In [1]:
%matplotlib inline
import matplotlib.pyplot as plt
from pycalphad import equilibrium
from pycalphad import Database, Model
import pycalphad.variables as v
In [2]:
dbf = Database('craldad_for_pandat.TDB')
phases = ['LIQUID', 'L12_FCC', 'BCC_B2', 'HCP_A3']
%time eq = equilibrium(dbf, ['AL', 'CO', 'NI', 'CR', 'VA'] , phases,\
{v.X('AL'): 0.20, v.X('CO'): 0.2, v.X('CR'): 0.2, v.T: 1373, v.P: 101325})
print(eq)
Components: AL CO CR NI VA
Phases: BCC_B2 HCP_A3 L12_FCC LIQUID [done]
Computing initial grid [4524 points, 742.1KB]
Computing convex hull [iteration 1]
progress 157375.713982 [1 conditions updated]
Refining convex hull
N-R convergence on mini-iteration 4 [BCC_B2]
N-R convergence on mini-iteration 4 [HCP_A3]
N-R convergence on mini-iteration 3 [L12_FCC]
N-R convergence on mini-iteration 2 [LIQUID]
Rebuilding grid [4540 points, 744.7KB]
Computing convex hull [iteration 2]
progress 3132.10946059 [1 conditions updated]
Refining convex hull
N-R convergence on mini-iteration 0 [BCC_B2]
N-R convergence on mini-iteration 1 [HCP_A3]
N-R convergence on mini-iteration 1 [L12_FCC]
N-R convergence on mini-iteration 1 [LIQUID]
Rebuilding grid [4556 points, 747.3KB]
Computing convex hull [iteration 3]
progress 2169.34747008 [1 conditions updated]
Refining convex hull
N-R convergence on mini-iteration 0 [BCC_B2]
N-R convergence on mini-iteration 1 [HCP_A3]
N-R convergence on mini-iteration 1 [L12_FCC]
N-R convergence on mini-iteration 1 [LIQUID]
Rebuilding grid [4572 points, 749.9KB]
Computing convex hull [iteration 4]
progress 1113.76824576 [1 conditions updated]
Refining convex hull
N-R convergence on mini-iteration 0 [BCC_B2]
N-R convergence on mini-iteration 1 [HCP_A3]
N-R convergence on mini-iteration 1 [L12_FCC]
N-R convergence on mini-iteration 1 [LIQUID]
Rebuilding grid [4588 points, 752.6KB]
Computing convex hull [iteration 5]
progress 542.637656309 [0 conditions updated]
Global search complete
Refining equilibrium
Chem pot progress 0.0
Energy progress 243.119468193
No progress
CPU times: user 11min 5s, sys: 11.9 s, total: 11min 17s
Wall time: 13min 30s
<xarray.Dataset>
Dimensions: (P: 1, T: 1, X_AL: 1, X_CO: 1, X_CR: 1, component: 4, internal_dof: 11, vertex: 4)
Coordinates:
* P (P) float64 1.013e+05
* T (T) float64 1.373e+03
* X_AL (X_AL) float64 0.2
* X_CO (X_CO) float64 0.2
* X_CR (X_CR) float64 0.2
* vertex (vertex) int64 0 1 2 3
* component (component) object 'AL' 'CO' 'CR' 'NI'
* internal_dof (internal_dof) int64 0 1 2 3 4 5 6 7 8 9 10
Data variables:
GM (P, T, X_AL, X_CO, X_CR) float64 -9.972e+04
NP (P, T, X_AL, X_CO, X_CR, vertex) float64 0.711 0.2797 ...
MU (P, T, X_AL, X_CO, X_CR, component) float64 -1.542e+05 ...
X (P, T, X_AL, X_CO, X_CR, vertex, component) float64 0.1729 ...
Y (P, T, X_AL, X_CO, X_CR, vertex, internal_dof) float64 0.1729 ...
Phase (P, T, X_AL, X_CO, X_CR, vertex) object 'L12_FCC' 'BCC_B2' ...
Attributes:
hull_iterations: 5
solve_iterations: 1
created: 2016-02-09 19:55:19.306799
engine: pycalphad 0.2.5+38.g143f2db.dirty
In [3]:
print(eq.GM)
print(eq.X)
print(eq.Y)
print(eq.Phase)
print(eq.MU)
print(eq.NP)
<xarray.DataArray 'GM' (P: 1, T: 1, X_AL: 1, X_CO: 1, X_CR: 1)>
array([[[[[-99718.37974555]]]]])
Coordinates:
* P (P) float64 1.013e+05
* T (T) float64 1.373e+03
* X_AL (X_AL) float64 0.2
* X_CO (X_CO) float64 0.2
* X_CR (X_CR) float64 0.2
<xarray.DataArray 'X' (P: 1, T: 1, X_AL: 1, X_CO: 1, X_CR: 1, vertex: 4, component: 4)>
array([[[[[[[ 1.72933019e-01, 1.82286085e-01, 2.07661172e-01,
4.37119724e-01],
[ 2.54676656e-01, 2.35896167e-01, 2.10020758e-01,
2.99406419e-01],
[ 5.00000059e-01, 1.22671835e-08, 9.42568851e-09,
4.99999919e-01],
[ nan, nan, nan,
nan]]]]]]])
Coordinates:
* P (P) float64 1.013e+05
* T (T) float64 1.373e+03
* X_AL (X_AL) float64 0.2
* X_CO (X_CO) float64 0.2
* X_CR (X_CR) float64 0.2
* vertex (vertex) int64 0 1 2 3
* component (component) object 'AL' 'CO' 'CR' 'NI'
<xarray.DataArray 'Y' (P: 1, T: 1, X_AL: 1, X_CO: 1, X_CR: 1, vertex: 4, internal_dof: 11)>
array([[[[[[[ 1.72933019e-01, 1.82286085e-01, 2.07661172e-01,
4.37119724e-01, 1.72933019e-01, 1.82286085e-01,
2.07661172e-01, 4.37119724e-01, 1.00000000e+00,
nan, nan],
[ 2.18906760e-02, 3.41108816e-01, 1.44613869e-01,
4.92385959e-01, 6.80831974e-07, 4.87462461e-01,
1.30683356e-01, 2.75427501e-01, 1.06426672e-01,
9.79103721e-09, 1.00000000e+00],
[ 2.24298273e-09, 1.16650004e-08, 6.48630181e-09,
9.99999670e-01, 3.09153000e-07, 9.99999961e-01,
1.28693629e-08, 1.23650723e-08, 1.38068975e-08,
3.07994506e-12, 1.00000000e+00],
[ nan, nan, nan,
nan, nan, nan,
nan, nan, nan,
nan, nan]]]]]]])
Coordinates:
* P (P) float64 1.013e+05
* T (T) float64 1.373e+03
* X_AL (X_AL) float64 0.2
* X_CO (X_CO) float64 0.2
* X_CR (X_CR) float64 0.2
* vertex (vertex) int64 0 1 2 3
* internal_dof (internal_dof) int64 0 1 2 3 4 5 6 7 8 9 10
<xarray.DataArray 'Phase' (P: 1, T: 1, X_AL: 1, X_CO: 1, X_CR: 1, vertex: 4)>
array([[[[[['L12_FCC', 'BCC_B2', 'BCC_B2', '']]]]]], dtype=object)
Coordinates:
* P (P) float64 1.013e+05
* T (T) float64 1.373e+03
* X_AL (X_AL) float64 0.2
* X_CO (X_CO) float64 0.2
* X_CR (X_CR) float64 0.2
* vertex (vertex) int64 0 1 2 3
<xarray.DataArray 'MU' (P: 1, T: 1, X_AL: 1, X_CO: 1, X_CR: 1, component: 4)>
array([[[[[[-154178.41917463, -92774.07801646, -65904.16677837,
-93475.41604963]]]]]])
Coordinates:
* P (P) float64 1.013e+05
* T (T) float64 1.373e+03
* X_AL (X_AL) float64 0.2
* X_CO (X_CO) float64 0.2
* X_CR (X_CR) float64 0.2
* component (component) object 'AL' 'CO' 'CR' 'NI'
<xarray.DataArray 'NP' (P: 1, T: 1, X_AL: 1, X_CO: 1, X_CR: 1, vertex: 4)>
array([[[[[[ 0.71100819, 0.27966724, 0.00932457, nan]]]]]])
Coordinates:
* P (P) float64 1.013e+05
* T (T) float64 1.373e+03
* X_AL (X_AL) float64 0.2
* X_CO (X_CO) float64 0.2
* X_CR (X_CR) float64 0.2
* vertex (vertex) int64 0 1 2 3
In [ ]:
Content source: richardotis/pycalphad-sandbox
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