diff --git a/CEP/Pipeline/test/regression_tests/long_baseline_pipeline_test.py b/CEP/Pipeline/test/regression_tests/long_baseline_pipeline_test.py
index 5e12e482a8e567b12edd168a1aea9839f453d37c..4daa3d5fad0ae01931f1b7d34bb1946cb44a6b53 100644
--- a/CEP/Pipeline/test/regression_tests/long_baseline_pipeline_test.py
+++ b/CEP/Pipeline/test/regression_tests/long_baseline_pipeline_test.py
@@ -1,376 +1,61 @@
-import math
+import pyrap.tables as pt
+import numpy
import sys
-def validate_image_equality(image_1_path, image_2_path, max_delta):
- import pyrap.images as pim
+def load_and_compare_data_sets(ms1, ms2):
+ # open the two datasets
+ ms1 = pt.table(ms1)
+ ms2 = pt.table(ms2)
- # 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)
+ #get the amount of rows in the dataset
+ n_row = len(ms1.getcol('DATA'))
+ n_complex_vis = 4
- 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
+ # create a target array with the same length as the datacolumn
+ div_array = numpy.zeros((n_row, 1, n_complex_vis), dtype=numpy.complex64)
+ ms1_array = ms1.getcol('DATA')
+ ms2_array = ms2.getcol('CORRECTED_DATA')
- return return_value
+ div_max = 0
+ for idx in xrange(n_row):
+ for idy in xrange(n_complex_vis):
+ div_value = ms1_array[idx][0][idy] - ms2_array[idx][0][idy]
+ if numpy.abs(div_value) > numpy.abs(div_max):
+ div_max = div_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)
+ div_array[idx][0][idy] = div_value
+ print "maximum different value between measurement sets: {0}".format(div_max)
+ # Use a delta of about float precision
+ if div_max > 1e-6:
+ print "The measurement sets are contained a different value"
+ print "failed delta test!"
+ return False
+ return True
if __name__ == "__main__":
- source_list_1, image_1, source_list_2, image_2, max_delta = None, None, None, None, None
+ ms_1, mw_2 = None, None
# Parse parameters from command line
error = False
- print sys.argv[1:5]
+ print sys.argv
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])
+ ms_1, mw_2 = sys.argv[1:3]
+ except Exception, e:
+ print e
+ print "usage: python {0} ms1 "\
+ " ms2 ".format(sys.argv[0])
+ print "The longbaseline is deterministic and should result in the same ms"
sys.exit(1)
- max_delta = None
- try:
- max_delta = float(sys.argv[5])
- except:
- max_delta = 0.0001
-
- sys.exit(0)
- # todo: Add delta test when we have validate test data
- #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)
+ if not error:
+ print "regression test:"
+ data_equality = load_and_compare_data_sets(ms_1, mw_2)
+ if not data_equality:
+ print "Regression test failed: exiting with exitstatus 1"
+ sys.exit(1)
+ print "Regression test Succeed!!"
+ sys.exit(0)