doc: fix sufficient comments in Present_data.py
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Skudalen
parent
503742e231
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588fa7d282
@ -22,10 +22,15 @@ def plot_df(df:DataFrame):
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plt.show()
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# Plots ndarrays after transformations
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# Input: X-values and Y-values
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# Output: None --> Plot
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def plot_array(N, y):
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plt.plot(N, np.abs(y))
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plt.show()
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# Plots two subplots with two dataframes in order to compare them
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# Input: Old dataframe, old title, new dataframe, new title
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# Output: None --> Plot
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def plot_compare_two_df(df_old, old_name, df_new, new_name):
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x = get_xory_from_df('x', df_old)
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y1 = get_xory_from_df('y', df_old)
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@ -38,6 +43,9 @@ def plot_compare_two_df(df_old, old_name, df_new, new_name):
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axis[1].set_title(new_name)
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plt.show()
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# Plots one set of MFCC data
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# Input: 2d array of MFCC data(frame, coefficients), data_label for description
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# Output: None -> Plot
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def plot_mfcc(mfcc_data, data_label:str):
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fig, ax = plt.subplots()
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mfcc_data= np.swapaxes(mfcc_data, 0 ,1)
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@ -51,6 +59,9 @@ def plot_mfcc(mfcc_data, data_label:str):
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ax.set_xlabel('Time(s)')
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plt.show()
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# Plots three sets of MFCC data
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# Input: 3 x (2d array of MFCC data(frame, coefficients)), 3 x (data_label for description)
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# Output: None -> Plot
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def plot_3_mfcc(mfcc_data1, data_label1:str, mfcc_data2, data_label2:str, mfcc_data3, data_label3:str):
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fig, axes = plt.subplots(nrows=3)
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@ -71,6 +82,9 @@ def plot_3_mfcc(mfcc_data1, data_label1:str, mfcc_data2, data_label2:str, mfcc_d
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plt.show()
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# Plots eight subplots with all EMG data from Subject 1 and Session 1
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# Input: list of 8 arrays of EMG data(datapoints), list of 8 data_labels for description
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# Output: None -> Plot
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def plot_all_emg_mfcc(data_list:list, label_list:list):
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fig, axes = plt.subplots(nrows=4, ncols=2)
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plt.subplots_adjust(hspace=1.4, wspace=0.4)
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@ -108,13 +122,15 @@ def pretty(dict):
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print('\t\t\t Type:', type(value[0][0]), type(value[0][1]))
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print('\t\t\t Sample:', value[0][0], value[0][1])
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# DATA FUNCTIONS: --------------------------------------------------------------:
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# The CSV_handler takes in data_type, but only for visuals.
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# The CSV_handler takes in data_type(soft, hard, softPP, hardPP)
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# E.g. handler = CSV_handler('soft')
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# Takes in handler and detailes to denoise.
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# Returns arrays and df
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# Denoices one set of EMG data
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# Input: CSV_handler and detailes for ID
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# Output: DataFrame(df)
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def denoice_dataset(handler:CSV_handler, subject_nr, which_arm, round, emg_nr):
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df = handler.get_df_from_data_dict(subject_nr, which_arm, round, emg_nr)
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@ -126,6 +142,9 @@ def denoice_dataset(handler:CSV_handler, subject_nr, which_arm, round, emg_nr):
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df_new = make_df_from_xandy(N, y_values, emg_nr)
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return df_new
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# Quick debug function for NN_handler dict
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# Input: NN_hanlder dict, nr of samples per person
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# Output: None -> prints if NaN
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def test_for_NaN(dict, samples_per_person):
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for key, value in dict.items():
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for i in range(samples_per_person):
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@ -133,10 +152,12 @@ def test_for_NaN(dict, samples_per_person):
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#print(df)
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print(df.isnull())
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# CASE FUNTIONS ----------------------------------------------------------------:
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# Takes in a df and compares the FFT and the wavelet denoising of the FFT
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# Returns None. Plots the two
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# Input: timestamp/EMG Dataframe
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# Output: None --> Plot
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def compare_with_wavelet_filter(data_frame):
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N_trans, cA, cD = wavelet_db4(data_frame)
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data_frame_freq = make_df_from_xandy(N_trans, cA, 1)
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@ -146,7 +167,7 @@ def compare_with_wavelet_filter(data_frame):
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plot_compare_two_df(data_frame_freq, 'Original data', data_frame_freq_filt, 'Analyzed data')
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# Loads three preset emg_1 datasets(subj1:session1, subj1:session2, subj2:session1), calculates mfcc for each and plots them.
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# Loads three preset EMG nr 1 datasets(subj1:session1, subj1:session2, subj2:session1), calculates mfcc for each and plots them.
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# Input: CSV_handler
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# Output: None --> Plot
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def mfcc_3_plots_1_1_2(csv_handler:CSV_handler):
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@ -165,7 +186,7 @@ def mfcc_3_plots_1_1_2(csv_handler:CSV_handler):
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plot_3_mfcc(mfcc_feat1, label_1, mfcc_feat2, label_2, mfcc_feat3, label_3)
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# Loads three preset emg_1 datasets(subj3:session1, subj3:session2, subj4:session1), calculates mfcc for each and plots them.
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# Loads three preset EMG nr 1 datasets(subj3:session1, subj3:session2, subj4:session1), calculates mfcc for each and plots them.
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# Input: CSV_handler
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# Output: None --> Plot
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def mfcc_3_plots_3_3_4(csv_handler:CSV_handler):
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@ -184,6 +205,9 @@ def mfcc_3_plots_3_3_4(csv_handler:CSV_handler):
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plot_3_mfcc(mfcc_feat1, label_1, mfcc_feat2, label_2, mfcc_feat3, label_3)
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# Loads preset emg 1-8 datasets(subj1 and session1) and calculates mfcc for each and plots them.
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# Input: CSV_handler
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# Output: None --> Plot
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def mfcc_all_emg_plots(csv_handler:CSV_handler):
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df1, samplerate1 = csv_handler.get_data( 1, 'left', 1, 1)
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df2, samplerate2 = csv_handler.get_data( 1, 'left', 1, 2)
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@ -216,7 +240,7 @@ def mfcc_all_emg_plots(csv_handler:CSV_handler):
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# MAIN: ------------------------------------------------------------------------:
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def main():
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if __name__ == "__main__":
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csv_handler = CSV_handler()
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csv_handler.load_data('soft')
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@ -224,13 +248,5 @@ def main():
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nn_handler.store_mfcc_samples()
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nn_handler.save_json_mfcc()
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'''
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dict = dl_data_handler.get_mfcc_samples_dict()
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subject1_df = dict.get(5)
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print(subject1_df)
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print(len(subject1_df.loc[1]))
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'''
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main()
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@ -37,10 +37,10 @@ Scripts to handle CSV files composed by 2 * 8 EMG sensors(left & right) devided
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2. Place the data files in the working directory
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3. (For now) Add the session filenames in the desired load_data() function
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4. Assuming NN analysis:
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1. Create `CSV_handler` object
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1. Create a `CSV_handler` object
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2. Load data with `load_data(CSV_handler, <datatype>)`
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3. Create `NN_handler` object with <CSV_handler> as input
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3. Create `NN_handler` object with `CSV_handler` as input
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4. Load MFCC data into the `NN_handler` with `store_mfcc_samples()`
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5. Run `save_json_mfcc()`
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6. Run `Neural_Network_Analysis.py`
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5. Run `save_json_mfcc()` to save samples in json
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6. Run `Neural_Network_Analysis.py` with desired config
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