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updating docs
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@karllark
karllark committed Apr 4, 2023
1 parent 7cc7a18 commit f7c852c
Showing 1 changed file with 56 additions and 35 deletions.
91 changes: 56 additions & 35 deletions docs/dust_extinction/model_flavors.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -52,7 +52,7 @@ Average models
ax.xaxis.set_major_formatter(ScalarFormatter())

ax.set_xlabel(r'$\lambda$ [$\mu$m]')
ax.set_ylabel(r'$A(x)/A(V)$')
ax.set_ylabel(r'$A(\lambda)/A(V)$')
ax.set_title('Ultraviolet to Near-Infrared Models')

ax.legend(loc='best')
Expand Down Expand Up @@ -135,10 +135,10 @@ R(V) (+ other variables) dependent prediction models
VCG04, GCC09, M14, F19, D22,
G23)

fig, ax = plt.subplots()
fig, ax = plt.subplots(ncols=2, figsize=(10, 4))

# generate the curves and plot them
x = np.arange(0.5,11.0,0.1)/u.micron
x = np.arange(1./30., 1./0.0912, 0.001)/u.micron

Rv = 3.1

Expand All @@ -150,17 +150,24 @@ R(V) (+ other variables) dependent prediction models
x.value >= ext_model.x_range[0],
x.value <= ext_model.x_range[1]))
yvals = ext_model(x[indxs])
ax.plot(1./x[indxs], yvals, label=ext_model.__class__.__name__)
ax[0].plot(1./x[indxs], yvals, label=ext_model.__class__.__name__)
ax[1].plot(1./x[indxs], yvals, label=ext_model.__class__.__name__)

ax.set_xscale('log')
ax.xaxis.set_major_formatter(ScalarFormatter())
for iax in ax:
iax.set_xscale('log')
iax.xaxis.set_major_formatter(ScalarFormatter())

ax.set_xlabel(r'$\lambda$ [$\mu$m]')
ax.set_ylabel('$A(x)/A(V)$')
iax.set_xlabel(r'$\lambda$ [$\mu$m]')
iax.set_ylabel(r'$A(\lambda)/A(V)$')

ax.set_title('R(V) = 3.1')
ax[0].set_title(f'UV-NIR R(V) = {Rv}')
ax[0].set_xlim(0.08, 3.0)
ax[1].set_title(f'NIR-MIR R(V) = {Rv}')
ax[1].set_xlim(1.0, 32.0)
ax[1].set_ylim(0.0, 0.50)

ax.legend(loc='best')
ax[0].legend(loc='best')
ax[1].legend(loc='best')
plt.tight_layout()
plt.show()

Expand All @@ -176,10 +183,10 @@ R(V) (+ other variables) dependent prediction models
VCG04, GCC09, M14, F19, D22,
G23)

fig, ax = plt.subplots()
fig, ax = plt.subplots(ncols=2, figsize=(10, 4))

# generate the curves and plot them
x = np.arange(0.5,11.0,0.1)/u.micron
x = np.arange(1./30., 1./0.0912, 0.001)/u.micron

Rv = 2.5

Expand All @@ -191,17 +198,24 @@ R(V) (+ other variables) dependent prediction models
x.value >= ext_model.x_range[0],
x.value <= ext_model.x_range[1]))
yvals = ext_model(x[indxs])
ax.plot(1./x[indxs], yvals, label=ext_model.__class__.__name__)
ax[0].plot(1./x[indxs], yvals, label=ext_model.__class__.__name__)
ax[1].plot(1./x[indxs], yvals, label=ext_model.__class__.__name__)

ax.set_xscale('log')
ax.xaxis.set_major_formatter(ScalarFormatter())
for iax in ax:
iax.set_xscale('log')
iax.xaxis.set_major_formatter(ScalarFormatter())

ax.set_xlabel(r'$\lambda$ [$\mu$m]')
ax.set_ylabel('$A(x)/A(V)$')
iax.set_xlabel(r'$\lambda$ [$\mu$m]')
iax.set_ylabel(r'$A(\lambda)/A(V)$')

ax.set_title('R(V) = 2.5')
ax[0].set_title(f'UV-NIR R(V) = {Rv}')
ax[0].set_xlim(0.08, 3.0)
ax[1].set_title(f'NIR-MIR R(V) = {Rv}')
ax[1].set_xlim(1.0, 32.0)
ax[1].set_ylim(0.0, 0.50)

ax.legend(loc='best')
ax[0].legend(loc='best')
ax[1].legend(loc='best')
plt.tight_layout()
plt.show()

Expand All @@ -217,10 +231,10 @@ R(V) (+ other variables) dependent prediction models
VCG04, GCC09, M14, F19, D22,
G23)

fig, ax = plt.subplots()
fig, ax = plt.subplots(ncols=2, figsize=(10, 4))

# generate the curves and plot them
x = np.arange(0.5,11.0,0.1)/u.micron
x = np.arange(1./30., 1./0.0912, 0.001)/u.micron

Rv = 5.5

Expand All @@ -232,17 +246,24 @@ R(V) (+ other variables) dependent prediction models
x.value >= ext_model.x_range[0],
x.value <= ext_model.x_range[1]))
yvals = ext_model(x[indxs])
ax.plot(1./x[indxs], yvals, label=ext_model.__class__.__name__)
ax[0].plot(1./x[indxs], yvals, label=ext_model.__class__.__name__)
ax[1].plot(1./x[indxs], yvals, label=ext_model.__class__.__name__)

ax.set_xscale('log')
ax.xaxis.set_major_formatter(ScalarFormatter())
for iax in ax:
iax.set_xscale('log')
iax.xaxis.set_major_formatter(ScalarFormatter())

ax.set_xlabel(r'$\lambda$ [$\mu$m]')
ax.set_ylabel('$A(x)/A(V)$')
iax.set_xlabel(r'$\lambda$ [$\mu$m]')
iax.set_ylabel(r'$A(\lambda)/A(V)$')

ax.set_title('R(V) = 5.5')
ax[0].set_title(f'UV-NIR R(V) = {Rv}')
ax[0].set_xlim(0.08, 3.0)
ax[1].set_title(f'NIR-MIR R(V) = {Rv}')
ax[1].set_xlim(1.0, 32.0)
ax[1].set_ylim(0.0, 0.50)

ax.legend(loc='best')
ax[0].legend(loc='best')
ax[1].legend(loc='best')
plt.tight_layout()
plt.show()

Expand Down Expand Up @@ -272,7 +293,7 @@ R(V) (+ other variables) dependent prediction models
ax.xaxis.set_major_formatter(ScalarFormatter())

ax.set_xlabel(r'$\lambda$ [$\mu$m]')
ax.set_ylabel('$A(x)/A(V)$')
ax.set_ylabel(r'$A(\lambda)/A(V)$')

ax.set_title('G16; $f_A = 1.0$; $R(V)_A$ variable')

Expand Down Expand Up @@ -306,7 +327,7 @@ R(V) (+ other variables) dependent prediction models
ax.xaxis.set_major_formatter(ScalarFormatter())

ax.set_xlabel(r'$\lambda$ [$\mu$m]')
ax.set_ylabel('$A(x)/A(V)$')
ax.set_ylabel(r'$A(\lambda)/A(V)$')

ax.set_title('G16; $f_A$ variable; $R(V)_A = 3.1$')

Expand Down Expand Up @@ -359,7 +380,7 @@ Grain models
ax.plot(lam[indxs], yvals, label=f"{ext_model.__class__.__name__} {cname}")

ax.set_xlabel('$\lambda$ [$\mu m$]')
ax.set_ylabel('$A(x)/A(V)$')
ax.set_ylabel(r'$A(\lambda)/A(V)$')
ax.set_title('Grain Models')

ax.set_xscale('log')
Expand Down Expand Up @@ -408,7 +429,7 @@ Grain models
ax.plot(lam[indxs], yvals, label=f"{ext_model.__class__.__name__} {cname}")

ax.set_xlabel('$\lambda$ [$\mu m$]')
ax.set_ylabel('$A(x)/A(V)$')
ax.set_ylabel(r'$A(\lambda)/A(V)$')
ax.set_title('Grain Models')

ax.set_xscale('log')
Expand Down Expand Up @@ -448,7 +469,7 @@ Grain models
ax.plot(lam[indxs], yvals, label=f"{ext_model.__class__.__name__} {cname}")

ax.set_xlabel('$\lambda$ [$\mu m$]')
ax.set_ylabel('$A(x)/A(V)$')
ax.set_ylabel(r'$A(\lambda)/A(V)$')
ax.set_title('Grain Models')

ax.set_xscale('log')
Expand Down Expand Up @@ -557,7 +578,7 @@ Shape fitting models
ax.set_ylim(1e-3,10.)

ax.set_xlabel('$\lambda$ [$\mu$m]')
ax.set_ylabel('$A(x)/A(V)$')
ax.set_ylabel(r'$A(\lambda)/A(V)$')

ax.set_title('P92')

Expand Down Expand Up @@ -597,7 +618,7 @@ Shape fitting models
ax.set_yscale('log')

ax.set_xlabel('$\lambda$ [$\mu$m]')
ax.set_ylabel('$A(x)/A(V)$')
ax.set_ylabel(r'$A(\lambda)/A(V)$')

ax.set_title('G21')

Expand Down

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