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abhiprime | Summary:This is an efficient prime number tester program created to check if the given number is a prime number or not.The file is availabe onhttps://pypi.org/project/abhiprime/#filesSteps to use:Open the code editor like jupyter or google colab where you can install packages. Like in Jupyter type-!pip install abhiprimeOne can also use command prompt. Open the path where python is installed and type-pip install abhiprimeOnce installation is done open your IDE and type-import abhiprimeorimport abhprime as apPress enterNow type- abhiprime.test_prime(any number which you want to check)If you are using alias name for abhiprime then type- ap.test_prime(any number which you want to check)If you get ModuleNotFoundError (It may happen if you install via command prompt), then make sure that path specified for python is correct and is added in environment path variable.If you are already using abhiprime then please upgrade it, using$ pip install --upgrade abhiprimeIf upgradation is throwing error, then uninstall the previous version and install the latest version.After upgradation, typeimport abhiprime as ap
from abhiprime import *Now you can use it to check the following operations:ap.test_prime(n)- This is an efficient prime number tester program created to check if the given number is a prime number or not.
ap.prev_prime(n)- used to give the just previous prime number value from the given number n.
ap.next_prime(n)- used to give the just next prime number value from the given number n.
ap.prime_upto(n)- gives a list of prime numbers upto the given number n.
ap.range_prime(n,m)- gives a list of prime numbers between numbers n and m.
ap.fib_prime(n)- gives a list of numbers which are prime and fibonacci also.
ap.prime_factor(n)- gives a list of numbers which are prime prime factors of the given number.Change Log6.0.0(15/07/2021)Eighth Release |
abhi-probability | No description available on PyPI. |
abhir1pdf | This is the homepage of our project. |
abhirpdf | This is the homepage of our project. |
abhishekcal | Failed to fetch description. HTTP Status Code: 404 |
abhishekcalculator | Failed to fetch description. HTTP Status Code: 404 |
abhishek-helloworld | Hello WorldThis is an example project demonstrating how to publish a python module to PyPI.InstallationRun the following to install:pipinstallhelloworldUsagefromhelloworldimportsay_hello# Generate "Hello, World!"say_hello()# Generate "Hello, Everybody!"say_hello("Everybody")Developing Hello WorldTo install helloworld, along with the tools you need to develop and run tests, run the following in your virutalenv:$pipinstall-e.[dev]$pipinstall-e.'[dev]'# for ZSH$pipinstall-e."[dev]"# for Windows |
abhishek-k | No description available on PyPI. |
abhistrongseries | This is a library to check whether a given number belongs to strong series or to print strong series for given n elementsChange Log0.0.1 (05/03/2023)First Release |
abhitest | Example PackageThis is a simple example package. You can useTest package |
abhiwin_package1 | UNKNOWN |
abhkumar10-helloworld | Hello WorldThis is an example project demonstrating how to publish a python module to PyPI.InstallationRun the following to install:pipinstallabhkumar10-helloworldUsagefromhelloworldimportsay_hello# Generate "Hello, World!"say_hello()# Generate "Hello, Everybody!"say_hello("Everybody")Developing Hello WorldTo install helloworld, along with the tools you need to develop and run tests, run the following in your virutalenv:$pipinstall-e.[dev]$pipinstall-e.'[dev]'# for ZSH$pipinstall-e."[dev]"# for Windows |
abhorrentTestPackage | No description available on PyPI. |
abi | UNKNOWN |
abi2doc | attribute documentation-----------------------`abi2doc` is a helper tool for generating and formatting sysfs attributedocumentation which is location under ``Documentation/ABI``\ in the kernelsource code.From a `rough estimate`_, there are around 2000 attributes that are undocumentedin the kernel... image:: https://plot.ly/~aishpant/1.png?share_key=8mG4JmyySLLYjbjTg7Uy62:target: https://plot.ly/~aishpant/1/?share_key=8mG4JmyySLLYjbjTg7Uy62:align: center:alt: sysfs line plot:width: 600pxThe ABI documentation format looks like the following:| What: (the full sysfs path of the attribute)| Date: (date of creation)| KernelVersion: (kernel version it first showed up in)| Contact: (primary contact)| Description: (long description on usage)`abi2doc` can fill in the '`Date`' and the '`KernelVersion`' fields with highaccuracy. The '`Contact`' details is prompted for once, and the others '`What`'and '`Description`' are prompted on every attribute.It also tries to collect description from various sources-- From the commit message that introduced the attribute- From comments around the attribute show/store functions or the attributedeclaring macro.- From the structure fields that map to the attribute.For eg. consider the attribute declaring macro `PORT_RO(dest_id)` and itsshow function `port_destid_show(...)`... code:: cstatic ssize_tport_destid_show(struct device *dev, struct device_attribute *attr,char *buf){struct rio_mport *mport = to_rio_mport(dev);if (mport)return sprintf(buf, "0x%04x\n", mport->host_deviceid);elsereturn -ENODEV;}The show functions typically contain a conversion to a driver private structand then one or many fields from it are put in the buffer.In the example above, the driver private structure is a struct of type`rio_mport` and the attribute `port_id` maps to the field `host_deviceid` inthe structure... code:: cstruct rio_mport {...int host_deviceid; /* Host device ID */struct rio_ops *ops; /* low-level architecture-dependent routines */unsigned char id; /* port ID, unique among all ports */...};There's a comment against `host_deviceid` here and this can be extracted.All sysfs attribute declaring macros are located in ``abi2doc/macros.txt``. Eachrow of `macros.txt` contains an attribute declaring macro space separated by thelocation of the attribute name in the macro - `DEVICE_ATTR 0`. This list is notcomplete. Please send a pull request if you find any that are not in the list.Usage-----Prerequisites:- Coccinelle - `install instructions`_spatch will need to be compiled with option `./configure --with-python=python3`- Python 3- Linux Kernel source code`abi2doc` is available on `PYPI`_. Install with ``pip3``:``pip3 install abi2doc``The library is currently tested against Python versions `3.4+`... code:: bashusage: abi2doc [-h] -f SOURCE_FILE -o OUTPUT_FILEHelper for documenting Linux Kernel sysfs attributesrequired arguments:-f SOURCE_FILE linux source file to document-o OUTPUT_FILE location of the generated sysfs ABI documentationoptional arguments:-h, --help show this help message and exitExample usage:.. code:: bashabi2doc -f drivers/video/backlight/lp855x_bl.c -o sysfs_doc.txtThe script will fill in the '`Date`' and the '`KernelVersion`' fields for foundattributes. The '`Contact`' details is prompted for once, and the others 'What'and '`Description`' are prompted on every attribute. The entered descriptionwill be followed by hints, as shown in a generated file below.::What: /sys/class/backlight/<backlight>/bled_modeDate: Oct, 2012KernelVersion: 3.7Contact: [email protected]:(WO) Write to the backlight mapping mode. The backlight currentcan be mapped for either exponential (value "0") or linearmapping modes (default).--------------------------------%%%%% Hints below %%%%%bled_mode DEVICE_ATTR drivers/video/backlight/lm3639_bl.c 220--------------------------------%%%%% store fn comments %%%%%/* backlight mapping mode */--------------------------------%%%%% commit message %%%%%commit 0f59858d511960caefb42c4535dc73c2c5f3136cAuthor: G.Shark Jeong <[email protected]>Date: Thu Oct 4 17:12:55 2012 -0700backlight: add new lm3639 backlight driverThis driver is a general version for LM3639 backlgiht + flash driver chipof TI.LM3639:The LM3639 is a single chip LCD Display Backlight driver + white LEDCamera driver. Programming is done over an I2C compatible interface.www.ti.com[[email protected]: code layout tweaks]Signed-off-by: G.Shark Jeong <[email protected]>Cc: Richard Purdie <[email protected]>Cc: Daniel Jeong <[email protected]>Cc: Randy Dunlap <[email protected]>Signed-off-by: Andrew Morton <[email protected]>Signed-off-by: Linus Torvalds <[email protected]>Expected time for the scripts to run =`(num of attrs x avg 4 min per attr)/num of cores`.Contributions-------------Contributions are welcome, whether it is in the form of code or documentation.Please refer to the `issues`_ tab for places that need help... _install instructions: https://github.com/coccinelle/coccinelle/.. _PYPI: https://pypi.org/project/abi2doc/.. _Coccinelle: http://coccinelle.lip6.fr/.. _rough estimate: https://github.com/aishpant/documentation-scripts/blob/master/result/output.csv.. _issues: https://github.com/aishpant/attribute-documentation/issues |
abi2fastq | Seehttps://github.com/olgabot/abi2fastq |
abi2le-mirror-bert-colinglab | No description available on PyPI. |
abi2solc | abi2solcA library for generating Solidity interfaces from ABIs.InstallationYou can install the latest release viapip:$pipinstallabi2solcOr clone the repo and usesetuptoolsfor the most up-to-date version:$pythonsetup.pyinstallUsage>>>importabi2solc>>>abi=[{'constant':False,'inputs':[{'name':'spender','type':'address'},...>>>interface=abi2solc.generate_interface(abi,"TestInterface")>>>print(interface)'''pragma solidity ^0.5.0;interface ExampleInterface {event Approval (address indexed tokenOwner, address indexed spender, uint256 tokens);event Transfer (address indexed from, address indexed to, uint256 tokens);function approve (address spender, uint256 tokens) external returns (bool success);function transfer (address to, uint256 tokens) external returns (bool success);function transferFrom (address from, address to, uint256 tokens) external returns (bool success);function allowance (address tokenOwner, address spender) external view returns (uint256 remaining);function balanceOf (address tokenOwner) external view returns (uint256 balance);function totalSupply () external view returns (uint256);}'''Supported VersionsBy default,abi2solcgenerates interfaces with pragma^0.5.0With thesolc4=Truekwarg, interfaces are generated with pragma^0.4.17Ifsolc4=Trueand the ABI also contains tuple types, anabstract base contractis generated with pragma^0.4.22TestsTo run the test suite:$toxTests make use ofpy-solc-x.DevelopmentThis project is still under active development and should be considered a beta. Comments, questions, criticisms and pull requests are welcomed! Feel free to open an issue if you encounter a problem or would like to suggest a new feature.LicenseThis project is licensed under theMIT license. |
abi3audit | abi3auditRead our blog post about how we find bugs withabi3audit!abi3auditscans Python extensions forabi3violations and inconsistencies.It can scan individual (unpackaged) shared objects, packaged wheels, or entire
package version histories.IndexMotivationInstallationUsageExamplesLimitationsLicensingMotivationCPython (the reference implementation of Python) defines a stable API and corresponding
ABI ("abi3"). In principle, any CPython extension can be built against this
API/ABI and will remain forward compatible with future minor versions of CPython.
In other words: if you build against the stable ABI for Python 3.5, your
extension should work without modification on Python 3.9.The stable ABI simplifies packaging of CPython extensions, since the packager
only needs to build oneabi3wheel that targets the minimum supported Python
version.To signal that a Python wheel containsabi3-compatible extensions,
the Python packaging ecosystem uses theabi3wheel tag, e.g.:pyrage-1.0.1-cp37-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.whlUnfortunately, there isno actual enforcementofabi3compliance
in Python extensions at install or runtime: a wheel (or independent
shared object) that is tagged asabi3is assumed to beabi3, but
is not validated in any way.To make matters worse, there isno formal connectionbetween the flag
(--py-limited-api)
that controls wheel tagging and the build macros
(Py_LIMITED_API)
that actually lock a Python extension into a specificabi3version.As a result: it is very easy to compile a Python extension for the wrongabi3version, or to tag a Python wheel asabi3without actually compiling it
asabi3-compatible.This has serious security and reliability implications: non-stable parts
of the CPython ABI can change between minor versions, resulting in crashes,
unpredictable behavior, or potentially exploitable memory corruption when
a Python extension incorrectly assumes the parameters of a function
or layout of a structure.Installationabi3auditis available viapip:pipinstallabi3auditUsageYou can runabi3auditas a standalone program, or viapython -m abi3audit:abi3audit--help
python-mabi3audit--helpTop-level:usage: abi3audit [-h] [--debug] [-v] [-R] [-o OUTPUT] [-S][--assume-minimum-abi3 ASSUME_MINIMUM_ABI3]SPEC [SPEC ...]Scans Python extensions for abi3 violations and inconsistenciespositional arguments:SPEC the files or other dependency specs to scanoptions:-h, --help show this help message and exit--debug emit debug statements; this setting also overrides`ABI3AUDIT_LOGLEVEL` and is equivalent to setting itto `debug`-v, --verbose give more output, including pretty-printed results foreach audit step-R, --report generate a JSON report; uses --output-o OUTPUT, --output OUTPUTthe path to write the JSON report to (default: stdout)-S, --strict fail the entire audit if an individual audit stepfails--assume-minimum-abi3 ASSUME_MINIMUM_ABI3assumed abi3 version (3.x, with x>=2) if it cannot bedetectedExamplesAudit a single shared object, wheel, or PyPI package:# audit a local copy of an abi3 extensionabi3auditprocmaps.abi3.so# audit a local copy of an abi3 wheelabi3auditprocmaps-0.5.0-cp36-abi3-manylinux2010_x86_64.whl# audit every abi3 wheel for the package 'procmaps' on PyPIabi3auditprocmapsShow additional detail (pretty tables and individual violations) while auditing:abi3auditprocmaps--verboseyields:[17:59:46] 👎 procmaps:procmaps-0.5.0-cp36-abi3-manylinux2010_x86_64.whl: procmaps.abi3.souses the Python 3.10 ABI, but is tagged for the Python 3.6 ABI┏━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┓┃ Symbol ┃ Version ┃┡━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━┩│ PyUnicode_AsUTF8AndSize │ 3.10 │└─────────────────────────┴─────────┘[17:59:47] 💁 procmaps: 2 extensions scanned; 1 ABI version mismatches and 0ABI violations foundGenerate a JSON report for each input:abi3auditprocmaps--report|python-mjson.toolyields:{"specs":{"procmaps":{"kind":"package","package":{"procmaps-0.5.0-cp36-abi3-manylinux2010_x86_64.whl":[{"name":"procmaps.abi3.so","result":{"is_abi3":true,"is_abi3_baseline_compatible":false,"baseline":"3.6","computed":"3.10","non_abi3_symbols":[],"future_abi3_objects":{"PyUnicode_AsUTF8AndSize":"3.10"}}}],"procmaps-0.6.1-cp37-abi3-manylinux_2_5_x86_64.manylinux1_x86_64.whl":[{"name":"procmaps.abi3.so","result":{"is_abi3":true,"is_abi3_baseline_compatible":true,"baseline":"3.7","computed":"3.7","non_abi3_symbols":[],"future_abi3_objects":{}}}]}}}}Limitationsabi3auditis abest-efforttool, with some of the same limitations asauditwheel. In particular:abi3auditcannot check fordynamicabi3 violations, such as an extension
that callsdlsym(3)to invoke a non-abi3 function at runtime.abi3auditcan confirm the presence of abi3-compatible symbols, but does
not have an exhaustive list of abi3-incompatiblesymbols. Instead, it looks
for violations by looking for symbols that start withPy_or_Py_that
are not in the abi3 compatibility list. This isunlikelyto result in false
positives, butcouldif an extension incorrectly uses those reserved
prefixes.When auditing a "bare" shared object (e.g.foo.abi3.so),abi3auditcannot
assume anything about the minimumintendedabi3 version. Instead, it
defaults to the lowest known abi3 version (abi3-cp32) and warns on any
version mismatches (e.g., a symbol that was only stabilized in 3.6).
This can result in false positives, so users are encouraged to audit entire
wheels or packages instead (since they contain the sufficient metadata).abi3auditconsiders the abi3 version when a symbol wasstabilized,
notintroduced. In other words:abi3auditwill produce a warning
when anabi3-cp36extension contains a function stabilized in 3.7, even
if that function was introduced in 3.6. This isnota false positive
(it is an ABI version mismatch), but it'sgenerallynot a source of bugs.abi3auditchecks both the "local" and "external" symbols for each extension,
for formats that support both. It does this to catch symbols that have been
inlined, such as_Py_DECREF. However, if the extension's symbol table
has been stripped, these may be missed.Licensingabi3auditis licensed under the MIT license.abi3auditincludes ASN.1 and Mach-O parsers generated from
definitions provided by theKaitai Structproject.
These vendored parsers are licensed by the Kaitai Struct authors under the MIT
license. |
abi3info | abi3infoabi3info exposes information about CPython's "limited API" (including the
stable ABI, calledabi3) as a Python library.Installationabi3info is available viapip:$pipinstallabi3infoUsageabi3info exposes limited API and stable ABI information in the form of a set
of top-level dictionaries, namely:importabi3infoabi3info.FEATURE_MACROSabi3info.MACROSabi3info.STRUCTSabi3info.TYPEDEFSabi3info.FUNCTIONSabi3info.DATASEach of these is a mapping of a name (either asstrorSymbol) to
a data model describing the kind of item (e.g.FeatureMacroorFunction).See the generated documentationfor
more details, including comprehensive type hints and explanations of each data
model.See also thestable_abi.tomlfile, taken from
the CPython sources, which describes each model and their semantics.ExamplesGet information about a particular function:fromabi3infoimportFUNCTIONSfromabi3info.modelsimportSymbolfunc=FUNCTIONS[Symbol("_Py_NegativeRefcount")]print(func.symbol,func.added,func.ifdef,func.abi_only)Get information about the feature macros that control the limited API:fromabi3infoimportFEATURE_MACROSprint(fmforfminFEATURE_MACROS.values())Licensingabi3info is licensed under the MIT license.abi3info is partially generated from metadata retrieved from theCPython sources,
which is licensed under thePSF license. |
abi42 | UNKNOWN |
abics | abICSabICS is a software framework for training a machine learning model to
reproduce first-principles energies and then using the model to perform
configurational sampling in disordered systems.
Specific emphasis is placed on multi-component solid state systems such as metal and oxide alloys.
The current version of abics can use neural network models implemented in aenet to be used as
the machine learning model. As of this moment, abICS can also generate Quantum Espresso, VASP,
and OpenMX input files for obtaining the reference training data for the machine learning model.Requirementpython3 (>=3.7)numpyscipytoml (for parsing input files)mpi4py (for parallel tempering)This requires one of the MPI implementationpymatgen (>=2019.12.3) (for using Structure as a configuration)This requires Cythonqe-tools (for parsing QE I/O)Install abICSPymatgen requires Cython but Cython will not be installed automatically,
please make sure that this is installed,$pip3installCythonmpi4py requires one of the MPI implementations such as OpenMPI,
please make sure that this is also installed.
In the case of using homebrew on macOS, for example,$brewinstallopen-mpiAfter installing Cython and MPI,$pip3installabicswill install abICS and dependencies.If you want to change the directory where abICS is installed,
add--useroption or--prefix=DIRECTORYoption to the above command as$pip3install--userabicsFor details ofpip, see the manual ofpipbypip3 help installIf you want to install abICS from source, seewiki pageLicenseThe distribution of the program package and the source codes follow GNU General Public License version 3 (GPL v3).Official pagehttps://www.pasums.issp.u-tokyo.ac.jp/abicsAuthorShusuke Kasamatsu, Yuichi Motoyama, Tatsumi Aoyama, Kazuyoshi YoshimiManualEnglish online manualJapnese online manualAPI reference |
abidcalculator | ascalculatorUnder contruction! Not ready for use yet! Currently experimenting and planning!Developed by Abid Sahariar from DIA (c)2023Example of How To Use (abidcalculator)Creating A Calculator |
abide-distributions | No description available on PyPI. |
abidrive | No description available on PyPI. |
abi-ds-utils | No description available on PyPI. |
abie | No description available on PyPI. |
abieos-python | abieos-pythonPython bindings forabieoslibrary. Implements Binary <> JSON conversion of EOS action data using ABIs.BuildingPrerequisitesabieos C++ library depends on a recent version of CMake (>=3.11) and GCC8. Please make sure these are available in your system.Statically linkedThis is the most comfortable to use, as we can just package the library as a wheel file and ship it. Patch toCMakeLists.txtis required to be able to build abieos as a static library.First you need to build abieos library itself:$gitsubmoduleupdate--init-recursive
$mkdirexternal/abieos/build
$cdexternal/abieos/build
$cmake..
$makeThen you can simply build the package usingsetuptools.$pythonsetup.pybuild--staticDynamically linkedIf you really want to dynamically link the bindings to the version of abieos installed on your system, you also can. At the time of writing these instructions (24.02.2020), EOSIO does not release a binary packages for any OS nor even supports installing the built library usingmakecommand. Assuming you've built the library and copied it manually into correct paths, you can simply build the bindings usingsetuptools.$pythonsetup.pybuildRunning testsWhen running tests you can run into problems because a version without C extensions will be used. As a workaround, you can copy the built file toabieos/:cpbuild/lib.*/abieos/_private*abieos# workaroundflake8
mypy
pytestUsageIf you're running Python on Linux or macOS, you can skip building and use a wheel instead.$pipinstallabieos-pythonIt is recommended to useEosAbiSerializerclass if possible, but if your use case requires access the original abieos API, it is available underabieos.private.With ABI in binary format (bytes)fromabieosimportEosAbiSerializers=EosAbiSerializer()s.set_abi_from_bin('eosio.token',abi)With ABI in hex format (str)fromabieosimportEosAbiSerializers=EosAbiSerializer()s.set_abi_from_hex('eosio.token',abi)With ABI in JSON format (dict)fromabieosimportEosAbiSerializers=EosAbiSerializer()s.set_abi_from_json('eosio.token',abi)dict -> bytess.json_to_bin('eosio.token',s.get_type_for_action('eosio.token','transfer'),{'from':'useraaaaaaaa','to':'useraaaaaaab','quantity':'0.0001 SYS','memo':'',})dict -> hex strings.json_to_hex('eosio.token',s.get_type_for_action('eosio.token','transfer'),{'from':'useraaaaaaaa','to':'useraaaaaaab','quantity':'0.0001 SYS','memo':'',})bytes -> dicts.bin_to_json('eosio.token',s.get_type_for_action('eosio.token','transfer'),b'`\x8c1\xc6\x18s\x15\xd6p\x8c1\xc6\x18s\x15\xd6\x01\x00\x00\x00\x00'b'\x00\x00\x00\x04SYS\x00\x00\x00\x00\x00')hex string -> dicts.hex_to_json('eosio.token',s.get_type_for_action('eosio.token','transfer'),'608C31C6187315D6708C31C6187315D60100000000000000045359530000000000')With custom JSON encoder/decoderfromabieosimportEosAbiSerializers=EosAbiSerializer(loads=simplejson.loads,dumps=simplejson.dumps) |
abiflows | The latest development version is always available from site <https://github.com/abinit/abiflows> |
abifpy | abifpy is a python module that extracts sequence and various other data from
Applied Biosystem’s, Inc. format (ABI) file. The module is python3-compatible
and was written based on theofficial specreleased by Applied Biosystems.A modified version of this module has been merged into theBiopython
project, available from version 1.58 onwards. If you already have Biopython
version >=1.58, there is no need to use abifpy. Despite that, I am keeping
the module available as a stand-alone for personal reasons :).abifpy provides the following items:classTrace(in_file)Class representing the trace filein_file.Trace object attributes and methodsseqString of base-called nucleotide sequence stored in the file.qualString of phred quality characters of the base-called sequence.qual_valList of phred quality values of the base-called sequence.idString of the sequence file name.nameString of the sample name entered prior to sequencing.trim(sequence[, cutoff=0.05])Returns a trimmed sequence using Richard Mott’s algorithm (used in phred)
with the probability cutoff of 0.05. Can be used onseq,qual, andqual_val.get_data(key)Returns metadata stored in the file, accepts keys fromtags(see below).export([out_file=””, fmt=’fasta’])Writes a fasta (fmt='fasta'), qual (fmt='qual'), or
fastq (fmt='fastq') file from the trace file. Default format isfasta.close()Closes the Trace file object.seq_remove_ambig(seq)Replaces extra ambigous base characters (K, Y, W, M, R, S) with ‘N’. Acceptsseqfor input.EXTRACTDictionary for determining which metadata are extracted.dataDictionary that contains the file metadata. The keys are values ofEXTRACT.tagsDictionary of tags with values of data directory class instance. Keys are tag name and
tag number, concatenated. Useget_data()to access values in eachtagsentry.Usage$ python
>>> from abifpy import Trace
>>> yummy = Trace('tests/3730.ab1')Or if you want to perform base trimming directly:>>> yummy = Trace('tests/3730.ab1', trimming=True)Sequence can be accessed with theseqattribute. Other attributes of note
arequalfor phred quality characters,qual_valfor phred quality values,idfor sequencing trace file name, andnamefor the sample name:>>> yummy.seq
'GGGCGAGCKYYAYATTTTGGCAAGAATTGAGCTCT...
>>> yummy.qual
'5$%%%\'%%!!!\'!+5;726@>A=3824DESHSS...
>>> yummy.qual_val
[20, 3, 4, 4, 4, 6, 4, 4, 0, 0, 0, 6, 0, 10, 20, 26, 22, 17, 21...
>>> yummy.id
'3730'
>>> yummy.name
'226032_C-ME-18_pCAGseqF'If trimming was not performed when instantiating, you can still do it afterwards:>>> yummy.trim(yummy.seq)The quality values itself can be trimmed as well:>>> yummy.trim(yummy.qual)Viewing the trace file metadata is easy. Use the values fromEXTRACTas the keys indata:>>> yummy.data['well']
'B9'
>>> yummy.data['model']
'3730'
>>> yummy.data['run start date']
datetime.date(2009, 12, 12)metadata not contained indatacan be viewed usingget_data()with one of the keys intagsas the argument, e.g.:>>> yummy.get_data('PTYP1')
'96-well'For more info on the meaning of these tags and the file metadata, consult theofficial spec.Installationpip install abifpy, orAdd the abifpy directory to your$PYTHONPATH(in.bashrcto make it persistent)Licenseabifpy is licensed under the MIT License.Copyright (c) 2011 by Wibowo Arindrarto <[email protected]>Permission 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. |
a-bigelow.cdk-eventbridge-partner-processors | Eventbridge SaaS Partner fURLsThis CDK Construct library provides CDK constructs for the 1st-party (i.e. developed by AWS) lambda fURL webhook receivers for:GitHubStripeTwilioUsage Examples (Simplified)These examples are consistent for all 3 primary exported constructs of this library:GitHubEventProcessorTwilioEventProcessorStripeEventProcessorNote: Click on the aboveView on Construct Hubbutton to view auto-generated examples in Python/GoTypescriptimport{GitHubEventProcessor}from'cdk-eventbridge-partner-processors';import{Stack,StackProps}from'aws-cdk-lib';import{Construct}from'constructs';import{EventBus}from'aws-cdk-lib/aws-events';import{Secret}from'aws-cdk-lib/aws-secretsmanager';exportclassMyStackWithABetterNameextendsStack{constructor(scope:Construct,id:string,props:StackProps){super(scope,id,props);//ThislibraryhasnoopiniononhowyoureferenceyourEventBus,//ItjustneedstofulfilltheIEventBusprotocolconstmyEventBus=newEventBus(this,'TheBestBusEver',{eventBusName:'TheGreatestBus'});//Thislibraryhasnoopiniononhowyoureferenceyoursecret,//ItjustneedstofulfilltheISecretprotocolconstmySecret=Secret.fromSecretNameV2(this,'MyNuclearCodeSecret','/home/recipes/icbm')//Functionwillautomaticallyreceivepermissionto://1.Posteventstothegivenbus//2.ReadthegivensecretconstgithubEventProcessor=newGitHubEventProcessor(this,'GitHubProcessor',{eventBus:myEventBus,webhookSecret:mySecret,lambdaInvocationAlarmThreshold:2000,})}}Golangpackagemainimport(partner"github.com/a-bigelow/cdk-eventbridge-partner-processors-go""github.com/aws/aws-cdk-go/awscdk/v2""github.com/aws/aws-cdk-go/awscdk/v2/awsevents""github.com/aws/constructs-go/constructs/v10""github.com/aws/jsii-runtime-go")typeClusterStackPropsstruct{awscdk.StackProps}funcNewClusterStack(scopeconstructs.Construct,idstring,props*ClusterStackProps)awscdk.Stack{varspropsawscdk.StackPropsifprops!=nil{sprops=props.StackProps}stack:=awscdk.NewStack(scope,&id,&sprops)// The code that defines your stack goes hereeventProps:=awsevents.EventBusProps{EventBusName:jsii.String("name")}eventBus:=awsevents.NewEventBus(stack,jsii.String("id"),&eventProps)secret:=secretsmanager.secret.fromSecretNameV2(scope,jsii.String("secret"),jsii.String("secretName"))partnerProcessor:=partner.GithubEventProcessor{EventBus:eventBus,WebhookSecret:secret,LambdaInvocationAlarmThreshold:2000,}_=partner.NewGitHubEventProcessor(stack,jsii.String("id"),partnerProcessor)returnstack}Disclaimer:warning: The Lambda Functions that handle the actual event processing in this Library are owned and maintained by Amazon Web Services. This CDK Construct library provides a thin deployment wrapper for these functions. Changes made to the S3 location of the functions will break this library. Until I have a way to deterministically track where these things are, please raise anissueif you have reason to believe that the functions have moved.AWS DocumentationSeeherefor additional information. |
abi-guesser | ABI Guesser PyA Pythonic version ofabi-guesser.
All credit goes tosamczsunfor the original development; this is a direct 1-to-1 replication of his work.Installationpip install abi_guesserUsage>>>fromabi_guesserimportguess_fragment>>>guess_fragment("0x38ed173900000000000000000000000000000000000000000000000000000000389fd9800000000000000000000000000000000000000000004607aa2b3b11fb9d4c000000000000000000000000000000000000000000000000000000000000000000a000000000000000000000000032baf5624fed24e66feaeb9c3fb1cad9e0960cc80000000000000000000000000000000000000000000000000000000064da7d310000000000000000000000000000000000000000000000000000000000000003000000000000000000000000dac17f958d2ee523a2206206994597c13d831ec7000000000000000000000000c02aaa39b223fe8d0a0e5c4f27ead9083c756cc2000000000000000000000000c8de43bfe33ff496fa14c270d9cb29bda196b9b5")'guessed_38ed1739(uint256,uint256,address[],address,uint256)'>>># swapExactTokensForTokens(uint256 amountIn, uint256 amountOutMin, address[] path, address to, uint256 deadline) |
abilian-core | AboutAbilian Core is an enterprise application development platform based on theFlask micro-framework, theSQLAlchemy ORM, good intentions and best practices (for some value of “best”).The full documentation is available onhttp://docs.abilian.com/.Goals & principlesDevelopment must be easy and fun (some some definition of “easy” and “fun”, of course)The less code (and configuration) we write, the betterLeverage existing reputable open source libraries and frameworks, such as SQLAlchemy and FlaskIt must lower errors, bugs, project’s time to deliver. It’s intended to be a rapid application development toolIt must promote best practices in software development, specially Test-Driven Development (as advocated by theGOOS book)FeaturesHere’s a short list of features that you may find appealing in Abilian:InfrastructurePlugin frameworkAsynchronous tasks (usingCelery)Security model and serviceDomain model and servicesPersistent domain object model, based on SQLAlchemyAuditContent management and servicesSimple file-based content repositoryIndexing serviceDocument preview and transformationSocialUsers, groups and social graph (followers)Activity streamsUser Interface and APIForms (based onWTForms)CRUD (Create, Retrieve, Edit/Update, Remove) interface from domain
modelsLabels and descriptions for each fieldVarious web utilities: view decorators, class-based views, Jinja2
filters, etc.A default UI based onBootstrap 3and
several carefully selected jQuery plugins such asSelect2REST and AJAX API helpersi18n: support for multi-language via Babel, with multiple translation
dictionariesManagement and adminInitial settings wizardAdmin and user settings frameworkSystem monitoring (usingSentry)Current statusAbilian Core is currently alpha software, in terms of API stability.It is currently used in several applications that have been developped
byAbilianover the last two years:Abilian SBE (Social Business Engine)- an enterprise 2.0
(social collaboration) platformAbilian EMS (Event Management System)Abilian CRM (Customer / Contact / Community Relationship Management
System)Abilian Le MOOC - a MOOC prototypeAbilian CMS - a Web CMSIn other words, Abilian Core is the foundation for a small, but growing,
family of business-critical applications that our customers intend us to
support in the coming years.So while Abilian Core APIs, object model and even architecture, may (and
most probably will) change due to various refactorings that are expected
as we can’t be expected to ship perfect software on the firt release, we
also intend to treat it as a valuable business asset and keep
maintaining and improving it in the foreseeable future.Roadmap & getting involvedIf you need help or for general discussions about the Abilian Platform, we
recommend joing theAbilian Usersforum on Google
Groups.For features and bug requests (or is it the other way around?), we
recommend that you use theGitHub issue
tracker.Read theContributing Guidefor more information.InstallIf you are a Python web developer (which is the primary target for this
project), you probably already know about:Python 3.8+VirtualenvPoetry (https://poetry.eustace.io/)So, after you have created and activated a virtualenv for the project,
just run:poetryTo use some features of the library, namely document and images
transformation, you will need to install the additional native packages,
using our operating system’s package management tools (dpkg,yum,brew…):A few image manipulation libraries (libpng,libjpeg)Thepoppler-utils,unoconv,LibreOffice,ImageMagickutilitiesLook at thefabfile.pyfor the exact list.TestingAbilian Core come with a full unit and integration testing suite. You
can run it withmake test(once your virtualenv has been activated and
all required dependencies have been installed, see above).Alternatively, you can usetoxto run the full test suite in an
isolated environment.LicenceAbilian Core is licensed under the LGPL.CreditsAbilian Core has been created by the development team at Abilian
(currently: Stefane and Bertrand), with financial support from our
wonderful customers, and R&D fundings from the French Government, the
Paris Region and the European Union.We are also specially grateful to:Armin Ronacherfor his work on Flask.Michael Bayerfor his work on
SQLAlchemy.Everyone who has been involved with and produced open source software
for the Flask ecosystem (Kiran Jonnalagadda and theHasGeekteam, Max Countryman, Matt Wright,
Matt Good, Thomas Johansson, James Crasta, and many others).The creators of Django, Pylons, TurboGears, Pyramid and Zope, for
even more inspiration.The whole Python community.LinksDiscussion list (Google Groups)DocumentationGitHub repositoryCorporate support |
abilian-crm-core | No description available on PyPI. |
abilian-devtools | Abilian Development Tools (abilian-devtoolsoradt)Abilian Development Tools (ADT) is a curated collection of Python development tools that includes formatters, testing frameworks, style checkers, type checkers, and supply chain audit tools. By addingabilian-devtools = '*'to your project'srequirements.inorpyproject.toml, you can access over 40+ curated projects and plugins.
Proper configuration and usage in your project is still required.
Additionally, the package provides a command-line interface (CLI) called adt to help users get started with various development tasks such as running tests, security audits, and code formatting.
ADT was developped atAbilianas a tool to help manage dozens of Python projects (open source or not). We hope it can be useful to others too.What this is?This is a curated, and opiniated, collection of best-of-breed Python development tools:Formatters (black,isort,docformatter)Testing frameworks (pytestand friends,nox)Style checkers (ruff,flake8and friends)Type checkers (mypy,pyright)Supply chain audit (pip-audit,safety,reuse,vulture,deptry)And more.Obviously, all the credit goes to the creators and maintainers of these wonderful projects. You have all our gratitude!UsageInstead of having to track all the 40+ projects and plugins we have curated, you just need to addabilian-devtools = '*'in your project'srequirements.inorpyproject.toml.You still need to properly configure and call them in your own projects.For example configuration, see, for instance,https://github.com/abilian/nua(Makefile,pyproject.toml,setup.cfg,tasks.py,noxfile.py...).CLI helperAs a bonus, we're providing a CLI calledadtwhich can help you get started:$ adt
adt (0.5.x)
Usage:
adt <command> [options] [arguments]
Options:
-V Show version and exit
-d Enable debug mode
-v Increase verbosity
Available commands:
all Run everything (linters and tests).
audit Run security audit.
bump-version Bump version in pyproject.toml, commit & apply tag.
check Run checker/linters on specified files or directories.
clean Cleanup cruft.
cruft Run cruft audit.
format Format code in specified files or directories.
help-make Helper to generate the `make help` message.
test Run tests.Why this?Wehave created Abilian DevTools to help us maintainour own projects, and we thought it could be useful to others.Here are some of the reasons why we have created this project:Streamlined Tool Collection: Abilian Devtools brings together a wide range of Python development tools in a single, curated package. This allows developers to focus on their work without spending time searching for and integrating individual tools.Consistency: By using a curated set of best-of-breed tools, our team can achieve a consistent level of code quality, style, and security across their projects.Simplified Dependency Management: Instead of managing individual dependencies for each tool, developers only need to add abilian-devtools to their project'srequirements.inorpyproject.toml. This makes it easier to maintain and update dependencies over time.Easy-to-use CLI: Theadtcommand-line utility simplifies common development tasks such as running tests, code formatting, and security audits. This can save time and effort, especially for those new to these tools.Up-to-date Toolset: Abilian Devtools aims to provide an up-to-date collection of tools, ensuring that developers have access to the latest features and improvements without having to manually track and update each tool.RoadmapHere are some ideas for future improvements:Support for additional tools: for instance, tools that deal with changelogs (viaConventional Commits), versioning, documentation...Monorepo support: Better support for monorepos.Customization: The curated nature of Abilian Devtools means that it comes with a predefined set of tools. Your project may require additional or alternative tools, or different settings. ADT could help managing (and updating) settings according to your own tastes and needs.Generating configuration and supporting files: Currently our projects are generated from a template by third-party tools (CruftorCookiecutter, usingthis template). ADT could help generating configuration files for the various tools (pyproject.toml,setup.cfg,noxfile.py,Makefile,tasks.py...).Updating configuration and supporting files: As tools and best practices evolves, and for long-running projects, configuration need to be adapted over time, which can become quite time-consuming, specially when you are working on many small projects (or monorepos). Tools likeCruftorMedikit) can help, but in our experience are too fragile. ADT could help with this.CI/CD: ADT could help with CI/CD integration.DiscussionOn GitHub(evergreen)On Reddit(May 2023)On AFPY Forum(May 2023, in French)ReferencesThis presentation from 2017was given at the Paris Open Source Summit (POSS). Many tools have evolved or appeared since then, but the general principles are still valid.This presentation from 2005was given (in French) at the "Rencontres Mondiales du Logiciel Libre" in Bordeaux. It's obviously outdated, but kept for nostalgic reasons ;) |
abilian-sbe | No description available on PyPI. |
abilian-sbe-next | No description available on PyPI. |
abi-maker | No description available on PyPI. |
abimap | abimapA helper for library maintainers to use symbol versioningWhy use symbol versioning?The main reason is to be able to keep the library[ABI]stable.If a library is intended to be used for a long time, it will need updates for
eventual bug fixes and/or improvement.
This can lead to changes in the[API]and, in the worst case, changes to the[ABI].Using symbol versioning, it is possible to make compatible changes and keep the
applications working without recompiling.
If incompatible changes were made (breaking the[ABI]), symbol versioning allows both
incompatible versions to live in the same system without conflict.
And even more uncommon situations, like an application to be linked to
different (incompatible) versions of the same library.For more information, I strongly recommend reading:[HOW_TO]How to write shared libraries, by Ulrich DrepperHow to add symbol versioning to my library?Adding version information to the symbols is easy.
Keeping the[ABI]stable, unfortunately, is not. This project intends to help in the first part.To add version information to symbols of a library, one can use version scripts (in Linux).
Version scripts are files used by linkers to map symbols to a given version.
It contains the symbols exported by the library grouped by the releases where they were introduced. For example:LIB_EXAMPLE_1_0_0
{
global:
symbol;
another_symbol;
local:
*;
};In this example, the releaseLIB_EXAMPLE_1_0_0introduces the symbolssymbolandanother_symbol.
The*wildcard inlocalcatches all other symbols, meaning onlysymbolandanother_symbolare globally exported as part of the library[API].If a compatible change is made, it would introduce a new release, like:LIB_EXAMPLE_1_0_0
{
global:
symbol;
another_symbol;
local:
*;
};
LIB_EXAMPLE_1_1_0
{
global:
new_symbol;
} LIB_EXAMPLE_1_0_0;The new releaseLIB_EXAMPLE_1_1_0introduces the symbolnew_symbol.
The*wildcard should be only in one version, usually in the oldest version.
The} LIB_EXAMPLE_1_0_0;part in the end of the new release means the new release depends on the old release.Suppose a new incompatible versionLIB_EXAMPLE_2_0_0released afterLIB_EXAMPLE_1_1_0. Its map would look like:LIB_EXAMPLE_2_0_0
{
global:
a_newer_symbol;
another_symbol;
new_symbol;
local:
*;
};The symbolsymbolwas removed (and that is why it was incompatible). And a new symbol was introduced,a_newer_symbol.Note that all global symbols in all releases were merged in a unique new release.Installation:At the command line:pip install abimapUsage:This project delivers a script,abimap. This is my first project in python, so feel free to point out ways to improve it.The sub-commandsupdateandnewexpect a list of symbols given in stdin. The list of symbols are words separated by non-alphanumeric characters (matches with the regular expression[a-zA-Z0-9_]+). For example:symbol, another, one_moreand:symbol
another
one_moreare valid inputs.The last sub-command,check, expects only the path to the map file to be
checked.tl;dr$ abimap update lib_example.map < symbols_listor (setting an output):$ abimap update lib_example.map -o new.map < symbols_listor:$ cat symbols_list | abimap update lib_example.map -o new.mapor (to create a new map):$ cat symbols_list | abimap new -r lib_example_1_0_0 -o new.mapor (to check the content of a existing map):$ abimap check my.mapor (to check the current version):$ abimap versionLong versionRunningabimap-hwill give:usage: abimap [-h] {update,new,check,version} ...
Helper tools for linker version script maintenance
optional arguments:
-h, --help show this help message and exit
Subcommands:
{update,new,check,version}
These subcommands have their own set of options
update Update the map file
new Create a new map file
check Check the map file
version Print version
Call a subcommand passing '-h' to see its specific optionsCall a subcommand passing ‘-h’ to see its specific options
There are four subcommands,update,new,check, andversionRunningabimap update-hwill give:usage: abimap update [-h] [-o OUT] [-i INPUT] [-d]
[--verbosity {quiet,error,warning,info,debug} | --quiet | --debug]
[-l LOGFILE] [-n NAME] [-v VERSION] [-r RELEASE]
[--no_guess] [--allow-abi-break] [-f] [-a | --remove]
file
positional arguments:
file The map file being updated
optional arguments:
-h, --help show this help message and exit
-o OUT, --out OUT Output file (defaults to stdout)
-i INPUT, --in INPUT Read from this file instead of stdio
-d, --dry Do everything, but do not modify the files
--verbosity {quiet,error,warning,info,debug}
Set the program verbosity
--quiet Makes the program quiet
--debug Makes the program print debug info
-l LOGFILE, --logfile LOGFILE
Log to this file
-n NAME, --name NAME The name of the library (e.g. libx)
-v VERSION, --version VERSION
The release version (e.g. 1_0_0 or 1.0.0)
-r RELEASE, --release RELEASE
The full name of the release to be used (e.g.
LIBX_1_0_0)
--no_guess Disable next release name guessing
--allow-abi-break Allow removing symbols, and to break ABI
-f, --final Mark the modified release as final, preventing later
changes.
-a, --add Adds the symbols to the map file.
--remove Remove the symbols from the map file. This breaks the
ABI.
A list of symbols is expected as the input. If a file is provided with '-i',
the symbols are read from the given file. Otherwise the symbols are read from
stdin.Runningabimap new-hwill give:usage: abimap new [-h] [-o OUT] [-i INPUT] [-d]
[--verbosity {quiet,error,warning,info,debug} | --quiet | --debug]
[-l LOGFILE] [-n NAME] [-v VERSION] [-r RELEASE]
[--no_guess] [-f]
optional arguments:
-h, --help show this help message and exit
-o OUT, --out OUT Output file (defaults to stdout)
-i INPUT, --in INPUT Read from this file instead of stdio
-d, --dry Do everything, but do not modify the files
--verbosity {quiet,error,warning,info,debug}
Set the program verbosity
--quiet Makes the program quiet
--debug Makes the program print debug info
-l LOGFILE, --logfile LOGFILE
Log to this file
-n NAME, --name NAME The name of the library (e.g. libx)
-v VERSION, --version VERSION
The release version (e.g. 1_0_0 or 1.0.0)
-r RELEASE, --release RELEASE
The full name of the release to be used (e.g.
LIBX_1_0_0)
--no_guess Disable next release name guessing
-f, --final Mark the new release as final, preventing later
changes.
A list of symbols is expected as the input. If a file is provided with '-i',
the symbols are read from the given file. Otherwise the symbols are read from
stdin.Runningabimap check-hwill give:usage: abimap check [-h]
[--verbosity {quiet,error,warning,info,debug} | --quiet | --debug]
[-l LOGFILE]
file
positional arguments:
file The map file to be checked
optional arguments:
-h, --help show this help message and exit
--verbosity {quiet,error,warning,info,debug}
Set the program verbosity
--quiet Makes the program quiet
--debug Makes the program print debug info
-l LOGFILE, --logfile LOGFILE
Log to this fileRunningabimap version-hwill give:usage: abimap version [-h]
optional arguments:
-h, --help show this help message and exitImport as a library:To use abimap in a project as a library:from abimap import symverDocumentation:Check inRead the docsReferences:[ABI](1,2,3,4)https://en.wikipedia.org/wiki/Application_binary_interface[API](1,2)https://en.wikipedia.org/wiki/Application_programming_interface[HOW_TO]https://www.akkadia.org/drepper/dsohowto.pdf, How to write shared libraries by Ulrich DrepperHistory0.3.2 (2019-08-05)Fixed broken builds due to changes in warning outputChanged tests to check error messagesAdded python 3.7 to testing matrixAdded requirement to verify SNI when checking URLs in docs0.3.1 (2018-08-20)Fixed bug when sorting releases: the older come firstAdded missing runtime requirement for setuptoolsAdded manpage generation0.3.0 (2018-08-03)Complete rename of the project to abimap0.2.5 (2018-07-26)Add tests using different program namesUse the command line application name in output stringsAdd a new entry point symver-smap for console scriptsSkip tests which use caplog if pytest version is < 3.4Added an alias for pytest in setup.cfg. This fixed setup.py for test target0.2.4 (2018-06-15)Removed dead code, removed executable file permissionRemoved appveyor related files0.2.3 (2018-06-15)Removed shebangs from scripts0.2.2 (2018-06-01)Fixed a bug in updates with provided release informationFixed a bug in get_info_from_release_string()0.2.1 (2018-05-30)Fixed a bug where invalid characters were accepted in release name0.2.0 (2018-05-29)Added version information in output filesAdded sub-command “version” to output name and versionAdded option “–final” to mark modified release as releasedPrevent releases marked with the special comment “# Released” to be modifiedAllow existing release updateDetect duplicated symbols given as input0.1.0 (2018-05-09)First release on PyPI. |
abimca | Autoencoder Based Iterative Modeling and Subsequence Clustering Algorithm (ABIMCA)This repository contains the python code for the Autoencoder Based Iterative Modeling and Subsequence Clustering Algorithm (ABIMCA)[^koehn] which is a deep learning method to separate multivariate time-series data (MTSD) into subsequences. It is beneficial in a variety of fields, to cluster MTSD into smaller segments or subsequences in an unsupervised manner. The ability to filter measurement data based on specific subsequences can improve downstream development products such as anomaly detection or machine diagnosis in condition based maintenance (CbM) strategies. Our algorithm is specifically useful for MTSD generated by a mechatronic system in a transient environment. It can be used offline as well as online for streaming data. It utilizes recurrent neural network (RNN) based Autoencoders (AE) by iteratively training a Base Autoencoder (BAE), generating a segmentation score and saving the intermediate parameters of the BAE to recognize previously identified subsequences.UsagePackage can be installed with pip$pipinstallabimcaor clone the repository, and cd into the directory. Then recommendation is to create a virtual environment after installing and using python 3.9 withpyenvpython-mvenv.venvactivate the environment
Linux:
$ source .venv/bin/activateWindows cmd:
C:> .venv\Scripts\activate.batThen install withpoetry$poetryinstallFinally run the minimal example inmain.py$python-mmainThe above graphic example was generated with therun_lorenz.pyscript. The minimal example is as follows:fromabimcaimportSubsequenceIdentifierimportnumpyasnpdefmain():# Generating random data. This will produce no class predictions or all points have the same class. For more reasonable results replace the data input with your mechatronic measurement data.# Number of datapoints (time-steps)n_p=300# Number of dimensions or featuresdim=5X=np.random.rand(n_p,dim)# Number of clustersn_c=5y=np.random.randint(n_c,size=n_p)# Compute online clusteringsi=SubsequenceIdentifier(disable_progress_bar=False)si.fit(X)print(f"Label array from online fitting:\n{si.label_array}")# Compute offline clusteringlabels=si.predict(X)print(f"Label array from online fitting:\n{labels}")if__name__=="__main__":main()References[^koehn]: Köhne, J. et al. Autoencoder based iterative modeling and multivariate time-series subsequence clustering algorithm |
abinator | AbinatorDefine ABI for your smart contract with dataclass-like style.Quick exampleDefine your abi asAbichild.Abi.to_abi()returns abi as json, so you can use it withweb3pyandweb3-premium.fromabinatorimportAbi,uint256,uint8classERC20Fragment(Abi):decimals:uint8@viewdefbalanceOf(account:address)->uint256:...ERC20Fragment.to_abi()# returns json with abiDocumentationState mutabilityYou can useview,pure,payabledecoratos for state mutabilty.fromabinatorimportAbi,uint256classContract(Abi):@viewdefbalanceOf(account:address)->uint256:...@payabledefdeposit():...@puredefsafe_add(a:uint256,b:uint256)->uint256:...EventsDefine events with child class ofEventinside your abi class.You can useindexeddecorator for topics.fromabinatorimportAbi,Event,address,uint256,indexedclassERC20Fragment(Abi):classTransfer(Event):from_:indexed(address)to:indexed(address)value:uint256Also there isanonymousfor event class:fromabinatorimportAbi,Event,anonymous,uint256classContract(Abi):@anonymousclassAnonymousEvent(Event):value:uint256Structs and tupleDefine structs with child class ofStructinside your abi class.fromabinatorimportAbi,Struct,payable,address,uint24,int24,uint256classNonfungiblePositionManager(Abi):classMintParams(Struct):token0:addresstoken1:addressfee:uint24tickLower:int24tickUpper:int24amount0Desired:uint256amount1Desired:uint256amount0Min:uint256amount1Min:uint256recipient:addressdeadline:uint256@payabledefmint(params:MintParams)->tuple[uint256,uint128,uint256,uint256]:... |
abineshpdf | This is the homepage of our projects. |
abing | Welcome to ABING!What is ABING?ABING는 AB테스트를 위한 라우팅 툴입니다. 일반적인 비즈니스 모델에서 사용할 수 있도록 ABING는 별도의 연동과정 없이 AB테스팅을 운영할 수 있도록 개발되었습니다. UI에서 실험 생성 및 설정 후 route API(/api/v1/experiments/route)에서 각 user id별(필요 시 uuid)로 할당만 받으면 자유롭게 AB테스팅을 할 수 있습니다.Getting startedInstallation아래와 같이 pip로 빠르게 설치할 수 있습니다.pipinstallabing서버 실행DB 초기화DB 연결을 위해 ABING은 다음 다섯개의 환경변수를 필요로 합니다.ABING_DBABING_DB_USERABING_DB_PASSWORDABING_DB_HOSTABING_DB_PORT따라서 아래와 같이 환경변수를 export해주세요exportABING_DB="abing"exportABING_DB_USER="abing"exportABING_DB_PASSWORD="$uperScretPassW0rd"exportABING_DB_HOST="abing-db.com"exportABING_DB_PORT=5432이후 migration을 위해 아래와 같이 명령어를 입력해주시면 됩니다(abing은 migration툴로 alembic을 사용하고 있으므로 정상적으로 마이그레이션이 되었다면, alembic migration 로그가 출력될 거에요)abinginitdb서버 실행abing은 uvicorn을 사용하여 서버를 실행합니다. 따라서 아래와 같이 커맨드를 날리면 바로 서버가 구동됩니다uvicornabing:app--reload--host"0.0.0.0"gunicorn과 같이 실행하고 싶으시다면 아래와 같이 uvicorn worker class와 같이 사용하시는걸 추천드립니다.gunicornabing:app-w4-kuvicorn.workers.UvicornWorker최초 관리자 계정 생성다른 유저 계정을 만들어주려면 최초 관리자 계정이 필요합니다. 따라서 아래와 같이createsuperuser명령어로 최초의 슈퍼계정을 만들어주세요.abingcreatesueruser여기까지 정상적으로 실행이 되셨다면, 배포하는 url의 루트(/)에서 슈퍼계정으로 로그인을 해보세요. 로그인이 정상적으로 되었다면 성공적으로 abing을 시작하셨습니다!실험 생성하기Experiments - List에서 Add a new experiment를 클릭하시거나, Experiments - Create를 눌러 실험 생성 메뉴로 갈 수 있습니다.각 항목을 입력하고, Create를 누르면 실험이 생성됩니다.Name(실험 명)은 고유할 필요는 없으나, 운영차원에서 코드와 함께 관리되는 것이 좋습니다.Create로 만들때에는 실험은 런칭이 되지 않은 상태로 리스트업됩니다.+Add Arm을 누르면 바로 생성할 Arm을 추가할 수 있습니다. 이 때 Traffic은 얼마나 유저를 해당 Arm에 보낼지에 대한 가중치로, 모든 Arm 합산 대비 해당 Arm의 비율로 내부적으로 계산되어 추후 Arm 선택 시 반영됩니다.각 Arm의 Feature는 해당 Arm이 선택되었을 때 보여줄 Feature flag입니다. 예를 들어 featurekey가fontColor라면value는#fff같이 입력하여 자유롭게 클라이언트에서 dynamic한 값으로 사용할 수 있습니다. 물론 key value는 문자열에 불과하기 때문에 사용자에 따라서는 분기를 위한 flag 수준으로만 사용해도 무방합니다(e.gkey에is_baseline,value에true이런 식으로 지정할 수 있습니다)실험 운영하기실험이 성공적으로 만들어졌다면Experiments - List에서 실험을 찾아볼 수 있습니다.실험은 운영 중인 실험, 최근에 만든 실험 순, 최근에 업데이트된 실험 순으로 자동 정렬되어 있습니다.Progress는 일종의 부가정보입니다. Progress가 100%에 도달한다고 해서 실험이 종료되거나 하지 않습니다.방근 만든 실험을 바로 런칭하고 싶다면 오른쪽 스위치를 누르기만 하면 됩니다.실험 정보를 보고 싶다면+를 눌러 펼쳐보거나 실험명에 있는 링크를 눌러 자세히 볼 수 있습니다.포함해야 하는 feature 혹은 Arm이 있는데 반영이 안되었다면 링크를 눌러 수정할 수 있습니다.Client에서 실험 라우팅하기/api/v1/experiments/route를 통해 현재 운영중인 모든 실험의 각 arm에 할당될 수 있습니다.parameter로user_id값만 보내주면 됩니다.user_id는 로그인이 없는 서비스라면 uuid값을 보내주는 것으로 충분합니다. 로그인마다 동일하게 보이려면 local storage에 uuid를 저장하기만 하면 됩니다(abing는 별도의 id 저장없이 동일한 traffic allocation이 가능하도록 디자인 되었습니다)/api/v1/experiments/{id}/route를 통해 특정 id의 실험에 한해 route받을 수도 있습니다.모든 parameter는/api/v1/experiments/route와 동일합니다.Future추가 기능MAB 기능 지원: Thomson sampling, Eplisilon Greedy, UCB 세가지 유형을 지원.SDK: 각 언어에 맞는 sdk를 지원하여, 다양한 플랫폼에서 활용할 수 있도록 함.실험 통계 기능: 각 실험화면에서 Sample size calculator, Significant calculator 및 Power analysis 등을 지원Tag 기능: Arm 태그 기능을 통해 특정 태그값을 가지고 있는 경우 특정 Arm으로 포함되도록 지원(e.g Control tag가 iOS이고 client에서 보내주는 User meta에 iOS가 포함되면 해당 Arm에 포함)사용성 증대: 실험 검색 기능, 카테고리 기능 추가기타 Bug fix: 에러 메시지 분류 등 |
abinitostudio | This is a long description. |
abin-sim | ABINInstalaçãoUtilize o pipx para instalar o abin no seu computador.sudo apt install pipx
pipx install abin_simA versão da aplicação no README será atualizada sempre que um novo build rodar.Se você estiver rodando no PopOS precisa criar um link simbólico para a aplicação ou adicionar $HOME/.local/bin no path do SOsudo ln -s $HOME/.local/bin/abin /usr/local/bin/abinConfiguraçãoApós realizar a instalação do App, será necessário iniciar o arquivo de configuração
Para isso execute a instrução abaixo:abin --configureA instrução acima criará um arquivo chamado 'settings.toml' em $HOME/abin/
Com o arquivo em mãos, altere o valor devault_tokenpara o seu token, o valor deverá focar entre " "vault_token-> Seu token de autenticação no VaultPara ter acesso seu token basta autenticar no Web UI do Vault.
Acesse no seu navegador o endereçohttps://aspirina.simtech.solutionsPS: O acesso devera ser solicitado ao time de SRE da SIMTechNa tela de login:MudeMethodparaUsernameEntre com o seu usuário emUsernameEntre com a sua senha emPasswordClique emMore OptionsEmMount PathdigitesimtechClique emSign inApós o login clique no boneco localizao no canto superior direito e, após, clique emCopy TokenImportante:O Token é válido por 30 dias, portanto lembre de renová-lo.FunçõesDESCRITIVOUso: abin
Retorna uma breve explicação sobre a funcionalidade do CLI.
Traz exemplos de uso e link para projeto no GitHubVERSIONUso: abin --version
Retorna a versão atualmente instalada no SO.
Para atualizá-la:
pipx upgrade abin_simCONFIGUREUso: abin --configure
Gera o arquivo de configuração da aplicação.LISTUso: abin list
Retorna a árvore de Secrets cadastradas no Path obedecendo a referência abaixp
╭─ Referência ──────────────────╮
│ │
│ Vault Environment: │
│ ├── (env)-(proj) │
│ │ ├── (api) │
│ │
╰─ Referência ──────────────────╯GETUso: abin get [OPTIONS]
╭─ Options ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ * --app TEXT Nome da aplicação que deseja recuperar os secrets. [default: None] [required] │
│ * --env TEXT Ambiente da aplicação que deseja recuperar (Envs possíveis: dev, qa, main). [default: None] [required] │
│ * --proj TEXT Projeto que deseja conectar para recuperar os secrets (Projs possíveis: sim, charrua) [default: None] [required] │
│ --file --no-file Cria um arquivo para cada path cadastrada no secrets. [default: file] │
│ --help Show this message and exit. │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
Examplo:
* Imprime os dados no StdOut (Tela)
abin get --app api-auth --env dev --proj sim --no-file
* Imprime os dados em arquivo (Com base no arquivo $HOME/abin/settings.toml)
abin get --app api-auth --env dev --proj simUPDATEUso: abin update [OPTIONS]
╭─ Options─────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ * --app TEXT Nome da aplicação que deseja recuperar os secrets. [default: None] [required] │
│ * --env TEXT Ambiente da aplicação que deseja recuperar (Envs possíveis: dev, qa, main). [default: None] [required] │
│ * --proj TEXT Projeto que deseja conectar para recuperar os secrets (Projs possíveis: sim, charrua) [default: None] [required] │
│ --secret TEXT Secret que será atualizada no Vault (Ex.: env, gcp.json, config.yaml ...) [default: env] │
│ * --file TEXT Arquivo com variárias de ambiente [default: None] [required] │
│ --help Show this message and exit. │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
Eexamplo:
abin update --app api-auth --env dev --proj sim --file .env (para atualizar outro secret use --secret NOME)COMPAREUso: abin compare [OPTIONS]
╭─ Options ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╮
│ * --app TEXT Nome da aplicação que deseja recuperar os secrets. [default: None] [required] │
│ * --env TEXT Ambiente da aplicação que deseja recuperar (Envs possíveis: dev, qa, main). [default: None] [required] │
│ * --proj TEXT Projeto que deseja conectar para recuperar os secrets (Projs possíveis: sim, charrua) [default: None] [required] │
│ --help Show this message and exit. │
╰──────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────╯
Eexamplo:
abin compare --app api-auth --env qa,dev --proj sim |
abiosgaming.py | UNKNOWN |
abiparser | abi_parserA simple way to parse your ABIs in Python.InstallationpipinstallabiparserfromabiparserimportContractABIabi=ContractABI('<PATH TO ABI JSON'>)# namename=abi.name# functionsfunctions=abi.functions# events - todo# events = abi.events# get function by namefunction=abi.get_function('<function_name>')# bytecode - todo# bytecode = abi.bytecode# get function by signature - todo# function = abi.get_function_by_signature('<function_signature>') |
abipy | AbiPy is a Python library to analyze the results produced byABINIT,
an open-source program for the ab-initio calculations of the physical properties of materials
within Density Functional Theory and Many-Body perturbation theory.
AbiPy also provides tools to generate input files and workflows to automate
ab-initio calculations and typical convergence studies.
AbiPy is interfaced withPymatgenallowing users to
benefit from the different tools and python objects available in the pymatgen ecosystem.
The official documentation is hosted ongithub pages.
AbiPy can be used in conjunction withmatplotlib,pandas,ipythonandjupyterthus providing a powerful and user-friendly environment for data analysis and visualization.
Check out ourgallery of plotting scriptsand thegallery of AbiPy workflows.
To learn more about the integration between jupyter and AbiPy, visit our collection ofnotebooksand theAbiPy lessons.
The latest development version is always available from <https://github.com/abinit/abipy> |
abi-pyspark-utils | No description available on PyPI. |
abiquo-api | Abiquo API Python ClientThis is a Python client for the Abiquo API. It allows to consume the API
in a dynamic way and to navigate its resources using its built-in linking
features.The project depends onrequestsand optionally onrequests_oauthlib,
if you prefer to use OAuth instead of Basic Authentication.InstallationYou can easily install the module with:pipinstallabiquo-apiUsageUsing the client is pretty straightforward. Here are some examples:Using HTTP Basic AuthenticationThis example shows how to get the list of existing datacenters and how to
navigate its links to create a rack in each of them:importjsonfromabiquo.clientimportAbiquofromabiquo.clientimportcheck_responseapi=Abiquo(API_URL,auth=(username,password))code,datacenters=api.admin.datacenters.get(headers={'Accept':'application/vnd.abiquo.datacenters+json'})print"Response code is:%s"%codefordcindatacenters:print"Creating rack in datacenter%s[%s]"%(dc.name,dc.location)code,rack=dc.follow('racks').post(data=json.dumps({'name':'New rack'}),headers={'accept':'application/vnd.abiquo.rack+json','content-type':'application/vnd.abiquo.rack+json'})check_response(201,code,rack)print"Response code is:%s"%codeprint"Created Rack:%s"%rack.nameNote that you don't need to care about pagination, the client handles it internally for you.Using an OpenID Bearer access tokenIf your platform uses OpenID and you have a Bearer access token, you can configure the client
as follows:importjsonfromabiquo.clientimportAbiquofromabiquo.authimportBearerTokenAuthapi=Abiquo(API_URL,auth=BearerTokenAuth(token))Using a token authenticationimportjsonfromabiquo.clientimportAbiquofromabiquo.authimportTokenAuthapi=Abiquo(API_URL,auth=TokenAuth(token))Using OAuthTo use OAuth first you have to register your client application in the Abiquo API. To do that, you can
use theregister.pyscript as follows, and it will register the application and generate the access
tokens:$pythonregister.pyAbiquoAPIendpoint:http://localhost/api
UsernameorOpenIDaccess_token(prefixedwith"openid:"):your-username
Password:your-password
Applicationname:MyCoolApp
Appkey:54e00f27-6995-40e8-aefe-75f76f514d89
Appsecret:eayP6ll3G02ypBhQBmg0398HYBldkf3B5Jqti73Z
Accesstoken:c9c9bd44-6812-4ddf-b39d-a27f86bf03da
Accesstokensecret:MifYOffkoPkhk33ZTiGOYnIg8irRjw7BlUCR2GUh7IQKv4omfENlMi/tr+gUdt5L8eRCSYKFQVhI4Npga6mXIVl1tCMHqTldYfqUJZdHr0c=If your Abiquo platform uses OpenID, then you can register your application using the Access Token as follows:$pythonregister.pyAbiquoAPIendpoint:http://localhost/apiUsernameorOpenIDaccess_token(prefixedwith"openid:"):openid:bac4564c-4522-450e-985b-5f880f02a3dd
Applicationname:MyCoolApp
Appkey:685df603-cb51-4ffa-bd7e-8b0235f5ac70
Appsecret:HtoICXYr2WENp5D1g7UjbifNizTFh1I3AW3ylEjm
Accesstoken:b1b2856e-5098-4a54-ae3c-d99b739f6770
Accesstokensecret:pBioSC7SNv/0lPRQWBiOr9uSXf8bIs6D2jVVAy2WkBq3Vr37efMKv3mTugk9+TlTAtrWPsPoPdHDGjEtbb5PBHKb2JKWUC9y+OZ44I4v9kk=Once you have the tokens, you just have to create the authentication object and pass it to the
Abiquo client as follows:fromrequests_oauthlibimportOAuth1fromabiquo.clientimportAbiquoAPP_KEY='54e00f27-6995-40e8-aefe-75f76f514d89'APP_SECRET='eayP6ll3G02ypBhQBmg0398HYBldkf3B5Jqti73Z'ACCESS_TOKEN='c9c9bd44-6812-4ddf-b39d-a27f86bf03da'ACCESS_TOKEN_SECRET='MifYOffkoPkhk33ZTiGOYnIg8irRjw7BlUCR2GUh7IQKv4omfENlMi/tr+gUdt5L8eRCSYKFQVhI4Npga6mXIVl1tCMHqTldYfqUJZdHr0c='oauth=OAuth1(APP_KEY,client_secret=APP_SECRET,resource_owner_key=ACCESS_TOKEN,resource_owner_secret=ACCESS_TOKEN_SECRET)api=Abiquo(API_URL,auth=oauth)And that's it! Now you can use the Abiquo client as shown in the Basic Authentication examples.Running the testsYou can run the unit tests as follows:pipinstallrequestsrequests_oauthlibhttpretty
python-munittestdiscover-vContributingThis project is still in an early development stage and is still incomplete. All
contributions are welcome, so feel free toraise a pull request.LicenseThe Abiquo API Java Client is licensed under the Apache License version 2. For
further details, see theLICENSEfile. |
abir | Abir--Python项目配置方案 yaml/environ安装pipinstallabir快速上手django project在settings.py中添加importabir# other settingsabir.load()# at the end of settings.py在项目根文件夹下添加config.yaml添加后的项目结构如下：├──project
│├──project
│|├──__init__.py
│|├──asgi.py
│|├──wsgi.py
│|├──urls.py
│|├──settings.py
│├──manange.py
│├──config.yaml# 添加到根下在yaml中添加对应的配置项# settings 中已配置，只希望修改部分配置项时，使用 dot 查询并修改：DATABASES.default.NAME:'db_name'DATABASES.default.HOST:'dh_host'# 未配置时，会添加配置项DATABASES.default.PORT:'port'DATABASES.default.USER:'db_user_name'DATABASES.default.PASSWORD:'db_password'# settings中无配置，或已配置，但希望全部替换，不使用 dot 查询：CACHE:default:BACKEND:'django_redis.cache.RedisCache'LOCATION:'redis://127.0.0.1:6379/1'OPTIONS:CLIENT_CLASS:'django_redis.client.DefaultClient'LANGUAGE_CODE:'zh-CN'USE_TZ:trueALLOWED_HOSTS:-*⚠️ dot.将会查询settings.py，并更新查询路径下的值。启动服务。pythonmanmage.pyrunserver# or wsgi其他python项目假设项目结构如下：├──project
│├──packagges
│├──modules……添加config_module.py (module名称可自定义)如下添加代码importabirabir.load(base_dir=BASE_DIR,conf_module='conf_module')# 如果config_module不在根下，输入完整查询路径即可，如：project.packageA.moduleB# confi_module 也可以是任何可设置property的对象：getattr and setattr添加config.yaml添加后的项目结构如下：├──project
│├──config_module.py
│├──config.yaml# 添加到根下执行应用，即可获取配置environment 通过环境变量来进行配置⚠️ 环境变量拥有最高优先级：当yaml/settings中存在配置，且环境变量中也存在，优先取环境变量的配置值，即：environ > yaml > settings（当load()在conf_module末尾调用时）前缀abir通过前缀ABIR_捕获环境变量。1. 字符串类型ABIR_LANGUAGE_CODE=es-us2. 其他类型abir读取环境变量时，会识别:定义，当定义为 :json ，将运行json.loads进行值转换，因此可以通过赋值环境变量为json-string的方式，来满足非字符串类型的配置ABIR_LANGUAGE_CODE=zh-CN
ABIR_USE_TZ:json=falseABIR_TIMEOUT:json=20ABIR_BLACK_UIDS:json=[101,39,847,11]ABIR_LIFETIME:json={"days":1,"key":"some-key"}# 注意 json-string 与 前端书写json的区别。以上配置，将会被abir解读为：LANGUAGE_CODE='zh-CN'USE_TZ=FalseTIMEOUT=20BLACK_UIDS=[101,39,847,11]LIFETIME={'days':1,'key':'some-key'} |
abirami | sample analog_modem |
abirami-hide-code | jupyterlab-hide-code: A JupyterLab Extension to Run and Hide Source CodeA button in JupyterLab to run the code cells and then to hide the code cells.
This JupyterLab extension was inspired by thejlab-hide-codeJupyterLab
extension from Aachen (Aix) Virtual Platform for Materials Processing.RequirementsJupyterLab >= 3.0Installpipinstalljupyterlab-hide-codeUninstallpipuninstalljupyterlab-hide-codeContributingBuildThejlpmcommand is JupyterLab's pinned version ofyarnthat is installed with JupyterLab. You may useyarnornpmin lieu ofjlpmbelow.# Clone the repo to your local environment# Move to jupyterlab-hide-code directory# Install dependenciesjlpm# Build Typescript sourcejlpmbuild# Link your development version of the extension with JupyterLabjupyterlabextensionlink.# Rebuild Typescript source after making changesjlpmbuild# Rebuild JupyterLab after making any changesjupyterlabbuildYou can watch the source directory and run JupyterLab in watch mode to watch for changes in the extension's source and automatically rebuild the extension and application.# Watch the source directory in another terminal tabjlpmwatch# Run jupyterlab in watch mode in one terminal tabjupyterlab--watchAcknowlegementsWe acknowledge support from the EPFL Open Science Fund via theOSSCARproject. |
abir-roy-package | This is a package for image processing and making caption from image.
abir_roy_package
ImageCaptioningModel()
data
Dataset()
Download()
transforms
BlurImage()
CropImage()
FlipImage()
RescaleImage()
RotateImage() |
abism | ABISM: Adaptive Background Interferometric Strehl MeterInstall:pipinstallabismor for developpers:pipinstall-Ugit+https://github.com/tinmarino/abismStart:from shell:abismimage.fitsWhat:A graphical user interface (GUI) to measure the strehl ratio.
Meaning the quality (.fits) image from a telescope with adaptive optics.Who:For observer astronomers using adaptive opticsHow:On the following image, we have a Strehl ratio 50% which is excellent.
Notice the warning that we are reaching the non-linearity detector limit.
We did not take time to measure the error of the measure if non linear, it is very detector dependant.More:from ipython:# Importfromabism.runimportrun_async# Launch (sm for Strehl Meter)sm=run_async('--verbose','1','./image.fits')# Print detailsprint(sm.state)License:Do whatever you want with the codeAuthors:Julien Girard, Martin Tourneboeuf |
abita-distributions | The package provides two probabitlity distribution functions Binomial distribution function and Gaussian distribution function.
The Binomial distribution function can be used by importing Binomial class with input values as probability value, number of data.
The Gaussian distribution function can be used by importing Gaussian class with input values mean and standard deviation. |
abitfrosty | No description available on PyPI. |
abito | abitoPython package for hypothesis testing. Suitable for using in A/B-testing software.
Tested for Python >= 3.5. Based on numpy and scipy.FeaturesConvenient interface to run significance tests.Support of ratio-samples. Linearization included (delta-method).Bootstrapping: can measure significance of any statistic, even quantiles. Multiprocessing is supported.Ntile-bucketing: compress samples to get better performance.Trim: get rid of heavy tails.Installationpip install abitoUsageThe most powerful tool in this package is the Sample:importabitoasabLet's draw some observations from Poisson distribution and initiate Sample instance from them.importnumpyasnpobservations=np.random.poisson(1,size=10**6)sample=ab.sample(observations)Now we can calculate any statistic in numpy-way.print(sample.mean())print(sample.std())print(sample.quantile(q=[0.05,0.95]))To compare with other sample we can use t_test or mann_whitney_u_test:observations_control=np.random.poisson(1.005,size=10**6)sample_control=Sample(observations_control)print(sample.t_test(sample_control))print(sample.mann_whitney_u_test(sample_control))BootstrapOr we can use bootstrap to compare any statistic:sample.bootstrap_test(sample_control,stat='mean',n_iters=100)To improve performance, it's better to provide observations in weighted form: unique values + counts. Or, we can compress samples, using built-in method:sample.reweigh(inplace=True)sample_control.reweigh(inplace=True)sample.bootstrap_test(sample_control,stat='mean',n_iters=10000)Now bootstrap is working lightning-fast. To improve performance further you can set parameter n_threads > 1 to run bootstrapping using multiprocessing.Compressobservations=np.random.normal(100,size=10**8)sample=ab.sample(observations)compressed=sample.compress(n_buckets=100,stat='mean')%timeitsample.std()%timeitcompressed.std() |
abi.tools.uigenerator | ============Basic user interface to use for generating Python user interface descriptions from Qt ui files.Installation------------To install, simply.. code:: bashpip install -U abi.tools.uigeneratorKeywords: abi user interface generatorPlatform: UNKNOWNClassifier: Development Status :: 3 - AlphaClassifier: Topic :: Utilities |
abi-writer | ABI-Documentation |
abjad | Abjad helps composers build up complex pieces of music notation in iterative and
incremental ways. Use Abjad to create a symbolic representation of all the notes, rests,
chords, tuplets, beams and slurs in any score. Because Abjad extends the Python
programming language, you can use Abjad to make systematic changes to music as you work.
Because Abjad wraps the LilyPond music notation package, you can use Abjad to control the
typographic detail of symbols on the page.Abjad’s documentation is available here:https://abjad.github.ioAbjad’s install instructions are tested on macOS and Linux.Abjad requires Python 3.10 or later:~$ python --version
Python 3.11.0Abjad requires LilyPond 2.23.6 or later.Make sure LilyPond is installed:http://lilypond.org/development.htmlMake sure LilyPond is callable from the commandline:$ lilypond --version
GNU LilyPond 2.23.80 (running Guile 2.2)Create a Python 3 virtual environment for Abjad:https://docs.python.org/3/tutorial/venv.htmlActivate the virtual environment and then use pip to install Abjad:~$ python -m pip install abjadStart Python, import Abjad, start making music notation:~$ python
>>> import abjad
>>> note = abjad.Note("c'4")
>>> abjad.show(note)Join the Abjad community:https://abjad.github.io/appendices/community.html |
abjad-ext-book | # abjad-ext-book
Abjad Book Extension |
abjad-ext-cli | # abjad-ext-cli
Abjad CLI Extension |
abjad-ext-ipython | # abjad-ext-ipython
Abjad IPython Extension |
abjad-ext-nauert | # abjad-ext-nauert
Abjad quantization extension, based on Paul Nauert’s Q-Grids |
abjad-ext-rmakers | Abjad’s rhythm-maker extension package.[](https://github.com/ambv/black) |
abjad-ext-tonality | # abjad-ext-tonality
Abjad tonal analysis extension |
abjadtools | convert abjad objects to its music21 counterpartutilities to make working with abjad less painfull |
ab-j-distributions | No description available on PyPI. |
abk2calculator | No description available on PyPI. |
abk2simplelib | No description available on PyPI. |
abk3calculator | No description available on PyPI. |
abk-bwp | Bing Wallpaper (currently MacOS only)Downloads daily Bing images and sets them as desktop wallpaperConfiguring the appPlease see the file abk_bwp/config/bwp_config.toml file. There are some setting you might want to change.
Like the image size, which should ideally correspond to the size of your display.
In the config file you will find also detailed explanation, what exactly each configuration item is for.Installing python dependencies:Installing with pyenv + virtual environment (Recommended)If you are like me and don't want to mix your python packages, you want to create python virtual environment before installing dependencies.
I use pyenv tool for that. Here are the steps on MacOS:install brew. Google for it if you don't have it alreadybrew install pyenv - will install pyenv toolbrew install pyenv-virtualenv - installs virtualenv pyenv versionpyenv versions - will show you currently installed python versions and virtual envs on your systempyenv install --list - will show you all available python versions you could install.pyenv install 3.8.9 - installs python 3.8.9 versionpyenv virtualenv 3.8.9 bwp - creates virtual environment [bing wall paper] with python 3.8.9cd <your_project_dir> - change into your project directory e.g.: cd abk_bwppyenv local bwp - setting the current directory to use [bwp] virtual environmentmake install - will install all needed python dependency packages into [bwp] virtual environment.make bwp - will download bing image and add title to the imageInstalling without pyenv or python virtual environmet. Note the app does not run with python 2.7If it is too many steps for you and just want to get it working "quick and dirty".
Warning: there might be some python packages, which might collide with already installed packages.cd abk_bwp - change to the project directorymake install - to install python dependency packages in default locationmake bwp - will download bing image and add title to the imageMakefile rulesThere are some Makefile rules, which are created for your convinience. For more help with rules type: make help
Here are some described in the tablemakefile ruledescriptionmake bwpexecutes the abk_bwp program, which dowloads Bing images and creates a desktop imagemake bwp_logexecutes the abk_bwp program, with tracing to the console and log filemake bwp_traceexecutes the abk_bwp program, with tracing to the consolemake bwp_desktop_enableexecutes the abk_bwp program, enables auto download (time configured in bwp_config.toml)make bwp_desktop_disableexecutes the abk_bwp program, disables auto download (time configured in bwp_config.toml)make bwp_ftv_enableWIP: executes the abk_bwp program, enables Samsung frame TV support (Not working yet)make bwp_ftv_disableWIP: executes the abk_bwp program, disables Samsung frame TV support (Not working yet)make installinstalls all required python packages for the appmake install_testinstalls all required python packages for app and additional packages to run the unit testmake install_devinstalls all required python packages for app and additional for test and developmentmake testruns unit testsmake test_vruns unit tests with verbosity enabledmake test_ffruns unit tests and fails fast on the first broken testmake test_vffruns unit tests fails fast on the first broken test with verbosity enabledmake test_1runs one specific unit testmake coverageruns unit tests with coveragemake coverageruns unit tests with coveragemake cleancleans some auto generated build filesmake settingsdisplays some Makefile settingsmake helpdisplays Makefile helpPython tracing:In order to debug python scripts, you could enable the traces in the
logging.yaml file by changing levels from CRITICAL to DEBUGScheduler / plist tracing / TroubleshootingThe project contains com.abk.bingwallpaper_debug.sh.plist file, which can be used to debug scheduler problems.Copy com.abk.bingwallpaper_debug.sh.plist to ~/Library/LaunchAgents/ directory.change to directory: cd ~/Labrary/LaunchAgentsload the scheduler with: launchctl load -w com.abk.bingwallpaper_debug.sh.pliststart the job with: launchctl start com.abk.bingwallpaper_debug.shcheck the job run: launchctl list | grep com.abk.bingwallpaper_debug.sh
if it return 0 the job ran successfullythe traces will be available in
/tmp/com.abk.bingwallpaper_debug.sh.stderr
and
/tmp/com.abk.bingwallpaper_debug.sh.stdoutafter troubleshooting don't forget to disable the job for the debug schedulerexecute following
launchctl stop com.abk.bingwallpaper_debug.sh
launchctl unload -w com.abk.bingwallpaper_debug.sh.plistdelete the debug file from ~/Labrary/LaunchAgents
rm com.abk.bingwallpaper_debug.sh.plist in: ~/Labrary/LaunchAgentsApp runs on:MacOS Monterey (local machine) / Python 3.10.6Linux Ubuntu 20.04 / Python 3.8.9Windows 10 / Python 3.8.9Pipeline Unit Tests ran on:Linux latest / Python 3.8.x, 3.9.x, 3.10.xMacOS latest / Python 3.8.x, 3.9.x, 3.10.xWindows latest / Python 3.8.x, 3.9.x, 3.10.x |
abkcalculator | No description available on PyPI. |
abkdependerlib | No description available on PyPI. |
abkp009-simple-calc | simple calculator16 Jan 2021Author: Abhishek Pandey
Change: initial checkin |
abksimplelib | No description available on PyPI. |
ablaevent | Ablaevent is developer-friendly, self-hosted product analytics. ablaevent-python is the python package. |
ablang | AbLang: A language model for antibodiesMotivation:General protein language models have been shown to summarize the semantics of protein sequences into representations that are useful for state-of-the-art predictive methods. However, for antibody specific problems, such as restoring residues lost due to sequencing errors, a model trained solely on antibodies may be more powerful. Antibodies are one of the few protein types where the volume of sequence data needed for such language models is available, e.g. in the Observed Antibody Space (OAS) database.Results:Here, we introduce AbLang, a language model trained on the antibody sequences in the OAS database. We demonstrate the power of AbLang by using it to restore missing residues in antibody sequence data, a key issue with B-cell receptor repertoire sequencing, e.g. over 40% of OAS sequences are missing the first 15 amino acids. AbLang restores the missing residues of antibody sequences better than using IMGT germlines or the general protein language model ESM-1b. Further, AbLang does not require knowledge of the germline of the antibody and is seven times faster than ESM-1b.Availability and implementation:AbLang is a python package available athttps://github.com/oxpig/AbLang.Install AbLangAbLang is freely available and can be installed with pip.pip install ablangor directly from github.pip install -U git+https://github.com/oxpig/AbLang.gitNB:If you use the argument "align=True", you need to manually install a version ofANARCIin the same environment. ANARCI can also be installed using bioconda; however, this version is maintained by a third party.conda install -c bioconda anarciAbLang use casesA Jupyter notebookshowing the different use cases of AbLang and its building blocks can be foundhere.Currently, AbLang can be used to generate three different representations/encodings for antibody sequences.Res-codings:These encodings are 768 values for each residue, useful for residue specific predictions.Seq-codings:These encodings are 768 values for each sequence, useful for sequence specific predictions. The same length of encodings for each sequence, means these encodings also removes the need to align antibody sequences.Res-likelihoods:These encodings are the likelihoods of each amino acid at each position in a given antibody sequence, useful for exploring possible mutations. The order of amino acids follows the ablang vocabulary.These representations can be used for a plethora of antibody design applications. As an example, we have used the res-likelihoods from AbLang to restore missing residues in antibody sequences due either to sequencing errors, such as ambiguous bases, or the limitations of the sequencing techniques used.Antibody sequence restorationRestoration of antibody sequences can be done using the "restore" mode as seen below.import ablang
heavy_ablang = ablang.pretrained("heavy") # Use "light" if you are working with light chains
heavy_ablang.freeze()
seqs = [
'EV*LVESGPGLVQPGKSLRLSCVASGFTFSGYGMHWVRQAPGKGLEWIALIIYDESNKYYADSVKGRFTISRDNSKNTLYLQMSSLRAEDTAVFYCAKVKFYDPTAPNDYWGQGTLVTVSS',
'*************PGKSLRLSCVASGFTFSGYGMHWVRQAPGKGLEWIALIIYDESNK*YADSVKGRFTISRDNSKNTLYLQMSSLRAEDTAVFYCAKVKFYDPTAPNDYWGQGTL*****',
]
heavy_ablang(seqs, mode='restore')The output of the above is seen below.array(['EVQLVESGPGLVQPGKSLRLSCVASGFTFSGYGMHWVRQAPGKGLEWIALIIYDESNKYYADSVKGRFTISRDNSKNTLYLQMSSLRAEDTAVFYCAKVKFYDPTAPNDYWGQGTLVTVSS','QVQLVESGGGVVQPGKSLRLSCVASGFTFSGYGMHWVRQAPGKGLEWIALIIYDESNKYYADSVKGRFTISRDNSKNTLYLQMSSLRAEDTAVFYCAKVKFYDPTAPNDYWGQGTLVTVSS'],dtype='<U121')For restoration of an unknown number of missing residues at the ends of antibody sequences, the "align" parameter can be set to True.seqs = [
'EV*LVESGPGLVQPGKSLRLSCVASGFTFSGYGMHWVRQAPGKGLEWIALIIYDESNKYYADSVKGRFTISRDNSKNTLYLQMSSLRAEDTAVFYCAKVKFYDPTAPNDYWGQGTLVTVSS',
'PGKSLRLSCVASGFTFSGYGMHWVRQAPGKGLEWIALIIYDESNK*YADSVKGRFTISRDNSKNTLYLQMSSLRAEDTAVFYCAKVKFYDPTAPNDYWGQGTL',
]
heavy_ablang(seqs, mode='restore', align=True)The output of the above is seen below.array(['EVQLVESGPGLVQPGKSLRLSCVASGFTFSGYGMHWVRQAPGKGLEWIALIIYDESNKYYADSVKGRFTISRDNSKNTLYLQMSSLRAEDTAVFYCAKVKFYDPTAPNDYWGQGTLVTVSS','QVQLVESGGGVVQPGKSLRLSCVASGFTFSGYGMHWVRQAPGKGLEWIALIIYDESNKYYADSVKGRFTISRDNSKNTLYLQMSSLRAEDTAVFYCAKVKFYDPTAPNDYWGQGTLVTVSS'],dtype='<U121')Citation@article{Olsen2022,
title={AbLang: An antibody language model for completing antibody sequences},
author={Tobias H. Olsen, Iain H. Moal and Charlotte M. Deane},
journal={bioRxiv},
doi={https://doi.org/10.1101/2022.01.20.477061},
year={2022}
} |
ablang2 | AbLang-2Addressing the antibody germline bias and its effect on language models for improved antibody designMotivation:The versatile pathogen-binding properties of antibodies have made them an extremely important class of biotherapeutics. However, therapeutic antibody development is a complex, expensive and time-consuming task, with the final antibody needing to not only have strong and specific binding, but also be minimally impacted by any developability issues. The success of transformer-based language models for language tasks and the availability of vast amounts of antibody sequences, has led to the development of many antibody-specific language models to help guide antibody discovery and design. Antibody diversity primarily arises from V(D)J recombination, mutations within the CDRs, or from a small number of mutations away from the germline outside the CDRs. Consequently, a significant portion of the variable domain of all natural antibody sequences remains germline. This affects the pre-training of antibody-specific language models, where this facet of the sequence data introduces a prevailing bias towards germline residues. This poses a challenge, as mutations away from the germline are often vital for generating specific and potent binding to a target, meaning that language models need be able to suggest key mutations away from germline.Results:In this study, we explored the implications of the germline bias, examining its impact on both general-protein and antibody-specific language models. We developed and trained a series of new antibody-specific language models optimised for predicting non-germline residues. We then compared our final model, AbLang-2, with current models and show how it suggests a diverse set of valid mutations with high cumulative probability. AbLang-2 is trained on both unpaired and paired data, and is the first freely available paired VH-VL language model (https://github.com/oxpig/AbLang2.git).Availability and implementation:AbLang2 is a python package available athttps://github.com/oxpig/AbLang2.git.Install AbLang2AbLang is freely available and can be installed with pip.pip install ablang2or directly from github.pip install -U git+https://github.com/oxpig/AbLang2.gitNB:If you want to have your returned output aligned (i.e. use the argument "align=True"), you need to manually installPandasand a version ofANARCIin the same environment. ANARCI can also be installed using bioconda; however, this version is maintained by a third party.conda install -c bioconda anarciAbLang2 usecasesAbLang2 can be used in different ways and for a variety of usecases. The central building blocks are the tokenizer, AbRep, and AbLang.Tokenizer: Converts sequences and amino acids to tokens, and vice versaAbRep: Generates residue embeddings from tokensAbLang: Generates amino acid likelihoods from tokensimport ablang2
# Download and initialise the model
ablang = ablang2.pretrained(model_to_use='ablang2-paired', random_init=False, ncpu=1, device='cpu')
seq = [
'EVQLLESGGEVKKPGASVKVSCRASGYTFRNYGLTWVRQAPGQGLEWMGWISAYNGNTNYAQKFQGRVTLTTDTSTSTAYMELRSLRSDDTAVYFCARDVPGHGAAFMDVWGTGTTVTVSS',
'DIQLTQSPLSLPVTLGQPASISCRSSQSLEASDTNIYLSWFQQRPGQSPRRLIYKISNRDSGVPDRFSGSGSGTHFTLRISRVEADDVAVYYCMQGTHWPPAFGQGTKVDIK'
]
# Tokenize input sequences
seqs = [f"{seq[0]}|{seq[1]}"] # Input needs to be a list
tokenized_seq = ablang.tokenizer(seqs, pad=True, w_extra_tkns=False, device="cpu")
# Generate rescodings
with torch.no_grad():
rescoding = ablang.AbRep(tokenized_seq).last_hidden_states
# Generate logits/likelihoods
with torch.no_grad():
likelihoods = ablang.AbLang(tokenized_seq)We have build a wrapper for specific usecases which can be explored via a the followingJupyter notebook.Citation@article{Olsen2024,
title={},
author={Tobias H. Olsen, Iain H. Moal and Charlotte M. Deane},
journal={in-preparation},
doi={},
year={2024}
} |
ablation | Ablation studies for evaluating XAI methods |
ablator | 🚀 ABLATOR is aDISTRIBUTED EXECUTION FRAMEWORKdesigned to enhance ablation studies in complex machine learning models. It automates the process of configuration and conducts multiple experiments in parallel.What are Ablation Studies?It involves removing specific parts of a neural network architecture or changing different aspects of the training process to examine their contributions to the model's performance.Why ABLATOR?As machine learning models grow in complexity, the number of components that need to be ablated also increases. This consequently expands the search space of possible configurations, requiring an efficient approach to horizontally scale multiple parallel experimental trials. ABLATOR is a tool that aids in the horizontal scaling of experimental trials.Instead of manually configuring and conducting multiple experiments with various hyperparameter settings, ABLATOR automates this process. It initializes experiments based on different hyperparameter configurations, tracks the state of each experiment, and provides experiment persistence on the cloud.Key FeaturesIt is a tool that simplifies the process of prototyping of models.It streamlines model experimentation and evaluation.It offers a flexible configuration system.It facilitates result interpretation through visualization."Auto-Trainer" feature reduces redundant coding tasks.ABLATOR vs. Without ABLATORLeft, ABLATOR efficiently conducts multiple trials in parallel based and log the experiment results.Right,manually, one would need to run trials sequentially, demanding more effort and independent analysis.InstallFor MacOS and Linux systems directly install via pip.pip install ablatorIf you are using Windows, you will need to install WSL using the official from Microsoft.WSL is a Linux subsystem and for ABLATOR purposes is identical to using Linux.Basic Concepts1. Create your Configurationfromtorchimportnnimporttorchfromablatorimport(ModelConfig,ModelWrapper,OptimizerConfig,TrainConfig,configclass,Literal,ParallelTrainer,SearchSpace,)fromablator.config.mpimportParallelConfig@configclassclassTrainConfig(TrainConfig):dataset:str="random"dataset_size:int@configclassclassModelConfig(ModelConfig):layer:Literal["layer_a","layer_b"]="layer_a"@configclassclassParallelConfig(ParallelConfig):model_config:ModelConfigtrain_config:TrainConfigconfig=ParallelConfig(experiment_dir="ablator-exp",train_config=TrainConfig(batch_size=128,epochs=2,dataset_size=100,optimizer_config=OptimizerConfig(name="sgd",arguments={"lr":0.1}),scheduler_config=None,),model_config=ModelConfig(),device="cpu",search_space={"model_config.layer":SearchSpace(categorical_values=["layer_a","layer_b"])},total_trials=2,)2. Define your ModelclassSimpleModel(nn.Module):def__init__(self,config:ModelConfig)->None:super().__init__()ifconfig.layer=="layer_a":self.param=nn.Parameter(torch.ones(100,1))else:self.param=nn.Parameter(torch.randn(200,1))defforward(self,x:torch.Tensor):x=self.paramreturn{"preds":x},x.sum().abs()classSimpleWrapper(ModelWrapper):defmake_dataloader_train(self,run_config:ParallelConfig):dl=[torch.rand(100)foriinrange(run_config.train_config.dataset_size)]returndldefmake_dataloader_val(self,run_config:ParallelConfig):dl=[torch.rand(100)foriinrange(run_config.train_config.dataset_size)]returndl3. Launch 🚀mywrapper=SimpleWrapper(SimpleModel)withParallelTrainer(mywrapper,config)asablator:ablator.launch(".")Learn More about ABLATOR ModulesConfiguration ModuleTraining ModuleExperiment and Metrics ModuleAnalysis ModuleTutorialsExplore a variety of tutorials and examples on how to utilize ABLATOR.
Ready to dive in? 👉Ablation TutorialsContribution GuidelinesABLATOR is open source, and we value contributions from our community!
Check out ourDevelopment Guidefor details on our development process and insights into the internals of the ABLATOR library.For any bugs or feature requests related to ABLATOR, please visit our GitHub Issues or reach out to slackAblator CommunityPlatformPurposeSupport LevelGitHub IssuesTo report issues or suggest new features.ABLATOR TeamSlackTo collaborate with fellow ABLATOR users.CommunityDiscordTo inquire about ABLATOR usage and collaborate with other ABLATOR enthusiasts.CommunityTwitterFor staying up-to-date on new features of Ablator.ABLATOR TeamReferences@inproceedings{fostiropoulos2023ablator,
title={ABLATOR: Robust Horizontal-Scaling of Machine Learning Ablation Experiments},
author={Fostiropoulos, Iordanis and Itti, Laurent},
booktitle={AutoML Conference 2023 (ABCD Track)},
year={2023}} |
ablator-ken-test | ABLATORA distributed experiment execution framework for ablation studies. ABLATOR provides a wrapper for your model and a Trainer class for you to prototype on your method and scale to thousands of experimental trials with 1 code change.Ablation studies are experiments used to identify the causal effects on a method performance. For example,does your novel layer really improve performance?What are Ablators?Ablators are materials that are depleted during operation (NASA). An experimental ABLATOR should not interfere with the experimental result.Why ABLATOR?Strictly typed configuration system prevents errors.Seamless prototyping to productionStateful experiment design. Stop, Resume, Share your experimentsAutomated analysis artifactsTemplate TrainingWhat is the difference with usingxxxComparison table with existing framework:FrameworkHPOConfigurationTrainingTuningAnalysisRay:white_check_mark::x::x::white_check_mark::x:Lighting:x::x::white_check_mark::x::x:Optuna:white_check_mark::x::x::x::white_check_mark:Hydra:x::white_check_mark::x::x::x:ABLATOR:heavy_check_mark::heavy_check_mark::heavy_check_mark::heavy_check_mark::heavy_check_mark:Features compared, hyperparameter selection (HPO), removing boilerplate code for configuring experiments (Configuration), removing boiler plate code for running experiments at scale (Tuning) and performing analysis on the hyperparameter selection (Analysis).Using:Ray: You will need to write boiler-plate code for integrating with a configuration system (i.e. Hydra), saving experiments artifacts or logging (i.e. integrate with Wandb).Lighting: You will need to write boiler-plate code for HPO (i.e. using Optuna), Configuring experiments (i.e. Hydra) and horizontal distributed execution (i.e. integrate with Ray)Hydra: The configuration system is not strongly typed (ABLATOR), and does not provide support for common ML use-cases where configuration attributes areDerived(inferred during run-time) orStateless(change between trials). Additionally, ABLATOR provides support for custom objects that are dynamically inferred and initialized during execution.ABLATOR: Combines Ray back-end, with Optuna for HPO and removes boiler-plate code for fault tollerant strategies, training, and analyzing the results.Integrating different tools, for distributed execution, fault tollerance, training, checkpointing and analysis iserror prone! Poor compatibility between tools, verisioning errors will lead to errors in your analysis.You can use ABLATOR with any other library i.e. PyTorch Lighting. Just wrap a Lighting model with ModelWrapper. For examples please lookexamplesSpend more time in the creative process of ML research and less time on dev-ops.Pre-Release - PhaseThe library is under active development and a lot of the API endpoints will be removed / renamed or their functionality changed without notice.InstallUse a python virtual enviroment to avoid version conflicts.pip install git+https://github.com/fostiropoulos/ablator.gitFor Developmentgit clone [email protected]:fostiropoulos/ablator.gitcd ablatorpip install -e .[dev] |
ablator-ken-test2 | ABLATORA distributed experiment execution framework for ablation studies. ABLATOR provides a wrapper for your model and a Trainer class for you to prototype on your method and scale to thousands of experimental trials with 1 code change.Ablation studies are experiments used to identify the causal effects on a method performance. For example,does your novel layer really improve performance?What are Ablators?Ablators are materials that are depleted during operation (NASA). An experimental ABLATOR should not interfere with the experimental result.Why ABLATOR?Strictly typed configuration system prevents errors.Seamless prototyping to productionStateful experiment design. Stop, Resume, Share your experimentsAutomated analysis artifactsTemplate TrainingWhat is the difference with usingxxxComparison table with existing framework:FrameworkHPOConfigurationTrainingTuningAnalysisRay:white_check_mark::x::x::white_check_mark::x:Lighting:x::x::white_check_mark::x::x:Optuna:white_check_mark::x::x::x::white_check_mark:Hydra:x::white_check_mark::x::x::x:ABLATOR:heavy_check_mark::heavy_check_mark::heavy_check_mark::heavy_check_mark::heavy_check_mark:Features compared, hyperparameter selection (HPO), removing boilerplate code for configuring experiments (Configuration), removing boiler plate code for running experiments at scale (Tuning) and performing analysis on the hyperparameter selection (Analysis).Using:Ray: You will need to write boiler-plate code for integrating with a configuration system (i.e. Hydra), saving experiments artifacts or logging (i.e. integrate with Wandb).Lighting: You will need to write boiler-plate code for HPO (i.e. using Optuna), Configuring experiments (i.e. Hydra) and horizontal distributed execution (i.e. integrate with Ray)Hydra: The configuration system is not strongly typed (ABLATOR), and does not provide support for common ML use-cases where configuration attributes areDerived(inferred during run-time) orStateless(change between trials). Additionally, ABLATOR provides support for custom objects that are dynamically inferred and initialized during execution.ABLATOR: Combines Ray back-end, with Optuna for HPO and removes boiler-plate code for fault tollerant strategies, training, and analyzing the results.Integrating different tools, for distributed execution, fault tollerance, training, checkpointing and analysis iserror prone! Poor compatibility between tools, verisioning errors will lead to errors in your analysis.You can use ABLATOR with any other library i.e. PyTorch Lighting. Just wrap a Lighting model with ModelWrapper. For examples please lookexamplesSpend more time in the creative process of ML research and less time on dev-ops.Pre-Release - PhaseThe library is under active development and a lot of the API endpoints will be removed / renamed or their functionality changed without notice.InstallUse a python virtual enviroment to avoid version conflicts.pip install git+https://github.com/fostiropoulos/ablator.gitFor Developmentgit clone [email protected]:fostiropoulos/ablator.gitcd ablatorpip install -e .[dev] |
ablator-ken-test3 | ABLATORA distributed experiment execution framework for ablation studies. ABLATOR provides a wrapper for your model and a Trainer class for you to prototype on your method and scale to thousands of experimental trials with 1 code change.Ablation studies are experiments used to identify the causal effects on a method performance. For example,does your novel layer really improve performance?What are Ablators?Ablators are materials that are depleted during operation (NASA). An experimental ABLATOR should not interfere with the experimental result.Why ABLATOR?Strictly typed configuration system prevents errors.Seamless prototyping to productionStateful experiment design. Stop, Resume, Share your experimentsAutomated analysis artifactsTemplate TrainingWhat is the difference with usingxxxComparison table with existing framework:FrameworkHPOConfigurationTrainingTuningAnalysisRay:white_check_mark::x::x::white_check_mark::x:Lighting:x::x::white_check_mark::x::x:Optuna:white_check_mark::x::x::x::white_check_mark:Hydra:x::white_check_mark::x::x::x:ABLATOR:heavy_check_mark::heavy_check_mark::heavy_check_mark::heavy_check_mark::heavy_check_mark:Features compared, hyperparameter selection (HPO), removing boilerplate code for configuring experiments (Configuration), removing boiler plate code for running experiments at scale (Tuning) and performing analysis on the hyperparameter selection (Analysis).Using:Ray: You will need to write boiler-plate code for integrating with a configuration system (i.e. Hydra), saving experiments artifacts or logging (i.e. integrate with Wandb).Lighting: You will need to write boiler-plate code for HPO (i.e. using Optuna), Configuring experiments (i.e. Hydra) and horizontal distributed execution (i.e. integrate with Ray)Hydra: The configuration system is not strongly typed (ABLATOR), and does not provide support for common ML use-cases where configuration attributes areDerived(inferred during run-time) orStateless(change between trials). Additionally, ABLATOR provides support for custom objects that are dynamically inferred and initialized during execution.ABLATOR: Combines Ray back-end, with Optuna for HPO and removes boiler-plate code for fault tollerant strategies, training, and analyzing the results.Integrating different tools, for distributed execution, fault tollerance, training, checkpointing and analysis iserror prone! Poor compatibility between tools, verisioning errors will lead to errors in your analysis.You can use ABLATOR with any other library i.e. PyTorch Lighting. Just wrap a Lighting model with ModelWrapper. For examples please lookexamplesSpend more time in the creative process of ML research and less time on dev-ops.Pre-Release - PhaseThe library is under active development and a lot of the API endpoints will be removed / renamed or their functionality changed without notice.InstallUse a python virtual enviroment to avoid version conflicts.pip install git+https://github.com/fostiropoulos/ablator.gitFor Developmentgit clone [email protected]:fostiropoulos/ablator.gitcd ablatorpip install -e .[dev] |
ablaze | No description available on PyPI. |
abl.cssprocessor | UNKNOWN |
able | ABleAble stands for Allthenticate's BLE Peripheral Library.
It serves the purpose of enabling the abstraction of using a BLE Peripheral on Ubuntu, MacOs and Windows based systems
without having to adapt your software to have platform specific support.In ProgressNote that this project is still undergoing work by the development team, the main things we are working on are:Cleaning up some bugs in the bluezdbus and macos backendMaking the gitlab/github repo public for contributionsSetup a slack for developers to reach out to us about issues or ideasMake logging/use clearer and update our documentation (for example we require a fork of bluez) and moving things from this readme to our docsGet our docs on read the docsSome more surprises :)How To'sQuick StartTo get started just runget_started.sh. This will install poetry and all of the project's dependencies, and drop you into the projects virtual environment.bash get_started.shWhenever you pull new updates we recommend running a quickpoetry installto get any updates. From there, please check out our examples to get started with the project!Upgrading Poetry InstallerIt may be the case that the current version of your Poetry package is outdated. Runscripts/check_poetry.shto check if there is a newer version of the poetry installer.bash scripts/check_poetry.shDocumentationWriting DocsWe follow the Sphinx docstring format for our docstrings, see the followingsiteon the complete specification, but the
general docstring will look like:"""
[Summary]
:param [ParamName]: [ParamDescription], defaults to [DefaultParamVal]
:type [ParamName]: [ParamType](, optional)
...
:raises [ErrorType]: [ErrorDescription]
...
:return: [ReturnDescription]
:rtype: [ReturnType]
"""Generating DocsTo generate the docs for local use you should just have to run the following inside of the poetry shell:make htmlThen open the html produced by Sphinx.Auto-Generating DocsIf you want to have docs magically update as you are writing them, run the following command:sphinx-autobuild source/ build/html/Testing AbleHow To Test?Running tests are easy, you need to only do a few things as a developer. First track down the IP of the companion you
plan to be using, we recommend a raspberry pi. Once you have that IP, export it as an environment variable as so:export ABLE_CENTRAL_IP="<IP>"Now, we can run the tests with the following command:poetry install && poetry run pytest testsRigorous TestingIf you want to do more rigorous testing over a long period of time and check for flaky tests, you will
have to modify thepyproject.toml. We already have the dependencies you need to run tests multiple times
to detect flakiness, all you need to do is modify the following line:addopts = "--flake-finder --flake-runs=1 --reruns 5 --reruns-delay 5"Into:addopts = "--flake-finder --flake-runs=10 --reruns 5 --reruns-delay 5"This will run each test 10 times, you can even modify it to be greater should you choose. You can also
modify thererunsandreruns-delayparameters to change how much time you should wait between failed tests,
maybe to let things simmer and how many reruns you will accept.Speedy TestsOur dependencies includepytest-fast-firstwhich will locally track which tests are quicker and will use AI and
deep learning (a json dictionary of times) to track and run tests that go faster first! Neat!Coming Soon To TestingWe are hoping to have unit tests coming soon for Able but right now are relying solely on hardware in the loop tests
to get things off the ground. Eventually we will detect if you have a companion set and if not, we will only run
the unit tests.SupportIf you have any questions on the use of this library feel free to reach out the head
[email protected] submit an issue on the repository.ContributingContributing is not currently enabled but once the repository is licensed we will be opening the project up for public contributions.AcknowledgementsThis project was inspired by the great work done by the developer team forBleakwhich is a fantastic platform agnostic bluetooth framework for a BLE client/central we would highly reccomend!We also took notes from the work done by Kevin Car with his companion libraryBless,
who made a great server supplement to Bleak whose work saved us from countless hours from fighting dbus and pyobjc! |
ablerConfig | ablerConfig简介这是一个用于处理配置信息的Python包。它可以帮助应用程序读取、写入和验证配置文件。该包暂时只供彭彭项目组自用。特点支持JSON5格式的配置文件。可以验证配置文件的格式和内容。可以使用默认值填充缺失的配置项。可以将配置文件转换为Python对象，以便更方便地访问和修改。支持内部配置文件和本地外部配置文件。依赖项pyjson5json5安装您可以使用pip安装abler_config：pipinstallablerConfig使用方法加载配置文件您可以使用ablerConfig.Config()函数加载配置文件。例如：importablerConfigasconfigconfig.Config(default='my-config',local_dir='ex-conf',show_message=my_show_message)引用配置项您可以使用ablerConfig.Config.object_of()方法引用配置文件中的配置对象，也可以使用ablerConfig.Config.value_of()方法将引用配置文件中的简单配置项。例如：fromablerConfigimportConfig,ConfigNodeconf=Config.object_of('alarm_control.state_send')addr=tuple(Config.value_of('net_gate.tt_addr')) |
able-recipe | Pythoninterface to Android Bluetooth Low Energy API.Code repository:https://github.com/b3b/ableDocumentation:https://herethere.me/ableChangelog:https://github.com/b3b/able/blob/master/CHANGELOG.rstQuick start development environmentableis included inPythonHereapp, together with theJupyter Notebookit could be used as a development environment.Usage example:https://herethere.me/pythonhere/examples/ble.htmlBuildThe following instructions are for building app withbuildozertool.able_reciperecipe should be added to buildozer.spec requirements:requirements = python3,kivy,android,able_recipeBluetooth permissions should be requested in buildozer.spec:android.permissions = BLUETOOTH, BLUETOOTH_ADMIN, BLUETOOTH_SCAN, BLUETOOTH_CONNECT, BLUETOOTH_ADVERTISE, ACCESS_FINE_LOCATIONApp configuration example:buildozer.specBuild with a local versionTo build app with a local (modified) version ofable,path toablerecipes directory should be set in buildozer.spec:p4a.local_recipes = /path/to/cloned/repo/recipesContributorsThanks,andfmartandreamerellodatmaniac95dgatfdwmoffattEnkumicahelhailesirHelaFayejacklinquanjuasiepoPapoKarloRoberWareRowatarorobgar2001sodefsooko_io |
abl.errorreporter | UNKNOWN |
abletondrumrack | No description available on PyPI. |
ableton-helpers | Ableton ScriptsUsefull scripts to operate abletonableton-change-versionChanges version of ableton project (now supports only > 11.1.5 and to 11.1.5 only) |
abletonparsing | AbletonParsingParse an Ableton ASD clip file and its warp markers in Python. This module has been tested with.asdfiles saved with Ableton 9 and Ableton 10.Installpip install abletonparsingAPIClip class:.loop_on - ( bool , READ/WRITE ) - Loop toggle is on.start_marker - ( float , READ/WRITE ) - Start marker in beats relative to 1.1.1.end_marker - ( float , READ/WRITE ) - End marker in beats relative to 1.1.1.loop_start - ( float , READ/WRITE ) - Loop start in beats relative to 1.1.1.loop_end - ( float , READ/WRITE ) - Loop end in beats relative to 1.1.1.hidden_loop_start - ( float , READ/WRITE ) - Hidden loop start in beats relative to 1.1.1.hidden_loop_end - ( float , READ/WRITE ) - Hidden loop end in beats relative to 1.1.1.warp_markers - ( list[WarpMarker] , READ/WRITE ) - List of warp markers.warp_on - ( bool , READ/WRITE ) - Warping is on.sr - ( float , READ/WRITE ) - Sample rate of audio dataWarpMarker class:.seconds - ( float , READ/WRITE ) - Position in seconds in the audio data..beats - ( float , READ/WRITE ) - Position in "beats" (typically quarter note) relative to 1.1.1Ifloop_onis false, thenloop_startwill equal thestart_marker, andloop_endwill equal theend_marker.Exampleimportabletonparsingimportlibrosaimportsoundfileassfimportpyrubberbandaspyrbbpm=130.audio_path='drums.wav'clip_path=audio_path+'.asd'audio_data,sr=librosa.load(audio_path,sr=None,mono=False)num_samples=audio_data.shape[1]clip=abletonparsing.Clip(clip_path,sr,num_samples)time_map=clip.get_time_map(bpm)# Time-stretch the audio to the requested bpm.output_audio=pyrb.timemap_stretch(audio_data.transpose(),sr,time_map)withsf.SoundFile('output.wav','w',sr,2,'PCM_24')asf:f.write(output_audio) |
abletoolz | AbletoolzAbletoolz is a Python command line tool to edit, fix and analyze Ableton sets. Primarily the purpose is to automate
things that aren't available and make your life easier.
It can:Run on one set, or an entire directory of sets. So you can fix/analyze etc everything with one command.Color all your tracks/clips with a random color gradients.Create a sample database of all your sample folders, which can then be used to automatically fix any broken samples in your ableton sets.Set all your Master/Cue outputs to a specific output, so if you buy a new audio interface you can fix all your master outs to point to 7/8 in one go.Validate all plugins in a set are installed. MacOS VST3s currently do not work for this.Fold/Unfold all tracks, and/or set track height and widths.Prepend the set version name to the beginning of the file.Append the number of bars of the track, and the bpm to the end of the file.Dump the XML of the set, in case you want to disect how they are structured or contribute to this project : )It also:Moves your original set files to a backup folder before writing any changes, so you are never at risk of losing anything.Supports both Windows and MacOS created sets.Works on Ableton 8.2+ sets(not every command works with older versions though).Preserves the original set modification time.Future plans:Figure out way to verify AU plugins on MacOs.Analyze audio clips and color them based on a Serato like gradient(red for bass, turqoise for hi end etc...)Build plugin parsers, that can read in the plugin saved buffer and attempt fixes or other things. For instance, a sampler that has a broken filepath could automatically be fixed.Figure out how ableton calculates CRC's for samples and use it to make perfect sample fixing. The current algorithm has a very low probability of being wrong, but this would guarantee each result is correct.Attempt to detect key based on non drum track midi notes.Installation:Minimum python required 3.10(https://www.python.org/downloads/)Open a command line shell and make sure you installed Python 3.10+ correctly:python -V # Should give you a versionOnce you verify you have python 3.10+, clone this repository using git clone, or download this repo's zip file and
extract it to a folder. Navigate to that folder on the command line, then run:pip install abletoolzThis will install abletoolz as a command in your command line, you can now callabletoolzfrom anywhere if the
installation completed successfully. (Create an issue if you run into any errors please!)Usage:-hPrint argument usage.-vVerbosity. For some commands, displays more information.Input - Parsing single or multiple sets.Only one input option can be used at a time.abletoolz setname.alsProcess single set.abletoolz setname.als folder/with/setsProcess single set and all sets within a directory(recursive)."abletoolz D:\somefolder"Finds all sets in directory and all subdirectories. If "backup", "Backup" or "abletoolz_backup" are in any
of the path hierarchy, those sets is skipped.NOTE: On Windows, do NOT include the ending backslash when you have quotes! There is a bug with powershell
in how it handles backslashes and how python interprets backslashes as escape characters:abletoolz "D:\somefolder\"# BADabletoolz "D:\somefolder"# GOODwithout quotes, backslashes are fine (but you'll need to use quotes if you have spaces in the directory path)abletoolz D:\somefolder\# GOODAnalysis - checking samples/tracks/plugins--check-samplesChecks relative and absolute sample paths and verifies if the file exists. Ableton will load the
sample as long as one of the two are valid. If relative path doesn't exist(Not collected and saved) only absolute path
is checked. By default only missing samples are displayed to reduce clutter, use-vto show all found samples as well.--check-pluginsChecks plugin VST paths and verifies they exist.Note: When loading a set, if Ableton finds the
same plugin name in a different path it will automatically fix any broken paths the next time you save your project. This
command attempts to find missing VSTs and show an updated path if it finds one that Ableton will most likely load.
Mac Audio Units/AU are not stored with paths, just plugin names. Mac OS is not supported for this yet.--list-tracksList track information.Create sample database(used for automatic sample fixing)--db folder/with/samplesBuild up a database of all samples that is used when
you run--fix-samples-collector--fix-samples-absolute. This file gets stored in your home directory.EditThese will only edit sets in memory unless you use-s/--saveexplicitly to commit changes.--fix-samples-collectGo through each sample reference in the ableton set, and if any are missing try to match them based on last modification date, file size and name from the database created with--db. Sample is copied into the set's
project folder, the same action as collect and save in ableton.--fix-samples-absoluteThe same thing as--fix-samples-collect, just doesn't
copy the sample and instead puts the full path. Note: on MacOS 10/9 sets,
this sometimes acts strange, so use--fix-samples-collectfor those.--gradient-tracksGenerate random gradients for tracks and clips. The results from this are limited, since
there are only 70 available colors in ableton, but sometimes you get some pretty good results!--unfoldor--foldunfolds/folds all tracks in set.--set-track-heightsSet arrangement track heights for all tracks, including groups and automation lanes. The values
will be different on different computers/OSes because it's based on your screen resolution, so first experiment
with this command and--set-track-widthson a set with different values and open it after to see how it looks. On my
setup the Min is 17, Default 68, Max 425 for track height.--set-track-widthsSet clip view track widths for all tracks. On my setup, Min 17, Default 24, Max 264.--master-outnumber to set Master audio output channels to. 1 correlates to stereo out 1/2, 2 to stereo out 3/4 etc.--cue-outset Cue audio output channels. Same numbering for stereo outputs as master out.Output - saving edited sets to disk-s,--saveSaves modified set in the same location as the original file. This only applies if you use options that actually alter
the set, not just analyze plugins/samples/etc. When you use this option, as a safety precaution the original file is stored under the same
directory as the original set under${CURRENT_SET_FOLDER}/abletoolz_backup/set_name__1.als. If that file exists, it will automatically
create a new one${CURRENT_SET_FOLDER}/abletoolz_backup/set_name__2.alsand keep increasing the number as files get created. That
way your previous versions are always still intact (be sure to clean this folder up if you run this a bunch of times).DisclaimerBefore usingEditoptions with save, experiment on a set you don't care about first and then open them in ableton to be sure the changes are what you expect. Because I understand how many hours of hard work go into set files,
I've put in multiple safeguards to prevent you losing anything:Original file is ALWAYS moved to the backup directory${CURRENT_SET_FOLDER}/abletoolz_backup/as described above,
so you can always re-open that
file if for some reason the newly created set breaks(so far I have not been able to break one, on Windows and MacOs).I use a non daemon thread to do the actual file write, which will not be forcibly killed if you Cntrl + C the script
during some long operation. Rather than rely on this, please just allow the script to finish processing to avoid any
issues, and make sure the options you're using do what you expect before executing a long running operation(hundreds
of sets can take a while).All other arguments only modify the set in memory and will only write those changes to a new set when you include-s-x,--xmlDumps the uncompressed set XML in same directory as set_name.xml Useful to understand set structure for
development. You can edit this xml file, rename it from.xmlto.alsand Ableton will load it! If you run with this
option multiple times, the previous xml file will be moved into theabletoolz_backupfolder with the same renaming behavior as-s/--save.--append-bars-bpmUsed with-s/--save, appends the longest clip or furthest arrangement bar length and bpm to the
set name. For example,myset.als-->myset_32bars_90bpm.als. Running this multiple times overwrites this section only (so your filename
wont keep growing).--prepend-versionPuts the ableton version used to create set at beginning of file name.ExamplesCheck all samples in setsabletoolz "D:\all_sets" --check-samplesabletoolz "D:\all_sets" --check-pluginsabletoolz "D:\all_sets\some_set.als" --list-tracksSet all master outs to stereo 1/2 and cue outs to 3/4abletoolz "D:\all_sets" -s --master-out 1 --cue-out 2Or a bunch of optionsabletoolz "D:\all_sets\myset.als" -s -x --master-out 1 --cue-out 1 --unfold \
--set-track-heights 68 --set-track-widths 24abletoolz "D:\all_sets\myset.als" -s --append-bars-bpm |
ablilcalculator | My python packageGet startedPre-requisitesInstall necessary dependencies for developementpipinstalltwinebuildsetuptoolsDevelopClone project and reset git historygitclonehttps://github.com/ablil/python-starterpypkg&&cdpypkg&&rm-rf.git&&gitinit&&gitadd.&&gitcommit-m'initial commit'Run your testsBuild and publishBuildrm-rfdist&&python-mbuildPublish to TestPyPipython-mtwineupload--repositorytestpypidist/*Publish to PyPipython-mtwineupload--repositorytestpypidist/* |
ablinfer | No description available on PyPI. |
abl.jquery | UNKNOWN |
abl.jquery.plugins.form | UNKNOWN |
abl.jquery.ui | UNKNOWN |