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🎲 rvg 🎲 - Random Values Generator

Testing PyPI release

Description

rvg is a Python 3 package utility to create random values of any Python 3 data type.

Its main purpose is to help in applications where reliable -in terms of type safety- random values are needed (e.g. statistics, machine learning, general testing etc), and in specific layouts (e.g. "I want a numpy structured array of random pairs of ints and floats and I want it now").

Installation

Either download the source code from the releases page or use pip:

pip install rvg

Current Status

Pre-release

For the time being, only numpy types are supported. More specifically:

  • numpy scalar data types
  • numpy arrays of scalar data types
  • numpy arrays of structured data types

After the alpha release, more features will be implemented, focusing mainly on Python 3 native types.

Usage

Right now, rvg provides the NumPyRVG interface to generate random numpy values.

>>> from rvg import NumPyRVG
>>> import numpy as np
>>> rand = NumPyRVG(limit=1000) # `limit`: lower and upper limit of numeric values to be generated

The functionalities of NumPyRVG include the generation of:

  • numpy scalar data types:
>>> rand(np.int8)
78
>>> rand(np.uint16)
866
>>> i = rand(np.int8)
>>> u = rand(np.uint16)
>>> f = rand(np.float32)
>>> d = rand(np.double)
>>> i, u, f, d
(-101, 720, -234.16493, -882.7847115143803)
>>> type(i), type(u), type(f), type(d)
(<class 'numpy.int8'>, <class 'numpy.uint16'>, <class 'numpy.float32'>, <class 'numpy.float64'>)
  • numpy array data types from scalar types:
>>> rand(np.uint8, length=3) # `length`: length of the output numpy array
array([ 72, 222, 146], dtype=uint8)
>>> rand(np.float32, length=4)
array([ 939.84973, -903.3939 , -805.2647 , -676.1155 ], dtype=float32)
  • numpy structured array data types (structured datatypes can be nested):
>>> type1 = np.dtype('int,float')
>>> type2 = np.dtype('double,uint,long')
>>> rand(type1)
(849, -626.7902777962995)
>>> type(rand(type1)) # single values that correspond to structured data types
<class 'tuple'>       # are considered to be tuples
>>> rand(type1, length=3)
array([(-594,  451.64958214), ( 965,  -22.77642568),
       (-713, -100.61156315)], dtype=[('f0', '<i8'), ('f1', '<f8')])
>>> rand(type2, length=3)
array([(506.01690599,  48,  946), (643.06826309, 363, -865),
       (264.05285682, 214,  395)],
      dtype=[('f0', '<f8'), ('f1', '<u8'), ('f2', '<i8')])
>>> type3 = np.dtype([('t1', type1), ('t2', type2)]) # a nested structured data type
>>> rand(type3, length=2)
array([((716,  434.4316939), (-143.98226673, 610, -354)),
       ((307, -894.4985342), ( 234.0804783 , 678,  327))],
      dtype=[('t1', [('f0', '<i8'), ('f1', '<f8')]), ('t2', [('f0', '<f8'), ('f1', '<u8'), ('f2', '<i8')])])