def test_delete(self):
inst = np_container(None)
inst.save(self.fn, model_name='a')
from pyemma._base.serialization.h5file import H5File
with H5File(self.fn) as f:
f.delete('a')
self.assertNotIn('a', f.models_descriptive.keys())
def test_rename(self):
inst = np_container(None)
inst.save(self.fn, model_name='a')
with H5File(self.fn, mode='a') as f:
f.rename('a', 'b')
models = f.models_descriptive.keys()
self.assertIn('b', models)
self.assertNotIn('a', models)
def save(self,
file_name,
model_name='default',
overwrite=False,
save_streaming_chain=False):
r""" saves the current state of this object to given file and name.
Parameters
-----------
file_name: str
path to desired output file
model_name: str, default='default'
creates a group named 'model_name' in the given file, which will contain all of the data.
If the name already exists, and overwrite is False (default) will raise a RuntimeError.
overwrite: bool, default=False
Should overwrite existing model names?
save_streaming_chain : boolean, default=False
if True, the data_producer(s) of this object will also be saved in the given file.
Examples
--------
>>> import pyemma, numpy as np
>>> from pyemma.util.contexts import named_temporary_file
>>> m = pyemma.msm.MSM(P=np.array([[0.1, 0.9], [0.9, 0.1]]))
>>> with named_temporary_file() as file: # doctest: +SKIP
... m.save(file, 'simple') # doctest: +SKIP
... inst_restored = pyemma.load(file, 'simple') # doctest: +SKIP
>>> np.testing.assert_equal(m.P, inst_restored.P) # doctest: +SKIP
"""
import six
if six.PY2:
raise NotImplementedError(
'This feature is only available on Python3. Consider upgrading.'
)
from pyemma._base.serialization.h5file import H5File
try:
with H5File(file_name=file_name) as f:
f.add_serializable(model_name,
obj=self,
overwrite=overwrite,
save_streaming_chain=save_streaming_chain)
except Exception as e:
msg = ('During saving the object {obj}") '
'the following error occurred: {error}'.format(obj=self,
error=e))
if isinstance(self, Loggable):
self.logger.exception(msg)
else:
logger.exception(msg)
raise
def load(cls, file_name, model_name='default'):
""" Loads a previously saved PyEMMA object from disk.
Parameters
----------
file_name : str or file like object (has to provide read method).
The file like object tried to be read for a serialized object.
model_name: str, default='default'
if multiple models are contained in the file, these can be accessed by
their name. Use :func:`pyemma.list_models` to get a representation of all stored models.
Returns
-------
obj : the de-serialized object
"""
from .h5file import H5File
with H5File(file_name, model_name=model_name, mode='r') as f:
return f.model
def load(cls, file_name, model_name='default'):
""" loads a previously saved object of this class from a file.
Parameters
----------
file_name : str or file like object (has to provide read method).
The file like object tried to be read for a serialized object.
model_name: str, default='default'
if multiple models are contained in the file, these can be accessed by
their name. Use func:`pyemma.list_models` to get a representation of all stored models.
Returns
-------
obj : the de-serialized object
"""
import six
if six.PY2:
raise NotImplementedError('This feature is only available on Python3. Consider upgrading.')
from .h5file import H5File
with H5File(file_name, model_name=model_name, mode='r') as f:
return f.model
def _load_cmp(self, pdb):
top = mdtraj.load(pdb).top
with H5File(self.f, mode='a') as fh:
fh.add_object('top', top)
restored = fh.model
assert top == restored
assert tuple(top.atoms) == tuple(restored.atoms)
assert tuple(top.bonds) == tuple(restored.bonds)
# mdtraj (1.9.1) does not impl eq for Residue...
def eq(self, other):
from mdtraj.core.topology import Residue
if not isinstance(other, Residue):
return False
return (self.index == other.index and self.resSeq == other.resSeq
and other.name == self.name
and tuple(other.atoms) == tuple(self.atoms))
from unittest import mock
with mock.patch('mdtraj.core.topology.Residue.__eq__', eq):
self.assertEqual(tuple(top.residues), tuple(restored.residues))
def main(argv=None):
import six
if six.PY2:
print('This tool is only available for Python3.')
sys.exit(1)
import argparse
from pyemma import load
from pyemma._base.serialization.h5file import H5File
parser = argparse.ArgumentParser()
parser.add_argument('--json', action='store_true', default=False)
parser.add_argument('files',
metavar='files',
nargs='+',
help='files to inspect')
parser.add_argument(
'--recursive',
action='store_true',
default=False,
help='If the pipeline of the stored estimator was stored, '
'gather these information as well. This will require to load the model, '
'so it could take a while, if the pipeline contains lots of data.')
parser.add_argument('-v', '--verbose', action='store_true', default=False)
args = parser.parse_args(argv)
# store found models by filename
from collections import defaultdict
models = defaultdict(dict)
for f in args.files:
try:
with H5File(f) as fh:
m = fh.models_descriptive
for k in m:
models[f][k] = m[k]
for model_name, values in m.items():
if values['saved_streaming_chain']:
restored = load(f)
models[f][model_name]['input_chain'] = [
repr(x) for x in restored._data_flow_chain()
]
except BaseException as e:
print(
'{} did not contain a valid PyEMMA model. Error was {err}. '
'If you are sure, that it does, please post an issue on Github'
.format(f, err=e))
if args.verbose:
import traceback
traceback.print_exc()
return 1
if not args.json:
from io import StringIO
buff = StringIO()
buff.write('PyEMMA models\n')
buff.write('=' * (buff.tell() - 1))
buff.write('\n' * 2)
for f in models:
buff.write('file: {}'.format(f))
buff.write('\n')
buff.write('-' * 80)
buff.write('\n')
model_file = models[f]
for i, model_name in enumerate(model_file):
attrs = model_file[model_name]
buff.write('{index}. name: {key}\n'
'created: {created}\n'
'{repr}\n'.format(key=model_name,
index=i + 1,
created=attrs['created_readable'],
repr=attrs['class_str']))
if attrs['saved_streaming_chain']:
buff.write('\n---------Input chain---------\n')
for j, x in enumerate(attrs['input_chain']):
buff.write('{index}. {repr}\n'.format(index=j + 1,
repr=x))
buff.write('-' * 80)
buff.write('\n')
buff.seek(0)
print(buff.read())
else:
import json
json.dump(models, fp=sys.stdout)
return 0
def test_model_not_existant(self):
inst = np_container(None)
inst.save(self.fn, 'foo')
with self.assertRaises(ValueError) as cm:
f = H5File(self.fn, model_name='bar')
self.assertIn('"bar" not found', cm.exception.args[0])
def test_delete(self):
inst = np_container(None)
inst.save(self.fn, model_name='a')
with H5File(self.fn, mode='a') as f:
f.delete('a')
self.assertNotIn('a', f.models_descriptive.keys())
