import torch

from torch.autograd import Variable

import matplotlib.pyplot as plt

from torchnet import meter

def plot_training(costs, accs):

    '''

    Plots curve of Cost vs epochs and Accuracy vs epochs for 'train' and 'valid' sets during training

    '''

    train_acc = accs['train']

    valid_acc = accs['valid']

    train_cost = costs['train']

    valid_cost = costs['valid']

    epochs = range(len(train_acc))

    plt.figure(figsize=(10, 5))

    plt.subplot(1, 2, 1,)

    plt.plot(epochs, train_acc)

    plt.plot(epochs, valid_acc)

    plt.legend(['train', 'valid'], loc='upper left')

    plt.title('Accuracy')

    plt.subplot(1, 2, 2)

    plt.plot(epochs, train_cost)

    plt.plot(epochs, valid_cost)

    plt.legend(['train', 'valid'], loc='upper left')

    plt.title('Cost')

    plt.show()

def n_p(x):

    '''convert numpy float to Variable tensor float'''    

    return Variable(torch.cuda.FloatTensor([x]), requires_grad=False)

def get_count(df, cat):

    '''

    Returns number of images in a study type dataframe which are of abnormal or normal

    Args:

    df -- dataframe

    cat -- category, "positive" for abnormal and "negative" for normal

    '''

    return df[df['Path'].str.contains(cat)]['Count'].sum()

if __name__=='main':

    pass