semvec/pypi-packages huggingface dataset:
interview-buddy 0.4
This package is made for beginners who are appearing for interview preparation and to crack coding interviews

# Interview Buddy
Interview Buddy is designed for beginners who are new to coding which has two ai-models in it **code-llama-7b** for code generation and **python-code-explainer** which makes it useful for generating code and understanding code for both beginner and intermediate programmers

## Installation

Install interview-buddy with cmd

```bash
  pip install interview-buddy==0.4
```

# Initialization for Basic Methods
```bash
from interview.interview import Interview
interview = Interview(data=10)
```

# Initialization for Generating Code 
```bash
from interview.interview import Interview
interview = Code_Generator("Generate")
```

# Initialize the Interview object with necessary data
## Methods
**a.** **factorial()**
This method calculates the factorial of a given number.

**Parameters:**
* **data:** Integer, the number for which factorial is to be calculated.
**Example:**
```bash
result = interview.factorial()
print("Factorial:", result)
```

**Parameters:**
* **User_input:** String, Enter the query which want to be generated.
**Example:**
```bash
result = interview.generate_code("Write me a code for Binary Tree")
print("Factorial:", result)
```

# Supported Methods
List all the supported methods along with a brief description of what each method does.

**fibonacci_series():** Generates a Fibonacci series.\
**palindrom():** Checks if a string is a palindrome.\
**bubble_sort():** Sorts a list using the bubble sort algorithm.\
**insertion_sort():** Sorts a list using the insertion sort algorithm.\
**anagram():** Checks if two strings are anagrams.\
**binary_search():** Performs binary search on a sorted list.\
**min_():** Finds the minimum value in a list.\
**max_():** Finds the maximum value in a list.\
**reverse():** Reverses a list.
**generate_code():** Which is used to generate code 

## Installation

Install interview-buddy with cmd

```bash
  pip install interview-buddy
```
    


## Authors

- [@RoboJunior](https://github.com/RoboJunior) 👨‍💻




## Dependencies

* Transformers
* Ctransformers

Transformers - Installation
```bash
  pip install transformers
```

Ctranformers - Installation
```bash
#Base ctransformers with no GPU acceleration
pip install ctransformers>=0.2.24

#Or with CUDA GPU acceleration
pip install ctransformers[cuda]>=0.2.24

#Or with ROCm GPU acceleration
CT_HIPBLAS=1 pip install ctransformers>=0.2.24 --no-binary ctransformers

#Or with Metal GPU acceleration for macOS systems
CT_METAL=1 pip install ctransformers>=0.2.24 --no-binary ctransformers

```
pybode 0.0.1
Support package for Bode diagramms
Requires Python: >=3.11

# Bode Plot with DHO800/900
A python support package plots a Bode plot with gain and phase. The program is tested with DHO914 as Oscilloscope and DG1022Z as function generator.


![Bode setup](/doc/bode.jpg)

## Test setup
Connect the DG1000Z and the DHO800/900 with same Ethernet as the computer. 

### Two Pin Component Frequency Characterisation 
Generator CH1 should be connected to the oscilloscope CH1 via a BNC or SMA cable. Same connection for CH2 but the channel 2 has a T-connector in between and the component is between plus and minus of the stub. The wire length of CH1 and CH2 should be the same and the cable at the end of the oscilloscope should be terminated with 50 Ohm.

### Circuit Transfer Function
Generator CH1 should be connected to the oscilloscope CH1 via a BNC or SMA cable.  
The generator CH2 is connected to the circuit input and the circuit output directly to oscilloscope CH2.
The wire length of CH1 and CH2 should be the same and the cable at the end of the oscilloscope should be terminated with 50 Ohm.

## Python Requirements
This package needs numpy/scipy/matplotlib/pylabdevs/pydg1000z/pydho800 environment. 

## Installing 

There is a Python and PyPi packages that can be installed using

```
pip install  numpy scipy matplotlib pylabdevs pydho800 pydg1000z pybode
```

## Usage
The basic syntax is `python bode.py MIN_FREQ MAX_FREQ [FREQ_COUNT]`, so if you, for example, want to test your DUT between 1kHz and 2.2Mhz, with 100 steps (default is 50),
you can do it like this: `python bode.py 1e3 2.2e6 100`.

By default only the Amplitude diagram is measured and plotted. If you also want to get the Phase diagram, you will have to specify the `--phase` flag.

If you want to use the measured data in another software like OriginLab or Matlab, you can export it to a semicolon-seperated CSV file with the `--output` option.

So a typical command line would like this: ```python bode.py 1e3 2.2e6 100 --osc_ip 10.0.0.123 --awg_ip 10.0.0.124  --phase --output out.csv```

To see the full list of possible options call `python bode.py --help`.

## License
This program is licensed under the MIT License. See [LICENSE](https://github.com/pmschueler/pybode/blob/main/LICENSE) file for more info.

## Acknowledgements
Based on the original work by [DS1054_BodePlotter](https://github.com/jbtronics/DS1054_BodePlotter) I have re-used code parts and the method of the measurement.
pytest-tipsi-testing 1.4.3
Better fixtures management. Various helpers
Keywords: asyncio,testing

pytest-tipsi-testing
====================

.. contents:: **Table of Contents**
    :backlinks: none

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

pytest-tipsi-testing is distributed on `PyPI <https://pypi.org>`_ as a universal
wheel and is available on Linux/macOS and Windows and supports
Python 3.5+.

.. code-block:: bash

    $ pip install pytest-tipsi-testing

License
-------

pytest-tipsi-testing is distributed under the terms of the
`MIT License <https://choosealicense.com/licenses/mit>`_.


Motivation and features
-----------------------

We want to make fixtures more predictable in pytest. So this plugin ensures that only loaded fixtures will be available when test is running.

Currently pytest allows fixtures with bigger scopes (session, module and etc.) to be instantiated even if test doesn't directly require them. This may cause some troubles in several cases, for example if you wan't to create some kind of cache in bigger fixture.

This means that all not required fixtures will be finished before test is started: even with bigger scope. So if you want to have some fixtures always awailable (eg. docker_start fixture with session scope) - you should make it autoused.


Also we're forcing correct order for fixtures with different scopes: session -> module -> class -> function.


Usecase
^^^^^^^

In conjunction with `pytest-tipsi-django <https://github.com/tipsi/pytest-tipsi-django>`_ you can make fixtures on different level than scope and share the same database state across different tests. It's quit helpful when you perform complex and long database setup and want to have small and readable test cases.

Please see ``pytest-tipsi-django`` documentation for more detailed description.


Fixtures
--------

vprint
^^^^^^

Print that works only when verbose mode is enabled ``-v``.

*Note*: you should add ``-s`` to see the output.


log_requests
^^^^^^^^^^^^

Use it when you want to write down all request/responses made with `requests <http://docs.python-requests.org/en/master/>`_ library. By default it will put docs into ``.doc`` dir or other directory supplied by ``DOCS_ROOT`` environmental variable.

.. code-block:: python

  def example(log_requests):
    import requests
    with log_request('out_file'):
      r = requests.get('http://echo.jsontest.com/key/value/one/two')
      assert r.status_code == 200, r


It will put json file into your ``.doc/{MODULE_PATH}.out_file.json``, in my case it was a ``.doc/tests.test_log_requests.out.json`` your case may be different depending on module you have used it in.
analyser 0.1.0
An analytical library for signal data analysis
Requires Python: >=3.6

# Analyser v1.0

The analyser Python package is designed to fetch and analyze signal data. It interfaces with a web API to retrieve signals and their values, offering functionalities to calculate statistical measures like mean and standard deviation. It supports filtering by groups, ensuring tailored analyses. The package also includes efficient data handling with batch retrieval, making it suitable for large datasets.

## Environment setup
Create virtual environment with Python version > 3.8.5
```
python3 -m venv .venv
```
## Activate virtual environment
```
source .venv/bin/activate
```
## Install requirements
```
pip install -r requirements.txt
```
## Run all Tests
```
pytest -v -s
```

## Useage
(Refer to unseage.md)

## Installation
(Refer to installation.md)
rinkrat 0.1.1
A command line tool to query NHL stats.
Requires Python: >=3.8
Keywords: NHL,hockey,stats

# rinkrat
Rinkrat is a command line tool to query the NHL's public API for statistics.

## Install
You can install rinkrat from PyPi

    python -m pip install rinkrat

It is supported by python 3.8 and above.

## Usage

    rinkrat [ command ] [ sub-command ] { parameters }

Rinkrat uses the command line interface to get statistics from the public NHL api. With the current version you can access team standings data and rank it based on several criteria:

### Commands
Commands give you access to different data. Sub-commands determine how that data is organized and presented. For command snippits and descriptions, please see the [commands](docs/commands) directory.
datanest 1.0.0
A simple, intuitive, pandas-based database.
Requires Python: >=3.7

# datanest

[![PyPI - Version](https://img.shields.io/pypi/v/datanest.svg)](https://pypi.org/project/datanest)
[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/datanest.svg)](https://pypi.org/project/datanest)
[![GitHub license](https://img.shields.io/badge/license-MIT-blue.svg)](https://raw.githubusercontent.com/praneethnamburi/datanest/main/LICENSE)
[![Documentation Status](https://readthedocs.org/projects/datanest/badge/?version=latest)](https://datanest.readthedocs.io)


*A simple, intuitive, pandas-based database.*

Perfect for handling data such as time series, images, or any
Python objects alongside their metadata. This tool encapsulates a pandas
DataFrame containing metadata and Python objects. It provides an intuitive
data and metadata retrieval syntax through keyword-arguments.

-----

## Installation

```console
pip install datanest
```

## Usage

`datanest.Database` is the core class that wraps a `pandas.DataFrame` object. Even before adding any data fields using the `add_data_field` method, the database can already be used to query rows from the encapsulated DataFrame with an intuitive keyword argument syntax.

```python
import datanest

# Load example DataFrame with columns: 
# participant_id (int), age (float), surgery_performed (bool), notes (str)
db = datanest.get_example_database()

# Retrieve all metadata
db()

# Retrieve metadata for participant 3
db(participant_id=3)

# Retrieve metadata for participants aged 50 to 60 who have not had surgery
db(age_lim=(50, 60), surgery_performed=True)

# Retrieve metadata for participants where the notes string contains the word interesting
db(notes_has='interesting')
```

The `add_data_field` method can be used to add arbitrary python objects to the database, and we can retrieve relevant data entries using the same keyword argument syntax.

```python
# Add heart rate data to the database, indexed by participant_id
db.add_data_field('heart_rate', datanest.get_example_data(), 'participant_id')

# Retrieve all heart rate time series data
db.heart_rate()

# Retrieve heart rate time series data for participant 3
db.heart_rate(participant_id=3)

# Retrieve heart rate time series for participants aged 50 to 60
db.heart_rate(age_lim=(50, 60))

# Retrieve heart rate time series for participants where the notes string contains the word interesting
db.heart_rate(notes_has='interesting')
```

## License

`datanest` is distributed under the terms of the [MIT license](LICENSE).

## Contact

[Praneeth Namburi](https://praneethnamburi.com)

Project Link: [https://github.com/praneethnamburi/datanest](https://github.com/praneethnamburi/datanest)


## Acknowledgments

This tool was developed as part of the ImmersionToolbox initiative at the [MIT.nano Immersion Lab](https://immersion.mit.edu). Thanks to NCSOFT for supporting this initiative.