Repository: climate Updated Branches: refs/heads/master 43b04e4f0 -> 817c854ba
CLIMATE-850 - Precipitation intensity and duration analyzer using hourly data - metrics.wet_spell_analysis has been added Project: http://git-wip-us.apache.org/repos/asf/climate/repo Commit: http://git-wip-us.apache.org/repos/asf/climate/commit/111d3a4b Tree: http://git-wip-us.apache.org/repos/asf/climate/tree/111d3a4b Diff: http://git-wip-us.apache.org/repos/asf/climate/diff/111d3a4b Branch: refs/heads/master Commit: 111d3a4bce322226807e334b0a35ba8bbec9e4f5 Parents: 9115096 Author: huikyole <huiky...@argo.jpl.nasa.gov> Authored: Mon Aug 15 21:04:28 2016 -0700 Committer: huikyole <huiky...@argo.jpl.nasa.gov> Committed: Mon Aug 15 21:04:28 2016 -0700 ---------------------------------------------------------------------- ocw/metrics.py | 44 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 44 insertions(+) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/climate/blob/111d3a4b/ocw/metrics.py ---------------------------------------------------------------------- diff --git a/ocw/metrics.py b/ocw/metrics.py index 62f4e3d..e29dc74 100644 --- a/ocw/metrics.py +++ b/ocw/metrics.py @@ -346,3 +346,47 @@ def calc_rmse(target_array, reference_array): ''' return (ma.mean((calc_bias(target_array, reference_array))**2))**0.5 + +def wet_spell_analysis(reference_array, threshold=0.1, nyear=1, dt=3.): + ''' Characterize wet spells using sub-daily (hourly) data + + :param reference_array: an array to be analyzed + :type reference_array: :class:'numpy.ma.core.MaskedArray' + + :param threshold: the minimum amount of rainfall [mm/hour] + :type threshold: 'float' + + :param nyear: the number of discontinous periods + :type nyear: 'int' + + :param dt: the temporal resolution of reference_array + :type dt: 'float' + ''' + nt = reference_array.shape[0] + if reference_array.ndim == 3: + 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] + + nt_each_year = nt/nyear + spell_duration = [] + peak_rainfall = [] + total_rainfall = [] + + for index in xy_indices: + for iyear in np.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) & (data0_temp.mask ==False))[0] + t_index = np.insert(t_index, 0, 0) + t_index = t_index + nt_each_year*iyear + for it in np.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): + spell_duration.append((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)
