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https://issues.apache.org/jira/browse/CLIMATE-791?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15327812#comment-15327812
]
ASF GitHub Bot commented on CLIMATE-791:
----------------------------------------
Github user lewismc commented on a diff in the pull request:
https://github.com/apache/climate/pull/341#discussion_r66834575
--- Diff: ocw/dataset_processor.py ---
@@ -68,8 +68,6 @@ def temporal_subset(month_start, month_end,
target_dataset, average_each_year=Fa
time_index = np.sort(time_index)
- time_index = time_index[np.where((time_index >= time_index_first) &
(time_index <= time_index_last))]
--- End diff --
Did you determine that this is not used?
> temporal_subset does not work for month_start > month_end
> ---------------------------------------------------------
>
> Key: CLIMATE-791
> URL: https://issues.apache.org/jira/browse/CLIMATE-791
> Project: Apache Open Climate Workbench
> Issue Type: Bug
> Reporter: Ibrahim Jarif
> Assignee: Ibrahim Jarif
> Priority: Minor
>
> The following function does not work as expected when the *month_start* is
> *greater* than *month_end*. The problem occurs with the last year in the
> *target_dataset*.
> {code}
> def temporal_subset(month_start, month_end, target_dataset,
> average_each_year=False):
> """ Temporally subset data given month_index.
> :param month_start: An integer for beginning month (Jan=1)
> :type month_start: :class:`int`
> :param month_end: An integer for ending month (Jan=1)
> :type month_end: :class:`int`
> :param target_dataset: Dataset object that needs temporal subsetting
> :type target_dataset: Open Climate Workbench Dataset Object
> :param average_each_year: If True, output dataset is averaged for each
> year
> :type average_each_year: :class:'boolean'
> :returns: A temporal subset OCW Dataset
> :rtype: Open Climate Workbench Dataset Object
> """
> if month_start > month_end:
> month_index = range(month_start,13)
> month_index.extend(range(1, month_end+1))
> else:
> month_index = range(month_start, month_end+1)
> dates = target_dataset.times
> months = np.array([d.month for d in dates])
> time_index = []
> for m_value in month_index:
> time_index = np.append(time_index, np.where(months == m_value)[0])
> if m_value == month_index[0]:
> time_index_first = np.min(np.where(months == m_value)[0])
> if m_value == month_index[-1]:
> time_index_last = np.max(np.where(months == m_value)[0])
> time_index = np.sort(time_index)
> time_index = time_index[np.where((time_index >= time_index_first) &
> (time_index <= time_index_last))]
> time_index = list(time_index)
> new_dataset = ds.Dataset(target_dataset.lats,
> target_dataset.lons,
> target_dataset.times[time_index],
> target_dataset.values[time_index,:],
> variable=target_dataset.variable,
> units=target_dataset.units,
> name=target_dataset.name)
> if average_each_year:
> nmonth = len(month_index)
> ntime = new_dataset.times.size
> nyear = ntime/nmonth
> averaged_time = []
> ny, nx = target_dataset.values.shape[1:]
> averaged_values =ma.zeros([nyear, ny, nx])
> for iyear in np.arange(nyear):
> # centered time index of the season between month_start and
> month_end in each year
> center_index = int(nmonth/2)+iyear*nmonth
> if nmonth == 1:
> center_index = iyear
> averaged_time.append(new_dataset.times[center_index])
> averaged_values[iyear,:] =
> ma.average(new_dataset.values[nmonth*iyear:nmonth*iyear+nmonth,:], axis=0)
> new_dataset = ds.Dataset(target_dataset.lats,
> target_dataset.lons,
> np.array(averaged_time),
> averaged_values,
> variable=target_dataset.variable,
> units=target_dataset.units,
> name=target_dataset.name)
>
> return new_dataset
> {code}
> Example:
> {code}
> month_start = 8
> month_end = 1
> time = [2000,1,1,0,0,
> 2000,2,1,0,0,
> 2000,3,1,0,0,
> 2000,4,1,0,0,
> 2000,5,1,0,0,
> 2000,6,1,0,0,
> 2000,7,1,0,0,
> 2000,8,1,0,0,
> 2000,9,1,0,0,
> 2000,10,1,0,0,
> 2000,11,1,0,0,
> 2000,12,1,0,0]
> {code}
> The returned dataset should have
> {code}
> time = [2000,1,1,0,0,
> 2000,8,1,0,0,
> 2000,9,1,0,0,
> 2000,10,1,0,0
> 2000,11,1,0,0
> 2000,12,1,0,0]
> {code} But it has {code} time = [2000,1,1,0,0] {code} All the other time
> values are removed.
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