Loading dataData can be loaded with arbitrary start and end times very simply:
md = ap.load_data('AURORAWATCHNET', 'LAN1', 'MagData',
In this case the selected portion of data crosses midnight and two data files must be loaded, concatenated and trimmed to get the desired time range. This is performed automatically by
auroraplot, the user need not be concerned with the format of the files or where they are located. It is even possible for the files to be downloaded on-the-fly using FTP or HTTP transfer protocols.
load_datareturns an object (of type
MagData) to the user containing the actual magnetic field data and various other metadata, such as a timestamp for each sample and the data units. Each object can store more multiple data channels but all data points must share the same timestamps, be of the same type and share the same units. Therefore it is not possible to store operating temperatures (units °C) in an object holding magnetic field strength (units tesla). The operating temperature data can be accessed as:
td = ap.load_data('AURORAWATCHNET', 'LAN1', 'TemperatureData',
Battery voltage (data type
VoltageData) can be accessed in a similar same way.
Plotting dataHigh-level plot functions enable the data be be plotted very simply, for the magnetic field data loaded previously
will produce a matplotlib figure with a title and the axes labelled with the correct units. Temperature and voltage data are plotted in the same way.
I have created some tools to make working with numpy's datetime64 and timedelta64 objects more convenient, including rounding functions (
floor) which round to an interval. They are useful for finding the start of an hour, or the end of a day. I have also created
Formatterclasses to sensibly label time axes using
timedelta64intervals. Tick marks are located on the nearest second, minute, hour, day, month or year boundary (or multiple thereof) depending on the time interval being displayed. Thanks to matplotlib's structure the labels are automatically regenerated with the most appropriate time units when a plot is zoomed.
Quiet-day curvesOther operations include the generation of quiet-day curves. These are the curves from which we measure geomagnetic activity and are of critical importance for AuroraWatch UK. There are are not flat but have a daily variation caused by the equatorial electrojet. The empirical algorithm selects the days (typically 5) with the least geomagnetic activity. A truncated Fourier series is used to guarantee that the quiet-day curves are cyclic, with the start and end points having the same magnitude and slope. This is essential otherwise our rolling plots would show up the discontinuities in the QDC at midnight, and would falsely cause step changes in the geomagnetic activity. An example QDC is shown below.
|Quiet-day curve for magnetometer at Lancaster ,UK. This is derived from recorded data|
and clearly shows the Sq current system caused by the equatorial electrojet.
From this we can see that even on a geomagnetically quiet day we would expect a 30nT variation in field strength seen by the magnetometer. The AuroraWatch threshold for minor geomagnetic activity is 50nT so this shows the importance of using a quiet-day curve instead of a flat line when calculating geomagnetic activity.
Stack plotsStack plots (also called magnetograms) are a convenient representation for magnetic field data from a set of magnetometers separated in latitude.Data from the northernmost instruments is placed at the top and that from the southernmost at the bottom. An example stackplot is shown below:
|Stackplot showing data from two Lancaster stations and from Ormskirk.|
The source code is available under a BSD-type license from Github.You will need python, along with the numpy (version 1.7), matplotlib and scipy python modules.auroraplot has been tested under Debian Linux (64 bit version) and Raspbian on the Raspberry Pi.