PyClimate 1.1. A set of C and Python routines for the ... - Starship

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an array with as many heterogeneous correlation maps )as columns (expansion coefficients) are in ycoefs. The ordering of the maps is as describedin previous routine.Function: def SCF(sigmas)This function returns the squared covariance fraction associated to each singular value,+*¤#,% or(') "$#&% (!scf#.- /0 1243 /0 1fromthe array5 returned by svd().Function: def CSCF(sigmas)This function returns the cumulative squared covariance fraction associated to a set ofsingular cscf#6- values,21, from the 2 3 /0array5 returned by svd().Function: def makemctest(Um,Vm,ldata,rdata,itimes,ielems)This function performs a Monte Carlo test on the stability of the left singular vectorsUm and the right singular vectors Vm obtained from svd() or truncated to the; /0 798: #leading ones (Um.shape[-1]==Vm.shape[-1]). It computes itimes simultaneoussubsamples (ielems long each) of each datafield ldata, rdata, and evaluates thecongruence coefficient of the master singular vectors to those derived from the subsamples.It returns a tuple of Numerical Python arrays (ucoefs,vcoefs) whichhold the congruence coefficient for each sample and each U/V master singular vector(ucoefs.shape=(itimes,Um.shape[-1])). ielems must be smaller thanlen(ldata).9 CCA (Canonical Correlation Analysis)Canonical correlation analysis can be performed by bpcca.py. This technique getsthe pattern decomposition of two fields such that the correlation between the temporalcoefficients of paired patterns is maximum. The Barnett and Preisendorfer [1] approachhas been implemented. In this approach an EOF prefiltering is performed and the CCAis carried out in EOF–coordinates. The user never deals with these coordinates but cancustomize some features of the EOF decomposition.The results are scaled for the canonical expansion coefficients to have unit variance.If the columns of< and= are the canonical expansion coefficients and> and? arethe canonical patterns, the fields@ andA can be partially (due to the EOF prefiltering)recovered as: BBThe internal calculus are carried out by means of singuar value decomposition of theprincipal component cross covariance matrix.E AF-F=G? @C-4.EClass: bpccaFunction: def init (sfield, zfield, retainedeofs=None)Given two two–dimensional datasets this constructor performs the CCA decompositionof the fields. sfield and zfield are the left and right fields respectively. The user canspecify the number of EOFs to be retained in each field as a tuple retainedeofs. Ifno retainedeofs is specified 70% rule is used: EOFs are retained until a 70% of thevariance is explained.Function: def leftpatterns()This method returns an array which columns are the left (those associated to the sfield)canonical patterns.12
Function: def rightpatterns()Idem for the right patterns.Function: def leftexpcoeffs()This method returns an array which columns are the left (those associated to the sfield)canonical canonical expansin coefficients.Function: def rightexpcoeffs()Idem for the right coeffiients.Function: def correlation()Gives the ordered canonical correlations of the expansion coefficients as an array.Function: def getvariancefractions()Returns a tuple containing the variance fraction explained by each canonical pattern.The first element of the tuple is the array of fractions corresponding to the left field.The second one is that of the right field.10 Multivariate linear filters: LinearFilter.pyFilters are used in climate analysis to atenuate components of irrelevant frequenciesfrom a broadband signal and to select the frequencies of interest for the problem underconsideration. The simplest of these filters are the linear filters and two of them areprovided: the Kolmogorov-Zurbenko filter and the Lanczos filter. In the current version,the filter does not handle missing values.Both of them filter the data by weighting the input with coefficientsHJI that depend onthe filter:O P IRQSOHTIVU¥IXWLThis common part of the computation is carried out by subclassing the LinearFilterclass.Class: LinearFilterMethod: LinearFilter.getfiltered(ifield)KMLNGiven an input field, the function returns the filtered data corresponding to each inputrecord. This function must be called in an iterative way. The filter has been implementedwith performance in mind, so that, in order to avoid several iterations througha possibly long dataset, it iterates just once, performing a multivariate filtering to eachrecord. This makes that during the initialization phase, the internal running buffer mustbe initialized (filled) until valid values can be obtained. During the initialization phaseof the filter, this function returns None. After the internal buffer is ready, the functionreturns valid filtered fields until the user arrives at the final record of the iteration.Method: LinearFilter.reset()This method resets the linear filter so that it is able to start computing with a data set ofa different shape avoiding the creation of a new instance of the class.10.1 KZ filter: KZFilter.pyThe Kolmogorov–Zurbenko digital filter is a very simple but powerful digital filter. Itsfeatures are documented elsewhere [7, 12].Class: KZFilterMethod: KZFilter. init (self,points,iterations,lowpass=1)Default (and only) constructor. points is an odd integer representing the number of13
Page 1 and 2: £¤¥Jon Sáenz , Juan Zubillaga
Page 3 and 4: needed are:gunzip PyClimate-1.0.tar
Page 5 and 6: 3 IO functions3.1 ASCII files: asci
Page 7 and 8: so that the time values created thi
Page 9 and 10: jQb]]]6 Simple multivariate statist
Page 11: temporal subsampling of the origina
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Page 17 and 18: 13 Other linksThere are other packa

PyClimate 1.1. A set of C and Python routines for the ... - Starship

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