diff --git a/.gitattributes b/.gitattributes
index 57d339eb510c55612e091cf23ecd46d978c096ee..dde50889be6e31064f07b7807ce6f04e8621ffd4 100644
--- a/.gitattributes
+++ b/.gitattributes
@@ -1407,6 +1407,7 @@ CEP/Pipeline/test/regression_tests/branch_imaging_regression.parset -text
CEP/Pipeline/test/regression_tests/calibration_pipeline_test.py eol=lf
CEP/Pipeline/test/regression_tests/calibrator_pipeline.py -text
CEP/Pipeline/test/regression_tests/imaging_pipeline.py -text
+CEP/Pipeline/test/regression_tests/imaging_pipeline_test.py eol=lf
CEP/Pipeline/test/regression_tests/jenkins_config/Branche[!!-~]regression[!!-~]calibrator[!!-~]pipeline[!!-~].config.xml -text
CEP/Pipeline/test/regression_tests/jenkins_config/Branche[!!-~]regression[!!-~]imaging[!!-~]pipeline[!!-~].config.xml -text
CEP/Pipeline/test/regression_tests/jenkins_config/Branche[!!-~]regression[!!-~]target[!!-~]pipeline.config.xml -text
diff --git a/CEP/Pipeline/test/regression_tests/imaging_pipeline_test.py b/CEP/Pipeline/test/regression_tests/imaging_pipeline_test.py
new file mode 100644
index 0000000000000000000000000000000000000000..30821350808654563ecf678f0331c6083a838000
--- /dev/null
+++ b/CEP/Pipeline/test/regression_tests/imaging_pipeline_test.py
@@ -0,0 +1,374 @@
+import math
+import sys
+
+def validate_image_equality(image_1_path, image_2_path, max_delta):
+ import pyrap.images as pim
+
+ # get the difference between the two images
+ print "comparing images from paths:"
+ print image_1_path
+ print image_2_path
+ im = pim.image('"{0}" - "{1}"'.format(image_1_path, image_2_path))
+ im.saveas("difference.IM2")
+ # get the stats of the image
+ stats_dict = im.statistics()
+ return_value = compare_image_statistics(stats_dict, max_delta)
+
+ if not return_value:
+ print "\n\n\n"
+ print "*"*30
+ print "Statistics of the produced image:"
+ im = pim.image("{0}".format(image_1_path))
+ stats_dict_single_image = im.statistics()
+ print stats_dict_single_image
+ print "\n\n\n"
+ print "Statistics of the compare image:"
+ im = pim.image("{0}".format(image_2_path))
+ stats_dict_single_image = im.statistics()
+ print stats_dict_single_image
+ print "\n\n\n"
+ print "difference between produced image and the baseline image:"
+ print "maximum delta: {0}".format(max_delta)
+ print stats_dict
+ print "*"*30
+
+ return return_value
+
+
+def _test_against_maxdelta(value, max_delta, name):
+ if math.fabs(value) > max_delta:
+ print "Dif found: '{0}' difference >{2}<is larger then " \
+ "the maximum accepted delta: {1}".format(name, max_delta, value)
+ return True
+ return False
+
+def compare_image_statistics(stats_dict, max_delta=0.0001):
+
+ return_value = False
+ found_incorrect_datapoint = False
+ for name, value in stats_dict.items():
+
+ if name == "rms":
+ found_incorrect_datapoint = _test_against_maxdelta(
+ float(value[0]), max_delta * 300, name)
+ elif name == "medabsdevmed":
+ found_incorrect_datapoint = _test_against_maxdelta(
+ float(value[0]), max_delta * 200, name)
+ elif name == "minpos":
+ pass
+ # this min location might move 100 points while still being the same image
+ elif name == "min":
+ found_incorrect_datapoint = _test_against_maxdelta(
+ float(value[0]), max_delta * 2000, name)
+ elif name == "maxpos":
+ pass
+ # this max location might move 100 points while still being the same image
+ elif name == "max":
+ found_incorrect_datapoint = _test_against_maxdelta(
+ float(value[0]), max_delta * 1500, name)
+ elif name == "sum":
+ found_incorrect_datapoint = _test_against_maxdelta(
+ float(value[0]), max_delta * 200000, name)
+ elif name == "quartile":
+ found_incorrect_datapoint = _test_against_maxdelta(
+ float(value[0]), max_delta * 4000, name)
+ elif name == "sumsq":
+ # tested with sum already
+ pass
+
+ elif name == "median":
+ found_incorrect_datapoint = _test_against_maxdelta(
+ float(value[0]), max_delta, name)
+ elif name == "npts":
+ pass # cannot be tested..
+ elif name == "sigma":
+ found_incorrect_datapoint = _test_against_maxdelta(
+ float(value[0]), max_delta * 300, name)
+ elif name == "mean":
+ found_incorrect_datapoint = _test_against_maxdelta(
+ float(value[0]), max_delta * 3, name)
+
+ # if we found an incorrect datapoint in this run or with previous
+ # results: results in true value if any comparison failed
+ return_value = return_value or found_incorrect_datapoint
+
+ return not return_value
+
+
+
+# from here sourcelist compare functions
+def validate_source_list_files(source_list_1_path, source_list_2_path, max_delta):
+ # read the sourcelist files
+ fp = open(source_list_1_path)
+ sourcelist1 = fp.read()
+ fp.close()
+
+ fp = open(source_list_2_path)
+ sourcelist2 = fp.read()
+ fp.close()
+
+ # convert to dataarrays
+ sourcelist_data_1 = convert_sourcelist_as_string_to_data_array(sourcelist1)
+ sourcelist_data_2 = convert_sourcelist_as_string_to_data_array(sourcelist2)
+
+ return compare_sourcelist_data_arrays(sourcelist_data_1, sourcelist_data_2, max_delta)
+
+
+def convert_sourcelist_as_string_to_data_array(source_list_as_string):
+ #split in lines
+ source_list_lines = source_list_as_string.split("\n")
+ entries_array = []
+
+ #get the format line
+ format_line_entrie = source_list_lines[0]
+
+ # get the format entries
+ entries_array.append([format_line_entrie.split(",")[0].split("=")[1].strip()])
+ for entry in format_line_entrie.split(',')[1:]:
+ entries_array.append([entry.strip()])
+
+ # scan all the lines for the actual data
+
+ for line in sorted(source_list_lines[2:]): # try sorting based on name (should work :P)
+ # if empty
+ if line == "":
+ continue
+ # add the data entries
+ for idx, entrie in enumerate(line.split(",")):
+ entries_array[idx].append(entrie.strip())
+
+ return entries_array
+
+def easyprint_data_arrays(data_array1, data_array2):
+ print "All data as red from the sourcelists:"
+ for (first_array, second_array) in zip(data_array1, data_array2):
+ print first_array
+ print second_array
+
+def compare_sourcelist_data_arrays(data_array1, data_array2, max_delta=0.0001):
+ """
+ Ugly function to compare two sourcelists.
+ It needs major refactoring, but for a proof of concept it works
+ """
+ print "######################################################"
+ found_incorrect_datapoint = False
+ for (first_array, second_array) in zip(data_array1, data_array2):
+
+ # first check if the format string is the same, we have a major fail if this happens
+ if first_array[0] != second_array[0]:
+ print "******************* problem:"
+ print "format strings not equal: {0} != {1}".format(first_array[0], second_array[0])
+ found_incorrect_datapoint = True
+
+ # Hard check on equality of the name of the found sources
+ if first_array[0] == "Name":
+ for (entrie1, entrie2) in zip(first_array[1:], second_array[1:]):
+ if entrie1 != entrie2:
+ print "The sourcelist entrie names are not the same: \n{0} !=\n {1}".format(entrie1, entrie2)
+ found_incorrect_datapoint = True
+
+ # Hard check on equality of the type of the found sources
+ elif first_array[0] == "Type":
+ for (entrie1, entrie2) in zip(first_array[1:], second_array[1:]):
+ if entrie1 != entrie2:
+ print "The sourcelist entrie types are not the same: {0} != {1}".format(entrie1, entrie2)
+ found_incorrect_datapoint = True
+
+ # soft check on the Ra: convert to float and compare the values
+ elif first_array[0] == "Ra":
+ for (entrie1, entrie2) in zip(first_array[1:], second_array[1:]):
+ entrie1_as_array = entrie1.split(":")
+ entrie1_as_float = float(entrie1_as_array[0]) * 3600 + float(entrie1_as_array[1]) * 60 + float(entrie1_as_array[2])# float("".join(entrie1.split(":")))
+ entrie2_as_array = entrie2.split(":")
+ entrie2_as_float = float(entrie2_as_array[0]) * 3600 + float(entrie2_as_array[1]) * 60 + float(entrie2_as_array[2])
+ if not math.fabs(entrie1_as_float - entrie2_as_float) < (max_delta * 10000) :
+ print "we have a problem Ra's are not the same within max_delta: {0} != {1} max_delta_ra = {2}".format(
+ entrie1, entrie2, max_delta * 10000)
+ found_incorrect_datapoint = True
+ elif first_array[0] == "Dec":
+ for (entrie1, entrie2) in zip(first_array[1:], second_array[1:]):
+ entrie1_as_array = entrie1.strip("+").split(".")
+ entrie1_as_float = float(entrie1_as_array[0]) * 3600 + float(entrie1_as_array[1]) * 60 + \
+ float("{0}.{1}".format(entrie1_as_array[2], entrie1_as_array[3]))
+ entrie2_as_array = entrie2.strip("+").split(".")
+ entrie2_as_float = float(entrie2_as_array[0]) * 3600 + float(entrie2_as_array[1]) * 60 + \
+ float("{0}.{1}".format(entrie2_as_array[2], entrie2_as_array[3]))
+ if not math.fabs(entrie1_as_float - entrie2_as_float) < (max_delta * 10000) :
+ print "Dec's are not the same within max_delta: {0} != {1} max_delta_ra = {2}".format(
+ entrie1, entrie2, max_delta * 10000)
+ found_incorrect_datapoint = True
+
+ elif first_array[0] == "I":
+ for (entrie1, entrie2) in zip(first_array[1:], second_array[1:]):
+ entrie1_as_float = float(entrie1)
+ entrie2_as_float = float(entrie2)
+ if not math.fabs(entrie1_as_float - entrie2_as_float) < (max_delta * 2000):
+ print "I's are not the same within max_delta {0} != {1} max_delta_I = {2} ".format(
+ entrie1_as_float, entrie2_as_float, max_delta * 1000)
+ found_incorrect_datapoint = True
+
+
+ elif first_array[0] == "Q":
+ for (entrie1, entrie2) in zip(first_array[1:], second_array[1:]):
+ entrie1_as_float = float(entrie1)
+ entrie2_as_float = float(entrie2)
+ if not math.fabs(entrie1_as_float - entrie2_as_float) < (max_delta * 1000):
+ print "Q's are not the same within max_delta {0} != {1} max_delta_I = {2} ".format(
+ entrie1_as_float, entrie2_as_float, max_delta * 1000)
+ found_incorrect_datapoint = True
+ elif first_array[0] == "U":
+ for (entrie1, entrie2) in zip(first_array[1:], second_array[1:]):
+ entrie1_as_float = float(entrie1)
+ entrie2_as_float = float(entrie2)
+ if not math.fabs(entrie1_as_float - entrie2_as_float) < (max_delta * 1000):
+ print "Q's are not the same within max_delta {0} != {1} max_delta_I = {2} ".format(
+ entrie1_as_float, entrie2_as_float, max_delta * 1000)
+ found_incorrect_datapoint = True
+
+ elif first_array[0] == "V":
+ for (entrie1, entrie2) in zip(first_array[1:], second_array[1:]):
+ entrie1_as_float = float(entrie1)
+ entrie2_as_float = float(entrie2)
+ if not math.fabs(entrie1_as_float - entrie2_as_float) < (max_delta * 1000):
+ print "V's are not the same within max_delta {0} != {1} max_delta_I = {2} ".format(
+ entrie1_as_float, entrie2_as_float, max_delta * 1000)
+ found_incorrect_datapoint = True
+
+ elif first_array[0] == "MajorAxis":
+ for (entrie1, entrie2) in zip(first_array[1:], second_array[1:]):
+ entrie1_as_float = float(entrie1)
+ entrie2_as_float = float(entrie2)
+ if not math.fabs(entrie1_as_float - entrie2_as_float) < (max_delta * 60000):
+ print "MajorAxis's are not the same within max_delta {0} != {1} max_delta_I = {2} ".format(
+ entrie1_as_float, entrie2_as_float, max_delta * 50000)
+ found_incorrect_datapoint = True
+
+ elif first_array[0] == "MinorAxis":
+ for (entrie1, entrie2) in zip(first_array[1:], second_array[1:]):
+ entrie1_as_float = float(entrie1)
+ entrie2_as_float = float(entrie2)
+ if not math.fabs(entrie1_as_float - entrie2_as_float) < (max_delta * 30000):
+ print "MinorAxis's are not the same within max_delta {0} != {1} max_delta_I = {2} ".format(
+ entrie1_as_float, entrie2_as_float, max_delta * 30000)
+ found_incorrect_datapoint = True
+
+ elif first_array[0] == "Orientation":
+ for (entrie1, entrie2) in zip(first_array[1:], second_array[1:]):
+ entrie1_as_float = float(entrie1)
+ entrie2_as_float = float(entrie2)
+ if not math.fabs(entrie1_as_float - entrie2_as_float) < (max_delta * 70000):
+ print "Orientation's are not the same within max_delta {0} != {1} max_delta_I = {2} ".format(
+ entrie1_as_float, entrie2_as_float, max_delta * 10000)
+ found_incorrect_datapoint = True
+
+ elif first_array[0].split("=")[0].strip() == "ReferenceFrequency":
+ for (entrie1, entrie2) in zip(first_array[1:], second_array[1:]):
+ entrie1_as_float = float(entrie1)
+ entrie2_as_float = float(entrie2)
+ if not math.fabs(entrie1_as_float - entrie2_as_float) < (max_delta * 10000000):
+ print "Orientation's are not the same within max_delta {0} != {1} max_delta_I = {2} ".format(
+ entrie1_as_float, entrie2_as_float, max_delta * 10000000)
+ found_incorrect_datapoint = True
+ elif first_array[0].split("=")[0].strip() == "SpectralIndex":
+ # Not known yet what will be in the spectral index: therefore do not test it
+ pass
+ else:
+ print "unknown format line entrie found: delta fails"
+ print first_array[0]
+ found_incorrect_datapoint = True
+
+ if found_incorrect_datapoint:
+ print "######################################################"
+ print "compared the following data arrays:"
+ easyprint_data_arrays(data_array1, data_array2)
+ print "######################################################"
+
+
+ # return inverse of found_incorrect_datapoint to signal delta test success
+ return not found_incorrect_datapoint
+
+
+# Test data:
+source_list_as_string = """
+format = Name, Type, Ra, Dec, I, Q, U, V, MajorAxis, MinorAxis, Orientation, ReferenceFrequency='6.82495e+07', SpectralIndex='[]'
+
+/data/scratch/klijn/out/awimage_cycle_0/image.restored_w0_i3_s3_g3, GAUSSIAN, 14:58:34.711, +71.42.19.636, 3.145e+01, 0.0, 0.0, 0.0, 1.79857e+02, 1.49783e+02, 1.24446e+02, 6.82495e+07, [0.000e+00]
+/data/scratch/klijn/out/awimage_cycle_0/image.restored_w0_i2_s2_g2, GAUSSIAN, 15:09:52.818, +70.48.01.625, 2.321e+01, 0.0, 0.0, 0.0, 2.23966e+02, 1.09786e+02, 1.32842e+02, 6.82495e+07, [0.000e+00]
+/data/scratch/klijn/out/awimage_cycle_0/image.restored_w0_i4_s4_g4, GAUSSIAN, 14:53:10.634, +69.29.31.920, 1.566e+01, 0.0, 0.0, 0.0, 1.25136e+02, 4.72783e+01, 6.49083e+01, 6.82495e+07, [0.000e+00]
+/data/scratch/klijn/out/awimage_cycle_0/image.restored_w0_i0_s0_g0, POINT, 15:20:15.370, +72.27.35.077, 1.151e+01, 0.0, 0.0, 0.0, 0.00000e+00, 0.00000e+00, 0.00000e+00, 6.82495e+07, [0.000e+00]
+/data/scratch/klijn/out/awimage_cycle_0/image.restored_w0_i1_s1_g1, POINT, 15:15:15.623, +66.54.31.670, 4.138e+00, 0.0, 0.0, 0.0, 0.00000e+00, 0.00000e+00, 0.00000e+00, 6.82495e+07, [0.000e+00]
+
+"""
+
+source_list_as_string2 = """
+format = Name, Type, Ra, Dec, I, Q, U, V, MajorAxis, MinorAxis, Orientation, ReferenceFrequency='6.82495e+07', SpectralIndex='[]'
+
+/data/scratch/klijn/out/awimage_cycle_0/image.restored_w0_i3_s3_g3, GAUSSIAN, 14:58:34.711, +71.42.19.636, 3.146e+01, 0.0, 0.0, 0.0, 1.79857e+02, 1.49783e+02, 1.24446e+02, 6.82496e+07, [0.000e+00]
+/data/scratch/klijn/out/awimage_cycle_0/image.restored_w0_i2_s2_g2, GAUSSIAN, 15:09:52.818, +70.48.01.625, 2.321e+01, 0.0, 0.0, 0.0, 2.23966e+02, 1.09786e+02, 1.32842e+02, 6.82495e+07, [0.000e+00]
+/data/scratch/klijn/out/awimage_cycle_0/image.restored_w0_i4_s4_g4, GAUSSIAN, 14:53:10.634, +69.29.31.920, 1.566e+01, 0.0, 0.0, 0.0, 1.25136e+02, 4.72783e+01, 6.49083e+01, 6.82495e+07, [0.000e+00]
+/data/scratch/klijn/out/awimage_cycle_0/image.restored_w0_i0_s0_g0, POINT, 15:20:15.370, +72.27.35.077, 1.151e+01, 0.0, 0.0, 0.0, 0.00000e+00, 0.00000e+00, 0.00000e+00, 6.82495e+07, [0.000e+00]
+/data/scratch/klijn/out/awimage_cycle_0/image.restored_w0_i1_s1_g1, POINT, 15:15:15.623, +66.54.31.670, 4.138e+00, 0.0, 0.0, 0.0, 0.00000e+00, 0.00000e+00, 0.00000e+00, 6.82495e+07, [0.000e+00]
+
+"""
+#entries_array = convert_sourcelist_as_string_to_data_array(source_list_as_string)
+#entries_array2 = convert_sourcelist_as_string_to_data_array(source_list_as_string2)
+
+#print compare_sourcelist_data_arrays(entries_array, entries_array2, 0.0001)
+
+image_data = {'rms': [ 0.], 'medabsdevmed':[ 0.], 'minpos': [0, 0, 0, 0]
+ , 'min':[ 0.], 'max': [ 0.],
+ 'quartile': [ 0.], 'sumsq': [ 0.], 'median': [ 0.], 'npts':[ 65536.],
+ 'maxpos': [0, 0, 0, 0], 'sigma': [ 0.], 'mean': [ 0.]}
+
+
+ #{'rms': array([ 0.52093363]), 'medabsdevmed': array([ 0.27387491]), 'minpos': array([156, 221, 0, 0],
+ #dtype=int32), 'min': array([-2.26162958]), 'max': array([ 24.01361465]), 'sum': array([ 1355.46549538]),
+ #'quartile': array([ 0.54873329]), 'sumsq': array([ 17784.62525496]), 'median': array([ 0.00240479]),
+ # 'npts': array([ 65536.]), 'maxpos': array([148, 199, 0, 0], dtype=int32),
+ # 'sigma': array([ 0.52052685]), 'mean': array([ 0.02068276])}
+
+image_data = {'rms': [ 0.52093363], 'medabsdevmed': [ 0.27387491], 'minpos': [[156, 221, 0, 0], "int32"],
+ 'min': [-2.26162958], 'max': [ 24.01361465], 'sum': [ 1355.46549538],
+ 'quartile' : [ 0.54873329], 'sumsq': [ 17784.62525496], 'median': [ 0.00240479],
+ 'npts': [ 65536.], 'maxpos':[ [148, 199, 0, 0], "int32"],
+ 'sigma': [ 0.52052685], 'mean': [ 0.02068276]}
+
+# print compare_image_statistics(image_data)
+
+
+
+if __name__ == "__main__":
+ source_list_1, image_1, source_list_2, image_2, max_delta = None, None, None, None, None
+ # Parse parameters from command line
+ error = False
+ print sys.argv[1:5]
+ try:
+ image_1, source_list_1, fist_1, image_2, source_list_2, fits_2 = sys.argv[1:7]
+ except:
+ print "Sourcelist comparison has been disabled! Arguments must still be provided"
+ print "usage: python {0} source_list_1_path "\
+ " image_1_path source_list_2_path image_2_path (max_delta type=float)".format(sys.argv[0])
+ sys.exit(1)
+
+ max_delta = None
+ try:
+ max_delta = float(sys.argv[5])
+ except:
+ max_delta = 0.0001
+
+ print "using max delta: {0}".format(max_delta)
+
+ if not error:
+ image_equality = validate_image_equality(image_1, image_2, max_delta)
+ # sourcelist comparison is still unstable default to true
+ sourcelist_equality = True #validate_source_list_files(source_list_1, source_list_2, max_delta)
+ if not (image_equality and sourcelist_equality):
+ print "Regression test failed: exiting with exitstatus 1"
+ print " image_equality: {0}".format(image_equality)
+ print " sourcelist_equality: {0}".format(sourcelist_equality)
+ sys.exit(1)
+
+ print "Regression test Succeed!!"
+ sys.exit(0)
+
+