semvec/pypi-packages huggingface dataset:
qcm-parser 0.1.5
A parser of QCM file
Requires Python: >=3.6

# QCM parser

It's a basic and quite strict QCM (Multiple Choices Questions) parser.

It parses a file .md file into Python objects.
That's all.

There's no rendering, no export. It must be done separetaly.

## Installation

It requires python>=3.7 and `markdown` package.

```bash
$ pip install qcm_parser
```

## Usage

In your script :

```python
from qcm_parser.parser import ParseQCM

qcm = ParseQCM.from_file("my_qcm_file.md")

print(qcm)
```

Will display your QCM as a text.

In memory, you `qcm` object is represented as a tree which holds parts.

Those parts holds questions, holding answers.

It is then easy to navigate through it like this :

```
print(qcm.title)
for part in qcm.parts:
    print(part.title)
    print(part.text)
    for question in part.questions:
        print(question.title)
        print(question.text)
        for answer in question.title:
            print(answer.answer)
```

## Modes

There's 2 modes :

- for web export (`qcm_part.from_file(filename, mode="web")`), the default one, `mode` named parameter can be ommitted.
- for pdf export (`qcm_part.from_file(filename, mode="pdf")`).

The `mode=web` should be used if you want to serve a QCM like I do [here](https://github.com/qkzk/qcm_alchemy)

It replaces `markdown` syntax with `html` syntax using `markdown` module.

The `mode=pdf` can be used to create a randomized QCM in a .md file, then to export it with pandoc.

## QCM file description

**Files should be utf-8 encoded text files with .md extensions.**

They should respect the [example](./example/example.md) format :

```markdown
# title

## part

the text of the part

### question with multiple choices

optional sub text. Can contains code, latex, whatever

- [x] right answer
- [ ] wrong answer a
- [ ] wrong answer b

### question with text allowed

- [t]
```

## Known limitations

Only a little subset of markdown is supported.

1. A title must be found
2. title, parts and questions can't have empty string after `#`, `##` or `###` tag

   ```mardown
   ## valid part

   ##
   wrong part
   ```

3. Code blocks with triple backticks are supported not `~~~`. Those will creates bugs.

   ````markdown
   # title

   ## part

   ### question

   ```python
   # comment
   def f():
       return 1
   ```
   ````

   is valid.

   but :

   ````markdown
   # title

   ## part

   ### question

   ```python
   # comment
   def f():
       return 1
   ```
   ````

   ```

   **is not valid.**

   The `# comment` line will be interpreted as a new title etc.
   ```
terraply 0.0.2
A wrapper to expand and exclude targets in terraform.
Requires Python: >=3.7

Terraply
========

A thin terraform wrapper that expands the -target argument using glob syntax and adds an -exclude
argument.


## Install

You must have terraform installed. Then:

```
pip install terraply
```

## Examples
```
terraply apply -target module.child-module-*.resource
```


```
terraply apply -target module.child-module-*.resource --exclude module.child-module-5
```
MCSimPython 0.1.6
Python vessel simulation environment
Requires Python: >=3.8
Keywords: mcsim,MCSimPython,DP,dynamic positioning,Marine Cybernetics

# MCSimPython

[![License: GPL v3](https://img.shields.io/badge/License-GPLv3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0) [![Python: v3.8](https://shields.io/badge/python-v3.8-green.svg)](https://www.python.org/downloads/release/python-380/) [![Python: v3.9](https://shields.io/badge/python-v3.9-green.svg)](https://www.python.org/downloads/release/python-390)

Vessel simulator used in master project and master thesis of M. Kongshaug, H. Mo and J. Hygen. The project is developed at Norwegian University of Science and Technology, Institue of Marine Technology. 

The python package is not complete and there is no guarantee for the validity of the vessel models.

The complete documentation can be found at: https://wave-model.readthedocs.io/en/latest/index.html

## How to use MCSimPython

### PyPi
The `MCSimPython` package can be installed from PyPi using `pip`:

`pip install MCSimPython`

### From GitHub:
Install from GitHub in the following:
- Clone the GitHub repository to your local computer.
- Create a virtual environment `py -m venv name-of-venv`
- Activate virtual environment `name-of-venv\scripts\activate`
- Update pip and setuptools: `py -m pip install --upgrade pip setuptools`
- Install the python package locally: `pip install .` (alternatively you can install as an editable `pip install -e .`)
- Verify that the python package `MCSimPython` has been installed properly by running a demo script, or simply in the command promt:
```
(venv) C:\path\to\dir> python
>>> import MCSimPython
>>>
```

## Demos

Demonstration of how the individual components of the python package can be used is given in `demos`. Some demos of combination of the different components are also given here. 

## Visualization

### RVG 6DOF model in beam sea
![6DOF RVG 2D Visualization](https://github.com/janerikhy/Wave-Model/blob/main/demos/animations/vessel_motion3d__rvg_waveangle_90.gif)

### RVG 6DOF model in head sea
![6DOF RVG 3D Visualization](https://github.com/janerikhy/Wave-Model/blob/main/demos/animations/vessel_motion3d__rvg_waveangle_180.gif)

<!---
### CSAD 6DOF model in multidirectional sea
![6DOF CSAD 2D Visualisation](https://github.com/janerikhy/Wave-Model/blob/main/demos/animations/vessel_motion3d_22.gif)
--->
osais 1.0.49
The osais Python lib for connecting AIs to OSAIS cloud
Requires Python: >=3
Keywords: osais,opensourceais

# osais
The osais Python lib for connecting AIs to OSAIS cloud

## Install

pip install osais

## Requirements 

Kept minimal (boto3, Jinja2, requests, schedule, watchdog, Werkzeug)

AI that includes osais package must have a ./_input and a ./_output directory, for receiving and generating files
Support of uploading to AWS S3 is provided

## Support

eric [at] incubiq [dot] com