import torch
import torch.nn as nn
import torchvision.models as models
import numpy as np
import torchvision.transforms as transforms
from PIL import Image
import time
import bioval.utils.gpu_manager as gpu_manager
import bioval.utils.distance as distance
import bioval.utils.aggregation as aggregation

from scipy.stats import pearsonr

class ConditionalEvaluation():
    """
    This class computes a set metrics for evaluation of conditional generation :
    1 - It computes TopK intraclass distance between generated and real.
    2 - It computes the interclass preservation of correlations between generated and real.
    3 - In the biology context, it measures the preservation of distance of generated classes
    from the control class in both real and generated images.

    Parameters
    ----------
    method : str, optional
        The method for computing the distance between the two tensors. The options are 'euclidean', 'cosine',
        'correlation', 'chebyshev', 'minkowski', and 'cityblock'. The default method is 'euclidean'.

    aggregate : str, optional
        The method for aggregating a 3D tensor into a 2D tensor (Or a 2D tensor to a 1D tensor in the case of control).
        The options are 'mean', 'median', and 'robust_mean'. The default aggregate method is 'mean'.

    Attributes
    ----------
    method : str
        The method for computing the distance between the two tensors.

    aggregate : str
        The method for aggregating a 3D tensor into a 2D tensor.

    _methods : dict
        A dictionary containing all the available methods for computing the distance between the two tensors.

    _aggregs : dict
        A dictionary containing all the available methods for aggregating a 3D tensor into a 2D tensor.
    """

    def __init__(self, method: str = 'euclidean',aggregate: str = 'mean',distributed_method: str = 'kid'):
        self._method = method
        self._distributed_method = distributed_method
        self._aggregate = aggregate
        self._methods = distance.get_distance_functions()
        self._distributed_methods = distance.get_distributed_distance_functions()
        self._aggregs = aggregation.get_aggregation_functions()
        self.inception = models.inception_v3(weights=models.Inception_V3_Weights.IMAGENET1K_V1, transform_input=False)
        # delete last layer of inception
        # Set the model to evaluation mode
        self.inception.fc = torch.nn.Identity()
        if self._method not in list(self._methods.keys()):
            raise ValueError(f"Method {self._method} not available. Available methods are {list(self._methods.keys())}")
        if self._distributed_method not in list(self._distributed_methods.keys()):
            raise ValueError(f"Distributed method {self._distributed_method} not available. Available methods are {list(self._distributed_methods.keys())}")
        if self._aggregate not in list(self._aggregs.keys()):
            raise ValueError(f"Aggregation method {self._aggregate} not available. Available aggregation methods are {list(self._aggregs.keys())}")

    @property
    def method(self):
        """
        Get the value of the 'method' property.
        Returns:
                str: The comparison method to be used for calculating the distance between arrays.
        """
        return self._method

    @method.setter
    def method(self, value):
        """
        Set the value of the 'method' property.

        Args:
            value (str): The comparison method to be used for calculating the distance between arrays.

        Raises:
            ValueError: If the value provided for 'method' is not in the list of valid methods.
        """
        if value not in self._methods:
            raise ValueError("Invalid method, choose from {}".format(self._methods))
        self._method = value

    @property
    def aggregate(self):
        """
        Get the value of the 'aggregate' property.

        Returns:
            str: The aggregation method to be used to convert 3D tensors to 2D tensors.
        """
        return self._aggregate

    @aggregate.setter
    def aggregate(self, value):
        """
        Set the value of the 'aggregate' property.

        Args:
            value (str): The aggregation method to be used to convert 3D tensors to 2D tensors.

        Raises:
            ValueError: If the value provided for 'aggregate' is not in the list of valid aggregations.
        """
        if value not in self._aggregs:
            raise ValueError("Invalid aggregation method, choose from {}".format(self._aggregs))
        self._aggregate = value

    def __call__(self, arr1: torch.Tensor, arr2: torch.Tensor, control = None, k_range=[1, 5, 10],aggregated=True,detailed_output=True,batch_size = 256,percent=0.1) -> dict:
        """
        This function is used to compare two tensors and return a dictionary with the scores of each of the three metrics.
        The comparison is performed based on the method and aggregation set for the class.
        The tensors for arr1 and arr2 should be either 2D or 3D tensors for embeddings, or 4D or 5D tensors
        for images. control is none if the control metric is not to be computed. If control metric is to be computed,
        the tensor for control should be either 1D or 2D tensor for embeddings, or 3D or 4D tensor for images.

        Args:

            - arr1: A torch.Tensor object of shape (N, I, H, W, C) or (N, H, W, C) or (N, I, F) or (N, F), where C is the number of channels,
            H is the height, and W is the width of the input image, and N is the number of classes, I the number of instances,
            and F is the number of features.
            - arr2: A torch.Tensor object of shape (N, I, H, W, C) or (N, H, W, C) or (N, I, F) or (N, F), where C is the number of channels,
            H is the height, and W is the width of the input image, and N is the number of classes, I the number of instances,
            and F is the number of features.
            - control: If not not None, it is a torch.Tensor object of shape (I, H, W, C) or (H, W, C) or (I, F) or (F), where C is the number of channels,
            H is the height, and W is the width of the input image, and I the number of instances, and F is the number of features.
            - k_range (List[int]): A list of values for the range of k for intraclass scores.

        Returns:

            dict: A dictionary with the scores for each metric computed.

        Examples:
        >>> topk = ConditionalEvaluation()
        >>> arr1 = torch.randn(100, 10)
        >>> arr2 = torch.randn(100, 10)
        >>> print(topk(arr1, arr2, k_range=[1, 5, 10, 20, 50, 100]))

        """
        # check if format is correct and prepare data if its in image format
        arr1,arr2,control = self._prepare_data_format(arr1, arr2,k_range,control,aggregated,batch_size)
        dict_score = {}
        #### Control metric
        if control is not None:
            #if aggregated:
            dict_score = self._compute_control_scores(arr1, arr2,control,dict_score,aggregated,detailed_output)
            #else:
                # Mention that control metric is not available for non aggregated data yet in an warning message
                #pass

        #### Inter class metric
        dict_score = self._compute_interclass_scores(arr1, arr2,dict_score,aggregated,detailed_output)
        #### Intra class metric
        dict_score = self._compute_intraclass_scores(arr1, arr2,k_range,dict_score,aggregated,detailed_output)
        dict_score = self._compute_overall_distributed_score(arr1, arr2,dict_score,aggregated,percent)
        return dict_score

    def _compute_interclass_scores(self,arr1: torch.Tensor, arr2: torch.Tensor,output : dict,aggregated=True,detailed_output=True) -> dict:
        """
        Computes the interclass scores of two matrices using the specified comparison method.
        Interclass metric is a metric that allows the comparison of classes of two different sets or matrices.
        It is computed by computing all distances between all pairs of classes of each of the two matrices and then
        compute the correlation between the two sets of distances.

        Args:

        - arr1: a tensor of shape (num_classes, num_features) representing the first array.
        - arr2: a tensor of shape (num_classes, num_features) representing the second array.
        - output: a dictionary containing the scores of the different metrics resulting from earlier computations.

        Returns:

        A dictionary containing the correlation score and the p-value score of the interclass metric comparison,
        in addition to other scores from earlier computations.

        """
        #### Inter class metric
        if aggregated:
            matrix_1 = self._methods[self._method](arr1, arr1)
            matrix_2 = self._methods[self._method](arr2, arr2)
        else:
            matrix_1 = self._distributional_distance_matrix(arr1, arr1)
            matrix_2 = self._distributional_distance_matrix(arr2, arr2)

        if detailed_output == True :
            output['interclass_matrix_1'] = matrix_1
            output['interclass_matrix_2'] = matrix_2

        # delete the diagonal of each matrix
        matrix_1 = matrix_1[~torch.eye(matrix_1.shape[0], dtype=bool)].view(matrix_1.shape[0], -1)
        matrix_2 = matrix_2[~torch.eye(matrix_2.shape[0], dtype=bool)].view(matrix_2.shape[0], -1)

        # flatten the matrices
        matrix_1 = matrix_1.flatten()
        matrix_2 = matrix_2.flatten()

        # Compute correlations between the two matrices
        corr,pvalue = self._compute_pearson_correlation(matrix_1, matrix_2)

        # add the correlation score to the dictionary
        output['inter_corr'] = corr
        if detailed_output == True :
            output['inter_p'] = pvalue
        return output

    def _compute_intraclass_scores(self,arr1: torch.Tensor, arr2: torch.Tensor,k_range : list,output : dict,aggregated=True,detailed_output=True) -> dict:
        """
        Computes the intraclass scores of two matrices using the specified comparison method in the initialization
        of the class. This metric compares the distance between the same class in arr1 and arr2. It also computes distances
        of a class in each array to all other classes in the other array. This is to measure the topk distance of a class
        in an array to others in the other array. The function takes two tensor inputs
        arr1 and arr2, a list of integer values k_range, and a dictionary output and returns a dictionary with
        evaluation scores. The function computes intraclass scores for the input tensors by computing the diagonal
        ranks of the comparison matrix between the tensors, and then calculates the mean ranks, top-k,
        and exact matching scores.

        Args:

        arr1: A tensor representing the first set of embeddings, with shape (N,F), with N being
        the number of classes and F the number of features.
        arr2: A tensor representing the second set of embeddings, with shape (N,F), with N being
        the number of classes and F the number of features.
        k_range: A list of integer values representing the number of top-k scores to be computed.
        output: A dictionary containing results of other evaluation metrics computed beforehand.

        Returns:

        output: A dictionary containing the results of the evaluation metric in addition to other metrics computed beforehand.
        """
        # get the matrix for comparison using the specified method

        if aggregated:
            matrix = self._methods[self._method](arr1, arr2)

        else:
            matrix = self._distributional_distance_matrix(arr1, arr2)
            # Compute the mean of diagonal values
            mean_diag = torch.mean(torch.diag(matrix))
            output["intra_" + self._distributed_method] = mean_diag.item()
        if detailed_output == True :
            output['matrix'] = matrix
        # compute the diagonal ranks of the comparison matrix
        ranks = self._compute_diag_ranks(matrix)
        # compute the scores for each value in k_range
        mean_ranks = torch.mean(ranks.float(), dim=0)

        # compute the scores for each value in k_range
        for k in k_range :
            number_values = k
            r = (ranks <= number_values).sum()
            r = (r/matrix.shape[0]) * 100
            output['intra_top'+str(k)] = r.item()
        if detailed_output == True :
            # add the mean ranks score to the dictionary
            output['mean_ranks'] = ((mean_ranks/matrix.shape[0]) * 100).item()
            # add the exact matching score to the dictionary
            r_exact = (ranks == 1).sum()
            output['exact_matching'] = ((r_exact/matrix.shape[0]) * 100).item()

        return output

    def _compute_control_scores(self,arr1: torch.Tensor, arr2: torch.Tensor,control: torch.Tensor,output : dict,aggregated=True,detailed_output=True) -> dict:
        """
        Computes the control scores of two matrices with control using the specified comparison method. We compute
        the distance between the control and the vector representing each class of the first array to get an array of distances.
        We compute the same score for the second array, and then compute the euclidean distance between the two arrays of distances.

        Args :

        arr1: A torch.Tensor object of shape  (N, F), where N is the number of classes and F is the number of features.
        arr2: A torch.Tensor object of shape  (N, F), where N is the number of classes and F is the number of features.
        control: A torch.Tensor object of shape  (F), where F is the number of features.
        output: A dictionary containing the results of the evaluation metrics computed by the function.

        Returns :

        output: A dictionary containing the results of the evaluation metrics computed by the function.
        """
        if aggregated :
            # Compute the distances between the control and the vectors of each class of the first array
            dist1 = torch.norm(arr1 - control, dim=-1)
            # Compute the distances between the control and the vectors of each class of the second array
            dist2 = torch.norm(arr2 - control, dim=-1)

            # Compute the euclidean distance between each element of the two arrays of distances
            score = torch.abs(dist1 - dist2)
            # Add all classes scores to a new dictionary inside existing dictionary :
            output['class_control_scores'] = score.tolist()
            output['control_score'] = torch.mean(score).item()

        else :
            distance_matrix_1 = np.zeros(arr1.shape[0])
            for i in range(arr1.shape[0]):
                distance = self._distributed_methods[self._distributed_method](arr1[i], control)
                distance_matrix_1[i] = distance
            distance_matrix_2 = np.zeros(arr2.shape[0])
            for i in range(arr2.shape[0]):
                distance = self._distributed_methods[self._distributed_method](arr2[i], control)
                distance_matrix_2[i] = distance

            # compute euclidean distance between each element of the two arrays of distances
            score = torch.abs(torch.from_numpy(distance_matrix_1) - torch.from_numpy(distance_matrix_2))
            # add all classes scores to a new dictionary inside existing dictionary :
            output['class_control_scores'] = score.tolist()
            # compute the mean of the scores wityh torch
            output['control_score'] = torch.mean(score).item()

        return output

    def _compute_overall_distributed_score(self,arr1: torch.Tensor, arr2: torch.Tensor,output : dict,aggregated=True,percent=0.1) -> dict:
        """
        Args:

        arr1: A tensor representing the first set of embeddings, with shape (N,F), with N being
        the number of classes and F the number of features.
        arr2: A tensor representing the second set of embeddings, with shape (N,F), with N being
        the number of classes and F the number of features.
        output: A dictionary containing results of other evaluation metrics computed beforehand.

        Returns:

        output: A dictionary containing the results of the evaluation metric in addition to other metrics computed beforehand.
        """
        if aggregated:
            pass
        else:
            if self._distributed_method == "kid":
                # change shape of arr1 from (N, I, F) to (n, F) where n = N*I
                arr1 = arr1.reshape(-1, arr1.shape[-1])
                # change shape of arr2 from (N, I, F) to (n, F) where n = N*I
                arr2 = arr2.reshape(-1, arr2.shape[-1])
            else :
                # sample uniformly 10% of instances of each class N, and convert to (n,F) where n = N*0.1
                arr1 = self._sample_uniformly(arr1, percent)
                arr2 = self._sample_uniformly(arr2, percent)

            distance = self._distributed_methods[self._distributed_method](arr1, arr2)
            output["overall_" + self._distributed_method] = distance
        return output

    def _prepare_data_format(self,arr1: torch.Tensor, arr2: torch.Tensor,k_range : list,control=None,aggregated=True,batch_size = 256) -> tuple:

        """
        Checks the input tensors, raises an error if they are not torch.Tensors,
        or if they do not have the same number of classes or features, or if they are not on the same device.
        It also checks the values of method and aggregate, raises an error if they are not in the list of defined methods or aggregations.
        The function then extracts the features from the arrays and control if they are composed of images, and aggregates
        the features of tensors into one vector of features.

        Args:

        arr1: A torch.Tensor object of shape (N, I, H, W, C) or (N, H, W, C) or (N, I, F) or (N, F), where C is the number of channels,
        H is the height, and W is the width of the input image, and N is the number of classes, I the number of instances,
        and F is the number of features.
        arr2: A torch.Tensor object of shape (N, I, H, W, C) or (N, H, W, C) or (N, I, F) or (N, F), where C is the number of channels,
        H is the height, and W is the width of the input image, and N is the number of classes, I the number of instances,
        and F is the number of features.
        k_range: A list of integers that indicates the values of k in the top-k similarity scoring metric.
        control: A torch.Tensor object of shape (I, H, W, C) or (H, W, C) or (I, F) or (F), where C is the number of channels,
        H is the height, and W is the width of the input image, and I the number of instances, and F is the number of features.

        Returns:

        A tuple containing two torch.Tensor objects that represent arr1 and arr2 and control after validation, extraction of features and aggregation.
        Raises:
        TypeError: if any of arr1 and arr2 is not a torch.Tensor object, or they are not of the same device, or they don't have float dtype.
        ValueError: if any of arr1 and arr2 have less than 2 classes, or they have different number of classes, or they don't have a valid number of dimensions, or the number of classes is less than the maximum value of k_range, or method or aggregate attributes are not valid.
        AssertionError: if the returned objects are not of type tuple.
        """
        # check if arr1 and arr2 are tensors of the same shape and raise error if not
        if isinstance(arr1, np.ndarray):
            arr1 = torch.tensor(arr1)
        if isinstance(arr2, np.ndarray):
            arr2 = torch.tensor(arr2)
        if isinstance(control, np.ndarray):
            control = torch.tensor(control)
        if isinstance(arr1, list):
            arr1 = torch.tensor(arr1)
        if isinstance(arr2, list):
            arr2 = torch.tensor(arr2)
        if isinstance(control, list):
            control = torch.tensor(control)
        if not isinstance(arr1, torch.Tensor):
            raise TypeError(f"First tensor should be a torch.Tensor but got {type(arr1)}")
        if not isinstance(arr2, torch.Tensor):
            raise TypeError(f"Second tensor should be a torch.Tensor but got {type(arr2)}")
        if not isinstance(control, torch.Tensor) and control is not None:
            raise TypeError(f"Non null Control tensor should be a torch.Tensor but got {type(control)}")
        # check if arr1 and arr2 are floats, and convert to float if they are not
        if arr1.dtype != torch.float:
            # try to convert to float and if error, raise error that input should have float dtype
            try:
                arr1 = arr1.float()
            except:
                raise TypeError(f"First tensor should have float dtype but got {arr1.dtype}")
        if arr2.dtype != torch.float:
            # try to convert to float and if error, raise error that input should have float dtype
            try:
                arr2 = arr2.float()
            except:
                raise TypeError(f"Second tensor should have float dtype but got {arr2.dtype}")
        if control is not None and control.dtype != torch.float:
            # try to convert to float and if error, raise error that input should have float dtype
            try:
                control = control.float()
            except:
                raise TypeError(f"Control tensor should have float dtype but got {control.dtype}")
        # Check if the number of classes is greater than 1
        if arr1.shape[0] < 2:
            raise ValueError(f"First tensor should have at least 2 classes but got {arr1.shape[0]}")
        if arr2.shape[0] < 2:
            raise ValueError(f"Second tensor should have at least 2 classes but got {arr2.shape[0]}")
        # control if the tensors have the same shape for the 0 dimension
        if arr1.shape[0] != arr2.shape[0]:
            raise ValueError(f"First tensor and second tensor should have the same number of classes in dimension 0 but got {arr1.shape[0]} and {arr2.shape[0]} respectively")
        # check if arr1 and arr2 are 2D or 3D or 4D or 5D tensors and raise error if not
        if arr1.ndim not in [2, 3,4,5]:
            raise ValueError(f"First tensor should be a 2D or 3D tensor for embeddings, or 4D or 5D tensor for images, but got {arr1.ndim}D tensor")
        if arr2.ndim not in [2, 3,4,5]:
            raise ValueError(f"Second tensor should be a 2D or 3D tensor for embeddings, or 4D or 5D tensor for images, but got {arr2.ndim}D tensor")
        if control is not None and control.ndim not in [1,2, 3,4]:
            raise ValueError(f"Control tensor should be a 1D or 2D or 3D or 4D tensor for images, but got {control.ndim}D tensor")
        # check if number of classes if less than max value of k_range
        if arr1.shape[0] < max(k_range):
            # modify k_range to have only values less than number of classes
            k_range = [k for k in k_range if k <= arr1.shape[0]]
        # check if both arrays are on the same device
        if arr1.device != arr2.device:
            raise ValueError(f"First tensor and second tensor should be on the same device but got {arr1.device} and {arr2.device} respectively")
        if control is not None and arr1.device != control.device:
            raise ValueError(f"Array tensors and Control tensor should be on the same device but got {arr1.device} and {control.device} respectively")
        # here the code for inception model
        if arr1.ndim in [4,5] :
            # call inception function
            arr1 = self._extract_inception_embeddings(arr1,batch_size)
        if arr2.ndim in [4,5] :
            # call inception function
            arr2 = self._extract_inception_embeddings(arr2,batch_size)
        if control is not None and control.ndim in [3,4] :
            # call inception function
            control = self._extract_inception_embeddings(control,batch_size)
        # control if both tensors have the same shape for the embedding dimension (if vector of size 2D or 3D)
        if arr1.ndim in [2, 3] and arr2.ndim in [2, 3] and arr1.shape[-1] != arr2.shape[-1]:
            raise ValueError(f"First tensor and second tensor should have the same number of features in dimension -1 but got {arr1.shape[-1]} and {arr2.shape[-1]} respectively")
        if control is not None and arr1.ndim in [2, 3] and control.ndim in [1, 2] and arr1.shape[-1] != control.shape[-1]:
            raise ValueError(f"First tensor and Control tensor should have the same number of features in dimension -1 but got {arr1.shape[-1]} and {control.shape[-1]} respectively")
        # check if method and aggregate attributes are valid
        if self._method not in self._methods and aggregated:
            raise ValueError(f"{self._method} not in list of defined methods. Please choose from {list(self._methods.keys())}")
        if self._aggregate not in self._aggregs and aggregated:
            raise ValueError(f"{self._aggregate} not in list of defined aggregations. Please choose from {list(self._aggregs.keys())}")
        # aggregate the arrays if they are 3D tensors
        if arr1.ndim == 3 and aggregated:
            arr1 = self._aggregs[self._aggregate](arr1)
        if arr2.ndim == 3 and aggregated:
            arr2 = self._aggregs[self._aggregate](arr2)
        if control is not None and control.ndim == 2 and aggregated:
            control = self._aggregs[self._aggregate](control,control=True)
        return arr1,arr2,control

    def _extract_inception_embeddings(self,images: torch.Tensor,batch_size = 256)  -> torch.Tensor:
        """
        Extract Inception embeddings from a batch of images.

        This function takes in a batch of images as a `torch.Tensor` and returns the embeddings generated
        by the Inception model. The input image can either be of shape (number of classes, number of images in classes, height, width, channels) or (number of classes, height, width, channels).
        The function performs the following steps:
            1. Resizes and crops the images to 299x299.
            2. Normalizes the images according to the mean and standard deviation specified.
            3. Passes the images through the Inception model and extracts the embeddings.
            4. Returns the embeddings with the original shape if input was 5 dimensional or reshaped if input was 4 dimensional, or unsqueezed if its 3D (control compound).

        Args:

            images (torch.Tensor): A batch of images with shape (number of classes, number of images in classes, height, width, channels) or (number of classes, height, width, channels),
            and for Control only : (number of classes, height, width, channels) or (height, width, channels).

        Returns:

            torch.Tensor: The embeddings generated from the Inception model for the given batch of images.

        Raises:

            ValueError: If the input image shape is not 4 or 5 dimensional.

        """
        original_transform = transform = transforms.Compose([
            transforms.Resize(299),
            transforms.CenterCrop(299),
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
        ])
        normalisation_values = [0.485, 0.456, 0.406]
        # add the number of channels to the normalisation values, by adding 0.406 to the list for each additional channel beyond 3, and deleting some normalisation values if nb of channels is less than 3
        normalisation_values = normalisation_values * (images.shape[-1]//3) + [0.485, 0.456, 0.406][:images.shape[-1]%3]
        second_normalisation_values = [0.229, 0.224, 0.225]
        # add the number of channels to the normalisation values, by adding 0.225 to the list for each additional channel beyond 3, and deleting some normalisation values if nb of channels is less than 3
        second_normalisation_values = second_normalisation_values * (images.shape[-1]//3) + [0.229, 0.224, 0.225][:images.shape[-1]%3]
        transform = transforms.Compose([
            transforms.Resize(299),
            transforms.CenterCrop(299),
            transforms.ToTensor(),
            transforms.Normalize(normalisation_values, second_normalisation_values)
        ])
        original_shape = images.shape
        # Convert images to a tensor
        if len(images.shape) == 5:
            # case when input is ('number of classes','number of images in classes', 'height','width', 'channels')
            images = images.reshape(original_shape[0]*original_shape[1], images.shape[2], images.shape[3], images.shape[4])
        elif len(images.shape) == 4:
            # case when input is ('number of classes', 'height','width', 'channels')
            pass
        elif len(images.shape) == 3:
            # case when input is ('height','width', 'channels')
            images = images.unsqueeze(0)
        else:
            raise ValueError("Input image shape should be either 5 or 4 or 3 dimensional")
        # get the device of the images
        device = images.device
        # move the inception model to the same device as the images
        self.inception.to(device)
        # Transfer the images to cpu
        images = images.cpu()
        images = images.numpy()
        images = images.astype(np.uint8)
        # if nb of channels is 3, convert the images to PIL images
        if images.shape[-1] == 3:
            images = [Image.fromarray(image.astype(np.uint8)) for image in images]
            images = torch.stack([original_transform(image) for image in images])
        # if nb of channels is 1, convert the images to PIL images
        elif images.shape[-1] == 1:
            images = [Image.fromarray(image.astype(np.uint8).squeeze()) for image in images]
            images = torch.stack([transform(image) for image in images])
        # if nb of channels is 4, convert the images to PIL images
        elif images.shape[-1] == 4:
            images = [Image.fromarray(image.astype(np.uint8)) for image in images]
            images = torch.stack([transform(image) for image in images])
            # if nb of channels is different from these values, convert each channel to an image and then stack them together
        else:
            final_images = []
            for c in range(images.shape[-1]):
                # extract the channel
                channel_images = images[:, :, :, c]
                # convert 1 channel images to 3 channels images by duplicating the channel 3 times
                channel_images = np.stack([channel_images, channel_images, channel_images], axis = -1)
                channel_images = channel_images.astype(np.uint8)

                # convert the channel to PIL images
                channel_images = [Image.fromarray(image.astype(np.uint8)) for image in channel_images]
                # stack the channel images together
                channel_images = torch.stack([original_transform(image) for image in channel_images])
                # deduplicate the channels to get back to 1 channel images
                channel_images = channel_images[:, 0, :, :]

                # append the channel images to the final images list
                final_images.append(channel_images)

            # stack the final images together
            images = torch.stack(final_images, dim = -1)
            # convert  (N, H, W, C) to (N, C, H, W)
            images = images.permute(0, 3, 1, 2)

        images_shape = images.shape

        # Forward pass the images through the model
        embeddings = self._process_images_in_batches(images, self.inception, device, batch_size)

        #with torch.no_grad():
            #embeddings = self.inception(images).detach()
        if len(original_shape) == 5:
            embeddings = embeddings.reshape(original_shape[0],original_shape[1],-1 )
        elif len(original_shape) == 4:
            embeddings = embeddings.reshape(images_shape[0], -1)
        return embeddings

    def _process_images_in_batches(self, images, inception, device, batch_size):
        num_images = len(images)
        embeddings = []

        with torch.no_grad():
            inception.eval()
            for i in range(0, num_images, batch_size):
                batch_images = images[i:i + min(batch_size, num_images - i)]

                # Handle images with different number of channels
                if batch_images.shape[1] != 3:
                    embeddings_per_channel = []
                    for c in range(batch_images.shape[1]):
                        # Expand the channel dimension to be (N, C, H, W) where C=3
                        channel_images = batch_images[:, c:c+1].expand(-1, 3, -1, -1)
                        channel_images = channel_images.to(device)

                        channel_embeddings = inception(channel_images)
                        channel_embeddings = channel_embeddings.detach()

                        del channel_images

                        # transfer batch embeddings to cpu
                        channel_embeddings = channel_embeddings.cpu()
                        embeddings_per_channel.append(channel_embeddings)

                    # Concatenate the embeddings for all channels
                    batch_embeddings = torch.cat(embeddings_per_channel, dim=1)
                else:
                    batch_images = batch_images.to(device)

                    batch_embeddings = inception(batch_images)
                    batch_embeddings = batch_embeddings.detach()

                    del batch_images

                    # transfer batch embeddings to cpu
                    batch_embeddings = batch_embeddings.cpu()
                embeddings.append(batch_embeddings)

        # Concatenate all the embeddings
        embeddings = torch.cat(embeddings, dim=0)

        # transfer embeddings to the device
        embeddings = embeddings.to(device)

        return embeddings

    def _distributional_distance_matrix(self,x: torch.Tensor, y: torch.Tensor) -> torch.Tensor:
        """
        Computes the distance matrix between two tensors of shape (C, I, F) using the given distance method.

        Args:
            x (torch.Tensor): First input tensor of shape (C, I, F).
            y (torch.Tensor): Second input tensor of shape (C, I, F).
            method (str): The distance method to be used. Can be 'swd' (sliced Wasserstein distance), 'mmd' (maximum mean
                        discrepancy), or 'kid' (kernel intrinsic discrepancy).

        Returns:
            torch.Tensor: The distance matrix of shape (C, C).
        """

        # Compute the distance between each pair of conditions using the given method
        distance_matrix = np.zeros((x.shape[0], x.shape[0]))
        for i in range(x.shape[0]):
            for j in range(x.shape[0]):
                distance = self._distributed_methods[self._distributed_method](x[i], y[j])
                distance_matrix[i, j] = distance

        # convert nd array to tensor
        distance_matrix = torch.from_numpy(distance_matrix)

        return distance_matrix

    @staticmethod
    def _compute_diag_ranks(matrix: torch.Tensor) -> torch.Tensor:
        """
        Computes the diagonal ranks of a given matrix.

        Args:

        matrix (torch.Tensor): 2D tensor of similarity scores between two arrays.

        Returns:

        torch.Tensor: 1D tensor of ranks of the diagonal elements in the input matrix.
                    Ranks start from 1, with 1 being the highest score.

        Example:

        >>> matrix = torch.tensor([[0.7, 0.2, 0.1], [0.3, 0.5, 0.2], [0.1, 0.3, 0.6]])
        >>> _compute_diag_ranks(matrix)
        tensor([3, 3, 3])
        """
        sorted_indices = torch.argsort(matrix, dim=1)
        rank_indices = torch.argsort(sorted_indices, dim=1)
        diagonal_indices = torch.arange(0,matrix.shape[0])
        # check if rank_indices or diagonal_indices is on gpu and if one of them is on gpu, put the other on the same gpu
        if rank_indices.device != diagonal_indices.device:
            if rank_indices.device == 'cpu':
                diagonal_indices = diagonal_indices.to(rank_indices.device)
            else:
                rank_indices = rank_indices.to(diagonal_indices.device)
        diagonal_ranks = torch.gather(rank_indices,dim=1,index=diagonal_indices.view(-1,1)) +1

        #flatten the tensor
        diagonal_ranks = diagonal_ranks.view(-1)

        return diagonal_ranks

    @staticmethod
    def _compute_pearson_correlation(matrix_1, matrix_2):
        """
        Calculates the Pearson correlation coefficient and the p-value between two matrices.

        Args:

            matrix_1 (torch.Tensor): First input matrix.
            matrix_2 (torch.Tensor): Second input matrix.

        Returns:

            A tuple containing the Pearson correlation coefficient (float) and the p-value (float) between the two matrices.

        Raises:

            TypeError: If either matrix_1 or matrix_2 is not a PyTorch Tensor.
        """
        # Compute the Pearson correlation between the entire vectors
        correlation, p_value = pearsonr(matrix_1.cpu().numpy(), matrix_2.cpu().numpy())

        # Return the vector-wise correlation and p-value
        return correlation, p_value

    def _sample_uniformly(self,arr,percentage=0.1):
        # sample from an aray of shape (N,I, F) uniformly along the I dimension to get a new array of shape (N, I', F), w@@here I' = I * percentage
        # get the number of samples to be taken from each array
        num_samples = int(arr.shape[1] * percentage)
        # get the indices of the samples to be taken
        indices = torch.randperm(arr.shape[1])[:num_samples]
        # get the samples
        arr = arr[:,indices,:]
        # convert N,I,F to N*I,F
        arr = arr.view(arr.shape[0]*arr.shape[1],arr.shape[2])
        return arr

# test the class
if __name__ == '__main__':

    best_gpu = gpu_manager.get_available_gpu()

    topk = ConditionalEvaluation(distributed_method='kid')
    """

    # test on 2D tensors
    arr1 = torch.randn(100, 10)
    arr2 = torch.randn(100, 10)
    print("2D tensors")
    start_time = time.time()
    print(topk(arr1, arr2, k_range=[1, 5, 10, 20, 50, 100]))
    print("Time elapsed: {:.2f}s".format(time.time() - start_time))

    # repreat test on 3D tensors
    arr1 = torch.randn(100, 10, 10)
    arr2 = torch.randn(100, 10, 10)
    print("3D tensors")
    start_time = time.time()
    print(topk(arr1, arr2, k_range=[1, 5, 10, 20, 50, 100]))
    print("Time elapsed: {:.2f}s".format(time.time() - start_time))

    # test on 4D tensors

    arr1 = torch.randn(100, 10, 10,3) * 256
    arr2 = torch.randn(100, 10, 10, 3) * 256
    print("4D tensors on CPU")
    start_time = time.time()
    print(topk(arr1, arr2, k_range=[1, 5, 10, 20, 50, 100]))
    print("Time elapsed: {:.2f}s".format(time.time() - start_time))

    # test on 5D tensors
    arr1 = torch.randn(100, 2, 10, 10, 3) * 256
    arr2 = torch.randn(100, 2, 10, 10, 3) * 256
    print("5D tensors on CPU")
    start_time = time.time()
    print(topk(arr1, arr2, k_range=[1, 5, 10, 20, 50, 100]))
    print("Time elapsed: {:.2f}s".format(time.time() - start_time))
    """

    if best_gpu is not None:
        # test on 2D tensors
        """
        arr1 = torch.randn(100, 10)
        arr2 = torch.randn(100, 10)
        arr1 = arr1.cuda(best_gpu)
        arr2 = arr2.cuda(best_gpu)
        print("2D tensors on GPU")
        start_time = time.time()
        print(topk(arr1, arr2, k_range=[1, 5, 10]))
        print("Time elapsed: {:.2f}s".format(time.time() - start_time))

        # repreat test on 3D tensors
        arr1 = torch.randn(110, 10, 10)
        arr2 = torch.randn(110, 10, 10)
        arr1 = arr1.cuda(best_gpu)
        arr2 = arr2.cuda(best_gpu)
        print("3D tensors on GPU")
        start_time = time.time()
        print(topk(arr1, arr2, k_range=[1, 5, 10]))
        print("Time elapsed: {:.2f}s".format(time.time() - start_time))

        arr1 = torch.randn(110, 10, 10,3) * 256
        arr2 = torch.randn(110, 10, 10, 3) * 256
        # pass the arrays to first gpu
        arr1 = arr1.cuda(best_gpu)
        arr2 = arr2.cuda(best_gpu)
        print("4D tensors on GPU")
        start_time = time.time()
        print(topk(arr1, arr2, k_range=[1, 5, 10]))
        print("Time elapsed: {:.2f}s".format(time.time() - start_time))

        # test on 4D tensors with 3D control
        arr1 = torch.randn(110, 10, 10,3) * 256
        arr2 = torch.randn(110, 10, 10, 3) * 256
        control = torch.randn(10, 10, 3) * 256
        # pass the arrays to first gpu
        arr1 = arr1.cuda(best_gpu)
        arr2 = arr2.cuda(best_gpu)
        control = control.cuda(best_gpu)
        print("4D tensors on GPU + 3D control")
        start_time = time.time()
        print(topk(arr1, arr2, control=control,k_range=[1, 5, 10]))
        print("Time elapsed: {:.2f}s".format(time.time() - start_time))

        # test on 4D tensors with 4D control
        arr1 = torch.randn(110, 10, 10,3) * 256
        arr2 = torch.randn(110, 10, 10, 3) * 256
        control = torch.randn(20,10, 10, 3) * 256
        # pass the arrays to first gpu
        arr1 = arr1.cuda(best_gpu)
        arr2 = arr2.cuda(best_gpu)
        control = control.cuda(best_gpu)
        print("4D tensors on GPU + 4D control")
        start_time = time.time()
        print(topk(arr1, arr2, control=control,k_range=[1, 5, 10]))
        print("Time elapsed: {:.2f}s".format(time.time() - start_time))

        # test on 5D tensors
        arr1 = torch.randn(110, 2, 10, 10, 3) * 256
        arr2 = torch.randn(110, 2, 10, 10, 3) * 256
        arr1 = arr1.cuda(best_gpu)
        arr2 = arr2.cuda(best_gpu)
        print("5D tensors on GPU")
        start_time = time.time()
        print(topk(arr1, arr2, k_range=[1, 5, 10]))
        print("Time elapsed: {:.2f}s".format(time.time() - start_time))

        # test on 5D tensors
        arr1 = torch.randn(8, 2, 10, 10, 3) * 256
        arr2 = torch.randn(8, 2, 10, 10, 3) * 256
        arr1 = arr1.cuda(best_gpu)
        arr2 = arr2.cuda(best_gpu)
        print("5D tensors on GPU, 8 classes")
        start_time = time.time()
        print(topk(arr1, arr2, k_range=[1, 5, 10]))
        print("Time elapsed: {:.2f}s".format(time.time() - start_time))

        # test on 5D tensors
        arr1 = torch.randn(50, 2, 10, 10, 3) * 256
        arr2 = torch.randn(50, 2, 10, 10, 3) * 256
        arr1 = arr1.cuda(best_gpu)
        arr2 = arr2.cuda(best_gpu)
        print("5D tensors on GPU, 50 classes")
        start_time = time.time()
        print(topk(arr1, arr2, k_range=[1, 5, 10]))
        print("Time elapsed: {:.2f}s".format(time.time() - start_time))

        # test on 5D tensors
        arr1 = torch.randn(150, 2, 10, 10, 3) * 256
        arr2 = torch.randn(150, 2, 10, 10, 3) * 256
        arr1 = arr1.cuda(best_gpu)
        arr2 = arr2.cuda(best_gpu)
        print("5D tensors on GPU, 150 classes")
        start_time = time.time()
        print(topk(arr1, arr2, k_range=[1, 5, 10]))
        print("Time elapsed: {:.2f}s".format(time.time() - start_time))

        # test on 5D tensors on Distributed KID
        arr1 = torch.randn(10, 100, 256, 256, 3) * 256
        arr2 = torch.randn(10, 100, 256, 256, 3) * 256
        arr1 = arr1.cuda(best_gpu)
        arr2 = arr2.cuda(best_gpu)

        print("5D tensors on GPU, 30 classes, aggregated with mean")
        start_time = time.time()
        print(topk(arr1, arr2, k_range=[1, 5, 10],detailed_output=False))
        print("Time elapsed: {:.2f}s".format(time.time() - start_time))
        """

        # test on 5D tensors on Distributed KID
        arr1 = torch.randn(30, 10, 256, 256, 1) * 256
        arr2 = torch.randn(30, 10, 256, 256, 1) * 256
        arr1 = arr1.cuda(best_gpu)
        arr2 = arr2.cuda(best_gpu)

        print("5D tensors on GPU, 30 classes, aggregated with mean")
        start_time = time.time()
        print(topk(arr1, arr2, k_range=[1, 5, 10],detailed_output=False))
        print("Time elapsed: {:.2f}s".format(time.time() - start_time))

        # test on 5D tensors on Distributed KID
        #arr1 = torch.randn(30, 20, 10, 10, 3) * 256
        #arr2 = torch.randn(30, 20, 10, 10, 3) * 256
        #arr1 = arr1.cuda(best_gpu)
        #arr2 = arr2.cuda(best_gpu)
        print("5D tensors on GPU, 30 classes, Distributed KID")
        start_time = time.time()
        print(topk(arr1, arr2, k_range=[1, 5, 10],aggregated=False,detailed_output=False))
        print("Time elapsed: {:.2f}s".format(time.time() - start_time))

        """
        # test on 5D tensors on Distributed KID
        arr1 = torch.randn(30, 10, 256)
        arr2 = torch.randn(30, 10, 256)
        arr1 = arr1.cuda(best_gpu)
        arr2 = arr2.cuda(best_gpu)
        print("3D tensors on GPU, 30 classes, aggregated with mean")
        start_time = time.time()
        print(topk(arr1, arr2, k_range=[1, 5, 10]))
        print("Time elapsed: {:.2f}s".format(time.time() - start_time))

        # test on 5D tensors on Distributed KID
        #arr1 = torch.randn(30, 20, 10, 10, 3) * 256
        #arr2 = torch.randn(30, 20, 10, 10, 3) * 256
        #arr1 = arr1.cuda(best_gpu)
        #arr2 = arr2.cuda(best_gpu)
        print("3D tensors on GPU, 30 classes, Distributed KID")
        start_time = time.time()
        print(topk(arr1, arr2, k_range=[1, 5, 10],aggregated=False))
        print("Time elapsed: {:.2f}s".format(time.time() - start_time))
        """

        # test on 4D tensors with 4D control
        arr1 = torch.randn(10,10, 256, 256,1) * 256
        arr2 = torch.randn(10,10, 256, 256, 1) * 256
        control = torch.randn(10,256, 256, 1) * 256
        # pass the arrays to first gpu
        arr1 = arr1.cuda(best_gpu)
        arr2 = arr2.cuda(best_gpu)
        control = control.cuda(best_gpu)
        print("5D tensors on GPU + 4D control on GPU")
        start_time = time.time()
        print(topk(arr1, arr2, control=control,k_range=[1, 5, 10],aggregated=False,detailed_output=False))
        print("Time elapsed: {:.2f}s".format(time.time() - start_time))


import sys
input = lambda: sys.stdin.readline().rstrip()
from collections import deque

def resolve():
    R, C = map(int, input().split())
    sy, sx = map(int, input().split())
    gy, gx = map(int, input().split())
    c = [list(input().split()) for _ in range(R)]

    dir = ((0, 1), (1, 0), (0, -1), (-1, 0))
    dist = [[float('inf')]*C for _ in range(R)]

    que = deque()
    que.append(((sy-1, sx-1), 0, set()))
    while len(que)>0:
        now = que.popleft()
        dist[now[0][0]][now[0][1]] = min(now[1], dist[now[0][0]][now[0][1]])
        for d in dir:
            next = (now[0][0]+d[0], now[0][1]+d[1])
            print(next)
            if 0<=next[0]<R and 0<=next[1]<C and c[next[0]][next[1]]=='.' and not next in now[2]:
                que.append((next, now[1]+1, now[2]|{now[0]}))

    print(dist[gy-1][gx-1])

if __name__ == '__main__':
    resolve()


from flask import g
from flask_resources import Resource, resource_requestctx, response_handler, route
from invenio_records_resources.resources.records.resource import (
    request_data,
    request_headers,
    request_search_args,
    request_view_args,
)

from .errors import ErrorHandlersMixin

#
# Resource
#
class BannerResource(ErrorHandlersMixin, Resource):
    """Banner resource."""

    def __init__(self, config, service):
        """Constructor."""
        super(BannerResource, self).__init__(config)
        self.service = service

    def create_url_rules(self):
        """Create the URL rules for the record resource."""
        routes = self.config.routes
        return [
            route("POST", routes["list"], self.create),
            route("GET", routes["item"], self.read),
            route("GET", routes["list"], self.search),
            route("DELETE", routes["item"], self.delete),
            route("PUT", routes["item"], self.update),
        ]

    @request_view_args
    @request_data
    @response_handler()
    def update(self):
        """Update a banner."""
        banner = self.service.update(
            id=resource_requestctx.view_args["banner_id"],
            identity=g.identity,
            data=resource_requestctx.data,
        )

        # disable expired banners
        self.service.disable_expired(identity=g.identity)

        return banner.to_dict(), 200

    @request_view_args
    @response_handler()
    def read(self):
        """Read a banner."""
        banner_id = resource_requestctx.view_args["banner_id"]
        banner = self.service.read(
            id=banner_id,
            identity=g.identity,
        )

        return banner.to_dict(), 200

    @request_search_args
    @response_handler(many=True)
    def search(self):
        """Perform a search over the banners."""
        banners = self.service.search(
            identity=g.identity,
            params=resource_requestctx.args,
        )

        return banners.to_dict(), 200

    @request_data
    @response_handler()
    def create(self):
        """Create a banner."""
        banner = self.service.create(
            g.identity,
            resource_requestctx.data or {},
        )

        # disable expired banners
        self.service.disable_expired(identity=g.identity)

        return banner.to_dict(), 201

    @request_headers
    @request_view_args
    def delete(self):
        """Delete a banner."""
        banner_id = resource_requestctx.view_args["banner_id"]
        banner = self.service.delete(
            id=banner_id,
            identity=g.identity,
        )

        return banner.to_dict(), 204


# https://onlinejudge.u-aizu.ac.jp/courses/lesson/1/ALDS1/4/ALDS1_4_B
# pythonでは便利な実装がすでにあるのでそれを使えるように
# 実装例はここにある https://docs.python.org/ja/3/library/bisect.html#searching-sorted-lists

from bisect import bisect_left
# 他にもbisect, bisect_leftがあったりするが、重複した値があったときに右側を返すか左側を返すかの違い
# bisectはrightと一緒

def find_exact_equal(a: list, x: int):
    i = bisect_left(a, x)
    if i != len(a) and a[i] == x:
        return 1  # 見つかったら1を返す。
    return 0  # 見つからなかったら0

n = input()
S = list(map(int, input().split()))
q = input()
T = list(map(int, input().split()))

cnt = 0
for t in T:
    cnt += find_exact_equal(S, t)

print(cnt)


from bkbase.dataflow.batch.conf import batch_conf
from bkbase.dataflow.batch.utils import batch_logger, batch_utils
from pyspark.sql.types import StructField, StructType

def create_hdfs_src_df(source_node, pipeline):
    schema = __get_input_schema(source_node)
    batch_logger.info("Get schema for {}: {}".format(source_node.node_id, schema))

    if isinstance(source_node.input, list):
        input_list = __check_input_path(source_node.input)
    else:
        raise Exception("Normal source node input only support list")

    batch_logger.info("start to generate Dataframe from path: {}".format(input_list))
    df = pipeline.spark.read.schema(schema).parquet(*input_list)
    batch_logger.info("Gernerated dataframe from path %s" % input_list)

    pipeline.check_and_add_monitor_input(input_list, source_node.node_id)
    return df

def create_hdfs_self_dependency_src_df(source_node, pipeline):
    schema = __get_input_schema(source_node)
    batch_logger.info("Get schema for {}: {}".format(source_node.node_id, schema))

    if isinstance(source_node.input, dict):
        input_list = source_node.input
    else:
        raise Exception("Self Dependency Source node input only support dict")

    origin_input_list = __check_input_path(input_list[batch_conf.SELF_DEPENDENCY_ORIGIN_KEY])
    df = pipeline.spark.read.schema(schema).parquet(*origin_input_list)
    if len(df.take(1)) != 0:
        pipeline.check_and_add_monitor_input(origin_input_list, source_node.node_id)
        return df
    batch_logger.info("Self dependency origin path is empty try to load from copy path %s" % input_list)

    copy_input_list = __check_input_path(input_list[batch_conf.SELF_DEPENDENCY_COPY_KEY])
    df = pipeline.spark.read.schema(schema).parquet(*copy_input_list)
    pipeline.check_and_add_monitor_input(copy_input_list, source_node.node_id)
    return df

def __get_input_schema(source_node):
    schema = []
    for field in source_node.fields:
        schema.append(StructField(field["field"], batch_utils.map_struct_type(field["type"])))

    for reserved_field in batch_conf.RESERVED_FIELDS:
        schema.append(StructField(reserved_field["field"], batch_utils.map_struct_type(reserved_field["type"])))

    return StructType(schema)

def __check_input_path(input_list):
    result_list = []
    for path in input_list:
        if batch_utils.is_hdfs_path_exist(path):
            result_list.append(path)
        else:
            batch_logger.info("Remove non-exist path from input list: {}".format(path))
    return result_list


import sys, re
from PyQt5.QtWidgets import QApplication, QDialog, QMessageBox
from PyQt5 import uic

class Dialog(QDialog):
    def __init__(self):
        QDialog.__init__(self)
        uic.loadUi("poll.ui", self)
        self.name.textChanged.connect(self.validarNombre)
        self.email.textChanged.connect(self.validarEmail)
        self.send.clicked.connect(self.validarFormulario)

    def validarNombre(self):
        nombre = self.name.text()
        validar = re.match('^[a-z\sáéíóúàèìòùäëïöüñ]+$', nombre, re.I)
        if nombre == "":
            self.name.setStyleSheet("border: 1px solid yellow")
            return False
        elif not validar:
            self.name.setStyleSheet("border: 1px solid red")
            return False
        else:
            self.name.setStyleSheet("border: 1px solid green")
            return True

    def validarEmail(self):
        email = self.email.text()
        validar = re.match('^[a-zA-Z0-9\._-]+@[a-zA-Z0-9-]{2,}[.][a-zA-Z]{2,4}$', email, re.I)
        if email == "":
            self.email.setStyleSheet("border: 1px solid yellow")
            return False
        elif not validar:
            self.email.setStyleSheet("border: 1px solid red")
            return False
        else:
            self.email.setStyleSheet("border: 1px solid green")
            return True

    def validarFormulario(self):
        if self.validarNombre() and self.validarEmail():
            QMessageBox.information(self, "Formulario correcto", "validación correcta", QMessageBox.Discard)
        else:
            QMessageBox.warning(self, "Formulario incorrecto", "Fallo en la validación", QMessageBox.Discard)

if __name__ == "__main__":
    app = QApplication(sys.argv)
    dialogo = Dialog()
    dialogo.show()
    app.exec_()


# generalized model fitting lab
#7/25

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import LogNorm
import math
from scipy import interpolate

# #let’s test x from -3 to 3 and y from -2 to 2
# x=np.linspace(-3,3,40)
# y=np.linspace(-2,2,40)
#
#
# #look up the documentation for meshgrid to find out what it does, particularly look at the examples at the bottom
# xv,yv=np.meshgrid(x,y)
#
#
# #calculate chi2 on the grid of values
# chi2=1/(3*np.exp(-((xv+1.01)**2+(yv+0.811)**2)/0.005)+np.exp(-((xv-
# 1)**2+yv**2)))
#
#
# #chi2 will naturally be a 1D array, but we need it to be a 2D array to
# # make a plot with imshow. np.reshape can handle that
# chi2=np.reshape(chi2,(len(x),len(y)))
#
#
# plt.imshow(chi2,origin='lower',extent=(-3,3,-2,2), norm=LogNorm()) #extent tells imshow what the x and y range are
# plt.colorbar()
#
# plt.show()

'''Create a “model” that is composed of 13 data points from x=-7 to x=+7 drawn from the function y = e −x . Create both a linear and cubic interpolation of the data. Evaluate the two
interpolations at 100 points from -7 to +7. Plot up the “model” and the two interpolations. You plot
should look like mine. Cubic fits produce “smoother” interpolations, but they are subject to massive
over/under-estimates if you have gaps in your model.'''
e = math.e
x= np.linspace(-7,7,13)
y = e**(-x**2)

f_lin = interpolate.interp1d(x, y)
f_cub = interpolate.interp1d(x, y, 'cubic')
print(f_lin)

xlinear = np.linspace(-7,7,100)
ylinear = f_lin(xlinear)

xcubic = np.linspace(-7,7,100)
ycubic = f_cub(xlinear)

plt.xlabel('x')
plt.ylabel('y')

# Supernova data
days = np.array([-4, -2.5, 1, 2])
m = np.array([1.46, 1.87, 1.08, 1.08])
offset = np.array([0.07, 0.09, 0.05, 0.05])

# plt.plot(xcubic, ycubic, '-', days, m, '.')
#
# plt.show()

# daysnew = np.linspace(-7,7,100)
# bestfit = f_cub(daysnew)
A = np.linspace(-4, 2, 100)
x_0 = np.linspace(-4, 2, 100)

chisq = np.zeros((100, 100))

def besttransformations(A, x_0, function):
    for a in range(0, len(A)):
        for x in range(0, len(x_0)):
            chisq[a][x] = ((function(days-x_0[x]) + A[a]-m)**2/(offset**2)).sum()

    print(A[np.unravel_index(np.argmin(chisq, axis=None), chisq.shape)[0]])
    print(x_0[np.unravel_index(np.argmin(chisq, axis=None), chisq.shape)[1]])

plt.imshow(chisq,origin='lower',extent=(-4,2,-4,2), norm =LogNorm()) #extent tells imshow what the x and y range are
plt.colorbar()

plt.show()

# first try different values of A / vertical offset, try lines for each and find minimum difference
# try x value offset, calc chisq

# plt.plot(days, m, 'o', xcubic-2.95, ycubic+1.07, '-')
# plt.show()


# to draw a bar graph
import matplotlib.pyplot as plt
x=[1,2,3,4,5,6]
y=[12,34,22,10,45,21]

tick_label=['one','two','three','four','five','six']
plt.bar(x,y,tick_label=tick_label,color=['green','blue'],width=.7)
plt.xlabel("x-axis")
plt.ylabel("y-axis")
plt.title('bar-graph')
plt.show()