Saravanan666/pypi-org huggingface dataset:
Package: concat
Concat======An experimental concatenative Python-based programming language.Examples are in the examples directory. To see the spec, go to[http://jmanuel1.github.io/concat-spec/](http://jmanuel1.github.io/concat-spec/).Building and uploading (on Windows)---------------------Change the version number in `setup.py`. **Upload will fail if you try to pushan existing version.**Delete the `dist` directory, if it exists.rmdir dist /SBuild source and pure Python-3 distributions.py -3 setup.py sdistpy -3 setup.py bdist_wheelUpload.twine upload dist/* [-r pypitest]Testing-------Run the tests under `coverage.py`:pip install coveragecoverage run setup.py testCombine the coverage data:coverage combine**Nota Bene**: If you have `concat` installed globally, make sure to create andenter a `virtualenv` before testing, so you don't end up running the installedversion.Related work------------For a similar idea that is more mature, check out[Nustack](https://github.com/BookOwl/nustack).
Package: concataudio
Concatenates multiple audio files from the given list and exports the result.pip install concataudioTested against Windows 10 / Python 3.10 / AnacondaConcatenatesmultipleaudiofilesfromthegivenlistandexportstheresult.Parameters:audio_file_list(list):Alistoffilepathsfortheaudiofilestobeconcatenated.output_file(str):Thepathtosavetheoutputconcatenatedaudiofile.file_format(str,optional):Theformatoftheoutputfile.Defaultsto'wav'.Example:fromconcataudioimportconcat_audio_filesaudio_file_list=[r"C:\welcheantwort.wav",r"C:\ProgramData\anaconda3\envs\soundtest\speech_orig.wav",r"C:\welcheantwort.wav"]concat_audio_files(audio_file_list,output_file='c:\\output_audio3.wav',file_format='wav')
Package: concatenate
concatenateConcatenate two objects of the same typeInstallationpipinstallconcatenateUsage>>>fromconcatenateimportconcatenate>>>>>># int>>>assertconcatenate(123,456)==123456>>>assertconcatenate(0xdead,0xbeef,base=16)==0xdeadbeef>>>assertconcatenate(0b1010,0b1100,base=2)==0b10101100>>>assertconcatenate(0o137,0o246,base=8)==0o137246>>>>>># str>>>assertconcatenate('foo','bar')=='foobar'>>>>>># dict>>>assertconcatenate({'a':1},{'b':2})=={'a':1,'b':2}>>>>>># list>>>assertconcatenate([1,2,3],[4,5,6])==[1,2,3,4,5,6]>>>>>># tuple>>>assertconcatenate((1,2,3),(4,5,6))==(1,2,3,4,5,6)>>>>>># set>>>assertconcatenate({1,2,3},{3,4,5})=={1,2,3,4,5}
Package: concat-map
ConcatMapTheConcatMapmodule provides a class for abstract indexing of
concatenated, fixed-size entities.  Basically, it's a way to access many
things as if they were one thing.InstallationTo install the module, run:pip install concatmapUsagefromconcatmapimportConcat# Create a list of lists of integerslsts=[list(range(n))forninrange(1,11)]# Create a Concat object from the list of listsc=Concat.from_arrays(*lsts)# Access elements in the Concat objectelement=c[50]# Access slices in the Concat objectslice=c[50:100]# iterate over the Concat object elementsforxinc:print(x)TestingTo run the unit tests for the module, run:make ; make testLicenseThis module is licensed under the MIT License.
Package: concat-remove-library
library_multConsider a string containing lowercase characters from the Portuguese alphabet. You can perform two types of operations on this string:Concat a lowercase character from the Portuguese alphabet at the end of the string.Remove the last character from the string. If the string is empty, it will remain empty.Given an integer k and two strings s and t, determine if you can convert s to t using exactly k operations described above on s. If possible, the program prints 'yes', otherwise it prints 'no'.For example, string s = [a, b, c] and string t = [d, e, f]. The number of moves k = 6. To convert s to t, we first remove all characters using 3 moves. Then we concatenate each of the characters of t in the order. In the sixth move, you will have the expected string s. If more movements are available than necessary, they can be eliminated by performing multiple removals on an empty string. If there are fewer movements, it would not be possible to create the new string.Input format The first line contains the string s, the starting string. The second line contains the string t, the desired string. The third line contains an integer k, the number of operations.Constrains a) 1 <= | s | <= 100 b) 1 <= | t | <= 100 c) 1 <= k <= 100 d) s and t consists of lowercase letters of the Portuguese alphabet, ascii [a-z]Output format Print 'yes' if you can get the string t by executing exactly k operations on the string s, and print 'no' otherwise.Example 1 bla bla bla bla blablabcde 8 yesExplanation It took 5 operations to reduce the string "blablablabla" to "blablab". Then, 3 concatenation operations (c, d, e) were needed to obtain the string "blablabcde". In this way, as it was possible to convert s to t using exactly k = 8 operations, the program printed "yes".
Package: concatrim
ConcatrimConcatrim is a python program to trim-and-concatenate media files. It depends onffmpeg.LicenseConcatrim is distributed as source code on this repository and PyPI underMITlicense.FeaturesTrim an input time-based media file based on given time spans and concatenate trimmed pieces into a single file.Mapping between time points in the original and trimmed file.InstallationDependenciesSystem applicationffmpegPython librariesSeerequirements.txt.From PyPIInstall usingpippipinstallconcatrimFrom sourceClone this repository and runsetup.pyto installpythonsetup.pyinstallUsageCaveatsAt the moment;the package only provides Python API's for its supported operations (no command line interface).the program only supports audio inputs.trim-then-concatenateTo trim a media file, useConcatrimmerclass fromconcatrimpackage.
Initiate an instance with the input file name and optionally configuration for padding. When you trim more than two parts of the input media, a silence padding (configured in milliseconds) will be inserted between each slice.fromconcatrimimportConcatrimmerc=Concatrimmer('input-file.mp3',1000)# will insert 1-second silences between slicesFrom here, you can set which part of the input we want to trim, usingadd_spansmethod.c.add_spans([1000,4000],[12000,22000])#  configures the program to extract two parts, 1-4 second and 12-22 second.When you're done adding spans, callconcatrimmethod, with a directory name you want to use to store trimmed output file.c.concatrim('../outputs')Additionally, we can passprefix,suffixarguments to rename the output file (suffixwill be added at before the extension name).c.concatrim('../outputs',suffix='.trimmmed')Timepoint conversionOnce you have all spans for trimming configured in aConcatrimmerobject, you can also ask for conversion between two time points; one from the original media file, and the other from the trimmed one. This conversion will consider the padding pauses that'd be inserted between spans. Again, all input and output of the conversion is in milliseconds.c.conv_to_trimmed(2100)# will return 1100c.conv_to_original(1100)# will return 2100c.conv_to_original(11100)# will return None because 11100 ms is trimmed outc.conv_to_original(12100)# will return 4100
Package: concat-video-g4
Concat videoConcat 2 video files with same codecs and params.
It use ffmpeg. Installffmpegand add it to PATH.InstallationPackage Installation from PyPi$pipinstallconcat-video-g4Package Installation from Source CodeThe source code is available onGitHub.Download and install the package:$gitclonehttps://github.com/Genzo4/concat_video
$cdconcat_video
$pipinstall-rrequirements.txt
$pipinstall.Usagefromconcat_video_g4importconcat_videoconcat_video('path_to_video_1.ts','path_to_video_2.ts','path_to_output_video.ts')ChangelogConcat videoОбъединение двух видео файлов с одинаковыми кодеками и параметрами в один файл.
Используется ffmpeg. Для использования установитеffmpegи пропишите его в PATH.УстановкаУстановка пакета с PyPi$pipinstallconcat-video-g4Установка пакета из исходного кодаИсходный код размещается наGitHub.Скачайте его и установите пакет:$gitclonehttps://github.com/Genzo4/concat_video
$cdconcat_video
$pipinstall-rrequirements.txt
$pipinstall.Использованиеfromconcat_video_g4importconcat_videoconcat_video('path_to_video_1.ts','path_to_video_2.ts','path_to_output_video.ts')Changelog
Package: concave-hull
concave_hullA very fast 2D concave hull algorithm.Credits goes to:https://github.com/mapbox/concavemanhttps://github.com/sadaszewski/concaveman-cppOnline document:https://concave-hull.readthedocs.io/en/latest/Installvia pippipinstall-Uconcave_hullfrom sourcegitclone--recursivehttps://github.com/cubao/concave_hull
pipinstall./concave_hullOrpipinstallgit+https://github.com/cubao/concave_hull.git(you can build wheels for later reuse bypip wheel git+https://github.com/cubao/concave_hull.git)UsageSignature:# importfromconcave_hullimportconcave_hull,concave_hull_indexes# get concave hull indexesconcave_hull_indexes(points:Union[numpy.ndarray,List,Tuple],*,concavity:float=2.0,length_threshold:float=0.0,# you can just ignore "convex_hull_indexes"convex_hull_indexes:numpy.ndarray[numpy.int32[m,1]]=None,)->numpy.ndarray[numpy.int32[m,1]]# get concave hull pointsconcave_hull(points:Union[numpy.ndarray,List,Tuple],...# same as)->Union[numpy.ndarray,List,Tuple]# P.S., we provide convex_hull (Graham scan)fromconcave_hullimportconvex_hull,convex_hull_indexesconcavityis a relative measure of concavity. 1 results in a relatively
detailed shape, Infinity results in a convex hull. You can use values lower
than 1, but they can produce pretty crazy shapes.length_threshold: when a segment length is under this threshold, it stops
being considered for further detalization. Higher values result in simpler
shapes.(document fromhttps://github.com/mapbox/concaveman)Example (see full code intest.py):importmatplotlib.pyplotaspltimportnumpyasnpfromscipy.spatialimportConvexHullfromconcave_hullimportconcave_hull,concave_hull_indexespoints=[]c=np.array([250,250])forxinnp.arange(100,400,5*np.pi):foryinnp.arange(100,400,5*np.pi):ifx>c[0]andy>c[1]:continuer=np.linalg.norm(c-[x,y])ifr>150:continuepoints.append([x,y])points=np.array(points)convex_hull=ConvexHull(points[:,:2])# it's already N-by-2, I'm just emphasizing# https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.ConvexHull.htmlplt.plot(points[:,0],points[:,1],"o")forsimplexinconvex_hull.simplices:plt.plot(points[simplex,0],points[simplex,1],"g-",alpha=0.5)idxes=concave_hull_indexes(points[:,:2],length_threshold=50,)# you can get coordinates by `points[idxes]`assertnp.all(points[idxes]==concave_hull(points,length_threshold=50))forf,tinzip(idxes[:-1],idxes[1:]):# noqaseg=points[[f,t]]plt.plot(seg[:,0],seg[:,1],"r-",alpha=0.5)# plt.savefig('hull.png')plt.show()Testsmake python_install
make python_test
Package: conceal
concealconceal is a python package for developing a digital/fractal camouflage imagefollowing in the footsteps of the article "Introducing the National Algorithim" by Sjef van Gaalen about the new camouflage patterns of The Netherlands (NFP), this package aims to create a digital/fractal camouflage based upon images of the environment and supplied required colorsInstallationgit clone https://github.com/michaelpeterswa/conceal.gitOr download the file manually.Release History0.0.1Opened Repository (08.18.2020)MetaMichael Peters -enter additional contact information hereDistributed under the ____ license. SeeLICENSEfor more information.
Package: concealed
concealedaboutpython util module to provide encryption and decryption using steganography using the least significant bits of the RGB values of an image on top of advanced encryption standard.reference for advanced encryption standard.installationpip install concealedusagereference python fileSupported formatsinputpngjpegwebpnote: apng or other animated image inputs will have unintended transformations in the encoded image.outputpngnote: output image will be in rgb / rgba color modes only.configsconcealed\data\config.inienvpython>=3.9.6changelogv2.0.0initial implementation as a python module.removed fastapi.validated webp support in input images.v1.0.0initial implementation using fastapi server.Feedback is appreciated. Thank you!
Package: concentration
ConcentrationRead Latest Documentation-Browse GitHub Code RepositoryStay focused on work when you want, and goof off when you don't. Concentration is a simple
Python 3 console utility to block distracting sites when you need to focus, while allowing you to easily
take timed breaks. Concentration uses the /etc/hosts file as the mechanism to
block sites, and works on Linux, Macintosh, and Windows.Installing Concentrationpip3 install concentrationOr, if pip is already set to use Python 3pip install concentrationOr, if you wanted concentration installed and ran in an isolated environment, consider usingpipx:pipx run concentrationUsing ConcentrationTo keep focused (blocking distracting sites):sudo concentration improveTo take a small 5 minute timed break:sudo concentration breakTo take a long 60 minute timed break:sudo concentration break -m 60You can cancel breaks withCtrl-C.To disable blocking until you explicitly enable it again:sudo concentration loseConfiguring ConcentrationYou can add more files to the blocked list by putting them in the following files (new line delimited):~/.concentration.distractors/etc/concentration.distractorsYou can make sure sites are visible even if concentration is enabled by putting them in the following files (new line delimited):~/.concentration.safe/etc/concentration.safeThanks and I hope you find concentration useful in your effort to get more done!~Timothy Crosley
Package: concentration-lib
Concentration bounds libraryconcentration_bounds: fixed sample size confidence bounds on the mean.concentration_variance_bounds: fixed sample size confidence bounds on the standard deviation.empirical_concentration_bounds: fixed sample size confidence bounds with data-dependent estimators rather than fixed parameters (e.g the variance in Bernstein bound is estimated from the data instead of being prior knowledge).To install : pip install concentration_libhttps://pypi.org/project/concentration-lib/To test : python -m pytest
Package: concentrationMetrics
The concentrationMetrics LibraryconcentrationMetrics is an MIT-licensedPythonpackage aimed at becoming a reference implementation of indexes used in the analysis of concentration, inequality and diversity measures.Overview of Main Featuresexhaustive collection of concentration and inequality indexes and metricssupports file input/output in both json and csv formatsdetailed mathematical documentationcomputation of confidence intervals via bootstrapingvisualization using matplotlibUsageUsing the library is quite straightforward. For example calculating the Gini and the HHI indexes
on randomly generated portfolio data:importnumpyasnpfromconcentrationMetricsimportIndex# Create some dataa=1.7portfolio=np.random.zipf(a,100)# Calculate the desired indexesindices=Index()hhi=indices.hhi(portfolio)gini=indices.gini(portfolio)# Compute the confidence interval around the HHI index valuelower_bound,val,upper_bound=indices.compute(portfolio,index='hhi',ci=0.95,samples=10000)# Calculate indexes on a dataframeBCI=pd.read_json(dataset_path+"BCI.json")y=BCI.valuesmyGroupIndex=cm.Index(data=y,index='simpson')myGroupIndex.print(6)Many more examples and tests are available in the examples and test directories.File structureconcentrationMetrics/model.pyThe library moduledatasets/Contains a variety of datasets useful for getting started with the ConcentrationMetricsexamples/Variety of usage examplesdocs/Sphinx generated documentationtests/testing the implementationAll indexes are currently implemented in concentrationMetrics/model.py as methods of the Index object.DependenciesThe main dependencies are the standard python datascience stack (numpy, pandas etc) and networkx. The full list is in requirements.txtmatplotlibnumpypandasscipynetworkxDatasetsVersion 0.5.0 includes datasets used primarily for testing and comparison with R implementations:hhbudget.csvIlocos.csvBCI.jsonComparison with R packagesatkinson_test.py compares the Atkinson function with the IC2/Atk function
Package: concentric
a connection manager for python for connecting to various databases.supported databases:oraclenetsuitemssqlmysqlverticaredshiftpostgresdb2 i-seriesoverviewconcentric is based off ofwaddlefor secrets
management, which means it is strongly coupled to aws kms for its key management.quick startcreate a waddle configuration fileoracle:
  host: localhost
  user: scott
  password: tiger
  sid: xewaddle in the password for securitywaddle add-secret -f /path/to/config.yml oracle.passworduse itfrom concentric.managers import setup_concentric
from concentric.managers import CachingConnectionManager as ccm

setup_concentric('/path/to/waddle_config.yml', '/path/to/another_config.yml')
conn = ccm.connect('oracle')
with conn.cursor() as cursor:
    cursor.execute('select sysdate as dt from dual')
    results = cursor.fetchall()contributingSample configuration files:db2hp3000mysqlnetsuiteoraclepostgresredshiftsnowflakesql serververtica
Package: concept
ConceptConceptis a technique that leverages CLIP and BERTopic-based techniques to perform Concept Modeling on images.Since topics are part of conversations and text, they do not represent the context of images well. Therefore, these clusters of images are
referred to as 'Concepts' instead of the traditional 'Topics'.Thus,Concept Modelingtakes inspiration from topic modeling techniques
to cluster images, find common concepts and model them both visually
using images and textually using topic representations.InstallationInstallation, with sentence-transformers, can be done usingpypi:pipinstallconceptQuick StartFirst, we need to download and extract 25.000 images from Unsplash used in the sentence-transformers
example:importosimportglobimportzipfilefromtqdmimporttqdmfromsentence_transformersimportutil# 25k images from Unsplashimg_folder='photos/'ifnotos.path.exists(img_folder)orlen(os.listdir(img_folder))==0:os.makedirs(img_folder,exist_ok=True)photo_filename='unsplash-25k-photos.zip'ifnotos.path.exists(photo_filename):#Download dataset if does not existutil.http_get('http://sbert.net/datasets/'+photo_filename,photo_filename)#Extract all imageswithzipfile.ZipFile(photo_filename,'r')aszf:formemberintqdm(zf.infolist(),desc='Extracting'):zf.extract(member,img_folder)img_names=list(glob.glob('photos/*.jpg'))Next, we only need to pass images toConcept:fromconceptimportConceptModelconcept_model=ConceptModel()concepts=concept_model.fit_transform(img_names)The resulting concepts can be visualized throughconcept_model.visualize_concepts():However, to get the full experience, we need to label the concept clusters with topics. To do this,
we need to create a vocabulary. We are going to feed our model with 50.000 nouns from the English
vocabulary:importrandomimportnltknltk.download("wordnet")fromnltk.corpusimportwordnetaswnall_nouns=[wordforsynsetinwn.all_synsets('n')forwordinsynset.lemma_names()if"_"notinword]selected_nouns=random.sample(all_nouns,50_000)Then, we can pass in the resultingselected_nounstoConcept:fromconceptimportConceptModelconcept_model=ConceptModel()concepts=concept_model.fit_transform(img_names,docs=selected_nouns)Again, the resulting concepts can be visualized. This time however, we can also see the generated topics
throughconcept_model.visualize_concepts():NOTE: UseConcept(embedding_model="clip-ViT-B-32-multilingual-v1")to select a model that supports 50+ languages.Search ConceptsWe can quickly search for specific concepts by embedding a search term and finding the cluster embeddings
that best represent them. As an example, let us search for the termbeachand see what we can find.
To do this, we simply run the following:>>>concept_model.find_concepts("beach")[(100,0.277577825349102),(53,0.27431058773894657),(95,0.25973751319723837),(77,0.2560122597417548),(97,0.25361988261846297)]Each tuple contains two values, the first is the concept cluster and the second the similarity to the
search term. The top 5 similar topics are returned.Now, let us visualize those concepts to see how well the search function works:concept_model.visualize_concepts(concepts=[100,53,95,77,97])
Package: concept-formation
Concept FormationThis is a Python library of algorithms that perform concept formation written by
Christopher MacLellan (http://www.christopia.net) and Erik Harpstead
(http://www.erikharpstead.net).OverviewIn this library, theCOBWEBandCOBWEB/3algorithms are implemented. These systems accept a stream of instances, which
are represented as dictionaries of attributes and values (where values can be
nominal for COBWEB and either numeric or nominal for COBWEB/3), and learns a
concept hierarchy. The resulting hierarchy can be used for clustering and
prediction.This library also includesTRESTLE,
an extension of COBWEB and COBWEB/3 that support structured and relational data
objects. This system employs partial matching to rename new objects to align
with previous examples, then categorizes these renamed objects.Lastly, we have extended the COBWEB/3 algorithm to support three key
improvements. First, COBWEB/3 now uses anunbiased estimatorto
calculate the standard deviation of numeric values. This is particularly useful
in situations where the number of available data points is low. Second,
COBWEB/3 supports online normalization of the continuous values, which is
useful in situations where numeric values are on different scales and helps to
ensure that numeric values do not impact the model more than nominal values.
Finally, it is assumed that there is some base noise in measuring continuous
values, this noise insures that the probability of any one value never exceeds
1, even when the standard deviation is small.InstallationYou can install this software using pip:pip install -U concept_formationYou can install the latest version of the code directly from github:pip install -U git+https://github.com/cmaclell/concept_formation@masterImportant LinksSource code:https://github.com/cmaclell/concept_formationDocumentation:http://concept-formation.readthedocs.orgExamplesWe have created a number of examples to demonstrate the basic functionality of
this library. The examples can be foundhere.Citing this SoftwareIf you use this software in a scientific publiction, then we would appreciate
citation of the following paper:MacLellan, C.J., Harpstead, E., Aleven, V., Koedinger K.R. (2016)TRESTLE: A
Model of Concept Formation in Structured Domains.
Advances in Cognitive Systems, 4, 131-150.Bibtex entry:@article{trestle:2016a,
author={MacLellan, C.J. and Harpstead, E. and Aleven, V. and Koedinger, K.R.},
title={TRESTLE: A Model of Concept Formation in Structured Domains},
journal={Advances in Cognitive Systems},
volume={4},
year={2016}
}
Package: conceptionlogicielle
Hello worldStart here :git clone https://gitlab.com/conception-logicielle/tp2-conception-corrige.git tp2Install requirements file :poetry installThen you can run the app :poetry run python tp2on some operating systems, python you might have to use cmd python3 or python2You can change words using env variables
e.g. on linux :export DEFAULT_MOT_UN=totoon some operating systems, python you might have to use cmd python3 or python2You can also download the package:pip install
Package: conceptionlogicielletp2
Hello worldStart here :git clone https://gitlab.com/conception-logicielle/tp2-conception-corrige.git tp2Install requirements file :poetry installThen you can run the app :poetry run python tp2on some operating systems, python you might have to use cmd python3 or python2You can change words using env variables
e.g. on linux :export DEFAULT_MOT_UN=totoon some operating systems, python you might have to use cmd python3 or python2You can also download the package:pip install