Saravanan666/pypi-org huggingface dataset:
Package: categoricaloutlier
Categorical Outlier is a tool to detect anomalous observations in categorical and DateTime features. Most of the techniques that we already have are focused on numeric features. There is no library available which can detect an outlier within categorical data. This package does that by building a profile using the past observations and gives an outlier score to a new observation on the basis of this profile. An example use of this package may be predicting unusual driving behavior. A driver who drives the same route(s) to reach maybe office will show an anomalous behavior if he takes an altogether different route on a particular day. He will get a high outlier score for this behavior. On the contrary, an uber driver drives to a new location every time and hence, a new destination will not be an anomalous behavior and hence will get a low score. The package also takes a combination of categorical features as input.
Package: categorical-variable-handling
Categorical Variable handlingThis is a package that contain some powerful Categorical feature handling algorithm :For that you have to install this library using pip commandThen observe you dataset and figure out which encoding technique you wants to usedAfter that call that library and do as follows:fromcategorical_variable_handlingimportCategoricalFeaturecolumn="Give here the list of Categorical column"dataframe="Here give the dataframe"encoding_type="Here give the encoding type e.g:'label','binary','ohe':one hot encoder"handle_na="this is a boolean value if you wants to handle the NaN value then make true else make false "cat=CategoricalFeature(df,categorical_features=c,encoding_type='ohe',handle_na=Truecat.fit_transform()#This will return the transformed value of the datasetBy. Manikant Kumar
Package: categorize
CategorizeCategorize is a simple, customizable CLI-tool for automation sorting your files by their extensions.InstallingFrom sources. Clone the repo:$gitclonehttps://github.com/kantegory/categorize.gitInstall package:$makeinstallThat's all, you awesome!Or from pip:$pipinstallcategorizeUsageSorting:$categorize--directory/path/to/your/directoryShow all categories and extensions:$categorize-config--showEdit pattern name:$categorize-config--edit-namepattern_namenew_nameEdit extensions for pattern name:$categorize-config--edit-extpattern_nameContactsEmail me, or contact me intelegram.
Package: categorize-cli
Categorize-CLIA command-line-tool to help you organize files in a given directoryCategorize-CLI v1.0.0A new update aimed to simplify the command-line-interface while increasing functionalityView the changelogfor all the new features!UsageExtensions:This command will organize the files based on the specified extension type (ex: image, audio, video) in the working directoryCategorize ext -t [command]Categorize ext --type [command]To organize all files based on extension (ex: .jpeg, .txt) use the--allflagCategorize ext --allCommands:[text, image, audio, video, word, powerpoint, excel, access, executables, pdf, archives, documents, media, safe]Keyword:This command will organize the files based on the specified keyword present in the file names in the working directoryCategorize key -k [command]Categorize key --keyword [command]Year-created:This command will organize files based on year created in the working directoryCategorize yearOtherIf you want to organize files belonging to another directory then you can just specify the path using this option-por--pathCategorize [command] --path [command]If you want to view the full output you can use the verbose flag-vor--verboseCategorize [command] --verboseCommand line interfaceUsage: Categorize [OPTIONS] COMMAND [ARGS]...

  Categorize files based on different categories

Options:
  --version  Show the version and exit.
  --help     Show this message and exit.

Commands:
  ext   Organize files based on specified extension (ex: image, video)
  key   Organize files based on specified keyword
  year  Organize files based on year created
Package: category
categoryCategorical transformation for data scienceInstallationpip install works for this library.pipinstallcategorySingle Category>>>fromcategoryimportCategory>>>book=Category(['a','b','c','Category_d','e','f','g','h','i','j'],pad_mst=False)>>>book.i2c[2]'c'>>>book.c2i[['Category_d','f']]array([3,5])You can setpad_msttoTrueto handle the missing token>>>fromcategoryimportCategory>>>book=Category(['a','b','c','Category_d','e','f','g','h','i','j'],pad_mst=True)>>>book.i2c[2]# the 1st token is the missing token, not 'a' any more'b'>>>book.c2i[['Stranger','Category_d','Unknown','f']]array([0,4,0,6])Multi-Category>>>fromcategoryimport(Category,MultiCategory)>>>cates=list(f"category{i}"foriinrange(1000))>>>multi_cate=MultiCategory(Category(cates,pad_mst=True))>>>multi_cate.string_to_index("category42, category108")array([42,108])You can also try to convert a list of strings, containing multicategorical info (which the data input is frequently used in tabular data), to nhot encoded array, and back>>>nhot=multi_cate.batch_strings_to_nhot(["category42, category108","category999"])>>>multi_cate.nhot_to_list(nhot)[0]["category42","category108"]PerformanceThe running speed of this library mostly depends on python dictionary and numpy operations. Though python is a 'slow' language, such application is pretty fast, and not easy to improve using other language.Here we compare the this library with theRust implementationReferencesGitHubPyPI packageRust implementationUsed inTai-Chi engine, a verstile user-friendly deep learning library
Package: catencfamily
catencfamilyThe module provides a way to encode categorical features in multiple but related ways using network analysis. Together, the family of multiple encodings serve as a numerical vector for every level of a categorical feature. The class transforms a group of categorical features into corresponding numerical features which can then be used either in unsupervised learning or in predictive analytics. To assist in unsupervised learning, it has methods to save a categorical feature as network graphs and plot them. The class has methods to extract unit vectors for every level of a categorical feature to help, for example, in understanding relationshsips between various levels. Extracted numerical vectors can directly be used in plotting, for example, in tensorflow's Embedding Projector. Class provides methods to get categories encoded for large datasets; one can, for example, take a sample of data at a time, have categories encoded, then take another sample and have categories similarly encoded. After a number of iterations, take either a mean or median to get final category-wise vectors. As the encodings are calculated using a group of network analysis operations, the family of encodings is extensible. The class provides one way, but a limited one to extend it.Installationpip install catencfamilyRequirementspython >= 3.7
pandas
numpy
networkx
cdlib
scikit-learn
matplotlib
pathlibLicense_MIT License. Any contribution is welcome!
Package: catenp
DESCRIPTIONPython client for extracting data from CATE archives to numpy arraysINSTALLATIONpip install catenpUSAGESee alsocatenp.catenumpy.Examplefrom catenp import Authenticate,DatabaseInfo,GetData

# Authenticate to the server
tk = Authenticate(serverAddress,serverPort,cateUserName,catePassword)

# (Optional) get server/ data info
info = DatabaseInfo(serverAddress,serverPort,cateUserName)
print("Info: ")
for kk in info: print("  ",kk,":",info[kk])

# Extract some data    
arr=GetData(serverAddress,serverPort,cateUserName,tstart,tstop,cstart,cstop)
Package: caterpillar-apitest
# caterpillar_apitest
## caterpillar_apitest 简介
caterpillar_apitest 为基于pytest及数据驱动封装的自定义接口自动化测试框架，通过xlsx格式的excel表格按照给定模板
编写数据，从而自动生成执行自动化测试用例的框架## caterpillar_apitest 安装方式`bash pip install-Ucaterpillar_apitest `## caterpillar_apitest 使用说明参照 [caterpillar_apitest使用详细说明](https://blog.csdn.net/redrose2100/article/details/121525929)### 1.1.11   发布日期：2021-12-03将text写入response返回值中### 1.1.10   发布日期：2021-12-02断言运算符in和not in 若操作数为布尔类型，转换为字符串后转换为均小写字母格式### 1.1.9   发布日期：2021-12-01put方法支持json格式数据### 1.1.8   发布日期：2021-12-01支持json格式数据### 1.1.7   发布日期：2021-11-30依赖和断言中语句之间使用分号间隔### 1.1.6   发布日期：2021-11-30依赖中增加对列表元素支持### 1.1.5   发布日期：2021-11-29断言中增加对列表元素断言的支持### 1.1.4   发布日期：2021-11-29解决断言中字符串中带有点误认为数据结构的问题，当断言中带有点作为一个整体时使用双引号或者单引号引起来### 1.1.3   发布日期：2021-11-29解决excel存在空白行误认为测试数据的bug### 1.1.2   发布日期：2021-11-25修复打印log异常的bug### 1.1.1   发布日期：2021-11-25接口请求完成后增加接口详细信息的打印### 1.1.0   发布日期：2021-11-25解析接口依赖时增加异常捕获### 1.0.17  发布日期：2021-11-24增加说明文档链接### 1.0.16  发布日期：2021-11-24修复bug（https://gitee.com/redrose2100/caterpillar_apitest/issues/I4JOTO）### 1.0.15  发布日期：2021-11-24解决接口返回json数据的异常的问题### 1.0.14  发布日期：2021-11-24增加 > 和 < 断言运算符### 1.0.13  发布日期：2021-11-24解决读取xlsx文件告警的问题### 1.0.12  发布日期：2021-11-24解决获取断言操作符异常的bug### 1.0.11  发布日期：2021-11-24解决data未写入ApiTest.case_data的问题### 1.0.10  发布日期：2021-11-24解决接口返回码为整数，期望值为字符串，不一致的问题### 1.0.9  发布日期：2021-11-24增加断言运算符处理方法### 1.0.8  发布日期：2021-11-24增加run方法封装### 1.0.7  发布日期：2021-11-24增加api_assert断言方法### 1.0.6  发布日期：2021-11-24增加框架中对接口依赖的处理### 1.0.5  发布日期：2021-11-24增加将接口返回数据写入数据结构中的操作### 1.0.4  发布日期：2021-11-24增加request方法### 1.0.3  发布日期：2021-11-23增加loads方法### 1.0.2  发布日期：2021-11-23增加获取指定目录下所有xlsx文件的测试数据（测试数据模板确定）### 1.0.1  发布日期：2021-11-23增加获取指定目录下所有xlsx文件
Package: caterpillar-common
# caterpillar_common#### 介绍
公共封装#### 软件架构
软件架构说明#### 安装教程pip install caterpillar_common#### 使用说明[日志功能使用帮助文档](docs/log.md)[csv文件读写帮助文档](docs/csv.md)[excel文件读写帮助文档](docs/excel.md)#### 版本修改记录1.0.6 发布时间：2021/09/09修复log功能的找不到目录的bug1.0.5 发布时间：2021/09/08增加从读取excel文件的功能，见 3. [excel文件读写帮助文档](docs/excel.md)1.0.3 发布时间：2021/08/16解决log功能不支持设置多个log名称的问题1.0.2 发布时间：2021/08/16增加csv文件读写功能，见 2.  [csv文件读写帮助文档](docs/csv.md)1.0.1  发布时间：2021/08/16增加日志功能，见 使用说明中的 1 [日志功能使用帮助文档](docs/log.md)#### 参与贡献Fork 本仓库新建 Feat_xxx 分支提交代码新建 Pull Request
Package: caterpillard
Caterpillar DiagramA generic innovative visualization technique for univariate time-series data capable of
forecasting the next-state transition using Markov chains.This is a software implementation of the proposed Caterpillar Diagram in the research
article titled"An innovative color-coding scheme for terrorism threat advisory
system".What is a Caterpillar Diagram?A Caterpillar Diagram is a visualization technique used for analyzing univariate
time-series data. It consists of a series of colored circles with varying radii. The
circle's color represents the direction of change in the time-series data, and the
circle's size shows its variation.It implements the innovative and intuitiveDifference of Differences (DoD)approach
to create a color schema. As proposed, it segregates the time-series data under analysis
into a cohort of three consecutive time units. Further, it utilizes the unsigned
differences between observations to assign a size to each cohort. This novel visualization
technique can segregate the time-series data using seven colors or five stages ofAggressive, Ascent, Descent, Controlled, and Status Quo.Further, the proposed mechanism utilizes the accumulated color information regarding each
cohort to forecast the next step transition using a stationary matrix of Markov Chains.AuthorsPrabal Pratap Singh - https://orcid.org/0000-0002-0738-7629Prof. Deepu Philip - https://orcid.org/0000-0002-4607-9020Installation instructionsTo installcaterpillardpackage from PyPI:(env)$pipinstallcaterpillardDocumentationThe documentation for the package is availablehereCompatibilityThis package has been tested on Python 3.9 and Python 3.10 across all major operating
systems likeLinux,MacOsandWindows.LicenseThis package is licensed underGNU Affero General Public License v3.0ContributionsPlease readCONTRIBUTING.mdfor more details.CiteThis package has been developed as a part of the doctoral research titled "Modeling &
Analysis of Terrorism" by Prabal Pratap Singh under the supervision of Prof. Deepu Philip
atIndian Institute of Technology Kanpur.If you utilize this package then please use the bibliography in IEEE format to cite this
package and the associated Journal article in your work:[1] Prabal Pratap Singh and Deepu Philip, “Caterpillar Diagram.” Jan. 12, 2023.
Accessed: Jan. 12, 2023. [Online]. Available:https://github.com/mechaprabal/caterpillard[2] P. P. Singh and D. Philip, “An innovative color-coding scheme for terrorism threat
advisory system,” Methodological Innovations, p. 20597991221144576, Dec. 2022, doi:
10.1177/20597991221144577.
Package: caterpillar-HTMLTestRunner
# caterpillar_HTMLTestRunner#### 介绍
用于存放HTMLTestRunner.py文件，并从此仓代码发布到pypi官方，从而可以使用pip install HTMLTestRunner 命令安装的方式安装#### 软件架构
软件架构说明#### 安装教程pip install caterpillar-HTMLTestRunner#### 使用说明使用举例代码参考[实例代码](docs/example.py)#### 修改记录1.0.2 晚上帮助文档和修改记录 2021/08/011.0.1 修改适配Python3 修改记录如下   2021/08/01第94行，将import StringIO修改成import io第539行，将self.outputBuffer = StringIO.StringIO()修改成self.outputBuffer = io.StringIO()第642行，将if not rmap.has_key(cls):修改成if not cls in rmap:第766行，将uo = o.decode(‘latin-1’)修改成uo = e第772行，将ue = e.decode(‘latin-1’)修改成ue = e第631行，将print >> sys.stderr, ‘nTime Elapsed: %s’ % (self.stopTime-self.startTime)修改成print(sys.stderr, ‘nTime Elapsed: %s’ % (self.stopTime-self.startTime))#### 参与贡献Fork 本仓库新建 Feat_xxx 分支提交代码新建 Pull Request
Package: caterpillar-log
# caterpillar_log
## caterpillar_log 简介
caterpillar_log 主要为log日志功能的高层封装，致力于提供非常便捷的写日志功能## caterpillar_log 安装方式`bash pip install-Ucaterpillar_log `## caterpillar_log 使用说明
参照 [caterpillar_log使用详细说明](https://blog.csdn.net/redrose2100/article/details/121503460)## caterpillar_log 发布记录### 1.0.2  发布日期：2021-11-23
* 完善caterpillar_log使用帮助文档### 1.0.1  发布日期：2021-11-23
* 支持直接导入的方式和自定义日志名称的方式
Package: caterpillar-pytest
# caterpillar_pytest#### 介绍
pytest功能增强#### 软件架构
软件架构说明#### 安装教程pip install caterpillar_pytest#### 使用说明待补充#### 参与贡献Fork 本仓库新建 Feat_xxx 分支提交代码新建 Pull Request#### 特技使用 Readme_XXX.md 来支持不同的语言，例如 Readme_en.md, Readme_zh.mdGitee 官方博客 [blog.gitee.com](https://blog.gitee.com)你可以 [https://gitee.com/explore](https://gitee.com/explore) 这个地址来了解 Gitee 上的优秀开源项目[GVP](https://gitee.com/gvp) 全称是 Gitee 最有价值开源项目，是综合评定出的优秀开源项目Gitee 官方提供的使用手册 [https://gitee.com/help](https://gitee.com/help)Gitee 封面人物是一档用来展示 Gitee 会员风采的栏目 [https://gitee.com/gitee-stars/](https://gitee.com/gitee-stars/)
Package: caterva
python-catervaPython wrapper forCaterva.InstallpipinstallcatervaDevelopment WorkflowClone repo and submodulesgitclone--recurse-submoduleshttps://github.com/Blosc/python-catervaInstall requirementspython-mpipinstall-rrequirements-build.txt
python-mpipinstall-rrequirements.txt
python-mpipinstall-rrequirements-tests.txtCompilepythonsetup.pybuild_ext--build-type=RelWithDebInfoRun testsPYTHONPATH=.pytestInstallingpython-mpipinstall.
Package: catesegy
DESCRIPTIONPython client for extracting data from CATE archives and writing as segy dataINSTALLATIONpip install catesegyUSAGESee alsocatenp.catesegy.Examplefrom catesegy import Authenticate,WriteFile

# Authenticate to the server
tk = Authenticate(serverAddress,serverPort,cateUserName,catePassword)

# Get the data and save as SEGY
WriteFile(serverAddress,serverPort,cateUserName,
    tstart,tstop,
    cstart,cstop,
    "test.sgy"
    )
Package: cateye
CateyeA hint-enabled search engine framework for biomedical classification systemsFeaturesHint: Show hints for search terms which can narrow down the results fast.Fallback: If no result satisfying the query, the system automatically eliminates less important search terms.Spelling correction: Build-in spelling correction for query terms.Abbreviation expansion: Pre-defined abbreviation list will be automatically applied during the searchSorted results: Sort the results according to the search history.Installation$gitclonehttps://github.com/jeroyang/cateye.git
$cdcateye
$pipinstall-e.Usage1. Run the Demo Site:$FLASK_APP=application.pyFLASK_ENV=developmentflaskrunThen browse the local sitehttp://127.0.0.1:5000/Try to search "rhinitis"2. Make your own site:2-1. Check the constants.py:Setup the essential variables in the constants.py:SITE_TITLE, SITE_SUBTITLE, TOKEN_FOLDER, SNIPPET_FOLDER, HINT_FOLDER, SPELLING_FILE, ABBREVIATION_FILE, INDEX_URLTheINDEX_URLwill be used in the Shove object, which can be a local URL starts with file:// please check the document ofShove.2-2. Data preparingFolders overview:data:The data source for the search engine, all information in this subfolders using the term id as their filenamesdata/token:The tokens of the documents, after lemmatizationdata/snippet:The HTML snippets of the documents, which will be shown on the search resultsdata/hint:The hints for each entitydata/spelling.txt:The formal spelling of your tokens (before normalization). If possible, sort the tokens with the frequency of usage, the most common word the first.data/abbreviation.txt:The abbreviations, one line for one abbreviation pair, using tab to separate the short form and long formCateye include some very basic text processing tools:
tokenizer (cateye.tokenize) and lemmatizer (cateye.lemmatize)The tokenize function will be used in two places: the first place is to cut your documents into tokens, and the second place is to cut your query into tokens.The lemmatizing function will normalize your tokens. If you wish to build a case-insensitive search engine, you may use lowercase lemmatizer on the tokens.2-3. Build the index:Run the command in the command line$cateyenewindexThis command read the files in thetoken_folderand build an on-disk index in theindex_url. It takes time depending on the size of your data.2-4. Run your application:$FLASK_APP=application.pyFLASK_ENV=developmentflaskrunLicenseFree software: MIT license
Package: catfact
forcats (in python)This is a port of the popular R package forcats to python.NOTE:I am in the process of moving the forcats module frommachow/siubato here.
In the meantime, you can install and use it from there.First, install siuba:pip install siubaThen, you can import forcats using...from siuba.dply.forcats import fct_lump, fct_reorder          # etc..
Package: cat-fact
cat-factcatFactGet random cat facts from the internetInstallation Instructions$pipinstallcat-factWant bleeding edge version?Create and activate virtual environment.$virtualenvvenv
$source./venv/bin/activateNow runpython setup.py install, this will installcatFactcommand into the virtual environmentIf runningcatFactsays command not found, deactivate and activate the virtual environment. That should fix it.Example$catFact
JuliusCeasar,HenriII,CharlesXI,andNapoleonwereallafraidofcats.
Package: catflow
CatFlowMachine learning aided catalysis reaction free energy calculation and post-analysis workflow, thus, analyzer for catalysis.As is known to all, cat is fluid and thus cat flows. 🐱Former Miko-AnalyzerInstallationTo install, clone the repository:git clone https://github.com/cloudac7/catflow.gitand then install withpip:cd catflow
pip install .AcknowledgementThis project is inspired by and built upon the following projects:ai2-kit: A toolkit featured artificial intelligence × ab initio for computational chemistry research.DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models.ASE: Atomic Simulation Environment.DPDispatcher: Generate and submit HPC jobs.Metadynminer: Reading, analysis and visualization of metadynamics HILLS files produced by Plumed. As well as its Python implementationMetadynminer.py.stringmethod: Python implementation of the string method to compute the minimum energy path.
Package: catflow-validate
catflow-validateThis Python package contains classes to validate input files for the CATFLOW hydrological model.
The package can be used in other Python software, as a standalone CLI script or it can be
pulled into a input files repository and can be run as a unittest.InstallPyPIpipinstallcatflow_validateUseCatflow-validate can be used as a python library or a command line interface.Usage: catlidate landuse [OPTIONS]

Options:
  -f, --filename TEXT  Filename for the landuse class definition file.
  -r, --recursive      Validate all referenced landuse class parameter files
                       recursively
  -v, --verbose        Print out verbose information on errors and warnings.
  -e, --extended       Print an extended report.
  --help               Show this message and exit.
Package: catflow-worker
catflow-workerConsumer/publisher loop for workers in an object recognition pipelineSetupInstallpre-commitin your virtualenv. Runpre-commit installafter cloning this repository.DevelopInstall:pip install --editable .[dev]Configure:export CATFLOW_S3_ENDPOINT_URL="your-endpoint-url"
export CATFLOW_AWS_ACCESS_KEY_ID="your-access-key-id"
export CATFLOW_AWS_SECRET_ACCESS_KEY="your-secret-access-key"
export CATFLOW_AWS_BUCKET_NAME="your-bucket-name"
export CATFLOW_AMQP_URL="amqp://username:password@hostname:port/"
export CATFLOW_AMQP_EXCHANGE="catflow"Run the sample loop:$ python -m catflow_worker
[*] Message received (ingest.video): da0b7b0a-c5e9-44dd-b67a-4fef9c5d86bd.mp4
[-] Content-Type binary/octet-stream
[-] Content-Length 92598
[*] Message received (detect.video): da0b7b0a-c5e9-44dd-b67a-4fef9c5d86bd.mp4
[-] Content-Type binary/octet-stream
[-] Content-Length 92598Buildpip install build
python -m buildTestpytestUsageDefine an async handler function:async def example_handler(
    msg: str, key: str, s3: Any, bucket: str
) -> Tuple[bool, Tuple[str, List[Any]]]:The handler functiongets: a message, the key the message was sent to, an S3 client, the name of an S3 bucket to useand returns: an error status, and a list of (key, message) tuples to publishThen start it like:worker = await Worker.create(handler, queue_name, topic_key)
await worker.work()Seecatflow_worker/main.pyfor a working example.
Package: catgrad
catgradYou like category theory? You like tinygrad? You love catgrad! ❤️catgrad is a bit different: instead of using autograd to train, youcompileyour model's reverse pass into static code.
This means your training loop can run without needing a deep learning framework
(not even catgrad!)Here is a linear model incatgrad:model = layers.linear(BATCH_TYPE, INPUT_TYPE, OUTPUT_TYPE)catgrad can compile this model into static python code:class CompiledModel:
    backend: ArrayBackend

    def predict(self, x1, x0):
        x2 = x0 @ x1
        return [x2]

    def step(self, x0, x1, x9):
        x4, x10 = (x0, x0)
        x11, x12 = (x1, x1)
        x16 = self.backend.constant(0.0001, Dtype.float32)
        # ... snip ...
        x18 = x17 * x5
        x2 = x10 - x18
        return [x2]... so you can train your model by just iteratingstep; no autograd needed:for i in range(0, NUM_ITER):
    p = step(p, x, y)Catgrad usesreverse derivativesandopen hypergraphsto transform a model into its backwards pass.
For details, seethis paper.Installpip install catgradExamplesTrain simple MLPs for theiris dataset:./data/get-iris-data.sh
python3 -m examples.iris (linear|simple|dense|hidden)Compilation TargetsTarget backends we plan to support soon:Python/numpyPython/tinygradC++/GGML