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[WIP] Functions to convert netcdfplus CVs to SimStore CVs #1005

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[WIP] Functions to convert netcdfplus CVs to SimStore CVs #1005

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f33daeb
Start of converting netcdfplus CV to SimStore
dwhswenson Apr 8, 2021
060a629
start to tests from SimStore CVs from netcdfplus
dwhswenson Apr 25, 2021
2801508
Merge remote-tracking branch 'upstream/master' into new_cv_from_old
dwhswenson Apr 25, 2021
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Start of converting netcdfplus CV to SimStore 
Still need to do some custom work on the func_config for MDTraj
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@dwhswenson
dwhswenson committed Apr 8, 2021
commit f33daeb538c93aa3e5ffa2d6573f03416dd9c896
46 changes: 46 additions & 0 deletions openpathsampling/experimental/storage/collective_variables.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,13 +7,59 @@
from openpathsampling.netcdfplus import StorableNamedObject
from ..simstore.serialization_helpers import get_uuid

def _func_config_from_netcdfplus(cv_requires_lists, cv_wrap_numpy,
cv_scalarize_singletons):
configs = []
if cv_requires_lists:
configs.extend(requires_lists_pre, requires_lists_post)
if cv_wrap_numpy:
configs.append(wrap_numpy)
if cv_scalarize_singletons:
configs.append(scalarize_singletons)
func_config = StorableFunctionConfig(configs)
return func_config

class CollectiveVariable(StorableFunction):
"""Wrapper around functions that map snapshots to values.

This specializes the generic :class:`.StorableFunction` for OPS
snapshots. In particular, it makes it so that trajectories and sampls
are seen as lists of snapshots.
"""
@staticmethod
def _remap_netcdfplus_dict(dct):
from openpathsampling.netcdfplus import ObjectJSON
# create configs from the netcdfplus CV
cv_time_reversible = dct.pop('cv_time_reversible', None)
cv_requires_lists = dct.pop('cv_requires_lists', None)
cv_wrap_numpy = dct.pop('cv_wrap_numpy', None)
cv_scalarize_singletons = dct.pop('cv_scalarize_numpy_singletons',
None)
dct['func_config'] = _func_config_from_netcdfplus(
cv_requires_lists, cv_wrap_numpy, cv_scalarize_singletons
)
# remap changed dict names
func_dct = None
func_names = iter(['f', 'cv_callable', 'featurizer', 'generator'])
while func_dct is None:
try:
func_dct = dct.pop(next(func_names), None)
except StopIteration:
raise RuntimeError("Unable to convert netcdfplus CV: ",
str(dct['name']))

dct['func'] = ObjectJSON.callable_from_dict(func_dct)
# set default dict keys not used in netcdfplus
dct['source'] = None
return dct

@classmethod
def from_netcdfplus_cv(cls, old_cv):
dct = old_cv.to_dict()
name = dct.pop('name')
dct = cls._remap_netcdfplus_dict(dct)
return cls.from_dict(dct).named(name)

def is_scalar(self, item):
# override is_scalar so that Trajectories and Samples are treated
# as iterables over snapshots
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