<?xml version="1.0" encoding="UTF-8"?>
<testsuites>
	<testsuite name="visstat_test-20260409020336" tests="33" file=".py" time="107.653" timestamp="2026-04-09T02:05:24" failures="0" errors="0" skipped="0">
		<testcase classname="visstat_test" name="test_antenna" time="7.215" timestamp="2026-04-09T02:03:43" file="/home/casatest/casa6/casatasks/tests/casashell_tests/test_task_visstat_casashell.py" line="281">
			<!--
            test_antenna
            &#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;-
            
            Test the antenna selection parameter
            
            Assert that selection with this parameter will return a different result than no selection.
        -->
		</testcase>
		<testcase classname="visstat_test" name="test_array" time="1.221" timestamp="2026-04-09T02:03:45" file="/home/casatest/casa6/casatasks/tests/casashell_tests/test_task_visstat_casashell.py" line="366">
			<!--
            test_array
            &#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;-
            
            Test the array selection parameter.
            
            Assert that checking an out of range array returns a NoneType, and valid selections retrun a dictionary
        -->
		</testcase>
		<testcase classname="visstat_test" name="test_axis" time="16.709" timestamp="2026-04-09T02:04:01" file="/home/casatest/casa6/casatasks/tests/casashell_tests/test_task_visstat_casashell.py" line="141">
			<!--
            test_axis
            &#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;
            
            Test the axis parameter values.
            visstat should return a dict and the keys should match the provided key list.
            
            This test iterates over all the possible axis values
        -->
		</testcase>
		<testcase classname="visstat_test" name="test_channelSelectFlags" time="4.774" timestamp="2026-04-09T02:04:06" file="/home/casatest/casa6/casatasks/tests/casashell_tests/test_task_visstat_casashell.py" line="578">
			<!--Visstat 02: Check channel selections, useflags=True, reportingaxes='ddid',correlation=corr, datacolumn=data, axis=amp-->
			<system-out><![CDATA[Call with spw='0:1~1', correlation=ll

s2 {'DATA_DESC_ID=0': {'firstquartile': 0.012998353689908981, 'isMasked': True, 'isWeighted': False, 'max': 23.695859909057617, 'maxDatasetIndex': 1, 'maxIndex': 1, 'mean': 1.51201536185032, 'medabsdevmed': 0.020134394988417625, 'median': 0.027283204719424248, 'min': 0.00037757985410280526, 'minDatasetIndex': 25, 'minIndex': 195, 'npts': 21119.0, 'rms': 5.254564972098979, 'stddev': 5.032440935625601, 'sum': 31932.25242691688, 'sumOfWeights': 21119.0, 'sumsq': 583105.1578786756, 'thirdquartile': 0.20560552179813385, 'variance': 25.325461770560278}}
Checking npts: 21119 vs 21119.0
Call with spw='0:1~1', correlation=rr

s2 {'DATA_DESC_ID=0': {'firstquartile': 0.013324417173862457, 'isMasked': True, 'isWeighted': False, 'max': 26.920883178710938, 'maxDatasetIndex': 4, 'maxIndex': 19, 'mean': 1.703732570450803, 'medabsdevmed': 0.02199349133297801, 'median': 0.028599174693226814, 'min': 0.00016617041546851397, 'minDatasetIndex': 27, 'minIndex': 173, 'npts': 21119.0, 'rms': 5.9277552741561355, 'stddev': 5.677772835275629, 'sum': 35981.12815535044, 'sumOfWeights': 21119.0, 'sumsq': 742085.3900242473, 'thirdquartile': 0.26254236698150635, 'variance': 32.237104368993855}}
Checking npts: 21119 vs 21119.0
Call with spw='0:1~1', correlation=ll,rr

s2 {'DATA_DESC_ID=0': {'firstquartile': 0.01315868180245161, 'isMasked': True, 'isWeighted': False, 'max': 26.920883178710938, 'maxDatasetIndex': 4, 'maxIndex': 39, 'mean': 1.6078739661505588, 'medabsdevmed': 0.021032736403867602, 'median': 0.027937160804867744, 'min': 0.00016617041546851397, 'minDatasetIndex': 27, 'minIndex': 347, 'npts': 42238.0, 'rms': 5.601282694003898, 'stddev': 5.365611869664173, 'sum': 67913.38058226732, 'sumOfWeights': 42238.0, 'sumsq': 1325190.5479029168, 'thirdquartile': 0.23095539212226868, 'variance': 28.789790735881063}}
Checking npts: 42238 vs 42238.0
Call with spw='0:1~2', correlation=ll

s2 {'DATA_DESC_ID=0': {'firstquartile': 0.015203726477921009, 'isMasked': True, 'isWeighted': False, 'max': 47.85862731933594, 'maxDatasetIndex': 9, 'maxIndex': 3, 'mean': 2.2685205557350705, 'medabsdevmed': 0.025584048125892878, 'median': 0.033365800976753235, 'min': 0.00037757985410280526, 'minDatasetIndex': 25, 'minIndex': 390, 'npts': 42238.0, 'rms': 8.247744697530607, 'stddev': 7.929728608521772, 'sum': 95817.77123313842, 'sumOfWeights': 42238.0, 'sumsq': 2873252.3086548215, 'thirdquartile': 0.2353196144104004, 'variance': 62.88059580480865}}
Checking npts: 42238 vs 42238.0
Call with spw='0:1~2', correlation=rr

s2 {'DATA_DESC_ID=0': {'firstquartile': 0.015641184523701668, 'isMasked': True, 'isWeighted': False, 'max': 50.03026580810547, 'maxDatasetIndex': 30, 'maxIndex': 39, 'mean': 2.4662003717613192, 'medabsdevmed': 0.027923745336011052, 'median': 0.03566293604671955, 'min': 3.5077806387562305e-05, 'minDatasetIndex': 38, 'minIndex': 65, 'npts': 42238.0, 'rms': 8.903693383964843, 'stddev': 8.555427781728099, 'sum': 104167.37130245389, 'sumOfWeights': 42238.0, 'sumsq': 3348449.376676098, 'thirdquartile': 0.2842475175857544, 'variance': 73.19534452836497}}
Checking npts: 42238 vs 42238.0
Call with spw='0:1~2', correlation=ll,rr

s2 {'DATA_DESC_ID=0': {'firstquartile': 0.015421337448060513, 'isMasked': True, 'isWeighted': False, 'max': 50.03026580810547, 'maxDatasetIndex': 30, 'maxIndex': 79, 'mean': 2.3673604637482013, 'medabsdevmed': 0.026692680083215237, 'median': 0.03441326133906841, 'min': 3.5077806387562305e-05, 'minDatasetIndex': 38, 'minIndex': 131, 'npts': 84476.0, 'rms': 8.581988361426047, 'stddev': 8.249056563527775, 'sum': 199985.1425355923, 'sumOfWeights': 84476.0, 'sumsq': 6221701.685330949, 'thirdquartile': 0.2572411596775055, 'variance': 68.04693418828066}}
Checking npts: 84476 vs 84476.0
Call with spw='0:1~4', correlation=ll

s2 {'DATA_DESC_ID=0': {'firstquartile': 0.018262511119246483, 'isMasked': True, 'isWeighted': False, 'max': 67.18834686279297, 'maxDatasetIndex': 1, 'maxIndex': 7, 'mean': 3.2328949655030006, 'medabsdevmed': 0.03320419928058982, 'median': 0.04149508289992809, 'min': 2.2739146515959874e-05, 'minDatasetIndex': 48, 'minIndex': 851, 'npts': 84476.0, 'rms': 11.716386189704192, 'stddev': 11.261598322870743, 'sum': 273102.0351058309, 'sumOfWeights': 84476.0, 'sumsq': 11596333.532833287, 'thirdquartile': 0.27172136306762695, 'variance': 126.82359678568513}}
Checking npts: 84476 vs 84476.0
Call with spw='0:1~4', correlation=rr

s2 {'DATA_DESC_ID=0': {'firstquartile': 0.018680663779377937, 'isMasked': True, 'isWeighted': False, 'max': 68.62496948242188, 'maxDatasetIndex': 26, 'maxIndex': 79, 'mean': 3.3996190574518157, 'medabsdevmed': 0.03522256435826421, 'median': 0.043506983667612076, 'min': 3.5077806387562305e-05, 'minDatasetIndex': 38, 'minIndex': 129, 'npts': 84476.0, 'rms': 12.239038012587871, 'stddev': 11.757477542335039, 'sum': 287186.2194972994, 'sumOfWeights': 84476.0, 'sumsq': 12654002.292281372, 'thirdquartile': 0.3134520947933197, 'variance': 138.2382781585128}}
Checking npts: 84476 vs 84476.0
Call with spw='0:1~4', correlation=ll,rr

s2 {'DATA_DESC_ID=0': {'firstquartile': 0.01846258156001568, 'isMasked': True, 'isWeighted': False, 'max': 68.62496948242188, 'maxDatasetIndex': 26, 'maxIndex': 159, 'mean': 3.3162570114774232, 'medabsdevmed': 0.03416486969217658, 'median': 0.0424413587898016, 'min': 2.2739146515959874e-05, 'minDatasetIndex': 48, 'minIndex': 1702, 'npts': 168952.0, 'rms': 11.98056252477037, 'stddev': 11.51247594176724, 'sum': 560288.2546031302, 'sumOfWeights': 168952.0, 'sumsq': 24250335.825114865, 'thirdquartile': 0.290507972240448, 'variance': 132.5371023097695}}
Checking npts: 168952 vs 168952.0
Call with spw='0:1~7', correlation=ll

s2 {'DATA_DESC_ID=0': {'firstquartile': 0.020986022427678108, 'isMasked': True, 'isWeighted': False, 'max': 70.78073120117188, 'maxDatasetIndex': 24, 'maxIndex': 13, 'mean': 3.965659174843849, 'medabsdevmed': 0.03952489420771599, 'median': 0.04782070219516754, 'min': 2.2739146515959874e-05, 'minDatasetIndex': 48, 'minIndex': 1487, 'npts': 147833.0, 'rms': 14.130246023570889, 'stddev': 13.562398173931312, 'sum': 586255.2927946859, 'sumOfWeights': 147833.0, 'sumsq': 29516906.334224183, 'thirdquartile': 0.31161126494407654, 'variance': 183.93864422825538}}
Checking npts: 147833 vs 147833.0
Call with spw='0:1~7', correlation=rr

s2 {'DATA_DESC_ID=0': {'firstquartile': 0.02118206024169922, 'isMasked': True, 'isWeighted': False, 'max': 73.75, 'maxDatasetIndex': 12, 'maxIndex': 139, 'mean': 4.075617324934261, 'medabsdevmed': 0.040772588923573494, 'median': 0.04898509383201599, 'min': 3.5077806387562305e-05, 'minDatasetIndex': 38, 'minIndex': 225, 'npts': 147833.0, 'rms': 14.45538727219714, 'stddev': 13.868989355124057, 'sum': 602510.7359970011, 'sumOfWeights': 147833.0, 'sumsq': 30890920.713062864, 'thirdquartile': 0.35131359100341797, 'variance': 192.3488657325444}}
Checking npts: 147833 vs 147833.0
Call with spw='0:1~7', correlation=ll,rr

s2 {'DATA_DESC_ID=0': {'firstquartile': 0.021086707711219788, 'isMasked': True, 'isWeighted': False, 'max': 73.75, 'maxDatasetIndex': 12, 'maxIndex': 279, 'mean': 4.0206382498889885, 'medabsdevmed': 0.040139758959412575, 'median': 0.048403069376945496, 'min': 2.2739146515959874e-05, 'minDatasetIndex': 48, 'minIndex': 2974, 'npts': 295666.0, 'rms': 14.293741180597857, 'stddev': 13.716637392172299, 'sum': 1188766.028791686, 'sumOfWeights': 295666.0, 'sumsq': 60407827.04728677, 'thirdquartile': 0.3312663435935974, 'variance': 188.14614134833926}}
Checking npts: 295666 vs 295666.0
Call with spw='0:1~13', correlation=ll

s2 {'DATA_DESC_ID=0': {'firstquartile': 0.02359769307076931, 'isMasked': True, 'isWeighted': False, 'max': 70.78073120117188, 'maxDatasetIndex': 24, 'maxIndex': 19, 'mean': 4.573634004135756, 'medabsdevmed': 0.04534465912729502, 'median': 0.05354195833206177, 'min': 2.2739146515959874e-05, 'minDatasetIndex': 48, 'minIndex': 2759, 'npts': 274547.0, 'rms': 16.001664561308168, 'stddev': 15.334144814094772, 'sum': 1255677.4949334562, 'sumOfWeights': 274547.0, 'sumsq': 70298656.77073619, 'thirdquartile': 0.3944782316684723, 'variance': 235.13599717962958}}
Checking npts: 274547 vs 274547.0
Call with spw='0:1~13', correlation=rr

s2 {'DATA_DESC_ID=0': {'firstquartile': 0.023458972573280334, 'isMasked': True, 'isWeighted': False, 'max': 73.75, 'maxDatasetIndex': 12, 'maxIndex': 253, 'mean': 4.598994075086379, 'medabsdevmed': 0.04547768831253052, 'median': 0.053455792367458344, 'min': 3.5077806387562305e-05, 'minDatasetIndex': 38, 'minIndex': 417, 'npts': 274547.0, 'rms': 16.050754617587558, 'stddev': 15.3778034395668, 'sum': 1262640.0263327176, 'sumOfWeights': 274547.0, 'sumsq': 70730644.13747361, 'thirdquartile': 0.4329370856285095, 'variance': 236.47683862595252}}
Checking npts: 274547 vs 274547.0
Call with spw='0:1~13', correlation=ll,rr

s2 {'DATA_DESC_ID=0': {'firstquartile': 0.02353147603571415, 'isMasked': True, 'isWeighted': False, 'max': 73.75, 'maxDatasetIndex': 12, 'maxIndex': 507, 'mean': 4.586314039611009, 'medabsdevmed': 0.04539835639297962, 'median': 0.05349326133728027, 'min': 2.2739146515959874e-05, 'minDatasetIndex': 48, 'minIndex': 5518, 'npts': 549094.0, 'rms': 16.026228385472407, 'stddev': 15.355980894727772, 'sum': 2518317.5212661745, 'sumOfWeights': 549094.0, 'sumsq': 141029300.90821227, 'thirdquartile': 0.4128650426864624, 'variance': 235.80614923924432}}
Checking npts: 549094 vs 549094.0
Call with spw='0:1~62', correlation=ll

s2 {'DATA_DESC_ID=0': {'firstquartile': 0.02503143437206745, 'isMasked': True, 'isWeighted': False, 'max': 72.1248779296875, 'maxDatasetIndex': 19, 'maxIndex': 1090, 'mean': 4.931429419886097, 'medabsdevmed': 0.047344045247882605, 'median': 0.055874770507216454, 'min': 2.2739146515959874e-05, 'minDatasetIndex': 48, 'minIndex': 13147, 'npts': 1309378.0, 'rms': 17.284456326640903, 'stddev': 16.566038874005862, 'sum': 6457105.190951617, 'sumOfWeights': 1309378.0, 'sumsq': 391179859.9531237, 'thirdquartile': 0.396679162979126, 'variance': 274.4336439750734}}
Checking npts: 1309378 vs 1309378.0
Call with spw='0:1~62', correlation=rr

s2 {'DATA_DESC_ID=0': {'firstquartile': 0.024585282430052757, 'isMasked': True, 'isWeighted': False, 'max': 73.75, 'maxDatasetIndex': 12, 'maxIndex': 1184, 'mean': 4.8928319861429355, 'medabsdevmed': 0.04655483830720186, 'median': 0.054879553616046906, 'min': 2.2130521756480448e-05, 'minDatasetIndex': 54, 'minIndex': 4277, 'npts': 1309378.0, 'rms': 17.151711039791177, 'stddev': 16.43902652644461, 'sum': 6406566.560351796, 'sumOfWeights': 1309378.0, 'sumsq': 385194380.2849973, 'thirdquartile': 0.4161178767681122, 'variance': 270.24159313714955}}
Checking npts: 1309378 vs 1309378.0
Call with spw='0:1~62', correlation=ll,rr

s2 {'DATA_DESC_ID=0': {'firstquartile': 0.02480573207139969, 'isMasked': True, 'isWeighted': False, 'max': 73.75, 'maxDatasetIndex': 12, 'maxIndex': 2369, 'mean': 4.912130703014791, 'medabsdevmed': 0.04696154408156872, 'median': 0.05538627877831459, 'min': 2.2130521756480448e-05, 'minDatasetIndex': 54, 'minIndex': 8555, 'npts': 2618756.0, 'rms': 17.218211610093668, 'stddev': 16.50266302757405, 'sum': 12863671.751303274, 'sumOfWeights': 2618756.0, 'sumsq': 776374240.2379606, 'thirdquartile': 0.40908554196357727, 'variance': 272.33788700165957}}
Checking npts: 2618756 vs 2618756.0
]]></system-out>
		</testcase>
		<testcase classname="visstat_test" name="test_corrLLRR" time="1.305" timestamp="2026-04-09T02:04:07" file="/home/casatest/casa6/casatasks/tests/casashell_tests/test_task_visstat_casashell.py" line="870">
			<!--Visstat 09: Test using reportingaxes=ddid, correlation=[LL,RR], datacolumn=float_data spw=[0,1,2,3]-->
		</testcase>
		<testcase classname="visstat_test" name="test_correlation" time="0.826" timestamp="2026-04-09T02:04:08" file="/home/casatest/casa6/casatasks/tests/casashell_tests/test_task_visstat_casashell.py" line="332">
			<!--
            test_correlation
            &#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;-
            
            Test the correlation parameter
            
            Assert that the selection with this parameter will return a different value than no selection.
        -->
		</testcase>
		<testcase classname="visstat_test" name="test_datacolCorrected" time="1.088" timestamp="2026-04-09T02:04:09" file="/home/casatest/casa6/casatasks/tests/casashell_tests/test_task_visstat_casashell.py" line="773">
			<!--Visstat 07: Default values with datacolum=corrected, reportingaxis=ddid-->
			<system-out><![CDATA[
Checking firstquartile: 0.024146264419 vs 0.02414626255631447

Checking isMasked: True vs True

Checking isWeighted: False vs False

Checking max: 73.75 vs 73.75

Checking maxDatasetIndex: 12 vs 12

Checking maxIndex: 2408 vs 2408

Checking mean: 4.837103133618731 vs 4.837103133596521

Checking medabsdevmed: 0.04501341888681054 vs 0.04501341888681054

Checking median: 0.05355948396027088 vs 0.05355948396027088

Checking min: 2.2130521756480448e-05 vs 2.2130521756480448e-05

Checking minDatasetIndex: 54 vs 54

Checking minIndex: 8692 vs 8692

Checking npts: 2660994.0 vs 2660994.0

Checking thirdquartile: 0.3291134536266327 vs 0.3291134536266327

Checking rms: 17.081207832906546 vs 17.081207832905257

Checking stddev: 16.382008276126726 vs 16.382008276132133

Checking sum: 12871502.415939873 vs 12871502.4158821

Checking sumsq: 776391995.3973862 vs 776391995.3972691

Checking variance: 268.3701951590845 vs 268.37019515926175
]]></system-out>
		</testcase>
		<testcase classname="visstat_test" name="test_datacolModel" time="0.489" timestamp="2026-04-09T02:04:10" file="/home/casatest/casa6/casatasks/tests/casashell_tests/test_task_visstat_casashell.py" line="599">
			<!--Visstat 03: Default values with datacolum=model, reportingaxis=ddid-->
			<system-out><![CDATA[
Checking firstquartile: 1.0 vs 1.0

Checking isMasked: True vs True

Checking isWeighted: False vs False

Checking max: 1.0 vs 1.0

Checking maxDatasetIndex: 0 vs 0

Checking maxIndex: 0 vs 0

Checking mean: 1.0 vs 1.0

Checking medabsdevmed: 0.0 vs 0.0

Checking median: 1.0 vs 1.0

Checking min: 1.0 vs 1.0

Checking minDatasetIndex: 0 vs 0

Checking minIndex: 0 vs 0

Checking npts: 2660994.0 vs 2660994.0

Checking thirdquartile: 1.0 vs 1.0

Checking rms: 1.0 vs 1.0

Checking stddev: 0.0 vs 0.0

Checking sum: 2660994.0 vs 2660994.0

Checking sumsq: 2660994.0 vs 2660994.0

Checking variance: 0.0 vs 0.0
]]></system-out>
		</testcase>
		<testcase classname="visstat_test" name="test_datacolMulti" time="3.141" timestamp="2026-04-09T02:04:13" file="/home/casatest/casa6/casatasks/tests/casashell_tests/test_task_visstat_casashell.py" line="833">
			<!--Visstat 08: Test when using reportingaxes='integration, datacolumn=data,corrected,model-->
		</testcase>
		<testcase classname="visstat_test" name="test_datacolumn" time="3.858" timestamp="2026-04-09T02:04:17" file="/home/casatest/casa6/casatasks/tests/casashell_tests/test_task_visstat_casashell.py" line="202">
			<!--
            test_datacolumn
            &#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;
            
            Check the data column parameter.
            
            Iterate over possible data column inputs and check that a dictionary is created by visstat
            also check that all the keys that should be present are there.
            
            (This last step may not be nessisary for this test)
        -->
		</testcase>
		<testcase classname="visstat_test" name="test_defaultValues" time="1.037" timestamp="2026-04-09T02:04:18" file="/home/casatest/casa6/casatasks/tests/casashell_tests/test_task_visstat_casashell.py" line="514">
			<!--Visstat 01: Default values-->
			<system-out><![CDATA[
Checking firstquartile: 0.024146264419 vs 0.02414626255631447

Checking isMasked: True vs True

Checking isWeighted: False vs False

Checking max: 73.75 vs 73.75

Checking maxDatasetIndex: 12 vs 12

Checking maxIndex: 2408 vs 2408

Checking mean: 4.837103133618731 vs 4.837103133596521

Checking medabsdevmed: 0.04501341888681054 vs 0.04501341888681054

Checking median: 0.05355948396027088 vs 0.05355948396027088

Checking min: 2.2130521756480448e-05 vs 2.2130521756480448e-05

Checking minDatasetIndex: 54 vs 54

Checking minIndex: 8692 vs 8692

Checking npts: 2660994.0 vs 2660994.0

Checking thirdquartile: 0.3291134536266327 vs 0.3291134536266327

Checking rms: 17.081207832906546 vs 17.081207832905257

Checking stddev: 16.382008276126726 vs 16.382008276132133

Checking sum: 12871502.415939873 vs 12871502.4158821

Checking sumsq: 776391995.3973862 vs 776391995.3972691

Checking variance: 268.3701951590845 vs 268.37019515926175
]]></system-out>
		</testcase>
		<testcase classname="visstat_test" name="test_doquantiles" time="1.320" timestamp="2026-04-09T02:04:19" file="/home/casatest/casa6/casatasks/tests/casashell_tests/test_task_visstat_casashell.py" line="1077">
			<!--test doquantiles parameter-->
		</testcase>
		<testcase classname="visstat_test" name="test_field" time="0.728" timestamp="2026-04-09T02:04:20" file="/home/casatest/casa6/casatasks/tests/casashell_tests/test_task_visstat_casashell.py" line="245">
			<!--
            test_field
            &#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;
            
            Test the field selection parameter.
            
            Assert that a selection using the field parameter returns a different result than no selection.
        -->
		</testcase>
		<testcase classname="visstat_test" name="test_handle_all_flagged_groups" time="0.341" timestamp="2026-04-09T02:04:20" file="/home/casatest/casa6/casatasks/tests/casashell_tests/test_task_visstat_casashell.py" line="1057">
			<!--visstat 13: handle all-flagged sub-selections in reportingaxes, CAS-12857-->
		</testcase>
		<testcase classname="visstat_test" name="test_intent" time="0.646" timestamp="2026-04-09T02:04:21" file="/home/casatest/casa6/casatasks/tests/casashell_tests/test_task_visstat_casashell.py" line="489">
			<!--
            test_intent
            &#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;-
            
            Test the intent parameter
            
            Assert that the specified selection creates a different dict than the default values.
        -->
		</testcase>
		<testcase classname="visstat_test" name="test_intentOnOff" time="0.431" timestamp="2026-04-09T02:04:21" file="/home/casatest/casa6/casatasks/tests/casashell_tests/test_task_visstat_casashell.py" line="908">
			<!--Visstat 10: Test using reportingaxes=field, datacolumn=corrected, intent=[on,off]-->
			<system-out><![CDATA[
check intent on rms
4.304297370622183
check intent off rms
4.304297370622183

check intent on max
19.147367477416992
check intent off max
19.147367477416992

check intent on min
-15.199692726135254
check intent off min
-15.199692726135254

check intent on sum
35087.679596841335
check intent off sum
35087.679596841335

check intent on median
4.278878211975098
check intent off median
4.278878211975098

check intent on stddev
0.42603291690877887
check intent off stddev
0.42603291690877887

check intent on mean
4.283164013286287
check intent off mean
4.283164013286287

check intent on rms
3.9222649429916068
check intent off rms
3.9222649429916068

check intent on max
5.5116047859191895
check intent off max
5.5116047859191895

check intent on min
2.200864553451538
check intent off min
2.200864553451538

check intent on sum
32089.27730822563
check intent off sum
32089.27730822563

check intent on median
3.910185933113098
check intent off median
3.910185933113098

check intent on stddev
0.2002944658536293
check intent off stddev
0.2002944658536293

check intent on mean
3.91714810891426
check intent off mean
3.91714810891426
]]></system-out>
		</testcase>
		<testcase classname="visstat_test" name="test_maxuvwdistance" time="5.085" timestamp="2026-04-09T02:04:26" file="/home/casatest/casa6/casatasks/tests/casashell_tests/test_task_visstat_casashell.py" line="470">
			<!--
            test_maxuvwdistance
            &#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;-
            
            Test the maxuvwdistance parameter
            
            Assert that the output is a python dict. Once again this selection seems to not change the values that are returned
        -->
		</testcase>
		<testcase classname="visstat_test" name="test_observation" time="1.103" timestamp="2026-04-09T02:04:27" file="/home/casatest/casa6/casatasks/tests/casashell_tests/test_task_visstat_casashell.py" line="386">
			<!--
            test_observation
            &#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;-
            
            Test the observation selection parameter
            
            Assert that checking an out of range observation ID returns a NoneType, and valid selections return a dictionary
        -->
		</testcase>
		<testcase classname="visstat_test" name="test_reportAxisField" time="10.314" timestamp="2026-04-09T02:04:38" file="/home/casatest/casa6/casatasks/tests/casashell_tests/test_task_visstat_casashell.py" line="728">
			<!--Visstat 06: Test using reportingaxes=field-->
		</testcase>
		<testcase classname="visstat_test" name="test_reportAxisInt" time="1.269" timestamp="2026-04-09T02:04:39" file="/home/casatest/casa6/casatasks/tests/casashell_tests/test_task_visstat_casashell.py" line="688">
			<!--Visstat 05: Test using reportingaxes=integration, datacolumn=float_data-->
			<system-out><![CDATA[rms 4.304297370622183
max 19.147367477416992
min -15.199692726135254
sum 35087.679596841335
median 4.278878211975098
stddev 0.42603291690877887
mean 4.283164013286287
rms 4.335384666829241
max 5.960114002227783
min 0.9407882690429688
sum 35488.134197711945
median 4.325183868408203
stddev 0.1700795650207272
mean 4.33204763155664
rms 4.3062036140575755
max 6.313568592071533
min 1.483452320098877
sum 35238.97358107567
median 4.286481618881226
stddev 0.19837352702878458
mean 4.301632517221151
rms 4.304297370622183
max 19.147367477416992
min -15.199692726135254
sum 35087.679596841335
median 4.278878211975098
stddev 0.42603291690877887
mean 4.283164013286287
rms 4.335384666829241
max 5.960114002227783
min 0.9407882690429688
sum 35488.134197711945
median 4.325183868408203
stddev 0.1700795650207272
mean 4.33204763155664
rms 4.3062036140575755
max 6.313568592071533
min 1.483452320098877
sum 35238.97358107567
median 4.286481618881226
stddev 0.19837352702878458
mean 4.301632517221151
rms 4.26021842669192
max 6.594586372375488
min 2.4618935585021973
sum 34870.10427117348
median 4.255388259887695
stddev 0.17545003865511732
mean 4.256604525289726
rms 4.304297370622183
max 19.147367477416992
min -15.199692726135254
sum 35087.679596841335
median 4.278878211975098
stddev 0.42603291690877887
mean 4.283164013286287
rms 4.335384666829241
max 5.960114002227783
min 0.9407882690429688
sum 35488.134197711945
median 4.325183868408203
stddev 0.1700795650207272
mean 4.33204763155664
rms 4.3062036140575755
max 6.313568592071533
min 1.483452320098877
sum 35238.97358107567
median 4.286481618881226
stddev 0.19837352702878458
mean 4.301632517221151
rms 4.26021842669192
max 6.594586372375488
min 2.4618935585021973
sum 34870.10427117348
median 4.255388259887695
stddev 0.17545003865511732
mean 4.256604525289726
rms 4.304297370622183
max 19.147367477416992
min -15.199692726135254
sum 35087.679596841335
median 4.278878211975098
stddev 0.42603291690877887
mean 4.283164013286287
rms 4.335384666829241
max 5.960114002227783
min 0.9407882690429688
sum 35488.134197711945
median 4.325183868408203
stddev 0.1700795650207272
mean 4.33204763155664
rms 4.3062036140575755
max 6.313568592071533
min 1.483452320098877
sum 35238.97358107567
median 4.286481618881226
stddev 0.19837352702878458
mean 4.301632517221151
rms 4.26021842669192
max 6.594586372375488
min 2.4618935585021973
sum 34870.10427117348
median 4.255388259887695
stddev 0.17545003865511732
mean 4.256604525289726
rms 3.9222649429916068
max 5.5116047859191895
min 2.200864553451538
sum 32089.27730822563
median 3.910185933113098
stddev 0.2002944658536293
mean 3.91714810891426
rms 3.802793856096218
max 6.305795192718506
min 0.28544679284095764
sum 31103.86206844449
median 3.79739773273468
stddev 0.21240224065093644
mean 3.796858162651916
rms 5.327318728125395
max 134.43093872070312
min -221.74778747558594
sum 32937.74573640153
median 3.9856526851654053
stddev 3.495085023068308
mean 4.020720915088079
rms 3.9222649429916068
max 5.5116047859191895
min 2.200864553451538
sum 32089.27730822563
median 3.910185933113098
stddev 0.2002944658536293
mean 3.91714810891426
rms 3.802793856096218
max 6.305795192718506
min 0.28544679284095764
sum 31103.86206844449
median 3.79739773273468
stddev 0.21240224065093644
mean 3.796858162651916
rms 5.327318728125395
max 134.43093872070312
min -221.74778747558594
sum 32937.74573640153
median 3.9856526851654053
stddev 3.495085023068308
mean 4.020720915088079
rms 4.00829066977956
max 5.441513538360596
min 1.3497779369354248
sum 32807.04150438309
median 4.002278804779053
stddev 0.16807248338680303
mean 4.004765808640507
rms 3.9222649429916068
max 5.5116047859191895
min 2.200864553451538
sum 32089.27730822563
median 3.910185933113098
stddev 0.2002944658536293
mean 3.91714810891426
rms 3.802793856096218
max 6.305795192718506
min 0.28544679284095764
sum 31103.86206844449
median 3.79739773273468
stddev 0.21240224065093644
mean 3.796858162651916
rms 5.327318728125395
max 134.43093872070312
min -221.74778747558594
sum 32937.74573640153
median 3.9856526851654053
stddev 3.495085023068308
mean 4.020720915088079
rms 4.00829066977956
max 5.441513538360596
min 1.3497779369354248
sum 32807.04150438309
median 4.002278804779053
stddev 0.16807248338680303
mean 4.004765808640507
rms 3.9222649429916068
max 5.5116047859191895
min 2.200864553451538
sum 32089.27730822563
median 3.910185933113098
stddev 0.2002944658536293
mean 3.91714810891426
rms 3.802793856096218
max 6.305795192718506
min 0.28544679284095764
sum 31103.86206844449
median 3.79739773273468
stddev 0.21240224065093644
mean 3.796858162651916
rms 5.327318728125395
max 134.43093872070312
min -221.74778747558594
sum 32937.74573640153
median 3.9856526851654053
stddev 3.495085023068308
mean 4.020720915088079
rms 4.00829066977956
max 5.441513538360596
min 1.3497779369354248
sum 32807.04150438309
median 4.002278804779053
stddev 0.16807248338680303
mean 4.004765808640507
]]></system-out>
		</testcase>
		<testcase classname="visstat_test" name="test_reportingaxes" time="14.643" timestamp="2026-04-09T02:04:54" file="/home/casatest/casa6/casatasks/tests/casashell_tests/test_task_visstat_casashell.py" line="163">
			<!--
            test_reportingaxes
            &#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;-
            
            Test the reportingaxes parameter.
            The output should be a dict and contain all the expected keys.
            
            Iterate over all the possible values.
        -->
		</testcase>
		<testcase classname="visstat_test" name="test_scan" time="0.708" timestamp="2026-04-09T02:04:54" file="/home/casatest/casa6/casatasks/tests/casashell_tests/test_task_visstat_casashell.py" line="349">
			<!--
            test_scan
            &#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;
        
            Test the scan selection parameter
        
            Assert that the selction with this parameter will return a different result than no selection.
        -->
		</testcase>
		<testcase classname="visstat_test" name="test_selectdata" time="1.029" timestamp="2026-04-09T02:04:55" file="/home/casatest/casa6/casatasks/tests/casashell_tests/test_task_visstat_casashell.py" line="262">
			<!--
            test_selectdata
            &#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;-
            
            Test the selectdata parameter
            
            Assert that the select data parameter prevents other selection fields from having an affect
            
            Assert that with selectdata=False and an active selection produces the same results as the task with no selection
        -->
		</testcase>
		<testcase classname="visstat_test" name="test_special_cases" time="0.910" timestamp="2026-04-09T02:04:56" file="/home/casatest/casa6/casatasks/tests/casashell_tests/test_task_visstat_casashell.py" line="656">
			<!--Visstat 04: Test of special cases-->
			<system-out><![CDATA[
antenna = 1 ; mean =  0.0

antenna = 2 ; mean =  1.0

antenna = 3 ; mean =  2.0

antenna = 4 ; mean =  3.0
]]></system-out>
		</testcase>
		<testcase classname="visstat_test" name="test_spw" time="0.875" timestamp="2026-04-09T02:04:57" file="/home/casatest/casa6/casatasks/tests/casashell_tests/test_task_visstat_casashell.py" line="228">
			<!--
            test_spw
            &#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;-
            
            Test the spectral window selection parameter.
            
            Assert that a selection using the spw parameter returns a different result than no selection.
        -->
		</testcase>
		<testcase classname="visstat_test" name="test_timeAvgAcrossScans" time="2.302" timestamp="2026-04-09T02:05:00" file="/home/casatest/casa6/casatasks/tests/casashell_tests/test_task_visstat_casashell.py" line="1013">
			<!--Visstat 12: Test of time averaging across scans-->
		</testcase>
		<testcase classname="visstat_test" name="test_timeAvgWithinScans" time="2.459" timestamp="2026-04-09T02:05:02" file="/home/casatest/casa6/casatasks/tests/casashell_tests/test_task_visstat_casashell.py" line="951">
			<!--Visstat 11: Test of time averaging within scans-->
		</testcase>
		<testcase classname="visstat_test" name="test_timeavg" time="5.014" timestamp="2026-04-09T02:05:07" file="/home/casatest/casa6/casatasks/tests/casashell_tests/test_task_visstat_casashell.py" line="406">
			<!--
            test_timeaverage
            &#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;-
            
            Test the timeaverage parameter
            
            Assert that the dict produced when timeaverage = True is different from the one produced when timeaverage=False
        -->
		</testcase>
		<testcase classname="visstat_test" name="test_timebin" time="2.337" timestamp="2026-04-09T02:05:09" file="/home/casatest/casa6/casatasks/tests/casashell_tests/test_task_visstat_casashell.py" line="425">
			<!--
            test_timebin
            &#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;-
            
            Test the timebin parameter
            
            Assert that the result when given a bin width for averaging is different than when none is given.
        -->
		</testcase>
		<testcase classname="visstat_test" name="test_timerange" time="0.652" timestamp="2026-04-09T02:05:10" file="/home/casatest/casa6/casatasks/tests/casashell_tests/test_task_visstat_casashell.py" line="315">
			<!--
            test_timerange
            &#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;
            
            Test the timerange selection parameter
            
            Assert that the selection with this parameter will return a different value than no selection.
        -->
		</testcase>
		<testcase classname="visstat_test" name="test_timespan" time="11.719" timestamp="2026-04-09T02:05:22" file="/home/casatest/casa6/casatasks/tests/casashell_tests/test_task_visstat_casashell.py" line="442">
			<!--
            test_timespan
            &#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;
            
            Test the timespan parameter
            
            Assert that all parameter settings give different results than the default output
        -->
		</testcase>
		<testcase classname="visstat_test" name="test_useflags" time="1.360" timestamp="2026-04-09T02:05:23" file="/home/casatest/casa6/casatasks/tests/casashell_tests/test_task_visstat_casashell.py" line="185">
			<!--
            test_useflags
            &#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;
            
            Check that the useflags parameter produces different results then when useflags = False.
        -->
		</testcase>
		<testcase classname="visstat_test" name="test_uvange" time="0.748" timestamp="2026-04-09T02:05:24" file="/home/casatest/casa6/casatasks/tests/casashell_tests/test_task_visstat_casashell.py" line="298">
			<!--
            test_uvrange
            &#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;&#45;
            
            Test the uvrange selection parameter
            
            Assert that the selection with this parameter will return a different value than no selection.
        -->
		</testcase>
	</testsuite>
</testsuites>
