Saravanan666/pypi-org huggingface dataset:
Package: metaflow-card
Hello World Metaflow CardThis repository is an example hello world card.You can install this card via :pip install metaflow-cardHow to use a helloworld card@card(type='helloworld',options={"attribute":"html"})@stepdeftrain(self):......self.html=some_html()# set some html to the self attributeHow to implement custom card moduleThe core namespace package name ismetaflow_extensions. Any new custom MetaflowCard should be present inside a folder with the following directory structure:some_random_dir/ # the name of this dir doesn't matter
├ setup.py
├ metaflow_extensions/ # namespace package name 
│  └ organizationA/ # NO __init__.py file as it is a namespace package. 
│      └ plugins/ # NO __init__.py file
│        └ cards/ # NO __init__.py file # This is a namespace package. 
│           └ my_card_module/  # Name of card_module
│               └ __init__.py. # This is the __init__.py is required to recoginize `my_card_module` as a package
│               └ somerandomfile.py. # Some file as a part of the package. 
.The__init__.pyof themetaflow_extensions.organizationA.plugins.cards.my_card_module, requires aCARDSattribute which needs to be alistof objects inheritingMetaflowCardclass. For example, the below__init__.pyfile exposes aMetaflowCardoftype"y_card2".frommetaflow.cardsimportMetaflowCardclassYCard(MetaflowCard):type="y_card2"ALLOW_USER_COMPONENTS=Truedef__init__(self,options={},components=[],graph=None):self._components=componentsdefrender(self,task):return"I am Y card%s"%'\n'.join([compforcompinself._components])CARDS=[YCard]Having this metaflow_extensions module present in the python path of your flow will make the cards usable within the flow.
Package: metaflow-card-uplot
Metaflow Card To Plot TimeseriesUsageInstall this card usingpip install metaflow-card-uplotUsage in@stepcode:@card(type='uplot',options={"artifact":"timeseries"})@stepdefstart(self):importpandasindex=pandas.date_range("2022-01-01",periods=60,freq="Min")series={'first':self._fake_series(len(index),0.1),'second':self._fake_series(len(index),1)}self.timeseries=pandas.DataFrame(series,index=index)self.next(self.end)def_fake_series(self,n,g):return[(random.random()-0.5)*0.1+math.sin(x*g)forxinrange(n)]
Package: metaflow-helper
# metaflow-helperConvenience utilities for common machine learning tasks on MetaflowUnit tests | Examples |———- | ——-  |![Build](https://github.com/fwhigh/metaflow-helper/actions/workflows/python36.yml/badge.svg) | ![Build](https://github.com/fwhigh/metaflow-helper/actions/workflows/examples36.yml/badge.svg) |![Build](https://github.com/fwhigh/metaflow-helper/actions/workflows/python37.yml/badge.svg) | ![Build](https://github.com/fwhigh/metaflow-helper/actions/workflows/examples37.yml/badge.svg) |![Build](https://github.com/fwhigh/metaflow-helper/actions/workflows/python38.yml/badge.svg) | ![Build](https://github.com/fwhigh/metaflow-helper/actions/workflows/examples38.yml/badge.svg) |## QuickstartYou can run the model selection tournament immediately like this.
Install a convenience package called metaflow-helper.`bash git clonehttps://github.com/fwhigh/metaflow-helper.gitcdmetaflow-helperpython-mpip install . `Then run a Metaflow example.
Examples need a few more packages, including Metaflow itself, which metaflow-helper doesn’t currently require.`bash python-mpip install-rexample-requirements.txtpythonexamples/model-selection/train.pyrun `You can visualize the example flow.`bash pythonexamples/model-selection/train.pyoutput-dot| dot-Grankdir=TB-Tpng-omodel-selection-flow.png`## Release procedure1. Bump VERSION in setup.py. This should contain the new version without the v
1. Rungit commit -a -m “Release v0.0.1”with VERSION version number
1. Rungit tag -a v0.0.1 -m “Release v0.0.1”with VERSION version number
1. Optional: runmake package1. Rungit push origin v0.0.1
Package: metaflow-sentry-logger
Sentry Logging Plugin for MetaflowEnabling the use ofSentrywithMetaflowSentry Logging Plugin for MetaflowInstallationUsageConfigurationExamplesUsing the step decoratorCalling using the--withcommand line argumentInstallationInstall the plugin using pip.pipinstallmetaflow-sentry-loggerFor developmentpipinstallmetaflow-sentry-logger[dev]UsageThe sentry plugin can be called using either the--with sentrycommand line argument or through the step decorator@sentry.ConfigurationOnly basic configuration is currently supported using environment variables.NameDescriptionRequiredLocationMETAFLOW_SENTRY_DSNThe DSN for the target sentry projectTrueEnvironment VariableMetaflow ConfigMETAFLOW_PROFILEUsed by this plugin to determine the environment.FalseEnvironment VariableExamplesSee the (examples)[examples] directory for some example flows or see the code snippets belowUsing the step decorator# examples/sample_flow.pyfrommetaflowimportFlowSpec,step,sentryclassMyFlow(FlowSpec):@step@sentrydefstart(self):print("Start step")@step@sentrydeferror(self):self.div_0=1/0@step@sentrydefend(self):print("final step")Calling using the--withcommand line argumentpythonexamples/sample_flow_with.pyrun--withsentry
Package: metafold
Metafold SDK for PythonInstallationpip install metafoldUsagefrommetafoldimportMetafoldClientaccess_token="..."project_id="123"metafold=MetafoldClient(access_token,project_id)assets=metafold.assets.list()print(assets[0].name)asset=metafold.assets.get("123")print(asset.name)Read thedocumentationfor more info or play around with one of theexamples.
Package: metaframe
metaframeMetaClass infrastructure to intercept instance creation/initialization enabling
modification of args/kwargs and instance.DocumentationRead the full documentation at readthedocs.org:metaframe documentationPython 2/3 SupportPython 2.7Python 3.2/3.3/3.4/3.5It also works with pypy and pypy3Installationmetaframeis self-contained with no external dependenciesFrom pypi:pip install metaframeFrom source:Place themetaframedirectory found in the sources inside your projectFeatures:MetaFramemetaclass to apply to any object
- With embedded staticmethod with_metaclass to enable inheritanceMetaFrameBaseclass from which classes can inherit3 hooks (classmethods)_new_pre: called before object creation_new_do: called for object creation_init_pre: called after object creation / before object initialization_init_do: called fo object initialization_init_post: called after object initializationUsage:A quick example which filters None and arguments which do not convert to float
from the kwargs before the object is created:from metaframe import MetaFrame


class A(MetaFrame.as_metaclass(object)):

    @classmethod
    def _new_do(cls, *args, **kwargs):

        nkwargs = dict()
        for key, val in kwargs.items():

            # Remove any argument with a value of None
            if val is None:
                continue

            try:
                val = float(val)
            except:
                continue

            nkwargs[key] = val

        # The only nuisance being the cumbersome call to _new_do
        # super doesn't work
        obj, args, kwargs = cls.__class__._new_do(cls, *args, **nkwargs)
        return obj, args, kwargs

    def __init__(self, **kwargs):
        for key, val in kwargs.items():
            print('key, val, type', key, val, type(val))


a = A(p1=72, p2=None, p3='hello', p4=None, p5='72.5')


# Now with a subclassed MetaB from MetaFrame
# Here super can be applied to find the higher in the hierarchy _new_do

class MetaB(MetaFrame):

    def _new_do(cls, *args, **kwargs):

        nkwargs = dict()
        for key, val in kwargs.items():

            # Remove any argument with a value of None
            if val is None:
                continue

            try:
                val = float(val)
            except:
                continue

            nkwargs[key] = val

        # super can be called directly
        obj, args, kwargs = super(MetaB, cls)._new_do(*args, **nkwargs)
        return obj, args, kwargs


class B(MetaB.as_metaclass()):
    def __init__(self, **kwargs):
        for key, val in kwargs.items():
            print('key, val, type', key, val, type(val))


b = B(p1=27, p2=None, p3='olleh', p4=None, p5='5.27')
Package: metafs
MetaFS is for generating a datastore of file metadata for rapid complex searchesBuild StatusInstallationTo install metafs, simply:$ pip install metafsor using easy_install:$ easy_install metafsor from source:$ python setup.py installGetting StartedThe Filer will not parse files above the max_parse_size provided when initializing (Default: 100000000) and can
use a specific magic file using magic_file when initializing.>>>importmetafs>>>filer=metafs.SQLiteFiler("./test.db")>>>filer.update("/")>>>filer.search("SELECT * FROM files")>>>filer.close()SQLiteFiler Tableshasheshash_id INTEGERhash TEXTmagicsmagic_id INTEGERmagic TEXTpathspath_id INTEGERpath TEXTmtime REALatime REALctime REALfilesfile_id INTEGERpath_id INTEGERfilename TEXTmagic_id INTEGERsize INTEGERmtime REALctime REALatime REALpeheadersfile_id INTEGERexport_dll_id INTEGERcompile_time INTEGERpetype TEXTdllsdll_id INTEGERname TEXTfunctionsfunction_id INTEGERname TEXTfrom_dll_id INTEGERfile_export_dllsfile_id INTEGERdll_id INTEGERfile_import_dllsfile_id INTEGERdll_id INTEGERfile_export_functionsfile_id INTEGERfunction_id INTEGERfile_import_functionsfile_id INTEGERfunction_id INTEGERfile_version_infofile_id INTEGERversion_info_field_id INTEGERversion_info_value_id INTEGERversion_info_fieldsversion_info_field_id INTEGERversion_info_field TEXTversion_info_valuesversion_info_value_id INTEGERversion_info_value TEXTsectionsfile_id INTEGERname TEXTsize INTEGERv_size INTEGERentropy REALanomaliesfile_id INTEGERanomaly TEXTMore InformationFree software: BSD license, see LICENSE.txt for details
Package: meta_func
# Metadata for FunctionsPython decorator function to track metadata on function calls# Example```python@meta_func(ignore_errors=True)def self_mult(n):sleep(0.2)return n*nprint(self_mult(10)) # => 100print(self_mult.log_info()) # => {'time_started': 1422042033.971449, 'warnings': None, 'args': (10,), 'error_info': None, 'time_ended': 1422042034.171857, 'time_elapsed': 0.200408, 'return_value': 100, 'kwargs': None}print(self_mult(15)) # => 225print(self_mult.log_info()) # => {'time_started': 1422042034.172171, 'warnings': None, 'args': (15,), 'error_info': None, 'time_ended': 1422042034.373299, 'time_elapsed': 0.201128, 'return_value': 225, 'kwargs': None}print(self_mult("foo")) # => Noneprint(self_mult.log_info()) # => {'time_started': 1422042034.373651, 'warnings': None, 'args': ('foo',), 'error_info': (<type 'exceptions.TypeError'>, TypeError("can't multiply sequence by non-int of type 'str'",), <traceback object at 0x10fcea098>), 'time_ended': 1422042034.574758, 'time_elapsed': 0.201107, 'return_value': None, 'kwargs': None}```# UsageDecorate a function with `@meta_func()` passing the optional keyword argument ignore_errors = True/False. ignore_errors tells `@meta_func` whether or not to raise any exception your decorated function raises or to solely log to the error_info attribute.# CompatibilityTested compatible with Py2.7 and Py3, but this code is provided as is with no warranty or guarantee, implied or explicit.# More InformationPlease see my blog post here: http://garrens.com/blog/2015/01/23/metadata-for-functions-python-decorator/
Package: metagen
Aboutmetagenis a command line utility to ease generation ofmetadata.xmlfilesfor Gentoo packages.
It is licensed under theGPL v2 licenseand hosted atgitweb.gentoo.org.
Please useGentoo's Bugzillato report bugs aboutmetagen.Example# metagen -e 'somebody@gentoo.org' -n 'Some Body' -t person -f -q
./metadata.xml written

# cat metadata.xml
<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE pkgmetadata SYSTEM 'https://www.gentoo.org/dtd/metadata.dtd'>
<pkgmetadata>
        <maintainer type="person">
                <email>somebody@gentoo.org</email>
                <name>Some Body</name>
        </maintainer>
</pkgmetadata>Usage# metagen --help
usage: metagen [-h] [--version] [--email EMAIL] [--name NAME] [--echangelog]
               [--desc DESC] [--type TYPE] [--long LONG] [--output OUTPUT]
               [--force] [--verbose] [--quiet] [-Q]

optional arguments:
  -h, --help            show this help message and exit
  --version             show program's version number and exit

maintainer arguments:
  --email EMAIL, -e EMAIL
                        Maintainer's email address
  --name NAME, -n NAME  Maintainer's name
  --echangelog, -m      Use name and email address from ECHANGELOG_USER
                        environmental variable. This is a shortcut for -e
                        <email> -n <name>
  --desc DESC, -d DESC  Description of maintainership
  --type TYPE, -t TYPE  Maintainer type as of GLEP 67; valid values are:
                        "person", "project", "unknown"

package arguments:
  --long LONG, -l LONG  Long description of package.

operation arguments:
  --output OUTPUT, -o OUTPUT
                        Specify location of output file.
  --force, -f           Force overwrite of existing metadata.
  --verbose, -v         Verbose. Output of file to stdout. (default)
  --quiet, -q           Squelch output of file to stdout.
  -Q                    Do not write file to disk.
Package: metagenome-atlas
ATLASDocumentationInstallAll dependencies are installed viacondausing thebiocondachannel.
The workflow and some dependencies require Python 3.The intended usage requiresconda.Further dependencies will be installed on the first execution of the assembly
or annotation protocol and re-used on subsequent executions of the protocols.For more information related to bioconda, see:https://bioconda.github.io/As a New EnvironmentWithconda, execute:conda create -n atlas -c bioconda -c conda-forge python>=3.6 snakemake pandas bbmap=37.78 click ruamel.yaml biopythonLoad the environment:source activate atlasInstallatlas:pip install -U metagenome-atlasor install the up to date atlas from github:git clone https://github.com/metagenome-atlas/atlas.git
cd atlas
pip install .Getting StartedAfter installing, one needs to download the required databases and create a sample configuration file.DatabasesTo download the databases and their respective metadata databases:atlas download -o ~/databasesThe downloads use approximately 100 GB of disk space.Configuration FileTo create a configuration file run:atlas make-config --database-dir ~/databases config.yaml ~/directory_with_fastqsSample names and file paths along with default settings will populate
config.yaml. ThisYAMLfile can be updated
with any text editor.Sample names should be A-Z characters and can be dash ("-") delimited.For complete documentation, please see:AssemblyAfter editing your configuration file and adjusting any additional parameters
we run assemblies across our samples using:atlas assemble config.yamlBy default, this will write results into our current working directory across
the total number of CPU cores available.LicenseBSD-3.DisclaimerThis material was prepared as an account of work sponsored by an agency of the
United States Government.  Neither the United States Government nor the United
States Department of Energy, nor Battelle, nor any of their employees, nor any
jurisdiction or organization that has cooperated in the development of these
materials, makes any warranty, express or implied, or assumes any legal
liability or responsibility for the accuracy, completeness, or usefulness or
any information, apparatus, product, software, or process disclosed, or
represents that its use would not infringe privately owned rights.Reference herein to any specific commercial product, process, or service by
trade name, trademark, manufacturer, or otherwise does not necessarily
constitute or imply its endorsement, recommendation, or favoring by the United
States Government or any agency thereof, or Battelle Memorial Institute. The
views and opinions of authors expressed herein do not necessarily state or
reflect those of the United States Government or any agency thereof.PACIFIC NORTHWEST NATIONAL LABORATORY operated by BATTELLE for the UNITED
STATES DEPARTMENT OF ENERGY under Contract DE-AC05-76RL01830
Package: metagenomix
metagenomixmetagenomixis a pipelinecreator,monitor,manager,exporterandmergerthat takes care of writing the command-lines for any shotgun metagenomics
software, either as bash scripts orSlurm/Torquejobs (incl. scratch space usage), based on user-definedconfigurationsfor databases, co-assemblies, strain foci, as well as software-specific or
computing resource parameters (incl. memory, scratch relocations, modules
and conda environments).Outputs are scripts that the user needs to run sequentially, which typically
can be handled by packages such assnakemake: this is not (yet)
used here asmetagenomixis only meant to facilitate the creation,
monitoring, management and access of shotgun metagenomic analyses results for
personalized pipelines including any software.Any software? Well, if not already in thesoftwares list,
someone will need to add it to metagenomix, following the instructions tocontribute code.In a nutshell,Please read the full documentation for more details, using theWiki pagesInstallationpip install metagenomixorpip install --upgrade git+https://github.com/FranckLejzerowicz/metagenomix.gitDepencenciesWhile a container solution with all the softwares and conda environments is
being develop, it currently is the responsibility of the user to install all
the databases and softwares that the pipeline will allow you to prepare
command-lines for. Some softwares necessitate to be present either as a
single binary file or with an entire folder (e.g., pre-trained models or
scripts). Since some softwares require the user to edit configurations
after install, some level of manual installation/tuning will be needed
before usage.UsageUsage: metagenomix [OPTIONS] COMMAND [ARGS]...

  Metagenomix command line manager

Options:
  --version  Show the version and exit.
  --help     Show this message and exit.

Commands:
  create   Write jobs for your pipeline configuration.
  export   Prepare an archive for specific pipeline outputs.
  manage   Edit the contents of your pipeline output folder.
  monitor  Check IO/job status of your pipeline configuration.
  merge    Combine the per-sample outputs into feature tables.Detailed explanations for each command atRunningWiki page
Package: metagentools
metagentoolsThis repo includes the code developed as part of a research project
aiming at using deep learning techniques to classify a wide range of
corona virus based on NG reads.The code is structured intogeneral codeandsubmodules:General codeis used across the project and can also be reused for
other projects:art: wrapper code to use ART-ILLUMINA read simulator package from
a jupyter notebook. MUST BE RUN LOCALLY.core: base classes used accross the packagewandb: utility classes to open, retrieve, use WandB runs and
artifacts accross the projectSubmodulescover code written for a specific part of the projectcnn_virus: all specific code related to the evaluation and
improvement of CNN Virus modelarchitecture: all code related to evaluated and trained
architecturesdata: data processing, iterators and parsing functions to handle
datautils: other utility functionsThis is a work in progress, and some functions or modules may be broken.
We may or may not be able to provide support depending on the when the
support is requested.Installation:pip install metagentoolsfor the stable versionpip install git+https://github.com/vtecftwy/metagentools.git@mainfor the latest committed versionRequirements:python 3.8 or higher is required for this packagepackage tested with python 3.10
Package: metagit
MetagitGit for Git RepositoriesIf you:have a directory full of Git reposwant to version-control the configuration of those reposlike GitThen you might find Metagit useful. It offers a Git-like experience for managing Git repos.DemoInitialize a Metagit repository:$ cd ~/projects/    # or wherever you store your Git projects, or use metagit -C
$ metagit init      # creates .metagit, a Git repo where tracked project configs are kept
Initialized Metagit repository in /home/dmtucker/projects
$ metagit status
Untracked projects
  (use "metagit add <project>..." to begin tracking)
	project1/
	project2/Track changes to project config:$ metagit add project1/ project2/    # start tracking some projects
$ git -C project1/ remote add foo git@somehost.com:foo/project1.git
$ rm -rf project2/
$ metagit status
Changes
  (use "metagit add/rm <project>..." to accept changes)
  (use "metagit restore <project>..." to undo changes)
	modified: project1
	deleted:  project2Git tracks lines in files. Metagit tracks config in projects.$ metagit diffdiff --git a/project1 b/project1index 515f483..93a874c 100644--- a/project1+++ b/project1@@ -3,3 +3,6 @@filemode = truebare = falselogallrefupdates = true+[remote "foo"]+	url = git@somehost.com:foo/project1.git+	fetch = +refs/heads/*:refs/remotes/foo/*diff --git a/project2 b/project2deleted file mode 100644index 515f483..0000000--- a/project2+++ /dev/null@@ -1,5 +0,0 @@-[core]-	repositoryformatversion = 0-	filemode = true-	bare = false-	logallrefupdates = trueThis example deletedproject2so as to demonstratemetagit restore(below), but keep in mind,ONLY CONFIGURATION IS TRACKED.
If you need all ofproject2back, you should restore from a backup.$metagitrestoreproject2# only restores git config! not branches/tags/etc.$metagitaddproject1# add the project again to keep the changes$metagitrmproject1# stop tracking a project (does not affect the actual project)About.metagit.metagitis just another Git repo in your projects folder. It is created bymetagit init, which automatically starts tracking it.
Whenmetagit addis used to start tracking a project, that project's.git/configfile is copied into.metagitand committed.$ git -C .metagit log
36cec91 (HEAD -> master) Remove project1
a1ed312 Add project1
185ed15 Add project2
e4d274d Add project1
1e16b5e Add .metagitPro tip: Consider a clone of the.metagitrepo a (Metagit) "clone" of your projects folder.
For example, push the.metagitrepo to GitHub to allow for easy provisioning of new dev machines:$mkdir~/projects
$cd~/projects/
$gitclonegit@github.com:dmtucker/.metagit.git
...
$metagitrestoreproject2
Package: metagraph
MetagraphPython library for running graph algorithms on a variety of hardware backends.
Data representing the graph will be automatically converted between available hardware options
to find an efficient solution.VisitReadTheDocs pagefor more details.Development EnvironmentTo create a new development environment:conda env create
conda activate mg
pre-commit install  # for black
python setup.py developTo run unit tests + coverage automaticallypytestTo build web documentationcd docs
make htmlTo build PDF documentationcd docs
make latexpdf
Package: metagym
MetaGymMetaGym provides abundant environments for benchmarking Reinforcement Learning and Meta Reinforcement LearningEnvironments UpdatingLiftSim：Simulator for Evelvator Dispatching (Sep, 2019)Quadrotor: 3D Quadrotor simulator for different tasks (Mar, 2020)Quadrupedal: Quadrupedal robot adapting to different terrains (Seq, 2021)MetaMaze: Meta maze environment for 3D visual navigation (Oct, 2021)Navigator2D: Simple 2D navigator meta environment (Oct, 2021)MetaLocomotion: Locomotion simulator with diverse geometries (June, 2022)