def get_data(self):
data = Data('exponential_data')
model = self.get_model()
function = model.lambdify('exp', ('t', 'a', 'k'), modules='numpy')
linspace = numpy.linspace(-1, 1, 300, endpoint=True)
data.array = numpy.array([ linspace, function(linspace, 2, 3) ])
return data
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_error_makes_ndarray(self):
data = Data()
data.error = (0.1, None)
assert_allclose(data.error[0], numpy.array(0.1))
eq_(data.error[1], None)
data.error = ([0.1, 0.2], None)
assert_allclose(data.error[0], numpy.array([0.1, 0.2]))
eq_(data.error[1], None)
data.error = (0.1, 0.2)
assert_allclose(data.error[0], numpy.array(0.1))
assert_allclose(data.error[1], numpy.array(0.2))
data.error = (0.1, [0.1, 0.2])
assert_allclose(data.error[0], numpy.array(0.1))
assert_allclose(data.error[1], numpy.array([0.1, 0.2]))
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]]))
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))
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 = [
def test_limits_symmetric(self):
data = Data()
data.array = numpy.array([ [-2, 1], [3, 4] ])
fit = Fit(data=data)
eq_(fit.limits, (-2, 2))
def test_limits(self):
data = Data()
data.array = numpy.array([ [1, 2], [3, 4] ])
fit = Fit(data=data)
eq_(fit.limits, (0, 2))
import os
from example_helper import save_example_fit
from scipy_data_fitting import Data, Model, Fit
#
# Example of a basic linear fit with error bars.
#
name = 'linear'
# Load data from a csv file.
data = Data(name)
data.path = os.path.join('examples', 'data', 'linear.csv')
data.genfromtxt_args['skip_header'] = 1
data.error_columns = (2, 3)
# 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', 'value': 1, 'units': 'm'},
import os
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 `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'}