semvec/pypi-packages huggingface dataset:
django-polls-dixonogb 0.1
A Django app to conduct web-based polls.
Requires Python: >=3.10

=====================
django-polls-dixonogb
=====================

django-polls-dixonogb is a Django app to conduct web-based polls. For each
question, visitors can choose between a fixed number of answers.

Detailed documentation is in the "docs" directory.

Quick start
-----------

1. Add "polls" to your INSTALLED_APPS setting like this::

    INSTALLED_APPS = [
        ...,
        "django_polls_dixonogb",
    ]

2. Include the polls URLconf in your project urls.py like this::

    path("polls/", include("django_polls_dixonogb.urls")),

3. Run ``python manage.py migrate`` to create the models.

4. Start the development server and visit the admin to create a poll.

5. Visit the ``/polls/`` URL to participate in the poll.
ilp-decoder 0.2.2
An implementation of ILP decoders for quantum error correction.
Requires Python: >=3.10

# ILP Decoder

An implementation of the ILP decoder for circuit-level noise.

Included is a `ilp_decoder.ILPDecoder` class that can be configured using a `stim.DetectorErrorModel` and decodes shot data, directly outputting predicted observables (without sinter), as well as a `ilp_decoder.ILPSinterDecoder` class, which subclasses `sinter.Decoder`, for interfacing with sinter.

## Installation

To install from pypi, run:

```
pip install ilp_decoder
```

To install from source, run:

```
pip install -e .
```

from the root directory.

## Usage

Here is an example of how the decoder can be used directly with Stim:

```python
import stim
import numpy as np
from ilp_decoder import ILPDecoder

num_shots = 100
d = 3
p = 0.001
circuit = stim.Circuit.generated(
    "surface_code:rotated_memory_x",
    rounds=d,
    distance=d,
    before_round_data_depolarization=p,
    before_measure_flip_probability=p,
    after_reset_flip_probability=p,
    after_clifford_depolarization=p
)

sampler = circuit.compile_detector_sampler()
shots, observables = sampler.sample(num_shots, separate_observables=True)

decoder = ILPDecoder.from_circuit(circuit)

predicted_observables = decoder.decode_batch(shots)
num_mistakes = np.sum(np.any(predicted_observables != observables, axis=1))

print(f"{num_mistakes}/{num_shots}")
```

### Sinter integration

To integrate with [sinter](https://github.com/quantumlib/Stim/tree/main/glue/sample), you can use the
`ilp_decoder.ILPSinterDecoder` class, which inherits from `sinter.Decoder`.
To use it, you can use the `custom_decoders` argument when using `sinter.collect`:

```python
import sinter
from ilp_decoder import ILPSinterDecoder, sinter_decoders

samples = sinter.collect(
    num_workers=4,
    max_shots=1_000_000,
    max_errors=1000,
    tasks=generate_example_tasks(),
    decoders=['ilp'],
    custom_decoders=sinter_decoders()
    print_progress=True,
)
```

### Advanced ILP solvers

You can use whatever ILP solver supported by `cvxpy` instead of the default one `highs`, though
`highs` should be one of the fastest open-source solver available. If you have access to `gurobi`,
you may gain some extra speed boost (not tested).
qore-client 0.1.7
Qore Client
Requires Python: >=3.8

# Qore Client

Qore Client is a Python client library for the Qore API.

## Prerequisites

First, install `uv` package installer:

```bash
# On macOS and Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# On Windows PowerShell
(Invoke-WebRequest -Uri "https://astral.sh/uv/install.ps1" -UseBasicParsing).Content | pwsh -Command -
```

## Installation

For users, simply install using pip:

```bash
pip install qore-client
```

## Usage Example

The code below is an example of creating a simple parquet file, uploading it to Qore Drive, and then downloading it again.

```python
access_key = "access_key"
secret_key = "secret_key"
folder_id = "folder_id"
parquet_file = "data.parquet"

from qore_client.client import QoreClient

client = QoreClient(access_key, secret_key)

data = {
    "name": ["Alice", "Bob", "Charlie", "David"],
    "age": [25, 30, 35, 40],
    "city": ["New York", "Los Angeles", "Chicago", "Houston"],
    "salary": [50000, 60000, 70000, 80000],
}

sample_df = pd.DataFrame(data)
sample_df.to_parquet(parquet_file)

create_file_response = client.create_file(folder_id, parquet_file)
get_file_response = client.get_file(response["id"])

df = pd.read_parquet(response2)

print(df)
#       name  age         city  salary
# 0    Alice   25     New York   50000
# 1      Bob   30  Los Angeles   60000
# 2  Charlie   35      Chicago   70000
# 3    David   40      Houston   80000
```

## Development Environment Setup

1. Clone the repository

```bash
git clone <repository-url>
```

2. Create a virtual environment and install dependencies

```bash
bash dev.sh
```

## Testing Development Versions

```bash
# Install the package from TestPyPI
uv pip install -i https://test.pypi.org/simple/ qore-client=={version}

# Install the package from PyPI
uv pip install qore-client=={version}
```

## CI/CD

This project supports automated testing and deployment through GitLab CI/CD.
rgmining-rsd 0.3.6
An implementation of Review Spammer Detection algorithm
Requires Python: >=3.11
Keywords: algorithm, graph, icdm, mining, review

An implementation of Review Spammer Detection
=============================================

|GPLv3| |Build Status| |Release|

|Logo|

This package provides an implementation of Review Spammer Detection
(RSD) introduced in this paper:

- `Guan Wang <https://www.linkedin.com/in/guanwang/>`__,
  `Sihong Xie <https://engineering.lehigh.edu/faculty/sihong-xie>`__,
  `Bing Liu <https://www.cs.uic.edu/~liub/>`__, and
  `Philip S. Yu <https://www.cs.uic.edu/~psyu/>`__,
  "`Review Graph Based Online Store Review Spammer Detection <https://ieeexplore.ieee.org/document/6137345?arnumber=6137345>`__,"
  Proc. of `IEEE 11th International Conference on Data Mining <https://ieeexplore.ieee.org/xpl/conhome/6135855/proceeding>`__, 2011, pp. 1242-1247.

See `the documents <https://rgmining.github.io/rsd/>`__ for more
information.

Installation
------------

Use ``pip`` to install this package.

::

    pip install --upgrade rgmining-rsd

License
-------

This software is released under The GNU General Public License Version
3, see `COPYING <COPYING>`__ for more detail.

.. |GPLv3| image:: https://img.shields.io/badge/license-GPLv3-blue.svg
   :target: https://www.gnu.org/copyleft/gpl.html
.. |Build Status| image:: https://github.com/rgmining/rsd/actions/workflows/python-lib.yaml/badge.svg
   :target: https://github.com/rgmining/rsd/actions/workflows/python-lib.yaml
.. |Release| image:: https://img.shields.io/badge/release-0.3.6-brightgreen.svg
   :target: https://pypi.org/project/rgmining-rsd/
.. |Logo| image:: https://rgmining.github.io/synthetic/_static/image.png
   :target: https://rgmining.github.io/rsd/