Repository: climate Updated Branches: refs/heads/master 9fa81d3bc -> d98d172f5
fixed Project: http://git-wip-us.apache.org/repos/asf/climate/repo Commit: http://git-wip-us.apache.org/repos/asf/climate/commit/1d7aaf7b Tree: http://git-wip-us.apache.org/repos/asf/climate/tree/1d7aaf7b Diff: http://git-wip-us.apache.org/repos/asf/climate/diff/1d7aaf7b Branch: refs/heads/master Commit: 1d7aaf7b5a9e0c72c86e5b6e0699e0c55b250497 Parents: 7187cf3 Author: huikyole <huiky...@argo.jpl.nasa.gov> Authored: Mon Jan 9 20:57:04 2017 -0800 Committer: huikyole <huiky...@argo.jpl.nasa.gov> Committed: Mon Jan 9 20:57:04 2017 -0800 ---------------------------------------------------------------------- ocw/metrics.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/climate/blob/1d7aaf7b/ocw/metrics.py ---------------------------------------------------------------------- diff --git a/ocw/metrics.py b/ocw/metrics.py index c796e96..2f56d75 100644 --- a/ocw/metrics.py +++ b/ocw/metrics.py @@ -330,7 +330,7 @@ def calc_correlation(target_array, reference_array): :param reference_array: an array of reference dataset :type reference_array: :class:'numpy.ma.core.MaskedArray' - :returns: pearson's correlation coefficient between the two input arrays + :returns: pearson's correlation coefficient between the two inumpy.t arrays :rtype: :class:'numpy.ma.core.MaskedArray' ''' @@ -418,10 +418,10 @@ def wet_spell_analysis(reference_array, threshold=0.1, nyear=1, dt=3.): ''' nt = reference_array.shape[0] if reference_array.ndim == 3: - reshaped_array = reference_array.reshape[nt, reference_array.size / nt] + reshaped_array = reference_array.reshape([nt, reference_array.size / nt]) else: reshaped_array = reference_array - xy_indices = np.where(reshaped_array.mask[0, :] == False)[0] + xy_indices = numpy.where(reshaped_array.mask[0, :] == False)[0] nt_each_year = nt / nyear spell_duration = [] @@ -429,16 +429,16 @@ def wet_spell_analysis(reference_array, threshold=0.1, nyear=1, dt=3.): total_rainfall = [] for index in xy_indices: - for iyear in np.arange(nyear): + for iyear in numpy.arange(nyear): data0_temp = reshaped_array[nt_each_year * iyear:nt_each_year * (iyear + 1), index] # time indices when precipitation rate is smaller than the # threshold [mm/hr] - t_index = np.where((data0_temp <= threshold) & + t_index = numpy.where((data0_temp <= threshold) & (data0_temp.mask == False))[0] - t_index = np.insert(t_index, 0, 0) + t_index = numpy.insert(t_index, 0, 0) t_index = t_index + nt_each_year * iyear - for it in np.arange(t_index.size - 1): + for it in numpy.arange(t_index.size - 1): if t_index[it + 1] - t_index[it] > 1: data1_temp = data0_temp[t_index[it] + 1:t_index[it + 1]] if not ma.is_masked(data1_temp): @@ -446,4 +446,4 @@ def wet_spell_analysis(reference_array, threshold=0.1, nyear=1, dt=3.): (t_index[it + 1] - t_index[it] - 1) * dt) peak_rainfall.append(data1_temp.max()) total_rainfall.append(data1_temp.sum()) - return np.array(spell_duration), np.array(peak_rainfall), np.array(total_rainfall) + return numpy.array(spell_duration), numpy.array(peak_rainfall), numpy.array(total_rainfall)
