def test_add_symbol(self): model = Model() model.add_symbol('x') model.add_symbol('why', string='y') eq_(len(model.symbols), 2) eq_(model.symbols['x'].name, 'x') eq_(model.symbols['why'].name, 'y')
def get_model(self): model = Model() model.test_symbols = ('x', 'y', 'z') model.add_symbols(*model.test_symbols) x, y, z = model.get_symbols(*model.test_symbols) model.expressions['exp'] = x + y model.replacements['rep_1'] = (x, y + z) model.replacements['rep_2'] = (z, x * x) model.replacements['rep_3'] = (x, y * z) model.replacement_groups['rep_g'] = ['rep_1', 'rep_2'] return model
def get_model(self): model = Model('exponential_model') symbols = ('f', 'a', 'k', 't', 'τ') model.add_symbols(*symbols) f, a, k, t, tau = model.get_symbols(*symbols) model.expressions['f'] = f model.expressions['exp'] = a * sympy.functions.exp(- k * t) model.replacements['exp'] = (f, model.expressions['exp']) model.replacements['tau'] = (k, tau**(-1) ) model.replacement_groups['all'] = ['exp', 'tau'] return model
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'}, {'symbol': 'x_0', 'guess': 1, 'units': 'm'}, ] # Use `numpy.polyfit` to do the 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 = [ {'symbol': 'A', 'value': 0.3, 'prefix': 'kilo', 'units': 'kV'}, {'symbol': 'ω', 'guess': 1, 'units': 'Hz'}, {'symbol': 'δ', 'guess': 1}, ]
def test_symbol(self): model = Model() model.add_symbol('x') eq_(model.symbol('x'), model.symbols['x'])
def test_get_symbols(self): model = Model() model.add_symbols('x', 'y', 'z') x, y, z = model.get_symbols('x', 'y', 'z') eq_(x, model.symbols['x']) eq_(y, model.symbols['y'])
def test_add_symbols(self): model = Model() model.add_symbols('x', 'y', 'z') eq_(len(model.symbols), 3) eq_(model.symbols['x'].name, 'x') eq_(model.symbols['y'].name, 'y')