semvec/pypi-packages huggingface dataset:
pypxr 0.0.1
A collection of utilities for reducing data and modeling Polarized Resonant Soft X-ray Reflectivity.
Requires Python: >=3.6

# PyPXR

Open-source python utilities for modeling Polarized X-ray Reflectivity using an anisotropic transfer matrix formalism. The roots of this package were originally forked from the refnx.reflect layer model developed by Andrew Nelson.

This package requires the use of [refnx](https://refnx.readthedocs.io/en/latest/index.html) in order to model and fit data.

Some functionalities have been forked and repurposed from other open-source utilities:

  - [D. Dietze : FSRStools](https://github.com/ddietze/FSRStools)
  - [M. Jeannin : pyGTM](https://github.com/pyMatJ/pyGTM)
  - [P. Dmitriev : PyATMM](https://github.com/kitchenknif/PyATMM)


Results produced from the main-branch uniaxial calculation have been published and can be found online

  - [ACS Appl. Mater. Interfaces 2022, 14, 2, 3455–3466 (DOI:10.1021/acsami.1c19948)](https://pubs.acs.org/doi/abs/10.1021/acsami.1c19948)


### Contact information:
 - Authors: Thomas J. Ferron & Daniel F. Sunday
 - Affiliation: Materials Measurement Laboratory / Material Science and Engineering Division / Polymers Processing Group
 - Website: 
    - [Detailed Documentation (ReadTheDocs)](https://p-rsoxr.readthedocs.io/en/latest/)
    - [Resonant Soft X-ray Scattering Group](https://www.nist.gov/programs-projects/resonant-soft-x-ray-scattering-rsoxs)
 - Email: daniel.sunday@nist.gov
dlt-sidestep 0.0.8
A library that helps you build dlt pipelines by side stepping the dlt library and making your code interactive
Keywords: python,databricks,dlt,delta live tables


### Installation

pip install in your Databricks Notebook

```python
%pip install dlt_sidestep
```

### Example Usage

**Note**: You must define a `pipeline_id` variable as `spark.conf.get("pipelines.id", None)`

**Note**: You must define a `g` variable as `globals()`
`

```python
from pyspark.sql.functions import *
from pyspark.sql.types import *
from dlt_sidestep import SideStep

pipeline_id =  spark.conf.get("pipelines.id", None)
g = globals()

if pipeline_id:
  import dlt

json_path = "/databricks-datasets/wikipedia-datasets/data-001/clickstream/raw-uncompressed-json/2015_2_clickstream.json"

step = """
@dlt.create_table(
  comment="The raw wikipedia click stream dataset, ingested from /databricks-datasets.",
  table_properties={
    "quality": "bronze"
  }
)
def clickstream_raw():
  return (
    spark.read.option("inferSchema", "true").json(json_path)
  )
"""
SideStep(step, pipeline_id, g)
df = clickstream_raw()
df.display()


step = """
@dlt.create_table(
  comment="Wikipedia clickstream dataset with cleaned-up datatypes / column names and quality expectations.",
  table_properties={
    "quality": "silver"
  }
)
@dlt.expect("valid_current_page", "current_page_id IS NOT NULL AND current_page_title IS NOT NULL")
@dlt.expect_or_fail("valid_count", "click_count > 0")
def clickstream_clean():
  return (
    dlt.read("clickstream_raw")
      .withColumn("current_page_id", expr("CAST(curr_id AS INT)"))
      .withColumn("click_count", expr("CAST(n AS INT)"))
      .withColumn("previous_page_id", expr("CAST(prev_id AS INT)"))
      .withColumnRenamed("curr_title", "current_page_title")
      .withColumnRenamed("prev_title", "previous_page_title")
      .select("current_page_id", "current_page_title", "click_count", "previous_page_id", "previous_page_title")
  )
"""
SideStep(step, pipeline_id, g)
df = clickstream_clean()
df.display()

```
trivia 0.1.0
Awesome trivia created by rochacbruno

# trivia

[![codecov](https://codecov.io/gh/rochacbruno/trivia/branch/main/graph/badge.svg?token=trivia_token_here)](https://codecov.io/gh/rochacbruno/trivia)
[![CI](https://github.com/rochacbruno/trivia/actions/workflows/main.yml/badge.svg)](https://github.com/rochacbruno/trivia/actions/workflows/main.yml)

Python client for Open trivia Database

## Install it from PyPI

```bash
pip install trivia
```

## Usage

```bash
trivia
# or
python -m trivia
```

You can pass arguments `[amount], [category], [level]`

```bash
# 15 questions, from category 18, level easy
trivia 15 18 easy
```

![](trivia.gif)

## Development

Read the [CONTRIBUTING.md](CONTRIBUTING.md) file.
wandb-utils 0.1.2
Utitlity functions and scripts to work with Weights \& Biases
Requires Python: >=3.7
Keywords: AI,ML,Optimization,Machine Learning,Deep Learning

# Weights & Biases Utilities

## What is it?

[Weight & Biases (wandb)](wandb.ai/site) is a experiment management and hyperparameter tuning utility for machine learning experiments. Please refer to [their website](https://wandb.ai/site) for more information. Weights & Biases Utitlities (wandb-utils), is library of some utility functions and command line interface built on top of the official wandb API. This is an unofficial library, meaning that it is not supported by the Weights & Biases team, but is managed by open source contributors and ML researchers.
schedule-data-processing 0.1.1
The `schedule_data_processing` library

# Installation

To install the package, run the following(you'll need at least Python 3.7):

```
pip install --index-url https://test.pypi.org/simple/ --no-deps schedule-data-processing
```

# Usage

## Security

From azure
```
export KEY_VAULT_NAME=zeroGTaskKey
```

Or run:
```
source security
```

## Merge

To generate the merged schedule dataset provide the keyword `merge`:

```
schedule_data merge
```

The output will be saved as 
```
output.csv
```


## Processing

To look up an individual flight record provide the keyword `lookup` 
followed by the flight number, e.g.:

```
schedule_data lookup ZG2362
```

To look up more than one flight record (the output JSON strings will be separated 
by newlines), provide a list of flights separated by commas, e.g.

```
schedule_data lookup ZG2361,ZG2362
```

Option: use the `--pretty` or `-p` option to pretty-print the JSON strings.

0 : unformatted

1 : formatted

2 : formatted and colorful

```
schedule_data lookup --pretty 2 ZG2361,ZG2362
```
perceptron-pypi-Jateendra 0.0.1
It is an implementation of Perceptron
Requires Python: >=3.7

# oneNeuron_pypi
oneNeuron_pypi

# How to use this

```python
from oneNeuron.perceptron import Perceptron
## get X and y and then use below commands
model = Perceptron(eta=eta, epochs=epochs)
model.fit(X, y)

```

# Reference -
[Official python docs](https://packaging.python.org/tutorials/packaging-projects/)

[github docs for github actions](https://docs.github.com/en/actions/guides/building-and-testing-python#publishing-to-package-registries)