polarpy.polarlike.PolarLike.get_simulated_dataset() - Code Metrics - Inspection of "fix sims" - grburgess/polarpy - Measure and Improve Code Quality continuously with Scrutinizer

Passed

Push — master ( b7666a...6a6b47 )

by J. Michael

created 2018-08-09 14:30 UTC

PolarLike.get_simulated_dataset() B

↳ Parent: polarpy.polarlike

Complexity

Conditions

Size

Total Lines	74
Code Lines	26

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
cc	6
eloc	26
nop	3
dl	0
loc	74
rs	8.3226
c	0
b	0
f	0

How to fix Long Method

import collections
class SomeClass:
    def some_method(self):
        """Do x and return foo."""
from contextlib import contextmanager
import matplotlib.pyplot as plt

import numpy as np


from astromodels import Parameter, Uniform_prior

from threeML import PluginPrototype

from threeML.io.plotting.step_plot import step_plot

from threeML.utils.binner import Rebinner

from threeML.utils.polarization.binned_polarization import BinnedModulationCurve

from threeML.utils.statistics.likelihood_functions import poisson_observed_poisson_background, \

    poisson_observed_gaussian_background

from polarpy.modulation_curve_file import ModulationCurveFile
from polarpy.polar_response import PolarResponse


class PolarLike(PluginPrototype):

    """
    Preliminary POLAR polarization plugin
    """

    def __init__(self, name, observation, background, response, interval_number=None, verbose=False):

        """



        :param interval_number:
        :param name:
        :param observation:
        :param background:
        :param response:

        :param verbose:

        """

        # if we pass a string, there may be multiple time intervals
        # saved so we must specify a time interval

        if isinstance(observation, str):
            assert interval_number is not None, 'must specify an interval number'

            # this is a file
            read_file = ModulationCurveFile.read(observation)

            # create the bmc
            observation = read_file.to_binned_modulation_curve(interval=interval_number)

        # the same applies for the background
        if isinstance(background, str):
            assert interval_number is not None, 'must specify an interval number'

            # this is a file
            read_file = ModulationCurveFile.read(background)

            background = read_file.to_binned_modulation_curve(interval=interval_number)

        assert isinstance(observation, BinnedModulationCurve), 'The observation must be a BinnedModulationCurve'

        assert isinstance(background, BinnedModulationCurve), 'The observation must be a BinnedModulationCurve'


        # attach the required variables

        self._observation = observation
        self._background = background

        self._observed_counts = observation.counts
        self._background_counts = background.counts
        self._background_count_errors = background.count_errors
        self._scale = observation.exposure / background.exposure
        self._exposure = observation.exposure
        self._background_exposure = background.exposure

        self._likelihood_model = None
        self._rebinner = None
        

        # now do some double checks

        assert len(self._observed_counts) == len(self._background_counts)

        self._n_synthetic_datasets = 0

        # set up the effective area correction

        self._nuisance_parameter = Parameter(
            "cons_%s" % name,
            1.0,
            min_value=0.8,
            max_value=1.2,
            delta=0.05,
            free=False,
            desc="Effective area correction for %s" % name)

        nuisance_parameters = collections.OrderedDict()
        nuisance_parameters[self._nuisance_parameter.name] = self._nuisance_parameter

        # pass to the plugin proto

        super(PolarLike, self).__init__(name, nuisance_parameters)


        # The following vectors are the ones that will be really used for the computation. At the beginning they just

        # point to the original ones, but if a rebinner is used and/or a mask is created through set_active_measurements,

        # they will contain the rebinned and/or masked versions

        self._current_observed_counts = self._observed_counts
        self._current_background_counts = self._background_counts
        self._current_background_count_errors = self._background_count_errors


        self._verbose = verbose

        # we can either attach or build a response

        assert isinstance(response, str) or isinstance(

            response, PolarResponse), 'The response must be a file name or a PolarResponse'

        if isinstance(response, PolarResponse):

            self._response = response

        else:

            self._response = PolarResponse(response)

        # attach the interpolators to the

        self._all_interp = self._response.interpolators

        # we also make sure the lengths match up here
        assert self._response.n_scattering_bins == len(
            self._observation.counts), 'observation counts shape does not agree with response shape'

    def use_effective_area_correction(self, lower=0.5, upper=1.5):
        """
        Use an area constant to correct for response issues

        :param lower:
        :param upper:
        :return:
        """

        self._nuisance_parameter.free = True
        self._nuisance_parameter.bounds = (lower, upper)
        self._nuisance_parameter.prior = Uniform_prior(lower_bound=lower, upper_bound=upper)
        if self._verbose:
            print('Using effective area correction')

    def fix_effective_area_correction(self, value=1):
        """

        fix the effective area correction to a particular values

        :param value:
        :return:
        """

        # allow the value to be outside the bounds
        if self._nuisance_parameter.max_value < value:

            self._nuisance_parameter.max_value = value + 0.1

        elif self._nuisance_parameter.min_value > value:

            self._nuisance_parameter.min_value = value = 0.1

        self._nuisance_parameter.fix = True
        self._nuisance_parameter.value = value

        if self._verbose:
            print('Fixing effective area correction')

    @property
    def effective_area_correction(self):
class SomeClass:
    def some_method(self):
        """Do x and return foo."""

        return self._nuisance_parameter

    def get_simulated_dataset(self, new_name=None, **kwargs):

        """
        Returns another Binned instance where data have been obtained by randomizing the current expectation from the

        model, as well as from the background (depending on the respective noise models)

        :return: an BinnedSpectrum or child instance
        """

        assert self._likelihood_model is not None, "You need to set up a model before randomizing"

        # Keep track of how many syntethic datasets we have generated

        self._n_synthetic_datasets += 1

        # Generate a name for the new dataset if needed
        if new_name is None:
            new_name = "%s_sim_%i" % (self.name, self._n_synthetic_datasets)

        # Generate randomized data depending on the different noise models

        # We remove the mask temporarily because we need the various elements for all channels. We will restore it

        # at the end

        # Get the source model for all channels (that's why we don't use the .folded_model property)

        

        # We remove the mask temporarily because we need the various elements for all channels. We will restore it

        # at the end

        original_rebinner = self._rebinner

        with self._without_rebinner():

            # Get the source model for all channels (that's why we don't use the .folded_model property)


        

            source_model_counts = self._get_model_counts()

            if self._background.is_poisson:
                _, background_model_counts = poisson_observed_poisson_background(
                            self._current_observed_counts, self._current_background_counts, self._scale, source_model_counts)

            else:

                _, background_model_counts = poisson_observed_gaussian_background(
                            self._current_observed_counts, self._current_background_counts, self._current_background_count_errors, source_model_counts)


            # Now randomize the expectations

            # Randomize expectations for the source

            randomized_source_counts = np.random.poisson(source_model_counts + background_model_counts)


            randomized_background_counts = np.random.poisson(background_model_counts)

            new_observation = self._observation.clone(new_counts=randomized_source_counts)

            new_background = self._background.clone(new_counts=randomized_background_counts)

            new_plugin = PolarLike(
                name=new_name,
                observation=new_observation,
                background=new_background,
                response=self._response,
                verbose=False,
            )

            # Apply the same selections as the current data set
            if original_rebinner is not None:

                # Apply rebinning, which also applies the mask
                new_plugin._apply_rebinner(original_rebinner)
class MyParent:
    def __init__(self):
        self._x = 1;
        self.y = 2;

class MyChild(MyParent):
    def some_method(self):
        return self._x    # Ok, since accessed from a child class

class AnotherClass:
    def some_method(self, instance_of_my_child):
        return instance_of_my_child._x   # Would be flagged as AnotherClass is not
                                         # a child class of MyParent

            

            return new_plugin

    def set_model(self, likelihood_model_instance):
        """
        Set the model to be used in the joint minimization. Must be a LikelihoodModel instance.
        :param likelihood_model_instance: instance of Model
        :type likelihood_model_instance: astromodels.Model
        """

        if likelihood_model_instance is None:
            return

        # if self._source_name is not None:

        #     # Make sure that the source is in the model
        #     assert self._source_name in likelihood_model_instance.sources, \
        #                                         "This XYLike plugin refers to the source %s, " \
        #                                         "but that source is not in the likelihood model" % (self._source_name)


        for k, v in likelihood_model_instance.free_parameters.items():


            if 'polarization.degree' in k:
                self._pol_degree = v
class Foo:
    def __init__(self, x=None):
        self.x = x

            if 'polarization.angle' in k:
                self._pol_angle = v
class Foo:
    def __init__(self, x=None):
        self.x = x

        # now we need to get the intergal flux

        _, integral = self._get_diff_flux_and_integral(likelihood_model_instance)

        self._integral_flux = integral
class Foo:
    def __init__(self, x=None):
        self.x = x

        self._likelihood_model = likelihood_model_instance

    def _get_diff_flux_and_integral(self, likelihood_model):

        n_point_sources = likelihood_model.get_number_of_point_sources()

        # Make a function which will stack all point sources (OGIP do not support spatial dimension)

        def differential_flux(scattering_edges):
class SomeClass:
    def some_method(self):
        """Do x and return foo."""
            fluxes = likelihood_model.get_point_source_fluxes(0, scattering_edges, tag=self._tag)

            # If we have only one point source, this will never be executed
            for i in range(1, n_point_sources):
                fluxes += likelihood_model.get_point_source_fluxes(i, scattering_edges, tag=self._tag)


            return fluxes

        # The following integrates the diffFlux function using Simpson's rule
        # This assume that the intervals e1,e2 are all small, which is guaranteed
        # for any reasonable response matrix, given that e1 and e2 are Monte-Carlo
        # scattering_edges. It also assumes that the function is smooth in the interval
        # e1 - e2 and twice-differentiable, again reasonable on small intervals for
        # decent models. It might fail for models with too sharp features, smaller
        # than the size of the monte carlo interval.

        def integral(e1, e2):
class SomeClass:
    def some_method(self):
        """Do x and return foo."""
            # Simpson's rule

            return (e2 - e1) / 6.0 * (differential_flux(e1) + 4 * differential_flux(
                (e1 + e2) / 2.0) + differential_flux(e2))

        return differential_flux, integral

    def _get_model_rate(self):

        # first we need to get the integrated expectation from the spectrum

        intergal_spectrum = np.array(
            [self._integral_flux(emin, emax) for emin, emax in zip(self._response.ene_lo, self._response.ene_hi)])


        # we evaluate at the center of the bin. the bin widths are already included
        eval_points = np.array(
            [[ene, self._pol_angle.value, self._pol_degree.value] for ene in self._response.energy_mid])


        expectation = []

        # create the model counts by summing over energy

        for i, interpolator in enumerate(self._all_interp):

            rate = np.dot(interpolator(eval_points), intergal_spectrum)

            expectation.append(rate)

        return np.array(expectation)

    def _get_model_counts(self):


        if self._rebinner is None:
            model_rate = self._get_model_rate()

        else:

            model_rate, = self._rebinner.rebin(self._get_model_rate)

        

        return self._nuisance_parameter.value * self._exposure * model_rate

    def get_log_like(self):
class SomeClass:
    def some_method(self):
        """Do x and return foo."""

        model_counts = self._get_model_counts()

        if self._background.is_poisson:

            loglike, bkg_model = poisson_observed_poisson_background(        self._current_observed_counts, self._current_background_counts,

                                                                     self._scale, model_counts)


        else:

            loglike, bkg_model = poisson_observed_gaussian_background(        self._current_observed_counts, self._current_background_counts,

                                                                      self._current_background_count_errors, model_counts)


        return np.sum(loglike)

    def inner_fit(self):
class SomeClass:
    def some_method(self):
        """Do x and return foo."""

        return self.get_log_like()

    def writeto(self, file_name):
        """
        Write the data to HDF5 modulation curve files. Both background and observation
        files are created
        :param file_name: the file name header. The .h5 extension is added automatically
        """
        # first create a file container
        observation_file = ModulationCurveFile.from_binned_modulation_curve(self._observation)

        background_file = ModulationCurveFile.from_binned_modulation_curve(self._background)

        observation_file.writeto("%s.h5" % file_name)

        background_file.writeto("%s_bak.h5" % file_name)



    @property
    def scattering_boundaries(self):
        """
        Energy boundaries of channels currently in use (rebinned, if a rebinner is active)

        :return: (sa_min, sa_max)
        """

        scattering_edges = np.array(self._observation.edges)

        sa_min, sa_max = scattering_edges[:-1], scattering_edges[1:]

        if self._rebinner is not None:
            # Get the rebinned chans. NOTE: these are already masked

            sa_min, sa_max = self._rebinner.get_new_start_and_stop(sa_min, sa_max)

 

        return sa_min, sa_max


        

    def display(self, ax=None, show_data=True, show_model=True, show_total=False, model_kwargs={}, data_kwargs={}):
# Bad:
# If array_param is modified inside the function, the next invocation will
# receive the modified object.
def some_function(array_param=[]):
    # ...

# Better: Create an array on each invocation
def some_function(array_param=None):
    array_param = array_param or []
    # ...
        """

        :param ax:
        :param show_data:
        :param show_model:
        :param show_total:
        :param model_kwargs:
        :param data_kwargs:
        :return:
        """

        sa_min, sa_max = self.scattering_boundaries
        

        if show_total:
            show_model = False
            show_data = False

        if ax is None:

            fig, ax = plt.subplots()

        else:

            fig = ax.get_figure()

        if show_total:

            total_rate =         self._current_observed_counts / self._exposure

            bkg_rate = self._current_background_counts / self._background_exposure

            total_errors = np.sqrt(total_rate)

            if self._background.is_poisson:

                bkg_errors = np.sqrt(bkg_rate)

            else:

                bkg_errors = self._current_background_count_errors

            ax.hlines(
                total_rate,
                sa_min,
                sa_max,
                color='#7D0505',
                **data_kwargs)
            ax.vlines(
                np.mean([self.scattering_boundaries],axis=1),

                total_rate - total_errors,
                total_rate + total_errors,
                color='#7D0505',
                **data_kwargs)

            ax.hlines(
                bkg_rate,
                sa_min,
                sa_max,
                color='#0D5BAE',
                **data_kwargs)
            ax.vlines(
                np.mean([self.scattering_boundaries],axis=1),

                bkg_rate - bkg_errors,
                bkg_rate + bkg_errors,
                color='#0D5BAE',
                **data_kwargs)

        if show_data:

            net_rate = (        self._observed_counts / self._exposure) - self._background_counts / self._background_exposure


            if self._background.is_poisson:

                errors = np.sqrt((        self._observed_counts / self._exposure) +

                                 (self._background_counts / self._background_exposure))

            else:

                errors = np.sqrt((        self._observed_counts / self._exposure) +

                                 (self._background.count_errors / self._background_exposure)**2)

            ax.hlines(net_rate, self._response.scattering_bins_lo, self._response.scattering_bins_hi, **data_kwargs)

            ax.vlines(self._response.scattering_bins, net_rate - errors, net_rate + errors, **data_kwargs)


        if show_model:
            step_plot(
                ax=ax,
                xbins=np.vstack([self._response.scattering_bins_lo, self._response.scattering_bins_hi]).T,

                y=self._get_model_counts() / self._exposure,
                **model_kwargs)

        ax.set_xlabel('Scattering Angle')
        ax.set_ylabel('Net Rate (cnt/s/bin)')

        return fig

    @property
    def observation(self):
class SomeClass:
    def some_method(self):
        """Do x and return foo."""
        return self._observation

    @property
    def background(self):
class SomeClass:
    def some_method(self):
        """Do x and return foo."""
        return self._background

    @contextmanager
    def _without__rebinner(self):

        # Store rebinner for later use

        rebinner = self._rebinner

        # Clean mask and rebinning

        self.remove_rebinning()


        # Execute whathever

        yield

        # Restore mask and rebinner (if any)



        if rebinner is not None:

            # There was a rebinner, use it. Note that the rebinner applies the mask by itself

            self._apply_rebinner(rebinner)




            

    def rebin_on_background(self, min_number_of_counts):
        """
        Rebin the spectrum guaranteeing the provided minimum number of counts in each background bin. This is usually

        required for spectra with very few background counts to make the Poisson profile likelihood meaningful.

        Of course this is not relevant if you treat the background as ideal, nor if the background spectrum has

        Gaussian errors.

        The observed spectrum will be rebinned in the same fashion as the background spectrum.

        To neutralize this completely, use "remove_rebinning"

        :param min_number_of_counts: the minimum number of counts in each bin
        :return: none
        """

        # NOTE: the rebinner takes care of the mask already

        assert self._background is not None, "This data has no background, cannot rebin on background!"


        rebinner = Rebinner(self._background_counts, min_number_of_counts, mask = None)


        self._apply_rebinner(rebinner)

    def rebin_on_source(self, min_number_of_counts):
        """
        Rebin the spectrum guaranteeing the provided minimum number of counts in each source bin.

        To neutralize this completely, use "remove_rebinning"

        :param min_number_of_counts: the minimum number of counts in each bin
        :return: none
        """

        # NOTE: the rebinner takes care of the mask already



        rebinner = Rebinner(self._observed_counts, min_number_of_counts, self._mask)

        self._apply_rebinner(rebinner)

    def _apply_rebinner(self, rebinner):

        self._rebinner = rebinner

        # Apply the rebinning to everything.
        # NOTE: the output of the .rebin method are the vectors with the mask *already applied*

        self._current_observed_counts, = self._rebinner.rebin(self._observed_counts)

        if self._background is not None:

            self._current_background_counts, = self._rebinner.rebin(self._background_counts)

            if self._background_count_errors is not None:
                # NOTE: the output of the .rebin method are the vectors with the mask *already applied*


                self._current_background_count_errors, = self._rebinner.rebin_errors(self._background_count_errors)


        if self._verbose:
            print("Now using %s bins" % self._rebinner.n_bins)

    def remove_rebinning(self):
        """
        Remove the rebinning scheme set with rebin_on_background.

        :return:
        """

        self._rebinner = None


1			import collections
			0 ignored issues – show Coding Style introduced 2018-08-09 12:46 UTC by Report Bug Copy Issue Report This module should have a docstring. The coding style of this project requires that you add a docstring to this code element. Below, you find an example for methods: class SomeClass: def some_method(self): """Do x and return foo.""" If you would like to know more about docstrings, we recommend to read PEP-257: Docstring Conventions. Loading history...
2			from contextlib import contextmanager
3			import matplotlib.pyplot as plt
			0 ignored issues – show introduced 2018-08-08 07:31 UTC by Report Bug Copy Issue Report Unable to import 'matplotlib.pyplot' Loading history...
4			import numpy as np
			0 ignored issues – show introduced 2018-08-08 07:31 UTC by Report Bug Copy Issue Report Unable to import 'numpy' Loading history...
5
6			from astromodels import Parameter, Uniform_prior
			0 ignored issues – show introduced 2018-08-08 07:31 UTC by Report Bug Copy Issue Report Unable to import 'astromodels' Loading history...
7			from threeML import PluginPrototype
			0 ignored issues – show introduced 2018-08-08 07:31 UTC by Report Bug Copy Issue Report Unable to import 'threeML' Loading history...
8			from threeML.io.plotting.step_plot import step_plot
			0 ignored issues – show introduced 2018-08-08 07:31 UTC by Report Bug Copy Issue Report Unable to import 'threeML.io.plotting.step_plot' Loading history...
9			from threeML.utils.binner import Rebinner
			0 ignored issues – show introduced 2018-08-09 12:46 UTC by Report Bug Copy Issue Report Unable to import 'threeML.utils.binner' Loading history...
10			from threeML.utils.polarization.binned_polarization import BinnedModulationCurve
			0 ignored issues – show introduced 2018-08-08 07:31 UTC by Report Bug Copy Issue Report Unable to import 'threeML.utils.polarization.binned_polarization' Loading history...
11			from threeML.utils.statistics.likelihood_functions import poisson_observed_poisson_background, \
			0 ignored issues – show introduced 2018-08-08 07:31 UTC by Report Bug Copy Issue Report Unable to import 'threeML.utils.statistics.likelihood_functions' Loading history...
12			poisson_observed_gaussian_background
13
14			from polarpy.modulation_curve_file import ModulationCurveFile
15			from polarpy.polar_response import PolarResponse
16
17
18			class PolarLike(PluginPrototype):
			0 ignored issues – show best-practice introduced 2018-08-09 12:46 UTC by Report Bug Copy Issue Report Too many instance attributes (21/7) Loading history... Unused Code introduced 2018-08-08 07:31 UTC by Report Bug Copy Issue Report The variable `__class__` seems to be unused. Loading history...
19			"""
20			Preliminary POLAR polarization plugin
21			"""
22
23			def __init__(self, name, observation, background, response, interval_number=None, verbose=False):
			0 ignored issues – show Coding Style introduced 2018-08-08 07:31 UTC by Report Bug Copy Issue Report This line is too long as per the coding-style (101/100). This check looks for lines that are too long. You can specify the maximum line length. Loading history... best-practice introduced 2018-08-08 07:31 UTC by Report Bug Copy Issue Report Too many arguments (7/5) Loading history...
24			"""
25
26
27
28			:param interval_number:
29			:param name:

grburgess / polarpy

Push — master ( b7666a...6a6b47 )

PolarLike.get_simulated_dataset() B

Complexity

Size

Duplication

Importance

How to fix Long Method

Long Method

Duplication Side-by-Side

Filter issues like