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.

clean_pdf() {
 pdftk $1 dump_data | \
  sed -e 's/\(InfoValue:\)\s.*/\1\ /g' | \
  pdftk $1 update_info - output clean-$1
 
 exiftool -all:all= clean-$1
 exiftool -all:all clean-$1
 exiftool -extractEmbedded -all:all clean-$1
 qpdf --linearize clean-$1 clean2-$1
 
 pdftk clean2-$1 dump_data
 exiftool clean2-$1
 pdfinfo -meta clean2-$1
}

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

clean_pdf my-unclean.pdf

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.

#!/usr/bin/env bash
username='xxx'
password='xxx'
host='ftp.host'
localBackupDir='/backups/host'
remoteDir='/public_html/'
cd $localBackupDir
rm -rf backup.3
mv backup.2 backup.3
mv backup.1 backup.2
mv backup.0 backup.1
cp -al backup.1 backup.0 #-al or -r
lftp -e "set ssl:verify-certificate no; \
         mirror --only-newer --parallel=4 $remoteDir $localBackupDir/backup.0;\
         exit"\
     -u $username,$password $host

At Quantego.com 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.

class CustomSeries(pandas.Series):
    @property
    def _constructor(self):
        return CustomSeries

    def custom_series_function(self):
        return 'OK'

class CustomDataFrame(pandas.DataFrame):
    "My custom dataframe"
    def __init__(self, *args, **kw):
        super(CustomDataFrame, self).__init__(*args, **kw)

    @property
    def _constructor(self):
        return CustomDataFrame

    _constructor_sliced = CustomSeries

    def custom_frame_function(self):
        return 'OK'

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.

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:

540 297 1860
+886-2-8663-8287
0870882993
0090 530 229 12 04
+66 (0) 28340463
058 218 0600
(2014-2015
062-21-8608888
886-5-2781880
011-81-27372-9341
03- 7722 5012
+886-2-8663-8287
+62 – 21 – 5694 2002
+34 918 380 082
+90 532 643 34 34
+7 495 228 3513
+ 7 702 270 38 13 + 7 777

While not matching:

9:00-17:00
2015(15:00

And here the regex:

(?!.*[a-zA-Z\,:])(?=(\D*\d){7,14})([\+\d\(]{1,2}.{6,23}\d)

To use it in Python

import re

rex = '(?!.*[a-zA-Z\,:])(?=(\D*\d){7,14})([\+\d\(]{1,2}.{6,23}\d)'
numbers = re.findall(rex, str_with_phone_numbers)

 

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.

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.

Docker is a great set of technologies. Once you are comfortable with using it, you are presented with a set of challenges, you didn't have before. To name some:

• log consolidation: How to retrieve log files from dozens of containers?
• monitoring: How much RAM and CPU is each container using?

There are a few articles on this topic out there. After reading them none of the solutions really hit me, but they all had some nice features which I chose to combine here. Continue reading →

Simple Python script to download files via Linksnappy.

#! /usr/bin/env python

import requests
import json
import sys

USERNAME = 'my username'
PASSWORD = 'my pass'

params = {'link': sys.argv[1],
'type': '',
'username': USERNAME,
'password': PASSWORD}

resp = requests.post('http://gen.linksnappy.com/genAPI.php',
data={'genLinks': json.dumps(params)})

url = json.loads(resp.text)['links'][0]['generated']

local_filename = url.split('/')[-1]

# http://stackoverflow.com/questions/16694907/how-to-download-large-file-in-python-with-requests-py
r = requests.get(url, stream=True)
    with open(local_filename, 'wb') as f:
        for chunk in r.iter_content(chunk_size=1024):
            if chunk: # filter out keep-alive new chunks
                f.write(chunk)
                f.flush()
print local_filename

I like to write my notes and reports in Markdown and then send them out in PDF. Gimli worked OK for a while but rasterizes files and doesn't work with UTF8-characters. I finally came across a similar project in Python and now I'm very happy with it. You can define a custom style sheet in your .profile and md2pdf will use it.