notebook.community



In [1]:

    
%cat 0Source_Citation.txt









    



Source and citation

- This notebook is a part of the `pytheos` package.
- Website: http://github.com/SHDShim/pytheos.
- How to cite: S.-H. Shim (2017) Pytheos - a python tool set for equations of state. DOI: 10.5281/zenodo.802392



In [2]:

    
%matplotlib inline
# %matplotlib notebook # for interactive

For high dpi displays.



In [3]:

    
%config InlineBackend.figure_format = 'retina'

0. General note

This example compares pressure calculated from pytheos and original publication for the gold scale by Tange 2008.

1. Global setup



In [4]:

    
import matplotlib.pyplot as plt
import numpy as np
from uncertainties import unumpy as unp
import pytheos as eos

3. Compare



In [5]:

    
eta = np.linspace(1., 0.65, 36)
print(eta)









    



[1.   0.99 0.98 0.97 0.96 0.95 0.94 0.93 0.92 0.91 0.9  0.89 0.88 0.87
 0.86 0.85 0.84 0.83 0.82 0.81 0.8  0.79 0.78 0.77 0.76 0.75 0.74 0.73
 0.72 0.71 0.7  0.69 0.68 0.67 0.66 0.65]



In [6]:

    
tange_mgo = eos.periclase.Tange2009()



In [7]:

    
tange_mgo.print_equations()









    



P_static:  vinet
P_thermal:  tange
P_anharmonic:  None
P_electronic:  None



In [8]:

    
tange_mgo.print_equations()









    



P_static:  vinet
P_thermal:  tange
P_anharmonic:  None
P_electronic:  None



In [9]:

    
tange_mgo.print_parameters()









    



Static:  OrderedDict([('v0', 74.698+/-0.001), ('k0', 160.63+/-0.18), ('k0p', 4.367+/-0.013)])
Thermal:  OrderedDict([('v0', 74.698+/-0.001), ('gamma0', 1.442+/-0.015), ('a', 0.138+/-0.019), ('b', 5.4+/-1.1), ('theta0', 761.0+/-13.0)])
Anharmonic:  None
Electronic:  None



In [10]:

    
v0 = 74.698



In [11]:

    
tange_mgo.three_r









    Out[11]:





24.943379399999998



In [12]:

    
v = v0 * (eta) 
temp = 3000.



In [13]:

    
p = tange_mgo.cal_p(v, temp * np.ones_like(v))



In [14]:

    
print('for T = ', temp)
for eta_i, p_i in zip(eta, p):
    print("{0: .3f} {1: .2f}".format(eta_i, p_i))









    



for T =  3000.0
 1.000  16.76+/-0.18
 0.990  18.44+/-0.18
 0.980  20.22+/-0.18
 0.970  22.10+/-0.19
 0.960  24.09+/-0.21
 0.950  26.18+/-0.22
 0.940  28.40+/-0.23
 0.930  30.74+/-0.25
 0.920  33.21+/-0.26
 0.910  35.82+/-0.28
 0.900  38.58+/-0.29
 0.890  41.48+/-0.30
 0.880  44.55+/-0.31
 0.870  47.79+/-0.33
 0.860  51.20+/-0.34
 0.850  54.81+/-0.35
 0.840  58.61+/-0.36
 0.830  62.63+/-0.37
 0.820  66.87+/-0.38
 0.810  71.34+/-0.39
 0.800  76.07+/-0.40
 0.790  81.06+/-0.41
 0.780  86.33+/-0.42
 0.770  91.90+/-0.43
 0.760  97.79+/-0.44
 0.750  104.01+/-0.46
 0.740  110.59+/-0.47
 0.730  117.55+/-0.48
 0.720  124.91+/-0.50
 0.710  132.70+/-0.52
 0.700  140.95+/-0.54
 0.690  149.69+/-0.56
 0.680  158.94+/-0.58
 0.670  168.75+/-0.61
 0.660  179.16+/-0.64
 0.650  190.19+/-0.67

Make comparison with the Vinet column in the table.



In [15]:

    
v = tange_mgo.cal_v(p, temp * np.ones_like(p), min_strain=0.6)
print(1.-(v/v0))









    



[0.   0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1  0.11 0.12 0.13
 0.14 0.15 0.16 0.17 0.18 0.19 0.2  0.21 0.22 0.23 0.24 0.25 0.26 0.27
 0.28 0.29 0.3  0.31 0.32 0.33 0.34 0.35]



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