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Diffstat (limited to 'numpy/lib/index_tricks.py')
| -rw-r--r-- | numpy/lib/index_tricks.py | 457 |
1 files changed, 457 insertions, 0 deletions
diff --git a/numpy/lib/index_tricks.py b/numpy/lib/index_tricks.py new file mode 100644 index 000000000..8e6f36e30 --- /dev/null +++ b/numpy/lib/index_tricks.py @@ -0,0 +1,457 @@ +## Automatically adapted for numpy Sep 19, 2005 by convertcode.py + +__all__ = ['unravel_index', + 'mgrid', + 'ogrid', + 'r_', 'c_', 's_', + 'index_exp', 'ix_', + 'ndenumerate','ndindex'] + +import sys +import numpy.core.numeric as _nx +from numpy.core.numeric import asarray, ScalarType, array +import math + +import function_base +import numpy.core.defmatrix as matrix +makemat = matrix.matrix + +# contributed by Stefan van der Walt +def unravel_index(x,dims): + """Convert a flat index into an index tuple for an array of given shape. + + e.g. for a 2x2 array, unravel_index(2,(2,2)) returns (1,0). + + Example usage: + p = x.argmax() + idx = unravel_index(p,x.shape) + x[idx] == x.max() + + Note: x.flat[p] == x.max() + + Thus, it may be easier to use flattened indexing than to re-map + the index to a tuple. + """ + if x > _nx.prod(dims)-1 or x < 0: + raise ValueError("Invalid index, must be 0 <= x <= number of elements.") + + idx = _nx.empty_like(dims) + + # Take dimensions + # [a,b,c,d] + # Reverse and drop first element + # [d,c,b] + # Prepend [1] + # [1,d,c,b] + # Calculate cumulative product + # [1,d,dc,dcb] + # Reverse + # [dcb,dc,d,1] + dim_prod = _nx.cumprod([1] + list(dims)[:0:-1])[::-1] + # Indeces become [x/dcb % a, x/dc % b, x/d % c, x/1 % d] + return tuple(x/dim_prod % dims) + +def ix_(*args): + """ Construct an open mesh from multiple sequences. + + This function takes n 1-d sequences and returns n outputs with n + dimensions each such that the shape is 1 in all but one dimension and + the dimension with the non-unit shape value cycles through all n + dimensions. + + Using ix_() one can quickly construct index arrays that will index + the cross product. + + a[ix_([1,3,7],[2,5,8])] returns the array + + a[1,2] a[1,5] a[1,8] + a[3,2] a[3,5] a[3,8] + a[7,2] a[7,5] a[7,8] + """ + out = [] + nd = len(args) + baseshape = [1]*nd + for k in range(nd): + new = _nx.asarray(args[k]) + if (new.ndim != 1): + raise ValueError, "Cross index must be 1 dimensional" + if issubclass(new.dtype.type, _nx.bool_): + new = new.nonzero()[0] + baseshape[k] = len(new) + new = new.reshape(tuple(baseshape)) + out.append(new) + baseshape[k] = 1 + return tuple(out) + +class nd_grid(object): + """ Construct a "meshgrid" in N-dimensions. + + grid = nd_grid() creates an instance which will return a mesh-grid + when indexed. The dimension and number of the output arrays are equal + to the number of indexing dimensions. If the step length is not a + complex number, then the stop is not inclusive. + + However, if the step length is a COMPLEX NUMBER (e.g. 5j), then the + integer part of it's magnitude is interpreted as specifying the + number of points to create between the start and stop values, where + the stop value IS INCLUSIVE. + + If instantiated with an argument of sparse=True, the mesh-grid is + open (or not fleshed out) so that only one-dimension of each returned + argument is greater than 1 + + Example: + + >>> mgrid = nd_grid() + >>> mgrid[0:5,0:5] + array([[[0, 0, 0, 0, 0], + [1, 1, 1, 1, 1], + [2, 2, 2, 2, 2], + [3, 3, 3, 3, 3], + [4, 4, 4, 4, 4]], + <BLANKLINE> + [[0, 1, 2, 3, 4], + [0, 1, 2, 3, 4], + [0, 1, 2, 3, 4], + [0, 1, 2, 3, 4], + [0, 1, 2, 3, 4]]]) + >>> mgrid[-1:1:5j] + array([-1. , -0.5, 0. , 0.5, 1. ]) + + >>> ogrid = nd_grid(sparse=True) + >>> ogrid[0:5,0:5] + [array([[0], + [1], + [2], + [3], + [4]]), array([[0, 1, 2, 3, 4]])] + + """ + def __init__(self, sparse=False): + self.sparse = sparse + def __getitem__(self,key): + try: + size = [] + typ = int + for k in range(len(key)): + step = key[k].step + start = key[k].start + if start is None: start=0 + if step is None: step=1 + if isinstance(step, complex): + size.append(int(abs(step))) + typ = float + else: + size.append(math.ceil((key[k].stop - start)/(step*1.0))) + if isinstance(step, float) or \ + isinstance(start, float) or \ + isinstance(key[k].stop, float): + typ = float + if self.sparse: + nn = map(lambda x,t: _nx.arange(x, dtype=t), size, \ + (typ,)*len(size)) + else: + nn = _nx.indices(size, typ) + for k in range(len(size)): + step = key[k].step + start = key[k].start + if start is None: start=0 + if step is None: step=1 + if isinstance(step, complex): + step = int(abs(step)) + if step != 1: + step = (key[k].stop - start)/float(step-1) + nn[k] = (nn[k]*step+start) + if self.sparse: + slobj = [_nx.newaxis]*len(size) + for k in range(len(size)): + slobj[k] = slice(None,None) + nn[k] = nn[k][slobj] + slobj[k] = _nx.newaxis + return nn + except (IndexError, TypeError): + step = key.step + stop = key.stop + start = key.start + if start is None: start = 0 + if isinstance(step, complex): + step = abs(step) + length = int(step) + if step != 1: + step = (key.stop-start)/float(step-1) + stop = key.stop+step + return _nx.arange(0, length,1, float)*step + start + else: + return _nx.arange(start, stop, step) + + def __getslice__(self,i,j): + return _nx.arange(i,j) + + def __len__(self): + return 0 + +mgrid = nd_grid(sparse=False) +ogrid = nd_grid(sparse=True) + +class concatenator(object): + """Translates slice objects to concatenation along an axis. + """ + def _retval(self, res): + if self.matrix: + oldndim = res.ndim + res = makemat(res) + if oldndim == 1 and self.col: + res = res.T + self.axis = self._axis + self.matrix = self._matrix + self.col = 0 + return res + + def __init__(self, axis=0, matrix=False, ndmin=1, trans1d=-1): + self._axis = axis + self._matrix = matrix + self.axis = axis + self.matrix = matrix + self.col = 0 + self.trans1d = trans1d + self.ndmin = ndmin + + def __getitem__(self,key): + trans1d = self.trans1d + ndmin = self.ndmin + if isinstance(key, str): + frame = sys._getframe().f_back + mymat = matrix.bmat(key,frame.f_globals,frame.f_locals) + return mymat + if type(key) is not tuple: + key = (key,) + objs = [] + scalars = [] + final_dtypedescr = None + for k in range(len(key)): + scalar = False + if type(key[k]) is slice: + step = key[k].step + start = key[k].start + stop = key[k].stop + if start is None: start = 0 + if step is None: + step = 1 + if isinstance(step, complex): + size = int(abs(step)) + newobj = function_base.linspace(start, stop, num=size) + else: + newobj = _nx.arange(start, stop, step) + if ndmin > 1: + newobj = array(newobj,copy=False,ndmin=ndmin) + if trans1d != -1: + newobj = newobj.swapaxes(-1,trans1d) + elif isinstance(key[k],str): + if k != 0: + raise ValueError, "special directives must be the"\ + "first entry." + key0 = key[0] + if key0 in 'rc': + self.matrix = True + self.col = (key0 == 'c') + continue + if ',' in key0: + vec = key0.split(',') + try: + self.axis, ndmin = \ + [int(x) for x in vec[:2]] + if len(vec) == 3: + trans1d = int(vec[2]) + continue + except: + raise ValueError, "unknown special directive" + try: + self.axis = int(key[k]) + continue + except (ValueError, TypeError): + raise ValueError, "unknown special directive" + elif type(key[k]) in ScalarType: + newobj = array(key[k],ndmin=ndmin) + scalars.append(k) + scalar = True + else: + newobj = key[k] + if ndmin > 1: + tempobj = array(newobj, copy=False, subok=True) + newobj = array(newobj, copy=False, subok=True, + ndmin=ndmin) + if trans1d != -1 and tempobj.ndim < ndmin: + k2 = ndmin-tempobj.ndim + if (trans1d < 0): + trans1d += k2 + 1 + defaxes = range(ndmin) + k1 = trans1d + axes = defaxes[:k1] + defaxes[k2:] + \ + defaxes[k1:k2] + newobj = newobj.transpose(axes) + del tempobj + objs.append(newobj) + if isinstance(newobj, _nx.ndarray) and not scalar: + if final_dtypedescr is None: + final_dtypedescr = newobj.dtype + elif newobj.dtype > final_dtypedescr: + final_dtypedescr = newobj.dtype + if final_dtypedescr is not None: + for k in scalars: + objs[k] = objs[k].astype(final_dtypedescr) + res = _nx.concatenate(tuple(objs),axis=self.axis) + return self._retval(res) + + def __getslice__(self,i,j): + res = _nx.arange(i,j) + return self._retval(res) + + def __len__(self): + return 0 + +# separate classes are used here instead of just making r_ = concatentor(0), +# etc. because otherwise we couldn't get the doc string to come out right +# in help(r_) + +class r_class(concatenator): + """Translates slice objects to concatenation along the first axis. + + For example: + >>> r_[array([1,2,3]), 0, 0, array([4,5,6])] + array([1, 2, 3, 0, 0, 4, 5, 6]) + + """ + def __init__(self): + concatenator.__init__(self, 0) + +r_ = r_class() + +class c_class(concatenator): + """Translates slice objects to concatenation along the second axis. + """ + def __init__(self): + concatenator.__init__(self, -1, ndmin=2, trans1d=0) + +c_ = c_class() + +class ndenumerate(object): + """ + A simple nd index iterator over an array. + + Example: + >>> a = array([[1,2],[3,4]]) + >>> for index, x in ndenumerate(a): + ... print index, x + (0, 0) 1 + (0, 1) 2 + (1, 0) 3 + (1, 1) 4 + """ + def __init__(self, arr): + self.iter = asarray(arr).flat + + def next(self): + return self.iter.coords, self.iter.next() + + def __iter__(self): + return self + + +class ndindex(object): + """Pass in a sequence of integers corresponding + to the number of dimensions in the counter. This iterator + will then return an N-dimensional counter. + + Example: + >>> for index in ndindex(3,2,1): + ... print index + (0, 0, 0) + (0, 1, 0) + (1, 0, 0) + (1, 1, 0) + (2, 0, 0) + (2, 1, 0) + + """ + + def __init__(self, *args): + if len(args) == 1 and isinstance(args[0], tuple): + args = args[0] + self.nd = len(args) + self.ind = [0]*self.nd + self.index = 0 + self.maxvals = args + tot = 1 + for k in range(self.nd): + tot *= args[k] + self.total = tot + + def _incrementone(self, axis): + if (axis < 0): # base case + return + if (self.ind[axis] < self.maxvals[axis]-1): + self.ind[axis] += 1 + else: + self.ind[axis] = 0 + self._incrementone(axis-1) + + def ndincr(self): + self._incrementone(self.nd-1) + + def next(self): + if (self.index >= self.total): + raise StopIteration + val = tuple(self.ind) + self.index += 1 + self.ndincr() + return val + + def __iter__(self): + return self + + + + +# You can do all this with slice() plus a few special objects, +# but there's a lot to remember. This version is simpler because +# it uses the standard array indexing syntax. +# +# Written by Konrad Hinsen <hinsen@cnrs-orleans.fr> +# last revision: 1999-7-23 +# +# Cosmetic changes by T. Oliphant 2001 +# +# + +class _index_expression_class(object): + """ + A nicer way to build up index tuples for arrays. + + For any index combination, including slicing and axis insertion, + 'a[indices]' is the same as 'a[index_exp[indices]]' for any + array 'a'. However, 'index_exp[indices]' can be used anywhere + in Python code and returns a tuple of slice objects that can be + used in the construction of complex index expressions. + """ + maxint = sys.maxint + def __init__(self, maketuple): + self.maketuple = maketuple + + def __getitem__(self, item): + if self.maketuple and type(item) != type(()): + return (item,) + else: + return item + + def __len__(self): + return self.maxint + + def __getslice__(self, start, stop): + if stop == self.maxint: + stop = None + return self[start:stop:None] + +index_exp = _index_expression_class(1) +s_ = _index_expression_class(0) + +# End contribution from Konrad. |