semvec/pypi-packages huggingface dataset:
tc-tui 0.1.0
A TUI for creating testcases for the ISP/OOP1 courses at TU Graz
Requires Python: >=3.6

# tc-tui

A TUI (Text-based User Interface) for creating testcases for C programs.
The testcases are in the format used with this testrunner https://gitlab.tugraz.at/testrunner/testrunner

## Features

- Create testcases for C programs by interactively running the program and capturing input/output
- Edit testcases for C programs
- Regenerate testcases with the previous inputs
- Reorder testcases and set public/private status
- Generate coverage reports

## Installation

### Option 1: Install from PyPI

```bash
pip install tc-tui
```

### Option 2: Install from source

```bash
git clone https://gitlab.tugraz.at/B3D2209CD12BEAA2/tc-tui.git
cd tc-tui
pip install -e .
```

## Dependencies

TestcaseHelper requires the following external dependencies:

- **gcc**: C compiler
- **make**: Build tool
- **gcov**: Coverage analysis tool (usually comes with gcc)
- **lcov**: Coverage report generator
- **genhtml**: HTML report generator (part of lcov package)

### Installing dependencies

#### Ubuntu/Debian

```bash
sudo apt-get install gcc make lcov
```

#### Fedora/RHEL

```bash
sudo dnf install gcc make lcov
```

#### macOS

```bash
brew install gcc make lcov
```

## Usage

Open a terminal in the assigment directory (which contains the source files, the `Makefile` and the testrunner binary) and run:

```bash
tc-tui
```

This will open the TUI in your terminal.

## Configuration

tc-tui uses an optional`config.ini` file for configuration. The default configuration is:

```ini
[files]
source_files = *.c
all_tests_file = test_all.toml
public_tests_file = test.toml

[coverage]
cov_executable = a_cov.out
output_dir = coverage_report/
```

You can create/modify this file to change the default settings.

## License

This project is licensed under the MIT License - see the LICENSE file for details.
dict-of-lists 1.0.0
A dictionary with empty list/sets pre-setted values
Requires Python: >=3.6

# DictOfLists & DictOfSets

This package is designed to simplify code when **all the values of a dictionary are intended to be `list` or `set` objects**.

## DictOfLists
If a key does not exist, it will automatically be initialized with an **empty list**.

## DictOfSets
If a key does not exist, it will automatically be initialized with an **empty set**.

---

By using these two dictionary types, there is **no need to manually check for key existence** or to add an empty list or set as its value throughout your code.  
These actions are handled **automatically** whenever a key is accessed.
ddstats 0.0.5
Fast drawdown & CED metrics in Rust with NumPy bindings
Requires Python: >=3.9

# ddstats

Fast drawdown & CED metrics in Rust with NumPy bindings.

## Overview

`ddstats` provides high-performance financial metrics, including drawdown and Expected Drawdown (CED), implemented in Rust and exposed to Python via NumPy bindings. This allows for fast computations directly from Python, leveraging Rust's speed and safety.

## Features

- **Drawdown metrics**: Compute maximum drawdown and related statistics.
- **CED (Conditional Expected Drawdown)**: Efficient calculation for risk analysis.
- **NumPy integration**: Seamless usage from Python with NumPy arrays.
- **Cross-platform**: Works on Windows, macOS, and Linux.

## Installation

You can install `ddstats` using pip:

```sh
pip install ddstats
```

Or build from source:

```sh
git clone https://github.com/integerQuant/ddstats.git
cd ddstats
pip install maturin
maturin develop
```

## Usage

```python
import numpy as np
import ddstats

x = np.array([0.01, -0.02, 0.03], dtype=float)
max_dd = ddstats.max_drawdown(x)
print("Max Drawdown:", max_dd)
```

## API

### `ddstats.max_drawdown(x: np.ndarray) -> float`

Computes the maximum drawdown of a time series.

- **x**: 1D NumPy array of floats.

### `ddstats.ced(x: np.ndarray, level: float) -> float`

Computes the Conditional Expected Drawdown at a given confidence level.

- **x**: 1D NumPy array of floats.
- **level**: Confidence level (e.g., 0.95).

## Building

This project uses [maturin](https://github.com/PyO3/maturin) for building Python bindings:

```sh
maturin build
```

## License

MIT License. See [LICENSE](LICENSE).
TC-TUI 1.0.0
A TUI for creating testcases for the ISP/OOP1 courses at TU Graz
Requires Python: >=3.6

# TC-GEN

A TUI (Text-based User Interface) for creating testcases for C programs.
The testcases are in the format used with this testrunner https://gitlab.tugraz.at/testrunner/testrunner

## Features

- Create testcases for C programs by interactively running the program and capturing input/output
- Edit testcases for C programs
- Regenerate testcases with the previous inputs
- Reorder testcases and set public/private status
- Generate coverage reports

## Installation

### Option 1: Install from PyPI

```bash
pip install testcasehelper
```

### Option 2: Install from source

```bash
git clone https://github.com/yourusername/TestcaseHelper.git
cd TestcaseHelper
pip install -e .
```

## Dependencies

TestcaseHelper requires the following external dependencies:

- **gcc**: C compiler
- **make**: Build tool
- **gcov**: Coverage analysis tool (usually comes with gcc)
- **lcov**: Coverage report generator
- **genhtml**: HTML report generator (part of lcov package)

### Installing dependencies

#### Ubuntu/Debian

```bash
sudo apt-get install gcc make lcov
```

#### Fedora/RHEL

```bash
sudo dnf install gcc make lcov
```

#### macOS

```bash
brew install gcc make lcov
```

## Usage

```bash
testcasehelper
```

This will open the TestcaseHelper TUI in your terminal.

## Configuration

TestcaseHelper uses a `config.ini` file for configuration. The default configuration is:

```ini
[files]
source_files = *.c
all_tests_file = test_all.toml
public_tests_file = test.toml

[coverage]
executable = a_cov.out
output_dir = coverage_report/
```

You can modify this file to change the default settings.

## License

This project is licensed under the MIT License - see the LICENSE file for details.