import random
import numpy as np
import tensorflow as tf
from tensorflow.keras.utils import Sequence


class UnetGen(Sequence):
    
    """Utility class for generating batches of temporal data.
    This class takes in a sequence of data-points gathered at
    equal intervals, along with time series parameters such as
    stride, length of history, etc., to produce batches for
    training/validation.
    # Arguments
        data: Indexable generator (such as list or Numpy array)
            containing consecutive data points (timesteps).
            The data should be at 2D, and axis 0 is expected
            to be the time dimension.
        targets: Targets corresponding to timesteps in `data`.
            It should have same length as `data`.
        length: Length of the output sequences (in number of timesteps).
        sampling_rate: Period between successive individual timesteps
            within sequences. For rate `r`, timesteps
            `data[i]`, `data[i-r]`, ... `data[i - length]`
            are used for create a sample sequence.
        stride: Period between successive output sequences.
            For stride `s`, consecutive output samples would
            be centered around `data[i]`, `data[i+s]`, `data[i+2*s]`, etc.
        start_index: Data points earlier than `start_index` will not be used
            in the output sequences. This is useful to reserve part of the
            data for test or validation.
        end_index: Data points later than `end_index` will not be used
            in the output sequences. This is useful to reserve part of the
            data for test or validation.
        shuffle: Whether to shuffle output samples,
            or instead draw them in chronological order.
        reverse: Boolean: if `true`, timesteps in each output sample will be
            in reverse chronological order.
        batch_size: Number of timeseries samples in each batch
            (except maybe the last one).
    # Returns
        A [Sequence](/utils/#sequence) instance.
    # Examples
    ```python
    from keras.preprocessing.sequence import TimeseriesGenerator
    import numpy as np
    data = np.array([[i] for i in range(50)])
    targets = np.array([[i] for i in range(50)])
    data_gen = TimeseriesGenerator(data, targets,
                                   length=10, sampling_rate=2,
                                   batch_size=2)
    assert len(data_gen) == 20
    batch_0 = data_gen[0]
    x, y = batch_0
    assert np.array_equal(x,
                          np.array([[[0], [2], [4], [6], [8]],
                                    [[1], [3], [5], [7], [9]]]))
    assert np.array_equal(y,
                          np.array([[10], [11]]))
    ```
    """   
    
    def __init__(self, data, targets, length,
                 sampling_rate = 1,
                 stride = 1,
                 start_index = 0,
                 end_index = None,
                 targettype = 'multi', # 'multi', 'simil', 'regres'
                 shuffle=False,
                 reverse=False,
                 batch_size=128,
                 aug = False,
                 expanddims=True):
        
       
        if len(data) != len(targets):
            print('Data and targets have to be' +
                             ' of same length. '
                             'Data length is {}'.format(len(data)) +
                             ' while target length is {}'.format(len(targets)))
            print('Target is assumed to be similarity parameter')
            targettype = 'simil'

        self.data = data
        self.targets = targets
        self.length = length
        self.sampling_rate = sampling_rate
        self.stride = stride
        self.start_index = start_index + length
        if end_index is None:
            end_index = len(data) - 1
        self.end_index = end_index
        self.targettype = targettype
        self.shuffle = shuffle
        self.reverse = reverse
        self.batch_size = batch_size
        self.aug = aug
        self.expanddims = expanddims
        
        if self.start_index > self.end_index:
            raise ValueError('`start_index+length=%i > end_index=%i` '
                             'is disallowed, as no part of the sequence '
                             'would be left to be used as current step.'
                             % (self.start_index, self.end_index))

    def __len__(self):
        return (self.end_index - self.start_index +
                self.batch_size * self.stride) // (self.batch_size * self.stride)

    def __getitem__(self, index):
        if self.shuffle:
            rows = np.random.randint(
                self.start_index, self.end_index + 1, size=self.batch_size)
        else:
            i = self.start_index + self.batch_size * self.stride * index
            rows = np.arange(i, min(i + self.batch_size *
                                    self.stride, self.end_index + 1), self.stride)
        if self.targettype == 'multi':
            samples = np.array([self.data[row-self.length:row:self.sampling_rate]
                                for row in rows], dtype = 'float64')
            
            targets = np.array([self.targets[row-self.length:row:self.sampling_rate]
                                for row in rows], dtype = 'float64')
            
            if self.expanddims:
                samples = np.expand_dims(samples, 2) 
                targets = np.expand_dims(targets, 2)
            else:
                samples = np.expand_dims(samples, 3)
                targets = np.squeeze(targets)
                
        if self.targettype == 'simil':
            samples = np.array([self.data[row - self.length:row:self.sampling_rate]
                                for row in rows])
            targets = np.array([self.targets[i] for i,row in enumerate(rows)])
            

        if self.targettype == 'regres':
            samples = np.array([self.data[row - self.length:row:self.sampling_rate]
                            for row in rows])
            targets = np.array([self.targets[row] for row in rows])

        if self.reverse:
            return samples[:, ::-1, ...], targets
        
        
        return samples, targets
74e368605d6c7de411be495f6844b343588c9342
<mask token>


class UnetGen(Sequence):
    <mask token>
    <mask token>

    def __len__(self):
        return (self.end_index - self.start_index + self.batch_size * self.
            stride) // (self.batch_size * self.stride)

    def __getitem__(self, index):
        if self.shuffle:
            rows = np.random.randint(self.start_index, self.end_index + 1,
                size=self.batch_size)
        else:
            i = self.start_index + self.batch_size * self.stride * index
            rows = np.arange(i, min(i + self.batch_size * self.stride, self
                .end_index + 1), self.stride)
        if self.targettype == 'multi':
            samples = np.array([self.data[row - self.length:row:self.
                sampling_rate] for row in rows], dtype='float64')
            targets = np.array([self.targets[row - self.length:row:self.
                sampling_rate] for row in rows], dtype='float64')
            if self.expanddims:
                samples = np.expand_dims(samples, 2)
                targets = np.expand_dims(targets, 2)
            else:
                samples = np.expand_dims(samples, 3)
                targets = np.squeeze(targets)
        if self.targettype == 'simil':
            samples = np.array([self.data[row - self.length:row:self.
                sampling_rate] for row in rows])
            targets = np.array([self.targets[i] for i, row in enumerate(rows)])
        if self.targettype == 'regres':
            samples = np.array([self.data[row - self.length:row:self.
                sampling_rate] for row in rows])
            targets = np.array([self.targets[row] for row in rows])
        if self.reverse:
            return samples[:, ::-1, ...], targets
        return samples, targets

<mask token>


class UnetGen(Sequence):
    <mask token>

    def __init__(self, data, targets, length, sampling_rate=1, stride=1,
        start_index=0, end_index=None, targettype='multi', shuffle=False,
        reverse=False, batch_size=128, aug=False, expanddims=True):
        if len(data) != len(targets):
            print('Data and targets have to be' +
                ' of same length. Data length is {}'.format(len(data)) +
                ' while target length is {}'.format(len(targets)))
            print('Target is assumed to be similarity parameter')
            targettype = 'simil'
        self.data = data
        self.targets = targets
        self.length = length
        self.sampling_rate = sampling_rate
        self.stride = stride
        self.start_index = start_index + length
        if end_index is None:
            end_index = len(data) - 1
        self.end_index = end_index
        self.targettype = targettype
        self.shuffle = shuffle
        self.reverse = reverse
        self.batch_size = batch_size
        self.aug = aug
        self.expanddims = expanddims
        if self.start_index > self.end_index:
            raise ValueError(
                '`start_index+length=%i > end_index=%i` is disallowed, as no part of the sequence would be left to be used as current step.'
                 % (self.start_index, self.end_index))

    def __len__(self):
        return (self.end_index - self.start_index + self.batch_size * self.
            stride) // (self.batch_size * self.stride)

    def __getitem__(self, index):
        if self.shuffle:
            rows = np.random.randint(self.start_index, self.end_index + 1,
                size=self.batch_size)
        else:
            i = self.start_index + self.batch_size * self.stride * index
            rows = np.arange(i, min(i + self.batch_size * self.stride, self
                .end_index + 1), self.stride)
        if self.targettype == 'multi':
            samples = np.array([self.data[row - self.length:row:self.
                sampling_rate] for row in rows], dtype='float64')
            targets = np.array([self.targets[row - self.length:row:self.
                sampling_rate] for row in rows], dtype='float64')
            if self.expanddims:
                samples = np.expand_dims(samples, 2)
                targets = np.expand_dims(targets, 2)
            else:
                samples = np.expand_dims(samples, 3)
                targets = np.squeeze(targets)
        if self.targettype == 'simil':
            samples = np.array([self.data[row - self.length:row:self.
                sampling_rate] for row in rows])
            targets = np.array([self.targets[i] for i, row in enumerate(rows)])
        if self.targettype == 'regres':
            samples = np.array([self.data[row - self.length:row:self.
                sampling_rate] for row in rows])
            targets = np.array([self.targets[row] for row in rows])
        if self.reverse:
            return samples[:, ::-1, ...], targets
        return samples, targets

<mask token>


class UnetGen(Sequence):
    """Utility class for generating batches of temporal data.
    This class takes in a sequence of data-points gathered at
    equal intervals, along with time series parameters such as
    stride, length of history, etc., to produce batches for
    training/validation.
    # Arguments
        data: Indexable generator (such as list or Numpy array)
            containing consecutive data points (timesteps).
            The data should be at 2D, and axis 0 is expected
            to be the time dimension.
        targets: Targets corresponding to timesteps in `data`.
            It should have same length as `data`.
        length: Length of the output sequences (in number of timesteps).
        sampling_rate: Period between successive individual timesteps
            within sequences. For rate `r`, timesteps
            `data[i]`, `data[i-r]`, ... `data[i - length]`
            are used for create a sample sequence.
        stride: Period between successive output sequences.
            For stride `s`, consecutive output samples would
            be centered around `data[i]`, `data[i+s]`, `data[i+2*s]`, etc.
        start_index: Data points earlier than `start_index` will not be used
            in the output sequences. This is useful to reserve part of the
            data for test or validation.
        end_index: Data points later than `end_index` will not be used
            in the output sequences. This is useful to reserve part of the
            data for test or validation.
        shuffle: Whether to shuffle output samples,
            or instead draw them in chronological order.
        reverse: Boolean: if `true`, timesteps in each output sample will be
            in reverse chronological order.
        batch_size: Number of timeseries samples in each batch
            (except maybe the last one).
    # Returns
        A [Sequence](/utils/#sequence) instance.
    # Examples
    ```python
    from keras.preprocessing.sequence import TimeseriesGenerator
    import numpy as np
    data = np.array([[i] for i in range(50)])
    targets = np.array([[i] for i in range(50)])
    data_gen = TimeseriesGenerator(data, targets,
                                   length=10, sampling_rate=2,
                                   batch_size=2)
    assert len(data_gen) == 20
    batch_0 = data_gen[0]
    x, y = batch_0
    assert np.array_equal(x,
                          np.array([[[0], [2], [4], [6], [8]],
                                    [[1], [3], [5], [7], [9]]]))
    assert np.array_equal(y,
                          np.array([[10], [11]]))
    ```
    """

    def __init__(self, data, targets, length, sampling_rate=1, stride=1,
        start_index=0, end_index=None, targettype='multi', shuffle=False,
        reverse=False, batch_size=128, aug=False, expanddims=True):
        if len(data) != len(targets):
            print('Data and targets have to be' +
                ' of same length. Data length is {}'.format(len(data)) +
                ' while target length is {}'.format(len(targets)))
            print('Target is assumed to be similarity parameter')
            targettype = 'simil'
        self.data = data
        self.targets = targets
        self.length = length
        self.sampling_rate = sampling_rate
        self.stride = stride
        self.start_index = start_index + length
        if end_index is None:
            end_index = len(data) - 1
        self.end_index = end_index
        self.targettype = targettype
        self.shuffle = shuffle
        self.reverse = reverse
        self.batch_size = batch_size
        self.aug = aug
        self.expanddims = expanddims
        if self.start_index > self.end_index:
            raise ValueError(
                '`start_index+length=%i > end_index=%i` is disallowed, as no part of the sequence would be left to be used as current step.'
                 % (self.start_index, self.end_index))

    def __len__(self):
        return (self.end_index - self.start_index + self.batch_size * self.
            stride) // (self.batch_size * self.stride)

    def __getitem__(self, index):
        if self.shuffle:
            rows = np.random.randint(self.start_index, self.end_index + 1,
                size=self.batch_size)
        else:
            i = self.start_index + self.batch_size * self.stride * index
            rows = np.arange(i, min(i + self.batch_size * self.stride, self
                .end_index + 1), self.stride)
        if self.targettype == 'multi':
            samples = np.array([self.data[row - self.length:row:self.
                sampling_rate] for row in rows], dtype='float64')
            targets = np.array([self.targets[row - self.length:row:self.
                sampling_rate] for row in rows], dtype='float64')
            if self.expanddims:
                samples = np.expand_dims(samples, 2)
                targets = np.expand_dims(targets, 2)
            else:
                samples = np.expand_dims(samples, 3)
                targets = np.squeeze(targets)
        if self.targettype == 'simil':
            samples = np.array([self.data[row - self.length:row:self.
                sampling_rate] for row in rows])
            targets = np.array([self.targets[i] for i, row in enumerate(rows)])
        if self.targettype == 'regres':
            samples = np.array([self.data[row - self.length:row:self.
                sampling_rate] for row in rows])
            targets = np.array([self.targets[row] for row in rows])
        if self.reverse:
            return samples[:, ::-1, ...], targets
        return samples, targets

import random
import numpy as np
import tensorflow as tf
from tensorflow.keras.utils import Sequence


class UnetGen(Sequence):
    """Utility class for generating batches of temporal data.
    This class takes in a sequence of data-points gathered at
    equal intervals, along with time series parameters such as
    stride, length of history, etc., to produce batches for
    training/validation.
    # Arguments
        data: Indexable generator (such as list or Numpy array)
            containing consecutive data points (timesteps).
            The data should be at 2D, and axis 0 is expected
            to be the time dimension.
        targets: Targets corresponding to timesteps in `data`.
            It should have same length as `data`.
        length: Length of the output sequences (in number of timesteps).
        sampling_rate: Period between successive individual timesteps
            within sequences. For rate `r`, timesteps
            `data[i]`, `data[i-r]`, ... `data[i - length]`
            are used for create a sample sequence.
        stride: Period between successive output sequences.
            For stride `s`, consecutive output samples would
            be centered around `data[i]`, `data[i+s]`, `data[i+2*s]`, etc.
        start_index: Data points earlier than `start_index` will not be used
            in the output sequences. This is useful to reserve part of the
            data for test or validation.
        end_index: Data points later than `end_index` will not be used
            in the output sequences. This is useful to reserve part of the
            data for test or validation.
        shuffle: Whether to shuffle output samples,
            or instead draw them in chronological order.
        reverse: Boolean: if `true`, timesteps in each output sample will be
            in reverse chronological order.
        batch_size: Number of timeseries samples in each batch
            (except maybe the last one).
    # Returns
        A [Sequence](/utils/#sequence) instance.
    # Examples
    ```python
    from keras.preprocessing.sequence import TimeseriesGenerator
    import numpy as np
    data = np.array([[i] for i in range(50)])
    targets = np.array([[i] for i in range(50)])
    data_gen = TimeseriesGenerator(data, targets,
                                   length=10, sampling_rate=2,
                                   batch_size=2)
    assert len(data_gen) == 20
    batch_0 = data_gen[0]
    x, y = batch_0
    assert np.array_equal(x,
                          np.array([[[0], [2], [4], [6], [8]],
                                    [[1], [3], [5], [7], [9]]]))
    assert np.array_equal(y,
                          np.array([[10], [11]]))
    ```
    """

    def __init__(self, data, targets, length, sampling_rate=1, stride=1,
        start_index=0, end_index=None, targettype='multi', shuffle=False,
        reverse=False, batch_size=128, aug=False, expanddims=True):
        if len(data) != len(targets):
            print('Data and targets have to be' +
                ' of same length. Data length is {}'.format(len(data)) +
                ' while target length is {}'.format(len(targets)))
            print('Target is assumed to be similarity parameter')
            targettype = 'simil'
        self.data = data
        self.targets = targets
        self.length = length
        self.sampling_rate = sampling_rate
        self.stride = stride
        self.start_index = start_index + length
        if end_index is None:
            end_index = len(data) - 1
        self.end_index = end_index
        self.targettype = targettype
        self.shuffle = shuffle
        self.reverse = reverse
        self.batch_size = batch_size
        self.aug = aug
        self.expanddims = expanddims
        if self.start_index > self.end_index:
            raise ValueError(
                '`start_index+length=%i > end_index=%i` is disallowed, as no part of the sequence would be left to be used as current step.'
                 % (self.start_index, self.end_index))

    def __len__(self):
        return (self.end_index - self.start_index + self.batch_size * self.
            stride) // (self.batch_size * self.stride)

    def __getitem__(self, index):
        if self.shuffle:
            rows = np.random.randint(self.start_index, self.end_index + 1,
                size=self.batch_size)
        else:
            i = self.start_index + self.batch_size * self.stride * index
            rows = np.arange(i, min(i + self.batch_size * self.stride, self
                .end_index + 1), self.stride)
        if self.targettype == 'multi':
            samples = np.array([self.data[row - self.length:row:self.
                sampling_rate] for row in rows], dtype='float64')
            targets = np.array([self.targets[row - self.length:row:self.
                sampling_rate] for row in rows], dtype='float64')
            if self.expanddims:
                samples = np.expand_dims(samples, 2)
                targets = np.expand_dims(targets, 2)
            else:
                samples = np.expand_dims(samples, 3)
                targets = np.squeeze(targets)
        if self.targettype == 'simil':
            samples = np.array([self.data[row - self.length:row:self.
                sampling_rate] for row in rows])
            targets = np.array([self.targets[i] for i, row in enumerate(rows)])
        if self.targettype == 'regres':
            samples = np.array([self.data[row - self.length:row:self.
                sampling_rate] for row in rows])
            targets = np.array([self.targets[row] for row in rows])
        if self.reverse:
            return samples[:, ::-1, ...], targets
        return samples, targets

import random
import numpy as np
import tensorflow as tf
from tensorflow.keras.utils import Sequence


class UnetGen(Sequence):
    
    """Utility class for generating batches of temporal data.
    This class takes in a sequence of data-points gathered at
    equal intervals, along with time series parameters such as
    stride, length of history, etc., to produce batches for
    training/validation.
    # Arguments
        data: Indexable generator (such as list or Numpy array)
            containing consecutive data points (timesteps).
            The data should be at 2D, and axis 0 is expected
            to be the time dimension.
        targets: Targets corresponding to timesteps in `data`.
            It should have same length as `data`.
        length: Length of the output sequences (in number of timesteps).
        sampling_rate: Period between successive individual timesteps
            within sequences. For rate `r`, timesteps
            `data[i]`, `data[i-r]`, ... `data[i - length]`
            are used for create a sample sequence.
        stride: Period between successive output sequences.
            For stride `s`, consecutive output samples would
            be centered around `data[i]`, `data[i+s]`, `data[i+2*s]`, etc.
        start_index: Data points earlier than `start_index` will not be used
            in the output sequences. This is useful to reserve part of the
            data for test or validation.
        end_index: Data points later than `end_index` will not be used
            in the output sequences. This is useful to reserve part of the
            data for test or validation.
        shuffle: Whether to shuffle output samples,
            or instead draw them in chronological order.
        reverse: Boolean: if `true`, timesteps in each output sample will be
            in reverse chronological order.
        batch_size: Number of timeseries samples in each batch
            (except maybe the last one).
    # Returns
        A [Sequence](/utils/#sequence) instance.
    # Examples
    ```python
    from keras.preprocessing.sequence import TimeseriesGenerator
    import numpy as np
    data = np.array([[i] for i in range(50)])
    targets = np.array([[i] for i in range(50)])
    data_gen = TimeseriesGenerator(data, targets,
                                   length=10, sampling_rate=2,
                                   batch_size=2)
    assert len(data_gen) == 20
    batch_0 = data_gen[0]
    x, y = batch_0
    assert np.array_equal(x,
                          np.array([[[0], [2], [4], [6], [8]],
                                    [[1], [3], [5], [7], [9]]]))
    assert np.array_equal(y,
                          np.array([[10], [11]]))
    ```
    """   
    
    def __init__(self, data, targets, length,
                 sampling_rate = 1,
                 stride = 1,
                 start_index = 0,
                 end_index = None,
                 targettype = 'multi', # 'multi', 'simil', 'regres'
                 shuffle=False,
                 reverse=False,
                 batch_size=128,
                 aug = False,
                 expanddims=True):
        
       
        if len(data) != len(targets):
            print('Data and targets have to be' +
                             ' of same length. '
                             'Data length is {}'.format(len(data)) +
                             ' while target length is {}'.format(len(targets)))
            print('Target is assumed to be similarity parameter')
            targettype = 'simil'

        self.data = data
        self.targets = targets
        self.length = length
        self.sampling_rate = sampling_rate
        self.stride = stride
        self.start_index = start_index + length
        if end_index is None:
            end_index = len(data) - 1
        self.end_index = end_index
        self.targettype = targettype
        self.shuffle = shuffle
        self.reverse = reverse
        self.batch_size = batch_size
        self.aug = aug
        self.expanddims = expanddims
        
        if self.start_index > self.end_index:
            raise ValueError('`start_index+length=%i > end_index=%i` '
                             'is disallowed, as no part of the sequence '
                             'would be left to be used as current step.'
                             % (self.start_index, self.end_index))

    def __len__(self):
        return (self.end_index - self.start_index +
                self.batch_size * self.stride) // (self.batch_size * self.stride)

    def __getitem__(self, index):
        if self.shuffle:
            rows = np.random.randint(
                self.start_index, self.end_index + 1, size=self.batch_size)
        else:
            i = self.start_index + self.batch_size * self.stride * index
            rows = np.arange(i, min(i + self.batch_size *
                                    self.stride, self.end_index + 1), self.stride)
        if self.targettype == 'multi':
            samples = np.array([self.data[row-self.length:row:self.sampling_rate]
                                for row in rows], dtype = 'float64')
            
            targets = np.array([self.targets[row-self.length:row:self.sampling_rate]
                                for row in rows], dtype = 'float64')
            
            if self.expanddims:
                samples = np.expand_dims(samples, 2) 
                targets = np.expand_dims(targets, 2)
            else:
                samples = np.expand_dims(samples, 3)
                targets = np.squeeze(targets)
                
        if self.targettype == 'simil':
            samples = np.array([self.data[row - self.length:row:self.sampling_rate]
                                for row in rows])
            targets = np.array([self.targets[i] for i,row in enumerate(rows)])
            

        if self.targettype == 'regres':
            samples = np.array([self.data[row - self.length:row:self.sampling_rate]
                            for row in rows])
            targets = np.array([self.targets[row] for row in rows])

        if self.reverse:
            return samples[:, ::-1, ...], targets
        
        
        return samples, targets