utils/utils.py

import matplotlib.pyplot as plt
import csv
from collections import OrderedDict
from typing import List, Tuple
import torch
import numpy as np
import subprocess
import os
from utils.Dataset import Jammer_Dataset
from torch.utils.data import DataLoader
from utils.Dataset import balance_data
import torch.nn.functional as F


def visualization(image, label):
        # Create a plot
    plt.figure(figsize=(10, 6))  # Adjust the figure size as needed
    plt.imshow(image.permute(1, 2, 0), origin='lower')
    plt.colorbar()
    plt.title(f'Spectrogram of class {label} ')
    plt.xlabel('Time')
    plt.ylabel('Channel')
    plt.show()


def save_balanced_data_to_csv(data, csv_filename):
    # Create the folder if it doesn't exist
    folder_path = "../Linkhalle_fewshot_dataset"
    os.makedirs(folder_path, exist_ok=True)

    csv_filepath = os.path.join(folder_path, csv_filename)

    # Check if the file already exists
    file_exists = os.path.isfile(csv_filepath)

    # Check if data is not empty and the file doesn't exist
    # print(f"data is following --------{data}")
    if data and not file_exists:
        with open(csv_filepath, 'w', newline='') as csvfile:
            fieldnames = ['Filename', 'Label']
            writer = csv.DictWriter(csvfile, fieldnames=fieldnames)

            # Write the header
            writer.writeheader()

            # Write the data
            for filename, label in data:
                writer.writerow({'Filename': filename, 'Label': label})
    elif not data:
        print("No data to save.")
    elif file_exists:
        print("File already exists. Data not written.")

def load_data_from_csv(csv_filename):
    dataset_training = "../Linkhalle_fewshot_dataset"
    csv_filepath = os.path.join(dataset_training, csv_filename)
    data = []
    with open(csv_filepath, 'r') as csvfile:
        reader = csv.DictReader(csvfile)
        for row in reader:
            if row['Filename'] and row['Label']:
                filename = row['Filename']
                label = int(row['Label'])
                data.append((filename, label))
    return data



def update(data, target, model, optimizer):
    model.send(data.location)
    optimizer.zero_grad()
    prediction = model(data)
    loss = F.mse_loss(prediction.view(-1), target)
    loss.backward()
    optimizer.step()
    return model


def get_parameters(net) -> List[np.ndarray]:
    return [val.cpu().numpy() for _, val in net.state_dict().items()]


def set_parameters(net, parameters: List[np.ndarray]):
    params_dict = zip(net.state_dict().keys(), parameters)
    state_dict = OrderedDict({k: torch.Tensor(v) for k, v in params_dict})
    net.load_state_dict(state_dict, strict=True)

def check_client_availability(client_name):
    try:
        # Run 'ping' command with a timeout of 2 seconds (adjust as needed)
        subprocess.check_call(["ping", "-c", "1", "-W", "2", client_name])
        return True  # Client is reachable
    except subprocess.CalledProcessError:
        return False  # Client is not reachable


def calculate_binary_metrics(true_labels, predicted_labels):
    """
    Calculate binary classification metrics using NumPy.
    """

    tp = np.sum(np.logical_and(predicted_labels == 1, true_labels == 1))
    fp = np.sum(np.logical_and(predicted_labels == 1, true_labels == 0))
    fn = np.sum(np.logical_and(predicted_labels == 0, true_labels == 1))
    tn = np.sum(np.logical_and(predicted_labels == 0, true_labels == 0))

    precision = tp / (tp + fp) if (tp + fp) > 0 else 0
    recall = tp / (tp + fn) if (tp + fn) > 0 else 0
    f1 = 2 * (precision * recall) / (precision + recall) if (precision + recall) > 0 else 0

    # F2 score with more emphasis on recall
    beta = 2
    f2 = (1 + beta**2) * (precision * recall) / ((beta**2 * precision) + recall) if (precision + recall) > 0 else 0

    # Accuracy
    accuracy = (tp + tn) / (tp + tn + fp + fn) if (tp + tn + fp + fn) > 0 else 0

    return precision, recall, f1, f2, accuracy



def create_test_set(
        root_dir,
        train_test_split_dir
    ):
    
        required_train_txt = ["test_files_0.8_0.8_0.8_0.8_0.8_0.8_0.0_0.8_0.8_0.8_0.8_0.8_split4.txt"]
                # Initialize lists to store filenames and labels
        print(f"required file ------------------------{required_train_txt}")
        filenames = []
        labels = []

        for filename in required_train_txt:
            with open(os.path.join(train_test_split_dir, filename), 'r') as file:
                lines = file.readlines()
                for line in lines:
                    parts = line.strip().split()
                    if len(parts) == 2:
                        filenames.append(os.path.join(root_dir, parts[0]))
                        labels.append(int(parts[1]))


        # Randomly shuffle the data and labels
        combined_data = list(zip(filenames, labels))
        #random.shuffle(combined_data)
        # combined_data = balance_data(n_shot,n_query,combined_data)
        testing_path = "tesing_file"
        os.makedirs(testing_path, exist_ok=True)
        test_file = os.path.join(testing_path, f'tesing_data_{4}.csv')
        save_balanced_data_to_csv(combined_data, test_file)
      


def create_test_dataloder(
    root_dir,
    path,
    batch,
    n_workers,
    norm_vals,
    transforms=None,
    
):
        train_data = load_data_from_csv(path)
        train_dataset = Jammer_Dataset(root_dir, train_data, norm_vals,transform=transforms)

        # Create DataLoader for training
        train_loader = DataLoader(
            train_dataset,
            batch_size=batch,
            num_workers=n_workers,
            pin_memory=True,
            shuffle=False,
        )


        return train_loader