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 filters 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`

.

Provided that you have installed the `scipy.stats`

package, the

`pert(low, peak, high)`

distribution is supported.

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()`

which 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:

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

For other files, we have the `file`

generator:

>>> s8e "file('iris.csv') | sample(1) | cli" 150,4,setosa,versicolor,virginica

Finally, we have `combinations`

and `permutations`

that are inherited from
itertools and behave exactly like those.

```
>>> s8e "'ABC' | permutations | cli"
```

However, the output of this is rather non-UNIXy, with the abstractions leaking through:

>>> s8e "'HT' | permutations | cli" ('H', 'T') ('T', 'H')

So to get a better output, we can use an *elementwise join* `elt_join`

:

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

which also takes a seperator as argument:

>>> s8e "'HT' | permutations | elt_join(';') | cli" H;T T;H

This is already supported by Jinja's `map`

function (notice the strings around `join`

):

>>> s8e "'HT' | permutations | map('join', ';') | cli" H;T T;H

We can thus count the number of permutations of a set of size 10:

>>> s8e "range(10) | permutations | len" 3628800

The `product`

generator takes two generators and computes a cross-product of
these. In addition,

## A warning about infinity

All generators are (potentially) 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, which is already finite, but
is much more helpful on an infinite generator. The above example is probably
better written as

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

However, much more interesting are the infinite random generators, 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" 4.98 4.42 2.05 2.29 3.34

### Selection and modifications

The `sample`

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) | round | shift(15) | sample(5) |cli" 33.731 22.204 16.763 17.04 18.668

Both `shift`

and `scale`

work on generators, so to add `sin(0.1)`

and
`sin(0.2)`

, we can run

```
>>> s8e "sin(0.1) | shift(sin(0.2)) | sample(10) | cli"
```

### 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 strings. Hence, we can write

```
>>> s8e "['win', 'draw', 'loss'] | choice | sample(6) | sort | cli"
draw
draw
loss
loss
loss
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 works as expected on a `Counter`

object (a
`dict`

type), so if we want the output sorted by key, we can run

>>> s8e "range(1,7) | choice | sample(100) | counter | sort | elt_join | cli" 42 # seed=42 1 17 2 21 3 12 4 21 5 13 6 16

There is a minor hack to sort by value, namely by `swap`

-ing the Counter twice:

>>> s8e "range(1,7) | choice | sample(100) | counter | swap | sort | swap | elt_join | cli" 42 # seed=42 3 12 5 13 6 16 1 17 2 21 4 21

The `swap`

filter does an element-wise reverse, with element-wise reverse
defined on a dictionary as a list of `(value, key)`

for each key-value pair in
the dictionary.

So, to get the three most common anagram strings, we can run

>>> s8e "words() | map('sort') | counter | swap | sort(reverse=True) | swap | sample(3) | map('first') | elt_join('') | cli" aeprs acerst opst

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"
```

The
`pert`

distribution
takes inputs `low`

, `peak`

, and `high`

:

```
>>> s8e "pert(10, 50, 90) | sample(100000) | hist(100) | gobble"
```

Although `hist`

is the most useful, one could imaging running `s8e`

on
timeseries, where a `line`

plot makes most sense:

```
>>> s8e "sin(22/700) | sample(200) | line"
```

The scatter function can also be used, but requires that the input stream is a
stream of pairs, which can be obtained either by the `product`

generator, or via
the `pair`

or `counter`

filter:

```
s8e "normal(100, 10) | sample(10**5) | round(0) | counter | scatter"
```

### Fourier

A fourier transform is offered as a filter `fft`

:

```
>>> samplitude "sin(0.1) | shift(sin(0.2)) | sample(1000) | fft | line | gobble"
```

## Your own filter

If you use Samplitude programmatically, you can register your own filter by sending a dictionary

{'name1' : filter1, 'name2' : filter2, #..., 'namen' : filtern, }

to the `samplitude`

function.

### Example: secretary problem

Suppose you want to emulate the secretary problem ...

#### Intermezzo: The problem

For those not familiar, you are a boss, Alice, who wants to hire a new secretary Bob. Suppose you want to hire the tallest Bob of all your candidates, but the candidates arrive in a stream, and you know only the number of candidates. For each candidate, you have to accept (hire) or reject the candidate. Once you have rejected a candidate, you cannot undo the decision.

The solution to this problem is to look at the first `n/e`

(`e~2.71828`

being
the Euler constant) candidates, and thereafter accept the first candidate taller
than all of the `n/e`

first candidates.

#### A Samplitude solution

Let `normal(170, 10)`

be the candidate generator, and let `n=100`

. We create a
filter `secretary`

that takes a stream and an integer (`n`

) and picks according
to the solution. In order to be able to assess the quality of the solution
later, the filter must forward the entire list of candidates; hence we annotate
the one we choose with `(c, False)`

for a candidate we rejected, and `(c, True)`

denotes the candidate we accepted.

def secretary(gen, n): import math explore = int(n / math.e) target = -float('inf') i = 0 # explore the first n/e candidates for c in gen: target = max(c, target) yield (c, False) i += 1 if i == explore: break _ok = lambda c, i, found: ((i == n-1 and not found) or (c > target and not found)) have_hired = False for c in gen: status = _ok(c, i, have_hired) have_hired = have_hired or status yield c, status i += 1 if i == n: return

Now, to emulate the secretary problem with Samplitude:

from samplitude import samplitude as s8e # insert above secretary function n = 100 filters = {'secretary': secretary} solution = s8e('normal(170, 10) | secretary(%d) | list' % n, filters=filters) solution = eval(solution) # Samplitude returns an eval-able string cands = map(lambda x: x[0], solution) opt = [s[0] for s in solution if s[1]][0] # the next line prints in which position the candidate is print(1+sorted(cands, reverse=True).index(opt), '/', n)

In about 67% of the cases we can expect to get one of the top candidates, whereas the remaining 33% of the cases will be uniformly distributed. Running 100k runs with a population of size 1000 reveals the structure.

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