sciris/sciris-arrays

Sciris Math and Array Tools

Reference for array utilities that simplify NumPy workflows. See full tutorial: docs/tutorials/tut_arrays.ipynb.

If you need more detail, use your MCP tools (Context7 or GitMCP) to look up current Sciris documentation, or consult the other Sciris skills.

Array Indexing

import numpy as np
import sciris as sc

data = np.random.rand(100)

# Find indices where condition is true
inds = sc.findinds(data > 0.9)           # vs (data>0.9).nonzero()[0]

# Find nearest value
ind = sc.findnearest(data, 0.5)          # vs np.argmin(abs(data - 0.5))

# Works on lists too (not just arrays)
ind = sc.findnearest([81, 78, 66, 25], 50)

Creating/Concatenating Arrays

sc.cat(1, 2, 3)                          # array([1, 2, 3])

# Add row to matrix — vs np.concatenate([data, np.atleast_2d(np.array([1,2]))])
data = sc.cat(data, [1, 2])

Missing Values

d0 = [1, 2, np.nan, 4, np.nan, 6, np.nan, np.nan, np.nan, 10]

d1 = sc.rmnans(d0)                       # Remove NaNs
d2 = sc.fillnans(d0, 0)                  # Replace with zeros
d3 = sc.fillnans(d0, 'linear')           # Linear interpolation

Smoothing

# 1D smoothing
smooth = sc.smooth(data, 7)              # Gaussian-like smoothing
roll = sc.rolling(data, 7)               # Rolling average

# 2D Gaussian smoothing
smooth2d = sc.gauss2d(raw_2d, scale=2)

Other Math

# Random rounding: 0.7 rounds up 70% of the time
rounded = sc.randround(data)

# Linear regression
m, b = sc.linregress(x, y)

# 3D plotting helpers
ax = sc.ax3d(121)                        # Create 3D subplot
sc.bar3d(data, ax=ax)                    # 3D bar plot