From 5d4ccdc42bcc8488524801226591d3c11927595d Mon Sep 17 00:00:00 2001 From: Justin Spilker Date: Thu, 5 May 2016 10:14:05 -0700 Subject: [PATCH] updated and cleaned up code to viewable standards added examples, license, requirements --- Data_objs.py | 146 ---- GenerateLensingGrid.py | 105 --- ImageModelMCMC.py | 201 ------ LICENSE.txt | 21 + Model_objs.py | 233 ------ README.md | 118 ++- SourceProfile.py | 100 --- __init__.py | 17 - .../Demo_lensmapping.py | 32 +- .../Demo_sourcelensing.py | 67 +- examples/Ex1_get_data_continuum.py | 105 +++ examples/Ex2_get_data_line.py | 118 +++ examples/Ex3_fit_continuum_SPT0202-61.py | 205 ++++++ examples/Ex4_fit_line_SPT0346-52_CO21.py | 121 ++++ examples/SPT0202-61_com.bin | Bin 0 -> 262088 bytes examples/SPT0202-61_ext.bin | Bin 0 -> 514088 bytes examples/SPT0346-52_models.png | Bin 0 -> 1071923 bytes examples/spt0346linedata.tar.gz | Bin 0 -> 953102 bytes get_ychains.py | 70 -- modelcal.py | 58 -- requirements.txt | 6 + utils.py | 293 -------- uvgrid.py | 66 -- uvimage.py | 75 -- LensModelMCMC.py => visilens/LensModelMCMC.py | 73 +- visilens/__init__.py | 7 + .../calc_likelihood.py | 312 ++++---- visilens/class_utils.py | 680 ++++++++++++++++++ RayTracePixels.py => visilens/lensing.py | 217 +++--- plot_images.py => visilens/plot_images.py | 137 ++-- triangleplot.py => visilens/triangleplot.py | 3 +- visilens/utils.py | 93 +++ 32 files changed, 1892 insertions(+), 1787 deletions(-) delete mode 100644 Data_objs.py delete mode 100644 GenerateLensingGrid.py delete mode 100644 ImageModelMCMC.py create mode 100644 LICENSE.txt delete mode 100644 Model_objs.py delete mode 100644 SourceProfile.py delete mode 100644 __init__.py rename Demo_lensmapping.py => examples/Demo_lensmapping.py (83%) mode change 100644 => 100755 rename Demo_sourcelensing.py => examples/Demo_sourcelensing.py (71%) mode change 100644 => 100755 create mode 100755 examples/Ex1_get_data_continuum.py create mode 100755 examples/Ex2_get_data_line.py create mode 100755 examples/Ex3_fit_continuum_SPT0202-61.py create mode 100755 examples/Ex4_fit_line_SPT0346-52_CO21.py create mode 100644 examples/SPT0202-61_com.bin create mode 100644 examples/SPT0202-61_ext.bin create mode 100644 examples/SPT0346-52_models.png create mode 100644 examples/spt0346linedata.tar.gz delete mode 100644 get_ychains.py delete mode 100644 modelcal.py create mode 100644 requirements.txt delete mode 100644 utils.py delete mode 100644 uvgrid.py delete mode 100644 uvimage.py rename LensModelMCMC.py => visilens/LensModelMCMC.py (90%) create mode 100644 visilens/__init__.py rename calc_likelihood.py => visilens/calc_likelihood.py (60%) create mode 100644 visilens/class_utils.py rename RayTracePixels.py => visilens/lensing.py (52%) rename plot_images.py => visilens/plot_images.py (71%) rename triangleplot.py => visilens/triangleplot.py (99%) create mode 100644 visilens/utils.py diff --git a/Data_objs.py b/Data_objs.py deleted file mode 100644 index 9131c9a..0000000 --- a/Data_objs.py +++ /dev/null @@ -1,146 +0,0 @@ -import numpy as np - -class Visdata(object): - """ - Class to hold all necessary info relating to one set of visibilities. - Auto-updates amp&phase or real&imag if those values are changed, but - MUST SET WITH, eg, visobj.amp = (a numpy array of the new values); - CANNOT USE, eg, visobj.amp[0] = newval, AS THIS DOES NOT CALL THE - SETTER FUNCTIONS. - """ - - def __init__(self,u,v,real,imag,sigma,ant1=None,ant2=None,PBfwhm=None,filename=None): - self.u = u - self.v = v - self.real = real - self.imag = imag - self.sigma = sigma - self.ant1 = ant1 - self.ant2 = ant2 - self.PBfwhm = PBfwhm - self.filename = filename - - @property - def uvdist(self): - return np.sqrt(self.u**2. + self.v**2.) - - @property - def real(self): - return self._real - @real.setter - def real(self,val): - self._real = val - # Setting amp & phase during __init__ will fail since imag is still unknown - # Doing so during conjugate() will also fail, but gives a ValueError - try: - self._amp = np.sqrt(self._real**2. + self.imag**2.) - self._phase = np.arctan2(self.imag,self._real) - except (AttributeError,ValueError): - self._amp = None - self._phase = None - - @property - def imag(self): - return self._imag - @imag.setter - def imag(self,val): - self._imag = val - try: - self._amp = np.sqrt(self.real**2. + self._imag**2.) - self._phase = np.arctan2(self._imag,self.real) - except (AttributeError,ValueError): - self._amp = None - self._phase = None - - @property - def amp(self): - return self._amp - @amp.setter - def amp(self,val): - self._amp = val - self._real = val * np.cos(self.phase) - self._imag = val * np.sin(self.phase) - - @property - def phase(self): - return self._phase - @phase.setter - def phase(self,val): - self._phase = val - self._real = self.amp * np.cos(val) - self._imag = self.amp * np.sin(val) - - def __add__(self,other): - return Visdata(self.u,self.v,self.real+other.real,self.imag+other.imag,\ - (self.sigma**-2. + other.sigma**-2.)**-0.5) - - def __sub__(self,other): - return Visdata(self.u,self.v,self.real-other.real,self.imag-other.imag,\ - (self.sigma**-2. + other.sigma**-2.)**-0.5) - - def conjugate(self): - u = np.concatenate((self.u,-self.u)) - v = np.concatenate((self.v,-self.v)) - real = np.concatenate((self.real,self.real)) - imag = np.concatenate((self.imag,-self.imag)) - sigma = np.concatenate((self.sigma,self.sigma)) - ant1 = np.concatenate((self.ant1,self.ant2)) - ant2 = np.concatenate((self.ant2,self.ant1)) - self.u = u - self.v = v - self.real = real - self.imag = imag - self.sigma = sigma - self.ant1 = ant1 - self.ant2 = ant2 - -class ImageData(object): - """ - Class to hold info relating to images. Simpler than visibility data. - Each parameter should be a 2D array (even the x&y coordinates). Also allows - for a mask to be passed, where non-zero points in the mask are considered bad. - - If you don't pass a noise map, one will be created as sqrt(data). - """ - - def __init__(self,x,y,data,header=None,sigma=None,mask=None,filename=None): - if noisemap is None: - noisemap = np.sqrt(np.abs(np.asarray(data))) - - if mask is None: - mask = np.zeros(x.shape) - - self.x = np.asarray(x) - self.y = np.asarray(y) - self.header = header - self.data = np.asarray(data) - self.noisemap = np.asarray(noisemap) - self.mask = np.asarray(mask) - self.filename = filename - - def __add__(self,other): - if isinstance(other,ImageData): - return ImageData(self.x,self.y,self.data+other.data,self.header,self.sigma,self.mask,self.filename) - else: return ImageData(self.x,self.y,self.data+other,self.header,self.sigma,self.mask,self.filename) - - def __subtract__(self,other): - if isinstance(other,ImageData): - return ImageData(self.x,self.y,self.data-self.other,self.header,self.sigma,self.mask,self.filename) - else: return ImageData(self.x,self.y,self.data-other,self.header,self.sigma,self.mask,self.filename) - - def __mul__(self,other): - if isinstance(other,ImageData): - return ImageData(self.x,self.y,self.data*self.other,self.header,self.sigma,self.mask,self.filename) - else: return ImageData(self.x,self.y,self.data*other,self.header,self.sigma,self.mask,self.filename) - - def __div__(self,other): - if isinstance(other,ImageData): - return ImageData(self.x,self.y,self.data/self.other,self.sigma,self.mask) - else: return ImageData(self.x,self.y,self.data/np.asarray(other),self.sigma,self.mask) - - def __truediv__(self,other): - if isinstance(other,ImageData): - return ImageData(self.x,self.y,self.data/self.other,self.sigma,self.mask) - else: return ImageData(self.x,self.y,self.data/np.asarray(other),self.sigma,self.mask) - - diff --git a/GenerateLensingGrid.py b/GenerateLensingGrid.py deleted file mode 100644 index 13fac15..0000000 --- a/GenerateLensingGrid.py +++ /dev/null @@ -1,105 +0,0 @@ -import numpy as np -from Data_objs import * -arcsec2rad = np.pi/(180.*3600.) -rad2arcsec =3600.*180./np.pi -deg2rad = np.pi/180. -rad2deg = 180./np.pi - -def GenerateLensingGrid(data=None,xmax=None,emissionbox=[-5,5,-5,5],fieldres=None,emitres=None): - """ - Routine to generate two grids for lensing. The first will be a lower-resolution - grid with resolution determined by fieldres and size determined - by xmax. The second is a much higher resolution grid which will be used for - the lensing itself, with resolution determined by emitres and size - determined from emissionbox - i.e., emissionbox should contain the coordinates - which conservatively encompass the real emission, so we only have to lens that part - of the field at high resolution. - - Since we're going to be FFT'ing with these coordinates, the resolution isn't - directly set-able. For the low-res full-field map, it instead is set to the next-higher - power of 2 from what would be expected from having ~4 resolution elements across - the synthesized beam. - - Inputs: - data: - Either a Visdata or ImageData object, used to determine the resolutions of - the two grids (based on the image size or maximum uvdistance in the dataset) - xmax: - Field size for the low-resolution grid in arcsec, which will extend from - (-xmax,-xmax) to (+xmax,+xmax), e.g. (-30,-30) to (+30,+30)arcsec. Should be - at least a bit bigger than the primary beam. Not needed for images. - emissionbox: - A 1x4 list of [xmin,xmax,ymin,ymax] defining a box (in arcsec) which contains - the source emission. Coordinates should be given in arcsec relative to the - pointing/image center. - fieldres,emitres: - Resolutions of the coarse, full-field and fine (lensed) field, in arcsec. - If not given, suitable values will be calculated from the visibilities. - fieldres is unnecessary for images. - - Returns: - If there are any Visdata objects in the datasets, returns: - xmapfield,ymapfield: - 2xN matrices containing x and y coordinates for the full-field, lower-resolution - grid, in arcsec. - xmapemission,ymapemission: - 2xN matrices containing x and y coordinates for the smaller, very high resolution - grid, in arcsec. - indices: - A [4x1] array containing the indices of xmapfield,ymapfield which overlap with - the high resolution grid. - - For any ImageData objects, returns nothing, but places additional attributes in each - image, corresponding to xmapemission,ymapemission, and indices (this lets us do this - for images with different native resolutions). - """ - - # Factors higher-resolution than (1/2*max(uvdist)) to make the field and emission grids - Nover_field = 4. - Nover_emission = 8. - - # Allow multiple visdata objects to be passed, pick the highest resolution point of all - uvmax = 0. - try: - for vis in data: - uvmax = max(uvmax,vis.uvdist.max()) - except TypeError: - uvmax = data.uvdist.max() - - # Calculate resolutions of the grids - if fieldres is None: fieldres = (2*Nover_field*uvmax)**-1. - else: fieldres *= arcsec2rad - if emitres is None: emitres = (2*Nover_emission*uvmax)**-1. - else: emitres *= arcsec2rad - - # Calculate the field grid size as a power of 2. - Nfield = 2**np.ceil(np.log2(2*np.abs(xmax)*arcsec2rad/fieldres)) - #print NFFT - - # Calculate the grid coordinates for the larger field. - fieldcoords = np.linspace(-np.abs(xmax),np.abs(xmax),Nfield) - xmapfield,ymapfield = np.meshgrid(fieldcoords,fieldcoords) - - # Calculate the indices where the high-resolution lensing grid meets the larger field grid - indices = np.round(np.interp(np.asarray(emissionbox),fieldcoords,np.arange(Nfield))) - - # Calculate the grid coordinates for the high-res lensing grid; the grids should meet at indices - #Nemx = 1 + np.abs(indices[1]-indices[0])*np.ceil((fieldcoords[1]-fieldcoords[0])/(emitres*rad2arcsec)) - #Nemy = 1 + np.abs(indices[3]-indices[2])*np.ceil((fieldcoords[1]-fieldcoords[0])/(emitres*rad2arcsec)) - #xemcoords = np.linspace(fieldcoords[indices[0]],fieldcoords[indices[1]],Nemx) - #yemcoords = np.linspace(fieldcoords[indices[2]],fieldcoords[indices[3]],Nemy) - #xmapemission,ymapemission = np.meshgrid(xemcoords,yemcoords) - - # Below verbatim reproduces yashar's grid - Nemx = 1 + np.abs(indices[1]-indices[0])*np.ceil((fieldcoords[1]-fieldcoords[0])/(2*emitres*rad2arcsec)) - Nemy = 1 + np.abs(indices[3]-indices[2])*np.ceil((fieldcoords[1]-fieldcoords[0])/(2*emitres*rad2arcsec)) - xemcoords = np.linspace(fieldcoords[indices[0]],fieldcoords[indices[1]],Nemx) - yemcoords = np.linspace(fieldcoords[indices[2]],fieldcoords[indices[3]],Nemy) - xmapemission,ymapemission = np.meshgrid(xemcoords,yemcoords) - xmapemission -= (xmapemission[0,1]-xmapemission[0,0]) - ymapemission -= abs((ymapemission[1,0]-ymapemission[0,0])) - #xmapemission -= 4.65484e-5 # i have no idea where this miniscule offset comes from - #ymapemission -= 4.65484e-5 - - - return xmapfield,ymapfield,xmapemission,ymapemission,indices diff --git a/ImageModelMCMC.py b/ImageModelMCMC.py deleted file mode 100644 index 5850fd0..0000000 --- a/ImageModelMCMC.py +++ /dev/null @@ -1,201 +0,0 @@ -import numpy as np -import scipy.sparse -import scipy.sparse.linalg -import emcee -from Model_objs import * -from GenerateLensingGrid import GenerateLensingGrid -from calc_likelihood import calc_im_lnlike_galfit - -arcsec2rad = np.pi/180/3600 - -def ImageModelMCMC(image,psf,sigma,lens,source,shear=None, - highresbox=[-3.,3.,-3.,3.],emitres=None, - #GALFITPARAMS GO HERE.... - cosmo=None,nwalkers=1e3,nburn=1e3,nstep=1e3,nthreads=1): - """ - Wrapper function which basically takes what the user wants and turns it into the - format needed for the acutal MCMC lens modeling. - - Inputs: - image: - An astropy.io PrimaryHDU object. Since we're mediating through galfit, - it's easiest just to work directly with these. We calculate the lensed - emission based on the header keywords CDELT_1&2 or CD1_1&2 and the field - size. - sigma: - An astropy.io PrimaryHDU object representing the uncertainty map - for the above image. - psf: - An astropy.io PrimaryHDU object representing the PSF through which the - input image above was convolved. Should have the same pixel scale as - the input image. - lens: - Any of the currently implemented lens objects. - source: - One or more of the currently implemented source objects; if more than - one source to be fit, should be a list of multiple sources. - shear: - An ExternalShear object, or None (default) if no external shear desired - highresbox: - A list of [xmin,xmax,ymin,ymax] which tells the region to perform the lensing - calculations at high resolution, to account for large magnification gradients. - Just has to conservatively encompass the lensed emission. - emitres: - Resolution of the above highresbox; if None, a suitable value is picked. - nwalkers: - Number of walkers to use in the mcmc process; see dan.iel.fm/emcee/current - for more details. - nburn: - Number of burn-in steps to take with the chain. - nstep: - Number of actual steps to take in the mcmc chains after the burn-in - nthreads: - Number of threads (read: cores) to use during the fitting, default 1. - - Returns: - mcmcresult: - A nested dict containing the chains requested. Will have all the MCMC - chain results, plus metadata about the run (initial params, data used, - etc.). Formatting still a work in progress. - chains: - The raw chain data, for testing. - blobs: - Everything else returned by the likelihood function; will have - magnifications and any modelcal phase offsets at each step; eventually - will remove this once get everything packaged up for mcmcresult nicely. - colnames: - Basically all the keys to the mcmcresult dict; eventually won't need - to return this once mcmcresult is packaged up nicely. - """ - - # Making these lists just makes later stuff easier since we now know the dtype - source = list(np.array([source]).flatten()) # Ensure source(s) are a list - data = list(np.array([data]).flatten()) # Same for dataset(s) - - # emcee isn't very flexible in terms of how it gets initialized; start by - # assembling the user-provided info into a form it likes - ndim, p0, colnames = 0, [], [] - # Lens first - if isinstance(lens,SIELens): - for key in ['x','y','M','e','PA']: - if not vars(lens)[key]['fixed']: - ndim += 1 - p0.append(vars(lens)[key]['value']) - colnames.append(key+'L') - # Then source(s) - for i,src in enumerate(source): - if isinstance(src,GaussSource): - for key in ['xoff','yoff','flux','width']: - if not vars(src)[key]['fixed']: - ndim += 1 - p0.append(vars(src)[key]['value']) - colnames.append(key+'S'+str(i)) - elif isinstance(src,SersicSource): - for key in ['xoff','yoff','flux','alpha','index','axisratio','PA']: - if not vars(src)[key]['fixed']: - ndim += 1 - p0.append(vars(src)[key]['value']) - colnames.append(key+'S'+str(i)) - # Then shear - if shear is not None: - for key in ['shear','shearangle']: - if not vars(shear)[key]['fixed']: - ndim += 1 - p0.append(vars(shear)[key]['value']) - colnames.append(key) - - - # Figure out the x&y coordinates of each pixel in the image and add - # them as attributes of the image object. - dx,dy = None, None - if 'CDELT1' in image.header: dx = image.header['CDELT1']*3600 # to arcsec - if 'CDELT2' in image.header: dy = image.header['CDELT2']*3600 - if 'CD1_1' in image.header and 'CD1_2' in image.header: - dx = 3600*np.sqrt(image.header['CD1_1']**2. + image.header['CD1_2']**2.) - if 'CD2_1' in image.header and 'CD2_2' in image.header: - dy = 3600*np.sqrt(image.header['CD2_1']**2. + image.header['CD2_2']**2.) - if dx is None or dy is None: - raise KeyError("I don't know the pixel scale of your image. Does your fits"\ - "header have CDELT1&2 or CD[12]_[12] in it?") - xm = np.arange(-dx*image.shape[0]/2.,+dx*image.shape[0]/2.,dx) - ym = np.arange(-dy*image.shape[1]/2.,+dy*image.shape[1]/2.,dy) - image.x,image.y = np.meshgrid(xm,ym) - - # Create our lensing grid coordinates now, since those shouldn't be - # recalculated with every call to the likelihood function - if emitres is None: emitres = np.min((image.x[0,1]-image.x[0,0],image.y[1,0]-image.y[0,0]))/4. - ixx = np.round(np.interp(np.asarray(highresbox)[:2],xm,np.arange(image.shape[0]))) - ixy = np.round(np.interp(np.asarray(highresbox)[2:],ym,np.arange(image.shape[1]))) - indices = np.concatenate((ixx,ixy)) - Nemx = 1 + np.abs(ixx[1]-ixx[0])*np.ceil(dx/emitres) - Nemy = 1 + np.abs(ixy[1]-ixy[0])*np.ceil(dy/emitres) - xemcoords = np.linspace(image.x[ixx[1],ixx[0]],image.x[ixx[0],ixx[1]],Nemx) - yemcoords = np.linspace(image.y[ixy[0],ixy[1]],image.y[ixy[1],ixy[0]],Nemy) - xemit,yemit = np.meshgrid(xemcoords,yemcoords) - image.xemit, image.yemit = xemit, yemit - image.indices = indices - - # Calculate some distances; we only need to calculate these once. - # This assumes multiple sources are all at same z; should be this - # way anyway or else we'd have to deal with multiple lensing planes - if cosmo is None: from astropy.cosmology import WMAP9 as cosmo - Dd = cosmo.angular_diameter_distance(lens.z).value - Ds = cosmo.angular_diameter_distance(source[0].z).value - Dds= cosmo.angular_diameter_distance_z1z2(lens.z,source[0].z).value - - p0 = np.array(p0) - # Create a ball of starting points for the walkers, gaussian ball of - # 10% width; if initial value is 0 (eg, astrometric shift), give a small sigma - initials = emcee.utils.sample_ball(p0,np.asarray([0.1*x if x else 0.05 for x in p0]),int(nwalkers)) - - # Create the sampler object; uses calc_likelihood function defined elsewhere - lenssampler = emcee.EnsembleSampler(nwalkers,ndim,calc_im_lnlike_galfit, - args = [image,sigma.data,psf.data,lens,source,shear,Dd,Ds,Dds], - #TODO: GALFIT PARAMS HERE?], - threads=nthreads) - - # Run burn-in phase - print "Running burn-in... " - pos,prob,rstate,mus = lenssampler.run_mcmc(initials,nburn,storechain=False) - lenssampler.reset() - - # Run actual chains - print "Done. Running chains... " - lenssampler.run_mcmc(pos,nstep,rstate0=rstate) - print "Mean acceptance fraction: ",np.mean(lenssampler.acceptance_fraction) - - blobs = lenssampler.blobs - #mus = np.asarray([[a[0] for a in l] for l in blobs]).flatten() - #colnames.append('mu') - - # Assemble the output. Want to return something that contains both the MCMC chains - # themselves, but also metadata about the run. - mcmcresult = {} - - try: # keep track of mercurial revision, for reproducibility's sake - import subprocess - mcmcresult['hghash'] = subprocess.check_output('hg id -i',shell=True).rstrip() - except: - mcmcresult['hghash'] = 'None' - - mcmcresult['datasets'] = [dset.filename for dset in data] # Data files used - - mcmcresult['lens_p0'] = lens # Initial params for lens,src(s),shear; also tells if fixed, priors, etc. - mcmcresult['source_p0'] = source - if shear: mcmcresult['shear_p0'] = shear - - if sourcedatamap: mcmcresult['sourcedatamap'] = sourcedatamap - - #mcmcresult['chains'] = np.core.records.fromarrays(np.c_[lenssampler.flatchain,mus].T,names=colnames) - mcmcresult['chains'] = np.core.records.fromarrays(np.c_[lenssampler.flatchain].T,names=colnames) - mcmcresult['lnlike'] = lenssampler.flatlnprobability - - - #if any(modelcal): mcmcresult['modelcal'] = {} - #for i in range(len(data)): - # if modelcal[i]: mcmcresult['modelcal']['phases_dset'+str(i)] = np.vstack(dphases[:,i]) - - return mcmcresult,lenssampler.flatchain,lenssampler.blobs,colnames - #return lenssampler.flatchain,lenssampler.blobs,colnames - - diff --git a/LICENSE.txt b/LICENSE.txt new file mode 100644 index 0000000..c252f25 --- /dev/null +++ b/LICENSE.txt @@ -0,0 +1,21 @@ +The MIT License (MIT) + +Copyright 2010-2016 Justin Spilker and contributors. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. \ No newline at end of file diff --git a/Model_objs.py b/Model_objs.py deleted file mode 100644 index 996130b..0000000 --- a/Model_objs.py +++ /dev/null @@ -1,233 +0,0 @@ -# Collection of classes for various useful lenses/sources/shear -import numpy as np -__all__ = ['SIELens','SersicSource','GaussSource','PointSource',\ - 'ExternalShear'] - -class SIELens: - """ - Class to hold parameters for an SIE lens. Each is going to be a dictionary, - holding the 'value' of the parameter, whether or not it should be 'fixed', - and a lower and upper bound 'prior'. Only exception is redshift, assumed - not to be a free parameter in any fitting procedure. - - Units of parameters are M: Msol; x,y: arcsec; e: 0-1; PA: degrees - """ - def __init__(self,z,x,y,M,e,PA): - # Do some input handling. - if not isinstance(x,dict): - x = {'value':x,'fixed':False,'prior':[-30.,30.]} - if not isinstance(y,dict): - y = {'value':y,'fixed':False,'prior':[-30.,30.]} - if not isinstance(M,dict): - M = {'value':M,'fixed':False,'prior':[1e7,1e15]} - if not isinstance(e,dict): - e = {'value':e,'fixed':False,'prior':[0.,1.]} - if not isinstance(PA,dict): - PA = {'value':PA,'fixed':False,'prior':[0.,180.]} - - if not all(['value' in d for d in [x,y,M,e,PA]]): - raise KeyError("All parameter dicts must contain the key 'value'.") - - if not 'fixed' in x: x['fixed'] = False - if not 'fixed' in y: y['fixed'] = False - if not 'fixed' in M: M['fixed'] = False - if not 'fixed' in e: e['fixed'] = False - if not 'fixed' in PA: PA['fixed'] = False - - if not 'prior' in x: x['prior'] = [-30.,30.] - if not 'prior' in y: y['prior'] = [-30.,30.] - if not 'prior' in M: M['prior'] = [1e7,1e15] - if not 'prior' in e: e['prior'] = [0.,1.] - if not 'prior' in PA: PA['prior'] = [0.,180.] - - self.z = z - self.x = x - self.y = y - self.M = M - self.e = e - self.PA = PA - -class SersicSource(object): - """ - Create a symmetric Sersic profile object, with - I(x,y) = A * exp(-bn*((r/reff)^(1/n)-1)), - where n is the Sersic index (0.5=gaussian,1=expdisk,4=elliptical), - reff encloses half the light, and r=sqrt(x**2+y**2). To make a non- - axisymmetric profile, we rescale x and y above to elliptical coords. - bn is just a number to make reff enclose half the light. - - Specified here by the integrated flux (NOT peak amplitude). This helps - avoid problems for large indices (even n~4), where tons of flux are - spread to large distances. - - If lensed is True, it's assumed this source is in the background, and we - lens it; otherwise it's assumed to be a foreground/unlensed object. - - Units of parameters are xoff,yoff: arcsec; flux: Jy; - width: reff in arcsec; axisratio: minor/major; PA: deg CCW from x-axis - - Note: if `lensed' is True, the positions are relative to the lens center, - otherwise they're relative to the field center (or (0,0) coordinates) - """ - - def __init__(self,z,lensed=True,xoff=None,yoff=None,flux=None,reff=None,\ - index=None,axisratio=None,PA=None): - # Do some input handling. - if not isinstance(xoff,dict): - xoff = {'value':xoff,'fixed':False,'prior':[-10.,10.]} - if not isinstance(yoff,dict): - yoff = {'value':yoff,'fixed':False,'prior':[-10.,10.]} - if not isinstance(flux,dict): - flux = {'value':flux,'fixed':False,'prior':[1e-5,1.]} # 0.01 to 1Jy source - if not isinstance(reff,dict): - reff = {'value':reff,'fixed':False,'prior':[0.,2.]} # arcsec - if not isinstance(index,dict): - index = {'value':index,'fixed':False,'prior':[0.3,4.]} - if not isinstance(axisratio,dict): - axisratio = {'value':axisratio,'fixed':False,'prior':[0.01,1.]} - if not isinstance(PA,dict): - PA = {'value':PA,'fixed':False,'prior':[0.,180.]} - - if not all(['value' in d for d in [xoff,yoff,flux,reff,index,axisratio,PA]]): - raise KeyError("All parameter dicts must contain the key 'value'.") - - if not 'fixed' in xoff: xoff['fixed'] = False - if not 'fixed' in yoff: yoff['fixed'] = False - if not 'fixed' in flux: flux['fixed'] = False - if not 'fixed' in reff: reff['fixed'] = False - if not 'fixed' in index: index['fixed'] = False - if not 'fixed' in axisratio: axisratio['fixed'] = False - if not 'fixed' in PA: PA['fixed'] = False - - if not 'prior' in xoff: xoff['prior'] = [-10.,10.] - if not 'prior' in yoff: yoff['prior'] = [-10.,10.] - if not 'prior' in flux: flux['prior'] = [1e-5,1.] - if not 'prior' in reff: reff['prior'] = [0.,2.] - if not 'prior' in index: index['prior'] = [1/3.,10] - if not 'prior' in axisratio: axisratio['prior'] = [0.01,1.] - if not 'prior' in PA: PA['prior'] = [0.,180.] - - self.z = z - self.lensed = lensed - self.xoff = xoff - self.yoff = yoff - self.flux = flux - self.reff = reff - self.index = index - self.axisratio = axisratio - self.PA = PA - - -class GaussSource(object): - """ - Convenience wrapper to create a symmetric Gaussian source profile, a special - case of a Sersic profile with n=0.5 and alpha=sqrt(2)*sigma, axis ratio = 1., PA=0. - - If lensed is True, it's assumed this source is in the background, and we - lens it; otherwise it's assumed to be a foreground/unlensed object. - - Units of parameters are xoff,yoff: arcsec; flux: Jy; width: Gaussian rms in arcsec - - Note: if `lensed' is True, the positions are relative to the lens center, - otherwise they're relative to the field center (or (0,0) coordinates) - """ - - def __init__(self,z,lensed=True,xoff=None,yoff=None,flux=None,width=None): - # Do some input handling. - if not isinstance(xoff,dict): - xoff = {'value':xoff,'fixed':False,'prior':[-10.,10.]} - if not isinstance(yoff,dict): - yoff = {'value':yoff,'fixed':False,'prior':[-10.,10.]} - if not isinstance(flux,dict): - flux = {'value':flux,'fixed':False,'prior':[1e-5,1.]} # 0.01 to 1Jy source - if not isinstance(width,dict): - width = {'value':width,'fixed':False,'prior':[0.,2.]} # arcsec - - if not all(['value' in d for d in [xoff,yoff,flux,width]]): - raise KeyError("All parameter dicts must contain the key 'value'.") - - if not 'fixed' in xoff: xoff['fixed'] = False - if not 'fixed' in yoff: yoff['fixed'] = False - if not 'fixed' in flux: flux['fixed'] = False - if not 'fixed' in width: width['fixed'] = False - - if not 'prior' in xoff: xoff['prior'] = [-10.,10.] - if not 'prior' in yoff: yoff['prior'] = [-10.,10.] - if not 'prior' in flux: flux['prior'] = [1e-5,1.] - if not 'prior' in width: width['prior'] = [0.,2.] - - self.z = z - self.lensed = lensed - self.xoff = xoff - self.yoff = yoff - self.flux = flux - self.width = width - -class PointSource(object): - """ - Creates an object which is unresolved by the data. - - The three parameters are the position (xoff,yoff), and integrated flux - (flux). - - If lensed is True, it's assumed this source is in the background, and we - lens it; otherwise it's assumed to be a foreground/unlensed object. - - Units of parameters are xoff,yoff: arcsec; flux: Jy - - Note: if `lensed' is True, the positions are relative to the lens center, - otherwise they're relative to the field center (or (0,0) coordinates) - """ - - def __init__(self,z,lensed=True,xoff=None,yoff=None,flux=None): - # Do some input handling. - if not isinstance(xoff,dict): - xoff = {'value':xoff,'fixed':False,'prior':[-10.,10.]} - if not isinstance(yoff,dict): - yoff = {'value':yoff,'fixed':False,'prior':[-10.,10.]} - if not isinstance(flux,dict): - flux = {'value':flux,'fixed':False,'prior':[1e-5,1.]} # 0.01 to 1Jy source - - if not all(['value' in d for d in [xoff,yoff,flux]]): - raise KeyError("All parameter dicts must contain the key 'value'.") - - if not 'fixed' in xoff: xoff['fixed'] = False - if not 'fixed' in yoff: yoff['fixed'] = False - if not 'fixed' in flux: flux['fixed'] = False - - if not 'prior' in xoff: xoff['prior'] = [-10.,10.] - if not 'prior' in yoff: yoff['prior'] = [-10.,10.] - if not 'prior' in flux: flux['prior'] = [1e-5,1.] - - self.z = z - self.lensed = lensed - self.xoff = xoff - self.yoff = yoff - self.flux = flux - - -class ExternalShear: - """ - Class to hold the two parameters relating to external shear. - - Units are shear: 0-1; shearangle: degrees rel to lens PA. - """ - def __init__(self,shear,shearangle): - # Do some input handling. - if not isinstance(shear,dict): - shear = {'value':shear,'fixed':False,'prior':[0.,1.]} - if not isinstance(shearangle,dict): - shearangle = {'value':shearangle,'fixed':False,'prior':[0.,180.]} - - if not all(['value' in d for d in [shear,shearangle]]): - raise KeyError("All parameter dicts must contain the key 'value'.") - - if not 'fixed' in shear: shear['fixed'] = False - if not 'fixed' in shearangle: shearangle['fixed'] = False - - if not 'prior' in shear: shear['prior'] = [0.,1.] - if not 'prior' in shearangle: shearangle['prior'] = [0.,180.] - - self.shear = shear - self.shearangle = shearangle - diff --git a/README.md b/README.md index c6b1cc5..90cdb86 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,119 @@ visilens ======== -Module for modeling gravitational lensing systems in which the data are not images but interferometric visibilities. -A couple general lensing examples are in the Demo* scripts. +Visilens is a python module for modeling gravitational lensing +systems observed by a radio/mm interferometer like ALMA or ATCA. +Because interferometers observe the Fourier components of the +sky and not actual images, every pixel in an interferometric +image is correlated with every other pixel. If you try to make +lens models using these images, you can get models which are just +plain wrong, or at least parameter uncertainties which aren't +estimated correctly. There can be residual calibration problems +in the data which might lead you to the wrong model. + +Visilens gets around this by modeling the interferometric +visibilities directly. This lets us take care of those calibration +uncertainties (from variations in the absolute flux scale, or +not knowing the antenna positions perfectly, or residual +atmospheric effects) directly. Visilens marginalizes over these +data uncertainties to arrive at models which take full advantage +of the signal in the data, with accurate uncertainty estimates. + +One of my favorite examples of how this works is shown below. We +initially got 1.5arcsec resolution imaging of one particular +source, SPT0346-52. The model kept suggesting the structure in the +upper-right part of the middle panel, and even we didn't believe +that could possibly be right - the model predicted three lensed +images, but there's only two in the image made from the data. +We've since gotten data down to 0.25arcsec resolution, which +exactly confirms the original model. This tells you that there's +information present in the visibilities which isn't obvious by eye +in any images. + +![SPT0346-52 lens models](/examples/SPT0346-52_models.png?raw=true) + +How to use visilens +------------------- + +There are a few demos and example usage scripts in the examples/ +folder, along with the data files to reproduce the models in those +examples. + +If you're new to lensing, I recommend playing around with the two +Demo scripts first, which I hope will help build intuition for how +the lens mapping / caustics change as you change the lens properties, +and how that affects the observed emission. + +The first two example scripts in that folder deal with getting data +out of CASA's measurement set format and into something more useful. +There's an example for continuum data and for spectral line data. + +The next two examples go through the model fitting process, and show +pretty much all the options / features of the code. + +If you need help using the code, please feel free to email me. + +Attribution +----------- + +If you find visilens useful for your work, please cite Hezaveh et +al. (2013), and Spilker et al. (2016): + + @ARTICLE{hezaveh13a, + author = {{Hezaveh}, Y.~D. and {Marrone}, D.~P. and {Fassnacht}, C.~D. and + {Spilker}, J.~S. and {Vieira}, J.~D. and {Aguirre}, J.~E. and + {Aird}, K.~A. and {Aravena}, M. and {Ashby}, M.~L.~N. and {Bayliss}, M. and + {Benson}, B.~A. and {Bleem}, L.~E. and {Bothwell}, M. and {Brodwin}, M. and + {Carlstrom}, J.~E. and {Chang}, C.~L. and {Chapman}, S.~C. and + {Crawford}, T.~M. and {Crites}, A.~T. and {De Breuck}, C. and + {de Haan}, T. and {Dobbs}, M.~A. and {Fomalont}, E.~B. and {George}, E.~M. and + {Gladders}, M.~D. and {Gonzalez}, A.~H. and {Greve}, T.~R. and + {Halverson}, N.~W. and {High}, F.~W. and {Holder}, G.~P. and + {Holzapfel}, W.~L. and {Hoover}, S. and {Hrubes}, J.~D. and + {Husband}, K. and {Hunter}, T.~R. and {Keisler}, R. and {Lee}, A.~T. and + {Leitch}, E.~M. and {Lueker}, M. and {Luong-Van}, D. and {Malkan}, M. and + {McIntyre}, V. and {McMahon}, J.~J. and {Mehl}, J. and {Menten}, K.~M. and + {Meyer}, S.~S. and {Mocanu}, L.~M. and {Murphy}, E.~J. and {Natoli}, T. and + {Padin}, S. and {Plagge}, T. and {Reichardt}, C.~L. and {Rest}, A. and + {Ruel}, J. and {Ruhl}, J.~E. and {Sharon}, K. and {Schaffer}, K.~K. and + {Shaw}, L. and {Shirokoff}, E. and {Stalder}, B. and {Staniszewski}, Z. and + {Stark}, A.~A. and {Story}, K. and {Vanderlinde}, K. and {Wei{\ss}}, A. and + {Welikala}, N. and {Williamson}, R.}, + title = "{ALMA Observations of SPT-discovered, Strongly Lensed, Dusty, Star-forming Galaxies}", + journal = {\apj}, + archivePrefix = "arXiv", + eprint = {1303.2722}, + keywords = {galaxies: high-redshift, galaxies: starburst, gravitational lensing: strong, techniques: interferometric}, + year = 2013, + month = apr, + volume = 767, + eid = {132}, + pages = {132}, + doi = {10.1088/0004-637X/767/2/132}, + adsurl = {http://adsabs.harvard.edu/abs/2013ApJ...767..132H}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} + } + + @ARTICLE{spilker16, + author = {{Spilker}, J. and {Marrone}, D. and {Aravena}, M. and {Bethermin}, M. and + {Bothwell}, M. and {Carlstrom}, J. and {Chapman}, S. and {Crawford}, T. and + {de Breuck}, C. and {Fassnacht}, C. and {Gonzalez}, A. and {Greve}, T. and + {Hezaveh}, Y. and {Litke}, K. and {Ma}, J. and {Malkan}, M. and + {Rotermund}, K. and {Strandet}, M. and {Vieira}, J. and {Weiss}, A. and + {Welikala}, N.}, + title = "{ALMA Imaging and Gravitational Lens Models of South Pole Telescope-Selected Dusty, Star-Forming Galaxies at High Redshifts}", + journal = {ArXiv e-prints}, + archivePrefix = "arXiv", + eprint = {1604.05723}, + keywords = {Astrophysics - Astrophysics of Galaxies}, + year = 2016, + month = apr, + adsurl = {http://adsabs.harvard.edu/abs/2016arXiv160405723S}, + adsnote = {Provided by the SAO/NASA Astrophysics Data System} + } + +License +------- + +visilens is free software provided under the MIT license. You can +read the legalese version in LICENSE.txt. \ No newline at end of file diff --git a/SourceProfile.py b/SourceProfile.py deleted file mode 100644 index 2c0b51c..0000000 --- a/SourceProfile.py +++ /dev/null @@ -1,100 +0,0 @@ -import numpy as np -from scipy.special import gamma -from Model_objs import * -#import warnings -arcsec2rad = (np.pi/(180.*3600.)) -rad2arcsec =3600.*180./np.pi -deg2rad = np.pi/180. - -def SourceProfile(xsource,ysource,source,lens): - """ - Creates the source-plane profile of the given Source. - - Inputs: - xsource,ysource: - Source-plane coordinates, in arcsec, on which to - calculate the luminosity profile of the source - - Source: - Any supported source-plane object, e.g. a GaussSource - object. The object will contain all the necessary - parameters to create the profile. - - Lens: - Any supported Lens object, e.g. an SIELens. We only need - this because, in the case of single lenses, the source - position is defined as offset from the lens centroid. If - there is more than one lens, or if the source is unlensed, - the source position is defined **relative to the field - center, aka (0,0) coordinates**. - - - Returns: - I: - The luminosity profile of the given Source. Has same - shape as xsource and ysource. Note: returned image has - units of flux / arcsec^2 (or whatever the x,y units are), - so to properly normalize, must multiply by pixel area. This - isn't done here since the lensing means the pixels likely - aren't on a uniform grid. - """ - - lens = list(np.array([lens]).flatten()) - - # First case: a circular Gaussian source. - if source.__class__.__name__=='GaussSource': - sigma = source.width['value'] - amp = source.flux['value']/(2.*np.pi*sigma**2.) - if source.lensed:# and len(lens)==1: - xs = source.xoff['value'] + lens[0].x['value'] - ys = source.yoff['value'] + lens[0].y['value'] - else: - xs = source.xoff['value'] - ys = source.yoff['value'] - - return amp * np.exp(-0.5 * (np.sqrt((xsource-xs)**2.+(ysource-ys)**2.)/sigma)**2.) - - elif source.__class__.__name__=='SersicSource': - if source.lensed:# and len(lens)==1: - xs = source.xoff['value'] + lens[0].x['value'] - ys = source.yoff['value'] + lens[0].y['value'] - else: - xs = source.xoff['value'] - ys = source.yoff['value'] - PA, ar = source.PA['value']*deg2rad, source.axisratio['value'] - reff, index = source.reff['value'], source.index['value'] - dX = (xsource-xs)*np.cos(PA) + (ysource-ys)*np.sin(PA) - dY = (-(xsource-xs)*np.sin(PA) + (ysource-ys)*np.cos(PA))/ar - R = np.sqrt(dX**2. + dY**2.) - - # Calculate b_n, to make reff enclose half the light; this approx from Ciotti&Bertin99 - # This approximation good to 1 in 10^4 for n > 0.36; for smaller n it gets worse rapidly!! - #if index < 0.35: warnings.warn("Sersic index n < 0.35 -- approximation to b_n is very bad in this regime!") - bn = 2*index - 1./3. + 4./(405*index) + 46./(25515*index**2) + 131./(1148175*index**3) - 2194697./(30690717750*index**4) - - # Backing out from the integral to R=inf of a general sersic profile - Ieff = source.flux['value'] * bn**(2*index) / (2*np.pi*reff**2 * ar * np.exp(bn) * index * gamma(2*index)) - - return Ieff * np.exp(-bn*((R/reff)**(1./index)-1.)) - - elif source.__class__.__name__=='PointSource': - if source.lensed:# and len(lens)==1: - xs = source.xoff['value'] + lens[0].x['value'] - ys = source.yoff['value'] + lens[0].y['value'] - return ValueError("Lensed point sources not working yet... try a"\ - "gaussian with small width instead...") - else: - xs = source.xoff['value'] - ys = source.yoff['value'] - - yloc = np.abs(xsource[0,:] - xs).argmin() - xloc = np.abs(ysource[:,0] - ys).argmin() - - m = np.zeros(xsource.shape) - m[xloc,yloc] += source.flux['value']/(xsource[0,1]-xsource[0,0])**2. - - return m - - - else: raise ValueError("So far only GaussSource, SersicSource, and "\ - "PointSource objects supported...") diff --git a/__init__.py b/__init__.py deleted file mode 100644 index 7f8036a..0000000 --- a/__init__.py +++ /dev/null @@ -1,17 +0,0 @@ -from Data_objs import Visdata,ImageData -from Model_objs import * -from utils import * -from RayTracePixels import * -from SourceProfile import SourceProfile -from GenerateLensingGrid import GenerateLensingGrid -from LensModelMCMC import LensModelMCMC -from ImageModelMCMC import ImageModelMCMC -from uvimage import uvimageslow -from plot_images import plot_images -from triangleplot import * -from calc_likelihood import * -from modelcal import model_cal -__all__ = ['Visdata','Model_objs','utils', - 'RayTracePixels','SourceProfile', - 'GenerateLensingGrid','uvimage','triangleplot', - 'plot_images'] diff --git a/Demo_lensmapping.py b/examples/Demo_lensmapping.py old mode 100644 new mode 100755 similarity index 83% rename from Demo_lensmapping.py rename to examples/Demo_lensmapping.py index a97ef22..70479f4 --- a/Demo_lensmapping.py +++ b/examples/Demo_lensmapping.py @@ -1,11 +1,10 @@ import numpy as np -from RayTracePixels import * -from Model_objs import * -from SourceProfile import * -import matplotlib.pyplot as pl +import sys; sys.path.append('..') +import visilens as vl from matplotlib.widgets import Slider,Button from astropy.cosmology import WMAP9 as cosmo - +import matplotlib.pyplot as pl +pl.ioff() xim = np.arange(-3,3,.03) yim = np.arange(-3,3,.03) @@ -17,28 +16,25 @@ MLens,eLens,PALens = 2.87e11,0.5,90. xSource,ySource,FSource,sSource = 0.216,-0.24,0.023,0.074 -Lens = SIELens(zLens,xLens,yLens,MLens,eLens,PALens) -Source = GaussSource(zSource,True,xSource,ySource,FSource,sSource) -Shear = ExternalShear(0.,0.) +Lens = vl.SIELens(zLens,xLens,yLens,MLens,eLens,PALens) +Shear= vl.ExternalShear(0.,0.) +lens = [Lens,Shear] +Source = vl.GaussSource(zSource,True,xSource,ySource,FSource,sSource) Dd = cosmo.angular_diameter_distance(zLens).value Ds = cosmo.angular_diameter_distance(zSource).value Dds = cosmo.angular_diameter_distance_z1z2(zLens,zSource).value -caustics = CausticsSIE(Lens,Dd,Ds,Dds,Shear) +caustics = vl.CausticsSIE(Lens,Dd,Ds,Dds,Shear) -xsource,ysource = LensRayTrace(xim,yim,Lens,Dd,Ds,Dds,shear=Shear) +xsource,ysource = vl.LensRayTrace(xim,yim,lens,Dd,Ds,Dds) f = pl.figure() ax = f.add_subplot(111,aspect='equal') pl.subplots_adjust(bottom=0.25,top=0.98) -#ax.plot(xim,yim,'g-') ax.plot(xsource,ysource,'b-') for i in range(caustics.shape[0]): ax.plot(caustics[i,0,:],caustics[i,1,:],'k-') -#ax.set_xlim(-0.5,0.5) -#ax.set_ylim(-0.5,0.5) - # Put in a bunch of sliders to control lensing parameters axcolor='lightgoldenrodyellow' @@ -71,10 +67,10 @@ def update(val): newDd = cosmo.angular_diameter_distance(zL).value newDs = cosmo.angular_diameter_distance(zS).value newDds= cosmo.angular_diameter_distance_z1z2(zL,zS).value - newLens = SIELens(zLens,xL,yL,10**ML,eL,PAL) - newShear = ExternalShear(sh,sha) - xsource,ysource = LensRayTrace(xim,yim,newLens,newDd,newDs,newDds,newShear) - caustics = CausticsSIE(newLens,Dd,Ds,Dds,newShear) + newLens = vl.SIELens(zLens,xL,yL,10**ML,eL,PAL) + newShear = vl.ExternalShear(sh,sha) + xsource,ysource = vl.LensRayTrace(xim,yim,[newLens,newShear],newDd,newDs,newDds) + caustics = vl.CausticsSIE(newLens,newDd,newDs,newDds,newShear) ax.cla() ax.plot(xsource,ysource,'b-') for i in range(caustics.shape[0]): diff --git a/Demo_sourcelensing.py b/examples/Demo_sourcelensing.py old mode 100644 new mode 100755 similarity index 71% rename from Demo_sourcelensing.py rename to examples/Demo_sourcelensing.py index b8a37c3..7d290c1 --- a/Demo_sourcelensing.py +++ b/examples/Demo_sourcelensing.py @@ -1,37 +1,36 @@ import numpy as np -from RayTracePixels import * -from Model_objs import * -from SourceProfile import SourceProfile -import matplotlib.pyplot as pl +import sys; sys.path.append('..') +import visilens as vl +import matplotlib.pyplot as pl; pl.ioff() import matplotlib.cm as cm from matplotlib.widgets import Slider,Button from astropy.cosmology import WMAP9 as cosmo -xim = np.arange(-2,2,.01) -yim = np.arange(-2,2,.01) +xim = np.arange(-3.,3.,.02) +yim = np.arange(-3.,3.,.02) xim,yim = np.meshgrid(xim,yim) zLens,zSource = 0.8,5.656 xLens,yLens = 0.,0. -MLens,eLens,PALens = 2.87e11,0.54,180-96.4 -xSource,ySource,FSource,sSource,nSource,arSource,PAsource = 0.216,0.24,0.023,0.074,0.5,0.7,45. -shear,shearangle = 0., 0. +MLens,eLens,PALens = 2.87e11,0.5,70. +xSource,ySource,FSource,sSource,nSource,arSource,PAsource = 0.216,0.24,0.02,0.1,0.8,0.7,120.-90 +shear,shearangle = 0.12, 120. -Lens = SIELens(zLens,xLens,yLens,MLens,eLens,PALens) -Source = SersicSource(zSource,True,xSource,ySource,FSource,sSource,nSource,arSource,PAsource) -Shear = ExternalShear(shear,shearangle) +Lens = vl.SIELens(zLens,xLens,yLens,MLens,eLens,PALens) +Shear = vl.ExternalShear(shear,shearangle) +Source = vl.SersicSource(zSource,True,xSource,ySource,FSource,sSource,nSource,arSource,PAsource) Dd = cosmo.angular_diameter_distance(zLens).value Ds = cosmo.angular_diameter_distance(zSource).value Dds = cosmo.angular_diameter_distance_z1z2(zLens,zSource).value -xsource,ysource = LensRayTrace(xim,yim,Lens,Dd,Ds,Dds,shear=Shear) +xsource,ysource = vl.LensRayTrace(xim,yim,[Lens,Shear],Dd,Ds,Dds) -imbg = SourceProfile(xim,yim,Source,Lens) -imlensed = SourceProfile(xsource,ysource,Source,Lens) -caustics = CausticsSIE(Lens,Dd,Ds,Dds,Shear) +imbg = vl.SourceProfile(xim,yim,Source,[Lens,Shear]) +imlensed = vl.SourceProfile(xsource,ysource,Source,[Lens,Shear]) +caustics = vl.CausticsSIE(Lens,Dd,Ds,Dds,Shear) f = pl.figure(figsize=(12,6)) ax = f.add_subplot(111,aspect='equal') @@ -47,6 +46,8 @@ ax.imshow(imbg,cmap=cmbg,extent=[xim.min(),xim.max(),yim.min(),yim.max()],origin='lower') ax.imshow(imlensed,cmap=cmlens,extent=[xim.min(),xim.max(),yim.min(),yim.max()],origin='lower') +mu = imlensed.sum()*(xim[0,1]-xim[0,0])**2 / Source.flux['value'] +ax.text(0.9,1.05,'$\\mu$ = {0:.2f}'.format(mu),transform=ax.transAxes) for i in range(caustics.shape[0]): ax.plot(caustics[i,0,:],caustics[i,1,:],'k-') @@ -100,18 +101,20 @@ def update(val): newDd = cosmo.angular_diameter_distance(zL).value newDs = cosmo.angular_diameter_distance(zS).value newDds= cosmo.angular_diameter_distance_z1z2(zL,zS).value - newLens = SIELens(zLens,xL,yL,10**ML,eL,PAL) - newShear = ExternalShear(sh,sha) - newSource = SersicSource(zS,True,xs,ys,Fs,ws,ns,ars,pas) - xs,ys = LensRayTrace(xim,yim,newLens,newDd,newDs,newDds,newShear) - imbg = SourceProfile(xim,yim,newSource,newLens) - imlensed = SourceProfile(xs,ys,newSource,newLens) - caustics = CausticsSIE(newLens,Dd,Ds,Dds,newShear) + newLens = vl.SIELens(zLens,xL,yL,10**ML,eL,PAL) + newShear = vl.ExternalShear(sh,sha) + newSource = vl.SersicSource(zS,True,xs,ys,Fs,ws,ns,ars,pas) + xs,ys = vl.LensRayTrace(xim,yim,[newLens,newShear],newDd,newDs,newDds) + imbg = vl.SourceProfile(xim,yim,newSource,[newLens,newShear]) + imlensed = vl.SourceProfile(xs,ys,newSource,[newLens,newShear]) + caustics = vl.CausticsSIE(newLens,newDd,newDs,newDds,newShear) ax.cla() ax.imshow(imbg,cmap=cmbg,extent=[xim.min(),xim.max(),yim.min(),yim.max()],origin='lower') ax.imshow(imlensed,cmap=cmlens,extent=[xim.min(),xim.max(),yim.min(),yim.max()],origin='lower') + mu = imlensed.sum()*(xim[0,1]-xim[0,0])**2 / newSource.flux['value'] + ax.text(0.9,1.05,'$\\mu$ = {0:.2f}'.format(mu),transform=ax.transAxes) for i in range(caustics.shape[0]): ax.plot(caustics[i,0,:],caustics[i,1,:],'k-') @@ -138,22 +141,4 @@ def reset(event): slns.reset(); slars.reset(); slPAs.reset() resbutton.on_clicked(reset) -#spt0346ax = pl.axes([0.56,ytop-15*ystep,0.08,0.04]) -#s0346button = Button(spt0346ax,'SPT0346-52',color=axcolor,hovercolor='0.975') -""" -sptsources = { - 'SPT0346-52':{'zL':0.8,'zS':5.6556,'xL':0.,'yL':0.,'ML':np.log10(3.73e11),'eL':0.55,'PAL':83.5, - 'ss':0.,'sa':0.,'xS':0.245,'yS':0.285,'FS':0.023,'ws':0.085}} - -def switchsource(sd): - slzL.set_val(sd['zL']); slzS.set_val(sd['zS']) - slxL.set_val(sd['xL']); slyL.set_val(sd['yL']) - slML.set_val(sd['ML']); sleL.set_val(sd['eL']); slPAL.set_val(sd['PAL']) - slss.set_val(sd['ss']); slsa.set_val(sd['sa']) - slxs.set_val(sd['xS']); slys.set_val(sd['yS']) - slFs.set_val(sd['FS']); slws.set_val(sd['ws']) - -#s0346button.on_clicked(switchsource(sptsources['SPT0346-52'])) -""" - pl.show() diff --git a/examples/Ex1_get_data_continuum.py b/examples/Ex1_get_data_continuum.py new file mode 100755 index 0000000..fe3d9c2 --- /dev/null +++ b/examples/Ex1_get_data_continuum.py @@ -0,0 +1,105 @@ +""" +Example 1, to be run within CASA. This script serves as a guideline +for how to get data out of a CASA ms and into a format which +visilens can use. We really don't need all that much information, +so we keep only the columns we need. + +To keep the number of visibilities low, we first average the data +a bit. In this particular case, the on-source integration times were +only ~60s, so we won't average in time. We will average down each of +the four ALMA basebands (spectral windows), since this is continuum +data and the fractional bandwidth from the lowest to highest observed +frequency is small. We'll also average the two orthogonal polarizations, +since the source is unpolarized. Last, for fitting, we need an +accurate estimate of the uncertainty on each visibility. The *relative* +uncertainties in the data are okay, but they're not on any absolute scale, +so we need to calculate what the re-scaling factor should be. To do this, +we take the difference between successive visibilities on each baseline +(these are strong sources, so unfortunately we can't just use the rms) +and re-scale the noise to match. In principle CASA's statwt also does +this, but I found that it sometimes gave bizarre results (some baselines +weighted 100x more than others for no obvious reason, etc.). If you +have better luck with it, feel free to use that instead! +""" + +import numpy as np +import os +c = 299792458.0 # in m/s + +# Path to the calibrated ms file, and the source name we want. +inms = 'Compact_0202_to_0418.cal.ms' +field = 'SPT0202-61' +spw = '0,1,2,3' + +# First we split out just the source we want from our ms file. +outms = field+'_'+inms[:3].lower()+'.ms' +os.system('rm -rf '+outms) +split(inms,outms,field=field,spw=spw,width=128,datacolumn='corrected', + keepflags=False) + +# Now we'll get the visibility columns we need, before manipulating them. +# data_desc_id is a proxy for the spw number. +ms.open(outms,nomodify=True) +visdata = ms.getdata(['uvw','antenna1','antenna2','data','sigma','data_desc_id']) +visdata['data'] = np.squeeze(visdata['data']) # ditch unnecessary extra dimension +ms.close() + +# Get the frequencies associated with each spw, because uvw coordinates are in m +tb.open(outms+'/SPECTRAL_WINDOW') +freqs = np.squeeze(tb.getcol('CHAN_FREQ')) # center freq of each spw +tb.close() + +# Get the primary beam size from the antenna diameter. Assumes homogeneous array, +# sorry CARMA users. +tb.open(outms+'/ANTENNA') +diam = np.squeeze(tb.getcol('DISH_DIAMETER'))[0] +PBfwhm = 1.2*(c/np.mean(freqs))/diam * (3600*180/np.pi) # in arcsec +tb.close() + +# Data and sigma have both polarizations; average them +visdata['data'] = np.average(visdata['data'],weights=(visdata['sigma']**-2.),axis=0) +visdata['sigma']= np.sum((visdata['sigma']**-2.),axis=0)**-0.5 + +# Convert uvw coords from m to lambda +for ispw in range(len(spw.split(','))): + visdata['uvw'][:,visdata['data_desc_id']==ispw] *= freqs[ispw]/c + +# Calculate the noise re-scaling, by differencing consecutive visibilities on the +# same baseline. Have to do an ugly double-loop here; would work better if we knew +# in advance how the data were ordered (eg time-sorted). We assume that we can +# re-scale the noise using the mean of the re-scalings from each baseline. +facs = [] +for ant1 in np.unique(visdata['antenna1']): + for ant2 in np.unique(visdata['antenna2']): + if ant1 < ant2: + thisbase = (visdata['antenna1']==ant1) & (visdata['antenna2']==ant2) + reals = visdata['data'].real[thisbase] + imags = visdata['data'].imag[thisbase] + sigs = visdata['sigma'][thisbase] + diffrs = reals - np.roll(reals,-1); diffis = imags - np.roll(imags,-1) + std = np.mean([diffrs.std(),diffis.std()]) + facs.append(std/sigs.mean()/np.sqrt(2)) + +facs = np.asarray(facs); visdata['sigma'] *= facs.mean() +print outms, '| mean rescaling factor: ',facs.mean(), '| rms/beam (mJy): ',1000*((visdata['sigma']**-2).sum())**-0.5 + + +# If we ever want to mess with the data after re-scaling the weights, we have to +# write them back to the ms file. But, CASA doesn't like that we've averaged +# the polarizations together, so we have to keep them separate for this purpose. +ms.open(outms,nomodify=False) +replace = ms.getdata(['sigma','weight']) +replace['sigma'] *= facs.mean() +replace['weight'] = replace['sigma']**-2. +ms.putdata(replace) +ms.close() + +# Create one single array of all this data, then save everything. +allarr = np.vstack((visdata['uvw'][0,:],visdata['uvw'][1,:],visdata['data'].real, + visdata['data'].imag,visdata['sigma'],visdata['antenna1'],visdata['antenna2'])) + +outfname = field+'_'+inms[:3].lower()+'.bin' +with open(outfname,'wb')as f: + allarr.tofile(f) + f.write(PBfwhm) + diff --git a/examples/Ex2_get_data_line.py b/examples/Ex2_get_data_line.py new file mode 100755 index 0000000..d467afd --- /dev/null +++ b/examples/Ex2_get_data_line.py @@ -0,0 +1,118 @@ +""" +Example 2, to be run from within CASA. This script shows a bit +more complicated example for getting data out of CASA and into +a useful format. In this case, we observed an object with ATCA +looking for CO(2-1) emission at z=5.7. The line is faint, so +there were many hours of observations, spread over several days +and several different array configurations. For this example, I +output one file per track, containing all the data from that +day's observations (though you could just as easily combine all +the data together and then run this). + +This time, we'll average over 200km/s, centered at the rest +frequency of the CO line. +""" + +import numpy as np +import os +c = 299792458.0 # in m/s + +width = '200km/s' +center = '0km/s' +restfreq = '34.64132GHz' # CO(2-1) obs frequency +outfolder = 'linedata' + +# All the files where the data are. Like I say, this was a lot of observing. +vis = ['100412/spt0346_100412.cal','100812/spt0346_100812.cal','100912/spt0346_100912.cal', # H214 +'102313/spt0346_102313.cal','102413/spt0346_102413.cal','102513/spt0346_102513.cal','102613/spt0346_102613.cal', # 6KM +'102713/spt0346_102713.cal','102813/spt0346_102813.cal','102913/spt0346_102913.cal','110813/spt0346_110813.cal', # 6KM +'011714/spt0346_011714.cal','011814/spt0346_011814.cal', # 1.5KM +'051014_0346/spt0346_051014.cal','051114_0346/spt0346_051114.cal', # 1.5KM +'051514/spt0346_051514.cal','051614/spt0346_051614.cal','051714/spt0346_051714.cal'] # 1.5KM + +# We'll average down in time, but don't want to reduce amplitude of sources far from phase center; +# this 'magic' factor follows Cotton2009 (effects of baseline-dependent time averaging +# of uv data). It assumes a 1% loss in amplitude due to smearing for a source at 1/4 +# of a PB FWHM (~100"/4 for 34.6GHz and 22m dishes), and is the max baseline over which we can time +# average in m. This value is specific to the ATCA 22m dishes and this frequency, but can be solved for with +# maxuvw = D / (1.2*loc/PBfwhm) * 0.244, where 0.244 solves 1/1.01 = sinc(x), loc = 0.25*PBfwhm +maxuvw = 18.0 + +splitvislist = [] +for i,date in enumerate(vis): + outms = date.split('/')[1][:-4]+'_'+center[:-4]+'_dv'+width[:-4]+'.cal' + os.system('rm -rf '+outms) + os.system('rm -rf '+outfolder+outms) + + # Average the data in both time and frequency, according to line with requested at top of script. + mstransform(vis=date,outputvis=outms,datacolumn='data',regridms=True,restfreq=restfreq, + mode='velocity',nchan=1,width=width,start=center, + timeaverage=True,timebin='120s',maxuvwdistance=maxuvw,timespan='scan') # average in time, unless uv coords more than maxuvw apart + splitvislist.append(folder+outms) + + # Similar to continuum script, get all the stuff we need + ms.open(outms,nomodify=True) + visdata = ms.getdata(['uvw','antenna1','antenna2','data','sigma','data_desc_id']) + visdata['data'] = np.squeeze(visdata['data']) + ms.close() + + # Get the frequency of this channel, for primary beam in arcsec below + tb.open(outms+'/SPECTRAL_WINDOW') + freqs = np.squeeze(tb.getcol('CHAN_FREQ')) + tb.close() + + # Get the primary beam size + tb.open(outms+'/ANTENNA') + diam = np.squeeze(tb.getcol('DISH_DIAMETER'))[0] # assumes all dishes are equal size + tb.close() + PBfwhm = 1.2*(c/np.mean(freqs))/diam * (3600*180/np.pi) # in arcsec + + # Current data+sigma has two polarizations/correlations, average them. + visdata['data'] = np.average(visdata['data'],weights=(visdata['sigma']**-2.),axis=0) + visdata['sigma'] = np.sum((visdata['sigma']**-2.),axis=0)**-0.5 + + # Convert uvw coords from m to wavelengths + visdata['uvw'] *= freqs/c + + # Now we re-scale the noise, based on differencing successive visibilities on the same baseline + facs = [] # for calculating a single rescale factor + for ant1 in np.unique(visdata['antenna1']): + for ant2 in np.unique(visdata['antenna2']): + if ant1 < ant2: + thisbase = (visdata['antenna1']==ant1) & (visdata['antenna2']==ant2) + reals = visdata['data'].real[:,thisbase] + imags = visdata['data'].imag[:,thisbase] + sigs = visdata['sigma'][:,thisbase] + diffrs = reals - np.roll(reals,-1); diffis = imags - np.roll(imags,-1) + std = np.mean([diffrs.std(),diffis.std()]) + facs.append(std/sigs.mean()/np.sqrt(2)) # for calculating a single rescaling + + facs = np.asarray(facs) + visdata['sigma'] *= facs.mean() + print outms, '| mean rescaling factor: ',facs.mean(), '| rms/beam (mJy): ',1000*((visdata['sigma']**-2).sum())**-0.5 + + # Write the new weights back to the ms file, except for having averaged polarizations together + ms.open(outms,nomodify=False) + replace = ms.getdata(['sigma','weight']) + replace['sigma'] *= facs.mean() + replace['weight'] = replace['sigma']**-2. + ms.putdata(replace) + ms.close() + + # Now we assemble the data to write it to the format that the visilens code expects. + allarr = np.vstack((visdata['uvw'][0,:],visdata['uvw'][1,:],visdata['data'].real,visdata['data'].imag,\ + visdata['sigma'],visdata['antenna1'],visdata['antenna2'])) + + # Write out the data + outfname = 'linedata/'+date.split('/')[1][:-4]+'_'+center[:-4]+'_dv'+width[:-4]+'.bin' + with open(outfname,'wb') as f: + allarr.tofile(f) + f.write(PBfwhm) + os.system('mv '+outms+' '+folder) + #os.system('rm -rf '+outms) + + +# The following are optional steps, if you want to put all the data together +# to play around with later. +# visbase = folder+'spt'+source+'_'+center[:-4]+'_dv'+width[:-4] +# concat(splitvislist,visbase+'.ms') diff --git a/examples/Ex3_fit_continuum_SPT0202-61.py b/examples/Ex3_fit_continuum_SPT0202-61.py new file mode 100755 index 0000000..d6b597d --- /dev/null +++ b/examples/Ex3_fit_continuum_SPT0202-61.py @@ -0,0 +1,205 @@ +""" +Example 3, to be run in python. This script runs through fitting +to the 870um continuum data of one source from our ALMA cycle 0 +data, SPT0202-61, which we generated in Example 1. It's a fairly +complex system, so it uses most all of the capabilities of the code. + +Here, we model the field using a standard SIE lens with external +tidal shear, which lenses two source-plane components of the +emission. There's also an additional unlensed source ~7" south +of the lensed emission detected at ~8-10sigma. + +In this case, we actually don't have spectroscopic redshifts for +either the lens or the background source, but the magnification +and source angular size don't actually need those. + +The observations were taken at 870um in two different array +configurations spaced months apart. We fit them jointly, but need +to allow some extra freedom in the model (the absolute flux scale, +for example, doesn't agree to better than the precision of the data). +""" + +import numpy as np +import matplotlib.pyplot as pl; pl.ioff() +import sys +sys.path.append('/Users/jspilker/Research/visilens') +import visilens as vl +import time + +# We'll use this to help name the output plots +plotfbase = 'SPT0202-61_twosersic' + +# These are the files that came out of the first example script, +# you should also have been able to download them from the same place +# you got this script. +comdata = vl.read_visdata('SPT0202-61_com.bin') +extdata = vl.read_visdata('SPT0202-61_ext.bin') + +# If you want/need to manipulate the data further, you can do that too: +# comdata.sigma *= 1.5 # really weird data, see what de-influencing it does + +# First we define what the lensing environment should look like. For each +# parameter, you must specify an initial guess for its value, can decide +# whether that parameter is fixed or free during fitting, and can impose +# a uniform square prior on its value (ie, it must be between the two values +# in the prior). A value is required, but if you don't specify fixed/free, +# it will not be fixed, and some very loose prior is placed on its value. +# Lens position is relative to the ALMA phase center, with +x west (sorry not +# sorry) and +y north, in arcseconds. +lens = [vl.SIELens(z=0.5, + x={'value':0.06,'fixed':False,'prior':[-1.5,1.5]}, + y={'value':2.18,'fixed':False,'prior':[1.,3.5]}, + M={'value':1.2e11,'fixed':False,'prior':[1e10,1e12]}, + e=0.4, # will be a free parameter, loose prior + PA={'value':70.,'prior':[-180.,180.]}), # deg east of north + + vl.ExternalShear( + shear={'value':0.22,'prior':[0.,0.8]}, + shearangle={'value':10.,'prior':[-90.,90.]})] + +# We define two sources which will be lensed by the above (only one +# didn't yield a good fit, as it turns out galaxies don't always look +# like well-behaved Sersic profiles). There's also an unlensed source +# in the field which we model simultaneously. For the lensed sources, +# their positions are relative to the position of the lens we defined +# earlier. For the unlensed source, they're again defined relative to +# the ALMA pointing center. Flux is in Jy (because our data amplitudes +# are also in Jy), sizes are in arcsec. +src = [vl.SersicSource(z=3.5, lensed=True, + xoff={'value':-0.1,'fixed':False,'prior':[-1.,1.]}, + yoff={'value':-0.08,'fixed':False,'prior':[-1.,1.]}, + flux={'value':0.003,'fixed':False,'prior':[0.,0.08]}, + reff={'value':0.07,'fixed':False,'prior':[0.,1.]}, + axisratio={'value':0.6,'fixed':False}, + index={'value':0.5,'fixed':False,'prior':[0.1,4.0]}, + PA={'value':90.,'fixed':False,'prior':[0.,180.]}), + + vl.SersicSource(z=3.5, lensed=True, + xoff={'value':0.03,'fixed':False,'prior':[-1.,1.]}, + yoff={'value':0.04,'fixed':False,'prior':[-1.,1.]}, + flux={'value':0.004,'fixed':False,'prior':[0.,0.08]}, + reff={'value':0.25,'fixed':False,'prior':[0.,1.]}, + axisratio={'value':0.7,'fixed':False}, + index={'value':1.,'fixed':False,'prior':[0.2,4.0]}, + PA={'value':10.,'fixed':False,'prior':[-90.,90.]}), + + # Extra unlensed source in the field. + vl.GaussSource(z=3.5,lensed=False, + xoff={'value':1.3,'prior':[0.8,1.8]}, + yoff={'value':-3.9,'prior':[-4.4,-3.4]}, + flux=0.003, + width=0.15)] + + +# These set how we set up the field. In this case, we'll model +# a region within +/- 15" of the phase center (you can check that +# this is sufficient by looking at comdata.uvdist.min()). The +# lensed emission is encompassed within the limits of highresbox, +# ie, between -2.5" and +2.5" in x and -0.5" and +4.5" in y. We +# simulate this region at higher resolution, in this case 0.01". +# The full field is simulated at 0.06". NOTE: the field grid size +# goes to the next-higher power of 2, so fieldres is a coarse limit +# to the actual resolution. In this case, the true resolution will +# be 30/512 = 0.0586", because the grid we asked for gets rounded up +# to 512. Using large grids makes the code take MUCH longer, so I +# strongly recommend choosing the field size and resolution carefully; +# there's a big difference between a 1024x1024 grid and a 512x512 grid! +xmax = 15. +highresbox = [-2.5, 2.5, -0.5, 4.5] +emitres, fieldres = 0.01, 0.06 + +# Okay, now onto some interferometric wizardry. The data were +# taken using short (~60s) integrations in two tracks separated +# by several months. First, we allow for some variation in the +# absolute flux scaling of the two tracks - they used different +# flux calibrators - and an astrometric shift between the two +# tracks. These can't vary too much from 1 (flux scale), or 0 +# (shift), but often are statistically different from those values. +scaleamp = True # allow flux re-scaling +shiftphase = True # allow astrometric shift + +# There's also the possibility that the data have antenna-based +# phase errors which could bias our model results. These could +# be due to imperfectly known antenna positions, or the fact that +# these observations were too short to allow atmospheric variations +# to average over the array. This is sort of like self-calibrating +# your data, except that in this case, we use a model of the field +# generated from the lensing geometry instead of from CLEAN +# components. This lets us marginalize over those phase errors, but +# you can also use this to identify possibly-bad antennas to remove. +# So, DO NOT self-cal your data beforehand! The math behind this +# technique is described in the appendix to Hezaveh et al. (2013), +# 762, 132. +modelcal = True + +# Now we do some setup for the MCMC run. These are the usual emcee +# parameters which govern how many chains are run and for how long. +nwalkers,nburn,nstep = 300,200,200 + +# For multiprocessing, there's a couple options. For a single machine, +# you can just specify the number of cores to use, like below. It can +# also be run under MPI, in which case you should set mpirun to True, +# and run your script using +# mpirun -np 64 --mca mpi_warn_on_fork 0 python Ex3_fit_continuum_SPT0202-61.py +# [that mpi_warn just turns off some harmless scare text I find annoying] +nthreads = 8 +mpirun = False + + +# Now we run the modeling! This will take awhile, get coffee or something. +# This returns a dictionary which contains all of the chains, magnifications, +# antenna phases, etc., as well as some meta-data about the run like initial +# conditions so you can reproduce a result. +t1 = time.time() +mcmcresult = vl.LensModelMCMC(data=[extdata,comdata],lens=lens,source=src,xmax=xmax,highresbox=highresbox,\ + fieldres=fieldres,emitres=emitres,scaleamp=scaleamp,shiftphase=shiftphase,\ + modelcal=modelcal,nwalkers=nwalkers,nburn=nburn,nstep=nstep,nthreads=nthreads,mpirun=mpirun) + +t2 = time.time() +print "total time: {0:.1f} hours for {1:.0f} samples".format((t2-t1)/3600.,nwalkers*(nburn+nstep)) + +# How to save the data is up to you; I find a gzipped python pickle file +# convenient. Can be loaded later with +# mcmc = pickle.load(gzip.open('chains_'+plotfbase+'.pzip')) +import pickle +import gzip +pickle.dump(mcmcresult,gzip.open('chains_'+plotfbase+'.pzip','wb')) + +# Now we make a big parameter covariance triangle plot. This returns a +# matplotlib figure object and array of axes objects, so you can tweak +# to your heart's content. +pl.ioff() +f,axesarray = vl.TrianglePlot_MCMC(mcmcresult,plotmag=True,plotnuisance=True) +f.savefig(plotfbase+'_triangle.png') +pl.close() + +# Now we image the data and compare to an image of the best model. The imaging +# is very dumb, so very slow. If you want to do a lot of imaging or more intensive +# imaging, I highly recommend outputting visibilities to uvfits, because I have +# not tried to re-implement all of CASA's imaging capabilities. You can set which/ +# how many contours get drawn with the kwargs mapcontours and rescontours (in +# multiples of the noise). In this case, we plot each dataset individually, and +# then both together. You can plot only both of them together by setting +# plotcombined=True, plotall=False. There are some other kwargs you can use to +# mess with the appearance, check the plot_images docs. +# We get four panels for each dataset (and both together): raw dirty image, +# model dirty image, residuals, and a high-res model of the lensed emission. +f,axarr = vl.plot_images([extdata,comdata],mcmcresult,imsize=300,pixsize=0.1, + limits=[-6,6,-4,8],mapcontours=np.array([-25,-15,-5,5,15,25,35,45,55,65]),plotall=True) +axarr[2][0].text(0.,-0.3,"Data contours: steps of 10$\sigma$ starting at $\pm$5; "\ + "Residual contours: steps of 1$\sigma$ starting at $\pm$2",\ + transform=axarr[2][0].transAxes) +f.savefig(plotfbase+'_panels.png') +pl.close() + +# Create histograms of the modelcal phase offsets. This lets us see if there +# are potentially-bad antennas (consistent, large phase errors) which could +# indicate an antenna position uncertainty or just bad data. +for i in range(2): + for j in range(mcmcresult['calphases_dset'+str(i)].shape[1]): + pl.hist(mcmcresult['calphases_dset'+str(i)][:,j]*180/np.pi,bins=50,histtype='step') + + pl.xlabel('Modelcal Phases, dataset {0:.0f}, degrees'.format(i)) + pl.savefig(plotfbase+'_phasesdset{0:.0f}.png'.format(i)) + pl.close() + diff --git a/examples/Ex4_fit_line_SPT0346-52_CO21.py b/examples/Ex4_fit_line_SPT0346-52_CO21.py new file mode 100755 index 0000000..61c6e55 --- /dev/null +++ b/examples/Ex4_fit_line_SPT0346-52_CO21.py @@ -0,0 +1,121 @@ +""" +Example 4, to be run in python. In this example, we fit the CO +emission using the data made from Ex2 (which you should be +able to download from the same place you got this script). + +For these data, I just fixed the lens properties to those +we got from the ALMA 870um data, because the ALMA data have +way higher signal-to-noise than these do. You could also do +one big joint fit, if you needed to. +""" + +import numpy as np +import matplotlib.pyplot as pl; pl.ioff() +import sys +sys.path.append('/Users/jspilker/Research/visilens') +import visilens as vl +import time +import glob + +# We match the file names made by our Example 2 script. +center = '0' +width = '200' + +# Read in the data from each day's observations... +datasets = [] +for f in sorted(glob.glob('spt0346linedata/spt0346_*_'+center+'_dv'+width+'*.bin')): + datasets.append(vl.read_visdata(f)) + +# and concatenate it all together. We could have done this earlier when +# we split out the data, but this way we keep open the option of re-scaling the +# amplitudes on different days if we ever have enough s/n for that. +atcadata = vl.concatvis(datasets) +atcadata.filename = 'All, '+center+'km/s, '+width+'km/s wide' + +# A plot name helper +plotfbase = 'SPT0346-52_CO21_'+center+'_dv'+width + + +# We set the lens parameters to the ALMA best-fit values. +# Lens positions are relative to the pointing center offset +# ALMA pointed at 03:46:41.19, -52:05:05.5, ATCA at 03:46:41.13, -52:05:02.1 +# to calculate offset, use astropy.coordinates & astropy.units: +# import astropy.units as u; import astropy.coordinates as ac +# alma = ac.SkyCoord('03h46m41.19s','-52d05m05.5s') +# atca = ac.SkyCoord('03h46m41.13s','-52d05m02.1s') +# x = xLens*u.arcsec - (alma.ra - atca.ra).to(u.arcsec)*np.cos(alma.dec) +# y = (alma.dec + yLens*u.arcsec - atca.dec).to(u.arcsec) + +# Best-fit lens to combined data, sersic + shear +# ALMA xL = 0.806, yL = 3.036 --> xL_atca = 0.253, yL_atca = -0.364 +lens = [vl.SIELens(z=0.8, + x={'value':0.253,'fixed':True,'prior':[-0.221,0.779]}, + y={'value':-0.364,'fixed':True,'prior':[-0.828,0.172]}, + M={'value':2.811e11,'fixed':True,'prior':[1e10,5e13]}, + e={'value':0.515,'fixed':True,'prior':[0.4,0.6]}, + PA={'value':70.90,'fixed':True,'prior':[60, 95]}), + + vl.ExternalShear( + shear={'value':0.119,'fixed':True}, + shearangle={'value':122.13,'fixed':True})] + +# We're just going to model the source as a simple symmetric Gaussian +src = vl.GaussSource(z=5.65, + xoff={'value':0.142,'fixed':False,'prior':[-1., 1.]}, + yoff={'value':0.30,'fixed':False,'prior':[-1., 1.]}, + flux={'value':0.000273,'fixed':False,'prior':[0.,0.002]}, + width={'value':0.108,'fixed':False,'prior':[0.02,0.5]}) + + +# Set up the gridding for the moedling. +# The ATCA primary beam is ~70arcsec, but a 38arcsec box keeps +# our grid to 512x512 instead of 1024x1024, and our shortest +# baselines don't care about this anyway. +xmax = 38. +highresbox = [0.3-2.5, 0.3+2.5, -0.36-2.5, -0.36+2.5] +emitres, fieldres = 0.03, 0.15 + +# In this case, we don't have much signal-to-noise, so we won't +# allow flux scaling or astrometric shifts +scaleamp, shiftphase = False, False + +# Similarly, we don't allow antenna-based phase errors. If these +# were significant, we'd see them in the delays across the ATCA +# bandwidth. Also, we observed many-hour tracks, so atmospheric +# effects have largely averaged out. +modelcal = False + +# Do some setup for the MCMC things and multiprocessing +nwalkers,nburn,nstep = 300,200,200 +nthreads=4 # use 4 cores for the calculations +mpirun = False + +# Now we do the actual MCMC calculations, as in the previous example +t1 = time.time() +mcmcresult = vl.LensModelMCMC(atcadata,lens,src,xmax=xmax,highresbox=highresbox,\ + fieldres=fieldres,emitres=emitres,scaleamp=scaleamp,shiftphase=shiftphase,\ + modelcal=modelcal,nwalkers=nwalkers,nburn=nburn,nstep=nstep,nthreads=nthreads,pool=None,mpirun=mpirun) + +t2 = time.time() +print "total time: {0:.1f} hours for {1:.0f} samples".format((t2-t1)/3600.,nwalkers*(nburn+nstep)) + +# We just dump out the results of the mcmc run. +import pickle +import gzip +pickle.dump(mcmcresult,gzip.open('chains_'+plotfbase+'.pzip','w')) + +# Plot triangle degeneracy plot. +f,axesarray = vl.TrianglePlot_MCMC(mcmcresult,plotmag=True,plotnuisance=True) +f.savefig(plotfbase+'_triangle.png') +pl.close() + + +# Plot images, and we're done! +if plotpanels: + f,axarr = vl.plot_images(atcadata,mcmcresult,imsize=500,pixsize=0.07, + limits=[-5,5,-5,5],mapcontours=np.array([-4,4,6,8,10])) + axarr[0][0].text(0.,-0.2,"Data contours: steps of 2$\sigma$ starting at $\pm$4; "\ + "Residual contours: Steps of 1$\sigma$ starting at $\pm$2",\ + transform=axarr[0][0].transAxes) + f.savefig(plotfbase+'_panels.png') + pl.close() diff --git a/examples/SPT0202-61_com.bin b/examples/SPT0202-61_com.bin new file mode 100644 index 0000000000000000000000000000000000000000..158230a32aa2951a7cf0020b0337a6da796bfc8a GIT binary patch literal 262088 zcmWigX*|?#7siz(MD{imQIe&|5)~(DlM*78T`96lDru7I=b%96F>`8}_^;=}EGpZlEay3TYx~#Bjr4g+42Gn2N?f}<9^GJJbDpaahd57`zfJCj!!M~(Aurm3R zoyNd}ZT_v1o?ZsDndk@A+cLnx;X7X7JRR03^_*i}}eA4LHH|9kK 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z>?T<15pnkL<^Jjlu?P=Q1cgXH`$K~MGd~7D>(JpV=L>dLbNe+i4o42LiEaS!7q$jV>;3?IAob^lol*0YPT9ATA4wjPkm%d4p|)^Z%bB zqq7FvcQSSlxAFfM1D-tt<4r*SW>+B EA3MsWQUCw| literal 0 HcmV?d00001 diff --git a/get_ychains.py b/get_ychains.py deleted file mode 100644 index 43339ac..0000000 --- a/get_ychains.py +++ /dev/null @@ -1,70 +0,0 @@ -# Script on how to get info from yashar's chains -import sys -sys.path.append('/home/jspilker/Research') -import visilens as vl -from scipy.io import loadmat -import numpy as np -arcsec2rad = np.pi/180./3600. - -a = loadmat('mcmc_chain.mat') - -lnlike,lenspars,srcpars = [],[],[] -for arr in a['chain_1'][1:]: - lnlike.append(arr[0]) - lenspars.append(arr[1]) - srcpars.append(arr[2]) - -lnlike = np.squeeze(np.asarray(lnlike)) -lenspars = np.squeeze(np.asarray(lenspars)) -srcpars = np.squeeze(np.asarray(srcpars)) -allpars = np.c_[lenspars,srcpars] -#allpars[:,4] *= -1 -#allpars[:,-1] *= -1 -xL,yL,ML,eL,PAL = allpars[:,3],allpars[:,4],allpars[:,0],allpars[:,1],allpars[:,2] -xS,yS,FS,RS = allpars[:,7],allpars[:,8],allpars[:,5],allpars[:,6] -allpars = np.c_[xL,yL,ML,eL,PAL,xS,yS,FS,RS] -#allpars[:,4] = 180.-allpars[:,4] - -# a pseudo-burnin -lnlike = lnlike[10000:] -allpars = allpars[10000:,:] - -chaindict = {} -cols = ['xL','yL','ML','eL','PAL','xoffS0','yoffS0','fluxS0','widthS0'] -chaindict['chains'] = np.core.records.fromarrays(allpars.transpose(),names=cols) -chaindict['lnlike'] = lnlike - -f,axesarray = vl.TrianglePlot_MCMC(chaindict,plotmag=True,plotnuisance=True) -f.savefig('yasharchains_triangle.png') - -fname = 'ALMA_2_unselcald.mat' -a = loadmat(fname) -comdata = vl.Visdata(a['u'].flatten(),a['v'].flatten(),a['vis_data'].flatten().real,a['vis_data'].flatten().imag,\ - a['sig'].flatten(),a['ant1'].flatten(),a['ant2'].flatten()) -#comdata = gl.visdata(comdata.u,comdata.v,a['vis_data'].flatten().real,a['vis_data'].flatten().imag,\ -# a['sig'].flatten(),a['ant1'].flatten(),a['ant2'].flatten()) - -comdata.sigma *= 0.0203 -comdata.filename = fname -comdata.PBfwhm = 21.0 # arcsec - -# All the baselines to ant12 are flagged (0 amp), remove them -good = np.where((comdata.ant1!=12) & (comdata.ant2!=12)) -comdata = vl.Visdata(comdata.u[good],comdata.v[good],comdata.real[good],comdata.imag[good],comdata.sigma[good],\ - comdata.ant1[good],comdata.ant2[good],filename=comdata.filename,PBfwhm=comdata.PBfwhm) - - -chaindict['lens_p0'] = vl.SIELens(0.263,xL.mean(),yL.mean(),ML.mean(),eL.mean(),PAL.mean()) -chaindict['source_p0'] = [vl.GaussSource(4.224,True,xS.mean(),yS.mean(),FS.mean(),RS.mean())] -chaindict['xmax'] = 40. -chaindict['highresbox'] = [-7, -2.5, 1.,5.5] -chaindict['emitres'] = 0.015 -chaindict['fieldres'] = (2*4*comdata.uvdist.max())**-1 / arcsec2rad -chaindict['modelcal'] = [True] -chaindict['data'] = [comdata.filename] - -f,axarr,ims = vl.plot_images(comdata,chaindict,imsize=250,pixsize=0.2,returnimages=True,\ - mapcontours=np.array([-28,-23,-18,-13,-8,-3,3,8,13,18,23,28,33]),\ - rescontours=np.array([-6,-5,-4,-3,-2,-1,1,2,3,4,5,6]),level=0.0017,limits=[-15,5,13,-7]) -f.savefig('yasharchains_panels.png') - diff --git a/modelcal.py b/modelcal.py deleted file mode 100644 index ae4f979..0000000 --- a/modelcal.py +++ /dev/null @@ -1,58 +0,0 @@ -import numpy as np -import scipy.sparse -from Data_objs import Visdata -import copy - -def model_cal(realdata,modeldata,dPhi_dphi=None,FdPC=None): - """ - Routine following Hezaveh+13 to implement perturbative phase corrections - to model visibility data. This routine is designed to start out from an - intermediate step in the self-cal process, since we can avoid doing a - lot of expensive matrix inversions that way, but if either of the - necessary arrays aren't provided, we calculate them. - - Inputs: - realdata,modeldata: - visdata objects containing the actual data and model-generated data - - dPhi_dphi: None, or pre-calculated - See H+13, App A. An N_ant-1 x M_vis matrix whose ik'th element is 1 - if the first antenna of the visibility is k, -1 if the second, or 0. - - FdPC: None, or pre-calculated - See H+13, App A, eq A2. This is an N_ant-1 x M_vis matrix, equal to - -inv(F)*dPhi_dphi*inv(C) in the nomenclature of H+13. This has the - matrix inversions that we want to avoid calculating at every MCMC - iteration (inverting C takes ~3s for M~5k, even with a sparse matrix). - - Outputs: - modelcaldata: - visdata object containing updated visibilities - - dphi: - Array of length N_ant-1 containing the implemented phase offsets - """ - - # If we don't have the pre-calculated arrays, do so now. It's expensive - # to do these matrix inversions at every MCMC step. - if np.any((FdPC is None, dPhi_dphi is None)): - uniqant = np.unique(np.asarray([realdata.ant1,realdata.ant2]).flatten()) - dPhi_dphi = np.zeros((uniqant.size-1,realdata.u.size)) - for j in range(1,uniqant.size): - dPhi_dphi[j-1,:] = (realdata.ant1==uniqant[j])-1*(realdata.ant2==uniqant[j]) - C = scipy.sparse.diags((realdata.sigma/realdata.amp)**-2.,0) - F = np.dot(dPhi_dphi,C*dPhi_dphi.T) - Finv = np.linalg.inv(F) - FdPC = np.dot(-Finv,dPhi_dphi*C) - - # Calculate current phase difference between data and model; wrap to +/- pi - deltaphi = realdata.phase - modeldata.phase - deltaphi = (deltaphi + np.pi) % (2 * np.pi) - np.pi - - dphi = np.dot(FdPC,deltaphi) - - modelcaldata = copy.deepcopy(realdata) - - modelcaldata.phase += np.dot(dPhi_dphi.T,dphi) - - return modelcaldata,dphi diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..0f56e0c --- /dev/null +++ b/requirements.txt @@ -0,0 +1,6 @@ +numpy +scipy +matplotlib +astropy +Pillow +emcee \ No newline at end of file diff --git a/utils.py b/utils.py deleted file mode 100644 index 3faa174..0000000 --- a/utils.py +++ /dev/null @@ -1,293 +0,0 @@ -import numpy as np -import scipy.stats as stats -from scipy.special import erfinv -from astropy.stats import sigma_clip -import copy -from Data_objs import Visdata -import astropy.constants as co -c = co.c.value # speed of light, in m/s -G = co.G.value # gravitational constant in SI units -Msun = co.M_sun.value # solar mass, in kg -Mpc = 1e6*co.pc.value # 1 Mpc, in m -arcsec2rad = (np.pi/(180.*3600.)) -rad2arcsec =3600.*180./np.pi - -__all__ = ['read_visdata','read_image','cart2pol','pol2cart','concatvis','bin_visibilities',\ - 'expsinc','box','sersic_area','thetaE'] - -def read_visdata(filename): - """ - Function to read in visibility data from file and create a visdata object - to hold it afterwards. So far only .bin files from get_visibilities.py are - supported; idea is eventually to be able to not mess with that and get straight - from a CASA ms, but don't currently know how to do that without bundling the - casacore utilities directly... - - Params: - filename - Name of file to read from. Should contain all the visibility data needed, - including u (Lambda), v (Lambda), real, imag, sigma, antenna1, and antenna 2. - - Returns: - visdata - A visdata object containing the data from filename. - """ - - if not filename.split('.')[-1].lower() in ['bin']: - raise ValueError('Only .bin files are supported for now...') - - data = np.fromfile(filename) - PBfwhm = data[-1] - data = data[:-1] - data = data.reshape(7,data.size/7) # bin files lose array shape, so reshape to match - - return Visdata(*data,PBfwhm=PBfwhm,filename=filename) - -def read_image(image,psf,noisemap=None,mask=None): - """ - Function to read in an image and create an ImageData object to hold it. - - Params: - image - numpy array, or name of .fits file to read from. Contains the actual image. - - psf - numpy array, or name of .fits file to read from. Contains PSF image. - - noisemap - numpy array, or name of .fits file to read from. An optional error map - on the image data. If None, the ImageData noisemap is initialized to be - sqrt(image). - - mask - numpy array, or name of .fits file to read from. An optional pixel - mask. Should be same size as image, where non-zero values will be - considered bad/masked. If None, initialized as zeros (no mask). - - Returns: - ImageData - An ImageData object. - """ - - return None - -def cart2pol(x,y): - """ - Convert a set of x,y Cartesian coordinates. Why isn't this in numpy or scipy already? - - Inputs: - x,y: Arrays or lists of coordinate pairs to transform - - Returns: - r,theta: Arrays of polar coordinates from the given x and y, theta in radians - """ - - x, y = np.asarray(x), np.asarray(y) - - r = np.sqrt(x**2. + y**2.) - theta = np.arctan2(y,x) - - return r, theta - -def pol2cart(r,theta): - """ - Convert a set of r,theta polar coordinates to Cartesian. - - Inputs: - r,theta: Arrays or lists of coordinate pairs to transform; assumes theta in radians - - Returns: - x,y: Arrays of Cartesian coordinates from the given r and theta - """ - - r, theta = np.asarray(r), np.asarray(theta) - - x = r * np.cos(theta) - y = r * np.sin(theta) - - return x,y - -def concatvis(visdatas): - """ - Concatenate multiple visibility sets into one larger set. - Does no consistency checking of any kind, so beware. - - :param visdatas: - List of visdata objects - - This method returns: - - * ``concatvis'' - The concatenated visibility set. - """ - - newu, newv, newr, newi = np.array([]),np.array([]),np.array([]),np.array([]) - news, newa1,newa2 = np.array([]),np.array([]),np.array([]) - - for vis in visdatas: - newu = np.concatenate((newu,vis.u)) - newv = np.concatenate((newv,vis.v)) - newr = np.concatenate((newr,vis.real)) - newi = np.concatenate((newi,vis.imag)) - news = np.concatenate((news,vis.sigma)) - newa1= np.concatenate((newa1,vis.ant1)) - newa2= np.concatenate((newa2,vis.ant2)) - - return Visdata(newu,newv,newr,newi,news,newa1,newa2,visdatas[0].PBfwhm,'Combined Data') - -def bin_visibilities(visdata,maxnewsize=None): - """ - WARNING: DOESN'T WORK CURRENTLY(?) - Bins up (ie, averages down) visibilities to reduce the total - number of them. Note that since we fit directly to the visibilities, - this is slightly different (and easier) than gridding in preparation for - imaging, as we won't need to FFT and so don't need a convolution function. - - :param visdata - A Visdata object. - :param maxnewsize = None - If desired, the maximum number of visibilities post-binning can - be specified. As long as this number meets other criteria (ie, - we don't have bin sizes smaller than an integration time or - bandwidth in wavelengths), the total number in the returned - Visdata will have fewer than maxnewsize visibilities. - - This method returns: - * ``BinnedVisibilities'' - A Visdata object containing binned visibilities. - """ - - if maxnewsize is None: maxnewsize = visdata.u.size/2 - - # Bins should be larger than an integration; strictly only valid for an EW array, - # and assumes a 20s integration time. Thus, this is a conservative estimate. - minbinsize = 20. * visdata.uvdist.max() / (24*3600.) - - # Bins should be smaller than the effective field size - maxbinsize = (visdata.PBfwhm * arcsec2rad)**-1 - - print minbinsize,maxbinsize - - # We're going to find a binning solution iteratively; this gets us set up - Nbins, binsizeunmet, Nvis, it, maxiter = [3000,3000], True, visdata.u.size, 0, 250 - - while (binsizeunmet or Nvis >= maxnewsize): - print Nbins - # Figure out how to bin up the data - counts,uedges,vedges,bins = stats.binned_statistic_2d( - visdata.u,visdata.v,values=visdata.real,statistic='count', - bins=Nbins) - - du, dv = uedges[1]-uedges[0], vedges[1]-vedges[0] - - # Check that our bins in u and v meet our conditions - if (du > minbinsize and du < maxbinsize and - dv > minbinsize and dv < maxbinsize): binsizeunmet = False - # Otherwise we have to adjust the number of bins to adjust their size... - #elif (du <= minbinsize or dv <= minbinsize): Nbins = int(Nbins/1.2) - #elif (du >= maxbinsize or dv >= maxbinsize): Nbins = int(Nbins*1.2) - elif du <= minbinsize: Nbins[0] = int(Nbins[0]/1.1); binsizeunmet=True - elif dv <= minbinsize: Nbins[1] = int(Nbins[1]/1.1); binsizeunmet=True - elif du >= maxbinsize: Nbins[0] = int(Nbins[0]*1.1); binsizeunmet=True - elif dv >= maxbinsize: Nbins[1] = int(Nbins[1]*1.1); binsizeunmet=True - - # If we still have more than the desired number of visibilities, make - # fewer bins (we'll loop after this). - if np.unique(bins).size > maxnewsize: Nbins[0],Nbins[1] = int(Nbins[0]/1.1),int(Nbins[1]/1.1) - Nvis = np.unique(bins).size - it += 1 - if it > maxiter: raise ValueError("It's impossible to split your data into that few bins! " - "Try setting maxnewsize to a larger value!") - print Nvis,du,dv - - - # Get us some placeholder arrays for the binned data - u,v,real,imag,sigma,ant1,ant2 = np.zeros((7,Nvis)) - - for i,filledbin in enumerate(np.unique(bins)): - # This tells us which visibilities belong to the current bin - points = np.where(bins==filledbin)[0] - # This unravels the indices to uedges,vedges from the binned_statistic binnumber - uloc = int(np.floor(filledbin/(vedges.size+1)) - 1) - vloc = int(filledbin - (vedges.size+1)*(uloc+1) - 1) - # Get our new data, place at center of uv bins - u[i],v[i] = uedges[uloc]+0.5*du, vedges[vloc]+0.5*dv - real[i],sumwt = np.average(visdata.real[points],weights=visdata.sigma[points]**-2.,returned=True) - imag[i] = np.average(visdata.imag[points],weights=visdata.sigma[points]**-2.) - sigma[i] = sumwt**-0.5 - # We can keep the antenna numbers if we've only selected points from the same baseline, - # otherwise get rid of them (CHECK IF MODELCAL FAILS WITH None ANTENNAS) - ant1[i] = visdata.ant1[points][0] if (visdata.ant1[points]==visdata.ant1[points][0]).all() else None - ant2[i] = visdata.ant2[points][0] if (visdata.ant2[points]==visdata.ant2[points][0]).all() else None - - return Visdata(u,v,real,imag,sigma,ant1,ant2,visdata.PBfwhm,'BIN{0}'.format(Nvis)+visdata.filename) - - -def box(u,v,deltau,deltav): - """ - Define a box/tophat/pillbox shaped convolution kernel. Bad for - reducing aliasing. - """ - - try: # Assumes u is already a 2D grid of values - conv = np.zeros(u.shape) - conv[u.shape[0]/2.,u.shape[1]/2.] = 1. - except: # Assumes u and v are still 1D coords - conv = np.zeros((u.size,v.size)) - conv[u.size/2.,v.size/2.] = 1. - - return conv - -def expsinc(u,v,deltau,deltav): - """ - Define a sinc*gaussian exponential convolution kernel, following - TMS Ch. 10. - """ - - alpha1 = 1.55 # See TMS, who cited... I forget, somebody. - alpha2 = 2.52 - - return np.sinc(u/(alpha1*deltau))*np.exp(-(u/(alpha2*deltau))**2.) * \ - np.sinc(v/(alpha1*deltav))*np.exp(-(v/(alpha2*deltav))**2.) - -def sersic_area(n,majaxis,axisratio): - """ - Calculate the total area (to R=infinity) of a Sersic profile. - Uses the Ciotti+Bertin 1999 approx to b_n. - - :param n - Sersic profile index. C&B1999 approx is good down to n~0.2 - - :param majaxis - Major axis of the profile, arbitrary units. - - :param axisratio - Ratio of minor/major axis. If >1, we use 1/axisratio instead. - - This method returns: - - * ``sersicarea'' - The profile area, in square-majaxis units. - """ - from scipy.special import gamma - # C&B1999 eq 18 - bn = 2*n - 1./3. + 4./(405.*n) + 46./(25515*n**2) + 131./(1148175*n**3) - 2194697./(30690717750*n**4) - # Graham&Driver 2005, eq 2. Note: exp(bn) term is due to using r_half [==r_eff] instead of r/r0, cf C&B1999 eq 4 - return 2*np.pi*n*majaxis**2 * axisratio * np.exp(bn) * gamma(2*n) / bn**(2*n) - - -def thetaE(ML,zL,zS,cosmo=None): - """ - Calculate the Einstein radius in arcsec of a lens of mass ML, - assuming redshifts zL and zS. If cosmo is None, WMAP9 - is assumed. - """ - - if cosmo is None: - from astropy.cosmology import WMAP9 - cosmo = WMAP9 - - Dd = WMAP9.angular_diameter_distance(zL).value # in Mpc - Ds = WMAP9.angular_diameter_distance(zS).value - Dds= WMAP9.angular_diameter_distance_z1z2(zL,zS).value - - thE = np.sqrt((4*G*ML*Msun*Dds) / (c**2 * Dd*Ds*Mpc)) * rad2arcsec - - return thE \ No newline at end of file diff --git a/uvgrid.py b/uvgrid.py deleted file mode 100644 index 1a3a00d..0000000 --- a/uvgrid.py +++ /dev/null @@ -1,66 +0,0 @@ -import numpy as np -from Data_objs import Visdata - -def box(u,v,du,dv): - return 1. - -def expsinc(u,v,du,dv): - a1, a2 = 1.55, 2.52 - return np.sinc(u/(a1*du))*np.exp(-(u/(a2*du))**2.) * \ - np.sinc(v/(a1*du))*np.exp(-(v/(a2*dv))**2.) - -def uvgrid(visdata,gridsize=256,binsize=2.0,convolution='box'): - """ - Grid a set of given visibilities onto a grid of gridsize x gridsize. - - Inputs: - visdata: - Any visdata object. - gridsize: - Size of grid to be binned onto, NxN. - binsize: - Size of uv bin. - convolution: - Which convolution kernel to use. Can be either 'box' or 'expsinc' - - Returns: - gridvis: - The visibilities gridded as requested. - """ - - if not convolution.lower() in ['box','expsinc']: - raise ValueError("Unrecognized convolution kernel; must be one of 'box','expsinc'") - - # Calculate the new u and v grids; create placeholders for the rest - nu = (np.arange(gridsize) - gridsize/2.)*binsize - newu,newv = np.meshgrid(nu,nu) - newr = np.zeros((gridsize,gridsize)) - newi = np.zeros((gridsize,gridsize)) - news = np.zeros((gridsize,gridsize)) - - i = np.round(visdata.u/binsize + gridsize/2. - 1) - j = np.round(visdata.v/binsize + gridsize/2.) - - if convolution.lower() == 'box': - conv_func, ninclude = box, 1 - elif convolution.lower() == 'expsinc': - conv_func, ninclude = expsinc, 7 - - incrange = np.arange(ninclude) - (ninclude-1)/2. - for k in range(visdata.u.size): - for l in incrange+j[k]: - for m in incrange+i[k]: - print k,l,m,j[k],i[k] - conv = conv_func(visdata.u[k]-newu[l,m],visdata.v[k]-newv[l,m],\ - binsize,binsize) - newr[l,m] += (visdata.real[k]*(visdata.sigma[k]**-2.)).sum()*conv - newi[l,m] += (visdata.imag[k]*(visdata.sigma[k]**-2.)).sum()*conv - news[l,m] += (visdata.sigma[k]**-2.).sum()**-0.5*conv - - newu = newu.reshape(gridsize**2) - newv = newv.reshape(gridsize**2) - newr = newr.reshape(gridsize**2) - newi = newi.reshape(gridsize**2) - news = news.reshape(gridsize**2) - - return visdata(newu,newv,newr,newi,news) diff --git a/uvimage.py b/uvimage.py deleted file mode 100644 index 5401912..0000000 --- a/uvimage.py +++ /dev/null @@ -1,75 +0,0 @@ -import numpy as np -from scipy.fftpack import fftshift,fft2 -from Data_objs import Visdata -import copy -from uvgrid import uvgrid -from utils import box,expsinc -arcsec2rad = np.pi/180./3600. -c = 2.99792458e8 # in m/s - -__all__ = ['uvimageslow'] - -def uvimageslow(visdata,imsize=256,pixsize=0.5,taper=0.): - """ - Invert a set of visibilities to the image plane in the slowest way possible - (ie, no gridding of visibilities, just straight-up summation). This gets - pretty darn close to imaging with CASA with natural weighting, no cleaning. - - Inputs: - visdata: - Any Visdata object - imsize: - Size of the output image in pixels - pixsize: - Pixel size, in arcseconds - taper: - Apply an additional gaussian taper to the - visibilities with sigma of this, in arcsec. - - Returns: - image: - A 2D array containing the image of the inverted visibilities - """ - - thisvis = copy.deepcopy(visdata) - - if taper > 0.: - uvsig = (taper * arcsec2rad)**-1. - scalefac = np.exp(-thisvis.uvdist**2/(2*uvsig**2.)) - thisvis.sigma /= scalefac - - x = np.linspace(-imsize*pixsize/2. * arcsec2rad,+imsize*pixsize/2. * arcsec2rad,imsize) - x,y = np.meshgrid(x,x) - # These offsets line us up with CASA's coord system - x -= pixsize/2. * arcsec2rad - y += pixsize/2. * arcsec2rad - - im = np.array(np.zeros(x.shape),dtype=complex) - - # Check to see if we have the conjugate visibilities, and image. - if np.all(thisvis.u[:thisvis.u.size/2] == -thisvis.u[thisvis.u.size/2:]): - for i in range(thisvis.u.size/2): - im += (thisvis.sigma[i]**-2)*(thisvis.real[i]+1j*thisvis.imag[i]) *\ - np.exp(2*np.pi*1j*((thisvis.u[i]*x)+(thisvis.v[i]*y))) - else: - for i in range(thisvis.u.size): - im += (thisvis.sigma[i]**-2)*(thisvis.real[i]+1j*thisvis.imag[i]) *\ - np.exp(2*np.pi*1j*((thisvis.u[i]*x)+(thisvis.v[i]*y))) - - # We only imaged the non-conjugate visibilities; fix and renormalize to Jy/beam units - im = 2*im.real - im /= (2*thisvis.sigma**-2).sum() - - return im - -def uvimage(visdata,imsize=256,pixsize=0.5,weighting='natural',convolution='expsinc'): - """ - Faster imaging by gridding then FFTs. - """ - - # First grid up the data... - binsize = (pixsize*imsize*arcsec2rad)**-1. - gridded_data = uvgrid(visdata,gridsize=imsize,binsize=binsize,convolution=convolution) - - - diff --git a/LensModelMCMC.py b/visilens/LensModelMCMC.py similarity index 90% rename from LensModelMCMC.py rename to visilens/LensModelMCMC.py index d1eb3e6..9e9b4c2 100644 --- a/LensModelMCMC.py +++ b/visilens/LensModelMCMC.py @@ -1,19 +1,21 @@ import numpy as np import scipy.sparse -import scipy.sparse.linalg import os import sys import emcee import copy -from Model_objs import * -from GenerateLensingGrid import GenerateLensingGrid +from astropy.cosmology import WMAP9 +from class_utils import * +from lensing import * +from utils import * from calc_likelihood import calc_vis_lnlike + arcsec2rad = np.pi/180/3600 -def LensModelMCMC(data,lens,source,shear=None, +def LensModelMCMC(data,lens,source, xmax=30.,highresbox=[-3.,3.,3.,-3.],emitres=None,fieldres=None, sourcedatamap=None, scaleamp=False, shiftphase=False, - modelcal=True,cosmo=None, + modelcal=True,cosmo=WMAP9, nwalkers=1e3,nburn=1e3,nstep=1e3,pool=None,nthreads=1,mpirun=False): """ Wrapper function which basically takes what the user wants and turns it into the @@ -24,12 +26,10 @@ def LensModelMCMC(data,lens,source,shear=None, One or more visdata objects; if multiple datasets are being fit to, should be a list of visdata objects. lens: - Any of the currently implemented lens objects. + Any of the currently implemented lens objects or ExternalShear. source: One or more of the currently implemented source objects; if more than one source to be fit, should be a list of multiple sources. - shear: - An ExternalShear object, or None (default) if no external shear desired xmax: (Half-)Grid size, in arcseconds; the grid will span +/-xmax in x&y highresbox: @@ -119,6 +119,12 @@ def LensModelMCMC(data,lens,source,shear=None, ndim += 1 p0.append(vars(ilens)[key]['value']) colnames.append(key+'L'+str(i)) + elif ilens.__class__.__name__=='ExternalShear': + for key in ['shear','shearangle']: + if not vars(ilens)[key]['fixed']: + ndim += 1 + p0.append(vars(ilens)[key]['value']) + colnames.append(key) # Then source(s) for i,src in enumerate(source): if src.__class__.__name__=='GaussSource': @@ -139,13 +145,6 @@ def LensModelMCMC(data,lens,source,shear=None, ndim += 1 p0.append(vars(src)[key]['value']) colnames.append(key+'S'+str(i)) - # Then shear - if shear is not None: - for key in ['shear','shearangle']: - if not vars(shear)[key]['fixed']: - ndim += 1 - p0.append(vars(shear)[key]['value']) - colnames.append(key) # Then flux rescaling; only matters if >1 dataset for i,t in enumerate(scaleamp[1:]): if t: @@ -163,7 +162,7 @@ def LensModelMCMC(data,lens,source,shear=None, # Get any model-cal parameters set up. The process involves some expensive # matrix inversions, but these only need to be done once, so we'll do them # now and pass the results as arguments to the likelihood function. See docs - # in modelcal.py for more info. + # in calc_likelihood.model_cal for more info. for i,dset in enumerate(data): if modelcal[i]: uniqant = np.unique(np.asarray([dset.ant1,dset.ant2]).flatten()) @@ -190,7 +189,7 @@ def LensModelMCMC(data,lens,source,shear=None, # Calculate some distances; we only need to calculate these once. # This assumes multiple sources are all at same z; should be this # way anyway or else we'd have to deal with multiple lensing planes - if cosmo is None: from astropy.cosmology import WMAP9 as cosmo + if cosmo is None: cosmo = WMAP9 Dd = cosmo.angular_diameter_distance(lens[0].z).value Ds = cosmo.angular_diameter_distance(source[0].z).value Dds= cosmo.angular_diameter_distance_z1z2(lens[0].z,source[0].z).value @@ -202,21 +201,27 @@ def LensModelMCMC(data,lens,source,shear=None, isangle = np.array([0.30 if 'PA' in s or 'angle' in s else 0.1 for s in colnames]) initials = emcee.utils.sample_ball(p0,np.asarray([isangle[i]*x if x else 0.05 for i,x in enumerate(p0)]),int(nwalkers)) + # All the lens objects know if their parameters have been altered since the last time + # we calculated the deflections. If all the lens pars are fixed, we only need to do the + # deflections once. This step ensures that the lens object we create the sampler with + # has these initial deflections. + for i,ilens in enumerate(lens): + if ilens.__class__.__name__ == 'SIELens': ilens.deflect(xemit,yemit,Dd,Ds,Dds) + elif ilens.__class__.__name__ == 'ExternalShear': ilens.deflect(xemit,yemit,lens[0]) + # Create the sampler object; uses calc_likelihood function defined elsewhere lenssampler = emcee.EnsembleSampler(nwalkers,ndim,calc_vis_lnlike, - args = [data,lens,source,shear,Dd,Ds,Dds,ug, + args = [data,lens,source,Dd,Ds,Dds,ug, xmap,ymap,xemit,yemit,indices, sourcedatamap,scaleamp,shiftphase,modelcal], threads=nthreads,pool=pool) - #return colnames,initials,lenssampler # Run burn-in phase print "Running burn-in... " #pos,prob,rstate,mus = lenssampler.run_mcmc(initials,nburn,storechain=False) for i,result in enumerate(lenssampler.sample(initials,iterations=nburn,storechain=False)): - # WTF, el gato? - print 'Burn-in step ',i,'/',nburn + if i%20==0: print 'Burn-in step ',i,'/',nburn pos,prob,rstate,blob = result @@ -225,7 +230,7 @@ def LensModelMCMC(data,lens,source,shear=None, # Run actual chains print "Done. Running chains... " for i,result in enumerate(lenssampler.sample(pos,rstate0=rstate,iterations=nstep,storechain=True)): - print 'Chain step ',i,'/',nstep + if i%20==0: print 'Chain step ',i,'/',nstep #lenssampler.run_mcmc(pos,nstep,rstate0=rstate) if mpirun: pool.close() @@ -255,7 +260,7 @@ def LensModelMCMC(data,lens,source,shear=None, # but it's fine. use --mca mpi_warn_on_fork 0 in the mpirun statement to disable try: import subprocess - gitd = os.path.dirname(__file__) + gitd = os.path.abspath(os.path.join(os.path.dirname(__file__),os.pardir)) mcmcresult['githash'] = subprocess.check_output('git --git-dir={0:s} --work-tree={1:s} '\ 'rev-parse HEAD'.format(gitd+'/.git',gitd),shell=True).rstrip() except: @@ -266,7 +271,6 @@ def LensModelMCMC(data,lens,source,shear=None, mcmcresult['lens_p0'] = lens # Initial params for lens,src(s),shear; also tells if fixed, priors, etc. mcmcresult['source_p0'] = source - if shear: mcmcresult['shear_p0'] = shear if sourcedatamap: mcmcresult['sourcedatamap'] = sourcedatamap mcmcresult['xmax'] = xmax @@ -291,6 +295,11 @@ def LensModelMCMC(data,lens,source,shear=None, if not vars(ilens)[key]['fixed']: ilens.__dict__[key]['value'] = np.median(c[key+'L'+str(i)]) pbest.append(np.median(c[key+'L'+str(i)])) + elif ilens.__class__.__name__ == 'ExternalShear': + for key in ['shear','shearangle']: + if not vars(ilens)[key]['fixed']: + ilens.__dict__[key]['value'] = np.median(c[key]) + pbest.append(np.median(c[key])) mcmcresult['best-fit']['lens'] = lens @@ -315,16 +324,6 @@ def LensModelMCMC(data,lens,source,shear=None, mcmcresult['best-fit']['source'] = source mcmcresult['best-fit']['magnification'] = np.median(mus[~bad],axis=0) - # now do shear, if any - if 'shear_p0' in mcmcresult.keys(): - shear = mcmcresult['shear_p0'] - for key in ['shear','shearangle']: - if not vars(shear)[key]['fixed']: - shear.__dict__[key]['value'] = np.median(c[key]) - pbest.append(np.median(c[key])) - mcmcresult['best-fit']['shear'] = shear - else: mcmcresult['best-fit']['shear'] = None - # Any amplitude scaling or astrometric shifts bfscaleamp = np.ones(len(data)) if 'scaleamp' in mcmcresult.keys(): @@ -349,7 +348,7 @@ def LensModelMCMC(data,lens,source,shear=None, mcmcresult['best-fit']['shiftphase'] = bfshiftphase mcmcresult['best-fit']['lnlike'] = calc_vis_lnlike(pbest,data,mcmcresult['best-fit']['lens'], - mcmcresult['best-fit']['source'],mcmcresult['best-fit']['shear'], + mcmcresult['best-fit']['source'], Dd,Ds,Dds,ug,xmap,ymap,xemit,yemit,indices, sourcedatamap,scaleamp,shiftphase,modelcal)[0] @@ -368,6 +367,4 @@ def LensModelMCMC(data,lens,source,shear=None, else: if any(modelcal): mcmcresult['calphases_dset0'] = dphases - return mcmcresult,lenssampler.flatchain,lenssampler.blobs,colnames - - + return mcmcresult \ No newline at end of file diff --git a/visilens/__init__.py b/visilens/__init__.py new file mode 100644 index 0000000..d9d4ecd --- /dev/null +++ b/visilens/__init__.py @@ -0,0 +1,7 @@ +from LensModelMCMC import LensModelMCMC +from utils import * +from class_utils import * +from lensing import * +from calc_likelihood import * +from triangleplot import * +from plot_images import * \ No newline at end of file diff --git a/calc_likelihood.py b/visilens/calc_likelihood.py similarity index 60% rename from calc_likelihood.py rename to visilens/calc_likelihood.py index 754df3a..3bae9b0 100644 --- a/calc_likelihood.py +++ b/visilens/calc_likelihood.py @@ -1,22 +1,22 @@ import numpy as np from PIL import Image from scipy.fftpack import fftshift,fft2 +from scipy.special import gamma from scipy.interpolate import RectBivariateSpline -from scipy.ndimage.filters import convolve -from astropy.io import fits import copy -from Model_objs import * -from RayTracePixels import * -from SourceProfile import SourceProfile -from Data_objs import Visdata -from modelcal import model_cal +from lensing import * +from class_utils import * +from utils import * + arcsec2rad = np.pi/180/3600 +rad2arcsec = 3600.*180./np.pi +deg2rad = np.pi/180. -__all__ = ['calc_vis_lnlike','calc_im_lnlike_galfit','pass_priors', - 'create_modelimage','fft_interpolate','logp','logl'] +__all__ = ['calc_vis_lnlike','pass_priors','SourceProfile', + 'create_modelimage','fft_interpolate','model_cal','logp','logl'] -def calc_vis_lnlike(p,data,lens,source,shear, +def calc_vis_lnlike(p,data,lens,source, Dd,Ds,Dds,ug,xmap,ymap,xemit,yemit,indices, sourcedatamap=None,scaleamp=False,shiftphase=False,modelcal=True): """ @@ -54,8 +54,8 @@ def calc_vis_lnlike(p,data,lens,source,shear, # First we'll take care of the priors implemented on the parameters. # If we pass them all, `pass_priors' returns updated versions of all the objects # Otherwise, we got False, and unpacking all those values raises a TypeError. - x = pass_priors(p,lens,source,shear,scaleamp,shiftphase) - try: thislens,thissource,thisshear,thisascale,thispshift = x + x = pass_priors(p,lens,source,scaleamp,shiftphase) + try: thislens,thissource,thisascale,thispshift = x except TypeError: return -np.inf,[np.nan] # Ok, if we've made it this far we can do the actual likelihood calculation @@ -67,7 +67,7 @@ def calc_vis_lnlike(p,data,lens,source,shear, for i,dset in enumerate(data): # Make a model of this field. - immap,mags = create_modelimage(thislens,thissource,thisshear,\ + immap,mags = create_modelimage(thislens,thissource,\ xmap,ymap,xemit,yemit,indices,Dd,Ds,Dds,sourcedatamap[i]) # Filter non-zero magnifications (only relevant if sourcedatamap) @@ -89,119 +89,8 @@ def calc_vis_lnlike(p,data,lens,source,shear, if np.isnan(lnL): return -np.inf,[np.nan] return lnL,[mus,dphases] - - -def calc_im_lnlike_galfit(p,image,sigma,psf,lens,source,shear, - Dd,Ds,Dds): - """ - Calculates log-likelihood of the given parameters. - - Inputs: - p: - A numpy array of arbitrary length, generated elsewhere to contain - all the quantities we're fitting for. Has to be this way for emcee - compatibility. - image: - An astropy.io PrimaryHDU object, modified to contain attributes - related to lensing calculations by ImageModelMCMC - sigma: - An ndarray representing the uncertainty map of the above image. - psf: - An ndarray representing the PSF the above image has been convolved with - lens: - A lens object; will contain info about any fixed params, priors, etc. - source: - A source object or list of them. - shear: - A shear object (or None) - - Returns: - lnL: - The log-likelihood of the model given the data. - mu: - The magnification(s) of the lensed source(s) - """ - - # First we'll take care of the priors implemented on the parameters. - # If we pass them all, `pass_priors' returns updated versions of all the objects - # Otherwise, we got False, and unpacking all those values raises a TypeError. - image = copy.deepcopy(image) - x = pass_priors(p,lens,source,shear,[False],[False]) - try: thislens,thissource,thisshear,foo,bar = x - except TypeError: return -np.inf,[np.nan] - - # Ok, if we've made it this far we can do the actual likelihood calculation - - # First, we'll set up the initial part of the galfit file, ie everything except source specification - # --- TODO --- - - # Do the raytracing for this set of lens & shear params - LensRayTrace(image.xemit,image.yemit,thislens,Dd,Ds,Dds,thisshear) - - # Now loop through all the datasets, fitting the requested sources to each. We'll also calculate - # magnifications for each source, defined as the sum of the output flux / input flux - # Thus, the returned magnification will be an array of length (# of unique sources) - lnL,mags = 0., np.zeros(len(thissource)) - - # Two arrays, one of the lensed sources, the other the full field and any unlensed sources - imsrc = np.zeros(image.xemit.shape) - immap = np.zeros(image.x.shape) - - for j,src in enumerate(thissource): - if thissource.lensed: - ims = SourceProfile(xsrc,ysrc,src,thislens) - imsrc += ims - mags[j] = ims.sum()*(image.xemit[0,1]-image.xemit[0,0])**2./src.flux['value'] - else: - with open(galfitfile,'a') as f: # Here we'll append the info about this source to the GALFIT file - if isinstance(src,GaussSource): # a symmetric gaussian - f.write('0) gaussian\n') - # Calculate source position in pixels, galfit measures origin at image lower left(?) - xloc = (src.xoff['value']+thislens.x['value']-image.x.min()) / (image.x[0,1]-image.x[0,0]) - yloc = (src.yoff['value']+thislens.y['value']-image.y.min()) / (image.y[1,0]-image.y[0,0]) - varx = s - f.write('1) {0:.1f} {1:.1f} {2:.0f} {3:.0f}'.format(xloc,yloc,int(src.xoff['fixed']),int(src.yoff['fixed']))) - # Add in "total magnitude" - Jingzhe, I don't know what this means exactly... - f.write('2) {0:.3f} {1:.0f}'.format(src.flux['value'],int(src.flux['fixed']))) - # And the source FWHM - fwhmpix = (2*np.sqrt(2*np.log(2))*src.width['value'])/(image.xmap[0,1]-image.xmap[0,0]) - f.write('3) {0:.3f} {1:.0f}'.format(fwhmpix,int(src.width['fixed']))) - # the rest we can just do; it's a circular gaussian - # not sure what the Z) row is actually doing.... - f.write('9) 1.0 0\n10) 0.0 0\nZ) 0') - elif isinstance(src,SersicSource): # A generic sersic profile, arbitrary PA - pass # TODO, blarg pixel coords are annoying - mags[j] = 1. - - - - # Try to reproduce matlab's antialiasing thing; this uses a 3lobe lanczos low-pass filter - imsrc = Image.fromarray(imsrc) - resize = np.array(imsrc.resize((int(image.indices[1]-image.indices[0]),int(image.indices[3]-image.indices[2])),Image.ANTIALIAS)) - immap[image.indices[2]:image.indices[3],image.indices[0]:image.indices[1]] += resize - - - # Okay, now we have our lensed emission model, let's convolve it with the PSF - # and subtract from the data before sending to galfit - imconv = convolve(immap*(image.x[0,1]-image.x[0,0])**2.,psf,mode='constant') - image.data -= imconv - image.writeto('galfit_input.fits') - # Now we run galfit on the difference image... - os.system('galfit '+galfitfile) - - # At this point, we read in either the model image (and do the chi-2 calculation ourselves) - # or the residual image, in which case it's just the sum of the resid/sigma**2. Below assumes we - # have the residual image... is that right? - resid = fits.open('galfit_output.fits')[0] - lnL = -(resid**2./sigma**2.).sum() - - # Last-ditch attempt to keep from hanging - if np.isnan(lnL): lnL = -np.inf - - return lnL,[mags] - -def pass_priors(p,lens,source,shear,scaleamp,shiftphase): +def pass_priors(p,lens,source,scaleamp,shiftphase): """ Figures out if any of the proposed values in `p' exceed the priors defined by each object. @@ -214,8 +103,6 @@ def pass_priors(p,lens,source,shear,scaleamp,shiftphase): Any of the currently implemented lens objects source Any one or a list of sources - shear - An external shear object, or None. scaleamp Either a single True/False value, list of these, or None. shiftphase @@ -230,7 +117,6 @@ def pass_priors(p,lens,source,shear,scaleamp,shiftphase): # in `p', we muse make copies to avoid effing things up. thislens = copy.deepcopy(lens) thissource = copy.deepcopy(source) - thisshear = copy.deepcopy(shear) thisascale = copy.deepcopy(scaleamp) thispshift = copy.deepcopy(shiftphase) @@ -241,6 +127,14 @@ def pass_priors(p,lens,source,shear,scaleamp,shiftphase): if not vars(ilens)[key]['fixed']: # A uniform prior if p[ip] < vars(ilens)[key]['prior'][0] or p[ip] > vars(ilens)[key]['prior'][1]: return False + thislens[i]._altered = True + thislens[i].__dict__[key]['value'] = p[ip] + ip += 1 + elif ilens.__class__.__name__=='ExternalShear': + for key in ['shear','shearangle']: + if not vars(ilens)[key]['fixed']: + if p[ip] < vars(ilens)[key]['prior'][0] or p[ip] > vars(ilens)[key]['prior'][1]: return False + thislens[i]._altered = True thislens[i].__dict__[key]['value'] = p[ip] ip += 1 # now do the source(s) @@ -263,13 +157,8 @@ def pass_priors(p,lens,source,shear,scaleamp,shiftphase): if p[ip] < vars(src)[key]['prior'][0] or p[ip] > vars(src)[key]['prior'][1]: return False thissource[i].__dict__[key]['value'] = p[ip] ip += 1 - # now do shear, if any - if shear is not None: - for key in ['shear','shearangle']: - if not vars(shear)[key]['fixed']: - if p[ip] < vars(shear)[key]['prior'][0] or p[ip] > vars(shear)[key]['prior'][1]: return False - thisshear.__dict__[key]['value'] = p[ip] - ip += 1 + + # Amplitude re-scaling for multiple datasets for i,t in enumerate(scaleamp): # only matters if >1 datasets if i==0: thisascale[i] = 1. elif t: @@ -287,10 +176,102 @@ def pass_priors(p,lens,source,shear,scaleamp,shiftphase): ip += 2 else: thispshift[i] = [0.,0.] # no shifting - return thislens,thissource,thisshear,thisascale,thispshift + return thislens,thissource,thisascale,thispshift + + +def SourceProfile(xsource,ysource,source,lens): + """ + Creates the source-plane profile of the given Source. + + Inputs: + xsource,ysource: + Source-plane coordinates, in arcsec, on which to + calculate the luminosity profile of the source + + Source: + Any supported source-plane object, e.g. a GaussSource + object. The object will contain all the necessary + parameters to create the profile. + + Lens: + Any supported Lens object, e.g. an SIELens. We only need + this because, in the case of single lenses, the source + position is defined as offset from the lens centroid. If + there is more than one lens, or if the source is unlensed, + the source position is defined **relative to the field + center, aka (0,0) coordinates**. + + Returns: + I: + The luminosity profile of the given Source. Has same + shape as xsource and ysource. Note: returned image has + units of flux / arcsec^2 (or whatever the x,y units are), + so to properly normalize, must multiply by pixel area. This + isn't done here since the lensing means the pixels likely + aren't on a uniform grid. + """ + + lens = list(np.array([lens]).flatten()) + + # First case: a circular Gaussian source. + if source.__class__.__name__=='GaussSource': + sigma = source.width['value'] + amp = source.flux['value']/(2.*np.pi*sigma**2.) + if source.lensed:# and len(lens)==1: + xs = source.xoff['value'] + lens[0].x['value'] + ys = source.yoff['value'] + lens[0].y['value'] + else: + xs = source.xoff['value'] + ys = source.yoff['value'] + + return amp * np.exp(-0.5 * (np.sqrt((xsource-xs)**2.+(ysource-ys)**2.)/sigma)**2.) + + elif source.__class__.__name__=='SersicSource': + if source.lensed:# and len(lens)==1: + xs = source.xoff['value'] + lens[0].x['value'] + ys = source.yoff['value'] + lens[0].y['value'] + else: + xs = source.xoff['value'] + ys = source.yoff['value'] + PA, ar = source.PA['value']*deg2rad, source.axisratio['value'] + reff, index = source.reff['value'], source.index['value'] + dX = (xsource-xs)*np.cos(PA) + (ysource-ys)*np.sin(PA) + dY = (-(xsource-xs)*np.sin(PA) + (ysource-ys)*np.cos(PA))/ar + R = np.sqrt(dX**2. + dY**2.) + + # Calculate b_n, to make reff enclose half the light; this approx from Ciotti&Bertin99 + # This approximation good to 1 in 10^4 for n > 0.36; for smaller n it gets worse rapidly!! + bn = 2*index - 1./3. + 4./(405*index) + 46./(25515*index**2) + 131./(1148175*index**3) - 2194697./(30690717750*index**4) + + # Backing out from the integral to R=inf of a general sersic profile + Ieff = source.flux['value'] * bn**(2*index) / (2*np.pi*reff**2 * ar * np.exp(bn) * index * gamma(2*index)) + + return Ieff * np.exp(-bn*((R/reff)**(1./index)-1.)) + + elif source.__class__.__name__=='PointSource': + if source.lensed:# and len(lens)==1: + xs = source.xoff['value'] + lens[0].x['value'] + ys = source.yoff['value'] + lens[0].y['value'] + return ValueError("Lensed point sources not working yet... try a"\ + "gaussian with small width instead...") + else: + xs = source.xoff['value'] + ys = source.yoff['value'] + + yloc = np.abs(xsource[0,:] - xs).argmin() + xloc = np.abs(ysource[:,0] - ys).argmin() + + m = np.zeros(xsource.shape) + m[xloc,yloc] += source.flux['value']/(xsource[0,1]-xsource[0,0])**2. + + return m + + + else: raise ValueError("So far only GaussSource, SersicSource, and "\ + "PointSource objects supported...") -def create_modelimage(lens,source,shear,xmap,ymap,xemit,yemit,indices, +def create_modelimage(lens,source,xmap,ymap,xemit,yemit,indices, Dd=None,Ds=None,Dds=None,sourcedatamap=None): """ Creates a model lensed image given the objects and map @@ -320,7 +301,7 @@ def create_modelimage(lens,source,shear,xmap,ymap,xemit,yemit,indices, Dds= cosmo.angular_diameter_distance_z1z2(lens[0].z,source[0].z).value # Do the raytracing for this set of lens & shear params - xsrc,ysrc = LensRayTrace(xemit,yemit,lens,Dd,Ds,Dds,shear) + xsrc,ysrc = LensRayTrace(xemit,yemit,lens,Dd,Ds,Dds) if sourcedatamap is not None: # ... then particular source(s) are specified for this map for jsrc in sourcedatamap: @@ -391,6 +372,61 @@ def fft_interpolate(visdata,immap,xmap,ymap,ug=None,scaleamp=1.,shiftphase=[0.,0 return interpdata +def model_cal(realdata,modeldata,dPhi_dphi=None,FdPC=None): + """ + Routine following Hezaveh+13 to implement perturbative phase corrections + to model visibility data. This routine is designed to start out from an + intermediate step in the self-cal process, since we can avoid doing a + lot of expensive matrix inversions that way, but if either of the + necessary arrays aren't provided, we calculate them. + + Inputs: + realdata,modeldata: + visdata objects containing the actual data and model-generated data + + dPhi_dphi: None, or pre-calculated + See H+13, App A. An N_ant-1 x M_vis matrix whose ik'th element is 1 + if the first antenna of the visibility is k, -1 if the second, or 0. + + FdPC: None, or pre-calculated + See H+13, App A, eq A2. This is an N_ant-1 x M_vis matrix, equal to + -inv(F)*dPhi_dphi*inv(C) in the nomenclature of H+13. This has the + matrix inversions that we want to avoid calculating at every MCMC + iteration (inverting C takes ~3s for M~5k, even with a sparse matrix). + + Outputs: + modelcaldata: + visdata object containing updated visibilities + + dphi: + Array of length N_ant-1 containing the implemented phase offsets + """ + + # If we don't have the pre-calculated arrays, do so now. It's expensive + # to do these matrix inversions at every MCMC step. + if np.any((FdPC is None, dPhi_dphi is None)): + import scipy.sparse + uniqant = np.unique(np.asarray([realdata.ant1,realdata.ant2]).flatten()) + dPhi_dphi = np.zeros((uniqant.size-1,realdata.u.size)) + for j in range(1,uniqant.size): + dPhi_dphi[j-1,:] = (realdata.ant1==uniqant[j])-1*(realdata.ant2==uniqant[j]) + C = scipy.sparse.diags((realdata.sigma/realdata.amp)**-2.,0) + F = np.dot(dPhi_dphi,C*dPhi_dphi.T) + Finv = np.linalg.inv(F) + FdPC = np.dot(-Finv,dPhi_dphi*C) + + # Calculate current phase difference between data and model; wrap to +/- pi + deltaphi = realdata.phase - modeldata.phase + deltaphi = (deltaphi + np.pi) % (2 * np.pi) - np.pi + + dphi = np.dot(FdPC,deltaphi) + + modelcaldata = copy.deepcopy(realdata) + + modelcaldata.phase += np.dot(dPhi_dphi.T,dphi) + + return modelcaldata,dphi + def logp(p): return 0.0 # flat prior, this is handled in calc_likelihood def logl(p,**kwargs): return calc_vis_lnlike(p,**kwargs)[0] # PT sampler can't deal with blobs diff --git a/visilens/class_utils.py b/visilens/class_utils.py new file mode 100644 index 0000000..ca60b73 --- /dev/null +++ b/visilens/class_utils.py @@ -0,0 +1,680 @@ +__all__ = ['Visdata','SersicSource','GaussSource','PointSource','SIELens','ExternalShear', + 'read_visdata','concatvis','bin_visibilities'] + +import numpy as np +import astropy.constants as co +from utils import cart2pol,pol2cart + +c = co.c.value # speed of light, in m/s +G = co.G.value # gravitational constant in SI units +Msun = co.M_sun.value # solar mass, in kg +Mpc = 1e6*co.pc.value # 1 Mpc, in m +arcsec2rad = (np.pi/(180.*3600.)) +rad2arcsec =3600.*180./np.pi +deg2rad = np.pi/180. + +class Visdata(object): + """ + Class to hold all necessary info relating to one set of visibilities. + Auto-updates amp&phase or real&imag if those values are changed, but + MUST SET WITH, eg, visobj.amp = (a numpy array of the new values); + CANNOT USE, eg, visobj.amp[0] = newval, AS THIS DOES NOT CALL THE + SETTER FUNCTIONS. + + Parameters: + u numpy ndarray + The Fourier plane u coordinates of the visibilities to follow. + v numpy ndarray + The Fourier plane v coordinates of the visibilities to follow. + real numpy ndarray + The real parts of the visibilities + imag numpy ndarray + The imaginary parts of the visibilities + ant1 numpy ndarray + The first antenna number or name of the visibility on each baseline + ant2 numpy ndarray + The second antenna number or name of the visibility on each baseline + PBfwhm float + The FWHM of the antenna primary beam at this wavelength (at present + assumes a homogeneous antenna array) + filename str + A filename associated with these data. + """ + + def __init__(self,u,v,real,imag,sigma,ant1=None,ant2=None,PBfwhm=None,filename=None): + self.u = u + self.v = v + self.real = real + self.imag = imag + self.sigma = sigma + self.ant1 = ant1 + self.ant2 = ant2 + self.PBfwhm = PBfwhm + self.filename = filename + + @property + def uvdist(self): + return np.sqrt(self.u**2. + self.v**2.) + + @property + def real(self): + return self._real + @real.setter + def real(self,val): + self._real = val + # Setting amp & phase during __init__ will fail since imag is still unknown + # Doing so during conjugate() will also fail, but gives a ValueError + try: + self._amp = np.sqrt(self._real**2. + self.imag**2.) + self._phase = np.arctan2(self.imag,self._real) + except (AttributeError,ValueError): + self._amp = None + self._phase = None + + @property + def imag(self): + return self._imag + @imag.setter + def imag(self,val): + self._imag = val + try: + self._amp = np.sqrt(self.real**2. + self._imag**2.) + self._phase = np.arctan2(self._imag,self.real) + except (AttributeError,ValueError): + self._amp = None + self._phase = None + + @property + def amp(self): + return self._amp + @amp.setter + def amp(self,val): + self._amp = val + self._real = val * np.cos(self.phase) + self._imag = val * np.sin(self.phase) + + @property + def phase(self): + return self._phase + @phase.setter + def phase(self,val): + self._phase = val + self._real = self.amp * np.cos(val) + self._imag = self.amp * np.sin(val) + + def __add__(self,other): + return Visdata(self.u,self.v,self.real+other.real,self.imag+other.imag,\ + (self.sigma**-2. + other.sigma**-2.)**-0.5) + + def __sub__(self,other): + return Visdata(self.u,self.v,self.real-other.real,self.imag-other.imag,\ + (self.sigma**-2. + other.sigma**-2.)**-0.5) + + def conjugate(self): + u = np.concatenate((self.u,-self.u)) + v = np.concatenate((self.v,-self.v)) + real = np.concatenate((self.real,self.real)) + imag = np.concatenate((self.imag,-self.imag)) + sigma = np.concatenate((self.sigma,self.sigma)) + ant1 = np.concatenate((self.ant1,self.ant2)) + ant2 = np.concatenate((self.ant2,self.ant1)) + self.u = u + self.v = v + self.real = real + self.imag = imag + self.sigma = sigma + self.ant1 = ant1 + self.ant2 = ant2 + +class SIELens(object): + """ + Class to hold parameters for an SIE lens, with each parameter (besides + redshift) a dictionary. + + Example format of each parameter: + x = {'value':x0,'fixed':False,'prior':[xmin,xmax]}, where x0 is the + initial/current value of x, x should not be a fixed parameter during fitting, + and the value of x must be between xmin and xmax. + + Note: in my infinite future free time, will probably replace e and PA with + the x and y components of the ellipticity, which are better behaved as e->0. + + Parameters: + z + Lens redshift. If unknown, any value can be chosen as long as it is + less than the source redshift you know/assume. + x, y + Position of the lens, in arcseconds relative to the phase center of + the data (or any other reference point of your choosing). +x is west + (sorry not sorry), +y is north. + M + Lens mass, in Msun. With the lens and source redshifts, sets the + overall "strength" of the lens. Can be converted to an Einstein radius + using theta_Ein = (4*G*M * D_LS / (c**2 * D_L * D_S))**0.5, in radians, + with G and c the gravitational constant and speed of light, and D_L, D_S + and D_LS the distances to the lens, source, and between the lens and source, + respectively. + e + Lens ellipticity, ranging from 0 (a circularly symmetric lens) to 1 (a very + elongated lens). + PA + Lens major axis position angle, in degrees east of north. + """ + def __init__(self,z,x,y,M,e,PA): + # Do some input handling. + if not isinstance(x,dict): + x = {'value':x,'fixed':False,'prior':[-30.,30.]} + if not isinstance(y,dict): + y = {'value':y,'fixed':False,'prior':[-30.,30.]} + if not isinstance(M,dict): + M = {'value':M,'fixed':False,'prior':[1e7,1e15]} + if not isinstance(e,dict): + e = {'value':e,'fixed':False,'prior':[0.,1.]} + if not isinstance(PA,dict): + PA = {'value':PA,'fixed':False,'prior':[0.,180.]} + + if not all(['value' in d for d in [x,y,M,e,PA]]): + raise KeyError("All parameter dicts must contain the key 'value'.") + + if not 'fixed' in x: x['fixed'] = False + if not 'fixed' in y: y['fixed'] = False + if not 'fixed' in M: M['fixed'] = False + if not 'fixed' in e: e['fixed'] = False + if not 'fixed' in PA: PA['fixed'] = False + + if not 'prior' in x: x['prior'] = [-30.,30.] + if not 'prior' in y: y['prior'] = [-30.,30.] + if not 'prior' in M: M['prior'] = [1e7,1e15] + if not 'prior' in e: e['prior'] = [0.,1.] + if not 'prior' in PA: PA['prior'] = [0.,180.] + + self.z = z + self.x = x + self.y = y + self.M = M + self.e = e + self.PA = PA + + # Here we keep a Boolean flag which tells us whether one of the lens + # properties has changed since the last time we did the lensing + # deflections. If everything is the same, we don't need to lens twice. + self._altered = True + + def deflect(self,xim,yim,Dd,Ds,Dds): + """ + Follow Kormann+1994 for the lensing deflections. + + Parameters: + xim, yim + 2D Arrays of image coordinates we're going to lens, + probably generated by np.meshgrid. + Dd, Ds, Dds + Distances to the lens, source and between the source + and lens (units don't matter as long as they're the + same). Can't be calculated only from lens due to source + distances. + """ + if self._altered: # Only redo if something is new. + ximage, yimage = xim.copy(), yim.copy() # for safety. + + f = 1. - self.e['value'] + fprime = np.sqrt(1. - f**2.) + + # K+94 parameterizes in terms of LOS velocity dispersion and then + # basically the Einstein radius. + sigma = ((self.M['value']*Ds*G*Msun*c**2.)/(4*np.pi**2. * Dd*Dds*Mpc))**(1/4.) + Xi0 = 4*np.pi * (sigma/c)**2. * (Dd*Dds/Ds) + + # Flip units, the recenter and rotate grid to lens center and major axis + ximage *= arcsec2rad; yimage *= arcsec2rad + ximage -= (self.x['value']*arcsec2rad) + yimage -= (self.y['value']*arcsec2rad) + if not np.isclose(self.PA['value'], 0.): + r,theta = cart2pol(ximage,yimage) + ximage,yimage = pol2cart(r,theta-(self.PA['value']*deg2rad)) + phi = np.arctan2(yimage,ximage) + + # Calculate the deflections, account for e=0 (the SIS), which has + # cancelling infinities. K+94 eq 27a. + if np.isclose(f, 1.): + dxs = -(Xi0/Dd)*np.cos(phi) + dys = -(Xi0/Dd)*np.sin(phi) + else: + dxs = -(Xi0/Dd)*(np.sqrt(f)/fprime)*np.arcsinh(np.cos(phi)*fprime/f) + dys = -(Xi0/Dd)*(np.sqrt(f)/fprime)*np.arcsin(np.sin(phi)*fprime) + + # Rotate and shift back to sky frame + if not np.isclose(self.PA['value'], 0.): + r,theta = cart2pol(dxs,dys) + dxs,dys = pol2cart(r,theta+(self.PA['value']*deg2rad)) + dxs *= rad2arcsec; dys *= rad2arcsec + + self.deflected_x = dxs + self.deflected_y = dys + self._altered = False + + +class ExternalShear(object): + """ + Class to hold the two parameters relating to an external tidal shear, + where each parameter is a dictionary. + + Example format of each parameter: + x = {'value':x0,'fixed':False,'prior':[xmin,xmax]}, where x0 is the + initial/current value of x, x should not be a fixed parameter during fitting, + and the value of x must be between xmin and xmax. + + Parameters: + shear: + The strength of the external shear. Should be 0 to 1 (although treating + other objects in the lensing environment like this is really only valid + for shear <~ 0.3). + shearangle + The position angle of the tidal shear, in degrees east of north. + """ + def __init__(self,shear,shearangle): + # Do some input handling. + if not isinstance(shear,dict): + shear = {'value':shear,'fixed':False,'prior':[0.,1.]} + if not isinstance(shearangle,dict): + shearangle = {'value':shearangle,'fixed':False,'prior':[0.,180.]} + + if not all(['value' in d for d in [shear,shearangle]]): + raise KeyError("All parameter dicts must contain the key 'value'.") + + if not 'fixed' in shear: shear['fixed'] = False + if not 'fixed' in shearangle: shearangle['fixed'] = False + + if not 'prior' in shear: shear['prior'] = [0.,1.] + if not 'prior' in shearangle: shearangle['prior'] = [0.,180.] + + self.shear = shear + self.shearangle = shearangle + + def deflect(self,xim,yim,lens): + """ + Calculate deflection following Keeton,Mao,Witt 2000. + + Parameters: + xim, yim + 2D Arrays of image coordinates we're going to lens, + probably generated by np.meshgrid. + lens + A lens object; we use this to shift the coordinate system + to be centered on the lens. + """ + + ximage,yimage = xim.copy(), yim.copy() + + ximage -= lens.x['value']; yimage -= lens.y['value'] + + if not np.isclose(lens.PA['value'], 0.): + r,theta = cart2pol(ximage,yimage) + ximage,yimage = pol2cart(r,theta-(lens.PA['value']*deg2rad)) + + # KMW2000, altered for our coordinate convention. + g,thg = self.shear['value'], (self.shearangle['value']-lens.PA['value'])*deg2rad + dxs = -g*np.cos(2*thg)*ximage - g*np.sin(2*thg)*yimage + dys = -g*np.sin(2*thg)*ximage + g*np.cos(2*thg)*yimage + + if not np.isclose(lens.PA['value'], 0.): + r,theta = cart2pol(dxs,dys) + dxs,dys = pol2cart(r,theta+(lens.PA['value']*deg2rad)) + + self.deflected_x = dxs; self.deflected_y = dys + +class SersicSource(object): + """ + Class to hold parameters of an elliptical Sersic light profile, ie + I(x,y) = A * exp(-bn*((r/reff)^(1/n)-1)), + where bn makes reff enclose half the light (varies with Sersic index), + and all the variable parameters are dictionaries. + + Example format of each parameter: + x = {'value':x0,'fixed':False,'prior':[xmin,xmax]}, where x0 is the + initial/current value of x, x should not be a fixed parameter during fitting, + and the value of x must be between xmin and xmax. + + Parameters: + z + Source redshift. Can be made up, as long as it's higher than + the lens redshift. + lensed + True/False flag determining whether this object is actually lensed + (in which case it gets run through the lensing equations) or not (in + which case it's simply added to the model of the field without lensing). + This also determines the convention for the source position coordinates, + see below. + x, y + Position of the source in arcseconds. If lensed is True, this position + is relative to the position of the lens (or the first lens in a list of + lenses). If lensed is False, this position is relative to the field + center (or (0,0) coordinates). +x is west (sorry not sorry), +y is north. + flux + Total integrated flux density of the source (ie, NOT peak pixel value), in + units of Jy. + reff + The source major axis in arcseconds. + index + The Sersic profile index n (0.5 is ~Gaussian, 1 is ~an exponential disk, 4 + is a de Vaucoleurs profile). Recommend that this not be much less than 0.3, + or the approximation to b(n) which makes reff enclose half the light begins + to break down. + axisratio + The source minor/major axis ratio, varying from 1 (circularly symmetric) to + 0 (highly elongated). + PA + Source position angle. If lensed is True, this is in degrees CCW from the + lens major axis (or first lens in a list of them). If lensed is False, this + is in degrees east of north. + """ + + def __init__(self,z,lensed=True,xoff=None,yoff=None,flux=None,reff=None,\ + index=None,axisratio=None,PA=None): + # Do some input handling. + if not isinstance(xoff,dict): + xoff = {'value':xoff,'fixed':False,'prior':[-10.,10.]} + if not isinstance(yoff,dict): + yoff = {'value':yoff,'fixed':False,'prior':[-10.,10.]} + if not isinstance(flux,dict): + flux = {'value':flux,'fixed':False,'prior':[1e-5,1.]} # 0.01 to 1Jy source + if not isinstance(reff,dict): + reff = {'value':reff,'fixed':False,'prior':[0.,2.]} # arcsec + if not isinstance(index,dict): + index = {'value':index,'fixed':False,'prior':[0.3,4.]} + if not isinstance(axisratio,dict): + axisratio = {'value':axisratio,'fixed':False,'prior':[0.01,1.]} + if not isinstance(PA,dict): + PA = {'value':PA,'fixed':False,'prior':[0.,180.]} + + if not all(['value' in d for d in [xoff,yoff,flux,reff,index,axisratio,PA]]): + raise KeyError("All parameter dicts must contain the key 'value'.") + + if not 'fixed' in xoff: xoff['fixed'] = False + if not 'fixed' in yoff: yoff['fixed'] = False + if not 'fixed' in flux: flux['fixed'] = False + if not 'fixed' in reff: reff['fixed'] = False + if not 'fixed' in index: index['fixed'] = False + if not 'fixed' in axisratio: axisratio['fixed'] = False + if not 'fixed' in PA: PA['fixed'] = False + + if not 'prior' in xoff: xoff['prior'] = [-10.,10.] + if not 'prior' in yoff: yoff['prior'] = [-10.,10.] + if not 'prior' in flux: flux['prior'] = [1e-5,1.] + if not 'prior' in reff: reff['prior'] = [0.,2.] + if not 'prior' in index: index['prior'] = [1/3.,10] + if not 'prior' in axisratio: axisratio['prior'] = [0.01,1.] + if not 'prior' in PA: PA['prior'] = [0.,180.] + + self.z = z + self.lensed = lensed + self.xoff = xoff + self.yoff = yoff + self.flux = flux + self.reff = reff + self.index = index + self.axisratio = axisratio + self.PA = PA + + +class GaussSource(object): + """ + Class to hold parameters of a circularly symmetric Gaussian light + profile, where all the variable parameters are dictionaries. + + Example format of each parameter: + x = {'value':x0,'fixed':False,'prior':[xmin,xmax]}, where x0 is the + initial/current value of x, x should not be a fixed parameter during fitting, + and the value of x must be between xmin and xmax. + + Parameters: + z + Source redshift. Can be made up, as long as it's higher than + the lens redshift. + lensed + True/False flag determining whether this object is actually lensed + (in which case it gets run through the lensing equations) or not (in + which case it's simply added to the model of the field without lensing). + This also determines the convention for the source position coordinates, + see below. + x, y + Position of the source in arcseconds. If lensed is True, this position + is relative to the position of the lens (or the first lens in a list of + lenses). If lensed is False, this position is relative to the field + center (or (0,0) coordinates). +x is west (sorry not sorry), +y is north. + flux + Total integrated flux density of the source (ie, NOT peak pixel value), in + units of Jy. + width + The Gaussian width (sigma) of the light profile, in arcseconds. + """ + + def __init__(self,z,lensed=True,xoff=None,yoff=None,flux=None,width=None): + # Do some input handling. + if not isinstance(xoff,dict): + xoff = {'value':xoff,'fixed':False,'prior':[-10.,10.]} + if not isinstance(yoff,dict): + yoff = {'value':yoff,'fixed':False,'prior':[-10.,10.]} + if not isinstance(flux,dict): + flux = {'value':flux,'fixed':False,'prior':[1e-5,1.]} # 0.01 to 1Jy source + if not isinstance(width,dict): + width = {'value':width,'fixed':False,'prior':[0.,2.]} # arcsec + + if not all(['value' in d for d in [xoff,yoff,flux,width]]): + raise KeyError("All parameter dicts must contain the key 'value'.") + + if not 'fixed' in xoff: xoff['fixed'] = False + if not 'fixed' in yoff: yoff['fixed'] = False + if not 'fixed' in flux: flux['fixed'] = False + if not 'fixed' in width: width['fixed'] = False + + if not 'prior' in xoff: xoff['prior'] = [-10.,10.] + if not 'prior' in yoff: yoff['prior'] = [-10.,10.] + if not 'prior' in flux: flux['prior'] = [1e-5,1.] + if not 'prior' in width: width['prior'] = [0.,2.] + + self.z = z + self.lensed = lensed + self.xoff = xoff + self.yoff = yoff + self.flux = flux + self.width = width + +class PointSource(object): + """ + Class to hold parameters of an (unlensed) object unresolved by + the data, where all the variable parameters are dictionaries. + + Example format of each parameter: + x = {'value':x0,'fixed':False,'prior':[xmin,xmax]}, where x0 is the + initial/current value of x, x should not be a fixed parameter during fitting, + and the value of x must be between xmin and xmax. + + NOTE: Having a lensed point source is not currently implemented. + + Parameters: + z + Source redshift. Can be made up, as long as it's higher than + the lens redshift. + lensed + True/False flag determining whether this object is actually lensed + (in which case it gets run through the lensing equations) or not (in + which case it's simply added to the model of the field without lensing). + This also determines the convention for the source position coordinates, + see below. + x, y + Position of the source in arcseconds. If lensed is False (it must be), + this position is relative to the field center (or (0,0) coordinates). + +x is west (sorry not sorry), +y is north. + flux + Total flux density of the source, in units of Jy. + """ + + def __init__(self,z,lensed=True,xoff=None,yoff=None,flux=None): + # Do some input handling. + if not isinstance(xoff,dict): + xoff = {'value':xoff,'fixed':False,'prior':[-10.,10.]} + if not isinstance(yoff,dict): + yoff = {'value':yoff,'fixed':False,'prior':[-10.,10.]} + if not isinstance(flux,dict): + flux = {'value':flux,'fixed':False,'prior':[1e-5,1.]} # 0.01 to 1Jy source + + if not all(['value' in d for d in [xoff,yoff,flux]]): + raise KeyError("All parameter dicts must contain the key 'value'.") + + if not 'fixed' in xoff: xoff['fixed'] = False + if not 'fixed' in yoff: yoff['fixed'] = False + if not 'fixed' in flux: flux['fixed'] = False + + if not 'prior' in xoff: xoff['prior'] = [-10.,10.] + if not 'prior' in yoff: yoff['prior'] = [-10.,10.] + if not 'prior' in flux: flux['prior'] = [1e-5,1.] + + self.z = z + self.lensed = lensed + self.xoff = xoff + self.yoff = yoff + self.flux = flux + + +def read_visdata(filename): + """ + Function to read in visibility data from file and create a visdata object + to hold it afterwards. So far only .bin files from get_visibilities.py are + supported; idea is eventually to be able to not mess with that and get straight + from a CASA ms, but don't currently know how to do that without bundling the + casacore utilities directly... + + Params: + filename + Name of file to read from. Should contain all the visibility data needed, + including u (Lambda), v (Lambda), real, imag, sigma, antenna1, and antenna 2. + + Returns: + visdata + A visdata object containing the data from filename. + """ + + if not filename.split('.')[-1].lower() in ['bin']: + raise ValueError('Only .bin files are supported for now...') + + data = np.fromfile(filename) + PBfwhm = data[-1] + data = data[:-1] + data = data.reshape(7,data.size/7) # bin files lose array shape, so reshape to match + + return Visdata(*data,PBfwhm=PBfwhm,filename=filename) + + +def concatvis(visdatas): + """ + Concatenate multiple visibility sets into one larger set. + Does no consistency checking of any kind, so beware. + + :param visdatas: + List of visdata objects + + This method returns: + + * ``concatvis'' - The concatenated visibility set. + """ + + newu, newv, newr, newi = np.array([]),np.array([]),np.array([]),np.array([]) + news, newa1,newa2 = np.array([]),np.array([]),np.array([]) + + for vis in visdatas: + newu = np.concatenate((newu,vis.u)) + newv = np.concatenate((newv,vis.v)) + newr = np.concatenate((newr,vis.real)) + newi = np.concatenate((newi,vis.imag)) + news = np.concatenate((news,vis.sigma)) + newa1= np.concatenate((newa1,vis.ant1)) + newa2= np.concatenate((newa2,vis.ant2)) + + return Visdata(newu,newv,newr,newi,news,newa1,newa2,visdatas[0].PBfwhm,'Combined Data') + +def bin_visibilities(visdata,maxnewsize=None): + """ + WARNING: DOESN'T WORK CURRENTLY(?) + Bins up (ie, averages down) visibilities to reduce the total + number of them. Note that since we fit directly to the visibilities, + this is slightly different (and easier) than gridding in preparation for + imaging, as we won't need to FFT and so don't need a convolution function. + + :param visdata + A Visdata object. + :param maxnewsize = None + If desired, the maximum number of visibilities post-binning can + be specified. As long as this number meets other criteria (ie, + we don't have bin sizes smaller than an integration time or + bandwidth in wavelengths), the total number in the returned + Visdata will have fewer than maxnewsize visibilities. + + This method returns: + * ``BinnedVisibilities'' - A Visdata object containing binned visibilities. + """ + + if maxnewsize is None: maxnewsize = visdata.u.size/2 + + # Bins should be larger than an integration; strictly only valid for an EW array, + # and assumes a 20s integration time. Thus, this is a conservative estimate. + minbinsize = 20. * visdata.uvdist.max() / (24*3600.) + + # Bins should be smaller than the effective field size + maxbinsize = (visdata.PBfwhm * arcsec2rad)**-1 + + print minbinsize,maxbinsize + + # We're going to find a binning solution iteratively; this gets us set up + Nbins, binsizeunmet, Nvis, it, maxiter = [3000,3000], True, visdata.u.size, 0, 250 + + while (binsizeunmet or Nvis >= maxnewsize): + print Nbins + # Figure out how to bin up the data + counts,uedges,vedges,bins = stats.binned_statistic_2d( + visdata.u,visdata.v,values=visdata.real,statistic='count', + bins=Nbins) + + du, dv = uedges[1]-uedges[0], vedges[1]-vedges[0] + + # Check that our bins in u and v meet our conditions + if (du > minbinsize and du < maxbinsize and + dv > minbinsize and dv < maxbinsize): binsizeunmet = False + # Otherwise we have to adjust the number of bins to adjust their size... + #elif (du <= minbinsize or dv <= minbinsize): Nbins = int(Nbins/1.2) + #elif (du >= maxbinsize or dv >= maxbinsize): Nbins = int(Nbins*1.2) + elif du <= minbinsize: Nbins[0] = int(Nbins[0]/1.1); binsizeunmet=True + elif dv <= minbinsize: Nbins[1] = int(Nbins[1]/1.1); binsizeunmet=True + elif du >= maxbinsize: Nbins[0] = int(Nbins[0]*1.1); binsizeunmet=True + elif dv >= maxbinsize: Nbins[1] = int(Nbins[1]*1.1); binsizeunmet=True + + # If we still have more than the desired number of visibilities, make + # fewer bins (we'll loop after this). + if np.unique(bins).size > maxnewsize: Nbins[0],Nbins[1] = int(Nbins[0]/1.1),int(Nbins[1]/1.1) + Nvis = np.unique(bins).size + it += 1 + if it > maxiter: raise ValueError("It's impossible to split your data into that few bins! " + "Try setting maxnewsize to a larger value!") + print Nvis,du,dv + + + # Get us some placeholder arrays for the binned data + u,v,real,imag,sigma,ant1,ant2 = np.zeros((7,Nvis)) + + for i,filledbin in enumerate(np.unique(bins)): + # This tells us which visibilities belong to the current bin + points = np.where(bins==filledbin)[0] + # This unravels the indices to uedges,vedges from the binned_statistic binnumber + uloc = int(np.floor(filledbin/(vedges.size+1)) - 1) + vloc = int(filledbin - (vedges.size+1)*(uloc+1) - 1) + # Get our new data, place at center of uv bins + u[i],v[i] = uedges[uloc]+0.5*du, vedges[vloc]+0.5*dv + real[i],sumwt = np.average(visdata.real[points],weights=visdata.sigma[points]**-2.,returned=True) + imag[i] = np.average(visdata.imag[points],weights=visdata.sigma[points]**-2.) + sigma[i] = sumwt**-0.5 + # We can keep the antenna numbers if we've only selected points from the same baseline, + # otherwise get rid of them (CHECK IF MODELCAL FAILS WITH None ANTENNAS) + ant1[i] = visdata.ant1[points][0] if (visdata.ant1[points]==visdata.ant1[points][0]).all() else None + ant2[i] = visdata.ant2[points][0] if (visdata.ant2[points]==visdata.ant2[points][0]).all() else None + + return Visdata(u,v,real,imag,sigma,ant1,ant2,visdata.PBfwhm,'BIN{0}'.format(Nvis)+visdata.filename) diff --git a/RayTracePixels.py b/visilens/lensing.py similarity index 52% rename from RayTracePixels.py rename to visilens/lensing.py index 4fb3a09..8a9f5f1 100644 --- a/RayTracePixels.py +++ b/visilens/lensing.py @@ -1,18 +1,23 @@ import numpy as np -from utils import cart2pol,pol2cart +import scipy.sparse +import copy +from class_utils import * +from utils import * +from astropy.cosmology import WMAP9 import astropy.constants as co + c = co.c.value # speed of light, in m/s G = co.G.value # gravitational constant in SI units Msun = co.M_sun.value # solar mass, in kg Mpc = 1e6*co.pc.value # 1 Mpc, in m -arcsec2rad = (np.pi/(180.*3600.)) +arcsec2rad = np.pi/(180.*3600.) rad2arcsec =3600.*180./np.pi deg2rad = np.pi/180. rad2deg = 180./np.pi -__all__ = ['LensRayTrace','RayTraceSIE','TraceExternalShear','CausticsSIE'] +__all__ = ['LensRayTrace','GenerateLensingGrid','thetaE','CausticsSIE'] -def LensRayTrace(xim,yim,lens,Dd,Ds,Dds,shear=None): +def LensRayTrace(xim,yim,lens,Dd,Ds,Dds): """ Wrapper to pass off lensing calculations to any number of functions defined below, accumulating lensing offsets from multiple lenses @@ -25,136 +30,110 @@ def LensRayTrace(xim,yim,lens,Dd,Ds,Dds,shear=None): yimage = yim.copy() for i,ilens in enumerate(lens): - if ilens.__class__.__name__ == 'SIELens': - dxt,dyt = xim.copy(),yim.copy() - dxt,dyt = RayTraceSIE(dxt,dyt,ilens,Dd,Ds,Dds) - ximage += dxt; yimage += dyt - - else: raise ValueError("Only SIE Lenses supported for now...") - - if shear is not None: - dxt,dyt = xim.copy(),yim.copy() - dxt,dyt = TraceExternalShear(dxt,dyt,lens[0],shear) - ximage += dxt; yimage += dyt + if ilens.__class__.__name__ == 'SIELens': ilens.deflect(xim,yim,Dd,Ds,Dds) + elif ilens.__class__.__name__ == 'ExternalShear': ilens.deflect(xim,yim,lens[0]) + ximage += ilens.deflected_x; yimage += ilens.deflected_y return ximage,yimage -def RayTraceSIE(xim,yim,SIELens,Dd,Ds,Dds): +def GenerateLensingGrid(data=None,xmax=None,emissionbox=[-5,5,-5,5],fieldres=None,emitres=None): """ - Routine to transform image-plane coordinates to source-plane coordinates - for an SIE lens. + Routine to generate two grids for lensing. The first will be a lower-resolution + grid with resolution determined by fieldres and size determined + by xmax. The second is a much higher resolution grid which will be used for + the lensing itself, with resolution determined by emitres and size + determined from emissionbox - i.e., emissionbox should contain the coordinates + which conservatively encompass the real emission, so we only have to lens that part + of the field at high resolution. + + Since we're going to be FFT'ing with these coordinates, the resolution isn't + directly set-able. For the low-res full-field map, it instead is set to the next-higher + power of 2 from what would be expected from having ~4 resolution elements across + the synthesized beam. Inputs: - ximage,yimage: - 2D arrays defining a grid of coordinates to map, in arcsec. These should - be relative to the field center, i.e., we'll use the lens locations to - recenter this grid on the lens and then shift back to the coordinate system - given. - - SIELens: - An SIELens object, containing mass, offset, etc. properties. - - Dd,Ds,Dds: - Angular diameter distances (in Mpc) to the lens, source, and lens-source, - respectively. These could be calculated by passing a Lens and Source object, - but since redshift is fixed, we can just calculate the distances once - elsewhere and use those values repeatedly. + data: + A Visdata object, used to determine the resolutions of + the two grids (based on the image size or maximum uvdistance in the dataset) + xmax: + Field size for the low-resolution grid in arcsec, which will extend from + (-xmax,-xmax) to (+xmax,+xmax), e.g. (-30,-30) to (+30,+30)arcsec. Should be + at least a bit bigger than the primary beam. Not needed for images. + emissionbox: + A 1x4 list of [xmin,xmax,ymin,ymax] defining a box (in arcsec) which contains + the source emission. Coordinates should be given in arcsec relative to the + pointing/image center. + fieldres,emitres: + Resolutions of the coarse, full-field and fine (lensed) field, in arcsec. + If not given, suitable values will be calculated from the visibilities. + fieldres is unnecessary for images. Returns: - xsource,ysource: - Source-plane coordinates for the grid points given in ximage, yimage, in arcsec. + If there are any Visdata objects in the datasets, returns: + xmapfield,ymapfield: + 2xN matrices containing x and y coordinates for the full-field, lower-resolution + grid, in arcsec. + xmapemission,ymapemission: + 2xN matrices containing x and y coordinates for the smaller, very high resolution + grid, in arcsec. + indices: + A [4x1] array containing the indices of xmapfield,ymapfield which overlap with + the high resolution grid. """ - - ximage,yimage = xim.copy(), yim.copy() - - # Following Kormann+ 1994 for the lensing. Easier to work with axis ratio than ellipticity - f = 1. - SIELens.e['value'] - fprime = np.sqrt(1. - f**2.) - - # K+94 parameterize lens in terms of LOS velocity dispersion; calculate here in m/s - sigma_lens = ((SIELens.M['value']*Ds*G*Msun*c**2.)/(4.*np.pi**2. * Dd*Dds*Mpc))**(1./4.) - # K+94, eq 15., units of Mpc; a polar coordinate radius normalization - Xi0 = 4 * np.pi * (sigma_lens/c)**2. * (Dd*Dds/Ds) - - ximage *= arcsec2rad - yimage *= arcsec2rad - - # Recenter grid on the lens - ximage -= SIELens.x['value']*arcsec2rad - yimage -= SIELens.y['value']*arcsec2rad - - # Rotate the coordinate system to align with the lens major axis - if not np.isclose(SIELens.PA['value'], 0.): - r,theta = cart2pol(ximage,yimage) - ximage,yimage = pol2cart(r,theta-(SIELens.PA['value']*deg2rad)) - - # Polar coordinate angle from lens centroid - phi = np.arctan2(yimage,ximage) - - # Calculate the transformation of the convergence term; K+94 eq 27a - # Need to account for case of ellipticity=0 (the SIS), which has canceling infinities - if np.isclose(f,1.): - dxs = - (Xi0/Dd)*np.cos(phi) - dys = - (Xi0/Dd)*np.sin(phi) - else: - dxs = - (Xi0/Dd)*(np.sqrt(f)/fprime)*np.arcsinh(np.cos(phi)*fprime/f) - dys = - (Xi0/Dd)*(np.sqrt(f)/fprime)*np.arcsin(np.sin(phi)*fprime) - - # Rotate and shift the coordinate system back to the sky frame - if not np.isclose(SIELens.PA['value'],0.): - r,theta = cart2pol(dxs,dys) - dxs,dys = pol2cart(r,theta+(SIELens.PA['value']*deg2rad)) - - dxs *= rad2arcsec - dys *= rad2arcsec + # Factors higher-resolution than (1/2*max(uvdist)) to make the field and emission grids + Nover_field = 4. + Nover_emission = 8. + + # Allow multiple visdata objects to be passed, pick the highest resolution point of all + uvmax = 0. + try: + for vis in data: + uvmax = max(uvmax,vis.uvdist.max()) + except TypeError: + uvmax = data.uvdist.max() + + # Calculate resolutions of the grids + if fieldres is None: fieldres = (2*Nover_field*uvmax)**-1. + else: fieldres *= arcsec2rad + if emitres is None: emitres = (2*Nover_emission*uvmax)**-1. + else: emitres *= arcsec2rad + + # Calculate the field grid size as a power of 2. + Nfield = 2**np.ceil(np.log2(2*np.abs(xmax)*arcsec2rad/fieldres)) + + # Calculate the grid coordinates for the larger field. + fieldcoords = np.linspace(-np.abs(xmax),np.abs(xmax),Nfield) + xmapfield,ymapfield = np.meshgrid(fieldcoords,fieldcoords) + + # Calculate the indices where the high-resolution lensing grid meets the larger field grid + indices = np.round(np.interp(np.asarray(emissionbox),fieldcoords,np.arange(Nfield))) + + # Calculate the grid coordinates for the high-res lensing grid; grids meet at indices. Some pixel-shifting reqd. + Nemx = 1 + np.abs(indices[1]-indices[0])*np.ceil((fieldcoords[1]-fieldcoords[0])/(2*emitres*rad2arcsec)) + Nemy = 1 + np.abs(indices[3]-indices[2])*np.ceil((fieldcoords[1]-fieldcoords[0])/(2*emitres*rad2arcsec)) + xemcoords = np.linspace(fieldcoords[indices[0]],fieldcoords[indices[1]],Nemx) + yemcoords = np.linspace(fieldcoords[indices[2]],fieldcoords[indices[3]],Nemy) + xmapemission,ymapemission = np.meshgrid(xemcoords,yemcoords) + xmapemission -= (xmapemission[0,1]-xmapemission[0,0]) + ymapemission -= abs((ymapemission[1,0]-ymapemission[0,0])) + + return xmapfield,ymapfield,xmapemission,ymapemission,indices - #print dxs,dys - - return dxs,dys - -def TraceExternalShear(xim,yim,lens,shear): +def thetaE(ML,zL,zS,cosmo=WMAP9): """ - Calculate deflections due to an external shear component. - - Inputs: - xim,yim: - Map coordinates to perform the shear on. - - lens: - A lens object; we use this to shift the coordinate system - to be centered on the lens. This should be a single lens, - not a list, since external shear isn't really meant to be - applied if there are multiple lenses anyway. - - shear: - An ExternalShear object, which gives the strength and angle of - the deflection. The angle is defined in degrees N of E. - - Returns: - xsource,ysource: - Deflections calculated for the given inputs. + Calculate the Einstein radius in arcsec of a lens of mass ML, + assuming redshifts zL and zS. If cosmo is None, WMAP9 + is assumed. ML is in solar masses. """ - ximage,yimage = xim.copy(),yim.copy() + Dd = cosmo.angular_diameter_distance(zL).value # in Mpc + Ds = cosmo.angular_diameter_distance(zS).value + Dds= cosmo.angular_diameter_distance_z1z2(zL,zS).value - ximage -= lens.x['value'] - yimage -= lens.y['value'] + thE = np.sqrt((4*G*ML*Msun*Dds) / (c**2 * Dd*Ds*Mpc)) * rad2arcsec - if not np.isclose(lens.PA['value'], 0.): - r,theta = cart2pol(ximage,yimage) - ximage,yimage = pol2cart(r,theta-(lens.PA['value']*deg2rad)) - - # Calculate contribution from shear term; see Keeton,Mao&Witt2000, altered for this coord convention - gamma,thg = shear.shear['value'],(shear.shearangle['value']-lens.PA['value'])*deg2rad - dxs = -gamma*np.cos(2*thg)*ximage - gamma*np.sin(2*thg)*yimage - dys = -gamma*np.sin(2*thg)*ximage + gamma*np.cos(2*thg)*yimage - - if not np.isclose(lens.PA['value'],0.): - r,theta = cart2pol(dxs,dys) - dxs,dys = pol2cart(r,theta+(lens.PA['value']*deg2rad)) - - return dxs,dys + return thE def CausticsSIE(SIELens,Dd,Ds,Dds,Shear=None): """ @@ -267,4 +246,4 @@ def CausticsSIE(SIELens,Dd,Ds,Dds,Shear=None): xr += SIELens.x['value'] yr += SIELens.y['value'] - return np.atleast_3d([[xr,yr],[xt,yt]]) + return np.atleast_3d([[xr,yr],[xt,yt]]) \ No newline at end of file diff --git a/plot_images.py b/visilens/plot_images.py similarity index 71% rename from plot_images.py rename to visilens/plot_images.py index ff4af2b..dbc7723 100644 --- a/plot_images.py +++ b/visilens/plot_images.py @@ -1,20 +1,80 @@ import numpy as np +import copy +from scipy.fftpack import fftshift,fft2 from scipy.ndimage.measurements import center_of_mass -from astropy.stats import sigma_clip -import matplotlib.pyplot as pl +import matplotlib.pyplot as pl; pl.ioff() import matplotlib.cm as cm from matplotlib.colors import SymLogNorm -from Model_objs import * -from Data_objs import * -from uvimage import uvimageslow -from calc_likelihood import create_modelimage,fft_interpolate -from modelcal import model_cal -from GenerateLensingGrid import GenerateLensingGrid +from class_utils import * +from calc_likelihood import * from utils import * -import copy +from lensing import * + +arcsec2rad = np.pi/180./3600. +c = 2.99792458e8 # in m/s + +__all__ = ['uvimageslow','plot_images'] + +def uvimageslow(visdata,imsize=256,pixsize=0.5,taper=0.): + """ + Invert a set of visibilities to the image plane in the slowest way possible + (ie, no gridding of visibilities, just straight-up summation). This gets + pretty darn close to imaging with CASA with natural weighting, no cleaning. + + Note: this is very slow, because it's very stupid (no gridding of visibilities + or anything). If you need to make large images of a lot of data, I strongly + recommend outputting your data to uvfits and imaging elsewhere, because I don't + particularly want to re-create all of CASA's :clean: functionality on my own. + + Inputs: + visdata: + Any Visdata object + imsize: + Size of the output image in pixels + pixsize: + Pixel size, in arcseconds + taper: + Apply an additional gaussian taper to the + visibilities with sigma of this, in arcsec. + + Returns: + image: + A 2D array containing the image of the inverted visibilities + """ + + thisvis = copy.deepcopy(visdata) + + if taper > 0.: + uvsig = (taper * arcsec2rad)**-1. + scalefac = np.exp(-thisvis.uvdist**2/(2*uvsig**2.)) + thisvis.sigma /= scalefac + + x = np.linspace(-imsize*pixsize/2. * arcsec2rad,+imsize*pixsize/2. * arcsec2rad,imsize) + x,y = np.meshgrid(x,x) + # These offsets line us up with CASA's coord system + x -= pixsize/2. * arcsec2rad + y += pixsize/2. * arcsec2rad + + im = np.array(np.zeros(x.shape),dtype=complex) + + # Check to see if we have the conjugate visibilities, and image. + if np.all(thisvis.u[:thisvis.u.size/2] == -thisvis.u[thisvis.u.size/2:]): + for i in range(thisvis.u.size/2): + im += (thisvis.sigma[i]**-2)*(thisvis.real[i]+1j*thisvis.imag[i]) *\ + np.exp(2*np.pi*1j*((thisvis.u[i]*x)+(thisvis.v[i]*y))) + else: + for i in range(thisvis.u.size): + im += (thisvis.sigma[i]**-2)*(thisvis.real[i]+1j*thisvis.imag[i]) *\ + np.exp(2*np.pi*1j*((thisvis.u[i]*x)+(thisvis.v[i]*y))) + + # We only imaged the non-conjugate visibilities; fix and renormalize to Jy/beam units + im = 2*im.real + im /= (2*thisvis.sigma**-2).sum() + + return im def plot_images(data,mcmcresult,returnimages=False,plotcombined=False,plotall=False, - imsize=512,pixsize=0.2,taper=0.,**kwargs): + imsize=256,pixsize=0.2,taper=0.,**kwargs): """ Create a four-panel figure from data and chains, showing data, best-fit model, residuals, high-res image. @@ -64,61 +124,10 @@ def plot_images(data,mcmcresult,returnimages=False,plotcombined=False,plotall=Fa # shorthand for later c = copy.deepcopy(mcmcresult['chains']) - """ - # Set up to create the model image. We'll assume the best-fit values are all the medians. - lens,source = copy.deepcopy(mcmcresult['lens_p0']), copy.deepcopy(mcmcresult['source_p0']) - for i,ilens in enumerate(lens): - if ilens.__class__.__name__ == 'SIELens': - for key in ['x','y','M','e','PA']: - if not vars(ilens)[key]['fixed']: - ilens.__dict__[key]['value'] = np.median(c[key+'L'+str(i)]) - # now do the source(s) - for i,src in enumerate(source): # Source is a list of source objects - if src.__class__.__name__ == 'GaussSource': - for key in ['xoff','yoff','flux','width']: - if not vars(src)[key]['fixed']: - src.__dict__[key]['value'] = np.median(c[key+'S'+str(i)]) - elif src.__class__.__name__ == 'SersicSource': - for key in ['xoff','yoff','flux','reff','index','axisratio','PA']: - if not vars(src)[key]['fixed']: - src.__dict__[key]['value'] = np.median(c[key+'S'+str(i)]) - elif src.__class__.__name__ == 'PointSource': - for key in ['xoff','yoff','flux']: - if not vars(src)[key]['fixed']: - src.__dict__[key]['value'] = np.median(c[key+'S'+str(i)]) - - # now do shear, if any - if 'shear_p0' in mcmcresult.keys(): - shear = mcmcresult['shear_p0'] - for key in ['shear','shearangle']: - if not vars(shear)[key]['fixed']: - shear.__dict__[key]['value'] = np.median(c[key]) - else: shear = None - - # Any amplitude scaling or astrometric shifts - scaleamp = np.ones(len(datasets)) - if 'scaleamp' in mcmcresult.keys(): - for i,t in enumerate(mcmcresult['scaleamp']): # only matters if >1 datasets - if i==0: pass - elif t: scaleamp[i] = np.median(c['ampscale_dset'+str(i)]) - else: pass - shiftphase = np.zeros((len(datasets),2)) - if 'shiftphase' in mcmcresult.keys(): - for i,t in enumerate(mcmcresult['shiftphase']): - if i==0: pass # only matters if >1 datasets - elif t: - shiftphase[i][0] = np.median(c['astromshift_x_dset'+str(i)]) - shiftphase[i][1] = np.median(c['astromshift_y_dset'+str(i)]) - else: pass # no shifting - - sourcedatamap = mcmcresult['sourcedatamap'] if 'sourcedatamap' in mcmcresult.keys() else None - modelcal = mcmcresult['modelcal'] if 'modelcal' in mcmcresult.keys() else [False]*len(datasets) - """ # Now all these things are saved separately, don't have to do the BS above lens = mcmcresult['best-fit']['lens'] source=mcmcresult['best-fit']['source'] - shear =mcmcresult['best-fit']['shear'] if 'shear' in mcmcresult['best-fit'].keys() else None scaleamp = mcmcresult['best-fit']['scaleamp'] if 'scaleamp' in mcmcresult['best-fit'].keys() else np.ones(len(datasets)) shiftphase=mcmcresult['best-fit']['shiftphase'] if 'shiftphase' in mcmcresult['best-fit'].keys() else np.zeros((len(datasets),2)) sourcedatamap = mcmcresult['sourcedatamap'] if 'sourcedatamap' in mcmcresult.keys() else None @@ -146,7 +155,7 @@ def plot_images(data,mcmcresult,returnimages=False,plotcombined=False,plotall=Fa mcmcresult['highresbox'],mcmcresult['fieldres'],mcmcresult['emitres']) # Create model image - immap,_ = create_modelimage(lens,source,shear,xmap,ymap,xemit,yemit,\ + immap,_ = create_modelimage(lens,source,xmap,ymap,xemit,yemit,\ ix,sourcedatamap) # And interpolate onto uv-coords of dataset @@ -210,13 +219,11 @@ def plot_images(data,mcmcresult,returnimages=False,plotcombined=False,plotall=Fa else: sig,unit = s,'Jy' axarr[row,2].text(0.1,0.1,"1$\sigma$ = {0:.1f}{1:s}".format(sig,unit), transform=axarr[row,2].transAxes,bbox=dict(fc='w')) - #axarr[i,3].imshow(immap,interpolation='nearest',\ - # extent=[xmap.min(),xmap.max(),xmap.max(),xmap.min()],cmap=cmap) # Give a zoomed-in view in the last panel # Create model image at higher res, remove unlensed sources src = [src for src in source if src.lensed] - imemit,_ = create_modelimage(lens,src,shear,xemit,yemit,xemit,yemit,\ + imemit,_ = create_modelimage(lens,src,xemit,yemit,xemit,yemit,\ [0,xemit.shape[1],0,xemit.shape[0]],sourcedatamap) images[row].append(imemit) diff --git a/triangleplot.py b/visilens/triangleplot.py similarity index 99% rename from triangleplot.py rename to visilens/triangleplot.py index 187e783..a0ad859 100644 --- a/triangleplot.py +++ b/visilens/triangleplot.py @@ -1,6 +1,5 @@ -import matplotlib.pyplot as pl +import matplotlib.pyplot as pl; pl.ioff() import matplotlib.cm as cm -from matplotlib.colors import LinearSegmentedColormap from matplotlib.ticker import MaxNLocator import scipy.ndimage import numpy as np diff --git a/visilens/utils.py b/visilens/utils.py new file mode 100644 index 0000000..dbcd87a --- /dev/null +++ b/visilens/utils.py @@ -0,0 +1,93 @@ +import numpy as np +import scipy.stats as stats +arcsec2rad = (np.pi/(180.*3600.)) + +__all__ = ['cart2pol','pol2cart','expsinc','box','sersic_area'] + +def cart2pol(x,y): + """ + Convert a set of x,y Cartesian coordinates. Why isn't this in numpy or scipy already? + + Inputs: + x,y: Arrays or lists of coordinate pairs to transform + + Returns: + r,theta: Arrays of polar coordinates from the given x and y, theta in radians + """ + + x, y = np.asarray(x), np.asarray(y) + + r = np.sqrt(x**2. + y**2.) + theta = np.arctan2(y,x) + + return r, theta + +def pol2cart(r,theta): + """ + Convert a set of r,theta polar coordinates to Cartesian. + + Inputs: + r,theta: Arrays or lists of coordinate pairs to transform; assumes theta in radians + + Returns: + x,y: Arrays of Cartesian coordinates from the given r and theta + """ + + r, theta = np.asarray(r), np.asarray(theta) + + x = r * np.cos(theta) + y = r * np.sin(theta) + + return x,y + + +def box(u,v,deltau,deltav): + """ + Define a box/tophat/pillbox shaped convolution kernel. Bad for + reducing aliasing. + """ + + try: # Assumes u is already a 2D grid of values + conv = np.zeros(u.shape) + conv[u.shape[0]/2.,u.shape[1]/2.] = 1. + except: # Assumes u and v are still 1D coords + conv = np.zeros((u.size,v.size)) + conv[u.size/2.,v.size/2.] = 1. + + return conv + +def expsinc(u,v,deltau,deltav): + """ + Define a sinc*gaussian exponential convolution kernel, following + TMS Ch. 10. + """ + + alpha1 = 1.55 # See TMS, who cited... I forget, somebody. + alpha2 = 2.52 + + return np.sinc(u/(alpha1*deltau))*np.exp(-(u/(alpha2*deltau))**2.) * \ + np.sinc(v/(alpha1*deltav))*np.exp(-(v/(alpha2*deltav))**2.) + +def sersic_area(n,majaxis,axisratio): + """ + Calculate the total area (to R=infinity) of a Sersic profile. + Uses the Ciotti+Bertin 1999 approx to b_n. + + :param n + Sersic profile index. C&B1999 approx is good down to n~0.2 + + :param majaxis + Major axis of the profile, arbitrary units. + + :param axisratio + Ratio of minor/major axis. If >1, we use 1/axisratio instead. + + This method returns: + + * ``sersicarea'' - The profile area, in square-majaxis units. + """ + from scipy.special import gamma + # C&B1999 eq 18 + bn = 2*n - 1./3. + 4./(405.*n) + 46./(25515*n**2) + 131./(1148175*n**3) - 2194697./(30690717750*n**4) + # Graham&Driver 2005, eq 2. Note: exp(bn) term is due to using r_half [==r_eff] instead of r/r0, cf C&B1999 eq 4 + return 2*np.pi*n*majaxis**2 * axisratio * np.exp(bn) * gamma(2*n) / bn**(2*n) \ No newline at end of file

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