hawc_hal.log_likelihood.log_likelihood() - Code Metrics - giacomov/hawc_hal - Measure and Improve Code Quality continuously with Scrutinizer

hawc_hal.log_likelihood.log_likelihood() A
last analyzed 2018-12-06 21:33 UTC

↳ Parent: hawc_hal.log_likelihood

Complexity

Conditions

Size

Total Lines	23
Code Lines	6

Duplication

Lines	0
Ratio	0 %

Importance

Changes

Metric	Value
eloc	6
dl	0
loc	23
rs	10
c	0
b	0
f	0
cc	1
nop	3

from numba import jit
class SomeClass:
    def some_method(self):
        """Do x and return foo."""
import numpy as np


# This function has two signatures in numba because if there are no sources in the likelihood model,
# then expected_model_counts is 0.0
@jit(["float64(float64[:], float64[:], float64[:])", "float64(float64[:], float64[:], float64)"],
     nopython=True, parallel=False)
def log_likelihood(observed_counts, expected_bkg_counts, expected_model_counts):  # pragma: no cover
    """
    Poisson log-likelihood minus log factorial minus bias. The bias migth be needed to keep the numerical value
    of the likelihood small enough so that there aren't numerical problems when computing differences between two
    likelihood values.

    :param observed_counts:
    :param expected_bkg_counts:
    :param expected_model_counts:
    :param bias:
    :return:
    """

    predicted_counts = expected_bkg_counts + expected_model_counts

    # Remember: because of how the DataAnalysisBin in map_tree.py initializes the maps,
    # observed_counts > 0 everywhere

    log_likes = observed_counts * np.log(predicted_counts) - predicted_counts

    return np.sum(log_likes)


1			from numba import jit
			0 ignored issues – show Coding Style introduced 2018-06-02 00:48 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			import numpy as np
3
4
5			# This function has two signatures in numba because if there are no sources in the likelihood model,
6			# then expected_model_counts is 0.0
7			@jit(["float64(float64[:], float64[:], float64[:])", "float64(float64[:], float64[:], float64)"],
8			nopython=True, parallel=False)
9			def log_likelihood(observed_counts, expected_bkg_counts, expected_model_counts): # pragma: no cover
10			"""
11			Poisson log-likelihood minus log factorial minus bias. The bias migth be needed to keep the numerical value
12			of the likelihood small enough so that there aren't numerical problems when computing differences between two
13			likelihood values.
14
15			:param observed_counts:
16			:param expected_bkg_counts:
17			:param expected_model_counts:
18			:param bias:
19			:return:
20			"""
21
22			predicted_counts = expected_bkg_counts + expected_model_counts
23
24			# Remember: because of how the DataAnalysisBin in map_tree.py initializes the maps,
25			# observed_counts > 0 everywhere
26
27			log_likes = observed_counts * np.log(predicted_counts) - predicted_counts
28
29			return np.sum(log_likes)
			0 ignored issues – show Coding Style introduced 2018-06-02 00:48 UTC by Report Bug Copy Issue Report Final newline missing Loading history...

giacomov / hawc_hal

hawc_hal.log_likelihood.log_likelihood() A last analyzed 2018-12-06 21:33 UTC

Complexity

Size

Duplication

Importance

Duplication Side-by-Side

Filter issues like

hawc_hal.log_likelihood.log_likelihood() A
last analyzed 2018-12-06 21:33 UTC