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)
