coexist.AccessData#
- class coexist.AccessData(access_path='.')[source]#
Bases:
object
Access (pun intended) data generated by a
coexist.Access
run; read it in usingcoexist.AccessData.read("access_seed<seed>")
.Examples
Suppose you run
coexist.Access.learn(random_seed = 123)
- then a directory “access_seed123/” would be generated. Access (yes, still intended) all data generated in a Python-friendly format using:>>> import coexist >>> data = coexist.AccessData("access_123") >>> data AccessData -------------------------------------------------------------------------- paths ╎ AccessPaths(...) parameters ╎ value min max sigma ╎ fp1 -0.005312 -5.0 10.0 0.024483 ╎ fp2 0.003409 -5.0 10.0 0.034576 ╎ fp3 0.296074 -5.0 10.0 2.078181 num_epochs ╎ 31 target ╎ 0.1 seed ╎ 123 epochs ╎ DataFrame(fp1_mean, fp2_mean, fp3_mean, fp1_std, fp2_std, ╎ fp3_std, overall_std) epochs_scaled ╎ DataFrame(fp1_mean, fp2_mean, fp3_mean, fp1_std, fp2_std, ╎ fp3_std, overall_std) results ╎ DataFrame(fp1, fp2, fp3, error) results_scaled ╎ DataFrame(fp1, fp2, fp3, error)
- Attributes:
- paths
AccessPaths
Struct-like object storing relevant paths in the given ACCES directory.
- parameters
pd.DataFrame
The optimum free parameters found (final or intermediate if ACCES is still running).
- parameters_scaled
pd.DataFrame
The optimum free parameters found, divided by
scaling
such that the initial variance in the parameter values was unity.- scaling
np.ndarray
A vector with the values to scale each parameter by - they are the initial variances (
sigma
).- population
int
The number of simulations to run in parallel within a single epoch, or number of parameter combinations to try at once.
- num_epochs
int
The number of epochs that were successfully executed.
- target
float
The target variance, where the initial parameter uncertainty must be decreased from 1 to
target
.- seed
int
The random number generator seed uniquely defining this ACCES run.
- epochs
np.ndarray
Matrix with columns [mean_param1, mean_param2, …, std_param1, std_param2, …, std_overall] with one row per epoch.
- epochs_scaled
np.ndarray
Same as
epochs
, scaled such that the initial variance (sigma
) becomes unity.- results
np.ndarray
Matrix with columns [param1, param2, …, error] for each parameter combination tried - i.e.
population * num_epochs
.- results_scaled
np.ndarray
Same as
results
, scaled such that the initial variance (sigma
) becomes unity.
- paths
- __init__(access_path='.')[source]#
Read in data generated by
coexist.Access
; the access_path can be either the “access_info_<hash>” directory itself, or its parent directory.
Methods
__init__
([access_path])Read in data generated by
coexist.Access
; the access_path can be either the "access_info_<hash>" directory itself, or its parent directory.copy
()Return copy of AccessData object.
empty
()Create an empty AccessData object that you can set attributes to directly.
legacy
(access_path)Read in data from legacy coexist-0.1.0 ACCES format; this is normally called automatically by
AccessData.read
.read
([access_path])Read in data generated by
coexist.Access
; the access_path can be either the "access_seed<hash>" directory itself, or its parent directory.save
(dirname)Save AccessData to a new directory at dirname.
- static empty()[source]#
Create an empty AccessData object that you can set attributes to directly.
Examples
Create an empty AccessData object:
>>> import coexist >>> data = coexist.AccessData.empty()
- static read(access_path='.')[source]#
Read in data generated by
coexist.Access
; the access_path can be either the “access_seed<hash>” directory itself, or its parent directory.Here for backwards-compatibility; you can instantiate the class directly with the
access_path
, e.g.AccessData(".")
.