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 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 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 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)
def cumprod(a, axis=None):
'''Cumulative product of input'''
if axis is None:
return _stats.cumulativeProduct(a)
else:
return _stats.cumulativeProduct(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 iqr(a, axis=None):
'''Interquartile range of input'''
if axis is None:
return _stats.iqr(a)
else:
return _stats.iqr(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)
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 skewness(a, axis=None):
'''Skewness of input'''
if axis is None:
return _stats.skewness(a)
else:
return _stats.skewness(a, axis)