def cumsum(a, axis=None, dtype=None): '''Cumulative sum of input''' dtype = _translatenativetype(dtype) if dtype is not None: a = a.cast(_translatenativetype(dtype).value) if axis is None: return _stats.cumulativeSum(a) else: return _stats.cumulativeSum(a, _jint(axis))
def prod(a, axis=None, dtype=None, keepdims=False): '''Product of input''' if dtype is None: if axis is None: return a.product(_empty_boolean_array) elif isinstance(axis, int): return a.product(_jint(axis)) elif isinstance(axis, tuple): return a.product([_jint(x) for x in axis]) dtval = _translatenativetype(dtype).value if axis is None: return _stats.typedProduct(a, dtval) return _stats.typedProduct(a, dtval, axis)
def quantile(a, q, axis=None): '''Quantile (or inverse cumulative distribution) function based on input a -- data q -- probability value(s) axis -- can be None''' q = _toList(q) if axis is None: if len(q) == 1: return _stats.quantile(a, q)[0] return _stats.quantile(a, q) else: if len(q) == 1: return _stats.quantile(a, axis, q)[0] return _stats.quantile(a, axis, q)
def skewness(a, axis=None): '''Skewness of input''' if axis is None: return _stats.skewness(a) else: return _stats.skewness(a, axis)
def residual(a, b, weight=None): '''Residual (sum of squared difference) of two inputs with optional weighting''' if weight is None: return _stats.residual(a, b) return _stats.weightedResidual(a, b, weight)
def iqr(a, axis=None): '''Interquartile range of input''' if axis is None: return _stats.iqr(a) else: return _stats.iqr(a, axis)
def kurtosis(a, axis=None): '''Kurtosis of input''' if axis is None: return _stats.kurtosis(a) else: return _stats.kurtosis(a, axis)
def cumprod(a, axis=None): '''Cumulative product of input''' if axis is None: return _stats.cumulativeProduct(a) else: return _stats.cumulativeProduct(a, axis)
def median(a, axis=None): '''Median of input''' if axis is None: return _stats.median(a) else: return _stats.median(a, axis)
def median(a, axis=None, keepdims=False): '''Median of input''' if axis is None: return _stats.median(a) else: return _stats.median(a, axis)
def cumsum(a, axis=None): '''Cumulative sum of input''' if axis is None: return _stats.cumulativeSum(a) else: return _stats.cumulativeSum(a, axis)