Python: module Numeric

Numeric (version 23.1)

index
c:\bin\lang\py22\lib\site-packages\numeric\numeric.py

Numeric module defining a multi-dimensional array and useful procedures for Numerical computation. Functions - array - NumPy Array construction - zeros - Return an array of all zeros - shape - Return shape of sequence or array - rank - Return number of dimensions - size - Return number of elements in entire array or a certain dimension - fromstring - Construct array from (byte) string - take - Select sub-arrays using sequence of indices - put - Set sub-arrays using sequence of 1-D indices - putmask - Set portion of arrays using a mask - reshape - Return array with new shape - repeat - Repeat elements of array - choose - Construct new array from indexed array tuple - cross_correlate - Correlate two 1-d arrays - searchsorted - Search for element in 1-d array - sum - Total sum over a specified dimension - average - Average, possibly weighted, over axis or array. - cumsum - Cumulative sum over a specified dimension - product - Total product over a specified dimension - cumproduct - Cumulative product over a specified dimension - alltrue - Logical and over an entire axis - sometrue - Logical or over an entire axis - allclose - Tests if sequences are essentially equal More Functions: - arrayrange (arange) - Return regularly spaced array - asarray - Guarantee NumPy array - sarray - Guarantee a NumPy array that keeps precision - convolve - Convolve two 1-d arrays - swapaxes - Exchange axes - concatenate - Join arrays together - transpose - Permute axes - sort - Sort elements of array - argsort - Indices of sorted array - argmax - Index of largest value - argmin - Index of smallest value - innerproduct - Innerproduct of two arrays - dot - Dot product (matrix multiplication) - outerproduct - Outerproduct of two arrays - resize - Return array with arbitrary new shape - indices - Tuple of indices - fromfunction - Construct array from universal function - diagonal - Return diagonal array - trace - Trace of array - dump - Dump array to file object (pickle) - dumps - Return pickled string representing data - load - Return array stored in file object - loads - Return array from pickled string - ravel - Return array as 1-D - nonzero - Indices of nonzero elements for 1-D array - shape - Shape of array - where - Construct array from binary result - compress - Elements of array where condition is true - clip - Clip array between two values - zeros - Array of all zeros - ones - Array of all ones - identity - 2-D identity array (matrix) (Universal) Math Functions add logical_or exp subtract logical_xor log multiply logical_not log10 divide maximum sin divide_safe minimum sinh conjugate bitwise_and sqrt power bitwise_or tan absolute bitwise_xor tanh negative invert ceil greater left_shift fabs greater_equal right_shift floor less arccos arctan2 less_equal arcsin fmod equal arctan hypot not_equal cos around logical_and cosh sign arccosh arcsinh arctanh

Modules

_numpy
copy

copy_reg
math

multiarray
pickle

string
types

Classes

Pickler

Pickler

Unpickler

Unpickler

class Pickler(Pickler)

Methods defined here:

save_array(self, object)

Data and non-method functions defined here:

__doc__ = None

__module__ = 'Numeric'

dispatch = {<type 'array'>: <function save_array at 0x00881318>, <type 'class'>: <function save_global at 0x0087CB88>, <type 'instance'>: <function save_inst at 0x0087CB48>, <type 'dict'>: <function save_dict at 0x0087CB08>, <type 'float'>: <function save_float at 0x0087C988>, <type 'function'>: <function save_global at 0x0087CB88>, <type 'int'>: <function save_int at 0x0087C908>, <type 'list'>: <function save_list at 0x0087CAC8>, <type 'long'>: <function save_long at 0x0087C948>, <type 'builtin_function_or_method'>: <function save_global at 0x0087CB88>, ...}

Methods inherited from Pickler:

__init__(self, file, bin=0)

dump(self, object)

get(self, i)

inst_persistent_id(self, object)

persistent_id(self, object)

put(self, i)

save(self, object, pers_save=0)

save_dict(self, object)

save_empty_tuple(self, object)

save_float(self, object, pack=<built-in function pack>)

save_global(self, object, name=None)

save_inst(self, object)

save_int(self, object)

save_list(self, object)

save_long(self, object)

save_none(self, object)

save_pers(self, pid)

save_reduce(self, callable, arg_tup, state=None)

save_string(self, object)

save_tuple(self, object)

save_unicode(self, object)

class Unpickler(Unpickler)

Methods defined here:

load_array(self)

Data and non-method functions defined here:

__doc__ = None

__module__ = 'Numeric'

dispatch = {'': <function load_eof at 0x0087CDA0>, '(': <function load_mark at 0x0087E748>, ')': <function load_empty_tuple at 0x0087D838>, '.': <function load_stop at 0x0087E788>, '0': <function load_pop at 0x0087DDC0>, '1': <function load_pop_mark at 0x0087DE00>, '2': <function load_dup at 0x0087DE40>, 'A': <function load_array at 0x0088AF48>, 'F': <function load_float at 0x0087D168>, 'G': <function load_binfloat at 0x0087D1A8>, ...}

Methods inherited from Unpickler:

__init__(self, file)

_is_string_secure(self, s): Return true if s contains a string that is safe to eval The definition of secure string is based on the implementation in cPickle. s is secure as long as it only contains a quoted string and optional trailing whitespace.

find_class(self, module, name)

load(self)

load_append(self)

load_appends(self)

load_binfloat(self, unpack=<built-in function unpack>)

load_binget(self)

load_binint(self)

load_binint1(self)

load_binint2(self)

load_binpersid(self)

load_binput(self)

load_binstring(self)

load_binunicode(self)

load_build(self)

load_dict(self)

load_dup(self)

load_empty_dictionary(self)

load_empty_list(self)

load_empty_tuple(self)

load_eof(self)

load_float(self)

load_get(self)

load_global(self)

load_inst(self)

load_int(self)

load_list(self)

load_long(self)

load_long_binget(self)

load_long_binput(self)

load_mark(self)

load_none(self)

load_obj(self)

load_persid(self)

load_pop(self)

load_pop_mark(self)

load_put(self)

load_reduce(self)

load_setitem(self)

load_setitems(self)

load_short_binstring(self)

load_stop(self)

load_string(self)

load_tuple(self)

load_unicode(self)

marker(self)

Functions

DumpArray(m, fp)

LoadArray(fp)

allclose(a, b, rtol=1.0000000000000001e-005, atol=1e-008): allclose(a,b,rtol=1.e-5,atol=1.e-8) Returns true if all components of a and b are equal subject to given tolerances. The relative error rtol must be positive and << 1.0 The absolute error atol comes into play for those elements of y that are very small or zero; it says how small x must be also.

alltrue(x, axis=0): Perform a logical_and over the given axis.

arange(...): arange(start, stop=None, step=1, typecode=None) Just like range() except it returns an array whose type can be specified by the keyword argument typecode.

argmax(a, axis=-1): argmax(a,axis=-1) returns the indices to the maximum value of the 1-D arrays along the given axis.

argmin(a, axis=-1): argmin(a,axis=-1) returns the indices to the minimum value of the 1-D arrays along the given axis.

argsort(a, axis=-1): argsort(a,axis=-1) return the indices into a of the sorted array along the given axis, so that take(a,result,axis) is the sorted array.

around(m, decimals=0): around(m, decimals=0) Round in the same way as standard python performs rounding. Returns always a float.

array(...): array(sequence, typecode=None, copy=1, savespace=0) will return a new array formed from the given (potentially nested) sequence with type given by typecode. If no typecode is given, then the type will be determined as the minimum type required to hold the objects in sequence. If copy is zero and sequence is already an array, a reference will be returned. If savespace is nonzero, the new array will maintain its precision in operations.

array_constructor(shape, typecode, thestr, Endian=1)

array_repr(a, max_line_width=None, precision=None, suppress_small=None)

array_str(a, max_line_width=None, precision=None, suppress_small=None)

arrayrange = arange(...): arange(start, stop=None, step=1, typecode=None) Just like range() except it returns an array whose type can be specified by the keyword argument typecode.

asarray(a, typecode=None, savespace=0): asarray(a,typecode=None) returns a as a NumPy array. Unlike array(), no copy is performed if a is already an array.

average(a, axis=0, weights=None, returned=0): average(a, axis=0, weights=None) Computes average along indicated axis. If axis is None, average over the entire array. Inputs can be integer or floating types; result is type Float. If weights are given, result is: sum(a*weights)/(sum(weights)) weights must have a's shape or be the 1-d with length the size of a in the given axis. Integer weights are converted to Float. Not supplying weights is equivalent to supply weights that are all 1. If returned, return a tuple: the result and the sum of the weights or count of values. The shape of these two results will be the same. raises ZeroDivisionError if appropriate when result is scalar. (The version in MA does not -- it returns masked values).

choose(...): choose(a, (b1,b2,...))

clip(m, m_min, m_max): clip(m, m_min, m_max) = every entry in m that is less than m_min is replaced by m_min, and every entry greater than m_max is replaced by m_max.

compress(condition, m, dimension=-1): compress(condition, x, dimension=-1) = those elements of x corresponding to those elements of condition that are "true". condition must be the same size as the given dimension of x.

concatenate(a, axis=0): concatenate(a, axis=0) joins the tuple of sequences in a into a single NumPy array.

convolve(a, v, mode=2): Returns the discrete, linear convolution of 1-D sequences a and v; mode can be 0 (valid), 1 (same), or 2 (full) to specify size of the resulting sequence.

cross_correlate(...): cross_correlate(a,v, mode=0)

cumproduct(x, axis=0): Sum the array over the given axis.

cumsum(x, axis=0): Sum the array over the given axis.

diagonal(a, offset=0, axis1=0, axis2=1): diagonal(a, offset=0, axis1=0, axis2=1) returns the given diagonals defined by the last two dimensions of the array.

dot(a, b): dot(a,b) returns matrix-multiplication between a and b. The product-sum is over the last dimension of a and the second-to-last dimension of b.

dump(object, file): dump(object, file) pickles (binary-writes) the object to an open file.

dumps(object): dumps(object) pickles (binary-writes) the object and returns the byte stream.

fromfunction(function, dimensions): fromfunction(function, dimensions) returns an array constructed by calling function on a tuple of number grids. The function should accept as many arguments as there are dimensions which is a list of numbers indicating the length of the desired output for each axis.

fromstring(...): fromstring(string, typecode='l', count=-1) returns a new 1d array initialized from the raw binary data in string. If count is positive, the new array will have count elements, otherwise it's size is determined by the size of string.

identity(n, typecode='l'): identity(n) returns the identity matrix of shape n x n.

indices(dimensions, typecode=None): indices(dimensions,typecode=None) returns an array representing a grid of indices with row-only, and column-only variation.

innerproduct(a, b): innerproduct(a,b) returns the dot product of two arrays, which has shape a.shape[:-1] + b.shape[:-1] with elements computed by summing the product of the elements from the last dimensions of a and b.

load(file): load(file) returns an array from the open file pointing to pickled data.

loads(str): loads(str) returns an array from a byte stream containing its pickled representation.

matrixmultiply = dot(a, b): dot(a,b) returns matrix-multiplication between a and b. The product-sum is over the last dimension of a and the second-to-last dimension of b.

nonzero(a): nonzero(a) returns the indices of the elements of a which are not zero, a must be 1d

ones(shape, typecode='l', savespace=0): ones(shape, typecode=Int, savespace=0) returns an array of the given dimensions which is initialized to all ones.

outerproduct(a, b): outerproduct(a,b) returns the outer product of two vectors. result(i,j) = a(i)*b(j) when a and b are vectors Will accept any arguments that can be made into vectors.

pickle_array(a)

product(x, axis=0): Product of the array elements over the given axis.

put(a, ind, v): put(a, ind, v) results in a[n] = v[n] for all n in ind If v is shorter than mask it will be repeated as necessary. In particular v can be a scalar or length 1 array. The routine put is the equivalent of the following (although the loop is in C for speed): ind = array(indices, copy=0) v = array(values, copy=0).astype(a, typecode()) for i in ind: a.flat[i] = v[i] a must be a contiguous Numeric array.

putmask(a, mask, v): putmask(a, mask, v) results in a = v for all places mask is true. If v is shorter than mask it will be repeated as necessary. In particular v can be a scalar or length 1 array.

rank(a): Get the rank of sequence a (the number of dimensions, not a matrix rank) The rank of a scalar is zero.

ravel(m): ravel(m) returns a 1d array corresponding to all the elements of it's argument.

repeat(a, repeats, axis=0): repeat elements of a repeats times along axis repeats is a sequence of length a.shape[axis] telling how many times to repeat each element. If repeats is an integer, it is interpreted as a tuple of length a.shape[axis] containing repeats. The argument a can be anything array(a) will accept.

reshape(...): reshape(a, (d1, d2, ..., dn)). Change the shape of a to be an n-dimensional array with dimensions given by d1...dn. Note: the size specified for the new array must be exactly equal to the size of the old one or an error will occur.

resize(a, new_shape): resize(a,new_shape) returns a new array with the specified shape. The original array's total size can be any size.

sarray(a, typecode=None, copy=0): sarray(a, typecode=None, copy=0) calls array with savespace=1.

searchsorted = binarysearch(...): binarysearch(a,v)

shape(a): Get the shape of sequence a

sign(m): sign(m) gives an array with shape of m with elements defined by sign function: where m is less than 0 return -1, where m greater than 0, a=1, elsewhere a=0.

size(a, axis=None): Get the number of elements in sequence a, or along a certain axis.

sometrue(x, axis=0): Perform a logical_or over the given axis.

sort(a, axis=-1): sort(a,axis=-1) returns array with elements sorted along given axis.

sum(x, axis=0): Sum the array over the given axis.

swapaxes(a, axis1, axis2): swapaxes(a, axis1, axis2) returns array a with axis1 and axis2 interchanged.

take(...): take(a, indices, axis=0). Selects the elements in indices from array a along the given axis.

trace(a, offset=0, axis1=0, axis2=1): trace(a,offset=0, axis1=0, axis2=1) returns the sum along diagonals (defined by the last two dimenions) of the array.

transpose(a, axes=None): transpose(a, axes=None) returns array with dimensions permuted according to axes. If axes is None (default) returns array with dimensions reversed.

vdot(a, b): Returns the dot product of 2 vectors (or anything that can be made into a vector). NB: this is not the same as `dot`, as it takes the conjugate of its first argument if complex and always returns a scalar.

where(condition, x, y): where(condition,x,y) is shaped like condition and has elements of x and y where condition is respectively true or false.

zeros(...): zeros((d1,...,dn),typecode='l',savespace=0) will return a new array of shape (d1,...,dn) and type typecode with all it's entries initialized to zero. If savespace is nonzero the array will be a spacesaver array.

Data
		Character = 'c' Complex = 'D' Complex0 = 'F' Complex16 = 'F' Complex32 = 'F' Complex64 = 'D' Complex8 = 'F' Float = 'd' Float0 = 'f' Float16 = 'f' Float32 = 'f' Float64 = 'd' Float8 = 'f' Int = 'l' Int0 = '1' Int16 = 's' Int32 = 'i' Int8 = '1' LittleEndian = 1 NewAxis = None PrecisionError = 'PrecisionError' PyObject = 'O' UInt = 'u' UInt16 = 'w' UInt32 = 'u' UInt8 = 'b' UnsignedInt16 = 'w' UnsignedInt32 = 'u' UnsignedInt8 = 'b' UnsignedInteger = 'u' __file__ = r'C:\bin\lang\py22\lib\site-packages\Numeric\Numeric.pyc' __name__ = 'Numeric' __version__ = '23.1' absolute = <ufunc 'absolute'> add = <ufunc 'add'> arccos = <ufunc 'arccos'> arccosh = <ufunc 'arccosh'> arcsin = <ufunc 'arcsin'> arcsinh = <ufunc 'arcsinh'> arctan = <ufunc 'arctan'> arctan2 = <ufunc 'arctan2'> arctanh = <ufunc 'arctanh'> bitwise_and = <ufunc 'bitwise_and'> bitwise_or = <ufunc 'bitwise_or'> bitwise_xor = <ufunc 'bitwise_xor'> ceil = <ufunc 'ceil'> conjugate = <ufunc 'conjugate'> cos = <ufunc 'cos'> cosh = <ufunc 'cosh'> divide = <ufunc 'divide'> divide_safe = <ufunc 'divide_safe'> e = 2.7182818284590451 equal = <ufunc 'equal'> exp = <ufunc 'exp'> fabs = <ufunc 'fabs'> floor = <ufunc 'floor'> floor_divide = <ufunc 'floor_divide'> fmod = <ufunc 'fmod'> greater = <ufunc 'greater'> greater_equal = <ufunc 'greater_equal'> hypot = <ufunc 'hypot'> invert = <ufunc 'invert'> left_shift = <ufunc 'left_shift'> less = <ufunc 'less'> less_equal = <ufunc 'less_equal'> log = <ufunc 'log'> log10 = <ufunc 'log10'> logical_and = <ufunc 'logical_and'> logical_not = <ufunc 'logical_not'> logical_or = <ufunc 'logical_or'> logical_xor = <ufunc 'logical_xor'> maximum = <ufunc 'maximum'> minimum = <ufunc 'minimum'> multiply = <ufunc 'multiply'> negative = <ufunc 'negative'> not_equal = <ufunc 'not_equal'> pi = 3.1415926535897931 power = <ufunc 'power'> remainder = <ufunc 'remainder'> right_shift = <ufunc 'right_shift'> sin = <ufunc 'sin'> sinh = <ufunc 'sinh'> sqrt = <ufunc 'sqrt'> subtract = <ufunc 'subtract'> tan = <ufunc 'tan'> tanh = <ufunc 'tanh'> true_divide = <ufunc 'true_divide'> typecodes = {'Character': 'c', 'Complex': 'FD', 'Float': 'fd', 'Integer': '1sil', 'UnsignedInteger': 'bwu'}