# Uniform prior

[3]:

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


## Description

[5]:

func.display()

• description: A function which is constant on the interval lower_bound - upper_bound and 0 outside the interval. The extremes of the interval are counted as part of the interval.
• formula: $f(x)=\begin{cases}0 & x < \text{lower_bound} \\\text{value} & \text{lower_bound} \le x \le \text{upper_bound} \\ 0 & x > \text{upper_bound} \end{cases}$
• parameters:
• lower_bound:
• value: 0.0
• desc: Lower bound for the interval
• min_value: -inf
• max_value: inf
• unit:
• is_normalization: False
• delta: 0.1
• free: True
• upper_bound:
• value: 1.0
• desc: Upper bound for the interval
• min_value: -inf
• max_value: inf
• unit:
• is_normalization: False
• delta: 0.1
• free: True
• value:
• value: 1.0
• desc: Value in the interval
• min_value: None
• 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)