# Gaussian

[3]:

# Parameters
func_name = "Gaussian"
wide_energy_range = True
x_scale = "linear"
y_scale = "linear"
linear_range = True


## Description

[5]:

func.display()

• description: A Gaussian function
• formula: $K \frac{1}{\sigma \sqrt{2 \pi}}\exp{\frac{(x-\mu)^2}{2~(\sigma)^2}}$
• parameters:
• F:
• value: 1.0
• desc: Integral between -inf and +inf. Fix this to 1 to obtain a Normal distribution
• min_value: None
• max_value: None
• unit:
• is_normalization: False
• delta: 0.1
• free: True
• mu:
• value: 0.0
• desc: Central value
• min_value: None
• max_value: None
• unit:
• is_normalization: False
• delta: 0.1
• free: True
• sigma:
• value: 1.0
• desc: standard deviation
• min_value: 1e-12
• max_value: None
• unit:
• is_normalization: False
• delta: 0.1
• 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)


## 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)


## $$\nu$$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)