---------------------------------------------------------------------------
RemoteTraceback                           Traceback (most recent call last)
RemoteTraceback: 
"""
Traceback (most recent call last):
  File "/path/to/htmd/site-packages/pyEMMA-2.1-py3.5-linux-x86_64.egg/pyemma/_base/estimator.py", line 372, in model
    return self._model
AttributeError: 'MaximumLikelihoodMSM' object has no attribute '_model'

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/path/to/htmd/site-packages/joblib/parallel.py", line 130, in __call__
    return self.func(*args, **kwargs)
  File "/path/to/htmd/site-packages/joblib/parallel.py", line 72, in __call__
    return [func(*args, **kwargs) for func, args, kwargs in self.items]
  File "/path/to/htmd/site-packages/joblib/parallel.py", line 72, in <listcomp>
    return [func(*args, **kwargs) for func, args, kwargs in self.items]
  File "/path/to/htmd/site-packages/pyEMMA-2.1-py3.5-linux-x86_64.egg/pyemma/_base/estimator.py", line 144, in _estimate_param_scan_worker
    res.append(estimator.model)
  File "/path/to/htmd/site-packages/pyEMMA-2.1-py3.5-linux-x86_64.egg/pyemma/_base/estimator.py", line 375, in model
    'Model has not yet been estimated. Call estimate(X) or fit(X) first')
AttributeError: Model has not yet been estimated. Call estimate(X) or fit(X) first

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "/path/to/htmd/multiprocessing/pool.py", line 119, in worker
    result = (True, func(*args, **kwds))
  File "/path/to/htmd/site-packages/joblib/parallel.py", line 140, in __call__
    raise TransportableException(text, e_type)
joblib.my_exceptions.TransportableException: TransportableException
___________________________________________________________________________
AttributeError                                     Wed Apr 20 13:32:14 2016
PID: 12654Python 3.5.1: /nfs/grid/software/hpcc/apps/Linux-x86_64-RHEL6/acellera/current/python/bin/python
...........................................................................
/path/to/htmd/site-packages/joblib/parallel.py in __call__(self=<joblib.parallel.BatchedCalls object>)
     67     def __init__(self, iterator_slice):
     68         self.items = list(iterator_slice)
     69         self._size = len(self.items)
     70 
     71     def __call__(self):
---> 72         return [func(*args, **kwargs) for func, args, kwargs in self.items]
        self.items = [(<function _estimate_param_scan_worker>, (MaximumLikelihoodMSM(connectivity='largest', cou...ble=True, sparse=False, statdist_constraint=None), {'lag': 51}, [array([  31, 1106,   31,  245,  245,  245, 1106,...  340,  950,  656, 1140,  718, 1116, 1162,  340]), array([ 277,   31,  598,  277,  616, 1186,  903,... 1031,  160,  797,  160,  493,  493,  493,  455]), array([ 363,  925,  925, 1106,  925,  245,  813,...  422,  121,  638,  398,  422,  398,  638,  638]), array([ 864,  277,   31,  245, 1182,   31,  581,...  492,  108,  778,  778,  492,  492,  465,  465]), array([ 864,  537,   31,  537,  135,  135,  277,...  972,  477,    7,  477,    7,  477,    7,  972]), array([ 864,   31,  581,  104,  804,  804,  301,... 1052,  461, 1211,  992, 1052,   73, 1211, 1052]), array([ 277, 1000,  980,  363,  864,  135,  242,...  485, 1183,  456, 1043,  456,  628,  456,  391]), array([ 804,  245,  868,  106,  850,  868,   31,... 1084,  154,  442,  442,  259,  766,  442,  161]), array([  31,   31,  245, 1186,  864,  245,  864,...  504,  841,  187,  504,  240,  240,  637,  504]), array([ 245,  581,  672,  672,  672,  664, 1130,...  451,  933,  933,  210,  155,  600,  155,  600])], None, None, False), {})]
     73 
     74     def __len__(self):
     75         return self._size
     76 

...........................................................................
/path/to/htmd/site-packages/joblib/parallel.py in <listcomp>(.0=<list_iterator object>)
     67     def __init__(self, iterator_slice):
     68         self.items = list(iterator_slice)
     69         self._size = len(self.items)
     70 
     71     def __call__(self):
---> 72         return [func(*args, **kwargs) for func, args, kwargs in self.items]
        func = <function _estimate_param_scan_worker>
        args = (MaximumLikelihoodMSM(connectivity='largest', cou...ble=True, sparse=False, statdist_constraint=None), {'lag': 51}, [array([  31, 1106,   31,  245,  245,  245, 1106,...  340,  950,  656, 1140,  718, 1116, 1162,  340]), array([ 277,   31,  598,  277,  616, 1186,  903,... 1031,  160,  797,  160,  493,  493,  493,  455]), array([ 363,  925,  925, 1106,  925,  245,  813,...  422,  121,  638,  398,  422,  398,  638,  638]), array([ 864,  277,   31,  245, 1182,   31,  581,...  492,  108,  778,  778,  492,  492,  465,  465]), array([ 864,  537,   31,  537,  135,  135,  277,...  972,  477,    7,  477,    7,  477,    7,  972]), array([ 864,   31,  581,  104,  804,  804,  301,... 1052,  461, 1211,  992, 1052,   73, 1211, 1052]), array([ 277, 1000,  980,  363,  864,  135,  242,...  485, 1183,  456, 1043,  456,  628,  456,  391]), array([ 804,  245,  868,  106,  850,  868,   31,... 1084,  154,  442,  442,  259,  766,  442,  161]), array([  31,   31,  245, 1186,  864,  245,  864,...  504,  841,  187,  504,  240,  240,  637,  504]), array([ 245,  581,  672,  672,  672,  664, 1130,...  451,  933,  933,  210,  155,  600,  155,  600])], None, None, False)
        kwargs = {}
     73 
     74     def __len__(self):
     75         return self._size
     76 

...........................................................................
/path/to/htmd/site-packages/pyEMMA-2.1-py3.5-linux-x86_64.egg/pyemma/_base/estimator.py in _estimate_param_scan_worker(estimator=MaximumLikelihoodMSM(connectivity='largest', cou...ble=True, sparse=False, statdist_constraint=None), params={'lag': 51}, X=[array([  31, 1106,   31,  245,  245,  245, 1106,...  340,  950,  656, 1140,  718, 1116, 1162,  340]), array([ 277,   31,  598,  277,  616, 1186,  903,... 1031,  160,  797,  160,  493,  493,  493,  455]), array([ 363,  925,  925, 1106,  925,  245,  813,...  422,  121,  638,  398,  422,  398,  638,  638]), array([ 864,  277,   31,  245, 1182,   31,  581,...  492,  108,  778,  778,  492,  492,  465,  465]), array([ 864,  537,   31,  537,  135,  135,  277,...  972,  477,    7,  477,    7,  477,    7,  972]), array([ 864,   31,  581,  104,  804,  804,  301,... 1052,  461, 1211,  992, 1052,   73, 1211, 1052]), array([ 277, 1000,  980,  363,  864,  135,  242,...  485, 1183,  456, 1043,  456,  628,  456,  391]), array([ 804,  245,  868,  106,  850,  868,   31,... 1084,  154,  442,  442,  259,  766,  442,  161]), array([  31,   31,  245, 1186,  864,  245,  864,...  504,  841,  187,  504,  240,  240,  637,  504]), array([ 245,  581,  672,  672,  672,  664, 1130,...  451,  933,  933,  210,  155,  600,  155,  600])], evaluate=None, evaluate_args=None, failfast=False)
    139     # deal with results
    140     res = []
    141 
    142     # deal with result
    143     if evaluate is None:  # we want full models
--> 144         res.append(estimator.model)
        res.append = <built-in method append of list object>
        estimator.model = undefined
    145     # we want to evaluate function(s) of the model
    146     elif _types.is_iterable(evaluate):
    147         values = []  # the function values the model
    148         for ieval in range(len(evaluate)):

...........................................................................
/path/to/htmd/site-packages/pyEMMA-2.1-py3.5-linux-x86_64.egg/pyemma/_base/estimator.py in model(self=MaximumLikelihoodMSM(connectivity='largest', cou...ble=True, sparse=False, statdist_constraint=None))
    370         """The model estimated by this Estimator"""
    371         try:
    372             return self._model
    373         except AttributeError:
    374             raise AttributeError(
--> 375                 'Model has not yet been estimated. Call estimate(X) or fit(X) first')
    376 
    377 
    378 
    379 

AttributeError: Model has not yet been estimated. Call estimate(X) or fit(X) first
___________________________________________________________________________
"""

The above exception was the direct cause of the following exception:

TransportableException                    Traceback (most recent call last)
/path/to/htmd/site-packages/joblib/parallel.py in retrieve(self)
    726             try:
--> 727                 self._output.extend(job.get())
    728             except tuple(self.exceptions) as exception:

/path/to/htmd/multiprocessing/pool.py in get(self, timeout)
    607         else:
--> 608             raise self._value
    609 

TransportableException: TransportableException
___________________________________________________________________________
AttributeError                                     Wed Apr 20 13:32:14 2016
PID: 12654Python 3.5.1: /nfs/grid/software/hpcc/apps/Linux-x86_64-RHEL6/acellera/current/python/bin/python
...........................................................................
/path/to/htmd/site-packages/joblib/parallel.py in __call__(self=<joblib.parallel.BatchedCalls object>)
     67     def __init__(self, iterator_slice):
     68         self.items = list(iterator_slice)
     69         self._size = len(self.items)
     70 
     71     def __call__(self):
---> 72         return [func(*args, **kwargs) for func, args, kwargs in self.items]
        self.items = [(<function _estimate_param_scan_worker>, (MaximumLikelihoodMSM(connectivity='largest', cou...ble=True, sparse=False, statdist_constraint=None), {'lag': 51}, [array([  31, 1106,   31,  245,  245,  245, 1106,...  340,  950,  656, 1140,  718, 1116, 1162,  340]), array([ 277,   31,  598,  277,  616, 1186,  903,... 1031,  160,  797,  160,  493,  493,  493,  455]), array([ 363,  925,  925, 1106,  925,  245,  813,...  422,  121,  638,  398,  422,  398,  638,  638]), array([ 864,  277,   31,  245, 1182,   31,  581,...  492,  108,  778,  778,  492,  492,  465,  465]), array([ 864,  537,   31,  537,  135,  135,  277,...  972,  477,    7,  477,    7,  477,    7,  972]), array([ 864,   31,  581,  104,  804,  804,  301,... 1052,  461, 1211,  992, 1052,   73, 1211, 1052]), array([ 277, 1000,  980,  363,  864,  135,  242,...  485, 1183,  456, 1043,  456,  628,  456,  391]), array([ 804,  245,  868,  106,  850,  868,   31,... 1084,  154,  442,  442,  259,  766,  442,  161]), array([  31,   31,  245, 1186,  864,  245,  864,...  504,  841,  187,  504,  240,  240,  637,  504]), array([ 245,  581,  672,  672,  672,  664, 1130,...  451,  933,  933,  210,  155,  600,  155,  600])], None, None, False), {})]
     73 
     74     def __len__(self):
     75         return self._size
     76 

...........................................................................
/path/to/htmd/site-packages/joblib/parallel.py in <listcomp>(.0=<list_iterator object>)
     67     def __init__(self, iterator_slice):
     68         self.items = list(iterator_slice)
     69         self._size = len(self.items)
     70 
     71     def __call__(self):
---> 72         return [func(*args, **kwargs) for func, args, kwargs in self.items]
        func = <function _estimate_param_scan_worker>
        args = (MaximumLikelihoodMSM(connectivity='largest', cou...ble=True, sparse=False, statdist_constraint=None), {'lag': 51}, [array([  31, 1106,   31,  245,  245,  245, 1106,...  340,  950,  656, 1140,  718, 1116, 1162,  340]), array([ 277,   31,  598,  277,  616, 1186,  903,... 1031,  160,  797,  160,  493,  493,  493,  455]), array([ 363,  925,  925, 1106,  925,  245,  813,...  422,  121,  638,  398,  422,  398,  638,  638]), array([ 864,  277,   31,  245, 1182,   31,  581,...  492,  108,  778,  778,  492,  492,  465,  465]), array([ 864,  537,   31,  537,  135,  135,  277,...  972,  477,    7,  477,    7,  477,    7,  972]), array([ 864,   31,  581,  104,  804,  804,  301,... 1052,  461, 1211,  992, 1052,   73, 1211, 1052]), array([ 277, 1000,  980,  363,  864,  135,  242,...  485, 1183,  456, 1043,  456,  628,  456,  391]), array([ 804,  245,  868,  106,  850,  868,   31,... 1084,  154,  442,  442,  259,  766,  442,  161]), array([  31,   31,  245, 1186,  864,  245,  864,...  504,  841,  187,  504,  240,  240,  637,  504]), array([ 245,  581,  672,  672,  672,  664, 1130,...  451,  933,  933,  210,  155,  600,  155,  600])], None, None, False)
        kwargs = {}
     73 
     74     def __len__(self):
     75         return self._size
     76 

...........................................................................
/path/to/htmd/site-packages/pyEMMA-2.1-py3.5-linux-x86_64.egg/pyemma/_base/estimator.py in _estimate_param_scan_worker(estimator=MaximumLikelihoodMSM(connectivity='largest', cou...ble=True, sparse=False, statdist_constraint=None), params={'lag': 51}, X=[array([  31, 1106,   31,  245,  245,  245, 1106,...  340,  950,  656, 1140,  718, 1116, 1162,  340]), array([ 277,   31,  598,  277,  616, 1186,  903,... 1031,  160,  797,  160,  493,  493,  493,  455]), array([ 363,  925,  925, 1106,  925,  245,  813,...  422,  121,  638,  398,  422,  398,  638,  638]), array([ 864,  277,   31,  245, 1182,   31,  581,...  492,  108,  778,  778,  492,  492,  465,  465]), array([ 864,  537,   31,  537,  135,  135,  277,...  972,  477,    7,  477,    7,  477,    7,  972]), array([ 864,   31,  581,  104,  804,  804,  301,... 1052,  461, 1211,  992, 1052,   73, 1211, 1052]), array([ 277, 1000,  980,  363,  864,  135,  242,...  485, 1183,  456, 1043,  456,  628,  456,  391]), array([ 804,  245,  868,  106,  850,  868,   31,... 1084,  154,  442,  442,  259,  766,  442,  161]), array([  31,   31,  245, 1186,  864,  245,  864,...  504,  841,  187,  504,  240,  240,  637,  504]), array([ 245,  581,  672,  672,  672,  664, 1130,...  451,  933,  933,  210,  155,  600,  155,  600])], evaluate=None, evaluate_args=None, failfast=False)
    139     # deal with results
    140     res = []
    141 
    142     # deal with result
    143     if evaluate is None:  # we want full models
--> 144         res.append(estimator.model)
        res.append = <built-in method append of list object>
        estimator.model = undefined
    145     # we want to evaluate function(s) of the model
    146     elif _types.is_iterable(evaluate):
    147         values = []  # the function values the model
    148         for ieval in range(len(evaluate)):

...........................................................................
/path/to/htmd/site-packages/pyEMMA-2.1-py3.5-linux-x86_64.egg/pyemma/_base/estimator.py in model(self=MaximumLikelihoodMSM(connectivity='largest', cou...ble=True, sparse=False, statdist_constraint=None))
    370         """The model estimated by this Estimator"""
    371         try:
    372             return self._model
    373         except AttributeError:
    374             raise AttributeError(
--> 375                 'Model has not yet been estimated. Call estimate(X) or fit(X) first')
    376 
    377 
    378 
    379 

AttributeError: Model has not yet been estimated. Call estimate(X) or fit(X) first
___________________________________________________________________________

During handling of the above exception, another exception occurred:

JoblibAttributeError                      Traceback (most recent call last)
<ipython-input-27-e1010bcd926d> in <module>()
      1 model=Model(dataTica)
      2 #model.markovModel(45,5)
----> 3 model.plotTimescales()

/path/to/htmd/site-packages/htmd/model.py in plotTimescales(self, lags, errors, nits, results, plot)
    129 
    130         from htmd.config import _config
--> 131         its = msm.its(self.data.St.tolist(), lags=lags, errors=errors, nits=nits, n_jobs=_config['ncpus'])
    132         if plot:
    133             plt.ion()

/path/to/htmd/site-packages/pyEMMA-2.1-py3.5-linux-x86_64.egg/pyemma/msm/api.py in timescales_msm(dtrajs, lags, nits, reversible, connected, errors, nsamples, n_jobs, show_progress)
    182     itsobj = _ImpliedTimescales(estimator, lags=lags, nits=nits, n_jobs=n_jobs,
    183                                 show_progress=show_progress)
--> 184     itsobj.estimate(dtrajs)
    185     return itsobj
    186 

/path/to/htmd/site-packages/pyEMMA-2.1-py3.5-linux-x86_64.egg/pyemma/_base/estimator.py in estimate(self, X, **params)
    341         if params:
    342             self.set_params(**params)
--> 343         self._model = self._estimate(X)
    344         self._estimated = True
    345         return self

/path/to/htmd/site-packages/pyEMMA-2.1-py3.5-linux-x86_64.egg/pyemma/msm/estimators/implied_timescales.py in _estimate(self, data)
    146         self._models, self._estimators = estimate_param_scan(self.estimator, data, param_sets, failfast=False,
    147                                                              return_estimators=True, n_jobs=self.n_jobs,
--> 148                                                              progress_reporter=self)
    149 
    150         ### PROCESS RESULTS

/path/to/htmd/site-packages/pyEMMA-2.1-py3.5-linux-x86_64.egg/pyemma/_base/estimator.py in estimate_param_scan(estimator, X, param_sets, evaluate, evaluate_args, failfast, return_estimators, n_jobs, progress_reporter)
    296 
    297     # container for model or function evaluations
--> 298     res = pool(task_iter)
    299 
    300     if progress_reporter is not None:

/path/to/htmd/site-packages/joblib/parallel.py in __call__(self, iterable)
    808                 # consumption.
    809                 self._iterating = False
--> 810             self.retrieve()
    811             # Make sure that we get a last message telling us we are done
    812             elapsed_time = time.time() - self._start_time

/path/to/htmd/site-packages/joblib/parallel.py in retrieve(self)
    755                     # a working pool as they expect.
    756                     self._initialize_pool()
--> 757                 raise exception
    758 
    759     def __call__(self, iterable):

JoblibAttributeError: JoblibAttributeError
___________________________________________________________________________
Multiprocessing exception:
...........................................................................
/path/to/htmd/runpy.py in _run_module_as_main(mod_name='ipykernel.__main__', alter_argv=1)
    165         sys.exit(msg)
    166     main_globals = sys.modules["__main__"].__dict__
    167     if alter_argv:
    168         sys.argv[0] = mod_spec.origin
    169     return _run_code(code, main_globals, None,
--> 170                      "__main__", mod_spec)
        mod_spec = ModuleSpec(name='ipykernel.__main__', loader=<_f...b/python3.5/site-packages/ipykernel/__main__.py')
    171 
    172 def run_module(mod_name, init_globals=None,
    173                run_name=None, alter_sys=False):
    174     """Execute a module's code without importing it

...........................................................................
/path/to/htmd/runpy.py in _run_code(code=<code object <module> at 0x7fab2b610db0, file "/...3.5/site-packages/ipykernel/__main__.py", line 1>, run_globals={'__builtins__': <module 'builtins' (built-in)>, '__cached__': '/nfs/grid/software/hpcc/apps/Linux-x86_64-RHEL6/...ges/ipykernel/__pycache__/__main__.cpython-35.pyc', '__doc__': None, '__file__': '/nfs/grid/software/hpcc/apps/Linux-x86_64-RHEL6/...lib/python3.5/site-packages/ipykernel/__main__.py', '__loader__': <_frozen_importlib_external.SourceFileLoader object>, '__name__': '__main__', '__package__': 'ipykernel', '__spec__': ModuleSpec(name='ipykernel.__main__', loader=<_f...b/python3.5/site-packages/ipykernel/__main__.py'), 'app': <module 'ipykernel.kernelapp' from '/nfs/grid/so.../python3.5/site-packages/ipykernel/kernelapp.py'>}, init_globals=None, mod_name='__main__', mod_spec=ModuleSpec(name='ipykernel.__main__', loader=<_f...b/python3.5/site-packages/ipykernel/__main__.py'), pkg_name='ipykernel', script_name=None)
     80                        __cached__ = cached,
     81                        __doc__ = None,
     82                        __loader__ = loader,
     83                        __package__ = pkg_name,
     84                        __spec__ = mod_spec)
---> 85     exec(code, run_globals)
        code = <code object <module> at 0x7fab2b610db0, file "/...3.5/site-packages/ipykernel/__main__.py", line 1>
        run_globals = {'__builtins__': <module 'builtins' (built-in)>, '__cached__': '/nfs/grid/software/hpcc/apps/Linux-x86_64-RHEL6/...ges/ipykernel/__pycache__/__main__.cpython-35.pyc', '__doc__': None, '__file__': '/nfs/grid/software/hpcc/apps/Linux-x86_64-RHEL6/...lib/python3.5/site-packages/ipykernel/__main__.py', '__loader__': <_frozen_importlib_external.SourceFileLoader object>, '__name__': '__main__', '__package__': 'ipykernel', '__spec__': ModuleSpec(name='ipykernel.__main__', loader=<_f...b/python3.5/site-packages/ipykernel/__main__.py'), 'app': <module 'ipykernel.kernelapp' from '/nfs/grid/so.../python3.5/site-packages/ipykernel/kernelapp.py'>}
     86     return run_globals
     87 
     88 def _run_module_code(code, init_globals=None,
     89                     mod_name=None, mod_spec=None,

...........................................................................
/path/to/htmd/site-packages/ipykernel/__main__.py in <module>()
      1 
      2 
----> 3 
      4 if __name__ == '__main__':
      5     from ipykernel import kernelapp as app
      6     app.launch_new_instance()
      7 
      8 
      9 
     10 

...........................................................................
/path/to/htmd/site-packages/traitlets/config/application.py in launch_instance(cls=<class 'ipykernel.kernelapp.IPKernelApp'>, argv=None, **kwargs={})
    591         
    592         If a global instance already exists, this reinitializes and starts it
    593         """
    594         app = cls.instance(**kwargs)
    595         app.initialize(argv)
--> 596         app.start()
        app.start = <bound method IPKernelApp.start of <ipykernel.kernelapp.IPKernelApp object>>
    597 
    598 #-----------------------------------------------------------------------------
    599 # utility functions, for convenience
    600 #-----------------------------------------------------------------------------

...........................................................................
/path/to/htmd/site-packages/ipykernel/kernelapp.py in start(self=<ipykernel.kernelapp.IPKernelApp object>)
    437         
    438         if self.poller is not None:
    439             self.poller.start()
    440         self.kernel.start()
    441         try:
--> 442             ioloop.IOLoop.instance().start()
    443         except KeyboardInterrupt:
    444             pass
    445 
    446 launch_new_instance = IPKernelApp.launch_instance

...........................................................................
/path/to/htmd/site-packages/zmq/eventloop/ioloop.py in start(self=<zmq.eventloop.ioloop.ZMQIOLoop object>)
    157             PollIOLoop.configure(ZMQIOLoop)
    158         return PollIOLoop.current(*args, **kwargs)
    159     
    160     def start(self):
    161         try:
--> 162             super(ZMQIOLoop, self).start()
        self.start = <bound method ZMQIOLoop.start of <zmq.eventloop.ioloop.ZMQIOLoop object>>
    163         except ZMQError as e:
    164             if e.errno == ETERM:
    165                 # quietly return on ETERM
    166                 pass

...........................................................................
/path/to/htmd/site-packages/tornado/ioloop.py in start(self=<zmq.eventloop.ioloop.ZMQIOLoop object>)
    878                 self._events.update(event_pairs)
    879                 while self._events:
    880                     fd, events = self._events.popitem()
    881                     try:
    882                         fd_obj, handler_func = self._handlers[fd]
--> 883                         handler_func(fd_obj, events)
        handler_func = <function wrap.<locals>.null_wrapper>
        fd_obj = <zmq.sugar.socket.Socket object>
        events = 1
    884                     except (OSError, IOError) as e:
    885                         if errno_from_exception(e) == errno.EPIPE:
    886                             # Happens when the client closes the connection
    887                             pass

...........................................................................
/path/to/htmd/site-packages/tornado/stack_context.py in null_wrapper(*args=(<zmq.sugar.socket.Socket object>, 1), **kwargs={})
    270         # Fast path when there are no active contexts.
    271         def null_wrapper(*args, **kwargs):
    272             try:
    273                 current_state = _state.contexts
    274                 _state.contexts = cap_contexts[0]
--> 275                 return fn(*args, **kwargs)
        args = (<zmq.sugar.socket.Socket object>, 1)
        kwargs = {}
    276             finally:
    277                 _state.contexts = current_state
    278         null_wrapper._wrapped = True
    279         return null_wrapper

...........................................................................
/path/to/htmd/site-packages/zmq/eventloop/zmqstream.py in _handle_events(self=<zmq.eventloop.zmqstream.ZMQStream object>, fd=<zmq.sugar.socket.Socket object>, events=1)
    435             # dispatch events:
    436             if events & IOLoop.ERROR:
    437                 gen_log.error("got POLLERR event on ZMQStream, which doesn't make sense")
    438                 return
    439             if events & IOLoop.READ:
--> 440                 self._handle_recv()
        self._handle_recv = <bound method ZMQStream._handle_recv of <zmq.eventloop.zmqstream.ZMQStream object>>
    441                 if not self.socket:
    442                     return
    443             if events & IOLoop.WRITE:
    444                 self._handle_send()

...........................................................................
/path/to/htmd/site-packages/zmq/eventloop/zmqstream.py in _handle_recv(self=<zmq.eventloop.zmqstream.ZMQStream object>)
    467                 gen_log.error("RECV Error: %s"%zmq.strerror(e.errno))
    468         else:
    469             if self._recv_callback:
    470                 callback = self._recv_callback
    471                 # self._recv_callback = None
--> 472                 self._run_callback(callback, msg)
        self._run_callback = <bound method ZMQStream._run_callback of <zmq.eventloop.zmqstream.ZMQStream object>>
        callback = <function wrap.<locals>.null_wrapper>
        msg = [<zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>]
    473                 
    474         # self.update_state()
    475         
    476 

...........................................................................
/path/to/htmd/site-packages/zmq/eventloop/zmqstream.py in _run_callback(self=<zmq.eventloop.zmqstream.ZMQStream object>, callback=<function wrap.<locals>.null_wrapper>, *args=([<zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>],), **kwargs={})
    409         close our socket."""
    410         try:
    411             # Use a NullContext to ensure that all StackContexts are run
    412             # inside our blanket exception handler rather than outside.
    413             with stack_context.NullContext():
--> 414                 callback(*args, **kwargs)
        callback = <function wrap.<locals>.null_wrapper>
        args = ([<zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>],)
        kwargs = {}
    415         except:
    416             gen_log.error("Uncaught exception, closing connection.",
    417                           exc_info=True)
    418             # Close the socket on an uncaught exception from a user callback

...........................................................................
/path/to/htmd/site-packages/tornado/stack_context.py in null_wrapper(*args=([<zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>],), **kwargs={})
    270         # Fast path when there are no active contexts.
    271         def null_wrapper(*args, **kwargs):
    272             try:
    273                 current_state = _state.contexts
    274                 _state.contexts = cap_contexts[0]
--> 275                 return fn(*args, **kwargs)
        args = ([<zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>],)
        kwargs = {}
    276             finally:
    277                 _state.contexts = current_state
    278         null_wrapper._wrapped = True
    279         return null_wrapper

...........................................................................
/path/to/htmd/site-packages/ipykernel/kernelbase.py in dispatcher(msg=[<zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>])
    271         if self.control_stream:
    272             self.control_stream.on_recv(self.dispatch_control, copy=False)
    273 
    274         def make_dispatcher(stream):
    275             def dispatcher(msg):
--> 276                 return self.dispatch_shell(stream, msg)
        msg = [<zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>, <zmq.sugar.frame.Frame object>]
    277             return dispatcher
    278 
    279         for s in self.shell_streams:
    280             s.on_recv(make_dispatcher(s), copy=False)

...........................................................................
/path/to/htmd/site-packages/ipykernel/kernelbase.py in dispatch_shell(self=<ipykernel.ipkernel.IPythonKernel object>, stream=<zmq.eventloop.zmqstream.ZMQStream object>, msg={'buffers': [], 'content': {'allow_stdin': True, 'code': 'model=Model(dataTica)\n#model.markovModel(45,5)\nmodel.plotTimescales()', 'silent': False, 'stop_on_error': True, 'store_history': True, 'user_expressions': {}}, 'header': {'date': '2016-04-20T13:32:13.414404', 'msg_id': '6F3FEF3733904DF4AEE3906F6BB806C2', 'msg_type': 'execute_request', 'session': 'D300C2F92BF54CF08BA9FE808E755A29', 'username': 'username', 'version': '5.0'}, 'metadata': {}, 'msg_id': '6F3FEF3733904DF4AEE3906F6BB806C2', 'msg_type': 'execute_request', 'parent_header': {}})
    223             self.log.error("UNKNOWN MESSAGE TYPE: %r", msg_type)
    224         else:
    225             self.log.debug("%s: %s", msg_type, msg)
    226             self.pre_handler_hook()
    227             try:
--> 228                 handler(stream, idents, msg)
        handler = <bound method Kernel.execute_request of <ipykernel.ipkernel.IPythonKernel object>>
        stream = <zmq.eventloop.zmqstream.ZMQStream object>
        idents = [b'D300C2F92BF54CF08BA9FE808E755A29']
        msg = {'buffers': [], 'content': {'allow_stdin': True, 'code': 'model=Model(dataTica)\n#model.markovModel(45,5)\nmodel.plotTimescales()', 'silent': False, 'stop_on_error': True, 'store_history': True, 'user_expressions': {}}, 'header': {'date': '2016-04-20T13:32:13.414404', 'msg_id': '6F3FEF3733904DF4AEE3906F6BB806C2', 'msg_type': 'execute_request', 'session': 'D300C2F92BF54CF08BA9FE808E755A29', 'username': 'username', 'version': '5.0'}, 'metadata': {}, 'msg_id': '6F3FEF3733904DF4AEE3906F6BB806C2', 'msg_type': 'execute_request', 'parent_header': {}}
    229             except Exception:
    230                 self.log.error("Exception in message handler:", exc_info=True)
    231             finally:
    232                 self.post_handler_hook()

...........................................................................
/path/to/htmd/site-packages/ipykernel/kernelbase.py in execute_request(self=<ipykernel.ipkernel.IPythonKernel object>, stream=<zmq.eventloop.zmqstream.ZMQStream object>, ident=[b'D300C2F92BF54CF08BA9FE808E755A29'], parent={'buffers': [], 'content': {'allow_stdin': True, 'code': 'model=Model(dataTica)\n#model.markovModel(45,5)\nmodel.plotTimescales()', 'silent': False, 'stop_on_error': True, 'store_history': True, 'user_expressions': {}}, 'header': {'date': '2016-04-20T13:32:13.414404', 'msg_id': '6F3FEF3733904DF4AEE3906F6BB806C2', 'msg_type': 'execute_request', 'session': 'D300C2F92BF54CF08BA9FE808E755A29', 'username': 'username', 'version': '5.0'}, 'metadata': {}, 'msg_id': '6F3FEF3733904DF4AEE3906F6BB806C2', 'msg_type': 'execute_request', 'parent_header': {}})
    386         if not silent:
    387             self.execution_count += 1
    388             self._publish_execute_input(code, parent, self.execution_count)
    389 
    390         reply_content = self.do_execute(code, silent, store_history,
--> 391                                         user_expressions, allow_stdin)
        user_expressions = {}
        allow_stdin = True
    392 
    393         # Flush output before sending the reply.
    394         sys.stdout.flush()
    395         sys.stderr.flush()

...........................................................................
/path/to/htmd/site-packages/ipykernel/ipkernel.py in do_execute(self=<ipykernel.ipkernel.IPythonKernel object>, code='model=Model(dataTica)\n#model.markovModel(45,5)\nmodel.plotTimescales()', silent=False, store_history=True, user_expressions={}, allow_stdin=True)
    194 
    195         reply_content = {}
    196         # FIXME: the shell calls the exception handler itself.
    197         shell._reply_content = None
    198         try:
--> 199             shell.run_cell(code, store_history=store_history, silent=silent)
        shell.run_cell = <bound method InteractiveShell.run_cell of <ipykernel.zmqshell.ZMQInteractiveShell object>>
        code = 'model=Model(dataTica)\n#model.markovModel(45,5)\nmodel.plotTimescales()'
        store_history = True
        silent = False
    200         except:
    201             status = u'error'
    202             # FIXME: this code right now isn't being used yet by default,
    203             # because the run_cell() call above directly fires off exception

...........................................................................
/path/to/htmd/site-packages/IPython/core/interactiveshell.py in run_cell(self=<ipykernel.zmqshell.ZMQInteractiveShell object>, raw_cell='model=Model(dataTica)\n#model.markovModel(45,5)\nmodel.plotTimescales()', store_history=True, silent=False, shell_futures=True)
   2718                 self.displayhook.exec_result = result
   2719 
   2720                 # Execute the user code
   2721                 interactivity = "none" if silent else self.ast_node_interactivity
   2722                 self.run_ast_nodes(code_ast.body, cell_name,
-> 2723                    interactivity=interactivity, compiler=compiler, result=result)
        interactivity = 'last_expr'
        compiler = <IPython.core.compilerop.CachingCompiler object>
   2724 
   2725                 # Reset this so later displayed values do not modify the
   2726                 # ExecutionResult
   2727                 self.displayhook.exec_result = None

...........................................................................
/path/to/htmd/site-packages/IPython/core/interactiveshell.py in run_ast_nodes(self=<ipykernel.zmqshell.ZMQInteractiveShell object>, nodelist=[<_ast.Assign object>, <_ast.Expr object>], cell_name='<ipython-input-27-e1010bcd926d>', interactivity='last', compiler=<IPython.core.compilerop.CachingCompiler object>, result=<IPython.core.interactiveshell.ExecutionResult object>)
   2826                     return True
   2827 
   2828             for i, node in enumerate(to_run_interactive):
   2829                 mod = ast.Interactive([node])
   2830                 code = compiler(mod, cell_name, "single")
-> 2831                 if self.run_code(code, result):
        self.run_code = <bound method InteractiveShell.run_code of <ipykernel.zmqshell.ZMQInteractiveShell object>>
        code = <code object <module> at 0x7faaef7f7ed0, file "<ipython-input-27-e1010bcd926d>", line 3>
        result = <IPython.core.interactiveshell.ExecutionResult object>
   2832                     return True
   2833 
   2834             # Flush softspace
   2835             if softspace(sys.stdout, 0):

...........................................................................
/path/to/htmd/site-packages/IPython/core/interactiveshell.py in run_code(self=<ipykernel.zmqshell.ZMQInteractiveShell object>, code_obj=<code object <module> at 0x7faaef7f7ed0, file "<ipython-input-27-e1010bcd926d>", line 3>, result=<IPython.core.interactiveshell.ExecutionResult object>)
   2880         outflag = 1  # happens in more places, so it's easier as default
   2881         try:
   2882             try:
   2883                 self.hooks.pre_run_code_hook()
   2884                 #rprint('Running code', repr(code_obj)) # dbg
-> 2885                 exec(code_obj, self.user_global_ns, self.user_ns)
        code_obj = <code object <module> at 0x7faaef7f7ed0, file "<ipython-input-27-e1010bcd926d>", line 3>
        self.user_global_ns = {'AWS': <class 'htmd.apps.aws.AWS'>, 'Acemd': <class 'htmd.acemd.acemd.Acemd'>, 'AcemdLocal': <class 'htmd.apps.acemdlocal.AcemdLocal'>, 'AdaptiveRun': <class 'htmd.adaptive.adaptiverun.AdaptiveRun'>, 'DisulfideBridge': <class 'htmd.builder.builder.DisulfideBridge'>, 'In': ['', "from htmd import *\nfrom glob import glob\nget_ipy...ic('matplotlib inline')\nhtmd.config(viewer='ngl')", "get_ipython().magic('ls')\nos.chdir('/hpc/grid/wi...pace/groups/neuro_cc/acellera/tmp/proteins/CCR6')", "sims = simlist(glob('prod/*/'), glob('prod/*/structure.pdb'))", "fsims = simfilter(sims, './filtered/', filtersel='not water')", "data = MetricSelfDistance.project(fsims, 'protei... 310 313 316 319 322 325 328', metric='contacts')", 'tica = TICA(data, 20)\ndataTica = tica.project(3)', 'dataTica.cluster(MiniBatchKMeans(n_clusters=3000), mergesmall=3)', 'dataTica.numFrames\ndataTica.plotTrajSizes()', 'dataTica.fstep=0.1;', 'model=Model(dataTica)\n#model.markovModel(45,5)\nmodel.plotTimescales(lags=list(range(1, 90, 10)))', 'model=Model(dataTica)\n#model.markovModel(45,5)\nmodel.plotTimescales()', "from htmd import *\nfrom glob import glob\nget_ipy...ic('matplotlib inline')\nhtmd.config(viewer='ngl')", "get_ipython().magic('ls')\nos.chdir('/hpc/grid/wi...pace/groups/neuro_cc/acellera/tmp/proteins/CCR6')", "sims = simlist(glob('prod/*/'), glob('prod/*/structure.pdb'))", "fsims = simfilter(sims, './filtered/', filtersel='not water')", "data = MetricSelfDistance.project(fsims, 'protei... 310 313 316 319 322 325 328', metric='contacts')", 'data.plotTrajSizes()\n#data.dropTraj()\n#tica = TICA(data, 20)\n#dataTica = tica.project(3)', 'data.plotTrajSizes()\ndata.dropTraj()\ndata.fstep=0.1\ntica = TICA(data, 20)\ndataTica = tica.project(3)', 'dataTica.cluster(MiniBatchKMeans(n_clusters=3000), mergesmall=3)', ...], 'KMeansTri': <class 'htmd.projections.kmeanstri.KMeansTri'>, 'Kinetics': <class 'htmd.kinetics.Kinetics'>, 'LSF': <class 'htmd.apps.lsf.LSF'>, 'Metric': <class 'htmd.projections.metric.Metric'>, ...}
        self.user_ns = {'AWS': <class 'htmd.apps.aws.AWS'>, 'Acemd': <class 'htmd.acemd.acemd.Acemd'>, 'AcemdLocal': <class 'htmd.apps.acemdlocal.AcemdLocal'>, 'AdaptiveRun': <class 'htmd.adaptive.adaptiverun.AdaptiveRun'>, 'DisulfideBridge': <class 'htmd.builder.builder.DisulfideBridge'>, 'In': ['', "from htmd import *\nfrom glob import glob\nget_ipy...ic('matplotlib inline')\nhtmd.config(viewer='ngl')", "get_ipython().magic('ls')\nos.chdir('/hpc/grid/wi...pace/groups/neuro_cc/acellera/tmp/proteins/CCR6')", "sims = simlist(glob('prod/*/'), glob('prod/*/structure.pdb'))", "fsims = simfilter(sims, './filtered/', filtersel='not water')", "data = MetricSelfDistance.project(fsims, 'protei... 310 313 316 319 322 325 328', metric='contacts')", 'tica = TICA(data, 20)\ndataTica = tica.project(3)', 'dataTica.cluster(MiniBatchKMeans(n_clusters=3000), mergesmall=3)', 'dataTica.numFrames\ndataTica.plotTrajSizes()', 'dataTica.fstep=0.1;', 'model=Model(dataTica)\n#model.markovModel(45,5)\nmodel.plotTimescales(lags=list(range(1, 90, 10)))', 'model=Model(dataTica)\n#model.markovModel(45,5)\nmodel.plotTimescales()', "from htmd import *\nfrom glob import glob\nget_ipy...ic('matplotlib inline')\nhtmd.config(viewer='ngl')", "get_ipython().magic('ls')\nos.chdir('/hpc/grid/wi...pace/groups/neuro_cc/acellera/tmp/proteins/CCR6')", "sims = simlist(glob('prod/*/'), glob('prod/*/structure.pdb'))", "fsims = simfilter(sims, './filtered/', filtersel='not water')", "data = MetricSelfDistance.project(fsims, 'protei... 310 313 316 319 322 325 328', metric='contacts')", 'data.plotTrajSizes()\n#data.dropTraj()\n#tica = TICA(data, 20)\n#dataTica = tica.project(3)', 'data.plotTrajSizes()\ndata.dropTraj()\ndata.fstep=0.1\ntica = TICA(data, 20)\ndataTica = tica.project(3)', 'dataTica.cluster(MiniBatchKMeans(n_clusters=3000), mergesmall=3)', ...], 'KMeansTri': <class 'htmd.projections.kmeanstri.KMeansTri'>, 'Kinetics': <class 'htmd.kinetics.Kinetics'>, 'LSF': <class 'htmd.apps.lsf.LSF'>, 'Metric': <class 'htmd.projections.metric.Metric'>, ...}
   2886             finally:
   2887                 # Reset our crash handler in place
   2888                 sys.excepthook = old_excepthook
   2889         except SystemExit as e:

...........................................................................
/hpc/grid/wip_cmg_wwmc/workspace/groups/neuro_cc/acellera/tmp/proteins/CCR6/<ipython-input-27-e1010bcd926d> in <module>()
      1 
      2 
----> 3 
      4 model=Model(dataTica)
      5 #model.markovModel(45,5)
      6 model.plotTimescales()
      7 
      8 
      9 
     10 

...........................................................................
/path/to/htmd/site-packages/htmd/model.py in plotTimescales(self=<htmd.model.Model object>, lags=array([  1,  10,  51,  92, 134, 175, 216, 257, 2...28, 669, 711, 752, 793, 834, 875, 917, 958, 999]), errors=None, nits=20, results=False, plot=True)
    126             lags = self._defaultLags()
    127         if nits is None:
    128             nits = np.min((self.data.K, 20))
    129 
    130         from htmd.config import _config
--> 131         its = msm.its(self.data.St.tolist(), lags=lags, errors=errors, nits=nits, n_jobs=_config['ncpus'])
        its = undefined
        msm.its = <function timescales_msm>
        self.data.St.tolist = <built-in method tolist of numpy.ndarray object>
        lags = array([  1,  10,  51,  92, 134, 175, 216, 257, 2...28, 669, 711, 752, 793, 834, 875, 917, 958, 999])
        errors = None
        nits = 20
        _config = {'ncpus': -2, 'viewer': 'ngl'}
    132         if plot:
    133             plt.ion()
    134             plt.figure()
    135             mplt.plot_implied_timescales(its, dt=self.data.fstep, units='ns')

...........................................................................
/path/to/htmd/site-packages/pyEMMA-2.1-py3.5-linux-x86_64.egg/pyemma/msm/api.py in timescales_msm(dtrajs=[array([  31, 1106,   31,  245,  245,  245, 1106,...  340,  950,  656, 1140,  718, 1116, 1162,  340]), array([ 277,   31,  598,  277,  616, 1186,  903,... 1031,  160,  797,  160,  493,  493,  493,  455]), array([ 363,  925,  925, 1106,  925,  245,  813,...  422,  121,  638,  398,  422,  398,  638,  638]), array([ 864,  277,   31,  245, 1182,   31,  581,...  492,  108,  778,  778,  492,  492,  465,  465]), array([ 864,  537,   31,  537,  135,  135,  277,...  972,  477,    7,  477,    7,  477,    7,  972]), array([ 864,   31,  581,  104,  804,  804,  301,... 1052,  461, 1211,  992, 1052,   73, 1211, 1052]), array([ 277, 1000,  980,  363,  864,  135,  242,...  485, 1183,  456, 1043,  456,  628,  456,  391]), array([ 804,  245,  868,  106,  850,  868,   31,... 1084,  154,  442,  442,  259,  766,  442,  161]), array([  31,   31,  245, 1186,  864,  245,  864,...  504,  841,  187,  504,  240,  240,  637,  504]), array([ 245,  581,  672,  672,  672,  664, 1130,...  451,  933,  933,  210,  155,  600,  155,  600])], lags=array([  1,  10,  51,  92, 134, 175, 216, 257, 2...28, 669, 711, 752, 793, 834, 875, 917, 958, 999]), nits=20, reversible=True, connected=True, errors=None, nsamples=50, n_jobs=-2, show_progress=True)
    179         raise NotImplementedError('Error estimation method'+errors+'currently not implemented')
    180 
    181     # go
    182     itsobj = _ImpliedTimescales(estimator, lags=lags, nits=nits, n_jobs=n_jobs,
    183                                 show_progress=show_progress)
--> 184     itsobj.estimate(dtrajs)
        itsobj.estimate = <bound method Estimator.estimate of ImpliedTimes...gs=None, n_jobs=-2, nits=20, show_progress=True)>
        dtrajs = [array([  31, 1106,   31,  245,  245,  245, 1106,...  340,  950,  656, 1140,  718, 1116, 1162,  340]), array([ 277,   31,  598,  277,  616, 1186,  903,... 1031,  160,  797,  160,  493,  493,  493,  455]), array([ 363,  925,  925, 1106,  925,  245,  813,...  422,  121,  638,  398,  422,  398,  638,  638]), array([ 864,  277,   31,  245, 1182,   31,  581,...  492,  108,  778,  778,  492,  492,  465,  465]), array([ 864,  537,   31,  537,  135,  135,  277,...  972,  477,    7,  477,    7,  477,    7,  972]), array([ 864,   31,  581,  104,  804,  804,  301,... 1052,  461, 1211,  992, 1052,   73, 1211, 1052]), array([ 277, 1000,  980,  363,  864,  135,  242,...  485, 1183,  456, 1043,  456,  628,  456,  391]), array([ 804,  245,  868,  106,  850,  868,   31,... 1084,  154,  442,  442,  259,  766,  442,  161]), array([  31,   31,  245, 1186,  864,  245,  864,...  504,  841,  187,  504,  240,  240,  637,  504]), array([ 245,  581,  672,  672,  672,  664, 1130,...  451,  933,  933,  210,  155,  600,  155,  600])]
    185     return itsobj
    186 
    187 
    188 def markov_model(P, dt_model='1 step'):

...........................................................................
/path/to/htmd/site-packages/pyEMMA-2.1-py3.5-linux-x86_64.egg/pyemma/_base/estimator.py in estimate(self=ImpliedTimescales(estimator=MaximumLikelihoodMSM...ags=None, n_jobs=-2, nits=20, show_progress=True), X=[array([  31, 1106,   31,  245,  245,  245, 1106,...  340,  950,  656, 1140,  718, 1116, 1162,  340]), array([ 277,   31,  598,  277,  616, 1186,  903,... 1031,  160,  797,  160,  493,  493,  493,  455]), array([ 363,  925,  925, 1106,  925,  245,  813,...  422,  121,  638,  398,  422,  398,  638,  638]), array([ 864,  277,   31,  245, 1182,   31,  581,...  492,  108,  778,  778,  492,  492,  465,  465]), array([ 864,  537,   31,  537,  135,  135,  277,...  972,  477,    7,  477,    7,  477,    7,  972]), array([ 864,   31,  581,  104,  804,  804,  301,... 1052,  461, 1211,  992, 1052,   73, 1211, 1052]), array([ 277, 1000,  980,  363,  864,  135,  242,...  485, 1183,  456, 1043,  456,  628,  456,  391]), array([ 804,  245,  868,  106,  850,  868,   31,... 1084,  154,  442,  442,  259,  766,  442,  161]), array([  31,   31,  245, 1186,  864,  245,  864,...  504,  841,  187,  504,  240,  240,  637,  504]), array([ 245,  581,  672,  672,  672,  664, 1130,...  451,  933,  933,  210,  155,  600,  155,  600])], **params={})
    338 
    339         """
    340         # set params
    341         if params:
    342             self.set_params(**params)
--> 343         self._model = self._estimate(X)
        self._model = undefined
        self._estimate = <bound method ImpliedTimescales._estimate of Imp...gs=None, n_jobs=-2, nits=20, show_progress=True)>
        X = [array([  31, 1106,   31,  245,  245,  245, 1106,...  340,  950,  656, 1140,  718, 1116, 1162,  340]), array([ 277,   31,  598,  277,  616, 1186,  903,... 1031,  160,  797,  160,  493,  493,  493,  455]), array([ 363,  925,  925, 1106,  925,  245,  813,...  422,  121,  638,  398,  422,  398,  638,  638]), array([ 864,  277,   31,  245, 1182,   31,  581,...  492,  108,  778,  778,  492,  492,  465,  465]), array([ 864,  537,   31,  537,  135,  135,  277,...  972,  477,    7,  477,    7,  477,    7,  972]), array([ 864,   31,  581,  104,  804,  804,  301,... 1052,  461, 1211,  992, 1052,   73, 1211, 1052]), array([ 277, 1000,  980,  363,  864,  135,  242,...  485, 1183,  456, 1043,  456,  628,  456,  391]), array([ 804,  245,  868,  106,  850,  868,   31,... 1084,  154,  442,  442,  259,  766,  442,  161]), array([  31,   31,  245, 1186,  864,  245,  864,...  504,  841,  187,  504,  240,  240,  637,  504]), array([ 245,  581,  672,  672,  672,  664, 1130,...  451,  933,  933,  210,  155,  600,  155,  600])]
    344         self._estimated = True
    345         return self
    346 
    347     def _estimate(self, X):

...........................................................................
/path/to/htmd/site-packages/pyEMMA-2.1-py3.5-linux-x86_64.egg/pyemma/msm/estimators/implied_timescales.py in _estimate(self=ImpliedTimescales(estimator=MaximumLikelihoodMSM...ags=None, n_jobs=-2, nits=20, show_progress=True), data=[array([  31, 1106,   31,  245,  245,  245, 1106,...  340,  950,  656, 1140,  718, 1116, 1162,  340]), array([ 277,   31,  598,  277,  616, 1186,  903,... 1031,  160,  797,  160,  493,  493,  493,  455]), array([ 363,  925,  925, 1106,  925,  245,  813,...  422,  121,  638,  398,  422,  398,  638,  638]), array([ 864,  277,   31,  245, 1182,   31,  581,...  492,  108,  778,  778,  492,  492,  465,  465]), array([ 864,  537,   31,  537,  135,  135,  277,...  972,  477,    7,  477,    7,  477,    7,  972]), array([ 864,   31,  581,  104,  804,  804,  301,... 1052,  461, 1211,  992, 1052,   73, 1211, 1052]), array([ 277, 1000,  980,  363,  864,  135,  242,...  485, 1183,  456, 1043,  456,  628,  456,  391]), array([ 804,  245,  868,  106,  850,  868,   31,... 1084,  154,  442,  442,  259,  766,  442,  161]), array([  31,   31,  245, 1186,  864,  245,  864,...  504,  841,  187,  504,  240,  240,  637,  504]), array([ 245,  581,  672,  672,  672,  664, 1130,...  451,  933,  933,  210,  155,  600,  155,  600])])
    143             self.estimator.show_progress = False
    144 
    145         # run estimation on all lag times
    146         self._models, self._estimators = estimate_param_scan(self.estimator, data, param_sets, failfast=False,
    147                                                              return_estimators=True, n_jobs=self.n_jobs,
--> 148                                                              progress_reporter=self)
        self = ImpliedTimescales(estimator=MaximumLikelihoodMSM...ags=None, n_jobs=-2, nits=20, show_progress=True)
    149 
    150         ### PROCESS RESULTS
    151         # if some results are None, estimation has failed. Warn and truncate models and lag times
    152         good = np.array([i for i, m in enumerate(self._models) if m is not None], dtype=int)

...........................................................................
/path/to/htmd/site-packages/pyEMMA-2.1-py3.5-linux-x86_64.egg/pyemma/_base/estimator.py in estimate_param_scan(estimator=MaximumLikelihoodMSM(connectivity='largest', cou...ble=True, sparse=False, statdist_constraint=None), X=[array([  31, 1106,   31,  245,  245,  245, 1106,...  340,  950,  656, 1140,  718, 1116, 1162,  340]), array([ 277,   31,  598,  277,  616, 1186,  903,... 1031,  160,  797,  160,  493,  493,  493,  455]), array([ 363,  925,  925, 1106,  925,  245,  813,...  422,  121,  638,  398,  422,  398,  638,  638]), array([ 864,  277,   31,  245, 1182,   31,  581,...  492,  108,  778,  778,  492,  492,  465,  465]), array([ 864,  537,   31,  537,  135,  135,  277,...  972,  477,    7,  477,    7,  477,    7,  972]), array([ 864,   31,  581,  104,  804,  804,  301,... 1052,  461, 1211,  992, 1052,   73, 1211, 1052]), array([ 277, 1000,  980,  363,  864,  135,  242,...  485, 1183,  456, 1043,  456,  628,  456,  391]), array([ 804,  245,  868,  106,  850,  868,   31,... 1084,  154,  442,  442,  259,  766,  442,  161]), array([  31,   31,  245, 1186,  864,  245,  864,...  504,  841,  187,  504,  240,  240,  637,  504]), array([ 245,  581,  672,  672,  672,  664, 1130,...  451,  933,  933,  210,  155,  600,  155,  600])], param_sets=({'lag': 1}, {'lag': 10}, {'lag': 51}, {'lag': 92}, {'lag': 134}, {'lag': 175}, {'lag': 216}, {'lag': 257}, {'lag': 298}, {'lag': 340}, {'lag': 381}, {'lag': 422}, {'lag': 463}, {'lag': 504}, {'lag': 546}, {'lag': 587}, {'lag': 628}, {'lag': 669}, {'lag': 711}, {'lag': 752}, ...), evaluate=None, evaluate_args=None, failfast=False, return_estimators=True, n_jobs=-2, progress_reporter=ImpliedTimescales(estimator=MaximumLikelihoodMSM...ags=None, n_jobs=-2, nits=20, show_progress=True))
    293                                                              failfast,
    294                                                              )
    295                  for i in range(len(param_sets)))
    296 
    297     # container for model or function evaluations
--> 298     res = pool(task_iter)
        res = undefined
        pool = Parallel(n_jobs=-2)
        task_iter = <generator object estimate_param_scan.<locals>.<genexpr>>
    299 
    300     if progress_reporter is not None:
    301         progress_reporter._progress_force_finish(0)
    302 

...........................................................................
/path/to/htmd/site-packages/joblib/parallel.py in __call__(self=Parallel(n_jobs=-2), iterable=<generator object estimate_param_scan.<locals>.<genexpr>>)
    805             if pre_dispatch == "all" or n_jobs == 1:
    806                 # The iterable was consumed all at once by the above for loop.
    807                 # No need to wait for async callbacks to trigger to
    808                 # consumption.
    809                 self._iterating = False
--> 810             self.retrieve()
        self.retrieve = <bound method Parallel.retrieve of Parallel(n_jobs=-2)>
    811             # Make sure that we get a last message telling us we are done
    812             elapsed_time = time.time() - self._start_time
    813             self._print('Done %3i out of %3i | elapsed: %s finished',
    814                         (len(self._output), len(self._output),

---------------------------------------------------------------------------
Sub-process traceback:
---------------------------------------------------------------------------
AttributeError                                     Wed Apr 20 13:32:14 2016
PID: 12654Python 3.5.1: /nfs/grid/software/hpcc/apps/Linux-x86_64-RHEL6/acellera/current/python/bin/python
...........................................................................
/path/to/htmd/site-packages/joblib/parallel.py in __call__(self=<joblib.parallel.BatchedCalls object>)
     67     def __init__(self, iterator_slice):
     68         self.items = list(iterator_slice)
     69         self._size = len(self.items)
     70 
     71     def __call__(self):
---> 72         return [func(*args, **kwargs) for func, args, kwargs in self.items]
        self.items = [(<function _estimate_param_scan_worker>, (MaximumLikelihoodMSM(connectivity='largest', cou...ble=True, sparse=False, statdist_constraint=None), {'lag': 51}, [array([  31, 1106,   31,  245,  245,  245, 1106,...  340,  950,  656, 1140,  718, 1116, 1162,  340]), array([ 277,   31,  598,  277,  616, 1186,  903,... 1031,  160,  797,  160,  493,  493,  493,  455]), array([ 363,  925,  925, 1106,  925,  245,  813,...  422,  121,  638,  398,  422,  398,  638,  638]), array([ 864,  277,   31,  245, 1182,   31,  581,...  492,  108,  778,  778,  492,  492,  465,  465]), array([ 864,  537,   31,  537,  135,  135,  277,...  972,  477,    7,  477,    7,  477,    7,  972]), array([ 864,   31,  581,  104,  804,  804,  301,... 1052,  461, 1211,  992, 1052,   73, 1211, 1052]), array([ 277, 1000,  980,  363,  864,  135,  242,...  485, 1183,  456, 1043,  456,  628,  456,  391]), array([ 804,  245,  868,  106,  850,  868,   31,... 1084,  154,  442,  442,  259,  766,  442,  161]), array([  31,   31,  245, 1186,  864,  245,  864,...  504,  841,  187,  504,  240,  240,  637,  504]), array([ 245,  581,  672,  672,  672,  664, 1130,...  451,  933,  933,  210,  155,  600,  155,  600])], None, None, False), {})]
     73 
     74     def __len__(self):
     75         return self._size
     76 

...........................................................................
/path/to/htmd/site-packages/joblib/parallel.py in <listcomp>(.0=<list_iterator object>)
     67     def __init__(self, iterator_slice):
     68         self.items = list(iterator_slice)
     69         self._size = len(self.items)
     70 
     71     def __call__(self):
---> 72         return [func(*args, **kwargs) for func, args, kwargs in self.items]
        func = <function _estimate_param_scan_worker>
        args = (MaximumLikelihoodMSM(connectivity='largest', cou...ble=True, sparse=False, statdist_constraint=None), {'lag': 51}, [array([  31, 1106,   31,  245,  245,  245, 1106,...  340,  950,  656, 1140,  718, 1116, 1162,  340]), array([ 277,   31,  598,  277,  616, 1186,  903,... 1031,  160,  797,  160,  493,  493,  493,  455]), array([ 363,  925,  925, 1106,  925,  245,  813,...  422,  121,  638,  398,  422,  398,  638,  638]), array([ 864,  277,   31,  245, 1182,   31,  581,...  492,  108,  778,  778,  492,  492,  465,  465]), array([ 864,  537,   31,  537,  135,  135,  277,...  972,  477,    7,  477,    7,  477,    7,  972]), array([ 864,   31,  581,  104,  804,  804,  301,... 1052,  461, 1211,  992, 1052,   73, 1211, 1052]), array([ 277, 1000,  980,  363,  864,  135,  242,...  485, 1183,  456, 1043,  456,  628,  456,  391]), array([ 804,  245,  868,  106,  850,  868,   31,... 1084,  154,  442,  442,  259,  766,  442,  161]), array([  31,   31,  245, 1186,  864,  245,  864,...  504,  841,  187,  504,  240,  240,  637,  504]), array([ 245,  581,  672,  672,  672,  664, 1130,...  451,  933,  933,  210,  155,  600,  155,  600])], None, None, False)
        kwargs = {}
     73 
     74     def __len__(self):
     75         return self._size
     76 

...........................................................................
/path/to/htmd/site-packages/pyEMMA-2.1-py3.5-linux-x86_64.egg/pyemma/_base/estimator.py in _estimate_param_scan_worker(estimator=MaximumLikelihoodMSM(connectivity='largest', cou...ble=True, sparse=False, statdist_constraint=None), params={'lag': 51}, X=[array([  31, 1106,   31,  245,  245,  245, 1106,...  340,  950,  656, 1140,  718, 1116, 1162,  340]), array([ 277,   31,  598,  277,  616, 1186,  903,... 1031,  160,  797,  160,  493,  493,  493,  455]), array([ 363,  925,  925, 1106,  925,  245,  813,...  422,  121,  638,  398,  422,  398,  638,  638]), array([ 864,  277,   31,  245, 1182,   31,  581,...  492,  108,  778,  778,  492,  492,  465,  465]), array([ 864,  537,   31,  537,  135,  135,  277,...  972,  477,    7,  477,    7,  477,    7,  972]), array([ 864,   31,  581,  104,  804,  804,  301,... 1052,  461, 1211,  992, 1052,   73, 1211, 1052]), array([ 277, 1000,  980,  363,  864,  135,  242,...  485, 1183,  456, 1043,  456,  628,  456,  391]), array([ 804,  245,  868,  106,  850,  868,   31,... 1084,  154,  442,  442,  259,  766,  442,  161]), array([  31,   31,  245, 1186,  864,  245,  864,...  504,  841,  187,  504,  240,  240,  637,  504]), array([ 245,  581,  672,  672,  672,  664, 1130,...  451,  933,  933,  210,  155,  600,  155,  600])], evaluate=None, evaluate_args=None, failfast=False)
    139     # deal with results
    140     res = []
    141 
    142     # deal with result
    143     if evaluate is None:  # we want full models
--> 144         res.append(estimator.model)
        res.append = <built-in method append of list object>
        estimator.model = undefined
    145     # we want to evaluate function(s) of the model
    146     elif _types.is_iterable(evaluate):
    147         values = []  # the function values the model
    148         for ieval in range(len(evaluate)):

...........................................................................
/path/to/htmd/site-packages/pyEMMA-2.1-py3.5-linux-x86_64.egg/pyemma/_base/estimator.py in model(self=MaximumLikelihoodMSM(connectivity='largest', cou...ble=True, sparse=False, statdist_constraint=None))
    370         """The model estimated by this Estimator"""
    371         try:
    372             return self._model
    373         except AttributeError:
    374             raise AttributeError(
--> 375                 'Model has not yet been estimated. Call estimate(X) or fit(X) first')
    376 
    377 
    378 
    379 

AttributeError: Model has not yet been estimated. Call estimate(X) or fit(X) first
___________________________________________________________________________