3. Dataset

The dataset classes aims to emulate some of the functionality of NumPy’s ndarray class.

Capabilities

• Indexing
• Slicing
• Arithmetic operations
• Mathematical and statistical functions

Key differences

• DataSet only holds multi-dimensional array of doubles
• Expandable with reserved space - makes arrays non-contiguous
• No strides
• No broadcasting
• No ellipse
• Incomplete implementation all NumPy’s methods

Implemented NumPy methods (1.3)

• Array attributes: shape, ndim, data, size, itemsize, nbytes
• Array methods: copy, fill, reshape, resize, transpose, flatten, squeeze, take, put, max, min, sum, prod, all, any, argmax, argmin
• Array creation: array, zeros, ones, linspace, logspace, arange, diag, diagflat, meshgrid, indices
• Array manipulation: tile, repeat, concatenate, vstack, hstack, dstack, array_split, split, vsplit, hsplit, dsplit
• Array modification: fill, append
• Maths: add, subtract, multiply, divide, negative, power, absolute, exp, log, log2, log10, expm1, log1p, sqrt, square, reciprocal, angle, conjugate, floor_divide, reminder, phase, signum, diff
• Trig: sin, cos, tan, arcsin, arccos, arctan, arctan2, hypot, sinh, cosh, tanh, arcsinh, arccosh, arctanh, deg2rad, rad2deg
• Rounding: rint, ceil, floor
• Stats: amax, amin, ptp, mean, std, var, cumprod, cumsum
• Random: rand, randint, random_integer, randn, exponential, poisson, seed

Non-NumPy

• Array methods: cast, minpos, maxpos, index
• Maths: cbrt
• Stats: rms, skew, kurtosis, median, iqr
• Import/export JAMA [1]

This is an example of DataSet usage:

import scisoftpy as dnp

# create a 1D dataset from 0 to 9 of doubles
a = dnp.arange(10)
# make it have 2 rows and 5 columns
a.shape = 2,5
a
# create new dataset with each element raised to power of 5/2
b = dnp.power(a,2.5)
# create new dataset that is sum of two datasets
c = a + b
# modify dataset in-place by dividing each element by corresponding
# element in other dataset
c /= a

# reassign a to a new (integer) dataset of 2 rows and 3 columns
a = dnp.array([[0, 1, 2], [3, 4, 5]])
# create new dataset from a slice of dataset which takes just the 2nd column
d = a[:,1]
# modify dataset and set a slice to a given value
a[1,0:1] = -2

# b is a double dataset (highest type that can contain all entries is used)
b = dnp.array([[-2, 2.3], [1.2, 9.3]])
# modify dataset and set a slice to the values in another dataset
a[:,1:] = b
# notice the values from b are truncated to integers
a

import Jama.Matrix as Matrix
#  make a dataset from a Jama Matrix
m = Matrix([[0,1,1.5],[2,3,3.5]])
dj = dnp.array(m) # directly
da = dnp.array(m.getArray()) # from Java array
db = dnp.array([m.getArray(), m.getArray()]) # from list of arrays

import scisoftpy.random as drd
# create dataset of shape 3,12 of uniform random numbers between 0 and 1
a = drd.rand((3,12))
# take item-wise sine
dnp.sin(a)
# create dataset of shape 3,4 of random integers from 0 to 11 inclusive
drd.randint(12, size=(3,4))

3.1. References

[1]	JAMA: http://math.nist.gov/javanumerics/jama/