Samplitude (s8e) is a statistical distributions command line tool

## Project description

# samplitude

CLI generation and plotting of random variables:

$ samplitude "sin(0.31415) | sample(6) | round | cli" 0.0 0.309 0.588 0.809 0.951 1.0

The word *samplitude* is a portmanteau of *sample* and *amplitude*. This
project also started as an étude, hence should be pronounced *sampl-étude*.

`samplitude`

is a chain starting with a *generator*, followed by zero or more
*filters*, followed by a consumer. Most generators are infinite (with the
exception of `range`

and `lists`

and possibly `stdin`

). Some of the filters can
turn infinite generators into finite generators (like `sample`

and `gobble`

),
and some filters can turn finite generators into infinite generators, such as
`choice`

.

*Consumers* are any filter that necessarily flush the input; `list`

, `cli`

,
`tojson`

, `unique`

, and the plotting tools, `hist`

, `scatter`

and `line`

are
examples of consumers. The `list`

consumer is a Jinja2 built-in, and other
Jinja2 consumers are `sum`

, `min`

, and `max`

:

samplitude "sin(0.31415) | sample(5) | round | max | cli" 0.951

For simplicity, **s8e** is an alias for samplitude.

## Generators

In addition to the standard `range`

function, we support infinite generators

`exponential(lambd)`

:`lambd`

is 1.0 divided by the desired mean.`uniform(a, b)`

: Get a random number in the range`[a, b)`

or`[a, b]`

depending on rounding.`gauss(mu, sigma)`

:`mu`

is the mean, and`sigma`

is the standard deviation.`normal(mu, sigma)`

: as above`lognormal(mu, sigma)`

: as above`triangular(low, high)`

: Continuous distribution bounded by given lower and upper limits, and having a given mode value in-between.`beta(alpha, beta)`

: Conditions on the parameters are`alpha > 0`

and`beta > 0`

. Returned values range between 0 and 1.`gamma(alpha, beta)`

: as above`weibull(alpha, beta)`

:`alpha`

is the scale parameter and`beta`

is the shape parameter.`pareto(alpha)`

: Pareto distribution.`alpha`

is the shape parameter.`vonmises(mu, kappa)`

:`mu`

is the mean angle, expressed in radians between 0 and`2*pi`

, and`kappa`

is the concentration parameter, which must be greater than or equal to zero. If kappa is equal to zero, this distribution reduces to a uniform random angle over the range 0 to`2*pi`

.

We have a special infinite generator (filter) that works on finite generators:

`choice`

,

whose behaviour is explained below.

For input from files, either use `words`

with a specified environment variable
`DICTIONARY`

, or pipe through

`stdin()`

that reads from `stdin`

.

If the file is a csv file, there is a `csv`

generator that reads a csv file with
Pandas and outputs the first column (if nothing else is specified). Specify the
column with either an integer index or a column name:

>>> s8e "csv('iris.csv', 'virginica') | counter | cli" 0 50 1 50 2 50

## A warning about infinity

All generators are infinite generators, and must be sampled with `sample(n)`

before consuming!

## Usage and installation

Install with

pip install samplitude

or to get bleeding release,

pip install git+https://github.com/pgdr/samplitude

### Examples

This is pure Jinja2:

>>> samplitude "range(5) | list" [0, 1, 2, 3, 4]

However, to get a more UNIXy output, we use `cli`

instead of `list`

:

>>> s8e "range(5) | cli" 0 1 2 3 4

To limit the output, we use `sample(n)`

:

>>> s8e "range(1000) | sample(5) | cli" 0 1 2 3 4

That isn't very helpful on the `range`

generator, but is much more helpful on an
infinite generator, such as the `uniform`

generator:

>>> s8e "uniform(0, 5) | sample(5) | cli" 3.3900198868059235 1.2002767137709318 0.40999391897569126 1.9394585953696264 4.37327472704115

We can round the output in case we don't need as many digits (note that `round`

is a generator as well and can be placed on either side of `sample`

):

>>> s8e "uniform(0, 5) | round(2) | sample(5) | cli" 4.58 4.33 1.87 2.09 4.8

### Selection and modifications

The `samplitude`

behavior is equivalent to the `head`

program, or from languages
such as Haskell. The `head`

alias is supported:

>>> samplitude "uniform(0, 5) | round(2) | head(5) | cli" 4.58 4.33 1.87 2.09 4.8

`drop`

is also available:

>>> s8e "uniform(0, 5) | round(2) | drop(2) | head(3) | cli" 1.87 2.09 4.8

To **shift** and **scale** distributions, we can use the `shift(s)`

and
`scale(s)`

filters. To get a Poisson point process starting at 15, we can run

>>> s8e "poisson(0.3) | shift(15)" # equivalent to exponential(0.3)...

### Choices and other operations

Using `choice`

with a finite generator gives an infinite generator that chooses
from the provided generator:

>>> samplitude "range(0, 11, 2) | choice | sample(6) | cli" 8 0 8 10 4 6

Jinja2 supports more generic lists, e.g., lists of string. Hence, we can write

```
>>> s8e "['win', 'draw', 'loss'] | choice | sample(6) | sort | cli"
draw
draw
draw
loss
win
win
```

... and as in Python, strings are also iterable:

```
>>> s8e "'HT' | cli"
H
T
```

... so we can flip six coins with

```
>>> s8e "'HT' | choice | sample(6) | cli"
H
T
T
H
H
H
```

We can flip 100 coins and count the output with `counter`

(which is
`collections.Counter`

)

>>> s8e "'HT' | choice | sample(100) | counter | cli" H 47 T 53

The `sort`

functionality does not work as expected on a `Counter`

object (a
`dict`

type), so if we want the output sorted, we pipe through `sort`

from
*coreutils*:

>>> s8e "range(1,7) | choice | sample(100) | counter | cli" | sort -n 1 24 2 17 3 18 4 16 5 14 6 11

Using `stdin()`

as a generator, we can pipe into `samplitude`

. Beware that
`stdin()`

flushes the input, hence `stdin`

(currently) does not work with
infinite input streams.

>>> ls | samplitude "stdin() | choice | sample(1) | cli" some_file

Then, if we ever wanted to shuffle `ls`

we can run

>>> ls | samplitude "stdin() | shuffle | cli" some_file

>>> cat FILE | samplitude "stdin() | cli" # NOOP; cats FILE

### The fun powder plot

For fun, if you have installed `matplotlib`

, we support plotting, `hist`

being
the most useful.

```
>>> samplitude "normal(100, 5) | sample(1000) | hist"
```

An exponential distribution can be plotted with `exponential(lamba)`

. Note that
the `cli`

output must be the last filter in the chain, as that is a command-line
utility only:

```
>>> s8e "normal(100, 5) | sample(1000) | hist | cli"
```

To **repress output after plotting**, you can use the `gobble`

filter to empty
the pipe:

```
>>> s8e "normal(100, 5) | sample(1000) | hist | gobble"
```

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