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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (CLIMATE-813) Fix PEP8 Violations in utils
Date Sat, 18 Jun 2016 10:33:05 GMT

    [ https://issues.apache.org/jira/browse/CLIMATE-813?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15337739#comment-15337739
] 

ASF GitHub Bot commented on CLIMATE-813:
----------------------------------------

Github user jarifibrahim commented on a diff in the pull request:

    https://github.com/apache/climate/pull/362#discussion_r67599123
  
    --- Diff: ocw/utils.py ---
    @@ -326,97 +339,107 @@ def calc_climatology_monthly(dataset):
             raise ValueError(error)
         else:
             values = reshape_monthly_to_annually(dataset).mean(axis=0)
    -        
    +
             # A year can commence from any month
             first_month = dataset.times[0].month
    -        times = np.array([datetime.datetime(1, first_month, 1) + relativedelta(months
= x) 
    -                for x in range(12)])
    +        times = np.array([dt.datetime(1, first_month, 1) +
    +                          relativedelta(months=x)
    +                          for x in range(12)])
             return values, times
     
    +
     def calc_time_series(dataset):
         ''' Calculate time series mean values for a dataset
     
    -    :param dataset: Dataset object 
    +    :param dataset: Dataset object
         :type dataset: :class:`dataset.Dataset`
     
         :returns: time series for the dataset of shape (nT)
         '''
     
    -    t_series =[]
    +    t_series = []
         for t in xrange(dataset.values.shape[0]):
    -        t_series.append(dataset.values[t,:,:].mean())
    -    
    +        t_series.append(dataset.values[t, :, :].mean())
    +
         return t_series
     
    +
     def get_temporal_overlap(dataset_array):
         ''' Find the maximum temporal overlap across the observation and model datasets
     
         :param dataset_array: an array of OCW datasets
         '''
    -    start_time =[]
    -    end_time =[]
    +    start_time = []
    +    end_time = []
         for dataset in dataset_array:
             start_time.append(dataset.time_range()[0])
             end_time.append(dataset.time_range()[1])
     
         return np.max(start_time), np.min(end_time)
     
    +
     def calc_subregion_area_mean_and_std(dataset_array, subregions):
    -    ''' Calculate area mean and standard deviation values for a given subregions using
datasets on common grid points
    +    ''' Calculate area mean and standard deviation values for a given
    +        subregions using datasets on common grid points
         :param dataset_array: An array of OCW Dataset Objects
    -    :type list:  
    +    :type list:
         :param subregions: list of subregions
         :type subregions: :class:`numpy.ma.array`
    -    :returns: area averaged time series for the dataset of shape (ntime, nsubregion)
    +    :returns: area averaged time series for the dataset of shape
    +              (ntime, nsubregion)
         '''
     
         ndata = len(dataset_array)
         dataset0 = dataset_array[0]
         if dataset0.lons.ndim == 1:
    -       lons, lats = np.meshgrid(dataset0.lons, dataset0.lats)
    +        lons, lats = np.meshgrid(dataset0.lons, dataset0.lats)
         else:
    -       lons = dataset0.lons
    -       lats = dataset0.lats
    +        lons = dataset0.lons
    +        lats = dataset0.lats
         subregion_array = np.zeros(lons.shape)
    -    mask_array = dataset_array[0].values[0,:].mask
    +    mask_array = dataset_array[0].values[0, :].mask
         # dataset0.values.shsape[0]: length of the time dimension
         # spatial average
    -    t_series =ma.zeros([ndata, dataset0.values.shape[0], len(subregions)])
    +    t_series = ma.zeros([ndata, dataset0.values.shape[0], len(subregions)])
         # spatial standard deviation
    -    spatial_std =ma.zeros([ndata, dataset0.values.shape[0], len(subregions)])
    +    spatial_std = ma.zeros([ndata, dataset0.values.shape[0], len(subregions)])
     
         for iregion, subregion in enumerate(subregions):
             lat_min, lat_max, lon_min, lon_max = subregion[1]
    -        y_index,x_index = np.where((lats >= lat_min) & (lats <= lat_max) &
(lons >= lon_min) & (lons <= lon_max))
    -        subregion_array[y_index,x_index] = iregion+1
    +        y_index, x_index = np.where((lats >= lat_min) & (
    +            lats <= lat_max) & (lons >= lon_min) & (lons <= lon_max))
    +        subregion_array[y_index, x_index] = iregion + 1
             for idata in np.arange(ndata):
    -            t_series[idata, :, iregion] = ma.mean(dataset_array[idata].values[:,y_index,
x_index], axis=1)
    -            spatial_std[idata, :, iregion] = ma.std(dataset_array[idata].values[:,y_index,
x_index], axis=1)
    -    subregion_array = ma.array(subregion_array, mask=mask_array) 
    +            t_series[idata, :, iregion] = ma.mean(dataset_array[idata].values[
    +                                                  :, y_index, x_index], axis=1)
    +            spatial_std[idata, :, iregion] = ma.std(
    +                dataset_array[idata].values[:, y_index, x_index], axis=1)
    +    subregion_array = ma.array(subregion_array, mask=mask_array)
         return t_series, spatial_std, subregion_array
     
    +
     def calc_area_weighted_spatial_average(dataset, area_weight=False):
         '''Calculate area weighted average of the values in OCW dataset
     
    -    :param dataset: Dataset object 
    +    :param dataset: Dataset object
         :type dataset: :class:`dataset.Dataset`
     
         :returns: time series for the dataset of shape (nT)
         '''
     
    -    if dataset.lats.ndim ==1:
    +    if dataset.lats.ndim == 1:
             lons, lats = np.meshgrid(dataset.lons, dataset.lats)
         else:
    -        lons = dataset.lons
    --- End diff --
    
    `lons` variable was not used anywhere.


> Fix PEP8 Violations in utils
> ----------------------------
>
>                 Key: CLIMATE-813
>                 URL: https://issues.apache.org/jira/browse/CLIMATE-813
>             Project: Apache Open Climate Workbench
>          Issue Type: Improvement
>            Reporter: Ibrahim Jarif
>            Assignee: Ibrahim Jarif
>             Fix For: 1.1
>
>




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