News : DjangoCon Europe 2026!

We’re excited to share that DjangoCon Europe returns in 2026 — this time in the historic and sun-soaked city of Athens, Greece 🇬🇷, with three days of talks from April 15–17, 2026!

This is the 18th edition of the Conference and it is organized by a team made up of Django practitioners from all levels. We welcome people from all over the world.

Our conference seeks to educate and develop new skills, best practices, and ideas for the benefit of attendees, developers, speakers, and everyone in our global Django Community, not least those watching the talks online.

This year it will take place in the beautiful city of Athens. Don't miss the chance to join us for three days of talks, workshops, and sprints.

Read more abot this event on the official webpage.

News : Hyperflask - web stack on top of Python !

The goal of the Hyperflask stack is to provide a single unified web stack, built on top of Python and proven technologies, where all components have been designed to work together seamlessly.

It intends to provide solo devs and small teams a solution that allows them to build and operate a website/web app with minimal boilerplate and overhead. All the focus can go to work on the actual product.

Read more on the official website.

News : xAI A.P.I. with regional endpoint on xai_sdk python package.

Grok is a family of Large Language Models (LLMs) developed by xAI.

Inspired by the Hitchhiker's Guide to the Galaxy, Grok is a maximally truth-seeking AI that provides insightful, unfiltered truths about the universe.

xAI offers an API for developers to programmatically interact with our Grok models. The same models power our consumer facing services such as Grok.com, the iOS and Android apps, as well as Grok in X experience.

If you want to use a regional endpoint, you need to specify the endpoint url when making request with SDK. In xAI SDK, this is specified through the api_host parameter.

Is not free models available for xAI A.P.I.

See this example from the official website:

import os

from xai_sdk import Client
from xai_sdk.chat import user

client = Client(
api_key=os.getenv("XAI_API_KEY"),
api_host="us-east-1.api.x.ai" # Without the https://
)

chat = client.chat.create(model="grok-4")
chat.append(user("What is the meaning of life?"))

completion = chat.sample()

Python 3.11.11 : Colab simple tests with Protocol Buffers - part 054.

Today, something simple into colab_google GitHub project, see catafest_071.ipynb - simple example with altair and vega-datasets python packages.

The result comes with some charts example.

Python 3.12.12 : Google colab example - satellite sentinel-2-l2a.

Tested today, location my home, this is the result of satellite sentinel-2-l2a - octomber 2025.

Python 3.12.12 : Google colab example of satellite detecting edges .

Simple colab project with google satellite detecting ...

I used these python packages:

%pip install segment-geospatial
%pip install leafmap
%pip install samgeo
%pip install localtileserver
%pip install fiona

This is the result:

News : Google DeepMind and Google Colab - part 001.

Today I tested Google DeepMind and Google Colab.

You can see my simple test on Suceava area ...

News : What’s new in Python 3.14.

... this news is old from five days ago .

Python 3.14 is the latest stable release of the Python programming language, with a mix of changes to the language, the implementation, and the standard library.

See the official webpage.

Python 3.8.5 : ... online python tool

You can found this good online tool for simple python programming. See the result with version and python modules used into this online tool.

Versiunea de Python: 3.8.5 (default, Jul 20 2020, 23:11:29) 
[GCC 9.3.0]

Module instalate:
- webencodings (0.5.1)
- urllib3 (1.25.9)
- toml (0.10.1)
- six (1.15.0)
- setuptools (47.0.0)
- retrying (1.3.3)
- requests (2.23.0)
- pytz (2020.1)
- pytoml (0.1.21)
- python-dateutil (2.8.1)
- pyparsing (2.4.7)
- progress (1.5)
- pip (20.1.1)
- pep517 (0.8.2)
- pandas (1.1.0)
- packaging (20.4)
- ordered-set (4.0.1)
- numpy (1.19.1)
- msgpack (1.0.0)
- lockfile (0.12.2)
- idna (2.9)
- html5lib (1.0.1)
- distro (1.5.0)
- distlib (0.3.0)
- contextlib2 (0.6.0)
- colorama (0.4.3)
- chardet (3.0.4)
- certifi (2020.4.5.1)
- CacheControl (0.12.6)
- appdirs (1.4.4)


** Process exited - Return Code: 0 **

Python 3.15 : PEP 810 - Explicit lazy imports

Python’s import system could be in for its biggest change in years.

Lazy imports are controlled, in the sense that deferred loading is only triggered by the importing code itself.

This avoids shifting responsibility onto downstream users and prevents accidental surprises in library behavior.

The new explicit lazy import mechanism enhances security by deferring module initialization until runtime, thereby minimizing the attack surface and preventing premature execution of potentially vulnerable or malicious code during application startup.

See on the official website.

Python 3.13.2 : ... Android Pydroid 3 application with flask ...

Python 3.10.7 : Krita and python - part 002.

A simple source code to export PNG file for Godot game engine as Texture2D .

from krita import *
from PyQt5.QtWidgets import QAction, QMessageBox
import os

class ExportGodotPNG(Extension):
    def __init__(self, parent):
        super().__init__(parent)

    def setup(self):
        pass

    def export_png(self):
        # Get the active document
        doc = Krita.instance().activeDocument()
        if not doc:
            QMessageBox.warning(None, "Error", "No document open! Please open a document and try again.")
            return

        # Create an InfoObject for PNG export
        info = InfoObject()
        info.setProperty("alpha", True)  # Keep alpha channel for transparency
        info.setProperty("compression", 0)  # No compression for maximum quality
        info.setProperty("interlaced", False)  # Disable interlacing
        info.setProperty("forceSRGB", True)  # Force sRGB for Godot compatibility

        # Build the output file path
        if doc.fileName():
            base_path = os.path.splitext(doc.fileName())[0]
        else:
            base_path = os.path.join(os.path.expanduser("~"), "export_godot")
        output_file = base_path + "_godot.png"

        # Export the document as PNG
        try:
            doc.exportImage(output_file, info)
            # Show success message with brief usage info
            QMessageBox.information(None, "Success", 
                f"Successfully exported as PNG for Godot: {output_file}\n\n"
                "This PNG has no compression, alpha channel support, and sRGB for Godot compatibility. "
                "To use in Godot, import the PNG and adjust texture settings as needed."
            )
        except Exception as e:
            QMessageBox.critical(None, "Error", f"Export failed: {str(e)}")

    def createActions(self, window):
        # Create only the export action in Tools > Scripts
        action_export = window.createAction("export_godot_png", "Export Godot PNG", "tools/scripts")
        action_export.triggered.connect(self.export_png)

# Register the plugin
Krita.instance().addExtension(ExportGodotPNG(Krita.instance()))

News : ... 2025 election for the PSF Board

The 2025 election for the PSF Board :

• Abigail Dogbe
• Jannis Leidel
• Sheena O’Connell
• Simon Willison

Python 3.13.0 : Script for python modules then installs them - updated with fix.

This script scans a folder full of .py files Python scripts, identifies all the external modules they import, filters out built-in ones, writes the installable ones to a requirements.txt file, and then installs them using pip—in parallel threads for speed.

I use the copilot and some comments are into my language, but I tested and works well:

NOTE: I updated with detection python modules based "from" and another issue: check if python module is instaled and step over that python module ...

This script will try to install many python modules, I can update to be better with these issues:

...some modules are default , some scripts are from another area, see Blender 3D with bpy python modules, some packages comes with same modules, this can be soleved with defined lists with unique items.

import subprocess
import sys
import os
import shutil
import importlib.util
import re
import concurrent.futures
from typing import List, Tuple, Set

class ModuleManager:
    def __init__(self):
        self.modules: Set[str] = set()
        self.pip_path = self._get_pip_path()

    def _get_pip_path(self) -> str:
        possible_path = os.path.join(sys.exec_prefix, "Scripts", "pip.exe")
        return shutil.which("pip") or (possible_path if os.path.exists(possible_path) else None)

    def extract_imports_from_file(self, file_path: str) -> List[Tuple[str, str]]:
        imports = []
        try:
            with open(file_path, 'r', encoding='utf-8') as file:
                for line in file:
                    # Detect 'import module'
                    import_match = re.match(r'^\s*import\s+([a-zA-Z0-9_]+)(\s+as\s+.*)?$', line)
                    if import_match:
                        module = import_match.group(1)
                        imports.append((module, line.strip()))
                        continue
                    
                    # Detect 'from module import ...'
                    from_match = re.match(r'^\s*from\s+([a-zA-Z0-9_]+)\s+import\s+.*$', line)
                    if from_match:
                        module = from_match.group(1)
                        imports.append((module, line.strip()))
        except FileNotFoundError:
            print(f"❌ Fișierul {file_path} nu a fost găsit.")
        except Exception as e:
            print(f"❌ Eroare la citirea fișierului {file_path}: {e}")
        return imports

    def scan_directory_for_py_files(self, directory: str = '.') -> List[str]:
        py_files = []
        for root, _, files in os.walk(directory):
            for file in files:
                if file.endswith('.py'):
                    py_files.append(os.path.join(root, file))
        return py_files

    def collect_unique_modules(self, directory: str = '.') -> None:
        py_files = self.scan_directory_for_py_files(directory)
        all_imports = []
        with concurrent.futures.ThreadPoolExecutor() as executor:
            future_to_file = {executor.submit(self.extract_imports_from_file, file_path): file_path for file_path in py_files}
            for future in concurrent.futures.as_completed(future_to_file):
                imports = future.result()
                all_imports.extend(imports)
        
        for module, _ in all_imports:
            self.modules.add(module)

    def is_module_installed(self, module: str) -> bool:
        return importlib.util.find_spec(module) is not None

    def run_pip_install(self, module: str) -> bool:
        if not self.pip_path:
            print(f"❌ Nu am găsit pip pentru {module}.")
            return False
        try:
            subprocess.check_call([self.pip_path, "install", module])
            print(f"✅ Pachetul {module} a fost instalat cu succes.")
            return True
        except subprocess.CalledProcessError as e:
            print(f"❌ Eroare la instalarea pachetului {module}: {e}")
            return False

    def check_and_install_modules(self) -> None:
        def process_module(module):
            print(f"\n🔎 Verific dacă {module} este instalat...")
            if self.is_module_installed(module):
                print(f"✅ {module} este deja instalat.")
            else:
                print(f"📦 Instalez {module}...")
                self.run_pip_install(module)
                # Re-verifică după instalare
                if self.is_module_installed(module):
                    print(f"✅ {module} funcționează acum.")
                else:
                    print(f"❌ {module} nu funcționează după instalare.")

        with concurrent.futures.ThreadPoolExecutor() as executor:
            executor.map(process_module, self.modules)

def main():
    print("🔍 Verific pip...")
    manager = ModuleManager()
    if manager.pip_path:
        print(f"✅ Pip este disponibil la: {manager.pip_path}")
    else:
        print("⚠️ Pip nu este disponibil.")
        return

    directory = sys.argv[1] if len(sys.argv) > 1 else '.'
    print(f"\n📜 Scanez directorul {directory} pentru fișiere .py...")
    manager.collect_unique_modules(directory)
    
    if not manager.modules:
        print("⚠️ Nu s-au găsit module în importuri.")
        return
    
    print(f"\nModule unice detectate: {', '.join(manager.modules)}")
    manager.check_and_install_modules()

if __name__ == "__main__":
    main()

Python 3.13.0 : Predicted XAU/USD with torch.

Testing the torch python package

import torch
import torch.nn as nn
import numpy as np

data = np.array([
    [1800.5, 1810.0, 1795.0, 1000, 1805.2],
    [1805.2, 1815.0, 1800.0, 1200, 1812.8],
    [1812.8, 1820.0, 1808.0, 1100, 1810.5],
    [1810.5, 1818.0, 1805.0, 1300, 1825.0],
    [1825.0, 1830.0, 1815.0, 1400, 1820.3],
    [1820.3, 1828.0, 1810.0, 1250, 1835.7]
])

X, y = torch.tensor(data[:, :4], dtype=torch.float32), torch.tensor(data[:, 4], dtype=torch.float32)
model = nn.Sequential(nn.Linear(4, 6), nn.ReLU(), nn.Linear(6, 4), nn.ReLU(), nn.Linear(4, 1))
optimizer = torch.optim.Adam(model.parameters())
loss_fn = nn.MSELoss()
for _ in range(3000):
    optimizer.zero_grad()
    y_pred = model(X).squeeze()
    loss = loss_fn(y_pred, y)
    loss.backward()
    optimizer.step()
prediction = model(torch.tensor([[1830.0, 1840.0, 1825.0, 1150]], dtype=torch.float32))
print("Predicted XAU/USD closing price:", round(prediction.item(), 2))

The result is :

python torch_001.py
Predicted XAU/USD closing price: 1819.57

Python 3.13.0 : Predicted XAU/USD with tensorflow.

This is the source code :

import tensorflow as tf
import numpy as np

data = np.array([
    [1800.5, 1810.0, 1795.0, 1000, 1805.2],
    [1805.2, 1815.0, 1800.0, 1200, 1812.8],
    [1812.8, 1820.0, 1808.0, 1100, 1810.5],
    [1810.5, 1818.0, 1805.0, 1300, 1825.0],
    [1825.0, 1830.0, 1815.0, 1400, 1820.3],
    [1820.3, 1828.0, 1810.0, 1250, 1835.7]
])

X, y = data[:, :4], data[:, 4]
model = tf.keras.Sequential([
    tf.keras.layers.Dense(6, activation='relu', input_shape=(4,)),
    tf.keras.layers.Dense(4, activation='relu'),
    tf.keras.layers.Dense(1)
])
model.compile(optimizer='adam', loss='mse')
model.fit(X, y, epochs=3000, verbose=0)
prediction = model.predict(np.array([[1830.0, 1840.0, 1825.0, 1150]]))
print("Predicted XAU/USD closing price:", round(prediction[0][0], 2))

The result is :

python tf_001.py
2025-08-30 21:11:13.966066: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.
C:\Python313\Lib\site-packages\google\protobuf\runtime_version.py:98: UserWarning: Protobuf gencode version 5.28.3 is exactly one major version older than the runtime version 6.31.1 at tensorflow/core/framework/attr_value.proto. Please update the gencode to avoid compatibility violations in the next runtime release.
...
Predicted XAU/USD closing price: 2.9

Python 3.13.0 : Predicted XAU/USD with MLPRegressor.

Testing the MLPRegressor from sklearn python package:

from sklearn.neural_network import MLPRegressor
import numpy as np

data = np.array([
    [1800.5, 1810.0, 1795.0, 1000, 1805.2],
    [1805.2, 1815.0, 1800.0, 1200, 1812.8],
    [1812.8, 1820.0, 1808.0, 1100, 1810.5],
    [1810.5, 1818.0, 1805.0, 1300, 1825.0],
    [1825.0, 1830.0, 1815.0, 1400, 1820.3],
    [1820.3, 1828.0, 1810.0, 1250, 1835.7]
])

X, y = data[:, :4], data[:, 4]
model = MLPRegressor(hidden_layer_sizes=(6, 4), max_iter=3000)
model.fit(X, y)

prediction = model.predict([[1830.0, 1840.0, 1825.0, 1150]])
print("Predicted XAU/USD closing price:", round(prediction[0], 2))

The answer is: Predicted XAU/USD closing price: 1836.68

News : Django Hijack

Log in and work on behalf of other users without having to know their credentials.

With Django Hijack, admins can impersonate and work on behalf of other users without having to know their credentials.

Comes with two versions available: v2.x and v3.x. The last v3.x provides a security first design, easy integration, customisation, out-of-the-box Django admin support and dark-mode. It is a complete rewrite and all former APIs are broken.

See the webpage project.

News : The geoai-py - part 001.

A powerful Python package for integrating Artificial Intelligence with geospatial data analysis and visualization

GeoAI bridges the gap between AI and geospatial analysis, providing tools for processing, analyzing, and visualizing geospatial data using advanced machine learning techniques. Whether you're working with satellite imagery, LiDAR point clouds, or vector data, GeoAI offers intuitive interfaces to apply cutting-edge AI models.

Today , I tested this python package named geoai-py. I used the pip tool:

pip install geoai-py
Successfully installed Flask-Caching-2.3.1 MarkupSafe-3.0.2 PySocks-1.7.1 PyYAML-6.0.2 absl-py-2.3.1 aenum-3.1.16 affine-2.4.0 aiohappyeyeballs-2.6.1 aiohttp-3.12.14 aiosignal-1.4.0 albucore-0.0.24 albumentations-2.0.8 aniso8601-10.0.1 annotated-types-0.7.0 antlr4-python3-runtime-4.9.3 anyio-4.9.0 anywidget-0.9.18 argon2-cffi-25.1.0 argon2-cffi-bindings-21.2.0 arrow-1.3.0 asttokens-3.0.0 beautifulsoup4-4.13.4 bitsandbytes-0.46.1 bleach-6.2.0 blinker-1.9.0 bqplot-0.12.45 branca-0.8.1 buildingregulariser-0.2.2 cachelib-0.13.0 cachetools-6.1.0 cffi-1.17.1 click-8.2.1 click-plugins-1.1.1.2 cligj-0.7.2 color-operations-0.2.0 comm-0.2.2 contextily-1.6.2 contourpy-1.3.2 cycler-0.12.1 datasets-4.0.0 decorator-5.2.1 defusedxml-0.7.1 dill-0.3.8 docstring-parser-0.17.0 duckdb-1.3.2 einops-0.8.1 eval-type-backport-0.2.2 ever-beta-0.5.1 executing-2.2.0 fastjsonschema-2.21.1 filelock-3.18.0 fiona-1.10.1 flask-3.1.1 flask-cors-6.0.1 flask-restx-1.3.0 folium-0.20.0 fonttools-4.59.0 fqdn-1.5.1 frozenlist-1.7.0 fsspec-2025.3.0 gdown-5.2.0 geoai-py-0.9.0 geographiclib-2.0 geojson-3.2.0 geopandas-1.1.1 geopy-2.4.1 gitdb-4.0.12 gitpython-3.1.44 grpcio-1.73.1 h11-0.16.0 httpcore-1.0.9 httpx-0.28.1 huggingface_hub-0.33.4 hydra-core-1.3.2 importlib-resources-6.5.2 ipyevents-2.0.2 ipyfilechooser-0.6.0 ipyleaflet-0.20.0 ipython-9.4.0 ipython-pygments-lexers-1.1.1 ipytree-0.2.2 ipyvue-1.11.2 ipyvuetify-1.11.3 ipywidgets-8.1.7 isoduration-20.11.0 itsdangerous-2.2.0 jedi-0.19.2 jinja2-3.1.6 joblib-1.5.1 jsonargparse-4.40.0 jsonnet-0.21.0 jsonpointer-3.0.0 jupyter-client-8.6.3 jupyter-core-5.8.1 jupyter-events-0.12.0 jupyter-leaflet-0.20.0 jupyter-server-2.16.0 jupyter-server-proxy-4.4.0 jupyter-server-terminals-0.5.3 jupyterlab-pygments-0.3.0 jupyterlab_widgets-3.0.15 kiwisolver-1.4.8 kornia-0.8.1 kornia_rs-0.1.9 leafmap-0.48.6 lightly-1.5.21 lightly_utils-0.0.2 lightning-2.5.2 lightning-utilities-0.14.3 localtileserver-0.10.6 mapclassify-2.10.0 maplibre-0.3.4 markdown-3.8.2 markdown-it-py-3.0.0 matplotlib-3.10.3 matplotlib-inline-0.1.7 mdurl-0.1.2 mercantile-1.2.1 mistune-3.1.3 morecantile-6.2.0 multidict-6.6.3 multiprocess-0.70.16 narwhals-1.48.0 nbclient-0.10.2 nbconvert-7.16.6 nbformat-5.10.4 numexpr-2.11.0 omegaconf-2.3.0 opencv-python-headless-4.12.0.88 overrides-7.7.0 overturemaps-0.15.0 pandas-2.3.1 pandocfilters-1.5.1 parso-0.8.4 planetary-computer-1.0.0 plotly-6.2.0 prettytable-3.16.0 prometheus-client-0.22.1 prompt_toolkit-3.0.51 propcache-0.3.2 psygnal-0.14.0 pure-eval-0.2.3 pyarrow-21.0.0 pycparser-2.22 pydantic-2.11.7 pydantic-core-2.33.2 pygments-2.19.2 pyogrio-0.11.0 pyparsing-3.2.3 pyproj-3.7.1 pystac-1.13.0 pystac-client-0.9.0 python-box-7.3.2 python-dateutil-2.9.0.post0 python-dotenv-1.1.1 python-json-logger-3.3.0 pytorch_lightning-2.5.2 pytz-2025.2 pywin32-311 pywinpty-2.0.15 pyzmq-27.0.0 rasterio-1.4.3 regex-2024.11.6 rfc3339-validator-0.1.4 rfc3986-validator-0.1.1 rich-14.0.0 rio-cogeo-5.4.2 rio-tiler-7.8.1 rioxarray-0.19.0 rtree-1.4.0 safetensors-0.5.3 scikit-learn-1.7.1 scooby-0.10.1 segmentation-models-pytorch-0.5.0 send2trash-1.8.3 sentry-sdk-2.33.0 server-thread-0.3.0 shapely-2.1.1 simpervisor-1.0.0 simsimd-6.5.0 six-1.17.0 smmap-5.0.2 sniffio-1.3.1 soupsieve-2.7 stack_data-0.6.3 stringzilla-3.12.5 tensorboard-2.20.0 tensorboard-data-server-0.7.2 tensorboardX-2.6.4 terminado-0.18.1 threadpoolctl-3.6.0 timm-1.0.17 tinycss2-1.4.0 tokenizers-0.21.2 torch-2.7.1 torchange-0.0.1 torchgeo-0.7.1 torchinfo-1.8.0 torchmetrics-1.7.4 torchvision-0.22.1 tornado-6.5.1 traitlets-5.14.3 traittypes-0.2.1 transformers-4.53.2 types-python-dateutil-2.9.0.20250708 typeshed-client-2.8.2 typing-inspection-0.4.1 tzdata-2025.2 uri-template-1.3.0 uvicorn-0.35.0 wandb-0.21.0 wcwidth-0.2.13 webcolors-24.11.1 webencodings-0.5.1 websocket-client-1.8.0 werkzeug-3.1.3 whitebox-2.3.6 whiteboxgui-2.3.0 widgetsnbextension-4.0.14 xarray-2025.7.1 xxhash-3.5.0 xyzservices-2025.4.0 yarl-1.20.1

You can find some example on the Open Geospatial Solutions - the official youtube channel

Let's see my testing python example:

>>> import geoai
>>> dir(geoai)
['AgricultureFieldDelineator', 'Any', 'AutoConfig', 'AutoModelForMaskGeneration', 

'AutoModelForMaskedImageModeling', 'AutoProcessor', 'BoundingBox', 'BuildingFootprintExtractor',

 'CLIPSegForImageSegmentation', 'CLIPSegProcessor', 'CLIPSegmentation', 'CarDetector', 

'ChangeDetection', 'CustomDataset', 'DetectionResult', 'Dict', 'ET', 'GroundedSAM', 'Image', 

'Iterable', 'List', 'Map', 'MapLibre', 'MultiPolygon', 'NonGeoDataset', 'ObjectDetector', 

'Optional', 'OrderedDict', 'ParkingSplotDetector', 'Path', 'Polygon', 'RandomRotation', 

'ShipDetector', 'SolarPanelDetector', 'Tuple', 'Union', 'Window', '__author__', 

'__builtins__', '__cached__', '__doc__', '__email__', '__file__', '__loader__', '__name__', 

'__package__', '__path__', '__spec__', '__version__', 'adaptive_regularization', 

'add_geometric_properties', 'analyze_vector_attributes', 'batch_vector_to_raster', 'bbox_to_xy',

 'box', 'boxes_to_vector', 'calc_stats', 'change_detection', 'classify', 'classify_image', 

'classify_images', 'clip_raster_by_bbox', 'coords_to_xy', 'create_overview_image', 

'create_split_map', 'create_vector_data', 'csv', 'cv2', 'dataclass', 'deeplabv3_resnet50', 

'dict_to_image', 'dict_to_rioxarray', 'download', 'download_file', 'download_model_from_hf', 

'download_naip', 'download_overture_buildings', 'download_pc_stac_item', 'edit_vector_data', 

'export_geotiff_tiles', 'export_geotiff_tiles_batch', 'export_tiles_to_geojson', 

'export_training_data', 'extract', 'extract_building_stats', 'fasterrcnn_resnet50_fpn_v2', 

'fcn_resnet50', 'features', 'geoai', 'geojson_to_coords', 'geojson_to_xy', 'get_device', 

'get_instance_segmentation_model', 'get_model_config', 'get_model_input_channels', 

'get_overture_data', 'get_raster_info', 'get_raster_info_gdal', 'get_raster_resolution', 

'get_raster_stats', 'get_vector_info', 'get_vector_info_ogr', 'glob', 'gpd', 'hf', 

'hf_hub_download', 'hybrid_regularization', 'image_segmentation', 'inspect_pth_file', 

'install_package', 'instance_segmentation', 'instance_segmentation_batch', 

'instance_segmentation_inference_on_geotiff', 'json', 'leafmap', 'logging', 'maplibregl', 

'mapping', 'mask_generation', 'maskrcnn_resnet50_fpn', 'masks_to_vector', 'math', 

'mosaic_geotiffs', 'ndimage', 'np', 'object_detection', 'object_detection_batch', 'orthogonalize',

 'os', 'pc_collection_list', 'pc_item_asset_list', 'pc_stac_download', 'pc_stac_search', 'pd', 

'pipeline', 'plot_batch', 'plot_images', 'plot_masks', 'plot_performance_metrics', 

'plot_prediction_comparison', 'plt', 'print_raster_info', 'print_vector_info', 'raster_to_vector',

 'raster_to_vector_batch', 'rasterio', 'read_pc_item_asset', 'read_raster', 'read_vector', 

'region_groups', 'regularization', 'regularize', 'requests', 'rotate', 'rowcol_to_xy', 'rxr', 

'segment', 'semantic_segmentation', 'semantic_segmentation_batch', 'set_proj_lib_path', 'shape', 

'show', 'stack_bands', 'subprocess', 'sys', 'temp_file_path', 'time', 'torch', 'torchgeo', 'tqdm',

 'train', 'train_MaskRCNN_model', 'train_classifier', 'train_instance_segmentation_model', 

'train_segmentation_model', 'transform_bounds', 'try_common_architectures', 'utils', 

'vector_to_geojson', 'vector_to_raster', 'view_image', 'view_pc_item', 'view_pc_items', 

'view_raster', 'view_vector', 'view_vector_interactive', 'visualize_vector_by_attribute', 

'warnings', 'write_colormap', 'xr']

Python 3.13.5 : Get bookmarks from Edge browser with python.

Today I tested with these python modules json and pathlib.

This python script will get all bookmarks from Edge browser:

import json
from pathlib import Path

bookmark_path = Path.home() / "AppData/Local/Microsoft/Edge/User Data/Default/Bookmarks"

with open(bookmark_path, "r", encoding="utf-8") as f:
    data = json.load(f)

# Exemplu: listăm toate titlurile bookmark-urilor
def extract_bookmarks(bookmark_node):
    bookmarks = []
    if "children" in bookmark_node:
        for child in bookmark_node["children"]:
            bookmarks.extend(extract_bookmarks(child))
    elif bookmark_node.get("type") == "url":
        bookmarks.append((bookmark_node["name"], bookmark_node["url"]))
    return bookmarks

all_bookmarks = extract_bookmarks(data["roots"]["bookmark_bar"])
for name, url in all_bookmarks:
    print(f"{name}: {url}")