Package: btrdb
BTrDB Bindings for PythonThese are BTrDB Bindings for Python allowing you painless and productive access to the Berkeley Tree Database (BTrDB). BTrDB is a time series database focusing on blazing speed with respect to univariate time series data at the nanosecond scale.Sample CodeOur goal is to make BTrDB as easy to use as possible, focusing on integration with other tools and the productivity of our users. In keeping with this we continue to add new features such as easy transformation to numpy arrays, pandas Series, etc. See the sample code below and then checkout ourdocumentationfor more in depth instructions.import btrdb

# connect to database
conn = btrdb.connect("192.168.1.101:4410")

# view time series streams found at provided collection path
streams = conn.streams_in_collection("USEAST_NOC1/90807")
for stream in streams:
    print(stream.name)

# retrieve a given stream by UUID and print out data points.  In value
# queries you will receive RawPoint instances which contain a time and value
# attribute
stream = conn.stream_from_uuid("71466a91-dcfe-42ea-9e88-87c51f847942")
for point, _ in stream.values(start, end):
    print(point)
>> RawPoint(1500000000000000000, 1.0)
>> RawPoint(1500000000100000000, 2.0)
>> RawPoint(1500000000200000000, 3.0)
...

# view windowed data.  Each StatPoint contains the time of the window and
# common statistical data such as min, mean, max, count, and standard
# deviation of values the window covers.  See docs for more details.
width = 300000000
depth = 20
for point, _ in stream.windows(start=start, end=end,
                               width=width, depth=depth):
>> StatPoint(1500000000000000000, 1.0, 2.0, 3.0, 3, 0.816496580927726)
>> StatPoint(1500000000300000000, 4.0, 5.0, 6.0, 3, 0.816496580927726)
>> StatPoint(1500000000600000000, 7.0, 8.0, 9.0, 3, 0.816496580927726)You can also easily work with a group of streams for when you need to evaluate data across multiple time series or serialize to disk.from btrdb.utils.timez import to_nanoseconds

start = to_nanoseconds(datetime(2018,1,1,9,0,0))
streams = db.streams(*uuid_list)

# convert stream data to numpy arrays
data = streams.filter(start=start).to_array()

# serialize stream data to disk as CSV
streams.filter(start=start).to_csv("data.csv")

# convert stream data to a pandas DataFrame
streams.filter(start=start).to_dataframe()
>>                    time  NOC_1/stream0  NOC_1/stream1
    0  1500000000000000000            NaN            1.0
    1  1500000000100000000            2.0            NaN
    2  1500000000200000000            NaN            3.0
    3  1500000000300000000            4.0            NaN
    4  1500000000400000000            NaN            5.0
    5  1500000000500000000            6.0            NaN
    6  1500000000600000000            NaN            7.0
    7  1500000000700000000            8.0            NaN
    8  1500000000800000000            NaN            9.0
    9  1500000000900000000           10.0            NaNInstallationSee our documentation oninstallingthe bindings for more detailed instructions. However, to quickly get started using the latest available versions you can usepipto install from pypi withcondasupport coming in the near future.$ pip install btrdbTestsThis project includes a suite of automated tests based uponpytest. For your convenience, aMakefilehas been provided with a target for evaluating the test suite. Use the following command to run the tests.$ make testAside from basic unit tests, the test suite is configured to usepytest-flake8for linting and style checking as well ascoveragefor measuring test coverage.Note that the test suite has additional dependencies that must be installed for them to successfully run:pip install -r tests/requirements.txt.ReleasesThis codebase uses github actions to control the release process. To create a new release of the software, runrelease.shwith arguments for the new version as shown below. Make sure you are in the master branch when running this script../release.sh 5 11 4This will tag and push the current commit and github actions will run the test suite, build the package, and push it to pypi. If any issues are encountered with the automated tests, the build will fail and you will have a tag with no corresponding release.After a release is created, you can manually edit the release description through github.DocumentationThe project documentation is written in reStructuredText and is built using Sphinx, which also includes the docstring documentation from thebtrdbPython package. For your convenience, theMakefileincludes a target for building the documentation:$ make htmlThis will build the HTML documentation locally indocs/build, which can be viewed usingopen docs/build/index.html. Other formats (PDF, epub, etc) can be built usingdocs/Makefile. The documentation is automatically built on every GitHub release and hosted onRead The Docs.Note that the documentation also requires Sphix and other dependencies to successfully build:pip install -r docs/requirements.txt.VersioningThis codebases uses a form ofSemantic Versioningto structure version numbers. In general, the major version number will track with the BTrDB codebase to transparently maintain version compatibility. Planned features between major versions will increment the minor version while any special releases (bug fixes, etc.) will increment the patch number.Pre-commit hookspip install pre-commitand then runpre-commit run --all-filesto comb through changes and make sure they are formatted correctly.pre-commit installand then rungit committo automatically run the pre-commit hooks on every commit.