def show_recipes(args):
drpsys = DrpSystem()
# Query instruments
if args.instrument:
name = args.instrument
res = [(name, drpsys.query_by_name(name))]
else:
res = drpsys.query_all().items()
# Function to print
if args.template:
this_recipe_print = print_recipe_template
else:
this_recipe_print = print_recipe
for name, theins in res:
# Per instrument
if theins:
for pipe in theins.pipelines.values():
for mode, recipe_fqn in pipe.recipes.items():
if not args.name or (recipe_fqn in args.name):
Cls = import_object(recipe_fqn)
this_recipe_print(Cls, name=recipe_fqn, insname=theins.name, pipename=pipe.name, modename=mode)
else:
print_no_instrument(name)
def test_recipe1():
drps = init_drp_system()
instrument = drps.get('MEGARA')
pipeline = instrument.pipelines.get('default')
recipe_fqn = pipeline.recipes.get('bias_image')
RecipeClass = import_object(recipe_fqn)
assert RecipeClass is BiasRecipe
def test_recipe1(drpmocker):
drpmocker.add_drp('MEGARA', load_drp)
insdrp = numina.drps.get_system_drps().query_by_name('MEGARA')
pipeline = insdrp.pipelines.get('default')
recipe_fqn = pipeline.recipes.get('MEGARA_BIAS_IMAGE')
RecipeClass = import_object(recipe_fqn)
assert RecipeClass is BiasRecipe
def test_bias(drpmocker):
BASE_URL = 'http://guaix.fis.ucm.es/~spr/megara_test/BIAS/%s'
images = ['e99d2937d2c29a27c0ba4eebfcf7918e',
'e99d2937d2c29a27c0ba4eebfcf7918e',
'e99d2937d2c29a27c0ba4eebfcf7918e']
fs = [download_cache(BASE_URL % i) for i in images]
ob = ObservationResult()
ob.instrument = 'MEGARA'
ob.mode = 'bias_image'
ob.frames = [DataFrame(filename=f.name) for f in fs]
drpmocker.add_drp('MEGARA', load_drp)
# Here we could directly import the required pipeline,
# but the idea is to test all the process
insdrp = numina.drps.get_system_drps().query_by_name(ob.instrument)
pipeline = insdrp.pipelines.get('default')
recipe_fqn = pipeline.recipes.get(ob.mode)
RecipeClass = import_object(recipe_fqn)
assert RecipeClass is BiasRecipe
# TODO: these should be created by a build_recipe_input method
recipe = BiasRecipe()
ri = recipe.create_input(obresult=ob)
result = recipe.run(ri)
# assert result.qc >= QC.UNKNOWN
# Checks on the image
hdulist = result.biasframe.open()
assert len(hdulist) == 1
hdu = hdulist[0]
assert hdu.shape == (4112, 4096)
data = hdu.data
mlevel = 0.0
block = data[1980:2020, 1980:2020]
mblock = block.mean()
sblock = block.std()
assert abs(mblock - mlevel) < 5 * sblock
# In the end, remove the files
for f in fs:
os.remove(f.name)
def test_recipe2():
drps = init_drp_system()
BASE_URL = 'http://guaix.fis.ucm.es/~spr/megara_test/BIAS/%s'
images = ['e99d2937d2c29a27c0ba4eebfcf7918e',
'e99d2937d2c29a27c0ba4eebfcf7918e',
'e99d2937d2c29a27c0ba4eebfcf7918e']
fs = [download_cache(BASE_URL % i) for i in images]
ob = ObservationResult()
ob.instrument = 'MEGARA'
ob.mode = 'bias_image'
ob.frames = [DataFrame(filename=f.name) for f in fs]
instrument = drps.get(ob.instrument)
pipeline = instrument.pipelines.get('default')
recipe_fqn = pipeline.recipes.get(ob.mode)
RecipeClass = import_object(recipe_fqn)
assert RecipeClass is BiasRecipe
# TODO: these should be created by a RecipeInputBuilder
recipe = BiasRecipe()
RR = BiasRecipe.RecipeRequirements # @UndefinedVariable
ri = RR(obresult=ob)
result = recipe.run(ri)
# assert result.qc >= QC.UNKNOWN
# Checks on the image
hdulist = result.biasframe.open()
assert len(hdulist) == 1
hdu = hdulist[0]
assert hdu.shape == (4112, 4096)
data = hdu.data
mlevel = 0.0
block = data[1980:2020, 1980:2020]
mblock = block.mean()
sblock = block.std()
assert abs(mblock - mlevel) < 5 * sblock
# In the end, remove the files
for f in fs:
os.remove(f.name)
def search_recipe_from_ob(self, obsres, pipeline):
_logger.info("Identifier of the observation result: %s", obsres.id)
_logger.info("instrument name: %s", obsres.instrument)
my_ins = self.drps.query_by_name(obsres.instrument)
if my_ins is None:
raise ValueError('no instrument named %r' % obsres.instrument)
my_pipe = my_ins.pipelines.get(pipeline)
if my_pipe is None:
raise ValueError('no pipeline named %r' % pipeline)
_logger.info("observing mode: %r", obsres.mode)
recipe_fqn = my_pipe.recipes.get(obsres.mode)
recipeclass = import_object(recipe_fqn)
return recipeclass
def search_recipe(self, ins, mode, pipeline):
recipe_fqn = self.search_recipe_fqn(ins, mode, pipeline)
klass = import_object(recipe_fqn)
return klass
