Saravanan666/pypi-org huggingface dataset:
Package: wellcompare
GarciaLabInfo-Code developed for research on modeling prion growth dynamics in budding yeast.
This package is used to compare 96 well plates that contain groupings of
experimental and control replicates. This code generates graphs, heatmaps,
and sumarries of your data, while also performing some basic statistics.Usage -Make a directory every screen that you run (Ex. "Screen1"). Inside of your
individual screen directory, create another folder called 'Data' and put the
excel file producefrom the Epoch2 plate reader inside. Then, from outside that
directory, run the package ($ python -m wellcompare) on the terminal.
The programwill create several directories for you. Some notable ones are
'Graphs', which contains graphs and data summaries, and 'Heatmaps'
(if -hm flag added) (temporarily disabled until further updates).Example Directory Structure:|- Outer_folder    <- run python -m wellcompare inside this outer directory|      |- Screen1|          |- Data|            |- screen1.xslx|      |- Screen2|          |- Data|            |- screen2.xslxRequirements -Also outlined in requirements.txt:Python3Pip3PandasNumpyMaplotlibScipySeabornArgParsexlrdSetup & Installation (MacOS)-Download Package (recommended):Type the following commands into terminal$ pip install wellcombine$ python -m wellcompare [options]-- OR --Clone Repo:Type the following commands into terminal$ git clonehttps://github.com/jacobian0208/well_compare.git$ pip install -r requirements.txt$ python -m wellcompare [options]Files & Directories -process.py : module that coordinates graphs and organizes experiment
analysisgraph.py : module to graph cleaned dataextract.py : module to extract and clean data from Epoch2 microplate reader
outputmain.py : main file to run package as scriptrequirements.txt : a list of required packages to install to run this
program correctlyLICENSE : an MIT license to allow use of this program
Package: weller
WellerInstallation:pip install wellerWhat is it?weller it's a modern library for caching. It add a TTL cache per item, auto values refresh and also so
easy interface for ruling thisWhat it includes:Weller - a automatic dispatcher for your perfect cacheStrictCached and LazyCached - a simply TTL cache per itemStrictAutoCached and LazyAutoCached - the same as the upper one, but with automatic values refreshStrict and Lazy differenceThis library has the concept of strict and lazy caches.First, the Lazy, deletes or refreshes only the data that you receiveSecond, the Strict, deletes or refreshes all data from a storage per any requestUsage examples:importasynciofromtypingimportAny,AnnotatedfromwellerimportWeller,Dependsfromweller.dispather.storageimportWellerMemoryStoragestorage=WellerMemoryStorage()# If first_long = True, then first get of data will download all the data. Default Falseweller=Weller(storage=storage,first_long=False)asyncdefget_db()->dict[str,Any]:return{"some_key":"some_data"}@weller.add("some_long",duration=5)asyncdefget_long_data(db:Annotated[dict,Depends(get_db)]):awaitasyncio.sleep(2)returndb.get("some_key")asyncdefmain():awaitweller.get("some_long")# it will take 2 secondsawaitweller.get("some_long")# but it will be done instantlyasyncio.run(main())You can see other examples in weller -> examplesDocumentation:In development and will be available soonRoadmap:Add redis storageWork and with stability and security
Package: wellfit
# WellFitTransit modeling with [starry](https://rodluger.github.io/starry/) and [celerite](https://celerite.readthedocs.io/en/stable/), capitalizing on gradients. Under development.### notebooksThis folder contains a few notebooks about fitting different transit models that we’ve collected over the year. They are there for inspiration and for basic examples. They will be deleted before the stable release of wellfit.### InstallationWhilewellfitis under development, you can install it by running the following in a terminal` git clonehttps://github.com/christinahedges/wellfit.gitcd wellfit pip install-e. `This will install in “development” mode, so you can edit the wellfit code and your changes will automatically run.
Package: wellknown-securitytxt
SecurityTXTSecurityTXT is a simple security.txt library.>>>fromsecuritytxtimportSecurityTXT>>>sec=SecurityTXT.from_url("adobe.com")>>>sec.contact['https://hackerone.com/adobe','mailto:psirt@adobe.com']>>>sec.signature'iQIzBAEBCAAdFiEEqvs1Pw7pNc/gvcvRX9Oj3XV3pEYFAmEBhSgACgk...'>>>sec.expiredFalseSecurityTXT allows you to easily retrieve, parse and manipulate security.txt files. It tries to follow the latestdraft RFCas closely as possible, while it still
parses security.txt files that contain minor mistakes. The package aims to be well-documented, thereby easy to use in
combination withpydoc.InstallationSecurityTXT is available on PyPI:$ python -m pip install wellknown-securitytxtThe package has only been tested with Python 3.6.8+Supported Features & Best–PracticesThe package has been build to support easy and automated retrieval and parsing of security.txt files. Therefore,
features include:Automated searching for security.txt files on specified host.Signature parsing for signed files.Allows for parsing unknown fields and comments that are present in security.txt file.Automated validity tests for parsed security.txt files.Every class and function is fully documented.Soon to be implemented:fail_silently: if a file format is invalid, continue parsing the rest of the lines instead of raising an error.
Package: wellmap
Many medium-throughput experiments produce data in 24-, 96-, or 384-well plate
format.  However, it can be a challenge to keep track of which wells (e.g. A1,
B2, etc.) correspond to which experimental conditions (e.g. genotype, drug
concentration, replicate number, etc.) for large numbers of experiments.  It
can also be a challenge to write analysis scripts flexible enough to handle the
different plate layouts that will inevitably come up as more and more
experiments are run.Thewellmappackage solves these challenges by introducing aTOML-based file
formatthat succinctly describes the organization of wells on plates.  The
file format is designed to be human-readable and -writable, so it can serve as
a standalone digital record.  The file format can also be easily parsed in
python and R to help write analysis scripts that will work regardless of how
you (or your collaborators) organize wells on your plates.CitationKundert, K. Wellmap: a file format for microplate layouts.BMC Res Notes14, 164 (2021).https://doi.org/10.1186/s13104-021-05573-0DocumentationSee the complete documentationhere.
Package: wellnote-downloader
Wellnote DownloaderWellnote Downloaderは、Wellnote からデータをダウンロードするツールです。ブラウザを自動操作し、ユーザーが一つづつクリックしたのと同じ作業を次々と繰り返すツールです。↓動作の様子実行例の見方： この文書で例示する実行例では、実行するコマンドを、$の後ろに書きます。続く行で実行結果の例を示しています。例えばファイルa.txtとb.txtが存在するフォルダでlsコマンドを実行すると２つのファイル名が表示されますが、その実行例は次のように示します。$ ls
a.txt b.txtセットアップ必要な環境は、コマンドターミナルがあり、Python が動き、FirefoxまたはChromeがインストールされている環境です。OSは特に指定しません。Firefox または Chrome をインストールする。Pythonをインストールする。
コマンドラインからpython3コマンドとpip3コマンドを実行できるか確認する$python3--version
Python3.11.0

$pip3--versionpip22.3frompip(python3.11)Wellnote Downloader をインストール$pip3installwellnote_downloader
長いので略コマンドラインからwellnote_downloaderコマンドを実行できるか確認する。$wellnote_downloader--version0.10.0# Command not found エラーが出る場合は、`wellnote_downloader` を `python -m wellnote_downloader` にすると動くかもしれません$python-mwellnote_downloader--version使い方アルバム内の写真・動画のダウンロード全部ダウンロードする場合$wellnote_downloaderalbum2015年の1月から2016年の12月までダウンロードする場合は以下のように実行します。$wellnote_downloaderalbum--start2015-01--end2016-12今いるフォルダ内にDownloadsというフォルダができているので、その中のファイルがダウンロードできているか確認します。ホーム画面の日記のダウンロード（コメント、スタンプ付き）全部ダウンロードする場合$wellnote_downloaderhome2015年の1月から2016年の12月までダウンロードする場合は以下のように実行します。$wellnote_downloaderhome--start2015-01--end2016-12今いるフォルダ内にDownloadsというフォルダができているので、その中のファイルがダウンロードできているか確認します。その他wellnoteのサーバーに過度な負荷がかかることが予見される使い方（ツールを改造してスピード調節部分を削除したり、並列でいくつも起動したりするなど）は絶対にやめてください。サーバーが落ちると、全員がダウンロードできなくなります。それにそのまま早期にサービス終了する可能性もありますので、常識の範囲内での利用をお願いします。途中で止める場合は、ターミナルウィンドウでCtrl-Cを押します。実行前にメールアドレスとパスワードを設定すると、入力を省略できます。$exportWELLNOTE_EMAIL=あなたのEmailアドレス
$exportWELLNOTE_PASSWORD=あなたのパスワードデフォルトではログインセッションを再利用するので、ユーザーを切り替えたくてもログインプロンプトがでないため切り替えられなくなってしまいます。その場合は一度--clear-profileオプションをつけて実行して下さい。$wellnote_downloaderhome--clear-profile
$wellnote_downloaderalbum--clear-profile画像の読み込みが遅すぎて日記の保存に間に合わないとき、ブラウザを操作するペースを遅くする事ができます。操作イベントを送る前に待つ時間をデフォルトの1秒から3秒に変えるには、以下のようにします。$wellnote_downloaderhome--interval3Command not found エラーが出る場合は、wellnote_downloaderをpython -m wellnote_downloaderにすると動くかもしれません$python-mwellnote_downloader--version0.10.0開発者Takahide NogayamaライセンスThis project is licensed under the MIT License - see theLICENSEfile for details貢献方法Please readCONTRIBUTING.mdfor details on our code of conduct, and the process for submitting pull requests to us.
Package: wellpathpy
# README## Contributors:[Robert Leckenby](https://github.com/Zabamund)[Brendon Hall](https://github.com/brendonhall)[Jørgen Kvalsvik](https://github.com/jokva)## Introductionwellpathpyis a LGPL-3.0 licensed library to import well deviations in (md, inc, azi) format, calculate their TVD values using a choice of methods and return them as positional logs in (tvd, northing, easting) format.## Featuresload well deviation in (md, inc, azi) format:meta data (header, rkb, dfe, rt)md, incl, azicalculate position log survey using one of these methods:minimum curvature methodradius of curvature methodtangential methodscalculate dog-leg severity from minimum curvaturecalculate depth references using header data if available: MD, TVD, TVDSSreturn interpolated deviation in (tvd, northing, easting) formatmove surface location to (0, 0, 0) or to (kb, mE, mN)convert to tvdss based on kb elevationresample deviation on regular steps with minimum curvature only## InstallationThis is work in progressFrom [pypi](https://pypi.org/project/wellpathpy/) with:pip install wellpathpy## Requirements[numpy](https://numpy.org/) version 1.16.2 or greater## TutorialsA tutorial is available on [wellpathpy.readthedocs.io](https://wellpathpy.readthedocs.io/en/latest/tutorial.html)## ContributingWe welcome all kinds of contributions, including code, bug reports, issues, feature requests, and documentation. The preferred way of submitting a contribution is to either make an issue on github or by forking the project on github and making a pull request.## Historywellpathpy started as a community project during the [May 2019 Transform event](https://agilescientific.com/blog/2019/5/18/transform-happened).
Package: wellping-ema-parser
WellPing-EMA-ParserConverts Stanford Communities Project EMA data from JSON to CSVThis package conveniently wraps anEMA_Parserobject for all your social network analysis needsUser NotesBefore running the script:Your target directory should have one JSON file with all participants' EMA dataAfter running the script:Your target directory will contain:Subject-wise CSV files of pings and answers (not shown in the screenshot below)Composite CSV of all subjectsA JSON file containing participant data with existenatial errors (to be parsed separately)An error log of parsing issues that didnotprevent subjects from inclusion in the CSV
Package: well-plate
Well Plate (well_plate)Makes nice visualizations of well plates!Installationpip install well_plateDependenciesnumpypandasplotlyOptionsWell numbers6122496384To add more seewell_plate/config.pyWell shape'circle''rect' (square)Add datausewp.add_data(); pass in a pd.Series with the index corresponding to well plate index (eg. "A1", "E4", ...)ExampleBasic exampleimportwell_platewp=well_plate.WellPlate(24)wp.plot()Change well sizeimportwell_platewp=well_plate.WellPlate(96,"rect")wp.plot()Add dataimportpandasaspdimportwell_platewp=well_plate.WellPlate(384,"rect")df=pd.read_csv("example1_data.csv",index_col=0)wp.add_data(df["mw_n"])wp.plot(key="mw_n")wp.heatmap(key="mw_n")
Package: well-profile
Introductionwell_profile is a tool to generate or load well profiles in 3D. Features are added as they
are needed; suggestions and contributions of all kinds are very welcome.DocumentationSee here for thecomplete well_profile package documentation.Getting StartedThese instructions will get you a copy of the project up and running on your local machine for development and testing purposes.Get well_profileUsers: Wheels for Python fromPyPIpip install well_profileDevelopers: Source code fromGithubgit clone https://github.com/pro-well-plan/well_profileQuick examplesColor by specified parameter:e.g. 'dls'|'dl'|'tvd'|'md'|'inc'|'azi'import well_profile as wp
well = wp.load('trajectory1.xlsx')     # LOAD WELL
well.plot(style={'color': 'dls', 'size': 5}).show()Also with dark mode:well.plot(style={'darkMode': True, 'color': 'dls', 'size': 5}).show()Plotting 3 wellbores:Well 1 -> excel file: trajectory1.xlsxWell 2 -> generated wellWell 3 -> excel file: trajectory2.xlsximport well_profile as wp
well_1 = wp.load('trajectory1.xlsx')      # LOAD WELL 1
well_2 = wp.get(6000, profile='J', kop=2000, eob=3000, build_angle=85, set_start={'east':2000})       # GET WELL 2 --> North: 0 m, East: 2000 m
well_3 = wp.load('trajectory2.xlsx', set_start={'north':-3000})        # LOAD WELL 3 --> North: -3000 m, East: 0 m
well_1.plot(add_well=[well_2, well_3],
            names=['first well name',
                   'second well name',
                   'third well name']).show()        # Generate 3D plot for well 1 including wells 2 and 3ContributingPlease readCONTRIBUTINGfor details on our code of conduct, and the process for submitting pull requests to us.HistoryThis tool was initially written and is maintained byPro Well Plan
ASas a free, simple, easy-to-use way to perform
torque and drag calculations along the well that can be tailored to our needs, and as contribution to the
free software community.LicenseThis project is licensed under the GNU Lesser General Public License v3.0 - see theLICENSEfile for detailsfor further information contactjuan@prowellplan.com
Package: wellpvt
Define pressure and temperature of a producing well, and get PVT properties of oil and gas produced.Installation$ pip install wellpvtProject Highlightssingle-phase propertiesoil: density, specific gravitygas: pseudo-critical properties, compressibility, density, formation volume factor, viscositymultiphase mixture properties TBAExample Usagedefine produced oil with 40 deg. APIfrom wellpvt import Oiloil = Oil(40)print(oil.api)>>40print(round(oil.calculate_sg(), 3))>>0.825print(round(oil.calculate_density(), 1))>>51.5Licensewellpvt is licensed under the MIT License
Package: wellradpy
WellRadPyWellRadPy ("Well Radius Python") is a Python package for calculating the radius
of influence and radius of investigation of pumping wells. These quantities
frequently need to be estimated in well hydraulics and aquifer testing (also
called well testing in petroleum engineering). In particular, calculating the
radius of investigation is critical for the design and the interpretation of
pumping tests. This package implements solutions for both drawdown and recovery
phases of pumping tests.Note that radius of influence and radius of investigation are different
concepts. Furthermore, there are various ways by which each radius may be
precisely defined. This package implements most definitions.ReferencesBresciani, E., Shandilya, R.N., Kang, P.K., Lee, S., 2020. Well radius of
influence and radius of investigation: What exactly are they and how to
estimate them? Journal of Hydrology, 583: 124646.https://doi.org/10.1016/j.jhydrol.2020.124646.Bresciani, E., Shandilya, R.N., Kang, P.K., Lee, S., 2020. Evolution of the
radius of investigation during recovery tests. Journal of Hydrology,
590: 125346.https://doi.org/10.1016/j.jhydrol.2020.125346.UsageWellRadPy is simple and its use is straightforward. The/examplesdirectory
in theGitHub repositorycontains scripts where the functions for calculating radius of influence and
radius of investigation are exemplified.Help can also be obtained by typinghelp(function_path)in the Python
console.InstallationFor simple useFrom the command line, typepip install wellradpy. This will download
the latest release of the package from the PyPI repository and install it.For developmentClone (or download) the sources from theGitHub repository.From the project directory, typepip install -e ..
This will install the current directory, so that you can both modify the
package and use it at the same time.DependenciesWellRadPy depends on the Python packages NumPy and SciPy (>= 1.2.0). These
will be automatically installed with any of the above installation methods.AuthorsEtienne Bresciani(etiennebresciani)LicenseThis project is licensed under the MIT License – see theLICENSE.txtfile for details.AcknowledgementsKorea Ministry of Science and ICT through the Korea Research Fellowship
program of the National Research Foundation of Korea (2016H1D3A1908042)Korea Institute of Science and Technology (KIST) through the Future Research
Program (2E29660, 2E30510)Korea Ministry of Environment (MOE) through the Subsurface Environment
Management (SEM) Project of the Korea Environment Industry & Technology
Institute (KEITI) (2018002440006)
Package: wells
wells is a collection of python utilities:Most common and general utilities:utils               common utilities. sequence, string and others.Other included utilities:bashscript_utils    read and write bashrc like config files (e.g. A=B)config              config parser. parse simple a = b config files. Because I don’t like ini sections.debug               debug utilities, like @trace_timefileutils           file io, like safe_read_file, safe_write_fileiputils             utilities to work with IP addressespy3utils            python 3 portability issue helper. similar tosix.redisutils          utilities when working with redis, including some converters for py3.shellutils          shell related utilitiesInstallationTo install this package:$pipinstallwellsBaisc Usage>>>importdatetime>>>fromwells.utilsimportsha1sum,to_json>>>sha1sum("abc")'a9993e364706816aba3e25717850c26c9cd0d89d'>>>to_json({"start_date":datetime.date.today()})'{"start_date": "2017-04-23"}'>>>importwells.utils>>>help(wells.utils)DocumentationExternal documentation is not available yet. Type help(<module_name>) to get
document in python REPL. You can also read examples in unit tests.LicenseCopyright (C) 2019  Yuanle Song <sylecn@gmail.com>This library is licensed under the Apache License, Version 2.0 (the “License”); you may not use this file except in compliance with the License. You may obtain a copy of the License athttps://www.apache.org/licenses/LICENSE-2.0Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.ChangeLogv1.0.0 2017-04-23
- init release.
Package: wells4hydrogeology
Wells 4 Hydrogeology (w4h)The w4h Python package is a package designed jointly by the Illinois State Geological Survey and Illinois State Water Survey.It is designed to read in geology data from wells and create a layered, gridded hydrogeologic model of a study region, all within a python environment, automating and performing tasks often carried out in a dedicated GIS software.The w4h package contains all the functions needed for getting N layers of a hydrogeology grid.
Though the source code is split into separate modules, all functions are designed to be accessed directly from the w4h module (example: w4h.read_study_area())The w4h module is designed to be flexible and customizable, allowing various kinds of data to be read in, with many different kinds of initial structures.Using well descriptions from these database tables, the code contained here extracts, manipulates, and organizes the data to be used for hydrogeologic modeling. The scripts here can be used for specific regions of interests/study areas within the state, or for the state as a whole.API DocumentationAPI DocumentationhereDependenciesThe w4h module has the following dependencies:numpygeopandas (and therefore pandas)rioxarray (and therefore xarray)matplotlibscipyowslibInputsRequired InputsRequired inputs include are shownherewikiOrganizationModulesThe package is organized by module, but all functions can be accessed directly using w4h.function_name() as well.core: general utility functions used throughoutclassify: functions for classifying the dataclean: functions for cleaning the dataexport: functions for exporting the data, both as tables and rasterslayers: functinos for generating layer(ed) modelsmapping: functions for mapping or performing geospatial analysisread: functions for reading in various filesIntended workflowDiagram for workflow availablehere.Disclaimerswells4hydrogeology is a python tool developed jointly by the Illinois State Water Survey and the Illinois State Geological Survey, both part of the Prairie Research Institute at the University of Illinois. By using this repository, you agree to the Terms of Use contained in theData Use License Agreement.
Package: well-schematics
well-schematicsmatplotlib code for drawing borehole/well schematic diagramsInstall$pipinstall-Uwell-schematicsUsageCheck out thedocumentation.>>>fromwell_schematicsimportdraw_simple>>>draw_simple(pzone_top=27,pzone_bottom=36,casing_top=-0.5,pzone_type="S")Contributing / roadmapI would love to have implementations of different types of schematics. Any
contributions at all arewelcome!The simplest well: casing with a production zone at the bottomDifferent types of production zones, particularly perforationsMultiple production zones separated by blanksDifferent pipe diametersRisers (pipe inside pipe)Drilled holeCemented zones outside pipeCement plugsAnnotations tied to each casing pipe/production zoneLicenseFree to modify and re-distribute under theMIT license.
Package: welltestpy
Welcome to welltestpyPurposewelltestpy provides a framework to handle, process, plot and analyse data from well based field campaigns.InstallationYou can install the latest version with the following command:pip install welltestpyOr from condaconda install -c conda-forge welltestpyDocumentation for welltestpyYou can find the documentation including tutorials and examples underhttps://welltestpy.readthedocs.io.Citing welltestpyIf you are using this package you can cite ourGroundwater publicationby:Müller, S., Leven, C., Dietrich, P., Attinger, S. and Zech, A. (2021):
How to Find Aquifer Statistics Utilizing Pumping Tests? Two Field Studies Using welltestpy.
Groundwater,https://doi.org/10.1111/gwat.13121To cite the code, please visit theZenodo page.Provided Subpackageswelltestpy.data# Subpackage to handle data from field campaignswelltestpy.estimate# Subpackage to estimate field parameterswelltestpy.process# Subpackage to pre- and post-process datawelltestpy.tools# Subpackage with tools for plotting and triagulationRequirementsNumPy >= 1.14.5SciPy >= 1.1.0AnaFlow >= 1.0.0SpotPy >= 1.5.0Matplotlib >= 3.0.0ContactYou can contact us viainfo@geostat-framework.org.LicenseMIT