Saravanan666/pypi-org huggingface dataset:
Package: mendotapy
mendotapyA Python 3.6+ package for analysis-readyLake Mendota ice phenologydataInstallingPyPipipinstallmendotapy==0.1.0GitHubpipinstall-egit+https://github.com/lgloege/mendotapy.git#egg=mendotapyUsing the PackageRead data into a dataframeimportmendotapydf=mendotapy.load()Summary plot of the dataimportmendotapymendotapy.plot()
Package: mend-sbom-export-cli
Mend SBOM CliGeneration SBOM reports in the SPDX or CycloneDx formatsSupported Operating SystemsPrerequisitesInstallationConfiguration ParametersExecution ExamplesSupported Operating SystemsLinux (Bash):CentOS, Debian, UbuntuWindows (PowerShell):10, 2012, 2016PrerequisitesPython 3.9+Mend user with admin permissionsInstallation$ pip install mend-sbom-export-cliNote:Depending on whether the package was installed as a root user or not, you need to make sure the package installation location was added to the$PATHenvironment variable.Configuration ParametersNote:Parameters can be specified as either command-line arguments, environment variables, or a combination of both.Command-line arguments take precedence over environment variables.CLI argumentEnv. VariableTypeRequiredDescription‑‑helpswitchNoShow help and exit‑‑api-keyWS_APIKEYstringNo*Mend API Key‑‑serviceWS_SERVICEUSERstringNo*Mend Service User email‑‑user-keyWS_USERKEYstringYesMend User Key (your own personal user key if Mend API Key provided or user key of service user)‑‑urlWS_WSS_URLstringYesMend Server URL‑‑productWS_PRODUCTTOKENstringNoEmpty String(Include all products). Comma-separated list of Mend Product Tokens that should be included‑‑projectWS_PROJECTTOKENstringNoEmpty String(Include all projects). Comma-separated list of Mend Project Tokens that should be included‑‑excludeWS_EXCLUDETOKENstringNoEmpty String(No exclusions).Commsa-separated list of Mend Project Tokens that should be excluded‑‑licensetextboolNoInclude full license text for all libraries (default:False)‑‑dirstringNoOutput directory for the report files (default:current folder)‑‑typestringNoReport format [spdxcdx] (default:spdx)‑‑threadsintNoNumber of threads to run in parallel for report generation (default:10)*One of the parameters must be specified (Api-key or Mend Service User email).The Service User or your user should have the rights to work with the requested org/product/projects.Execution ExamplesUsing command-line arguments only:sbom_export_cli--user-keyWS_USERKEY--api-keyWS_APIKEY--url$WS_WSS_URL--product`ProductToken1`,`ProductToken2`--project`ProjectToken`--dir$OUTPUT_DIRECTORYUsing environment variables:exportWS_USERKEY=xxxxxxxxxxxexportWS_APIKEY=xxxxxxxxxxxexportWS_WSS_URL=https://saas.mend.io

sbom_export_cli--product`ProductToken`Note:Either form is accepted. For the rest of the examples, the latter form would be usedNote:In the following examples, $WS_USERKEY, $WS_APIKEY and $WS_WSS_URL are assumed to have been exported as environment variables.Create CycloneDx SBOM reports$sbom_export_cli--project"$WS_PROJECTTOKEN"--dir$HOME/reports--typecdxCreate SPDX reports$sbom_export_cli--product"$WS_PRODUCTTOKEN"--dir$HOME/reports--licensetextTrue
Package: meneco-gui
A graphical user interface for menecoInstallationmeneco-gui is implemented using PyQt. Unfortunately PyQt4 is not as easily installed via pip.
So you first need to get your hands  dirty and install it manually.
If your distribution does not provide PyQt4 packages you can find instructions for installing PyQt4here.You can then install meneco-gui by running:$ pip install --user meneco-guiThe executable scripts can then be found in ~/.local/bin.UsageTypical usage is:$ meneco-gui.py
Package: menelaus
Menelaus implements algorithms for the purposes of drift detection. Drift
detection is a branch of machine learning focused on the detection of unforeseen
shifts in data. The relationships between variables in a dataset are rarely
static and can be affected by changes in both internal and external factors,
e.g. changes in data collection techniques, external protocols, and/or
population demographics. Both undetected changes in data and undetected model
underperformance pose risks to the users thereof. The aim of this package is to
enable monitoring of data and machine learning model performance.For full documentation, see:GitHub:https://github.com/mitre/menelausReadTheDocs:https://menelaus.readthedocs.io/en/latest/
Package: menhir.contenttype.document
menhir.contenttype.documentprovides a text-centered content forDolmenbasedGrokapplications. This document type has a field
allowing a WYSIWYG edition and indexes itself for full-text searches.SchemaADocumenthas a dedicated schema defining a text field, in addition
of the baseIDescriptiveSchema:>>> from dolmen.app.content import IDescriptiveSchema
>>> from menhir.contenttype.document import IDocument

>>> IDocument.isOrExtends(IDescriptiveSchema)
True

>>> from dolmen.content import schema
>>> from menhir.contenttype.document import Document

>>> IDocument in schema.bind().get(Document)
TrueThe text field is called “body”, as it is the document’s body text:>>> for attr, doc in IDocument.namesAndDescriptions():
...   print attr, ':', doc
body : <zope.schema...Text object at ...>FactoryIn order to create aDocumentcontent, the current user will have to
be granted thedolmen.content.Addpermission, from thedolmen.app.securitypackage:>>> from dolmen.content import require
>>> require.bind().get(Document)
'dolmen.content.Add'FormsThe WYSIWYG widget is provided bydolmen.widget.tinymce, using the
javascript library TinyMCE and jQuery. The Add form and the Edit form
will display the widget, sincemenhir.contenttype.documentprovides aFieldsCustomizeradapter (seedolmen.forms.cruddocumentation):>>> from dolmen.forms.crud import Edit
>>> mydoc = Document(title=u'Some title', body=u"Some body")

>>> from zope.publisher.browser import TestRequest
>>> request = TestRequest()

>>> editform = Edit(mydoc, request)
>>> editform.update()

>>> editform.fields['body'].mode
<Marker TINYMCE.INPUT>IndexationADocumentis indexed in full-text, using anISearchableTextadapter:>>> from zope.index.text.interfaces import ISearchableText
>>> indexer = ISearchableText(mydoc)
>>> indexer.getSearchableText()
(u'Some title', u'', u'Some body\n\n')

>>> mydoc.body = u'<p>Rich content comes in <strong>HTML</strong></p>'
>>> indexer.getSearchableText()
(u'Some title', u'', u'Rich content comes in **HTML**\n\n')

>>> mydoc.description = u"I'm described"
>>> indexer.getSearchableText()
(u'Some title', u"I'm described", u'Rich content comes in **HTML**\n\n')ViewADocumenthas its very own index view, allowing it to render in a
simple way, without the default field names (seedolmen.forms.crud):>>> from zope.component import getMultiAdapter
>>> view = getMultiAdapter((mydoc, request), name='index')

>>> from dolmen.app.layout import Page
>>> isinstance(view, Page)
True

>>> print view.content()
<div class="document">
  <h1>Some title</h1>
  <p>Rich content comes in <strong>HTML</strong></p>
</div>Changelog0.2 (2011-02-14)Updated to useIDescriptiveContentfromdolmen.app.contentinstead ofIBaseContentfromdolmen.contentthat non longer
exists : a document now has adescriptionattribute in addition totitle. This change has been echoed in the indexer.Updated dependencies.0.1 (2010-07-20)Initial release
Package: menhir.contenttype.file
menhir.contenttype.fileprovides a content type forDolmenbasedGrokapplications.SchemaThemenhir.contenttype.fileprovides a very simple schema,
extending theIDescriptiveSchemainterface, fromdolmen.app.content:>>> from dolmen.app.content import IDescriptiveSchema
>>> from menhir.contenttype.file import IFile

>>> IFile.isOrExtends(IDescriptiveSchema)
TrueTheIFileinterface describes the data field, that is to hold the
uploaded file or given bytes:>>> for attr, doc in IFile.namesAndDescriptions():
...   print attr, ':', doc
data : <dolmen.file.field.FileField object at ...>ThisIFileis directly provided by theFileobject, as defines it as
its schema:>>> from menhir.contenttype.file import File
>>> from dolmen.content import Content

>>> somefile = File(title=u"My nice file", data="Some file data")
>>> IFile.providedBy(somefile)
True

>>> from dolmen.content import schema
>>> IFile in schema.bind().get(somefile)
True

>>> somefile.title
u'My nice file'StorageThe file uses a Blob storage for the data:>>> somefile.data
<dolmen.blob.file.BlobValue object at ...>

>>> print somefile.data.data
Some file dataIconThe content registers an icon, thanks to thedolmen.app.contentpackage:>>> from zope.component import getMultiAdapter
>>> from zope.publisher.browser import TestRequest

>>> request = TestRequest()
>>> icon = getMultiAdapter((somefile, request), name="icon")
>>> print icon
<zope.browserresource.icon.IconView object at ...>Changelog0.3 (2011-02-14)The schema now inherits fromIDescriptiveSchemafromdolmen.app.contentand no longer fromIBaseContentfromdolmen.content. This adds adescriptionattribute along with
the already existingtitle.0.2 (2010-07-20)Corrected package archive MANIFEST, to include missing icon0.1 (2010-07-16)Initial release
Package: menhir.contenttype.image
menhir.contenttype.imageprovides an image-centered content type
forDolmenbasedGrokapplications.SchemaThemenhir.contenttype.imageImagecontent provides a custom schema,
extending theIDescriptiveSchemainterface, fromdolmen.app.content:>>> from dolmen.app.content import IDescriptiveSchema
>>> from menhir.contenttype.image import IImage

>>> IImage.isOrExtends(IDescriptiveSchema)
TrueTheIImageinterface describes the image field, that is to store the
uploaded image data. The field comes fromdolmen.file:>>> for attr, doc in IImage.namesAndDescriptions():
...   print attr, ':', doc
image : <dolmen.file.field.ImageField object at ...>FactoryThemenhir.contenttype.imageImagecontent uses a ZODB Blob to
store the data. Theimageattribute of the factory class,Image,
is a blob property fromdolmen.blob:>>> from menhir.contenttype.image import Image
>>> Image.image
<dolmen.blob.property.BlobProperty object at ...>The instanciation is fairly straightforward:>>> import os.path
>>> path = os.path.join(os.path.dirname(__file__), 'image.png')

>>> imagefile = open(path)
>>> image = Image(title=u"My Image", image=imagefile)
>>> imagefile.close()The factory is protected by a commondolmen.app.securityright:>>> from dolmen.content import require
>>> print require.bind().get(image)
dolmen.content.AddIconThe content registers an icon, thanks to thedolmen.app.contentpackage:>>> from zope.component import getMultiAdapter
>>> from zope.publisher.browser import TestRequest

>>> request = TestRequest()
>>> icon = getMultiAdapter((image, request), name="icon")
>>> print icon
<zope.browserresource.icon.IconView object at ...>ViewThe content registers its own index view. It displays the image and a
download link. To be able to display the content, we need it persisted
in a locatable tree, as the links depend on the urls:>>> from zope.component.hooks import getSite
>>> site = getSite()
>>> site['image'] = imageWe can now summon the view and render it. The view is adolmen.app.layoutPage:>>> from dolmen.app.layout import Page
>>> index = getMultiAdapter((image, request), name="index")
>>> isinstance(index, Page)
True

>>> index.update()
>>> print index.content()
<h1>My Image</h1>
<div class="content">
  <p class="download">
    <a href="http://127.0.0.1/image/++download++image"
       title="Download">Download</a>
    &mdash;
    <span>1 KB</span>
  </p>
  <div class="image">
    <a href="http://127.0.0.1/image/++thumbnail++image.large"
       class="image-link" title="My Image">
      <img src="http://127.0.0.1/image/++thumbnail++image.preview" />
    </a>
  </div>
</div>Changelog0.4 (2011-02-14)The schema now inherits fromIDescriptiveSchemafromdolmen.app.contentand no longer fromIBaseContentfromdolmen.content. This adds adescriptionattribute along with
the already existingtitle.0.3 (2011-01-13)Resources are now registered and declared thanks tofanstatic.0.2 (2010-07-27)Packaging corrections.0.1 (2010-07-21)Initial release
Package: menkeyshow.conway
Conway's Game_of_LifePython implementation ofConway's Game of LifeConway's Game of Life is an automaton simulation with rather simple rules and a so called zero-player game.
After marking the status (alive/dead) of the cells, the programm simulates the life according to the following rules:Any live cell with fewer than two live neighbours dies, as if by underpopulation.Any live cell with two or three live neighbours lives on to the next generation.Any live cell with more than three live neighbours dies, as if by overpopulation.Any dead cell with exactly three live neighbours becomes a live cell, as if by reproduction.
Package: menlo-syncro
Synster -- Python API for Menlo Syncro boxDependenciesNeeds:python3-pyserialpython3-Quick'n Dirty SnippetsTry this:>>> from rbp import Message, Device, ControlBytes, Errors, Commands
>>> dev = Device("/dev/ttyUSB0")
>>> m = Message(src=0x0, dest=0xff, cmd=Commands.ECHO, data='hi!')
>>> r = dev.req(m)
>>> print(r)Test like this, if you already have a Menlo Syncro attached:$ export SYNCSTER_PORT=/dev/ttyUSB0
$ cd syncster/syncster
$ pytest-3 -s rbp.py
Package: menovideo
Data-efficient-video-transformerthis repo is for menovideo associated with the paper'Data Efficient Video Transformer for Violence Detection' (DeVTR)one of big challenges facing researchers in computer vision with transformers especially in video tasks is the need for large data and high computational resources , our method called DeVTR (Data Efficient Video Transformer for Violence Detection) to overcame these challenges (he need for large data and high computational resources )In this work, we propose a data-efficient video transformer (DeVTr) based on the transformer network as a Spatio-temporal learning method with a pre-trained 2d-Convolutional neural network (2d-CNN) as an embedding layer for the input data. The model has been trained and tested on the Real-life violence dataset (RLVS) and achieved an accuracy of 96.25%. A comparison of the result for the suggested method with previous techniques illustrated that the suggested method provides the best result among all the other studies for violence event detection.Results and benchmarkingthe model achieved 96.25% based on RLVS dataset and also worth to mention that it was better than TimeSformer in both memory efficiency and convergence speed and accuracyComparing results of DeVTr vs other methods based on RLVS Datasetsaliency map for random video of violence actionmenvideo packagethemenovideo packagehelp you build video action recognation / video understanding  model  based on1-  build using our Novel model DeVTR with full costmaztion
2-  video dataset reader and preprocessing to easly read videos and make them as pytorch ready dataloaders
3-  Timedistributed warper similar to keras timedistributed warper which can help you easly build (classical CNN+LSTM )this is new novel transformer network combined with Conv net to build a highly accuract video action recognation model with limited data and hw rescourcessimple usageinstallpip install menovideoimport itimport menovideo.menovideo as menoformer
import menovideo.videopre as vide_readerinit DeVTr model without pre-trained wightsmodel = menoformer.DeVTr()init DeVTr with pre-trained wigths
the trained wights  can bedownloaded from this urlwight = 'drive/MyDrive/Colab Notebooks/transformers/violance-detaction-myresearch/vg19bn40convtransformer-ep-0.pth'
model2 = menoformer.DeVTr(w= wight , base ='default')using the video reader and pre-processing helpers
parameters is :pandas dataframe contain the path and label of each videonumber of frames for the singal videoRGB is the number of color channlesh is the hieght of the frame for each videow is the width of the frame for each videovalid_dataset = vide_reader.TaskDataset(valid_df,timesep=time_stp,rgb=RGB,h=H,w=W)for detlied example of using the labrary usepackage_test.ipynbplease use pytorch 1.9 for the pre-trained modelTo cite our paper/code:@INPROCEEDINGS{9530829,  author={Abdali, Almamon Rasool},  booktitle={2021 IEEE International Conference on Communication, Networks and Satellite (COMNETSAT)},   title={Data Efficient Video Transformer for Violence Detection},   year={2021},  volume={},  number={},  pages={195-199},  doi={10.1109/COMNETSAT53002.2021.9530829}}
Package: menpo-PyVRML97
VRML97 Scenegraph modelling objects for PythonThis project provides a core semantic model for VRML97 objects which
is close to (but not identical to) that specified in the VRML97 spec.
It is primarily used for the OpenGLContext project’s VRML97 rendering
engine, but can also be used for generating, parsing or processing VRML97
scenegraphs.We have forked this to provide a correct setup.py. No other changes have been made.Originally fromhttp://pyopengl.sourceforge.net/context/
Package: menrva
main/MenrvaGetting StartedDownload links:SSH clone URL: ssh://git@git.jetbrains.space/odysseus/main/menrva.gitHTTPS clone URL:https://git.jetbrains.space/odysseus/main/menrva.gitThese instructions will get you a copy of the project up and running on your local machine for development and testing purposes.PrerequisitesWhat things you need to install the software and how to install them.ExamplesDeploymentAdd additional notes about how to deploy this on a production system.ResourcesAdd links to external resources for this project, such as CI server, bug tracker, etc.
Package: mensor
Mensor is a graph-based computation engine for computing measures and metrics.
It:defines a new grammar for extracting measures and metrics that is designed to
be intuitive and capable (it can do almost(?) anything that makes sense to
do with metrics and measures).makes measure and metric definitions explicit and shareable, and their
computations transparent and reproducible.allows multiple data sources to be stitched together on the fly without users
having to explicitly write the code / query required to join the data sources.is agnostic as to how data is stored or accessed, and new data backends are
relatively simple to write.allows for local ad-hoc definitions of additional data sources for exploration
by data scientists or other technically minded folk, decoupling it from
deployment into production services.Note:Mensor is currently still under heavy development, and intrusive
changes to the API are expected. To minimise the impact on downstream projects,
Mensor will strictly adhere to semantic versioning. In particular, any
incompatible or sufficiently adventurous change to classes expected to be used
outside of Mensor itself will result in a minor version bump. If you pin the
version of Mensor used in your project usingmensor>=x.y(.z)?<x.y+1(e.g.mensor>=0.1.2<0.2), you should be protected from any code churn and can
upgrade to newer versions of Mensor after reading the release notes at your
leisure.
Package: ment
mentWhat is this?mentis a tool towrite daily logs in markdown quicklysynthesize daily logs based on categorySynthesizing daily logs is like sorting loose-leaf notebook.InstallationpipinstallmentUsagestart edittingmTypem<Enter>.Default,it meansvim ~/ment_dir/<todays_date>/diary.mdIf you want to switch editor, lookconfigurationsynthesize by tagmsynthe<tag_name>Then, it extracts contents followed by "# <tag_name>" from daily logs,
and outputs~/ment_dir/synthe/<tag_name>/synthe_<tag_name>.md.If you want to list tags,m list.To synthesize recent 7days document,m week.
It outputs~/ment_dir/synthe/week/synthe_week.md.To read synthesized documents,m read <tag_name>.To update already synthesized documents,m update.configurationIf you want to change editor and directory, please set environment variable.exportMENT_DIR="/path/to/documents"exportMENT_EDITOR="your editor"Default,MENT_DIR is~/ment_dir/directory structure~/ment_dir/
├── 2021-03-27
│   └── 2021-03-27.md
├── 2021-03-28
│   └── 2021-03-28.md
├── 2021-03-29
│   └── 2021-03-29.md
├── 2021-03-30
│   └── 2021-03-30.md
└── synthe
    ├── tag1
    │   └── synthe_tag1.md
    ├── tag2
    │   └── synthe_tag2.md
    ├── tag3
    │   └── synthe_tag3.md
    └── weeks.mdcompletionbash-completion file formentisbash_completion_for_ment.catbash_completion_for_ment>>~/.bash_completionsource~/.bashrc
Package: mentaldistancechecker
Mental Distance Checker"MentalDistanceChecker" is a python program that estimates psychological distance from sentences and elapsed time.RequirementPython 3.8.6speechrecognitiontransformersfugashiipadicEnvironments underpipenv for Windowsandpipenv for macare tested.InstallationInstall packages with pip(pipenv) command.pipinstallspeechrecognition
pipinstalltransformers
pipinstallfugashi
pipinstallipadicUsagepip install mentaldistancecheckerfrommentaldistancecheckerimportmentaldistancecheckerNoteThis is the version under development.AuthorTaichi Kezukamusashino universityemail:s1922011@stu.musashino-u.ac.jpLicense"MentalDistanceChecker" is underMIT license.Thank you!
Package: mental-health-and-prevalence
Mental Health"Mental health includes our emotional, psychological, and social well-being. It affects how we think, feel, and act. It also helps determine how we handle stress, relate to others, and make healthy choices. 1. Mental health is important at every stage of life, from childhood and adolescence through adulthood."(Mental Health Dataset | Kaggle, License CC0: Public Domain)prevalence-by-mental-and-substance-use-disorder.csvThere are Seven columns('Schizophrenia', 'Bipolar_disorder', 'Eating_disorders', 'Anxiety_disorders', 'Drug_use_disorders', 'Depressive_disorders', 'Alcohol_use_disorders') storedthese symptoms preference.prevalence-of-depression-males-vs-females.csvThe prevalence-of-depression-males-vs-females.csv file include prevalense of depression separated by gender and popunation of each country.How to install mental-health-and-prevalence==0.0.1$ pip install mental-health-and-prevalence==0.0.1
$ pip install pandas
$ pip install matplotlib
$ pip install GitPython
Package: mentalics
Mentalics[!WARNING]This library is not stable yet.mentalicsis designed primarily to read, write, and analyze NSKeyedArchiver-generated archives as Python object graphs. It is designed to reflect theNSCoderinterface, handle circular references, and efficiently work through unknown classes and files.UsageExploring an unknown archive (not stable):frommentalicsimportExplorerwithopen("my.plist","rb")asfile:explorer=Explorer(file)print(explorer._classes)Decoding a known archive:frommentalicsimportUnarchiver,NSCodingfromdataclassesimportdataclass@dataclassclassMyClass(NSCoding):my_attr:strdef__init_from_archive__(self,decoder)->"NSCoding":my_attr=decoder.decode("myAttr")returnself.__init__(my_attr)defencode_archive(self,encoder)->None:encoder.encode(self.my_attr,for_key="myAttr")withopen("my.plist","rb")asfile:dearchiver=Unarchiver(file)dearchiver.set_class(MyClass,"MyClass")root=dearchiver.decode()print(root)Whymentalics?There are many other libraries for parsing plists and archives:plistlibandbplist-python(plists, not archives),bpylistandbpylist2,plistutils,ccl-bplist, and probably others.However, there is mismatched support for desirable features:Handling circular references (ccl-bplist)NSCoder/NSCoding-like interface (bpylist, bpylist2)Simple handling of NSValues (none)Exploring archives with unknown structure (ccl-bplist)Generating code for classes from archives (none)All of these are either a part ofmentalicsor a planned feature.
Package: mental-models
mental_modelsExtracts human mental models from text and facilitates mental model comparison and contrasting. An alternative implementation of "AutoMap," see: J Diesner, K M Carley, "AutoMap1.2 - Extract, analyze, represent, and compare mental models from texts" (2003) for more background details.Installpip install mental_models
#  then Build as belowBuildThis library assumes that several models and datasets are available. You can run the command below to build them.#  ... need a small language model 
python -m spacy download en_core_web_sm
# ... adds in WordNet tokenization for spaCy
python -m nltk.downloader wordnet
python -m nltk.downloader omw