# -*- coding: utf-8 -*-
import json
import numpy as np
import pandas as pd
import yaml
from rich.progress import track
from .coefficients import CoefficientManager
from .compute_theory import make_predictions
from .loader import load_datasets
[docs]
class FitManager:
"""
Class to collect all the fit information,
load the results, compute best theory predictions.
Attributes
----------
path: pathlib.Path
path to fit location
name: srt
fit name
label: str, optional
fit label if any otherwise guess it from the name
config: dict
configuration dictionary
has_posterior: bool
True if the fi contains the full posterrio distribution,
False if only cl bounds are stored (external fits for benchmark)
results: pandas.DataFrame
fit results, they need to be loaded by `load_results`
Parameters
----------
path: pathlib.Path
path to fit location
name: srt
fit name
label: str, optional
fit label if any otherwise guess it from the name
"""
def __init__(self, path, name, label=None):
self.path = path
self.name = name
self.label = (
r"${\rm %s}$" % name.replace("_", r"\ ") if label is None else label
)
# load the configuration file
self.config = self.load_configuration()
self.has_posterior = self.config.get("has_posterior", True)
self.results = None
self.datasets = None
def __repr__(self):
return self.name
def __eq__(self, comapre_name):
return self.name == comapre_name
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def load_results(self):
"""
Load posterior distribution of a fit.
If the fit is produced by and external source it loads
the results. Results are stored in a class attribute
"""
file = "results"
if self.has_posterior:
file = "fit_results"
with open(f"{self.path}/{self.name}/{file}.json", encoding="utf-8") as f:
results = json.load(f)
# if the posterior is from single parameter fits
# then each distribution might have a different number of samples
is_single_param = results.get("single_parameter_fits", False)
if is_single_param:
del results["single_parameter_fits"]
num_samples = []
for key in results["samples"].keys():
num_samples.append(len(results["samples"][key]))
num_samples_min = min(num_samples)
for key in results["samples"].keys():
results["samples"][key] = np.random.choice(
results["samples"][key], num_samples_min, replace=False
)
# TODO: support pariwise posteriors
# Be sure columns are sorted, otherwise can't compute theory...
results["samples"] = pd.DataFrame(results["samples"]).sort_index(axis=1)
results["best_fit_point"] = pd.DataFrame(
[results["best_fit_point"]]
).sort_index(axis=1)
self.results = results
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def load_configuration(self):
"""Load configuration yaml card.
Returns
-------
dict
configuration card
"""
with open(f"{self.path}/{self.name}/{self.name}.yaml", encoding="utf-8") as f:
config = yaml.safe_load(f)
return config
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def load_datasets(self):
"""Load all datasets."""
self.datasets = load_datasets(
self.config["data_path"],
self.config["datasets"],
self.config["coefficients"],
self.config["order"],
self.config["use_quad"],
self.config["use_theory_covmat"],
False, # t0 is not used here because in the report we look at the experimental chi2
self.config.get("use_multiplicative_prescription", False),
self.config.get("theory_path", None),
self.config.get("rot_to_fit_basis", None),
self.config.get("uv_couplings", False),
self.config.get("external_chi2", False),
)
@property
def smeft_predictions(self):
"""Compute |SMEFT| predictions for each replica.
Returns
-------
np.ndarray:
|SMEFT| predictions for each replica
"""
smeft = []
for rep in track(
range(self.n_replica),
description=f"[green]Computing SMEFT predictions for each replica of {self.name}...",
):
smeft.append(
make_predictions(
self.datasets,
self.results["samples"].iloc[rep, :],
self.config["use_quad"],
self.config.get("use_multiplicative_prescription", False),
)
)
return np.array(smeft)
@property
def smeft_predictions_best_fit(self):
"""Compute |SMEFT| predictions for the best fit point.
Returns
-------
np.ndarray:
|SMEFT| predictions for the best fit
"""
predictions = make_predictions(
self.datasets,
self.results["best_fit_point"].iloc[0, :],
self.config["use_quad"],
self.config.get("use_multiplicative_prescription", False),
)
# Add a dimension to match the shape of the replica predictions
return np.array([predictions])
@property
def coefficients(self):
"""coefficient manager"""
return CoefficientManager.from_dict(self.config["coefficients"])
@property
def n_replica(self):
"""Number of replicas"""
return self.results["samples"].shape[0]