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abdelrahman-obfuscate | No description available on PyPI. |
abdesign | ABDesignAPIThe ABDesign API provides functions and data structures for dealing with Immunoglobulin sequences. It contains computational methods to 'humanize' an antibody by CDR-grafting.Note:A local installation of ANARCI is needed for this tool. You can downlod ANARCIhere.IgObjectCore data structure. All params are set on initialization, either by using create_annotation(), or manually.seqrequiredchain_typeiso_typespeciesannotationregionsexport_to_json()HumanizationTo 'humanize' a queried sequence, a human template sequence must be provided in form of an IgObject, too. It is also possible to indicate the wanted annotation types for the hybrid IgObject. By default, all annotation systems provided by this package are applied.replace_cdr(ig_query:IgObject, ig_target:IgObject, annotation_types) |
abdi | No description available on PyPI. |
abdlmutii | No description available on PyPI. |
abdo | No description available on PyPI. |
abdoalmajeed | printChange Log0.0.1 (15/11/2021)First Release |
abdoconvo | No description available on PyPI. |
abdo-obfuscate | No description available on PyPI. |
abdoTheBest | GeNNGeNN (generative neural networks) is a high-level interface for text applications using PyTorch RNN's.FeaturesPreprocessing:Parsing txt, json, and csv files.NLTK, regex and spacy tokenization support.GloVe and fastText pretrained embeddings, with the ability to fine-tune for your data.Architectures and customization:GPT2 with small, medium, and large variants.LSTM and GRU, with variable size.Variable number of layers and batches.Dropout.Text generation:Random seed sampling from the n first tokens in all instances, or the most frequent token.Top-K sampling for next token prediction with variable K.Nucleus sampling for next token prediction with variable probability threshold.Text Summarization:All GPT2 variants can be trained to perform text summarization.Getting startedHow to installpipinstallgennPrerequisitesPyTorch 1.4.0pipinstalltorch==1.4.0Pytorch Transformerspipinstallpytorch_transformersNumPypipinstallnumpyfastTextpipinstallfasttextUse the package managerpipto install genn.UsageText Generation:RNNs (You can switch LSTMGenerator with GRUGenerator:fromgennimportPreprocessing,LSTMGenerator,GRUGenerator#LSTM exampleds=Preprocessing("data.txt")gen=LSTMGenerator(ds,nLayers=2,batchSize=16,embSize=64,lstmSize=16,epochs=20)#Train the modelgen.run()# Generate 5 new documentsprint(gen.generate_document(5))GPT2 Generator:#GPT2 examplegen=GPT2("data.txt",taskToken="Movie:",epochs=7,variant="medium")#Train the modelgen.run()#Generate 10 new documentsprint(gen.generate_document(10))Text Summarization:GPT2 Summarizer:#GPT2 Summarizer examplefromgennimportGPT2Summarizersumm=GPT2Summarizer("data.txt",epochs=3,batch_size=8)#Train the modelsumm.run()#Create 5 summaries of a source documentsrc_doc="This is the source document to summarize"print(summ.summarize_document(n=5,setSeed=src_doc))For more examples on how to use Preprocessing, please refer tothis file.For more examples on how to use LSTMGenerator and GRUGenerator, please refer tothis file.For more examples on how to use GPT2, please refer tothis file.For more examples on how to use GPT2Summarizer, please refer tothis file.ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.LicenseDistributed under the MIT License. SeeLICENSEfor more information. |
abdoulaziz | No description available on PyPI. |
abdoutilities | Various utilities, displaying a random quoteChange Log0.0.1 (07/09/2022)First Release |
abdpdf | No description available on PyPI. |
abdttsslug | abdttsSum of squareFree software: MIT licenseDocumentation:https://abdttsslug.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.0.1 (2021-04-10)First release on PyPI.0.0.2 (2021-04-10)Minor fixes.0.0.3 (2021-04-10)Minor fixes |
abdu | No description available on PyPI. |
abduct | Capture stdout/stderr and optionally release when an exception occurs.fromabductimportcaptured,out,errwithcaptured(out())asstdout:...withcaptured(out(),err())as(stdout,stderr):...@captured(out(),err())...Installation:$pipinstallabductWhen stdout or stderr is captured, the relatedsys.stdoutorsys.stderrobject is replaced with aStringIOobject for the
life of the context.ExamplesIt’s often useful to capture the output of a block of code. Abduct
makes this easy:withcaptured(out())asstdout:print('hello!')assertstdout.getvalue()=='hello!'Sometimes you may want to hide the output of some codeunlesssomething goes wrong. In this case, simply specifyrelease_on_exception=True:withcaptured(out(release_on_exception=True)):print('Really important message!')ifblow_up:raiseRuntimeError()In this case,Really important message!will be printed onstdoutif the exception is raised.If you’d like to capture the output, but still write through tostdoutorstderr, use thetee=Trueparameter:withcaptured(err(tee=True))asstderr:sys.stderr.write('Error!')assertstderr.getvalue()=='Error!'In this case,Error!is capturedandwritten tostderrat the same time.Changelog2.0.1Added tests for the decorator usage.2.0.0Feature: “Create a write-through option for output.”Backwards-incompatible change:stdoutandstderrmethods are nowoutanderrrespectively.1.0.4Fixed Travis release criteria.1.0.3Refactored test runner.1.0.2Fixed README and description.1.0.1Travis config now defers to tox.Added examples to README.1.0.0Actual working code. Yay!0.0.1Initial release. |
abductive-learning | Failed to fetch description. HTTP Status Code: 404 |
abdul-987-pdf | This is the home page of our project |
abdul-hello-pdf | This is the home page of our project |
abdulkerim | No description available on PyPI. |
abdullahfunctions | python-example-packageThis is a starter repo for creating a new python package.TestsSimply run:pytestCode covergeGenerate coverage report with:py.test --cov=myPackage tests/script:- pip install --user --upgrade setuptools wheel twine numpy- python3 setup.py sdist bdist_wheel- python3 -m twine upload --repository-urlhttps://test.pypi.org/legacy/dist/*before_script:- bumpversion minor setup.py |
abdullapdf | No description available on PyPI. |
abdullokhpdf | <bound method Path.read_text of PosixPath(‘README.md’)> |
abdulmajed | No description available on PyPI. |
abdulmz-test | No description available on PyPI. |
abdulpdf | No description available on PyPI. |
abdulpdf2text | This is a simple exmaple of my pdf2text project publication |
abdupy | Failed to fetch description. HTTP Status Code: 404 |
abdurion | Aggregation Package Details- This package computes the mean, median, min, max, sum of positives, and count of negatives of a list. |
abdur-phone-number-validator | No description available on PyPI. |
abdur-shopping-cart | Simple Shopping CartThis is a simple shopping cart that allows you to add items into the cart
and display the state of the cart.InstallationTo install the library run the below command in your virtual environmentpip install abdur-shopping-cartCode styleThis project makes use of black with default settings to format the code
and flake 8 as a linter.Usage`
from abdur_shopping_cart.shopping_cart import ShoppingCartcart = ShoppingCart()
print(cart.add_to_cart("cornflakes", 1))
print(cart.add_to_cart("cornflakes", 1))
print(cart.add_to_cart("weetabix", 1))
print(cart.calculate_state())
`APIadd_to_cart(product_name, quantity)Takes in a product name as a string, quantity as an integer
Returns a success/failurecalculate_state()Calculates the current state of the cart
Returns the product names, quantities, sub_total, tax and totalTestingThe project uses pytest to run its testsOtherThe .gitignore file was generated using gitignore.iohttps://www.toptal.com/developers/gitignore/ |
abdur-test-utilities | A basic utility to i can publish a package. |
abduvakhkhobpdf | This is our first page. |
abduy-dist | No description available on PyPI. |
abeattacks | abeattacksThis python module contain the attacks against ABE schemes presented
at Black Hat Europe 2021 by Antonio de la Piedra and Marloes Venema.At the CT-RSA 2021 conference, Venema and Alpár presented attacks against 11 ABE and MA-ABE schemes, including the highly cited DAC-MACS scheme with applications to the cloud.We demonstrate the practicality of the attacks providing:
- A decryption attack against DAC-MACS, where a single user is able to decrypt ciphertexts with policies she cannot satisfy. This user does not even need to collude with other users or corrupt an authority.
- A decryption attack with corruption of one of the authorities against the YJ14-MA-ABE scheme.
- A decryption attack against the YCT14 scheme where two users collude in order to obtain a decryption key based on the work of Tan et al. and Herranz (2019).RequirementsInstall CHARM using the following commands:add-apt-repository ppa:deadsnakes/ppa -y
apt update -y
apt install python3.7 python3.7-dev -y
apt install python3-virtualenv -y
apt install build-essential sudo python3-dev wget flex bison python3-pip libssl-dev libgmp10 libgmp-dev git openssl -y
rm -f /usr/bin/python && ln -s /usr/bin/python3.7 /usr/bin/python
rm -f /usr/bin/python3 && ln -s /usr/bin/python3.7 /usr/bin/python3
git clone https://github.com/JHUISI/charm
git checkout 55d82436d5da1a830fb16d6536700d9d61c0149d
./configure.sh
python3.7 -m pip install -r requirements.txt
cd charm/deps/pbc
make
ldconfig
cd charm/
make
make install
ldconfigReferences- https://www.blackhat.com/eu-21/briefings/schedule/index.html#practical-attacks-against-attribute-based-encryption-25058
- https://eprint.iacr.org/2020/460 |
abecalculator | This is a simple calculator function which add, subract, multiply or divide two numbers.Change log0.1 (16/02/2020)First Release |
abeci | abeciCreateperfect pangrams, sentences with exactly one of each letter in the English alphabet.Generate PangramsAfter installingabecias a package, you can run:abeci-pangramsThis script makes a text file with this name, inresultsby default:./results/std4_max1_has_2x_3x/pangrams_1024_0x1_128x2_512x3_1024x4.txtIf theeffects/source.pfile is missing,The script uses Google's English Corpus of all books published in 2008.The script will wiret aneffects/source.pto speed up future calls to the script.it also:writes a log file such as2022-04-07T0200.log.writes intermediate files in aneffectsdirectory.The file is named after the current date and UTC time.Run the help command for options:abeci-pangrams -hAPI UsageNo programatic api is documented at this time:Look tosrc/modules/savePangrams.pyfor inspirationLocal InstallationDependenciesInstallMinicondaor Anaconda.conda env create -f environment.yamlorconda env update -f environment.yamlThen, activate the installed environment:conda activate abeciAfter installing dependencies, you can run:python src/pangrams.pyRun the help command for options:python src/pangrams.py -hBuild Locally w/ pipUpgrade pip and build with pippython -m pip install --upgrade pip
pip install buildInstall LocallyUsing the conda environment, install locally with pip:VIRTUAL_ENV=$CONDA_PREFIX pip install --src $VIRTUAL_ENV/src -e .TestAfter installing locally, run:bash run_test.shPublishPublishing happens on release. The following two links were inspirations:Publish to pipPublish to conda |
abed | A command line tool for easily managing benchmark experiments |
abedpdf | This is the homepage of our package |
abedy-gitlab-client | Gitlab ClientThis package provides the ability to communicate with the Gitlab API to access groups and project details based on a givenAPI_TOKEN.Some more updated docs will follow soon. |
abeec | abeec 🐝 --- an ABC samplerabeecis a sampler to perform Approximate Bayesian Computation (ABC) --- i.e., likelihood free posterior inference! It is based on the algorithm presented inIshida et al. (2015).Author: Nestor Espinoza ([email protected])Statement of needWhile for Cosmological applications an ABC sampler has already been published by the team of Ishida et al (cosmoabc---check their repository!),
there was a need to develop a more general scheme to allow some flexibility to the sampler. For instance, doing arbitrary prior distributions (e.g., with priors that might be correlated)
was not straightforward to implement, as well as have external functions for distances and simulators that could all benefit from a common parallelization scheme. On top of that, I wanted
a simple sampler that used the most basicpythonlibraries (e.g.,numpyandscipy) at its core. That's whereabeeccomes into place.Using the libraryTo perform ABC on a given dataset, you need three ingredients:Apriorfrom which to draw points.Adistanceto compute distances from simulated datasets to your dataset.And asimulator, to simulate datasets to compare against your dataset.Inabeec, it is expected the user will provideclassesdefining theprior, thedistanceand thesimulator. All the sampler does it take those and apply the iterative importance
sampling scheme outlined inIshida et al. (2015), giving back a sample from the posterior. Once those classes are written, one might simply run the
sampler as:import abeec
from your_script import prior, distance, simulator
samples = abeec.sample(prior, distance, simulator)The best is to check the examples underexamples.InstallationInstallation is as simple as:python setup.py installOr via PyPi:pip install abeecLicence and attributionRead theLICENCEfile for licencing details on how to use the code. If you make use of this code, please citeIshida et al. (2015)and link back
to this repository. |
abei | Python Library for Graphical Programming |
abejacli | No description available on PyPI. |
abejaruntime | No description available on PyPI. |
abeja-sdk | No description available on PyPI. |
abel | Vectorfromabel.linalg.vectorimportVectora,b=Vector([1,2]),Vector([3,4])c,d=Vector([1,2,3]),Vector([4,5,6])asserta.shape==b.shape==(1,2)assertc.shape==d.shape==(1,3)Additionasserta+a==Vector([2,4])asserta+b==Vector([4,6])assertb+b==Vector([6,8])Subtractionasserta-b==Vector([-2,-2])assertb-a==Vector([2,2])Scalingasserta*5==Vector([5,10])assert5*a==Vector([5,10])asserta/2==Vector([0.5,1.0])Dot (inner) productasserta@b==11asserta@a==5Norm (length)asserta.norm()-2.236<0.001assertb.norm()-5<0.001AngleassertVector.angle(a,a)<0.001assertVector.angle(a,b)-0.1799<0.001assertVector.angle(a,b)==Vector.angle(b,a)Vector projectionassertVector.proj(a,a)==aassertVector.proj(a,b)==Vector([1.32,1.76])assertVector.proj(b,a)==Vector([2.2,4.4])Scalar projectionassertVector.scalproj(a,b)-4.919<0.01assertVector.scalproj(b,a)-2.2<0.01assertVector.scalproj(a,a)-2.236<0.01assertVector.scalproj(b,b)-5<0.1Cross productassertVector.cross(c,d)==Vector([-3,6,-3])Average (arithmetic mean)assertVector.average(Vector([2,1]),Vector([4,2]))==Vector([3.0,1.5])CollinearityVectors are collinear iff one is a scalar multiple of the other.assertVector.collinear(Vector([2,1]),Vector([4,2]))assertVector.collinear(Vector([-3,4,1]),Vector([-15,20,5]))assertnotVector.collinear(Vector([0,1]),Vector([1,0]))Linear independenceA set of vectors is linearly independent iff all vectors in it are pairwise non-collinear.assertVector.linindep(Vector([0,1]),Vector([1,0]))assertVector.linindep(Vector([1,1]),Vector([2,1]))assertnotVector.linindep(Vector([1,2,3]),Vector([0,0,1]),Vector([0,0,2])) |
abel-airflow | UNKNOWN |
abelian | abelianis a Python library for computations on elementary locally compact abelian groups (LCAs).
The elementary LCAs are the groups R, Z, T = R/Z, Z_n and direct sums of these.
The Fourier transformation is defined on these groups.
Withabelianit is possible to sample, periodize and perform Fourier
analysis on elementary LCAs using homomorphisms between groups.Classes and methodsThe most important classes are listed below. The software contains many other functions and methods not listed.TheLCAclass represents elementary LCAs, i.e. R, Z, T = R/Z, Z_n and direct sums of these groups.Fundamental methods: identity LCA, direct sums, equality, isomorphic, element projection, Pontryagin dual.TheHomLCAclass represents homomorphisms between LCAs.Fundamental methods: identity morphism, zero morphism, equality, composition, evaluation, stacking, element-wise operations, kernel, cokernel, image, coimage, dual (adjoint) morphism.TheLCAFuncclass represents functions from LCAs to complex numbers.Fundamental methods: evaluation, composition, shift (translation), pullback, pushforward, point-wise operators (i.e. addition).ExampleThe following example shows Fourier analysis on a hexagonal lattice.We create a Gaussian on R^2 and a homomorphism for sampling.fromabelianimportLCA,HomLCA,LCAFunc,voronoifrommathimportexp,pi,sqrtZ=LCA(orders=[0],discrete=[True])R=LCA(orders=[0],discrete=[False])# Create the Gaussian function on R^2function=LCAFunc(lambdax:exp(-pi*sum(j**2forjinx)),domain=R**2)# Create an hexagonal sampling homomorphism (lattice on R^2)phi=HomLCA([[1,1/2],[0,sqrt(3)/2]],source=Z**2,target=R**2)phi=phi*(1/7)# Downcale the hexagonfunction_sampled=function.pullback(phi)Next we approximate the two-dimensional integral of the Gaussian.# Approximate the two dimensional integral of the Gaussianscaling_factor=phi.A.det()integral_sum=0forelementinphi.source.elements_by_maxnorm(list(range(20))):integral_sum+=function_sampled(element)print(integral_sum*scaling_factor)# 0.999999997457763We use the FFT to move approximate the Fourier transform of the Gaussian.# Sample, periodize and take DFT of the Gaussianphi_p=HomLCA([[10,0],[0,10]],source=Z**2,target=Z**2)periodized=function_sampled.pushforward(phi_p.cokernel())dual_func=periodized.dft()# Interpret the output of the DFT on R^2phi_periodize_ann=phi_p.annihilator()# Compute a Voronoi transversal function, interpret on R^2sigma=voronoi(phi.dual(),norm_p=2)factor=phi_p.A.det()*scaling_factortotal_error=0forelementindual_func.domain.elements_by_maxnorm():value=dual_func(element)coords_on_R=sigma(phi_periodize_ann(element))# The Gaussian is invariant under Fourier transformation, so we can# compare the error using the analytical expressiontrue_val=function(coords_on_R)approximated_val=abs(value)total_error+=abs(true_val-approximated_val*factor)asserttotal_error<10e-15Please seethe documentationfor more examples and information. |
abeliantensors | Introductionabeliantensors is a Python 3 package that implements U(1) and Zn symmetry preserving
tensors, as described by Singh et al. inarXiv: 0907.2994andarXiv: 1008.4774. abeliantensors has been designed
for use in tensor network algorithms, and works well with thencon function.InstallationIf you just want to use the library:pip install --user git+https://github.com/mhauru/abeliantensorsIf you also want to modify and develop the librarygit clone https://github.com/mhauru/abeliantensors
cd abeliantensors
pip install --user -e .[tests]after which you can run the test suite by just callingpytest.Usageabeliantensors exports classesTensorU1,TensorZ2, andTensorZ3. Other
cyclic groups Zn can be implemented with one-liners, see the filesymmetrytensors.pyfor examples. abeliantensors also exports a class calledTensor, that is just a wrapper around regular numpy ndarrays, but that
implements the exact same interface as the symmetric tensor classes. This
allows for easy switching between utilizing and not utilizing the symmetry
preserving tensors by simply changing the class that is imported.Each symmetric tensor has, in addition to its tensor elements, the following
pieces of what we call form data:shapedescribes the dimensions of the tensors, just like with numpy arrays.
The difference is that for symmetric tensors the dimension of each index
isn't just a number, but a list of numbers, that sets how the vector space is
partitioned by the irreducible representations (irreps) of the symmetry. So
for instanceshape=[[2,3], [5,4]]could be the shape of a Z2 symmetric
matrix of dimensions 5 x 9, where the first 2 rows and 5 columns are
associated with one of the two irreps of Z2, and the remaining 3 rows and 4
columns with the other.qhapeis likeshape, but lists the irrep charges instead of the
dimensions. Irrep charges are often also called quantum numbers, hence the q.
In the above exampleqhape=[[0,1], [0,1]]would mark the first part of both
the row and column space to belong to the trivial irrep of charge 0, and the
second part to the irrep with charge 1. For Zn the possible charges are 0, 1,
..., n, for U(1) they are all positive and negative integers.dirsis a list of 1s and -1s, that gives a direction to each index: either
1 for outgoing or -1 for ingoing.chargeis an integer, the irrep charge associated to tensor. In most cases
you wantcharge=0, which is also the default when creating new tensors.Note that each element of the tensor is associated with one irrep charge for
each of the indices. The symmetry property is then that an element can only be
non-zero if the charges from each index, multiplied by the direction of that
index, add up to the charge of the tensor. Addition of charges for Zn tensors
is modulo n. For instance for acharge=0TensorZ2object this means that
the charges on each leg must add up to an even number for an element to be
non-zero. The whole point of this library is to store and use such symmetric
tensors in an efficient way, where we don't waste memory or computation time on
the elements we know are zero by symmetry, and can't accidentally let them be
non-zero.Here's a simple nonsense example of how abeliantensors can be used:import numpy as np
from abeliantensors import TensorZ2
# Create a symmetric tensor from an ndarray. All elements that should be zero
# by symmetry are simply discarded, whether they are zero or not.
sigmaz = np.array([[1, 0], [0, -1]])
sigmaz = TensorZ2.from_ndarray(
sigmaz, shape=[[1, 1], [1, 1]], qhape=[[0, 1], [0, 1]], dirs=[1, -1]
)
# Create a random symmetric tensor.
a = TensorZ2.random(
shape=[[3, 2], [2, 4], [4, 4], [1, 1]],
qhape=[[0, 1]] * 4,
dirs=[-1, 1, 1, -1],
)
# Do a singular value decomposition of a tensor, thinking of it as a matrix
# with some of the indices combined to a single matrix index, like one does
# with numpy.reshape. Here we combine indices 0 and 2 to form the left matrix
# index, and 1 and 3 to form the right one. The indices are reshaped back to
# the original form after the SVD, so U and V are in this case order-3 tensors.
U, S, V = a.svd([0, 2], [1, 3])
# You can also do a truncated SVD, in this case to truncating to dimension 4.
U, S, V = a.svd([0, 2], [1, 3], chis=4)
# We can contract tensors together easily using the ncon package.
# Note that conjugation flips the direction of each index, as well as the
# charge of the tensor, which in this case though is 0.
from ncon import ncon
aadg = ncon((a, a.conjugate()), ([1, 2, -1, -2], [1, 2, -11, -12]))
# Finally, knowing that aadg is Hermitian, do an eigenvalue
# decomposition of it, this time truncating not to a specific dimension, but
# to a maximum relative truncation error of 1e-5.
E, U = aadg.eig([0, 1], [2, 3], hermitian=True, eps=1e-5)There are many other user-facing methods and features, for more, see
theAPI docs.Demo and performanceThe folderdemohas an implementation of Levin and Nave'sTRG
algorithm, and a script
that runs it on the square lattice Ising model, using both symmetric tensors
of the TensorZ2 class and dense Tensors, and compares the run times.
Below is a plot of how long it takes to run a single TRG step at various
bond dimensions for both of them.Note that both axes are logarithmic.At low bond dimensions the simpleTensorclass outperformsTensorZ2, because
keeping track of the symmetry structure imposes an overhead. The time
complexity of the overhead is subleading as a function of bond dimension, and
as one goes to higher bond dimensions the symmetric tensors become faster.
Asymptotically both have the same scaling as a function of bond dimension, but
the prefactor is smaller forTensorZ2by a factor of 1/4. This is because
instead of multiplying or decomposing anmxmmatrix at costm**3, we
are multiplying twom/2bym/2matrices, at a total cost of2*(m/2)**3 = (m**3)/4. For larger symmetry groups, the asymptotic benefit would be
greater. For instance forTensorZ3, we should see an approximately 9-fold
speed-up.Similar results can be obtained for other algorithms, although the cross-over
point in bond dimension will be different.Design and structureThe implementation is built on top of numpy, and the block-wise sparse
structure of the symmetry preserving tensors is implemented with Python
dictionaries. Here's a quick summary of what each file does.tensorcommon.py: A parent class of all the other classes,TensorCommon,
that implements some higher-level features using the lower-level methods.abeliantensor.py: All the fun is in here. Implements the classAbelianTensor, that is the parent of all the symmetric tensor classes. This
includes implementations of various common tensor operations, such as
contractions and decompositions, preserving and making use of the block-wise
sparse structure these tensors have.tensor.py:Tensor, the wrapper class for numpy arrays. It is designed so that
any call to a method of theAbelianTensorclass is also a valid call to a
similarly named method of theTensorclass. All the symmetry-related
information is simply discarded and some underlying numpy function is called.
Even if one doesn't use symmetry preserving tensors, theTensorclass provides
some neat convenience functions, such as an easy-to-read one-liner for the
transpose-reshape-decompose-reshape-transpose procedure for singular value and
eigenvalue decompositions of tensors.symmetrytensors.py: A small file that simply creates subclasses ofAbelianTensorfor specific symmetry groups. If you need something other than
Z2, Z3 and U(1), check this file to see how you could add what you need.TestsThetestsfolder has plenty of tests for the various classes. They can be run
by callingpytest, provided abeliantensors was installed with the extras optiontests.Most of the tests are based on generating a random instance of one of the
"fancy" tensor classes in this package, and confirming that the following
diagram commutes:Fancy tensor ─── map to numpy ndarray ───> ndarray
│ │
│ │
Do the thing Do the thing
│ │
│ │
V V
Fancy tensor ─── map to numpy ndarray ───> ndarrayTwo command line arguments can be provided,--n_iterswhich sets how many
times each test is run, with different random tensors each time (100 by
default), and--tensorclasswhich can be used to specify which
tensorclass(es) the tests are run on (by default all of them). Here's an
example of how one might run a specific test repeatedly:pytest tests/test_tensors.py::test_to_and_from_ndarray --tensorclass TensorZ2 --n_iters 1000 |
abellin | No description available on PyPI. |
abelmokadem-awsapilib | A python library that exposes AWS services that are not covered by boto3, through the usage of undocumented APIs.Documentation:https://awsapilib.readthedocs.org/en/latestDevelopment WorkflowThe workflow supports the following stepslinttestbuilddocumentuploadgraphThese actions are supported out of the box by the corresponding scripts under _CI/scripts directory with sane defaults based on best practices.
Sourcing setup_aliases.ps1 for windows powershell or setup_aliases.sh in bash on Mac or Linux will provide with handy aliases for the shell of all those commands prepended with an underscore.The bootstrap script creates a .venv directory inside the project directory hosting the virtual environment. It uses pipenv for that.
It is called by all other scripts before they do anything. So one could simple start by calling _lint and that would set up everything before it tried to actually lint the projectOnce the code is ready to be delivered the _tag script should be called accepting one of three arguments, patch, minor, major following the semantic versioning scheme.
So for the initial delivery one would call$ _tag –minorwhich would bump the version of the project to 0.1.0 tag it in git and do a push and also ask for the change and automagically update HISTORY.rst with the version and the change provided.So the full workflow after git is initialized is:repeat as necessary (of course it could be test - code - lint :) )codelinttestcommit and pushdevelop more through the code-lint-test cycletag (with the appropriate argument)buildupload (if you want to host your package in pypi)document (of course this could be run at any point)Important InformationThis template is based on pipenv. In order to be compatible with requirements.txt so the actual created package can be used by any part of the existing python ecosystem some hacks were needed.
So when building a package out of thisdo notsimple call$ python setup.py sdist bdist_eggas this will produce an unusable artifact with files missing.Instead use the provided build and upload scripts that create all the necessary files in the artifact.Project FeaturesPlease look into the usage files.History0.0.1 (26-04-2021)First code creation0.1.0 (11-05-2021)Initial release0.1.1 (17-05-2021)Filtering out failed accounts from checking their update status0.1.2 (17-05-2021)Fixed a timing issue with getting the active service catalog product on account creation.0.2.0 (18-05-2021)Exposed governed and non governed regions and a small fix with latest update changes.0.2.1 (18-05-2021)Dynamically retrieving updatable information about control tower.0.2.2 (19-05-2021)Added some blocking on actions to prevent race conditions. |
abel-pytorch | How to decay your Learning Rate (PyTorch)PyTorch implementation ofABELLRScheduler based on weight-norm. If you find this work interesting, do consider starring the repository. If you use this in your research, don't forget to cite!Original paperDocsInstallationWIP - not available on PyPi yet.pip install abel-pytorchUsageimporttorchfromtorchimportnn,optimfromabelimportABELmodel=resnet18()optim=optim.SGD(model.parameters(),1e-3)scheduler=ABEL(optim,0.9)fori,(images,labels)inenumerate(trainloader):# forward pass...optim.step()scheduler.step()Cite original paper:@article{lewkowycz2021decay,
title={How to decay your learning rate},
author={Lewkowycz, Aitor},
journal={arXiv preprint arXiv:2103.12682},
year={2021}
}Cite this work:@misc{abel2021pytorch,
author = {Vaibhav Balloli},
title = {A PyTorch implementation of ABEL},
year = {2021},
howpublished = {\url{https://github.com/tourdeml/abel-pytorch}}
} |
abeluna | AbelunaA simple GUI to-do/task manager with CalDAV support. In theory, Abeluna should support any CalDAV server, but currently onlyNextcloudandRadicaleare tested.The goal of this application is to become a desktop version of Nextcloud's Tasks app. As such, not all functionality in theicalendar's VTODOare supported, only those that are used by Nextcloud. On the other hand, there some non-standard fields used by Nextcloud that are supported by Abeluna, such as the ability to hide subtasks.ScreenshotsInstallationFrom PyPIFirst, install two packages,libnotifyandgobject-introspection. On other distributions besides Arch Linux, these names may be different. For example, on Debian-based systems,gobject-introspectionislibgirepository1.0-dev.$pipinstallabeluna
$abelunaAURIf you are using Arch Linux and do not wish to install through PIP, you can install the AUR packageabelunafor the latest version.Usage$abelunaIn the GUI, calendars can be added throughSettings > Calendar settings. General settings, such as the timezone and synchronization schedule can be accessed throughSettings > General settings.Future PlansSupport for desktop notifications.Support for recurring tasks.Add common keyboard shortcuts. |
abem | Acoustic Boundary Element Method (abem)AcousticBEM is a small library and example programs for solving the acoustic
Helmholtz equation using the Boundary Element Method. The library is a
re-implementation of the core components of Stephen Kirkup's ABEM Fortran
library and its example programs.
The original Fortran code and the book "The Boundary Element Method in
Acoustics" are available on his website:http://www.boundary-element-method.com/. |
abe-mocks | UNKNOWN |
abenity | A Python library for using the Abenity API. |
abeona | abeona v0.45.0A simple transcriptome assembler based on kallisto and Cortex graphs.Abeona consists of the following stages:Assembly of reads into a De Bruijn graphPruning of tips and low-coverage unitigsPartitioning of the De Bruijn graph into subgraphsGeneration of candidate transcripts by simple path traversalFiltering of candidate transcripts by kallistoInstallationThe easiest way to install abeona is into acondaenvironment.After activating the conda environment, run:condainstallabeona-cconda-forge-cbiocondaUsageThe principal command isabeona assemble. This command assembles transcripts from cleaned
short-read RNA-seq reads in FASTA or FASTQ format. A description of command arguments is
available with the command:abeonaassemble--helpSpecifying input read dataAbeona is designed to be run on reads from one biological sample at a time.
Abeona uses sequencing reads in two stages: for De Bruijn-graph construction,
and for candidate transcript filtering with kallisto. The first stage accepts
paired-end, single-end, or both types of reads through the--fastx-*arguments.
The reads for the second stage are specified with the--kallisto-fastx-*arguments.
Kallisto only accepts single-end or paired-end reads, so input to this stage
is also restricted in that manner.Toy Example# Let's create a FASTA consisting of sub-reads from two transcripts: AAAAACCC and AAAAAGGG$forsinAAAAACCAAAAAGGAAAACCCAAAAGGG;doforiin$(seq13);doecho-e">_\n$s">>input.fa;done;done# Now feed the fasta to the graph assembly step with --fastx-single and to the kallisto filtering
# step with --kallisto-fastx-single.$abeonaassemble-k5-m4--fastx-singleinput.fa--kallisto-fastx-single\input.fa--kallisto-fragment-length7--kallisto-sd1-otest--no-linksNEXTFLOW~version0.31.1Launching`assemble.nf`[determined_allen]-revision:11c20ed355[bootstrap_samples:100,fastx_forward:null,fastx_reverse:null,fastx_single:/Users/winni/tmp/input.fa,initial_contigs:null,jobs:2,kallisto_fastx_forward:null,kallisto_fastx_reverse:null,kallisto_fastx_single:/Users/winni/tmp/input.fa,kallisto_fragment_length:7.0,kallisto_sd:1.0,kmer_size:5,max_paths_per_subgraph:0,memory:4,merge_candidates_before_kallisto:false,min_tip_length:0,min_unitig_coverage:4,out_dir:test,quiet:false,resume:false,mccortex:mccortex5,mccortex_args:--sort--force-m4G][warmup]executor>local[26/119d41]Submittedprocess>fullCortexGraph[fc/585605]Submittedprocess>cleanCortexGraph[dd/40b5fc]Submittedprocess>pruneCortexGraphOfTips[36/f63343]Submittedprocess>traverseCortexSubgraphs[23/6d9033]Submittedprocess>candidateTranscripts(1)[d5/05d417]Submittedprocess>buildKallistoIndices(1)[ac/e36d53]Submittedprocess>kallistoQuant(1)[ec/2b258d]Submittedprocess>filter_transcripts(1)[49/d4c7e3]Submittedprocess>concatTranscripts# View the resulting assembled transcripts$zcattest/all_transcripts/transcripts.fa.gz>g0_p0prop_bs_est_counts_ge_1=0.98AAAAAGGG>g0_p1prop_bs_est_counts_ge_1=1.0AAAAACCCDevelopmentconda env create -f environment.yml my-dev-env
conda activate my-dev-env
make testLicenseAbeona is distributed under the terms of theApache License, Version 2.0.CitingIf you use abeona in your research, please cite:Akhter S, Kretzschmar WW, Nordal V, Delhomme N, Street NR, Nilsson O, Emanuelsson O, Sundström JF. Integrative Analysis of Three RNA Sequencing Methods Identifies Mutually Exclusive Exons of MADS-Box Isoforms During Early Bud Development in Picea abies. Front. Plant Sci. 9, 1–18 (2018).ChangelogVersion 0.45.0Date:XXXNew featuresabeona assembleMccortex is now used for pruning by defaultThe command line argument--prune-tips-with-mccortexis now deprecated.
Instead use--no-prune-tips-with-mccortex.New iterative pruning strategy--prune-tips-iteratively.Version 0.44.0Date:2019-03-26This version skips commits made for the 0.43.0 tag.New featuresReads that share kmers with subgraphs that are skipped are now reported in theunassembled_readsdirectory.Version 0.42.0Date:2018-12-17Interface ChangesCleanup now deletes all directories in output dir except forall_transcripts/transcripts.fa.gzCleanup is now on by defaultCleanup can be turned off with--no-cleanupflagall_transcripts/transcripts.fa.gzis unzipped and stored astranscripts.fato conform
to the convention set by Trinity and Oases for output file namesVersion 0.41.0Date:2018-12-13Interface changesRemove--kallisto-fastx-*arguments. Being able to separately specify reads to graph building
and kallisto has not been all that useful, and it increases the complexity of the code.Add default value of--kmer-sizefor--min-tip-length.FixesThere are several ways in which kallisto can fail due to no reads pseudoaligning to a subgraph’s
candidate transcripts. When this happens, abeona now catches the error and silently ignores the
subgraph.Version 0.40.0Date:2018-11-17New featuresAdd--no-linksargument to turn off link use in candidate transcript creationAdd--max-junctionsargument to allow fast skipping of subgraphs with too many junctionsFixesProperly assign reads to all subgraphs to which they are assignableSolve high-mem use problem by creating links only on assigned readsVersion 0.36.0Date:2018-10-25New featuresGraph traversal now uses linksFixesLots of improvements toabeona readsto improve memory and filehandle useVersion 0.33.0Date:2018-10-17New featuresUse kmer mapping (abeona reads) to assign reads to subgraphs before quantification of
candidate transcripts with kallistoFixesAdd missing conda dependencyseqtktoenvironment.ymlfor travis CI |
abepdf | No description available on PyPI. |
abe-python | UNKNOWN |
aberdeen | Simple python script for taking a directory of markdown files and
generating/storing the backend of a blog.The goal is to enable quick editing of simple text-files and posting them to a
database via a push to a git branch (default ‘publish’).Upon running, a python JSON object is created from each file found. There is a
markdown header extracted from the file indicating post title, date posted,
authors, tags, etc. The content of the post is converted automatically to html
and added to the final object in the path.The resulting objects (currently) are sent to a MongoDB session and saved to the
specified collection.This process is strictly a ‘model’ management system, any view and controller
must be built/managed by you.(The name comes from the “Aberdeen” fish hook 🎣)RequirementsPython 3.4MarkdownSupported Databasesmongodb -asyncio_mongoInstallationCopy thepost-update,aberdeen.pyand config files to the directoryhooksin the git repository on the server. Edit the config file to your
specifications. This will be updated when uploaded to pypi.Server SetupOn your server, create a bare git repository, something like ‘blog_data’. This
will simply hold all your markdown (or maybeothertype) files. Create a
‘publish’ branch in addition to another ‘working’ one (presumably ‘master’). Add
the post-update webhook and configuration as explained in Installation. Clone
the repo to your working computer.UsageThis program requires a key-value pair header in each of the markdown files that
have typical elements required for blogging---
title : Post Title
date : Mar 15, 2015
tags : Example
Feeling Happy
XYZ
author: Me
---
# My New Post
This is a great post! *All* my markdown worksThe ‘tags’ attribute in this example will generate a list of strings; for more
information on how the metadata header works,read this.Aberdeen creates a python ‘time’ object from the ‘date’ attribute. It will try
to be smart about the style of the date, and there are a few ways to interpret
the datetime from the string, but it has to be accepted in some form or another
by thestrptimefunction of python ‘time’ library. The first way to work will be saved, so it
rewards consistency. It is recommended you put in a time field if you care about
that, else it will default to midnight of the determined date.Maybe this can be specified in the config file? (that’s not implemented yet.)This kind of information is great for storing in NoSQL databases, so MongoDB is
the only database currently supported. The content of the markdown is converted
to HTML and added to the result as ‘html_content’ field. The objects are sorted
in terms of date and written to the database. The previous table or collection
will dropped and the new items added. (*NO GUARANTEE*that the items will be
in the same order).Other ThingsRemeber this does not have any HTML structure or view-support for a blog. This
strictly converts one form of a model (markdown files) to another (database
entries). The view/controllers are totally up to you for retreiving and
displaying the posts.Always assume that the database collection/table will beerasedupon every
push. The idea is the database reflects the files, so changing a file will
replace that entry in the database. It is recommended to NOT use fixed links to
posts. It is suggested to used date+title as a unique identifier. Alternatively,
you could store a unique post id in the metadata field, if you want some
assurance that things will be fixed (but it’s up to you to keep track of the and
their uniqueness).LICENSEApache 2.0 |
abero | runnerAnalyze multiple files for similarity and/or uniqueness.
Finding similarities of works duplicated from one or more part of different works to create a seemingly unique one can be difficult because of different strategies being used, but this can be done better with a software like runner.RequirementsArkivistpip install arkivistUsageInstall the latest abero package, upcoming versions might introduce unannounced changes, so a virtual environment is a must have before installation.pipinstall-UaberoTo integrate abero into your Python codes, check the code snippet below:importaberoabero.analyze(directory,extension="txt",threshold=80,template=None,skipnames=0,group=0,unzip=0,reset=0)CLI Usage# usage: runner [-h] -d directory [-e extension] [-c control] [-t threshold] [-u unzip] [-s skipnames] [-g group] [-r reset]pyrunner.py-d"<path_to_files>"-e"txt"-c"<path_to_control_file>"-t1-u1-s1-r1-d <path>- Full path of the dirctory containing the files to analyze.-e <txt>- List of allowed file extensions to analyze.-c <*.txt>- Full path of the control file.-t <80>- Threshold level for uniqueness, treats similarity below threshold as unique (1-100; default = 0)-u <0>- Unzip/extract ZIP files (0-1; default = 0)-s <0>- Skip files with common names (0-1; default = 0)-g <1>- Only compare if files contains the same identifier (0-1; default = 1)Example:student1*_set1*.py >> student2*_set1*.py-r <0>- Reset analytics before execution (0-1; default = 0)Control FileControl file contains words or phrases, checked line-by-line, that are deem allowed to be contained in all files to analyzed; therefore, if found on the test files, it will not be flagged as duplicate work.FeaturesUnzip featureFile comparisonThreshold levelsSkip / group compareDiff tool, content viewerDid you know?The repository nameaberowas inspired from the words aberrant and runner (Latin), which may mean deviating or being absent. |
aberquota | UNKNOWN |
abess | Overviewabess(Adaptive BEst Subset Selection) library aims to solve general best subset selection, i.e.,
find a small subset of predictors such that the resulting model is expected to have the highest accuracy.
The selection for best subset shows great value in scientific researches and practical application.
For example, clinicians wants to know whether a patient is health or not
based on the expression level of a few of important genes.This library implements a generic algorithm framework to find the optimal solution in an extremely fast way[1].
This framework now supports the detection of best subset under:linear regression,(multi-class) classification,censored-response modeling[2],multi-response modeling (a.k.a. multi-tasks learning), etc.
It also supports the variants of best subset selection likegroup best subset selection[3]andnuisance best subset selection[4].
Especially, the time complexity of (group) best subset selection for linear regression is certifiably polynomial[1][3].Quick startInstall the stable abess Python package from Pypi:$pipinstallabessBest subset selection for linear regression on a simulated dataset in Python:fromabess.linearimportLinearRegressionfromabess.datasetsimportmake_glm_datasim_dat=make_glm_data(n=300,p=1000,k=10,family="gaussian")model=LinearRegression()model.fit(sim_dat.x,sim_dat.y)See more examples analyzed with Python in the tutorials; the notebooks are availablehere.Runtime PerformanceTo show the power of abess in computation,
we assess its timings of the CPU execution (seconds) on synthetic datasets, and compare to
state-of-the-art variable selection methods.
The variable selection and estimation results are deferred toperformance.We compare abess Python package with scikit-learn on linear and logistic regression.
Results are presented in the below figure, and can be reproduce by running the commands in shell:$python./simulation/Python/timings.pywe obtain the runtime comparison picture:abess reaches a high efficient performance especially in linear regression where it gives the fastest solution.Open source softwareabess is a free software and its source code are publicly available inGithub.
The core framework is programmed in C++, and user-friendly R and Python interfaces are offered.
You can redistribute it and/or modify it under the terms of theGPL-v3 License.
We welcome contributions for abess, especially stretching abess to
the other best subset selection problems.CitationIf you useabessor reference our tutorials in a presentation or publication, we would appreciate citations of our library[5].Jin Zhu, Liyuan Hu, Junhao Huang, Kangkang Jiang, Yanhang Zhang, Shiyun Lin, Junxian Zhu, Xueqin Wang (2022). “abess: A Fast Best Subset Selection Library in Python and R.” Journal of Machine Learning Research (Accepted).The corresponding BibteX entry:@article{zhu2022abess,author={JinZhuandLiyuanHuandJunhaoHuangandKangkangJiangandYanhangZhangandShiyunLinandJunxianZhuandXueqinWang},title={abess:AFastBestSubsetSelectionLibraryinPythonandR},journal={JournalofMachineLearningResearch(Accepted)},year={2022}}References[1](1,2)Junxian Zhu, Canhong Wen, Jin Zhu, Heping Zhang, and Xueqin Wang (2020). A polynomial algorithm for best-subset selection problem. Proceedings of the National Academy of Sciences, 117(52):33117-33123.[2]Pölsterl, S (2020). scikit-survival: A Library for Time-to-Event Analysis Built on Top of scikit-learn. J. Mach. Learn. Res., 21(212), 1-6.[3](1,2)Yanhang Zhang, Junxian Zhu, Jin Zhu, and Xueqin Wang (2022). A Splicing Approach to Best Subset of Groups Selection. INFORMS Journal on Computing (Accepted). doi:10.1287/ijoc.2022.1241.[4]Qiang Sun and Heping Zhang (2020). Targeted Inference Involving High-Dimensional Data Using Nuisance Penalized Regression, Journal of the American Statistical Association, DOI: 10.1080/01621459.2020.1737079.[5]Zhu Jin, Xueqin Wang, Liyuan Hu, Junhao Huang, Kangkang Jiang, Yanhang Zhang, Shiyun Lin, and Junxian Zhu. “abess: A Fast Best-Subset Selection Library in Python and R.” Journal of Machine Learning Research 23, no. 202 (2022): 1-7. |
abeverage-calcalc | Failed to fetch description. HTTP Status Code: 404 |
abeverage-calculator | Hey! Please install my package usingpipinstallabeverage-calculatorThen it can be run from the command line like:calc-w"mass of earth"Or from python like:fromcalcalc.CalCalcimport*calcualte('mass of earth') |
abexlib | Failed to fetch description. HTTP Status Code: 404 |
abexp | ABexpABexpis aPythonlibrary which aims to support users along the entire end-to-end A/B test experiment flow
(see picture below). It contains A/B testing modules which use both frequentist and bayesian statistical approaches
including bayesian generalized linear model (GLM).InstallationThis library is distributed onPyPIand
can be installed withpip. The latest release is version0.0.1.$ pip install abexpThe command above will automatically install all the dependencies listed inrequirements.txt. Please visit theinstallationpage for more details.Getting startedA short example, illustrating it use:import abexpCompute the minimum sample size needed for an A/B test experiment with two variants, so called control and treatment
groups.from abexp.core.design import SampleSize
c = 0.33 # conversion rate control group
t = 0.31 # conversion rate treatment group
sample_size = SampleSize.ssd_prop(prop_contr=c, prop_treat=t) # minimum sample size per each groupDocumentationFor more information please read the fulldocumentationandtutorials.Info for developersThe source code of the project is available onGitHub.$ git clone https://github.com/PlaytikaResearch/abexp.gitYou can install the library and the dependencies with one of the following commands:$ pip install . # install library + dependencies
$ pip install .[develop] # install library + dependencies + developer-dependencies
$ pip install -r requirements.txt # install dependencies
$ pip install -r requirements-dev.txt # install developer-dependenciesAs suggested by the authors ofpymc3andpandoc, we highly recommend to install these dependencies withconda:$ conda install -c conda-forge pandoc
$ conda install -c conda-forge pymc3To create the fileabexp.whlfor the installation withpiprun the following command:$ python setup.py sdist bdist_wheelTo create the HTML documentation run the following commands:$ cd docs
$ make htmlRun testsTests can be executed withpytestrunning the following commands:$ cd tests
$ pytest # run all tests
$ pytest test_testmodule.py # run all tests within a module
$ pytest test_testmodule.py -k test_testname # run only 1 testLicenseMIT License |
abf | UNKNOWN |
abfab.plone | This is the server-side add-on implementing AbFab for Plone.It requireshttps://github.com/ebrehault/abfab-voltoto be installed on the client side.Why? What for?The main objective behind AbFab is to provide a way to make frontend easy, fun, and pleasant.Client-side technics do improve the user experience, nevertheless they should not damage the developer experience. Bundling is not scalable, adding a new page to an existing app should not involve to re-deploy the entire thing.AbFab is not meant to be a gigantic framework covering thousands of use cases. It targets small features that could probably be implemented in more classical ways, but you just do not want to deploy too many things (like a database, a bunch of backend endpoints, a security layer, a frontend app, etc.), or maybe you do not want to pollute your existing stack with extra dependencies just to achieve a small widget in one given page of your entire app.AbFab is an all-in-one platform allowing to develop simple frontend components that can be published anywhere.DescriptionAbFab is a web application publication environment. It provides the essential services any web application needs:a secured and fast backend storage,a minimalistic yet powerful frontend component framework (Svelte),a light JavaScript runtime offering routing and backend connectivity.Components are written in Svelte, they are compiled in the browser (you do not need a local NPM), stored in the Plone site (in a soup, seehttps://pypi.org/project/souper.plone/), and can be published to any page as a web component.Simple things must be simpleNo bundle and no static files: You will not have to use NPM, you will not need to bundle your code. All the components and data are on the server, there is no need to generate and deploy static files.Code-splitting: Each component is compiled automatically and independently, and each page of your app will load only the needed components.Client-side navigation: Navigation from one page to another is performed by loading only the missing data and the application renders it on the client-side, so the application is always fast. It behaves as a Single-Page-App, but it’s not.Component approach: Components are an efficient way to structure an app (HTML is built that way actually). You should be able use them outside the SPA pattern.Do you need to learn a new technology? NO :)LOW CODE: To develop an AbFab app, you will just need HTML, CSS and (simple) JavaScript. Svelte could be considered as a templating layer, it is very simple to learn and to use and will not be a blocker.LOW DEPLOYMENT: AbFab is not just a frontend solution, it comes with backend capabilities, your component are stored in the site directly.LOW BUILD: Components can be developed directly from the AbFab online interface. No NPM, no bundling.InstallationInstall abfab.plone withpip:$ pip install abfab.ploneOr using buildout:[buildout]
...
eggs =
abfab.ploneand then runningbin/buildoutRaise the upload max size limit inzope.conf:<dos_protection>
form-memory-limit 4MB
</dos_protection>Check the rest of the installation in the abfab-volto’s READMEhttps://github.com/ebrehault/abfab-volto#installation.AuthorEric BréhaultInspirationDawn French, Jennifer Saunders and Joanna Lumley.ContributeIssue Tracker:https://github.com/ebrehault/abfab.plone/issuesSource Code:https://github.com/ebrehault/abfab.ploneLicenseThe project is licensed under the GPLv2.ContributorsEric BREHAULT,[email protected] (2023-10-28)Fix permission Check.
[ebrehault]1.0.1 (2023-10-03)Clean up.
[ebrehault]1.0a2 (2023-10-03)Check permissions.
[ebrehault]1.0a1 (2023-09-20)Initial release.
[ebrehault] |
abfallwirtschaftfulda | Python: Abfallwirtschaft Fulda![Support my work on Patreon][patreon-shield]Asynchronous Python client for the Abfallwirtschaft Fulda API.AboutThis package allows you to request waste pickup days from Abfallwirtschaft Fulda
programmatically. It is mainly created to allow third-party programs to use
or respond to this data.An excellent example of this might be Home Assistant, which allows you to write
automations, e.g., play a Google Home announcement in the morning when it is
trash pickup day.InstallationpipinstallabfallwirtschaftfuldaUsageimportasynciofromabfallwirtschaftfuldaimportAbfallwirtschaftFulda,WASTE_TYPE_NON_RECYCLABLEasyncdefmain(loop):"""Show example on stats from Abfallwirtschaft Fulda."""asyncwithAbfallwirtschaftFulda(district_id=7,town_id=40,loop=loop)astw:awaittw.update()pickup=awaittw.next_pickup(WASTE_TYPE_NON_RECYCLABLE)print("Next pickup for Non-recyclable:",pickup)if__name__=="__main__":loop=asyncio.get_event_loop()loop.run_until_complete(main(loop))Changelog & ReleasesThis repository keeps a change log usingGitLab's releasesfunctionality. The format of the log is based onKeep a Changelog.Releases are based onSemantic Versioning, and use the format
ofMAJOR.MINOR.PATCH. In a nutshell, the version will be incremented
based on the following:MAJOR: Incompatible or major changes.MINOR: Backwards-compatible new features and enhancements.PATCH: Backwards-compatible bugfixes and package updates.ContributingThis is an active open-source project. We are always open to people who want to
use the code or contribute to it.We've set up a separate document for ourcontribution guidelines.Thank you for being involved! :heart_eyes:Setting up development environmentIn case you'd like to contribute, aMakefilehas been included to ensure a
quick start.makevenvsource./venv/bin/activate
makedevNow you can start developing, runmakewithout arguments to get an overview
of all make goals that are available (including description):$make
AsynchronousPythonclientforAbfallwirtschaftFulda.
Usage:makehelpShowsthismessage.makedevSetupadevelopmentenvironment.makelintRunalllinters.makelint-blackRunlintingusingblack&blacken-docs.makelint-flake8Runlintingusingflake8(pycodestyle/pydocstyle).makelint-pylintRunlintingusingPyLint.makelint-mypyRunlintingusingMyPy.maketestRuntestsquicklywiththedefaultPython.makecoverageCheckcodecoveragequicklywiththedefaultPython.makeinstallInstallthepackagetotheactivePython'ssite-packages.makecleanRemovesbuild,test,coverageandPythonartifacts.makeclean-allRemovesallvenv,build,test,coverageandPythonartifacts.makeclean-buildRemovesbuildartifacts.makeclean-pycRemovesPythonfileartifacts.makeclean-testRemovestestandcoverageartifacts.makeclean-venvRemovesPythonvirtualenvironmentartifacts.makedistBuildssourceandwheelpackage.makereleaseReleasebuildonPyPmaketoxRuntestsoneveryPythonversionwithtox.makevenvCreatePythonvenvenvironment.LicenseMIT LicenseCopyright (c) 2019 Stephan BeierPermission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE. |
abf-explorer | ABF ExplorerABF_Exploreris asimplegraphical application for quickly viewing axon binary format (ABF) files from electrophysiology experiments without writing a bunch of boilerplate python/matlab/etc. This graphical tool allows you to quickly scroll through files, read the basic metadata, and take a look at the data.We use the excellent pyABF package (https://github.com/swharden/pyABF) byScott W Hardento do the hard work of parsing ABF files.UseThis is a graphical application for inspecting physiology data in ABF files. ABF Explorer supports Python > 3.7.Installpipinstallabf_explorerLaunch GUIabf_explorerCommand line options-dor--startup-dirspecify path to a directory containing ABF files.Use:abf_explorer-dpath/to/abfsKeyboard shorcutsTABwill plot the current selectioncwill clear the current plotSample dataSample data (abf's) can be downloaded fromthis repository.Changelogabf_explorer==0.6Date: 2021-04-21Reason: Bump PyQt5 version to fix BigSur GUI failure. |
abfn | ABFNABFN text filter rules for indic languagesref:https://www.unicode.org/L2/L2016/16161-indic-text-seg.pdf |
abfy | Failed to fetch description. HTTP Status Code: 404 |
abgleich | ABGLEICHSYNOPSISabgleichis a simple ZFS sync tool. It displays source and target ZFS zpool, dataset and snapshot trees. It creates meaningful snapshots only if datasets have actually been changed. It compares a source zpool tree to a target, backup zpool tree. It pushes backups from a source to a target. It cleanes up older snapshots on the source side if they are present on the target side. It runs on a command line and produces nice, user-friendly, human-readable, colorized output. It also includes a GUI.CLI EXAMPLEGUI EXAMPLEsnapbackupcleanupINSTALLATIONThe base CLI tool can be installed as follows:pipinstall-vUabgleichAn installation also including a GUI can be triggered by running:pipinstall-vUabgleich[gui]RequiresCPython3.6 or later, aUnix shellandssh. GUI support requiresQt5in addition. Tested withOpenZFS0.8.x on Linux.abgleich, CPython and the Unix shell must only be installed on one of the involved systems. Any remote system will be contacted via ssh and provided with direct ZFS commands.INITIALIZATIONAll actions involving a remote host assume thatsshwith public key authentication instead of passwords is correctly configured and working.Let's assume that everything insource_tank/dataand below should be synced withtarget_tank/some_backup/data.source_tankandtarget_tankare zpools.datais the "prefix" for the source zpool,some_backup/datais the corresponding "prefix" for the target zpool. Forabgleichto work,source_tank/dataandtarget_tank/some_backupmust exist.target_tank/some_backup/datamust not exist. The latter will be created byabgleich. It is highly recommended to set the mountpoint oftarget_tank/some_backuptononebefore runningabgleichfor the first time.Rights to run the following commands are required:commandsourcetargetzfs listxxzfs getxxzfs snapshotxzfs sendxzfs receivexzfs destroyxconfig.yamlComplete example configuration file:source:zpool:tank_ssdprefix:host:localhostuser:port:target:zpool:tank_hddprefix:BACKUP_SOMEMACHINEhost:bigdatauser:zfsadminport:include_root:yeskeep_snapshots:2keep_backlog:Truealways_changed:nowritten_threshold:1048576check_diff:yessuffix:_backupdigits:2ignore:-home/user/CACHE-home/user/CCACHEssh:compression:nocipher:[email protected]:target_samba_noshare:yestarget_autosnapshot_ignore:yeszpooldefines the name of the zpools on source and target sides. Theprefixvalue defines a "path" to a dataset underneath thezpool, so the name of the zpool itself is not part of theprefix. Theprefixcan be empty on either side. Prefixes can differ between source and target side.hostspecifies a value used byssh. It does not have to be an actual host name. It can also be an alias from ssh's configuration. If ahostis set tolocalhost,sshwont be used and theuserfield can be left empty or omitted. Both source and target can be remote hosts orlocalhostat the same time. Theportparameter specifies a customsshport. It can be left empty or omitted.sshwill then use its defaults or configuration to determine the correct port.include_rootindicates whether{zpool}{/{prefix}}should be included in all operations.keep_snapshotsis an integer and must be greater or equal to1. It specifies the number of snapshots that are kept per dataset on the source side when a cleanup operation is triggered.keep_backlogis either an integer or a boolean. It specifies if (or how many) snapshots are kept on the target side if the target side is cleaned. Snapshots that are part of the overlap with the source side are never considered for removal.suffixcontains the name suffix for new snapshots.Whether or not snapshots are generated is based on the following sequence of checks:Dataset is ignored: NODataset has no snapshot: YESIf thealways_changedconfiguration option is set toyes: YESIf thetaggingconfiguration option underneathcompatibilityis set to yes and the last snapshot of the dataset has not been tagged byabgleichas a backup: YESwrittenproperty of dataset equals0: NODataset is a volume: YESIf thewritten_thresholdconfiguration is set and thewrittenproperty of dataset is larger than the value ofwritten_threshold: YESIf thecheck_diffconfiguration option is set tono: YESIfzfs diffproduces any output relative to the last snapshot: YESOtherwise: NOSettingalways_changedtoyescausesabgleichto beliefe that all datasets have always changed since the last snapshot, completely ignoring what ZFS actually reports. No diff will be produced & checked for values ofwrittenlower thanwritten_threshold. Checking diffs can be completely deactivated by settingcheck_difftono.digitsspecifies how many digits are used for a decimal number describing the n-th snapshot per dataset per day as part of the name of new snapshots.ignorelists stuff underneath theprefixwhich will be ignored by this tool, i.e. no snapshots, backups or cleanups.sshallows to fine-tune the speed of backups. In fast local networks, it is best to setcompressiontonobecause the compression is usually slowing down the transfer. However, for low-bandwidth transmissions, it makes sense to set it toyes. For significantly better speed in fast local networks, make sure that both the source and the target system support a common cipher, which is accelerated byAES-NIon both ends. Thesshport can be specified per side via theportconfiguration option, i.e. for source and/or target.Custom pre- and post-processing can be applied aftersendand beforereceiveper side via shell commands specified in theprocessingconfiguration option (underneathsourceandtarget). This can be useful for a custom transfer compression based on e.g.lzmaorbzip2.compatibilityadds options for makingabgleichmore compatible with other tools. Iftarget_samba_noshareis active, thesharesmbproperty will - as part of backup operations - be set toofffor{zpool}{/{prefix}}on the target side, preventing sharing/exposing backup datasets by accident. Iftarget_autosnapshot_ignoreis active, thecom.sun:auto-snapshotproperty will - similarly as part of backup operations - be set tofalsefor{zpool}{/{prefix}}on the target side, tellingzfs-auto-snapshotto ignore the dataset.USAGEAll potentially changing or destructive actions are listed in detail before the user is asked to confirm them. None of the commands listed below create, change or destroy a zpool, dataset or snapshot on their own without the user's explicit consent.abgleich tree config.yaml [source|target]Show ZFS tree with snapshots, disk space and compression ratio. Appendsourceortarget(optional).abgleich snap config.yamlDetermine which datasets on the source side have been changed since last snapshot. Generate snapshots on the source side where applicable.abgleich compare config.yamlCompare source ZFS tree with target ZFS tree. See what is missing where.abgleich backup config.yamlSend (new) datasets and new snapshots from source to target.abgleich cleanup config.yaml [source|target]Cleanup older local snapshots on source side if they are present on both sides. Of those snapshots present on both sides, keep at leastkeep_snapshotsnumber of snapshots on source side. Or: Cleanup older snapshots on target side. Beyond the overlap with source, keep at leastkeep_backlogsnapshots. Ifkeep_backlogisFalse, all snapshots older than the overlap will be removed. Ifkeep_backlogisTrue, no snapshots will be removed. Ifabgleich cleanruns against the target side, an extra warning will be displayed and must be confirmed by the user before any dangerous actions are attempted.abgleich wizard config.yamlRuns a sequence ofsnap,backupandcleanupin a wizard GUI. This command is only available ifabgleichwas installed with GUI support.SPEEDabgleichuses Python'stype hintsand enforces them withtypeguardat runtime. It furthermore makes countless assertions.The enforcement of types and assertions can be controlled through thePYTHONOPTIMIZEenvironment variable. If set to0(the implicit default value), all checks are activated.abgleichwill run slow. For safety, this mode is highly recommended. For significantly higher speed, all type checks and most assertions can be deactivated by settingPYTHONOPTIMIZEto1or2, e.g.PYTHONOPTIMIZE=1 abgleich tree config.yaml. This is not recommended. You may want to check if another tool or configuration has altered this environment variable by runningecho $PYTHONOPTIMIZE. |
abgleich-pkg-wasix | AbgleichThis package has the following repositories:Abgleich@githubAbgleich-binder@githubpypi-test-repoFirst time runTLDRfrom abgleich_pkg.abgleich import setupEnvironment
setupEnvironment()detailedimport os
import logging
from abgleich_pkg.abgleich import setlogging, printGlobals, setupEnvironment
os.chdir(os.path.dirname(os.path.realpath(__file__)))
setlogging()
printGlobals()
setupEnvironment()start dockercd docker
docker-compose -f docker-compose.yml upFürs Internet:Freigabe von ip auf port:15432
dbconnstr='postgresql+psycopg2://postgres:[email protected]:15432/mydb'
Also:
port:15432
user:postgres
pwd:secret
server:x.x.x.x
database:mydbexample mainimport os
import logging
from abgleich_pkg.abgleich import setlogging, prepareParams, checkDebugEnv, printGlobals, setupEnvironment, write2CSV, writeObj, write2DF, prepareFilterCSV, writeObj, getTuples, calcvalues, \
no_abacus, no_names, baseDataDir, tuplefn, tablename, chunksize, agents, threshold, filterOnAlgo, op, dbconnstr
def main():
setlogging()
prepareParams()
printGlobals()
tuples = getTuples(no_abacus, no_names, f'{baseDataDir}/{tuplefn}')
result = calcvalues(agents, chunksize, tuples)
writeObj(result, f'{baseDataDir}/{tablename}.result')
finallist = prepareFilterCSV(result, threshold, filterOnAlgo, op)
writeObj(finallist, f'{baseDataDir}/{tablename}.finallist')
write2CSV(finallist, f'{baseDataDir}/{tablename}.csv')
write2DF(finallist, f'{baseDataDir}/{tablename}.df')
if os.name == 'posix':
try:
os.chdir('docker')
os.system('./do_csv_upload.sh')
except OSError:
print("Something wrong with specified directory. Exception- ", sys.exc_info())
finally:
print("Current directory is-", os.getcwd())
print('run do_csv_upload.sh manually!')
else:
logging.warning(f'system: {os.name}. load make take some time....')
write2Database(finallist, dbconnstr, tablename)
if __name__ == "__main__":
os.chdir(os.path.dirname(os.path.realpath(__file__)))
print(f'cwd: {os.curdir}')
if True:
setlogging()
printGlobals()
setupEnvironment()
else:
main()callme:python3 main.py --no_abacus=all --no_names=all --calcTuples=j --tuplesfile=abgleich.tuples --tablename=ABGLEICHinstallation method (try)import sys
import subprocess
subprocess.check_call([sys.executable, '-m', 'pip', 'install', '--upgrade', '--index-url=https://test.pypi.org/simple', '--no-deps', 'abgleich-pkg-wasix'])
import abgleich_pkgThis markdown :-)You can useGithub-flavored Markdownto write more content.📦 setup.py (for humans)This repo exists to providean example setup.pyfile, that can be used
to bootstrap your next Python project. It includes some advanced
patterns and best practices forsetup.py, as well as some
commented–out nice–to–haves.For example, thissetup.pyprovides a$ python setup.py uploadcommand, which creates auniversal wheel(andsdist) and uploads
your package toPyPiusingTwine, without the need for an annoyingsetup.cfgfile. It also creates/uploads a new git tag, automatically.In short,setup.pyfiles can be daunting to approach, when first
starting out — even Guido has been heard saying, "everyone cargo cults
thems". It's true — so, I want this repo to be the best place to
copy–paste from :)Check out the example!Installationcdyour_project# Download the setup.py file:# download with wgetwgethttps://raw.githubusercontent.com/navdeep-G/setup.py/master/setup.py-Osetup.py# download with curlcurl-Ohttps://raw.githubusercontent.com/navdeep-G/setup.py/master/setup.pyTo DoTests via$ setup.py test(if it's concise).Pull requests are encouraged!More ResourcesWhat is setup.py?on Stack OverflowOfficial Python Packaging User GuideThe Hitchhiker's Guide to PackagingCookiecutter template for a Python packageLicenseThis is free and unencumbered software released into the public domain.Anyone is free to copy, modify, publish, use, compile, sell, or
distribute this software, either in source code form or as a compiled
binary, for any purpose, commercial or non-commercial, and by any means. |
abglibpythonpro | Failed to fetch description. HTTP Status Code: 404 |
abg-python | abg_pythonSome Python utilities common to a lot of my packages and repositories (perhaps most notablyFIREstudioand, in part,Firefly).If you use any part of this code for direct calculation please put a nice thank you in your acknowledgements ("Some of the calculations presented in this work rely on public code developed by Alex Gurvich, available at github.com/agurvich/abg_python" or something like that). If you use it as a dependency for something else I've built, acknowledge that codebase instead (e.g. FIRE studio or Firefly).Installationabg_pythonis available from PyPi bypipinstallabg_pythonor you can build the source yourself by cloning this repository and, from the top level directory, executingpipinstall-e. |
abhakliste | AbhaklisteAbhakliste is a minimal task runner that prints a list of tasks and their status.
It runs a collection of dependent task which can be shell commands or python functions in sequence
and checks for error. Even if one task fails, it continues to run the rest of the tasks.
The goal of this project is to provide a minimal task runner with a low overhead API.⚙️ InstallationInstall the project withpippipinstallabhakliste🎨 FeaturesLow Overhead Task RunnerVisual summary of task resultsWritten in pure python (no modules)Supports Python 3.8+💡 Usage ExamplesimportsubprocessfromabhaklisteimportAbhakliste# set up runnerabhaker=Abhakliste()# run code contextwithabhaker.run_context(desc="Run ls"):subprocess.check_output("ls")# run cli commandabhaker.run_cmd("ls",desc="Run ls")# run functiondefrun_ls():subprocess.check_output("ls")abhaker.run_func(run_ls,desc="Run ls")# raise an error if a run failedabhaker.raise_on_error()📜 DocumentationFor further examples on how to use the modules and a detailed API reference, see thedocumentation. |
abhaypdf | No description available on PyPI. |
abhaypdf1234 | This is the homepage of our project. |
abhaypython | No description available on PyPI. |
abhay-python-day2 | No description available on PyPI. |
abhaysuvagiya | No description available on PyPI. |
abhealer | abhealerAn Areca Backup helper programLicense: Apache-2.0Documentation:https://abhealer.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand thePyPackageTemplateproject template.History0.1.0 (2017-07-13)First release on PyPI. |
ab-helper | No description available on PyPI. |
abhi | UNKNOWN |
abhi1234-gen | No description available on PyPI. |
abhiarmstrongseries | This is a library to check whether a given number belongs to armstrong series or to print armstrong series for given n elementsChange Log0.0.1 (05/03/2023)First Release |
abhicalc16 | Abhi’s calcChange Log0.0.2 (19/04/2020)First Release |
abhicalc164 | Failed to fetch description. HTTP Status Code: 404 |
abhiDarekar | No description available on PyPI. |
abhieaiesbcalc | No description available on PyPI. |
abhi-example | # Example PackageThis is a simple example package. You can use
[Github-flavored Markdown](https://guides.github.com/features/mastering-markdown/)
to write your content. |
abhifibonacciseries | This is a library to check whether a given number belongs to fibonacci series or to print fibonacci for given n elementsChange Log0.0.2 (05/03/2023)Second Release |
abhi-general | No description available on PyPI. |
abhilash99 | # Example PackageThis is a simple example package. You can use
[Github-flavored Markdown](https://guides.github.com/features/mastering-markdown/)
to write your content. |
abhiml | Failed to fetch description. HTTP Status Code: 404 |
abhinav-distributions | No description available on PyPI. |
abhinavPY | # abhinavPY
fun times ahead |
abhinavt-package | Abhinav TP twine Testing |
abhipdf | This is the homepage of our project. |