Python 3.12.1 : pipx tool .

The pip is a general-purpose package installer for both libraries and apps with no environment isolation. pipx is made specifically for application installation, as it adds isolation yet still makes the apps available in your shell: pipx creates an isolated environment for each application and its associated packages.

Install the pipx tool :

python -m pip install --user pipx
Collecting pipx
  Downloading pipx-1.4.3-py3-none-any.whl.metadata (17 kB)
  ...

Upgrade the pipx tool:

python -m pip install --user --upgrade pipx

Using pipx to install an application by running :

python -m pipx install pyos
⡿ installing pyos  installed package pyos 0.8.0, installed using Python 3.12.1
  These apps are now globally available
    - psh.exe
    - pyos.exe
done! ✨ 🌟 ✨

Show the Python packages on the environment:

python -m pipx list
venvs are in C:\Users\catafest\AppData\Local\pipx\pipx\venvs
apps are exposed on your $PATH at C:\Users\catafest\.local\bin
manual pages are exposed at C:\Users\catafest\.local\share\man
   package pyos 0.8.0, installed using Python 3.12.1
    - psh.exe
    - pyos.exe

If an application installed by pipx requires additional packages, you can add them with pipx inject, and this can be seen with the list argument.

python -m pipx inject pyos matplotlib
  injected package matplotlib into venv pyos
done! ✨ 🌟 ✨
...
python -m pipx list
venvs are in C:\Users\catafest\AppData\Local\pipx\pipx\venvs
apps are exposed on your $PATH at C:\Users\catafest\.local\bin
manual pages are exposed at C:\Users\catafest\.local\share\man
   package pyos 0.8.0, installed using Python 3.12.1
    - psh.exe
    - pyos.exe
...    
python -m pipx list --include-injected
venvs are in C:\Users\catafest\AppData\Local\pipx\pipx\venvs
apps are exposed on your $PATH at C:\Users\catafest\.local\bin
manual pages are exposed at C:\Users\catafest\.local\share\man
   package pyos 0.8.0, installed using Python 3.12.1
    - psh.exe
    - pyos.exe
    Injected Packages:
      - matplotlib 3.8.3
      - test-py 0.3

This adds the matplotlib package to pyosenvironment.

If I try to inject into another environment name, then I will get an error:

python -m pipx inject catafest matplotlib
Can't inject 'matplotlib' into nonexistent Virtual Environment 'catafest'. Be sure to install the package first
with 'pipx install catafest' before injecting into it.

Create a Python file named test.py with this source code:

# test.py

# Requirements:
# requests
#
# The list of requirements is terminated by a blank line or an empty comment line.

import sys
import requests
project = sys.argv[1]
pipx_data = requests.get(f"https://pypi.org/pypi/{project}/json").json()
print(pipx_data["info"]["version"])

You can run it with:

python -m pipx run test.py pipx
1.4.3

I don't know how advanced the environment is built and I tested some simple scenarios but I found some inconsistencies in the scripts created by the user that can be run other than with a simple run and on several environments in the same folder. Theoretically, there should be such functionality.

Python 3.12.1 : The kaitai python module and IDE online tool.

Kaitai Struct is a declarative language used to describe various binary data structures, laid out in files or in memory: i.e. binary file formats, network stream packet formats, etc.

The main idea is that a particular format is described in Kaitai Struct language (.ksy file) and then can be compiled with ksc into source files in one of the supported programming languages. These modules will include a generated code for a parser that can read the described data structure from a file or stream and give access to it in a nice, easy-to-comprehend API.

Let's install the Python module:

python3 -m pip install --upgrade kaitaistruct
Python was not found; run without arguments to install from the Microsoft Store, or disable this shortcut from Settings > Manage App Execution Aliases.

C:\PythonProjects\kaitai_001>python -m pip install --upgrade kaitaistruct
Collecting kaitaistruct
  Downloading kaitaistruct-0.10-py2.py3-none-any.whl.metadata (2.5 kB)
Downloading kaitaistruct-0.10-py2.py3-none-any.whl (7.0 kB)
Installing collected packages: kaitaistruct
Successfully installed kaitaistruct-0.10

The Kaitai compiler can be downloaded from the official website.

After installation, you can use the compiler ...

kaitai-struct-compiler.bat --version
kaitai-struct-compiler 0.10
...
kaitai-struct-compiler.bat --help
kaitai-struct-compiler 0.10
Usage: kaitai-struct-compiler [options] ...

  ...                source files (.ksy)
  -t, --target   target languages (graphviz, csharp, rust, all, perl, java, go, cpp_stl, php, lua, python, nim, html, ruby, construct, javascript)
  -d, --outdir 
                           output directory (filenames will be auto-generated); on Unix-like shells, the short form `-d` requires arguments to be preceded by `--`
  -I, --import-path ;;...
                           .ksy library search path(s) for imports (see also KSPATH env variable)
  --cpp-namespace 
                           C++ namespace (C++ only, default: none)
  --cpp-standard 
                           C++ standard to target (C++ only, supported: 98, 11, default: 98)
  --go-package    Go package (Go only, default: none)
  --java-package 
                           Java package (Java only, default: root package)
  --java-from-file-class 
                           Java class to be invoked in fromFile() helper (default: io.kaitai.struct.ByteBufferKaitaiStream)
  --dotnet-namespace 
                           .NET Namespace (.NET only, default: Kaitai)
  --php-namespace 
                           PHP Namespace (PHP only, default: root package)
  --python-package 
                           Python package (Python only, default: root package)
  --nim-module     Path of Nim runtime module (Nim only, default: kaitai_struct_nim_runtime)
  --nim-opaque     Directory of opaque Nim modules (Nim only, default: directory of generated module)
  --opaque-types    opaque types allowed, default: false
  --ksc-exceptions         ksc throws exceptions instead of human-readable error messages
  --ksc-json-output        output compilation results as JSON to stdout
  --verbose         verbose output
  --no-auto-read           disable auto-running `_read` in constructor
  --read-pos               `_read` remembers attribute positions in stream
  --debug                  same as --no-auto-read --read-pos (useful for visualization tools)
  --help                   display this help and exit
  --version                output version information and exit

Steps to use this tool with Python. You need to use a defined kaitai file format for your file type - for example, gif file format, compile this kaitai then you can use it in this manner:

from kaitaistruct import __version__ as ks_version, KaitaiStruct, KaitaiStream, BytesIO
import mmap
print('kaitai version : ', ks_version)
f = open("python_giphy.gif", "rb")
with mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ) as buf:
    stream = KaitaiStream(BytesIO(buf))
    obj1 = print(stream)
    obj2 = print(stream)
    obj3 = print(stream)
stream.close()

I only test a little but is a great tool.

Kaitai Struct is free and open-source software, licensed under the following terms: Compiler and visualizer — GPLv3+ and these Runtime libraries:

• C++/STL — MIT
• C# — MIT
• Go — MIT
• Java — MIT
• JavaScript — Apache v2
• Lua — MIT
• Nim — MIT
• Perl — MIT
• PHP — MIT
• Python — MIT
• Ruby — MIT
• Rust — MIT
• Swift — MIT

Is easier to understand if you use the IDE on the web. On the left side you can see a cloud icon for upload, first, select the kaitai GIF type from formats/image/gif.ksy from web IDE, then select a GIF file from your computer and upload.

The default IDE looks like this:

You can use the kaitai online IDE.

Python 3.10.12 : YOLOv9 with GELAN-C model on colab notebook - part 045.

Today I tested YOLOv9 with GELAN-C model. Find the notebook on my GitHub repo.

Python 3.10.12 : Few example for CUDA and NVCC - part 044.

NVCC use CUDA C/C++ source code and allows developers to write high-performance GPU-accelerated applications by leveraging the power of NVIDIA GPUs for parallel processing tasks.

Today I test some simple examples with this tool on Google Colab using the nvcc4jupyter python package.

You need to install it with the pip and know how to use the CUDA C/C++ source code, or use the basic example from documentation.

pip install nvcc4jupyter

I change some source code because is need to install this library and I don't have time to learn and test.

But this will allow me to test better, because on my desktop I don't have a good hardware.

This is the source I change and I cut the source code linked on error_handling.h.

This is the changed source code , you can see more on my GitHub repo for Google Colab ! !

#include 
//#include "error_handling.h"

const int DSIZE = 4096;
const int block_size = 256;

// vector add kernel: C = A + B
__global__ void vadd(const float *A, const float *B, float *C, int ds){
    int idx = threadIdx.x + blockIdx.x * blockDim.x;
    if (idx < ds) {
        C[idx] = A[idx] + B[idx];
    }
}

int main(){
    float *h_A, *h_B, *h_C, *d_A, *d_B, *d_C;

    // allocate space for vectors in host memory
    h_A = new float[DSIZE];
    h_B = new float[DSIZE];
    h_C = new float[DSIZE];

    // initialize vectors in host memory to random values (except for the
    // result vector whose values do not matter as they will be overwritten)
    for (int i = 0; i < DSIZE; i++) {
        h_A[i] = rand()/(float)RAND_MAX;
        h_B[i] = rand()/(float)RAND_MAX;
    }

    // allocate space for vectors in device memory
    cudaMalloc(&d_A, DSIZE*sizeof(float));
    cudaMalloc(&d_B, DSIZE*sizeof(float));
    cudaMalloc(&d_C, DSIZE*sizeof(float));
    //cudaCheckErrors("cudaMalloc failure"); // error checking

    // copy vectors A and B from host to device:
    cudaMemcpy(d_A, h_A, DSIZE*sizeof(float), cudaMemcpyHostToDevice);
    cudaMemcpy(d_B, h_B, DSIZE*sizeof(float), cudaMemcpyHostToDevice);
    //cudaCheckErrors("cudaMemcpy H2D failure");

    // launch the vector adding kernel
    vadd<<<(DSIZE+block_size-1)/block_size, block_size>>>(d_A, d_B, d_C, DSIZE);
    //cudaCheckErrors("kernel launch failure");

    // wait for the kernel to finish execution
    cudaDeviceSynchronize();
    //cudaCheckErrors("kernel execution failure");

    cudaMemcpy(h_C, d_C, DSIZE*sizeof(float), cudaMemcpyDeviceToHost);
    //cudaCheckErrors("cudaMemcpy D2H failure");

    printf("A[0] = %f\n", h_A[0]);
    printf("B[0] = %f\n", h_B[0]);
    printf("C[0] = %f\n", h_C[0]);
    return 0;
}

This is result ...

A[0] = 0.840188
B[0] = 0.394383
C[0] = 0.000000

Python 3.12.1 : Simple view for sqlite table with PyQt6.

This is the source code that show you how to use QSqlDatabase, QSqlQuery, QSqlTableModel:

import sys
from PyQt6.QtWidgets import QApplication, QMainWindow, QTableView
from PyQt6.QtSql import QSqlDatabase, QSqlQuery, QSqlTableModel

class MainWindow(QMainWindow):
    def __init__(self):
        super().__init__()

        # Initialize the database
        self.init_db()

        # Set up the GUI
        self.table_view = QTableView(self)
        self.setCentralWidget(self.table_view)

        # Set up the model and connect it to the database
        self.model = QSqlTableModel(self)
        self.model.setTable('files')
        self.model.select()
        self.table_view.setModel(self.model)

    def init_db(self):
        # Connect to the database
        db = QSqlDatabase.addDatabase('QSQLITE')
        db.setDatabaseName('file_paths.db')
        if not db.open():
            print('Could not open database')
            sys.exit(1)

    def create_table(self):
        # Create the 'files' table if it doesn't exist
        query = QSqlQuery()
        query.exec('CREATE TABLE IF NOT EXISTS files (id INTEGER PRIMARY KEY, path TEXT)')

if __name__ == '__main__':
    app = QApplication(sys.argv)
    window = MainWindow()
    window.show()
    sys.exit(app.exec())

Python 3.12.1 : Simple browser with PyQt6-WebEngine.

This is a source code for a minimal browser with PyQt6 and PyQt6-WebEngine.

You need to install it with the pip tool:

pip install PyQt6 PyQt6-WebEngine

You can update the pip tool with

python.exe -m pip install --upgrade pip

Create a python script use the source code and run it.

Let's see the source code:

import sys
from PyQt6.QtCore import QUrl
from PyQt6.QtGui import QKeySequence, QAction
from PyQt6.QtWidgets import QApplication, QMainWindow, QLineEdit, QToolBar
from PyQt6.QtWebEngineWidgets import QWebEngineView
 
class MainWindow(QMainWindow):
    def __init__(self):
        super().__init__()
         
        # Create a web view
        self.web_view = QWebEngineView()
        self.web_view.setUrl(QUrl("127.0.0.1"))
        self.setCentralWidget(self.web_view)
 
        # Create a toolbar
        toolbar = QToolBar()
        self.addToolBar(toolbar)
         
        # Add a back action to the toolbar
        back_action = QAction("Back", self)
        back_action.setShortcut(QKeySequence("Back"))
        back_action.triggered.connect(self.web_view.back)
        toolbar.addAction(back_action)
         
        # Add a forward action to the toolbar
        forward_action = QAction("Forward", self)
        forward_action.setShortcut(QKeySequence("Forward"))
        forward_action.triggered.connect(self.web_view.forward)
        toolbar.addAction(forward_action)
         
        # Add a reload action to the toolbar
        reload_action = QAction("Reload", self)
        reload_action.setShortcut(QKeySequence("Refresh"))
        reload_action.triggered.connect(self.web_view.reload)
        toolbar.addAction(reload_action)
         
        # Add a search bar to the toolbar
        self.search_bar = QLineEdit()
        self.search_bar.returnPressed.connect(self.load_url)
        toolbar.addWidget(self.search_bar)
         
         
    def load_url(self):
        url = self.search_bar.text()
        if not url.startswith("http"):
            url = "https://" + url
        self.web_view.load(QUrl(url))
         
if __name__ == "__main__":
    app = QApplication(sys.argv)
    window = MainWindow()
    window.show()
    app.exec()

News : How to use ShellExecuteA with Python programming language.

I just discovery this option to use ShellExecuteA in Python.

Let's see some example:

import ctypes
import sys

def is_admin():
    try:
        return ctypes.windll.shell32.IsUserAnAdmin()
    except:
        return False

if not is_admin():
    ctypes.windll.shell32.ShellExecuteW(None, "runas", sys.executable, __file__, None, 1)

... and another example:

import sys
import ctypes

#fix unicode access
import sys
if sys.version_info[0] >= 3:
    unicode = str

def run_as_admin(argv=None, debug=False):
    shell32 = ctypes.windll.shell32
    if argv is None and shell32.IsUserAnAdmin():
        return True    
    if argv is None:
        argv = sys.argv
    if hasattr(sys, '_MEIPASS'):
        # Support pyinstaller wrapped program.
        arguments = map(unicode, argv[1:])
    else:
        arguments = map(unicode, argv)
    argument_line = u' '.join(arguments)
    executable = unicode(sys.executable)
    if debug:
        print('Command line: ', executable, argument_line)
    ret = shell32.ShellExecuteW(None, u"runas", executable, argument_line, None, 1)
    if int(ret) <= 32:
        return False
    return None  

if __name__ == '__main__':
    ret = run_as_admin()
    if ret is True:
        print ('I have admin privilege.')
        input('Press ENTER to exit.')
    elif ret is None:
        print('I am elevating to admin privilege.')
        input('Press ENTER to exit.')
    else:
        print('Error(ret=%d): cannot elevate privilege.' % (ret, ))

News : NVIDIA Kaolin library provides a PyTorch API.

NVIDIA Kaolin library provides a PyTorch API for working with a variety of 3D representations and includes a growing collection of GPU-optimized operations such as modular differentiable rendering, fast conversions between representations, data loading, 3D checkpoints, differentiable camera API, differentiable lighting with spherical harmonics and spherical gaussians, powerful quadtree acceleration structure called Structured Point Clouds, interactive 3D visualizer for jupyter notebooks, convenient batched mesh container and more ... from GitHub repo - kaolin.

See this example from NVIDIA:

NVIDIA Kaolin library has introduced the SurfaceMesh class to make it easier to track attributes such as faces, vertices, normals, face_normals, and others associated with surface meshes.

Python 3.12.1 : Create a simple color palette.

Today I tested a simple source code for a color palette created with PIL python package.

This is the result:

Let's see the source code:

from PIL import Image, ImageDraw

# 640x640 pixeli with 10x10 squares 64x64 
img = Image.new('RGB', (640, 640))
draw = ImageDraw.Draw(img)

# color list
colors = []
# for 100 colors you need to set steps with 62,62,64 
# or you can change 64,62,62 some little changes 
for r in range(0, 256, 62):
    for g in range(0, 256, 62):
        for b in range(0, 256, 64):
            colors.append((r, g, b))

# show result of colors and size up 100 items 
print(colors)
print(len(colors))

# create 10x10 colors and fill the image 
for i in range(10):
    for j in range(10):
        x0 = j * 64
        y0 = i * 64
        x1 = x0 + 64
        y1 = y0 + 64
        color = colors[i*10 + j]  # Selectarea culorii din lista
        draw.rectangle([x0, y0, x1, y1], fill=color)

# save teh image
img.save('rgb_color_matrix.png')

Python 3.10.12 : LaTeX on colab notebook - part 043.

Today I tested with a simple LaTeX examples in the Colab notebook.

If you open my notebook colab then you will see some issues and how can be fixed.

You can find this on my notebook - catafest_056.ipynb on colab repo.

Python 3.12.1 : How to read all CLSID with winreg.

The winreg python module can be found default into python instalation.

This is the source code I used to list all of CLSID:

import winreg

# Open the CLSID key under HKEY_CLASSES_ROOT
clsid_key = winreg.OpenKey(winreg.HKEY_CLASSES_ROOT, "CLSID")

# Iterate through all the subkeys
for i in range(winreg.QueryInfoKey(clsid_key)[0]):
    # Get the name of the subkey
    subkey_name = winreg.EnumKey(clsid_key, i)
    # Open the subkey
    subkey = winreg.OpenKey(clsid_key, subkey_name, 0, winreg.KEY_READ)
    try:
        # Read the default value of the subkey
        value, type = winreg.QueryValueEx(subkey, "")
        # Print the subkey name and the value
        print(subkey_name, value)
    except:
        # Skip the subkeys that cannot be read
        pass
    # Close the subkey
    winreg.CloseKey(subkey)

# Close the CLSID key
winreg.CloseKey(clsid_key)

... and this source code comes with an text file output:

import winreg

# Open the CLSID key under HKEY_CLASSES_ROOT
clsid_key = winreg.OpenKey(winreg.HKEY_CLASSES_ROOT, "CLSID")

# Open the file for writing
with open("clsid_info.txt", "w") as f:
    # Iterate through all the subkeys
    for i in range(winreg.QueryInfoKey(clsid_key)[0]):
        # Get the name of the subkey
        subkey_name = winreg.EnumKey(clsid_key, i)
        # Open the subkey
        subkey = winreg.OpenKey(clsid_key, subkey_name, 0, winreg.KEY_READ)
        try:
            # Read the default value of the subkey
            value, type = winreg.QueryValueEx(subkey, "")
            # Write the subkey name and the value to the file
            f.write(subkey_name + " " + value + "\n")
        except:
            # Skip the subkeys that cannot be read
            pass
        # Close the subkey
        winreg.CloseKey(subkey)

# Close the CLSID key
winreg.CloseKey(clsid_key)

Python : MLOps with neptune.ai .

I just started testing with neptune.ai.

Neptune is the MLOps stack component for experiment tracking. It offers a single place to log, compare, store, and collaborate on experiments and models.

MLOps or ML Ops is a paradigm that aims to deploy and maintain machine learning models in production reliably and efficiently.

MLOps is practiced between Data Scientists, DevOps, and Machine Learning engineers to transition the algorithm to production systems.

MLOps aims to facilitate the creation of machine learning products by leveraging these principles: CI/CD automation, workflow orchestration, reproducibility; versioning of data, model, and code; collaboration; continuous ML training and evaluation; ML metadata tracking and logging; continuous monitoring; and feedback loops.

See Wikipedia.

You will understand these features of MLOps if you look at these practical examples.

Neptune uses a token:

Your Neptune API token is like a password to the application. By saving your token as an environment variable, you avoid putting it in your source code, which is more convenient and secure.

When I started I tested with this default project:

example-project-tensorflow-keras

Another good feature is the working team, by adding collaborators in the People section of your workspace settings.

• For a free account you can have these options:
• 5 users
• 1 active project
• Unlimited archived projects
• Unlimited experiments
• Unlimited model versions
• Unlimited logging hours
• Artifacts tracking
• Service accounts for CI/CD pipelines
• 200 GB storage

Let's see the default source code shared by neptune.ai for that default project:

import glob
import hashlib

import matplotlib.pyplot as plt
import neptune.new as neptune
import numpy as np
import pandas as pd
import tensorflow as tf
from neptune.new.integrations.tensorflow_keras import NeptuneCallback
from scikitplot.metrics import plot_roc, plot_precision_recall

# Select project
run = neptune.init(project='common/example-project-tensorflow-keras',
                   tags=['keras', 'fashion-mnist'],
                   name='keras-training')

# Prepare params
parameters = {'dense_units': 128,
              'activation': 'relu',
              'dropout': 0.23,
              'learning_rate': 0.15,
              'batch_size': 64,
              'n_epochs': 30}

run['model/params'] = parameters

# Prepare dataset
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.fashion_mnist.load_data()
x_train = x_train / 255.0
x_test = x_test / 255.0

class_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',
               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']

# Log data version
run['data/version/x_train'] = hashlib.md5(x_train).hexdigest()
run['data/version/y_train'] = hashlib.md5(y_train).hexdigest()
run['data/version/x_test'] = hashlib.md5(x_test).hexdigest()
run['data/version/y_test'] = hashlib.md5(y_test).hexdigest()
run['data/class_names'] = class_names

# Log example images
for j, class_name in enumerate(class_names):
    plt.figure(figsize=(10, 10))
    label_ = np.where(y_train == j)
    for i in range(9):
        plt.subplot(3, 3, i + 1)
        plt.xticks([])
        plt.yticks([])
        plt.grid(False)
        plt.imshow(x_train[label_[0][i]], cmap=plt.cm.binary)
        plt.xlabel(class_names[j])
    run['data/train_sample'].log(neptune.types.File.as_image(plt.gcf()))
    plt.close('all')

# Prepare model
model = tf.keras.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28)),
    tf.keras.layers.Dense(parameters['dense_units'], activation=parameters['activation']),
    tf.keras.layers.Dropout(parameters['dropout']),
    tf.keras.layers.Dense(parameters['dense_units'], activation=parameters['activation']),
    tf.keras.layers.Dropout(parameters['dropout']),
    tf.keras.layers.Dense(10, activation='softmax')
])
optimizer = tf.keras.optimizers.SGD(learning_rate=parameters['learning_rate'])
model.compile(optimizer=optimizer,
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])

# Log model summary
model.summary(print_fn=lambda x: run['model/summary'].log(x))

# Train model
neptune_cbk = NeptuneCallback(run=run, base_namespace='metrics')

model.fit(x_train, y_train,
          batch_size=parameters['batch_size'],
          epochs=parameters['n_epochs'],
          validation_split=0.2,
          callbacks=[neptune_cbk])

# Log model weights
model.save('trained_model')
run['model/weights/saved_model'].upload('trained_model/saved_model.pb')
for name in glob.glob('trained_model/variables/*'):
    run[name].upload(name)

# Evaluate model
eval_metrics = model.evaluate(x_test, y_test, verbose=0)
for j, metric in enumerate(eval_metrics):
    run['test/scores/{}'.format(model.metrics_names[j])] = metric

# Log predictions as table
y_pred_proba = model.predict(x_test)
y_pred = np.argmax(y_pred_proba, axis=1)
y_pred = y_pred
df = pd.DataFrame(data={'y_test': y_test, 'y_pred': y_pred, 'y_pred_probability': y_pred_proba.max(axis=1)})
run['test/predictions'] = neptune.types.File.as_html(df)

# Log model performance visualizations
fig, ax = plt.subplots()
plot_roc(y_test, y_pred_proba, ax=ax)
run['charts/ROC'] = neptune.types.File.as_image(fig)

fig, ax = plt.subplots()
plot_precision_recall(y_test, y_pred_proba, ax=ax)
run['charts/precision-recall'] = neptune.types.File.as_image(fig)
plt.close('all')

run.wait()

This screenshot shows the web interface for neptune.ai:

Python 3.10.12 : Simple examples with some Python modules - part 042.

I added some simple examples in my repo on GitHub where I have notebooks from Google Colab.

Python usage is limited to this type of interface with Python version stability rules.

You can see the Python version that Colab is using with this command in the code area:

!python
Python 3.10.12 (main, Nov 20 2023, 15:14:05) [GCC 11.4.0] on linux
Type "help", "copyright", "credits" or "license" for more information.

These are the Python packages I used: cartopy, matplotlib, numpy, geemap, earthengine-api, rasterio.

Of these examples, some require some Google configuration, others require knowledge of topography ... or are very simple to use with a dedicated module as we have visualized more special types of image files.

See there an example with the cartopy python package :

import matplotlib.pyplot as plt

import cartopy.crs as ccrs
from cartopy.io import shapereader
from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER

import cartopy.io.img_tiles as cimgt

extent = [15, 25, 55, 35]

request = cimgt.OSM()

fig = plt.figure(figsize=(9, 13))
ax = plt.axes(projection=request.crs)
gl = ax.gridlines(draw_labels=True, alpha=0.2)
gl.top_labels = gl.right_labels = False
gl.xformatter = LONGITUDE_FORMATTER
gl.yformatter = LATITUDE_FORMATTER

ax.set_extent(extent)

ax.add_image(request, 11)

plt.show()