semvec/pypi-packages huggingface dataset:
HM1507-3GUI 0.4b0
Module for saving data from the HAMEG HM1507-3 oscilloscope to the PC.
Requires Python: >=3.6

# HM1507_3GUI package

This module provides a GUI for saving data from the HAMEG HM1507-3 oscilloscope to the PC as .png, .svg and .csv files. It requires the DSMVLib module.
spdx-validator 0.1.7
Simple SPDX Package validator
Requires Python: >=3.6

<!--
SPDX-FileCopyrightText: 2020 Henrik Sandklef <hesa@sandklef.com>

SPDX-License-Identifier: GPL-3.0-or-later
-->

&nbsp;

[![REUSE status][1]][2]

[1]: https://api.reuse.software/badge/github.com/hesa/spdx-validator
[2]: https://api.reuse.software/info/github.com/hesa/spdx-validator

# spdx-validator

Simple validator of package SBoM

# Installation

```
git https://github.com/hesa/spdx-validator.git
cd spdx-validator
pip install .
```

# Supported SPDX versions

* [2.2](https://spdx.github.io/spdx-spec/) through this [JSON schema](https://github.com/spdx/spdx-spec/blob/development/v2.2.1/schemas/spdx-schema.json)

## Supported formats

* yaml

* JSON

# Using spdx_validator

## Basic use

Assuming you have an SPDX file, `project.json`, you would like to validate:

```
$ spdx-validator project.json
$ echo $?
0
```

If you don't see any printout and the return code is `0`, the file is valid.

## Verbose printut

Assuming you have an SPDX file, `project.spdx.yaml`, you would like to validate:

```
$ spdx-validator example-data/project.spdx.yml --verbose
Determine file suffix:  OK, .yml
Read data from file: OK
Validating spdx data : OK
$ echo $?
0
```

## Recursive use

As above you have an SPDX file, `project.json`, you would like to
validate. But this time you want to check all the relationships
recursively.

```
$ spdx-validator project.json -r
$ echo $?
0
```

If you don't see any printout and the return code is `0`, the file is valid.

# License

The program is licensed under GPL-3.0-only

The data (in the var directory) may be under other licenses.
pycnysr 0.0.2
a simple multi directory watcher and syncer
Requires Python: >=3.9,<4.0
Keywords: files,synchronization,rsync,watcher

# pycnysr

A simple directory watcher and syncer

# usage

Based on a YAML config file (see the [example configuration file](config.dist.yaml)), this module synchronizes a local directory to one or more local or remote directories, using [rsync](https://rsync.samba.org) as the underlying backend.

It uses two levels of exclusion and inclusion in order to avoid useless expensive rsync calls. At a first level, files that don't need to be monitored
can be excluded at a cheap cost. At a second level, only files that need to be
really rsynced can be configured and fine tuned.

It should be able to run on Windows, Linux and macOS and generate optional notifications.

# in practice

```yaml
my-repository:
  destinations: [
    "my-host:~/my-repository/"
  ]
  event_handler:
    excludes: [
      ".*tmp.*"
    ]
    includes: [
      ".*/api/.*",
      ".*/conf/.*",
    ]
  notify: true
  rsync:
    filters: [
      "- ***/*.pyc",
      "- ***/__pycache__/",
      "+ api/***",
      "+ conf/***",
      "- *"
    ]
    options: [
      '--archive',
      '--delete',
      '--rsh=ssh'
    ]
  source: ~/Sites/my-repository/
```

Given this `config.yaml` file, changes in the source directory `~/Sites/my-repository/` will be propagated to `my-host:~/my-repository/` via SSH with options eventually passed in the `rsync.options` list.

First, only files not excluded in the `event_handler.excludes` (by default: [])
and included by the `event_handler.includes` (by default: ['.*']) will be
passed to the rsync process.

Then, rsync is called with the [filter rules](https://download.samba.org/pub/rsync/rsync.1) built from the `rsync.filters` list.

Tet's run the watcher:

```console
❯ pycnysr --config config.yaml
2022-10-13 21:03:17 INFO set log level to INFO
2022-10-13 21:03:17 INFO rsync binary is /opt/homebrew/bin/rsync
2022-10-13 21:03:17 INFO using config /Users/laurent/Sites/pycnysr/config.yaml
2022-10-13 21:03:17 INFO syncing repository named my-repository located in /Users/laurent/Sites/pycnysr to ['/Users/laurent/Downloads/dest/']
2022-10-13 21:03:17 INFO observers all initialized
````

On another console, create a file:

```console
touch api/new-file.txt
```
See the file synchronized:

```console
2022-10-13 21:03:23 INFO synchronizing /Users/laurent/Sites/pycnysr/new-file.txt
```

# warning

Use with care. early development.

# license

This project is licensed under the terms of the MIT license.
pedpy 1.0.0b1
pedpy is a python module for pedestrian movement analysis.
Requires Python: >=3.8

![ci workflow](https://github.com/PedestrianDynamics/pedestrian-trajectory-analyzer/actions/workflows/ci.yml/badge.svg)

# pedpy

`pedpy` is a python module for pedestrian movement analysis. 
It implements different measurement methods for density, velocity and flow.

This repo is a port from the original `JPSreport` to a Python implementation, and will provide the same functionalities.

## Getting started
### Setup Python
For setting up your Python Environment a Python version >= 3.8 is recommended (our code is tested with 3.8).
To avoid conflicts with other libraries/applications the usage of virtual environments is recommended, see [Python Documentation](https://docs.python.org/3/library/venv.html) for more detail.

### Installing pedpy
To install the latest **stable** version of `pedpy` and its dependencies from PyPI:
```bash
python3 -m pip install pedpy
```

If you want to install the current version in the repository which might be unstable, you can do so via:
```bash
python3 -m pip install --pre --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple/ pedpy
```

### Usage

The usage of `pedpy` is demonstrated in the [demo notebook](usage.ipynb).
You can either download the notebook and [demo files](demos/bottleneck/) 
from the GitHub repository or clone the whole repository with:

```bash 
git clone https://github.com/PedestrianDynamics/pedestrian-trajectory-analyzer.git
```

For using the notebook some additional libraries need to be installed, mainly 
for plotting.
You can install the needed libraries with:

```bash
python3 -m pip install jupyter matplotlib
```

Afterwards you can start a jupyter server with:
```bash
jupyter notebook
```

After navigating to the notebook, you can see how the library can be used 
for different kinds of analysis.

Some examples how the computed values can be visualized are also shown in the notebook, e.g., density/velocity profiles, fundamental diagrams, N-T-diagrams, etc.

![voronoi](figs/voronoi_diagrams.png)

![density](figs/density_comparison.png)
simple-dl-toolkit 0.0.2
Simple deep learning toolkit
Requires Python: >=3.6


[English](README.en.md) | [简体中文](README.md)

# 介绍
simpledl是Simple Deep Learning Toolkit的缩写，基于PyTorch，旨在用极简的接口构建神经网络和训练，能方便地复用模型，以及修改训练流程的各个环节，主要用于复现经典模型和进行一些实验。


# 关键接口
## Data
不同任务的数据格式和处理方法都不相同，如果对数据进行抽象处理的话，会使整个代码变得极其复杂，simpledl没有针对数据抽象单独的类，而是复用PyTorch的Dataset，只要求返回的batch数据是dict格式，例如
```python
batch = {
    'src': src_seq,
    'src_size': src_size, 
    'tgt': tgt_seq
}
```
在simpledl/data中提供了一些常用的数据处理类，可以方便地复用，比如TranslationDataset，根据两种语言的文本文件，构建一个Dataset。


## Model
Model的接口是forward函数，表示模型的前向计算，函数的参数可以是batch的key，或者是batch，输出也要求是一个dict，比如
```python
def forward(self, src, src_size, tgt):
    # do something 
    output_logits = calc_output_logits()
    return {
        'logits': output_logits,
    }
```

## Loss
Loss的接口是数据的一个batch和模型的前向计算的输出，输出是dict，要求有key为loss，便于后面进行梯度更新 比如
```python
def forward(self, batch, out):
    return {
        'loss': loss_calc(batch, out),
    }
```

## Optimizer
使用PyTorch的Optimizer，进行简单的包装，可以根据参数str构建需要的optimizer


## Trainer
Trainer是主要的类，包括构建数据集、Model、Loss、Optimizer、callbacks。callbacks是训练过程中使用的，用于统计每一步训练的结果、保存断点、模型评估、earlystopping等操作。

训练的流程如下:
```python
def train(...):
    prepare_callbacks()
    restore_training_if_necessary()
    for e in training_epochs:
        call_callbacks_epoch_begin()
        for batch in dataset:
            call_callbacks_batch_begin()
            model_out = forward_model(batch)  # 根据batch，调用Model的forward函数
            loss = calc_loss(model_out, batch)
            gradient_update_step()  # 进行优化
            call_callbacks_batch_end()  # 统计结果、保存断点、调整学习率等
        call_callbacks_epoch_end()    
        evaluate_if_necessary()
```
通过修改trainer，可以方便地修改数据、模型、以及训练的每个环节


# 示例
## [Machine Translation: Transformer, IWSLT2014](examples/translation-iwslt14-en-de/README.md)
BLEU结果: 
| Model | de -> en | en -> de |
| :--:  |  :--:    |   :--:   |
| Transformer | 33.27 | 27.72 | 
| Tied Transformers | 35.10 | 29.07 | 
| fairseq | 34.54 | 28.61 | 
| Ours | 34.36 | 28.33 | 


# Reference
fairseq </br>
AllenNLP </br>
[Read TFRecord](https://github.com/vahidk/tfrecord/blob/master/tfrecord/reader.py)</br>