diff --git a/cognitive_load _analysis/adicht_utils.py b/cognitive_load _analysis/adicht_utils.py index 26f6350632708570cfa556dce803a354a9ca0076..2a7275a19694dd9ba195193c18240769afce1099 100644 --- a/cognitive_load _analysis/adicht_utils.py +++ b/cognitive_load _analysis/adicht_utils.py @@ -169,7 +169,7 @@ def atmung_process(atmung_signal_:np.ndarray, sampling_rate_=1000): #peak_signals, info = nk.rsp_peaks(rsp_cleaned) #amplitude = nk.rsp_amplitude(rsp_cleaned, peak_signals) # TODO perform process and select breath rate or RVT - rsp_rate = nk.rsp_rate(rsp_cleaned, sampling_rate=sampling_rate_) + rsp_rate =nk.rsp_rate(rsp_cleaned, sampling_rate=sampling_rate_) return rsp_rate @@ -339,7 +339,7 @@ def slice_channels(channels: list, indx_start: np.ndarray, indx_end: np.ndarray, post_process = 2 * post_process data = slice_signals(channel, indx_start, indx_end, take_mean, post_process) if channel.name == "Atmung": - post_process = 3 * post_process + post_process = 3 * post_processt data = slice_signals(channel, indx_start, indx_end, take_mean, post_process) else: data = slice_signals(channel, indx_start, indx_end) @@ -349,7 +349,8 @@ def slice_channels(channels: list, indx_start: np.ndarray, indx_end: np.ndarray, if mean == False: normalized_data = np.subtract(data,data[0]).astype(np.float32) else: - normalized_data = data - data[0] + #normalized_data = data - data[0] # uncomment for baseline subraction + normalized_data = data channels_df[channel.name] = np.delete(normalized_data, 0) return channels_df, Error @@ -406,10 +407,10 @@ def slice_channels_for_analysis(channels: list, indx_start: np.ndarray, indx_end else: data = slice_signals(channel, indx_start, indx_end, take_mean, post_process) - # baseline subtraction + # uncomment for baseline subtraction if base_normalization: baseline = np.mean(data[0]) - data = data - baseline + #data = data - baseline #data = np.subtract(data, baseline).astype(np.float16) #data = signal.detrend(data, axis=0, type="constant", bp= baseline,overwrite_data=False) diff --git a/cognitive_load _analysis/smell_load_analysis.py b/cognitive_load _analysis/smell_load_analysis.py index 4080a685a0971795e59ad248b6a11707e138ea64..1fc49323293aad6c84b22fb78929ff8a569798ad 100644 --- a/cognitive_load _analysis/smell_load_analysis.py +++ b/cognitive_load _analysis/smell_load_analysis.py @@ -55,7 +55,7 @@ def read_adicht_file(file: str, front_offset: int = 24e3, back_offset: int = 24e slice_end = np.delete(slice_end, baseline_ids[0, 1]) # replacing baseline tick positions - slice_start.put(baseline_ids[0, 0], 0) + slice_start.put(baseline_ids[0, 0], 1) slice_end.put(0, baseline_tickpositions[0, 1]) # Removing baseline_end tick positions @@ -88,7 +88,7 @@ def get_data_for_analysis(directory: str = "smell_adicht_files/", channels: list = ['Atmung', 'Hautleitfähigkeit', 'HR'], start_block: list = ['baseline_start'], end_block: list = ['baseline_end'], offeset_sec: int = 24, - return_signals:bool=True) \ + return_signals:bool=False) \ -> [np.ndarray, np.ndarray, np.ndarray, np.ndarray]: """ Main function that collects data from all subjects