示例#1
0
    def similarity(self, image, method='correlation'):
        """ Calculate similarity of Brain_Data() instance with single Brain_Data or Nibabel image

            Args:
                image: Brain_Data or Nibabel instance of weight map

            Returns:
                pexp: Outputs a vector of pattern expression values

        """

        if not isinstance(image, Brain_Data):
            if isinstance(image, nib.Nifti1Image):
                image = Brain_Data(image)
            else:
                raise ValueError(
                    "Image is not a Brain_Data or nibabel instance")
        dim = image.shape()

        # Check to make sure masks are the same for each dataset and if not create a union mask
        # This might be handy code for a new Brain_Data method
        if np.sum(self.nifti_masker.mask_img.get_data() == 1) != np.sum(
                image.nifti_masker.mask_img.get_data() == 1):
            new_mask = intersect_masks(
                [self.nifti_masker.mask_img, image.nifti_masker.mask_img],
                threshold=1,
                connected=False)
            new_nifti_masker = NiftiMasker(mask_img=new_mask)
            data2 = new_nifti_masker.fit_transform(self.to_nifti())
            image2 = new_nifti_masker.fit_transform(image.to_nifti())
        else:
            data2 = self.data
            image2 = image.data

        # Calculate pattern expression
        if method is 'dot_product':
            if len(image2.shape) > 1:
                if image2.shape[0] > 1:
                    pexp = []
                    for i in range(image2.shape[0]):
                        pexp.append(np.dot(data2, image2[i, :]))
                    pexp = np.array(pexp)
                else:
                    pexp = np.dot(data2, image2)
            else:
                pexp = np.dot(data2, image2)
        elif method is 'correlation':
            if len(image2.shape) > 1:
                if image2.shape[0] > 1:
                    pexp = []
                    for i in range(image2.shape[0]):
                        pexp.append(pearson(image2[i, :], data2))
                    pexp = np.array(pexp)
                else:
                    pexp = pearson(image2, data2)
            else:
                pexp = pearson(image2, data2)
        return pexp
示例#2
0
    def similarity(self, image, method='correlation'):
        """ Calculate similarity of Brain_Data() instance with single Brain_Data or Nibabel image

            Args:
                self: Brain_Data instance of data to be applied
                image: Brain_Data or Nibabel instance of weight map

            Returns:
                pexp: Outputs a vector of pattern expression values

        """

        if not isinstance(image, Brain_Data):
            if isinstance(image, nib.Nifti1Image):
                image = Brain_Data(image)
            else:
                raise ValueError("Image is not a Brain_Data or nibabel instance")
        dim = image.shape()

        # Check to make sure masks are the same for each dataset and if not create a union mask
        # This might be handy code for a new Brain_Data method
        if np.sum(self.nifti_masker.mask_img.get_data()==1)!=np.sum(image.nifti_masker.mask_img.get_data()==1):
            new_mask = intersect_masks([self.nifti_masker.mask_img, image.nifti_masker.mask_img], threshold=1, connected=False)
            new_nifti_masker = NiftiMasker(mask_img=new_mask)
            data2 = new_nifti_masker.fit_transform(self.to_nifti())
            image2 = new_nifti_masker.fit_transform(image.to_nifti())
        else:
            data2 = self.data
            image2 = image.data


        # Calculate pattern expression
        if method is 'dot_product':
            if len(image2.shape) > 1:
                if image2.shape[0]>1:
                    pexp = []
                    for i in range(image2.shape[0]):
                        pexp.append(np.dot(data2, image2[i,:]))
                    pexp = np.array(pexp)
                else:
                    pexp = np.dot(data2, image2)
            else:
                pexp = np.dot(data2, image2)
        elif method is 'correlation':
            if len(image2.shape) > 1:
                if image2.shape[0]>1:
                    pexp = []
                    for i in range(image2.shape[0]):
                        pexp.append(pearson(image2[i,:], data2))
                    pexp = np.array(pexp)
                else:
                    pexp = pearson(image2, data2)
            else:
                pexp = pearson(image2, data2)
        return pexp
示例#3
0
def apply_mask(data=None,
               weight_map=None,
               mask=None,
               method='dot_product',
               save_output=False,
               output_dir='.'):
    """ Apply Nifti weight map to Nifti Images. 
 
        Args:
            data: nibabel instance of data to be applied
            weight_map: nibabel instance of weight map
            mask: binary nibabel mask
            method: type of pattern expression (e.g,. 'dot_product','correlation')
            save_output: Boolean indicating whether or not to save output to csv file.
            output_dir: Directory to use for writing all outputs
            **kwargs: Additional parameters to pass

        Returns:
            pexp: Outputs a vector of pattern expression values

    """

    if mask is not None:
        if type(mask) is not nib.nifti1.Nifti1Image:
            raise ValueError("Mask is not a nibabel instance")
    else:
        mask = nib.load(
            os.path.join(resource_dir, 'MNI152_T1_2mm_brain_mask_dil.nii.gz'))

    if type(data) is not nib.nifti1.Nifti1Image:
        raise ValueError("Data is not a nibabel instance")

    nifti_masker = NiftiMasker(mask_img=mask)
    data_masked = nifti_masker.fit_transform(data)

    if type(weight_map) is not nib.nifti1.Nifti1Image:
        raise ValueError("Weight_map is not a nibabel instance")

    weight_map_masked = nifti_masker.fit_transform(weight_map)

    # Calculate pattern expression
    if method is 'dot_product':
        pexp = np.dot(data_masked, np.transpose(weight_map_masked)).squeeze()
    elif method is 'correlation':
        pexp = pearson(data_masked, weight_map_masked)

    if save_output:
        np.savetxt(os.path.join(output_dir,
                                "Pattern_Expression_" + method + ".csv"),
                   pexp,
                   delimiter=",")

    return pexp
示例#4
0
def apply_mask(data=None, weight_map=None, mask=None, method='dot_product', save_output=False, output_dir='.'):
    """ Apply Nifti weight map to Nifti Images. 
 
        Args:
            data: nibabel instance of data to be applied
            weight_map: nibabel instance of weight map
            mask: binary nibabel mask
            method: type of pattern expression (e.g,. 'dot_product','correlation')
            save_output: Boolean indicating whether or not to save output to csv file.
            output_dir: Directory to use for writing all outputs
            **kwargs: Additional parameters to pass

        Returns:
            pexp: Outputs a vector of pattern expression values

    """ 

    if mask is not None:
        if type(mask) is not nib.nifti1.Nifti1Image:
            raise ValueError("Mask is not a nibabel instance")
    else:
        mask = nib.load(os.path.join(resource_dir,'MNI152_T1_2mm_brain_mask_dil.nii.gz'))
    
    if type(data) is not nib.nifti1.Nifti1Image:
        raise ValueError("Data is not a nibabel instance")
    
    nifti_masker = NiftiMasker(mask_img=mask)
    data_masked = nifti_masker.fit_transform(data)

    if type(weight_map) is not nib.nifti1.Nifti1Image:
        raise ValueError("Weight_map is not a nibabel instance")
    
    weight_map_masked = nifti_masker.fit_transform(weight_map)

    # Calculate pattern expression
    if method is 'dot_product':
        pexp = np.dot(data_masked,np.transpose(weight_map_masked)).squeeze()
    elif method is 'correlation':
        pexp = pearson(data_masked,weight_map_masked)

    if save_output:
        np.savetxt(os.path.join(output_dir,"Pattern_Expression_" + method + ".csv"), pexp, delimiter=",")

    return pexp
示例#5
0
def apply_mask(data=None,
               weight_map=None,
               mask=None,
               method='dot_product',
               save_output=False,
               output_dir='.'):
    """ Apply Nifti weight map to Nifti Images.

        Args:
            data: nibabel instance of data to be applied
            weight_map: nibabel instance of weight map
            mask: binary nibabel mask
            method: type of pattern expression (e.g,. 'dot_product','correlation')
            save_output: Boolean indicating whether or not to save output to csv file.
            output_dir: Directory to use for writing all outputs
            **kwargs: Additional parameters to pass

        Returns:
            pexp: Outputs a vector of pattern expression values

    """

    if mask is not None:
        if type(mask) is not nib.nifti1.Nifti1Image:
            raise ValueError("Mask is not a nibabel instance")
    else:
        mask = nib.load(
            os.path.join(get_resource_path(),
                         'MNI152_T1_2mm_brain_mask.nii.gz'))

    if type(data) is not nib.nifti1.Nifti1Image:
        if type(data) is str:
            if os.path.isfile(data):
                data = nib.load(data)
        elif type(data) is list:
            data = nib.funcs.concat_images(data)
        else:
            raise ValueError(
                "Data is not a nibabel instance, list of files, or a valid file name."
            )

    nifti_masker = NiftiMasker(mask_img=mask)
    data_masked = nifti_masker.fit_transform(data)
    if len(data_masked.shape) > 2:
        data_masked = data_masked.squeeze()

    if type(weight_map) is not nib.nifti1.Nifti1Image:
        if type(weight_map) is str:
            if os.path.isfile(weight_map):
                data = nib.load(weight_map)
        elif type(weight_map) is list:
            weight_map = nib.funcs.concat_images(weight_map)
        else:
            raise ValueError(
                "Weight_map is not a nibabel instance, list of files, or a valid file name."
            )

    weight_map_masked = nifti_masker.fit_transform(weight_map)
    if len(weight_map_masked.shape) > 2:
        weight_map_masked = weight_map_masked.squeeze()

    # Calculate pattern expression
    pexp = pd.DataFrame()
    for w in range(0, weight_map_masked.shape[0]):
        if method == 'dot_product':
            pexp = pexp.append(pd.Series(
                np.dot(data_masked, np.transpose(weight_map_masked[w, :]))),
                               ignore_index=True)
        elif method == 'correlation':
            pexp = pexp.append(pd.Series(
                pearson(data_masked, weight_map_masked[w, :])),
                               ignore_index=True)
    pexp = pexp.T

    if save_output:
        pexp.to_csv(
            os.path.join(output_dir, "Pattern_Expression_" + method + ".csv"))
        # np.savetxt(os.path.join(output_dir,"Pattern_Expression_" + method + ".csv"), pexp, delimiter=",")

    return pexp
示例#6
0
def apply_mask(data=None, weight_map=None, mask=None, method='dot_product', save_output=False, output_dir='.'):
    """ Apply Nifti weight map to Nifti Images.

        Args:
            data: nibabel instance of data to be applied
            weight_map: nibabel instance of weight map
            mask: binary nibabel mask
            method: type of pattern expression (e.g,. 'dot_product','correlation')
            save_output: Boolean indicating whether or not to save output to csv file.
            output_dir: Directory to use for writing all outputs
            **kwargs: Additional parameters to pass

        Returns:
            pexp: Outputs a vector of pattern expression values

    """

    if mask is not None:
        if type(mask) is not nib.nifti1.Nifti1Image:
            raise ValueError("Mask is not a nibabel instance")
    else:
        mask = nib.load(os.path.join(get_resource_path(),'MNI152_T1_2mm_brain_mask.nii.gz'))

    if type(data) is not nib.nifti1.Nifti1Image:
        if type(data) is str:
            if os.path.isfile(data):
                data = nib.load(data)
        elif type(data) is list:
            data = nib.funcs.concat_images(data)
        else:
            raise ValueError("Data is not a nibabel instance, list of files, or a valid file name.")

    nifti_masker = NiftiMasker(mask_img=mask)
    data_masked = nifti_masker.fit_transform(data)
    if len(data_masked.shape) > 2:
        data_masked = data_masked.squeeze()

    if type(weight_map) is not nib.nifti1.Nifti1Image:
        if type(weight_map) is str:
            if os.path.isfile(weight_map):
                data = nib.load(weight_map)
        elif type(weight_map) is list:
            weight_map = nib.funcs.concat_images(weight_map)
        else:
            raise ValueError("Weight_map is not a nibabel instance, list of files, or a valid file name.")

    weight_map_masked = nifti_masker.fit_transform(weight_map)
    if len(weight_map_masked.shape) > 2:
        weight_map_masked = weight_map_masked.squeeze()

    # Calculate pattern expression
    pexp = pd.DataFrame()
    for w in range(0, weight_map_masked.shape[0]):
        if method == 'dot_product':
            pexp = pexp.append(pd.Series(np.dot(data_masked,np.transpose(weight_map_masked[w,:]))), ignore_index=True)
        elif method == 'correlation':
            pexp = pexp.append(pd.Series(pearson(data_masked,weight_map_masked[w,:])), ignore_index=True)
    pexp = pexp.T

    if save_output:
        pexp.to_csv(os.path.join(output_dir,"Pattern_Expression_" + method + ".csv"))
        # np.savetxt(os.path.join(output_dir,"Pattern_Expression_" + method + ".csv"), pexp, delimiter=",")

    return pexp