In [1]:
%matplotlib inline
import os
import yt
yt.mylog.setLevel("WARNING")
import numpy as np
import matplotlib
matplotlib.rcParams['font.family'] = 'stixgeneral'
matplotlib.rcParams['figure.dpi'] = 150
import matplotlib.pyplot as plt
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from yt_conduction_fields import *
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ds = yt.load('/home/ychen/data/0only_1022_h1_10Myr/data/MHD_Jet_10Myr_hdf5_plt_cnt_1050')
sp1 = ds.sphere([0,0,0], (180, 'kpc'))
sp2 = ds.sphere([0,0,0], (2, 'kpc'))
sp = sp1 - sp2
ds.current_time.in_units('Myr')
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In [4]:
plot = yt.SlicePlot(ds, 'y', 'jet ')
plot.zoom(4)
plot.set_zlim('jet ', 1E-6, 1)
plot.annotate_contour('jet ', ncont=4, clim=(1E-3, 1E-1), take_log=True)
plot.show()
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plot = yt.SlicePlot(ds, 'y', 'xray_emissivity_0.1_100_keV')
plot.zoom(4)
plot.show()
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plot = yt.SlicePlot(ds, 'y', 'xray_cooling_time', )
plot.set_unit('xray_cooling_time', 'Gyr')
plot.zoom(4)
plot.show()
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plot = yt.SlicePlot(ds, 'y', 'temperature')
plot.zoom(4)
plot.show()
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plot = yt.SlicePlot(ds, 'y', 'entropy', data_source=sp)
plot.set_zlim('entropy', 30, 250)
plot.zoom(4)
plot.show()
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plot = yt.SlicePlot(ds, 'y', 'density')
plot.zoom(4)
plot.show()
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plot = yt.SlicePlot(ds, 'y', 'temperature_gradient_magnitude', data_source=sp, width=(240, 'kpc'))
plot.set_unit('temperature_gradient_magnitude', 'K/pc')
plot.show()
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plot = yt.SlicePlot(ds, 'y', 'temperature_gradient_x', data_source=sp, width=(240, 'kpc'))
plot.set_unit('temperature_gradient_x', 'K/pc')
plot.set_cmap('temperature_gradient_x', 'seismic')
plot.set_zlim('temperature_gradient_x', -1E3, 1E3)
plot.show()
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plot = yt.SlicePlot(ds, 'y', 'spitzer_conduction_coefficient', data_source=sp, width=(240, 'kpc'))
plot.set_zlim('spitzer_conduction_coefficient', 1E29, 1E32)
plot.show()
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plot = yt.SlicePlot(ds, 'y', 'spitzer_heat_flux_x', data_source=sp, width=(240, 'kpc'))
plot.set_log('spitzer_heat_flux_x' , True, linthresh=1E-3)
plot.set_cmap('spitzer_heat_flux_x', 'seismic')
plot.set_zlim('spitzer_heat_flux_x', -1E-1, 1E-1)
plot.show()
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plot = yt.SlicePlot(ds, 'y', 'spitzer_heat_flux_divergence', data_source=sp, width=(240, 'kpc'))
plot.set_log('spitzer_heat_flux_divergence' , True, linthresh=1E-25)
plot.set_cmap('spitzer_heat_flux_divergence', 'seismic_r')
plot.set_zlim('spitzer_heat_flux_divergence', -1E-23, 1E-23)
plot.show()
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plot = yt.SlicePlot(ds, 'y', 'spitzer_heating_rate', data_source=sp, width=(240, 'kpc'))
plot.set_log('spitzer_heating_rate' , True, linthresh=1E40)
plot.set_cmap('spitzer_heating_rate', 'seismic')
plot.set_zlim('spitzer_heating_rate', -1E42, 1E42)
plot.show()
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plot = yt.SlicePlot(ds, 'y', 'total_heating_rate', data_source=sp, width=(240, 'kpc'))
plot.set_log('total_heating_rate' , True, linthresh=1E40)
plot.set_cmap('total_heating_rate', 'seismic')
plot.set_zlim('total_heating_rate', -1E42, 1E42)
plot.show()
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#plot = yt.SlicePlot(ds, 'y', 'total_cooling_time', data_source=sp, width=(240, 'kpc'))
plot.set_log('total_cooling_time' , False)
plot.set_cmap('total_cooling_time', 'seismic')
plot.set_zlim('total_cooling_time', -1E2, 1E2)
plot.show()
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extrema = {'entropy': (20, 324)}
logs = {'entropy': True}
fields = ['spitzer_heating_rate', 'xray_luminosity_0.1_100_keV', 'cell_mass']
prof_entropy = yt.create_profile(sp, 'entropy', fields, weight_field=None,
extrema=extrema, logs=logs)
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plt.step(prof_entropy.x, -prof_entropy['xray_luminosity_0.1_100_keV'], label='X-ray Cooling')
plt.step(prof_entropy.x, prof_entropy['spitzer_heating_rate'], label='Spitzer Thermal Conduction')
plt.step(prof_entropy.x, prof_entropy['spitzer_heating_rate']-prof_entropy['xray_luminosity_0.1_100_keV'], label='Total')
plt.step(prof_entropy.x, 0.1*prof_entropy['spitzer_heating_rate']-prof_entropy['xray_luminosity_0.1_100_keV'],
label=r'Total $f_s$=0.1')
plt.ylim(-1E46, 1E46)
plt.yscale('symlog', linthreshy=1E42)
yticks = [-1E46, -1E45, -1E44, -1E43, -1E42, 0, 1E42, 1E43, 1E44, 1E45, 1E46]
plt.yticks(yticks, yticks)
plt.ylabel('Heating/Cooling rate (erg/s)')
plt.semilogx()
plt.xlim(20, 324)
entropy_ticks = [20, 30, 40, 50, 60, 70, 80, 90, 100, 200, 300]
plt.xticks(entropy_ticks, entropy_ticks)
plt.xlabel(r'entropy (cm$^2\ $ keV)')
plt.axhline(0, ls=':', color='grey')
plt.text(22, 1E45, '%.0f Myr' % ds.current_time.in_units('Myr'))
plt.legend(loc=4)
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extrema = {'entropy': (20, 250), 'spherical_radius': ((10, 'kpc'), (150, 'kpc'))}
fields = ['spitzer_heating_rate', 'xray_luminosity_0.1_100_keV', 'cell_mass']
prof = yt.create_profile(sp, ['spherical_radius', 'entropy'], fields, weight_field=None,
extrema=extrema)
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from mpl_toolkits.axes_grid1 import make_axes_locatable
divider = make_axes_locatable(ax)
ax2 = divider.append_axes("bottom", 1.3, pad=0.1, sharex=ax)
pp = yt.PhasePlot.from_profile(prof, )
pp.set_unit('spherical_radius', 'kpc')
pp.set_cmap('spitzer_heating_rate', 'RdBu_r')
pp.set_log('spitzer_heating_rate', False)
pp.set_zlim('spitzer_heating_rate', -1E44, 1E44)
plot2 = pp['spitzer_heating_rate']
entropy_ticks = [20, 30, 40, 50, 60, 70, 80, 90, 100, 200]
plot2.axes.set_yticks(entropy_ticks)
plot2.axes.set_yticklabels(entropy_ticks)
r_ticks = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
plot2.axes.set_xticks(r_ticks)
plot2.axes.set_xticklabels(r_ticks)
plot2
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from yt.visualization.plot_container import FieldTransform
from yt.visualization.tick_locators import LogLocator
linthresh = 1E43
symlog_transform = FieldTransform('symlog', None, LogLocator())
pp._field_transform['spitzer_heating_rate'] = symlog_transform
pp._field_transform['spitzer_heating_rate'].func = linthresh
pp._setup_plots()
pp.annotate_text(12, 200, '%.0f Myr' % ds.current_time.in_units('Myr'))
print(pp._field_transform['spitzer_heating_rate'])
pp.set_zlim('spitzer_heating_rate', -5E44, 5E44)
plot2 = pp['spitzer_heating_rate']
entropy_ticks = [20, 30, 40, 50, 60, 70, 80, 90, 100, 200]
plot2.axes.set_yticks(entropy_ticks)
plot2.axes.set_yticklabels(entropy_ticks)
r_ticks = [10, 20, 30, 40, 50, 60, 70, 80, 90, 100]
plot2.axes.set_xticks(r_ticks)
plot2.axes.set_xticklabels(r_ticks)
plot2
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In [70]:
keV = yt.units.keV
cm = yt.units.cm
kappa = 1*(keV**(5/2)*cm**3)
kappa.convert_to_base()
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heating_rate = prof['spitzer_heating_rate'].flatten()
null = plt.hist(np.log10(heating_rate[heating_rate>0]), bins=20, histtype='step', label='heating')
null = plt.hist(np.log10(-heating_rate[heating_rate<0]), bins=20, histtype='step', label='cooling')
plt.legend()
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