openpathsampling.storage.AnalysisStorage

class openpathsampling.storage.AnalysisStorage(filename, caching_mode='analysis')[source]

Open a storage in read-only and do caching useful for analysis.

__init__(filename, caching_mode='analysis')[source]

Open a storage in read-only and do caching useful for analysis.

Parameters
  • filename (str) – The filename of the storage to be opened

  • caching_mode (str) – The caching mode to be used. Default is analysis which will cache lots of usually relevant object. If you have a decent size system and lots of memory you might want to try unlimited which will not load all objects but keep every object you load. This is fastest but might crash for large storages.

Methods

__init__(filename[, caching_mode])

Open a storage in read-only and do caching useful for analysis.

analysis_cache_sizes()

Cache Sizes for analysis sessions

cache_for_analysis(storage)

Run specific caching useful for later analysis sessions.

cache_image()

Return an dict containing information about all caches

check_version()

close

`close(self)`

createCompoundType

`createCompoundType(self, datatype, datatype_name)`

createDimension

`createDimension(self, dimname, size=None)`

createEnumType

`createEnumType(self, datatype, datatype_name, enum_dict)`

createGroup

`createGroup(self, groupname)`

createVLType

`createVLType(self, datatype, datatype_name)`

createVariable

`createVariable(self, varname, datatype, dimensions=(), zlib=False, complevel=4, shuffle=True, fletcher32=False, contiguous=False, chunksizes=None, endian='native', least_significant_digit=None, fill_value=None, chunk_cache=None)`

create_dimension(dim_name[, size])

Initialize a new dimension in the storage.

create_store(name, store[, register_attr])

Create a special variable type obj.name that can hold storable objects

create_type_delegate(var_type)

Create a variable value delegator for var_type

create_variable(var_name, var_type, dimensions)

Create a new variable in the netCDF storage.

create_variable_delegate(var_name)

Create a delegate property that wraps the netcdf.Variable and takes care of type conversions

default_cache_sizes()

Cache sizes for standard sessions for medium production and analysis.

delncattr

`delncattr(self,name,value)`

filepath

`filepath(self,encoding=None)`

finalize_stores()

Run initializations for all added stores.

find_store(obj)

Return the default store used for an storable object

fromcdl

`fromcdl(cdlfilename, ncfilename=None, mode='a',format='NETCDF4')`

get_value_parameters(value)

Compute netcdfplus compatible parameters to store a value

get_var_types()

List all allowed variable type to be used in create_variable

get_variables_by_attributes

`get_variables_by_attribute(self, **kwargs)`

getncattr

`getncattr(self,name)`

identify_var_type(instance)

Identify common python and numpy types

idx(obj)

Return the index used to store the given object in this storage

isopen

`isopen(self)`

list_storable_objects()

Return a list of storable object base classes

list_stores()

Return a list of registered stores

load(uuid)

Load an object from the storage

lowmemory_cache_sizes()

Cache sizes for very low memory

memtest_cache_sizes()

Cache Sizes for memtest debugging sessions

ncattrs

`ncattrs(self)`

no_cache_sizes()

Set cache sizes to no caching at all.

production_cache_sizes()

Cache Sizes for production runs

register_store(name, store[, register_attr])

Add a object store to the file

renameAttribute

`renameAttribute(self, oldname, newname)`

renameDimension

`renameDimension(self, oldname, newname)`

renameGroup

`renameGroup(self, oldname, newname)`

renameVariable

`renameVariable(self, oldname, newname)`

repr_json(obj)

Return the JSON representation in the storage if available

save(obj[, idx])

Save a storable object into the correct Storage in the netCDF file

set_always_mask

`set_always_mask(self, True_or_False)`

set_auto_chartostring

`set_auto_chartostring(self, True_or_False)`

set_auto_mask

`set_auto_mask(self, True_or_False)`

set_auto_maskandscale

`set_auto_maskandscale(self, True_or_False)`

set_auto_scale

`set_auto_scale(self, True_or_False)`

set_caching_mode([mode])

Set default values for all caches

set_fill_off

`set_fill_off(self)`

set_fill_on

`set_fill_on(self)`

set_ncstring_attrs

`set_ncstring_attrs(self, True_or_False)`

setncattr

`setncattr(self,name,value)`

setncattr_string

`setncattr_string(self,name,value)`

setncatts

`setncatts(self,attdict)`

sync

`sync(self)`

sync_all()

Convenience function to use self.cvs and self at once.

to_uuid_chunks(x)

tocdl

`tocdl(self, coordvars=False, data=False, outfile=None)`

unlimited_cache_sizes()

Set cache sizes to no caching at all.

update_delegates()

Updates the set of delegates in self.vars

update_storable_classes()

var_type_to_nc_type(var_type)

Return the compatible netCDF variable type for var_type

write_meta()

Attributes

USE_FEATURE_SNAPSHOTS

cmptypes

data_model

dimensions

disk_format

enumtypes

file_format

file_size

file_size_str

filename

groups

keepweakref

name

string name of Group instance

objects

Return a dictionary of all objects stored.

parent

path

support_simtk_unit

tags

variables

vltypes