semvec/pypi-packages huggingface dataset:
waveletnn 0.3.4
WaveletNN blocks and losses
License: MIT
Requires Python: >=3.11
Keywords: wavelet, dwt, idwt, pytorch

# Wavelet Neural Networks

> [![PyPI - Version](https://img.shields.io/pypi/v/waveletnn?style=flat)](https://pypi.org/project/waveletnn/)
[![GitHub License](https://img.shields.io/github/license/Scurrra/WaveletNN-PyTorch?style=flat)](https://github.com/Scurrra/WaveletNN-PyTorch/tree/master?tab=MIT-1-ov-file)


> `pip install waveletnn`

Package provides the implementation of orthonormal and biorthogonal wavelet transforms via convolutions. Batch multi-channel one- and -two-dimensional data is supported. For analysis kernels of even length are supported, while for inverse transform (synthesis) kernels are required to have length `4k + 2`. The blocks are:

- `OrthonormalWaveletBlock1D`
- `OrthonormalWaveletBlock2D`
- `BiorthogonalWaveletBlock1D`
- `BiorthogonalWaveletBlock2D`
- `InverseWaveletBlock1D`
- `InverseWaveletBlock2D`

Package provides loss functions for wavelet's kernels regularizations to preserve features of both orthonormal (`OrthonormalWaveletRegularization`) and biorthogonal (`BiorthogonalWaveletRegularization`) wavelets while training. The features are admissibility (sum of coeffs), orthogonality and regularity. For more info on training see [notebooks directory](notebooks/).

The package can use wavelets from the [PyWavelets](https://pywavelets.readthedocs.io/) library. If the library is not yet installed use `pip install waveletnn[pywt]` for full installation. The wavelet blocks can be constructed by providing the name of the wavelet to be used (see [pywt.wavelist()](https://pywavelets.readthedocs.io/en/latest/ref/wavelets.html#built-in-wavelets-wavelist)) or by manually providing scaling filter for orthonormal blocks and scaling and wavelet analysis filters for biorthogonal wavelets. Note, that results of transform blocks are different from the results of the `pywt` functions: the approximation from each level is half its input no matter what signal extension mode is used. 

The signal extension (padding) is performed via separate module `PadSequence`. Extension modes are: 
- "constant" -- ```... v  v | x1 x2 ... xn | v  v ...```, with default `v = 0` (same as in `torch`, default behavior corresponds to "zero" padding in `pywt`)
- "circular" -- ```... xn-1 xn | x1 x2 ... xn | x1 x2 ...``` (same as in `torch`, corresponds to "periodic" padding in `pywt`)
- "replicate" -- ```... x1 x1 | x1 x2 ... xn | xn xn ...``` (same as in `torch`, corresponds to "constant" padding in `pywt`)
- "reflect" -- ```... x3 x2 | x1 x2 ... xn | xn-1 xn-2 ...``` (same as in `torch` and `pywt`)
- "antireflect" -- ```... (2*x1 - x3) (2*x1 - x2) | x1 x2 ... xn | (2*xn - xn-1) (2*xn - xn-2) ...``` (same as in `pywt`, not present in `torch`)

For examples on usage see [notebooks directory](notebooks/).
rc-tidevice3 0.1.6
forked from codeskyblue/tidevice3, wrapper for pymobiledevice3 for easy use with iphone device
License: MIT
Requires Python: >=3.8
Keywords: automation, device, ios, iphone, mobile, pymobiledevice3, testing

# tidevice3
[![PyPI version](https://badge.fury.io/py/tidevice3.svg)](https://badge.fury.io/py/tidevice3)
[![codecov](https://codecov.io/gh/codeskyblue/tidevice3/graph/badge.svg?token=twFRe9igek)](https://codecov.io/gh/codeskyblue/tidevice3)

wrapper for pymobiledevice3 to make it more easy to use.


# Install

Mac

```bash
pip install tidevice3

# or install as Isolated environment
brew install pipx
pipx install tidevice3
```

Linux

```bash
# required by pytun-pmd3
sudo apt install python3-dev gcc pipx
pipx install tidevice3
pipx ensurepath
```

# CLI Usage

iOS >= 17 `screenshot,app:ps` connect through Ethernet over USB (NCM device) instead of usbmuxd
So tunneld should start first.

```bash
# start tunneld for iOS>=17
# launch process (pmd3 remote start-tunnel) when new usb device connected
# root required
$ sudo t3 tunneld
```

Basic usage

```bash
$ t3 list
...

# enable developer mode and mount develoepr image
$ t3 developer

# install & uninstall
$ t3 install https://....ipa
$ t3 install ./some.ipa
$ t3 uninstall com.example

# take screenshot
$ t3 screenshot out.png

# reboot
$ t3 reboot

# file operation
$ t3 fsync <ls|rm|pull|push> [Arguments...]

# app
$ t3 app <ps|list|launch|kill|instal|uninstall|foreground>

# install
# alias for app install
$ t3 install <URL or LocalIPA>

# screenrecord
$ t3 screenrecord out.mp4

# relay (like iproxy LOCAL_PORT DEVICE_PORT)
$ t3 relay 8100 8100
$ t3 relay 8100 8100 --source 0.0.0.0 --daemonize

# show help
$ t3 --help
```

# API Usage
The API alone is insufficient for all operations; combining it with the pymobiledevice3 library can accomplish more things.

```python
from tidevice3.api import list_devices, connect_service_provider, screenshot, app_install

for d in list_devices(usb=True):
    print("UDID:", d.Identifier)
    service_provider = connect_service_provider(d.Identifier)
    pil_im = screenshot(service_provider)
    pil_im.save("screenshot.png")

    # install ipa from URL or local
    app_install(service_provider, "https://example.org/some.ipa")
```

# iOS 17 support
- Mac (supported)
- Windows (https://github.com/doronz88/pymobiledevice3/issues/569)
- Linux (https://github.com/doronz88/pymobiledevice3/issues/566)

Mac,Windows,Linux all supported iOS<17

# WDA
其实WDA启动可以不用XCUITest，下面是具体的方法（适用于iOS >= 15)

- iOS企业重签名方法 https://zhuanlan.zhihu.com/p/673521212
- iOS WDA脱离xcode & tidevice运行自动化 https://zhuanlan.zhihu.com/p/673319266

# DEVELOP & CONTRIBUTE
see [DEVELOP.md](DEVELOP.md)

# LICENSE
[MIT](LICENSE)