memorymanagement
Provides memory management support.
The only Python implementation currently supported is the one made by the CPython team. The rest remain untested.
Table of Contents
Modules
cleaning
Provides the class Cleaner, which allows you to flag references stored in memory to eventually erase them.
It is also possible to modify the list of flagged references through its methods.
pointers
Provides a safe implementation of pointers for Python.
Class Pointer
The pointer itself. Imitates the behaviour of C pointers. Pointer points to a reference, not to an object stored in memory.
Decorator pointerize
Allows functions to receive pointers instead of values.
Installation
You can install the memorymanagement package from PyPI as follows:
pip install memorymanagement
Usage
Class Cleaner
# Imports
# Make your imports here
from memorymanagement import Cleaner # Importing class Cleaner
Right after imports are done, I recommend to initialize an instance of class Cleaner, so no arguments are needed. This is the optimal use this class was designed for.
# Initialize cleaner object
cleaner=Cleaner()
Create, for example, these global variables:
value_1=10
value_2=50
value_3=100
Update the list of flagged references like one of the following:
-
Including all new global variables:
cleaner.update() print(cleaner.flagged)
Output:
["value_1","value_2","value_3"]
-
Excluding some variables:
cleaner.update(exclude="value_2") print(cleaner.flagged)
Output:
["value_1","value_3"]
cleaner.update(exclude=["value_2","value_3"]) print(cleaner.flagged)
Output:
["value_1"]
-
Include again some variables:
After having excluded some variables
cleaner.update(exclude=["value_2","value_3"])
You can reintroduce them
# Create a new variable and update value_4=150 cleaner.update(exclude="value_4",include="value_2") # "include" can also be a list of strings print(cleaner.flagged)
Output:
["value_1","value_2"]
You can also directly include or exclude references like so:
cleaner.exclude(<name_var_1>,<name_var_2>,...)
cleaner.include(<name_var_1>,<name_var_2>,...)
To flag all the excluded variables at once, you can use:
cleaner.include_all(exclude=[<var_1>,<var_2>,...]) # exclude: optional keyword argument, accepts strings and iterables of strings
You can also exclude all the flagged variables like so:
cleaner.exclude_all(include=[<var_1>,<var_2>,...]) # include: optional keyword argument, accepts strings and iterables of strings
And reset the cleaner's parameters like so:
cleaner.purge()
Class Pointer
Example:
from memorymanagement import Pointer
a=10
x=Pointer(a)
print(f"a:\n{a}\n\nPointer:\n{x.value}\n\n")
a=20
print(f"a:\n{a}\n\nPointer:\n{x.value}\n\n")
a=10
x.value=20
print(f"a:\n{a}\n\nPointer:\n{x.value}")
Output:
a:
10
Pointer:
10
a:
20
Pointer:
20
a:
20
Pointer:
20
Decorator pointerize
Example:
from memorymanagement import pointerize
@pointerize
def myFunction(value:int):
value=20
return
a=10
print(f"Value: {a}")
myFunction(Pointer(a))
print(f"Value: {a}")
Output:
Value: 10
Value: 20
Contribution
To contribute to this project fork this repository and clone your fork. Pull requests will be revised by the owner before being accepted or rejected.
There are two branches:
Pull requests from TestPyPI to PyPI will only be done by the owner when a new release is ready.
Branch merging
To merge branches properly with PyPI branch in your cloned repository, you will need to have the .gitattributes file in the PyPI branch and execute the following commands, while in repo directory, in your PowerShell:
git config merge.keepPyPIFiles.name "Keep README.md and setup.cfg from PyPI branch on merge"
git config merge.keepPyPIFiles.driver "bash -c 'cp $(git rev-parse --show-toplevel)/$3 $2'"
This way, README.md and setup.cfg files will not be overwritten in the PyPI branch.
1 maintainer
Metadata
1 maintainer
Release files for memorymanagement 1.8.0
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Source distribution (sdist)
| File | Size | Uploaded | Provenance | |
|---|---|---|---|---|
| memorymanagement-1.8.0.tar.gz | 17.5 kB | Without provenance | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| memorymanagement-1.8.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 35.3 kB
Release files / memorymanagement-1.8.0.tar.gz
| Download URL | memorymanagement-1.8.0.tar.gz |
|---|---|
| Size | 17.5 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.14.8
|
Release files / memorymanagement-1.8.0-py3-none-any.whl
| Download URL | memorymanagement-1.8.0-py3-none-any.whl |
|---|---|
| Size | 17.8 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
3aa4fcc8aa7acb0973689c5c5d9f1468603ed6a36f07d67bfd98191e4e081665
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| Upload date | |
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Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/7.0.0 CPython/3.14.8
|
Security for memorymanagement 1.8.0
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