import pandas as pd
import tifffile
import numpy as np
import yaml
import pathlib as pl
import datetime as dt
import xml.etree.ElementTree as ET
import logging
import os
import datetime
from readlif.reader import LifFile


class LIFReaderGio(LifFile):

    def __init__(self, lif_file: str):

        super().__init__(lif_file)

    def load_all_metadata(self):
        """
        Load all metadata in the initialized LIF file
        only including sets with more than one frame (i.e. exclude snapshots)
        :return: pd.DataFrame with each row containing metadata of one measurement
        """

        root = ET.fromstring(self.xml_header)  # this is the full metadata as XML
        all_metadata_df = pd.DataFrame()

        # iterate all measurements
        for fle_ind, measurement in enumerate(self.get_iter_image()):
            this_measurement = self.get_image(fle_ind)
            lif_metadata = pd.Series()
            lif_metadata["Label"] = this_measurement.name
#            lif_metadata["Measu"] = fle_ind
            if this_measurement.dims.t > 1:
                #information about time between frames if more than one frame
                # converting from seconds to milliseconds
                cycle = float(
                    this_measurement.info["settings"]["FrameTime"])  # milliseconds per frame, Leica gives microseconds
                lif_metadata["Cycle"] = 1000 * cycle
                lif_metadata['SampFreq'] = this_measurement.info["scale"][3]  # frames per second?
            else:
                lif_metadata["Cycle"] = -1
                lif_metadata['SampFreq'] = -1  # frames per second?
            lif_metadata["Lambda"] = 0  # TODO
            # convert from meters to micrometers
            lif_metadata["PxSzX"] = this_measurement.info["scale"][0]
            lif_metadata["PxSzY"] = this_measurement.info["scale"][1]  # y-size

            lif_metadata['FrameSizeX'] = this_measurement.dims.x  # pixel number in x
            lif_metadata['FrameSizeY'] = this_measurement.dims.y  # pixel number in y
            lif_metadata['NumFrames'] = this_measurement.dims.t  # pixel number in t
            lif_metadata['Comment'] = "Leica .lif file"

            # extract measurement time - which is only in the XML of the full LIF file, and not in this_measurement
            # see /pyview/view/python_core/measurement_list/importers.py
            # i.e. if changes are necessary here, do them also there
            #  time stamps are not correct - I do not know why yet (15.6.2022)
            # that is: there are less time stamps in the XML file than measurements in the .lif file
            # therefore, I cannot attribute the right time to each measurements
            print('Now using UTC from first frame in measu: ', fle_ind)
            # timestamp of first frame in measurement measu!
            time = root.findall(".//TimeStampList")[fle_ind].text[:15]
            timeStamp = int(time, 16)
            # windows uses 1. Januar<y 1601 as reference
            # https://gist.github.com/Mostafa-Hamdy-Elgiar/9714475f1b3bc224ea063af81566d873
            EPOCH_AS_FILETIME = 116444736000000000  # January 1, 1970 as MS file time
            HUNDREDS_OF_NANOSECONDS = 10000000
            measurementtime = datetime.datetime.utcfromtimestamp((timeStamp - EPOCH_AS_FILETIME) / HUNDREDS_OF_NANOSECONDS)
            print('Lif-File time in importers.py: ', measurementtime)  # for debugging
            # UTC, e.g. 1623229504.482
            UTC = measurementtime.timestamp()
            # meta_info.update({'UTCTime':UTC})
            lif_metadata['UTC'] = UTC
            # MTime is the time passed with respect to the very first measurement in this animal
            time = root.findall(".//TimeStampList")[0].text[:15]
            timeStamp = int(time, 16)
            measurementtime_first = datetime.datetime.utcfromtimestamp(
                (timeStamp - EPOCH_AS_FILETIME) / HUNDREDS_OF_NANOSECONDS)
            MTime = measurementtime - measurementtime_first
            # format this timedelta
            minutes, seconds = divmod(MTime.seconds + MTime.days * 86400, 60)
            hours, minutes = divmod(minutes, 60)
            lif_metadata['MTime'] = '{:02d}:{:02d}:{:02d}'.format(hours, minutes, seconds)

            all_metadata_df = all_metadata_df.append(pd.DataFrame(lif_metadata).T, ignore_index=True)
        return all_metadata_df

    def load_data(self, measu):

        this_measurement = self.get_image(measu)
        dims = this_measurement.dims
        # dimensions are x, y, z, t, m. We are interested in x, y, t
        img_data = np.zeros((dims.x, dims.y, dims.t), dtype=np.float)

        frame_list = [i for i in this_measurement.get_iter_t(c=0, z=0)]
        for count, frame in enumerate(frame_list):
            img_data[:, :, count] = np.asarray(frame)

        return img_data
# end LIFReaderGio


def read_lif(lif_file, measu):
    """
    Read a measurement from a Leica lif file into numpy array.
    implemented May 2022, tested with data from Marco Paoli, Toulouse
    :param str lif_file: path of lif file
    :param int measu: which measurement in the lif file to load
    :return: numpy.ndarray in XYT format
    """

    lif_reader_wrapper = LIFReaderGio(lif_file)
    return lif_reader_wrapper.load_data(measu)
# end read_lif


def read_tif_2Dor3D(tif_file, flip_y=True, return_3D=False, load_data=True):
    """
    Read a TIF file into numpy array. TIF file axes are assumed to be TYX or YX. Also works for OME Tiff files,
    e.g. Live Acquisition, or FIJI
    :param str tif_file: path of tif file
    :param bool flip_y: whether to flip Y axis
    :param bool return_3D: whether to convert 2D to 3D if required
    :param bool load_data: if True loads data and returns else first return value is None
    :return: data, metadata
    data: numpy.ndarray in XY or XYT format
    metadata: dictionary if present, else None
    
    # if tif_file is str, convert it to path
    Read a TIF file into numpy array. TIF file axes are assumed to be TYX or YX. Also works for OME Tiff files,
    e.g. Live Acquisition, or FIJI
    :param str tif_file: path of tif file
    :param bool flip_y: whether to flip Y axis
    :param bool return_3D: whether to convert 2D to 3D if required
    :param bool load_data: if True loads data and returns else first return value is None
    :return: data, metadata
    data: numpy.ndarray in XY or XYT format
    metadata: dictionary if present, else None
    
    """
    # if tif_file is str, convert it to path
    # a476b63c975103c2cd6357311bbcd521129766f9
    if type(tif_file) == str:
        tif_file = pl.Path(tif_file)

    # load metadata
    # tif_file=animal_list[10]
    with tifffile.TiffFile(tif_file) as tif:
            metadata = tif.imagej_metadata
            # imagej_metadata does not work any more or never worked on stack - read metadata from first frame
            if metadata is None:
                metadata = tif.pages[0].description

    # extract XML tree from metadata into root
    try:
        root = ET.fromstring(metadata)
        metadata_present = True

        # define namespace for OME data
        # this uses xTree OME syntax
        # https://docs.python.org/3/library/xml.etree.elementtree.html#xml.etree.ElementTree.Element
        ns = {
            "d": "http://www.openmicroscopy.org/Schemas/OME/2013-06"    
        }
        # now get all infos that we put into settings file
        meta_info = root.find("./d:Image/d:Pixels", ns).attrib
        # result is a dictionary, for example:
     #        {'ID': 'Pixels:1-0',
     # 'DimensionOrder': 'XYTZC',
     # 'Type': 'uint16',
     # 'SizeX': '1392',
     # 'SizeY': '1040',
     # 'SizeZ': '1',
     # 'SizeC': '1',
     # 'SizeT': '160',
     # 'PhysicalSizeX': '6.45',
     # 'PhysicalSizeY': '6.45',
     # 'PhysicalSizeZ': '1000',
     # 'SignificantBits': '14'}
        # acquisition date as string, e.g. '2021-09-19T16:49:28'
        AcquisitionDate = root.find("./d:Image/d:AcquisitionDate", ns).text
        meta_info.update({'AcquisitionDate':AcquisitionDate})
        # binning info, e.g. '1x1'
        Binning = root.find("./d:Image/d:Pixels/d:Channel/d:DetectorSettings", ns).attrib["Binning"]
        meta_info.update({'Binning':Binning})
     # frame interval
        # relative time of secoond image (first image looks unsafe - often it is blanck. Therefore use frames 2 and 3)
        time_frame1 = root.find("./d:Image/d:Pixels/d:Plane[2]", ns).attrib["DeltaT"]
        # relative time of third image
        time_frame2 = root.find("./d:Image/d:Pixels/d:Plane[3]", ns).attrib["DeltaT"]
        GDMfreq = (float(time_frame2) - float(time_frame1))
        GDMfreq = int(GDMfreq*1000 + 0.5) # unit is ms, rounded
        meta_info.update({'GDMfreq':str(GDMfreq)})
    # exposure time for frame 2 - expecting that to be uniform
        ExposureTime_ms = float(root.find("./d:Image/d:Pixels/d:Plane[2]", ns).attrib["ExposureTime"])
        ExposureTime_ms = int(1000*ExposureTime_ms) # value in Andor is in seconds
        meta_info.update({'ExposureTime_ms':str(ExposureTime_ms)})
    # columns in .settings that need to be filled here:
    # get the tif file, including the last directory
        this_filename = tif_file.parts
        dbb = this_filename[-2] +'/'+ this_filename[-1]
        meta_info.update({'dbb':dbb})
        meta_info.update({'Label':this_filename[-1]})
        # PxSzX
        # replace the Andor name "PhysicalSizeX' with the Galizia name PsSzX
        meta_info['PsSzX'] = meta_info.pop('PhysicalSizeX')
        meta_info['PsSzY'] = meta_info.pop('PhysicalSizeY')
        # PxSzY, e.g. 1.5625
    # When was this measurement taken?
    # first get the time when the measurement was started
        measurementtime = dt.datetime.fromisoformat(AcquisitionDate)
    # now add the time of the first frame, since measurement start time ie equal for all measurements in one loop
        measurementtime_delta = dt.timedelta(seconds=float(time_frame1))
        measurementtime = measurementtime + measurementtime_delta
        # StartTime, e.g. 10:05:04
        StartTime = measurementtime.strftime('%H:%M:%S')
        meta_info.update({'StartTime':StartTime})
        # UTC, e.g. 1623229504.482
        UTC = measurementtime.timestamp()
        meta_info.update({'UTCTime':UTC})
    except:
        metadata_present = False
        meta_info = None

    # load data
    if load_data:
        with tifffile.TiffFile(tif_file) as tif:
                imagej_hyperstack = tif.asarray()

        if len(imagej_hyperstack.shape) == 3:  # 3D data in TYX format

            if flip_y:
                imagej_hyperstack = np.flip(imagej_hyperstack, axis=1)

            imagej_hyperstack = imagej_hyperstack.swapaxes(0, 2)  # return in XYT format

        # read 2D tif data
        elif len(imagej_hyperstack.shape) == 2:  # 2D data in YX format

            if flip_y:
                imagej_hyperstack = np.flip(imagej_hyperstack, axis=0)

            imagej_hyperstack = imagej_hyperstack.swapaxes(0, 1)  # YX to XY format
            if return_3D:
                imagej_hyperstack = np.stack([imagej_hyperstack], axis=2)
    else:  # i.e., if load_data is false
        imagej_hyperstack = None

    return imagej_hyperstack, meta_info
# end read_ometif_metadict


def read_single_file_fura_tif(tif_file):
    """
    Read FURA data from <tif_file>. Assume input file has the format TWYX, where W is wavelength and
    this dimension has size 2.
    :param str tif_file: absolute path of the file on file system
    :rtype: data_340, data_380
    data_340: 340nm data as an numpy.ndarray, format XYT
    data_380: 380nm data as an numpy.ndarray, format XYT
    """

    data_in = tifffile.imread(tif_file)

    data_in = np.flip(data_in, axis=1)  # format TWYX

    # split data, each will have format TYX
    data_340 = data_in[:, 1, :, :]
    data_380 = data_in[:, 0, :, :]

    return data_340.swapaxes(0, 2), data_380.swapaxes(0, 2)  # return in format XYT


def write_tif_2Dor3D(array_xy_or_xyt, tif_file, dtype=None, scale_data=False, labels=None):
    """
    Write a 2D or a 3D numpy array to a TIFF file with data type format <dtype>. If <dtype> is None, data is written
    in its own data type. Else, the function will try to safely cast data in <array_xy_or_xyt> to <dtype>.
    If it is not possible and <scale_data> is True, data is scaled to fit the dynamic range of <dtype> and
    written to disc. Else an error is raised
    :param numpy.ndarray array_xy_or_xyt: array to be written
    :param str tif_file: name of file to which data is to be written
    :param dtype: data type format to use. Must be a valid numerical numpy dtype
    (https://numpy.org/doc/stable/reference/arrays.scalars.html)
    :param str|Sequence labels: a str or 1 member sequence when <array_xy_or_xyt> is 2D, else a sequence
    with the same size as the last (3rd) dimension of <array_xy_or_xyt>
    """

    if dtype is None:
        array_cast = array_xy_or_xyt
    else:
        if issubclass(dtype, np.integer):
            info = np.iinfo(dtype)
        elif issubclass(dtype, np.flexible):
            info = np.finfo(dtype)
        else:
            raise ValueError(
                "Invalid dtype. Please specify a valid numerical numpy dtype "
                "(https://numpy.org/doc/stable/reference/arrays.scalars.html)")

        if np.can_cast(array_xy_or_xyt, dtype):

            array_cast = array_xy_or_xyt.astype(dtype)

        elif scale_data:

            array_min, array_max = array_xy_or_xyt.min(), array_xy_or_xyt.max()
            array_xy_or_xyt_0_1 = (array_xy_or_xyt - array_min) / (array_max - array_min)

            array_scaled = info.min + array_xy_or_xyt_0_1 * (info.max - info.min)
            array_cast = array_scaled.astype(dtype)

        else:
            raise ValueError(
                f"The values in the specified array could not be safely cast into the specified dtype ({dtype})."
                f"If you want the values in the specified array to be scaled into the dynamic range of {dtype}, "
                f"set the argument <scale_data> to True")

    # flip Y axis
    array_cast = np.flip(array_cast, axis=1)

    if type(labels) is str:
        labels = [labels]

    if len(array_cast.shape) == 2:
        array_to_write = array_cast.swapaxes(0, 1)  # from XY to YX
        if labels is not None:
            assert len(labels) == 1, \
                f"Expected one label to write along with a one page TIF. Got ({len(labels)})"
    elif len(array_cast.shape) == 3:
        array_to_write = array_cast.swapaxes(0, 2)  # from XYT to TYX
        if labels is not None:
            assert len(labels) == array_cast.shape[2], \
                f"Expected {array_cast.shape[2]} labels two write along with array with shape {array_cast.shape}. " \
                f"Got {len(labels)}"
    else:
        raise ValueError("This function can only write 2D or 3D arrays")

    kwargs = {"description": None}
    if labels is not None:
        kwargs["description"] = "Labels=" + ";;".join(labels)

    tifffile.imwrite(tif_file, data=array_to_write, **kwargs)


def read_check_yml_file(yml_filename, expected_type=None):
    """
    Reads flags from <filename>, applies some checks and returns them
    :param yml_filename: str, path of a .yml file
    :param expected_type: any, if specified, as assertion error is raised if contents
    of the yml file is not of the specfied type
    :return: any, depending of contents of the yml file
    """

    with open(yml_filename, 'r') as fle:
        yml_contents = yaml.load(fle, yaml.SafeLoader)

    if expected_type is not None:
        assert type(yml_contents) is expected_type, f"YML file {yml_filename} was expected to contain " \
                                                    f"{expected_type} data," \
                                                    f"found, {type(yml_contents)} instead"
    return yml_contents


def write_yml(yml_filename, to_write):

    with open(yml_filename, 'w') as fle:
        yaml.dump(to_write, fle, Dumper=yaml.SafeDumper)

def read_lsm(path):
    """ takes a path to a lsm file, reads the file with the tifffile lib and
   returns a np array
   """
    data_cut = tifffile.imread(path)
    data_cut_rot = np.swapaxes(data_cut, 0, 2)
    data_cut_rot_flip = np.flip(data_cut_rot, axis=1)

    return data_cut_rot_flip


def load_pst(filename):
    """
    read tillvision based .pst files as uint16.
    """
    # filename can have an extension (e.g. .pst), or not
    # reading stack size from inf
    # inf_path = os.path.splitext(filename)[0] + '.inf'
    # this does not work for /data/030725bR.pst\\dbb10F, remove extension by hand,
    # assuming it is exactly 3 elements

    if filename.endswith(".pst") or filename.endswith(".ps"):
        filepath = pl.Path(filename)
    else:
        filepath = pl.Path(f"{filename}.pst")
        if not filepath.is_file():
            filepath = filepath.with_suffix(".ps")

    assert filepath.is_file(), \
        f"Could not find either of the following raw data files:\n{filename}.pst\n{filename}.ps"

    meta = {}
    with open(filepath.with_suffix(".inf"), 'r') as fh:
        #    fh.next()
        for line in fh.readlines():
            try:
                k, v = line.strip().split('=')
                meta[k] = v
            except:
                pass
    # reading stack from pst
    shape = np.int32((meta['Width'], meta['Height'], meta['Frames']))

    expected_units = np.prod(shape)

    assert filepath.stat().st_size >= 2 * expected_units, \
        f"Expected at least {2 * expected_units} bytes in {filepath}. Found {filepath.stat().st_size}"

    raw = np.fromfile(filepath, dtype='int16', count=expected_units)
    data = np.reshape(raw, shape, order='F')

    # was swapping x, y axes; commented out to retain original order
    # data  = data.swapaxes(0,1)

    # data is upside down as compared to what we see in TillVision
    data = np.flip(data, axis=1)
    data = data.astype('uint16')
    return data