# Source code for fibtortuosity.fibtortuosity

    # Copyright 2016 Joshua Taillon
    #
    # Licensed under the Apache License, Version 2.0 (the "License");
    # you may not use this file except in compliance with the License.
    # You may obtain a copy of the License at
    #
    #     http://www.apache.org/licenses/LICENSE-2.0
    #
    # Unless required by applicable law or agreed to in writing, software
    # distributed under the License is distributed on an "AS IS" BASIS,
    # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
    # See the License for the specific language governing permissions and
    # limitations under the License.

    import sys
    from datetime import datetime

    from libtiff import TIFFfile, TIFFimage
    import matplotlib
    import numpy as np
    import skfmm
    from matplotlib import pyplot as plt

    # from memory_profiler import profile
    import gc

    __version__ = '0.2.4'

    __all__ = ['tortuosity_from_labels_x', 'tortuosity_from_labels_y',
               'tortuosity_from_labels_z', 'calculate_geodesic_distance',
               'save_results', 'load_results', 'run_full_analysis_lsm_ysz',
               'save_as_tiff', 'tortuosity_profile', 'plot_tort_prof',
               'save_profile_to_csv', 'load_profile_from_csv', '_geo_dist',
               '_calc_interface', '_calc_tort', '_calc_euc_x']

    def run_full_analysis_lsm_ysz(electrolyte_file,
                                  electrolyte_and_pore_file,
                                  electrolyte_and_lsm_file,
                                  electrolyte_and_ysz_file,
                                  date,
                                  phase,
                                  direction,
                                  npzfile=None,
                                  units='nm',
                                  delay=0,
                                  calculate_all=False,
                                  load_from_prev_run=True,
                                  create_hspy_sigs=True,
                                  save_avizo_tiff=True,
                                  tort_profile=True,
                                  save_tort_prof=True,
                                  in_ipython=True,
                                  send_text=False,
                                  text_email=None,
                                  text_number=None,
                                  text_carrier=None):
        """
        Run full tortuosity analysis for LSM/YSZ, calling other functions as
        necessary.

        Parameters
        ----------
        electrolyte_file: str
            Name of bulk electrolyte LabelField
        electrolyte_and_pore_file: str
            Name of LabelField containing bulk electrolyte and pore
        electrolyte_and_lsm_file: str
            Name of LabelField containing bulk electrolyte and LSM
        electrolyte_and_ysz_file: str
            Name of LabelField containing bulk electrolyte and YSZ
        date: str
            date string to be used in output filenames
        phase: str
            phase label to be used in output filenames
        direction: str
            direction of analysis ('x', 'y', or 'z')
        npzfile: str or None
            filename of previously saved data to use (if loading from disk)
        units: str
            units for labeling purpose
        delay: int
            delay to wait before running analysis (useful if running more than
            one at a time)
        calculate_all: bool
            flag to indicate that geodesic distance etc. should be calculated
            from scratch (takes a while)
        load_from_prev_run: bool
            flag to indicate that data should be loaded from disk, using the
            file specified in ``npzfile``
        create_hspy_sigs: bool
            flag to indicate if hyperspy signals should be created (to help
            visualize the results)
        save_avizo_tiff: bool

        tort_profile: bool

        save_tort_prof: bool

        in_ipython: bool
            flag to control if notifications from ipython will be shown.
            Requires the "Notifyme!" `extension <https://gist.github.com/jat255/bde
            d66839b424e20eb8bd1dc2336f841>`_
        send_text: bool
            flag to control if text should be sent (requires |texting|_ and
            |keyring|_ packages)
        text_email: string
            email address to send text from (see |textingTextServer|_)
        text_number: string
            number to send text to (see |textingTextServer|_)
        text_carrier: string
            carrier of number to send to (see |textingTextServer|_)

        Returns
        -------
        results: dict
            results is a dictionary containing all the various parameters that
            were calculated during the analysis

        Notes
        -----
        To authenticate your email server and store the password in your local
        keyring, run the following before trying to send texts:

        >>> import keyring
        ... keyring.set_password('python_TextServer', emailaddress, password)

        Examples
        --------

        >>> res = run_full_analysis_lsm_ysz(electrolyte_file="bulkYSZ.tif",
        ...                                 electrolyte_and_pore_file="bulkYSZandPRE.tif",
        ...                                 electrolyte_and_lsm_file="bulkYSZandLSM.tif",
        ...                                 electrolyte_and_ysz_file="bulkYSZandYSZ.tif",
        ...                                 date='2015-05-06',
        ...                                 phase='Pore',
        ...                                 direction='x',
        ...                                 npzfile=None,
        ...                                 units='nm',
        ...                                 delay=0,
        ...                                 calculate_all=True,
        ...                                 load_from_prev_run=False,
        ...                                 create_hspy_sigs=True,
        ...                                 save_avizo_tiff=True,
        ...                                 tort_profile=True,
        ...                                 save_tort_prof=True,
        ...                                 in_ipython=False,
        ...                                 send_text=True,
        ...                                 text_email="email@server.com",
        ...                                 text_number="8008675309",
        ...                                 text_carrier="verizon")

        .. |texting| replace:: ``texting``
        .. _texting: https://bitbucket.org/jat255/jat255-python-modules/src/09e8acf65230dd3f84df8b25f84000d2eec02755/texting/?at=master
        .. |keyring| replace:: ``keyring``
        .. _keyring: https://pypi.python.org/pypi/keyring
        .. |textingTextServer| replace:: ``texting.TextServer``
        .. _textingTextServer: https://bitbucket.org/jat255/jat255-python-modules/src/09e8acf65230dd3f84df8b25f84000d2eec02755/texting/texting/texting.py?at=master&fileviewer=file-view-default#texting.py-7

        """
        # Dictionary for phase -> filename mapping:
        filedict = {'Pore':electrolyte_and_pore_file,
                    'LSM':electrolyte_and_lsm_file,
                    'YSZ':electrolyte_and_ysz_file}

        # Check to make sure that both calculate and load options are specified
        if calculate_all:
            if load_from_prev_run:
                print("Both loading and calculating were specified. Calculating "
                      "is given preference, so data will not be loaded from disk.")
                sys.stdout.flush()
            load_from_prev_run = False

        # Dictionary to hold all the results
        results = {}

        # Imports
        if in_ipython:
            from IPython import get_ipython
            ipython = get_ipython()

            ipython.magic('load_ext notifyme')
            ipython.magic('notifysound default')

        if send_text:
            try:
                # noinspection PyUnresolvedReferences
                from texting.texting import TextServer
                txt_srv = TextServer(text_email, text_number, text_carrier)
            except ImportError as e:
                print('Could not import texting library. Is it installed?')
                send_text = False

        def get_time():
            return time.time()

        def done_text_send(text_start_time, text_end_time, process):
            """
            Send a text message with descriptive information about calculation
            times using the ``TextServer`` class

            Parameters
            ----------
            text_start_time: datetime.time object
                Starting time of interval
            text_end_time: datetime.time object
                Ending time of interval
            process: str
                Descriptive string to include in the message (usually what the
                name of the process was)
            """
            time_used = text_end_time - text_start_time
            hours_hold = int(time_used / 3600)
            minutes_hold = int(time_used / 60 - int(hours_hold * 60))
            seconds_hold = int(time_used - int(minutes_hold * 60))
            formatted_time = str(hours_hold).zfill(2) + ':' + str(
                minutes_hold).zfill(2) + ':' + str(seconds_hold).zfill(2)
            #
            msg = ('\nDONE\nProcess: ' + str(process) + '\nTime required: ' +
                   str(formatted_time))

            # The actual mail send
            txt_srv.send_message(msg)

        # Delay (if requested):
        import time
        if delay > 0:
            print("Waiting %i seconds before running..." % delay)
            time.sleep(delay)

        # Calculating geodesic, euclidean, and tortuosity
        if calculate_all:
            fn_string = 'tortuosity_from_labels_' + direction
            fn = globals()[fn_string]
            print("Cathode data file is " + filedict[phase])
            sys.stdout.flush()

            start_time = get_time()
            g, e, t, d = fn(electrolyte_file,
                            filedict[phase],
                            phase,
                            units=units,
                            save_output=False,
                            print_output=True)
            results['g'] = g
            results['e'] = e
            results['t'] = t
            results['d'] = d
            print("Data stored in keys \'g\', \'e\', \'t\', and \'d\'")
            print("")
            if in_ipython:
                ipython.magic('notifyme $phase - $direction completed')
            if send_text:
                done_text_send(start_time,
                               get_time(),
                               phase + '-' + direction + ' done')

        # Saving results:
        if calculate_all:
            print("Saving results to disk...")
            sys.stdout.flush()
            start_time = get_time()
            save_results(fname=None,
                         geo_d=g,
                         euc_d=e,
                         tort=t,
                         desc=d,
                         phase=phase,
                         direction=direction)
            if in_ipython:
                ipython.magic('notifyme $phase - $direction saved')
            if send_text:
                done_text_send(start_time,
                               get_time(),
                               phase + '-' + direction + ' saved')

        # Loading data (after, instead of calculating from scratch):
        if load_from_prev_run:
            if npzfile is None:
                print("No .npz file was supplied for loading...")
                raise ValueError('npzfile not defined')
            print("Loading data from previous run...")
            sys.stdout.flush()
            g, e, t, d = load_results(npzfile)
            print("Old data loaded from " + npzfile)
            results['g'] = g
            results['e'] = e
            results['t'] = t
            results['d'] = d
            print("Stored in keys \'g\', \'e\', \'t\', and \'d\'")
            print("")
            sys.stdout.flush()

        # Visualizing results (requires hyperspy):
        if create_hspy_sigs:
            import hyperspy.signals as signals

            g_s = signals.Image(g)
            g_s.metadata.General.title = phase + "_" + direction + " Geodesic"

            e_s = signals.Image(e)
            e_s.metadata.General.title = phase + "_" + direction + " Euclidean"

            t_s = signals.Image(t)
            t_s.metadata.General.title = phase + "_" + direction + " Tortuosity"

            # for i in [g_s, e_s, t_s]:
            #   i.plot()

            results['g_s'] = g_s
            results['e_s'] = e_s
            results['t_s'] = t_s
            print("Hyperspy signals are in values \'g_s\', \'e_s\', and \'t_s\'")
            print("")
            sys.stdout.flush()

        # Saving as tiff for Avizo:
        if save_avizo_tiff:
            save_as_tiff(date + "_" + phase + "_" + direction + "-tort.tif",
                         t,
                         'float32',
                         d)
            print("")
            sys.stdout.flush()

        # Average tortuosity profile:
        if tort_profile:
            print("Calculating average tortuosity profile")
            sys.stdout.flush()

            t_avg, e_avg = tortuosity_profile(t,
                                              e,
                                              axis=direction)
            f = plot_tort_prof(t_avg,
                               e_avg,
                               direction,
                               sns_fontscale=2,
                               sns_color_num=1)
            plt.tight_layout()
            matplotlib.rc('image', cmap='gray')

            results['f'] = f
            results['t_avg'] = t_avg
            results['e_avg'] = e_avg
            print("Average tortuosity plot is in variable \'f\', profiles are "
                  "in \'t_avg\' and \'e_avg\'")
            print("")
            sys.stdout.flush()

        # Save profile to disk:
        if save_tort_prof:
            save_profile_to_csv(date + '_' +
                                phase + '_' +
                                direction + '-tort-profile.csv',
                                e_avg / 1000,  # divide by 1000 to get microns
                                t_avg,
                                direction,
                                phase)

        return results

    def calculate_geodesic_distance(electrolyte_fname,
                                    phase_fname,
                                    units='nm',
                                    print_output=True,
                                    ):
        """
        Calculate the geodesic distance of an indexed tif file.

        .. note:: Deprecated. This function will work, but it is better to use
                  :func:`tortuosity_from_labels_x`,
                  :func:`tortuosity_from_labels_y`,
                  and :func:`tortuosity_from_labels_z`.

        Parameters
        ----------
        electrolyte_fname: str
            Filename of a 3D tiff file that contains only the bulk electrolyte
            labelfield (as 1 values)
        phase_fname: str
            Filename of a 3D tiff file that contains the bulk electrolyte and the
            phase for which the geodesic distance is to be calculated
        units: str
            units of the given dimensions
        print_output: bool
            switch to control whether output is printed

        Returns
        -------
        d: np.array
            Numpy array with geodesic distance from supplied surface
        desc: str
            description string to be used when saving the file (for proper
            reading into Avizo)
        """

        # Load electrolyte data
        electrolyte_tif = TIFFfile(electrolyte_fname)
        electrolyte_array = electrolyte_tif.get_tiff_array()
        electrolyte_data = electrolyte_array[::].astype('int8')
        if print_output:
            print('Loaded electrolyte data.')

        # Load phase to be calculated data
        cathode_tif = TIFFfile(phase_fname)
        cathode_array = cathode_tif.get_tiff_array()
        cathode_data = cathode_array[::].astype('int8')
        if print_output:
            print('Loaded cathode data.')

        # Get information from Avizo-saved tiff file
        ifd = electrolyte_tif.get_first_ifd()
        size_x = ifd.get_value('ImageWidth')
        size_y = ifd.get_value('ImageLength')
        size_z = electrolyte_tif.get_depth()
        image_description = ifd.get_value('ImageDescription').split(b'\0',1)[0]
        if print_output:
            print('ImageDescription is: {}'.format(image_description))

        # Calculate some parameters from the image input
        bb_xmin, bb_xmax, bb_ymin, bb_ymax, bb_zmin, bb_zmax = \
            [eval(i) for i in image_description.split()[1:]]
        bound_x = bb_xmax - bb_xmin
        bound_y = bb_ymax - bb_ymin
        bound_z = bb_zmax - bb_zmin
        if print_output:
            print("Bounding box dimensions are: ({0:.2f}, {1:.2f}, "
                  "{2:.2f})".format(bound_x, bound_y, bound_z) + " " + units)

        vox_x = (bb_xmax - bb_xmin) / float(size_x - 1)
        vox_y = (bb_ymax - bb_ymin) / float(size_y - 1)
        vox_z = (bb_zmax - bb_zmin) / float(size_z - 1)
        if print_output:
            print("Voxel dimensions are: ({0:.2f}, {1:.2f}, "
                  "{2:.2f})".format(vox_x, vox_y, vox_z) + " " + units)

        # Switch electrolyte tag (1) to a negative value to create zero-contour
        cathode_data[electrolyte_data == 1] = -1

        # Mask the voxels where phase is not present:
        mask = cathode_data == 0
        cathode_data = np.ma.MaskedArray(cathode_data, mask)

        # Do geodesic distance calculation
        start_time = datetime.now()
        if print_output:
            print('Starting calculation at: {}'.format(start_time))
        # Calculate geodesic distance:
        d = skfmm.distance(cathode_data, dx=[vox_z, vox_y, vox_x])
        end_time = datetime.now()
        if print_output:
            print('Calculation took: {}'.format(end_time - start_time))

        # Set distances within the bulk electrolyte to zero:
        # d.data[electrolyte_data == 1] = 0

        return d.data, image_description

    def tortuosity_from_labels_x(electrolyte_fname,
                                 phase_fname,
                                 phase,
                                 units='nm',
                                 print_output=True,
                                 save_output=False):
        """
        Calculate the tortuosity (normal to electrolyte interface, x-direction)
        from supplied label fields

        Parameters
        ----------
        electrolyte_fname: str
            Filename of a 3D tiff file that contains only the bulk electrolyte
            labelfield (as 1 values)
        phase_fname: str
            Filename of a 3D tiff file that contains the bulk electrolyte and the
            phase for which the geodesic distance is to be calculated
        phase: str
            Phase that is being calculated. i.e. 'pore', 'LSM', 'YSZ', etc.
        units: str
            units of the given dimensions
        print_output: bool
            switch to control whether output is printed
        save_output: bool
            switch to control whether data is saved to .npz files for later recall

        Returns
        --------
        geo_d: np.array
            Numpy array with geodesic distance from supplied surface
        euc_d: np.array
            Numpy array with euclidean distance from electrolyte
        tort: np.array
            Numpy array with tortuosity at each point
        desc: str
            description string to be used when saving the file with
            :func:`save_as_tiff` (for proper reading into Avizo)
        """

        if print_output:
            print('Starting calculation on ' + phase + ' in x direction.')
            sys.stdout.flush()

        global_start_time = datetime.now()

        # Load electrolyte data
        start_time = datetime.now()
        electrolyte_tif = TIFFfile(electrolyte_fname)
        electrolyte_array = electrolyte_tif.get_tiff_array()
        electrolyte_data = electrolyte_array[::].astype('int8')
        end_time = datetime.now()
        if print_output:
            print('Loaded electrolyte data in {} seconds.'.format(end_time -
                                                                  start_time))
            sys.stdout.flush()

        # Load phase to be calculated data
        start_time = datetime.now()
        cathode_tif = TIFFfile(phase_fname)
        cathode_array = cathode_tif.get_tiff_array()
        cathode_data = cathode_array[::].astype('int8')
        end_time = datetime.now()
        if print_output:
            print('Loaded cathode data in {} seconds.'.format(end_time -
                                                              start_time))
            sys.stdout.flush()

        # Get information from Avizo-saved tiff file
        ifd = electrolyte_tif.get_first_ifd()
        size_x = ifd.get_value('ImageWidth')
        size_y = ifd.get_value('ImageLength')
        size_z = electrolyte_tif.get_depth()
        image_description = ifd.get_value('ImageDescription').split(b'\0',1)[0]
        if print_output:
            print('ImageDescription is: {}'.format(image_description))
            sys.stdout.flush()

        # Calculate some parameters from the image input
        bb_xmin, bb_xmax, bb_ymin, bb_ymax, bb_zmin, bb_zmax = \
            [eval(i) for i in image_description.split()[1:]]
        bound_x = bb_xmax - bb_xmin
        bound_y = bb_ymax - bb_ymin
        bound_z = bb_zmax - bb_zmin
        if print_output:
            print("Bounding box dimensions are: ({0:.2f}, {1:.2f}, "
                  "{2:.2f})".format(bound_x, bound_y, bound_z) + " " + units)
            sys.stdout.flush()

        vox_x = (bb_xmax - bb_xmin) / float(size_x - 1)
        vox_y = (bb_ymax - bb_ymin) / float(size_y - 1)
        vox_z = (bb_zmax - bb_zmin) / float(size_z - 1)
        if print_output:
            print("Voxel dimensions are: ({0:.2f}, {1:.2f}, "
                  "{2:.2f})".format(vox_x, vox_y, vox_z) + " " + units)
            sys.stdout.flush()

        # Switch electrolyte tag (1) to a negative value to create zero-contour
        cathode_data[electrolyte_data == 1] = -1

        # Mask the voxels where phase is not present:
        cathode_mask = cathode_data == 0

        cathode_ma = np.ma.MaskedArray(cathode_data, cathode_mask)
        del cathode_mask
        gc.collect()

        # Do geodesic distance calculation
        start_time = datetime.now()
        if print_output:
            print('Starting geodesic calculation at: {}'.format(start_time))
            sys.stdout.flush()
        geo_d = _geo_dist(cathode_ma, vox_x, vox_y, vox_z)
        end_time = datetime.now()
        if print_output:
            print('Geodesic calculation took: {}'.format(end_time -
                                                         start_time))
            sys.stdout.flush()

        # Calculating zero reference for each [x,y] point for euclidean distance
        start_time = datetime.now()
        x_interface = _calc_interface(electrolyte_data)
        end_time = datetime.now()
        if print_output:
            print("Calculating zero distance interface took: "
                  "{}".format(end_time - start_time))
            sys.stdout.flush()

        # Calculate euclidean distance
        start_time = datetime.now()
        # noinspection PyTypeChecker
        euc_d = _calc_euc_x(x_interface, electrolyte_data, vox_x)
        end_time = datetime.now()
        if print_output:
            print("Calculating euclidean distance took: {}".format(end_time -
                                                                   start_time))
            sys.stdout.flush()

        # Calculate tortuosity
        start_time = datetime.now()
        tort = _calc_tort(geo_d, euc_d, electrolyte_data)
        end_time = datetime.now()
        if print_output:
            print("Calculating tortuosity took: {}".format(end_time - start_time))
            sys.stdout.flush()

        start_time = datetime.now()
        if save_output:
            if print_output:
                print("Saving data...")
                sys.stdout.flush()
            save_results(fname=None,
                         direction='x',
                         phase=phase,
                         geo_d=geo_d.data,
                         euc_d=euc_d,
                         tort=tort.data,
                         desc=image_description)
            end_time = datetime.now()
            if print_output:
                print("Saving data took: {}".format(end_time - start_time))
                sys.stdout.flush()

        end_time = datetime.now()
        if print_output:
            print("Total execution time was: {}".format(end_time -
                                                        global_start_time))
            sys.stdout.flush()

        return geo_d.data, euc_d, tort.data, image_description

    # @profile
    def tortuosity_from_labels_y(electrolyte_fname,
                                 phase_fname,
                                 phase,
                                 units='nm',
                                 print_output=True,
                                 save_output=False):
        """
        Calculate the tortuosity (parallel to electrolyte interface, y-direction)
        from supplied label fields

        Parameters
        ----------
        electrolyte_fname: str
            Filename of a 3D tiff file that contains only the bulk electrolyte
            labelfield (as 1 values)
        phase_fname: str
            Filename of a 3D tiff file that contains the bulk electrolyte and the
            phase for which the geodesic distance is to be calculated
        phase: str
            Phase that is being calculated. i.e. 'pore', 'LSM', 'YSZ', etc.
        units: str
            units of the given dimensions
        print_output: bool
            switch to control whether output is printed
        save_output: bool
            switch to control whether data is saved to .npz files for later recall

        Returns
        --------
        geo_d: np.array
            Numpy array with geodesic distance from supplied surface
        euc_d: np.array
            Numpy array with euclidean distance from electrolyte
        tort: np.array
            Numpy array with tortuosity at each point
        desc: str
            description string to be used when saving the file with
            :func:`save_as_tiff` (for proper reading into Avizo)
        """

        global_start_time = datetime.now()

        if print_output:
            print('Starting calculation on ' + phase + ' in y direction.')
            sys.stdout.flush()

        # Load electrolyte data
        start_time = datetime.now()
        electrolyte_tif = TIFFfile(electrolyte_fname)
        electrolyte_array = electrolyte_tif.get_tiff_array()
        electrolyte_data = electrolyte_array[::].astype('int8')
        end_time = datetime.now()
        if print_output:
            print('Loaded electrolyte data in {} seconds.'.format(end_time -
                                                                  start_time))
            sys.stdout.flush()

        # Load phase to be calculated data
        start_time = datetime.now()
        cathode_tif = TIFFfile(phase_fname)
        cathode_array = cathode_tif.get_tiff_array()
        cathode_data = cathode_array[::].astype('int8')
        end_time = datetime.now()
        if print_output:
            print('Loaded cathode data in {} seconds.'.format(end_time -
                                                              start_time))
            sys.stdout.flush()

        # Get information from Avizo-saved tiff file
        ifd = electrolyte_tif.get_first_ifd()
        size_x = ifd.get_value('ImageWidth')
        size_y = ifd.get_value('ImageLength')
        size_z = electrolyte_tif.get_depth()
        image_description = ifd.get_value('ImageDescription').split(b'\0',1)[0]
        if print_output:
            print('ImageDescription is: {}'.format(image_description))
            sys.stdout.flush()

        # Calculate some parameters from the image input
        bb_xmin, bb_xmax, bb_ymin, bb_ymax, bb_zmin, bb_zmax = \
            [eval(i) for i in image_description.split()[1:]]
        bound_x = bb_xmax - bb_xmin
        bound_y = bb_ymax - bb_ymin
        bound_z = bb_zmax - bb_zmin
        if print_output:
            print("Bounding box dimensions are: ({0:.2f}, {1:.2f}, "
                  "{2:.2f})".format(bound_x, bound_y, bound_z) + " " + units)
            sys.stdout.flush()

        vox_x = (bb_xmax - bb_xmin) / float(size_x - 1)
        vox_y = (bb_ymax - bb_ymin) / float(size_y - 1)
        vox_z = (bb_zmax - bb_zmin) / float(size_z - 1)
        if print_output:
            print("Voxel dimensions are: ({0:.2f}, {1:.2f}, "
                  "{2:.2f})".format(vox_x, vox_y, vox_z) + " " + units)
            sys.stdout.flush()

        # Set electrolyte area to zero in cathode data
        cathode_data[cathode_data == 1] = 0

        # Mask the voxels where phase is not present:
        cathode_mask = cathode_data == 0

        # Switch XZ zero-plane to a negative value to create zero-contour
        cathode_data[:,0,:] = -1

        cathode_ma = np.ma.MaskedArray(cathode_data, cathode_mask)
        del cathode_mask
        gc.collect()

        # Do geodesic distance calculation
        start_time = datetime.now()
        if print_output:
            print('Starting geodesic calculation at: {}'.format(start_time))
            sys.stdout.flush()
        geo_d = _geo_dist(cathode_ma, vox_x, vox_y, vox_z)
        end_time = datetime.now()
        if print_output:
            print('Geodesic calculation took: {}'.format(end_time -
                                                         start_time))
            sys.stdout.flush()

        # Do not need to calculating zero reference for the y-direction (use
        # just plane)
        # start_time = datetime.now()
        # x_interface = _calc_interface(electrolyte_data)
        # end_time = datetime.now()
        # if print_output:
        #     print("Calculating zero distance interface took: "
        #           "{}".format(end_time - start_time))

        # Calculate euclidean distance
        start_time = datetime.now()

        y_interface = np.zeros_like(electrolyte_data[:,0,:], dtype=np.float32)
        euc_d = np.zeros_like(electrolyte_data, dtype=np.float32)

        for (z,x), value in np.ndenumerate(y_interface):
            # set the value at each [x,z] point to a numpy array that ranges the
            # z dimension times the vox_y size
            # so this value can be compared with that calculated geodesically
            euc_d[z,:,x] = np.arange(len(euc_d[z,:,x])) * vox_y

        euc_d[euc_d < 0] = 0    # set all negative values to zero,
        #                         since they are on the other side of the
        #                         electrolyte (probably don't need this for y-dir)
        end_time = datetime.now()
        if print_output:
            print("Calculating euclidean distance took: {}".format(end_time -
                                                                   start_time))
            sys.stdout.flush()

        # Calculate tortuosity
        start_time = datetime.now()
        tort = _calc_tort(geo_d, euc_d, electrolyte_data)
        end_time = datetime.now()
        if print_output:
            print("Calculating tortuosity took: {}".format(end_time - start_time))
            sys.stdout.flush()

        start_time = datetime.now()
        if save_output:
            if print_output:
                print("Saving data...")
                sys.stdout.flush()
            save_results(fname=None,
                         direction='y',
                         phase=phase,
                         geo_d=geo_d.data,
                         euc_d=euc_d,
                         tort=tort.data,
                         desc=image_description)
            end_time = datetime.now()
            if print_output:
                print("Saving data took: {}".format(end_time - start_time))
                sys.stdout.flush()

        end_time = datetime.now()
        if print_output:
            print("Total execution time was: {}".format(end_time -
                                                        global_start_time))
            sys.stdout.flush()

        return geo_d.data, euc_d, tort.data, image_description

    def tortuosity_from_labels_z(electrolyte_fname,
                                 phase_fname,
                                 phase,
                                 units='nm',
                                 print_output=True,
                                 save_output=False):
        """
        Calculate the tortuosity (parallel to electrolyte interface, z-direction)
        from supplied label fields

        Parameters
        ----------
        electrolyte_fname: str
            Filename of a 3D tiff file that contains only the bulk electrolyte
            labelfield (as 1 values)
        phase_fname: str
            Filename of a 3D tiff file that contains the bulk electrolyte and the
            phase for which the geodesic distance is to be calculated
        phase: str
            Phase that is being calculated. i.e. 'pore', 'LSM', 'YSZ', etc.
        units: str
            units of the given dimensions
        print_output: bool
            switch to control whether output is printed
        save_output: bool
            switch to control whether data is saved to .npz files for later recall

        Returns
        --------
        geo_d: np.array
            Numpy array with geodesic distance from supplied surface
        euc_d: np.array
            Numpy array with euclidean distance from electrolyte
        tort: np.array
            Numpy array with tortuosity at each point
        desc: str
            description string to be used when saving the file with
            :func:`save_as_tiff` (for proper reading into Avizo)
        """

        global_start_time = datetime.now()

        if print_output:
            print('Starting calculation on ' + phase + ' in z direction.')
            sys.stdout.flush()

        # Load electrolyte data
        start_time = datetime.now()
        electrolyte_tif = TIFFfile(electrolyte_fname)
        electrolyte_array = electrolyte_tif.get_tiff_array()
        electrolyte_data = electrolyte_array[::].astype('int8')
        end_time = datetime.now()
        if print_output:
            print('Loaded electrolyte data in {} seconds.'.format(end_time -
                                                                  start_time))
            sys.stdout.flush()

        # Load phase to be calculated data
        start_time = datetime.now()
        cathode_tif = TIFFfile(phase_fname)
        cathode_array = cathode_tif.get_tiff_array()
        cathode_data = cathode_array[::].astype('int8')
        end_time = datetime.now()
        if print_output:
            print('Loaded cathode data in {} seconds.'.format(end_time -
                                                              start_time))
            sys.stdout.flush()

        # Get information from Avizo-saved tiff file
        ifd = electrolyte_tif.get_first_ifd()
        size_x = ifd.get_value('ImageWidth')
        size_y = ifd.get_value('ImageLength')
        size_z = electrolyte_tif.get_depth()
        image_description = ifd.get_value('ImageDescription').split(b'\0',1)[0]
        if print_output:
            print('ImageDescription is: {}'.format(image_description))
            sys.stdout.flush()

        # Calculate some parameters from the image input
        bb_xmin, bb_xmax, bb_ymin, bb_ymax, bb_zmin, bb_zmax = \
            [eval(i) for i in image_description.split()[1:]]
        bound_x = bb_xmax - bb_xmin
        bound_y = bb_ymax - bb_ymin
        bound_z = bb_zmax - bb_zmin
        if print_output:
            print("Bounding box dimensions are: ({0:.2f}, {1:.2f}, "
                  "{2:.2f})".format(bound_x, bound_y, bound_z) + " " + units)
            sys.stdout.flush()

        vox_x = (bb_xmax - bb_xmin) / float(size_x - 1)
        vox_y = (bb_ymax - bb_ymin) / float(size_y - 1)
        vox_z = (bb_zmax - bb_zmin) / float(size_z - 1)
        if print_output:
            print("Voxel dimensions are: ({0:.2f}, {1:.2f}, "
                  "{2:.2f})".format(vox_x, vox_y, vox_z) + " " + units)
            sys.stdout.flush()

        # Set electrolyte area to zero in cathode data
        cathode_data[cathode_data == 1] = 0

        # Mask the voxels where phase is not present:
        cathode_mask = cathode_data == 0

        # Switch XY zero-plane to a negative value to create zero-contour
        cathode_data[0,:,:] = -1

        cathode_ma = np.ma.MaskedArray(cathode_data, cathode_mask)
        del cathode_mask
        gc.collect()

        # Do geodesic distance calculation
        start_time = datetime.now()
        if print_output:
            print('Starting geodesic calculation at: {}'.format(start_time))
            sys.stdout.flush()
        geo_d = _geo_dist(cathode_ma, vox_x, vox_y, vox_z)
        end_time = datetime.now()
        if print_output:
            print('Geodesic calculation took: {}'.format(end_time -
                                                         start_time))
            sys.stdout.flush()

        # Do not need to calculating zero reference for the y-direction (use
        # just plane)
        # start_time = datetime.now()
        # x_interface = _calc_interface(electrolyte_data)
        # end_time = datetime.now()
        # if print_output:
        #     print("Calculating zero distance interface took: "
        #           "{}".format(end_time - start_time))

        # Calculate euclidean distance
        start_time = datetime.now()

        z_interface = np.zeros_like(electrolyte_data[0,:,:], dtype=np.float32)
        euc_d = np.zeros_like(electrolyte_data, dtype=np.float32)

        for (y,x), value in np.ndenumerate(z_interface):
            # set the value at each [x,z] point to a numpy array that ranges the
            # z dimension times the vox_z size
            # so this value can be compared with that calculated geodesically
            euc_d[:,y,x] = np.arange(len(euc_d[:,y,x])) * vox_z

        euc_d[euc_d < 0] = 0    # set all negative values to zero,
        #                         since they are on the other side of the
        #                         electrolyte (probably don't need this for y-dir)
        end_time = datetime.now()
        if print_output:
            print("Calculating euclidean distance took: {}".format(end_time -
                                                                   start_time))
            sys.stdout.flush()

        # Calculate tortuosity
        start_time = datetime.now()
        tort = _calc_tort(geo_d, euc_d, electrolyte_data)
        end_time = datetime.now()
        if print_output:
            print("Calculating tortuosity took: {}".format(end_time - start_time))
            sys.stdout.flush()

        start_time = datetime.now()
        if save_output:
            if print_output:
                print("Saving data...")
                sys.stdout.flush()
            save_results(fname=None,
                         direction='z',
                         phase=phase,
                         geo_d=geo_d.data,
                         euc_d=euc_d,
                         tort=tort.data,
                         desc=image_description)
            end_time = datetime.now()
            if print_output:
                print("Saving data took: {}".format(end_time - start_time))
                sys.stdout.flush()

        end_time = datetime.now()
        if print_output:
            print("Total execution time was: {}".format(end_time -
                                                        global_start_time))
            sys.stdout.flush()

        return geo_d.data, euc_d, tort.data, image_description

    def save_results(fname=None,
                     phase="",
                     direction="",
                     geo_d=None,
                     euc_d=None,
                     tort=None,
                     desc=None):
        """
        Saves all the results into a compressed numpy archive

        Parameters
        -----------
        geo_d: numpy array
            Geodesic distance array
        euc_d: numpy array
            Euclidean distance array
        tort: numpy array
            Tortuosity array
        desc: str
            Image description string
        """
        if fname is None:
            fname = datetime.now().strftime("%Y-%m-%d_%H.%M.%S_" + phase + '_' +
                                            direction + '_all-results.npz')

        np.savez_compressed(fname,
                            geo_d=geo_d,
                            euc_d=euc_d,
                            tort=tort,
                            image_description=desc
                            )

        print("Saved results to " + fname)

    def load_results(fname):
        """
        Loads the results that have been previously saved with :func:`save_results`

        Parameters
        -----------
        fname: str
            filename to load

        Returns
        --------
        geo_d: numpy array
            Geodesic distance array
        euc_d: numpy array
            Euclidean distance array
        tort: numpy array
            Tortuosity array
        desc: str
            Image description string
        """
        npzfile = np.load(fname)
        geo_d = npzfile['geo_d']
        euc_d = npzfile['euc_d']
        tort = npzfile['tort']
        desc = npzfile['image_description']

        # if description isn't None, cast it as string
        if desc.dtype != 'O':
            desc = str(desc)

        return geo_d, euc_d, tort, desc

    def tortuosity_profile(tort, euc, axis='x'):
        """
        Calculate the average tortuosity along an axis

        Parameters
        ----------
        tort: Numpy array (output of :func:`tortuosity_from_labels_x`...)
            Contains the tortuosity data, with 0 values in the masked areas
        euc: Numpy array (output of :func:`tortuosity_from_labels_x`...)
            Contains the euclidean distance at each point
        axis: str
            Axis along which to calculate profile. Should be 'x', 'y', or 'z'

        Returns
        -------
        tort_prof: 1D Numpy array
            1D profile of average tortuosity at each point in the array
            (ignoring the masked areas)
        euc_prof: 1D Numpy array
            1D profile of the average euclidean distance at each point
        """

        # Set all 0 tortuosity values to nans so they are ignored when calculating
        # the average:
        tort[tort == 0] = np.nan

        axis_dict = {'x':2, 'y':1, 'z':0}

        # Figure out the correct axis:
        axes = [0, 1, 2]
        axes.remove(axis_dict[axis])
        axes = tuple(axes)

        # Calculate averages along relevant axes (ignoring nans):
        euc_prof = np.nanmean(euc, axis=axes)
        tort_prof = np.nanmean(tort, axis=axes)

        return tort_prof, euc_prof

    def plot_tort_prof(tort_prof,
                       euc_prof,
                       direction,
                       units='nm',
                       figsize=None,
                       sns_style='white',
                       sns_context='paper',
                       sns_fontscale=1.5,
                       sns_cmap=None,
                       sns_color_num=0,
                       sns_kw=None,):
        """
        Plot a profile of the average tortuosity.

        Parameters
        ----------
        tort_prof: np.array
            tortuosity profile (output of :func:`tortuosity_profile`)
        euc_prof: np.array
            euclidean profile (output of :func:`tortuosity_profile`)
        direction: str
            direction for labeling
        units: str
            units of distance for labeling
        figsize : tuple of integers, optional, default: None
            width, height in inches. If not provided, defaults to rc
            figure.figsize.
        sns_style: str
            default style for seaborn ('white', 'dark', 'darkgrid', etc.)
        sns_context: str
            context for plotting with seaborn
        sns_fontscale: float
            font scale to pass to seaborn
        sns_cmap: list, str, tuple
            colormap for use with seaborn
            default is [light blue, dark teal, blue, red, green, orange]
            If str or tuple, then these parameters are passed to the
            seaborn.color_palette() function
        sns_color_num: int
            index of color (in ``sns_cmap``) to use
        sns_kw: dict
            additional arguments to be passed to seaborn.set_style
            (see http://goo.gl/WvLdc6 for details)

        Returns
        -------
        matplotlib figure, <matplotlib axes>
            Handle to figure that is plotted (and axes array if subplots)
        """
        # Import and setup seaborn styles
        if sns_kw is None:
            sns_kw = {}
        if sns_cmap is None:
            sns_cmap = ["#4C72B1", "#004040", "#023FA5", "#8E063B",
                        "#098C09", "#EF9708"]
        import seaborn as sns
        matplotlib.rcParams['mathtext.fontset'] = 'stixsans'
        sns.set(rc={'image.cmap': 'Greys_r'})
        sns.set_style(sns_style, sns_kw)
        sns.set_context(sns_context, font_scale=sns_fontscale)
        if type(sns_cmap) is tuple:
            palette = sns.color_palette(*sns_cmap)
        else:
            palette = sns.color_palette(sns_cmap)
        sns.color_palette(palette)

        if units is 'um':
            units = "${\mu}m$"

        fig = plt.figure(figsize=figsize)
        plt.plot(euc_prof, tort_prof, color=palette[sns_color_num])
        plt.xlabel('Euclidean distance (' + direction + " direction, " +
                   units + ")")
        plt.ylabel("Average tortuosity")
        plt.tight_layout()

        return fig

    def save_profile_to_csv(fname,
                            x,
                            y,
                            direction,
                            phase,
                            x_name='Euc_d',
                            y_name='tort',
                            fmt='%10.5f',
                            delimiter=',',
                            ):
        """
        Saves two profiles (x and y) into a text file.

        Parameters
        ----------
        fname: str
            filename to save to (no checks are done before overwriting)
        x: 1D np.array
            X-profile that will be used as first column
            should be a one dimensional numpy row vector
        y: np.array or list of np.arrays
            can be 1D np.array, or a list of them, with data in rows.
            Each row will be transformed to a column in the text files
        direction: str
            direction for labeling
        phase: str
            phase for labeling
        x_name: str
            Name of header in x-column
        y_name: str
            Name of header in y-column
        fmt: str
            Format for decimals to use
        delimiter: str
            Delimiter to use in the csv file
        """
        np.savetxt(fname, np.vstack((x,y)).transpose(),
                   header=phase + " " + direction + "-direction \n" + x_name +
                          delimiter + '\t' + y_name,
                   fmt=fmt,
                   delimiter=delimiter)
        print('Saved', fname)

    def load_profile_from_csv(fname,
                              delimiter=',',
                              skiprows=2):
        """
        Loads columns of a text file into 1D numpy arrays.

        Parameters
        ----------
        fname: str
            filename to load from
        delimiter: str
            character to use as delimiter
        skiprows: int
            number of header rows to skip at beginning of file

        Returns
        --------
        list of 1D np.array objects, 1 for each column in the data
        """
        data = np.loadtxt(fname,
                          delimiter=delimiter,
                          skiprows=skiprows).transpose()
        return [i[0] for i in np.vsplit(data, len(data))]

    def save_as_tiff(fname, data, dtype, desc):
        """
        Save a numpy array as tiff (useful for transferring data back to Avizo)

        Parameters
        ----------
        fname: str
            Filename to save to
        data: np.array
            Data array to be written to image file
        dtype: str
            data type to save as. For some reason, euclidean distance works
            best as 'uint32', while the others are fine as 'float32'.
        desc: str
            Image description that includes bounding box info
        """
        tiff = TIFFimage(data.astype(dtype),
                         description=desc)
        tiff.write_file(fname, compression='lzw')

    def _geo_dist(data, vox_x, vox_y, vox_z):
        """
        Internal helper method to call scikit-fmm distance method

        Parameters
        ----------
        data: masked data array to calculate on
        vox_x: x voxel size
        vox_y: y voxel size
        vox_z: z voxel size

        Returns
        -------
        d: distance transform as calculated by scikit-fmm
        """
        d = skfmm.distance(data, dx=[vox_z, vox_y, vox_x])

        # Convert dtype from float64 to float32 to save memory (almost half):
        d = np.ma.asarray(d, dtype='float32')

        # Set distances within the bulk electrolyte to zero:
        d.data[d.data < 0] = 0
        return d

    def _calc_interface(electrolyte_data):
        """
        Calculates interface between bulk electrolyte and the cathode

        Parameters
        ----------
        electrolyte_data: label data for bulk electrolyte

        Returns
        -------
        x_interface: 2D numpy array
            x values of interface at each y and z locations
        """
        # create a single plane array with same x and y dimensions
        x_interface = np.zeros_like(electrolyte_data[:, :, 0], dtype=int)
        # visit each point on the array
        for (x, y), value in np.ndenumerate(x_interface):
            # set the value at each point to the first place where the electrolyte
            # value is 0 (which is the boundary between electrolyte and cathode)
            x_interface[x, y] = np.where(electrolyte_data[x, y, :] == 0)[0][0]

        return x_interface

    def _calc_tort(geo_d, euc_d, electrolyte_data):
        """
        Calculates tortuosity from geodesic and euclidean distances

        Parameters
        ----------
        geo_d: Geodesic distance (Masked Array)
        euc_d: Euclidean distance (Numpy array)
        electrolyte_data: label data for bulk electrolyte

        Returns
        -------
        tort: tortuosity numpy array
        """

        old = np.seterr(divide='ignore')

        # Calculate tortuosity by dividing geo_d by euc_d
        tort = np.divide(geo_d, euc_d)
        # Clean up the data, removing values < 1 and any infinities
        tort[electrolyte_data == 1] = 0     # 0 so it will be ignored in bulk YSZ
        tort[np.logical_and(tort < 1, tort > 0)] = 1
        tort[np.isinf(tort)] = 1
        tort[np.isnan(tort)] = 1

        # tort should never be very large (greater than 2), so we can
        # safely represent it with a half precision float
        # 53.7MiB before, 44.0MiB after, so saving 18%
        # tort = np.ma.asarray(tort, dtype='float16')

        np.seterr(**old)

        return tort

    def _calc_euc_x(x_interface, electrolyte_data, vox_x):
        """
        Calculates euclidean distance from electrolyte interface

        Parameters
        ----------
        x_interface: Numpy array containing x value of boundary for electrolyte
            for all y and z coordinates
        electrolyte_data: label data for bulk electrolyte
        vox_x: float
            voxel x-size

        Returns
        -------
        euc_d: euclidean distance numpy array
        """
        # create euclidean distance array of the same shape as the others
        euc_d = np.zeros_like(electrolyte_data, dtype=np.float32)
        # visit each [x,y] location in this array:
        for (x,y), value in np.ndenumerate(x_interface):
            # set the value at each [x,y] point to a numpy array that ranges the
            # x dimension minus the zero reference from before times the vox_x size
            # so this value can be compared with that calculated geodesically
            euc_d[x,y] = (np.arange(len(euc_d[x,y])) - x_interface[x,y]) * vox_x

        euc_d[euc_d < 0] = 0    # set all negative values to zero,
        #                         since they are on the other side of the
        #                         electrolyte

        return euc_d

        # How to create multiple processes for jobs:
        ############################################
        #
        # # Setup parallel stuff:
        # manager = multiprocessing.Manager()
        # return_dict = manager.dict()
        # jobs = []
        #
        # Calculate geodesic distance:
        # p = multiprocessing.Process(target=_geo_dist, args=('geo',
        #                                                     return_dict,
        #                                                     cathode_data,
        #                                                     vox_x,
        #                                                     vox_y,
        #                                                     vox_z))
        # jobs.append(p)
        # p.start()
        # if print_output:
        #     print('Started geo calc')
        #
        # Calculate euclidean distance:
        # p = multiprocessing.Process(target=_geo_dist, args=('euc',
        #                                                     return_dict,
        #                                                     all_data,
        #                                                     vox_x,
        #                                                     vox_y,
        #                                                     vox_z))
        # jobs.append(p)
        # p.start()
        # if print_output:
        #     print('Started euc calc')
        #
        # if print_output:
        #     print('Jobs list:' + repr(jobs))
        #
        # for proc in jobs:
        #     proc.join()
        #
        # geo_d = return_dict['geo']
        # euc_d = return_dict['euc']