{ "metadata": { "name": "LSL DRX Tutorial" }, "nbformat": 2, "worksheets": [ { "cells": [ { "cell_type": "markdown", "source": [ "# Basics" ] }, { "cell_type": "markdown", "source": [ "The LSL interface for DRX (beam formed) data is similar to that of TBN. ", "", "First, download a snippet of DRX data:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "# This may take a bit...", "import os", "import urllib2", "from lsl.reader import drx", "", "if not os.path.exists('temp.drx'):", " fh1 = urllib2.urlopen('http://lda10g.alliance.unm.edu/data1/055917_000007389_Jupiter.dat')", " fh2 = open('temp.drx', 'wb')", " fh2.write(fh1.read(drx.FrameSize*300))", " fh1.close()", " fh2.close()", " ", "print \"DRX Size: %.1f kB\" % (os.path.getsize('temp.drx')/1024.)" ], "language": "python", "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "DRX Size: 1209.4 kB" ] } ], "prompt_number": 1 }, { "cell_type": "markdown", "source": [ "To read in a chunk of data:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from lsl.reader import drx, errors", "", "# Read in the first 5 frames", "fh = open('temp.drx', 'rb')", "for i in xrange(5):", " try:", " frame = drx.readFrame(fh)", " except errors.syncError:", " continue", " except errors.eofError:", " break", " ", " print \"Beam: %i, Tuning: %i, Pol.: %i\" % frame.parseID()", " print \"-> Time tag: %.6f s\" % frame.getTime()", " print \"-> Mean value: %.3f + %.3f j\" % (frame.data.iq.mean().real, frame.data.iq.mean().imag)", "fh.close()" ], "language": "python", "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Beam: 1, Tuning: 1, Pol.: 1", "-> Time tag: 1324515670.601744 s", "-> Mean value: -0.009 + 0.000 j", "Beam: 1, Tuning: 1, Pol.: 0", "-> Time tag: 1324515670.601953 s", "-> Mean value: -0.018 + 0.010 j", "Beam: 1, Tuning: 1, Pol.: 1", "-> Time tag: 1324515670.601953 s", "-> Mean value: 0.000 + 0.010 j", "Beam: 1, Tuning: 1, Pol.: 0", "-> Time tag: 1324515670.602162 s", "-> Mean value: 0.011 + -0.014 j", "Beam: 1, Tuning: 1, Pol.: 1", "-> Time tag: 1324515670.602162 s", "-> Mean value: -0.020 + -0.006 j" ] } ], "prompt_number": 2 }, { "cell_type": "markdown", "source": [ "The time tags reported in the above example are in seconds since the Unix epoch. These can be converted to Python datetime instances through:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from datetime import datetime", "from lsl.reader import drx, errors", "", "# Read in the first 5 frames", "fh = open('temp.drx', 'rb')", "for i in xrange(5):", " try:", " frame = drx.readFrame(fh)", " except errors.syncError:", " continue", " except errors.eofError:", " break", " ", " print \"Beam: %i, Tuning: %i, Pol.: %i\" % frame.parseID()", " print \"-> Time tag: %.6f s (%s)\" % (frame.getTime(), datetime.utcfromtimestamp(frame.getTime()))", " print \"-> Mean value: %.3f + %.3f j\" % (frame.data.iq.mean().real, frame.data.iq.mean().imag)", "fh.close()" ], "language": "python", "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Beam: 1, Tuning: 1, Pol.: 1", "-> Time tag: 1324515670.601744 s (2011-12-22 01:01:10.601744)", "-> Mean value: -0.009 + 0.000 j", "Beam: 1, Tuning: 1, Pol.: 0", "-> Time tag: 1324515670.601953 s (2011-12-22 01:01:10.601953)", "-> Mean value: -0.018 + 0.010 j", "Beam: 1, Tuning: 1, Pol.: 1", "-> Time tag: 1324515670.601953 s (2011-12-22 01:01:10.601953)", "-> Mean value: 0.000 + 0.010 j", "Beam: 1, Tuning: 1, Pol.: 0", "-> Time tag: 1324515670.602162 s (2011-12-22 01:01:10.602162)", "-> Mean value: 0.011 + -0.014 j", "Beam: 1, Tuning: 1, Pol.: 1", "-> Time tag: 1324515670.602162 s (2011-12-22 01:01:10.602162)", "-> Mean value: -0.020 + -0.006 j" ] } ], "prompt_number": 3 }, { "cell_type": "markdown", "source": [ "## Creating Spectra" ] }, { "cell_type": "markdown", "source": [ "Similar to TBW and TBN data, you can easily create spectra with SpecMaster. The following example reads and processes the first two frames in a DRX file:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline", "import numpy", "from lsl.correlator import fx as fxc", "from lsl.misc.mathutil import to_dB", "from matplotlib import pyplot as plt", "", "fh = open('temp.drx', 'rb')", "frame1 = drx.readFrame(fh)", "frame2 = drx.readFrame(fh)", "srate = frame1.getSampleRate() # Data sample rate in Hz", "cFreq = frame1.getCentralFreq() # Data tuning in Hz", "", "# SpecMaster expects 2-D data", "data = numpy.zeros((2,frame1.data.iq.size), dtype=frame1.data.iq.dtype)", "data[0,:] = frame1.data.iq", "data[1,:] = frame2.data.iq", "", "freq, spec = fxc.SpecMaster(data, LFFT=512, SampleRate=srate, CentralFreq=cFreq) ", "", "fig = plt.figure()", "ax = fig.gca()", "ax.plot(freq/1e6, to_dB(spec[0,:]), label='B%i,T%i,P%i' % frame1.parseID())", "ax.plot(freq/1e6, to_dB(spec[1,:]), label='B%i,T%i,P%i' % frame2.parseID())", "ax.set_xlabel('Frequency [MHz]')", "ax.set_ylabel('PSD [arb. dB]')", "ax.legend(loc=0)", "plt.show()" ], "language": "python", "outputs": [ { "output_type": "stream", "stream": "stderr", "text": [ "ERROR: Magic function `matplotlib` not found." ] }, { "output_type": "display_data", "png": 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hazDXGyWa7O5ucuJi5G9vh7AWJoVFUPPjsnxoLrGAYfp0ePjh3IArrAKvsCR6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\nJSLRsUnFlrIlTsJH14VDsF0bRRLXdcophdYgUD7cNBxB2xOPY4+Fo48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} ], "prompt_number": 4 }, { "cell_type": "markdown", "source": [ "To integrate down you need to read in more data and save it to a NumpyArray. To read in 50 frames from each tuning/polarization use:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "data = numpy.zeros((4,50*4096), dtype=numpy.complex64)", "count = [0,0,0,0]", "", "for i in xrange(50):", " for j in xrange(2):", " frame = drx.readFrame(fh)", " srate = frame.getSampleRate() # Data sample rate in Hz", " cFreq = frame.getCentralFreq() # Data tuning in Hz", " ", " beam,tune,pol = frame.parseID()", " k = 2*(tune-1) + pol", " data[k, count[k]*4096:(count[k]+1)*4096] = frame.data.iq", " count[k] += 1", " ", "freq, spec = fxc.SpecMaster(data, LFFT=512, SampleRate=srate, CentralFreq=cFreq) ", "", "fig = plt.figure()", "ax = fig.gca()", "ax.plot(freq/1e6, to_dB(spec[0,:]), label='B%i,T%i,P%i' % (beam,tune,0))", "ax.plot(freq/1e6, to_dB(spec[1,:]), label='B%i,T%i,P%i' % (beam,tune,1))", "ax.set_xlabel('Frequency [MHz]')", "ax.set_ylabel('PSD [arb. dB]')", "ax.legend(loc=0)", "plt.show()" ], "language": "python", "outputs": [ { "output_type": "display_data", "png": 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0qK+o2GhtrnmMqDtBXu5gU0xBcYVZ66lRktDpdFetwtq7d290Orl6Vghrl5gI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\nyfL9Fl3zRJw1gazP/pR+yotsKXqNzk5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} ], "prompt_number": 5 }, { "cell_type": "markdown", "source": [ "## Computing Stokes Parameters" ] }, { "cell_type": "markdown", "source": [ "LSL also supports computing all four Stokes parameters from data using the StokesMaster function. Using this function requires a little more setup since the function needs to know which singals are from which beam/tuning/polarization. This is done by first creating \"dummy\" lsl.common.stations.Antenna instances to store the mapping:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "from lsl.common import stations", "", "antennas = []", "for i in xrange(2):", " if i / 2 == 0:", " newAnt = stations.Antenna(1)", " else:", " newAnt = stations.Antenna(2)", " if i % 2 == 0:", " newAnt.pol = 0", " else:", " newAnt.pol = 1", " newAnt.digitizer = i+1", " antennas.append(newAnt)", " ", "for ant in antennas:", " print str(ant)" ], "language": "python", "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "Antenna 1: stand=0, polarization=0; digitizer 1; status is 0", "Antenna 1: stand=0, polarization=1; digitizer 2; status is 0" ] } ], "prompt_number": 6 }, { "cell_type": "markdown", "source": [ "Now the data can be passed into StokesMaster and the results plotted:" ] }, { "cell_type": "code", "collapsed": false, "input": [ "freq, spec = fxc.StokesMaster(data, antennas, LFFT=512, SampleRate=srate, CentralFreq=cFreq)", "print freq.shape, spec.shape", "", "fig = plt.figure()", "ax = fig.gca()", "ax.plot(freq/1e6, spec[0,0,:], label='I')", "ax.plot(freq/1e6, spec[1,0,:], label='Q')", "ax.plot(freq/1e6, spec[2,0,:], label='U')", "ax.plot(freq/1e6, spec[3,0,:], label='V')", "ax.set_xlabel('Frequency [MHz]')", "ax.set_ylabel('PSD [arb. dB]')", "ax.legend(loc=0)", "plt.show()" ], "language": "python", "outputs": [ { "output_type": "stream", "stream": "stdout", "text": [ "(512,) (4, 1, 512)" ] }, { "output_type": "display_data", "png": 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kCSGEzZg5EyZMUInA3h6+/lptL7pkCdx5p7Wju7acThJdurTseSRJCCEu8Ouv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\nNVrk5El1aYBDAwbgb29PYmEhAB579uB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AAElFTkSuQmCC\n" } ], "prompt_number": 7 }, { "cell_type": "markdown", "source": [ "Note that the shape of the output spectra is 3-D in the case of StokesMaster as opposed to 2-D for SpecMaster. The extra dimension stores the four Stokes parameters in the other of I, Q, U, and V." ] } ] } ] }