def test_ramp_fit_step(fits_input): """Make sure the RampFitStep runs without error.""" fname = fits_input[0].header['filename'].replace('.fits', '_rampfitstep.fits') RampFitStep.call(datamodels.open(fits_input), output_file=fname, save_results=True)
def test_ramp_fit_nirspec(self): """ Regression test of ramp_fit step performed on NIRSpec data. This is a single integration dataset. """ input_file = self.get_data(self.test_dir, 'jw00023001001_01101_00001_NRS1_jump.fits') result, result_int = RampFitStep.call(input_file, save_opt=True, opt_name='rampfit_opt_out.fits', name='RampFit' ) output_file = result.meta.filename result.save(output_file) result.close() outputs = [(output_file, 'jw00023001001_01101_00001_NRS1_ramp_fit.fits'), ('rampfit_opt_out_fitopt.fits', 'jw00023001001_01101_00001_NRS1_opt.fits', ['primary','slope','sigslope','yint','sigyint', 'pedestal','weights','crmag']) ] self.compare_outputs(outputs)
def test_ramp_fit_nirspec(self): """ Regression test of ramp_fit step performed on NIRSpec data. This is a single integration dataset. """ input_file = self.get_data(self.test_dir, 'jw00023001001_01101_00001_NRS1_jump.fits') result, result_int = RampFitStep.call(input_file, save_opt=True, opt_name='rampfit_opt_out.fits', name='RampFit' ) output_file = result.meta.filename result.save(output_file) result.close() outputs = [(output_file, 'jw00023001001_01101_00001_NRS1_ramp_fit.fits'), ('rampfit_opt_out_fitopt.fits', 'jw00023001001_01101_00001_NRS1_opt.fits', ['primary','slope','sigslope','yint','sigyint', 'pedestal','weights','crmag']) ] self.compare_outputs(outputs)
def test_fake_pedestals(darkcases, rates, pedestals): '''Test ramp-fit step with fake data.''' # open ramp to get data shape and headers m = RampModel(darkcases) tgroup = m.meta.exposure.group_time rates = np.float(rates) pedestals = np.float(pedestals) nrows = int(m.meta.subarray.xsize) ncols = int(m.meta.subarray.ysize) ngroups = int(m.meta.exposure.ngroups) nints = int(m.meta.exposure.nints) # create fake ramps with known slope and pedestal new_data = np.zeros((nints, ngroups, nrows, ncols), dtype=np.float32) for i in np.arange(0, ngroups): for j in np.arange(4, 2044): for k in np.arange(4, 2044): new_data[0, i, j, k] = pedestals + rates * ((i + 1) * tgroup) # save it m.data = new_data m.err = np.zeros((nints, ngroups, nrows, ncols), dtype=np.float32) fake_data_outname = darkcases[:-5] + "_rate" + str( rates) + "_pedestal" + str( pedestals) + "_test_fake_pedestals_uncal.fits" m.save(fake_data_outname, overwrite=True) output, outint = RampFitStep.call( m, output_file=fake_data_outname[:-5] + "_rate.fits", save_opt=True, opt_name=fake_data_outname[:-5] + "_rate_opt.fits") optoutput = fits.open(fake_data_outname[:-5] + "rate_opt.fits") # check pedestal clip = sigma_clip(optoutput['PEDESTAL'].data) clip.data[clip.mask] = np.nan meanped = np.nanmean(clip.data) assert np.allclose(pedestals, meanped, rtol=2, atol=2) == True optoutput.close()
def test_ramp_fit_miri1(self): """ Regression test of ramp_fit step performed on MIRI data. """ input_file = self.get_data(self.test_dir, 'jw00001001001_01101_00001_MIRIMAGE_jump.fits') result = RampFitStep.call(input_file, save_opt=True, opt_name='rampfit1_opt_out.fits') output_file = result[0].save(path=result[0].meta.filename.replace('jump','rampfit')) int_output = result[1].save(path=result[1].meta.filename.replace('jump','rampfit_int')) result[0].close() result[1].close() outputs = [(output_file, 'jw00001001001_01101_00001_MIRIMAGE_ramp_fit.fits'), (int_output, 'jw00001001001_01101_00001_MIRIMAGE_int.fits'), ('rampfit1_opt_out_fitopt.fits', 'jw00001001001_01101_00001_MIRIMAGE_opt.fits') ] self.compare_outputs(outputs)
def test_extensions(darkcases): '''Test ramp-fit step with fake data.''' # open ramp to get data shape and headers m = RampModel(darkcases) fake_data_outname = darkcases[:-5] + "_test_extensions_uncal.fits" output, outint = RampFitStep.call(m, save_opt=True, opt_name=fake_data_outname[:-5] + "_rate_opt.fits") with fits.open(fake_data_outname[:-5] + "_rate_opt.fits") as h: # check all optional outputs are there assert h[1] == h['SLOPE'] assert h[2] == h['SIGSLOPE'] assert h[3] == h['YINT'] assert h[4] == h['SIGYINT'] assert h[5] == h['PEDESTAL'] assert h[6] == h['WEIGHTS'] assert h[7] == h['CRMAG']
def test_ramp_fit_niriss(self): """ Regression test of ramp_fit step performed on NIRISS data. """ input_file = self.get_data( self.test_dir, 'jw00034001001_01101_00001_NIRISS_jump.fits') result, result_int = RampFitStep.call(input_file, save_opt=True, opt_name='rampfit_opt_out.fits') output_file = result.meta.filename result.save(output_file) result.close() outputs = [(output_file, 'jw00034001001_01101_00001_NIRISS_ramp_fit.fits'), ('rampfit_opt_out_fitopt.fits', 'jw00034001001_01101_00001_NIRISS_uncal_opt.fits', [ 'primary', 'slope', 'sigslope', 'yint', 'sigyint', 'pedestal', 'weights', 'crmag' ])] self.compare_outputs(outputs)
def test_ramp_fit_miri1(self): """ Regression test of ramp_fit step performed on MIRI data. """ input_file = self.get_data(self.test_dir, 'jw00001001001_01101_00001_MIRIMAGE_jump.fits') result = RampFitStep.call(input_file, save_opt=True, opt_name='rampfit1_opt_out.fits') output_file = result[0].save(path=result[0].meta.filename.replace('jump','rampfit')) int_output = result[1].save(path=result[1].meta.filename.replace('jump','rampfit_int')) result[0].close() result[1].close() outputs = [(output_file, 'jw00001001001_01101_00001_MIRIMAGE_ramp_fit.fits'), (int_output, 'jw00001001001_01101_00001_MIRIMAGE_int.fits'), ('rampfit1_opt_out_fitopt.fits', 'jw00001001001_01101_00001_MIRIMAGE_opt.fits') ] self.compare_outputs(outputs)
def test_ramp_fit_niriss(self): """ Regression test of ramp_fit step performed on NIRISS data. """ input_file = self.get_data(self.test_dir, 'jw00034001001_01101_00001_NIRISS_jump.fits') result, result_int = RampFitStep.call(input_file, save_opt=True, opt_name='rampfit_opt_out.fits' ) output_file = result.meta.filename result.save(output_file) result.close() outputs = [(output_file, 'jw00034001001_01101_00001_NIRISS_ramp_fit.fits'), ('rampfit_opt_out_fitopt.fits', 'jw00034001001_01101_00001_NIRISS_uncal_opt.fits', ['primary','slope','sigslope','yint','sigyint', 'pedestal','weights','crmag']) ] self.compare_outputs(outputs)
def test_dq(darkcases): '''Test ramp-fit step with fake data.''' # open ramp to get data shape and headers m = RampModel(darkcases) # create one saturated pixel m.data[0, :, 250, 250] = m.data[0, :, 250, 250] + 57000 m.pixeldq[250, 250] = 2.0 # add in jump to one of the pixels m.data[0, 2:, 500, 500] = m.data[0, 2:, 500, 500] + 5000 m.groupdq[0, 2, 500, 500] = 4.0 output, outint = RampFitStep.call(m) # check DQ array before_satdq = m.pixeldq[250, 250] after_satdq = output.dq[250, 250] before_CRdq = m.groupdq[0, 2, 500, 500] after_CRdq = output.dq[500, 500] assert before_satdq == after_satdq assert before_CRdq == after_CRdq
def test_CR_handling(darkcases, rates, pedestals): '''Test ramp-fit step with fake data.''' # open ramp to get data shape and headers m = RampModel(darkcases) tgroup = m.meta.exposure.group_time rates = np.float(rates) pedestals = np.float(pedestals) ngroups = 10 nints = 1 nrows = int(m.meta.subarray.xsize) ncols = int(m.meta.subarray.ysize) m.meta.exposure.ngroups = ngroups m.meta.exposure.ngroup = ngroups m.meta.exposure.nints = nints m.err = np.zeros((nints, ngroups, nrows, ncols), dtype=np.float32) m.groupdq = np.zeros((nints, ngroups, nrows, ncols), dtype=np.float32) # create fake ramps with known slope and pedestal new_data = np.zeros((nints, ngroups, nrows, ncols), dtype=np.float32) for ints in np.arange(0, nints): for i in np.arange(0, ngroups): for j in np.arange(4, 2044): for k in np.arange(4, 2044): new_data[ints, i, j, k] = pedestals + rates * ((i + 1) * tgroup) # add in jump to one of the pixels new_data[0, 2:, 500, 500] = new_data[0, 2:, 500, 500] + (rates * 5) m.groupdq[0, 2, 500, 500] = 4.0 # add in two jumps to one of the pixels new_data[0, 2:, 740, 740] = new_data[0, 2:, 740, 740] + (rates * 5) new_data[0, 6:, 740, 740] = new_data[0, 6:, 740, 740] + (rates * 6) m.groupdq[0, 2, 740, 740] = 4.0 m.groupdq[0, 6, 740, 740] = 4.0 # save it m.data = new_data fake_data_outname = darkcases[:-5] + "_rate" + str( rates) + "_pedestal" + str(pedestals) + "_test2_uncal.fits" # m.save(fake_data_outname,overwrite=True) output, outint = RampFitStep.call( m, output_file=fake_data_outname[:-5] + "rate.fits", save_opt=True, opt_name=fake_data_outname[:-5] + "rate_opt.fits") optoutput = fits.open(fake_data_outname[:-5] + "rate_opt.fits") # check output rates in regular output clip = sigma_clip(output.data) clip.data[clip.mask] = np.nan clip.data[output.dq != 0] = np.nan meanrate = np.nanmean(clip.data) assert np.allclose(meanrate, rates, rtol=8, atol=8) == True # check output rates in INTS output if nints > 1: for i in np.arange(0, nints): clip = sigma_clip(outint.data[nints, :, :]) clip.data[clip.mask] = np.nan clip.data[output.dq != 0] = np.nan meanrate = np.nanmean(clip.data) assert np.allclose(meanrate, rates, rtol=8, atol=8) == True # CR rates from rate_opt.fits file ratebeforeCR1 = optoutput['SLOPE'].data[0, 0, 740, 740] rateafterCR1 = optoutput['SLOPE'].data[0, 1, 740, 740] ratebeforeCR2 = optoutput['SLOPE'].data[0, 1, 740, 740] rateafterCR2 = optoutput['SLOPE'].data[0, 2, 740, 740] assert np.allclose(ratebeforeCR1, rateafterCR1, rtol=1e-2, atol=1e-2) == True assert np.allclose(ratebeforeCR2, rateafterCR2, rtol=1e-2, atol=1e-2) == True # # check to make sure slope is weighted average of intervals # weights = optoutput['WEIGHTS'].data # interval1 = optoutput['SLOPE'].data[0,0,740,740]*weights[0,0,740,740] # interval2 = optoutput['SLOPE'].data[0,1,740,740]*weights[0,1,740,740] # interval3 = optoutput['SLOPE'].data[0,2,740,740]*weights[0,2,740,740] # calc = (interval1 + interval2 + interval3)/(weights[0,0,740,740] + weights[0,1,740,740] +weights[0,2,740,740]) # print(calc) # other integrations shouldn't have CR hit if nints > 1: int2_noCRbefore = optoutput['SLOPE'].data[1, 0, 740, 740] int2_noCRafter1 = optoutput['SLOPE'].data[1, 1, 740, 740] int2_noCRafter2 = optoutput['SLOPE'].data[1, 2, 740, 740] assert int2_noCRbefore == rates assert int2_noCRafter1 == 0.0 assert int2_noCRafter2 == 0.0 # CR rates for pix with no CR hit ratebefore = optoutput['SLOPE'].data[0, 0, 800, 800] rateafter = optoutput['SLOPE'].data[0, 1, 800, 800] assert ratebefore == output.data[800, 800] assert rateafter == 0.0 # Check CR magnitude # right now this is just calculated as the difference # between the two group values for the pixel. Is that right? manualCRmag = new_data[0, 2, 500, 500] - new_data[0, 1, 500, 500] pipeCRmag = optoutput['CRMAG'].data[0, 0, 500, 500] assert np.allclose(manualCRmag, pipeCRmag, rtol=1, atol=1) == True manualCRmag = new_data[0, 2, 740, 740] - new_data[0, 1, 740, 740] pipeCRmag = optoutput['CRMAG'].data[0, 0, 740, 740] assert np.allclose(manualCRmag, pipeCRmag, rtol=1, atol=1) == True manualCRmag = new_data[0, 6, 740, 740] - new_data[0, 5, 740, 740] pipeCRmag = optoutput['CRMAG'].data[0, 1, 740, 740] assert np.allclose(manualCRmag, pipeCRmag, rtol=1, atol=1) == True optoutput.close()
def test_ramp_fit_step(fits_input): """Make sure the RampFitStep runs without error.""" RampFitStep.call(datamodels.open(fits_input), save_results=True)