Merge pull request #62 from Pica4x6/traces_deletion

Traces + Bias improvements

megaradrp/drp.yaml

@@ -83,7 +83,7 @@ modes:
 pipelines:
   default:
     recipes:
-      bias_image: megaradrp.recipes.BiasRecipe
+      bias_image: megaradrp.recipes.calibration.bias.BiasRecipe
       dark_image: megaradrp.recipes.DarkRecipe
       fiber_flat_image: megaradrp.recipes.FiberFlatRecipe
       mos_image: megaradrp.recipes.scientific.FiberMOSRecipe2

megaradrp/recipes/__init__.py

@@ -17,5 +17,5 @@
 # along with Megara DRP.  If not, see <http://www.gnu.org/licenses/>.
 #
 
-from .calibration import BiasRecipe, DarkRecipe, FiberFlatRecipe
-from .scientific import FiberMOSRecipe
+from .calibration import DarkRecipe, FiberFlatRecipe
+from .scientific import FiberMOSRecipe

megaradrp/recipes/calibration/__init__.py

@@ -19,11 +19,10 @@
 
 '''Calibration Recipes for Megara'''
 
-from .base import BiasRecipe, DarkRecipe
+from .base import DarkRecipe
 from .base import PseudoFluxCalibrationRecipe
 from .flat import TraceMapRecipe, TwilightFiberFlatRecipe
 from .flat import FiberFlatRecipe
-from .base import ArcRecipe
 from .base import LCB_IFU_StdStarRecipe, FiberMOS_StdStarRecipe
 from .base import SensitivityFromStdStarRecipe, S_And_E_FromStdStarsRecipe
-from .base import BadPixelsMaskRecipe, LinearityTestRecipe
+from .base import BadPixelsMaskRecipe, LinearityTestRecipe

megaradrp/recipes/calibration/arc.py

@@ -38,7 +38,6 @@
 # FIXME: remove this later
 from numina.core.products import LinesCatalog
 from scipy.interpolate import interp1d
-import matplotlib.pyplot as plt
 from numina.array.wavecal.arccalibration import arccalibration_direct
 from numina.array.wavecal.arccalibration import fit_solution
 from numina.array.wavecal.arccalibration import gen_triplets_master

megaradrp/recipes/calibration/base.py

@@ -32,7 +32,6 @@
 from numina.core import Product, DataProductRequirement, Requirement
 from numina.core.products import ArrayType
 from numina.core.requirements import ObservationResultRequirement
-from numina.core import RecipeError
 from numina.array.combine import median as c_median
 from numina.flow import SerialFlow
 from numina.flow.processing import BiasCorrector
@@ -50,63 +49,6 @@
 _logger = logging.getLogger('numina.recipes.megara')
 
 
-class BiasRecipe(MegaraBaseRecipe):
-    '''Process BIAS images and create MASTER_BIAS.'''
-
-    obresult = ObservationResultRequirement()
-
-    biasframe = Product(MasterBias)
-
-    def __init__(self):
-        super(BiasRecipe, self).__init__(
-            version="0.1.0"
-        )
-
-    def run(self, rinput):
-        return self.process(rinput.obresult)
-
-    def process(self, obresult):
-        _logger.info('starting bias reduction')
-
-        if not obresult.images:
-            raise RecipeError('Frame list is empty')
-
-        cdata = []
-
-        o_c = OverscanCorrector()
-        t_i = TrimImage()
-
-        basicflow = SerialFlow([o_c, t_i])
-
-        try:
-            for frame in obresult.images:
-                hdulist = frame.open()
-                hdulist = basicflow(hdulist)
-                cdata.append(hdulist)
-
-            _logger.info('stacking %d images using median', len(cdata))
-
-            data = c_median([d[0].data for d in cdata], dtype='float32')
-            template_header = cdata[0][0].header
-            hdu = fits.PrimaryHDU(data[0], header=template_header)
-        finally:
-            for hdulist in cdata:
-                hdulist.close()
-
-        hdr = hdu.header
-        hdr = self.set_base_headers(hdr)
-        hdr['IMGTYP'] = ('BIAS', 'Image type')
-        hdr['NUMTYP'] = ('MASTER_BIAS', 'Data product type')
-        hdr['CCDMEAN'] = data[0].mean()
-
-        varhdu = fits.ImageHDU(data[1], name='VARIANCE')
-        num = fits.ImageHDU(data[2], name='MAP')
-        hdulist = fits.HDUList([hdu, varhdu, num])
-        _logger.info('bias reduction ended')
-
-        result = self.create_result(biasframe=hdu)
-        return result
-
 
 class DarkRecipe(MegaraBaseRecipe):
 
@@ -240,24 +182,6 @@ def add_wcs(hdr):
         return result
 
 
-class ArcRecipe(MegaraBaseRecipe):
-
-    master_bias = MasterBiasRequirement()
-    obresult = ObservationResultRequirement()
-
-    fiberflat_frame = Product(MasterFiberFlat)
-    fiberflat_rss = Product(MasterFiberFlat)
-    traces = Product(ArrayType)
-
-    def __init__(self):
-        super(ArcRecipe, self).__init__(
-            version="0.1.0"
-        )
-
-    def run(self, rinput):
-        pass
-
-
 class LCB_IFU_StdStarRecipe(MegaraBaseRecipe):
 
     master_bias = MasterBiasRequirement()

megaradrp/recipes/calibration/bias.py

@@ -0,0 +1,67 @@
+import logging
+
+from astropy.io import fits
+
+from megaradrp.core import MegaraBaseRecipe
+from megaradrp.processing import OverscanCorrector, TrimImage
+from megaradrp.products import MasterBias
+
+from numina.array.combine import median as c_median
+from numina.core import Product, RecipeError
+from numina.core.requirements import ObservationResultRequirement
+from numina.flow import SerialFlow
+
+_logger = logging.getLogger('numina.recipes.megara')
+
+
+class BiasRecipe(MegaraBaseRecipe):
+    '''Process BIAS images and create MASTER_BIAS.'''
+
+    obresult = ObservationResultRequirement()
+    biasframe = Product(MasterBias)
+
+    def __init__(self):
+        super(BiasRecipe, self).__init__(version="0.1.0")
+
+    def run(self, rinput):
+        return self.process(rinput.obresult)
+
+    def process(self, obresult):
+        _logger.info('starting bias reduction')
+
+        if not obresult.images:
+            raise RecipeError('Frame list is empty')
+
+        cdata = []
+        o_c = OverscanCorrector()
+        t_i = TrimImage()
+        basicflow = SerialFlow([o_c, t_i])
+
+        try:
+            for frame in obresult.images:
+                hdulist = frame.open()
+                hdulist = basicflow(hdulist)
+                cdata.append(hdulist)
+
+            _logger.info('stacking %d images using median', len(cdata))
+
+            data = c_median([d[0].data for d in cdata], dtype='float32')
+            template_header = cdata[0][0].header
+            hdu = fits.PrimaryHDU(data[0], header=template_header)
+        finally:
+            for hdulist in cdata:
+                hdulist.close()
+
+        hdr = hdu.header
+        hdr = self.set_base_headers(hdr)
+        hdr['IMGTYP'] = ('BIAS', 'Image type')
+        hdr['NUMTYP'] = ('MASTER_BIAS', 'Data product type')
+        hdr['CCDMEAN'] = data[0].mean()
+
+        varhdu = fits.ImageHDU(data[1], name='VARIANCE')
+        num = fits.ImageHDU(data[2], name='MAP')
+        hdulist = fits.HDUList([hdu, varhdu, num])
+        _logger.info('bias reduction ended')
+
+        result = self.create_result(biasframe=hdu)
+        return result

megaradrp/recipes/calibration/flat.py

@@ -173,13 +173,12 @@ def run(self, rinput):
         hs = 3
         step1 = 2
         background1 = 150.0
-        npred = 1
         maxdis1 = 2.0
 
         _logger.info('find peaks in column %i', cstart)
 
         central_peaks = init_traces(data, center=cstart, hs=hs,
-                                background=background1, npred=npred)
+                                background=background1)
 
         _logger.info(' %i peaks found', len(central_peaks))
 

megaradrp/recipes/calibration/tests/test_bias.py

@@ -0,0 +1,65 @@
+import os
+
+import pytest
+
+from numina.tests.testcache import download_cache
+
+from numina.core import import_object
+from numina.core.pipeline import DrpSystem
+from numina.core import ObservationResult
+from numina.core import DataFrame
+from megaradrp.recipes.calibration.bias import BiasRecipe
+from megaradrp.loader import megara_drp_load
+
+@pytest.mark.remote
+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', megara_drp_load)
+
+    # Here we could directly import the required pipeline,
+    # but the idea is to test all the process
+    insdrp = DrpSystem().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)