def test_load_data(self): for data_file in self.data_files: data = Data() data.path = data_file['filename'] data.genfromtxt_args['delimiter'] = data_file['delimiter'] if data_file['headers']: data.genfromtxt_args['skip_header'] = 1 yield assert_allclose, data.load_data(), self.raw_data
def test_load_data_with_scale(self): data_file = self.data_files[0] data = Data() data.path = data_file['filename'] data.genfromtxt_args['delimiter'] = data_file['delimiter'] data.scale = (2, 5) raw_data_scaled = [ [ 2 * x for x in self.raw_data[0] ], [ 5 * x for x in self.raw_data[1] ], ] assert_allclose(data.load_data(), raw_data_scaled)
def test_load_error_with_scale(self): raw_error = numpy.array(self.error) data = Data() data.path = self.error_file['filename'] data.scale = (2, 10) data.error_columns = ((1, 2), 0) assert_allclose(data.error[0], 2 * numpy.array([raw_error[1], raw_error[2]])) assert_allclose(data.error[1], 10 * raw_error[0]) del data._error data.error_columns = (0, (2, 1)) assert_allclose(data.error[0], 2 * raw_error[0]) assert_allclose(data.error[1], 10 * numpy.array([raw_error[2], raw_error[1]])) del data._error data.error_columns = ((1, 3), (0, 2)) assert_allclose(data.error[0], 2 * numpy.array([raw_error[1], raw_error[3]])) assert_allclose(data.error[1], 10 * numpy.array([raw_error[0], raw_error[2]]))
def test_load_error(self): raw_error = numpy.array(self.error) data = Data() data.path = self.error_file['filename'] data.error_columns = (1, None) assert_allclose(data.error[0], raw_error[1]) eq_(data.error[1], None) del data._error data.error_columns = (None, 1) eq_(data.error[0], None) assert_allclose(data.error[1], raw_error[1]) del data._error data.error_columns = (2, 1) assert_allclose(data.error[0], raw_error[2]) assert_allclose(data.error[1], raw_error[1]) del data._error data.error_columns = ((1, 2), None) assert_allclose(data.error[0], numpy.array([raw_error[1], raw_error[2]])) eq_(data.error[1], None) del data._error data.error_columns = (None, (1, 2)) eq_(data.error[0], None) assert_allclose(data.error[1], numpy.array([raw_error[1], raw_error[2]])) del data._error data.error_columns = ((1, 2), 0) assert_allclose(data.error[0], numpy.array([raw_error[1], raw_error[2]])) assert_allclose(data.error[1], raw_error[0]) del data._error data.error_columns = (0, (2, 1)) assert_allclose(data.error[0], raw_error[0]) assert_allclose(data.error[1], numpy.array([raw_error[2], raw_error[1]])) del data._error data.error_columns = ((1, 3), (0, 2)) assert_allclose(data.error[0], numpy.array([raw_error[1], raw_error[3]])) assert_allclose(data.error[1], numpy.array([raw_error[0], raw_error[2]]))
import os import numpy from example_helper import save_example_fit from scipy_data_fitting import Data, Model, Fit # # Example of a basic linear fit. # This example demonstrates how to use a custom `fit_function`. # 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'},
import os import sympy from example_helper import save_example_fit from scipy_data_fitting import Data, Model, Fit # # Example of a fit to a sine wave with error bars. # name = 'wave' # 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 = [