def fitwin(x,y):
def fit(x, params):
a, b, c, d = params
O1 = 0.9295 #principal lunar
M2 = 1.9324 #principal lunar
return a * (numpy.cos(2*pi*O1*x + b)) + c * (numpy.cos(2*pi*M2*x + d))
a = FitParam("O1 Amp", 0., -10., 10.)
b = FitParam("O1 Shift", 0., -10., 10.,logscale="True")
c = FitParam("M2 Amp", 0., -10., 10.)
d = FitParam("M2 Shift", 0., -10., 10.,logscale="True")
params = [a, b, c, d]
values = guifit(x, y, fit, params, xlabel="Time (s)", ylabel="amplitude (ft)")
return values
def test():
x = np.linspace(-10, 10, 1000)
y = np.cos(1.5*x)+np.random.rand(x.shape[0])*.2
def fit(x, params):
a, b = params
return np.cos(b*x)+a
a = FitParam("Offset", 1., 0., 2.)
b = FitParam("Frequency", 2., 1., 10., logscale=True)
params = [a, b]
values = guifit(x, y, fit, params, xlabel="Time (s)", ylabel="Power (a.u.)")
print(values)
print([param.value for param in params])
def gaussfit(x,y):
if not isinstance(x,np.ndarray):
x=np.array(x)
y=np.array(y)
from guiqwt.widgets.fit import FitParam, guifit
def fit(x, params):
A, mu, FWHM,b = params
return A*np.exp(-4*math.log(2)*np.power((x-mu),2)/math.pow(FWHM,2))+b
A = FitParam("Amplitude", y.max(), 0., y.max())
mu = FitParam("mu", x[len(x)/2], x[0], x[-1])
FWHM = FitParam("FWHM", (x.max()-x.min())/4, 0., x.max()-x.min())
b = FitParam("Offset", 0, 0., y.max())
params = [A, mu, FWHM,b]
values = guifit(x, y, fit, params, xlabel="Time (s)", ylabel="Power (a.u.)",auto_fit=True)
return(values)
def fitwin(x, y):
def fit(x, params):
a, b, c, d = params
O1 = 0.9295 #principal lunar
M2 = 1.9324 #principal lunar
return a * (numpy.cos(2 * pi * O1 * x +
b)) + c * (numpy.cos(2 * pi * M2 * x + d))
a = FitParam("O1 Amp", 0., -10., 10.)
b = FitParam("O1 Shift", 0., -10., 10., logscale="True")
c = FitParam("M2 Amp", 0., -10., 10.)
d = FitParam("M2 Shift", 0., -10., 10., logscale="True")
params = [a, b, c, d]
values = guifit(x,
y,
fit,
params,
xlabel="Time (s)",
ylabel="amplitude (ft)")
return values
def gaussfit(x, y):
if not isinstance(x, np.ndarray):
x = np.array(x)
y = np.array(y)
from guiqwt.widgets.fit import FitParam, guifit
def fit(x, params):
A, mu, FWHM, b = params
return A * np.exp(-4 * math.log(2) * np.power(
(x - mu), 2) / math.pow(FWHM, 2)) + b
A = FitParam("Amplitude", y.max(), 0., y.max())
mu = FitParam("mu", x[len(x) / 2], x[0], x[-1])
FWHM = FitParam("FWHM", (x.max() - x.min()) / 4, 0., x.max() - x.min())
b = FitParam("Offset", 0, 0., y.max())
params = [A, mu, FWHM, b]
values = guifit(x,
y,
fit,
params,
xlabel="Time (s)",
ylabel="Power (a.u.)",
auto_fit=True)
return (values)
def graphicalfit(self):
#SHOW = True # Show test in GUI-based test launcher
x=self.x()
y=self.y()
def fitfn(x, params):
#ignore the x
for index, key in enumerate(self.fit_params):
self.fit_params[key] = float(params[index])
res = self.fn(**self.getparams())
if len(x)!=len(self.x()):
s = pandas.Series(res, index=self.x())
res = s[x].values
return res
self.graphical_params=list()
for index, key in enumerate(self.fit_params):
fp=FitParam(key, self.fit_params[key],0.001*abs(self.fit_params[key]),10.0*abs(self.fit_params[key]),logscale=False,steps=2000,format='%.8f')
self.graphical_params.append(fp)
values = guifit(x, y, fitfn, self.graphical_params, xlabel="x-axis", ylabel="y-axis")
if values is None:
self.gfit_concluded = False
else:
self.gfit_concluded = True
self.comment("Graphical fit finished with the following values: ")
self.comment(values)
self.comment([param.value for param in self.graphical_params])
self.sqerror = self.getsqerror()
self.comment("Fit completed with sqerror = " + str(self.sqerror))
self.comment("Obtained parameter values: ")
self.comment(dict(self.getparams()))
# evaluate the performed fit in fitdata
self.fitdata = pandas.Series(data = self.fn(**self.getparams()), index = self.x(), \
name = 'fitfunction: '+ self.func)
return values
interactive = InteractiveFitSettings()
if not interactive.edit(size=(640, 1)):
sys.exit(0)
else:
config['file'] = interactive.filename
config['function'] = interactive.funcname
config['xcol'] = interactive.xcol
config['ycol'] = interactive.ycol
config['skiprows'] = interactive.skiprows
with open(config_file, 'w') as f:
pickle.dump(config, f)
# Open data file
name, extension = os.path.splitext(interactive.filename)
if extension == 'csv':
delimiter = ','
else:
delimiter = None
xdata, ydata = np.loadtxt(
interactive.filename, delimiter=delimiter,
usecols=(interactive.xcol, interactive.ycol),
unpack=True, skiprows=interactive.skiprows
)
# Fit
#x = np.linspace(-10, 10, 1000)
#y = np.cos(1.5*x) + np.random.rand(x.shape[0])*.2
#func = Sine(x, y)
func = getattr(sys.modules[__name__], interactive.funcname)(xdata, ydata)
values = guifit(func.xdata, func.ydata, func.func, func.params)