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

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Class: JDTimeHandlerFunction: def init (self,units)The creator parses a COARDS–type units attribute of the netCDF time variable (ex:hours since 1958-01-01 0:0) and computes the offset and the scale factor to get JulianDays from the netCDF time variable for each record.Function: def getdatefields(self,tvalue,listlen=6)This method scales the value of tvalue using the scaling factor corresponding to theunits attribute and adds the offset computed from the units attribute, getting the JulianDay corresponding to the time coordinate tvalue. The Julian Day value is used tocreate a broken date structure, and its values (year,month,day,hour,minute,second) arereturned as a list, with as many members as those requested by listlen.Function: def gettimevalue(self,datefields,listlen=6)This function is the inverse of the previous one. It creates a Julian Day from the valuesin the list datefields (as many values as the value of listlen), computes the offset fromthe origin of coordinates and scales the value to the units corresponding to the unitsattribute of the time variable used to cretae the instance of this class. Due to sometruncations which appear during the floating point operations, this function is onlyaccurate to about 12 significant figures. So, if you are creating a netCDF file fromscratch, it is more accurate and fast just to add equal time intervals using a fixed timestep from record to record.5 Distribution functionsDCDFLIB.C 1.1 is a library of C Routines for Cumulative Distribution Functionsdeveloped by Barry W. Brown, James Lovato and Kathy Russell, Department of Biomathematics,The University of Texas, M.D. Anderson Cancer Center. The libraryallows the determination of cumulative distribution functions, inverses, and the parametersof the following statistical distributions: Beta, Binomial, , Noncentral ,, Noncentral , Gamma, Negative Binomial, Normal, Poisson, Student’s and Non-central . The file pydcdflib.py provides access to this library from Python. Foreach one of these distributions, a class has been created that wraps the parameters inthe call to the functions of this routine and some C functions that wrap the originalC calls to make them available to the Python interpreter. The same names have beenused in the wrapping of the parameters, so that the original documentation of the DCD-FLIB.C library is valid for each of the classes in this Python extension. The class ispassed to a function with the same name of the original one (with py prepended tothe name of the wrapper function). The fields of each class are updated by the wrapperfunctions. The code of the original C library is untouched. Examples on the useof these wrappers can be obtained from the testDCDFLIB.py script in the sourcedirectory of the distribution and from the enclosed exdcdflib.py file. There is anerror when computing the parameters for the F distribution using the value which=3.This seems to be linked to the properties of the F distribution and, in case it were abug, it also exists in the original DCDFLIB.C, as the same problem appears using theC file testF.c, provided in the source directory. The documentation of the originalDCDFLIB.C library is distributed within the doc subdirectory of the distribution.8
jQb]]]6 Simple multivariate statistical toolsThe main goal of file mvarstatools.py is to provide some routines that are beingused by others like svdeofs.py. However, some of them may be interesting bythemselves, and they are thus documented in this section.Function: def center(dataset)Given an input multivariate dataset with arbitrary dimensions (with the restrictionthat first dimension is time), this function returns the centered !#"$&%(' dataset,*,+.- +with the ')" / sample average ' .Function: def standardize(dataset)Given an input multivariate dataset with arbitrary dimensions (with - the +1032 restriction465 , " that first dimension is time), this function returns the standardized datasetwith 7 -the sample standard deviation 7 - "Function: def covariancematrix(X,Y)Given two input datasets @* +:9;- +10=? 8. /and A , with first dimension time and second dimension9;D 0 D =FE 9HG 0 G =/ space, this function returns the S–mode covariance matrix .BC"Function: def correlationmatrix(X,Y)Given two input datasets and , with first dimension time @and A second dimension/ space, this function BI"returns the S–mode correlation matrix from the stand-E - - ardized J and datasets.Function: def detrend(dataset,tvalues,order=1)From the univariate time series dataset, this function returns a tuple (detrended,trendcoefs).The trend is computed adjusting by means of linear least squares a polinomialto the data, V where is order. The tuple holds the detrended time*,PQHRTSU Q L KLNMO"series as the first member and the the trend U Q coefficients as the second element of thetuple.Function: def congruence(pattern1,pattern2)Given two spatial patterns pattern1 W (X.Z), this function returns the^ ? ^`_a7 EOF analysis^ _ bcbcongruence coefficient [ Y\ Z " ]) and pattern2 ( WX>Y? b [3, 13]. ^A subroutine is provided in the file svdeofs.py to perform the decomposition of adataset in its spatial eigenvectors (Empirical Orthogonal Functions, EOFs), the fractionof variance explained by each mode and its temporal expansion coefficients [11, 15](PCs). One of the problems of this technique in climatological analysis is the singularityof covariance matrices when the number of spatial degrees of freedom is biggerthan the number of temporal samples. To avoid this problem, the routine is basedon the SVD decomposition of the data @d"fe Qcgih matrix . For this implementation,means a temporal sample and qr"lknmomim`V a grid point or observing site."lknmimomNpCorrections derived from quadrature or area factors [2, 10] must be applied prior to thisroutine. There is an example on the use of these function in file exsvdeofs.py.NEW: Since version 1.1, the input data set may be arbitrarily shaped, with the onlyrestriction that the leading axis must be the time axis.Function: def svdeofs(dataset, pcscaling=0)Given a two dimensional dataset ptsuVas described above, this function returns atuple v ,w ,x . v"ye z Y{ mimom { z6| h is a pfs~} matrix, where each column z> is a vectorof length p and is the € –eth PC of the field. Each element < in the one dimensional9
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: so that the time values created thi
Page 11 and 12: temporal subsampling of the origina
Page 13 and 14: Function: def rightpatterns()Idem f
Page 15 and 16: hh(2)(3)‚¦”“•” ‡Method
Page 17 and 18: 13 Other linksThere are other packa

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