{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Flux calibration\n", "\n", "Our science goal is to monitor the flux density variations of methanol masers. What we actually measure is antenna temperature in Kelvins. What we want is flux density in Jy.\n", "\n", "We use known flux calibrators - continuum point sources that are well characterised and stable - to give us a conversion factor between antenna temperature and flux density. This is known as the Point Source Sensitivity (PSS). The inverse of this number is the gain $G$. \n", "\n", "The relation between the flux density $S$ of an unresolved source and the antenna temperature $T_A$ due to that source is given by \n", "$$ S = \\frac{2kT_A}{eA}$$\n", " \n", "where $k$ is Boltzmann's constant, $e$ is the apperture efficiency and $A$ is the geometric area of the aperture.\n", "\n", "We define the gain as\n", "$$G= \\frac{T_A}{S} $$\n", "\n", "A common measure of antenna performance is the system equivalent flux density (SEFD), which is the flux density that a source would have to have in order to double the measured system temperature.\n", "$$SEFD = \\frac {T_{sys}}{G}$$\n", "\n", "The 26-m has slight variations in pointing, so in order to make sure we accurately measure the peak flux of the source, we do a series of drift scans at the expected north and south half-power points, as well as the centre. Basically we park the telescope at a coordinate just ahead of where the source currently is in the sky, and wait for earth's rotation to carry the telescope beam across the source. The advantage of this is that you don't have to worry about whether your telescope can drive at a consistent speed across the source. The 26-m tracks perfectly at sidereal rate, but is not very good at higher speeds. Other telescopes may choose to use on-the-fly scans, where they sample the power at a high rate while slewing across the source.\n" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from astropy.io import fits as pyfits\n", "import os\n", "from scipy import stats\n", "#display figures inline \n", "%matplotlib inline " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Select an observation to work on and open the fits file" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fitsfile = 'data/continuum_drift_scans/2004d142_18h49m34s_Cont_sharmila_VIRGO_A.fits'\n", "#fitsfile = 'data/continuum_drift_scans/2004d143_15h04m53s_Cont_sharmila_HYDRA_A.fits'\n", "hdulist = pyfits.open(fitsfile) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Examine the fits file" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Filename: data/continuum_drift_scans/2004d142_18h49m34s_Cont_sharmila_VIRGO_A.fits\n", "No. Name Type Cards Dimensions Format\n", "0 PRIMARY PrimaryHDU 25 () \n", "1 04.5S BinTableHDU 60 1R x 13C [1D, 1D, 1D, 8A, 1D, 8A, 1D, 27A, 1D, 1D, 28A, 1D, 1D] \n", "2 Drift BinTableHDU 94 100R x 23C [1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D] \n", "3 Drift BinTableHDU 90 1540R x 23C [1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D] \n", "4 Drift BinTableHDU 90 1540R x 23C [1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D] \n", "5 Drift BinTableHDU 90 1540R x 23C [1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D, 1D] \n" ] } ], "source": [ "hdulist.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are several data tables in this file. \n", "\n", "The first header data unit (HDU) contains information on the observation. \n", "\n", "The second one has information on the 4.5 cm feed system and was intended to have calibration data. This data is not necessarily up to date, so we generally disregard it.\n", "\n", "The third unit has the noise diode firing, used to convert from raw counts to Kelvins.\n", "\n", "The next three binary tables are the drift scans themselves, starting at the north offset position.\n" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "SIMPLE = T \n", "BITPIX = 8 \n", "NAXIS = 0 \n", "EXTEND = T \n", "DATE = '2004-05-21T18:49:34' / file creation date (YYYY-MM-DDThh:mm:ss UT) \n", "COMMENT information about the object, from scheduler task \n", "OBJECT = 'VIRGO A ' / Name of object \n", "LONGITUD= 187.705916666667 / Longitude of object \n", "LATITUDE= 12.3911111111111 / Latitude of object \n", "EQUINOX = '2000.0 ' \n", "COMMENT information about the scan, from scheduler task \n", "OBSERVER= 'S Goedhart' / Principal Investigator \n", "OBSLOCAL= 'sharmila' / On-site observer \n", "PROJNAME= '6.7-GHz continuum calibration for methanol maser monitoring' / Short \n", "PROPOSAL= '2003.006' / Observing proposal ID \n", "COMMENT information about the antenna \n", "TELESCOP= 'HartRAO 26m Antenna' / Telescope name \n", "UPGRADE = 'All new panels - radially setting Ring 5' / Antenna surface status \n", "FOCUS = 7.8955078125 / Sub-reflector focus \n", "TILT = -0.76171875 / Sub-reflector tilt \n", "TAMBIENT= 281.32456741333 / [K] Ambient temperature \n", "PRESSURE= 871. / [mbar] Atmospheric pressure \n", "HUMIDITY= 54.5306573486328 / [%] Humidity \n", "SIMULATE= 0 / Simulation ? \n", "SCANDIST= 0.64 / Scan length (deg) " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hdulist[0].header" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A note on the field called 'upgrade' - during this time the dish surface was being replaced with solid panels. Sometimes panels were missing, or mis-aligned. Depending on which ring was being worked on, this could have a significant impact on the aperture efficiency and the point source sensitivity.\n", "\n", "Extract the data" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [], "source": [ "noise_cal = hdulist[2]\n", "drift1 = hdulist[3]\n", "drift2 = hdulist[4]\n", "drift3 = hdulist[5]" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "XTENSION= 'BINTABLE' / binary table extension \n", "BITPIX = 8 / 8-bit bytes \n", "NAXIS = 2 / 2-dimensional binary table \n", "NAXIS1 = 184 / width of table in bytes \n", "NAXIS2 = 1540 / number of rows in table \n", "PCOUNT = 0 / size of special data area \n", "GCOUNT = 1 / one data group (required keyword) \n", "TFIELDS = 23 / number of fields in each row \n", "EXTNAME = 'Drift ' / name of this binary table extension \n", "FRONTEND= '04.5S ' / Frontend ID \n", "CENTFREQ= 6668.518 / [MHz] Backend centre frequency \n", "BANDWDTH= 16. / [MHz] Bandwidth of backend \n", "TCAL1 = 14.2 / [K] Cal noise diode value for channel 1 \n", "TCALSIG1= 0. / [K] uncertainty in TCAL1 \n", "TCALFRQ1= 6670. / [Hz] Frequency the calibration applies to \n", "TCALDAT1= '19000101' / Calibration date \n", "TCAL2 = 21. / [K] Cal noise diode value for channel 2 \n", "TCALSIG2= 0. / [K] uncertainty in TCAL2 \n", "TCALFRQ2= 6670. / [Hz] Frequency the calibration applies to \n", "TCALDAT2= '19000101' / Calibration date \n", "SCANDIST= 0.64 / Scan length (deg) \n", "TTYPE1 = 'MJD ' / label for field \n", "TFORM1 = '1D ' / format of field \n", "TUNIT1 = 'days ' \n", "TTYPE2 = 'Count1 ' / label for field \n", "TFORM2 = '1D ' / format of field \n", "TUNIT2 = 'Hz ' \n", "TTYPE3 = 'Count2 ' / label for field \n", "TFORM3 = '1D ' / format of field \n", "TUNIT3 = 'Hz ' \n", "TTYPE4 = 'Hour_Angle' / label for field \n", "TFORM4 = '1D ' / format of field \n", "TUNIT4 = 'deg ' \n", "TTYPE5 = 'Declination' / label for field \n", "TFORM5 = '1D ' / format of field \n", "TUNIT5 = 'deg ' \n", "TTYPE6 = 'Azimuth ' / label for field \n", "TFORM6 = '1D ' / format of field \n", "TUNIT6 = 'deg ' \n", "TTYPE7 = 'Elevation' / label for field \n", "TFORM7 = '1D ' / format of field \n", "TUNIT7 = 'deg ' \n", "TTYPE8 = 'RA_Apparent' / label for field \n", "TFORM8 = '1D ' / format of field \n", "TUNIT8 = 'deg ' \n", "TTYPE9 = 'Dec_Apparent' / label for field \n", "TFORM9 = '1D ' / format of field \n", "TUNIT9 = 'deg ' \n", "TTYPE10 = 'RA_Mean ' / label for field \n", "TFORM10 = '1D ' / format of field \n", "TUNIT10 = 'deg ' \n", "TTYPE11 = 'Dec_Mean' / label for field \n", "TFORM11 = '1D ' / format of field \n", "TUNIT11 = 'deg ' \n", "TTYPE12 = 'RA_B1950' / label for field \n", "TFORM12 = '1D ' / format of field \n", "TUNIT12 = 'deg ' \n", "TTYPE13 = 'Dec_B1950' / label for field \n", "TFORM13 = '1D ' / format of field \n", "TUNIT13 = 'deg ' \n", "TTYPE14 = 'RA_J2000' / label for field \n", "TFORM14 = '1D ' / format of field \n", "TUNIT14 = 'deg ' \n", "TTYPE15 = 'Dec_J2000' / label for field \n", "TFORM15 = '1D ' / format of field \n", "TUNIT15 = 'deg ' \n", "TTYPE16 = 'Galactic_Long' / label for field \n", "TFORM16 = '1D ' / format of field \n", "TUNIT16 = 'deg ' \n", "TTYPE17 = 'Galactic_Lat' / label for field \n", "TFORM17 = '1D ' / format of field \n", "TUNIT17 = 'deg ' \n", "TTYPE18 = 'Ecliptic_Long' / label for field \n", "TFORM18 = '1D ' / format of field \n", "TUNIT18 = 'deg ' \n", "TTYPE19 = 'Ecliptic_Lat' / label for field \n", "TFORM19 = '1D ' / format of field \n", "TUNIT19 = 'deg ' \n", "TTYPE20 = 'Uiser_Long' / label for field \n", "TFORM20 = '1D ' / format of field \n", "TUNIT20 = 'deg ' \n", "TTYPE21 = 'User_Lat' / label for field \n", "TFORM21 = '1D ' / format of field \n", "TUNIT21 = 'deg ' \n", "TTYPE22 = 'HA_Error' / label for field \n", "TFORM22 = '1D ' / format of field \n", "TUNIT22 = 'deg ' \n", "TTYPE23 = 'Dec_Error' / label for field \n", "TFORM23 = '1D ' / format of field \n", "TUNIT23 = 'deg ' " ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "drift1.header" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Most of the fields are actually just the position in different formats. We are interested in the signal 'Count1' and 'Count2'. " ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "ColDefs(\n", " name = 'MJD'; format = '1D'; unit = 'days'\n", " name = 'Count1'; format = '1D'; unit = 'Hz'\n", " name = 'Count2'; format = '1D'; unit = 'Hz'\n", " name = 'Hour_Angle'; format = '1D'; unit = 'deg'\n", " name = 'Declination'; format = '1D'; unit = 'deg'\n", " name = 'Azimuth'; format = '1D'; unit = 'deg'\n", " name = 'Elevation'; format = '1D'; unit = 'deg'\n", " name = 'RA_Apparent'; format = '1D'; unit = 'deg'\n", " name = 'Dec_Apparent'; format = '1D'; unit = 'deg'\n", " name = 'RA_Mean'; format = '1D'; unit = 'deg'\n", " name = 'Dec_Mean'; format = '1D'; unit = 'deg'\n", " name = 'RA_B1950'; format = '1D'; unit = 'deg'\n", " name = 'Dec_B1950'; format = '1D'; unit = 'deg'\n", " name = 'RA_J2000'; format = '1D'; unit = 'deg'\n", " name = 'Dec_J2000'; format = '1D'; unit = 'deg'\n", " name = 'Galactic_Long'; format = '1D'; unit = 'deg'\n", " name = 'Galactic_Lat'; format = '1D'; unit = 'deg'\n", " name = 'Ecliptic_Long'; format = '1D'; unit = 'deg'\n", " name = 'Ecliptic_Lat'; format = '1D'; unit = 'deg'\n", " name = 'Uiser_Long'; format = '1D'; unit = 'deg'\n", " name = 'User_Lat'; format = '1D'; unit = 'deg'\n", " name = 'HA_Error'; format = '1D'; unit = 'deg'\n", " name = 'Dec_Error'; format = '1D'; unit = 'deg'\n", ")" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "noise_cal.columns" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Noise calibration" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A noise diode, when activated, generates a consistent signal corresponding to a temperature $T_{cal}$(K). We use this signal to calibrate the system gain, enabling us convert from raw counts (or voltage, as the case may be) to Kelvins. The frequency and duration of noise diode firings depends entirely on the gain stability of the system. One does not want to fire the noise diode during observations, because it increases the system noise when it is on, affecting sensitivity. Most systems will have a design requirement that the gain remain stable, or behave in a linear manner, over the duration of a typical scan. Some causes of gain variation could be temperature variations, or the bending RF cables as the telescope moves around." ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.plot(noise_cal.data['Count1'])\n", "#plt.axis('tight')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So what we want to do is measure the amplitude of the jump between the system level counts and the noise diode firing. We're going to detect at which index the jumps occur, and we can either fit a linear function to the two sections, or simply take the mean. In most cases, the counts should remain very stable for the duration of the noise diode calibration cycle. The noise diode values are give in the header of the file as TCAL1 and TCAL2 for the two channels (which correspond to left and right circular).\n", "\n", "The diff function will calculate the difference between adjacent values in an array. This is a quick way to find where there are abrupt changes in a data-series." ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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STy3usZz8euIT+rIvETmLgQ4W4EbgOXc/5O4R8EngtlU+poE2VwkJMouDBcAaAUdnNBwm\nImeWWu0DWKEtwPMdzw8DN52+0e/+xadIVDaTrI8yNFqlMFIhP1wlSQqaObyeo9FIUG/UqTUiwnqF\nH8+/yJHyj5muHaGYC9gytoGrN2xg08go2URAJhGQsgz1ZpVqM6TSqHDsRI0jx+scnYqI6jAS5FlX\nzDM6lCedTJKwJKlEkmCoyci6iOF1EYlkkxPTSaaPJ5maghNhidnaLKXaLPlUwOVDV3Dl6OWsH8nT\nzByjmj5C1aaglaDZSEEzTWU+S2k6y8zxHPVqmo2XGZs3w4YN0Goa1WqCejVJi4hEtkIyWyXyCsdL\nIdNzIbPlKhkLKKSG+aej3+Rlw907fclWwHuf+HNeuWUz60YyJFsB88dGmXphhNnjBZLpBqlMRDIT\nEdkcVWaoMEMyHbFxNGDjWJ4NowHWyOJRjmY9S63eohLVqdUjapHTrGVp1bM0oxTZkVnSw9MkCzOk\nkwnSFEhToF5LcGSmxLFSiblKyGgwxKaRUa4YG6VQAE9V8FRIk4haDeo1o1YzGlGCZpSg0UiQag0R\nsJ68byCfLJAfrpArVsgOVcgEIclcSERIqVpm8miZF46VmZqC8MWtTP/gal48sIVg/TTFK39EduOP\nCEbLjI1kGBvJsG44TSaVJpNMk0okmQ0rTM2XmC6XyLGOVxZu5GVDryafzuHJGkcbB5msf48T1ePM\nR/OU6/M0G0ky0UbS9U2ko/VcflmOrZdn2XpFhuJQkmQSkkmYKTV59kDEge/X+eHhiLAWUYsi6o2I\nzPAM+Q3HSA0fJ5OLKCY2MsRmhhLrGRmLKI5VCIYrJDxDfW6E8MQwpbkmx5s/4Hjj/zHdmGRd6go2\nZ7axOb2NBCnmGtPMNaeoxJ/Q4LUhvJ4nHYRkiiWSQYlsFvKpgCBdIMjkKBaSFAtJ8rkkLx5xDh1y\nDv3QqZRTBJk8Q5mATDpBxY4T2lFCO87YcJYrNwxz9eZhMhnj2Ow8x0vzXDGykZ2v3bHo/81/OPgd\nHvryowz7VoaaW8k1N0EiglSVVrJCIl8iEczi2RmSSaeYvIwCl5GKxpiaco4eb3BsukExG3D1pnW8\n7Ip1FIvGoelJDs8e5mh4hHQaCvkU+VySei3B7IwxO2NUwhSZRJZMMkculaE4VqG4fo786Dz5nJFP\nBeRTeWilOVGqcWKuysx8lbDSolJ1alXI+CgbUlexIXMVo/kiPvIDqsH3mU/9EG8lSDSGoF7AGgUS\nzQBr5PEoR7WSpBImqFUTFIoR6zbUGB6rkcpVqTRCaq0K9WYNpwXmuDvNWp5ovkh9bpifffk1/Ntf\nvKY378YdzN173ujFYma/Auxw99+In78duMnd/2PHNl58zb+glZqnmaiS3rKO5OUbaVRzkGhiqSqe\nqoI1SZIhQZqU5xlObGY0tZl1mU3MVSocC48zUztOzWZeeuPyRBVr5kk0AxLNPLl0jkIuTTHIkEw5\nYb1CJapQbVVwmjhNWjRpRkmiWpqokqHVSpDNNcnmmmSyTi4xTCExSpAcodYqM9P8MXP+AnWvkKxu\nxOc30ppfTyIJiVREIh2RydfJBFUy+RqeqFOrQbUG9RpYwkkkmyRSTayVxqM8rXoOojz5VIFCJiDI\n5GgmQmrMULVZ/uDn38Xv33bLovP9K//lL/nm5HcIa3XCeg3LhORGZkkWZiBTJuEpzDNYK03Gh8n6\nKDlGadRTzFUqzNdCqs0Klq5h6SqWqpJMJEmSJmkZEgkgWYNkDbcGVh+hFa4jKq1r/2JkypAuk0i2\nKKSGKWaGCdIB8/V5SvUZ5psnaDTA6wGtaoA306TTkEo7qZSTTDqJVItEskkrNU89dZxacoqGldu/\nqI08XsvTrBZoVNr/Tb1eIJ8qMBoUGBlxEmM/opb7IbOtSYqp9YxwFbnaVurlIUrzEfNhRKUe0STC\nLcKtQdry5GyYIFmkmT3GdLCP+ez/JRttppZ5gWzlGnJz28k0LiPtRTI+RCrdxIOjNHJHqSWPU42q\nVBt16s0aLW8B4A4JS5BLZwhyaYJsO9DSifYj1RyB8DIas5dRq6SIMkepZ16klpwiqmaozeepzOVI\npCKyIyVShRKplJOv/gT56k+Qrm6hnn2BSv4g5dxBsBaZxhiZ5npS5LF0iKfLtBIViAJa1WGi+SKN\nhtFKhrSSZRpWpdFs0mg2aXmTbNYoFIyhgpFINah7hbpXaHmDXOsycs2NZJrrCasRpVqJsDlLy1uk\nvUgykaAyto/GB55f9P/mL/3xB/jbw59mS+qnqOeep5o6QsIzJJp5rJXDK8NE8yPUZkeJGpAqHsOG\njkF+mmw6SS6TIpdNUm2GzDdOUOEELW+Ri7ZQaG6h4JtpNox6o0m90SCVbpEPnHzeSWUaNLxG3atE\nrTrNSkBtfojK7BCNhuPJCp4OsWREJpEjl8qRTWXJpBOk05BJQz05wyw/ZC7xI2qUyFevJTH7MppT\n17T/iMjPk8iVsUwIqZBWKsSTVSzRxBItLNHCm2la9SyNWpZWPffS+1KilQVP4N4eoErlKiSDEonc\nHG+85lYe/g9/AMDExAQTExMvndP77rsPd7fzem8e8GB5DbDL3XfEz+8BWu7+QMc2PsivUVaHO5TL\n7bmldLr37YdRyPOzz3PN6DVkU9mz73CBtFpg1n5cjJ+VWMHg+3MvHOcVH3olrT+ZWrTutbt2YcDE\nrl3n/wNkATM772AZ9DmWrwPbzOwaM8sAbwWeXOVjkkuAGQwNXZhQAQjSAa/c8MpVDRVov9FfjFA5\n+bNWYtO6Ap4u0+3vxHItZCjbfW5QLr6BDhZ3bwC/A3wR2A885u7fW92jEpELYSiXg2Sd+XJz0bow\nCinmFSz9YtAn73H3LwBfWO3jEJELy8ygkefYTIXi0NCCdZVmmZH84vuvZHUMdI9FRNaWRLPAsZnF\n91JVmyGjgXos/ULBIiIDI9Xqfi9VrRUyOqRg6RcKFhEZGCkPmCot7rHUPWSdgqVvKFhEZGCkCZju\nEiyRlVk/rGDpFwoWERkYGev+eXVNC9kwrMn7fqFgEZGBkUsEnCgvnmNpJkI2rlOPpV8oWERkYORS\nAbPhwh5LrQakQ0YLCpZ+oWARkYGRTwWUqguDpVQCy5QJ0gqWfqFgEZGBUcgUmKssHAqbmwNPhxTS\nmmPpFwoWERkYQ5mA+frCHsv0bPtbTdPJC/TBbnLOFCwiMjCGsgHl04Ll2ExIsqlhsH6iYBGRgTGc\nD6g0FgbL8dkyKVew9BMFi4gMjJGgQKWxcI5lqhSScs2v9BMFi4gMjNFCQK21sMdyYj4kY+qx9BMF\ni4gMjHVDi4NlphySTShY+omCRUQGxvpigYjTeizlMrmkgqWfKFhEZGCMFQMatvDriUuVkHxKcyz9\nRMEiIgNjJAggE1KtnqqVKqHuuu8zChYRGRhBOiCZDZmbO1Wbr4UUMgqWfqJgEZGBUUgXsExIqXSq\nVq6XGcoqWPqJgkVEBkaQDiBTXtBjCaOQ4ZzmWPqJgkVEBkaQDvDUwqGwSiNkOK8eSz9RsIjIwAjS\nAa3kwqGwajNsT+pL31CwiMjAyKVytKzObKn5Uq3WKrNuSMHSTxQsIjIwzIyUBxyfPXWTZJ2QsaLm\nWPqJgkVEBkqagOn5drDU6+BJDYX1GwWLiAyUTCJgeq4dLHNzkMqHFHSDZF9RsIjIQMklCsyU2x+d\nXypBIqc77/vNeQeLmf2qmX3XzJpmdv1p6+4xs4Nm9qyZ3dJRv8HMnonXPdRRz5rZY3H9q2Z2dce6\nO83sQPx4Z0f9WjN7Ot7nk2am7yUVWQNyyYBS5VSPJZEtK1j6zEp6LM8Avwz8XWfRzLYDbwW2AzuA\nD5uZxas/Aux0923ANjPbEdd3AlNx/UHggbitMeB9wI3x414zG4n3eQD4YLzPibgNEbnEBamA2fBU\nsFgmpJDR5H0/Oe9gcfdn3f1Al1W3AY+6e+Tuh4DngJvM7HKg6O774u0+DtweL98K7I6XnwBeHy+/\nEdjj7jPuPgPsBd4UB9XrgE/F2+3uaEtELmGFTIH5WjtYSiXwtIbC+s2FmGO5Ajjc8fwwsKVLfTKu\nE//7PIC7N4BZM1t/hrbGgBl3b3VpS0QuYYVMwFytPccyN9e+KkzB0l9SZ1ppZnuBzV1WvdfdP3th\nDums/OybLLRr166XlsfHxxkfH+/h4YjIxVTMBXy/fqrH0kxqjqUXJiYmmJiY6ElbZwwWd7/5PNqc\nBLZ2PL+Sdk9jMl4+vX5yn6uAF8wsBYy4+5SZTQLjHftsBb4MTAOjZpaIey1Xxm101RksIjLYivmA\nMDo1x9K0kEJacywrdfof3ffdd995t9WroTDrWH4SuMPMMmZ2LbAN2OfuLwIlM7spniN5B/CZjn3u\njJffDHwpXt4D3GJmo2a2DrgZ+KK7O/AV4Ffj7e4EPt2j1yIifWw0X6DSbAfLTCkCIJ3URaH9ZCWX\nG/+ymT0PvAb4nJl9AcDd9wOPA/uBLwB3xUEAcBfwl8BB4Dl3fyqufxRYb2YHgd8F7o7bmgbeD3wN\n2AfcF0/iA7wH+P14n3VxGyJyiRspBNRa7a8nnp4LyZiGwfrNGYfCzsTd/wb4myXW3Q/c36X+DeC6\nLvUa8JYl2noYeLhL/QfATed21CIy6IZzAaRnqdXgRLlMZkTB0m90572IDJQgHZAptL+TZaYckk9q\nfqXfKFhEZKAUMgVSQZlSCUqVkFxSPZZ+o2ARkYESpAOSuXaPZa6qe1j6kYJFRAbKyWAplWCuWqaQ\nUbD0GwWLiAyUQrqAZdo9lnIUUsxqjqXfKFhEZKAE6QDS5Xaw1EOKefVY+o2CRUQGSpAO8FR7KKzS\nCBlWsPQdBYuIDJQgHdBKhkxPQzNRpphTsPQbBYuIDJRCpkAzEfLCC5AdChnSd7H0HQWLiAyUIB3Q\nsDKTk5Ap6HLjfqRgEZGBEqQDIkImJyEdKFj6kYJFRAZKNpmlSYPJHzdI5fVdLP1IwSIiA8XMyCUC\nfny8QjKn72LpRwoWERk4uWRAM1EmkdVQWD9SsIjIwMmnA0iHkFGw9CMFi4gMnMLJYElrjqUfKVhE\nZOAMZQuQLtNKqsfSjxQsIjJwhrLtHksrGVLQDZJ9R8EiIgNnKBuQzIc0E+qx9CMFi4gMnEKmQL4Y\nUkdzLP1IwSIiAydIB2SHy0SuHks/UrCIyMAJUgHZQkjNdYNkP1KwiMjACdIBP/sLs5hBOple7cOR\n0yhYRGTgFDIFXn7dcQ2D9SkFi4gMnCAdcCw8pmDpUwoWERk4J4NF8yv9ScEiIgMnSAccDzUU1q8U\nLCIycArpgoKlj513sJjZfzWz75nZP5nZX5vZSMe6e8zsoJk9a2a3dNRvMLNn4nUPddSzZvZYXP+q\nmV3dse5OMzsQP97ZUb/WzJ6O9/mkmenSEJE1IkgHHCtrjqVfraTHsgf4SXd/NXAAuAfAzLYDbwW2\nAzuAD5uZxft8BNjp7tuAbWa2I67vBKbi+oPAA3FbY8D7gBvjx70dAfYA8MF4nxNxGyKyBgTpgHJU\n1ueE9anzDhZ33+vurfjp08CV8fJtwKPuHrn7IeA54CYzuxwouvu+eLuPA7fHy7cCu+PlJ4DXx8tv\nBPa4+4y7zwB7gTfFQfU64FPxdrs72hKRS9zJQFGPpT/1ao7l14HPx8tXAIc71h0GtnSpT8Z14n+f\nB3D3BjBrZuvP0NYYMNMRbJ1ticgl7mSgKFj6U+pMK81sL7C5y6r3uvtn423+EKi7+ycuwPF14xfp\n54hIn3opWFIKln50xmBx95vPtN7M/h3wi5wauoJ272Frx/Mrafc0Jjk1XNZZP7nPVcALZpYCRtx9\nyswmgfGOfbYCXwamgVEzS8S9livjNrratWvXS8vj4+OMj48vtamIDICTwaI5lt6ZmJhgYmKiJ22Z\n+/l1AOKJ9w8Cr3X34x317cAnaE+2bwH+Fni5u7uZPQ28C9gHfA74kLs/ZWZ3Ade5+2+Z2R3A7e5+\nRzx5/3XgesCAbwDXu/uMmT0OPOHuj5nZnwHfdvc/63Kcfr6vUUT604nKCcY+MMa9r72XXeO7Vvtw\nLklmhrvb2bdc7Iw9lrP4H0AG2Btf9PV/3P0ud98fv+nvBxrAXR3v7HcBHwPywOfd/am4/lHgETM7\nCEwBdwB1OQqOAAAG4ElEQVS4+7SZvR/4WrzdffEkPsB7gE+a2R8B34zbEJE1QHMs/e28eyyDQj0W\nkUuPu5N6f4qHdjzE79z4O6t9OJeklfRYdOe9iAwcMyNIB/qssD6lYBGRgVRIFzQU1qcULCIykIJ0\noGDpUwoWERlICpb+pWARkYG0aWgTGwsbV/swpAtdFSYiIovoqjAREekbChYREekpBYuIiPSUgkVE\nRHpKwSIiIj2lYBERkZ5SsIiISE8pWEREpKcULCIi0lMKFhER6SkFi4iI9JSCRUREekrBIiIiPaVg\nERGRnlKwiIhITylYRESkpxQsIiLSUwoWERHpKQWLiIj0lIJFRER6SsEiIiI9pWAREZGeOu9gMbP3\nm9k/mdm3zexLZra1Y909ZnbQzJ41s1s66jeY2TPxuoc66lkzeyyuf9XMru5Yd6eZHYgf7+yoX2tm\nT8f7fNLM0uf7WkREpHdW0mP5gLu/2t1/Gvg0cC+AmW0H3gpsB3YAHzYzi/f5CLDT3bcB28xsR1zf\nCUzF9QeBB+K2xoD3ATfGj3vNbCTe5wHgg/E+J+I25AwmJiZW+xD6hs7FKToXp+hc9MZ5B4u7z3U8\nHQKOx8u3AY+6e+Tuh4DngJvM7HKg6O774u0+DtweL98K7I6XnwBeHy+/Edjj7jPuPgPsBd4UB9Xr\ngE/F2+3uaEuWoF+aU3QuTtG5OEXnojdSK9nZzP4L8A6gQrtHAXAF8NWOzQ4DW4AoXj5pMq4T//s8\ngLs3zGzWzNbHbR3u0tYYMOPurS5tiYjIKjpjj8XM9sZzIqc//g2Au/+hu18FPAz894txwIBfpJ8j\nIiLnw91X/ACuAv45Xr4buLtj3VPATcBm4Hsd9bcBH+nY5jXxcgo4Fi/fAfxZxz5/Tnv+xoBjQCKu\n/0vgqSWOzfXQQw899Dj3x/lmwnkPhZnZNnc/GD+9DfhWvPwk8Akz+2+0h6e2Afvc3c2sZGY3Afto\nD6F9qGOfO2kPob0Z+FJc3wPcb2ajtMPkZuA9cVtfAX4VeCze99PdjtPdrVtdREQuDIv/qj/3Hc0+\nBbwSaALfB37L3Y/G694L/DrQAN7t7l+M6zcAHwPywOfd/V1xPQs8AvwMMAXcEU/8Y2a/Brw3/rF/\n5O674/q1wCdpz7d8E3i7u0fn9WJERKRnzjtYREREurlk77w3sx3xDZoHzew9q308F5OZbTWzr5jZ\nd83sn83sZM9wLL4g44CZ7YmHGNcEM0ua2bfM7LPx8zV5Lsxs1Mw+ZWbfM7P9ZnbTGj4X98S/I8+Y\n2SfiG7XXxLkws78ysyNm9kxHbcnXvtRN70u5JIPFzJLAn9K+QXM78DYze9XqHtVFFQG/5+4/CbwG\n+O349d8N7HX3V9Cex7p7FY/xYns3sJ/2pCSs3XPxEO1h6FcBPwU8yxo8F2Z2DfAbwPXufh2QpH2x\n0Fo5Fw/Tfn/s1PW1L3HT+xmz45IMFtr31Dzn7ofieZdP0r7AYE1w9xfd/dvx8jzwPdoXUnTeiLpm\nbio1syuBXwT+kvZFILAGz0X8qRX/yt3/CsDdG+4+yxo8F0CJ9h9ggZmlgAB4gTVyLtz972l/Ykmn\npV57t5veb+QMLtVgeemGy9jJGyvXnPgvs58BngY2ufuReNURYNMqHdbF9iDwn4BWR20tnotrgWNm\n9rCZfdPM/qeZFViD58Ldp4EPAj+iHSgz7r6XNXguOiz12pe6UX1Jl2qw6IoEwMyGaH9EzrtP+wie\n9gXqa+A8mdkvAUfd/Vuc6q0ssFbOBe17xK4HPuzu1wNlThvqWSvnwsxeBvwucA3tN84hM3t75zZr\n5Vx0s4zXfsbzcqkGyySwteP5VhYm7iUv/rTnJ4BH3P3kPT5HzGxzvP5y4OhqHd9F9HPArWb2A+BR\n4F+b2SOszXNxGDjs7l+Ln3+KdtC8uAbPxc8C/9vdp9y9Afw17Rut1+K5OGmp34nT30+vjGtLulSD\n5eu0Pz35GjPL0J54enKVj+miiT+k86PAfnfv/KidkzeiwhluKr2UuPt73X2ru19Le3L2y+7+Dtbm\nuXgReN7MXhGX3gB8F/gsa+xc0L5o4TVmlo9/X95A++KOtXguTlrqd+JJ4A4zy8T3D26jfZP7ki7Z\n+1jM7E20P78sCXzU3f94lQ/pojGzXwD+DvgOp7qs99D+n+Fx2h/Bcwh4S/yp0WuCmb0W+AN3vzX+\nSoY1dy7M7NW0L2LI0L6x+ddo/46sxXPxn2m/gbZo32T974Eia+BcmNmjwGuBDbTnU94HfIYlXvtS\nN70v2f6lGiwiIrI6LtWhMBERWSUKFhER6SkFi4iI9JSCRUREekrBIiIiPaVgERGRnlKwiIhITylY\nRESkp/4/KY0idKXVcBwAAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "jumps1 = np.diff(noise_cal.data['Count1'])\n", "jumps2 = np.diff(noise_cal.data['Count2'])\n", "plt.plot(jumps1) \n", "plt.plot(jumps2) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The spike corresponds to when the noise diode turns on. The drop corresponds to when the noise diode turns off." ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counts per Kelvin for channel 1 is 12139.79 +- 89.41\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#find the array indices at which the noise diode turns on and off for channel 1\n", "nd_on_pos1 = np.argmax(jumps1)+1\n", "nd_off_pos1 = np.argmin(jumps1)+1\n", "#there are two segments where the noise diode is off. We concatenate these two segments\n", "nd_off_count1 = np.concatenate((noise_cal.data['Count1'][:nd_on_pos1], noise_cal.data['Count1'][nd_off_pos1:]))\n", "nd_off_mean1 = np.mean(nd_off_count1)\n", "nd_off_sem1 = stats.sem(nd_off_count1)\n", "\n", "nd_on_count1 = noise_cal.data['Count1'][nd_on_pos1:nd_off_pos1]\n", "nd_on_mean1 = np.mean(nd_on_count1)\n", "nd_on_sem1 = stats.sem(nd_on_count1)\n", "err_1 = np.sqrt(nd_on_sem1**2 + nd_off_sem1**2)\n", "\n", "#verify that our selection criteria were correct\n", "plt.plot(nd_on_count1)\n", "plt.plot(nd_off_count1)\n", "#counts per Kelvin\n", "count_K1 = (nd_on_mean1 - nd_off_mean1)/noise_cal.header['TCAL1']\n", "print('Counts per Kelvin for channel 1 is %.2f +- %.2f'%(count_K1, err_1))" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Counts per Kelvin for channel 2 is 10631.11 +- 84.79\n" ] }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#repeat for second polarisation\n", "#find the array indices at which the noise diode turns on and off for channel 2\n", "nd_on_pos2 = np.argmax(jumps2)+1\n", "nd_off_pos2 = np.argmin(jumps2)+1\n", "#there are two segments where the noise diode is off. We concatenate these two segments\n", "nd_off_count2 = np.concatenate((noise_cal.data['Count2'][:nd_on_pos2], noise_cal.data['Count2'][nd_off_pos2:]))\n", "nd_off_mean2 = np.mean(nd_off_count2)\n", "nd_off_sem2 = stats.sem(nd_off_count2)\n", "\n", "nd_on_count2 = noise_cal.data['Count2'][nd_on_pos2:nd_off_pos2]\n", "nd_on_mean2 = np.mean(nd_on_count2)\n", "nd_on_sem2 = stats.sem(nd_on_count2)\n", "err_2 = np.sqrt(nd_on_sem2**2 + nd_off_sem2**2)\n", "\n", "#verify that our selection criteria were correct\n", "plt.plot(nd_on_count2)\n", "plt.plot(nd_off_count2)\n", "#counts per Kelvin\n", "count_K2 = (nd_on_mean2 - nd_off_mean2)/noise_cal.header['TCAL2']\n", "print('Counts per Kelvin for channel 2 is %.2f +- %.2f'%(count_K2, err_2))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This was just a demonstration of what is being done by the online system. The same conversion (labelled HZperK) is already in the header." ] }, { "cell_type": "code", "execution_count": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "XTENSION= 'BINTABLE' / binary table extension \n", "BITPIX = 8 / 8-bit bytes \n", "NAXIS = 2 / 2-dimensional binary table \n", "NAXIS1 = 184 / width of table in bytes \n", "NAXIS2 = 100 / number of rows in table \n", "PCOUNT = 0 / size of special data area \n", "GCOUNT = 1 / one data group (required keyword) \n", "TFIELDS = 23 / number of fields in each row \n", "EXTNAME = 'Drift ' / name of this binary table extension \n", "FRONTEND= '04.5S ' / Frontend ID \n", "CENTFREQ= 6668.518 / [MHz] Backend centre frequency \n", "BANDWDTH= 16. / [MHz] Bandwidth of backend \n", "TCAL1 = 14.2 / [K] Cal noise diode value for channel 1 \n", "TCALSIG1= 0. / [K] uncertainty in TCAL1 \n", "TCALFRQ1= 6670. / [Hz] Frequency the calibration applies to \n", "TCALDAT1= '19000101' / Calibration date \n", "TCAL2 = 21. / [K] Cal noise diode value for channel 2 \n", "TCALSIG2= 0. / [K] uncertainty in TCAL2 \n", "TCALFRQ2= 6670. / [Hz] Frequency the calibration applies to \n", "TCALDAT2= '19000101' / Calibration date \n", "SCANDIST= 0.64 / Scan length (deg) \n", "HZPERK1 = 12139.7854916614 / [Hz/K] Counter calibration \n", "HZKERR1 = 0.872808623974454 / [Hz/K] Counter cal error \n", "HZPERK2 = 10631.1095866918 / [Hz/K] Counter calibration \n", "HZKERR2 = 0.576776168333796 / [Hz/K] Counter cal error \n", "TTYPE1 = 'MJD ' / label for field \n", "TFORM1 = '1D ' / format of field \n", "TUNIT1 = 'days ' \n", "TTYPE2 = 'Count1 ' / label for field \n", "TFORM2 = '1D ' / format of field \n", "TUNIT2 = 'Hz ' \n", "TTYPE3 = 'Count2 ' / label for field \n", "TFORM3 = '1D ' / format of field \n", "TUNIT3 = 'Hz ' \n", "TTYPE4 = 'Hour_Angle' / label for field \n", "TFORM4 = '1D ' / format of field \n", "TUNIT4 = 'deg ' \n", "TTYPE5 = 'Declination' / label for field \n", "TFORM5 = '1D ' / format of field \n", "TUNIT5 = 'deg ' \n", "TTYPE6 = 'Azimuth ' / label for field \n", "TFORM6 = '1D ' / format of field \n", "TUNIT6 = 'deg ' \n", "TTYPE7 = 'Elevation' / label for field \n", "TFORM7 = '1D ' / format of field \n", "TUNIT7 = 'deg ' \n", "TTYPE8 = 'RA_Apparent' / label for field \n", "TFORM8 = '1D ' / format of field \n", "TUNIT8 = 'deg ' \n", "TTYPE9 = 'Dec_Apparent' / label for field \n", "TFORM9 = '1D ' / format of field \n", "TUNIT9 = 'deg ' \n", "TTYPE10 = 'RA_Mean ' / label for field \n", "TFORM10 = '1D ' / format of field \n", "TUNIT10 = 'deg ' \n", "TTYPE11 = 'Dec_Mean' / label for field \n", "TFORM11 = '1D ' / format of field \n", "TUNIT11 = 'deg ' \n", "TTYPE12 = 'RA_B1950' / label for field \n", "TFORM12 = '1D ' / format of field \n", "TUNIT12 = 'deg ' \n", "TTYPE13 = 'Dec_B1950' / label for field \n", "TFORM13 = '1D ' / format of field \n", "TUNIT13 = 'deg ' \n", "TTYPE14 = 'RA_J2000' / label for field \n", "TFORM14 = '1D ' / format of field \n", "TUNIT14 = 'deg ' \n", "TTYPE15 = 'Dec_J2000' / label for field \n", "TFORM15 = '1D ' / format of field \n", "TUNIT15 = 'deg ' \n", "TTYPE16 = 'Galactic_Long' / label for field \n", "TFORM16 = '1D ' / format of field \n", "TUNIT16 = 'deg ' \n", "TTYPE17 = 'Galactic_Lat' / label for field \n", "TFORM17 = '1D ' / format of field \n", "TUNIT17 = 'deg ' \n", "TTYPE18 = 'Ecliptic_Long' / label for field \n", "TFORM18 = '1D ' / format of field \n", "TUNIT18 = 'deg ' \n", "TTYPE19 = 'Ecliptic_Lat' / label for field \n", "TFORM19 = '1D ' / format of field \n", "TUNIT19 = 'deg ' \n", "TTYPE20 = 'Uiser_Long' / label for field \n", "TFORM20 = '1D ' / format of field \n", "TUNIT20 = 'deg ' \n", "TTYPE21 = 'User_Lat' / label for field \n", "TFORM21 = '1D ' / format of field \n", "TUNIT21 = 'deg ' \n", "TTYPE22 = 'HA_Error' / label for field \n", "TFORM22 = '1D ' / format of field \n", "TUNIT22 = 'deg ' \n", "TTYPE23 = 'Dec_Error' / label for field \n", "TFORM23 = '1D ' / format of field \n", "TUNIT23 = 'deg ' " ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "noise_cal.header" ] }, { "cell_type": "code", "execution_count": 28, "metadata": { "collapsed": false }, "outputs": [], "source": [ "count_K1 = noise_cal.header['HZPERK1']\n", "count_K2 = noise_cal.header['HZPERK2']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Measure the peak antenna temperature\n", "\n", "## Load and plot data" ] }, { "cell_type": "code", "execution_count": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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jFUB/Ve3JZCw35UJCNmuWWdpBFomIckXjxsCmTa6joEwJLCETkSYADlfVpwFA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\nMAyjyZhBZhiGYRiG0WT+H+imKfHNmyaBAAAAAElFTkSuQmCC\n", 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64Lp6u3dq5Ni5wDGZ7XeBvdKNTgrtgw9KKyHTK8vqov+ipOBWr4abb4aePWNH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\nfjf7/XKS+Xefat1VjC/VXxtbsCD7+zh2bOxoqtf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SMlNUTU3Qrx+sumrSkaTzCVk51doZY8pDY6PbluPM+PvuC7fcEjwuxyWeTHFY\nQmaKqqkpGKZdTnxCNnZssnEYY8pDUxOsvDKMHAkbbOCOleMNW6dOMHhw8Hju3ORiMfGyhMwUVbkm\nZD17BvvLlycXhzGmPIwfD/PmuRHYXjkmZJCehNlo8eplCZkpqnJNyHwNGQQjq4wxtcv3d62rgG/B\nPfYI9i0hq14V8FE0laRcE7JwZ/6FC5OLwxhTHoYPd9vevYNj5VpD1q0brLmm27/9dnjqqWTjMfGw\nhMwUVbkmZH37BvuWkBljfEf+IUMSDaNg8+a57e23w1FHJRuLiUd90gGY6rLppklHkJ0lZMaYfA4/\nvHzLL3A1eUuWuP3w3GSmelhCZmpCr17BvnXqN8ZE3Xdf0hHkN2kSHHMMjB5tCVm1siZLUxPCfUPC\nqwkYY0wl6N8fFi1y+7auZXWKNSETkX4iMkpEponIVBHZVkQGi8irIvKWiDwmIr1zXLuniLwjIu+L\nyHlxxmmKo1KW9bCEzBhTiSZOdFurIatOcdeQ3QQ8paqDgM2BacDtwLmqujnwKHBO9CIR6QTcAuwJ\nbAwcLiKDYo7VdNCoUUlHkF9zMwwdCoceCtdfn3Q0xhjTNn60uCVk1Sm2hExE+gI7qOodAKrarKrz\ngfVV9aXUaWOAg7NcPhSYrqofq2oTcD/w07hiNcVR7utEdurkZuResQL+8Y+kozHGJO3445OOoG0s\nIatucdaQrQfMFpE7RWSCiNwmIj2BKSLik6tDgLWzXLsmMCP0eGbqmCljfoLF3/wm2Tjy8R36p02D\nCROSjcUYkwxVqK+HP/856Ujaptxvek3HxJmQ1QNDgL+o6hBgMXAecBxwkoi8AfQCso15s/XsK9hl\nlyUdQW6jRwf7TzyRXBzGmOTU1bkuDF27Jh1J24RXHDHVJ85pL2YCM1V1fOrxKOB8Vb0U2ANARDYA\n9sly7Wek15ytnXq9DMP9dMtAQ0MDDQ0NHY3btJOvRu/UKdk48gmPTlJL+ytCY2MjjX4WT2Nq2B57\nwIsvJh2FiYtojN9KIvIi8GtVfU9EhgPdgetUdbaI1AEjgedVdWTkunrgXWBXYBYwDjhcVadFztM4\n4zdtc9FFcOWVMGsWrLFG0tFk9/jjsP/+bv+yy4LlU0zlEBFUtUwXuWkbK8OS4afBqcR/eh/7ihWV\nsQ6nSZev/Ir7v/MU4F4RmYQbZXklbsTku7gRlzN9MiYiA0TkSXADAIDfAaOBqcAD0WTMlJ+5c2Gz\nzWD11ZM8aus8AAAgAElEQVSOJLf99gv2W1qSi8NUBhG5QESmiMhkEblPRLqKyCGpYytEJOfCOzZ1\nj4nDCy+4tS39rP2mesQ6U7+qTgK2jhy+OfUTPXcWoeZLVX0aeDrO+ExxLV0KZ51VOXdtlXh3bEpH\nRNYFjgcGqeoyEXkAOAx4HTgQ+Fuea/3UPcNwXTDGi8hjdmNpOqqhwXXuX7QIevZMOhpTTBXy1Wkq\nwfLl0KVL0lG0bvp0t7Wh46YVC4AmoEeqG0UP4DNVfUdV32vlWpu6x8SmWzd3A2yqiyVkpmiWL4fO\nnZOOonUDB7qtzdhv8lHVecD1wKe4vqzfqOqYAi+3qXvKXCXcPObSqZObuseSsupiCZkpmkqpIfNe\neqn1c0ztEpGBwOnAusAAoJeIHFHg5dYgXqb8oJ533kk2jo6or4e99oITTkg6ElNMsfYhM7Wl0hKy\n119POgJT5rYCXlHVuQAi8giwHXBvAdfa1D1l6vHH3Xa99ZKNoyPef99tx41LNg7TurZM21PQtBep\ndSTXBVqAT1S1LO4tbMh4eRGBhx+Ggw5KOpLWVfKw91rXnmkv2lOGichgXPK1NbAUN03POFW9NfX8\nC8DZqvpmlmtt6p4yVQ1/+/53WH99eK+13oymrOQrv3LWkInIesAZwN64u71ZgABriMhawBPAn1T1\n46JHbCrOokVuG5541ZgkdbQMU9VJInI38AYukZsA3CYiB+JGiq8KPCkiE1V1LxEZANymqvuoarOI\n+Kl7OgH/sBGWpths6p7qkrOGTEQeBG4DGlOjhMLPdQZ2xk36+vPYo8zB7i7Lx4wZrrP80qWVMe1F\nNdwl16pCa8isDDPZVMPfvv8d1lsPPvww2VhM27Srhgw4UlWzrTMJsJaqPgM80+HoTFVoaoK11qqM\nZMzUDCvDTFWr5KTSZMr39fkvEclYejXVr6IxtohMRaq0Dv3lvN6mKRorw0xVs4SsuuRLyCYAT4nI\nd+vLi0gD8CTw65jjMhWmUuYg8yopeTTtZmWYqWrWh6y65EzIVPVi4AVgtIj0EpGDgLuBA1T12VIF\naCrDzJlubpxKYQlZ9bMyzFQ7S8iqS96vUFUdISJLcHeaALuq6vvxh2UqzT77tH5OOXnhBRiSc1lo\nUy2sDDNRm20WTA5b6ZYtSzoCU0w5a8hE5HEReRxoAFYDvgFuSB1/rETxmQpQifPgDBoEXVO9iz75\nJNlYTDysDDPZ1NXBz36WdBQdc+21bjtnDuy0kxt1uXhxsjGZjss37UVD5JDi5vABUFUdG2NcBbEh\n48n79lvo2TN4XCn/HStWuCbW886Dq6+unLhNm6a9aIgcsjLMsPHGMGqU21ayt96CwYODxx9+WNmr\nD9SKdk17oaqNsUVkqsZttyUdQfv4UZZXX51sHCY+VoaZqOXL4bPPqqMPafR3mD8/mThM8eRrsnxS\nRA4Jj1AKPddDRA4VkafiDc+UO5s+wpQrK8NM1AUXwIIF1ZmQLViQTBymePJ16j8W+B1wuYisAD7H\nVff3T133AHB07BGasvXxx3DKKUlHYUxOVoaZNDNmuG01JGTRaYaWLk0mDlM8+ZosvwIuBS4Vkf7A\n91NPfaKqX5QiOFPeDjww6QiKp6XFVhmoNlaGmVyqISGL/g6WkFW+gmaOShVeVoCZNOHO/JVu2TLo\n3j3pKExcrAwzAG+/7bbVkJB1jaxBsWRJMnGY4rE6AdNuffsG+716JRdHMTQ3Jx2BMSZu06a5bTUk\nZNEbYqshq3yWkJl269PHbd98E/bdF9ZcM9l42urOO+GHP3T7K1YkG4sxJl7hWe0raZm3XKK/wzff\nJBOHKZ42JWQisrKIbB5XMKaySGomlbo6uOceNw9OJTnmGFhlFbdvCVltsDKsdoVHIUqrs9hVntNP\nTzoC01GtJmQiMlZE+ojIysCbwO0i8qf4QzPlrqkJDjoINt3UTbJaic0Afk5OWxOuelkZZgDGpqYB\nvuaaZOMopnfeSToCU0yF1JD1VdUFwEHA3ao6FBgWb1imEixaBEceWVmLikc1Nbmt1ZBVNSvDDAcc\n4LbnnJNsHMW04YZJR2CKqZCErJOIrAH8HHgydczW+jD85z+VuY5lmF+c1xKyqmZlmDGm7BWSkP0e\nGA18oKrjRGQg8H4hLy4i/URklIhME5GpIrKtiAwVkfEiMjG13TrHtReIyBQRmSwi94lI12znmWT1\n7590BB3jEzJrsqxq7S7DjDGmVHIuLv7dCSKrqOrcdr24yF3AWFW9Q0TqgZ7Av4E/qupoEdkLOFdV\nd45cty7wPDBIVZeJyAPAU6p6V+Q8W5g3IS0tbtmk5ubKXj5p001hyhT46CNYd92kozGFKHRx8dD5\n7S7D4mZlWOn4jvzV9s8t4jr033gjTJ7syjRTvvKVX4XUkL0mIg+JyN4ihY9NEZG+wA6qegeAqjar\n6nzc8iV+Bqt+wGdZLl8ANAE9UolcjxznmYTsuafbVnIyBvDMM25rTZZVrV1lmDGV4OOPgxGWRx0F\n116baDimAwqpIavDdYA9DtgaeBC4U1Xz9h4SkS2AvwFTgcG40U2nAasBL+P6cNQBP1bVGVmuPwG4\nHlgCjFbVX2Y5x+4uE1JNd5vrrw9PPeW2pvy1o4asXWVYKVgZVjoi8MAD8POfJx1J8c2eDd/7XvDY\nPlLlq0M1ZKraoqrPqOphwPG4xXjHp4aSb5fn0npgCPAXVR0CLAYuAP4BnKqq6wBnAHdkCXggcDqw\nLjAA6CUiR7QWqzHtUVdnNWTVrANlmKki/fvDDjskHUU8ossomcrU6oQFIrIqcARwFPAl8DvgcVyt\n1yhc0pTNTGCmqo5PPR6FS8iGquqw0LHbs1y7FfCK7/chIo8A2wH3Rk8cPnz4d/sNDQ00NDS09isZ\nk6alBb79NukoTC6NjY00Nja2+/oOlGGmijQ3V8cM/dn4hKxTJ7u5rGSFNFm+B/wTuENVZ0aeO19V\nr8pz7YvAr1X1PREZjusLtitwpqqOFZFdgatUdevIdYNxydfWwFJgJDBOVW+NnGfV/QkRge23h5df\nTjqSjhOBHj1g8eKkIzGFaEeTZbvLsLhZGVYaX37pasi+/hr69Us6muJTdTX99fUu8bSPVPnKV34V\nkpB9V2Kk+mL0Sk2yWMgbD8bVgHUBPgCOBdYHbgW64vqHnaSqE0VkAHCbqu6TuvZcXNNCCzABl9g1\nRV7fCrOEDBgAb7zhtpWumvrD1YJ2JGTtLsPiZmVYaYwYAZdcAgsXQq9eSUcTj/BwFftIla+OJmT3\nAb8FVgDjcSMkb1LVxBegsMIsOf36udE91XC3aQlZZWlHQmZlWI3zf+PffgvduycbS1wsIasM7erU\nn5puAmCT1N3kAcDTuP4WGSMeTW359lvXzGdMubIyzADcERo21q1bcnHEbfz41s8x5S3fKMtxqW29\niHTGFWaPp5oNLf+uUe+84+7EWlqqp4PsI4+4rRVoVcfKMJO2dmU1z0JnN8iVL19C5j+6fwM+BnoB\nY0Xk+8D8mOMyZWr2bLddsaJ6CjffD+6225KNwxSdlWGmZpZFCydktfI7V5t8016sJiJn4gq1G1LH\nzsQlcf+OOzBTnqolCQvzNX0bbZRsHKborAwzNZOchPvGdepUvSNKq1m+hKwT0LtUgZjKUI1z3NRK\ngV2DrAwzLFuWdASlEW2y/PBDGDIkmVhM++RLyL5Q1ctLFompCM3NSUdQfEuXuu3998OZZyYbiykq\nK8PMdwnZAw8kG0fcoqNHbV7FylPI4uLGfKcaZ7RfdVW3tU79xlSXcFJSjWtYhtVHqleqsayudvkS\nsmF5njM1av/93baaho9vtBFss03SUZgYWBlW4/bZx21PPDHZOJJgCVnlyZmQ+XUkjcnmsceSjqC4\nzjjDbW/PtrKqqUhWhpmxY922Fm+4/vhHmDgx6ShMW1iTpWmXahtt6Zszjj8+2TiMMcW3fHnSEZTG\nRx/BT3/q9sePh5tuSjYe0zaWkJmCvf12sF8tk8J61ZZgGmMCP/hB0hGUxrrrpk91sfLKiYVi2sES\nMlOwzTYL9nfcMbk4jDGmNRdcEOzvumtycZRa+Oay2m6cq50lZKZdrEbJGFPOHnww6QiScdFFwX50\n5KUpb5aQGRNRjZPfGlNrPvkk6QiS8cMfwujRbt9qyCqLJWSmzar9ruuSS5KOwBjTUXvv7baXXZZs\nHEmwhcYrkyVkps06dUo6gnhNmZJ0BMaYjvI3josWJRtHEpqa3LZWlo2qFpaQmTar9hoy1aQjMMZ0\n1Guvue2SJcnGkQQ/zceyZW5uRZsktjJU+VerKaZNNoFDDoGtt046knhV0yoExtSqBQvcthYTspVW\ncttly9zcimusEaxaYMqX1ZCZgn37LRxxRNA3o9r4Wa0fegj22y/ZWIwxHeNriWoxIRs6FG64AZYu\ndY+jC4+b8mQJmSnI88/D3LmwyipJRxKfLbYI9p94Irk4jDEdoxr0o/JJSa1ZdVX46iu339ycbCym\nMJaQmVY1N7uJFRcsgL59k44mXhdemHQExpiOWrEiGHxUqwlZ165u+SSozYENlcj6kJlW+Sr/3r2h\nrspT+C5dko7AGNNR4fm3arHJElxC9uWXbt8SsspgCZlpVTghq3bhRYgXLqyN39mYavLf/wb7112X\n3hWhlnTtGuxbQlYZqry+wxSDT8hWXz3ZOEohnJCde25ycRhj2uf884P9s86qrXUsw8IJ2eLFycVh\nCmcJmWmVT8j69Us2jlIIT6T4yCPJxWGMaZ+XX046gvLgE7LOnYMpQEx5izUhE5F+IjJKRKaJyFQR\n2VZEhorIeBGZmNpmndUq27Vxxmpy85MKVvsM/ZBeQ/bVVzB/fnKxGGNMe/mEbKONYMSIZGMxhYm7\nhuwm4ClVHQRsDkwDrgEuVtUtgUtTjwu91pTYNtsE/Q9Eko2lFHxC5psrv/giuViMMaa9/Nxjkycn\nG4cpXGwJmYj0BXZQ1TsAVLVZVecDnwN+8oR+wGdtuNaU2Lhx8PDDbr/aR1hC0GQ5dKjb2vw9xphK\ntNFGbnviiW5bq33pKkmcX7HrAbNF5E4RmSAit4lID+B84HoR+RS4FrigDdeaEmppcdubb3bbWkjI\nfvlL93Pwwa5A85NLGmNMJfHl9fbbu+3zzycXSzH9/veujK5GcU57UQ8MAX6nquNF5EZc8vVj4FRV\nfVREDgHuAHYr4NrzcU2caYYPH/7dfkNDAw0NDTH8KrUpWjtUC02We+7pfgB69LAasnLQ2NhIY2Nj\n0mGYCvDoo2676qowZ06ysZSDceNg883hyCPd4y+/LK/R8medBVddFcwbNy3VMWnQoNzX3Hqr6997\nzz2uS8mhh8LYsfHHWgqiqvG8sEh/4FVVXS/1+Ce4hGwHVe2TOibAN6rat4Brz1fVfSPnaVzxG9eZ\nv2dP14/s9ddh333h8ceTjqp0tt0WbrzRbU35EBFUtSpuD6wMK56mpmBi5912g2efdUsomeBmesyY\n8mq6FIGPP4bvf9897tTJtczk+3/r3dv1a1Z1S9ztt5/b/+or+N73ShJ2h+Qrv2JrhFLVL4AZIrJB\n6tAwYArwvojslDq2C/BeG641JeSb6/y0F7XQZBlWX29NlsZUirlzg/0+fZKLoxydfbbblmMrR3hp\nK99NZv58mD4dzjvPTdAdFp7kNjwqfvXVYfTo+OIshbhn6j8FuFdEugAfAMcCDwK3ikhXYAlwAoCI\nDABuU9V98lxrSsg31/mErBz/mOPUubM1WRpTKcI3jGecAUOGJBdLuTnySLdqQbncVI8bB9ek5lfI\nNrXQXnvBq68G+7l6IkXL508+KVqIiYg1IVPVSUB0nrE3gG2ynDsL2Cf0ONu1poT8h93PQ1Yuf8yl\nYjVkxlSO8Jfz4MFBZ3YTTIFRLjfVDz0UjN6fMiUY1e75ZAxcrdnnn7vBZX7kKMCsWcF30gWpoYGV\n3kRdY1+xpi18AfdZamKScvljLhWrITOmMgwe7L6gvZ49k4ulHPVIzVGQ9E31hAmw3XbBTT6kN1lm\n8+67MGCA6/x/5ZXB8TXXDAZuXHWV2/o+hJXKEjKTUy2OsgxbuhTuvjvpKIwxrXnrLfj3v4PHtVZW\ntcbP2r/jjq4TfVKeesrVfv3lL8Gx8HJ12bzzTrD/XqTH+Zlnpj/2fdC8Bx+M/6a6pSX9fVta4P77\n2/dalpCZrB5+GC67LP1YJYxgKaYXXoAHHkg6CmNMIfzyQBMnJhtHOQrXGH74YTIxXHwxXHJJ5vFw\nQnbggZnP+3kws/H9m71oF5NDD4XXXis8xvbYaSfYZ5/g8UcfweGHt++14u7UbyrUr38N33wTPH7l\nFdhii+TiMcaYQlg5lalHaFr1FSvcduZMWGut0sVwxRXZj/uRkp9/DrNnd+w9wqMusx1be21X49be\nJu2//AXefNN9H/o5015+OX1Ub2tNsPlYDZnJKnqnMXBg0DHUGGNMZdp9d9f3au21g+QsTgsXwqhR\nuZ/3NWRHHOGSmyOOaP97zZ7tVlkJ17r577IVK1wSGp5GY8ECePvtwl//8svhjjvSm1HBJbxLl7r3\n97V20QEGy5e7io58LCEzWUXb3X0fhFoU/eMztUNELhCRKSIyWUTuE5GuIrKyiDwrIu+JyDMi0i/H\ntR+LyFsiMlFExpU69mo3YQL8739JR1E5wk1/vnyfPj3+9x05Eg45JPfzCxa47aefum0hc8hdckl6\nM6E3YgQ88gi8+GJwzCdk4RYf75xzYLPNXK1Xvn6Hn3/unv/qq/TjPunaYQc46STXrcd/JrN9h/7j\nH/l/L0vITFbRjpaVPnqlI669NukITBJEZF3geGCIqm4GdAIOwy3j9qyqbgA8l3qcjQINqrqlqg7N\ncY5ppx/9CLbcMrMjt8lu1VWDfZ8sbLRR7qbEYvjpT+GZZ9KPHXNM+mPf5++DD9y2kKmGfv97VxOW\nS7g1509/cls/ItP/7jNnwt//7vZffz39+tVWcwMCvPCkw2G+hrFPH/d6ECS52ZpPW2MJmcmQbRRO\nLdaQderktq2NAjJVawHQBPQQkXqgBzAL2B+4K3XOXcABeV7DxvvFxK9/WC3rGMatPtRj/Lnngv1o\nwlRMjz3mljcKi97cf/hhMLUSQLduhb22///P9ZyvvfKLqvuEzCd8U6cG5y9enH79nDnptWzZ5jcb\nODBI7pYtCyal9asFhBOyQm8aLCEzGbLdoSQ9f00S/B+8JWS1SVXnAdcDn+ISsW9U9VlgdVX9MnXa\nl0Cu5ZoVGCMib4jI8bEHXGP8F/cuuyQbR6UIN8mFa6lKXbZHE6nFi9Ob8rKNtPRuuilIdPzv8/TT\nbo3ll14Kzlu2LL1/3OefB33HFi50z4cT1HPPzXyv8PXR0ZzgEkmfkD36aDAlh2+yXL7c9Z2bO7fw\nCcZr8GvWtKa5Of3DWqssIattIjIQOB1YFxgA9BKRI8PnpFYGzzU/+PaquiWwF3CyiOwQY7g1J1si\nsccepY+jUuSaxT6uebqyJTHgasjCTYrffps+xVK+7jEbbRSUyz4x23NP2Hdf+OEPg/O++CI9CVp3\n3eD8nXd2fdry1bCBa8acPdv9u22TsbaQ4//tojVs4JpKDzkE7rqr8O8Q+9o1GZqaYOWVMzsw1prW\n/mBN1dsKeEVV5wKIyCPAj4EvRKS/qn4hImsAWf9SVPXz1Ha2iDwKDAVeip43fPjw7/YbGhpoyLVw\nn0kTTTDmzYOVVkomlkqQa0Sl75pRLB995EZLHnVU+vG+fd26lV26uFqjF16A4cMz540Ll7t1da5J\n9W9/c8sthWONJjmrh+qpDz00vRP/8uXB/Gvz5sGzz2YfaKAa1LxNnOg66Z9+eu7f9Z57cj939dVu\n+9lnjYwY0Zj7xBBLyEyGpiZXsPmE7Ouvk40nKf36uT9eU7PeAS4Rke7AUmAYMA5YDBwNXJ3a/it6\noYj0ADqp6kIR6QnsDlye7U3CCZlpP0vG8stVE1bshGybbbLPJxZOyLp3h733dj/9+qUvMB5OyOrr\nYddd3XfSQw+l157tsAOcfHLw2CdS3bu7ZLBfZOzzGWcE+0uXZiaM4EZdRqfBuPHG3L/rn/+c/bj/\nXQGWL2/g1FMbQoPDshYDgDVZmix+8Yv0KuXoB7tW+D/g5ctdn4vZs21JllqiqpOAu4E3gLdSh/8O\nXAXsJiLvAbukHiMiA0TkydR5/YGXROR/wOvAE6oaY/dpY/LL1Y/p+efdqMVCqMKiRcHjwYNhXGRC\nl1yTu/bu7bavvJJ+3E/SOmyY20YTMnDNktHnNtkEbrkl832GDm3/yNvrrw865Rfi/fezHw/fHNxy\nC2ywQWGvZwmZyfDee65W7JFHard2DOB3v3PbTz91/QBqbekoA6p6japuoqqbqerRqtqkqvNUdZiq\nbqCqu6vqN6lzZ6nqPqn9D1V1i9TPpqr6x2R/k+qyYEEwf5UpjE/Ibr8987n//rew13jooSCxAreG\n6AsvuAQonKhl40chhkd4QpCQ+fK2d++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BYeq6Vy9//JFHYJtt/L5qhd2TcYdF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\njQHozsZYAUx/bRhl02Fl1l9F5Qs0W4f1TP21MVYclOUFbAH8FngauA8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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#construct an array for the x-axis in terms of scan offset from the centre\n", "scandist = hdulist[0].header['SCANDIST']\n", "offset = np.linspace(-scandist/2.0, scandist/2.0, len(drift1.data['MJD']))\n", "\n", "#extract the scan data, convert to Kelvins and plot\n", "plt.figure(figsize=[10,5])\n", "plt.suptitle('North offset scan')\n", "north_scan_LCP = drift1.data['Count1']/count_K1\n", "north_scan_RCP = drift1.data['Count2']/count_K2\n", "ax = plt.subplot(121)\n", "plt.plot(offset, north_scan_LCP, label = 'LCP')\n", "plt.xlabel('RA offset (deg)')\n", "plt.ylabel('Tsys (K)')\n", "plt.legend()\n", "lim = plt.axis('tight')\n", "ax = plt.subplot(122)\n", "plt.plot(offset, north_scan_RCP, label = 'RCP')\n", "plt.xlabel('RA offset (deg)')\n", "plt.ylabel('Tsys (K)')\n", "plt.legend()\n", "lim = plt.axis('tight')\n", "\n", "plt.figure(figsize=[10,5])\n", "plt.suptitle('Central (on-source) scan')\n", "on_scan_LCP = drift2.data['Count1']/count_K1\n", "on_scan_RCP = drift2.data['Count2']/count_K2\n", "ax = plt.subplot(121)\n", "plt.plot(offset, on_scan_LCP, label = 'LCP')\n", "plt.xlabel('RA offset (deg)')\n", "plt.ylabel('Tsys (K)')\n", "plt.legend()\n", "lim = plt.axis('tight')\n", "ax = plt.subplot(122)\n", "plt.plot(offset, on_scan_RCP, label = 'RCP')\n", "plt.xlabel('RA offset (deg)')\n", "plt.ylabel('Tsys (K)')\n", "plt.legend()\n", "lim = plt.axis('tight')\n", "\n", "plt.figure(figsize=[10,5])\n", "plt.suptitle('South offset scan')\n", "south_scan_LCP = drift3.data['Count1']/count_K1\n", "south_scan_RCP = drift3.data['Count2']/count_K2\n", "ax = plt.subplot(121)\n", "plt.plot(offset, south_scan_LCP, label = 'LCP')\n", "plt.xlabel('RA offset (deg)')\n", "plt.ylabel('Tsys (K)')\n", "plt.legend()\n", "lim = plt.axis('tight')\n", "ax = plt.subplot(122)\n", "plt.plot(offset, south_scan_RCP, label = 'RCP')\n", "plt.xlabel('RA offset (deg)')\n", "plt.ylabel('Tsys (K)')\n", "plt.legend()\n", "lim = plt.axis('tight')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What can we tell about the telescope by looking at this observation?\n", "\n", "The pointing is slightly off. If we had a perfect pointing model, the peak of the scan would always be at 0 RA offset, and the north and south offset scans would have half the power of the central scan. \n", "\n", "The system temperature for the LCP signal chain is about 68 K and for RCP about 80 K.\n", "\n", "$T_{sys} = T_{cmb} + T_{gal} + T_{atm} + T_{spill} + T_{sw} + + T_{loss} + T_{rx}$\n", "\n", "where\n", "\n", "$T_{cmb}$ ... cosmic microwave background\n", "\n", "$T_{gal}$ ... galactic background\n", "\n", "$T_{atm}$ ... atmospheric emission\n", "\n", "$T_{spill}$ ... ground radiation\n", "\n", "$T_{sw}$ ... standing wave pattern\n", "\n", "$T_{loss}$ ... ohmic losses, losses in feed\n", "\n", "$T_{rx}$ ... receiver noise temperature\n", "\n", "\n", "\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Fit a baseline to remove systemic contributions\n", "\n", "We are only interested in the temperature contributed by the source, so we want to subract off the system temperature contribution. We see that the baseline level changes through during the scan, and the scan also crosses the first sidelobes. \n", "\n", "What do we fit then? This is a fairly strong source, we can clealy see the antenna beam pattern. Looking carefully we can see a deflection point at the base of the central Gaussian. This is the first null of the beam. At these points we will not be receiving any power from the source.\n", "\n", "The second table in the fits file gives us useful information on the 4.5cm feed system, including information about the beam." ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "XTENSION= 'BINTABLE' / binary table extension \n", "BITPIX = 8 / 8-bit bytes \n", "NAXIS = 2 / 2-dimensional binary table \n", "NAXIS1 = 143 / width of table in bytes \n", "NAXIS2 = 1 / number of rows in table \n", "PCOUNT = 0 / size of special data area \n", "GCOUNT = 1 / one data group (required keyword) \n", "TFIELDS = 13 / number of fields in each row \n", "EXTNAME = '04.5S ' / name of this binary table extension \n", "FEEDTYPE= 'Diagonal' / Feedtype \n", "BMOFFHA = -0.46 / Beam offset in HA \n", "BMOFFDEC= 0.02 / Beam offset in Dec \n", "HPBW = 0.116 / Half-power beamwidth in degrees \n", "FNBW = 0.27 / Beamwidth to first nulls in degrees \n", "SNBW = 0.54 / Beamwidth to second nulls, in degrees \n", "DICHROIC= '(Off) ' / Dichroic reflector position ON or OFF \n", "PHASECAL= '(Off) ' / Mark 4 VLBI phase calibration ON or OFF \n", "NOMTSYS = 45. / [K] Nominal minimum system temperature \n", "TTYPE1 = 'PSS_Value' / label for field \n", "TFORM1 = '1D ' / format of field \n", "TDISP1 = 'G.3 ' \n", "TUNIT1 = 'Jy/K ' \n", "TTYPE2 = 'PSS_Sigma' / label for field \n", "TFORM2 = '1D ' / format of field \n", "TDISP2 = 'G.3 ' \n", "TUNIT2 = 'Jy/K ' \n", "TTYPE3 = 'PSS_Freq' / label for field \n", "TFORM3 = '1D ' / format of field \n", "TDISP3 = 'G.3 ' \n", "TUNIT3 = 'MHz ' \n", "TTYPE4 = 'PSS_Date' / label for field \n", "TFORM4 = '8A ' / format of field \n", "TTYPE5 = 'PSS_BW ' / label for field \n", "TFORM5 = '1D ' / format of field \n", "TDISP5 = 'G.3 ' \n", "TUNIT5 = 'MHz ' \n", "TTYPE6 = 'PSS_Source' / label for field \n", "TFORM6 = '8A ' / format of field \n", "TTYPE7 = 'PSS_Flux' / label for field \n", "TFORM7 = '1D ' / format of field \n", "TDISP7 = 'G.3 ' \n", "TUNIT7 = 'Jy ' \n", "TTYPE8 = 'Feedsys ' / label for field \n", "TFORM8 = '27A ' / format of field \n", "TTYPE9 = 'FeedSys_RF_Hi' / label for field \n", "TFORM9 = '1D ' / format of field \n", "TDISP9 = 'G.3 ' \n", "TTYPE10 = 'FeedSys_RF_Lo' / label for field \n", "TFORM10 = '1D ' / format of field \n", "TDISP10 = 'G.3 ' \n", "TTYPE11 = 'Mixer ' / label for field \n", "TFORM11 = '28A ' / format of field \n", "TTYPE12 = 'Mixer_RF_Lo' / label for field \n", "TFORM12 = '1D ' / format of field \n", "TDISP12 = 'G.3 ' \n", "TUNIT12 = 'MHz ' \n", "TTYPE13 = 'Mixer_RF_Hi' / label for field \n", "TFORM13 = '1D ' / format of field \n", "TDISP13 = 'G.3 ' \n", "TUNIT13 = 'MHz ' " ] }, "execution_count": 30, "metadata": {}, "output_type": "execute_result" } ], "source": [ "hdulist[1].header" ] }, { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [], "source": [ "hpbw = hdulist[1].header['HPBW'] #half power beamwidth\n", "hfnbw = hdulist[1].header['FNBW']/2.0 #half beamwidth to first nulls" ] }, { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from scipy.optimize import curve_fit\n", "\n", "def gauss_lin(x, *p):\n", " \"\"\"\n", " Gaussian plus first-order polynomial to fit the telescope beam.\n", " Note that the width of the Gaussian is hard-coded to the half-power beamwidth\n", " \"\"\"\n", " amp, mu, a, c = p\n", " sigma = hpbw/(2*np.sqrt(2*np.log(2))) #a bit messy but not sure how to pass a constant through the curve fitting\n", " return amp*np.exp(-(x-mu)**2/(2.*sigma**2)) + a*x + c\n", "\n", "\n", "def fit_drift(offset, drift_power, p0):\n", " \"\"\"Fit a Gaussian beam plus first-order polynomial to driftscan and\n", " plot the results.\n", " \"\"\"\n", " gaus_coeff, var_matrix = curve_fit(gauss_lin, offset, drift_power, p0)\n", " gauss_fit = gauss_lin(offset, *gaus_coeff)\n", " plt.plot(offset, drift_power)\n", " plt.plot(offset, gauss_fit, color='red')\n", " return gaus_coeff, var_matrix\n", "\n" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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GM3fu/lfHa9fyadQFbK3bgQX746nYL4F6P3+QzTEdaN+1JgMEKkTDBdVhRwZQ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\noKnl3Xe7SU9iYwPHVKgQ+AI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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "p0 = [3, 0, 0.1, 80]\n", "coeff, covar_matx = fit_drift(offset, north_scan_RCP, p0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Virgo A is partially resolved by the 26-m telescope, so what we see is not a perfect Gaussian. We don't get a perfect fit, but it can help us disentangle the various components and zoom in on an area in which we can do a more specialised fit.\n", "\n", "We have a good estimate for the baseline, and the centre of the Gaussian. I plot them below, as well as the centre of the beam and the position of the first nulls to confirm whether the fit works." ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python2.7/site-packages/matplotlib/collections.py:590: FutureWarning: elementwise comparison failed; returning scalar instead, but in the future will perform elementwise comparison\n", " if self._edgecolors == str('face'):\n" ] }, { "data": { "image/png": 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X+ENeM3vNhdyOfF7qo3N3Hyunnw5f3EuvNn1ZMNdOGHjxX3bcYmhvONpXs/2n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\n6I576SV47bXq29esgV9+gfX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ASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "p = np.poly1d([coeff[2], coeff[3]]) #construct a polynomial function from fit results\n", "plt.plot(offset, north_scan_RCP)\n", "plt.plot(offset, p(offset))\n", "lim = plt.axis('tight')\n", "plt.vlines(coeff[1], lim[2], lim[3])\n", "plt.vlines(coeff[1]-hfnbw, lim[2], lim[3])\n", "plt.vlines(coeff[1]+hfnbw, lim[2], lim[3])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Visually, this seems to make sense. Lets continue" ] }, { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#Get indices where the offset values are within the main beam\n", "main_beam = np.where(np.logical_and(offset >= coeff[1]-hfnbw, offset <= coeff[1]+hfnbw))[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "I am now going to introduce the concept of masked arrays. A mask is basically an array of boolean values (0 = False, 1 = True)\n", "Numpy has a module called ma (for masked array). This module has many of the standard numpy functions, but now with and option to have a mask. I am going to mask the main beam, then fit a higher order polynomial through the data outside of the main beam.\n", "\n", "Masked arrays can also be used to flag RFI." ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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b+AHHmCsvrVtHN2A2ji78DUW4AXgPWAXkcWbeglGjRhEWFsZvv/1mnfOiiy4q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\nvnOn42HmhRdCv37wxBPQpg3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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#mask the main beam for polynomial fit to the baseline\n", "mask = np.zeros(len(offset))\n", "mask[main_beam] = 1\n", "offset_masked = np.ma.array(offset,mask=mask)\n", "amp_masked = np.ma.array(north_scan_RCP, mask=mask)\n", "\n", "#fit a polynomial of any order\n", "lin_first_null = np.poly1d(np.ma.polyfit(x=offset_masked, y=amp_masked, deg=3))\n", "plt.plot(offset, north_scan_RCP, color='k')\n", "plt.plot(offset, lin_first_null(offset), color='r')\n", "lim = plt.axis('tight')\n", "plt.vlines(coeff[1], lim[2], lim[3])\n", "plt.vlines(coeff[1]-hfnbw, lim[2], lim[3])\n", "plt.vlines(coeff[1]+hfnbw, lim[2], lim[3])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Subtract the line fit through the first nulls, then try to fit a Gaussian to the main beam data only" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def gauss(x, *p):\n", " \"\"\"\n", " Gaussian for fitting the beam \n", " \"\"\"\n", " amp, mu = p\n", " sigma = hpbw/(2*np.sqrt(2*np.log(2))) #a bit messy but not sure how to pass a constant through the curve fitting\n", " return amp*np.exp(-(x-mu)**2/(2.*sigma**2))\n", " \n", "def fit_gauss(offset, drift_power, p0):\n", " \"\"\"Fit a Gaussian beam plus first-order\n", " plot the results.\n", " \"\"\"\n", " gaus_coeff, var_matrix = curve_fit(gauss, offset, drift_power, p0)\n", " gauss_fit = gauss(offset, *gaus_coeff)\n", " plt.plot(offset, drift_power)\n", " plt.plot(offset, gauss_fit)\n", " return gaus_coeff, var_matrix\n", " \n", " " ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Amplitude of peak is 1.95 K\n" ] }, { "data": { "image/png": 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8zvEdvSF3MPffD3/8I9JN0EYJlSMpLg/lhS+XohjPgQPw8cd2RyXsIMlb1Elr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\nPe3DO3BmcntWLm0PJ7pBfg9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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "base_sub = north_scan_RCP - lin_first_null(offset)\n", "p0 = [3, 0]\n", "coeff, covar_matx = fit_gauss(offset, base_sub, p0)\n", "lim = plt.axis('tight')\n", "plt.hlines(0, lim[0], lim[1])\n", "print('Amplitude of peak is %.2f K'%coeff[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The old data reduction program at HartRAO would fit a 2nd order polynomial through the top of the peak, because the beam isn't really a Gaussian - its actually a modified sin(x)/x function.\n", "\n", "However, for the moment, the Gaussian fit seems to work pretty well." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that I think I have a strategy that will work, I am going to combine it all into a function. \n", "\n", "Note the text inside the triple quotes. This is known as a docstring and will be displayed when you ask for inline help. I came back to this work after a week of doing other stuff, and found I could not remember what all the p0 parameters were because the ordering is quite arbitrary. So writing your documentation as you go along is a really good idea!\n", "\n" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def fit_beam(offset, drift_power, p0, hfnbw):\n", " \"\"\"\n", " Fit a Gaussian beam plus linear baseline to driftscan and\n", " plot the results.\n", " Use the initial fit to determine the location of the first nulls. \n", " Then fit a linear baseline through the first nulls and subtract off.\n", " Fit a parabola to the top of the main beam to get an accurate fit\n", " the peak amplitude.\n", " \n", " Parameters:\n", " offset : array of floats, shape (N)\n", " coordinates in drive (or drift) direction of scan.\n", " drift_power : array of floats, shape (N)\n", " measured signal amplitude as a function of offset position\n", " p0 : array of floats, shape (4)\n", " initial guess for fitting\n", " p0[0] = height of Gaussian component\n", " p0[1] = centre x position of Gaussian component\n", " p0[2] = slope of linear component\n", " p0[3] = y-intercept of linear component\n", " hfnbw : float\n", " half of beam-width to first nulls\n", " hhpbw : float\n", " half of beam-width at half-power point\n", " \n", " returns:\n", " offset : pointing offset in degrees\n", " peak: peak of Gaussian after baseline subtraction\n", " \n", " \"\"\"\n", " \n", " coeff, covar_matrix = curve_fit(gauss_lin, offset, drift_power, p0)\n", " fit = gauss_lin(offset, *coeff)\n", " plt.figure(figsize=[10,5])\n", " ax = plt.subplot(121)\n", " plt.plot(offset, drift_power)\n", " plt.plot(offset, fit)\n", " lim = plt.axis('tight')\n", " plt.ylabel('Tsys (K)')\n", " plt.xlabel('Offset from centre (deg)')\n", " plt.vlines(coeff[1], lim[2], lim[3])\n", " plt.vlines(coeff[1]-hfnbw, lim[2], lim[3])\n", " plt.vlines(coeff[1]+hfnbw, lim[2], lim[3])\n", " \n", " #Get indices where the offset values are within the main beam\n", " main_beam = np.where(np.logical_and(offset >= coeff[1]-hfnbw, offset <= coeff[1]+hfnbw))[0]\n", " \n", " #Mask the main beam then do a polynomial fit to the baseline outside of the main beam.\n", " mask = np.zeros(len(offset))\n", " mask[main_beam] = 1\n", " offset_masked = np.ma.array(offset,mask=mask)\n", " amp_masked = np.ma.array(drift_power, mask=mask)\n", " lin_first_null = np.poly1d(np.ma.polyfit(offset_masked, amp_masked, 3))\n", " \n", " #Subtract the polynomial fitted to the baseline, then fit 2nd-order polynomial through the top of the beam.\n", " base_sub = drift_power - lin_first_null(offset)\n", " ax = plt.subplot(122)\n", " \n", " gauss_p0 = [p0[0], p0[1]] # initial guess for just the Gaussian component\n", " coeff, covar_matx = fit_gauss(offset, base_sub, gauss_p0)\n", " lim = plt.axis('tight')\n", " plt.hlines(0, lim[0], lim[1])\n", " peak = coeff[0]\n", " offset = coeff[1]\n", " print('Amplitude of peak is %.2f K'%coeff[0])\n", " \n", " \n", " return offset, peak\n", " " ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Amplitude of peak is 1.95 K\n" ] }, { "data": { "text/plain": [ "(-0.010999513553385158, 1.9516589740683365)" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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OUWHe6RA7UkLPYkjbRne3T3avlJqmXnkFrvxiP33xxcxPme/1/c+MyqfTcZADtV1e37cK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\nDRrYm9j553ttL75o342BRo1Ct4GVa9cue5xBg4JrBLpTokdjRVASi3+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V+i+w0BgT6F9F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\nGgAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "p0 = [3, 0, 0.1, 80]\n", "fit_beam(offset, north_scan_RCP, p0, hfnbw)" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Amplitude of peak is 3.36 K\n" ] }, { "data": { "text/plain": [ "(-0.020095183613374024, 3.3577925558485178)" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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CFxafLi9xFRVtOt5CaUGmgqSmy9fl/+1vQ3JyYPf9+9t9PWSgd1oqpQJvV5XvA+Vf/xr4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\nfPstjBrlyR/+bQDxifFMXD8xz6999qxnFMxlyxa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vcOfkO4k/H+83mXbsgAkTPPlFi0xs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\nTX758pTH4+PNwoB+/Ux8yuuvN9OFSUlmpD021tQrVcrs77vPyw+iI+N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KGaU6pKotRORB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\nYrFYLJYA8/8i+WqvSc60agAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "p0 = [3, 0, 0.1, 80]\n", "fit_beam(offset, on_scan_RCP, p0, hfnbw)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Selection of initial guess" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Amplitude of peak is 3.36 K\n" ] }, { "data": { "text/plain": [ "(-0.020095185150923931, 3.3577925558550494)" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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CFxafLi9xFRVtOt5CaUGmgqSmy9fl/+1vQ3JyYPf9+9t9PWSgd1oqpQJvV5XvA+Vf/xr4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\nfHnK4/HxZmFAv34mPuX115vpwqQkM9IeG2vqlSpl9vff7+UH0ZHxsst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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#kick the initial values to see how sensitive the fit is\n", "p0 = [30, 0, 0.1, 800]\n", "fit_beam(offset, on_scan_RCP, p0, hfnbw)" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Amplitude of peak is 30.52 K\n" ] }, { "data": { "text/plain": [ "(0.29250867505951639, 30.518135327637111)" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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hMLaMc3cBnYr93AmnlayE4gWZCZ+UFGeG/pde8jpJ5VlBFllO/KI1ZcoU78KU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\nx6zdu5YBrf1VkFXmoSTXBvWHgw2Irb6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by/cup/mw5rw++3WGtB3C+PvHR6wxtmKFGdH5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\n8Ltci8USHnzwAZw5A+++6z+Zb//5Nl2u7UKFol4K2+YArEE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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "#kick the initial values to see how sensitive the fit is\n", "p0 = [3, 0.2, 0.1, 80]\n", "fit_beam(offset, on_scan_RCP, p0, hfnbw)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Well,that seems reasonable enough - your guess for the linear component can be outrageous and the curve fitter will still converge, but your guess for the centre of the gaussian should be at least somewhere inside the main beam. We are already making the assumption that the first nulls are covered by the scan (ie the pointing model isn't terribly off) and so we are probably ok with assuming that the centre of the source is near the centre of the scan but system temperature can vary considerably so its good that the fit is not sensitive to the guess at the baseline level.\n", "\n", "##For discussion\n", "\n", "* If you were automating this process, how would you check that the fit was good, and what would you do if it wasn't?\n", "* Is a first-order polynomial fit good enough to correct the baseline drift? Should we try a higher order in some cases?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##Apply the fit to all the scans\n", " \n", "We have six scans in total to fit. We then need to calculate a pointing correction in the north-south direction, and apply a correction to the measured 'on-source' amplitude.\n", "\n", "We treat each polarisation separately, since they have different gains.\n", "\n", "I am going to introduce a new data structure now - the dictionary. This is a list of key - value pairs. The fits headers are an example of a dictionary.\n", "\n", "##For discussion\n", "\n", "Do you think the pointing correction will be different between the polarisations? \n", "\n" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs": [], "source": [ "LCP = {}\n", "#set initial values for the fitting\n", "nom_Tsys = 70 # expected Tsys to set the baseline height\n", "beam_centre = 0 # we're hoping the beam is somewhere close to the centre of the scan\n", "beam_height = 3\n", "slope = 0.1\n", "\n", "p0 = [beam_height, beam_centre, slope, nom_Tsys]" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Amplitude of peak is 1.97 K\n", "Amplitude of peak is 3.25 K\n", "Amplitude of peak is 1.19 K\n" ] }, { "data": { "image/png": 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rw7zWAoXW2mUqxsJTY0wZOQmhe0KLMTHkZs6BjHJaW12nEZFQcqC1jJQQLshS\nE1JJjk2D1Cq1kEWIgBVkxph04CJr7W8ArLU91tpGoMBa+6pvtxeBfxrpbQKVTwKrt6+XlpgKpiTk\nuo4yokVT88ldVkpNjeskIhJKKtvKSOkK3YIMYJLxbluqIIsMgWwhywOqjTGPGGM2GmMeNsakAtuN\nMdf79vkQMGuY11vgRWPMO8aYmwOYUwKgsrmSxL4skuNDtEesT35mPvGTS6mrc51EREJJZXspqV2h\n24cMYO4kr2O/CrLIEMiCLA5YDvzSWrscaAXuwOtH9gVjzDvABGC4X6ULrLXLgKuALxpjLgpgVvGz\nsoYyUrvziAtoL8Xxy8/Mp2dCmbfupogIXgt/ZWsFb72Q6zrKiFYuyKPwH8tobHSdRPwhkB+XB4AD\n1tq3fY+fAL5hrf0OcAWAMWY+cM1QL7bWHvJ9rzbGPAWcC7x6/H6rVq0a2C4sLKSwsNB/fwI5ZWX1\nZVTvyaNpmuskI8vLyKMj5RXa210nkeEUFRVRVFTkOoZEkcrmSmxbFvSEdgt/XmYeRclvUl3hOon4\nQ8AKMmvtYWNMhTFmvrV2D3AZ3u3KHF+RFQPcCTxw/GuNMSlArLW22Xeb8x+Au4Y6zuCCTEJHWYM3\nS3+oN6XnZ+bTnlSqFrIQdvyF1l13DXkqEPGb0vrSkF0yabDcjFyazOM0NblOIv4wpoLMGLMIyAX6\ngH3W2l1jfP8vAb83xiQAe4FPAjcaY/rnbv+ztfa3vmNMBx621l4DTAWeNMb0Z/y9tfaFMR5TQkBZ\nfRk0XExCguskI8vLzKMlrozWVovGkIgIHL2gDHV5GXnU9JaphT9CDFuQGWPygNuAq4GDQCXeJ9Y0\nY8xM4FngP6215cO9h7V2C3DOcU//zPd1/L6V+G5fWmtLgaUn8weR0LK3rgzqb3IdY1QTEycSTzLV\nbdXAZNdxRCQElNZ7y76Futnps6nrOUhLWw8BnlZUgmCkTv0/AlYDi6y1F1trb7DWfsRaezGwEHgO\n+I9ghJTws/uId0ILhzUiM8hj1X2ltLS4TiIioWD7wfBoIUuMSyQzfjIN9oDrKOIHIxVk/2KtXWut\nHWqVv5nW2hestf8cqGASvjp7Oqlpr4KmmeFRkFlvCSX1wxARgP3NpWHRQgYwPSWXRspcxxA/GKkg\ne9oYk3j8k8aYJUBRwBJJ2NvfuJ/YthnQFxcWBVlql1eQhfoABBEJjv1NZVA313WMMZmdlk9znAqy\nSDBSQbZSpirrAAAgAElEQVQRWOMb8QiAMaYQ71blZwKcS8LYzsNlTI73ri77+hyHGYP0Pm9yxY4O\n10lExLXFy9s50lILzdNdRxmT3Ix8DraW8YUvuE4i4zVsQWatvRNYB/zVGDPBGPMB4HfA+621a4MV\nUMJLTQ1cf2MZh3Z4BVl6uuNAYzApxmsh6+x0nUREXHu3ohwaZ4ON5bXXXKcZ3dxM7/y1ebPrJDJe\nIw7LsNZ+zxjTjtdaBnCptbY48LEkXB06BGR6Hfpnz4ZPftJ1otFdc0E+//OyCjIRwTt/1edx6aVw\nwQWuw4xufo5XkKWqD2zYG7aFzBiz2hizGigEcoAG4B7f888EKZ+EGWuBDO+E9tprEBvrOtHoPnrN\nLExaFS3t6kQmEvUyvUlh3/c+10HG5owZXkEWDudaGdlILWQ/PW578MyZYdBVW1zo62OghSwnx3Wa\nsYmLiSOhczqbSvfz3ovnuY4jIc4Y8xu8OROPWGvPHGafn+Gtw9sG3GSt3RTEiDIevoKsp8d1kLGZ\nmTEVEptp7mzBWx5awtWwBZm1tiiIOSRCDG4hi493nWbsOg/nc/ufS/nqJ1WQyageAX6O16f2BMaY\nq4F51toCY8wKvOXhVgYxn4xHRhlUnE9vr+sgY2OMgYZcJs0tA4a8PpAwMdIty+eMMR8aPMpy0M9S\njDEfNsasCWw8CTctXS2Q0AItU4kZaQxvqKn3mv1FRmOtfRWoH2GX64BHffu+CWQYY6YEI5v4QZi1\nkAGcOdNbk1fC20i3LD8J3ArcZYzpBQ7h3bKc6nvd48CNAU8oYeVgWzk05AIGE05LQ9bne1fGIuM3\nA6gY9PgAMBOochNHxspaG5YF2eT4fN49UIa1hNd5V44x0i3LI8B3gO8YY6YCc3w/2metPRyMcBJ+\nKprCYw24E9TnwWkbXKeQyHH8x+KQ/W5XrVo1sF1YWEhhYWHgEsmoattroS8OOjLC5pYlwJSEfF7q\nKmXTJli+3HUaGayoqIiioqIx7Tum1Uh9BZiKMBlVRWt4rAF3At8ty74+wutWq4Sig8CsQY9n+p47\nweCCTNwrrfdax4CwaiGbmpgPmS/S3Ow6iRzv+Autu+66a9h99dEjfnWwJVxbyLyC7LOfdR1EIsAz\nwCcAjDErgQZrrW5XhriODlhx5dGCLJwGJU1L8s5f7e2uk8h4jKmFTGQs7r4bntxSBvUXuo5y8tqz\nIKaXP62u51dkuk4jIcwY8wfgYiDbGFMBfBeIB7DWPmStXWOMudoYUwK04vXHlRDX2IjXj9R3QXnH\nHW7znIxpyd7yb909faidJXydVEFmjMkCZlprtwYoj4Sxxx4DLtwL9fk8/bTrNCfLQH0+rQlloIJM\nRmCtvWEM+9wajCziP52deB36D53FFVdAygnzC4SupJhU6EynouEwEB5rcMqJRi2ljTGvGGMm+oqx\nDcCvjDH/GfhoEm66uvtHKM3l3HNdpzkF9fn0TixVPwyRKNTaysAIy9WrXac5OX19QH0eX/yWpr4I\nZ2Np20y31jYBHwB+Z609F7gssLEkHHXEHYbuFG7+xESmTXOd5hT4pr645RbXQUQk2LyCzLtlGU79\nx8A3AEFT94S9sRRkscaYacA/A8/5ntPSSXKCjiSvdSwpyXWSk1dRgTc6NLOU7m7XaUQk2JpauiHt\nIDTMGX3nENPRgSa3jgBjKcj+L/BXYK+19i1jzFygOLCxJBzV2r1QN5fkZNdJTt7MmQyc0BITXacR\nkWArr6+AlqnQm+A6yklra2Pg/FWl8bxhaywF2cvW2sXW2lsArLV7rbX/FOBcEoZ60vZC/VyvP0M4\n8p3QNNO1SPTZ1ximU/YAaWkMnL+mTnWdRk7VWAqyN4wxf/IN49ZHlQwrJttrIaupcZ3kFDXOgfQK\nysrDaIpuEfGL/S1H5yALN//yL5DcqVuW4W4sBdkC4GG8iQ5LjDE/NMbMD2wsCUcTZnstZOE0XHyw\npWckQVs2r24ZclJ1EYlgle2lLJmdz0svuU5y8mJiYGb6dEiphTjNDhuuRi3IrLV91toXrLUfAW7G\nW1D8bd90GOcHPKGEjbbEvfz6J3O5+27XSU7Npk2oY6xIlKrqLGN6ch7vfa/rJKcmLibWG5CQsc91\nFDlFY5mHLNsY86/GmA3A7cCtQDbwVeCxAOeTMFHb3ExvbCvLC6Z6/RnC1Fn5+SRNU0EmEk3e+17Y\nsr/UWxMyTMXG4vWB0wVl2BrLLcvXgXTgemvt1dbaJ6213dbad4AHAxtPwkX2/L3Y2nwmTAjvboaX\nn51HX7pOaCLRZN06IKOMacnhW5DdcgsDLfyvvOI6jZyKMfUhs9b+X2vtAWNMjDFmYv8PrLVhenNK\n/MlaINPrP5aa6jrN+BRkz6NnYonrGCISTIlNEN/GlNTJrpOcsi98gYGCrKvLdRo5FWMpyH7vWzop\nFdgG7DTGfD3AuSSMtLcDWd4Iy3Dt0N9vYU4BfRnF3kSLIhIdskqgbh4JCeHdwn/bTV5B1tPjOomc\nimELMmNM/8Ljp/uWTno/8DyQC3w88NEkXHR1ETEtZAtzCmBSMaefocUoRKKGryBbt851kPH5xLX5\npM3WaiPhaqQWsrd83+OMMfF4Bdlqa203WjpJBunpwWshq88nLm7U3UNaVkom9CZQeviI6ygiEiy+\nguzaa10HGZ+8jDzaEkvp6tJHdDgaqSDrb7t9CCgHJgCvGGPmAI0BziVhpKcHr4Wsbq7rKP5R67WS\nrV3rOoiIBIWvIPvQh1wHGZ/0pHRibRI1HbqgDEcjFWQ5xpj/A8QD9wAvAv8Hb5HxvwQhm4SJts4u\nSKvENIXforxDqiuArGLuv991EBEJtI99jEF9yFynGb/k9nnc8k0NTApHIxVksUAaXsvY4K8UIMx7\nCom/lJTAhz+7D5qnEx8TAWczGGghC8dF0kVk7KyFxx5joCCLhMUBYxu985eEn5F6/By21t4VtCQS\nln77W3indC9MDf8RlgPqCuC0J1SQiUS4tjYgvhWSGqB5hus4fpHaWUBdVjEbNsBZZ7lOIydjLNNe\niAyrpYWB/mPXXec6jZ/Uercsw32AgoiMrLmZgQFJL66NjI/DxNZ5kFXC2We7TiIna6TfwMuClkLC\nVlcXkLWXyfFzefRR12n8pK4AskqwGkwsEtG8gsy7XZmd7TqNfyQ065ZluBq2ILPW1gYziISn/jnI\nktsjZIQlQOdE6EqjNabSdRIRCaB9+xgoyGIio4GM+CavhV+zU4WfCPkVFFcOHQImFbNv43zXUfyr\ntoDKDl1likSyw4fxipe6ecTHu07jH6YzA3qSYEKV6yhyklSQybjU1fdCZinUzXMdxb/qCvjbtmLq\n6lwHEZFAaWlhoIVs4ULXafyjt5eBqXskvKggk3Fpjd8HrZN5fnXkDEn89a8ZmPqivNx1GhEJlP6C\nLKYhci4ovYJsnvqRhSEVZDIuzQl7oHZ+xDT3AyxfzsAVZmen6zQiEij1Le2QWg1Ns1xH8ZveXnwj\nxTU5bLhRQSbj0pxQDLXzI2JCxX6xsQy0kKkgE4lcle2l0JCLsbGuo/jN4FuWL77oOo2cDBVkMi5t\nSV4LWSSJjcVr8s8s5fH/6XMdR0QC5FBniXfxFUEeeICBC8rPf951GjkZKshkXDrTIrQg606F9kwe\nfOyA6zgiEiDVvV6H/kiaBPqyy/AuKLNK6O3T1BfhRAWZnLLeXujL8AqySFiUt99Af7i6Api0x2kW\nEQmcekq49oJ5bN3qOomfdaZDdwoxEw+5TiInQQWZnLK6pg5IO8RbL+RywQWu0/hPZqb3fU7qAnLP\nUkEmEqka4/Zw3oIC5kdWI7+ntoCMuRppGU5UkMkp23FoL7HNuZxzVlxEderPyPC+X3TaQszknW7D\niEjANCftYsGkBa5jBEZdARvLNNIynKggk1O2vWoPiS2R1SEWwBhvfqK5ExfRnKiCTCQSNXc20xPX\nQN6kyJny4hi1WtMy3Kggk1O2p6aYlI5IbOuH1FTInbCI5iQVZCKRaMeRPdiaAiamRejHoG/qC6t+\n/WEjQn8TJRjeLt1D2/7ILMgApqXMoju2gabOJtdRRMTP/vjiLqhZwIQJrpMExrI58zSXYphRQSan\n7PXde2iriNyCLDEhBmoW8EbJLtdRRMTPypt3Q83CiCzI1qyB//rhfMgqob2j13UcGSMVZHLqJkXe\nHGSDxcVBX9UivvMz3bYUiSR33glvlOyG2gUkR84yvAOuugrOXjwB05FNWf0+13FkjFSQyUmzFpat\nbISEFq64YLrrOAETFwfULKQhXgWZSCT5/vfhcPduPnXdAmIi+FMwrn4hu2rUwh8uIvhXUQKlqws2\nV+yB2gJ+dl8EzXdxnPh4oGYRzYk6oYlEii1bANMHWcUc2hahU174xDcuYletLijDhQoyOWk//jGQ\nsxNqFpGS4jpN4MTFAdWLqOzayc03+/7cIhLWHngAmFgBHZlUH4zADmSDtO1byK+e0gVluAhoQWaM\nyTDGPGGM2WmM2WGMWWmMWWKMWW+M2WqMecYYkzbMa680xuwyxhQbY+4IZE45Od/+NpC9E6pPIynJ\ndZrAiYvDWxMuYx+/eqSL3/3OdSIRGa+YGCB7N9QsIG3IT58IUrOIQz1qIQsXgW4huw9YY61dBCwG\ndgK/Ar5urV0MPAV87fgXGWNigfuBK4HTgBuMMYsCnFVORs4OqF4UkR1i+yUnA72J0DgbsoojcjSW\nSLTxCrJdULOQ1FTXaQKsZqH3Z5WwELCCzBiTDlxkrf0NgLW2x1rbCBRYa1/17fYi8E9DvPxcoMRa\nW26t7Qb+CFwfqKxyCnK8FrJILsgGrp6rF0H2roj+s4pEi5gYYNJupicu4LOfdZ0mwFqmgOmlurXa\ndRIZg0C2kOUB1caYR4wxG40xDxtjUoHtxpj+4upDwFDrVswAKgY9PuB7TkJBXAek7+fys+dF9Ail\niRN9GzWLIGcnubku04iIPxgDZO/mm59bwLXXuk4TaAZqFvHBW3ZG1HrDkSqQH6dxwHLgl9ba5UAr\ncAfwKeALxph3gAlA1xCv1WIPoSyrGOrzeeH5eNdJAiox0bdRvQhydtDT4zSOiPhB/y3LBVkLXUcJ\njpqF/G2HbluGg7gAvvcB4IC19m3f4yeAb1hrvwNcAWCMmQ9cM8RrD3Jsy9ks3/udYNWqVQPbhYWF\nFBYWjje3jMbXfywafPjD8PjfzoDzf0pHjes00amoqIiioiLXMSRC9MQ2QWIDszMidFHx41Uv8gZh\nScgLWEFmrT1sjKkwxsy31u4BLsO7XZljra02xsQAdwIPDPHyd4ACY0wuUAl8GLhhqOMMLsgkSHz9\nx6KBtXh/1knFVG7vprc3nthY16miy/EXWnfddZe7MBL26mK9C8qkxAjubzFYzULIe9l1ChmDQP9G\nfgn4vTFmC94oyx/gjZjcjTfi8oC19rcAxpjpxpjnwBsAANwK/BXYATxurVWJHypydnj9qqKAtUBP\nMjMnzObvu3ZrLjKRMFcbuw2qTz/aJSHS+frASugLaEFmrd1irT3HWrvEWvsBa22jtfZn1toFvq9v\nDtq30lp7zaDHz/v2mWet/WEgc8pJyo6eFrLly71OwHPTzoTJ73L4sOtEIjIetbHb4cjpJCS4ThJ4\nmzcDDbmQWgXxbd4FpoSsKGmzFX/p6euBrBKoiewlR/rdcQf09sL8jDNhyrvcd5/rRCIyHu/s2w5H\nzojoEeL9FiwA+uKgbh7x03bRNdQQOgkZUfArKf7055dLoXk69ETHpFzGeF+Lp3otZCISvqwFJm/j\n9MmnH53WJoLF+wbCJzWfgZm6jSNH3OaRkakgk5Py88e3Q80iom3Q28ULzoQpW13HEJFxqKipg4QW\nrr5wVlTMy9U/AOn2j59JV/q7zJ7tNo+MTAWZnJSGhHehajFLl7pOElynTcuHlBpIbHQdRURO0cYK\nr/9YT3cUVGODLJvhdbmQ0KaCTE5KQ+K7UHXmQFN4tIiNieW0nNMpuHCb6ygicoq2HdkO1aeTlOQ6\nSfBYC8tnHO1y0dfnOJAMSwWZnJSm5K1QtTjqCjKAhVln0pioq0yRcLW73msh++pXXScJrjnpcyCx\nGZLraG93nUaGo4JMxqytu432+AquXjk/KguyMyafSWuqCjKRcLWnfhuzEs9g0iTXSYLLGANHzoDJ\n79La6jqNDEcFmYzZ9iPbmWQXcOF5UViNAUunnUlHhjr2CxhjrjTG7DLGFBtj7hji54XGmEZjzCbf\n150ucspRxsCmg9up2Hi66yhOXLfS60fW1uY6iQwnkGtZSgT5y1/g9t9vpap7MWaF6zRunDt7Kb3Z\nW+izfcQYXctEK2NMLHA/3nJwB4G3jTHPDLGayCvW2uuCHlBO0N4OpB6hu7cbmqe5juPE5YvP5OW5\naiELZfpUkTF5/HEoafZGWHZ0uE7jxvSMSdCexa4jJa6jiFvnAiXW2nJrbTfwR+D6IfaLrqF8IezQ\nIWDKFqhaQrT+s5w5+UxsjlrIQpkKMhmTI0fw5uE6cmbUFmTGAIeW8/uXN7qOIm7NACoGPT7ge24w\nC5xvjNlijFljjImOtcZC1KFDwNTNcDjK5usZ5MwpZ9IxcRstLVo/KVTplqWMyf4KC2d5IyyjepRO\n5Vn84JGNzO/6CDfe6DqMODKWT7SNwCxrbZsx5irgaWD+8TutWrVqYLuwsJDCwkI/RZTBWlqAaZug\n5ArXUZzJSs4iri+N8oZ9QK7rOFGjqKiIojHOpK6CTMYkY+YhsDHQMiVqW8gAOLQczv8J72qwZTQ7\nCMwa9HgWXivZAGtt86Dt540xvzTGZFlr6wbvN7ggk8Dp7sZrIXvthPEXUSWj80x21W9FBVnwHH+h\ndddddw27r25Zypgk526FqjMBE9UtZNedsxymbeSnP1WzfxR7BygwxuQaYxKADwPPDN7BGDPFGG9x\nHmPMuYA5vhiT4GntaoOMcqhZ5DqKUzk9y9jdtMl1DBmGCjIZk5qEDV7rEER1C1lv0xToToWMcqqr\nXacRF6y1PcCtwF+BHcDj1tqdxpjPGWM+59vtg8C7xpjNwL3AR9ykFYDSlnfJ6FnIbx5OcB3FqeSG\n5by5X31gQ5UKMhmTuqQNfPWjZ7FkCVx6qes07vT2ApVnwfQNTJ7sG+wgUcda+7y1doG1dp619oe+\n5x6y1j7k2/6FtfYMa+1Sa+351to33CaObg/9ZTOte5fyyU96SwlFq7efOYvDbOS221wnkaGoIJMx\nOcwG8pPPYvNm+NznRt8/UvX24rUUTvOuMteudZtHREa3r3Mz3RXRO8Ky3wfemweJzdz7sK4kQ5EK\nMhlVVXMNJDWS3jvXdRTnjhZkGwCIjXWbR0TGYOpmOLTMdQrnfvJjM3BBqUXGQ48KMhnVqyUb4NAy\nbJ9+XXJzgYPnwoy3wfR5BZqIhKzevl6Y4k1qHe3i4/G6XEzbyLp1rtPI8fQJK6N6s2IDHDqLnh7X\nSdz75S+hrmIKdGTApN1RPeJUJBzsrt0NLVOhM911FOfi4xlo4b/sMujsdJ1IBlNBJqPaUv0OVJ7l\nzeUT5RITITMTqDgPZq1XQSYS4t46+JbXqi0kJHBMH9j3vMdtHjmWCjIZ1bY6r4UsP991khBy4DyY\nqYJMJNS9dfAtpvauYMsW10nci48H6gogpRaS63jrLdeJZDAVZDKimrYamrsaOHPGvKie7uIEaiET\nCXkbNsB/r3uLhCPnesVIlIuPB2wMMUeWDgxMktChgkxG9JUfv0NW9zIS4vWrcoyqxZCxj4b2RtdJ\nRGQYz6zpoDlxJzHVS1WQ4d2yfPppyGo/F2a+6TqOHEefsjKi3xe9wf7XztPJ7Hh98aQ0LueNA2+y\nSSuRiISkKrMZahbS3pSscxhgDFx/PSTXeF0uJLSoIJORzXodDqggO9769bBi+nm8cWA9y5e7TiMi\nQzlg34SD51JVhc5hgyQcOQ9mvgFE8bIFIUgFmQyrt68XZrwJFefx6quu04SWlSthyaTzvIJVRELS\nAY6OsEyI7mUsjxHTOh26JsCkYtdRZBAVZDKsHdU7oHUytOW4jhKSpnRcCLPWk5Km+UBEQtEB+xZU\nnsMll0B2tus0oaO3FzhwHkkFum0ZSlSQybDWH1gPFecDaFbnobRPgvo8Flyi0UoioaaqpYo2WwPV\ni3hDS7sfo7eXgZHiEjpUkMmw1h9Y7823Bcyb5zhMCLr+eshquoTaCet49FG0NpxICHlt/2vMTTwf\nbCwTJ7pOE1ruuAMK562kY9IbzJnjOo30U0Emw3q94vWBFjJ1iD3RokXwzRsuoT6jiJtugkOHXCcS\nkX6v7n+VhMMXsXAh6gN7nFtugWd/tQyyitl/uBmrvv0hQQWZDKmmrYbDLYfJ7Dmdz38ectSNbEgr\np72Hloz1ENulFjKREPLq/lfZ+ORFpKVBQYHrNKEnNSkBDi+FGW/R1eU6jYAKMhnGK+WvMKP3Aupr\nY/nUpyBGvylDyknLxNbOg+lva9Z+kRDR3NnM7prdUHk29fWu04SwfRdD7is0N7sOIqCCTIaxrnwd\n8+MvATRcfCRxcUDZJZC3TgWZSIhYf2A989OWQ2+izl8jKS+E3HW0tbkOIqCCTIbxUuk6slu8gkz9\nx4aXmAiUXg5z/6qCTCRErNn2KpuevgjwZqeXof39Dxdgpm/iR/e0sWaN6zSigkxOUNVSRcnhSn79\nvWWAWshGMmMGXrP/1C1UN+veiEgoeL3yb7DfK8jUt3N455+TSlLDUn65+nW+8Q3XaUQFmZygqLyI\nhMMXgY0F1EI2muS4ZNj3Hn7x1xdobXWdRiS6tXS1sL1uI+xTQTYWabWFkLuO1FTXSUQFmZxgXfk6\nEg5eMvBYTf4ji4sDiq/mr3vX8Otfu04jEt1eKX+F0zLOhm6vwlBBNrIjb3p9YDWS3j0VZHKCdeXr\niK04WpDphDYyryC7Cgqep6tbf1kiLq0tXcvyjMsBeO974ZlnHAcKdRXnw5StJKdrqKVrKsjkGGX1\nZTR0NGAPLwbgxz+G2bMdhwpxcXFAQx60ZVNptYySiEtrS9fyX1/3CrKLLoKFCx0HCnU9yXBgJYeS\ntD6eayrI5BjPlzzP8olXUVfr/Wp84hOag2w0cXG+jd3XssM+7TSLSLSqrobK5koOtxyGQ8uBQf83\nZVirVwPFV3MwRcMsXdNHrRxjTfEaZndcPfA4JcVhmDAxcNLf8UHebP4Tq1drHRKRYJs8GX6+5gUu\nyb1EA5JOQnIyUHw1+xPX8NZbOne5pIJMAFi3Dkx8O3/b9zeWTbx84PnkZIehwsSXv+y1JKa1nE1D\nUzfX3bzVdSSRqPTywdVckXftwGOt0Ti6pCSgZgE9XXGsuHab6zhRTQ26AkBxMZD7CkumLiElJnPg\n+dhYd5nCxe23e99nfsvwg7c/BKf/CVjiNJNI1InrYFPDi3y28MGBpzQD/eiSkgAMFF8NBWuAMx0n\nil5qIRPAN/nr/GcpX3s1X/iC6zThKSEB2P4hOO1P/OAHujQXCarcdcTXLYa2o/M3XH75CPsLMKjL\nxZ5rYP5zTrNEOxVkAkBcfB8sepIDL3xAk5ueooQEoPJsiOnle4+84zqOSHRZ8Axxe68beJiT442y\nlJEN9LMrL4QpW6lurXYZJ6qpIBMA9vWuh7ZsqF0w8NymTQ4DhSHvxGZg0yeJO/s3ruOIRI0+2wcL\nVtOz/WhBppnnx2aghawnGUqu5O6/PKW+d46oIBMA3mx5AnZ88JjnNGT85Ays+bnlRlrzHqetWx1Y\nRIJhfcV66EinreLoBeUrrzgMFEZmzoTzz4dvfxvMjg9xz//+iXoty+uECjKhz/axvuHEguz00x0F\nClMDBVnTTPr2r2DB+5+krQ0OHHAaSyTi/WHbH+Ddjx7znCa0HpuUFPj73+GGG8AWXwUz3qaqucZ1\nrKikgkx448AbJMWmQfVpA8/Nn681LE/WQEEGsOnTHJjyEF/5Csya5SySSMTr6evhD1v/B7Z9xHWU\nsJaQAHSnQMkVfPVXT7qOE5VUkAm/3fxbknd/4pjnNKHiyTumINv1fsjYz+6Wt53lGc3EibB+vesU\nIuPzQvFL1O3Nh/q5rqOEtYHz17YbeP7gfzvNEq1UkEW5tu42/rDlCYqf+Pgxz3/0o8O8QIaVkNA/\npw/QF8cZLf/K3sn3OM00kuZm2LjRdQqRU7d9O/zytd/Duzcc8/yuXY4ChbGBgmzPNTBpN3tq9zjN\nE41UkEW53739FHFVK6B5xjHPf/ObjgKFsYQE71Zvv2mHP82RtL9C+n53oUahgRsSzs44u57nS1fD\nux8beG79eliwYIQXyZAGCrK+eNj6cX67+bcu40QlFWRRrLsbbnnoEaYfuWnguZgY6O11lymczZoF\nl1xy9PHa1elkln0GLvyhu1CjUEEm4ertt4ElvyO+/Gpvyh6frCx3mcLZsX1gP8lPXnyUnr4eZ3mi\nkQqyKPaBz+2Ayds4L/P6gedSUryiTE7eWWfBvffCzTcffe7IU1+H0/+HeeeUUlHhLttwwqWvYEkJ\nbNjgOoWEkoMHLZz9IJ1//9wxzxcUOAoU5o4pyKpPp7t6Ns/ueZazz9ZFerAE9KPXGJNhjHnCGLPT\nGLPDGLPSGHOuMeZtY8wm3/dzhnltuTFmq2+/twKZM1o9W/0zeOfz/PqhJP7xH73nli93mykSHHPy\nasuGN7/M3lmreCsEf4vDpYXskkvg7LNdp5BQsqXxFcDAPm86/v5zmEaHn5oTzgVvfIX/XP+fbNgA\nHR1OIkWdQLeF3AessdYuAhYDO4H/AO601i4DvuN7PBQLFFprl1lrzw1wzqhT114Hpz8O73wegMJC\n7/mFC91lihQDHfv7vXEb5K9lS+2bTvKMpKEBXnrp6OOnnoKPfWz4/f2pZwx3Qx5+GL70pbHtK9Hl\n6eofwxtfAbwKrK/PbZ5wZ8xxLeY7/4my+nKY/g5dXa5SRZeAFWTGmHTgImvtbwCstT3W2kbgEJDu\n22d0da8AACAASURBVC0DODjS24z1eGpSPTkPvvMg7L4OWqYCDKxfqduV43f33fDk4Gl8OifCCz/h\n/vLPh0yfjO5u7/t3vwuXXeadjLu64AMfgMcegwceOPE1L74Iv/rV0cf19fDFL57a8bdsGdvt0nvu\ngfvv1/9vOdaWw1so79gEm28ceE6/I+N3zG3LvjhuWvRlOO8eOjudRYoqgfz4zQOqjTGPGGM2GmMe\nNsakAN8AfmqM2Q/8GPi3YV5vgReNMe8YY24eZh8ADh/2mltVxY9NU2cT975xL7x29K++vyCLjXUU\nKoKkpcGVVx735Lsfpf5ANve+cS+7d/vvWH198OqrJ/+6/lsQNYMm5G5qOrp9++3H7v/ww3D55cf2\nj1u3Dn75S/jd744WeGO1dOnY9utv9ajWescyyI/+/iMuT7sNehMHnrviCjjjDIehIsDMmceuAXpu\n/Gcgfy3bq0JvHpENG+C551yn8K9AFmRxwHLgl9ba5UArXvH1a+DL1trZwG3AcKswX+C7rXkV8EVj\nzEVD7bRq1Sq+971VwCqee67omJ8dPqwibSg/e/NnXDHvCqg5en+ypcUralescBgsgiQnw1e+MvgZ\nA88+xN2v/gcLLzn53umbNjHkgr8bN8J73uNtd3V5v/PH27fv6HZFhdc69dRTJ+63Z5hph3buhM9+\n9sTnGxq87zfeCFu3jpz/VO3dC1AErAJWsWrVqsAcSMLG9iPbebH0RS5MOrYz/623wrvvOgoVIXbt\n8uYn7Hft5emw/qv8+J3vhFy3gRtugPe9z3UKP7PWBuQLmAqUDXp8IfAc0DToOQM0juG9vgt8dYjn\nrbXWbttmLVh70UX2GGDtHXfYqNX/9zNYXVudzf6PbLunZo/1PuK9r7vvdhDwFAz1ZwpVv/iFPebv\nGKz9+brHLV+eaxvaG47Zd7Q/F1j7/PPe98FefdV7rq/P2v/f3nnHSVFkD/z7WJawu6CiEgQBBT0P\nj6QiBkTPDHrqKSCKHGe4n+nUU0TRM8CJAROGOwNGEEExnooBRTHhqYCgCAqoGEAkh10WWHbf74/X\nTffM9OzOLBtml/p+PvXprtDVb7p7ql9XvXp1xRWJ+Rs3WtqPP1q8Y8dEmUoLU6cmpvncfXeQ9vHH\nZV+P225TfeWV4PeUdSuLi6PP7V2rSmu7qirUpGc5kzjh6RN01Cej9N//Dp6LoqLqlqp2EfO/y87X\nOkOaa6fjZla3WDHssUfZbUgmUlr7VWk9ZKq6FPhZRHxXmUcDXwMLRORwL+1IIOG7XERyRKSRt58L\nHAsk/fbxh0s+jbCZXrSonD+glnLdu9fRt0Nf9to5dm54/BCVY9uJssc7sW0/WNCb0yaexubi9Lpv\nV65MTDvM6zdevdrvTQpYsAAOOcT2Bw2CYcNgcWkWmxG88kryPL+HDIi0MZkzJ/b/N3QonHRS6ueO\n6rF7553Uj69JnHwyTJxY3VLEcsMNnq+vDOLdH95l7vK5XHjAhRQWBuk1ZbZwjaQol5Kp1/Nli3/4\nHxIZQbpmEjWByjbhvgR4WkRmY7MsbwbOB24XkVnACOD/AERkNxHxR4SbAx96ZT4FXlPVyclO4nel\nRg1PVpcxYmGh2RJlEjN/nckL815gxJEjYtKzs53tWGUQdU2Li4E3R5FXrxGDXh6UlpG/P50/qiFq\n3Rr++9/YtPfeC5ZGmjoVhg+HVatSPh1Q+v/n8ZCxQdS0+I4d4cgj7Tu7rOEOVXj66cS0eNJR6GoS\nr7wCzzwTmzZ2rF3D6uKmm+Dee6vv/D6qNoy2cctGLpp0EXcdexf169ZnzBho3766paudHHVUXML0\n86FeAWNnj60WeaLItCHUiqBSFTJVna2q3VS1s6qeqqprVXW6qnZX1S6qerCqfuGVXaKqJ3j733v5\nXVT1D6paqqvz0jTl6rIhW7fO7LIyhaLiIi547QJGHDmCJg2bxEwRd357KodwD9mNN9q2sBDQLK77\n/XhWFa6iz8Q+FBYVRh4fj98A+RMwwoTT5s5NTb68vLLLhI3+w5SUwC+/BPHVq03ZfPRR6Ns36D3b\ntMlcqpQ2o3LGDPjXv+Css2DcOLNxGzYM7rgjsWxhapeqRhJv1zdlivUyVieZ8NJ7+mlo3Bhu/uBm\n9m26L3/+vTkcmzPHHAY7Kp6EnmjNgtceYsjkq1lekBkzbDLh2UyVoiKbMV4WtcLJQWk3prqn62bC\nVOzzz4c+941gp4Y7cU7XcwB4+eVqFmo7IKyQdepkPWZ+j0e3Lg159YxXycnO4YgxR5gDmDIY5M3w\n37Ch9HJTpqRmZJ+Ky4qw0uXz7LOJitqAAbDHHjYL8/nnYaedLH3JEvjgg9iy4Y+kl14yNyG+rf7A\ngaa8Dh8OTzxRtny1mQR/dtVAJrz0Fi8Gdp/GqA9Gc3+v+6tbnO2GSy+NS1hyAAfn/IX+E85hw4bq\nH7pM1hETZdpR3SxcCIMHl12uVihkpfWQTZmS/Cvqp0pc89mXqboVQoDRb37EK0se5u6eT1JH7JaH\nX6iuh6xyCA9ZRq0RWi+rHk+f+jT99+0Pf4PRM0ZTXJKowcc7vNyyxRTqf/4z+ryDB0PnzmXL17u3\n9Wy1axed37p1tM1Z//7QrFlieqpLQ/3lL8H+qafCCy/E5scPvW6v1K9fdpnKJh2F7Mcf7cOjotmU\ntQL69KdgwmPU27QbH38MP/xgeT16wIQJFX9OB9we4bK9T5MRvPvpMk4cUf1j2f6zKQKTJ9vH36+/\nwi67RI8iVAcbNpg8qf6ParxC1qtX7I+dPNleMt9/H6Ql66Vq0yZ6SGbFCrOf2BZ8hSydJSdmzqx4\n9wGL1iyCvv3g5Se47rIWgLkx8JkwIdoFgmPbCfeQJbPRExHOan85PDWA4S8/RbdHuvHC3BcoLilm\n9Gh78cR/cMyda24obrml9PMns78dPRrOPddcnOy4IzRvHl3uX/8KjPJbtUp+nnSnnj/7bGpygkLW\nJmi4Enb4CXadCy0zcP2pSmJbnDRX1IdgaS+SZ5+1ocQ77oDrr7f266uv0u9VmzrVXqpRz8HGLRsZ\nt6kffHUGzD+R7783JaxPH8u/4AL7QHBUPDFOYn2K68ELE/hf3dt4Y8EbKde1fHnynqs//KF8vhTD\n7eJxx8GZZ8Juu1k8yhzjt98S/R8OH57+RCefJk3go49KL7NypYVUR8pq/NyUN9+EXXcN4scdZ9tw\nd398A/HTT3C/1/O9bh3svDNce61ps2ecYRf5hhuskSkv/rBMaQrZxRebQvnLL2Y/07272dqUNSQV\nz9df2zVo2jQuoyGcOP5E+GgoLOzFywutt6VDB1sEG6BfP+edv7Lwr+vdd9tajPG8+KINy732GsA4\nloxQ7p/7EiM/vJMr376SRa+ewWWr+3DD/3Uh/O3Uq1dsPU8+CX/9a2L9US/lnXayYcWwg1f/vzJq\nlClnZ5xh8fDz1L8/3Hln9O9cuhSoUwT18r1QEOxnh/bLk6d1oCgXNud5IRceiZajNqAa9Fj7Svzw\n4YENYpgjjjDlZMSIxLwGDUzROfzwxLx0KG05ovfeM2P7a66xF8748ZZeVJTerMfRo21bWGg+9QYP\ntmewaEsxZ0wcSF6dnWGKfX2cfbaV9SerVNUyX9sjUSMnJSXA6j05euWL/OXlk5l05iQObFn2yoZ/\n+IM5nD3uODNL8Gd/g72/Xn/dZoyPG2f3NHzuDRss3rChxVessLriP1RL+xD49FM46KDEdH/m+dCh\nsOeeZf6MrYwfbx0/n39u/8Fk+P8fX+kra5JqjVfIAJ56yr70w9Pww4pQ+EYtWAB77x3E27WzHqJR\no+wFdt99Qd4779iyMuUhlR6yBx6wbtaXX4YWLUzOyK+SMvC9U5eU2IMyYACs2LAC/gK92vfi608v\n2VrWH74dOxbOO88pY5WJf18uvzw6/7TT4lOE49ucymkdTmXUMzO4XJ7j3sX9uXfkChhwECztAqva\nW8hvDoVNYOOONG0a+zf2v8YuuyxcdTFkFfG/2Rv4Zd0GCjYXUFBUwIaiDaxvVgD7bGCXIwtYu7kA\nDtoA9Qp4ad0GOL4A6hXwWv0COCNWcdq9XT7L1uQzXQugV0mgNMUrUFv3Q3n5zWPziuLKbc6FolyO\nPLwe776beJ1qK1u2BBMgfCPgMWMSFbL16+H9963NGzHCXJ60b28mGo89ZmW+/LL8Cpn/DL3xBqxd\nCzvskFjGn2BRXGwKoP8RWlQUvDxToXNn66kvLLQJHm+8Aaf23US3W8/iq4VruO+g1/hCTTtNdcKK\no3Lw3xfNiw7hiZOf4MTxJ/J8v+fp2aZnqcctW2bP9UMP2XvWV8j891FBgSkrAwfaB+fOOwfH7r67\nrXzy9NNWT5S5BCQ+G1u22EfN99/HKmNFRbGTjB55xP5L6aygcu21ti3r/enrAf4oQlmuOmqFQgbW\nfRhWyMKEFbKoseUff4z+GjjmmLI12mTMmmXbP/4x1lN6PL4C5rvI2BZ7rt9+83raei3kpAknwcIj\n6bnpdu4MvcB+9zvbzpxpSqCj8ujUKf3nx3/JXd5/f2B/eOc2yFsKrT6BpnOgzfvQ9THIXQYNV0GD\ntfx5ej24pi6UWCjROtZjlbUZsrwtkF0nm+7jc8jNziUnO4fcernkZufy4245UJzLmwstnR1MGWq7\nWx6sbgZFObTrkMc3MwKFqX2bXKbcnEdevTza7JZL/pp6JFOUGjWK9f6dDjumMNmhJrN+fezwysCB\nNpzctWuQFm/cf801gd3d7Nlw6KFwqzcPffz4oKfq0kttYfbyMHx4sP/ZZ2ZPeNFFpvCpwtVX20ed\nz8aN8Pbbtu+3t2+9Ze4T/N6y776zepYvtxnoe+9tW7/n1X/2C7N+47hxp7M+fxcY/yockAHGdA7a\ntw8UigUL4MS9T2TCaRPoM7EPdxxzB4O6DCq9Ao/wpB7/Ob7//ujVQHr2NFc9kyfbfyOqN9hn9erY\neHa2KXJvvhmbXlBg7Uq4bU7F5mztWut1PvnkIK0shSx+tmqZXh+SeYytCQFIydv4J5+Yp/KiItUZ\nMxLz77pLtWHD6GNVVZcvV128WHXu3HS88QZh82bVkSMDb/ibN6vecYflnXSSbe+/37Z5eUEdmzeX\nfZ78fDuufn3VJUtU+d1/tekdTfWhzx9SUO3TJ/l1adky9d+TKVATXTN7+Pc8OqxSUP3ll7Kf53B4\na3KxvvNBvlJ/jdJwhd710FKl0WIlZ7ml1d2gyBYFe1aiuPLKWI/XjzyiOnas7TdubHl+GT8ce2xQ\nfvJk1bZtk8s4bVp6vykczj5b9aOPVD//3OL77bf1Gaj29mdbg99+XX21apMmya9Bhw6x9yuqzAcf\n2Pb882PTf/01OG7WrOj7H8WgQUEd/fsH+089pfrkk6Xfs6VLbeWIcBvqy33VVapNm9r+0KGqF10U\nd/xek5QrdtPrplyn+3Sw57ZHj+TnclQu4Wv917+qigTxlSutzFe/faX7/HsfPfvls3XVhlVJ68nO\ntm3fvkH6Aw8k3tNffok+f0WFn3+2utesCdL22CNR5uJi1e++C+IjR1rZwkLVNm1s/z//Sf36gWqr\nVlpq+1XtjdK2hFQVsn32se3bbwcNeziMHKmakxN97KJFsfGsrERF6fPPy74RfigoiI2ffHJs3FfI\nXnzR4pMnW3zlSnug/eVknnlG9bPPVDt18o7NXapHP3CmcumeetOYj7bK4C8v4Yc99wz2GzYs/WHK\nRGqyQvbEE2U/q/Pnp9e4LFpkz4Eff/5528YvkXT66cnl2rTJPjiiWLHCjg8vkwSqRxwRW85/XuPD\nvHmWP3lykHbAAapr16b2+666KlGmTFDIgOOBb4AFwNVJytzn5c8Gukbkb/2dubnJr8Fee8X//sTg\nL6H197/Hpo8aZe2br1iD6vvvx9Z38cWqjz+uunp1bFrUeRo3Vj3vvNLv2fffB88hmHLmy5302J2/\nUU4/RbmkvbLHlITf2bp1bPyLL0xRdFQupd3nGTOCcus3rdcLX7tQm9/ZXEdPH62btmxKWs8ppwTp\no0cn1rvLLqmdv7xh4UKr21+KDkxHiOe55yzPZ9Qoi48YETyPDz4Y5M+fb+1a2dcP1WTtSrKMmhDC\nDdrw4WXfiP/+19bci0+/+ebkDeI11ySmzZ9vStmUKapbtlha796pafavvhobP+KI2LivkIXTVFVn\nz7Z9/wW5NeQuVY4aqlzVRDnmSiU7X0H1pZeSy7D//qoXXKA6bFjiQ5jp1GSF7OGHy35Go57P0sIP\nP1gPEqjWrRu8mB9/XPXTT+1j4f77Vb/9tvxyL18ePOd+6NUrtkyy56242PJ//DFI69bN0uLLvvJK\nYlpUr151K2RAFrAQaAtkA7OA38eV6Q287u13B/4XUc/W3xnueYgPrVurrloVKM1RZaLWHPXDbrvF\nxtu0sXqGDo3tkW3WzO5zQYHq7run9xyWFjZutPYSVM89N5RXp0hp96YpYlc2VXrcqtQtjKyjZ0/b\n+h/OW7ak9Qg7yol//Zs3T7wnr78elCspsXfU54s/16PHHq2t7m6lIz8aqb+s/SWmHrBOiJIS6zlP\n1iaOHRvby1reEPW/+vprTZCpY0dLKy5WPeYYO/cjj1ie33Zde61tr7wyUMhGj7bjXnjB4kcdZb3V\nCxZE9/5ZQDVZ25IsoyaEcIPmd9mXFiZONCUqPv3009O7yd9+G3Tbh7s9J0wwheuWW1Kva+edE9Ns\nWCYIqqrvvmv7c+aoDUPt9ZrSt48ydAflxPOVxj+lfM7SeksynZqskPnD0qWFc85JnrfvvolpK1fa\nkDzYsLXfw+Yv4l2RxJ83jN8jMm6c9aLUqxc8u6qqGzYExx54YGJ9V1xhPTfxvy9aDlSrt905GHgz\nFB8KDI0r8xBweij+DdAsrkzK/9n27VUPOyzxuvnhrbdSb3Patg3qmTAhNu/QQ4OhpYoKq1f7L7AS\nPaT3D0rnMcrJfzUl7LwDlW7/UbILSq0jvsfOUTWAmVosX554T0aNMmWlZctAsVq2zI77fPHnesy/\nz9Gc4TvpYY8fphw+XGn9gZJdoKecYsPaoNq5c/J7HnXO+OB/gCYL/uhYONx0k3VstGsXm163buxv\niz+uXz/b/uMfgUJ2xx3BbwmHXr1KkwvVZG1LsoyaEADdf3/7FV9/bUOS4R/epUtsfNw41UmTyr7J\nZYV584IHcOzYIP2pp7a97qiwpnCNsscUpcetKgNOVK5ppJx9mHLAA2YnlEIdzZsHyl/nzhX1d616\narJCtm6d6q23ln6fDjwwMS0rS/Xpp62OqJeS/yWZmxsMBX7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AyW6gPNu9guxt1f4TWn+/a00opWLUJdf7v1AmJ7tz/OX5/jFkiYnuHF+FPy3IVEjU1sLu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\n15qRQr17exmpQYPwaUW8B+5uuyMqLRaL5UgyYIAZ/VWzZmh8xYrxNy1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v7Lncey9kZAz+PDlpORTlF7Fqb2cfWEvIEpdrCZmIDAUuVNXFAKraoqp1gF9V3wzt9hpw\nbR/HjwUux0ngBjd8xUS1DTUbyG0tIj0lfNPrzxwxk3X71pGUBP/3fzZSyRjjnq4VsnPHnNxavO0+\nNO5DvF35dsdnS8gSl5sVsklAjYg8IiKrROQhEckCNojIVaF9rgfG9XH8fTjVM6t3xJm1VWvJb559\nSiMrezqt8DTK9peRlNzKzTfD00+H79zGGNNVaytMnX6MzEnrOWv0Wad0rnPGnMP7e9/nP/8TLrnE\nJoZNZCc5FeeAzz0HuE1VV4jI/cAdwC3AAyLyPeBZ4LjnARG5AqhW1dUiUtLfRRYuXNjxvqSkhJKS\nfnc3UWDtvrUMa5p10hPB9mZI2hCGZw3nQN4WOFhkIy3jSGlpKaWlpV6HYUyHlhZoyV9DUX4RmSmZ\np3Sus0adxY/e+BFPfBe2besxpYZJKG4mZLuAXaq6IvR5CXCnqt4NXAYgIkXAx3s59nzgEyJyOZAO\n5IjI/6jq53ru2DUhM7Fh7b615B67MqwVMoCZw2dSWrAetlpCFk96PmgtWrTIu2CMwUmaGgvfPeXm\nSoDpBdPZe2QvBxsPkpSUa31gE5hrTZaqWgVUhpIugPk4zZWFACKSBNwFHDd7i6p+V1XHqeok4Abg\nH70lYyb2qCqrd3/Ay38Mb5MlOAlZW+G68J7UGGN6aG2Fxvz3wpKQ+ZJ8zB4xm1V7V5GUZBWyROb2\nKMuvAI+LyFqcUZY/Bm4Ukc04Iy53qeofAERktIi80Md5rN4RJ3Yf3k0SKVA/POwJ2enDT6c130nI\nrEJmjHFLays05K465f5j7c4efTYr96zE54N/+zeYNy8spzUxxs0mS1R1LXBOj80PhP703HcPvTRf\nqupSYKkrAZqIK6spY3zGaawj/J1XZ46YSWPOQgDExuUaY1xy5Fg9zZk7mF4Qnql7zh59Ns+VP8eI\nUIlk+fKwnNbEGJup30TUxpqNjEouBuDIkfCeuyi/CIbuhOQGq5AZY1wTOLSejPrppPrCM3XPWaPO\nYuWelSTZb+SEZv/8JqIef7WMVx6fAYR/vp1UXyrUToXCMput3xjjmvK6tWQdOSNs5yvKL6K6vppm\n34GwndPEHkvITESV126EGqdC5soEiNWnw/D11jHWGOOalbvXMCSMCZkvycfpw08nmGyDkhKZJWQm\noupSyqDGqZA1N7twgf3FULCJL33JOvYbY9zxetkatrwVvoQMYNaIWdRaQpbQLCEzEVNTXwNJLXBk\nJOBSQlZTDAVl7p3fJDwRWSwi+0Skz9+eIvKAiAREZK2InBnJ+Iy7WttaYfg6qJod1vPOHD6TmqQP\nwnpOE1ssITMRU7a/zKlghZYmveYaFy6yfzoUbAJsCRLjmkeABX19GZrQeqqq+oEv0ctciyZ2bTmw\nBY4WwLGhYT3vrBGz2N5oCVkis4TMRMwTf9/Y0Vx59dVwTs8JUcKh1g/DtkFSsy3Sa1yhqm8C/fW+\n/gTwx9C+y4FcERkRidiM+9ZUrYGq8DZXgjNtz6H09SA2VX+isoTMRMzb5WUdHfqzsty5hjan46sf\nC3lbrEJmvDIGqOzyeRcw1qNYTBi1tcGt318L+8LbXAmQm56LrykPcreF/dwmNlhCZiKmPrOzQnbz\nze5dJ+mA02xpCZnxUM+piW2ISRxoaoKDKRuc0dwuSD84C0Y4XRPt/pV4XJ2p35iu9uP0IfvVr+CS\nS1y8UKhjfzB4NZMmuXgdY3q3GxjX5fPY0LZeLVy4sON9z4XUTXQpKwMKN0D1aa6cP+XATBjxAWy6\nmttug4cecuUyJoJKS0spLS0d0L6iMTw3gIhoLMcfbiJCtP59/OOtOua/NIZvNh/inp8NrjA72P+u\n9PN+z7GRb8Azf4zaqS+i+d8q2oX+7jxdHEtEJgLPqerMXr67HLhNVS8XkXnA/ara6+qEdg+LLZLS\nAHfkwU8OQVtK2O8vI+Y/SXX+EvjTEj7+cXj++fCe33ivv/uXNVmaiPjOvZvQmumkpbr/I/eHnxd3\njLQ0JtxE5AngbWCaiFSKyC0icquI3Aqgqi8CW0WkAngQ+LKH4ZpwKtgMB6ZAW4orp/fVzHIqZEB6\nuiuXMFHMmixNRBxMKYP900kbd+J9T9WlZ06DF8pwuu3YKuMmvFT1xgHsc1skYjER1qW5csWK8J/+\nkjP8PDZ0JyQ3kp9vGVmisQqZiYgjqRUQ9DNxovvXys/Mh5Z0GLKX3FzYutX9axpj4ltbG1DYOTDp\n7LPDf41H/5jC5GGTIS/AhAnhP7+JbpaQGdft3Qt7GgNQ62fUqAhdtMZptqyrgw0bInRNY0zcam4G\nhm+AGnc69LebmF0MhRttPd4EZAmZcd1NNwF5FVA7lZycCF10//SOJZSsz7Qx5lQ1NeE0WdbM4PLL\n3bvOhMxiKCyzia0TkCVkxnUjRirkByDoj2BC1tmx3xIyY8ypqjvaADm7IOjnhRfcu87YdGfanh/9\nyO5dicYSMuO6oaNqoDUFGodFLCH79Q+mkTVxMxDq+2GMMafgf1/eTPLhKWirOyMs282bMgMKnep+\nQ4OrlzJRxhIy47ptdU7/MYAhQyJzzanD/BzLCgD2lGmMOTWtrXDHzzeSfGCG69cqOX0avuEBSGpx\n+q2ZhGEJmXHdpmqn/xi4t4ZlT5PzJtKSsReSGy0hM8ackoYGoHADqXXudugHyEzJJKN1JORuY+tW\n2LnT9UuaKGEJmXHdQZ/TfwxAIjQtWGZ6Mr7DE2DYVmuyNMackoYGYPgGMuvdr5AB5DY7HfvnzIHp\n0yNySRMFLCEzrjuW1Vkhi5TUVEg66Ie8gCVkxphTcvQoUFDGlCGRSciGtczoGCVu/cgShyVkxlXr\n10NDZmcfskhJTQX2+yHfEjJjzKmZOLkFcndQ4JsSkes5FbKNEbmWiR6WkBlXzZypMKyCwPKpEZ2g\nNTUVmvf5Ib/cOsYaY07a0aNA7nY4PIohGZFZzuicScUdIy1N4nA9IRORXBFZIiJlIrJRROaJyGwR\neUdEPhCRZ0XkuLF3IjJORF4XkQ0isl5Evup2rMYFmfuhLZnRw/KYEZlqPxCqkAWLIC9AbW3krmuM\niS+1tUB+OdT6yciIzDXvurWYpOHt6/GaRBGJCtkvgRdVtRiYBZQBDwPfVtVZwF+Ab/VyXDNwu6qe\nBswD/k1EiiMQrwmn/ADUTiUtLbKXTUrCGUiQH+D22yN7bWNM/AgGgbwABIsilpANyxiGrzXbmYjW\nJAxXEzIRGQpcqKqLAVS1RVXrAL+qvhna7TXg2p7HqmqVqq4JvT+Ck8iNdjNe44LQkkk+nwfXPjQO\nMoKQctSDixtj4kEwiFMhC/rJzIzcdTOOToeCzZG7oPGc2xWySUCNiDwiIqtE5CERyQI2iMhVoX2u\nB8b1dxIRmQicCSx3M1jjgrzId+jvoElwYLKTFBpjzEnYsoXQ0m+Rq5ABZDUUOYmgSRhuJ2TJwBzg\nN6o6B6gH7gBuAb4sIu8D2UCfy6iKSDawBPhaqFJmYoQIHRUyzwTtpmaMOXlVVXT0IUuPTJ9+ALKb\n7N6VaJJdPv8uYJeqrgh9XgLcqap3A5cBiEgR8PHeDhaRFODPwGOq+kxv+yxcuLDjfUlJCSUlJeGK\n3YRDfgDe9ahCBk51Li/Azp0wfrx3YZiTV1paSmlpqddhmAR1tKkRsqvg4MSINlnmNBdB/t8jd0Hj\nOVcTMlWtEpFKESlS1XJgPk5zZaGq1ohIEnAX8Nuex4qIAL8HNqrq/X1do2tCZqKNelohu+ce+PaT\nfhj3Dv/xH/Doo56EYU5RzwetRYsWeReMSTjVzVugfiK0JUe0yTK3tQhGWIUskURilOVXgMdFZC3O\nKMsfAzeKyGacjvq7VPUPACIyWkReCB33IeCzwMUisjr0Z0EE4jXhkrnf6cfVkO/N5TPpqJA99pgn\nIRhjYtjSpbD4rwHGZzlV/lmzInftXJ3kjLL09dmjx8QZt5ssUdW1wDk9Nj8Q+tNz3z2Emi9VdRk2\ncW1s87j/WGYmoakvnKdM1citpWmMiX3PPAPklzN1WBE7IjwlWGZaKtSNg2FbAVvQMhFYwmPck9+5\nqLgXbrwRHnlgNKTWQ9ohm7HfGDMoQ4YAeQFGpUX+PpaTQ2hQ0maarEiWECwhM65QxfMKWXo6XHut\nODHkBZwlUIwxZoCysoD8ckanFUX82i0tdIwSr6+P+OWNBywhM6645hogr4LrPuKnsNC7ODIy6Jix\n325qxpjBEAHyA5w+KvIVsvXrsYQswVhCZlzxzDNAXoBv3DyV6mrv4khOxunYn19uFTJjzKAc08OQ\nfpDTJ4yJ+LWdJstpkF/OEZuBMyFYQmZcopAfwJ/n4RxkIeOziqzJ0hgzKEuWwN33O90uUlMi/6uy\noICOClmFLTaSECwhM+7IDIIKeRl5XkfC2ZOnQl6FJWTGmAG7/npCa1gWOZX2CLv/fuDwaEg7xJXX\nHYp8ACbiLCEz7gh16JcomGcit9XpQ2YJmTFmUEIjxVNSIn/pvDyceRxDcym2tEQ+BhNZlpAZd3i5\nqHgPGTocfE1UHzrgdSjGmFjiYYWsQ6jZ8nvf8zAGExGWkBl3eL2oeBeCQNDPloMBr0MxxsSS0INl\nNCRk5baKUtyzhMyE3fr1eD4p7HFqp7LziPWMNeEhIgtEZJOIBETkjl6+LxCRv4nIGhFZLyJf8CBM\nc6pCFTIvmiwBAgE6EjKbHDb+WUJmwu773yeqKmQA1PrZ1WAVMnPqRMQH/ApYAMzAWZu3uMdutwGr\nVfUMoAS4V0S8rLOYwcoIQlIL1Bfi83kTwtSpOAlZgc3WnwgsITNh9/TTCnkB3nouiipkQT9VTZaQ\nmbCYC1So6nZVbQaeBK7qsc9eICf0PgcIqqp1y44hl3wq4CRDiLdr4IYqZMeaIryYpok4S8hM+GXU\ngigzJuZ7HQkAt98OI1KmUt1iTZYmLMYAlV0+7wpt6+oh4DQR2QOsBb4WodhMGLz6Kvx9dYDzpzsP\nlWlpHgbTkAetqRzRKg+DMJFgCZkJq9ZWnObKoJ+hQ72f8gJg8mT40nV+djcE+OY3vY7GxIGBlCq+\nC6xR1dHAGcCvRWSIu2GZcHnnHSC/nPFZRbS2hta09MjLLwPBIg6lWIU/3lmfBhNWzc04Hfprp3pb\n5u+hMLMQDrbw8uu1/BzvJ6s1MW03MK7L53E4VbKuzgf+E0BVt4jINmAa8H7Pky1cuLDjfUlJCSUl\nJeGN1gxadjaQH2BC9pUkeVy2mD0b+K2f+vQA8GFvgzGDVlpaSmlp6YD2tYTMhFVzM5BXwTdujqL+\nY0BWljjD10cEgHO9DsfEtvcBv4hMBPYAnwZu7LHPJmA+8JaIjMBJxrb2drKuCZmJIvnlTBpa5HUU\nTkIYLKJhlM17EYt6PmQtWrSoz32tydKEVXMzpIwMMGtsFI2wBDIzgdqpNGZaPzJzakKd828DXgY2\nAk+papmI3Coit4Z2+zFwtoisBV4Dvq2qtd5EbAarrc0ZmDR1mPcPlj4fUOvnWJY1WcY7q5CZsGpu\nBh1WgT/vNq9D6SYzEwj62aR2UzOnTlVfAl7qse3BLu/3A1dGOi4THnVtVdCSQeGQXK9DcRKyYBFN\nQ6xCFu+sQmbCqrkZWocGmJoXXRWyrCw61oSz+XyMMf2pbnUmtk5P9zqS9grZVFpyttCmbV6HY1xk\nCZkJq5ojtZDUSkFmgdehdNPeZEl+gOpqr6MxxkSzDXudGfpTU72OJJSQNWUjDXlU1lWecH8Tuywh\nM2FVURsg9bAfiaYhlkBODs5STnkVHD7sdTTGmGj2VpmzhqXXIyyBzlUCav0Eaq3LRTyLgh83E0+2\n1lWQVh9dzZUAQ4cCRwtA2tgVDHodjjEmSj36KB1rWGZmeh1NZ0Km+/0EgpaQxTNLyExY3XlPgEPb\nvB+Z1NPQoQDidOyvsZuaMaZ3n/sckB9gbKafgijoeZGUBLfcAtlNRZQHrWN/PLOEzIRXtC0qHpKd\nHXpTO5WdR2zqC2NMH6QNhm2h0Bcd9zER+OEP4cgOP/c/ag+T8cwSMhNeeRXOaMYo09GlrdZP5VG7\nqRlj+pBTCQ35PPRrD9dL6iEjg45Fxk38cjUhE5FcEVkiImUislFE5onIbBF5R0Q+EJFn+1rfTUQW\niMgmEQlLsuwsAAAgAElEQVSIyB1uxmnCY906IC8QlRWyDkFnTUtjjOlVqP/YWWd5HUinjAzgwGQY\nuoPm1mavwzEucbtC9kvgRVUtBmYBZcDDOLNWzwL+Anyr50Ei4gN+BSwAZgA3ikixy7GaU/ToklpI\naoH6Qq9D6VVLCxSPnMqysgD33ut1NMaYaDR0sjMHWTRJSwNa0uHwaLYf3O51OMYlriVkIjIUuFBV\nF4Oz3Iiq1gF+VX0ztNtrwLW9HD4XqFDV7araDDwJXOVWrCY8jmW19x+Lrikv2vl8cNpIZ3LYh3+v\nXodjjIlCef5ybv6E92tYdtW1y0VZtVX445WbFbJJQI2IPCIiq0TkIRHJAjaISHtydT0wrpdjxwBd\nZ8DbFdpmolgQp//YrbeeeF+vSGM+qLBpR5BVq7yOxhgTbY6kOiMso1KwiAcet35k8crNhCwZmAP8\nRlXnAPXAHcAtwJdF5H0gG+htIRsrX8Sg/W1O/7Hvf9/rSPr2ysvSsYTS8897HY0xJtocSS9nQnZ0\nVcg6BP1UHLAKWbxyc3HxXcAuVV0R+rwEuFNV7wYuAxCRIuDjvRy7m+6Vs3Gh8x1n4cKFHe9LSkoo\nKSk51bjNSappqYDg/M6ZpaNQSwtOs2peBarneR2OGYDS0lJKS0u9DsMkgObWZhrTdjIue7LXofQu\nWMShZHuSjFeuJWSqWiUilSJSpKrlwHyc5spCVa0RkSTgLuC3vRz+PuAXkYnAHuDTwI29XadrQma8\nVdUcgNr/R2ur15H07Ykn4BO/8EN+ALU6bEzo+aC1aNEi74IxcW3bwW2kNY4lMy0KFrHsTa2foxlW\nIYtXbo+y/ArwuIisxRll+WOcEZObcUZc7lLVPwCIyGgReQGcAQDAbcDLwEbgKVUtczlWc4pqqaB4\nhJ8RI7yOpG9XXklHk2Vbm9fRGGOiSXmwnLQjRSS72XZ0Kg5OpCm1isaWRq8jMS5w9cdOVdcC5/TY\n/EDoT89999Cl+VJVXwJecjM+Ez4HGg7QShPf/rfCqFiQt19BP8yzCpkxprtAMEDqET8pKV5H0oe2\nZFKOTmBL7RZOG36a19GYMIv2X50mRlTUVpDdNJW0tOic8qKrFa84fchaWi0jM8Z0emF5OTWbiqI3\nIQNSDxURqLVmy3hkCZkJi4raCrIao/jJsouzZ+SDJnGoeb/XoRhjosi7AWdS2KhtsgTkgN8WGY9T\nlpCZsAjUBkhvmEpqlPaFPU7QT+k6e8o0xnRqynaWTYrGB8szz3ReW6uLCATt3hWPLCEzYRGoDZBe\n74+dhKx2KhurKryOwhgTJRqaG2hJrYG68VFZIXvvPRg/HvLUz9/XWoUsHg0oIRORYhH5mIhcJiLT\n3Q7KxJ5AMMD6pX6OHvU6kgEKjbQ0xhhwul3ogUmgvqiskCUnw4MPwq61RWyrs3tXPOrzOUBEJgG3\nA5fjTNS6B2eRwlEiMhZ4HrhPVbdHIE4T5cqDAaj1OxOvxoKgH6Y953UUxpgoEajtXFQ8GitkEIrr\n8BhIP8i7qw4zb84Qr0MyYdTfj93PgIeAb4QW+O4gIinAxcA9wKfcC8/EgtqGWlpaW0huKuS667yO\nZoCsQmaM6aI86PQfA8jK8jiYPqSkAJoEtVM57+MV6N4zvQ7JhFF/CdlnVbW3dSYBxqrqK8ArLsRk\nYkwgGGDCED/1YyX65yBr17F8kiIS/VN1GGPctXl/AGrPBWBIlBaeOppSg37ILwcsIYsn/f36fEZE\n0npuFJHZQKlrEZmYE6gNMD47hjr0A76mPGhLpuZojdehGGOiwOb9ToXs7LO9jqRvHU2pwSLItwp/\nvOkvIVsFvCgime0bRKQEeAH4ostxmRgSCAYYmxEbc5C1S00Fav02fNwYA0BFqA9ZNK/Fe8YZoTe1\n7RUyE0/6TMhU9S7gdeBlEckWkWuA/wGuVtVXIxWgiX6B2gBDmqJ7MsWenIRsqs14bYyhrrGO+uYj\ncHh0VA9M6miFCBZZH9g41O+vUFX9kYg04FTLAC5RVfspMN0EagM88YOvwi6vIxm411+HObf7qai1\nuciMSXSBWqfKX47w9a97HU3/Ro6EqqBVyOJRf9NedJ0ToBAIAL8IdYBWVf2Ey7GZGKCqbK7pHC4e\nK4qLIfmQn0DtX9mxAyZM8DoiY4xXyoPljE4vYnQJfOELXkfTv/JyyMkZDr5mgkeD5Gfmex2SCZP+\nKmT39nivOPOQEXpvDDuD+zl8KAkaYuumkJICLfv8LF0XYOL1oPYTbQZBRBYA9wM+4GFV/Vkv+5QA\n9wEpwH5VLYlkjGbgyoPljEotIhgDA5OcEaACQWeRcUvI4kefCZmqlkYwDhOj7vtj7FXHAHw+oHYq\n+5or6P6sYUz/RMQH/AqYjzNp9goReVZVy7rskwv8GrhMVXeJSIE30ZqBKA+WMzLlYxyKoYFJBJ1B\nSfPGzvM6EhMmfXbqF5EXROT6rqMsu3yXKSKfFpEX3Q3PRLugOjP0x6TGYdCaClnVXkdiYstcoEJV\nt4cmzX4SuKrHPv8E/FlVdwGo6v4Ix2gGoTxYznBfdC4q3qdgEcsrrEt3POlv2oubgZnA+yKyTkRe\nEZFXRWQdsBIoBj4fiSBNdNq+HR57qTwmK2Qdgn6bz8cM1higssvnXaFtXfmBPBF5XUTeF5GbIhad\nGRRVpTxYTmFSbM2lSK3fmTvNxI3+miyrgbuBu0VkJNDe7XmHqlZFIjgT3T75ScAfgE1Xex3Kyaud\nCnkB2touiJ1VBozXBtLjMAWYA1wCZALviMi7vY1SX7hwYcf7kpISSkpKwhOlGZB99ftIS04jrS0v\n5ipklUftYTLalZaWUlpaOqB9BzRzVCgBsyTMdJOVhTMXTixXyGr9kFfBsWOQkeF1MCZG7AbGdfk8\njuMnfanE6cjfADSIyBvAbJzR6t10TchM5JUHyynKL+KmUA3zsce8jWcgXn0VLr3ST2V9uS3/FuV6\nPmQtWrSoz32tJmBOWs5QhbwKqPWTne11NCcp1GQZzZNBmqjzPuAXkYkikgp8Gni2xz5/BS4QEV+o\nH+65wMYIx2kGoD0hiyXjxgGNw0hNSmdf/T6vwzFhYgmZOWmpw6qgJZ2Vb+VyxRUwpmcvmij3yCMw\nNtMPeYGoXi7FRBdVbQFuA17GSbKeUtUyEblVRG4N7bMJ+BvwAbAceEhVLSGLQuXBcoryipgzBx59\n1OtoBqZ9VZSxmX7Kg9aPLF4MKiETkTwRmeVWMCa2HElzRlgmJTk3sq1bvY5ocL7wBRiRMhXyKmhp\nsYnIzMCp6kuqOk1Vp6rqT0LbHlTVB7vs83NVPU1VZ6rqA95Fa/rTXiHLyIidCaLb+7uOzSiy9Xjj\nyAkTMhFZKiI5IpKHM7ryYRG5z/3QTLQ76AswPsvP6ac7T2wxNUIpxNecCy3pVFnZ35iE1J6QNTcT\nW536gdFpViGLJwOpkA1V1UPANcD/qOpcnAkRTYKrJcAFxUUxtah4T83NQK2fCltk3JiE09rWytYD\nW5maN5WmpthLyEamOLP1m/gwkITMJyKjgE8BL4S2WfuOYVtdDE8KG3LsGFA7lS0H7KZmTKLZUbeD\nEdkjyEjJoLk5dqr8w4c7r4U+q5DFk4EkZD/A6by6RVXfE5Ep9DJ0uzcikisiS0SkTEQ2isg8EZkr\nIitEZHXo9Zw+jv2OiGwITUr7vyKSNvD/LBMR+QGmFcRBQhb0s62uwutQjDER1t5cWV4OGzbEToUs\nKwuuuQYKkqay5cAW2rTN65BMGAwkIfuHqs5S1X8FUNUtqnrtAM//S+BFVS0GZgFlwD3AXap6Js7E\ns/f0PEhEJgL/AsxR1Zk4C/jeMMBrmghoaW2DvAq++tmpXodyStLTgVo/W+usQmZMomkfYfnFLzqf\nYyUhAyfWtsZsGmvzeOhPlSc+wES9gSRk74rI/4nI5TKI2edEZChwoaouBmeouKrWAXuBoaHdcnEm\nWezpENAMZIpIMs5M173tZzxy8dU74Wg+uZlDvA7llLzyChD0s/2QJWTGJJryYDmThxbx5pvO51hK\nyFpb4fOfB4JFvFVm9694MJCEbBrwEPA5oEJEfiIiA5lFbxJQIyKPiMgqEXkoNEHincC9IrIT+C/g\nOz0PVNVa4F5gJ7AHOKiqrw3sP8lEwrJNm2D/dK/DOGWjR8Ok3KnsOFyBqnWNNCaRlAfLWfZs56+z\nWErIliwJvan1s7/NErJ4cMKETFXbVPUVVb0Bpxnx88CK0HQY5/dzaDLOWm6/UdU5QD1O8vV74Kuq\nOh64HVjc88BQP7V/ByYCo4FsEfnMoP7LjLsK4iMhA0hpHUqGL4s9h/d4HYoxJoLKg+U883BsJmQd\ngkXsV+vYHw9OOGGBiBQAn8GpkO3DmaH6OZx12ZbgJE292QXsUtUVoc9LcBKyuao6v8u2h3s59mzg\nbVUNhmJ4GjgfeLznjrYwr0cKNsG+mV5HERZtbTAxu5iy/WWMyYmx5QYSwGAW5zVmoBqaG6g6UgV1\nnbPBxsooS4CxY2HXLiDop1ZKvQ7HhMFAZpB6G3gMuEpVuy6g+76I/K6vg1S1SkQqRaRIVctx5i7b\nAIwUkYtUdSnwEaC31H4T8D0RyQAaQ8e+19t1bGFejxRs4jS53usowqKiApJfK2bjORuZP9mm2Is2\ng1mc15iBqqjdwlCdSHVb56/BWKqQpaeH3gSLqBWrkMWDgSRk0zTUuUZEkoDs0ESxqOpPT3DsV4DH\nQwvwbgFuBv4E/Do0jUUD8KXQuUfjrPf2cVVdKyL/g7OIbxuwCvjvQf/XGdckDd/EH74zzeswwqZl\n7wzKamypQWMSxcuryqje2L3bRSwlZB0Tch+YzCHZSXNrMym+GPoPMMcZSEL2uIj8P6AVWAEMFZFf\nqupx01X0pKprgZ7zjL0PnNvLvnuAj3f5fA+9TIlhvHew8SBtyfVMKYyj5r39xZTt/7PXURhjIqS8\ndhPUFHfb5vN5FMxJ6EjIWtPI1tFsP7gdf35szwuZ6Prs1B+abgLgtFBF7GrgJZw+Yze5H5qJVpv3\nb0aC08jKGvAsKNGvxulDZoxJDNsOl8H+7gnZwCd28t7tt8OFFzrvc9v8toRSHOhvlGV7n61kEUnB\nScieU9VmbOmkhLVpE8y7whlhGUvl/f48/TRweDT1xxqpbaj1OhxjTARsP1LWUSF7/nmPgzkJt9wC\nDz7ovM9TW0IpHvSXkLU/KzwIbAeygaUiMgGoczkuE6VqaoCCTWjN9Jh6muzP6NEAQubR6ZTVWJXM\nmHjXpm1UNpTD/umMGAEXXOB1RCenvYk1T4vYvH+zt8GYU9ZfQlYoIl8HUoBfAK8BX8dZZPyvEYjN\nRCER4moOMujsyDsh05otjUkEOw7uYIgvD5qGMHkyDB0KsTgvdHtCVsgMu3fFgf469fuA2F4Xx4Rd\naytxl5C1hdblHZFUbBUyYxJA2f4yxqQXU3whvPCC19GcvPaEbHTqDP5uo8RjXn8JWZWq2oQ/ppvG\npmYYtg1qY3tR8a4aG53XzW8V0zq+1NNYjDHuK6spo5BifJkwJIbLDkmhNq685FE0tTax/+h+CjIL\nvA3KnLSBrGVpTIetB7fCoTHQkn7inWNEQej+VfGOVciMSQRl+8t47YliXn7Z60hOTXNz+zthRuEM\nu3/FuP4SMpuy3Bzny99zmivT4ycfY/p0OPdc4OAk9tXvo76p3uuQjDEuKttfdtwcZLGovULW1gYz\nCmew0ZotY1qfCVn7OpLGdBPqP/bss14HEl633w60JZPPVDYHbbSSMfFKVSmrKePTlxTz+997Hc2p\nmTQJPvtZp2+vJWSxz5oszeAUboT9xXEz5UW7T33Ked291potjYlnNUdrAOGpxYVsjoNnL7+/S4Vs\nvyVkscwSMjNg69cDw9dD9elxMylsu44E02bsNyauldWU4astBoTLLvM6mlOXlGQVsnhhCZkZsJmz\nWp0my5oZfPjDXkfjkhq7qRkTz8r2l5FZ7/QfmzPH42DCwOdzKmTjcsZR11jHwcaDXodkTpIlZGbg\nhm2DowVwLCfumiw7VJ/Ouup1XkdhjHFJWU0Z2ceceRQzMz0OJgz27YOf/hREhOJC63IRyywhMwNX\nuAGqT/c6CncFi9h1aJeNtDQmTpXtLyPloFMhS031OJgwWL/eeW1osGbLWGcJmRm4UP+x5P6mE451\nbSnkNE2zm5oxcWpjzUZmjZrhdRhhc/So83rkCMwosCWUYpklZGbghq+HmtM6luuIV6kHZlqzpTFx\n6EDDAQ4dO0Rh6gR+9jOvowmP9oSsudkqZLHOEjIzcMM3xH+FDBjSMJMP9n3gdRjGmDBbV72O04ef\nTmODkJHhdTThceONzmtLiyVksc4SMjMgza3NSEGA/7i1mD/9yeto3FXQOssqZKZfIrJARDaJSEBE\n7uhnv3NEpEVErolkfKZ36/Y5CVlDQ3x06Ae4I/TTN2ECTMydSHV9NUeajngblDkplpCZAamorcBX\nP5YvfCaDyy/3Ohp3rF7tvL65ZCbLyi0hM70TER/wK2ABMAO4UUSOW4cntN/PgL8B8TouOaasr17P\nQz+ayV/+QtxUyLryJfmYVjDNRlrGKEvIzIA8+Q+nQ39+vteRuOeMM0JvDo+mqaWFfUf2eRqPiVpz\ngQpV3a6qzcCTwFW97PcVYAlQE8ngTN/WVa+D6pkcPBifCRnAzOHWBzZWWUJmTqilBX7wu/W07DmN\noUO9jsZd3/0ugMA+u6mZPo0BKrt83hXa1kFExuAkab8NbdLIhGb6oqqsr17fMXVPvN7LZo+YbX1g\nY1Scd8824dDQAAzfQPrW60iK8xS+Y16ifbP4YN8HzJ8839N4TFQaSHJ1P3CnqqqICP00WS5cuLDj\nfUlJCSUlJacan+lF5aFKMlIyqDtaAMRnQtbYCLNGzOKFwAteh2JCSktLKS0tHdC+lpCZE3ISsnUM\n2bDQ61Bc19QUelM9k1W73/E0FhO1dgPjunweh1Ml6+os4EknF6MA+JiINKvqsz1P1jUhM+5ZX72e\nmcNnUhX6nJPjaTiuWLwYrv2c8zCpqkjcLqkSO3o+ZC1atKjPfeO83mHCIXioHoZWMiZtmtehuK4j\nIds3k1fWWJOl6dX7gF9EJopIKvBpoFuipaqTVXWSqk7C6Uf2r70lYyZy2kdYtounCtkLXQpiI7JH\n4EvysefwHu8CMifFEjJzQmuqPoCaGQwbmuJ1KK47diz0pvp0aiijpa3F03hM9FHVFuA24GVgI/CU\nqpaJyK0icqu30Zm+rKtex5iUmR2f4ykhmx/qWfHee87rrBGzrB9ZDHI1IRORXBFZIiJlIrJRROaJ\nyFwRWSEiq0Ov5wz0WDdjNX37oHoNVJ0R9zP0Q5cKWdMQODSGFds2eRqPiU6q+pKqTlPVqar6k9C2\nB1X1wV72vVlVn458lKarddXrePahzoQsLc3DYMIsNRWys+GPf3Q+zxpuCVkscrtC9kvgRVUtBmYB\nZcA9wF2qeiZwd+jzQI81EXbuubAh6CRkidAdoT0h+/a3gb1zeCOwytN4jDGnrrm1mfJgOVodP2tY\n9nTmmc7rihWhClm1JWSxxrWETESGAheq6mJwyvyqWgfsBdqLxbk4HWQHeqyJsPfeg/d2roG9Z8b9\nCEvobLKcOxfYcxbrg5aQGRPryoPljM0Zy9G6OJmevxft9+emJmuyjFVujrKcBNSIyCPAbGAl8DXg\nTmCZiPwcJyE8b6DHqupRF+M1PbS1AUkt7GtbD/tmkXT6CQ+JeTfdBD4fXHstjPv/5rCh1vphGxPr\nVletZmbBmfxlpfM5Hqv9GpqMpaUFiguLqait4FjLMdKS46htNs65mZAlA3OA21R1hYjcD3wHJwH7\nqqr+RUSuBxYDlw7g2Dtxmji7sTl83NPSAuSXw+HR0DQkLm9iPS1Y4PwBGNZ4Jpvr1tCmbSRJApQH\no9Rg5vExpjer965GquZ0fC6L4w4wLS2QnpzO5GGT2bR/E7NHzvY6JDNAbiZku4Bdqroi9HkJTkI2\nV1Xnd9n28ACPvbO3i9gcPu5paQFGriGv6QxqISGaLLvKII/c1EICwQDTCuJ/yo9oNZh5fIzpzaqq\nVcz0df4KmRaH/zu3PzBv2QKXXNLZbGkJWexw7VesqlYBlSJSFNo0H9gABETkotC2jwDlgzjWRFBz\nMzByDam1Tm/RREvIkpPBnz2HlXtXeh2KMeYkqSqr967Gn32m16G4qr3J8tbQxCuzR8xmTdUa7wIy\ng+b2r9ivAI+LyFqckZL/CdwK3CMia4AfAV8CEJHRIvJCP8f+2OVYTQ9OhWw1SdXOqtuJ0GTZVUoK\n+LPOYtVe69hvTKzadnAb2anZZMtwr0OJqLNGnWUPkzHG1aWTVHUt0HOesfeBc3vZdw/w8RMcayKo\nuVlh5Bra3nVK3olYIZuUMYfX9v7M61CMMSdp9d7VnDnqTJoOeR2Ju3o+MM8ZNYfVVautD2wMsX8l\n06cdB3dCWzJVgdFAYlbIJqXNYdXeVagOZD1pY0y0WV21mjkj53RO+hynDvVIOPMz88nLyKOitsKb\ngMygWUJm+rRy33uwey7gZGKJlpA1NsJfnygkJy3HbmrGxKhVe1c5FbI4T8i+8Y3O9//yL3DkiNNs\n+f6e970LygyKJWSmV3/+M/zmmfaEzDE8sbpg8Prr8NRTMG/sPJbvXu51OMaYk7C6ajVzRjkVsm99\nC9591+uI3PGZz3S+f/hheOYZOHv02azcY/3IYoUlZKZXX/wibDjYmZC9/Tbce6/HQXlk3th5vFP5\njtdhGGMGae/hvTS1NjEuZxxHjkBOjrMcXLy6u8tMnVlZ1rE/1lhCZnrV1NIKo1bBnrMBmDIFMjI8\nDsoj5409j3d3x+ljtTFxbNXeVZw58kx+/WvhpZdg2DCvI3LXV7/a+T4zs3vHfhP9LCEzvWoeWgZH\nRkGjcwdLS+DVNzIPncmm/Zuob6r3OhRjzCC8t/s9zh1zLl/5CqxeHf8Jmc/X+T4pqbNjfyAY8C4o\nM2CWkJleNQ/v3n8sNdXDYDz2wC/SmTl8ppX+jYkxy3cv59yxnW2U8Z6QJXeZyOrGG51Xa7aMHZaQ\nmeNs3w6M6Z6QJWKFrP1p89gx60dmTKxR1Y4KWbt4T8i6VsiCQefVOvbHDkvIzHGamzkuIUu0SWHB\nmYcMnITM+pEZE1sCtQFy0nIYkT2iY9uIEf0cEAe6JmQAS5fCOaPPsVHiMSIBf82aEznUWA/5m6Hq\nDK9D8VTXhKy9QmYTxBoTG5bv6t5cCTBypEfBREjPhOyuu2DumLmsqVpDU2ucT8QWBywhM8dZVb2c\nlOAZ0JLudSieak/IAMYPHY8vyce2g9u8C8gYM2DLdy/v1lwJkB7nt7T2lowVKzq3DUkbwtS8qaze\nu9qboMyAWUJmjrOyehlDDlzQ8fnAAQ+D8VBubud7EeGiCRexdPtS7wIyxgzY8t3Lya0/lz17OrfF\n+2oj7f99Y8c6r1lZzut5Y8/jnV3WBzbaWUJmjvPo0jcZeqgzIeuamCSS2293Xpua4AtfgLPyS7jl\nh697GpMx5sQaWxrZWLORf/7YHG64wetoIi8jA370Izj9dOfz+ePO5+3Kt70NypyQJWSmm5a2Fo7m\nLad5y4d4+unErY4B3Hab87pzJ/zxj/DN60pgYqn1IzMmyq3eu5rpBdOhJaNb810i2LMHhg51qmMt\nLc6288ZZhSwWWEJmuvnlk2uhbjzpmscnP5m41bGuOpLSoB+SWgjst35kxkSzN3e+yQXjnCp/Y6PH\nwUTYqFHOa3JyZ0I2ZdgUjrUco7Ku0rvAzAlZQma6ueepZbDzAtpspQ0ArrkGZs5s/ySwvYTF/yj1\nMCJjzIm8seMNPjzhw16H4amuCZmIWJUsBlhCZrppHfMm7LiQ1lavI4kO8+dDfdcVk7aX8LOnSr0K\nxxhzAq1trbxV+RYXjL/gxDvHsa4JGcD5Y60fWbSzhMx0aNM26gtLYceHLSELSUqCt7vew7aXWD8y\nY6LY+ur1jMga0W1C2ETUMyG7cMKFLN1ho8SjmSVkBnCqQKWb1pKdVACHxrF7t9cRRYdjx3psCPpB\nWgnU2mK9xkQja6509EzIzhl9Dltqt1DbUOtdUKZflpAZAD71Kbjki68xSecDYAWgvghULOBvFX/z\nOhBjTC/e2PkGF46/0OswPNczIUvxpXD+uPNtLsUoZgmZAaCyEpj8dya0zvc6lKjS20SSRUkf46WK\nlyIfjIkaIrJARDaJSEBE7ujl+8+IyFoR+UBE3hKRWV7EmWhU1SpkIcnJ8NRTsHdv57aLJ17M69tt\nLsVoZQmZAUBSjsG4txnbUuJ1KFHte9+DgkPzWbZzGTUHGrwOx3hARHzAr4AFwAzgRhEp7rHbVuDD\nqjoL+CHw35GNMjFt2r+JjOQMJuRO8DoUzyUnO6/LlnVuu3iSJWTRzBIyA0Bj4TtQM4P7f2oTj/Xn\n0ktBjuWSsv9Mps4v9Toc4425QIWqblfVZuBJ4KquO6jqO6paF/q4HBgb4RgT0itbXuHSyZfS2grP\nPON1NN5qT8i6dj+ZM2oOlXWV1NTXeBOU6ZclZAaAw4WvwtZLvA4j6vRssvT5oLUV2jZ/jEMjrNky\nQY0Bus6wuSu0rS//DLzoakQGgFe3vspHp3yUNWvgk5/0OhpvtSdk3bYlJXPB+Aso3V4a8XjMiVlC\nZgA4WPgCVHys4/P553sYTBRr7yibvuty8L9g018kpgH/o4vIxcAtwHH9zEx4NbU28caON/jIpI/Q\n3Nz9u699jYSb7Lo9IVuzpvv2iydezD+2/SPyAZkT6iWHDh8RyQUeBk7DuYndArQBvw5duwX4sqr2\nutpYqK/G+8AuVb3SzVij0S9+AV/9au9POuG0s24nzem7ofI8ABYsgJes+NNNXh4Eg7BypVMhS6md\nBdLGuup1zBph/bUTzG5gXJfP43CqZN2EOvI/BCxQ1T5XhV24cGHH+5KSEkpKSsIVZ0J5p/IdphdM\nJ1xqsUwAACAASURBVD8zn3U9lkvy+XofoBPP2n9v/OQn8OMfd25fMHUBVzxxBaqKJNpfigdKS0sp\nLS0d0L4u/6rnl8CLqnqdiCQDWcBfgbtU9WUR+RhwD3BxH8d/DdgIDHE5zqj0jW84S/dMnOjudZ7b\n/Dwtmz4G6gPg6FF3rxeLfL7O19WrAQQ2XseSjUuiLiGrqYEnn4SvfMXrSOLW+4BfRCYCe4BPAzd2\n3UFExgNPA59V1Yr+TtY1ITMnr73/GEBdXffvkhKwLaivB/kZhTNo0zY27d9EcWHPsSgm3Ho+ZC1a\ntKjPfV37MRWRocCFqroYQFVbQp1c9wJDQ7vl4jxt9nb8WOBynApbwqbxkSizP/bec7C5swDZuXaj\naf//6D/+w3ltT8yAjoQs2vzv/zqVVeMOVW0BbgNexnlgfEpVy0TkVhG5NbTb3cAw4LcislpE3vMo\n3ITxytZX+OiUjwLw3HPdv0vEQlBfCZmIcPnUy3kxYN0ao42bFbJJQI2IPALMBlbiVLzuBJaJyM9x\nEsLz+jj+PuBbQI6LMUaN1tYev+xD3O6idPjYYdYcWAZbngLg4ovhgQfcvWYsOf307v8G3W5yu+dy\npOkIG2s2MqNwRsRjM95R1ZeAl3pse7DL+y8CX4x0XIlq7+G9VNRWcN4459fJ73/f/ftETMhSUvr+\n7nL/5dz37n184/xvRC4gc0JuJmTJwBzgNlVdISL3A9/BScC+qqp/EZHrgcXApV0PFJErgGpVXS0i\nJf1dJF76XyQnQ1kZTJ/efbvbCdnfKv6GP/181h1z8t6CgsQs7w9U96RZuLb4WpZsXMLdF93tVUhx\nbzB9MExier78eRZMXUCqL7XX79PTIxxQFOiv7/FHJn2Ef3r6nzh07BA5aQlR84gJbiZku3A647d3\n2F+Ck5DNVdX5XbY93Mux5wOfEJHLgXQgR0T+R///9s4zTIoqa8DvmdSEIYMSFQUVkGgAVFTEhKiI\nIoYVAXUxgMiiH2ZdXTO66q5iBBUjCsoaMAGCKCICElSQpCgiiJIHmHy/H7cvVV1dHYaZ7umZue/z\n9FO56nR19b2nzj1BqUHeHSuT/8WGDY5CZoYqE13ke+IPEzmq2oV8F1yuUyfq7lUerxXzgsMv4LJ3\nL+OOE+5IWQfZc8/VQ5gnRfLUTHFK4oNhqZq8u+JdBnYcuHf56KNhvitUrHr1chCqnImmkNXMqsmx\nLY5l2ppp9G/XP3lCWaKSMFuIUmojsE5EDg2uOgX4AVglIicG1/UCVvoce6tSqoVS6iDgIuAzP2Ws\nsuGuO2YUMW/4dlmyM28n03+azl9fOAl71q5N3PUqA16FrFFedxSKeevnJeX6q1eXTEkvKNAJMr1D\nOBZLZSEnP4fZv8ymd+vee9e521KA7OwkC5UC+L1cv/JKsEwe0PfQvkz5cUpyhbJEJdGDUyOA10Rk\nCdARuA+4ChgjIouBe4ErAUSkqYhMjXCeKpHsyd2ImPlEKmRjZ7zHju9O4P1J9fauM87rFn+8Q8if\nfioM6TSEFxe9mJTrH3IIjPOzKUdg5Eg99fNPtFgqA9PWTKNb827UreZUGfG+tAwdmmShUoDGjZ35\nTp3gjTdg0CB4/HG9rn+7/kxdNZXcwlz/E1iSTkIVMqXUEqXU0UqpTkqp85RS25VSC5RS3ZRSnZVS\nxyilFgX3/V0pdabPOT5XSvVNpJypQrIVsikrJ8L3F+5dHjoUevRI3PUqA3l5ocuBAFza6VImLZvE\n7oLk5Av5M1j1ZPlyePrp8O2bNunphAnwWzA7lvULtFRW3l7+Nv0O67d3ed48WLHC2b5unf6fVjXc\nQ5ZLl8Lf/qbna9bU08bZjem4f0c+XfNp8oWz+GKb6XJk7lydL8rgVr6MQpaTk5hr/5HzB8tyvoQf\nnRJ8V1yR+CS0FZ0mTUKX09Kgee3mdG3WlbeXvZ0UGYxSeP/9MGxY+HaTBPLGGx1fRKOQXX11YpV8\niyWZ7C7YzQcrP2DA4QP2ruveXf9HGjXSyw0blpNwKYr75WxAuwFMWjap/ISxhGAVsgSiFHz1lf82\nEV2e6OKLQ/c3GJP7Tz8lRrYJSybQuuA8yHdy7tphrdjUqQNvvgkmlqRePa08Dz96OP/95r9JKaWU\nnx/ffps2wY4den7qVK2cPfssLFiQONkslmTy/or36d68O/vV3A8IbUPnzNHLVTHC0vDII+Hr3DEx\n/dv254OVH5BXmBe+oyXpWIUsgSxdCscdF3u/Xbv0NCfHyZJvLGSJyJqvlGLct+NY/EJomiSrkMXH\nBRfAP/8JZ56pIxiPOgrOPPRMtu7ZylfrImjgZYixkLk7n6++0gr+ek+a5S++0NM//oCzztLztk6p\npbLw+vev87cOf9u7vGGDs81a++HKK6MHNDSp1YRO+3eySWJTBKuQpQCmEx0yBE7Tiab3KmR79ujp\nzz/rkj3/+U/przf7l9kUFWTCb91D1u+/f+nPXZUwv9uSJZAmaYzsNpL/zCuDHygGxkLmruJw3HF6\nCHzChMjH2fqklsrE1j1bmbV2Fv3aOP5j7uE4+4KplVI/txf3y9zgToN5cXFygpLiZcmSxOfgTEWs\nQlYGKKULT3sxDULr1nBnlLyhl17qzM+Zo6cmH5lRyI4+Go44Av7xj9LL+9SCpzir8ZW4K1K1bQtN\nm5b+3FWJxYtDl4d0HsJnP3/Gqs2rEnpdPwuZIZpC5iU3Vw+dJzrXncWSCF5d+ip9DukTktjU/Sxb\nC1nke+AOThpw+AC++PULNuZsTI5QcdC5M0yfXt5SJB+rkJUBb7+tHUeXLw9dbxSyNWu0ojVqVHiN\nNYBvfKrcmWHM3NzQqR+NG+vh0Xj4eevPzPhpBqc2vDxkfTJqZlZ2agVqMbLbSO6ZfU+Zn3v6dDj4\nYD1vGlO/3+yvv+I/55o1enrQQaWTzWLxIz8/PB9YWaGU4tmFz3LVkVeFrHcrZNZCFp9Clp2VzXlt\nzuOVJa8kR6g48Ua0VwWsQlYGGEWsXZRyhllZOv9L377w3HPxn9tYyNxs2+bMi2j/ID+lzo/bPnic\nc1r8nYziWiHrq6J5uKw44ABnfmT3kXy8+mN+/OvHMr3G55/rYWtwGqq33grfr3ZtHdb+6qvRz7d5\nMzzwgJ43iSJLwtCh/i8XFovhoINCrf9lyVfrvqKguIATD9Q5xvfs0dezFrJQIhUP8So7l3e5nPGL\nxlOsUu/NvLi46kSGW4WsDIj0FuhuHNyFXqdGSn/rg7GMuf9Y7shMw0svAfi/Er76KrRsCVv2bOGN\nH17h5WtHsHNn6D7WQrbvnBqsxPrmm/DhlNqM6j6K2z+7vUyv4X7bz88PbVDdjVVOjraYdujgrJs9\nW5dOcvPcc/Daa86yeVZ373Yig1euhJtvdvYxAQKgk9OWhT+jpXKwbZsT0Wv4/XdYuDAx13t24bNc\necSVe8uVbdumq4y429yqmHssHpo10+3H9u3OumNbHEsgI5CSOcnuu6/qVFqwClkZEMk07lbU3M6m\n770X/XyffebMb9miz+Mesvz4Yz31hnjDYb7nmzcPfvkFxswZA8v6U7ilGRdcoP3GjjxS79O7t++h\nlihMnqwz4Zvf+aKLdPLFf3T/Bws3LGT6T/vmBHHjjY71yuBukPLyQq2k7oL0OTm6bp/7BSA7Ozzh\nr0kOaTAvCXfe6UQGv/giPPSQnt+zB044IXRIdOvW+L+TpXLTqRMcc0z4+kS86G3YuYEPVn7AkM5D\nwra5X06y/OuMV2kefVT3V7/+CnWdwgaICKO6j+Lxrx8vP+EisGJF/Kl+KjrWqFsGRFLI3G9rJWmY\nTj7ZmZ88WXeobuXOdM6ffOI90md8k2BNs+wNPDT9OfjccTbr3FlbRBYuhCeeiF8+i6Z/f33/Bg2C\nBg30uqwsqJ5ZncdPf5wRH41gydVLyEovWc/w8MN66PGWW5x17mdsxozQmqPuXHW5uVCjRqhClpkZ\nqoBdd13483jOOVrBd0dkuYfLa9TQU7eltqo0kpbY/PqrfzuYCFeIx79+nIEdB9KgRoO964wiZnxv\nIfQ/YHFITw+1jhkubn8xt8y4hWV/LqNdoyj+N0lk9+5QS35lx1rISskPPzjO0V6OOsqZj1cha9s2\nfJ0ejnTo2lVPw/2E/PXrunWBE++BRZfBjuZ712dlaatOVazzVlYYP5VHH9VT0wn0Pawvreq14v4v\n7t+n8xYXa7+xbdtg5szQ/GKFhTobuZdbb9XT6tX1sKVRwmrXDk2OWa1a5CARd6dq9nEPVfpVk7BU\nfH780V95GjfOKb8VC79o3bJWyLbnbmfconFcf8z1IevNy4E7yjiS/1RVJj9f/8f9fqtARoBhRw3T\nIynliHlmlApVsEErktEC3Co6ViGLwl9/OY7Uftx/P7Rvr4d2YhGv35h3KMlLIOA8kOFvDlob8HaU\nWwOLoN1k+PKWkPWBgFbIShJkYAnFXTMPtIVp+XI9BPD82c/zzIJnIiSLbcSvv0Y+b04O9OypKwH0\n6gX//jeccUZ0WQYP1lOltBU1J0cPbx5wgFNGBrRCFinhsHto3XRyPXs665YsceatQlZ5aNsW/ve/\n8PVDh8ZnPY+k/JS1QjZ2/ljOaH0GLeu2DFlvntUnnyzb61Um6tbVkdqRFDKAEd1GMHXVVFZuXplc\n4VyYdqVv3/Dgg4YNExcokgpYhSwKZ53lpBrw4+WXw9fNnq3L0+wr0craTJigrRW5uZGGizKYO1db\nacaP12uKiot4bcfVMP0B2B1a1M36WJQeb2Z8cKJum9RqwrNnPctZL15Cj9O8+Si+pGXLkl2rT5/I\n1opAwHFinj3bWW9+4w4dnAz9gQDc45OZY948RyFbvtyxhrmtu717O52vVchSmx07SmYlOu88//V+\nnXd+vm6HzPMfKaKxLBWyzbs389jXj3HnieFJHe3weWy2boUBA7RC5pdcGqButbqM7DaSuz+/O/wE\nZcDYsdrFIxpXXunMu61hSuk2Z/Lk0ssxebIObkg1rEIWBXcZDj/SfO7eBRfoAs6JQCnty/Ptt5Ei\niDL3drqzZunp2PljSVOZsPiysL2tQlZ6/O6h+7k4p805ZK+9iDnNz2VXbh4iOsACDi1xZ5WdHbkR\nyc2NHVXWqZOeuocv58515vPyHNnbtQtPfOvFKmSpzaZN8e0XKzFwYWF4AMfpp2trRfPmenskP9qy\nVMju++I+BrQbwKENDg3b5lXIhg8vu+tWNpYtgwsv1PN+6SRGdhvJ9J+ms2TjkvCNpWT8eHjFle5M\nJHxY0u2i8/XXzvxNNznzRlHbs2ffElsvWKCjgFMNq5BFwS8HmBs/heyPPyLvn52tG7KSnM9N7EK5\nzmtqUREs2biEe2bfw/kZL4IKP7lVyErPvfeGr/N2Th023Qe79qfd7ZdCWkGJLWOGWKHfRiG7P4Lb\n2gMP6Ghc9zPkPudzz4XK/v330a/nVsi8aVTKgtWrbRWB0hCvr437d/TzdX32WahfP3Tdt986Hen9\n9ydeIVu9ZTUTlkzwtY5BeLtrhy7jw08hqxWoxd097+baj65FlfGYs1+f402X4sY9PPnuu868ifau\nUQOuCs0NHJEffnCUsDp14jumNHz2mTNSFS9WIYtCLIUsWiZo05HUq+esy8mJXEJpxAid98nQvTt8\n+WX4ftWrR5PICSvaU7yTi96+iMdOf4w6RYf47l2SMjsWf9y/r8GrWKdJGrzzKltydsH5F0G6dowo\naW6dePwLIbzzNNSpo4ct3Za0Wq78wK+9FvulwM3Gjfo537VLBw6UNYccEh7QYgnn5Ze1pcpLWSlk\nfudxD4W++27k56Ys+nOlFFd/cDW39riVxtmNfffp27f016msTJ0K8+f7b4uUcHXoEUPZU7CHV5fG\nyDBdQtwKmfdZ++AD/z7P4O4f3VGi48dH7qs7d3bKGrZv7wzLJyNH3bXXwt//XrJjrEIWhVhDMl7/\nDLfj9MKF2qRvioUbInXCSoWGaRcV6fqV7tw+cVvI0gqYXn8A7bNPZGDHgRH9K2INyVri4zLPaHBa\nmv79brhBLxcWAoXVaPblO4DA4F6QvYFDgyMva9bE10B07hx9uzlHrHB/d+dZK7RgQ8SXjEg+Qjt2\nOB12Ivx4vMMZVYHCQt22xKvMDB7s78sYreYp6MCO4cN1x2GIZpGcMwfOPFPPu5+hP/+MrJCVpJRX\nJCYsmcC23G2M7D4yrv2t5T+UPn1CI/7dRPrPpqel89SZT3Hj9BvLtMal+7cxymBhofaNPvtsOP74\n+M7jTUTs/h5jxjhBS0uW6MA8k/vT9OnJqOKwLy4dViGLQrQbOnduaMRZ48ahWvtff+lhHK/Pj0k1\n4aW4OPRhLSrSy97O3s9C5lhOZoIUQd+h5GxPZ/LlT/L77/Cvf/l/B7/hNkvpSUvTyXsffRSmTXMl\n8i0IwKS3YPUZcNWRbGs2GaUUv/0WnzLTpImeVq+us+n/9ptubE/U1WP2KlOxHLlXr3bma9bUMrpl\n9yOST87Wrc7bqTuHmeHrr7Uj8b5SFXNJmXanpOVivM+QUcgiKVnr1sFTT4VaIaOl55k6FT78EBYt\nCn1O1q1zlD5TtQL0cFJpUxSs2bKGG6fdyPNnP09GWny9qJ9yavEnmp9h12ZdGXrEUC5797IyG7p0\n93Hm+czPD010HQ/PPedUFAGnDcvP175m7uwI1arpPIvgtCfJqHNqFbJ9ZNAg3dAYlNKdWqQbOnGi\nE7FmaNs2tEE880z9wJmEmoaMjNBGy31Nd+djru0+XqlQS8p//6sdNPfKklYA5w6COr/A5DdJl4yo\nNS7794+8zRI/3j93WpqO0AVtPTCsXIn25Zt9O7w1iZ8OuIOz3jiLXwu+BeL/A+/erS2nzZppM//M\nmaHbY7Wdl1+uo51GjtQN5CmnaItu587x174zjBvnKGQffRT+5vnOO/5RUX/9BaNGRZcTqmY9QvMc\nREpNEol33nHm777bSWcTSSHze96iWcjMy+ARR4Qr7sZaMd1VnKK0v11uYS4XTL6AO064gy5NusR9\nXCKGzysrt8eo8HbHCXewdc9W/j3332VyPbdCZvpL74uEn7LkHZJ/+eXQNmnAACcKE0LrX450GVaL\nivR+JXHN2FesQrYPKKWjPsaOdda5tXW/H85bS/Kzz+Cf//Q/vxliNKkQMjNDlaqnn9YN3KBBoQ+r\n8TXr4mqHzINkOsCuXbUieP75QI0/YeAZUG0bvPYh5GdTVORvtTBvD1WlPliiefDB0GW3QtSgAf6s\nOw6eWczprU7n+gVnwyVn8OZ3k8gtLJlJQSRciYqVhLhVKxg2TBe7N8dmZ+uoyvvu8z8m0tChCRQA\nnY+vqAhuu83ZHmn46Jtv9PVjUZUtZCVVyNzcdZeureo+nxc/C5z72VniCbJzW+ejWWGV0p1sNGft\nWBSrYoa+P5RW9Vpxbddro+7rVSLtkGX8xCrjl5meyZvnv8mjcx/l/RXvl/p6fhYy73PYpQt86iqp\nef75oRYv42bhtb5++qnz/BYUOP8fdynC+fN1abhhw/RyIms474tCVgXfP0Mxw0luC5n7RhqL1nvv\nRXaob9jQX/Exx4NTNywjQ/uGNW2qIz6aNnUK8LofMGO9atdON3Bjx0Z2SGx1ygwCIy4nb8El8Nk9\noJxXDHdOF4NRxLz+Q5Z9o0EDXe5o9Gi97I6kHTEiyoFFAe447Tp27B4K7Sbz/KJnGf7JULj4BALr\nTybv56NgUwfIK9kr/76MLsQKGDCNm1+WfzOsPmMGkFbIjNn5jM7NJ68wj/ueyIf6eSz7M58Fi/LJ\nyc2jQ6d8fszPh0PyeGdZPoVK7/vXtnwWLMrj8I75/PNf+XBCHv/bkc/yafnkFeWRX5RPflHlSzhV\nUKAVGPMb+ClkGzdqt4hobNyoa6m+/nro+pJYyIqLtSLWqVN4BQ93+xetsyks1EOhXlavhtatIx9n\nUEpx07SbWLNlDdMHTd9bQNyPuXOdequWxHBg3QOZcuEUznrjLN696F2ObXFs7IN82LAhVCEzL3n5\n+aEK/vbtoSNDkyfDpEnO8lNPwWOPhaet2LrVedYLCiIPl7tT/eTn6zZtxQrdF5ekT/zjD9hvP/+X\nkz//jDyqEI0qr5C5G71ly/SP4+6cMjK0Gf7337VlwVCjhnNsZmaoeb56dceKZR4QYynLyIBDD9V+\nDgcfDB07OscZa8C334bL6fbhMQ/Ayk1reXLKHXzxyxcMP/AZHn0oPJW7N/pEKefNNVYnbImfCy5w\nFDI3q1ZFOmIXUDP4W1SHpZcyue+l5Kg/OfS0z5DWn8Fhr0GjH6gTqMf2X1tw/lvNaJLdhNqB2mRn\nZe/9pKelI4iO5uwgLEtLY+L3UFhcSEFRgZ4WF4TNFxQX7N1n5558OCsP0vODnzyaH5jP7vx8tmzP\nY0aDfBiaT1rtPA49MJ+Va0L33TsvivnFAVo+HtA1PP+eBYUB+kzI4pc1WVAU4Li/ssjZngVdA7yw\nKIvqWVl8PiPAnxuzoCiLwU0CFGbp+QwJUK96TbLSswik63OOp4Sx5CnOW2/BwIG6rUhLc5Qdt9LT\npIkO22/nKTHo9s+bPRumTCmdQrZhgx66Li4OHx1wd5JbtkT+PgUF4WlQFi3SIwGgHbi9xe4NxaqY\n0Z+OZtpP05g1ZBY1Mmv47xjE6zpiSQzdmnfj1XNfpd/Efky+YDInHHhCic/RtKlT9g/gsMP0tKBA\nO+Ibtm6Nnk2gRg2t7HgtyPn5zrO+cGHsFxjQrhPNm0ObNtqVwy9NxcUXa9cMb3/ZuLFWFM8/31m3\ncqWODt9vv/DzbN3qH5XvpsorZG6N/fDD9Y1ctMhZZ5QkEX1Dzf5ZWc4DUauW07i1bOmE2YKz3i8C\nzl0UGpyx82gRd0opFm5YSM9Hn+Efy6ZwzdHX8PSwp3n2iejjj/feqy1zoB/2Bg2qpn9Ooij50Fr4\neFFBAbzxUiMKFl9Iwz8u1FGwUsSizetYu2U9f+auZ2PORnbm7WR73nbW71xPTn4OxaqYYlWMQnHM\nkGLW11JM+VGRkZZBZlrm3mlmeqazHJwPZAbIrJZJs9oBGpPFxl+10kRRFse3z+KHpQG2fJvFAe2z\n2PZtgEM7ZPHqjVm0b6P30Z/gfGEAVDrFwMdzdeoW8/JwxtXwzDN6/svnnPWvPKEbKbnAuQ93PwkT\ngoEvZw+CwT30sfn5MPQ6uBIfs28FZuBAPd21K7QtGThQB2yYRNN+ud6Mf17v3pEtAr/8ol/EvOlQ\nPvggfF+j8O3ZE66QRSsj56awMFzZc78YLlrkr5Dl5Odw5ftX8sv2X/h8yOfUqx6j9/LB7ztZQhk/\nHq64ouTHnd76dF7v/zr93+rPmFPGcFmX8GTjsfDzaV6xIrRe7rZt0bMJGIXM+6IxeLATpDB8eHxZ\nBAYOdJKoe5MfGyZO1IECflHuxhWpuFhHyx92WGiwgWHxYj0UG2v0osp3yd6O1K1lgxM5KaItWqYj\nKS6GIUN0hFLduo5jYkZG6E035zJKVjxKkHdcu6CogG/Wf8Onaz7l7eVvs6dwD5cdeRlvH72K+tXD\nk05lZoaPyx94oJOCIzOzbMLRLQ7xKmQvvaSfGz+F7KefnDe0vedT6RxUryUH1WtZahlj8Y/3nee7\nfXsYdQbcOBP4GQYNh/97H2q3gsP3A7Uluh/RMceEWnqNMmY46CDdwY8ZA5dcErrN3Zmb/9U11+gX\nluuu2+evl/Ls2qUt5uaNe+FCPcxnCtdHo25dPWQJ/j45Xbvq0lign8GfforsLwjaiu5VyOLNCVdQ\nEH1I089it3jjYi5++2K6N+/OtEunxbSM+ZGd7aTlsERmyBCt+BcUaL/Pli1h7dr4jj3l4FOYPWQ2\n/d7sx5x1c3jktEeoW61uzOO8ioh72ZtJoLBQK2QDBuhIcm+ppZo19f/Cz2/b/WzFk0XAXU+4qAh+\n/FErVd62LVYU/Ntv61ES8Pf9jPdlpko79SvlhEibxmfbNn9NWSR0W3GxLvoMWmN3a/QmPQE4jaM5\nf7ROLLcwF+qvYu7GGTy/8Hmu/fBaerzQgwZjGnDtR9eSW5jLU2c+xaoRq7j9hNtDlDFj/oXQaCu3\n/JbEEa9C5jjHhytkPXrot6xrrin7oswl4Zpr4LvvtEXVdKwmp5r7OYuFGaLysnq1UyP2wQd1nU03\n7s7crVyk0hC7iPQWkR9FZJWI3BRhn/8Gty8RkZhhgqNH647xk0+cde7nINozUaeO0xH4VQNxW+1v\nvtm/lqm73VqwIFwhizcn3H33RVfe3Fn1N+/ezGWTruOUCadxa49befGcF+NWxtzpWiA5qQwqA2lp\nWtkx7ji6lFv8tG3UlvlD55ORlkH7p9rzypJXKCqOXlLDq9D4KTinn+64BVWvrofyv/rKsRAba5R7\n6Nz7UlHSyh7uwJPCQh0k51bkjG94QYF2W/L2o2bZrTP4yRCvg3/CLWQiUhcYBxwOKOByoBgYG7x+\nITBMKTXfc1wL4GVgv+Bxzyml/luWsk2bpjPpnn66/uHNkMCyZeH7HtSqELJytb9MRh45GXlsljwW\nb8hj/u95bN+VBwfnsX2/PO59MI+rhun9FpAH3fJ4eE4eIybl8eA3e9iWu42tuVv3Trfu2crmPZvZ\nmbcTBjbn+RUH0L55Szru35H+bfvTqXEnX0uYG5Nm4dZbnQACN+XZwVcFoilk11yjo2nB/ceNnGSq\nevXQnHbJxt0Ru4fBduyIlZg4PrZvjx6B527Qhg93IlWVCgYOlDMikg48CZwCrAfmi8h7Sqnlrn36\nAK2VUoeISDfgaaB7tPO+GkyK7nYGdt+npUt1qpOmTbXy4f5PuxWyWCWvInVa7t/97LO1T417KCne\nqM/HHgtd3r49tCN78EEYcsMKnl7wNC8sfJmdcy9CZi3j0psaxneBIN6k21YhKxmlyRFXO1CbZ856\nhi9//ZJbZtzCvV/cy6juo7i4/cVUT6vDzJmhLwZeZd7vxe7kk7Vv4po1/u2M3zHedjdadZ2L55t9\nmwAAIABJREFUL4Y33ghdt3mzExxglK8774Q77tDzxuqVlxeaU9T8x9LS9P/Q/Z/yjnAVFTlln2Ll\nZEzGkOV/gA+VUueLSAZQE3gXuF0p9YmInAGMAU7yHFcAjFJKLRaRbGChiExzN3oA7614j7zCPPKK\n8uKfBufXrsuDwXl80yiP3NZ5UKyVqKt+zIP/y9urfJlSNxQFtJ9McPrwbwFemFKNQEaAQHqA+ucE\nqFE9wEe/B6jeJcCenQH21A1A/QB/7ApQq16A6pnVaVKrCfWq1aNutbrUq16PetXqUb96fRrVbMQ5\nfdN4/d59jYB8h5tvPo8VK8K3JDK81xJdIevcWb9hHXusjlzTuX8itxzuwItIdSkTxauvwgkuf113\nox3tmXzhBd2Bx0NRUXSFzPs2aRIpFxTonGkpQFdgtVJqLYCITATOAdxtU19gAoBSap6I1BWR/ZVS\nUardaiINj5iafWPH6vvg7qBq1nQ6hcaNQ/PfaRn11Nt5gLYKTJkSruDUrasju43FPZ4OfP/9w+tK\n1q0Ls77MgwPmw4GfQ7u3OWnCRi7teCk7H/oOdjajLN4Xk5FbqjIRyWeqJPQ4oAezh8xm5tqZPL3g\naW6efjNtsk5m3mu9+anrKbSs2xIRCVPI/Kxy1ao5JZ78FDIRrVCZCicQ3lZEq6nbtq3/epOJYPbs\n8G0mibb7P5mf7wzx3nabjrB3J1/3/r+OP96J7PTLyegmoQqZiNQBjldKDQZQShUC20VkA2DKe9ZF\nv2WGoJTaCGwMzueIyHKgKaGNHs9/+zyB9MBepSiQHuD3dQH++D3Aab0C1MysSf3q9UP3CU7nbA+w\nZHaADscF+GlVgN/WakVrR1DhmjUjQM8eQQWsOPxWjf9IO9P6cft3+keYPhpyR8SqQenwfqlSvfSn\nVi3l2zBZC1li8fMNbNJEO5ampentoQ6tkZ0S3H6Ht9xSpmLGxOvP1bdvfNFKRiF9/vnwdAlefvgh\nukIWKUN9abO+lyHNgHWu5d+AbnHs0xwosULWvbuueGBYu1ZnzXfj9hv97jv/8777LvTrF76+Zk2t\n3LnbjZYttYN006ah+44YAU884XPytEKoto2Mpn9AjY2Q/QfUXQsNl0Oj5fScugJ6Hwa/nACfPsKy\nh06kbp10xrg60HXroEULf9njwW3Ns8SmpMN7kRAReh3Ui14H9eKv3X9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/glWrlg7ftlgs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\nYrFYLBZLOfP/6466RiA3sE8AAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "LCP['north_pos'], LCP['north_height'] = fit_beam(offset, north_scan_LCP, p0, hfnbw)\n", "LCP['centre_pos'], LCP['centre_height'] = fit_beam(offset, on_scan_LCP, p0, hfnbw)\n", "LCP['south_pos'], LCP['south_height'] = fit_beam(offset, south_scan_LCP, p0, hfnbw)\n" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'centre_height': 3.248198723781011, 'north_height': 1.9722877109613168, 'south_height': 1.191878746810932, 'centre_pos': -0.014946913255602956, 'north_pos': -0.0050754851934369403, 'south_pos': -0.012771754720146699}\n" ] } ], "source": [ "print LCP" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Amplitude of peak is 1.95 K\n", "Amplitude of peak is 3.36 K\n", "Amplitude of peak is 1.22 K\n" ] }, { "data": { "image/png": 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OUWHe6RA7UkLPYkjbRne3T3avlJqmXnkFrvxiP33xxcxPme/1/c+MyqfTcZADtV1e37cK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\nXPv1tew+5N3s/JZWv2J1xRWRfmVBClm3bjkn+r3uOm95927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CFxafLi9xFRVtOt5CaUGmgqSmy9fl/+1vQ3JyYPf9+9t9PWSgd1oqpQJvV5XvA+Vf/xr4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XS315PeOGjwOMsRgeq9zeZamxWpXZ0QgvprX+FPi0x7kXHfYrgEvcHZc4cZV4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\nneQee2j/S0FNCFgfbb4C4Jgc1CdAP2ACqEuBncBMoBPQELgfOAloBlz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fDF02z9cqVXQu\nM5PiIhUDUOJJSqotxoRqsVgsFouleDE5xy68MLFypBqiSrFKKyKlV3iLxVJolFIpUW/D9mEWS9kj\nUv9VqhUyi8VisVgsllQgJYcsLRaLxWKxWEoTViGzWCwWi8ViSTAppZCJyGEiMk1E1ojIVBGpEbBP\nBRGZLyJLRGSliDyQZPI1FJGZIrJCRJaLyPXJIpuz3/9EZIuILIuTXH1EZLWIfC8iAbUYQET+7Wxf\nKiId4iFXLLKJSCsRmSci+0Tk5njJVQD5BjvX7FsRmSMi7ZNMvnMc+RaLyCIR6RlP+eKN7b9KXj5n\nv7j1Ycncf8UiXyL7sDLZfymlUuaDrgpwmzM/Cngwwn6VnGk54CvglGSRD6gLHOvMVwG+A45OBtmc\nbacCHYBlcZApHcgGsoAMYIn/WgB9gcnO/AnAV3H6LWOR7QigEzAauDkechVQvi5AdWe+T7yuXQHk\nq+yZbwdkx/Maxvtj+6+4Xb+49GHJ3H8VQL6E9GFltf9KKQsZcDZg0sS+DAwI2kkptceZzURf2F9L\nXjQgBvmUUpuVUkuc+d3AKqBeMsjmyPQF8Fsc5AHojL6Jf1RK5QBvAOf49jkkt1JqPlBDROokg2xK\nqW1KqYVAThzkKYx885RSO53F+UCDJJPPW4G2CrA9jvIlAtt/lbB8jlzx6sOSuf+KSb4E9mFlsv9K\nNYWsjlJqizO/BQi8sUUkTUSWOPvMVEqtTCb5DCKShX6Tm1+yYgEFlC1O1Ac2eJZ/dtblt088/pix\nyJZICirfX4HJJSpRKDHJJyIDRGQVuq5t3Ia/EoTtv4pGsvVhydx/RTp3svRhZbL/KnWZ+kVkGtos\n7ucO74JSSkmEHD9KqTzgWBGpDnwqIt2VUrOSRT6nnSrAO8BI500zaWSLI7HK4M/pEg/Zk+H6RCNm\n+USkB3AlcHLJiRNGTPIppd4H3heRU4FXgJYlKlUJY/uv5JAvTiRz/xXP8xSGMtl/lTqFTCl1eqRt\njqNmXaXUZhE5EtiaT1s7ReRj9Bj5rGSRT0QygHeBCc4PWiwU57WLExuBhp7lhug3kWj7NHDWlTSx\nyJZIYpLPcYR9HuijlIrXUDQU8Poppb4QkXIicrhSakeJS1dC2P4r8fLFkWTuv4LOnUx9WJnsv1Jt\nyHIScLkzfzkQ1hmISC0TfSMiFYHTgcVJJJ8ALwArlVKPx0mumGRLAAuB5iKSJSKZwIVoOb1MAi4D\nEJETgd89wxaJls2QiKzy+conIo2A94AhSqnsgDYSLV9T5/+AiHQEKM3KWAzY/qtoJFsflsz9V6zy\nGeLdh5XN/qs4Ig6S5QMcBkwH1gBTgRrO+nrAx858e+AbdFTEt8CtSSbfKUCeI99i59MnGWRzll8H\nNgH70WPofylhuc5ER2plA7c7664BrvHs85SzfSnQMY6/Z1TZ0EMrG4CdaCfi9UCVJJLvv8AOz322\nIF6yxSjfbcByR7YvgOPjKV+8P7b/Knn5nOW49WHJ3H/FIl8i+7Cy2H/Z0kkWi8VisVgsCSbVhiwt\nFovFYrFYSh1WIbNYLBaLxWJJMFYhs1gsFovFYkkwViGzWCwWi8ViSTBWIbNYLBaLxWJJMFYhs1gs\nFovFYkkwViFLIUSkgYh8ICJrRCRbRB53smab7a+LyFIRGSkiLUVkiYgsEpEmBTzP5U4m7KBtrTzt\nHlXU71TSiEg3EelSDO2cJSJ3R9hW6NIxIvKoU3bDYrFYLCmMVchSBCcj8HvAe0qpFkALdIX5+53t\ndYFOSqljlFJPAOcCbyuljlNKrSvg6a5AJ1sMYoCn3R+88pmsxUlGD+CkoA0ikl6Adm4GnomwrSjJ\n/p4Bbi3C8RaLxWIpBViFLHXoCexVSr0MhwoQ3whc6ZRYmQrUF5HFIvJPYCQwTEQ+E5FKIvKxY9la\nJiKDAETkOBGZJSILRWSKiNQVkfPRtfNeFZFvRKSCEUBE+vrabSwi34nIy8AyoKGIPOyc41vPebqL\nyGwReV9E1orIgyJyqYgscPYLs+CJSBURedHZvlREBjrrzxCRuY6F7i0Rqeys/1FE7nbWf+tYCLPQ\nmZVvdL7LKSLykoiME5GvgDFO+YtPnGvwuYiEFYcVkYZApnJKnojIUSIyzznPaN++tzrfa6nXoiYi\nd4rIahH5QkReE5Gbnd/xeyBLnHI5FovFYklNSl1xcUtE2gCLvCuUUrtEZD3QFOgPfKSU6gCHLGq7\nlFKPish5wEalVD9nWzVnqPNJoL9SaoeIXAjcr5T6q4gMB25WSn3jO99kERnnaTcLaAZcqpRa4Jzn\nGHT5lyOAr0Xkc+fw9kArdHmOH4DnlVKdReR64G9o5dLLncBvSqn2jsw1RKQWcAfQSym1V0RGATcB\n96GtVNuUUseJyDDgFqXU1V55nXb+irb+dVFKKRH5DF0KI1tETgCeBnr5ZDkZXc7G8ATwH6XUBBG5\nzqwUkTOAZs73SgM+cIYj9wEDnWuQ6bS10NPeYqAL8AkWi8ViSUmsQpY65DcsFjRcaNZ9C4wVkQfR\nStuXItIWreRNd0Ya09H136K1F7TtJ6XUAmf+ZOA1pet1bRWR2cDxwB/A1x4LUzbwqXPMcvSwop9e\n6IKuACilfheRs4DWwFxH5kxgrueY95zpN2gFKNJ3edtRxqqgFaG3PaOtmQGyNAJ+8SyfhB4SBpgA\njHHmzwDOEBFTDLoy0ByoCryvlDoAHBCRD30ybQKyAs5rsVgslhTBKmSpw0rgfO8KEamGVhay0UVi\nA1FKfS8iHYB+wGjHKjQRWKGUCvSvIna/qD99y37lx7Sz37Muz7OcR+T7NEgpnKaUuiTC/qbNg1Ha\nBNjjTNOA341VMR9i9Y97QCn1XMiBIiN9x/vbEormh2axWCyWJMf6kKUISqnPgEoicikcckh/BHhR\nKbUv2rGiIyb3KaVeBcYCHdBV7I8QkROdfTJEpLVzyC6gWiHE/AK4UETSROQIoCuwgNiVGS/TgOGe\n71AD+Ao4WUSaOusqi0jzfNrZhbZQhaGU+gP4wfGbM4EJ7QN2/YlQhXcOcJEzP9iz/lO0T5/xa6vv\nXIc5QH8RKe9Y5foRqoAdCfyYz/ewWCwWSynGKmSpxbnABSKyBq1Q7QH+7tnut7KY5XbAfGco7Z/A\naKVUDtriNkZEluD6MQG8BIwTn1N/hPMcmldKTUQPjy4FPgNuVUptdfaJZAGKtG00UNMJEFgCdFdK\nbUdHgL4uIkvRw5VhTvi+Nj8EzjVO/QHyDwb+6pxjOXB2QHtzgI6e5ZHAcBH5Fu2PppzvPw14DZjn\nbHsLqKKUWghMQl+byegAiJ2e9joA8wLOa7FYLJYUQbQ7j8ViKQoiMgMYrJT6Jd+dg4+vrJT6U0Qq\nAbOBq5VSS0SkBTBWKRWkCFosFoslRbAWMouleBgLXFuE459zLJSLgHeUUkuc9dcCDxVVOIvFYrEk\nN9ZCZrFYLBaLxZJgrIXMYrFYLBaLJcFYhcxisVgsFoslwViFzGKxWCwWiyXBWIXMYrFYLBaLJcFY\nhcxisVgsFoslwViFzGKxWCwWiyXB/D8q2hMrdFzyTgAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "RCP = {}\n", "RCP['north_pos'], RCP['north_height'] = fit_beam(offset, north_scan_RCP, p0, hfnbw)\n", "RCP['centre_pos'], RCP['centre_height'] = fit_beam(offset, on_scan_RCP, p0, hfnbw)\n", "RCP['south_pos'], RCP['south_height'] = fit_beam(offset, south_scan_RCP, p0, hfnbw)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Use a Gaussian approximation to find the pointing offset. Then calculate a pointing correction to be applied to the peak amplitude." ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Amplitude correction factor due to pointing error is 1.020\n" ] }, { "data": { "text/plain": [ "[]" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "x = [-hpbw/2, 0, hpbw/2]\n", "ns = [LCP['north_height'], LCP['centre_height'], LCP['south_height'] ]\n", "p0 = [LCP['centre_height'], 0.0]\n", "ns_coeff, ns_var_matrix = curve_fit(gauss, x, ns, p0)\n", "pointcor = np.exp(np.log(2) * (ns_coeff[1]**2) / (hpbw/2)**2)\n", "print(\"Amplitude correction factor due to pointing error is %.3f\"%pointcor)\n", "smooth = np.linspace(-2*hpbw, 2*hpbw, 100)\n", "ns_fits = gauss(smooth, *ns_coeff)\n", "plt.figure(figsize=[6,6])\n", "plt.plot(x, ns, 'ro', markersize=5)\n", "plt.plot(smooth, ns_fits, 'k:', linewidth=3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## For discussion\n", "\n", "It is not always possible to get a really good fit to the data points using a Gaussian approximation. Also, the beam isn't really Gaussian past the half-power points. In practise, the method I have shown you only works if the pointing offset is small. We have found that if the pointing correction is greater than 1.3 (30%) that particular data point in the time-series will exhibit as an outlier. Throughout most of our monitoring program, the pointing corrections were less than 5%. However, the telescope's surface was upgraded from perforated aluminium to solid sheets during 2003-2004, and the additional weight changed the telescope's balance, affecting the pointing.\n", "\n", "Generally, the telescope control system already applies a pointing model when observing a source. This pointing model will include factors such as encoder offsets and gravitational deformation. Factors that are dependent only on the telescope's pointing direction are encoded into the model. Differential heating of the structure during the day can also affect the pointing, but without having a very detailed mechanical model and temperature sensors placed across the structure, we cannot model this. Hence the calibration that we have just worked through. " ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def pointcor(scan, hpbw):\n", " \"\"\"\n", " Calculate a pointing correction factor for continuum driftscans.\n", " \"\"\"\n", " x = [-hpbw/2, 0, +hpbw/2]\n", " ns = [scan['north_height'], scan['centre_height'], scan['south_height'] ]\n", " p0 = [scan['centre_height'], 0.0]\n", " ns_coeff, ns_var_matrix = curve_fit(gauss, x, ns, p0)\n", " pointcor = np.exp(np.log(2) * (ns_coeff[1]**2) / (hpbw/2)**2)\n", " print(\"Amplitude correction factor due to pointing error is %.3f\"%pointcor)\n", " return pointcor\n", " " ] }, { "cell_type": "code", "execution_count": 50, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Amplitude correction factor due to pointing error is 1.016\n", "Amplitude correction factor due to pointing error is 1.020\n" ] } ], "source": [ "RCP['pc'] = pointcor(RCP, hpbw)\n", "LCP['pc'] = pointcor(LCP, hpbw)" ] }, { "cell_type": "code", "execution_count": 51, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "centre_height 3.35779255585\n", "north_height 1.95165897407\n", "south_height 1.22467709684\n", "centre_pos -0.0200951836134\n", "pc 1.01598997979\n", "north_pos -0.0109995135534\n", "south_pos -0.0196116807345\n" ] } ], "source": [ "#a neater way to view the contents of your dictionary\n", "for key, item in RCP.iteritems():\n", " print key, item" ] }, { "cell_type": "code", "execution_count": 52, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "centre_height 3.24819872378\n", "north_height 1.97228771096\n", "south_height 1.19187874681\n", "centre_pos -0.0149469132556\n", "pc 1.01995003636\n", "north_pos -0.00507548519344\n", "south_pos -0.0127717547201\n" ] } ], "source": [ "for key, item in LCP.iteritems():\n", " print key, item" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dictionaries make very handy containers because you can store pretty much anything in them. You could also add an array of values to your dictionary - for example the scan data itself, if you didn't want to keep on having to track down each HDU." ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [], "source": [ "LCP['Ta_on_corr'] = LCP['centre_height']*LCP['pc']\n", "RCP['Ta_on_corr'] = RCP['centre_height']*RCP['pc']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# The flux density of the calibrators" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For single dish observations, the flux standards defined by Ott et al. (1994) (http://adsabs.harvard.edu/full/1994A&A...284..331O) are still in use (http://adsabs.harvard.edu/abs/1977A%26A....61...99B is an earlier flux scale). At HartRAO we generally observe three flux calibrators: 3C123, Virgo A and Hydra A. Most of the sources, with the exception of 3C286 and 3C295 (both too faint to be of much use to us) have shown variability on the time-scale of a decade. So it is generally a good idea to have multiple calibrators. Three is the minimum number that would enable you to determine which source has undergone intrinsic variation. \n", "\n", "The spectra of the sources are characterised by a frequency dependent expression with three coefficients. \n", "$$\\text{log } S\\text{[Jy]} = a + b * \\text{log }\\nu \\text{[MHz]} + c * \\text{log}^2 \\nu \\text{[MHz]}$$\n", "\n", "Table 5 of Ott et al. (1994) is shown below:\n", "" ] }, { "cell_type": "code", "execution_count": 54, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def S_ott(a, b, c, nu):\n", " #evaluate the Ott flux polynomial\n", " return 10**(a + b * np.log10(nu) + c * np.log10(nu)**2)" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Flux density of Virgo A at 6668.518 MHz is 58.70 Jy\n", "Flux density of Hydra A at 6668.518 MHz is 9.96 Jy\n" ] } ], "source": [ "nu = drift1.header['CENTFREQ']\n", "VirA = S_ott(4.484, -0.603, -0.0280, nu)\n", "HydA = S_ott(4.728, -1.025, 0.0130, nu) #3C218 in Ott table\n", "print('Flux density of Virgo A at %.3f MHz is %.2f Jy'%(nu, VirA))\n", "print('Flux density of Hydra A at %.3f MHz is %.2f Jy'%(nu, HydA))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that this is the total flux of the source. Each polarisation of the reciever only picks up half of this flux.\n", "\n", "##To do\n", "Write a conditional that checks the source name and calculates the correct flux polynomial" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [], "source": [ "LCP['PSS'] = VirA/(2*LCP['Ta_on_corr'])\n", "RCP['PSS'] = VirA/(2*RCP['Ta_on_corr'])" ] }, { "cell_type": "code", "execution_count": 57, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Point source sensitivity of LCP channel is 8.86 Jy/K\n", "Point source sensitivity of RCP channel is 8.60 Jy/K\n" ] } ], "source": [ "print('Point source sensitivity of LCP channel is %.2f Jy/K'%LCP['PSS'])\n", "print('Point source sensitivity of RCP channel is %.2f Jy/K'%RCP['PSS'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Note\n", "\n", "There is one additional thing we have to correct for if we are using Virgo A as our flux calibrator. Virgo A at 6.7 GHz is slightly resolved by the 26m telescope. The correction factor is described by Baars (1973, http://adsabs.harvard.edu/abs/1973ITAP...21..461B) and is dependent on the ratio between the angular size of the source and the half-power beam-width of the telescope.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Exercises\n", "\n", "* What is the SEFD of the system?\n", "* What is the aperture efficiency of the telescope?\n", "* Repeat the exercise for an observation taken in early 2003 and then for any observation taken after mid-2005. What do you find? Why do you think this has happened?\n", "\n", "# Advanced excercise\n", "* Write a dictionary to contain the coefficients for all the calibrators.\n", "* Write a function that will return the flux density of a given calibrator at any frequency in the range covered by the model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.10" } }, "nbformat": 4, "nbformat_minor": 0 }