In [1]:
%matplotlib inline
import matplotlib
matplotlib.style.use('fivethirtyeight')
import paramselect
import os
import fnmatch
def recursive_glob(start, pattern):
matches = []
for root, dirnames, filenames in os.walk(start):
for filename in fnmatch.filter(filenames, pattern):
matches.append(os.path.join(root, filename))
return matches
datasets = paramselect.load_datasets(sorted(recursive_glob('Al-Ni', '*.json')))
In [2]:
from paramselect import fit, _multiphase_error
from paramselect import multi_phase_fit
from paramselect import _multiphase_fitting_system
from pycalphad import Database, equilibrium
%time dbf = fit('input.json', datasets, saveall=True)
FITTING: BCC_B2
9 endmembers (6 distinct by symmetry)
ENDMEMBER: ('AL', 'AL', 'VA')
SYMMETRIC_ENDMEMBERS: [('AL', 'AL', 'VA')]
ENDMEMBER: ('AL', 'NI', 'VA')
['CPM_FORM']: datasets found: 1
(T*log(T),) rss: 14.988883895 AIC: 175.267766573
(T*log(T), T**2) rss: 0.287388338954 AIC: -75.8029363477
(T*log(T), T**2, 1/T) rss: 0.00998663905176 AIC: -288.816459148
(T*log(T), T**2, 1/T, T**3) rss: 0.00726733042695 AIC: -307.159440568
['SM_FORM']: datasets found: 1
(T,) rss: 0.00256968120974 AIC: -379.694299563
['HM_FORM']: datasets found: 2
(1,) rss: 173532.435506 AIC: 786.167787589
SYMMETRIC_ENDMEMBERS: [('AL', 'NI', 'VA'), ('NI', 'AL', 'VA')]
ENDMEMBER: ('AL', 'VA', 'VA')
['CPM_FORM']: datasets found: 0
['SM_FORM']: datasets found: 1
(T,) rss: 0.0 AIC: -inf
['HM_FORM']: datasets found: 1
(1,) rss: 0.0 AIC: -inf
SYMMETRIC_ENDMEMBERS: [('AL', 'VA', 'VA'), ('VA', 'AL', 'VA')]
ENDMEMBER: ('NI', 'NI', 'VA')
SYMMETRIC_ENDMEMBERS: [('NI', 'NI', 'VA')]
ENDMEMBER: ('NI', 'VA', 'VA')
['CPM_FORM']: datasets found: 0
['SM_FORM']: datasets found: 0
['HM_FORM']: datasets found: 1
(1,) rss: 0.0 AIC: -inf
SYMMETRIC_ENDMEMBERS: [('NI', 'VA', 'VA'), ('VA', 'NI', 'VA')]
ENDMEMBER: ('VA', 'VA', 'VA')
['CPM_FORM']: datasets found: 0
['SM_FORM']: datasets found: 0
['HM_FORM']: datasets found: 0
SYMMETRIC_ENDMEMBERS: [('VA', 'VA', 'VA')]
15 distinct binary interactions
INTERACTION: ('AL', ('AL', 'NI'), 'VA')
ENDMEMBERS FROM INTERACTION: [('AL', 'AL', 'VA'), ('AL', 'NI', 'VA')]
['CPM_FORM', 'CPM_MIX']: datasets found: 0
['SM_FORM', 'SM_MIX']: datasets found: 0
['HM_FORM', 'HM_MIX']: datasets found: 2
[YS] rss: 56928744.6099 AIC: 73.4292437969
[YS, YS*Z] rss: 54621570.9005 AIC: 75.2637577365
[YS, YS*Z, YS*Z**2] rss: 54621570.9005 AIC: 77.2637577365
[YS, YS*Z, YS*Z**2, YS*Z**3] rss: 54621570.9005 AIC: 79.2637577365
[45262.9 0.0 0.0 0.0 0 0 0 0 0 0]
INTERACTION: ('AL', ('AL', 'VA'), 'VA')
ENDMEMBERS FROM INTERACTION: [('AL', 'AL', 'VA'), ('AL', 'VA', 'VA')]
['CPM_FORM', 'CPM_MIX']: datasets found: 0
['SM_FORM', 'SM_MIX']: datasets found: 0
['HM_FORM', 'HM_MIX']: datasets found: 0
[0.0 0.0 0.0 0.0 0 0 0 0 0 0]
INTERACTION: ('AL', ('NI', 'VA'), 'VA')
ENDMEMBERS FROM INTERACTION: [('AL', 'NI', 'VA'), ('AL', 'VA', 'VA')]
['CPM_FORM', 'CPM_MIX']: datasets found: 0
['SM_FORM', 'SM_MIX']: datasets found: 0
['HM_FORM', 'HM_MIX']: datasets found: 2
[YS] rss: 1943407.32761 AIC: 59.9198133796
[YS, YS*Z] rss: 1483161.22585 AIC: 60.8387453249
[YS, YS*Z, YS*Z**2] rss: 1483161.22585 AIC: 62.8387453249
[YS, YS*Z, YS*Z**2, YS*Z**3] rss: 1483161.22585 AIC: 64.8387453249
[-28577.7 0.0 0.0 0.0 0 0 0 0 0 0]
INTERACTION: (('AL', 'NI'), 'NI', 'VA')
ENDMEMBERS FROM INTERACTION: [('AL', 'NI', 'VA'), ('NI', 'NI', 'VA')]
['CPM_FORM', 'CPM_MIX']: datasets found: 0
['SM_FORM', 'SM_MIX']: datasets found: 0
['HM_FORM', 'HM_MIX']: datasets found: 2
[YS] rss: 672770.000307 AIC: 55.6766351893
[YS, YS*Z] rss: 596240.153321 AIC: 57.1935952268
[YS, YS*Z, YS*Z**2] rss: 596240.153321 AIC: 59.1935952268
[YS, YS*Z, YS*Z**2, YS*Z**3] rss: 596240.153321 AIC: 61.1935952268
[-20275.1 0.0 0.0 0.0 0 0 0 0 0 0]
INTERACTION: (('AL', 'NI'), 'VA', 'VA')
ENDMEMBERS FROM INTERACTION: [('AL', 'VA', 'VA'), ('NI', 'VA', 'VA')]
['CPM_FORM', 'CPM_MIX']: datasets found: 0
['SM_FORM', 'SM_MIX']: datasets found: 0
['HM_FORM', 'HM_MIX']: datasets found: 2
[YS] rss: 56406289.2126 AIC: 37.6961824422
[YS, YS*Z] rss: 56406289.2126 AIC: 39.6961824422
[YS, YS*Z, YS*Z**2] rss: 56406289.2126 AIC: 41.6961824422
[YS, YS*Z, YS*Z**2, YS*Z**3] rss: 56406289.2126 AIC: 43.6961824422
[-150055.0 0.0 0.0 0.0 0 0 0 0 0 0]
INTERACTION: (('AL', 'VA'), 'NI', 'VA')
ENDMEMBERS FROM INTERACTION: [('AL', 'NI', 'VA'), ('VA', 'NI', 'VA')]
['CPM_FORM', 'CPM_MIX']: datasets found: 0
['SM_FORM', 'SM_MIX']: datasets found: 0
['HM_FORM', 'HM_MIX']: datasets found: 2
[YS] rss: 20012403.7667 AIC: 69.2474513105
[YS, YS*Z] rss: 12817704.8052 AIC: 69.4653518443
[YS, YS*Z, YS*Z**2] rss: 12817704.8052 AIC: 71.4653518443
[YS, YS*Z, YS*Z**2, YS*Z**3] rss: 12817704.8052 AIC: 73.4653518443
[41335.6 0.0 0.0 0.0 0 0 0 0 0 0]
INTERACTION: (('AL', 'VA'), 'VA', 'VA')
ENDMEMBERS FROM INTERACTION: [('AL', 'VA', 'VA'), ('VA', 'VA', 'VA')]
['CPM_FORM', 'CPM_MIX']: datasets found: 0
['SM_FORM', 'SM_MIX']: datasets found: 0
['HM_FORM', 'HM_MIX']: datasets found: 1
[YS] rss: 0.0 AIC: -inf
[YS, YS*Z] rss: 0.0 AIC: -inf
[YS, YS*Z, YS*Z**2] rss: 0.0 AIC: -inf
[YS, YS*Z, YS*Z**2, YS*Z**3] rss: 0.0 AIC: -inf
[67135.0 0.0 0.0 0.0 0 0 0 0 0 0]
INTERACTION: ('NI', ('NI', 'VA'), 'VA')
ENDMEMBERS FROM INTERACTION: [('NI', 'NI', 'VA'), ('NI', 'VA', 'VA')]
['CPM_FORM', 'CPM_MIX']: datasets found: 0
['SM_FORM', 'SM_MIX']: datasets found: 0
['HM_FORM', 'HM_MIX']: datasets found: 0
[0.0 0.0 0.0 0.0 0 0 0 0 0 0]
INTERACTION: (('NI', 'VA'), 'VA', 'VA')
ENDMEMBERS FROM INTERACTION: [('NI', 'VA', 'VA'), ('VA', 'VA', 'VA')]
['CPM_FORM', 'CPM_MIX']: datasets found: 0
['SM_FORM', 'SM_MIX']: datasets found: 0
['HM_FORM', 'HM_MIX']: datasets found: 1
[YS] rss: 0.0 AIC: -inf
[YS, YS*Z] rss: 0.0 AIC: -inf
[YS, YS*Z, YS*Z**2] rss: 0.0 AIC: -inf
[YS, YS*Z, YS*Z**2, YS*Z**3] rss: 0.0 AIC: -inf
[34418.8 0.0 0.0 0.0 0 0 0 0 0 0]
INTERACTION: (('AL', 'NI'), ('AL', 'NI'), 'VA')
ENDMEMBERS FROM INTERACTION: [('AL', 'AL', 'VA'), ('AL', 'NI', 'VA'), ('NI', 'AL', 'VA'), ('NI', 'NI', 'VA')]
['CPM_FORM', 'CPM_MIX']: datasets found: 0
['SM_FORM', 'SM_MIX']: datasets found: 0
['HM_FORM', 'HM_MIX']: datasets found: 1
[YS] rss: 22681384.5193 AIC: 52.8111652424
[YS, YS*Z] rss: 22358706.7018 AIC: 54.7681790943
[YS, YS*Z, YS*Z**2] rss: 22358706.7018 AIC: 56.7681790943
[YS, YS*Z, YS*Z**2, YS*Z**3] rss: 22358706.7018 AIC: 58.7681790943
[-152236.0 0.0 0.0 0.0 0 0 0 0 0 0]
INTERACTION: (('AL', 'NI'), ('AL', 'VA'), 'VA')
ENDMEMBERS FROM INTERACTION: [('AL', 'AL', 'VA'), ('AL', 'VA', 'VA'), ('NI', 'AL', 'VA'), ('NI', 'VA', 'VA')]
['CPM_FORM', 'CPM_MIX']: datasets found: 0
['SM_FORM', 'SM_MIX']: datasets found: 0
['HM_FORM', 'HM_MIX']: datasets found: 0
[0.0 0.0 0.0 0.0 0 0 0 0 0 0]
INTERACTION: (('AL', 'NI'), ('NI', 'VA'), 'VA')
ENDMEMBERS FROM INTERACTION: [('AL', 'NI', 'VA'), ('AL', 'VA', 'VA'), ('NI', 'NI', 'VA'), ('NI', 'VA', 'VA')]
['CPM_FORM', 'CPM_MIX']: datasets found: 0
['SM_FORM', 'SM_MIX']: datasets found: 0
['HM_FORM', 'HM_MIX']: datasets found: 0
[0.0 0.0 0.0 0.0 0 0 0 0 0 0]
INTERACTION: (('AL', 'VA'), ('AL', 'VA'), 'VA')
ENDMEMBERS FROM INTERACTION: [('AL', 'AL', 'VA'), ('AL', 'VA', 'VA'), ('VA', 'AL', 'VA'), ('VA', 'VA', 'VA')]
['CPM_FORM', 'CPM_MIX']: datasets found: 0
['SM_FORM', 'SM_MIX']: datasets found: 0
['HM_FORM', 'HM_MIX']: datasets found: 0
[0.0 0.0 0.0 0.0 0 0 0 0 0 0]
INTERACTION: (('AL', 'VA'), ('NI', 'VA'), 'VA')
ENDMEMBERS FROM INTERACTION: [('AL', 'NI', 'VA'), ('AL', 'VA', 'VA'), ('VA', 'NI', 'VA'), ('VA', 'VA', 'VA')]
['CPM_FORM', 'CPM_MIX']: datasets found: 0
['SM_FORM', 'SM_MIX']: datasets found: 0
['HM_FORM', 'HM_MIX']: datasets found: 0
[0.0 0.0 0.0 0.0 0 0 0 0 0 0]
INTERACTION: (('NI', 'VA'), ('NI', 'VA'), 'VA')
ENDMEMBERS FROM INTERACTION: [('NI', 'NI', 'VA'), ('NI', 'VA', 'VA'), ('VA', 'NI', 'VA'), ('VA', 'VA', 'VA')]
['CPM_FORM', 'CPM_MIX']: datasets found: 0
['SM_FORM', 'SM_MIX']: datasets found: 0
['HM_FORM', 'HM_MIX']: datasets found: 0
[0.0 0.0 0.0 0.0 0 0 0 0 0 0]
FITTING: LIQUID
2 endmembers (2 distinct by symmetry)
ENDMEMBER: ('AL',)
SYMMETRIC_ENDMEMBERS: [('AL',)]
ENDMEMBER: ('NI',)
SYMMETRIC_ENDMEMBERS: [('NI',)]
1 distinct binary interactions
INTERACTION: (('AL', 'NI'),)
ENDMEMBERS FROM INTERACTION: [('AL',), ('NI',)]
['CPM_FORM', 'CPM_MIX']: datasets found: 0
['SM_FORM', 'SM_MIX']: datasets found: 0
['HM_FORM', 'HM_MIX']: datasets found: 1
[YS] rss: 94032758.7985 AIC: 350.82392177
[YS, YS*Z] rss: 74222612.3634 AIC: 348.3290088
[YS, YS*Z, YS*Z**2] rss: 5870112.86861 AIC: 302.122303974
[YS, YS*Z, YS*Z**2, YS*Z**3] rss: 5504592.61518 AIC: 302.900773131
[-197437.0 14423.4 61412.5 0.0 0 0 0 0 0 0]
FITTING: AL3NI2
4 endmembers (4 distinct by symmetry)
ENDMEMBER: ('AL', 'AL', 'NI')
['CPM_FORM']: datasets found: 0
['SM_FORM']: datasets found: 1
(T,) rss: 0.0 AIC: -inf
['HM_FORM']: datasets found: 1
(1,) rss: 0.0 AIC: -inf
SYMMETRIC_ENDMEMBERS: [('AL', 'AL', 'NI')]
ENDMEMBER: ('AL', 'NI', 'NI')
['CPM_FORM']: datasets found: 0
['SM_FORM']: datasets found: 0
['HM_FORM']: datasets found: 1
(1,) rss: 0.0 AIC: -inf
SYMMETRIC_ENDMEMBERS: [('AL', 'NI', 'NI')]
ENDMEMBER: ('NI', 'AL', 'NI')
['CPM_FORM']: datasets found: 0
['SM_FORM']: datasets found: 0
['HM_FORM']: datasets found: 1
(1,) rss: 0.0 AIC: -inf
SYMMETRIC_ENDMEMBERS: [('NI', 'AL', 'NI')]
ENDMEMBER: ('NI', 'NI', 'NI')
['CPM_FORM']: datasets found: 0
['SM_FORM']: datasets found: 0
['HM_FORM']: datasets found: 1
(1,) rss: 0.0 AIC: -inf
SYMMETRIC_ENDMEMBERS: [('NI', 'NI', 'NI')]
5 distinct binary interactions
INTERACTION: ('AL', ('AL', 'NI'), 'NI')
ENDMEMBERS FROM INTERACTION: [('AL', 'AL', 'NI'), ('AL', 'NI', 'NI')]
['CPM_FORM', 'CPM_MIX']: datasets found: 0
['SM_FORM', 'SM_MIX']: datasets found: 0
['HM_FORM', 'HM_MIX']: datasets found: 0
[0.0 0.0 0.0 0.0 0 0 0 0 0 0]
INTERACTION: (('AL', 'NI'), 'AL', 'NI')
ENDMEMBERS FROM INTERACTION: [('AL', 'AL', 'NI'), ('NI', 'AL', 'NI')]
['CPM_FORM', 'CPM_MIX']: datasets found: 0
['SM_FORM', 'SM_MIX']: datasets found: 0
['HM_FORM', 'HM_MIX']: datasets found: 0
[0.0 0.0 0.0 0.0 0 0 0 0 0 0]
INTERACTION: (('AL', 'NI'), 'NI', 'NI')
ENDMEMBERS FROM INTERACTION: [('AL', 'NI', 'NI'), ('NI', 'NI', 'NI')]
['CPM_FORM', 'CPM_MIX']: datasets found: 0
['SM_FORM', 'SM_MIX']: datasets found: 0
['HM_FORM', 'HM_MIX']: datasets found: 0
[0.0 0.0 0.0 0.0 0 0 0 0 0 0]
INTERACTION: ('NI', ('AL', 'NI'), 'NI')
ENDMEMBERS FROM INTERACTION: [('NI', 'AL', 'NI'), ('NI', 'NI', 'NI')]
['CPM_FORM', 'CPM_MIX']: datasets found: 0
['SM_FORM', 'SM_MIX']: datasets found: 0
['HM_FORM', 'HM_MIX']: datasets found: 0
[0.0 0.0 0.0 0.0 0 0 0 0 0 0]
INTERACTION: (('AL', 'NI'), ('AL', 'NI'), 'NI')
ENDMEMBERS FROM INTERACTION: [('AL', 'AL', 'NI'), ('AL', 'NI', 'NI'), ('NI', 'AL', 'NI'), ('NI', 'NI', 'NI')]
['CPM_FORM', 'CPM_MIX']: datasets found: 0
['SM_FORM', 'SM_MIX']: datasets found: 0
['HM_FORM', 'HM_MIX']: datasets found: 0
[0.0 0.0 0.0 0.0 0 0 0 0 0 0]
FITTING: AL3NI5
1 endmembers (1 distinct by symmetry)
ENDMEMBER: ('AL', 'NI')
['CPM_FORM']: datasets found: 0
['SM_FORM']: datasets found: 1
(T,) rss: 0.0 AIC: -inf
['HM_FORM']: datasets found: 1
(1,) rss: 0.0 AIC: -inf
SYMMETRIC_ENDMEMBERS: [('AL', 'NI')]
0 distinct binary interactions
FITTING: FCC_L12
16 endmembers (5 distinct by symmetry)
ENDMEMBER: ('AL', 'AL', 'AL', 'AL', 'VA')
SYMMETRIC_ENDMEMBERS: [('AL', 'AL', 'AL', 'AL', 'VA')]
ENDMEMBER: ('AL', 'AL', 'AL', 'NI', 'VA')
['CPM_FORM']: datasets found: 0
['SM_FORM']: datasets found: 1
(T,) rss: 0.0 AIC: -inf
['HM_FORM']: datasets found: 3
(1,) rss: 126001.34 AIC: 37.2321434624
SYMMETRIC_ENDMEMBERS: [('AL', 'AL', 'AL', 'NI', 'VA'), ('AL', 'AL', 'NI', 'AL', 'VA'), ('AL', 'NI', 'AL', 'AL', 'VA'), ('NI', 'AL', 'AL', 'AL', 'VA')]
ENDMEMBER: ('AL', 'AL', 'NI', 'NI', 'VA')
['CPM_FORM']: datasets found: 0
['SM_FORM']: datasets found: 1
(T,) rss: 0.0 AIC: -inf
['HM_FORM']: datasets found: 2
(1,) rss: 100238640.5 AIC: 38.846128612
SYMMETRIC_ENDMEMBERS: [('AL', 'AL', 'NI', 'NI', 'VA'), ('AL', 'NI', 'NI', 'AL', 'VA'), ('NI', 'AL', 'AL', 'NI', 'VA'), ('NI', 'NI', 'AL', 'AL', 'VA')]
ENDMEMBER: ('AL', 'NI', 'NI', 'NI', 'VA')
['CPM_FORM']: datasets found: 1
(T*log(T),) rss: 5.67271997875 AIC: 113.082797874
(T*log(T), T**2) rss: 0.137679848376 AIC: -122.900750615
(T*log(T), T**2, 1/T) rss: 0.0340600362496 AIC: -210.296354526
(T*log(T), T**2, 1/T, T**3) rss: 0.00463296494425 AIC: -335.971727303
['SM_FORM']: datasets found: 1
(T,) rss: 0.0008948303987 AIC: -447.208086804
['HM_FORM']: datasets found: 3
(1,) rss: 66785.4610365 AIC: 735.209885321
SYMMETRIC_ENDMEMBERS: [('AL', 'NI', 'NI', 'NI', 'VA'), ('NI', 'AL', 'NI', 'NI', 'VA'), ('NI', 'NI', 'AL', 'NI', 'VA'), ('NI', 'NI', 'NI', 'AL', 'VA')]
ENDMEMBER: ('NI', 'NI', 'NI', 'NI', 'VA')
SYMMETRIC_ENDMEMBERS: [('NI', 'NI', 'NI', 'NI', 'VA')]
10 distinct binary interactions
INTERACTION: ('AL', 'AL', 'AL', ('AL', 'NI'), 'VA')
ENDMEMBERS FROM INTERACTION: [('AL', 'AL', 'AL', 'AL', 'VA'), ('AL', 'AL', 'AL', 'NI', 'VA')]
['CPM_FORM', 'CPM_MIX']: datasets found: 0
['SM_FORM', 'SM_MIX']: datasets found: 0
['HM_FORM', 'HM_MIX']: datasets found: 1
[YS] rss: 12879.7647059 AIC: 30.3902381939
[YS, YS*Z] rss: 7879.76470588 AIC: 30.9161599682
[YS, YS*Z, YS*Z**2] rss: 4.39439700419e-25 AIC: -162.252891011
[YS, YS*Z, YS*Z**2, YS*Z**3] rss: 1.107646539e-23 AIC: -150.571658829
[5512.0 -533.333 -1952.0 0.0 0 0 0 0 0 0]
INTERACTION: ('AL', 'AL', ('AL', 'NI'), 'NI', 'VA')
ENDMEMBERS FROM INTERACTION: [('AL', 'AL', 'AL', 'NI', 'VA'), ('AL', 'AL', 'NI', 'NI', 'VA')]
['CPM_FORM', 'CPM_MIX']: datasets found: 0
['SM_FORM', 'SM_MIX']: datasets found: 0
['HM_FORM', 'HM_MIX']: datasets found: 1
[YS] rss: 133130.117647 AIC: 37.3972467714
[YS, YS*Z] rss: 2.11764705882 AIC: 6.2509167832
[YS, YS*Z, YS*Z**2] rss: 3.87740912134e-26 AIC: -169.536135718
[YS, YS*Z, YS*Z**2, YS*Z**3] rss: 2.12288149393e-24 AIC: -155.527803612
[3768.0 2752.0 32.0 0.0 0 0 0 0 0 0]
INTERACTION: ('AL', ('AL', 'NI'), 'NI', 'NI', 'VA')
ENDMEMBERS FROM INTERACTION: [('AL', 'AL', 'NI', 'NI', 'VA'), ('AL', 'NI', 'NI', 'NI', 'VA')]
['CPM_FORM', 'CPM_MIX']: datasets found: 0
['SM_FORM', 'SM_MIX']: datasets found: 0
['HM_FORM', 'HM_MIX']: datasets found: 1
[YS] rss: 184858.529412 AIC: 38.3820383169
[YS, YS*Z] rss: 13160.5294118 AIC: 32.4549322988
[YS, YS*Z, YS*Z**2] rss: 5.16987882846e-26 AIC: -168.673089501
[YS, YS*Z, YS*Z**2, YS*Z**3] rss: 1.31444169214e-23 AIC: -150.058135396
[-5196.0 3125.33 2522.67 0.0 0 0 0 0 0 0]
INTERACTION: (('AL', 'NI'), 'NI', 'NI', 'NI', 'VA')
ENDMEMBERS FROM INTERACTION: [('AL', 'NI', 'NI', 'NI', 'VA'), ('NI', 'NI', 'NI', 'NI', 'VA')]
['CPM_FORM', 'CPM_MIX']: datasets found: 0
['SM_FORM', 'SM_MIX']: datasets found: 0
['HM_FORM', 'HM_MIX']: datasets found: 2
[YS] rss: 74250.9411765 AIC: 69.2912343942
[YS, YS*Z] rss: 21350.9411765 AIC: 63.8131066054
[YS, YS*Z, YS*Z**2] rss: 9341.5 AIC: 60.8533327074
[YS, YS*Z, YS*Z**2, YS*Z**3] rss: 9341.5 AIC: 62.8533327074
[5122.0 1226.67 -1704.0 0.0 0 0 0 0 0 0]
INTERACTION: ('AL', 'AL', ('AL', 'NI'), ('AL', 'NI'), 'VA')
ENDMEMBERS FROM INTERACTION: [('AL', 'AL', 'AL', 'AL', 'VA'), ('AL', 'AL', 'AL', 'NI', 'VA'), ('AL', 'AL', 'NI', 'AL', 'VA'), ('AL', 'AL', 'NI', 'NI', 'VA')]
['CPM_FORM', 'CPM_MIX']: datasets found: 0
['SM_FORM', 'SM_MIX']: datasets found: 0
['HM_FORM', 'HM_MIX']: datasets found: 0
[0.0 0.0 0.0 0.0 0 0 0 0 0 0]
INTERACTION: ('AL', ('AL', 'NI'), ('AL', 'NI'), 'NI', 'VA')
ENDMEMBERS FROM INTERACTION: [('AL', 'AL', 'AL', 'NI', 'VA'), ('AL', 'AL', 'NI', 'NI', 'VA'), ('AL', 'NI', 'AL', 'NI', 'VA'), ('AL', 'NI', 'NI', 'NI', 'VA')]
['CPM_FORM', 'CPM_MIX']: datasets found: 0
['SM_FORM', 'SM_MIX']: datasets found: 0
['HM_FORM', 'HM_MIX']: datasets found: 0
[0.0 0.0 0.0 0.0 0 0 0 0 0 0]
INTERACTION: (('AL', 'NI'), ('AL', 'NI'), 'NI', 'NI', 'VA')
ENDMEMBERS FROM INTERACTION: [('AL', 'AL', 'NI', 'NI', 'VA'), ('AL', 'NI', 'NI', 'NI', 'VA'), ('NI', 'AL', 'NI', 'NI', 'VA'), ('NI', 'NI', 'NI', 'NI', 'VA')]
['CPM_FORM', 'CPM_MIX']: datasets found: 0
['SM_FORM', 'SM_MIX']: datasets found: 0
['HM_FORM', 'HM_MIX']: datasets found: 0
[0.0 0.0 0.0 0.0 0 0 0 0 0 0]
INTERACTION: ('AL', ('AL', 'NI'), ('AL', 'NI'), ('AL', 'NI'), 'VA')
ENDMEMBERS FROM INTERACTION: [('AL', 'AL', 'AL', 'AL', 'VA'), ('AL', 'AL', 'AL', 'NI', 'VA'), ('AL', 'AL', 'NI', 'AL', 'VA'), ('AL', 'AL', 'NI', 'NI', 'VA'), ('AL', 'NI', 'AL', 'AL', 'VA'), ('AL', 'NI', 'AL', 'NI', 'VA'), ('AL', 'NI', 'NI', 'AL', 'VA'), ('AL', 'NI', 'NI', 'NI', 'VA')]
['CPM_FORM', 'CPM_MIX']: datasets found: 0
['SM_FORM', 'SM_MIX']: datasets found: 0
['HM_FORM', 'HM_MIX']: datasets found: 0
[0.0 0.0 0.0 0.0 0 0 0 0 0 0]
INTERACTION: (('AL', 'NI'), ('AL', 'NI'), ('AL', 'NI'), 'NI', 'VA')
ENDMEMBERS FROM INTERACTION: [('AL', 'AL', 'AL', 'NI', 'VA'), ('AL', 'AL', 'NI', 'NI', 'VA'), ('AL', 'NI', 'AL', 'NI', 'VA'), ('AL', 'NI', 'NI', 'NI', 'VA'), ('NI', 'AL', 'AL', 'NI', 'VA'), ('NI', 'AL', 'NI', 'NI', 'VA'), ('NI', 'NI', 'AL', 'NI', 'VA'), ('NI', 'NI', 'NI', 'NI', 'VA')]
['CPM_FORM', 'CPM_MIX']: datasets found: 0
['SM_FORM', 'SM_MIX']: datasets found: 0
['HM_FORM', 'HM_MIX']: datasets found: 0
[0.0 0.0 0.0 0.0 0 0 0 0 0 0]
INTERACTION: (('AL', 'NI'), ('AL', 'NI'), ('AL', 'NI'), ('AL', 'NI'), 'VA')
ENDMEMBERS FROM INTERACTION: [('AL', 'AL', 'AL', 'AL', 'VA'), ('AL', 'AL', 'AL', 'NI', 'VA'), ('AL', 'AL', 'NI', 'AL', 'VA'), ('AL', 'AL', 'NI', 'NI', 'VA'), ('AL', 'NI', 'AL', 'AL', 'VA'), ('AL', 'NI', 'AL', 'NI', 'VA'), ('AL', 'NI', 'NI', 'AL', 'VA'), ('AL', 'NI', 'NI', 'NI', 'VA'), ('NI', 'AL', 'AL', 'AL', 'VA'), ('NI', 'AL', 'AL', 'NI', 'VA'), ('NI', 'AL', 'NI', 'AL', 'VA'), ('NI', 'AL', 'NI', 'NI', 'VA'), ('NI', 'NI', 'AL', 'AL', 'VA'), ('NI', 'NI', 'AL', 'NI', 'VA'), ('NI', 'NI', 'NI', 'AL', 'VA'), ('NI', 'NI', 'NI', 'NI', 'VA')]
['CPM_FORM', 'CPM_MIX']: datasets found: 0
['SM_FORM', 'SM_MIX']: datasets found: 0
['HM_FORM', 'HM_MIX']: datasets found: 1
[YS] rss: 2201164.28144 AIC: 45.8134909915
[YS, YS*Z] rss: 2016857.1472 AIC: 47.5511529701
[YS, YS*Z, YS*Z**2] rss: 2016857.1472 AIC: 49.5511529701
[YS, YS*Z, YS*Z**2, YS*Z**3] rss: 2016857.1472 AIC: 51.5511529701
[-4257290.0 0.0 0.0 0.0 0 0 0 0 0 0]
FITTING: AL3NI1
1 endmembers (1 distinct by symmetry)
ENDMEMBER: ('AL', 'NI')
['CPM_FORM']: datasets found: 0
['SM_FORM']: datasets found: 1
(T,) rss: 0.0 AIC: -inf
['HM_FORM']: datasets found: 1
(1,) rss: 0.0 AIC: -inf
SYMMETRIC_ENDMEMBERS: [('AL', 'NI')]
0 distinct binary interactions
Total number of features: 86
Compiling phase models
Starting fit
MAIN ITERATION 1
ERROR SCALE 1327605.03352
ERROR SCALE 1320110.16085 (NO ZPF)
FEATURES [('AL3NI5', ('AL', 'NI'), 0, 1, 1.0), ('AL3NI5', ('AL', 'NI'), 0, T, 0.001), ('FCC_L12', ('AL', 'AL', 'AL', 'NI', 'VA'), 0, 1, 1.0), ('FCC_L12', ('AL', 'AL', 'AL', 'NI', 'VA'), 0, T, 0.001)]
MSE 1.0 ([ 0. 0. 0. 0.])
MSE 1.0 ([ 0. 0. 0. 0.])
MSE 1.00000700637 ([ 1. 0. 0. 0.])
MSE 0.999988681946 ([-1.618034 0. 0. 0. ])
MSE 0.998892082177 ([-177.98374 0. 0. 0. ])
MSE 0.997192995405 ([-801.7713934 0. 0. 0. ])
MSE 1.00201473254 ([-1811.08102539 0. 0. 0. ])
MSE 0.997192995405 ([-801.7713934 0. 0. 0. ])
MSE 0.997192995405 ([-801.7713934 0. 0. 0. ])
MSE 0.997195727606 ([-801.7713934 1. 0. 0. ])
MSE 0.99718858033 ([-801.7713934 -1.618034 0. 0. ])
MSE 0.999728691098 ([-801.7713934 -177.98374 0. 0. ])
MSE 0.99718858033 ([-801.7713934 -1.618034 0. 0. ])
MSE 0.997011036124 ([-801.7713934 -68.98373726 0. 0. ])
MSE 0.997011036124 ([-801.7713934 -68.98373726 0. 0. ])
MSE 0.997013063337 ([-801.7713934 -68.98373726 1. 0. ])
MSE 0.997007786016 ([-801.7713934 -68.98373726 -1.618034 0. ])
MSE 0.996866929362 ([-801.7713934 -68.98373726 -142.67055911 0. ])
MSE 0.997235696746 ([-801.7713934 -68.98373726 -370.89834051 0. ])
MSE 0.996866929362 ([-801.7713934 -68.98373726 -142.67055911 0. ])
MSE 0.996866929362 ([-801.7713934 -68.98373726 -142.67055911 0. ])
MSE 0.996866929384 ([-801.7713934 -68.98373726 -142.67055911 1. ])
MSE 0.996866929419 ([-801.7713934 -68.98373726 -142.67055911 -1.618034 ])
MSE 0.996866929362 ([-801.7713934 -68.98373726 -142.67055911 0. ])
MSE 0.99686692937 ([ -8.01771393e+02 -6.89837373e+01 -1.42670559e+02 -6.18033975e-01])
MSE 0.996866929365 ([ -8.01771393e+02 -6.89837373e+01 -1.42670559e+02 3.81966000e-01])
MSE 0.996866929362 ([ -8.01771393e+02 -6.89837373e+01 -1.42670559e+02 -6.11171860e-05])
MSE 0.996866929363 ([ -8.01771393e+02 -6.89837373e+01 -1.42670559e+02 1.56701165e-01])
MSE 0.996866929362 ([ -8.01771393e+02 -6.89837373e+01 -1.42670559e+02 5.98545170e-02])
MSE 0.996866929362 ([ -8.01771393e+02 -6.89837373e+01 -1.42670559e+02 2.89112214e-02])
MSE 0.996866929362 ([ -8.01771393e+02 -6.89837373e+01 -1.42670559e+02 1.42034271e-02])
MSE 0.996866929362 ([ -8.01771393e+02 -6.89837373e+01 -1.42670559e+02 7.03511539e-03])
MSE 0.996866929362 ([ -8.01771393e+02 -6.89837373e+01 -1.42670559e+02 3.48267651e-03])
MSE 0.996866929362 ([ -8.01771393e+02 -6.89837373e+01 -1.42670559e+02 1.71795952e-03])
MSE 0.996866929362 ([ -8.01771393e+02 -6.89837373e+01 -1.42670559e+02 6.56202125e-04])
MSE 0.996866929362 ([ -8.01771393e+02 -6.89837373e+01 -1.42670559e+02 2.50646901e-04])
MSE 0.996866929362 ([ -8.01771393e+02 -6.89837373e+01 -1.42670559e+02 9.57385941e-05])
MSE 0.996866929362 ([ -8.01771393e+02 -6.89837373e+01 -1.42670559e+02 3.65688878e-05])
MSE 0.996866929362 ([ -8.01771393e+02 -6.89837373e+01 -1.42670559e+02 -2.33446871e-05])
MSE 0.996866929362 ([ -8.01771393e+02 -6.89837373e+01 -1.42670559e+02 1.39680718e-05])
MSE 0.996866929362 ([ -8.01771393e+02 -6.89837373e+01 -1.42670559e+02 -8.91687674e-06])
MSE 1.00069824923 ([ -1.60354279e+03 -1.37967475e+02 -2.85341118e+02 -1.78337535e-05])
MSE 0.996866929362 ([ -8.01771393e+02 -6.89837373e+01 -1.42670559e+02 -8.91687674e-06])
MSE 0.996866698774 ([ -8.00771393e+02 -6.89837373e+01 -1.42670559e+02 -8.91687674e-06])
MSE 0.996866344173 ([ -7.99153359e+02 -6.89837373e+01 -1.42670559e+02 -8.91687674e-06])
MSE 0.996863768798 ([ -7.74867736e+02 -6.89837373e+01 -1.42670559e+02 -8.91687674e-06])
MSE 0.996870511218 ([ -7.35572771e+02 -6.89837373e+01 -1.42670559e+02 -8.91687674e-06])
MSE 0.996863768798 ([ -7.74867736e+02 -6.89837373e+01 -1.42670559e+02 -8.91687674e-06])
MSE 0.996863768798 ([ -7.74867736e+02 -6.89837373e+01 -1.42670559e+02 -8.91687674e-06])
MSE 0.996866406354 ([ -7.74867736e+02 -6.79837373e+01 -1.42670559e+02 -8.91687674e-06])
MSE 0.99685950686 ([ -7.74867736e+02 -7.06017713e+01 -1.42670559e+02 -8.91687674e-06])
MSE 0.999335782411 ([ -7.74867736e+02 -2.46967477e+02 -1.42670559e+02 -8.91687674e-06])
MSE 0.99685950686 ([ -7.74867736e+02 -7.06017713e+01 -1.42670559e+02 -8.91687674e-06])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42670559e+02 -8.91687674e-06])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42670559e+02 -8.91687674e-06])
MSE 0.996688345521 ([ -7.74867736e+02 -1.37967475e+02 -1.41670559e+02 -8.91687674e-06])
MSE 0.996688356976 ([ -7.74867736e+02 -1.37967475e+02 -1.44288593e+02 -8.91687674e-06])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42670559e+02 -8.91687674e-06])
MSE 0.996688341146 ([ -7.74867736e+02 -1.37967475e+02 -1.43288593e+02 -8.91687674e-06])
MSE 0.996688339474 ([ -7.74867736e+02 -1.37967475e+02 -1.42288593e+02 -8.91687674e-06])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42670574e+02 -8.91687674e-06])
MSE 0.996688338442 ([ -7.74867736e+02 -1.37967475e+02 -1.42660773e+02 -8.91687674e-06])
MSE 0.996688338442 ([ -7.74867736e+02 -1.37967475e+02 -1.42666821e+02 -8.91687674e-06])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42668986e+02 -8.91687674e-06])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42669839e+02 -8.91687674e-06])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42670221e+02 -8.91687674e-06])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42670402e+02 -8.91687674e-06])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42670487e+02 -8.91687674e-06])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42670532e+02 -8.91687674e-06])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42670549e+02 -8.91687674e-06])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42670565e+02 -8.91687674e-06])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42670555e+02 -8.91687674e-06])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42670561e+02 -8.91687674e-06])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42670558e+02 -8.91687674e-06])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42670560e+02 -8.91687674e-06])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42670559e+02 -8.91687674e-06])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42670559e+02 -8.91687674e-06])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42670559e+02 -8.91687674e-06])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42670559e+02 -8.91687674e-06])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42670559e+02 -8.91687674e-06])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42670559e+02 -8.91687674e-06])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42670559e+02 -8.91687674e-06])
MSE 0.996688338463 ([-774.8677356 -137.96747452 -142.67055915 0.99999108])
MSE 0.996688338499 ([-774.8677356 -137.96747452 -142.67055915 -1.61804292])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42670559e+02 -8.91687674e-06])
MSE 0.99668833845 ([ -7.74867736e+02 -1.37967475e+02 -1.42670559e+02 -6.18042892e-01])
MSE 0.996688338445 ([ -7.74867736e+02 -1.37967475e+02 -1.42670559e+02 3.81957083e-01])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42670559e+02 -3.05225154e-05])
MSE 0.996688338442 ([ -7.74867736e+02 -1.37967475e+02 -1.42670559e+02 1.83146332e-01])
MSE 0.996688338442 ([ -7.74867736e+02 -1.37967475e+02 -1.42670559e+02 6.99501611e-02])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42670559e+02 3.46946989e-02])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42670559e+02 1.72650149e-02])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42670559e+02 8.60420516e-03])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42670559e+02 4.28570733e-03])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42670559e+02 2.12900765e-03])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42670559e+02 8.07697602e-04])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42670559e+02 3.03002089e-04])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42670559e+02 1.10225563e-04])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42670559e+02 3.65914844e-05])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42670559e+02 8.46576994e-06])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42670559e+02 -1.71694961e-05])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42670559e+02 -2.27729672e-06])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42670559e+02 -1.20690968e-05])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42670559e+02 -6.38078291e-06])
MSE 0.996688338441 ([ -7.74867736e+02 -1.37967475e+02 -1.42670559e+02 -1.01209176e-05])
Preserved Files: []
---------------------------------------------------------------------------
ValueError Traceback (most recent call last)
<ipython-input-2-a0a996c9733e> in <module>()
3 from paramselect import _multiphase_fitting_system
4 from pycalphad import Database, equilibrium
----> 5 get_ipython().magic("time dbf = fit('input.json', datasets, saveall=True)")
/home/rotis/anaconda/envs/calphadpy3/lib/python3.5/site-packages/IPython/core/interactiveshell.py in magic(self, arg_s)
2334 magic_name, _, magic_arg_s = arg_s.partition(' ')
2335 magic_name = magic_name.lstrip(prefilter.ESC_MAGIC)
-> 2336 return self.run_line_magic(magic_name, magic_arg_s)
2337
2338 #-------------------------------------------------------------------------
/home/rotis/anaconda/envs/calphadpy3/lib/python3.5/site-packages/IPython/core/interactiveshell.py in run_line_magic(self, magic_name, line)
2255 kwargs['local_ns'] = sys._getframe(stack_depth).f_locals
2256 with self.builtin_trap:
-> 2257 result = fn(*args,**kwargs)
2258 return result
2259
<decorator-gen-60> in time(self, line, cell, local_ns)
/home/rotis/anaconda/envs/calphadpy3/lib/python3.5/site-packages/IPython/core/magic.py in <lambda>(f, *a, **k)
191 # but it's overkill for just that one bit of state.
192 def magic_deco(arg):
--> 193 call = lambda f, *a, **k: f(*a, **k)
194
195 if callable(arg):
/home/rotis/anaconda/envs/calphadpy3/lib/python3.5/site-packages/IPython/core/magics/execution.py in time(self, line, cell, local_ns)
1165 else:
1166 st = clock2()
-> 1167 exec(code, glob, local_ns)
1168 end = clock2()
1169 out = None
<timed exec> in <module>()
/home/rotis/git/pycalphad-fitting/paramselect.py in fit(input_fname, datasets, saveall, resume)
1806 if (len(selected_feature_indices) > 0) and np.any(feature_scores[selected_feature_indices] > 1e-6):
1807 res_output = fmin_powell(sumsqerr, np.full_like(selected_feature_indices, 0, dtype=np.float),
-> 1808 full_output=True, ftol=1e-3, xtol=0.1)
1809 print('RES OUTPUT', res_output)
1810 coefs = res_output[0]
/home/rotis/anaconda/envs/calphadpy3/lib/python3.5/site-packages/scipy/optimize/optimize.py in fmin_powell(func, x0, args, xtol, ftol, maxiter, maxfun, full_output, disp, retall, callback, direc)
2280 'return_all': retall}
2281
-> 2282 res = _minimize_powell(func, x0, args, callback=callback, **opts)
2283
2284 if full_output:
/home/rotis/anaconda/envs/calphadpy3/lib/python3.5/site-packages/scipy/optimize/optimize.py in _minimize_powell(func, x0, args, callback, xtol, ftol, maxiter, maxfev, disp, direc, return_all, **unknown_options)
2351 fx2 = fval
2352 fval, x, direc1 = _linesearch_powell(func, x, direc1,
-> 2353 tol=xtol * 100)
2354 if (fx2 - fval) > delta:
2355 delta = fx2 - fval
/home/rotis/anaconda/envs/calphadpy3/lib/python3.5/site-packages/scipy/optimize/optimize.py in _linesearch_powell(func, p, xi, tol)
2173 def myfunc(alpha):
2174 return func(p + alpha*xi)
-> 2175 alpha_min, fret, iter, num = brent(myfunc, full_output=1, tol=tol)
2176 xi = alpha_min*xi
2177 return squeeze(fret), p + xi, xi
/home/rotis/anaconda/envs/calphadpy3/lib/python3.5/site-packages/scipy/optimize/optimize.py in brent(func, args, brack, tol, full_output, maxiter)
1912 options = {'xtol': tol,
1913 'maxiter': maxiter}
-> 1914 res = _minimize_scalar_brent(func, brack, args, **options)
1915 if full_output:
1916 return res['x'], res['fun'], res['nit'], res['nfev']
/home/rotis/anaconda/envs/calphadpy3/lib/python3.5/site-packages/scipy/optimize/optimize.py in _minimize_scalar_brent(func, brack, args, xtol, maxiter, **unknown_options)
1944 full_output=True, maxiter=maxiter)
1945 brent.set_bracket(brack)
-> 1946 brent.optimize()
1947 x, fval, nit, nfev = brent.get_result(full_output=True)
1948 return OptimizeResult(fun=fval, x=x, nit=nit, nfev=nfev,
/home/rotis/anaconda/envs/calphadpy3/lib/python3.5/site-packages/scipy/optimize/optimize.py in optimize(self)
1817 else:
1818 u = x + rat
-> 1819 fu = func(*((u,) + self.args)) # calculate new output value
1820 funcalls += 1
1821
/home/rotis/anaconda/envs/calphadpy3/lib/python3.5/site-packages/scipy/optimize/optimize.py in myfunc(alpha)
2172 """
2173 def myfunc(alpha):
-> 2174 return func(p + alpha*xi)
2175 alpha_min, fret, iter, num = brent(myfunc, full_output=1, tol=tol)
2176 xi = alpha_min*xi
/home/rotis/anaconda/envs/calphadpy3/lib/python3.5/site-packages/scipy/optimize/optimize.py in function_wrapper(*wrapper_args)
287 def function_wrapper(*wrapper_args):
288 ncalls[0] += 1
--> 289 return function(*(wrapper_args + args))
290
291 return ncalls, function_wrapper
/home/rotis/git/pycalphad-fitting/paramselect.py in sumsqerr(x)
1800 obj_callables=temp_obj,
1801 grad_callables=temp_grad,
-> 1802 hess_callables=temp_hess)
1803 print('MSE', np.nanmean(iter_error**2) / error_scale, '({})'.format(x))
1804 return np.nanmean(iter_error**2) / error_scale
/home/rotis/git/pycalphad-fitting/paramselect.py in _multiphase_error(dbf, data, datasets, **kwargs)
1419 comps = sorted(data['components'])
1420 phases = sorted(data['phases'].keys())
-> 1421 errors = multi_phase_fit(dbf, comps, phases, data, datasets, None, **kwargs)
1422 data_rows = len(errors.all())
1423
/home/rotis/git/pycalphad-fitting/paramselect.py in multi_phase_fit(dbf, comps, phases, inpd, datasets, x, param_symbols, obj_callables, grad_callables, hess_callables)
1060 callables=phase_obj_callables, grad_callables=phase_grad_callables,
1061 hess_callables=phase_hess_callables, model=phase_models,
-> 1062 tmpman=tmpman)
1063 #print('MULTI_EQDATA', multi_eqdata)
1064 # Does there exist only a single phase in the result with zero internal degrees of freedom?
/home/rotis/git/pycalphad/pycalphad/core/tempfilemanager.py in wrapper(*args, **kwargs)
68 else:
69 kwargs['tmpman'] = tmpman
---> 70 return func(*args, **kwargs)
71 return wrapper
/home/rotis/git/pycalphad/pycalphad/core/equilibrium.py in equilibrium(dbf, comps, phases, conditions, output, model, verbose, pbar, broadcast, calc_opts, nprocs, tmpman, return_grids, **kwargs)
899 else:
900 # Single-process job; don't create child processes
--> 901 properties = _solve_eq_at_conditions(dbf, comps, properties, phase_records, callable_dict, verbose)
902
903 # Compute equilibrium values of any additional user-specified properties
/home/rotis/git/pycalphad/pycalphad/core/tempfilemanager.py in wrapper(*args, **kwargs)
65 with self:
66 kwargs['tmpman'] = self
---> 67 return func(*args, **kwargs)
68 else:
69 kwargs['tmpman'] = tmpman
/home/rotis/git/pycalphad/pycalphad/core/equilibrium.py in _solve_eq_at_conditions(dbf, comps, properties, phase_records, callable_dict, verbose, tmpman)
453 l_hessian, gradient_term = _build_multiphase_system(dbf, comps, phases, cur_conds, site_fracs, phase_fracs,
454 l_constraints, constraint_jac, constraint_hess,
--> 455 l_multipliers, callable_dict, phase_records)
456 # Equation 18.18 in Nocedal and Wright
457 if m != n:
/home/rotis/git/pycalphad/pycalphad/core/equilibrium.py in _build_multiphase_system(dbf, comps, phases, cur_conds, site_fracs, phase_fracs, l_constraints, constraint_jac, constraint_hess, l_multipliers, callable_dict, phase_records)
310 var_offset:var_offset + phase_dof[phase_idx]] = \
311 phase_frac * np.squeeze(hess(*itertools.chain([cur_conds['P'], cur_conds['T']],
--> 312 site_fracs[var_offset:var_offset + phase_dof[phase_idx]])
313 ))[2:, 2:] # Remove P,T hessian part
314 # Phase fraction / site fraction cross derivative
/home/rotis/git/pycalphad-fitting/paramselect.py in <lambda>(pn, *args)
1793 temp_hess = {phase_name: lambda *args, pn=phase_name: np.sum([h(*args)
1794 for h in itertools.chain(hess_funcs[pn],
-> 1795 temp_hess_funcs[pn])],
1796 axis=0)
1797 for phase_name in sorted(data['phases'].keys())}
/home/rotis/git/pycalphad-fitting/paramselect.py in <listcomp>(.0)
1792 for phase_name in sorted(data['phases'].keys())}
1793 temp_hess = {phase_name: lambda *args, pn=phase_name: np.sum([h(*args)
-> 1794 for h in itertools.chain(hess_funcs[pn],
1795 temp_hess_funcs[pn])],
1796 axis=0)
/home/rotis/git/pycalphad/pycalphad/core/sympydiff_utils.py in hess_argwrapper(*args)
102 resarray = list(itertools.chain(*(f(*args) for f in hess)))
103 result = np.zeros((len(args), len(args)) + resarray[0].shape)
--> 104 result[triu_indices] = resarray
105 axes = tuple(range(len(result.shape)))
106 # Upper triangular is filled; also need to fill lower triangular
ValueError: shape mismatch: value array of shape (74,) could not be broadcast to indexing result of shape (66,)
In [ ]:
print(dbf.to_string(fmt='tdb', groupby='phase'))
In [ ]:
datasets = paramselect.load_datasets(sorted(recursive_glob('Al-Ni', '*.json')))
paramselect.plot_parameters(dbf, ["AL", "NI", "VA"], "BCC_B2", ["AL", ["AL", "NI"], "VA"], [[0, 1]], datasets)
In [ ]:
datasets = paramselect.load_datasets(sorted(recursive_glob('Al-Ni', '*.json')))
paramselect.plot_parameters(dbf, ["AL", "NI", "VA"], "BCC_B2", ["NI", ["AL", "NI"], "VA"], [[0, 1]], datasets)
In [ ]:
datasets = paramselect.load_datasets(sorted(recursive_glob('Al-Ni', '*.json')))
paramselect.plot_parameters(dbf, ["AL", "NI", "VA"], "BCC_B2", ["NI", ["AL", "VA"], "VA"], [[0, 1]], datasets)
In [ ]:
datasets = paramselect.load_datasets(sorted(recursive_glob('Al-Ni', '*.json')))
paramselect.plot_parameters(dbf, ["AL", "NI", "VA"], "BCC_B2", ["AL", ["NI", "VA"], "VA"], [[0, 1]], datasets)
In [ ]:
datasets = paramselect.load_datasets(sorted(recursive_glob('Al-Ni', '*.json')))
paramselect.plot_parameters(dbf, ["AL", "NI", "VA"], "BCC_B2", ["VA", ["AL", "NI"], "VA"], [[0, 1]], datasets)
In [ ]:
datasets = paramselect.load_datasets(sorted(recursive_glob('Al-Ni', '*.json')))
paramselect.plot_parameters(dbf, ["AL", "NI", "VA"], "BCC_B2", [["AL", "NI"], ["AL", "NI"], "VA"], [[0, 1]], datasets)
In [ ]:
paramselect.plot_parameters(dbf, ["AL", "NI", "VA"], "BCC_B2", ["AL", "NI", "VA"], [[0, 1]], datasets)
In [ ]:
paramselect.plot_parameters(dbf, ["AL", "NI", "VA"], "FCC_L12", ["AL", "NI", "NI", "NI", "VA"], [[0,1,2,3]], datasets)
In [ ]:
paramselect.plot_parameters(dbf, ["AL", "NI", "VA"], "FCC_L12", ["AL", "AL", "NI", "NI", "VA"], [[0,1,2,3]], datasets)
In [ ]:
paramselect.plot_parameters(dbf, ["AL", "NI", "VA"], "FCC_L12", ["AL", "AL", "AL", "NI", "VA"], [[0,1,2,3]], datasets)
In [ ]:
paramselect.plot_parameters(dbf, ["AL", "NI", "VA"], "FCC_L12", [["AL", "NI"], "NI", "NI", "NI", "VA"], [[0,1,2,3]], datasets)
In [ ]:
paramselect.plot_parameters(dbf, ["AL", "NI", "VA"], "FCC_L12", [["AL", "NI"], "NI", "NI", "AL", "VA"], [[0,1,2,3]], datasets)
In [ ]:
paramselect.plot_parameters(dbf, ["AL", "NI", "VA"], "FCC_L12", [["AL", "NI"], "NI", "AL", "AL", "VA"], [[0,1,2,3]], datasets)
In [ ]:
paramselect.plot_parameters(dbf, ["AL", "NI", "VA"], "FCC_L12", ["AL", ["AL", "NI"], "AL", "AL", "VA"], [[0,1,2,3]], datasets)
In [ ]:
paramselect.plot_parameters(dbf, ["AL", "NI", "VA"], "FCC_L12", [["AL", "NI"], ["AL", "NI"], ["AL", "NI"], ["AL", "NI"], "VA"], [[0,1,2,3]], datasets)
In [ ]:
paramselect.plot_parameters(dbf, ["AL", "NI", "VA"], "AL3NI2", ["AL", ["AL", "NI"], "NI"], None, datasets)
In [ ]:
paramselect.plot_parameters(dbf, ["AL", "NI", "VA"], "AL3NI2", ["AL", ["AL", "NI"], "VA"], None, datasets)
In [ ]:
paramselect.plot_parameters(dbf, ["AL", "NI", "VA"], "AL3NI2", ["AL", "NI", ["NI", "VA"]], None, datasets)
In [ ]:
paramselect.plot_parameters(dbf, ["AL", "NI", "VA"], "LIQUID", [["AL", "NI"]], None, datasets)
In [ ]:
dbf.to_file('AlNi.tdb', if_exists='overwrite')
In [ ]:
from paramselect import multi_plot
from pycalphad import binplot, Database
import pycalphad.variables as v
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(9,6))
dbf = Database('2016-04-25 15:45:29.550255-ALNI-iter4.tdb')
binplot(dbf, ['AL', 'NI', 'VA'], sorted(dbf.phases.keys()),
{v.X('NI'): (0,1,0.02), v.T: (300, 2000, 30), v.P: 101325}, ax=fig.gca(), eq_kwargs={'_approx': True})
multi_plot(None, ['AL', 'NI', 'VA'], sorted(dbf.phases.keys()), datasets, ax=fig.gca())
plt.show()
In [ ]:
from pycalphad import calculate,Database
import pycalphad.variables as v
import matplotlib.pyplot as plt
%matplotlib inline
import numpy as np
result = calculate(Database('AlNi.tdb'), ['AL', 'NI', 'VA'], ['FCC_L12', 'LIQUID'], pdens=5000, T=1645, P=1e5)
In [ ]:
fig = plt.figure(figsize=(12,12))
bcc_energies_x = result.X.sel(component='NI').values[np.nonzero(result.Phase.values == 'FCC_L12')]
bcc_energies_y = result.GM.values[np.nonzero(result.Phase.values == 'FCC_L12')]
fig.gca().scatter(bcc_energies_x, bcc_energies_y, color='b')
liq_energies_x = result.X.sel(component='NI').values[np.nonzero(result.Phase.values == 'LIQUID')]
liq_energies_y = result.GM.values[np.nonzero(result.Phase.values == 'LIQUID')]
fig.gca().scatter(liq_energies_x, liq_energies_y, color='r')
fig.gca().plot([0, 1], [-239801.46331355, -102505.10323622], color='k', linestyle='--')
#fig.gca().plot([0, 1], [-1.147e5, -6.06e4], color='k', linestyle='--')
fig.gca().set_ylim((-150000, -5e4))
fig.gca().set_xlim((0.7, 1))
#fig.gca().axvline(0.733, color='k', linestyle='--')
plt.show()
Content source: richardotis/pycalphad-fitting
Similar notebooks: