Saravanan666/pypi-org huggingface dataset:
Package: dmp
dmpGoogle'sDiff Match and Patchlibrary, packaged for modern Python.Installdmp is supported on Python 2.7 or Python 3.4 or newer.  You can install it from PyPI:python-mpipinstalldmpUsageTo make it possible to coexist with upstream diff-match-patch (and to reduce
boilerplate), this makes the normalAPIavailable to import asdmpinstead
ofdiff_match_patch. The rest of the API remains unchanged, although helper
functions may be added in future updates.Generating a patchset (analogous to unified diff) between two texts:fromdmpimportdiff_match_patchdmp=diff_match_patch()patches=dmp.patch_make(text1,text2)diff=dmp.patch_toText(patches)Applying a patchset to a text can then be done with:fromdmpimportdiff_match_patchdmp=diff_match_patch()patches=dmp.patch_fromText(diff)new_text,_=dmp.patch_apply(patches,text)Original READMEThe Diff Match and Patch libraries offer robust algorithms to perform the
operations required for synchronizing plain text.Diff:Compare two blocks of plain text and efficiently return a list of differences.Diff DemoMatch:Given a search string, find its best fuzzy match in a block of plain text. Weighted for both accuracy and location.Match DemoPatch:Apply a list of patches onto plain text. Use best-effort to apply patch even when the underlying text doesn't match.Patch DemoOriginally built in 2006 to power Google Docs, this library is now available in C++, C#, Dart, Java, JavaScript, Lua, Objective C, and Python.ReferenceAPI- Common API across all languages.Line or Word Diffs- Less detailed diffs.Plain Text vs. Structured Content- How to deal with data like XML.Unidiff- The patch serialization format.Support- Newsgroup for developers.LanguagesAlthough each language port of Diff Match Patch uses the same API, there are some language-specific notes.C++C#DartJavaJavaScriptLuaObjective-CPythonA standardized speed test tracks therelative performance of diffsin each language.AlgorithmsThis library implementsMyer's diff algorithmwhich is generally considered to be the best general-purpose diff. A layer ofpre-diff speedups and post-diff cleanupssurround the diff algorithm, improving both performance and output quality.This library also implements aBitap matching algorithmat the heart of aflexible matching and patching strategy.
Package: dmp-cli
A command-line-interface fordjango-mako-plus.Documentation:https://knowsuchagency.github.io/dmp-cliSource:https://github.com/knowsuchagency/dmp-cliInstallationpipenv install -d dmp-cli
# or
pip install dmp-cliUsageTo create a newdjango-mako-plusproject usedmp new project [NAME]Similarly, for new appsdmp new app [NAME]These are just shortcuts forpython3 -m django startproject --template=http://cdn.rawgit.com/doconix/django-mako-plus/master/project_template.zip [NAME]
# and
python3 manage.py startapp --template=http://cdn.rawgit.com/doconix/django-mako-plus/master/app_template.zip --extension=py,htm,html [NAME]… but use the templates on your local filesystem from the currently installed version ofDMP, rather than fetching the templates from github.
Package: dm.pdb
Pdb, the Python debugger, has some limitations.
This extension tries to get rid of some of them.Things donesetting breakpointsAllowfilenameto specify a module as well.Allow setting breakpoints from outside viado_break.
This is useful for debug setups.exception displayWe store the exception in thePdbinstance.
A new commandexception(abbreviatede) callsprint_exceptionon the stored exception value.wherecommand improvementseach frame identifies its level (the top frame is at level 0,
the one below it at level 1, etc…)the current frame is marked as suchwheregets optional argumentsnumberandendnumbercontrols how many frames are displayed (default:maxint),endat what level the display ends, negative numbers count from
the bottom (default:-1, i.e. the bottom frame).new commandframe level(abbr:f)switch to call frame atlevelwithout argument, provide info about current frameallow to customize the display of thewherecommand
and of tracebacks
e.g. to display additional debugging information as
provided by Zope (for example).This can be done by customizinggetAdditionalFrameInfo.Things not yet doneReturning fromdebugoccasionally returns tostoprather than where we entered the recursive debugger.
This is often nasty (and should be fixed)zpdbThe modulezpdbcontains a debugger targeting Zope development. It displays
additional debug information often used for Zope development
(–>__traceback_info__,__traceback_supplement__). It depends
on the packagezExceptions.Version history1.4new commandti[nfo]command forzpdbto control the display
of__traceback_info__and__traceback_supplement__information1.3Python 3 compatibility1.2fixed:do_breaksometimes failed to resolve functions correctly.1.1improved handling of errors in command argument parsingPython 2.6 compatibility1.0.4work around Python monkey patchingset_traceinside a doctest.1.0.3fixdebugto use the currentPdbclass, not Python’s.1.0.2let external calls todo_breakwork even after the firstrun.
Package: dm.plone.advancedquery
Small package to allow the use ofProducts.AdvancedQuery(version 4+)
in Plone 5.2+.It provides a (required) adapter for Plone’sCatalogTooland an (optional) adapter for Plone’sExtendedPathIndex.You must ensure that itsconfigure.zcmlis “executed” at startup.
This usually is ensured by extending thebuildoutdefinitionzcmlwithdm.plone.advancedquery.
Package: dm.plonepatches.reload
This package provides patches to improveplone.reload.Calling itspatch_top_level_reloadapplies a patch
for (#10)
making it possible to reload top level modules in place.Calling itspatch_handle_super_and_decoratorsapplies a patch
for (#1)
and (#11)
improving the reload on methods involvingsuperand classmethods
(in Python 3). In addition, it
improves the reload of simple decoratored functions/methods.BE WARNED: even with all patches applied,plone.reloadis not yet perfect. For example, it still cannot change reliably
function default values for parameters.
Should you observe surprising behaviour after
you made a reload, restart and see whether the behaviour changes.History1.1newpatch_handle_super_and_decoratorspatch_handle_super_and_methodsis now an alias forpatch_handle_super_and_decorators1.0initial version
Package: dmponline
DMPonlineA Python interface to the DMPonline API.Usage example:fromdmponlineimportDMPonline# initialize DMPonline classdmp_api=DMPonline(<YOUR-DMPONLINE-API-TOKEN>,token_user=<YOUR-DMPONLINE-USER-EMAIL>)# retreive pandas dataframe with all DMPs in your organizationdmps=dmp_api.plan_statistics(params={'remove_tests':'false'})Command lineThe package also ships a command line script to retreive an overview of all questions in a particular DMP.Usage example:question_overview-iDMPONLINE_PLAN_ID-t<YOUR-DMPONLINE-API-TOKEN>
Package: dmppl
dmppl - Dave McEwan's Personal Python LibraryLibrary is split into several modules:base - Useful in almost every project.color - Color (colour in English) related stuff.fx - Fixed point arithmetic using NumPy data types.math - Useful for mathematical code.nd - ND-array operations.prng - Pseudo-Random Number Generators. Currently just xoroshiro128+.stats - Statistics. Currently just for binary classifiers.test - Helpers for unit testing.toml - Save/load TOML files with optional compression.vcd - Value Change Dump (from Verilog) reader and writer.yaml - Extended YAML parser, useful for implementing DSLs on top of YAML.Some useful scripts are provided indmppl/scriptswhich show how to use some
parts of the library modules.beamer-times - Report the time breakdown for beamer presentations by reading
annotations in the LaTeX.parvec - Generate pseudorandom parameter vectors for design space
exploration with repeatable results.vcd-utils - Convert VCD (Verilog IEEE1364) files to/from YAML and CSV,
extract information, or cleanup dodgy VCDs using the forgiving reader
with strict writer.svg2png - Simple wrapper around inkscape to export SVGs to PNGs.Some experiments are given indmppl/experimentswhich are likely not useful
to most people.relest - (Relationship Estimation) Model SoC signal relations.
Part of my PhD project presented at LOD2019.eva - (EVent Analysis) Measure and visualize correlations between
measurements.
Part of my PhD project.Usessemantic versioningSupports multiple versions of Python (2.7, 3.6, 3.7, 3.8).At some point I'll drop support for 2.7 and <3.7, probably when distros stop
including them which isn't likely soon.Dropping support for <3.6 allows use of type annotations.Dropping support for <3.8 allows use of newer language features such as the
walrus operator, and getting rid of version-specific hacks like
`openCsvKwargs'.
Don't necessarily need to wait for distros to support 3.8+, only wait for
3.6+ since altinstall+venv seems to be the recommended way forward.
low priorityKeeping support for old versions allows compatibility with other programs
which have built-in Python shells,
TODO: Collate version dependencies for various programs and decide what I
want to support: Inkscape, Blender, nextpnr, yosys, ...pip packages are not specified with versions as it is assumed (naively) that
the latest releases won't break anything.See theMakefileto wrap up common actions and provide examples of how to run
things.make venvto create a virtual environment for each supported version.make unittestto perform all unit tests.make unittest-coverageto perform all unit tests collecting code coverage
and produce HTML reports.make distto build a setuptools distribution.make cleanto remove all generated files.TODO:Upload to PyPI.mypyIntegrate relest-ffnnImplement vch, vchliteExpand documentation hereSphinx/readthedocsChange convention to snake case for idiomatic Python and rust?
Package: dm.profile
“profile.Profile” improvementI started the implementation of this module in order to improve
the profiler’s calibration using statistical methods.
Unfortunately, variance proved to be far too high and my calibration trials
did not converge even for large sample sets.
I had to abandon this approach.The code is still there (in case anyone would like to look what I have
tried) but I do not use it.“pstats.Stats” improvementBeside the profiler’s not so good calibration, I was not
satisfied with “pstats” format especially for the caller and callee analysis
and with its insistence to write to “stdout”.Therefore, I derived a new classStatsfrompstats.Statsand added new
methodsshowStats,showCallersandshowCalleeswhich
correspond toprint_stats,print_callersandprint_callees.
They write to a file set withsetOutputFile(defaultsys.stdout)
and use a different output format. ForshowCallersandshowCalleesit is much more readable than the format used byprint_callersandprint_callees. I am not sure that this is also the case for the
format used forshowStatsversusprint_stats.When you are using “Stats.print_callers” or “Stats.print_callees”
and find their output unreadable, then a switch to “dm.profile.Stats”
may be profitable for you.The methodshowHeaderoutputs header information (with involved files,
top level functions, number of calls and profiled time).
Package: dmpy
DistributedMakefor Python.This project usesSemantic versioning.Installation from pypipip install dmpyInstallation from githubpip install git+git://github.com/kvg/dmpy.git attrsExamplesContents of an example dmpy script:# example.py
from dmpy import DistributedMake, get_dm_arg_parser

args = get_dm_arg_parser().parse_args()
m = DistributedMake(args_object=args)

m.add("test_output_file", None, "echo 'hi world'")
m.execute()Then run example.py:# get a dry-run of all commands to be executed
python example.py

# run all commands
python example.py -rTestsmake testBugsPlease raise an issue onthe github pageto report a bug.DevelopmentWe now have a Pipfile and Pipfile.lock for use withpipenv!Please always update your Pipfile.lock (pipenv lock) before making a PR.
Package: dm-pybloom
dm_pybloom=======.. image:: https://travis-ci.org/jaybaird/python-bloomfilter.svg?branch=master:target: https://travis-ci.org/jaybaird/python-bloomfilter``dm_pybloom`` is a module that includes a Bloom Filter data structure along withan implmentation of Scalable Bloom Filters as discussed in:P. Almeida, C.Baquero, N. Preguiça, D. Hutchison, Scalable Bloom Filters,(GLOBECOM 2007), IEEE, 2007.Bloom filters are great if you understand what amount of bits you need to setaside early to store your entire set. Scalable Bloom Filters allow your bloomfilter bits to grow as a function of false positive probability and size.A filter is "full" when at capacity: M * ((ln 2 ^ 2) / abs(ln p)), where Mis the number of bits and p is the false positive probability. When capacityis reached a new filter is then created exponentially larger than the lastwith a tighter probability of false positives and a larger number of hashfunctions... code-block:: python>>> from dm_pybloom import BloomFilter>>> f = BloomFilter(capacity=1000, error_rate=0.001)>>> [f.add(x) for x in range(10)][False, False, False, False, False, False, False, False, False, False]>>> all([(x in f) for x in range(10)])True>>> 10 in fFalse>>> 5 in fTrue>>> f = BloomFilter(capacity=1000, error_rate=0.001)>>> for i in xrange(0, f.capacity):...     _ = f.add(i)>>> (1.0 - (len(f) / float(f.capacity))) <= f.error_rate + 2e-18True>>> from dm_pybloom import ScalableBloomFilter>>> sbf = ScalableBloomFilter(mode=ScalableBloomFilter.SMALL_SET_GROWTH)>>> count = 10000>>> for i in xrange(0, count):...     _ = sbf.add(i)...>>> (1.0 - (len(sbf) / float(count))) <= sbf.error_rate + 2e-18True# len(sbf) may not equal the entire input length. 0.01% error is well# below the default 0.1% error threshold. As the capacity goes up, the# error will approach 0.1%.
Package: dmri-amico
AMICO - Accelerated Microstructure Imaging via Convex OptimizationImplementation of the linear framework for Accelerated Microstructure Imaging via Convex Optimization (AMICO) described inDaducci, Alessandro, et al. 2015DocumentationThe documentation, including theinstallation guideas well as a series of demos/tutorials to show how to use AMICO to fit different models to your data, can be found in thewiki.SupportFor questions, requesting assistance, suggesting enhancements or new ideas as well as for reporting bugs, pleaseopen an issue.
Package: dmri-commit
The reconstructions recovered with existing tractography algorithms are not really quantitative even though diffusion MRI is a quantitative modality by nature. As a matter of fact, several techniques have been proposed in recent years to estimate, at the voxel level, intrinsic micro-structural features of the tissue, such as axonal density and diameter, by using multi-compartment models.
Convex Optimization Modeling for Microstructure Informed Tractography (COMMIT) implements a novel framework to re-establish the link between tractography and tissue micro-structure.Starting from an input set of candidate fiber-tracts, which can be estimated using standard fiber-tracking techniques, COMMIT models the diffusion MRI signal in each voxel of the image as a linear combination of the restricted and hindered contributions generated in every location of the brain by these candidate tracts. Then, COMMIT seeks for the effective contribution of each of them such that they globally fit the measured signal at best.These weights can be easily estimated by solving a convenient global convex optimization problem and using efficient algorithms. Results clearly demonstrated the benefits of the proposed formulation, opening new perspectives for a more quantitative and biologically-plausible assessment of the structural connectivity in the brain.
Package: dmri-dicelib
Diffusion Imaging and Connectivity Estimation (DICE) libraryVarious algorithms developed by the DICE lab to process/manipulate diffusion MRI objects, e.g. streamlines, tractograms etc.InstallationOpen the system shell, go to the folder where you downloaded this repository and run:$pipinstall.-UThe library is now available in your Python interpreter and can be imported as:>>>importdicelibAlso, all thedice_*.pyscripts in thebin/folder will be available in your system path.
Package: dmriprep
[Documentation]
[Support at neurostars.org]AboutThe preprocessing of diffusion MRI (dMRI) involves numerous steps to clean and standardize
the data before fitting a particular model.
Generally, researchers create ad-hoc preprocessing workflows for each dataset,
building upon a large inventory of available tools.
The complexity of these workflows has snowballed with rapid advances in
acquisition and processing.
dMRIPrep is an analysis-agnostic tool that addresses the challenge of robust and
reproducible preprocessing for whole-brain dMRI data.
dMRIPrep automatically adapts a best-in-breed workflow to the idiosyncrasies of
virtually any dataset, ensuring high-quality preprocessing without manual intervention.
dMRIPrep equips neuroscientists with an easy-to-use and transparent preprocessing
workflow, which can help ensure the validity of inference and the interpretability
of results.The workflow is based onNipypeand
encompasses a large set of tools from other neuroimaging packages.
This pipeline was designed to provide the best software implementation for each state of
preprocessing, and will be updated as newer and better neuroimaging software
becomes available.dMRIPrep performs basic preprocessing steps such as head-motion correction,
susceptibility-derived distortion correction, eddy current correction, etc.
providing outputs that can be easily submitted to a variety of diffusion models.Getting involvedWe welcome all contributions!
We’d like to ask you to familiarize yourself with ourcontributing guidelines.
For ideas for contributing todMRIPrep, please see the current list ofissues.
For making your contribution, we use the GitHub flow, which is
nicely explained in the chapterContributing to a Projectin Pro Git
by Scott Chacon and also in theMaking a change sectionof our guidelines.
If you’re still not sure where to begin, feel free to pop intoMattermostand introduce yourself!
Our project maintainers will do their best to answer any question or concerns and will be happy to help you find somewhere to get started.Want to learn more about our future plans for developingdMRIPrep?
Please take a look at ourmilestones boardandproject roadmap.We ask that all contributors todMRIPrepacross all project-related spaces (including but not limited to: GitHub, Mattermost, and project emails), adhere to ourcode of conduct.
Package: dmr-kaitai
DMR Kaitai protocolsThis repository containsKaitai-IOdefinitions of various protocols that can be met in DMR
networks, both amateur and professionalContributingPlease follow these stepsAdd or update.ksydefinition fileRun re-compilation script./rebuild-all.sh(generates .py from .ksy and formats them usingblackformatter)Provide at least one example dataset to check the implementation withAnd specifically avoid these mistakes:Modifying generated python sources / dissectors after generatingSupportedETSICSBK (all from TS 102 361-2)Data Blocks (Rate 1, 1/2, 3/4 confirmed and unconfirmed)Data Headers (UDT, Response, Unconfirmed, Confirmed, Short Data Defined, Short Status Precoded, Proprietary)UDP IPv4 Compressed headerFULL LC (Full Link Control, Group/Individual voice, GPS Info, Talker Alias + continuation blocks)HAMHomebrew 2015 (per DL5DI, G4KLX, DG1HT 2015)Mmdvm 2020 (with non-standardized features in mmdvm-host, dmr-gateway, hblink and others)HyteraData Delivery StatesData Transmit ProtocolGPS DataHDAP (Hytera DMR Application Protocol)HRNP (Hytera Radio Network Protocol)HSTRP (Hytera Simple Transport Reliability Protocol)IPSC (with separate definition for IPSC Heartbeat)LP (Location Protocol)RCP (Radio Control Protocol)RRS (Radio Registration Service)RTTP (Real-time Transport Protocol)TP (Telemetry Protocol)TMP (Text Message Protocol)MotorolaARS (Automatic Registration Service)MBXML (Motorola Binary XML) - only partially supported nowTMP (Text Message Protocol)Testingrunmake test(or pytest directly like thisenv PYTHONPATH=. pytest -vrpP)ToolsIf you install throughpip3 install dmr-kaitaior manually from repo usingpython3 setup.py install, you should be
provided with command-line tools,
to pass hex payloads of various protocols to and see what is inside. Tools support multiple hex payloads, each hex
string is treated as separate PDU to parse and pretty-printUsage example> debug-hytera-hrnp 7e0400002010000100189b6002040005006400000001c403
{'block': 0,
 'checksum': 39776,
 'data': {'checksum': 196,
          'data': {'data': {'data': b'd\x00\x00\x00\x01'},
                   'message_length': 5,
                   'service_type': <ServiceTypes.radio_identification_request: 4>},
          'is_reliable_message': False,
          'message_footer': b'\x03',
          'message_header': <MessageHeaderTypes.radio_control_protocol: 2>},
 'destination_id': 16,
 'header_identifier': b'~',
 'hrnp_packet_length': 24,
 'opcode': <Opcodes.data: 0>,
 'packet_number': 1,
 'source_id': 32,
 'version': 4}Full listing of available toolsdebug-homebrewdebug-mmdvmdebug-hytera-rcpdebug-hytera-hdapdebug-hytera-hrnpdebug-hytera-hstrpdebug-hytera-tmpdebug-hytera-lpdebug-hytera-ipscdebug-hytera-rrsdebug-hytera-ddsdebug-hytera-dtpdebug-motorola-arsdebug-motorola-tmpLicenseAGPLv3.0 see LICENSE file for more info
Package: dm-robotics-agentflow
AgentFlow: A Modular Toolkit for Scalable RL ResearchOverviewAgentFlowis a library for composing Reinforcement-Learning agents. The core
features that AgentFlow provides are:tools for slicing, transforming, and composingspecstools for encapsulating and composing RL-tasks.Unlike the standard RL setup, which assumes a single environment and an agent,AgentFlowis designed for the single-embodiment, multiple-task regime. This
was motivated by the robotics use-case, which frequently requires training RL
modules for various skills, and then composing them (possibly with non-learned
controllers too).Instead of having to implement a separate RL environment for each skill and
combine them ad hoc, withAgentFlowyou can define one or moreSubTaskswhichmodifya timestep from a single top-level environment, e.g. adding
observations and defining rewards, or isolating a particular sub-system of the
environment, such as a robot arm.You thencomposeSubTasks with regular RL-agents to form modules, and use a
set of graph-building operators to define the flow of these modules over time
(hence the nameAgentFlow).The graph-building step is entirely optional, and is intended only for use-cases
that require something like a (possibly learnable, possibly stochastic)
state-machine.ComponentsControl FlowExamples