我们从Python开源项目中，提取了以下15个代码示例，用于说明如何使用__builtin__.max()。
def limit(value, min=negative_infinite, max=positive_infinite): """Limit a numeric value to the specified range""" return maximum(min, minimum(value, max))
def _get_clipfn(size, signed=True): maxval = _get_maxval(size, signed) minval = _get_minval(size, signed) return lambda val: __builtin__.max(min(val, maxval), minval)
def max(cp, size): _check_params(len(cp), size) if len(cp) == 0: return 0 return __builtin__.max(abs(sample) for sample in _get_samples(cp, size))
def minmax(cp, size): _check_params(len(cp), size) max_sample, min_sample = 0, 0 for sample in _get_samples(cp, size): max_sample = __builtin__.max(sample, max_sample) min_sample = __builtin__.min(sample, min_sample) return min_sample, max_sample
def maxpp(cp, size): _check_params(len(cp), size) sample_count = _sample_count(cp, size) prevextremevalid = False prevextreme = None max = 0 prevval = getsample(cp, size, 0) val = getsample(cp, size, 1) prevdiff = val - prevval for i in range(1, sample_count): val = getsample(cp, size, i) diff = val - prevval if diff * prevdiff < 0: if prevextremevalid: extremediff = abs(prevval - prevextreme) if extremediff > max: max = extremediff prevextremevalid = True prevextreme = prevval prevval = val if diff != 0: prevdiff = diff return max
def _postprocess_eval_line(self, evalline, fname, span): lines = evalline.split('\n') # If line ended on '\n', last element is ''. We remove it and # add the trailing newline later manually. trailing_newline = (lines[-1] == '') if trailing_newline: del lines[-1] lnum = linenumdir(span[0], fname) if self._linenums else '' clnum = lnum if self._contlinenums else '' linenumsep = '\n' + lnum clinenumsep = '\n' + clnum foldedlines = [self._foldline(line) for line in lines] outlines = [clinenumsep.join(lines) for lines in foldedlines] result = linenumsep.join(outlines) # Add missing trailing newline if trailing_newline: trailing = '\n' if self._linenums: # Last line was folded, but no linenums were generated for # the continuation lines -> current line position is not # in sync with the one calculated from the last line number unsync = ( len(foldedlines) and len(foldedlines[-1]) > 1 and not self._contlinenums) # Eval directive in source consists of more than one line multiline = span[1] - span[0] > 1 if unsync or multiline: # For inline eval directives span[0] == span[1] # -> next line is span[0] + 1 and not span[1] as for # line eval directives nextline = max(span[1], span[0] + 1) trailing += linenumdir(nextline, fname) else: trailing = '' return result + trailing
def _get_smart_fold_pos(line, start, end): linelen = end - start ispace = line.rfind(' ', start, end) # The space we waste for smart folding should be max. 1/3rd of the line if ispace != -1 and ispace >= start + (2 * linelen) // 3: return ispace else: return end
def sizeof(self,ix): if isinstance(ix,int): n = ix+1 elif isinstance(ix,slice): n = ix.stop elif isinstance(ix,(list,np.ndarray)): n = max(ix)+1 else: assert 0,ix if not isinstance(n,int): raise IndexError return n
def length(a): try: return __builtin__.max(np.asarray(a).shape) except ValueError: return 1
def max(a, d=0, nargout=0): if d or nargout: raise NotImplementedError return np.amax(a)
def __setitem__(self,index,value): #import pdb; pdb.set_trace() indices = self.compute_indices(index) try: if len(indices) == 1: np.asarray(self).reshape(-1,order="F").__setitem__(indices,value) else: np.asarray(self).__setitem__(indices,value) except (ValueError,IndexError): #import pdb; pdb.set_trace() if not self.size: new_shape = [self.sizeof(s) for s in indices] self.resize(new_shape,refcheck=0) np.asarray(self).__setitem__(indices,value) elif len(indices) == 1: # One-dimensional resize is only implemented for # two cases: # # a. empty matrices having shape [0 0]. These # matries may be resized to any shape. A[B]=C # where A=[], and B is specific -- A[1:10]=C # rather than A[:]=C or A[1:end]=C if self.size and not isvector_or_scalar(self): raise IndexError("One-dimensional resize " "works only on vectors, and " "row and column matrices") # One dimensional resize of scalars creates row matrices # ai = 3 # a(4) = 1 # 3 0 0 1 n = self.sizeof(indices[0]) # zero-based if max(self.shape) == 1: new_shape = list(self.shape) new_shape[-1] = n else: new_shape = [(1 if s==1 else n) for s in self.shape] self.resize(new_shape,refcheck=0) np.asarray(self).reshape(-1,order="F").__setitem__(indices,value) else: new_shape = list(self.shape) if self.flags["C_CONTIGUOUS"]: new_shape[0] = self.sizeof(indices[0]) elif self.flags["F_CONTIGUOUS"]: new_shape[-1] = self.sizeof(indices[-1]) self.resize(new_shape,refcheck=0) np.asarray(self).__setitem__(indices,value)
