from __future__ import print_function

import time

import numpy as np

np.random.seed(1234)

from functools import reduce

import math as m

import scipy.io

#import theano

#import theano.tensor as T

from scipy.interpolate import griddata

from sklearn.preprocessing import scale

#from utils import augment_EEG, cart2sph, pol2cart

#import lasagne

# from lasagne.layers.dnn import Conv2DDNNLayer as ConvLayer

#from lasagne.layers import Conv2DLayer, MaxPool2DLayer, InputLayer

#from lasagne.layers import DenseLayer, ElemwiseMergeLayer, FlattenLayer

#from lasagne.layers import ConcatLayer, ReshapeLayer, get_output_shape

#from lasagne.layers import Conv1DLayer, DimshuffleLayer, LSTMLayer, SliceLayer

def azim_proj(pos):

    """

    Computes the Azimuthal Equidistant Projection of input point in 3D Cartesian Coordinates.

    Imagine a plane being placed against (tangent to) a globe. If

    a light source inside the globe projects the graticule onto

    the plane the result would be a planar, or azimuthal, map

    projection.

    :param pos: position in 3D Cartesian coordinates

    :return: projected coordinates using Azimuthal Equidistant Projection

    """

    [r, elev, az] = cart2sph(pos[0], pos[1], pos[2])

    return pol2cart(az, m.pi / 2 - elev)

def gen_images(locs, features, n_gridpoints, normalize=True,

               augment=False, pca=False, std_mult=0.1, n_components=2, edgeless=False):

    """

    Generates EEG images given electrode locations in 2D space and multiple feature values for each electrode

    :param locs: An array with shape [n_electrodes, 2] containing X, Y

                        coordinates for each electrode.

    :param features: Feature matrix as [n_samples, n_features]

                                Features are as columns.

                                Features corresponding to each frequency band are concatenated.

                                (alpha1, alpha2, ..., beta1, beta2,...)

    :param n_gridpoints: Number of pixels in the output images

    :param normalize:   Flag for whether to normalize each band over all samples

    :param augment:     Flag for generating augmented images

    :param pca:         Flag for PCA based data augmentation

    :param std_mult     Multiplier for std of added noise

    :param n_components: Number of components in PCA to retain for augmentation

    :param edgeless:    If True generates edgeless images by adding artificial channels

                        at four corners of the image with value = 0 (default=False).

    :return:            Tensor of size [samples, colors, W, H] containing generated

                        images.

    """

    feat_array_temp = []

    nElectrodes = locs.shape[0]     # Number of electrodes

    # Test whether the feature vector length is divisible by number of electrodes

    assert features.shape[1] % nElectrodes == 0

    n_colors = features.shape[1] // nElectrodes

    for c in range(int(n_colors)):

        feat_array_temp.append(features[:, c * nElectrodes : nElectrodes * (c+1)])

    if augment:

        if pca:

            for c in range(n_colors):

                feat_array_temp[c] = augment_EEG(feat_array_temp[c], std_mult, pca=True, n_components=n_components)

        else:

            for c in range(n_colors):

                feat_array_temp[c] = augment_EEG(feat_array_temp[c], std_mult, pca=False, n_components=n_components)

    nSamples = features.shape[0]

    # Interpolate the values

    grid_x, grid_y = np.mgrid[

                     min(locs[:, 0]):max(locs[:, 0]):n_gridpoints*1j,

                     min(locs[:, 1]):max(locs[:, 1]):n_gridpoints*1j

                     ]

    temp_interp = []

    for c in range(n_colors):

        temp_interp.append(np.zeros([nSamples, n_gridpoints, n_gridpoints]))

    # Generate edgeless images

    if edgeless:

        min_x, min_y = np.min(locs, axis=0)

        max_x, max_y = np.max(locs, axis=0)

        locs = np.append(locs, np.array([[min_x, min_y], [min_x, max_y],[max_x, min_y],[max_x, max_y]]),axis=0)

        for c in range(n_colors):

            feat_array_temp[c] = np.append(feat_array_temp[c], np.zeros((nSamples, 4)), axis=1)

    # Interpolating

    for i in range(nSamples):

        for c in range(n_colors):

            temp_interp[c][i, :, :] = griddata(locs, feat_array_temp[c][i, :], (grid_x, grid_y),

                                    method='cubic', fill_value=np.nan)

        print('Interpolating {0}/{1}\r'.format(i+1, nSamples), end='\r')

    # Normalizing

    for c in range(n_colors):

        if normalize:

            temp_interp[c][~np.isnan(temp_interp[c])] = \

                scale(temp_interp[c][~np.isnan(temp_interp[c])])

        temp_interp[c] = np.nan_to_num(temp_interp[c])

    return np.swapaxes(np.asarray(temp_interp), 0, 1)     # swap axes to have [samples, colors, W, H]

def build_cnn(input_var=None, w_init=None, n_layers=(4, 2, 1), n_filters_first=32, imsize=32, n_colors=3):

    """

    Builds a VGG style CNN network followed by a fully-connected layer and a softmax layer.

    Stacks are separated by a maxpool layer. Number of kernels in each layer is twice

    the number in previous stack.

    input_var: Theano variable for input to the network

    outputs: pointer to the output of the last layer of network (softmax)

    :param input_var: theano variable as input to the network

    :param w_init: Initial weight values

    :param n_layers: number of layers in each stack. An array of integers with each

                    value corresponding to the number of layers in each stack.

                    (e.g. [4, 2, 1] == 3 stacks with 4, 2, and 1 layers in each.

    :param n_filters_first: number of filters in the first layer

    :param imSize: Size of the image

    :param n_colors: Number of color channels (depth)

    :return: a pointer to the output of last layer

    """

    weights = []        # Keeps the weights for all layers

    count = 0

    # If no initial weight is given, initialize with GlorotUniform

    if w_init is None:

        w_init = [lasagne.init.GlorotUniform()] * sum(n_layers)

    # Input layer

    network = InputLayer(shape=(None, n_colors, imsize, imsize),

                                        input_var=input_var)

    for i, s in enumerate(n_layers):

        for l in range(s):

            network = Conv2DLayer(network, num_filters=n_filters_first * (2 ** i), filter_size=(3, 3),

                          W=w_init[count], pad='same')

            count += 1

            weights.append(network.W)

        network = MaxPool2DLayer(network, pool_size=(2, 2))

    return network, weights

def build_convpool_max(input_vars, nb_classes, imsize=32, n_colors=3, n_timewin=3):

    """

    Builds the complete network with maxpooling layer in time.

    :param input_vars: list of EEG images (one image per time window)

    :param nb_classes: number of classes

    :param imsize: size of the input image (assumes a square input)

    :param n_colors: number of color channels in the image

    :param n_timewin: number of time windows in the snippet

    :return: a pointer to the output of last layer

    """

    convnets = []

    w_init = None

    # Build 7 parallel CNNs with shared weights

    for i in range(n_timewin):

        if i == 0:

            convnet, w_init = build_cnn(input_vars[i], imsize=imsize, n_colors=n_colors)

        else:

            convnet, _ = build_cnn(input_vars[i], w_init=w_init, imsize=imsize, n_colors=n_colors)

        convnets.append(convnet)

    # convpooling using Max pooling over frames

    convpool = ElemwiseMergeLayer(convnets, theano.tensor.maximum)

    # A fully-connected layer of 512 units with 50% dropout on its inputs:

    convpool = DenseLayer(lasagne.layers.dropout(convpool, p=.5),

            num_units=512, nonlinearity=lasagne.nonlinearities.rectify)

    # And, finally, the output layer with 50% dropout on its inputs:

    convpool = lasagne.layers.DenseLayer(lasagne.layers.dropout(convpool, p=.5),

            num_units=nb_classes, nonlinearity=lasagne.nonlinearities.softmax)

    return convpool

def build_convpool_conv1d(input_vars, nb_classes, imsize=32, n_colors=3, n_timewin=3):

    """

    Builds the complete network with 1D-conv layer to integrate time from sequences of EEG images.

    :param input_vars: list of EEG images (one image per time window)

    :param nb_classes: number of classes

    :param imsize: size of the input image (assumes a square input)

    :param n_colors: number of color channels in the image

    :param n_timewin: number of time windows in the snippet

    :return: a pointer to the output of last layer

    """

    convnets = []

    w_init = None

    # Build 7 parallel CNNs with shared weights

    for i in range(n_timewin):

        if i == 0:

            convnet, w_init = build_cnn(input_vars[i], imsize=imsize, n_colors=n_colors)

        else:

            convnet, _ = build_cnn(input_vars[i], w_init=w_init, imsize=imsize, n_colors=n_colors)

        convnets.append(FlattenLayer(convnet))

    # at this point convnets shape is [numTimeWin][n_samples, features]

    # we want the shape to be [n_samples, features, numTimeWin]

    convpool = ConcatLayer(convnets)

    convpool = ReshapeLayer(convpool, ([0], n_timewin, get_output_shape(convnets[0])[1]))

    convpool = DimshuffleLayer(convpool, (0, 2, 1))

    # input to 1D convlayer should be in (batch_size, num_input_channels, input_length)

    convpool = Conv1DLayer(convpool, 64, 3)

    # A fully-connected layer of 512 units with 50% dropout on its inputs:

    convpool = DenseLayer(lasagne.layers.dropout(convpool, p=.5),

            num_units=512, nonlinearity=lasagne.nonlinearities.rectify)

    # And, finally, the output layer with 50% dropout on its inputs:

    convpool = DenseLayer(lasagne.layers.dropout(convpool, p=.5),

            num_units=nb_classes, nonlinearity=lasagne.nonlinearities.softmax)

    return convpool

def build_convpool_lstm(input_vars, nb_classes, grad_clip=110, imsize=32, n_colors=3, n_timewin=3):

    """

    Builds the complete network with LSTM layer to integrate time from sequences of EEG images.

    :param input_vars: list of EEG images (one image per time window)

    :param nb_classes: number of classes

    :param grad_clip:  the gradient messages are clipped to the given value during

                        the backward pass.

    :param imsize: size of the input image (assumes a square input)

    :param n_colors: number of color channels in the image

    :param n_timewin: number of time windows in the snippet

    :return: a pointer to the output of last layer

    """

    convnets = []

    w_init = None

    # Build 7 parallel CNNs with shared weights

    for i in range(n_timewin):

        if i == 0:

            convnet, w_init = build_cnn(input_vars[i], imsize=imsize, n_colors=n_colors)

        else:

            convnet, _ = build_cnn(input_vars[i], w_init=w_init, imsize=imsize, n_colors=n_colors)

        convnets.append(FlattenLayer(convnet))

    # at this point convnets shape is [numTimeWin][n_samples, features]

    # we want the shape to be [n_samples, features, numTimeWin]

    convpool = ConcatLayer(convnets)

    convpool = ReshapeLayer(convpool, ([0], n_timewin, get_output_shape(convnets[0])[1]))

    # Input to LSTM should have the shape as (batch size, SEQ_LENGTH, num_features)

    convpool = LSTMLayer(convpool, num_units=128, grad_clipping=grad_clip,

        nonlinearity=lasagne.nonlinearities.tanh)

    # We only need the final prediction, we isolate that quantity and feed it

    # to the next layer.

    convpool = SliceLayer(convpool, -1, 1)      # Selecting the last prediction

    # A fully-connected layer of 256 units with 50% dropout on its inputs:

    convpool = DenseLayer(lasagne.layers.dropout(convpool, p=.5),

            num_units=256, nonlinearity=lasagne.nonlinearities.rectify)

    # And, finally, the output layer with 50% dropout on its inputs:

    convpool = DenseLayer(lasagne.layers.dropout(convpool, p=.5),

            num_units=nb_classes, nonlinearity=lasagne.nonlinearities.softmax)

    return convpool

def build_convpool_mix(input_vars, nb_classes, grad_clip=110, imsize=32, n_colors=3, n_timewin=3):

    """

    Builds the complete network with LSTM and 1D-conv layers combined

    :param input_vars: list of EEG images (one image per time window)

    :param nb_classes: number of classes

    :param grad_clip:  the gradient messages are clipped to the given value during

                        the backward pass.

    :param imsize: size of the input image (assumes a square input)

    :param n_colors: number of color channels in the image

    :param n_timewin: number of time windows in the snippet

    :return: a pointer to the output of last layer

    """

    convnets = []

    w_init = None

    # Build 7 parallel CNNs with shared weights

    for i in range(n_timewin):

        if i == 0:

            convnet, w_init = build_cnn(input_vars[i], imsize=imsize, n_colors=n_colors)

        else:

            convnet, _ = build_cnn(input_vars[i], w_init=w_init, imsize=imsize, n_colors=n_colors)

        convnets.append(FlattenLayer(convnet))

    # at this point convnets shape is [numTimeWin][n_samples, features]

    # we want the shape to be [n_samples, features, numTimeWin]

    convpool = ConcatLayer(convnets)

    convpool = ReshapeLayer(convpool, ([0], n_timewin, get_output_shape(convnets[0])[1]))

    reformConvpool = DimshuffleLayer(convpool, (0, 2, 1))

    # input to 1D convlayer should be in (batch_size, num_input_channels, input_length)

    conv_out = Conv1DLayer(reformConvpool, 64, 3)

    conv_out = FlattenLayer(conv_out)

    # Input to LSTM should have the shape as (batch size, SEQ_LENGTH, num_features)

    lstm = LSTMLayer(convpool, num_units=128, grad_clipping=grad_clip,

        nonlinearity=lasagne.nonlinearities.tanh)

    lstm_out = SliceLayer(lstm, -1, 1)

    # Merge 1D-Conv and LSTM outputs

    dense_input = ConcatLayer([conv_out, lstm_out])

    # A fully-connected layer of 256 units with 50% dropout on its inputs:

    convpool = DenseLayer(lasagne.layers.dropout(dense_input, p=.5),

            num_units=512, nonlinearity=lasagne.nonlinearities.rectify)

    # And, finally, the 10-unit output layer with 50% dropout on its inputs:

    convpool = DenseLayer(convpool,

            num_units=nb_classes, nonlinearity=lasagne.nonlinearities.softmax)

    return convpool

def iterate_minibatches(inputs, targets, batchsize, shuffle=False):

    """

    Iterates over the samples returing batches of size batchsize.

    :param inputs: input data array. It should be a 4D numpy array for images [n_samples, n_colors, W, H] and 5D numpy

                    array if working with sequence of images [n_timewindows, n_samples, n_colors, W, H].

    :param targets: vector of target labels.

    :param batchsize: Batch size

    :param shuffle: Flag whether to shuffle the samples before iterating or not.

    :return: images and labels for a batch

    """

    if inputs.ndim == 4:

        input_len = inputs.shape[0]

    elif inputs.ndim == 5:

        input_len = inputs.shape[1]

    assert input_len == len(targets)

    if shuffle:

        indices = np.arange(input_len)

        np.random.shuffle(indices)

    for start_idx in range(0, input_len, batchsize):

        if shuffle:

            excerpt = indices[start_idx:start_idx + batchsize]

        else:

            excerpt = slice(start_idx, start_idx + batchsize)

        if inputs.ndim == 4:

            yield inputs[excerpt], targets[excerpt]

        elif inputs.ndim == 5:

            yield inputs[:, excerpt], targets[excerpt]

def train(images, labels, fold, model_type, batch_size=32, num_epochs=5):

    """

    A sample training function which loops over the training set and evaluates the network

    on the validation set after each epoch. Evaluates the network on the training set

    whenever the

    :param images: input images

    :param labels: target labels

    :param fold: tuple of (train, test) index numbers

    :param model_type: model type ('cnn', '1dconv', 'maxpool', 'lstm', 'mix')

    :param batch_size: batch size for training

    :param num_epochs: number of epochs of dataset to go over for training

    :return: none

    """

    num_classes = len(np.unique(labels))

    (X_train, y_train), (X_val, y_val), (X_test, y_test) = reformatInput(images, labels, fold)

    X_train = X_train.astype("float32", casting='unsafe')

    X_val = X_val.astype("float32", casting='unsafe')

    X_test = X_test.astype("float32", casting='unsafe')

    # Prepare Theano variables for inputs and targets

    input_var = T.TensorType('floatX', ((False,) * 5))()

    target_var = T.ivector('targets')

    # Create neural network model (depending on first command line parameter)

    print("Building model and compiling functions...")

    # Building the appropriate model

    if model_type == '1dconv':

        network = build_convpool_conv1d(input_var, num_classes)

    elif model_type == 'maxpool':

        network = build_convpool_max(input_var, num_classes)

    elif model_type == 'lstm':

        network = build_convpool_lstm(input_var, num_classes, 100)

    elif model_type == 'mix':

        network = build_convpool_mix(input_var, num_classes, 100)

    elif model_type == 'cnn':

        input_var = T.tensor4('inputs')

        network, _ = build_cnn(input_var)

        network = DenseLayer(lasagne.layers.dropout(network, p=.5),

                             num_units=256,

                             nonlinearity=lasagne.nonlinearities.rectify)

        network = DenseLayer(lasagne.layers.dropout(network, p=.5),

                             num_units=num_classes,

                             nonlinearity=lasagne.nonlinearities.softmax)

    else:

        raise ValueError("Model not supported ['1dconv', 'maxpool', 'lstm', 'mix', 'cnn']")

    # Create a loss expression for training, i.e., a scalar objective we want

    # to minimize (for our multi-class problem, it is the cross-entropy loss):

    prediction = lasagne.layers.get_output(network)

    loss = lasagne.objectives.categorical_crossentropy(prediction, target_var)

    loss = loss.mean()

    params = lasagne.layers.get_all_params(network, trainable=True)

    updates = lasagne.updates.adam(loss, params, learning_rate=0.001)

    # Create a loss expression for validation/testing. The crucial difference

    # here is that we do a deterministic forward pass through the network,

    # disabling dropout layers.

    test_prediction = lasagne.layers.get_output(network, deterministic=True)

    test_loss = lasagne.objectives.categorical_crossentropy(test_prediction,

                                                            target_var)

    test_loss = test_loss.mean()

    # As a bonus, also create an expression for the classification accuracy:

    test_acc = T.mean(T.eq(T.argmax(test_prediction, axis=1), target_var),

                      dtype=theano.config.floatX)

    # Compile a function performing a training step on a mini-batch (by giving

    # the updates dictionary) and returning the corresponding training loss:

    train_fn = theano.function([input_var, target_var], loss, updates=updates)

    # Compile a second function computing the validation loss and accuracy:

    val_fn = theano.function([input_var, target_var], [test_loss, test_acc])

    # Finally, launch the training loop.

    print("Starting training...")

    best_validation_accu = 0

    # We iterate over epochs:

    for epoch in range(num_epochs):

        # In each epoch, we do a full pass over the training data:

        train_err = 0

        train_batches = 0

        start_time = time.time()

        for batch in iterate_minibatches(X_train, y_train, batch_size, shuffle=False):

            inputs, targets = batch

            train_err += train_fn(inputs, targets)

            train_batches += 1

        # And a full pass over the validation data:

        val_err = 0

        val_acc = 0

        val_batches = 0

        for batch in iterate_minibatches(X_val, y_val, batch_size, shuffle=False):

            inputs, targets = batch

            err, acc = val_fn(inputs, targets)

            val_err += err

            val_acc += acc

            val_batches += 1

        av_train_err = train_err / train_batches

        av_val_err = val_err / val_batches

        av_val_acc = val_acc / val_batches

        # Then we print the results for this epoch:

        print("Epoch {} of {} took {:.3f}s".format(

            epoch + 1, num_epochs, time.time() - start_time))

        print("  training loss:\t\t{:.6f}".format(av_train_err))

        print("  validation loss:\t\t{:.6f}".format(av_val_err))

        print("  validation accuracy:\t\t{:.2f} %".format(av_val_acc * 100))

        if av_val_acc > best_validation_accu:

            best_validation_accu = av_val_acc

            # After training, we compute and print the test error:

            test_err = 0

            test_acc = 0

            test_batches = 0

            for batch in iterate_minibatches(X_test, y_test, batch_size, shuffle=False):

                inputs, targets = batch

                err, acc = val_fn(inputs, targets)

                test_err += err

                test_acc += acc

                test_batches += 1

            av_test_err = test_err / test_batches

            av_test_acc = test_acc / test_batches

            print("Final results:")

            print("  test loss:\t\t\t{:.6f}".format(av_test_err))

            print("  test accuracy:\t\t{:.2f} %".format(av_test_acc * 100))

            # Dump the network weights to a file like this:

            np.savez('weights_lasg_{0}'.format(model_type), *lasagne.layers.get_all_param_values(network))

    print('-'*50)

    print("Best validation accuracy:\t\t{:.2f} %".format(best_validation_accu * 100))

    print("Best test accuracy:\t\t{:.2f} %".format(av_test_acc * 100))

'''

if __name__ == '__main__':

    from utils import reformatInput

    # Load electrode locations

    print('Loading data...')

    locs = scipy.io.loadmat('../Sample data/Neuroscan_locs_orig.mat')

    locs_3d = locs['A']

    locs_2d = []

    # Convert to 2D

    for e in locs_3d:

        locs_2d.append(azim_proj(e))

    feats = scipy.io.loadmat('../Sample data/FeatureMat_timeWin.mat')['features']

    print ('Feats Shape: ',feats.shape)

    subj_nums = np.squeeze(scipy.io.loadmat('../Sample data/trials_subNums.mat')['subjectNum'])

    # Leave-Subject-Out cross validation

    fold_pairs = []

    for i in np.unique(subj_nums):

        ts = subj_nums == i

        tr = np.squeeze(np.nonzero(np.bitwise_not(ts)))

        ts = np.squeeze(np.nonzero(ts))

        np.random.shuffle(tr)  # Shuffle indices

        np.random.shuffle(ts)

        fold_pairs.append((tr, ts))

    # CNN Mode

    print('Generating images...')

    # Find the average response over time windows

    av_feats = reduce(lambda x, y: x+y, [feats[:, i*192:(i+1)*192] for i in range(feats.shape[1] / 192)])

    av_feats = av_feats / (feats.shape[1] / 192)

    images = gen_images(np.array(locs_2d),

                                  av_feats,

                                  32, normalize=False)

    print('\n')

    # Class labels should start from 0

    print('Training the CNN Model...')

    train(images, np.squeeze(feats[:, -1]) - 1, fold_pairs[2], 'cnn')

    # Conv-LSTM Mode

    print('Generating images for all time windows...')

    images_timewin = np.array([gen_images(np.array(locs_2d),

                                                    feats[:, i * 192:(i + 1) * 192], 32, normalize=False) for i in

                                         range(feats.shape[1] / 192)

                                         ])

    print('\n')

    print('Training the LSTM-CONV Model...')

    train(images_timewin, np.squeeze(feats[:, -1]) - 1, fold_pairs[2], 'mix')

    print('Done!')

'''