# 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)