colname}_maxima'].copy() for index, _ in df.iterrows(): i_pks = 0 # iterable to keep track of consecutive min-min and max-max versus min-max - n_min = df.loc[index, 'angle_new_minima'].size # number of minima in window - n_max = df.loc[index, 'angle_new_maxima'].size # number of maxima in window + n_min = df.loc[index, f'{angle_colname}_new_minima'].size # number of minima in window + n_max = df.loc[index, f'{angle_colname}_new_maxima'].size # number of maxima in window if n_min > 0 and n_max > 0: # if the first minimum occurs before the first maximum, start with the minimum - if df.loc[index, 'angle_new_maxima'][0] > df.loc[index, 'angle_new_minima'][0]: + if df.loc[index, f'{angle_colname}_new_maxima'][0] > df.loc[index, f'{angle_colname}_new_minima'][0]: # only continue if there are enough minima and maxima to perform operations - while i_pks < df.loc[index, 'angle_new_minima'].size - 1 and i_pks < df.loc[index, 'angle_new_maxima'].size: + while i_pks < df.loc[index, f'{angle_colname}_new_minima'].size - 1 and i_pks < df.loc[index, f'{angle_colname}_new_maxima'].size: # if the next minimum comes before the next maximum, we have two minima in a row, and should keep the larger one - if df.loc[index, 'angle_new_minima'][i_pks+1] < df.loc[index, 'angle_new_maxima'][i_pks]: + if df.loc[index, f'{angle_colname}_new_minima'][i_pks+1] < df.loc[index, f'{angle_colname}_new_maxima'][i_pks]: # if the next minimum is smaller than the current minimum, keep the next minimum and discard the current minimum - if df.loc[index, angle_colname][df.loc[index, 'angle_new_minima'][i_pks+1]] < df.loc[index, angle_colname][df.loc[index, 'angle_new_minima'][i_pks]]: - df.at[index, 'angle_new_minima'] = np.delete(df.loc[index, 'angle_new_minima'], i_pks) + if df.loc[index, angle_colname][df.loc[index, f'{angle_colname}_new_minima'][i_pks+1]] < df.loc[index, angle_colname][df.loc[index, f'{angle_colname}_new_minima'][i_pks]]: + df.at[index, f'{angle_colname}_new_minima'] = np.delete(df.loc[index, f'{angle_colname}_new_minima'], i_pks) # otherwise, keep the current minimum and discard the next minimum else: - df.at[index, 'angle_new_minima'] = np.delete(df.loc[index, 'angle_new_minima'], i_pks+1) + df.at[index, f'{angle_colname}_new_minima'] = np.delete(df.loc[index, f'{angle_colname}_new_minima'], i_pks+1) i_pks -= 1 # if the current maximum comes before the current minimum, we have two maxima in a row, and should keep the larger one - if i_pks >= 0 and df.loc[index, 'angle_new_minima'][i_pks] > df.loc[index, 'angle_new_maxima'][i_pks]: + if i_pks >= 0 and df.loc[index, f'{angle_colname}_new_minima'][i_pks] > df.loc[index, f'{angle_colname}_new_maxima'][i_pks]: # if the current maximum is smaller than the previous maximum, keep the previous maximum and discard the current maximum - if df.loc[index, angle_colname][df.loc[index, 'angle_new_maxima'][i_pks]] < df.loc[index, angle_colname][df.loc[index, 'angle_new_maxima'][i_pks-1]]: - df.at[index, 'angle_new_maxima'] = np.delete(df.loc[index, 'angle_new_maxima'], i_pks) + if df.loc[index, angle_colname][df.loc[index, f'{angle_colname}_new_maxima'][i_pks]] < df.loc[index, angle_colname][df.loc[index, f'{angle_colname}_new_maxima'][i_pks-1]]: + df.at[index, f'{angle_colname}_new_maxima'] = np.delete(df.loc[index, f'{angle_colname}_new_maxima'], i_pks) # otherwise, keep the current maximum and discard the previous maximum else: - df.at[index, 'angle_new_maxima'] = np.delete(df.loc[index, 'angle_new_maxima'], i_pks-1) + df.at[index, f'{angle_colname}_new_maxima'] = np.delete(df.loc[index, f'{angle_colname}_new_maxima'], i_pks-1) i_pks -= 1 i_pks += 1 # or if the first maximum occurs before the first minimum, start with the maximum - elif df.loc[index, 'angle_new_maxima'][0] < df.loc[index, 'angle_new_minima'][0]: + elif df.loc[index, f'{angle_colname}_new_maxima'][0] < df.loc[index, f'{angle_colname}_new_minima'][0]: # only continue if there are enough minima and maxima to perform operations - while i_pks < df.loc[index, 'angle_new_minima'].size and i_pks < df.loc[index, 'angle_new_maxima'].size-1: + while i_pks < df.loc[index, f'{angle_colname}_new_minima'].size and i_pks < df.loc[index, f'{angle_colname}_new_maxima'].size-1: # if the next maximum comes before the current minimum, we have two maxima in a row, and should keep the larger one - if df.loc[index, 'angle_new_minima'][i_pks] > df.loc[index, 'angle_new_maxima'][i_pks+1]: + if df.loc[index, f'{angle_colname}_new_minima'][i_pks] > df.loc[index, f'{angle_colname}_new_maxima'][i_pks+1]: # if the next maximum is smaller than the current maximum, keep the next maximum and discard the current maximum - if df.loc[index, angle_colname][df.loc[index, 'angle_new_maxima'][i_pks+1]] > df.loc[index, angle_colname][df.loc[index, 'angle_new_maxima'][i_pks]]: - df.at[index, 'angle_new_maxima'] = np.delete(df.loc[index, 'angle_new_maxima'], i_pks) + if df.loc[index, angle_colname][df.loc[index, f'{angle_colname}_new_maxima'][i_pks+1]] > df.loc[index, angle_colname][df.loc[index, f'{angle_colname}_new_maxima'][i_pks]]: + df.at[index, f'{angle_colname}_new_maxima'] = np.delete(df.loc[index, f'{angle_colname}_new_maxima'], i_pks) # otherwise, keep the current maximum and discard the next maximum else: - df.at[index, 'angle_new_maxima'] = np.delete(df.loc[index, 'angle_new_maxima'], i_pks+1) + df.at[index, f'{angle_colname}_new_maxima'] = np.delete(df.loc[index, f'{angle_colname}_new_maxima'], i_pks+1) i_pks -= 1 # if the current minimum comes before the current maximum, we have two minima in a row, and should keep the larger one - if i_pks > 0 and df.loc[index, 'angle_new_minima'][i_pks] < df.loc[index, 'angle_new_maxima'][i_pks]: + if i_pks > 0 and df.loc[index, f'{angle_colname}_new_minima'][i_pks] < df.loc[index, f'{angle_colname}_new_maxima'][i_pks]: # if the current minimum is smaller than the previous minimum, keep the previous minimum and discard the current minimum - if df.loc[index, angle_colname][df.loc[index, 'angle_new_minima'][i_pks]] < df.loc[index, angle_colname][df.loc[index, 'angle_new_minima'][i_pks-1]]: - df.at[index, 'angle_new_minima'] = np.delete(df.loc[index, 'angle_new_minima'], i_pks-1) + if df.loc[index, angle_colname][df.loc[index, f'{angle_colname}_new_minima'][i_pks]] < df.loc[index, angle_colname][df.loc[index, f'{angle_colname}_new_minima'][i_pks-1]]: + df.at[index, f'{angle_colname}_new_minima'] = np.delete(df.loc[index, f'{angle_colname}_new_minima'], i_pks-1) # otherwise, keep the current minimum and discard the previous minimum else: - df.at[index, 'angle_new_minima'] = np.delete(df.loc[index, 'angle_new_minima'], i_pks) + df.at[index, f'{angle_colname}_new_minima'] = np.delete(df.loc[index, f'{angle_colname}_new_minima'], i_pks) i_pks -= 1 i_pks += 1 # for some peculiar reason, if a single item remains in the row for angle_new_minima or # angle_new_maxima, it could be either a scalar or a vector. - for col in ['angle_new_minima', 'angle_new_maxima']: + for col in [f'{angle_colname}_new_minima', f'{angle_colname}_new_maxima']: df.loc[df.apply(lambda x: type(x[col].tolist())==int, axis=1), col] = df.loc[df.apply(lambda x: type(x[col].tolist())==int, axis=1), col].apply(lambda x: [x]) - df['angle_extrema_values'] = df.apply(lambda x: [x[angle_colname][i] for i in np.concatenate([x['angle_new_minima'], x['angle_new_maxima']])], axis=1) + df[f'{angle_colname}_extrema_values'] = df.apply(lambda x: [x[angle_colname][i] for i in np.concatenate([x[f'{angle_colname}_new_minima'], x[f'{angle_colname}_new_maxima']])], axis=1) return @@ -583,8 +583,8 @@ def extract_peak_angular_velocity( pd.DataFrame The dataframe with the forward and backward peak angular velocity """ - df['forward_peak_ang_vel'] = np.empty((len(df), 0)).tolist() - df['backward_peak_ang_vel'] = np.empty((len(df), 0)).tolist() + df[f'forward_peak_{velocity_colname}'] = np.empty((len(df), 0)).tolist() + df[f'backward_peak_{velocity_colname}'] = np.empty((len(df), 0)).tolist() # for each window for index, row in df.iterrows(): @@ -597,11 +597,11 @@ def extract_peak_angular_velocity( # if the peak is a maximum and there is another peak after it if peak_index in row[angle_maxima_colname] and j < len(l_extrema_indices) - 1: # compute the forward peak angular velocity, defined by the maximum negative angular velocity between the two peaks - df.loc[index, 'forward_peak_ang_vel'].append(np.abs(min(row[velocity_colname][l_extrema_indices[j]:l_extrema_indices[j+1]]))) + df.loc[index, f'forward_peak_{velocity_colname}'].append(np.abs(min(row[velocity_colname][l_extrema_indices[j]:l_extrema_indices[j+1]]))) # if the peak is a minimum and there is another peak after it elif peak_index in row[angle_minima_colname] and j < len(l_extrema_indices) - 1: # compute the backward peak angular velocity, defined by the maximum positive angular velocity between the two peaks - df.loc[index, 'backward_peak_ang_vel'].append(np.abs(max(row[velocity_colname][l_extrema_indices[j]:l_extrema_indices[j+1]]))) + df.loc[index, f'backward_peak_{velocity_colname}'].append(np.abs(max(row[velocity_colname][l_extrema_indices[j]:l_extrema_indices[j+1]]))) return @@ -698,6 +698,6 @@ def extract_spectral_domain_features(config, df_windowed, sensor, l_sensor_colna df_windowed = pd.concat([df_windowed, cc_cols], axis=1) - df_windowed = df_windowed.rename(columns={f'cc_{cc_nr}': f'cc_{cc_nr}_{sensor}' for cc_nr in range(1,config.n_coefficients_cc+1)}).rename(columns={'window_start': 'time'}) + df_windowed = df_windowed.rename(columns={f'cc_{cc_nr}': f'cc_{cc_nr}_{sensor}' for cc_nr in range(1,config.n_coefficients_cc+1)}).rename(columns={'window_start': DataColumns.TIME}) return df_windowed From 38cede3b2d28e197a85134f478eed809468176c4 Mon Sep 17 00:00:00 2001 From: Erikpostt Date: Tue, 22 Oct 2024 14:07:42 +0200 Subject: [PATCH 2/8] Solve feature names error --- src/paradigma/gait/gait_analysis.py | 56 ++++++++++++++--------------- 1 file changed, 28 insertions(+), 28 deletions(-) diff --git a/src/paradigma/gait/gait_analysis.py b/src/paradigma/gait/gait_analysis.py index a0e94f4..cd236e0 100644 --- a/src/paradigma/gait/gait_analysis.py +++ b/src/paradigma/gait/gait_analysis.py @@ -167,25 +167,25 @@ def extract_arm_activity_features(df: pd.DataFrame, config: ArmActivityFeatureEx del df # transform the angle from the temporal domain to the spectral domain using the fast fourier transform - df_windowed['angle_freqs'], df_windowed['angle_fft'] = signal_to_ffts( + df_windowed[f'{config.angle_colname}_freqs'], df_windowed[f'{config.angle_colname}_fft'] = signal_to_ffts( sensor_col=df_windowed[config.angle_colname], window_type=config.window_type, sampling_frequency=config.sampling_frequency) # obtain the dominant frequency of the angle signal in the frequency band of interest # defined by the highest peak in the power spectrum - df_windowed['angle_dominant_frequency'] = df_windowed.apply( - lambda x: get_dominant_frequency(signal_ffts=x['angle_fft'], - signal_freqs=x['angle_freqs'], + df_windowed[f'{config.angle_colname}_dominant_frequency'] = df_windowed.apply( + lambda x: get_dominant_frequency(signal_ffts=x[f'{config.angle_colname}_fft'], + signal_freqs=x[f'{config.angle_colname}_freqs'], fmin=config.power_band_low_frequency, fmax=config.power_band_high_frequency ), axis=1 ) - df_windowed = df_windowed.drop(columns=['angle_fft', 'angle_freqs']) + df_windowed = df_windowed.drop(columns=[f'{config.angle_colname}_fft', f'{config.angle_colname}_freqs']) # compute the percentage of power in the frequency band of interest (i.e., the frequency band of the arm swing) - df_windowed['angle_perc_power'] = df_windowed[config.angle_colname].apply( + df_windowed[f'{config.angle_colname}_perc_power'] = df_windowed[config.angle_colname].apply( lambda x: compute_perc_power( sensor_col=x, fmin_band=config.power_band_low_frequency, @@ -197,49 +197,49 @@ def extract_arm_activity_features(df: pd.DataFrame, config: ArmActivityFeatureEx ) ) - # note to eScience: why are the columns 'angle_new_minima', 'angle_new_maxima', - # 'angle_minima_deleted' and 'angle_maxima deleted' created here? Should a copy + # note to eScience: why are the columns f'{config.angle_colname}_new_minima', f'{config.angle_colname}_new_maxima', + # f'{config.angle_colname}_minima_deleted' and f'{config.angle_colname}_maxima deleted' created here? Should a copy # of 'df_windowed' be created inside 'extract_angle_extremes' to prevent this from # happening? # determine the extrema (minima and maxima) of the angle signal extract_angle_extremes( df=df_windowed, angle_colname=config.angle_colname, - dominant_frequency_colname='angle_dominant_frequency', + dominant_frequency_colname=f'{config.angle_colname}_dominant_frequency', sampling_frequency=config.sampling_frequency ) df_windowed = df_windowed.drop(columns=[config.angle_colname]) # calculate the change in angle between consecutive extrema (minima and maxima) of the angle signal inside the window - df_windowed['angle_amplitudes'] = extract_range_of_motion( - angle_extrema_values_col=df_windowed['angle_extrema_values'] + df_windowed[f'{config.angle_colname}_amplitudes'] = extract_range_of_motion( + angle_extrema_values_col=df_windowed[f'{config.angle_colname}_extrema_values'] ) - df_windowed = df_windowed.drop(columns=['angle_extrema_values']) + df_windowed = df_windowed.drop(columns=[f'{config.angle_colname}_extrema_values']) # aggregate the changes in angle between consecutive extrema to obtain the range of motion - df_windowed['range_of_motion'] = df_windowed['angle_amplitudes'].apply(lambda x: np.mean(x) if len(x) > 0 else 0).replace(np.nan, 0) + df_windowed['range_of_motion'] = df_windowed[f'{config.angle_colname}_amplitudes'].apply(lambda x: np.mean(x) if len(x) > 0 else 0).replace(np.nan, 0) - df_windowed = df_windowed.drop(columns=['angle_amplitudes']) + df_windowed = df_windowed.drop(columns=[f'{config.angle_colname}_amplitudes']) # compute the forward and backward peak angular velocity using the extrema of the angular velocity extract_peak_angular_velocity( df=df_windowed, velocity_colname=config.velocity_colname, - angle_minima_colname='angle_minima', - angle_maxima_colname='angle_maxima' + angle_minima_colname=f'{config.angle_colname}_minima', + angle_maxima_colname=f'{config.angle_colname}_maxima' ) - df_windowed = df_windowed.drop(columns=['angle_minima','angle_maxima', 'angle_new_minima', - 'angle_new_maxima', config.velocity_colname]) + df_windowed = df_windowed.drop(columns=[f'{config.angle_colname}_minima',f'{config.angle_colname}_maxima', f'{config.angle_colname}_new_minima', + f'{config.angle_colname}_new_maxima', config.velocity_colname]) # compute aggregated measures of the peak angular velocity for dir in ['forward', 'backward']: - df_windowed[f'{dir}_peak_ang_vel_mean'] = df_windowed[f'{dir}_peak_ang_vel'].apply(lambda x: np.mean(x) if len(x) > 0 else 0) - df_windowed[f'{dir}_peak_ang_vel_std'] = df_windowed[f'{dir}_peak_ang_vel'].apply(lambda x: np.std(x) if len(x) > 0 else 0) + df_windowed[f'{dir}_peak_{config.velocity_colname}_mean'] = df_windowed[f'{dir}_peak_{config.velocity_colname}'].apply(lambda x: np.mean(x) if len(x) > 0 else 0) + df_windowed[f'{dir}_peak_{config.velocity_colname}_std'] = df_windowed[f'{dir}_peak_{config.velocity_colname}'].apply(lambda x: np.std(x) if len(x) > 0 else 0) - df_windowed = df_windowed.drop(columns=[f'{dir}_peak_ang_vel']) + df_windowed = df_windowed.drop(columns=[f'{dir}_peak_{config.velocity_colname}']) # compute statistics of the temporal domain accelerometer signals df_windowed = extract_temporal_domain_features(config, df_windowed, l_gravity_stats=['mean', 'std']) @@ -296,8 +296,8 @@ def detect_other_arm_activities(df: pd.DataFrame, config: FilteringGaitConfig, c [f'{x}_power_above_tremor' for x in config.l_accelerometer_cols] + \ [f'cc_{i}_accelerometer' for i in range(1, 13)] + [f'cc_{i}_gyroscope' for i in range(1, 13)] + \ [f'grav_{x}_mean' for x in config.l_accelerometer_cols] + [f'grav_{x}_std' for x in config.l_accelerometer_cols] + \ - ['range_of_motion', 'forward_peak_ang_vel_mean', 'backward_peak_ang_vel_mean', 'forward_peak_ang_vel_std', - 'backward_peak_ang_vel_std', 'angle_perc_power', 'angle_dominant_frequency'] + \ + ['range_of_motion', f'forward_peak_{config.velocity_colname}_mean', f'backward_peak_{config.velocity_colname}_mean', 'forward_peak_{config.velocity_colname}_std', + f'backward_peak_{config.velocity_colname}_std', f'{config.angle_colname}_perc_power', f'{config.angle_colname}_dominant_frequency'] + \ [f'{x}_dominant_frequency' for x in config.l_accelerometer_cols] X = df.loc[:, clf.feature_names_in_] @@ -341,8 +341,8 @@ def quantify_arm_swing(df: pd.DataFrame, config: ArmSwingQuantificationConfig) - del df # create peak angular velocity - df_filtered.loc[:, 'peak_ang_vel'] = df_filtered.loc[:, ['forward_peak_ang_vel_mean', 'backward_peak_ang_vel_mean']].mean(axis=1) - df_filtered = df_filtered.drop(columns=['forward_peak_ang_vel_mean', 'backward_peak_ang_vel_mean']) + df_filtered.loc[:, 'peak_{config.velocity_colname}'] = df_filtered.loc[:, ['forward_peak_{config.velocity_colname}_mean', 'backward_peak_{config.velocity_colname}_mean']].mean(axis=1) + df_filtered = df_filtered.drop(columns=['forward_peak_{config.velocity_colname}_mean', 'backward_peak_{config.velocity_colname}_mean']) # Segmenting @@ -365,7 +365,7 @@ def quantify_arm_swing(df: pd.DataFrame, config: ArmSwingQuantificationConfig) - time_colname=DataColumns.TIME, segment_nr_colname=DataColumns.SEGMENT_NR, window_step_size_s=config.window_step_size, - l_metrics=['range_of_motion', 'peak_ang_vel'], + l_metrics=['range_of_motion', 'peak_{config.velocity_colname}'], l_aggregates=['median'], l_quantiles=[0.95] ) @@ -394,7 +394,7 @@ def quantify_arm_swing_io(path_to_feature_input: Union[str, Path], path_to_predi assert df_features[DataColumns.TIME].equals(df_predictions[DataColumns.TIME]) # Subset features - l_feature_cols = [DataColumns.TIME, 'range_of_motion', 'forward_peak_ang_vel_mean', 'backward_peak_ang_vel_mean'] + l_feature_cols = [DataColumns.TIME, 'range_of_motion', 'forward_peak_{config.velocity_colname}_mean', 'backward_peak_{config.velocity_colname}_mean'] df_features = df_features[l_feature_cols] # Concatenate features and predictions @@ -407,7 +407,7 @@ def quantify_arm_swing_io(path_to_feature_input: Union[str, Path], path_to_predi metadata_time.file_name = 'arm_swing_time.bin' metadata_samples.channels = ['range_of_motion_median', 'range_of_motion_quantile_95', - 'peak_ang_vel_median', 'peak_ang_vel_quantile_95'] + 'peak_{config.velocity_colname}_median', 'peak_{config.velocity_colname}_quantile_95'] metadata_samples.units = ['deg', 'deg', 'deg/s', 'deg/s'] metadata_time.channels = [DataColumns.TIME, 'segment_duration_ms'] From 72458a0fe7342eeaba16dd4e60cd72f82b4aeeba Mon Sep 17 00:00:00 2001 From: Erikpostt Date: Tue, 22 Oct 2024 14:32:31 +0200 Subject: [PATCH 3/8] Update test files --- docs/notebooks/gait/gait_analysis.ipynb | 16 +++++++------- src/paradigma/gait/feature_extraction.py | 2 +- src/paradigma/gait/gait_analysis.py | 14 ++++++------ src/paradigma/gait/gait_analysis_config.py | 22 ++++++++++++++----- .../gait/arm_activity_meta.json | 8 +++---- .../5.quantification/gait/arm_swing_meta.json | 4 ++-- 6 files changed, 38 insertions(+), 28 deletions(-) diff --git a/docs/notebooks/gait/gait_analysis.ipynb b/docs/notebooks/gait/gait_analysis.ipynb index 430f8f4..97a11de 100644 --- a/docs/notebooks/gait/gait_analysis.ipynb +++ b/docs/notebooks/gait/gait_analysis.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 2, "metadata": { "tags": [ "parameters" @@ -53,7 +53,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -70,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -87,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -104,7 +104,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -121,7 +121,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -138,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ diff --git a/src/paradigma/gait/feature_extraction.py b/src/paradigma/gait/feature_extraction.py index 8ba1dc3..2d60d68 100644 --- a/src/paradigma/gait/feature_extraction.py +++ b/src/paradigma/gait/feature_extraction.py @@ -603,7 +603,7 @@ def extract_peak_angular_velocity( # compute the backward peak angular velocity, defined by the maximum positive angular velocity between the two peaks df.loc[index, f'backward_peak_{velocity_colname}'].append(np.abs(max(row[velocity_colname][l_extrema_indices[j]:l_extrema_indices[j+1]]))) - return + return df def extract_temporal_domain_features(config: IMUConfig, df_windowed:pd.DataFrame, l_gravity_stats=['mean', 'std']) -> pd.DataFrame: diff --git a/src/paradigma/gait/gait_analysis.py b/src/paradigma/gait/gait_analysis.py index cd236e0..bd13e90 100644 --- a/src/paradigma/gait/gait_analysis.py +++ b/src/paradigma/gait/gait_analysis.py @@ -224,7 +224,7 @@ def extract_arm_activity_features(df: pd.DataFrame, config: ArmActivityFeatureEx df_windowed = df_windowed.drop(columns=[f'{config.angle_colname}_amplitudes']) # compute the forward and backward peak angular velocity using the extrema of the angular velocity - extract_peak_angular_velocity( + df_windowed = extract_peak_angular_velocity( df=df_windowed, velocity_colname=config.velocity_colname, angle_minima_colname=f'{config.angle_colname}_minima', @@ -296,7 +296,7 @@ def detect_other_arm_activities(df: pd.DataFrame, config: FilteringGaitConfig, c [f'{x}_power_above_tremor' for x in config.l_accelerometer_cols] + \ [f'cc_{i}_accelerometer' for i in range(1, 13)] + [f'cc_{i}_gyroscope' for i in range(1, 13)] + \ [f'grav_{x}_mean' for x in config.l_accelerometer_cols] + [f'grav_{x}_std' for x in config.l_accelerometer_cols] + \ - ['range_of_motion', f'forward_peak_{config.velocity_colname}_mean', f'backward_peak_{config.velocity_colname}_mean', 'forward_peak_{config.velocity_colname}_std', + ['range_of_motion', f'forward_peak_{config.velocity_colname}_mean', f'backward_peak_{config.velocity_colname}_mean', f'forward_peak_{config.velocity_colname}_std', f'backward_peak_{config.velocity_colname}_std', f'{config.angle_colname}_perc_power', f'{config.angle_colname}_dominant_frequency'] + \ [f'{x}_dominant_frequency' for x in config.l_accelerometer_cols] X = df.loc[:, clf.feature_names_in_] @@ -341,8 +341,8 @@ def quantify_arm_swing(df: pd.DataFrame, config: ArmSwingQuantificationConfig) - del df # create peak angular velocity - df_filtered.loc[:, 'peak_{config.velocity_colname}'] = df_filtered.loc[:, ['forward_peak_{config.velocity_colname}_mean', 'backward_peak_{config.velocity_colname}_mean']].mean(axis=1) - df_filtered = df_filtered.drop(columns=['forward_peak_{config.velocity_colname}_mean', 'backward_peak_{config.velocity_colname}_mean']) + df_filtered.loc[:, f'peak_{config.velocity_colname}'] = df_filtered.loc[:, [f'forward_peak_{config.velocity_colname}_mean', f'backward_peak_{config.velocity_colname}_mean']].mean(axis=1) + df_filtered = df_filtered.drop(columns=[f'forward_peak_{config.velocity_colname}_mean', f'backward_peak_{config.velocity_colname}_mean']) # Segmenting @@ -365,7 +365,7 @@ def quantify_arm_swing(df: pd.DataFrame, config: ArmSwingQuantificationConfig) - time_colname=DataColumns.TIME, segment_nr_colname=DataColumns.SEGMENT_NR, window_step_size_s=config.window_step_size, - l_metrics=['range_of_motion', 'peak_{config.velocity_colname}'], + l_metrics=['range_of_motion', f'peak_{config.velocity_colname}'], l_aggregates=['median'], l_quantiles=[0.95] ) @@ -394,7 +394,7 @@ def quantify_arm_swing_io(path_to_feature_input: Union[str, Path], path_to_predi assert df_features[DataColumns.TIME].equals(df_predictions[DataColumns.TIME]) # Subset features - l_feature_cols = [DataColumns.TIME, 'range_of_motion', 'forward_peak_{config.velocity_colname}_mean', 'backward_peak_{config.velocity_colname}_mean'] + l_feature_cols = [DataColumns.TIME, 'range_of_motion', f'forward_peak_{config.velocity_colname}_mean', f'backward_peak_{config.velocity_colname}_mean'] df_features = df_features[l_feature_cols] # Concatenate features and predictions @@ -407,7 +407,7 @@ def quantify_arm_swing_io(path_to_feature_input: Union[str, Path], path_to_predi metadata_time.file_name = 'arm_swing_time.bin' metadata_samples.channels = ['range_of_motion_median', 'range_of_motion_quantile_95', - 'peak_{config.velocity_colname}_median', 'peak_{config.velocity_colname}_quantile_95'] + f'peak_{config.velocity_colname}_median', f'peak_{config.velocity_colname}_quantile_95'] metadata_samples.units = ['deg', 'deg', 'deg/s', 'deg/s'] metadata_time.channels = [DataColumns.TIME, 'segment_duration_ms'] diff --git a/src/paradigma/gait/gait_analysis_config.py b/src/paradigma/gait/gait_analysis_config.py index f44d0e8..627a0d4 100644 --- a/src/paradigma/gait/gait_analysis_config.py +++ b/src/paradigma/gait/gait_analysis_config.py @@ -205,12 +205,12 @@ def __init__(self) -> None: self.initialize_column_names() self.d_channels_values = { - "angle_perc_power": "proportion", + f"{self.angle_colname}_perc_power": "proportion", "range_of_motion": "deg", - "forward_peak_ang_vel_mean": DataUnits.ROTATION, - "forward_peak_ang_vel_std": DataUnits.ROTATION, - "backward_peak_ang_vel_mean": DataUnits.ROTATION, - "backward_peak_ang_vel_std": DataUnits.ROTATION, + f"forward_peak_{self.velocity_colname}_mean": DataUnits.ROTATION, + f"forward_peak_{self.velocity_colname}_std": DataUnits.ROTATION, + f"backward_peak_{self.velocity_colname}_mean": DataUnits.ROTATION, + f"backward_peak_{self.velocity_colname}_std": DataUnits.ROTATION, "std_norm_acc": DataUnits.GRAVITY, "grav_accelerometer_x_mean": DataUnits.GRAVITY, "grav_accelerometer_x_std": DataUnits.GRAVITY, @@ -233,7 +233,7 @@ def __init__(self) -> None: "accelerometer_z_power_tremor": "X", "accelerometer_z_power_above_tremor": "X", "accelerometer_z_dominant_frequency": DataUnits.FREQUENCY, - "angle_dominant_frequency": DataUnits.FREQUENCY, + f"{self.angle_colname}_dominant_frequency": DataUnits.FREQUENCY, } for sensor in ["accelerometer", "gyroscope"]: @@ -243,11 +243,19 @@ def __init__(self) -> None: class FilteringGaitConfig(IMUConfig): + def initialize_column_names( + self + ) -> None: + + self.angle_colname=DataColumns.ANGLE + self.velocity_colname=DataColumns.VELOCITY + def __init__(self) -> None: super().__init__() self.classifier_file_name = "gait_filtering_classifier.pkl" self.set_filenames_values("arm_activity") + self.initialize_column_names() @@ -257,6 +265,8 @@ def __init__(self) -> None: super().__init__() self.set_filenames_values("arm_activity") + self.angle_colname = DataColumns.ANGLE + self.velocity_colname = DataColumns.VELOCITY self.pred_other_arm_activity_proba_colname = DataColumns.PRED_OTHER_ARM_ACTIVITY_PROBA self.pred_other_arm_activity_colname = DataColumns.PRED_OTHER_ARM_ACTIVITY diff --git a/tests/data/3.extracted_features/gait/arm_activity_meta.json b/tests/data/3.extracted_features/gait/arm_activity_meta.json index 4a6c7a5..2c60fd9 100644 --- a/tests/data/3.extracted_features/gait/arm_activity_meta.json +++ b/tests/data/3.extracted_features/gait/arm_activity_meta.json @@ -28,10 +28,10 @@ "channels": [ "angle_perc_power", "range_of_motion", - "forward_peak_ang_vel_mean", - "forward_peak_ang_vel_std", - "backward_peak_ang_vel_mean", - "backward_peak_ang_vel_std", + "forward_peak_velocity_mean", + "forward_peak_velocity_std", + "backward_peak_velocity_mean", + "backward_peak_velocity_std", "std_norm_acc", "grav_accelerometer_x_mean", "grav_accelerometer_x_std", diff --git a/tests/data/5.quantification/gait/arm_swing_meta.json b/tests/data/5.quantification/gait/arm_swing_meta.json index a4fce64..af7f641 100644 --- a/tests/data/5.quantification/gait/arm_swing_meta.json +++ b/tests/data/5.quantification/gait/arm_swing_meta.json @@ -30,8 +30,8 @@ "channels": [ "range_of_motion_median", "range_of_motion_quantile_95", - "peak_ang_vel_median", - "peak_ang_vel_quantile_95" + "peak_velocity_median", + "peak_velocity_quantile_95" ], "units": [ "deg", From a4d47bd3f07900e3f53ff8cc02a9083d7c6fc0f1 Mon Sep 17 00:00:00 2001 From: Erikpostt Date: Tue, 22 Oct 2024 14:40:42 +0200 Subject: [PATCH 4/8] Update dependencies and add nbstripout --- poetry.lock | 605 +++++++++++++++++++++++++------------------------ pyproject.toml | 1 + 2 files changed, 307 insertions(+), 299 deletions(-) diff --git a/poetry.lock b/poetry.lock index 52a2257..14e34f1 100644 --- a/poetry.lock +++ b/poetry.lock @@ -482,73 +482,73 @@ test = ["pytest"] 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@@ -0,0 +1,3 @@ +*.ipynb filter=nbstripout +*.zpln filter=nbstripout +*.ipynb diff=ipynb From 1e207b076dfb514b6be2623a9b145cdc13ae97ec Mon Sep 17 00:00:00 2001 From: Erikpostt Date: Thu, 24 Oct 2024 09:58:03 +0200 Subject: [PATCH 6/8] Attempt at stripping outputs from jupyter notebooks --- docs/notebooks/gait/gait_analysis.ipynb | 336 ++++++++++++------------ 1 file changed, 168 insertions(+), 168 deletions(-) diff --git a/docs/notebooks/gait/gait_analysis.ipynb b/docs/notebooks/gait/gait_analysis.ipynb index 97a11de..3ed031e 100644 --- a/docs/notebooks/gait/gait_analysis.ipynb +++ b/docs/notebooks/gait/gait_analysis.ipynb @@ -1,171 +1,171 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Gait analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "from paradigma.preprocessing_config import IMUPreprocessingConfig\n", - "from paradigma.gait.gait_analysis import extract_gait_features_io, detect_gait_io, extract_arm_activity_features_io, detect_other_arm_activities_io, quantify_arm_swing_io\n", - "from paradigma.gait.gait_analysis_config import GaitFeatureExtractionConfig, GaitDetectionConfig, ArmActivityFeatureExtractionConfig, FilteringGaitConfig, ArmSwingQuantificationConfig\n", - "from paradigma.imu_preprocessing import preprocess_imu_data_io" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "tags": [ - "parameters" - ] - }, - "outputs": [], - "source": [ - "branch = 'gait'\n", - "sensor = 'imu'\n", - "\n", - "path_to_data = '../../../tests/data'\n", - "path_to_classifier = os.path.join(path_to_data, '0.classification', branch)\n", - "path_to_sensor_data = os.path.join(path_to_data, '1.sensor_data', sensor)\n", - "path_to_preprocessed_data = os.path.join(path_to_data, '2.preprocessed_data', sensor)\n", - "path_to_extracted_features = os.path.join(path_to_data, '3.extracted_features', branch)\n", - "path_to_predictions = os.path.join(path_to_data, '4.predictions', branch)\n", - "path_to_quantification = os.path.join(path_to_data, '5.quantification', branch)\n", - "\n", - "# Cell has the tag 'parameters', so it won't overwrite the parameters when running the notebook in tests" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Preprocessing" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "config = IMUPreprocessingConfig()\n", - "preprocess_imu_data_io(path_to_sensor_data, path_to_preprocessed_data, config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Extract gait features" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "config = GaitFeatureExtractionConfig()\n", - "extract_gait_features_io(path_to_preprocessed_data, path_to_extracted_features, config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Detect gait" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "config = GaitDetectionConfig()\n", - "detect_gait_io(path_to_extracted_features, path_to_predictions, path_to_classifier, config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Extract arm actvitiy features" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "config = ArmActivityFeatureExtractionConfig()\n", - "extract_arm_activity_features_io(path_to_preprocessed_data, path_to_extracted_features, config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Filter gait" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "config = FilteringGaitConfig()\n", - "detect_other_arm_activities_io(path_to_extracted_features, path_to_predictions, path_to_classifier, config)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Quantify arm swing" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "config = ArmSwingQuantificationConfig()\n", - "quantify_arm_swing_io(path_to_extracted_features, path_to_predictions, path_to_quantification, config)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "paradigma-Fn6RLG4_-py3.11", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.11.5" - } + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Gait analysis" + ] }, - "nbformat": 4, - "nbformat_minor": 2 + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from paradigma.preprocessing_config import IMUPreprocessingConfig\n", + "from paradigma.gait.gait_analysis import extract_gait_features_io, detect_gait_io, extract_arm_activity_features_io, detect_other_arm_activities_io, quantify_arm_swing_io\n", + "from paradigma.gait.gait_analysis_config import GaitFeatureExtractionConfig, GaitDetectionConfig, ArmActivityFeatureExtractionConfig, FilteringGaitConfig, ArmSwingQuantificationConfig\n", + "from paradigma.imu_preprocessing import preprocess_imu_data_io" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "tags": [ + "parameters" + ] + }, + "outputs": [], + "source": [ + "branch = 'gait'\n", + "sensor = 'imu'\n", + "\n", + "path_to_data = '../../../tests/data'\n", + "path_to_classifier = os.path.join(path_to_data, '0.classification', branch)\n", + "path_to_sensor_data = os.path.join(path_to_data, '1.sensor_data', sensor)\n", + "path_to_preprocessed_data = os.path.join(path_to_data, '2.preprocessed_data', sensor)\n", + "path_to_extracted_features = os.path.join(path_to_data, '3.extracted_features', branch)\n", + "path_to_predictions = os.path.join(path_to_data, '4.predictions', branch)\n", + "path_to_quantification = os.path.join(path_to_data, '5.quantification', branch)\n", + "\n", + "# Cell has the tag 'parameters', so it won't overwrite the parameters when running the notebook in tests" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Preprocessing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "config = IMUPreprocessingConfig()\n", + "preprocess_imu_data_io(path_to_sensor_data, path_to_preprocessed_data, config)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Extract gait features" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "config = GaitFeatureExtractionConfig()\n", + "extract_gait_features_io(path_to_preprocessed_data, path_to_extracted_features, config)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Detect gait" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "config = GaitDetectionConfig()\n", + "detect_gait_io(path_to_extracted_features, path_to_predictions, path_to_classifier, config)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Extract arm actvitiy features" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "config = ArmActivityFeatureExtractionConfig()\n", + "extract_arm_activity_features_io(path_to_preprocessed_data, path_to_extracted_features, config)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Filter gait" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "config = FilteringGaitConfig()\n", + "detect_other_arm_activities_io(path_to_extracted_features, path_to_predictions, path_to_classifier, config)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Quantify arm swing" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "config = ArmSwingQuantificationConfig()\n", + "quantify_arm_swing_io(path_to_extracted_features, path_to_predictions, path_to_quantification, config)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "paradigma-Fn6RLG4_-py3.11", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 } From ed3bf29b22b9cd15ffdc948434175a4aa30d8194 Mon Sep 17 00:00:00 2001 From: Erikpostt Date: Thu, 24 Oct 2024 11:15:13 +0200 Subject: [PATCH 7/8] Small corrected of order of extrema --- src/paradigma/gait/feature_extraction.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/paradigma/gait/feature_extraction.py b/src/paradigma/gait/feature_extraction.py index 2d60d68..c4d50fb 100644 --- a/src/paradigma/gait/feature_extraction.py +++ b/src/paradigma/gait/feature_extraction.py @@ -512,7 +512,7 @@ def extract_angle_extremes( for col in [f'{angle_colname}_new_minima', f'{angle_colname}_new_maxima']: df.loc[df.apply(lambda x: type(x[col].tolist())==int, axis=1), col] = df.loc[df.apply(lambda x: type(x[col].tolist())==int, axis=1), col].apply(lambda x: [x]) - df[f'{angle_colname}_extrema_values'] = df.apply(lambda x: [x[angle_colname][i] for i in np.concatenate([x[f'{angle_colname}_new_minima'], x[f'{angle_colname}_new_maxima']])], axis=1) + df[f'{angle_colname}_extrema_values'] = df.apply(lambda x: [x[angle_colname][i] for i in sorted(np.concatenate([x[f'{angle_colname}_new_minima'], x[f'{angle_colname}_new_maxima']]))], axis=1) return From 2bf7be6d369a93ea3d92e62be88af10cabaad7e9 Mon Sep 17 00:00:00 2001 From: Erikpostt Date: Thu, 24 Oct 2024 11:26:26 +0200 Subject: [PATCH 8/8] Solve test issue --- poetry.lock | 171 ++++++++++-------- .../gait/arm_activity_values.bin | Bin 272208 -> 272208 bytes 2 files changed, 93 insertions(+), 78 deletions(-) diff --git a/poetry.lock b/poetry.lock index 14e34f1..e072b73 100644 --- a/poetry.lock +++ b/poetry.lock @@ -669,88 +669,103 @@ devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benc [[package]] name = "frozenlist" -version = "1.4.1" +version = "1.5.0" description = "A list-like structure which implements collections.abc.MutableSequence" optional = false python-versions = ">=3.8" files = [ - {file = 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