The MERLIN system


MERLIN, the Multi-Element Radio Linked Interferometer Network, radio telescope array that is spread throughout central England and Wales. The interesting aspect of MERLIN for our uses is high-quality, minimally diverse location identifiers are available publicly. They make a reasonably useful test case for upoints objects across small geographic distances.

According to the official MERLIN documentation the locations of the array elements are:

Name Latitude Longitude
Cambridge 52°10′06.48″N 000°02′23.25″E
Darnhall 53°08′38.40″N 002°32′45.57″W
Defford 52°05′27.61″N 002°08′09.62″W
Knocking 52°46′40.83″N 003°00′39.55″W
Lovell Telescope 53°14′10.50″N 002°18′25.74″W
Mark II 53°13′51.62″N 002°18′34.16″W
Pickmere 53°16′44.42″N 002°26′41.98″W

Using Point objects

We can create a Python dictionary containing the locations of the array very simply:

>>> from upoints.point import (Point, KeyedPoints)
>>> from upoints import utils
>>> MERLIN = KeyedPoints({
...     'Cambridge': Point((52, 10, 6.48), (0, 2, 23.25)),
...     'Darnhall': Point((53, 8, 38.4), (-2, -32, -45.57)),
...     'Defford': Point((52, 5, 27.61), (-2, -8, -9.62)),
...     'Knocking': Point((52, 46, 40.83), (-3, -0, -39.55)),
...     'Lovell Telescope': Point((53, 14, 10.5), (-2, -18, -25.74)),
...     'Mark II': Point((53, 13, 51.62), (-2, -18, -34.16)),
...     'Pickmere': Point((53, 16, 44.42), (-2, -26, -41.98)),
... })

As a simple smoke test the MERLIN website contains a location page which states the longest baseline in the array is Cambridge to Knocking at 217 kM, and also that the shortest baseline is between the Jodrell Bank site and Pickmere at 11.2 kM. The Point object’s distance() method can calculate these distances for us quite simply:

>>> "%.3f kM" % MERLIN['Cambridge'].distance(MERLIN['Knocking'])
'217.312 kM'
>>> "%.3f kM" % MERLIN['Lovell Telescope'].distance(MERLIN['Pickmere'])
'10.322 kM'
>>> "%.3f kM" % MERLIN['Mark II'].distance(MERLIN['Pickmere'])
'10.469 kM'


The web page gives the shortest baseline as the distance from Pickmere to Jodrell Bank, but doesn’t give a location for Jodrell Bank. However, as can be seen from the example above the two array elements based at Jodrell Bank(Lovell Telescope and the Mark II) are giving a plausible value.

Using dump_xearth_markers()

The MERLIN website also contains a layman description page that has a nice map showing the locations of the array elements, we can create a similar image with xplanet or xearth:

>>> print("\n".join(utils.dump_xearth_markers(MERLIN)))
52.168467 0.039792 "Cambridge"
53.144000 -2.545992 "Darnhall"
52.091003 -2.136006 "Defford"
52.778008 -3.010986 "Knocking"
53.236250 -2.307150 "Lovell Telescope"
53.231006 -2.309489 "Mark II"
53.279006 -2.444994 "Pickmere"

The map on the website contains a few more locations presumably to help the viewer with orientation, but the image below is useful as a good approximation. And, of course, the locations could be supplemented either by hand, or by using one of the other upoints supported databases.

xearth displaying array locations

Examining local solar time

Imagine the contrived example that we were allowed access to each of the locations and we’re hoping to catch the end of an imaginary partial eclipse occurring at 05:45 UTC on 2007-09-20 we can find the best location to view from quite simply. Clearly, the most important factor is whether the Sun will be visible at the given time and this can be calculated very easily:

>>> import datetime
>>> for name, rise in MERLIN.sunrise(, 9, 20)):
...     if rise > datetime.time(5, 45): continue
...     print(name)
...     print("     - sunrise @ %s UTC" % rise.strftime("%H:%M"))
     - sunrise @ 05:41 UTC

This simple code snippet shows us that we should set up our equipment at the Cambridge site, which lucky for me is only a short trip up the road:

>>> Home = Point(52.015, -0.221)
>>> print("%i kM" % Home.distance(MERLIN['Cambridge']))
24 kM

Comparisons with other Point-type objects

In our contrived example above we may wish to travel only if the weather will be warm enough that we’re unlikely to freeze to death(that risk is only acceptable for a full eclipse), and we can use the other upoints tools to find closest weather station quite easily:

>>> from upoints import weather_stations
>>> ICAO_stations_database = urllib.urlopen("")
>>> ICAO_stations = weather_stations.Stations(ICAO_stations_database, "ICAO")
>>> calc_distance = lambda (name, location): MERLIN['Cambridge'].distance(location)
>>> station_id, station_data = sorted(ICAO_stations.items(), key=calc_distance)[0]
>>> print(station_data)
Cambridge (N52.200°; E000.183°)

The calc_distance() function simply returns the distance from the Cambridge MERLIN station to the provided station, and we use it as the sorting method to discover the closest weather station from the NOAA database. The station_id and station_data variables are set to the first result from the sorted list of station distances, which thanks to the calc_distance() sorting method are the details of the closest weather station.

As we’re already using Python we may as well use Python to fetch the weather data for the station using the ever useful pymetar library.

>>> report = pymetar.ReportFetcher(station_id).FetchReport()
>>> report_decoded = pymetar.ReportParser().ParseReport(report)
>>> print("%i°C @ %s" % (report_decoded.getTemperatureCelsius(),
...                      report_decoded.getISOTime()))
10°C @ 2007-11-28 19:20:00Z