我们从Python开源项目中，提取了以下50个代码示例，用于说明如何使用numpy.greater_equal()。
def _constrained_sum_sample_pos(n, total): # in this setting, there will be no empty groups generated by this function n = int(n) total = int(total) normalized_list = [int(total) + 1] while sum(normalized_list) > total and np.greater_equal(normalized_list, np.zeros(n)).all(): indicator = True while indicator: normalized_list = list(map(round, map(lambda x: x * total, np.random.dirichlet(np.ones(n), 1).tolist()[0]))) normalized_list = list(map(int, normalized_list)) indicator = len(normalized_list) - np.count_nonzero(normalized_list) != 0 sum_ = 0 for ind, q in enumerate(normalized_list): if ind < len(normalized_list) - 1: sum_ += q # TODO: there is a bug here; sometimes it assigns -1 to the end of the array, but pass the while condition normalized_list[len(normalized_list) - 1] = abs(total - sum_) assert sum(normalized_list) == total, "ERROR: the constrainedSumSamplePos-sampled list does not sum to #edges." return map(str, normalized_list)
def equal(x1, x2): """ Return (x1 == x2) element-wise. Unlike `numpy.equal`, this comparison is performed by first stripping whitespace characters from the end of the string. This behavior is provided for backward-compatibility with numarray. Parameters ---------- x1, x2 : array_like of str or unicode Input arrays of the same shape. Returns ------- out : ndarray or bool Output array of bools, or a single bool if x1 and x2 are scalars. See Also -------- not_equal, greater_equal, less_equal, greater, less """ return compare_chararrays(x1, x2, '==', True)
def not_equal(x1, x2): """ Return (x1 != x2) element-wise. Unlike `numpy.not_equal`, this comparison is performed by first stripping whitespace characters from the end of the string. This behavior is provided for backward-compatibility with numarray. Parameters ---------- x1, x2 : array_like of str or unicode Input arrays of the same shape. Returns ------- out : ndarray or bool Output array of bools, or a single bool if x1 and x2 are scalars. See Also -------- equal, greater_equal, less_equal, greater, less """ return compare_chararrays(x1, x2, '!=', True)
def greater_equal(x1, x2): """ Return (x1 >= x2) element-wise. Unlike `numpy.greater_equal`, this comparison is performed by first stripping whitespace characters from the end of the string. This behavior is provided for backward-compatibility with numarray. Parameters ---------- x1, x2 : array_like of str or unicode Input arrays of the same shape. Returns ------- out : ndarray or bool Output array of bools, or a single bool if x1 and x2 are scalars. See Also -------- equal, not_equal, less_equal, greater, less """ return compare_chararrays(x1, x2, '>=', True)
def less_equal(x1, x2): """ Return (x1 <= x2) element-wise. Unlike `numpy.less_equal`, this comparison is performed by first stripping whitespace characters from the end of the string. This behavior is provided for backward-compatibility with numarray. Parameters ---------- x1, x2 : array_like of str or unicode Input arrays of the same shape. Returns ------- out : ndarray or bool Output array of bools, or a single bool if x1 and x2 are scalars. See Also -------- equal, not_equal, greater_equal, greater, less """ return compare_chararrays(x1, x2, '<=', True)
def greater(x1, x2): """ Return (x1 > x2) element-wise. Unlike `numpy.greater`, this comparison is performed by first stripping whitespace characters from the end of the string. This behavior is provided for backward-compatibility with numarray. Parameters ---------- x1, x2 : array_like of str or unicode Input arrays of the same shape. Returns ------- out : ndarray or bool Output array of bools, or a single bool if x1 and x2 are scalars. See Also -------- equal, not_equal, greater_equal, less_equal, less """ return compare_chararrays(x1, x2, '>', True)
def test_NotImplemented_not_returned(self): # See gh-5964 and gh-2091. Some of these functions are not operator # related and were fixed for other reasons in the past. binary_funcs = [ np.power, np.add, np.subtract, np.multiply, np.divide, np.true_divide, np.floor_divide, np.bitwise_and, np.bitwise_or, np.bitwise_xor, np.left_shift, np.right_shift, np.fmax, np.fmin, np.fmod, np.hypot, np.logaddexp, np.logaddexp2, np.logical_and, np.logical_or, np.logical_xor, np.maximum, np.minimum, np.mod ] # These functions still return NotImplemented. Will be fixed in # future. # bad = [np.greater, np.greater_equal, np.less, np.less_equal, np.not_equal] a = np.array('1') b = 1 for f in binary_funcs: assert_raises(TypeError, f, a, b)
def test_identity_equality_mismatch(self): a = np.array([np.nan], dtype=object) with warnings.catch_warnings(): warnings.filterwarnings('always', '', FutureWarning) assert_warns(FutureWarning, np.equal, a, a) assert_warns(FutureWarning, np.not_equal, a, a) with warnings.catch_warnings(): warnings.filterwarnings('error', '', FutureWarning) assert_raises(FutureWarning, np.equal, a, a) assert_raises(FutureWarning, np.not_equal, a, a) # And the other do not warn: with np.errstate(invalid='ignore'): np.less(a, a) np.greater(a, a) np.less_equal(a, a) np.greater_equal(a, a)
def ntron_pulse(amplitude=1.0, rise_time=80e-12, hold_time=170e-12, fall_time=1.0e-9, sample_rate=12e9): delay = 2.0e-9 # Wait a few TCs for the rising edge duration = delay + hold_time + 6.0*fall_time # Wait 6 TCs for the slow decay pulse_points = int(duration*sample_rate) if pulse_points < 320: duration = 319/sample_rate # times = np.arange(0, duration, 1/sample_rate) times = np.linspace(0, duration, 320) else: pulse_points = 64*np.ceil(pulse_points/64.0) duration = (pulse_points-1)/sample_rate # times = np.arange(0, duration, 1/sample_rate) times = np.linspace(0, duration, pulse_points) rise_mask = np.less(times, delay) hold_mask = np.less(times, delay + hold_time)*np.greater_equal(times, delay) fall_mask = np.greater_equal(times, delay + hold_time) wf = rise_mask*np.exp((times-delay)/rise_time) wf += hold_mask wf += fall_mask*np.exp(-(times-delay-hold_time)/fall_time) return amplitude*wf
def build(self, input_shape): super().build(input_shape) self.mask = np.ones(self.W_shape) assert mask.shape[0] == mask.shape[1] filter_size = self.mask.shape[0] filter_center = filter_size / 2 self.mask[math.ceil(filter_center):] = 0 self.mask[math.floor(filter_center):, math.ceil(filter_center):] = 0 if self.mono: if self.mask_type == 'A': self.mask[math.floor(filter_center), math.floor(filter_center)] = 0 else: op = np.greater_equal if self.mask_type == 'A' else np.greater for i in range(self.n_channels): for j in range(self.n_channels): if op(i, j): self.mask[math.floor(filter_center), math.floor(filter_center), i::self.n_channels, j::self.n_channels] = 0 self.mask = K.variable(self.mask)
def points_in_front(self, points, inverted=False, ret_indices=False): ''' Given an array of points, return the points which lie either on the plane or in the half-space in front of it (i.e. in the direction of the plane normal). points: An array of points. inverted: When `True`, invert the logic. Return the points that lie behind the plane instead. ret_indices: When `True`, return the indices instead of the points themselves. ''' sign = self.sign(points) if inverted: mask = np.less_equal(sign, 0) else: mask = np.greater_equal(sign, 0) indices = np.flatnonzero(mask) return indices if ret_indices else points[indices]
def amedian (inarray,numbins=1000): """ Calculates the COMPUTED median value of an array of numbers, given the number of bins to use for the histogram (more bins approaches finding the precise median value of the array; default number of bins = 1000). From G.W. Heiman's Basic Stats, or CRC Probability & Statistics. NOTE: THIS ROUTINE ALWAYS uses the entire passed array (flattens it first). Usage: amedian(inarray,numbins=1000) Returns: median calculated over ALL values in inarray """ inarray = N.ravel(inarray) (hist, smallest, binsize, extras) = ahistogram(inarray,numbins,[min(inarray),max(inarray)]) cumhist = N.cumsum(hist) # make cumulative histogram otherbins = N.greater_equal(cumhist,len(inarray)/2.0) otherbins = list(otherbins) # list of 0/1s, 1s start at median bin cfbin = otherbins.index(1) # get 1st(!) index holding 50%ile score LRL = smallest + binsize*cfbin # get lower read limit of that bin cfbelow = N.add.reduce(hist[0:cfbin]) # cum. freq. below bin freq = hist[cfbin] # frequency IN the 50%ile bin median = LRL + ((len(inarray)/2.0-cfbelow)/float(freq))*binsize # MEDIAN return median
def atmin(a,lowerlimit=None,dimension=None,inclusive=1): """ Returns the minimum value of a, along dimension, including only values less than (or equal to, if inclusive=1) lowerlimit. If the limit is set to None, all values in the array are used. Usage: atmin(a,lowerlimit=None,dimension=None,inclusive=1) """ if inclusive: lowerfcn = N.greater else: lowerfcn = N.greater_equal if dimension == None: a = N.ravel(a) dimension = 0 if lowerlimit == None: lowerlimit = N.minimum.reduce(N.ravel(a))-11 biggest = N.maximum.reduce(N.ravel(a)) ta = N.where(lowerfcn(a,lowerlimit),a,biggest) return N.minimum.reduce(ta,dimension)
def parse(cls, func): if isinstance(func, six.string_types): func = func.lower().strip() if func in [np.equal, '=', 'eq', '-eq', '==', 'is', 'equal', 'equal to']: return cls.eq elif func in [np.not_equal, '<>', 'ne', '-ne', '!=', 'not', 'not_equal', 'not equal to']: return cls.ne elif func in [np.greater, '>', 'gt', '-gt', 'above', 'after', 'greater', 'greater than']: return cls.gt elif func in [np.less, '<', 'lt', '-lt', 'below', 'before', 'less', 'less than']: return cls.lt elif func in [np.greater_equal, '>=', 'ge', '-ge', 'greater_equal', 'greater than or equal to']: return cls.ge elif func in [np.less_equal, '<=', 'le', '-le', 'less_equal', 'less than or equal to']: return cls.le raise ValueError('Invalid Comparison name: %s'%func) # ### Control Condition classes #
