Upload input_preprocess.py

input_preprocess.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Fri Sep 13 16:13:29 2024
+
+This script generates preprocessed data from wireless communication scenarios, 
+including token generation, patch creation, and data sampling for machine learning models.
+
+@author: salikha4
+"""
+
+import numpy as np
+import os
+from tqdm import tqdm
+import time
+import pickle
+import DeepMIMOv3
+
+#%% Scenarios List
+def scenarios_list():
+    """Returns an array of available scenarios."""
+    return np.array([
+        'city_18_denver', 'city_15_indianapolis', 'city_19_oklahoma', 
+        'city_12_fortworth', 'city_11_santaclara', 'city_7_sandiego'
+    ])
+
+#%% Token Generation
+def tokenizer(scenario_names, gen_raw=True):
+    """
+    Generates tokens by preparing and preprocessing the dataset.
+
+    Args:
+        scenario_idxs (list): Indices of the scenarios.
+        patch_gen (bool): Whether to generate patches. Defaults to True.
+        patch_size (int): Size of each patch. Defaults to 16.
+        gen_deepMIMO_data (bool): Whether to generate DeepMIMO data. Defaults to False.
+        gen_raw (bool): Whether to generate raw data. Defaults to False.
+        save_data (bool): Whether to save the preprocessed data. Defaults to False.
+    
+    Returns:
+        preprocessed_data, sequence_length, element_length: Preprocessed data and related dimensions.
+    """
+    
+    # Patch generation or loading
+    deepmimo_data = [DeepMIMO_data_gen(scenario_name) for scenario_name in scenario_names]
+    n_scenarios = len(scenario_names)
+    
+    patches = [patch_maker(deepmimo_data[scenario_idx]) for scenario_idx in range(n_scenarios)]
+    patches = np.vstack(patches)
+    
+    # Define dimensions
+    patch_size = patches.shape[2]
+    n_patches = patches.shape[1]
+    n_masks_half = int(0.15 * n_patches / 2)
+    sequence_length = n_patches + 1
+    element_length = patch_size
+    
+    word2id = {'[CLS]': 0.2 * np.ones((patch_size)), '[MASK]': 0.1 * np.ones((patch_size))}
+
+    # Generate preprocessed channels
+    preprocessed_data = []
+    for user_idx in tqdm(range(len(patches)), desc="Processing items"):
+        sample = make_sample(user_idx, patches, word2id, n_patches, n_masks_half, patch_size, gen_raw=gen_raw)
+        preprocessed_data.append(sample)
+            
+    return preprocessed_data
+
+#%% Patch Creation
+def patch_maker(data, patch_size=16, norm_factor=1e6):
+    """
+    Creates patches from the dataset based on the scenario.
+
+    Args:-
+        patch_size (int): Size of each patch.
+        scenario (str): Selected scenario for data generation.
+        gen_deepMIMO_data (bool): Whether to generate DeepMIMO data.
+        norm_factor (int): Normalization factor for channels.
+
+    Returns:
+        patch (numpy array): Generated patches.
+    """
+    idxs = np.where(data['user']['LoS'] != -1)[0]
+    
+    # Reshaping and normalizing channels
+    original_ch = data['user']['channel'][idxs]
+    flat_channels = original_ch.reshape((original_ch.shape[0], -1)).astype(np.csingle)
+    flat_channels_complex = np.hstack((flat_channels.real, flat_channels.imag)) * norm_factor
+    
+    # Create patches
+    n_patches = flat_channels_complex.shape[1] // patch_size
+    patch = np.zeros((len(idxs), n_patches, patch_size))
+    for idx in range(n_patches):
+        patch[:, idx, :] = flat_channels_complex[:, idx * patch_size:(idx + 1) * patch_size]
+    
+    return patch
+
+
+#%% Data Generation for Scenario Areas
+def DeepMIMO_data_gen(scenario):
+    """
+    Generates or loads data for a given scenario.
+
+    Args:
+        scenario (str): Scenario name.
+        gen_deepMIMO_data (bool): Whether to generate DeepMIMO data.
+        save_data (bool): Whether to save generated data.
+    
+    Returns:
+        data (dict): Loaded or generated data.
+    """
+    
+    parameters, row_column_users, n_ant_bs, n_ant_ue, n_subcarriers = get_parameters(scenario)
+    
+    deepMIMO_dataset = DeepMIMOv3.generate_data(parameters)
+    uniform_idxs = uniform_sampling(deepMIMO_dataset, [1, 1], len(parameters['user_rows']), 
+                                    users_per_row=row_column_users[scenario]['n_per_row'])
+    data = select_by_idx(deepMIMO_dataset, uniform_idxs)[0]
+        
+    return data
+
+#%%%
+def get_parameters(scenario):
+    
+    n_ant_bs = 32 #32
+    n_ant_ue = 1
+    n_subcarriers = 32 #32
+    scs = 30e3
+        
+    row_column_users = {
+    'city_18_denver': {
+        'n_rows': 85,
+        'n_per_row': 82
+    },
+    'city_15_indianapolis': {
+        'n_rows': 80,
+        'n_per_row': 79
+    },
+    'city_19_oklahoma': {
+        'n_rows': 82,
+        'n_per_row': 75
+    },
+    'city_12_fortworth': {
+        'n_rows': 86,
+        'n_per_row': 72
+    },
+    'city_11_santaclara': {
+        'n_rows': 47,
+        'n_per_row': 114
+    },
+    'city_7_sandiego': {
+        'n_rows': 71,
+        'n_per_row': 83
+    }}
+    
+    parameters = DeepMIMOv3.default_params()
+    parameters['dataset_folder'] = './scenarios'
+    parameters['scenario'] = scenario
+    
+    if scenario == 'O1_3p5':
+        parameters['active_BS'] = np.array([4])
+    elif scenario in ['city_18_denver', 'city_15_indianapolis']:
+        parameters['active_BS'] = np.array([3])
+    else:
+        parameters['active_BS'] = np.array([1])
+        
+    if scenario == 'Boston5G_3p5':
+        parameters['user_rows'] = np.arange(row_column_users[scenario]['n_rows'][0],
+                                            row_column_users[scenario]['n_rows'][1])
+    else:
+        parameters['user_rows'] = np.arange(row_column_users[scenario]['n_rows'])
+    parameters['bs_antenna']['shape'] = np.array([n_ant_bs, 1]) # Horizontal, Vertical 
+    parameters['bs_antenna']['rotation'] = np.array([0,0,-135]) # (x,y,z)
+    parameters['ue_antenna']['shape'] = np.array([n_ant_ue, 1])
+    parameters['enable_BS2BS'] = False
+    parameters['OFDM']['subcarriers'] = n_subcarriers
+    parameters['OFDM']['selected_subcarriers'] = np.arange(n_subcarriers)
+    
+    parameters['OFDM']['bandwidth'] = scs * n_subcarriers / 1e9
+    parameters['num_paths'] = 20
+    
+    return parameters, row_column_users, n_ant_bs, n_ant_ue, n_subcarriers
+
+
+#%% Sample Generation
+def make_sample(user_idx, patch, word2id, n_patches, n_masks, patch_size, gen_raw=False):
+    """
+    Generates a sample for each user, including masking and tokenizing.
+
+    Args:
+        user_idx (int): Index of the user.
+        patch (numpy array): Patches data.
+        word2id (dict): Dictionary for special tokens.
+        n_patches (int): Number of patches.
+        n_masks (int): Number of masks.
+        patch_size (int): Size of each patch.
+        gen_raw (bool): Whether to generate raw tokens.
+
+    Returns:
+        sample (list): Generated sample for the user.
+    """
+    
+    tokens = patch[user_idx]
+    input_ids = np.vstack((word2id['[CLS]'], tokens))
+    
+    real_tokens_size = int(n_patches / 2)
+    masks_pos_real = np.random.choice(range(0, real_tokens_size), size=n_masks, replace=False)
+    masks_pos_imag = masks_pos_real + real_tokens_size
+    masked_pos = np.hstack((masks_pos_real, masks_pos_imag)) + 1
+    
+    masked_tokens = []
+    for pos in masked_pos:
+        original_masked_tokens = input_ids[pos].copy()
+        masked_tokens.append(original_masked_tokens)
+        if not gen_raw:
+            rnd_num = np.random.rand()
+            if rnd_num < 0.1:
+                input_ids[pos] = np.random.rand(patch_size)
+            elif rnd_num < 0.9:
+                input_ids[pos] = word2id['[MASK]']
+
+    return [input_ids, masked_tokens, masked_pos]
+
+
+#%% Sampling and Data Selection
+def uniform_sampling(dataset, sampling_div, n_rows, users_per_row):
+    """
+    Performs uniform sampling on the dataset.
+
+    Args:
+        dataset (dict): DeepMIMO dataset.
+        sampling_div (list): Step sizes along [x, y] dimensions.
+        n_rows (int): Number of rows for user selection.
+        users_per_row (int): Number of users per row.
+
+    Returns:
+        uniform_idxs (numpy array): Indices of the selected samples.
+    """
+    cols = np.arange(users_per_row, step=sampling_div[0])
+    rows = np.arange(n_rows, step=sampling_div[1])
+    uniform_idxs = np.array([j + i * users_per_row for i in rows for j in cols])
+    
+    return uniform_idxs
+
+def select_by_idx(dataset, idxs):
+    """
+    Selects a subset of the dataset based on the provided indices.
+
+    Args:
+        dataset (dict): Dataset to trim.
+        idxs (numpy array): Indices of users to select.
+
+    Returns:
+        dataset_t (list): Trimmed dataset based on selected indices.
+    """
+    dataset_t = []  # Trimmed dataset
+    for bs_idx in range(len(dataset)):
+        dataset_t.append({})
+        for key in dataset[bs_idx].keys():
+            dataset_t[bs_idx]['location'] = dataset[bs_idx]['location']
+            dataset_t[bs_idx]['user'] = {k: dataset[bs_idx]['user'][k][idxs] for k in dataset[bs_idx]['user']}
+    
+    return dataset_t
+
+#%% Save and Load Utilities
+def save_var(var, path):
+    """
+    Saves a variable to a pickle file.
+
+    Args:
+        var (object): Variable to be saved.
+        path (str): Path to save the file.
+
+    Returns:
+        None
+    """
+    path_full = path if path.endswith('.p') else (path + '.pickle')    
+    with open(path_full, 'wb') as handle:
+        pickle.dump(var, handle)
+
+def load_var(path):
+    """
+    Loads a variable from a pickle file.
+
+    Args:
+        path (str): Path of the file to load.
+
+    Returns:
+        var (object): Loaded variable.
+    """
+    path_full = path if path.endswith('.p') else (path + '.pickle')
+    with open(path_full, 'rb') as handle:
+        var = pickle.load(handle)
+    
+    return var
+
+#%%