giovanni 0.0.1

# giovanni

A CLI tool used to calculate the likelihood of a shiny Pokemon encounter for a given number of resets, or simulate the distribution of required SRs for a given generation of Pokemon.

### Encounter odds:

Compute the probability of encountering a shiny in Gen 4 with a single encounter:

` $ python -m giovanni odds --gen 4 --verbose [INFO] 2020-12-22 16:07:54 [giovanni.cli.odds] - Probability of shiny encounter after 1 soft reset: 0.012% `

The probability of encountering a shiny in Gen 6 with 5000 SRs and a shiny charm equipped:

` $ python -m giovanni odds --gen 6 --soft_resets 5000 --charm [INFO] 2020-12-22 16:07:57 [giovanni.cli.odds] - Probability of shiny encounter after 5,000 soft resets: 91.301% `

### Montecarlo simulation:

A simulation of Gen 6 soft resets required to encounter a shiny:

` $ python -m giovanni simulate --gen 6 -n 5000 simulation: 100%|██████████████████████████████████████████████████████████████████| 5000/5000 [00:13<00:00, 365.02it/s] [INFO] 2020-12-22 15:53:43 [giovanni.cli.simulate] - Montecarlo simulation results -- Average 3980.136 Std Dev 3983.669 Max 32989.000 Min 1.000 Name: Required encounters (gen=6, num_trials=5000, swarm_size=1), dtype: float64 `

To simulate, e.g., SS/HG soft resets required to encounter a shiny starter use the swarm_size argument:

` python -m giovanni simulate --gen 4 -n 5000 --swarm_size 3 simulation: 100%|██████████████████████████████████████████████████████████████████| 5000/5000 [00:15<00:00, 329.87it/s] [INFO] 2020-12-22 15:51:01 [giovanni.cli.simulate] - Montecarlo simulation results -- Average 2737.669 Std Dev 2717.707 Max 22760.000 Min 1.000 Name: Required encounters (gen=4, num_trials=5000, swarm_size=3), dtype: float64 `

### Notes

• No considerations are made around [shiny locks](https://pokemon-shiny-hunting.fandom.com/wiki/Shiny_Locks). Make sure that you are aware whether the ‘Mon you’re hunting can be found in shiny form.

• This project only covers the main titles in the game series. It does not cover offshoots or mobile games in the series.

• The current implementation only takes into account base shiny rates by generation (see future work)

### Future work

A list of things that would be cool to implement:

• Considerations around encounter method:

  • Wild/SR

  • Masuda

  • Chaining

  • Etc.

• Gen 8

## Installing

### Installing from PyPi:

giovanni is available on public PyPi:

`bash $ pip install giovanni `

### Get setup for local dev:

Set up by cloning and building from Git:

`bash $ git clone https://github.com/tgsmith61591/giovanni.git $ cd giovanni $ make requirements lint-requirements testing-requirements `

Run a quick test to make sure everything is copacetic:

`bash $ make test `