diff options
Diffstat (limited to 'numpy/lib/function_base.py')
| -rw-r--r-- | numpy/lib/function_base.py | 138 |
1 files changed, 93 insertions, 45 deletions
diff --git a/numpy/lib/function_base.py b/numpy/lib/function_base.py index 3b8ddf099..3f293ae4c 100644 --- a/numpy/lib/function_base.py +++ b/numpy/lib/function_base.py @@ -117,15 +117,16 @@ def histogram(a, bins=10, range=None, normed=False, weights=None): if weights is not None: weights = asarray(weights) if np.any(weights.shape != a.shape): - raise ValueError, 'weights should have the same shape as a.' + raise ValueError( + 'weights should have the same shape as a.') weights = weights.ravel() a = a.ravel() if (range is not None): mn, mx = range if (mn > mx): - raise AttributeError, \ - 'max must be larger than min in range parameter.' + raise AttributeError( + 'max must be larger than min in range parameter.') if not iterable(bins): if range is None: @@ -138,7 +139,8 @@ def histogram(a, bins=10, range=None, normed=False, weights=None): else: bins = asarray(bins) if (np.diff(bins) < 0).any(): - raise AttributeError, 'bins must increase monotonically.' + raise AttributeError( + 'bins must increase monotonically.') # Histogram is an integer or a float array depending on the weights. if weights is None: @@ -244,8 +246,9 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None): try: M = len(bins) if M != D: - raise AttributeError, 'The dimension of bins must be equal ' \ - 'to the dimension of the sample x.' + raise AttributeError( + 'The dimension of bins must be equal'\ + ' to the dimension of the sample x.') except TypeError: bins = D*[bins] @@ -334,12 +337,13 @@ def histogramdd(sample, bins=10, range=None, normed=False, weights=None): s = hist.sum() for i in arange(D): shape = ones(D, int) - shape[i] = nbin[i]-2 + shape[i] = nbin[i] - 2 hist = hist / dedges[i].reshape(shape) hist /= s - if (hist.shape != nbin-2).any(): - raise RuntimeError('Internal Shape Error') + if (hist.shape != nbin - 2).any(): + raise RuntimeError( + "Internal Shape Error") return hist, edges @@ -429,23 +433,30 @@ def average(a, axis=None, weights=None, returned=False): # Sanity checks if a.shape != wgt.shape : if axis is None : - raise TypeError, "Axis must be specified when shapes of a and weights differ." + raise TypeError( + "Axis must be specified when shapes of a "\ + "and weights differ.") if wgt.ndim != 1 : - raise TypeError, "1D weights expected when shapes of a and weights differ." + raise TypeError( + "1D weights expected when shapes of a and "\ + "weights differ.") if wgt.shape[0] != a.shape[axis] : - raise ValueError, "Length of weights not compatible with specified axis." + raise ValueError( + "Length of weights not compatible with "\ + "specified axis.") # setup wgt to broadcast along axis - wgt = np.array(wgt, copy=0, ndmin=a.ndim).swapaxes(-1,axis) + wgt = np.array(wgt, copy=0, ndmin=a.ndim).swapaxes(-1, axis) scl = wgt.sum(axis=axis) if (scl == 0.0).any(): - raise ZeroDivisionError, "Weights sum to zero, can't be normalized" + raise ZeroDivisionError( + "Weights sum to zero, can't be normalized") - avg = np.multiply(a,wgt).sum(axis)/scl + avg = np.multiply(a, wgt).sum(axis)/scl if returned: - scl = np.multiply(avg,0) + scl + scl = np.multiply(avg, 0) + scl return avg, scl else: return avg @@ -513,7 +524,8 @@ def asarray_chkfinite(a): a = asarray(a) if (a.dtype.char in typecodes['AllFloat']) \ and (_nx.isnan(a).any() or _nx.isinf(a).any()): - raise ValueError, "array must not contain infs or NaNs" + raise ValueError( + "array must not contain infs or NaNs") return a def piecewise(x, condlist, funclist, *args, **kw): @@ -612,8 +624,8 @@ def piecewise(x, condlist, funclist, *args, **kw): condlist.append(~totlist) n += 1 if (n != n2): - raise ValueError, "function list and condition list " \ - "must be the same" + raise ValueError( + "function list and condition list must be the same") zerod = False # This is a hack to work around problems with NumPy's # handling of 0-d arrays and boolean indexing with @@ -683,7 +695,8 @@ def select(condlist, choicelist, default=0): n = len(condlist) n2 = len(choicelist) if n2 != n: - raise ValueError, "list of cases must be same length as list of conditions" + raise ValueError( + "list of cases must be same length as list of conditions") choicelist = [default] + choicelist S = 0 pfac = 1 @@ -791,7 +804,8 @@ def gradient(f, *varargs): elif n == N: dx = list(varargs) else: - raise SyntaxError, "invalid number of arguments" + raise SyntaxError( + "invalid number of arguments") # use central differences on interior and first differences on endpoints @@ -885,7 +899,8 @@ def diff(a, n=1, axis=-1): if n == 0: return a if n < 0: - raise ValueError, 'order must be non-negative but got ' + repr(n) + raise ValueError( + "order must be non-negative but got " + repr(n)) a = asanyarray(a) nd = len(a.shape) slice1 = [slice(None)]*nd @@ -1596,22 +1611,49 @@ def disp(mesg, device=None, linefeed=True): # return number of input arguments and # number of default arguments -import re + def _get_nargs(obj): + import re + + terr = re.compile(r'.*? takes (exactly|at least) (?P<exargs>(\d+)|(\w+))' + + r' argument(s|) \((?P<gargs>(\d+)|(\w+)) given\)') + def _convert_to_int(strval): + try: + result = int(strval) + except ValueError: + if strval=='zero': + result = 0 + elif strval=='one': + result = 1 + elif strval=='two': + result = 2 + # How high to go? English only? + else: + raise + return result + if not callable(obj): - raise TypeError, "Object is not callable." + raise TypeError( + "Object is not callable.") if sys.version_info[0] >= 3: + # inspect currently fails for binary extensions + # like math.cos. So fall back to other methods if + # it fails. import inspect - spec = inspect.getargspec(obj) - nargs = len(spec.args) - if spec.defaults: - ndefaults = len(spec.defaults) - else: - ndefaults = 0 - if inspect.ismethod(obj): - nargs -= 1 - return nargs, ndefaults - elif hasattr(obj,'func_code'): + try: + spec = inspect.getargspec(obj) + nargs = len(spec.args) + if spec.defaults: + ndefaults = len(spec.defaults) + else: + ndefaults = 0 + if inspect.ismethod(obj): + nargs -= 1 + return nargs, ndefaults + except: + pass + + if hasattr(obj,'func_code'): fcode = obj.func_code nargs = fcode.co_argcount if obj.func_defaults is not None: @@ -1621,19 +1663,21 @@ def _get_nargs(obj): if isinstance(obj, types.MethodType): nargs -= 1 return nargs, ndefaults - terr = re.compile(r'.*? takes exactly (?P<exargs>\d+) argument(s|) \((?P<gargs>\d+) given\)') + try: obj() return 0, 0 except TypeError, msg: m = terr.match(str(msg)) if m: - nargs = int(m.group('exargs')) - ndefaults = int(m.group('gargs')) + nargs = _convert_to_int(m.group('exargs')) + ndefaults = _convert_to_int(m.group('gargs')) if isinstance(obj, types.MethodType): nargs -= 1 return nargs, ndefaults - raise ValueError, 'failed to determine the number of arguments for %s' % (obj) + + raise ValueError( + "failed to determine the number of arguments for %s" % (obj)) class vectorize(object): @@ -1717,11 +1761,13 @@ class vectorize(object): self.otypes = otypes for char in self.otypes: if char not in typecodes['All']: - raise ValueError, "invalid otype specified" + raise ValueError( + "invalid otype specified") elif iterable(otypes): self.otypes = ''.join([_nx.dtype(x).char for x in otypes]) else: - raise ValueError, "output types must be a string of typecode characters or a list of data-types" + raise ValueError( + "Invalid otype specification") self.lastcallargs = 0 def __call__(self, *args): @@ -1730,8 +1776,8 @@ class vectorize(object): nargs = len(args) if self.nin: if (nargs > self.nin) or (nargs < self.nin_wo_defaults): - raise ValueError, "mismatch between python function inputs"\ - " and received arguments" + raise ValueError( + "Invalid number of arguments") # we need a new ufunc if this is being called with more arguments. if (self.lastcallargs != nargs): @@ -3052,7 +3098,8 @@ def delete(arr, obj, axis=None): if isinstance(obj, (int, long, integer)): if (obj < 0): obj += N if (obj < 0 or obj >=N): - raise ValueError, "invalid entry" + raise ValueError( + "invalid entry") newshape[axis]-=1; new = empty(newshape, arr.dtype, arr.flags.fnc) slobj[axis] = slice(None, obj) @@ -3197,8 +3244,9 @@ def insert(arr, obj, values, axis=None): if isinstance(obj, (int, long, integer)): if (obj < 0): obj += N if obj < 0 or obj > N: - raise ValueError, "index (%d) out of range (0<=index<=%d) "\ - "in dimension %d" % (obj, N, axis) + raise ValueError( + "index (%d) out of range (0<=index<=%d) "\ + "in dimension %d" % (obj, N, axis)) newshape[axis] += 1; new = empty(newshape, arr.dtype, arr.flags.fnc) slobj[axis] = slice(None, obj) |