def get_fit_for_fitting(self): fit = Fit('exponential_fit', data=self.get_data(), model=self.get_model()) fit.expression = 'exp' fit.independent = {'symbol': 't'} fit.parameters = [ {'symbol': 'a', 'guess': 10}, {'symbol': 'k', 'guess': 20}, ] return fit
def test_all_variables(self): fit = Fit(model=Model()) symbols = ('t', 'u', 'x', 'm', 'D', 'k', 'τ', 'a', 'b') fit.model.add_symbols(*symbols) fit.free_variables = ['t', 'u'] fit.independent = {'symbol': 'x'} fit.parameters = [ {'symbol': 'm', 'guess': 2}, {'symbol': 'D', 'guess': 2}, {'symbol': fit.model.symbol('k'), 'value': 3}, {'symbol': 'τ', 'value': 4}, ] fit.constants = [ {'symbol': 'a'}, {'symbol': 'b'}, ] eq_(fit.all_variables, tuple( fit.model.symbol(s) for s in symbols ))
def test_function_with_more_symbols(self): fit = Fit() fit.model = Model() symbols = ('x', 'a', 'b', 'c', 'd', 'e', 'f') fit.model.add_symbols(*symbols) x, a, b, c, d, e, f = fit.model.get_symbols(*symbols) fit.model.expressions['exp'] = a * f + b * e + x * c + d fit.expression = 'exp' fit.independent = {'symbol': 'x'} fit.parameters = [ {'symbol': 'a', 'guess': 2}, {'symbol': 'b', 'guess': 3}, {'symbol': 'c', 'value': 4, 'prefix': 'kilo'}, {'symbol': 'd', 'value': 5}, ] fit.constants = [ {'symbol': 'e', 'value': 10}, {'symbol': 'f', 'value': 12, 'prefix': 'milli'}, ] eq_(fit.function(2, 4, 7), 8075.048)
# 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)