Gcalc

Gcalc is a game probability calculator.

Gcalc is not merely a gacha simulator but more of a generic probability calculator.

Usage

```bash

Basic

gcalc

SUBCOMMANDS:

cond Conditional calculation

qual Conditional calculation but only prints result

range Prints range of calculations

reference Create a reference file

option Create an option file

For cond and qual

-b, --budget Budget of total cost -C, --cost Cost per try

For range

-c, --count Counts to execute -S, --start Starting index to print

Global option

--constant <constant>      Constant value to be added into probability

-f, --format Table format(csv|console|gfm) --fallback Set csv value fallback {rollback|ignore|none} [default: none] -h, --help Print help information -l, --column Column mapping --noheader CSV without header -o, --out File to write output -O, --option

Install

binary bash cargo install gcalc --features binary --locked

libary toml [dependencies] gcalc = "0.2.0"

Demo

```bash

Print records of probability 0.2(20%) with budget of 100 and cost of 20 for each iteration.

Target probability is 0.6

gcalc cond 0.2 --budget 100 --cost 20 -f console --precision 2 -T percentage -t 0.6

Print from 0 to 10 as github markdown formatted table, which has a precision of

2 digits. Each try has cost of 1000.

gcalc range --probability 0.2 --count 10 --format gfm --precision 2 --cost 1000

Print probability changes illustrated as csv formatted table, which has a

precision of 2 digits. Target probability is 0.8.

gcalc cond --probability 0.2 --format csv --precision 2 --target 0.8

Print how many counts are required to reach 0.99 probability with 0.01 change of each try

Qual subcommand uses geometric-series formula if no reference was given as an argument

Which is efficient for very small probability cases

gcalc qual --probability 0.001 -f gfm --target 0.99 --precision 2

Use reference file

gcalc --ref ref.csv

Use reference from stdin

cat ref.csv | gcalc --refin

Read options from option file

gcalc range --option option.json ```

Results of prior usages are,

```bash

gcalc cond --probability 0.2 --budget 100 --cost 20 -f console --precision 2 -T percentage -t 0.6

+-------+-------------+------+ | count | probability | cost | +-------+-------------+------+ | 1 | 20.00% | 20 | +-------+-------------+------+ | 2 | 36.00% | 40 | +-------+-------------+------+ | 3 | 48.80% | 60 | +-------+-------------+------+ | 4 | 59.04% | 80 | +-------+-------------+------+ | 5 | 67.23% | 100 | +-------+-------------+------+

gcalc range --probability 0.2 --count 10 --format gfm --precision 2 --cost 1000

| count | probability | cost | |-------+-------------+-------| | 1 | 0.20 | 1000 | | 2 | 0.36 | 2000 | | 3 | 0.48 | 3000 | | 4 | 0.59 | 4000 | | 5 | 0.67 | 5000 | | 6 | 0.73 | 6000 | | 7 | 0.79 | 7000 | | 8 | 0.83 | 8000 | | 9 | 0.86 | 9000 | | 10 | 0.89 | 10000 |

gcalc cond --probability 0.2 --format csv --precision 2 --target 0.8

count,probability,cost 1,0.20,0.0 2,0.36,0.0 3,0.48,0.0 4,0.59,0.0 5,0.67,0.0 6,0.73,0.0 7,0.79,0.0 8,0.83,0.0

gcalc qual --probability 0.001 -f gfm --target 0.99 --precision 2

| count | probability | cost | |-------+-------------+------| | 4602 | 0.99 | 0 | ```

Reference file example csv count,probability,constant,cost 1,0.1,0,50 2,0.2,0,40 3,0.3,0,30 4,0.4,0,20 5,0.5,0,10

option file example

json { "count": 10, "prob_type": "Percentage", "prob_precision": 2, "budget": null, "fallback": "None", "no_header": false, "strict": false, "target": null, "column_map": { "count": 0, "probability": 1, "constant": 2, "cost": 3 }, "format": "GFM", "csv_ref": { "File": "ref.csv" }, "out_option": "Console" } "csv_ref" is a tuple which has variant of... ``` "csv_ref" : { "Raw" : "count,probability,constant,cost 1,0.1,0.0,10" }

or

"csvref" : { "File": "filenameinstring" }

or

"csv_ref" : "None", ```

Advanced usage

Column mapping

You can read existing csv file without changing order of original source with column option. You can type any character if given colun is not used by gcalc.

Currently, reference csv requires all count,cost,probability,constant columns. This behaviour might change in the future though.

Default order of columns are

```bash

Example csv content...

db,type,count,constant,cost,probability t1,a,1,0.1,30,0.1 t1,b,2,0.1,20,0.1 t1,c,3,0.1,10,0.1 t1,a,4,0.2,30,0.1 t1,b,5,0.2,20,0.2 t1,c,6,0.2,10,0.3

Example usage

gcalc --ref ref.csv range --count 6 --column db,type,count,constant,cost,probability

You can also write as

gcalc --ref ref.csv range --count 6 --column ,,count,constant,cost,probability ```

Strict Read

Gcalc doesn't match every try for corresponding reference's record by default.

```bash

Example csv file

count,probability,constant,cost 1,0.5,0,50 2,0.2,0,40 3,0.3,0,30 4,0.4,0,20 5,0.5,0,10

Range from 1 to 10

From 6th try, 5th record values are used

gcalc range --count 10 --ref ref.csv ```

User can disable this behaviour with strict flag

```bash gcalc range --count 10 --ref ref.csv --strict

Previous command yields error

Invalid csv error = Empty row in index: 6 ```

Fallback

Gcalc tries to parse CSV value as intended data type. But user can define fallback behaviour for such situations.

```bash

Example csv file

count,probability,constant,cost 1,0.5,0.2,50 2,0.2,-,40 3,0.3,0.1,30 4,0.4,-,20 5,0.5,-,10

Default value is 'none'

Option is case insensitive, btw

gcalc --ref ref.csv --constant 0.7 --fallback rollback|ignore|none On previous example, at the third record which is3,0.3,0.1,30```

About

Why not use a simple formula?

Well because, real life usages are not clear cut demonstrated geometric sequences. Sometimes there is bonus for a specific gacha stage and there is also a so-called confirmed gacha system, which makes it hard to use geometric series formula.

Ok, why not use other gacha simulators or calculators?

First, simulation is not calculation. The major reasoning of this crate is about expectation and calibration, especially game development in mind.

Second, existing calculators only consider fixed value of probability. However there are plenty of contents that utilize bonus probability on specific steps and there are some gachas that have different probability for different situations.

Third, those are hard to integrate with other systems. Most of them are either simple programs with integrated front end (GUI) which can be only losely connected to other game development tools at the most and automation is nearly impossible.

Goal