# Load data from a csv file.
data = Data(name)
data.path = os.path.join('examples','data', 'wave.csv')
data.genfromtxt_args['skip_header'] = 1
data.error = (0.1, 0.05)
# Create a wave model.
model = Model(name)
model.add_symbols('t', 'A', 'ω', 'δ')
A, t, ω, δ = model.get_symbols('A', 't', 'ω', 'δ')
model.expressions['wave'] = A * sympy.functions.sin(ω * t + δ)
model.expressions['frequency'] = ω / (2 * sympy.pi)
# Create the fit using the data and model.
fit = Fit(name, data=data, model=model)
fit.expression = 'wave'
fit.independent = {'symbol': 't', 'name': 'Time', 'units': 's'}
fit.dependent = {'name': 'Voltage', 'prefix': 'kilo', 'units': 'kV'}
fit.parameters = [
{'symbol': 'A', 'value': 0.3, 'prefix': 'kilo', 'units': 'kV'},
{'symbol': 'ω', 'guess': 1, 'units': 'Hz'},
{'symbol': 'δ', 'guess': 1},
]
fit.quantities = [
{'expression': 'frequency', 'name': 'Frequency', 'units': 'Hz'},
{'expression': 1 / model.expressions['frequency'] , 'name': 'Period', 'units': 's'},
]
# Save the fit to disk.
save_example_fit(fit)
#
name = 'linear_polyfit'
# Load data from a csv file.
data = Data(name)
data.path = os.path.join('examples', 'data', 'linear.csv')
data.genfromtxt_args['skip_header'] = 1
# Create a linear model.
model = Model(name)
model.add_symbols('t', 'v', 'x_0')
t, v, x_0 = model.get_symbols('t', 'v', 'x_0')
model.expressions['line'] = v * t + x_0
# Create the fit using the data and model.
fit = Fit(name, data=data, model=model)
fit.expression = 'line'
fit.independent = {'symbol': 't', 'name': 'Time', 'units': 's'}
fit.dependent = {'name': 'Distance', 'units': 'm'}
fit.parameters = [
{'symbol': 'v', 'guess': 1, 'units': 'm/s'},
{'symbol': 'x_0', 'guess': 1, 'units': 'm'},
]
# Use `numpy.polyfit` to do the fit.
fit.options['fit_function'] = lambda f, x, y, p0, **op: (numpy.polyfit(x, y, 1), )
# Save the fit to disk.
save_example_fit(fit)
#
name = 'linear_scaled'
# Load data from a csv file.
data = Data(name)
data.path = os.path.join('examples', 'data', 'linear.csv')
data.genfromtxt_args['skip_header'] = 1
# Assume the data was not saved in SI base units.
data.scale = ('micro', 'kilo')
# Create a linear model.
model = Model(name)
model.add_symbols('t', 'v', 'x_0')
t, v, x_0 = model.get_symbols('t', 'v', 'x_0')
model.expressions['line'] = v * t + x_0
# Create the fit using the data and model.
fit = Fit(name, data=data, model=model)
fit.expression = 'line'
fit.independent = {'symbol': 't', 'name': 'Time', 'prefix': 'micro', 'units': 'µs'}
fit.dependent = {'name': 'Distance', 'prefix': 'kilo', 'units': 'km'}
fit.parameters = [
{'symbol': 'v', 'guess': 1, 'prefix': 10**9, 'units': 'km/µs'},
{'symbol': 'x_0', 'value': 1, 'prefix': 'kilo', 'units': 'km'},
]
# Save the fit to disk.
save_example_fit(fit)
