def setup(self): import datetime as d import os # Change here DATA_PATH where StepBiasCombine will search for files workdir = os.path.abspath(os.path.dirname(__file__)) self.data_path = os.path.join(workdir, "test_data") self.patch("corral.conf.settings.DATA_PATH", self.data_path) # self.patch("corral.conf.settings.PAWPRINT_PATH", "/my/test/path") cleanstate = models.State(name='cleaned', order=2, is_error=False) self.save(cleanstate) self.session.commit() # generate a new master dark init_date = string2datetime("2016-09-14T02:01:23.4") darkcomb = models.Combination(created_at=init_date, modified_at=init_date, exptime=60., mean_jd=2016.32234214, imagetype='Master Dark') self.save(darkcomb) darkmaster = models.MasterCalib( imagetype="Master Dark", modified_at=init_date, exptime=darkcomb.exptime, mean_jd=darkcomb.mean_jd) darkmaster.combination = darkcomb self.save(darkmaster) self.session.add(darkmaster) self.session.add(darkcomb) self.session.commit() dark_data = 50. + np.random.rand(100, 100) hdr = {'exptime': darkcomb.exptime, 'jd': darkcomb.mean_jd} darkcomb.writefile(img_obj=dark_data, hdr_dict=hdr) # generate combination material self.num_flat = 5 init_date = string2datetime("2016-09-14T02:01:23.4") for i in range(self.num_flat): aflat = models.CalibFile( imagetype="Flat", state=cleanstate, observation_date=init_date + i * d.timedelta(minutes=5), jd=2016.32234214, exptime=60., xbinning=1, ybinning=1, state_count=0) self.save(aflat) for aflat in self.session.query(models.CalibFile).all(): flat_data = 50. + np.random.rand(100, 100) hdr = {'exptime': aflat.exptime} aflat.writefile(img_obj=flat_data, hdr_dict=hdr)
def setup(self): import datetime as d from astropy.io import fits import os # Change here DATA_PATH where StepBiasCombine will search for files workdir = os.path.abspath(os.path.dirname(__file__)) self.data_path = os.path.join(workdir, "test_data") self.patch("corral.conf.settings.DATA_PATH", self.data_path) # self.patch("corral.conf.settings.PAWPRINT_PATH", "/my/test/path") cleanstate = models.State(name='cleaned', order=2, is_error=False) self.save(cleanstate) self.session.commit() self.num_dark = 5 init_date = string2datetime("2016-09-14T02:01:23.4") for i in range(self.num_dark): adark = models.CalibFile( imagetype="Dark", state=cleanstate, observation_date=init_date + i * d.timedelta(minutes=5), jd=2016.32234214, exptime=60., xbinning=1, ybinning=1, state_count=0) self.save(adark) for adark in self.session.query(models.CalibFile).all(): adark_dir = os.path.dirname(adark.get_path()) if not os.path.exists(adark_dir): os.makedirs(adark_dir) dark_data = 50. + np.random.rand(100, 100) hdu = fits.PrimaryHDU(dark_data) hdu.writeto(adark.get_path(), clobber=True)
def setup(self): self.bad_keys = settings.CLEANER_ATTR['imagetype'].keys() # Create the states that StepCalCleaner uses rawstate = models.State(name='raw', order=1, is_error=False) self.save(rawstate) cleanstate = models.State(name='cleaned', order=2, is_error=False) self.save(cleanstate) self.session.commit() for abadkey in self.bad_keys: acalib = models.CalibFile( imagetype=abadkey, state=rawstate, observation_date=string2datetime("2016-09-14T02:01:23.4"), jd=2016.32234214, exptime=60., xbinning=1, ybinning=1, state_count=0) self.save(acalib)