doesn't dump file unless asked, reads good var in assert func and sav… (

#28)

* doesn't dump file unless asked, reads good var in assert func and save with correct name

* no restriction on numpy

.circleci/config.yml

@@ -17,7 +17,7 @@ aliases:
  - &create_conda_env
  name: create_conda_env
  environment:
- PKGS: "cdms2 cdat_info testsrunner udunits2 'numpy<1.14.6'"
+ PKGS: "cdms2 cdat_info testsrunner udunits2 numpy"
  CHANNELS: "-c cdat/label/nightly -c conda-forge -c cdat"
  command: |
  export PATH=$WORKDIR/miniconda/bin:$PATH
@@ -181,10 +181,10 @@ workflows:
  genutil:
  jobs:
  - macos_genutil_py2
- - macos_genutil_py3:
- requires:
- - macos_genutil_py2
+ - macos_genutil_py3
+ # requires:
+ # - macos_genutil_py2
  - linux_genutil_py2
- - linux_genutil_py3:
- requires:
- - linux_genutil_py2
+ - linux_genutil_py3
+ #  requires:
+ # - linux_genutil_py2

tests/test_genutil_statistics.py

@@ -3,46 +3,56 @@
 import unittest
 import cdat_info
 import numpy
+dump = False
 
 class GENUTIL(unittest.TestCase):
- def assertArraysEqual(self,A,B):
- self.assertTrue(numpy.ma.allclose(A,B))
+ def assertArraysEqual(self,A,nm,skip=False, dump=dump):
+ B = self.good(nm)
+ print("A-B max difference", numpy.abs(A-B).max(), skip)
+ print("A-B % difference", numpy.abs((A-B)/A).max(), skip)
+ if dump:
+ self.out.write(A, id=nm)
+ self.out.sync()
+
+ if not skip:
+ self.assertTrue(numpy.ma.allclose(A,B))
 
  def testStatisitcs(self):
  f=cdms2.open(os.path.join(cdat_info.get_sampledata_path(),'clt.nc'))
 
  u=f('u',time=slice(0,1),level=slice(0,1),squeeze=1)
  v=f('v',time=slice(0,1),plev1=slice(0,1),squeeze=1)
 
- f=cdms2.open(os.path.join(cdat_info.get_sampledata_path(),"genutil_statistics.nc"))
- self.out = cdms2.open("genutil_statistics_new.nc","w")
+ self.good = cdms2.open(os.path.join(cdat_info.get_sampledata_path(),"genutil_statistics.nc"))
+ if dump:
+ self.out = cdms2.open("genutil_statistics_new.nc","w")
  print('Lagged correlation')
  nm = "lagged_corr"
- self.assertArraysEqual(genutil.statistics.laggedcorrelation(u,v,axis=0), f( nm+"_1"))
- self.assertArraysEqual(genutil.statistics.laggedcorrelation(u,v,axis=0,lag=4), f( nm+"_2"))
- self.assertArraysEqual(genutil.statistics.laggedcorrelation(u,v,axis=0,lag=4,noloop=1), f( nm+"_3"))
- self.assertArraysEqual(genutil.statistics.laggedcorrelation(u,v,axis=0,lag=[4,8,10]), f(nm+"_4"))
+ self.assertArraysEqual(genutil.statistics.laggedcorrelation(u,v,axis=0), nm+"_1")
+ self.assertArraysEqual(genutil.statistics.laggedcorrelation(u,v,axis=0,lag=4), nm+"_2")
+ self.assertArraysEqual(genutil.statistics.laggedcorrelation(u,v,axis=0,lag=4,noloop=1), nm+"_3")
+ self.assertArraysEqual(genutil.statistics.laggedcorrelation(u,v,axis=0,lag=[4,8,10]), nm+"_4")
 
 
  print('Lagged covariance')
  nm = "lagged_cov"
- self.assertArraysEqual(genutil.statistics.laggedcovariance(u,v,axis=0), f(nm+"_1"))
- self.assertArraysEqual(genutil.statistics.laggedcovariance(u,v,axis=0,lag=4), f(nm+"_2"))
- self.assertArraysEqual(genutil.statistics.laggedcovariance(u,v,axis=0,lag=4,noloop=1), f(nm+"_3"))
- self.assertArraysEqual(genutil.statistics.laggedcovariance(u,v,axis=0,lag=[4,8,10]), f(nm+"_4"))
+ self.assertArraysEqual(genutil.statistics.laggedcovariance(u,v,axis=0), nm+"_1")
+ self.assertArraysEqual(genutil.statistics.laggedcovariance(u,v,axis=0,lag=4), nm+"_2")
+ self.assertArraysEqual(genutil.statistics.laggedcovariance(u,v,axis=0,lag=4,noloop=1), nm+"_3")
+ self.assertArraysEqual(genutil.statistics.laggedcovariance(u,v,axis=0,lag=[4,8,10]), nm+"_4")
 
  print('Auto correlation')
  nm = "auto_corr"
- self.assertArraysEqual(genutil.statistics.autocorrelation(u,axis=0), f(nm+"_1"))
- self.assertArraysEqual(genutil.statistics.autocorrelation(u,axis=0,lag=4), f(nm+"_2"))
- self.assertArraysEqual(genutil.statistics.autocorrelation(u,axis=0,lag=4,noloop=1), f(nm+"_3"))
- self.assertArraysEqual(genutil.statistics.autocorrelation(u,axis=0,lag=[4,8,10]), f(nm+"_4"))
+ self.assertArraysEqual(genutil.statistics.autocorrelation(u,axis=0), nm+"_1")
+ self.assertArraysEqual(genutil.statistics.autocorrelation(u,axis=0,lag=4), nm+"_2")
+ self.assertArraysEqual(genutil.statistics.autocorrelation(u,axis=0,lag=4,noloop=1), nm+"_3")
+ self.assertArraysEqual(genutil.statistics.autocorrelation(u,axis=0,lag=[4,8,10]), nm+"_4")
 
  print('Auto covariance')
  nm = "auto_cov"
- self.assertArraysEqual(genutil.statistics.autocovariance(u,axis=0), f(nm+"_1"))
- self.assertArraysEqual(genutil.statistics.autocovariance(u,axis=0,lag=4), f(nm+"_2"))
- self.assertArraysEqual(genutil.statistics.autocovariance(u,axis=0,lag=4,noloop=1), f(nm+"_3"))
- self.assertArraysEqual(genutil.statistics.autocovariance(u,axis=0,lag=[4,8,10]), f(nm+"_4"))
+ self.assertArraysEqual(genutil.statistics.autocovariance(u,axis=0), nm+"_1")
+ self.assertArraysEqual(genutil.statistics.autocovariance(u,axis=0,lag=4), nm+"_2")
+ self.assertArraysEqual(genutil.statistics.autocovariance(u,axis=0,lag=4,noloop=1), nm+"_3")
+ self.assertArraysEqual(genutil.statistics.autocovariance(u,axis=0,lag=[4,8,10]), nm+"_4")
  f.close()