Saravanan666/pypi-org huggingface dataset:
Package: m01.fake
This package provides a fake mongodb implementation and is quite handy for
testing.CHANGES3.0.4 (2022-02-06)bugfix: fixed TyoeError if the deque get sliced in skip cursor method3.0.3 (2018-11-24)bugfix: fix find_one_and_update and ensure that upsert is working.3.0.2 (2017-01-11)bugfix: fix $setOnInsert, _id as a part of $setOnInsert didn’t get applied
to the new inserted document3.0.1 (2015-12-24)make implementation compatible with final pymongo 3.2 version3.0.0 (2015-11-11)initial releaseimplemented update operatorimplemented find operatoruse 3.0.0 as package version and reflect pymongo > 3.0.0 compatibilitym01.mongofake replacement, support pymongo >= 3.0.0
Package: m01.fs
This package provides a mongodb file implementation without GridFS for Zope3.
This means, such files are limited to the mongo document size, currently 16MB.
As you probably know, you should not use GridFS for small binary data because
you will double the number of queries. This package will offer a file which
stores the meta and file data in one document.CHANGES3.1.4 (2019-07-16)bugfix: fix height in ImageObject. There was a typo and the height didn’t
get stored in the database3.1.3 (2018-05-02)bugfix: added missing import3.1.2 (2018-05-02)bugfix: added missing m01.mongo.item import in m01.fs.itembugfix: implemented width and height field property for IImage objectsfeature: added ImageObjectBaseTest base test class3.1.1 (2018-03-01)bugfix: inherit IFileSchema from IFileBaseSchema3.1.0 (2018-02-16)feature: implemented IImage and image object, image chunk reader and writer
Note: the new interface IFileBaseSchema is the shared schema for IFileSchema
and IImageSchema.3.0.1 (2017-12-27)bugfix: FileContainerItem was using the wrong (storage) interface. Changed
IFileStorageItem to IFileContainerItem in FileContainerItemcleanup, fix tests3.0.0 (2015-11-11)support pymongo >= 3.0.0 and use 3.0.0 as package version and reflect
pymongo >= 3.0.0 compatibility1.0.1 (2015-09-28)removed bson dependency, pymongo uses it’s own bson implementation1.0.0 (2015-03-17)feature: replace internal method setContentType with setContentTypeAndEncoding
and also set encoding if given.bugfix: fix apply encoding value. The value u’None’ was applied if no encoding
was given instead of None.0.5.1 (2013-03-19)bugfix: remove notifyRemove method. This is a left over from m01.gridfs and
not required since the item it self stores the image data0.5.0 (2012-11-18)initial release
Package: m01.grid
This package provides a mongodb grid based file implementation for Zope3.CHANGES3.0.0 (2018-04-24)feature: added skipMD5Check to ChunkWriter for skip the filemd5 hash checkmake it compatible with new mongo client setup in m01.mongo > 3.0.*. Because
the connection pool factory concept is gone in m01.mongoensure acknowledged file collection. Because with w=0, ‘filemd5’ might run
before the final chunks are written.adjust test setupsupport pymongo >= 3.0.0 and use 3.0.0 as package version and reflect
pymongo >= 3.0.0 compatibility0.5.0 (2012-11-17)initial release
Package: m01.i18n
This package provides a i18n aware content base class for Zope3 using the
mongodb instead of ZODB.CHANGES3.1.0 (2018-04-24)feature: added I18nMongoObjectcleanup: I18nMongoItemBase moved from m01.i18n.item to m01.i18n.base.3.0.0 (2017-01-11)feature: added level 2 test using mongodb stub server and use the fake mongodb
setup for default test runnersupport pymongo >= 3.0.0 and use 3.0.0 as package version and reflect
pymongo >= 3.0.0 compatibility0.5.0 (2013-06-28)initial release
Package: m01.mongofake
This package provides a FAKE mongoDB implementation and is quite handy for
testing.CHANGES1.0.1 (2015-03-17)bugfix: reflect changes in pymongo > 2.8.0. The dta basename checker method
_check_name method was moved from pymongo.database to pymongo.helpers. Make
import work for all pymongo versions.1.0.0 (2013-03-06)Added support for Python 3.3.Dropped support for Python 2.4 and 2.5.0.2.1 (2013-02-02)added realistic FakeCollection.remove return valuesbugfix around FakeCollection.save0.2.0 (2012-12-16)feature: support pymongo 2.4 API and support new safe write concerninclude m01.stub for testing mongofake components and compare with real
pymongo output. Use –all option for real pymongo and mongodb tests.implemented some basic tests and run them with m01.stub and m01.mongofake
librariesimplemented correct response for collection update method based on new
pymongo 2.4. More will follow if more tests get implementedswitch from lovely.importchecker to p01.recipe.setup importchecker which
doesn’t warn about bad svn url during buildout process0.1.1 (2012-12-10)inproved dictify method, also convert single SON object to dictadd some initial tests0.1.0 (2012-05-22)First independent release from m01.mongo.testing
Package: m01.searcher
This package provides a mongodb based search filter implementation for Zope3.CHANGES3.0.0 (2015-11-10)support pymongo >= 3.0.0 and use 3.0.0 as package version and reflect
pymongo >= 3.0.0 compatibility0.5.1 (2012-12-10)switch back to simpler memcached based p01.session concept since mongodb
based m01.session implementation doesn’t scale as excpected.0.5.0 (2012-11-18)initial release
Package: m01.zfs
This package provides a mongodb file implementation without GridFS for Zope3.
This means, such files are limited to the mongo document size, currently 16MB.
As you probably know, you should not use GridFS for small binary data because
you will double the number of queries. This package will offer a file which
stores the meta and file data in one document. This implementation will
compress the data with zlib. See m01.fs for a non compressed implementation.CHANGES3.0.2 (2018-03-08)bugfix: remove context from CHunkReaderBase class and use data and fname
from context or given data dict3.0.1 (2018-03-06)bugfix: set content size (csize) on adding upload item3.0.0 (2015-11-10)support pymongo >= 3.0.0 and use 3.0.0 as package version and reflect
pymongo >= 3.0.0 compatibility0.6.1 (2015-09-28)removed bson dependency, pymongo uses it’s own bson implementation0.6.0 (2015-03-17)feature: replace internal method setContentType with setContentTypeAndEncoding
and also set encoding if given.0.5.0 (2013-11-23)initial release
Package: m05-miniproject-MaryamLucas
M05-MiniProjectRepository of the MiniProject M05.ObjectiveIn the context of reproducibility, the objective of this mini-project is to apply reproducible research methodologies to different problems of prediction, working with two specific cases:Wine quality,data set.Boston house,data setData sets from UCI Machine Learning Repositories.Execution - DocumentationThe set of instructions to run this project and its complete documentation are avaliablehereSuportGroup/Alumni:Naderi, MaryamContact.Stel, LucasContact.LicenseMIT licenseMore info.
Package: m17
Python package for M17 radio and radio-over-IP protocolsM17 is a new, experimental radio protocol similar to DMR/MotoTRBO, P25, D-STAR, and others.
It uses the Codec2 vocoder by David Rowe.SeeM17 Projectfor more details.This code will be pushed up tothe M17 project's newly created Githubeventually.
As for now, it's all by yours truly.This package has full Python support for handling M17 addresses, framing and parsing,
and including a full Python native VoIP client (for developers).FeaturesBasepython -m m17.address <callsign>- print the encoded M17 base40 representation of the callsigns givenseeframes.pyandframer.pyin the source for M17 frame classes and example usage.[Codec2]python -m m17.audio_test 3200where 3200 can also be 1600, 1200, or other supported Codec2 bitrate. Takes your microphone, encodes and decodes it into Codec2, and plays it back. Useful for getting your microphone input tuned properly for Codec2.InstallationPipYou can install withpip install m17, and get a basic feature set
including framing and M17 address translation.However, to get all features that useCodec2, including the full M17 VoIP node
andaudio_test, you must first haveCodec2installed on
your system, including theCodec2development headers, andCython. Once that's
complete, you can thenpip install m17[Codec2]to install all features.Note that installingCythonseparately beforepycodec2seems to be
required in order to make sure it's available forpycodec2's setup
process.Here's an example for Arch Linux.pacman -Syu base-devel codec2 python python-pip python-setuptools
pip install --upgrade pip numpy Cython wheel setuptools
pip install m17[Codec2]Naturally you need the typical compilation tools, which on Arch arebase-devel.On systems that separate development headers, you need those too for
Codec2, i.e. bothcodec2andcodec2-dev, or whatever the appropriate
names are for your distro.On many Ubuntu and Debian systems, the codec2 packages in the distro
repositories is too out of date for use with pycodec2, so you may need
to follow theupstream Codec2 instructionsand theupstream pycodec2 instructionsto get a fully working installation of both.You may email me with clearly described problems regarding installation
and I will do my best to help.TODO:https://github.com/joerick/cibuildwheel
Package: m1key
This package is based on resume. A python package which describes my journey till now.Possible Commands                   |      Output
1.   m1key -h                           |  Help
2.   m1key -i                           |  Introduction
3.   m1key -t                           |  Gives Tree structure of all commands
4.   m1key -w                           |  Takes you to my portfolio website
5.   m1key project -h                   |  Gives help commands about project
6.   m1key project -s image _location   |  Produces an image by extracting the 2 dominant colors of given image
7.   m1key project -t                   |  Opens up the camera and puts a prop on your face in realtime.
8.   m1key project -o                   |  Take you to oneml website, which is platform where one can visualize ML algos.
9.   m1key resume -d                    |  Download Resume
10.  m1key skills / m1key skills -a     |  List down all my skills
11.  m1key education -a                 |  List down all my educational reports
Package: m1l0-protobufs
M1L0 ProtobufsProtobufs for micro services used by the M1L0 frameworkCurrent list of protobufs:protobufs/imagebuilderImage builder serviceprotobufs/dataserviceData pipeline serviceThe python package contains the python client needed to interface with the grpc services.To use the client:from m1l0_services.imagebuilder import image_builder_pb2
from m1l0_services.imagebuilder import image_builder_pb2_grpcThe protobufs are packaged into a PyPI package and published at the following location:https://pypi.org/project/m1l0-protobufs/Using bufhttps://docs.buf.build# shows which packages will be built; used as debugging

buf build -o -#format=json | jq '.file[] | .package' | sort | uniq | head

buf ls-files

buf lint # apply liniting to protobuf filesReferencesPublishing packages on PYPITODO:Setup type checkingIssues:Compiling grpc_python_pluginThe grpc_python_plugin is required to use buf generateTo build from source:git clone --recursive https://github.com/grpc/grpc
cmake .
cmake --build . --target pluginsUnable to call buf generate as its failing with following error:Unknown flag: --descriptor_set_inAbove fixed by updating protoc to 3.18.0
Package: m1-project-agne
BasketballCrawlerWelcome to the Python package designed for web crawling. This project aims to scrape data from the website "https://www.krepsinis.net" related to basketball articles. The data is extracted using web scraping techniques and organized into a Pandas DataFrame.InstallationUsing a Package ManagerYou can install the crawler as a package using pip:pipinstallm1_project_agneOr using poetry:poetryaddm1_project_agneCloning the RepositoryYou can also clone the repository and install the dependencies using poetry:gitclonehttps://github.com/agnesema/m1_project_agnecdm1_project_agne
poetryinstallUsageTo use this project, you can import thecrawlfunction frommain.pyand call it with the desired parameters. Here is an example:fromm1_project_agne.mainimportcrawlresult=crawl(source="krepsinis",return_format="csv",time_limit=10,search_word="rytas")print(result)StructureThe project is structured as follows:m1_project_agne/:Main package directory.init.py:Package initialization file.krepsinis.py:Crawler for the Krepsinis website.main.py:Main script for the crawler package.tests/:Test scripts for the package.init.py:Initialization file for tests.test.py:Test scriptsLicenceThis project is licensed under the MIT license.
Package: m209
Author:Brian Neal <bgneal@gmail.com>Version:0.1Date:July 4, 2013Home Page:https://bitbucket.org/bgneal/m209/License:MIT License (see LICENSE.txt)Documentation:https://m209.readthedocs.orgSupport:https://bitbucket.org/bgneal/m209/issuesTheM-209is a mechanical cipher machine used by the US military during World
War II and up to the Korean War. The M-209 is also known as the CSP-1500 by
the US Navy. The M-209 is an example of a Hagelin device, a family of
mechanical cipher machines created by Swedish inventorBoris Hagelin.This project is a Python 3 library and command-line utility for encrypting and
decrypting text by simulating the operation of an actual M-209 device.Please see thedocumentationfor library usage, tutorials and
references.
Package: m26
m26 - calculations for sports like running, cycling, and swimmingFeaturesCreate Distances in either miles, kilometers, or yards.Translates Distances to the other units of measure.Specify ElapsedTime either in ‘hh:mm:ss’ strings, or int second values.Calculates Speed from a given Distance and ElapsedTime - per mile, per kilometer, and per yard.Calculates pace_per_mile and seconds_per_mile for a given Speed.Projects one Speed to another Distance with either a simple or algorithmic formula.RunWalkCalculator calculates pace and mph from given time intervals and paces.Calculates the Age of person, and age_graded times.Calculates five standard heart-rate training zones based on Age.Quick startInstallation:$pipinstallm26Use:>>>importm26>>>d1=m26.Distance(26.2)>>>d1<Distance value:26.2 uom:m>>>>d1.as_miles()26.2>>>d1.as_kilometers()42.1648128>>>d1.as_yards()46112.0>>>d2=m26.Distance(50.0,m26.Constants.uom_kilometers())>>>d2<Distance value:50.0 uom:k>>>>d3=m26.Distance(7040,m26.Constants.uom_yards())>>>d3<Distance value:7040.0 uom:y>>>>d4=m26.Distance(10.0,m26.Constants.uom_kilometers())>>>d4.add(d1)>>>d4<Distance value:52.1648128 uom:k>>>>d4.subtract(d3)>>>d4<Distance value:45.7274368 uom:k>>>>t1=m26.ElapsedTime('3:47:30')>>>t1<ElapsedTime hh:3.0 mm:47.0 ss:30.0 secs:13650.0>>>>t2=m26.ElapsedTime(3662)>>>t2<ElapsedTime hh:1 mm:1 ss:2.0 secs:3662.0>>>>s=m26.Speed(d1,t1)>>>s<Speed dist:<Distance value:26.2 uom:m> etime:<ElapsedTime hh:3.0 mm:47.0 ss:30.0 secs:13650.0>>>>>s.mph()6.90989010989011>>>s.kph()11.120390189010989>>>s.yph()12161.406593406595>>>s.seconds_per_mile()520.9923664122138>>>s.pace_per_mile()'8:40.99'>>>s.projected_time(d2,'simple')'04:29:46'>>>s.projected_time(d2,'riegel')'04:32:32'>>>result=m26.RunWalkCalculator.calculate('2:30','9:16','0:45','17:00',31.0)>>>result{
  "avg_mph": 5.4292343387471,
  "avg_ppm": "11:03.07",
  "miles": 31.0,
  "proj_miles": 31.0,
  "proj_time": "05:42:35",
  "run_hhmmss": "2:30",
  "run_ppm": "9:16",
  "walk_hhmmss": "0:45",
  "walk_ppm": "17:00"
}>>>a=m26.AgeCalculator.calculate('1960-10-01','2015-10-18')>>>a<Age value:55.044490075290895>>>>a2=m26.Age(58.1)>>>a2<Age value:58.1>>>>graded=s.age_graded(a,a2)>>>graded<Speed dist:<Distance value:26.2 uom:m> etime:<ElapsedTime hh:3 mm:51 ss:47.61402391772026 secs:13907.61402391772>>>>>zones=a2.training_zones()>>>zones[
  {
    "age": 58.1,
    "max": 161.9,
    "pct_max": 0.95,
    "pulse": 153.805,
    "zone": 1
  },
  {
    "age": 58.1,
    "max": 161.9,
    "pct_max": 0.9,
    "pulse": 145.71,
    "zone": 2
  },
  {
    "age": 58.1,
    "max": 161.9,
    "pct_max": 0.85,
    "pulse": 137.615,
    "zone": 3
  },
  {
    "age": 58.1,
    "max": 161.9,
    "pct_max": 0.8,
    "pulse": 129.52,
    "zone": 4
  },
  {
    "age": 58.1,
    "max": 161.9,
    "pct_max": 0.75,
    "pulse": 121.42500000000001,
    "zone": 5
  }
]Source CodeSeem26-py at GitHub.ChangelogVersion 0.2.12020/02/19. Version 0.2.1,  Jinja2 upgrade2017/09/27. Version 0.2.0,  Converted to the pytest testing framework; coverage at 100%.2015/10/31. Initial Production release.
Package: m269-lib
This library aims to provide simple, readable and documented implementations
of commonly taught data structures and algorithms (DSA).
It was created for M269, our DSA course, but we hope it's of use by other courses.The current version implementsbags (multisets), with a Python dictionaryqueues, with Python lists and linked listsstacks, Python lists and linked lists.The library uses a small subset of Python's language and standard library,
to make it easier to understand.All modules, classes and methods have docstrings.
All classes have usage examples.
All methods indicate their worst-case complexity.For installation and usage instructions,
see thedocumentation.
Package: m2-beats2audio
Beats2Audiobeats2audiois an utility to create audio files with clicks at designated
onset times as provided in a configuration file. Onset times are specified in
a text file defining one location per line, in milliseconds. This utility was
developed in the context of the publication "Simple and Cheap Setup for
Measuring Timed Responses to Auditory Stimuli", Miguel. et al. (under review).InstallationFrom pypi (requires pip >= 20.0.1):pip install m2-beats2audioFrom sources:git clone https://github.com/m2march/beats2audio.git
    cd beats2audio
    python setup.py installUsageGiven a input file as the following, specifying onsets at 500 ms intervals
(seeexamples/iso_500.txt):0
500
1000
1500
2000
2500beats2audiotakes as a positional parameter the onset times file and
specifies an output file with the-oflag. I.e.:`beats2audio -o 500.wav examples/iso_500.txt`The utility can producewavandmp3files, according to the extension of
the output file or via the-fflag. More options are explained in the help
text-h.
Package: m2c
m2cworkflowpip install m2cmkdiryour-project-namecdyour-project-namem2c initm2c buildm2c runm2c debugm2c unittestm2c packcodingdesgin apimodel.txtm2c apicodedesgin objmodel.txtm2c objcodecoding logic with api/obj...coding unittest...m2c unittestm2c packdone
Package: m2controller
why we create a robot for people learning programming?Inspired by this idea:
"It is important to teach problem solving or teach to the problem not to the tools so this would be like you are trying to teach people about how engines work. Before, you know you could start by the more traditional approach, would be to say we're going to teach you all about screwdrivers and wrenches and you even have a course on screwdrivers, a course on wrenches, and all these things and its a very difficult way to do it. A much better way would be like here's the engine. Now let's take it apart. How are we going to take it apart? Oh, you need a screwdriver, that's what the screwdrivers for. You need a wrench, that's what the wrench is for. Then a very important thing happens, whcih is that the relevance of the tools becomes apparent." --- Elon MaskWe create these robots. You first see these robots working, them take them apart, understanding their mechatronic working principles, understanding their programming design principles. In the meantime, the relevance of the software and hardware parts becomes apparent.
Package: m2dp
M2DP pythonpython implementation of the M2DP algorithm. Original repository can be foundhere.Introduction:Multiview 2D projection (M2DP) is a global descriptor of input point cloud.Input:
      data        n*3     Point cloud. Each row is [x y z]
Output:
      desM2DP     192*1   M2DP descriptor of the input cloud data
      A           64*128  Signature matrixDetails of M2DP can be found in the following paper:Li He, Xiaolong Wang and Hong Zhang, M2DP: A Novel 3D Point Cloud
Descriptor and Its Application in Loop Closure Detection, IROS 2016.Li He, Dept. of Computing Science, University of Albertalhe2@ualberta.caRequirementPython 3.6NumpyScikit-LearnusageFollow the below format to extract features from pointcloud:from m2dp import M2DP
des,A=M2DP(point_cloud)AcknowledgementThank@jubaer145for your help in this project.Reference@inproceedings{he2016m2dp,
  title={M2DP: A novel 3D point cloud descriptor and its application in loop closure detection},
  author={He, Li and Wang, Xiaolong and Zhang, Hong},
  booktitle={2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages={231--237},
  year={2016},
  organization={IEEE}
}CopyrightSeeLICENSEfor details.
Package: m2lk
m2lkWotLK M2 files parserfromm2lkimportM2FileReader,M2,M2SkinwithM2FileReader(filename)asreader:m2=M2()m2.load(reader)withM2FileReader(skin_filename)asreader:skin=M2Skin()skin.load(reader)And thenm2can be used as astructure.M2FieldFeild's value can be obtained withvalueproperty. For example:m2.valuem2.name.valueThe value is a JSON-srializable data.M2ArrayFieldM2ArrayField is a subclass of M2Field. Its item can be obtained by index. For example:iflen(m2.sequences)>0:sequence_0_field=m2.sequences[0]# M2Field instancesequence_0_field.movespeed.value# floatM2StructM2Struct is a subclass of M2Field.fieldsproperty is a dict{field_name: field}.
For example M2 structure fields can be obtained withlist(m2.fields).Structure field can be obtained by attribute:getattr(obj, field_name). For example:sequences_field=m2.sequences# M2ArrayFieldStructure field's value can be obtained by name:obj[field_name].
For example JSON-serializable value ofsequencesfield:sequences_value=m2['sequences']# The same as m2.sequences.valueNote:M2andM2Skinare subclasses ofM2Struct.JSONA m2 value can be saved to a json file:importjsonm2_json=json.dumps(m2.value)And then loaded to a M2 structure:m2=M2()m2.from_json(json.loads(m2_json))The same is for M2Skin:importjsonskin_json=json.dumps(skin.value)skin_new=M2Skin()skin_new.from_json(json.loads(skin_json))
Package: m2m
Copyright (c) 2022 Jérémie DECOCK (www.jdhp.org)Web site:http://www.jdhp.org/software_en.html#m2mOnline documentation:https://jdhp.gitlab.io/m2mExamples:https://jdhp.gitlab.io/m2m/gallery/Notebooks:https://gitlab.com/jdhp/sensors-m2m-daemons-notebooksSource code:https://gitlab.com/jdhp/sensors-m2m-daemonsIssue tracker:https://gitlab.com/jdhp/sensors-m2m-daemons/issuesM2M Daemons on PyPI:https://pypi.org/project/m2mM2M Daemons on Anaconda Cloud:https://anaconda.org/jdhp/m2mDescriptionM2M DaemonsNote:This project is still in beta stage, so the API is not finalized yet.DependenciesPython >= 3.6Installation (Gnu/Linux)You can install M2M Daemons with these commands:conda deactivate
python3 -m venv env
source env/bin/activate
python3 -m pip install --upgrade pip
python3 -m pip install -r requirements.txt
pip install m2mDocumentationOnline documentation:https://jdhp.gitlab.io/m2mAPI documentation:https://jdhp.gitlab.io/m2m/api.htmlExample usageTODOBug reportsTo search for bugs or report them, please use the M2M Daemons Bug Tracker at:https://gitlab.com/jdhp/sensors-m2m-daemons/issuesLicenseThis project is provided under the terms and conditions of theMIT License.