Repository: climate Updated Branches: refs/heads/master 6156eff08 -> ae0ade234
CLIMATE-863 - Address warning, etc within Travis-CI log output Project: http://git-wip-us.apache.org/repos/asf/climate/repo Commit: http://git-wip-us.apache.org/repos/asf/climate/commit/ae0ade23 Tree: http://git-wip-us.apache.org/repos/asf/climate/tree/ae0ade23 Diff: http://git-wip-us.apache.org/repos/asf/climate/diff/ae0ade23 Branch: refs/heads/master Commit: ae0ade2341e63592a7d2b294190b39f60588e7bf Parents: 6156eff Author: Ibrahim Jarif <jarifibra...@gmail.com> Authored: Thu Nov 10 23:17:47 2016 +0530 Committer: Ibrahim Jarif <jarifibra...@gmail.com> Committed: Thu Nov 10 23:17:47 2016 +0530 ---------------------------------------------------------------------- .noserc | 2 +- .travis.yml | 3 +++ ocw/data_source/local.py | 2 -- ocw/dataset_processor.py | 45 +++++++++++++++++++++++++++---------------- 4 files changed, 32 insertions(+), 20 deletions(-) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/climate/blob/ae0ade23/.noserc ---------------------------------------------------------------------- diff --git a/.noserc b/.noserc index 59b8a69..a1be0c2 100644 --- a/.noserc +++ b/.noserc @@ -17,7 +17,7 @@ [nosetests] where=ocw/tests/ -verbosity=3 +verbosity=2 with-coverage=1 cover-package=ocw nocapture=1 http://git-wip-us.apache.org/repos/asf/climate/blob/ae0ade23/.travis.yml ---------------------------------------------------------------------- diff --git a/.travis.yml b/.travis.yml index 9cac19c..a1c4e8b 100644 --- a/.travis.yml +++ b/.travis.yml @@ -49,6 +49,9 @@ install: - source activate test-environment - python setup.py install + # Install coverage plugin + - pip install coverage + # Workaround to get travis build working with matplotlib before_script: - "export DISPLAY=:99.0" http://git-wip-us.apache.org/repos/asf/climate/blob/ae0ade23/ocw/data_source/local.py ---------------------------------------------------------------------- diff --git a/ocw/data_source/local.py b/ocw/data_source/local.py index 626dee5..fecf25c 100644 --- a/ocw/data_source/local.py +++ b/ocw/data_source/local.py @@ -509,9 +509,7 @@ def load_dataset_from_multiple_netcdf_files(variable_name, y_index, x_index = numpy.where(mask_dataset.values == mask_value) times = [] - nfile = len(nc_files) for ifile, file in enumerate(nc_files): - print('NC file ' + str(ifile + 1) + '/' + str(nfile), file) file_object0 = load_file(file, variable_name, lat_name=lat_name, lon_name=lon_name, time_name=time_name) values0 = file_object0.values http://git-wip-us.apache.org/repos/asf/climate/blob/ae0ade23/ocw/dataset_processor.py ---------------------------------------------------------------------- diff --git a/ocw/dataset_processor.py b/ocw/dataset_processor.py index 6579677..b4c1b41 100755 --- a/ocw/dataset_processor.py +++ b/ocw/dataset_processor.py @@ -146,9 +146,8 @@ def temporal_rebin_with_time_index(target_dataset, nt_average): """ nt = target_dataset.times.size if nt % nt_average != 0: - msg = ('Warning: length of time dimension must ' - 'be a multiple of nt_average') - print(msg) + logger.warning('Length of time dimension must ' + 'be a multiple of nt_average') # nt2 is the length of time dimension in the rebinned dataset nt2 = nt // nt_average binned_dates = target_dataset.times[np.arange(nt2) * nt_average] @@ -301,7 +300,8 @@ def spatial_regrid(target_dataset, new_latitudes, new_longitudes, for axis in (0, 1): q_shifted = np.roll(values_original, shift=shift, axis=axis) idx = ~q_shifted.mask * values_original.mask - values_original.data[idx] = q_shifted[idx] + indices = np.where(idx)[0] + values_original.data[indices] = q_shifted[indices] new_values[i] = map_coordinates(values_original, [new_lats_indices.flatten(), new_lons_indices.flatten()], @@ -310,8 +310,10 @@ def spatial_regrid(target_dataset, new_latitudes, new_longitudes, # Make a masking map using nearest neighbour interpolation -use this to # determine locations with MDI and mask these qmdi = np.zeros_like(values_original) - qmdi[values_original.mask == True] = 1. - qmdi[values_original.mask == False] = 0. + values_true_indices = np.where(values_original.mask == True)[0] + values_false_indices = np.where(values_original.mask == False)[0] + qmdi[values_true_indices] = 1. + qmdi[values_false_indices] = 0. qmdi_r = map_coordinates(qmdi, [new_lats_indices.flatten( ), new_lons_indices.flatten()], order=1).reshape(new_lats.shape) mdimask = (qmdi_r != 0.0) @@ -932,13 +934,17 @@ def _rcmes_spatial_regrid(spatial_values, lat, lon, lat2, lon2, order=1): lati = lati.copy() # Now, we'll set points outside the boundaries to lie along an edge - loni[loni > lon.max()] = lon.max() - loni[loni < lon.min()] = lon.min() + loni_max_indices = np.where(loni > lon.max()) + loni_min_indices = np.where(loni < lon.min()) + loni[loni_max_indices] = lon.max() + loni[loni_min_indices] = lon.min() # To deal with the "hard" break, we'll have to treat y differently, # so we're just setting the min here... - lati[lati > lat.max()] = lat.max() - lati[lati < lat.min()] = lat.min() + lati_max_indices = np.where(lati > lat.max()) + lati_min_indices = np.where(lati < lat.min()) + lati[lati_max_indices] = lat.max() + lati[lati_min_indices] = lat.min() # We need to convert these to (float) indicies # (xi should range from 0 to (nx - 1), etc) @@ -972,7 +978,8 @@ def _rcmes_spatial_regrid(spatial_values, lat, lon, lat2, lon2, order=1): for axis in (0, 1): q_shifted = np.roll(spatial_values, shift=shift, axis=axis) idx = ~q_shifted.mask * spatial_values.mask - spatial_values.data[idx] = q_shifted[idx] + indices = np.where(idx) + spatial_values.data[indices] = q_shifted[indices] # Now we actually interpolate # map_coordinates does cubic interpolation by default, @@ -994,8 +1001,10 @@ def _rcmes_spatial_regrid(spatial_values, lat, lon, lat2, lon2, order=1): # Make second map using nearest neighbour interpolation -use this to # determine locations with MDI and mask these qmdi = np.zeros_like(spatial_values) - qmdi[spatial_values.mask == True] = 1. - qmdi[spatial_values.mask == False] = 0. + spatial_indices_true = np.where(spatial_values.mask) + spatial_indices_false = np.where(not spatial_values.mask) + qmdi[spatial_indices_true] = 1. + qmdi[spatial_indices_false] = 0. qmdi_r = map_coordinates(qmdi, [lati, loni], order=order) qmdi_r = qmdi_r.reshape([nlat2, nlon2]) mdimask = (qmdi_r != 0.0) @@ -1057,14 +1066,15 @@ def _rcmes_calc_average_on_new_time_unit(data, dates, unit): :returns: meanstorem, newTimesList :rtype: 3D numpy masked array the same shape as the input array, list of python datetime objects + :raises: ValueError """ # Check if the user-selected temporal grid is valid. If not, EXIT acceptable = ((unit == 'full') | (unit == 'annual') | (unit == 'monthly') | (unit == 'daily')) if not acceptable: - print('Error: unknown unit type selected for time averaging: EXIT') - return - 1, - 1, - 1, - 1 + raise ValueError('Error: unknown unit type selected ' + 'for time averaging: EXIT') nt, ny, nx = data.shape if unit == 'full': @@ -1131,14 +1141,15 @@ def _rcmes_calc_average_on_new_time_unit_K(data, dates, unit): :returns: meanstorem, newTimesList :rtype: 3D numpy masked array the same shape as the input array, list of python datetime objects + :raises: ValueError """ # Check if the user-selected temporal grid is valid. If not, EXIT acceptable = ((unit == 'full') | (unit == 'annual') | (unit == 'monthly') | (unit == 'daily')) if not acceptable: - print('Error: unknown unit type selected for time averaging: EXIT') - return - 1, - 1, - 1, - 1 + raise ValueError('Error: unknown unit type selected ' + 'for time averaging: EXIT') # Calculate arrays of: annual timeseries: year (2007,2007), # monthly time series: year-month (200701,200702),
