pep8ed , added test suite, and conda recipe

.travis.yml

@@ -0,0 +1,22 @@
+os:
+    - linux
+    - osx
+dist: trusty
+sudo: false
+before_install:
+- if [ "$TRAVIS_OS_NAME" = "linux" ]; then wget https://repo.continuum.io/miniconda/Miniconda2-latest-Linux-x86_64.sh -O miniconda.sh; fi
+- export PATH="$HOME/miniconda/bin:$PATH"
+- bash miniconda.sh -b -p $HOME/miniconda
+- conda config --set always_yes yes --set changeps1 no
+- conda update -y -q conda
+script:
+- conda install -c uvcdat/label/nightly -c conda-forge -c uvcdat genutil nose image-compare flake8 matplotlib
+- export UVCDAT_ANONYMOUS_LOG=False
+- echo $TRAVIS_BRANCH
+- export TRAVIS_PR_BRANCH=$TRAVIS_BRANCH
+- echo $TRAVIS_EVENT_TYPE
+- echo $TRAVIS_PULL_REQUEST
+- python setup.py install
+- python run_tests.py -v2 -n2
+after_success:
+    - if [ "$TRAVIS_BRANCH" == "master" -a "$TRAVIS_PULL_REQUEST" == "false" ]; then conda install conda-build && conda install anaconda-client && bash ci-support/conda_upload.sh ; fi

LICENSE

@@ -0,0 +1,13 @@
+BSD 3-clause license
+Copyright (c) 2015-2017, conda-forge
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
+
+1. Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
+
+2. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
+
+3. Neither the name of the copyright holder nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

conda/meta.yaml

@@ -0,0 +1,43 @@
+{% set name = "ensometrics" %}
+{% set version = "0.1" %}
+
+package:
+    name: {{ name|lower }}
+    version: {{ version }}
+
+source:
+    git_url: https://github.com/eguil/Enso_metrics.git
+    git_rev: master
+
+
+build:
+  number: 0
+  skip: True  # [win or py3k]
+  script: python setup.py install
+
+requirements:
+  build:
+    - python
+    - setuptools
+  run:
+    - python
+    - cdms2
+    - numpy
+    - udunits2
+    - genutil
+
+test:
+  command:
+    - export UVCDAT_ANONYMOUS_LOG=false && python run_tests.py -v2
+
+about:
+    home:  http://github.com/eguil/Enso_metrics
+    license: 'CCLRC'
+    license_file: LICENSE
+    summary: "Library to compute ENSO metrics"
+
+extra:
+  recipe-maintainers:
+    - doutriaux1
+    - eguil
+    - lee1043

lib/EnsoMetricsGraph.py

@@ -5,39 +5,40 @@
 import matplotlib.pyplot as plt
 
 
-def EnsoMetricsTable(models,metrics, figName):
+def EnsoMetricsTable(models, metrics, figName):
 
-    fig=plt.figure()
+    fig = plt.figure()
     ax = fig.add_subplot(111)
     ax.xaxis.set_visible(False)
     ax.yaxis.set_visible(False)
     # Labels
     colLabels = ("ENSO Amplitude", r'$\mu$')
     rowLabels = models
     # make the table
-    the_table = ax.table(cellText=metrics,colWidths=[.2,.2],
-          colLabels=colLabels,rowLabels=rowLabels,colLoc='center',loc='center')
+    the_table = ax.table(cellText=metrics, colWidths=[.2, .2],
+                         colLabels=colLabels, rowLabels=rowLabels, colLoc='center', loc='center')
     # height of rows
     table_props = the_table.properties()
     table_cells = table_props['child_artists']
-    for cell in table_cells: cell.set_height(0.08)
+    for cell in table_cells:
+        cell.set_height(0.08)
     # size of fonts
     the_table.set_fontsize(12)
-    #the_table.scale(1.5, 1.5)
-    plt.tight_layout(rect=[0.05,0.15,0.95,.95])
+    # the_table.scale(1.5, 1.5)
+    plt.tight_layout(rect=[0.05, 0.15, 0.95, .95])
 
     plt.show()
-    #plt.savefig(figName+'.jpeg')
+    # plt.savefig(figName+'.jpeg')
 
-    #colLabels=("Model", "ENSO Amplitude", "Mu")
-    #nrows, ncols = len(clust_data)+1, len(colLables)
-    #hcell, wcell = 0.3, 1.
-    #hpad, wpad = 0, 0
-    #fig=plt.figure(figsize=(ncols*wcell+wpad, nrows*hcell+hpad))
-    #ax = fig.add_subplot(111)
-    #ax.axis('off')
-    ##do the table
-    #the_table = ax.table(cellText=clust_data,
+    # colLabels=("Model", "ENSO Amplitude", "Mu")
+    # nrows, ncols = len(clust_data)+1, len(colLables)
+    # hcell, wcell = 0.3, 1.
+    # hpad, wpad = 0, 0
+    # fig=plt.figure(figsize=(ncols*wcell+wpad, nrows*hcell+hpad))
+    # ax = fig.add_subplot(111)
+    # ax.axis('off')
+    # do the table
+    # the_table = ax.table(cellText=clust_data,
     #          colLabels=colLabels,
     #          loc='center')
-    #plt.savefig("table.png")
+    # plt.savefig("table.png")

lib/EnsoMetricsLib.py

@@ -1,21 +1,22 @@
-import os
 import cdms2 as cdm
 import numpy as npy
 import cdutil as cdu
-from genutil import statistics
-from cdms2.selectors import Selector
-from monthly_variability_statistics import  interannual_variabilty_std_annual_cycle_removed, get_slope_linear_regression_from_anomaly 
+from monthly_variability_statistics import interannual_variabilty_std_annual_cycle_removed
+from monthly_variability_statistics import get_slope_linear_regression_from_anomaly
 import MV2 as mv
 
 #
 # Libray to compute ENSO metrics
 # These procedures have file names as inputs and metric as output
 #
-def Ensoampln3 (sstfile, sstname):
-    ensoampl = Ensoampl(sstfile,sstname, nino3)
+
+
+def Ensoampln3(sstfile, sstname):
+    ensoampl = Ensoampl(sstfile, sstname, nino3)  # noqa ??? We need to pass nino3
     return ensoampl
 
-def EnsoAmpl (sstfile, sstname, ninobox):
+
+def EnsoAmpl(sstfile, sstname, ninobox):
     '''
     The EnsoAmpl() function compute the SST standard deviation in a ninobox
 
@@ -48,21 +49,21 @@ def EnsoAmpl (sstfile, sstname, ninobox):
     cdm.setAutoBounds('on')
 
     # Define metric attributes
-    Name   = 'ENSO amplitude'
-    Units  = 'C'
-    Method = 'Standard deviation of SST in '+ninobox
-    Ref    = 'Using CDAT std dev calculation'
+    Name = 'ENSO amplitude'
+    Units = 'C'
+    Method = 'Standard deviation of SST in ' + ninobox
+    Ref = 'Using CDAT std dev calculation'
 
     # Open file and get time dimension
     fi = cdm.open(sstfile)
-    ssth = fi[sstname] # Create variable handle
+    ssth = fi[sstname]  # Create variable handle
     # Number of months and years
     timN = ssth.shape[0]
     yearN = timN / 12
 
     # define ninobox
-    if ninobox =='nino3':
-        nbox = cdu.region.domain(latitude=(-5.,5.),longitude=(-150,-90))
+    if ninobox == 'nino3':
+        nbox = cdu.region.domain(latitude=(-5., 5.), longitude=(-150, -90))
     else:
         print '!!! ninobox not defined in EnsoAmpl', ninobox
     # Read SST in box
@@ -73,11 +74,12 @@ def EnsoAmpl (sstfile, sstname, ninobox):
     sstStd = interannual_variabilty_std_annual_cycle_removed(sst)
 
     # Create output
-    amplMetric = {'name':Name, 'value':sstStd, 'units':Units, 'method':Method, 'nyears':yearN, 'ref':Ref}
+    amplMetric = {'name': Name, 'value': sstStd, 'units': Units, 'method': Method, 'nyears': yearN, 'ref': Ref}
 
     return amplMetric
 
-def EnsoMu (sstfile, tauxfile, sstname, tauxname):
+
+def EnsoMu(sstfile, tauxfile, sstname, tauxname):
     '''
     The EnsoMu() function compute the regression of nino4 tauxA over nino3 sstA
 
@@ -110,39 +112,39 @@ def EnsoMu (sstfile, tauxfile, sstname, tauxname):
     cdm.setAutoBounds('on')
 
     # Define metric attributes
-    Name   = 'Bjerknes feedback (mu)'
-    Units  = '10-3 N/m2/C'
+    Name = 'Bjerknes feedback (mu)'
+    Units = '10-3 N/m2/C'
     Method = 'Regression of nino4 tauxA over nino3 sstA'
-    Ref    = 'Using CDAT regression calculation'
+    Ref = 'Using CDAT regression calculation'
 
     # Open file and get time dimension
     fsst = cdm.open(sstfile)
     ftaux = cdm.open(tauxfile)
-    ssth = fsst[sstname] # Create variable handle
+    ssth = fsst[sstname]  # Create variable handle
     # Number of months and years
     timN = ssth.shape[0]
     yearN = timN / 12
 
     # Read SST and Taux in boxes
-    n3box = cdu.region.domain(latitude=(-5.,5.),longitude=(-150,-90))
+    n3box = cdu.region.domain(latitude=(-5., 5.), longitude=(-150, -90))
     sst = fsst(sstname, n3box)
 
-    n4box = cdu.region.domain(latitude=(-5.,5.),longitude=(160,210))
+    n4box = cdu.region.domain(latitude=(-5., 5.), longitude=(160, 210))
     taux = ftaux(tauxname, n4box)
 
     # Average and compute regression of interannual anomaly
-    muSlope = get_slope_linear_regression_from_anomaly(taux,sst, 0) # (all points)
-    muSlopePlus = get_slope_linear_regression_from_anomaly(taux,sst, 1) # (positive SSTA = El Nino)
-    muSlopeNeg = get_slope_linear_regression_from_anomaly(taux,sst, -1) # (negative SSTA = La Nina)
+    muSlope = get_slope_linear_regression_from_anomaly(taux, sst, 0)  # (all points)
+    muSlopePlus = get_slope_linear_regression_from_anomaly(taux, sst, 1)  # (positive SSTA = El Nino)
+    muSlopeNeg = get_slope_linear_regression_from_anomaly(taux, sst, -1)  # (negative SSTA = La Nina)
 
     # Change units
-    muSlope     = muSlope * 1000.
+    muSlope = muSlope * 1000.
     muSlopePlus = muSlopePlus * 1000.
-    muSlopeNeg  = muSlopeNeg * 1000.
+    muSlopeNeg = muSlopeNeg * 1000.
 
     # Create output
-    muMetric = {'name':Name, 'value':muSlope, 'units':Units, 'method':Method, 'nyears':yearN, 'ref':Ref, \
-                'nonlinearity':muSlopeNeg-muSlopePlus}
+    muMetric = {'name': Name, 'value': muSlope, 'units': Units, 'method': Method, 'nyears': yearN, 'ref': Ref,
+                'nonlinearity': muSlopeNeg - muSlopePlus}
 
     return muMetric
 
@@ -153,9 +155,9 @@ def computeAnom(var1d, nYears):
     :param var:
     :return:
     '''
-    varAC = npy.ma.ones([12], dtype='float32')*0.
+    varAC = npy.ma.ones([12], dtype='float32') * 0.
     for m in range(12):
-        d = var1d[m::12] # select indices for month m every 12 months
-        varAC[m]   = mv.average(d) # average along time axis
-    varInter = var1d - npy.tile(varAC, nYears) # compute anomaly
+        d = var1d[m::12]  # select indices for month m every 12 months
+        varAC[m] = mv.average(d)  # average along time axis
+    varInter = var1d - npy.tile(varAC, nYears)  # compute anomaly
     return varInter

lib/__init_.py

@@ -0,0 +1,3 @@
+from EnsoMetricsLib import Ensoampln3, EnsoAmpl, EnsoMu, computeAnom  # noqa
+from metricsCollectionsLib import defCollection, metricReqs  # noqa
+from EnsoMetricsGraph import EnsoMetricsTable  # noqa

lib/metricsCollectionsLib.py

@@ -4,25 +4,28 @@
 # Draft version
 #
 # Define metrics collections
+
+
 def defCollection(MC=True):
-# Name, list of metrics
+    # Name, list of metrics
     metrics_collection = {
-        'MC1':{'long_name':'Metrics collection Q1','list_of_metrics':['EnsoAmpl','EnsoMu'],
-               'description':'Describe which science question this collection is about'},
+        'MC1': {'long_name': 'Metrics collection Q1', 'list_of_metrics': ['EnsoAmpl', 'EnsoMu'],
+                'description': 'Describe which science question this collection is about'},
     }
     if MC:
         return metrics_collection
     else:
         return metrics_collection[MC]
 
 # List of metrics requirements (var name and reference obs)
+
+
 def metricReqs(VOR=True):
     var_obs_requirements = {
-        'EnsoAmpl':{'nbvar':1,'var_names':['sst'],'ref_obs':['HadiSST1.1']},
-        'EnsoMu':{'nbvar':2,'var_names':['sst','taux'],'ref_obs':['HadiSST1.1','ERA-interim']},
+        'EnsoAmpl': {'nbvar': 1, 'var_names': ['sst'], 'ref_obs': ['HadiSST1.1']},
+        'EnsoMu': {'nbvar': 2, 'var_names': ['sst', 'taux'], 'ref_obs': ['HadiSST1.1', 'ERA-interim']},
     }
     if VOR:
         return var_obs_requirements
     else:
         return var_obs_requirements[VOR]
-