HiddenMarkov2D fails

[mcongiu]

I am trying to run hiddenMarkov2D motion correction.

Two months ago I managed without any issue but now I am getting this error:

---

TypeErrorTraceback (most recent call last)
<ipython-input-10-e5b947635e34> in <module>()
      3 dataset = correction_approach.correct(
      4     sequences, dataset_path, channel_names=['a'],
----> 5     trim_criterion=0.95)
      6 
      7 # Export the time averages for a manuscript figure.

C:\Users\mcongiu\Anaconda3\envs\sima\lib\site-packages\sima\motion\motion.pyc in correct(self, dataset, savedir, channel_names, info, correction_channels, trim_criterion)
    121         else:
    122             mc_sequences = sequences
--> 123         displacements = self.estimate(sima.ImagingDataset(mc_sequences, None))
    124         disp_dim = displacements[0].shape[-1]
    125         max_disp = np.ceil(

C:\Users\mcongiu\Anaconda3\envs\sima\lib\site-packages\sima\imaging.pyc in __init__(self, sequences, savedir, channel_names, read_only)
    147             if channel_names is None:
    148                 self.channel_names = [
--> 149                     str(x) for x in range(self.frame_shape[-1])]
    150             else:
    151                 self.channel_names = channel_names

C:\Users\mcongiu\Anaconda3\envs\sima\lib\site-packages\sima\imaging.pyc in frame_shape(self)
    214     def frame_shape(self):
    215         if not hasattr(self, '_frame_shape'):
--> 216             self._frame_shape = self.sequences[0].shape[1:]
    217         return self._frame_shape
    218 

C:\Users\mcongiu\Anaconda3\envs\sima\lib\site-packages\sima\sequence.pyc in shape(self)
    165     @property
    166     def shape(self):
--> 167         return (len(self),) + self._get_frame(0).shape
    168 
    169     def apply_displacements(self, displacements, frame_shape=None):

C:\Users\mcongiu\Anaconda3\envs\sima\lib\site-packages\sima\sequence.pyc in __len__(self)
    584     def __len__(self):
    585         if self._len is None:
--> 586             self._len = sum(1 for _ in self)
    587         return self._len
    588 

C:\Users\mcongiu\Anaconda3\envs\sima\lib\site-packages\sima\sequence.pyc in <genexpr>((_,))
    584     def __len__(self):
    585         if self._len is None:
--> 586             self._len = sum(1 for _ in self)
    587         return self._len
    588 

C:\Users\mcongiu\Anaconda3\envs\sima\lib\site-packages\sima\sequence.pyc in __iter__(self)
    535                          for _ in range(self._num_channels)],
    536                         axis=2), 0)
--> 537                  for _ in range(self._num_planes)], 0)
    538 
    539     def _get_frame(self, n):

C:\Users\mcongiu\Anaconda3\envs\sima\lib\site-packages\future\builtins\newnext.pyc in newnext(iterator, default)
     57         except AttributeError:
     58             try:
---> 59                 return iterator.next()
     60             except AttributeError:
     61                 raise TypeError("'{0}' object is not an iterator".format(

C:\Users\mcongiu\Anaconda3\envs\sima\lib\site-packages\sima\sequence.pyc in _iter_pages(self)
    567             else:
    568                 idx += 1
--> 569                 yield np.array(images).astype(float)
    570         images.close()
    571 

TypeError: float() argument must be a string or a number

---

this is my code:

from __future__ import print_function
from builtins import input
from builtins import range
import sima
import sima.motion
import sima.segment
from sima.ROI import ROIList
#import simaWorkflowUtils #ImportError: No module named simaWorkflowUtils
import os, sys
import json
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
utils_dir = os.path.join(os.getcwd(), 'utils')
sys.path.append(utils_dir)
import glob
import pandas as pd
import itertools

basedir = 'C:\\Users\\Data\\2P\\ebc'
tif = 'file.tif'
tiff_file = os.path.join(basedir, tif)
sequences = [sima.Sequence.create('TIFF', tiff_file)]
dataset_path = os.path.join(basedir, 'dataset.sima')

correction_approach = sima.motion.HiddenMarkov2D(num_states_retained=30,   
                                                 max_displacement=[20, 30])
dataset = correction_approach.correct(
    sequences, dataset_path, channel_names=['ch'],
    trim_criterion=0.95)

dataset.export_averages([os.path.join(basedir, 'avg.tif')])
dataset.export_frames([[[os.path.join(basedir, 'corrected.tif')]]], fmt='TIFF16')