Pressure

In [1]:

from PASCal.app import fit
import numpy as np

from PASCal.app import fit import numpy as np

In [2]:

# Define example data from the app
data = np.loadtxt((line for line in """
#Variable Pressure Data for Ag3CoCN6 Phase II
0.190	1	6.6985	11.5782	6.5554	90	78.522	90
0.226	1	6.6934	11.5391	6.5661	90	78.521	90.0000
0.290	1	6.68	11.468	6.577	90	78.47	90.0000
0.294	1	6.6745	11.497	6.5707	90	78.473	90.0000
0.395	1	6.6415	11.4251	6.5959	90	78.284	90.0000
0.55	1	6.605	11.295	6.629	90	78.21	90.0000
0.78	1	6.553	11.196	6.646	90	77.967	90.0000
0.90	1	6.539	11.156	6.654	90	77.871	90.0000
0.92	1	6.5333	11.154	6.6507	90	77.91	90.0000
1.08	1	6.505	11.116	6.663	90	77.73	90.0000
1.87	1	6.4172	10.922	6.696	90	77.584	90.0000
3.02	1	6.2904	10.739	6.747	90	77.39	90.0000
5.13	1	6.0657	10.493	6.833	90	77.09	90.0000
6.92	1	5.91	10.333	6.891	90	77.01	90.0000
7.65	1	5.8657	10.27	6.908	90	76.921	90.0000
""".splitlines()))

# Define example data from the app data = np.loadtxt((line for line in """ #Variable Pressure Data for Ag3CoCN6 Phase II 0.190 1 6.6985 11.5782 6.5554 90 78.522 90 0.226 1 6.6934 11.5391 6.5661 90 78.521 90.0000 0.290 1 6.68 11.468 6.577 90 78.47 90.0000 0.294 1 6.6745 11.497 6.5707 90 78.473 90.0000 0.395 1 6.6415 11.4251 6.5959 90 78.284 90.0000 0.55 1 6.605 11.295 6.629 90 78.21 90.0000 0.78 1 6.553 11.196 6.646 90 77.967 90.0000 0.90 1 6.539 11.156 6.654 90 77.871 90.0000 0.92 1 6.5333 11.154 6.6507 90 77.91 90.0000 1.08 1 6.505 11.116 6.663 90 77.73 90.0000 1.87 1 6.4172 10.922 6.696 90 77.584 90.0000 3.02 1 6.2904 10.739 6.747 90 77.39 90.0000 5.13 1 6.0657 10.493 6.833 90 77.09 90.0000 6.92 1 5.91 10.333 6.891 90 77.01 90.0000 7.65 1 5.8657 10.27 6.908 90 76.921 90.0000 """.splitlines()))

In [3]:

x = data[:, 0]
x_error = data[:, 1]
unit_cells = data[:, 2:]

x = data[:, 0] x_error = data[:, 1] unit_cells = data[:, 2:]

In [4]:

fit_results = fit(x, x_error, unit_cells, {"data_type": "pressure"})

fit_results = fit(x, x_error, unit_cells, {"data_type": "pressure"})

Performing fit with options=Options(data_type=<PASCalDataType.PRESSURE: 'Pressure'>, eulerian_strain=True, finite_strain=True, use_pc=False, pc_val=None, deg_poly_strain=5, deg_poly_vol=5)

In [5]:

from PASCal.plotting import plot_strain, plot_volume

from PASCal.plotting import plot_strain, plot_volume

In [6]:

fit_results.plot_strain()

fit_results.plot_strain()

In [7]:

fit_results.plot_volume()

fit_results.plot_volume()

In [8]:

fit_results.plot_indicatrix(plot_size=600)

fit_results.plot_indicatrix(plot_size=600)

In [9]:

fit_results.plot_compressibility()

fit_results.plot_compressibility()

In [10]:

import pprint
pprint.pprint(fit_results.named_coefficients)

import pprint pprint.pprint(fit_results.named_coefficients)

{'B0': array([24.62696904, 12.08767507]),
 'BPrime': array([ 4.        , 11.85340906]),
 'CalEpsilon0': array([ 0.0390179 ,  0.00341754, -0.00167155]),
 'CalLambda': array([-0.08036248, -0.03591663,  0.01856326]),
 'CalNu': array([0.33258547, 0.68308275, 0.5307593 ]),
 'CalPc': array([0.08172067, 0.17238386, 0.18292445]),
 'CalPress': array([[-0.60188815, -0.49905322, -0.22628737, -0.25686882,  0.06956871,
         0.55851053,  1.15186482,  1.34221086,  1.39677469,  1.65210928,
         2.67473018,  3.78753886,  5.50135179,  6.63916142,  7.02527641],
       [-0.15553569, -0.09481268,  0.07002167,  0.05126458,  0.25517921,
         0.57638424,  0.99320692,  1.13355797,  1.17440708,  1.36929596,
         2.2153013 ,  3.26867169,  5.21463635,  6.76822512,  7.35083189],
       [ 0.12718238,  0.16177168,  0.25873287,  0.24747641,  0.37285064,
         0.58332567,  0.87883973,  0.98375769,  1.01479011,  1.16586422,
         1.87661684,  2.87545369,  5.00825043,  6.94324167,  7.71646062]]),
 'PcCoef': array([0., 0., 0.]),
 'SigB0': array([0.93058209, 1.55899714]),
 'SigBPrime': array(['n/a', 1.6479251595018671], dtype=object),
 'SigV0': array([1.76380397, 1.98806401]),
 'V0': array([495.06956949, 503.21719632]),
 'VolCoef': 24.44571205846455,
 'VolCoefErr': 1.3225221768973972,
 'XCal': array([[  0.06509574,  -0.31922425,  -0.86740335,  -0.89767243,
         -1.56091231,  -2.34228427,  -3.22969031,  -3.61592132,
         -3.67654026,  -4.12985614,  -5.84832291,  -7.5991747 ,
         -9.86740606, -11.32977399, -11.85236066],
       [  0.11418795,  -0.1449885 ,  -0.49067565,  -0.50991107,
         -0.94537698,  -1.50490227,  -2.21386698,  -2.54868225,
         -2.60271925,  -3.01979356,  -4.81403252,  -6.99905572,
        -10.37897833, -12.89171925, -13.85366046],
       [ -0.0330655 ,   0.1825966 ,   0.39993694,   0.41108415,
          0.64789259,   0.92339164,   1.24466649,   1.38878934,
          1.41167397,   1.58518302,   2.28308192,   3.06148596,
          4.16981335,   4.94228395,   5.22903306]])}