Пример #1
0
    def fitter(self,
               xax,
               data,
               err=None,
               quiet=True,
               veryverbose=False,
               debug=False,
               parinfo=None,
               **kwargs):
        """
        Run the fitter using mpfit.
        
        kwargs will be passed to _make_parinfo and mpfit.

        Parameters
        ----------
        xax : SpectroscopicAxis 
            The X-axis of the spectrum
        data : ndarray
            The data to fit
        err : ndarray (optional)
            The error on the data.  If unspecified, will be uniform unity
        parinfo : ParinfoList
            The guesses, parameter limits, etc.  See
            `pyspeckit.spectrum.parinfo` for details
        quiet : bool
            pass to mpfit.  If False, will print out the parameter values for
            each iteration of the fitter
        veryverbose : bool
            print out a variety of mpfit output parameters
        debug : bool
            raise an exception (rather than a warning) if chi^2 is nan
        """

        if parinfo is None:
            parinfo, kwargs = self._make_parinfo(debug=debug, **kwargs)
        else:
            log.debug("Using user-specified parinfo dict")
            # clean out disallowed kwargs (don't want to pass them to mpfit)
            #throwaway, kwargs = self._make_parinfo(debug=debug, **kwargs)

        self.xax = xax  # the 'stored' xax is just a link to the original
        if hasattr(xax, 'as_unit') and self.fitunits is not None:
            # some models will depend on the input units.  For these, pass in an X-axis in those units
            # (gaussian, voigt, lorentz profiles should not depend on units.  Ammonia, formaldehyde,
            # H-alpha, etc. should)
            xax = copy.copy(xax)
            # xax.convert_to_unit(self.fitunits, quiet=quiet)
            xax = xax.as_unit(self.fitunits, quiet=quiet, **kwargs)
        elif self.fitunits is not None:
            raise TypeError("X axis does not have a convert method")

        if np.any(np.isnan(data)) or np.any(np.isinf(data)):
            err[np.isnan(data) + np.isinf(data)] = np.inf
            data[np.isnan(data) + np.isinf(data)] = 0

        if np.any(np.isnan(err)):
            raise ValueError(
                "One or more of the error values is NaN."
                "  This is not allowed.  Errors can be infinite "
                "(which is equivalent to giving zero weight to "
                "a data point), but otherwise they must be positive "
                "floats.")
        elif np.any(err < 0):
            raise ValueError("At least one error value is negative, which is "
                             "not allowed as negative errors are not "
                             "meaningful in the optimization process.")

        for p in parinfo:
            log.debug(p)
        log.debug("\n".join([
            "%s %i: tied: %s value: %s" %
            (p['parname'], p['n'], p['tied'], p['value']) for p in parinfo
        ]))

        mp = mpfit(self.mpfitfun(xax, data, err),
                   parinfo=parinfo,
                   quiet=quiet,
                   debug=debug,
                   **kwargs)
        mpp = mp.params
        if mp.perror is not None: mpperr = mp.perror
        else: mpperr = mpp * 0
        chi2 = mp.fnorm

        if mp.status == 0:
            if "parameters are not within PARINFO limits" in mp.errmsg:
                log.warn(parinfo)
            raise mpfitException(mp.errmsg)

        for i, (p, e) in enumerate(zip(mpp, mpperr)):
            self.parinfo[i]['value'] = p
            self.parinfo[i]['error'] = e

        if veryverbose:
            log.info("Fit status: {0}".format(mp.status))
            log.info("Fit error message: {0}".format(mp.errmsg))
            log.info("Fit message: {0}".format(mpfit_messages[mp.status]))
            for i, p in enumerate(mpp):
                log.info("{0}: {1} +/- {2}".format(self.parinfo[i]['parname'],
                                                   p, mpperr[i]))
            log.info("Chi2: {0} Reduced Chi2: {1}  DOF:{2}".format(
                mp.fnorm, mp.fnorm / (len(data) - len(mpp)),
                len(data) - len(mpp)))

        self.mp = mp
        self.mpp = self.parinfo.values
        self.mpperr = self.parinfo.errors
        self.mppnames = self.parinfo.names
        self.model = self.n_modelfunc(self.parinfo,
                                      **self.modelfunc_kwargs)(xax)
        log.debug("Modelpars: {0}".format(self.mpp))
        if np.isnan(chi2):
            if debug:
                raise ValueError("Error: chi^2 is nan")
            else:
                log.warn("Warning: chi^2 is nan")
        return mpp, self.model, mpperr, chi2
Пример #2
0
    def fitter(self, xax, data, err=None, quiet=True, veryverbose=False,
               debug=False, parinfo=None, **kwargs):
        """
        Run the fitter using mpfit.
        
        kwargs will be passed to _make_parinfo and mpfit.

        Parameters
        ----------
        xax : SpectroscopicAxis 
            The X-axis of the spectrum
        data : ndarray
            The data to fit
        err : ndarray (optional)
            The error on the data.  If unspecified, will be uniform unity
        parinfo : ParinfoList
            The guesses, parameter limits, etc.  See
            `pyspeckit.spectrum.parinfo` for details
        quiet : bool
            pass to mpfit.  If False, will print out the parameter values for
            each iteration of the fitter
        veryverbose : bool
            print out a variety of mpfit output parameters
        debug : bool
            raise an exception (rather than a warning) if chi^2 is nan
        """

        if parinfo is None:
            parinfo, kwargs = self._make_parinfo(debug=debug, **kwargs)
        else:
            log.debug("Using user-specified parinfo dict")
            # clean out disallowed kwargs (don't want to pass them to mpfit)
            #throwaway, kwargs = self._make_parinfo(debug=debug, **kwargs)

        self.xax = xax # the 'stored' xax is just a link to the original
        if hasattr(xax,'as_unit') and self.fitunits is not None:
            # some models will depend on the input units.  For these, pass in an X-axis in those units
            # (gaussian, voigt, lorentz profiles should not depend on units.  Ammonia, formaldehyde,
            # H-alpha, etc. should)
            xax = copy.copy(xax)
            # xax.convert_to_unit(self.fitunits, quiet=quiet)
            xax = xax.as_unit(self.fitunits, quiet=quiet, **kwargs)
        elif self.fitunits is not None:
            raise TypeError("X axis does not have a convert method")

        if np.any(np.isnan(data)) or np.any(np.isinf(data)):
            err[np.isnan(data) + np.isinf(data)] = np.inf
            data[np.isnan(data) + np.isinf(data)] = 0

        if np.any(np.isnan(err)):
            raise ValueError("One or more of the error values is NaN."
                             "  This is not allowed.  Errors can be infinite "
                             "(which is equivalent to giving zero weight to "
                             "a data point), but otherwise they must be positive "
                             "floats.")
        elif np.any(err<0):
            raise ValueError("At least one error value is negative, which is "
                             "not allowed as negative errors are not "
                             "meaningful in the optimization process.")

        for p in parinfo: log.debug( p )
        log.debug( "\n".join(["%s %i: tied: %s value: %s" % (p['parname'],p['n'],p['tied'],p['value']) for p in parinfo]) )

        mp = mpfit(self.mpfitfun(xax,data,err),parinfo=parinfo,quiet=quiet,debug=debug,**kwargs)
        mpp = mp.params
        if mp.perror is not None: mpperr = mp.perror
        else: mpperr = mpp*0
        chi2 = mp.fnorm

        if mp.status == 0:
            if "parameters are not within PARINFO limits" in mp.errmsg:
                log.warning( parinfo )
            raise mpfitException(mp.errmsg)

        for i,(p,e) in enumerate(zip(mpp,mpperr)):
            self.parinfo[i]['value'] = p
            self.parinfo[i]['error'] = e

        if veryverbose:
            log.info("Fit status: {0}".format(mp.status))
            log.info("Fit error message: {0}".format(mp.errmsg))
            log.info("Fit message: {0}".format(mpfit_messages[mp.status]))
            for i,p in enumerate(mpp):
                log.info("{0}: {1} +/- {2}".format(self.parinfo[i]['parname'],
                                                    p,mpperr[i]))
            log.info("Chi2: {0} Reduced Chi2: {1}  DOF:{2}".format(mp.fnorm,
                                                                   mp.fnorm/(len(data)-len(mpp)),
                                                                   len(data)-len(mpp)))

        self.mp = mp
        self.mpp = self.parinfo.values
        self.mpperr = self.parinfo.errors
        self.mppnames = self.parinfo.names
        self.model = self.n_modelfunc(self.parinfo,**self.modelfunc_kwargs)(xax)
        log.debug("Modelpars: {0}".format(self.mpp))
        if np.isnan(chi2):
            if debug:
                raise ValueError("Error: chi^2 is nan")
            else:
                log.warning("Warning: chi^2 is nan")
        return mpp,self.model,mpperr,chi2
Пример #3
0
    def fitter(self, xax, data, err=None, quiet=True, veryverbose=False,
            debug=False, parinfo=None, **kwargs):
        """
        Run the fitter using mpfit.
        
        kwargs will be passed to _make_parinfo and mpfit.

        Parameters
        ----------
        xax : SpectroscopicAxis 
            The X-axis of the spectrum
        data : ndarray
            The data to fit
        err : ndarray (optional)
            The error on the data.  If unspecified, will be uniform unity
        parinfo : ParinfoList
            The guesses, parameter limits, etc.  See
            `pyspeckit.spectrum.parinfo` for details
        quiet : bool
            pass to mpfit.  If False, will print out the parameter values for
            each iteration of the fitter
        veryverbose : bool
            print out a variety of mpfit output parameters
        debug : bool
            raise an exception (rather than a warning) if chi^2 is nan
        """

        if parinfo is None:
            parinfo, kwargs = self._make_parinfo(debug=debug, **kwargs)
        else:
            if debug: print "Using user-specified parinfo dict"
            # clean out disallowed kwargs (don't want to pass them to mpfit)
            #throwaway, kwargs = self._make_parinfo(debug=debug, **kwargs)

        self.xax = xax # the 'stored' xax is just a link to the original
        if hasattr(xax,'convert_to_unit') and self.fitunits is not None:
            # some models will depend on the input units.  For these, pass in an X-axis in those units
            # (gaussian, voigt, lorentz profiles should not depend on units.  Ammonia, formaldehyde,
            # H-alpha, etc. should)
            xax = copy.copy(xax)
            xax.convert_to_unit(self.fitunits, quiet=quiet)
        elif self.fitunits is not None:
            raise TypeError("X axis does not have a convert method")

        if np.any(np.isnan(data)) or np.any(np.isinf(data)):
            err[np.isnan(data) + np.isinf(data)] = np.inf
            data[np.isnan(data) + np.isinf(data)] = 0

        if debug:
            for p in parinfo: print p
            print "\n".join(["%s %i: tied: %s value: %s" % (p['parname'],p['n'],p['tied'],p['value']) for p in parinfo])

        mp = mpfit(self.mpfitfun(xax,data,err),parinfo=parinfo,quiet=quiet,**kwargs)
        mpp = mp.params
        if mp.perror is not None: mpperr = mp.perror
        else: mpperr = mpp*0
        chi2 = mp.fnorm

        if mp.status == 0:
            if "parameters are not within PARINFO limits" in mp.errmsg:
                print parinfo
            raise mpfitException(mp.errmsg)

        for i,(p,e) in enumerate(zip(mpp,mpperr)):
            self.parinfo[i]['value'] = p
            self.parinfo[i]['error'] = e

        if veryverbose:
            print "Fit status: ",mp.status
            print "Fit error message: ",mp.errmsg
            print "Fit message: ",mpfit_messages[mp.status]
            for i,p in enumerate(mpp):
                print self.parinfo[i]['parname'],p," +/- ",mpperr[i]
            print "Chi2: ",mp.fnorm," Reduced Chi2: ",mp.fnorm/len(data)," DOF:",len(data)-len(mpp)

        self.mp = mp
        self.mpp = self.parinfo.values
        self.mpperr = self.parinfo.errors
        self.mppnames = self.parinfo.names
        self.model = self.n_modelfunc(self.parinfo,**self.modelfunc_kwargs)(xax)
        if debug:
            print "Modelpars: ",self.mpp
        if np.isnan(chi2):
            if debug:
                raise ValueError("Error: chi^2 is nan")
            else:
                print "Warning: chi^2 is nan"
        return mpp,self.model,mpperr,chi2