Skip to main content

A package to load, manipulate and visualize SNOM and AFM data.

Overview

The package contains several classes, one for each implemented measurement type (so far: SNOM/AFM images, approach/deproach curves and 3D scans (2D approach curves)). These classes need the path to the measurement folder as well as the channels as input. The classes will then load the data of all specified channels as well as the measurement parameters and the header information of the measurement files. The data can then be manipulated and plotted. Each manipulation changes the data in the memory and also the parameter dictionaries if necessary. The data can then also be saved with the changes.

The package will also create a folder in the users home directory to store several files like a config file, a plot memory, a matplotlib style file and a general plotting parameters file. Making it easier to adjust the package to your needs.

Installation

The package can be installed via pip:

pip install snom-analysis

If you install via pip all dependencies will be installed automatically. I recommend to use a virtual environment.

Documentation

The documentation can be found at https://snom-analysis.readthedocs.io/en/latest/index.html

Metadata

Release files for snom-analysis 0.2.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for snom-analysis 0.2.0
File Size Uploaded Provenance Actions
snom_analysis-0.2.0.tar.gz 101.7 kB Without provenance Details for snom_analysis-0.2.0.tar.gz

Built distribution (wheel)

Table of built distributions (wheels) for snom-analysis 0.2.0
File Interpreter ABI Platform Actions
snom_analysis-0.2.0-py3-none-any.whl Python 3 none any Details for snom_analysis-0.2.0-py3-none-any.whl

Total release size: 212.0 kB

Release files / snom_analysis-0.2.0.tar.gz

Download URL snom_analysis-0.2.0.tar.gz
Size 101.7 kB
Tags Source
SHA-256 checksum
How to use checksums
f1894e3d6de90a7852827fbcbb9bc86ad387bba56195b0775434cdbae4ebf218
BLAKE2b-256 checksum
How to use checksums
1d8f782612e546f8ff1fc4c18f2db252cbb17fffef9e0c51512983271a28d65a
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.1.0 CPython/3.13.2

Release files / snom_analysis-0.2.0-py3-none-any.whl

Download URL snom_analysis-0.2.0-py3-none-any.whl
Size 110.3 kB
Tags Python 3
SHA-256 checksum
How to use checksums
7f0f8eca23f0d575a244e26406dd8f032e847b1af1a6cdbf78f674cbeced5eb5
BLAKE2b-256 checksum
How to use checksums
5f15a25607d25024eb09700782b7b4f84782dd7b9a2c46a6235a0d4a3804358e
Upload date
Uploaded using Trusted Publishing?
What is trusted publishing?
No
Uploaded via twine/6.1.0 CPython/3.13.2

Security for snom-analysis 0.2.0

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.

No provenance found

None of the files in this release were uploaded with provenance metadata.

A 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.

To learn more about protecting yourself from malicious actors, visit our package security documentation.

Anthropic, PBC Visionary sponsor Bloomberg Visionary sponsor Hudson River Trading Visionary sponsor Meta Visionary sponsor NVIDIA Visionary sponsor Microsoft Sustainability sponsor Depot Continuous Integration AWS Cloud computing and Security Sponsor Datadog Monitoring Fastly CDN Google Download Analytics Sentry Error logging StatusPage Status page