15296154421

Committed 28 May 2025 09:07AM UTC coverage: 67.817% (+0.02%) from 67.793%

Build # 15296154421

Build Type

push

github

Committed by

web-flow

Commit Message

Merge pull request #334 from Zeitsperre/typo-adjustments

Typo adjustments, black formatting, drop Python 3.9, support Python 3.13

Run Details

24 of 99 new or added lines in 15 files covered. (24.24%)

13 existing lines in 7 files now uncovered.

3753 of 5534 relevant lines covered (67.82%)

2.03 hits per line

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0.0

/src/spotpy/examples/spot_setup_standardnormal.py

"""
Copyright 2015 by Tobias Houska
This file is part of Statistical Parameter Estimation Tool (SPOTPY).

:author: Tobias Houska

This example implements the Standard Normal function into SPOT.
"""

import numpy as np

import spotpy


class spot_setup(object):
    def __init__(self, mean=0, std=1):
        self.params = [spotpy.parameter.Uniform("x", -5, 5, 1.5, 3.0)]
        self.mean = mean
        self.std = std

    def parameters(self):
        return spotpy.parameter.generate(self.params)

    def simulation(self, x):
        simulations = (1.0 / (self.std * np.sqrt(2 * np.pi))) ** (
            (-1.0 / 2.0) * (((x - self.mean) / self.std) ** 2)
        )
        return simulations

    def evaluation(self):
        observations = [0]
        return observations

    def objectivefunction(self, simulation, evaluation):
        objectivefunction = -spotpy.objectivefunctions.rmse(
            evaluation=evaluation, simulation=simulation
        )
        return objectivefunction

1	"""
2	Copyright 2015 by Tobias Houska
3	This file is part of Statistical Parameter Estimation Tool (SPOTPY).
4
5	:author: Tobias Houska
6
7	This example implements the Standard Normal function into SPOT.
8	"""
9
UNCOV 10	import numpy as np	×
11
12	import spotpy	×
13
14
15	class spot_setup(object):	×
16	def __init__(self, mean=0, std=1):	×
17	self.params = [spotpy.parameter.Uniform("x", -5, 5, 1.5, 3.0)]	×
18	self.mean = mean	×
19	self.std = std	×
20
21	def parameters(self):	×
22	return spotpy.parameter.generate(self.params)	×
23
24	def simulation(self, x):	×
25	simulations = (1.0 / (self.std * np.sqrt(2 * np.pi))) ** (	×
26	(-1.0 / 2.0) * (((x - self.mean) / self.std) ** 2)
27	)
28	return simulations	×
29
30	def evaluation(self):	×
31	observations = [0]	×
32	return observations	×
33
34	def objectivefunction(self, simulation, evaluation):	×
35	objectivefunction = -spotpy.objectivefunctions.rmse(	×
36	evaluation=evaluation, simulation=simulation
37	)
38	return objectivefunction	×

thouska / spotpy / 15296154421

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