Framework of fast implementation data processing and operating pipelines
Project description
Informatics, sourced from its original meaning: the sciences concerned with gathering, manipulating, storing, retrieving, and classifying recorded information.
It is designed to enable users solve complex problems in science, engineering, and other domains efficiently and accurately. Its powerful capabilities are achieved through a combination of cutting-edge software engineering techniques and the elegance of Python’s functional programming paradigm. The strength of highly modular and extensible architecture allows users to quickly assemble and customize data processing pipelines to satisfy their specific needs. Whether it’s data cleaning, transformation, analysis, or visualization, informatics provides a rich set of tools and functions to facilitate these tasks.
Informatics is built to serve for science as well as engineer domains. It provides ready-made solutions for common tasks like feature engineering, model training, evaluation, deployment, and more. Refer the documentation for a detailed information about its essential designs, functions, as well as applied scopes.
Main Features
Here list a few of things that informatics featured:
Powerful integration capability for various utilities (e.g. functions, frames, packages, and etc.) in Python ecosystem.
Universal processing interface designed in high dimensionality to guarantee consistency of calling for different types of data.
Scripting on basis of functional programming paradigm, with properties of robust performance, and easy decoupling for extension.
Intuitive combination of data processing units, for fast experiments, validation, or building for upper applications.
Documentation in details for not only basic functions, but the tutorials, interpretation for essential concepts, examples of applications, and such like.
License
Apache License v2.0
Getting Help
For usage questions about functions, API reference and example code snippet on documentation would be helpful. While for other issues and suggestions, post your advice here or mail the author.
- Authors:
Chen Zhang
- Version:
0.0.4
- Created on:
Mar 12, 2024
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
Built Distributions
File details
Details for the file informatics-0.0.5rc0-cp312-cp312-win_amd64.whl
.
File metadata
- Download URL: informatics-0.0.5rc0-cp312-cp312-win_amd64.whl
- Upload date:
- Size: 730.9 kB
- Tags: CPython 3.12, Windows x86-64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.0.0 CPython/3.11.8
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 0721fd85bba8f79cd7f64d5759bbe21b2b60f57f335200e2325a5d6ca32db696 |
|
MD5 | e5a3ab0caf2f33ea8c6f63270d19c37a |
|
BLAKE2b-256 | 72722a9a92098233adcd103f5313cb360f1c7b779d6a1a5f87bb13dec5a40de4 |
File details
Details for the file informatics-0.0.5rc0-cp312-cp312-manylinux_2_17_x86_64.whl
.
File metadata
- Download URL: informatics-0.0.5rc0-cp312-cp312-manylinux_2_17_x86_64.whl
- Upload date:
- Size: 931.6 kB
- Tags: CPython 3.12, manylinux: glibc 2.17+ x86-64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.0.0 CPython/3.11.8
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | e99ae84d2bf7e4bcbd6a5ed812a212576750826513d8b62675e897aecd7e46df |
|
MD5 | a145980e85dc580695f5c74748261646 |
|
BLAKE2b-256 | f47148f15e1b974aa234e6ae95deb7e256efe216fc894d7a8a16eb3b17bcf48a |
File details
Details for the file informatics-0.0.5rc0-cp312-cp312-macosx_10_9_x86_64.whl
.
File metadata
- Download URL: informatics-0.0.5rc0-cp312-cp312-macosx_10_9_x86_64.whl
- Upload date:
- Size: 808.6 kB
- Tags: CPython 3.12, macOS 10.9+ x86-64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.0.0 CPython/3.11.8
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 44b5abe97c8e2aaedfb0c7140531f85835b47f62932385db9ce20c3c37e2d203 |
|
MD5 | 39627097d2458a20d628d4a6767fdc6d |
|
BLAKE2b-256 | 4ec5fa412525352320ee3833d2f54fe95d0280aea0933f0248c2c20a71631952 |
File details
Details for the file informatics-0.0.5rc0-cp311-cp311-win_amd64.whl
.
File metadata
- Download URL: informatics-0.0.5rc0-cp311-cp311-win_amd64.whl
- Upload date:
- Size: 758.8 kB
- Tags: CPython 3.11, Windows x86-64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.0.0 CPython/3.11.8
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 4631b806391257e0b6de38e62d899283228f3d749b10e7a0732f9239e75d1ca4 |
|
MD5 | 46ddf3b1330aeccb8b5cf4e734e80cb4 |
|
BLAKE2b-256 | 8ddcfe4bee549aeeab7e51e911f56d67fb638595e97993ed0d41f56e97ab1179 |
File details
Details for the file informatics-0.0.5rc0-cp311-cp311-manylinux_2_17_x86_64.whl
.
File metadata
- Download URL: informatics-0.0.5rc0-cp311-cp311-manylinux_2_17_x86_64.whl
- Upload date:
- Size: 944.7 kB
- Tags: CPython 3.11, manylinux: glibc 2.17+ x86-64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.0.0 CPython/3.11.8
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 05cda90037071b9c24e3b7d4e8eb3f68701f8029242d1e8abf1e48bf1a19d9ee |
|
MD5 | 9eda8f2ebb0fdb8c715b2d8d2420dd53 |
|
BLAKE2b-256 | 8fe09a3c6b93b5a0f936bc039e0b95849f39f444e1b3b3596dcb344a6893157e |
File details
Details for the file informatics-0.0.5rc0-cp311-cp311-macosx_10_9_x86_64.whl
.
File metadata
- Download URL: informatics-0.0.5rc0-cp311-cp311-macosx_10_9_x86_64.whl
- Upload date:
- Size: 823.2 kB
- Tags: CPython 3.11, macOS 10.9+ x86-64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.0.0 CPython/3.11.8
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 62df0b5e71052fa787287b3d74055d55487619dcef36b0588f62d1312fe32cb4 |
|
MD5 | 67532b9afd57e7fd2f7ad9cbd3685ea7 |
|
BLAKE2b-256 | d8b83fff4e320b8a235d76d46789eb3ce66f7d17f37c460725b5f101696607fc |
File details
Details for the file informatics-0.0.5rc0-cp310-cp310-win_amd64.whl
.
File metadata
- Download URL: informatics-0.0.5rc0-cp310-cp310-win_amd64.whl
- Upload date:
- Size: 757.5 kB
- Tags: CPython 3.10, Windows x86-64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.0.0 CPython/3.11.8
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 6c5696841593c78c94cb142001980acf272577b9cc721037d4c1be91e735e6bb |
|
MD5 | 4569c028e810dfa2c5ed31882a74d9ba |
|
BLAKE2b-256 | e5f27cfcc4349e87fd8e0f376772eb2627030e4d6c4d2067c5706270deeb6d17 |
File details
Details for the file informatics-0.0.5rc0-cp310-cp310-manylinux_2_17_x86_64.whl
.
File metadata
- Download URL: informatics-0.0.5rc0-cp310-cp310-manylinux_2_17_x86_64.whl
- Upload date:
- Size: 944.3 kB
- Tags: CPython 3.10, manylinux: glibc 2.17+ x86-64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.0.0 CPython/3.11.8
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | ccd4d39d5b4b8ab880af6690b239497a25aae190c591aad83ca7623d151b6efe |
|
MD5 | 4786b8987ff90d0ca1ce7d55b5d8f34c |
|
BLAKE2b-256 | ab6143512a9f9813058dfeed2ce5b9e63250ad6d257ee246fa5ee1b41531a25d |
File details
Details for the file informatics-0.0.5rc0-cp310-cp310-macosx_10_9_x86_64.whl
.
File metadata
- Download URL: informatics-0.0.5rc0-cp310-cp310-macosx_10_9_x86_64.whl
- Upload date:
- Size: 821.7 kB
- Tags: CPython 3.10, macOS 10.9+ x86-64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.0.0 CPython/3.11.8
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 3553f29b6b7e3c797daef8c837c7621835fe18f9d6fc9ee4a8105472938bd71c |
|
MD5 | cb7d38508a6f61637daf3efae7fcbc51 |
|
BLAKE2b-256 | 7a813af1a549397a84c551a393cfd63cd8ebe96e66c7194a8df1290bf85b80e4 |
File details
Details for the file informatics-0.0.5rc0-cp39-cp39-win_amd64.whl
.
File metadata
- Download URL: informatics-0.0.5rc0-cp39-cp39-win_amd64.whl
- Upload date:
- Size: 758.4 kB
- Tags: CPython 3.9, Windows x86-64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.0.0 CPython/3.11.8
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | c18693306756801ec4637080b88477451e99dec7a80db19c9080bc84bef43f8a |
|
MD5 | 7cfd4ecf2b06309a19c7f4bc853340da |
|
BLAKE2b-256 | ceec6abe2948563b9a8beb2c3e95edfc0d9ee2047aa3771d7457f081a0e67fa4 |
File details
Details for the file informatics-0.0.5rc0-cp39-cp39-manylinux_2_17_x86_64.whl
.
File metadata
- Download URL: informatics-0.0.5rc0-cp39-cp39-manylinux_2_17_x86_64.whl
- Upload date:
- Size: 946.2 kB
- Tags: CPython 3.9, manylinux: glibc 2.17+ x86-64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.0.0 CPython/3.11.8
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | 9ddf17c8fe77222f229d8c16085c28441a25ff62e305b376a21309f9e0bb1b42 |
|
MD5 | 39be46e1ed44ef5de67eea72d77e0b92 |
|
BLAKE2b-256 | f4d4e4f9b5f9aac363f4d843c4762720c9e664e43aef8d4f681ddbafd0a6dec0 |
File details
Details for the file informatics-0.0.5rc0-cp39-cp39-macosx_10_9_x86_64.whl
.
File metadata
- Download URL: informatics-0.0.5rc0-cp39-cp39-macosx_10_9_x86_64.whl
- Upload date:
- Size: 821.9 kB
- Tags: CPython 3.9, macOS 10.9+ x86-64
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/5.0.0 CPython/3.11.8
File hashes
Algorithm | Hash digest | |
---|---|---|
SHA256 | c91ffa1f31d13bf6d010177ced6ae4394d6a61d81152ac15cae4fce1101e15a1 |
|
MD5 | 2d7dc9c9b4a39983cde7ade8d62527eb |
|
BLAKE2b-256 | 8690b5f954d2b2280eae67287ee067bdb5188059e850f4acd24e094280868550 |