Saravanan666/pypi-org huggingface dataset:
Package: diffit
Diff-it: Data DifferOverviewPrerequisitesExtras for macOSGetting Started(macOS Users) Upgrading GNU MakeCreating the Local EnvironmentLocal Environment MaintenanceHelpRunning the Test HarnessFAQsOverviewdiffitwill report differences between two data sets with similar schema.Refer toDiffit's documentationfor detailed instructions.PrerequisitesGNU makePython 3 Interpreter.We recommend installing pyenvDockerGetting StartedMakesteris used as the Integrated Developer Platform.(macOS Users only) Upgrading GNU MakeFollowthese notesto getGNU make.Creating the Local EnvironmentGet the code and change into the top levelgitproject directory:git clone git@github.com:loum/diffit.git && cd diffitNOTE:Run all commands from the top-level directory of thegitrepository.For first-time setup, get theMakester project:git submodule update --initInitialise the environment:make init-devLocal Environment MaintenanceKeepMakester projectup-to-date with:git submodule update --remote --mergeHelpThere should be amaketarget to get most things done. Check the help for more information:make helpRunning the Test HarnessWe usepytest. To run the tests:make testsFAQsQ. Why do I getWARNING: An illegal reflective access operation has occurred?Seems to be related to the JVM version being used. Java 8 will suppress the warning. To check available Java versions on your Mac try/usr/libexec/java_home -V. Then:export JAVA_HOME=$(/usr/libexec/java_home -v <java_version>)top
Package: diffjeom
diffjeomDifferential geometry powered byjax. Given a
metric function, this package lets you compute:Christoffel symbols of the second kind(get_christoffel2);The Riemann curvature tensor(get_riemann);The Ricci tensor(get_ricci_tensor);The Ricci scalar/scalar curvature(get_ricci_scalar).These work with the usual jax transformations likejitandvmap.The functionscheck_christoffel2_sym,check_riemann_sym,check_diff_bianchiandcheck_ricci_tensor_symare provided to check
identities for these objects.Getting startedInstall withpipinstalldiffjeomThis will install jax and jaxlib if you don't already have them. The tests
require numpy and can be run withpytest. Check them out for some usage
examples.
Package: diffjpeg-jax
DiffJPEG: A Jax ImplementationThis is a Jax implementation of the differentiable JPEG compression algorithm, based on thePyTorch implementation.RequirementsJAXInstallationCan be installed with pip:pipinstalldiffjpeg_jaxUsageUnlike the PyTorch version, this is ML library agnostic, so it simply is implemented as a function.fromdiffjpeg_jaximportdiff_jpegimg=...# (H, W, C)jpeg=diff_jpeg(img,quality=75)Note: The implementation is not wrapped in JIT, so make sure to do that if you want to. For batch processing just use vmap.
Package: diff-json-lr
diff_jsonPython versionBasic Usageimportjsonfromdiff_jsonimportJSONDiffold_json=Nonenew_json=None# JSON data can be passed as an encoded string...withopen("old_version.json")asov:old_json=ov.read()withopen("new_version.json")asnv:new_json=nv.read()# ...or as a Python object (dict, list, or tuple)# old_json = {'a': 1}# new_json = {'a': 2, 'b': 77}diff=JSONDiff(old_json,new_json)diff.run()patch_data=diff.get_patch()print(json.dumps(patch_data,indent=2))"""[{"op": "replace","path": "/a","value": 2},{"op": "add","path": "/b","value": 77}]"""NOTE: The value ofpatch_dataabove will be used in examples below.Classes by process sectionPathfindingEach XPath within both JSON documents is cataloged. The pathfinding module provides a sortable XPath representation
class, and a sibling class that searches for an XPath and an optional wildcard for children or all descendants.diff_json.pathfinding.XPathimportdiff_json.pathfinding.XPathxpath1=XPath([1,"op"])xpath2=XPath.from_string("/1/op")Represents an XPath as alistof segments, such as[1, "op"]. When cast to a string, it will output the proper XPath"/1/op". Each XPath has an ID string representing the structure, allowing for a list of XPath objects to be sorted.diff_json.pathfinding.XPathMatchfromdiff_json.pathfindingimportXPath,XPathMatchxpath=XPath([1,"op"])xpm1=XPathMatch([1,"op"],"**")xpm2=XPathMatch.from_path_string("/1/op/**")xpm3=xpath.to_match("**")An XPathMatch allows for searching a list of XPath objects to find the descendants of a given path. If no wildcard is
provided, it will match that path only. The wildcard"*"will find the path's direct children, while"**"will find
all descendants.MappingJSON documents passed in for diffing are first mapped into a listing of XPaths pointing to a representative object.
While slower in finding the difference, this process greatly increases the speed of creating output data.diff_json.mapping.JSONElementfromdiff_json.pathfindingimportXPathfromdiff_json.mappingimportJSONElementxpath=XPath([1,"op"])element=JSONElement(xpath,patch_data[1]['op'])Represents a single value within a JSON document, and holds a variety of metadata about that element.diff_json.mapping.JSONMapfromdiff_json.mappingimportJSONMapmap=JSONMap(patch_data)This map of the entire JSON document holds a listing of all XPaths discovered, pointing to their respective JSONElement.Diffingdiff_json.diffing.JSONDifffromdiff_json.diffingimportJSONDiffdiff=JSONDiff([],patch_data)The diff itself. During initialization, the two JSON documents will be passed to create two JSONMap objects. The diff is
not calculated immediately, but only oncediff.run()is called. Once calculated, a patch document can be obtained by
callingdiff.get_patch(), or it can be passed to an output generator. The JSONDiff constructor takes a number of
parameters that determine how the diff is executed: any paths that are ignored from diffing, which paths to count
as a diff operation, whether to track movement of elements within an array, etc. See the class docstring for complete
information.
Package: difflearn
difflearnThis is a python tool packages for differential network inference (DNI).This package mainly contains:Differential network inference models:Pinv;NetDiff;BDgraph;JGL;JGLCV;Expression profiles simulation algorithms:distributions:Gaussian;Exponential;Mixed;network structures:random;hub;block;scale-free;Visulization tools and some useful utilities.Requirements:Before installation, you should:installpytorchyourself according to your environment;installR languageand R packages as follows:JGLinstall.packages("JGL")BDgraph:install.packages("BDgraph")NetDiff:library(devtools)install_git("https://gitlab.com/tt104/NetDiff.git")Please note:
If you have several different versions ofR, you should specify the version installed with above packages with:importosos.environ["R_HOME"]="your path to R"InstallationEasily run:pip install difflearnQuick Startfromdifflearn.simulationimport*fromdifflearn.modelsimportRandom,Pinv,NetDiff,BDGraph,JointGraphicalLasso,JointGraphicalLassoCVfromdifflearn.utilsimport*fromdifflearn.visualizationimportshow_matriximportmatplotlib.pyplotaspltdata_params={'p':10,'n':1000,'sample_n':100,'repeats':1,'sparsity':[0.1,0.1],'diff_ratio':[0.5,0.5],'parallel_loops':1,'net_rand_mode':'BA','diff_mode':'hub','target_type':'float','distribution':'Gaussian','usage':'comparison',}data=ExpressionProfilesParallel(**data_params)modelrandom=Random()modelPinv=Pinv()modelBDgraph=BDGraph()modelNetDiff=NetDiff()modelJGL=JointGraphicalLasso()modelJGLCV=JointGraphicalLassoCV()(sigma,delta,*X)=data[0]modelrandom.fit(X)modelPinv.fit(X)modelBDgraph.fit(X)modelNetDiff.fit(X)modelJGL.fit(X)modelJGLCV.fit(X)fig,axs=plt.subplots(4,2,figsize=(7,7))show_matrix(vec2mat(delta)[0],ax=axs[0][0],title='Ground Truth')axs[0][1].set_visible(False)show_matrix(modelrandom.delta,ax=axs[1][0],title='Random')show_matrix(modelPinv.delta,ax=axs[1][1],title='Pinv')show_matrix(modelBDgraph.delta,ax=axs[2][0],title='BDgraph')show_matrix(modelNetDiff.delta,ax=axs[2][1],title='NetDiff')show_matrix(modelJGL.delta,ax=axs[3][0],title='JGL')show_matrix(modelJGLCV.delta,ax=axs[3][1],title='JGLCV')plt.tight_layout()fig.set_dpi(300)plt.show()
Package: difflibakik
This is a library for the subject ‘Differential Equations’Short documentation:function - function, which we want to solve
h - step
x0, y0 - initial conditiona,b - range
n - number of iterationseuler(function,h,x0,y0)improved_euler(function,h,x0,y0)runge_kutta2(function,h,x0,y0)runge_kutta3(function,h,x0,y0)runge_kutta4(function,h,x0,y0)simpson_rule(function,a,b,n,rng=[],result=0)trapezoidal_rule(function,a,b,n,rng=[],result=0)Change Log0.0.1 (17/12/2021)First Release
Package: diff-llm
DiffLLMExtending the next token prediction setting into "diff prediction".SetupCreate virtual environment:conda create -n diff-llm python=3.9
conda activate diff-llm
pip install -r requirements.txtExport secrets:export $(grep -v '^#' secrets.txt | xargs)Export env vars:export PYTHONPATH=.UsageCreate datasetpython src/create_dataset.py \
    --output-dir ./dataset \
    --page-names '["Deep learning", "Ancient Greece", "Ted Chiang"]' \
    --n-revisions 10
Package: difflume
Welcome to DiffLume! 🎉Ever wanted to spot the differences between text files in a jiffy? DiffLume's got your back!What's Cool About It?Split view? Nope, try three-panel view for comparing text files! 📄We're buddies with Python 3.10 and above 🐍We play well with Linux, MacOS, and Windows (especially the shiny new Windows Terminal)Ever heard of CouchDB? We can peek into its revisions 🛋️Shuffling between revisions? Use the][and}{keysKeep content in sync across panels 🔄Go full-screen or pick your favorite diff view mode, your choice!Getting StartedGet it via pip:python3-mpipinstall--userDiffLumeTo kick things off, just pop this in your terminal:difflume# orpython3-mdifflumeMore of a Docker person? We got you:dockerrun-it--rmyakim/difflumeLicenseChill, it'sMIT.Big Thanks 🙌This project was generated withyakimka/cookiecutter-pyproject.PS. This readme is generated by Chat GPT 😅
Package: diffmah
diffmahInstallationThe latest release of diffmah is available for installation with pip or conda-forge:$ conda install -c conda-forge diffmahTo install diffmah into your environment from the source code:$ cd /path/to/root/diffmah
$ pip install .Environment configurationFor a typical development environment in conda-forge:$ conda create -c conda-forge -n diffit python=3.9 numpy jax pytest ipython jupyter matplotlib scipy h5py diffmahProject dataData for this project can be foundat this URL.DocumentationOnline documentation for Diffmah is availablediffmah.readthedocs.io.Scripts and demo notebooksThediffmah_fitter_demo.ipynbnotebook demonstrates how to fit the MAH of a simulated halo with a diffmah approximation. Seehistory_fitting_script.pyfor an example of how to fit the MAHs of a large number of simulated halos in parallel with mpi4py.Citing diffmahThe diffmah paperhas been published by theOpen Journal of Astrophysics. Citation information for the paper can be found atthis ADS link, copied below for convenience:@ARTICLE{2021OJAp....4E...7H,
       author = {{Hearin}, Andrew P. and {Chaves-Montero}, Jon{\'a}s and {Becker}, Mathew R. and {Alarcon}, Alex},
        title = "{A Differentiable Model of the Assembly of Individual and Populations of Dark Matter Halos}",
      journal = {The Open Journal of Astrophysics},
     keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies},
         year = 2021,
        month = jul,
       volume = {4},
       number = {1},
          eid = {7},
        pages = {7},
          doi = {10.21105/astro.2105.05859},
archivePrefix = {arXiv},
       eprint = {2105.05859},
 primaryClass = {astro-ph.CO},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2021OJAp....4E...7H},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
Package: diffmanifests
diffmanifestsdiffmanifestsis a tool used to see deeper differences between manifests via Gerrit & Gitiles API.RequirementPython >= 3.7InstallpipinstalldiffmanifestsUpdatepipinstalldiffmanifests--upgradeRundiffmanifests--config-fileconfig.json--manifest1-filemanifest1.xml--manifest2-filemanifest2.xml--output-fileoutput.jsonSettingsDiff Manifestsparameters can be set in the directoryconfig.An example of configuration inconfig.json:{"gerrit":{"pass":"pass","url":"http://localhost:80","user":"user"},"gitiles":{"pass":"pass","retry":1,"timeout":-1,"url":"http://localhost:80","user":"user"}}FeatureDiff Manifestssupports to compare commit 2 with commit 1 in diagram A/B/C.LicenseProject License can be foundhere.Referencegit-repo/subcmds/diffmanifests
Package: diff-match-patch
diff-match-patchGoogle'sDiff Match and Patchlibrary, packaged for modern Python.Installdiff-match-patch is supported on Python 3.7 or newer.
You can install it from PyPI:python-mpipinstalldiff-match-patchUsageGenerating a patchset (analogous to unified diff) between two texts:fromdiff_match_patchimportdiff_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:fromdiff_match_patchimportdiff_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: diff-match-patch-cython
Install withpip install diff-match-patch-cythonand then import like normalimport diff_match_patch.Build with Cython - mucho faster.Python bundle documentation on BitBucket:https://bitbucket.org/spookylukey/diff-match-patchDocumentation on the Google site:http://code.google.com/p/google-diff-match-patch/See README.original.txt for the original README.This is an unofficial package of the Python versions for PyPI, by
Luke Planthttp://lukeplant.me.uk/Seehttps://bitbucket.org/spookylukey/diff-match-patchfor packaging issues.Version 20121119Upstream release
Added Cython support.Version 20120106Upstream releaseVersion 20111010Upstream releaseVersion 20110725.1Python 3 packaging bug (python3 directory excluded)Other packaging bugs (extra files included).Version 20110725Initial PyPI package
Package: diff_match_patch_python_python2_fork
A Python extension module that wraps google-diff-match-patch’s C++ implementation for performing very fast string comparisons.google-diff-match-patch is a Google library for computing differences between text files (<http://code.google.com/p/google-diff-match-patch>). Thare are implementations in various languages. Although there is a Python port, it’s slow on very large documents, and I have a need for speed. I wanted to use the C++ implementation, but I’m a Python guy so I’d prefer to use it from Python.@leutloff determined that the C++ port could be even faster by replacing the Qt 4 dependency with the standard C++ library primitives. So he rewrote the module at <https://github.com/leutloff/diff-match-patch-cpp-stl>.This project is an extension module for Python using @leutloff’s library so Python code
can call into the native library easily. It works in both Python 2 and Python 3.ExampleFirst:pip3 install diff_match_patch_pythonThen write (this is Python 3):from diff_match_patch import diff

changes = diff("Hello world.", "Goodbye moon.",
        timelimit=0, checklines=False)

for op, length in changes:
        if op == "-": print ("next", length, "characters are deleted")
        if op == "=": print ("next", length, "characters are in common")
        if op == "+": print ("next", length, "characters are inserted")The module also provides a method calleddiff_bytesfor performing a diff on a bytes array.In Python 2, the methods are nameddiff_unicode(unicode strings) anddiff_str(str strings).Thetimelimitargument is the maximum running time in seconds if you want to ensure the result comes quickly. According to the Google docs, the diff will stop working after the time is exceeded and will return a valid diff, but it might not be the best one.checklinesis also a Google thing and might speed up diffs that are over lined-based text like code.The diff methods also take acounts_onlyargument which isTrueby default. Set it toFalseto have the returned value be an array of tuples of operations and corresponding strings rather than operations and the lengths of those strings.DependenciesTo build from source, you will need:Python development headers (Debian packagepython-dev)The diff-match-patch library, which you can clone usinggit submodule update--init.BuildBuild the binary module using:python setup.py installOr you may find pre-built binaries stored in the git repository in the build directory.
Your mileage may vary with these depending on whether your system is compatible.
Package: diffop
A Python package for finite difference derivatives in any number of dimensions.Features:Differentiate arrays of any number of dimensions along any axisPartial derivatives of any desired orderAccuracy order can be specifiedAccurate treatment of grid boundaryIncludes standard operators from vector calculus like gradient, divergence and curlCan handle uniform and non-uniform gridsCan handle arbitrary linear combinations of derivatives with constant and variable coefficientsFully vectorized for speedCalculate raw finite difference coefficients for any order and accuracy for uniform and non-uniform grids
Package: diffpanstarrs
diffpanstarrsA small utility package to download and process Pan-STARRS data in the sense of
difference imaging.InstallationSimply clone and install using pip:git clone git@gitlab.epfl.ch:dux/diffpanstarrs.git
  cd diffpanstarrs
  pip install .Requires python ≥ 3.7.One must also have a working installation of sextractor. For now, sextractor must
be set in the path under the aliasessexorsextractor.UsageSee the example intests/test_simple_download_and_process.py:fromdiffpanstarrsimportdownloadAndProcess# feed the routine all the parameters like the coordinates, the size of the# images to download, which color channels, etc.res=downloadAndProcess(RA=69.5619,DEC=-12.28745,hsize=512,workdir='myworkdir',name="HE0435-1223",channels=['g'])# plot the light curves:res.plotCurves()# plot the difference images and variability image:res.plotDiffImg(crop=30)
Package: diffport
Diffport is a databasesummary diffreporting tool. It helps in maintaining,
diffing and reporting summaries from a database based on a set ofwatchers. It
currently works (and is tested) under postgres.UsageDiffport works by using a set ofwatchersto take snapshots of database at
different times and then producing a difference report when asked for it.
It uses a config file specifying these watchers and their own specific
configuration needs. An example follows:# diffport.yaml
- name: number-of-rows
  config:
    groupby:
      - <groupby-column-one>
      - <groupby-column-two>
    table: <table-name>

- name: tables-in-schema
  config:
    - <schema-one>
    - <schema-two>The first watcher (number-of-rows) keeps the count of rows in table<table-name, grouped by columns<groupby-column-one>and<groupby-column-two>. The second watchertables-in-schemakeeps the list
of tables in each of the schema provided initsconfig.For more details and usage instructions, head over to the project’s
documentationhere.
Package: diff-predictor
diff_predictorDiff_predictor is a prediction package for multiple particle tracking data and
is intended for use alongside diff_classifier (https://github.com/Nance-Lab/diff_classifier).
It contains methods intended to transform and predict on MPT data, as well as methods for
analyzing prediction results including feature importance.
To get started using these components in your own software, please go to the
repository README_.
.. _README:https://github.com/Nance-Lab/diff_predictor/blob/main/README.mddiff_predictoris licensed under the terms of the MIT license. See the file
"LICENSE" for information on the history of this software, terms & conditions
for usage, and a DISCLAIMER OF ALL WARRANTIES.
All trademarks referenced herein are property of their respective holders.
Package: diff_py
The simple diff tool which is written by Python. The diff result can be printed in console or to html file.Usageusage:diff_py[-h][-c|-CNUM|-u|-UNUM|-n|-HOUTPUT_FILE][-v]FILESFILESThesimpledifftoolwhichiswrittenbyPython.positionalarguments:FILEScanbe"FILE1 FILE2","DIR1 DIR2","FILE DIR",or"DIR
                        FILE"optionalarguments:-h,--helpshowthishelpmessageandexit-c,--contextoutput3linesofcopiedcontext-CNUMoutputNUMlinesofcopiedcontext-u,--unifiedoutput3linesofunifiedcontext-UNUMoutputNUMlinesofunifiedcontext-n,--ndiffoutputPythonDiffer-stylecontext-HOUTPUT_FILE,--htmlOUTPUT_FILEoutputHTMLformatcontext-v,--versionshowprogram'sversionnumberandexit