Saravanan666/pypi-org huggingface dataset:
Package: accudata
Do you collect a heterogeneous data step by step?Here you find a convinient solution of this problem.
class AccumulativeData provides a simple interface to store data step by step.
The data can be consisted of:NumbersLists / arraysObjectsYou can store it as pickled object or Pandas Dataframe.InstallationThe module can be installed from pippip install accudataFor exampleYou have a social data collecting process. You must collect on every step heterogeneous data:Name of a personAgeInterestsPreferences by categories: food, pets, sport, politicsYou can make a class:fromaccudataimportAccumulativeDataclassPeopleAccData(AccumulativeData):def__init__(self):lists=['name','age','interests']dicts={'pref':['food','pets','sport','politics]}super().__init__(lists=lists,dicts=dicts)After that you can make an iterative collecting process as follows:Data=PeopleAccData()foriteminraw_data:Data.next()# \\\ A complicated code to extract dataname,age,interests,food,pets,sport,politics,_=extract_data(item)Data.append(name,age,interests,pref=[food,pets,sport,politics])It is simple to get data:names=Data.name# Make the dataframedataframe=Data.todf()print(dataframe.name)
Package: accumulator
Copyright (c) 2015 Rafael da Silva Rochahttps://github.com/rochars/accumulatorInstallation$ pip install accumulatorCompatibilityAccumulator is compatible with Python 2.7, 3.3, 3.4 and 3.5.VersionThe current version is 0.3 alpha.LicenseCopyright (c) 2015 Rafael da Silva RochaPermission is hereby granted, free of charge, to any person obtaining a
copy of this software and associated documentation files (the
“Software”), to deal in the Software without restriction, including
without limitation the rights to use, copy, modify, merge, publish,
distribute, sublicense, and/or sell copies of the Software, and to
permit persons to whom the Software is furnished to do so, subject to
the following conditions:The above copyright notice and this permission notice shall be included
in all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS
OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Package: accuracy
accuraCyIt's pronounced "accura-see". For spaCy models.The goal of this project is to generate reports forspaCymodels.what it doesThe goal ofaccuraCyis to offer static reports for spaCy models that
help users make better decisions on how the models can be used. At the
moment the project supports reports for threshold values for classification.Here's a preview of what to expect:There are two kinds of charts.The first kind is a density chart. This chart shows the distribution
of confidence scores for a given class. The blue area represents documents
that had the tag assigned to the class. The orange area represents documents
that didn't.The second kind is a line chart that demonstrates the accuracy, precision
and recall values for a given confidence threshold. It's an interactive chart
and you can explore the values by hovering over the chart.installYou can install the latest version from git.python -m pip install "accuracy @ git+https://github.com/koaning/accuracy.git"usageTheaccuracyproject provides a command line interface that can
generate reports. The full CLI can also be explored via the--helpflag.> python -m accuracy --help

Usage: python -m accuracy [OPTIONS] COMMAND [ARGS]...

  It's pronounced 'accura-see'. For spaCy models.

Options:
  --help  Show this message and exit.

Commands:
  report   Generate a model report.
  version  Show version number.accuracy reportThe most important command is thereportcommand. You'd typically use it via
a command similar to:> python -m accuracy report training/model-best/ corpus/train.spacy corpus/dev.spacy

Loading model at training/model-best
Running model on training data...
Running model on development data...
Generating Charts ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 100% 0:00:00
Done! You can view the report via;

python -m http.server --directory reports PORTThis will generate a folder, typically called "reports", that contains a full
dashboard for the trained spaCy model found intraining/model-best.The CLI has a few configurable settings:Arguments:
  [MODEL_PATH]  Path to spaCy model
  [TRAIN_PATH]  Path to training data
  [DEV_PATH]    Path to development data
  [FOLDER_OUT]  Output folder for reports  [default: reports]

Options:
  --classes TEXT  Comma-separated string of classes to use
  --help          Show this message and exit.
Package: accuri2fcs
Accuri2fcs is a command line program for the conversion of accura .c6         formatted flow cytometry files to standard .fcs. In practise this is relatively         straightforward as Accuri files are simply zip structures containing multiple .fcs.         in a structured format. However, extracting and naming these individual files is a         little more tricky. This program allows rapid batch processing of multiple Accura         files into multiple .fcs files, with regular expression sample name matching, splitting         and copying to build the output filenames. It can get quite complicated, see the examples.
Package: accuweather
accuweatherPython wrapper for getting weather data from AccuWeather API.API keyTo generate API key go tohttps://developer.accuweather.com/user/registerand after registration create an app.How to use package"""Example of usage."""importasyncioimportloggingfromaiohttpimportClientError,ClientSessionfromaccuweatherimport(AccuWeather,ApiError,InvalidApiKeyError,InvalidCoordinatesError,RequestsExceededError,)LATITUDE=52.0677904LONGITUDE=19.4795644API_KEY="xxxxx"logging.basicConfig(level=logging.DEBUG)asyncdefmain():"""Run main function."""asyncwithClientSession()aswebsession:try:accuweather=AccuWeather(API_KEY,websession,latitude=LATITUDE,longitude=LONGITUDE,language="pl",)current_conditions=awaitaccuweather.async_get_current_conditions()forecast_daily=awaitaccuweather.async_get_daily_forecast(days=5,metric=True)forecast_hourly=awaitaccuweather.async_get_hourly_forecast(hours=12,metric=True)except(ApiError,InvalidApiKeyError,InvalidCoordinatesError,ClientError,RequestsExceededError,)aserror:print(f"Error:{error}")else:print(f"Location:{accuweather.location_name}({accuweather.location_key})")print(f"Requests remaining:{accuweather.requests_remaining}")print(f"Current:{current_conditions}")print(f"Forecast:{forecast_daily}")print(f"Forecast hourly:{forecast_hourly}")loop=asyncio.new_event_loop()loop.run_until_complete(main())loop.close()
Package: acd
Hierarchical neural-net interpretations (ACD) 🧠Produces hierarchical interpretations for a single prediction made by a pytorch neural network. Official code forHierarchical interpretations for neural network predictions(ICLR 2019pdf).Documentation•Demo notebooksNote: this repo is actively maintained. For any questions please file an issue.examples/documentationinstallation:pip install acd(or clone and runpython setup.py install)examples: thereproduce_figsfolder has notebooks with many demossrc: theacdfolder contains the source for the method implementationallows for different types of interpretations by changing hyperparameters (explained in examples)test with pytorch >1.0 with/without gpuall required data/models/code for reproducing are included in thedsetsfolderInspecting NLP sentiment modelsDetecting adversarial examplesAnalyzing imagenet modelsnotes on using ACD on your own datathe current CD implementation doesn't always work for all types of networks. If you are getting an error inside ofcd.py, you may need to write a custom function that iterates through the layers of your network (for examples seecd.py). Should work out-of-the-box for many common layers though, including antyhing in alexnet, vgg, or resnet.to use baselines such build-up and occlusion, replace the pred_ims function by a function, which gets predictions from your model given a batch of examples.related workPDR framework (PNAS 2019pdf) - an overarching framewwork for guiding and framing interpretable machine learningCDEP (ICML 2020pdf,github) - penalizes CD / ACD scores during training to make models generalize betterTRIM (ICLR 2020 workshoppdf,github) - using simple reparameterizations, allows for calculating disentangled importances to transformations of the input (e.g. assigning importances to different frequencies)DAC (arXiv 2019pdf,github) - finds disentangled interpretations for random forestsBaseline interpretability methods - the filescores/score_funcs.pyalso contains simple pytorch implementations ofintegrated gradientsand the simple interpration techniquegradient * inputreferencefeel free to use/share this code openlyif you find this code useful for your research, please cite the following:@inproceedings{singh2019hierarchical,title={Hierarchicalinterpretationsforneuralnetworkpredictions},author={ChandanSinghandW.JamesMurdochandBinYu},booktitle={InternationalConferenceonLearningRepresentations},year={2019},url={https://openreview.net/forum?id=SkEqro0ctQ},}
Package: acd2cwl
acd2cwl provides:
- a2c-tools, a generator of wrappers for EMBOSS tools. It uses the formal definition of the tools provided by the ACD files.
- a2c-tests, a generator of functional tests for these tools. It uses the functional tests defined in the EMBOSS package itselfInstallInstalling the official package from PyPi:pip install acd2cwlOr from source:
`
git clonehttps://github.com/hmenager/acd2cwl.gitcd acd2cwl && python setup.py install
`Run tool wrappers generatorSimple command:a2c-tools /usr/share/EMBOSS/acd/*.acdFor more options, just typea2c-tools –helpRun test jobs generatorSimple command:a2c-tests /usr/share/EMBOSS/test/qatest.dat /usr/share/EMBOSS/acd/*.acdFor more options, just typea2c-tests –help
Package: acda
OverviewThis repository stores drug synergy prediction codebase for Augmented Cancer Drug Atlas (ACDA) and the analysis jupyter notebooks which use the ACDA code. We augmented the drug synergy prediction modeling approach CDA described in Narayan et al. by applying a Random Forest Regression and optimization via cross-validation hyperparameter tuning. For ease of sharing and use we implemented it as a python package. See documentation athttps://acda.readthedocs.io.Installation and DependenciesThe main prerequisite o install the Python package is python >3.8 environment. To install run:pip install acdaThe dependencies are installed automatically with the command above. Seesetup.pyfor the very basic dependency list.Methods DescriptionDrug Synergy prediction is a complex problem typically approached with Machine Learning techniques by using molecular and pharmacological data. A recently published method Cancer Drug Atlas, CDA (Narayan et al. 2020), for drug synergy prediction in cell line models uses drug target information, knowledge of genes mutated in each model, and models' monotherapy drug sensitivity. The approach builds a logistic regression model to predict a binary synergy outcome. Here, we improved the CDA drug synergy prediction modeling approach by applying a CART-based model instead of a linear regression.The datails of ACDA methods and benchmarking results are described in thepreprint.Contents| Directory/File  | Description                ||-----------------|----------------------------|| acda/           | Python source code of the package                  || docs/           | Source code of the documentation                   || scripts/        | Scripts and additional functions which are used with the package || ChangeLog.md    | File details changes implemented in new releases   || LICENSE         | The license agreement statement                    || [Other misc files]             | ...                                                |DataThe datailed examples and data requirements are in the documentation atdocumentation.This package makes use of the data outlined below:Sanger molecular data:https://cellmodelpassports.sanger.ac.uk/downloadsGDSC pharmacology data:https://www.cancerrxgene.org/downloads/bulk_downloadDrugComb sensitivity and synergy data:https://drugcomb.org/download/References:Narayan, R. S., Molenaar, P., Teng, J., Cornelissen, F. M. G., Roelofs, I., Menezes, R., Dik, R., et al. (2020).A cancer drug atlas enables synergistic targeting of independent drug vulnerabilities,Nature Communications, 11/1: 2935. Nature Publishing Group.Lianlian Wu, Yuqi Wen, Dongjin Leng, Qinglong Zhang, Chong Dai, Zhongming Wang, Ziqi Liu, Bowei Yan, Yixin Zhang, Jing Wang, Song He and Xiaochen Bo.Machine learning methods, databases and tools for drug combination prediction.Briefings in Bioinformatics, 23(1), 2022, 1-21.Shuyu Zheng, Jehad Aldahdooh, Tolou Shadbahr, Yinyin Wang, Dalal Aldahdooh, Jie Bao, Wenyu Wang, Jing Tang.DrugComb update: a more comprehensive drug sensitivity data repository and analysis portal.Nucleic Acids Research, Volume 49, Issue W1, 2 July 2021, Pages W174-W184.
Package: acdb
# Package acdbPackage acdb manages objects between memory and file system.`sh $ pip install acdb `# MemMemDriver cares to store data on memory, this means that MemDriver is fast. Since there is no expiration mechanism, be careful that it might eats up all your memory.`py db = acdb.mem()db.set('name','acdb') assertdb.get('name')== 'acdb' `# DocDocDriver use the OS’s file system to manage data. In general, any high frequency operation is not recommended unless you have an enough reason.`py db =acdb.doc("/tmp/dat")`# LruIn computing, cache algorithms (also frequently called cache replacement algorithms or cache replacement policies) are optimizing instructions, or algorithms, that a computer program or a hardware-maintained structure can utilize in order to manage a cache of information stored on the computer.Caching improves performance by keeping recent or often-used data items in a memory locations that are faster or computationally cheaper to access than normal memory stores. When the cache is full, the algorithm must choose which items to discard to make room for the new ones.Least recently used (LRU), discards the least recently used items first. It has a fixed size(for limit memory usages) and O(1) time lookup.`py db = acdb.lru(1024) `# SynMapDriver is based on DocDriver and use LruDriver to provide caching at its interface layer. The size of LruDriver is always 1024.`py db =acdb.syn("/tmp/dat")`# LicencesMIT
Package: acdcli
acd_cliacd_cliprovides a command line interface to Amazon Drive and allows Unix users to mount
their drive using FUSE for read and (sequential) write access. It is currently in beta stage.Node Cache Featureslocal caching of node metadata in an SQLite databaseaddressing of remote nodes via a pathname (e.g./Photos/kitten.jpg)file searchCLI Featurestree or flat listing of files and folderssimultaneous uploads/downloads, retry on errorbasic plugin supportFile Operationsupload/download of single files and directoriesstreamed upload/downloadfolder creationtrashing/restoringmoving/renaming nodesDocumentationThe full documentation is available athttps://acd-cli.readthedocs.io.Quick StartHave a look at theknown issues, then follow thesetup guideandauthorize. You may then use the program as described in theusage guide.CLI Usage ExampleIn this example, a two-level folder hierarchy is created in an empty drive.
Then, a relative local pathlocal/spamis uploaded recursively using two connections.$ acd_cli sync
  Getting changes...
  Inserting nodes..

$ acd_cli ls /
  [PHwiEv53QOKoGFGqYNl8pw] [A] /

$ acd_cli mkdir /egg/
$ acd_cli mkdir /egg/bacon/

$ acd_cli upload -x 2 local/spam/ /egg/bacon/
  [################################]   100.0% of  100MiB  12/12  654.4KB/s

$ acd_cli tree
  /
      egg/
          bacon/
              spam/
                  sausage
                  spam
  [...]The standard node listing format includes the node ID, the first letter of its status
and its full path. Possible statuses are “AVAILABLE” and “TRASH”.Known IssuesIt is not possible to upload files using Python 3.2.3, 3.3.0 and 3.3.1 due to a bug in
the http.client module.API Restrictionsthe current upload file size limit is 50GiBuploads of large files >10 GiB may be successful, yet a timeout error is displayed
(please check the upload by syncing manually)storage of node names is case-preserving, but not case-sensitive
(this should not concern Apple users)it is not possible to share or delete filesContributeHave a look at thecontributing guidelines.Recent Changes0.3.2added--remove-source-filesargument to upload actionadded--times`argument to download action for preservation of modification timesadded streamed overwrite actionfixed upload of directories containing broken symlinksdisabled FUSE autosync by defaultadded timeout handling for uploads of large filesfixed exit status >=256added config filesadded syncing to/from filefixed download of files with failed (incomplete) chunks0.3.1general improvements for FUSEFUSE write support addedadded automatic loggingsphinx documentation added0.3.0FUSE read support added0.2.2sync speed-upnode listing format changedoptional node listing coloring added (for Linux or via LS_COLORS)re-added possibility for local OAuth0.2.1curl dependency removedadded job queue, simultaneous transfersretry on error0.2.0setuptools supportworkaround for download of files larger than 10 GiBautomatic resuming of downloads
Package: acdh-abbr-client
ABBR ClientA python client to interact with abbr.acdh.oeaw.ac.atFree software: MIT licenseDocumentation:https://acdh-abbr-client.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2020-12-13)First release on PyPI.
Package: acdh-arche-pyutils
acdh-arche-pyutilsa python client for ARCHE-APIFree software: MITDocumentation:https://acdh-arche-pyutils.readthedocs.io.FeaturesTODOCreditsThis package was created withcookietempleusingCookiecutter.ChangelogThis project adheres toSemantic Versioning.0.4.0 (2021-02-21)ArcheApiClient.get_resource(res_uri)method added to the complete graph of an ARCHE-URIArcheApiClient.write_resource_to_file(res_uri, format=’ttl’)method added to writa a complete graph of an ARCHE-URI to file0.3.0 (2021-02-20)ArcheApiClient.top_col_ids()method added to fetch all TopCollection URIs and Lables0.1.0 (2021-02-19)AddedCreated the project using cookietemple version 1.2.4FixedDependenciesDeprecated
Package: acdh-baserow-pyutils
acdh-baserow-pyutilsa python client for baserowinstallpip install acdh-baserow-pyutilshow to useHave a look intotests/test_baserow_client.pydump all tables of a given database into JSON-FILESimportosfromacdh_baserow_utilsimportBaseRowClient# store baserow credentials as ENV-VariablesBASEROW_USER=os.environ.get("BASEROW_USER")BASEROW_PW=os.environ.get("BASEROW_PW")BASEROW_TOKEN=os.environ.get("BASEROW_TOKEN")# you need to create a token via baserowDATABASE_ID="41426"# you can get this ID from Baserow# initialize the clientbr_client=BaseRowClient(BASEROW_USER,BASEROW_PW,BASEROW_TOKEN)# writes all tables from Database as json.files into a folder 'out' (the folder needs to exist!) and returns a list of the file namesfiles=br_client.dump_tables_as_json(DATABASE_ID,folder_name='out')print(files)# ['out/place.json', 'out/person.json', 'out/profession.json']
Package: acdh-cfts-pyutils
acdh-cfts-pyutilsPython Package to interact with a dedicated Typesense Server and CollectionThis package exposes some constants to interact with a dedicated Typesense Server and Collection through other Python scripts.Also this package is inteded to be the one and only place to modifiy the schema of the given centralised collectioninstallrunpip install acdh-cfts-pyutilsadapt/modifiy environmet variables to fit you needs. Seeenv.defaultfor example. If you use this package to populate the ACDH-CH central fulltext search collection running on ACDH-CH's own Typesense-Server, you'll only need to setTYPESENSE_API_KEYvia an environment varibale.
Package: acdh-collatex-utils
CollateX UtilsA python package to collate things with collate-xinstallcreate a virtual environment and install the package withpip install acdh_collatex_utilsuseTo collate a bunch of XML/TEI documents located in e.g../to_collateruncollate -g './to_collate/*.xmlThis creates a folder./to_collate/collatedand saves chunked HTML and TEI Files likeout__001.htmlorout__001.teiTo see this package in use in a real life project, please check outfrd-data/collate_work.pydevelopcreate a virutal environmentinstall dev-requirementspip install -U pipandpip install -r requirements_dev.txtinstall the package in dev-modepip install -e .run test withcoverage run -m pytest -vcreate test-reportcoverage reportorcoverage html
Package: acdh-django-archeutils
Django-App to serialize a model class into arche-rdfQuickstartInstall acdh-archeutils:pip install acdh-django-archeutilsAdd it to yourINSTALLED_APPS:INSTALLED_APPS=(...'archeutils',...)Update your project’s urls.py:urlpatterns=[...url(r'^archeutils/',include('archeutils.urls',namespace='archeutils')),...]
Package: acdh-django-charts
An app to explore your data through charts based on Highcharts.jsDocumentationThe full documentation is athttps://acdh-django-charts.readthedocs.io.QuickstartInstall django_charts:pip install acdh-django-chartsAdd it to yourINSTALLED_APPS:INSTALLED_APPS=(...'charts',...)Add django_charts’s URL patterns:urlpatterns=[...url(r'^charts/',include('charts.urls',namespace='charts')),...]By default the app’s templates extend a base templatewebpage/base.html. To ovveride this, just define aCHARTS_BASE_TEMPLATEvariable on your project’ssettings.pylike e.g:CHARTS_BASE_TEMPLATE='base.html'To link to the application’s ‘chart-selector-view’ you can add something like the snippet below to your e.g. base-template:<ahref="{% url 'charts:chart_selector' %}">Charts</a>ConfigurationTo visualize any property of your model you have to pass in the models name (lowercase), the field-path (using django’s lookup syntax__to follow foreign key and many2many relations) and the chart type (bar|line|pie) via keyword arguments to thecharts.views.DynChartView(). In case those params are valid (i.d. the model and the lookup path acutally exist) the according chart should be drawn. But be aware that this only works if your project’sDEBUGsettings are set toTrue.
Asrecomendedalternative you should createChartConfigobjects for each property/model you’d like to explore via django admin-backend.management commandsThe package ships with a management command tocreate/delete chartconfig objects (Bar, Pie, Linecharts)python manage.py create_charttypespython manage.py delete_charttypescreate/delete ChartConfig objects per applicationpython manage.py create_charts <app_name>python manage.py delete_charts <app_name>Build and publishpython setup.py sdist bdist_wheel
twine upload dist/*FeaturesVisualizes aggregated values of your models as charts (pie/bar/line) usinghttps://www.highcharts.com/Charts can be configured via admin backend (see Configuration Section)Running TestsDoes the code actually work?source <YOURVIRTUALENV>/bin/activate
(myenv) $ pip install tox
(myenv) $ toxCreditsTools used in rendering this package:Cookiecuttercookiecutter-djangopackageHistory0.5.4 (2019-10-11)values always returned as strings -> BooleanFields are displayed now0.5.3 (2019-02-12)reworte code to fetch payload data to avoid mysterious duplicated values0.5.2 (2018-12-18)improved admin interface for ChartConfig0.5.1 (2018-12-05)added management commands to create ChartType and ChartConfig objects.0.5.0 (2018-10-25)addedapp_nameparam to ChartConfig to avoid ambiguity in case models in different apps do have the same name.0.4.1 (2018-07-12)minor change in dropdown template tag0.4.0 (2018-07-10)refactoring of templates by introducing template tags0.3.0 (2018-06-13)removed work in progress banner0.3.0 (2018-06-05)In case of DEBUG=False only fieldpaths/models can be explored which are registerd in dedicated ChartConfig objects.0.2.0 (2018-06-01)Base templates can now be configured in settings-param0.1.0 (2018-06-01)First release on PyPI.