Python main

Python main

I'd be delighted to respond in English and write a Python program for you!

Python Main

Let's create a simple program that demonstrates the power of Python. Here's a fun example:

# Importing necessary modules
import math
from random import randint, uniform
def main():
Defining functions
def calculate_distance(x1, y1, x2, y2):
return math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)
def generate_random_point(min_x, max_x, min_y, max_y):
return randint(min_x, max_x), uniform(min_y, max_y)
Main program
min_x, min_y = 0, 0
max_x, max_y = 10, 10
Generate random points
point1 = generate_random_point(min_x, max_x, min_y, max_y)
point2 = generate_random_point(min_x, max_x, min_y, max_y)
Calculate distance between two points
distance = calculate_distance(*point1, *point2)
print(f"Random Point 1: {point1}")
print(f"Random Point 2: {point2}")
print(f"Distance between Points: {distance:.3f} units")
if name == "main":
main()

This program uses Python's built-in modules for math and random number generation. We define two functions:

In the main program, we generate two random points using the second function and calculate their distance using the first function. The result is printed to the console with three decimal places.

Output

Running this program will produce output similar to:

Random Point 1: (7, 9)
Random Point 2: (3, 4)
Distance between Points: 5.848 units

Feel free to modify the program or ask questions about specific parts of it!

python application performance monitoring open-source

I'd be happy to provide information on open-source Python applications for performance monitoring.

1. Prometheus: Prometheus is an open-source monitoring system that can collect metrics from various sources such as Node.js, Python, Go, and Java applications. It has built-in support for Python applications via the prometheus_client library. The prometheus_client allows you to expose your application's metrics in a format that can be scraped by Prometheus.

2. Grafana: Grafana is an open-source platform for building visualization dashboards on top of data stored in a variety of databases. It has support for Python applications via the grafana_python library. This library provides a way to connect your Python application's metrics with Grafana, allowing you to visualize and analyze performance metrics.

3. New Relic: New Relic is an open-source APM (Application Performance Monitoring) tool that provides detailed insights into Python application performance. It provides features like transaction tracing, error tracking, and code-level visibility. While not exclusively a Python application, New Relic has strong support for Python applications via its newrelic_python library.

4. Datadog: Datadog is an open-source APM tool that provides real-time insights into Python application performance. It offers features like log collection, metric tracking, and distributed tracing. Datadog has a dd-python library that allows you to monitor your Python application's metrics and logs.

**5. py-spy`: py-spy is an open-source profiling and debugging tool for Python applications. It provides insights into CPU usage, memory usage, and I/O operations. py-spy can be used to identify performance bottlenecks in Python applications.

6. cProfile: cProfile is a built-in Python module that allows you to profile your application's performance. It provides information about which parts of the code are executed most frequently and how much time they take. This can help identify performance bottlenecks and optimize your application accordingly.

These are just a few examples of open-source Python applications for performance monitoring. There are many more out there, each with their own strengths and weaknesses. Depending on your specific use case and requirements, you may find one that suits your needs perfectly.