diff --git a/models/dayabay_v0.py b/models/dayabay_v0.py
index 24486295e2a10763f6ca3a213900d93afaf17355..84493f11cace4e1d56962c14216e34a7aca022ea 100644
--- a/models/dayabay_v0.py
+++ b/models/dayabay_v0.py
@@ -138,7 +138,8 @@ def model_dayabay_v0():
with Graph(close=True) as g:
storage ^= load_parameters({'path': 'ibd' , 'load': datasource/'parameters/pdg2012.yaml'})
storage ^= load_parameters({'path': 'ibd.csc' , 'load': datasource/'parameters/ibd_constants.yaml'})
- storage ^= load_parameters({'path': 'conversion' , 'load': datasource/'parameters/conversion_thermal_power.py'})
+ storage ^= load_parameters({'path': 'conversion' , 'load': datasource/'parameters/conversion_thermal_power.yaml'})
+ storage ^= load_parameters({'path': 'conversion' , 'load': datasource/'parameters/conversion_oscprob_argument.yaml'})
storage ^= load_parameters({ 'load': datasource/'parameters/baselines.yaml'})
diff --git a/models/dayabay_v1.py b/models/dayabay_v1.py
index fa706ad9cb4f2d8e70d5b8a4ae2a54de353b34da..266db4ff568d8115eb861b58eea7b1e5740e812a 100644
--- a/models/dayabay_v1.py
+++ b/models/dayabay_v1.py
@@ -1,10 +1,182 @@
from dagflow.bundles.load_parameters import load_parameters
+from multikeydict.nestedmkdict import NestedMKDict
+from multikeydict.visitor import NestedMKDictVisitor
from pathlib import Path
+from tabulate import tabulate
-from pprint import pprint
+from typing import Union, Tuple, List, Optional
+import pandas as pd
+from pandas import DataFrame
+pd.set_option('display.max_rows', None)
+pd.set_option('display.max_colwidth', 100)
+
+from dagflow.graph import Graph
+from dagflow.graphviz import savegraph
+from dagflow.lib.Sum import Sum
+
+from gindex import GNIndex
+
+class ParametersVisitor(NestedMKDictVisitor):
+ __slots__ = ('_kwargs', '_data', '_localdata', '_path')
+ _kwargs: dict
+ _data: List[dict]
+ _localdata: List[dict]
+ _paths: List[Tuple[str, ...]]
+ _path: Tuple[str, ...]
+ # _npars: List[int]
+
+ def __init__(self, kwargs: dict):
+ self._kwargs = kwargs
+ # self._npars = []
+
+ @property
+ def data(self):
+ return self._data
+
+ def start(self, dct):
+ self._data = []
+ self._path = ()
+ self._paths = []
+ self._localdata = []
+
+ def enterdict(self, k, v):
+ if self._localdata:
+ self.exitdict(self._path, None)
+ self._path = k
+ self._paths.append(self._path)
+ self._localdata = []
+
+ def visit(self, key, value):
+ try:
+ dct = value.to_dict(**self._kwargs)
+ except AttributeError:
+ return
+
+ subkey = key[len(self._path):]
+ subkeystr = '.'.join(subkey)
+
+ if self._path:
+ dct['path'] = f'.. {subkeystr}'
+ else:
+ dct['path'] = subkeystr
+
+ self._localdata.append(dct)
+
+ def exitdict(self, k, v):
+ if self._localdata:
+ self._data.append({
+ 'path': f"group: {'.'.join(self._path)} [{len(self._localdata)}]"
+ })
+ self._data.extend(self._localdata)
+ self._localdata = []
+ if self._paths:
+ del self._paths[-1]
+
+ self._path = self._paths[-1] if self._paths else ()
+
+ def stop(self, dct):
+ pass
+
+class ParametersWrapper(NestedMKDict):
+ def to_dict(self, **kwargs) -> list:
+ return self.visit(ParametersVisitor(kwargs)).data
+
+ def to_df(self, *, columns: Optional[List[str]]=None, **kwargs) -> DataFrame:
+ dct = self.to_dict(**kwargs)
+ if columns is None:
+ columns = ['path', 'value', 'central', 'sigma', 'label']
+ df = DataFrame(dct, columns=columns)
+ for key in ('central', 'sigma'):
+ if df[key].isna().all():
+ del df[key]
+ else:
+ df[key].fillna('-', inplace=True)
+
+ df['value'].fillna('-', inplace=True)
+ df['label'].fillna('', inplace=True)
+ return df
+
+ def to_string(self, **kwargs) -> str:
+ df = self.to_df()
+ return df.to_string(**kwargs)
+
+ def to_table(self, *, df_kwargs: dict={}, **kwargs) -> str:
+ df = self.to_df(**df_kwargs)
+ kwargs.setdefault('headers', df.columns)
+ ret = tabulate(df, **kwargs)
+
+ return ret
+
+ def to_latex(self, *, return_df: bool=False, **kwargs) -> Union[str, Tuple[str, DataFrame]]:
+ df = self.to_df(label_from='latex', **kwargs)
+ tex = df.to_latex(escape=False)
+
+ if return_df:
+ return tex, df
+
+ return tex
def model_dayabay_v1():
- datasource = Path('data/dayabay-v1')
- vars = load_parameters({'path': 'ibd', 'load': datasource/'parameters/pdg2020.yaml'})
+ storage = ParametersWrapper({}, sep='.')
+ datasource = Path('data/dayabay-v1')
+
+ index = GNIndex.from_dict({
+ ('s', 'site'): ('EH1', 'EH2', 'EH3'),
+ ('d', 'detector'): ('AD11', 'AD12', 'AD21', 'AD22', 'AD31', 'AD32', 'AD33', 'AD34'),
+ ('p', 'period'): ('6AD', '8AD', '7AD'),
+ ('r', 'reactor'): ('DB1', 'DB2', 'LA1', 'LA2', 'LA3', 'LA4'),
+ ('i', 'isotope'): ('U235', 'U238', 'Pu239', 'Pu241'),
+ ('b', 'background'): ('acc', 'lihe', 'fastn', 'amc', 'alphan'),
+ })
+ idx_r= index.sub('r')
+ idx_rd= index.sub(('r', 'd'))
+ idx_ri= index.sub(('r', 'i'))
+ list_reactors = idx_r.values
+ list_dr = idx_rd.values
+ list_reactors_isotopes = idx_ri.values
+
+ with Graph(close=True) as g:
+ storage ^= load_parameters({'path': 'ibd' , 'load': datasource/'parameters/pdg2020.yaml'})
+ storage ^= load_parameters({'path': 'ibd.csc' , 'load': datasource/'parameters/ibd_constants.yaml'})
+ storage ^= load_parameters({'path': 'conversion' , 'load': datasource/'parameters/conversion_thermal_power.py'})
+ storage ^= load_parameters({'path': 'conversion' , 'load': datasource/'parameters/conversion_oscprob_argument.py'})
+
+ storage ^= load_parameters({ 'load': datasource/'parameters/baselines.yaml'})
+
+ storage ^= load_parameters({'path': 'detector' , 'load': datasource/'parameters/detector_nprotons_correction.yaml'})
+ storage ^= load_parameters({'path': 'detector' , 'load': datasource/'parameters/detector_eres.yaml'})
+
+ storage ^= load_parameters({'path': 'reactor' , 'load': datasource/'parameters/reactor_thermal_power_nominal.yaml' , 'replicate': list_reactors })
+ storage ^= load_parameters({'path': 'reactor' , 'load': datasource/'parameters/offequilibrium_correction.yaml' , 'replicate': list_reactors_isotopes })
+
+ nuisanceall = Sum('nuisance total')
+ storage['stat.nuisance.all'] = nuisanceall
+
+ (output for output in storage['stat.nuisance_parts'].walkvalues()) >> nuisanceall
+
+ storage['parameter.normalized.detector.eres.b_stat'].value = 1
+ storage['parameter.normalized.detector.eres.a_nonuniform'].value = 2
+
+ print('Everything')
+ print(storage.to_table())
+
+ print('Constants')
+ print(storage['parameter.constant'].to_table())
+
+ print('Constrained')
+ print(storage['parameter.constrained'].to_table())
+
+ print('Normalized')
+ print(storage['parameter.normalized'].to_table())
+
+ print('Stat')
+ print(storage['stat'].to_table())
+
+ # print('Parameters (latex)')
+ # print(storage['parameter'].to_latex())
+ #
+ # print('Constants (latex)')
+ # tex = storage['parameter.constant'].to_latex(columns=['path', 'value', 'label'])
+ # print(tex)
- pprint(vars.object)
+ savegraph(g, "output/dayabay_v0.dot", show='all')