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casa
$Rev:20696$
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casa
$Rev:20696$
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00001 ########################################################################## 00002 # imfit_test.py 00003 # 00004 # Copyright (C) 2008, 2009 00005 # Associated Universities, Inc. Washington DC, USA. 00006 # 00007 # This script is free software; you can redistribute it and/or modify it 00008 # under the terms of the GNU Library General Public License as published by 00009 # the Free Software Foundation; either version 2 of the License, or (at your 00010 # option) any later version. 00011 # 00012 # This library is distributed in the hope that it will be useful, but WITHOUT 00013 # ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or 00014 # FITNESS FOR A PARTICULAR PURPOSE. See the GNU Library General Public 00015 # License for more details. 00016 # 00017 # You should have received a copy of the GNU Library General Public License 00018 # along with this library; if not, write to the Free Software Foundation, 00019 # Inc., 675 Massachusetts Ave, Cambridge, MA 02139, USA. 00020 # 00021 # Correspondence concerning AIPS++ should be adressed as follows: 00022 # Internet email: aips2-request@nrao.edu. 00023 # Postal address: AIPS++ Project Office 00024 # National Radio Astronomy Observatory 00025 # 520 Edgemont Road 00026 # Charlottesville, VA 22903-2475 USA 00027 # 00028 # <author> 00029 # Dave Mehringer 00030 # </author> 00031 # 00032 # <summary> 00033 # Test suite for the CASA task imcollapse 00034 # </summary> 00035 # 00036 # <reviewed reviwer="" date="" tests="" demos=""> 00037 # </reviewed 00038 # 00039 # <prerequisite> 00040 # <ul> 00041 # <li> <linkto class="task_imcollapse.py:description">imcollapse</linkto> 00042 # </ul> 00043 # </prerequisite> 00044 # 00045 # <etymology> 00046 # Test for the imcollapse task 00047 # </etymology> 00048 # 00049 # <synopsis> 00050 # Test the imcollapse task and the ia.collapse() method upon which it is built. 00051 # </synopsis> 00052 # 00053 # <example> 00054 # 00055 # This test runs as part of the CASA python unit test suite and can be run from 00056 # the command line via eg 00057 # 00058 # `echo $CASAPATH/bin/casapy | sed -e 's$ $/$'` --nologger --log2term -c `echo $CASAPATH | awk '{print $1}'`/code/xmlcasa/scripts/regressions/admin/runUnitTest.py test_imcollapse[test1,test2,...] 00059 # 00060 # </example> 00061 # 00062 # <motivation> 00063 # To provide a test standard for the imcollapse task to ensure 00064 # coding changes do not break the associated bits 00065 # </motivation> 00066 # 00067 00068 ########################################################################### 00069 import shutil 00070 import casac 00071 from tasks import * 00072 from taskinit import * 00073 from __main__ import * 00074 import unittest 00075 00076 good_image = "collapse_in.fits" 00077 masked_image = "im_w_mask.im" 00078 datapath=os.environ.get('CASAPATH').split()[0]+'/data/regression/unittest/imcollapse/' 00079 00080 00081 def run_collapse( 00082 imagename, function, axes, outfile, region, box, chans, 00083 stokes, mask, overwrite, stretch=False 00084 ): 00085 myia = iatool() 00086 myia.open(imagename) 00087 res = myia.collapse( 00088 function=function, axes=axes, outfile=outfile, 00089 region=region, box=box, chans=chans, stokes=stokes, 00090 mask=mask, overwrite=overwrite, stretch=stretch 00091 ) 00092 myia.close() 00093 myia.done() 00094 return res 00095 00096 def run_imcollapse( 00097 imagename, function, axes, outfile, region, box, chans, 00098 stokes, mask, overwrite, wantreturn, stretch=False 00099 ): 00100 return imcollapse( 00101 imagename=imagename, function=function, axes=axes, 00102 outfile=outfile, region=region, box=box, chans=chans, 00103 stokes=stokes, mask=mask, overwrite=overwrite, 00104 wantreturn=wantreturn, stretch=stretch 00105 ) 00106 00107 class imcollapse_test(unittest.TestCase): 00108 00109 def setUp(self): 00110 shutil.copy(datapath + good_image, good_image) 00111 self.tabular_spectral_image = datapath + "longZax" 00112 00113 def tearDown(self): 00114 os.remove(good_image) 00115 00116 def checkImage(self, gotImage, expectedName): 00117 expected = iatool() 00118 expected.open(expectedName) 00119 got = iatool() 00120 if type(gotImage) == str: 00121 got.open(gotImage) 00122 else: 00123 got = gotImage 00124 self.assertTrue(all(got.shape() == expected.shape())) 00125 diffData = got.getchunk() - expected.getchunk() 00126 self.assertTrue(abs(diffData).max() == 0) 00127 gotCsys = got.coordsys() 00128 expectedCsys = expected.coordsys() 00129 diffPixels = gotCsys.referencepixel()['numeric'] - expectedCsys.referencepixel()['numeric'] 00130 self.assertTrue(abs(diffPixels).max() == 0) 00131 fracDiffRef = ( 00132 gotCsys.referencevalue()['numeric'] - expectedCsys.referencevalue()['numeric'] 00133 )/expectedCsys.referencevalue()['numeric']; 00134 self.assertTrue(abs(fracDiffRef).max() <= 1.5e-6) 00135 beam = got.restoringbeam() 00136 self.assertTrue(len(beam) == 3) 00137 self.assertTrue(abs(beam["major"]["value"] - 1) < 1.5e-6) 00138 self.assertTrue(abs(beam["minor"]["value"] - 1) < 1.5e-6) 00139 self.assertTrue(abs(beam["positionangle"]["value"] - 40) < 1.5e-6) 00140 got.close() 00141 got.done() 00142 expected.close() 00143 expected.done() 00144 00145 def test_exceptions(self): 00146 """imcollapse: Test various exception cases""" 00147 00148 bogus = "mybogus.im" 00149 def testit( 00150 imagename, function, axes, outfile, region, 00151 box, chans, stokes, mask, overwrite, wantreturn 00152 ): 00153 for i in [0,1]: 00154 if (i==0 and len(imagename) > 0 and imagename != bogus): 00155 self.assertRaises( 00156 Exception, run_collapse, imagename, 00157 function, axes, outfile, region, box, 00158 chans, stokes, mask, overwrite 00159 ) 00160 else: 00161 self.assertFalse( 00162 run_imcollapse( 00163 imagename, function, axes, 00164 outfile, region, box, chans, 00165 stokes, mask, overwrite, 00166 wantreturn 00167 ) 00168 ) 00169 # no image name given 00170 testit("", "mean", 0, "", "", "", "", "", "", False, True) 00171 # bad image name given 00172 testit(bogus, "mean", 0, "", "", "", "", "", "", False, True) 00173 # no function given 00174 testit(good_image, "", 0, "", "", "", "", "", "", False, True) 00175 # bogus function given 00176 testit(good_image, "bogus function", 0, "", "", "", "", "", "", False, True) 00177 # bogus region given 00178 testit(good_image, "mean", 0, "", "bogus_region", "", "", "", "", False, True) 00179 #bogus box 00180 testit(good_image, "mean", 0, "", "", "abc", "", "", "", False, True) 00181 # another bogus box 00182 testit(good_image, "mean", 0, "", "", "0,0,1000,1000", "", "", "", False, True) 00183 # no axes 00184 testit(good_image, "mean", "", "", "", "", "", "", "", False, True) 00185 # bogus axes 00186 testit(good_image, "mean", 10, "", "", "", "", "", "", False, True) 00187 00188 def test_1(self): 00189 """imcollapse: average full image collapse along axis 0""" 00190 expected = "collapse_avg_0.fits" 00191 shutil.copy(datapath + expected, expected) 00192 for i in [0, 1]: 00193 for axis in (0 ,"r", "right"): 00194 outname = "test_1_" + str(i) + "_" + str(axis) + ".im" 00195 if i == 0: 00196 mytool = run_collapse( 00197 good_image, "mean", axis, outname, "", "", 00198 "", "", "", False 00199 ) 00200 self.assertTrue(type(mytool) == type(ia)) 00201 self.checkImage(mytool, expected) 00202 self.checkImage(outname, expected) 00203 else: 00204 for wantreturn in [True, False]: 00205 outname = outname + str(wantreturn) 00206 mytool = run_imcollapse( 00207 good_image, "mean", 0, outname, "", "", 00208 "", "", "", False, wantreturn 00209 ) 00210 if (wantreturn): 00211 self.assertTrue(type(mytool) == type(ia)) 00212 self.checkImage(mytool, expected) 00213 else: 00214 self.assertTrue(mytool == None) 00215 self.checkImage(outname, expected) 00216 shutil.rmtree(outname) 00217 00218 def test_2(self): 00219 """imcollapse: average full image collapse along axis 2""" 00220 expected = "collapse_avg_2.fits" 00221 shutil.copy(datapath + expected, expected) 00222 for i in [0, 1]: 00223 for axis in (2, "f", "freq"): 00224 outname = "test_2_" + str(i) + str(axis) + ".im" 00225 if i == 0: 00226 mytool = run_collapse( 00227 good_image, "mean", axis, outname, "", "", 00228 "", "", "", False 00229 ) 00230 self.assertTrue(type(mytool) == type(ia)) 00231 self.checkImage(mytool, expected) 00232 self.checkImage(outname, expected) 00233 else: 00234 for wantreturn in [True, False]: 00235 outname = outname + str(wantreturn) 00236 mytool = run_imcollapse( 00237 good_image, "mean", 2, outname, "", "", 00238 "", "", "", False, wantreturn 00239 ) 00240 if (wantreturn): 00241 self.assertTrue(type(mytool) == type(ia)) 00242 self.checkImage(mytool, expected) 00243 else: 00244 self.assertTrue(mytool == None) 00245 self.checkImage(outname, expected) 00246 shutil.rmtree(outname) 00247 00248 def test_3(self): 00249 """imcollapse: average full image collapse along axis 2 and check output overwritability""" 00250 expected = "collapse_sum_1.fits" 00251 shutil.copy(datapath + expected, expected) 00252 box = "1,1,2,2" 00253 chans = "1~2" 00254 stokes = "qu" 00255 for i in [0, 1]: 00256 outname = "test_3_" + str(i) + ".im" 00257 if i == 0: 00258 mytool = run_collapse( 00259 good_image, "sum", 1, outname, "", box, 00260 chans, stokes, "", False 00261 ) 00262 # check that can overwrite previous output. Then check output image 00263 mytool = run_collapse( 00264 good_image, "sum", 1, outname, "", box, 00265 chans, stokes, "", True 00266 ) 00267 self.assertTrue(type(mytool) == type(ia)) 00268 self.checkImage(mytool, expected) 00269 self.checkImage(outname, expected) 00270 else: 00271 for wantreturn in [True, False]: 00272 outname = outname + str(wantreturn) 00273 # check that can overwrite previous output. Then check output image 00274 mytool = run_imcollapse( 00275 good_image, "sum", 1, outname, "", box, 00276 chans, stokes, "", False, wantreturn 00277 ) 00278 mytool = run_imcollapse( 00279 good_image, "sum", 1, outname, "", box, 00280 chans, stokes, "", True, wantreturn 00281 ) 00282 if (wantreturn): 00283 self.assertTrue(type(mytool) == type(ia)) 00284 self.checkImage(mytool, expected) 00285 else: 00286 self.assertTrue(mytool == None) 00287 self.checkImage(outname, expected) 00288 shutil.rmtree(outname) 00289 00290 def test_4(self): 00291 """imcollapse: not specifying an output image is ok""" 00292 expected = "collapse_avg_2.fits" 00293 shutil.copy(datapath + expected, expected) 00294 for i in [0, 1]: 00295 if i == 0: 00296 mytool = run_collapse( 00297 good_image, "mean", 2, "", "", "", 00298 "", "", "", False 00299 ) 00300 self.assertTrue(type(mytool) == type(ia)) 00301 self.checkImage(mytool, expected) 00302 else: 00303 mytool = run_imcollapse( 00304 good_image, "mean", 2, "", "", "", 00305 "", "", "", False, True 00306 ) 00307 self.assertTrue(type(mytool) == type(ia)) 00308 self.checkImage(mytool, expected) 00309 00310 def test_5(self): 00311 """imcollapse: not wanting a tool returned results in None being returned""" 00312 expected = "collapse_avg_2.fits" 00313 shutil.copy(datapath + expected, expected) 00314 mytool = run_imcollapse( 00315 good_image, "mean", 2, "", "", "", 00316 "", "", "", False, False 00317 ) 00318 self.assertTrue(mytool == None) 00319 00320 00321 def test_6(self): 00322 """imcollapse: memory only images can be collapsed""" 00323 mytool = run_collapse( 00324 good_image, "mean", 2, "", "", "", 00325 "", "", "", False 00326 ) 00327 mytool2 = mytool.collapse("mean", 3) 00328 expected = [3, 3, 1, 1] 00329 self.assertTrue(all(mytool2.shape() == expected)) 00330 00331 mytool = run_imcollapse( 00332 good_image, "mean", 2, "", "", "", 00333 "", "", "", False, True 00334 ) 00335 mytool2 = mytool.collapse("mean", 3) 00336 expected = [3, 3, 1, 1] 00337 self.assertTrue(all(mytool2.shape() == expected)) 00338 00339 def test_7(self): 00340 """imcollapse: verify collapsing along multiple axes works""" 00341 expected = "collapse_avg_0_1.fits" 00342 shutil.copy(datapath + expected, expected) 00343 for i in [0, 1]: 00344 for axes in ([0, 1], ["r", "d"], ["right", "dec"]): 00345 if i == 0: 00346 mytool = run_collapse( 00347 good_image, "mean", axes, "", "", "", 00348 "", "", "", False 00349 ) 00350 self.assertTrue(type(mytool) == type(ia)) 00351 self.checkImage(mytool, expected) 00352 else: 00353 mytool = run_imcollapse( 00354 good_image, "mean", [0, 1], "", "", "", 00355 "", "", "", False, True 00356 ) 00357 self.assertTrue(type(mytool) == type(ia)) 00358 self.checkImage(mytool, expected) 00359 00360 def test_8(self): 00361 """imcollapse: test both OTF and permanent masking works""" 00362 xx = iatool() 00363 good_image_im = "collapse_in.im" 00364 xx.fromfits(good_image_im, good_image) 00365 xx.calcmask(good_image_im + "<78") 00366 xx.close() 00367 xx.done() 00368 mytool = F 00369 axes = 3 00370 for i in [0,1]: 00371 for j in [0, 1, 2]: 00372 mask = good_image_im + ">7" 00373 if j == 0: 00374 xx.open(good_image_im) 00375 xx.maskhandler("set", "") 00376 xx.close() 00377 xx.done() 00378 if j == 1: 00379 mask = "" 00380 xx.open(good_image_im) 00381 xx.maskhandler("set", "mask0") 00382 xx.close() 00383 xx.done() 00384 for func in ["mean", "median"]: 00385 for outfile in ["", "test_8_"+str(i) + str(j) + func]: 00386 if i == 0: 00387 mytool = run_collapse( 00388 good_image_im, func, axes, outfile, "", "", 00389 "", "", mask, False 00390 ) 00391 self.assertTrue(type(mytool) == type(ia)) 00392 else: 00393 mytool = run_imcollapse( 00394 good_image_im, func, axes, outfile, "", "", 00395 "", "", mask, False, True 00396 ) 00397 self.assertTrue(type(mytool) == type(ia)) 00398 npts = mytool.statistics()["npts"] 00399 mytool.close() 00400 mytool.done() 00401 if (j == 0): 00402 self.assertTrue(npts == 25) 00403 elif (j == 1): 00404 self.assertTrue(npts == 26) 00405 else: 00406 self.assertTrue(npts == 24) 00407 00408 def test_CAS_3418(self): 00409 """imcollapse: Test seperate code for median due to performance issues""" 00410 for i in range(0,4): 00411 xx = iatool() 00412 xx.open(good_image) 00413 exp = xx.statistics(robust=T, axes=i)["median"] 00414 xx.done() 00415 mytool = run_collapse( 00416 good_image, "median", i, "", "", "", 00417 "", "", "", False 00418 ) 00419 zz = mytool.subimage("", dropdeg=T) 00420 got = zz.getchunk() 00421 self.assertTrue((got == exp).all()) 00422 00423 mytool = run_imcollapse( 00424 good_image, "median", i, "", "", "", 00425 "", "", "", False, True 00426 ) 00427 zz = mytool.subimage("", dropdeg=T) 00428 got = zz.getchunk() 00429 self.assertTrue((got == exp).all()) 00430 mytool.done() 00431 zz.done() 00432 00433 def test_stretch(self): 00434 """ imcollapse: Test stretch parameter""" 00435 yy = iatool() 00436 yy.open(good_image) 00437 mycs = yy.coordsys().torecord() 00438 yy.done() 00439 maskim = "ymask" 00440 yy.fromshape(maskim,[3,3,1,1]) 00441 yy.addnoise() 00442 yy.setcoordsys(mycs) 00443 yy.done() 00444 for i in [0,1]: 00445 if i == 1: 00446 yy = run_collapse( 00447 good_image, "mean", 0, "", "", "", "", 00448 "", maskim + ">0", False, stretch=True 00449 ) 00450 else: 00451 yy = run_imcollapse( 00452 good_image, "mean", 0, "", "", "", "", 00453 "", maskim + ">0", False, True, True 00454 ) 00455 self.assertTrue(type(yy) == type(ia)) 00456 00457 def test_CAS3737(self): 00458 """ imcollapse: test tabular spectral axis has correct collapsed reference value """ 00459 image = self.tabular_spectral_image 00460 chans = "2445~2555" 00461 mytool = run_collapse( 00462 image, "mean", 2, "", "", "", 00463 chans, "", "", False 00464 ) 00465 expected = 98318505973583.641 00466 got = mytool.toworld([0,0,0])["numeric"][2] 00467 mytool.done() 00468 frac = got/expected - 1 00469 self.assertTrue(frac < 1e-6 and frac > -1e-6) 00470 00471 mytool = run_imcollapse( 00472 image, "mean", 2, "", "", "", 00473 chans, "", "", False, True 00474 ) 00475 got = mytool.toworld([0,0,0])["numeric"][2] 00476 mytool.done() 00477 frac = got/expected - 1 00478 self.assertTrue(frac < 1e-6 and frac > -1e-6) 00479 00480 def test_beams(self): 00481 """test per plane beams""" 00482 myia = iatool() 00483 myia.fromshape("", [10, 10, 10, 4]) 00484 myia.setrestoringbeam( 00485 major="4arcsec", minor="3arcsec", 00486 pa="20deg", channel=1, polarization=1 00487 ) 00488 for i in range (myia.shape()[2]): 00489 for j in range(myia.shape()[3]): 00490 major = qa.quantity(4 + i + j, "arcsec") 00491 minor = qa.quantity(2 + i + 0.5*j, "arcsec") 00492 pa = qa.quantity(10*i + j, "deg") 00493 myia.setrestoringbeam( 00494 major=major, minor=minor, pa=pa, 00495 channel=i, polarization=j 00496 ) 00497 reg = rg.box(blc=[1,1,1,1], trc=[2,2,2,2]) 00498 collapsed = myia.collapse(function="mean", axes=2, outfile="", region=reg) 00499 beam = collapsed.restoringbeam() 00500 self.assertTrue(len(beam) == 3) 00501 self.assertTrue(beam["major"] == qa.quantity(6, "arcsec")) 00502 self.assertTrue(beam["minor"] == qa.quantity(3.5, "arcsec")) 00503 self.assertTrue(beam["positionangle"] == qa.quantity(11, "deg")) 00504 myia.done() 00505 collapsed.done() 00506 00507 00508 00509 def suite(): 00510 return [imcollapse_test]
1.8.0
