Saravanan666/pypi-org huggingface dataset:
Package: tcf
# tcf cli## 什么是 tcftcf 全称为 tencent cloud function，是腾讯云无服务器云函数 SCF（Serverless Cloud Function）产品的命令行工具。通过 tcf 命令行工具，您可以方便的实现函数打包、部署、本地调试，也可以方便的生成云函数的项目并基于 demo 项目进一步的开发。tcf 通过一个函数模板配置文件，完成函数及相关周边资源的描述，并基于配置文件实现本地代码及配置部署到云端的过程。目前 tcf 以开源形式发布，您可以在本项目中查看命令行源代码及更多帮助文档，并可以通过项目 issue 反馈问题。## 功能特性通过 tcf 命令行工具，你可以：快速初始化云函数项目在本地开发及测试你的云函数代码使用模拟的 COS、CMQ、Ckafka、API 网关等触发器事件来触发函数运行验证 TCSAM 模板配置文件打包、上传函数代码，创建函数及更新函数配置## 运行环境tcf 可以在 Windows、Linux、Mac 上运行。tcf 基于 Python 开发完成，因此在安装及运行前需要系统中安装有 Python 环境。更详细信息可见[安装及配置](https://github.com/tencentyun/tcfcli/blob/master/docs/安装与配置.md)。## 快速入门### 安装 tcf#### 前置依赖在安装 tcf 前，请确保系统中已经安装好了如下软件：Python 2.7+ 或 Python 3.6+pipgit对应的开发语言（如Node.js 8.9等）#### 通过 pip 安装 tcf通过使用如下命令完成 tcf 安装：`bash $ pip install tcf `通过执行如下命令及输出确保 tcf 安装已成功：`bash $ tcf--versionTCF CLI, version 0.1.0 `### 配置 tcf从腾讯云控制台获取到账号的 APPID，SecretId及 SecretKey 信息，并配置到 tcf 中，作为 tcf 调用云 API 时的认证信息。例如获取到的账号 APPID 为 1253970223，SecretId 和 SecretKey 分别为 AKIxxxxxxxxxx 及 uxxlxxxxxxxx，期望在广州区使用云函数。则通过如下命令完成 tcf 的配置
：`bash $ tcf configure set--regionap-guangzhou--appid1253970223--secret-idAKIxxxxxxxxxx--secret-keyuxxlxxxxxxxx `### 初始化模板项目选择进入到合适的代码目录，例如cd ~。通过执行如下命令，创建运行环境为 Node.js 8.9，名称为 testscf 的项目。`bash $ tcf init--runtimenodejs8.9--nametestscf `此命令会在当前目录下创建 testscf 项目目录。### 本地触发函数执行$ cd testscf进入项目目录。通过执行如下命令，本地模拟触发函数。`bash $ tcf native invoke-ttemplate.yaml--no-event`注：当前仅Node.js runtime支持该能力，并且需要升级TCF CLI >= 0.2.0版本。为保证部署云端和本地运行的结果一致，建议本地安装的runtime版本和云端版本保持一致。例如，如在云端使用Node.js 8.9，则本机建议也安装Node.js 8.x版本。### 打包项目执行$ cd testscf进入项目目录。可以通过ls命令看到，当前项目目录下包括了 README 说明文档，hello_world 代码目录，template.yaml 配置文件。通过执行如下打包命令：` $ tcf package--template-filetemplate.yaml `tcf 会依据 template.yaml 文件内的描述，将 hello_world 代码目录内的代码生成部署程序包，并生成 deploy 配置文件。此时再次通过ls命令，可以看到项目目录内多了 deploy.yaml 部署用配置文件，以及类似32b29935-1ec1-11e9-be82-9801a7af1801.zip的部署包。### 部署云函数通过执行如下命令，完成本地代码包及函数配置部署到云端：`bash $ tcf deploy--template-filedeploy.yaml `运行成功完成部署后，可以通过进入腾讯云云函数的控制台，检查函数是否已经成功部署。## 详细使用指导[快速开始](https://github.com/tencentyun/tcfcli/blob/master/docs/快速开始.md)[安装与配置](https://github.com/tencentyun/tcfcli/blob/master/docs/安装与配置.md)[初始化示例项目](https://github.com/tencentyun/tcfcli/blob/master/docs/初始化示例项目.md)[打包部署](https://github.com/tencentyun/tcfcli/blob/master/docs/打包部署.md)[本地调试(native invoke)](https://github.com/tencentyun/tcfcli/blob/master/docs/%E6%9C%AC%E5%9C%B0%E8%B0%83%E8%AF%95(native%20invoke).md)[本地调试(local invoke)](https://github.com/tencentyun/tcfcli/blob/master/docs/%E6%9C%AC%E5%9C%B0%E8%B0%83%E8%AF%95(local%20invoke).md)[测试模板](https://github.com/tencentyun/tcfcli/blob/master/docs/测试模板.md)[模板文件](https://github.com/tencentyun/tcfcli/blob/master/docs/模板文件.md)[TCSAM说明](https://github.com/tencentyun/tcfcli/blob/master/docs/specs/tencentcloud%20sam%20version%202018-11-11-zh-cn.md)[常见问题 FAQ](https://github.com/tencentyun/tcfcli/blob/master/docs/常见问题%20FAQ.md)
Package: tcfetch
tc-fetchFetch artifacts from taskcluster.This is a small utility application designed to download and fetch
logs fromTaskCluster-based CI systems.In particular it's built for use withMozilla's
CIandweb-platform-testsCI.Command line usagetcfetch [--out-dir <path>] [--artifact-name <name>] [--filter-jobs <expression>]* <repo> <commit>By default tcfetch is configured to fetch web-platform-tests results
in wptreport format.Validreponames are:mozilla-central,mozilla-beta,autoland,try- Mozilla
repositories hosted onhg.mozilla.org.wpt- Theweb-platform-testsrepository.commitmust be the hash of a commit in the corresponding
repository. For Mozilla repositories the minimum commit prefix is 12
characters. For web-platform-tests, anything non-ambiguous should
work.--out-dir- The path to put the downloaded artifact files.--artifact-name- The name of the artifact to download (currently
implemented as a suffix match on the full path).--filter-jobs- A filter string used to select the task names to
include. This is a string that's interpreted as a regex. If the string
starts with!, any matching jobs are excluded. If the string starts
with^(after removing any!), it's used as a regexp against the
full task name, otherwise it's used as a substring match.For example to fetch all Firefox logs from web-platform-tests commit
0f123ad and put them in a directory calledlogs:tcfetch --out-dir logs --filter-jobs '-firefox-' wpt 0f123ad
Package: tcfg
tcfgfromtcfgimportcfg,Option@cfg.json('cfg.json')classConfig:# year must be a string. It has a default value of '2023'year='2023'# load must be a boolean. It has a default value of trueload=true@cfg.toml('cfg.toml')classConfig2:# year must be a string. Default to ''year=str# load must be a boolean. Default to falseload=bool# Any config class will save to the specified file path unless overridden.# This includes config classes that are in other config classes.# This means you can have one master config object in your code that uses many config files.@cfg.yaml('nested.tml')classNested:# Must be a bool. Defaults to falseenabled=false# Must be an int. Defaults to 8081port=8081# Can specify multiple specific options.scope=Option('public','private',default='private')# Must be a list# Literals are defaults and types are used for valid element types. Literal types are also used in element types.extensions=['reload',dict]options={# wildcard type used for validation of extra values not specified as a key in this dict'*':dict,'open':false,# can have recursive nesting'deep_nesting':{'random':int}}@cfg.yaml('cfg.yml')classConfig3:nested=Nested# nested is a sub/nested config section
Package: tcga-paired-purification
TCGA paired purificationThe tcga_paired_purification packages allows researchers to input common paired experiments (i.e. cancer paired with normal samples) and use that as a basis to remove contamination due to non-cancerous components. The package was specifically wrote to deal with the normal contamination in TCGA datasets.Download PackageDownload the tcga_paired_purification package by:pip install git+https://github.com/jeffliu6068/tcga_paired_purification.gitorpip install tcga_paired_purificationImportOnce installed, import the package by:import tcga_paired_purificationIntuition: How DEA Works to Identify Differentially Expressed GenesThe package takes into account 3 seperate information:Contaminated expression data (i.e. TCGA cancer RNA-seq)Mean and standard deviations of the distributions of normal dataPurity of the contaminated data (i.e. copy number variation or histological assessment)By taking into account the ratio of normal vs cancer proportion via copy number variation, we can remove normal contamination from the paired cancer expression data as shown in the TCGA dataset.Available Tools in the tcga_paired_purification Packagetcga_paired_purificationimport tcga_paired_purification as tpp

purified_df = tpp.tcga_paired_purification(input_data_cancer, input_data_normal, purity_df)input_data_cancer is the input dataframe with genes (row) x samples (columns)input_data_normal is a dataframe with the information of the means and standard deviation of the paired normal distribution of each gene and with columns 'mean' and 'std'purity_df is a dataframe with samples (rows) x purity (columns)AuthorsTa-Chun (Jeff) Liu-jeffliu6068Sir Walter Fred Bodmer FRS FRSE-SupervisionLicenseThis project is licensed under the MIT License - see theLICENSE.mdfile for detailsAcknowledgmentsHat tip to anyone whose code was usedInspiration: Thank you for all that has contributed ideas and expertise to make this possible. Let's advance science together.
Package: tc-gcs
Thumbor Google Cloud StorageThumbor Storage, Thumbor Loader and Thumbor Result Storage forGoogle Cloud Storage, akaGCS.The thumbor storage can be customized as follows:Storages,Image Loaders,Result Storages.Installationpip install tc-gcsAuthentiationAuthentication is handled by the Google Cloud Storage SDK, see google-cloud-storage SDKdocumentation.ConfigurationStorage settingsTC_GCS_STORAGE_PROJECT_ID = '' # GCS project id for Storage
TC_GCS_STORAGE_BUCKET_ID = '' # GCS bucket id for Storage
TC_GCS_STORAGE_ROOT_PATH = '' # GCS path prefix for Storage bucketKey settingsTC_GCS_RANDOMIZE_KEYS=False # Adds some randomization in the GCS keys for the Storage and Result Storage. Defaults to False for Backwards Compatibility, set it to True for performance.
TC_GCS_ROOT_IMAGE_NAME='root_image' # Sets a default name for requested images ending with a trailing /. Those images will be stored in result_storage and storage under the name set in this configuration.
Package: tcgplayer-api
TCGPlayer-APIA Python client library for theTCGPlayer APIInstallationpipinstalltcgplayer-apiNote that the latest version of tcgplayer-api will only be compatible with newer version of Python. This is to allow tcgplayer-api to take advantage of the latest language/library features and to reduce the technical debt of maintaining tcgplayer-api. See the table below for officially supported versions:tcgplayer-apiPython0.0.1>=3.6Example Usage>>> from tcgplayer_api.auth import BearerAuth
>>> from tcgplayer_api.client import TCGPlayerClient


>>> PUBLIC_KEY = "x"
>>> PRIVATE_KEY = "y"
>>> auth = BearerAuth(public_key=PUBLIC_KEY, private_key=PRIVATE_KEY)

>>> client = TCGPlayerClient(auth=auth)
>>> client.list_all_categories()
{'totalItems': 61,
 'success': True,
 'errors': [],
 'results': [{'categoryId': 55,
   'name': 'Architect TCG',
   'modifiedOn': '2018-09-17T15:21:54.233',
   'displayName': 'Architect TCG',
   'seoCategoryName': 'Architect TCG',
   'sealedLabel': 'Sealed Products',
   'nonSealedLabel': 'Singles',
   'conditionGuideUrl': 'https://store.tcgplayer.com/',
   'isScannable': True,
   'popularity': 0},
  ...
 ]
}Roadmap to v1.0.0Fix support for post and other request types besides getAdd pagination support for responsesAdd module/func docstringsAdd basic documentationAdd parameters to v1.37.0.jsonImprove endpoint methods' docstrings to include arg infoAdd more meaningful error messages when required params are missing or for various status codes
Package: tcgshuffle
使い方fromtcgshuffleimportevaluateevaluate(評価したいシャッフル関数,デッキ枚数)出力position_prob_map: 元々i番目にあったカードが、シャッフル後デッキ内のどこに移動した頻度neighbor_prob_map: 元々i番目にあったカードとj番目にあったカードについて、シャッフル後にそれらが隣り合っていた頻度position_badness: position_prob_mapの頻度が、理想頻度(1 / デッキ枚数)とどの程度乖離しているかneighbor_badness: neighbor_prob_map、理想頻度(1 / (デッキ枚数 - 1))とどの程度乖離しているかtotal_position_badness: 総合評価として、理想頻度を1とした場合にposition_badnessの偏りがどの程度大きいかを示す（もっとも悪い数字をデッキ枚数分取り出し平均したもの）total_neighbor_badness: 総合評価として、理想頻度を1とした場合にneighbor_badnessの偏りがどの程度大きいかを示す（もっとも悪い数字をデッキ枚数分取り出し平均したもの）シャッフル関数定義方法説明準備中...basic example 1ディールシャッフル（6つの束）ファローシャッフル（5回連続）ヒンドゥーシャッフル（4回シャカシャカする作業を1回）ファローシャッフル（5回連続）ヒンドゥーシャッフル（4回シャカシャカする作業を1回）カット（3分割）fromtcgshuffleimportDeck,Shuffler,evaluatedefshuffle(d:Deck,s:Shuffler)->Deck:s.deal(d,[6])s.fallow(d,5,0.1,0.1)s.over_hand(d,[4],0.1)s.fallow(d,5,0.1,0.1)s.over_hand(d,[4],0.1)s.cut(d,3,0.1)returndposition_prob_map,neighbor_prob_map,position_badness,neighbor_badness,total_position_badness,total_neighbor_badness\=evaluate(shuffle,60)total_position_badness = 0.21536666666666665
total_neighbor_badness = 0.21140000000000003basic example 2ヒンドゥーシャッフル（5回シャカシャカ、4回シャカシャカ、3回シャカシャカ、・・・と21回行う）fromtcgshuffleimportDeck,Shuffler,evaluatedefshuffle(d:Deck,s:Shuffler):s.hindu(d,[5,4,3,4,5,4,3,4,5,4,3,4,5,4,3,4,5,4,3,4,5],0.4)returndposition_prob_map,neighbor_prob_map,position_badness,neighbor_badness,total_position_badness,total_neighbor_badness\=evaluate(shuffle,60)total_position_badness = 0.4182666666666666
total_neighbor_badness = 13.671499999999998advanced example 1２束に分けるそれぞれの束をファローシャッフル（8回連続）２束を合わせる（上に載せるだけ）fromtcgshuffleimportDeck,Shuffler,evaluatedefshuffle(d:Deck,s:Shuffler):ds=s.split(d,2,0.1)s.super_fallow(ds,8,0.1,0.1)d=s.merge(ds)returndposition_prob_map,neighbor_prob_map,position_badness,neighbor_badness,total_position_badness,total_neighbor_badness\=evaluate(shuffle,60)total_position_badness = 1.2804000000000002
total_neighbor_badness = 1.3671999999999995advanced example 2ディールシャッフル（6つの束）以下を3回繰り返す束を2つに分ける（元の束 -> [A, B]）各束をさらに3つに分ける（A -> [A1, A2, A3], B -> [B1, B2, B3]）A1とB1、A2とB2、A3とB3をそれぞれ合わせるA1+B1、A2+B2、A3+B3にそれぞれ、ファローシャッフル（3回連続）A1+B1、A2+B2、A3+B3を合わせるヒンドゥーシャッフル（4回シャカシャカする作業を3回実施）fromtcgshuffleimportDeck,Shuffler,evaluatedefshuffle(d:Deck,s:Shuffler):s.deal(d,[6])foriinrange(3):sp2=s.split(d,2)sp6=s.super_split(sp2,3)pile1=s.merge([sp6[0],sp6[3]])pile2=s.merge([sp6[1],sp6[4]])pile3=s.merge([sp6[2],sp6[5]])s.fallow(pile1,3,0.1,0.1)s.fallow(pile2,3,0.1,0.1)s.fallow(pile3,3,0.1,0.1)d=s.merge([pile1,pile2,pile3])s.hindu(d,[4,4,4],0.1)returndposition_prob_map,neighbor_prob_map,position_badness,neighbor_badness,total_position_badness,total_neighbor_badness\=evaluate(shuffle,60)total_position_badness = 0.23553333333333332
total_neighbor_badness = 0.2083
Package: tchan
tchan - Telegram Channel scraperPython library and command-line interface to scrape Telegram public channels.
Since this scraper uses Telegram Channel Web preview,it won't workfor:Public channels with "Restrict saving content" option enabledPrivate channelsPublic GroupsPrivate GroupsIt's also not possible to retrieve comments, since they're made on a group.Installingpipinstalltchan# Python library onlypipinstalltchan[cli]# Library + CLIUsing as a librayfromtchanimportChannelScraperscraper=ChannelScraper()formessageinscraper.messages("tchantest"):print(f"New message ({message.type}) from{message.channel}:")print(f"  id={message.id}")print(f"  created_at={message.created_at.isoformat()}")print(f"  text={message.text}")# TODO: add more parametersUsing as a command-line toolScrape one or many channels and save all messages tomessages.csv:tchanmessages.csvchannel1[channel2...channelN]TestsTo run all tests, execute:maketest# or just `pytest`Make sure to install development requirements.Tests were made on a channel created for this task:tchantest.
Package: tchappui-webdrivers
install-webdriversEasy-to-use script to install the latest versions of chromedriver
and geckodriver on Travis-CI.InstallationYou can install this command directly from PyPI using pip as follows:$ pip install tchappui-webdriversUser guideAfter having installed tchappui-webdrivers from PyPI,
simply install the last stable versions of chromedriver and
geckdriver using the install-webdrivers command. Here is a sample
.travis.yml file as an example django project:dist: bionic
language: python

python:
  - '3.8'

addons:
  # ajouter uniquement les navigateurs utilisés
  chrome: stable
  firefox: latest

branches:
  only:
    - staging

install:
  - pip install tchappui-webdrivers

before_script:
  - install-webdrivers --path /usr/local/bin/

env:
  global:
    # we suppose the settings for Travis are in a dedicated file
    - DJANGO_SETTINGS_MODULE="config.settings.travis"

services:
  - postgresql

script:
  - python manage.py testThen, in your tests, start both chrome and firefox in headless mode:fromseleniumimportwebdriverchrome_options=webdriver.ChromeOptions()chrome_options.add_argument('--headless')chrome_options.add_argument('--disable-gpu')chrome_options.add_argument('--remote-debugging-port=9222')chrome_options.add_argument('--window-size=1920x1080')firefox_options=webdriver.FirefoxOptions()firefox_options.headless=Truefirefox_options.add_argument('--window-size=1920x1080')History0.1.0 (2020-05-01)First release on PyPI.
Package: tchappui-zevents
ZEventsEasy to use generic events system.Free software: MIT licenseDocumentation:https://zevents.readthedocs.io.FeaturesEventManager class orchestrating subscriptions, unsubscriptions and notificationsGeneric Event class usable to implement custom eventsGeneric TriggerEvent triggering the processing of queues in EventManagerStandard events collection: TickEvent, QuitEventInstall$pipinstalltchappui-zeventsExampleA simple example of how to use ZEvents can be found in theUsage sectionof the documentation.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2019-02-04)First release on PyPI.
Package: tchart
Minimal chart renderer for fixed size canvas for Python.Installationpip install tchartUsagesimple way:fromtchartimportTchartt=Tchart(height=10,width=80)data=[12.1,-2,100]chart=t.render(data)print('\n'.join(chart))extended way:fromtchartimportTchartfromtchart.renderersimportBoxRendererfromtchart.decoratorsimportAxisDecoratort=Tchart(height=10,width=80,renderer=BoxRenderer(),decorators=[AxisDecorator(),])data=[12.1,-2,100]chart=t.render(data)print('\n'.join(chart))ExamplesPlease check theexamplesdirectory.examples/show_all_renderers.pyexamples/show_all_decorators.pyexamples/show_stacked_decorators.pyBugsBugs or suggestions? Visit theissue tracker.
Package: tChat
tChat:speech_balloon: A Twitch chat UI for your terminal.Installationpip install tChatUsagetChat -ch <channel name>For example:python3 src/main.py -ch shroudwhereshroudis a Twitch streamer and we want to join his chat using tChat.If it is your first time, you probably don't have a configuration file, therefore the program will ask you two things: your Twitch nickname and your OAuth token.IMPORTANT: Use this website to get your oauth token:Twitch Chat OAuth Password GeneratorTroubleshootingBlank nicknamesBe careful with the colors of your terminal. Sometimes it's gonna look like that the nickname is blank, but it is not, actually. You can try changing your terminal color background and see what fits better for you.LicenseMIT
Package: tc-hivemind-backend
# Hivemind-backend[![Maintainability](https://api.codeclimate.com/v1/badges/1f01c7faab6d09fa3a83/maintainability)](https://codeclimate.com/github/TogetherCrew/hivemind-backend/maintainability)
[![Test Coverage](https://api.codeclimate.com/v1/badges/1f01c7faab6d09fa3a83/test_coverage)](https://codeclimate.com/github/TogetherCrew/hivemind-backend/test_coverage)This repository is designed to be a shared library for our hivemind etl sctips and bot codes.
Package: tciaclient
TCIA (The Cancer Imaging Archive) Download Client for PythonThis Python package uses the official TCIA REST API to enable downloads fromwww.cancerimagingarchive.netfrom within Python scripts and Jupyter Notebooks.The documentation can be found athttps://moritzschwyzer.github.io/tciaclient/. This PyPI/Conda package is based on source code of the TCIA-API-SDKhttps://github.com/TCIA-Community/TCIA-API-SDK.Installpip install tciaclientHow to useStep 1: Import theTCIAClientfrom thetciaclient.corepackage.fromtciaclient.coreimportTCIAClientStep 2: Create an instance of theTCIAClient.tc=TCIAClient()Step 3: Specify the collection you want to download (find the name onhttps://wiki.cancerimagingarchive.net/display/Public/Collections).collection_name="NSCLC-Radiomics"Step 4: Get the series information of the chosen collection. In this example, we specify that we only want series that are CT scans.series=tc.get_series(collection=collection_name,modality="CT")Step 5: Download the dataset to the specified path.download_path="./tcia-downloads"fori,sinenumerate(series):print(i)tc.get_image(seriesInstanceUid=s["SeriesInstanceUID"],downloadPath=download_path,zipFileName=str(i).zfill(3)+"-"+collection_name+".zip")TCIA Data Usage Policies and RestrictionsInformation regarding data usage policies and restrictions can be found onhttps://wiki.cancerimagingarchive.net/display/Public/Data+Usage+Policies+and+Restrictions
Package: tcia-utils
OverviewThetcia_utilspackage contains functions to simplify common tasks one might perform when interacting with The Cancer Imaging Archive (TCIA) via Jupyter/Python. Learn more about TCIA and its open-access datasets athttps://www.cancerimagingarchive.net/.  Please be sure to comply with theTCIA Data Usage Policy.Installationpip install tcia_utilsUsageTo import functions related to Wordpress, which holds metadata for TCIA datasets:from tcia_utils import wordpressTo import functions related to the NBIA software, which holds TCIA's DICOM radiology data:from tcia_utils import nbiaTo import functions related to the PathDB software, which holds TCIA's histopathology data:from tcia_utils import pathdbTo import functions related to Datacite, which holds information about citations and Digital Object Identifiers (DOIs) for TCIA datasets:from tcia_utils import dataciteExample notebooks demonstrating tcia_utils functionality can be found athttps://github.com/kirbyju/TCIA_Notebooks.
Package: tcid50-calculator
TCID50_CalculatorPip Package Referencehttps://pypi.org/project/tcid50-calculator/What does this calculate?Calculate cumulative infected and uninfected wells adding infected wells totaling everything
under dilution and uninfected wells totaling everything above dilution.Find PD. PD=(A-50)/(A-B)A=% response above 50% closest to 50B=% response below 50% closest to 50Find TCID 50Log(10) TCID50 = Dilution giving above 50% response minus PDTake reciprocal of TCID50 and multiply by 100 to get TCID50/mLMultiply by 0.69 to get pfu/mLOutput all the aforementioned step results per calculation to be executed.How to run a new calculation?InputInput should be acsvwith the following layout (start and end block per calculation in the file if you want one file to calculate multiple results):STARTDilution,Infected,Uninfected0.00001,8,00.000001,5,30.0000001,1,70.00000001,0,8ENDSTARTDilution,Infected,Uninfected0.00001,8,00.000001,5,30.0000001,1,70.00000001,0,8ENDHow to run?Install python 3.x:https://www.python.org/downloads/Install pip:https://pip.pypa.io/en/stable/installing/Install the pip package via command line: pip install tcid50_calculatorExecute the module via command line: tcid50_calculator calculate_tcid50 "FULL_PATH_TO_CSV"Example Command:python ./TCID50_Calculator/application/main.py calculate_tcid50 "C:\Users\USER\Desktop\Projects\TCID50_Calculator\test.csv"OutputOutput will match the same inputcsvin the same directory.It will have the same name of the provided csv, with_resultsuffixed.The result file will have additional columns for cumulative totals, Percent_Infected, PD, TCID50, TCID50/mL, and pfu/mL.STARTDilution,Infected,Uninfected,Cumulative_Infected,Cumulative_Uninfected,Percent_Infected,BLANK_COLUMN,PD,TCID50,TCID50_per_mL,pfu_per_mL0.00001,8,0,14,0,100,,0.29,0.000000512,0.0000000195,0.00000001340.000001,5,3,6,3,66.70.0000001,1,7,1,10,9.10.00000001,0,8,0,18,0ENDSTARTDilution,Infected,Uninfected,Cumulative_Infected,Cumulative_Uninfected,Percent_Infected,BLANK_COLUMN,PD,TCID50,TCID50_per_mL,pfu_per_mL0.00001,8,0,14,0,100,,0.29,0.000000512,0.0000000195,0.00000001340.000001,5,3,6,3,66.70.0000001,1,7,1,10,9.10.00000001,0,8,0,18,0END
Package: tcim
tcim - terminal commands in menusAn easy way to call hard to remember or complex terminal commands or scripts from a menu.supported menu systemsdmenuxdg desktop menufilescommands.tsvcontains your commands and is located in~/.config/tcim.
It can be edited using the menu (see usage) or manually using your favourite text editor.
Since it is a.tsvfile, you could even use a spreadsheet program like libreoffice calc.file formatcommands.tsvis a tab-separated values file:name <tab> comment <tab> commandexample:list filesystems	report file system space usage	df -hIf you don't want to keep the terminal open after the command has finished, prepend the command with[NOKEEP].
If you don't want to run the command in a terminal, prepend the command with[NOTERM].The command can't contain double quotes".
If you need double quotes, move the command into a script file and use it in the command field.installationpip install tcimusagedmenuCalltcim-dmenuto list the available commands in a dmenu.
Currentlyxtermis used to display the result of the command and it is expected to be installed.xdg desktop menuCalltcim-update-xdg-menuto update your desktop menu, used by many desktop environments.
Once called, everything (e.g. editing the commands-file) is available from the menu.uninstallpip uninstall tcimdoesn't uninstall .desktop menu entries created bytcim-update-xdg-menu.
You can use this command to get rid of them:xdg-desktop-menu uninstall command-menu.directory ~/.local/share/applications/command-*todonew field for optional working directorysupport more menu systems (e.g. rofi, openbox)