cdxcore documentation

This module contains a number of lightweight tools, developed for managing data analytics and machine learning projects.

Install using:

pip install -U cdxcore

Documentation can be found here: https://quantitative-research.de/docs/cdxcore. cdxcore is best used with Python 3.12 and above, but is tested vs Python 3.10 onwards.

Highlights

Dynamic plotting: simple live/animated plots built on Matplotlib.
Config management: validated, discoverable configurations with automatic help.
Versioning & caching: code-versioned I/O and reproducible hashing for pipelines.
PrettyObject: dictionary-like objects that allow attribute access.
Utilities: formatting helpers, binary I/O, shared-memory arrays, and more.

Main Functionality

cdxcore.dynaplot is a framework for simple dynamic graphs with matplotlib. It has a simple methodology for animated updates for graphs (e.g. during training runs), and allows generation of plot layouts without knowing upfront the number of plots (e.g. for plotting a list of features).

cdxcore.config allows robust management of configurations. It automates help, validation checking, and detects misspelled configuration arguments.

from cdxcore.config import Config, Int, Float

class Network(object):
    def __init__( self, config ):
        self.depth      = config("depth", 1, Int>0, "Depth of the network")
        self.width      = config("width", 1, Int>0, "Width of the network")
        self.activation = config("activation", "selu", str, "Activation function")
        config.done() # see below

config = Config()
config.network.depth         = 10
config.network.width         = 100
config.network.activation    = 'relu'

network = Network(config.network)
config.done()

cdxcore.subdir wraps various file and directory functions into convenient objects. Useful if files have common extensions.

from cdxcore.subdir import SubDir
import numpy as np
root   = SubDir("!")   # current temp directory
subdir = root("test")  # sub-directory 'test'
subdir.write("data", np.zeros((10,2)))
data   = subdir.read("data")

Caching: SubDir supports code-versioned file i/o which is used by @cdxcore.subdir.SubDir.cache for an efficient code-versioned caching protocol for functions and objects:

from cdxcore.subdir import SubDir
cache   = SubDir("!/.cache;*.bin")

@cache.cache("0.1")
def f(x,y):
   return x*y

_ = f(1,2)    # function gets computed and the result cached
_ = f(1,2)    # restore result from cache
_ = f(2,2)    # different parameters: compute and store result

Code versioning is implemented in cdxcore.version:

from cdxcore.version import version

@version("0.0.1")
def f(x):
    return x

print( f.version.full )   # -> 0.0.1

Hashing (which is used for caching above) is implemented in cdxcore.uniquehash:

class A(object):
    def __init__(self, x):
        self.x = x
        self._y = x*2  # protected member will not be hashed by default

from cdxcore.uniquehash import UniqueHash
uniqueHash = UniqueHash(length=12)
a = A(2)
print( uniqueHash(a) ) # --> "2d1dc3767730"

cdxcore.pretty provides a PrettyObject class whose objects operate like dictionaries. This is for users who prefer attribute . notation over item access when building structured output.
```
from cdxcore.pretty import PrettyObject
pdct = PrettyObject(z=1)

pdct.num_samples = 1000
pdct.num_batches = 100
pdct.method = "signature"
```

General purpose utilities

cdxcore.verbose provides user-controllable context output for providing progress updates to users.
cdxcore.util offers a number of utility functions such as standard formatting for dates, big numbers, lists, dictionaries etc.
cdxcore.npio provides a low level binary i/o interface for numpy files.
cdxcore.npshm provides shared memory numpy arrays.
cdxcore.rolling provides robust rolling statistics: exponentially weighted location and scale estimators which flag outliers, for regular and irregular time steps.

Metadata

Release files for cdxcore 0.1.96

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

Source distribution (sdist)

Source distribution for cdxcore 0.1.96
File	Size	Uploaded
cdxcore-0.1.96.tar.gz	280.4 kB	Oct 6, 2026	Details

Built distribution (wheel)

Table of built distributions (wheels) for cdxcore 0.1.96
File	Interpreter	ABI	Platform	Reset
cdxcore-0.1.96-py3-none-any.whl 219.9 kB Oct 6, 2026	Python 3	none	any	Details

Total release size: 500.3 kB

Release files / cdxcore-0.1.96.tar.gz

Download URL	cdxcore-0.1.96.tar.gz
Size	280.4 kB
Tags	Source
SHA-256 checksum How to use checksums	`54177acf4063a69c6c164c80715b7f1f13303ede2ca6faffd8d8c9fcc0b89297`
BLAKE2b-256 checksum How to use checksums	`354aaa54024d697a42a8c0326b645ab9ca8333997130fd22a6f7790593ae5e5c`
Upload date	Oct 6, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.13.5`

Release files / cdxcore-0.1.96-py3-none-any.whl

Download URL	cdxcore-0.1.96-py3-none-any.whl
Size	219.9 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`7d44d7b8e2444badc9590b8e321c126a6965f6081bb9959e53087cd232db381e`
BLAKE2b-256 checksum How to use checksums	`ef82b87772f6b1a8d35f164ed9cfd621a1d017327f14fbea0bf8a60b546fe1cc`
Upload date	Oct 6, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/7.0.0 CPython/3.13.5`