diff --git a/analysis/1_preprocessing_physio.py b/analysis/1_preprocessing_physio.py index 8df2aad..6cedbfc 100644 --- a/analysis/1_preprocessing_physio.py +++ b/analysis/1_preprocessing_physio.py @@ -16,15 +16,6 @@ mne.set_log_level(verbose="WARNING") -def fig2img(fig): - """Convert a Matplotlib figure to a PIL Image and return it""" - buffer = io.BytesIO() - fig.savefig(buffer) - buffer.seek(0) - img = PIL.Image.open(buffer) - return img - - def qc_physio(df, info, sub, plot_ecg=[], plot_ppg=[]): """Quality control (QC) of physiological signals.""" @@ -32,7 +23,7 @@ def qc_physio(df, info, sub, plot_ecg=[], plot_ppg=[]): nk.ecg_plot(df, info) # Save ECG plot fig = plt.gcf() img = ill.image_text( - sub, color="black", size=100, x=-0.82, y=0.90, image=fig2img(fig) + sub, color="black", size=100, x=-0.82, y=0.90, image=ill.fig2img(fig) ) plt.close(fig) # Do not show the plot in the console plot_ecg.append(img) @@ -43,7 +34,7 @@ def qc_physio(df, info, sub, plot_ecg=[], plot_ppg=[]): nk.ppg_plot(df, info) # Save ECG plot fig = plt.gcf() img = ill.image_text( - sub, color="black", size=100, x=-0.82, y=0.90, image=fig2img(fig) + sub, color="black", size=100, x=-0.82, y=0.90, image=ill.fig2img(fig) ) plot_ppg.append(img) plt.close(fig) @@ -66,7 +57,7 @@ def qc_eeg(raw, sub, plot_psd=[]): psd = raw.compute_psd(picks="eeg", n_fft=256 * 20, fmax=80).to_data_frame() psd.plot(x="freq", y=ch_names, ax=ax, logy=True) - img = ill.image_mosaic([fig2img(i) for i in [fig1, fig2]], nrows=1, ncols=2) + img = ill.image_mosaic([ill.fig2img(i) for i in [fig1, fig2]], nrows=1, ncols=2) # To image plot_psd.append(img) @@ -86,46 +77,47 @@ def qc_hep(epochs, reject_log, sub, plot_hep=[]): hep.to_data_frame().plot(x="time", y=["AF7", "AF8"], ax=ax[1]) # To image - plot_hep.append(fig2img(fig)) + plot_hep.append(ill.fig2img(fig)) plt.close("all") return plot_hep def qc_heo(power, itc, sub, plot_heo=[]): - fig1 = power.plot_topo( + fig, ax = plt.subplots(2, 2, figsize=(8, 8)) + # fig.tight_layout() + power.plot( baseline=None, mode="logratio", - title=f"{sub}: Time-frequency Power", vmin=0, - # vmax=1e-7, - fig_facecolor="white", - fig_background=None, - font_color="black", + axes=ax[0], show=False, ) + ax[0][0].set_title("Power (AF7)") + ax[0][1].set_title("Power (AF8)") - fig2 = itc.plot_topo( + itc.plot( title="Inter-Trial coherence", vmin=0, - vmax=1.0, cmap="CMRmap", - fig_facecolor="white", - fig_background=None, - font_color="black", + axes=ax[1], show=False, ) + ax[1][0].set_title("Inter-Trial coherence (AF7)") + ax[1][1].set_title("Inter-Trial coherence (AF8)") - img = ill.image_mosaic([fig2img(i) for i in [fig1, fig2]], nrows=1, ncols=2) - plot_heo.append(img) + fig.tight_layout() + fig.suptitle(f"{sub}") + + plot_heo.append(ill.fig2img(fig)) plt.close("all") return plot_heo def analyze_hep(raw, events): - out = {} + out = {} # Initialize info dictionary # From Zaccaro et al. (preprint): Epochs were not baseline-corrected (Petzschner et al., 2019). - # This decision was made to exclude confounding residual CFA evoked by ECG waves that precede + # This decision was made to exclude confounding the evoked signal by ECG waves that precede # the R-peak (P and Q-waves). epochs = mne.Epochs( @@ -144,8 +136,12 @@ def analyze_hep(raw, events): # Autoreject try: + original_epochs = epochs.copy() ar = autoreject.AutoReject(verbose=False, picks="eeg") - epochs, out["autoreject_log"] = ar.fit_transform(epochs, return_log=True) + epochs = ar.fit_transform(original_epochs) + out["autoreject_log"] = ar.get_reject_log(original_epochs.copy().pick("eeg")) + # reject_log.plot("horizontal", aspect="auto") + # p2 = reject_log.plot_epochs(original_epochs.copy().pick("eeg")) except IndexError: out["autoreject_log"] = None out["epochs"] = epochs @@ -172,7 +168,7 @@ def analyze_hep(raw, events): rez[f"HEP_Amplitude_400_600_{ch}"] = hep2[ch].mean() # Compute Time-frequency Power - freqs = np.logspace(*np.log10([6, 60]), num=16) # freqs of interest (log-spaced) + freqs = np.logspace(*np.log10([3, 60]), num=20) # freqs of interest (log-spaced) n_cycles = freqs / 2.0 # different number of cycle per frequency power, itc = mne.time_frequency.tfr_morlet( epochs, @@ -192,140 +188,6 @@ def analyze_hep(raw, events): return rez, out -# Processing ================================================================================= - - -def process_tap(sub, path_eeg, path_beh, qc_tap_ecg=[]): - # Open TAP file - file = [file for file in os.listdir(path_eeg) if "TAP" in file] - file = path_eeg + [f for f in file if ".vhdr" in f][0] - tap = mne.io.read_raw_brainvision(file, preload=True, verbose=False) - - # Load behavioral data - file = [file for file in os.listdir(path_beh) if "TAP" in file] - file = path_beh + [f for f in file if ".tsv" in f][0] - tap_beh = pd.read_csv(file, sep="\t") - - # Find events and crop just before (1 second +/-) first and after last - events = nk.events_find( - tap["PHOTO"][0][0], - threshold_keep="below", - duration_min=50, - duration_max=500, - ) - # In milliseconds (= /2 for 2000 Hz) - onsets_photo = events["onset"] / (tap.info["sfreq"] / 1000) - - # If long first event, remove it - if (len(onsets_photo) == 421) & ( - np.diff(onsets_photo)[0] > np.mean(np.diff(onsets_photo)[1:61]) * 3 - ): - onsets_photo = onsets_photo[1::] - - # Manual fix - # plt.vlines(onsets_photo, 0, 1, color="blue") - # plt.vlines(tap_beh["Tapping_Times"].values + onsets_photo[0], 1, 2, color="red") - if sub in ["sub-01", "sub-17", "sub-22", "sub-26"]: - onsets_photo = onsets_photo[1::] - - if len(onsets_photo) != 420: - print(f" - WARNING: Number of events is not 420 ({len(onsets_photo)})") - - # Correct for delay between photo and behavioral data - onsets_beh = tap_beh["Tapping_Times"].values + onsets_photo[0] - - # Compute correlation - if scipy.stats.pearsonr(onsets_photo, onsets_beh)[0] < 0.999: - # plt.scatter(onsets_beh, onsets_photo) - print(f" - WARNING: Correlation between photo and beh onsets is low.") - - # Preprocess physio - bio = tap.to_data_frame().bfill() # Backfill missing values at the beginning - bio, info = nk.bio_process( - ecg=bio["ECG"].values, - rsp=bio["RSP"].values, - sampling_rate=tap.info["sfreq"], - ) - - # QC - qc_tap_ecg, _ = qc_physio(bio, info, sub, plot_ecg=qc_tap_ecg, plot_ppg=None) - - # Epoch around each tap - epochs = nk.epochs_create( - bio, - onsets_beh, # onsets_photo - sampling_rate=tap.info["sfreq"], - epochs_start=-4, - epochs_end=4, - ) - - # Closest R-peak to each tap - dat = pd.DataFrame() # Initialize dataframe - for _, e in epochs.items(): - # Filter df at 0 - at_index = e.iloc[np.argmin(np.abs(e.index)), :] - # R-peaks - rpeaks = e[e["ECG_R_Peaks"] == 1].index.values - r = np.nan if len(rpeaks) == 0 else rpeaks[np.argmin(np.abs(rpeaks))] - r_pre = np.nan if sum(rpeaks < 0) == 0 else np.max(rpeaks[rpeaks < 0]) - r_post = np.nan if sum(rpeaks >= 0) == 0 else np.min(rpeaks[rpeaks >= 0]) - # RSP - peaks - p = e[e["RSP_Peaks"] == 1].index.values - rsp_peak = np.nan if len(p) == 0 else p[np.argmin(np.abs(p))] - rsp_pre = np.nan if sum(p < 0) == 0 else np.max(p[p < 0]) - rsp_post = np.nan if sum(p >= 0) == 0 else np.min(p[p >= 0]) - # RSP - troughs - t = e[e["RSP_Troughs"] == 1].index.values - rsp_trough = np.nan if len(t) == 0 else t[np.argmin(np.abs(t))] - rsp_trough_pre = np.nan if sum(t < 0) == 0 else np.max(t[t < 0]) - rsp_trough_post = np.nan if sum(t >= 0) == 0 else np.min(t[t >= 0]) - - dat = pd.concat( - [ - dat, - pd.DataFrame( - { - "ECG_Rate": at_index["ECG_Rate"], - "Closest_R": r, - "Closest_R_Pre": r_pre, - "Closest_R_Post": r_post, - "ECG_Phase_Atrial": at_index["ECG_Phase_Atrial"], - "ECG_Phase_Ventricular": at_index["ECG_Phase_Ventricular"], - "Closest_RSP_Peak": rsp_peak, - "Closest_RSP_Peak_Pre": rsp_pre, - "Closest_RSP_Peak_Post": rsp_post, - "Closest_RSP_Trough": rsp_trough, - "Closest_RSP_Trough_Pre": rsp_trough_pre, - "Closest_RSP_Trough_Post": rsp_trough_post, - "RSP_Phase": at_index["RSP_Phase"], - "RSP_Phase_Completion": at_index["RSP_Phase_Completion"], - }, - index=[0], - ), - ], - axis=0, - ) - - # TODO: HEP amplitude of closest (pre) R-peak - - # Compute tapping rate - tap_beh["Tapping_Rate"] = np.nan - for cond in tap_beh["Condition"].unique(): - tap_times = tap_beh[tap_beh["Condition"] == cond]["Tapping_Times"].values - np.diff(tap_times) - rate = nk.signal_rate( - tap_times, - sampling_rate=1000, - show=False, - interpolation_method="monotone_cubic", - ) - tap_beh.loc[tap_beh["Condition"] == cond, "Tapping_Rate"] = rate - - # Merge with behavioral data - dat = pd.concat([tap_beh, dat.reset_index(drop=True)], axis=1) - return dat, qc_tap_ecg - - # Variables ================================================================================== # Change the path to your local data folder. # The data can be downloaded from OpenNeuro (TODO). @@ -345,7 +207,6 @@ def process_tap(sub, path_eeg, path_beh, qc_tap_ecg=[]): qc_rs_ppg = [] qc_rs_hep = [] qc_rs_heo = [] -qc_tap_ecg = [] qc_hct_psd = [] qc_hct_ecg = [] qc_hct_ppg = [] @@ -355,7 +216,7 @@ def process_tap(sub, path_eeg, path_beh, qc_tap_ecg=[]): # sub = "sub-19" # Loop through participants ================================================================== -for sub in meta["participant_id"].values[0::]: +for sub in meta["participant_id"].values[0:8:]: # Print progress and comments print(sub) print(" * " + meta[meta["participant_id"] == sub]["Comments"].values[0]) @@ -369,125 +230,127 @@ def process_tap(sub, path_eeg, path_beh, qc_tap_ecg=[]): file = path_beh + [f for f in file if ".tsv" in f][0] dfsub = pd.read_csv(file, sep="\t") - # # Resting State ========================================================================== - - # # Preprocessing -------------------------------------------------------------------------- - # print(" - RS - Preprocessing") - - # # Open RS assessment - # file = [file for file in os.listdir(path_beh) if "RS" in file] - # file = path_beh + [f for f in file if ".tsv" in f][0] - # rs_beh = pd.read_csv(file, sep="\t").drop(["participant_id"], axis=1) - # dfsub = pd.concat([dfsub, rs_beh.add_prefix("RS_")], axis=1) - - # # Open RS file - # file = [file for file in os.listdir(path_eeg) if "RS" in file] - # file = path_eeg + [f for f in file if ".vhdr" in f][0] - # rs = mne.io.read_raw_brainvision(file, preload=True) - - # # Detect onset of RS - # events = nk.events_find( - # rs.to_data_frame()["PHOTO"], - # threshold_keep="below", - # duration_min=int(rs.info["sfreq"] * 5), - # ) - # assert len(events["onset"]) == 1 # Check that there is only one event - - # # Filter EEG - # rs = rs.set_montage("standard_1020") - # rs, _ = mne.set_eeg_reference(rs, ["TP9", "TP10"]) - # rs = rs.notch_filter(np.arange(50, 251, 50), picks="eeg") - # rs = rs.filter(1, 60, picks="eeg") - # rs = nk.mne_crop( - # rs, smin=events["onset"][0], smax=events["onset"][0] + events["duration"][0] - # ) - - # # Fix for recording interruption - # if sub in ["sub-10", "sub-16", "sub-20"]: # Cut till before the nan - # first_na = np.where(rs.to_data_frame()[["AF7"]].isna())[0][0] - # rs = nk.mne_crop(rs, smin=0, smax=first_na - 1) - # if sub in ["sub-15", "sub-19"]: # Take second half - # last_na = np.where(rs.to_data_frame()[["AF7"]][0:800000].isna())[0][-1] - # rs = nk.mne_crop(rs, smin=last_na, smax=None) - - # # QC - # qc_rs_psd = qc_eeg(rs, sub, plot_psd=qc_rs_psd) - - # # Heartbeat Evoked Potentials (HEP) ------------------------------------------------------- - # print(" - RS - HEP") - - # # Preprocess physio - # ecg, info = nk.bio_process( - # ecg=rs["ECG"][0][0], - # ppg=rs["PPG_Muse"][0][0], - # sampling_rate=rs.info["sfreq"], - # ) - - # # Find R-peaks - # events, _ = nk.events_to_mne(ecg["ECG_R_Peaks"].values.nonzero()[0]) - - # rs_hep, out = analyze_hep(rs, events) - - # # QC - # qc_rs_ecg, qc_rs_ppg = qc_physio( - # ecg, info, sub, plot_ecg=qc_rs_ecg, plot_ppg=qc_rs_ppg - # ) - # qc_rs_hep = qc_hep(out["epochs"], out["autoreject_log"], sub, plot_hep=qc_rs_hep) - # qc_rs_heo = qc_heo(out["timefrequency"], out["itc"], sub, plot_heo=qc_rs_heo) - - # # Add Features - # if sub not in ["sub-06", "sub-09"]: - # dfsub = pd.concat([dfsub, rs_hep.add_prefix("RS_")], axis=1) - # out["df"]["participant_id"] = sub - # out["df"]["Condition"] = "RestingState" - # df_hep = pd.concat([df_hep, out["df"]], axis=0) - - # Tapping Task =========================================================================== - print(" - TAP - Preprocessing") - - if sub in ["sub-09"]: - print(" - WARNING: Skipping TAP for this participant (No ECG).") - elif sub in ["sub-31"]: - print(" - WARNING: Skipping TAP for this participant (ECG electrode fell).") - else: - tap, qc_tap_ecg = process_tap(sub, path_eeg, path_beh, qc_tap_ecg) - df_tap = pd.concat([df_tap, tap], axis=0) - - -# # Heartbeat Counting Task (HCT) =========================================================== - -# # Preprocessing -------------------------------------------------------------------------- -# print(" - HCT - Preprocessing") -# # Open HCT file -# file = [file for file in os.listdir(path_eeg) if "HCT" in file] -# file = path_eeg + [f for f in file if ".vhdr" in f][0] -# hct = mne.io.read_raw_brainvision(file, preload=True, verbose=False) - -# # Filter EEG -# hct = hct.set_montage("standard_1020") -# hct, _ = mne.set_eeg_reference(hct, ["TP9", "TP10"]) -# hct = hct.notch_filter(np.arange(50, 251, 50), picks="eeg") -# hct = hct.filter(1, 60, picks="eeg") - -# # Find events and crop just before (1 second +/-) first and after last -# events = nk.events_find( -# hct["PHOTO"][0][0], threshold_keep="below", duration_min=15000 -# ) -# if sub in ["sub-16"]: -# events["onset"] = events["onset"][0:-1] -# events["duration"] = events["duration"][0:-1] -# start_end = [events["onset"][0], events["onset"][-1] + events["duration"][-1]] -# if sub in ["sub-13"]: -# start_end[0] = 2178 -# hct = nk.mne_crop(hct, smin=start_end[0] - 2000, smax=start_end[1] + 2000) - -# qc_hct_psd = qc_eeg(hct, sub, plot_psd=qc_hct_psd) - -# # Find events (again as data was cropped) and epoch -# events = nk.events_find( -# hct["PHOTO"][0][0], threshold_keep="below", duration_min=15000 -# ) -# assert len(events["onset"]) == 6 # Check that there are 6 epochs (the 6 intervals) + # Resting State ========================================================================== + + # Preprocessing -------------------------------------------------------------------------- + print(" - RS - Preprocessing") + + # Open RS assessment + file = [file for file in os.listdir(path_beh) if "RS" in file] + file = path_beh + [f for f in file if ".tsv" in f][0] + rs_beh = pd.read_csv(file, sep="\t").drop(["participant_id"], axis=1) + dfsub = pd.concat([dfsub, rs_beh.add_prefix("RS_")], axis=1) + + # Open RS file + file = [file for file in os.listdir(path_eeg) if "RS" in file] + file = path_eeg + [f for f in file if ".vhdr" in f][0] + rs = mne.io.read_raw_brainvision(file, preload=True) + + # Detect onset of RS + events = nk.events_find( + rs.to_data_frame()["PHOTO"], + threshold_keep="below", + duration_min=int(rs.info["sfreq"] * 5), + ) + assert len(events["onset"]) == 1 # Check that there is only one event + + # Filter EEG + rs = rs.set_montage("standard_1020") + rs, _ = mne.set_eeg_reference(rs, ["TP9", "TP10"]) + rs = rs.notch_filter(np.arange(50, 251, 50), picks="eeg") + rs = rs.filter(1, 60, picks="eeg") + rs = nk.mne_crop( + rs, smin=events["onset"][0], smax=events["onset"][0] + events["duration"][0] + ) + + # Fix for recording interruption + if sub in ["sub-10", "sub-16", "sub-20"]: # Cut till before the nan + first_na = np.where(rs.to_data_frame()[["AF7"]].isna())[0][0] + rs = nk.mne_crop(rs, smin=0, smax=first_na - 1) + if sub in ["sub-15", "sub-19"]: # Take second half + last_na = np.where(rs.to_data_frame()[["AF7"]][0:800000].isna())[0][-1] + rs = nk.mne_crop(rs, smin=last_na, smax=None) + if sub in ["sub-24"]: # Crop out nans at the beginning + + def consecutive_nans(ch="AF7"): + nans = np.where(rs.to_data_frame()[ch].isna())[0] + return np.max(np.split(nans, np.where(np.diff(nans) != 1)[0] + 1)[0]) + + nans = np.max([consecutive_nans(ch) for ch in ["AF7", "ECG", "PPG_Muse"]]) + rs = nk.mne_crop(rs, smin=nans, smax=None) + + # QC + qc_rs_psd = qc_eeg(rs, sub, plot_psd=qc_rs_psd) + + # Heartbeat Evoked Potentials (HEP) ------------------------------------------------------- + print(" - RS - HEP") + + # Preprocess physio + bio, info = nk.bio_process( + ecg=rs["ECG"][0][0], + ppg=rs["PPG_Muse"][0][0], + sampling_rate=rs.info["sfreq"], + ) + + # Find R-peaks + events, _ = nk.events_to_mne(bio["ECG_R_Peaks"].values.nonzero()[0]) + + rs_hep, out = analyze_hep(rs, events) + + # QC + qc_rs_ecg, qc_rs_ppg = qc_physio( + bio, info, sub, plot_ecg=qc_rs_ecg, plot_ppg=qc_rs_ppg + ) + qc_rs_hep = qc_hep(out["epochs"], out["autoreject_log"], sub, plot_hep=qc_rs_hep) + qc_rs_heo = qc_heo(out["timefrequency"], out["itc"], sub, plot_heo=qc_rs_heo) + + # Add Features + if sub not in ["sub-06", "sub-09"]: + dfsub = pd.concat([dfsub, rs_hep.add_prefix("RS_")], axis=1) + out["df"]["participant_id"] = sub + out["df"]["Condition"] = "RestingState" + df_hep = pd.concat([df_hep, out["df"]], axis=0) + + # Hear Rate Variability (HRV) ------------------------------------------------------------- + hrv = nk.hrv(bio["ECG_R_Peaks"].values.nonzero()[0], sampling_rate=rs.info["sfreq"]) + idx = ["MeanNN", "SDNN", "RMSSD", "SampEn", "HF", "HFD", "LFHF", "IALS", "Ca", "AI"] + hrv = hrv[["HRV_" + s for s in idx]] + dfsub = pd.concat([dfsub, hrv], axis=1) + + # Heartbeat Counting Task (HCT) =========================================================== + + # Preprocessing -------------------------------------------------------------------------- + print(" - HCT - Preprocessing") + # Open HCT file + file = [file for file in os.listdir(path_eeg) if "HCT" in file] + file = path_eeg + [f for f in file if ".vhdr" in f][0] + hct = mne.io.read_raw_brainvision(file, preload=True, verbose=False) + + # Filter EEG + hct = hct.set_montage("standard_1020") + hct, _ = mne.set_eeg_reference(hct, ["TP9", "TP10"]) + hct = hct.notch_filter(np.arange(50, 251, 50), picks="eeg") + hct = hct.filter(1, 60, picks="eeg") + + # Find events and crop just before (1 second +/-) first and after last + events = nk.events_find( + hct["PHOTO"][0][0], threshold_keep="below", duration_min=15000 + ) + if sub in ["sub-16"]: + events["onset"] = events["onset"][0:-1] + events["duration"] = events["duration"][0:-1] + start_end = [events["onset"][0], events["onset"][-1] + events["duration"][-1]] + if sub in ["sub-13"]: + start_end[0] = 2178 + hct = nk.mne_crop(hct, smin=start_end[0] - 2000, smax=start_end[1] + 2000) + + qc_hct_psd = qc_eeg(hct, sub, plot_psd=qc_hct_psd) + + # Find events (again as data was cropped) and epoch + events = nk.events_find( + hct["PHOTO"][0][0], threshold_keep="below", duration_min=15000 + ) + assert len(events["onset"]) == 6 # Check that there are 6 epochs (the 6 intervals) # # HCT - HEP ------------------------------------------------------------------------------ # print(" - HCT - HEP") @@ -573,36 +436,36 @@ def process_tap(sub, path_eeg, path_beh, qc_tap_ecg=[]): # df = pd.concat([df, dfsub], axis=0) -# # Clean up and Save data +# Clean up and Save data # df = pd.merge(meta, df) -# # Keep only columns that do not end with number or _R +# Keep only columns that do not end with number or _R # df = df.filter(regex="^(?!.*[0-9]$)(?!.*_R$).*") # df.to_csv("../data/data.csv", index=False) -# df_hep.to_csv("../data/data_hep.csv", index=False) -df_tap.to_csv("../data/data_tap.csv", index=False) +df_hep.to_csv("../data/data_hep.csv", index=False) # Save figures -qc_rs_psd = ill.image_mosaic(qc_rs_psd, ncols=2, nrows="auto") -qc_rs_psd.save("figures/signals_qc_rs_psd.png") -qc_rs_ecg = ill.image_mosaic(qc_rs_ecg, ncols=2, nrows="auto") -qc_rs_ecg.save("figures/signals_qc_rs_ecg.png") -qc_rs_ppg = ill.image_mosaic(qc_rs_ppg, ncols=2, nrows="auto") -qc_rs_ppg.save("figures/signals_qc_rs_ppg.png") -qc_rs_hep = ill.image_mosaic(qc_rs_hep, ncols=2, nrows="auto") -qc_rs_hep.save("figures/signals_qc_rs_hep.png") -qc_rs_heo = ill.image_mosaic(qc_rs_heo, ncols=2, nrows="auto") -qc_rs_heo.save("figures/signals_qc_rs_heo.png") -qc_tap_ecg = ill.image_mosaic(qc_tap_ecg, ncols=2, nrows="auto") -qc_tap_ecg.save("figures/signals_qc_tap_ecg.png") -qc_hct_ecg = ill.image_mosaic(qc_hct_ecg, ncols=2, nrows="auto") -qc_hct_ecg.save("figures/signals_qc_hct_ecg.png") -qc_hct_ppg = ill.image_mosaic(qc_hct_ppg, ncols=2, nrows="auto") -qc_hct_ppg.save("figures/signals_qc_hct_ppg.png") -qc_hct_psd = ill.image_mosaic(qc_hct_psd, ncols=2, nrows="auto") -qc_hct_psd.save("figures/signals_qc_hct_psd.png") -qc_hct_hep = ill.image_mosaic(qc_hct_hep, ncols=2, nrows="auto") -qc_hct_hep.save("figures/signals_qc_hct_hep.png") -qc_hct_heo = ill.image_mosaic(qc_hct_heo, ncols=2, nrows="auto") -qc_hct_heo.save("figures/signals_qc_hct_heo.png") +ill.image_mosaic(qc_rs_psd, ncols=4, nrows="auto").save("figures/signals_qc_rs_psd.png") +ill.image_mosaic(qc_rs_ecg, ncols=4, nrows="auto").save("figures/signals_qc_rs_ecg.png") +ill.image_mosaic(qc_rs_ppg, ncols=4, nrows="auto").save("figures/signals_qc_rs_ppg.png") +ill.image_mosaic(qc_rs_hep, ncols=4, nrows="auto").save("figures/signals_qc_rs_hep.png") +ill.image_mosaic(qc_rs_heo, ncols=4, nrows="auto").save("figures/signals_qc_rs_heo.png") + +print("Done!") + +ill.image_mosaic(qc_hct_psd, ncols=4, nrows="auto").save( + "figures/signals_qc_hct_psd.png" +) +ill.image_mosaic(qc_hct_ecg, ncols=4, nrows="auto").save( + "figures/signals_qc_hct_ecg.png" +) +ill.image_mosaic(qc_hct_ppg, ncols=4, nrows="auto").save( + "figures/signals_qc_hct_ppg.png" +) +ill.image_mosaic(qc_hct_hep, ncols=4, nrows="auto").save( + "figures/signals_qc_hct_hep.png" +) +ill.image_mosaic(qc_hct_heo, ncols=4, nrows="auto").save( + "figures/signals_qc_hct_heo.png" +) print("Done!") diff --git a/analysis/figures/signals_qc_rs_ecg.png b/analysis/figures/signals_qc_rs_ecg.png index 0e64e61..7d29045 100644 Binary files a/analysis/figures/signals_qc_rs_ecg.png and b/analysis/figures/signals_qc_rs_ecg.png differ diff --git a/analysis/figures/signals_qc_rs_heo.png b/analysis/figures/signals_qc_rs_heo.png index 0978ebf..c3044b9 100644 Binary files a/analysis/figures/signals_qc_rs_heo.png and b/analysis/figures/signals_qc_rs_heo.png differ diff --git a/analysis/figures/signals_qc_rs_hep.png b/analysis/figures/signals_qc_rs_hep.png index 6fcc067..105610f 100644 Binary files a/analysis/figures/signals_qc_rs_hep.png and b/analysis/figures/signals_qc_rs_hep.png differ diff --git a/analysis/figures/signals_qc_rs_ppg.png b/analysis/figures/signals_qc_rs_ppg.png index 6116a4e..455eb26 100644 Binary files a/analysis/figures/signals_qc_rs_ppg.png and b/analysis/figures/signals_qc_rs_ppg.png differ diff --git a/analysis/figures/signals_qc_rs_psd.png b/analysis/figures/signals_qc_rs_psd.png index b1c114c..ebc10ec 100644 Binary files a/analysis/figures/signals_qc_rs_psd.png and b/analysis/figures/signals_qc_rs_psd.png differ diff --git a/analysis/figures/signals_qc_tap_ecg.png b/analysis/figures/signals_qc_tap_ecg.png deleted file mode 100644 index 1469017..0000000 Binary files a/analysis/figures/signals_qc_tap_ecg.png and /dev/null differ diff --git a/data/data_hep.csv b/data/data_hep.csv index a28c0c4..64130bd 100644 --- a/data/data_hep.csv +++ b/data/data_hep.csv @@ -1,18538 +1,3508 @@ time,AF7,AF8,ECG,PPG_Muse,participant_id,Condition --0.3,1.6694999719880204,-3.1978278188183014,-0.026508943158528205,130405.30590230667,sub-01,RestingState --0.298,1.7112909399456482,-2.816851479494673,-0.02561309440217334,130405.21830054291,sub-01,RestingState --0.296,1.8727233738274494,-2.240282097739759,-0.024923529976592227,130404.98105495235,sub-01,RestingState --0.294,2.130387304134474,-1.5151514240776642,-0.02436587312625243,130404.75223880608,sub-01,RestingState --0.292,2.4464725210785336,-0.6995856169897089,-0.02361078667460341,130404.52059023066,sub-01,RestingState --0.29,2.774433722410784,0.1440951760828974,-0.02283372784129591,130404.23850067846,sub-01,RestingState --0.288,3.0660636311362506,0.9576831073524614,-0.022082568170061097,130404.08697421994,sub-01,RestingState --0.286,3.278868061636881,1.6945468268393755,-0.021241738067325724,130404.05720827695,sub-01,RestingState --0.284,3.3812258219486626,2.3231273684706735,-0.020395552788483887,130404.06346675717,sub-01,RestingState --0.282,3.3562676280359227,2.829276844888828,-0.019653417411502897,130403.99960990509,sub-01,RestingState --0.28,3.2028534465256273,3.214563530299193,-0.01895771490219152,130403.9316824968,sub-01,RestingState --0.278,2.934357466410507,3.4920154223170536,-0.018185976794033106,130403.73663500663,sub-01,RestingState --0.276,2.575795019066067,3.6805604619775507,-0.017528651404798534,130403.51863975567,sub-01,RestingState --0.274,2.159761083317962,3.7988645930823854,-0.016781295107262433,130403.35020352779,sub-01,RestingState --0.272,1.7222406106241392,3.860486902344002,-0.016096416824764842,130403.11192333793,sub-01,RestingState --0.27,1.2975410560606342,3.8692918384495956,-0.01537053407115539,130402.87620420626,sub-01,RestingState --0.268,0.9148119650247668,3.8189070544717696,-0.014575648748535728,130402.72718792406,sub-01,RestingState --0.266,0.5964830007068158,3.69580353594324,-0.013735941643556527,130402.78164857526,sub-01,RestingState --0.264,0.35760124099843754,3.4839832081594104,-0.012991862927498404,130402.94879579388,sub-01,RestingState --0.262,0.20661498980499263,3.1719149825155037,-0.012386854395811803,130403.23877204907,sub-01,RestingState --0.26,0.14662383918819086,2.7587446334700165,-0.011798394626364068,130403.5437415197,sub-01,RestingState --0.258,0.17595007416346903,2.25838238130992,-0.011263444616833325,130403.7531886024,sub-01,RestingState --0.256,0.2891926182668058,1.7022757710803733,-0.010586302182978319,130403.88999321568,sub-01,RestingState --0.254,0.478533294344578,1.1392715865399898,-0.010026273116064367,130403.94716757127,sub-01,RestingState --0.252,0.7339265820454592,0.6320280201441256,-0.009761818760604652,130403.85352781552,sub-01,RestingState --0.25,1.0429489021629814,0.25014417553237145,-0.009292876613522225,130403.5829884669,sub-01,RestingState --0.248,1.3903723912325185,0.06032872476301939,-0.008866970749616773,130403.15698778832,sub-01,RestingState --0.246,1.7578759979596283,0.11634601508572087,-0.008407946271113244,130402.63921302579,sub-01,RestingState --0.244,2.1245659384458127,0.45002821327561726,-0.007875672185189336,130402.1202679782,sub-01,RestingState --0.242,2.4678277899122563,1.0640151919252994,-0.00749884728931024,130401.73066485759,sub-01,RestingState --0.24,2.7650093425534457,1.9292180294199084,-0.007275985231242524,130401.43748303948,sub-01,RestingState --0.238,2.9955852781744046,2.9860368323212074,-0.006852690901123436,130401.1512042063,sub-01,RestingState --0.236,3.1437297912199025,4.149627100189506,-0.006358606448885756,130400.85781886033,sub-01,RestingState --0.234,3.2008404213501223,5.319536751809889,-0.00597731321404045,130400.62578018992,sub-01,RestingState --0.232,3.1668711768529,6.390885875909728,-0.005715022548911771,130400.53597354131,sub-01,RestingState --0.23,3.051268080287005,7.267439580989922,-0.0054964741312688884,130400.42131953896,sub-01,RestingState --0.228,2.872522558976617,7.873443136315325,-0.005396713852472321,130400.38488805962,sub-01,RestingState --0.226,2.6558787750966752,8.162353496758255,-0.00518522785257354,130400.26752035292,sub-01,RestingState --0.224,2.4304573980381337,8.122731074886074,-0.004966040723271977,130400.09694708257,sub-01,RestingState --0.222,2.224905031621901,7.7780411427147715,-0.004845723461958715,130400.00386702838,sub-01,RestingState --0.22,2.0624304231796673,7.18195671656586,-0.004697929684193984,130400.12125169593,sub-01,RestingState --0.218,1.9573492580077725,6.410815117262254,-0.004601621258880037,130400.14806648575,sub-01,RestingState --0.216,1.9123734848197285,5.552296061985383,-0.004440834614990988,130400.0512381277,sub-01,RestingState --0.214,1.9174266105252238,4.693613361188901,-0.004221715480591889,130400.04516621439,sub-01,RestingState --0.212,1.9510534887740214,3.9104156911779557,-0.004000155741994594,130399.84545454533,sub-01,RestingState --0.21,1.9835474868638547,3.2578286160723917,-0.0038215647505996254,130399.64737109904,sub-01,RestingState --0.208,1.9813827321480324,2.764982680566968,-0.0037557709887099337,130399.49708276807,sub-01,RestingState --0.206,1.9125814886175339,2.4339579825561115,-0.0034617234055860156,130399.30715739493,sub-01,RestingState --0.204,1.7523665120860852,2.243537169551911,-0.0032129258173533674,130399.04180800536,sub-01,RestingState --0.202,1.4874087575115407,2.1550465487980777,-0.003119552350107293,130398.95800542744,sub-01,RestingState --0.2,1.1184693636199252,2.1199121493078796,-0.002926498071181248,130399.02069199464,sub-01,RestingState --0.198,0.6615254434605833,2.0889608924803587,-0.0030027141091845044,130399.06970827682,sub-01,RestingState --0.196,0.14605086653621355,2.020281198937329,-0.0030874351487814714,130399.25588534608,sub-01,RestingState --0.194,-0.3889539671423083,1.8850205193433633,-0.003158038439121084,130399.4913331072,sub-01,RestingState --0.192,-0.8997604113079458,1.671497032963385,-0.0032514466093658775,130399.70139077354,sub-01,RestingState --0.19,-1.3446968313258971,1.3839206820430718,-0.0033847966065312486,130399.83670284934,sub-01,RestingState --0.188,-1.6907007571736945,1.0388177756058838,-0.0033883175476763132,130399.88778833109,sub-01,RestingState --0.186,-1.9175843843063258,0.6609290542532938,-0.0033391654300966235,130399.84097693351,sub-01,RestingState --0.184,-2.02106118972462,0.2772366781631061,-0.0033862873963904864,130399.83968792384,sub-01,RestingState --0.182,-2.012858437482342,-0.08765083168102847,-0.003380951821954737,130399.81185549522,sub-01,RestingState --0.18,-1.918818970412421,-0.4159435362861997,-0.0034576590154218457,130399.80018656715,sub-01,RestingState --0.178,-1.7752252542516411,-0.6992219630762746,-0.003435291477714672,130399.63977272715,sub-01,RestingState --0.176,-1.6228401351995732,-0.9376203820088519,-0.003296439254907901,130399.47756105823,sub-01,RestingState --0.174,-1.5009358051464958,-1.1380104792252006,-0.0030996962538156017,130399.40188263217,sub-01,RestingState --0.172,-1.4413782857242574,-1.3094975253990242,-0.003096199241890779,130399.4038500678,sub-01,RestingState --0.17,-1.4638143160716037,-1.4590940181490621,-0.0028964497348807966,130399.45006784263,sub-01,RestingState --0.168,-1.5739697297002408,-1.5897330551791227,-0.0026868087817849087,130399.36687584799,sub-01,RestingState --0.166,-1.7641309700055903,-1.6985330653191306,-0.002724175189916617,130399.39024762572,sub-01,RestingState --0.164,-2.0154475966339227,-1.777291721691545,-0.0029611211041212252,130399.5177747625,sub-01,RestingState --0.162,-2.302049404261964,-1.8146895937414724,-0.0030115957892047464,130399.86248303941,sub-01,RestingState --0.16,-2.5957245215780187,-1.7992759115859425,-0.003238487329715966,130400.27661126184,sub-01,RestingState --0.158,-2.8710033429440664,-1.7244601169418456,-0.003692421689503188,130400.68439620068,sub-01,RestingState --0.156,-3.1096118795321366,-1.5930647564546063,-0.003945409453817065,130401.12406716424,sub-01,RestingState --0.154,-3.303461845503346,-1.420890057351058,-0.004273613317211862,130401.62208276805,sub-01,RestingState --0.152,-3.4556193921845084,-1.2380804317114484,-0.004553080285381865,130402.09540366344,sub-01,RestingState --0.15,-3.5784703471434978,-1.0862728672818895,-0.004720175164817097,130402.46675712337,sub-01,RestingState --0.148,-3.690503346264863,-1.014370304379616,-0.004858887865962491,130402.77751017631,sub-01,RestingState --0.146,-3.8124584888778283,-1.0721184635821164,-0.004951678682355586,130403.02956241515,sub-01,RestingState --0.144,-3.9629857343993624,-1.3022941634482919,-0.0044463384981419385,130403.26901289012,sub-01,RestingState --0.142,-4.154609775409569,-1.7334083523984725,-0.003296957396651059,130403.40559701512,sub-01,RestingState --0.14,-4.390636227539727,-2.3726112555965657,-0.002007274571010873,130403.41387381256,sub-01,RestingState --0.138,-4.663557131165324,-3.201775992438664,-0.001159449179654119,130403.25607191326,sub-01,RestingState --0.136,-4.955557173237032,-4.176756678839385,-0.00047150102032096804,130403.20703867036,sub-01,RestingState --0.134,-5.24154051063975,-5.231114581145085,0.00010878203345392931,130403.2473541386,sub-01,RestingState --0.132,-5.493491533348999,-6.284076673074269,0.0006846262759995341,130403.2481512892,sub-01,RestingState --0.13,-5.684814321039607,-7.249279011507147,0.0013580509474573018,130403.30052578039,sub-01,RestingState --0.128,-5.794397150837904,-8.045438414596676,0.0030984369105662665,130403.50403663488,sub-01,RestingState --0.126,-5.809874282530615,-8.607007969834374,0.005489899565638234,130403.86962347355,sub-01,RestingState --0.124,-5.729069333855896,-8.891797760608558,0.007651743001932125,130404.31858887365,sub-01,RestingState --0.122,-5.560357871757402,-8.886982110765862,0.009056410504577842,130405.00437584784,sub-01,RestingState --0.12,-5.3211868279865815,-8.610385466001471,0.009212641329270759,130405.64440298501,sub-01,RestingState --0.118,-5.034629258733776,-8.10673527501744,0.00854757108480456,130406.31811397572,sub-01,RestingState --0.116,-4.725395743262767,-7.440666056754005,0.007387209112976772,130407.08551560377,sub-01,RestingState --0.114,-4.415659571839837,-6.686261652737813,0.005711601442162443,130407.7040705563,sub-01,RestingState --0.112,-4.122107896708992,-5.916847036543088,0.0035917074337459185,130408.12425373126,sub-01,RestingState --0.11,-3.8542574444890216,-5.195460740681575,0.0012852326673888357,130408.51758819529,sub-01,RestingState --0.108,-3.6140013841300798,-4.56672533462373,-0.0011953587505647819,130408.9295115333,sub-01,RestingState --0.106,-3.3964535966651987,-4.052514653591845,-0.0034466591119571024,130409.25262890082,sub-01,RestingState --0.104,-3.1916918371300755,-3.6504763760048093,-0.005102039544793852,130409.52082767973,sub-01,RestingState --0.102,-2.9876204415498893,-3.3370038389312016,-0.005852170818844828,130409.66906377212,sub-01,RestingState --0.1,-2.773406927017641,-3.074452608527247,-0.006277363798655292,130409.79559023054,sub-01,RestingState --0.098,-2.5425202943567755,-2.8201156348087775,-0.0065814072804617095,130410.0289687923,sub-01,RestingState 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--0.042,-5.664088988893557,0.16881749057872097,-0.006007430071525357,109134.68203124993,sub-02,HCT --0.04,-6.794499079514309,0.2302209119477804,-0.005383909200124134,109137.53218749996,sub-02,HCT --0.038,-8.124400740566749,0.3191188407214447,-0.004791782170699054,109140.2678125,sub-02,HCT --0.036,-8.956963195688159,0.4426423825229192,-0.004380727468912178,109142.98156249992,sub-02,HCT --0.034,-8.695161016299,0.5991461118543534,-0.00497628004217404,109145.51427083333,sub-02,HCT --0.032,-6.988398285773736,0.778416190225151,-0.005815576739842075,109148.03901041667,sub-02,HCT --0.03,-3.8123296903902335,0.9644011396709482,-0.007298628394928526,109150.40447916671,sub-02,HCT --0.028,0.5215452115545395,1.1392700224561674,-0.008874718857987322,109152.77666666666,sub-02,HCT --0.026,5.450671122568102,1.2878869848440528,-0.008926704796079633,109154.8926041667,sub-02,HCT --0.024,10.297883152208238,1.4013725499924552,-0.005073458799476306,109156.9680208333,sub-02,HCT --0.022,14.428511826885124,1.478519165472142,0.0012344257019807027,109159.12187500003,sub-02,HCT --0.02,17.400490952796442,1.5250896166254473,0.013514135941077254,109161.2673958333,sub-02,HCT --0.018,19.049917964884642,1.5508236534553268,0.03195590366764616,109163.31437500002,sub-02,HCT --0.016,19.50734688782639,1.5651732859290526,0.053829912282526524,109165.08953125007,sub-02,HCT --0.014,19.146057232796682,1.5735012729260927,0.07642241689066097,109166.96057291659,sub-02,HCT --0.012,18.457937693429397,1.5746012473835034,0.1060996258010467,109168.80468749999,sub-02,HCT --0.01,17.91005937383383,1.560570171242309,0.1413274760047595,109170.98473958336,sub-02,HCT --0.008,17.811717086861194,1.5192400351902444,0.18002477422356594,109173.42958333333,sub-02,HCT --0.006,18.22411021412966,1.4383814540458137,0.22375451639294638,109176.00994791662,sub-02,HCT --0.004,18.954055277973595,1.3106356225606064,0.271061131954193,109178.64536458324,sub-02,HCT 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--0.192,-4.331962348340213,-4.234153326318977,-0.003666666143376811,140036.5514423077,sub-08,HCT --0.19,-3.9840261656422538,-3.884027078673745,-0.0028873411072895695,140037.0565705128,sub-08,HCT --0.188,-3.752550871277858,-3.6674645619678095,-0.002546389233649302,140037.35560897438,sub-08,HCT --0.186,-3.6342368987991827,-3.5810833087327354,-0.001984947971560849,140037.9673076923,sub-08,HCT --0.184,-3.5958756115412793,-3.5881194101807923,-0.0009765766861117957,140038.63733974358,sub-08,HCT --0.182,-3.5858564900569774,-3.6318557946929992,0.0007638440088601038,140039.16362179484,sub-08,HCT --0.18,-3.549852669154967,-3.6532328099977867,0.002247139324213211,140039.22355769234,sub-08,HCT --0.178,-3.4461695464960953,-3.6076264217773093,0.003300895938539329,140039.25192307698,sub-08,HCT --0.176,-3.256305424044569,-3.4758037541042883,0.004583892808892787,140038.60464743586,sub-08,HCT --0.174,-2.9882003862966404,-3.2661986829083256,0.0048036620905921356,140037.99519230766,sub-08,HCT --0.172,-2.671842648735817,-3.0089102073382623,0.00603914514869804,140037.51089743595,sub-08,HCT --0.17,-2.348890380755614,-2.7435192139817652,0.00783688585894803,140037.02227564104,sub-08,HCT --0.168,-2.0598059906835062,-2.5049529571488995,0.00978425814985083,140036.9692307692,sub-08,HCT --0.166,-1.8322992134549227,-2.311938204991715,0.010858260551377988,140037.96266025645,sub-08,HCT --0.164,-1.6742940145506677,-2.1611621380816715,0.012371367244766307,140039.19711538462,sub-08,HCT --0.162,-1.5735475042934315,-2.029181277873094,0.014164832031402072,140040.64695512824,sub-08,HCT --0.16,-1.5033210008099736,-1.8811744359776081,0.015955788978876975,140042.29679487183,sub-08,HCT --0.158,-1.4322554470828999,-1.6836428657849634,0.017697129730517288,140044.19519230767,sub-08,HCT --0.156,-1.3352136011327174,-1.4170007095588995,0.01784418908974681,140046.22003205126,sub-08,HCT --0.154,-1.2013951607361077,-1.0838539350264114,0.01763865308215222,140047.8362179487,sub-08,HCT 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--0.3,-9.37730995729191,-1.0576353285182631,0.0003383135958104348,104910.53535814615,sub-12,HCT --0.298,-4.642763259282582,-0.829548863999587,0.0002645141431871294,104911.16077949444,sub-12,HCT --0.296,-0.5943491951196198,-0.6875431722895016,0.00033511423145425536,104911.68047752803,sub-12,HCT --0.294,1.5533091256144016,-0.5939503949980617,0.0005156159549104897,104912.29838483142,sub-12,HCT --0.292,1.1140522300871192,-0.5104437082157871,0.0007327159053547252,104912.8090589888,sub-12,HCT --0.29,-1.867851718001164,-0.41121270610383087,0.00047620629551914525,104913.42004915733,sub-12,HCT --0.288,-6.612242266262461,-0.2919066128763875,-2.406634427983391e-05,104914.09813904495,sub-12,HCT --0.286,-11.793592951549273,-0.17157032798328237,1.3969766241827086e-05,104914.85298455057,sub-12,HCT --0.284,-15.886905926263623,-0.08759370684777741,-4.954990322908632e-05,104915.77257724716,sub-12,HCT --0.282,-17.572671303754763,-0.08440843737199673,0.0002987094738548901,104916.84508426966,sub-12,HCT --0.28,-16.09075145693295,-0.19948576790412081,0.0006656706225270445,104917.76976825847,sub-12,HCT --0.278,-11.455775625492807,-0.45046209447245367,0.0007121002755181935,104918.58202247189,sub-12,HCT --0.276,-4.460750388386476,-0.8270063536172272,0.0009012769370467986,104919.57127808989,sub-12,HCT --0.274,3.524706361985393,-1.289994646743522,0.0014549045438991125,104920.44301264045,sub-12,HCT --0.272,10.903536200890441,-1.7781105665126808,0.0016636163177970568,104921.25410814604,sub-12,HCT --0.27,16.256376548017542,-2.220518846103538,0.0015781238707455088,104922.04813904493,sub-12,HCT --0.268,18.687233344212753,-2.551994506374979,0.001282651389530399,104922.88117977527,sub-12,HCT --0.266,18.023512839948218,-2.726467591079137,0.0011673158680258154,104923.59627809007,sub-12,HCT --0.264,14.831836540973159,-2.725840691116822,0.001202070381788707,104924.40754915726,sub-12,HCT --0.262,10.24174382483201,-2.5619045428084766,0.0015468319800371934,104925.30663623592,sub-12,HCT --0.26,5.631534689560704,-2.2712761896109503,0.0017345566630617474,104926.18198735958,sub-12,HCT --0.258,2.2722448108105064,-1.9053732804560541,0.0022301235760353697,104927.04146769663,sub-12,HCT --0.256,1.008212176701716,-1.5182908272447213,0.002173885692650632,104927.7295997191,sub-12,HCT --0.254,2.0629849087986485,-1.1560344015335542,0.002109529287348539,104928.36025280901,sub-12,HCT --0.252,5.022013478634199,-0.8498634084373581,0.0020808289068012563,104928.81516853938,sub-12,HCT --0.25,8.973688095444116,-0.6147169054262255,0.0020276837668856697,104929.23939606747,sub-12,HCT --0.248,12.770714785393524,-0.4520188490381217,0.002171264060936407,104929.76811797754,sub-12,HCT --0.246,15.33668420448755,-0.355391358754269,0.002256872391356436,104930.21611657304,sub-12,HCT --0.244,15.92871344694034,-0.316387772507718,0.0024361053815216173,104930.54269662924,sub-12,HCT --0.242,14.29330944773626,-0.3283519513856133,0.0026589636413987274,104930.83781601125,sub-12,HCT --0.24,10.682763131870512,-0.3873186145930873,0.0025802690347637787,104931.42507022475,sub-12,HCT --0.238,5.740997784617439,-0.48945561898655204,0.0026065422642700883,104932.22303370782,sub-12,HCT --0.236,0.29564151205102657,-0.6269119336138129,0.0024754074620873196,104933.19852528095,sub-12,HCT --0.234,-4.872251960673033,-0.7840274707817712,0.002537956484887429,104934.30663623595,sub-12,HCT --0.232,-9.213161109957507,-0.9356877777395677,0.002882218362782338,104935.27025983148,sub-12,HCT --0.23,-12.498450175261114,-1.0489436750149486,0.0029037946840157884,104936.45860252803,sub-12,HCT --0.228,-14.787568292630596,-1.088302101320129,0.002498595613334799,104938.15319522472,sub-12,HCT --0.226,-16.305379973509016,-1.0231647387349272,0.0023013564422279783,104939.98226825846,sub-12,HCT --0.224,-17.278782574358434,-0.8349252243469155,0.002538954368039891,104941.57191011228,sub-12,HCT --0.222,-17.788855829201406,-0.5221899124444083,0.002522938930067337,104942.71822331465,sub-12,HCT --0.22,-17.699844562302626,-0.10175865210989375,0.002961183905441578,104943.81509831468,sub-12,HCT --0.218,-16.6833289630579,0.39494483857482177,0.0032525184587810676,104944.83655196632,sub-12,HCT --0.216,-14.322718537563853,0.9291471138832793,0.0033734246712140347,104946.05179073037,sub-12,HCT --0.214,-10.285864351925994,1.4637680536245454,0.003315845670441937,104947.12064606749,sub-12,HCT --0.212,-4.50685111632517,1.971033527590047,0.0029875033592682973,104947.63542837078,sub-12,HCT --0.21,2.686543923624692,2.437115307894693,0.002628370191704368,104948.18230337078,sub-12,HCT --0.208,10.535681828012617,2.8618294153274766,0.0021457035118017834,104948.98170646066,sub-12,HCT --0.206,17.94639401041221,3.254170388531777,0.001270714144585795,104950.02022471903,sub-12,HCT --0.204,23.699673551360586,3.6254783520555356,0.00041484833492737516,104951.029389045,sub-12,HCT --0.202,26.72776033646388,3.9820430605132553,-0.00023800241772668716,104952.08641151682,sub-12,HCT --0.2,26.39167775484537,4.320461264892681,-0.0009249864647052052,104953.11348314607,sub-12,HCT --0.198,22.679932654313582,4.626745491052919,-0.0018051855103818311,104954.10839185395,sub-12,HCT --0.196,16.280697379280888,4.880086658157254,-0.0027005227990542545,104955.2740870787,sub-12,HCT --0.194,8.495033884435465,5.060221244484456,-0.003093890361005742,104956.44610252815,sub-12,HCT --0.192,0.9984849799445665,5.15551296281439,-0.0032536164503497514,104957.61372893256,sub-12,HCT --0.19,-4.502201164459332,5.17001739439302,-0.0028403492313997854,104958.90154494379,sub-12,HCT --0.188,-6.641873784817047,5.125927951516819,-0.002020486738506911,104960.30516151687,sub-12,HCT --0.186,-4.711624556644217,5.060399469560487,-0.0005133105613611112,104961.50684691014,sub-12,HCT --0.184,1.1606335354921766,5.017856567482537,0.0018091582158119466,104962.51566011237,sub-12,HCT --0.182,10.011596650043582,5.039128051053556,0.0048349887795565245,104963.57977528086,sub-12,HCT --0.18,20.22544419164791,5.1509747221135065,0.008353324878532816,104964.44480337085,sub-12,HCT --0.178,29.861370662185323,5.358116538360069,0.011306569112859897,104964.99104634835,sub-12,HCT --0.176,37.047323826570974,5.640700072117812,0.013844211379329516,104965.3225772472,sub-12,HCT --0.174,40.36396548573647,5.958043301016271,0.0158068706928235,104965.4742977528,sub-12,HCT --0.172,39.13993120413674,6.25752021264904,0.01743482133147843,104965.6994733146,sub-12,HCT --0.17,33.58231285207469,6.487424607985883,0.01829251194692621,104966.0180477528,sub-12,HCT --0.168,24.71755374517351,6.609165271201098,0.018478998656601006,104966.56818820225,sub-12,HCT --0.166,14.152743190413945,6.606116081695404,0.018092993424847874,104967.14501404486,sub-12,HCT --0.164,3.713558646881472,6.48685854907217,0.01802835727317467,104967.75775983147,sub-12,HCT --0.162,-4.945560316835807,6.2816035343056305,0.01798561556391757,104968.4596558988,sub-12,HCT --0.16,-10.67490568236388,6.033439269893164,0.017742427594658706,104969.42078651693,sub-12,HCT --0.158,-13.031558759843692,5.786456061124877,0.01748478440344428,104970.32798455057,sub-12,HCT --0.156,-12.305600853496482,5.574224043871927,0.017077211634669867,104971.02471910111,sub-12,HCT --0.154,-9.37542078235773,5.4115425894266425,0.016636563049872752,104971.72057584267,sub-12,HCT --0.152,-5.43375571773733,5.291831174716817,0.016620126737145274,104972.14982443818,sub-12,HCT --0.15,-1.6547130815156472,5.190959578046114,0.01715214281658069,104972.78809691021,sub-12,HCT --0.148,1.109795541604362,5.075911569921197,0.01733412927374094,104973.76000702249,sub-12,HCT --0.146,2.5238004638478135,4.916357599031531,0.01710528597736031,104974.93318117979,sub-12,HCT --0.144,2.810086115170889,4.695316534649336,0.016968336162354894,104976.05912921345,sub-12,HCT --0.142,2.6303577981533466,4.4159831647147785,0.016561219411445876,104977.4210674158,sub-12,HCT --0.14,2.838468969372065,4.102852990422656,0.015981291536702733,104978.91091994384,sub-12,HCT --0.138,4.188782304178902,3.7965138845121986,0.014889296664117906,104980.60670646075,sub-12,HCT --0.136,7.080387186032285,3.543472974169441,0.013246238872531164,104982.44231039325,sub-12,HCT --0.134,11.399912372616265,3.383376097283339,0.010375779042034553,104984.0363764045,sub-12,HCT --0.132,16.512214043596966,3.3373131647296956,0.007264690334271323,104985.46716994385,sub-12,HCT --0.13,21.400929527225088,3.3997512556554246,0.004133253671832129,104986.65403792133,sub-12,HCT --0.128,24.92143217577176,3.5366090425422665,0.0013681186791133103,104987.60495084271,sub-12,HCT --0.126,26.10843038899013,3.690459512494649,-0.0012556678857748417,104988.41829353942,sub-12,HCT --0.124,24.438708294741758,3.791446276229034,-0.00285773200801376,104989.29501404492,sub-12,HCT --0.122,19.976226751469955,3.7718172601972415,-0.003969777404151934,104990.0214185393,sub-12,HCT --0.12,13.361539002734066,3.5807782145631832,-0.004843888137397836,104990.79174859554,sub-12,HCT --0.118,5.633213992328442,3.1962511729789735,-0.005316701746073016,104991.4049157303,sub-12,HCT --0.116,-2.049650865311669,2.6311097316782215,-0.005470613708732548,104992.15670646066,sub-12,HCT --0.114,-8.706615960814348,1.9324002949793624,-0.005976442625802575,104993.23974719102,sub-12,HCT --0.112,-13.780895150646925,1.1737205008867446,-0.006208182878896945,104994.72257724729,sub-12,HCT --0.11,-17.243663064889972,0.4421040991151137,-0.006495119280339398,104996.19775280898,sub-12,HCT --0.108,-19.536773288868837,-0.17778330502261416,-0.0067027754108586165,104997.50509129216,sub-12,HCT --0.106,-21.366322207307633,-0.6187403008555076,-0.00708324124547418,104998.64185393261,sub-12,HCT --0.104,-23.401688587425426,-0.8423869706724693,-0.007418751643236844,104999.9267907303,sub-12,HCT --0.102,-25.987298070417285,-0.8445357768101246,-0.007784626354472813,105001.23476123596,sub-12,HCT 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