Saravanan666/pypi-org huggingface dataset:
Package: diffpy.structure
diffpy.structurestorage and manipulation of crystal structure dataThe diffpy.structure package provides objects for storing atomic
coordinates, displacement parameters and other crystal structure data.
diffpy.structure supports import and export of structure data in several
structure formats such as CIF, PDB, and xyz.  It provides conversion
between fractional and absolute Cartesian coordinates, functions for
symmetry expansion of atom sites in the asymmetric unit and generation
of symmetry constraints for atom positions and displacement parameters.
diffpy.structure includes definitions of all space groups in over 500
symmetry settings.To learn more about diffpy.structure library see the
user manual athttp://diffpy.github.io/diffpy.structure.REQUIREMENTSThe diffpy.structure package requires Python 3.7 or later or 2.7 and
the following software:setuptools- software distribution tools for PythonNumPy- numerical mathematics and fast array operations for PythonWe recommend to useAnaconda Pythonas it allows to install all software dependencies together with
diffpy.structure.  For other Python distributions it is necessary to
install the required software separately.  As an example on Ubuntu
Linux the required software can be installed withsudo aptitude install python3-setuptools python3-numpydiffpy.structure also uses thePyCifRWlibrary, which is automatically deployed during the
installation process.INSTALLATIONThe preferred method is to use Anaconda Python and install from the
“diffpy” channel of Anaconda packagesconda config --add channels diffpy
conda install diffpy.structurediffpy.structure is also included in the “diffpy-cmi” collection
of packages for structure analysisconda install diffpy-cmiAnother installation option is to useeasy_installto download and
install the latest release fromPython Package Indexeasy_install diffpy.structureIf you prefer to install from sources, navigate to the source archive
directory and runpython setup.py installYou may need to usesudowith system Python so it is allowed to
copy files to system directories.  If sudo is not available, check
the usage info frompython setup.py install--helpfor options to
install to user-writable locations.  The installation integrity can be
verified by changing to the HOME directory and runningpython -m diffpy.structure.tests.runDEVELOPMENTdiffpy.structure is an open-source software developed as a part of the
DiffPy-CMI complex modeling initiative at the Brookhaven National
Laboratory.  The diffpy.structure sources are hosted athttps://github.com/diffpy/diffpy.structure.Feel free to fork the project and contribute.  To install diffpy.structure
in a development mode, where the sources are directly used by Python
rather than copied to a system directory, usepython setup.py develop --userACKNOWLEDGEMENTSpace group codes inspacegroupmod.pyandmmlibspacegroups.pyoriginate from the pymmlib project,http://pymmlib.sourceforge.net.CONTACTSFor more information on diffpy.structure please visit the project web-pagehttp://www.diffpy.org/or email Prof. Simon Billinge atsb2896@columbia.edu.
Package: diffpy.utils
diffpy.utilsGeneral purpose shared utilities for the diffpy libraries.The diffpy.utils package provides functions for extracting array data from
variously formatted text files and wx GUI utilities used by the PDFgui
program.  The package also includes an interpolation function based on the
Whittaker-Shannon formula that can be used to resample a PDF or other profile
function over a new grid.For more information about the diffpy.utils library, see the users manual athttp://diffpy.github.io/diffpy.utils.INSTALLATIONThe preferred method is to useMiniconda Pythonand install from the “conda-forge” channel of Conda packages.To add “conda-forge” to the conda channels, run the following in a terminal.conda config --add channels conda-forgeWe want to install our packages in a suitable conda environment.
The following creates and activates a new environment nameddiffpy-utilsconda create -n diffpy-utils python=3
conda activate diffpy-utilsThen, to fully installdiffpy.utilsin our active environment, runconda install diffpy.utilsAnother option is to usepipto download and install the latest release fromPython Package Index.
To install usingpipinto yourdiffpy-utilsenvironment, we will also have to install dependenciespip install numpy
pip install diffpy.utilsFor those planning to use functions in thediffpy.utils.wxmodule, you will also need to installwxPython.
Both of the following lines will install this package.conda install wxPython
pip install wxPythonIf you prefer to install from sources, after installing the dependencies, obtain the source archive fromGitHub. Once installed,cdinto yourdiffpy.utilsdirectory
and run the followingpip install -e .To check the installation integrity, if the following passes all checks, you are good!pip install pytest
python -m diffpy.utils.tests.runDEVELOPMENTdiffpy.utils is an open-source software developed as a part of the
DiffPy-CMI complex modeling initiative at the Brookhaven National
Laboratory.  The diffpy.utils sources are hosted athttps://github.com/diffpy/diffpy.utils.Feel free to fork the project and contribute.  To install diffpy.utils
in a development mode, with its sources being directly used by Python
rather than copied to a package directory, usepython setup.py develop --userCONTACTSFor more information on diffpy.utils please visit the project web-pagehttp://www.diffpy.org/or email Prof. Simon Billinge atsb2896@columbia.edu.
Package: diffractsim
Diffractsim: A diffraction simulator for exploring and visualizing physical opticsFlexible, and easy-to-use Python diffraction simulator that focuses on visualizing physical optics phenomena.
The simulator uses mainly scalar diffraction techniques for light propagation, provides an interface for simulation set up, and includes several plotting options, counting with CIE Color matching functions for accurate color reproduction.The basic use of this simulator using the angular spectrum method is described in thisarticle.
Take a look to thevideosto see the animated simulations!
Package: diffrax
DiffraxNumerical differential equation solvers in JAX. Autodifferentiable and GPU-capable.Diffrax is aJAX-based library providing numerical differential equation solvers.Features include:ODE/SDE/CDE (ordinary/stochastic/controlled) solvers;lots of different solvers (includingTsit5,Dopri8, symplectic solvers, implicit solvers);vmappableeverything(including the region of integration);using a PyTree as the state;dense solutions;multiple adjoint methods for backpropagation;support for neural differential equations.From a technical point of view, the internal structure of the library is pretty cool -- all kinds of equations (ODEs, SDEs, CDEs) are solved in a unified way (rather than being treated separately), producing a small tightly-written library.Installationpip install diffraxRequires Python 3.9+, JAX 0.4.13+, andEquinox0.10.11+.DocumentationAvailable athttps://docs.kidger.site/diffrax.Quick examplefromdiffraximportdiffeqsolve,ODETerm,Dopri5importjax.numpyasjnpdeff(t,y,args):return-yterm=ODETerm(f)solver=Dopri5()y0=jnp.array([2.,3.])solution=diffeqsolve(term,solver,t0=0,t1=1,dt0=0.1,y0=y0)Here,Dopri5refers to the Dormand--Prince 5(4) numerical differential equation solver, which is a standard choice for many problems.CitationIf you found this library useful in academic research, please cite:(arXiv link)@phdthesis{kidger2021on,title={{O}n {N}eural {D}ifferential {E}quations},author={Patrick Kidger},year={2021},school={University of Oxford},}(Also consider starring the project on GitHub.)See also: other libraries in the JAX ecosystemjaxtyping: type annotations for shape/dtype of arrays.Equinox: neural networks.Optax: first-order gradient (SGD, Adam, ...) optimisers.Optimistix: root finding, minimisation, fixed points, and least squares.Lineax: linear solvers.BlackJAX: probabilistic+Bayesian sampling.Orbax: checkpointing (async/multi-host/multi-device).sympy2jax: SymPy<->JAX conversion; train symbolic expressions via gradient descent.Eqxvision: computer vision models.Levanter: scalable+reliable training of foundation models (e.g. LLMs).PySR: symbolic regression. (Non-JAX honourable mention!)
Package: diffs
diffsdiffsmonitors for any changes in arbitrary shell command output between the initial state and the current output.Installationpip3installdiffsPython 3.6 (or newer) is required.UsagediffsCMDThe initialCMDoutput is stored at the beginning.
Then the command is run again periodically and the output is compared against the initial output.
If there are differences, they are shown in the standard diff format. The unchaged lines are not shown.ExamplesShow how many packets have been sent since the script has been started:diffs ifconfig wlp2s0Find new applications opening network ports (shows only changes between the initial state):diffs sudo netstat -tulpnMonitor changes in a file:$diffscat/etc/resolv.confDifference between starting date and the current date:diffs dateFinding newly spawned processes:diffs ps a -o pid,cmd
Package: diffsim
DiffSimIntroductionDiffSim provides an atomistic approach to efficiently simulate Bragg coherent x-ray diffraction imaging (BCDI) diffraction patterns by factorising and eliminating certain redundancies in the conventional approach. The method used is able to reduce the computation time by several orders of magnitude without compromising the recovered phase information and therefore enables feasible atomistic simulations on nanoscale crystals with arbitrary lattice distortions.Please cite the following article when using Diffsim in published work:Ahmed Mokhtar, David Serban and Marcus Newton, Simulation of Bragg coherent diffraction imaging, J. Phys. Commun. Volume 6, 055003 (2022)InstallationDiffSim requires:Python >= 3.7Numba >= 0.58.1NumPy >= 1.25.0scipympi4pyInstallation via pip:$ pip install diffsimReporting BugsPlease send any bugs, problems, and proposals to:Bonsu.Devel@gmail.comor visit:http://github.com/bonsudev/diffsimLibrary Usage and ExamplesExample scripts are available in the examples folder.Version HistoryVersion 1.2 🗓 ️(02/11/2023)🔧 Minor bug fixes.Version 1.1 🗓 ️(29/10/2023)✨ Refactored code for speed improvements.
✨ Added attenuation and refraction corrections.
🔧 Minor bug fixes.Version 1.0 🗓 ️(14/09/2022)✨ First major release.LicenceGNU GPLv3
Package: diffsky
Diffsky is a python library based on JAX for producing mock catalogs based ondiffstaranddsps.InstallationThe latest release of diffsky is available for installation with pip or conda-forge:$ conda install -c conda-forge diffskyTo install diffsky into your environment from the source code:$ cd /path/to/root/diffsky
$ pip install .Conda environmentFor a typical development environment in conda-forge:$ conda create -c conda-forge -n diffsky_env python=3.9 numpy jax pytest ipython jupyter matplotlib scipy h5py diffmah diffstar dsps diffskyDocumentationOnline documentation for diffsky is available atdiffsky.readthedocs.io.TestingTo run the suite of unit tests:$ cd /path/to/root/diffsky
$ pytestTo build html of test coverage:$ pytest -v --cov --cov-report html
$ open htmlcov/index.html
Package: diffsort
diffsort - Differentiable Sorting NetworksOfficial implementation for ourICLR 2022 Paper "Monotonic Differentiable Sorting Networks"and ourICML 2021 Paper "Differentiable Sorting Networks for Scalable Sorting and Ranking Supervision".
In this work, we leverage classic sorting networks and relax them to propose a new differentiable sorting function: diffsort.
This allows propagating gradients through (an approximation of) the sorting / ranking function / operation.
Herein, diffsort outperforms existing differentiable sorting functions on the four-digit MNIST and the SVHN sorting tasks.
In this repo, we present the PyTorch implementation of differentiable sorting networks.Video @Youtube.💻 Installationdiffsortcan be installed via pip from PyPI withpipinstalldiffsortOr from source, e.g., in a virtual environment likevirtualenv-ppython3.env1
..env1/bin/activate
pipinstall.👩‍💻 UsageimporttorchfromdiffsortimportDiffSortNetvector_length=2**4vectors=torch.randperm(vector_length,dtype=torch.float32,device='cpu',requires_grad=True).view(1,-1)vectors=vectors-5.# sort using a bitonic-sorting-networksorter=DiffSortNet('bitonic',vector_length,steepness=5)sorted_vectors,permutation_matrices=sorter(vectors)print(sorted_vectors)🧪 ExperimentsYou can find the main experiment in thisColab notebook.You can run the four-digit MNIST experiment aspythonexperiments/main.py-n5-modd_even-s10-dmnistor for the bitonic networkpythonexperiments/main.py-n16-mbitonic-s20-dmnistor for SVHNpythonexperiments/main.py-n5-modd_even-s10-dsvhn📖 Citing@inproceedings{petersen2022monotonic,title={Monotonic Differentiable Sorting Networks},author={Petersen, Felix and Borgelt, Christian and Kuehne, Hilde and Deussen, Oliver},booktitle={International Conference on Learning Representations (ICLR)},year={2022}}@inproceedings{petersen2021diffsort,title={Differentiable Sorting Networks for Scalable Sorting and Ranking Supervision},author={Petersen, Felix and Borgelt, Christian and Kuehne, Hilde and Deussen, Oliver},booktitle={International Conference on Machine Learning (ICML)},year={2021}}Licensediffsortis released under the MIT license. SeeLICENSEfor additional details about it.
Package: diffstar
diffstarInstallationThe latest release of diffstar is available for installation with either pip or conda-forge:$ conda install -c conda-forge diffstarAlternatively, to install diffstar into your environment from the source code:$ cd /path/to/root/diffstar
$ pip install .Environment configurationFor a typical development environment with conda-forge:$ conda create -c conda-forge -n diffit python=3.9 numpy numba flake8 pytest jax ipython jupyter matplotlib scipy h5py diffmah diffstarProject dataData for this project can be foundat this URL.Scripts and demo notebooksThediffstar_fitter_demo.ipynbnotebook demonstrates how to fit the SFH of a simulated galaxy with a diffstar approximation.Seehistory_fitting_script.pyfor an example of how to fit the SFHs of a large number of simulated galaxies in parallel with mpi4py.Citing diffstarThe Diffstar paperhas been published inMonthly Notices of the Royal Astronomical Society. Citation information for the paper can be found atthis ADS link, copied below for convenience:@ARTICLE{2023MNRAS.518..562A,
       author = {{Alarcon}, Alex and {Hearin}, Andrew P. and {Becker}, Matthew R. and {Chaves-Montero}, Jon{\'a}s},
        title = "{Diffstar: a fully parametric physical model for galaxy assembly history}",
      journal = {MNRAS},
     keywords = {galaxies: evolution, galaxies: fundamental parameters, galaxies: star formation, Astrophysics - Astrophysics of Galaxies, Astrophysics - Cosmology and Nongalactic Astrophysics},
         year = 2023,
        month = jan,
       volume = {518},
       number = {1},
        pages = {562-584},
          doi = {10.1093/mnras/stac3118},
archivePrefix = {arXiv},
       eprint = {2205.04273},
 primaryClass = {astro-ph.GA},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2023MNRAS.518..562A},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}
Package: diffstrings
diffstringsShow difference of strings in color. It works on both terminal and ipython.It extends difflib.SequenceMatcher class and provides a direct function too.InstallationpipinstalldiffstringsAPIExample ResultColor explanation:Green: to addYellow: to replace. (Remove yellow colored then add with green colored strings)Red: to be removedExample texts# Truth text to compare withtruth="""Lorem ipsum dolor sit amet,consectetur adipiscing elit,  sed do eiusmod tempor incididunt ut labore et dolore magna aliqua. Ut enim ad minim veniam, quis nostrud exercitation ullamco laboris nisi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in voluptate velit esse cillum dolore eu fugiat nulla pariatur. Excepteur sint occaecat cupidatat non proident, sunt in culpa qui officia deserunt mollit anim id est laborum.f"""# Supposedly wrong text to comparepredict="""Lorem furio dlfgor sit amet, consectetur apiscbgfing elit, sed do eiusmod tempor inciddfbunt ut labore et ddslore magna aliqua. Ut enim ad minim veniam, quis nostrud exercsfsation ullamco laboris assi ut aliquip ex ea commodo consequat. Duis aute irure dolor in reprehenderit in volbcvbte velit esse cilldm doldsre eu fugiat nulla pafdgatur. Excepteur sint occaecat cupidatat non pident, sunt in culpa qui asafficia desert mollit anim id est lfdgarum."""Using classfromdiffstringsimportSequenceMatchers=SequenceMatcher(None,predict,truth,autojunk=False)print(s.diff_strings())print(s.diff_strings(True))Using functionfromdiffstringsimportdiff_strings

print(diff_strings(predict,truth))print(diff_strings(predict,truth,show_change_on_seq2=True))
Package: difftacular
ThisBreezyplugin adds a few commands that
perform variants on the basicbrz diffcommand.brzdiff-mainlineperforms a diff between the specified revisions,
reflecting only changes originated in the mainline: that is, it first
applies any merges introduced between the old and new revisions to a
temporary copy of the old tree, and then performs a diff between that
temporary copy and the new tree.  For example, this can be used to see
changes introduced while resolving conflicts during a manual merge.brzdiff-ignore-branchesperforms a diff between the specified
revisions, but ignoring merges from the specified branches.brzpreview-diffperforms a preview diff in the style used by Launchpad
merge proposals: it shows the diff that would be introduced by merging the
tip of the source branch into the target branch.  If a prerequisite branch
is also given using the--prerequisite-branchoption, then it will merge
the tip of the prerequisite branch into a temporary copy of the target
branch, and then show the diff that would be introduced by merging the tip
of the source branch into that temporary copy.
Package: diff-timer
diff_timerThis package includes several timers base on Python.Timer:base on listHeapTimer:base on heapWheelTimer:base on Timing WheelExplanationcontroller:The Object which is used to create different timerstick:The time interval which is determined by youtps:tick per secondInstallationpip install diff_timer
pip install --upgrade diff_timerHow to use1、Instantiationfromdiff_timerimportControllerINTERVAL=5tps=50controller=Controller()controller.initTimer("WHEEL",INTERVAL,tps)2、We need a time driverTIME_GAP=1/tps# 1 / 50 = 0.02curTick=0whileTrue:time.sleep(TIME_GAP)controller.tick(curTick)curTick+=13、set timer at any place that you wantfromdiff_timerimportsetTimeOutdeftest():print("this is a test func")setTimeOut(test,10,"test_func")example: test.py
Package: diff-tool
Diff ToolDisplay a diff between two files in HTML.Running this tool requires two files to compare. It will output thedifference to an HTML file which can be viewed in a browser to see what changed between files.Install# Install toolpip3installdiff-tool# Install locallymakeinstallUsageUsage:
    diff-tool --file1 /path/to/file1.txt --file2 /path/to/file2.txt --output path/to/diff.html

Options:
    -h, --help            show this help message and exit
    -f1 FILE1, --file1 FILE1
                            The path to the base file to compare a second file to.
    -f2 FILE2, --file2 FILE2
                            The path to the second file compared to the base file.
    -o OUTPUT, --output OUTPUT
                            The path/name to the output file where the diff will be stored.Development# Get a comprehensive list of development toolsmakehelp# Run the tool locallyvenv/bin/pythondiff_tool/diff_files.py--help
Package: difftree
difftreedifftree is a tool that diffs two directories. It recursively walks through the directories
and lists files that are missing on either side.By defaultdifftreeonly checks whether a file is present or not, so files
present on both sides but with different file sizes, permissions or content
will not be listed. However, checking any of those can be enabled via
the-s,-p, and-zflags respectively.Usage> difftree dir-a/ dir-b/
dir-a/                 <-> dir-b/
                       <-  file-only-in-dirb.txt
file-only-in-dir-a.txt  ->The following options are available:-h, --help            show this help message and exit
  -p, --check-perms     Diff file permissions
  -s, --check-sizes     Diff file sizes
  -z, --check-hashes    Diff file hashes
  -d, --dir-norecurse   Show missing directories as a single entry (don't show files in the directory)
  -e exclude_regex, --exclude exclude_regex
                        Exclude files matching this regexInstallThe recommended way to installdifftreeis viapipx:pipx difftreeAlternativesUNIX built-indiff -rq dir1 dir2works quite well.rsync --dry-run -r --itemize-changes dir1 dir2also works quite well, but
doesn't list files that are present indir2but missing indir1. It requires you
to parse the quite terse change format e.g.>fcsT....and>f+++++++.
Package: diff-trigger
变更触发器应用场景周期性的执行某个任务， 将任务的结果存储于磁盘中。 当每次检测到结果和上次不同时， 更新外部存储的结果并触发回调任务安装pip install diff_trigger使用举个栗子fromdiff_triggerimportwatchdiffimportrequestsimportloggingdeftrigger(old,new):logging.info('data change from{}to{}'.format(old,new))@watchdiff(dbpath='/var/cache/pythonjob',callback=trigger,key='somekey')defjob(url):r=requests.get(url)returnr.contentclassSomeClass:@watchdiff(dbpath='/var/cache/pythonjob',callback=trigger)defjob(self,url):r=requests.get(url)returnr.content说明定义一个有两个参数的回调函数，第一个参数会被传递为旧的结果，第二个参数会被传递为新的结果。根据实际情况使用这个数据使用watchdiff作为装饰器，填入本地的数据地址，以及所要指定的触发函数名。可以手工指定一个键，函数结果会存放在这个键下面。如果不指定键名，会根据函数的调用参数来生成一个键在类中使用时，整个函数的签名计算会有整个实例的序列化信息， 因此尽量不要在体积较大的类中使用。或者使用staticmethod如果在类中作为装饰器使用，需要注意的是实例的改变会更改本地数据库中的键值，导致无法触发回调函数。只有在实例不变的情况下才会有效。或者使用staticmethod
Package: diffupy
IntroductionDiffuPy is a generalizable Python implementation of the numerous label propagation algorithms. DiffuPy supports generic
graph formats such asJSON, CSV, GraphML, or GML. Check
outDiffuPy’s documentation here.InstallationThe latest stable code can be installed fromPyPIwith:$python3-mpipinstalldiffupyThe most recent code can be installed from the source onGitHubwith:$python3-mpipinstallgit+https://github.com/multipaths/DiffuPy.gitFor developers, the repository can be cloned fromGitHuband installed in
editable mode with:$gitclonehttps://github.com/multipaths/DiffuPy.git$cddiffupy$python3-mpipinstall-e.Command Line InterfaceThe following commands can be used directly from your terminal:1.Run a diffusion analysisThe following command will run a diffusion method on a given network with the given data.  More informationhere.$python3-mdiffupydiffuse--network=<path-to-network-file>--data=<path-to-data-file>--method=<method>2.Generate a kernel with one of the seven methods implementedGenerates the regularised Laplacian kernel of a given graph. More information in thedocumentation.$python3-mdiffupykernel--network=<path-to-network-file>DisclaimerDiffuPy is a scientific software that has been developed in an academic capacity, and thus comes with no warranty or
guarantee of maintenance, support, or back-up of data.