ms762wu/EMG_Biometrics_2021
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feat: make func to store mfcc samples in dl_handler

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Skudalen 2021-07-07 10:46:31 +02:00
parent 31cf8a110d
commit 39b4841a09
1 changed files with 36 additions and 21 deletions
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57
Handle_emg_data.py
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@ -503,12 +503,19 @@ class DL_data_handler:
def __init__(self, csv_handler:CSV_handler) -> None:
self.csv_handler = csv_handler
# Should med 4 sessions * split nr of samples per person. Each sample is structured like this: [sample_df, samplerate]
self.samples_per_subject = {1: [],
2: [],
3: [],
4: [],
5: []
}
self.reg_samples_per_subject = {1: [],
2: [],
3: [],
4: [],
5: []
}
# Should med 4 sessions * (~150, 208) of mfcc samples per person. One DataFrame per subject
self.mfcc_samples_per_subject = {1: None,
2: None,
3: None,
4: None,
5: None
}
def get_samples_dict(self):
return self.samples_per_subject
@ -555,6 +562,14 @@ class DL_data_handler:
return tot_session_df
def reshape_session_df_to_signal(self, df:DataFrame):
main_df = df[['timestamp', 1]].rename(columns={1: 'emg'})
for i in range(2, 17):
adding_df = df[['timestamp', i]].rename(columns={i: 'emg'})
main_df = pd.concat([main_df, adding_df], ignore_index=True)
samplerate = get_samplerate(main_df)
return main_df, samplerate
def store_samples(self, split_nr) -> None:
for subject_nr in range(5):
subj_samples = []
@ -572,9 +587,10 @@ class DL_data_handler:
df_finished, samplerate = self.reshape_session_df_to_signal(df)
subj_samples.append([df_finished, samplerate])
self.samples_per_subject[subject_nr+1] = subj_samples
self.reg_samples_per_subject[subject_nr+1] = subj_samples
def make_mfcc_df_from_session_df(self, session_df):
def make_mfcc_df_from_session_df(self, session_df) -> DataFrame:
session_df.rename(columns={0:'timestamp'}, inplace=True)
samplerate = get_samplerate(session_df)
#attach_func = lambda list_1, list_2: list_1.tolist().extend(list_2.tolist())
@ -596,26 +612,25 @@ class DL_data_handler:
return result_df
def store_mfcc_samples(self):
def store_mfcc_samples(self) -> None:
for subject_nr in range(5):
subj_samples = []
for session_nr in range(4):
list_of_emg = self.get_emg_list(subject_nr+1, session_nr+1)
tot_session_df = self.make_subj_sample(list_of_emg)
# TESTING FOR NAN
if tot_session_df.isnull().values.any():
print('NaN in: subject', subject_nr+1, 'session:', session_nr+1, 'where? HERE')
pass
mfcc_df_i = self.make_mfcc_df_from_session_df(tot_session_df)
subj_samples.append(mfcc_df_i)
result_df = pd.concat(subj_samples, axis=1, ignore_index=True)
self.mfcc_samples_per_subject[subject_nr+1] = result_df
def reshape_session_df_to_signal(self, df:DataFrame):
main_df = df[['timestamp', 1]].rename(columns={1: 'emg'})
for i in range(2, 17):
adding_df = df[['timestamp', i]].rename(columns={i: 'emg'})
main_df = pd.concat([main_df, adding_df], ignore_index=True)
samplerate = get_samplerate(main_df)
return main_df, samplerate
def save_mfcc(self, json_path=JSON_PATH):
def save_json_ed1(self, json_path=JSON_PATH):
# dictionary to store mapping, labels, and MFCCs
data = {