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Multi-frequency inversion with EMagPy

This notebook compared a mutli-coil instruments (CMD Explorer) and a multi-frequency instrument (GEM-2) synthetically.

[1]:
import numpy as np
import matplotlib.pyplot as plt
import sys
sys.path.append('../src')
from emagpy import Problem
[2]:
# parameters for the synthetic model
nlayer = 2 # number of layers
npos = 20 # number of positions/sampling locations
conds = np.ones((npos, nlayer))*[10, 50] # EC in mS/m
x = np.linspace(0.1, 2, npos)[:,None]
depths = 0 + 2/(1+np.exp(-4*(x-1))) # depth of model

# defines coils configuration, frequency and height above the ground
coilsA = ['VCP1.48f10000h0', 'VCP2.82f10000h0', 'VCP4.49f10000h0',
        'HCP1.48f10000h0', 'HCP2.82f10000h0', 'HCP4.49f10000h0']
coilsB = ['VCP2.82f300h0', 'VCP2.82f600h0', 'VCP2.82f900h0',
        'VCP2.82f12000h0', 'VCP2.82f2000h0', 'VCP2.82f24000h0']
coilsList = [coilsA, coilsB]

# foward modelling
ks = []
for i, coils in enumerate(coilsList):
    k = Problem()
    k.setModels([depths], [conds])
    _ = k.forward(forwardModel='FSlin', coils=coils, noise=0.0)
    ks.append(k)
k.showResults() # display original model
k.show() # display ECa computed from forward modelling

for k, fm in zip(ks, ['FSlin']*len(coilsList)):
    k.setInit(depths0=[0.5], fixedDepths=[False],
              conds0=[20, 20], fixedConds=[False, False]) # set initial values
    # invert using ROPE solver (RObust Parameter Estimation)
    k.invert(forwardModel=fm, method='ROPE', regularization='l1', alpha=0,
             bnds=[(0.01, 3), (0, 80), (0, 80)], rep=1000, njobs=-1)
  0%|                                                    | 0/20 [00:00<?, ?it/s]
Forward modelling
Forward modelling
100%|██████████████████████████████████████████| 20/20 [00:00<00:00, 158.83it/s]
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.
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']
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']
50 input vectors with 3 parameters
Generating 126 parameters:
(40, 51)
(77, 101)
(116, 151)
(157, 201)
50 input vectors with 3 parameters
Generating 126 parameters:
(38, 51)
(86, 101)
(121, 151)
(170, 201)
50 input vectors with 3 parameters
Generating 126 parameters:
(46, 51)
(80, 101)
(113, 151)
(156, 201)
50 input vectors with 3 parameters
Generating 126 parameters:
(43, 51)
(82, 101)
(122, 151)
(168, 201)
50 input vectors with 3 parameters
Generating 126 parameters:
50 input vectors with 3 parameters
Generating 126 parameters:
(36, 51)
(68, 101)
(50, 51)
(97, 151)
(100, 101)
(119, 201)
(137, 151)
(150, 251)
13 input vectors with 3 parameters
Generating 126 parameters:
(6, 14)
(15, 27)
(26, 40)
(34, 53)
(41, 66)
(52, 79)
(64, 92)
(70, 105)
(75, 118)
(85, 131)
(93, 144)
(102, 157)
(108, 170)
(115, 183)
50 input vectors with 3 parameters
Generating 126 parameters:
(124, 196)
(134, 209)
(48, 51)
(89, 101)
(126, 151)
50 input vectors with 3 parameters
Generating 126 parameters:
(39, 51)
(84, 101)
(124, 151)
(170, 201)
13 input vectors with 3 parameters
Generating 126 parameters:
(11, 14)
(17, 27)
(28, 40)
(36, 53)
(39, 66)
(45, 79)
(53, 92)
(65, 105)
(76, 118)
(83, 131)
(90, 144)
(98, 157)
(104, 170)
(115, 183)
(121, 196)
(126, 209)
13 input vectors with 3 parameters
Generating 126 parameters:
(5, 14)
(8, 27)
(14, 40)
(20, 53)
(26, 66)
(35, 79)
(46, 92)
(50, 105)
13 input vectors with 3 parameters
Generating 126 parameters:
(55, 118)
(5, 14)
(64, 131)
(16, 27)
(71, 144)
(21, 40)
(74, 157)
(27, 53)
(83, 170)
(29, 66)
(91, 183)
(34, 79)
(97, 196)
(39, 92)
(104, 209)
(45, 105)
(111, 222)
(50, 118)
(115, 235)
(62, 131)
(120, 248)
(66, 144)
(123, 261)
(70, 157)
(134, 274)
(76, 170)
(87, 183)
(94, 196)
(98, 209)
(105, 222)
(113, 235)
(123, 248)
(130, 261)
13 input vectors with 3 parameters
Generating 126 parameters:
(14, 14)
(27, 27)
(40, 40)
(53, 53)
(63, 66)
(70, 79)
(82, 92)
(88, 105)
(96, 118)
(103, 131)
(115, 144)
(124, 157)
(137, 170)
13 input vectors with 3 parameters
Generating 126 parameters:
(8, 14)
(18, 27)
(20, 40)
(24, 53)
13 input vectors with 3 parameters
Generating 126 parameters:
(36, 66)
(5, 14)
(40, 79)
(10, 27)
(42, 92)
(16, 40)
(48, 105)
(24, 53)
(61, 118)
(35, 66)
(74, 131)
(44, 79)
(57, 92)(84, 144)

(86, 157)
(63, 105)
(91, 170)
(73, 118)
(94, 183)
(78, 131)
(101, 196)(89, 144)

(93, 157)(104, 209)

(115, 222)
(94, 170)
(106, 183)(128, 235)

(116, 196)
(125, 209)
(136, 222)
13 input vectors with 3 parameters
Generating 126 parameters:
(6, 14)
(16, 27)
(17, 40)
(21, 53)
13 input vectors with 3 parameters
Generating 126 parameters:
(11, 14)
(24, 27)
(26, 66)
(33, 40)
(30, 79)
(41, 53)
(40, 92)
(48, 105)(49, 66)

(58, 118)
(61, 79)
(65, 131)
(70, 92)
(70, 144)
(83, 105)
(79, 157)
(94, 118)
(88, 170)
(103, 131)
(95, 183)
(112, 144)
(99, 196)
(105, 209)(124, 157)

(133, 170)
(112, 222)
(118, 235)
(123, 248)
(131, 261)
50 input vectors with 3 parameters
Generating 126 parameters:
(50, 51)
(97, 101)
(135, 151)
13 input vectors with 3 parameters
Generating 126 parameters:
(3, 14)
(9, 27)
(18, 40)
(21, 53)
(25, 66)
(30, 79)
(35, 92)
(41, 105)
(43, 118)
(49, 131)
(51, 144)
(63, 157)
(72, 170)
(75, 183)
(87, 196)
(90, 209)
(95, 222)
(104, 235)
(109, 248)
(114, 261)
(120, 274)
(131, 287)
50 input vectors with 3 parameters
Generating 126 parameters:
(48, 51)
(98, 101)
(138, 151)
13 input vectors with 3 parameters
Generating 126 parameters:
(14, 14)
(27, 27)
(32, 40)
(45, 53)
(58, 66)
(68, 79)
(78, 92)
(81, 105)
(94, 118)
(103, 131)
(116, 144)
(129, 157)
50 input vectors with 3 parameters
Generating 126 parameters:
(44, 51)
(93, 101)
(132, 151)
13 input vectors with 3 parameters
Generating 126 parameters:
(11, 14)
(15, 27)
(17, 40)
(21, 53)
(25, 66)
(36, 79)
(38, 92)
(46, 105)
(47, 118)
(51, 131)
(60, 144)
(73, 157)
(79, 170)
(83, 183)
(85, 196)
13 input vectors with 3 parameters(93, 209)
(97, 222)
(108, 235)

Generating 126 parameters:
13 input vectors with 3 parameters
Generating 126 parameters:
(116, 248)
(3, 14)
(119, 261)
(10, 27)
(127, 274)
(16, 40)
(18, 53)
(24, 66)
(29, 79)
(37, 92)
(41, 105)
(44, 118)
(47, 131)
(52, 144)
(57, 157)
(69, 170)
(73, 183)
(76, 196)
(84, 209)
(96, 222)
(103, 235)
(111, 248)
(112, 261)
(117, 274)
(124, 287)
(128, 300)
(5, 14)
(9, 27)
(17, 40)
(19, 53)
(22, 66)
(26, 79)
(30, 92)
(35, 105)
(48, 118)
(53, 131)
(57, 144)
(61, 157)
(65, 170)
(67, 183)
(72, 196)
(75, 209)
(82, 222)
(91, 235)
(104, 248)
(114, 261)
(116, 274)
(121, 287)
(125, 300)
(133, 313)
13 input vectors with 3 parameters
Generating 126 parameters:
(8, 14)
(21, 27)
(32, 40)
(44, 53)
(52, 66)
(61, 79)
(74, 92)
(79, 105)
(89, 118)
(102, 131)
(109, 144)
(113, 157)
(122, 170)
(129, 183)
13 input vectors with 3 parameters
Generating 126 parameters:
(3, 14)
(6, 27)
(10, 40)
50 input vectors with 3 parameters
Generating 126 parameters:
(19, 53)
(22, 66)
(24, 79)
(28, 92)
(46, 51)
(32, 105)
(41, 118)
(51, 131)
(61, 144)
(94, 101)
(64, 157)
(66, 170)
(70, 183)
(137, 151)
(76, 196)
(81, 209)
(86, 222)
13 input vectors with 3 parameters
Generating 126 parameters:
(94, 235)
(107, 248)
(12, 14)
(20, 27)
(120, 261)
(27, 40)
(132, 274)
(40, 53)
(50, 66)
(60, 79)
(68, 92)
(76, 105)
(88, 118)
(95, 131)
(105, 144)
(111, 157)
(122, 170)
(132, 183)
13 input vectors with 3 parameters
Generating 126 parameters:
(12, 14)
(21, 27)
(28, 40)
(38, 53)
(51, 66)
(55, 79)
(59, 92)
(62, 105)
(74, 118)
(82, 131)
(94, 144)
(104, 157)
(109, 170)
(119, 183)
(132, 196)
13 input vectors with 3 parameters
Generating 126 parameters:
(4, 14)
(13, 27)
(21, 40)
(29, 53)
(33, 66)
(36, 79)
(37, 92)
(40, 105)
(50, 118)
(59, 131)
(72, 144)
(78, 157)
(87, 170)
(94, 183)
(103, 196)
(112, 209)
(124, 222)
(130, 235)
13 input vectors with 3 parameters
Generating 126 parameters:
(2, 14)
(4, 27)
(9, 40)
(13, 53)
(15, 66)
(17, 79)
(23, 92)
(23, 105)
(23, 118)
(25, 131)
(25, 144)
(29, 157)
(30, 170)
(33, 183)
(41, 196)
(47, 209)
(53, 222)
(57, 235)
(61, 248)
(62, 261)
(64, 274)
(66, 287)
(69, 300)
(82, 313)
(84, 326)
(91, 339)
(93, 352)
(101, 365)
(105, 378)
(114, 391)
(120, 404)
(122, 417)
(125, 430)
(135, 443)
13 input vectors with 3 parameters
Generating 126 parameters:
(5, 14)
(11, 27)
(12, 40)
(17, 53)
(24, 66)
(29, 79)
(33, 92)
(36, 105)
(43, 118)
(48, 131)
(55, 144)
(62, 157)
(67, 170)
(73, 183)
(76, 196)
(87, 209)
(90, 222)
(101, 235)
(104, 248)
(107, 261)
(119, 274)
(130, 287)
13 input vectors with 3 parameters
Generating 126 parameters:
(7, 14)
(10, 27)
(12, 40)
(23, 53)
(26, 66)
(29, 79)
(34, 92)
(34, 105)
(39, 118)
(45, 131)
(46, 144)
(48, 157)
(52, 170)
(61, 183)
(64, 196)
(69, 209)
(72, 222)
13 input vectors with 3 parameters
Generating 126 parameters:
(12, 14)
(19, 27)
(31, 40)
(38, 53)
(48, 66)
(54, 79)
(58, 92)
(70, 105)
(79, 118)
(80, 131)
(82, 144)
(86, 157)
(89, 170)
Stopping samplig
(100, 183)

*** Final SPOTPY summary ***
Total Duration: 1.58 seconds
Total Repetitions: 1000
Maximal objective value: -0.664459
Corresponding parameter setting:
x0: 0.834298
x1: 14.7193
x2: 52.4592
******************************

(107, 196)
(116, 209)
(122, 222)
(125, 235)
(135, 248)
(75, 235)
(78, 248)
(87, 261)
(89, 274)
50 input vectors with 3 parameters(93, 287)

Generating 126 parameters:
(95, 300)
(100, 313)
13 input vectors with 3 parameters
Generating 126 parameters:
(105, 326)
(111, 339)
(5, 14)
(112, 352)
(33, 51)
(11, 27)
(117, 365)
(123, 378)
(14, 40)
(127, 391)
(19, 53)
(66, 101)(29, 66)

(38, 79)
(41, 92)
(46, 105)
(113, 151)
(51, 118)
(57, 131)
(60, 144)
(60, 157)
(159, 201)
(65, 170)
(67, 183)
(76, 196)
(81, 209)
(86, 222)
(91, 235)
(95, 248)
(101, 261)
(108, 274)
(111, 287)
(113, 300)
(121, 313)
(126, 326)
13 input vectors with 3 parameters
Generating 126 parameters:
(6, 14)
(15, 27)
(18, 40)
(24, 53)
(32, 66)
(34, 79)
(47, 92)
(54, 105)
(58, 118)
(61, 131)
(64, 144)
(69, 157)
(81, 170)
(86, 183)
(91, 196)
(95, 209)
(106, 222)
(111, 235)
(121, 248)
(128, 261)
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: 1.62 seconds
Total Repetitions: 1000
Maximal objective value: -0.810151
Corresponding parameter setting:
x0: 0.53724
x1: 14.5167
x2: 51.2382
******************************

13 input vectors with 3 parameters
Generating 126 parameters:
(14, 14)
(25, 27)
(33, 40)
(43, 53)
(48, 66)
(61, 79)
(74, 92)
(79, 105)
Stopping samplig
(92, 118)

*** Final SPOTPY summary ***
Total Duration: 1.56 seconds
Total Repetitions: 1000
Maximal objective value: -0.747463
Corresponding parameter setting:
x0: 0.668899
x1: 36.5868
x2: 50.8884
******************************

(105, 131)
(118, 144)
(127, 157)
13 input vectors with 3 parameters
Generating 126 parameters:
(14, 14)
(25, 27)
(36, 40)
(47, 53)
(57, 66)
(69, 79)
(82, 92)
(92, 105)
(102, 118)
(110, 131)
(121, 144)
(132, 157)
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:
(44, 51)
(85, 101)
(128, 151)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 1.55 seconds
Total Repetitions: 1000
Maximal objective value: -0.319511
Corresponding parameter setting:
x0: 1.79489
x1: 10.3945
x2: 52.1814
******************************

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:
13 input vectors with 3 parameters
Generating 126 parameters:
(49, 51)
(14, 14)
(26, 27)
(86, 101)
(37, 40)
(44, 53)
(113, 151)
(52, 66)
(64, 79)
(160, 201)
(73, 92)
(84, 105)
(91, 118)
(100, 131)
(110, 144)
(115, 157)
(126, 170)
13 input vectors with 3 parameters
Generating 126 parameters:
(10, 14)
(22, 27)
(33, 40)
(45, 53)
(56, 66)
(68, 79)
(81, 92)
(92, 105)
(103, 118)
(112, 131)
(119, 144)
(125, 157)
(134, 170)
50 input vectors with 3 parameters
Generating 126 parameters:
(35, 51)
(84, 101)
(129, 151)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 1.64 seconds
Total Repetitions: 1000
Maximal objective value: -0.514981
Corresponding parameter setting:
x0: 1.37693
x1: 10.8767
x2: 49.5088
******************************

13 input vectors with 3 parameters
Generating 126 parameters:
(4, 14)
(14, 27)
(18, 40)
(28, 53)
(29, 66)
(39, 79)
(41, 92)
(48, 105)
(58, 118)
(70, 131)
(79, 144)
(87, 157)
(92, 170)
(97, 183)
(107, 196)
(120, 209)
(129, 222)
50 input vectors with 3 parameters
Generating 126 parameters:
(34, 51)
(78, 101)
(118, 151)
(166, 201)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 1.85 seconds
Total Repetitions: 1000
Maximal objective value: -0.387578
Corresponding parameter setting:
x0: 0.868831
x1: 11.3199
x2: 50.5278
******************************

Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 1.68 seconds
Total Repetitions: 1000
Maximal objective value: -0.85269
Corresponding parameter setting:
x0: 0.733494
x1: 32.3101
x2: 51.7401
******************************

708 of 1000, maximal objective function=-0.74079, time remaining: 00:00:01
3 Subset: Run 82 of 126 (best like=-0.74079)
677 of 1000, maximal objective function=-0.675198, time remaining: 00:00:01
3 Subset: Run 51 of 126 (best like=-0.675198)
13 input vectors with 3 parameters
Generating 126 parameters:
(11, 14)
(19, 27)
(25, 40)
(32, 53)
(33, 66)
(36, 79)
(45, 92)
(51, 105)
(62, 118)
(74, 131)
(81, 144)
(90, 157)
(97, 170)
(100, 183)
(112, 196)
(118, 209)
(124, 222)
(131, 235)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 1.82 seconds
Total Repetitions: 1000
Maximal objective value: -0.416835
Corresponding parameter setting:
x0: 1.31005
x1: 10.6654
x2: 51.9599
******************************

13 input vectors with 3 parameters
Generating 126 parameters:
(6, 14)
(19, 27)
(25, 40)
(29, 53)
(42, 66)
(51, 79)
(57, 92)
(61, 105)
(69, 118)
(71, 131)
(84, 144)
(92, 157)
(97, 170)
(106, 183)
(113, 196)
(120, 209)
(122, 222)
(126, 235)
13 input vectors with 3 parameters
Generating 126 parameters:
(3, 14)
(12, 27)
(25, 40)
(28, 53)
(37, 66)
(40, 79)
(44, 92)
(50, 105)
(54, 118)
(58, 131)
(62, 144)
(67, 157)
(68, 170)
(77, 183)
(81, 196)
(83, 209)
(92, 222)
(97, 235)
(100, 248)
(109, 261)
(118, 274)
(129, 287)
13 input vectors with 3 parameters
Generating 126 parameters:
(10, 14)
(12, 27)
(24, 40)
(31, 53)
(43, 66)
(49, 79)
(54, 92)
(58, 105)
(62, 118)
(75, 131)
(79, 144)
(88, 157)
(91, 170)
(99, 183)
(105, 196)
13 input vectors with 3 parameters
Generating 126 parameters:
(111, 209)
(117, 222)
(9, 14)
(120, 235)
(16, 27)
(133, 248)
(22, 40)
(35, 53)
(48, 66)
(52, 79)
(60, 92)
(64, 105)
(70, 118)
(78, 131)
(91, 144)
(98, 157)
(111, 170)
(116, 183)
(124, 196)
(136, 209)
623 of 1000, maximal objective function=-0.842117, time remaining: 00:00:01
2 Subset: Run 123 of 126 (best like=-0.842117)
13 input vectors with 3 parameters
Generating 126 parameters:
(10, 14)
(22, 27)
(32, 40)
(44, 53)
(49, 66)
(61, 79)
(72, 92)
(80, 105)
(90, 118)
(103, 131)
(115, 144)
(127, 157)
772 of 1000, maximal objective function=-0.900086, time remaining: 00:00:01
4 Subset: Run 20 of 126 (best like=-0.900086)
669 of 1000, maximal objective function=-0.64833, time remaining: 00:00:01
3 Subset: Run 43 of 126 (best like=-0.64833)
13 input vectors with 3 parameters
Generating 126 parameters:
13 input vectors with 3 parameters(14, 14)

Generating 126 parameters:
(25, 27)
(5, 14)
(32, 40)
(15, 27)
(42, 53)
(21, 40)
(53, 66)
(29, 53)
(60, 79)
(32, 66)
(71, 92)(37, 79)

(38, 92)(77, 105)

(82, 118)
(49, 105)
13 input vectors with 3 parameters
Generating 126 parameters:
(7, 14)
(54, 118)
(93, 131)
(11, 27)
(13, 40)
(58, 131)(22, 53)

(102, 144)
(35, 66)
(41, 79)
(60, 144)
(45, 92)
(110, 157)
(52, 105)
(59, 118)
(67, 157)
(115, 170)
(63, 131)
(71, 144)
(74, 157)
(76, 170)
(125, 183)(77, 170)

(82, 183)
(82, 183)
(90, 196)
(93, 209)
(138, 196)
(94, 222)
(85, 196)
(96, 235)
(96, 248)
(102, 261)
(87, 209)
(105, 274)
(108, 287)
(110, 300)
(97, 222)
(112, 313)
(120, 326)
(128, 339)
(102, 235)
(111, 248)
(119, 261)
(124, 274)
(132, 287)
620 of 1000, maximal objective function=-0.764547, time remaining: 00:00:01
2 Subset: Run 120 of 126 (best like=-0.764547)
646 of 1000, maximal objective function=-0.679937, time remaining: 00:00:01
3 Subset: Run 20 of 126 (best like=-0.679937)
13 input vectors with 3 parameters
Generating 126 parameters:
(8, 14)
(13, 27)
(19, 40)
(27, 53)
(35, 66)
(40, 79)
(45, 92)
(50, 105)
(54, 118)
(56, 131)
(59, 144)
(62, 157)
(68, 170)
(73, 183)
(85, 196)
(96, 209)
(99, 222)
(110, 235)
(114, 248)
(124, 261)
(130, 274)
13 input vectors with 3 parameters
Generating 126 parameters:
(10, 14)
(12, 27)
(15, 40)
(22, 53)
(33, 66)
(37, 79)
(38, 92)
(50, 105)
(53, 118)
(64, 131)
(65, 144)
50 input vectors with 3 parameters
Generating 126 parameters:
(68, 157)
(74, 170)
(78, 183)
(34, 51)
(84, 196)
(85, 209)
(82, 101)
(87, 222)
(99, 235)
(102, 248)
(127, 151)
(111, 261)
(118, 274)
(123, 287)
(125, 300)
(137, 313)
881 of 1000, maximal objective function=-0.436862, time remaining: 00:00:00
5 Subset: Run 3 of 126 (best like=-0.436862)
50 input vectors with 3 parameters
Generating 126 parameters:
(40, 51)
(84, 101)
(127, 151)
50 input vectors with 3 parameters
Generating 126 parameters:
(47, 51)
(96, 101)
(134, 151)
13 input vectors with 3 parameters
Generating 126 parameters:
(9, 14)
(21, 27)
(33, 40)
(36, 53)
(38, 66)
(47, 79)
(52, 92)
(54, 105)
(63, 118)
(72, 131)
(85, 144)
(88, 157)
(89, 170)
(95, 183)
(100, 196)
(108, 209)
(117, 222)
(119, 235)
(126, 248)
13 input vectors with 3 parameters
Generating 126 parameters:
(13, 14)
(19, 27)
(26, 40)
(33, 53)
(38, 66)
(40, 79)
(45, 92)
(50, 105)
(58, 118)
(65, 131)
(76, 144)
(81, 157)
(86, 170)
(96, 183)
(98, 196)
(101, 209)
(103, 222)
(110, 235)
(122, 248)
(127, 261)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 2.1 seconds
Total Repetitions: 1000
Maximal objective value: -0.436862
Corresponding parameter setting:
x0: 1.37384
x1: 9.37053
x2: 47.2649
******************************

13 input vectors with 3 parameters
Generating 126 parameters:
(14, 14)
(25, 27)
13 input vectors with 3 parameters
Generating 126 parameters:
(27, 40)
(11, 14)
(21, 27)
(32, 53)
(34, 40)
(39, 66)
(47, 53)
(49, 79)
(54, 66)
(65, 79)
(60, 92)
(78, 92)
(67, 105)
(89, 105)
(72, 118)
(95, 118)
(85, 131)
(102, 131)
(85, 144)
(115, 144)
(96, 157)
(97, 170)(128, 157)

(99, 183)
(111, 196)
(113, 209)
(115, 222)
(122, 235)
(130, 248)
13 input vectors with 3 parameters
Generating 126 parameters:
(5, 14)
(15, 27)
(28, 40)
(35, 53)
(41, 66)
(46, 79)
(51, 92)
(54, 105)
13 input vectors with 3 parameters
Generating 126 parameters:
(66, 118)
(11, 14)
(16, 27)
(69, 131)
(28, 40)
(38, 53)
(78, 144)
(46, 66)
(50, 79)
(81, 157)
(60, 92)
(66, 105)
(93, 170)
(70, 118)
(81, 131)
(101, 183)
(90, 144)
13 input vectors with 3 parameters
Generating 126 parameters:
(104, 196)
(94, 157)
(104, 170)(107, 209)

(116, 183)
(110, 222)
(7, 14)
(127, 196)
(113, 235)
(16, 27)
(116, 248)
(23, 40)
(120, 261)
(28, 53)
(128, 274)
(33, 66)
(43, 79)
(48, 92)
(53, 105)
(62, 118)
(68, 131)
(76, 144)
(84, 157)
(90, 170)
(92, 183)
(102, 196)
(109, 209)
(121, 222)
(128, 235)
13 input vectors with 3 parameters
Generating 126 parameters:
(12, 14)
(18, 27)
(23, 40)
(36, 53)
(45, 66)
(57, 79)
(70, 92)
(82, 105)
(94, 118)
(105, 131)
(117, 144)
(128, 157)
13 input vectors with 3 parameters
Generating 126 parameters:
13 input vectors with 3 parameters
Generating 126 parameters:
(5, 14)
(5, 14)
(18, 27)
(6, 27)
(21, 40)
(9, 40)
(24, 53)
(16, 53)
(28, 66)
(17, 66)
(33, 79)
(26, 79)
(28, 92)
(46, 92)
(31, 105)
(48, 105)
(34, 118)
(57, 118)
(45, 131)
(62, 131)
(47, 144)
(73, 144)
(49, 157)
(74, 157)
(62, 170)
(78, 170)
13 input vectors with 3 parameters
Generating 126 parameters:
(69, 183)
(4, 14)
(83, 183)
(6, 27)
(14, 40)
(73, 196)
(94, 196)
(79, 209)
(19, 53)
(105, 209)
(84, 222)
(22, 66)
13 input vectors with 3 parameters
Generating 126 parameters:
(106, 222)
(3, 14)
(86, 235)
(4, 27)
(26, 79)
(111, 235)
(7, 40)
(93, 248)
(13, 53)
(16, 66)
(33, 92)
(119, 248)
(25, 79)
(30, 92)
(106, 261)
(31, 105)
(124, 261)
(33, 118)
(39, 105)
(45, 131)
(111, 274)
50 input vectors with 3 parameters
Generating 126 parameters:
(55, 144)
(127, 274)
(41, 118)
(65, 157)
(116, 287)
(47, 51)
(74, 170)
(81, 183)
(43, 131)
(85, 196)
(117, 300)
(93, 101)
(98, 209)
(46, 144)
(111, 222)
(137, 151)
(116, 235)
(121, 313)
(116, 248)
(122, 261)
(57, 157)
(135, 274)
(133, 326)
(58, 170)
(61, 183)
(66, 196)
(71, 209)
(73, 222)
(77, 235)
(82, 248)
(85, 261)
(87, 274)
(95, 287)
(104, 300)
(109, 313)
(111, 326)
(114, 339)
(117, 352)
13 input vectors with 3 parameters
Generating 126 parameters:
(8, 14)
(124, 365)
(14, 27)
(22, 40)
(127, 378)
(35, 53)
(45, 66)
(54, 79)
(67, 92)
(73, 105)
(79, 118)
(80, 131)
(84, 144)
(92, 157)
(96, 170)
(99, 183)
(103, 196)
(107, 209)
(111, 222)
(117, 235)
(120, 248)
(133, 261)
13 input vectors with 3 parameters
Generating 126 parameters:
(11, 14)
(12, 27)
(15, 40)
(21, 53)
(28, 66)
(32, 79)
(36, 92)
(42, 105)
(48, 118)
(61, 131)
(65, 144)
13 input vectors with 3 parameters
Generating 126 parameters:
(66, 157)
(67, 170)
(70, 183)
(74, 196)
(9, 14)
(79, 209)
(82, 222)
(87, 235)
(12, 27)
(91, 248)
(94, 261)
(99, 274)
(16, 40)
(107, 287)
(114, 300)
(118, 313)
(20, 53)
(125, 326)
(133, 339)
(23, 66)
(24, 79)
(26, 92)
(28, 105)
(30, 118)
(39, 131)
(39, 144)
(44, 157)
(45, 170)
(46, 183)
(52, 196)
(55, 209)
(56, 222)
(63, 235)
(67, 248)
(70, 261)
(82, 274)
(84, 287)
(89, 300)
(93, 313)
(97, 326)
(106, 339)
(109, 352)
(113, 365)
(115, 378)
(118, 391)
(129, 404)
13 input vectors with 3 parameters
Generating 126 parameters:
(4, 14)
(9, 27)
(10, 40)
(12, 53)
(16, 66)
(23, 79)
(25, 92)
(34, 105)
(35, 118)
(41, 131)
(43, 144)
(45, 157)
(47, 170)
(47, 183)
(51, 196)
(53, 209)
(54, 222)
(67, 235)
(78, 248)
(78, 261)
(79, 274)
(80, 287)
(84, 300)
13 input vectors with 3 parameters
Generating 126 parameters:
(85, 313)
(14, 14)
(91, 326)
(93, 339)
(21, 27)
(94, 352)
(33, 40)
(95, 365)
(37, 53)
(96, 378)
(44, 66)
(97, 391)
(54, 79)
(100, 404)
(60, 92)
(104, 417)
(68, 105)
(112, 430)
(77, 118)
(118, 443)
(85, 131)
(123, 456)
(97, 144)
(125, 469)
(101, 157)
(125, 482)
(107, 170)
(134, 495)
(120, 183)
(133, 196)
13 input vectors with 3 parameters
Generating 126 parameters:
(9, 14)
(14, 27)
(26, 40)
(31, 53)
(36, 66)
(39, 79)
(39, 92)
(40, 105)
(42, 118)
(45, 131)
(49, 144)
(54, 157)
(57, 170)
(60, 183)
(70, 196)
(76, 209)
(86, 222)
(92, 235)
(101, 248)
(106, 261)
(109, 274)
(109, 287)
(111, 300)
(115, 313)
(118, 326)
(124, 339)
(125, 352)
(129, 365)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 2.61 seconds
Total Repetitions: 1000
Maximal objective value: -0.4173
Corresponding parameter setting:
x0: 2.11962
x1: 10.5822
x2: 54.6761
******************************

Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 1.03 seconds
Total Repetitions: 1000
Maximal objective value: -0.172654
Corresponding parameter setting:
x0: 1.99798
x1: 10.0786
x2: 50.3983
******************************

13 input vectors with 3 parameters
Generating 126 parameters:
(2, 14)
(4, 27)
(8, 40)
(10, 53)
(10, 66)
(14, 79)
(17, 92)
(21, 105)
(30, 118)
(35, 131)
(45, 144)
(52, 157)
(54, 170)
(63, 183)
(69, 196)
(72, 209)
(81, 222)
(83, 235)
(85, 248)
(91, 261)
(103, 274)
(112, 287)
(122, 300)
(134, 313)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 2.86 seconds
Total Repetitions: 1000
Maximal objective value: -0.74079
Corresponding parameter setting:
x0: 0.557945
x1: 39.6826
x2: 50.0374
******************************

Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 1.1 seconds
Total Repetitions: 1000
Maximal objective value: -0.259725
Corresponding parameter setting:
x0: 1.97025
x1: 10.0035
x2: 50.7608
******************************

Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 2.67 seconds
Total Repetitions: 1000
Maximal objective value: -0.712575
Corresponding parameter setting:
x0: 0.611044
x1: 23.6314
x2: 51.0763
******************************

13 input vectors with 3 parameters
Generating 126 parameters:
(9, 14)
(22, 27)
(28, 40)
(32, 53)
(36, 66)
(39, 79)
(49, 92)
(61, 105)
(65, 118)
(73, 131)
(79, 144)
(85, 157)
(94, 170)
(96, 183)
(104, 196)
(115, 209)
(117, 222)
(122, 235)
(135, 248)
13 input vectors with 3 parameters
Generating 126 parameters:
(12, 14)
(23, 27)
(33, 40)
(43, 53)
(51, 66)
(57, 79)
(65, 92)
(78, 105)
(85, 118)
(98, 131)
(104, 144)
(113, 157)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 2.91 seconds
Total Repetitions: 1000
Maximal objective value: -0.480381
Corresponding parameter setting:
x0: 0.712043
x1: 45.147
x2: 50.6729
******************************

(120, 170)
(129, 183)
13 input vectors with 3 parameters
Generating 126 parameters:
(2, 14)
(6, 27)
(7, 40)
(8, 53)
(12, 66)
(15, 79)
(24, 92)
(26, 105)
(33, 118)
13 input vectors with 3 parameters
Generating 126 parameters:
(43, 131)
(48, 144)
(13, 14)
(52, 157)
(22, 27)
(62, 170)
(31, 40)
(66, 183)
(35, 53)
(70, 196)
(42, 66)
(72, 209)
(47, 79)
(75, 222)
(77, 235)
(57, 92)
(80, 248)
(65, 105)
(89, 261)
(72, 118)
(93, 274)
(73, 131)
(106, 287)
(85, 144)
(111, 300)
(87, 157)
(113, 313)
(92, 170)
(114, 326)
(95, 183)
(121, 339)
(100, 196)
(123, 352)
(110, 209)
(135, 365)
(111, 222)
(119, 235)
(122, 248)
(135, 261)
13 input vectors with 3 parameters
Generating 126 parameters:
(6, 14)
(8, 27)
(10, 40)
(12, 53)
(17, 66)
(19, 79)
(24, 92)
(26, 105)
(36, 118)
(42, 131)
(45, 144)
(47, 157)
(50, 170)
(54, 183)
(67, 196)
(72, 209)
(75, 222)
(78, 235)
(80, 248)
(85, 261)
(86, 274)
(90, 287)
(98, 300)
(98, 313)
(100, 326)
(106, 339)
(109, 352)
(121, 365)
(129, 378)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 1.19 seconds
Total Repetitions: 1000
Maximal objective value: -0.443909
Corresponding parameter setting:
x0: 1.98907
x1: 10.6236
x2: 50.3106
******************************

Stopping samplig
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 2.77 seconds
Total Repetitions: 1000
Maximal objective value: -0.318786
Corresponding parameter setting:
x0: 1.75336
x1: 9.73778
x2: 50.4351
******************************


*** Final SPOTPY summary ***
Total Duration: 2.77 seconds
Total Repetitions: 1000
Maximal objective value: -0.49281
Corresponding parameter setting:
x0: 1.15405
x1: 11.8678
x2: 51.959
******************************

Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 1.39 seconds
Total Repetitions: 1000
Maximal objective value: -0.369329
Corresponding parameter setting:
x0: 1.81662
x1: 9.74184
x2: 48.7195
******************************

Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 2.95 seconds
Total Repetitions: 1000
Maximal objective value: -0.553827
Corresponding parameter setting:
x0: 0.597298
x1: 41.6305
x2: 51.0788
******************************

100%|████████████████████████████████████████| 20/20 [00:00<00:00, 12550.28it/s]
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']
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']
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']
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']
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:
(33, 51)
(73, 101)
(107, 151)
(156, 201)
50 input vectors with 3 parameters
Generating 126 parameters:
(39, 51)
(79, 101)
(125, 151)
(171, 201)
50 input vectors with 3 parameters
Generating 126 parameters:
50 input vectors with 3 parameters
Generating 126 parameters:
(43, 51)
(42, 51)
(90, 101)
(85, 101)
(129, 151)
(108, 151)
(148, 201)
50 input vectors with 3 parameters
Generating 126 parameters:
(44, 51)
(93, 101)
50 input vectors with 3 parameters
Generating 126 parameters:
(139, 151)
(47, 51)
(96, 101)
(142, 151)
50 input vectors with 3 parameters
Generating 126 parameters:
(51, 51)50 input vectors with 3 parameters
Generating 126 parameters:

(39, 51)
(93, 101)
(135, 151)
(88, 101)
(119, 151)
(162, 201)
13 input vectors with 3 parameters
Generating 126 parameters:
(12, 14)
13 input vectors with 3 parameters
Generating 126 parameters:
(23, 27)
(10, 14)
(33, 40)
(19, 27)
(46, 53)
(27, 40)
(57, 66)
(38, 53)
(66, 79)
(47, 66)
(78, 92)
(55, 79)
(86, 105)
(65, 92)
(96, 118)
(74, 105)
(108, 131)
(85, 118)
(116, 144)
(93, 131)
(125, 157)
(104, 144)
(133, 170)
(115, 157)
(125, 170)
(134, 183)
13 input vectors with 3 parameters
Generating 126 parameters:
(8, 14)
(17, 27)
(25, 40)
(34, 53)
(45, 66)
(49, 79)
(54, 92)
(65, 105)
(71, 118)
(78, 131)
(91, 144)
(99, 157)
13 input vectors with 3 parameters
Generating 126 parameters:
(105, 170)
(12, 14)(118, 183)

(127, 196)
(19, 27)
(28, 40)
(37, 53)
(46, 66)
(50, 79)
(61, 92)
(73, 105)
(84, 118)
(93, 131)
(98, 144)
(104, 157)
(114, 170)
(121, 183)
(132, 196)
13 input vectors with 3 parameters
Generating 126 parameters:
(12, 14)
(17, 27)
(27, 40)
(40, 53)
(47, 66)
(55, 79)
(60, 92)
13 input vectors with 3 parameters
Generating 126 parameters:
(68, 105)
(77, 118)(12, 14)

(87, 131)
(23, 27)
(92, 144)
(33, 40)
(101, 157)
(43, 53)
(110, 170)
(51, 66)
(117, 183)
(61, 79)
(128, 196)
(73, 92)
(84, 105)
(93, 118)
(105, 131)
(117, 144)
(126, 157)
13 input vectors with 3 parameters
Generating 126 parameters:
(12, 14)
(23, 27)
(30, 40)
(41, 53)
(49, 66)
(56, 79)
(67, 92)
(78, 105)
(90, 118)
(100, 131)
(113, 144)
(123, 157)
(135, 170)
13 input vectors with 3 parameters
Generating 126 parameters:
(12, 14)
(21, 27)
(26, 40)
(33, 53)
(44, 66)
(53, 79)
(60, 92)
(69, 105)
(82, 118)
(88, 131)
(98, 144)
(108, 157)
(121, 170)
(131, 183)
13 input vectors with 3 parameters
Generating 126 parameters:
(12, 14)
(22, 27)
(29, 40)
(40, 53)
(53, 66)
(62, 79)
(72, 92)
(84, 105)
(90, 118)
(100, 131)
(111, 144)
(122, 157)
(133, 170)
13 input vectors with 3 parameters
Generating 126 parameters:
(8, 14)
(17, 27)
(29, 40)
13 input vectors with 3 parameters
Generating 126 parameters:
(41, 53)
(47, 66)
(12, 14)
(55, 79)
(22, 27)
(60, 92)
(34, 40)
(71, 105)
(47, 53)
(81, 118)
(59, 66)(92, 131)

(102, 144)(68, 79)

(81, 92)(107, 157)

(113, 170)
(90, 105)
(120, 183)
(103, 118)
(127, 196)
(114, 131)
(123, 144)
(135, 157)
13 input vectors with 3 parameters
Generating 126 parameters:
(12, 14)
(19, 27)
(26, 40)
(38, 53)
(48, 66)
(60, 79)
(73, 92)
(79, 105)
(87, 118)
(98, 131)
13 input vectors with 3 parameters
Generating 126 parameters:
(106, 144)
(12, 14)
(111, 157)
(25, 27)
(117, 170)
(34, 40)
(123, 183)
(42, 53)
(133, 196)
(50, 66)
(54, 79)
(62, 92)
(69, 105)
(74, 118)
(86, 131)
(96, 144)
(109, 157)
(116, 170)
(121, 183)
(128, 196)
13 input vectors with 3 parameters
Generating 126 parameters:
(13, 14)
(26, 27)
(37, 40)
(47, 53)
(57, 66)
(69, 79)
(80, 92)
(90, 105)
(101, 118)
(113, 131)
(125, 144)
(131, 157)
13 input vectors with 3 parameters
Generating 126 parameters:
(7, 14)
(14, 27)
(21, 40)
(31, 53)
(37, 66)
(49, 79)
(56, 92)
(65, 105)
(75, 118)
(80, 131)
(92, 144)
(103, 157)
(113, 170)
(123, 183)
(133, 196)
13 input vectors with 3 parameters
Generating 126 parameters:
(7, 14)
(14, 27)
(23, 40)
(33, 53)
(43, 66)
(51, 79)
(56, 92)
(66, 105)
(78, 118)
(84, 131)
(93, 144)
(102, 157)
(110, 170)
(117, 183)
(127, 196)
13 input vectors with 3 parameters
Generating 126 parameters:
(9, 14)
(20, 27)
(23, 40)
(31, 53)
(34, 66)
(42, 79)
(51, 92)
(60, 105)
(73, 118)
(83, 131)
(91, 144)
(102, 157)
(115, 170)
(119, 183)
(121, 196)
(130, 209)
13 input vectors with 3 parameters
Generating 126 parameters:
(5, 14)
(12, 27)
(18, 40)
(24, 53)
(34, 66)
(45, 79)
(50, 92)
(56, 105)
(67, 118)
(73, 131)
(78, 144)
13 input vectors with 3 parameters
Generating 126 parameters:(88, 157)

(97, 170)
(9, 14)
(108, 183)
(22, 27)
(113, 196)
(122, 209)
(28, 40)
(132, 222)(35, 53)

(44, 66)
(47, 79)
(60, 92)
(70, 105)
(82, 118)
(87, 131)
(94, 144)
(101, 157)
(112, 170)
(118, 183)
(123, 196)
(133, 209)
13 input vectors with 3 parameters
Generating 126 parameters:
(9, 14)
(20, 27)
(28, 40)
(36, 53)
(45, 66)
(54, 79)
(64, 92)
(72, 105)
(80, 118)
(88, 131)
(96, 144)
(104, 157)
(111, 170)
(118, 183)
(124, 196)
(129, 209)
13 input vectors with 3 parameters
Generating 126 parameters:
(6, 14)
(14, 27)
(19, 40)
(28, 53)
(33, 66)
(41, 79)
(44, 92)
(50, 105)
(62, 118)
50 input vectors with 3 parameters
Generating 126 parameters:
(70, 131)
(78, 144)
(83, 157)
(87, 170)
(47, 51)
(94, 183)
(101, 196)
(88, 101)
(109, 209)
(118, 222)
(124, 235)
(120, 151)
(129, 248)
13 input vectors with 3 parameters
Generating 126 parameters:
(9, 14)
(165, 201)
(14, 27)
(22, 40)
(27, 53)
(32, 66)
(45, 79)
(55, 92)
(57, 105)
(66, 118)
(73, 131)
(83, 144)
(95, 157)
(104, 170)
(114, 183)
(121, 196)
(134, 209)
13 input vectors with 3 parameters
Generating 126 parameters:
(11, 14)
(20, 27)
(32, 40)
(45, 53)
(54, 66)
(60, 79)
(67, 92)
(80, 105)
(88, 118)
(94, 131)
(101, 144)
(110, 157)
(113, 170)
(125, 183)
(129, 196)
50 input vectors with 3 parameters
Generating 126 parameters:
(47, 51)
(97, 101)
(144, 151)
13 input vectors with 3 parameters
Generating 126 parameters:
(9, 14)
(12, 27)
(20, 40)13 input vectors with 3 parameters
(27, 53)
(34, 66)
(47, 79)
(55, 92)
(62, 105)
(68, 118)
(76, 131)
(84, 144)
(91, 157)
(103, 170)
(114, 183)
(118, 196)
(122, 209)
(132, 222)

Generating 126 parameters:
(9, 14)
(15, 27)
(19, 40)
(22, 53)
50 input vectors with 3 parameters
Generating 126 parameters:
(30, 66)
(36, 79)
(45, 92)
(53, 105)
(59, 118)(48, 51)
(91, 101)
50 input vectors with 3 parameters
Generating 126 parameters:
(38, 51)(140, 151)

(88, 101)
(132, 151)
50 input vectors with 3 parameters
Generating 126 parameters:
(42, 51)

(89, 101)
(71, 131)
(81, 144)
(88, 157)
(125, 151)
(92, 170)
(98, 183)
(105, 196)
(109, 209)
(118, 222)
(170, 201)
(126, 235)
13 input vectors with 3 parameters
Generating 126 parameters:
(5, 14)
(14, 27)
(24, 40)
(32, 53)
(38, 66)
(43, 79)
(52, 92)
(60, 105)
(67, 118)
(71, 131)
(80, 144)
(87, 157)
(97, 170)
(107, 183)
(113, 196)
(123, 209)
(131, 222)
50 input vectors with 3 parameters
Generating 126 parameters:
(43, 51)
(88, 101)
(129, 151)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 1.64 seconds
Total Repetitions: 1000
Maximal objective value: -0.394705
Corresponding parameter setting:
x0: 1.23815
x1: 40.7034
x2: 54.7713
******************************

50 input vectors with 3 parameters
Generating 126 parameters:
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.
(42, 51)
(69, 101)
(118, 151)
Initialize database...
['csv', 'hdf5', 'ram', 'sql', 'custom', 'noData']
(161, 201)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 1.6 seconds
Total Repetitions: 1000
Maximal objective value: -0.501463
Corresponding parameter setting:
x0: 1.69346
x1: 11.9696
x2: 42.9569
******************************

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: 1.62 seconds
Total Repetitions: 1000
Maximal objective value: -0.323973
Corresponding parameter setting:
x0: 2.7717
x1: 16.9525
x2: 48.6417
******************************

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']
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 1.66 seconds
Total Repetitions: 1000
Maximal objective value: -0.165175
Corresponding parameter setting:
x0: 1.15551
x1: 6.56935
x2: 49.3553
******************************

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: 1.69 seconds
Total Repetitions: 1000
Maximal objective value: -0.307681
Corresponding parameter setting:
x0: 2.82445
x1: 31.4141
x2: 51.8472
******************************

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

*** Final SPOTPY summary ***
Total Duration: 1.67 seconds
Total Repetitions: 1000
Maximal objective value: -0.308205
Corresponding parameter setting:
x0: 1.41816
x1: 4.26296
x2: 52.4677
******************************

13 input vectors with 3 parameters
Generating 126 parameters:
(14, 14)
(26, 27)
(35, 40)
(44, 53)
(57, 66)
(69, 79)
(82, 92)
(88, 105)
(98, 118)
(109, 131)
(116, 144)
(128, 157)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 1.75 seconds
Total Repetitions: 1000
Maximal objective value: -0.300868
Corresponding parameter setting:
x0: 1.35551
x1: 6.66836
x2: 50.9897
******************************

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


*** Final SPOTPY summary ***
Total Duration: 1.84 seconds
Total Repetitions: 1000
Maximal objective value: -0.284322
Corresponding parameter setting:
x0: 0.214914
x1: 30.7975
x2: 51.5905
******************************

(49, 51)
(96, 101)
(134, 151)
13 input vectors with 3 parameters
Generating 126 parameters:
(11, 14)
(23, 27)
(27, 40)
(34, 53)
(44, 66)
(54, 79)
(65, 92)
(73, 105)
(81, 118)
(93, 131)
(101, 144)
(108, 157)
(115, 170)
(127, 183)
13 input vectors with 3 parameters
Generating 126 parameters:
(7, 14)
(14, 27)
(20, 40)
(30, 53)
(39, 66)
13 input vectors with 3 parameters
(47, 79)
Generating 126 parameters:
(53, 92)
(65, 105)
(70, 118)
(78, 131)
(10, 14)
(91, 144)
(96, 157)
(100, 170)
(14, 27)
(109, 183)
(115, 196)
(124, 209)
(26, 40)
(134, 222)
(36, 53)
(43, 66)
(52, 79)
(62, 92)
(68, 105)
(75, 118)
(85, 131)
(95, 144)
(104, 157)
(112, 170)
(118, 183)
(129, 196)
13 input vectors with 3 parameters
Generating 126 parameters:
(9, 14)
(18, 27)
(28, 40)
(38, 53)
(45, 66)
(53, 79)
(63, 92)
(72, 105)
13 input vectors with 3 parameters
Generating 126 parameters:
(82, 118)
(89, 131)
(13, 14)
(101, 144)
(22, 27)
(113, 157)
(34, 40)
(123, 170)
(43, 53)
(134, 183)
(55, 66)
(68, 79)
(75, 92)
(88, 105)
(98, 118)
(110, 131)
(122, 144)
(134, 157)
634 of 1000, maximal objective function=-0.497316, time remaining: 00:00:01
3 Subset: Run 8 of 126 (best like=-0.497316)
13 input vectors with 3 parameters
Generating 126 parameters:
651 of 1000, maximal objective function=-0.548826, time remaining: 00:00:01
3 Subset: Run 25 of 126 (best like=-0.548826)
(10, 14)
(23, 27)
(36, 40)
(41, 53)
(53, 66)
(65, 79)
(77, 92)
(88, 105)
(95, 118)
(106, 131)
(115, 144)
13 input vectors with 3 parameters
Generating 126 parameters:
(127, 157)
(12, 14)
842 of 1000, maximal objective function=-0.298666, time remaining: 00:00:00
4 Subset: Run 90 of 126 (best like=-0.298666)
(16, 27)
(20, 40)
(32, 53)
(39, 66)
(50, 79)
(56, 92)
(61, 105)
(66, 118)
(76, 131)
(84, 144)
(96, 157)
(96, 170)
(102, 183)
(107, 196)
(117, 209)
(123, 222)
643 of 1000, maximal objective function=-0.429917, time remaining: 00:00:01
3 Subset: Run 17 of 126 (best like=-0.429917)
(124, 235)
(137, 248)
754 of 1000, maximal objective function=-0.43195, time remaining: 00:00:01
4 Subset: Run 2 of 126 (best like=-0.43195)
574 of 1000, maximal objective function=-0.334244, time remaining: 00:00:01
2 Subset: Run 74 of 126 (best like=-0.334244)
13 input vectors with 3 parameters
Generating 126 parameters:
(12, 14)
(22, 27)
(30, 40)
(41, 53)
(52, 66)
(63, 79)
637 of 1000, maximal objective function=-0.431509, time remaining: 00:00:01
3 Subset: Run 11 of 126 (best like=-0.431509)
(72, 92)
(84, 105)
(95, 118)
(104, 131)
(108, 144)
(119, 157)
(128, 170)
13 input vectors with 3 parameters
Generating 126 parameters:
(14, 14)
(26, 27)
626 of 1000, maximal objective function=-0.625851, time remaining: 00:00:01
3 Subset: Run 0 of 126 (best like=-0.625851)
(36, 40)
(38, 53)
(40, 66)
(45, 79)
(48, 92)
(61, 105)
(64, 118)
(68, 131)
(74, 144)
(86, 157)
(87, 170)
(89, 183)
(94, 196)
(100, 209)
(113, 222)
(118, 235)
(118, 248)
(122, 261)
(130, 274)
13 input vectors with 3 parameters
Generating 126 parameters:
(6, 14)
(13, 27)
(18, 40)
(24, 53)
(33, 66)
(37, 79)
(43, 92)
(52, 105)
(56, 118)
(62, 131)
(74, 144)
(80, 157)
(88, 170)
(92, 183)
(97, 196)
(102, 209)
(113, 222)
(126, 235)
13 input vectors with 3 parameters
Generating 126 parameters:
(6, 14)
(14, 27)
(20, 40)
(25, 53)
(31, 66)
(37, 79)
(48, 92)
(56, 105)
(69, 118)
(71, 131)
(76, 144)
(86, 157)
(92, 170)
(96, 183)
(106, 196)
(111, 209)
(119, 222)
(129, 235)
13 input vectors with 3 parameters
Generating 126 parameters:
(9, 14)
(20, 27)
(28, 40)
(41, 53)
(48, 66)
(58, 79)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 2.23 seconds
Total Repetitions: 1000
Maximal objective value: -0.298666
Corresponding parameter setting:
x0: 1.39067
x1: 39.8558
x2: 48.9364
******************************

(70, 92)
(78, 105)
(86, 118)
(95, 131)
(105, 144)
(115, 157)
(125, 170)
(132, 183)
13 input vectors with 3 parameters
Generating 126 parameters:
(6, 14)
(19, 27)
(30, 40)
(35, 53)
(45, 66)
(50, 79)
(56, 92)
(62, 105)
(74, 118)
(79, 131)
(86, 144)
(90, 157)
(101, 170)
(108, 183)
(120, 196)
(124, 209)
(127, 222)
13 input vectors with 3 parameters
Generating 126 parameters:
(8, 14)
(15, 27)
(19, 40)
(30, 53)
(39, 66)
(50, 79)
(62, 92)
(73, 105)
(75, 118)
(82, 131)
(90, 144)
(96, 157)
(101, 170)
Stopping samplig
(107, 183)

*** Final SPOTPY summary ***
Total Duration: 2.33 seconds
Total Repetitions: 1000
Maximal objective value: -0.259351
Corresponding parameter setting:
x0: 1.35817
x1: 30.4647
x2: 50.2922
******************************

(114, 196)
(124, 209)
(135, 222)
13 input vectors with 3 parameters
Generating 126 parameters:
13 input vectors with 3 parameters
Generating 126 parameters:
(11, 14)
(8, 14)
(22, 27)
(17, 27)
(33, 40)
(23, 40)
(41, 53)
(30, 53)
(47, 66)
(43, 66)
(56, 79)
(55, 79)
(66, 92)
50 input vectors with 3 parameters
Generating 126 parameters:
(61, 92)
(74, 105)(50, 51)

(68, 105)
(91, 101)
(78, 118)
(74, 118)
(135, 151)
(86, 131)
(80, 131)
(93, 144)
(87, 144)
(103, 157)
(93, 157)
(111, 170)
(98, 170)
(119, 183)
(104, 183)
(130, 196)
(112, 196)
(119, 209)
(131, 222)
50 input vectors with 3 parameters
Generating 126 parameters:
(47, 51)
(89, 101)
(138, 151)
13 input vectors with 3 parameters
Generating 126 parameters:
(9, 14)
(17, 27)
(26, 40)
(38, 53)
(46, 66)
(53, 79)
(59, 92)
(66, 105)
(72, 118)
(81, 131)
(87, 144)
(93, 157)
(103, 170)
(107, 183)
(113, 196)
(119, 209)
(125, 222)
(129, 235)
50 input vectors with 3 parameters
Generating 126 parameters:
(49, 51)
(86, 101)
(119, 151)
(158, 201)
13 input vectors with 3 parameters
Generating 126 parameters:
(8, 14)
(16, 27)
(21, 40)
(27, 53)
(37, 66)
(48, 79)
(58, 92)
(66, 105)
(78, 118)
(86, 131)
(92, 144)
(105, 157)
(111, 170)
13 input vectors with 3 parameters
Generating 126 parameters:
(122, 183)
(10, 14)
(23, 27)
(34, 40)
(130, 196)
(45, 53)
(57, 66)
(67, 79)
(78, 92)
(90, 105)
(103, 118)
(114, 131)
(123, 144)
(136, 157)
13 input vectors with 3 parameters
Generating 126 parameters:
(13, 14)
(25, 27)
(37, 40)
(45, 53)
(53, 66)
(61, 79)
(72, 92)
(82, 105)
(92, 118)
(103, 131)
(110, 144)
(120, 157)
(131, 170)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 2.49 seconds
Total Repetitions: 1000
Maximal objective value: -0.431509
Corresponding parameter setting:
x0: 1.45728
x1: 14.0322
x2: 49.2156
******************************

13 input vectors with 3 parameters
Generating 126 parameters:
(11, 14)
(14, 27)
(23, 40)
(30, 53)
(37, 66)
(44, 79)
(51, 92)
(56, 105)
(65, 118)
(72, 131)
(79, 144)
(87, 157)
(93, 170)
(98, 183)
(102, 196)
(114, 209)
(125, 222)
(132, 235)
13 input vectors with 3 parameters
Generating 126 parameters:
(9, 14)
(18, 27)
(29, 40)
(35, 53)
(42, 66)
(52, 79)
(60, 92)
(66, 105)
(74, 118)
(85, 131)
(95, 144)
(100, 157)
(113, 170)
(121, 183)
(128, 196)
13 input vectors with 3 parameters
Generating 126 parameters:
(13, 14)
(16, 27)
(23, 40)
(35, 53)
(39, 66)
(41, 79)
(50, 92)
(59, 105)
(72, 118)
(76, 131)
(79, 144)
(92, 157)
(105, 170)
(110, 183)
(117, 196)
(123, 209)
(128, 222)
13 input vectors with 3 parameters
Generating 126 parameters:
(13, 14)
(25, 27)
(38, 40)
(51, 53)
(58, 66)
(71, 79)
(80, 92)
(89, 105)
(98, 118)
(109, 131)
(118, 144)
(127, 157)
13 input vectors with 3 parameters
Generating 126 parameters:
(10, 14)
(17, 27)
(22, 40)
(28, 53)
(35, 66)
(45, 79)
(54, 92)
(56, 105)
(58, 118)
(66, 131)
(71, 144)
(80, 157)
(92, 170)
(103, 183)
(108, 196)
(116, 209)
(125, 222)
(128, 235)
13 input vectors with 3 parameters
Generating 126 parameters:
13 input vectors with 3 parameters
Generating 126 parameters:
(11, 14)
(19, 27)
(10, 14)
(27, 40)
(37, 53)
(47, 66)
(20, 27)
(53, 79)
(60, 92)
(66, 105)
(26, 40)
(70, 118)
(76, 131)
(89, 144)
(33, 53)
(100, 157)
(104, 170)
(110, 183)
(37, 66)
(119, 196)
(123, 209)
(129, 222)
(43, 79)
(47, 92)
(53, 105)
(58, 118)
(62, 131)
(69, 144)
(78, 157)
(87, 170)
(92, 183)
(99, 196)
(103, 209)
(111, 222)
(115, 235)
(119, 248)
(121, 261)
(125, 274)
(133, 287)
13 input vectors with 3 parameters
Generating 126 parameters:
(12, 14)
(16, 27)
13 input vectors with 3 parameters
Generating 126 parameters:
(29, 40)
(14, 14)
(24, 27)
(41, 53)
(32, 40)
(36, 53)
(50, 66)
(49, 66)
(55, 79)
(59, 79)
(67, 92)
(64, 92)
(79, 105)
(70, 105)
(89, 118)
(98, 131)
(77, 118)
(109, 144)
(83, 131)
(117, 157)
(130, 170)
(87, 144)
(97, 157)
(101, 170)
(109, 183)
(114, 196)
(118, 209)
(125, 222)
(133, 235)
13 input vectors with 3 parameters
Generating 126 parameters:
(11, 14)
(19, 27)
(28, 40)
(35, 53)
(46, 66)
(57, 79)
(64, 92)
(72, 105)
(85, 118)
(94, 131)
(107, 144)
(116, 157)
(123, 170)
(136, 183)
13 input vectors with 3 parameters
Generating 126 parameters:
(9, 14)
(18, 27)
(24, 40)
(31, 53)
(38, 66)
(50, 79)
(53, 92)
(61, 105)
(70, 118)
(79, 131)
(89, 144)
(98, 157)
(105, 170)
(108, 183)
(115, 196)
(126, 209)
13 input vectors with 3 parameters
Generating 126 parameters:
(11, 14)
(19, 27)
(25, 40)
(33, 53)
(42, 66)
(47, 79)
(51, 92)
(61, 105)
(68, 118)
(81, 131)
(91, 144)
(100, 157)
(105, 170)
(110, 183)
(118, 196)
(128, 209)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 2.77 seconds
Total Repetitions: 1000
Maximal objective value: -0.334244
Corresponding parameter setting:
x0: 2.52722
x1: 25.8764
x2: 54.4993
******************************

13 input vectors with 3 parameters
Generating 126 parameters:
(2, 14)
(5, 27)
(8, 40)
(10, 53)
(13, 66)
(20, 79)
(23, 92)
(24, 105)
(25, 118)
(29, 131)
(31, 144)
(37, 157)
(41, 170)
(43, 183)
(45, 196)
(48, 209)
(58, 222)
(70, 235)
(78, 248)
(82, 261)
(86, 274)
(90, 287)
(93, 300)
(96, 313)
(100, 326)
(104, 339)
(106, 352)
(111, 365)
(114, 378)
(117, 391)
(120, 404)
13 input vectors with 3 parameters
Generating 126 parameters:
(125, 417)
(138, 430)
(12, 14)
(24, 27)
(32, 40)
(37, 53)
(45, 66)
(53, 79)
(62, 92)
(72, 105)
(77, 118)
(83, 131)
(86, 144)
(91, 157)
(96, 170)
(108, 183)
(114, 196)
(124, 209)
(130, 222)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 1.17 seconds
Total Repetitions: 1000
Maximal objective value: -0.696116
Corresponding parameter setting:
x0: 1.99411
x1: 13.2552
x2: 44.0775
******************************

Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 1.35 seconds
Total Repetitions: 1000
Maximal objective value: -0.412909
Corresponding parameter setting:
x0: 1.23263
x1: 2.25534
x2: 48.0825
******************************

Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 3.21 seconds
Total Repetitions: 1000
Maximal objective value: -0.237866
Corresponding parameter setting:
x0: 1.94176
x1: 36.5763
x2: 52.3619
******************************

50 input vectors with 3 parametersStopping samplig

*** Final SPOTPY summary ***
Total Duration: 1.58 seconds
Total Repetitions: 1000
Maximal objective value: -0.393238
Corresponding parameter setting:
x0: 2.24235
x1: 13.0904
x2: 46.4456
******************************


Generating 126 parameters:
(43, 51)
(76, 101)
(115, 151)
(165, 201)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 3.36 seconds
Total Repetitions: 1000
Maximal objective value: -0.260096
Corresponding parameter setting:
x0: 1.59398
x1: 43.3694
x2: 48.8646
******************************

Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 3.42 seconds
Total Repetitions: 1000
Maximal objective value: -0.404059
Corresponding parameter setting:
x0: 1.33703
x1: 48.036
x2: 47.639
******************************

Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 3.62 seconds
Total Repetitions: 1000
Maximal objective value: -0.286832
Corresponding parameter setting:
x0: 2.29642
x1: 22.1482
x2: 52.2526
******************************

555 of 1000, maximal objective function=-0.399622, time remaining: 00:00:02
2 Subset: Run 55 of 126 (best like=-0.399622)
13 input vectors with 3 parameters
Generating 126 parameters:
(7, 14)
(15, 27)
(22, 40)
(27, 53)
(35, 66)
(41, 79)
(44, 92)
(53, 105)
(56, 118)
(65, 131)
(78, 144)
(84, 157)
(89, 170)
(99, 183)
(105, 196)
(112, 209)
(120, 222)
(127, 235)
13 input vectors with 3 parameters
Generating 126 parameters:
(12, 14)
(17, 27)
(20, 40)
(27, 53)
(29, 66)
(35, 79)
(38, 92)
(43, 105)
(46, 118)
(50, 131)
(56, 144)
(65, 157)
(67, 170)
(68, 183)
(76, 196)
(83, 209)
(90, 222)
(96, 235)
(99, 248)
(106, 261)
(110, 274)
(113, 287)
(120, 300)
(128, 313)
13 input vectors with 3 parameters
Generating 126 parameters:
(4, 14)
(13, 27)
(17, 40)
(19, 53)
(26, 66)
(36, 79)
(37, 92)
(46, 105)
(49, 118)
(53, 131)
(54, 144)
(58, 157)
(61, 170)
(63, 183)
(68, 196)
(77, 209)
(81, 222)
(88, 235)
(97, 248)
(103, 261)
(105, 274)
(105, 287)
(106, 300)
(110, 313)
(115, 326)
(119, 339)
(121, 352)
(124, 365)
(126, 378)
13 input vectors with 3 parameters
Generating 126 parameters:
(5, 14)
(6, 27)
(17, 40)
(25, 53)
(38, 66)
(40, 79)
(45, 92)
(48, 105)
(52, 118)
(52, 131)
(60, 144)
(63, 157)
(65, 170)
(68, 183)
(71, 196)
(76, 209)
(83, 222)
(84, 235)
(90, 248)
(93, 261)
(97, 274)
(100, 287)
(104, 300)
(107, 313)
(112, 326)
(116, 339)
(118, 352)
(120, 365)
(123, 378)
(126, 391)
13 input vectors with 3 parameters
Generating 126 parameters:
(2, 14)
(7, 27)
(11, 40)
(11, 53)
(22, 66)
(27, 79)
(32, 92)
(34, 105)
(46, 118)
(50, 131)
(55, 144)
(57, 157)
(60, 170)
(62, 183)
(65, 196)
(74, 209)
(77, 222)
(79, 235)
(79, 248)
(85, 261)
(90, 274)
(92, 287)
(97, 300)
(103, 313)
(109, 326)
(109, 339)
(111, 352)
(113, 365)
(115, 378)
(122, 391)
(124, 404)
(126, 417)
13 input vectors with 3 parameters
Generating 126 parameters:
(4, 14)
(5, 27)
(8, 40)
(11, 53)
(13, 66)
(15, 79)
(18, 92)
(26, 105)
(26, 118)
(34, 131)
(37, 144)
(44, 157)
(47, 170)
(48, 183)
(54, 196)
(55, 209)
(68, 222)
(69, 235)
(73, 248)
(76, 261)
(79, 274)
(83, 287)
(86, 300)
(99, 313)
(101, 326)
(102, 339)
(104, 352)
(107, 365)
(110, 378)
(113, 391)
(123, 404)
(126, 417)
13 input vectors with 3 parameters
Generating 126 parameters:
(3, 14)
(6, 27)
(9, 40)
(15, 53)
(17, 66)
(28, 79)
(37, 92)
(41, 105)
(52, 118)
(65, 131)
(69, 144)
(73, 157)
(79, 170)
(85, 183)
(93, 196)
(96, 209)
(98, 222)
(102, 235)
(104, 248)
(107, 261)
(112, 274)
(115, 287)
(119, 300)
(120, 313)
(125, 326)
(129, 339)
Stopping samplig

*** Final SPOTPY summary ***
Total Duration: 3.34 seconds
Total Repetitions: 1000
Maximal objective value: -0.0761489
Corresponding parameter setting:
x0: 1.95346
x1: 10.226
x2: 50.088
******************************

../_images/gallery_nb_multi-frequencies_2_6.png
../_images/gallery_nb_multi-frequencies_2_7.png
[3]:
# figure
titles = ['(a) Explorer', '(b) GEM-2', '(c)', '(d)']
fig, axs = plt.subplots(1, 2, figsize=(10, 3), sharex=True, sharey=True)
axs = axs.flatten()
for i, k in enumerate(ks):
    ax = axs[i]
    k.showResults(ax=ax, vmin=0, vmax=60, maxDepth=5, errorbar=True, dist=False)
    ax.step(-np.vstack([depths, depths[-1,:]]), 'r-', where='post')
    if i % 2 == 0:
        fig.axes[-1].remove() # remove colorbar
    else:
        ax.set_ylabel('')
    if i < 2:
        ax.set_xlabel('')
    ax.set_title(titles[i])
ax.set_xticks(np.arange(0, 21, 5))
fig.tight_layout()
../_images/gallery_nb_multi-frequencies_3_0.png