semvec/pypi-packages huggingface dataset:
zax 0.7
ZAX
Requires Python: <3.11,>=3.8
Keywords: fallout, fallout2

# ZAX
[![Build status](https://github.com/BGforgeNet/ZAX/workflows/build/badge.svg)](https://github.com/BGforgeNet/ZAX/actions?query=workflow%3Abuild) <a href="#"><img align="right" src="https://raw.githubusercontent.com/BGforgeNet/zax/master/docs/zax.png" width="45%" alt="ZAX screenshot" title="ZAX screenshot"/></a>
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ZAX is a Fallout 2 game manager. Its purpose is to unify and simplify game and mods configuration. It should work with any game based on Fallout 2 engine.

### Installation

#### Windows
Download `zax.exe` from the [latest release page](https://github.com/BGforgeNet/zax/releases/latest) and launch it.

#### Linux / Mac / other
- There's a x64 [binary](https://github.com/BGforgeNet/zax/releases/latest) for Linux.
- ZAX is also available in PyPi:
    ```bash
    pip3 install zax
    zax
    ```

### Info
- [Forums](https://forums.bgforge.net/viewforum.php?f=34)
- [Development](docs/development.md)
- [Changelog](docs/changelog.md)
amlhpc 0.2.2
Emulate Slurm/PBS/LSF HPC scheduler in Azure ML
Requires Python: <4.0,>=3.8

# amlhpc

Package to provide a -just enough- Slurm or PBS experience on Azure Machine Learning. Use the infamous sbatch/qsub/sinfo to submit
jobs and get insight into the state of the HPC system through a familiar way. Allow applications to interact with AML without 
the need to re-program another integration.

For the commands to function, the following environment variables have to be set:
```
SUBSCRIPTION=<guid of you Azure subscription e.g. 12345678-1234-1234-1234-1234567890ab>
CI_RESOURCE_GROUP=<name of the resource group where your Azure Machine Learning Workspace is created>
CI_WORKSPACE=<name of your Azure MAchine Learning Workspace>
```

In the Azure Machine Learning environment, the CI_RESOURCE_GROUP and CI_WORKGROUP are normally set, so you only need to export SUBSCRIPTION.

# sinfo

Show the available partitions. sinfo does not take any options.
```
(azureml_py38) azureuser@login-vm:~/cloudfiles/code/Users/username$ sinfo
PARTITION       AVAIL   VM_SIZE                 NODES   STATE
f16s            UP      STANDARD_F16S_V2        37
hc44            UP      STANDARD_HC44RS         3
hbv2            UP      STANDARD_HB120RS_V2     4
login-vm        UP      STANDARD_DS12_V2        None
```

# squeue

Show the queue with historical jobs. squeue does not take any options.
```
(azureml_py38) azureuser@login-vm:~/cloudfiles/code/Users/username$ squeue
JOBID                           NAME            PARTITION       STATE   TIME
crimson_root_52y4l9yfjd         sbatch  	f16s
polite_lock_v8wyc9gnx9          runscript.sh    f16s
```

# sbatch

Submit a job, either as a command through the `--wrap` option or a (shell) script. sbatch uses several options, which are explained in sbatch --help.
Quite a bit of sbatch options are supported such as running multi-node MPI jobs with the option to set the amount of nodes to be used.
Also array jobs are supported with the default `--array` option.

Some additional options are introduced to support e.g. the data-handling methods available in AML. These are explaned in [data.md](data.md). 
```
(azureml_py38) azureuser@login-vm:~/cloudfiles/code/Users/username$ sbatch -p f16s --wrap="hostname"
gifted_engine_yq801rygm2
```
```
(azureml_py38) azureuser@login-vm:~/cloudfiles/code/Users/username$ sbatch --help
usage: sbatch [-h] [-a ARRAY] -p PARTITION [-N NODES] [-w WRAP] [script]

sbatch: submit jobs to Azure Machine Learning

positional arguments:
  script                script to be executed

optional arguments:
  -h, --help            show this help message and exit
  -a ARRAY, --array ARRAY
                        index for array jobs
  -p PARTITION, --partition PARTITION
                        set compute partition where the job should be run. Use <sinfo> to view available partitions
  -N NODES, --nodes NODES
                        amount of nodes to use for the job
  -w WRAP, --wrap WRAP  command line to be executed, should be enclosed with quotes
```

If you encounter a scenario or option that is not supported yet or behaves unexpected, please create an issue and explain the option and the scenario.
investment-calculator 1.1.0
This package is used to calculate your investment returns.
Requires Python: >=3.6

# InvestmentCalculatorPackage
## Introduction

This python package provides simple functions to calculate investment returns for SIP (Systematic Investment Plan) and Lumpsum investments. It aims to help users easily compute returns without needing to perform complex calculations manually.

## Installation

You can install the `investment_calculator` package via pip:

```bash
pip install investment_calculator
```

## Usage

### Calculating SIP Returns

A systematic investment plan (SIP) is an investment vehicle offeredto investors, allowing them to invest small amounts periodically instead of lumpsums. The frequency of investment is usually weekly, monthly or quarterly.

To calculate returns for SIP investments:

First, prepare a CSV file with columns: amount, rate_of_interest, and number_of_years containing investment details. Here's an example:

```csv
amount,rate_of_interest,number_of_years
20000,12,8
30000,10,15
```
The above csv file indicates that an investment of INR 20,000 was made every month at 12% per annum for the first 8 years, and then, an amount INR 30,000 was invested monthly at 10% per annum for the next 15 years. 

Next, use the SIPCalculator class to process this CSV:

```python
from investment_calculator import SIPCalculator

sip = SIPCalculator()
location = '/home/user/investment.csv'
sip.update_investments(csv_location = location)
print("Total Investment Value: {0}".format(str(sip.final_investment_amount))
```

### Calculating Lumpsum Returns

A lumpsum investment in mutual funds is a one-time payment made in full at the beginning of an investment period. It is a single, large payment made upfront, without any subsequent payments.

To calculate returns for Lumpsum investments:

```python
from investment_calculator import LumpsumCalculator

lumpsum = LumpsumCalculator(amount = 1_000_000, rate_of_interest = 8, investment_term_in_years = 15)
print("Total Investment Value: {0}".format(str(lumpsum.final_investment_amount))
```

Update the values of the parameters for your needs.

### Calculating SWP

SWP (Systematic Withdrawal Plan) is a feature offered to investors where the investor can withdraw a fixed amount of money at regular intervals (monthly, quarterly, etc.) from their investment.

To calculate returns for Lumpsum investments:
```python
from investment_calculator import *

swp = SWPCalculator(
    initial_investment_amount = 1_000_000,
    rate_of_interest = 7,
    initial_monthly_withdrawal = 5_000,
    inflation_rate = 7,
    number_of_years = 30
)

investment_df = swp.get_calculated_swp()
print(investment_df)
```

This functions returns a dataframe which tells you how much money remains of your investment at the end of each year. It also accounts for inflation.

## Contributing
Contributions are welcome! Please fork the repository and submit a pull request.