Python numpy 模块，isposinf() 实例源码

def __add__(self, other):

        assert isinstance(other, ShapeFunction), "Can only add other shape function"

        assert self.name == other.name, "Cannot add shapes of different features"

        new_splits = self.splits.copy()
        new_vals = self.values.copy()

        for split, val in zip(other.splits, other.values):
            idx = np.searchsorted(new_splits, split, side='right')
            new_val = val
            if split in new_splits:
                idx_2 = np.argwhere(new_splits == split)
                new_vals[idx_2] = new_vals[idx_2] + new_val
            elif idx == len(new_splits) and (~np.isposinf(split)):
                new_splits = np.append(new_splits, split)
                new_vals = np.append(new_vals, new_val)
            elif np.isposinf(split):
                new_vals[-1] = new_vals[-1] + new_val
            else:
                new_splits = np.insert(new_splits, idx, split)
                new_vals = np.insert(new_vals, idx, new_val)

        return ShapeFunction(new_splits, new_vals, self.name)

def get_mask_invalid(matrix):
    mask = np.isposinf(matrix) + np.isneginf(matrix) + np.isnan(matrix)
    return mask

def set_logp_to_neg_inf(X, logp, bounds):
    """Set `logp` to negative infinity when `X` is outside the allowed bounds.

    # Arguments
        X: tensorflow.Tensor
            The variable to apply the bounds to
        logp: tensorflow.Tensor
            The log probability corrosponding to `X`
        bounds: list of `Region` objects
            The regions corrosponding to allowed regions of `X`

    # Returns
        logp: tensorflow.Tensor
            The newly bounded log probability
    """
    conditions = []
    for l, u in bounds:
        lower_is_neg_inf = not isinstance(l, tf.Tensor) and np.isneginf(l)
        upper_is_pos_inf = not isinstance(u, tf.Tensor) and np.isposinf(u)

        if not lower_is_neg_inf and upper_is_pos_inf:
            conditions.append(tf.greater(X, l))
        elif lower_is_neg_inf and not upper_is_pos_inf:
            conditions.append(tf.less(X, u))
        elif not (lower_is_neg_inf or upper_is_pos_inf):
            conditions.append(tf.logical_and(tf.greater(X, l), tf.less(X, u)))

    if len(conditions) > 0:
        is_inside_bounds = conditions[0]
        for condition in conditions[1:]:
            is_inside_bounds = tf.logical_or(is_inside_bounds, condition)

        logp = tf.select(
            is_inside_bounds,
            logp,
            tf.fill(tf.shape(X), config.dtype(-np.inf))
        )

    return logp

def _numpy(self, data, weights, shape):
        q = self.quantity(data)
        self._checkNPQuantity(q, shape)
        self._checkNPWeights(weights, shape)
        weights = self._makeNPWeights(weights, shape)
        newentries = weights.sum()

        import numpy

        selection = numpy.isnan(q)
        numpy.bitwise_not(selection, selection)
        subweights = weights.copy()
        subweights[selection] = 0.0
        self.nanflow._numpy(data, subweights, shape)

        # switch to float here like in bin.py else numpy throws
        # TypeError on trivial integer cases such as:
        # >>> q = numpy.array([1,2,3,4])
        # >>> np.divide(q,1,q)
        # >>> np.floor(q,q)
        q = numpy.array(q, dtype=numpy.float64)
        neginfs = numpy.isneginf(q)
        posinfs = numpy.isposinf(q)

        numpy.subtract(q, self.origin, q)
        numpy.divide(q, self.binWidth, q)
        numpy.floor(q, q)
        q = numpy.array(q, dtype=numpy.int64)
        q[neginfs] = LONG_MINUSINF
        q[posinfs] = LONG_PLUSINF

        selected = q[weights > 0.0]

        selection = numpy.empty(q.shape, dtype=numpy.bool)
        for index in numpy.unique(selected):
            if index != LONG_NAN:
                bin = self.bins.get(index)
                if bin is None:
                    bin = self.value.zero()
                    self.bins[index] = bin

                numpy.not_equal(q, index, selection)
                subweights[:] = weights
                subweights[selection] = 0.0
                bin._numpy(data, subweights, shape)

        # no possibility of exception from here on out (for rollback)
        self.entries += float(newentries)