""" Python wrappers for Orthogonal Distance Regression (ODRPACK). Classes ======= Data -- stores the data and weights to fit against RealData -- stores data with standard deviations and covariance matrices Model -- stores the model and its related information Output -- stores all of the output from an ODR run ODR -- collects all data and runs the fitting routine Exceptions ========== odr_error -- error sometimes raised inside odr() and can be raised in the fitting functions to tell ODRPACK to halt the procedure odr_stop -- error to raise in fitting functions to tell ODRPACK that the data or parameters given are invalid Use === Basic use: 1) Define the function you want to fit against: def f(B, x): return B[0]*x + B[1] B is a vector of the parameters. x is an array of the current x values. (Same format as the x passed to Data or RealData. Return an array in the same format as y passed to Data or RealData.) 2) Create a Model. linear = Model(f) 3) Create a Data or RealData instance. mydata = Data(x, y, wd=1./power(sx,2), we=1./power(sy,2)) or mydata = RealData(x, y, sx=sx, sy=sy) 4) Instantiate ODR with your data, model and initial parameter estimate. myodr = ODR(mydata, linear, beta0=[1., 2.]) 5) Run the fit. myoutput = myodr.run() 6) Examine output. myoutput.pprint() Read the docstrings and the accompanying tests for more advanced usage. Notes ===== * Array formats -- FORTRAN stores its arrays in memory column first, i.e. an array element A(i, j, k) will be next to A(i+1, j, k). In C and, consequently, NumPy, arrays are stored row first: A[i, j, k] is next to A[i, j, k+1]. For efficiency and convenience, the input and output arrays of the fitting function (and its Jacobians) are passed to FORTRAN without transposition. Therefore, where the ODRPACK documentation says that the X array is of shape (N, M), it will be passed to the Python function as an array of shape (M, N). If M==1, the one-dimensional case, then nothing matters; if M>1, then your Python functions will be dealing with arrays that are indexed in reverse of the ODRPACK documentation. No real biggie, but watch out for your indexing of the Jacobians: the i,j'th elements (@f_i/@x_j) evaluated at the n'th observation will be returned as jacd[j, i, n]. Except for the Jacobians, it really is easier to deal with x[0] and x[1] than x[:,0] and x[:,1]. Of course, you can always use the transpose() function from scipy explicitly. * Examples -- See the accompanying file test/test.py for examples of how to set up fits of your own. Some are taken from the User's Guide; some are from other sources. * Models -- Some common models are instantiated in the accompanying module models.py . Contributions are welcome. Credits ======= * Thanks to Arnold Moene and Gerard Vermeulen for fixing some killer bugs. Robert Kern robert.kern@gmail.com """ import numpy from scipy.odr import __odrpack odr = __odrpack.odr odr_error = __odrpack.odr_error odr_stop = __odrpack.odr_stop def _conv(obj, dtype=None): """ Convert an object to the preferred form for input to the odr routine. """ if obj is None: return obj else: if dtype is None: obj = numpy.asarray(obj) else: obj = numpy.asarray(obj, dtype) if obj.shape == (): # Scalar. return obj.dtype.type(obj) else: return obj def report_error(info): """ Interprets the return code of the odr routine. Parameters ---------- info : int The return code of the odr routine. Returns ------- problems : list(str) A list of messages about why the odr() routine stopped. """ stopreason = ('Blank', 'Sum of squares convergence', 'Parameter convergence', 'Both sum of squares and parameter convergence', 'Iteration limit reached')[info % 5] if info >= 5: # questionable results or fatal error I = (info/10000 % 10, info/1000 % 10, info/100 % 10, info/10 % 10, info % 10) problems = [] if I[0] == 0: if I[1] != 0: problems.append('Derivatives possibly not correct') if I[2] != 0: problems.append('Error occured in callback') if I[3] != 0: problems.append('Problem is not full rank at solution') problems.append(stopreason) elif I[0] == 1: if I[1] != 0: problems.append('N < 1') if I[2] != 0: problems.append('M < 1') if I[3] != 0: problems.append('NP < 1 or NP > N') if I[4] != 0: problems.append('NQ < 1') elif I[0] == 2: if I[1] != 0: problems.append('LDY and/or LDX incorrect') if I[2] != 0: problems.append('LDWE, LD2WE, LDWD, and/or LD2WD incorrect') if I[3] != 0: problems.append('LDIFX, LDSTPD, and/or LDSCLD incorrect') if I[4] != 0: problems.append('LWORK and/or LIWORK too small') elif I[0] == 3: if I[1] != 0: problems.append('STPB and/or STPD incorrect') if I[2] != 0: problems.append('SCLB and/or SCLD incorrect') if I[3] != 0: problems.append('WE incorrect') if I[4] != 0: problems.append('WD incorrect') elif I[0] == 4: problems.append('Error in derivatives') elif I[0] == 5: problems.append('Error occured in callback') elif I[0] == 6: problems.append('Numerical error detected') return problems else: return [stopreason] class Data(object): """ The Data class stores the data to fit. Each argument is attached to the member of the instance of the same name. The structures of x and y are described in the Model class docstring. If y is an integer, then the Data instance can only be used to fit with implicit models where the dimensionality of the response is equal to the specified value of y. The structures of wd and we are described below. meta is an freeform dictionary for application-specific use. we weights the effect a deviation in the response variable has on the fit. wd weights the effect a deviation in the input variable has on the fit. To handle multidimensional inputs and responses easily, the structure of these arguments has the n'th dimensional axis first. These arguments heavily use the structured arguments feature of ODRPACK to conveniently and flexibly support all options. See the ODRPACK User's Guide for a full explanation of how these weights are used in the algorithm. Basically, a higher value of the weight for a particular data point makes a deviation at that point more detrimental to the fit. we -- if we is a scalar, then that value is used for all data points (and all dimensions of the response variable). If we is a rank-1 array of length q (the dimensionality of the response variable), then this vector is the diagonal of the covariant weighting matrix for all data points. If we is a rank-1 array of length n (the number of data points), then the i'th element is the weight for the i'th response variable observation (single-dimensional only). If we is a rank-2 array of shape (q, q), then this is the full covariant weighting matrix broadcast to each observation. If we is a rank-2 array of shape (q, n), then we[:,i] is the diagonal of the covariant weighting matrix for the i'th observation. If we is a rank-3 array of shape (q, q, n), then we[:,:,i] is the full specification of the covariant weighting matrix for each observation. If the fit is implicit, then only a positive scalar value is used. wd -- if wd is a scalar, then that value is used for all data points (and all dimensions of the input variable). If wd = 0, then the covariant weighting matrix for each observation is set to the identity matrix (so each dimension of each observation has the same weight). If wd is a rank-1 array of length m (the dimensionality of the input variable), then this vector is the diagonal of the covariant weighting matrix for all data points. If wd is a rank-1 array of length n (the number of data points), then the i'th element is the weight for the i'th input variable observation (single-dimensional only). If wd is a rank-2 array of shape (m, m), then this is the full covariant weighting matrix broadcast to each observation. If wd is a rank-2 array of shape (m, n), then wd[:,i] is the diagonal of the covariant weighting matrix for the i'th observation. If wd is a rank-3 array of shape (m, m, n), then wd[:,:,i] is the full specification of the covariant weighting matrix for each observation. fix -- fix is the same as ifixx in the class ODR. It is an array of integers with the same shape as data.x that determines which input observations are treated as fixed. One can use a sequence of length m (the dimensionality of the input observations) to fix some dimensions for all observations. A value of 0 fixes the observation, a value > 0 makes it free. meta -- optional, freeform dictionary for metadata """ def __init__(self, x, y=None, we=None, wd=None, fix=None, meta={}): self.x = _conv(x) self.y = _conv(y) self.we = _conv(we) self.wd = _conv(wd) self.fix = _conv(fix) self.meta = meta def set_meta(self, **kwds): """ Update the metadata dictionary with the keywords and data provided by keywords. Example ------- data.set_meta(lab="Ph 7; Lab 26", title="Ag110 + Ag108 Decay") """ self.meta.update(kwds) def __getattr__(self, attr): """ Dispatch aatribute access to the metadata dictionary. """ if attr in self.meta.keys(): return self.meta[attr] else: raise AttributeError, "'%s' not in metadata" % attr class RealData(Data): """ The RealData class stores the weightings as actual standard deviations and/or covariances. The weights needed for ODRPACK are generated on-the-fly with __getattr__ trickery. sx and sy are standard deviations of x and y and are converted to weights by dividing 1.0 by their squares. E.g. wd = 1./numpy.power(sx, 2) covx and covy are arrays of covariance matrices and are converted to weights by performing a matrix inversion on each observation's covariance matrix. E.g. we[i] = numpy.linalg.inv(covy[i]) # i in range(len(covy)) # if covy.shape == (n,q,q) These arguments follow the same structured argument conventions as wd and we only restricted by their natures: sx and sy can't be rank-3, but covx and covy can be. Only set *either* sx or covx (not both). Setting both will raise an exception. Same with sy and covy. The argument and member fix is the same as Data.fix and ODR.ifixx: It is an array of integers with the same shape as data.x that determines which input observations are treated as fixed. One can use a sequence of length m (the dimensionality of the input observations) to fix some dimensions for all observations. A value of 0 fixes the observation, a value > 0 makes it free. """ def __init__(self, x, y=None, sx=None, sy=None, covx=None, covy=None, fix=None, meta={}): if (sx is not None) and (covx is not None): raise ValueError, "cannot set both sx and covx" if (sy is not None) and (covy is not None): raise ValueError, "cannot set both sy and covy" # Set flags for __getattr__ self._ga_flags = {} if sx is not None: self._ga_flags['wd'] = 'sx' else: self._ga_flags['wd'] = 'covx' if sy is not None: self._ga_flags['we'] = 'sy' else: self._ga_flags['we'] = 'covy' self.x = _conv(x) self.y = _conv(y) self.sx = _conv(sx) self.sy = _conv(sy) self.covx = _conv(covx) self.covy = _conv(covy) self.fix = _conv(fix) self.meta = meta def _sd2wt(self, sd): """ Convert standard deviation to weights. """ return 1./numpy.power(sd, 2) def _cov2wt(self, cov): """ Convert covariance matrix(-ices) to weights. """ from numpy.dual import inv if len(cov.shape) == 2: return inv(cov) else: weights = numpy.zeros(cov.shape, float) for i in range(cov.shape[-1]): # n weights[:,:,i] = inv(cov[:,:,i]) return weights def __getattr__(self, attr): lookup_tbl = {('wd', 'sx'): (self._sd2wt, self.sx), ('wd', 'covx'): (self._cov2wt, self.covx), ('we', 'sy'): (self._sd2wt, self.sy), ('we', 'covy'): (self._cov2wt, self.covy)} if attr not in ('wd', 'we'): if attr in self.meta.keys(): return self.meta[attr] else: raise AttributeError, "'%s' not in metadata" % attr else: func, arg = lookup_tbl[(attr, self._ga_flags[attr])] if arg is not None: return apply(func, (arg,)) else: return None class Model(object): """ The Model class stores information about the function you wish to fit. It stores the function itself, at the least, and optionally stores functions which compute the Jacobians used during fitting. Also, one can provide a function that will provide reasonable starting values for the fit parameters possibly given the set of data. The initialization method stores these into members of the same name. fcn -- fit function: fcn(beta, x) --> y fjacb -- Jacobian of fcn wrt the fit parameters beta: fjacb(beta, x) --> @f_i(x,B)/@B_j fjacd -- Jacobian of fcn wrt the (possibly multidimensional) input variable: fjacd(beta, x) --> @f_i(x,B)/@x_j extra_args -- if specified, extra_args should be a tuple of extra arguments to pass to fcn, fjacb, and fjacd. Each will be called like the following: apply(fcn, (beta, x) + extra_args) estimate -- provide estimates of the fit parameters from the data: estimate(data) --> estbeta implicit -- boolean variable which, if TRUE, specifies that the model is implicit; i.e fcn(beta, x) ~= 0 and there is no y data to fit against. meta -- an optional, freeform dictionary of metadata for the model Note that the fcn, fjacb, and fjacd operate on NumPy arrays and return a NumPy array. estimate takes an instance of the Data class. Here are the rules for the shapes of the argument and return arrays: x -- if the input data is single-dimensional, then x is rank-1 array; i.e. x = array([1, 2, 3, ...]); x.shape = (n,) If the input data is multi-dimensional, then x is a rank-2 array; i.e. x = array([[1, 2, ...], [2, 4, ...]]); x.shape = (m, n) In all cases, it has the same shape as the input data array passed to odr(). m is the dimensionality of the input data, n is the number of observations. y -- if the response variable is single-dimensional, then y is a rank-1 array; i.e. y = array([2, 4, ...]); y.shape = (n,) If the response variable is multi-dimensional, then y is a rank-2 array; i.e. y = array([[2, 4, ...], [3, 6, ...]]); y.shape = (q, n) where q is the dimensionality of the response variable. beta -- rank-1 array of length p where p is the number of parameters; i.e. beta = array([B_1, B_2, ..., B_p]) fjacb -- if the response variable is multi-dimensional, then the return array's shape is (q, p, n) such that fjacb(x,beta)[l,k,i] = @f_l(X,B)/@B_k evaluated at the i'th data point. If q == 1, then the return array is only rank-2 and with shape (p, n). fjacd -- as with fjacb, only the return array's shape is (q, m, n) such that fjacd(x,beta)[l,j,i] = @f_l(X,B)/@X_j at the i'th data point. If q == 1, then the return array's shape is (m, n). If m == 1, the shape is (q, n). If m == q == 1, the shape is (n,). """ def __init__(self, fcn, fjacb=None, fjacd=None, extra_args=None, estimate=None, implicit=0, meta=None): self.fcn = fcn self.fjacb = fjacb self.fjacd = fjacd if extra_args is not None: extra_args = tuple(extra_args) self.extra_args = extra_args self.estimate = estimate self.implicit = implicit self.meta = meta def set_meta(self, **kwds): """ Update the metadata dictionary with the keywords and data provided here. Example ------- set_meta(name="Exponential", equation="y = a exp(b x) + c") """ self.meta.update(kwds) def __getattr__(self, attr): """ Dispatch attribute access to the metadata. """ if attr in self.meta.keys(): return self.meta[attr] else: raise AttributeError, "'%s' not in metadata" % attr class Output(object): """ The Output class stores the output of an ODR run. Takes one argument for initialization: the return value from the function odr(). Attributes ---------- beta -- estimated parameter values [beta.shape == (q,)] sd_beta -- standard errors of the estimated parameters [sd_beta.shape == (p,)] cov_beta -- covariance matrix of the estimated parameters [cov_beta.shape == (p, p)] Optional Attributes ------------------- Present if odr() was run with "full_output=1". delta -- array of estimated errors in input variables [delta.shape == data.x.shape] eps -- array of estimated errors in response variables [eps.shape == data.y.shape] xplus -- array of x + delta [xplus.shape == data.x.shape] y -- array of y = fcn(x + delta) [y.shape == data.y.shape] res_var -- residual variance [scalar] sum_sqare -- sum of squares error [scalar] sum_square_delta -- sum of squares of delta error [scalar] sum_square_eps -- sum of squares of eps error [scalar] inv_condnum -- inverse condition number [scalar] (cf. ODRPACK UG p. 77) rel_error -- relative error in function values computed within fcn [scalar] work -- final work array [array] work_ind -- indices into work for drawing out values [dictionary] (cf. ODRPACK UG p. 83) info -- reason for returning (as output by ODRPACK) [integer] (cf. ODRPACK UG p. 38) stopreason -- "info" interpreted into English [list of strings] """ def __init__(self, output): self.beta = output[0] self.sd_beta = output[1] self.cov_beta = output[2] if len(output) == 4: # full output self.__dict__.update(output[3]) self.stopreason = report_error(self.info) def pprint(self): """ Pretty-print important results. """ print 'Beta:', self.beta print 'Beta Std Error:', self.sd_beta print 'Beta Covariance:', self.cov_beta if hasattr(self, 'info'): print 'Residual Variance:',self.res_var print 'Inverse Condition #:', self.inv_condnum print 'Reason(s) for Halting:' for r in self.stopreason: print ' %s' % r class ODR(object): """ The ODR class gathers all information and coordinates the running of the main fitting routine. Members of instances of the ODR class have the same names as the arguments to the initialization routine. Parameters ---------- Required: data -- instance of the Data class model -- instance of the Model class beta0 -- a rank-1 sequence of initial parameter values. Optional if model provides an "estimate" function to estimate these values. Optional: delta0 -- a (double-precision) float array to hold the initial values of the errors in the input variables. Must be same shape as data.x . ifixb -- sequence of integers with the same length as beta0 that determines which parameters are held fixed. A value of 0 fixes the parameter, a value > 0 makes the parameter free. ifixx -- an array of integers with the same shape as data.x that determines which input observations are treated as fixed. One can use a sequence of length m (the dimensionality of the input observations) to fix some dimensions for all observations. A value of 0 fixes the observation, a value > 0 makes it free. job -- an integer telling ODRPACK what tasks to perform. See p. 31 of the ODRPACK User's Guide if you absolutely must set the value here. Use the method set_job post-initialization for a more readable interface. iprint -- an integer telling ODRPACK what to print. See pp. 33-34 of the ODRPACK User's Guide if you absolutely must set the value here. Use the method set_iprint post-initialization for a more readable interface. errfile -- string with the filename to print ODRPACK errors to. *Do Not Open This File Yourself!* rptfile -- string with the filename to print ODRPACK summaries to. *Do Not Open This File Yourself!* ndigit -- integer specifying the number of reliable digits in the computation of the function. taufac -- float specifying the initial trust region. The default value is 1. The initial trust region is equal to taufac times the length of the first computed Gauss-Newton step. taufac must be less than 1. sstol -- float specifying the tolerance for convergence based on the relative change in the sum-of-squares. The default value is eps**(1/2) where eps is the smallest value such that 1 + eps > 1 for double precision computation on the machine. sstol must be less than 1. partol -- float specifying the tolerance for convergence based on the relative change in the estimated parameters. The default value is eps**(2/3) for explicit models and eps**(1/3) for implicit models. partol must be less than 1. maxit -- integer specifying the maximum number of iterations to perform. For first runs, maxit is the total number of iterations performed and defaults to 50. For restarts, maxit is the number of additional iterations to perform and defaults to 10. stpb -- sequence (len(stpb) == len(beta0)) of relative step sizes to compute finite difference derivatives wrt the parameters. stpd -- array (stpd.shape == data.x.shape or stpd.shape == (m,)) of relative step sizes to compute finite difference derivatives wrt the input variable errors. If stpd is a rank-1 array with length m (the dimensionality of the input variable), then the values are broadcast to all observations. sclb -- sequence (len(stpb) == len(beta0)) of scaling factors for the parameters. The purpose of these scaling factors are to scale all of the parameters to around unity. Normally appropriate scaling factors are computed if this argument is not specified. Specify them yourself if the automatic procedure goes awry. scld -- array (scld.shape == data.x.shape or scld.shape == (m,)) of scaling factors for the *errors* in the input variables. Again, these factors are automatically computed if you do not provide them. If scld.shape == (m,), then the scaling factors are broadcast to all observations. work -- array to hold the double-valued working data for ODRPACK. When restarting, takes the value of self.output.work . iwork -- array to hold the integer-valued working data for ODRPACK. When restarting, takes the value of self.output.iwork . Other Members (not supplied as initialization arguments): output -- an instance if the Output class containing all of the returned data from an invocation of ODR.run() or ODR.restart() """ def __init__(self, data, model, beta0=None, delta0=None, ifixb=None, ifixx=None, job=None, iprint=None, errfile=None, rptfile=None, ndigit=None, taufac=None, sstol=None, partol=None, maxit=None, stpb=None, stpd=None, sclb=None, scld=None, work=None, iwork=None): self.data = data self.model = model if beta0 is None: if self.model.estimate is not None: self.beta0 = _conv(self.model.estimate(self.data)) else: raise ValueError( "must specify beta0 or provide an estimater with the model" ) else: self.beta0 = _conv(beta0) self.delta0 = _conv(delta0) # These really are 32-bit integers in FORTRAN (gfortran), even on 64-bit # platforms. # XXX: some other FORTRAN compilers may not agree. self.ifixx = _conv(ifixx, dtype=numpy.int32) self.ifixb = _conv(ifixb, dtype=numpy.int32) self.job = job self.iprint = iprint self.errfile = errfile self.rptfile = rptfile self.ndigit = ndigit self.taufac = taufac self.sstol = sstol self.partol = partol self.maxit = maxit self.stpb = _conv(stpb) self.stpd = _conv(stpd) self.sclb = _conv(sclb) self.scld = _conv(scld) self.work = _conv(work) self.iwork = _conv(iwork) self.output = None self._check() def _check(self): """ Check the inputs for consistency, but don't bother checking things that the builtin function odr will check. """ x_s = list(self.data.x.shape) if isinstance(self.data.y, numpy.ndarray): y_s = list(self.data.y.shape) if self.model.implicit: raise odr_error("an implicit model cannot use response data") else: # implicit model with q == self.data.y y_s = [self.data.y, x_s[-1]] if not self.model.implicit: raise odr_error("an explicit model needs response data") self.set_job(fit_type=1) if x_s[-1] != y_s[-1]: raise odr_error("number of observations do not match") n = x_s[-1] if len(x_s) == 2: m = x_s[0] else: m = 1 if len(y_s) == 2: q = y_s[0] else: q = 1 p = len(self.beta0) # permissible output array shapes fcn_perms = [(q, n)] fjacd_perms = [(q, m, n)] fjacb_perms = [(q, p, n)] if q == 1: fcn_perms.append((n,)) fjacd_perms.append((m, n)) fjacb_perms.append((p, n)) if m == 1: fjacd_perms.append((q, n)) if p == 1: fjacb_perms.append((q, n)) if m == q == 1: fjacd_perms.append((n,)) if p == q == 1: fjacb_perms.append((n,)) # try evaluating the supplied functions to make sure they provide # sensible outputs arglist = (self.beta0, self.data.x) if self.model.extra_args is not None: arglist = arglist + self.model.extra_args res = self.model.fcn(*arglist) if res.shape not in fcn_perms: print res.shape print fcn_perms raise odr_error("fcn does not output %s-shaped array" % y_s) if self.model.fjacd is not None: res = self.model.fjacd(*arglist) if res.shape not in fjacd_perms: raise odr_error( "fjacd does not output %s-shaped array" % (q, m, n)) if self.model.fjacb is not None: res = self.model.fjacb(*arglist) if res.shape not in fjacb_perms: raise odr_error( "fjacb does not output %s-shaped array" % (q, p, n)) # check shape of delta0 if self.delta0 is not None and self.delta0.shape != self.data.x.shape: raise odr_error( "delta0 is not a %s-shaped array" % self.data.x.shape) def _gen_work(self): """ Generate a suitable work array if one does not already exist. """ n = self.data.x.shape[-1] p = self.beta0.shape[0] if len(self.data.x.shape) == 2: m = self.data.x.shape[0] else: m = 1 if self.model.implicit: q = self.data.y elif len(self.data.y.shape) == 2: q = self.data.y.shape[0] else: q = 1 if self.data.we is None: ldwe = ld2we = 1 elif len(self.data.we.shape) == 3: ld2we, ldwe = self.data.we.shape[1:] else: # Okay, this isn't precisely right, but for this calculation, # it's fine ldwe = 1 ld2we = self.data.we.shape[1] if self.job % 10 < 2: # ODR not OLS lwork = (18 + 11*p + p*p + m + m*m + 4*n*q + 6*n*m + 2*n*q*p + 2*n*q*m + q*q + 5*q + q*(p+m) + ldwe*ld2we*q) else: # OLS not ODR lwork = (18 + 11*p + p*p + m + m*m + 4*n*q + 2*n*m + 2*n*q*p + 5*q + q*(p+m) + ldwe*ld2we*q) if isinstance(self.work, numpy.ndarray) and self.work.shape == (lwork,)\ and self.work.dtype.str.endswith('f8'): # the existing array is fine return else: self.work = numpy.zeros((lwork,), float) def set_job(self, fit_type=None, deriv=None, var_calc=None, del_init=None, restart=None): """ Sets the "job" parameter is a hopefully comprehensible way. If an argument is not specified, then the value is left as is. The default value from class initialization is for all of these options set to 0. ========= ===== ===================================================== Parameter Value Meaning ========= ===== ===================================================== fit_type 0 explicit ODR 1 implicit ODR 2 ordinary least-squares deriv 0 forward finite differences 1 central finite differences 2 user-supplied derivatives (Jacobians) with results checked by ODRPACK 3 user-supplied derivatives, no checking var_calc 0 calculate asymptotic covariance matrix and fit parameter uncertainties (V_B, s_B) using derivatives recomputed at the final solution 1 calculate V_B and s_B using derivatives from last iteration 2 do not calculate V_B and s_B del_init 0 initial input variable offsets set to 0 1 initial offsets provided by user in variable "work" restart 0 fit is not a restart 1 fit is a restart ========= ===== ===================================================== The permissible values are different from those given on pg. 31 of the ODRPACK User's Guide only in that one cannot specify numbers greater than the last value for each variable. If one does not supply functions to compute the Jacobians, the fitting procedure will change deriv to 0, finite differences, as a default. To initialize the input variable offsets by yourself, set del_init to 1 and put the offsets into the "work" variable correctly. """ if self.job is None: job_l = [0, 0, 0, 0, 0] else: job_l = [self.job / 10000 % 10, self.job / 1000 % 10, self.job / 100 % 10, self.job / 10 % 10, self.job % 10] if fit_type in (0, 1, 2): job_l[4] = fit_type if deriv in (0, 1, 2, 3): job_l[3] = deriv if var_calc in (0, 1, 2): job_l[2] = var_calc if del_init in (0, 1): job_l[1] = del_init if restart in (0, 1): job_l[0] = restart self.job = (job_l[0]*10000 + job_l[1]*1000 + job_l[2]*100 + job_l[3]*10 + job_l[4]) def set_iprint(self, init=None, so_init=None, iter=None, so_iter=None, iter_step=None, final=None, so_final=None): """ Set the iprint parameter for the printing of computation reports. If any of the arguments are specified here, then they are set in the iprint member. If iprint is not set manually or with this method, then ODRPACK defaults to no printing. If no filename is specified with the member rptfile, then ODRPACK prints to stdout. One can tell ODRPACK to print to stdout in addition to the specified filename by setting the so_* arguments to this function, but one cannot specify to print to stdout but not a file since one can do that by not specifying a rptfile filename. There are three reports: initialization, iteration, and final reports. They are represented by the arguments init, iter, and final respectively. The permissible values are 0, 1, and 2 representing "no report", "short report", and "long report" respectively. The argument iter_step (0 <= iter_step <= 9) specifies how often to make the iteration report; the report will be made for every iter_step'th iteration starting with iteration one. If iter_step == 0, then no iteration report is made, regardless of the other arguments. If the rptfile is None, then any so_* arguments supplied will raise an exception. """ if self.iprint is None: self.iprint = 0 ip = [self.iprint / 1000 % 10, self.iprint / 100 % 10, self.iprint / 10 % 10, self.iprint % 10] # make a list to convert iprint digits to/from argument inputs # rptfile, stdout ip2arg = [[0, 0], # none, none [1, 0], # short, none [2, 0], # long, none [1, 1], # short, short [2, 1], # long, short [1, 2], # short, long [2, 2]] # long, long if (self.rptfile is None and (so_init is not None or so_iter is not None or so_final is not None)): raise odr_error( "no rptfile specified, cannot output to stdout twice") iprint_l = ip2arg[ip[0]] + ip2arg[ip[1]] + ip2arg[ip[3]] if init is not None: iprint_l[0] = init if so_init is not None: iprint_l[1] = so_init if iter is not None: iprint_l[2] = iter if so_iter is not None: iprint_l[3] = so_iter if final is not None: iprint_l[4] = final if so_final is not None: iprint_l[5] = so_final if iter_step in range(10): # 0..9 ip[2] = iter_step ip[0] = ip2arg.index(iprint_l[0:2]) ip[1] = ip2arg.index(iprint_l[2:4]) ip[3] = ip2arg.index(iprint_l[4:6]) self.iprint = ip[0]*1000 + ip[1]*100 + ip[2]*10 + ip[3] def run(self): """ Run the fitting routine with all of the information given. Returns ------- output : Output instance This object is also assigned to the attribute .output . """ args = (self.model.fcn, self.beta0, self.data.y, self.data.x) kwds = {'full_output': 1} kwd_l = ['ifixx', 'ifixb', 'job', 'iprint', 'errfile', 'rptfile', 'ndigit', 'taufac', 'sstol', 'partol', 'maxit', 'stpb', 'stpd', 'sclb', 'scld', 'work', 'iwork'] if self.delta0 is not None and self.job % 1000 / 10 == 1: # delta0 provided and fit is not a restart self._gen_work() d0 = numpy.ravel(self.delta0) self.work[:len(d0)] = d0 # set the kwds from other objects explicitly if self.model.fjacb is not None: kwds['fjacb'] = self.model.fjacb if self.model.fjacd is not None: kwds['fjacd'] = self.model.fjacd if self.data.we is not None: kwds['we'] = self.data.we if self.data.wd is not None: kwds['wd'] = self.data.wd if self.model.extra_args is not None: kwds['extra_args'] = self.model.extra_args # implicitly set kwds from self's members for attr in kwd_l: obj = getattr(self, attr) if obj is not None: kwds[attr] = obj self.output = Output(apply(odr, args, kwds)) return self.output def restart(self, iter=None): """ Restarts the run with iter more iterations. Parameters ---------- iter : int, optional ODRPACK's default for the number of new iterations is 10. Returns ------- output : Output instance This object is also assigned to the attribute .output . """ if self.output is None: raise odr_error, "cannot restart: run() has not been called before" self.set_job(restart=1) self.work = self.output.work self.iwork = self.output.iwork self.maxit = iter return self.run() #### EOF #######################################################################