Saravanan666/pypi-org huggingface dataset:
Package: pyhcl
pyhclImplements a parser forHCL (HashiCorp Configuration
Language)in Python. This
implementation aims to be compatible with the original golang version of
the parser.pyhcl does not support HCL2 (which is what modern terraform uses). You might tryhttps://pypi.org/project/python-hcl2/instead (though I’ve never personally tried it).The grammar and many of the tests/fixtures were copied/ported from the
golang parser into pyhcl. All releases are tested with a variety of
python versions from Python 2.7 onward.This version has been modified to work with terraform 0.12 syntax.
It should be backward compatible with earlier versions.
It doesn’t cover every situation.  See discussion in pull request:https://github.com/virtuald/pyhcl/pull/57Installationpip install pyhclUsageThis module is intended to be used in mostly the same way that one would
use the json module in python, and load/loads/dumps are implemented.import hcl

with open('file.hcl', 'r') as fp:
    obj = hcl.load(fp)Currently the dumps function outputs JSON, and not HCL.Convert HCL to JSONpyhcl comes with a script that you can use to easily convert HCL to JSON,
similar to the json.tool that comes with python:hcltool INFILE [OUTFILE]Structure ValidationSimilar to JSON, the output of parsing HCL is a python dictionary with
no defined structure. The golang library for HCL implements support for
parsing HCL according to defined objects, but this implementation does
not currently support such constructs.Instead, I recommend that you use tools designed to validate JSON, such
as theschematicslibrary.SyntaxSingle line comments start with#or//Multi-line comments are wrapped in/*and*/Values are assigned with the syntaxkey = value(whitespace
doesn’t matter). The value can be any primitive: a string, number,
boolean, object, or list.Strings are double-quoted and can contain any UTF-8 characters.
Example:"Hello, World"Numbers are assumed to be base 10. If you prefix a number with 0x, it
is treated as a hexadecimal. If it is prefixed with 0, it is treated
as an octal. Numbers can be in scientific notation: “1e10”.Boolean values:true,falseArrays can be made by wrapping it in[]. Example:["foo", "bar", 42]. Arrays can contain primitives and other
arrays, but cannot contain objects. Objects must use the block syntax
shown below.Objects and nested objects are created using the structure shown below:variable "ami" {
    description = "the AMI to use"
}TestingTo run the tests:pip install -r testing-requirements.txt
tests/run_tests.shDebug ModeTo enable debug mode:import hcl
hcl.parser.DEBUG = TrueAuthorsDustin Spicuzza (dustin@virtualroadside.com)Note: This project is not associated with Hashicorp
Package: py-hcl
PyHCLPyHCL is a hardware construct language likeChiselbut more lightweight and more relaxed to use.
As a novel hardware construction framework embedded in Python, PyHCL supports several useful features include object-oriented, functional programming,
and dynamically typed objects.The goal of PyHCL is providing a complete design and verification tool flow for heterogeneous computing systems flexibly using the same design methodology.PyHCL is powered byFIRRTL, an intermediate representation for digital circuit design. With the FIRRTL
compiler framework, PyHCL-generated circuits can be compiled to the widely-used HDL Verilog.Getting StartedInstalling PyHCL$pipinstallpy-hclWriting A Full AdderPyHCL defines modules using only simple Python syntax that looks like this:frompy_hclimport*classFullAdder(Module):io=IO(a=Input(Bool),b=Input(Bool),cin=Input(Bool),sum=Output(Bool),cout=Output(Bool),)# Generate the sumio.sum<<=io.a^io.b^io.cin# Generate the carryio.cout<<=io.a&io.b|io.b&io.cin|io.a&io.cinCompiling To FIRRTLCompiling module by callingcompile_to_firrtl:compile_to_firrtl(FullAdder,'full_adder.fir')Will generate the following FIRRTL codes:circuit FullAdder :
  module FullAdder :
    input clock : Clock
    input reset : UInt<1>
    input FullAdder_io_a : UInt<1>
    input FullAdder_io_b : UInt<1>
    input FullAdder_io_cin : UInt<1>
    output FullAdder_io_sum : UInt<1>
    output FullAdder_io_cout : UInt<1>

    node _T_0 = xor(FullAdder_io_a, FullAdder_io_b)
    node _T_1 = xor(_T_0, FullAdder_io_cin)
    FullAdder_io_sum <= _T_1
    node _T_2 = and(FullAdder_io_a, FullAdder_io_b)
    node _T_3 = and(FullAdder_io_b, FullAdder_io_cin)
    node _T_4 = or(_T_2, _T_3)
    node _T_5 = and(FullAdder_io_a, FullAdder_io_cin)
    node _T_6 = or(_T_4, _T_5)
    FullAdder_io_cout <= _T_6Compiling To VerilogWhile FIRRTL is generated, PyHCL's job is complete. To further compile to Verilog, theFIRRTL compiler frameworkis required:$firrtl-ifull_adder.firThenFullAdder.vwill be generated:moduleFullAdder(inputclock,inputreset,inputFullAdder_io_a,inputFullAdder_io_b,inputFullAdder_io_cin,outputFullAdder_io_sum,outputFullAdder_io_cout);wire_T_0;wire_T_2;wire_T_3;wire_T_4;wire_T_5;assign_T_0=FullAdder_io_a^FullAdder_io_b;assign_T_2=FullAdder_io_a&FullAdder_io_b;assign_T_3=FullAdder_io_b&FullAdder_io_cin;assign_T_4=_T_2|_T_3;assign_T_5=FullAdder_io_a&FullAdder_io_cin;assignFullAdder_io_sum=_T_0^FullAdder_io_cin;assignFullAdder_io_cout=_T_4|_T_5;endmoduleFeaturesSupports multiple data types:UInt,SInt,Vector,Bundle,Clock,Memory, and casual combination between them.Supports object-oriented inheritance, can compose modules by writing fewer codes.Supports a bunch of convenient operations, such as the addition ofUInts,SInts,Vectors andBundles.Supports the parameterization of variables, such as bit width, with the syntax facilities of the host language Python.TODOSupports more operationsPyHCL's verification facility
Package: pyhclrs
pyhclrsPython wrapper formartinohmann/hcl-rs.SetuppipinstallpyhclrsUsage>>>importpyhclrs>>>pyhclrs.loads("""variable "docker_ports" {...   type = list(object({...     internal = number...     external = number...     protocol = string...   }))...   default = [...     {...       internal = 8300...       external = 8300...       protocol = "tcp"...     }...   ]... }"""){'variable':{'docker_ports':{'type':'${list(object({ internal = number, external = number, protocol = string }))}','default':[{'internal':8300,'external':8300,'protocol':'tcp'}]}}}
Package: pyhcm
Python Huawei Cloud MessagingWrapper of huawei cloud messaging(Push Kit)for sending push notification using python.Home pagehttps://pypi.org/project/pyhcm/Installpip install pyhcmExampleSend notification to topicCLIENT_ID = "00000000"
CLIENT_SECRET = "0000000000000000000000000000000000000000000000000000000000000000"

HCMNotification(CLIENT_ID, CLIENT_SECRET).notify_topic_subscribers('test_topic', 'test title', 'test body')Send notification to userCLIENT_ID = "00000000"
CLIENT_SECRET = "0000000000000000000000000000000000000000000000000000000000000000"

HCMNotification(CLIENT_ID, CLIENT_SECRET).notify_single_device('token_xyz', 'test title', 'test body')
Package: pyhcrf
pyhcrfA hidden (state) conditional random field (HCRF) written in Python and Cython.This package is a fork of the originalpyhcrf, written byDirko Coetsee, featuring Python 3 and Windows support
with a cleaner code base maintained byMartin Larralde.Overviewpyhcrfimplements a HCRF model with asklearn-inspired
interface. The model classifies sequences according to a latent state sequence.
This package provides methods to learn parameters from example sequences and to
classify new sequences. See thepaperby Wanget al.and thereportby Dirko Coetsee.StatesEach state is numbered0, 1, ..., num_states - 1. The state machine starts
instate 0and ends innum_states - 1. Currently the state transitions are
constrained so that, on each element in the input sequence, the state machine
either stays in the current state or advances to a state represented by the
next number.  This default can be changed by setting thetransitionsand
correspondingtransition_parametersproperties.Dependenciespyhcrfdepends onnumpy,scipy(for the LM-BFGS optimiser andscipy.sparse),
and also needscythonfor building from source.ExampleX=[array([[1.,-0.82683403,2.48881337],[1.,-1.07491808,1.55848197],[1.,6.7814359,4.01074595]]),array([[1.,-3.01165932,-2.15972362],[1.,-3.41449473,-2.2668825]]),array([[1.,-2.64921323,-1.20159641],[1.,0.31139394,1.58841159]]),array([[1.,5.85226017,2.43317499],[1.,-1.57598266,-2.07585778]]),array([[1.,-0.32999744,-2.70695361],[1.,0.44311988,0.36400733]]),array([[1.,-0.05301562,3.95424435],[1.,3.04540498,-3.25040276]]),array([[1.,-4.29117715,0.9167861],[1.,-3.22775884,1.83277224]]),array([[1.,-2.80856491,1.95630489],[1.,1.62290542,-0.7457237]]),array([[1.,-2.32682366,2.60844469],[1.,2.12320609,1.04483217]]),array([[1.,-4.17616178,4.09969658],[1.,0.67287935,-5.67652159]])]y=[0,1,0,1,1,0,1,0,0,0]>>>frompyhcrfimportHCRF>>>fromsklearn.metricsimportconfusion_matrix>>>model=HCRF(num_states=3,random_seed=3,optimizer_kwargs={'maxfun':200})>>>model.fit(X,y)>>>pred=model.predict(X)>>>confusion_matrix(y,pred)array([[12,0],[0,8]])InstallationDownload/clone and runpython setup.py build_ext --inplace
python setup.py installLicenseThe original code, and all contributions made subsequently in this fork, are
licensed with BSD-2-Clause.
Package: pyhctb
pyhctbThis is an API layer for getting school bus transit coordinates fromHere Comes The Bus.To install this package, simply run:pipinstallpyhctbTo usepyhctb, run the following command, replacingUSERNAME,PASSWORD, andSCHOOL_CODEwith your own credentials:python-mpyhctb--userUSERNAME--passwordPASSWORD--codeSCHOOL_CODEYou should get a final output that looks something like the following (but with actual values):{"legacyID":"aaaaaaaa-bbbb-cccc-dddd-eeeeeeeeeeee","name":"Johnny Everyboy","timeSpanId":"ffffffff-gggg-hhhh-iiii-jjjjjjjjjjjj","latitude":"70.92797","longitude":"-74.29596"}
Package: pyhdbsql
pyhdbsqlCommand line app that connects to a HANA database and call an sql statement. The credentials of the database are stored in the config.yaml.The outcome of aSELECT- statement can be stored in an output-file given in the config-file.For uploading the data can be read from a csv-file of a path given in the config-file.Examplepyhdbsql <sql-statement>or explicitlypyhdbsql SELECT * FROM TESTTABLE;config.yaml###### HANA DB
HDB_HOST : db.com
HDB_USER : 'dbuser'
HDB_PWD : 'pwd123!'
HDB_PORT : 443

BATCHSIZE : 1  # uinteger, 0: no batches, 1: cursor.execute(), >1:cursor.executemany()
INFO_BATCHSIZE : 10000 # logging info when this number of records have been uploaded. Only used for BATCH_SIZE = 1, value 0: No info


### output file
OUTPUT_FILE : ./sqlout.csv

### input file for executemany
INPUT_CSV : ./records.csv
Package: pyhdf
pyhdfpyhdf is a python wrapper around the NCSA HDF version 4 library.
The SD (Scientific Dataset), VS (Vdata) and V (Vgroup) API's
are currently implemented. NetCDF files can also be
read and modified. It supports both Python 2 and Python 3.Note:The sourceforge pyhdfwebsiteandprojectare out-of-date.
The original author of pyhdf have abandoned the project and it is
currently maintained ingithub.Version 0.9.x was calledpython-hdf4in PyPI because at that time we didn't haveaccessto thepyhdf packagein PyPI.  For version
0.10.0 and onward, please installpyhdfinstead ofpython-hdf4.InstallationSeepyhdf installation instructionsordoc/install.rst.DocumentationSeepyhdf documentation.Additional documentation on the HDF4 format can be found in theHDF4 Support Page.ExamplesExample python programs using the pyhdf package
can be found inside theexamples/subdirectory.
Package: pyhdfe
An overview of the package, examples, and other documentation can be found onRead the Docs.PyHDFE is a Python 3 implementation of algorithms for absorbing high dimensional fixed effects. This package was created byJeff Gortmakerin collaboration withAnya Tarascina.What PyHDFE won’t do is provide a convenient interface for running regressions. Instead, the package is meant to be incorporated into statistical projects that would benefit from performant fixed effect absorption. Another goal is facilitating fair comparison of algorithms that have been previously implemented in various languages with different convergence criteria.Development of the package has been guided by code made publicly available by many researchers and practitioners. For a full list of papers and software cited in this documentation, refer to thereferencessection of the documentation.InstallationThe PyHDFE package has been tested onPythonversions 3.6 through 3.9. TheSciPy instructionsfor installing related packages is a good guide for how to install a scientific Python environment. A good choice is theAnaconda Distribution, since, along with many other packages that are useful for scientific computing, it comes packaged with PyHDFE’s only required dependencies:NumPyandSciPy.You can install the current release of PyHDFE withpip:pip install pyhdfeYou can upgrade to a newer release with the--upgradeflag:pip install --upgrade pyhdfeIf you lack permissions, you can install PyHDFE in your user directory with the--userflag:pip install --user pyhdfeAlternatively, you can download a wheel or source archive fromPyPI. You can find the latest development code onGitHuband the latest development documentationhere.Bugs and RequestsPlease use theGitHub issue trackerto submit bugs or to request features.
Package: pyhdfs-client
pyhdfs-client : Powerful HDFS Client for pythonWhy it's fast powerful?Native hdfs client offers much better performance than webhdfs clients. However calling native client for hadoop operations have an additional overhead of starting jvm. pyhdfs-client brings the performance of native hdfs client without any overhead of starting jvm on every command execution.FeaturesHDFS client for pythonEasy to integrate with python applicationsBetter Performance than webhdfs clientsProvide native hadoop client performance without any overheadSupport both UNIX and WindowsWhats new in 0.1.3?Multiple instances of HDFS client enabled.[fix] Temporary folder deletion[fix] Java process shutdown issues on UNIXInstallationpip install pyhdfs-clientRequirements:  hadoop binaries and py4j installedSample Usage>>> from pyhdfs_client.pyhdfs_client import HDFSClient
>>> hdfs_client = HDFSClient()
>>> ret, out, err = hdfs_client.run(['-ls', '/'])
>>> print(out)
Found 1 items
drwxr-xr-x   - gp supergroup          0 2021-03-21 01:10 /f1
>>> hdfs_client.stop() # to terminate hdfs clientContributionAny contribution for enhancements and bug fixes is welcome.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.2 (2021-03-23)added UNIX Support0.1.1 (2021-03-22)First release on PyPI.
Package: pyhdfview
PyHdfViewA simple command line viewer for HDF5 files.NOTE:This is still an early protype.UsageList groups of a database:$hv../mosaik-hdf5/tests/data/testdb.hdf5List../mosaik-hdf5/tests/data/testdb.hdf5:/•RelationsList sub-groups of a group:$hv../mosaik-hdf5/tests/data/testdb.hdf5:/RelationsList../mosaik-hdf5/tests/data/testdb.hdf5:/Relations•DB-0.hdf5db•Sim-0.0.0•Sim-1.0.0•Sim-1.0.1Print dataset:$hv../mosaik-hdf5/tests/data/testdb.hdf5:/Relations/Sim-0.0.0Dataset../mosaik-hdf5/tests/data/testdb.hdf5:/Relations/Sim-0.0.0[b'DB-0.hdf5db'b'Sim-1.0.0'b'Sim-1.0.1']Print attributes:$hvattrs../mosaik-hdf5/tests/data/testdb.hdf5:/Relations/Sim-0.0.0Attributesof../mosaik-hdf5/tests/data/testdb.hdf5:/Relations/Sim-0.0.0•spam:eggs•foo:barChangeLog for PyHdfView0.2 – 2014-10-13[FIX] Added colorama to dependencies.0.1 – 2014-07-24Initial release. Currently it can:list sub-groups of a file / groupprint a datasetprint attributesAuthorsPyHdfView has been developed by Stefan Scherfke.
Package: pyhdpowerview
StatuspyhdpowerviewPython3 interface implementation for Powerview HubNotesThis is for use withHome-AssistantUsagefrompyhdpowerviewimportPowerView# Connect via IPpv=PowerView('192.168.1.50')# Return an array of shade Id objectsshades=pv.get_shades()# Return shade objectshade=pv.get_shade(shade_id)# Return attributes of shadeprint(shade.id)print(shade.name)print(shade.position)# Position represented 0 (close) to 100 (open)print(shade.battery_level)# Battery level %# Update shade positionpv.get_status(shade_id)# Close shadepv.close_shade(shade_id)# Open Shadepv.open_shade(shade_id)# Stop shade in motionpv.stop_shade(shade_id)# Set shade to specific position (0 - 100)pv.set_shade_position(shade_id,55)
Package: pyhdust
Analysis tools for multi-technique astronomical data andhdustmodels.Full documentation atpyhdust.readthedocs.io.Abouthdustcode: Carciofi & Bjorkman (2006,2008).pyhdustshould be independent of plataform (Linux, Mac, Windows) and compatible with any version of Python (superior or equal to 3.4).How to install/uninstallI strongly suggest users to use thepipplataform:pipinstallpyhdustIf your are not the root of the system, add the flag--userto the command above. Dependencies (as filters efficiency and stellar evolution models) are included in the subdirectories.WarningNever combinesudowith--user! Otherwise you will face critical permission problems for your packages!NoteTo use thepyhdustscripts, the binaries path of your pip installation directory must be in systemPATH. If you don’t find them, adapt the following command to your$HOME/.bashrc:PATH=$PATH:~/.local/bin/pyhdustrequires numpy, six and astropy modules. Optionally, it makes use of:emceematplotlibpandaspIDLyscipywgetxmltodictTo onlyupdatethe package:pipinstall-U--no-depspyhdust-Uforces the upgrade and--no-depsdo not reinstall the dependent packages.For other options, consult thefull documentation.How to use the modulesTo make use of all routines, the suggestion is to import them as follows:importpyhdustashdtimportpyhdust.beatlasasbatimportpyhdust.fieldstarsasflsimportpyhdust.imagesasimgimportpyhdust.inputasinpimportpyhdust.interftoolsasinttimportpyhdust.jdcalasjdcalimportpyhdust.poltoolsaspoltimportpyhdust.phcasphcimportpyhdust.rotstarsasrotimportpyhdust.singscatassstimportpyhdust.spectoolsassptimportpyhdust.statsassttLicenseThe code is free, available under the terms of theGNU GPL v3.0 license.
Package: pyhdwallet
pyhdwalletA Python lib for working withBIP32(Hierarchical Deterministic Wallets)This project aims to provide an API similar tobitcoincashjs2-libconcerning HD utilities.Don't trust. Verify.Sample Usage>>>frombinasciiimportunhexlify>>>frompyhdwalletimportECPair,HDNode>>>frompyhdwallet.hashutilsimportsha256>>>frompyhdwallet.networksimportBITCOIN_TESTNET>>>>>>>>>SEED=b'5636fa7760cca11a5ef1212c56fe0f5e576004e371b88a53780994ece7b6fe8f6923bd5ba3ab0688b0dbb865dbfef37894a39bf2ce9b11315c5413d510a1eee1'>>>seed_buffer=unhexlify(SEED)>>>>>>hdnode=HDNode.from_seed(seed_buffer)>>>print("xprv:",hdnode.to_base58())xprv:xprv9s21ZrQH143K2KoRMrztFLQfsKWMADqGKYXSoPRpLapf13Q4cMcFWJeNangQ2XRucXfkoQscg4dk7w3vtfStFZNM1z4DnxfRh4XYJkT1gAg>>>print("xpub:",hdnode.neutered().to_base58())xpub:xpub661MyMwAqRbcEostTtXtcUMQRMLqZgZ7gmT3bmqRtvMdsqjD9tvW46xrS53Se68UQLRAYGjSkpm5Cje9We12dE3CAK1p4W3xFauG5aX1Z8u>>>print("address:",hdnode.get_address())address:17avao5dfvwuq1ugvo5wN7sUSNUjMa93BX>>>>>>hdnode_testnet=HDNode.from_seed(seed_buffer,network=BITCOIN_TESTNET)>>>print("tprv (testnet):",hdnode_testnet.to_base58())tprv(testnet):tprv8ZgxMBicQKsPd92x2RrPQz2fBSvZPjsGf6SZforGpZK8ne99bix1241pVxr42tpDyyCXoWVNqRDYanbg1snq4cdwYdGXTKPUcAGxkXczrnh>>>>>>hdnode2=HDNode.from_base58("xprv9s21ZrQH143K2KoRMrztFLQfsKWMADqGKYXSoPRpLapf13Q4cMcFWJeNangQ2XRucXfkoQscg4dk7w3vtfStFZNM1z4DnxfRh4XYJkT1gAg")>>>>>>child=hdnode.derive_path("m/0/1'/1")>>>print("address child:",child.get_address())addresschild:14fW6kaeFiPees7FWNkvNFxewqhX7zSj2r>>>print("child hdnode: ",child)childhdnode:HDNode(keyPair=ECPair(privKey=85992014414924097038377675237145575004969559361289161063865857372311461164262,pubKey=b'\x02E\x8aEg\xc9\x03\xaf@\x12\xe1\xa4a"}\xfa1)\xfcM\x1e.\x05\xe6\x11\xcc\xac\x17\xdf\xc2\x02\xb12',compressed=True,network=BitcoinMainnet),chainCode=b'\xe9>(\x84\xb2\x83"\x80c8)\xdb\xbf\x1d\xe8\xa9*1\x15\x10s\xc5x\xb1E\xfc)ONuC>',depth=3,index=1,parentFingerprint=1079081844)>>>>>>ecpair=child.get_keypair()>>>print("public key:",ecpair.pubkey_buffer)publickey:b'\x02E\x8aEg\xc9\x03\xaf@\x12\xe1\xa4a"}\xfa1)\xfcM\x1e.\x05\xe6\x11\xcc\xac\x17\xdf\xc2\x02\xb12'>>>print("wif:",ecpair.to_wif())wif:L3bGjEiQ6TynvKskm567Kh2rKrrs2JmgACV81Zb6MFFx9JyMnJNV>>>ecpair2=ECPair.from_wif("L1HZ6Vo7RJtXBzx75o2N2AhpXFVVuXVatqu6Xsy4iWNtmPphhJiz")>>>print("wif2:",ecpair2.to_wif())wif2:L1HZ6Vo7RJtXBzx75o2N2AhpXFVVuXVatqu6Xsy4iWNtmPphhJiz>>>>>>signature=ecpair.sign(sha256(b"Message"))>>>print("signature valid:",ecpair.verify(sha256(b"Message"),signature))signaturevalid:TrueDocumentationSee thedocumentationfor more info.
Package: py-hdwallet
py-hdwalletHD wallet generator.WARNING: This package has not been peer reviewed or audited.  Use at your own risk!Read more in thedocumentation on ReadTheDocs.View the change log.Quickstartpipinstallpy-hdwalletDeveloper SetupIf you would like to hack on py-hdwallet, please check out theSnake Charmers
Tactical Manualfor information on how we do:TestingPull RequestsCode StyleDocumentationDevelopment Environment SetupYou can set up your dev environment with:gitclonegit@github.com:ethereum/py-hdwallet.gitcdpy-hdwallet
virtualenv-ppython3venv
.venv/bin/activate
pipinstall-e.[dev]Testing SetupDuring development, you might like to have tests run on every file save.Show flake8 errors on file change:# Test flake8when-changed-v-s-r-1hdwallet/tests/-c"clear; flake8 hdwallet tests && echo 'flake8 success' || echo 'error'"Run multi-process tests in one command, but without color:# in the project root:pytest--numprocesses=4--looponfail--maxfail=1# the same thing, succinctly:pytest-n4-f--maxfail=1Run in one thread, with color and desktop notifications:cdvenv
ptw--onfail"notify-send -t 5000 'Test failure ⚠⚠⚠⚠⚠' 'python 3 test on py-hdwallet failed'"../tests../hdwalletRelease setupFor Debian-like systems:apt install pandocTo release a new version:makereleasebump=$$VERSION_PART_TO_BUMP$$How to bumpversionThe version format for this repo is{major}.{minor}.{patch}for stable, and{major}.{minor}.{patch}-{stage}.{devnum}for unstable (stagecan be alpha or beta).To issue the next version in line, specify which part to bump,
likemake release bump=minorormake release bump=devnum. This is typically done from the
master branch, except when releasing a beta (in which case the beta is released from master,
and the previous stable branch is released from said branch). To include changes made with each
release, update "docs/releases.rst" with the changes, and apply commit directly to master
before release.If you are in a beta version,make release bump=stagewill switch to a stable.To issue an unstable version when the current version is stable, specify the
new version explicitly, likemake release bump="--new-version 4.0.0-alpha.1 devnum"