Source code for

""" .. _Regrid-at-api:

   **Regrid_AT** --- Regrids multiple cubes to a common resolution.

   This module defines the Regrid_AT class.
import admit as admit 
from admit.AT import AT
from admit.Summary import SummaryEntry
import admit.util.bdp_types as bt
import admit.util.Table
import admit.util.utils as utils
from admit.bdp.SpwCube_BDP import SpwCube_BDP
import numpy as np
import os as os
from copy import deepcopy

  import casa
  import taskinit
  print "WARNING: No CASA; Regrid task cannot function."

[docs]class Regrid_AT(AT): """Creates a regridded version of multiple datacubes. Capable of regridding in both spatial and spectral dimensions. Default behavior is to take a set of N cubes and (a) regrid the pixel size to the minimum pixel size of all the images and (b) to remap the images so that they all fit on the minimum-area square patch of sky that encloses each image. User may specify an alterative pixel size. User may also specify spectral regridding. If spectral regridding is desired, the default behavior is to regrid so a common set of frequencies with mininum/maximum frequency those of the collection of cubes and channel width the minimum channel width in the collection of cubes. Alternatively, user may specify a desired channel width in Hz. See also :ref:`Regrid-AT-Design` for the design document. **Keywords** **do_spatial_regrid**: bool Whether or not to do spatial regridding. The default is to do spatial regridding. If set to true (or left default), the pix_scale keyword will be used to determine the appropriate pixel size to regrid the images to. **pix_scale**: double The pixel size on which to regrid all the images. If negative, then the default is the minimum pixel scale for all images. Unit is arcsec. **Default**: -1.0. **do_freq_regrid**: bool Whether or not to do frequency regridding. The default is to only do spatial regridding. If set to true, the chan_width keyword will be used to determine the appropriate channel width to regrid the images to. **chan_width**: double The channel width with which to regrid the images. If negative, then the default is the minimum such channel width. Unit is Hz. **Default**: -1.0. **Input BDPs** **SpwCube_BDP**: count: `varies` (minimum 2) Input cubes, as from `Ingest_AT <Ingest_AT.html>`_, `ContinuumSub_AT <ContinuumSub_AT.html>`_ or `LineCube_AT <LineCube_AT.html>`_. **Output BDPs** **SpwCube_BDP**: count: `varies` (equal to input count) Regridded cubes. Parameters ---------- keyval : dict, optional Dictionary of keyword:value pairs. Attributes ---------- _version : string Version ID string. """ def __init__(self, **keyval): keys = { "do_spatial_regrid": True, "pix_scale" : -1.0, "do_freq_regrid": False, "chan_width" : -1.0 } AT.__init__(self,keys,keyval) self._version = "1.1.0" self.set_bdp_in([(SpwCube_BDP,0,bt.REQUIRED)]) self.set_bdp_out([(SpwCube_BDP,0)])
[docs] def summary(self): """Returns the summary dictionary from the AT, for merging into the ADMIT Summary object. Regrid_AT adds the following to ADMIT summary:: Key type Description -------- ------ ----------- regrid list Info about smoothing done """ if hasattr(self,"_summary"): return self._summary else: return {}
[docs] def run(self): """ The run method creates the BDP Parameters ---------- None Returns ------- None """ self._summary = {} dt = utils.Dtime("Regrid") dt.tag("start") do_spatial_regrid = self.getkey("do_spatial_regrid") pix_scale = self.getkey("pix_scale") do_freq_regrid=self.getkey("do_freq_regrid") chan_width = self.getkey("chan_width") pix_size=[] chan_size=[] im_size =[] pix_wc_x = [] pix_wc_y = [] pix_wc_nu=[] src_dec = [] RADPERARCSEC = 4.848137E-6 ia = taskinit.iatool() for ibdp in self._bdp_in: # Convert input CASA images to numpy arrays. istem = ibdp.getimagefile(bt.CASA) ifile = ibdp.baseDir() + istem h = casa.imhead(ifile, mode='list') pix_size.append(np.abs(h['cdelt1'])) # pix scale in asec @todo QA ? chan_size.append(np.abs(h['cdelt3'])) # grab the pixels pix_x = h['shape'][0] pix_y = h['shape'][1] pix_nu= h['shape'][2] mycs = ia.coordsys(axes=[0,1,2]) # getting all four corners handles the case of images where # x-y axis not aligned with RA-dec for xpix in [0,pix_x]: for ypix in [0,pix_y]: x = mycs.toworld([xpix,ypix])['numeric'][0] y = mycs.toworld([xpix,ypix])['numeric'][1] pix_wc_x.append(x) pix_wc_y.append(y) nu= mycs.toworld([pix_x,pix_y,0])['numeric'][2] pix_wc_nu.append(nu) nu= mycs.toworld([pix_x,pix_y,pix_nu])['numeric'][2] pix_wc_nu.append(nu) ia.close() min_ra = np.min(pix_wc_x) max_ra = np.max(pix_wc_x) min_dec = np.min(pix_wc_y) max_dec = np.max(pix_wc_y) mean_ra = 0.5*(min_ra + max_ra) mean_dec = 0.5*(min_dec + max_dec) if(pix_scale < 0): pix_scale = np.min(pix_size) else: pix_scale = pix_scale * RADPERARCSEC npix_ra = int((max_ra - min_ra) / pix_scale * np.cos(mean_dec)) npix_dec = (max_dec - min_dec) / pix_scale npix_dec = (int(npix_dec) if npix_dec == int(npix_dec) else int(npix_dec) + 1) min_nu = np.min(pix_wc_nu) max_nu = np.max(pix_wc_nu) mean_nu = 0.5*(min_nu + max_nu) if(chan_width < 0): chan_width = np.min(chan_size) npix_nu =int((max_nu - min_nu)/chan_width)+1 # now regrid everything innames =[] outnames = [] incdelt = [] outcdelt = [] #========================================================= #@todo - check if bdp_ins refer to same input file. # If so, the current code will fail because the output # file name is fixed to $INPUT_regrid. A valid use case # is "regrid the same input file different ways" -- which # is not supported in the current code but should be #========================================================= for ibdp in self._bdp_in: istem = ibdp.getimagefile(bt.CASA) ifile = ibdp.baseDir() + istem ostem = "%s_regrid/" % (istem) ofile = self.baseDir() + ostem # save the input/output file names innames.append(istem) outnames.append(ostem) header=casa.imregrid(imagename=ifile,template='get') # save the input cdelt1,2,3 for summary table incdelt.append((header['csys']['direction0']['cdelt'][0]/RADPERARCSEC,header['csys']['direction0']['cdelt'][1]/RADPERARCSEC,utils.freqtovel(mean_nu,header['csys']['spectral2']['wcs']['cdelt']))) if(do_spatial_regrid): header['csys']['direction0']['cdelt'] = [ -1*pix_scale,pix_scale] header['csys']['direction0']['crval'] = [ mean_ra,mean_dec] header['shap'][0] = npix_ra header['shap'][1] = npix_dec header['csys']['direction0']['crpix'] = [npix_ra/2,npix_dec/2] if(do_freq_regrid): header['csys']['spectral2']['wcs']['crval']=min_nu header['shap'][2] = npix_nu chan_size = np.abs(header['csys']['spectral2']['wcs']['cdelt']) header['csys']['spectral2']['wcs']['cdelt'] = chan_width flux_correction = chan_width/chan_size casa.imregrid(imagename=ifile,output=ofile,template=header) # save the output cdelt1,2,3 for summary table newhead = casa.imregrid(imagename=ofile,template='get') outcdelt.append((newhead['csys']['direction0']['cdelt'][0]/RADPERARCSEC,newhead['csys']['direction0']['cdelt'][1]/RADPERARCSEC,utils.freqtovel(mean_nu,newhead['csys']['spectral2']['wcs']['cdelt']))) if(do_freq_regrid): print flux_correction ia.calc(pixels=ofile.replace(r"/",r"\/")+'*'+str(flux_correction)) ia.done() obdp = admit.SpwCube_BDP(ostem) self.addoutput(obdp) # make a table for summary atable = admit.util.Table() atable.columns = ["Input Image","cdelt1", "cdelt2", "cdelt3", "Regridded Image","cdelt1","cdelt2","cdelt3"] atable.units = ["","arcsec", "arcsec", "km/s", "","arcsec","arcsec","km/s"] for i in range(len(innames)): atable.addRow([ innames[i],incdelt[i][0],incdelt[i][1],incdelt[i][2], outnames[i],outcdelt[i][0],outcdelt[i][1],outcdelt[i][2] ]) #keys = "pixsize=%.4g naxis1=%d naxis2=%d mean_ra=%0.4f mean_dec=%0.4f reffreq=%g chan_width=%g" % (pix_scale/(4.848137E-6), npix_ra, npix_dec, mean_ra,mean_dec,min_nu,chan_width) taskargs = "pix_scale = " + str(self.getkey("pix_scale")) + " chan_width = "+str(self.getkey("chan_width")) self._summary["regrid"] = SummaryEntry(atable.serialize(),"Regrid_AT",,taskargs) dt.tag("done") dt.end()