autofit.DatabasePaths#

class autofit.DatabasePaths(session, name: Optional[str] = None, path_prefix: Optional[str] = None, is_identifier_in_paths=True, parent=None, save_all_samples=False, unique_tag: Optional[str] = None)[source]#

Bases: AbstractPaths

Manages the path structure for NonLinearSearch output, for analyses both not using and using the search API. Use via non-linear searches requires manual input of paths, whereas the search API manages this using the search attributes.

The output path within which the Paths objects path structure is contained is set via PyAutoConf, using the command:

from autoconf import conf conf.instance = conf.Config(output_path=”path/to/output”)

If we assume all the input strings above are used with the following example names:

name = “name” path_prefix = “folder_0/folder_1”

The output path of the NonLinearSearch results will be:

/path/to/output/folder_0/folder_1/name

Parameters:

name – The name of the non-linear search, which is used as a folder name after the path_prefix. For searchs this name is the name.
path_prefix – A prefixed path that appears after the output_path but before the name variable.
is_identifier_in_paths – If True output path and symlink path terminate with an identifier generated from the search and model

Methods

`completed`
`create_child`	Create a paths object which is the child of some parent paths object.
`for_sub_analysis`
`is_object`
`load_array`	Load an array from the database
`load_fits`	Load a fits file from the database
`load_json`	Load a json file from the database
`load_latent_samples`
`load_object`
`load_samples`	Load samples from the database
`load_samples_info`
`load_samples_summary`	Load samples summary from the database.
`load_search_internal`
`remove_object`
`remove_search_internal`
`restore`	Copy files from the `.zip` file to the samples folder.
`save_all`
`save_array`	Save an array as a json file in the database
`save_fits`	Save a fits file in the database
`save_identifier`
`save_json`	Save a dictionary as a json file in the database
`save_latent_samples`	Save latent variables.
`save_object`
`save_parent_identifier`
`save_samples`	Save samples to the database
`save_samples_summary`	Save samples summary to the database.
`save_search_internal`
`save_summary`
`save_unique_tag`
`zip_remove`	Remove files from both the symlinked folder and the output directory
`zip_remove_nuclear`	When multiple model-fits are performed using the same path_prefix and name, the results are populated in the same folder with different unique identifiers.

Attributes

`fit`
`identifier`
`image_path`	The path to the image folder.
`is_complete`
`is_grid_search`
`non_linear_name`
`output_path`	The path to the output information for a search.
`parent`	The search performed before this search.
`profile_path`	The path to the profile folder.
`samples`
`search_internal_path`	The path to the samples folder.

property parent: AbstractPaths#: The search performed before this search. For example, a search that is then compared to searches during a grid search.

create_child(name: Optional[str] = None, path_prefix: Optional[str] = None, is_identifier_in_paths: Optional[bool] = None, identifier: Optional[str] = None) → DatabasePaths[source]#

Create a paths object which is the child of some parent paths object. This is done during a GridSearch so that results can be stored in the correct directory. It also allows database fit objects to be related correctly.

If no instance is set the prior median model is used to ensure that the parent object is queryable.

Parameters:

identifier –
name –
path_prefix –
is_identifier_in_paths – If False then this path’s identifier will not be added to its output path.

Return type:

A new paths object

zip_remove()[source]#: Remove files from both the symlinked folder and the output directory

save_json(name, object_dict: Union[dict, list], prefix: str = '')[source]#

Save a dictionary as a json file in the database

Parameters:

name – The name of the json
object_dict – The dictionary to save

load_json(name: str, prefix: str = '') → Union[dict, list][source]#

Load a json file from the database

Parameters:: name – The name of the json
Return type:: The loaded dictionary

save_array(name, array: ndarray)[source]#

Save an array as a json file in the database

Parameters:

name – The name of the array
array – The array to save

load_array(name: str) → ndarray[source]#

Load an array from the database

Parameters:: name – The name of the array
Return type:: The loaded array

save_fits(name: str, hdu, prefix: str = '')[source]#

Save a fits file in the database

Parameters:

name – The name of the fits file
hdu – The hdu to save

load_fits(name: str, prefix: str = '')[source]#

Load a fits file from the database

Parameters:: name – The name of the fits file
Return type:: The loaded hdu

save_samples(samples)[source]#: Save samples to the database

save_latent_samples(latent_samples)[source]#: Save latent variables. These are values computed from an instance and output during analysis.

load_samples()[source]#: Load samples from the database