In [1]:

    

import numpy as np

%matplotlib inline
import matplotlib.pyplot as plt
import astropy.units as u

import pysac.mhs_atmosphere as atm


    

    




WARNING:traits.has_traits:DEPRECATED: traits.has_traits.wrapped_class, 'the 'implements' class advisor has been deprecated. Use the 'provides' class decorator.

In [7]:

    

#Read in the VAL3C model
empirical_data = atm.hs_atmosphere.read_VAL3c_MTW(MTW_file=False)[:-26]

# Create a Z array at the interpolated resolution and interpolate.
ZZ = u.Quantity(np.linspace(empirical_data['Z'][0], empirical_data['Z'][-1], 128), unit=empirical_data['Z'].unit)
table = atm.hs_atmosphere.interpolate_atmosphere(empirical_data, ZZ)

# Create a figure and make space for the axes.
fig, ax = plt.subplots(gridspec_kw={'right':0.775, 'left':0.13}, figsize=(15,8))

# Shortcut all the Mm conversion.
Z = empirical_data['Z'].to(u.Mm)

lrho, = ax.plot(Z, empirical_data['rho'].quantity.si, 'x', color='blue')
lrho_i, = ax.plot(ZZ.to(u.Mm), table['rho'].quantity.si, color='blue')

ax2 = ax.twinx()
lp, = ax2.plot(Z, empirical_data['p'].to(u.Pa), 'x', color='green')
lp_i, = ax2.plot(ZZ.to(u.Mm), table['p'].to(u.Pa), color='green')


ax3 = ax.twinx()
ax3.spines["right"].set_position(("axes", 1.18))
lt, = ax3.plot(Z, empirical_data['T'], 'x', color='red')
lt_i, = ax3.plot(ZZ.to(u.Mm), table['T'], color='red')


# Set primary axes properties and labels
ax.semilogy()
ax.set_ylabel(r"Density [{}]".format(lrho._yorig.unit._repr_latex_()))
ax.set_xlabel(r"Height [{}]".format(lrho._xorig.unit._repr_latex_()))
ax.set_xlim(Z[0].value-0.1, Z[-1].value+0.1)


# Pressure Axis
ax2.semilogy()
ax2.set_ylabel(r"Pressure [{}]".format(lp._yorig.unit._repr_latex_()))


# Temp axis
ax3.set_ylabel(r"Temperature [{}]".format(lt._yorig.unit._repr_latex_()))

# Set the colours for the ticks and the labels.
ax.tick_params(axis='y', colors=lrho.get_color())
ax2.tick_params(axis='y', colors=lp.get_color())
ax3.tick_params(axis='y', colors=lt.get_color())

ax.yaxis.label.set_color(lrho.get_color())
ax2.yaxis.label.set_color(lp.get_color())
ax3.yaxis.label.set_color(lt.get_color())

fig


    

    
Out[7]:

In [9]:

    

table


    

    
Out[9]:




<Table length=128>

Z
p
T
rho
mu
km
dyn / cm2
K
g / cm3

float64
float64
float64
float64
float64
-75.0
212505.285616
6604.88886241
3.91130749015e-07
1.23884247641
-61.7716535433
191134.085836
6535.97139044
3.64314548677e-07
1.23991852077
-48.5433070866
171912.141679
6467.77302488
3.39336885969e-07
1.24092846946
-35.3149606299
154623.307127
6400.28626236
3.16071709454e-07
1.24139725517
-22.0866141732
139073.173502
6333.50367778
2.94401609869e-07
1.24179325032
-8.85826771654
125086.88339
6267.41792354
2.74217227613e-07
1.24193097891
4.37007874016
112507.16441
6202.02172868
2.55416700857e-07
1.24203404157
17.5984251969
101192.560727
6137.30789813
2.37905151491e-07
1.24206681019
30.8267716535
91015.8424149
6073.26931188
2.21594206315e-07
1.24209957968
...
...
...
...
...
1485.94488189
1.54484544456
6427.90214702
2.84626158446e-12
1.22118002624
1499.17322835
1.43029930466
6456.81485707
2.62007548044e-12
1.22014894192
1512.4015748
1.32424645333
6485.85761651
2.41186388514e-12
1.21911872818
1525.62992126
1.22605713605
6515.03101031
2.22019840416e-12
1.21750150977
1538.85826772
1.13514829288
6544.33562606
2.04376415443e-12
1.21574296774
1552.08661417
1.05098009624
6573.77205401
1.88135074375e-12
1.21398696572
1565.31496063
0.973052745277
6603.34088703
1.73184396709e-12
1.21223350005
1578.54330709
0.900903498059
6633.0427207
1.59421816284e-12
1.21048256706
1591.77165354
0.834103923713
6662.87815324
1.46752917643e-12
1.2087341631
1605.0
0.772257358365
6692.84778558
1.35090788316e-12
1.2069882845

In [14]:

    

fig = plt.figure()
plt.plot(table['T'],'x')
fig


    

    
Out[14]:

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