tessl/pypi-scipp
Multi-dimensional data arrays with labeled dimensions for scientific computing
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Pending
reduction-operations.mddocs/
Reduction Operations
Statistical reduction functions along dimensions with comprehensive NaN handling and uncertainty propagation.
Capabilities
Statistical Reductions
Functions for computing statistics along specified dimensions with automatic uncertainty propagation.
def sum(x, dim=None):
"""Sum along dimensions"""
def mean(x, dim=None):
"""Mean along dimensions"""
def std(x, dim=None):
"""Standard deviation along dimensions"""
def var(x, dim=None):
"""Variance along dimensions"""
def median(x, dim=None):
"""Median along dimensions"""
def min(x, dim=None):
"""Minimum along dimensions"""
def max(x, dim=None):
"""Maximum along dimensions"""
def all(x, dim=None):
"""Check if all values are true"""
def any(x, dim=None):
"""Check if any values are true"""NaN-Handling Reductions
Statistical functions that ignore NaN values during computation.
def nansum(x, dim=None):
"""Sum along dimensions ignoring NaN"""
def nanmean(x, dim=None):
"""Mean along dimensions ignoring NaN"""
def nanstd(x, dim=None):
"""Standard deviation ignoring NaN"""
def nanvar(x, dim=None):
"""Variance ignoring NaN"""
def nanmedian(x, dim=None):
"""Median ignoring NaN"""
def nanmin(x, dim=None):
"""Minimum ignoring NaN"""
def nanmax(x, dim=None):
"""Maximum ignoring NaN"""Install with Tessl CLI
npx tessl i tessl/pypi-scipp