From 591b70a1bd7c622dbb2ef9f98dd81aad51855aa8 Mon Sep 17 00:00:00 2001 From: Claudia Onorato Date: Tue, 6 Oct 2020 00:53:17 -0400 Subject: [PATCH 01/25] feature extraction outline --- .editorconfig | 3 + backend/backend/config/constants.py | 26 ++++++++ backend/backend/features/__init__.py | 24 ++++++++ backend/backend/features/constants.py | 29 +++++++++ backend/backend/features/extraction.py | 60 +++++++++++++++++++ backend/backend/features/pipeline/__init__.py | 8 +++ backend/backend/features/pipeline/utils.py | 37 ++++++++++++ backend/backend/features/utils.py | 48 +++++++++++++++ backend/backend/predict.py | 12 ++++ 9 files changed, 247 insertions(+) create mode 100644 backend/backend/config/constants.py create mode 100644 backend/backend/features/__init__.py create mode 100644 backend/backend/features/constants.py create mode 100644 backend/backend/features/extraction.py create mode 100644 backend/backend/features/pipeline/__init__.py create mode 100644 backend/backend/features/pipeline/utils.py create mode 100644 backend/backend/features/utils.py create mode 100644 backend/backend/predict.py diff --git a/.editorconfig b/.editorconfig index e89330a6..2308a7a9 100644 --- a/.editorconfig +++ b/.editorconfig @@ -8,6 +8,9 @@ indent_size = 2 insert_final_newline = true trim_trailing_whitespace = true +[*.py] +indent_size = 4 + [*.md] max_line_length = off trim_trailing_whitespace = false diff --git a/backend/backend/config/constants.py b/backend/backend/config/constants.py new file mode 100644 index 00000000..624f6b66 --- /dev/null +++ b/backend/backend/config/constants.py @@ -0,0 +1,26 @@ +SLEEP_STAGES_VALUES = { + "W": 0, + "N1": 1, + "N2": 2, + "N3": 3, + "REM": 4 +} +N_STAGES = len(SLEEP_STAGES_VALUES.keys()) + +EEG_CHANNELS = [ + 'EEG Fpz-Cz', + 'EEG Pz-Oz' +] + +EPOCH_DURATION = 30 # in seconds + +DATASET_SAMPLE_RATE = 100 # in Hz +OPENBCI_CYTON_SAMPLE_RATE = 250 # in Hz +OPENBCI_GANGLION_SAMPLE_RATE = 200 # in Hz + +AGE_FEATURE_BINS = [ + [0, 49], + [50, 59], + [60, 84], + [85, 110] +] diff --git a/backend/backend/features/__init__.py b/backend/backend/features/__init__.py new file mode 100644 index 00000000..2aeaaaf5 --- /dev/null +++ b/backend/backend/features/__init__.py @@ -0,0 +1,24 @@ +import numpy as np + +from backend.features.extraction import ( + get_eeg_features, + get_categorical_features, +) + + +def get_features(signal, age, sex): + """Returns the raw features + Input + ------- + raw_signal: MNE.Raw object with signals with or without annotations + age: Age of the subject + sex: Sex of the subject + + Returns + ------- + - features X in a vector of (nb_epochs, nb_features) + """ + X_eeg = get_eeg_features(signal) + X_categorical = get_categorical_features(age, sex, X_eeg.shape[0]) + + return np.append(X_categorical, X_eeg, axis=1) diff --git a/backend/backend/features/constants.py b/backend/backend/features/constants.py new file mode 100644 index 00000000..85a16682 --- /dev/null +++ b/backend/backend/features/constants.py @@ -0,0 +1,29 @@ +from backend.config.constants import ( + DATASET_SAMPLE_RATE, + EPOCH_DURATION, +) + +NYQUIST_FREQ = DATASET_SAMPLE_RATE / 2 +MAX_TIME = EPOCH_DURATION - 1. / DATASET_SAMPLE_RATE + +DELTA = "delta" +THETA = "theta" +ALPHA = "alpha" +SIGMA = "sigma" +BETA = "beta" + +FREQ_BANDS_RANGE = { + DELTA: [0.5, 4.5], + THETA: [4.5, 8.5], + ALPHA: [8.5, 11.5], + SIGMA: [11.5, 15.5], + BETA: [15.5, 30] +} + +FREQ_BANDS_ORDERS = { + DELTA: 5, + THETA: 8, + ALPHA: 9, + SIGMA: 9, + BETA: 14 +} diff --git a/backend/backend/features/extraction.py b/backend/backend/features/extraction.py new file mode 100644 index 00000000..6a2b48ac --- /dev/null +++ b/backend/backend/features/extraction.py @@ -0,0 +1,60 @@ +"""Feature extraction tools based off a two channel EEG recording""" +import numpy as np + +from backend.config.constants import ( + EEG_CHANNELS, + AGE_FEATURE_BINS, +) +from backend.features.utils import ( + drop_other_channels, + crop_raw_data, + convert_to_epochs, +) + + +def get_eeg_features(raw_data): + features_file = [] + + for channel in EEG_CHANNELS: + chan_data = drop_other_channels(raw_data.copy(), channel) + # TODO: input actual bed & out of bed times + chan_data = crop_raw_data(chan_data, 0, 100) + X_file_channel = convert_to_epochs( + chan_data + ) + + X_features = feature_union.transform(X_file_channel) + features_file.append(X_features) + + print( + f"Done extracting {X_features.shape[1]} features " + f"on {X_features.shape[0]} epochs for {channel}\n" + ) + + return np.hstack(tuple(features_file)) + + +def get_categorical_features(age, sex, nb_epochs): + """Returns the categorical feature matrix + Input + ------- + age: Age of the subject + sex: Sex of the subject + nb_epochs: corresponds to the nb of epochs which will be analyzed. + + Returns + ------- + Array of size (nb_epochs,nb_categorical_features), which contains + (duplicated) value for all epochs because it concerns the same subject. + """ + age_category = next( + category_index + for category_index, age_range in enumerate(AGE_FEATURE_BINS) + if age >= age_range[0] and age <= age_range[1] + ) + + X_categorical = [sex, age_category] + + return np.array( + X_categorical * nb_epochs + ).reshape(nb_epochs, len(X_categorical)) diff --git a/backend/backend/features/pipeline/__init__.py b/backend/backend/features/pipeline/__init__.py new file mode 100644 index 00000000..6107588b --- /dev/null +++ b/backend/backend/features/pipeline/__init__.py @@ -0,0 +1,8 @@ +from sklearn.pipeline import FeatureUnion + + +feature_union = FeatureUnion([ + ('time_domain', time_domain_pipeline), + ('frequency_domain', frequency_domain_pipeline), + ('subband_time_domain', subband_feature_union) +], n_jobs=1) diff --git a/backend/backend/features/pipeline/utils.py b/backend/backend/features/pipeline/utils.py new file mode 100644 index 00000000..75472009 --- /dev/null +++ b/backend/backend/features/pipeline/utils.py @@ -0,0 +1,37 @@ +def get_data_from_epochs(epochs): + """ + epochs: mne.Epochs + + returns np array of shape (nb_epochs, sampling_rate*epoch_length) + """ + return epochs.get_data().squeeze() + + +def get_transformer(get_feature): + + def get_one_feature_per_epoch(X, get_feature): + """ + X: Input matrix (nb_epochs, sampling_rate*epoch_length) + get_feature: callable + generates one feature for each epoch + + returns matrix (nb_epoch,1) + """ + return [[get_feature(epoch)] for epoch in X] + + return lambda X: get_one_feature_per_epoch(X, get_feature) + + +def get_transformer_list(get_features): + + def get_feature_list_per_epoch(X, get_features): + """ + X: Input matrix (nb_epochs, sampling_rate*epoch_length) + get_feature: callable + generates a list of features for each epoch + + returns matrix (nb_epoch,nb_features) + """ + return [get_features(epoch) for epoch in X] + + return lambda X: get_feature_list_per_epoch(X, get_features) diff --git a/backend/backend/features/utils.py b/backend/backend/features/utils.py new file mode 100644 index 00000000..952d753d --- /dev/null +++ b/backend/backend/features/utils.py @@ -0,0 +1,48 @@ +import mne + +from backend.features.constants import MAX_TIME + + +def drop_other_channels(raw_data, channel_to_keep): + """ + returns: mne.Raw with the two EEG channels + """ + raw_data.drop_channels( + [ch for ch in raw_data.info['ch_names'] if ch != channel_to_keep]) + + return raw_data + + +def crop_raw_data( + raw_data, + bed_seconds, + out_of_bed_seconds +): + """Returns cropped raw signal + Input + ------- + raw_data: instance of mne.Raw + bed_seconds: number of seconds between start of recording & moment at + which the subjet went to bed (in seconds) + out_of_bed_seconds: number of seconds between start of recording & moment + at which the subjet got out of bed (in seconds) + """ + raw_data.crop( + tmin=bed_seconds, + tmax=min(out_of_bed_seconds, raw_data.times[-1]) + ) + + return raw_data + + +def convert_to_epochs(raw_data): + """ + returns: mne.Epochs + """ + return mne.Epochs( + raw=raw_data, + tmin=0., + tmax=MAX_TIME, + preload=True, + baseline=None, + verbose=False) diff --git a/backend/backend/predict.py b/backend/backend/predict.py new file mode 100644 index 00000000..397fa7f9 --- /dev/null +++ b/backend/backend/predict.py @@ -0,0 +1,12 @@ +"""defines functions to predict sleep stages based off EEG signals""" +from backend.features import get_features + + +def predict(raw_eeg, age, sex): + """ + raw_eeg: instance of mne.io.RawArray + Should contain 2 channels (1: FPZ-CZ, 2: PZ-OZ) + sex: should either be 'F' or 'M' + age: indicates the subject's age + """ + X_openbci, y_openbci = get_features(raw_eeg, age, sex) From 42bff7abfa6fa3805e1f80c2288db0b01fb3518b Mon Sep 17 00:00:00 2001 From: Claudia Onorato Date: Tue, 6 Oct 2020 01:02:38 -0400 Subject: [PATCH 02/25] added outline for feature pipeline extraction --- backend/backend/features/extraction.py | 2 + backend/backend/features/pipeline/__init__.py | 21 +++- .../pipeline/frequency_domain_transformers.py | 2 + .../features/pipeline/subband_transformers.py | 2 + .../pipeline/time_domain_transformers.py | 101 ++++++++++++++++++ 5 files changed, 123 insertions(+), 5 deletions(-) create mode 100644 backend/backend/features/pipeline/frequency_domain_transformers.py create mode 100644 backend/backend/features/pipeline/subband_transformers.py create mode 100644 backend/backend/features/pipeline/time_domain_transformers.py diff --git a/backend/backend/features/extraction.py b/backend/backend/features/extraction.py index 6a2b48ac..684b4275 100644 --- a/backend/backend/features/extraction.py +++ b/backend/backend/features/extraction.py @@ -5,6 +5,7 @@ EEG_CHANNELS, AGE_FEATURE_BINS, ) +from backend.features.pipeline import get_feature_union from backend.features.utils import ( drop_other_channels, crop_raw_data, @@ -23,6 +24,7 @@ def get_eeg_features(raw_data): chan_data ) + feature_union = get_feature_union() X_features = feature_union.transform(X_file_channel) features_file.append(X_features) diff --git a/backend/backend/features/pipeline/__init__.py b/backend/backend/features/pipeline/__init__.py index 6107588b..c73ea413 100644 --- a/backend/backend/features/pipeline/__init__.py +++ b/backend/backend/features/pipeline/__init__.py @@ -1,8 +1,19 @@ from sklearn.pipeline import FeatureUnion +from backend.features.pipeline.time_domain_transformers import ( + get_time_domain_pipeline +) +from backend.features.pipeline.frequency_domain_transformers import ( + get_frequency_domain_pipeline +) +from backend.features.pipeline.subband_transformers import ( + get_subband_feature_union +) -feature_union = FeatureUnion([ - ('time_domain', time_domain_pipeline), - ('frequency_domain', frequency_domain_pipeline), - ('subband_time_domain', subband_feature_union) -], n_jobs=1) + +def get_feature_union(): + return FeatureUnion([ + ('time_domain', get_time_domain_pipeline()), + ('frequency_domain', get_frequency_domain_pipeline()), + ('subband_time_domain', get_subband_feature_union()) + ], n_jobs=1) diff --git a/backend/backend/features/pipeline/frequency_domain_transformers.py b/backend/backend/features/pipeline/frequency_domain_transformers.py new file mode 100644 index 00000000..bac350a9 --- /dev/null +++ b/backend/backend/features/pipeline/frequency_domain_transformers.py @@ -0,0 +1,2 @@ +def get_frequency_domain_pipeline(): + pass diff --git a/backend/backend/features/pipeline/subband_transformers.py b/backend/backend/features/pipeline/subband_transformers.py new file mode 100644 index 00000000..408f7d9a --- /dev/null +++ b/backend/backend/features/pipeline/subband_transformers.py @@ -0,0 +1,2 @@ +def get_subband_feature_union(): + pass diff --git a/backend/backend/features/pipeline/time_domain_transformers.py b/backend/backend/features/pipeline/time_domain_transformers.py new file mode 100644 index 00000000..c6841a84 --- /dev/null +++ b/backend/backend/features/pipeline/time_domain_transformers.py @@ -0,0 +1,101 @@ +import numpy as np +from scipy.stats import (skew, kurtosis) +from sklearn.pipeline import FeatureUnion, Pipeline +from sklearn.preprocessing import (FunctionTransformer) + + +from backend.features.pipeline.utils import ( + get_transformer, + get_transformer_list, + get_data_from_epochs_transformer, +) + + +def get_zero_crossing_rate(signal): + """ + Multiplies signal by itself shifted by one. + If the signal crosses the horizontal axis, + the sign will be negative and vice-versa. + + Returns nb of time the signal crossed the horizontal axis + """ + return ((signal[:-1] * signal[1:]) < 0).sum() + + +def get_mean_crossing_rate(signal): + return get_zero_crossing_rate(signal - np.mean(signal)) + + +def hjorth(X): + """ Compute Hjorth mobility and complexity of a time series from either two + [source: https://github.com/forrestbao/pyeeg] + cases below: + 1. X, the time series of type list (default) + 2. D, a first order differential sequence of X (if D is provided, + recommended to speed up) + In case 1, D is computed using Numpy's Difference function. + Notes + ----- + To speed up, it is recommended to compute D before calling this function + because D may also be used by other functions whereas computing it here + again will slow down. + Parameters + ---------- + X + list + a time series + D + list + first order differential sequence of a time series + Returns + ------- + As indicated in return line (mobility, complexity) + """ + D = np.diff(X) + D = D.tolist() + + D.insert(0, X[0]) # pad the first difference + D = np.array(D) + + n = len(X) + + M2 = float(sum(D ** 2)) / n + TP = sum(np.array(X) ** 2) + M4 = 0 + for i in range(1, len(D)): + M4 += (D[i] - D[i - 1]) ** 2 + M4 = M4 / n + + # Hjorth Mobility and Complexity + mobility = np.sqrt(M2 / TP) + complexity = np.sqrt( + float(M4) * TP / M2 / M2 + ) + return [mobility, complexity] + + +def get_time_domain_pipeline(): + mean_transformer = FunctionTransformer( + get_transformer(np.mean), validate=True) + std_transformer = FunctionTransformer( + get_transformer(np.std), validate=True) + skew_transformer = FunctionTransformer( + get_transformer(skew), validate=True) + kurtosis_transformer = FunctionTransformer( + get_transformer(kurtosis), validate=True) + mean_crossing_rate_transformer = FunctionTransformer( + get_transformer(get_mean_crossing_rate), validate=True) + hjorth_transformer = FunctionTransformer( + get_transformer_list(hjorth), validate=True) + + return Pipeline([ + ('epochs_to_data', get_data_from_epochs_transformer), + ('time_domain_features', FeatureUnion([ + ('mean', mean_transformer), + ('std', std_transformer), + ('skew', skew_transformer), + ('kurtosis', kurtosis_transformer), + ('mean-crossing-rate', mean_crossing_rate_transformer), + ('hjorth', hjorth_transformer) + ], n_jobs=1)) + ]) From f25e371e6257f2dc3fb529230e7c4802d7be777e Mon Sep 17 00:00:00 2001 From: Claudia Onorato Date: Tue, 6 Oct 2020 20:37:32 -0400 Subject: [PATCH 03/25] renamed --- .../{frequency_domain_transformers.py => frequency_domain.py} | 0 .../pipeline/{time_domain_transformers.py => time_domain.py} | 0 .../pipeline/{subband_transformers.py => time_subband.py} | 0 3 files changed, 0 insertions(+), 0 deletions(-) rename backend/backend/features/pipeline/{frequency_domain_transformers.py => frequency_domain.py} (100%) rename backend/backend/features/pipeline/{time_domain_transformers.py => time_domain.py} (100%) rename backend/backend/features/pipeline/{subband_transformers.py => time_subband.py} (100%) diff --git a/backend/backend/features/pipeline/frequency_domain_transformers.py b/backend/backend/features/pipeline/frequency_domain.py similarity index 100% rename from backend/backend/features/pipeline/frequency_domain_transformers.py rename to backend/backend/features/pipeline/frequency_domain.py diff --git a/backend/backend/features/pipeline/time_domain_transformers.py b/backend/backend/features/pipeline/time_domain.py similarity index 100% rename from backend/backend/features/pipeline/time_domain_transformers.py rename to backend/backend/features/pipeline/time_domain.py diff --git a/backend/backend/features/pipeline/subband_transformers.py b/backend/backend/features/pipeline/time_subband.py similarity index 100% rename from backend/backend/features/pipeline/subband_transformers.py rename to backend/backend/features/pipeline/time_subband.py From 0883070870a3e79c31a15befceb94b419d314b08 Mon Sep 17 00:00:00 2001 From: Claudia Onorato Date: Tue, 6 Oct 2020 21:09:36 -0400 Subject: [PATCH 04/25] added frequency features --- .../features/pipeline/frequency_domain.py | 111 +++++++++++++++++- backend/backend/features/pipeline/utils.py | 13 ++ backend/setup.cfg | 2 + 3 files changed, 125 insertions(+), 1 deletion(-) diff --git a/backend/backend/features/pipeline/frequency_domain.py b/backend/backend/features/pipeline/frequency_domain.py index bac350a9..96b07aa1 100644 --- a/backend/backend/features/pipeline/frequency_domain.py +++ b/backend/backend/features/pipeline/frequency_domain.py @@ -1,2 +1,111 @@ +import numpy as np +from sklearn.pipeline import FeatureUnion, Pipeline +from sklearn.preprocessing import (FunctionTransformer) + +from backend.features.pipeline.utils import ( + get_psds_from_epochs_transformer, +) +from backend.features.constants import ( + FREQ_BANDS_RANGE, +) + + +def get_mean_psds(psds_with_freqs, are_relative=False): + """EEG power band feature extraction. + Input + ------- + psds_with_freqs: tuple which contains + - (nb_epochs, nb_chan=1, nb_freqs) psds amplitudes + - (nb_freqs,) corresponding frequency values + + are_relative: boolean which indicates if the mean band powers + for each subband are relative to the total power or not. + + Returns + ------- + X : numpy array of shape [n_samples, nb_subband=5] + Transformed data. + """ + psds = psds_with_freqs[0] + freqs = psds_with_freqs[1] + + if are_relative: + psds /= np.sum(psds, axis=-1, keepdims=True) + + X = [] + for fmin, fmax in FREQ_BANDS_RANGE.values(): + psds_band = psds[:, :, (freqs >= fmin) & (freqs < fmax)].mean(axis=-1) + X.append(psds_band.reshape(len(psds), -1)) + + return np.concatenate(X, axis=1) + + +def get_sefd_on_all_epochs(psds_with_freqs): + """SEFd on all epochs + """ + SUBBAND_FREQ_SEFD = [8., 16.] + + psds = psds_with_freqs[0].squeeze() + freqs = psds_with_freqs[1] + + psds = psds[:, (freqs >= SUBBAND_FREQ_SEFD[0]) + & (freqs < SUBBAND_FREQ_SEFD[1])] + freqs = freqs[(freqs >= SUBBAND_FREQ_SEFD[0]) + & (freqs < SUBBAND_FREQ_SEFD[1])] + + def get_sefd(psd, freqs): + """Spectral edge frequency difference + Input + ------- + psd: array of the power spectrum density for one epoch + freqs: array of the frequencies + + Returns + ------- + Difference between the frequencies under which + cumulates 95 and 50 percent of the power + """ + assert len(psd) == len( + freqs), "All PSD value must have a corresponding frequency value" + + CUMUL_POWER_RATIO = [0.50, 0.95] + + total_power = np.sum(psd) + cumul_power = 0 + + lower_freq = None + upper_freq = None + + for amp, freq in zip(psd, freqs): + cumul_power += amp + if cumul_power >= CUMUL_POWER_RATIO[1] * total_power: + upper_freq = freq + break + elif lower_freq is None and cumul_power >= CUMUL_POWER_RATIO[0] * total_power: + lower_freq = freq + + return upper_freq - lower_freq + + return [[get_sefd(one_epoch_psd, freqs)] for one_epoch_psd in psds] + + def get_frequency_domain_pipeline(): - pass + absolute_mean_psds_transformer = FunctionTransformer( + get_mean_psds, validate=False) + relative_mean_psds_transformer = FunctionTransformer( + lambda psds_with_freq: get_mean_psds( + psds_with_freq, + are_relative=True + ), validate=False) + + sefd_transformer = FunctionTransformer( + get_sefd_on_all_epochs, validate=False) + + return Pipeline([ + ('get_psds_from_epochs', get_psds_from_epochs_transformer), + ('frequency_domain_features', FeatureUnion([ + ('absolute_mean_power_band', absolute_mean_psds_transformer), + ('relative_mean_power_band', relative_mean_psds_transformer), + ('sefd', sefd_transformer) + ], n_jobs=1)) + ]) diff --git a/backend/backend/features/pipeline/utils.py b/backend/backend/features/pipeline/utils.py index 75472009..ca2ba215 100644 --- a/backend/backend/features/pipeline/utils.py +++ b/backend/backend/features/pipeline/utils.py @@ -1,3 +1,6 @@ +from mne.time_frequency import psd_welch + + def get_data_from_epochs(epochs): """ epochs: mne.Epochs @@ -7,6 +10,16 @@ def get_data_from_epochs(epochs): return epochs.get_data().squeeze() +def get_psds_from_epochs(epochs): + """Extracts power spectrum densities from epochs + Returns + -------- + psds with associated frequencies calculated with the welch method. + """ + psds, freqs = psd_welch(epochs, fmin=0.5, fmax=30.) + return psds, freqs + + def get_transformer(get_feature): def get_one_feature_per_epoch(X, get_feature): diff --git a/backend/setup.cfg b/backend/setup.cfg index f35c53a3..6b489246 100644 --- a/backend/setup.cfg +++ b/backend/setup.cfg @@ -2,3 +2,5 @@ ignore = D203 exclude = __pycache__,old,build,dist max-complexity = 10 +# a bit bigger than prettier's printWidth because it's not a strict limit +max-line-length = 90 From e95ffd80520e1c1d8b7522bfe290d54bcd639ac3 Mon Sep 17 00:00:00 2001 From: Claudia Onorato Date: Thu, 8 Oct 2020 15:39:47 -0400 Subject: [PATCH 05/25] renamed package backend to classification --- backend/{backend => classification}/config/constants.py | 0 backend/{backend => classification}/features/__init__.py | 2 +- backend/{backend => classification}/features/constants.py | 2 +- backend/{backend => classification}/features/extraction.py | 6 +++--- .../features/pipeline/__init__.py | 6 +++--- .../features/pipeline/frequency_domain.py | 4 ++-- .../features/pipeline/time_domain.py | 2 +- .../features/pipeline/time_subband.py | 0 .../{backend => classification}/features/pipeline/utils.py | 0 backend/{backend => classification}/features/utils.py | 2 +- backend/{backend => classification}/predict.py | 2 +- 11 files changed, 13 insertions(+), 13 deletions(-) rename backend/{backend => classification}/config/constants.py (100%) rename backend/{backend => classification}/features/__init__.py (91%) rename backend/{backend => classification}/features/constants.py (90%) rename backend/{backend => classification}/features/extraction.py (91%) rename backend/{backend => classification}/features/pipeline/__init__.py (64%) rename backend/{backend => classification}/features/pipeline/frequency_domain.py (97%) rename backend/{backend => classification}/features/pipeline/time_domain.py (98%) rename backend/{backend => classification}/features/pipeline/time_subband.py (100%) rename backend/{backend => classification}/features/pipeline/utils.py (100%) rename backend/{backend => classification}/features/utils.py (94%) rename backend/{backend => classification}/predict.py (87%) diff --git a/backend/backend/config/constants.py b/backend/classification/config/constants.py similarity index 100% rename from backend/backend/config/constants.py rename to backend/classification/config/constants.py diff --git a/backend/backend/features/__init__.py b/backend/classification/features/__init__.py similarity index 91% rename from backend/backend/features/__init__.py rename to backend/classification/features/__init__.py index 2aeaaaf5..0e3e3d35 100644 --- a/backend/backend/features/__init__.py +++ b/backend/classification/features/__init__.py @@ -1,6 +1,6 @@ import numpy as np -from backend.features.extraction import ( +from classification.features.extraction import ( get_eeg_features, get_categorical_features, ) diff --git a/backend/backend/features/constants.py b/backend/classification/features/constants.py similarity index 90% rename from backend/backend/features/constants.py rename to backend/classification/features/constants.py index 85a16682..d27d612d 100644 --- a/backend/backend/features/constants.py +++ b/backend/classification/features/constants.py @@ -1,4 +1,4 @@ -from backend.config.constants import ( +from classification.config.constants import ( DATASET_SAMPLE_RATE, EPOCH_DURATION, ) diff --git a/backend/backend/features/extraction.py b/backend/classification/features/extraction.py similarity index 91% rename from backend/backend/features/extraction.py rename to backend/classification/features/extraction.py index 684b4275..1ad4033e 100644 --- a/backend/backend/features/extraction.py +++ b/backend/classification/features/extraction.py @@ -1,12 +1,12 @@ """Feature extraction tools based off a two channel EEG recording""" import numpy as np -from backend.config.constants import ( +from classification.config.constants import ( EEG_CHANNELS, AGE_FEATURE_BINS, ) -from backend.features.pipeline import get_feature_union -from backend.features.utils import ( +from classification.features.pipeline import get_feature_union +from classification.features.utils import ( drop_other_channels, crop_raw_data, convert_to_epochs, diff --git a/backend/backend/features/pipeline/__init__.py b/backend/classification/features/pipeline/__init__.py similarity index 64% rename from backend/backend/features/pipeline/__init__.py rename to backend/classification/features/pipeline/__init__.py index c73ea413..67674092 100644 --- a/backend/backend/features/pipeline/__init__.py +++ b/backend/classification/features/pipeline/__init__.py @@ -1,12 +1,12 @@ from sklearn.pipeline import FeatureUnion -from backend.features.pipeline.time_domain_transformers import ( +from classification.features.pipeline.time_domain_transformers import ( get_time_domain_pipeline ) -from backend.features.pipeline.frequency_domain_transformers import ( +from classification.features.pipeline.frequency_domain_transformers import ( get_frequency_domain_pipeline ) -from backend.features.pipeline.subband_transformers import ( +from classification.features.pipeline.subband_transformers import ( get_subband_feature_union ) diff --git a/backend/backend/features/pipeline/frequency_domain.py b/backend/classification/features/pipeline/frequency_domain.py similarity index 97% rename from backend/backend/features/pipeline/frequency_domain.py rename to backend/classification/features/pipeline/frequency_domain.py index 96b07aa1..3a36c2ac 100644 --- a/backend/backend/features/pipeline/frequency_domain.py +++ b/backend/classification/features/pipeline/frequency_domain.py @@ -2,10 +2,10 @@ from sklearn.pipeline import FeatureUnion, Pipeline from sklearn.preprocessing import (FunctionTransformer) -from backend.features.pipeline.utils import ( +from classification.features.pipeline.utils import ( get_psds_from_epochs_transformer, ) -from backend.features.constants import ( +from classification.features.constants import ( FREQ_BANDS_RANGE, ) diff --git a/backend/backend/features/pipeline/time_domain.py b/backend/classification/features/pipeline/time_domain.py similarity index 98% rename from backend/backend/features/pipeline/time_domain.py rename to backend/classification/features/pipeline/time_domain.py index c6841a84..2cb69001 100644 --- a/backend/backend/features/pipeline/time_domain.py +++ b/backend/classification/features/pipeline/time_domain.py @@ -4,7 +4,7 @@ from sklearn.preprocessing import (FunctionTransformer) -from backend.features.pipeline.utils import ( +from classification.features.pipeline.utils import ( get_transformer, get_transformer_list, get_data_from_epochs_transformer, diff --git a/backend/backend/features/pipeline/time_subband.py b/backend/classification/features/pipeline/time_subband.py similarity index 100% rename from backend/backend/features/pipeline/time_subband.py rename to backend/classification/features/pipeline/time_subband.py diff --git a/backend/backend/features/pipeline/utils.py b/backend/classification/features/pipeline/utils.py similarity index 100% rename from backend/backend/features/pipeline/utils.py rename to backend/classification/features/pipeline/utils.py diff --git a/backend/backend/features/utils.py b/backend/classification/features/utils.py similarity index 94% rename from backend/backend/features/utils.py rename to backend/classification/features/utils.py index 952d753d..7856057a 100644 --- a/backend/backend/features/utils.py +++ b/backend/classification/features/utils.py @@ -1,6 +1,6 @@ import mne -from backend.features.constants import MAX_TIME +from classification.features.constants import MAX_TIME def drop_other_channels(raw_data, channel_to_keep): diff --git a/backend/backend/predict.py b/backend/classification/predict.py similarity index 87% rename from backend/backend/predict.py rename to backend/classification/predict.py index 397fa7f9..f98a62cd 100644 --- a/backend/backend/predict.py +++ b/backend/classification/predict.py @@ -1,5 +1,5 @@ """defines functions to predict sleep stages based off EEG signals""" -from backend.features import get_features +from classification.features import get_features def predict(raw_eeg, age, sex): From 9e1185f79f825c3c60d7110e5d09579d406a543d Mon Sep 17 00:00:00 2001 From: Claudia Onorato Date: Thu, 15 Oct 2020 18:33:53 -0400 Subject: [PATCH 06/25] added time subband --- backend/classification/features/extraction.py | 17 ++++- .../features/pipeline/frequency_domain.py | 2 +- .../features/pipeline/time_subband.py | 65 ++++++++++++++++++- 3 files changed, 80 insertions(+), 4 deletions(-) diff --git a/backend/classification/features/extraction.py b/backend/classification/features/extraction.py index 1ad4033e..70bddc21 100644 --- a/backend/classification/features/extraction.py +++ b/backend/classification/features/extraction.py @@ -13,13 +13,26 @@ ) -def get_eeg_features(raw_data): +def get_eeg_features(raw_data, in_bed_time, out_of_bed_time): + """Returns the continuous feature matrix + Input + ------- + raw_signal: MNE.Raw object with signals with or without annotations + in_bed_time: timespan, in seconds, from which the subject started + the recording and went to bed + out_of_bed_time: timespan, in seconds, from which the subject + started the recording and got out of bed + + Returns + ------- + Array of size (nb_epochs, nb_continuous_features) + """ features_file = [] for channel in EEG_CHANNELS: chan_data = drop_other_channels(raw_data.copy(), channel) # TODO: input actual bed & out of bed times - chan_data = crop_raw_data(chan_data, 0, 100) + chan_data = crop_raw_data(chan_data, in_bed_time, out_of_bed_time) X_file_channel = convert_to_epochs( chan_data ) diff --git a/backend/classification/features/pipeline/frequency_domain.py b/backend/classification/features/pipeline/frequency_domain.py index 3a36c2ac..763a06bb 100644 --- a/backend/classification/features/pipeline/frequency_domain.py +++ b/backend/classification/features/pipeline/frequency_domain.py @@ -1,6 +1,6 @@ import numpy as np from sklearn.pipeline import FeatureUnion, Pipeline -from sklearn.preprocessing import (FunctionTransformer) +from sklearn.preprocessing import FunctionTransformer from classification.features.pipeline.utils import ( get_psds_from_epochs_transformer, diff --git a/backend/classification/features/pipeline/time_subband.py b/backend/classification/features/pipeline/time_subband.py index 408f7d9a..f06f67dd 100644 --- a/backend/classification/features/pipeline/time_subband.py +++ b/backend/classification/features/pipeline/time_subband.py @@ -1,2 +1,65 @@ +import numpy as np +from scipy.signal import butter +from sklearn.pipeline import FeatureUnion, Pipeline +from sklearn.preprocessing import FunctionTransformer + + +from classification.features.constants import ( + FREQ_BANDS_ORDERS, + FREQ_BANDS_RANGE, + NYQUIST_FREQ, +) +from classification.features.pipeline.utils import ( + get_data_from_epochs_transformer, + get_transformer, +) + + +def get_signal_mean_energy(signal): + """ + signal: array of (nb_sample_per_epoch,) + """ + return np.sum(signal**2) * 1e6 + + +def get_pipeline_per_subband(subband_name: str): + """ + Constructs a pipeline to extract the specified subband related features. + Output: + sklearn.pipeline.Pipeline object containing all steps to calculate time-domain feature on the specified subband. + """ + + freq_range = FREQ_BANDS_RANGE[subband_name] + order = FREQ_BANDS_ORDERS[subband_name] + + assert len( + freq_range) == 2, "Frequency range must only have 2 elements: [lower bound frequency, upper bound frequency]" + + bounds = [freq / NYQUIST_FREQ for freq in freq_range] + b, a = butter(order, bounds, btype='bandpass') + + def filter_epochs_in_specified_subband(epochs): + return epochs.copy().filter( + l_freq=bounds[0], + h_freq=bounds[1], + method='iir', + n_jobs=1, + iir_params={ + 'a': a, + 'b': b + }, verbose=False) + + return Pipeline([ + ('filter', FunctionTransformer(filter_epochs_in_specified_subband, validate=False)), + ('get-values', get_data_from_epochs_transformer), + ('mean-energy', FunctionTransformer( + get_transformer(get_signal_mean_energy), validate=True + )), + ]) + + def get_subband_feature_union(): - pass + return FeatureUnion([( + f"{band_name}-filter", + get_pipeline_per_subband(band_name) + ) for band_name in FREQ_BANDS_ORDERS.keys()], n_jobs=1) From dea9e43b56e3d7261a4cb69e00a5ae0176b06cb9 Mon Sep 17 00:00:00 2001 From: Claudia Onorato Date: Sat, 17 Oct 2020 18:03:57 -0400 Subject: [PATCH 07/25] convert sd file to decimal --- backend/app.py | 8 +- .../features/pipeline/__init__.py | 12 +- .../features/pipeline/frequency_domain.py | 4 +- .../features/pipeline/time_domain.py | 4 +- .../features/pipeline/time_subband.py | 4 +- backend/classification/file_loading.py | 103 ++++++++++++++++++ backend/readme.md | 35 ++++++ backend/requirements-dev.txt | 1 + backend/requirements.txt | 8 ++ backend/setup.cfg | 4 +- 10 files changed, 168 insertions(+), 15 deletions(-) create mode 100644 backend/classification/file_loading.py create mode 100644 backend/readme.md create mode 100644 backend/requirements-dev.txt diff --git a/backend/app.py b/backend/app.py index 4f0b0a0c..43ea94b5 100644 --- a/backend/app.py +++ b/backend/app.py @@ -3,6 +3,8 @@ from waitress import serve from http import HTTPStatus +from classification.file_loading import get_raw_array +from classification.predict import predict app = Flask(__name__) @@ -17,7 +19,7 @@ def hello(): return "Hello, World!" -@app.route('/analyze_sleep', methods=['POST']) +@app.route('/analyze-sleep', methods=['GET']) def analyze_sleep(): if 'file' not in request.files: return 'Missing file', HTTPStatus.BAD_REQUEST @@ -29,8 +31,8 @@ def analyze_sleep(): if not allowed_file(file.filename): return 'File format not allowed', HTTPStatus.BAD_REQUEST - file_content = file.read() - form_data = request.form.to_dict() + raw_array = get_raw_array(file) + # predict(raw_array) return '' diff --git a/backend/classification/features/pipeline/__init__.py b/backend/classification/features/pipeline/__init__.py index 67674092..d998654c 100644 --- a/backend/classification/features/pipeline/__init__.py +++ b/backend/classification/features/pipeline/__init__.py @@ -1,13 +1,13 @@ from sklearn.pipeline import FeatureUnion -from classification.features.pipeline.time_domain_transformers import ( - get_time_domain_pipeline +from classification.features.pipeline.time_domain import ( + get_time_domain_pipeline, ) -from classification.features.pipeline.frequency_domain_transformers import ( - get_frequency_domain_pipeline +from classification.features.pipeline.frequency_domain import ( + get_frequency_domain_pipeline, ) -from classification.features.pipeline.subband_transformers import ( - get_subband_feature_union +from classification.features.pipeline.time_subband import ( + get_subband_feature_union, ) diff --git a/backend/classification/features/pipeline/frequency_domain.py b/backend/classification/features/pipeline/frequency_domain.py index 763a06bb..b336a438 100644 --- a/backend/classification/features/pipeline/frequency_domain.py +++ b/backend/classification/features/pipeline/frequency_domain.py @@ -3,7 +3,7 @@ from sklearn.preprocessing import FunctionTransformer from classification.features.pipeline.utils import ( - get_psds_from_epochs_transformer, + get_psds_from_epochs, ) from classification.features.constants import ( FREQ_BANDS_RANGE, @@ -90,6 +90,8 @@ def get_sefd(psd, freqs): def get_frequency_domain_pipeline(): + get_psds_from_epochs_transformer = FunctionTransformer( + get_psds_from_epochs, validate=False) absolute_mean_psds_transformer = FunctionTransformer( get_mean_psds, validate=False) relative_mean_psds_transformer = FunctionTransformer( diff --git a/backend/classification/features/pipeline/time_domain.py b/backend/classification/features/pipeline/time_domain.py index 2cb69001..bd48d8e8 100644 --- a/backend/classification/features/pipeline/time_domain.py +++ b/backend/classification/features/pipeline/time_domain.py @@ -7,7 +7,7 @@ from classification.features.pipeline.utils import ( get_transformer, get_transformer_list, - get_data_from_epochs_transformer, + get_data_from_epochs, ) @@ -75,6 +75,8 @@ def hjorth(X): def get_time_domain_pipeline(): + get_data_from_epochs_transformer = FunctionTransformer( + get_data_from_epochs, validate=False) mean_transformer = FunctionTransformer( get_transformer(np.mean), validate=True) std_transformer = FunctionTransformer( diff --git a/backend/classification/features/pipeline/time_subband.py b/backend/classification/features/pipeline/time_subband.py index f06f67dd..60b664f0 100644 --- a/backend/classification/features/pipeline/time_subband.py +++ b/backend/classification/features/pipeline/time_subband.py @@ -10,7 +10,7 @@ NYQUIST_FREQ, ) from classification.features.pipeline.utils import ( - get_data_from_epochs_transformer, + get_data_from_epochs, get_transformer, ) @@ -51,7 +51,7 @@ def filter_epochs_in_specified_subband(epochs): return Pipeline([ ('filter', FunctionTransformer(filter_epochs_in_specified_subband, validate=False)), - ('get-values', get_data_from_epochs_transformer), + ('get-values', FunctionTransformer(get_data_from_epochs, validate=False)), ('mean-energy', FunctionTransformer( get_transformer(get_signal_mean_energy), validate=True )), diff --git a/backend/classification/file_loading.py b/backend/classification/file_loading.py new file mode 100644 index 00000000..ad184ea9 --- /dev/null +++ b/backend/classification/file_loading.py @@ -0,0 +1,103 @@ +""" +Function utilities to convert data acquired on an OpenBCI +Cyton board using the SD card logging strategy. + +TODO: We should look into optimizing this conversion. We currently +convert one line at a time, while a vectorized approach would be much more efficient, +as the conversion of a line does not depend on the other lines. + +The file conversion is based off the code written to convert +the raw hexadecimal data to signed decimal values in the OpenBCI GUI: +[https://github.com/OpenBCI/OpenBCI_GUI/blob/f907e6a23e4fe640179ff8421044c641d45f2c12/OpenBCI_GUI/DataLogging.pde#L1554-L1595] +The Cyton board logging format is also described here: +[https://docs.openbci.com/docs/02Cyton/CytonSDCard#data-logging-format] +""" +from io import StringIO +from mne import create_info +from mne.io import RawArray +import numpy as np + +from classification.config.constants import ( + EEG_CHANNELS, + OPENBCI_CYTON_SAMPLE_RATE, +) + +ADS1299_Vref = 4.5 +ADS1299_gain = 24. +SCALE_uV_PER_COUNT = ADS1299_Vref / ((2**23) - 1) / ADS1299_gain * 1000000 + +FILE_COLUMN_OFFSET = 1 +CYTON_TOTAL_NB_CHANNELS = 8 + + +def get_raw_array(file): + """Converts a file following the Cyton board SD card logging format into a mne.RawArray + Input: + - file: received as an input file + Returns: + - mne.RawArray of the two EEG channels of interest + """ + csv_file_content = StringIO(file.stream.read().decode("UTF8")) + + eeg_raw = [] + for line in csv_file_content.readlines(): + line_splitted = line.split(',') + + if len(line_splitted) >= CYTON_TOTAL_NB_CHANNELS: + eeg_raw.append(get_decimals_from_hexadecimal_strings(line_splitted)) + + eeg_raw = SCALE_uV_PER_COUNT * np.array(eeg_raw, dtype='object') + + raw_object = RawArray( + np.transpose(eeg_raw), + info=create_info( + ch_names=EEG_CHANNELS, + sfreq=OPENBCI_CYTON_SAMPLE_RATE, + ch_types='eeg'), + verbose=False, + ) + + print('First sample values: ', raw_object[:, 0]) + print('Second sample values: ', raw_object[:, 1]) + print('Number of samples: ', raw_object.n_times) + print('Duration of signal (h): ', raw_object.n_times / (3600 * OPENBCI_CYTON_SAMPLE_RATE)) + print('Channel names: ', raw_object.ch_names) + + return raw_object + + +def get_decimals_from_hexadecimal_strings(lines): + """Converts the array of hexadecimal strings to an array of decimal values of the EEG channels + Input: + - lines: splitted array of two complement hexadecimal + Returns: + - array of decimal values for each EEG channel of interest + """ + return np.array([ + convert_hexadecimal_to_signed_decimal(hex_value) + for hex_value in lines[FILE_COLUMN_OFFSET:FILE_COLUMN_OFFSET + len(EEG_CHANNELS)] + ]) + + +def convert_hexadecimal_to_signed_decimal(hex_value): + """Converts the hexadecimal value encoded on OpenBCI Cyton SD card to signed decimal + Input: + - hex_value: signed hexadecimal value + Returns: + - decimal value + """ + return get_twos_complement(hex_value) if len(hex_value) % 2 == 0 else 0 + + +def get_twos_complement(hexstr): + """Converts a two complement hexadecimal value in a string to a signed float + Input: + - hex_value: signed hexadecimal value + Returns: + - decimal value + """ + bits = len(hexstr) * 4 + value = int(hexstr, 16) + if value & (1 << (bits - 1)): + value -= 1 << bits + return value diff --git a/backend/readme.md b/backend/readme.md new file mode 100644 index 00000000..256b487b --- /dev/null +++ b/backend/readme.md @@ -0,0 +1,35 @@ +# Backend + +## Setup + +Create a new virtual environment to isolate Python packages. + +```bash +virtualenv -p /usr/local/bin/python3.7 venv +``` + +Activate your virtual environment. + +```bash +source venv/bin/activate +``` + +Install the required dependencies. + +```bash +pip install -r requirements.txt requirements-dev.txt +``` + +## Run it locally + +Activate your virtual environment. + +```bash +source venv/bin/activate +``` + +If you want to run the backend with hot reload enabled (you must have installed the development requirements), run the following command. + +```bash +hupper -m waitress app:app +``` diff --git a/backend/requirements-dev.txt b/backend/requirements-dev.txt new file mode 100644 index 00000000..e0720e14 --- /dev/null +++ b/backend/requirements-dev.txt @@ -0,0 +1 @@ +hupper==1.10.2 diff --git a/backend/requirements.txt b/backend/requirements.txt index d1a74b1e..8cdd0c76 100644 --- a/backend/requirements.txt +++ b/backend/requirements.txt @@ -1,3 +1,11 @@ Flask==1.1.2 Flask-Cors==1.10.3 waitress==1.4.4 + +mne==0.21.0 +numpy==1.19.2 +scipy==1.5.2 +joblib==0.17.0 +scikit-learn==0.23.2 +sklearn==0.0 +threadpoolctl==2.1.0 diff --git a/backend/setup.cfg b/backend/setup.cfg index 6b489246..425d152b 100644 --- a/backend/setup.cfg +++ b/backend/setup.cfg @@ -2,5 +2,5 @@ ignore = D203 exclude = __pycache__,old,build,dist max-complexity = 10 -# a bit bigger than prettier's printWidth because it's not a strict limit -max-line-length = 90 +max-line-length = 120 +aggressive = 5 From d0024116f617cc5631c8145029e905799484081e Mon Sep 17 00:00:00 2001 From: Claudia Onorato Date: Sun, 18 Oct 2020 01:27:16 -0400 Subject: [PATCH 08/25] linked sd card file uploaded to feature extraction --- backend/app.py | 16 +++++- backend/classification/config/constants.py | 8 +++ backend/classification/exceptions.py | 13 +++++ backend/classification/features/__init__.py | 14 +++-- backend/classification/features/constants.py | 2 + backend/classification/features/extraction.py | 12 +++- backend/classification/features/utils.py | 24 +++++++- backend/classification/file_loading.py | 2 + backend/classification/predict.py | 27 +++++++-- backend/classification/validation.py | 55 +++++++++++++++++++ backend/setup.cfg | 2 +- 11 files changed, 158 insertions(+), 17 deletions(-) create mode 100644 backend/classification/exceptions.py create mode 100644 backend/classification/validation.py diff --git a/backend/app.py b/backend/app.py index 43ea94b5..d4c106f4 100644 --- a/backend/app.py +++ b/backend/app.py @@ -21,6 +21,18 @@ def hello(): @app.route('/analyze-sleep', methods=['GET']) def analyze_sleep(): + """ + Request payload example + { + "file": File(...), + "device": "CYTON", + "sex": "F", + "age": "23", + "stream_start": 1602895800000, + "bedtime": 1602898320000, + "wakeup": 1602931800000 + } + """ if 'file' not in request.files: return 'Missing file', HTTPStatus.BAD_REQUEST file = request.files['file'] @@ -32,7 +44,9 @@ def analyze_sleep(): return 'File format not allowed', HTTPStatus.BAD_REQUEST raw_array = get_raw_array(file) - # predict(raw_array) + + form_data = request.form.to_dict() + predict(raw_array, **form_data) return '' diff --git a/backend/classification/config/constants.py b/backend/classification/config/constants.py index 624f6b66..7a89dc73 100644 --- a/backend/classification/config/constants.py +++ b/backend/classification/config/constants.py @@ -13,6 +13,7 @@ ] EPOCH_DURATION = 30 # in seconds +MIN_FILE_SIZE = EPOCH_DURATION # in seconds DATASET_SAMPLE_RATE = 100 # in Hz OPENBCI_CYTON_SAMPLE_RATE = 250 # in Hz @@ -24,3 +25,10 @@ [60, 84], [85, 110] ] +ACCEPTED_AGE_RANGE = [AGE_FEATURE_BINS[0][0], AGE_FEATURE_BINS[-1][-1]] + +# based from subject description file (see header) https://physionet.org/content/sleep-edfx/1.0.0/SC-subjects.xls +SEX_FEATURE = { + 'F': 1, + 'M': 2 +} diff --git a/backend/classification/exceptions.py b/backend/classification/exceptions.py new file mode 100644 index 00000000..c4ff3b3a --- /dev/null +++ b/backend/classification/exceptions.py @@ -0,0 +1,13 @@ +class ClassificationError(Exception): + """Base errors for application errors that can occur""" + pass + + +class TimestampsError(ClassificationError): + """Raised when timestamps are incoherent or doesn't fit with the provided file""" + pass + + +class FileSizeError(ClassificationError): + """Raised when file is either too big or too small""" + pass diff --git a/backend/classification/features/__init__.py b/backend/classification/features/__init__.py index 0e3e3d35..0142bbf5 100644 --- a/backend/classification/features/__init__.py +++ b/backend/classification/features/__init__.py @@ -6,19 +6,23 @@ ) -def get_features(signal, age, sex): +def get_features(signal, age, sex, in_bed_seconds, out_of_bed_seconds): """Returns the raw features Input ------- - raw_signal: MNE.Raw object with signals with or without annotations - age: Age of the subject - sex: Sex of the subject + - raw_signal: MNE.Raw object with signals with or without annotations + - age: Age of the subject + - sex: Sex of the subject + - in_bed_seconds: timespan, in seconds, from which the subject started + the recording and went to bed + - out_of_bed_seconds: timespan, in seconds, from which the subject + started the recording and got out of bed Returns ------- - features X in a vector of (nb_epochs, nb_features) """ - X_eeg = get_eeg_features(signal) + X_eeg = get_eeg_features(signal, in_bed_seconds, out_of_bed_seconds) X_categorical = get_categorical_features(age, sex, X_eeg.shape[0]) return np.append(X_categorical, X_eeg, axis=1) diff --git a/backend/classification/features/constants.py b/backend/classification/features/constants.py index d27d612d..b258c65e 100644 --- a/backend/classification/features/constants.py +++ b/backend/classification/features/constants.py @@ -27,3 +27,5 @@ SIGMA: 9, BETA: 14 } + +DATASET_HIGH_PASS_FREQ = 0.5 diff --git a/backend/classification/features/extraction.py b/backend/classification/features/extraction.py index 70bddc21..33243433 100644 --- a/backend/classification/features/extraction.py +++ b/backend/classification/features/extraction.py @@ -4,6 +4,10 @@ from classification.config.constants import ( EEG_CHANNELS, AGE_FEATURE_BINS, + DATASET_SAMPLE_RATE, +) +from classification.features.constants import ( + DATASET_HIGH_PASS_FREQ, ) from classification.features.pipeline import get_feature_union from classification.features.utils import ( @@ -31,13 +35,17 @@ def get_eeg_features(raw_data, in_bed_time, out_of_bed_time): for channel in EEG_CHANNELS: chan_data = drop_other_channels(raw_data.copy(), channel) - # TODO: input actual bed & out of bed times chan_data = crop_raw_data(chan_data, in_bed_time, out_of_bed_time) + chan_data = chan_data.resample(DATASET_SAMPLE_RATE) + chan_data = chan_data.filter(l_freq=DATASET_HIGH_PASS_FREQ, h_freq=None) + X_file_channel = convert_to_epochs( - chan_data + chan_data, in_bed_time ) feature_union = get_feature_union() + print('Started feature ext on epochs: ', X_file_channel) + X_features = feature_union.transform(X_file_channel) features_file.append(X_features) diff --git a/backend/classification/features/utils.py b/backend/classification/features/utils.py index 7856057a..927a9eb4 100644 --- a/backend/classification/features/utils.py +++ b/backend/classification/features/utils.py @@ -1,6 +1,7 @@ import mne +import numpy as np -from classification.features.constants import MAX_TIME +from classification.features.constants import MAX_TIME, EPOCH_DURATION, DATASET_SAMPLE_RATE def drop_other_channels(raw_data, channel_to_keep): @@ -29,18 +30,35 @@ def crop_raw_data( """ raw_data.crop( tmin=bed_seconds, - tmax=min(out_of_bed_seconds, raw_data.times[-1]) + tmax=min(out_of_bed_seconds, raw_data.times[-1]), + include_tmax=False, ) return raw_data -def convert_to_epochs(raw_data): +def convert_to_epochs(raw_data, in_bed_time): """ returns: mne.Epochs """ + # We must provide an event list to create an Epochs. We create a mock one, whereas + # it has, as its first column, the sample index, and at its third column, the event id. + # The sample index must correspond to the first sample index for each epoch (30s, not overlapping) + # Because we will not use the event id (we will predict it), we will keep it at 0. + # See here for more info: https://mne.tools/stable/auto_tutorials/intro/plot_20_events_from_raw.html + # NOTE: Events are considered in the time frame as before we've cropped + # the signal. We then have to add to all values the bedtime sample index offset. + sample_indexes = np.arange(raw_data.n_times) + (in_bed_time * DATASET_SAMPLE_RATE) + nb_epochs = raw_data.n_times // (DATASET_SAMPLE_RATE * EPOCH_DURATION) + events = np.zeros((nb_epochs, 3)) + events[:, 0] = sample_indexes[::(DATASET_SAMPLE_RATE * EPOCH_DURATION)] + events = events.astype('int') + + print(f'Will create {nb_epochs} epochs of duration {EPOCH_DURATION} after resampling to {DATASET_SAMPLE_RATE}.') + return mne.Epochs( raw=raw_data, + events=events, tmin=0., tmax=MAX_TIME, preload=True, diff --git a/backend/classification/file_loading.py b/backend/classification/file_loading.py index ad184ea9..3cfc240d 100644 --- a/backend/classification/file_loading.py +++ b/backend/classification/file_loading.py @@ -5,6 +5,8 @@ TODO: We should look into optimizing this conversion. We currently convert one line at a time, while a vectorized approach would be much more efficient, as the conversion of a line does not depend on the other lines. +TODO: Consider cropping file (from bed to wake up time) here, before the for loop. Have to consider +not all lines hold sample values (i.e. first line with comment and second line with a single timestamp). The file conversion is based off the code written to convert the raw hexadecimal data to signed decimal values in the OpenBCI GUI: diff --git a/backend/classification/predict.py b/backend/classification/predict.py index f98a62cd..934e70d2 100644 --- a/backend/classification/predict.py +++ b/backend/classification/predict.py @@ -1,12 +1,29 @@ """defines functions to predict sleep stages based off EEG signals""" from classification.features import get_features +from classification.validation import validate -def predict(raw_eeg, age, sex): +def predict(raw_eeg, age, sex, stream_start, bedtime, wakeup): """ - raw_eeg: instance of mne.io.RawArray + Input: + - raw_eeg: instance of mne.io.RawArray Should contain 2 channels (1: FPZ-CZ, 2: PZ-OZ) - sex: should either be 'F' or 'M' - age: indicates the subject's age + - sex: should either be 'F' or 'M' + - age: indicates the subject's age + - start_stream: UNIX timestamp of time at which recording started + - bedtime: UNIX timestamp of time at which to start classification (W before) + - wakeup: UNIX timestamp of time at which to end classification (W after) + """ - X_openbci, y_openbci = get_features(raw_eeg, age, sex) + age, stream_start, bedtime, wakeup = int(age), int(stream_start), int(bedtime), int(wakeup) + + in_bed_seconds = bedtime - stream_start + out_of_bed_seconds = wakeup - stream_start + + validate(raw_eeg, age, sex, in_bed_seconds, out_of_bed_seconds) + + X_openbci = get_features( + raw_eeg, age, sex, in_bed_seconds, out_of_bed_seconds + ) + + # print(X_openbci, X_openbci.shape) diff --git a/backend/classification/validation.py b/backend/classification/validation.py new file mode 100644 index 00000000..c13e557b --- /dev/null +++ b/backend/classification/validation.py @@ -0,0 +1,55 @@ +from classification.config.constants import ( + MIN_FILE_SIZE, + ACCEPTED_AGE_RANGE, + SEX_FEATURE, +) +from classification.exceptions import ( + TimestampsError, + FileSizeError, + ClassificationError, +) + + +def validate(raw_eeg, age, sex, in_bed_seconds, out_of_bed_seconds): + _validate_timestamps(in_bed_seconds, out_of_bed_seconds) + _validate_file_with_timestamps(raw_eeg, out_of_bed_seconds) + _validate_age(age) + _validate_sex(sex) + + +def _validate_timestamps(in_bed_seconds, out_of_bed_seconds): + has_positive_timespan = in_bed_seconds > 0 and out_of_bed_seconds > 0 + has_got_out_of_bed_after_in_bed = out_of_bed_seconds > in_bed_seconds + has_respected_minimum_bed_time = (out_of_bed_seconds - in_bed_seconds) > MIN_FILE_SIZE + + if not( + has_positive_timespan + and has_got_out_of_bed_after_in_bed + and has_respected_minimum_bed_time + ): + raise TimestampsError() + + +def _validate_file_with_timestamps(raw_eeg, out_of_bed_seconds): + has_raw_respected_minimum_file_size = raw_eeg.times[-1] > MIN_FILE_SIZE + is_raw_at_least_as_long_as_out_of_bed = raw_eeg.times[-1] >= out_of_bed_seconds + + if not has_raw_respected_minimum_file_size: + raise FileSizeError() + + if not is_raw_at_least_as_long_as_out_of_bed: + raise TimestampsError() + + +def _validate_age(age): + is_in_accepted_range = ACCEPTED_AGE_RANGE[0] <= int(age) <= ACCEPTED_AGE_RANGE[1] + + if not(is_in_accepted_range): + raise ClassificationError('invalid age') + + +def _validate_sex(sex): + is_in_accepted_sex_choices = sex in SEX_FEATURE.keys() + + if not(is_in_accepted_sex_choices): + raise ClassificationError() diff --git a/backend/setup.cfg b/backend/setup.cfg index 425d152b..39647c50 100644 --- a/backend/setup.cfg +++ b/backend/setup.cfg @@ -1,5 +1,5 @@ [flake8] -ignore = D203 +ignore = D203, W503 exclude = __pycache__,old,build,dist max-complexity = 10 max-line-length = 120 From b4bff8ae7b42339702b4b5bb9417ddc3d81b2361 Mon Sep 17 00:00:00 2001 From: Claudia Onorato Date: Sun, 18 Oct 2020 01:37:22 -0400 Subject: [PATCH 09/25] fix docstring on create epochs & sex value --- backend/classification/features/extraction.py | 4 +- backend/classification/features/utils.py | 38 +++++++++++-------- backend/classification/predict.py | 2 +- 3 files changed, 27 insertions(+), 17 deletions(-) diff --git a/backend/classification/features/extraction.py b/backend/classification/features/extraction.py index 33243433..cd384577 100644 --- a/backend/classification/features/extraction.py +++ b/backend/classification/features/extraction.py @@ -5,6 +5,7 @@ EEG_CHANNELS, AGE_FEATURE_BINS, DATASET_SAMPLE_RATE, + SEX_FEATURE, ) from classification.features.constants import ( DATASET_HIGH_PASS_FREQ, @@ -75,8 +76,9 @@ def get_categorical_features(age, sex, nb_epochs): for category_index, age_range in enumerate(AGE_FEATURE_BINS) if age >= age_range[0] and age <= age_range[1] ) + sex_value = SEX_FEATURE[sex] - X_categorical = [sex, age_category] + X_categorical = [sex_value, age_category] return np.array( X_categorical * nb_epochs diff --git a/backend/classification/features/utils.py b/backend/classification/features/utils.py index 927a9eb4..cf52d9e1 100644 --- a/backend/classification/features/utils.py +++ b/backend/classification/features/utils.py @@ -38,27 +38,35 @@ def crop_raw_data( def convert_to_epochs(raw_data, in_bed_time): - """ + """Converts the raw object to an Epochs + Input: + - raw_data: instance of mne.RawArray, that has been previously cropped to tmin=in_bed_time + - in_bed_time: number of seconds between start of recording & moment at + which the subjet went to bed (in seconds) returns: mne.Epochs """ - # We must provide an event list to create an Epochs. We create a mock one, whereas - # it has, as its first column, the sample index, and at its third column, the event id. - # The sample index must correspond to the first sample index for each epoch (30s, not overlapping) - # Because we will not use the event id (we will predict it), we will keep it at 0. - # See here for more info: https://mne.tools/stable/auto_tutorials/intro/plot_20_events_from_raw.html - # NOTE: Events are considered in the time frame as before we've cropped - # the signal. We then have to add to all values the bedtime sample index offset. - sample_indexes = np.arange(raw_data.n_times) + (in_bed_time * DATASET_SAMPLE_RATE) - nb_epochs = raw_data.n_times // (DATASET_SAMPLE_RATE * EPOCH_DURATION) - events = np.zeros((nb_epochs, 3)) - events[:, 0] = sample_indexes[::(DATASET_SAMPLE_RATE * EPOCH_DURATION)] - events = events.astype('int') + def get_events(): + # We must provide an event list to create an Epochs. We create a mock one, whereas + # it has, as its first column, the sample index, and at its third column, the event id. + # The sample index must correspond to the first sample index for each epoch (30s, not overlapping) + # Because we will not use the event id (we will predict it), we will keep it at 0. + # See here for more info: https://mne.tools/stable/auto_tutorials/intro/plot_20_events_from_raw.html + # NOTE: Events indexes are considered in the time frame as before we've cropped + # the signal. We then have to add to all values the bedtime sample index offset. + # Otherwise, all samples prior the bedtime will be dropped, when creating the Epochs! + sample_indexes = np.arange(raw_data.n_times) + (in_bed_time * DATASET_SAMPLE_RATE) + nb_epochs = raw_data.n_times // (DATASET_SAMPLE_RATE * EPOCH_DURATION) + events = np.zeros((nb_epochs, 3)) + events[:, 0] = sample_indexes[::(DATASET_SAMPLE_RATE * EPOCH_DURATION)] + events = events.astype('int') + + print(f'Will create {nb_epochs} epochs of duration {EPOCH_DURATION} after resampling to {DATASET_SAMPLE_RATE}.') - print(f'Will create {nb_epochs} epochs of duration {EPOCH_DURATION} after resampling to {DATASET_SAMPLE_RATE}.') + return events return mne.Epochs( raw=raw_data, - events=events, + events=get_events(), tmin=0., tmax=MAX_TIME, preload=True, diff --git a/backend/classification/predict.py b/backend/classification/predict.py index 934e70d2..bbc20545 100644 --- a/backend/classification/predict.py +++ b/backend/classification/predict.py @@ -26,4 +26,4 @@ def predict(raw_eeg, age, sex, stream_start, bedtime, wakeup): raw_eeg, age, sex, in_bed_seconds, out_of_bed_seconds ) - # print(X_openbci, X_openbci.shape) + print(X_openbci[0], X_openbci.shape) From 4028b5e4d9f218e0247b5ce6481192e823dd5053 Mon Sep 17 00:00:00 2001 From: Claudia Onorato Date: Sun, 18 Oct 2020 01:46:07 -0400 Subject: [PATCH 10/25] added bad request when raising classification exception --- backend/app.py | 11 +++++++---- backend/classification/exceptions.py | 6 +++--- 2 files changed, 10 insertions(+), 7 deletions(-) diff --git a/backend/app.py b/backend/app.py index d6aecdce..0e8adcab 100644 --- a/backend/app.py +++ b/backend/app.py @@ -5,6 +5,7 @@ from classification.file_loading import get_raw_array from classification.predict import predict +from classification.exceptions import ClassificationError app = Flask(__name__) @@ -43,10 +44,12 @@ def analyze_sleep(): if not allowed_file(file.filename): return 'File format not allowed', HTTPStatus.BAD_REQUEST - raw_array = get_raw_array(file) - - form_data = request.form.to_dict() - predict(raw_array, **form_data) + try: + raw_array = get_raw_array(file) + form_data = request.form.to_dict() + predict(raw_array, **form_data) + except ClassificationError as e: + return e.message, HTTPStatus.BAD_REQUEST with open("assets/mock_response.json", "r") as mock_response_file: return mock_response_file.read() diff --git a/backend/classification/exceptions.py b/backend/classification/exceptions.py index c4ff3b3a..e2478de2 100644 --- a/backend/classification/exceptions.py +++ b/backend/classification/exceptions.py @@ -1,13 +1,13 @@ class ClassificationError(Exception): """Base errors for application errors that can occur""" - pass + message = "An error occured while calculating sleep stages." class TimestampsError(ClassificationError): """Raised when timestamps are incoherent or doesn't fit with the provided file""" - pass + message = "Received file, stream start time, bedtime or wakeup time are incoherent" class FileSizeError(ClassificationError): """Raised when file is either too big or too small""" - pass + message = "Received file is either too big or too small" From 5e5d7b54a16e644620a2d91109feed3daed7f73a Mon Sep 17 00:00:00 2001 From: Claudia Onorato Date: Sun, 18 Oct 2020 22:04:29 -0400 Subject: [PATCH 11/25] fixed constants & requirements --- backend/app.py | 2 +- backend/classification/config/constants.py | 35 ++++++++----------- backend/classification/features/extraction.py | 12 +++---- backend/classification/validation.py | 19 +++++----- backend/requirements.txt | 3 -- 5 files changed, 30 insertions(+), 41 deletions(-) diff --git a/backend/app.py b/backend/app.py index 0e8adcab..2aa67346 100644 --- a/backend/app.py +++ b/backend/app.py @@ -20,7 +20,7 @@ def status(): return "" -@app.route('/analyze-sleep', methods=['GET']) +@app.route('/analyze-sleep', methods=['POST']) def analyze_sleep(): """ Request payload example diff --git a/backend/classification/config/constants.py b/backend/classification/config/constants.py index 7a89dc73..ef9dbd80 100644 --- a/backend/classification/config/constants.py +++ b/backend/classification/config/constants.py @@ -1,34 +1,29 @@ -SLEEP_STAGES_VALUES = { - "W": 0, - "N1": 1, - "N2": 2, - "N3": 3, - "REM": 4 -} -N_STAGES = len(SLEEP_STAGES_VALUES.keys()) +from enum import Enum + + +class Sex(Enum): + # based from subject description file (see header) + # https://physionet.org/content/sleep-edfx/1.0.0/SC-subjects.xls + F = 1 + M = 2 + EEG_CHANNELS = [ 'EEG Fpz-Cz', 'EEG Pz-Oz' ] -EPOCH_DURATION = 30 # in seconds -MIN_FILE_SIZE = EPOCH_DURATION # in seconds +EPOCH_DURATION = 30 +FILE_MINIMUM_DURATION = EPOCH_DURATION -DATASET_SAMPLE_RATE = 100 # in Hz -OPENBCI_CYTON_SAMPLE_RATE = 250 # in Hz -OPENBCI_GANGLION_SAMPLE_RATE = 200 # in Hz +DATASET_SAMPLE_RATE = 100 +OPENBCI_CYTON_SAMPLE_RATE = 250 +OPENBCI_GANGLION_SAMPLE_RATE = 200 AGE_FEATURE_BINS = [ [0, 49], [50, 59], [60, 84], - [85, 110] + [85, 125] ] ACCEPTED_AGE_RANGE = [AGE_FEATURE_BINS[0][0], AGE_FEATURE_BINS[-1][-1]] - -# based from subject description file (see header) https://physionet.org/content/sleep-edfx/1.0.0/SC-subjects.xls -SEX_FEATURE = { - 'F': 1, - 'M': 2 -} diff --git a/backend/classification/features/extraction.py b/backend/classification/features/extraction.py index cd384577..4acf7546 100644 --- a/backend/classification/features/extraction.py +++ b/backend/classification/features/extraction.py @@ -5,7 +5,7 @@ EEG_CHANNELS, AGE_FEATURE_BINS, DATASET_SAMPLE_RATE, - SEX_FEATURE, + Sex, ) from classification.features.constants import ( DATASET_HIGH_PASS_FREQ, @@ -40,9 +40,7 @@ def get_eeg_features(raw_data, in_bed_time, out_of_bed_time): chan_data = chan_data.resample(DATASET_SAMPLE_RATE) chan_data = chan_data.filter(l_freq=DATASET_HIGH_PASS_FREQ, h_freq=None) - X_file_channel = convert_to_epochs( - chan_data, in_bed_time - ) + X_file_channel = convert_to_epochs(chan_data, in_bed_time) feature_union = get_feature_union() print('Started feature ext on epochs: ', X_file_channel) @@ -76,10 +74,8 @@ def get_categorical_features(age, sex, nb_epochs): for category_index, age_range in enumerate(AGE_FEATURE_BINS) if age >= age_range[0] and age <= age_range[1] ) - sex_value = SEX_FEATURE[sex] + sex_value = Sex[sex].value X_categorical = [sex_value, age_category] - return np.array( - X_categorical * nb_epochs - ).reshape(nb_epochs, len(X_categorical)) + return np.array(X_categorical * nb_epochs).reshape(nb_epochs, -1) diff --git a/backend/classification/validation.py b/backend/classification/validation.py index c13e557b..01fbe24f 100644 --- a/backend/classification/validation.py +++ b/backend/classification/validation.py @@ -1,7 +1,7 @@ from classification.config.constants import ( - MIN_FILE_SIZE, + FILE_MINIMUM_DURATION, ACCEPTED_AGE_RANGE, - SEX_FEATURE, + Sex, ) from classification.exceptions import ( TimestampsError, @@ -20,7 +20,7 @@ def validate(raw_eeg, age, sex, in_bed_seconds, out_of_bed_seconds): def _validate_timestamps(in_bed_seconds, out_of_bed_seconds): has_positive_timespan = in_bed_seconds > 0 and out_of_bed_seconds > 0 has_got_out_of_bed_after_in_bed = out_of_bed_seconds > in_bed_seconds - has_respected_minimum_bed_time = (out_of_bed_seconds - in_bed_seconds) > MIN_FILE_SIZE + has_respected_minimum_bed_time = (out_of_bed_seconds - in_bed_seconds) > FILE_MINIMUM_DURATION if not( has_positive_timespan @@ -31,12 +31,13 @@ def _validate_timestamps(in_bed_seconds, out_of_bed_seconds): def _validate_file_with_timestamps(raw_eeg, out_of_bed_seconds): - has_raw_respected_minimum_file_size = raw_eeg.times[-1] > MIN_FILE_SIZE - is_raw_at_least_as_long_as_out_of_bed = raw_eeg.times[-1] >= out_of_bed_seconds + has_raw_respected_minimum_file_size = raw_eeg.times[-1] > FILE_MINIMUM_DURATION if not has_raw_respected_minimum_file_size: raise FileSizeError() + is_raw_at_least_as_long_as_out_of_bed = raw_eeg.times[-1] >= out_of_bed_seconds + if not is_raw_at_least_as_long_as_out_of_bed: raise TimestampsError() @@ -49,7 +50,7 @@ def _validate_age(age): def _validate_sex(sex): - is_in_accepted_sex_choices = sex in SEX_FEATURE.keys() - - if not(is_in_accepted_sex_choices): - raise ClassificationError() + try: + Sex[sex] + except KeyError: + raise ClassificationError('invalid sex') diff --git a/backend/requirements.txt b/backend/requirements.txt index 8cdd0c76..246c7204 100644 --- a/backend/requirements.txt +++ b/backend/requirements.txt @@ -5,7 +5,4 @@ waitress==1.4.4 mne==0.21.0 numpy==1.19.2 scipy==1.5.2 -joblib==0.17.0 scikit-learn==0.23.2 -sklearn==0.0 -threadpoolctl==2.1.0 From 09ef601892066e954516d454cff47f0589c71fcc Mon Sep 17 00:00:00 2001 From: Claudia Onorato Date: Sun, 18 Oct 2020 22:52:49 -0400 Subject: [PATCH 12/25] moved form data to int in parent file --- backend/app.py | 20 +++++++++++-- backend/classification/features/__init__.py | 27 +++++++++--------- backend/classification/features/extraction.py | 19 +++++-------- backend/classification/predict.py | 28 ++++++++----------- backend/classification/validation.py | 17 +++-------- 5 files changed, 54 insertions(+), 57 deletions(-) diff --git a/backend/app.py b/backend/app.py index 2aa67346..8b9d1cb1 100644 --- a/backend/app.py +++ b/backend/app.py @@ -6,6 +6,7 @@ from classification.file_loading import get_raw_array from classification.predict import predict from classification.exceptions import ClassificationError +from classification.config.constants import Sex app = Flask(__name__) @@ -44,10 +45,25 @@ def analyze_sleep(): if not allowed_file(file.filename): return 'File format not allowed', HTTPStatus.BAD_REQUEST + form_data = request.form.to_dict() + + try: + age = int(form_data['age']) + sex = Sex[form_data['sex']] + stream_start = int(form_data['stream_start']) + bedtime = int(form_data['bedtime']) + wakeup = int(form_data['wakeup']) + except (KeyError, ValueError): + return 'Missing or invalid request parameters', HTTPStatus.BAD_REQUEST + try: raw_array = get_raw_array(file) - form_data = request.form.to_dict() - predict(raw_array, **form_data) + predict(raw_array, info={ + 'sex': sex, + 'age': age, + 'in_bed_seconds': bedtime - stream_start, + 'out_of_bed_seconds': wakeup - stream_start + }) except ClassificationError as e: return e.message, HTTPStatus.BAD_REQUEST diff --git a/backend/classification/features/__init__.py b/backend/classification/features/__init__.py index 0142bbf5..51a0623d 100644 --- a/backend/classification/features/__init__.py +++ b/backend/classification/features/__init__.py @@ -6,23 +6,24 @@ ) -def get_features(signal, age, sex, in_bed_seconds, out_of_bed_seconds): +def get_features(signal, info): """Returns the raw features - Input - ------- - - raw_signal: MNE.Raw object with signals with or without annotations - - age: Age of the subject - - sex: Sex of the subject - - in_bed_seconds: timespan, in seconds, from which the subject started - the recording and went to bed - - out_of_bed_seconds: timespan, in seconds, from which the subject - started the recording and got out of bed - + Input: + - raw_eeg: instance of mne.io.RawArray + Should contain 2 channels (1: FPZ-CZ, 2: PZ-OZ) + - info: dict + Should contain the following keys: + - sex: instance of Sex enum + - age: indicates the subject's age + - in_bed_seconds: timespan, in seconds, from which + the subject started the recording and went to bed + - out_of_bed_seconds: timespan, in seconds, from which + the subject started the recording and got out of bed Returns ------- - features X in a vector of (nb_epochs, nb_features) """ - X_eeg = get_eeg_features(signal, in_bed_seconds, out_of_bed_seconds) - X_categorical = get_categorical_features(age, sex, X_eeg.shape[0]) + X_eeg = get_eeg_features(signal, info['in_bed_seconds'], info['out_of_bed_seconds']) + X_categorical = get_categorical_features(info['age'], info['sex'], X_eeg.shape[0]) return np.append(X_categorical, X_eeg, axis=1) diff --git a/backend/classification/features/extraction.py b/backend/classification/features/extraction.py index 4acf7546..3fe7bd73 100644 --- a/backend/classification/features/extraction.py +++ b/backend/classification/features/extraction.py @@ -5,11 +5,8 @@ EEG_CHANNELS, AGE_FEATURE_BINS, DATASET_SAMPLE_RATE, - Sex, -) -from classification.features.constants import ( - DATASET_HIGH_PASS_FREQ, ) +from classification.features.constants import DATASET_HIGH_PASS_FREQ from classification.features.pipeline import get_feature_union from classification.features.utils import ( drop_other_channels, @@ -18,14 +15,14 @@ ) -def get_eeg_features(raw_data, in_bed_time, out_of_bed_time): +def get_eeg_features(raw_data, in_bed_seconds, out_of_bed_seconds): """Returns the continuous feature matrix Input ------- raw_signal: MNE.Raw object with signals with or without annotations - in_bed_time: timespan, in seconds, from which the subject started + in_bed_seconds: timespan, in seconds, from which the subject started the recording and went to bed - out_of_bed_time: timespan, in seconds, from which the subject + out_of_bed_seconds: timespan, in seconds, from which the subject started the recording and got out of bed Returns @@ -36,11 +33,11 @@ def get_eeg_features(raw_data, in_bed_time, out_of_bed_time): for channel in EEG_CHANNELS: chan_data = drop_other_channels(raw_data.copy(), channel) - chan_data = crop_raw_data(chan_data, in_bed_time, out_of_bed_time) + chan_data = crop_raw_data(chan_data, in_bed_seconds, out_of_bed_seconds) chan_data = chan_data.resample(DATASET_SAMPLE_RATE) chan_data = chan_data.filter(l_freq=DATASET_HIGH_PASS_FREQ, h_freq=None) - X_file_channel = convert_to_epochs(chan_data, in_bed_time) + X_file_channel = convert_to_epochs(chan_data, in_bed_seconds) feature_union = get_feature_union() print('Started feature ext on epochs: ', X_file_channel) @@ -74,8 +71,6 @@ def get_categorical_features(age, sex, nb_epochs): for category_index, age_range in enumerate(AGE_FEATURE_BINS) if age >= age_range[0] and age <= age_range[1] ) - sex_value = Sex[sex].value - - X_categorical = [sex_value, age_category] + X_categorical = [sex.value, age_category] return np.array(X_categorical * nb_epochs).reshape(nb_epochs, -1) diff --git a/backend/classification/predict.py b/backend/classification/predict.py index bbc20545..07c6dbb7 100644 --- a/backend/classification/predict.py +++ b/backend/classification/predict.py @@ -3,27 +3,21 @@ from classification.validation import validate -def predict(raw_eeg, age, sex, stream_start, bedtime, wakeup): +def predict(raw_eeg, info): """ Input: - raw_eeg: instance of mne.io.RawArray Should contain 2 channels (1: FPZ-CZ, 2: PZ-OZ) - - sex: should either be 'F' or 'M' - - age: indicates the subject's age - - start_stream: UNIX timestamp of time at which recording started - - bedtime: UNIX timestamp of time at which to start classification (W before) - - wakeup: UNIX timestamp of time at which to end classification (W after) - + - info: dict + Should contain the following keys: + - sex: instance of Sex enum + - age: indicates the subject's age + - in_bed_seconds: timespan, in seconds, from which + the subject started the recording and went to bed + - out_of_bed_seconds: timespan, in seconds, from which + the subject started the recording and got out of bed """ - age, stream_start, bedtime, wakeup = int(age), int(stream_start), int(bedtime), int(wakeup) - - in_bed_seconds = bedtime - stream_start - out_of_bed_seconds = wakeup - stream_start - - validate(raw_eeg, age, sex, in_bed_seconds, out_of_bed_seconds) - - X_openbci = get_features( - raw_eeg, age, sex, in_bed_seconds, out_of_bed_seconds - ) + validate(raw_eeg, info) + X_openbci = get_features(raw_eeg, info) print(X_openbci[0], X_openbci.shape) diff --git a/backend/classification/validation.py b/backend/classification/validation.py index 01fbe24f..587f928e 100644 --- a/backend/classification/validation.py +++ b/backend/classification/validation.py @@ -1,7 +1,6 @@ from classification.config.constants import ( FILE_MINIMUM_DURATION, ACCEPTED_AGE_RANGE, - Sex, ) from classification.exceptions import ( TimestampsError, @@ -10,11 +9,10 @@ ) -def validate(raw_eeg, age, sex, in_bed_seconds, out_of_bed_seconds): - _validate_timestamps(in_bed_seconds, out_of_bed_seconds) - _validate_file_with_timestamps(raw_eeg, out_of_bed_seconds) - _validate_age(age) - _validate_sex(sex) +def validate(raw_eeg, info): + _validate_timestamps(info['in_bed_seconds'], info['out_of_bed_seconds']) + _validate_file_with_timestamps(raw_eeg, info['out_of_bed_seconds']) + _validate_age(info['age']) def _validate_timestamps(in_bed_seconds, out_of_bed_seconds): @@ -47,10 +45,3 @@ def _validate_age(age): if not(is_in_accepted_range): raise ClassificationError('invalid age') - - -def _validate_sex(sex): - try: - Sex[sex] - except KeyError: - raise ClassificationError('invalid sex') From cd1ea6d3646fc91a82edf374a840d8a8938134a0 Mon Sep 17 00:00:00 2001 From: Claudia Onorato Date: Sun, 18 Oct 2020 23:41:25 -0400 Subject: [PATCH 13/25] updated min age --- backend/classification/config/constants.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/backend/classification/config/constants.py b/backend/classification/config/constants.py index ef9dbd80..c95fcab2 100644 --- a/backend/classification/config/constants.py +++ b/backend/classification/config/constants.py @@ -21,7 +21,7 @@ class Sex(Enum): OPENBCI_GANGLION_SAMPLE_RATE = 200 AGE_FEATURE_BINS = [ - [0, 49], + [12, 49], [50, 59], [60, 84], [85, 125] From 908192950aa4e41bb119465547a687ec7f9b1c03 Mon Sep 17 00:00:00 2001 From: Claudia Onorato Date: Mon, 19 Oct 2020 15:59:56 -0400 Subject: [PATCH 14/25] call from FE to BE works --- backend/assets/mock_response.json | 2 +- web/src/requests/analyze-sleep.js | 2 +- .../views/analyze_sleep/upload_form/constants.js | 2 +- web/src/views/analyze_sleep/upload_form/index.js | 14 ++++++++------ 4 files changed, 11 insertions(+), 9 deletions(-) diff --git a/backend/assets/mock_response.json b/backend/assets/mock_response.json index 12677da9..926c530f 100644 --- a/backend/assets/mock_response.json +++ b/backend/assets/mock_response.json @@ -1,5 +1,5 @@ { - "board": "CYTHON", + "board": "CYTON", "subject": { "age": 28, "sex": "F" diff --git a/web/src/requests/analyze-sleep.js b/web/src/requests/analyze-sleep.js index b223f439..1ee95971 100644 --- a/web/src/requests/analyze-sleep.js +++ b/web/src/requests/analyze-sleep.js @@ -3,6 +3,6 @@ import Axios from 'axios-observable'; import { objectToFormData } from './object-to-formdata'; export const analyzeSleep = (formData) => - Axios.post(`${SERVER_URL}/analyze_sleep`, objectToFormData(formData), { + Axios.post(`${SERVER_URL}/analyze-sleep`, objectToFormData(formData), { headers: { 'Content-Type': 'multipart/form-data' }, }); diff --git a/web/src/views/analyze_sleep/upload_form/constants.js b/web/src/views/analyze_sleep/upload_form/constants.js index 825e4e79..2cab9365 100644 --- a/web/src/views/analyze_sleep/upload_form/constants.js +++ b/web/src/views/analyze_sleep/upload_form/constants.js @@ -1,6 +1,6 @@ export const MAX_FILE_UPLOAD_SIZE = 1.6e9; export const ACCEPTED_FILE_TYPE = 'text/plain'; -export const ACCEPTED_FILE_EXTENSION = '.txt'; +export const ACCEPTED_FILE_EXTENSION = '.TXT'; export const DEVICES = { CYTON: 'CYTON', GANGLION: 'GANGLION', diff --git a/web/src/views/analyze_sleep/upload_form/index.js b/web/src/views/analyze_sleep/upload_form/index.js index d0126faf..1447e04c 100644 --- a/web/src/views/analyze_sleep/upload_form/index.js +++ b/web/src/views/analyze_sleep/upload_form/index.js @@ -30,12 +30,14 @@ const filterOutDateTimeFields = (data) => const mergeDateTimeFields = (data) => filterInDateTimeFields(data).map((fieldPrefix) => [ fieldPrefix, - new Date( - Object.entries(data) - .filter(([fieldName, value]) => fieldName.startsWith(fieldPrefix)) - .map(([fieldName, value]) => value) - .join(' '), - ).getTime(), + Math.floor( + new Date( + Object.entries(data) + .filter(([fieldName, value]) => fieldName.startsWith(fieldPrefix)) + .map(([fieldName, value]) => value) + .join(' '), + ).getTime() / 1000, + ), ]); const postForm = async (formData, setResponse, setPostFormError, setIsFormSubmitted) => { From 0146d5afde38fae9f84f2bf37f947fbff1b9f1b2 Mon Sep 17 00:00:00 2001 From: Claudia Onorato Date: Mon, 19 Oct 2020 23:12:12 -0400 Subject: [PATCH 15/25] changed file extension checks --- backend/app.py | 5 ++--- backend/classification/config/constants.py | 2 ++ web/src/views/analyze_sleep/upload_form/constants.js | 2 +- web/src/views/analyze_sleep/upload_form/index.js | 5 ++++- 4 files changed, 9 insertions(+), 5 deletions(-) diff --git a/backend/app.py b/backend/app.py index 8b9d1cb1..fe1154d5 100644 --- a/backend/app.py +++ b/backend/app.py @@ -6,14 +6,13 @@ from classification.file_loading import get_raw_array from classification.predict import predict from classification.exceptions import ClassificationError -from classification.config.constants import Sex +from classification.config.constants import Sex, ALLOWED_FILE_EXTENSIONS app = Flask(__name__) def allowed_file(filename): - ALLOWED_EXTENSIONS = {'txt', 'csv'} - return '.' in filename and filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS + return '.' in filename and filename.lower().endswith(tuple(ALLOWED_FILE_EXTENSIONS)) @app.route("/") diff --git a/backend/classification/config/constants.py b/backend/classification/config/constants.py index c95fcab2..9a92613e 100644 --- a/backend/classification/config/constants.py +++ b/backend/classification/config/constants.py @@ -8,6 +8,8 @@ class Sex(Enum): M = 2 +ALLOWED_FILE_EXTENSIONS = {'txt', 'csv'} + EEG_CHANNELS = [ 'EEG Fpz-Cz', 'EEG Pz-Oz' diff --git a/web/src/views/analyze_sleep/upload_form/constants.js b/web/src/views/analyze_sleep/upload_form/constants.js index 2cab9365..825e4e79 100644 --- a/web/src/views/analyze_sleep/upload_form/constants.js +++ b/web/src/views/analyze_sleep/upload_form/constants.js @@ -1,6 +1,6 @@ export const MAX_FILE_UPLOAD_SIZE = 1.6e9; export const ACCEPTED_FILE_TYPE = 'text/plain'; -export const ACCEPTED_FILE_EXTENSION = '.TXT'; +export const ACCEPTED_FILE_EXTENSION = '.txt'; export const DEVICES = { CYTON: 'CYTON', GANGLION: 'GANGLION', diff --git a/web/src/views/analyze_sleep/upload_form/index.js b/web/src/views/analyze_sleep/upload_form/index.js index 1447e04c..ed2ec8a8 100644 --- a/web/src/views/analyze_sleep/upload_form/index.js +++ b/web/src/views/analyze_sleep/upload_form/index.js @@ -77,7 +77,10 @@ const UploadForm = () => { required: 'A valid .txt raw EEG file must be selected.', validate: (files) => { const file = files[0]; - if (file.type !== ACCEPTED_FILE_TYPE || !file.name.endsWith(ACCEPTED_FILE_EXTENSION)) { + if ( + file.type !== ACCEPTED_FILE_TYPE || + !file.name.toLowerCase().endsWith(ACCEPTED_FILE_EXTENSION) + ) { return 'A valid .txt raw OpenBCI EEG file must be selected.'; } else if (file.size >= MAX_FILE_UPLOAD_SIZE) { return 'File is too large. Must be less than 1.6 Gb.'; From ffb625252cd284a72cdd617aa75c269b75512e29 Mon Sep 17 00:00:00 2001 From: Claudia Onorato Date: Wed, 21 Oct 2020 23:13:28 -0400 Subject: [PATCH 16/25] Apply suggestions from code review Co-authored-by: Anes Belfodil --- backend/app.py | 2 +- backend/classification/config/constants.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/backend/app.py b/backend/app.py index fe1154d5..64c37bb3 100644 --- a/backend/app.py +++ b/backend/app.py @@ -12,7 +12,7 @@ def allowed_file(filename): - return '.' in filename and filename.lower().endswith(tuple(ALLOWED_FILE_EXTENSIONS)) + return filename.lower().endswith(ALLOWED_FILE_EXTENSIONS) @app.route("/") diff --git a/backend/classification/config/constants.py b/backend/classification/config/constants.py index 9a92613e..80692c6d 100644 --- a/backend/classification/config/constants.py +++ b/backend/classification/config/constants.py @@ -8,7 +8,7 @@ class Sex(Enum): M = 2 -ALLOWED_FILE_EXTENSIONS = {'txt', 'csv'} +ALLOWED_FILE_EXTENSIONS = ('.txt', '.csv') EEG_CHANNELS = [ 'EEG Fpz-Cz', From 558d30d75e9be47b454f0f71baa06ebf3fd3d13f Mon Sep 17 00:00:00 2001 From: Claudia Onorato Date: Thu, 22 Oct 2020 00:20:38 -0400 Subject: [PATCH 17/25] added filtering exploration for OpenBCI Cyton data & analyzed resampling (before or after epoch) --- ai/feature_extraction.ipynb | 546 ++++++++++++++++++++++++++++++------ ai/prediction_openbci.ipynb | 127 +++++---- 2 files changed, 524 insertions(+), 149 deletions(-) diff --git a/ai/feature_extraction.ipynb b/ai/feature_extraction.ipynb index 96e0bd7e..617c4442 100644 --- a/ai/feature_extraction.ipynb +++ b/ai/feature_extraction.ipynb @@ -16,9 +16,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 71, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], "source": [ "%load_ext autoreload\n", "%autoreload 2" @@ -26,7 +35,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 137, "metadata": {}, "outputs": [], "source": [ @@ -74,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 73, "metadata": {}, "outputs": [], "source": [ @@ -124,7 +133,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 74, "metadata": {}, "outputs": [ { @@ -240,7 +249,7 @@ "4 21720.0 1989-04-05 16:48:00 607798080.0 " ] }, - "execution_count": 4, + "execution_count": 74, "metadata": {}, "output_type": "execute_result" } @@ -259,12 +268,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 75, "metadata": {}, "outputs": [ { "data": { - "image/png": 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" ] @@ -348,7 +357,7 @@ "1 25620.0 1989-04-25 14:50:00 609519000.0 0 " ] }, - "execution_count": 5, + "execution_count": 75, "metadata": {}, "output_type": "execute_result" } @@ -388,7 +397,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 76, "metadata": {}, "outputs": [], "source": [ @@ -425,7 +434,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 77, "metadata": {}, "outputs": [ { @@ -487,7 +496,7 @@ "24 31680.0 1989-05-22 16:02:00 611856120.0 0 " ] }, - "execution_count": 7, + "execution_count": 77, "metadata": {}, "output_type": "execute_result" } @@ -506,7 +515,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 138, "metadata": {}, "outputs": [], "source": [ @@ -523,7 +532,11 @@ "\n", " return raw_data\n", " \n", - "def convert_to_epochs(raw_data, annotations_stage_dict=DATASET_SLEEP_STAGES_VALUES):\n", + "def convert_to_epochs(\n", + " raw_data,\n", + " annotations_stage_dict=DATASET_SLEEP_STAGES_VALUES,\n", + " sfreq=SAMPLING_FREQ\n", + "):\n", " \"\"\"\n", " returns:\n", " mne.Epochs, where the epochs are only choosen if the subject was in bed.\n", @@ -547,7 +560,7 @@ " events=events,\n", " event_id=event_id,\n", " tmin=0.,\n", - " tmax=MAX_TIME,\n", + " tmax=EPOCH_DURATION - 1. / sfreq,\n", " preload=True,\n", " baseline=None,\n", " verbose=False)\n", @@ -586,7 +599,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 79, "metadata": {}, "outputs": [], "source": [ @@ -653,7 +666,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 80, "metadata": {}, "outputs": [], "source": [ @@ -677,7 +690,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 81, "metadata": {}, "outputs": [], "source": [ @@ -725,7 +738,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 82, "metadata": {}, "outputs": [], "source": [ @@ -745,7 +758,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 83, "metadata": {}, "outputs": [], "source": [ @@ -774,7 +787,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 84, "metadata": {}, "outputs": [], "source": [ @@ -782,6 +795,7 @@ "\n", "def hjorth(X):\n", " \"\"\" Compute Hjorth mobility and complexity of a time series from either two\n", + " [source: https://github.com/forrestbao/pyeeg]\n", " cases below:\n", " 1. X, the time series of type list (default)\n", " 2. D, a first order differential sequence of X (if D is provided,\n", @@ -839,7 +853,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 85, "metadata": {}, "outputs": [], "source": [ @@ -848,7 +862,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 86, "metadata": {}, "outputs": [], "source": [ @@ -877,7 +891,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 87, "metadata": {}, "outputs": [], "source": [ @@ -903,7 +917,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 88, "metadata": {}, "outputs": [], "source": [ @@ -950,7 +964,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 89, "metadata": {}, "outputs": [], "source": [ @@ -1012,7 +1026,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 90, "metadata": {}, "outputs": [], "source": [ @@ -1021,7 +1035,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 91, "metadata": {}, "outputs": [], "source": [ @@ -1054,7 +1068,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 92, "metadata": {}, "outputs": [], "source": [ @@ -1076,7 +1090,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 93, "metadata": {}, "outputs": [], "source": [ @@ -1115,7 +1129,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 94, "metadata": {}, "outputs": [], "source": [ @@ -1124,7 +1138,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 95, "metadata": {}, "outputs": [], "source": [ @@ -1144,7 +1158,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 96, "metadata": {}, "outputs": [], "source": [ @@ -1165,7 +1179,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 97, "metadata": {}, "outputs": [ { @@ -2426,7 +2440,16 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 98, + "metadata": {}, + "outputs": [], + "source": [ + "from scipy.signal import butter, freqz, cheby1, cheby2" + ] + }, + { + "cell_type": "code", + "execution_count": 99, "metadata": {}, "outputs": [], "source": [ @@ -2451,86 +2474,429 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "### OpenBCI file preprocessing\n", + "___\n", + "\n", + "#### 1. Highpass filter (remove DC)\n", + "\n", "We will transform this signal into a similar one to those found in the dataset. By looking at one recordings in the dataset, in EdfBrowser, and at file info, we see the following characteristics for the EEG channels:\n", "\n", - "| Label | Sample frequency | Physical maximum | Physical minimum | Physical dimension | Digital maximum | Digital minimum | Prefilter |\n", + "| Label | Sample frequency | Physical maximum | Physical minimum | Physical dimension | Digital maximum | Digital minimum | Prefilter |\n", "|-|-|-|-|-| -|-|-|\n", "| EEG Fpz-Cz | 100 Hz| +189 | -192 | uV | +2047 | -2048 | HP:0.5Hz LP:100Hz |\n", "| EEG Pz-Oz | 100 Hz| +197 | -199 | uV | +2047 | -2048 | HP:0.5Hz LP:100Hz |\n", "\n", - "We see that there's a high pass filter applied at 0.5 Hz, a low pass filter applied at 100 Hz. We also resamply at 100 Hz." + "We see that there's a high pass filter applied at 0.5 Hz, a low pass filter applied at 100 Hz.\n", + "\n", + "We will first look to choose filter with which we will high-pass the signal. We decided to go for an IIR, because it is faster to run, and has a steeper curve, than an FIR filter. We then have to also select the filter type and the filter order." ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 100, "metadata": {}, "outputs": [ + { + "data": { + "image/png": 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4bTwePxBwzi3z6/Yrvz6bgBafDOuc24o3pPsxvJ7mm/B+fqq+A3WANo7z6XLOrcW7IH4cr7120MbTW/0vCz6Cd//n23j1/zHe8NOOeAjvYvyItfC0Xuf9BNdX8O7NLscbMfDx5uli0h/Au4f723jDnbPxHjzUsL7Dx8WXhvcFwGG8Ya4Hge92cN++ifdFxEa8B7aV+Mu6wpN4f28bgdeB5/EegBVpa6Nm/oJ3vF8EHnbOveAv/0+8c+yYX87imG3aap+2/h5bKrsvJ5+y3Hy+CX+I8reAv/nnzntbStcBv8Rrt7fwbr34pp//NrxhvH/Gu0+2+e/Z/gTvfuTY5yo0OZc78PncJufcZrznB6zBC2yvIOZcxnsifymwz8wOtJLNV/392onXpt91/s8OmVmWeU9JbhgJMhHYaGY1eOfPUrwvl87ULLzg/x07+UTmL59FfiLSgzQ8KVFEpMcw72cfLnPO/Ut7abu4HuPwej2GunPwYWnez3383DnX0R4VkfOaeT+580PnXEsPRGuedhjeFxWJTr/1KyIiHaCeXRGRFvjDHecCPz4Xga7Iu4E/hPxG8373+D14PXrLurteIiISnxTsiog0Y2YjgSN4D3P6XjdXRySeGN5PGx3GG8a8Be8eYRERkU6nYcwiIiIiIiISd9SzKyIiIiIiInFHwa6IiIiIiIjEnYTursDpGjBggBs2bFh3V0NERERERES6wLp16w445zLPNp/zLtgdNmwYa9eu7e5qiIiIiIiISBcws52dkY+GMYuIiIiIiEjcUbArIiIiIiIicUfBroiIiIiIiMSd8+6eXRERERERkXMpFAqxZ88eTpw40d1ViSvJyckMGTKExMTELslfwa6IiIiIiEgb9uzZQ9++fRk2bBhm1t3ViQvOOQ4ePMiePXsYPnx4l5ShYcwiIiIiIiJtOHHiBBkZGQp0O5GZkZGR0aW95Qp2RURERERE2qFAt/N1dZsq2BUREREREenhUlNTW1xeXFzMkiVLzijPr33tazz88MOnLK+srOSaa67hyiuv5K9//Ss33ngjR44caVKPsrIyfvnLX55RueeKgl0RERERERFp9OKLL3LFFVfw+uuvc9111/H888/Tv3//JmnOJNgNh8OdWc12KdgVERERERE5TzjnuOuuu8jJyWHSpElUVFQ0rlu3bh3jx4+nsLCQKVOmUF5eDsCTTz7JVVddRX5+Ph/96Eepra1tNf/169fzhS98geXLl1NQUMDx48cZNmwYBw4caJLui1/8In/9618pKCjgkUceIRKJ8PnPf56rrrqK0aNH86Mf/QiAVatWcd111zF16lRyc3O7oEVap6cxi4iIiIiIdNDXnytl8ztVnZpn7uA0vnpTXofSLlu2jK1bt7J582b2799Pbm4ut912G6FQiDlz5rB8+XIyMzNZvHgx8+fPZ8GCBcycOZNPf/rTANx333385Cc/Yc6cOS3mX1BQwDe+8Q3Wrl3L448/3mo9vv3tb/Pwww/zu9/9DoAnnniCfv368dprr1FXV8e1117LDTfcAEBJSQmbNm3qsqcut0bBroiIiIiIyHli9erVzJo1i2AwyODBg5kwYQIAW7duZdOmTUyePBmASCTCoEGDANi0aRP33XcfR44cobq6milTpnR6vV544QU2btzYeP/w0aNH2b59O0lJSVx99dXnPNAFBbsiIiIiIiId1tEe2HPNOUdeXh5r1qw5ZV1xcTHPPvss+fn5LFy4kFWrVnVJ+Y899tgpgfSqVatISUnp9PI6QvfsioiIiIiInCfGjRvH4sWLiUQilJeXs3LlSgBycnKorKxsDHZDoRClpaUAHDt2jEGDBhEKhfjFL37RKfXo27cvx44da5yfMmUK//u//0soFAJg27Zt1NTUdEpZZ0o9uyIiIiIiIueJGTNmsGLFCnJzc8nKymLs2LEAJCUlsWTJEu6++26OHj1KOBxm3rx55OXl8cADD3DNNdeQmZnJNddc0yRIPVOjR48mGAySn59PcXExc+fOpaysjDFjxuCcIzMzk2efffasyzkb5pzr1gqcrqKiIrd27druroaIiIiIiLxLbNmyhZEjR3Z3NeJSS21rZuucc0Vnm7eGMYuIiIiIiEjcUbArIiIiIiIicUfBroiIiIiIiMQdBbsiIiIiIiISdxTsioiIiIiISNxRsCsiIiIiIiJxR8GuiIiIiIhID7dnzx6mTZtGdnY2l156KXPnzqW+vr5D2xYXF7NkyZIzLnv58uWMHj2agoICioqKeOmll844r3Opy4JdM1tgZhVmtqmddFeZWdjMbu6quoiIiIiIiJyvnHPMnDmT6dOns337drZt20Z1dTXz588/JW04HD7r8prnMXHiRDZs2MD69etZsGABt99++1mXcS50Zc/uQuCDbSUwsyDwHeCFLqyHiIiIiIjIeWvFihUkJycze/ZsAILBII888ggLFiygtraWhQsXMnXqVCZMmMDEiRNxznHXXXeRk5PDpEmTqKioaMxr3bp1jB8/nsLCQqZMmUJ5eTkA119/PfPmzaOoqIhHH320SfmpqamYGQA1NTWN0z1dQldl7JxbbWbD2kk2B/gNcFVX1UNERERERKTT/OGLsO8fnZvnRVfAh77d6urS0lIKCwubLEtLSyMrK4sdO3YAUFJSwsaNG0lPT2fp0qVs3bqVzZs3s3//fnJzc7ntttsIhULMmTOH5cuXk5mZyeLFi5k/fz4LFiwAoL6+nrVr17ZYh2XLlvGlL32JiooKfv/733fSjnetLgt222Nm7wFmAB9Awa6IiIiIiMgZmzx5Munp6QCsXr2aWbNmEQwGGTx4MBMmTABg69atbNq0icmTJwMQiUQYNGhQYx633HJLq/nPmDGDGTNmsHr1ar7yla/w5z//uQv3pnN0W7ALfA+41zkXba8b3MzuAO4AyMrKOgdVExERERERaUEbPbBdJTc395QHTFVVVbFr1y4uu+wySkpKSElJaTcf5xx5eXmsWbOmxfUdyWPcuHG89dZbHDhwgAEDBnRsB7pJdz6NuQj4lZmVATcDPzCz6S0ldM494Zwrcs4VZWZmnss6ioiIiIiIdKuJEydSW1vLokWLAK9H9p577qG4uJg+ffqckn7cuHEsXryYSCRCeXk5K1euBCAnJ4fKysrGYDcUClFaWtpu+Tt27MA5B3jDpevq6sjIyOis3esy3daz65wb3jBtZguB3znnnu2u+oiIiIiIiPREZsayZcu48847eeCBB4hGo9x44408+OCDLaafMWMGK1asIDc3l6ysLMaOHQtAUlISS5Ys4e677+bo0aOEw2HmzZtHXl5em+X/5je/YdGiRSQmJtK7d28WL158Xjykyhoi9E7P2Oxp4HpgALAf+CqQCOCc+2GztAvxgt12f/ypqKjItXbTtIiIiIiISGfbsmULI0eO7O5qxKWW2tbM1jnnis427658GvOs00hb3FX1EBERERERkXef7rxnV0RERERERKRLKNgVERERERGRuKNgV0REREREROKOgl0RERERERGJOwp2RUREREREJO4o2BUREREREenh9uzZw7Rp08jOzubSSy9l7ty51NfXd2jb4uJilixp91de2/Xaa6+RkJDQKXmdCwp2RUREREREejDnHDNnzmT69Ols376dbdu2UV1dzfz5809JGw6Hz7q8lvKIRCLce++93HDDDWed/7miYFdERERERKQHW7FiBcnJycyePRuAYDDII488woIFC6itrWXhwoVMnTqVCRMmMHHiRJxz3HXXXeTk5DBp0iQqKioa81q3bh3jx4+nsLCQKVOmUF5eDsD111/PvHnzKCoq4tFHHz2lDo899hgf/ehHGThw4LnZ6U6Q0N0VEBEREREROV9859Xv8MahNzo1z8vTL+feq+9tdX1paSmFhYVNlqWlpZGVlcWOHTsAKCkpYePGjaSnp7N06VK2bt3K5s2b2b9/P7m5udx2222EQiHmzJnD8uXLyczMZPHixcyfP58FCxYAUF9fz9q1a08pf+/evSxbtoyVK1fy2muvdeKedy0FuyIiIiIiIue5yZMnk56eDsDq1auZNWsWwWCQwYMHM2HCBAC2bt3Kpk2bmDx5MuANTR40aFBjHrfcckuLec+bN4/vfOc7BALn18BgBbsiIiIiIiId1FYPbFfJzc095aFQVVVV7Nq1i8suu4ySkhJSUlLazcc5R15eHmvWrGlxfWt5rF27lo9//OMAHDhwgOeff56EhASmT59+mntybp1fobmIiIiIiMi7zMSJE6mtrWXRokWA1yN7zz33UFxcTJ8+fU5JP27cOBYvXkwkEqG8vJyVK1cCkJOTQ2VlZWOwGwqFKC0tbbf8t99+m7KyMsrKyrj55pv5wQ9+0OMDXVCwKyIiIiIi0qOZGcuWLeOZZ54hOzubESNGkJyczIMPPthi+hkzZpCdnU1ubi633norY8eOBSApKYklS5Zw7733kp+fT0FBAS+//PK53JVzypxz3V2H01JUVORaumlaRERERESkK2zZsoWRI0d2dzXiUktta2brnHNFZ5u3enZFREREREQk7ijYFRERERERkbijYFdERERERETijoJdERERERERiTsKdkVERERERCTuKNgVERERERGRuKNgV0REREREpIfbs2cP06ZNIzs7m0svvZS5c+dSX1/foW2Li4tZsmTJGZe9atUq+vXrR0FBAQUFBXzjG98447zOJQW7IiIiIiIiPZhzjpkzZzJ9+nS2b9/Otm3bqK6uZv78+aekDYfDZ11eS3lcd911rF+/nvXr13P//fefdRnngoJdERERERGRHmzFihUkJycze/ZsAILBII888ggLFiygtraWhQsXMnXqVCZMmMDEiRNxznHXXXeRk5PDpEmTqKioaMxr3bp1jB8/nsLCQqZMmUJ5eTkA119/PfPmzaOoqIhHH320W/azsyV0dwVERERERETOF/sefJC6LW90ap69Rl7ORV/+cqvrS0tLKSwsbLIsLS2NrKwsduzYAUBJSQkbN24kPT2dpUuXsnXrVjZv3sz+/fvJzc3ltttuIxQKMWfOHJYvX05mZiaLFy9m/vz5LFiwAID6+nrWrl3bYh3WrFlDfn4+gwcP5uGHHyYvL6+T9r7rKNgVERERERE5z02ePJn09HQAVq9ezaxZswgGgwwePJgJEyYAsHXrVjZt2sTkyZMBiEQiDBo0qDGPW265pcW8x4wZw86dO0lNTeX5559vHE7d0ynYFRERERER6aC2emC7Sm5u7ikPmKqqqmLXrl1cdtlllJSUkJKS0m4+zjny8vJYs2ZNi+tbyyMtLa1x+sYbb+TOO+/kwIEDDBgw4DT24tzTPbsiIiIiIiI92MSJE6mtrWXRokWA1yN7zz33UFxcTJ8+fU5JP27cOBYvXkwkEqG8vJyVK1cCkJOTQ2VlZWOwGwqFKC0tbbf8ffv24ZwD4NVXXyUajZKRkdFZu9dlFOyKiIiIiIj0YGbGsmXLeOaZZ8jOzmbEiBEkJyfz4IMPtph+xowZZGdnk5uby6233srYsWMBSEpKYsmSJdx7773k5+dTUFDAyy+/3G75S5YsYdSoUeTn53P33Xfzq1/9CjPr1H3sCtYQoZ8vioqKXGs3TYuIiIiIiHS2LVu2MHLkyO6uRlxqqW3NbJ1zruhs81bProiIiIiIiMQdBbsiIiIiIiISdxTsioiIiIiISNxRsCsiIiIiIiJxR8GuiIiIiIiIxB0FuyIiIiIiIhJ3FOyKiIiIiIj0cHv27GHatGlkZ2dz6aWXMnfuXOrr6zu0bXFxMUuWLDmr8letWkVBQQF5eXmMHz/+rPI6VxTsioiIiIiI9GDOOWbOnMn06dPZvn0727Zto7q6mvnz55+SNhwOn3V5zfM4cuQId955J7/97W8pLS3lmWeeOesyzgUFuyIiIiIiIj3YihUrSE5OZvbs2QAEg0EeeeQRFixYQG1tLQsXLmTq1KlMmDCBiRMn4pzjrrvuIicnh0mTJlFRUdGY17p16xg/fjyFhYVMmTKF8vJyAK6//nrmzZtHUVERjz76aJPyf/nLXzJz5kyysrIAGDhw4Dna87OT0N0VEBEREREROV/89dfbOLC7ulPzHHBxKtd9bESr60sDRpWNAAAgAElEQVRLSyksLGyyLC0tjaysLHbs2AFASUkJGzduJD09naVLl7J161Y2b97M/v37yc3N5bbbbiMUCjFnzhyWL19OZmYmixcvZv78+SxYsACA+vp61q5de0r527ZtIxQKcf3113Ps2DHmzp3Lrbfe2okt0DUU7IqIiIiIiJznJk+eTHp6OgCrV69m1qxZBINBBg8ezIQJEwDYunUrmzZtYvLkyQBEIhEGDRrUmMctt9zSYt7hcJh169bx4osvcvz4ccaOHct73/teRoxoPUDvCRTsioiIiIiIdFBbPbBdJTc395QHTFVVVbFr1y4uu+wySkpKSElJaTcf5xx5eXmsWbOmxfWt5TFkyBAyMjJISUkhJSWFcePGsWHDhh4f7OqeXRERERERkR5s4sSJ1NbWsmjRIsDrkb3nnnsoLi6mT58+p6QfN24cixcvJhKJUF5ezsqVKwHIycmhsrKyMdgNhUKUlpa2W/60adN46aWXCIfD1NbW8sorrzBy5MhO3MOuoWBXRERERESkBzMzli1bxjPPPEN2djYjRowgOTmZBx98sMX0M2bMIDs7m9zcXG699VbGjh0LQFJSEkuWLOHee+8lPz+fgoICXn755XbLHzlyJB/84AcZPXo0V199NbfffjujRo3q1H3sCuac6+46nJaioiLX0k3TIiIiIiIiXWHLli3nRU/m+ailtjWzdc65orPNWz27IiIiIiIiEncU7IqIiIiIiEjcUbArIiIiIiIicUfBroiIiIiIiMQdBbsiIiIiIiISd7os2DWzBWZWYWabWln/CTPbaGb/MLOXzSy/q+oiIiIiIiIi7y5d2bO7EPhgG+vfBsY7564AHgCe6MK6iIiIiIiInLf27NnDtGnTyM7O5tJLL2Xu3LnU19d3aNvi4mKWLFlyxmV/97vfpaCggIKCAkaNGkUwGOTQoUNnnN+50mXBrnNuNdBqCzjnXnbOHfZn/w4M6aq6iIiIiIiInK+cc8ycOZPp06ezfft2tm3bRnV1NfPnzz8lbTgcPuvymufx+c9/nvXr17N+/Xoeeughxo8fT3p6+lmX09V6yj27nwL+0N2VEBERERER6WlWrFhBcnIys2fPBiAYDPLII4+wYMECamtrWbhwIVOnTmXChAlMnDgR5xx33XUXOTk5TJo0iYqKisa81q1bx/jx4yksLGTKlCmUl5cDcP311zNv3jyKiop49NFHW63L008/zaxZs7p2hztJQndXwMw+gBfsvr+NNHcAdwBkZWWdo5qJiIiIiIg0tXLhE1TsfKtT8xw49BI+UHxHq+tLS0spLCxssiwtLY2srCx27NgBQElJCRs3biQ9PZ2lS5eydetWNm/ezP79+8nNzeW2224jFAoxZ84cli9fTmZmJosXL2b+/PksWLAAgPr6etauXdtqPWpra/njH//I448/3gl73fW6Ndg1s9HAj4EPOecOtpbOOfcE/j29RUVF7hxVT0RERERE5LwwefLkxqHFq1evZtasWQSDQQYPHsyECRMA2Lp1K5s2bWLy5MkARCIRBg0a1JjHLbfc0mYZzz33HNdee+15MYQZujHYNbMsYCnwr865bd1VDxERERERkY5qqwe2q+Tm5p7ygKmqqip27drFZZddRklJCSkpKe3m45wjLy+PNWvWtLi+vTx+9atfnTdDmKFrf3roaWANkGNme8zsU2b2GTP7jJ/kfiAD+IGZrTez1vvLRURERERE3qUmTpxIbW0tixYtArwe2XvuuYfi4mL69OlzSvpx48axePFiIpEI5eXlrFy5EoCcnBwqKysbg91QKERpaWmH6nD06FH+8pe/MG3atE7aq67XZT27zrk2Q37n3O3A7V1VvoiIiIiISDwwM5YtW8add97JAw88QDQa5cYbb+TBBx9sMf2MGTNYsWIFubm5ZGVlMXbsWACSkpJYsmQJd999N0ePHiUcDjNv3jzy8vLarcOyZcu44YYbOtSD3FOYc+fXLbBFRUWurZumRUREREREOtOWLVsYOXJkd1cjLrXUtma2zjlXdLZ595SfHhIRERERERHpNAp2RUREREREJO4o2BUREREREZG4o2BXRERERESkHefbs47OB13dpgp2RURERERE2pCcnMzBgwcV8HYi5xwHDx4kOTm5y8rosp8eEhERERERiQdDhgxhz549VFZWdndV4kpycjJDhgzpsvwV7IqIiIiIiLQhMTGR4cOHd3c15DRpGLOIiIiIiIjEHQW7IiIiIiIiEncU7IqIiIiIiEjcUbArIiIiIiIicUfBroiIiIiIiMQdBbsiIiIiIiISdxTsioiIiIiISNxRsCsiIiIiIiJxR8GuiIiIiIiIxB0FuyIiIiIiIhJ3FOyKiIiIiIhI3FGwKyIiIiIiInFHwa6IiIiIiIjEHQW7IiIiIiIiEncU7IqIiIiIiEjcUbArIiIiIiIicUfBroiIiIiIiMQdBbsiIiIiIiISdxTsioiIiIiISNxRsCsiIiIiIiJxR8GuiIiIiIiIxB0FuyIiIiIiIhJ3FOyKiIiIiIhI3FGwKyIiIiIiInFHwa6IiIiIiIjEHQW7IiIiIiIiEncU7IqIiIiIiEjcUbArIiIiIiIicUfBroiIiIiIiMQdBbsiIiIiIiISdxTsioiIiIiISNxRsCsiIiIiIiJxR8GuiIiIiIiIxB0FuyIiIiIiIhJ3FOyKiIiIiIhI3FGwKyIiIiIiInFHwa6IiIiIiIjEHQW7IiIiIiIiEncU7IqIiIiIiEjcUbArIiIiIiIicUfBroiIiIiIiMSdLgt2zWyBmVWY2aZW1puZfd/MdpjZRjMb01V1ERERERERkXeXruzZXQh8sI31HwKy/dcdwP92YV1ERERERETkXSShqzJ2zq02s2FtJJkGLHLOOeDvZtbfzAY558q7qk4iItK5XDRKJBIhGgkTCYeJhr13F40QjUZx/it2uvn8yekIURclGvbyi0YiLbzCfnkNaaJEI2FwrmnFzGImrVmtY9YFAgSDQQIJCQSCQQLBBIIJQSwQJBizLJAQJBiMmQ8EIGAEAgHMAlgggJl577HTFvDSBLx5HHj/7Tmvys7hXNSvvvPWxUy7mP0yf7+8/THvnz9tBmYBfz3+MovZ/mR5/pxftjcHxJTdtKlOtp+dbFYzrKEd/YUN5TY9DDHzzQ8DzY6RgeG3U+Mya5z29q/pPjfWI2baAg3rT+Z5st1a1thOTdqn2bpWnJJvC8WYBdosX0REukaXBbsd8B5gd8z8Hn+Zgl2RHqbsQA2fXrSW46FId1dFTpdzBKMhEiN1JEbqSYjWkxCpbzKdEKknKVJPYtRfHrM+GA1jLkLARWPeG6Yj2CnRUTfsIn7w14z5a9vbWuTcsmbvDdOtLT9dnXlOx3wh0Wa9zjCQPx/j/xabt702b+tLn3ZXCKD2kTPVncFuh5nZHXhDncnKyurm2oi8+2wur2J7RTWTRl5IWu/z4mMjPkWjBE8cJ6G2luCJGgJ1tQRP1BKsryVQV0ugvhYL1WLh49575DhEjmOuI19SBMCSMEsCSwKSMOsNlgAEwYJ+z1rQS8vJafPXxy7zljf0zAUAA9dwweww/9rQwF8O5q83vz7eOq9Hzlvn9YwG/LwacvNeXm9pQ69oY6Dr3MmA3O+hM385/jYOB0RxRP35KBAl6s83rGueDhzO/N5Xi+kRpHnerqHvtIV2b61X8ORy57dZw1zjKudwDQ3YuDAas11M0tbKa3VZK/W1Nta1u/3pbuOaTba0rqU83SnLXQfKtlbbwVvbstMJLE+t/6nnRU/48sW1OdvaeXHuah57ZseLnnDcO1N3f0lzls6XU6wbmqwHHaUO686r1r3AxTHzQ/xlp3DOPQE8AVBUVHQ+trPIea2mLgzAV2/K5eL0Pt1cm/gRjUSorTpK1YHDHNl3gKMVhzl28DA1R45QW1XFieoq6mqPEaqrJhKqwUWOExvMNGVgyZj1xgK9CST0I5h8EQnBZBKCvUiwBIIWJIgRdEZCNEowHCUYChOsrydYdwI7XoPV1RKIhglEjxOIVnm9t9EI5iKYi3rz/rRFIwSTggSSEgn2SiKYlEAgOYlgUiKBXklYYpBo0IgGIRx0RBIgFIT6QLTxdSIQoS4Q4biFOU6IE4EwdS5MHWFOEKKOECcIccLVc5wQIYsSDRjhAEQDEPFf0QBErYX3mGlnRmJiL4IJiSQEk0hITCIxIYmEQCIJgQQS/ffY6ZaWNUwHLUgwECRoQRICCY3zCZZwcnk0StBFCEbDBKMRgpEIAedNByJhgtEwgWiYYCRMIBIiGA157+EQgWiIQKTem4+EMP89EK3HwmFvWTREIFzvp/PSWqSeYLgOi4YJ4Ag0fJlAw9cOJ5f5X0Oc3XVdIKHZK9h02oLN1geazlsgJl3se6BZHsGW01rAy9Ni1wc6sM786bZednIbWkvPqcuapLWYZdbCslbmIWbamk23kGfjl0s0TXem7xp2LSLdaO6vftop+XRnsPtb4C4z+xVwDXBU9+uK9Ey19V7PYJ+kYDfX5PwUjTqqKo+zc9ObvP36a+x7cyPHj+4hGjnR+kaWTCChDwmJKSSnZNCrzzCSU/uSnNSLXoEgSRi9omF6hepJrqkm4ehhAkcOQOU7uMoKXH19y9kmJRHo25dgaiqBvn0J9E0lmJnmvade5K3rm0og1VsXSEmB5F6cCEY5FqynyuqosjoOU8thV82R+qMcqTvCkRNHvPe6IxypK+dY/THqInXtto1hpCSmNHklJySTHEwmOSGZ3gm96etPN8wnx84He9MroRe9gt4rKZhEUiCJxGCiNx9IIinozSdYQuv3TUYjUHcM6quhrtqfPuZN1/vzdcfgRDXUHYH6GgjV+q/jTefr/WWhWs74e/BgEiQkx7ySINgr5r0P9Gq+LMnbrsl0EgQTT74HYqYblgcSm80nnEwbCPrTCa3PKygSEZEeqsuCXTN7GrgeGGBme4CvAokAzrkfAs8DNwI7gFpgdlfVRUTOTrXfs5vSS0OY29IQ1B56p4ZD5TUc2HuUfW9u4ei+LUTq3sRFjwAQTBxA6oAr6N23P73T+pHSvx990y+g38B0+mf2J9XVEzywl9DOXdSXlVG/cyf1W18nVF4O0WY9u2YEMzJIyMggYcAAEoYVEhyQQcKATG9+gLc8eMEFBNLSCCQlNW7qnKOqvop9NfuoqK1gf+1+//0t9tfuZ3/Nfg4dPsSRuiNEXcs9ygmWQL9e/ejfqz/9k/szNG0o+b3ySUtKIzUptTGATU1MPfmedHK+d0JvAtYJPwwQjUJdFZw4AsePwvEj/nTs+9FTlzUEuKHajpUTSIReqZCUCol9ILE3JKVAn3RIHOItS+rjr2s2ndjbezUEsIkxwWzs8oReXhApIiIiZ6Urn8Y8q531DviPripfRDpPbX2YYMDoldCVv1Z2folEoux78yjlO45wqLyWQ+/UcGR/LeH6KiKht4mG3saFd+FcPRZIYMDFlzOsYDq5140lY8hgwvv3e4Fs2U7qy7ZRv34n9WVlHNuzh2OhUGM5gdRUkoYOpXdBAf2mTSNxyBASMjObBLGW0PJHeTgaZm/1XsqOlrGn8lUqdsYEtDX72V+7/5TeV8MY0HsAA/sMJKtvFlcOvJL+vfpzQfIFXkDb8Er23lMTU7vmKbORMBw/BDUHoKYSag9AzcGYaf/VMH3iCLQSkHs7FoTkftC7PyT39977Z0GvNOjV13slpfrTqZDUN2Y61U+X6gWiIiIicl5QN42ItKumLkKfpOC7/qczao7Wsav0IDs3HWT35kPUn4jgnKN3ymGCwZ0Q3kHdUe8h86npGVwyZgKXjLmKi0fkEikro3btOo7/v2+zfV0JkUOHGvO1Xr1Iysqi12WX0XfSRJKGDfNeQ4cSzMhot92P1h3l7aNvU1ZV5r0fLaOsqoxdx3YRjoYb0yUGEhnYZyAX9rmQvIw8JmRNaJwf2GcgF6VcREbvDBIDiV3TgOAFsdX7oKocjr0T8+5P11R4wevxw7T6QKc+6ZCSCX0GwMCR3nuf9JNBbHL/UwPbpFQNtxUREXmXUbArIu2qqQuT+i4cwhyNOirKqti5yQtwK3cdAyClXxKXjhlAqLaEN9f9kSN7j2IWYNCIy7lyyq0MGzmKlIOHOV7yOrWP/ZC3NmzAHT8OQOLFF5N63XUkj76CXpdcQtLQoSRcdFHjb4u2xjnHvpp9vHHoDcqqypoEtofrDjemSwgkkNU3i+H9hvOBiz/AsH7DGJY2jIv7Xkx6cnrXfmERjXqB66G34eiepkHssZhgtnkPbDAJ+g6CtMFe8NoQyKb4r8bpTOh9gYb4ioiISIe8+65eReS01dZH3jUPpzpRE2LXZi+43VV6iBPVIczgokv68d7plzB0VAa9+0b4vx88wtvr1zEsfww5V17FwPoobvNman/xDIe2fINDkQgEAvS6PIf+H/0ofQrH0HvMGBIvvLBj9QifYPPBzWys3MiGyg1srNxIxfGKxvXpyekMSxvGhKwJDO83nGFpwxjebziDUweTEOjCj/ZwHRzeCYff9oLa2PfDO6H5Q6mS+0HfwV4ge2Heyem0wScD3D4Z6nUVERGRTqdgV0TaVVMfjuuHU1UfPsEbf9/Hzn8cZP/bR3EOklMTGZqXwdBRGVycm05yije0d9emjSz55sOcOFbF1VnZDPrLq4QWPcNBvOHIvUePJuOOT9NnTCG9C/IJ9u3bbvnOOfZW720S2L5x6A3CzhuCPCR1CEUXFZGfmU9uRi7D+w2nX69+Xdcg0agXvFZshgPbYwLaMq/HNnZ4cWIKpA+HASNgxBS4YLg33y8L0gZ5D28SERER6Qbxe/UqIp2mpi5MSlL8fVycqA6x7v928o+Ve4iEowwc2pfCG4cxdFQGA4emEQic7G2MRiK8/PTPeOW5paQ6Y+y2XaRteJOk943lgo/9E73HjKF3Xh4W87Tj1tRH6psEthsqN3DwxEEAeif0ZtSAUXwy75PkZ+ZzReYVDOg9oMvagJqDUFEK+zfD/k1egFvxBoRqTqbpM8ALYLPGeu8NAe0FwyF1oHplRUREpEeKv6tXEel0NXURBvfvwocWnWOhuggbXtzN6y/spL4uQs41F3H1R4aTNqD3KWldNErFi3/i/576CZV1tbznUBVjktPImPtZ+k29iYSMjA6VWROq4a97/8qKnStYvXc1NX4wOTRtKO8b/D7yM/MZnTma7Auyu2YYcugEHNjaNKjdv9l7WFSD3uneUOMx/woDc+HCUTAgG5LTOr8+IiIiIl1Mwa6ItKu2PkyfOOjZjYSjbH7pHV57vozjVfUMzx/ANVMvIeM9qaekrd+zl6PLlrH198spSUkgGggwdsglXHnfHSSPGtWhBz0dOXGEVXtW8eLOF3n5nZepj9aTnpzOB4d9kPFDxlMwsIALki/o/B11Do7sgt2vwK6/w+5XveDWRbz1wV6QmQOXToALc/3ANg9SL1QvrYiIiMSN8//qVUS6XHVd5Ly+Z9dFHdte28+rz71F1YETDM7uz42fuYKLLml632v0+HGO/elPHFm6jOpX/s7WQRm8ndmf9AsyuOmLX2XAsEvaLWt/zX5W7F7BiztfZO3+tURchEEpg/hYzseYNHQSBZkFBDv7acKREOzbCLtegd1+cHus3FuX1BeGFMH7P+sFtBeOgvRLIHj+Hk8RERGRjtDVjoi0q7Y+TMp5+DRm5xw7Nx3k78++xcG91Qy4OJWPzMknK7fpT/DUvf02h366kKrnnydaXU390It5/doxHDx2lIIpH2b8v3yKhDbuxd1VtYs/7/ozL+56kY2VGwG4pN8l3DbqNiYOnUhuem7n/uTP8cOw+zWv53b3K7B3HYRqvXX9smDotZD1Xrj4Gi/A1U/1iIiIyLuQgl0RaVM06ryfHjrPenbLdxxhzbNvUr7jKGmZvbnhU3lcVjgQi33o1IkTHHziCQ4++WNISCBtyhQqc0ewesUfsGiYqZ/7MtnXvK/F/GtDtSzdvpSlO5ay/fB2AHIzcrn7yruZmDWRS/q33wvcYeF62PkSbPs/eOsvULnFW25BuOgKGHOrF9hefA30e0/nlSsiIiJyHju/rl5F5JyrDXn3eab2Oj96Bw/urebvy9+ibOMB+qQlMf6fcxh57SCCwUCTdNWrV7PvgW8S2r2btJtuIn3e3fz198v4x+9/w6DsHD589xfoN/DU38Q9ePwgT7/xNE+/8TRV9VXkZ+Zz71X3MiFrAoNTB3fejtQcgO0vwLY/wo4VUH8MEpK9XttRH4WLr4b3FEKvU+83FhEREREFuyLSjto677dee/oDqpxzrPvDTl557i2SkhN47/RLGP2Bi0lsFqSH9u1j/4MPceyFF0i65BKyFi6kdtBAfv3IQxzYvZOrpt3MtR/7F4IJTfd3d9Vufrb5Zzy741nqI/VMyJrA7FGzyc/M76wdgIotsO0PXg/u7lcBB6kXwaiZkPMhGD4ekvp0TnkiIiIica5nX72KSLerqfd6dlN6cM+uizr+tmQHG1bsZsTVF3LdLSNITmn6U0kuFOLQUz+n8vHHIRIhc9480v55Fmt+u4R1j36L3n3T+OiXvs6wgsIm25UeLOWnm37Kn3b+iaAFmXrpVD6Z90mG9xt+9hUP10HZS17v7bY/ek9QBhhUAOPvhZwPwkX5EAi0nY+IiIiInELBroi0qcbv2U3poT270UiUlU+9wRt/38foCUN4/83ZTe7LBagtKWHf175O3bZtpI4fz4VfuY/d+/ey9EtzOXagkism3MB1n5hN79S+gNdLvOadNSwoXcAr5a+QmpjK7LzZfGLkJ8jsk3l2FQ7XwRu/h9Jl8OYKqK+GhN5wyfXw/s/BiCmQ1onDoUVERETepXrm1auI9BiNwW4PfEBVOBThhR+X8vaGA1x903CKbhzW5KnH4cOHqXj4YY7+ZikJgwYx5PHHcPmj+cPPnmTHa2sYcPFQPvz1/8d7Ls/10kfDvFD2Aj8t/SlvHHqDgb0Hck/hPdw84mZSk87y3tiKLVDyFGx4Go4fgr6D4Ip/ghEfhOHjNDxZREREpJP1vKtXEelRav1hzH162E8P1Z8I8/z/bmTv1iNcd8sIRn9gSOM6F41ydOlSKr77MJGaGjJu/xTp//ZvbFj9In/7z//ARaNc98/FFH54OsGEBE6ET/Cb7b/hqc1Psbd6L8P7Decb7/sGH7nkIyQGE9uoRTvqqr0e3JJFsOdVCCTC5R+GMf8Kl3xAPwkkIiIi0oUU7IpIm2rqvZ7d1B7Us3u8up7fPbaByt3VTJqdS841FzWuO7F1K/u+9nWOv/46vYsKuej++zkccPzym/OpLHuL4VcWMfG2z9BvoLfN+or1fOVvX6GsqowrB17JvVfdy/iLxxOwM7xP1jnYWwIlP4NNv/GGKQ/IgRu+Bfkfh5QBndEEIiIiItKOnnP1KiI9UsMw5p7yO7vHDp3gue+vp+rgCW78zBUMG30yeDz01M/Z/+1vE0xLY9BDD5F8wyT+tvgp1r/wPKn9L+Cmz36R7Guuxcw4Hj7O468/zlObn2JQyiB+NOlHvO89Lf+mbofUHoKNi72hyhWlkNgH8mb6v4F7NZi1n4eIiIiIdJqecfUqIj1WTZ3/NOYeMIz5yP5alj/6OvW1Yabenc/g7Asa1x148kkq/+u/SZ04kUHffIAdW/7Bqs/9O7VHj3LlBz/CtR/7V3r18e6LLdlfwv0v38/Oqp3cknMLny38LCmJKadfoWgUylZ7w5S3PAeRehg8Bj7yPe+3cJPTOmvXRUREROQ0KdgVkTbV1veM39mt3HWM5x5bD8D0z40hM+vkk5MP/M8POPD446R9+MP0uWcey3/4PcrWr+PCSy5j+hfu56JLswE4Hj7O90u+zy+2/ILBqYP58Q0/5ppB15x+ZZyDN34HKx/yenGT+0PhbO9e3Iuu6LR9FhEREZEzp2BXRNpUXRchKRggKaH7fuv1ne1H+P3/bCCpdwJT5xZwwUVeL6xzjsr//m8OPvlj+s2cyYmPTmPJF+4mkBDkA8V3UDDlwwT8h0Ct27+O+/92P7uO7eKWnFv4XOHn6JN4mk9Ads77PdyVD8K+jZBxGUz/IeTNgMTkzt5tERERETkLCnZFpE219WH69Oq+Icxl/zjAH5/YRN/0ZKbOLaBvuhdUOufY/+BDHH7qKfrP+jjpn/9Pfvafd9H/okHM/PLX6Zvu3ctbG6rlsdcfa+zN/ckNP+HqQVefXiWcg+1/glUPwjuvwwXDvCD3in+CoD5GRURERHoiXaWJSJtq6iKkdNMQ5m2v7uPFhVvIGJLKTXPy6d03CfB+Wmjf177OkV//mvRPfpKBX7yXv/5yIccOVvLhud9tDHTX7lvL/S/fz+5ju5l1+SzmjZl3er25zsGbK7ye3L1roX8WTH3ce6ry2fwkkYiIiIh0OQW7/5+9Ow+Pqrr/OP6+k30PIYEAYUkIO4QdFFFBBAUVcN+3Wpdal1ar1mpta6uttmq1P7WtVhGtG1oBlX0VVDaBAAl7gBAgZN9mkpnMzPn9MRCIIZAAQyB8Xs/jI3POuTfn/sE8+XDO/R4ROSq7001EE6zsrl+UwzefbKFtaiyX3Z9GcJjv68q43ex76mlKp02j5X33kvDwwxTmZPPD11PpPXI07br1wFHt4NXVr/Lhpg9JikzinUveYXDi4MZNIGuxL+TuXgbRSb6iU/1uhsBgPzytiIiIiJxsCrsiclR2l/uUF6favGwf33y8hU5p8Vzy014EHqgEbaqr2fP445TPnEXCww8R/7OfYYxh/jtvEhwWzvk33cHK3JU88+0z5FTkcHOPm3mo/0ONW83d+a0v5O5aClFtYNzffMcHBYb46WlFRERExB8UdkXkqBwuzyld2XWUuVjy6VbadI7h0nt7ExDgK4zldbnY88tHqJg/n1aPP07Ln9wJwMali8jJ3MDFP/05/04za1YAACAASURBVN35Ka+teY32Ue1595J3GZQ4qOE/OHs5LHwOdiyGyNZw6Qsw8A4VnhIRERE5QynsishR2Z1uWkY0smrxCVj66RaqXR5G3NL9UNCtqiLnwYewL1lC698+TdzNNwNQZa9g8fv/IbFzV+bFZjJpzSTGJY/jd+f+ruGrufYCmPEYZPwPIhLgkudh0E8gKMxfjygiIiIip4DCrogcld3lJiLk1HxV7FxfwNZVeQy+PJm4Nr7jhbx2O7vv/zmOFSto86c/EnvNNTXjv/3kAyrLysgf14YpmZO4odsNPDn0SWxWA45JMgY2fO4Luq4KGPEbGPYABEf46/FERERE5BRS2BWRo3I4PYQH+38bs6vKzeKPNtMiMZyBl3QEwFNezu5776MyPZ22L75AzBVX1Izfn7WN9DkzqOgdw5TSWdzd524e7P8glmUd+4eV7YOvH4HNM6DdIJjwOrTq7q9HExEREZEmoLArIkdV4XQTeQpWdpdPz6KiyMlVvxpAQJANT0kJ2XffQ9XGjbR7+WWiLxlTM9Z4vcx5+/+oDoVpiek8MvAR7ux957F/iDGw5gOY/RR4nDDmOTjnZ2BrunOERURERMQ/FHZFpF5ujxen2+v3asz7d5SxbmEOvS9sR5vUWNzFxWTf+RNc27eT9NprRF00stb4lXOmk7d9G9/3LeDJC57hmq7X1HPnw5Rkw/SHIGshdDwPxv8DWnb20xOJiIiISFNT2BWRejmqPQB+rcbs8XhZ+MFGImJCOHdiZ4wx7Hv6t7iyskh6800ih59Xa/zevF0s/O/bFMY5ufeG3zE2ZezRf4DXC6v+A/N+71vZHfc3GHQX2BrwXq+IiIiInLEUdkWkXnanG8CvBarWzs2mcI+dsff1ITgskJL/feE7XuiJJ+oE3f32/bzyygO0qjaMuutnXHKsoFu4HaY/CLu+hZSRcMWr0KKj355FRERERE4fCrsiUi+707ey668CVSX7Haz8aied+yeQ0i+B6j172P/cc4QPHkzc7bfVGptdls1jH93DkKxA2o8aziWDrqr/xl4PLHsTFvwJAoJ9Baj63QwNKV4lIiIiIs2Cwq6I1MvhOrCy64d3do0xLPrvJgKCbJx/Q1eM18veJ38DQJs//xnrsG3GW4q3cN/sexm+KpTQFjFMvO0X9d84bxNM+znsWQVdx8Llr0B0m5M+fxERERE5vSnsiki9Kvy4jXnjd/vYs6WEETd3IyImhKL33vOdpfvccwQntasZty5/HT+b9zO6Z0UQUxbAmEd+TnBo2JFvuvJtmPUkBEfC1f+B3ldrNVdERETkLKWwKyL1cjj9U6DKXurku8+30bZLLD3Pa4tz2zbyXnqZyIsuIuaqK2vGLd+3nAcXPEhb4knbEk77fr1IHXJu3Rt63DDr17DyLegyBia8AZEJJ3XOIiIiInJmUTlSEamX/cA25pN99NDST7fidnkZcXM38LjZ+/gT2CIiaPPsH7AOrMQuyF7A/fPup11kO27dfy7G4+GiO++r6a9RWQz/vcYXdIc9CDd+rKArIiIiIgq7IlK/gwWqIk/iNuad6wrY9kMeg8Z1pEViBAVv/pOqzEwSn/0DgfHxACzavYhHFj1Ct7huPJf0K3YuX86QCdcSm/ijd28Lt8Pbo2HnUhj/fzDmT2Dz3zFJIiIiInLmUNgVkXodLFAVfpK2Mbuq3Cz+aDNxbSPoP6YjlevWUfCvfxEzYQLRo0cDsLN0J08ueZJucd3458g3WPb+ZGJbt2HIhGtq32zHN/DWReAohNumwYBbT8ocRURERKR5UNgVkXrVHD0UdHLC7rJpWVSUOBl5S3esaid7n/g1ga1a0fopXxVmR7WDXy76JYG2QF4Z8QobZ8+heN8eLvrJfQQGBx+60ap34f0rISoR7l4Anc6r5yeKiIiIyNlKBapEpF4Ol5vQIBuBASf+72K5WaWsX5RDnxFJJKbEkPvc87h27KDDu+8QEB2NMYbff/97tpds55+j/0mEw8ayzz+my9BhJPcb6LuJxw1znoblb0LqaLjmPxAac8JzExEREZHmRyu7IlKvCqf7pJyx63F7WfjBJiJjQzhnQgr277+n+P33aXHrrUSc66uu/OGmD5m5YyYP9n+QYW2HsWDSv7FsNkbcdrfvJlWl8NH1vqB7zv1w0ycKuiIiIiJSL63siki9HC7PSXlfd82cbIr22rns/jQCXA52PfkbgpOTafXILwFYvX81f1v5N0a0H8Fdfe4ia81Ksn5YwQU330l0fAIUZcGHN0DRdrj87zDozhOek4iIiIg0bwq7IlIv+0lY2S3OtbNqxk5SB7aiU1o8e5/4Ne78fDp99CG2sDDyHfk8uvhR2ka25fnhz2Nh8d2nHxLTqjUDxk2And/CJ7cABm79ApIvODkPJyIiIiLNmrYxi0i97C43ESdw7JDxGhb9dzOBwTaGX9eFsjlzKJ02jfh77yUsLY1qbzW/Wvwr7NV2Xhn5ClHBUexMX83+rK0MmXgtAes+hMkTICIefjpfQVdEREREGkxhV0TqZXd6CA8+/m3M21bnsXdrCcOuTiWkupzc3/2e0J49if/ZfQC8vOplVuet5vfn/p6uLbpijGHZ5x8T1TKeXs7FMP1BSD4f7poLLTufrMcSERERkbOAwq6I1MvhchN5Aiu76xfmEJMQRvdzEtn322fw2u20ffEFrKAgZu6YyQcbP+DmHjczLmUcALsz1rF3y0YGd3ITsOINGHIP3DQFwmJP1iOJiIiIyFlCYVdE6uVb2T2+sJufXc6+7aX0GZFE2dSpVCxcSMIjvyQkNZVtxdv43Xe/o3+r/jw68NGaa5Z9/jERYQH0qfgKRvwGxv0VAlRaQEREREQaz69h17KsSy3L2mxZ1jbLsn59hP4OlmUttCxrjWVZ6yzLGufP+YhI4/je2T2+bczrF+cQGGwjpaNh//PPEz5kCHG33Ua5q5xfLvolEUERvHThSwQFBAGQs3EDuzPXMzh6C4HnPwwXPn4yH0VEREREzjJ+C7uWZQUArwNjgZ7AjZZl9fzRsKeBT40x/YEbgDf8NR8RaTyH03NcBaqq7NVsXbGfrkNaU/SHpwFo++fnMRY8vfRpdpfv5m8X/o2E8ISaa5a9/WfCA1ykXXwZXPx7sKyT9BQiIiIicjby58ruEGCbMSbLGOMCPgYm/GiMAaIP/DkG2OvH+YhII7jcXlweLxHHUaBq43f7cFd76eTejGPlSlr/5jcEtWvHOxveYcHuBTw66FEGth5YM37flN+xK6eUgX1aEXT5iwq6IiIiInLC/Bl22wG7D/ucc6DtcL8HbrEsKweYATzox/mISCM4XG6ARr+za7yGDYtzaNM5Gs8HrxPauzcxV13J93u/5x9r/sGlnS7llh63HLpg2T9ZNmsOocEW/R56E2wqJSAiIiIiJ66pf6u8EZhkjEkCxgHvW5ZVZ06WZd1jWdYqy7JW5efnn/JJipyN7C4PQKOrMe/KKKSsoIrO4fuozskh/uf3k2vP5YlvniAlJoU/DPsD1sGV29WT2f/Fs2RVtGTghBsJjog62Y8hIiIiImcpf4bdPUD7wz4nHWg73F3ApwDGmO+BUCD+xzcyxvzbGDPIGDMoISHhx90i4gd254GV3UYWqFq/aA/h0cGETXuD0J49CT5/GI8segSX18UrI14hPCjcN3DdFJj+EMuqBhESHk7/cT9+y0FERERE5Pj5M+yuBLpYlpVsWVYwvgJU0380JhsYBWBZVg98YVdLtyKngYNhN6IR25hL8hxkZxbSuVUZnuydxP/8fl5Y+QIbCjfw3HnP0Smmk2/gxi/hi3vJjzuPbble+o+dQEh4hB+eQkRERETOVn4Lu8YYN/AAMBvYiK/qcoZlWc9aljX+wLBHgbsty0oHPgLuMMYYf81JRBrOcWAbc3gjClRt+GYPNsui5YL/ENK9O9+muJiyZQp39b6LUR1H+QZtnQdT7oR2A1juHkZQaBgDxo0/+o1FRERERBqp8WeKNIIxZga+wlOHtz1z2J8zgfP8OQcROT4VB1d2G/jObrXLw6bv9tG+dTW2BZlEvvwCL678Kz3ievBA/wd8g3YsgU9uhlbdKRz5GpufeoIh468mLFLv6oqIiIjIydXgsGtZVjug4+HXGGO+8cekRKTpHazG3NCwu3XFfpwON4lZUwjp2pVJLTdSkF/A30f+nUBbIOxeAR9eDy06wa1TWf7uewQGBzPw8iv9+BQiIiIicrZq0G+xlmW9AFwPZAKeA80GUNgVaabsTt9f9Yacs2uMYd2iHFpEewlftJTqPz3Oh5tf5aouV5GWkAZ718IH10BUa7htGsXlLjYtXcyAceMJj47x96OIiIiIyFmooSu7E4FuxhinPycjIqcPeyO2Me/bXkphTgW9iucTkprKs2ELiXJH8YsBv4C8jfD+lRAaDbdNh6hEVvz3VQICAxl0xVX+fgwREREROUs1tEBVFhDkz4mIyOnl4Dm7YUHHXtndsCiH4CBD/PoZ7Lp6CKsL1vCLAb8gtrIEJk+AgGC4bRrEtqc0bz+Z3yygz6hLiGwR5+/HEBEREZGzVENXdh3AWsuy5gM1q7vGmIf8MisRaXIOp5vw4ABsNuuo4+ylTravzqdD2RpCOrbl2dC5pEWncWXHS+CdS8BdBT+ZAy07A7By+mdYlsXg8VefiscQERERkbNUQ8PudOqekSsizZjd5WnQFuaMJXvxeg2JGdP5/vZuFFev4I2hb2L76hewPwNungKtugNQXlTAhoVz6TXiYqJaxvv7EURERETkLNagsGuMec/fExGR04vd6T5mcSqPx0vGkj0kVO0kLD6Il2OXc13X6+m5bQmsnwIjn4Yuo2vGr5z+OcYYhky41t/TFxEREZGz3FHDrmVZnxpjrrMsaz2+6su1GGPS/DYzEWlSDpeb8OCj/3tY1pp8HKUuUrfMYPolQcSEteDB+CHw4Q3Q7TI4/9GasfaSYtbPm02P80cS06q1v6cvIiIiIme5Y63sPnzg/5f7eyIicnqxOz1EHmMb8/pFOYR5SokK2c9HHYp5tudjRH9xv+8s3SvfBNuhGngrv/wfHreboVde5+eZi4iIiIgcI+waY/Yd+P+uUzMdETld2F1u4iKC6+0vyKlg37ZSUnfM58MhVfRr3Z/xyyeDyw63fwmhh87PdZSVkj53Bt2HX0iLxLanYvoiIiIicpZr0NFDlmWdY1nWSsuyKizLclmW5bEsq8zfkxORpuN7Z7f+fw9bvygHm3ET7U5nXg8XT7lCsXJWwcQ3agpSHfTD11Nxu1xa1RURERGRU6ah5+z+H3AjsBUIA34KvO6vSYlI03O4PESEHLlAldNRzZbv99I6dzkfDSjmhoQBdF37KZz3MPSaWGtsZUU5a2d/RddzhtOyXftTMXURERERkQaHXYwx24AAY4zHGPMucKn/piUiTa3CWX+Bqo3f7cPtgejy78gcEMv9a2ZA8oVw0TN1xq6ZOR1XZSXnXHW9v6csIiIiIlKjoefsOizLCgbSLct6EdhHI4KyiJxZjDH1ruwar2Hd7G3ElG7nq77ZPFJqERmRANe8AwG1v1KcDjurZ04ndfC5JHTodIpmLyIiIiLS8MB664GxPwfsQBJwtb8mJSJNy+n24vEaIo5QjTl7YyHl5RBTvAR7nzDGFuXBdZMhIr7O2G8+eBen3a5VXRERERE55Y51zu4EIMkY8/qBz4uBVvjO3P0e2Ob3GYrIKWd3ugGOWKBq7f82EOwqY3HXtTy5bw/W5a9CuwF1xm3+finr5s9i8IRraJ2S6vc5i4iIiIgc7lgru48D0w/7HAIMBEYAP/PTnESkiTlcHgDCg2tvYy7Nd5CT4yG2YClJnUpJ6Xsb9L+lzvWlefuZ++9/0Ca1G+ddV7dfRERERMTfjhV2g40xuw/7vNQYU2SMyQYi/DgvEWlCdpdvZTfyR9uYV3+8Egsvm5KWck90Z7j0L3Wu9bjdfP3aixhjuOzhxwgIbGhpABERERGRk+dYYbfF4R+MMQ8c9jHh5E9HRE4HB7cxhx8WdqtdHjavryC6OJ1zUosIv+4DCAypc+13U/7Lvq2bGX3PA8S0SjxlcxYREREROdyxwu5yy7Lu/nGjZVn3Aiv8MyURaWp2p28bc8Rh25hXfjAfjy2MsriFXHzlOxDdts51u9avZcW0z+hz0Ri6D7vglM1XREREROTHjrW/8JfAVMuybgJWH2gbiO/d3Yn+nJiINB3HgW3MB6sxG2PIXJJLqMfGRbeeg5U8vO41pSXM/L+XiGubxMg77jml8xURERER+bGjhl1jTB4wzLKsi4BeB5q/NsYs8PvMRKTJVNSs7Pq+Ipa+/SrOkDRiA6aRfPHf64w3Xi8z33iFKnsFVz/1R4JCQk/pfEVEREREfqxBlWMOhFsFXJGzxMGV3fCQAKgqY9uSAqzQasb99hGwrDrjf/h6KjvX/sCou+4noUOnUzxbEREREZG6jvXOroichQ6+sxsZEsiKT+/FHdCfSFs2Ldp1rDM2d/tWlnw0mdTB59J39NhTPVURERERkSPSmSAiUofd6cZmQciO+az4tggrpAWDRrevM87pcPD1qy8SEduCMfc9hHWEVV8RERERkaaglV0RqcPuctMmuIpVMx+mReEgLOOm57gBtcYYY5j39uuU5u1n3EO/IiwyqolmKyIiIiJSl8KuiNThcHr4TcAkphZ4qYzsT2K8h5Cw2htBMhbPZ9O3ixl27U0kde9Vz51ERERERJqGwq6I1JFStIiE4OUkbe2MMzSOHmN61uov2pvD/HfepH3PPgy58tommqWIiIiISP0UdkWkNnsh1+e+xBthrWhj74+Fl5TBbWu63S4XX736IoHBIYx98FFstoAmnKyIiIiIyJEp7IpIbTMeJTPYRasthoKW/UnqFEZIeFBN9zf/fZf8nVlc+rNfEBUX34QTFRERERGpn8KuiByy4X+YjC94Ni6F4Vs64gyNo+uFKTXd21YtZ82sLxkwbgKdBw5pwomKiIiIiBydwq6I+FTkwdePsiypDxH5pVhB/bBZhuS+vtXb8sICZr/5d1p16sz5N93RtHMVERERETkGhV0RAWPgy19gXHbebNWGi1cHsr/VAJK6tyAkPAiv18OMf/wNT3U1lz38OIFBQce+p4iIiIhIE1LYFRFY9wls/prvz7mTLXs30ienDc7QOFIHJwKwbcX35GzcwEU/uY+4tu2aeLIiIiIiIsemsCtytivdAzMex7QfyhvVOYzbGklxy35gGZL7JgCQtXoloZFR9LxgZBNPVkRERESkYRR2Rc5mxsD0B8Fbzffn3UN6Xjrj1gezp/VgSAwjNCII4/WyY+0PdEzrr2OGREREROSMobArcjZb/R5sn4+5+A+8nvUF5xTF4SkOwxMSS2hKFAB5O7NwlJaQ0n9QE09WRERERKThFHZFzlbFu2D2U5B8Ad+168G6/HXcua0deW0G48EQ0zkagKw1K8Gy6NR3QBNPWERERESk4RR2Rc5GXi9M+zlgYcb/H2+k/5MUW2tafLuRvHbnkB3oJTI6BIAda1aR2LkL4TGxTTtnEREREZFGUNgVORutfBt2LoFLnmOpPZt1Bet4KL8f5UGtqDLhbA7yEBkSiKOslH3btpDcT1uYRUREROTMorArcrYp3A7zfgepozH9b+XN9DdpG96GTou2UNhzDFiwNchDeEgAu9JXgzF6X1dEREREzjgKuyJnE68Hpt4PAUEw/jWW7F3K+oL1PBQyFueWreS17EtouwiqbBARHEjWmlWERcfQOiW1qWcuIiIiItIoCrsiZ5NV78DuZTD2RUxUG95Y+wbtItvR9/s87PGpVFQGENgpAoCwIIud6atJ7jsAy6avChERERE5s+g3WJGzRUUezP8jpIyAtOtZsmcJGYUZ3Jt8CxUzZ1Ey5Gosm4WnTSgAlTk7qKooJ1lbmEVERETkDKSwK3K2mPsMVDtg3N8wULOqOzzT4K2qIjcomXZdY6k88K2wf+MaLMtGRx05JCIiIiJnIIVdkbPBzm8h/SM47yGI78I3Od+QUZjBPX3upvzTz6hOO5+yUg+pA1thd7oJCrDITl9Nm67dCYuMaurZi4iIiIg0msKuSHPnqYavH4WYDnD+rzDG8Eb6GyRFJjHa3gnnli0U97sCy2aR0i8Bu9NNnM3J/qxtqsIsIiIiImcshV2R5m75PyF/I4x9AYLDWbR7EZmFmdyTdg/ln/0PwsPJqWxJu66xhEUFY3d5SKncDaD3dUVERETkjKWwK9Kcle6BRX+BrpdC93EYY3gz/U2SIpMY2+pCymbMxLrkOkrzq+g8oBUADpebdvZdRLSII6FjchM/gIiIiIjI8fFr2LUs61LLsjZblrXNsqxf1zPmOsuyMi3LyrAs60N/zkfkrDP7N+B1+1Z1gSV7lrCxaCP3pN2D4+tZmMpKCjpfiGVBSr8EACoqXSSU7iS53yAsy2rK2YuIiIiIHLdAf93YsqwA4HVgNJADrLQsa7oxJvOwMV2AJ4HzjDHFlmW18td8RM462+ZD5lQY+RS06ATAOxveITEikctSLiPnV9cR3LMHu3ZD266xhEcHAxCQv4tAj5Pk/gObcPIiIiIiIifGnyu7Q4BtxpgsY4wL+BiY8KMxdwOvG2OKAYwxeX6cj8jZw+2EGY9BXGcY9hAA6/PX88P+H7ilxy14Mjbh3LwZ27gbKdnvIHXAoX9niszfirFsdOzTr6lmLyIiIiJywvwZdtsBuw/7nHOg7XBdga6WZX1rWdYyy7Iu9eN8RM4e374GRdth3F8hKBSASRmTiAqK4pqu11D86adY4eHsb9HHt4W5/6Gw26IoC2fLjoSERzTV7EVERERETlhTF6gKBLoAI4Abgbcsy4r98SDLsu6xLGuVZVmr8vPzT/EURc4wxTthyd+g5wRIHQXA7vLdzMuexzXdriG0ykvZ1zOIuWwcWetLaNvl0Bbm8sICYirz8bTr3oQPICIiIiJy4vwZdvcA7Q/7nHSg7XA5wHRjTLUxZgewBV/4rcUY829jzCBjzKCEhAS/TVikWZj5a7AC4JI/1zS9n/k+NsvGLT1uoeyrrzCVlZhRV1Gc66ipwgywY+0qAII69jzl0xYREREROZn8GXZXAl0sy0q2LCsYuAGY/qMxU/Gt6mJZVjy+bc1ZfpyTSPO2aQZsmQkjfg0xvrcGSqpKmLptKpclX0ZCWALFn3xKSI8e7C6PAQtS+h/6B6Ss1asoC4gktFXbpnoCEREREZGTwm9h1xjjBh4AZgMbgU+NMRmWZT1rWdb4A8NmA4WWZWUCC4HHjDGF/pqTSLPmcsDMJyChB5zzs5rmTzZ/QqW7ktt73U7VhgycmzbR4rpr2b46n7apsUTEhADgrq4me8NadoV3IDIkqKmeQkRERETkpPDb0UMAxpgZwIwftT1z2J8N8MiB/0TkRCx5CUqz4Y6vIcAXVp0eJx9u+pDh7YbTpUUXcv/xR6zgYNyDLqJ4cQa9r+9ac/meTRlUV1WxK7oDY0L8+tUgIiIiIuJ3TV2gSkROhoKt8N1rkHYDdBpe0zx9+3SKqoq4s9edeF0uyr7+mqiLR7FziwMs6Dzg0BbmHWtWYQsMJCcsiYiQgKZ4ChERERGRk0ZhV+RMZwzM+BUEhsGYP9Y0e42XyRmT6RHXg8GJg6lYvBhPaSkxEyey7Yc82nSOqdnCDLBj7Q+0SOlOtS2I8GCt7IqIiIjImU1hV+RMl/EFZC2Ci56GyEOVlRftXsTOsp3c2ftOLMuidOo0AhLicaX0o2ivndSBh8aW5uVStGc30V3SALSyKyIiIiJnPIVdkTOZsxxm/wYS02DwXbW63st4j7YRbRndcTTuoiIqFi8m5vIr2J5e6NvC3P+wI4fW/ABAaEovACK0sisiIiIiZziFXZEz2aK/QHkuXP4K2A6txqbnp7M6bzW39ryVQFsgZV/PALebmIkT2b76wBbm2MO3MK8itnUbqiPjAa3sioiIiMiZT2FX5Ey1PwOWvQkDboOkQbW6Jm2YRHRwNFd1uQqA0qlTCenZg6rYJAr32Ok84NCqbrXLSfaGdST3H0SlywOgd3ZFRERE5IynsCtyJjIGvv4VhMbAxb+v1ZVdls387Plc3+16woPCcW7dSlVGBrETJrB9TR4AnfsfqsKck7kBt8tJcv9B2A+E3QgdPSQiIiIiZziFXZEz0fopkP2dL+iGx9Xqmpw5mUBbIDd2vxGA0mnTIDCQ6MsvJ2ttAa06RhHZIrRm/I41qwgMCiapZ2/sTjcA4cHaxiwiIiIiZzaFXZEzjcsOc38HbfpB/1trdRVVFTF121Su6HwFCeEJGI+H0ulfEnn++VTZIsnbWUbKYau64Au77XunERQcgt3lJjjQRlCAvhpERERE5Mym32hFzjTfvgble+HSv4Ct9l/hTzZ9gtPj5PaetwNg/34Z7rw8YiZMYEd6PgDJfQ+F3eJ9eyjZv4/k/r53fh1OD5HawiwiIiIizYDCrsiZpDQHvn0Vel0FHc+t1VXpruSjTR9xYdKFpMSm+IZPnYotOprIi0aStTaf2NbhxLWJqLlmx5pVACT384Vdu9OtLcwiIiIi0iwo7IqcSeb+DjAw+g91ur7c/iXFzmLu6HUHAJ6KCsrnzSN63Fhc1RZ7t5SQ0q/2FuasNauIa5tEbOtEAOwut87YFREREZFmQWFX5EyRvRw2fAbDHoTYDrW6PF4P72W8R5/4PgxsPRCA8tmzMVVVxE6cyK4NhXi9plbYra6qIidzfc0WZgCHy6MzdkVERESkWVDYFTkTeL0w6wmIagPn/aJO98LdC8kuz+b2XrdjWRYApV9MJbhTJ0L79iVrbT4RMcG06hhVc012Rjoet7tW2K1wunXskIiIiIg0Cwq7ImeCdR/D3jW+o4ZCIut0T8qYRFJkEhd3uBgAV04OjlWriJk4AU+1l+yMQpL7JWDZFP6BKAAAIABJREFUrJprdqxZRVBIKO2696ppczg9emdXRERERJoFhV2R052zAub9AdoNhD7X1elek7eG9Px0but1GwE2X1AtnTYNLIuY8ePJzizC7fLW2sJsjCFrzSo69OlHYFBQTbvdpZVdEREREWkeFHZFTndLX4GKXLj0hTpHDQFM2jCJmJAYJnSeAPiCbOm06YQPHUpQ27bsSM8nJDyQtl1ja64pzMmmvCCflMO2MIOvGrMKVImIiIhIc6CwK3I6K94F3/3Dt6LbfnCd7p2lO1m4eyE3dLuB8KBwACpXr6Y6O5uYCRPwerzsXFdIxz4tCQg49Nd9x9ofAOjUb2Ct+9ldHsJVoEpEREREmgGFXZHT2dxnwLLBxb87Yvd7me8RZAvixu431rSVTp2GFR5O9JjR7N1WSpW9us6RQzvWrCK+Qyei4w+rzuzx4nJ7idTKroiIiIg0Awq7Iqernd9C5lQY/guISarTXVhZyPRt0xmfOp6WYS0B8FZVUTZzJtGjR2OLiGDH2nwCgmx06Nmy5jqnw8GeTRm1qjCDrzgVQLje2RURERGRZkBhV+R05PXArF9DdBIMe+iIQz7a9BHV3mpu73l7TVv5/Pl4KyqImTjBV4QqPZ/2PeIIOmxrcvb6tXg9HlL6/eh9XZcbgAhVYxYRERGRZkBhV+R0tPa/kLsORv8BgsPrdDuqHXy8+WNGth9Jp5hONe2l06YR2KYN4UOHkp9dTkWRs84W5qw1qwgJj6BN1+6173kw7GplV0RERESaAYVdkdNNVRnM/yO0Hwq9rz7ikGnbp1HqLOWO3nfUtFXn5WFf+i0x48dj2Wxkrc3Hslkkp8XXjDHGsGPtKjr26UdAYO1Qaz+wjTlCBapEREREpBnQEo7I6WbJS2DPg5s+Bsuq0+3xepicMZm+CX3p36p/TXvZl1+B10vMBN8RRDvSC2jbJYbQyEPn6Obt2I69uKjO+7rgO3YIIFwFqkRERESkGdDKrsjppCgLlr0BfW+CdgOPOGTB7gXkVORwR687atqMMZROnUpo3zRCUpIp2e+gaK+d5L61tzBnfrMAW0AgKQOH1Lmv3eVb2Y3UNmYRERERaQYUdkVOJ3N+C7YgGPXMEbuNMUzaMIn2Ue0Z2X5kTbtz40acW7cSO3EiAFlr8wFqva/rrq4mc+kiUgefQ3h0TJ17H3xnN1wFqkRERESkGVDYFTld7PgGNn0F5/8Sotscccja/LWsK1jHbT1vI8B2KJSWTJ2KFRRE9Nixvlul55PQIYqouNCaMdtXLaeqvIzeI0cf8d4VThWoEhEREZHmQ2FX5HTg9cCsJyGmA5z7QL3DJm2YRGxILBNSJ9S0mepqyr76msiRIwmIjcVe6iQ3q4yUfvG1rt2waC6RLePpmNbviPd21BSoUtgVERERkTOfwq7I6WD1ZNi/AcY8C0FhRxyys3QnC3cv5Ppu1xMWeGhMxZKleIqKiDmwhXlHegEAyYdtYS4ryGdn+mp6XzgKm+3I25QPnrMbFqRtzCIiIiJy5lPYFWlqVaWw4E/QYRj0nFjvsPcz3yfIFsQN3W+o1V46dSoBcXFEnj8c8L2vG9MqjLg2ETVjMhfPB2PoNeLIW5jBV405LCiAAFvdCtAiIiIiImcahV2Rprb4RXAUwti/HPGoIYCiqiKmbZ/GFZ2vID7s0PZkT0kJFQsXEn35ZVhBQTgd1ezZVExKvwSsA/cyXi8bFs+jfa80Ylsn1jsNu8ujLcwiIiIi0mwo7Io0pcLtsPxf0P8WaNO33mGfbPoEp8fJbb1uq9VeNnMmprq6pgrzrg2FeL2mVhXmnI0bKN2fW29hqoMcTjcRIdrCLCIiIiLNg8KuSFOa/RQEhsJFv613SJW7io82fcSIpBGkxKTU6iudOo2QLl0I6dED8G1hDo8JpnWn6Jox6xfOJSQ8gi5Dhx11KhVOD+HBWtkVERERkeZBYVekqWxfAFtmwgWPQlTreodN3z6dYmcxt/e6vVa7M2sHlenpxEyciGVZuKs97MooIrlvAtaB926dDjtbl31L9/MuICg45KjTcbjcRGplV0RERESaCYVdkabgccOs30CLTnDO/fUO8xovkzMn07tlbwa2Hlirr3TaNLDZiL7icgByNhbjdnpI6Xvond5N3y7GXe2i98gxx5yS3aWVXRERERFpPhR2RZrCD+9C/kYY8ycIrH/FddHuRewq28XtvW+vKTgFvqJTpdOnE3HeeQS1agX4tjAHhwXSrluLmnEbFs4lvkMnWqekHnNKdr2zKyIiIiLNiMKuyKlWWQwLn4dO50P3y4869L2M92gX2Y6LO1xcq92xbBnuffuImTgBAK/Hy451BXTs3ZKAQN9f6/zsneRu30rvEaNrBeX6OJxuIrSyKyIiIiLNhMKuyKm2+EWoKoFL/1zvUUMA6fnprM5bza09byXQVjuElnz2GbaYGKIu9oXg3KxSqiqqa1Vh3rBwLraAQHqcP6JB09LRQyIiIiLSnCjsipxK+Vtgxb9hwG2Q2OeoQ9/LeI+o4CiuTL2yVru7uJjyufOIGT8eW4hvC3TWmgICAm106BUHgMddTeaShaQOGkp4dMwxp2WMwe50Ex6sbcwiIiIi0jwo7IqcSnOegqBwGPn0UYftLtvN/Oz5XN/tesKDwmv1lU6b5jtb95prAF9QzVqbT/seLQgO9a3Mbv9hBVXlZcc8W/cgl8eL22u0sisiIiIizYbCrsipsnUebJ0DFzwGkQlHHfr+xvexWTZu6n5TrXZjDCVTPiO0bxqh3boCUJBTQXlRFcmHb2FeMIfIuJZ07Nu/QVNzOD0ARGhlV0RERESaCYVdkVPBUw2zfwNxKTD0vqMOLakqYeq2qVyecjkJ4bVDceWatbi2b6fFtdfWtGWtzceyIDnNd+RQeWEBO9PX0OvCi7HZGhZeK5xuAMK1sisiIiIizYR+sxU5FVa9AwWb4YaPIDD4qEM/3fIple5Kbu95e52+kilTsIWHEz12bE3bjrX5tEmNJSzKd9+MxfMxxkvvERfXub4+DtfBlV19JYiIiIhI86CVXRF/cxT5jhpKvhC6jT3qUKfHyYcbP2R4u+Gktqh9Nq6nvJyyWbOIvuwybBERAJTmOyjcY6+pwmy8XjYsmkv7nn2ITWzT4CnaXb6VXZ2zKyIiIiLNhcKuiL8t+gs4y4551BDAV9u/orCqkDt63VGnr+zrrzGVlcRee01NW9baAgCS+/q2MOdsyqB0f26DC1MdZHceDLta2RURERGR5kFhV8Sf8jbByrdh4J3QutdRh3qNl/cy36NHXA+GJA6p018y5TNCunUjtM+hI4t2rM0nvn0k0fFhgK8wVXBYOF2GDmvUNO0HClTp6CERERERaS4UdkX8xRhfUargSBj51DGHL8lZwo7SHdze63asH60AV2VmUpWRQew119T0Ocpc7MsqrdnC7HTY2bL8O7qfdwFBIaGNmqrjwDbmSK3sioiIiEgzobAr4i9b58L2+TDiCYhoeczhkzImkRiRyJhOY+r0lXz2GVZwMDHjr6hp25GeD4aasLv5uyW4XU76jKx7/bHYXQdXdhV2RURERKR5UNgV8YeDRw21TIXBdx9z+IaCDazav4pbetxCkC2oVp+3spLSL78i6pJLCIiJqWnPWltAdEIYcW19xarWL5xDfPuOtO7cpdHTPfTOrrYxi4iIiEjz4Newa1nWpZZlbbYsa5tlWb8+yrirLcsylmUN8ud8RE6ZFW9B4Va45PljHjUE8F7Ge0QGRXJ1l6vr9JXNno23vLxWYSpXpZuczUWk9I3HsiwKsneSu20LvUeOrrMFuiEcTjeWBWFBCrsiIiIi0jz4LexalhUAvA6MBXoCN1qW1fMI46KAh4Hl/pqLyCllL4TFf4HOo6DLsbcU76nYw5xdc7i267VEBkfW6S+Z8hnBHTsSPnhwTVvW2ny8bkPnAa0A2LBoLraAQHqcP/L4puzyEBEceFxBWURERETkdOTPld0hwDZjTJYxxgV8DEw4wrg/Ai8AVX6ci8ips+h5cFb4VnUbEB4/yPwAGzZu6nFTnT5nVhaVP/xA7LXX1Aqim5fnEp0QRuvkaDzuajK/WUjnQUMIj46pc4+GsDvdqsQsIiIiIs2KP8NuO2D3YZ9zDrTVsCxrANDeGPO1H+chcursz4RV78Dgu6BV92MOL3WW8vnWzxmbPJbEiMQ6/SVTPoPAQGImTqxpqyiuImdzMd2GJmJZFlk/rKSyvOy4ClMdZHd5VIlZRERERJqVJitQZVmWDXgZeLQBY++xLGuVZVmr8vPz/T85keNhDMx+EkKiYcSTDbrkvxv/S6W7kjt631H3di4XpVOnEjVyJIHx8TXtW1bsBwPdhrYGfIWpIuNa0rFv/+OeusPpJlzFqURERESkGfFn2N0DtD/sc9KBtoOigN7AIsuydgLnANOPVKTKGPNvY8wgY8yghIQEP05Z5ARsmQVZi3xBNzzumMPLXeV8kPkBozqMomuLrnX7FyzAU1xcqzCVMYbNy3NJTIkhJiGc8qICdq5dTa8LR2GzHX9YrXC6deyQiIiIiDQr/gy7K4EulmUlW5YVDNwATD/YaYwpNcbEG2M6GWM6AcuA8caYVX6ck4h/uF0w+ymI7+bbwtwAH278kPLqcu5Nu/eI/SVTPiOwTRsizjuvpq1gdwVFe+10O8e35Tlz8QKM8dJrxMUnNH2HtjGLiIiISDPjt7BrjHEDDwCzgY3Ap8aYDMuynrUsa7y/fq5Ik/j+H1C0HS59HgKCjjncXm1ncuZkRiSNoEfLHnX6XTl7sH/3HbFXXYUVcGjFdvPyXGyBFqkDW2GMYcOiuST17E2LxLYnNH27SwWqRERERKR58etSjjFmBjDjR23P1DN2hD/nIuI3Jdmw+K/Q4wpIbdgK60ebPqLMVca9fY+8qlv6v88BiL36qpo2r8fLlpX76dQnntCIIHIyN1CSu49zr77xhB/B7nQToW3MIiIiItKMNFmBKpFmY9aTviOGLvlzg4Y7qh1MzpjM8HbD6R3fu06/cbsp+fx/RAwfTlDbQyu2uzcWU1nmottQ3xbm9QvnEBwWRpehw074ERxODxHaxiwiIiIizYjCrsiJ2DoXNn0FFzwGse2PPR74dPOnFDuL631Xt2LpUtz799cqTAW+LcwhEYF07N0Sp8PBlmXf0n3YhQSFhJ7QIxhjsLvcRKgas4iIiIg0Iwq7IserugpmPAYtu8C5DzTokkp3Je9mvMu5bc6lX6t+RxxTMuUzAlq2JGrEiJo2V6WbHWvz6TKoNQGBNjYuWYjb5aT3RaNP+DGqqr14DarGLCIiIiLNisKuyPH69lUo3gGX/Q0Cgxt0yWdbPqOoqoj7+t53xP7qvDwqFi0i9sqJWMGH7rl9TR7uai/dhiZijCF93kxaJXcmsXPdI4say+5yAxCplV0RERERaUYUdkWOR9EOWPoy9LoKUkY06BKnx8m7G95lSOIQBrQecMQxpV9MBY+HmKuvrtW+eXkuMa3CaJ0czd7NGynI3knf0eOwLOsEH8T3vi5oZVdEREREmheFXZHGMgZmPgG2QLjkuQZf9r+t/yO/Mr/eVV3j9VLy+eeEDx5MSHJyTXt5URV7tpTQbWgilmWRPncGwWHh9DjvwhN+FIAKp29lV+/sioiIiEhzorAr0libZ8DW2TDi1xDdsPNtXR4X/1n/Hwa0GsCg1oOOOMaxYgXV2dl1ClNtWZELBroOScRRVsqWZUvpecFFBIWeWGGqmp/rOhh2tbIrIiIiIs2Hwq5IY7gcMPPXkNADhh55hfZIpm6byn7Hfu7te2+9W49LpnyGLTqaqDFjatqMMWxelkub1BhiEsLYsHAuHrebvqPHnvCjHGR3aRuziIiIiDQ/CrsijbHkJSjNhstegoCgBl1S7anm7fVvk5aQxrltzj3iGHdxMeVz5hBzxRXYDluxzc8upzjX4StM5fWybv4sknr0Jr59x5PyOAB2bWMWERERkWZIYVekoQq2wXevQdoN0Om8Bl/2ZdaX7LPv4760++pd1S378ktMdTWx111bq33z8lxsgRadB7Ri17o1lO7PPamrunBY2NXKroiIiIg0Iwq7Ig1hDMx8DAJDYfSzDb6s2lvNv9f9m14tezG83fB6bm0omTKF0LQ0Qrt1q2n3eLxsXbmf5LR4QiOCWDt3JmHRMaQOGXbCj3M4x4FtzHpnV0RERESaE4VdkYbInAbbF8BFT0NU6wZfNiNrBnsq9nBf3/pXdavS03Fu3UbsNbWPG9qdWURleTXdhiZSVpBP1g8r6DNyNIFBDds+3VAHqzGHB2sbs4iIiIg0Hwq7IsfirIBZT0JiHxh0V4Mvc3vdvLX+LbrHdefCpPqPCSqeMgUrPJzocZfVat+8PJfQiCA69GrJ+gWzMRjSLj65W5jBV4050GYREqivAxERERFpPvTbrcixfPMilO+Fy16GgIZv9Z21cxa7ynYd9V1dT0UFZTNnET1uLAGRETXtzko3O9IL6DKoFeBl/YI5JPcbSEyrhq8qN5Td6SE8OKDeOYqIiIiInIkUdkWOJm8TfP869L8F2g9p8GUer4d/r/s3XVp0YWSHkfWOK/nkU4zDQYvrb6jVvn11Hp5qL93OacP2H5ZjLy6i7+hxx/0YR+NwufW+roiIiIg0Owq7IvUxBmb8CoIj4eI/NOrSubvmsqN0B/em3YvNOvJfM6/LRdGkSYSfew5hfXrX6tu8LJfY1uG06hRF+pwZRMUnkNx/4HE/ytEcXNkVEREREWlOFHZF6rP+M9i5BEY9AxHxDb7Ma7z8a92/6BzTmdEdR9c7rnTaNNz5+bT86U9rtZcVVrJ3awndhrameN9esjekkzbqUmw2/wRSu8tNpFZ2RURERKSZUdgVOZKqMpjzFLTtDwPvaNSlC7IXsK1kG3en3V3vqq7xeCh6+z+E9uxJxLDaRwltWbEfgK5DElk3bwa2gAD6XDTmuB6jIRxOD+E6Y1dEREREmhmFXZEjWfRnqMiDy16CRqyoGmP4Z/o/6RTdiUs7XVrvuPJ583Ht2kXLe+6uVRjKGMPmZbm07RJLWLSNjEXzSR0yjIjYFif0OEdT4XQTEaJtzCIiIiLSvCjsivxY7gZY/i/fim67xr0nu2j3IjYXb+butLsJqCckG2MofOstgjp2IGp07W3OebvKKdnvoNvQRLZ8v5QqewV9/XDc0OFUoEpEREREmiOFXZHDeb2+olRhsb53dRvBGMM/1/2TpMgkxiXXXznZsWwZVRs20PKuu7ACagfizctzCQi00XlAAulzZhDXNon2vfoc16M0lN2lbcwiIiIi0vwo7IocbtV/IPt7GP0shMc16tIle5aQWZjJPWn3EGirPzwWvvU2AQnxxEyYUKvd4/GydeV+OqXFU7I/m33bNtN39Fi/n39rd7qJUDVmEREREWlmFHZFDiraAXOfgc6joN/NjbrUGMO/0v9F24i2XN758nrHVW7IwP7dd7S8/XZsISG1+rIziqiqqKb7OYmkz51BYHAIPS8cdVyP0lBer8Hh8mgbs4iIiIg0Owq7IuDbvjzt52ALhPH/gEaupi7IXsC6gnX8NO2nBNmC6h1X+Pbb2KKiiL3hhjp9m5flEhYVRKvkEDYuXUT38y4gNCKy0Y/SGJXVHgAVqBIRERGRZkdhVwRg5Vuw61u45HmIadeoS50eJ39d9VdSY1O5MvXKese5du6kfPZsWtx4IwGRtUOs01HNznUFpA5qzeZvF+F2Ov1emAp8W5gBvbMrIiIiIs2Owq5I4XaY93tIHQ39b2n05e9nvs+eij08Pvjxo7+r+867WEFBxN12a52+bT/k4XF76TqkNelzZ9I6JZXE1K6Nnktj2V2+ld1IbWMWERERkWZGYVfObl4vTHsAbEEw/rVGb1/Od+Tz1rq3GNF+BOe2PbfecdV5eZR+8QUxV11JYHx8nf7Ny3NpkRhOdeVuCnOy6Tu6/mrOJ9OhlV1tYxYRERGR5kVhV85uK/4F2d/B2L9AdNtGX/7amtdweV08Nuixo44rnjwZ4/HQ8ic/qdNXVlDJvm2ldB2ayLp5MwkJj6D7sAsaPZfjcTDsqkCViIiIiDQ3Crty9ircDvP+AF0ugb43NvryjIIMpm6byq09bqVDdId6x3nKyij+6GOiL72U4A51x21ZkQtA+x6hbFn2LT0vvIig0NBGz+d4OFwHC1Qp7IqIiIhI86KwK2cnrwem3g+BwXDFq43evmyM4YWVLxAXGsc9afccdWzxx5/gtdtp+dO7jnifTctyadsllp1rl+D1uE9JYaqD7K4DK7vaxiwiIiIizYzCrpydlr0Ju5fB2Bchuk2jL5+1cxZr8tbw8ICHiQyu/3ggb1UVRZMnEzF8OKE9e9bp37ulhNK8SroObcW6ebNI6tmblkn1rxKfbDXv7GplV0RERESaGYVdOfsUbIUFf4SuYyHt+kZfXumu5KVVL9EjrgcTOk846tjSqVPxFBTQ8u67j9j/w+xdhEUHExKyl7L8/aesMNVBdueBasw6ekhEREREmhmFXTm71GxfDoUr/t7o7csAkzZMYr9jP08MeYIAW/3bf43bTeF/3iG0bxrhQwbX6c/bVcbuzCL6jWrP+oWzCI+JpcuQ+is6+4PjwDbmMG1jFhEREZFmRmFXzi7fvw45K2DcXyEqsdGX59pzeWfDO4zpOIaBrQcedWz5nDlU795Ny5/+FOsIoXr1rF0EhwXSsVcwO1avovfI0QQEBjV6TieiwukhOMBGcKC+CkRERESkedFvuHL2yN8CC/4E3S6DPtce1y1e/uFlDIZHBj1y1HHGGAreepvg5GSiRo2q01+ca2f72nz6jGjHxqXzMBjSRl16XHP6//buOzyqKv/j+PukV0JCqAEERASldwUVpXcVsPe29ra7dl3Xvq6rK1ZE7K6gqIgIUkVAlCIdKdJDCYSWMkmmnt8fM/iLkMCQQpLh83qeeZi599wz5+ZwZ+53TiuNPJeH+Gi16oqIiIhI6FGwKycHnxcm3AZRcTDolRJ1X162ZxlTNk/h2jOvJS0h7ahpHfN+wrlmjb9VN+zIy2zJ1K1ERITR8tw6rJw1lSbtOpJUq/Zxl6m0HE4vcRqvKyIiIiIhSHe5cnKY/xrsWAzDxkDi8QeVPuvjhYUvUCu2Fje2PHIJocPtGz2aiNq1SRo86Ih9OfsLWL9gNy3PS2PLsvnkZR2kbZ+Bx12msuBwqmVXREREREKTWnYl9O1ZCz88Cy0GQ8thJcri243fsnrfau7tcC9xkXFHTZu/fDl5CxeSct11mKioI/Yvnb4NgNYXpLFgwufUanQqjdoeffxveXG4PMRr2SERERERCUEKdiW0eT2B7ssJMPDlEnVfdrgd/HfJf2md2pqBTY7dArvv3XcJS0qi+ogjxwXnZbv4bd5OmnWtw461CzmYsYuuF19a5ARWJ0Key0u8ujGLiIiISAhSsCuhbf5I2LkEBr4ECbVKlMW7K99lb/5eHuz8IGHm6JeMc9MmcmbMJOXKKwhPiD9i/4pZ6Xg9Ptr2qs+Cr8aR2uAUmnbqWqJylQWH00Oclh0SERERkRCkYFdC1+7fYPbzcMZQOPPiEmWRnpPOR6s/YnCTwbSu2fqY6feNGYOJjib5qquO2OfM97By9nZObVeTvVuXsX/ndrpcfGmRE1idKA6XhwR1YxYRERGREKRgV0KTx+XvvhydCAP+U6LuywAvL36Z8LBw7ml/zzHTujMyyJr4LdWHDSMiJeWI/at+3I6rwEv7Pg355atxJNerT7Ou3UpUrrKS5/QSpwmqRERERCQEKdiV0DT1Edi1DAb9FxJqliiLRRmLmLFtBje2vJHa8ceewXn/Bx+Cz0fK9dcfsc/j8rJ8ZjoNz0ghK/M39m7bQteLLiEsrGIDTYfLozG7IiIiIhKSFOxK6FnxOSwaDWfdCWcMKVEWXp+XFxa+QL34elx75rXHTO/Zt48Dn39OtYEDiKp/5Bq8a+bvIj/HTbu+Dfnly7FUr12X5t3OK1HZyorH66PA7dNszCIiIiISkhTsSmjZvRom3g0Nz4ZeT5Y4my9//5L1B9Zzf8f7iYmIOWb6Pf9+Cet2k3rrbUfs83p9LJm2lTpNknA6NrJn80Y6XziCsPCKbdXNc3sBNEGViIiIiIQkBbsSOgqyYNzVEFMNRrwP4ZElyibblc3rS1+nfa329DmlzzHT5y1aRNaECdS44QaimzQ+Yv/vi3aTu99J+74NWfDlWKrVrMUZ555forKVpTynP9hVy66IiIiIhCIFuxIarIUJt8OBLTDiA0isU+KsRi0fxUHnQR7q/NAx17+1bjcZTz1FZL16pN76lyP3+yxLvt9KjbQEDOns2rCOzkOHEx5RskC8LOU6PYCCXREREREJTQp2JTT89CqsnQS9n4JTzi5xNr/t+43/rfkfF512ES1qtDhm+v0ffYzz9w3UfuwxwmJjj9i/efleDmTk0b6ffwbmhJQanNmjd4nLV5byXIFgV92YRURERCQEKdiVqm/zHJj5T/96umfdUeJs8tx5PDjnQVJiU7i/w/3HTO/OyCDzjTdIOP98Ei84sluytZZfv99CtZqxxMTuYcfa1XQaMoyIyIpv1QVwOA+N2VXLroiIiIiEnnINdo0x/Ywx64wxG4wxDxWx/35jzG/GmBXGmJnGmFPKszwSgrJ3wvgboEZTGPpGidfTBXhx0Ytszd7KC+e8QFJ00jHT737+BfD5qP3oI0Xu3772AHu25tC+T0MWfD2OuKTqtOrZt8TlK2uOP7oxq2VXREREREJPuQW7xphw4A2gP3AGcLkx5ozDki0FOlprWwPjgRfLqzwSgjwu+PxacOXBpZ9AdGKJs5q+dTpf/v4lN7a6kU51Oh0zfe7cueRMnUrqrbcSVb9+kWl+/X4rcUlRVKuRxbZVy+k4+GIio6JLXMay5nBpzK6IiIiIhK7ybNlasoECAAAgAElEQVTtDGyw1m6y1rqAscDQwgmstT9Ya/MCL38Bio4aRIoy7THYvhCGvg41Ty9xNhmODJ6c/yQta7Tk9ra3HzO9z+kk4+lniGrcmJQbri86z81Z7Fh3gLa9GrLom3HEJlajTe/+JS5jechzBWZjVjdmEREREQlB5RnspgHphV5vD2wrzo3AlHIsj4SSleNh4Sjoeju0vLjE2Xh9Xh6Z9whun5t/nfsvIsOOPZ523+h3cW/bRp0nHicsKqrINEu+30p0XASp9fPYvOxXOgy8kKiYIyewqkiHujHHqRuziIiIiISgSjFBlTHmKqAj8O9i9t9ijFlsjFmcmZl5Ygsnlc+eNTDxLmh4ln/25VJ4f/X7LMpYxCNdHqFhtYbHTO/ato1977xDtQEDiD/rrCLT7NuRy+ble2l9fn1+nTSemPgE2vYdVKpylodDE1SpZVdEREREQlF5Brs7gAaFXtcPbPsTY0wv4FFgiLXWWVRG1tp3rLUdrbUda9asWS6FlSqiIBvGXQVRCTD8fQgv+czGq/au4o2lb9C3UV+Gnjr0mOmttWQ8/QwmMpJaDz5YbLol07YSER1OvaYeNi7+hXb9hxAdF1ficpaXPJeHmMgwwsNKPqmXiIiIiEhlVZ7B7iLgNGNMY2NMFHAZMLFwAmNMO2AU/kB3TzmWRUKBtfDN7bB/M4x4H6rVLXFWDreDB+c8SGpcKo93fRwTxCzOOdOn45g7l5r33E1k7VpFpsnem8/vi/Zw5jn1WDrlS6JiY2nff0iJy1mecp0eteqKiIiISMgqt2DXWusB7gSmAmuAz621q40xTxljDt39/xtIAL4wxiwzxkwsJjsRmP8arPkWej0JjbqXKqvnFzzP9tztQS8z5HM42P3c80Q3b07yFVcUm27ptG0YA6e0MKxfOJ92/QYTk5BQqrKWlzyXVzMxi4iIiEjIKtc7XWvtZGDyYdueKPS8V3m+v4SQLfNgxpPQYgicfVepsvp+y/d8s/Ebbml9Cx1qdwjqmMw338STkUHaKy9jIoq+bBxZTtbM30Xzs+qyYubXREZF037AsbtHVxSH00NclCanEhEREZHQVCkmqBI5quxd8MX1kNIEhr4BQXQ5Ls7O3J08Nf8pWqe25tY2twZ1TMH69ez/8COShg8jrl27YtMtn5mOz+ujSdtI1s2fS5s+A4irduxW44ricHnUsisiIiIiIUt3ulK5ufPhi2vBlQvXToSYaiXOyuvz8vDch/Hh44VzXwhqmSFrLbufeprw+Hhq/fWvxabLy3ax6scdNO1QizVzviU8MpKOgy4qcVlPBIfTS7XYkk/wJSIiIiJSmallVyovjws+vxbSF8KFb0KtFqXK7t2V77JkzxIe7fIoDRIbHPsAIHviRPIWL6bm3/5KRHJykWmstfzwyVp8Xkvzs+L5be4sWvfsS3z1otNXFnkuD/HqxiwiIiIiIUrBrlROPi98/Rf4fSoMehnOLF0r6bI9y3hr+VsMaDyAQU2CW/PWm5XF7hf/TWybNlQfNqzYdOsWZLBlxV66XtiEtT9NIiwsjI5DLi5VeU8Eh9NLnGZjFhEREZEQpWBXKh9rYdJ9sPor6PVP6HhDqbLLdeXy0NyHqBNfh8e6PhbUMkMAma++ivfAAeo8+Q9MWNGXSs7+AuaOXU/dpkk0ah3N6tkzaXl+HxJTUktV5hPB4fKQEK2WXREREREJTWrWkcrFWpj+OCz5ELrfD93vLXWWzy14jl2OXXzQ7wMSoxKDOiZ/5SoOfDaW5KuuIqZF0d2nrbXM+mgNPgs9r23B4m8/BKDzhcNLXeYTIc/pJU4TVImIiIhIiFLLrlQuc1/yr6fb6Wbo+cSx0x/Dd5u+49tN33Jr61tpV6v4mZQLs14vGf/8J+GpNah5d/HLHK2es4Ptaw/QbVhTCnJ2snLWNM487wKqpdYqdbnLm8vjw+X1acyuiIiIiIQsBbtSeSwYBbOegdaXQf8XS7XEEMD2nO0888sztKvVjptb3xz0cQc//5yCVauo/eBDhCcW3RKclZnHT19uoEGLZOo09jD+uSeIr57M2ZdcVaoynyh5Lg+Alh4SERERkZClYFcqh2X/gykPQPNB/rV0ixkjGyyn18lDcx8C4PlzniciLLigzrVtG3te+S9xXbtSbeCAItP4fJaZH64hLDyMDv1T+PLZxwkPD2f4Y8+QkJxSqnKfKA6XF4B4TVAlIiIiIiFKwa5UvN8mwjd3QOPzYNgYCC9dAOb2ufn7j39neeZynjz7SdIS0oI6zpudTfqtt2GMoe5T/yx2IqvlM9PZtSGLTgNrMuX1p/G43Qx/9GmS69QrVblPpDynv2U3ThNUiYiIiEiIUrOOVKyNs+DLGyGtI1z2P4iMKVV2PuvjiZ+e4If0H3ikyyP0bdQ3qOOs282Oe+/FlZ5OwzHvEtWwYZHp9u90sOCbTTQ4I45lU0aSl5XFJY8/S2rDRqUq94mW61Q3ZhEREREJbbrTlYqzbQGMvRJSm8GVn0N0Qqmys9by3ILnmLRpEne3u5vLm18e9HEZzzyLY/7P1H32WeI7dy4yndfrY8YHvxER5eHg9s84uDuDYQ//kzpNm5Wq3BUhT92YRURERCTEqRuzVIxdK+DTEZBYF67+GmKTS53la0tfY9y6cVx/5vXc1OqmoI878NFHHBw3jho330T1YRcXm27J91vZs3U/EWYy+7ZvYfB9D9PgzNalLndFcBzqxqzZmEVEREQkRCnYlRNv7+/w8UUQnQjXfAMJpV+q571V7zF65WiGNxvOfR3uK3a87eFyfviB3S/8i8Tevah5333FpsvclsOiSRuJipjOvh2/0/+O+zm1Q9EtwFWBIzAbc4K6MYuIiIhIiNKdrpxYB9Phowv9ywpd8w1Ub1DqLD9f9zmv/PoK/Rv157EujwUd6BasXcuOv/6NmBYtqPevf2GKmQHa6/Yx/f1VeAu+Jz9vLb1vvpPm3c4rdbkrksPp78asCapEREREJFQp2JUTJ3cPfDQUnDlw/XeQ2rTUWU7eNJlnfnmGc+ufy7PnPEt4WHDBm3vPHtJvu53wxETqv/UWYXFxxaZd8O1G9mz4Bq9rLedeeT2te/Urdbkr2h/r7GrMroiIiIiEKN3pyolxMN0/RjdnF1w9Aeq0KnWWs9Nn88i8R+hYpyP/Oe8/RIZFBnWcLz+f7XfciffgQRp9+gmRtYvvRr1zw0EWffMJXtcqulx0KZ2GDCt1uSuDXKcXYyA2Ui27IiIiIhKaFOxK+du+GD67HDwFcMU4aNil1Fku3LWQv87+Ky1SWvDaBa8RExHckkXW52PnQw9TsGoV9V9/jZgzzig2rdvpZeLL7+Ap+JXWPQfQ7dKrSl3uyiLP6SEuMpywsOC6fIuIiIiIVDWaoErK16ov4f0BEBUHN06HxueWOsuVmSu5a9ZdNKzWkLd6vUV8ZHzQx2aOHEnO1KnU+tvfSOzZ86hpv37pfRz75tCobXd63XRr0GOBqwKHy0ucJqcSERERkRCmu10pH9bCjy/C7Oeg4dlw6ScQX6PU2f5+4HdunXErKTEpjOo9iuox1YM+9uCECex7exTVRwwn5Ybrj5p2zmcTSF8xgeR6rbjogb8XO3lVVeVwejQTs4iIiIiENN3tStlzF8A3d8Cq8dDmChj8X4iILnW26dnp3DL9FmLCYxjdZzS14oJfsihv8WJ2Pf4EcV26UOfxx4/aSrt6zhwWTRhDVFxjLn/mH4SFh9641jyXR2vsioiIiEhIU7ArZSt3D4y9ErYvhJ7/gO73+ZcZKqUMRwY3T78Zj8/DB/0+oH5i/aCPdW3bxvY77yIqLY36I1/FREUVm3bV7BlMfXskYRF1uejBx4iND24scFXjcHo1E7OIiIiIhDTd7UrZ2f0b/O9ScGTCJR/DGUPKJNv9Bfu5ZfotHHQeZEzfMZxa/dSgj/VmZ5N+621gLQ3efovwpKQi01mfj3njPmbhhC8Ii2hIx6G3U7957TIpf2XkcHlIiS8+6BcRERERqeoU7ErZWD8Nxt8A0QlwwxSo165Mst2Vu4u7f7ibnbk7ebvX25xZ48ygj7VuN9vvuQdXejoNx7xLVKNGRaZzu5xMffO/rPt5LuFRLWnd+2q6DS9+luZQ4HB6aJBS/NrCIiIiIiJVnYJdKR1rYcHbMPUR/9q5l4+FavXKJOufdvzEQ3Mfwu1z8+r5r9KxTsfgi+XzkfHU0+T9/At1n32W+M6di0yXl3WQr198mowN64iIPYeOgy7m7GFNQ2rm5aLkubzEa8yuiIiIiIQwBbtScl43THkAFr8HzQfBxe9AVPDLABXHZ32MWjGKt5a9xanVT+WVHq/QKKlR8MXKzWXnAw+SO2sWNW65herDLi4y3b7t6Xz1wpPk7NtHZPwgul3Sj/Z9Twn5QBcg1+khTmN2RURERCSE6W5XSib/IHxxLWya7Z+E6oInoAyW5zlYcJCH5z3MvB3zGNRkEI93fZy4yOC72zo3b2b7HXfi2rqV2o8+SvJVVxaZbuvKZUx8+Tm87jAi40fQ46rzaNUj+EmvqjJrLXkur5YeEhEREZGQprtdOX77N/knotq/GYa+Ce2KDiiP1+q9q7l/9v1k5mfyeNfHGdFsxHG1subMns3Ov/0dExlJw/feI75L0V2XV86axox33yA8MoWI+AvpfUMXTu9Sp0zOoSpwenx4fZa4aHVjFhEREZHQpWBXgufzwa/vw4wnISwcrvkGGnUrdbbWWsb/Pp7nFzxPamwqH/b7kFY1Wx3X8ftGjSLz1ZFEt2hOg9deIzIt7ch0Ph9zx37Eom/GEx3fhPCYgfS9pT1N2tYs9TlUJQ6nB0BLD4mIiIhISNPdrgQncz18ezds+xkanwdDRkJyo1Jnm+/J55lfnmHixomcXe9sXjjnBZJjkoM+3udwsPPhR8iZNo1qgwZR9+mnCIuNPSKd2+Xk+9dfZv2Cn4ip1o7w2PMZdHtb6jdPKfU5VDV5Li8A8erGLCIiIiIhTHe7cnQeF/z0X5jzb4iM83dbbnsFlMEkTtuyt3Hf7Pv4/cDv3NbmNv7S+i+EhwXftda1bRvb77gT58aN1HrgAVKuv67Ibs+OgweY8O+nydj4O3HJ5xMR25HBd7elTuOi19wNdQ7XoZZddWMWERERkdClYFeKl74IJt4FmWug5TDo9wIk1CqTrGdtm8Vj8x4jLCyMN3q+wTn1zzmu43PnzmPH3/6GARqMfoeEbkV3p96bvpWv//VPHAcPEpdyITEJpzPknrbUSEsog7OomhxOf8tunFp2RURERCSE6W5XjuTMgVnPwIJRUC0NrvgcmvUtk6w9Pg+vL32dMavGcEaNM3i5x8ukJRw5vrY41lr2v/cee/7zMtFNm1L/jdeJatCgyLRbVizl25efJyw8kuhqlxKf3ICh97YlqWbwszuHokNjdhM0QZWIiIiIhDAFu/Jn66fCpPshewd0vgV6Pg7RiWWS9d78vTw450EWZixkRLMRPNj5QaLDo4M+3pefz65HHyN78mQS+/Wj3nPPEhZXdOC6Zt5svn/zFRJq1MXtGUBSrVoMuacdCcnBv1+oygt0Y9Y6uyIiIiISynS3K365mfD9Q7BqPNRsDjdOgwZFL91zvFxeF1+s/4J3VryDw+3gmW7PMLTp0OPLY/sOtt91F861a6l5//3UuPmmYpclWjp1ErPeH0VK2mnkO/pQ85QaDLqrDbEJUWVxOlXeoW7Mmo1ZREREREKZ7nZPdtbC8s9g6iPgckCPR6D7fRBR+sDQZ31M3jyZ15e+Tvbu3fTIbEqvLhdzwXEGuo6ff2bHffdjvV4ajHqbhHPPLeZULL98NZb5n39KQo0WOHJ7kdasJgNvb01UrP6rH/LHBFXqxiwiIiIiIUwRwMls/yaYdB9smg0NuvqXE6p5eqmztdYyd8dcXl3yKhnpmzgnvSE1t9YHXz5Lf/sU16YMet5wG5ExMUfNp2DdejJfG0nujJlEnXoqDd54nahGjYp+T5+Pme+9w/LpkwiPPgMi+nPWkMa06dmAiEgFdYX90bKrCapEREREJITpbvdklLESfhoJq770Lyc08D/Q4QYICyt11sszl/PKr6+wccMKum6pw9nb04iIiqBN//50GHghK2ZO5ZevxrJrw3oG3/sgqQ0bHZGHa8sWMl97nezJkwmLjyf17ruoce21hMXHF/meHpebL575FzvX/UJ4dDta9byMrhc2JT5J43OLkufyEGYgOqL09S0iIiIiUlkp2D1ZWAub58BPr8LGmRCVAF1vg7PuhGp1S539poObGLl0JEtXzaHjppq02lmPyJgY2g0ZRIdBFxFXzb+mbbdLrqR+izOZ/NpLfProX7ng+r/Q8vzeGGNw79xJ5ptvkvX1BExUFDVuuokaN95AePXqxb7vlhUZfDfyRQpy1pOcdgGD7rmJWqdUK/X5hLJcp4f46IhixzyLiIiIiIQCBbshwJmXR1hYWNHdgr0eWPONP8jdtRzia0HPJ6DjDRCbXOr3znBk8Nbyt5i7eDLtNlZnSEY9omJjaT9sKO37DyE28cjA85RWbbnmxdeY/NpLTBs1kq1LFtLG4cMxfjwAyVdcQeotNxNRs2ax73twdx5zxq1i44L38Hm207r3lfS68TIFcEHIc3o1OZWIiIiIhDzd8VZhXo+bJVO+5ZcvxxIdF8/g+x+ibtPAmFtXHiz7FOa/Bge3Qo2mMHgktL4UIo8+VjYYWc4s3l35LtN++oKWvycwMLM2UfHxdLzkQtr1G0xMfMJRj4+vnsyFd/yVH5/5B8sX/sx2l4dz+/fhtHvvJ7JevWKPc+a5WfTdFlbMXI8z9yusN5O+t99Py/MuKPU5nSwcLg9xmpxKREREREKcgt2jyMs6SIEjl5R69Su6KH9irWXj4gX8+MkYDmbsolHbDuzfsZ1x/3iQ8y+/ktZxv2MWjYb8/VC/M/R9Dk4fUCZjctNz0vl247d8P2csp62Nos++VKITEuh0+TDa9hlIdDHr3hbmzc1l/4cfsv/9D0hzOEjp2YOFBQeZtnMjnt9W0Kpu3SNaaH1eH7/N28mCiZvJz9kPngkYshj6wOM0ad+p1Od1MnE4PSRocioRERERCXG64y3GpiWLmPLmKxTk5tCyRy+6XXo1CckpFV0sMrdtYfaHo9m2ajkpaQ0Y9vA/adS2A/npq5jyytPM+PhDdibtptc5XYg8715o2LX075mXydQtU5myeQpbN6+hy2/JnLevGtHVEul69SW06dX/mDMrA/gKCjjw6f/YN3o03oMHSezdi9S77iKmWTOaZx1k8uv/Yfro19m2egW9b76T6Lg4fD7LttX7+Pnrjezf6aBmAw+u3K/w+PIZ/uhT1G/RstTnd7JxuLzERallV0RERERCm7HWVnQZjkvTBml26bLlJNZILZf8vR4388Z+zOJvv6Jmw0bUP7MVy6dNITwigi4XXUL7gUOJjDrxs/zmZWcx//NPWDFjKtHx8Zw94gpan9WJ8E0zYM0k+H0qlnB+Ce/L/FXZ1GxwCoP/+gjJdYrvEnw02a5sZm6dyXebv2NRxiLC3JbzdzQhbY2XqJhYuo24kla9+h3zb+ErKMDx00/kTJ9B7g8/4M3KIr57d2recw+xrf4cqFqfj4XfjOencZ+QkFKTU9pexc6NkeRluahWM5Yzu0Xwy/hXMGFhDHvkKWo1alKiczvZDXptLrUTYxhznVrERURERKTyMcb8aq3tWOp8qlqw27BGsv374F50u/Qa2vYdQFhY2bVQZe3JYNKrL5KxYT1t+gykx9U3EhEVxYFdO5jz6ftsWPQLiak1OfeK6zj97HNPyGRIXo+bpd9P4pcvx+IqyKftud05q1k4sVunQ/oCsD6oVh9aDYcut0K1umxe9iuTX3sJ6/PR7477adqxS1DvVeAp4MftPzJ502Tm7piL2+emQUID+nraEzl7K/n7D9Dy/N6cc8V1f8yuXGSZs7LI/fFHf4A7bx42P5+watVI6HEeySNGENep6CArKzOP9Qt3s2r2IvanfwU2n9qnDaDLhRcSFbWbiS8/TUxCIsMffZrkumkl+nsKnP/SbFqlJTHy8nYVXRQRERERkSOctMFuu7Zt7GOXDGXL8iXUadqM3jffWSYtfOsX/MS0t0diraXvrXfTrGv3I9JsW7WC2R+/S+aWTdRt1pzzr7mZuqedXur3Loq1lk1LFvLjR2M4kLGTRvWT6FFnMzUcq/0JareC5gOg+UCo0xoOC7yz9uzm21eeZ/emDXS+cATdLrmKsPAjfxhw+9ws2LWAyZsmM3PbTPI8eaTGptKvUT96JHZlxzc/sHnpYlIbNqLXjbeT1vyMIsvr3r2HnJkzyJ0xA8fCReDxEFGrFom9epLYqxdxnTphIiOPOC4/x8Xvi/ewfmEGuzdnA1DvtOo0ahXPhoWfsm3lEhq2asuOtaupXrsuwx59isSU8mnVP1l0fnYGPVvU4vmLW1d0UUREREREjnDSBrsdO3a0ixYtYu38Ocz+cDT5Odl0HHQRZw2/nMjo459l2ONyMfvjMSyf9h11mjZj0D0PkFSrTrHpfT4vq2fPZN7Yj8jLOkiL7j3ofvm1VEstfpmc47V38+/MHv0KWzduIyXGRY+a62mcmA2NusHpA+H0/pB8SlDn9sMH77Bi5vc0bNmagXc/QFRiAhsObmDl3pWsyFzBj+k/csB5gMTIRHo36k3/xv1pl9KWpd99w4KvxmHCwzl7xBW06zeY8Ig/D/F2bt5MzowZ5MyYQcHyFQBENWpEYu9eJPbqRUyrVpgiJsVyO71sWpbJ+oW7SV+zH+uz1EhLoFnn2pzWqTaJKf56tD4fiyd9zdzPPqR2k6Zc/NCTRS5lJMen5T+mcmmnBjw+qOgfLkREREREKtJJHewuXrwYgPzcHOZ88j6rfphGUq3a9Lrxdhq17RB0Xvt37mDSf18gc+tmOgy6iHMuv4bwiCNbH4viys9j4TfjWTzpa4wJo+Pgi+g0ZBhRMbHHf1KuPHy7VpG1fgG/zprNik0FRIV5OLvObtp0aUV4i0FwWm+IO74Jsqy17HTsZM6UceyYMBt3FPzYfi87quUAUD26Ol3qdmFA4wF0T+tOVHgUW1csY+Z7b3Fg1w6ade1Oj2tuIrFGKj6XC9emTTjXraNgzVpy583FtWEjADEtW5LYqxeJvXsRfeqpR5bDZ8neV8De9Bw2Ls1k8/JMPC4fCSnRNOtUh2ada1MjrfilirL3ZhKXVJ2IIlqG5fhYa2nyyGTuuuA07u/drKKLIyIiIiJyBAW7hWz/bRXTRr/OgZ3bad7tPHpccxPx1ZOPms9vc39gxug3CI+Kov/t95V4+ZrszD3M+d8HrJs/h4TkFLpffi1nnHP+ES2a1lrysg6StXUdWRt+JWvberJ27yB7fzZZeT6y3dFYDAZLm2ZJnD38MmLP6HNca+JmObNYvXc1K/auYNXeVazcu5L9BfsBqJ0TxwVLaxOVZ6kzqDvnDrqcBtUa/DHuOPfAfmZ/9C7r5s8hKbUmZ3c9j1r5bpzr1uFcvw7n5i3g8QBgoqKIbdfOH+D26klk3bp/lKHA4Wbfjlz27XAE/s1l/04HbqcXgOi4CE7tUIvTO9em7qnVMWHlP+5Z/l+ey8MZT0zl4f7N+ct5R/4wISIiIiJS0apEsGuM6Qe8CoQD71prXzhsfzTwEdAB2Adcaq3dcrQ8iwp2ATxuNwsnfMHCCZ8TGR3DOVdeT6vzex8RdLoLCpj5/tusnj2DtOZnMvDuv5fJzM471q1h9kejydiwntpNmnL6WeeQs30DWTu3kLV3L1nZ+Xi8fz4mLtJLUkIkSTVSSKrbgKRGLUhrdy4paUWv62ut5YDzALscu8jIzWCXY9cfj/UH1rM1e+sfaZskNaFlaktap7amZc2WNKveDG++kylv/IdNSxbRvHsPel5yFd4dO1g6ZRK/rlqCz+el6QEHjdN3Ex74fxFZrx7Rp59O9OnNiDn9dKKbNSPqlFPwEcaBjLw/AtpDAa7joPOPMkTHR5CalkBKWkLg33hqNkgkPKL06/1KyWTmOOn07AyevrAlV3c9dld4EREREZETrdIHu8aYcGA90BvYDiwCLrfW/lYoze1Aa2vtrcaYy4CLrLWXHi3f4oLdQ/btSGfG6DfYvmYV9Vu0pNfNd1AjrQHgX6N20n//xf6d2+l68aWcNezyIidtOiaXA3IyIHe3/5GzG3IzsDm7Wbt2J3PWuMl1hhEV5iEpsoBqUS6qJ8VRrVYdkuqfStKpbUg6/Swik/+/RdRnfeS589hXsM8fwObuIsPx/wHtoedOr/NPRYkJj6FOfB0aJTWidY1WtIo/jWa2FjFZ+Xj27sOzdy+evZl49+3Dk7kX9769rMnLYm18JAkFLsKA7NhoajoKaJVQh8SmLbENTsVXqwHe6rVxusMpcLjIz3GTn+umINf/PGdfAT6f//9OWLghuW48NdLiqZGWQI1AcBuXFHVCZqyW4G3Z66DHS7N55dI2XNSu6B9VREREREQqUlUIds8CnrTW9g28fhjAWvt8oTRTA2l+NsZEABlATXuUQh0r2AX/xEarfpjOnE/fx+0soPOQYcQnVWP2Jx8QFRvLgOuu4pRTG4KnAOvKw7qdWFc+Plc+Prd/m89TgM/lxFeQhceRiSdvN15HJt78vbjdDjwGPAa8xofbGLwmHE9sEu6YJNwxyeSFJ1GQXIe8mOrkRcSS78qnID+PPFcuTmc+TmceBc48nK4C3K4C3M4Cwm0YER6IcYcT5Qkjxh1BCvEkmQSq2XgSbAyxvihifJFEesOJ8BiMy4fP7cWbX4A7Nx+vF3xhkYUeEfjCo/DFJUBsAjY6DhsVi8OXyd4DP4KJIC75Arw0xXqL/nuGhRtiEyKJSYgiNjGSmIRIqtWIpUZ9f3BbvXYc4eFqra0KVu/MYuDIeYy6ugN9zyx+IjYRERERkYpSVhGNrCIAAAusSURBVMFuxLGTlFgakF7o9Xbg8AVf/0hjrfUYY7KAGsDe4jLN3JrNm7dMBcAUGxP7txszApjHz1+NAyAsvAE+24fvPozEmgwwhwK0cCAh8CgfUYFHSecSzgk8/iSC/6/BYpa99WL9QTngPRSgY/GEpeJLaYIzLJyC8HDyw7wUhFnyDf5/w/jjtdsAxgluYH/gsQ1YWsKTkQrjDVwzCdHleemLiIiIiFS8KnHHa4y5Bbgl8NJ5x+h+q0qe29tlUSQpuVSO8mOGnBjd/1Wiw1R3VZvqr2pT/VVdqruqTfVXdanuqrbTyyKT8gx2dwANCr2uH9hWVJrtgW7MSfgnqvoTa+07wDsAxpjFZdGkLRVD9Vd1qe6qNtVf1ab6q7pUd1Wb6q/qUt1VbcaYo49bDVJ5DrRcBJxmjGlsjIkCLgMmHpZmInBt4PlwYNbRxuuKiIiIiIiIBKPcWnYDY3DvBKbiHxT7nrV2tTHmKWCxtXYiMAb42BizAf9I0MvKqzwiIiIiIiJy8ijXMbvW2snA5MO2PVHoeQEw4jizfacMiiYVR/VXdanuqjbVX9Wm+qu6VHdVm+qv6lLdVW1lUn/ltvSQiIiIiIiISEXR4qgiIiIiIiIScipVsGuM6WeMWWeM2WCMeaiI/dHGmHGB/QuMMY0K7Xs4sH2dMabviSy3BFV39xtjfjPGrDDGzDTGnFJon9cYsyzwOHwSMzkBgqi/64wxmYXq6aZC+641xvweeFx7+LFSvoKou1cK1dt6Y8zBQvt07VUwY8x7xpg9xpgil9QzfiMD9bvCGNO+0D5dexUoiLq7MlBnK40x840xbQrt2xLYvqysZhyV4xNE/fUwxmQV+ox8otC+o37uSvkKou7+XqjeVgW+61IC+3TtVTBjTANjzA+BuGC1MeaeItKU3XeftbZSPPBPYrURaAJEAcuBMw5LczvwduD5ZcC4wPMzAumjgcaBfMIr+pxOlkeQdXc+EBd4ftuhugu8zq3ocziZH0HW33XA60UcmwJsCvybHHieXNHndLI8gqm7w9LfhX+ywEOvde1VfB2eC7QHVhWzfwAwBTBAV2BBYLuuvcpfd2cfqhOg/6G6C7zeAqRW9DmczI8g6q8HMKmI7cf1uavHia+7w9IOxr/ay6HXuvYqvv7qAu0DzxOB9UXcd5bZd19latntDGyw1m6y1rqAscDQw9IMBT4MPB8P9DTGmMD2sdZap7V2M7AhkJ+cGMesO2vtD9bavMDLX/CvuyyVQzDXXnH6AtOttfuttQeA6UC/ciqnHOl46+5y4LMTUjIJirV2Dv7VCIozFPjI+v0CVDfG1EXXXoU7Vt1Za+cH6gb0vVfpBHHtFac035lSBo6z7vS9V8lYa3dZa5cEnucAa4C0w5KV2XdfZQp204D0Qq+3c+SJ/5HGWusBsoAaQR4r5ed4//434v+15pAYY8xiY8wvxpgLy6OAclTB1t+wQFeS8caYBsd5rJSPoP/+gaEDjYFZhTbr2qv8iqtjXXtVy+HfexaYZoz51RhzSwWVSY7tLGPMcmPMFGPMmYFtuvaqCGNMHP5A6MtCm3XtVSLGPyS1HbDgsF1l9t1XrksPiRzOGHMV0BE4r9DmU6y1O4wxTYBZxpiV1tqNFVNCKca3wGfWWqcx5i/4e1hcUMFlkuNzGTDeWusttE3Xnkg5M8acjz/Y7V5oc/fAtVcLmG6MWRtorZLKYwn+z8hcY8wAYAJwWgWXSY7PYOAna23hVmBde5WEMSYB/w8R91prs8vrfSpTy+4OoEGh1/UD24pMY4yJAJKAfUEeK+UnqL+/MaYX8CgwxFrrPLTdWrsj8O8mYDb+X3jkxDlm/Vlr9xWqs3eBDsEeK+XqeP7+l3FYVy5de1VCcXWsa68KMMa0xv+ZOdRau+/Q9kLX3h7gazT0qtKx1mZba3MDzycDkcaYVHTtVSVH+97TtVeBjDGR+APdT621XxWRpMy++ypTsLsIOM0Y09gYE4X/P+jhs4NOBA7NujUc/4BzG9h+mfHP1twY/y9vC09QuSWIujPGtANG4Q909xTanmyMiQ48TwW6Ab+dsJILBFd/dQu9HIJ/fAXAVKBPoB6TgT6BbXJiBPO5iTGmOf6JHH4utE3XXtUwEbgmMDNlVyDLWrsLXXuVnjGmIfAVcLW1dn2h7fHGmMRDz/HXXZGzykrFMcbUCcwLgzGmM/575n0E+bkrFcsYk4S/F+E3hbbp2qsEAtfVGGCNtfblYpKV2XdfpenGbK31GGPuxF/gcPwzhq42xjwFLLbWTsT/h/nYGLMB/8D0ywLHrjbGfI7/Rs0D3HFYVz0pR0HW3b+BBOCLwHfHNmvtEKAFMMoY48P/RfKCtVY33CdQkPV3tzFmCP7raz/+2Zmx1u43xjyN/8sf4KnDugtJOQqy7sD/WTk28OPgIbr2KgFjzGf4Z31NNcZsB/4BRAJYa98GJuOflXIDkAdcH9ina6+CBVF3T+CfV+TNwPeex1rbEagNfB3YFgH8z1r7/Qk/gZNcEPU3HLjNGOMB8oHLAp+hRX7uVsApnLSCqDuAi4Bp1lpHoUN17VUO3YCrgZXGmGWBbY8ADaHsv/vMn+99RERERERERKq+ytSNWURERERERKRMKNgVERERERGRkKNgV0REREREREKOgl0REREREREJOQp2RUREREREJOQo2BURESnEGOM1xiwr9GhU0WUqS8aYdsaYMYHn1xljXj9s/2xjTMejHD/WGHNaeZdTRESktCrNOrsiIiKVRL61tm1xO40xEdZaz4ksUBl7BHimFMe/BTwA3Fw2xRERESkfatkVERE5hkAL6ERjzCxgZmDb340xi4wxK4wx/yyU9lFjzHpjzDxjzGfGmL8Ftv/RYmqMSTXGbAk8DzfG/LtQXn8JbO8ROGa8MWatMeZTY4wJ7OtkjJlvjFlujFlojEk0xswxxrQtVI55xpg2h51HItDaWrs8iHMeUqh1e50xZnNg11yglzFGP5iLiEilpi8qERGRP4s1xiwLPN9srb0o8Lw9/kBxvzGmD3Aa0BkwwERjzLmAA7gMaIv/O3YJ8Osx3u9GIMta28kYEw38ZIyZFtjXDjgT2An8BHQzxiwExgGXWmsXGWOqAfnAGOA64F5jTDMgpoigtiOw6rBtlxpjuhd63RTAWjsRmAhgjPkc+DGw3WeM2QC0CeLcREREKoyCXRERkT8rrhvzdGvt/sDzPoHH0sDrBPzBbyLwtbU2D8AYMzGI9+sDtDbGDA+8Tgrk5QIWWmu3B/JaBjQCsoBd1tpFANba7MD+L4DHjTF/B24APijiveoCmYdtG2etvfPQC2PM7MI7jTEP4P+bvFFo8x6gHgp2RUSkElOwKyIiEhxHoecGeN5aO6pwAmPMvUc53sP/Dx+KOSyvu6y1Uw/LqwfgLLTJy1G+t621ecaY6cBQ4BKgQxHJ8g9776MyxvQCRgDnHrYrJpCXiIhIpaUxuyIiIsdvKnCDMSYBwBiTZoypBcwBLjTGxAbGxw4udMwW/j8AHX5YXrcZYyIDeTUzxsQf5b3XAXWNMZ0C6RMLjZ99FxgJLLLWHiji2DUEuikfizHmFOANYIS19vDAthlHdocWERGpVNSyKyIicpystdOMMS2AnwNzRuUCV1lrlxhjxgHL8Xf1XVTosJeAz40xtwDfFdr+Lv7uyUsCE1BlAhce5b1dxphLgdeMMbH4W1h7AbnW2l+NMdnA+8Ucu9YYk2SMSbTW5hzjNK8DagATAue401o7wBhTG3+35oxjHC8iIlKhjLW2ossgIiISkowxT+IPQl86Qe9XD5gNNLfW+opJcx+QY619t4TvcR+Qba0dU+KCioiInADqxiwiIhICjDHXAAuAR4sLdAPe4s9jgY/XQeDDUhwvIiJyQqhlV0REREREREKOWnZFREREREQk5CjYFRERERERkZCjYFdERERERERCjoJdERERERERCTkKdkVERERERCTkKNgVERERERGRkPN/6kgBI0gI/M8AAAAASUVORK5CYII=\n", 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "SAMPLING_FREQ_OPENBCI = 250\n", + "NYQUIST_FREQ_OPENBCI = SAMPLING_FREQ_OPENBCI/2\n", + "WOR_N = 4000\n", + "\n", + "CUTOFF_FREQ = 0.5\n", + "\n", + "hz = np.linspace(0, NYQUIST_FREQ_OPENBCI, WOR_N)\n", + "ideal_filter = [1 if CUTOFF_FREQ <= f else 0 for f in hz]\n", + "for filter_name, filter_type in [\n", + " ('butter', butter),\n", + " ('cheby1 with rp=0.2', lambda order, freq, **kwargs: cheby1(order, 0.2, freq, **kwargs)),\n", + " ('cheby1 with rp=0.5', lambda order, freq, **kwargs: cheby1(order, 0.5, freq, **kwargs)),\n", + " ('cheby1 with rp=1.0', lambda order, freq, **kwargs: cheby1(order, 1, freq, **kwargs)),\n", + " ('cheby2', lambda order, freq, **kwargs: cheby2(order, 30, freq - .1, **kwargs))\n", + "]: \n", + " fig, ax = plt.subplots(figsize = (16, 8))\n", + " ax.plot(hz, ideal_filter, label=\"Ideal filter\")\n", + "\n", + " \n", + " for order in range(3,8):\n", + " b, a = filter_type(order, CUTOFF_FREQ, fs=SAMPLING_FREQ_OPENBCI, btype='highpass')\n", + " w, h = freqz(b, a, worN=WOR_N)\n", + " ax.plot(hz, abs(h), label=f\"Order {order}\")\n", + " ax.set_ylim([0, 1.5])\n", + " ax.set_xlim([0, CUTOFF_FREQ + 1.5])\n", + "\n", + " ax.legend()\n", + " ax.set_title(f\"Frequency response of different orders for highpass with {filter_name} at {CUTOFF_FREQ} Hz\")\n", + " ax.set_xlabel(\"Frequency (Hz)\")\n", + " ax.set_ylabel(\"Gain\")\n", + "\n", + " plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can also look the most promising ones, with the mne helper functions, in the DB scale." + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No data specified. Sanity checks related to the length of the signal relative to the filter order will be skipped.\n", + "Setting up high-pass filter at 0.5 Hz\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No data specified. Sanity checks related to the length of the signal relative to the filter order will be skipped.\n", + "Setting up high-pass filter at 0.5 Hz\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "No data specified. Sanity checks related to the length of the signal relative to the filter order will be skipped.\n", + "Setting up high-pass filter at 0.5 Hz\n", + "\n" + ] + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, { "name": "stdout", "output_type": "stream", "text": [ - "Convert to dataset's sampling freq: 100Hz\n" + "No data specified. Sanity checks related to the length of the signal relative to the filter order will be skipped.\n", + "Setting up high-pass filter at 0.5 Hz\n", + "\n" ] }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for title,(b,a) in [\n", + " ('butter of order 5', butter(5, CUTOFF_FREQ, fs=SAMPLING_FREQ_OPENBCI, btype='highpass')),\n", + " ('butter of order 6', butter(6, CUTOFF_FREQ, fs=SAMPLING_FREQ_OPENBCI, btype='highpass')),\n", + " ('butter of order 7', butter(7, CUTOFF_FREQ, fs=SAMPLING_FREQ_OPENBCI, btype='highpass')),\n", + " ('cheby1 of order 6', cheby1(6, 0.2, CUTOFF_FREQ, fs=SAMPLING_FREQ_OPENBCI, btype='highpass')),\n", + "]:\n", + " mne.viz.plot_filter(\n", + " mne.filter.create_filter(\n", + " method='iir',\n", + " data=None,\n", + " l_freq=CUTOFF_FREQ,\n", + " h_freq=None,\n", + " sfreq=SAMPLING_FREQ_OPENBCI,\n", + " iir_params={'b':b, 'a':a}\n", + " ),\n", + " sfreq=SAMPLING_FREQ_OPENBCI,\n", + " freq=[0, CUTOFF_FREQ, CUTOFF_FREQ, 1.5],\n", + " gain=[0, 0, 1, 1],\n", + " fscale='linear',\n", + " show=True,\n", + " title=f'Filter {title}'\n", + " );" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Results\n", + "___\n", + "For the high pass filter, we will choose a Chebyshev type I filter of order 6, as we can see it is both stable, and has a gain of approximately 0dB at 0.5Hz, which is important because the delta band starts at 0.5 Hz.\n", + "\n", + "Furthermore, it is also said here it is well to use a Chebyshev type I filter before downsampling, as we will do once we have our epochs objects. https://dsp.stackexchange.com/questions/55953/chebyshev-i-or-chebyshev-ii" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 34, + "execution_count": 102, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "print(f\"Convert to dataset's sampling freq: {SAMPLING_FREQ}Hz\")\n", - "raw_openbci.resample(SAMPLING_FREQ)\n", - "raw_openbci.filter(l_freq=0.5, h_freq=None)\n", - "raw_openbci.info" + "b,a = cheby1(6, 0.2, CUTOFF_FREQ, fs=SAMPLING_FREQ_OPENBCI, btype='highpass')\n", + "raw_openbci.filter(\n", + " l_freq=CUTOFF_FREQ,\n", + " h_freq=None,\n", + " method='iir',\n", + " iir_params={'b':b, 'a':a},\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### 2. Resampling\n", + "\n", + "One consideration is that we have trained on data that has a different sampling rate than the equipment that we're using. The dataset sampling rate is at 100 Hz, whereas the sampling rate of an OpenBCI Cyton is at 250 Hz.\n", + "\n", + "There are three possible ways to deal with this:\n", + "- Not resample: since we're extracting a fixed number of features for each epoch, the base number of samples in each epoch will not be reflected to the classifier. So, technically, we could just ignore this difference. On the other hand, some feature extraction can result in different outcome based on the sampling rate, which could introduce error in our pipeline.\n", + "- Resample on raw data: As the library puts it, \"downsampling raw data effectively jitters trigger timings\". We can also put as an input the events, that are modified accordingly to the jitter and then returned.\n", + "- Resample on epochs data: As the library puts it, \"performing resampling after epoching can introduce edge artifacts on every epoch\". We could then remove a small bit at the start and at the end of each epochs, but that could potentially take out a lot of information needed in the classification.\n", + "\n", + "As for the performance, resampling can be quite long. Not resampling can take also quite some time, as we would extract data based on 250 samples instead of 100. The time it takes then relies on the algorithms complexity we use for the extraction." + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0 0 0]\n", + " [ 7500 0 0]\n", + " [ 15000 0 0]\n", + " ...\n", + " [9217500 0 0]\n", + " [9225000 0 0]\n", + " [9232500 0 0]] (1232, 3)\n" + ] + } + ], + "source": [ + "events, annot_event_id = mne.events_from_annotations(\n", + " raw_openbci,\n", + " event_id=ELECTROPHYSIOLOGIST_SLEEP_STAGES,\n", + " chunk_duration=EPOCH_DURATION,\n", + " verbose=False)\n", + "print(events, events.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 110, + "metadata": {}, + "outputs": [], + "source": [ + "raw_openbci_resampled_prior_epoching = raw_openbci.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 111, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 7.17 s, sys: 1.56 s, total: 8.74 s\n", + "Wall time: 9.41 s\n" + ] + } + ], + "source": [ + "%%time\n", + "_, modified_events = raw_openbci_resampled_prior_epoching.resample(\n", + " sfreq=SAMPLING_FREQ,\n", + " events=events\n", + ")\n", + "\n", + "# We can see the modified events, when resampling, only changed the events onset to match the change to the sampling rate.\n", + "sum(~(modified_events[:,0] == events[:,0]*SAMPLING_FREQ/SAMPLING_FREQ_OPENBCI)), sum(~(modified_events[:,2] == events[:,2]))" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [], + "source": [ + "del raw_openbci_resampled_prior_epoching" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After testing the first option, which was to pass the events to the resample function, we did not see any difference as getting events from annotations after applying resampling method on the raw objet." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Conclusion\n", + "\n", + "We have chose to apply the resample step on the raw data. Indeed, we rather introduce a small jitter at the start of our epochs, than introduce edge effects in all of our epochs. Considering that the EEG we're looking at are not event related, and we're looking at the general state of the subject, we'd rather have jitters, than potentially introducing errors in the features calculations." + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0 0 0]\n", + " [ 3000 0 0]\n", + " [ 6000 0 0]\n", + " ...\n", + " [3687000 0 0]\n", + " [3690000 0 0]\n", + " [3693000 0 0]] (1232, 3)\n" + ] + } + ], + "source": [ + "raw_openbci.resample(sfreq=SAMPLING_FREQ)\n", + "\n", + "events, annot_event_id = mne.events_from_annotations(\n", + " raw_openbci,\n", + " event_id=ELECTROPHYSIOLOGIST_SLEEP_STAGES,\n", + " chunk_duration=EPOCH_DURATION,\n", + " verbose=False)\n", + "print(events, events.shape)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Wrapping up\n", + "___\n", + "\n", + "We finally use the raw_data to extract the feature matrix." ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 142, "metadata": {}, "outputs": [ { @@ -2555,7 +2921,7 @@ " chan_data = drop_other_channels(raw_data.copy(), channel) \n", " X_file_channel, y_file_channel = convert_to_epochs(\n", " chan_data,\n", - " ELECTROPHYSIOLOGIST_SLEEP_STAGES\n", + " ELECTROPHYSIOLOGIST_SLEEP_STAGES,\n", " )\n", " \n", " X_features = feature_union.transform(X_file_channel)\n", @@ -2615,7 +2981,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 143, "metadata": {}, "outputs": [], "source": [ @@ -2623,8 +2989,8 @@ " np.save(\"./data/X_openbci-age-continuous.npy\", X_openbci)\n", " np.save(\"./data/y_openbci-age-continuous.npy\", y_openbci)\n", "else:\n", - " np.save(\"./data/X_openbci_HP.npy\", X_openbci)\n", - " np.save(\"./data/y_openbci_HP.npy\", y_openbci)" + " np.save(\"./data/X_openbci_HP_PRIOR.npy\", X_openbci)\n", + " np.save(\"./data/y_openbci_HP_PRIOR.npy\", y_openbci)" ] }, { @@ -2636,12 +3002,12 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 144, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -2682,7 +3048,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.2" + "version": "3.6.8" } }, "nbformat": 4, diff --git a/ai/prediction_openbci.ipynb b/ai/prediction_openbci.ipynb index 4ba24c28..db911293 100644 --- a/ai/prediction_openbci.ipynb +++ b/ai/prediction_openbci.ipynb @@ -10,9 +10,18 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 23, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", @@ -39,7 +48,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 24, "metadata": {}, "outputs": [ { @@ -58,8 +67,8 @@ " X = np.load(\"./data/X_openbci-age-continuous.npy\", allow_pickle=True)\n", " y = np.load(\"./data/y_openbci-age-continuous.npy\", allow_pickle=True)\n", "else: \n", - " X = np.load(\"data/X_openbci_HP.npy\", allow_pickle=True)\n", - " y = np.load(\"data/y_openbci_HP.npy\", allow_pickle=True)\n", + " X = np.load(\"data/X_openbci_HP_PRIOR.npy\", allow_pickle=True)\n", + " y = np.load(\"data/y_openbci_HP_PRIOR.npy\", allow_pickle=True)\n", "\n", "X = np.vstack(X)\n", "y = np.hstack(y)\n", @@ -70,7 +79,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 25, "metadata": {}, "outputs": [ { @@ -95,7 +104,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -117,15 +126,15 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 27, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 382 ms, sys: 24.6 ms, total: 407 ms\n", - "Wall time: 93.5 ms\n" + "CPU times: user 428 ms, sys: 34.8 ms, total: 462 ms\n", + "Wall time: 249 ms\n" ] } ], @@ -137,31 +146,31 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 28, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[368 0 1 0 10]\n", - " [ 8 1 23 0 26]\n", - " [ 8 1 304 36 4]\n", - " [ 6 0 20 216 0]\n", - " [ 4 0 33 0 163]]\n", + "[[366 2 1 0 10]\n", + " [ 7 0 20 0 31]\n", + " [ 8 1 314 26 4]\n", + " [ 4 0 24 214 0]\n", + " [ 3 1 30 0 166]]\n", " precision recall f1-score support\n", "\n", - " W 0.93 0.97 0.95 379\n", - " N1 0.50 0.02 0.03 58\n", - " N2 0.80 0.86 0.83 353\n", - " N3 0.86 0.89 0.87 242\n", - " REM 0.80 0.81 0.81 200\n", + " W 0.94 0.97 0.95 379\n", + " N1 0.00 0.00 0.00 58\n", + " N2 0.81 0.89 0.85 353\n", + " N3 0.89 0.88 0.89 242\n", + " REM 0.79 0.83 0.81 200\n", "\n", - " accuracy 0.85 1232\n", - " macro avg 0.78 0.71 0.70 1232\n", - "weighted avg 0.84 0.85 0.83 1232\n", + " accuracy 0.86 1232\n", + " macro avg 0.69 0.71 0.70 1232\n", + "weighted avg 0.82 0.86 0.84 1232\n", "\n", - "Agreement score (Cohen Kappa): 0.8041517082411541\n" + "Agreement score (Cohen Kappa): 0.8129614640807172\n" ] } ], @@ -175,12 +184,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 29, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -386,7 +395,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -397,36 +406,36 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 31, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[[369 0 3 0 7]\n", - " [ 2 1 27 0 28]\n", - " [ 2 2 320 28 1]\n", - " [ 0 0 23 219 0]\n", - " [ 0 0 33 0 167]]\n", + "[[367 0 4 0 8]\n", + " [ 2 0 27 0 29]\n", + " [ 2 2 328 19 2]\n", + " [ 0 0 24 218 0]\n", + " [ 0 0 30 0 170]]\n", " precision recall f1-score support\n", "\n", " W 0.99 0.97 0.98 379\n", - " N1 0.33 0.02 0.03 58\n", - " N2 0.79 0.91 0.84 353\n", - " N3 0.89 0.90 0.90 242\n", - " REM 0.82 0.83 0.83 200\n", + " N1 0.00 0.00 0.00 58\n", + " N2 0.79 0.93 0.86 353\n", + " N3 0.92 0.90 0.91 242\n", + " REM 0.81 0.85 0.83 200\n", "\n", - " accuracy 0.87 1232\n", - " macro avg 0.76 0.73 0.72 1232\n", - "weighted avg 0.85 0.87 0.86 1232\n", + " accuracy 0.88 1232\n", + " macro avg 0.70 0.73 0.72 1232\n", + "weighted avg 0.84 0.88 0.86 1232\n", "\n", - "Agreement score (Cohen Kappa): 0.8303076750565739\n" + "Agreement score (Cohen Kappa): 0.837859800239543\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -457,7 +466,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -473,7 +482,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -481,28 +490,28 @@ "output_type": "stream", "text": [ "[[367 4 7 0 1]\n", - " [ 0 7 21 0 30]\n", - " [ 0 1 305 47 0]\n", - " [ 0 0 18 224 0]\n", - " [ 0 0 30 0 170]]\n", + " [ 0 7 26 0 25]\n", + " [ 0 2 320 31 0]\n", + " [ 0 0 19 223 0]\n", + " [ 0 0 27 0 173]]\n", " precision recall f1-score support\n", "\n", " W 1.00 0.97 0.98 379\n", - " N1 0.58 0.12 0.20 58\n", - " N2 0.80 0.86 0.83 353\n", - " N3 0.83 0.93 0.87 242\n", - " REM 0.85 0.85 0.85 200\n", + " N1 0.54 0.12 0.20 58\n", + " N2 0.80 0.91 0.85 353\n", + " N3 0.88 0.92 0.90 242\n", + " REM 0.87 0.86 0.87 200\n", "\n", - " accuracy 0.87 1232\n", - " macro avg 0.81 0.75 0.75 1232\n", - "weighted avg 0.86 0.87 0.86 1232\n", + " accuracy 0.88 1232\n", + " macro avg 0.82 0.76 0.76 1232\n", + "weighted avg 0.88 0.88 0.87 1232\n", "\n", - "Agreement score (Cohen Kappa): 0.8278301223456617\n" + "Agreement score (Cohen Kappa): 0.845981289980649\n" ] }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -547,7 +556,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.2" + "version": "3.6.8" } }, "nbformat": 4, From 99acbe71aa7d737ddc592b9e9617aa9f9345db36 Mon Sep 17 00:00:00 2001 From: Claudia Onorato Date: Thu, 22 Oct 2020 00:22:50 -0400 Subject: [PATCH 18/25] renamed utils to preprocessing --- backend/classification/features/extraction.py | 2 +- backend/classification/features/{utils.py => preprocessing.py} | 0 2 files changed, 1 insertion(+), 1 deletion(-) rename backend/classification/features/{utils.py => preprocessing.py} (100%) diff --git a/backend/classification/features/extraction.py b/backend/classification/features/extraction.py index 3fe7bd73..1a73a5f0 100644 --- a/backend/classification/features/extraction.py +++ b/backend/classification/features/extraction.py @@ -8,7 +8,7 @@ ) from classification.features.constants import DATASET_HIGH_PASS_FREQ from classification.features.pipeline import get_feature_union -from classification.features.utils import ( +from classification.features.preprocessing import ( drop_other_channels, crop_raw_data, convert_to_epochs, diff --git a/backend/classification/features/utils.py b/backend/classification/features/preprocessing.py similarity index 100% rename from backend/classification/features/utils.py rename to backend/classification/features/preprocessing.py From 1afa17074ad9b02699dc969527e944fe4f5f3e3c Mon Sep 17 00:00:00 2001 From: Claudia Onorato Date: Thu, 22 Oct 2020 00:59:10 -0400 Subject: [PATCH 19/25] added HP filter & refactored to preprocessing module --- backend/classification/features/constants.py | 2 + backend/classification/features/extraction.py | 21 ++---- .../classification/features/preprocessing.py | 67 +++++++++++++++++-- 3 files changed, 67 insertions(+), 23 deletions(-) diff --git a/backend/classification/features/constants.py b/backend/classification/features/constants.py index b258c65e..d4510847 100644 --- a/backend/classification/features/constants.py +++ b/backend/classification/features/constants.py @@ -29,3 +29,5 @@ } DATASET_HIGH_PASS_FREQ = 0.5 +HIGH_PASS_FILTER_ORDER = 6 +HIGH_PASS_MAX_RIPPLE_DB = 0.2 diff --git a/backend/classification/features/extraction.py b/backend/classification/features/extraction.py index 1a73a5f0..9e92c053 100644 --- a/backend/classification/features/extraction.py +++ b/backend/classification/features/extraction.py @@ -4,15 +4,9 @@ from classification.config.constants import ( EEG_CHANNELS, AGE_FEATURE_BINS, - DATASET_SAMPLE_RATE, ) -from classification.features.constants import DATASET_HIGH_PASS_FREQ from classification.features.pipeline import get_feature_union -from classification.features.preprocessing import ( - drop_other_channels, - crop_raw_data, - convert_to_epochs, -) +from classification.features.preprocessing import preprocess def get_eeg_features(raw_data, in_bed_seconds, out_of_bed_seconds): @@ -30,19 +24,12 @@ def get_eeg_features(raw_data, in_bed_seconds, out_of_bed_seconds): Array of size (nb_epochs, nb_continuous_features) """ features_file = [] + feature_union = get_feature_union() for channel in EEG_CHANNELS: - chan_data = drop_other_channels(raw_data.copy(), channel) - chan_data = crop_raw_data(chan_data, in_bed_seconds, out_of_bed_seconds) - chan_data = chan_data.resample(DATASET_SAMPLE_RATE) - chan_data = chan_data.filter(l_freq=DATASET_HIGH_PASS_FREQ, h_freq=None) - - X_file_channel = convert_to_epochs(chan_data, in_bed_seconds) - - feature_union = get_feature_union() - print('Started feature ext on epochs: ', X_file_channel) + chan_data = preprocess(raw_data, channel, in_bed_seconds, out_of_bed_seconds) - X_features = feature_union.transform(X_file_channel) + X_features = feature_union.transform(chan_data) features_file.append(X_features) print( diff --git a/backend/classification/features/preprocessing.py b/backend/classification/features/preprocessing.py index cf52d9e1..72790d2a 100644 --- a/backend/classification/features/preprocessing.py +++ b/backend/classification/features/preprocessing.py @@ -1,20 +1,52 @@ import mne import numpy as np +from scipy.signal import cheby1 -from classification.features.constants import MAX_TIME, EPOCH_DURATION, DATASET_SAMPLE_RATE +from classification.config.constants import ( + # TODO: adapt pipeline to adjust whether data is being sent from ganglion or cyton + OPENBCI_CYTON_SAMPLE_RATE, +) +from classification.features.constants import ( + MAX_TIME, + EPOCH_DURATION, + DATASET_SAMPLE_RATE, + DATASET_HIGH_PASS_FREQ, + HIGH_PASS_FILTER_ORDER, + HIGH_PASS_MAX_RIPPLE_DB, +) -def drop_other_channels(raw_data, channel_to_keep): - """ - returns: mne.Raw with the two EEG channels +def preprocess(raw_data, channel, bed_seconds, out_of_bed_seconds): + """Returns preprocessed epochs of the specified channel + Input + ------- + raw_data: instance of mne.Raw + channel: channel to preprocess + bed_seconds: number of seconds between start of recording & moment at + which the subjet went to bed (in seconds) + out_of_bed_seconds: number of seconds between start of recording & moment + at which the subjet got out of bed (in seconds) """ + raw_data = raw_data.copy() + + raw_data = _drop_other_channels(raw_data, channel) + raw_data = _crop_raw_data(raw_data, bed_seconds, out_of_bed_seconds) + raw_data = _apply_high_pass_filter(raw_data) + raw_data = raw_data.resample(DATASET_SAMPLE_RATE) + raw_data = _convert_to_epochs(raw_data, bed_seconds) + + return raw_data + + +def _drop_other_channels(raw_data, channel_to_keep): + """returns mne.Raw with only the channel to keep""" raw_data.drop_channels( [ch for ch in raw_data.info['ch_names'] if ch != channel_to_keep]) return raw_data -def crop_raw_data( +def _crop_raw_data( raw_data, bed_seconds, out_of_bed_seconds @@ -37,7 +69,30 @@ def crop_raw_data( return raw_data -def convert_to_epochs(raw_data, in_bed_time): +def _apply_high_pass_filter(raw_data): + """Returns high passed raw signal + Input + ------- + raw_data: instance of mne.Raw + """ + b, a = cheby1( + HIGH_PASS_FILTER_ORDER, + HIGH_PASS_MAX_RIPPLE_DB, + DATASET_HIGH_PASS_FREQ, + fs=OPENBCI_CYTON_SAMPLE_RATE, + btype='highpass', + ) + raw_data.filter( + l_freq=DATASET_HIGH_PASS_FREQ, + h_freq=None, + method='iir', + iir_params={'b': b, 'a': a}, + ) + + return raw_data + + +def _convert_to_epochs(raw_data, in_bed_time): """Converts the raw object to an Epochs Input: - raw_data: instance of mne.RawArray, that has been previously cropped to tmin=in_bed_time From 778b6bce5f1884f4731d3970a76b4c625ea742b2 Mon Sep 17 00:00:00 2001 From: Claudia Onorato Date: Thu, 22 Oct 2020 10:07:47 -0400 Subject: [PATCH 20/25] switched to fixe length epochs --- backend/classification/features/constants.py | 1 - .../classification/features/preprocessing.py | 37 +++---------------- 2 files changed, 6 insertions(+), 32 deletions(-) diff --git a/backend/classification/features/constants.py b/backend/classification/features/constants.py index d4510847..4dd21316 100644 --- a/backend/classification/features/constants.py +++ b/backend/classification/features/constants.py @@ -4,7 +4,6 @@ ) NYQUIST_FREQ = DATASET_SAMPLE_RATE / 2 -MAX_TIME = EPOCH_DURATION - 1. / DATASET_SAMPLE_RATE DELTA = "delta" THETA = "theta" diff --git a/backend/classification/features/preprocessing.py b/backend/classification/features/preprocessing.py index 72790d2a..d5843cd8 100644 --- a/backend/classification/features/preprocessing.py +++ b/backend/classification/features/preprocessing.py @@ -1,5 +1,4 @@ import mne -import numpy as np from scipy.signal import cheby1 from classification.config.constants import ( @@ -7,7 +6,6 @@ OPENBCI_CYTON_SAMPLE_RATE, ) from classification.features.constants import ( - MAX_TIME, EPOCH_DURATION, DATASET_SAMPLE_RATE, DATASET_HIGH_PASS_FREQ, @@ -33,7 +31,7 @@ def preprocess(raw_data, channel, bed_seconds, out_of_bed_seconds): raw_data = _crop_raw_data(raw_data, bed_seconds, out_of_bed_seconds) raw_data = _apply_high_pass_filter(raw_data) raw_data = raw_data.resample(DATASET_SAMPLE_RATE) - raw_data = _convert_to_epochs(raw_data, bed_seconds) + raw_data = _convert_to_epochs(raw_data) return raw_data @@ -92,38 +90,15 @@ def _apply_high_pass_filter(raw_data): return raw_data -def _convert_to_epochs(raw_data, in_bed_time): +def _convert_to_epochs(raw_data): """Converts the raw object to an Epochs Input: - raw_data: instance of mne.RawArray, that has been previously cropped to tmin=in_bed_time - - in_bed_time: number of seconds between start of recording & moment at - which the subjet went to bed (in seconds) returns: mne.Epochs """ - def get_events(): - # We must provide an event list to create an Epochs. We create a mock one, whereas - # it has, as its first column, the sample index, and at its third column, the event id. - # The sample index must correspond to the first sample index for each epoch (30s, not overlapping) - # Because we will not use the event id (we will predict it), we will keep it at 0. - # See here for more info: https://mne.tools/stable/auto_tutorials/intro/plot_20_events_from_raw.html - # NOTE: Events indexes are considered in the time frame as before we've cropped - # the signal. We then have to add to all values the bedtime sample index offset. - # Otherwise, all samples prior the bedtime will be dropped, when creating the Epochs! - sample_indexes = np.arange(raw_data.n_times) + (in_bed_time * DATASET_SAMPLE_RATE) - nb_epochs = raw_data.n_times // (DATASET_SAMPLE_RATE * EPOCH_DURATION) - events = np.zeros((nb_epochs, 3)) - events[:, 0] = sample_indexes[::(DATASET_SAMPLE_RATE * EPOCH_DURATION)] - events = events.astype('int') - - print(f'Will create {nb_epochs} epochs of duration {EPOCH_DURATION} after resampling to {DATASET_SAMPLE_RATE}.') - - return events - - return mne.Epochs( + return mne.make_fixed_length_epochs( raw=raw_data, - events=get_events(), - tmin=0., - tmax=MAX_TIME, + duration=EPOCH_DURATION, preload=True, - baseline=None, - verbose=False) + verbose=None, + ) From 73463386897426c78b7c263248560fc550d4ab7b Mon Sep 17 00:00:00 2001 From: Claudia Onorato Date: Thu, 22 Oct 2020 10:16:52 -0400 Subject: [PATCH 21/25] added underscores --- .../features/pipeline/frequency_domain.py | 10 +++++----- .../features/pipeline/time_domain.py | 18 +++++++++--------- .../features/pipeline/time_subband.py | 8 ++++---- backend/classification/file_loading.py | 12 ++++++------ 4 files changed, 24 insertions(+), 24 deletions(-) diff --git a/backend/classification/features/pipeline/frequency_domain.py b/backend/classification/features/pipeline/frequency_domain.py index b336a438..b0dfba9e 100644 --- a/backend/classification/features/pipeline/frequency_domain.py +++ b/backend/classification/features/pipeline/frequency_domain.py @@ -10,7 +10,7 @@ ) -def get_mean_psds(psds_with_freqs, are_relative=False): +def _get_mean_psds(psds_with_freqs, are_relative=False): """EEG power band feature extraction. Input ------- @@ -40,7 +40,7 @@ def get_mean_psds(psds_with_freqs, are_relative=False): return np.concatenate(X, axis=1) -def get_sefd_on_all_epochs(psds_with_freqs): +def _get_sefd_on_all_epochs(psds_with_freqs): """SEFd on all epochs """ SUBBAND_FREQ_SEFD = [8., 16.] @@ -93,15 +93,15 @@ def get_frequency_domain_pipeline(): get_psds_from_epochs_transformer = FunctionTransformer( get_psds_from_epochs, validate=False) absolute_mean_psds_transformer = FunctionTransformer( - get_mean_psds, validate=False) + _get_mean_psds, validate=False) relative_mean_psds_transformer = FunctionTransformer( - lambda psds_with_freq: get_mean_psds( + lambda psds_with_freq: _get_mean_psds( psds_with_freq, are_relative=True ), validate=False) sefd_transformer = FunctionTransformer( - get_sefd_on_all_epochs, validate=False) + _get_sefd_on_all_epochs, validate=False) return Pipeline([ ('get_psds_from_epochs', get_psds_from_epochs_transformer), diff --git a/backend/classification/features/pipeline/time_domain.py b/backend/classification/features/pipeline/time_domain.py index bd48d8e8..1685fdee 100644 --- a/backend/classification/features/pipeline/time_domain.py +++ b/backend/classification/features/pipeline/time_domain.py @@ -11,7 +11,7 @@ ) -def get_zero_crossing_rate(signal): +def _get_zero_crossing_rate(signal): """ Multiplies signal by itself shifted by one. If the signal crosses the horizontal axis, @@ -22,12 +22,12 @@ def get_zero_crossing_rate(signal): return ((signal[:-1] * signal[1:]) < 0).sum() -def get_mean_crossing_rate(signal): - return get_zero_crossing_rate(signal - np.mean(signal)) +def _get_mean_crossing_rate(signal): + return _get_zero_crossing_rate(signal - np.mean(signal)) -def hjorth(X): - """ Compute Hjorth mobility and complexity of a time series from either two +def _hjorth(X): + """ Compute _hjorth mobility and complexity of a time series from either two [source: https://github.com/forrestbao/pyeeg] cases below: 1. X, the time series of type list (default) @@ -66,7 +66,7 @@ def hjorth(X): M4 += (D[i] - D[i - 1]) ** 2 M4 = M4 / n - # Hjorth Mobility and Complexity + # hjorth Mobility and Complexity mobility = np.sqrt(M2 / TP) complexity = np.sqrt( float(M4) * TP / M2 / M2 @@ -86,9 +86,9 @@ def get_time_domain_pipeline(): kurtosis_transformer = FunctionTransformer( get_transformer(kurtosis), validate=True) mean_crossing_rate_transformer = FunctionTransformer( - get_transformer(get_mean_crossing_rate), validate=True) + get_transformer(_get_mean_crossing_rate), validate=True) hjorth_transformer = FunctionTransformer( - get_transformer_list(hjorth), validate=True) + get_transformer_list(_hjorth), validate=True) return Pipeline([ ('epochs_to_data', get_data_from_epochs_transformer), @@ -98,6 +98,6 @@ def get_time_domain_pipeline(): ('skew', skew_transformer), ('kurtosis', kurtosis_transformer), ('mean-crossing-rate', mean_crossing_rate_transformer), - ('hjorth', hjorth_transformer) + ('_hjorth', hjorth_transformer) ], n_jobs=1)) ]) diff --git a/backend/classification/features/pipeline/time_subband.py b/backend/classification/features/pipeline/time_subband.py index 60b664f0..d4db68d3 100644 --- a/backend/classification/features/pipeline/time_subband.py +++ b/backend/classification/features/pipeline/time_subband.py @@ -15,14 +15,14 @@ ) -def get_signal_mean_energy(signal): +def _get_signal_mean_energy(signal): """ signal: array of (nb_sample_per_epoch,) """ return np.sum(signal**2) * 1e6 -def get_pipeline_per_subband(subband_name: str): +def _get_pipeline_per_subband(subband_name: str): """ Constructs a pipeline to extract the specified subband related features. Output: @@ -53,7 +53,7 @@ def filter_epochs_in_specified_subband(epochs): ('filter', FunctionTransformer(filter_epochs_in_specified_subband, validate=False)), ('get-values', FunctionTransformer(get_data_from_epochs, validate=False)), ('mean-energy', FunctionTransformer( - get_transformer(get_signal_mean_energy), validate=True + get_transformer(_get_signal_mean_energy), validate=True )), ]) @@ -61,5 +61,5 @@ def filter_epochs_in_specified_subband(epochs): def get_subband_feature_union(): return FeatureUnion([( f"{band_name}-filter", - get_pipeline_per_subband(band_name) + _get_pipeline_per_subband(band_name) ) for band_name in FREQ_BANDS_ORDERS.keys()], n_jobs=1) diff --git a/backend/classification/file_loading.py b/backend/classification/file_loading.py index 3cfc240d..670ad338 100644 --- a/backend/classification/file_loading.py +++ b/backend/classification/file_loading.py @@ -46,7 +46,7 @@ def get_raw_array(file): line_splitted = line.split(',') if len(line_splitted) >= CYTON_TOTAL_NB_CHANNELS: - eeg_raw.append(get_decimals_from_hexadecimal_strings(line_splitted)) + eeg_raw.append(_get_decimals_from_hexadecimal_strings(line_splitted)) eeg_raw = SCALE_uV_PER_COUNT * np.array(eeg_raw, dtype='object') @@ -68,7 +68,7 @@ def get_raw_array(file): return raw_object -def get_decimals_from_hexadecimal_strings(lines): +def _get_decimals_from_hexadecimal_strings(lines): """Converts the array of hexadecimal strings to an array of decimal values of the EEG channels Input: - lines: splitted array of two complement hexadecimal @@ -76,22 +76,22 @@ def get_decimals_from_hexadecimal_strings(lines): - array of decimal values for each EEG channel of interest """ return np.array([ - convert_hexadecimal_to_signed_decimal(hex_value) + _convert_hexadecimal_to_signed_decimal(hex_value) for hex_value in lines[FILE_COLUMN_OFFSET:FILE_COLUMN_OFFSET + len(EEG_CHANNELS)] ]) -def convert_hexadecimal_to_signed_decimal(hex_value): +def _convert_hexadecimal_to_signed_decimal(hex_value): """Converts the hexadecimal value encoded on OpenBCI Cyton SD card to signed decimal Input: - hex_value: signed hexadecimal value Returns: - decimal value """ - return get_twos_complement(hex_value) if len(hex_value) % 2 == 0 else 0 + return _get_twos_complement(hex_value) if len(hex_value) % 2 == 0 else 0 -def get_twos_complement(hexstr): +def _get_twos_complement(hexstr): """Converts a two complement hexadecimal value in a string to a signed float Input: - hex_value: signed hexadecimal value From 804f157b71e683e7a11a2df009c235e20f1735fe Mon Sep 17 00:00:00 2001 From: Claudia Onorato Date: Thu, 22 Oct 2020 10:27:46 -0400 Subject: [PATCH 22/25] converted fct name --- backend/classification/features/__init__.py | 4 ++-- backend/classification/features/extraction.py | 2 +- backend/classification/features/preprocessing.py | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/backend/classification/features/__init__.py b/backend/classification/features/__init__.py index 51a0623d..dc66d99b 100644 --- a/backend/classification/features/__init__.py +++ b/backend/classification/features/__init__.py @@ -2,7 +2,7 @@ from classification.features.extraction import ( get_eeg_features, - get_categorical_features, + get_non_eeg_features, ) @@ -24,6 +24,6 @@ def get_features(signal, info): - features X in a vector of (nb_epochs, nb_features) """ X_eeg = get_eeg_features(signal, info['in_bed_seconds'], info['out_of_bed_seconds']) - X_categorical = get_categorical_features(info['age'], info['sex'], X_eeg.shape[0]) + X_categorical = get_non_eeg_features(info['age'], info['sex'], X_eeg.shape[0]) return np.append(X_categorical, X_eeg, axis=1) diff --git a/backend/classification/features/extraction.py b/backend/classification/features/extraction.py index 9e92c053..494a9b00 100644 --- a/backend/classification/features/extraction.py +++ b/backend/classification/features/extraction.py @@ -40,7 +40,7 @@ def get_eeg_features(raw_data, in_bed_seconds, out_of_bed_seconds): return np.hstack(tuple(features_file)) -def get_categorical_features(age, sex, nb_epochs): +def get_non_eeg_features(age, sex, nb_epochs): """Returns the categorical feature matrix Input ------- diff --git a/backend/classification/features/preprocessing.py b/backend/classification/features/preprocessing.py index d5843cd8..24f9e6c1 100644 --- a/backend/classification/features/preprocessing.py +++ b/backend/classification/features/preprocessing.py @@ -100,5 +100,5 @@ def _convert_to_epochs(raw_data): raw=raw_data, duration=EPOCH_DURATION, preload=True, - verbose=None, + verbose=False, ) From 94c828422c5af4c0d5454b509625613b6aae57ac Mon Sep 17 00:00:00 2001 From: Claudia Onorato Date: Thu, 22 Oct 2020 10:33:49 -0400 Subject: [PATCH 23/25] deleted min between out of bed seconds and file max, because validation has already checked that and returned error otherwise --- backend/classification/features/preprocessing.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/backend/classification/features/preprocessing.py b/backend/classification/features/preprocessing.py index 24f9e6c1..4a188184 100644 --- a/backend/classification/features/preprocessing.py +++ b/backend/classification/features/preprocessing.py @@ -60,7 +60,7 @@ def _crop_raw_data( """ raw_data.crop( tmin=bed_seconds, - tmax=min(out_of_bed_seconds, raw_data.times[-1]), + tmax=out_of_bed_seconds, include_tmax=False, ) From 4e1b2c1e9a9a0bfb896b56b046efba29812740d9 Mon Sep 17 00:00:00 2001 From: Claudia Onorato Date: Thu, 22 Oct 2020 10:35:21 -0400 Subject: [PATCH 24/25] renamed file content var --- backend/classification/file_loading.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/backend/classification/file_loading.py b/backend/classification/file_loading.py index 670ad338..9dc44683 100644 --- a/backend/classification/file_loading.py +++ b/backend/classification/file_loading.py @@ -39,10 +39,10 @@ def get_raw_array(file): Returns: - mne.RawArray of the two EEG channels of interest """ - csv_file_content = StringIO(file.stream.read().decode("UTF8")) + file_content = StringIO(file.stream.read().decode("UTF8")) eeg_raw = [] - for line in csv_file_content.readlines(): + for line in file_content.readlines(): line_splitted = line.split(',') if len(line_splitted) >= CYTON_TOTAL_NB_CHANNELS: From 307cd95379c07c633d52227237b0014c046f50a8 Mon Sep 17 00:00:00 2001 From: Claudia Onorato Date: Thu, 22 Oct 2020 10:41:49 -0400 Subject: [PATCH 25/25] gets srate from raw object (preprocessing independant of acquisition card) --- backend/classification/features/preprocessing.py | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/backend/classification/features/preprocessing.py b/backend/classification/features/preprocessing.py index 4a188184..321f02a8 100644 --- a/backend/classification/features/preprocessing.py +++ b/backend/classification/features/preprocessing.py @@ -1,10 +1,6 @@ import mne from scipy.signal import cheby1 -from classification.config.constants import ( - # TODO: adapt pipeline to adjust whether data is being sent from ganglion or cyton - OPENBCI_CYTON_SAMPLE_RATE, -) from classification.features.constants import ( EPOCH_DURATION, DATASET_SAMPLE_RATE, @@ -73,11 +69,12 @@ def _apply_high_pass_filter(raw_data): ------- raw_data: instance of mne.Raw """ + sampling_rate = raw_data.info['sfreq'] b, a = cheby1( HIGH_PASS_FILTER_ORDER, HIGH_PASS_MAX_RIPPLE_DB, DATASET_HIGH_PASS_FREQ, - fs=OPENBCI_CYTON_SAMPLE_RATE, + fs=sampling_rate, btype='highpass', ) raw_data.filter(