# Optimizer - simple

```
[1]:
```

```
import nexus as nx
import math
import numpy as np
from scipy.signal import argrelextrema
import matplotlib.pyplot as plt
# ------------------------- Fe layer --------------------------
# the thickness is made to be fitted
# set with boundaries between 0 and 10.000
lay_Fe = nx.Layer(id = "Fe",
material = nx.Material.Template(nx.lib.material.Fe_enriched),
thickness = nx.Var(2000, 0, 1e5, True))
site = nx.Hyperfine(magnetic_field = 33)
lay_Fe.material.hyperfine_sites = [site]
sample = nx.Sample(id = "simple layer",
layers = [lay_Fe],
geometry = "f")
beam = nx.Beam()
beam.LinearSigma()
exp = nx.Experiment(beam = beam,
objects = sample,
isotope = nx.lib.moessbauer.Fe57,
id = "my exp")
detuning = np.arange(-200, 200.1, 0.2)
energy_spec = nx.EnergySpectrum(exp,
detuning,
electronic = True)
intensity = energy_spec()
plt.plot(detuning, intensity)
plt.xlabel('detuning (Gamma)')
plt.ylabel('intensity')
plt.show()
print(min(intensity))
```

```
0.4495182509432407
```

```
[2]:
```

```
# define your own Optimizer derived from nx.Optimizer
class NexusOptimizer(nx.Optimizer):
# this is just the standard initialization of the class
# keep it like this
def __init__(self, measurements, id):
super().__init__(measurements, id)
# the implementation of the residual, the value to be minimized
def Residual(self):
# here we need to calculate the energy spectrum and get its minimum
intensity = energy_spec()
# the residual is 'min(intensity) - 0' because we target the intensity minimum to be zero (minimized)
residual = min(intensity)
return residual
# initialize the optimizer with the method(s) to be used in the optimizer implementation
optimizer = NexusOptimizer(measurements = [energy_spec],
id = "opt id")
# let's see what the residual returns
print(optimizer.Residual())
optimizer.Evaluate()
```

```
0.4495182509432407
Run Optimizer instance with id: opt id
Starting optimizer with 1 provided measurement dependencies and 1 fit parameter(s):
no. | id | initial value | min | max
0 | | 2000 | 0 | 100000
Using 0 equality constraint(s) on parameter(s):
Using 0 inequality constraint(s).
Residual start value: 0.449518
Calling ceres solver with fit method LevMar
Ceres Solver Report: Iterations: 16, Initial cost: 1.010333e-01, Final cost: 1.624058e-06, Termination: CONVERGENCE
Optimizer finished with 1 fit parameter(s):
no. | id | fit value | initial value | min | max
0 | | 100000 | 2000 | 0 | 100000
and 0 equality constraint(s) on parameter(s):
and 0 inequality constraint(s).
Optimized residual from 0.449518 to 0.00180225
Optimizer instance finished. id:opt id
```

```
[3]:
```

```
plt.plot(detuning, energy_spec.result)
plt.xlabel('detuning (Gamma)')
plt.ylabel('intensity')
plt.show()
```