Merge branch 'master' into feat/workspace-poiless

Author: matthewfeickert

docs/examples/notebooks/binderexample/StatisticalAnalysis.ipynb

@@ -148,8 +148,8 @@
     }
    ],
    "source": [
-    "print(f'Samples:\\n {workspace.samples}')\n",
-    "print(f'Parameters:\\n {workspace.parameters}')"
+    "print(f'Samples:\\n {pdf.config.samples}')\n",
+    "print(f'Parameters:\\n {pdf.config.parameters}')"
    ]
   },
   {

src/pyhf/cli/spec.py

@@ -6,6 +6,7 @@
 
 from pyhf.workspace import Workspace
 from pyhf import modifiers
+from pyhf import parameters
 from pyhf import utils
 
 log = logging.getLogger(__name__)
@@ -72,14 +73,16 @@ def inspect(workspace, output_file, measurement):
     ]
     result['modifiers'] = dict(ws.modifiers)
 
+    parset_descr = {
+        parameters.paramsets.unconstrained: 'unconstrained',
+        parameters.paramsets.constrained_by_normal: 'constrained_by_normal',
+        parameters.paramsets.constrained_by_poisson: 'constrained_by_poisson',
+    }
+
+    model = ws.model()
+
     result['parameters'] = sorted(
-        (
-            parname,
-            modifiers.registry[result['modifiers'][parname]]
-            .required_parset([], [])['paramset_type']
-            .__name__,
-        )
-        for parname in ws.parameters
+        (k, parset_descr[type(v['paramset'])]) for k, v in model.config.par_map.items()
     )
     result['systematics'] = [
         (
@@ -97,7 +100,7 @@ def inspect(workspace, output_file, measurement):
 
     maxlen_channels = max(map(len, ws.channels))
     maxlen_samples = max(map(len, ws.samples))
-    maxlen_parameters = max(map(len, ws.parameters))
+    maxlen_parameters = max(map(len, [p for p, _ in result['parameters']]))
     maxlen_measurements = max(map(lambda x: len(x[0]), result['measurements']))
     maxlen = max(
         [maxlen_channels, maxlen_samples, maxlen_parameters, maxlen_measurements]
@@ -174,7 +177,7 @@ def inspect(workspace, output_file, measurement):
     '--modifier-type',
     default=[],
     multiple=True,
-    type=click.Choice(modifiers.uncombined.keys()),
+    type=click.Choice(modifiers.histfactory_set.keys()),
 )
 @click.option('--measurement', default=[], multiple=True, metavar='<MEASUREMENT>...')
 def prune(

src/pyhf/infer/calculators.py

@@ -628,7 +628,7 @@ def expected_value(self, nsigma):
             >>> samples = normal.sample((100,))
             >>> dist = pyhf.infer.calculators.EmpiricalDistribution(samples)
             >>> dist.expected_value(nsigma=1)
-            6.15094381209505
+            6.15094381209...
 
             >>> import pyhf
             >>> import numpy.random as random

src/pyhf/mixins.py

@@ -18,7 +18,6 @@ def __init__(self, *args, **kwargs):
         super().__init__(*args, **kwargs)
         self.channels = []
         self.samples = []
-        self.parameters = []
         self.modifiers = []
         # keep track of the width of each channel (how many bins)
         self.channel_nbins = {}
@@ -30,7 +29,6 @@ def __init__(self, *args, **kwargs):
             for sample in channel['samples']:
                 self.samples.append(sample['name'])
                 for modifier_def in sample['modifiers']:
-                    self.parameters.append(modifier_def['name'])
                     self.modifiers.append(
                         (
                             modifier_def['name'],  # mod name
@@ -40,7 +38,6 @@ def __init__(self, *args, **kwargs):
 
         self.channels = sorted(list(set(self.channels)))
         self.samples = sorted(list(set(self.samples)))
-        self.parameters = sorted(list(set(self.parameters)))
         self.modifiers = sorted(list(set(self.modifiers)))
         self.channel_nbins = {
             channel: self.channel_nbins[channel] for channel in self.channels

src/pyhf/modifiers/__init__.py

@@ -1,214 +1,47 @@
-import logging
-
-from pyhf import exceptions
-from pyhf import get_backend
-
-log = logging.getLogger(__name__)
-
-registry = {}
-
-
-def validate_modifier_structure(modifier):
-    """
-    Check if given object contains the right structure for modifiers
-    """
-    required_methods = ['required_parset']
-
-    for method in required_methods:
-        if not hasattr(modifier, method):
-            raise exceptions.InvalidModifier(
-                f'Expected {method:s} method on modifier {modifier.__name__:s}'
-            )
-    return True
-
-
-def add_to_registry(
-    cls, cls_name=None, constrained=False, pdf_type='normal', op_code='addition'
-):
-    """
-    Consistent add_to_registry() function that handles actually adding thing to the registry.
-
-    Raises an error if the name to register for the modifier already exists in the registry,
-    or if the modifier does not have the right structure.
-    """
-    global registry
-    cls_name = cls_name or cls.__name__
-    if cls_name in registry:
-        raise KeyError(f'The modifier name "{cls_name:s}" is already taken.')
-    # validate the structure
-    validate_modifier_structure(cls)
-    # set is_constrained
-    cls.is_constrained = constrained
-    if constrained:
-        tensorlib, _ = get_backend()
-        if not hasattr(tensorlib, pdf_type):
-            raise exceptions.InvalidModifier(
-                f'The specified pdf_type "{pdf_type:s}" is not valid for {cls_name:s}({cls.__name__:s}). See pyhf.tensor documentation for available pdfs.'
-            )
-        cls.pdf_type = pdf_type
-    else:
-        cls.pdf_type = None
-
-    if op_code not in ['addition', 'multiplication']:
-        raise exceptions.InvalidModifier(
-            f'The specified op_code "{op_code:s}" is not valid for {cls_name:s}({cls.__name__:s}). See pyhf.modifier documentation for available operation codes.'
-        )
-    cls.op_code = op_code
-
-    registry[cls_name] = cls
-
-
-def modifier(*args, **kwargs):
-    """
-    Decorator for registering modifiers. To flag the modifier as a constrained modifier, add `constrained=True`.
-
-
-    Args:
-        name (:obj:`str`): the name of the modifier to use. Use the class name by default. (default: None)
-        constrained (:obj:`bool`): whether the modifier is constrained or not. (default: False)
-        pdf_type (:obj:`str): the name of the pdf to use from tensorlib if constrained. (default: normal)
-        op_code (:obj:`str`): the name of the operation the modifier performs on the data (e.g. addition, multiplication)
-
-    Returns:
-        modifier
-
-    Raises:
-        ValueError: too many keyword arguments, or too many arguments, or wrong arguments
-        TypeError: provided name is not a string
-        pyhf.exceptions.InvalidModifier: object does not have necessary modifier structure
-    """
-    #
-    # Examples:
-    #
-    #   >>> @modifiers.modifier
-    #   >>> ... class myCustomModifier(object):
-    #   >>> ...   @classmethod
-    #   >>> ...   def required_parset(cls, sample_data, modifier_data): pass
-    #
-    #   >>> @modifiers.modifier(name='myCustomNamer')
-    #   >>> ... class myCustomModifier(object):
-    #   >>> ...   @classmethod
-    #   >>> ...   def required_parset(cls, sample_data, modifier_data): pass
-    #
-    #   >>> @modifiers.modifier(constrained=False)
-    #   >>> ... class myUnconstrainedModifier(object):
-    #   >>> ...   @classmethod
-    #   >>> ...   def required_parset(cls, sample_data, modifier_data): pass
-    #   >>> ...
-    #   >>> myUnconstrainedModifier.pdf_type
-    #   None
-    #
-    #   >>> @modifiers.modifier(constrained=True, pdf_type='poisson')
-    #   >>> ... class myConstrainedCustomPoissonModifier(object):
-    #   >>> ...   @classmethod
-    #   >>> ...   def required_parset(cls, sample_data, modifier_data): pass
-    #   >>> ...
-    #   >>> myConstrainedCustomGaussianModifier.pdf_type
-    #   'poisson'
-    #
-    #   >>> @modifiers.modifier(constrained=True)
-    #   >>> ... class myCustomModifier(object):
-    #   >>> ...   @classmethod
-    #   >>> ...   def required_parset(cls, sample_data, modifier_data): pass
-    #
-    #   >>> @modifiers.modifier(op_code='multiplication')
-    #   >>> ... class myMultiplierModifier(object):
-    #   >>> ...   @classmethod
-    #   >>> ...   def required_parset(cls, sample_data, modifier_data): pass
-    #   >>> ...
-    #   >>> myMultiplierModifier.op_code
-    #   'multiplication'
-
-    def _modifier(name, constrained, pdf_type, op_code):
-        def wrapper(cls):
-            add_to_registry(
-                cls,
-                cls_name=name,
-                constrained=constrained,
-                pdf_type=pdf_type,
-                op_code=op_code,
-            )
-            return cls
-
-        return wrapper
-
-    name = kwargs.pop('name', None)
-    constrained = bool(kwargs.pop('constrained', False))
-    pdf_type = str(kwargs.pop('pdf_type', 'normal'))
-    op_code = str(kwargs.pop('op_code', 'addition'))
-    # check for unparsed keyword arguments
-    if kwargs:
-        raise ValueError(f'Unparsed keyword arguments {kwargs.keys()}')
-    # check to make sure the given name is a string, if passed in one
-    if not isinstance(name, str) and name is not None:
-        raise TypeError(f'@modifier must be given a string. You gave it {type(name)}')
-
-    if not args:
-        # called like @modifier(name='foo', constrained=False, pdf_type='normal', op_code='addition')
-        return _modifier(name, constrained, pdf_type, op_code)
-    if len(args) == 1:
-        # called like @modifier
-        if not callable(args[0]):
-            raise ValueError('You must decorate a callable python object')
-        add_to_registry(
-            args[0],
-            cls_name=name,
-            constrained=constrained,
-            pdf_type=pdf_type,
-            op_code=op_code,
-        )
-        return args[0]
-    raise ValueError(
-        f'@modifier must be called with only keyword arguments, @modifier(name=\'foo\'), or no arguments, @modifier; ({len(args):d} given)'
-    )
-
-
-from pyhf.modifiers.histosys import histosys, histosys_combined
-from pyhf.modifiers.lumi import lumi, lumi_combined
-from pyhf.modifiers.normfactor import normfactor, normfactor_combined
-from pyhf.modifiers.normsys import normsys, normsys_combined
-from pyhf.modifiers.shapefactor import shapefactor, shapefactor_combined
-from pyhf.modifiers.shapesys import shapesys, shapesys_combined
-from pyhf.modifiers.staterror import staterror, staterror_combined
-
-uncombined = {
-    'histosys': histosys,
-    'lumi': lumi,
-    'normfactor': normfactor,
-    'normsys': normsys,
-    'shapefactor': shapefactor,
-    'shapesys': shapesys,
-    'staterror': staterror,
-}
-
-combined = {
-    'histosys': histosys_combined,
-    'lumi': lumi_combined,
-    'normfactor': normfactor_combined,
-    'normsys': normsys_combined,
-    'shapefactor': shapefactor_combined,
-    'shapesys': shapesys_combined,
-    'staterror': staterror_combined,
-}
+from pyhf.modifiers.histosys import histosys_builder, histosys_combined
+from pyhf.modifiers.lumi import lumi_builder, lumi_combined
+from pyhf.modifiers.normfactor import normfactor_builder, normfactor_combined
+from pyhf.modifiers.normsys import normsys_builder, normsys_combined
+from pyhf.modifiers.shapefactor import shapefactor_builder, shapefactor_combined
+from pyhf.modifiers.shapesys import shapesys_builder, shapesys_combined
+from pyhf.modifiers.staterror import staterror_builder, staterror_combined
 
 __all__ = [
-    "combined",
+    "histfactory_set",
     "histosys",
+    "histosys_builder",
     "histosys_combined",
     "lumi",
+    "lumi_builder",
     "lumi_combined",
     "normfactor",
+    "normfactor_builder",
     "normfactor_combined",
     "normsys",
+    "normsys_builder",
     "normsys_combined",
     "shapefactor",
+    "shapefactor_builder",
     "shapefactor_combined",
     "shapesys",
+    "shapesys_builder",
     "shapesys_combined",
     "staterror",
+    "staterror_builder",
     "staterror_combined",
 ]
 
 
 def __dir__():
     return __all__
+
+
+histfactory_set = {
+    "histosys": (histosys_builder, histosys_combined),
+    "lumi": (lumi_builder, lumi_combined),
+    "normfactor": (normfactor_builder, normfactor_combined),
+    "normsys": (normsys_builder, normsys_combined),
+    "shapefactor": (shapefactor_builder, shapefactor_combined),
+    "shapesys": (shapesys_builder, shapesys_combined),
+    "staterror": (staterror_builder, staterror_combined),
+}