diff options
Diffstat (limited to 'silx/math/fit/fittheories.py')
| -rw-r--r-- | silx/math/fit/fittheories.py | 34 |
1 files changed, 17 insertions, 17 deletions
diff --git a/silx/math/fit/fittheories.py b/silx/math/fit/fittheories.py index f733d1a..6b19a38 100644 --- a/silx/math/fit/fittheories.py +++ b/silx/math/fit/fittheories.py @@ -213,7 +213,7 @@ class FitTheories(object): """ pcoeffs = numpy.polyfit(x, y, n) - constraints = numpy.zeros((n + 1, 3), numpy.float) + constraints = numpy.zeros((n + 1, 3), numpy.float64) return pcoeffs, constraints def estimate_quadratic(self, x, y): @@ -298,7 +298,7 @@ class FitTheories(object): :return: List of peak indices """ # add padding - ysearch = numpy.ones((len(y) + 2 * fwhm,), numpy.float) + ysearch = numpy.ones((len(y) + 2 * fwhm,), numpy.float64) ysearch[0:fwhm] = y[0] ysearch[-1:-fwhm - 1:-1] = y[len(y)-1] ysearch[fwhm:fwhm + len(y)] = y[:] @@ -389,7 +389,7 @@ class FitTheories(object): xw = x yw = y - bg - cons = numpy.zeros((len(param), 3), numpy.float) + cons = numpy.zeros((len(param), 3), numpy.float64) # peak height must be positive cons[0:len(param):3, 0] = CPOSITIVE @@ -405,10 +405,10 @@ class FitTheories(object): shape = [max(1, int(x)) for x in (param[1:len(param):3])] cons[1:len(param):3, 1] = min(xw) * numpy.ones( shape, - numpy.float) + numpy.float64) cons[1:len(param):3, 2] = max(xw) * numpy.ones( shape, - numpy.float) + numpy.float64) # ensure fwhm is positive cons[2:len(param):3, 0] = CPOSITIVE @@ -420,7 +420,7 @@ class FitTheories(object): full_output=True) # set final constraints based on config parameters - cons = numpy.zeros((len(fittedpar), 3), numpy.float) + cons = numpy.zeros((len(fittedpar), 3), numpy.float64) peak_index = 0 for i in range(len(peaks)): # Setup height area constrains @@ -524,7 +524,7 @@ class FitTheories(object): # get the number of found peaks npeaks = len(fittedpar) // 3 estimated_parameters = [] - estimated_constraints = numpy.zeros((4 * npeaks, 3), numpy.float) + estimated_constraints = numpy.zeros((4 * npeaks, 3), numpy.float64) for i in range(npeaks): for j in range(3): estimated_parameters.append(fittedpar[3 * i + j]) @@ -579,7 +579,7 @@ class FitTheories(object): fittedpar, cons = self.estimate_height_position_fwhm(x, y) npeaks = len(fittedpar) // 3 newpar = [] - newcons = numpy.zeros((4 * npeaks, 3), numpy.float) + newcons = numpy.zeros((4 * npeaks, 3), numpy.float64) # find out related parameters proper index if not self.config['NoConstraintsFlag']: if self.config['SameFwhmFlag']: @@ -640,7 +640,7 @@ class FitTheories(object): fittedpar, cons = self.estimate_height_position_fwhm(x, y) npeaks = len(fittedpar) // 3 newpar = [] - newcons = numpy.zeros((5 * npeaks, 3), numpy.float) + newcons = numpy.zeros((5 * npeaks, 3), numpy.float64) # find out related parameters proper index if not self.config['NoConstraintsFlag']: if self.config['SameFwhmFlag']: @@ -741,7 +741,7 @@ class FitTheories(object): fittedpar, cons = self.estimate_height_position_fwhm(x, y) npeaks = len(fittedpar) // 3 newpar = [] - newcons = numpy.zeros((8 * npeaks, 3), numpy.float) + newcons = numpy.zeros((8 * npeaks, 3), numpy.float64) main_peak = 0 # find out related parameters proper index if not self.config['NoConstraintsFlag']: @@ -841,7 +841,7 @@ class FitTheories(object): newcons[8 * i + 7, 1] = self.config['MinStepTailHeightRatio'] newcons[8 * i + 7, 2] = self.config['MaxStepTailHeightRatio'] # if self.config['NoConstraintsFlag'] == 1: - # newcons=numpy.zeros((8*npeaks, 3),numpy.float) + # newcons=numpy.zeros((8*npeaks, 3),numpy.float64) if npeaks > 0: if g_term: if self.config['PositiveHeightAreaFlag']: @@ -931,7 +931,7 @@ class FitTheories(object): self.config["FwhmPoints"] * (x[1] - x[0])] # fwhm: default value # Setup constrains - newcons = numpy.zeros((3, 3), numpy.float) + newcons = numpy.zeros((3, 3), numpy.float64) if not self.config['NoConstraintsFlag']: # Setup height constrains if self.config['PositiveHeightAreaFlag']: @@ -983,7 +983,7 @@ class FitTheories(object): position = (xx[0] + xx[-1]) / 2.0 fwhm = xx[-1] - xx[0] largest = [height, position, fwhm, beamfwhm] - cons = numpy.zeros((4, 3), numpy.float) + cons = numpy.zeros((4, 3), numpy.float64) # Setup constrains if not self.config['NoConstraintsFlag']: # Setup height constrains @@ -1056,7 +1056,7 @@ class FitTheories(object): x[len(x)//2], # center: middle of x range self.config["FwhmPoints"] * (x[1] - x[0])] # fwhm: default value - newcons = numpy.zeros((3, 3), numpy.float) + newcons = numpy.zeros((3, 3), numpy.float64) # Setup constrains if not self.config['NoConstraintsFlag']: # Setup height constraints @@ -1123,7 +1123,7 @@ class FitTheories(object): npeaks = len(peaks) if not npeaks: fittedpar = [] - cons = numpy.zeros((len(fittedpar), 3), numpy.float) + cons = numpy.zeros((len(fittedpar), 3), numpy.float64) return fittedpar, cons fittedpar = [0.0, 0.0, 0.0, 0.0, 0.0] @@ -1153,7 +1153,7 @@ class FitTheories(object): fittedpar[4] = search_fwhm # setup constraints - cons = numpy.zeros((5, 3), numpy.float) + cons = numpy.zeros((5, 3), numpy.float64) cons[0, 0] = CFIXED # the number of gaussians if npeaks == 1: cons[1, 0] = CFIXED # the delta between peaks @@ -1337,7 +1337,7 @@ function, parameters list, configuration function and description. def test(a): from silx.math.fit import fitmanager - x = numpy.arange(1000).astype(numpy.float) + x = numpy.arange(1000).astype(numpy.float64) p = [1500, 100., 50.0, 1500, 700., 50.0] y_synthetic = functions.sum_gauss(x, *p) + 1 |