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"""Converts results from the compiled RAT code to python dataclasses."""
import json
from dataclasses import dataclass
from pathlib import Path
from typing import Any, Optional, Union
import numpy as np
import ratapi.rat_core
from ratapi.utils.enums import Procedures
bayes_results_subclasses = [
"predictionIntervals",
"confidenceIntervals",
"dreamParams",
"dreamOutput",
"nestedSamplerOutput",
]
bayes_results_fields = {
"param_fields": {
"predictionIntervals": [],
"confidenceIntervals": [],
"dreamParams": [
"nParams",
"nChains",
"nGenerations",
"parallel",
"CPU",
"jumpProbability",
"pUnitGamma",
"nCR",
"delta",
"steps",
"zeta",
"outlier",
"adaptPCR",
"thinning",
"epsilon",
"ABC",
"IO",
"storeOutput",
],
"dreamOutput": ["runtime", "iteration"],
"nestedSamplerOutput": ["logZ", "logZErr"],
},
"list_fields": {
"predictionIntervals": ["reflectivity"],
"confidenceIntervals": [],
"dreamParams": [],
"dreamOutput": [],
"nestedSamplerOutput": [],
},
"double_list_fields": {
"predictionIntervals": ["sld"],
"confidenceIntervals": [],
"dreamParams": [],
"dreamOutput": [],
"nestedSamplerOutput": [],
},
"array_fields": {
"predictionIntervals": ["sampleChi"],
"confidenceIntervals": ["percentile65", "percentile95", "mean"],
"dreamParams": ["R"],
"dreamOutput": ["allChains", "outlierChains", "AR", "R_stat", "CR"],
"nestedSamplerOutput": ["nestSamples", "postSamples"],
},
}
results_fields = {
"list_fields": ["reflectivity", "simulation", "shiftedData", "backgrounds", "resolutions"],
"double_list_fields": ["sldProfiles", "layers", "resampledLayers"],
}
def get_field_string(field: str, value: Any, array_limit: int):
"""Return a string representation of class fields where large arrays are represented by their shape.
An array will be displayed as just its shape if it is multidimensional or 1D and longer than ``array_limit``.
Parameters
----------
field : str
The name of the field in the RAT output class.
value : Any
The value of the given field in the RAT output class.
array_limit : int
The maximum length of 1D arrays which will be fully displayed.
Returns
-------
field_string : str
The string representation of the field in the RAT output class.
Examples
--------
>>> get_field_string("data", 130, 5)
"data = 130"
>>> get_field_string("data", array([1, 2, 3, 4, 5]), 10)
"data = [1 2 3 4 5]"
>>> get_field_string("data", array([1, 2, 3, 4, 5]), 3)
"data = Data array: [5],"
>>> get_field_string("data", array([[1, 2, 3], [4, 5, 6]]), 10)
"data = Data array: [2 x 3],"
"""
array_text = "Data array: "
if isinstance(value, list) and len(value) > 0:
if isinstance(value[0], np.ndarray):
array_strings = [f"{array_text}[{' x '.join(str(i) for i in array.shape)}]" for array in value]
field_string = f"{field} = [{', '.join(str(string) for string in array_strings)}],\n"
elif isinstance(value[0], list) and len(value[0]) > 0 and isinstance(value[0][0], np.ndarray):
array_strings = [
[f"{array_text}[{' x '.join(str(i) for i in array.shape)}]" for array in sub_list] for sub_list in value
]
list_strings = [f"[{', '.join(string for string in list_string)}]" for list_string in array_strings]
field_string = f"{field} = [{', '.join(list_strings)}],\n"
else:
field_string = f"{field} = {str(value)},\n"
elif isinstance(value, np.ndarray):
if value.ndim == 1 and value.size < array_limit:
field_string = f"{field} = {str(value) if value.size > 0 else '[]'},\n"
else:
field_string = f"{field} = {array_text}[{' x '.join(str(i) for i in value.shape)}],\n"
else:
field_string = f"{field} = {str(value)},\n"
return field_string
class RATResult:
"""A mixin class which truncates arrays when the class is displayed."""
def __str__(self):
output = f"{self.__class__.__name__}(\n"
for key, value in self.__dict__.items():
output += "\t" + get_field_string(key, value, 100)
output += ")"
return output
@dataclass
class CalculationResults(RATResult):
"""The goodness of fit from the Abeles calculation.
Parameters
----------
chiValues : np.ndarray
The chi-squared value for each contrast.
sumChi : float
The sum of the chiValues array.
"""
chiValues: np.ndarray
sumChi: float
@dataclass
class ContrastParams(RATResult):
"""The experimental parameters for each contrast.
Parameters
----------
scalefactors : np.ndarray
The scalefactor values for each contrast.
bulkIn : np.ndarray
The bulk in values for each contrast.
bulkOut : np.ndarray
The bulk out values for each contrast.
subRoughs : np.ndarray
The substrate roughness values for each contrast.
resample : np.ndarray
An array containing whether each contrast was resampled.
"""
scalefactors: np.ndarray
bulkIn: np.ndarray
bulkOut: np.ndarray
subRoughs: np.ndarray
resample: np.ndarray
@dataclass
class Results:
"""The results of a RAT calculation.
Parameters
----------
reflectivity : list
The reflectivity curves for each contrast,
with the same range as the data
(``data_range`` in the contrast's ``Data`` object)
simulation : list
The reflectivity curves for each contrast,
which can be a wider range to allow extrapolation
(``simulation_range`` in the contrast's ``Data`` object).
shiftedData : list
The data with scalefactors and background corrections applied.
backgrounds : list
The background for each contrast defined over the simulation range.
resolutions : list
The resolution for each contrast defined over the simulation range.
sldProfiles : list
The SLD profiles for each contrast.
layers : list
The array of layer parameter values for each contrast.
resampledLayers : list
If resampling is used, the array of layer parameter values for each contrast after resampling has been
performed.
calculationResults : CalculationResults
The chi-squared fit results from the final calculation and fit.
contrastParams : ContrastParams
The experimental parameters for the contrasts.
fitParams : np.ndarray
The best fit value of the parameter with name ``fitNames[i]``.
fitNames : list[str]
The names of the fit parameters, where ``fitNames[i]`` is the name
of the parameter with value given in ``fitParams[i]``.
"""
reflectivity: list
simulation: list
shiftedData: list
backgrounds: list
resolutions: list
sldProfiles: list
layers: list
resampledLayers: list
calculationResults: CalculationResults
contrastParams: ContrastParams
fitParams: np.ndarray
fitNames: list[str]
def __str__(self):
output = ""
for key, value in self.__dict__.items():
output += get_field_string(key, value, 100)
return output
def save(self, filepath: Union[str, Path] = "./results.json"):
"""Save the Results object to a JSON file.
Parameters
----------
filepath : str or Path
The path to where the results file will be written.
"""
filepath = Path(filepath).with_suffix(".json")
json_dict = write_core_results_fields(self)
filepath.write_text(json.dumps(json_dict))
@classmethod
def load(cls, path: Union[str, Path]) -> Union["Results", "BayesResults"]:
"""Load a Results object from file.
Parameters
----------
path : str or Path
The path to the results json file.
"""
path = Path(path)
input_data = path.read_text()
results_dict = json.loads(input_data)
results_dict = read_core_results_fields(results_dict)
if all(key in results_dict for key in bayes_results_subclasses):
results_dict = read_bayes_results_fields(results_dict)
return BayesResults(
reflectivity=results_dict["reflectivity"],
simulation=results_dict["simulation"],
shiftedData=results_dict["shiftedData"],
backgrounds=results_dict["backgrounds"],
resolutions=results_dict["resolutions"],
sldProfiles=results_dict["sldProfiles"],
layers=results_dict["layers"],
resampledLayers=results_dict["resampledLayers"],
calculationResults=CalculationResults(**results_dict["calculationResults"]),
contrastParams=ContrastParams(**results_dict["contrastParams"]),
fitParams=np.array(results_dict["fitParams"]),
fitNames=results_dict["fitNames"],
predictionIntervals=PredictionIntervals(**results_dict["predictionIntervals"]),
confidenceIntervals=ConfidenceIntervals(**results_dict["confidenceIntervals"]),
dreamParams=DreamParams(**results_dict["dreamParams"]),
dreamOutput=DreamOutput(**results_dict["dreamOutput"]),
nestedSamplerOutput=NestedSamplerOutput(**results_dict["nestedSamplerOutput"]),
chain=np.array(results_dict["chain"]),
)
else:
return Results(
reflectivity=results_dict["reflectivity"],
simulation=results_dict["simulation"],
shiftedData=results_dict["shiftedData"],
backgrounds=results_dict["backgrounds"],
resolutions=results_dict["resolutions"],
sldProfiles=results_dict["sldProfiles"],
layers=results_dict["layers"],
resampledLayers=results_dict["resampledLayers"],
calculationResults=CalculationResults(**results_dict["calculationResults"]),
contrastParams=ContrastParams(**results_dict["contrastParams"]),
fitParams=np.array(results_dict["fitParams"]),
fitNames=results_dict["fitNames"],
)
@dataclass
class PredictionIntervals(RATResult):
"""The Bayesian prediction intervals for 95% and 65% confidence.
For ``reflectivity`` and ``sld``, each list item is an array
with five rows. The rows represent:
- 0: the 5th percentile;
- 1: the 35th percentile;
- 2: the mean value of the interval;
- 3: the 65th percentile;
- 4: the 95th percentile.
Parameters
----------
reflectivity : list
The prediction interval data for reflectivity of each contrast.
SLD : list
The prediction interval data for SLD of each contrast.
sampleChi : np.ndarray
The value of sumChi at each point of the Markov chain.
"""
reflectivity: list
sld: list
sampleChi: np.ndarray
@dataclass
class ConfidenceIntervals(RATResult):
"""The 65% and 95% confidence intervals for the best fit results.
Parameters
----------
percentile95 : np.ndarray
The 95% confidence intervals for each fit parameter.
percentile65 : np.ndarray
The 65% confidence intervals for each fit parameter.
mean : np.ndarray
The mean values for each fit parameter.
"""
percentile95: np.ndarray
percentile65: np.ndarray
mean: np.ndarray
@dataclass
class DreamParams(RATResult):
"""The parameters used by the inner DREAM algorithm.
Parameters
----------
nParams : float
The number of parameters used by the algorithm.
nChains : float
The number of MCMC chains used by the algorithm.
nGenerations : float
The number of DE generations calculated per iteration.
parallel : bool
Whether the algorithm should run chains in parallel.
CPU : float
The number of processor cores used for parallel chains.
jumpProbability : float
A probability range for the size of jumps when performing subspace sampling.
pUnitGamma : float
The probability that the scaling-down factor of jumps will be ignored
and a larger jump will be taken for one iteration.
nCR : float
The number of crossovers performed each iteration.
delta : float
The number of chain mutation pairs proposed each iteration.
steps : float
The number of MCMC steps to perform between conversion checks.
zeta : float
The ergodicity of the algorithm.
outlier : str
What test should be used to detect outliers.
adaptPCR : bool
Whether the crossover probability for differential evolution should be
adapted by the algorithm as it runs.
thinning : float
The thinning rate of each Markov chain (to reduce memory intensity)
epsilon : float
The cutoff threshold for Approximate Bayesian Computation (if used)
ABC : bool
Whether Approximate Bayesian Computation is used.
IO : bool
Whether the algorithm should perform IO writes of the model in parallel.
storeOutput : bool
Whether output model simulations are performed.
R : np.ndarray
An array where row ``i`` is the list of chains
with which chain ``i`` can mutate.
"""
nParams: float
nChains: float
nGenerations: float
parallel: bool
CPU: float
jumpProbability: float
pUnitGamma: float
nCR: float
delta: float
steps: float
zeta: float
outlier: str
adaptPCR: bool
thinning: float
epsilon: float
ABC: bool
IO: bool
storeOutput: bool
R: np.ndarray
@dataclass
class DreamOutput(RATResult):
"""The diagnostic output information from DREAM.
Parameters
----------
allChains : np.ndarray
An ``nGenerations`` x ``nParams + 2`` x ``nChains`` size array,
where ``chain_k = DreamOutput.allChains[:, :, k]``
is the data of chain ``k`` in the final iteration;
for generation i of the final iteration, ``chain_k[i, j]`` represents:
- the sampled value of parameter ``j`` for ``j in 0:nParams``;
- the associated log-prior for those sampled values for ``j = nParams + 1``;
- the associated log-likelihood for those sampled values for ``j = nParams + 2``.
outlierChains : np.ndarray
A two-column array where ``DreamOutput.AR[i, 1]`` is the index of a chain
and ``DreamOutput.AR[i, 0]`` is the length of that chain when it was removed
for being an outlier.
runtime : float
The runtime of the DREAM algorithm in seconds.
iteration : float
The number of iterations performed.
AR : np.ndarray
A two-column array where ``DreamOutput.AR[i, 0]`` is an iteration number
and ``DreamOutput.AR[i, 1]`` is the average acceptance rate of chain step
proposals for that iteration.
R_stat : np.ndarray
An array where ``DreamOutput.R_stat[i, 0]`` is an iteration number and
``DreamOutput.R_stat[i, j]`` is the convergence statistic for parameter ``j``
at that iteration (where chains are indexed 1 to ``nParams`` inclusive).
CR : np.ndarray
A four-column array where ``DreamOutput.CR[i, 0]`` is an iteration number,
``and DreamOutput.CR[i, j]`` is the selection probability of the ``j``'th crossover
value for that iteration.
"""
allChains: np.ndarray
outlierChains: np.ndarray
runtime: float
iteration: float
AR: np.ndarray
R_stat: np.ndarray
CR: np.ndarray
@dataclass
class NestedSamplerOutput(RATResult):
"""The output information from the Nested Sampler (ns).
Parameters
----------
logZ : float
The natural logarithm of the evidence Z for the parameter values.
logZErr : float
The estimated uncertainty in the final value of logZ.
nestSamples : np.ndarray
``NestedSamplerOutput.nestSamples[i, j]`` represents the values
sampled at iteration ``i``, where this value is:
- the value sampled for parameter ``j``, for ``j`` in ``0:nParams``,
- the minimum log-likelihood for ``j = nParams + 1``.
postSamples : np.ndarray
The posterior values at the points sampled in ``NestedSamplerOutput.nestSamples``.
"""
logZ: float
logZErr: float
nestSamples: np.ndarray
postSamples: np.ndarray
@dataclass
class BayesResults(Results):
"""The results of a Bayesian RAT calculation.
Parameters
----------
predictionIntervals : PredictionIntervals
The prediction intervals.
confidenceIntervals : ConfidenceIntervals
The 65% and 95% confidence intervals for the best fit results.
dreamParams : DreamParams
The parameters used by DREAM, if relevant.
dreamOutput : DreamOutput
The output from DREAM if DREAM was used.
nestedSamplerOutput : NestedSamplerOutput
The output from nested sampling if ns was used.
chain : np.ndarray
The MCMC chains for each parameter.
The ``i``'th column of the array contains the chain for parameter ``fitNames[i]``.
"""
predictionIntervals: PredictionIntervals
confidenceIntervals: ConfidenceIntervals
dreamParams: DreamParams
dreamOutput: DreamOutput
nestedSamplerOutput: NestedSamplerOutput
chain: np.ndarray
def save(self, filepath: Union[str, Path] = "./results.json"):
"""Save the BayesResults object to a JSON file.
Parameters
----------
filepath : str or Path
The path to where the results file will be written.
"""
filepath = Path(filepath).with_suffix(".json")
json_dict = write_core_results_fields(self)
# Take each of the subclasses in a BayesResults instance and switch the numpy arrays to lists
for subclass_name in bayes_results_subclasses:
subclass = getattr(self, subclass_name)
subclass_dict = {}
for field in bayes_results_fields["param_fields"][subclass_name]:
subclass_dict[field] = getattr(subclass, field)
for field in bayes_results_fields["list_fields"][subclass_name]:
subclass_dict[field] = [result_array.tolist() for result_array in getattr(subclass, field)]
for field in bayes_results_fields["double_list_fields"][subclass_name]:
subclass_dict[field] = [
[result_array.tolist() for result_array in inner_list] for inner_list in getattr(subclass, field)
]
for field in bayes_results_fields["array_fields"][subclass_name]:
subclass_dict[field] = getattr(subclass, field).tolist()
json_dict[subclass_name] = subclass_dict
json_dict["chain"] = self.chain.tolist()
filepath.write_text(json.dumps(json_dict))
def write_core_results_fields(results: Union[Results, BayesResults], json_dict: Optional[dict] = None) -> dict:
"""Modify the values of the fields that appear in both Results and BayesResults when saving to a json file.
Parameters
----------
results: Union[Results, BayesResults]
The results or BayesResults object we are writing to json.
json_dict: Optional[dict]
The dictionary containing the json output.
Returns
-------
json_dict: dict
The output json dict updated with the fields that appear in both Results and BayesResults.
"""
if json_dict is None:
json_dict = {}
for field in results_fields["list_fields"]:
json_dict[field] = [result_array.tolist() for result_array in getattr(results, field)]
for field in results_fields["double_list_fields"]:
json_dict[field] = [
[result_array.tolist() for result_array in inner_list] for inner_list in getattr(results, field)
]
json_dict["calculationResults"] = {}
json_dict["calculationResults"]["chiValues"] = results.calculationResults.chiValues.tolist()
json_dict["calculationResults"]["sumChi"] = results.calculationResults.sumChi
json_dict["contrastParams"] = {}
for field in results.contrastParams.__dict__:
json_dict["contrastParams"][field] = getattr(results.contrastParams, field).tolist()
json_dict["fitParams"] = results.fitParams.tolist()
json_dict["fitNames"] = results.fitNames
return json_dict
def read_core_results_fields(results_dict: dict) -> dict:
"""Modify the values of the fields that appear in both Results and BayesResults when loading a json file.
Parameters
----------
results_dict: Optional[dict]
The dictionary containing the json input.
Returns
-------
results_dict: dict
The input json dict with the fields that appear in both Results and BayesResults converted to numpy arrays
where necessary.
"""
for field in results_fields["list_fields"]:
results_dict[field] = [np.array(result_array) for result_array in results_dict[field]]
for field in results_fields["double_list_fields"]:
results_dict[field] = [
[np.array(result_array) for result_array in inner_list] for inner_list in results_dict[field]
]
results_dict["calculationResults"]["chiValues"] = np.array(results_dict["calculationResults"]["chiValues"])
for field in results_dict["contrastParams"]:
results_dict["contrastParams"][field] = np.array(results_dict["contrastParams"][field])
return results_dict
def read_bayes_results_fields(results_dict: dict) -> dict:
"""Modify the values of the fields that appear only in BayesResults when loading a json file.
Parameters
----------
results_dict: Optional[dict]
The dictionary containing the json input.
Returns
-------
results_dict: dict
The input json dict with the fields that appear in both Results and BayesResults converted to numpy arrays
where necessary.
"""
for subclass_name in bayes_results_subclasses:
subclass_dict = {}
for field in bayes_results_fields["param_fields"][subclass_name]:
subclass_dict[field] = results_dict[subclass_name][field]
for field in bayes_results_fields["list_fields"][subclass_name]:
subclass_dict[field] = [np.array(result_array) for result_array in results_dict[subclass_name][field]]
for field in bayes_results_fields["double_list_fields"][subclass_name]:
subclass_dict[field] = [
[np.array(result_array) for result_array in inner_list]
for inner_list in results_dict[subclass_name][field]
]
for field in bayes_results_fields["array_fields"][subclass_name]:
subclass_dict[field] = np.array(results_dict[subclass_name][field])
results_dict[subclass_name] = subclass_dict
return results_dict
def make_results(
procedure: Procedures,
output_results: ratapi.rat_core.OutputResult,
bayes_results: Optional[ratapi.rat_core.OutputBayesResult] = None,
) -> Union[Results, BayesResults]:
"""Initialise a python Results or BayesResults object using the outputs from a RAT calculation.
Parameters
----------
procedure : Procedures
The procedure used by the calculation.
output_results : ratapi.rat_core.OutputResult
The C++ output results from the calculation.
bayes_results : Optional[ratapi.rat_core.OutputBayesResult]
The optional extra C++ Bayesian output results from a Bayesian calculation.
Returns
-------
Results or BayesResults
A result object containing the results of the calculation, of type
Results for non-Bayesian procedures and BayesResults for Bayesian procedures.
"""
calculation_results = CalculationResults(
chiValues=output_results.calculationResults.chiValues,
sumChi=output_results.calculationResults.sumChi,
)
contrast_params = ContrastParams(
scalefactors=output_results.contrastParams.scalefactors,
bulkIn=output_results.contrastParams.bulkIn,
bulkOut=output_results.contrastParams.bulkOut,
subRoughs=output_results.contrastParams.subRoughs,
resample=output_results.contrastParams.resample,
)
if procedure in [Procedures.NS, Procedures.DREAM]:
prediction_intervals = PredictionIntervals(
reflectivity=bayes_results.predictionIntervals.reflectivity,
sld=bayes_results.predictionIntervals.sld,
sampleChi=bayes_results.predictionIntervals.sampleChi,
)
confidence_intervals = ConfidenceIntervals(
percentile95=bayes_results.confidenceIntervals.percentile95,
percentile65=bayes_results.confidenceIntervals.percentile65,
mean=bayes_results.confidenceIntervals.mean,
)
dream_params = DreamParams(
nParams=bayes_results.dreamParams.nParams,
nChains=bayes_results.dreamParams.nChains,
nGenerations=bayes_results.dreamParams.nGenerations,
parallel=bool(bayes_results.dreamParams.parallel),
CPU=bayes_results.dreamParams.CPU,
jumpProbability=bayes_results.dreamParams.jumpProbability,
pUnitGamma=bayes_results.dreamParams.pUnitGamma,
nCR=bayes_results.dreamParams.nCR,
delta=bayes_results.dreamParams.delta,
steps=bayes_results.dreamParams.steps,
zeta=bayes_results.dreamParams.zeta,
outlier=bayes_results.dreamParams.outlier,
adaptPCR=bool(bayes_results.dreamParams.adaptPCR),
thinning=bayes_results.dreamParams.thinning,
epsilon=bayes_results.dreamParams.epsilon,
ABC=bool(bayes_results.dreamParams.ABC),
IO=bool(bayes_results.dreamParams.IO),
storeOutput=bool(bayes_results.dreamParams.storeOutput),
R=bayes_results.dreamParams.R,
)
dream_output = DreamOutput(
allChains=bayes_results.dreamOutput.allChains,
outlierChains=bayes_results.dreamOutput.outlierChains,
runtime=bayes_results.dreamOutput.runtime,
iteration=bayes_results.dreamOutput.iteration,
AR=bayes_results.dreamOutput.AR,
R_stat=bayes_results.dreamOutput.R_stat,
CR=bayes_results.dreamOutput.CR,
)
nested_sampler_output = NestedSamplerOutput(
logZ=bayes_results.nestedSamplerOutput.logZ,
logZErr=bayes_results.nestedSamplerOutput.logZErr,
nestSamples=bayes_results.nestedSamplerOutput.nestSamples,
postSamples=bayes_results.nestedSamplerOutput.postSamples,
)
results = BayesResults(
reflectivity=output_results.reflectivity,
simulation=output_results.simulation,
shiftedData=output_results.shiftedData,
backgrounds=output_results.backgrounds,
resolutions=output_results.resolutions,
sldProfiles=output_results.sldProfiles,
layers=output_results.layers,
resampledLayers=output_results.resampledLayers,
calculationResults=calculation_results,
contrastParams=contrast_params,
fitParams=output_results.fitParams,
fitNames=output_results.fitNames,
predictionIntervals=prediction_intervals,
confidenceIntervals=confidence_intervals,
dreamParams=dream_params,
dreamOutput=dream_output,
nestedSamplerOutput=nested_sampler_output,
chain=bayes_results.chain,
)
else:
results = Results(
reflectivity=output_results.reflectivity,
simulation=output_results.simulation,
shiftedData=output_results.shiftedData,
backgrounds=output_results.backgrounds,
resolutions=output_results.resolutions,
sldProfiles=output_results.sldProfiles,
layers=output_results.layers,
resampledLayers=output_results.resampledLayers,
calculationResults=calculation_results,
contrastParams=contrast_params,
fitParams=output_results.fitParams,
fitNames=output_results.fitNames,
)
return results
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