Making a Dobsonian from an Ikea Bedside Table

The Ikea RAST bedside table is cheap, made of pine, and happens to be almost a perfect fit for a typical 8" Newtonian OTA.
The upper and lower shelves just about fit an 8" OTA, and if you remove the left vertical plank in the photo above, and screw it to the back of the two shelves, you have the beginnings of a Dobsonian base.

I used an 8" f/4 Newtonian (an AT8IN actually) with its tube rings, and bolted two halves of a 12" diameter chopping board to the tube rings to serve as altitude bearings.  I had to use a number of fender washers to get the spacing right, but the diameter of the AT8IN is almost perfect for the Ikea table (about three washers were required to get the right spacing).



A significant advantage of this scheme is that by removing the trunnion bearings (which are held on with two M6 hex-head bolts on each side) and attaching a dovetail, the scope can be used for imaging.

Also, the scope has an extended-length aluminum tube of 2mm thickness, which is incredibly rigid (the original AstroTech tube was 0.8mm thick steel) while weighing the same as the original tube. In addition, because the tube is extended, focus can be achieved with a Paracorr without racking out the focuser, which improves the rigidity of the imaging train significantly.



Here's another view of the 12" diameter chopping board. I used the same type of chopping board as the azimuth base (and printed setting circles as well). It was necessary to cut V-shaped channels in the side of the Ikea bedside table to support the trunnion bearings.

The bearing surface is a rough glass fiber (FRP) strip that I purchased from AstroGoods.com and the bearings are virgin teflon blocks. The azimuth bearing surface is also an FRP strip from AstroGoods (I did not go for the vinyl LP record trick here).

I have used this setup to track Mars at 320X during the last opposition and while it's a tricky affair, it's actually usable. While the AstroTech 8" f/4 has a huge secondary, for Mars it was quite acceptable due to the small exit pupil.






I replaced the secondary collimation bolts with stainless steel M4 hardware:



There is a CatsEye hotspot on the primary mirror:



And all steel fasteners have been replaced with hex-head stainless steel hardware. Also note the seam in the faux carbon fiber covering: the aluminum tube underneath is unpainted, and I wrapped the tube in vinyl carbon fiber lookalike (used for wrapping cars).


The primary collimation locking bolts have been relocated next to the collimation knobs, and the primary support springs have been replaced with much stronger ones. This allows the Newtonian to hold collimation very well when used for imaging:


In addition, the telescope has a 2" Feathertouch focuser which is incredibly smooth and a joy to use.



I don't like to think about how much I've spent on this project in total. It is certainly several multiples of the price of a stock 8" dobsonian. However, this scope is better than a stock 8" dobsonian or a stock 8" imaging Newtonian in all possible ways.






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Parsing VPC Flow Logs with Pandas

Here's a trivial code snippet to parse AWS VPC Flow Logs. This is extremely useful when setting up permissive security groups and then tightening them up later.

This script will (probably) fail if there are too many VPC flow log files (and therefore the Python interpreter would run out of memory). However it's nice to see that Pandas read_csv can read S3 URL's directly (even gzip'ped CSV files).

You can also filter for REJECT  rule and find out all the IP's that have been attempting to attack you.


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from boto.s3.connection import S3Connection
import pandas as pd
import os

srcbucket = 'flowlogs-bucket-orly'
aws_access_key = 'AKIAxxx'
aws_secret_key = 'oRCIxxx'

os.environ['AWS_ACCESS_KEY_ID'] = aws_access_key
os.environ['AWS_SECRET_ACCESS_KEY'] = aws_secret_key

cols = [ 'timestamp', 'version', 'accountid', 'interfaceid', 'srcaddr', 'dstaddr', 'srcport', 'dstport', 'protocol', 'packets', 'bytes', 'start', 'end', 'action', 'logstatus']

# iterate over all the VPC flowlogs in the bucket
conn = S3Connection(aws_access_key, aws_secret_key)
bucket = conn.get_bucket(srcbucket)

count = 0
for key in bucket.list():
    keystr = key.name.encode('utf-8')
    if 'eni-' in keystr:
        s3url = 's3://' + srcbucket + '/' + keystr
        count = count + 1
        
        if (count == 1):
            df = pd.read_csv(s3url, delim_whitespace=True, header=None, names=cols, low_memory=True)
            df = df [ df['action'] == 'ACCEPT']
        else:
            df2 = pd.read_csv(s3url, delim_whitespace=True, header=None, names=cols, low_memory=True)
            df2 = df2 [ df2['action'] == 'ACCEPT']
            df = df.append(df2, ignore_index=True)
        
        print 'Processed ' + keystr

src = df.groupby(['srcaddr', 'dstaddr', 'dstport']).size().reset_index()
src.columns = [ 'srcaddr', 'dstaddr', 'dstport', 'count' ]

n = src.sort_values(by = ['count', 'srcaddr', 'dstaddr'], ascending=False)

print(n)
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