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| #!/usr/bin/env python | |
| # coding:utf-8 | |
| """ | |
| name : dataset.py, | |
| version : 1.0.0, | |
| url : https://github.com/abubakar-sani/RFF, | |
| license : MIT License, | |
| copyright : Copyright 2021 by Abubakar Sani Ali, Khalifa University, | |
| author : Abubakar Sani Ali, | |
| email : [email protected], | |
| date : 9/5/2022, | |
| description : Dataset module that returns the processed data, | |
| """ | |
| import math | |
| import os | |
| import numpy as np | |
| import pandas as pd | |
| import json | |
| base_dir = os.path.abspath('') | |
| raw_dir = f'{base_dir}/datasets/raw/spectral_scans_QC9880_ht20_background' | |
| processed_dir = f'{base_dir}/datasets/processed/spectral_scans_QC9880_ht20_background' | |
| def load_spectral_scans(jammer, jammed_freq, jammer_dist, jamming_power, channels, n_features, n_scans=10): | |
| if jammer == 'combined': | |
| scenario = f'samples_chamber_{jammer_dist}cm_{jamming_power}dBm' | |
| elif jammer == 'none': | |
| interference = np.random.choice(['high', 'medium', 'low'], p=[0.4, 0.4, 0.2]) | |
| if interference == 'high': | |
| scenario = f'samples_office_None' | |
| elif interference == 'low': | |
| scenario = f'samples_lab_None' | |
| else: | |
| scenario = f'samples_chamber_None' | |
| else: | |
| scenario = f'samples_chamber_{jammed_freq}MHz_{jammer_dist}cm_{jamming_power}dBm' | |
| # Process the dataset and generate the raw numpy (to processing it every run) | |
| if not os.path.isfile(f'{processed_dir}/{scenario}.npy'): | |
| spectral_data = [] | |
| for channel in channels: | |
| channel_data = np.empty((0, n_features), int) | |
| for scan in range(n_scans): | |
| if jammer == 'combined': | |
| scenario = f'samples_chamber_{channel}MHz_{jammer_dist}cm_{jamming_power}dBm' | |
| df = pd.read_csv(f'{raw_dir}/{scenario}_{scan}.csv') | |
| temp_data = df.where(df['freq1'] == channel).dropna() | |
| temp_data = temp_data.to_numpy() | |
| channel_data = np.append(channel_data, temp_data, axis=0) | |
| spectral_data.append(channel_data) | |
| if not os.path.exists(processed_dir): | |
| os.makedirs(processed_dir) | |
| if jammer == 'combined': | |
| scenario = f'samples_chamber_{jammer_dist}cm_{jamming_power}dBm' | |
| np.save(f'{processed_dir}/{scenario}', np.array(spectral_data, dtype='object')) | |
| spectral_data = np.load(f'{processed_dir}/{scenario}.npy', allow_pickle=True) | |
| return spectral_data | |
| def get_feats_dirs(scenario, scan=1): | |
| spectral_df = pd.read_csv(f'{raw_dir}/{scenario}_{scan}.csv') | |
| features = spectral_df.columns | |
| n_features = len(features) | |
| return features, n_features | |
| def process_data(data, channels, length, stride, time_step, mode=0): | |
| if mode == 0: | |
| min_len = len(data[0]) | |
| for i in range(len(data)): | |
| min_len = len(data[i]) if len(data[i]) < min_len else min_len | |
| t_samples = min_len | |
| else: | |
| t_samples = len(data) | |
| n_samples = math.floor((t_samples - length) / stride) | |
| if mode == 1: | |
| # Breaking data into batches | |
| data = data[0:t_samples] | |
| batches = [] | |
| for sample in range(n_samples): | |
| batch = [] | |
| for i in range(length): | |
| batch_sample = data[(sample * stride) + i] | |
| batch.append(batch_sample) | |
| batches.append(batch) | |
| else: | |
| batches = [] | |
| for sample in range(n_samples): | |
| batch = [] | |
| for i in range(len(channels)): | |
| channel_data = data[i] | |
| channel_samples = channel_data[(sample * stride):(length + (sample * stride)), :] | |
| batch.append(channel_samples) | |
| batches.append(batch) | |
| processed_data = np.array(batches[time_step % len(batches)]) | |
| return processed_data | |