Skip to main content

Apply auto smoothing to a time series data.

Project description

Auto Smooth

License Code style: black

Apply data smoothing/filtering to a time series by automatically selecting parameters.

Currently available smoothing/filtering techniques in the package:

  • Savitzky–Golay filter

Quickstart

from auto_smooth import auto_savgol

# apply savgol filter
data_filtered = auto_savgol(data)

>>> wl_best=7, po_best=2

original_vs_smooth

Savitzky-Golay Filtering

Savitzky–Golay (Abraham Savitzky and Marcel J. E. Golay) filter is a type of low-pass filter used for smoothing noisy data.^1 It is based on local least-squares fitting.^2

auto_savgol method applies a Savitzky–Golay filter using the scipy savgol_filter() method.

from auto_smooth import auto_savgol

# apply savgol filter
data_filtered = auto_savgol(data)

# pass window-length and polynomial-order arguments
data_filtered = auto_savgol(data, wl_min=10, wl_max=30, po_min=2, po_max=10)

References

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

auto_smooth-0.1.0.tar.gz (47.5 kB view details)

Uploaded Source

Built Distribution

auto_smooth-0.1.0-py3-none-any.whl (7.9 kB view details)

Uploaded Python 3

File details

Details for the file auto_smooth-0.1.0.tar.gz.

File metadata

  • Download URL: auto_smooth-0.1.0.tar.gz
  • Upload date:
  • Size: 47.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.6

File hashes

Hashes for auto_smooth-0.1.0.tar.gz
Algorithm Hash digest
SHA256 33f2fb54de5314d7f4fd42f31f7dead0b5acafe8d07d54a7d2c6b37e5e140144
MD5 be6544c49455d4792c6a955c60175008
BLAKE2b-256 a0e917c5266e7e61abd9bca3277239dcea865beca26494e2677a74a67d6795a9

See more details on using hashes here.

File details

Details for the file auto_smooth-0.1.0-py3-none-any.whl.

File metadata

  • Download URL: auto_smooth-0.1.0-py3-none-any.whl
  • Upload date:
  • Size: 7.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/4.0.2 CPython/3.9.6

File hashes

Hashes for auto_smooth-0.1.0-py3-none-any.whl
Algorithm Hash digest
SHA256 dc50e945dc5fd6f38dabc3bef513daa3bb1fac5fa12be868d54f780a692311b0
MD5 91d3d27f2e85770e97a3279a59ece4ac
BLAKE2b-256 5db868b5e3b3f026361978689811bb757c18aec2acefb9749c5b5f590b977076

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page