Documentación Sepy

_images/sepy.png

Sepy (Supercritical Extraction with Python) es una biblioteca open source orientada a cálculos de extracción con fluidos supercriticos.

Preprocesamiento de los datos

[['Boldo_0W', 'Boldo_160W', 'Boldo_200W', 'Boldo_280W', 'Boldo_400W'], ['Brasil_0W', 'Brasil_200W', 'Brasil_280W', 'Brasil_400W'], ['Uruguay_0W', 'Uruguay_200W', 'Uruguay_280W', 'Uruguay_400W', 'Uruguay_600W'], ['Romero_0W', 'Romero_200W', 'Romero_280W', 'Romero_400W'], ['Congorosa_0W', 'Congorosa_200W', 'Congorosa_280W', 'Congorosa_400W']]
[[   minutos  rendimiento
0    0.000       0.0000
1    6.683       0.9460
2    6.953       1.1193
3   17.053       2.3520
4   25.069       2.8202
5   34.586       3.2030
6   41.586       3.3810
7   52.353       3.5706
8   69.019       3.7933
9   84.019       3.6280,    minutos  rendimiento
0    0.000        0.000
1    7.250        1.127
2   20.117        2.557
3   25.200        2.835
4   35.917        3.113
5   43.033        3.272
6   53.333        3.406
7   72.950        3.591
8   88.100        3.686,    minutos  rendimiento
0    0.000        0.000
1    6.850        1.095
2   17.250        2.350
3   25.333        2.872
4   34.750        3.228
5   41.400        3.383
6   58.317        3.555,    minutos  rendimiento
0    0.000        0.000
1    7.383        1.093
2   18.333        2.339
3   25.333        2.861
4   37.750        3.157
5   43.883        3.301
6   55.750        3.700
7   68.150        3.700
8   82.750        3.821,    minutos  rendimiento
0    0.000        0.000
1    6.833        1.013
2   18.250        2.206
3   25.483        2.656
4   34.900        2.991
5   41.533        3.433
6   53.183        3.650
7   68.150        3.829
8   82.750        3.934], [   minutos  rendimiento
0     0.00       0.0000
1     3.00       0.0508
2    17.58       0.4874
3    25.00       0.7331
4    35.23       1.0231
5    40.65       1.1210
6    50.37       1.1710
7    70.28       1.2448
8    96.00       1.3626,    minutos  rendimiento
0     0.00        0.000
1     5.00        0.100
2    16.45        0.761
3    25.27        1.073
4    35.53        1.317
5    42.28        1.438
6    55.08        1.532
7    72.85        1.643
8    87.20        1.766,     minutos  rendimiento
0     0.000        0.000
1     6.733        0.270
2    16.450        0.761
3    17.400        0.846
4    25.270        1.073
5    26.000        1.126
6    35.530        1.317
7    37.067        1.365
8    42.280        1.438
9    45.300        1.522
10   55.080        1.532
11   58.750        1.697
12   77.000        1.760
13   87.200        1.766,    minutos  rendimiento
0    0.000        0.000
1    6.900        0.174
2   17.367        0.622
3   26.050        0.797
4   38.500        1.006
5   45.900        1.062
6   58.683        1.184
7   77.517        1.354
8   90.183        1.493], [    minutos  rendimiento
0     0.000        0.000
1     6.667        0.200
2     7.550        0.283
3    17.117        0.750
4    25.583        0.977
5    35.000        1.146
6    41.667        1.231
7    52.450        1.335
8    59.000        1.365
9    79.333        1.450
10   96.733        1.344,    minutos  rendimiento
0    0.000        0.000
1    6.967        0.095
2   17.750        0.234
3   26.150        0.298
4   36.667        0.345
5   44.667        0.384
6   53.083        0.416
7   74.867        0.451
8   84.917        0.506,    minutos  rendimiento
0    0.000        0.000
1    7.417        0.078
2   17.750        0.226
3   25.717        0.294
4   37.033        0.365
5   44.567        0.395
6   55.667        0.426
7   73.250        0.480
8   86.667        0.540,    minutos  rendimiento
0    0.000        0.000
1    7.583        0.074
2   19.417        0.221
3   28.167        0.357
4   37.917        0.432
5   44.200        0.462
6   54.883        0.498
7   70.233        0.556
8   81.850        0.579,    minutos  rendimiento
0    0.000        0.000
1    7.000        0.092
2   17.500        0.221
3   25.100        0.381
4   38.450        0.469
5   46.450        0.520
6   57.167        0.564
7   70.100        0.618], [   minutos  rendimiento
0    0.000        0.000
1    7.283        1.031
2   17.333        2.284
3   24.750        2.790
4   34.117        3.215
5   40.817        3.379
6   56.705        3.621
7   73.972        3.767
8   84.972        3.833,    minutos  rendimiento
0    0.000        0.000
1    6.683        1.456
2   15.767        2.491
3   23.083        2.916
4   32.150        3.300
5   38.667        3.350
6   48.250        3.438
7   64.533        3.553
8   79.917        3.719,    minutos  rendimiento
0    0.000        0.000
1    7.333        1.399
2   21.083        2.378
3   28.483        2.976
4   38.317        3.619
5   45.117        3.750
6   55.650        3.908
7   70.867        4.124
8   84.867        4.292,    minutos  rendimiento
0    0.000        0.000
1    7.400        1.461
2   17.467        2.412
3   25.800        3.128
4   35.200        3.239
5   45.800        3.254], [   minutos  rendimiento
0     0.00       0.0000
1     5.00       0.0080
2     6.83       0.0250
3    16.27       0.2689
4    24.33       0.3520
5    33.80       0.4290
6    39.45       0.4292
7    50.47       0.4336
8    64.15       0.5580
9    79.05       0.6090,     minutos  rendimiento
0     0.000        0.000
1     5.000        0.119
2     6.917        0.154
3     7.480        0.230
4    16.983        0.634
5    18.120        0.638
6    25.833        0.879
7    27.030        0.788
8    33.983        0.948
9    37.330        0.908
10   44.580        0.957
11   49.783        1.012
12   51.067        1.092
13   55.700        1.023
14   69.667        1.155
15   74.830        1.182
16   87.100        1.317
17   91.233        1.281,      minutos  rendimiento
0   0.000000        0.000
1   6.470000        0.140
2  15.720000        0.498
3  22.580000        0.638
4  31.870000        0.712
5  38.830000        0.721
6  49.220000        0.721
7  65.000000        0.756
8  79.816667        0.884,    minutos  rendimiento
0     0.00        0.000
1     7.47        0.089
2    18.10        0.319
3    26.27        0.559
4    35.83        0.659
5    42.25        0.800
6    52.00        0.803
7    68.73        0.864
8    81.43        1.042]]
minutos rendimiento minutos rendimiento minutos rendimiento minutos rendimiento minutos rendimiento
0 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
1 6.667 0.200 6.967 0.095 7.417 0.078 7.583 0.074 7.000 0.092
2 7.550 0.283 17.750 0.234 17.750 0.226 19.417 0.221 17.500 0.221
3 17.117 0.750 26.150 0.298 25.717 0.294 28.167 0.357 25.100 0.381
4 25.583 0.977 36.667 0.345 37.033 0.365 37.917 0.432 38.450 0.469
5 35.000 1.146 44.667 0.384 44.567 0.395 44.200 0.462 46.450 0.520
6 41.667 1.231 53.083 0.416 55.667 0.426 54.883 0.498 57.167 0.564
7 52.450 1.335 74.867 0.451 73.250 0.480 70.233 0.556 70.100 0.618
8 59.000 1.365 84.917 0.506 86.667 0.540 81.850 0.579 NaN NaN
9 79.333 1.450 NaN NaN NaN NaN NaN NaN NaN NaN
10 96.733 1.344 NaN NaN NaN NaN NaN NaN NaN NaN
minutos rendimiento minutos rendimiento minutos rendimiento minutos rendimiento minutos rendimiento matriz
0 0.000 0.0000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Boldo
1 6.683 0.9460 7.250 1.127 6.850 1.095 7.383 1.093 6.833 1.013 Boldo
2 6.953 1.1193 20.117 2.557 17.250 2.350 18.333 2.339 18.250 2.206 Boldo
3 17.053 2.3520 25.200 2.835 25.333 2.872 25.333 2.861 25.483 2.656 Boldo
4 25.069 2.8202 35.917 3.113 34.750 3.228 37.750 3.157 34.900 2.991 Boldo
5 34.586 3.2030 43.033 3.272 41.400 3.383 43.883 3.301 41.533 3.433 Boldo
6 41.586 3.3810 53.333 3.406 58.317 3.555 55.750 3.700 53.183 3.650 Boldo
7 52.353 3.5706 72.950 3.591 NaN NaN 68.150 3.700 68.150 3.829 Boldo
8 69.019 3.7933 88.100 3.686 NaN NaN 82.750 3.821 82.750 3.934 Boldo
9 84.019 3.6280 NaN NaN NaN NaN NaN NaN NaN NaN Boldo
minutos rendimiento minutos rendimiento minutos rendimiento minutos rendimiento minutos rendimiento matriz
0 0.00 0.0000 0.00 0.000 0.000 0.000 0.000 0.000 NaN NaN Brasil
1 3.00 0.0508 5.00 0.100 6.733 0.270 6.900 0.174 NaN NaN Brasil
2 17.58 0.4874 16.45 0.761 16.450 0.761 17.367 0.622 NaN NaN Brasil
3 25.00 0.7331 25.27 1.073 17.400 0.846 26.050 0.797 NaN NaN Brasil
4 35.23 1.0231 35.53 1.317 25.270 1.073 38.500 1.006 NaN NaN Brasil
5 40.65 1.1210 42.28 1.438 26.000 1.126 45.900 1.062 NaN NaN Brasil
6 50.37 1.1710 55.08 1.532 35.530 1.317 58.683 1.184 NaN NaN Brasil
7 70.28 1.2448 72.85 1.643 37.067 1.365 77.517 1.354 NaN NaN Brasil
8 96.00 1.3626 87.20 1.766 42.280 1.438 90.183 1.493 NaN NaN Brasil
9 NaN NaN NaN NaN 45.300 1.522 NaN NaN NaN NaN Brasil
10 NaN NaN NaN NaN 55.080 1.532 NaN NaN NaN NaN Brasil
11 NaN NaN NaN NaN 58.750 1.697 NaN NaN NaN NaN Brasil
12 NaN NaN NaN NaN 77.000 1.760 NaN NaN NaN NaN Brasil
13 NaN NaN NaN NaN 87.200 1.766 NaN NaN NaN NaN Brasil
minutos rendimiento minutos rendimiento minutos rendimiento minutos rendimiento minutos rendimiento matriz
0 0.000 0.0000 0.000 0.000 0.000000 0.000 0.000 0.000 0.000 0.000 Boldo
1 6.683 0.9460 7.250 1.127 6.850000 1.095 7.383 1.093 6.833 1.013 Boldo
2 6.953 1.1193 20.117 2.557 17.250000 2.350 18.333 2.339 18.250 2.206 Boldo
3 17.053 2.3520 25.200 2.835 25.333000 2.872 25.333 2.861 25.483 2.656 Boldo
4 25.069 2.8202 35.917 3.113 34.750000 3.228 37.750 3.157 34.900 2.991 Boldo
5 34.586 3.2030 43.033 3.272 41.400000 3.383 43.883 3.301 41.533 3.433 Boldo
6 41.586 3.3810 53.333 3.406 58.317000 3.555 55.750 3.700 53.183 3.650 Boldo
7 52.353 3.5706 72.950 3.591 NaN NaN 68.150 3.700 68.150 3.829 Boldo
8 69.019 3.7933 88.100 3.686 NaN NaN 82.750 3.821 82.750 3.934 Boldo
9 84.019 3.6280 NaN NaN NaN NaN NaN NaN NaN NaN Boldo
0 0.000 0.0000 0.000 0.000 0.000000 0.000 0.000 0.000 NaN NaN Brasil
1 3.000 0.0508 5.000 0.100 6.733000 0.270 6.900 0.174 NaN NaN Brasil
2 17.580 0.4874 16.450 0.761 16.450000 0.761 17.367 0.622 NaN NaN Brasil
3 25.000 0.7331 25.270 1.073 17.400000 0.846 26.050 0.797 NaN NaN Brasil
4 35.230 1.0231 35.530 1.317 25.270000 1.073 38.500 1.006 NaN NaN Brasil
5 40.650 1.1210 42.280 1.438 26.000000 1.126 45.900 1.062 NaN NaN Brasil
6 50.370 1.1710 55.080 1.532 35.530000 1.317 58.683 1.184 NaN NaN Brasil
7 70.280 1.2448 72.850 1.643 37.067000 1.365 77.517 1.354 NaN NaN Brasil
8 96.000 1.3626 87.200 1.766 42.280000 1.438 90.183 1.493 NaN NaN Brasil
9 NaN NaN NaN NaN 45.300000 1.522 NaN NaN NaN NaN Brasil
10 NaN NaN NaN NaN 55.080000 1.532 NaN NaN NaN NaN Brasil
11 NaN NaN NaN NaN 58.750000 1.697 NaN NaN NaN NaN Brasil
12 NaN NaN NaN NaN 77.000000 1.760 NaN NaN NaN NaN Brasil
13 NaN NaN NaN NaN 87.200000 1.766 NaN NaN NaN NaN Brasil
0 0.000 0.0000 0.000 0.000 0.000000 0.000 0.000 0.000 NaN NaN Congorosa
1 5.000 0.0080 5.000 0.119 6.470000 0.140 7.470 0.089 NaN NaN Congorosa
2 6.830 0.0250 6.917 0.154 15.720000 0.498 18.100 0.319 NaN NaN Congorosa
3 16.270 0.2689 7.480 0.230 22.580000 0.638 26.270 0.559 NaN NaN Congorosa
4 24.330 0.3520 16.983 0.634 31.870000 0.712 35.830 0.659 NaN NaN Congorosa
5 33.800 0.4290 18.120 0.638 38.830000 0.721 42.250 0.800 NaN NaN Congorosa
6 39.450 0.4292 25.833 0.879 49.220000 0.721 52.000 0.803 NaN NaN Congorosa
7 50.470 0.4336 27.030 0.788 65.000000 0.756 68.730 0.864 NaN NaN Congorosa
8 64.150 0.5580 33.983 0.948 79.816667 0.884 81.430 1.042 NaN NaN Congorosa
9 79.050 0.6090 37.330 0.908 NaN NaN NaN NaN NaN NaN Congorosa
10 NaN NaN 44.580 0.957 NaN NaN NaN NaN NaN NaN Congorosa
11 NaN NaN 49.783 1.012 NaN NaN NaN NaN NaN NaN Congorosa
12 NaN NaN 51.067 1.092 NaN NaN NaN NaN NaN NaN Congorosa
13 NaN NaN 55.700 1.023 NaN NaN NaN NaN NaN NaN Congorosa
14 NaN NaN 69.667 1.155 NaN NaN NaN NaN NaN NaN Congorosa
15 NaN NaN 74.830 1.182 NaN NaN NaN NaN NaN NaN Congorosa
16 NaN NaN 87.100 1.317 NaN NaN NaN NaN NaN NaN Congorosa
17 NaN NaN 91.233 1.281 NaN NaN NaN NaN NaN NaN Congorosa
minutos rendimiento minutos rendimiento minutos rendimiento minutos rendimiento minutos rendimiento
matriz
Boldo 0.000 0.0000 0.000 0.000 0.000000 0.000 0.000 0.000 0.000 0.000
Boldo 6.683 0.9460 7.250 1.127 6.850000 1.095 7.383 1.093 6.833 1.013
Boldo 6.953 1.1193 20.117 2.557 17.250000 2.350 18.333 2.339 18.250 2.206
Boldo 17.053 2.3520 25.200 2.835 25.333000 2.872 25.333 2.861 25.483 2.656
Boldo 25.069 2.8202 35.917 3.113 34.750000 3.228 37.750 3.157 34.900 2.991
Boldo 34.586 3.2030 43.033 3.272 41.400000 3.383 43.883 3.301 41.533 3.433
Boldo 41.586 3.3810 53.333 3.406 58.317000 3.555 55.750 3.700 53.183 3.650
Boldo 52.353 3.5706 72.950 3.591 NaN NaN 68.150 3.700 68.150 3.829
Boldo 69.019 3.7933 88.100 3.686 NaN NaN 82.750 3.821 82.750 3.934
Boldo 84.019 3.6280 NaN NaN NaN NaN NaN NaN NaN NaN
Brasil 0.000 0.0000 0.000 0.000 0.000000 0.000 0.000 0.000 NaN NaN
Brasil 3.000 0.0508 5.000 0.100 6.733000 0.270 6.900 0.174 NaN NaN
Brasil 17.580 0.4874 16.450 0.761 16.450000 0.761 17.367 0.622 NaN NaN
Brasil 25.000 0.7331 25.270 1.073 17.400000 0.846 26.050 0.797 NaN NaN
Brasil 35.230 1.0231 35.530 1.317 25.270000 1.073 38.500 1.006 NaN NaN
Brasil 40.650 1.1210 42.280 1.438 26.000000 1.126 45.900 1.062 NaN NaN
Brasil 50.370 1.1710 55.080 1.532 35.530000 1.317 58.683 1.184 NaN NaN
Brasil 70.280 1.2448 72.850 1.643 37.067000 1.365 77.517 1.354 NaN NaN
Brasil 96.000 1.3626 87.200 1.766 42.280000 1.438 90.183 1.493 NaN NaN
Brasil NaN NaN NaN NaN 45.300000 1.522 NaN NaN NaN NaN
Brasil NaN NaN NaN NaN 55.080000 1.532 NaN NaN NaN NaN
Brasil NaN NaN NaN NaN 58.750000 1.697 NaN NaN NaN NaN
Brasil NaN NaN NaN NaN 77.000000 1.760 NaN NaN NaN NaN
Brasil NaN NaN NaN NaN 87.200000 1.766 NaN NaN NaN NaN
Congorosa 0.000 0.0000 0.000 0.000 0.000000 0.000 0.000 0.000 NaN NaN
Congorosa 5.000 0.0080 5.000 0.119 6.470000 0.140 7.470 0.089 NaN NaN
Congorosa 6.830 0.0250 6.917 0.154 15.720000 0.498 18.100 0.319 NaN NaN
Congorosa 16.270 0.2689 7.480 0.230 22.580000 0.638 26.270 0.559 NaN NaN
Congorosa 24.330 0.3520 16.983 0.634 31.870000 0.712 35.830 0.659 NaN NaN
Congorosa 33.800 0.4290 18.120 0.638 38.830000 0.721 42.250 0.800 NaN NaN
Congorosa 39.450 0.4292 25.833 0.879 49.220000 0.721 52.000 0.803 NaN NaN
Congorosa 50.470 0.4336 27.030 0.788 65.000000 0.756 68.730 0.864 NaN NaN
Congorosa 64.150 0.5580 33.983 0.948 79.816667 0.884 81.430 1.042 NaN NaN
Congorosa 79.050 0.6090 37.330 0.908 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 44.580 0.957 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 49.783 1.012 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 51.067 1.092 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 55.700 1.023 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 69.667 1.155 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 74.830 1.182 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 87.100 1.317 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 91.233 1.281 NaN NaN NaN NaN NaN NaN 
Index(['minutos', 'rendimiento', 'minutos', 'rendimiento', 'minutos',
       'rendimiento', 'minutos', 'rendimiento', 'minutos', 'rendimiento'],
      dtype='object')
Extracción 1 Extracción 2 Extracción 3 Extracción 4 Extracción 5
$t$ [=] min $e$ $t$ [=] min $e$ $t$ [=] min $e$ $t$ [=] min $e$ $t$ [=] min $e$
matriz
Boldo 0.000 0.0000 0.000 0.000 0.000000 0.000 0.000 0.000 0.000 0.000
Boldo 6.683 0.9460 7.250 1.127 6.850000 1.095 7.383 1.093 6.833 1.013
Boldo 6.953 1.1193 20.117 2.557 17.250000 2.350 18.333 2.339 18.250 2.206
Boldo 17.053 2.3520 25.200 2.835 25.333000 2.872 25.333 2.861 25.483 2.656
Boldo 25.069 2.8202 35.917 3.113 34.750000 3.228 37.750 3.157 34.900 2.991
Boldo 34.586 3.2030 43.033 3.272 41.400000 3.383 43.883 3.301 41.533 3.433
Boldo 41.586 3.3810 53.333 3.406 58.317000 3.555 55.750 3.700 53.183 3.650
Boldo 52.353 3.5706 72.950 3.591 NaN NaN 68.150 3.700 68.150 3.829
Boldo 69.019 3.7933 88.100 3.686 NaN NaN 82.750 3.821 82.750 3.934
Boldo 84.019 3.6280 NaN NaN NaN NaN NaN NaN NaN NaN
Brasil 0.000 0.0000 0.000 0.000 0.000000 0.000 0.000 0.000 NaN NaN
Brasil 3.000 0.0508 5.000 0.100 6.733000 0.270 6.900 0.174 NaN NaN
Brasil 17.580 0.4874 16.450 0.761 16.450000 0.761 17.367 0.622 NaN NaN
Brasil 25.000 0.7331 25.270 1.073 17.400000 0.846 26.050 0.797 NaN NaN
Brasil 35.230 1.0231 35.530 1.317 25.270000 1.073 38.500 1.006 NaN NaN
Brasil 40.650 1.1210 42.280 1.438 26.000000 1.126 45.900 1.062 NaN NaN
Brasil 50.370 1.1710 55.080 1.532 35.530000 1.317 58.683 1.184 NaN NaN
Brasil 70.280 1.2448 72.850 1.643 37.067000 1.365 77.517 1.354 NaN NaN
Brasil 96.000 1.3626 87.200 1.766 42.280000 1.438 90.183 1.493 NaN NaN
Brasil NaN NaN NaN NaN 45.300000 1.522 NaN NaN NaN NaN
Brasil NaN NaN NaN NaN 55.080000 1.532 NaN NaN NaN NaN
Brasil NaN NaN NaN NaN 58.750000 1.697 NaN NaN NaN NaN
Brasil NaN NaN NaN NaN 77.000000 1.760 NaN NaN NaN NaN
Brasil NaN NaN NaN NaN 87.200000 1.766 NaN NaN NaN NaN
Congorosa 0.000 0.0000 0.000 0.000 0.000000 0.000 0.000 0.000 NaN NaN
Congorosa 5.000 0.0080 5.000 0.119 6.470000 0.140 7.470 0.089 NaN NaN
Congorosa 6.830 0.0250 6.917 0.154 15.720000 0.498 18.100 0.319 NaN NaN
Congorosa 16.270 0.2689 7.480 0.230 22.580000 0.638 26.270 0.559 NaN NaN
Congorosa 24.330 0.3520 16.983 0.634 31.870000 0.712 35.830 0.659 NaN NaN
Congorosa 33.800 0.4290 18.120 0.638 38.830000 0.721 42.250 0.800 NaN NaN
Congorosa 39.450 0.4292 25.833 0.879 49.220000 0.721 52.000 0.803 NaN NaN
Congorosa 50.470 0.4336 27.030 0.788 65.000000 0.756 68.730 0.864 NaN NaN
Congorosa 64.150 0.5580 33.983 0.948 79.816667 0.884 81.430 1.042 NaN NaN
Congorosa 79.050 0.6090 37.330 0.908 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 44.580 0.957 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 49.783 1.012 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 51.067 1.092 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 55.700 1.023 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 69.667 1.155 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 74.830 1.182 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 87.100 1.317 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 91.233 1.281 NaN NaN NaN NaN NaN NaN

Datos operación Yerba Romero

[   0.  200.  280.  400.]
tr =  [ 7.89494813  7.34917765  7.84414133  8.27932474]

Yerba Romero - 400.0 W
X0 =  [ 0.03833  0.03719  0.04292  0.03254] <class 'numpy.ndarray'>
xo = 0.03253999999999999, gamma = 2.1692508973607825, yr = 0.0074, TAO = 5.531852105882878

array([[ 0.01    ,  1.      ],
       [ 0.01    ,  0.5     ],
       [ 0.030321,  1.5     ]])

[  1.00000000e-02   7.40000000e+00   1.74670000e+01   2.58000000e+01
   3.52000000e+01   4.58000000e+01] <class 'numpy.ndarray'>
[ 0.01        0.8937927   2.10971312  3.11619616  4.25155446  5.53185211] <class 'numpy.ndarray'>
[ 0.0001   0.01461  0.02412  0.03128  0.03239  0.03254]

Datos operación Yerba uruguay

[   0.  200.  280.  400.  600.]
tr =  [ 6.90315722  7.06313825  7.19084665  6.90573444  6.96705883]

Yerba Uruguay - 600.0 W
X0 =  [ 0.0145   0.00506  0.0054   0.00579  0.00618] <class 'numpy.ndarray'>
xo = 0.00618, gamma = 1.8798398443346804, yr = 0.0013, TAO = 10.061634577110048

array([[ 0.01    ,  1.      ],
       [ 0.01    ,  0.5     ],
       [ 0.030321,  1.5     ]])

[  1.00000000e-02   1.00472813e+00   2.51182033e+00   3.60266802e+00
   5.51882810e+00   6.66708882e+00   8.20532759e+00   1.00616346e+01] <class 'numpy.ndarray'>
[ 0.0001   0.00092  0.00221  0.00381  0.00469  0.0052   0.00564  0.00618]

fin yu

Datos de operación yerba bolbo

tr =  [ 6.0855294   6.4236725   7.25440914  6.17722886  6.18946974]
[ 0.28606652  0.27100794  0.23997354  0.28181994  0.28126258]

['bolbo' 'bolbo' 'bolbo' 'bolbo' 'bolbo' 'brasil' 'brasil' 'brasil'
 'brasil' 'congorosa' 'congorosa' 'congorosa' 'congorosa']
Flujo $scCO_2$ [=] gr/min Masa matriz vegetal [=] gr Ultra Sonido [=] W
Matriz vegetal Extracciones
bolbo Extracción 1 10.13199 35.4183 0.0
Extracción 2 9.42132 34.7640 160.0
Extracción 3 8.39833 34.9969 200.0
Extracción 4 9.92660 35.2232 280.0
Extracción 5 9.98890 35.5145 400.0
brasil Extracción 1 9.02448 35.3788 0.0
Extracción 2 9.43613 34.8116 200.0
Extracción 3 9.12394 35.1161 280.0
Extracción 4 8.88352 35.5042 400.0
congorosa Extracción 1 10.15210 34.3674 0.0
Extracción 2 9.37822 35.7523 200.0
Extracción 3 10.20529 34.6646 280.0
Extracción 4 9.49983 34.2914 400.0 
{'Densidad gr/cm3': {'bolbo': 1.25, 'brasil': 1.34, 'congorosa': 1.31},
 'Porosidad': {'bolbo': 0.7208, 'brasil': 0.7179, 'congorosa': 0.7137}}
Densidad gr/cm3 Porosidad
bolbo 1.25 0.7208
brasil 1.34 0.7179
congorosa 1.31 0.7137
minutos rendimiento TAO minutos rendimiento TAO minutos rendimiento TAO minutos rendimiento TAO minutos rendimiento TAO
0 0.000 0.0000 0.000000 0.000 0.000 0.000000 0.000 0.000 0.000000 0.000 0.000 0.000000 0.000 0.000 0.000000
1 6.683 0.9460 1.230783 7.250 1.127 1.185647 6.850 1.095 1.257276 7.383 1.093 1.275159 6.833 1.013 1.103972
2 6.953 1.1193 1.280508 20.117 2.557 3.289884 17.250 2.350 3.166133 18.333 2.339 3.166395 18.250 2.206 2.948556
3 17.053 2.3520 3.140587 25.200 2.835 4.121145 25.333 2.872 4.649719 25.333 2.861 4.375404 25.483 2.656 4.117154
4 25.069 2.8202 4.616864 35.917 3.113 5.873776 34.750 3.228 6.378152 37.750 3.157 6.520013 34.900 2.991 5.638609
5 34.586 3.2030 6.369575 43.033 3.272 7.037509 41.400 3.383 7.598719 43.883 3.301 7.579278 41.533 3.433 6.710268
6 41.586 3.3810 7.658739 53.333 3.406 8.721945 58.317 3.555 10.703732 55.750 3.700 9.628893 53.183 3.650 8.592497
7 52.353 3.5706 9.641657 72.950 3.591 11.930060 NaN NaN NaN 68.150 3.700 11.770567 68.150 3.829 11.010636
8 69.019 3.7933 12.710972 88.100 3.686 14.407653 NaN NaN NaN 82.750 3.821 14.292214 82.750 3.934 13.369481
9 84.019 3.6280 15.473466 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 
280.0
Yerba Bolbo - 400.0 W
X0 =  [ 0.037933  0.03686   0.03555   0.03821   0.03934 ] <class 'numpy.ndarray'>
xo = 0.03934, gamma = 1.7408662464183382, yr = 0.0059, TAO = 13.369481303875684

array([[ 0.01    ,  1.      ],
       [ 0.01    ,  0.5     ],
       [ 0.030321,  1.5     ]])

[  3.00000000e-03   1.10397179e+00   2.94855630e+00   4.11715398e+00
   5.63860903e+00   6.71026788e+00   8.59249697e+00   1.10106363e+01
   1.33694813e+01] <class 'numpy.ndarray'>
[ 0.001    0.01013  0.02206  0.02656  0.02991  0.03433  0.0365   0.03829
  0.03934]

Datos de operación yerba brasil

[   0.  200.  280.  400.]
tr =  [ 6.27556792  5.90557503  6.16106767  6.39774269]

Yerba Brasil - 400.0 W
X0 =  [ 0.013626  0.01766   0.01766   0.01493 ] <class 'numpy.ndarray'>
xo = 0.01493, gamma = 1.6007817435333078, yr = 0.0059, TAO = 14.096065489128808

array([[ 0.01    ,  1.      ],
       [ 0.01    ,  0.5     ],
       [ 0.030321,  1.5     ]])

[  1.00000000e-04   1.07850539e+00   2.71455118e+00   4.07174862e+00
   6.01774748e+00   7.17440544e+00   9.17245391e+00   1.21163047e+01
   1.40960655e+01] <class 'numpy.ndarray'>
[ 0.0001   0.00174  0.00622  0.00797  0.01006  0.01062  0.01184  0.01354
  0.01493]

Datos de operación yerba congorosa

tr =  [ 5.42987582  6.11480591  5.44828666  5.78986569]

Yerba Congorosa - 400.0 W
X0 =  [ 0.00609  0.01317  0.00884  0.01042] <class 'numpy.ndarray'>
xo = 0.01042, gamma = 1.6039805840774504, yr = 0.0059, TAO = 14.064229522040602

array([[ 0.01    ,  1.      ],
       [ 0.01    ,  0.5     ],
       [ 0.030321,  1.5     ]])

[  1.00000000e-02   1.29018537e+00   3.12615196e+00   4.53723824e+00
   6.18839916e+00   7.29723317e+00   8.98121006e+00   1.18707417e+01
   1.40642295e+01] <class 'numpy.ndarray'>
[ 0.0001   0.00089  0.00319  0.00559  0.00659  0.008    0.00803  0.00864
  0.01042]

Modelo Lack

Ajuste de los parámetros del modelo 1: Modelo Lack

active_mask: array([0, 0])
       cost: 0.018300978793627454
        fun: array([-0.11809526,  0.37330728,  0.02808377,  0.00718511, -0.00462037,  0.        ])
       grad: array([  8.33403474e-04,  -1.54227422e-08])
        jac: array([[  0.00000000e+00,  -6.35444581e-09],
      [  0.00000000e+00,  -6.35444434e-09],
      [  2.06735361e-02,  -3.11057628e-01],
      [  3.17151307e-02,  -1.76298562e-01],
      [  0.00000000e+00,   0.00000000e+00],
      [  0.00000000e+00,   0.00000000e+00]])
    message: 'Both ftol and xtol termination conditions are satisfied.'
       nfev: 22
       njev: 12
 optimality: 6.7744917811128669e-08
     status: 4
    success: True
          x: array([  5.53594363e-05,   1.20746610e+00])

/home/andres-python/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:5: RuntimeWarning: invalid value encountered in log
/home/andres-python/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:4: RuntimeWarning: overflow encountered in exp
/home/andres-python/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:5: RuntimeWarning: overflow encountered in exp
/home/andres-python/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:14: RuntimeWarning: overflow encountered in exp
/home/andres-python/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:17: RuntimeWarning: invalid value encountered in log

[<matplotlib.lines.Line2D at 0x7f203f10ff98>]
_images/output_44_1.png 
/home/andres-python/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:4: RuntimeWarning: overflow encountered in exp

(1.6759521390448247, 3.7030513340813194, inf)

[<matplotlib.lines.Line2D at 0x7f203f0cd240>]
_images/output_46_1.png 
NOMBRE: Congorosa
_images/output_52_1.png 
NOMBRE: Congorosa
_images/output_53_1.png

Modelo Sovova

nan nan

/home/andres-python/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:9: RuntimeWarning: overflow encountered in exp
/home/andres-python/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:10: RuntimeWarning: overflow encountered in exp
/home/andres-python/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:28: RuntimeWarning: invalid value encountered in log

0.032539999999999993

active_mask: array([0, 0, 0])
       cost: 0.067573820707988189
        fun: array([ 1.81182816, -0.78683877, -0.26158694,  0.02518217, -0.00921332,
       0.09234707, -0.00521931, -0.0395414 ,  0.09887852])
       grad: array([  4.91259316e-02,  -2.61716140e-06,  -1.03208360e-05])
        jac: array([[  1.64962614e-05,  -7.38580870e-12,  -2.72356004e-09],
      [  5.71402153e-06,  -1.02692647e-13,  -1.42693543e-06],
      [  2.03829982e-06,  -3.70461323e-14,  -1.24830113e-06],
      [  7.58134626e+01,  -1.35986181e-06,  -6.79077966e+01],
      [  5.16927813e+01,  -9.15747753e-07,  -6.36407023e+01],
      [  8.24606189e+00,  -1.49376459e-07,  -1.20250951e+01],
      [  2.71129286e+01,  -5.00329665e-07,  -4.89541775e+01],
      [  1.19393497e+01,  -2.25101586e-07,  -2.87292828e+01],
      [  7.43796280e-01,  -1.26455894e-08,  -2.13102359e+00]])
    message: 'xtol termination condition is satisfied.'
       nfev: 31
       njev: 16
 optimality: 0.000515952907784016
     status: 3
    success: True
          x: array([ 0.01040376,  4.62716147,  0.008613  ])

[<matplotlib.lines.Line2D at 0x7effbc368080>]
_images/output_63_1.png 
  File "<ipython-input-337-5e85ffa89130>", line 1
    x: array([ 0.03829315,  1.70819818,  0.01795442]) #0
     ^
SyntaxError: invalid syntax
_images/output_65_0.png 
array([ 0.00017276,  0.00280808,  0.00535308,  0.0078023 ,  0.01015068,
        0.01239369,  0.01452747,  0.01654892,  0.01845584,  0.02024697,
        0.02192205,  0.02348181,  0.02492799,  0.02626321,  0.02749096,
        0.02861545,  0.02964145,  0.03057426,  0.03141945,  0.03218284,
        0.0328703 ,  0.0334877 ,  0.03404079,  0.03453511,  0.03497599,
        0.03536845,  0.03571721,  0.03602665,  0.03630083,  0.03654345,
        0.03675791,  0.03694729,  0.03711438,  0.03726167,  0.03739143,
        0.03750567,  0.0376062 ,  0.03769461,  0.03777233,  0.03784064,
        0.03790064,  0.03795334,  0.03799961,  0.03804023,  0.03807588,
        0.03810716,  0.0381346 ,  0.03815868,  0.0381798 ,  0.03819832])

(0.22974968576541693,
 24.199480533020207,
 5.9097726601403524,
 23.083006310073888,
 0.023157530596497361,
 3.292814746661628)

Help on function plot in module matplotlib.pyplot:

plot(args, **kwargs)
    Plot y versus x as lines and/or markers.

    Call signatures::

        plot([x], y, [fmt], data=None, **kwargs)
        plot([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs)

    The coordinates of the points or line nodes are given by *x, y.

    The optional parameter fmt is a convenient way for defining basic
    formatting like color, marker and linestyle. It's a shortcut string
    notation described in the Notes section below.

    >>> plot(x, y)        # plot x and y using default line style and color
    >>> plot(x, y, 'bo')  # plot x and y using blue circle markers
    >>> plot(y)           # plot y using x as index array 0..N-1
    >>> plot(y, 'r+')     # ditto, but with red plusses

    You can use .Line2D properties as keyword arguments for more
    control on the  appearance. Line properties and fmt can be mixed.
    The following two calls yield identical results:

    >>> plot(x, y, 'go--', linewidth=2, markersize=12)
    >>> plot(x, y, color='green', marker='o', linestyle='dashed',
            linewidth=2, markersize=12)

    When conflicting with fmt, keyword arguments take precedence.

    Plotting labelled data

    There's a convenient way for plotting objects with labelled data (i.e.
    data that can be accessed by index obj['y']). Instead of giving
    the data in x and y, you can provide the object in the data
    parameter and just give the labels for x and y::

    >>> plot('xlabel', 'ylabel', data=obj)

    All indexable objects are supported. This could e.g. be a dict, a
    pandas.DataFame or a structured numpy array.


    Plotting multiple sets of data

    There are various ways to plot multiple sets of data.

    - The most straight forward way is just to call plot multiple times.
      Example:

      >>> plot(x1, y1, 'bo')
      >>> plot(x2, y2, 'go')

    - Alternatively, if your data is already a 2d array, you can pass it
      directly to x, y. A separate data set will be drawn for every
      column.

      Example: an array a where the first column represents the x
      values and the other columns are the y columns::

      >>> plot(a[0], a[1:])

    - The third way is to specify multiple sets of [x], y, [fmt]
      groups::

      >>> plot(x1, y1, 'g^', x2, y2, 'g-')

      In this case, any additional keyword argument applies to all
      datasets. Also this syntax cannot be combined with the data
      parameter.

    By default, each line is assigned a different style specified by a
    'style cycle'. The fmt and line property parameters are only
    necessary if you want explicit deviations from these defaults.
    Alternatively, you can also change the style cycle using the
    'axes.prop_cycle' rcParam.

    Parameters
    ----------
    x, y : array-like or scalar
        The horizontal / vertical coordinates of the data points.
        x values are optional. If not given, they default to
        [0, ..., N-1].

        Commonly, these parameters are arrays of length N. However,
        scalars are supported as well (equivalent to an array with
        constant value).

        The parameters can also be 2-dimensional. Then, the columns
        represent separate data sets.

    fmt : str, optional
        A format string, e.g. 'ro' for red circles. See the Notes
        section for a full description of the format strings.

        Format strings are just an abbreviation for quickly setting
        basic line properties. All of these and more can also be
        controlled by keyword arguments.

    data : indexable object, optional
        An object with labelled data. If given, provide the label names to
        plot in x and y.

        .. note::
            Technically there's a slight ambiguity in calls where the
            second label is a valid fmt. plot('n', 'o', data=obj)
            could be plt(x, y) or plt(y, fmt). In such cases,
            the former interpretation is chosen, but a warning is issued.
            You may suppress the warning by adding an empty format string
            plot('n', 'o', '', data=obj).


    Other Parameters
    ----------------
    scalex, scaley : bool, optional, default: True
        These parameters determined if the view limits are adapted to
        the data limits. The values are passed on to autoscale_view.

    kwargs : `.Line2D` properties, optional
        *kwargs* are used to specify properties like a line label (for
        auto legends), linewidth, antialiasing, marker face color.
        Example::

        >>> plot([1,2,3], [1,2,3], 'go-', label='line 1', linewidth=2)
        >>> plot([1,2,3], [1,4,9], 'rs',  label='line 2')

        If you make multiple lines with one plot command, the kwargs
        apply to all those lines.

        Here is a list of available `.Line2D` properties:

          agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array
      alpha: float (0.0 transparent through 1.0 opaque)
      animated: bool
      antialiased or aa: bool
      clip_box: a `.Bbox` instance
      clip_on: bool
      clip_path: [(`~matplotlib.path.Path`, `.Transform`) | `.Patch` | None]
      color or c: any matplotlib color
      contains: a callable function
      dash_capstyle: ['butt' | 'round' | 'projecting']
      dash_joinstyle: ['miter' | 'round' | 'bevel']
      dashes: sequence of on/off ink in points
      drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post']
      figure: a `.Figure` instance
      fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none']
      gid: an id string
      label: object
      linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]
      linewidth or lw: float value in points
      marker: :mod:`A valid marker style <matplotlib.markers>`
      markeredgecolor or mec: any matplotlib color
      markeredgewidth or mew: float value in points
      markerfacecolor or mfc: any matplotlib color
      markerfacecoloralt or mfcalt: any matplotlib color
      markersize or ms: float
      markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]
      path_effects: `.AbstractPathEffect`
      picker: float distance in points or callable pick function ``fn(artist, event)``
      pickradius: float distance in points
      rasterized: bool or None
      sketch_params: (scale: float, length: float, randomness: float)
      snap: bool or None
      solid_capstyle: ['butt' | 'round' |  'projecting']
      solid_joinstyle: ['miter' | 'round' | 'bevel']
      transform: a :class:`matplotlib.transforms.Transform` instance
      url: a url string
      visible: bool
      xdata: 1D array
      ydata: 1D array
      zorder: float

    Returns
    -------
    lines
        A list of `.Line2D` objects representing the plotted data.


    See Also
    --------
    scatter : XY scatter plot with markers of variing size and/or color (
        sometimes also called bubble chart).


    Notes
    -----
    **Format Strings

    A format string consists of a part for color, marker and line::

        fmt = '[color][marker][line]'

    Each of them is optional. If not provided, the value from the style
    cycle is used. Exception: If line is given, but no marker,
    the data will be a line without markers.

    Colors

    The following color abbreviations are supported:

    =============    ===============================
    character        color
    =============    ===============================
    'b'          blue
    'g'          green
    'r'          red
    'c'          cyan
    'm'          magenta
    'y'          yellow
    'k'          black
    'w'          white
    =============    ===============================

    If the color is the only part of the format string, you can
    additionally use any  matplotlib.colors spec, e.g. full names
    ('green') or hex strings ('#008000').

    Markers

    =============    ===============================
    character        description
    =============    ===============================
    '.'          point marker
    ','          pixel marker
    'o'          circle marker
    'v'          triangle_down marker
    '^'          triangle_up marker
    '<'          triangle_left marker
    '>'          triangle_right marker
    '1'          tri_down marker
    '2'          tri_up marker
    '3'          tri_left marker
    '4'          tri_right marker
    's'          square marker
    'p'          pentagon marker
    '*'          star marker
    'h'          hexagon1 marker
    'H'          hexagon2 marker
    '+'          plus marker
    'x'          x marker
    'D'          diamond marker
    'd'          thin_diamond marker
    '|'          vline marker
    '_'          hline marker
    =============    ===============================

    Line Styles

    =============    ===============================
    character        description
    =============    ===============================
    '-'          solid line style
    '--'         dashed line style
    '-.'         dash-dot line style
    ':'          dotted line style
    =============    ===============================

    Example format strings::

        'b'    # blue markers with default shape
        'ro'   # red circles
        'g-'   # green solid line
        '--'   # dashed line with default color
        'k^:'  # black triangle_up markers connected by a dotted line

    .. note::
        In addition to the above described arguments, this function can take a
        data keyword argument. If such a data argument is given, the
        following arguments are replaced by data[<arg>]:

        * All arguments with the following names: 'x', 'y'.

  File "<ipython-input-104-c3b0085c4838>", line 1
    cielo, la persona de mkt es la que tiene que ofrecer un listado de servicios y productos:
                    ^
SyntaxError: invalid syntax

Preprocesamiento de los datos

[['Boldo_0W', 'Boldo_160W', 'Boldo_200W', 'Boldo_280W', 'Boldo_400W'], ['Brasil_0W', 'Brasil_200W', 'Brasil_280W', 'Brasil_400W'], ['Uruguay_0W', 'Uruguay_200W', 'Uruguay_280W', 'Uruguay_400W', 'Uruguay_600W'], ['Romero_0W', 'Romero_200W', 'Romero_280W', 'Romero_400W'], ['Congorosa_0W', 'Congorosa_200W', 'Congorosa_280W', 'Congorosa_400W']]
[[   minutos  rendimiento
0    0.000       0.0000
1    6.683       0.9460
2    6.953       1.1193
3   17.053       2.3520
4   25.069       2.8202
5   34.586       3.2030
6   41.586       3.3810
7   52.353       3.5706
8   69.019       3.7933
9   84.019       3.6280,    minutos  rendimiento
0    0.000        0.000
1    7.250        1.127
2   20.117        2.557
3   25.200        2.835
4   35.917        3.113
5   43.033        3.272
6   53.333        3.406
7   72.950        3.591
8   88.100        3.686,    minutos  rendimiento
0    0.000        0.000
1    6.850        1.095
2   17.250        2.350
3   25.333        2.872
4   34.750        3.228
5   41.400        3.383
6   58.317        3.555,    minutos  rendimiento
0    0.000        0.000
1    7.383        1.093
2   18.333        2.339
3   25.333        2.861
4   37.750        3.157
5   43.883        3.301
6   55.750        3.700
7   68.150        3.700
8   82.750        3.821,    minutos  rendimiento
0    0.000        0.000
1    6.833        1.013
2   18.250        2.206
3   25.483        2.656
4   34.900        2.991
5   41.533        3.433
6   53.183        3.650
7   68.150        3.829
8   82.750        3.934], [   minutos  rendimiento
0     0.00       0.0000
1     3.00       0.0508
2    17.58       0.4874
3    25.00       0.7331
4    35.23       1.0231
5    40.65       1.1210
6    50.37       1.1710
7    70.28       1.2448
8    96.00       1.3626,    minutos  rendimiento
0     0.00        0.000
1     5.00        0.100
2    16.45        0.761
3    25.27        1.073
4    35.53        1.317
5    42.28        1.438
6    55.08        1.532
7    72.85        1.643
8    87.20        1.766,     minutos  rendimiento
0     0.000        0.000
1     6.733        0.270
2    16.450        0.761
3    17.400        0.846
4    25.270        1.073
5    26.000        1.126
6    35.530        1.317
7    37.067        1.365
8    42.280        1.438
9    45.300        1.522
10   55.080        1.532
11   58.750        1.697
12   77.000        1.760
13   87.200        1.766,    minutos  rendimiento
0    0.000        0.000
1    6.900        0.174
2   17.367        0.622
3   26.050        0.797
4   38.500        1.006
5   45.900        1.062
6   58.683        1.184
7   77.517        1.354
8   90.183        1.493], [    minutos  rendimiento
0     0.000        0.000
1     6.667        0.200
2     7.550        0.283
3    17.117        0.750
4    25.583        0.977
5    35.000        1.146
6    41.667        1.231
7    52.450        1.335
8    59.000        1.365
9    79.333        1.450
10   96.733        1.344,    minutos  rendimiento
0    0.000        0.000
1    6.967        0.095
2   17.750        0.234
3   26.150        0.298
4   36.667        0.345
5   44.667        0.384
6   53.083        0.416
7   74.867        0.451
8   84.917        0.506,    minutos  rendimiento
0    0.000        0.000
1    7.417        0.078
2   17.750        0.226
3   25.717        0.294
4   37.033        0.365
5   44.567        0.395
6   55.667        0.426
7   73.250        0.480
8   86.667        0.540,    minutos  rendimiento
0    0.000        0.000
1    7.583        0.074
2   19.417        0.221
3   28.167        0.357
4   37.917        0.432
5   44.200        0.462
6   54.883        0.498
7   70.233        0.556
8   81.850        0.579,    minutos  rendimiento
0    0.000        0.000
1    7.000        0.092
2   17.500        0.221
3   25.100        0.381
4   38.450        0.469
5   46.450        0.520
6   57.167        0.564
7   70.100        0.618], [   minutos  rendimiento
0    0.000        0.000
1    7.283        1.031
2   17.333        2.284
3   24.750        2.790
4   34.117        3.215
5   40.817        3.379
6   56.705        3.621
7   73.972        3.767
8   84.972        3.833,    minutos  rendimiento
0    0.000        0.000
1    6.683        1.456
2   15.767        2.491
3   23.083        2.916
4   32.150        3.300
5   38.667        3.350
6   48.250        3.438
7   64.533        3.553
8   79.917        3.719,    minutos  rendimiento
0    0.000        0.000
1    7.333        1.399
2   21.083        2.378
3   28.483        2.976
4   38.317        3.619
5   45.117        3.750
6   55.650        3.908
7   70.867        4.124
8   84.867        4.292,    minutos  rendimiento
0    0.000        0.000
1    7.400        1.461
2   17.467        2.412
3   25.800        3.128
4   35.200        3.239
5   45.800        3.254], [   minutos  rendimiento
0     0.00       0.0000
1     5.00       0.0080
2     6.83       0.0250
3    16.27       0.2689
4    24.33       0.3520
5    33.80       0.4290
6    39.45       0.4292
7    50.47       0.4336
8    64.15       0.5580
9    79.05       0.6090,     minutos  rendimiento
0     0.000        0.000
1     5.000        0.119
2     6.917        0.154
3     7.480        0.230
4    16.983        0.634
5    18.120        0.638
6    25.833        0.879
7    27.030        0.788
8    33.983        0.948
9    37.330        0.908
10   44.580        0.957
11   49.783        1.012
12   51.067        1.092
13   55.700        1.023
14   69.667        1.155
15   74.830        1.182
16   87.100        1.317
17   91.233        1.281,      minutos  rendimiento
0   0.000000        0.000
1   6.470000        0.140
2  15.720000        0.498
3  22.580000        0.638
4  31.870000        0.712
5  38.830000        0.721
6  49.220000        0.721
7  65.000000        0.756
8  79.816667        0.884,    minutos  rendimiento
0     0.00        0.000
1     7.47        0.089
2    18.10        0.319
3    26.27        0.559
4    35.83        0.659
5    42.25        0.800
6    52.00        0.803
7    68.73        0.864
8    81.43        1.042]]
minutos rendimiento minutos rendimiento minutos rendimiento minutos rendimiento minutos rendimiento
0 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
1 6.667 0.200 6.967 0.095 7.417 0.078 7.583 0.074 7.000 0.092
2 7.550 0.283 17.750 0.234 17.750 0.226 19.417 0.221 17.500 0.221
3 17.117 0.750 26.150 0.298 25.717 0.294 28.167 0.357 25.100 0.381
4 25.583 0.977 36.667 0.345 37.033 0.365 37.917 0.432 38.450 0.469
5 35.000 1.146 44.667 0.384 44.567 0.395 44.200 0.462 46.450 0.520
6 41.667 1.231 53.083 0.416 55.667 0.426 54.883 0.498 57.167 0.564
7 52.450 1.335 74.867 0.451 73.250 0.480 70.233 0.556 70.100 0.618
8 59.000 1.365 84.917 0.506 86.667 0.540 81.850 0.579 NaN NaN
9 79.333 1.450 NaN NaN NaN NaN NaN NaN NaN NaN
10 96.733 1.344 NaN NaN NaN NaN NaN NaN NaN NaN
minutos rendimiento minutos rendimiento minutos rendimiento minutos rendimiento minutos rendimiento matriz
0 0.000 0.0000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Boldo
1 6.683 0.9460 7.250 1.127 6.850 1.095 7.383 1.093 6.833 1.013 Boldo
2 6.953 1.1193 20.117 2.557 17.250 2.350 18.333 2.339 18.250 2.206 Boldo
3 17.053 2.3520 25.200 2.835 25.333 2.872 25.333 2.861 25.483 2.656 Boldo
4 25.069 2.8202 35.917 3.113 34.750 3.228 37.750 3.157 34.900 2.991 Boldo
5 34.586 3.2030 43.033 3.272 41.400 3.383 43.883 3.301 41.533 3.433 Boldo
6 41.586 3.3810 53.333 3.406 58.317 3.555 55.750 3.700 53.183 3.650 Boldo
7 52.353 3.5706 72.950 3.591 NaN NaN 68.150 3.700 68.150 3.829 Boldo
8 69.019 3.7933 88.100 3.686 NaN NaN 82.750 3.821 82.750 3.934 Boldo
9 84.019 3.6280 NaN NaN NaN NaN NaN NaN NaN NaN Boldo
minutos rendimiento minutos rendimiento minutos rendimiento minutos rendimiento minutos rendimiento matriz
0 0.00 0.0000 0.00 0.000 0.000 0.000 0.000 0.000 NaN NaN Brasil
1 3.00 0.0508 5.00 0.100 6.733 0.270 6.900 0.174 NaN NaN Brasil
2 17.58 0.4874 16.45 0.761 16.450 0.761 17.367 0.622 NaN NaN Brasil
3 25.00 0.7331 25.27 1.073 17.400 0.846 26.050 0.797 NaN NaN Brasil
4 35.23 1.0231 35.53 1.317 25.270 1.073 38.500 1.006 NaN NaN Brasil
5 40.65 1.1210 42.28 1.438 26.000 1.126 45.900 1.062 NaN NaN Brasil
6 50.37 1.1710 55.08 1.532 35.530 1.317 58.683 1.184 NaN NaN Brasil
7 70.28 1.2448 72.85 1.643 37.067 1.365 77.517 1.354 NaN NaN Brasil
8 96.00 1.3626 87.20 1.766 42.280 1.438 90.183 1.493 NaN NaN Brasil
9 NaN NaN NaN NaN 45.300 1.522 NaN NaN NaN NaN Brasil
10 NaN NaN NaN NaN 55.080 1.532 NaN NaN NaN NaN Brasil
11 NaN NaN NaN NaN 58.750 1.697 NaN NaN NaN NaN Brasil
12 NaN NaN NaN NaN 77.000 1.760 NaN NaN NaN NaN Brasil
13 NaN NaN NaN NaN 87.200 1.766 NaN NaN NaN NaN Brasil
minutos rendimiento minutos rendimiento minutos rendimiento minutos rendimiento minutos rendimiento matriz
0 0.000 0.0000 0.000 0.000 0.000000 0.000 0.000 0.000 0.000 0.000 Boldo
1 6.683 0.9460 7.250 1.127 6.850000 1.095 7.383 1.093 6.833 1.013 Boldo
2 6.953 1.1193 20.117 2.557 17.250000 2.350 18.333 2.339 18.250 2.206 Boldo
3 17.053 2.3520 25.200 2.835 25.333000 2.872 25.333 2.861 25.483 2.656 Boldo
4 25.069 2.8202 35.917 3.113 34.750000 3.228 37.750 3.157 34.900 2.991 Boldo
5 34.586 3.2030 43.033 3.272 41.400000 3.383 43.883 3.301 41.533 3.433 Boldo
6 41.586 3.3810 53.333 3.406 58.317000 3.555 55.750 3.700 53.183 3.650 Boldo
7 52.353 3.5706 72.950 3.591 NaN NaN 68.150 3.700 68.150 3.829 Boldo
8 69.019 3.7933 88.100 3.686 NaN NaN 82.750 3.821 82.750 3.934 Boldo
9 84.019 3.6280 NaN NaN NaN NaN NaN NaN NaN NaN Boldo
0 0.000 0.0000 0.000 0.000 0.000000 0.000 0.000 0.000 NaN NaN Brasil
1 3.000 0.0508 5.000 0.100 6.733000 0.270 6.900 0.174 NaN NaN Brasil
2 17.580 0.4874 16.450 0.761 16.450000 0.761 17.367 0.622 NaN NaN Brasil
3 25.000 0.7331 25.270 1.073 17.400000 0.846 26.050 0.797 NaN NaN Brasil
4 35.230 1.0231 35.530 1.317 25.270000 1.073 38.500 1.006 NaN NaN Brasil
5 40.650 1.1210 42.280 1.438 26.000000 1.126 45.900 1.062 NaN NaN Brasil
6 50.370 1.1710 55.080 1.532 35.530000 1.317 58.683 1.184 NaN NaN Brasil
7 70.280 1.2448 72.850 1.643 37.067000 1.365 77.517 1.354 NaN NaN Brasil
8 96.000 1.3626 87.200 1.766 42.280000 1.438 90.183 1.493 NaN NaN Brasil
9 NaN NaN NaN NaN 45.300000 1.522 NaN NaN NaN NaN Brasil
10 NaN NaN NaN NaN 55.080000 1.532 NaN NaN NaN NaN Brasil
11 NaN NaN NaN NaN 58.750000 1.697 NaN NaN NaN NaN Brasil
12 NaN NaN NaN NaN 77.000000 1.760 NaN NaN NaN NaN Brasil
13 NaN NaN NaN NaN 87.200000 1.766 NaN NaN NaN NaN Brasil
0 0.000 0.0000 0.000 0.000 0.000000 0.000 0.000 0.000 NaN NaN Congorosa
1 5.000 0.0080 5.000 0.119 6.470000 0.140 7.470 0.089 NaN NaN Congorosa
2 6.830 0.0250 6.917 0.154 15.720000 0.498 18.100 0.319 NaN NaN Congorosa
3 16.270 0.2689 7.480 0.230 22.580000 0.638 26.270 0.559 NaN NaN Congorosa
4 24.330 0.3520 16.983 0.634 31.870000 0.712 35.830 0.659 NaN NaN Congorosa
5 33.800 0.4290 18.120 0.638 38.830000 0.721 42.250 0.800 NaN NaN Congorosa
6 39.450 0.4292 25.833 0.879 49.220000 0.721 52.000 0.803 NaN NaN Congorosa
7 50.470 0.4336 27.030 0.788 65.000000 0.756 68.730 0.864 NaN NaN Congorosa
8 64.150 0.5580 33.983 0.948 79.816667 0.884 81.430 1.042 NaN NaN Congorosa
9 79.050 0.6090 37.330 0.908 NaN NaN NaN NaN NaN NaN Congorosa
10 NaN NaN 44.580 0.957 NaN NaN NaN NaN NaN NaN Congorosa
11 NaN NaN 49.783 1.012 NaN NaN NaN NaN NaN NaN Congorosa
12 NaN NaN 51.067 1.092 NaN NaN NaN NaN NaN NaN Congorosa
13 NaN NaN 55.700 1.023 NaN NaN NaN NaN NaN NaN Congorosa
14 NaN NaN 69.667 1.155 NaN NaN NaN NaN NaN NaN Congorosa
15 NaN NaN 74.830 1.182 NaN NaN NaN NaN NaN NaN Congorosa
16 NaN NaN 87.100 1.317 NaN NaN NaN NaN NaN NaN Congorosa
17 NaN NaN 91.233 1.281 NaN NaN NaN NaN NaN NaN Congorosa
minutos rendimiento minutos rendimiento minutos rendimiento minutos rendimiento minutos rendimiento
matriz
Boldo 0.000 0.0000 0.000 0.000 0.000000 0.000 0.000 0.000 0.000 0.000
Boldo 6.683 0.9460 7.250 1.127 6.850000 1.095 7.383 1.093 6.833 1.013
Boldo 6.953 1.1193 20.117 2.557 17.250000 2.350 18.333 2.339 18.250 2.206
Boldo 17.053 2.3520 25.200 2.835 25.333000 2.872 25.333 2.861 25.483 2.656
Boldo 25.069 2.8202 35.917 3.113 34.750000 3.228 37.750 3.157 34.900 2.991
Boldo 34.586 3.2030 43.033 3.272 41.400000 3.383 43.883 3.301 41.533 3.433
Boldo 41.586 3.3810 53.333 3.406 58.317000 3.555 55.750 3.700 53.183 3.650
Boldo 52.353 3.5706 72.950 3.591 NaN NaN 68.150 3.700 68.150 3.829
Boldo 69.019 3.7933 88.100 3.686 NaN NaN 82.750 3.821 82.750 3.934
Boldo 84.019 3.6280 NaN NaN NaN NaN NaN NaN NaN NaN
Brasil 0.000 0.0000 0.000 0.000 0.000000 0.000 0.000 0.000 NaN NaN
Brasil 3.000 0.0508 5.000 0.100 6.733000 0.270 6.900 0.174 NaN NaN
Brasil 17.580 0.4874 16.450 0.761 16.450000 0.761 17.367 0.622 NaN NaN
Brasil 25.000 0.7331 25.270 1.073 17.400000 0.846 26.050 0.797 NaN NaN
Brasil 35.230 1.0231 35.530 1.317 25.270000 1.073 38.500 1.006 NaN NaN
Brasil 40.650 1.1210 42.280 1.438 26.000000 1.126 45.900 1.062 NaN NaN
Brasil 50.370 1.1710 55.080 1.532 35.530000 1.317 58.683 1.184 NaN NaN
Brasil 70.280 1.2448 72.850 1.643 37.067000 1.365 77.517 1.354 NaN NaN
Brasil 96.000 1.3626 87.200 1.766 42.280000 1.438 90.183 1.493 NaN NaN
Brasil NaN NaN NaN NaN 45.300000 1.522 NaN NaN NaN NaN
Brasil NaN NaN NaN NaN 55.080000 1.532 NaN NaN NaN NaN
Brasil NaN NaN NaN NaN 58.750000 1.697 NaN NaN NaN NaN
Brasil NaN NaN NaN NaN 77.000000 1.760 NaN NaN NaN NaN
Brasil NaN NaN NaN NaN 87.200000 1.766 NaN NaN NaN NaN
Congorosa 0.000 0.0000 0.000 0.000 0.000000 0.000 0.000 0.000 NaN NaN
Congorosa 5.000 0.0080 5.000 0.119 6.470000 0.140 7.470 0.089 NaN NaN
Congorosa 6.830 0.0250 6.917 0.154 15.720000 0.498 18.100 0.319 NaN NaN
Congorosa 16.270 0.2689 7.480 0.230 22.580000 0.638 26.270 0.559 NaN NaN
Congorosa 24.330 0.3520 16.983 0.634 31.870000 0.712 35.830 0.659 NaN NaN
Congorosa 33.800 0.4290 18.120 0.638 38.830000 0.721 42.250 0.800 NaN NaN
Congorosa 39.450 0.4292 25.833 0.879 49.220000 0.721 52.000 0.803 NaN NaN
Congorosa 50.470 0.4336 27.030 0.788 65.000000 0.756 68.730 0.864 NaN NaN
Congorosa 64.150 0.5580 33.983 0.948 79.816667 0.884 81.430 1.042 NaN NaN
Congorosa 79.050 0.6090 37.330 0.908 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 44.580 0.957 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 49.783 1.012 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 51.067 1.092 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 55.700 1.023 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 69.667 1.155 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 74.830 1.182 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 87.100 1.317 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 91.233 1.281 NaN NaN NaN NaN NaN NaN 
Index(['minutos', 'rendimiento', 'minutos', 'rendimiento', 'minutos',
       'rendimiento', 'minutos', 'rendimiento', 'minutos', 'rendimiento'],
      dtype='object')
Extracción 1 Extracción 2 Extracción 3 Extracción 4 Extracción 5
$t$ [=] min $e$ $t$ [=] min $e$ $t$ [=] min $e$ $t$ [=] min $e$ $t$ [=] min $e$
matriz
Boldo 0.000 0.0000 0.000 0.000 0.000000 0.000 0.000 0.000 0.000 0.000
Boldo 6.683 0.9460 7.250 1.127 6.850000 1.095 7.383 1.093 6.833 1.013
Boldo 6.953 1.1193 20.117 2.557 17.250000 2.350 18.333 2.339 18.250 2.206
Boldo 17.053 2.3520 25.200 2.835 25.333000 2.872 25.333 2.861 25.483 2.656
Boldo 25.069 2.8202 35.917 3.113 34.750000 3.228 37.750 3.157 34.900 2.991
Boldo 34.586 3.2030 43.033 3.272 41.400000 3.383 43.883 3.301 41.533 3.433
Boldo 41.586 3.3810 53.333 3.406 58.317000 3.555 55.750 3.700 53.183 3.650
Boldo 52.353 3.5706 72.950 3.591 NaN NaN 68.150 3.700 68.150 3.829
Boldo 69.019 3.7933 88.100 3.686 NaN NaN 82.750 3.821 82.750 3.934
Boldo 84.019 3.6280 NaN NaN NaN NaN NaN NaN NaN NaN
Brasil 0.000 0.0000 0.000 0.000 0.000000 0.000 0.000 0.000 NaN NaN
Brasil 3.000 0.0508 5.000 0.100 6.733000 0.270 6.900 0.174 NaN NaN
Brasil 17.580 0.4874 16.450 0.761 16.450000 0.761 17.367 0.622 NaN NaN
Brasil 25.000 0.7331 25.270 1.073 17.400000 0.846 26.050 0.797 NaN NaN
Brasil 35.230 1.0231 35.530 1.317 25.270000 1.073 38.500 1.006 NaN NaN
Brasil 40.650 1.1210 42.280 1.438 26.000000 1.126 45.900 1.062 NaN NaN
Brasil 50.370 1.1710 55.080 1.532 35.530000 1.317 58.683 1.184 NaN NaN
Brasil 70.280 1.2448 72.850 1.643 37.067000 1.365 77.517 1.354 NaN NaN
Brasil 96.000 1.3626 87.200 1.766 42.280000 1.438 90.183 1.493 NaN NaN
Brasil NaN NaN NaN NaN 45.300000 1.522 NaN NaN NaN NaN
Brasil NaN NaN NaN NaN 55.080000 1.532 NaN NaN NaN NaN
Brasil NaN NaN NaN NaN 58.750000 1.697 NaN NaN NaN NaN
Brasil NaN NaN NaN NaN 77.000000 1.760 NaN NaN NaN NaN
Brasil NaN NaN NaN NaN 87.200000 1.766 NaN NaN NaN NaN
Congorosa 0.000 0.0000 0.000 0.000 0.000000 0.000 0.000 0.000 NaN NaN
Congorosa 5.000 0.0080 5.000 0.119 6.470000 0.140 7.470 0.089 NaN NaN
Congorosa 6.830 0.0250 6.917 0.154 15.720000 0.498 18.100 0.319 NaN NaN
Congorosa 16.270 0.2689 7.480 0.230 22.580000 0.638 26.270 0.559 NaN NaN
Congorosa 24.330 0.3520 16.983 0.634 31.870000 0.712 35.830 0.659 NaN NaN
Congorosa 33.800 0.4290 18.120 0.638 38.830000 0.721 42.250 0.800 NaN NaN
Congorosa 39.450 0.4292 25.833 0.879 49.220000 0.721 52.000 0.803 NaN NaN
Congorosa 50.470 0.4336 27.030 0.788 65.000000 0.756 68.730 0.864 NaN NaN
Congorosa 64.150 0.5580 33.983 0.948 79.816667 0.884 81.430 1.042 NaN NaN
Congorosa 79.050 0.6090 37.330 0.908 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 44.580 0.957 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 49.783 1.012 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 51.067 1.092 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 55.700 1.023 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 69.667 1.155 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 74.830 1.182 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 87.100 1.317 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 91.233 1.281 NaN NaN NaN NaN NaN NaN

Datos operación Yerba Romero

[   0.  200.  280.  400.]
tr =  [ 7.89494813  7.34917765  7.84414133  8.27932474]

Yerba Romero - 400.0 W
X0 =  [ 0.03833  0.03719  0.04292  0.03254] <class 'numpy.ndarray'>
xo = 0.03253999999999999, gamma = 2.1692508973607825, yr = 0.0074, TAO = 5.531852105882878

array([[ 0.01    ,  1.      ],
       [ 0.01    ,  0.5     ],
       [ 0.030321,  1.5     ]])

[  1.00000000e-02   7.40000000e+00   1.74670000e+01   2.58000000e+01
   3.52000000e+01   4.58000000e+01] <class 'numpy.ndarray'>
[ 0.01        0.8937927   2.10971312  3.11619616  4.25155446  5.53185211] <class 'numpy.ndarray'>
[ 0.0001   0.01461  0.02412  0.03128  0.03239  0.03254]

Datos operación Yerba uruguay

[   0.  200.  280.  400.  600.]
tr =  [ 6.90315722  7.06313825  7.19084665  6.90573444  6.96705883]

Yerba Uruguay - 600.0 W
X0 =  [ 0.0145   0.00506  0.0054   0.00579  0.00618] <class 'numpy.ndarray'>
xo = 0.00618, gamma = 1.8798398443346804, yr = 0.0013, TAO = 10.061634577110048

array([[ 0.01    ,  1.      ],
       [ 0.01    ,  0.5     ],
       [ 0.030321,  1.5     ]])

[  1.00000000e-02   1.00472813e+00   2.51182033e+00   3.60266802e+00
   5.51882810e+00   6.66708882e+00   8.20532759e+00   1.00616346e+01] <class 'numpy.ndarray'>
[ 0.0001   0.00092  0.00221  0.00381  0.00469  0.0052   0.00564  0.00618]

fin yu

Datos de operación yerba bolbo

tr =  [ 6.0855294   6.4236725   7.25440914  6.17722886  6.18946974]
[ 0.28606652  0.27100794  0.23997354  0.28181994  0.28126258]

['bolbo' 'bolbo' 'bolbo' 'bolbo' 'bolbo' 'brasil' 'brasil' 'brasil'
 'brasil' 'congorosa' 'congorosa' 'congorosa' 'congorosa']
Flujo $scCO_2$ [=] gr/min Masa matriz vegetal [=] gr Ultra Sonido [=] W
Matriz vegetal Extracciones
bolbo Extracción 1 10.13199 35.4183 0.0
Extracción 2 9.42132 34.7640 160.0
Extracción 3 8.39833 34.9969 200.0
Extracción 4 9.92660 35.2232 280.0
Extracción 5 9.98890 35.5145 400.0
brasil Extracción 1 9.02448 35.3788 0.0
Extracción 2 9.43613 34.8116 200.0
Extracción 3 9.12394 35.1161 280.0
Extracción 4 8.88352 35.5042 400.0
congorosa Extracción 1 10.15210 34.3674 0.0
Extracción 2 9.37822 35.7523 200.0
Extracción 3 10.20529 34.6646 280.0
Extracción 4 9.49983 34.2914 400.0 
{'Densidad gr/cm3': {'bolbo': 1.25, 'brasil': 1.34, 'congorosa': 1.31},
 'Porosidad': {'bolbo': 0.7208, 'brasil': 0.7179, 'congorosa': 0.7137}}
Densidad gr/cm3 Porosidad
bolbo 1.25 0.7208
brasil 1.34 0.7179
congorosa 1.31 0.7137
minutos rendimiento TAO minutos rendimiento TAO minutos rendimiento TAO minutos rendimiento TAO minutos rendimiento TAO
0 0.000 0.0000 0.000000 0.000 0.000 0.000000 0.000 0.000 0.000000 0.000 0.000 0.000000 0.000 0.000 0.000000
1 6.683 0.9460 1.230783 7.250 1.127 1.185647 6.850 1.095 1.257276 7.383 1.093 1.275159 6.833 1.013 1.103972
2 6.953 1.1193 1.280508 20.117 2.557 3.289884 17.250 2.350 3.166133 18.333 2.339 3.166395 18.250 2.206 2.948556
3 17.053 2.3520 3.140587 25.200 2.835 4.121145 25.333 2.872 4.649719 25.333 2.861 4.375404 25.483 2.656 4.117154
4 25.069 2.8202 4.616864 35.917 3.113 5.873776 34.750 3.228 6.378152 37.750 3.157 6.520013 34.900 2.991 5.638609
5 34.586 3.2030 6.369575 43.033 3.272 7.037509 41.400 3.383 7.598719 43.883 3.301 7.579278 41.533 3.433 6.710268
6 41.586 3.3810 7.658739 53.333 3.406 8.721945 58.317 3.555 10.703732 55.750 3.700 9.628893 53.183 3.650 8.592497
7 52.353 3.5706 9.641657 72.950 3.591 11.930060 NaN NaN NaN 68.150 3.700 11.770567 68.150 3.829 11.010636
8 69.019 3.7933 12.710972 88.100 3.686 14.407653 NaN NaN NaN 82.750 3.821 14.292214 82.750 3.934 13.369481
9 84.019 3.6280 15.473466 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 
280.0
Yerba Bolbo - 400.0 W
X0 =  [ 0.037933  0.03686   0.03555   0.03821   0.03934 ] <class 'numpy.ndarray'>
xo = 0.03934, gamma = 1.7408662464183382, yr = 0.0059, TAO = 13.369481303875684

array([[ 0.01    ,  1.      ],
       [ 0.01    ,  0.5     ],
       [ 0.030321,  1.5     ]])

[  3.00000000e-03   1.10397179e+00   2.94855630e+00   4.11715398e+00
   5.63860903e+00   6.71026788e+00   8.59249697e+00   1.10106363e+01
   1.33694813e+01] <class 'numpy.ndarray'>
[ 0.001    0.01013  0.02206  0.02656  0.02991  0.03433  0.0365   0.03829
  0.03934]

Datos de operación yerba brasil

[   0.  200.  280.  400.]
tr =  [ 6.27556792  5.90557503  6.16106767  6.39774269]

Yerba Brasil - 400.0 W
X0 =  [ 0.013626  0.01766   0.01766   0.01493 ] <class 'numpy.ndarray'>
xo = 0.01493, gamma = 1.6007817435333078, yr = 0.0059, TAO = 14.096065489128808

array([[ 0.01    ,  1.      ],
       [ 0.01    ,  0.5     ],
       [ 0.030321,  1.5     ]])

[  1.00000000e-04   1.07850539e+00   2.71455118e+00   4.07174862e+00
   6.01774748e+00   7.17440544e+00   9.17245391e+00   1.21163047e+01
   1.40960655e+01] <class 'numpy.ndarray'>
[ 0.0001   0.00174  0.00622  0.00797  0.01006  0.01062  0.01184  0.01354
  0.01493]

Datos de operación yerba congorosa

tr =  [ 5.42987582  6.11480591  5.44828666  5.78986569]

Yerba Congorosa - 400.0 W
X0 =  [ 0.00609  0.01317  0.00884  0.01042] <class 'numpy.ndarray'>
xo = 0.01042, gamma = 1.6039805840774504, yr = 0.0059, TAO = 14.064229522040602

array([[ 0.01    ,  1.      ],
       [ 0.01    ,  0.5     ],
       [ 0.030321,  1.5     ]])

[  1.00000000e-02   1.29018537e+00   3.12615196e+00   4.53723824e+00
   6.18839916e+00   7.29723317e+00   8.98121006e+00   1.18707417e+01
   1.40642295e+01] <class 'numpy.ndarray'>
[ 0.0001   0.00089  0.00319  0.00559  0.00659  0.008    0.00803  0.00864
  0.01042]

Modelo Lack

Ajuste de los parámetros del modelo 1: Modelo Lack

active_mask: array([0, 0])
       cost: 0.018300978793627454
        fun: array([-0.11809526,  0.37330728,  0.02808377,  0.00718511, -0.00462037,  0.        ])
       grad: array([  8.33403474e-04,  -1.54227422e-08])
        jac: array([[  0.00000000e+00,  -6.35444581e-09],
      [  0.00000000e+00,  -6.35444434e-09],
      [  2.06735361e-02,  -3.11057628e-01],
      [  3.17151307e-02,  -1.76298562e-01],
      [  0.00000000e+00,   0.00000000e+00],
      [  0.00000000e+00,   0.00000000e+00]])
    message: 'Both ftol and xtol termination conditions are satisfied.'
       nfev: 22
       njev: 12
 optimality: 6.7744917811128669e-08
     status: 4
    success: True
          x: array([  5.53594363e-05,   1.20746610e+00])

/home/andres-python/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:5: RuntimeWarning: invalid value encountered in log
/home/andres-python/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:4: RuntimeWarning: overflow encountered in exp
/home/andres-python/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:5: RuntimeWarning: overflow encountered in exp
/home/andres-python/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:14: RuntimeWarning: overflow encountered in exp
/home/andres-python/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:17: RuntimeWarning: invalid value encountered in log

[<matplotlib.lines.Line2D at 0x7f203f10ff98>]
_images/output_44_1.png 
/home/andres-python/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:4: RuntimeWarning: overflow encountered in exp

(1.6759521390448247, 3.7030513340813194, inf)

[<matplotlib.lines.Line2D at 0x7f203f0cd240>]
_images/output_46_1.png 
NOMBRE: Congorosa
_images/output_52_1.png 
NOMBRE: Congorosa
_images/output_53_1.png

Modelo Sovova

nan nan

/home/andres-python/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:9: RuntimeWarning: overflow encountered in exp
/home/andres-python/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:10: RuntimeWarning: overflow encountered in exp
/home/andres-python/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:28: RuntimeWarning: invalid value encountered in log

0.032539999999999993

active_mask: array([0, 0, 0])
       cost: 0.067573820707988189
        fun: array([ 1.81182816, -0.78683877, -0.26158694,  0.02518217, -0.00921332,
       0.09234707, -0.00521931, -0.0395414 ,  0.09887852])
       grad: array([  4.91259316e-02,  -2.61716140e-06,  -1.03208360e-05])
        jac: array([[  1.64962614e-05,  -7.38580870e-12,  -2.72356004e-09],
      [  5.71402153e-06,  -1.02692647e-13,  -1.42693543e-06],
      [  2.03829982e-06,  -3.70461323e-14,  -1.24830113e-06],
      [  7.58134626e+01,  -1.35986181e-06,  -6.79077966e+01],
      [  5.16927813e+01,  -9.15747753e-07,  -6.36407023e+01],
      [  8.24606189e+00,  -1.49376459e-07,  -1.20250951e+01],
      [  2.71129286e+01,  -5.00329665e-07,  -4.89541775e+01],
      [  1.19393497e+01,  -2.25101586e-07,  -2.87292828e+01],
      [  7.43796280e-01,  -1.26455894e-08,  -2.13102359e+00]])
    message: 'xtol termination condition is satisfied.'
       nfev: 31
       njev: 16
 optimality: 0.000515952907784016
     status: 3
    success: True
          x: array([ 0.01040376,  4.62716147,  0.008613  ])

[<matplotlib.lines.Line2D at 0x7effbc368080>]
_images/output_63_1.png 
  File "<ipython-input-337-5e85ffa89130>", line 1
    x: array([ 0.03829315,  1.70819818,  0.01795442]) #0
     ^
SyntaxError: invalid syntax
_images/output_65_0.png 
array([ 0.00017276,  0.00280808,  0.00535308,  0.0078023 ,  0.01015068,
        0.01239369,  0.01452747,  0.01654892,  0.01845584,  0.02024697,
        0.02192205,  0.02348181,  0.02492799,  0.02626321,  0.02749096,
        0.02861545,  0.02964145,  0.03057426,  0.03141945,  0.03218284,
        0.0328703 ,  0.0334877 ,  0.03404079,  0.03453511,  0.03497599,
        0.03536845,  0.03571721,  0.03602665,  0.03630083,  0.03654345,
        0.03675791,  0.03694729,  0.03711438,  0.03726167,  0.03739143,
        0.03750567,  0.0376062 ,  0.03769461,  0.03777233,  0.03784064,
        0.03790064,  0.03795334,  0.03799961,  0.03804023,  0.03807588,
        0.03810716,  0.0381346 ,  0.03815868,  0.0381798 ,  0.03819832])

(0.22974968576541693,
 24.199480533020207,
 5.9097726601403524,
 23.083006310073888,
 0.023157530596497361,
 3.292814746661628)

Help on function plot in module matplotlib.pyplot:

plot(args, **kwargs)
    Plot y versus x as lines and/or markers.

    Call signatures::

        plot([x], y, [fmt], data=None, **kwargs)
        plot([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs)

    The coordinates of the points or line nodes are given by *x, y.

    The optional parameter fmt is a convenient way for defining basic
    formatting like color, marker and linestyle. It's a shortcut string
    notation described in the Notes section below.

    >>> plot(x, y)        # plot x and y using default line style and color
    >>> plot(x, y, 'bo')  # plot x and y using blue circle markers
    >>> plot(y)           # plot y using x as index array 0..N-1
    >>> plot(y, 'r+')     # ditto, but with red plusses

    You can use .Line2D properties as keyword arguments for more
    control on the  appearance. Line properties and fmt can be mixed.
    The following two calls yield identical results:

    >>> plot(x, y, 'go--', linewidth=2, markersize=12)
    >>> plot(x, y, color='green', marker='o', linestyle='dashed',
            linewidth=2, markersize=12)

    When conflicting with fmt, keyword arguments take precedence.

    Plotting labelled data

    There's a convenient way for plotting objects with labelled data (i.e.
    data that can be accessed by index obj['y']). Instead of giving
    the data in x and y, you can provide the object in the data
    parameter and just give the labels for x and y::

    >>> plot('xlabel', 'ylabel', data=obj)

    All indexable objects are supported. This could e.g. be a dict, a
    pandas.DataFame or a structured numpy array.


    Plotting multiple sets of data

    There are various ways to plot multiple sets of data.

    - The most straight forward way is just to call plot multiple times.
      Example:

      >>> plot(x1, y1, 'bo')
      >>> plot(x2, y2, 'go')

    - Alternatively, if your data is already a 2d array, you can pass it
      directly to x, y. A separate data set will be drawn for every
      column.

      Example: an array a where the first column represents the x
      values and the other columns are the y columns::

      >>> plot(a[0], a[1:])

    - The third way is to specify multiple sets of [x], y, [fmt]
      groups::

      >>> plot(x1, y1, 'g^', x2, y2, 'g-')

      In this case, any additional keyword argument applies to all
      datasets. Also this syntax cannot be combined with the data
      parameter.

    By default, each line is assigned a different style specified by a
    'style cycle'. The fmt and line property parameters are only
    necessary if you want explicit deviations from these defaults.
    Alternatively, you can also change the style cycle using the
    'axes.prop_cycle' rcParam.

    Parameters
    ----------
    x, y : array-like or scalar
        The horizontal / vertical coordinates of the data points.
        x values are optional. If not given, they default to
        [0, ..., N-1].

        Commonly, these parameters are arrays of length N. However,
        scalars are supported as well (equivalent to an array with
        constant value).

        The parameters can also be 2-dimensional. Then, the columns
        represent separate data sets.

    fmt : str, optional
        A format string, e.g. 'ro' for red circles. See the Notes
        section for a full description of the format strings.

        Format strings are just an abbreviation for quickly setting
        basic line properties. All of these and more can also be
        controlled by keyword arguments.

    data : indexable object, optional
        An object with labelled data. If given, provide the label names to
        plot in x and y.

        .. note::
            Technically there's a slight ambiguity in calls where the
            second label is a valid fmt. plot('n', 'o', data=obj)
            could be plt(x, y) or plt(y, fmt). In such cases,
            the former interpretation is chosen, but a warning is issued.
            You may suppress the warning by adding an empty format string
            plot('n', 'o', '', data=obj).


    Other Parameters
    ----------------
    scalex, scaley : bool, optional, default: True
        These parameters determined if the view limits are adapted to
        the data limits. The values are passed on to autoscale_view.

    kwargs : `.Line2D` properties, optional
        *kwargs* are used to specify properties like a line label (for
        auto legends), linewidth, antialiasing, marker face color.
        Example::

        >>> plot([1,2,3], [1,2,3], 'go-', label='line 1', linewidth=2)
        >>> plot([1,2,3], [1,4,9], 'rs',  label='line 2')

        If you make multiple lines with one plot command, the kwargs
        apply to all those lines.

        Here is a list of available `.Line2D` properties:

          agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array
      alpha: float (0.0 transparent through 1.0 opaque)
      animated: bool
      antialiased or aa: bool
      clip_box: a `.Bbox` instance
      clip_on: bool
      clip_path: [(`~matplotlib.path.Path`, `.Transform`) | `.Patch` | None]
      color or c: any matplotlib color
      contains: a callable function
      dash_capstyle: ['butt' | 'round' | 'projecting']
      dash_joinstyle: ['miter' | 'round' | 'bevel']
      dashes: sequence of on/off ink in points
      drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post']
      figure: a `.Figure` instance
      fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none']
      gid: an id string
      label: object
      linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]
      linewidth or lw: float value in points
      marker: :mod:`A valid marker style <matplotlib.markers>`
      markeredgecolor or mec: any matplotlib color
      markeredgewidth or mew: float value in points
      markerfacecolor or mfc: any matplotlib color
      markerfacecoloralt or mfcalt: any matplotlib color
      markersize or ms: float
      markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]
      path_effects: `.AbstractPathEffect`
      picker: float distance in points or callable pick function ``fn(artist, event)``
      pickradius: float distance in points
      rasterized: bool or None
      sketch_params: (scale: float, length: float, randomness: float)
      snap: bool or None
      solid_capstyle: ['butt' | 'round' |  'projecting']
      solid_joinstyle: ['miter' | 'round' | 'bevel']
      transform: a :class:`matplotlib.transforms.Transform` instance
      url: a url string
      visible: bool
      xdata: 1D array
      ydata: 1D array
      zorder: float

    Returns
    -------
    lines
        A list of `.Line2D` objects representing the plotted data.


    See Also
    --------
    scatter : XY scatter plot with markers of variing size and/or color (
        sometimes also called bubble chart).


    Notes
    -----
    **Format Strings

    A format string consists of a part for color, marker and line::

        fmt = '[color][marker][line]'

    Each of them is optional. If not provided, the value from the style
    cycle is used. Exception: If line is given, but no marker,
    the data will be a line without markers.

    Colors

    The following color abbreviations are supported:

    =============    ===============================
    character        color
    =============    ===============================
    'b'          blue
    'g'          green
    'r'          red
    'c'          cyan
    'm'          magenta
    'y'          yellow
    'k'          black
    'w'          white
    =============    ===============================

    If the color is the only part of the format string, you can
    additionally use any  matplotlib.colors spec, e.g. full names
    ('green') or hex strings ('#008000').

    Markers

    =============    ===============================
    character        description
    =============    ===============================
    '.'          point marker
    ','          pixel marker
    'o'          circle marker
    'v'          triangle_down marker
    '^'          triangle_up marker
    '<'          triangle_left marker
    '>'          triangle_right marker
    '1'          tri_down marker
    '2'          tri_up marker
    '3'          tri_left marker
    '4'          tri_right marker
    's'          square marker
    'p'          pentagon marker
    '*'          star marker
    'h'          hexagon1 marker
    'H'          hexagon2 marker
    '+'          plus marker
    'x'          x marker
    'D'          diamond marker
    'd'          thin_diamond marker
    '|'          vline marker
    '_'          hline marker
    =============    ===============================

    Line Styles

    =============    ===============================
    character        description
    =============    ===============================
    '-'          solid line style
    '--'         dashed line style
    '-.'         dash-dot line style
    ':'          dotted line style
    =============    ===============================

    Example format strings::

        'b'    # blue markers with default shape
        'ro'   # red circles
        'g-'   # green solid line
        '--'   # dashed line with default color
        'k^:'  # black triangle_up markers connected by a dotted line

    .. note::
        In addition to the above described arguments, this function can take a
        data keyword argument. If such a data argument is given, the
        following arguments are replaced by data[<arg>]:

        * All arguments with the following names: 'x', 'y'.

  File "<ipython-input-104-c3b0085c4838>", line 1
    cielo, la persona de mkt es la que tiene que ofrecer un listado de servicios y productos:
                    ^
SyntaxError: invalid syntax

Preprocesamiento de los datos

[['Boldo_0W', 'Boldo_160W', 'Boldo_200W', 'Boldo_280W', 'Boldo_400W'], ['Brasil_0W', 'Brasil_200W', 'Brasil_280W', 'Brasil_400W'], ['Uruguay_0W', 'Uruguay_200W', 'Uruguay_280W', 'Uruguay_400W', 'Uruguay_600W'], ['Romero_0W', 'Romero_200W', 'Romero_280W', 'Romero_400W'], ['Congorosa_0W', 'Congorosa_200W', 'Congorosa_280W', 'Congorosa_400W']]
[[   minutos  rendimiento
0    0.000       0.0000
1    6.683       0.9460
2    6.953       1.1193
3   17.053       2.3520
4   25.069       2.8202
5   34.586       3.2030
6   41.586       3.3810
7   52.353       3.5706
8   69.019       3.7933
9   84.019       3.6280,    minutos  rendimiento
0    0.000        0.000
1    7.250        1.127
2   20.117        2.557
3   25.200        2.835
4   35.917        3.113
5   43.033        3.272
6   53.333        3.406
7   72.950        3.591
8   88.100        3.686,    minutos  rendimiento
0    0.000        0.000
1    6.850        1.095
2   17.250        2.350
3   25.333        2.872
4   34.750        3.228
5   41.400        3.383
6   58.317        3.555,    minutos  rendimiento
0    0.000        0.000
1    7.383        1.093
2   18.333        2.339
3   25.333        2.861
4   37.750        3.157
5   43.883        3.301
6   55.750        3.700
7   68.150        3.700
8   82.750        3.821,    minutos  rendimiento
0    0.000        0.000
1    6.833        1.013
2   18.250        2.206
3   25.483        2.656
4   34.900        2.991
5   41.533        3.433
6   53.183        3.650
7   68.150        3.829
8   82.750        3.934], [   minutos  rendimiento
0     0.00       0.0000
1     3.00       0.0508
2    17.58       0.4874
3    25.00       0.7331
4    35.23       1.0231
5    40.65       1.1210
6    50.37       1.1710
7    70.28       1.2448
8    96.00       1.3626,    minutos  rendimiento
0     0.00        0.000
1     5.00        0.100
2    16.45        0.761
3    25.27        1.073
4    35.53        1.317
5    42.28        1.438
6    55.08        1.532
7    72.85        1.643
8    87.20        1.766,     minutos  rendimiento
0     0.000        0.000
1     6.733        0.270
2    16.450        0.761
3    17.400        0.846
4    25.270        1.073
5    26.000        1.126
6    35.530        1.317
7    37.067        1.365
8    42.280        1.438
9    45.300        1.522
10   55.080        1.532
11   58.750        1.697
12   77.000        1.760
13   87.200        1.766,    minutos  rendimiento
0    0.000        0.000
1    6.900        0.174
2   17.367        0.622
3   26.050        0.797
4   38.500        1.006
5   45.900        1.062
6   58.683        1.184
7   77.517        1.354
8   90.183        1.493], [    minutos  rendimiento
0     0.000        0.000
1     6.667        0.200
2     7.550        0.283
3    17.117        0.750
4    25.583        0.977
5    35.000        1.146
6    41.667        1.231
7    52.450        1.335
8    59.000        1.365
9    79.333        1.450
10   96.733        1.344,    minutos  rendimiento
0    0.000        0.000
1    6.967        0.095
2   17.750        0.234
3   26.150        0.298
4   36.667        0.345
5   44.667        0.384
6   53.083        0.416
7   74.867        0.451
8   84.917        0.506,    minutos  rendimiento
0    0.000        0.000
1    7.417        0.078
2   17.750        0.226
3   25.717        0.294
4   37.033        0.365
5   44.567        0.395
6   55.667        0.426
7   73.250        0.480
8   86.667        0.540,    minutos  rendimiento
0    0.000        0.000
1    7.583        0.074
2   19.417        0.221
3   28.167        0.357
4   37.917        0.432
5   44.200        0.462
6   54.883        0.498
7   70.233        0.556
8   81.850        0.579,    minutos  rendimiento
0    0.000        0.000
1    7.000        0.092
2   17.500        0.221
3   25.100        0.381
4   38.450        0.469
5   46.450        0.520
6   57.167        0.564
7   70.100        0.618], [   minutos  rendimiento
0    0.000        0.000
1    7.283        1.031
2   17.333        2.284
3   24.750        2.790
4   34.117        3.215
5   40.817        3.379
6   56.705        3.621
7   73.972        3.767
8   84.972        3.833,    minutos  rendimiento
0    0.000        0.000
1    6.683        1.456
2   15.767        2.491
3   23.083        2.916
4   32.150        3.300
5   38.667        3.350
6   48.250        3.438
7   64.533        3.553
8   79.917        3.719,    minutos  rendimiento
0    0.000        0.000
1    7.333        1.399
2   21.083        2.378
3   28.483        2.976
4   38.317        3.619
5   45.117        3.750
6   55.650        3.908
7   70.867        4.124
8   84.867        4.292,    minutos  rendimiento
0    0.000        0.000
1    7.400        1.461
2   17.467        2.412
3   25.800        3.128
4   35.200        3.239
5   45.800        3.254], [   minutos  rendimiento
0     0.00       0.0000
1     5.00       0.0080
2     6.83       0.0250
3    16.27       0.2689
4    24.33       0.3520
5    33.80       0.4290
6    39.45       0.4292
7    50.47       0.4336
8    64.15       0.5580
9    79.05       0.6090,     minutos  rendimiento
0     0.000        0.000
1     5.000        0.119
2     6.917        0.154
3     7.480        0.230
4    16.983        0.634
5    18.120        0.638
6    25.833        0.879
7    27.030        0.788
8    33.983        0.948
9    37.330        0.908
10   44.580        0.957
11   49.783        1.012
12   51.067        1.092
13   55.700        1.023
14   69.667        1.155
15   74.830        1.182
16   87.100        1.317
17   91.233        1.281,      minutos  rendimiento
0   0.000000        0.000
1   6.470000        0.140
2  15.720000        0.498
3  22.580000        0.638
4  31.870000        0.712
5  38.830000        0.721
6  49.220000        0.721
7  65.000000        0.756
8  79.816667        0.884,    minutos  rendimiento
0     0.00        0.000
1     7.47        0.089
2    18.10        0.319
3    26.27        0.559
4    35.83        0.659
5    42.25        0.800
6    52.00        0.803
7    68.73        0.864
8    81.43        1.042]]
minutos rendimiento minutos rendimiento minutos rendimiento minutos rendimiento minutos rendimiento
0 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
1 6.667 0.200 6.967 0.095 7.417 0.078 7.583 0.074 7.000 0.092
2 7.550 0.283 17.750 0.234 17.750 0.226 19.417 0.221 17.500 0.221
3 17.117 0.750 26.150 0.298 25.717 0.294 28.167 0.357 25.100 0.381
4 25.583 0.977 36.667 0.345 37.033 0.365 37.917 0.432 38.450 0.469
5 35.000 1.146 44.667 0.384 44.567 0.395 44.200 0.462 46.450 0.520
6 41.667 1.231 53.083 0.416 55.667 0.426 54.883 0.498 57.167 0.564
7 52.450 1.335 74.867 0.451 73.250 0.480 70.233 0.556 70.100 0.618
8 59.000 1.365 84.917 0.506 86.667 0.540 81.850 0.579 NaN NaN
9 79.333 1.450 NaN NaN NaN NaN NaN NaN NaN NaN
10 96.733 1.344 NaN NaN NaN NaN NaN NaN NaN NaN
minutos rendimiento minutos rendimiento minutos rendimiento minutos rendimiento minutos rendimiento matriz
0 0.000 0.0000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Boldo
1 6.683 0.9460 7.250 1.127 6.850 1.095 7.383 1.093 6.833 1.013 Boldo
2 6.953 1.1193 20.117 2.557 17.250 2.350 18.333 2.339 18.250 2.206 Boldo
3 17.053 2.3520 25.200 2.835 25.333 2.872 25.333 2.861 25.483 2.656 Boldo
4 25.069 2.8202 35.917 3.113 34.750 3.228 37.750 3.157 34.900 2.991 Boldo
5 34.586 3.2030 43.033 3.272 41.400 3.383 43.883 3.301 41.533 3.433 Boldo
6 41.586 3.3810 53.333 3.406 58.317 3.555 55.750 3.700 53.183 3.650 Boldo
7 52.353 3.5706 72.950 3.591 NaN NaN 68.150 3.700 68.150 3.829 Boldo
8 69.019 3.7933 88.100 3.686 NaN NaN 82.750 3.821 82.750 3.934 Boldo
9 84.019 3.6280 NaN NaN NaN NaN NaN NaN NaN NaN Boldo
minutos rendimiento minutos rendimiento minutos rendimiento minutos rendimiento minutos rendimiento matriz
0 0.00 0.0000 0.00 0.000 0.000 0.000 0.000 0.000 NaN NaN Brasil
1 3.00 0.0508 5.00 0.100 6.733 0.270 6.900 0.174 NaN NaN Brasil
2 17.58 0.4874 16.45 0.761 16.450 0.761 17.367 0.622 NaN NaN Brasil
3 25.00 0.7331 25.27 1.073 17.400 0.846 26.050 0.797 NaN NaN Brasil
4 35.23 1.0231 35.53 1.317 25.270 1.073 38.500 1.006 NaN NaN Brasil
5 40.65 1.1210 42.28 1.438 26.000 1.126 45.900 1.062 NaN NaN Brasil
6 50.37 1.1710 55.08 1.532 35.530 1.317 58.683 1.184 NaN NaN Brasil
7 70.28 1.2448 72.85 1.643 37.067 1.365 77.517 1.354 NaN NaN Brasil
8 96.00 1.3626 87.20 1.766 42.280 1.438 90.183 1.493 NaN NaN Brasil
9 NaN NaN NaN NaN 45.300 1.522 NaN NaN NaN NaN Brasil
10 NaN NaN NaN NaN 55.080 1.532 NaN NaN NaN NaN Brasil
11 NaN NaN NaN NaN 58.750 1.697 NaN NaN NaN NaN Brasil
12 NaN NaN NaN NaN 77.000 1.760 NaN NaN NaN NaN Brasil
13 NaN NaN NaN NaN 87.200 1.766 NaN NaN NaN NaN Brasil
minutos rendimiento minutos rendimiento minutos rendimiento minutos rendimiento minutos rendimiento matriz
0 0.000 0.0000 0.000 0.000 0.000000 0.000 0.000 0.000 0.000 0.000 Boldo
1 6.683 0.9460 7.250 1.127 6.850000 1.095 7.383 1.093 6.833 1.013 Boldo
2 6.953 1.1193 20.117 2.557 17.250000 2.350 18.333 2.339 18.250 2.206 Boldo
3 17.053 2.3520 25.200 2.835 25.333000 2.872 25.333 2.861 25.483 2.656 Boldo
4 25.069 2.8202 35.917 3.113 34.750000 3.228 37.750 3.157 34.900 2.991 Boldo
5 34.586 3.2030 43.033 3.272 41.400000 3.383 43.883 3.301 41.533 3.433 Boldo
6 41.586 3.3810 53.333 3.406 58.317000 3.555 55.750 3.700 53.183 3.650 Boldo
7 52.353 3.5706 72.950 3.591 NaN NaN 68.150 3.700 68.150 3.829 Boldo
8 69.019 3.7933 88.100 3.686 NaN NaN 82.750 3.821 82.750 3.934 Boldo
9 84.019 3.6280 NaN NaN NaN NaN NaN NaN NaN NaN Boldo
0 0.000 0.0000 0.000 0.000 0.000000 0.000 0.000 0.000 NaN NaN Brasil
1 3.000 0.0508 5.000 0.100 6.733000 0.270 6.900 0.174 NaN NaN Brasil
2 17.580 0.4874 16.450 0.761 16.450000 0.761 17.367 0.622 NaN NaN Brasil
3 25.000 0.7331 25.270 1.073 17.400000 0.846 26.050 0.797 NaN NaN Brasil
4 35.230 1.0231 35.530 1.317 25.270000 1.073 38.500 1.006 NaN NaN Brasil
5 40.650 1.1210 42.280 1.438 26.000000 1.126 45.900 1.062 NaN NaN Brasil
6 50.370 1.1710 55.080 1.532 35.530000 1.317 58.683 1.184 NaN NaN Brasil
7 70.280 1.2448 72.850 1.643 37.067000 1.365 77.517 1.354 NaN NaN Brasil
8 96.000 1.3626 87.200 1.766 42.280000 1.438 90.183 1.493 NaN NaN Brasil
9 NaN NaN NaN NaN 45.300000 1.522 NaN NaN NaN NaN Brasil
10 NaN NaN NaN NaN 55.080000 1.532 NaN NaN NaN NaN Brasil
11 NaN NaN NaN NaN 58.750000 1.697 NaN NaN NaN NaN Brasil
12 NaN NaN NaN NaN 77.000000 1.760 NaN NaN NaN NaN Brasil
13 NaN NaN NaN NaN 87.200000 1.766 NaN NaN NaN NaN Brasil
0 0.000 0.0000 0.000 0.000 0.000000 0.000 0.000 0.000 NaN NaN Congorosa
1 5.000 0.0080 5.000 0.119 6.470000 0.140 7.470 0.089 NaN NaN Congorosa
2 6.830 0.0250 6.917 0.154 15.720000 0.498 18.100 0.319 NaN NaN Congorosa
3 16.270 0.2689 7.480 0.230 22.580000 0.638 26.270 0.559 NaN NaN Congorosa
4 24.330 0.3520 16.983 0.634 31.870000 0.712 35.830 0.659 NaN NaN Congorosa
5 33.800 0.4290 18.120 0.638 38.830000 0.721 42.250 0.800 NaN NaN Congorosa
6 39.450 0.4292 25.833 0.879 49.220000 0.721 52.000 0.803 NaN NaN Congorosa
7 50.470 0.4336 27.030 0.788 65.000000 0.756 68.730 0.864 NaN NaN Congorosa
8 64.150 0.5580 33.983 0.948 79.816667 0.884 81.430 1.042 NaN NaN Congorosa
9 79.050 0.6090 37.330 0.908 NaN NaN NaN NaN NaN NaN Congorosa
10 NaN NaN 44.580 0.957 NaN NaN NaN NaN NaN NaN Congorosa
11 NaN NaN 49.783 1.012 NaN NaN NaN NaN NaN NaN Congorosa
12 NaN NaN 51.067 1.092 NaN NaN NaN NaN NaN NaN Congorosa
13 NaN NaN 55.700 1.023 NaN NaN NaN NaN NaN NaN Congorosa
14 NaN NaN 69.667 1.155 NaN NaN NaN NaN NaN NaN Congorosa
15 NaN NaN 74.830 1.182 NaN NaN NaN NaN NaN NaN Congorosa
16 NaN NaN 87.100 1.317 NaN NaN NaN NaN NaN NaN Congorosa
17 NaN NaN 91.233 1.281 NaN NaN NaN NaN NaN NaN Congorosa
minutos rendimiento minutos rendimiento minutos rendimiento minutos rendimiento minutos rendimiento
matriz
Boldo 0.000 0.0000 0.000 0.000 0.000000 0.000 0.000 0.000 0.000 0.000
Boldo 6.683 0.9460 7.250 1.127 6.850000 1.095 7.383 1.093 6.833 1.013
Boldo 6.953 1.1193 20.117 2.557 17.250000 2.350 18.333 2.339 18.250 2.206
Boldo 17.053 2.3520 25.200 2.835 25.333000 2.872 25.333 2.861 25.483 2.656
Boldo 25.069 2.8202 35.917 3.113 34.750000 3.228 37.750 3.157 34.900 2.991
Boldo 34.586 3.2030 43.033 3.272 41.400000 3.383 43.883 3.301 41.533 3.433
Boldo 41.586 3.3810 53.333 3.406 58.317000 3.555 55.750 3.700 53.183 3.650
Boldo 52.353 3.5706 72.950 3.591 NaN NaN 68.150 3.700 68.150 3.829
Boldo 69.019 3.7933 88.100 3.686 NaN NaN 82.750 3.821 82.750 3.934
Boldo 84.019 3.6280 NaN NaN NaN NaN NaN NaN NaN NaN
Brasil 0.000 0.0000 0.000 0.000 0.000000 0.000 0.000 0.000 NaN NaN
Brasil 3.000 0.0508 5.000 0.100 6.733000 0.270 6.900 0.174 NaN NaN
Brasil 17.580 0.4874 16.450 0.761 16.450000 0.761 17.367 0.622 NaN NaN
Brasil 25.000 0.7331 25.270 1.073 17.400000 0.846 26.050 0.797 NaN NaN
Brasil 35.230 1.0231 35.530 1.317 25.270000 1.073 38.500 1.006 NaN NaN
Brasil 40.650 1.1210 42.280 1.438 26.000000 1.126 45.900 1.062 NaN NaN
Brasil 50.370 1.1710 55.080 1.532 35.530000 1.317 58.683 1.184 NaN NaN
Brasil 70.280 1.2448 72.850 1.643 37.067000 1.365 77.517 1.354 NaN NaN
Brasil 96.000 1.3626 87.200 1.766 42.280000 1.438 90.183 1.493 NaN NaN
Brasil NaN NaN NaN NaN 45.300000 1.522 NaN NaN NaN NaN
Brasil NaN NaN NaN NaN 55.080000 1.532 NaN NaN NaN NaN
Brasil NaN NaN NaN NaN 58.750000 1.697 NaN NaN NaN NaN
Brasil NaN NaN NaN NaN 77.000000 1.760 NaN NaN NaN NaN
Brasil NaN NaN NaN NaN 87.200000 1.766 NaN NaN NaN NaN
Congorosa 0.000 0.0000 0.000 0.000 0.000000 0.000 0.000 0.000 NaN NaN
Congorosa 5.000 0.0080 5.000 0.119 6.470000 0.140 7.470 0.089 NaN NaN
Congorosa 6.830 0.0250 6.917 0.154 15.720000 0.498 18.100 0.319 NaN NaN
Congorosa 16.270 0.2689 7.480 0.230 22.580000 0.638 26.270 0.559 NaN NaN
Congorosa 24.330 0.3520 16.983 0.634 31.870000 0.712 35.830 0.659 NaN NaN
Congorosa 33.800 0.4290 18.120 0.638 38.830000 0.721 42.250 0.800 NaN NaN
Congorosa 39.450 0.4292 25.833 0.879 49.220000 0.721 52.000 0.803 NaN NaN
Congorosa 50.470 0.4336 27.030 0.788 65.000000 0.756 68.730 0.864 NaN NaN
Congorosa 64.150 0.5580 33.983 0.948 79.816667 0.884 81.430 1.042 NaN NaN
Congorosa 79.050 0.6090 37.330 0.908 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 44.580 0.957 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 49.783 1.012 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 51.067 1.092 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 55.700 1.023 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 69.667 1.155 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 74.830 1.182 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 87.100 1.317 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 91.233 1.281 NaN NaN NaN NaN NaN NaN 
Index(['minutos', 'rendimiento', 'minutos', 'rendimiento', 'minutos',
       'rendimiento', 'minutos', 'rendimiento', 'minutos', 'rendimiento'],
      dtype='object')
Extracción 1 Extracción 2 Extracción 3 Extracción 4 Extracción 5
$t$ [=] min $e$ $t$ [=] min $e$ $t$ [=] min $e$ $t$ [=] min $e$ $t$ [=] min $e$
matriz
Boldo 0.000 0.0000 0.000 0.000 0.000000 0.000 0.000 0.000 0.000 0.000
Boldo 6.683 0.9460 7.250 1.127 6.850000 1.095 7.383 1.093 6.833 1.013
Boldo 6.953 1.1193 20.117 2.557 17.250000 2.350 18.333 2.339 18.250 2.206
Boldo 17.053 2.3520 25.200 2.835 25.333000 2.872 25.333 2.861 25.483 2.656
Boldo 25.069 2.8202 35.917 3.113 34.750000 3.228 37.750 3.157 34.900 2.991
Boldo 34.586 3.2030 43.033 3.272 41.400000 3.383 43.883 3.301 41.533 3.433
Boldo 41.586 3.3810 53.333 3.406 58.317000 3.555 55.750 3.700 53.183 3.650
Boldo 52.353 3.5706 72.950 3.591 NaN NaN 68.150 3.700 68.150 3.829
Boldo 69.019 3.7933 88.100 3.686 NaN NaN 82.750 3.821 82.750 3.934
Boldo 84.019 3.6280 NaN NaN NaN NaN NaN NaN NaN NaN
Brasil 0.000 0.0000 0.000 0.000 0.000000 0.000 0.000 0.000 NaN NaN
Brasil 3.000 0.0508 5.000 0.100 6.733000 0.270 6.900 0.174 NaN NaN
Brasil 17.580 0.4874 16.450 0.761 16.450000 0.761 17.367 0.622 NaN NaN
Brasil 25.000 0.7331 25.270 1.073 17.400000 0.846 26.050 0.797 NaN NaN
Brasil 35.230 1.0231 35.530 1.317 25.270000 1.073 38.500 1.006 NaN NaN
Brasil 40.650 1.1210 42.280 1.438 26.000000 1.126 45.900 1.062 NaN NaN
Brasil 50.370 1.1710 55.080 1.532 35.530000 1.317 58.683 1.184 NaN NaN
Brasil 70.280 1.2448 72.850 1.643 37.067000 1.365 77.517 1.354 NaN NaN
Brasil 96.000 1.3626 87.200 1.766 42.280000 1.438 90.183 1.493 NaN NaN
Brasil NaN NaN NaN NaN 45.300000 1.522 NaN NaN NaN NaN
Brasil NaN NaN NaN NaN 55.080000 1.532 NaN NaN NaN NaN
Brasil NaN NaN NaN NaN 58.750000 1.697 NaN NaN NaN NaN
Brasil NaN NaN NaN NaN 77.000000 1.760 NaN NaN NaN NaN
Brasil NaN NaN NaN NaN 87.200000 1.766 NaN NaN NaN NaN
Congorosa 0.000 0.0000 0.000 0.000 0.000000 0.000 0.000 0.000 NaN NaN
Congorosa 5.000 0.0080 5.000 0.119 6.470000 0.140 7.470 0.089 NaN NaN
Congorosa 6.830 0.0250 6.917 0.154 15.720000 0.498 18.100 0.319 NaN NaN
Congorosa 16.270 0.2689 7.480 0.230 22.580000 0.638 26.270 0.559 NaN NaN
Congorosa 24.330 0.3520 16.983 0.634 31.870000 0.712 35.830 0.659 NaN NaN
Congorosa 33.800 0.4290 18.120 0.638 38.830000 0.721 42.250 0.800 NaN NaN
Congorosa 39.450 0.4292 25.833 0.879 49.220000 0.721 52.000 0.803 NaN NaN
Congorosa 50.470 0.4336 27.030 0.788 65.000000 0.756 68.730 0.864 NaN NaN
Congorosa 64.150 0.5580 33.983 0.948 79.816667 0.884 81.430 1.042 NaN NaN
Congorosa 79.050 0.6090 37.330 0.908 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 44.580 0.957 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 49.783 1.012 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 51.067 1.092 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 55.700 1.023 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 69.667 1.155 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 74.830 1.182 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 87.100 1.317 NaN NaN NaN NaN NaN NaN
Congorosa NaN NaN 91.233 1.281 NaN NaN NaN NaN NaN NaN

Datos operación Yerba Romero

[   0.  200.  280.  400.]
tr =  [ 7.89494813  7.34917765  7.84414133  8.27932474]

Yerba Romero - 400.0 W
X0 =  [ 0.03833  0.03719  0.04292  0.03254] <class 'numpy.ndarray'>
xo = 0.03253999999999999, gamma = 2.1692508973607825, yr = 0.0074, TAO = 5.531852105882878

array([[ 0.01    ,  1.      ],
       [ 0.01    ,  0.5     ],
       [ 0.030321,  1.5     ]])

[  1.00000000e-02   7.40000000e+00   1.74670000e+01   2.58000000e+01
   3.52000000e+01   4.58000000e+01] <class 'numpy.ndarray'>
[ 0.01        0.8937927   2.10971312  3.11619616  4.25155446  5.53185211] <class 'numpy.ndarray'>
[ 0.0001   0.01461  0.02412  0.03128  0.03239  0.03254]

Datos operación Yerba uruguay

[   0.  200.  280.  400.  600.]
tr =  [ 6.90315722  7.06313825  7.19084665  6.90573444  6.96705883]

Yerba Uruguay - 600.0 W
X0 =  [ 0.0145   0.00506  0.0054   0.00579  0.00618] <class 'numpy.ndarray'>
xo = 0.00618, gamma = 1.8798398443346804, yr = 0.0013, TAO = 10.061634577110048

array([[ 0.01    ,  1.      ],
       [ 0.01    ,  0.5     ],
       [ 0.030321,  1.5     ]])

[  1.00000000e-02   1.00472813e+00   2.51182033e+00   3.60266802e+00
   5.51882810e+00   6.66708882e+00   8.20532759e+00   1.00616346e+01] <class 'numpy.ndarray'>
[ 0.0001   0.00092  0.00221  0.00381  0.00469  0.0052   0.00564  0.00618]

fin yu

Datos de operación yerba bolbo

tr =  [ 6.0855294   6.4236725   7.25440914  6.17722886  6.18946974]
[ 0.28606652  0.27100794  0.23997354  0.28181994  0.28126258]

['bolbo' 'bolbo' 'bolbo' 'bolbo' 'bolbo' 'brasil' 'brasil' 'brasil'
 'brasil' 'congorosa' 'congorosa' 'congorosa' 'congorosa']
Flujo $scCO_2$ [=] gr/min Masa matriz vegetal [=] gr Ultra Sonido [=] W
Matriz vegetal Extracciones
bolbo Extracción 1 10.13199 35.4183 0.0
Extracción 2 9.42132 34.7640 160.0
Extracción 3 8.39833 34.9969 200.0
Extracción 4 9.92660 35.2232 280.0
Extracción 5 9.98890 35.5145 400.0
brasil Extracción 1 9.02448 35.3788 0.0
Extracción 2 9.43613 34.8116 200.0
Extracción 3 9.12394 35.1161 280.0
Extracción 4 8.88352 35.5042 400.0
congorosa Extracción 1 10.15210 34.3674 0.0
Extracción 2 9.37822 35.7523 200.0
Extracción 3 10.20529 34.6646 280.0
Extracción 4 9.49983 34.2914 400.0 
{'Densidad gr/cm3': {'bolbo': 1.25, 'brasil': 1.34, 'congorosa': 1.31},
 'Porosidad': {'bolbo': 0.7208, 'brasil': 0.7179, 'congorosa': 0.7137}}
Densidad gr/cm3 Porosidad
bolbo 1.25 0.7208
brasil 1.34 0.7179
congorosa 1.31 0.7137
minutos rendimiento TAO minutos rendimiento TAO minutos rendimiento TAO minutos rendimiento TAO minutos rendimiento TAO
0 0.000 0.0000 0.000000 0.000 0.000 0.000000 0.000 0.000 0.000000 0.000 0.000 0.000000 0.000 0.000 0.000000
1 6.683 0.9460 1.230783 7.250 1.127 1.185647 6.850 1.095 1.257276 7.383 1.093 1.275159 6.833 1.013 1.103972
2 6.953 1.1193 1.280508 20.117 2.557 3.289884 17.250 2.350 3.166133 18.333 2.339 3.166395 18.250 2.206 2.948556
3 17.053 2.3520 3.140587 25.200 2.835 4.121145 25.333 2.872 4.649719 25.333 2.861 4.375404 25.483 2.656 4.117154
4 25.069 2.8202 4.616864 35.917 3.113 5.873776 34.750 3.228 6.378152 37.750 3.157 6.520013 34.900 2.991 5.638609
5 34.586 3.2030 6.369575 43.033 3.272 7.037509 41.400 3.383 7.598719 43.883 3.301 7.579278 41.533 3.433 6.710268
6 41.586 3.3810 7.658739 53.333 3.406 8.721945 58.317 3.555 10.703732 55.750 3.700 9.628893 53.183 3.650 8.592497
7 52.353 3.5706 9.641657 72.950 3.591 11.930060 NaN NaN NaN 68.150 3.700 11.770567 68.150 3.829 11.010636
8 69.019 3.7933 12.710972 88.100 3.686 14.407653 NaN NaN NaN 82.750 3.821 14.292214 82.750 3.934 13.369481
9 84.019 3.6280 15.473466 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 
280.0
Yerba Bolbo - 400.0 W
X0 =  [ 0.037933  0.03686   0.03555   0.03821   0.03934 ] <class 'numpy.ndarray'>
xo = 0.03934, gamma = 1.7408662464183382, yr = 0.0059, TAO = 13.369481303875684

array([[ 0.01    ,  1.      ],
       [ 0.01    ,  0.5     ],
       [ 0.030321,  1.5     ]])

[  3.00000000e-03   1.10397179e+00   2.94855630e+00   4.11715398e+00
   5.63860903e+00   6.71026788e+00   8.59249697e+00   1.10106363e+01
   1.33694813e+01] <class 'numpy.ndarray'>
[ 0.001    0.01013  0.02206  0.02656  0.02991  0.03433  0.0365   0.03829
  0.03934]

Datos de operación yerba brasil

[   0.  200.  280.  400.]
tr =  [ 6.27556792  5.90557503  6.16106767  6.39774269]

Yerba Brasil - 400.0 W
X0 =  [ 0.013626  0.01766   0.01766   0.01493 ] <class 'numpy.ndarray'>
xo = 0.01493, gamma = 1.6007817435333078, yr = 0.0059, TAO = 14.096065489128808

array([[ 0.01    ,  1.      ],
       [ 0.01    ,  0.5     ],
       [ 0.030321,  1.5     ]])

[  1.00000000e-04   1.07850539e+00   2.71455118e+00   4.07174862e+00
   6.01774748e+00   7.17440544e+00   9.17245391e+00   1.21163047e+01
   1.40960655e+01] <class 'numpy.ndarray'>
[ 0.0001   0.00174  0.00622  0.00797  0.01006  0.01062  0.01184  0.01354
  0.01493]

Datos de operación yerba congorosa

tr =  [ 5.42987582  6.11480591  5.44828666  5.78986569]

Yerba Congorosa - 400.0 W
X0 =  [ 0.00609  0.01317  0.00884  0.01042] <class 'numpy.ndarray'>
xo = 0.01042, gamma = 1.6039805840774504, yr = 0.0059, TAO = 14.064229522040602

array([[ 0.01    ,  1.      ],
       [ 0.01    ,  0.5     ],
       [ 0.030321,  1.5     ]])

[  1.00000000e-02   1.29018537e+00   3.12615196e+00   4.53723824e+00
   6.18839916e+00   7.29723317e+00   8.98121006e+00   1.18707417e+01
   1.40642295e+01] <class 'numpy.ndarray'>
[ 0.0001   0.00089  0.00319  0.00559  0.00659  0.008    0.00803  0.00864
  0.01042]

Modelo Lack

Ajuste de los parámetros del modelo 1: Modelo Lack

active_mask: array([0, 0])
       cost: 0.018300978793627454
        fun: array([-0.11809526,  0.37330728,  0.02808377,  0.00718511, -0.00462037,  0.        ])
       grad: array([  8.33403474e-04,  -1.54227422e-08])
        jac: array([[  0.00000000e+00,  -6.35444581e-09],
      [  0.00000000e+00,  -6.35444434e-09],
      [  2.06735361e-02,  -3.11057628e-01],
      [  3.17151307e-02,  -1.76298562e-01],
      [  0.00000000e+00,   0.00000000e+00],
      [  0.00000000e+00,   0.00000000e+00]])
    message: 'Both ftol and xtol termination conditions are satisfied.'
       nfev: 22
       njev: 12
 optimality: 6.7744917811128669e-08
     status: 4
    success: True
          x: array([  5.53594363e-05,   1.20746610e+00])

/home/andres-python/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:5: RuntimeWarning: invalid value encountered in log
/home/andres-python/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:4: RuntimeWarning: overflow encountered in exp
/home/andres-python/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:5: RuntimeWarning: overflow encountered in exp
/home/andres-python/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:14: RuntimeWarning: overflow encountered in exp
/home/andres-python/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:17: RuntimeWarning: invalid value encountered in log

[<matplotlib.lines.Line2D at 0x7f203f10ff98>]
_images/output_44_1.png 
/home/andres-python/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:4: RuntimeWarning: overflow encountered in exp

(1.6759521390448247, 3.7030513340813194, inf)

[<matplotlib.lines.Line2D at 0x7f203f0cd240>]
_images/output_46_1.png 
NOMBRE: Congorosa
_images/output_52_1.png 
NOMBRE: Congorosa
_images/output_53_1.png

Modelo Sovova

nan nan

/home/andres-python/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:9: RuntimeWarning: overflow encountered in exp
/home/andres-python/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:10: RuntimeWarning: overflow encountered in exp
/home/andres-python/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:28: RuntimeWarning: invalid value encountered in log

0.032539999999999993

active_mask: array([0, 0, 0])
       cost: 0.067573820707988189
        fun: array([ 1.81182816, -0.78683877, -0.26158694,  0.02518217, -0.00921332,
       0.09234707, -0.00521931, -0.0395414 ,  0.09887852])
       grad: array([  4.91259316e-02,  -2.61716140e-06,  -1.03208360e-05])
        jac: array([[  1.64962614e-05,  -7.38580870e-12,  -2.72356004e-09],
      [  5.71402153e-06,  -1.02692647e-13,  -1.42693543e-06],
      [  2.03829982e-06,  -3.70461323e-14,  -1.24830113e-06],
      [  7.58134626e+01,  -1.35986181e-06,  -6.79077966e+01],
      [  5.16927813e+01,  -9.15747753e-07,  -6.36407023e+01],
      [  8.24606189e+00,  -1.49376459e-07,  -1.20250951e+01],
      [  2.71129286e+01,  -5.00329665e-07,  -4.89541775e+01],
      [  1.19393497e+01,  -2.25101586e-07,  -2.87292828e+01],
      [  7.43796280e-01,  -1.26455894e-08,  -2.13102359e+00]])
    message: 'xtol termination condition is satisfied.'
       nfev: 31
       njev: 16
 optimality: 0.000515952907784016
     status: 3
    success: True
          x: array([ 0.01040376,  4.62716147,  0.008613  ])

[<matplotlib.lines.Line2D at 0x7effbc368080>]
_images/output_63_1.png 
  File "<ipython-input-337-5e85ffa89130>", line 1
    x: array([ 0.03829315,  1.70819818,  0.01795442]) #0
     ^
SyntaxError: invalid syntax
_images/output_65_0.png 
array([ 0.00017276,  0.00280808,  0.00535308,  0.0078023 ,  0.01015068,
        0.01239369,  0.01452747,  0.01654892,  0.01845584,  0.02024697,
        0.02192205,  0.02348181,  0.02492799,  0.02626321,  0.02749096,
        0.02861545,  0.02964145,  0.03057426,  0.03141945,  0.03218284,
        0.0328703 ,  0.0334877 ,  0.03404079,  0.03453511,  0.03497599,
        0.03536845,  0.03571721,  0.03602665,  0.03630083,  0.03654345,
        0.03675791,  0.03694729,  0.03711438,  0.03726167,  0.03739143,
        0.03750567,  0.0376062 ,  0.03769461,  0.03777233,  0.03784064,
        0.03790064,  0.03795334,  0.03799961,  0.03804023,  0.03807588,
        0.03810716,  0.0381346 ,  0.03815868,  0.0381798 ,  0.03819832])

(0.22974968576541693,
 24.199480533020207,
 5.9097726601403524,
 23.083006310073888,
 0.023157530596497361,
 3.292814746661628)

Help on function plot in module matplotlib.pyplot:

plot(args, **kwargs)
    Plot y versus x as lines and/or markers.

    Call signatures::

        plot([x], y, [fmt], data=None, **kwargs)
        plot([x], y, [fmt], [x2], y2, [fmt2], ..., **kwargs)

    The coordinates of the points or line nodes are given by *x, y.

    The optional parameter fmt is a convenient way for defining basic
    formatting like color, marker and linestyle. It's a shortcut string
    notation described in the Notes section below.

    >>> plot(x, y)        # plot x and y using default line style and color
    >>> plot(x, y, 'bo')  # plot x and y using blue circle markers
    >>> plot(y)           # plot y using x as index array 0..N-1
    >>> plot(y, 'r+')     # ditto, but with red plusses

    You can use .Line2D properties as keyword arguments for more
    control on the  appearance. Line properties and fmt can be mixed.
    The following two calls yield identical results:

    >>> plot(x, y, 'go--', linewidth=2, markersize=12)
    >>> plot(x, y, color='green', marker='o', linestyle='dashed',
            linewidth=2, markersize=12)

    When conflicting with fmt, keyword arguments take precedence.

    Plotting labelled data

    There's a convenient way for plotting objects with labelled data (i.e.
    data that can be accessed by index obj['y']). Instead of giving
    the data in x and y, you can provide the object in the data
    parameter and just give the labels for x and y::

    >>> plot('xlabel', 'ylabel', data=obj)

    All indexable objects are supported. This could e.g. be a dict, a
    pandas.DataFame or a structured numpy array.


    Plotting multiple sets of data

    There are various ways to plot multiple sets of data.

    - The most straight forward way is just to call plot multiple times.
      Example:

      >>> plot(x1, y1, 'bo')
      >>> plot(x2, y2, 'go')

    - Alternatively, if your data is already a 2d array, you can pass it
      directly to x, y. A separate data set will be drawn for every
      column.

      Example: an array a where the first column represents the x
      values and the other columns are the y columns::

      >>> plot(a[0], a[1:])

    - The third way is to specify multiple sets of [x], y, [fmt]
      groups::

      >>> plot(x1, y1, 'g^', x2, y2, 'g-')

      In this case, any additional keyword argument applies to all
      datasets. Also this syntax cannot be combined with the data
      parameter.

    By default, each line is assigned a different style specified by a
    'style cycle'. The fmt and line property parameters are only
    necessary if you want explicit deviations from these defaults.
    Alternatively, you can also change the style cycle using the
    'axes.prop_cycle' rcParam.

    Parameters
    ----------
    x, y : array-like or scalar
        The horizontal / vertical coordinates of the data points.
        x values are optional. If not given, they default to
        [0, ..., N-1].

        Commonly, these parameters are arrays of length N. However,
        scalars are supported as well (equivalent to an array with
        constant value).

        The parameters can also be 2-dimensional. Then, the columns
        represent separate data sets.

    fmt : str, optional
        A format string, e.g. 'ro' for red circles. See the Notes
        section for a full description of the format strings.

        Format strings are just an abbreviation for quickly setting
        basic line properties. All of these and more can also be
        controlled by keyword arguments.

    data : indexable object, optional
        An object with labelled data. If given, provide the label names to
        plot in x and y.

        .. note::
            Technically there's a slight ambiguity in calls where the
            second label is a valid fmt. plot('n', 'o', data=obj)
            could be plt(x, y) or plt(y, fmt). In such cases,
            the former interpretation is chosen, but a warning is issued.
            You may suppress the warning by adding an empty format string
            plot('n', 'o', '', data=obj).


    Other Parameters
    ----------------
    scalex, scaley : bool, optional, default: True
        These parameters determined if the view limits are adapted to
        the data limits. The values are passed on to autoscale_view.

    kwargs : `.Line2D` properties, optional
        *kwargs* are used to specify properties like a line label (for
        auto legends), linewidth, antialiasing, marker face color.
        Example::

        >>> plot([1,2,3], [1,2,3], 'go-', label='line 1', linewidth=2)
        >>> plot([1,2,3], [1,4,9], 'rs',  label='line 2')

        If you make multiple lines with one plot command, the kwargs
        apply to all those lines.

        Here is a list of available `.Line2D` properties:

          agg_filter: a filter function, which takes a (m, n, 3) float array and a dpi value, and returns a (m, n, 3) array
      alpha: float (0.0 transparent through 1.0 opaque)
      animated: bool
      antialiased or aa: bool
      clip_box: a `.Bbox` instance
      clip_on: bool
      clip_path: [(`~matplotlib.path.Path`, `.Transform`) | `.Patch` | None]
      color or c: any matplotlib color
      contains: a callable function
      dash_capstyle: ['butt' | 'round' | 'projecting']
      dash_joinstyle: ['miter' | 'round' | 'bevel']
      dashes: sequence of on/off ink in points
      drawstyle: ['default' | 'steps' | 'steps-pre' | 'steps-mid' | 'steps-post']
      figure: a `.Figure` instance
      fillstyle: ['full' | 'left' | 'right' | 'bottom' | 'top' | 'none']
      gid: an id string
      label: object
      linestyle or ls: ['solid' | 'dashed', 'dashdot', 'dotted' | (offset, on-off-dash-seq) | ``'-'`` | ``'--'`` | ``'-.'`` | ``':'`` | ``'None'`` | ``' '`` | ``''``]
      linewidth or lw: float value in points
      marker: :mod:`A valid marker style <matplotlib.markers>`
      markeredgecolor or mec: any matplotlib color
      markeredgewidth or mew: float value in points
      markerfacecolor or mfc: any matplotlib color
      markerfacecoloralt or mfcalt: any matplotlib color
      markersize or ms: float
      markevery: [None | int | length-2 tuple of int | slice | list/array of int | float | length-2 tuple of float]
      path_effects: `.AbstractPathEffect`
      picker: float distance in points or callable pick function ``fn(artist, event)``
      pickradius: float distance in points
      rasterized: bool or None
      sketch_params: (scale: float, length: float, randomness: float)
      snap: bool or None
      solid_capstyle: ['butt' | 'round' |  'projecting']
      solid_joinstyle: ['miter' | 'round' | 'bevel']
      transform: a :class:`matplotlib.transforms.Transform` instance
      url: a url string
      visible: bool
      xdata: 1D array
      ydata: 1D array
      zorder: float

    Returns
    -------
    lines
        A list of `.Line2D` objects representing the plotted data.


    See Also
    --------
    scatter : XY scatter plot with markers of variing size and/or color (
        sometimes also called bubble chart).


    Notes
    -----
    **Format Strings

    A format string consists of a part for color, marker and line::

        fmt = '[color][marker][line]'

    Each of them is optional. If not provided, the value from the style
    cycle is used. Exception: If line is given, but no marker,
    the data will be a line without markers.

    Colors

    The following color abbreviations are supported:

    =============    ===============================
    character        color
    =============    ===============================
    'b'          blue
    'g'          green
    'r'          red
    'c'          cyan
    'm'          magenta
    'y'          yellow
    'k'          black
    'w'          white
    =============    ===============================

    If the color is the only part of the format string, you can
    additionally use any  matplotlib.colors spec, e.g. full names
    ('green') or hex strings ('#008000').

    Markers

    =============    ===============================
    character        description
    =============    ===============================
    '.'          point marker
    ','          pixel marker
    'o'          circle marker
    'v'          triangle_down marker
    '^'          triangle_up marker
    '<'          triangle_left marker
    '>'          triangle_right marker
    '1'          tri_down marker
    '2'          tri_up marker
    '3'          tri_left marker
    '4'          tri_right marker
    's'          square marker
    'p'          pentagon marker
    '*'          star marker
    'h'          hexagon1 marker
    'H'          hexagon2 marker
    '+'          plus marker
    'x'          x marker
    'D'          diamond marker
    'd'          thin_diamond marker
    '|'          vline marker
    '_'          hline marker
    =============    ===============================

    Line Styles

    =============    ===============================
    character        description
    =============    ===============================
    '-'          solid line style
    '--'         dashed line style
    '-.'         dash-dot line style
    ':'          dotted line style
    =============    ===============================

    Example format strings::

        'b'    # blue markers with default shape
        'ro'   # red circles
        'g-'   # green solid line
        '--'   # dashed line with default color
        'k^:'  # black triangle_up markers connected by a dotted line

    .. note::
        In addition to the above described arguments, this function can take a
        data keyword argument. If such a data argument is given, the
        following arguments are replaced by data[<arg>]:

        * All arguments with the following names: 'x', 'y'.

  File "<ipython-input-104-c3b0085c4838>", line 1
    cielo, la persona de mkt es la que tiene que ofrecer un listado de servicios y productos:
                    ^
SyntaxError: invalid syntax

Indices and tables