Saravanan666/pypi-org huggingface dataset:
Package: chunkedfile
ChunkedFileA file handle that groups subsequence writes together and periodically writes
them as separate files. Useful for file systems like GCS FUSE that do not
support file appending. Pathlib can be patched to replace files opened in
append mode with chunked files, so external libraries need not be changed.UsageUsingChunkedFile:importtimeimportchunkedfile# Write chunks up to every 10 minutes.withchunkedfile.ChunkedFile('filename.txt',600)asf:# The first chunk will be written on the first write after the sleep.f.write('Lorem\n')time.sleep(1000)f.write('Ipsum\n')# The last chunk is automatically written when the file is closed.f.write('Dolor\n')f.write('Sit\n')# The directory now contains two files:# - filename.txt-1-20210904T130400# - filename.txt-2-20210904T132100Usingpathlib.Path:importtimeimportpathlibimportchunkedfile# Path objects opened in append mode will be replaced by chunked files.chunkedfile.patch_pathlib_append(600)# Write chunks up to every 10 minutes.withpathlib.Path('filename.txt').open('a')asf:# The first chunk will be written during the sleep command.f.write('Lorem\n')f.write('Ipsum\n')time.sleep(1000)# The last chunk is automatically written when the file is closed.f.write('Dolor\n')f.write('Sit\n')# The directory now contains two files:# - filename.txt-1-20210904T130400# - filename.txt-2-20210904T132100
Package: chunkmuncher
# ChunkMuncher
Repackaging of the “chunk” module, originally written by Sjoerd Mullender,sjoerd@acm.orgThis module, along with some others, has been deprecated, and was removed in Python 3.13.0. As a result, some have decided to repackage modules we use often. This package will be for the chunk module, and I soon will be including the aifc and wave readers and writers. I can see room for improvements, so I plan on doing some.
Pull requests are welcome, as are issue reports.
Upgrades and tests are intended to follow.## Description from the original documentationThis module provides an interface for reading files that use EA IFF 85
chunks. This format is used in at least the Audio Interchange File
Format (AIFF/AIFF-C) and the Real Media File Format (RMFF). The WAVE
audio file format is closely related and can also be read using this
module.
Package: chunk-segmentor
# Chunk分词器使用指南环境依赖：python 3.6 (暂时只支持python3)## 主要功能1. 能够输出名词短语2. 支持词性输出，名词短语词性为np3. 支持名词短语以限定词+中心词的形式输出>不可分割的名词短语是不存在限定词+中心词的形式的，如“机器学习”，而“经典机器学习算法”可拆解为“经典_机器学习_算法”## Step 1 安装软件包推荐新建一个python的虚拟环境（可跳过）```bashconda create --name chunk_seg python=3.6.5```### pip安装```bashpip install git+https://www.github.com/keras-team/keras-contrib.gitpip install chunk-segmentor```### 手动安装```bashgit clone https://github.com/stevewyl/chunk_segmentorcd chunk_segmentorpip install -r requirements.txtpython setup.py install```### 额外安装```bash# 若你的机器安装有GPU，利用GPU加速预测速度pip install tensorflow-gpu==1.9.0```### 安装错误1. ImportError: cannot import name 'normalize_data_format'```bashpip install -U keras```## Step 2 如何使用* 第一次import的时候，会自动下载模型和字典数据* 支持单句和多句文本的输入格式，建议以列表的形式传入分词器```pythonfrom chunk_segmentor import Chunk_Segmentorcutter = Chunk_Segmentor()s = '这是一个能够输出名词短语的分词器，欢迎试用！'res = [item for item in cutter.cut([s] * 10000)] # 1080ti上耗时12s# 提供两个版本，accurate为精确版，fast为快速版但召回会降低一些，默认精确版cutter = Chunk_Segmentor(mode='accurate')cutter = Chunk_Segmentor(mode='fast')# 限定词+中心词的形式, 默认开启cutter.cut(s, qualifier=False)# 是否输出词性， 默认开启cutter.cut(s, pos=False)# 输出格式（词列表，词性列表，chunk集合）[(['这', '是', '一个', '能够', '输出', '名词_短语', '的', '分词器', ',', '欢迎', '试用', '!'],['rzv', 'vshi', 'mq', 'v', 'vn', 'np', 'ude1', 'np', 'w', 'v', 'v', 'w'],['分词器', '名词_短语'])...]```## Step 3 后续更新若存在新的模型和字典数据，会提示你是否需要更新## To-Do Lists1. 提升限定词和名词短语的准确性 ---> 新的模型2. char模型存在GPU调用内存溢出的问题 ---> 使用cnn提取Nchar信息来代替embedding的方式，缩小模型规模3. 自定义字典，支持不同粒度的切分4. 多进程模型加载和预测
Package: chunksum
chunksumPrint FastCDC rolling hash chunks and checksums.usage: chunksum [-h] [-n ALG_NAME] [-f CHUNKSUMS_FILE] [-i INCR_FILE] [-m]
                [path ...]

Print FastCDC rolling hash chunks and checksums.

positional arguments:
  path                  path to compute chunksums

optional arguments:
  -h, --help            show this help message and exit
  -n ALG_NAME, --alg-name ALG_NAME
                        chunksum algorithm name.
  -f CHUNKSUMS_FILE, --chunksums-file CHUNKSUMS_FILE
                        chunksum file path, `-' for standard output.
  -i INCR_FILE, --incr-file INCR_FILE
                        incremental updates file path
  -m, --multi-process   same number of multi-processes as cpu

alg-name:
  Format "fc[k|m|g][0-9][sha2|blake2b|blake2s][32]".

  For example, "fck4sha2", means using FastCDC("fc") with an
  average chunk size of 2**8=256KB("k8") and using sha256("sha2")
  to calculate the checksum.

  "fcm4blake2b32" means using FastCDC with an average chunk size
  of 2**4=16MB("m4") and using "blake2b" to calculate and output
  a checksum of length "32" bytes(save storage).

  For large files, you may using large chunk size, such as "m4",
  to reduce the number of chunks.

  (default: fck4sha2)

chunksums-file and incr-file:
  You can specify the previous chunksums file if you want to
  resume a previous check, or if you want to find the incremental
  updates (new files) of the directory.

Examples:

  $ chunksum /etc > ~/etc.chunksums

  $ chunksum -n fcm4blake2b32 -m ~/Videos

  $ chunksum -n fcm4blake2b32 -f ~/Videos/chunksums ~/Videos

  $ chunksum -n fcm4blake2b32 -f ~/chunksums -i ~/chunksums.incr ~/Videos
Package: chunksv
chunksvPython wrapper forcsv.readerthat can process files in predefined chunks.purposeThis library allows a user to partition a filestream into partitions of a predefined size. It was initially
motivated by the need to process large CSV files from AWS S3 while keeping application code clean.package installation and usageThe package is available on PyPI:python-mpipinstallchunksvThe library can be imported and used as follows:importchunksvwithopen("file.csv","r")asf:rows=chunksv.reader(f,max_bytes=<sizeofeachpartition>,header=[<optionalcolumnslist>])When thereaderobject has consumed enough rows to reach themax_byteslimit, it will raiseStopIteration. To
consume more rows from the input stream, callreader.resume():whilenotrows.empty:current_partition=[rforrinrows]<processpartitionhere>rows.resume()
Package: chunkup
ChunkupGiven an audio file, and a tab delimited file, split up audio file into
smaller chunks.Requirements & CaveatsImports pysox, therefore has the same dependencies as pysox, namely:Required prerequisite are the development libraries of sox at
version 14.3.x, i.e. the header files and libraries to link against.
Specifically you need sox.h in your include path and libsox.so and
libsox.a in your link path. Pysox will not compile against any sox
version prior to
14.3.0.chunkup willonlychunk up audio. Any other effects (resampling
etc) should be applied to the audio file before running chunkup.At the moment, chunkup will operate fairly blindly. If you give it
start and end times for a chunk that actually lie outside of the
provided audio file, it’ll return a bunch of sox errors that look
like:trim: End position is after expected end of audio.
trim: Last 1 position(s) not reached.Installationpip install chunkupAfter installing chunkup, thechunkupcommand line script should be
added to your path, and should be available from anywhere. Test it out
by opening a new terminal and runningchunkup -hUsageThis usage example pulls downEpisode #20: I Want to Break
Freeof
Reply All. The mp3 is 41.5M. You can getreplyallcreak.txtfromhttps://github.com/JoFrhwld/chunkup/blob/master/examples/replyallcreak.txtcurl -L -o examples/reply_all_podcast.mp3 http://bit.ly/chunkup
mkdir chunks

chunkup examples/reply_all_podcast.mp3 examples/replyallcreak.txt chunks/Chunk namingchunks/now contains 104 chunks of speech I annotated, largely to
separate out the three speakers in the first segment. The default naming
conventions of chunks are:[n]-[basename]-[col1]-[col3].wavWhere[n]is the numeric index of the chunk,[basename]is the
base name of the original soundfile,[col1]is the value from column
1 inreplyallcreak.txt(in this case, speaker ID), and[col3]is
the value from column 3 inreplyallcreak.txt(in this case, the
onset time of the chunk in ss.ms). Available variables for naming chunks
are:naming variabledescription[n]chunk number[basename]base name of original audio file[col0-9]value from any the given column in the chunk fileConfigurationYou can change the chunk naming convention either at the command line
usig the-nor--namingflag.$ chunkup -n [col1]-[n].wav examples/reply_all_podcast.mp3 examples/replyallcreak.txt chunks/Other config options includeoptiondescription-s,--startColumn index (starting with 1) for the start time, in ss.ms format-e,--endColumn index (starting with 1) for the end time, in ss.ms format--headerInclude if chunk file has a headerAll config options can be defined in a config file and passed to
chunkup.py with the prefix+. A sample config file for this data
would be:--naming
[n]-[basename]-[col1]-[col3].wav
--start
3
--end
4If saved to config.txt, it could be passed to chunkup.py like so:$ chunkup +config.txt examples/reply_all_podcast.mp3 examples/replyallcreak.txt chunks/
Package: chunky3d
chunky3dA 3D array-like NumPy-based data structure for large sparsely-populated volumesBuildIntroductionThis library provides a data structure,Sparse, which represents 3D volumetric data
and supports a subset ofnp.ndarrayfeatures.Example>>> import numpy as np
>>> from chunky3d import Sparse

>>> s = Sparse(shape=(64, 64, 64))
>>> s[0, 0, 0]
0

>>> s.dtype
numpy.float64

>>> s.nchunks
8

>>> s.nchunks_initialized
0

>>> s[1, 2, 3] = 3
>>> s.nchunks_initialized
1

>>> s[:2, 2, 3:5]
array([[0., 0.],
       [3., 0.]])Featureschunky3d.sparse_func- a collection of functions for analyzing chunked arrays, including
morphological operations (opening, closing), thinning, connected componentsFast load and save usingmsgpackOperations on arrays using.run(), with possible acceleration usingmultiprocessingmultiprocessing-based acceleration in most of existingsparse_funcAccelerated lookup usingnumbaInterpolation (point probe)Origin and spacing: representing 3D space with non-uniform spacing for different axesEasy visualization of arrays withdtype=np.uint8viachunky3d.k3d_connector.get_k3d_object()
Package: chunkyp
chunkypA small and concise data preprocessing library inspired by common NLP preprocessing workflows.Supportsray.Installationchunkyp is available on PyPi.pipinstallchunkypFor the dev version you can run the following.gitclonehttps://github.com/neophocion/chunkypcdchunkyp
pipinstall-e.UsageThe simplest way to get started is to look at the Jupyter notebooks innotebooks/A small example:fromchunkypimportres=pipe(records,# a list, or iterator across, dictsp('field',lambdax:x.lower()),p('field',lambdax:x.upper(),'new_field'),p(['field1','field2'],lambdax,y:len(x.split())==y,'new_field2'),)res=list(res)res
Package: churchdir
IntroductionInstallingRunning the programPhotosChangelogIntroductionThis program creates an address book from a spreadsheet file.  The address book
that is created is an html file which can be put on the Internet (a password
protected location[1]is strongly recommended), or on a local, in-house
server.  The spreadsheet file contains your lists of addresses.  This file is
saved in csv format.The churchdir program was created using the Python programing language and it
can be run on any computer (Windows, Mac, Linux, etc.) on which Python is
installed.  Python[2]is free to download[3]and use.Python 2.7 or 3.x will work.  3.x is recommended.InstallingOnce Python is installed on your computer (see above) install the Church
Directory software by executing this command from the command line:easy_install churchdirThis will download the program from a repository of Python programs from
pypi.python.org and install it on your computer.Running the programThis program works via the command line.  Create a directory in which to work
and then make it your working directory:C:\> mkdir mydirectory
C:\> cd mydirectoryThen execute the program like this:C:\mydirectory> churchdir-quickstartYou will then be asked a series of questions; most specifically, the name of
the input file.  If the input file doesn’t exist, a sample one will be created
for you.(You should use this csv file to begin your address list since it
contains the correct headers.  Use a spreadsheet program to edit.)After all the questions are answered, the output file (the address book) is
created in the current working directory.PhotosPhotos, specified in the “Photo URL” column of the input file, should be a head
shot of one person, preferably 96x96 pixels.  A secondary, larger photo may be
specified in the “Photo Secondary” column.  When a user visits the html page
and if he clicks on the head shot photo, the secondary photo appears.Changelogversion 0.1: intial releaseversion 0.2: program now better handles bad csv input filesversion 0.3: bug fix: erroneous error message was being shown.Footnotes[1]How to password protect a directory using .htaccess:link[2]An author of Python tutorial books explains Python:youtube video[3]Download Python:https://www.python.org/downloads/
Package: churchkey
churchkeya tool tunneling ssh over http proxy;churchkey tunnels ssh sessions through http proxy, so that users can log into
remote server via http proxy if direct access is not working; churchkey uses
httpconnectmethod and only works with http proxies supporting this method;churchkey supports two http authentication methods:basicanddigest;technical details are described inrfc 2817;alternative projects includecorkscrewandproxytunnel;installinstall via pip:pip install churchkeyusagecreate a file~/.ssh/proxyauthwith proxy username and password:{username}:{password}edit~/.ssh/configas follows:Host {host}
    HostName {hostname}
    Port {port}
    User {user}
    ProxyCommand churchkey {proxy_host} {proxy_port} %h %p ~/.ssh/proxyauthlog into remote server as usual:ssh {host}licenseCopyright (C) 2016-2018 Cyker WayThis program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
GNU General Public License for more details.You should have received a copy of the GNU General Public License
along with this program.  If not, seehttp://www.gnu.org/licenses/.
Package: church-of-jesus-christ-api
This Python package implements an unofficial API for accessing data from churchofjesuschrist.org.
The endpoints are the same ones used by the Church’s website and mobile app, and are therefore
subject to change in the future.By default, most methods can be called with default parameters and will return information for the
user’s unit. In the case where a method is to request information for a member, the default
is to return information about the current user.In order to get unit numbers for other units that the user has access to, use user_details to find
different unit numbers available to the user.Full documentation with examples can be found onread the docs
Package: churchrepl
A simple REPL for lambda calculusUse:when calling churchrepl and new REPL will be opened.churchrepl [-f --file file [file ...]] [-v --verbose]Flags:(optional) Read definitions and expressions from a file before loading the normal REPL.-f|--file file [file ...](optional) print debugging and verbose output.-v|--verboseChurch repl file structure:The EBNF grammar is as follows*:(* church-lambda EBNF *)
                  (* --- meta --- *)
 program = {line};
 line = (define | function);
 define = "@" alias ":" function;

                  (* --- lambdas --- *)
 function = lambda | application;
 lambda = "λ" variable "." expr;
 application = '(' expr  expr ')';
 expr = (lambda | application | variable | alias );

                  (* --- primitives --- *)
 variable = /[a-z]/;
 alias = /[_A-Z][_A-Z0-9]*/;A simple example program:@ID: λx.x
@APPLY: λf.λx.(f x)
@TRUE: λx.λy.x
@FALSE: λx.λy.y
@ZERO: λf.λx.x
@SUCC: λn.λf.λx.(f ((n f) x))
@ONE: (SUCC ZERO)
@TWO: (SUCC ONE)
@THREE: (SUCC TWO)
@FOUR: (SUCC THREE)
(SUCC ZERO)
(SUCC ONE)
(SUCC TWO)
(SUCC THREE)
(SUCC FOUR)Note: The λ (lambda, unicode u03bb) is equivalent to a backslash as far as this program is concerned.
When using the repl it may be easier to use a backslash.
Package: church.sermonaudio
Introduction===============A simple add-on for the Plone Content Management System which provides a "sermon audio" contenttype and a custom folder view which allows users to view and playsermons.Changelog=========1.0a9------------------- Now displays sermons in a reverse sorted manner [David Bain]1.0a8-------------------- Fixed script bug that prevented update without reuploading mp3 [David Bain]- Changed styles so that the default width of player is bit wider [David Bain]1.0a7--------------------- Fixed missing files from broken 1.0a6 release [David Bain]1.0a6--------------------- Added a catalog index (date) [David Bain]- sermon.pt view now sorts sermon results by data index [David Bain]1.0a4-------------------- A documentation release [David Bain]1.0a3--------------------- Mostly a version bump and beginnings of some documentation [David Bain]1.0a--------------------- Initial release [David Bain]- Added collective.mediaelementjs [David Bain]- Custom folder view allows users to show sermons in a folder and play usingmediaelementjs [David Bain]- Added a javascript validator to prevent non-mp3 files from being addedchurch.sermonaudio Installation-------------------------------To install church.sermonaudio using zc.buildout and the plone.recipe.zope2instancerecipe to manage your project, you can do this:* Add ``church.sermonaudio`` to the list of eggs to install, e.g.:[buildout]...eggs =...church.sermonaudio* Re-run buildout, e.g. with:$ ./bin/buildoutUsage------Use `Site Setup` > `Add-ons` and activate `Sermon Audio`.Create a folder for your sermons and select `Display` > `sermons`.This creates a custom view for your sermons. Now add sermon audioto your new folder. Visitors viewing your folder will see acustom audio player.
Package: churchtools
pychurchtoolsThis is a python wrapper for the ChurchTools API.
The API from ChurchTools is provided using the Swagger toolset.Note:This library is WIP.
Some endpoints are not implemented at all and the others are only partly
implemented.InstallationTo install, you can use "pip install ." in the source directory.
Type "pip uninstall churchtools" to remove the library.Dependencies:pydanticrequestsUsageAuthenticationVia a cookieLogin to churchtools in your browser and copy the cookie.
The cookie object looks something like this:cookie={'ChurchTools_ct_<church_name>':'some_random_text',}Pass this object to the init function of theChurchToolsclass or set the
fieldcookiein theChurchToolsobject.Via REST APITheChurchToolsclass provides a methodloginto log you in and set the
cookie automatically.ct.login('your_email','your_password')ExampleYou can execute the following in a python script to test the functionality.fromchurchtoolsimportChurchToolsc=ChurchTools('https://<church_name>.church.tools')c.login('your_email','your_password')events=c.events.list()print('Upcoming event:',events[0].__repr__())print(events[0].dict())print()me=c.general.whoami()print('Logged in as:',me.__repr__())print()my_events=c.person.events(me.id)print('Your next events:')[print(f'-{e.__repr__()}')foreinmy_events]NotesSet thedebuggingfield from 0 - 2 for none to many debug messages.Times are in UTC
Package: churneval
Package "churneval"Version:1.3Author:Soumi DeMaintained by:Soumi De <soumi.de@res.christuniversity.in>Description:churneval is a package to evaluate models used in churn classification. The evaluation metrics include accuracy, sensitivity, specificity, precision, F1-score and top decile lift. The package also contains functions to plot lift curve and gain curve of a model.License:GPL-3Date:12th November, 2021Function:get_performance_metrics               Function that returns evaluation metricsUsage:from churneval import get_performance_metricsget_performance_metrics(model_name, true_class, predicted_class, predicted_probs)Arguments:model_name: 		Abbreviated name of the churn model (in text)true_class: 		A dataframe of true class labels with shape (n,1)predicted_class: 	An array of binary predicted class with shape (n,)predicted_probs:	An array of predicted class probabilities with shape (n,)Returned Values:A dataframe consisting of elements given below:Model_Name: Abbreviated name of the churn modelAccuracy:		Accuracy of churn modelConfusion Matrix:	A 2X2 array representing confusion matrixPrecision:		Precision valueSensitivity:		Sensitivity valueSpecificity:		Specificity valueF1-score:		F1-scoreROC_score:		Area under the curvetop_dec_lift:		Top decile lift valueFunction:top_decile_lift               Function that returns top decile lift of a sampleUsage:from churneval import top_decile_lifttop_decile_lift(true_class, predicted_probs)Arguments:true_class: 		A dataframe of true class labels with shape (n,1)predicted_probs:	An array of predicted class probabilities with shape (n,)Returned Values:A float object with top decile lift valueFunction:lift_curve               Function that plots lift curve of a modelUsage:from churneval import lift_curvelift_curve(true_class, predicted_probs)Arguments:true_class: 		A dataframe of true class labels with shape (n,1)predicted_probs:	An array of predicted class probabilities with shape (n,)Returned Values:A plot that shows lift curvex-axis: Proportion of datay-axis: Lift of the modelFunction:gain_curve               Function that plots gain curve of a modelUsage:from churneval import gain_curvegain_curve(true_class, predicted_probs)Arguments:true_class: 		A dataframe of true class labels with shape (n,1)predicted_probs:	An array of predicted class probabilities with shape (n,)Returned Values:A plot that shows lift curvex-axis: Proportion of datay-axis: Gain of the model
Package: churn-framework
Churn FrameworkEncontre métricas que melhor separam clientes recorrentes de clientes que cancelaram o contrato durante a joranada de uso do produto/serviço.Auxílio na definição de possíveis pontos de corte para potenciais métricas de um possível indício de Churn durante a jornada de um cliente.Identificação de pontos de corte que melhor diferenciam clientes de sucesso de clientes que sofreram churnIdentificação de métricas cujo ponto de corte determinam um alerta para Churn durante os meses da jornada do clienteInstalaçãoDependências:Python (>=3.6)SeabornTqdmInstalar:pipinstallchurn-frameworkDocumentaçãoDescrição das funcionalidades de cada função presente no script churn_framework:Funçõesget_great_value- Função que encontra o valor ideal para um ou mais features com base em restrições definidas que melhor dividem clientes recorrentes de clientes que sofreram Churn, para cada mês da jornada do cliente.plot_metrics- Método que plota métricas de avaliação para cada valor testado no framework em um determinado mês, enfatizando o ponto ótimo encontrado.get_confusion_matrix- Método que retorna valores de matriz de confusão Cliente / Churn para um mês específico.plot_matrix- Método que traça a matriz de confusão Cliente / Churn.Também fornecemos um notebook passo a passo. Para visualizá-lo, basta digitar jupyter notebook dentro do diretório/churn_framework/.LicenseMIT