============================= test session starts ==============================
platform linux -- Python 3.12.9, pytest-9.1.1, pluggy-1.6.0 -- /home/casatest/venv/bin/python3.12
cachedir: .pytest_cache
rootdir: /home/casatest/casa6/casatestutils/nosedir/test_task_visstat
configfile: pytest.ini
collecting ... collected 36 items


test_task_visstat.py::visstat_test::test_antenna 
test_antenna
---------------------------

Test the antenna selection parameter

Assert that selection with this parameter will return a different result than no selection. 
PASSED

test_task_visstat.py::visstat_test::test_array 2026-10-09 15:58:53	SEVERE	visstat::ms::statistics	Exception Reported: MSSelectionNullSelection : The selected table has zero rows.
2026-10-09 15:58:53	SEVERE	visstat::::casa	Task visstat raised an exception of class RuntimeError with the following message: MSSelectionNullSelection : The selected table has zero rows.
2026-10-09 15:58:53	SEVERE	visstat::ms::statistics	Exception Reported: MSSelectionNullSelection : The selected table has zero rows.
2026-10-09 15:58:53	SEVERE	visstat::::casa	Task visstat raised an exception of class RuntimeError with the following message: MSSelectionNullSelection : The selected table has zero rows.

test_array
-------------

Test the array selection parameter.

Assert that checking an out of range array returns a NoneType, and valid selections retrun a dictionary 
PASSED

test_task_visstat.py::visstat_test::test_axis 
test_axis
------------------------------

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 
PASSED

test_task_visstat.py::visstat_test::test_channelSelectFlags 
Visstat 02: Check channel selections, useflags=True, reportingaxes='ddid',correlation=corr, datacolumn=data, axis=amp 
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': 4.02063824988892, 'medabsdevmed': 0.040139758959412575, 'median': 0.048403069376945496, 'min': 2.2739146515959874e-05, 'minDatasetIndex': 48, 'minIndex': 2974, 'npts': 295666.0, 'rms': 14.293741180597895, 'stddev': 13.71663739217234, 'sum': 1188766.0287916868, 'sumOfWeights': 295666.0, 'sumsq': 60407827.047287084, 'thirdquartile': 0.3312663435935974, 'variance': 188.1461413483404}}
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': 4.57363400413573, 'medabsdevmed': 0.04534465912729502, 'median': 0.05354195833206177, 'min': 2.2739146515959874e-05, 'minDatasetIndex': 48, 'minIndex': 2759, 'npts': 274547.0, 'rms': 16.001664561308115, 'stddev': 15.33414481409489, 'sum': 1255677.4949334553, 'sumOfWeights': 274547.0, 'sumsq': 70298656.77073571, 'thirdquartile': 0.3944782316684723, 'variance': 235.1359971796332}}
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': 4.598994075086256, 'medabsdevmed': 0.04547768831253052, 'median': 0.053455792367458344, 'min': 3.5077806387562305e-05, 'minDatasetIndex': 38, 'minIndex': 417, 'npts': 274547.0, 'rms': 16.050754617587547, 'stddev': 15.377803439566847, 'sum': 1262640.0263327179, 'sumOfWeights': 274547.0, 'sumsq': 70730644.13747351, 'thirdquartile': 0.4329370856285095, 'variance': 236.47683862595397}}
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': 4.586314039611128, 'medabsdevmed': 0.04539835639297962, 'median': 0.05349326133728027, 'min': 2.2739146515959874e-05, 'minDatasetIndex': 48, 'minIndex': 5518, 'npts': 549094.0, 'rms': 16.02622838547249, 'stddev': 15.355980894727814, 'sum': 2518317.5212661736, 'sumOfWeights': 549094.0, 'sumsq': 141029300.90821368, 'thirdquartile': 0.4128650426864624, 'variance': 235.80614923924563}}
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': 4.931429419886412, 'medabsdevmed': 0.047344045247882605, 'median': 0.055874770507216454, 'min': 2.2739146515959874e-05, 'minDatasetIndex': 48, 'minIndex': 13147, 'npts': 1309378.0, 'rms': 17.284456326641575, 'stddev': 16.566038874005407, 'sum': 6457105.190951668, 'sumOfWeights': 1309378.0, 'sumsq': 391179859.95315397, 'thirdquartile': 0.396679162979126, 'variance': 274.4336439750583}}
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': 4.892831986142966, 'medabsdevmed': 0.04655483830720186, 'median': 0.054879553616046906, 'min': 2.2130521756480448e-05, 'minDatasetIndex': 54, 'minIndex': 4277, 'npts': 1309378.0, 'rms': 17.151711039791266, 'stddev': 16.439026526444376, 'sum': 6406566.560351839, 'sumOfWeights': 1309378.0, 'sumsq': 385194380.2850013, 'thirdquartile': 0.4161178767681122, 'variance': 270.2415931371418}}
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': 4.912130703014519, 'medabsdevmed': 0.04696154408156872, 'median': 0.05538627877831459, 'min': 2.2130521756480448e-05, 'minDatasetIndex': 54, 'minIndex': 8555, 'npts': 2618756.0, 'rms': 17.21821161009346, 'stddev': 16.50266302757408, 'sum': 12863671.751303246, 'sumOfWeights': 2618756.0, 'sumsq': 776374240.2379415, 'thirdquartile': 0.40908554196357727, 'variance': 272.33788700166053}}
Checking npts: 2618756 vs 2618756.0
PASSED

test_task_visstat.py::visstat_test::test_complex_operator 
test_complex_operator
----------------------------

Check the different values of the complex operator parameter.

Iterate over possible complex operator and check that a dictionary is created by visstat
also check that all the keys that should be present are there. 
PASSED

test_task_visstat.py::visstat_test::test_corrLLRR 
Visstat 09: Test using reportingaxes=ddid, correlation=[LL,RR], datacolumn=float_data spw=[0,1,2,3] 
PASSED

test_task_visstat.py::visstat_test::test_correlation 
test_correlation
-------------------

Test the correlation parameter

Assert that the selection with this parameter will return a different value than no selection. 
PASSED

test_task_visstat.py::visstat_test::test_datacolCorrected 
Visstat 07: Default values with datacolum=corrected, reportingaxis=ddid 

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 4.837103133596972

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 17.081207832904955

Checking stddev: 16.382008276126726 vs 16.382008276131568

Checking sum: 12871502.415939873 vs 12871502.415881826

Checking sumsq: 776391995.3973862 vs 776391995.3972417

Checking variance: 268.3701951590845 vs 268.37019515924317
PASSED

test_task_visstat.py::visstat_test::test_datacolModel 
Visstat 03: Default values with datacolum=model, reportingaxis=ddid 

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
PASSED

test_task_visstat.py::visstat_test::test_datacolMulti 
Visstat 08: Test when using reportingaxes='integration, datacolumn=data,corrected,model 
PASSED

test_task_visstat.py::visstat_test::test_defaultValues 
Visstat 01: Default values 

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 4.837103133596972

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 17.081207832904955

Checking stddev: 16.382008276126726 vs 16.382008276131568

Checking sum: 12871502.415939873 vs 12871502.415881826

Checking sumsq: 776391995.3973862 vs 776391995.3972417

Checking variance: 268.3701951590845 vs 268.37019515924317
PASSED

test_task_visstat.py::visstat_test::test_doquantiles 
test doquantiles parameter 
PASSED

test_task_visstat.py::visstat_test::test_field 
test_field
--------------

Test the field selection parameter.

Assert that a selection using the field parameter returns a different result than no selection. 
PASSED

test_task_visstat.py::visstat_test::test_handle_all_flagged_groups 2026-10-09 15:59:07	WARN	visstat::::casa	DATA_DESC_ID=1 -- No valid points found

visstat 13: handle all-flagged sub-selections in reportingaxes, CAS-12857 
PASSED

test_task_visstat.py::visstat_test::test_intent 
test_intent
-----------------

Test the intent parameter

Assert that the specified selection creates a different dict than the default values. 
PASSED

test_task_visstat.py::visstat_test::test_intentOnOff 
Visstat 10: Test using reportingaxes=field, datacolumn=corrected, intent=[on,off] 

check intent on rms
4.304297370622206
check intent off rms
4.304297370622206

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
0.4260329169087803
check intent off stddev
0.4260329169087803

check intent on mean
4.283164013286309
check intent off mean
4.283164013286309

check intent on rms
3.9222649429916214
check intent off rms
3.9222649429916214

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
0.20029446585363156
check intent off stddev
0.20029446585363156

check intent on mean
3.917148108914265
check intent off mean
3.917148108914265
PASSED

test_task_visstat.py::visstat_test::test_maxuvwdistance 
test_maxuvwdistance
-----------------------

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 
PASSED

test_task_visstat.py::visstat_test::test_observation 2026-10-09 15:59:10	SEVERE	visstat::ms::statistics	Exception Reported: MSSelectionNullSelection : The selected table has zero rows.
2026-10-09 15:59:10	SEVERE	visstat::::casa	Task visstat raised an exception of class RuntimeError with the following message: MSSelectionNullSelection : The selected table has zero rows.
2026-10-09 15:59:10	SEVERE	visstat::ms::statistics	Exception Reported: MSSelectionNullSelection : The selected table has zero rows.
2026-10-09 15:59:10	SEVERE	visstat::::casa	Task visstat raised an exception of class RuntimeError with the following message: MSSelectionNullSelection : The selected table has zero rows.

test_observation
-----------------------

Test the observation selection parameter

Assert that checking an out of range observation ID returns a NoneType, and valid selections return a dictionary 
PASSED

test_task_visstat.py::visstat_test::test_reportAxisField 
Visstat 06: Test using reportingaxes=field 
PASSED

test_task_visstat.py::visstat_test::test_reportAxisInt 
Visstat 05: Test using reportingaxes=integration, datacolumn=float_data 
rms 4.304297370622206
max 19.147367477416992
min -15.199692726135254
sum 35087.679596841335
median 4.278878211975098
stddev 0.4260329169087803
mean 4.283164013286309
rms 4.3353846668292695
max 5.960114002227783
min 0.9407882690429688
sum 35488.134197711945
median 4.325183868408203
stddev 0.1700795650207275
mean 4.33204763155664
rms 4.306203614057598
max 6.313568592071533
min 1.483452320098877
sum 35238.97358107567
median 4.286481618881226
stddev 0.19837352702878472
mean 4.301632517221148
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median 4.286481618881226
stddev 0.19837352702878472
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rms 4.260218426691952
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median 4.255388259887695
stddev 0.17545003865511774
mean 4.256604525289735
rms 4.304297370622206
max 19.147367477416992
min -15.199692726135254
sum 35087.679596841335
median 4.278878211975098
stddev 0.4260329169087803
mean 4.283164013286309
rms 4.3353846668292695
max 5.960114002227783
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median 4.325183868408203
stddev 0.1700795650207275
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rms 4.306203614057598
max 6.313568592071533
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stddev 0.17545003865511774
mean 4.256604525289735
rms 4.304297370622206
max 19.147367477416992
min -15.199692726135254
sum 35087.679596841335
median 4.278878211975098
stddev 0.4260329169087803
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rms 4.3353846668292695
max 5.960114002227783
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rms 4.306203614057598
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stddev 0.16807248338680333
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rms 3.9222649429916214
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stddev 0.20029446585363156
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rms 3.8027938560962418
max 6.305795192718506
min 0.28544679284095764
sum 31103.86206844449
median 3.79739773273468
stddev 0.2124022406509363
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mean 4.004765808640521
PASSED

test_task_visstat.py::visstat_test::test_reportingaxes 
test_reportingaxes
-----------------------------

Test the reportingaxes parameter.
The output should be a dict and contain all the expected keys.

Iterate over all the possible values. 
PASSED

test_task_visstat.py::visstat_test::test_scan 
test_scan
------------

Test the scan selection parameter

Assert that the selction with this parameter will return a different result than no selection. 
PASSED

test_task_visstat.py::visstat_test::test_selectdata 
test_selectdata
-----------------------

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 
PASSED

test_task_visstat.py::visstat_test::test_sigma 
test_sigma
------------------------------

Test the axis parameter with a sigma. 
PASSED

test_task_visstat.py::visstat_test::test_sigma_spectrum 
test_sigma_spectrum
------------------------------

Test the axis parameter with a sigma_spectrum. 
PASSED

test_task_visstat.py::visstat_test::test_special_cases 
Visstat 04: Test of special cases 

antenna = 1 ; mean =  0.0

antenna = 2 ; mean =  1.0

antenna = 3 ; mean =  2.0

antenna = 4 ; mean =  3.0
PASSED

test_task_visstat.py::visstat_test::test_spw 
test_spw
---------------

Test the spectral window selection parameter.

Assert that a selection using the spw parameter returns a different result than no selection. 
PASSED

test_task_visstat.py::visstat_test::test_timeAvgAcrossScans 
Visstat 12: Test of time averaging across scans 
PASSED

test_task_visstat.py::visstat_test::test_timeAvgWithinScans 
Visstat 11: Test of time averaging within scans 
PASSED

test_task_visstat.py::visstat_test::test_timeavg 
test_timeaverage
---------------------

Test the timeaverage parameter

Assert that the dict produced when timeaverage = True is different from the one produced when timeaverage=False 
PASSED

test_task_visstat.py::visstat_test::test_timebin 
test_timebin
-------------------

Test the timebin parameter

Assert that the result when given a bin width for averaging is different than when none is given. 
PASSED

test_task_visstat.py::visstat_test::test_timerange 
test_timerange
------------------

Test the timerange selection parameter

Assert that the selection with this parameter will return a different value than no selection. 
PASSED

test_task_visstat.py::visstat_test::test_timespan 
test_timespan
------------------

Test the timespan parameter

Assert that all parameter settings give different results than the default output 
PASSED

test_task_visstat.py::visstat_test::test_useflags 
test_useflags
----------------------

Check that the useflags parameter produces different results then when useflags = False. 
PASSED

test_task_visstat.py::visstat_test::test_uvange 
test_uvrange
--------------------

Test the uvrange selection parameter

Assert that the selection with this parameter will return a different value than no selection. 
PASSED

test_task_visstat.py::visstat_test::test_weight 
test_weight
------------------------------

Test the axis parameter with a weight. 
PASSED

- generated xml file: /home/casatest/casa6/casatestutils/nosedir/xml/test_task_visstat/nose.xml -
======================== 36 passed, 1 warning in 45.17s ========================
Namespace(list=False, verbose=False, dry_run=False, classes=None, file=None, mapfile=<_io.TextIOWrapper name='/home/casatest/casa6/casatestutils/casatestutils/component_to_test_map.json' mode='r' encoding='utf-8'>, branch=None, merge_target=None, pkg=None, work_dir=None, ncores=2, test_paths='/home/casatest/casa6/casatasks/tests/tasks,/home/casatest/casa6/casatasks/tests/scripts', test_list=None, test_config=None, test_group='Documentation,visstat', pmode=None, bamboo=False, cachedir=None, ignore_list='test_casampi,test_task_msview,test_task_imview,test_task_plotms', run_skipped=False, only_skipped=False)

Operating system: Linux

Testing Components['Documentation', 'visstat']

No Tests for Component: Documentation
Arguments Sent Direct To Pytest :  []

Tests to Ignore:  ['test_casampi', 'test_task_msview', 'test_task_imview', 'test_task_plotms']
Running 1 Test(s)
Cleaning: /home/casatest/casa6/casatestutils/nosedir/
Tests: ['/home/casatest/casa6/casatasks/tests/tasks/test_task_visstat.py']

Setting Working Directory: /home/casatest/casa6/casatestutils/nosedir/test_task_visstat/
Test Directory: /home/casatest/casa6/casatestutils/nosedir/test_task_visstat

Running Command: pytest --junitxml=/home/casatest/casa6/casatestutils/nosedir/xml/test_task_visstat/nose.xml -s --disable-pytest-warnings --verbose -ra --tb=short /home/casatest/casa6/casatestutils/nosedir/test_task_visstat/