semvec/pypi-packages huggingface dataset:
clebs-puc-eng-dados-csv-converter 0.1.1
Convert csv to json. Publishing only for learning purposes at PUC.
Requires Python: >=3.9,<4.0

# File Converter

CSV to JSON converter.

## Introduction

Teaching how to deploy a lib on PyPi at PUC using Poetry.



### What this project can do

Read a **csv** file or a **folder** with csv`s and convert them to **JSON**.
This project is a program running on terminal, preferably install with pipx:


```bash
pipx install clebs-puc-csv-converter
```

To use, just type in:

```bash
csv_converter --help
```

This will list all available options.
mpi-channels 0.1.4
A RemoteChannel implementation built on top of MPI RMA
Requires Python: >=3.8

# Remote Channels via MPI RMA (One Sided Communication)

Use one-sided MPI communication (built on `mpi4py`) to coordinate task-based
parallelism via channels. The `RemoteChannel` class is a message queue.
"Producer" tasks can place messages into the queue using the (blocking) `put`
-- or the (non-blocking) `putf` -- methods. "Consumer" task can take messages
from the queue using the (blocking) `take` -- or the (non-blocking) `takef` --
methods.

This illustrates how you can use the `RemoteChannel` workflow to perform
parallel work:
![RemoteChannel](examples/illustrations/RemoteChannel.png)

## Installation

```
pip install mpi-channels
```

## Usage

* Create a `RemoteChannel` and ensure it knows how many messages to expect:

```python
from mpi_channels import RemoteChannel

# Make a Remote Channel (on MPI rank 0)
inputs = RemoteChannel(buff_size, message_size)
inputs.claim(data_size)
```

The `claim` method increments the expected number of messages (a counter used
to determine of a `take` call should wait for more data).

* Place data into the channel:

```python
# Put data into channel by iterating over data source and putting each element
# into the channel one-by-one. `putf` is non-blocking.
if rank == 0:
    # data = data source
    for elt in data:
        inputs.putf(elt)
```

The data `elt` must be a python object with a `len` (*hint:* scalars should be
wrapped in a single-item tuple: `inputs.putf((val,))` -- the trailing comman
ensures that `len((val,)) = 1`). The `putf` method returns a
`concurrent.Futures.Future` object and does not block execution if the channel
is full.

* Take data from the channel:

```python
# Take data from the channel
if rank > 0:
    # data_size is the expected data size -- make sure that it's big enough.
    for i in range(data_size):
        p = inputs.take()
        # if there is no more data, p = None
        if p is None:
            break
        # Do some work with p
```

The `take` method blocks until data can be taken from the channel. If the
channel is empty (that is more data have been taken from it than the sum of all
`inputs.claim(N)` calls until that point), then `take` returns `None`. Note
that `p` is an object with lenght. If `p` is a scalar with value `val` then
`p=[val]`.
dymanic-load 0.0.2
Load module, function from string

Dynamically load modules for Python
===================================

Load python module, or member of module from string.

Example Usage
-------------
::

    from dynamic_load import dynamic_load
    dattime_module =  dynamic_load("datetime") # Load datetime module
    dattime_module.datetime.now()
    time_function = dynamic_load("time.time")
    time_function()
seanomalydetection 0.1.0
Welcome to SEAnomalyDetection

# SEAnomalyDetection

A package for detection of anomalies in photovoltaic energy production.

Having an issue? Report it on [GitHub](https://github.com/MarekWadinger/seanomalydetection)


## Installation
```sh
pip install seanomalydetection
```

## Usage
Preprocessing and feature engineering on input data.
```python
from seanomalydetection.anomaly_detection import json_to_df

df = json_to_df('se_daily.json')
```

Works with pd.DataFrame too!

```python
from seanomalydetection import se_api_process
from seanomalydetection.anomaly_detection import json_to_df

data = se_api_process.get_se_as_df('src/seanomalydetection/se_daily.json')
df = json_to_df(data)
```
Split the dataset into train set and test set.
```python
from seanomalydetection.anomaly_detection import train_test_split

dt_train, df_test = train_test_split(df)
```

Train ensemble of outlier classifiers for all Modules with estimated outlier rate.
```python
from seanomalydetection.anomaly_detection import train_model

clf_models = train_model(df_train)
```
Return dfs containing outlier tags and outlier scores for test set.
```python
from seanomalydetection.anomaly_detection import evaluate_models

df_outliers, df_scores = evaluate_models(df_test, clf_models)
```

###List significantly anomalous Modules (specified by min_anomalies)
```python
from seanomalydetection.anomaly_detection import list_most_anomalous

most_anomalous = list_most_anomalous(df_outliers, min_anomalies=10)
```
###Plot time-series and SMA of significantly anomalous Modules 
(specified by min_anomalies)
```python
from seanomalydetection.anomaly_detection import plot_anomaly

plot_anomaly(df_outliers, df_scores, window=7, min_anomalies=10)
```
### Report printout
```python
from seanomalydetection.anomaly_detection import report_anomaly

report_anomaly(df_outliers)
```

## Tidy detection
Too much programming? We have easier solution.
```python
from seanomalydetection.anomaly_detection import tidy_detection

df_outliers, df_scores = tidy_detection('se_daily.json')
```
yaqd-gdrive 2021.10.0
Daemon interface for writing files to Google Drive
Requires Python: >=3.7

# yaqd-gdrive

[![PyPI version](https://badge.fury.io/py/yaqd-gdrive.svg)](https://badge.fury.io/py/yaqd-gdrive)
[![Conda](https://img.shields.io/conda/vn/conda-forge/yaqd-gdrive)](https://anaconda.org/conda-forge/yaqd-gdrive)
[![yaq](https://img.shields.io/badge/framework-yaq-orange)](https://yaq.fyi/)
[![black](https://img.shields.io/badge/code--style-black-black)](https://black.readthedocs.io/)
[![ver](https://img.shields.io/badge/calver-YYYY.0M.MICRO-blue)](https://calver.org/)
[![log](https://img.shields.io/badge/change-log-informational)](https://gitlab.com/yaq/yaqd-gdrive/-/blob/master/CHANGELOG.md)

yaq daemon interface to Google Drive

This package contains the following daemon(s):
- [gdrive](https://yaq.fyi/daemons/gdrive/)
yaqd-edaq 2021.10.0
yaq daemons for edaq sensor recording systems
Requires Python: >=3.7

# yaqd-edaq

[![PyPI](https://img.shields.io/pypi/v/yaqd-edaq)](https://pypi.org/project/yaqd-edaq)
[![Conda](https://img.shields.io/conda/vn/conda-forge/yaqd-edaq)](https://anaconda.org/conda-forge/yaqd-edaq)
[![yaq](https://img.shields.io/badge/framework-yaq-orange)](https://yaq.fyi/)
[![black](https://img.shields.io/badge/code--style-black-black)](https://black.readthedocs.io/)
[![ver](https://img.shields.io/badge/calver-YYYY.M.MICRO-blue)](https://calver.org/)
[![log](https://img.shields.io/badge/change-log-informational)](https://gitlab.com/yaq/yaqd-edaq/-/blob/master/CHANGELOG.md)

yaq daemons for edaq sensor recording systems

This package contains the following daemon(s):

- https://yaq.fyi/daemons/edaq-isopod

## calibration

Following is an example you might use to apply callibration to the quadmf.

```python
import yaqc

edaq = yaqc.Client(38000)
config = edaq.get_config()


print('Calibrate which channel?')
chno = input()
print(f'Will calibrate {chno}')
edaq.remove_calibration(chno)


calibpoints = config.get('calibpoints', 3)
calibvalues = config.get('calibvalues', [4,7,10])

for i in range (1,calibpoints+1):
    print(f'place probe in pH={calibvalues[i-1]} buffer, then press enter')
    input()
    edaq.add_calibration_point(chno, i, calibvalues[i-1])

print('Calibration Complete')
```
flax-extra 0.0.2
The package provides extra flexibility to Flax using ideas originated at Trax
Requires Python: >=3.9

# Flax extra

[![readthedocs](https://readthedocs.org/projects/flax_extra/badge/?version=latest&style=flat)](https://flax_extra.readthedocs.io)
[![codecov](https://codecov.io/gh/manifest/flax-extra/branch/main/graph/badge.svg?token=LDR4XJG8B5)](https://codecov.io/gh/manifest/flax-extra)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black#readme)
[![Package version](https://shields.io/pypi/v/flax_extra)](https://pypi.org/project/flax_extra)

The package provides extra flexibility to [Flax](github.com/google/flax) using ideas originated at [Trax](https://github.com/google/trax).

Extras include:
- Support of combinators (serial, parallel, branch, etc.) backed by [Redex](https://github.com/manifest/redex).
- A modular training loop working nicely with [Optax](https://github.com/deepmind/optax) optimizers and metrics.
    - Pluggable logging to stdout.
    - Parallel execution of training and evaluation tasks.
    - Regular and the best metric checkpoints.
    - [Tensorboard](https://www.tensorflow.org/tensorboard) integration.
- Additional linen modules.

Check out [documentation](https://flax-extra.readthedocs.io), introductory tutorials, or examples that include:
- [Perceiver IO](https://arxiv.org/abs/2107.14795)
    - [Classification model](https://flax-extra.readthedocs.io/en/latest/notebooks/perceiver_classification/example) pretrained on images from [ImageNet](https://ieeexplore.ieee.org/document/5206848).
    - [Masked-language model](https://flax-extra.readthedocs.io/en/latest/notebooks/perceiver_language_modeling/example) pretrained using a large text corpus obtained by combining [English Wikipedia and C4](https://arxiv.org/abs/1910.10683).
    - [Autoencoder model](https://flax-extra.readthedocs.io/en/latest/notebooks/perceiver_autoencoding/example) pretrained on multimodal input (audio, video, and label) of the [Kinetics-700-2020](https://arxiv.org/abs/2010.10864) dataset.
yaqd-ti 2021.10.0
yaq daemons for interfacing with Texas Instruments integrated circuits
Requires Python: >=3.7

# yaqd-ti

[![PyPI](https://img.shields.io/pypi/v/yaqd-ti)](https://pypi.org/project/yaqd-ti)
[![Conda](https://img.shields.io/conda/vn/conda-forge/yaqd-ti)](https://anaconda.org/conda-forge/yaqd-ti)
[![yaq](https://img.shields.io/badge/framework-yaq-orange)](https://yaq.fyi/)
[![black](https://img.shields.io/badge/code--style-black-black)](https://black.readthedocs.io/)
[![ver](https://img.shields.io/badge/calver-YYYY.0M.MICRO-blue)](https://calver.org/)
[![log](https://img.shields.io/badge/change-log-informational)](https://gitlab.com/yaq/yaqd-ti/-/blob/master/CHANGELOG.md)


yaq daemons for interfacing with Texas Instruments integrated circuits

This package contains the following daemon(s):

- [ads1115](https://yaq.fyi/daemons/ads1115)
time-series-predictor 3.0.6
Time Series Predictor
Requires Python: >=3.6

# Time Series Predictor

[![PyPI version](https://badge.fury.io/py/time-series-predictor.svg)](https://badge.fury.io/py/time-series-predictor) [![Documentation Status](https://readthedocs.org/projects/time-series-predictor/badge/?version=latest)](https://time-series-predictor.readthedocs.io/en/latest/?badge=latest) [![travis](https://app.travis-ci.com/krypton-unite/time_series_predictor.svg?branch=master)](https://app.travis-ci.com/github/krypton-unite/time_series_predictor) [![codecov](https://codecov.io/gh/krypton-unite/time_series_predictor/branch/master/graph/badge.svg)](https://codecov.io/gh/krypton-unite/time_series_predictor) [![GitHub license](https://img.shields.io/github/license/krypton-unite/time_series_predictor)](https://github.com/krypton-unite/time_series_predictor) [![Requirements Status](https://requires.io/github/krypton-unite/time_series_predictor/requirements.svg?branch=master)](https://requires.io/github/krypton-unite/time_series_predictor/requirements/?branch=master)

## Usage

Please refer to the following examples:

- [example_flights_dataset.ipynb](https://time-series-predictor.readthedocs.io/en/latest/notebooks/example_flights_dataset.html)
- [example_oze_challenge.ipynb](https://time-series-predictor.readthedocs.io/en/latest/notebooks/example_oze_challenge.html)

## Pre requisistes installation

### Windows

```powershell
.\scrips\init-env.ps1
```

#### Linux or MacOS

```bash
./scripts/init-env.sh
```

--------

## Development

```terminal
pip install -e .[dev]
```

--------

## Test

```terminal
pip install -e .[test]
pytest -s
```

--------

## Build docs

### Windows Requirements

#### Make

In an elevated powershell prompt install it by:

```powershell
choco install cmake
```

#### TexLive

Download and install TexLive from [ctan.org](http://mirror.ctan.org/systems/texlive/tlnet/install-tl-windows.exe)

#### Perl requirement

In an elevated powershell prompt install it by:

```powershell
choco install activeperl
```

#### CPANM requirement

In an elevated powershell prompt install it by:

```powershell
curl -L https://cpanmin.us | perl - App::cpanminus
```

#### Latex indent requirement

```powershell
git clone https://github.com/cmhughes/latexindent.pl.git
cd latexindent.pl/helper-scripts
perl latexindent-module-installer.pl
```

#### ImageMagick requirement

In an elevated powershell prompt install it by:

```powershell
choco install imagemagick
```

----------------

### Build process

```powershell
pip install -e .[docs]
# cd docs
.\make html
.\make latex
# sphinx-build docs/source docs/build
.\build\latex\make
```