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Re-applied Black formatting to tardis after excluding undesired directories #1213

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Jul 29, 2020
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Re-applied Black formatting to tardis after excluding undesired directories #1213

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1dd138e
Added directories to exclude in black configuration file
jaladh-singhal Jun 30, 2020
9a93016
Added black to tardis environment file
jaladh-singhal Jun 30, 2020
98bf7bd
Excluded setup related files from black configuration
jaladh-singhal Jul 1, 2020
a2de4c4
Applied black formatting to remaining files
jaladh-singhal Jul 1, 2020
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32 changes: 28 additions & 4 deletions asv/benchmarks/benchmarks.py
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -6,17 +6,21 @@

LINE_SIZE = 10000000


class TimeSuite:
"""
An example benchmark that times the performance of various kinds
of iterating over dictionaries in Python.
"""

def setup(self):
self.line = np.arange(LINE_SIZE, 1, -1).astype(np.float64)

def time_binarysearch(self):
for _ in range(LINE_SIZE):
montecarlo.binary_search_wrapper(self.line, np.random.random() * LINE_SIZE, 0, LINE_SIZE - 1)
montecarlo.binary_search_wrapper(
self.line, np.random.random() * LINE_SIZE, 0, LINE_SIZE - 1
)

def time_compute_distance2outer(self):
for _ in range(1000000):
Expand All @@ -25,7 +29,7 @@ def time_compute_distance2outer(self):
montecarlo.compute_distance2outer_wrapper(0.3, 1.0, 1.0)
montecarlo.compute_distance2outer_wrapper(0.3, -1.0, 1.0)
montecarlo.compute_distance2outer_wrapper(0.5, 0.0, 1.0)

def time_compute_distance2inner(self):
for _ in range(1000000):
montecarlo.compute_distance2inner_wrapper(1.5, -1.0, 1.0)
Expand All @@ -34,8 +38,28 @@ def time_compute_distance2inner(self):

def time_compute_distance2line(self):
for _ in range(1000000):
montecarlo.compute_distance2line_wrapper(2.20866912e+15, -0.251699059004, 1.05581082105e+15, 1.06020910733e+15, 1693440.0, 5.90513983371e-07, 1.0602263591e+15, 1.06011723237e+15, 2)
montecarlo.compute_distance2line_wrapper(2.23434667994e+15, -0.291130548401, 1.05581082105e+15, 1.06733618121e+15, 1693440.0, 5.90513983371e-07, 1.06738407486e+15, 1.06732933961e+15, 3)
montecarlo.compute_distance2line_wrapper(
2.20866912e15,
-0.251699059004,
1.05581082105e15,
1.06020910733e15,
1693440.0,
5.90513983371e-07,
1.0602263591e15,
1.06011723237e15,
2,
)
montecarlo.compute_distance2line_wrapper(
2.23434667994e15,
-0.291130548401,
1.05581082105e15,
1.06733618121e15,
1693440.0,
5.90513983371e-07,
1.06738407486e15,
1.06732933961e15,
3,
)

def time_compute_distance2electron(self):
for _ in range(1000000):
Expand Down
70 changes: 42 additions & 28 deletions docs/physics/plasma/plasma_plots/lte_ionization_balance.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -7,7 +7,7 @@
import numpy as np
import pandas as pd

#Making 2 Figures for ionization balance and level populations
# Making 2 Figures for ionization balance and level populations

plt.figure(1).clf()
ax1 = plt.figure(1).add_subplot(111)
Expand All @@ -16,73 +16,87 @@
ax2 = plt.figure(2).add_subplot(111)

# expanding the tilde to the users directory
#atom_fname = os.path.join(os.path.dirname(base.__file__), 'data', 'atom_data.h5')
# atom_fname = os.path.join(os.path.dirname(base.__file__), 'data', 'atom_data.h5')

# reading in the HDF5 File
atom_data = AtomData.from_hdf(atom_fname)

#The atom_data needs to be prepared to create indices. The Class needs to know which atomic numbers are needed for the
#calculation and what line interaction is needed (for "downbranch" and "macroatom" the code creates special tables)
atom_data.prepare_atom_data([14], 'scatter')
# The atom_data needs to be prepared to create indices. The Class needs to know which atomic numbers are needed for the
# calculation and what line interaction is needed (for "downbranch" and "macroatom" the code creates special tables)
atom_data.prepare_atom_data([14], "scatter")

#Initializing the NebularPlasma class using the from_abundance class method.
#This classmethod is normally only needed to test individual plasma classes
#Usually the plasma class just gets the number densities from the model class
# Initializing the NebularPlasma class using the from_abundance class method.
# This classmethod is normally only needed to test individual plasma classes
# Usually the plasma class just gets the number densities from the model class
assert True, (
'This script needs a proper rewrite and should use the new'
'"assemble_plasma" function.')
"This script needs a proper rewrite and should use the new"
'"assemble_plasma" function.'
)
# TODO: Uncomment and fix the next line
# lte_plasma = assemble_plasma({'Si':1.0}, 1e-14*u.g/u.cm**3, atom_data, 10*u.day)
lte_plasma = None
lte_plasma.update_radiationfield([10000], [1.0])

#Initializing a dataframe to store the ion populations and level populations for the different temperatures
# Initializing a dataframe to store the ion populations and level populations for the different temperatures
ion_number_densities = pd.DataFrame(index=lte_plasma.ion_populations.index)
level_populations = pd.DataFrame(index=lte_plasma.level_populations.ix[14, 1].index)
level_populations = pd.DataFrame(
index=lte_plasma.level_populations.ix[14, 1].index
)
t_rads = np.linspace(2000, 20000, 100)

#Calculating the different ion populations and level populuatios for the given temperatures
# Calculating the different ion populations and level populuatios for the given temperatures
for t_rad in t_rads:
lte_plasma.update_radiationfield([t_rad], ws=[1.0])
#getting total si number density
# getting total si number density
si_number_density = lte_plasma.number_densities.get_value(14, 0)
#Normalizing the ion populations
# Normalizing the ion populations
ion_density = lte_plasma.ion_populations / si_number_density
ion_number_densities[t_rad] = ion_density

#normalizing the level_populations for Si II
current_level_population = lte_plasma.level_populations[0].ix[14, 1] / lte_plasma.ion_populations.get_value((14, 1), 0)
# normalizing the level_populations for Si II
current_level_population = lte_plasma.level_populations[0].ix[
14, 1
] / lte_plasma.ion_populations.get_value((14, 1), 0)

#normalizing with statistical weight
# normalizing with statistical weight
current_level_population /= atom_data.levels.ix[14, 1].g

level_populations[t_rad] = current_level_population

ion_colors = ['b', 'g', 'r', 'k']
ion_colors = ["b", "g", "r", "k"]

for ion_number in [0, 1, 2, 3]:
current_ion_density = ion_number_densities.ix[14, ion_number]
ax1.plot(current_ion_density.index, current_ion_density.values, '%s-' % ion_colors[ion_number],
label='Si %s W=1.0' % tardis.util.base.int_to_roman(ion_number + 1).upper())
ax1.plot(
current_ion_density.index,
current_ion_density.values,
"%s-" % ion_colors[ion_number],
label="Si %s W=1.0"
% tardis.util.base.int_to_roman(ion_number + 1).upper(),
)


#only plotting every 5th radiation temperature
# only plotting every 5th radiation temperature
t_rad_normalizer = colors.Normalize(vmin=2000, vmax=20000)
t_rad_color_map = plt.cm.ScalarMappable(norm=t_rad_normalizer, cmap=plt.cm.jet)

for t_rad in t_rads[::5]:
ax2.plot(level_populations[t_rad].index, level_populations[t_rad].values, color=t_rad_color_map.to_rgba(t_rad))
ax2.plot(
level_populations[t_rad].index,
level_populations[t_rad].values,
color=t_rad_color_map.to_rgba(t_rad),
)
ax2.semilogy()

t_rad_color_map.set_array(t_rads)
cb = plt.figure(2).colorbar(t_rad_color_map)

ax1.set_xlabel('T [K]')
ax1.set_ylabel('Number Density Fraction')
ax1.set_xlabel("T [K]")
ax1.set_ylabel("Number Density Fraction")
ax1.legend()

ax2.set_xlabel('Level Number for Si II')
ax2.set_ylabel('Number Density Fraction')
cb.set_label('T [K]')
ax2.set_xlabel("Level Number for Si II")
ax2.set_ylabel("Number Density Fraction")
cb.set_label("T [K]")

plt.show()
104 changes: 66 additions & 38 deletions docs/physics/plasma/plasma_plots/nebular_ionization_balance.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -9,7 +9,7 @@
import numpy as np
import pandas as pd

#Making 2 Figures for ionization balance and level populations
# Making 2 Figures for ionization balance and level populations

plt.figure(1).clf()
ax1 = plt.figure(1).add_subplot(111)
Expand All @@ -18,97 +18,125 @@
ax2 = plt.figure(2).add_subplot(111)

# expanding the tilde to the users directory
atom_fname = os.path.expanduser('~/.tardis/si_kurucz.h5')
atom_fname = os.path.expanduser("~/.tardis/si_kurucz.h5")

# reading in the HDF5 File
atom_data = base.AtomData.from_hdf(atom_fname)

#The atom_data needs to be prepared to create indices. The Class needs to know which atomic numbers are needed for the
#calculation and what line interaction is needed (for "downbranch" and "macroatom" the code creates special tables)
atom_data.prepare_atom_data([14], 'scatter')
# The atom_data needs to be prepared to create indices. The Class needs to know which atomic numbers are needed for the
# calculation and what line interaction is needed (for "downbranch" and "macroatom" the code creates special tables)
atom_data.prepare_atom_data([14], "scatter")

#Initializing the NebularPlasma class using the from_abundance class method.
#This classmethod is normally only needed to test individual plasma classes
#Usually the plasma class just gets the number densities from the model class
nebular_plasma = plasma.NebularPlasma.from_abundance(10000, 0.5, {'Si': 1}, 1e-13, atom_data, 10.)
# Initializing the NebularPlasma class using the from_abundance class method.
# This classmethod is normally only needed to test individual plasma classes
# Usually the plasma class just gets the number densities from the model class
nebular_plasma = plasma.NebularPlasma.from_abundance(
10000, 0.5, {"Si": 1}, 1e-13, atom_data, 10.0
)


#Initializing a dataframe to store the ion populations and level populations for the different temperatures
# Initializing a dataframe to store the ion populations and level populations for the different temperatures
ion_number_densities = pd.DataFrame(index=nebular_plasma.ion_populations.index)
level_populations = pd.DataFrame(index=nebular_plasma.level_populations.ix[14, 1].index)
level_populations = pd.DataFrame(
index=nebular_plasma.level_populations.ix[14, 1].index
)
t_rads = np.linspace(2000, 20000, 100)

#Calculating the different ion populations and level populuatios for the given temperatures
# Calculating the different ion populations and level populuatios for the given temperatures
for t_rad in t_rads:
nebular_plasma.update_radiationfield(t_rad, w=1.0)
#getting total si number density
# getting total si number density
si_number_density = nebular_plasma.number_density.get_value(14)
#Normalizing the ion populations
# Normalizing the ion populations
ion_density = nebular_plasma.ion_populations / si_number_density
ion_number_densities[t_rad] = ion_density

#normalizing the level_populations for Si II
current_level_population = nebular_plasma.level_populations.ix[14, 1] / nebular_plasma.ion_populations.ix[14, 1]
#normalizing with statistical weight
# normalizing the level_populations for Si II
current_level_population = (
nebular_plasma.level_populations.ix[14, 1]
/ nebular_plasma.ion_populations.ix[14, 1]
)
# normalizing with statistical weight
current_level_population /= atom_data.levels.ix[14, 1].g

level_populations[t_rad] = current_level_population

ion_colors = ['b', 'g', 'r', 'k']
ion_colors = ["b", "g", "r", "k"]

for ion_number in [0, 1, 2, 3]:
current_ion_density = ion_number_densities.ix[14, ion_number]
ax1.plot(current_ion_density.index, current_ion_density.values, '%s-' % ion_colors[ion_number],
label='Si %s W=1.0' % tardis.util.base.int_to_roman(ion_number + 1).upper())
ax1.plot(
current_ion_density.index,
current_ion_density.values,
"%s-" % ion_colors[ion_number],
label="Si %s W=1.0"
% tardis.util.base.int_to_roman(ion_number + 1).upper(),
)


#only plotting every 5th radiation temperature
# only plotting every 5th radiation temperature
t_rad_normalizer = colors.Normalize(vmin=2000, vmax=20000)
t_rad_color_map = plt.cm.ScalarMappable(norm=t_rad_normalizer, cmap=plt.cm.jet)

for t_rad in t_rads[::5]:
ax2.plot(level_populations[t_rad].index, level_populations[t_rad].values, color=t_rad_color_map.to_rgba(t_rad))
ax2.plot(
level_populations[t_rad].index,
level_populations[t_rad].values,
color=t_rad_color_map.to_rgba(t_rad),
)
ax2.semilogy()

#Calculating the different ion populations for the given temperatures with W=0.5
# Calculating the different ion populations for the given temperatures with W=0.5
ion_number_densities = pd.DataFrame(index=nebular_plasma.ion_populations.index)
for t_rad in t_rads:
nebular_plasma.update_radiationfield(t_rad, w=0.5)
#getting total si number density
# getting total si number density
si_number_density = nebular_plasma.number_density.get_value(14)
#Normalizing the ion populations
# Normalizing the ion populations
ion_density = nebular_plasma.ion_populations / si_number_density
ion_number_densities[t_rad] = ion_density

#normalizing the level_populations for Si II
current_level_population = nebular_plasma.level_populations.ix[14, 1] / nebular_plasma.ion_populations.ix[14, 1]
#normalizing with statistical weight
# normalizing the level_populations for Si II
current_level_population = (
nebular_plasma.level_populations.ix[14, 1]
/ nebular_plasma.ion_populations.ix[14, 1]
)
# normalizing with statistical weight
current_level_population /= atom_data.levels.ix[14, 1].g

level_populations[t_rad] = current_level_population

#Plotting the ion fractions
# Plotting the ion fractions

for ion_number in [0, 1, 2, 3]:
print("w=0.5")
current_ion_density = ion_number_densities.ix[14, ion_number]
ax1.plot(current_ion_density.index, current_ion_density.values, '%s--' % ion_colors[ion_number],
label='Si %s W=0.5' % tardis.util.base.int_to_roman(ion_number + 1).upper())
ax1.plot(
current_ion_density.index,
current_ion_density.values,
"%s--" % ion_colors[ion_number],
label="Si %s W=0.5"
% tardis.util.base.int_to_roman(ion_number + 1).upper(),
)

for t_rad in t_rads[::5]:
ax2.plot(level_populations[t_rad].index, level_populations[t_rad].values, color=t_rad_color_map.to_rgba(t_rad),
linestyle='--')
ax2.plot(
level_populations[t_rad].index,
level_populations[t_rad].values,
color=t_rad_color_map.to_rgba(t_rad),
linestyle="--",
)
ax2.semilogy()

t_rad_color_map.set_array(t_rads)
cb = plt.figure(2).colorbar(t_rad_color_map)

ax1.set_xlabel('T [K]')
ax1.set_ylabel('Number Density Fraction')
ax1.set_xlabel("T [K]")
ax1.set_ylabel("Number Density Fraction")
ax1.legend()

ax2.set_xlabel('Level Number for Si II')
ax2.set_ylabel('Number Density Fraction')
cb.set_label('T [K]')
ax2.set_xlabel("Level Number for Si II")
ax2.set_ylabel("Number Density Fraction")
cb.set_label("T [K]")

plt.show()
15 changes: 8 additions & 7 deletions docs/physics/pyplot/plot_mu_in_out_packet.py
View file
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Original file line number Diff line number Diff line change
@@ -1,12 +1,13 @@
from pylab import *
from astropy import units as u, constants as const

x, y = x, y = mgrid[1:1000, 1:1000]
mu_in = x / 500. - 1
mu_out = y / 500. - 1
mu_in = x / 500.0 - 1
mu_out = y / 500.0 - 1
v = 1.1e4 * u.km / u.s
doppler_fac = (1-mu_in * v/const.c)/(1-mu_out * v/const.c)
xlabel('$\mu_{\\rm in}$')
ylabel('$\mu_{\\rm out}$')
imshow(np.rot90(doppler_fac), extent=[-1, 1, -1, 1], cmap='bwr')
doppler_fac = (1 - mu_in * v / const.c) / (1 - mu_out * v / const.c)
xlabel("$\mu_{\\rm in}$")
ylabel("$\mu_{\\rm out}$")
imshow(np.rot90(doppler_fac), extent=[-1, 1, -1, 1], cmap="bwr")
colorbar()
show()
show()
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