Exponential cutoff

[3]:

# Parameters
func_name = "Exponential_cutoff"
wide_energy_range = True
x_scale = "log"
y_scale = "log"
linear_range = False

Description

[5]:

func.display()

description: An exponential cutoff
formula: $ K \exp{(-x/xc)} $
parameters:
- K:
  - value: 1.0
  - desc: Normalization
  - min_value: None
  - max_value: None
  - unit:
  - is_normalization: True
  - delta: 0.1
  - free: True
- xc:
  - value: 100.0
  - desc: cutoff
  - min_value: 1.0
  - max_value: None
  - unit:
  - is_normalization: False
  - delta: 10.0
  - free: True

Shape

The shape of the function.

If this is not a photon model but a prior or linear function then ignore the units as these docs are auto-generated

[6]:

fig, ax = plt.subplots()


ax.plot(energy_grid, func(energy_grid), color=blue)

ax.set_xlabel("energy (keV)")
ax.set_ylabel("photon flux")
ax.set_xscale(x_scale)
ax.set_yscale(y_scale)

../_images/notebooks_Exponential_cutoff_8_0.png

F\(_{\nu}\)

The F\(_{\nu}\) shape of the photon model if this is not a photon model, please ignore this auto-generated plot

[7]:

fig, ax = plt.subplots()

ax.plot(energy_grid, energy_grid * func(energy_grid), red)


ax.set_xlabel("energy (keV)")
ax.set_ylabel(r"energy flux (F$_{\nu}$)")
ax.set_xscale(x_scale)
ax.set_yscale(y_scale)

../_images/notebooks_Exponential_cutoff_10_0.png

\(\nu\)F\(_{\nu}\)

The \(\nu\)F\(_{\nu}\) shape of the photon model if this is not a photon model, please ignore this auto-generated plot

[8]:

fig, ax = plt.subplots()

ax.plot(energy_grid, energy_grid**2 * func(energy_grid), color=green)


ax.set_xlabel("energy (keV)")
ax.set_ylabel(r"$\nu$F$_{\nu}$")
ax.set_xscale(x_scale)
ax.set_yscale(y_scale)

../_images/notebooks_Exponential_cutoff_12_0.png