SSH Port Forwarding Guide

2021-10-22

1625 words 8 mins read

Port forwarding

Setting up proper port forwarding for SSH connections, servers or just test purposes is crucial but isn’t as straight forward as your connection should be. This post aims to be a quick guide for different use cases.

Convert Python Scripts to Windows Executables (.py to .exe) - Pyinstaller and Pandas | 2022 WORKING VERSION

2021-06-25

python windows

902 words 5 mins read

Python and pandas are a great team for data science. But what if you need to deliver a script to a Windows client who does not have Python installed?

This is a quick tutorial for converting Python scripts (.py) to standalone, one-file Windows executables (.exe).

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pyinstaller -F my_script.py

Get a 20Mb exe file that is fully functional on every Windows machine!

An Introduction to HyperLogLog

2021-04-13

data visualization data-science

2369 words 12 mins read

HLL

Have you ever wondered how HyperLogLog works? Or have you never heard of it at all? In this post I explain the wonderful algorithm of Flajolet et al. from scratch and in a very simple manner.

Life Hacking: Adding Geographical Filters to Flat Search Websites with Turf.js

2021-02-18

javscript

1496 words 8 mins read

Milano Metro Stops

Flat hunting can be a big pain - particularly for European metropoles. Most flat search platforms already offer good filter options but until now, I didn’t see any with custom geographic filters. Apart from drawing your own area of interest on a map, you seldomly find filters such as “How far is the next metro stop?” or “How close are important facilities?”.

This article will show a quick and universal workaround for custom geographic filters based on the fantastic pure javascript GIS Turf.js. I implement a buffer based on Milano’s metro stops, verify that the flat is located within the buffer and open the respective offer in a new browser tab.

Using HyperLogLog with Leaflet-Hexbins

2021-02-11

leaflet data visualization data-science

1263 words 6 mins read

Leaflet hexbins

Leaflet-Hexbins is a powerful tool to aggregate data and visualize it quickly on a map. It can size the hexbin radius based on a feature values. But what if we used HyperLogLog data to preserve user-privacy? This post will provide a convenient way to quickly perform on-the-fly HyperLogLog unions for leaflet-hexbins.

No more backend-constraint for HyperLogLog

There are many HyperLogLog implementations out there but few work as well as postgres-hll and js-hll. They can even talk to each other by exchanging hexstrings! Usually postgres-hll is pretty much the standard extension to use, when dealing with HyperLogLog but is obviously bound to a postgres database running somewhere. With js-hll, the backend-constraint for HyperLogLog is gone, just so that users can perform any HyperLogLog action in their frontend!

HyperLogLog Javascript (js-hll) & Postgres (postgres-hll): making them talk to each other!

2021-02-11

leaflet data visualization data-science

844 words 4 mins read

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hex_array = ["/x138b7f010101c101e3024104e1058305a105c3072207810922098209a209c30b220c410ce20e210e410e610ee11002106310a210e31323138513a213c2142114421463156115a115e416011661168316a216e1186218a31927194119611aa81b411b611be11c411d431dc21e22218121a121e122a322e12324236123c224c12641266127c3288329442a012a612b012be22cc52d412e612e812fa12fc1302131843282338133e335023541356235a23623364138233863388139813a023a623a823d033d413e613ea13f4140a1414141e242014322438143a24406458145a145e546e14721474248014822486249214a234ae14b414b614be34ca24cc14d424d614da14e214fa250225101514552e453215423548154c355c1564558e3590359415b215b415d625da25f225f836122614162426422648164a16681672367c168016822686168a169216943698169c56b426b816d016e616ee16f416fc17102718471e17326742474e177027742776277e2782378a27901794179817a027a617aa17b217b817c627ce57d817dc47e837ee3800280848181826582a382c1832584e18502854285628607864187e5880188618883898289e28a248a438aa18ac18ae28b028b218b818ca28e028ea58f278fe4904190e192c192e1932193e2940194e2958395c2962296a496e198229923994499e19a619aa59ae39be29ce29e219e419ee49f039f219f63a001a041a181a1c2a202a241a341a462a483a4e3a501a564a5c1a622a6e1a801a867a941a981aaa3aae1ab24abe1ac01acc2ad62ae01ae21afc1b001b082b0a2b103b182b1e2b2a2b2e2b321b3a2b541b581b5e4b6e1b7e1b804b821b922ba21ba41ba83bbe3bc41bc82be04bec3bf01c021c0a2c204c262c2e1c3c1c542c681c782c861c8a1c923ca21cac1cf61d042d0a2d1a2d222d3e1d5a1d5e2d681d6e2d741d7e1d822d961d9c2da03da61da81db42db63db81dc01dd27dd44dde3de22dea1dec4df69dfe1e043e082e162e281e301e321e3c7e521e562e5a2e6a8e6c4e721e742e764e782e821e8e1e925ea45eac2eb81eba5ecc1ece4edc2ede2ee02eea1eec3eee1ef83efa1f082f0a7f121f1c1f2c3f3c2f405f464f501f521f642f661f682f6a3f724f781f8e1f924f981f9a1fa01fbe1fc03fc41fcc2fdc2fe81fea1ffc3ffe1",
"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",
"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"]

hex_array.reduce(function (first, second) {
   return first.union(
     hll.fromHexString(
       second.replace(/\\\\/g,"/"))
       .hllSet)}, new hll.HLL(11, 5)
    ).cardinality();
// 567

HyperLogLog is a fantastic algorithm to quickly count distinct elements in a set. As it consumes few memory and is really fast, it’s perfectly suited for big data. For a nice, visual explanation step over to the Redis guys on Youtube. However, this article is not about explaining HyperLogLog but rather understanding how to deal with backend/frontend communication between javascript and postgres by exchanging hexstrings between the two amazing libraries postgres-hll for postgres and js-hll for javascript.

Querying Postgres with Python Fastapi Backend and Leaflet-Geoman Frontend - Applying Geometry Filters

2021-02-10

leaflet data visualization

1368 words 7 mins read

Geometry Filter Popup

Querying Postgres with Python Fastapi Backend and Leaflet & Geoman Fronted - Applying Geometry Filters

How to query a database with a user-defined geometry drawn on a Leaflet frontend?

A very common use case for map applications are custom filters. Either thematic, temporal or value-based filters play a significant role but probably in a geographical context the most dominant one might be spatial filtering. In this blog post, I describe a simple boilerplate setup based on postgres, fastapi and geoman.