quantum_fitter package

Subpackages

• quantum_fitter.readout_tools package
  • Module contents
• quantum_fitter.standard_operating_functions package
  • Module contents
• quantum_fitter.tests package
  • Submodules
  • quantum_fitter.tests.test_fit module
  • quantum_fitter.tests.test_installation module
  • Module contents

Submodules

quantum_fitter.hello_world module

quantum_fitter.hello_world.greeter(name: str) → str

Make a string with a greeting

Parameters

name – The name of the person to greet

quantum_fitter.hello_world.hello_world() → None

Print hello world :)

quantum_fitter.hello_world.myadd(x: float, y: float) → float

Module contents

To use the easy Qfit package, you need to first initialize an instance by ins = quantum_fitter.Qfit(x, y, model, params_init); where params_init is a list for initial value

Then, use ins.do_fit() to fit through lmfit. If you want to change any parameters’ properties, use ins.set_params() to alter.

One example:

# define a function method

def gaussian(x, amp, cen, wid):

1-d gaussian: gaussian(x, amp, cen, wid) return (amp / (sqrt(2*pi) * wid)) * exp(-(x-cen)**2 / (2*wid**2))

# set the data value x = np.linspace(0, 10, 500) y = gaussian(x, 8, 5, 0.6) + np.random.randn(500)

# create a list for initial value (in sequence of parameters in function method) params_ini = [5, 5, 1]

# create an instance for Qfit, pass the data and initial value into it (params_ini can be empty) a = qf.QFit(x, y, gaussian, params_ini)

# Set the property of amp. Mind here the value have to redefine a.set_params(name=’amp’, value=’5’, vary, minimum, maximum, expression, brute_step)

a.do_fit() a.pretty_print() plt.show()