Saravanan666/pypi-org huggingface dataset:
Package: kdDesktopAssistant
# kdDesktopAssistant
桌面助手，替代系统默认的桌面。参考360桌面和安卓桌面开发。# 截图
![截图](/screenshot/桌面设置4.png)# 特色
- 支持自定义背景图片，每个页面有独立的背景图片+1
- 支持设置主页 +
- 支持轮播背景图片 -
- 支持打开网址，在浏览器里新开一个标签页，或者在单独做成一个APP +
- 支持打开其他文件，比如脚本 +
- 支持打开文件夹 +
- 支持托盘按钮 +
- 支持导出配置 -
- 支持分类
- 支持缩放图标
- 支持直接复制粘贴网址、目录、其他文件的快捷方式
- 支持自动整理桌面
- 支持在线换壁纸
- 支持显示本地桌面（上拉mainwindow)
- 增加左右切换桌面的箭头
Package: kddg
kddg - The Kortemme Lab DDG Database Repository===============================================Database APIs for a database used by our lab to store data about mutageneses, macromolecular complexes, experimental measurements, and computational methods. This database is used primarily for DDG predictions at present.Note: This code is unsupported and provided as is (see LICENSE).Coding convention=================- General rules      : The PEP 8 conventions (https://www.python.org/dev/peps/pep-0008/) are mostly sensible.- Blank lines        : See "Blank Lines" in PEP 8. Particularly, surround top-level function and class definitions with two blank lines.- Line length        : Can be more than 79 characters long. We have widescreen technology.- Whitespace         : Use spaces instead of tabs, with indents set to 4 spaces.- Natural language   : Please do not use contractions.- Nerdy references   : Allowed / encouraged.- Shiny?             : Shiny! See above.Installation============Once the PyPI package is up and running (it is currently not)...This package can be installed via:::pip install kddgTo install via pip and allow git push/pulling, use:In a virtualenv:::pip install -e git+ssh://git@github.com/Kortemme-Lab/kddg.git#egg=PackageIn your user-directory:::pip install --user -e git+ssh://git@github.com/Kortemme-Lab/kddg.git#egg=PackageIn your user-directory, without using SSH:::pip install --user -e git+https://github.com/Kortemme-Lab/kddg.git#egg=Package
Package: kdecolepy
kdecole-pyTranscription de la librairie NodeJSkdecole-apien Python.Pour plus d'information, veuillez vous reporter au README du projet sus-cité (https://github.com/maelgangloff/kdecole-api)UtilisationInstaller ce package pour utiliser ce package dans votre projet:pipinstallkdecolepyDes exemples de programme sont disponibles dans le dossierexamples.
Package: kde-gpu
ï»¿kde_gpuKernel density estimator using Nadaraya-Watson with GPU (CUDA)AuthorChen ChenRunning Environment SetupYou have to have cupy installed to be able to use GPU!!
Seehttps://github.com/cupy/cupySimilar toscipy.kde_gaussianandstatsmodels.nonparametric.kernel_density.KDEMultivariateConditional, we implemented nadaraya waston kernel density and kernel conditional probability estimator using cuda through cupy. However, it is much faster than cpu version and it maximise the use of GPU memory.Make a folder with name "data" in current directory. Then copy ORL and CroppedYaleB dataset inside. Please make sure you have the following file tree structure:|--- kde_gpu\***|---version.py \***|--- nadaraya_watson.py \***|--- conditional_probability.py \*|--- setup.py \*|--- example.py \*|--- README.md \Installkde_gpuwith following command: (Please usepip3if the defaultpythonin your computer ispython2)$ pip install -e .This command will runsetup.pywhere we specify the dependencies required to runnmf. The dependencies we require are:"scipy>=1.0.0",
       "pandas>=0.20.2",Please note that if the version number of installed package in your machine is lower than the stated version number,pipwill uninstall your out-of-date package and install the one with version number greater than or equal to the stated one insetup.py.Example"""
@author: chen.chen.adl@gmail.com
"""

#import kernel_smoothing
from scipy import stats
import pandas as pd
import cupy as cp
import numpy as np
import time


rv = stats.expon(0,1)

x = rv.rvs(size=10000)

density_real = rv.pdf(x)

t1=time.time()
kde_scipy=stats.gaussian_kde(x.T,bw_method='silverman')
kde_scipy=kde_scipy(x.T)
print(time.time()-t1)

t1=time.time()
kde_cupy=kde(cp.asarray(x.T),bw_method='silverman')
print(time.time()-t1)



df = pd.DataFrame({'x1':x,'kde_scipy':kde_scipy,
                   'kde_cupy':cp.asnumpy(kde_cupy).squeeze(),'real density':density_real})

df['scipy_mean_absolute_error']=np.abs(df['kde_scipy']-df['real density'])
df['cupy_mean_absolute_error']=np.abs(df['kde_cupy']-df['real density'])
print(df.mean())


rv = stats.truncnorm(-3,2,30,10)
nsample=10000
x = cp.asarray(rv.rvs(nsample))
ycondx = cp.asarray(cp.random.rand(nsample))
y = 10*(ycondx-0.5)+x

cdf_conditional_real = ycondx
df = pd.DataFrame({'y':cp.asnumpy(y),'x':cp.asnumpy(x),'real density':cp.asnumpy(cdf_conditional_real)})

df['nadaraya watson']= kernel_smoothing_ecdf(y,x)
df['nw_error']=np.abs(df['nadaraya watson']-df['real density'])
df.mean()
Package: kdeLF
kdeLFkdeLFis an MIT licensed Python implementation of Yuan et al.’smethod for estimating luminosity functions via Kernel Density Estimation (KDE). It is a wrapper to compiled fortran code that does the heavy lifting, and is therefore relatively fast. We are open to all questions, feedback, commentary, and suggestions as long as they are constructive. Discussions should always come in the form of git issues.DocumentationRead the docs atkdelf.readthedocs.io.Installation and testUsing pipThe recommended way to install the stable version ofkdeLFis usingpip:pip install -U kdeLFWe have uploaded several.whlfiles to thePyPIweb to support as many platforms as possible. If your platforms happens to be an exception, then the pip installation may fail. In this situation, you need to install theIntel fortran Compilerfirst, and then try the pip installation again.  If you have problems installing, please open an issue atGitHub.From sourceYou can also installkdeLFafter a download fromGitHub. Note that this requires aIntel fortran Compilerto be installed in advance.git clone https://github.com/yuanzunli/kdeLF.git
cd kdeLF
pip install .Test the installationTo make sure that the installation went alright, you can execute the built-in test program inkdeLFby running the following command:python3 -m kdeLF.test_kdeLFCitationPlease cite the following papers if you found this code useful in your research:Yuan, Z., Zhang, X., Wang, J., Cheng, X., & Wang, W. 2022, ApJS, 248, 1 (arXiv,ADS,BibTeX).Yuan, Z., Jarvis, M. J., & Wang, J. 2020, ApJS, 248, 1 (arXiv,ADS,BibTeX).
Package: kdenlive-tools
Command line tool to fiddle with kdenlive projects.List producers.Replace producer’s clips.Free software: BSD license.InstallationFirst, if thepython-lxmlpackage is not installed on your system, you will
need XML libraries:$ sudo aptitude install libxml2-dev libxslt-dev zlib1g-devOnly then you’ll be able to install the package and its python dependencies:$ pip install kdenlive-toolsDocumentationhttps://kdenlive-tools.readthedocs.org/DevelopmentUnit-testsTo run the all tests run:$ toxReleaseTo release a new minor version:$ bumpversion --versbose --allow-dirty minor
$ python setup.py register -r testpypi
$ python setup.py sdist upload -r testpypi
$ git push --tagsChangelog0.1.0 (2014-07-27)First release on PyPI.
Package: kdewallet
This is a simple project that wraps thekwallet-querycommand.
It was made to be flexible you can use whichever syntax suites your fancy
the way that kde structures their password storage is
wallet name -> folder name -> entry nameyou can install this package via PyPIpip install kdewalletthis module can be used in 3 ways here's a quick overviewfrom kdewallet import KDEWallet
my_pw = KDEWallet.wallet.folder.entry
my_pw = KDEWallet[wallet][folder][entry]
# or mix n match
my_pw = KDEWallt[wallet].folder.[entry]


# these are the more procedural style options
my_pw = KDEWallet(wallet, folder).get_entry(entry)
my_pw = KDEWallet(wallet).get_entry(entry, folder=folder)

# setting is supported as well
KDEWallet.wallet.folder.entry = 'really secure'For a more detailed example we'll assume we have two wallets just like this.work       # wallet 1
  servera  # folder 1
    root   # entry 1 in folder 1
    userx  # etnry 2 in folder 1

  serverb
    userz
    usery

personal  # wallet 2
  gmail   # folder 1 in wallet 2
    frank # entry 1  
    maddy

  banking     # etc
    evil bank
    meh credit unionso given that setup of data some examples of how you might use this would be as followsfrom kdewallet import KDEWallet
my_wallet = KDEWallet(wallet='work')
my_pw = my_wallet.get_entry(entry='root', folder='servera')
function_that_needs_password(my_pw)
other_pw = my_wallet.get_entry('frank', 'gmail', 'personal')
function_that_needs_password(other_pw)This could use a bit more work still, but the core tests are passing
most functionality seems to be working well enough for now. Not sure why I went mad with the
alternate syntax styles but ya gotta have fun sometimes I guess.Currently my todos for this project are:find if there is a more secure way to pass pw to kwallet-query than printfadd defult wallet/folder support to object accessmake it clearer whats happening with propertieswrite more rigorous tests
Package: kdezero
K_DeZeroOverviewThis library is an my improved version of "Deep Learning from Basic 3".
(In production)RequirementPython3.8If you use gpu, cupyIf you want to visualize the model, install graphvizFor details, see requirements.txtUsageMethod1:pip install kdezeroMethod2:git clone https://github.com/kotabrog/K_DeZero.git
cd kdezero
python setup.py sdist
pip install dist\kdezero-2.1.tar.gzThen you will be able to use "kdezero".sampleLook atsamples.FeaturesThis library can:Backpropagation-enabled variables can be used like numpyContains basic machine learning partsSome support gpuVisualize model configuration using graphvizAuthortwitterLicenceMIT
Package: kdFileFinder
# kdFileFinder
使用python3和pyqt5开发的，可以使用鼠标快速导航的开源文件管理器# 使用
- 单击打开目录
- 双击打开文件
- 右键空白处返回上一层目录
- 点击+添加书签或新建标签页
- 点击-删除书签或关闭标签页# 截图
![screenshot](/screenshot/screen-v1.0.png “截图”)# 安装kdFileFinder
- 安装python和pip，[下载地址](https://www.python.org/downloads/下载地址)
- 在终端(比如cmd或者xterm)安装PyQt5。输入命令pip install kdFileFinder
- 双击快捷方式即可启动
Package: kDic
Open Kage's useful tools and class to public.
(Long time used and upgraded)
But, this is develope version.
So, suddenly it will be big change when I got some more good idea.
seperate to kDict from kmiscInstallpip3installkDictCustom Dictionary ClassConvert Dictionary to Object style DictionaryContentsCreate tree type itemsAdded New commandsPut()    : Put value at a itemGet()    : Get value of itemDel()    : Delete itemUpdate() : Update value at itemPrint()  : Print dictionaryDiff()   : Compare two dictionaryCheck()  : Check put the value is same as the item(key)'s valueList()   : Return list of keys valueProper() : Show/Set/Update property at the item.Find()   : Find data in the dictionaryLoad()   : Load saved data from fileSave()   : dictionary save to fileSort()   : Sort dictionaryFirstKey(): Get first KeyFirstKey(): Get first KeyAdded property at each keyInitialize dictionaryfromkmiscimportkDictroot=kDict.kDict()fromkmiscimportkDict>>>test={'a':123,'b':{'c':{'ddd'},'e':{}}}root=kDict.kDict(test)Add new data>>>root.tree.apple.color='red'or>>>root.tree.apple.Put('color','red')or>>>root.tree.apple['color']='red'Get data>>>root.tree.apple.color.Get()or>>>root.tree.apple.Get('color')Print dictionary>>>root.Print()>>>root.tree.Print()Set property at Apple's colorSet readonly>>>root.tree.apple.color.Proper('readonly',True)Try change data>>>root.tree.apple.Put('color','white')itemisreadonly>>>root.tree.Print(){'color':{'._d':'red','._p':{'readonly':True}}}Unset readonly>>>root.tree.apple.color.Proper('readonly',False)Try change data>>>root.tree.apple.Put('color','white')>>>root.tree.Print(){'color':{'._d':'red','._p':{'readonly':True}}}Sample Dictionary:{'a':123,'b':{'c':set(['ddd']),'e':{},'z':123},'tree':{'apple':{'color':{'._d':'white','._p':{'readonly':False}}},'banana':{'banana2':{'._d':'white','._p':{}},'banana3':{'._d':'yellow','._p':{}},'color':{'._d':'yellow','._p':{'readonly':True}}},'yellow':{'monkey':{'._d':'white','._p':{}}}}}Find readonly property item path>>>root.Find('readonly',property=True)['tree/banana/color']Find apple key path>>>root.Find('apple',mode='key')['tree/apple']Find white color data path>>>root.Find('white')['tree/apple/color','tree/yellow/monkey','tree/banana/banana2']Find 123 data path>>>root.Find('white')['a','b/z']Find white color data path in key and value>>>root.Find('yellow',mode='all')['tree/yellow','tree/banana/color','tree/banana/banana3']Save Data (always use root if not then save partial data)>>>fromkmiscimportkDict>>>kDict.kDict._dfile_='<dict file name>'>>>root.Save()Load Data (always use root if not then load at key)>>>fromkmiscimportkDict>>>kDict.kDict._dfile_='<dict file name>'>>>root.Load()
Package: k-diffusion
k-diffusionAn implementation ofElucidating the Design Space of Diffusion-Based Generative Models(Karras et al., 2022) for PyTorch, with enhancements and additional features, such as improved sampling algorithms and transformer-based diffusion models.Installationk-diffusioncan be installed via PyPI (pip install k-diffusion) but it will not include training and inference scripts, only library code that others can depend on. To run the training and inference scripts, clone this repository and runpip install -e <path to repository>.TrainingTo train models:$./train.py--configCONFIG_FILE--nameRUN_NAMEFor instance, to train a model on MNIST:$./train.py--configconfigs/config_mnist_transformer.json--nameRUN_NAMEThe configuration file allows you to specify the dataset type. Currently supported types are"imagefolder"(finds all images in that folder and its subfolders, recursively),"cifar10"(CIFAR-10), and"mnist"(MNIST)."huggingface"Hugging Face Datasetsis also supported.Multi-GPU and multi-node training is supported withHugging Face Accelerate. You can configure Accelerate by running:$accelerateconfigthen running:$acceleratelaunchtrain.py--configCONFIG_FILE--nameRUN_NAMEEnhancements/additional featuresk-diffusion has support for training transformer-based diffusion models (likeDiTbut improved).k-diffusion supports a soft version ofMin-SNR loss weightingfor improved training at high resolutions with less hyperparameters than the loss weighting used in Karras et al. (2022).k-diffusion has wrappers forv-diffusion-pytorch,OpenAI diffusion, andCompVis diffusionmodels allowing them to be used with its samplers and ODE/SDE.k-diffusion implementsDPM-Solver, which produces higher quality samples at the same number of function evalutions as Karras Algorithm 2, as well as supporting adaptive step size control.DPM-Solver++(2S) and (2M)are implemented now too for improved quality with low numbers of steps.k-diffusion supportsCLIPguided sampling from unconditional diffusion models (seesample_clip_guided.py).k-diffusion supports log likelihood calculation (not a variational lower bound) for native models and all wrapped models.k-diffusion can calculate, during training, theFIDandKIDvs the training set.k-diffusion can calculate, during training, the gradient noise scale (1 / SNR), fromAn Empirical Model of Large-Batch Training,https://arxiv.org/abs/1812.06162).To doLatent diffusion
Package: kdl
快代理api SDK依赖环境python2.7 到 python3.7从快代理购买相应产品获取订单的secret_id和secret_key获取安装:安装 Python SDK 前，请先获取订单对应的`secret_id`和`secret_key`，请严格保管，避免泄露。通过pip安装(推荐)您可以通过pip将SDK安装到您的项目中：pip install kdl如果您的项目环境尚未安装pip，可参考：Ubuntu/Debian安装pip：apt-getinstallpython-setuptoolsCentOS安装pip：yum installpython-setuptoolsMacOS安装pip:curlhttps://bootstrap.pypa.io/get-pip.py-oget-pip.py;pythonget-pip.py您还可以参考pip官网进行安装。通过源码包安装前往Github 代码托管地址下载最新代码，解压后:$cdapi-sdk$pythonsetup.pyinstall示例以私密代理订单使用为例:# -*- coding: utf-8 -*-"""
   私密代理使用示例
   接口鉴权说明：
   目前支持的鉴权方式有 "token" 和 "hmacsha1" 两种，默认使用 "token"鉴权。
   所有方法均可添加关键字参数sign_type修改鉴权方式。
"""importkdlauth=kdl.Auth("secret_id","secret_key")client=kdl.Client(auth)# 获取订单到期时间, 返回时间字符串expire_time=client.get_order_expire_time()print("expire time",expire_time)# 获取ip白名单, 返回ip列表ip_whitelist=client.get_ip_whitelist()print("ip whitelist",ip_whitelist)# 设置ip白名单，参数类型为字符串或列表或元组# 成功则返回True, 否则抛出异常client.set_ip_whitelist([])client.set_ip_whitelist("127.0.0.1, 192.168.0.139")print(client.get_ip_whitelist())client.set_ip_whitelist(tuple())# 提取私密代理ip, 第一个参数为提取的数量, 其他参数以关键字参数的形式传入(不需要传入signature和timestamp)# 具体有哪些参数请参考帮助中心: "https://help.kuaidaili.com/api/getdps/"# 返回ip列表# 注意：若您使用的是python2, 且在终端调用，或在文件中调用且没有加 "# -*- coding: utf-8 -*-" 的话# 传入area参数时，请传入unicode类型，如 area=u'北京,上海'ips=client.get_dps(2,sign_type='hmacsha1',format='json',pt=2,area='北京,上海,广东')print("dps proxy: ",ips)# 检测私密代理有效性： 返回 ip: true/false 组成的dictips=client.get_dps(2,sign_type='token',format='json')valids=client.check_dps_valid(ips)print("valids: ",valids)# 获取计数版ip余额（仅私密代理计数版）balance=client.get_ip_balance(sign_type='hmacsha1')print("balance: ",balance)您可以在examples目录下找到更详细的示例参考资料查看API列表了解API鉴权
Package: kdLaunchPad
# kdLaunchPad
这是一个会话启动器，一键启动多个程序。# 起因我为什么要创建这个程序呢？每次开机启动的时候，我们都要打开好几个程序或者文件，比如浏览器，QQ，文件管理器。有时候从桌面快捷方式打开，有时候从文件管理器打开，有时候从开始菜单打开。这很浪费时间。或许我们可以使用系统自带的自动启动功能，但是那个功能太弱了，无法定制话启动方案。比如我上午上班的时候打开工作文档，中午打开音乐播放器听歌。每次启动系统的时候，我们想让电脑自动打开不同的程序或文件。但系统自带的自动启动功能只能设置一种启动方案。所以，我创建了这个程序，实现一键打开多个程序或文件的功能，而且开机每次启动的程序或文件可以随便选。# 截图
![kdLaunchPad_screenshot](screenshot/screen-2019-03-04-22-52-46.png.jpg “截图”)# 安装kdLaunchPad
- 安装python和pip，[下载地址](https://www.python.org/downloads/下载地址)
- 在终端(比如cmd或者xterm)安装PyQt5。输入命令pip install PyQt5
- 双击kdLaunchPad.py即可运行# 打包
## 方法一
pyinstaller -F kdLaunchPad.py最后复制ui文件和image目录到dist目录下即可。## 方法二pyinstaller –add-data=”kdLaunchPad.ui;.” –add-data=”launchSession.ui;.”  –add-data=”sessionItem.ui;.” -p=”image;.”  -c kdLaunchPad.py最后需要复制image文件夹到dist目录
Package: kdlines
kdlinesDensity heatmaps of many (time-)series using kernel density estimation using scikit-learn.This package is at alpha stage and experimental.Requirementsscikit-learn
numpy
matplotlibInstallationpip install kdlinesBasic example"""A simple example based on numpy arrays and pandas DataFrame"""importnumpyasnpimportpandasaspdimportmatplotlib.pyplotaspltfromkdlinesimportKernelDensityLines# generate test data, 1000 series each of length 100x_n=100y_n=1000# resolution of the heatmap y axis (i.e. number of grid cells)y_res=100# arrays and generated datax=np.linspace(0,x_n,x_n)ys=np.empty((y_n,x_n))randg=np.random.default_rng(seed=645)foriinrange(y_n):ys[i]=np.sin(-x/3)+0.5*randg.standard_normal(1)+x*0.1+11# Data frames with datetime index to simulate time series with timestampsys_df=pd.DataFrame(index=x,data=ys.T)ys_df.index=pd.date_range("2020-01-01",periods=x_n,freq="D")# Estimation on the pandas DataFramekde_df=KernelDensityLines(y_res=y_res,kde_kernel='linear',bandwidth='scott')kde_df.fit(y_lines=ys_df)# Plotting as subplot# sharex is False in this case since imshow is not plotted, only labeled,# with timestampsfig,axs=plt.subplots(2,1,figsize=(8,8),sharey=True,sharex=False)axs[0].plot(ys_df.index,ys_df.to_numpy(),lw=1)kde_df.plot(ax=axs[1])fig.tight_layout()MIT LicenseCopyright (c) 2022 Reinert Huseby KarlsenPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
Package: kdlutils
githubActionsThis repository contains a small collection of utility functions.Installing the library in colab!gitclonehttps://github.com/kaustubh-sadekar/dlutils.git!sudopython3dlutils/setup.pyinstallExample code for testing in colabimportsyssys.path.append("dlutils/")importkdlutilsinput_dim=(1,600,800)layersList=[{'conv':(1,7,3,1,1,1)},{'mp':(2,2,0,1)},{'conv':(7,14,3,1,1,1)},{'mp':(2,2,0,1)},{'conv':(14,30,3,1,1,1)},{'mp':(2,2,0,1)}]out=kdlutils.kdlutils.getOutShape(input_dim,layersList)print(out)Documentation and new codes will be uploaded soon.
Package: kdl-wagtail-page
Very basic but reusable Wagtail page models for KDL projects.Install it with pip or pipenv using the following package name:kdl-wagtail-pageRequirements: wagtail 2+, django 2+, python 3.5+The idea is to have a single code base for our Wagtail components.=> we can just install and use, no coding necessary for simple use cases=> we don’t have to maintain and upgrade a lot of small variants of the same
code which was copied and adapted for each projectFor contributors:runs out of the box with minimal settingskeep it generic and relatively simple (code and interface)if you need something more advanced (but reusable), create a new page typeif you need something very specific: subclass into your own projectkeep it focused, it is a package about pages (not menus, search or blogs)keep it backward-compatibleautomate the upgradeskeep it compatible with wagtail and Django 2keep it compatible with kdl-bare-bones base.html template blocks
Package: kdmapi
kdmapiKDMAPI (Keppy's Direct MIDI API)wrapper for Pythonkdmapi provides both C bindings for OmniMIDI.dll and a Python-friendly wrapper for themAMidobackend is also provided, instructions on how to use it are belowRequires Python 3.8 or greaterInstallationpip3installkdmapiYou will also need to haveOmniMIDIinstalledInstructionsfromkdmapiimportKDMAPI# Initialize the deviceKDMAPI.InitializeKDMAPIStream()# Send a short 32-bit MIDI message dataKDMAPI.SendDirectData(0x0)# Close the deviceKDMAPI.TerminateKDMAPIStream()Mido backendYou can use KDMAPI as aMidooutput backendimportmido# Set KDMAPI as MIDO backendmido.set_backend("kdmapi.mido_backend")# Open MIDI filemidi_file=mido.MidiFile("your_file.mid")withmido.open_output()asout:formsginmidi_file.play():out.send(msg)License#
# Copyright (C) 2022 Sebastiano Barezzi
#
# SPDX-License-Identifier: LGPL-3.0-or-later
#
Package: kdmp-parser
Python building forkdmp-parserThis C++ library parses Windows kernelfulldumps (.dump /fin WinDbg),BMPdumps (.dump /kain WinDbg) as well as more recent dump types that were introduced in ~2022.The library supports loading 64-bit dumps and provides read access to things like:The context record,The exception record,The bugcheck parameters,The physical memory.The Python bindings were authored byhugsy&masthoon. Refer to theproject page on Githubfor documentation, issues and pull requests.Installing from PyPIThe easiest way is simply to:pipinstallkdmp_parserInstalling using PIPRun the following after installingCMakeandPython3.8+ /pip:cd src/python
pip install requirements.txt
pip install .To create a wheel pacakge:cd src/python
pip wheel .UsageGet context, print the program counterimportkdmp_parserdmp=kdmp_parser.KernelDumpParser("full.dmp")assertdmp.type==kdmp_parser.DumpType.FullDumpprint(f"Dump RIP={dmp.context.Rip:#x}")Read a virtual memory page at address pointed by RIPimportkdmp_parserdmp=kdmp_parser.KernelDumpParser("full.dmp")dmp.read_virtual_page(dmp.context.Rip)Explore the physical memoryimportkdmp_parserdmp=kdmp_parser.KernelDumpParser("full.dmp")pml4=dmp.directory_table_baseprint(f"{pml4=:#x}")dmp.read_physical_page(pml4)Translate a virtual address into a physical addressimportkdmp_parserdmp=kdmp_parser.KernelDumpParser("full.dmp")VA=dmp.context.RipPA=dmp.translate_virtual(VA)print(f"{VA=:#x}->{PA=:#x}")AuthorsAxel '@0vercl0k' SouchetContributors