diff options
Diffstat (limited to 'numpy/lib/function_base.py')
| -rw-r--r-- | numpy/lib/function_base.py | 52 |
1 files changed, 26 insertions, 26 deletions
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index 4285bf793..91eace2ff 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -351,7 +351,7 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None): # Compute the bin number each sample falls into. Ncount = {} for i in arange(D): - Ncount[i] = digitize(sample[:,i], edges[i]) + Ncount[i] = digitize(sample[:, i], edges[i]) # Using digitize, values that fall on an edge are put in the right bin. # For the rightmost bin, we want values equal to the right @@ -362,7 +362,7 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None): if not np.isinf(mindiff): decimal = int(-log10(mindiff)) + 6 # Find which points are on the rightmost edge. - on_edge = where(around(sample[:,i], decimal) == around(edges[i][-1], + on_edge = where(around(sample[:, i], decimal) == around(edges[i][-1], decimal))[0] # Shift these points one bin to the left. Ncount[i][on_edge] -= 1 @@ -392,11 +392,11 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None): hist = hist.reshape(sort(nbin)) for i in arange(nbin.size): j = ni.argsort()[i] - hist = hist.swapaxes(i,j) - ni[i],ni[j] = ni[j],ni[i] + hist = hist.swapaxes(i, j) + ni[i], ni[j] = ni[j], ni[i] # Remove outliers (indices 0 and -1 for each dimension). - core = D*[slice(1,-1)] + core = D*[slice(1, -1)] hist = hist[core] # Normalize if normed is True @@ -1196,7 +1196,7 @@ def sort_complex(a): array([ 1.+2.j, 2.-1.j, 3.-3.j, 3.-2.j, 3.+5.j]) """ - b = array(a,copy=True) + b = array(a, copy=True) b.sort() if not issubclass(b.dtype.type, _nx.complexfloating): if b.dtype.char in 'bhBH': @@ -1269,7 +1269,7 @@ def unique(x): if tmp.size == 0: return tmp tmp.sort() - idx = concatenate(([True],tmp[1:]!=tmp[:-1])) + idx = concatenate(([True], tmp[1:]!=tmp[:-1])) return tmp[idx] except AttributeError: items = sorted(set(x)) @@ -1736,7 +1736,7 @@ def cov(m, y=None, rowvar=1, bias=0, ddof=None): rowvar = 1 if rowvar: axis = 0 - tup = (slice(None),newaxis) + tup = (slice(None), newaxis) else: axis = 1 tup = (newaxis, slice(None)) @@ -1744,7 +1744,7 @@ def cov(m, y=None, rowvar=1, bias=0, ddof=None): if y is not None: y = array(y, copy=False, ndmin=2, dtype=float) - X = concatenate((X,y), axis) + X = concatenate((X, y), axis) X -= X.mean(axis=1-axis)[tup] if rowvar: @@ -1820,7 +1820,7 @@ def corrcoef(x, y=None, rowvar=1, bias=0, ddof=None): d = diag(c) except ValueError: # scalar covariance return 1 - return c/sqrt(multiply.outer(d,d)) + return c/sqrt(multiply.outer(d, d)) def blackman(M): """ @@ -1916,7 +1916,7 @@ def blackman(M): return array([]) if M == 1: return ones(1, float) - n = arange(0,M) + n = arange(0, M) return 0.42-0.5*cos(2.0*pi*n/(M-1)) + 0.08*cos(4.0*pi*n/(M-1)) def bartlett(M): @@ -2022,8 +2022,8 @@ def bartlett(M): return array([]) if M == 1: return ones(1, float) - n = arange(0,M) - return where(less_equal(n,(M-1)/2.0),2.0*n/(M-1),2.0-2.0*n/(M-1)) + n = arange(0, M) + return where(less_equal(n, (M-1)/2.0), 2.0*n/(M-1), 2.0-2.0*n/(M-1)) def hanning(M): """ @@ -2120,7 +2120,7 @@ def hanning(M): return array([]) if M == 1: return ones(1, float) - n = arange(0,M) + n = arange(0, M) return 0.5-0.5*cos(2.0*pi*n/(M-1)) def hamming(M): @@ -2216,8 +2216,8 @@ def hamming(M): if M < 1: return array([]) if M == 1: - return ones(1,float) - n = arange(0,M) + return ones(1, float) + n = arange(0, M) return 0.54-0.46*cos(2.0*pi*n/(M-1)) ## Code from cephes for i0 @@ -2285,7 +2285,7 @@ def _chbevl(x, vals): b0 = vals[0] b1 = 0.0 - for i in range(1,len(vals)): + for i in range(1, len(vals)): b2 = b1 b1 = b0 b0 = x*b1 - b2 + vals[i] @@ -2364,7 +2364,7 @@ def i0(x): ## End of cephes code for i0 -def kaiser(M,beta): +def kaiser(M, beta): """ Return the Kaiser window. @@ -2487,7 +2487,7 @@ def kaiser(M,beta): from numpy.dual import i0 if M == 1: return np.array([1.]) - n = arange(0,M) + n = arange(0, M) alpha = (M-1)/2.0 return i0(beta * sqrt(1-((n-alpha)/alpha)**2.0))/i0(float(beta)) @@ -2592,7 +2592,7 @@ def msort(a): ``np.msort(a)`` is equivalent to ``np.sort(a, axis=0)``. """ - b = array(a,subok=True,copy=True) + b = array(a, subok=True, copy=True) b.sort(0) return b @@ -2841,7 +2841,7 @@ def _compute_qth_percentile(sorted, q, axis, out): else: indexer[axis] = slice(i, i+2) j = i + 1 - weights = array([(j - index), (index - i)],float) + weights = array([(j - index), (index - i)], float) wshape = [1]*sorted.ndim wshape[axis] = 2 weights.shape = wshape @@ -2926,8 +2926,8 @@ def trapz(y, x=None, dx=1.0, axis=-1): nd = len(y.shape) slice1 = [slice(None)]*nd slice2 = [slice(None)]*nd - slice1[axis] = slice(1,None) - slice2[axis] = slice(None,-1) + slice1[axis] = slice(1, None) + slice2[axis] = slice(None, -1) try: ret = (d * (y[slice1] +y [slice2]) / 2.0).sum(axis) except ValueError: # Operations didn't work, cast to ndarray @@ -3212,7 +3212,7 @@ def delete(arr, obj, axis=None): if stop == N: pass else: - slobj[axis] = slice(stop-numtodel,None) + slobj[axis] = slice(stop-numtodel, None) slobj2 = [slice(None)]*ndim slobj2[axis] = slice(stop, None) new[slobj] = arr[slobj2] @@ -3253,9 +3253,9 @@ def delete(arr, obj, axis=None): new = empty(newshape, arr.dtype, arr.flags.fnc) slobj[axis] = slice(None, obj) new[slobj] = arr[slobj] - slobj[axis] = slice(obj,None) + slobj[axis] = slice(obj, None) slobj2 = [slice(None)]*ndim - slobj2[axis] = slice(obj+1,None) + slobj2[axis] = slice(obj+1, None) new[slobj] = arr[slobj2] else: if obj.size == 0 and not isinstance(_obj, np.ndarray): |