Defozo

# G-code Post-processing Script for Temperature Management during Ironing Sections
# This script modifies G-code files to set a specific lower temperature at the start of the ironing section and reverts to the previously set temperature after the ironing section ends. This modification is particularly useful when printing with hardened steel nozzles at higher temperatures, which can lead to clogging issues when the filament is not being extruded.

# How It Works:
# - The script scans the G-code for temperature commands and ironing section markers.
# - It inserts a command to reduce the temperature at the start of ironing to a specified lower level and another to revert it back to the previous temperature after ironing ends.

# Quickstart Guide:
# 1. Download and save this script.
# 2. Setup in PrusaSlicer:

Defozo

# G-code Filament Management Enhancement Post-processing Script
# This script modifies G-code files by removing the initial 'M600' filament change command and all 'T' series toolhead change commands, facilitating better filament management during prints.
#
# Problem Description:
# The standard "Color change" feature in PrusaSlicer has significant limitations:
# 1. It only allows filament changes between layers, which restricts the timing of such changes and can impact print quality and detail.
# 2. It requires that all used filaments share similar properties, such as melting temperature and extrusion rates, limiting material versatility.
#
# Alternative Approach:
# An alternative method involves using "Custom G-code" settings to manage filament changes:

Defozo

# Displays graph amplitudes x decibels from selected microphone

import pyaudio
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.animation import FuncAnimation

# Constants
FORMAT = pyaudio.paInt16  # Audio format (16-bit PCM)
CHANNELS = 1             # Mono audio

Defozo

# AWS ECS Comprehensive Cleanup Script
#
# Description:
# This PowerShell script is tailored for a thorough cleanup of Amazon Elastic Container Service (ECS) resources across multiple AWS regions.
# It is particularly beneficial in instances where AWS credentials have been compromised, leading to the unauthorized and potentially 
# malicious creation of numerous ECS resources, including clusters, services, and task definitions.
#
# The script systematically automates the deletion process. It iterates over a predefined list of AWS regions, identifies and deletes 
# all ECS task definitions, then proceeds to identify all ECS clusters within each region, stops and deletes all services within those 
# clusters, and finally deletes the clusters themselves.

Defozo

/*
This Pipedream workflow is designed to integrate with Toggl and Habitica. 
For every 1 minute logged in Toggl, it scores a specified task in Habitica. 
The workflow is structured as follows:

1. Toggl is used as the trigger.
2. The `get_record_or_create` step retrieves or creates a record.
   - Uses the "Data Store" feature in Pipedream.
   - The key for the data store is the ID from the Toggl payload (`steps.trigger.event.payload.id`).
   - The value stored is the duration from the Toggl payload (`steps.trigger.event.payload.duration`).