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# This file was automatically generated by SWIG (http://www.swig.org). 

# Version 2.0.4 

# 

# Do not make changes to this file unless you know what you are doing--modify 

# the SWIG interface file instead. 

 

 

 

from sys import version_info 

if version_info >= (2,6,0): 

    def swig_import_helper(): 

        from os.path import dirname 

        import imp 

        fp = None 

        try: 

            fp, pathname, description = imp.find_module('_simulator', [dirname(__file__)]) 

        except ImportError: 

            import _simulator 

            return _simulator 

        if fp is not None: 

            try: 

                _mod = imp.load_module('_simulator', fp, pathname, description) 

            finally: 

                fp.close() 

            return _mod 

    _simulator = swig_import_helper() 

    del swig_import_helper 

else: 

    import _simulator 

del version_info 

try: 

    _swig_property = property 

except NameError: 

    pass # Python < 2.2 doesn't have 'property'. 

def _swig_setattr_nondynamic(self,class_type,name,value,static=1): 

    if (name == "thisown"): return self.this.own(value) 

    if (name == "this"): 

        if type(value).__name__ == 'SwigPyObject': 

            self.__dict__[name] = value 

            return 

    method = class_type.__swig_setmethods__.get(name,None) 

    if method: return method(self,value) 

    if (not static): 

        self.__dict__[name] = value 

    else: 

        raise AttributeError("You cannot add attributes to %s" % self) 

 

def _swig_setattr(self,class_type,name,value): 

    return _swig_setattr_nondynamic(self,class_type,name,value,0) 

 

def _swig_getattr(self,class_type,name): 

    if (name == "thisown"): return self.this.own() 

    method = class_type.__swig_getmethods__.get(name,None) 

    if method: return method(self) 

    raise AttributeError(name) 

 

def _swig_repr(self): 

    try: strthis = "proxy of " + self.this.__repr__() 

    except: strthis = "" 

    return "<%s.%s; %s >" % (self.__class__.__module__, self.__class__.__name__, strthis,) 

 

try: 

    _object = object 

    _newclass = 1 

except AttributeError: 

    class _object : pass 

    _newclass = 0 

 

 

class simulator(_object): 

    """Proxy of C++ casac::simulator class""" 

    __swig_setmethods__ = {} 

    __setattr__ = lambda self, name, value: _swig_setattr(self, simulator, name, value) 

    __swig_getmethods__ = {} 

    __getattr__ = lambda self, name: _swig_getattr(self, simulator, name) 

    __repr__ = _swig_repr 

    def __init__(self): 

        """__init__(self) -> simulator""" 

        this = _simulator.new_simulator() 

        try: self.this.append(this) 

        except: self.this = this 

    __swig_destroy__ = _simulator.delete_simulator 

    __del__ = lambda self : None; 

    def open(self, *args, **kwargs): 

        """ 

        open(self, ms = string("")) -> bool 

 

        Summary 

                Construct a simulator tool and creating a new MeasurementSet 

 

        Description 

                 

            This is used to construct { t simulator} tools.  A simulator tool 

            can either be instantiated from an existing MeasurementSet, predicting 

            and/or corrupting data on the given coordinates, or it can be used to 

            create a fundamentally new MeasurementSet from descriptions of the 

            array configuration and the observational parameters.  This 

            is useful for making a simulator 

            tool which will make a MeasurementSet from scratch.  In order to 

            do this, you must also run {        t setconfig}, { t setfield},  

            {   t setspwindow}, {       t setfeed}, and {       t settimes}.  Creating the 

            actual MS is performed by { t observe}.  Data can be {      t predict}-ed 

            and then {  t corrupted}-ed.  In this 

            example, we read in the antenna coordinates from an ASCII file: 

           

 

        Input Parameters: 

                ms               MeasurementSet to be created  

                 

        Example: 

                 

             

            tabname = 'VLAC.LOCAL.TAB' 

            asciifile = 'VLAC.LOCAL.STN' 

            mytab=table.create() 

            mytab.fromascii(tabname, asciifile); 

            xx=[]; yy:=[]; zz:=[]; diam:=[]; 

            xx = mytab.getcol('X');   

            yy = mytab.getcol('Y'); 

            zz = mytab.getcol('Z'); 

            diam = mytab.getcol('DIAM'); 

            # 

            sm.open('NEW1.ms') 

            # do configuration 

            posvla = me.observatory('vla');  #  me.observatory('ALMA') also works! 

            sm.setconfig(telescopename='VLA', x=xx, y=yy, z=zz, dishdiameter=diam,  

                         mount='alt-az', antname='VLA', 

                         coordsystem='local', referencelocation=posvla); 

 

            # Initialize the spectral windows     

            sm.setspwindow(spwname='CBand', freq='5GHz', 

                           deltafreq='50MHz', 

                           freqresolution='50MHz', 

                           nchannels=1, 

                           stokes='RR RL LR LL'); 

            sm.setspwindow(spwname='LBand', freq='1.420GHz', 

                           deltafreq='3.2MHz', 

                           freqresolution='3.2MHz', 

                           nchannels=32, 

                           stokes='RR LL'); 

             

            # Initialize the source and calibrater 

            sm.setfield(sourcename='My cal', 

                        sourcedirection=['J2000','00h0m0.0','+45.0.0.000'], 

                        calcode='A'); 

            sm.setfield(sourcename='My source', 

                        sourcedirection=['J2000','01h0m0.0','+47.0.0.000']); 

           

            sm.setlimits(shadowlimit=0.001, elevationlimit='8.0deg'); 

            sm.setauto(autocorrwt=0.0); 

             

            sm.settimes(integrationtime='10s', usehourangle=F,  

                        referencetime=me.epoch('utc', 'today')); 

             

            sm.observe('My cal', 'LBand', starttime='0s', stoptime='300s'); 

            sm.observe('My source', 'LBand', starttime='310s', stoptime='720s'); 

            sm.observe('My cal', 'CBand', starttime='720s', stoptime='1020s'); 

            sm.observe('My source', 'CBand', starttime='1030s', stoptime='1500s'); 

             

            sm.setdata(spwid=1, fieldid=1); 

            sm.predict(imagename='M31.MOD'); 

            sm.setdata(spwid=2, fieldid=2); 

            sm.predict(imagename='BigLBand.MOD'); 

            sm.close(); 

             

           

        -------------------------------------------------------------------------------- 

                       

        """ 

        return _simulator.simulator_open(self, *args, **kwargs) 

 

    def openfromms(self, *args, **kwargs): 

        """ 

        openfromms(self, ms = string("")) -> bool 

 

        Summary 

                Construct a simulator tool using an already existing  MS 

 

        Description 

                 

            This is used to construct { t simulator} tools operating on 

            an existing MS.  Data can be predicted and/or corrupted on the MS's given coordinates. 

           

 

        Input Parameters: 

                ms               MeasurementSet to be processed 'MS'  

                 

        Example: 

                 

             

            sm.openfromms('3C273XC1.MS'); 

            sm.predict('3C273XC1.imagename'); 

            sm.setnoise(simplenoise='10mJy'); 

            sm.setgain(interval='100s', amplitude=0.01); 

            sm.corrupt(); 

            sm.close(); 

             

             

           

        -------------------------------------------------------------------------------- 

                       

        """ 

        return _simulator.simulator_openfromms(self, *args, **kwargs) 

 

    def close(self): 

        """ 

        close(self) -> bool 

 

        Summary 

                Close the newsimulator tool 

 

        Description 

                 

            This is used to close {     t newsimulator} tools. Note that the 

            data is written to disk. This is a synonym for done. 

           

        -------------------------------------------------------------------------------- 

                       

        """ 

        return _simulator.simulator_close(self) 

 

    def done(self): 

        """ 

        done(self) -> bool 

 

        Summary 

                Close the newsimulator tool 

 

        Description 

                 

            This is used to close and { t newsimulator} tools. Note that the 

            data is written to disk. This is a synonym for close. 

           

        -------------------------------------------------------------------------------- 

                       

        """ 

        return _simulator.simulator_done(self) 

 

    def name(self): 

        """ 

        name(self) -> string 

 

        Summary 

                Provide the name of the attached MeasurementSet 

 

        Description 

                 

            Returns the name of the attached MeasurementSet. 

           

        -------------------------------------------------------------------------------- 

                       

        """ 

        return _simulator.simulator_name(self) 

 

    def summary(self): 

        """ 

        summary(self) -> bool 

 

        Summary 

                Summarize the current state 

 

        Description 

                 

            Writes a summary of the properties of the simulator to the 

            default logger. 

           

        -------------------------------------------------------------------------------- 

                       

        """ 

        return _simulator.simulator_summary(self) 

 

    def type(self): 

        """ 

        type(self) -> string 

 

        Summary 

                Return the type of this tool 

 

        Description 

                 

            This function returns the string `simulator'.  It is used so that  in 

            a script, you can make sure this  variable is a simulator 

                ool. 

           

        -------------------------------------------------------------------------------- 

                       

        """ 

        return _simulator.simulator_type(self) 

 

    def settimes(self, *args, **kwargs): 

        """ 

        settimes(self, integrationtime = initialize_variant("10s"), usehourangle = True,  

            referencetime = initialize_variant("50000.0d")) -> bool 

 

        Summary 

                Set integration time, {em etc.} 

 

        Description 

                 The start and stop times are referenced to 

            {   t referencetime}.  Use either starttime/stoptime or startha/stopha. 

            If the hour angles are specified, then the start and stop times are 

            calculated such that the start time is later than the reference time, 

            but less than one day later.  The hour angles refer to the first 

            source observed. 

           

 

        Input Parameters: 

                integrationtime  Integration time 10s  

                usehourangle     Use starttime/stoptime as hour angles - else they are referenced to referencetime true  

                referencetime    Reference time for starttime and stoptime. Epoch Measure . E.g me.epoch('UTC', '50000.0d') 50000.0d epoch measure  

                 

        -------------------------------------------------------------------------------- 

                       

        """ 

        return _simulator.simulator_settimes(self, *args, **kwargs) 

 

    def observe(self, *args, **kwargs): 

        """ 

        observe(self, sourcename = string(""), spwname = string(""), starttime = initialize_variant("0s"),  

            stoptime = initialize_variant("3600s"),  

            add_observation = False,  

            state_sig = True, state_ref = False,  

            state_cal = 0.0, state_load = 0.0, state_sub_scan = 0,  

            state_obs_mode = string("OBSERVE_TARGET.ON_SOURCE"),  

            observer = string("CASA simulator"),  

            project = string("CASA simulation")) -> bool 

 

        Summary 

                Observe a given configuration 

 

        Description 

                 

            Observe a given source with a given spectral window for the specified 

            times, including start, stop, integration, and gap times.  The start 

            and stop times are referenced to {  t referencetime}.  Use either 

            starttime/stoptime or startha/stopha.  If the hour angles are 

            specified, then the start and stop times are calculated such that the 

            start time is later than the reference time, but less than one day 

            later.  The hour angles refer to the first source observed. 

           

 

        Input Parameters: 

                sourcename       Name of source or field (must be specified) None  

                spwname          Unique user-supplied name for this spectral window None  

                starttime        Start time referenced to referenceepoch 0s  

                stoptime         Stop time referenced to referenceepoch 3600s  

                add_observation  Add a new line to the OBSERVATION subtable for this call false  

                state_sig        a new line will be added to STATE if the following don't match true  

                state_ref        false  

                state_cal        0.0  

                state_load       0.0  

                state_sub_scan   0  

                state_obs_mode   OBSERVE\_TARGET.ON\_SOURCE  

                observer         CASA simulator  

                project          CASA simulation  

                 

        -------------------------------------------------------------------------------- 

                       

        """ 

        return _simulator.simulator_observe(self, *args, **kwargs) 

 

    def observemany(self, *args, **kwargs): 

        """ 

        observemany(self, sourcenames = std::vector< string >(1, ""), spwname = string(""),  

            starttimes = std::vector< string >(1, ""),  

            stoptimes = std::vector< string >(1, ""),  

            directions = std::vector< string >(1, ""),  

            add_observation = False, state_sig = True,  

            state_ref = False, state_cal = 0.0, state_load = 0.0,  

            state_sub_scan = 0, state_obs_mode = string("OBSERVE_TARGET.ON_SOURCE"),  

            observer = string("CASA simulator"),  

            project = string("CASA simulation")) -> bool 

 

        Summary 

                Observe a given configuration 

 

        Description 

                 

            Observe given sources with a given spectral window for the specified 

            times, including start, stop, integration, and gap times.  The start 

            and stop times are referenced to {  t referencetime}.  Use either 

            starttime/stoptime or startha/stopha.  If the hour angles are 

            specified, then the start and stop times are calculated such that the 

            start time is later than the reference time, but less than one day 

            later.  The hour angles refer to the first source observed. 

           

 

        Input Parameters: 

                sourcenames      Name of sources None  

                spwname          Unique user-supplied name for this spectral window None  

                starttimes       Start times referenced to referenceepoch 0s  

                stoptimes        Stop time referenced to referenceepoch 3600s  

                directions        

                add_observation  Add a new line to the OBSERVATION subtable for this call false  

                state_sig        a new line will be added to STATE if the following don't match true  

                state_ref        false  

                state_cal        0.0  

                state_load       0.0  

                state_sub_scan   0  

                state_obs_mode   OBSERVE\_TARGET.ON\_SOURCE  

                observer         CASA simulator  

                project          CASA simulation  

                 

        -------------------------------------------------------------------------------- 

                       

        """ 

        return _simulator.simulator_observemany(self, *args, **kwargs) 

 

    def setlimits(self, *args, **kwargs): 

        """ 

        setlimits(self, shadowlimit = 1e-6, elevationlimit = initialize_variant("10deg")) -> bool 

 

        Summary 

                Set limits for observing 

 

        Description 

                 

            Data are flagged for two conditions: 

            egin{description} 

            \item[Below elevation limit] If either of the antennas point below the 

            specified elevation limit then the data are flagged. The elevation 

            is calculated correctly for antennas at different locations (such as 

            occurs in VLBI). 

            \item[Shadowing] If one antenna shadows another such that the 

            fractional (geometric) blockage is greater than the specified limit 

            then the data are flagged. No correction for blockage is made for 

            shadowed but non-flagged points. 

            nd{description} 

           

 

        Input Parameters: 

                shadowlimit      Maximum fraction of geometrically shadowed area before flagging occurs 1e-6 1e-6  

                elevationlimit   Minimum elevation angle before flagging occurs 10deg  

                 

        -------------------------------------------------------------------------------- 

                       

        """ 

        return _simulator.simulator_setlimits(self, *args, **kwargs) 

 

    def setauto(self, autocorrwt = 0.0): 

        """ 

        setauto(self, autocorrwt = 0.0) -> bool 

 

        Summary 

                Set autocorrelation weight 

 

        Description 

                 

           

 

        Input Parameters: 

                autocorrwt       Weight to assign autocorrelations (0=none) 0.0 0.0  

                 

        -------------------------------------------------------------------------------- 

                       

        """ 

        return _simulator.simulator_setauto(self, autocorrwt) 

 

    def setconfig(self, *args, **kwargs): 

        """ 

        setconfig(self, telescopename = string("VLA"), x = initialize_vector(1, (double)0),  

            y = initialize_vector(1, (double)0),  

            z = initialize_vector(1, (double)0),  

            dishdiameter = initialize_vector(1, (double)0),  

            offset = initialize_vector(1, (double)0),  

            mount = std::vector< string >(1, ""), antname = std::vector< string >(1, ""),  

            padname = std::vector< string >(1, ""),  

            coordsystem = string("global"),  

            referencelocation = initialize_variant("ALMA")) -> bool 

 

        Summary 

                Set the antenna configuration 

 

        Description 

                 

            Set the positions of the antennas.  Note that the name of the telescope will 

            control which voltage pattern is applied to the data. 

           

 

        Input Parameters: 

                telescopename    Name of the telescope we are simulating (determines VP) VLA 'VLA'  

                x                Vector of x values of all antennas [currently m] 0 []  

                y                Vector of y values of all antennas [currently m] 0 []  

                z                Vector of z values of all antennas [currently m] 0 []  

                dishdiameter     Vector of diameters of all antennas [currently m] 0 []  

                offset           Vector of offset of all antennas [currently m] 0 []  

                mount            Vector of mount types of all antennas (recognized mounts are 'ALT-AZ', 'EQUATORIAL', 'X-Y', 'ORBITING', 'BIZARRE' ALT-AZ []  

                antname          Vector of names of all antennas A []  

                padname          Vector of names of pads or stations P []  

                coordsystem      Coordinate system of antenna positions [x,y,z], possibilities are 'global', 'local' , 'longlat' global 'global'  

                referencelocation        Reference location [required for local coords] Position Measure of Coordinates of array location. E.g me.position('ITRF', '30.5deg', -20.2deg', 6000km') or me.observatory('ALMA') ALMA position measure  

                 

        Example: 

                 

             

            diam := [25, 25, 25, 25, 25] 

            xx := [50, 100, 150, 200, 250] 

            yy := [2, -5, -20, -50, -100] 

            zz := [-0.5, -1.0, -1.5, -2.0, -2.5] 

            posvla := dm.observatory('vla'); 

            sm.setconfig(telescopename='VLA', x=xx, y=yy, z=zz, dishdiameter=diam,  

                         mount='alt-az', antname='VLA', 

                         coordsystem='local', referencelocation=posvla); 

             

           

        -------------------------------------------------------------------------------- 

                       

        """ 

        return _simulator.simulator_setconfig(self, *args, **kwargs) 

 

    def setknownconfig(self, *args, **kwargs): 

        """ 

        setknownconfig(self, arrayname = string("VLA")) -> bool 

 

        Summary 

                Set the antenna configuration to a 

            known array 

 

        Description 

                 

            Sets the configuration to a known array such as VLAA, VLBA, EVN or 

            ATCA6.0A. The arrays are those known to 

            simhelper. All the information needed by 

            setconfig is filled in. 

           

 

        Input Parameters: 

                arrayname        Name of the telescope configurationwe are simulating VLA 'VLA'  

                 

        Example: 

                 

             

            sm.setknownconfig('ATCA6.0A'); 

             

           

        -------------------------------------------------------------------------------- 

                       

        """ 

        return _simulator.simulator_setknownconfig(self, *args, **kwargs) 

 

    def setfeed(self, *args, **kwargs): 

        """ 

        setfeed(self, mode = string(""), x = initialize_vector(1, (double)0),  

            y = initialize_vector(1, (double)0), pol = std::vector< string >(1, "")) -> bool 

 

        Summary 

                Set the feed parameters 

 

        Description 

                 

            The goal is to let the feed parameters be specified for each antenna and 

            each spectral window.  At this moment, you only have the choice between 

            'perfect R L' and 'perfect X Y' (i.e., you cannot invent your own 

            corrupted feeds yet).  Doesn't need to be run if you want perfect R and 

            L feeds.   

           

 

        Input Parameters: 

                mode             Mode for specifying feed parameters (currently, perfect only)  

                x                Some very secretive feed array parameter x 0  

                y                Some more very secretive feed array parameter y 0  

                pol              Guess its the polarization of feed arrays... your guess is as good as mine....if you know better let us know please ! R  

                 

        -------------------------------------------------------------------------------- 

                       

        """ 

        return _simulator.simulator_setfeed(self, *args, **kwargs) 

 

    def setfield(self, *args, **kwargs): 

        """ 

        setfield(self, sourcename = string("SOURCE"), sourcedirection = initialize_variant(""),  

            calcode = string(""), distance = initialize_variant("0m")) -> bool 

 

        Summary 

                Set one or more observed fields 

 

        Description 

                 

            Set one or more observed fields, including name, coordinates, calibration code. 

            Can be invoked multiple times for a complex observation. 

            Must be invoked at least once before {      t observe}. 

             

            If the distance to the object is set then the phase term includes a 

            curvature for the near-field effect at the center of the image. 

           

 

        Input Parameters: 

                sourcename       Name of source or field (must be specified) SOURCE 'unknown'  

                sourcedirection  Direction Measure of Coordinates of source to be observed. E.g me.direction('J2000', '30.5deg','-20.2deg').  

                calcode          Calibration code 'OBJ'  

                distance         Distance to the object 0m  

                 

        Example: 

                 

             

            sm.setconfig(telescopename='VLA', x=xx, y=yy, z=zz, dishdiameter=diam,  

                         mount='alt-az', antname='VLA', 

                         coordsystem='local', referencelocation=dm.observatory('vla')); 

 

            sm.setspwindow(spwname='XBAND', freq='8GHz', deltafreq='50MHz', 

                           freqresolution='50MHz', nchannels=1, stokes='RR 

                           LL'); 

            dir0 = me.direction('B1950',  '16h00m0.0', '50d0m0.000') 

            sm.setfield(sourcename='SIMU1', sourcedirection=dir0); 

            sm.observe('SIMU1', 'XBAND', integrationtime='10s', usehourangle=T, 

                       starttime='0s', stoptime='3600s',  

            referencetime=reftime); 

             

             

           

        -------------------------------------------------------------------------------- 

                       

        """ 

        return _simulator.simulator_setfield(self, *args, **kwargs) 

 

    def setmosaicfield(self, *args, **kwargs): 

        """ 

        setmosaicfield(self, sourcename = string("SOURCE"), calcode = string(""),  

            fieldcenter = initialize_variant(""), xmosp = 1,  

            ymosp = 1, mosspacing = initialize_variant("1arcsec"),  

            distance = initialize_variant("0m")) -> bool 

 

        Summary 

                Set observed mosaic fields 

 

        Description 

                 

            Set mosaic fields by internally invoking {  t setfield} multiple times. 

            Currently only handle a rectangular mosaicing pattern. 

            Either setfield or setmosaicfield must be invoked at least once before {    t observe}. 

             

            If the distance to the object is set then the phase term includes a 

            curvature for the near-field effect at the center of the image. 

           

 

        Input Parameters: 

                sourcename       Name of source or field (must be specified). SOURCE 'unknown'  

                calcode          Calibration code ''  

                fieldcenter      Coordinates of mosaic field center MDirection  

                xmosp            Number of mosaic pointing in horizontal direction 1  

                ymosp            Number of mosaic pointing in vertical direction 1  

                mosspacing       Spacing between mosaic pointings 1arcsec  

                distance         Distance to the object 0m  

                 

        Example: 

                 

             

            sm.setconfig(telescopename='VLA', x=xx, y=yy, z=zz, dishdiameter=diam, 

                         mount='alt-az', antname='VLA', 

                         coordsystem='local', referencelocation=dm.observatory('vla')); 

                                                                                         

            sm.setspwindow(spwname='XBAND', freq='8GHz', deltafreq='50MHz', 

                           freqresolution='50MHz', nchannels=1, stokes='RR 

                           LL'); 

            dir0 = me.direction('B1950',  '16h00m0.0', '50d0m0.000') 

            sm.setmosaicfield(sourcename='SIMU1', fieldcenter=dir0, 

                              xmosp=2, ymosp=2, mosspacing='154.5arcsec'); 

            sm.settimes(integrationtime='10s'); 

            sm.observe('SIMU1_1', 'XBAND', starttime='0s', stoptime='100s'); 

            sm.observe('SIMU1_2', 'XBAND', starttime='110s', stoptime='210s'); 

            sm.observe('SIMU1_3', 'XBAND', starttime='220s', stoptime='320s'); 

            sm.observe('SIMU1_4', 'XBAND', starttime='330s', stoptime='430s'); 

             

             

           

        -------------------------------------------------------------------------------- 

                       

        """ 

        return _simulator.simulator_setmosaicfield(self, *args, **kwargs) 

 

    def setspwindow(self, *args, **kwargs): 

        """ 

        setspwindow(self, spwname = string("XBAND"), freq = initialize_variant("8.0e9Hz"),  

            deltafreq = initialize_variant("50e6Hz"),  

            freqresolution = initialize_variant("50.e6Hz"),  

            refcode = string("TOPO"), nchannels = 1,  

            stokes = string("RR LL")) -> bool 

 

        Summary 

                Set one or more spectral windows 

 

        Description 

                 

        Set one or more spectral windows for the observations, including starting 

        frequency, number of channels, channel increment and resolution, and stokes 

        parameters observed.  Can be invoked multiple times for a complex observation. 

        Must be invoked at least once before {  t observe}. 

 

 

        Input Parameters: 

                spwname          Unique user-supplied name for this spectral window XBAND 'XBAND'  

                freq             Starting frequency 8.0e9Hz  

                deltafreq        Frequency increment per channel 50e6Hz  

                freqresolution   Frequency resolution per channel 50.e6Hz  

                refcode          Spectral reference code e.g. LSRK, TOPO, BARY TOPO LSRK LSRD BARY GEO TOPO GALACTO LGROUP CMB  

                nchannels        Number of channels 1  

                stokes           Stokes types to simulate RR LL 'RR LL'  

                 

        Example: 

                 

 

        To simulate a two spectral window (or two IF's in VLA jargon) data 

        set, use setpwid as follows (here we are simulating 16 channels, 50MHz 

        wide channel for each spectral window) 

 

 

        sm.setspwindow(spwname='CBAND', freq='2GHz', deltafreq='50MHz', 

                           freqresolution='50MHz', nchannels=16, stokes='RR LL'); 

 

        sm.setspwindow(spwname='SBAND', freq='5GHz', deltafreq='50MHz', 

                           freqresolution='50MHz', nchannels=16, stokes='RR LL'); 

 

 

 

        Note that the spwname is used in {      t observe} to determine which spectral window 

        is used. 

 

 

        -------------------------------------------------------------------------------- 

                       

        """ 

        return _simulator.simulator_setspwindow(self, *args, **kwargs) 

 

    def setdata(self, *args, **kwargs): 

        """ 

        setdata(self, spwid = initialize_vector(1, (int)0), fieldid = initialize_vector(1, (int)0),  

            msselect = string("")) -> bool 

 

        Summary 

                Set the data parameters selection for subsequent processing 

 

        Description 

                 

        This setup tool function selects which data are to be used 

        subsequently. After invocation of setdata, only the selected data are 

        operated on.  

 

 

 

        Input Parameters: 

                spwid            Spectral Window Ids (0 relative) to select 0  

                fieldid          Field Ids (0 relative) to select 0  

                msselect         TQL select string applied as a logical 'and' with the other selections String  

                 

        -------------------------------------------------------------------------------- 

                       

        """ 

        return _simulator.simulator_setdata(self, *args, **kwargs) 

 

    def predict(self, *args, **kwargs): 

        """ 

        predict(self, imagename = std::vector< string >(1, ""), complist = string(""),  

            incremental = False) -> bool 

 

        Summary 

                Predict astronomical data from an image 

 

        Description 

                 

        Predict astronomical data from an image.  The (u,v) coordinates already 

        exist, either from a MeasurementSet we have read in or by generating the 

        MeasurementSet coordinates and empty data through {     t create()}.  We 

        simply predict onto these coordinates. 

 

 

        Input Parameters: 

                imagename        Name of image from which to predict visibilities  

                complist         Name of component list String  

                incremental      Add this model to the existing Data Visibilities? false  

                 

        -------------------------------------------------------------------------------- 

                       

        """ 

        return _simulator.simulator_predict(self, *args, **kwargs) 

 

    def setoptions(self, *args, **kwargs): 

        """ 

        setoptions(self, ftmachine = string("ft"), cache = 0, tile = 16, gridfunction = string("SF"),  

            location = initialize_variant("ALMA"),  

            padding = 1.3, facets = 1,  

            maxdata = 2000.0, wprojplanes = 1) -> bool 

 

        Summary 

                Set various processing options 

 

        Description 

                 

 

        For most of these, set the options for {        t predict} details.  See also { t imager} help 

        for more details. 

 

        To simulate single dish data, use gridft=SD and gridfunction=PB. 

 

 

 

        Input Parameters: 

                ftmachine        Fourier transform machine. Possibilities are 'ft', 'sd' ft 'ft'  

                cache            Size of gridding cache in complex pixels 0  

                tile             Size of a gridding tile in pixels (in 1 dimension) 16  

                gridfunction     Gridding function. String: 'SF'|'BOX'|'PB' SF 'SF'  

                location         Location used in phase rotations. Position Measure of Coordinates of array location. E.g me.position('ITRF', '30.5deg', '-20.2deg', '6000km') or me.observatory('ALMA') ALMA position measure  

                padding          Padding factor in image plane (\>=1.0) 1.3  

                facets           Number of facets 1  

                maxdata          Maximum data to write to a single TSM file (MB) 2000.0  

                wprojplanes      Number of projection planes when using wproject as the ft-machine 1  

                 

        Example: 

                 

 

        sm.setoptions(cache=10000000, tile=32, gridfunction='BOX', me.location('vla')) 

 

 

        -------------------------------------------------------------------------------- 

                       

        """ 

        return _simulator.simulator_setoptions(self, *args, **kwargs) 

 

    def setvp(self, *args, **kwargs): 

        """ 

        setvp(self, dovp = True, usedefaultvp = True, vptable = string(""),  

            dosquint = True, parangleinc = initialize_variant("360deg"),  

            skyposthreshold = initialize_variant("180deg"),  

            pblimit = 1.0e-2) -> bool 

 

        Summary 

                Set the voltage pattern model for subsequent processing 

 

        Description 

                 

        Set the voltage pattern model (and hence, the primary beam) used for a Telecope. 

        There are currently two ways to set the voltage pattern: by using the extensive 

        list of defaults which the system knows about, or by creating a voltage pattern 

        description with the vpmanager.  The default voltage patterns 

        include both a high and a low frequency VP for the WSRT, a VP for each observing 

        band at the AT, several VP's for the VLA, including the approrpiate beam squint for 

        each observing band, and  Gaussian for the BIMA dishes. 

        If you are simulating a telescope which doesn't yet exist, you will need to supply 

        a model voltage pattern using the vpmanager. 

 

 

        Input Parameters: 

                dovp             Multiply by the voltage pattern (ie, primary beam) when simulating true  

                usedefaultvp     Look up the default VP for this telescope and frequency? true  

                vptable          If usedefaultvp is false, provide a VP Table made with vpmanager Table  

                dosquint         Activate the beam squint in the VP model true  

                parangleinc      Parallactice angle increment for squint application 360deg  

                skyposthreshold  Position threshold on the sky for feed arrays ?? 180deg  

                pblimit          Primary beam limit to use in feed arrays ? 1.0e-2  

                 

        Example: 

                 

 

        sm.setvp(dovp=T, usedefaultvp=F, vptable='MyAlternateVLAPBModel.TAB', dosquint=F); 

 

 

        -------------------------------------------------------------------------------- 

                       

        """ 

        return _simulator.simulator_setvp(self, *args, **kwargs) 

 

    def corrupt(self): 

        """ 

        corrupt(self) -> bool 

 

        Summary 

                Corrupt the data with visibility errors 

 

        Description 

                 

        Add errors specified by the {   t set} functions (such as noise,  

        gains, polarization leakage, bandpass, etc) to the visibility data. 

        The errors are applied to the MODEL\_DATA, and written to the 

        DATA and CORRECTED\_DATA columns.   Note that { t corrupt} handles 

        only visibility-plane effects, not image-plane effects such as 

        pointing errors and voltage patterns, which get applied in {    t predict}. 

        Note, the function applies errors to both cross- and auto-correlation 

        data; The auto-correlation data are corrupted properly only for 

        the thermalnoise set by {       t setnoise}. 

 

 

 

        Example: 

                 

 

        sm,openfromms('3C273XC1.MS'); 

        sm.predict('3C273XC1.FAKE.IMAGE'); 

        sm.setnoise( mode='simplenoise', simplenoise='0.1Jy'); 

        sm.setpa( mode='calculate'); 

        sm.corrupt(); 

 

 

        -------------------------------------------------------------------------------- 

                       

        """ 

        return _simulator.simulator_corrupt(self) 

 

    def reset(self): 

        """ 

        reset(self) -> bool 

 

        Summary 

                Reset the corruption terms 

 

        Description 

                 

        Reset the visibility corruption terms: this means that {        t corrupt} 

        introduces no errors. 

 

        -------------------------------------------------------------------------------- 

                       

        """ 

        return _simulator.simulator_reset(self) 

 

    def setbandpass(self, *args, **kwargs): 

        """ 

        setbandpass(self, mode = string("calculate"), table = string(""), interval = initialize_variant("3600s"),  

            amplitude = initialize_vector(1, (double)0.0)) -> bool 

 

        Summary 

                Set the bandpasses 

 

        Description 

                 

        Set the level of bandpass errors. The error distributions are normal, mean 

        zero, with the variances as specified. (Not yet implemented). 

 

 

        Input Parameters: 

                mode             Mode of operation. String: 'calculate'|'table' calculate 'calculate'  

                table            Name of table ''  

                interval         Coherence interval e.g. '1h' 3600s  

                amplitude        Variances errors in amplitude and phase 0.0  

                 

        -------------------------------------------------------------------------------- 

                       

        """ 

        return _simulator.simulator_setbandpass(self, *args, **kwargs) 

 

    def setapply(self, *args, **kwargs): 

        """ 

        setapply(self, table = string(""), type = string(""), t = 0.0, field = initialize_variant(""),  

            interp = string("linear"),  

            select = string(""), calwt = False,  

            spwmap = initialize_vector(1, (int)-1), opacity = 0.0) -> bool 

 

        Summary 

                Arrange for corruption by existing cal tables 

 

        Description 

                 

        Arrange for corruption by existing cal tables, in a manner exactly analogous to 

        calibrater.setapply.  

 

 

        Input Parameters: 

                table            Calibration table name  

                type             Component type B BPOLY G GSPLINE D P T TOPAC GAINCURVE  

                t                Interpolation interval (seconds) 0.0  

                field            Select on field  

                interp           Interpolation type (in time) aipslin nearest linear  

                select           TAQL selection string. Default is no selection.  

                calwt            Calibrate weights? false  

                spwmap           Spectral windows to apply -1  

                opacity          Array-wide zenith opacity (for type='TOPAC') 0.0  

                 

        -------------------------------------------------------------------------------- 

                       

        """ 

        return _simulator.simulator_setapply(self, *args, **kwargs) 

 

    def setgain(self, *args, **kwargs): 

        """ 

        setgain(self, mode = string("fbm"), table = string(""), interval = initialize_variant("10s"),  

            amplitude = initialize_vector(1, (double)0.01)) -> bool 

 

        Summary 

                Set the gains 

 

        Description 

                 

        Set the level of gain errors.  Gain drift is implemented as fractional brownian 

        motion with rms amplitude as specified.  Interval is not currently used,  

        but future statistical models for gain errors (e.g. simple Gaussian) will use it. 

 

 

 

        Input Parameters: 

                mode             Mode of operation. String: 'fbm' fbm 'fbm'  

                table            Optional name of table to write ''  

                interval         timescale for gain variations NOT USED 10s  

                amplitude        amplitude scale (RMS) for gain variations [real,imag] or scalar 0.01 []  

                 

        -------------------------------------------------------------------------------- 

                       

        """ 

        return _simulator.simulator_setgain(self, *args, **kwargs) 

 

    def settrop(self, *args, **kwargs): 

        """ 

        settrop(self, mode = string("screen"), table = string(""), pwv = 3.0,  

            deltapwv = 0.15, beta = 1.1, windspeed = 7.) -> bool 

 

        Summary 

                Set tropospheric gain corruptions 

 

        Description 

                 

        Set the atmosphere.  

 

 

        Input Parameters: 

                mode             Mode of operation - screen or individual antennas screen 'screen'  

                table            Name of cal table ''  

                pwv              total precipitable water vapour in mm 3.0  

                deltapwv         RMS PWV fluctuations *as a fraction of PWV parameter* 0.15  

                beta             exponent of fractional brownian motion 1.1  

                windspeed        wind speed for screen type corruption (m/s) 7.  

                 

        -------------------------------------------------------------------------------- 

                       

        """ 

        return _simulator.simulator_settrop(self, *args, **kwargs) 

 

    def setpointingerror(self, *args, **kwargs): 

        """ 

        setpointingerror(self, epjtablename = string(""), applypointingoffsets = False,  

            dopbcorrection = False) -> bool 

 

        Summary 

                Set the Pointing error 

 

        Description 

                 

        Set the pointing error from a calpointing table 

 

 

        Input Parameters: 

                epjtablename     Name of a table that has E-Jones errors for Pointing  

                applypointingoffsets     Apply pointing offsets false  

                dopbcorrection   apply primary beam correction false  

                 

        -------------------------------------------------------------------------------- 

                       

        """ 

        return _simulator.simulator_setpointingerror(self, *args, **kwargs) 

 

    def setleakage(self, *args, **kwargs): 

        """ 

        setleakage(self, mode = string("constant"), table = string(""), amplitude = initialize_vector(1, (double)0.01),  

            offset = initialize_vector(1, (double)0.)) -> bool 

 

        Summary 

                Set the polarization leakage 

 

        Description 

                 

        Set the level of polarization leakage between feeds. 

        Currently, no time dependence is available. 

 

 

        Input Parameters: 

                mode             Mode of operation. String: 'constant' constant 'constant'  

                table            Optional name of table to write ''  

                amplitude        Magnitude of pol leakage [real,imag] 0.01 []  

                offset           Meam of pol leakage [real,imag] 0. []  

                 

        -------------------------------------------------------------------------------- 

                       

        """ 

        return _simulator.simulator_setleakage(self, *args, **kwargs) 

 

    def oldsetnoise(self, *args, **kwargs): 

        """ 

        oldsetnoise(self, mode = string("calculate"), table = string(""), simplenoise = initialize_variant("0.0Jy"),  

            antefficiency = 0.8,  

            correfficiency = 0.85, spillefficiency = 0.85,  

            tau = 0.1, trx = 50, tatmos = 230.0,  

            tcmb = 2.7) -> bool 

 

        Summary 

                Set the noise level fixed sigma (mode=simplenoise) or 

           Brown's equation (mode=calculate) OBSOLETE VERSION 

 

        Description 

                 

        Set various system parameters from which the thermal (ie, random additive) noise level  

        will be calculated.   

 

        For mode=simplenoise, one specifies the standard deviation for the noise to be 

        added to real and imaginary parts of the visibility. 

 

        For mode=calculate, the noise will vary with dish diameter, antenna efficiency, 

        system temperature, opacity, sky temperature, etc.  The noise will increase with 

        the airmass if {        t tau} is greater than zero.  The noise is calculated according to 

        the {\it Brown Equation} (ie, R.L. Brown's calculation of MMA sensitivity, 3Oct95): 

        egin{equation} 

        \Delta S = rac{ 4 \sqrt{2} [ T_{rx}e^{ au A} + T_{atm} ( e^{   au A} - psilon_{l}) + T_{cmb}] } 

                        {  psilon_{q} psilon_{a} \pi D^2 \sqrt{\Delta  

        u \Delta t }} 

        nd{equation} 

 

 

        Input Parameters: 

                mode             Mode of operation. String: 'simplenoise'|'calculate' calculate 'simplenoise' 'calculate'  

                table            Name of noise table - not currently implemented ''  

                simplenoise      Level of noise (if mode=simplenoise) 0.0Jy  

                antefficiency    antenna efficiency 0.8 0.8  

                correfficiency   Correlation efficiency 0.85 0.85  

                spillefficiency  Forward spillover efficiency 0.85 0.85  

                tau              Atmospheric Opacity 0.1 0.1  

                trx              Receiver temp (ie, all non-atmospheric Tsys contributions) [K] 50 50  

                tatmos           (Physical, not Brightness) Temperature of atmosphere [K] 230.0 230.0  

                tcmb             Temperature of cosmic microwave background [K] 2.7 2.7  

                 

        -------------------------------------------------------------------------------- 

                       

        """ 

        return _simulator.simulator_oldsetnoise(self, *args, **kwargs) 

 

    def setnoise(self, *args, **kwargs): 

        """ 

        setnoise(self, mode = string("simplenoise"), table = string(""), simplenoise = initialize_variant("0.1Jy"),  

            pground = initialize_variant("560mbar"),  

            relhum = 20.0,  

            altitude = initialize_variant("5000m"),  

            waterheight = initialize_variant("200m"),  

            pwv = initialize_variant("1mm"), tatmos = 250.0,  

            tau = 0.1, antefficiency = 0.8, spillefficiency = 0.85,  

            correfficiency = 0.88, trx = 50,  

            tground = 270.0, tcmb = 2.73, OTF = True,  

            senscoeff = 0., rxtype = 0) -> bool 

 

        Summary 

                Set the noise level fixed sigma (mode=simplenoise) or 

           Brown's equation using the ATM model for frequency-dependent atmospheric 

           opacity (mode=tsys-atm) or Brown's equation, manually specifying the zenith 

           opacity (constant across the band) and atmospheric temperature  

           (mode=tsys-manual) 

 

        Description 

                 

        Set various system parameters from which the thermal (ie, random additive) noise level  

        will be calculated.   

 

        For mode=simplenoise, one specifies the standard deviation for the noise to be 

        added to real and imaginary parts of the visibility. 

 

        For mode=tsys-atm or tsys-atm, the noise will vary with dish diameter, antenna efficiency, 

        system temperature, opacity, sky temperature, etc.  The noise will increase with 

        the airmass if tau is greater than zero.  The noise is calculated according to 

        the Brown Equation (ie, R.L. Brown's calculation of MMA sensitivity, 3Oct95): 

        dS = 4 sqrt(2) [ T\_rx e\^{tau A} + T\_atm ( e\^{tau A} - epsilon\_l) + T\_cmb] /  

                       [ epsilon\_q epsilon\_a pi D\^2 sqrt{Delta nu Delta t} ] 

 

        For mode=tsys-atm, the sky brightness temperature is calculated using an atmospheric model created for the user-input PWV. 

        For mode=tsys-manual, the user specifies the sky brightness temperature  

        manually. 

 

 

        Input Parameters: 

                mode             Mode of operation. simplenoise 'simplenoise' 'tsys-atm' 'tsys-manual'  

                table            Name of optional cal table to write ''  

                simplenoise      Level of noise if not calculated 0.1Jy  

                pground          Ground pressure for ATM model (if tsys-atm) 560mbar  

                relhum           ground relative humidity for ATM model (if tsys-atm) 20.0  

                altitude         site altitude for ATM model (if tsys-atm) 5000m  

                waterheight      Height of water layer for ATM model (if tsys-atm) 200m  

                pwv              Precipitable Water Vapor ATM model (if tsys-atm) 1mm  

                tatmos           Temperature of atmosphere [K] (if tsys-manual) 250.0  

                tau              Zenith Atmospheric Opacity (if tsys-manual) 0.1  

                antefficiency    Antenna efficiency 0.8  

                spillefficiency  Forward spillover efficiency 0.85  

                correfficiency   Correlation efficiency 0.88  

                trx              Receiver temp (ie, all non-atmospheric Tsys contributions) [K] 50  

                tground          Temperature of ground/spill [K] 270.0  

                tcmb             Temperature of cosmic microwave background [K] 2.73  

                OTF              calculate noise on-the-fly (WARNING: only experts with high-RAM machines should use False) true  

                senscoeff        sensitivity constant (1./sqrt(2) for interferometer [default]; 1. for total power) 0.  

                rxtype           Receiver type; 0=2SB, 1=DSB e.g. ALMA B9 0  

                 

        -------------------------------------------------------------------------------- 

                       

        """ 

        return _simulator.simulator_setnoise(self, *args, **kwargs) 

 

    def setpa(self, *args, **kwargs): 

        """ 

        setpa(self, mode = string("calculate"), table = string(""), interval = initialize_variant("10s")) -> bool 

 

        Summary 

                Corrupt phase by the parallactic angle 

 

        Description 

                 

        Corrupt phase by the parallactic angle 

 

 

        Input Parameters: 

                mode             Mode of operation. String: 'calculate'|'table' calculate 'calculate'  

                table            Name of table ''  

                interval         Interval for parallactic angle application, e.g. '10s' 10s  

                 

        -------------------------------------------------------------------------------- 

                       

        """ 

        return _simulator.simulator_setpa(self, *args, **kwargs) 

 

    def setseed(self, seed = 185349251): 

        """ 

        setseed(self, seed = 185349251) -> bool 

 

        Summary 

                Set the seed for the random number generator 

 

        Description 

                 

 

 

 

        Input Parameters: 

                seed             Seed 185349251 185349251  

                 

        -------------------------------------------------------------------------------- 

                       

        """ 

        return _simulator.simulator_setseed(self, seed) 

 

simulator_swigregister = _simulator.simulator_swigregister 

simulator_swigregister(simulator) 

 

# This file is compatible with both classic and new-style classes.