michaeltoohig

why this code ?

P.S. How to quickly run postgres (using docker)

Don’t Feed the Beast – the Great Recruitment Agency Infestation

“Don’t move to that London” warned my northern grandfather once. “It’s full of spivs”.

The Oxford Dictionary (somewhat chauvinistically) defines a spiv as:

A man, typically a flashy dresser, who makes a living by disreputable dealings

“But I work in IT” I told him. “engineers aren’t like that”.

This is a simple example of how to use loguru in your flask application

Just create a new InterceptHandler and add it to your app. Different settings should be configured in your config file, so that it is easy to change settings.

Logging is then as easy as:

from loguru import logger

logger.info("I am logging from loguru!")

Docker compose as a systemd unit

Create file /etc/systemd/system/[email protected]. SystemD calling binaries using an absolute path. In my case is prefixed by /usr/local/bin, you should use paths specific for your environment.

[Unit]
Description=%i service with docker compose
PartOf=docker.service
After=docker.service

my database had 72k annotations at the time I ran these benchmarks, here's the result:

$ python scripts/batch_bench.py conf/development-app.ini dumb
Memory summary: start
      types |   # objects |   total size
=========== | =========== | ============
       dict |       13852 |     12.46 MB
  frozenset |         349 |     11.85 MB
VM: 327.29Mb

	import { encode } from "fast-png";
	import type { GeoTIFFImage, ReadRasterResult } from "geotiff";
	import { fromUrl, Pool } from "geotiff";
	import type { RasterSourceSpecification } from "maplibre-gl";
	import maplibregl from "maplibre-gl";

	const tileSize = 256;
	const decoderPool = new Pool();

	/**

	<script setup lang="ts">
	import { storeToRefs } from 'pinia';
	const notifyStore = useNotifyStore();
	const { notifications } = storeToRefs(notifyStore);
	</script>

	<template>
	....
	<div class="toast toast-end toast-top">
	<div v-for="notification in notifications" :class="notification.type">

	import os
	import pickle
	import warnings

	import numpy as np
	import pandas as pd
	from sklearn.model_selection import train_test_split
	from tensorflow.keras.callbacks import EarlyStopping
	from tensorflow.keras.layers import Dense
	from tensorflow.keras.layers import Dropout

	# Rate limiting with Celery + Django + Redis
	# Multiple Fixed Windows Algorithm inspired by Figma https://www.figma.com/blog/an-alternative-approach-to-rate-limiting/
	# and Celery's sometimes ambiguous, vague, and one-paragraph documentation
	#
	# Celery's Task is subclassed and the is_rate_okay function is added


	# celery.py or however your App is implemented in Django
	import os
	import math

	#!/usr/bin/env python
	# coding: UTF-8

	'''
	Setup apc mini colors without Ablton Live

	----------

	> brew install portmidi