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_scale_with_mixed_gives_numbers(self):
data = Data()
data.scale = (2, 'Avogadro constant')
assert_array_almost_equal(data.scale, (2, 6.022140857e+23))
def test_scale_with_strings_gives_numbers(self):
data = Data()
data.scale = ('kilo', 'milli')
assert_almost_equal(data.scale, (1000, 0.001))
def test_scale_with_numbers_gives_numbers(self):
data = Data()
data.scale = (1, 2)
eq_(data.scale, (1, 2))
from scipy_data_fitting import Data, Model, Fit
#
# Example of a basic linear fit.
# This example demonstrates how to use `prefix` for unit conversions.
#
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'},
