import tensorflow as tf
import numpy as np

from config import load_args
from lip_model.losses import cer
from lip_model.modules import embedding, sinusoid_encoding, multihead_attention, \
  feedforward, label_smoothing
from lip_model.visual_frontend import VisualFrontend
from util.tf_util import shape_list

config = load_args()

class TransformerTrainGraph():
  def __init__(self,
               x,
               y,
               is_training=True,
               reuse=None,
               embed_input=False,
               go_token_index=2,
               chars=None):

    self.is_training = is_training
    self.x = x

    if config.featurizer:
      vid_inp = x[0] if type(x) is tuple or type(x) is list else x
      istarget = tf.not_equal(vid_inp, 0)
      self.padding_mask = tf.to_float(tf.reduce_any(istarget, axis=[2,3,4]))

      with tf.variable_scope('visual_frontend', reuse=reuse):
        self.visual_frontend = VisualFrontend(vid_inp)
        vid_inp = self.visual_frontend.output
      vid_inp = vid_inp * tf.expand_dims(self.padding_mask,-1)
      # pad = 30
      # x = tf.keras.layers.ZeroPadding1D(padding=(pad, pad))(x)
      if type(x) is tuple or type(x) is list:
        x = [vid_inp] + list(x[1:])
      else:
        x = vid_inp

    if is_training:
      self.prev = y
      self.y = y
    else:
      # This is the partial prediction used for the autoregression -
      # augmented by one more element on every step when autoregression is on
      self.prev = y[0]
      self.y = y[1] # This is the whole ground truth transcription

    self.alignment_history = {} # to be filled in by decoder

    self.go_token_idx = go_token_index

    # define decoder inputs
    self.decoder_inputs = tf.concat(
      (tf.ones_like(self.prev[:, :1]) * go_token_index, self.prev[:, :-1]), -1)  # 2:<S>

    # Encoder
    self.enc = x
    with tf.variable_scope("encoder", reuse=reuse) as scope:
      self.enc = self.encoder_body(self.enc, is_training)

    # import ipdb; ipdb.set_trace()
    # Decoder
    self.dec = self.decoder_inputs

    top_scope = tf.get_variable_scope() # this is a hack to be able to use same model
    self.chars = chars # needed for decoding with external LM

    # --------------- index to char dict for summaries --------------------------------
    if chars is not None:
      keys = tf.constant( np.arange(len(chars)) , dtype=tf.int64)
      values = tf.constant(chars , dtype=tf.string)
      self.char_table = tf.contrib.lookup.HashTable(
                      tf.contrib.lookup.KeyValueTensorInitializer(keys, values), '')

    with tf.variable_scope("decoder", reuse=reuse) as scope:
      self.dec = self.decoder_body(self.enc, self.dec, is_training, top_scope=top_scope)
      if type(self.dec) == tuple:
        self.preds, self.scores, self.dec = self.dec # Inference graph output

    self.add_loss_and_metrics(reuse, is_training)

    if config.tb_eval:
      self.add_tb_summaries()
    self.tb_sum = tf.summary.merge_all()

  def project_output(self):
    return True

  def decoder_body(self, enc, dec, is_training, top_scope=None):
    # Initialize the masks for the pads from here,
    #  because after positional embeddings are added, nothing will be 0
    if config.mask_pads: # Guard this for backwards compatibility
      key_masks_enc = tf.sign(tf.abs(tf.reduce_sum(enc, axis=-1))) # (N, T_k)
      key_masks_dec = tf.cast( tf.sign(tf.abs(dec)), 'float32' ) # (N, T_k)
      query_masks_dec = tf.cast( tf.sign(tf.abs(dec)), 'float32' ) # (N, T_k)
    else:
      key_masks_enc = key_masks_dec = query_masks_dec = None

    ## Embedding
    dec = self.decoder_embeddings(dec, is_training)

    for i in range(config.num_blocks):
      with tf.variable_scope("num_blocks_{}".format(i)):
        ## self-attention
        dec, alignmets = multihead_attention(queries=dec,
                                       query_masks=query_masks_dec,
                                       keys=dec,
                                       key_masks=key_masks_dec,
                                       num_units=config.hidden_units,
                                       num_heads=config.num_heads,
                                       dropout_rate=config.dropout_rate,
                                       is_training=is_training,
                                       causality=True,
                                       scope="self_attention")
        # self.alignment_history["dec_self_att_{}".format(i)] = alignmets # save for tb

        ## vanilla attention
        dec, alignmets = multihead_attention(queries=dec,
                                       query_masks=query_masks_dec,
                                       keys=enc,
                                       key_masks=key_masks_enc,
                                       num_units=config.hidden_units,
                                       num_heads=config.num_heads,
                                       dropout_rate=config.dropout_rate,
                                       is_training=is_training,
                                       causality=False,
                                       scope="vanilla_attention")

        self.alignment_history["enc_dec_attention_{}".format(i)] = alignmets # save for tb

        ## Feed Forward
        dec = feedforward(dec, num_units=[4 * config.hidden_units, config.hidden_units])

    return dec

  def decoder_embeddings(self, decoder_inputs, is_training):
    dec = embedding(decoder_inputs,
                         vocab_size=config.n_labels,
                         num_units=config.hidden_units,
                         scale=True,
                         scope="dec_embed")

    # if self.is_training:
    #   dec = dec[:,:self.out_last_non_pad_idx]

    ## Positional Encoding
    pos = self.positional_encoding(decoder_inputs, scope='dec_pe')
    # if self.is_training:
    #   pos = pos[:,:self.out_last_non_pad_idx]
    dec += pos

    ## Dropout
    dec = tf.layers.dropout(dec,
                                 rate=config.dropout_rate,
                                 training=tf.convert_to_tensor(is_training))
    return dec

  def positional_encoding(self, inp, scope):
    if config.sinusoid:
      return sinusoid_encoding(inp,
                               num_units=config.hidden_units,
                               zero_pad=False,
                               scale=False,
                               scope=scope,
                               T  = config.maxlen
                               )
    else:
      return embedding(
        tf.tile(tf.expand_dims(tf.range(tf.shape(inp)[1]), 0),
                [tf.shape(inp)[0], 1]),
        vocab_size=config.maxlen,
        num_units=config.hidden_units,
        zero_pad=False,
        scale=False,
        scope="dec_pe")

  def encoder_body(self, enc, is_training):

    num_blocks = config.num_blocks

    if config.mask_pads: # Guard this for backwards compatibility
      # Initialize the masks for the pads from here,
      # because after positional embeddings are added, nothing will be 0
      key_masks = tf.sign(tf.abs(tf.reduce_sum(enc, axis=-1))) # (N, T_k)
      query_masks = tf.sign(tf.abs(tf.reduce_sum(enc, axis=-1))) # (N, T_k)
    else:
      key_masks = query_masks = None

    enc = self.encoder_embeddings(enc, is_training)

    for i in range(num_blocks):
      with tf.variable_scope("num_blocks_{}".format(i)):
        ### Multihead Attention
        enc, alignmets = multihead_attention(queries=enc,
                                       query_masks=query_masks,
                                       keys=enc,
                                       key_masks=key_masks,
                                       num_units=config.hidden_units,
                                       num_heads=config.num_heads,
                                       dropout_rate=config.dropout_rate,
                                       is_training=is_training,
                                       causality=False)
        # key_masks = query_masks = None #

        ### Feed Forward
        enc = feedforward(enc, num_units=[4 * config.hidden_units, config.hidden_units])

        # self.alignment_history["enc_self_att_{}".format(i)] = alignmets # save for tb

    return enc

  def encoder_embeddings(self, x, is_training, embed_input=0):
    # Embedding
    if embed_input:
      enc = embedding(x,
                      vocab_size=config.n_input_vocab,
                      num_units=config.hidden_units,
                      scale=True,
                      scope="enc_embed")
    else:
      enc = x

    ## Positional Encoding
    feat_dim = shape_list(enc)[-1]
    # if input features are not same size as transformer units, make a linear projection
    if not feat_dim == config.hidden_units:
      enc = tf.layers.dense(enc, config.hidden_units)

    enc += self.positional_encoding(enc, scope='enc_pe')

    if embed_input:
      ## Dropout
      enc = tf.layers.dropout(enc,
                                   rate=config.dropout_rate,
                                   training=tf.convert_to_tensor(is_training))
    return enc

  def add_loss_and_metrics(self, reuse, is_training):
    # Final linear projection
    if self.project_output():
      # with tf.variable_scope("ctc_conv1d_net/ctc_probs", reuse=reuse) as scope:
        self.logits = tf.layers.dense(self.dec, config.n_labels, reuse=reuse)
    else:
      assert(self.dec.get_shape()[-1].value == config.n_labels)
      self.logits = self.dec

    if config.test_aug_times:
      self.logits_aug = self.logits
      self.logits = tf.reduce_mean(self.logits, 0, keep_dims=True)

    self.istarget = tf.to_float(tf.not_equal(self.y, 0))

    # Loss
    self.y_one_hot = tf.one_hot(self.y, depth=config.n_labels)
    self.y_smoothed = label_smoothing(self.y_one_hot)

    self.loss = tf.nn.softmax_cross_entropy_with_logits(logits=self.logits,
                                                        labels=self.y_smoothed)

    # we want to know when to stop so learn padding as well
    self.mean_loss = tf.reduce_sum(self.loss) / (tf.reduce_sum(self.istarget))
    self.logprobs = tf.log(tf.nn.softmax(self.logits))

    if not 'infer' in config.graph_type:
      self.preds = tf.to_int32(tf.argmax(self.logits, axis=-1))
      self.cer, self.cer_per_sample = cer(self.y_one_hot, self.logits, return_all=True)
    else:
      self.preds = tf.to_int32(self.preds)
      one_hot_from_preds = tf.one_hot(self.preds, depth=config.n_labels)
      self.cer, self.cer_per_sample = cer(self.y_one_hot,
                                          one_hot_from_preds,
                                          return_all=True)

  def add_tb_summaries(self):
    from util.tb_util import add_gif_summary, colorize_image

    fps = 10
    timeline = False

    # ----------------    Add video summaries -------------------------------
    bs = int(self.visual_frontend.output.shape[0])
    b_id = 0
    non_pad_inds = tf.cast(tf.where(self.padding_mask[b_id] > 0)[:, 0], tf.int64)
    fr_in, to_in = non_pad_inds[0], non_pad_inds[-1] + 1 # For masking out input paddings
    add_gif_summary('1-video_input',
                self.visual_frontend.input[b_id][fr_in:to_in], fps=fps, timeline=timeline)
    if not config.test_aug_times:
      add_gif_summary('2-input_to_resnet',
              self.visual_frontend.aug_out[b_id][fr_in:to_in], fps=fps, timeline=timeline)
    else:
      # Viz the different test augmentations
      add_gif_summary('2-input_to_resnet',
                      tf.concat([ self.visual_frontend.aug_out[b_id][fr_in:to_in]
                            for b_id in xrange(bs) ], axis=2), fps=fps, timeline=timeline)

    # ----------------   Add text summaries -------------------------------
    pred_strings_tf = self.char_table.lookup(tf.cast(self.preds, tf.int64))
    joined_pred = tf.string_join(
      tf.split(pred_strings_tf, pred_strings_tf.shape[1], 1))[ :, 0]
    gt_strings_tf = self.char_table.lookup(tf.cast(self.y, tf.int64))
    joined_gt = tf.string_join(
      tf.split(gt_strings_tf, pred_strings_tf.shape[1], 1))[:, 0]
    joined_all = tf.string_join([joined_gt, joined_pred], ' --> ')
    tf.summary.text('Predictions', joined_all)

    # ----------------   Add image summaries -------------------------------
    all_atts = []
    for layer_name, alignment_history in self.alignment_history.items():
      for att_head_idx, attention_images in enumerate(alignment_history):
        all_atts.append(attention_images)
    avg_att = tf.exp(tf.reduce_mean(tf.log(all_atts), axis=0))

    # Permute and reshape (batch, t_dec, t_enc) --> (batch, t_enc, t_dec, 1)
    attention_img = tf.expand_dims(tf.transpose(avg_att, [0, 2, 1]), -1)
    attention_img *= 255 # Scale to range [0, 255]

    b_id = 0 # visualize only the first sample of the batch
    to_out =  tf.where( self.preds[b_id]> 0 )[-1][0] + 1 # To mask output paddings                                                                                |~
    color_img = tf.map_fn( colorize_image, (attention_img[:, fr_in:to_in, :to_out]) )
    tf.summary.image("3-enc_dec_attention", color_img)

    # ----------------   Add image with subs summaries -------------------------------
    # import ipdb; ipdb.set_trace()
    add_gif_summary('4-subs',
          self.visual_frontend.input[b_id][fr_in:to_in], fps=fps, timeline=timeline,
          attention=attention_img[b_id][fr_in:to_in, :to_out,0], pred=joined_pred[b_id])

  @classmethod
  def get_input_shapes_and_types(cls, batch=0):

    input_types = []

    input_shape = []
    if config.featurizer:
      input_shape += [ (config.time_dim, config.img_width, config.img_height, config.img_channels) ]
      input_types +=['float32']
    else:
      input_shape += [ (config.time_dim, config.feat_dim) ]
      input_types +=['float32']

    if batch:
      input_shape = [ (config.batch_size,) + shape for shape in input_shape ]

    return input_shape, input_types

  @classmethod
  def get_target_shapes_and_types(cls, batch=0):
    target_shape = [ ( config.time_dim, ) ]
    if batch:
      target_shape = [ (config.batch_size,) + shape for shape in target_shape ]
    target_types = ['int64']
    return target_shape, target_types

  @classmethod
  def get_model_input_target_shapes_and_types(cls, batch_dims=1):
    return cls.get_input_shapes_and_types(batch=batch_dims),\
           cls.get_target_shapes_and_types(batch=batch_dims)


import mysql.connector
from nwd.new_word_detection import NWD
from nwd.domain_classifier import Classifier
from nwd.preprocessing import SUB_SYMBOL
from datetime import date, timedelta, datetime
import sys
import numpy as np

db = mysql.connector.connect(user='root', db='thesis')
cursor = db.cursor()

print('Enter text type: ', end='')
text_type = input()
print('Enter min frequency: ', end='')
min_freq = int(input())
print('Enter threshold: ', end='')
threshold = int(input())
start_date = date(2018, 1, 1)
end_date = date(2019, 1, 1)
date_range = timedelta(days=1)

all_words = set()
target_mapping = {
    'ETtoday星光雲': '時尚流行',
    'ETtoday運動雲': '體育運動',
    '社會': '社會議題',
    '生活': '生活消費',
    '國際': '國內外政治',
    '政治': '國內外政治',
    '地方': '社會議題',
    'ETtoday寵物雲': '休閒娛樂',
    '財經': '金融經濟',
    'ETtoday健康雲': '健康醫療',
    'ETfashion': '時尚流行',
    '民生消費': '生活消費',
    '軍武': '國內外政治',
    'ETtoday車雲': '生活消費',
    'ETtoday遊戲雲': '休閒娛樂',
    '3C家電': '生活消費',
    '新奇': '休閒娛樂',
    '保險': '金融經濟',
    '法律': '社會議題',
    '雲論': '社會議題'
}
target_names = sorted(list(set(target_mapping.values())))
model_path = 'model/text_clf_svm_log.pkl'
text_clf = Classifier(model_path)

while start_date < end_date:
    if text_type == 'ptt':
        sql = f'SELECT uniID,content FROM articles_ptt WHERE datetime_pub>="{start_date.isoformat()}" AND datetime_pub<"{(start_date+date_range).isoformat()}"'
    elif text_type == 'news':
        # sql = f'SELECT content FROM articles limit {limit}'
        sql = f'SELECT uniID,content FROM articles WHERE datetime_pub>="{start_date.isoformat()}" AND datetime_pub<"{(start_date+date_range).isoformat()}"'
        # sql = f'SELECT content FROM articles limit {offset},{limit}'
    else:
        print('Unknown text type')
        sys.exit()
    cursor.execute(sql)

    docs = []
    docs_id = []
    length = 0
    for post in cursor.fetchall():
        length += 1
        if text_type == 'ptt':
            parentID, content = post
            doc = [content]
            sql = f'SELECT content FROM articles_comment WHERE parentID="{parentID}"'
            cursor.execute(sql)
            doc += [comment[0] for comment in cursor.fetchall()]
            docs_id.append(post[0])
            docs.append(SUB_SYMBOL.join(doc))
        elif text_type == 'news':
            docs_id.append(post[0])
            docs.append(post[1])
    print('length of docs:', len(docs))

    nwd = NWD(max_len=3, min_freq=min_freq, min_pmi=threshold, min_entropy=threshold)
    new_words = nwd.fit_detect(docs)

    for i, doc in enumerate(docs):
        docs[i] = ' '.join(doc)

    domains = []
    for new_word in new_words:
        indices = new_word[4]
        pred_docs = [docs[i] for i in indices]
        proba = text_clf.predict(pred_docs)
        domains.append(proba)

    vals = []
    for i, prob in enumerate(domains):
        new_word = ''.join(new_words[i][0])
        articles_id = map(lambda k: docs_id[k], new_words[i][4])
        articles_id = list(articles_id)
        if new_word not in all_words:
            vals.append((new_word, new_words[i][1], new_words[i][2], new_words[i][3], 2, str(articles_id), target_names[np.argmax(prob)], str(prob.tolist()), text_type, start_date))
            all_words.add(new_word)

    sql = f'INSERT INTO new_words (word,freq,pmi,entropy,dict_type,articles_id,domain,domain_prob,origin,detect_time) VALUES (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)'
    cursor.executemany(sql, vals)
    db.commit()

    start_date += date_range


"""Image series

The Series class is a subclassed Numpy.ndarray enhancing the array with relevant medical image methods and attributes.

  Typical example usage:

  si = Series('input')

"""

import copy

import matplotlib.colors
import numpy as np
# from numpy.compat import basestring
import logging
from pathlib import PurePath
import pydicom.dataset
import pydicom.datadict
# import numpy.core._multiarray_umath

import imagedata.axis
import imagedata.formats
import imagedata.readdata as readdata
import imagedata.formats.dicomlib.uid
from imagedata.header import Header

logger = logging.getLogger(__name__)

class DoNotSetSlicesError(Exception):
    pass

class Series(np.ndarray):
    """Series -- a multidimensional array of medical imaging pixels.

    The Series class is a subclassed Numpy.ndarray enhancing the ndarray
    with relevant medical image methods and attributes.

    Series(data, input_order='none', opts=None, shape=(0,), dtype=float, order=None, template=None, geometry=None)

    Examples:

        Read the contents of an input directory

        >>> image = Series('directory/')

        Make a Series instance from a Numpy.ndarray

        >>> a = np.eye(128)
        >>> image = Series(a)

    Args:
        data: (array_like or URL)
            Input data, either explicit as np.ndarray, np.uint16, np.float32, or by URL to input data.
        input_order (str): How to sort the input data. Typical values are:
                * 'none' : 3D volume or 2D slice (default).
                * 'time' : Time-resolved data.
                * 'b' : Diffusion data with variable b values.
                * 'te' : Varying echo times.
                * 'fa' : Varying flip angles.

        opts: Dict of input options, mostly for format specific plugins (argparse.Namespace or dict)
        shape: Tuple of ints, specifying shape of input data.
        dtype: Numpy data type. Default: float
        template: Input data to use as template for DICOM header (Series, array_like or URL)
        geometry: Input data to use as template for geometry (Series, array_like or URL)
        order: Row-major (C-style) or column-major (Fortran-style) order, {'C', 'F'}, optional

    Returns:
        Series instance
    """

    name = "Series"
    description = "Image series"
    authors = "Erling Andersen"
    version = "1.1.1"
    url = "www.helse-bergen.no"

    def __new__(cls, data, input_order='none',
                opts=None, shape=(0,), dtype=float, buffer=None, offset=0,
                strides=None, order=None,
                template=None, geometry=None, axes=None):

        if issubclass(type(template), Series):
            template = template.header
        if issubclass(type(geometry), Series):
            geometry = geometry.header
        if issubclass(type(data), np.ndarray):
            logger.debug('Series.__new__: data ({}) is subclass of np.ndarray'.format(type(data)))
            obj = np.asarray(data).view(cls)
            # Initialize attributes to defaults
            # cls.__init_attributes(cls, obj)
            # obj.header = Header() # Already set in __array_finalize__

            # set the new 'input_order' attribute to the value passed
            obj.header.input_order = input_order

            if issubclass(type(data), Series):
                # Copy attributes from existing Series to newly created obj
                obj.header = copy.copy(data.header)  # carry forward attributes
                obj.input_order = data.input_order
                obj.header.add_template(data.header)  # Includes DicomHeaderDict
                obj.header.add_geometry(data.header, data.header)

            # obj.header.set_default_values() # Already done in __array_finalize__
            if axes is not None:
                obj.header.axes = copy.copy(axes)
            obj.header.add_template(template)
            obj.header.add_geometry(template, geometry)
            return obj
        logger.debug('Series.__new__: data is NOT subclass of Series, type {}'.format(type(data)))

        # Assuming data is url to input data
        if isinstance(data, np.compat.basestring) or issubclass(type(data), PurePath):
            urls = data
        elif isinstance(data, list):
            urls = data
        else:
            if np.ndim(data) == 0:
                obj = np.asarray([data]).view(cls)
            else:
                obj = np.asarray(data).view(cls)
            # cls.__init_attributes(cls, obj)
            obj.header = Header()
            obj.header.input_order = input_order
            obj.header.input_format = type(data)
            if np.ndim(data) == 0:
                obj.header.axes = [imagedata.axis.UniformAxis('number', 0, 1)]
            obj.header.set_default_values(obj.axes if axes is None else axes)
            obj.header.add_template(template)
            obj.header.add_geometry(template, geometry)
            return obj

        # Read input, hdr is dict of attributes
        hdr, si = readdata.read(urls, input_order, opts)

        obj = np.asarray(si).view(cls)
        assert obj.header, "No Header found in obj.header"

        # set the new 'input_order' attribute to the value passed
        obj.header.input_order = input_order
        obj.header.input_format = hdr.input_format
        # Copy attributes from hdr dict to newly created obj
        logger.debug('Series.__new__: Copy attributes from hdr dict to newly created obj')
        if axes is not None:
            obj.axes = copy.copy(axes)
        elif hdr.axes is not None:
            obj.axes = hdr.axes
        obj.header.set_default_values(obj.axes)
        obj.header.add_template(hdr)
        obj.header.add_geometry(hdr, hdr)
        # for attr in __attributes(hdr):
        #     __set_attribute(obj.header, attr, __get_attribute(template, attr))
        #     setattr(obj.header, attr, hdr[attr])
        # Store any template and geometry headers,
        obj.header.add_template(template)
        obj.header.add_geometry(template, geometry)
        # Finally, we must return the newly created object
        return obj

    def __array_finalize__(self, obj) -> None:
        # logger.debug("Series.__array_finalize__: entered obj: {}".format(type(obj)))
        # ``self`` is a new object resulting from
        # ndarray.__new__(Series, ...), therefore it only has
        # attributes that the ndarray.__new__ constructor gave it -
        # i.e. those of a standard ndarray.
        #
        # We could have got to the ndarray.__new__ call in 3 ways:
        # From an explicit constructor - e.g. Series():
        #    obj is None
        #    (we're in the middle of the Series.__new__
        #    constructor, and self.__input_order will be set when we return to
        #    Series.__new__)
        if obj is None:
            return
        # From view casting - e.g arr.view(Series):
        #    obj is arr
        #    (type(obj) can be Series)
        # From new-from-template - e.g Series[:3]
        #    type(obj) is Series
        #
        # Note that it is here, rather than in the __new__ method,
        # that we set the default value for 'input_order', because this
        # method sees all creation of default objects - with the
        # Series.__new__ constructor, but also with arr.view(Series).
        # logger.debug("Series.__array_finalize__: obj: {}".format(type(obj)))

        if issubclass(type(obj), Series):
            # Copy attributes from obj to newly created self
            # logger.debug('Series.__array_finalize__: Copy attributes from {}'.format(type(obj)))
            # self.__dict__ = obj.__dict__.copy()  # carry forward attributes
            self.header = copy.copy(obj.header)  # carry forward attributes
        else:
            self.header = Header()
            self.header.set_default_values(self.axes)

        # We do not need to return anything

    def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
        # logger.debug('Series.__array_ufunc__ method: %s' % method)
        args = []
        in_no = []
        # logger.debug('Series.__array_ufunc__ inputs: %s %d' % (
        #    type(inputs), len(inputs)))
        for i, input_ in enumerate(inputs):
            if isinstance(input_, Series):
                in_no.append(i)
                args.append(input_.view(np.ndarray))
                # logger.debug('                       input %d: Series' % i)
                # logger.debug('                       input %s: ' % type(input_))
                # logger.debug('                       input spacing {} '.format(
                #        input_.spacing))
            else:
                args.append(input_)

        outputs = kwargs.pop('out', None)
        out_no = []
        # logger.debug('Series.__array_ufunc__ inputs: %d' % len(inputs))
        if outputs:
            out_args = []
            for j, output in enumerate(outputs):
                if isinstance(output, Series):
                    out_no.append(j)
                    out_args.append(output.view(np.ndarray))
                else:
                    out_args.append(output)
            kwargs['out'] = tuple(out_args)
        else:
            outputs = (None,) * ufunc.nout

        info = {}
        if in_no:
            info['inputs'] = in_no
        if out_no:
            info['outputs'] = out_no

        results = super(Series, self).__array_ufunc__(ufunc, method,
                                                      *args, **kwargs)
        if results is NotImplemented:
            return NotImplemented

        if method == 'at':
            if isinstance(inputs[0], Series):
                inputs[0].header = info
            return

        if ufunc.nout == 1:
            if np.isscalar(results):
                return results  # Do not pack scalar results as Series object
            results = (results,)

        results = tuple((np.asarray(result).view(Series)
                         if output is None else output)
                        for result, output in zip(results, outputs))
        if results and isinstance(results[0], Series):
            # logger.debug('Series.__array_ufunc__ add info to results:\n{}'.format(info))
            results[0].header = self._unify_headers(inputs)
            results[0].setDicomAttribute('WindowCenter', results[0].max() / 2)
            results[0].setDicomAttribute('WindowWidth', results[0].max())

        return results[0] if len(results) == 1 else results

    @staticmethod
    def _unify_headers(inputs: tuple) -> Header:
        """Unify the headers of the inputs.

        Typical usage is in expressions like c = a + b where at least
        one of the arguments is a Series instance.
        This function will provide a header for the result of
        the expression.

        Inputs:
        - inputs: a tuple of arguments (ndarray or Series)
        Return:
        - header: a Header() class
        """

        header = None
        for i, input_ in enumerate(inputs):
            if issubclass(type(input_), Series):
                if input_.header is None:
                    logger.warning('Series._unify_headers: new header')
                    header = Header()
                    header.input_order = imagedata.formats.INPUT_ORDER_NONE
                else:
                    logger.debug('Series._unify_headers: copy header')
                    header = copy.copy(input_.header)
                    header.input_order = input_.input_order
                    header.set_default_values(input_.axes)
                    header.add_template(input_.header)  # Includes DicomHeaderDict
                    header.add_geometry(input_.header, input_.header)

                # Here we could have compared the headers of
                # the arguments and resolved discrepancies.
                # The simplest resolution, however, is to take the
                # header of the first argument.
        return header

    def __getitem__(self, item):
        """__getitem__(self, item)

        Called to implement evaluation of self[item]. For sequence types, the
        accepted items should be integers and slice objects. Note that the
        special interpretation of negative indexes (if the class wishes to
        emulate a sequence type) is up to the __getitem__() method. If item is
        of an inappropriate type, TypeError may be raised; if of a value
        outside the set of indexes for the sequence (after any special
        interpretation of negative values), IndexError should be raised. For
        mapping types, if item is missing (not in the container), KeyError
        should be raised. Note: for loops expect that an IndexError will be
        raised for illegal indexes to allow proper detection of the end of the
        sequence.
        """

        def _set_geometry(_ret, _todo):
            for attr, value in _todo:
                try:
                    setattr(_ret, attr, value)
                except AttributeError:
                    pass

        def _number_of_ellipses(_items):
            n = 0  # type: int
            for _item in _items:
                if _item is Ellipsis:
                    n += 1
            return n

        def _calculate_spec(obj, _items):
            _slicing = False
            _spec = {}
            if isinstance(obj, Series):
                # Calculate slice range
                try:
                    for _dim in range(obj.ndim):  # Loop over actual array shape
                        _spec[_dim] = (0, obj.shape[_dim], 1, obj.axes[_dim])  # Initial start,stop,step,axis
                except (AttributeError, NameError):
                    raise ValueError('No header in _calculate_spec')

                # Determine how to loop over slice spec and items
                if _number_of_ellipses(_items) > 1:
                    raise IndexError('Multiple ellipses are not allowed.')

                # Assume Ellipsis anywhere in items
                index_spec = []
                index_item = []
                for _item in range(len(_items)):
                    if _items[_item] is not Ellipsis:
                        index_spec.append(_item)
                        index_item.append(_item)
                    else:  # Ellipsis
                        remaining_items = len(_items) - _item - 1
                        index_spec += range(len(_spec) - remaining_items, len(_spec))
                        index_item += range(len(_items) - remaining_items, len(_items))
                        break
                if len(index_spec) != len(index_item):
                    raise IndexError('Index problem: spec length %d, items length %d' %
                                     (len(index_spec), len(index_item)))

                for _item in index_item:
                    # If any item is of unknown type, we will not slice the data
                    if not isinstance(_items[_item], slice) and not isinstance(_items[_item], int):
                        return _slicing, _spec
                for _dim, _item in zip(index_spec, index_item):
                    if isinstance(_items[_item], slice):
                        _start = _items[_item].start or _spec[_dim][0]
                        _stop = _items[_item].stop or _spec[_dim][1]
                        _step = _items[_item].step or _spec[_dim][2]
                        if _start < 0:
                            _start = obj.shape[_dim] + _start
                        if _stop < 0:
                            _stop = obj.shape[_dim] + _stop
                        _spec[_dim] = (_start, _stop, _step, obj.axes[_dim])
                        _slicing = True
                    elif isinstance(_items[_item], int):
                        _start = _items[_item] or _spec[_dim][0]
                        if _start < 0:
                            _start = obj.shape[_dim] + _start
                        _stop = _start + 1
                        _step = 1
                        _spec[_dim] = (_start, _stop, _step, obj.axes[_dim])
                        _slicing = True
            return _slicing, _spec

        if getattr(self, 'header', None) is None:
            return super(Series, self).__getitem__(item)
        if isinstance(item, tuple):
            items = item
        else:
            items = (item,)
        slicing, spec = _calculate_spec(self, items)

        todo = []  # Collect header changes, apply after ndarray slicing
        reduce_dim = False  # Whether the slicing reduces the dimension
        if slicing:
            # Here we slice the header information
            new_axes = []
            for i in range(self.ndim):
                # Slice dimension i
                start, stop, step, axis = spec[i]
                new_axes.append(axis[start:stop:step])

                if axis.name == 'slice':
                    # Select slice of sliceLocations
                    sloc = self.__get_sliceLocations(spec[i])
                    todo.append(('sliceLocations', sloc))
                    # Select slice of imagePositions
                    ipp = self.__get_imagePositions(spec[i])
                    todo.append(('imagePositions', None))  # Wipe existing positions
                    if ipp is not None:
                        todo.append(('imagePositions', ipp))
                elif axis.name == imagedata.formats.input_order_to_dirname_str(self.input_order):
                    # Select slice of tags
                    tags = self.__get_tags(spec)
                    todo.append(('tags', tags))
                    if len(tags[0]) == 1:
                        reduce_dim = True
            # Select slice of DicomHeaderDict
            hdr = self.__get_DicomHeaderDict(spec)
            todo.append(('DicomHeaderDict', hdr))

        # Slicing the ndarray is done here
        ret = super(Series, self).__getitem__(item)
        if slicing and issubclass(type(ret), Series):
            # noinspection PyUnboundLocalVariable
            todo.append(('axes', new_axes[-ret.ndim:]))
            if reduce_dim:
                # Must copy the ret object before modifying. Otherwise, ret is a view to self.
                ret.header = copy.copy(ret.header)
                if ret.axes[-ret.ndim].name in ['slice', 'row', 'column', 'color']:
                    ret.input_order = imagedata.formats.INPUT_ORDER_NONE
                else:
                    raise IndexError('Unexpected axis {} after slicing'.format(ret.axes[0].name))
            _set_geometry(ret, todo)
            new_uid = ret.header.new_uid()
            ret.setDicomAttribute('SeriesInstanceUID', new_uid)
            # ret.setDicomAttribute('SeriesInstanceUID', ret.header.new_uid())
            ret.seriesInstanceUID = new_uid
        return ret

    def __get_sliceLocations(self, spec):
        # logger.debug('__get_sliceLocations: enter')
        try:
            sl = self.sliceLocations
        except ValueError:
            return None
        start, stop, step = 0, self.slices, 1
        if spec[0] is not None:
            start = spec[0]
        if spec[1] is not None:
            stop = spec[1]
        if spec[2] is not None:
            step = spec[2]
        sl = np.array(sl[start:stop:step])
        # logger.debug('__get_sliceLocations: exit')
        return sl

    def __get_imagePositions(self, spec):
        # logger.debug('__get_imagePositions: enter')
        try:
            ipp = self.imagePositions
        except ValueError:
            return None
        start, stop, step = 0, self.slices, 1
        if spec[0] is not None:
            start = spec[0]
        if spec[1] is not None:
            stop = spec[1]
        if spec[2] is not None:
            step = spec[2]
        ippdict = {}
        j = 0
        # logger.debug('__get_imagePositions: start,stop={},{}'.format(spec[0], stop))
        for i in range(start, stop, step):
            if i < 0:
                raise ValueError('i < 0')
            ippdict[j] = ipp[i]
            j += 1
        # logger.debug('__get_imagePositions: exit')
        return ippdict

    def __get_DicomHeaderDict(self, specs):
        try:
            slices = len(self.DicomHeaderDict)
        except ValueError:
            return None
        slice_spec = slice(0, slices, 1)
        assert len(self.tags) > 0, "No tags defined"
        tags = len(self.tags[0])
        tag_spec = slice(0, tags, 1)
        for d in specs:
            start, stop, step, axis = specs[d]
            if axis.name == 'slice':
                slice_spec = slice(start, stop, step)
            elif axis.name == imagedata.formats.input_order_to_dirname_str(self.input_order):
                tag_spec = slice(start, stop, step)
        # DicomHeaderDict[slice].tuple(tagvalue, filename, dicomheader)
        hdr = {}
        j = 0
        for s in range(slice_spec.start, slice_spec.stop, slice_spec.step):
            _slice = min(s, slices - 1)
            hdr[j] = list()
            for t in range(tag_spec.start, tag_spec.stop, tag_spec.step):
                try:
                    tag = self.tags[_slice][t]
                except IndexError:
                    raise IndexError("Could not get tag for slice {}, tag {}".format(_slice, t))
                try:
                    hdr[j].append(
                        self.__find_tag_in_hdr(self.DicomHeaderDict[_slice], tag)
                    )
                except TypeError:
                    return None
            j += 1

        return hdr

    def __repr__(self):
        return super().__repr__()

    def __str__(self):
        try:
            patientID = self.patientID
        except ValueError:
            patientID = ''
        try:
            patientName = self.patientName
        except ValueError:
            patientName = ''
        try:
            seriesDescription = self.seriesDescription
        except ValueError:
            seriesDescription = ''
        try:
            seriesNumber = self.seriesNumber
        except ValueError:
            seriesNumber = 0
        return """Patient: {} {}\nStudy  Time: {} {}\nSeries Time: {} {}\nSeries #{}: {}\nShape: {}, dtype: {}, input order: {}""".format(
            patientID, patientName,
            self.getDicomAttribute('StudyDate'), self.getDicomAttribute('StudyTime'),
            self.getDicomAttribute('SeriesDate'), self.getDicomAttribute('SeriesTime'),
            seriesNumber, seriesDescription,
            imagedata.formats.shape_to_str(self.shape), self.dtype,
            imagedata.formats.input_order_to_dirname_str(self.input_order))

    @staticmethod
    def __find_tag_in_hdr(hdr_list, find_tag):
        for tag, filename, hdr in hdr_list:
            if tag == find_tag:
                return tag, filename, hdr
        return None

    def __get_tags(self, specs):
        try:
            tmpl_tags = self.tags
            last_slice = len(self.tags) - 1  # known_slices might be less than actual data shape
            tags = len(self.tags[0])
        except ValueError:
            return None
        slice_spec = slice(0, self.slices, 1)
        tag_spec = slice(0, tags, 1)
        for d in specs:
            start, stop, step, axis = specs[d]
            if axis.name == "slice":
                slice_spec = slice(start, stop, step)
            elif axis.name == imagedata.formats.input_order_to_dirname_str(self.input_order):
                tag_spec = slice(start, stop, step)
        # tags: dict[slice] is np.array(tags)
        new_tags = {}
        j = 0
        for s in range(slice_spec.start, slice_spec.stop, slice_spec.step):
            new_tags[j] = list()
            for t in range(tag_spec.start, tag_spec.stop, tag_spec.step):
                # Limit slices to the known slices. Duplicates last slice and/or tag if too few.
                try:
                    new_tags[j].append(tmpl_tags[s][t])
                except IndexError:
                    raise IndexError("Could not get tag for slice {}, tag {}".format(s, t))
            j += 1
        return new_tags

    def write(self, url, opts=None, formats=None):
        """Write Series image

        Args:
            self: Series array
            directory_name: directory name
            filename_template: template including %d for image number
            opts: Output options (argparse.Namespace or dict)
            formats: list of output formats, overriding opts.output_format (list or str)
        """
        logger.debug('Series.write: url    : {}'.format(url))
        logger.debug('Series.write: formats: {}'.format(formats))
        logger.debug('Series.write: opts   : {}'.format(opts))
        readdata.write(self, url, formats=formats, opts=opts)

    @property
    def input_order(self):
        """str: Input order

        How to sort input files:
            * INPUT_ORDER_NONE ('none'): No sorting.
            * INPUT_ORDER_TIME ('time'): Sort on image time (acquisition time or trigger time).
            * INPUT_ORDER_B ('b'): Sort on b value.
            * INPUT_ORDER_FA ('fa'): Sort on flip angle.
            * INPUT_ORDER_TE ('te'): Sort on echo time.
            * INPUT_ORDER_FAULTY ('faulty'): Correct erroneous attributes.

        Returns:
            Current input order.

        Raises:
            ValueError: when order is illegal.
        """
        return self.header.input_order

    @input_order.setter
    def input_order(self, order):
        if order in imagedata.formats.input_order_set:
            self.header.input_order = order
        else:
            raise ValueError("Unknown input order: {}".format(order))

    @property
    def input_format(self):
        """str: Input format

        Possible input formats depend on the available `formats` plugins,
        and include `'dicom'`, `'itk'` and `'nifti'`."""
        return self.header.input_format

    @input_format.setter
    def input_format(self, fmt):
        self.header.input_format = fmt

    @property
    def input_sort(self):
        """int: Input order

        How to sort output files:
            * SORT_ON_SLICE: Run over slices first
            * SORT_ON_TAG  : Run over input order first, then slices

        Returns:
            The input order.

        Raises:
            ValueError: when input order is not defined.
        """
        try:
            if self.header.input_sort is not None:
                return self.header.input_sort
        except AttributeError:
            pass
        raise ValueError("Input sort order not set.")

    @input_sort.setter
    def input_sort(self, order):
        if order is None or order in imagedata.formats.sort_on_set:
            self.header.input_sort = order
        else:
            raise ValueError("Unknown sort order: {}".format(order))

    @property
    def sort_on(self):
        """int: Output order

        How to sort output files:
            * SORT_ON_SLICE: Run over slices first
            * SORT_ON_TAG  : Run over input order first, then slices

        Returns:
            Current output order.
        Raises:
            ValueError: when output order is not defined.
        """
        try:
            if self.header.sort_on is not None:
                return self.header.sort_on
        except AttributeError:
            pass
        raise ValueError("Output sort order not set.")

    @sort_on.setter
    def sort_on(self, order):
        if order in imagedata.formats.sort_on_set:
            self.header.sort_on = order
        else:
            raise ValueError("Unknown sort order: {}".format(order))

    @property
    def shape(self):
        """tuple: Matrix shape

        Raises:
            IndexError: always when set (do not set shape). Should set axes instead.
        """
        return super(Series, self).shape

    @shape.setter
    def shape(self, s):
        raise IndexError('Should set axes instead of shape.')

    @property
    def rows(self):
        """int: Number of rows

        Raises:
            ValueError: when number of rows is not defined.
        """
        try:
            row_axis = self.find_axis('row')
            return len(row_axis)
        except ValueError:
            _color = 0
            if self.color:
                _color = 1
            if self.ndim - _color < 2:
                raise ValueError("{}D dataset has no rows".format(self.ndim))
            return self.shape[-2 - _color]

    @property
    def columns(self):
        """int: Number of columns

        Raises:
            ValueError: when number of columns is not defined.
        """
        try:
            column_axis = self.find_axis('column')
            return len(column_axis)
        except ValueError:
            _color = 0
            if self.color:
                _color = 1
            if self.ndim - _color < 1:
                raise ValueError("Dataset has no columns")
            return self.shape[-1 - _color]

    @property
    def slices(self):
        """int: Number of slices

        Raises:
            ValueError: when number of slices is not defined.
            DoNotSetSlicesError: Always (do not set slices)
        """
        try:
            slice_axis = self.find_axis('slice')
            # logger.debug("Series.slices: {}D dataset slice_axis {}".format(self.ndim, slice_axis))
            return len(slice_axis)
        except ValueError:
            _color = 0
            if self.color:
                _color = 1
            if self.ndim - _color < 3:
                logger.debug("Series.slices: {}D dataset has no slices".format(self.ndim))
                # raise ValueError("{}D dataset has no slices".format(self.ndim))
                return 1
            logger.debug("Series.slices: {}D dataset slice from shape ({}) {}".format(
                self.ndim, self.shape, self.shape[-3 - _color]))
            return self.shape[-3 - _color]

    @slices.setter
    def slices(self, nslices):
        raise DoNotSetSlicesError('Do not set slices=%d explicitly. Slices are inferred from the shape.' %
                                  nslices)

    @property
    def sliceLocations(self):
        """numpy.array: Slice locations

        Sorted numpy array of slice locations, in mm.

        Raises:
            ValueError: When no slice locations are defined.
        """
        try:
            if self.header.sliceLocations is not None:
                return self.header.sliceLocations
            # Some image formats do not provide slice locations.
            # If orientation and imagePositions are set, slice locations can
            # be calculated.
            if self.header.orientation is not None and self.header.imagePositions is not None:
                logger.debug(
                    'sliceLocations: calculate {} slice from orientation and imagePositions'.format(self.slices))
                loc = np.empty(self.slices)
                normal = self.transformationMatrix[0, :3]
                for _slice in range(self.slices):
                    loc[_slice] = np.inner(normal, self.imagePositions[_slice].flatten())
                self.header.sliceLocations = loc
                return self.header.sliceLocations
        except AttributeError:
            pass
        raise ValueError("Slice locations are not defined.")

    @sliceLocations.setter
    def sliceLocations(self, loc):
        if loc is not None:
            self.header.sliceLocations = np.sort(loc)
        else:
            self.header.sliceLocations = None

    @property
    def DicomHeaderDict(self):
        """dict: DICOM header dictionary

        DicomHeaderDict[slice].tuple(tagvalue, filename, dicomheader)

        Raises:
            ValueError: when DICOM header is not set.

        Examples:
            Get values for slice=0:

            >>> tagvalue, filename, dicomheader = image.DicomHeaderDict()[0]
        """
        # logger.debug('Series.DicomHeaderDict: here')
        try:
            if self.header.DicomHeaderDict is not None:
                # logger.debug('Series.DicomHeaderDict: return')
                # logger.debug('Series.DicomHeaderDict: return {}'.format(type(self.header.DicomHeaderDict)))
                # logger.debug('Series.DicomHeaderDict: return {}'.format(self.header.DicomHeaderDict.keys()))
                return self.header.DicomHeaderDict
        except AttributeError:
            pass
        raise ValueError("Dicom Header Dict is not set.")

    @DicomHeaderDict.setter
    def DicomHeaderDict(self, dct):
        self.header.DicomHeaderDict = dct

    @property
    def tags(self):
        """dict[slice] of numpy.array(tags): Image tags for each slice

        Image tags can be an array of:
            - time points
            - diffusion weightings (b values)
            - flip angles

        Setting the tags will adjust the tags in DicomHeaderDict too.

        tags is a dict with (slice keys, tag array)

        dict[slice] is np.array(tags)

        Examples:

            >>>self.tags[slice][tag]

        Raises:
            ValueError: when tags are not set.
        """
        try:
            if self.header.tags is not None:
                return self.header.tags
        except AttributeError:
            pass
        return None

    @tags.setter
    def tags(self, tags):
        self.header.tags = {}
        hdr = {}
        for s in tags.keys():
            self.header.tags[s] = np.array(tags[s])
            hdr[s] = list()
            max_t = len(self.header.DicomHeaderDict[s]) - 1
            for t in range(len(tags[s])):
                if t <= max_t:
                    wrongtag, filename, dcm = self.header.DicomHeaderDict[s][t]
                    hdr[s].append(
                        (tags[s][t], filename, dcm)
                    )
                else:
                    # Copy last DicomHeaderDict
                    wrongtag, filename, dcm = self.header.DicomHeaderDict[s][max_t]
                    hdr[s].append(
                        (tags[s][t], None, dcm)
                    )
        self.header.DicomHeaderDict = hdr

    @property
    def axes(self):
        """list of Axis: axes objects

        Raises:
            ValueError: when the axes are not set.
        """
        try:
            if self.header.axes is not None:
                return self.header.axes
        except AttributeError:
            pass
        # Calculate axes from image shape
        self.header.axes = []
        shape = super(Series, self).shape
        if len(shape) < 1:
            return None
        if shape[-1] == 3 and self.dtype == np.uint8:
            _mono_shape = shape[:-1]
        else:
            _mono_shape = shape
        _max_known_shape = min(3, len(_mono_shape))
        _labels = ['slice', 'row', 'column'][-_max_known_shape:]
        while len(_labels) < self.ndim:
            _labels.insert(0, 'unknown')

        i = 0
        for d in super(Series, self).shape:
            self.header.axes.append(
                imagedata.axis.UniformLengthAxis(
                    _labels[i], 0, d, 1
                )
            )
            i += 1
        return self.header.axes

    @axes.setter
    def axes(self, ax):
        # Verify that axes shape match ndarray shape
        # Verify that axis names are used once only
        used_name = {}
        for i, axis in enumerate(ax):
            # if len(axis) != self.shape[i]:
            #     raise IndexError("Axis length {}  must match array shape {}".format(
            #         [len(x) for x in ax], self.shape))
            if axis.name in used_name:
                raise ValueError("Axis name {} is used multiple times.".format(axis.name))
            used_name[axis.name] = True
        self.header.axes = ax

        # Update spacing from new axes
        try:
            spacing = self.spacing
        except ValueError:
            spacing = np.array((1.0, 1.0, 1.0))
        for i, direction in enumerate(['slice', 'row', 'column']):
            try:
                axis = self.find_axis(direction)
                spacing[i] = axis.step
            except ValueError:
                pass
        self.header.spacing = spacing

    def find_axis(self, name):
        """Find axis with given name

        Args:
            name: Axis name to search for

        Returns:
            axis object with given name

        Raises:
            ValueError: when no axis object has given name

        Usage:
            >>> axis = si.find_axis('slice')
        """
        for axis in self.axes:
            if axis.name == name:
                return axis
        raise ValueError("No axis object with name %s exist" % name)

    @property
    def spacing(self):
        """numpy.array([ds,dr,dc]): spacing

        Given as ds,dr,dc in mm.
        2D image will return ds=1.

        Raises:
            ValueError: when spacing is not set.
            ValueError: when spacing is not a tuple of 3 coordinates

        Usage:
            >>> ds, dr, dc = si.spacing
            >>> si.spacing = ds, dr, dc
        """
        try:
            # if self.header.spacing is not None:
            #    return self.header.spacing
            slice_axis = self.find_axis('slice')
            ds = slice_axis.step
        except ValueError as e:
            if self.header.spacing is not None:
                ds = self.header.spacing[0]
            else:
                raise ValueError("Spacing is unknown: {}".format(e))
        try:
            row_axis = self.find_axis('row')
            column_axis = self.find_axis('column')
            return np.array((ds, row_axis.step, column_axis.step))
        except ValueError as e:
            raise ValueError("Spacing is unknown: {}".format(e))

    @spacing.setter
    def spacing(self, *args):
        if args[0] is None:
            return
        logger.debug("spacing.setter {} {}".format(len(args), args))
        for arg in args:
            logger.debug("spacing.setter arg {} {}".format(len(arg), arg))
        # Invalidate existing transformation matrix
        self.header.transformationMatrix = None
        # Handle both tuple and component spacings
        if len(args) == 3:
            spacing = np.array(args)
        elif len(args) == 1:
            arg = args[0]
            if len(arg) == 3:
                spacing = np.array(arg)
            elif len(arg) == 1:
                arg0 = arg[0]
                if len(arg0) == 3:
                    spacing = np.array(arg0)
                else:
                    raise ValueError("Length of spacing in setSpacing(): %d" % len(arg0))
            else:
                raise ValueError("Length of spacing in setSpacing(): %d" % len(arg))
        else:
            raise ValueError("Length of spacing in setSpacing(): %d" % len(args))
        try:
            slice_axis = self.find_axis('slice')
            slice_axis.step = spacing[0]
        except ValueError as e:
            # Assume 2D image with no slice dimension
            pass
        try:
            row_axis = self.find_axis('row')
            column_axis = self.find_axis('column')
            row_axis.step = spacing[1]
            column_axis.step = spacing[2]
            self.header.spacing = np.array(spacing)
        except ValueError as e:
            raise ValueError("Spacing cannot be set: {}".format(e))

    @property
    def imagePositions(self):
        """dict of numpy.array([z,y,x]): imagePositions

        The [z,y,x] coordinates of the upper left hand corner (first pixel)
        of each slice.

        dict(imagePositions[slice]) of [z,y,x] in mm, as numpy array

        When setting, the position list is added to existing imagePositions.
        Overlapping dict keys will replace exisiting imagePosition for given slice.

        Examples:
            >>> z,y,x = si.imagePositions[slice]

        Examples:
            >>> for slice in range(slices):
            >>>     si.imagePositions = {
            >>>         slice: si.getPositionForVoxel(np.array([slice,0,0]))
            >>>     }

        Raises:
            ValueError: when imagePositions are not set.
            AssertionError: when positions have wrong shape or datatype.
        """
        # logger.debug('Series.imagePositions.get:')
        try:
            if self.header.imagePositions is not None:
                if len(self.header.imagePositions) > self.slices:
                    # Truncate imagePositions to actual number of slices.
                    # Could be the result of a slicing operation.
                    ipp = {}
                    for z in range(self.slices):
                        ipp[z] = \
                            self.header.imagePositions[z]
                    self.header.imagePositions = ipp
                elif len(self.header.imagePositions) < self.slices and \
                        len(self.header.imagePositions) == 1:
                    # Some image formats define one imagePosition only.
                    # Could calculate the missing imagePositions from origin and
                    # orientation.
                    # Set imagePositions for additional slices
                    logger.debug(
                        'Series.imagePositions.get: 1 positions only.  Calculating the other {} positions'.format(
                            self.slices - 1))
                    m = self.transformationMatrix
                    for _slice in range(1, self.slices):
                        self.header.imagePositions[_slice] = \
                            self.getPositionForVoxel(np.array([_slice, 0, 0]),
                                                     transformation=m)
                return self.header.imagePositions
        except AttributeError:
            pass
        raise ValueError("No imagePositions set.")

    @imagePositions.setter
    def imagePositions(self, poslist):
        if poslist is None:
            self.header.imagePositions = None
            return
        assert isinstance(poslist, dict), "ImagePositions is not dict() (%s)" % type(poslist)
        # Invalidate existing transformation matrix
        # self.header.transformationMatrix = None
        try:
            if self.header.imagePositions is None:
                self.header.imagePositions = dict()
        except AttributeError:
            self.header.imagePositions = dict()
        # logger.debug("imagePositions set for keys {}".format(poslist.keys()))
        for _slice in poslist.keys():
            pos = poslist[_slice]
            # logger.debug("imagePositions set _slice {} to {}".format(_slice,pos))
            assert isinstance(pos, np.ndarray), "Wrong datatype of position (%s)" % type(pos)
            assert len(pos) == 3, "Wrong size of pos (is %d, should be 3)" % len(pos)
            self.header.imagePositions[_slice] = np.array(pos)

    @property
    def orientation(self):
        """numpy.array: Orientation

        The direction cosines of the first row and the first column with respect
        to the patient.
        These attributes shall be provided as a pair.
        Row value (column index) for the z,y,x axes respectively,
        followed by the column value (row index) for the z,y,x axes respectively.

        Raises:
            ValueError: when orientation is not set.
            AssertionError: when len(orient) != 6
        """
        try:
            if self.header.orientation is not None:
                return self.header.orientation
        except AttributeError:
            pass
        raise ValueError("No orientation set.")

    @orientation.setter
    def orientation(self, orient):
        if orient is None:
            self.header.transformationMatrix = None
            self.header.orientation = None
            return
        assert len(orient) == 6, "Wrong size of orientation"
        # Invalidate existing transformation matrix
        # self.header.transformationMatrix = None
        self.header.orientation = np.array(orient)

    @property
    def seriesNumber(self):
        """int: Series number

        DICOM series number.

        Raises:
            ValueError: when series number is not set.
            ValueError: when series number cannot be converted to int.
        """
        try:
            if self.header.seriesNumber is not None:
                return self.header.seriesNumber
        except AttributeError:
            pass
        raise ValueError("No series number set.")

    @seriesNumber.setter
    def seriesNumber(self, sernum):
        if sernum is None:
            self.header.seriesNumber = None
            return
        try:
            self.header.seriesNumber = int(sernum)
        except AttributeError:
            raise ValueError("Cannot convert series number to integer")

    @property
    def seriesDescription(self):
        """str: Series description

        DICOM series description.

        Raises:
            ValueError: When series description is not set.
            AssertionError: when series description is not str
        """
        try:
            if self.header.seriesDescription is not None:
                return self.header.seriesDescription
        except AttributeError:
            pass
        raise ValueError("No series description set.")

    @seriesDescription.setter
    def seriesDescription(self, descr):
        if descr is None:
            self.header.seriesDescription = None
            return
        assert isinstance(descr, str), "Given series description is not str"
        self.header.seriesDescription = descr

    @property
    def imageType(self):
        """list of str: Image type

        DICOM image type

        Raises:
            ValueError: when image type is not set.
            TypeError: When imagetype is not printable.
        """
        try:
            if self.header.imageType is not None:
                return self.header.imageType
        except AttributeError:
            pass
        raise ValueError("No image type set.")

    @imageType.setter
    def imageType(self, imagetype):
        if imagetype is None:
            self.header.imageType = None
            return
        self.header.imageType = list()
        try:
            for s in imagetype:
                self.header.imageType.append(str(s))
        except AttributeError:
            raise TypeError("Given image type is not printable (is %s)" % type(imagetype))

    @property
    def studyInstanceUID(self):
        """str: Study instance UID

        DICOM study instance UID

        Raises:
            ValueError: when study instance UID is not set.
            TypeError: When uid is not printable.
        """
        try:
            if self.header.studyInstanceUID is not None:
                return self.header.studyInstanceUID
        except AttributeError:
            pass
        raise ValueError("No study instance UID set.")

    @studyInstanceUID.setter
    def studyInstanceUID(self, uid):
        if uid is None:
            self.header.studyInstanceUID = None
            return
        try:
            self.header.studyInstanceUID = str(uid)
        except AttributeError:
            raise TypeError("Given study instance UID is not printable")

    @property
    def studyID(self):
        """str: Study ID

        DICOM study ID

        Raises:
            ValueError: when study ID is not set.
            TypeError: When id is not printable
        """
        try:
            if self.header.studyID is not None:
                return self.header.studyID
        except AttributeError:
            pass
        raise ValueError("No study ID set.")

    @studyID.setter
    def studyID(self, id):
        if id is None:
            self.header.studyID = None
            return
        try:
            self.header.studyID = str(id)
        except AttributeError:
            raise TypeError("Given study ID is not printable")

    @property
    def seriesInstanceUID(self):
        """str: Series instance UID

        DICOM series instance UID

        Raises:
            ValueError: when series instance UID is not set
            TypeError: When uid is not printable
        """
        try:
            if self.header.seriesInstanceUID is not None:
                return self.header.seriesInstanceUID
        except AttributeError:
            pass
        raise ValueError("No series instance UID set.")

    @seriesInstanceUID.setter
    def seriesInstanceUID(self, uid):
        if uid is None:
            self.header.seriesInstanceUID = None
            return
        try:
            if isinstance(uid, str):
                self.header.seriesInstanceUID = uid
            else:
                self.header.seriesInstanceUID = str(uid)
        except AttributeError:
            raise TypeError("Given series instance UID is not printable")

    @property
    def frameOfReferenceUID(self):
        """str: Frame of reference UID

        DICOM frame of reference UID

        Raises:
            ValueError: when frame of reference UID is not set
            TypeError: When uid is not printable
        """
        try:
            if self.header.frameOfReferenceUID is not None:
                return self.header.frameOfReferenceUID
        except AttributeError:
            pass
        raise ValueError("No frame of reference UID set.")

    @frameOfReferenceUID.setter
    def frameOfReferenceUID(self, uid):
        if uid is None:
            self.header.frameOfReferenceUID = None
            return
        try:
            self.header.frameOfReferenceUID = str(uid)
        except AttributeError:
            raise TypeError("Given frame of reference UID is not printable")

    @property
    def SOPClassUID(self):
        """DICOM SOP Class UID

        Raises:
            ValueError: when SOP Class UID is not set
        """
        try:
            if self.header.SOPClassUID is not None:
                return self.header.SOPClassUID
        except AttributeError:
            pass
        raise ValueError("No SOP Class UID set.")

    @SOPClassUID.setter
    def SOPClassUID(self, uid):
        if uid is None:
            self.header.SOPClassUID = None
            return
        try:
            self.header.SOPClassUID = imagedata.formats.dicomlib.uid.get_uid_for_storage_class(uid)
        except ValueError:
            raise

    @property
    def accessionNumber(self):
        """str: Accession number

        DICOM accession number

        Raises:
            ValueError: when accession number is not set
            TypeError: When accno is not printable
        """
        try:
            if self.header.accessionNumber is not None:
                return self.header.accessionNumber
        except AttributeError:
            pass
        raise ValueError("No accession number set.")

    @accessionNumber.setter
    def accessionNumber(self, accno):
        if accno is None:
            self.header.accessionNumber = None
            return
        try:
            self.header.accessionNumber = str(accno)
        except AttributeError:
            raise TypeError("Given accession number is not printable")

    @property
    def patientName(self):
        """str: Patient name

        DICOM patient name

        Raises:
            ValueError: when patient name is not set
            TypeError: When patnam is not printable
        """
        try:
            if self.header.patientName is not None:
                return self.header.patientName
        except AttributeError:
            pass
        raise ValueError("No patient name set.")

    @patientName.setter
    def patientName(self, patnam):
        if patnam is None:
            self.header.patientName = None
            return
        try:
            self.header.patientName = str(patnam)
        except AttributeError:
            raise TypeError("Given patient name is not printable")

    @property
    def patientID(self):
        """str: Patient ID

        DICOM patient ID

        Raises:
            ValueError: when patient ID is not set
            TypeError: When patID is not printable
        """
        try:
            if self.header.patientID is not None:
                return self.header.patientID
        except AttributeError:
            pass
        raise ValueError("No patient ID set.")

    @patientID.setter
    def patientID(self, patid):
        if patid is None:
            self.header.patientID = None
            return
        try:
            self.header.patientID = str(patid)
        except AttributeError:
            raise TypeError("Given patient ID is not printable")

    @property
    def patientBirthDate(self):
        """str: Patient birth date

        DICOM patient birth date

        Raises:
            ValueError: when patient birth date is not set.
            TypeError: When patient birth date is not printable.
        """
        try:
            if self.header.patientBirthDate is not None:
                return self.header.patientBirthDate
        except AttributeError:
            pass
        raise ValueError("No patient birthdate set.")

    @patientBirthDate.setter
    def patientBirthDate(self, patbirdat):
        if patbirdat is None:
            self.header.patientBirthDate = None
            return
        try:
            self.header.patientBirthDate = str(patbirdat)
        except AttributeError:
            raise TypeError("Given patient birth date is not printable")

    @property
    def color(self):
        """bool: Color interpretation

        Whether the array stores a color image, and the
        last index represents the color components

        Raises:
            ValueError: when color interpretation is not set
            TypeError: When color is not bool
        """
        try:
            if self.header.color is not None:
                return self.header.color
        except AttributeError:
            pass
        raise ValueError("No Color Interpretation is set.")

    @color.setter
    def color(self, color):
        if color is None:
            self.header.color = False
            return
        try:
            self.header.color = bool(color)
        except AttributeError:
            raise TypeError("Given color is not a boolean.")

    @property
    def photometricInterpretation(self):
        """str: Photometric Interpretation

        DICOM Photometric Interpretation

        Raises:
            ValueError: when photometric interpretation is not set
            TypeError: When photometric interpretation is not printable
        """
        try:
            if self.header.photometricInterpretation is not None:
                return self.header.photometricInterpretation
        except AttributeError:
            pass
        raise ValueError("No Photometric Interpretation is set.")

    @photometricInterpretation.setter
    def photometricInterpretation(self, string):
        if string is None:
            self.header.photometricInterpretation = None
            return
        try:
            self.header.photometricInterpretation = str(string)
        except AttributeError:
            raise TypeError("Given phometric interpretation is not printable")

    # noinspection PyPep8Naming
    @property
    def transformationMatrix(self):
        """numpy.array: Transformation matrix

        If the transformation matrix is not set, an attempt will be made to calculate it
        from spacing, imagePositions and orientation.

        When setting the transformation matrix, spacing and slices must be set in advance.
        A new transformation matrix will also impact orientation and  imagePositions.

        Raises:
            ValueError: Transformation matrix cannot be constructed.
        """

        # def normalize(v):
        #     """Normalize a vector
        #
        #     https://stackoverflow.com/questions/21030391/how-to-normalize-an-array-in-numpy
        #
        #     :param v: 3D vector
        #     :return: normalized 3D vector
        #     """
        #     norm=np.linalg.norm(v, ord=1)
        #     if norm==0:
        #         norm=np.finfo(v.dtype).eps
        #     return v/norm

        debug = None
        # debug = True

        try:
            if self.header.transformationMatrix is not None:
                return self.header.transformationMatrix

            # Calculate transformation matrix
            logger.debug('Series.transformationMatrix: Calculate transformation matrix')
            ds, dr, dc = self.spacing
            slices = len(self.header.imagePositions)
            T0 = self.header.imagePositions[0].reshape(3, 1)  # z,y,x
            Tn = self.header.imagePositions[slices - 1].reshape(3, 1)
            orient = self.orientation

            colr = np.array(orient[3:]).reshape(3, 1)
            colc = np.array(orient[:3]).reshape(3, 1)
            if slices > 1:
                logger.debug('Series.transformationMatrix: multiple slices case (slices={})'.format(slices))
                # Calculating normal vector based on first and last slice should be the correct method.
                k = (T0 - Tn) / (1 - slices)
                # Will just calculate normal to row and column to match other software.
                # k = np.cross(colr, colc, axis=0) * ds
            else:
                logger.debug('Series.transformationMatrix: single slice case')
                k = np.cross(colr, colc, axis=0)
                # k = normalize(k) * ds
                k = k * ds
            logger.debug('Series.transformationMatrix: k={}'.format(k.T))
            # logger.debug("q: k {} colc {} colr {} T0 {}".format(k.shape,
            #    colc.shape, colr.shape, T0.shape))
            A = np.eye(4)
            A[:3, :4] = np.hstack([
                k,
                colr * dr,
                colc * dc,
                T0])
            if debug:
                logger.debug("A:\n{}".format(A))
            self.header.transformationMatrix = A
            return self.header.transformationMatrix
        except AttributeError:
            pass
        raise ValueError('Transformation matrix cannot be constructed.')

    @transformationMatrix.setter
    def transformationMatrix(self, m):
        self.header.transformationMatrix = m
        # if M is not None:
        #    ds,dr,dc = self.spacing
        #    # Set imagePositions for first slice
        #    z,y,x = M[0:3,3]
        #    self.imagePositions = ({0: np.array([z,y,x])})
        #    # Set slice orientation
        #    orient = []
        #    orient.append(M[2,2]/dr)
        #    orient.append(M[1,2]/dr)
        #    orient.append(M[0,2]/dr)
        #    orient.append(M[2,1]/dc)
        #    orient.append(M[1,1]/dc)
        #    orient.append(M[0,1]/dc)
        #    self.orientation = orient
        #    # Set imagePositions for additional slices
        #    for _slice in range(1,self.slices):
        #        self.imagePositions = {
        #            _slice: self.getPositionForVoxel(np.array([_slice,0,0]),
        #                transformation=M)
        #        }

    def get_transformation_components_xyz(self):
        """Get origin and direction from transformation matrix in xyz convention.

        Returns:
            tuple: Tuple of
                - Origin np.array size 3.
                - Orientation np.array size 6 (row, then column directional cosines) (DICOM convention).
                - Normal vector np.array size 3 (slice direction).
        """
        m = self.transformationMatrix
        ds, dr, dc = self.spacing

        # origin
        try:
            ipp = self.imagePositions
            if len(ipp) > 0:
                ipp = ipp[0]
            else:
                ipp = np.array([0, 0, 0])
        except ValueError:
            ipp = np.array([0, 0, 0])
        if ipp.shape == (3, 1):
            ipp.shape = (3,)
        z, y, x = ipp[:]
        origin = np.array([x, y, z])

        # orientation
        # Reverse orientation vectors from zyx to xyz
        try:
            orientation = [
                self.orientation[2], self.orientation[1], self.orientation[0],
                self.orientation[5], self.orientation[4], self.orientation[3]]
        except ValueError:
            orientation = [1, 0, 0, 0, 1, 0]

        n = m[:3, 0][::-1].reshape(3)
        if self.slices == 1:
            n = n / ds

        return origin, np.array(orientation), n

    @property
    def timeline(self):
        """numpy.array: Timeline in seconds, as numpy array of floats
                Delta time is given as seconds. First image is t=0.
                Length of array is number of tags.

        Raises:
            ValueError: tags for dataset is not time tags
        """
        if self.input_order == imagedata.formats.INPUT_ORDER_TIME:
            timeline = [0.0]
            for t in range(1, len(self.tags[0])):
                timeline.append(self.tags[0][t] - self.tags[0][0])
            return np.array(timeline)
        else:
            raise ValueError("No timeline tags are available. Input order: {}".format(self.input_order))

    @property
    def bvalues(self):
        """numpy.array: b-values in s/mm2, as numpy array of floats
                Length of array is number of tags.

        Raises:
            ValueError: tags for dataset is not b tags
        """
        if self.input_order == imagedata.formats.INPUT_ORDER_B:
            return np.array(self.tags[0])
        else:
            raise ValueError("No b-value tags are available. Input order: {}".format(self.input_order))

    def getDicomAttribute(self, keyword, slice=0, tag=0):
        """Get named DICOM attribute.

        Args:
            keyword (str): name or dicom tag
            slice (int): optional slice to get attribute from (default: 0)
            tag (int): optional tag to get attribute from (default: 0)

        Returns:
            DICOM attribute
        """

        if self.DicomHeaderDict is None:
            return None
        if issubclass(type(keyword), str):
            _tag = pydicom.datadict.tag_for_keyword(keyword)
        else:
            _tag = keyword
        if _tag is None:
            return None
        tg, fname, im = self.DicomHeaderDict[slice][tag]
        if _tag in im:
            return im[_tag].value
        else:
            return None

    def setDicomAttribute(self, keyword, value, slice=None, tag=None):
        """Set named DICOM attribute.

        Args:
            keyword (str): name or dicom tag
            value: new value for DICOM attribute
            slice (int): optional slice to set attribute for (default: all)
            tag (int): optional tag to set attribute for (default: all)
        Raises:
            ValueError: When no DICOM tag is set.
        """

        if self.DicomHeaderDict is None:
            return None
        if issubclass(type(keyword), str):
            _tag = pydicom.datadict.tag_for_keyword(keyword)
        else:
            _tag = keyword
        if _tag is None:
            raise ValueError('No DICOM tag set')
        slices = [i for i in range(self.slices)]
        tags = [i for i in range(len(self.tags[0]))]
        if slice is not None:
            slices = [slice]
        if tag is not None:
            tags = [tag]
        for s in slices:
            for t in tags:
                tg, fname, im = self.DicomHeaderDict[s][t]
                # if _tag in im:
                #     im[_tag].value = value
                # else:
                #     VR = pydicom.datadict.dictionary_VR(_tag)
                #     im.add_new(_tag, VR, value)
                # Always make a new attribute to avoid cross-talk after copying Series instances.
                VR = pydicom.datadict.dictionary_VR(_tag)
                im.add_new(_tag, VR, value)

    def getPositionForVoxel(self, r, transformation=None):
        """Get patient position for center of given voxel r

        Use Patient Position and Image Orientation to calculate world
        coordinates for given voxel

        Args:
            r (numpy.array): (s,r,c) of voxel in voxel coordinates
            transformation (numpy.array, optional): transformation matrix when different from self.transformationMatrix

        Returns:
            numpy.array((z,y,x)): position of voxel in world coordinates (mm)
        """

        if transformation is None:
            transformation = self.transformationMatrix
        # q = self.getTransformationMatrix()

        # V = np.array([[r[2]], [r[1]], [r[0]], [1]])  # V is [x,y,z,1]
        # if len(r) == 3 or (len(r) == 1 and len(r[0] == 3)):
        #     p = np.vstack((r.reshape((3, 1)), [1]))
        # elif len(r) == 4:
        #     p = r.reshape((4, 1))
        try:
            p = r.reshape((4, 1))
        except ValueError:
            p = np.vstack((r.reshape((3, 1)), [1]))

        newposition = np.dot(transformation, p)

        return newposition[:3]  # z,y,x

    def getVoxelForPosition(self, p, transformation=None):
        """ Get voxel for given patient position p

        Use Patient Position and Image Orientation to calculate world
        coordinates for given voxel

        Args:
            p (numpy.array): (z,y,x) of voxel in world coordinates (mm)
            transformation (numpy.array, optional): transformation matrix when different from self.transformationMatrix

        Returns:
            numpy.array((s,r,c)): of voxel in voxel coordinates
        """

        if transformation is None:
            transformation = self.transformationMatrix
        # q = self.getTransformationMatrix()

        # V = np.array([[p[2]], [p[1]], [p[0]], [1]])    # V is [x,y,z,1]

        try:
            pt = p.reshape((4, 1))
        except ValueError:
            pt = np.vstack((p.reshape((3, 1)), [1]))  # type: np.ndarray

        qinv = np.linalg.inv(transformation)
        r = np.dot(qinv, pt)

        # z,y,x
        # return np.array([int(r[2,0]+0.5),int(r[1,0]+0.5),int(r[0,0]+0.5)], dtype=int)
        # return int(r+0.5)[:3]
        return (r + 0.5).astype(int)[:3]

    def deepcopy(self):
        """Create a copy using deepcopy."""
        a = Series(np.copy(self), template=self, geometry=self)
        a.header.DicomHeaderDict = imagedata.header.deepcopy_DicomHeaderDict(self.header.DicomHeaderDict)
        return a

    def to_rgb(self, colormap='Greys_r', lut=None, norm='linear'):
        """Create an RGB color image of self.

        Args:
            colormap (str): Matplotlib colormap name. Defaults: 'Greys_r'.
            lut (int): Number of rgb quantization levels. Default: None, lut is calculated from the voxel values.
            norm (str or matplotlib.colors.Normalize): Normalization method. Either linear/log, or
                the `.Normalize` instance used to scale scalar data to the [0, 1] range before mapping to colors using colormap.

        Returns:
            Series: RGB Series object
        """

        if self.color:
            return self

        import matplotlib as mpl
        import matplotlib.pyplot as plt
        from imagedata.viewer import get_window_level

        if lut is None:
            lut = 256 if self.dtype.kind == 'f' else (self.max().item()) + 1
        if isinstance(norm, str):
            if norm == 'linear':
                norm = matplotlib.colors.Normalize
            elif norm == 'log':
                norm = matplotlib.colors.LogNorm
            # elif norm == 'centered':
            #     norm = matplotlib.colors.CenteredNorm
            else:
                raise ValueError('Unknown normalization function: {}'.format(norm))
        if not issubclass(type(colormap), matplotlib.colors.Colormap):
            colormap = plt.get_cmap(colormap, lut)
        colormap.set_bad(color='k')  # Important for log display of non-positive values
        colormap.set_under(color='k')
        colormap.set_over(color='w')
        if type(norm) == type:
            window, level, vmin, vmax = get_window_level(self, norm, window=None, level=None)
            norm = norm(vmin=vmin, vmax=vmax)
        data = norm(self)
        # if np.issubdtype(self.dtype, np.floating):
        if self.dtype.kind == 'f':
            rgb = Series(
                colormap(data, bytes=True)[..., :3],  # Strip off alpha color
                input_order=self.input_order,
                geometry=self,
                axes=self.axes + [imagedata.axis.VariableAxis('rgb', ['r', 'g', 'b'])]
            )
        else:
            rgb = Series(
                colormap(data, bytes=True)[..., :3],  # Strip off alpha color
                input_order=self.input_order,
                geometry=self,
                axes=self.axes + [imagedata.axis.VariableAxis('rgb', ['r', 'g', 'b'])]
            )

        rgb.header.photometricInterpretation = 'RGB'
        rgb.header.color = True
        rgb.header.add_template(self.header)
        return rgb

    def show(self, im2=None, fig=None, colormap='Greys_r', norm='linear', colorbar=None, window=None, level=None,
             link=False):
        """Show image

        With ideas borrowed from Erlend Hodneland (2021).

        Args:
            im2 (Series or list of Series): Series or list of Series which will be displayed in addition to self.
            fig (matplotlib.plt.Figure, optional): if already exist
            colormap (str): color map for display. Default: 'Greys_r'
            norm (str or matplotlib.colors.Normalize): Normalization method. Either linear/log, or
                the `.Normalize` instance used to scale scalar data to the [0, 1] range before mapping to colors using colormap.
            colorbar (bool): Display colorbar with image. Default: None: determine colorbar based on colormap and norm.
            window (number): window width of signal intensities. Default is DICOM Window Width.
            level (number): window level of signal intensities. Default is DICOM Window Center.
            link (bool): whether scrolling is linked between displayed images. Default: False

        Raises:
            ValueError: when image is not a subclass of Series, or too many viewports are requested.
            IndexError: when there is a mismatch with images and viewports.
        """
        from imagedata.viewer import Viewer, default_layout
        import matplotlib.pyplot as plt

        # im2 can be single image or list of images
        images = list()
        images.append(self)
        if im2 is not None:
            if issubclass(type(im2), list):
                images += im2  # Join lists of Series
            else:
                images.append(im2)  # Append single Series instance

        # Create or connect to canvas
        if fig is None:
            fig = plt.figure()

        axes = default_layout(fig, len(images))
        try:
            viewer = Viewer(images, fig=fig, ax=axes,
                            colormap=colormap, norm=norm, colorbar=colorbar, window=window, level=level, link=link)
        except AssertionError:
            raise
        v = viewer.connect()
        plt.tight_layout()
        plt.show()
        viewer.disconnect()

    def get_roi(self, roi=None, color='r', follow=False, vertices=False, im2=None, fig=None, colormap='Greys_r',
                norm='linear', colorbar=None, window=None, level=None, link=False, single=False):
        """Let user draw ROI on image

        Args:
            roi: Predefined vertices (optional). Dict of slices, index as [tag,slice] or [slice], each is list of (x,y) pairs.
            color (str): Color of polygon ROI. Default: 'r'.
            follow: (bool) Copy ROI to next tag. Default: False.
            vertices (bool): Return both grid mask and dictionary of vertices. Default: False.
            im2 (Series or list of Series): Series or list of Series which will be displayed in addition to self.
            fig (matplotlib.plt.Figure, optional) if already exist
            colormap (str): colour map for display. Default: 'Greys_r'
            norm (str or matplotlib.colors.Normalize): Normalization method. Either linear/log, or
                the `.Normalize` instance used to scale scalar data to the [0, 1] range before mapping to colors using colormap.
            colorbar (bool): Display colorbar with image. Default: None: determine colorbar based on colormap and norm.
            window (number): window width of signal intensities. Default is DICOM Window Width.
            level (number): window level of signal intensities. Default is DICOM Window Center.
            link (bool): whether scrolling is linked between displayed objects. Default: False.
            single (bool): draw ROI in single slice per tag. Default: False.

        Returns:
            If vertices, tuple of grid mask and vertices_dict. Otherwise, grid mask only.
                - grid mask: Series object with voxel=1 inside ROI.
                  Series object with shape (nz,ny,nx) from original image,
                  dtype ubyte. Voxel inside ROI is 1, 0 outside.
                - vertices_dict: if vertices: Dictionary of vertices.

        Raises:
            ValueError: when image is not a subclass of Series, or too many viewports are requested.
            IndexError: when there is a mismatch with images and viewports.
        """
        from imagedata.viewer import Viewer, default_layout, grid_from_roi
        import matplotlib.pyplot as plt

        # im2 can be single image or list of images
        images = list()
        images.append(self)
        if im2 is not None:
            if issubclass(type(im2), list):
                images += im2  # Join lists of Series
            else:
                images.append(im2)  # Append single Series instance

        # Create or connect to canvas
        if fig is None:
            fig = plt.figure()

        axes = default_layout(fig, len(images))
        try:
            viewer = Viewer(images, fig=fig, ax=axes, follow=follow,
                            colormap=colormap, norm=norm, colorbar=colorbar, window=window, level=level, link=link)
        except AssertionError:
            raise
        v = viewer.connect_draw(roi=roi, color=color)
        plt.tight_layout()
        plt.show()
        # vertices = viewer.get_roi()
        viewer.disconnect_draw()
        if follow:
            input_order = self.input_order
        else:
            input_order = 'none'
        try:
            # new_roi = Series(grid_from_roi(self, viewer.get_roi()), input_order=input_order, template=self, geometry=self)
            new_grid = grid_from_roi(self, viewer.get_roi(), single=single)
        except IndexError:
            if follow:
                new_grid = np.zeros_like(self, dtype=np.ubyte)
            else:
                new_grid = np.zeros((self.slices, self.rows, self.columns), dtype=np.ubyte)
        new_roi = Series(new_grid, input_order=input_order, template=self, geometry=self)
        new_roi.seriesDescription = 'ROI'
        new_roi.setDicomAttribute('WindowCenter', .5)
        new_roi.setDicomAttribute('WindowWidth', 1)
        if vertices:
            return new_roi, viewer.get_roi()  # Return grid and vertices
        else:
            return new_roi  # Return grid only