hgraph-analytics
C++-first numerical analytics for hgraph. The package owns the numerical
analytical family migrated from core—diff, count, clip, ewma, and
pct_change—plus quantile, shaped-array array_std, shaped-array
rolling_window, window_values, array_get_item, cumulative_sum,
correlation, generic std/var, trailing rolling_mean, scheduled
resample, and the EWMA parameter conversion helpers.
For installation, semantics, and Python/C++ usage patterns, see the
analytics user guide.
Runnable examples covering scalar pipelines, rolling windows, and shaped-array
analytics are in python/examples.
import hgraph as hg
import hgraph_analytics as hga
change = hga.pct_change(
value,
period=12,
divide_by_zero=hg.DivideByZero.NAN,
)
bounded = hga.clip(change, -0.25, 0.25)
smoothed = hga.ewma(bounded, alpha=0.2)
median = hga.quantile(observations, 0.5)
volatility = hga.array_std(observations, ddof=1)
recent = hga.rolling_window(value, period=20, min_window_period=5)
window = hga.window_values(hg.to_window(value, 20))
running = hga.cumulative_sum(observations, axis=0)
correlation = hga.correlation(observations, rowvar=False)
sample_volatility = hga.std(hg.to_window(change, 20, 20), ddof=1)
moving_average = hga.rolling_mean(value, period=20)
The result is fractional: 0.05 denotes five percent. period counts valid
source observations and must be positive. The operator does not infer
dataframe ordering, elapsed-time sampling, market sessions, or financial price
adjustment.
Native consumers link hgraph::analytics, call
hgraph::analytics::register_analytics_operators(), and wire
the markers in hgraph::analytics, including diff, count, clip, ewma,
pct_change, window_values, array_get_item, cumulative_sum,
correlation, quantile, array_std, rolling_window, std_, var_,
rolling_mean, and resample.
See the hgraph user-guide migration note for the complete Python and C++ name mapping from the former core API.
Metadata
Release files for hgraph-analytics 0.8.34
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| hgraph_analytics-0.8.34.tar.gz | 42.2 kB | With provenance | Details |
Built distributions (wheels)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| hgraph_analytics-0.8.34-cp312-abi3-win_amd64.whl | CPython 3.12 | abi3 | Windows x86-64 | Details |
| hgraph_analytics-0.8.34-cp312-abi3-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl | CPython 3.12 | abi3 | Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64 | Details |
| hgraph_analytics-0.8.34-cp312-abi3-macosx_26_0_arm64.whl | CPython 3.12 | abi3 | macOS 26.0+ ARM64 | Details |
Total release size: 12.6 MB
Release files / hgraph_analytics-0.8.34.tar.gz
| Download URL | hgraph_analytics-0.8.34.tar.gz |
|---|---|
| Size | 42.2 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
c4a8527ffe6ea4dd128f6ae9c9f5c8c6f150086aba16fee2a393134109f35d73
|
|
BLAKE2b-256 checksum How to use checksums |
a78eda734df7d28e5f2e898303e402092b8fa29deefe867f8b2a837df4353ab1
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Oct 18, 2026.
Transparency logRelease files / hgraph_analytics-0.8.34-cp312-abi3-win_amd64.whl
| Download URL | hgraph_analytics-0.8.34-cp312-abi3-win_amd64.whl |
|---|---|
| Size | 7.6 MB |
| Tags | CPython 3.12 Windows x86-64 abi3 |
|
SHA-256 checksum How to use checksums |
ba191ee37ba03369c91019145beb9f0b88104c0af7a75b2e1e7e5d23539ac78b
|
|
BLAKE2b-256 checksum How to use checksums |
b45502b6f089af9e61f1b49a3a094901c82815f69c6272c2451e0794c2f31e81
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Oct 18, 2026.
Transparency logRelease files / hgraph_analytics-0.8.34-cp312-abi3-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
| Download URL | hgraph_analytics-0.8.34-cp312-abi3-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl |
|---|---|
| Size | 2.8 MB |
| Tags | CPython 3.12 Linux glibc 2.27+ x86-64 Linux glibc 2.28+ x86-64 abi3 |
|
SHA-256 checksum How to use checksums |
8375ec1a76f66d980a598f844454636fbce6eaea8dfb48c2cb418ac0c176ae02
|
|
BLAKE2b-256 checksum How to use checksums |
1e5a970e843b44a4617429317920ca4ac7c5decb6c66fddf5da6cd9b52d53cc3
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Oct 18, 2026.
Transparency logRelease files / hgraph_analytics-0.8.34-cp312-abi3-macosx_26_0_arm64.whl
| Download URL | hgraph_analytics-0.8.34-cp312-abi3-macosx_26_0_arm64.whl |
|---|---|
| Size | 2.2 MB |
| Tags | CPython 3.12 abi3 macOS 26.0+ ARM64 |
|
SHA-256 checksum How to use checksums |
cb95469191de42c05f9364ed1e541a181ab9dc919e35da43d6e31b1d70914506
|
|
BLAKE2b-256 checksum How to use checksums |
4a20a8214248152a878b17ec2e128b27a29f831869f014908c487de1a34d58c9
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/7.0.0 CPython/3.13.14
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Oct 18, 2026.
Transparency logSecurity for hgraph-analytics 0.8.34
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
Full provenance
All 4 files in this release have been uploaded with provenance metadata.
Attestation for hgraph_analytics-0.8.34.tar.gz
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Oct 18, 2026.
Transparency logA note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.