Keep all macOS Software Up-to-Date with a Single Command

Updates are important. While not as great as on Linux, you can use different package managers to manage all installed software and easily keep applications updated. Here the command I use. Requires homebrew, homebrew-cask-upgrade and mas.


Find sites vulnerable to WordPress Content Injection Vulnerability

WordPress’ update cycle is reaching the speed of Windows XP. Even Google is sending out warnings, urging site owners to update.  For me they were not accurate, but there are still many vulnerable sites out there.

One could – for example – use Nerdydata to search the internet’s source code for vulnerable WP versions. A simple search across their “Popular sites” dataset reveals close to 300 matches.

Regex used:  ver=4.7(\.1)?' 

Using the same trick, you could also identify vulnerable WP installs you are managing. Here a GIST to a short Python script.

Optimize Spamassassin Detection Rate

Email is a terrible way to communicate and should be avoided where possible. Unfortunately it is also the lowest common denominator on the web and will continue to be for the near future.

In the early days of the internet it was easy to run your own mailserver. Due to the absurd quantity of spam this task got increasingly harder and many tech-savvy people gave up and switched to Gmail or other services. This is a pity because a decentralized email infrastructure is harder to surveil, subpoena or shut down. I encourage everyone to run their own mail service if possible.

In this guide I will summarize the steps needed to get an effective spamassassin (SA) setup. Continue reading

Cheaply retrieve data from Amazon AWS Glacier

When it launched Amazon Glacier was applauded for providing a super-cheap long-term storage solution. While there are no surprises when uploading and storing files, retrieving them can get expensive. The pricing reflects the fact that Amazon needs to retrieve your files from tape, which is expensive and takes a long time. Several users reported high charges after retrieving their backups. To its defence, Amazon published a very detailed FAQ on this topic.

The key to getting your files back on the cheap is time. You can retrieve 5% of your total files for free each month, but that amount is calculated per hour, rather than per month or day. Example: You keep 500GB in Glacier. 500GB*5%/30/24=36MB/hour.

That’s great to know, but how can you keep retrieving 36MB for days or months without doing it manually? If you’re on OSX or Linux you can use mt-aws-glacier. It’s a Perl script to track and retrieve your Glacier files. Using mtglacier for slow retrieval isn’t straight forward. There is an issue to improve it, but for now let’s work with what’s available.

The tool has two commands to restore files. One initiates the job on Amazon’s side. After the job completes (takes about 4h), the files can be downloaded. With that in mind, we need to tell mtglacier to only request a few files and then sleep for another hour. There is no way to limit the file size to be requested, but the number of files can be limited. As long as you have many small files, this works great.

If your hourly allowance is 35MB and your average file size is 10MB, 3 files per hour is about right with some space for error.

Hope this helps.

Yahoo: Email not accepted for policy reasons

Yahoo failed as internet company for a reason. Try sending an email with a link to a bank website. E.g. CIMB (Popular across Asia)

Your email will be rejected by Yahoo. Just awesome…

Workaround: Use a shortlink to hide your URL. E.g. Now your phishing emails will arrive safely. 😉

Download Uber ride history to Python Pandas

With Uber rides this cheap and self-driving cars around the corner, I doubt that future generations will have their own cars. Except for extreme use cases, like commuting from the countryside.

Personally I spent EUR 91 on Uber this year (2 months) and it got mit 260 km. That’s 0.31 EUR/km.

There is an API to download your rides, but getting receipts/prices didn’t work for me, so I had to scrape them from the website directly. Hope Uber allows getting prices from their API at some point.

This is the code I used. The Uber-logins aren’t automated yet.

New Release of invoice2data

Thanks to some awesome contributors, there is a new release for invoice2data. This Python package allows you to get structured data from PDF invoices. Major enhancements:

  • powerful Yaml-based template format for new invoice issuers.
  • improved date-parseing thanks to dateparser.
  • improved PDF conversion thanks to new feature in xpdf
  • better testing and CI
  • option to add multiple keywords and regex to each field
  • option to define currency and date format (day or month first?)

All details and download on Github.

Unit testing for Jupyter (iPython) notebooks

At Quantego, we do most high-level work that supports energy analysts in Jupyter Notebooks. This allows us to pull several Java and Python packages together for a highly productive work environment.

Sample notebooks are hosted on Github and distributed with our Docker images. Of course we prefer for our sample notebooks to work, when people run them. They also uncover potential problems, by running at a very high level and thus using almost all available features.

If you have a similar setup – for example in a data analytics-driven environment – the following could work for you as well:

  1. Make sure your notebooks run correctly when running “Run All”. When testing you may try different things and run cells out of order. For automatic testing to work, they should run all in sequence.
  2. Test locally with

    This will convert your notebooks to HTML. We’re not intersted in the output, only potential errors during conversion. This only works with Jupyter and iPython >=4. Previous versions simply ignore errors.
  3. Next you could just run the same command in an isolated Docker container or in a CI step

    A full working example for CircleCI can be found in our sample-repo.

Shell Function to Remove all Metadata from PDF

A handy function to remove all metadata from a PDF file. When done it will show all the remaining metadata for inspection. Needs pdftk and exiftool installed.

Combines commands from here and here. Good job, guys.

After adding this snippet in ~/.profile or copy and pasting it in the shell, you can just run

Incremental FTP backups

If you happen to only have FTP access to a server or account (CPanel) you’re looking after, LFTP is an efficient tool to keep incremental backups. This will make hard links of the previous backup and updated it, copying and storing only changed files.

Extend Pandas DataFrame with custom functions and attributes

At we love working with Pandas Dataframes. We use them to store and analyze results from simulation runs. On top of our data matrix and a multi-level index we also need to accommodate custom plotting functions and attributes from the previous simulation run.

Subclassing  pandas.DataFrame for this task was a no-brainer. The new version 0.16.1 (to be released in the next days) includes some fixes to make working with subclasses of complex data-frames (DF) easier. Here an example of what can be done. First define two new classes for  pandas.Series (single col DF) and  pandas.DataFrame . You can define new functions or attributes, as needed.

Notice  _constructor  and  _constructor_sliced . They make sure you get the correct class back, when slicing the DF.

Via  self  you have convenient access to all Pandas functions and can even roll your own.

Regex to find phone numbers in every format

I couldn’t find a truly universal regular expression (regex) to match phone numbers, no matter from which country and in which format. They all seemed to be limited in some way. Even named entity extraction APIs require you to set a country to find phone numbers.

In the end I rolled my own regex. It simply looks for a certain amount of numbers and characters generally used to make phone numbers human-readable. If you are looking to match longer or shorter numbers, you can just change the quantifiers. Some examples it will match:

While not matching:

And here the regex:

To use it in Python


Let me know, if this is useful for you or if you find space for improvement. Currently the biggest issue I see is that the matching ranges between numbers and total chars are unrelated. Due to many filling chars higher values are needed. Those can lead to false negatives. Best test it for yourself.

Python clipboard access

I was using Python and Jinja2 to generate some tables with 100+ rows for WordPress. This package saved me the extra step to open a file and copy+paste it from there.

There should be many other uses to integrate it into semi-automated workflows.

Check it out here.

Extract structured data from PDF invoices

Most invoices exist in electronic format. They are generated from structured data and need to be entered as structured data. It’s a shame that we still need humans to manually extract data points, like amount, date or issuer from it.

In the last days, I tried a few online invoicing solutions, like shoeboxed, but none of them does a good job at automatically recognizing new invoices. Some do it manually and charge accordingly.

Currently I don’t see a way to automatically get the data. PDFs are simply not made for this. the best we can do is to add templates for a specific invoice format and use that to extract the data. I have created a proof of concept library, which is open source on github.

If you have any thoughts on what to improve or would like to extend this to use it in a production accounting, let me know.