Effect of noise and height on the inversion

This study has been demonstrated in the EMagPy paper McLachlan et al. (2021).

All inversions are performed with the ROPE solver on a two-layer model with a varying depth. (a) Inversion with 0% noise with device on the ground. (b) Inversion with 5% noise on the ground. (c) Inversion with 0% noise at 1 m above the ground (d) Inversion with 5% noise at 1 m above the ground. The red line represents the true interface between the two layers.

[1]:

import numpy as np
import matplotlib.pyplot as plt
import sys
sys.path.append('../src') # add path where emagpy is
from emagpy import Problem

letters = ['a','b','c','d','e','f','g','h','i','j']

[2]:

# parameters for the synthetic model
nlayer = 2 # number of layer
npos = 20 # number of sampling positions
conds = np.ones((npos, nlayer))*[20, 100]
x = np.linspace(0.1, 2, npos)[:,None]
depths = 0.65 + 0.2 * np.sin(x*np.pi*2) # wave
coils0 = ['VCP1.48f10000h0', 'VCP2.82f10000h0', 'VCP4.49f10000h0',
'HCP1.48f10000h0', 'HCP2.82f10000h0', 'HCP4.49f10000h0']
coils1 = ['VCP1.48f10000h1', 'VCP2.82f10000h1', 'VCP4.49f10000h1',
'HCP1.48f10000h1', 'HCP2.82f10000h1', 'HCP4.49f10000h1']
coils = [coils0, coils0, coils1, coils1]
noises = [0, 0.05, 0, 0.05]
ks = []
# generate ECa using forward model
for i in range(4):
k = Problem()
k.setModels([depths], [conds])
_ = k.forward(forwardModel='FSlin', coils=coils[i], noise=noises[i])
ks.append(k)

# invert
for k in ks:
k.setInit(depths0=np.array([0.5]), fixedDepths=[False])
k.invert(forwardModel='FSlin', method='ROPE', regularization='l1',
bnds=[(0.05, 2.5), (5, 150), (5, 150)], rep=1000, njobs=-1)

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Forward modelling
Forward modelling
Forward modelling
Forward modelling

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Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
50 input vectors with 3 parameters
Generating 126 parameters:
(51, 51)
(82, 101)
(111, 151)
(150, 201)
13 input vectors with 3 parameters
Generating 126 parameters:
(3, 14)
(6, 27)
(10, 40)
(14, 53)
(17, 66)
(27, 79)
(34, 92)
(36, 105)
(41, 118)
(46, 131)
(58, 144)
(71, 157)
(84, 170)
(91, 183)
(104, 196)
(106, 209)
(111, 222)
(116, 235)
(129, 248)
50 input vectors with 3 parameters
Generating 126 parameters:
(43, 51)
(78, 101)
(127, 151)
50 input vectors with 3 parameters
Generating 126 parameters:
(43, 51)
(88, 101)
(123, 151)
(169, 201)
50 input vectors with 3 parameters
Generating 126 parameters:
(47, 51)
(73, 101)
(120, 151)
(148, 201)
50 input vectors with 3 parameters
Generating 126 parameters:
50 input vectors with 3 parameters
Generating 126 parameters:
(43, 51)
(67, 101)(43, 51)

(104, 151)
(82, 101)
(123, 151)
(153, 201)
(165, 201)
13 input vectors with 3 parameters
Generating 126 parameters:
(5, 14)
(18, 27)
(28, 40)
(41, 53)
(51, 66)
(59, 79)
(72, 92)
(83, 105)
(84, 118)
(97, 131)
(102, 144)
(109, 157)
(118, 170)
(130, 183)
13 input vectors with 3 parameters
Generating 126 parameters:
(5, 14)
(16, 27)
(19, 40)
(22, 53)
(30, 66)
(38, 79)
(48, 92)
(52, 105)
(60, 118)
(71, 131)
(82, 144)
(92, 157)
(99, 170)
(103, 183)
(116, 196)
(126, 209)
13 input vectors with 3 parameters
Generating 126 parameters:
(10, 14)
(15, 27)
(18, 40)
(30, 53)
(37, 66)
(41, 79)
(49, 92)
(57, 105)
(69, 118)
(75, 131)
(86, 144)
(93, 157)
(104, 170)
(113, 183)
(116, 196)
(122, 209)
(127, 222)
13 input vectors with 3 parameters
Generating 126 parameters:
(8, 14)
(15, 27)
(20, 40)
(21, 53)
(24, 66)
(30, 79)
(38, 92)
(49, 105)
(56, 118)
(65, 131)
(71, 144)
(74, 157)
(77, 170)
(83, 183)
(93, 196)
(103, 209)
(106, 222)
(112, 235)
(116, 248)
(123, 261)
(136, 274)
13 input vectors with 3 parameters
Generating 126 parameters:
(11, 14)
(15, 27)
(26, 40)
(33, 53)
(45, 66)
(54, 79)
(59, 92)
(69, 105)
(71, 118)
(84, 131)
(85, 144)
(97, 157)
(102, 170)
(106, 183)
(112, 196)
(118, 209)
(121, 222)
(128, 235)
50 input vectors with 3 parameters
Generating 126 parameters:
(38, 51)
(75, 101)
(113, 151)
(159, 201)
13 input vectors with 3 parameters
Generating 126 parameters:
(7, 14)
(16, 27)
(27, 40)
(38, 53)
(50, 66)
(56, 79)
13 input vectors with 3 parameters
Generating 126 parameters:
(61, 92)
(7, 14)
(71, 105)
(12, 27)
(77, 118)
(20, 40)
(89, 131)
(26, 53)
(97, 144)
(30, 66)
(109, 157)
(38, 79)
(113, 170)
(51, 92)
(126, 183)
13 input vectors with 3 parameters
Generating 126 parameters:
(57, 105)
(7, 14)
(17, 27)
(61, 118)
(30, 40)
(69, 131)
(37, 53)
(78, 144)
(47, 66)
(82, 157)
(51, 79)
(89, 170)
(61, 92)
(99, 183)
(71, 105)
(103, 196)
(84, 118)
(104, 209)
(94, 131)
(110, 222)
(105, 144)
(120, 235)
(115, 157)
(125, 248)
(119, 170)
(138, 261)
(125, 183)
(138, 196)
13 input vectors with 3 parameters
Generating 126 parameters:
(4, 14)
(5, 27)
(8, 40)
(10, 53)
(14, 66)
(19, 79)
(23, 92)
(27, 105)
(32, 118)
(41, 131)
(46, 144)
(56, 157)
(60, 170)
(63, 183)
(70, 196)
(73, 209)
(78, 222)
(88, 235)
(93, 248)
(98, 261)
(105, 274)
(106, 287)
(113, 300)
(115, 313)
(117, 326)
(120, 339)
(123, 352)
(131, 365)
50 input vectors with 3 parameters
Generating 126 parameters:
(26, 51)
(76, 101)
(125, 151)
(154, 201)
13 input vectors with 3 parameters
Generating 126 parameters:
(9, 14)
(18, 27)
(21, 40)
(28, 53)
(35, 66)
(47, 79)
(51, 92)
(60, 105)
(63, 118)
(69, 131)
(74, 144)
(77, 157)
(82, 170)
(87, 183)
(99, 196)
(100, 209)
(108, 222)
(118, 235)
(123, 248)
(133, 261)
13 input vectors with 3 parameters
Generating 126 parameters:
(4, 14)
(6, 27)
(11, 40)
(20, 53)
(29, 66)
(34, 79)
(43, 92)
(51, 105)
(63, 118)
(73, 131)
(79, 144)
(87, 157)
(94, 170)
(103, 183)
(113, 196)
(122, 209)
(134, 222)
13 input vectors with 3 parameters
Generating 126 parameters:
(8, 14)
(10, 27)
(17, 40)
(23, 53)
(27, 66)
(31, 79)
(35, 92)
(37, 105)
(39, 118)
(45, 131)
(58, 144)
(63, 157)
(67, 170)
(73, 183)
(78, 196)
(84, 209)
(90, 222)
(97, 235)
(104, 248)
(109, 261)
(111, 274)
(115, 287)
(128, 300)
13 input vectors with 3 parameters
Generating 126 parameters:
(12, 14)
(15, 27)
(24, 40)
(32, 53)
(43, 66)
(52, 79)
(63, 92)
13 input vectors with 3 parameters
Generating 126 parameters:
(72, 105)
(9, 14)
(84, 118)
(13, 27)
(93, 131)
(18, 40)
(103, 144)
(21, 53)
(114, 157)
(25, 66)
(120, 170)
(30, 79)
(132, 183)
(39, 92)
(47, 105)
(59, 118)
(64, 131)
(70, 144)
(76, 157)
(82, 170)
(92, 183)
(96, 196)
(103, 209)
(107, 222)
(120, 235)
(124, 248)
(128, 261)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 1.51 seconds
Total Repetitions: 1000
Maximal objective value: -0.897718
Corresponding parameter setting:
x0: 0.596152
x1: 15.4208
x2: 99.3871
******************************

13 input vectors with 3 parameters
Generating 126 parameters:
(5, 14)
(7, 27)
(16, 40)
(23, 53)
(29, 66)
(35, 79)
(39, 92)
(44, 105)
(45, 118)
(50, 131)
(59, 144)
(62, 157)
(66, 170)
(68, 183)
(71, 196)
(76, 209)
(83, 222)
(88, 235)
(93, 248)
(98, 261)
(104, 274)
(107, 287)
(113, 300)
(119, 313)
(125, 326)
(132, 339)
13 input vectors with 3 parameters
Generating 126 parameters:
(6, 14)
(10, 27)
(14, 40)
(24, 53)
(30, 66)
(37, 79)
(39, 92)
(47, 105)
(59, 118)
(62, 131)
(70, 144)
(82, 157)
(91, 170)
(95, 183)
(101, 196)
(110, 209)
(112, 222)
(116, 235)
(118, 248)
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
(121, 261)
(127, 274)
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
13 input vectors with 3 parameters
Generating 126 parameters:
(5, 14)
(9, 27)
(22, 40)
(35, 53)
(39, 66)
(42, 79)
(50, 92)
(58, 105)
(64, 118)
(70, 131)
(80, 144)
(86, 157)
(91, 170)
(95, 183)
(106, 196)
(109, 209)
(112, 222)
(124, 235)
(134, 248)
13 input vectors with 3 parameters
Generating 126 parameters:
(7, 14)
(16, 27)
(19, 40)
(32, 53)
(37, 66)
(41, 79)
(43, 92)
(53, 105)
(63, 118)
(69, 131)
(79, 144)
(84, 157)
(94, 170)
(102, 183)
(109, 196)
(112, 209)
(120, 222)
(125, 235)
(138, 248)
13 input vectors with 3 parameters
Generating 126 parameters:
(10, 14)
(16, 27)
(27, 40)
(35, 53)
(45, 66)
(48, 79)
(60, 92)
(68, 105)
(77, 118)
(84, 131)
(92, 144)
(99, 157)
(109, 170)
(120, 183)
(122, 196)
(131, 209)
13 input vectors with 3 parameters
Generating 126 parameters:
(11, 14)
(24, 27)
(32, 40)
(41, 53)
(51, 66)
(56, 79)
(58, 92)
(64, 105)
(71, 118)
(77, 131)
(83, 144)
(96, 157)
(100, 170)
(104, 183)
(114, 196)
(126, 209)
13 input vectors with 3 parameters
Generating 126 parameters:
(8, 14)
(16, 27)
(21, 40)
(30, 53)
(37, 66)
(41, 79)
13 input vectors with 3 parameters(50, 92)

Generating 126 parameters:
(53, 105)
(11, 14)
(17, 27)
(58, 118)
(18, 40)
(66, 131)
(28, 53)
(75, 144)
(32, 66)
(37, 79)
(82, 157)
(47, 92)
(92, 170)
(56, 105)
(98, 183)
(67, 118)
(103, 196)
(74, 131)
(113, 209)
(80, 144)
(122, 222)
(83, 157)
(134, 235)
(86, 170)
(99, 183)
(101, 196)
(108, 209)
(113, 222)
(124, 235)
(136, 248)
13 input vectors with 3 parameters
Generating 126 parameters:
(14, 14)
(24, 27)
(28, 40)
(34, 53)
(39, 66)
(49, 79)
(62, 92)
(68, 105)
(77, 118)
(90, 131)
(92, 144)
(100, 157)
(107, 170)
(117, 183)
(122, 196)
(131, 209)
13 input vectors with 3 parameters
Generating 126 parameters:
(4, 14)
(10, 27)
(16, 40)
(22, 53)
(27, 66)
(31, 79)
(38, 92)
(51, 105)
(54, 118)
(57, 131)
(66, 144)
(73, 157)
(86, 170)
(89, 183)
(96, 196)
(103, 209)
(106, 222)
(110, 235)
(119, 248)
(121, 261)
(127, 274)
50 input vectors with 3 parameters
Generating 126 parameters:
Stopping samplig
(34, 51)

*** Final SPOTPY summary ***
Total Duration: 1.55 seconds
Total Repetitions: 1000
Maximal objective value: -0.865145
Corresponding parameter setting:
x0: 0.857068
x1: 20.4576
x2: 100.507
******************************

(56, 101)
(102, 151)
(134, 201)
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 1.54 seconds
Total Repetitions: 1000
Maximal objective value: -1.28523
Corresponding parameter setting:
x0: 0.583511
x1: 27.4793
x2: 103.433
******************************

Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 1.53 seconds
Total Repetitions: 1000
Maximal objective value: -1.08361
Corresponding parameter setting:
x0: 0.921057
x1: 21.2493
x2: 103.282
******************************

50 input vectors with 3 parameters
Generating 126 parameters:
(41, 51)
(72, 101)
(96, 151)
(117, 201)
(162, 251)
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
50 input vectors with 3 parameters
Initialize database...
Generating 126 parameters:
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
(50, 51)
(92, 101)
(119, 151)
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
(149, 201)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 1.57 seconds
Total Repetitions: 1000
Maximal objective value: -1.1589
Corresponding parameter setting:
x0: 0.803061
x1: 23.0295
x2: 101.067
******************************

13 input vectors with 3 parameters
Generating 126 parameters:
(9, 14)
(21, 27)
(24, 40)
(34, 53)
(41, 66)
(48, 79)
(57, 92)
(66, 105)
(69, 118)
(81, 131)
(90, 144)
(93, 157)
(99, 170)
(105, 183)
(117, 196)
(121, 209)
(125, 222)
(132, 235)
Stopping samplig
50 input vectors with 3 parameters
Generating 126 parameters:

*** Final SPOTPY summary ***
Total Duration: 1.58 seconds
Total Repetitions: 1000
Maximal objective value: -0.988562
Corresponding parameter setting:
x0: 0.646406
x1: 17.7803
x2: 101.025
******************************

(46, 51)
(77, 101)
(112, 151)
(139, 201)
13 input vectors with 3 parameters
Generating 126 parameters:
(9, 14)
(11, 27)
(20, 40)
(23, 53)
(28, 66)
(37, 79)
(42, 92)
(48, 105)
(54, 118)
(63, 131)
(69, 144)
(72, 157)
(78, 170)
(82, 183)
(86, 196)
(95, 209)
(99, 222)
(107, 235)
(112, 248)
(116, 261)
(128, 274)
50 input vectors with 3 parameters
Generating 126 parameters:
(49, 51)
(97, 101)
(141, 151)
50 input vectors with 3 parameters
Generating 126 parameters:
(42, 51)
50 input vectors with 3 parameters
Generating 126 parameters:
(70, 101)
(30, 51)
(105, 151)
(71, 101)(145, 201)

(117, 151)
(154, 201)
13 input vectors with 3 parameters
Generating 126 parameters:
(4, 14)
(17, 27)
(23, 40)
(28, 53)
(35, 66)
(39, 79)
(42, 92)
(45, 105)
(53, 118)
(61, 131)
(67, 144)
(80, 157)
(86, 170)
(88, 183)
(92, 196)
(95, 209)
(99, 222)
(101, 235)
(110, 248)
(113, 261)
(119, 274)
(123, 287)
(129, 300)
50 input vectors with 3 parameters
Generating 126 parameters:
(31, 51)
(71, 101)
(96, 151)
(120, 201)
(154, 251)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 1.63 seconds
Total Repetitions: 1000
Maximal objective value: -1.29137
Corresponding parameter setting:
x0: 0.562848
x1: 14.616
x2: 103.806
******************************

13 input vectors with 3 parameters
Generating 126 parameters:
(14, 14)
(16, 27)
(24, 40)
(27, 53)
(35, 66)
(48, 79)
(58, 92)
(60, 105)
(72, 118)
(78, 131)
(85, 144)
(89, 157)
(96, 170)
(102, 183)
(107, 196)
(110, 209)
(118, 222)
(128, 235)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 1.55 seconds
Total Repetitions: 1000
Maximal objective value: -0.911443
Corresponding parameter setting:
x0: 0.784087
x1: 20.1076
x2: 100.927
******************************

50 input vectors with 3 parameters
Generating 126 parameters:
(31, 51)
(77, 101)
(114, 151)
(148, 201)
13 input vectors with 3 parameters
Generating 126 parameters:
(9, 14)
(14, 27)
(22, 40)
(35, 53)
13 input vectors with 3 parameters
Generating 126 parameters:
(44, 66)
(9, 14)
(12, 27)
(51, 79)
(17, 40)
(23, 53)
(31, 66)
(58, 92)
(33, 79)
(38, 92)
(42, 105)
(49, 118)
(69, 105)
(56, 131)
(63, 144)
(65, 157)
(77, 118)
(71, 170)
(80, 183)
(88, 196)
(82, 131)
(99, 209)
(106, 222)
(116, 235)
(92, 144)
(119, 248)
(125, 261)
(130, 274)
(99, 157)
(105, 170)
(108, 183)
(109, 196)
(122, 209)
(123, 222)
(134, 235)
13 input vectors with 3 parameters
Generating 126 parameters:
(10, 14)
(18, 27)
(25, 40)
(34, 53)
(42, 66)
(51, 79)
(58, 92)
(62, 105)
(68, 118)
(74, 131)
(78, 144)
(87, 157)
(100, 170)
(105, 183)
(116, 196)
(127, 209)
13 input vectors with 3 parameters
Generating 126 parameters:
(14, 14)
(19, 27)
(23, 40)
(35, 53)
(41, 66)
(45, 79)
(50, 92)
(57, 105)
(70, 118)
(79, 131)
(92, 144)
(99, 157)
(102, 170)
(107, 183)
(116, 196)
(128, 209)
13 input vectors with 3 parameters
Generating 126 parameters:
(2, 14)
(11, 27)
(18, 40)
(26, 53)
(39, 66)
(50, 79)
(55, 92)
(60, 105)
972 of 1000, maximal objective function=-0.87257, time remaining: 00:00:00
5 Subset: Run 94 of 126 (best like=-0.87257)
(68, 118)
13 input vectors with 3 parameters
Generating 126 parameters:
(77, 131)
(5, 14)
(8, 27)
(83, 144)
(13, 40)
(17, 53)
(87, 157)
(19, 66)
(22, 79)
(95, 170)
(26, 92)
(30, 105)
(98, 183)
(32, 118)
(107, 196)
(36, 131)
(41, 144)
(49, 157)
(110, 209)
(54, 170)
(62, 183)
(117, 222)
(71, 196)
(74, 209)
(74, 222)
(81, 235)
(130, 235)
(90, 248)
(96, 261)
(101, 274)
(104, 287)
(111, 300)
(114, 313)
(118, 326)
(127, 339)
13 input vectors with 3 parameters
Generating 126 parameters:
(7, 14)
(10, 27)
(23, 40)
(27, 53)
(39, 66)
(43, 79)
(46, 92)
(53, 105)
13 input vectors with 3 parameters
Generating 126 parameters:
(60, 118)
(65, 131)
(7, 14)
(70, 144)
(9, 27)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 2.03 seconds
Total Repetitions: 1000
Maximal objective value: -0.87257
Corresponding parameter setting:
x0: 0.513463
x1: 17.9633
x2: 99.9631
******************************

(13, 40)
(82, 157)
(19, 53)
(87, 170)
(21, 66)
(95, 183)
(26, 79)
(97, 196)
(101, 209)
(35, 92)
(103, 222)
(40, 105)
(109, 235)
(44, 118)
(112, 248)
(50, 131)
(118, 261)
(57, 144)
(131, 274)
(62, 157)
(65, 170)
(68, 183)
(73, 196)
(78, 209)
(86, 222)
(89, 235)
(92, 248)
(97, 261)
(103, 274)
(104, 287)
(115, 300)
(123, 313)
(124, 326)
(129, 339)
50 input vectors with 3 parameters
Generating 126 parameters:
(39, 51)
(73, 101)
(106, 151)
676 of 1000, maximal objective function=-1.23227, time remaining: 00:00:01
3 Subset: Run 50 of 126 (best like=-1.23227)
(146, 201)
50 input vectors with 3 parameters
Generating 126 parameters:
(46, 51)
(95, 101)
(122, 151)
(151, 201)
686 of 1000, maximal objective function=-1.07558, time remaining: 00:00:01
3 Subset: Run 60 of 126 (best like=-1.07558)
13 input vectors with 3 parameters
Generating 126 parameters:
13 input vectors with 3 parameters
Generating 126 parameters:
(6, 14)
(8, 14)
(18, 27)
(24, 40)
(15, 27)
(31, 53)
(34, 66)
(38, 79)
(24, 40)(47, 92)

(57, 105)
(65, 118)
(72, 131)
(30, 53)
(85, 144)
(96, 157)
(101, 170)
(43, 66)
(107, 183)
(117, 196)
(125, 209)
(47, 79)
(128, 222)
(51, 92)
(64, 105)
(74, 118)
(79, 131)
(86, 144)
(90, 157)
(94, 170)
(104, 183)
(112, 196)
(118, 209)
(125, 222)
(133, 235)
661 of 1000, maximal objective function=-1.44663, time remaining: 00:00:01
3 Subset: Run 35 of 126 (best like=-1.44663)
687 of 1000, maximal objective function=-1.03658, time remaining: 00:00:01
3 Subset: Run 61 of 126 (best like=-1.03658)
672 of 1000, maximal objective function=-1.30416, time remaining: 00:00:01
3 Subset: Run 46 of 126 (best like=-1.30416)
13 input vectors with 3 parameters
Generating 126 parameters:
(7, 14)
(13, 27)
(26, 40)
(32, 53)
(41, 66)
(51, 79)
(57, 92)
(69, 105)
(76, 118)
(86, 131)
(93, 144)
(99, 157)
(104, 170)
(114, 183)
(122, 196)
(130, 209)
693 of 1000, maximal objective function=-0.968147, time remaining: 00:00:01
3 Subset: Run 67 of 126 (best like=-0.968147)
13 input vectors with 3 parameters
Generating 126 parameters:
(5, 14)
(11, 27)
(19, 40)
(25, 53)
(31, 66)
(39, 79)
(46, 92)
(56, 105)
(61, 118)
(72, 131)
(79, 144)
(87, 157)
(92, 170)
(95, 183)
(102, 196)
(104, 209)
(117, 222)
(120, 235)
(126, 248)
13 input vectors with 3 parameters
Generating 126 parameters:
(14, 14)
(23, 27)
(31, 40)
(38, 53)
(47, 66)
(59, 79)
(72, 92)
(76, 105)
(83, 118)
(94, 131)
(101, 144)
(108, 157)
(112, 170)
(118, 183)
(131, 196)
13 input vectors with 3 parameters
Generating 126 parameters:
(12, 14)
(20, 27)
(27, 40)
(33, 53)
(38, 66)
(50, 79)
(55, 92)
(62, 105)
(69, 118)
(76, 131)
(88, 144)
(94, 157)
(106, 170)
(118, 183)
(128, 196)
50 input vectors with 3 parameters
Generating 126 parameters:
(47, 51)
(87, 101)
(118, 151)
(147, 201)
13 input vectors with 3 parameters
Generating 126 parameters:
(11, 14)
(16, 27)
(29, 40)
(40, 53)
(45, 66)
(54, 79)
(60, 92)
(71, 105)
(84, 118)
(90, 131)
(97, 144)
(109, 157)
(116, 170)
(128, 183)
13 input vectors with 3 parameters
Generating 126 parameters:
(9, 14)
(15, 27)
(20, 40)
(28, 53)
(33, 66)
(41, 79)
13 input vectors with 3 parameters
Generating 126 parameters:
(51, 92)
(61, 105)
(67, 118)
(14, 14)
(71, 131)
(75, 144)
(79, 157)
(16, 27)
(85, 170)
(89, 183)
(99, 196)
(22, 40)
(105, 209)
(109, 222)
(119, 235)
(30, 53)
(132, 248)
(40, 66)
(47, 79)
(56, 92)
(63, 105)
(68, 118)
(80, 131)
(89, 144)
(95, 157)
(107, 170)
(120, 183)
(130, 196)
689 of 1000, maximal objective function=-1.07469, time remaining: 00:00:01
3 Subset: Run 63 of 126 (best like=-1.07469)
13 input vectors with 3 parameters
Generating 126 parameters:
(6, 14)
(16, 27)
(26, 40)
(39, 53)
(44, 66)
(49, 79)
(53, 92)
(61, 105)
(67, 118)
(71, 131)
(76, 144)
(77, 157)
(82, 170)
(89, 183)
(95, 196)
(106, 209)
(112, 222)
(114, 235)
(125, 248)
(133, 261)
13 input vectors with 3 parameters
Generating 126 parameters:
(7, 14)
(9, 27)
(15, 40)
(27, 53)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 1.11 seconds
Total Repetitions: 1000
Maximal objective value: -0.875408
Corresponding parameter setting:
x0: 0.416375
x1: 14.3785
x2: 99.2236
******************************

(36, 66)
(48, 79)
(60, 92)
(68, 105)
(70, 118)
(72, 131)
(77, 144)
(80, 157)
(84, 170)
(91, 183)
(103, 196)
(109, 209)
(115, 222)
(126, 235)
13 input vectors with 3 parameters
Generating 126 parameters:
(14, 14)
(25, 27)
(30, 40)
(39, 53)
(40, 66)
(43, 79)
(55, 92)
(60, 105)
(66, 118)
(69, 131)
(73, 144)
(81, 157)
(90, 170)
(98, 183)
(100, 196)
(106, 209)
(113, 222)
(121, 235)
(127, 248)
13 input vectors with 3 parameters
Generating 126 parameters:
(7, 14)
13 input vectors with 3 parameters
Generating 126 parameters:
(16, 27)
(13, 14)
(17, 27)
(22, 40)
(18, 40)
(32, 53)
(37, 66)
(40, 79)
(22, 53)
(51, 92)
(56, 105)
(64, 118)
(34, 66)
(69, 131)
(82, 144)
(86, 157)
(44, 79)
(99, 170)
(104, 183)
(111, 196)
(56, 92)
(122, 209)
(126, 222)
13 input vectors with 3 parameters
Generating 126 parameters:
(7, 14)
(61, 105)
(10, 27)
(18, 40)
(24, 53)
(71, 118)
(27, 66)
(32, 79)
(38, 92)
(82, 131)
(45, 105)
(55, 118)
(62, 131)
(89, 144)
(67, 144)
(74, 157)
(79, 170)
(102, 157)
(84, 183)
(93, 196)
(96, 209)
(105, 170)
(103, 222)
(108, 235)
(120, 248)
(118, 183)
(121, 261)
(124, 274)
(134, 287)
(126, 196)
13 input vectors with 3 parameters
Generating 126 parameters:
(14, 14)
(18, 27)
(21, 40)
(25, 53)
(27, 66)
(33, 79)
(40, 92)
(46, 105)
(49, 118)
(58, 131)
(65, 144)
(71, 157)
(77, 170)
(90, 183)
(102, 196)
(113, 209)
(126, 222)
13 input vectors with 3 parameters
Generating 126 parameters:
(4, 14)
(12, 27)
(18, 40)
(24, 53)
(30, 66)
(43, 79)
(50, 92)
(51, 105)
(57, 118)
(63, 131)
(72, 144)
(85, 157)
(91, 170)
(102, 183)
(113, 196)
(118, 209)
13 input vectors with 3 parameters
Generating 126 parameters:
(122, 222)
(14, 14)
(126, 235)
13 input vectors with 3 parameters
Generating 126 parameters:
(27, 27)
(2, 14)
(30, 40)
(13, 27)
(34, 53)
(18, 40)
(22, 53)
(46, 66)
(32, 66)
(51, 79)
(36, 79)
(56, 92)
(46, 92)
(63, 105)
(56, 105)
(67, 118)
(64, 118)
(77, 131)
(70, 131)
(78, 144)
(72, 144)
(87, 157)
(78, 157)
(97, 170)
(80, 170)
(104, 183)
(90, 183)
13 input vectors with 3 parameters
Generating 126 parameters:
(106, 196)
(103, 196)
(3, 14)
(109, 209)
(115, 209)
(113, 222)
(9, 27)
(125, 222)
(121, 235)
(15, 40)
(136, 235)
(124, 248)
(28, 53)
(130, 261)
(35, 66)
(40, 79)
(50, 92)
13 input vectors with 3 parameters
Generating 126 parameters:
(57, 105)
(4, 14)
(6, 27)
(60, 118)
(10, 40)
(16, 53)
(16, 66)
(69, 131)
(20, 79)
(22, 92)
(28, 105)
(78, 144)
(34, 118)
(38, 131)
(42, 144)
(90, 157)
(47, 157)
(53, 170)
(58, 183)
(97, 170)
(70, 196)
(78, 209)
(100, 183)
(81, 222)
(94, 235)
(99, 248)
(108, 196)
(108, 261)
(111, 274)
(120, 287)
(113, 209)
(125, 300)
(136, 313)
(124, 222)
(126, 235)
13 input vectors with 3 parameters
Generating 126 parameters:
(7, 14)
(14, 27)
(17, 40)
(25, 53)
(28, 66)
(35, 79)
(46, 92)
(52, 105)
(55, 118)
(61, 131)
(72, 144)
(82, 157)
(87, 170)
(92, 183)
(96, 196)
(102, 209)
(111, 222)
(120, 235)
(133, 248)
13 input vectors with 3 parameters
Generating 126 parameters:
(6, 14)
(11, 27)
(19, 40)
(32, 53)
(39, 66)
(47, 79)
(53, 92)
(62, 105)
(66, 118)
(72, 131)
(78, 144)
(87, 157)
(91, 170)
(100, 183)
(109, 196)
(118, 209)
(124, 222)
(132, 235)
13 input vectors with 3 parameters
Generating 126 parameters:
(2, 14)
(9, 27)
(16, 40)
(26, 53)
(31, 66)
(35, 79)
(38, 92)
(43, 105)
(46, 118)
(59, 131)
(65, 144)
(68, 157)
(76, 170)
(84, 183)
(90, 196)
(99, 209)
(100, 222)
(111, 235)
(115, 248)
(125, 261)
(129, 274)
13 input vectors with 3 parameters
Generating 126 parameters:
(4, 14)
(4, 27)
(16, 40)
(19, 53)
(20, 66)
(30, 79)
(32, 92)
(37, 105)
(41, 118)
(46, 131)
(47, 144)
(53, 157)
(55, 170)
(61, 183)
(64, 196)
(68, 209)
(73, 222)
(81, 235)
(91, 248)
(93, 261)
(96, 274)
(97, 287)
(100, 300)
(104, 313)
(109, 326)
(113, 339)
(117, 352)
(122, 365)
(131, 378)
13 input vectors with 3 parameters
Generating 126 parameters:
(9, 14)
(17, 27)
(21, 40)
(27, 53)
(31, 66)
(39, 79)
(44, 92)
(49, 105)
(53, 118)
(57, 131)
(60, 144)
(67, 157)
(72, 170)
(72, 183)
(83, 196)
(89, 209)
(100, 222)
(105, 235)
(111, 248)
(112, 261)
(114, 274)
(119, 287)
(124, 300)
(127, 313)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 1.05 seconds
Total Repetitions: 1000
Maximal objective value: -0.904149
Corresponding parameter setting:
x0: 0.460337
x1: 20.8
x2: 99.9291
******************************

Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 0.99 seconds
Total Repetitions: 1000
Maximal objective value: -1.38383
Corresponding parameter setting:
x0: 0.896784
x1: 29.6582
x2: 106.307
******************************

13 input vectors with 3 parameters
Generating 126 parameters:
(8, 14)
(19, 27)
(28, 40)
(41, 53)
(46, 66)
(51, 79)
(55, 92)
(58, 105)
(63, 118)
(71, 131)
(75, 144)
(78, 157)
(84, 170)
(89, 183)
(93, 196)
(106, 209)
(116, 222)
(122, 235)
(125, 248)
(131, 261)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 2.41 seconds
Total Repetitions: 1000
Maximal objective value: -0.877121
Corresponding parameter setting:
x0: 0.734379
x1: 18.0511
x2: 99.3818
******************************

13 input vectors with 3 parameters
Generating 126 parameters:
(9, 14)
(13, 27)
(20, 40)
(27, 53)
(33, 66)
(46, 79)
(49, 92)
(61, 105)
(70, 118)
(78, 131)
(83, 144)
(87, 157)
(89, 170)
(91, 183)
(101, 196)
(114, 209)
(127, 222)
13 input vectors with 3 parameters
Generating 126 parameters:
(3, 14)
(11, 27)
(14, 40)
(24, 53)
(29, 66)
(42, 79)
(55, 92)
(61, 105)
(67, 118)
(71, 131)
(78, 144)
(85, 157)
(90, 170)
(101, 183)
(107, 196)
(117, 209)
(118, 222)
(124, 235)
(129, 248)
13 input vectors with 3 parameters
Generating 126 parameters:
(14, 14)
(27, 27)
(33, 40)
(39, 53)
(41, 66)
(52, 79)
(62, 92)
(66, 105)
(69, 118)
(72, 131)
(84, 144)
(95, 157)
(100, 170)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 2.61 seconds
Total Repetitions: 1000
Maximal objective value: -1.07959
Corresponding parameter setting:
x0: 0.919912
x1: 23.0394
x2: 102.274
******************************

(109, 183)
(115, 196)
(117, 209)
(123, 222)
(133, 235)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 1.15 seconds
Total Repetitions: 1000
Maximal objective value: -0.870548
Corresponding parameter setting:
x0: 0.513447
x1: 17.6233
x2: 100.01
******************************

13 input vectors with 3 parameters
Generating 126 parameters:
13 input vectors with 3 parameters
Generating 126 parameters:
(12, 14)
(11, 14)
(18, 27)
(23, 27)
(26, 40)
(27, 40)
(34, 53)
(32, 53)
(43, 66)
(36, 66)
(54, 79)
(46, 79)
(62, 92)
(54, 92)
(66, 105)
(64, 105)
(71, 118)
(77, 118)
(80, 131)
(81, 131)
(89, 144)
(86, 144)
(90, 157)
(93, 157)
(98, 170)
(97, 170)
(109, 183)
(101, 183)
(119, 196)
(103, 196)
(122, 209)
(128, 222)
(112, 209)
(114, 222)
(125, 235)
(127, 248)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 2.53 seconds
Total Repetitions: 1000
Maximal objective value: -0.944238
Corresponding parameter setting:
x0: 0.653257
x1: 18.7701
x2: 100.45
******************************

13 input vectors with 3 parameters
Generating 126 parameters:
(5, 14)
(5, 27)
(7, 40)
(16, 53)
(18, 66)
(26, 79)
(32, 92)
(38, 105)
(42, 118)
(45, 131)
(49, 144)
(59, 157)
(65, 170)
(69, 183)
(75, 196)
(86, 209)
(98, 222)
(106, 235)
(111, 248)
(117, 261)
(121, 274)
(122, 287)
(126, 300)
13 input vectors with 3 parameters
Generating 126 parameters:
(6, 14)
(13, 27)
(19, 40)
(22, 53)
(27, 66)
(34, 79)
(36, 92)
(38, 105)
(41, 118)
(48, 131)
(52, 144)
(63, 157)
(68, 170)
(76, 183)
(83, 196)
(89, 209)
(95, 222)
(101, 235)
(103, 248)
(108, 261)
(115, 274)
(117, 287)
(122, 300)
(130, 313)

100%|████████████████████████████████████████| 20/20 [00:00<00:00, 20738.22it/s]

Stopping samplig
Stopping samplig

*** Final SPOTPY summary ***

*** Final SPOTPY summary ***
Total Duration: 2.52 seconds
Total Repetitions: Total Duration: 2.67 seconds1000

Total Repetitions: Maximal objective value: -1.070391000

Corresponding parameter setting:
Maximal objective value: -0.875831
Corresponding parameter setting:
x0: 0.766408
x1: 19.4197
x2: 101.249
******************************

x0: 0.86008
x1: 20.7455
x2: 100.499
******************************

Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 2.72 seconds
Total Repetitions: 1000
Maximal objective value: -1.07558
Corresponding parameter setting:
x0: 0.34276
x1: 9.323
x2: 96.8252
******************************

Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 2.71 seconds
Total Repetitions: 1000
Maximal objective value: -0.966446
Corresponding parameter setting:
x0: 0.545657
x1: 20.2531
x2: 100.955
******************************

Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']



50 input vectors with 3 parameters
Generating 126 parameters:
(32, 51)
(80, 101)
(106, 151)
(130, 201)
50 input vectors with 3 parameters
Generating 126 parameters:
(27, 51)
(75, 101)
(113, 151)
(157, 201)
50 input vectors with 3 parameters
Generating 126 parameters:
(46, 51)
(95, 101)
(143, 151)
50 input vectors with 3 parameters
Generating 126 parameters:
(51, 51)
(89, 101)
(138, 151)
50 input vectors with 3 parameters
Generating 126 parameters:
(51, 51)
(101, 101)
(141, 151)
50 input vectors with 3 parameters
Generating 126 parameters:
(28, 51)50 input vectors with 3 parameters
Generating 126 parameters:

(58, 101)
(44, 51)
(102, 151)
(71, 101)
(144, 201)
(120, 151)
(146, 201)
50 input vectors with 3 parameters
Generating 126 parameters:
(44, 51)
(84, 101)
(134, 151)
13 input vectors with 3 parameters
Generating 126 parameters:
(14, 14)
(21, 27)
(33, 40)
(41, 53)
(48, 66)
(60, 79)
(63, 92)
(66, 105)
(71, 118)
(76, 131)
(78, 144)
(80, 157)
(86, 170)
(97, 183)
(105, 196)
(111, 209)
(122, 222)
(126, 235)
13 input vectors with 3 parameters
Generating 126 parameters:
(7, 14)
13 input vectors with 3 parameters
Generating 126 parameters:
(17, 27)
(14, 14)
(24, 40)
(17, 27)
(29, 53)
(23, 40)
(38, 66)
(36, 53)
(45, 79)
(45, 66)
(58, 92)
(54, 79)
(60, 105)
(67, 92)
(73, 118)
(85, 131)
(69, 105)
(98, 144)
(72, 118)
(109, 157)
(85, 131)
(115, 170)
(90, 144)
(119, 183)
(102, 157)
(129, 196)
(106, 170)
(116, 183)
(120, 196)
(128, 209)
13 input vectors with 3 parameters
Generating 126 parameters:
(10, 14)
(23, 27)
(33, 40)
(40, 53)
(50, 66)
(58, 79)
(67, 92)
(74, 105)
(87, 118)
(99, 131)
(108, 144)
(115, 157)
(117, 170)
(124, 183)
(133, 196)
13 input vectors with 3 parameters
Generating 126 parameters:
(10, 14)
(14, 27)
(16, 40)
(20, 53)
(25, 66)
(29, 79)
(35, 92)
(42, 105)
(49, 118)
(52, 131)
(58, 144)
(64, 157)
(75, 170)
(86, 183)
(98, 196)
(103, 209)
(108, 222)
(113, 235)
(119, 248)
(123, 261)
(131, 274)
13 input vectors with 3 parameters
Generating 126 parameters:
(6, 14)
(9, 27)
(21, 40)
(25, 53)
(30, 66)
(39, 79)
(52, 92)
(55, 105)
(65, 118)
(71, 131)
(76, 144)
(79, 157)
(86, 170)
(90, 183)
(99, 196)
(107, 209)
(120, 222)
(122, 235)
(129, 248)
13 input vectors with 3 parameters
Generating 126 parameters:
(11, 14)
(14, 27)
(14, 40)
(15, 53)
(19, 66)
(28, 79)
(31, 92)
(35, 105)
(41, 118)
(46, 131)
(47, 144)
(50, 157)
(53, 170)
(55, 183)
(57, 196)
(62, 209)
13 input vectors with 3 parameters
Generating 126 parameters:
(68, 222)
(4, 14)
(75, 235)
(14, 27)
(76, 248)
(16, 40)
(81, 261)
(17, 53)
(86, 274)
(25, 66)(89, 287)

(96, 300)
(36, 79)
(104, 313)
(45, 92)
(104, 326)
(51, 105)
(108, 339)
(52, 118)
(112, 352)
(57, 131)
(118, 365)
(59, 144)
(118, 378)
(62, 157)
(122, 391)
(64, 170)
(131, 404)
(71, 183)
(74, 196)
(76, 209)
(79, 222)
(82, 235)
(83, 248)
(85, 261)
(86, 274)
(87, 287)
(90, 300)
(97, 313)
(100, 326)
(113, 339)
(117, 352)
(130, 365)
13 input vectors with 3 parameters
Generating 126 parameters:
(13, 14)
(19, 27)
(27, 40)
(34, 53)
(36, 66)
(42, 79)
(51, 92)
(59, 105)
(63, 118)
(74, 131)
(82, 144)
(85, 157)
(94, 170)
(100, 183)
(106, 196)
(107, 209)
(112, 222)
(121, 235)
(130, 248)
13 input vectors with 3 parameters
Generating 126 parameters:
(4, 14)
(9, 27)
(19, 40)
(23, 53)
(34, 66)
(38, 79)
(47, 92)
(56, 105)
(60, 118)
(66, 131)
(71, 144)
(75, 157)
(79, 170)
(82, 183)
(91, 196)
(100, 209)
(108, 222)
(120, 235)
(131, 248)
13 input vectors with 3 parameters
Generating 126 parameters:
(1, 14)
(6, 27)
13 input vectors with 3 parameters
Generating 126 parameters:
(10, 40)
(7, 14)
(18, 53)
(16, 27)
(25, 66)
(26, 79)
(24, 40)
(30, 92)
(32, 53)
(38, 105)
(43, 66)
(48, 79)
(44, 118)
(54, 92)
(49, 131)
(62, 105)
(56, 144)
(68, 118)
(57, 157)
(77, 131)
(58, 170)
(84, 144)
(67, 183)(87, 157)

(94, 170)
(68, 196)
(105, 183)
(70, 209)
(112, 196)
(83, 222)
(121, 209)
(88, 235)
(130, 222)
(94, 248)
(98, 261)
(104, 274)
(114, 287)
(119, 300)
(122, 313)
(131, 326)
13 input vectors with 3 parameters
Generating 126 parameters:
(13, 14)
(21, 27)
(32, 40)
(35, 53)
(41, 66)
(46, 79)
(55, 92)
(59, 105)
(67, 118)
(74, 131)
(77, 144)
(90, 157)
(94, 170)
(97, 183)
(104, 196)
(110, 209)
(115, 222)
(118, 235)
(124, 248)
(130, 261)
13 input vectors with 3 parameters
Generating 126 parameters:
(10, 14)
(15, 27)
(18, 40)
(23, 53)
(26, 66)
(30, 79)
(43, 92)
(46, 105)
(54, 118)
(65, 131)
(77, 144)
(80, 157)
(89, 170)
(96, 183)
(101, 196)
(105, 209)
(109, 222)
(122, 235)
(125, 248)
(135, 261)
13 input vectors with 3 parameters
Generating 126 parameters:
(2, 14)
(4, 27)
(10, 40)
(14, 53)
(26, 66)
(33, 79)
(43, 92)
(46, 105)
(52, 118)
(61, 131)
(62, 144)
(65, 157)
(70, 170)
(78, 183)
(80, 196)
(83, 209)
(86, 222)
(99, 235)
(107, 248)
(112, 261)
(117, 274)
(119, 287)
(122, 300)
(128, 313)
13 input vectors with 3 parameters
Generating 126 parameters:
(9, 14)
(18, 27)
(30, 40)
(36, 53)
(41, 66)
(44, 79)
(50, 92)
(57, 105)
(68, 118)
(75, 131)
(80, 144)
(84, 157)
(92, 170)
(98, 183)
(106, 196)
(109, 209)
(113, 222)
(114, 235)
(118, 248)
(122, 261)
(132, 274)
13 input vectors with 3 parameters
Generating 126 parameters:
(6, 14)
(13, 27)
(16, 40)
(18, 53)
(23, 66)
(29, 79)
(32, 92)
(42, 105)
(47, 118)
(54, 131)
(61, 144)
(72, 157)
(77, 170)
(87, 183)
(91, 196)
(92, 209)
(95, 222)
(98, 235)
(103, 248)
(110, 261)
(115, 274)
(117, 287)
(119, 300)
(123, 313)
(132, 326)
13 input vectors with 3 parameters
Generating 126 parameters:
(4, 14)
(12, 27)
(16, 40)
(20, 53)
(29, 66)
(36, 79)
(41, 92)
(46, 105)
(53, 118)
(63, 131)
(67, 144)
(70, 157)
(77, 170)
(84, 183)
(85, 196)
(92, 209)
(105, 222)
(108, 235)
(121, 248)
(126, 261)
13 input vectors with 3 parameters
Generating 126 parameters:
(4, 14)
(14, 27)
(21, 40)
(26, 53)
(36, 66)
(41, 79)
(48, 92)
(49, 105)
(57, 118)
(64, 131)
(69, 144)
(73, 157)
(77, 170)
(80, 183)
(83, 196)
(88, 209)
(101, 222)
(111, 235)
(117, 248)
(121, 261)
(128, 274)
13 input vectors with 3 parameters
Generating 126 parameters:
(14, 14)
(25, 27)
(29, 40)
(42, 53)
(55, 66)
(68, 79)
(74, 92)
(86, 105)
(92, 118)
(103, 131)
(115, 144)
(123, 157)
(136, 170)
13 input vectors with 3 parameters
Generating 126 parameters:
(5, 14)
(12, 27)
(15, 40)
(17, 53)
(19, 66)
(21, 79)
(26, 92)
(26, 105)
(26, 118)
(27, 131)
(28, 144)
(32, 157)
(34, 170)
(35, 183)
(41, 196)
(52, 209)
(53, 222)
(58, 235)
(60, 248)
(64, 261)
(73, 274)
(79, 287)
(80, 300)
(81, 313)
(90, 326)
(91, 339)
(98, 352)
(98, 365)
(102, 378)
(104, 391)
(105, 404)
(111, 417)
(116, 430)
(118, 443)
(127, 456)
13 input vectors with 3 parameters
Generating 126 parameters:
(3, 14)
(16, 27)
(20, 40)
(28, 53)
(37, 66)
(50, 79)
(56, 92)
(68, 105)
(76, 118)
(88, 131)
(101, 144)
(114, 157)
(118, 170)
(124, 183)
(134, 196)
13 input vectors with 3 parameters
Generating 126 parameters:
(8, 14)
(9, 27)
(10, 40)
(12, 53)
(20, 66)
(27, 79)
(36, 92)
(42, 105)
(54, 118)
(64, 131)
50 input vectors with 3 parameters
Generating 126 parameters:
(73, 144)
(86, 157)
(91, 170)
(92, 183)
(43, 51)
(97, 196)
(109, 209)
(120, 222)
(82, 101)(123, 235)

(128, 248)
(114, 151)
(145, 201)
13 input vectors with 3 parameters
Generating 126 parameters:
(9, 14)
(15, 27)
(27, 40)
(35, 53)
(40, 66)
(42, 79)
(49, 92)
(56, 105)
(62, 118)
(72, 131)
(78, 144)
(91, 157)
(101, 170)
(113, 183)
(121, 196)
(134, 209)
50 input vectors with 3 parameters
Generating 126 parameters:
(43, 51)
(74, 101)
(96, 151)
(136, 201)
50 input vectors with 3 parameters
Generating 126 parameters:
(31, 51)
(77, 101)
(105, 151)
(149, 201)
13 input vectors with 3 parameters
Generating 126 parameters:
(6, 14)
(7, 27)
(9, 40)
(22, 53)
(30, 66)
(33, 79)
(39, 92)
(51, 105)
(54, 118)
(64, 131)
(67, 144)
(70, 157)
(76, 170)
(87, 183)
(91, 196)
(95, 209)
(99, 222)
(101, 235)
(105, 248)
(112, 261)
(115, 274)
(122, 287)
(126, 300)
13 input vectors with 3 parameters
Generating 126 parameters:
(10, 14)
(13, 27)
(15, 40)
(28, 53)
(36, 66)
(38, 79)
(42, 92)
(45, 105)
(48, 118)
(52, 131)
(57, 144)
(60, 157)
(66, 170)
(71, 183)
(77, 196)
(86, 209)
(96, 222)
(102, 235)
(106, 248)
(113, 261)
(117, 274)
(122, 287)
(134, 300)
50 input vectors with 3 parameters
Generating 126 parameters:
50 input vectors with 3 parameters
Generating 126 parameters:
(38, 51)
(23, 51)(75, 101)

(63, 101)
(122, 151)
(113, 151)
(159, 201)
(148, 201)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 1.54 seconds
Total Repetitions: 1000
Maximal objective value: -1.44187
Corresponding parameter setting:
x0: 0.76636
x1: 13.7661
x2: 102.354
******************************

Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 1.54 seconds
Total Repetitions: 1000
Maximal objective value: -1.16971
Corresponding parameter setting:
x0: 0.710645
x1: 15.6785
x2: 98.2325
******************************

Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 1.53 seconds
Total Repetitions: 1000
Maximal objective value: -1.77318
Corresponding parameter setting:
x0: 0.890652
x1: 38.9738
x2: 110.22
******************************

Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
50 input vectors with 3 parameters
Generating 126 parameters:
(32, 51)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 1.55 seconds
Total Repetitions: 1000
Maximal objective value: -1.27424
Corresponding parameter setting:
x0: 0.554559
x1: 7.51151
x2: 94.2252
******************************

Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
13 input vectors with 3 parameters
Generating 126 parameters:
(10, 14)
(14, 27)
(24, 40)
(31, 53)
(40, 66)
(51, 79)
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData'](61, 92)

(71, 105)
(82, 118)
(93, 131)
(100, 144)
(62, 101)
(112, 157)
(122, 170)
(128, 183)
(107, 151)
(145, 201)
50 input vectors with 3 parameters
Generating 126 parameters:
(40, 51)
(69, 101)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 1.56 seconds
Total Repetitions: 1000
Maximal objective value: -1.50048
Corresponding parameter setting:
x0: 0.616655
x1: 15.7475
x2: 99.0592
******************************

(108, 151)
(146, 201)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 1.66 seconds
Total Repetitions: 1000
Maximal objective value: -1.3866
Corresponding parameter setting:
x0: 0.808644
x1: 18.3113
x2: 98.9487
******************************

50 input vectors with 3 parameters
Generating 126 parameters:
(46, 51)
(73, 101)
(111, 151)
(144, 201)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 1.61 seconds
Total Repetitions: 1000
Maximal objective value: -1.75437
Corresponding parameter setting:
x0: 0.749347
x1: 33.6098
x2: 103.406
******************************

Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 1.66 seconds
Total Repetitions: 1000
Maximal objective value: -1.27885
Corresponding parameter setting:
x0: 0.69587
x1: 15.704
x2: 101.109
******************************

13 input vectors with 3 parameters
Generating 126 parameters:
(13, 14)
(21, 27)
(26, 40)
(27, 53)
(30, 66)
(43, 79)
(47, 92)
(50, 105)
(54, 118)
(57, 131)
(65, 144)
(72, 157)
(79, 170)
(88, 183)
(93, 196)
(103, 209)
(111, 222)
(122, 235)
(123, 248)
(132, 261)
13 input vectors with 3 parameters
Generating 126 parameters:
(11, 14)
(17, 27)
(19, 40)
(29, 53)
(41, 66)
(46, 79)
(54, 92)
(56, 105)
(67, 118)
(77, 131)
(82, 144)
(86, 157)
(97, 170)
(108, 183)
13 input vectors with 3 parameters
Generating 126 parameters:
(116, 196)
(11, 14)
(126, 209)
13 input vectors with 3 parameters
Generating 126 parameters:
(21, 27)
(4, 14)
(6, 27)
(34, 40)
(14, 40)
(47, 53)
(20, 53)
(48, 66)
(26, 66)
(54, 79)
(31, 79)
(57, 92)
(35, 92)
(67, 105)
(42, 105)
(80, 118)
(49, 118)
(85, 131)
(56, 131)
(90, 144)
(61, 144)
(95, 157)
(66, 157)
(101, 170)
(78, 170)
(105, 183)
(84, 183)
(110, 196)
(88, 196)
(114, 209)
(91, 209)
(117, 222)
(95, 222)
(128, 235)
(97, 235)
(104, 248)
(114, 261)
(118, 274)
(131, 287)
13 input vectors with 3 parameters
Generating 126 parameters:
(6, 14)
(10, 27)
(21, 40)
(26, 53)
(32, 66)
(45, 79)
(57, 92)
(68, 105)
(81, 118)
(87, 131)
(90, 144)
(92, 157)
(97, 170)
(105, 183)
(113, 196)
(126, 209)
13 input vectors with 3 parameters
Generating 126 parameters:
(14, 14)
(26, 27)
(38, 40)
(41, 53)
(44, 66)
(51, 79)
(57, 92)
(59, 105)
(61, 118)
(65, 131)
(77, 144)
(82, 157)
(86, 170)
(91, 183)
(98, 196)
(104, 209)
(112, 222)
(125, 235)
(135, 248)
13 input vectors with 3 parameters
Generating 126 parameters:
(12, 14)
(19, 27)
(32, 40)
(38, 53)
(43, 66)
(52, 79)
(55, 92)
(63, 105)
(69, 118)
(82, 131)
(89, 144)
(97, 157)
(110, 170)
(119, 183)
(126, 196)
13 input vectors with 3 parameters
Generating 126 parameters:
(6, 14)
(12, 27)
(22, 40)
(25, 53)
(37, 66)
(46, 79)
(50, 92)
(61, 105)
(69, 118)
(78, 131)
(85, 144)
(95, 157)
(101, 170)
(110, 183)
13 input vectors with 3 parameters
Generating 126 parameters:
(115, 196)
(127, 209)
(11, 14)
(20, 27)
(28, 40)
(40, 53)
(47, 66)
(56, 79)
(69, 92)
(75, 105)
(86, 118)
(92, 131)
(96, 144)
(109, 157)
(121, 170)
13 input vectors with 3 parameters
Generating 126 parameters:
(128, 183)
(5, 14)
(15, 27)
(23, 40)
(28, 53)
(34, 66)
(45, 79)
(47, 92)
(50, 105)
(59, 118)
(66, 131)
(72, 144)
(83, 157)
(95, 170)
(107, 183)
(115, 196)
(121, 209)
(127, 222)
13 input vectors with 3 parameters
Generating 126 parameters:
(9, 14)
(14, 27)
(17, 40)
(24, 53)
(32, 66)
(41, 79)
(48, 92)
(54, 105)
(60, 118)
(72, 131)
(77, 144)
(78, 157)
(86, 170)
(95, 183)
(106, 196)
(109, 209)
876 of 1000, maximal objective function=-1.43176, time remaining: 00:00:00
(112, 222)4 Subset: Run 124 of 126 (best like=-1.43176)

(121, 235)
13 input vectors with 3 parameters
Generating 126 parameters:
(133, 248)
(6, 14)
(13, 27)
(18, 40)
(23, 53)
(28, 66)
(31, 79)
(42, 92)
(52, 105)
(53, 118)
(57, 131)
(64, 144)
(72, 157)
(75, 170)
(80, 183)
(87, 196)
(94, 209)
(97, 222)
(104, 235)
(109, 248)
(121, 261)
(123, 274)
(126, 287)
13 input vectors with 3 parameters
Generating 126 parameters:
(9, 14)
678 of 1000, maximal objective function=-1.57939, time remaining: 00:00:01
3 Subset: Run 52 of 126 (best like=-1.57939)
(15, 27)
(20, 40)
(29, 53)
(35, 66)
(40, 79)
(48, 92)
(58, 105)
(68, 118)
(74, 131)
(75, 144)
(80, 157)
645 of 1000, maximal objective function=-2.11889, time remaining: 00:00:01
3 Subset: Run 19 of 126 (best like=-2.11889)(85, 170)

(96, 183)
(102, 196)
(105, 209)
(115, 222)
(122, 235)
(127, 248)
651 of 1000, maximal objective function=-1.83837, time remaining: 00:00:01
3 Subset: Run 25 of 126 (best like=-1.83837)
683 of 1000, maximal objective function=-1.77892, time remaining: 00:00:01
3 Subset: Run 57 of 126 (best like=-1.77892)
659 of 1000, maximal objective function=-1.81529, time remaining: 00:00:01
3 Subset: Run 33 of 126 (best like=-1.81529)
638 of 1000, maximal objective function=-1.97162, time remaining: 00:00:01
3 Subset: Run 12 of 126 (best like=-1.97162)
623 of 1000, maximal objective function=-1.73093, time remaining: 00:00:01
2 Subset: Run 123 of 126 (best like=-1.73093)
13 input vectors with 3 parameters
Generating 126 parameters:
(3, 14)
(9, 27)
(22, 40)
(28, 53)
(33, 66)
(46, 79)
(53, 92)
(65, 105)
(78, 118)
(85, 131)
(89, 144)
(102, 157)
(108, 170)
(114, 183)
(118, 196)
(125, 209)
(135, 222)
50 input vectors with 3 parameters
Generating 126 parameters:
(46, 51)
(81, 101)
(119, 151)
(152, 201)
50 input vectors with 3 parameters
Generating 126 parameters:
(51, 51)
(96, 101)
(145, 151)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 2.15 seconds
Total Repetitions: 1000
Maximal objective value: -1.37487
Corresponding parameter setting:
x0: 0.838814
x1: 24.7306
x2: 99.3064
******************************

50 input vectors with 3 parameters
Generating 126 parameters:
(31, 51)
(74, 101)
(102, 151)
(143, 201)
13 input vectors with 3 parameters
Generating 126 parameters:
(9, 14)
(15, 27)
(26, 40)
(34, 53)
(45, 66)
(56, 79)
(69, 92)
(74, 105)
(79, 118)
(87, 131)
(97, 144)
(108, 157)
(112, 170)
(119, 183)
(125, 196)
(129, 209)
13 input vectors with 3 parameters
Generating 126 parameters:
(14, 14)
(16, 27)
(25, 40)
(30, 53)
(40, 66)
(53, 79)
(59, 92)
(65, 105)
(69, 118)
(82, 131)
(93, 144)
(97, 157)
(103, 170)
(112, 183)
(114, 196)
(118, 209)
(126, 222)
13 input vectors with 3 parameters
Generating 126 parameters:
(12, 14)
(23, 27)
13 input vectors with 3 parameters
Generating 126 parameters:
(33, 40)
(34, 53)
(44, 66)
(53, 79)
(6, 14)
(56, 92)
13 input vectors with 3 parameters
Generating 126 parameters:
(61, 105)
(74, 118)
(13, 27)
(78, 131)
(83, 144)
(5, 14)
(93, 157)
(99, 170)
(22, 40)
(112, 183)
(10, 27)
(124, 196)
(127, 209)
(31, 53)
(17, 40)
(42, 66)
(24, 53)
(51, 79)
(37, 66)
(57, 92)
(45, 79)
(62, 105)
(52, 92)
(68, 118)
(60, 105)
(72, 131)
(63, 118)
(76, 144)
(64, 131)
(82, 157)
(75, 144)
(90, 170)
(80, 157)
(102, 183)
(85, 170)
(113, 196)
(95, 183)
(119, 209)
(105, 196)
(127, 222)
(118, 209)
(131, 222)
13 input vectors with 3 parameters
Generating 126 parameters:
(5, 14)
(7, 27)
(17, 40)
(23, 53)
(31, 66)
(34, 79)
(44, 92)
(52, 105)
(57, 118)
(61, 131)
(65, 144)
(68, 157)
(81, 170)
(88, 183)
(99, 196)
(110, 209)
(113, 222)
(121, 235)
(124, 248)
(133, 261)
13 input vectors with 3 parameters
Generating 126 parameters:
(12, 14)
(22, 27)
(30, 40)
(43, 53)
(49, 66)
(61, 79)
(64, 92)
(72, 105)
(78, 118)
(85, 131)
(94, 144)
(100, 157)
(111, 170)
(122, 183)
(134, 196)
13 input vectors with 3 parameters
Generating 126 parameters:
(8, 14)
(14, 27)
(24, 40)
(29, 53)
(36, 66)
(44, 79)
(48, 92)
(51, 105)
(58, 118)
(65, 131)
(71, 144)
(74, 157)
(77, 170)
(86, 183)
(91, 196)
(96, 209)
(109, 222)
(114, 235)
(126, 248)
13 input vectors with 3 parameters
Generating 126 parameters:
(12, 14)
(19, 27)
(29, 40)
13 input vectors with 3 parameters
Generating 126 parameters:
(40, 53)
(49, 66)
(51, 79)
(7, 14)
(60, 92)
(71, 105)
(75, 118)
(77, 131)
(14, 27)
(82, 144)
(95, 157)
(104, 170)
(23, 40)
(109, 183)
(119, 196)
(122, 209)
(32, 53)
(135, 222)
(40, 66)
(44, 79)
13 input vectors with 3 parameters
Generating 126 parameters:
(46, 92)
(4, 14)
(59, 105)
(9, 27)
(67, 118)
(12, 40)
(78, 131)
(18, 53)
(87, 144)
(22, 66)
(95, 157)
(32, 79)
(99, 170)
(38, 92)
(104, 183)
(42, 105)
(46, 118)
(107, 196)
(47, 131)
(117, 209)
(56, 144)
(125, 222)
(64, 157)
(138, 235)
(69, 170)
(73, 183)
(77, 196)
(84, 209)
(86, 222)
(95, 235)
(105, 248)
(110, 261)
(120, 274)
(126, 287)
13 input vectors with 3 parameters
Generating 126 parameters:
(4, 14)
(8, 27)
(9, 40)
(21, 53)
(26, 66)
(31, 79)
(44, 92)
(52, 105)
(57, 118)
(64, 131)
(70, 144)
(79, 157)
(90, 170)
(98, 183)
(111, 196)
(124, 209)
(129, 222)
13 input vectors with 3 parameters
Generating 126 parameters:
(7, 14)
(9, 27)
(17, 40)
(19, 53)
(22, 66)
(23, 79)
(33, 92)
(36, 105)
(43, 118)
(55, 131)
(59, 144)
(64, 157)
(69, 170)
(74, 183)
(85, 196)
(98, 209)
(105, 222)
(110, 235)
(114, 248)
(121, 261)
(122, 274)
(125, 287)
(131, 300)
13 input vectors with 3 parameters
Generating 126 parameters:
(5, 14)
(11, 27)
(18, 40)
(25, 53)
(31, 66)
(39, 79)
(48, 92)
(52, 105)
(65, 118)
50 input vectors with 3 parameters
Generating 126 parameters:
(75, 131)
(80, 144)
(29, 51)
(85, 157)
(87, 170)
(64, 101)
(96, 183)
(104, 196)
(88, 151)
(111, 209)
(124, 201)
(121, 222)
(128, 235)
(168, 251)
13 input vectors with 3 parameters
Generating 126 parameters:
(1, 14)
(2, 27)
(8, 40)
(13, 53)
(25, 66)
(31, 79)
(37, 92)
(46, 105)
(49, 118)
(57, 131)
(68, 144)
(72, 157)
(84, 170)
(96, 183)
(101, 196)
(107, 209)
(120, 222)
(125, 235)
(131, 248)
13 input vectors with 3 parameters
Generating 126 parameters:
(12, 14)
13 input vectors with 3 parameters
Generating 126 parameters:
(14, 27)
(13, 14)
(26, 27)
(37, 40)
(25, 40)
(44, 53)
(52, 66)
(25, 53)
(64, 79)
(70, 92)
(80, 105)
(27, 66)
(85, 118)
(94, 131)
(101, 144)
(30, 79)
(108, 157)
(112, 170)
(123, 183)
(37, 92)
(132, 196)
(40, 105)
(44, 118)
(47, 131)
(52, 144)
(53, 157)
(57, 170)
(59, 183)
(62, 196)
(65, 209)
(70, 222)
(78, 235)
(81, 248)
(85, 261)
(87, 274)
(90, 287)
(95, 300)
(100, 313)
(109, 326)
(118, 339)
(125, 352)
(128, 365)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 2.46 seconds
Total Repetitions: 1000
Maximal objective value: -1.64672
Corresponding parameter setting:
x0: 0.834234
x1: 29.9397
x2: 103.518
******************************

13 input vectors with 3 parameters
Generating 126 parameters:
(10, 14)
(22, 27)
(25, 40)
(38, 53)
(43, 66)
(55, 79)
(61, 92)
(67, 105)
(79, 118)
(91, 131)
(95, 144)
(108, 157)
(112, 170)
(125, 183)
(134, 196)
13 input vectors with 3 parameters
Generating 126 parameters:
(14, 14)
(22, 27)
(33, 40)
(43, 53)
(56, 66)
(67, 79)
(78, 92)
(87, 105)
(99, 118)
(111, 131)
(120, 144)
(126, 157)
13 input vectors with 3 parameters
Generating 126 parameters:
(12, 14)
(22, 27)
(32, 40)
(34, 53)
(46, 66)
(57, 79)
(65, 92)
(71, 105)
(77, 118)
(89, 131)
(95, 144)
(101, 157)
(107, 170)
13 input vectors with 3 parameters
Generating 126 parameters:
(116, 183)
(12, 14)
(128, 196)
(21, 27)
(27, 40)
(33, 53)
(42, 66)
(46, 79)
(58, 92)
(71, 105)
(75, 118)
(86, 131)
(96, 144)
(103, 157)
(115, 170)
(125, 183)
(130, 196)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 0.99 seconds
Total Repetitions: 1000
Maximal objective value: -1.3691
Corresponding parameter setting:
x0: 0.454437
x1: 24.3716
x2: 92.9005
******************************

13 input vectors with 3 parameters
Generating 126 parameters:
(2, 14)
(3, 27)
(4, 40)
(4, 53)
(12, 66)
(14, 79)
(17, 92)
(19, 105)
(23, 118)
(24, 131)
(30, 144)
(32, 157)
(37, 170)
(42, 183)
(46, 196)
(51, 209)
(56, 222)
(61, 235)
(63, 248)
(68, 261)
(72, 274)
(83, 287)
(86, 300)
(91, 313)
(97, 326)
(99, 339)
(101, 352)
(110, 365)
(119, 378)
(122, 391)
(129, 404)
13 input vectors with 3 parameters
Generating 126 parameters:
(3, 14)
(7, 27)
(17, 40)
(21, 53)
(22, 66)
(25, 79)
(38, 92)
(45, 105)
(48, 118)
(60, 131)
(64, 144)
(68, 157)
(69, 170)
(72, 183)
(78, 196)
(84, 209)
(89, 222)
13 input vectors with 3 parameters
Generating 126 parameters:
(94, 235)
(11, 14)
(22, 27)
(28, 40)
(100, 248)
(34, 53)
(46, 66)
(53, 79)
(106, 261)
(60, 92)
(69, 105)
(75, 118)
(110, 274)
(84, 131)
(91, 144)
(99, 157)
(123, 287)
(109, 170)
(114, 183)
(120, 196)
(127, 300)
(131, 209)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 1.07 seconds
Total Repetitions: 1000
Maximal objective value: -1.67798
Corresponding parameter setting:
x0: 0.362054
x1: 13.0913
x2: 97.6184
******************************

13 input vectors with 3 parameters
Generating 126 parameters:
(14, 14)
(26, 27)
(36, 40)
(44, 53)
(50, 66)
(58, 79)
(70, 92)
(76, 105)
(81, 118)
(87, 131)
(93, 144)
(103, 157)
(116, 170)
(122, 183)
(133, 196)
13 input vectors with 3 parameters
Generating 126 parameters:
(10, 14)
(13, 27)
(23, 40)
(26, 53)
(35, 66)
(40, 79)
(43, 92)
(47, 105)
(55, 118)
(65, 131)
(70, 144)
(75, 157)
(76, 170)
(82, 183)
(88, 196)
(101, 209)
(112, 222)
(115, 235)
(120, 248)
(125, 261)
(134, 274)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 2.67 seconds
Total Repetitions: 1000
Maximal objective value: -1.76626
Corresponding parameter setting:
x0: 0.596586
x1: 31.3454
x2: 100.454
******************************

13 input vectors with 3 parameters
Generating 126 parameters:
(9, 14)
13 input vectors with 3 parameters
Generating 126 parameters:
(21, 27)
(31, 40)
(39, 53)
(4, 14)
(41, 66)
(52, 79)
(59, 92)
(16, 27)(65, 105)

(74, 118)
(83, 131)
(96, 144)
(26, 40)
(104, 157)
(116, 170)
(32, 53)
(127, 183)
(36, 66)
(39, 79)
(43, 92)
(49, 105)
(56, 118)
(60, 131)
(64, 144)
(67, 157)
(73, 170)
(77, 183)
(83, 196)
(87, 209)
(90, 222)
(96, 235)
(102, 248)
(113, 261)
(118, 274)
(126, 287)
13 input vectors with 3 parameters
Generating 126 parameters:
(12, 14)
13 input vectors with 3 parameters
Generating 126 parameters:
(23, 27)
(14, 14)
(26, 27)
(34, 40)
(31, 40)
(40, 53)
(48, 66)
(55, 79)
(37, 53)
(63, 92)
(70, 105)
(82, 118)
(40, 66)
(88, 131)
(97, 144)
(108, 157)
(49, 79)
(120, 170)
(129, 183)
(52, 92)
(61, 105)
(65, 118)
(75, 131)
(84, 144)
(96, 157)
(101, 170)
(107, 183)
(119, 196)
(122, 209)
(125, 222)
(128, 235)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 1.19 seconds
Total Repetitions: 1000
Maximal objective value: -1.57009
Corresponding parameter setting:
x0: 0.640393
x1: 26.664
x2: 105.233
******************************

Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 2.73 seconds
Total Repetitions: 1000
Maximal objective value: -1.49897
Corresponding parameter setting:
x0: 0.677961
x1: 20.6027
x2: 94.5585
******************************


100%|████████████████████████████████████████| 20/20 [00:00<00:00, 31619.33it/s]

Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 2.79 seconds
Total Repetitions: 1000
Maximal objective value: -1.71509
Corresponding parameter setting:
x0: 0.445602
x1: 37.1517
x2: 86.3062
******************************

13 input vectors with 3 parameters
Generating 126 parameters:
(3, 14)
(3, 27)
(4, 40)
(6, 53)
(6, 66)
(9, 79)
(10, 92)
(13, 105)
(17, 118)
(19, 131)
(21, 144)
(21, 157)
(21, 170)
(22, 183)
(23, 196)
(26, 209)
(30, 222)
(39, 235)
(40, 248)
(43, 261)
(43, 274)
(45, 287)
(51, 300)
(54, 313)
(57, 326)
(59, 339)
(61, 352)
(62, 365)
(63, 378)
(65, 391)
(66, 404)
(68, 417)
(69, 430)
(74, 443)
(75, 456)
(77, 469)
(77, 482)
(78, 495)
(80, 508)
(81, 521)
(83, 534)
(83, 547)
(84, 560)
(87, 573)
(87, 586)
(89, 599)
(89, 612)
(91, 625)
(93, 638)
(96, 651)
(100, 664)
(103, 677)
(106, 690)
(107, 703)
(112, 716)
(112, 729)
(113, 742)
(117, 755)
(124, 768)
(126, 781)
13 input vectors with 3 parameters
Generating 126 parameters:
(2, 14)
(2, 27)
(3, 40)
Stopping samplig
(4, 53)

*** Final SPOTPY summary ***
Total Duration: 2.82 seconds
Total Repetitions: 1000
Maximal objective value: -1.40283
Corresponding parameter setting:
x0: 1.22143
x1: 30.5194
x2: 118.413
******************************

(5, 66)
(5, 79)
(8, 92)
(8, 105)
(12, 118)
(14, 131)
(16, 144)
(20, 157)
(20, 170)
(21, 183)
(23, 196)
(24, 209)
Stopping samplig
(25, 222)

*** Final SPOTPY summary ***
Total Duration: 1.25 seconds
Total Repetitions: 1000
Maximal objective value: -1.86962
Corresponding parameter setting:
x0: 0.619342
x1: 14.3573
x2: 104.105
******************************

(30, 235)
(32, 248)
(34, 261)
(36, 274)
(40, 287)
(41, 300)
(49, 313)
(51, 326)
(54, 339)
(56, 352)
(58, 365)
(66, 378)
(66, 391)
(69, 404)
(69, 417)
(75, 430)
(75, 443)
(81, 456)
(81, 469)
(83, 482)
(84, 495)
(85, 508)
(89, 521)
(90, 534)
(94, 547)
(99, 560)
(101, 573)
(104, 586)
(106, 599)
(109, 612)
(111, 625)
(113, 638)
(114, 651)
(117, 664)
(122, 677)
(123, 690)
(123, 703)
(130, 716)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 2.83 seconds
Total Repetitions: 1000
Maximal objective value: -1.97639
Corresponding parameter setting:
x0: 0.583492
x1: 25.1303
x2: 100.77
******************************

Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 2.9 seconds
Total Repetitions: 1000
Maximal objective value: -1.67881
Corresponding parameter setting:
x0: 1.70049
x1: 27.6532
x2: 127
******************************

Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Initialize database...
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.



Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Initialize database...Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']

['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
50 input vectors with 3 parameters
Generating 126 parameters:
(49, 51)
(84, 101)
(132, 151)
13 input vectors with 3 parameters
Generating 126 parameters:
(6, 14)
(19, 27)
(26, 40)
(26, 53)
(37, 66)
(38, 79)
(39, 92)
(43, 105)
(44, 118)
(52, 131)
(54, 144)
(65, 157)
(70, 170)
(83, 183)
(85, 196)
(92, 209)
(94, 222)
(99, 235)
(103, 248)
(105, 261)
(110, 274)
(118, 287)
(126, 300)
13 input vectors with 3 parameters
Generating 126 parameters:
(5, 14)
(11, 27)
(12, 40)
(25, 53)
(30, 66)
(33, 79)
(45, 92)
(46, 105)
(59, 118)
(71, 131)
(73, 144)
(81, 157)
(91, 170)
(103, 183)
(108, 196)
(118, 209)
(122, 222)
(129, 235)
50 input vectors with 3 parameters
Generating 126 parameters:
(40, 51)
(82, 101)
(127, 151)
50 input vectors with 3 parameters
Generating 126 parameters:
(35, 51)
(83, 101)
(126, 151)
50 input vectors with 3 parameters
Generating 126 parameters:
(48, 51)
(86, 101)
(128, 151)
50 input vectors with 3 parameters
Generating 126 parameters:
(40, 51)
(83, 101)
(132, 151)
50 input vectors with 3 parameters
Generating 126 parameters:
(29, 51)
(79, 101)
(126, 151)
50 input vectors with 3 parameters
Generating 126 parameters:
(34, 51)
(67, 101)
(115, 151)
(152, 201)
50 input vectors with 3 parameters
Generating 126 parameters:
(39, 51)
(79, 101)
(123, 151)
(163, 201)
50 input vectors with 3 parameters
Generating 126 parameters:
(27, 51)
(66, 101)
(115, 151)
(155, 201)
13 input vectors with 3 parameters
Generating 126 parameters:
(6, 14)
(6, 27)
(10, 40)
(15, 53)
(18, 66)
(29, 79)
(35, 92)
(41, 105)
(46, 118)
(53, 131)
(58, 144)
(61, 157)
(65, 170)
(77, 183)
(79, 196)
(91, 209)
(101, 222)
(108, 235)
(110, 248)
(117, 261)
(123, 274)
(128, 287)
13 input vectors with 3 parameters
Generating 126 parameters:
(11, 14)
(14, 27)
(17, 40)
(20, 53)
(31, 66)
(43, 79)
(49, 92)
(53, 105)
(65, 118)
(69, 131)
(80, 144)
(82, 157)
(95, 170)
(104, 183)
(106, 196)
(109, 209)
(121, 222)
(133, 235)
13 input vectors with 3 parameters
Generating 126 parameters:
(14, 14)
(22, 27)
(28, 40)
(39, 53)
(42, 66)
(47, 79)
(48, 92)
(57, 105)
(64, 118)
(66, 131)
(77, 144)
(79, 157)
(85, 170)
(91, 183)
(104, 196)
(116, 209)
(126, 222)
13 input vectors with 3 parameters
Generating 126 parameters:
(8, 14)
(18, 27)
(31, 40)
(36, 53)
(49, 66)
(56, 79)
(63, 92)
(74, 105)
(82, 118)
(95, 131)
(107, 144)
(117, 157)
(127, 170)
13 input vectors with 3 parameters
Generating 126 parameters:
(10, 14)
(14, 27)
(24, 40)
(33, 53)
(44, 66)
(52, 79)
(61, 92)
(65, 105)
(75, 118)
(83, 131)
(92, 144)
(103, 157)
(110, 170)
(121, 183)
(134, 196)
13 input vectors with 3 parameters
Generating 126 parameters:
(9, 14)
(12, 27)
(22, 40)
(35, 53)
(41, 66)
(45, 79)
(50, 92)
(53, 105)
(61, 118)
(67, 131)
(72, 144)
(76, 157)
(82, 170)
(90, 183)
(94, 196)
(95, 209)
(98, 222)
(110, 235)
(111, 248)
(113, 261)
(126, 274)
13 input vectors with 3 parameters
Generating 126 parameters:
(5, 14)
(10, 27)
(16, 40)
(21, 53)
(25, 66)
(28, 79)
(38, 92)
(46, 105)
(56, 118)
(60, 131)
(71, 144)
(76, 157)
(80, 170)
(82, 183)
(86, 196)
(93, 209)
(97, 222)
(98, 235)
(105, 248)
(107, 261)
(117, 274)
(121, 287)
(123, 300)
(136, 313)
13 input vectors with 3 parameters
Generating 126 parameters:
(3, 14)
(12, 27)
(20, 40)
(25, 53)
(28, 66)
(34, 79)
(39, 92)
(47, 105)
(54, 118)
(59, 131)
(67, 144)
(75, 157)
(88, 170)
(93, 183)13 input vectors with 3 parameters
(95, 196)
(108, 209)
(114, 222)
(120, 235)
(127, 248)

Generating 126 parameters:
(6, 14)
(12, 27)
(15, 40)
(19, 53)
(31, 66)
(34, 79)
(38, 92)
(41, 105)
(46, 118)
(49, 131)
(58, 144)
(71, 157)
(74, 170)
(78, 183)
(89, 196)
(93, 209)
(99, 222)
(102, 235)
(110, 248)
(117, 261)
(123, 274)
(130, 287)
13 input vectors with 3 parameters
Generating 126 parameters:
(6, 14)
(7, 27)
(11, 40)
(23, 53)
(27, 66)
(28, 79)
(32, 92)
(35, 105)
(40, 118)
(43, 131)
(45, 144)
(48, 157)
(59, 170)
(63, 183)
(66, 196)
(73, 209)
(75, 222)
(82, 235)
(85, 248)
(90, 261)
(95, 274)
(99, 287)
(104, 300)
(116, 313)
(120, 326)
(128, 339)
13 input vectors with 3 parameters
Generating 126 parameters:
(8, 14)
(10, 27)
(13, 40)
(18, 53)
(28, 66)
(29, 79)
(34, 92)
(39, 105)
(41, 118)
(45, 131)
(58, 144)
(62, 157)
(68, 170)
(79, 183)
(87, 196)
(89, 209)
(100, 222)
(110, 235)
(113, 248)
(119, 261)
(128, 274)
13 input vectors with 3 parameters
Generating 126 parameters:
(4, 14)
(8, 27)
(16, 40)
(19, 53)
(25, 66)
(28, 79)
(35, 92)
(41, 105)
(53, 118)
(55, 131)
(64, 144)
(65, 157)
(66, 170)
(67, 183)
(78, 196)
(82, 209)
(95, 222)
(102, 235)
(110, 248)
(114, 261)
(126, 274)
13 input vectors with 3 parameters
Generating 126 parameters:
(4, 14)
(10, 27)
(21, 40)
(33, 53)
(45, 66)
(53, 79)
(60, 92)
(69, 105)
(77, 118)
(82, 131)
(84, 144)
(86, 157)
(93, 170)
(97, 183)
(101, 196)
(113, 209)
(119, 222)
(126, 235)
13 input vectors with 3 parameters
Generating 126 parameters:
(11, 14)
(19, 27)
(19, 40)
(22, 53)
(25, 66)
(34, 79)
(42, 92)
(51, 105)
13 input vectors with 3 parameters
Generating 126 parameters:
(54, 118)
(4, 14)
(60, 131)
(7, 27)
(72, 144)
(11, 40)
(80, 157)
(24, 53)
(85, 170)
(31, 66)(95, 183)

(96, 196)
(36, 79)
(39, 92)
(101, 209)
(112, 222)(52, 105)

(122, 235)(64, 118)

(68, 131)
(133, 248)
(74, 144)
(84, 157)
13 input vectors with 3 parameters
Generating 126 parameters:
(2, 14)
(3, 27)
(16, 40)
(17, 53)
(18, 66)
50 input vectors with 3 parameters
Generating 126 parameters:
(21, 79)
(43, 51)
(33, 92)
(97, 170)
(43, 105)
(92, 101)
(105, 183)
(45, 118)
(123, 151)
(118, 196)
(51, 131)(124, 209)

(151, 201)
(124, 222)
(54, 144)
(128, 235)
(65, 157)
(67, 170)
(72, 183)
(75, 196)
(78, 209)
(79, 222)
(83, 235)
(88, 248)
(88, 261)
(101, 274)
(113, 287)
(122, 300)
(123, 313)
(123, 326)
(124, 339)
(128, 352)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 1.99 seconds
Total Repetitions: 1000
Maximal objective value: -0.984736
Corresponding parameter setting:
x0: 0.866727
x1: 25.845
x2: 100.722
******************************

Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
13 input vectors with 3 parameters
Generating 126 parameters:
(8, 14)
(17, 27)
(17, 40)
(22, 53)
(24, 66)
(26, 79)
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
(31, 92)
(42, 105)
(44, 118)
(50, 131)
(55, 144)
(56, 157)
(62, 170)
(65, 183)
(78, 196)
(79, 209)
(84, 222)
13 input vectors with 3 parameters
Generating 126 parameters:
(87, 235)
(90, 248)
(3, 14)
(96, 261)
(4, 27)
(101, 274)
(7, 40)
(106, 287)
(7, 53)
(109, 300)
(18, 66)
(112, 313)
(21, 79)
(119, 326)
(34, 92)
(122, 339)
(40, 105)
(128, 352)
(41, 118)
(42, 131)
(42, 144)
(42, 157)
(43, 170)
(44, 183)
747 of 1000, maximal objective function=-1.14651, time remaining: 00:00:01
3 Subset: Run 121 of 126 (best like=-1.14651)
(45, 196)
(54, 209)
(56, 222)
(58, 235)
(60, 248)
(69, 261)
778 of 1000, maximal objective function=-0.713691, time remaining: 00:00:01
(78, 274)
4 Subset: Run 26 of 126 (best like=-0.713691)
(88, 287)
(93, 300)
(95, 313)
(100, 326)
(108, 339)
(112, 352)
(115, 365)
(126, 378)
13 input vectors with 3 parameters
Generating 126 parameters:
(14, 14)
(17, 27)
406 of 1000, maximal objective function=-1.2084, time remaining: 00:00:03
1 Subset: Run 405 of 500 (best like=-1.2084)
(19, 40)
13 input vectors with 3 parameters
Generating 126 parameters:
(28, 53)
(8, 14)
(29, 66)
(15, 27)
(37, 79)
(18, 40)
(40, 92)
(23, 53)
(48, 105)
(28, 66)
(53, 118)
(32, 79)
413 of 1000, maximal objective function=-1.40304, time remaining: 00:00:03
(57, 131)1 Subset: Run 412 of 500 (best like=-1.40304)
(34, 92)

(37, 105)
(70, 144)
(41, 118)
(72, 157)
(44, 131)
(74, 170)
(45, 144)
(79, 183)
(52, 157)
(83, 196)
(56, 170)
(86, 209)
(59, 183)
(99, 222)
(63, 196)
(102, 235)
(72, 209)
303 of 1000, maximal objective function=-0.981803, time remaining: 00:00:05
1 Subset: Run 302 of 500 (best like=-0.981803)
(105, 248)
(83, 222)
(108, 261)
(87, 235)
(109, 274)
(90, 248)
(110, 287)
(93, 261)
433 of 1000, maximal objective function=-1.19337, time remaining: 00:00:03
(123, 300)
(97, 274)1 Subset: Run 432 of 500 (best like=-1.19337)

(124, 313)
(103, 287)
(127, 326)
(110, 300)
(112, 313)
(115, 326)
(118, 339)
(125, 352)
393 of 1000, maximal objective function=-1.22345, time remaining: 00:00:03
1 Subset: Run 392 of 500 (best like=-1.22345)
(129, 365)
571 of 1000, maximal objective function=-1.32632, time remaining: 00:00:01
2 Subset: Run 71 of 126 (best like=-1.32632)
411 of 1000, maximal objective function=-1.11742, time remaining: 00:00:03
1 Subset: Run 410 of 500 (best like=-1.11742)
449 of 1000, maximal objective function=-1.32885, time remaining: 00:00:02
1 Subset: Run 448 of 500 (best like=-1.32885)
882 of 1000, maximal objective function=-0.780094, time remaining: 00:00:00
5 Subset: Run 4 of 126 (best like=-0.780094)
426 of 1000, maximal objective function=-1.33835, time remaining: 00:00:03
1 Subset: Run 425 of 500 (best like=-1.33835)
4 Subset: Run 17 of 126 (best like=-0.858251)
770 of 1000, maximal objective function=-0.858251, time remaining: 00:00:01
860 of 1000, maximal objective function=-0.860743, time remaining: 00:00:00
4 Subset: Run 108 of 126 (best like=-0.860743)
857 of 1000, maximal objective function=-1.05363, time remaining: 00:00:00
4 Subset: Run 105 of 126 (best like=-1.05363)
13 input vectors with 3 parameters
Generating 126 parameters:
(9, 14)
(11, 27)
(14, 40)
(22, 53)
(26, 66)
(35, 79)
(35, 92)
(38, 105)
(39, 118)
(41, 131)
(44, 144)
(51, 157)
(60, 170)
(62, 183)
(64, 196)
(66, 209)
(71, 222)
(72, 235)
(78, 248)
(83, 261)
(87, 274)
(89, 287)
(90, 300)
(95, 313)
(98, 326)
(103, 339)
(107, 352)
(111, 365)
(122, 378)
(123, 391)
(124, 404)
(126, 417)
13 input vectors with 3 parameters
Generating 126 parameters:
(10, 14)
(16, 27)
(16, 40)
(24, 53)
(24, 66)
(29, 79)
(31, 92)
(33, 105)
(34, 118)
(37, 131)
(42, 144)
(47, 157)
(58, 170)
(63, 183)
(67, 196)
(75, 209)
(83, 222)
(90, 235)
(103, 248)
(105, 261)
(106, 274)
(112, 287)
(116, 300)
(117, 313)
(130, 326)
13 input vectors with 3 parameters
Generating 126 parameters:
(9, 14)
(11, 27)
(13, 40)
(15, 53)
(24, 66)
(28, 79)
(39, 92)
(49, 105)
(56, 118)
(62, 131)
(74, 144)
(84, 157)
(87, 170)
(93, 183)
(96, 196)
(100, 209)
(107, 222)
(115, 235)
(121, 248)
(132, 261)
50 input vectors with 3 parameters
Generating 126 parameters:
(50, 51)
(91, 101)
(141, 151)
13 input vectors with 3 parameters
Generating 126 parameters:
(6, 14)
(11, 27)
(15, 40)
(16, 53)
(20, 66)
(25, 79)
(37, 92)
(42, 105)
(50, 118)
(58, 131)
(70, 144)
(75, 157)
(78, 170)
(84, 183)
(93, 196)
(98, 209)
(102, 222)
(113, 235)
(114, 248)
(120, 261)
(129, 274)
50 input vectors with 3 parameters
Generating 126 parameters:
(33, 51)
(82, 101)
(114, 151)
(160, 201)
13 input vectors with 3 parameters
Generating 126 parameters:
(5, 14)
(16, 27)
(20, 40)
(29, 53)
(31, 66)
(36, 79)
(40, 92)
(45, 105)
(51, 118)
(64, 131)
(77, 144)
(83, 157)
(95, 170)
(97, 183)
(100, 196)
(112, 209)
(122, 222)
(126, 235)
13 input vectors with 3 parameters
Generating 126 parameters:
(1, 14)
(12, 27)
(20, 40)
(33, 53)
(37, 66)
(45, 79)
(48, 92)
(53, 105)
(57, 118)
(57, 131)
(62, 144)
(65, 157)
(66, 170)
(78, 183)
(83, 196)
(87, 209)
Stopping samplig
(93, 222)

*** Final SPOTPY summary ***
(106, 235)
Total Duration: 2.26 seconds
Total Repetitions: 1000
Maximal objective value: -0.780094
Corresponding parameter setting:
x0: 0.783997
x1: 16.2991
x2: 99.4886
******************************

(115, 248)
(123, 261)
(125, 274)
(126, 287)
13 input vectors with 3 parameters
Generating 126 parameters:
(2, 14)
(9, 27)
(11, 40)
(20, 53)
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
(26, 66)
(31, 79)
(34, 92)
(39, 105)
(46, 118)
(54, 131)
(59, 144)
13 input vectors with 3 parameters(65, 157)

Generating 126 parameters:
(77, 170)
(6, 14)
(84, 183)
(12, 27)
(89, 196)
(15, 40)
(91, 209)
Initialize database...
(15, 53)(93, 222)

['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
(104, 235)
(22, 66)
(106, 248)
(31, 79)
(114, 261)
(36, 92)
(114, 274)
(37, 105)
(117, 287)
(46, 118)
(121, 300)
(55, 131)
(124, 313)
(64, 144)
(129, 326)
(71, 157)
(74, 170)
(76, 183)
(79, 196)
(83, 209)
(84, 222)
(88, 235)
(95, 248)
(98, 261)
(103, 274)
(109, 287)
(114, 300)
(118, 313)
(130, 326)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 2.36 seconds
Total Repetitions: 1000
Maximal objective value: -0.860743
Corresponding parameter setting:
x0: 0.670773
x1: 19.251
x2: 101.15
******************************

Stopping samplig
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm
*** Final SPOTPY summary ***
Total Duration: 2.35 seconds
Total Repetitions: 1000
Maximal objective value: -1.05363
Corresponding parameter setting:
x0: 0.467364
x1: 22.3244
x2: 95.899
******************************

with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
50 input vectors with 3 parameters
Generating 126 parameters:
13 input vectors with 3 parameters
Generating 126 parameters:
(9, 14)
(18, 27)
(26, 40)
(51, 51)
(30, 53)
(33, 66)
(36, 79)
(94, 101)(41, 92)

(42, 105)
(50, 118)
(57, 131)
(144, 151)
(64, 144)
(70, 157)
(73, 170)
(76, 183)
(79, 196)
(88, 209)
(90, 222)
(97, 235)
13 input vectors with 3 parameters
Generating 126 parameters:
(100, 248)
(109, 261)
(122, 274)
13 input vectors with 3 parameters(126, 287)
13 input vectors with 3 parameters
Generating 126 parameters:

Generating 126 parameters:
(14, 14)
(3, 14)
(16, 27)(22, 27)

(6, 14)(23, 40)(30, 40)

(36, 53)(29, 53)

(41, 66)(42, 66)

(43, 79)
(43, 79)
(44, 92)
(46, 92)
(57, 105)
(53, 105)
(60, 118)
(64, 118)
(66, 131)
(75, 131)
(74, 144)
(76, 144)
(77, 157)
(87, 157)
(80, 170)
(92, 170)
(83, 183)
(94, 183)
(91, 196)
(98, 196)
(101, 209)
(102, 209)
(108, 222)
(106, 222)
(113, 235)
(119, 235)
(116, 248)
(122, 248)
(119, 261)
(125, 261)
(125, 274)
(129, 274)
(138, 287)

(6, 27)
(16, 40)
(21, 53)
(23, 66)
(33, 79)
(35, 92)
(37, 105)
(40, 118)
(51, 131)
(54, 144)
(63, 157)
(63, 170)
(71, 183)
(74, 196)
(75, 209)
(86, 222)
(89, 235)
(93, 248)
(106, 261)
(113, 274)
(123, 287)
(129, 300)
50 input vectors with 3 parameters
Generating 126 parameters:
(32, 51)
(70, 101)
(90, 151)
(130, 201)Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 2.53 seconds
Total Repetitions: 1000
Maximal objective value: -0.858251
Corresponding parameter setting:
x0: 0.750859
x1: 15.5626
x2: 102.113
******************************

50 input vectors with 3 parameters
Generating 126 parameters:
(30, 51)
(72, 101)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 2.59 seconds
Total Repetitions: 1000
Maximal objective value: -1.02615
Corresponding parameter setting:
x0: 0.694129
x1: 19.6872
x2: 94.8369
******************************

(122, 151)
(169, 201)
50 input vectors with 3 parameters
Generating 126 parameters:
(47, 51)
(80, 101)
(116, 151)
(165, 201)
50 input vectors with 3 parameters
Generating 126 parameters:
(49, 51)
(88, 101)
(134, 151)
13 input vectors with 3 parameters
Generating 126 parameters:
(4, 14)
(9, 27)
(14, 40)
(18, 53)
(27, 66)
(40, 79)
(45, 92)
(55, 105)
(59, 118)
(63, 131)
(71, 144)
(83, 157)
(93, 170)
(98, 183)
(110, 196)
(120, 209)
(133, 222)
13 input vectors with 3 parameters
Generating 126 parameters:
(4, 14)
(14, 27)
(19, 40)
(31, 53)
(36, 66)
(40, 79)
(42, 92)
(49, 105)
(56, 118)
(64, 131)
(74, 144)
(77, 157)
(85, 170)
(90, 183)
(101, 196)
(106, 209)
(113, 222)
(126, 235)
13 input vectors with 3 parameters
Generating 126 parameters:
(7, 14)
(18, 27)
(22, 40)
(29, 53)
(32, 66)
(42, 79)
(43, 92)
(53, 105)
(63, 118)
(72, 131)
(76, 144)
(83, 157)
(85, 170)
(88, 183)
(93, 196)13 input vectors with 3 parameters

Generating 126 parameters:
(98, 209)(11, 14)

(13, 27)
(99, 222)
(16, 40)
(101, 235)
(104, 248)
(28, 53)
(107, 261)(35, 66)

(39, 79)(107, 274)

(110, 287)
(42, 92)
(114, 300)
(53, 105)
(116, 313)
(56, 118)
(120, 326)
(57, 131)(127, 339)

(61, 144)
(64, 157)
(69, 170)
(78, 183)
(83, 196)
(89, 209)
(96, 222)
(98, 235)
(110, 248)
(113, 261)
(120, 274)
(124, 287)
(131, 300)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 3.1 seconds
Total Repetitions: 1000
Maximal objective value: -0.713691
Corresponding parameter setting:
x0: 0.64059
x1: 10.1344
x2: 95.6019
******************************

50 input vectors with 3 parameters
Generating 126 parameters:
(41, 51)
(79, 101)
(110, 151)
(150, 201)
13 input vectors with 3 parameters
Generating 126 parameters:
(3, 14)
(9, 27)
(18, 40)
(25, 53)
(35, 66)
(45, 79)
(55, 92)
(64, 105)
(70, 118)
(82, 131)
(92, 144)
(99, 157)
(108, 170)
(116, 183)
(124, 196)
(133, 209)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 3.35 seconds
Total Repetitions: 1000
Maximal objective value: -0.938715
Corresponding parameter setting:
x0: 0.622525
x1: 11.0783
x2: 100.941
******************************

13 input vectors with 3 parameters
Generating 126 parameters:
(3, 14)
(11, 27)
(21, 40)
(22, 53)
(32, 66)
(34, 79)
(36, 92)
(42, 105)
(46, 118)
(48, 131)
(55, 144)
(58, 157)
(59, 170)
(61, 183)
(63, 196)
(63, 209)
(66, 222)
(69, 235)
(69, 248)
(82, 261)
(91, 274)
(92, 287)
(95, 300)
(108, 313)
(111, 326)
(119, 339)
(128, 352)
13 input vectors with 3 parameters
Generating 126 parameters:
(12, 14)
(19, 27)
(25, 40)
(30, 53)
(36, 66)
(39, 79)
(42, 92)
(54, 105)
(56, 118)
(65, 131)
(77, 144)
(85, 157)
(96, 170)
(108, 183)
(111, 196)
(124, 209)
(137, 222)
13 input vectors with 3 parameters13 input vectors with 3 parameters
Generating 126 parameters:
(8, 14)
(12, 27)
(17, 40)
(22, 53)
(26, 66)
(34, 79)
(41, 92)
(47, 105)
(52, 118)
(59, 131)
(64, 144)

Generating 126 parameters:
(72, 157)
(12, 14)
(78, 170)
(24, 27)
(32, 40)
(80, 183)
(38, 53)
(47, 66)
(86, 196)
(50, 79)
(52, 92)
(97, 209)
(59, 105)
(62, 118)
(110, 222)(66, 131)

(78, 144)
(83, 157)
(119, 235)
(90, 170)
(94, 183)
(122, 248)
(100, 196)
(103, 209)
(125, 261)
(104, 222)
(114, 235)
(131, 274)
(121, 248)
(126, 261)
13 input vectors with 3 parameters
Generating 126 parameters:
(1, 14)
(3, 27)
(15, 40)
(16, 53)
(22, 66)
(35, 79)
(40, 92)
(44, 105)
(54, 118)13 input vectors with 3 parameters
(59, 131)
(69, 144)
(80, 157)
(84, 170)
(92, 183)
(97, 196)
(101, 209)
(105, 222)
(110, 235)
(114, 248)
(119, 261)
(124, 274)
Generating 126 parameters:
(3, 14)
(130, 287)
(15, 27)

(18, 40)
(20, 53)
(22, 66)
(27, 79)
(40, 92)
(41, 105)
(42, 118)
(43, 131)
(43, 144)
(47, 157)
(50, 170)
(52, 183)
(55, 196)
(56, 209)
(69, 222)
(71, 235)
(73, 248)
(75, 261)
13 input vectors with 3 parameters
Generating 126 parameters:
(78, 274)
(80, 287)
(4, 14)
(85, 300)
(10, 27)
(90, 313)
(17, 40)(92, 326)

(94, 339)
(30, 53)
(94, 352)
(36, 66)
(105, 365)(43, 79)
(49, 92)
(57, 105)
(66, 118)
(72, 131)
(78, 144)
(81, 157)
(87, 170)
(92, 183)
(96, 196)
(104, 209)
(117, 222)
(123, 235)
(135, 248)

(105, 378)
(108, 391)
(119, 404)
(123, 417)
(125, 430)
(128, 443)
50 input vectors with 3 parameters
Generating 126 parameters:
(50, 51)
(89, 101)
(122, 151)
(161, 201)
50 input vectors with 3 parameters
Generating 126 parameters:
(34, 51)
(69, 101)
(97, 151)
(133, 201)
13 input vectors with 3 parameters
Generating 126 parameters:
(14, 14)
(24, 27)
(34, 40)
(44, 53)
(53, 66)
(64, 79)
(72, 92)548 of 1000, maximal objective function=-1.25135, time remaining: 00:00:02

2 Subset: Run 48 of 126 (best like=-1.25135)
(80, 105)13 input vectors with 3 parameters
Generating 126 parameters:

(7, 14)
(89, 118)
(7, 27)
(91, 131)
678 of 1000, maximal objective function=-0.954987, time remaining: 00:00:02
(95, 144)(13, 40)

3 Subset: Run 52 of 126 (best like=-0.954987)
(21, 53)
(107, 157)
(22, 66)
(115, 170)
(28, 79)
(121, 183)
(32, 92)
(130, 196)
(40, 105)
(43, 118)
951 of 1000, maximal objective function=-0.772004, time remaining: 00:00:00
5 Subset: Run 73 of 126 (best like=-0.772004)
(51, 131)
734 of 1000, maximal objective function=-0.860987, time remaining: 00:00:01
3 Subset: Run 108 of 126 (best like=-0.860987)
(54, 144)
(58, 157)
(60, 170)
626 of 1000, maximal objective function=-1.2084, time remaining: 00:00:02
3 Subset: Run 0 of 126 (best like=-1.2084)
(73, 183)
(86, 196)
(94, 209)
(94, 222)
(97, 235)
(97, 248)
(103, 261)
(107, 274)
(116, 287)
(122, 300)
(127, 313)
878 of 1000, maximal objective function=-0.90469, time remaining: 00:00:01
5 Subset: Run 0 of 126 (best like=-0.90469)
700 of 1000, maximal objective function=-1.08605, time remaining: 00:00:02
3 Subset: Run 74 of 126 (best like=-1.08605)
719 of 1000, maximal objective function=-1.04777, time remaining: 00:00:02
3 Subset: Run 93 of 126 (best like=-1.04777)
711 of 1000, maximal objective function=-1.11742, time remaining: 00:00:02
3 Subset: Run 85 of 126 (best like=-1.11742)
13 input vectors with 3 parameters
Generating 126 parameters:
(11, 14)
(19, 27)
(31, 40)
(37, 53)
(37, 66)
(46, 79)
(49, 92)
(54, 105)
(58, 118)
(65, 131)
(70, 144)
(70, 157)
(78, 170)
(88, 183)
(95, 196)
(108, 209)
(113, 222)
(119, 235)
(132, 248)
13 input vectors with 3 parameters
Generating 126 parameters:
(4, 14)
(9, 27)
(13, 40)
(16, 53)
(22, 66)
(23, 79)
(25, 92)
(29, 105)
(32, 118)
(34, 131)
(36, 144)
(41, 157)
(43, 170)
(44, 183)
(45, 196)
(48, 209)
(50, 222)
(62, 235)
(65, 248)
(67, 261)
(71, 274)
(79, 287)
(80, 300)
(82, 313)
(86, 326)
(89, 339)
(96, 352)Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 4.22 seconds
Total Repetitions: 1000
Maximal objective value: -0.772004
Corresponding parameter setting:
x0: 0.387249
x1: 13.9032
x2: 98.0336
******************************

(99, 365)
(104, 378)
(111, 391)
(116, 404)
(123, 417)
(135, 430)
569 of 1000, maximal objective function=-1.28332, time remaining: 00:00:02
2 Subset: Run 69 of 126 (best like=-1.28332)
13 input vectors with 3 parameters13 input vectors with 3 parameters
Generating 126 parameters:
(6, 14)
(9, 27)
(15, 40)
(26, 53)
(33, 66)

Generating 126 parameters:
(39, 79)
(45, 92)
(6, 14)(49, 105)

(57, 118)
(7, 27)
(62, 131)
(66, 144)
(15, 40)
(77, 157)
(22, 53)
(88, 170)
(26, 66)(93, 183)
(101, 196)
(109, 209)
(119, 222)

(131, 235)
(30, 79)
(34, 92)
(37, 105)
(45, 118)
(50, 131)
(60, 144)
(67, 157)
(73, 170)
(78, 183)
(80, 196)
(85, 209)
(90, 222)
(92, 235)
(94, 248)
(97, 261)
(103, 274)
(109, 287)
(112, 300)
(116, 313)
(128, 326)
353 of 1000, maximal objective function=-1.41092, time remaining: 00:00:04298 of 1000, maximal objective function=-1.11074, time remaining: 00:00:05
1 Subset: Run 352 of 500 (best like=-1.41092)

1 Subset: Run 297 of 500 (best like=-1.11074)
13 input vectors with 3 parameters
Generating 126 parameters:
(5, 14)
(15, 27)
(25, 40)
(36, 53)
(47, 66)
(52, 79)
(61, 92)
(65, 105)
(72, 118)
(77, 131)
(80, 144)
(91, 157)
(98, 170)
(109, 183)
(113, 196)
(120, 209)
(126, 222)
13 input vectors with 3 parameters
Generating 126 parameters:
(7, 14)
(13, 27)
(24, 40)
(34, 53)
(47, 66)
(52, 79)
(58, 92)
(66, 105)
(73, 118)
(77, 131)
(87, 144)
(92, 157)
(96, 170)
(107, 183)
(114, 196)
(122, 209)
(129, 222)
13 input vectors with 3 parameters
Generating 126 parameters:
(7, 14)
(11, 27)
(17, 40)
(19, 53)
(29, 66)
(40, 79)
(43, 92)
(46, 105)
(51, 118)
(55, 131)
(59, 144)
(64, 157)
(67, 170)
(70, 183)
(83, 196)
(96, 209)
(103, 222)
(106, 235)
(112, 248)
(121, 261)
(127, 274)
13 input vectors with 3 parameters
Generating 126 parameters:
(10, 14)
(17, 27)
(26, 40)
(28, 53)
(33, 66)
(36, 79)
(39, 92)
(41, 105)
(43, 118)
(48, 131)
(57, 144)
(70, 157)
(76, 170)
(76, 183)
(83, 196)
(93, 209)
(96, 222)
(98, 235)
(108, 248)
(117, 261)
(118, 274)
(121, 287)
(126, 300)
13 input vectors with 3 parameters
Generating 126 parameters:
(3, 14)
(16, 27)
(22, 40)
(29, 53)
(37, 66)
(42, 79)
(55, 92)
(57, 105)
(59, 118)
(68, 131)
(81, 144)
(83, 157)
(85, 170)13 input vectors with 3 parameters
Generating 126 parameters:

(91, 183)
(93, 196)
(5, 14)(100, 209)
(101, 222)
(101, 235)
(105, 248)
(108, 261)
(112, 274)

(121, 287)
(11, 27)
(128, 300)
(16, 40)
(21, 53)
(25, 66)
(30, 79)
(40, 92)
(45, 105)
(49, 118)
(62, 131)
(63, 144)
(65, 157)
(70, 170)
(82, 183)
(95, 196)
(102, 209)
(107, 222)
(111, 235)
(117, 248)
(119, 261)
(130, 274)
13 input vectors with 3 parametersStopping samplig

*** Final SPOTPY summary ***
Total Duration: 4.75 seconds
Total Repetitions: 1000
Maximal objective value: -0.901662
Corresponding parameter setting:
x0: 0.774779
x1: 17.9484
x2: 100.696
******************************

Generating 126 parameters:
(5, 14)
(14, 27)
(16, 40)
(22, 53)
(30, 66)
(38, 79)
(40, 92)13 input vectors with 3 parameters
Generating 126 parameters:
(4, 14)
(7, 27)
(17, 40)
(27, 53)
(28, 66)
(32, 79)
(45, 92)
(54, 105)
(59, 118)
(65, 131)
(74, 144)
(76, 157)
(83, 170)
(96, 183)
(100, 196)
(108, 209)
(121, 222)
(128, 235)

(47, 105)
(55, 118)
(68, 131)
(72, 144)
(81, 157)
(93, 170)
(102, 183)
(114, 196)
(118, 209)
(126, 222)
13 input vectors with 3 parameters
Generating 126 parameters:
(8, 14)
(19, 27)
(32, 40)
(36, 53)
(43, 66)
(53, 79)
(66, 92)
(76, 105)
(83, 118)
(83, 131)
(84, 144)
(85, 157)
(89, 170)
(101, 183)
(111, 196)
(116, 209)
(124, 222)
(136, 235)
13 input vectors with 3 parameters
Generating 126 parameters:
(11, 14)
(13, 27)
(15, 40)
(20, 53)
(33, 66)
(40, 79)
(52, 92)
(55, 105)
(58, 118)13 input vectors with 3 parameters
Generating 126 parameters:
(6, 14)
(11, 27)
(17, 40)
(20, 53)
(26, 66)
(34, 79)
(41, 92)
(47, 105)
(54, 118)
(57, 131)
(68, 144)
(79, 157)
(87, 170)

(91, 183)
(63, 131)
(68, 144)
(96, 196)
(72, 157)
(98, 209)
(81, 170)
(101, 222)(90, 183)

(95, 196)
(107, 235)(97, 209)

(98, 222)
(111, 248)
(111, 235)
(112, 248)
(119, 261)
(115, 261)
(121, 274)
(122, 274)
(123, 287)
(132, 300)
(127, 287)
13 input vectors with 3 parameters
Generating 126 parameters:
(7, 14)
(10, 27)
(14, 40)
(27, 53)
(36, 66)
(40, 79)
(44, 92)
(54, 105)
(56, 118)
(66, 131)
(74, 144)
(83, 157)
(85, 170)
(92, 183)
(98, 196)
(102, 209)
(115, 222)
(119, 235)
(127, 248)
13 input vectors with 3 parameters
Generating 126 parameters:
(6, 14)
(10, 27)
(13, 40)
(18, 53)
(27, 66)
(30, 79)
(43, 92)
(46, 105)
(55, 118)
(59, 131)
(69, 144)
(82, 157)
(85, 170)
(86, 183)
(91, 196)
(92, 209)
(95, 222)
(99, 235)
(109, 248)
(114, 261)
(116, 274)
(122, 287)
(128, 300)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 5.33 seconds
Total Repetitions: 1000
Maximal objective value: -0.682506
Corresponding parameter setting:
x0: 0.466812
x1: 10.8672
x2: 100.048
******************************

13 input vectors with 3 parameters
Generating 126 parameters:
(14, 14)
(19, 27)
(20, 40)
(32, 53)
(42, 66)
(44, 79)
(50, 92)
(55, 105)
(59, 118)
(62, 131)
(67, 144)
(70, 157)
(77, 170)
(81, 183)
(93, 196)
(104, 209)
(107, 222)
(111, 235)
(123, 248)
(127, 261)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 5.44 seconds
Total Repetitions: 1000
Maximal objective value: -0.860987
Corresponding parameter setting:
x0: 0.419067
x1: 16.6214
x2: 99.6154
******************************

13 input vectors with 3 parameters
Generating 126 parameters:
(5, 14)
(9, 27)
(10, 40)
(19, 53)
(24, 66)
(27, 79)
(30, 92)
(35, 105)
(37, 118)
(41, 131)
(43, 144)
(53, 157)
(56, 170)
(63, 183)
(67, 196)
(78, 209)
(83, 222)
(87, 235)
(97, 248)
(99, 261)
(101, 274)
(105, 287)
(107, 300)
(112, 313)
(115, 326)
(117, 339)
(118, 352)
(120, 365)
(125, 378)
(127, 391)
50 input vectors with 3 parametersStopping samplig

*** Final SPOTPY summary ***
Total Duration: 3.24 seconds
Total Repetitions: 1000
Maximal objective value: -0.825856
Corresponding parameter setting:
x0: 0.458791
x1: 12.4056
x2: 102.381
******************************

Generating 126 parameters:
(36, 51)
(69, 101)
(96, 151)
(130, 201)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 3.7 seconds
Total Repetitions: 1000
Maximal objective value: -1.18806
Corresponding parameter setting:
x0: 0.681257
x1: 18.3659
x2: 111.204
******************************

13 input vectors with 3 parameters
Generating 126 parameters:
(11, 14)
(15, 27)
(21, 40)
(22, 53)
(26, 66)
(29, 79)
(33, 92)
(36, 105)
(39, 118)
(51, 131)
(59, 144)
(65, 157)
(70, 170)
(77, 183)
(84, 196)
(94, 209)
(101, 222)
(108, 235)
(115, 248)
(125, 261)
(130, 274)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 5.97 seconds
Total Repetitions: 1000
Maximal objective value: -0.781878
Corresponding parameter setting:
x0: 0.595734
x1: 13.4421
x2: 100.705
******************************

980 of 1000, maximal objective function=-0.922854, time remaining: 00:00:00
5 Subset: Run 102 of 126 (best like=-0.922854)
877 of 1000, maximal objective function=-1.08307, time remaining: 00:00:01
4 Subset: Run 125 of 126 (best like=-1.08307)
13 input vectors with 3 parameters
Generating 126 parameters:
929 of 1000, maximal objective function=-0.795268, time remaining: 00:00:00
5 Subset: Run 51 of 126 (best like=-0.795268)
(5, 14)
(11, 27)
(14, 40)
(18, 53)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 6.06 seconds
Total Repetitions: 1000
Maximal objective value: -0.922854
Corresponding parameter setting:
x0: 0.80205
x1: 14.9476
x2: 104.617
******************************

(25, 66)
(29, 79)
(35, 92)
(38, 105)
(43, 118)
(49, 131)
(53, 144)
(55, 157)
(64, 170)
(67, 183)
(72, 196)
(75, 209)
(76, 222)
(82, 235)
(87, 248)
(94, 261)
(96, 274)
(99, 287)
(106, 300)
(114, 313)
(119, 326)
(121, 339)
(121, 352)
(125, 365)
(128, 378)
13 input vectors with 3 parameters
Generating 126 parameters:
(8, 14)
(18, 27)
(23, 40)
(30, 53)
(34, 66)
(40, 79)
(47, 92)
(52, 105)
(60, 118)
(64, 131)
(70, 144)
(81, 157)
(88, 170)
(93, 183)
(96, 196)
(98, 209)
(99, 222)
(105, 235)
(107, 248)
(114, 261)
(119, 274)
(123, 287)
(128, 300)
50 input vectors with 3 parameters
Generating 126 parameters:
(39, 51)
(65, 101)
(110, 151)
(155, 201)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 6.32 seconds
Total Repetitions: 1000
Maximal objective value: -0.795268
Corresponding parameter setting:
x0: 0.726439
x1: 13.3183
x2: 99.2644
******************************

726 of 1000, maximal objective function=-0.879469, time remaining: 00:00:02
3 Subset: Run 100 of 126 (best like=-0.879469)
517 of 1000, maximal objective function=-1.11074, time remaining: 00:00:04
2 Subset: Run 17 of 126 (best like=-1.11074)
13 input vectors with 3 parameters
Generating 126 parameters:
(6, 14)
(7, 27)
(9, 40)
(13, 53)
(16, 66)
(20, 79)
(33, 92)
(36, 105)
(42, 118)
(49, 131)
(57, 144)
(60, 157)
(73, 170)
(86, 183)
(91, 196)
(91, 209)
(96, 222)
(100, 235)
(102, 248)
(113, 261)
(115, 274)
(119, 287)
(124, 300)
(134, 313)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 6.77 seconds
Total Repetitions: 1000
Maximal objective value: -1.08307
Corresponding parameter setting:
x0: 0.68219
x1: 15.3047
x2: 109.04
******************************

13 input vectors with 3 parameters
Generating 126 parameters:
(4, 14)
(15, 27)
(23, 40)
(25, 53)
(33, 66)
(37, 79)
(46, 92)
(52, 105)
(64, 118)
(73, 131)
(81, 144)
(90, 157)
(92, 170)
(94, 183)
(102, 196)
(104, 209)
(114, 222)
(115, 235)
(126, 248)
13 input vectors with 3 parameters
Generating 126 parameters:
(11, 14)
(15, 27)
(24, 40)
(35, 53)
(44, 66)
(50, 79)
(59, 92)
(68, 105)
(77, 118)
(83, 131)
(91, 144)
(103, 157)
(113, 170)
(119, 183)
(121, 196)
(127, 209)
13 input vectors with 3 parameters
Generating 126 parameters:
(6, 14)
(15, 27)
(21, 40)
(23, 53)
(29, 66)
(34, 79)
(37, 92)
(43, 105)
(47, 118)
(55, 131)
(63, 144)
(76, 157)
(82, 170)
(87, 183)
(92, 196)
(97, 209)
(100, 222)
(106, 235)
(118, 248)
(122, 261)
(123, 274)
(129, 287)
13 input vectors with 3 parametersStopping samplig

*** Final SPOTPY summary ***
Total Duration: 4.96 seconds
Total Repetitions: 1000
Maximal objective value: -0.879469
Corresponding parameter setting:
x0: 0.681186
x1: 19.3151
x2: 101.531
******************************

Generating 126 parameters:
(10, 14)
(13, 27)
(21, 40)
(29, 53)
(36, 66)
(38, 79)
(41, 92)
(46, 105)
(51, 118)
(58, 131)
(62, 144)
(66, 157)
(72, 170)
(76, 183)
(81, 196)
(88, 209)
(97, 222)
(101, 235)
(108, 248)
(116, 261)
(121, 274)
(126, 287)
13 input vectors with 3 parameters
Generating 126 parameters:
(3, 14)
(10, 27)
(18, 40)
(23, 53)
(28, 66)
(33, 79)
(35, 92)
(43, 105)
(51, 118)
(57, 131)
(61, 144)
(68, 157)
(78, 170)
(80, 183)
(93, 196)
(99, 209)
(101, 222)
(104, 235)
(109, 248)
(118, 261)
(129, 274)
13 input vectors with 3 parameters
Generating 126 parameters:
(5, 14)
(9, 27)
(14, 40)
(18, 53)
(23, 66)
(28, 79)
(31, 92)
(44, 105)
(51, 118)
(54, 131)
(57, 144)
(61, 157)
(63, 170)
(67, 183)
(75, 196)
(78, 209)
(79, 222)
(84, 235)
(92, 248)
(97, 261)
(99, 274)
(106, 287)
(110, 300)
(117, 313)
(121, 326)
(126, 339)
13 input vectors with 3 parameters
Generating 126 parameters:
(7, 14)
(12, 27)
(16, 40)
(18, 53)
(23, 66)
(35, 79)
(38, 92)
(42, 105)
(45, 118)
(51, 131)
(55, 144)
(57, 157)
(61, 170)
(66, 183)
(74, 196)
(78, 209)
(85, 222)
(91, 235)
(94, 248)
(101, 261)
(102, 274)
(104, 287)
(106, 300)
(114, 313)
(125, 326)
(133, 339)

100%|██████████████████████████████████████████| 20/20 [00:00<00:00, 913.46it/s]

Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 5.61 seconds
Total Repetitions: 1000
Maximal objective value: -0.913765
Corresponding parameter setting:
x0: 0.568823
x1: 23.6363
x2: 100.248
******************************

Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']



Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.

['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']Initialize database...

['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
261 of 1000, maximal objective function=-1.71913, time remaining: 00:00:06287 of 1000, maximal objective function=-1.35085, time remaining: 00:00:05
1 Subset: Run 286 of 500 (best like=-1.35085)

1 Subset: Run 260 of 500 (best like=-1.71913)
225 of 1000, maximal objective function=-1.70311, time remaining: 00:00:07353 of 1000, maximal objective function=-1.41279, time remaining: 00:00:04
1 Subset: Run 352 of 500 (best like=-1.41279)

1 Subset: Run 224 of 500 (best like=-1.70311)
288 of 1000, maximal objective function=-1.23781, time remaining: 00:00:05
1 Subset: Run 287 of 500 (best like=-1.23781)
314 of 1000, maximal objective function=-1.42539, time remaining: 00:00:04222 of 1000, maximal objective function=-1.29254, time remaining: 00:00:07
1 Subset: Run 221 of 500 (best like=-1.29254)

1 Subset: Run 313 of 500 (best like=-1.42539)
131 of 1000, maximal objective function=-1.55936, time remaining: 00:00:13
1 Subset: Run 130 of 500 (best like=-1.55936)
260 of 1000, maximal objective function=-1.09639, time remaining: 00:00:06
1 Subset: Run 259 of 500 (best like=-1.09639)
219 of 1000, maximal objective function=-1.52918, time remaining: 00:00:07
1 Subset: Run 218 of 500 (best like=-1.52918)
136 of 1000, maximal objective function=-1.5868, time remaining: 00:00:13
1 Subset: Run 135 of 500 (best like=-1.5868)
212 of 1000, maximal objective function=-1.19529, time remaining: 00:00:07
1 Subset: Run 211 of 500 (best like=-1.19529)
188 of 1000, maximal objective function=-1.50158, time remaining: 00:00:09
1 Subset: Run 187 of 500 (best like=-1.50158)
212 of 1000, maximal objective function=-1.59053, time remaining: 00:00:07
1 Subset: Run 211 of 500 (best like=-1.59053)
196 of 1000, maximal objective function=-1.51849, time remaining: 00:00:08
1 Subset: Run 195 of 500 (best like=-1.51849)
210 of 1000, maximal objective function=-1.0264, time remaining: 00:00:07
1 Subset: Run 209 of 500 (best like=-1.0264)
50 input vectors with 3 parameters
Generating 126 parameters:
(43, 51)
(83, 101)
(113, 151)
(144, 201)
50 input vectors with 3 parameters
Generating 126 parameters:
(41, 51)
(82, 101)
(132, 151)
50 input vectors with 3 parameters
Generating 126 parameters:
(44, 51)
(94, 101)
(117, 151)
(167, 201)
50 input vectors with 3 parameters
Generating 126 parameters:
(39, 51)
(77, 101)
(124, 151)
(173, 201)
50 input vectors with 3 parameters
Generating 126 parameters:
(38, 51)
(88, 101)
523 of 1000, maximal objective function=-1.35085, time remaining: 00:00:04
2 Subset: Run 23 of 126 (best like=-1.35085)
577 of 1000, maximal objective function=-1.41279, time remaining: 00:00:03
2 Subset: Run 77 of 126 (best like=-1.41279)
(131, 151)
500 of 1000, maximal objective function=-1.52202, time remaining: 00:00:04
2 Subset: Run 0 of 126 (best like=-1.52202)
601 of 1000, maximal objective function=-1.23781, time remaining: 00:00:03
2 Subset: Run 101 of 126 (best like=-1.23781)
445 of 1000, maximal objective function=-1.59109, time remaining: 00:00:05
1 Subset: Run 444 of 500 (best like=-1.59109)
463 of 1000, maximal objective function=-1.29254, time remaining: 00:00:05312 of 1000, maximal objective function=-1.55936, time remaining: 00:00:09566 of 1000, maximal objective function=-1.29595, time remaining: 00:00:03
1 Subset: Run 462 of 500 (best like=-1.29254)

1 Subset: Run 311 of 500 (best like=-1.55936)

2 Subset: Run 66 of 126 (best like=-1.29595)
451 of 1000, maximal objective function=-1.09639, time remaining: 00:00:05
1 Subset: Run 450 of 500 (best like=-1.09639)
473 of 1000, maximal objective function=-1.24879, time remaining: 00:00:04
1 Subset: Run 472 of 500 (best like=-1.24879)
13 input vectors with 3 parameters
Generating 126 parameters:
(4, 14)
(9, 27)
(15, 40)
(21, 53)
(29, 66)
(33, 79)
(43, 92)
(47, 105)
(49, 118)
(61, 131)
(72, 144)
(76, 157)
(80, 170)
(89, 183)
(93, 196)
(96, 209)
(96, 222)
(103, 235)
(109, 248)
(112, 261)
(116, 274)
(117, 287)
(121, 300)
(127, 313)
291 of 1000, maximal objective function=-1.40589, time remaining: 00:00:10
1 Subset: Run 290 of 500 (best like=-1.40589)
427 of 1000, maximal objective function=-1.19529, time remaining: 00:00:05
1 Subset: Run 426 of 500 (best like=-1.19529)
407 of 1000, maximal objective function=-1.50158, time remaining: 00:00:06
1 Subset: Run 406 of 500 (best like=-1.50158)
322 of 1000, maximal objective function=-1.49058, time remaining: 00:00:08
1 Subset: Run 321 of 500 (best like=-1.49058)
381 of 1000, maximal objective function=-1.2779, time remaining: 00:00:07
1 Subset: Run 380 of 500 (best like=-1.2779)
50 input vectors with 3 parameters
Generating 126 parameters:
(44, 51)
(85, 101)50 input vectors with 3 parameters50 input vectors with 3 parameters
Generating 126 parameters:
(35, 51)
(79, 101)
Generating 126 parameters:

(47, 51)
(99, 151)
(108, 151)
(138, 201)

(89, 101)(142, 201)
500 of 1000, maximal objective function=-1.0264, time remaining: 00:00:04

2 Subset: Run 0 of 126 (best like=-1.0264)
(129, 151)
50 input vectors with 3 parameters
Generating 126 parameters:
(51, 51)
(78, 101)
(127, 151)
13 input vectors with 3 parameters
Generating 126 parameters:
(13, 14)
(22, 27)
(24, 40)
(37, 53)
(46, 66)
(51, 79)
(59, 92)
(72, 105)
(75, 118)
(83, 131)
(88, 144)
(93, 157)
(94, 170)
(100, 183)
(107, 196)
(108, 209)
(114, 222)
(120, 235)
(133, 248)
13 input vectors with 3 parameters
Generating 126 parameters:
(9, 14)
(17, 27)
(25, 40)
(31, 53)
(37, 66)
(45, 79)
(55, 92)
(62, 105)
(72, 118)
(76, 131)
(82, 144)
(95, 157)
(106, 170)
(109, 183)
(121, 196)
(133, 209)
13 input vectors with 3 parameters50 input vectors with 3 parameters
Generating 126 parameters:
(11, 14)
(20, 27)
(26, 40)
(34, 53)
(36, 66)
(46, 79)
(51, 92)
(59, 105)
(65, 118)
(68, 131)
(71, 144)
(84, 157)
(88, 170)
(90, 183)
Generating 126 parameters:

(94, 196)
(97, 209)
(110, 222)
(34, 51)
(117, 235)
(121, 248)
(134, 261)(72, 101)

(122, 151)
(162, 201)
13 input vectors with 3 parameters
Generating 126 parameters:
(2, 14)
(5, 27)
(14, 40)
(16, 53)
(24, 66)
(28, 79)
(34, 92)
(40, 105)
(53, 118)
(64, 131)
(75, 144)
(80, 157)
(87, 170)
(91, 183)
(96, 196)
(100, 209)
(104, 222)
(110, 235)
(113, 248)
(115, 261)
(123, 274)
(132, 287)
13 input vectors with 3 parameters
Generating 126 parameters:
13 input vectors with 3 parameters
Generating 126 parameters:
(9, 14)
(14, 27)
(13, 14)
(23, 40)(20, 27)

(23, 40)
(31, 53)
(29, 53)(37, 66)

(49, 79)(35, 66)
13 input vectors with 3 parameters
(41, 79)
Generating 126 parameters:
(10, 14)(58, 92)
(65, 105)
(19, 27)
(78, 118)
(26, 40)
(91, 131)
(32, 53)
(94, 144)
(45, 66)
(107, 157)
(55, 79)
(116, 170)
(66, 92)
(126, 183)
(75, 105)
13 input vectors with 3 parameters
Generating 126 parameters:
(6, 14)
(84, 118)

(14, 27)
(45, 92)
(90, 131)
(49, 105)

(54, 118)
(20, 40)(60, 131)
(98, 144)
(67, 144)

(80, 157)(111, 157)

(117, 170)(86, 170)
(23, 53)
(99, 183)
(33, 66)
(106, 196)

(113, 209)
(127, 183)
(119, 222)
(125, 235)
(131, 248)
(42, 79)
(51, 92)
(57, 105)
(70, 118)
(73, 131)
(79, 144)
(87, 157)
(96, 170)
(106, 183)
(113, 196)
(121, 209)
(127, 222)
50 input vectors with 3 parameters
Generating 126 parameters:
(40, 51)
(80, 101)
(120, 151)
(159, 201)
50 input vectors with 3 parameters
Generating 126 parameters:
(34, 51)
50 input vectors with 3 parameters
Generating 126 parameters:
(33, 51)
(70, 101)(79, 101)

(114, 151)
(113, 151)
(158, 201)(154, 201)

50 input vectors with 3 parameters
Generating 126 parameters:
(37, 51)
(79, 101)
(123, 151)
(169, 201)
13 input vectors with 3 parameters
Generating 126 parameters:
(7, 14)
(13, 27)
(20, 40)
(24, 53)
(29, 66)
(34, 79)
(47, 92)
(60, 105)
(65, 118)
(69, 131)
(71, 144)
(71, 157)
(74, 170)
13 input vectors with 3 parameters(76, 183)
13 input vectors with 3 parameters
Generating 126 parameters:
(80, 196)
(86, 209)
(3, 14)
(90, 222)(7, 27)
(10, 40)
(12, 53)

Generating 126 parameters:
(25, 66)(12, 14)
(38, 79)

(39, 92)
(43, 105)
(45, 118)

(101, 235)
(58, 131)
(114, 248)(60, 144)
(64, 157)
(74, 170)
(77, 183)
(80, 196)
(82, 209)
(82, 222)
(83, 235)
(85, 248)

(88, 261)
(123, 261)
(21, 27)
(129, 274)
(95, 274)
(27, 40)
(40, 53)
(108, 287)
(49, 66)
(111, 300)
(53, 79)
(114, 313)
(65, 92)
(70, 105)(127, 326)

(75, 118)
(85, 131)
(98, 144)
(108, 157)
(118, 170)
(127, 183)
13 input vectors with 3 parameters
Generating 126 parameters:
(5, 14)
(8, 27)
(10, 40)
(18, 53)
(21, 66)
(23, 79)
(26, 92)
(29, 105)
(32, 118)
(37, 131)
(40, 144)
(44, 157)
(54, 170)
(56, 183)
(60, 196)
(65, 209)
(69, 222)
(73, 235)
(75, 248)
(86, 261)
(92, 274)
(98, 287)
(111, 300)
(116, 313)
(121, 326)
(134, 339)
737 of 1000, maximal objective function=-1.28963, time remaining: 00:00:02
3 Subset: Run 111 of 126 (best like=-1.28963)
729 of 1000, maximal objective function=-1.41279, time remaining: 00:00:02915 of 1000, maximal objective function=-1.04123, time remaining: 00:00:01
5 Subset: Run 37 of 126 (best like=-1.04123)
654 of 1000, maximal objective function=-1.44845, time remaining: 00:00:03
3 Subset: Run 28 of 126 (best like=-1.44845)

3 Subset: Run 103 of 126 (best like=-1.41279)
13 input vectors with 3 parameters
Generating 126 parameters:
(5, 14)
(10, 27)656 of 1000, maximal objective function=-1.41042, time remaining: 00:00:03
3 Subset: Run 30 of 126 (best like=-1.41042)

(16, 40)
(21, 53)
(23, 66)
(27, 79)
(29, 92)
(39, 105)
(43, 118)
(49, 131)
(55, 144)770 of 1000, maximal objective function=-1.23732, time remaining: 00:00:02750 of 1000, maximal objective function=-1.29254, time remaining: 00:00:02
3 Subset: Run 124 of 126 (best like=-1.29254)

(59, 157)
13 input vectors with 3 parameters
Generating 126 parameters:
(65, 170)
(6, 14)(69, 183)
(71, 196)

(75, 209)
(80, 222)(13, 27)
(25, 40)
(83, 235)

(92, 248)
(31, 53)463 of 1000, maximal objective function=-1.39591, time remaining: 00:00:07
(95, 261)(32, 66)

(97, 274)
(45, 79)
(102, 287)
(50, 92)
(110, 300)
(59, 105)
(112, 313)

(68, 118)1 Subset: Run 462 of 500 (best like=-1.39591)

(120, 326)(76, 131)
(78, 144)

(121, 339)
(91, 157)
(132, 352)
(101, 170)
(114, 183)

4 Subset: Run 18 of 126 (best like=-1.23732)
(117, 196)
(119, 209)
(127, 222)
572 of 1000, maximal objective function=-1.09639, time remaining: 00:00:04
2 Subset: Run 72 of 126 (best like=-1.09639)
13 input vectors with 3 parameters
Generating 126 parameters:
(7, 14)
(17, 27)
(22, 40)
(32, 53)
(44, 66)
(49, 79)
(60, 92)
(65, 105)
(72, 118)757 of 1000, maximal objective function=-1.1348, time remaining: 00:00:02
(77, 131)
(85, 144)
(93, 157)
(103, 170)
4 Subset: Run 5 of 126 (best like=-1.1348)

(107, 183)
(119, 196)
(128, 209)
452 of 1000, maximal objective function=-1.40589, time remaining: 00:00:07
1 Subset: Run 451 of 500 (best like=-1.40589)
532 of 1000, maximal objective function=-1.19529, time remaining: 00:00:05
13 input vectors with 3 parameters2 Subset: Run 32 of 126 (best like=-1.19529)

Generating 126 parameters:
(11, 14)
(12, 27)
(21, 40)
(25, 53)572 of 1000, maximal objective function=-1.46669, time remaining: 00:00:04
2 Subset: Run 72 of 126 (best like=-1.46669)
617 of 1000, maximal objective function=-1.28895, time remaining: 00:00:04
2 Subset: Run 117 of 126 (best like=-1.28895)

(29, 66)
(31, 79)
(42, 92)
(45, 105)
(48, 118)
(61, 131)590 of 1000, maximal objective function=-1.20339, time remaining: 00:00:04
2 Subset: Run 90 of 126 (best like=-1.20339)

(64, 144)
(65, 157)
(69, 170)
(74, 183)
(79, 196)
(89, 209)
(96, 222)
(99, 235)
(106, 248)773 of 1000, maximal objective function=-1.0264, time remaining: 00:00:02

4 Subset: Run 21 of 126 (best like=-1.0264)
13 input vectors with 3 parameters(113, 261)

Generating 126 parameters:
(121, 274)
(3, 14)
(124, 287)
(8, 27)(129, 300)

(9, 40)
(13, 53)
(19, 66)
(23, 79)
(34, 92)
(39, 105)
(41, 118)
(51, 131)
(53, 144)
(62, 157)
(64, 170)
(67, 183)
(73, 196)
(78, 209)
(81, 222)
(86, 235)
(93, 248)
(102, 261)
(109, 274)
(109, 287)
(117, 300)
(124, 313)
(130, 326)
13 input vectors with 3 parameters
Generating 126 parameters:
(4, 14)
(10, 27)
(15, 40)
(22, 53)
(27, 66)
(37, 79)
(40, 92)
(45, 105)
(48, 118)
(49, 131)
(61, 144)
(64, 157)
(67, 170)
(76, 183)
(77, 196)
(81, 209)
(89, 222)
(91, 235)
(92, 248)
(93, 261)
(96, 274)
(99, 287)
(104, 300)
(105, 313)
(106, 326)
(110, 339)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 6.52 seconds
Total Repetitions: 1000
Maximal objective value: -1.01225
Corresponding parameter setting:
x0: 0.620577
x1: 8.52243
x2: 108.484
******************************

(110, 352)
(115, 365)
(120, 378)
(125, 391)Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.

(125, 404)
(126, 417)
13 input vectors with 3 parameters
Generating 126 parameters:
Initialize database...
(4, 14)
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
(7, 27)
(9, 40)
(14, 53)
(21, 66)13 input vectors with 3 parameters
(34, 79)
(34, 92)
(37, 105)

Generating 126 parameters:
(5, 14)
(41, 118)
(14, 27)
(18, 40)
(28, 53)
(42, 131)
(32, 66)
(35, 79)
(36, 92)
(49, 105)
(46, 144)
(58, 118)
(59, 157)(61, 131)
(65, 144)
(69, 157)
(67, 170)
(75, 183)
(80, 196)

(85, 209)
(72, 170)(87, 222)

(95, 235)(83, 183)

(91, 196)(102, 248)
(103, 209)
(111, 222)
(116, 235)
(128, 248)

(111, 261)
(112, 274)
(115, 287)
(124, 300)
(125, 313)
(129, 326)
13 input vectors with 3 parameters
Generating 126 parameters:
(12, 14)
(23, 27)
(31, 40)
(38, 53)
50 input vectors with 3 parameters(46, 66)
(59, 79)
(63, 92)
(70, 105)
(81, 118)
(85, 131)

Generating 126 parameters:
(41, 51)(93, 144)
(101, 157)

(106, 170)
(76, 101)(118, 183)
(124, 196)

(137, 209)
(120, 151)
(166, 201)
13 input vectors with 3 parameters
Generating 126 parameters:
(3, 14)
(7, 27)
(11, 40)
(21, 53)
(34, 66)
(38, 79)
(48, 92)
(58, 105)
(68, 118)
(73, 131)
(79, 144)
(88, 157)
(97, 170)
(105, 183)
(111, 196)
(117, 209)
(130, 222)
13 input vectors with 3 parameters
Generating 126 parameters:
(11, 14)
(15, 27)
(27, 40)
(34, 53)
(43, 66)
(48, 79)
(61, 92)
(69, 105)
(80, 118)
(86, 131)
(88, 144)
(92, 157)
(98, 170)
(105, 183)
(112, 196)
(125, 209)
(128, 222)
13 input vectors with 3 parameters
Generating 126 parameters:
(3, 14)
(11, 27)
(16, 40)
(23, 53)
(36, 66)
(40, 79)
(51, 92)
(56, 105)
(58, 118)
(65, 131)
(69, 144)
(73, 157)
(76, 170)
(83, 183)
(88, 196)
(94, 209)
(95, 222)
(104, 235)
(113, 248)
(116, 261)
(119, 274)
(124, 287)
(127, 300)
13 input vectors with 3 parameters13 input vectors with 3 parameters
Generating 126 parameters:
(7, 14)
(9, 27)
(15, 40)

Generating 126 parameters:
(10, 14)
(18, 53)
(23, 27)
(21, 66)
(27, 40)
(30, 79)
(32, 53)
(36, 92)(36, 66)

(49, 79)
(43, 105)
(62, 92)
(46, 118)(65, 105)

(73, 118)
(77, 131)
(54, 131)
(90, 144)
(103, 157)
(67, 144)(114, 170)
(127, 183)

50 input vectors with 3 parameters(75, 157)
(78, 170)
(87, 183)
(97, 196)
(98, 209)
(107, 222)
(117, 235)
(126, 248)
Generating 126 parameters:

13 input vectors with 3 parameters
Generating 126 parameters:
(13, 14)
(50, 51)(26, 27)

(37, 40)(89, 101)
(125, 151)
(158, 201)

(50, 53)
(63, 66)
(74, 79)
(76, 92)
(79, 105)
(90, 118)
(98, 131)
(105, 144)
(113, 157)
(119, 170)
(125, 183)
(138, 196)
13 input vectors with 3 parameters
Generating 126 parameters:
(5, 14)
(15, 27)
(26, 40)
(28, 53)
(31, 66)
(41, 79)
(45, 92)
(53, 105)
(57, 118)
(64, 131)
(75, 144)
(85, 157)
(87, 170)
(90, 183)
(101, 196)
(104, 209)
(107, 222)
(117, 235)
(117, 248)
(124, 261)
(125, 274)
(131, 287)
13 input vectors with 3 parameters
Generating 126 parameters:
(3, 14)
(6, 27)
(11, 40)
(14, 53)
(21, 66)
(30, 79)
(32, 92)
(37, 105)
(44, 118)
(47, 131)
(54, 144)
(63, 157)
(65, 170)
(66, 183)
(69, 196)
(73, 209)
(80, 222)
(87, 235)
(88, 248)
(91, 261)
(96, 274)
(98, 287)
(103, 300)
(113, 313)
(126, 326)
13 input vectors with 3 parameters
Generating 126 parameters:
(5, 14)
(6, 27)
(9, 40)
(20, 53)
(31, 66)
(44, 79)
(47, 92)
(59, 105)
(63, 118)
(67, 131)
(69, 144)
(72, 157)
(75, 170)
(88, 183)
(94, 196)
(98, 209)
(102, 222)
(103, 235)
(103, 248)
(105, 261)
(108, 274)
(110, 287)
(111, 300)
(114, 313)
(116, 326)
(117, 339)
(129, 352)
13 input vectors with 3 parameters
Generating 126 parameters:
(7, 14)
(17, 27)
(22, 40)
(27, 53)
(32, 66)
(40, 79)
(45, 92)
(54, 105)
(60, 118)
(62, 131)
(74, 144)
(80, 157)
(85, 170)
(93, 183)
(104, 196)
(109, 209)
(114, 222)
(120, 235)
(132, 248)
13 input vectors with 3 parameters
Generating 126 parameters:
(5, 14)
(17, 27)
(21, 40)
(32, 53)
(44, 66)
(54, 79)
(67, 92)
(79, 105)
(92, 118)
(101, 131)
(105, 144)
(113, 157)
(121, 170)
(131, 183)
13 input vectors with 3 parameters
Generating 126 parameters:
(6, 14)
(19, 27)
(20, 40)
(24, 53)
(32, 66)
(37, 79)
(41, 92)
(45, 105)
(51, 118)
(60, 131)
(63, 144)
(67, 157)
(72, 170)
(76, 183)
(86, 196)
(91, 209)
(93, 222)
(106, 235)
(113, 248)
(116, 261)
(121, 274)
(134, 287)
13 input vectors with 3 parametersStopping samplig
Generating 126 parameters:
(7, 14)
(19, 27)
(25, 40)
(31, 53)
(35, 66)

*** Final SPOTPY summary ***
Total Duration: 7.87 seconds
Total Repetitions: 1000
Maximal objective value: -1.29254
Corresponding parameter setting:
x0: 0.518399
x1: 13.9564
x2: 101.268
(38, 79)
******************************

(44, 92)Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.

(45, 105)
(56, 118)
(59, 131)
(68, 144)
(71, 157)
(73, 170)
(78, 183)
(86, 196)Initialize database...
(97, 209)
(103, 222)
(110, 235)
(116, 248)
(129, 261)

['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
13 input vectors with 3 parameters
Generating 126 parameters:
(4, 14)
(16, 27)
(17, 40)
(19, 53)
(23, 66)
(31, 79)
(43, 92)
(53, 105)
(62, 118)
(72, 131)
(81, 144)
(87, 157)
(91, 170)
(103, 183)
(107, 196)874 of 1000, maximal objective function=-1.23695, time remaining: 00:00:01
4 Subset: Run 122 of 126 (best like=-1.23695)
Stopping samplig
(109, 209)
(114, 222)

*** Final SPOTPY summary ***
Total Duration: 7.9 seconds
Total Repetitions: 1000
Maximal objective value: -1.1348
Corresponding parameter setting:
x0: 0.755556
x1: 8.68515
x2: 102.32
******************************

13 input vectors with 3 parameters
Generating 126 parameters:
(6, 14)
(11, 27)
(117, 235)
Starting the ROPE algotrithm with 1000 repetitions...(123, 248)
13 input vectors with 3 parameters
Generating 126 parameters:
(135, 261)
(4, 14)
(5, 27)
(7, 40)
(14, 53)
(17, 66)

Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
(18, 79)
(20, 92)
(24, 105)
(26, 118)
(27, 131)
(27, 144)
(29, 157)
(32, 170)
(43, 183)
(47, 196)
(48, 209)

(49, 222)
829 of 1000, maximal objective function=-1.41279, time remaining: 00:00:02
4 Subset: Run 77 of 126 (best like=-1.41279)
(24, 40)
(51, 235)
(30, 53)
(52, 248)
(43, 66)
(56, 261)
(55, 79)
(58, 274)
(66, 92)
(61, 287)
(79, 105)
(70, 300)(89, 118)

(101, 131)
(71, 313)
(72, 326)
(72, 339)
Initialize database...(81, 352)
(86, 365)
(90, 378)
(95, 391)
(101, 404)
(113, 417)
(120, 430)
(128, 443)
(106, 144)
(117, 157)
(127, 170)

['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
878 of 1000, maximal objective function=-1.37176, time remaining: 00:00:01922 of 1000, maximal objective function=-1.35952, time remaining: 00:00:01
5 Subset: Run 0 of 126 (best like=-1.37176)

5 Subset: Run 44 of 126 (best like=-1.35952)
573 of 1000, maximal objective function=-1.25262, time remaining: 00:00:06
2 Subset: Run 73 of 126 (best like=-1.25262)
969 of 1000, maximal objective function=-1.20208, time remaining: 00:00:00
5 Subset: Run 91 of 126 (best like=-1.20208)
681 of 1000, maximal objective function=-1.09639, time remaining: 00:00:04
3 Subset: Run 55 of 126 (best like=-1.09639)
563 of 1000, maximal objective function=-1.40589, time remaining: 00:00:06
2 Subset: Run 63 of 126 (best like=-1.40589)
793 of 1000, maximal objective function=-1.42042, time remaining: 00:00:02
4 Subset: Run 41 of 126 (best like=-1.42042)
640 of 1000, maximal objective function=-1.19529, time remaining: 00:00:05875 of 1000, maximal objective function=-1.04608, time remaining: 00:00:01
3 Subset: Run 14 of 126 (best like=-1.19529)

4 Subset: Run 123 of 126 (best like=-1.04608)
813 of 1000, maximal objective function=-1.09656, time remaining: 00:00:0213 input vectors with 3 parameters
Generating 126 parameters:
(2, 14)
(10, 27)
(11, 40)
(17, 53)
(22, 66)
(25, 79)
(34, 92)
(36, 105)
4 Subset: Run 61 of 126 (best like=-1.09656)

(43, 118)
(45, 131)
(49, 144)
(57, 157)
(61, 170)
(74, 183)
(86, 196)
(94, 209)
(104, 222)
(108, 235)
(109, 248)
(122, 261)
(131, 274)
958 of 1000, maximal objective function=-1.0264, time remaining: 00:00:00
5 Subset: Run 80 of 126 (best like=-1.0264)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 8.38 seconds
Total Repetitions: 1000
Maximal objective value: -1.20208
Corresponding parameter setting:
x0: 0.662624
x1: 12.8604
x2: 113.854
******************************

Starting the ROPE algotrithm with 1000 repetitions...
Initializing the  RObust Parameter Estimation (ROPE) algorithm  with  1000  repetitions
The objective function will be maximized
Warning: Burn-in samples and total number of repetions are not compatible.
SPOTPY will automatically adjust the number of total repetitions.
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
340 of 1000, maximal objective function=-1.6328, time remaining: 00:00:04
1 Subset: Run 339 of 500 (best like=-1.6328)
Stopping samplig13 input vectors with 3 parametersStopping samplig

*** Final SPOTPY summary ***
Total Duration: 8.76 seconds
Total Repetitions: 1000
Maximal objective value: -1.23695
Corresponding parameter setting:
x0: 1.16837
x1: 10.1068
x2: 114.083
******************************

Generating 126 parameters:
(13, 14)
(21, 27)
(23, 40)
(28, 53)

(35, 66)

*** Final SPOTPY summary ***
Total Duration: 8.75 seconds
Total Repetitions: 1000
Maximal objective value: -1.35611
Corresponding parameter setting:
x0: 0.823588
x1: 9.92185
x2: 105.351
******************************
(36, 79)

(40, 92)
(51, 105)
(56, 118)
(63, 131)
(67, 144)
(69, 157)
(70, 170)
(77, 183)
(87, 196)
(89, 209)
(94, 222)
(99, 235)
(109, 248)
(112, 261)
(116, 274)
(122, 287)
(124, 300)
(130, 313)
13 input vectors with 3 parameters
Generating 126 parameters:
(4, 14)
(6, 27)
(8, 40)
(18, 53)
(21, 66)
(29, 79)
(34, 92)
(37, 105)
(39, 118)
(43, 131)
(49, 144)
(58, 157)
(61, 170)
(62, 183)
(64, 196)
(74, 209)
(77, 222)
(79, 235)
(79, 248)
(81, 261)
(84, 274)
(88, 287)
(91, 300)
(99, 313)
(108, 326)
(109, 339)
(111, 352)
(113, 365)
(116, 378)
(119, 391)
(130, 404)
13 input vectors with 3 parameters
Generating 126 parameters:
(12, 14)
(18, 27)
(28, 40)
(35, 53)
(39, 66)
(44, 79)
(49, 92)13 input vectors with 3 parameters
Generating 126 parameters:

(6, 14)
(56, 105)
(13, 27)
(63, 118)
(16, 40)
(69, 131)
(20, 53)
(78, 144)
(25, 66)
(82, 157)
(32, 79)
(36, 92)
(93, 170)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 8.98 seconds
Total Repetitions: 1000
Maximal objective value: -1.35045
Corresponding parameter setting:
x0: 0.450388
x1: 9.80417
x2: 83.4925
******************************

(99, 183)
(112, 196)
(122, 209)
(126, 222)
13 input vectors with 3 parameters
Generating 126 parameters:
(10, 14)
(15, 27)
(20, 40)
(41, 105)
(33, 53)
(46, 118)
(49, 131)
(42, 66)
(50, 144)
(48, 79)
(53, 157)
(59, 92)(58, 170)

(61, 183)
(65, 105)
(62, 196)
(74, 118)(68, 209)

(80, 131)
(92, 144)13 input vectors with 3 parameters
Generating 126 parameters:

(5, 14)
(100, 157)
(11, 27)
(105, 170)
(20, 40)
(24, 53)(109, 183)

(26, 66)
(119, 196)
(29, 79)
(129, 209)
(35, 92)
(42, 105)
(46, 118)
(56, 131)
(63, 144)
(69, 157)
(74, 170)
(79, 183)
(83, 196)
(87, 209)
(93, 222)
(104, 235)
(109, 248)
(116, 261)
(126, 274)
13 input vectors with 3 parameters
Generating 126 parameters:
(11, 14)
(14, 27)
(72, 222)
(18, 40)
(80, 235)
(22, 53)
(86, 248)
(24, 66)
(89, 261)
(27, 79)
(31, 92)
(31, 105)
(36, 118)
(39, 131)
(49, 144)
(55, 157)
(64, 170)
(66, 183)
(77, 196)
(82, 209)
(85, 222)
(91, 235)(102, 274)

(104, 248)(105, 287)
(110, 300)

(106, 261)(113, 313)
(117, 326)
(117, 274)
(119, 287)
(123, 300)

(125, 313)
(130, 326)
(125, 339)
(130, 352)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 8.79 seconds
Total Repetitions: 1000
Maximal objective value: -1.0264
Corresponding parameter setting:
x0: 0.368417
x1: 8.55014
x2: 96.7543
******************************

13 input vectors with 3 parameters
Generating 126 parameters:
(4, 14)
(11, 27)
(18, 40)
(22, 53)
(27, 66)
(32, 79)
(35, 92)
(40, 105)
(45, 118)
(52, 131)
(54, 144)
(61, 157)
(65, 170)
(71, 183)
(75, 196)
(79, 209)
(83, 222)
(87, 235)
(91, 248)
(97, 261)
(103, 274)
(106, 287)
(108, 300)
(119, 313)
(122, 326)
(124, 339)
(137, 352)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 9.02 seconds
Total Repetitions: 1000
Maximal objective value: -1.04608
Corresponding parameter setting:
x0: 0.525319
x1: 12.7998
x2: 96.7266
******************************

50 input vectors with 3 parameters
Generating 126 parameters:
(45, 51)
(82, 101)
(117, 151)
(159, 201)
13 input vectors with 3 parameters
Generating 126 parameters:
(5, 14)
(7, 27)
(10, 40)
(14, 53)
(15, 66)
(15, 79)
(19, 92)
(22, 105)
(27, 118)
(28, 131)
(28, 144)
(34, 157)
(36, 170)
(40, 183)
(44, 196)
(57, 209)
(58, 222)
(61, 235)
(63, 248)
(66, 261)
(70, 274)
(78, 287)
(79, 300)
(86, 313)
(98, 326)
(99, 339)
(110, 352)
(120, 365)
(131, 378)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 9.6 seconds
Total Repetitions: 1000
Maximal objective value: -1.09656
Corresponding parameter setting:
x0: 0.709244
x1: 8.46663
x2: 107.164
******************************

268 of 1000, maximal objective function=-1.6838, time remaining: 00:00:05
1 Subset: Run 267 of 500 (best like=-1.6838)
13 input vectors with 3 parameters
Generating 126 parameters:
(3, 14)
(7, 27)
(12, 40)
(14, 53)
(15, 66)
(21, 79)
(23, 92)
(25, 105)
(30, 118)
(33, 131)
(46, 144)
(50, 157)
(57, 170)
(62, 183)
(63, 196)
(69, 209)Stopping samplig

*** Final SPOTPY summary ***(73, 222)

(74, 235)
Total Duration: 9.77 seconds
Total Repetitions: 1000
Maximal objective value: -1.32625
Corresponding parameter setting:
x0: 0.89623
x1: 19.3652
x2: 101.956
******************************

(78, 248)
(80, 261)
(82, 274)
(91, 287)
(103, 300)
(103, 313)
(110, 326)
(115, 339)
(120, 352)
(121, 365)164 of 1000, maximal objective function=-1.58965, time remaining: 00:00:10
(124, 378)
(126, 391)
13 input vectors with 3 parameters
Generating 126 parameters:
(11, 14)
(15, 27)
(18, 40)
(20, 53)
(25, 66)

1 Subset: Run 163 of 500 (best like=-1.58965)
(27, 79)
(30, 92)
(33, 105)
(38, 118)
(49, 131)
(56, 144)
(59, 157)
(62, 170)
963 of 1000, maximal objective function=-1.40134, time remaining: 00:00:00
5 Subset: Run 85 of 126 (best like=-1.40134)
(65, 183)
(75, 196)
(78, 209)
(81, 222)
(81, 235)
(83, 248)
(87, 261)
(93, 274)
(100, 287)
(101, 300)
(104, 313)
(106, 326)
(111, 339)
(122, 352)
(126, 365)
13 input vectors with 3 parameters
Generating 126 parameters:
(9, 14)
(12, 27)
(15, 40)
(18, 53)
(20, 66)
(32, 79)
(34, 92)
(41, 105)
(54, 118)
(64, 131)
(66, 144)
(66, 157)
(69, 170)
(72, 183)
(74, 196)
(75, 209)
(77, 222)
(77, 235)
(81, 248)
(85, 261)
(89, 274)
(94, 287)
(105, 300)
(107, 313)
(117, 326)
(120, 339)
(121, 352)
(132, 365)
708 of 1000, maximal objective function=-1.25262, time remaining: 00:00:04
3 Subset: Run 82 of 126 (best like=-1.25262)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 10.05 seconds
Total Repetitions: 1000
Maximal objective value: -1.03815
Corresponding parameter setting:
x0: 0.623571
x1: 12.4051
x2: 105.313
******************************

13 input vectors with 3 parameters
Generating 126 parameters:
(2, 14)
(4, 27)
(7, 40)
(11, 53)
(18, 66)
(26, 79)
(33, 92)
(39, 105)
(50, 118)
(63, 131)
(71, 144)
(76, 157)
(84, 170)
(94, 183)
(97, 196)
(104, 209)
(109, 222)
(119, 235)
(127, 248)
707 of 1000, maximal objective function=-1.35102, time remaining: 00:00:04
3 Subset: Run 81 of 126 (best like=-1.35102)
780 of 1000, maximal objective function=-1.19198, time remaining: 00:00:03
4 Subset: Run 28 of 126 (best like=-1.19198)
220 of 1000, maximal objective function=-1.31305, time remaining: 00:00:07
1 Subset: Run 219 of 500 (best like=-1.31305)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 10.53 seconds
Total Repetitions: 1000
Maximal objective value: -1.40134
Corresponding parameter setting:
x0: 0.763613
x1: 11.4156
x2: 107.108
******************************

682 of 1000, maximal objective function=-1.39559, time remaining: 00:00:02
3 Subset: Run 56 of 126 (best like=-1.39559)
13 input vectors with 3 parameters50 input vectors with 3 parameters
Generating 126 parameters:
(35, 51)
(73, 101)
(106, 151)
(138, 201)

Generating 126 parameters:
(9, 14)
(15, 27)
(20, 40)
(26, 53)
(30, 66)
(35, 79)
(38, 92)
(51, 105)
(54, 118)
(65, 131)
(77, 144)
(85, 157)
(88, 170)
(96, 183)
(99, 196)
(106, 209)
(107, 222)
(109, 235)
(119, 248)
(132, 261)
13 input vectors with 3 parameters
Generating 126 parameters:
(5, 14)
(7, 27)
(9, 40)
(15, 53)
(23, 66)
(26, 79)
(28, 92)
(29, 105)
(31, 118)
(34, 131)
(35, 144)
(37, 157)
(40, 170)
(45, 183)
(51, 196)
(53, 209)
(55, 222)
(58, 235)
(61, 248)
(69, 261)
(71, 274)
(76, 287)
(78, 300)
(79, 313)
(80, 326)
(80, 339)
(82, 352)
(82, 365)
(83, 378)
(86, 391)
(91, 404)
(93, 417)
(99, 430)
(106, 443)
(113, 456)
13 input vectors with 3 parameters(117, 469)
(121, 482)
(125, 495)
(130, 508)

Generating 126 parameters:
(7, 14)
(13, 27)
(15, 40)
(17, 53)
(20, 66)
(24, 79)
(28, 92)
(30, 105)
(35, 118)
13 input vectors with 3 parameters
Generating 126 parameters:
(41, 131)
(44, 144)
(6, 14)
(54, 157)(19, 27)
(24, 40)
(31, 53)
(38, 66)
(44, 79)
(51, 92)
(64, 105)
(77, 118)
(90, 131)
(94, 144)
(100, 157)
(109, 170)
(117, 183)
(120, 196)

(123, 209)
(58, 170)
(125, 222)
(62, 183)
(131, 235)
(66, 196)
(69, 209)
(72, 222)
(76, 235)
(79, 248)
(84, 261)
(91, 274)
(95, 287)
(97, 300)
(100, 313)
(100, 326)
(104, 339)
(108, 352)
(110, 365)
(118, 378)
(129, 391)
13 input vectors with 3 parameters
Generating 126 parameters:
(6, 14)
(16, 27)
(25, 40)
(31, 53)
(42, 66)
(47, 79)
(53, 92)
(66, 105)
(76, 118)
(88, 131)
(98, 144)
(102, 157)
(105, 170)
(111, 183)
(119, 196)
(126, 209)
13 input vectors with 3 parameters
Generating 126 parameters:
(5, 14)
(10, 27)
(15, 40)
(24, 53)
(27, 66)
(32, 79)
(39, 92)
(52, 105)
(54, 118)
(56, 131)
(59, 144)
(63, 157)
(68, 170)
(72, 183)
(79, 196)
(86, 209)
(94, 222)
(98, 235)
(105, 248)
(111, 261)
(118, 274)
(127, 287)
13 input vectors with 3 parameters
Generating 126 parameters:
(7, 14)
(11, 27)
(22, 40)
(28, 53)
(41, 66)
(50, 79)
(61, 92)
(66, 105)
(69, 118)
(72, 131)
(82, 144)
(93, 157)
(103, 170)
(106, 183)
(118, 196)
(128, 209)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 11.52 seconds
Total Repetitions: 1000
Maximal objective value: -1.05426
Corresponding parameter setting:
x0: 0.898026
x1: 9.60285
x2: 110.851
******************************

13 input vectors with 3 parameters
Generating 126 parameters:
(5, 14)
(13, 27)
(16, 40)
(17, 53)
(20, 66)
(26, 79)
(30, 92)
(33, 105)
(34, 118)
(41, 131)
(49, 144)
(51, 157)
(55, 170)
(67, 183)
(74, 196)
(80, 209)
(82, 222)
(87, 235)
(97, 248)
(103, 261)
(106, 274)
(110, 287)
(121, 300)
(124, 313)
(129, 326)
783 of 1000, maximal objective function=-1.68245, time remaining: 00:00:01
4 Subset: Run 31 of 126 (best like=-1.68245)
476 of 1000, maximal objective function=-1.58965, time remaining: 00:00:04
1 Subset: Run 475 of 500 (best like=-1.58965)
863 of 1000, maximal objective function=-1.25262, time remaining: 00:00:02
4 Subset: Run 111 of 126 (best like=-1.25262)
50 input vectors with 3 parameters
Generating 126 parameters:
13 input vectors with 3 parameters
(51, 51)
Generating 126 parameters:
(10, 14)
(99, 101)
(138, 151)(14, 27)
(16, 40)
(19, 53)

(22, 66)
(31, 79)
(36, 92)
(49, 105)
(62, 118)
(71, 131)
(73, 144)
(74, 157)
(84, 170)
(84, 183)
(91, 196)
(95, 209)
(96, 222)
865 of 1000, maximal objective function=-1.27694, time remaining: 00:00:02
4 Subset: Run 113 of 126 (best like=-1.27694)
(101, 235)
(104, 248)
(105, 261)
(107, 274)
(120, 287)
(133, 300)
13 input vectors with 3 parameters
Generating 126 parameters:
(8, 14)
(17, 27)
(19, 40)50 input vectors with 3 parameters
Generating 126 parameters:
(38, 51)
(88, 101)
(128, 151)

(25, 53)
(30, 66)
(43, 79)
(46, 92)
(52, 105)
(59, 118)
(70, 131)
(75, 144)
(85, 157)
(97, 170)
(100, 183)
(110, 196)
(113, 209)
(116, 222)
(125, 235)
(132, 248)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 5.85 seconds
Total Repetitions: 1000
Maximal objective value: -1.35642
Corresponding parameter setting:
x0: 0.539181
x1: 31.6057
x2: 100.295
******************************

13 input vectors with 3 parameters
Generating 126 parameters:
(11, 14)
(14, 27)
(17, 40)
(19, 53)
(19, 66)
(25, 79)
(26, 92)
(26, 105)
(29, 118)
(29, 131)
(34, 144)
(37, 157)
(41, 170)
(52, 183)
(52, 196)
(53, 209)
(66, 222)
(69, 235)
(74, 248)
(78, 261)
(80, 274)
(80, 287)
(91, 300)
(93, 313)
(95, 326)
(98, 339)
(104, 352)
(104, 365)
(105, 378)
(107, 391)
(119, 404)
(125, 417)
(129, 430)
533 of 1000, maximal objective function=-1.31305, time remaining: 00:00:04
2 Subset: Run 33 of 126 (best like=-1.31305)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 5.06 seconds
Total Repetitions: 1000
Maximal objective value: -1.68245
Corresponding parameter setting:
x0: 0.411516
x1: 7.14934
x2: 104.595
******************************

13 input vectors with 3 parameters
Generating 126 parameters:
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 13.0 seconds
Total Repetitions: 1000
Maximal objective value: -1.25262
Corresponding parameter setting:
x0: 0.434901
x1: 9.81048
x2: 99.7709
******************************

(10, 14)
(11, 27)
(18, 40)
(27, 53)
(39, 66)
(48, 79)
(57, 92)
(68, 105)
(75, 118)
(78, 131)
(85, 144)
(90, 157)
(91, 170)
(98, 183)
(105, 196)
(118, 209)
(127, 222)
13 input vectors with 3 parameters
Generating 126 parameters:
(7, 14)
(14, 27)
(18, 40)
(28, 53)
(30, 66)
(35, 79)
(43, 92)
(48, 105)
(54, 118)
(59, 131)
(72, 144)
(78, 157)
(82, 170)
(87, 183)
(91, 196)
(95, 209)
(107, 222)
(118, 235)
(121, 248)
(128, 261)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 12.97 seconds
Total Repetitions: 1000
Maximal objective value: -1.24029
Corresponding parameter setting:
x0: 0.59731
x1: 15.7225
x2: 97.4114
******************************

13 input vectors with 3 parameters
Generating 126 parameters:
(5, 14)
(13, 27)
(22, 40)
(27, 53)
(32, 66)
(35, 79)
(43, 92)
(48, 105)
(56, 118)
(65, 131)
(70, 144)
(79, 157)
(92, 170)
(97, 183)
(110, 196)
(114, 209)
(118, 222)
(130, 235)
13 input vectors with 3 parameters
Generating 126 parameters:
(5, 14)
(12, 27)
(18, 40)
(25, 53)
(38, 66)
(44, 79)
(45, 92)
(50, 105)
(56, 118)
(56, 131)
(59, 144)
(65, 157)
(65, 170)
(75, 183)
(86, 196)
(91, 209)
(100, 222)
(106, 235)
(112, 248)
(113, 261)
(118, 274)
(123, 287)
(124, 300)
(129, 313)
790 of 1000, maximal objective function=-1.36985, time remaining: 00:00:02
4 Subset: Run 38 of 126 (best like=-1.36985)
856 of 1000, maximal objective function=-1.25819, time remaining: 00:00:01
4 Subset: Run 104 of 126 (best like=-1.25819)
13 input vectors with 3 parameters
Generating 126 parameters:
(13, 14)
(20, 27)
(24, 40)
(27, 53)
(33, 66)
(42, 79)
(51, 92)
(61, 105)
(64, 118)
(76, 131)
(83, 144)
(91, 157)
(100, 170)
(108, 183)
(120, 196)
(131, 209)
13 input vectors with 3 parameters
Generating 126 parameters:
(7, 14)
(15, 27)
(23, 40)
(28, 53)
(36, 66)
(49, 79)
(55, 92)
(67, 105)
(70, 118)
(77, 131)
(82, 144)
(94, 157)
(107, 170)
(114, 183)
(116, 196)
(123, 209)
(131, 222)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 6.83 seconds
Total Repetitions: 1000
Maximal objective value: -1.25819
Corresponding parameter setting:
x0: 0.380839
x1: 20.3474
x2: 91.554
******************************

Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 7.64 seconds
Total Repetitions: 1000
Maximal objective value: -1.36985
Corresponding parameter setting:
x0: 0.500434
x1: 8.20173
x2: 99.5374
******************************


[3]:

# graph of inverted values
titles = ['(a) at 0 m (no noise)', '(b) at 0 m (with 5% noise)',
'(c) at 1 m (no noise)', '(d) at 1 m (with 5% noise)']
fig, axs = plt.subplots(1, 4, sharex=True, sharey=True, figsize=(14,3))
for i in range(4):
ax = axs[i]
ks[i].showResults(ax=ax, vmin=0, vmax=120, maxDepth=2, errorbar=True)
rmseDepths = np.sqrt(np.sum((ks[i].depths[0][:,0] - depths[:,0])**2)/len(depths[:,0]))
ax.set_title('{:s} \nRMSE$_{{depth}}$={:.2f}'.format(titles[i], rmseDepths))
ax.step(np.arange(depths.shape[0])+0.5, -depths, 'r', where='post') # true depth
if i < 3:
fig.axes[-1].remove()
if i > 0:
ax.set_ylabel('')

[4]:

# graph of apparent values
titles = ['at 0 m (no noise)', 'at 0 m (with 5% noise)',
'at 1 m (no noise)', 'at 1 m (with 5% noise)']
fig, axs = plt.subplots(1, 4, sharex=True, sharey=True, figsize=(14,3))
for i in range(4):
ax = axs[i]
ks[i].show(ax=ax, vmin=10, vmax=100)
ax.set_title('({:s}) {:s}'.format(letters[i], titles[i]))
if i > 0:
ax.get_legend().remove()
ax.set_ylabel('')