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FIA - Fluxomers Iterative Algorithm SYNOPSIS: fia_analysis {-i[=--fia] PROJFILE | -t[=--ftbl] PROJFILE} [-o[=--output_file] OUTPUTFILE] USAGE: fia_analysis performs the following two main steps: 1) Metabolic pathway analysis. Here FIA transforms the text file representing the metabolic network into mathematical matrices required later for the MFA optimization process. 2) Metabolic fluxes evaluation. Here FIA tries to find the fluxes that best suit the given metabolic system. The output log of this stage is a file called <PROJFILE>.out, or else if specified by the "-o" flag. fia_analysis supports two types of input file formats: FIA and 13CFlux FTBL. The two are very much alike, except for the fact that the FIA format supports only declaration of uni-directional fluxes, and does not contain the FLUXES and INEQUALITIES sections. For bi-directional fluxes, FIA simply defines two seperated fluxes. When analyzing 13CFlux FTBL files, fia_analysis looks for C=0 constraints in the FLUXES XCH section in order to determine directionality of fluxes. OPTIONS: -i --fia Specifies that the metabolic pathway input file <PROJFILE> is a FIA formated file. All fluxes are assumed unidirectional (for bi-directional specify 2 seperated fluxes). -t --ftbl Specifies that the metabolic pathway input file <PROJFILE> is a 13CFlux FTBL formated file. Flux is assumed unidirectional unless C=0 constraint is applied to it in the FLUXES XCH section. The FIA formated file will be saved under <PROJFILE>.fia . -o --output_file optional. Specifies the name of the result log output file. The default file name is PROJFILE.out at least one of the above must be supplied. USAGE EXAMPLE: fia_analysis -t temp.ftbl Runs anaylsis & evaluation of the network defined in the FTBL formated input file temp.ftbl . same as: fia_analysis --ftbl temp.ftbl fia_analysis --fia temp.fia -a | Runs only network analysis on the given FIA formated input file. Same as: fia_analysis -i temp.fia -a fia_analysis -i temp.fia --analyze_only fia_analysis --fia temp.fia --analyze_only fia_analysis temp.fia -i -e Runs only the fluxes evaluation on the FIA formated input file temp.fia . This assumes analysis has been done before on the temp.fia file. Same as: fia_analysis -i temp.fia --evaluate_only fia_analysis --fia temp.fia -e fia_analysis --fia temp.fia --evaluate_only
Version: 0.7.0
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MyWriter A file-handler replacement that duplicates the actions applied to the hanlder to a log file handler as well. |
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FtblFile This class is the main object of the FIA algorithm. |
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scipy.array |
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scipy.sparse matrix |
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list |
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list |
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string |
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umfpack = um.UmfpackContext()
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prog_width = 40
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display_prog = False
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ALLOW_THREADS = 1
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BUFSIZE = 10000
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CLIP = 0
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ERR_CALL = 3
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ERR_DEFAULT = 0
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ERR_DEFAULT2 = 2084
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ERR_IGNORE = 0
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ERR_LOG = 5
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ERR_PRINT = 4
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ERR_RAISE = 2
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ERR_WARN = 1
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FLOATING_POINT_SUPPORT = 1
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FPE_DIVIDEBYZERO = 1
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FPE_INVALID = 8
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FPE_OVERFLOW = 2
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FPE_UNDERFLOW = 4
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False_ = False
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Inf = inf
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Infinity = inf
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MAXDIMS = 32
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NAN = nan
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NINF = -inf
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NZERO = -0.0
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NaN = nan
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PINF = inf
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PZERO = 0.0
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RAISE = 2
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SHIFT_DIVIDEBYZERO = 0
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SHIFT_INVALID = 9
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SHIFT_OVERFLOW = 3
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SHIFT_UNDERFLOW = 6
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ScalarType =
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True_ = True
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UFUNC_BUFSIZE_DEFAULT = 10000
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UFUNC_PYVALS_NAME =
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WRAP = 1
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__package__ = None
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absolute = <ufunc 'absolute'>
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add = <ufunc 'add'>
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arccosh = <ufunc 'arccosh'>
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arcsinh = <ufunc 'arcsinh'>
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arctan = <ufunc 'arctan'>
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arctan2 = <ufunc 'arctan2'>
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bitwise_and = <ufunc 'bitwise_and'>
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bitwise_not = <ufunc 'invert'>
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bitwise_or = <ufunc 'bitwise_or'>
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bitwise_xor = <ufunc 'bitwise_xor'>
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c_ = <numpy.lib.index_tricks.CClass object at 0xee37d0>
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cast = {<type 'numpy.int64'>: <function <lambda> at 0xdcec80>,
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ceil = <ufunc 'ceil'>
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conj = <ufunc 'conjugate'>
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conjugate = <ufunc 'conjugate'>
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cos = <ufunc 'cos'>
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cosh = <ufunc 'cosh'>
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deg2rad = <ufunc 'deg2rad'>
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degrees = <ufunc 'degrees'>
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divide = <ufunc 'divide'>
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e = 2.71828182846
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equal = <ufunc 'equal'>
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exp = <ufunc 'exp'>
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exp2 = <ufunc 'exp2'>
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expm1 = <ufunc 'expm1'>
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fabs = <ufunc 'fabs'>
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floor = <ufunc 'floor'>
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floor_divide = <ufunc 'floor_divide'>
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fmax = <ufunc 'fmax'>
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fmin = <ufunc 'fmin'>
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fmod = <ufunc 'fmod'>
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frexp = <ufunc 'frexp'>
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greater = <ufunc 'greater'>
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greater_equal = <ufunc 'greater_equal'>
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hypot = <ufunc 'hypot'>
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index_exp = <numpy.lib.index_tricks.IndexExpression object at
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inf = inf
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infty = inf
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invert = <ufunc 'invert'>
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isfinite = <ufunc 'isfinite'>
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isinf = <ufunc 'isinf'>
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isnan = <ufunc 'isnan'>
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ldexp = <ufunc 'ldexp'>
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left_shift = <ufunc 'left_shift'>
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less = <ufunc 'less'>
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less_equal = <ufunc 'less_equal'>
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little_endian = True
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log1p = <ufunc 'log1p'>
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logaddexp = <ufunc 'logaddexp'>
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logaddexp2 = <ufunc 'logaddexp2'>
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logical_and = <ufunc 'logical_and'>
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logical_not = <ufunc 'logical_not'>
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logical_or = <ufunc 'logical_or'>
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logical_xor = <ufunc 'logical_xor'>
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maximum = <ufunc 'maximum'>
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mgrid = <numpy.lib.index_tricks.nd_grid object at 0xee3610>
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minimum = <ufunc 'minimum'>
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mod = <ufunc 'remainder'>
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modf = <ufunc 'modf'>
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multiply = <ufunc 'multiply'>
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nan = nan
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nbytes = {<type 'numpy.int64'>: 8, <type 'numpy.int16'>: 2, <t
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negative = <ufunc 'negative'>
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newaxis = None
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not_equal = <ufunc 'not_equal'>
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ogrid = <numpy.lib.index_tricks.nd_grid object at 0xee3650>
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ones_like = <ufunc 'ones_like'>
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pi = 3.14159265359
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r_ = <numpy.lib.index_tricks.RClass object at 0xee3710>
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rad2deg = <ufunc 'rad2deg'>
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radians = <ufunc 'radians'>
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reciprocal = <ufunc 'reciprocal'>
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remainder = <ufunc 'remainder'>
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right_shift = <ufunc 'right_shift'>
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rint = <ufunc 'rint'>
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s_ = <numpy.lib.index_tricks.IndexExpression object at 0xee3910>
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sctypeDict =
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sctypeNA =
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sctypes =
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sign = <ufunc 'sign'>
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signbit = <ufunc 'signbit'>
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sin = <ufunc 'sin'>
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sinh = <ufunc 'sinh'>
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square = <ufunc 'square'>
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subtract = <ufunc 'subtract'>
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tan = <ufunc 'tan'>
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tanh = <ufunc 'tanh'>
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true_divide = <ufunc 'true_divide'>
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trunc = <ufunc 'trunc'>
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typeDict =
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typeNA =
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typecodes =
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This function returns a vector which is 1/vec.
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Fast sparse diagonal matrix creator. Simply returns a sparse matrix with a as its diagonal.
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This function transforms input with ( )'s into all the possible sets of inputs with 0's and 1's replacing the ( )s.
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This function returns all the possible permutations of no_of_atoms atoms, seperated by the number of 1's in them. (indices_to_change) specifies which of the atoms can be changed (and the counting is then done only on these atoms). For example, if we call: create_ms_isotopomers_dict(4,[0,1,2]) we get: [['000x'], ['001x', '010x', '100x'], ['011x', '101x', '110x'], ['111x']] The first element represents all 0's vec (for indices_to_change), the second a vector with only one 1 element, the third with two 1's elements, and the third with three 1's elements. This function is used for mass-spectrometry measurements analysis.
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Converts number to binary format with at (length) bits.
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This fucntion saves the input vector in text TAB seperated format. Used for MATLAB debugging purposes. |
This fucntion saves the input matrix in text TAB seperated format loadable by MATLAB. Used for MATLAB debugging purposes. |
This function generates the initial point given for the optimization process. It is based upon the standard init point finding methods of iterior point algorithms: In order to find a valid solution for S*u = 0, U* u <= s we solve: min(s) s.t.: [I,-I]*u + [0*i,10*i] >= s Su = S_b We then use this initial point with the propogation equation, resulting with the first point for the algorithm. |
Main MFA optimization objective function value. This function calculates || self.tG * x - self.tB || for a given u vector by finding the valid x vector and then substituting it in the objective function. |
Main MFA optimization objective function gradient calculation. This function calculates d (|| self.tG * x - self.tB ||) / du for a given u vector. |
Random values in a given shape. Create an array of the given shape and propagate it with random samples from a uniform distribution over ``[0, 1)``. Parameters ---------- d0, d1, ..., dn : int Shape of the output. Returns ------- out : ndarray, shape ``(d0, d1, ..., dn)`` Random values. See Also -------- random Notes ----- This is a convenience function. If you want an interface that takes a shape-tuple as the first argument, refer to `random`. Examples -------- >>> np.random.rand(3,2) array([[ 0.14022471, 0.96360618], #random [ 0.37601032, 0.25528411], #random [ 0.49313049, 0.94909878]]) #random |
Returns zero-mean, unit-variance Gaussian random numbers in an array of shape (d0, d1, ..., dn). Note: This is a convenience function. If you want an interface that takes a tuple as the first argument use numpy.random.standard_normal(shape_tuple). |
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ScalarType
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cast
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index_exp
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nbytes
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sctypeDict
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sctypeNA
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sctypes
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typeDict
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typeNA
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typecodes
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