[pre-commit.ci] auto fixes from pre-commit.com hooks

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Author: pre-commit-ci[bot]

alea/examples/gaussian_model.py

@@ -7,9 +7,9 @@
 
 
 class GaussianModel(StatisticalModel):
-    """A model of a gaussian measurement, where the model has parameters mu and sigma. For
-    illustration, we show how required nominal parameters can be added to the init sigma is fixed in
-    this example.
+    """A model of a gaussian measurement, where the model has parameters mu and sigma. illustration,
+    we show how required nominal parameters can be added to the init sigma is fixed in For this
+    example.
 
     Args:
         parameter_definition (dict or list, optional (default=None)):

alea/model.py

@@ -156,8 +156,8 @@ def _generate_data(self, **kwargs):
 
     @_needs_data
     def ll(self, **kwargs) -> float:
-        """Likelihod function, returns the loglikelihood for the given parameters. The parameters
-        are passed as keyword arguments, positional arguments are not possible. If a parameter is
+        """Likelihod function, returns the loglikelihood for the given parameters. are passed as
+        keyword arguments, positional arguments are not possible. If a parameter is The parameters
         not given, the default value is used.
 
         Keyword Args:
@@ -171,8 +171,8 @@ def ll(self, **kwargs) -> float:
         return self._ll(**parameters)
 
     def generate_data(self, **kwargs) -> Union[dict, list]:
-        """Generate data for the given parameters. The parameters are passed as keyword arguments,
-        positional arguments are not possible. If a parameter is not given, the default value is
+        """Generate data for the given parameters. positional arguments are not possible. If a
+        parameter is not given, the default value is The parameters are passed as keyword arguments,
         used.
 
         Raises:
@@ -324,8 +324,8 @@ def cost(args):
     def fit(
         self, verbose: Optional[bool] = False, fit_strategy: Optional[dict] = None, **kwargs
     ) -> Tuple[dict, float]:
-        """Fit the model to the data by maximizing the likelihood. Return a dict containing best-fit
-        values of each parameter, and the value of the likelihood evaluated there. While the
+        """Fit the model to the data by maximizing the likelihood. values of each parameter, and the
+        value of the likelihood evaluated there. While the Return a dict containing best-fit
         optimization is a minimization, the likelihood returned is the __maximum__ of the
         likelihood.
 
@@ -565,9 +565,9 @@ def confidence_interval(
         asymptotic_dof: Optional[int] = None,
         fit_strategy: Optional[dict] = None,
     ) -> Tuple[float, float]:
-        """Uses self.fit to compute confidence intervals for a certain named parameter. If the
-        parameter is a rate parameter, and the model has expectation values implemented, the bounds
-        will be interpreted as bounds on the expectation value, so that the range in the fit is
+        """Uses self.fit to compute confidence intervals for a certain named parameter. parameter is
+        a rate parameter, and the model has expectation values implemented, the bounds If the will
+        be interpreted as bounds on the expectation value, so that the range in the fit is
         parameter_interval_bounds/mus. Otherwise the bound is taken as-is.
 
         Args:
@@ -680,8 +680,8 @@ def get_model_from_name(statistical_model: str):
 
 
 class MinuitWrap:
-    """Wrapper for functions to be called by Minuit. Initialized with a function f and a Parameters
-    instance.
+    """Wrapper for functions to be called by Minuit. instance. Initialized with a function f and a
+    Parameters.
 
     Attributes:
         func: function wrapped

alea/models/blueice_extended_model.py

@@ -85,9 +85,9 @@ def data(self) -> Union[dict, list]:
 
     @data.setter
     def data(self, data: Union[dict, list]):
-        """Overrides default setter. Will also set the data of the blueice ll. Data-sets are
-        expected to be in the form of a list of one or more structured arrays representing the data-
-        sets of one or more likelihood terms.
+        """Overrides default setter. expected to be in the form of a list of one or more structured
+        arrays representing the data- Will also set the data of the blueice ll. Data-sets are sets
+        of one or more likelihood terms.
 
         Args:
             data (dict or list): Data of the statistical model.
@@ -127,9 +127,9 @@ def data(self, data: Union[dict, list]):
         self.is_data_set = True
 
     def get_source_name_list(self, likelihood_name: str) -> list:
-        """Return a list of source names for a given likelihood term. The order is the same as used
-        in the ``source`` column of the data, so this can be used to map the indices provided in the
-        data to a source name.
+        """Return a list of source names for a given likelihood term. in the ``source`` column of
+        the data, so this can be used to map the indices provided in the The order is the same as
+        used data to a source name.
 
         Args:
             likelihood_name (str): Name of the likelihood.

alea/parameters.py

@@ -205,11 +205,11 @@ def _check_parameter_consistency(self):
 
 
 class ConditionalParameter:
-    """This class is used to define a parameter that depends on another parameter. It has the same
-    attributes as the Parameter class but each of them can be a dictionary with keys being the
-    values of the conditioning parameter and values being the corresponding values of the
-    conditional parameter. Calling the object with the conditioning parameter value as an argument
-    will return a corresponding Parameter object with the correct values.
+    """This class is used to define a parameter that depends on another parameter. attributes as the
+    Parameter class but each of them can be a dictionary with keys being the It has the same values
+    of the conditioning parameter and values being the corresponding values of the conditional
+    parameter. Calling the object with the conditioning parameter value as an argument will return a
+    corresponding Parameter object with the correct values.
 
     Attributes:
         name (str): The name of the parameter.
@@ -389,8 +389,8 @@ def from_config(cls, config: Dict[str, dict]):
 
     @classmethod
     def from_list(cls, names: List[str]):
-        """Creates a Parameters object from a list of parameter names. Everything else is set to
-        default values.
+        """Creates a Parameters object from a list of parameter names. default values. Everything
+        else is set to.
 
         Args:
             names (List[str]): List of parameter names.

alea/runner.py

@@ -239,11 +239,12 @@ def runner_arguments():
     def update_poi(
         model, poi: str, generate_values: Dict[str, float], nominal_values: Dict[str, float] = {}
     ):
-        """Update the poi according to poi_expectation. First, it will check if poi_expectation is
-        provided, if not so, it will do nothing. Second, it will check if poi is provided, if so, it
-        will raise error. Third, it will check if poi ends with _rate_multiplier, if not so, it will
-        raise error. Finally, it will update poi to the correct value according to poi_expectation
-        using the get_expectation_values method of model, under specified nominal_values.
+        """Update the poi according to poi_expectation. provided, if not so, it will do nothing.
+        Second, it will check if poi is provided, if so, it First, it will check if poi_expectation
+        is will raise error. Third, it will check if poi ends with _rate_multiplier, if not so, it
+        will raise error. Finally, it will update poi to the correct value according to
+        poi_expectation using the get_expectation_values method of model, under specified
+        nominal_values.
 
         Args:
             poi (str): parameter of interest

alea/submitter.py

@@ -22,9 +22,9 @@
 
 
 class Submitter:
-    """Submitter base class that generate the submission script from the configuration. It is
-    initialized by the configuration file, and the configuration file should contain the arguments
-    of __init__ method of the Submitter.
+    """Submitter base class that generate the submission script from the configuration. initialized
+    by the configuration file, and the configuration file should contain the arguments It is of
+    __init__ method of the Submitter.
 
     Attributes:
         statistical_model (str): the name of the statistical model
@@ -323,8 +323,8 @@ def filename_kwargs(self, runner_args: dict) -> dict:
         return needed_kwargs
 
     def computation_tickets_generator(self):
-        """Get the submission script for the current configuration. It generates the submission
-        script for each combination of the computation options.
+        """Get the submission script for the current configuration. script for each combination of
+        the computation options. It generates the submission.
 
         For Runner from to_zip, to_vary and in_common:
             - First, generate the combined computational options directly.
@@ -390,8 +390,8 @@ def already_done(self, i_args: dict) -> bool:
         return is_done
 
     def combined_tickets_generator(self):
-        """Get the combined submission script for the current configuration. ``self.combine_n_jobs``
-        jobs will be combined into one submission script.
+        """Get the combined submission script for the current configuration. jobs will be combined
+        into one submission script. ``self.combine_n_jobs``
 
         Yields:
             (str, str): the combined submission script and name output_filename
@@ -454,8 +454,8 @@ def update_runner_args(
         parameters_fittable: List[str],
         parameters_not_fittable: List[str],
     ):
-        """Update the runner arguments' generate_values and nominal_values. If the argument is
-        fittable, it will be added to generate_values, otherwise it will be added to nominal_values.
+        """Update the runner arguments' generate_values and nominal_values. fittable, it will be
+        added to generate_values, otherwise it will be added to nominal_values. If the argument is.
 
         Args:
             runner_args (dict): the arguments of Runner

alea/submitters/local.py

@@ -108,9 +108,9 @@ def submit(
         true_name: str = "true",
         confidence_levels: List[float] = [0.6827, 0.8, 0.9, 0.95],
     ):
-        """Read the likelihood ratio from the output files and calculate the Neyman threshold. The
+        """Read the likelihood ratio from the output files and calculate the Neyman threshold.
         threshold will be saved into a json file. The threshold will be sorted based on the elements
-        of poi.
+        The of poi.
 
         Args:
             free_name: the name of the free hypothesis
@@ -443,10 +443,10 @@ def get_confidence_interval_thresholds(
         limit_threshold_interpolation,
         asymptotic_dof: Optional[int] = 1,
     ):
-        """Get confidence interval threshold function from limit_threshold file. If the
-        limit_threshold file does not contain the threshold, it will interpolate the threshold from
-        the existing threshold, using the RegularGridInterpolator, so in this case the threshold is
-        not exact.
+        """Get confidence interval threshold function from limit_threshold file. limit_threshold
+        file does not contain the threshold, it will interpolate the threshold from If the the
+        existing threshold, using the RegularGridInterpolator, so in this case the threshold is not
+        exact.
 
         Args:
             poi (str): parameter of interest

alea/submitters/slurm.py

@@ -21,9 +21,9 @@
 
 
 class SubmitterSlurm(Submitter):
-    """Submitter for slurm cluster, using utilix.batchq.submit_job. The default batchq arguments are
-    defined in BATCHQ_DEFAULT_ARGUMENTS. You can also overwrite them by passing them inside
-    configuration file.
+    """Submitter for slurm cluster, using utilix.batchq.submit_job. defined in
+    BATCHQ_DEFAULT_ARGUMENTS. You can also overwrite them by passing them inside The default batchq
+    arguments are configuration file.
 
     Keyword Args:
         slurm_configurations (dict): The configurations for utilix.batchq.submit_job.
@@ -83,8 +83,8 @@ def _check_batchq_arguments(self):
             )
 
     def submit(self, **kwargs):
-        """Submits job to batch queue which actually runs the analysis. Overwrite the
-        BATCHQ_DEFAULT_ARGUMENTS by configuration file. If debug is True, only submit the first job.
+        """Submits job to batch queue which actually runs the analysis. BATCHQ_DEFAULT_ARGUMENTS by
+        configuration file. If debug is True, only submit the first job. Overwrite the.
 
         Keyword Args:
             jobname (str): The name of the job.

alea/template_source.py

@@ -14,8 +14,8 @@
 
 
 class TemplateSource(HistogramPdfSource):
-    """A source defined with a template histogram. The parameters are set in self.config.
-    "templatename", "histname", "analysis_space" must be in self.config.
+    """A source defined with a template histogram. "templatename", "histname", "analysis_space" must
+    be in self.config. The parameters are set in self.config.
 
     Attributes:
         config (dict): The configuration of the source.
@@ -273,8 +273,8 @@ def simulate(self, n_events: int):
 
 
 class CombinedSource(TemplateSource):
-    """Source that is a weighted sums of histograms. Useful e.g. for safeguard. The first histogram
-    is the base histogram, and the rest are added to it with weights. The weights can be set as
+    """Source that is a weighted sums of histograms. is the base histogram, and the rest are added
+    to it with weights. The weights can be set as Useful e.g. for safeguard. The first histogram
     shape parameters in the config.
 
     Args:
@@ -364,8 +364,8 @@ def build_histogram(self):
 
 
 class SpectrumTemplateSource(TemplateSource):
-    """Reweighted template source by 1D spectrum. The first axis of the template is assumed to be
-    reweighted.
+    """Reweighted template source by 1D spectrum. reweighted. The first axis of the template is
+    assumed to be.
 
     Args:
         spectrum_name:

alea/utils.py

@@ -456,8 +456,8 @@ def clip_limits(value) -> Tuple[float, float]:
 
 
 def can_assign_to_typing(value_type, target_type) -> bool:
-    """Check if value_type can be assigned to target_type. This is useful when converting Runner's
-    argument into strings.
+    """Check if value_type can be assigned to target_type. argument into strings. This is useful
+    when converting Runner's.
 
     Args:
         value_type: type of the value, might be float, int, etc.
@@ -483,8 +483,8 @@ def add_i_batch(filename: str) -> str:
 
 
 def search_filename_pattern(filename: str) -> str:
-    """Return pattern for a given existing filename. This is needed because sometimes the filename
-    is not appended by "_{i_batch:d}". We need to distinguish between the two cases and return the
+    """Return pattern for a given existing filename. is not appended by "_{i_batch:d}". We need to
+    distinguish between the two cases and return the This is needed because sometimes the filename
     correct pattern.
 
     Returns:
@@ -625,8 +625,8 @@ def convert_to_vary(to_vary: Dict[str, List]) -> List[Dict[str, Any]]:
 
 
 def convert_to_in_common(in_common: Dict[str, Any]) -> Dict[str, Any]:
-    """Expand the values in in_common, according to the itertools.product method, if necessary. This
-    usually happens to the hypotheses.
+    """Expand the values in in_common, according to the itertools.product method, if necessary.
+    usually happens to the hypotheses. This.
 
     Example:
         >>> convert_to_in_common({'hypotheses': ['free', {'a': [1, 2], 'b': [3, 4]}]})
@@ -653,9 +653,9 @@ def convert_to_in_common(in_common: Dict[str, Any]) -> Dict[str, Any]:
 
 
 def compute_variations(to_zip, to_vary, in_common) -> list:
-    """Compute variations of Runner from to_zip, to_vary and in_common. By priority, the order is
-    to_zip, to_vary, in_common. The values in to_zip will overwrite the keys in to_vary and
-    in_common. The values in to_vary will overwrite the keys in in_common.
+    """Compute variations of Runner from to_zip, to_vary and in_common. to_zip, to_vary, in_common.
+    The values in to_zip will overwrite the keys in to_vary and By priority, the order is in_common.
+    The values in to_vary will overwrite the keys in in_common.
 
     Args:
         to_zip (dict): variations to be zipped
@@ -728,8 +728,8 @@ def signal_multiplier_estimator(
     iteration=100,
     diagnostic=False,
 ) -> float:
-    """Estimate the best-fit signal multiplier using perturbation theory. The method tries to solve
-    the critial point of the likelihood function by perturbation theory, where the likelihood
+    """Estimate the best-fit signal multiplier using perturbation theory. the critial point of the
+    likelihood function by perturbation theory, where the likelihood The method tries to solve
     function is defined as the binned Poisson likelihood function, given signal, background models
     and data.