import numpy as np
import nibabel as nb
import os
import sys
import nighresjava
from ..io import load_volume, save_volume
from ..utils import _output_dir_4saving, _fname_4saving, \
                    _check_topology_lut_dir, _check_available_memory


def segmentation_statistics(segmentation, intensity=None, template=None,
                            statistics=None, output_csv=None,
                            atlas=None, skip_first=True, ignore_zero=True,
                            save_data=False, overwrite=False, output_dir=None,
                            file_name=None):
    """ Segmentation Statistics

    Compute various statistics of image segmentations

    Parameters
    ----------
    segmentation: niimg
        Input segmentation image
    intensity: niimg, optional
        Input intensity image for intensity-based statistics
    template: niimg, optional
        Input template segmentation for comparisons
    statistics: [str] 
        Statistics to compute. Available options include:
        "Voxels", "Volume", "Center_of_mass",
        "Mean_intensity", "Std_intensity",
        "10_intensity","25_intensity","50_intensity","75_intensity","90_intensity",
        "Median_intensity","IQR_intensity",
        "SNR_intensity","rSNR_intensity",
		"Volumes", "Dice_overlap", "Jaccard_overlap", "Volume_difference",
        "False_positives","False_negatives",
        "Dilated_Dice_overlap","Dilated_false_positive","Dilated_false_negative",
        "Dilated_false_negative_volume","Dilated_false_positive_volume",
        "Center_distance",
        "Detected_clusters", "False_detections",
        "Cluster_numbers", "Mean_cluster_sizes", "Cluster_maps",
        "Average_surface_distance", "Average_surface_difference", 
        "Average_squared_surface_distance", "Hausdorff_distance"
    output_csv: str
        File name of the statistics file to generate or expand
    atlas: str, optional
        File name of an atlas file defining the segmentation labels
    skip_first: bool, optional
        Whether to skip the first segmentation label (usually representing the
        background, default is True)
    ignore_zero: bool, optional
        Whether to ignore zero intensity values in the intensity image
        (default is True)
    save_data: bool, optional
        Save output data to file (default is False)
    overwrite: bool, optional
        Overwrite existing results (default is False)
    output_dir: str, optional
        Path to desired output directory, will be created if it doesn't exist
    file_name: str, optional
        Desired base name for output files with file extension
        (suffixes will be added)

    Returns
    ----------
    dict
        Dictionary collecting outputs under the following keys
        (suffix of output files in brackets)

        * csv (str): The csv statistics file
        * map (niimg): Map of the estimated statistic, if relevant (stat-map)

    Notes
    ----------
    Original Java module by <NAME>.
    """

    print('\nSegmentation statistics')

    # make sure that saving related parameters are correct
    if save_data:
        output_dir = _output_dir_4saving(output_dir,segmentation)

        map_file = os.path.join(output_dir,
                        _fname_4saving(file_name=file_name,
                                   rootfile=segmentation,
                                   suffix='stat-map'))

        csv_file = os.path.join(output_dir, output_csv)

        if overwrite is False \
            and os.path.isfile(csv_file) :
                # check that the denoised data is the same too
                print("append results to existing csv file")

        if overwrite is True:
            # delete current stats file to start from the beginning
            os.remove(csv_file)
    else:
        csv_file = output_csv

    # start virtual machine, if not already running
    try:
        mem = _check_available_memory()
        nighresjava.initVM(initialheap=mem['init'], maxheap=mem['max'])
    except ValueError:
        pass
    # create algorithm instance
    stats = nighresjava.StatisticsSegmentation()

    # load first image and use it to set dimensions and resolution
    img = load_volume(segmentation)
    data = img.get_data()
    affine = img.affine
    header = img.header
    resolution = [x.item() for x in header.get_zooms()]
    dimensions = data.shape

    stats.setDimensions(dimensions[0], dimensions[1], dimensions[2])
    stats.setResolutions(resolution[0], resolution[1], resolution[2])

    stats.setSegmentationImage(nighresjava.JArray('int')(
                                    (data.flatten('F')).astype(int).tolist()))
    stats.setSegmentationName(_fname_4saving(rootfile=segmentation))

    # other input images, if any
    if intensity is not None:
        data = load_volume(intensity).get_data()
        stats.setIntensityImage(nighresjava.JArray('float')(
                                    (data.flatten('F')).astype(float)))
        stats.setIntensityName(_fname_4saving(rootfile=intensity))

    if template is not None:
        data = load_volume(template).get_data()
        stats.setTemplateImage(nighresjava.JArray('int')(
                                    (data.flatten('F')).astype(int).tolist()))
        stats.setTemplateName(_fname_4saving(rootfile=template))

    # set algorithm parameters
    if atlas is not None:
        stats.setAtlasFile(atlas)

    stats.setSkipFirstLabel(skip_first)
    stats.setIgnoreZeroIntensities(ignore_zero)
    
    stats.setStatisticNumber(len(statistics))
    for idx,stat in enumerate(statistics): stats.setStatisticAt(idx, stat)

    stats.setSpreadsheetFile(csv_file)

    # execute the algorithm
    try:
        stats.execute()

    except:
        # if the Java module fails, reraise the error it throws
        print("\n The underlying Java code did not execute cleanly: ")
        print(sys.exc_info()[0])
        raise
        return

    # reshape output to what nibabel likes
    output = False
    for st in statistics:
        if st=="Cluster_maps":
            output=True

    if (output):
        data = np.reshape(np.array(stats.getOutputImage(),
                                       dtype=np.int32), dimensions, 'F')
        header['cal_min'] = np.nanmin(data)
        header['cal_max'] = np.nanmax(data)
        output = nb.Nifti1Image(data, affine, header)

        if save_data:
            save_volume(map_file, output)

    csv_file = stats.getOutputFile()

    if output:
        return {'csv': csv_file, 'map': output}
    else:
        return {'csv': csv_file}

import numpy as np
import nibabel as nb
import os
import sys
import nighresjava
from ..io import load_volume, save_volume
from ..utils import _output_dir_4saving, _fname_4saving, \
                    _check_topology_lut_dir, _check_available_memory


def segmentation_statistics(segmentation, intensity=None, template=None,
                            statistics=None, output_csv=None,
                            atlas=None, skip_first=True, ignore_zero=True,
                            save_data=False, overwrite=False, output_dir=None,
                            file_name=None):
    """ Segmentation Statistics

    Compute various statistics of image segmentations

    Parameters
    ----------
    segmentation: niimg
        Input segmentation image
    intensity: niimg, optional
        Input intensity image for intensity-based statistics
    template: niimg, optional
        Input template segmentation for comparisons
    statistics: [str] 
        Statistics to compute. Available options include:
        "Voxels", "Volume", "Center_of_mass",
        "Mean_intensity", "Std_intensity",
        "10_intensity","25_intensity","50_intensity","75_intensity","90_intensity",
        "Median_intensity","IQR_intensity",
        "SNR_intensity","rSNR_intensity",
		"Volumes", "Dice_overlap", "Jaccard_overlap", "Volume_difference",
        "False_positives","False_negatives",
        "Dilated_Dice_overlap","Dilated_false_positive","Dilated_false_negative",
        "Dilated_false_negative_volume","Dilated_false_positive_volume",
        "Center_distance",
        "Detected_clusters", "False_detections",
        "Cluster_numbers", "Mean_cluster_sizes", "Cluster_maps",
        "Average_surface_distance", "Average_surface_difference", 
        "Average_squared_surface_distance", "Hausdorff_distance"
    output_csv: str
        File name of the statistics file to generate or expand
    atlas: str, optional
        File name of an atlas file defining the segmentation labels
    skip_first: bool, optional
        Whether to skip the first segmentation label (usually representing the
        background, default is True)
    ignore_zero: bool, optional
        Whether to ignore zero intensity values in the intensity image
        (default is True)
    save_data: bool, optional
        Save output data to file (default is False)
    overwrite: bool, optional
        Overwrite existing results (default is False)
    output_dir: str, optional
        Path to desired output directory, will be created if it doesn't exist
    file_name: str, optional
        Desired base name for output files with file extension
        (suffixes will be added)

    Returns
    ----------
    dict
        Dictionary collecting outputs under the following keys
        (suffix of output files in brackets)

        * csv (str): The csv statistics file
        * map (niimg): Map of the estimated statistic, if relevant (stat-map)

    Notes
    ----------
    Original Java module by <NAME>.
    """

    print('\nSegmentation statistics')

    # make sure that saving related parameters are correct
    if save_data:
        output_dir = _output_dir_4saving(output_dir,segmentation)

        map_file = os.path.join(output_dir,
                        _fname_4saving(file_name=file_name,
                                   rootfile=segmentation,
                                   suffix='stat-map'))

        csv_file = os.path.join(output_dir, output_csv)

        if overwrite is False \
            and os.path.isfile(csv_file) :
                # check that the denoised data is the same too
                print("append results to existing csv file")

        if overwrite is True:
            # delete current stats file to start from the beginning
            os.remove(csv_file)
    else:
        csv_file = output_csv

    # start virtual machine, if not already running
    try:
        mem = _check_available_memory()
        nighresjava.initVM(initialheap=mem['init'], maxheap=mem['max'])
    except ValueError:
        pass
    # create algorithm instance
    stats = nighresjava.StatisticsSegmentation()

    # load first image and use it to set dimensions and resolution
    img = load_volume(segmentation)
    data = img.get_data()
    affine = img.affine
    header = img.header
    resolution = [x.item() for x in header.get_zooms()]
    dimensions = data.shape

    stats.setDimensions(dimensions[0], dimensions[1], dimensions[2])
    stats.setResolutions(resolution[0], resolution[1], resolution[2])

    stats.setSegmentationImage(nighresjava.JArray('int')(
                                    (data.flatten('F')).astype(int).tolist()))
    stats.setSegmentationName(_fname_4saving(rootfile=segmentation))

    # other input images, if any
    if intensity is not None:
        data = load_volume(intensity).get_data()
        stats.setIntensityImage(nighresjava.JArray('float')(
                                    (data.flatten('F')).astype(float)))
        stats.setIntensityName(_fname_4saving(rootfile=intensity))

    if template is not None:
        data = load_volume(template).get_data()
        stats.setTemplateImage(nighresjava.JArray('int')(
                                    (data.flatten('F')).astype(int).tolist()))
        stats.setTemplateName(_fname_4saving(rootfile=template))

    # set algorithm parameters
    if atlas is not None:
        stats.setAtlasFile(atlas)

    stats.setSkipFirstLabel(skip_first)
    stats.setIgnoreZeroIntensities(ignore_zero)
    
    stats.setStatisticNumber(len(statistics))
    for idx,stat in enumerate(statistics): stats.setStatisticAt(idx, stat)

    stats.setSpreadsheetFile(csv_file)

    # execute the algorithm
    try:
        stats.execute()

    except:
        # if the Java module fails, reraise the error it throws
        print("\n The underlying Java code did not execute cleanly: ")
        print(sys.exc_info()[0])
        raise
        return

    # reshape output to what nibabel likes
    output = False
    for st in statistics:
        if st=="Cluster_maps":
            output=True

    if (output):
        data = np.reshape(np.array(stats.getOutputImage(),
                                       dtype=np.int32), dimensions, 'F')
        header['cal_min'] = np.nanmin(data)
        header['cal_max'] = np.nanmax(data)
        output = nb.Nifti1Image(data, affine, header)

        if save_data:
            save_volume(map_file, output)

    csv_file = stats.getOutputFile()

    if output:
        return {'csv': csv_file, 'map': output}
    else:
        return {'csv': csv_file}

Segmentation Statistics      Compute various statistics of image segmentations      Parameters     ----------     segmentation: niimg         Input segmentation image     intensity: niimg, optional         Input intensity image for intensity-based statistics     template: niimg, optional         Input template segmentation for comparisons     statistics: [str]          Statistics to compute. Available options include:         "Voxels", "Volume", "Center_of_mass",         "Mean_intensity", "Std_intensity",         "10_intensity","25_intensity","50_intensity","75_intensity","90_intensity",         "Median_intensity","IQR_intensity",         "SNR_intensity","rSNR_intensity", 		"Volumes", "Dice_overlap", "Jaccard_overlap", "Volume_difference",         "False_positives","False_negatives",         "Dilated_Dice_overlap","Dilated_false_positive","Dilated_false_negative",         "Dilated_false_negative_volume","Dilated_false_positive_volume",         "Center_distance",         "Detected_clusters", "False_detections",         "Cluster_numbers", "Mean_cluster_sizes", "Cluster_maps",         "Average_surface_distance", "Average_surface_difference",          "Average_squared_surface_distance", "Hausdorff_distance"     output_csv: str         File name of the statistics file to generate or expand     atlas: str, optional         File name of an atlas file defining the segmentation labels     skip_first: bool, optional         Whether to skip the first segmentation label (usually representing the         background, default is True)     ignore_zero: bool, optional         Whether to ignore zero intensity values in the intensity image         (default is True)     save_data: bool, optional         Save output data to file (default is False)     overwrite: bool, optional         Overwrite existing results (default is False)     output_dir: str, optional         Path to desired output directory, will be created if it doesn't exist     file_name: str, optional         Desired base name for output files with file extension         (suffixes will be added)      Returns     ----------     dict         Dictionary collecting outputs under the following keys         (suffix of output files in brackets)          * csv (str): The csv statistics file         * map (niimg): Map of the estimated statistic, if relevant (stat-map)      Notes     ----------     Original Java module by <NAME>. # make sure that saving related parameters are correct # check that the denoised data is the same too # delete current stats file to start from the beginning # start virtual machine, if not already running # create algorithm instance # load first image and use it to set dimensions and resolution # other input images, if any # set algorithm parameters # execute the algorithm # if the Java module fails, reraise the error it throws # reshape output to what nibabel likes