From bcd2301ba97234a8e93cd301dd187d035cc3acc6 Mon Sep 17 00:00:00 2001 From: ctena Date: Tue, 7 Mar 2023 10:42:43 +0100 Subject: [PATCH 01/43] Cell measures: Corrected bug on serial write --- nes/nc_projections/default_nes.py | 12 ++++++++---- tests/2.4-test_cell_area.py | 4 +++- 2 files changed, 11 insertions(+), 5 deletions(-) diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py index a5cdb8a..da83b2b 100644 --- a/nes/nc_projections/default_nes.py +++ b/nes/nc_projections/default_nes.py @@ -342,7 +342,7 @@ class Nes(object): """ d = self.__dict__ - state = {k: d[k] for k in d if k not in ['comm', 'variables', 'netcdf']} + state = {k: d[k] for k in d if k not in ['comm', 'variables', 'netcdf', 'cell_measures']} return state @@ -380,8 +380,10 @@ class Nes(object): nessy.netcdf = None if copy_vars: nessy.variables = nessy._get_lazy_variables() + nessy.cell_measures = deepcopy(self.cell_measures) else: nessy.variables = {} + nessy.cell_measures = {} return nessy @@ -2388,11 +2390,13 @@ class Nes(object): else: # if serial: if serial and self.size > 1: - data = self._gather_data() + data = self._gather_data(self.variables) + c_measures = self._gather_data(self.cell_measures) if self.master: new_nc = self.copy(copy_vars=False) new_nc.set_communicator(MPI.COMM_SELF) new_nc.variables = data + new_nc.cell_measures = c_measures if type == 'NES': new_nc.__to_netcdf_py(path) elif type == 'CAMS_RA': @@ -2924,7 +2928,7 @@ class Nes(object): return data_list - def _gather_data(self): + def _gather_data(self, data_to_gather): """ Gather all the variable data into the MPI rank 0 to perform a serial write. @@ -2934,7 +2938,7 @@ class Nes(object): Variables dictionary with all the data from all the ranks. """ - data_list = deepcopy(self.variables) + data_list = deepcopy(data_to_gather) for var_name in data_list.keys(): if self.info and self.master: print("Gathering {0}".format(var_name)) diff --git a/tests/2.4-test_cell_area.py b/tests/2.4-test_cell_area.py index 4c9b152..bd4fb39 100644 --- a/tests/2.4-test_cell_area.py +++ b/tests/2.4-test_cell_area.py @@ -54,7 +54,8 @@ print('Rank {0:03d}: Calculate grid cell area: {1}'.format(rank, nessy.cell_meas # WRITE st_time = timeit.default_timer() -nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size)) +nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), serial=True) +print("SERIE "*20) comm.Barrier() result.loc['write', test_name] = timeit.default_timer() - st_time @@ -192,3 +193,4 @@ del nessy if rank == 0: result.to_csv(result_path) + -- GitLab From 5d86c03d1c997c36a24ecf02df027e0d57624301 Mon Sep 17 00:00:00 2001 From: ctena Date: Tue, 7 Mar 2023 10:48:08 +0100 Subject: [PATCH 02/43] Cell measures: Corrected bug on serial write (CHANGELOG update) --- CHANGELOG.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 5017be6..6bcc27c 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,11 @@ # NES CHANGELOG +### 1.1.1 +* Release date: ??? +* Changes and new features: + * Bugs fixing: + * Bug on `cell_methods` serial write ([#53](https://earth.bsc.es/gitlab/es/NES/-/issues/53)) + ### 1.1.0 * Release date: 2023/03/02 * Changes and new features: -- GitLab From 80a5ff0ad95d5a9639c57f258e4c40d0305e9886 Mon Sep 17 00:00:00 2001 From: ctena Date: Tue, 7 Mar 2023 10:49:47 +0100 Subject: [PATCH 03/43] Cell measures: Corrected bug on serial write (CHANGELOG update) --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 6bcc27c..663be2a 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -10,6 +10,7 @@ * Release date: 2023/03/02 * Changes and new features: * Improve Lat-Lon to Cartesian coordinates method (used in Providentia). + * Horizontal interpolation: Conservative * Function to_shapefile() to create shapefiles from a NES object without losing data from the original grid and being able to select the time and level. * Function from_shapefile() to create a new grid with data from a shapefile after doing a spatial join. * Function create_shapefile() can now be used in parallel. -- GitLab From 5b1a614bf8aca438277bc4430e0295eb3418ce1f Mon Sep 17 00:00:00 2001 From: Alba Vilanova Date: Tue, 7 Mar 2023 14:59:48 +0100 Subject: [PATCH 04/43] Temporal --- tests/2.1-test_spatial_join.py | 82 +- tests/2.3-test_bounds.py | 4 +- tutorials/5.Geospatial/5.2.Spatial_Join.ipynb | 1338 ++++++++++++++--- 3 files changed, 1248 insertions(+), 176 deletions(-) diff --git a/tests/2.1-test_spatial_join.py b/tests/2.1-test_spatial_join.py index 8aedaba..b6f7da6 100644 --- a/tests/2.1-test_spatial_join.py +++ b/tests/2.1-test_spatial_join.py @@ -4,6 +4,7 @@ import sys from mpi4py import MPI import pandas as pd import timeit +import numpy as np from nes import * comm = MPI.COMM_WORLD @@ -47,6 +48,19 @@ comm.Barrier() result.loc['calculate', test_name] = timeit.default_timer() - st_time print('SPATIAL JOIN - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) +# ADD TIMEZONES +timezones = nessy.shapefile['tzid'].values.reshape([nessy.lat['data'].shape[0], + nessy.lon['data'].shape[-1]]) +timezones = np.repeat(timezones[np.newaxis, :, :], [len(nessy.lev['data'])], axis=0) +timezones = np.repeat(timezones[np.newaxis, :, :, :], [len(nessy.time)], axis=0) +nessy.variables['tz'] = {'data': timezones,} + +# WRITE +st_time = timeit.default_timer() +nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True) +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + comm.Barrier() if rank == 0: print(result.loc[:, test_name]) @@ -78,9 +92,21 @@ nessy = from_shapefile(shapefile_path, method='centroid', projection=projection, n_lat=n_lat, n_lon=n_lon) comm.Barrier() result.loc['calculate', test_name] = timeit.default_timer() - st_time - print('FROM SHAPEFILE - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) +# ADD TIMEZONES +timezones = nessy.shapefile['tzid'].values.reshape([nessy.lat['data'].shape[0], + nessy.lon['data'].shape[-1]]) +timezones = np.repeat(timezones[np.newaxis, :, :], [len(nessy.lev['data'])], axis=0) +timezones = np.repeat(timezones[np.newaxis, :, :, :], [len(nessy.time)], axis=0) +nessy.variables['tz'] = {'data': timezones,} + +# WRITE +st_time = timeit.default_timer() +nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True) +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + comm.Barrier() if rank == 0: print(result.loc[:, test_name]) @@ -108,6 +134,19 @@ comm.Barrier() result.loc['calculate', test_name] = timeit.default_timer() - st_time print('SPATIAL JOIN - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) +# ADD TIMEZONES +timezones = nessy.shapefile['tzid'].values.reshape([nessy.lat['data'].shape[0], + nessy.lon['data'].shape[-1]]) +timezones = np.repeat(timezones[np.newaxis, :, :], [len(nessy.lev['data'])], axis=0) +timezones = np.repeat(timezones[np.newaxis, :, :, :], [len(nessy.time)], axis=0) +nessy.variables['tz'] = {'data': timezones,} + +# WRITE +st_time = timeit.default_timer() +nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True) +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + comm.Barrier() if rank == 0: print(result.loc[:, test_name]) @@ -139,9 +178,21 @@ nessy = from_shapefile(shapefile_path, method='nearest', projection=projection, n_lat=n_lat, n_lon=n_lon) comm.Barrier() result.loc['calculate', test_name] = timeit.default_timer() - st_time - print('FROM SHAPEFILE - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) +# ADD TIMEZONES +timezones = nessy.shapefile['tzid'].values.reshape([nessy.lat['data'].shape[0], + nessy.lon['data'].shape[-1]]) +timezones = np.repeat(timezones[np.newaxis, :, :], [len(nessy.lev['data'])], axis=0) +timezones = np.repeat(timezones[np.newaxis, :, :, :], [len(nessy.time)], axis=0) +nessy.variables['tz'] = {'data': timezones,} + +# WRITE +st_time = timeit.default_timer() +nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True) +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + comm.Barrier() if rank == 0: print(result.loc[:, test_name]) @@ -170,6 +221,19 @@ comm.Barrier() result.loc['calculate', test_name] = timeit.default_timer() - st_time print('SPATIAL JOIN - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) +# ADD TIMEZONES +timezones = nessy.shapefile['tzid'].values.reshape([nessy.lat['data'].shape[0], + nessy.lon['data'].shape[-1]]) +timezones = np.repeat(timezones[np.newaxis, :, :], [len(nessy.lev['data'])], axis=0) +timezones = np.repeat(timezones[np.newaxis, :, :, :], [len(nessy.time)], axis=0) +nessy.variables['tz'] = {'data': timezones,} + +# WRITE +st_time = timeit.default_timer() +nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True) +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + comm.Barrier() if rank == 0: print(result.loc[:, test_name]) @@ -202,9 +266,21 @@ nessy = from_shapefile(shapefile_path, method='intersection', projection=project n_lat=n_lat, n_lon=n_lon) comm.Barrier() result.loc['calculate', test_name] = timeit.default_timer() - st_time - print('FROM SHAPEFILE - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) +# ADD TIMEZONES +timezones = nessy.shapefile['tzid'].values.reshape([nessy.lat['data'].shape[0], + nessy.lon['data'].shape[-1]]) +timezones = np.repeat(timezones[np.newaxis, :, :], [len(nessy.lev['data'])], axis=0) +timezones = np.repeat(timezones[np.newaxis, :, :, :], [len(nessy.time)], axis=0) +nessy.variables['tz'] = {'data': timezones,} + +# WRITE +st_time = timeit.default_timer() +nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True) +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + comm.Barrier() if rank == 0: print(result.loc[:, test_name]) diff --git a/tests/2.3-test_bounds.py b/tests/2.3-test_bounds.py index 3bee2ed..adf5671 100644 --- a/tests/2.3-test_bounds.py +++ b/tests/2.3-test_bounds.py @@ -142,9 +142,7 @@ nessy_5.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info comm.Barrier() result.loc['write', test_name] = timeit.default_timer() - st_time -# REOPENcomm.Barrier() -result.loc['write', test_name] = timeit.default_timer() - st_time - +# REOPEN nessy_6 = open_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True) # LOAD DATA AND EXPLORE BOUNDS diff --git a/tutorials/5.Geospatial/5.2.Spatial_Join.ipynb b/tutorials/5.Geospatial/5.2.Spatial_Join.ipynb index 8ebefee..426045a 100644 --- a/tutorials/5.Geospatial/5.2.Spatial_Join.ipynb +++ b/tutorials/5.Geospatial/5.2.Spatial_Join.ipynb @@ -16,6 +16,7 @@ "from nes import *\n", "import geopandas as gpd\n", "import pandas as pd\n", + "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "pd.options.mode.chained_assignment = None" @@ -82,7 +83,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2666: UserWarning: Shapefile does not exist. It will be created now.\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2759: UserWarning: Shapefile does not exist. It will be created now.\n", " warnings.warn(msg)\n" ] } @@ -219,7 +220,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### Add data of last timestep" + "### Save timezones data as variable" ] }, { @@ -228,7 +229,7 @@ "metadata": {}, "outputs": [], "source": [ - "grid.keep_vars('O3')" + "timezones = grid.shapefile['tzid'].values.reshape([grid.lat['data'].shape[0], grid.lon['data'].shape[-1]])" ] }, { @@ -237,7 +238,7 @@ "metadata": {}, "outputs": [], "source": [ - "grid.load()" + "timezones = np.repeat(timezones[np.newaxis, :, :], [len(grid.lev['data'])], axis=0)" ] }, { @@ -246,13 +247,1163 @@ "metadata": {}, "outputs": [], "source": [ - "grid.shapefile['O3'] = grid.variables['O3']['data'][-1, -1, :].ravel()" + "timezones = np.repeat(timezones[np.newaxis, :, :, :], [len(grid.time)], axis=0)" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, + "outputs": [], + "source": [ + "grid.variables['tz'] = {'data': timezones,}" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! 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It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable PBLH was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable RLWTOA was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable RSWIN was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable U10 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable USTAR was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable V10 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable RMOL was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable T2 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable relative_humidity_2m was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable T was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable U was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable V was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable SH2O was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable SMC was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable STC was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable AERO_ACPREC was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable AERO_CUPREC was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable AERO_DEPDRY was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable AERO_OPT_R was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable DRE_SW_TOA was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable DRE_SW_SFC was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable DRE_LW_TOA was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable DRE_LW_SFC was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable ENG_SW_SFC was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable ADRYDEP was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable WETDEP was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable PH_NO2 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable HSUM was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable POLR was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aerosol_optical_depth_dim was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aerosol_optical_depth was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable satellite_AOD_dim was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable satellite_AOD was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aerosol_loading_dim was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aerosol_loading was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable clear_sky_AOD_dim was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable clear_sky_AOD was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable layer_thickness was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable mid_layer_pressure was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable interface_pressure was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable relative_humidity was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable mid_layer_height was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable mid_layer_height_agl was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable air_density was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable dry_pm10_mass was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable dry_pm2p5_mass was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable QC was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable QR was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable QS was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable QG was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aero_dust_001 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aero_dust_002 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aero_dust_003 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aero_dust_004 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aero_dust_005 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aero_dust_006 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aero_dust_007 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aero_dust_008 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aero_ssa_001 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aero_ssa_002 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aero_ssa_003 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aero_ssa_004 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aero_ssa_005 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aero_ssa_006 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aero_ssa_007 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aero_ssa_008 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aero_om_001 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aero_om_002 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aero_om_003 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aero_om_004 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aero_om_005 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aero_om_006 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aero_bc_001 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aero_bc_002 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aero_so4_001 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aero_no3_001 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aero_no3_002 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aero_no3_003 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aero_nh4_001 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aero_unsp_001 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aero_unsp_002 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aero_unsp_003 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aero_unsp_004 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aero_unsp_005 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable NO2 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable NO was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable O3 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable NO3 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable N2O5 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable HNO3 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable HONO was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable PNA was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable H2O2 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable NTR was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable ROOH was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable FORM was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable ALD2 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable ALDX was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable PAR was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable CO was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable MEPX was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable MEOH was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable FACD was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable PAN was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable PACD was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable AACD was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable PANX was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable OLE was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable ETH was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable IOLE was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable TOL was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable CRES was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable OPEN was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable MGLY was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable XYL was not loaded. 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Variable aerosol_extinction_DUST_2 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aerosol_extinction_DUST_3 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aerosol_extinction_DUST_4 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aerosol_extinction_DUST_5 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aerosol_extinction_DUST_6 was not loaded. 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It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aerosol_extinction_UNSPC_1 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aerosol_extinction_UNSPC_2 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aerosol_extinction_UNSPC_3 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aerosol_extinction_UNSPC_4 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n", + "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2300: UserWarning: WARNING!!! Variable aerosol_extinction_UNSPC_5 was not loaded. It will not be written.\n", + " warnings.warn(msg)\n" + ] + } + ], + "source": [ + "grid.to_netcdf('grid_with_tz.nc')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "grid_with_tz = open_netcdf('grid_with_tz.nc')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "grid_with_tz.keep_vars('tz')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "grid_with_tz.load()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'data': array([[[['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Aden'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ...,\n", + " 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'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ...,\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Toronto', 'America/Iqaluit', 'America/Pangnirtung',\n", + " ..., 'Asia/Tomsk', 'Asia/Tomsk', 'Asia/Krasnoyarsk']],\n", + " \n", + " [['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Aden'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ...,\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " 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[[['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Aden'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ...,\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Toronto', 'America/Iqaluit', 'America/Pangnirtung',\n", + " ..., 'Asia/Tomsk', 'Asia/Tomsk', 'Asia/Krasnoyarsk']],\n", + " \n", + " [['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Aden'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ...,\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Toronto', 'America/Iqaluit', 'America/Pangnirtung',\n", + " ..., 'Asia/Tomsk', 'Asia/Tomsk', 'Asia/Krasnoyarsk']],\n", + " \n", + " [['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Aden'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ...,\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Toronto', 'America/Iqaluit', 'America/Pangnirtung',\n", + " ..., 'Asia/Tomsk', 'Asia/Tomsk', 'Asia/Krasnoyarsk']],\n", + " \n", + " ...,\n", + " \n", + " [['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Aden'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ...,\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Toronto', 'America/Iqaluit', 'America/Pangnirtung',\n", + " ..., 'Asia/Tomsk', 'Asia/Tomsk', 'Asia/Krasnoyarsk']],\n", + " \n", + " [['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Aden'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ...,\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Toronto', 'America/Iqaluit', 'America/Pangnirtung',\n", + " ..., 'Asia/Tomsk', 'Asia/Tomsk', 'Asia/Krasnoyarsk']],\n", + " \n", + " [['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Aden'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ...,\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Toronto', 'America/Iqaluit', 'America/Pangnirtung',\n", + " ..., 'Asia/Tomsk', 'Asia/Tomsk', 'Asia/Krasnoyarsk']]],\n", + " \n", + " \n", + " [[['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Aden'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ...,\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Toronto', 'America/Iqaluit', 'America/Pangnirtung',\n", + " ..., 'Asia/Tomsk', 'Asia/Tomsk', 'Asia/Krasnoyarsk']],\n", + " \n", + " [['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Aden'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ...,\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Toronto', 'America/Iqaluit', 'America/Pangnirtung',\n", + " ..., 'Asia/Tomsk', 'Asia/Tomsk', 'Asia/Krasnoyarsk']],\n", + " \n", + " [['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Aden'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ...,\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Toronto', 'America/Iqaluit', 'America/Pangnirtung',\n", + " ..., 'Asia/Tomsk', 'Asia/Tomsk', 'Asia/Krasnoyarsk']],\n", + " \n", + " ...,\n", + " \n", + " [['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Aden'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ...,\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Toronto', 'America/Iqaluit', 'America/Pangnirtung',\n", + " ..., 'Asia/Tomsk', 'Asia/Tomsk', 'Asia/Krasnoyarsk']],\n", + " \n", + " [['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Aden'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ...,\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Toronto', 'America/Iqaluit', 'America/Pangnirtung',\n", + " ..., 'Asia/Tomsk', 'Asia/Tomsk', 'Asia/Krasnoyarsk']],\n", + " \n", + " [['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Aden'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ...,\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Toronto', 'America/Iqaluit', 'America/Pangnirtung',\n", + " ..., 'Asia/Tomsk', 'Asia/Tomsk', 'Asia/Krasnoyarsk']]],\n", + " \n", + " \n", + " ...,\n", + " \n", + " \n", + " [[['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Aden'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ...,\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Toronto', 'America/Iqaluit', 'America/Pangnirtung',\n", + " ..., 'Asia/Tomsk', 'Asia/Tomsk', 'Asia/Krasnoyarsk']],\n", + " \n", + " [['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Aden'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ...,\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Toronto', 'America/Iqaluit', 'America/Pangnirtung',\n", + " ..., 'Asia/Tomsk', 'Asia/Tomsk', 'Asia/Krasnoyarsk']],\n", + " \n", + " [['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Aden'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ...,\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Toronto', 'America/Iqaluit', 'America/Pangnirtung',\n", + " ..., 'Asia/Tomsk', 'Asia/Tomsk', 'Asia/Krasnoyarsk']],\n", + " \n", + " ...,\n", + " \n", + " [['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Aden'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ...,\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Toronto', 'America/Iqaluit', 'America/Pangnirtung',\n", + " ..., 'Asia/Tomsk', 'Asia/Tomsk', 'Asia/Krasnoyarsk']],\n", + " \n", + " [['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Aden'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ...,\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Toronto', 'America/Iqaluit', 'America/Pangnirtung',\n", + " ..., 'Asia/Tomsk', 'Asia/Tomsk', 'Asia/Krasnoyarsk']],\n", + " \n", + " [['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Aden'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ...,\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Toronto', 'America/Iqaluit', 'America/Pangnirtung',\n", + " ..., 'Asia/Tomsk', 'Asia/Tomsk', 'Asia/Krasnoyarsk']]],\n", + " \n", + " \n", + " [[['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Aden'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ...,\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Toronto', 'America/Iqaluit', 'America/Pangnirtung',\n", + " ..., 'Asia/Tomsk', 'Asia/Tomsk', 'Asia/Krasnoyarsk']],\n", + " \n", + " [['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Aden'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ...,\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Toronto', 'America/Iqaluit', 'America/Pangnirtung',\n", + " ..., 'Asia/Tomsk', 'Asia/Tomsk', 'Asia/Krasnoyarsk']],\n", + " \n", + " [['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Aden'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ...,\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Toronto', 'America/Iqaluit', 'America/Pangnirtung',\n", + " ..., 'Asia/Tomsk', 'Asia/Tomsk', 'Asia/Krasnoyarsk']],\n", + " \n", + " ...,\n", + " \n", + " [['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Aden'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ...,\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Toronto', 'America/Iqaluit', 'America/Pangnirtung',\n", + " ..., 'Asia/Tomsk', 'Asia/Tomsk', 'Asia/Krasnoyarsk']],\n", + " \n", + " [['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Aden'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ...,\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Toronto', 'America/Iqaluit', 'America/Pangnirtung',\n", + " ..., 'Asia/Tomsk', 'Asia/Tomsk', 'Asia/Krasnoyarsk']],\n", + " \n", + " [['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Aden'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ...,\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Toronto', 'America/Iqaluit', 'America/Pangnirtung',\n", + " ..., 'Asia/Tomsk', 'Asia/Tomsk', 'Asia/Krasnoyarsk']]],\n", + " \n", + " \n", + " [[['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Aden'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ...,\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Toronto', 'America/Iqaluit', 'America/Pangnirtung',\n", + " ..., 'Asia/Tomsk', 'Asia/Tomsk', 'Asia/Krasnoyarsk']],\n", + " \n", + " [['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Aden'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ...,\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Toronto', 'America/Iqaluit', 'America/Pangnirtung',\n", + " ..., 'Asia/Tomsk', 'Asia/Tomsk', 'Asia/Krasnoyarsk']],\n", + " \n", + " [['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Aden'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ...,\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Toronto', 'America/Iqaluit', 'America/Pangnirtung',\n", + " ..., 'Asia/Tomsk', 'Asia/Tomsk', 'Asia/Krasnoyarsk']],\n", + " \n", + " ...,\n", + " \n", + " [['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Aden'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ...,\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Toronto', 'America/Iqaluit', 'America/Pangnirtung',\n", + " ..., 'Asia/Tomsk', 'Asia/Tomsk', 'Asia/Krasnoyarsk']],\n", + " \n", + " [['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Aden'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ...,\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Toronto', 'America/Iqaluit', 'America/Pangnirtung',\n", + " ..., 'Asia/Tomsk', 'Asia/Tomsk', 'Asia/Krasnoyarsk']],\n", + " \n", + " [['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Aden'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ['nan', 'nan', 'nan', ..., 'Asia/Riyadh', 'Asia/Riyadh',\n", + " 'Asia/Riyadh'],\n", + " ...,\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Pangnirtung', 'America/Pangnirtung',\n", + " 'America/Pangnirtung', ..., 'Asia/Tomsk', 'Asia/Tomsk',\n", + " 'Asia/Tomsk'],\n", + " ['America/Toronto', 'America/Iqaluit', 'America/Pangnirtung',\n", + " ..., 'Asia/Tomsk', 'Asia/Tomsk', 'Asia/Krasnoyarsk']]]],\n", + " dtype=object),\n", + " 'dimensions': ('time', 'lev', 'rlat', 'rlon'),\n", + " 'grid_mapping': 'rotated_pole',\n", + " 'coordinates': 'lat lon'}" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "grid_with_tz.variables['tz']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Add ozone data of last timestep" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [], + "source": [ + "grid.keep_vars('O3')" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "grid.load()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "grid.shapefile['O3'] = grid.variables['O3']['data'][-1, -1, :].ravel()" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, "outputs": [ { "data": { @@ -390,7 +1541,7 @@ "[95121 rows x 3 columns]" ] }, - "execution_count": 11, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -408,7 +1559,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 21, "metadata": {}, "outputs": [ { @@ -444,22 +1595,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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Europe/Stockholm\n", - "\n", - "[22500 rows x 2 columns]" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "regular_grid.shapefile" ] @@ -679,22 +1690,9 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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ffTXSeS0VKTG4+3up72slPSBpuKQPzKyfJKW+r40rSACtj3WokLoc1/gxFWfLOnTL6fEb1hi2f1T1iiuu0AUXXKDRo0c3+mZ93nnn6auvvtLgwYN14oknZlx2e8iQITr++OOVSCS011571S+7ve+++2rs2LFas2aNFixYkHXZ7XRTp07VCSecoNGjR6tPnz6RfsYZM2Zon3320X777acuXbroyCOPjHReSzW57LaZVUjq4O6fpW4vlPQfksZIWu/u081siqQd3f0XjT0Wy24jHxhKKr5X1/1Ge+7xzUjH+qb50hcPSEob1rGuyaTQgnkMcWHZ7Wg1hm9KeiA15tZR0l3u/qiZPSdpnpmdKeldSSe0OHIAbY51/YG8/CjZV//XYOZzbj2FUtMWl93mQj1odVr6H3suvQd6CaHm9BiiYIXW+DWnx8AiegCazeSNfqoHpaW5rxWJAUCzde74rtZ/tJXk0Aq4u9avX6/y8iY+IZaGtZIANNtO3W/WPz45V+vWVcllTZ/QBCvjrShO5eXlzZocx6uBViGfY/zUC1quU9kGDdjh2oI+Zz4/WYbGMZQEAAiQGAAAARIDACBAjQGtQrbxZeoF7UcurzV1idzQYwAABEgMAIAAiQEAECAxAAACFJ+RN8VY3A5oDL+TuaHHAAAIkBgAAAESAwAgQI0BGRVj4li2S3Y2dhwQp6i/a22tFkGPAQAQIDEAAAIkBgBAgMQAAAhQfG4HWmOxtjXGjPalrRWc09FjAAAESAwAgACJAQAQoMbQRjFGD+RfW64rpKPHAAAIkBgAAAESAwAgQGIAAAQoPgNAFu2l2NwQPQYAQIDEAAAIkBgAAAESAwAgQGIAAAQiJwYzKzOzF8zskdT9IWa2xMxqzazGzIbHFyYAoFCa02O4QNKKtPvXSbrS3YdI+mXqPgCglYuUGMysv6Txkm5N2+ySeqRu95T0Xn5DAwAUQ9QJbjMk/UJS97RtF0p6zMxuUDLBHJTpRDObLGmyJFVVVeUeKZolfWIOK60CaI4mewxmdrSkte6+rMGucyVd5O47S7pI0u8yne/us9w94e6JysrKFgcMAIhXlB7DSEkTzOwoSeWSepjZnZK+r2TdQZLuVTjMBABopZrsMbj7Ze7e390HSKqW9Gd3P1nJmsIhqcMOk7QytigBAAXTkkX0zpL032bWUdJmpeoIKD3ZFgKj9gAgk2YlBndfJGlR6vZiSfvnPyQAQDEx8xkAECAxAAACJAYAQIAruLVjUa9ORZEaaF/oMQAAAiQGAECAxAAACFBjQJOoRaAtivp73R7RYwAABEgMAIAAiQEAECAxAAACFJ+RN1GvGsfV5VAMFJujo8cAAAiQGAAAARIDACBAjQGxiDqeS70B+UYtoeXoMQAAAiQGAECAxAAACJAYAAABis8oKgrOQOmhxwAACJAYAAABEgMAIECNAUWVbTIStQc0B5Pa8oseAwAgQGIAAARIDACAAIkBABCg+IyS1FgxkcI0GoryO0GBOjp6DACAAIkBABAgMQAAAtQY0Opw1TcgXvQYAACByInBzMrM7AUzeyRt2/lm9pqZvWJm18UTIgCgkJozlHSBpBWSekiSmX1X0jGS9nX3LWbWN4b4AAAFFqnHYGb9JY2XdGva5nMlTXf3LZLk7mvzHx4AoNCi9hhmSPqFpO5p23aXNNrMrpa0WdIl7v5cwxPNbLKkyZJUVVXVsmiBBihEI6qGvx9MeMuuyR6DmR0taa27L2uwq6OkXpJGSPq5pHlmZg3Pd/dZ7p5w90RlZWU+YgYAxChKj2GkpAlmdpSkckk9zOxOSasl3e/uLmmpmdVJ6iPpw9iiBQDErskeg7tf5u793X2ApGpJf3b3kyU9KOkwSTKz3SV9Q9K6GGMFABRASya43SbpNjN7WdKXkialeg8AUHKoKUTXrMTg7oskLUrd/lLSyfkPCQBQTMx8BgAESAwAgACJAQAQYHVVtBkNi4tMeEM6JrhFR48BABAgMQAAAiQGAECAGgOAdim95kC9IUSPAQAQIDEAAAIkBgBAgBoDgDaL2kFu6DEAAAIkBgBAgMQAAAiQGAAAAYrPaDNYNA8NMYktN/QYAAABEgMAIEBiAAAEqDEAaBe4UE909BgAAAESAwAgQGIAAARIDACAAMVnAO0Cxebo6DEAAAIkBgBAgMQAAAhQYwDQLjDBLTp6DACAAIkBABAgMQAAAiQGAECA4jOAdimXK/61l4J15B6DmZWZ2Qtm9kiD7ZeYmZtZn/yHBwAotOYMJV0gaUX6BjPbWdJYSe/mMygAQPFESgxm1l/SeEm3Nth1o6RfSPI8xwUAKJKoNYYZSiaA7ts3mNkESf9w9xfNLOuJZjZZ0mRJqqqqyj3SItnlpl81+5w3f/qzGCIBUGjtpabQUJM9BjM7WtJad1+Wtq2rpMsl/bKp8919lrsn3D1RWVnZomABAPGL0mMYKWmCmR0lqVxSD0l/kDRQ0vbeQn9Jz5vZcHd/P65gAQDxazIxuPtlki6TJDM7VNIl7n58+jFm9rakhLuviyFGAEABMcENABBo1gQ3d18kaVGG7QPyE07x5FJkLsRj5Vu+C+PZftZCFeBzmaQEoHH0GAAAARIDACBAYgAABNrVInqlPPZfKIVqg0I9z6ofFuRp0I6010lt6egxAAACJAYAQIDEAAAIkBgAAIE2WXymyNx+7Drv7Prbq354SxEjQWtGwTlEjwEAECAxAAACJAYAQKDN1BioKyC93tAQ9YfcxDX2XgqLH5ZSXaGx9ihGnPQYAAABEgMAIEBiAAAESAwAgECbKT4DjclWmKYoHSpUoTPq8+SzSF1KxWapNArw2dBjAAAESAwAgACJAQAQMHcv2JMlEgmvqamJ/XmY7IZCaaxG0diEu1werxBKbRy+NYqzdpDv18fMlrl74mvPk9dnAQC0eiQGAECAxAAACJAYAAABJrgBLZBLgbkx2YqLpTwZCm0PPQYAQIDEAAAIkBgAAIE2OcEtHZPdgNy9+dOfFTuEVqFQNSAmuAEAioLEAAAIkBgAAAESAwAgwAS3DL5z4ZL622/MGFHESACg8CL3GMyszMxeMLNHUvevN7NXzWy5mT1gZjvEFyYAoFCaM5R0gaQVafcXStrH3feV9Lqky/IZGACgOCIlBjPrL2m8pFu3b3P3x919W+ruEkn98x8eAKDQotYYZkj6haTuWfafIemeTDvMbLKkyZJUVVXV3PhilV5LaOk5jdUiojwPtQyUAia05SZ94lm+J7sV46p6TfYYzOxoSWvdfVmW/ZdL2iZpTqb97j7L3RPunqisrGxRsACA+EXpMYyUNMHMjpJULqmHmd3p7ieb2SRJR0sa44VcWwMAEJsmewzufpm793f3AZKqJf05lRTGSbpU0gR33xRznACAAmnJBLdfK1lzWGhmtWb2P3mKCQBQRM2a4ObuiyQtSt3eNYZ48i6XAnMu57f0eQCgVLAkBgAgQGIAAARIDACAQJtfRG9h3b31t8d2OKGIkTSusRoFk9+Qb0xki0/DCWmFurpbPtFjAAAESAwAgACJAQAQIDEAAAJtvvjcWlFwRktRYC4N2VZeLcaqqVHRYwAABEgMAIAAiQEAEKDGUETUEZAP1BJaj1KuK6SjxwAACJAYAAABEgMAIECNASgh1AtQCugxAAACJAYAQIDEAAAIkBgAAAGKz0CRUXBGqaHHAAAIkBgAAAESAwAgQI0BKDBqCih19BgAAAESAwAgQGIAAARIDACAQJsvPo/tcEKxQwAoOKNVoccAAAiQGAAAARIDACDQJmsMpVxXeGPGiGKHgHZql5t+1eQxDWshUc5p6jHQ+tBjAAAEIicGMyszsxfM7JHU/R3NbKGZrUx97xVfmACAQmlOj+ECSSvS7k+R9KS77ybpydR9AEArFykxmFl/SeMl3Zq2+RhJs1O3Z0s6Nr+hAQCKwdy96YPM5kuaJqm7pEvc/Wgz+8Tdd0g75mN3/9pwkplNljRZkqqqqvZ/55138hZ8ulIuOGdDIbp9irM4m0uxOE4UokubmS1z90TD7U32GMzsaElr3X1ZLk/s7rPcPeHuicrKylweAgBQQFE+rjpS0gQzO0pSuaQeZnanpA/MrJ+7rzGzfpLWxhkoAKAwmuwxuPtl7t7f3QdIqpb0Z3c/WdIfJU1KHTZJ0kOxRQkAKJiWTHCbLmmemZ0p6V1JrW+QvwioK7QtjY2hp4/353usvdRqCWhbmpUY3H2RpEWp2+sljcl/SACAYmLmMwAgQGIAAARIDACAQKtdXbWUJ7RRYG7bohaS28LkrrbwM6D56DEAAAIkBgBAgMQAAAi02hpDKfvOhUuC+9QcEFXUSXHp+/I52Y2aAiR6DACABkgMAIAAiQEAECAxAAACka7gli+JRMJramry8lilPMGtMRSi25ZcirW5FIvjfB4Kzu1XzldwAwC0LyQGAECAxAAACDDBDWiBhuP4pTReX0qxoHWhxwAACJAYAAABEgMAIEBiAAAEKD4XAJPaALQm9BgAAAESAwAgQGIAAARabY1hYd29wf1SWlSPmkL7kcsksobn5PMKbEA+0GMAAARIDACAAIkBABAgMQAAAq32Cm6NKaVCtEQxuq1h1VK0FVzBDQAQCYkBABAgMQAAAq12gltr8p0Ll9Tfpt7QOlFXQHtCjwEAEGgyMZhZuZktNbMXzewVM7sytX2ImS0xs1ozqzGz4fGHCwCIW5ShpC2SDnP3z82sk6TFZrZA0n9IutLdF5jZUZKuk3RofKECAAqhycTgyYkOn6fudkp9eeqrR2p7T0nvxREgAKCwIhWfzaxM0jJJu0qa6e7PmtmFkh4zsxuUHJI6KMu5kyVNlqSqqqq8BN2U9JVXS2GyGwXn1omCM9qrSMVnd//K3YdI6i9puJntI+lcSRe5+86SLpL0uyznznL3hLsnKisr8xU3ACAmzfpUkrt/ImmRpHGSJkm6P7XrXkkUnwGgDYjyqaRKM9shdbuLpMMlvapkTeGQ1GGHSVoZV5AAgMKJUmPoJ2l2qs7QQdI8d3/EzD6R9N9m1lHSZqXqCKCmAKB1i/KppOWShmbYvljS/nEEBQAoHmY+AwACJAYAQIDEAAAIkBgAAAESAwAgQGIAAARIDACAAFdwQ7vAgnhAdPQYAAABEgMAIEBiAAAESAwAgADFZ7QZFJiB/KDHAAAIkBgAAAESAwAgQI0hTxbW3dvsc3a56VcxRAIALUOPAQAQIDEAAAIkBgBAgMQAAAi0+eJzLkVhtB5MagPyjx4DACBAYgAABEgMAIBAm68xlLL08fFSnuwWdRw//Wdo7JwoPyu1A6B46DEAAAIkBgBAgMQAAAiYuxfsyRKJhNfU1BTs+VqzYtQcGNcH2hczW+buiYbb6TEAAAIkBgBAgMQAAAiQGAAAASa4laiWTn7Ldn4xCsyNxU/BGyg9TfYYzKzczJaa2Ytm9oqZXZm273wzey21/bp4QwUAFEKUHsMWSYe5++dm1knSYjNbIKmLpGMk7evuW8ysb5yBAgAKo8nE4MmJDp+n7nZKfbmkcyVNd/ctqePWxhUkAKBwItUYzKxM0jJJu0qa6e7Pmtnukkab2dWSNku6xN2fy3DuZEmTJamqqipvgbcnLR2HZxwfQHNE+lSSu3/l7kMk9Zc03Mz2UTKp9JI0QtLPJc0zM8tw7ix3T7h7orKyMo+hAwDi0KyPq7r7J5IWSRonabWk+z1pqaQ6SX3yHiEAoKCifCqp0sx2SN3uIulwSa9KelDSYantu0v6hqR18YUKACiEKDWGfpJmp+oMHSTNc/dHzOwbkm4zs5clfSlpkhdyRT4AQCxYXRUA2ilWVwUAREJiAAAESAwAgACJAQAQIDEAAAIkBgBAgMQAAAiQGAAAARIDACBAYgAABEgMAIAAiQEAECAxAAACJAYAQIDEAAAIkBgAAAESAwAgUNAruJnZh5LeacFD9FHpX1e61GMs9fik0o+x1OOTSj/GUo9PKv0Y8xHft929suHGgiaGljKzmkyXoSslpR5jqccnlX6MpR6fVPoxlnp8UunHGGd8DCUBAAIkBgBAoLUlhlnFDiCCUo+x1OOTSj/GUo9PKv0YSz0+qfRjjC2+VlVjAADEr7X1GAAAMZ4otFkAAAUGSURBVCMxAAACJZcYzOwEM3vFzOrMLNFg32VmtsrMXjOz72U5f0czW2hmK1Pfe8Uc7z1mVpv6etvMarMc97aZvZQ6ribOmBo871Qz+0dajEdlOW5cql1XmdmUQsWXeu7rzexVM1tuZg+Y2Q5ZjitoGzbVJpZ0U2r/cjMbFndMDZ5/ZzP7i5mtSP3NXJDhmEPN7NO01/+XBY6x0desBNpwj7S2qTWzDWZ2YYNjCtqGZnabma01s5fTtkV6X8vb37G7l9SXpD0l7SFpkaRE2va9JL0oqbOkgZLekFSW4fzrJE1J3Z4i6doCxv4rSb/Msu9tSX2K0J5TJV3SxDFlqfbcRdI3Uu28VwFjPEJSx9Tta7O9ZoVswyhtIukoSQskmaQRkp4t8GvbT9Kw1O3ukl7PEOOhkh4p9O9d1Nes2G2Y4TV/X8lJX0VrQ0kHSxom6eW0bU2+r+Xz77jkegzuvsLdX8uw6xhJc919i7u/JWmVpOFZjpuduj1b0rHxRBoyM5P0Q0l3F+L58my4pFXu/qa7fylprpLtWBDu/ri7b0vdXSKpf6GeuxFR2uQYSXd40hJJO5hZv0IF6O5r3P351O3PJK2QtFOhnj9PitqGDYyR9Ia7t2R1hhZz96clfdRgc5T3tbz9HZdcYmjETpL+L+3+amX+I/imu6+Rkn84kvoWIDZJGi3pA3dfmWW/S3rczJaZ2eQCxbTdv6W66bdl6YJGbdtCOEPJ/yAzKWQbRmmTkmk3MxsgaaikZzPs/lcze9HMFpjZ3gUNrOnXrGTaUFK1sv9jV8w2lKK9r+WtLTvmclJLmdkTkr6VYdfl7v5QttMybCvIZ20jxvsjNd5bGOnu75lZX0kLzezV1H8GscYn6WZJ/6lkW/2nksNdZzR8iAzn5rVto7ShmV0uaZukOVkeJrY2zCBKmxTtdzIIwqybpPskXejuGxrsfl7JoZHPU/WlByXtVsDwmnrNSqUNvyFpgqTLMuwudhtGlbe2LEpicPfDczhttaSd0+73l/RehuM+MLN+7r4m1SVdm0uM6ZqK18w6Spooaf9GHuO91Pe1ZvaAkt2+vLypRW1PM/utpEcy7IratjmL0IaTJB0taYynBkwzPEZsbZhBlDaJvd2aYmadlEwKc9z9/ob70xOFu//JzH5jZn3cvSCLw0V4zYrehilHSnre3T9ouKPYbZgS5X0tb23ZmoaS/iip2sw6m9lAJTP20izHTUrdniQpWw8knw6X9Kq7r86008wqzKz79ttKFltfznRsvjUYrz0uy/M+J2k3MxuY+s+pWsl2LAgzGyfpUkkT3H1TlmMK3YZR2uSPkk5NfbJmhKRPt3f3CyFV1/qdpBXu/l9ZjvlW6jiZ2XAl/+bXFyi+KK9ZUdswTdYefzHbME2U97X8/R0XqtIe9UvJN6/VkrZI+kDSY2n7Lley6v6apCPTtt+q1CeYJPWW9KSklanvOxYg5tslndNg279I+lPq9i5KfkLgRUmvKDl8Uqj2/IOklyQtT/2S9GsYX+r+UUp+quWNQsaXeu5VSo6N1qa+/qcU2jBTm0g6Z/trrWTXfWZq/0tK+xRdgdptlJJDBcvT2u6oBjH+W6q9XlSysH9QAePL+JqVUhumYuiq5Bt9z7RtRWtDJRPUGklbU++FZ2Z7X4vr75glMQAAgdY0lAQAKAASAwAgQGIAAARIDACAAIkBABAgMQAAAiQGAEDg/wP7WxjXdm7NvQAAAABJRU5ErkJggg==\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "fig, ax = plt.subplots(1, figsize=(20, 7))\n", "\n", -- GitLab From 38a204bd2e1fa672a5b76ef64e88f53cac234218 Mon Sep 17 00:00:00 2001 From: Alba Vilanova Date: Tue, 7 Mar 2023 15:02:04 +0100 Subject: [PATCH 05/43] Temporal --- nes/nc_projections/default_nes.py | 55 ++++++++++++++++++-- nes/nc_projections/points_nes.py | 2 +- nes/nc_projections/points_nes_ghost.py | 2 +- nes/nc_projections/points_nes_providentia.py | 2 +- 4 files changed, 54 insertions(+), 7 deletions(-) diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py index fcb4397..31b3b4a 100644 --- a/nes/nc_projections/default_nes.py +++ b/nes/nc_projections/default_nes.py @@ -8,7 +8,7 @@ import numpy as np import pandas as pd import datetime from xarray import open_dataset -from netCDF4 import Dataset, num2date, date2num +from netCDF4 import Dataset, num2date, date2num, stringtochar from mpi4py import MPI from cfunits import Units from numpy.ma.core import MaskError @@ -1415,6 +1415,7 @@ class Nes(object): """ units = self.__parse_time_unit(time.units) + if not hasattr(time, 'calendar'): calendar = 'standard' else: @@ -1469,7 +1470,6 @@ class Nes(object): if self.master: nc_var = self.netcdf.variables['time'] time_data, units, calendar = self.__parse_time(nc_var) - time = num2date(time_data, units, calendar=calendar) time = [aux.replace(second=0, microsecond=0) for aux in time] else: @@ -2188,12 +2188,59 @@ class Nes(object): for i, (var_name, var_dict) in enumerate(self.variables.items()): if var_dict['data'] is not None: + + # Get dimensions + var_dims = ('time', 'lev',) + self._var_dim + + # Get data type + if 'dtype' in var_dict.keys(): + var_dtype = var_dict['dtype'] + if var_dtype != var_dict['data'].dtype: + msg = "WARNING!!! " + msg += "Different data types for variable {0}. ".format(var_name) + msg += "Input dtype={0}. Data dtype={1}.".format(var_dtype, + var_dict['data'].dtype) + warnings.warn(msg) + try: + var_dict['data'] = var_dict['data'].astype(var_dtype) + except Exception as e: # TODO: Detect exception + raise e("It was not possible to cast the data to the input dtype.") + else: + var_dtype = var_dict['data'].dtype + + # Transform objects into strings + if var_dtype == np.dtype(object): + var_dict['data'] = var_dict['data'].astype(str) + var_dtype = var_dict['data'].dtype + + print(var_dtype) + print(var_dict['data'].dtype) + print('first', var_dict['data']) + # Convert list of strings to chars for parallelization + try: + unicode_type = len(max(var_dict['data'].flatten(), key=len)) + print('Unicode', unicode_type) + print(var_dict['data'].dtype == np.dtype(' 0, complevel=self.zip_lvl) else: if self.balanced: @@ -2203,7 +2250,7 @@ class Nes(object): else: chunk_size = None chunk_size = self.comm.bcast(chunk_size, root=0) - var = netcdf.createVariable(var_name, var_dict['data'].dtype, ('time', 'lev',) + self._var_dim, + var = netcdf.createVariable(var_name, var_dtype, var_dims, zlib=self.zip_lvl > 0, complevel=self.zip_lvl, chunksizes=chunk_size) if self.info: diff --git a/nes/nc_projections/points_nes.py b/nes/nc_projections/points_nes.py index 9c3d859..5a2edaa 100644 --- a/nes/nc_projections/points_nes.py +++ b/nes/nc_projections/points_nes.py @@ -369,7 +369,7 @@ class PointsNes(Nes): # Convert list of strings to chars for parallelization try: - unicode_type = len(max(var_dict['data'], key=len)) + unicode_type = len(max(var_dict['data'].flatten(), key=len)) if ((var_dict['data'].dtype == np.dtype(' Date: Tue, 7 Mar 2023 15:02:39 +0100 Subject: [PATCH 06/43] Temporal 2 --- tests/2.1-test_spatial_join.py | 445 +++++++++++++++++---------------- 1 file changed, 224 insertions(+), 221 deletions(-) diff --git a/tests/2.1-test_spatial_join.py b/tests/2.1-test_spatial_join.py index b6f7da6..4059b80 100644 --- a/tests/2.1-test_spatial_join.py +++ b/tests/2.1-test_spatial_join.py @@ -51,8 +51,11 @@ print('SPATIAL JOIN - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapef # ADD TIMEZONES timezones = nessy.shapefile['tzid'].values.reshape([nessy.lat['data'].shape[0], nessy.lon['data'].shape[-1]]) +print('Shape 0', timezones.shape) timezones = np.repeat(timezones[np.newaxis, :, :], [len(nessy.lev['data'])], axis=0) +print('Shape 1', timezones.shape) timezones = np.repeat(timezones[np.newaxis, :, :, :], [len(nessy.time)], axis=0) +print('Shape 2', timezones.shape) nessy.variables['tz'] = {'data': timezones,} # WRITE @@ -66,225 +69,225 @@ if rank == 0: print(result.loc[:, test_name]) sys.stdout.flush() -# ====================================================================================================================== -# =================================== CENTROID FROM NEW FILE =================================================== -# ====================================================================================================================== - -test_name = '2.1.2.New_file_centroid' -if rank == 0: - print(test_name) - -# DEFINE PROJECTION -st_time = timeit.default_timer() -projection = 'regular' -lat_orig = 41.1 -lon_orig = 1.8 -inc_lat = 0.2 -inc_lon = 0.2 -n_lat = 100 -n_lon = 100 - -# SPATIAL JOIN -# Method can be centroid, nearest and intersection -nessy = from_shapefile(shapefile_path, method='centroid', projection=projection, - lat_orig=lat_orig, lon_orig=lon_orig, - inc_lat=inc_lat, inc_lon=inc_lon, - n_lat=n_lat, n_lon=n_lon) -comm.Barrier() -result.loc['calculate', test_name] = timeit.default_timer() - st_time -print('FROM SHAPEFILE - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) - -# ADD TIMEZONES -timezones = nessy.shapefile['tzid'].values.reshape([nessy.lat['data'].shape[0], - nessy.lon['data'].shape[-1]]) -timezones = np.repeat(timezones[np.newaxis, :, :], [len(nessy.lev['data'])], axis=0) -timezones = np.repeat(timezones[np.newaxis, :, :, :], [len(nessy.time)], axis=0) -nessy.variables['tz'] = {'data': timezones,} - -# WRITE -st_time = timeit.default_timer() -nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True) -comm.Barrier() -result.loc['write', test_name] = timeit.default_timer() - st_time - -comm.Barrier() -if rank == 0: - print(result.loc[:, test_name]) -sys.stdout.flush() - -# ====================================================================================================================== -# =================================== NEAREST EXISTING FILE =================================================== -# ====================================================================================================================== - -test_name = '2.1.3.Existing_file_nearest' -if rank == 0: - print(test_name) - -# READ -st_time = timeit.default_timer() -nessy = open_netcdf(original_path, parallel_method=parallel_method) -comm.Barrier() -result.loc['read', test_name] = timeit.default_timer() - st_time - -# SPATIAL JOIN -# Method can be centroid, nearest and intersection -st_time = timeit.default_timer() -nessy.spatial_join(shapefile_path, method='nearest') -comm.Barrier() -result.loc['calculate', test_name] = timeit.default_timer() - st_time -print('SPATIAL JOIN - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) - -# ADD TIMEZONES -timezones = nessy.shapefile['tzid'].values.reshape([nessy.lat['data'].shape[0], - nessy.lon['data'].shape[-1]]) -timezones = np.repeat(timezones[np.newaxis, :, :], [len(nessy.lev['data'])], axis=0) -timezones = np.repeat(timezones[np.newaxis, :, :, :], [len(nessy.time)], axis=0) -nessy.variables['tz'] = {'data': timezones,} - -# WRITE -st_time = timeit.default_timer() -nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True) -comm.Barrier() -result.loc['write', test_name] = timeit.default_timer() - st_time - -comm.Barrier() -if rank == 0: - print(result.loc[:, test_name]) -sys.stdout.flush() - -# ====================================================================================================================== -# =================================== NEAREST FROM NEW FILE =================================================== -# ====================================================================================================================== - -test_name = '2.1.4.New_file_nearest' -if rank == 0: - print(test_name) - -# DEFINE PROJECTION -st_time = timeit.default_timer() -projection = 'regular' -lat_orig = 41.1 -lon_orig = 1.8 -inc_lat = 0.2 -inc_lon = 0.2 -n_lat = 100 -n_lon = 100 - -# SPATIAL JOIN -# Method can be centroid, nearest and intersection -nessy = from_shapefile(shapefile_path, method='nearest', projection=projection, - lat_orig=lat_orig, lon_orig=lon_orig, - inc_lat=inc_lat, inc_lon=inc_lon, - n_lat=n_lat, n_lon=n_lon) -comm.Barrier() -result.loc['calculate', test_name] = timeit.default_timer() - st_time -print('FROM SHAPEFILE - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) - -# ADD TIMEZONES -timezones = nessy.shapefile['tzid'].values.reshape([nessy.lat['data'].shape[0], - nessy.lon['data'].shape[-1]]) -timezones = np.repeat(timezones[np.newaxis, :, :], [len(nessy.lev['data'])], axis=0) -timezones = np.repeat(timezones[np.newaxis, :, :, :], [len(nessy.time)], axis=0) -nessy.variables['tz'] = {'data': timezones,} - -# WRITE -st_time = timeit.default_timer() -nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True) -comm.Barrier() -result.loc['write', test_name] = timeit.default_timer() - st_time - -comm.Barrier() -if rank == 0: - print(result.loc[:, test_name]) -sys.stdout.flush() - - -# ====================================================================================================================== -# =================================== INTERSECTION EXISTING FILE =================================================== -# ====================================================================================================================== - -test_name = '2.1.5.Existing_file_intersection' -if rank == 0: - print(test_name) +# # ====================================================================================================================== +# # =================================== CENTROID FROM NEW FILE =================================================== +# # ====================================================================================================================== + +# test_name = '2.1.2.New_file_centroid' +# if rank == 0: +# print(test_name) + +# # DEFINE PROJECTION +# st_time = timeit.default_timer() +# projection = 'regular' +# lat_orig = 41.1 +# lon_orig = 1.8 +# inc_lat = 0.2 +# inc_lon = 0.2 +# n_lat = 100 +# n_lon = 100 + +# # SPATIAL JOIN +# # Method can be centroid, nearest and intersection +# nessy = from_shapefile(shapefile_path, method='centroid', projection=projection, +# lat_orig=lat_orig, lon_orig=lon_orig, +# inc_lat=inc_lat, inc_lon=inc_lon, +# n_lat=n_lat, n_lon=n_lon) +# comm.Barrier() +# result.loc['calculate', test_name] = timeit.default_timer() - st_time +# print('FROM SHAPEFILE - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) + +# # ADD TIMEZONES +# timezones = nessy.shapefile['tzid'].values.reshape([nessy.lat['data'].shape[0], +# nessy.lon['data'].shape[-1]]) +# timezones = np.repeat(timezones[np.newaxis, :, :], [len(nessy.lev['data'])], axis=0) +# timezones = np.repeat(timezones[np.newaxis, :, :, :], [len(nessy.time)], axis=0) +# nessy.variables['tz'] = {'data': timezones,} + +# # WRITE +# st_time = timeit.default_timer() +# nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True) +# comm.Barrier() +# result.loc['write', test_name] = timeit.default_timer() - st_time + +# comm.Barrier() +# if rank == 0: +# print(result.loc[:, test_name]) +# sys.stdout.flush() + +# # ====================================================================================================================== +# # =================================== NEAREST EXISTING FILE =================================================== +# # ====================================================================================================================== + +# test_name = '2.1.3.Existing_file_nearest' +# if rank == 0: +# print(test_name) + +# # READ +# st_time = timeit.default_timer() +# nessy = open_netcdf(original_path, parallel_method=parallel_method) +# comm.Barrier() +# result.loc['read', test_name] = timeit.default_timer() - st_time + +# # SPATIAL JOIN +# # Method can be centroid, nearest and intersection +# st_time = timeit.default_timer() +# nessy.spatial_join(shapefile_path, method='nearest') +# comm.Barrier() +# result.loc['calculate', test_name] = timeit.default_timer() - st_time +# print('SPATIAL JOIN - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) + +# # ADD TIMEZONES +# timezones = nessy.shapefile['tzid'].values.reshape([nessy.lat['data'].shape[0], +# nessy.lon['data'].shape[-1]]) +# timezones = np.repeat(timezones[np.newaxis, :, :], [len(nessy.lev['data'])], axis=0) +# timezones = np.repeat(timezones[np.newaxis, :, :, :], [len(nessy.time)], axis=0) +# nessy.variables['tz'] = {'data': timezones,} + +# # WRITE +# st_time = timeit.default_timer() +# nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True) +# comm.Barrier() +# result.loc['write', test_name] = timeit.default_timer() - st_time + +# comm.Barrier() +# if rank == 0: +# print(result.loc[:, test_name]) +# sys.stdout.flush() + +# # ====================================================================================================================== +# # =================================== NEAREST FROM NEW FILE =================================================== +# # ====================================================================================================================== + +# test_name = '2.1.4.New_file_nearest' +# if rank == 0: +# print(test_name) + +# # DEFINE PROJECTION +# st_time = timeit.default_timer() +# projection = 'regular' +# lat_orig = 41.1 +# lon_orig = 1.8 +# inc_lat = 0.2 +# inc_lon = 0.2 +# n_lat = 100 +# n_lon = 100 + +# # SPATIAL JOIN +# # Method can be centroid, nearest and intersection +# nessy = from_shapefile(shapefile_path, method='nearest', projection=projection, +# lat_orig=lat_orig, lon_orig=lon_orig, +# inc_lat=inc_lat, inc_lon=inc_lon, +# n_lat=n_lat, n_lon=n_lon) +# comm.Barrier() +# result.loc['calculate', test_name] = timeit.default_timer() - st_time +# print('FROM SHAPEFILE - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) + +# # ADD TIMEZONES +# timezones = nessy.shapefile['tzid'].values.reshape([nessy.lat['data'].shape[0], +# nessy.lon['data'].shape[-1]]) +# timezones = np.repeat(timezones[np.newaxis, :, :], [len(nessy.lev['data'])], axis=0) +# timezones = np.repeat(timezones[np.newaxis, :, :, :], [len(nessy.time)], axis=0) +# nessy.variables['tz'] = {'data': timezones,} + +# # WRITE +# st_time = timeit.default_timer() +# nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True) +# comm.Barrier() +# result.loc['write', test_name] = timeit.default_timer() - st_time + +# comm.Barrier() +# if rank == 0: +# print(result.loc[:, test_name]) +# sys.stdout.flush() + + +# # ====================================================================================================================== +# # =================================== INTERSECTION EXISTING FILE =================================================== +# # ====================================================================================================================== + +# test_name = '2.1.5.Existing_file_intersection' +# if rank == 0: +# print(test_name) -# READ -st_time = timeit.default_timer() -nessy = open_netcdf(original_path, parallel_method=parallel_method) -comm.Barrier() -result.loc['read', test_name] = timeit.default_timer() - st_time - -# SPATIAL JOIN -# Method can be centroid, nearest and intersection -st_time = timeit.default_timer() -nessy.spatial_join(shapefile_path, method='intersection') -comm.Barrier() -result.loc['calculate', test_name] = timeit.default_timer() - st_time -print('SPATIAL JOIN - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) - -# ADD TIMEZONES -timezones = nessy.shapefile['tzid'].values.reshape([nessy.lat['data'].shape[0], - nessy.lon['data'].shape[-1]]) -timezones = np.repeat(timezones[np.newaxis, :, :], [len(nessy.lev['data'])], axis=0) -timezones = np.repeat(timezones[np.newaxis, :, :, :], [len(nessy.time)], axis=0) -nessy.variables['tz'] = {'data': timezones,} - -# WRITE -st_time = timeit.default_timer() -nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True) -comm.Barrier() -result.loc['write', test_name] = timeit.default_timer() - st_time - -comm.Barrier() -if rank == 0: - print(result.loc[:, test_name]) -sys.stdout.flush() - - -# ====================================================================================================================== -# =================================== INTERSECTION FROM NEW FILE =================================================== -# ====================================================================================================================== - -test_name = '2.1.6.New_file_intersection' -if rank == 0: - print(test_name) - -# DEFINE PROJECTION -st_time = timeit.default_timer() -projection = 'regular' -lat_orig = 41.1 -lon_orig = 1.8 -inc_lat = 0.2 -inc_lon = 0.2 -n_lat = 100 -n_lon = 100 - -# SPATIAL JOIN -# Method can be centroid, nearest and intersection -nessy = from_shapefile(shapefile_path, method='intersection', projection=projection, - lat_orig=lat_orig, lon_orig=lon_orig, - inc_lat=inc_lat, inc_lon=inc_lon, - n_lat=n_lat, n_lon=n_lon) -comm.Barrier() -result.loc['calculate', test_name] = timeit.default_timer() - st_time -print('FROM SHAPEFILE - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) - -# ADD TIMEZONES -timezones = nessy.shapefile['tzid'].values.reshape([nessy.lat['data'].shape[0], - nessy.lon['data'].shape[-1]]) -timezones = np.repeat(timezones[np.newaxis, :, :], [len(nessy.lev['data'])], axis=0) -timezones = np.repeat(timezones[np.newaxis, :, :, :], [len(nessy.time)], axis=0) -nessy.variables['tz'] = {'data': timezones,} - -# WRITE -st_time = timeit.default_timer() -nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True) -comm.Barrier() -result.loc['write', test_name] = timeit.default_timer() - st_time - -comm.Barrier() -if rank == 0: - print(result.loc[:, test_name]) -sys.stdout.flush() - -if rank == 0: - result.to_csv(result_path) +# # READ +# st_time = timeit.default_timer() +# nessy = open_netcdf(original_path, parallel_method=parallel_method) +# comm.Barrier() +# result.loc['read', test_name] = timeit.default_timer() - st_time + +# # SPATIAL JOIN +# # Method can be centroid, nearest and intersection +# st_time = timeit.default_timer() +# nessy.spatial_join(shapefile_path, method='intersection') +# comm.Barrier() +# result.loc['calculate', test_name] = timeit.default_timer() - st_time +# print('SPATIAL JOIN - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) + +# # ADD TIMEZONES +# timezones = nessy.shapefile['tzid'].values.reshape([nessy.lat['data'].shape[0], +# nessy.lon['data'].shape[-1]]) +# timezones = np.repeat(timezones[np.newaxis, :, :], [len(nessy.lev['data'])], axis=0) +# timezones = np.repeat(timezones[np.newaxis, :, :, :], [len(nessy.time)], axis=0) +# nessy.variables['tz'] = {'data': timezones,} + +# # WRITE +# st_time = timeit.default_timer() +# nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True) +# comm.Barrier() +# result.loc['write', test_name] = timeit.default_timer() - st_time + +# comm.Barrier() +# if rank == 0: +# print(result.loc[:, test_name]) +# sys.stdout.flush() + + +# # ====================================================================================================================== +# # =================================== INTERSECTION FROM NEW FILE =================================================== +# # ====================================================================================================================== + +# test_name = '2.1.6.New_file_intersection' +# if rank == 0: +# print(test_name) + +# # DEFINE PROJECTION +# st_time = timeit.default_timer() +# projection = 'regular' +# lat_orig = 41.1 +# lon_orig = 1.8 +# inc_lat = 0.2 +# inc_lon = 0.2 +# n_lat = 100 +# n_lon = 100 + +# # SPATIAL JOIN +# # Method can be centroid, nearest and intersection +# nessy = from_shapefile(shapefile_path, method='intersection', projection=projection, +# lat_orig=lat_orig, lon_orig=lon_orig, +# inc_lat=inc_lat, inc_lon=inc_lon, +# n_lat=n_lat, n_lon=n_lon) +# comm.Barrier() +# result.loc['calculate', test_name] = timeit.default_timer() - st_time +# print('FROM SHAPEFILE - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) + +# # ADD TIMEZONES +# timezones = nessy.shapefile['tzid'].values.reshape([nessy.lat['data'].shape[0], +# nessy.lon['data'].shape[-1]]) +# timezones = np.repeat(timezones[np.newaxis, :, :], [len(nessy.lev['data'])], axis=0) +# timezones = np.repeat(timezones[np.newaxis, :, :, :], [len(nessy.time)], axis=0) +# nessy.variables['tz'] = {'data': timezones,} + +# # WRITE +# st_time = timeit.default_timer() +# nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True) +# comm.Barrier() +# result.loc['write', test_name] = timeit.default_timer() - st_time + +# comm.Barrier() +# if rank == 0: +# print(result.loc[:, test_name]) +# sys.stdout.flush() + +# if rank == 0: +# result.to_csv(result_path) -- GitLab From e9999299a339511d47076a3234626f2889f93f36 Mon Sep 17 00:00:00 2001 From: Alba Vilanova Date: Tue, 7 Mar 2023 15:18:22 +0100 Subject: [PATCH 07/43] Remove serial=True and print --- tests/2.4-test_cell_area.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/tests/2.4-test_cell_area.py b/tests/2.4-test_cell_area.py index bd4fb39..fde0b56 100644 --- a/tests/2.4-test_cell_area.py +++ b/tests/2.4-test_cell_area.py @@ -54,8 +54,7 @@ print('Rank {0:03d}: Calculate grid cell area: {1}'.format(rank, nessy.cell_meas # WRITE st_time = timeit.default_timer() -nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), serial=True) -print("SERIE "*20) +nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size)) comm.Barrier() result.loc['write', test_name] = timeit.default_timer() - st_time -- GitLab From 7e261bbbf1cef38b6cc228f35509314915281f5e Mon Sep 17 00:00:00 2001 From: ctena Date: Tue, 7 Mar 2023 18:18:16 +0100 Subject: [PATCH 08/43] Horizontal Interpolation Conservative improvement usage of memory --- nes/methods/horizontal_interpolation.py | 103 ++++++++++---------- tests/3.3-test_horiz_interp_conservative.py | 26 +++-- 2 files changed, 73 insertions(+), 56 deletions(-) diff --git a/nes/methods/horizontal_interpolation.py b/nes/methods/horizontal_interpolation.py index 897327f..58a2abc 100644 --- a/nes/methods/horizontal_interpolation.py +++ b/nes/methods/horizontal_interpolation.py @@ -4,6 +4,7 @@ import sys import warnings import numpy as np import pandas as pd +from geopandas import GeoSeries import os import nes from mpi4py import MPI @@ -13,6 +14,8 @@ from datetime import datetime from warnings import warn import copy import pyproj +import gc +import psutil # CONSTANTS NEAREST_OPTS = ['NearestNeighbour', 'NearestNeighbours', 'nn', 'NN'] @@ -33,7 +36,7 @@ def interpolate_horizontal(self, dst_grid, weight_matrix_path=None, kind='Neares weight_matrix_path : str, None Path to the weight matrix to read/create. kind : str - Kind of horizontal methods. Accepted values: ['NearestNeighbour', 'Conservative']. + Kind of horizontal interpolation. Accepted values: ['NearestNeighbour', 'Conservative']. n_neighbours : int Used if kind == NearestNeighbour. Number of nearest neighbours to interpolate. Default: 4. info : bool @@ -94,7 +97,7 @@ def interpolate_horizontal(self, dst_grid, weight_matrix_path=None, kind='Neares # Apply weights for var_name, var_info in self.variables.items(): if info and self.master: - print("\t{var} horizontal methods".format(var=var_name)) + print("\t{var} horizontal interpolation".format(var=var_name)) sys.stdout.flush() src_shape = var_info['data'].shape if isinstance(dst_grid, nes.PointsNes): @@ -207,7 +210,7 @@ def get_weights_idx_t_axis(self, dst_grid, weight_matrix_path, kind, n_neighbour weight_matrix_path : str, None Path to the weight matrix to read/create. kind : str - Kind of horizontal methods. Accepted values: ['NearestNeighbour', 'Conservative']. + Kind of horizontal interpolation. Accepted values: ['NearestNeighbour', 'Conservative']. n_neighbours : int Used if kind == NearestNeighbour. Number of nearest neighbours to interpolate. Default: 4. only_create : bool @@ -301,7 +304,7 @@ def get_weights_idx_xy_axis(self, dst_grid, weight_matrix_path, kind, n_neighbou weight_matrix_path : str, None Path to the weight matrix to read/create. kind : str - Kind of horizontal methods. Accepted values: ['NearestNeighbour', 'Conservative']. + Kind of horizontal interpolation. Accepted values: ['NearestNeighbour', 'Conservative']. n_neighbours : int Used if kind == NearestNeighbour. Number of nearest neighbours to interpolate. Default: 4. only_create : bool @@ -540,83 +543,83 @@ def create_area_conservative_weight_matrix(self, dst_nes, wm_path=None, info=Fal if info and self.master: print("\tCreating area conservative Weight Matrix") sys.stdout.flush() + + my_crs = pyproj.CRS.from_proj4("+proj=latlon") # Common projection for both shapefiles + # Get a portion of the destiny grid if dst_nes.shapefile is None: dst_nes.create_shapefile() dst_grid = copy.deepcopy(dst_nes.shapefile) - # Get the complete source grid + # Formatting Destination grid + dst_grid.to_crs(crs=my_crs, inplace=True) + dst_grid['FID_dst'] = dst_grid.index + + # Preparing Source grid if self.shapefile is None: self.create_shapefile() src_grid = copy.deepcopy(self.shapefile) - if self.parallel_method == 'T': - # All process has the same shapefile - pass - else: + # Formatting Source grid + src_grid.to_crs(crs=my_crs, inplace=True) + + # Serialize index intersection function to avoid memory problems + if self.size > 1 and self.parallel_method != 'T': src_grid = self.comm.gather(src_grid, root=0) + dst_grid = self.comm.gather(dst_grid, root=0) if self.master: src_grid = pd.concat(src_grid) - src_grid = self.comm.bcast(src_grid) - - my_crs = pyproj.CRS.from_proj4("+proj=latlon") - # Normalizing projections - dst_grid.to_crs(crs=my_crs, inplace=True) - dst_grid['FID_dst'] = dst_grid.index - dst_grid = dst_grid.reset_index() - - # src_grid.to_crs(crs=pyproj.Proj(proj='geocent', ellps='WGS84', datum='WGS84').crs, inplace=True) - src_grid.to_crs(crs=my_crs, inplace=True) - src_grid['FID_src'] = src_grid.index + dst_grid = pd.concat(dst_grid) + if self.master: + src_grid['FID_src'] = src_grid.index + src_grid = src_grid.reset_index() + dst_grid = dst_grid.reset_index() + fid_src, fid_dst = dst_grid.sindex.query_bulk(src_grid.geometry, predicate='intersects') + + # Calculate intersected areas and fractions + intersection_df = pd.DataFrame(columns=["FID_src", "FID_dst"]) + + intersection_df['FID_src'] = np.array(src_grid.loc[fid_src, 'FID_src'], dtype=np.uint32) + intersection_df['FID_dst'] = np.array(dst_grid.loc[fid_dst, 'FID_dst'], dtype=np.uint32) + + intersection_df['geometry_src'] = src_grid.loc[fid_src, 'geometry'].values + intersection_df['geometry_dst'] = dst_grid.loc[fid_dst, 'geometry'].values + del src_grid, dst_grid, fid_src, fid_dst + # Split the array into smaller arrays in order to scatter the data among the processes + intersection_df = np.array_split(intersection_df, self.size) + else: + intersection_df = None - src_grid = src_grid.reset_index() + intersection_df = self.comm.scatter(intersection_df, root=0) if info and self.master: print("\t\tGrids created and ready to interpolate") sys.stdout.flush() - - # Get intersected areas - inp, res = dst_grid.sindex.query_bulk(src_grid.geometry, predicate='intersects') - - # Calculate intersected areas and fractions - intersection = pd.DataFrame(columns=["FID_src", "FID_dst"]) - intersection['INP'] = np.array(inp, dtype=np.uint32) - intersection['RES'] = np.array(res, dtype=np.uint32) - intersection['FID_src'] = np.array(src_grid.loc[inp, 'FID_src'], dtype=np.uint32) - intersection['FID_dst'] = np.array(dst_grid.loc[res, 'FID_dst'], dtype=np.uint32) - - intersection['geometry_src'] = src_grid.loc[inp, 'geometry'].values - intersection['geometry_dst'] = dst_grid.loc[res, 'geometry'].values - if True: # No Warnings Zone warnings.filterwarnings('ignore') - intersection['intersect_area'] = intersection.apply( - lambda x: x['geometry_src'].intersection(x['geometry_dst']).buffer(0).area, axis=1) - intersection.drop(intersection[intersection['intersect_area'] <= 0].index, inplace=True) - - intersection["src_area"] = src_grid.loc[intersection['FID_src'], 'geometry'].area.values + intersection_df['weight'] = np.array(intersection_df.apply( + lambda x: x['geometry_src'].intersection(x['geometry_dst']).buffer(0).area / x['geometry_src'].area, + axis=1), dtype=np.float64) + intersection_df.drop(columns=["geometry_src", "geometry_dst"], inplace=True) + gc.collect() warnings.filterwarnings('default') - intersection['weight'] = intersection['intersect_area'] / intersection["src_area"] - # Format & Clean - intersection.drop(columns=["geometry_src", "geometry_dst", "src_area", "intersect_area"], inplace=True) - if info and self.master: print("\t\tWeights calculated. Formatting weight matrix.") sys.stdout.flush() # Initialising weight matrix if self.parallel_method != 'T': - intersection = self.comm.gather(intersection, root=0) + intersection_df = self.comm.gather(intersection_df, root=0) if self.master: if self.parallel_method != 'T': - intersection = pd.concat(intersection) - intersection = intersection.set_index(['FID_dst', intersection.groupby('FID_dst').cumcount()]).rename_axis( + intersection_df = pd.concat(intersection_df) + intersection_df = intersection_df.set_index(['FID_dst', intersection_df.groupby('FID_dst').cumcount()]).rename_axis( ('FID', 'level')).sort_index() - intersection.rename(columns={"FID_src": "idx"}, inplace=True) + intersection_df.rename(columns={"FID_src": "idx"}, inplace=True) weight_matrix = dst_nes.copy() weight_matrix.time = [datetime(year=2000, month=1, day=1, hour=0, second=0, microsecond=0)] weight_matrix._time = [datetime(year=2000, month=1, day=1, hour=0, second=0, microsecond=0)] @@ -629,7 +632,7 @@ def create_area_conservative_weight_matrix(self, dst_nes, wm_path=None, info=Fal weight_matrix.set_communicator(MPI.COMM_SELF) - weight_matrix.set_levels({'data': np.arange(intersection.index.get_level_values('level').max() + 1), + weight_matrix.set_levels({'data': np.arange(intersection_df.index.get_level_values('level').max() + 1), 'dimensions': ('lev',), 'units': '', 'positive': 'up'}) @@ -653,7 +656,7 @@ def create_area_conservative_weight_matrix(self, dst_nes, wm_path=None, info=Fal # Filling Weight matrix variables for aux_lev in weight_matrix.lev['data']: - aux_data = intersection.xs(level='level', key=aux_lev) + aux_data = intersection_df.xs(level='level', key=aux_lev) weight_matrix.variables['weight']['data'][0, aux_lev, aux_data.index] = aux_data.loc[:, 'weight'].values weight_matrix.variables['idx']['data'][0, aux_lev, aux_data.index] = aux_data.loc[:, 'idx'].values # Re-shaping diff --git a/tests/3.3-test_horiz_interp_conservative.py b/tests/3.3-test_horiz_interp_conservative.py index 5198124..a10f27d 100644 --- a/tests/3.3-test_horiz_interp_conservative.py +++ b/tests/3.3-test_horiz_interp_conservative.py @@ -16,9 +16,12 @@ result_path = "Times_test_3.3_horiz_interp_conservative.py_{0}_{1:03d}.csv".form result = pd.DataFrame(index=['read', 'calculate', 'write'], columns=['3.3.1.Only interp', '3.3.2.Create_WM', "3.3.3.Use_WM", "3.3.4.Read_WM"]) -# NAMEE src_path = "/gpfs/projects/bsc32/models/NES_tutorial_data/MONARCH_d01_2022111512.nc" +src_type = 'NAMEE' var_list = ['O3'] +# src_path = "/gpfs/projects/bsc32/models/NES_tutorial_data/nox_no_201505.nc" +# src_type = 'CAMS_glob_antv21' +# var_list = ['nox_no'] # ====================================================================================================================== # ====================================== Only interp ===================================================== @@ -52,13 +55,15 @@ y_0 = -797137.125 dst_nes = create_nes(comm=None, info=False, projection='lcc', lat_1=lat_1, lat_2=lat_2, lon_0=lon_0, lat_0=lat_0, nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0, parallel_method=parallel_method, times=src_nes.get_full_times()) +dst_type = "IP" + comm.Barrier() result.loc['read', test_name] = timeit.default_timer() - st_time st_time = timeit.default_timer() # INTERPOLATE -interp_nes = src_nes.interpolate_horizontal(dst_grid=dst_nes, kind='Conservative') +interp_nes = src_nes.interpolate_horizontal(dst_grid=dst_nes, kind='Conservative', info=False) # interp_nes = src_nes.interpolate_horizontal(dst_grid=dst_nes, kind='Conservative', weight_matrix_path='T_WM.nc') comm.Barrier() result.loc['calculate', test_name] = timeit.default_timer() - st_time @@ -104,18 +109,21 @@ y_0 = -797137.125 dst_nes = create_nes(comm=None, info=False, projection='lcc', lat_1=lat_1, lat_2=lat_2, lon_0=lon_0, lat_0=lat_0, nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0, parallel_method=parallel_method, times=src_nes.get_full_times()) +dst_type = "IP" + comm.Barrier() result.loc['read', test_name] = timeit.default_timer() - st_time # Cleaning WM -if os.path.exists("CONS_WM_NAMEE_to_IP.nc") and rank == 0: - os.remove("CONS_WM_NAMEE_to_IP.nc") +if os.path.exists("CONS_WM_{0}_to_{1}.nc".format(src_type, dst_type)) and rank == 0: + os.remove("CONS_WM_{0}_to_{1}.nc".format(src_type, dst_type)) comm.Barrier() # INTERPOLATE st_time = timeit.default_timer() wm_nes = src_nes.interpolate_horizontal(dst_grid=dst_nes, kind='Conservative', info=True, - weight_matrix_path="CONS_WM_NAMEE_to_IP.nc", only_create_wm=True) + weight_matrix_path="CONS_WM_{0}_to_{1}.nc".format(src_type, dst_type), + only_create_wm=True) comm.Barrier() result.loc['calculate', test_name] = timeit.default_timer() - st_time @@ -160,6 +168,8 @@ y_0 = -797137.125 dst_nes = create_nes(comm=None, info=False, projection='lcc', lat_1=lat_1, lat_2=lat_2, lon_0=lon_0, lat_0=lat_0, nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0, parallel_method=parallel_method, times=src_nes.get_full_times()) +dst_type = "IP" + comm.Barrier() result.loc['read', test_name] = timeit.default_timer() - st_time @@ -210,12 +220,15 @@ y_0 = -797137.125 dst_nes = create_nes(comm=None, info=False, projection='lcc', lat_1=lat_1, lat_2=lat_2, lon_0=lon_0, lat_0=lat_0, nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0, parallel_method=parallel_method, times=src_nes.get_full_times()) +dst_type = "IP" + comm.Barrier() result.loc['read', test_name] = timeit.default_timer() - st_time # INTERPOLATE st_time = timeit.default_timer() -interp_nes = src_nes.interpolate_horizontal(dst_grid=dst_nes, kind='Conservative', weight_matrix_path="CONS_WM_NAMEE_to_IP.nc") +interp_nes = src_nes.interpolate_horizontal(dst_grid=dst_nes, kind='Conservative', + weight_matrix_path="CONS_WM_{0}_to_{1}.nc".format(src_type, dst_type)) comm.Barrier() result.loc['calculate', test_name] = timeit.default_timer() - st_time @@ -232,3 +245,4 @@ sys.stdout.flush() if rank == 0: result.to_csv(result_path) + print("TEST PASSED SUCCESSFULLY!!!!!") -- GitLab From 70331ec01f8897891bf6b31716a606ef7d0a7e10 Mon Sep 17 00:00:00 2001 From: ctena Date: Tue, 7 Mar 2023 18:20:52 +0100 Subject: [PATCH 09/43] Horizontal Interpolation Conservative improvement usage of memory (CHANGELOG) --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 663be2a..c79027a 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -5,6 +5,7 @@ * Changes and new features: * Bugs fixing: * Bug on `cell_methods` serial write ([#53](https://earth.bsc.es/gitlab/es/NES/-/issues/53)) + * Horizontal Interpolation Conservative: Improvement on memory usage when calculating the weight matrix ([#54](https://earth.bsc.es/gitlab/es/NES/-/issues/54)) ### 1.1.0 * Release date: 2023/03/02 -- GitLab From 6648cfe4888fffb0c5b8c40f72d5be2d3cfd6a8b Mon Sep 17 00:00:00 2001 From: Alba Vilanova Date: Wed, 8 Mar 2023 12:47:37 +0100 Subject: [PATCH 10/43] #49 Write 2D string data --- nes/nc_projections/default_nes.py | 106 +- tests/2.1-test_spatial_join.py | 461 ++--- tutorials/5.Geospatial/5.2.Spatial_Join.ipynb | 1485 ++++++----------- 3 files changed, 827 insertions(+), 1225 deletions(-) diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py index ac0daaf..38a8f8a 100644 --- a/nes/nc_projections/default_nes.py +++ b/nes/nc_projections/default_nes.py @@ -88,7 +88,7 @@ class Nes(object): """ def __init__(self, comm=None, path=None, info=False, dataset=None, xarray=False, parallel_method='Y', avoid_first_hours=0, avoid_last_hours=0, first_level=0, last_level=None, create_nes=False, - balanced=False, times=None, **kwargs): + balanced=False, times=None, strlen=75, **kwargs): """ Initialize the Nes class @@ -246,6 +246,9 @@ class Nes(object): # Writing options self.zip_lvl = 0 + # Get string length + self.strlen = strlen + # Dimensions information self._var_dim = None self._lat_dim = None @@ -318,6 +321,7 @@ class Nes(object): del self.lat_bnds del self._lon_bnds del self.lon_bnds + del self.strlen del self.shapefile for cell_measure in self.cell_measures.keys(): if self.cell_measures[cell_measure]['data'] is not None: @@ -1797,10 +1801,21 @@ class Nes(object): self.read_axis_limits['x_min']:self.read_axis_limits['x_max']] data = data.reshape(1, 1, data.shape[-2], data.shape[-1]) elif len(var_dims) == 3: - data = nc_var[self.read_axis_limits['t_min']:self.read_axis_limits['t_max'], - self.read_axis_limits['y_min']:self.read_axis_limits['y_max'], - self.read_axis_limits['x_min']:self.read_axis_limits['x_max']] - data = data.reshape(data.shape[-3], 1, data.shape[-2], data.shape[-1]) + if 'strlen' in var_dims: + data = nc_var[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'], + self.read_axis_limits['x_min']:self.read_axis_limits['x_max'], + :] + data_aux = np.empty(shape=(data.shape[0], data.shape[1]), dtype=np.object) + for lat_n in range(data.shape[0]): + for lon_n in range(data.shape[1]): + data_aux[lat_n, lon_n] = ''.join( + data[lat_n, lon_n].tostring().decode('ascii').replace('\x00', '')) + data = data_aux.reshape(1, 1, data_aux.shape[-2], data_aux.shape[-1]) + else: + data = nc_var[self.read_axis_limits['t_min']:self.read_axis_limits['t_max'], + self.read_axis_limits['y_min']:self.read_axis_limits['y_max'], + self.read_axis_limits['x_min']:self.read_axis_limits['x_max']] + data = data.reshape(data.shape[-3], 1, data.shape[-2], data.shape[-1]) elif len(var_dims) == 4: data = nc_var[self.read_axis_limits['t_min']:self.read_axis_limits['t_max'], self.read_axis_limits['z_min']:self.read_axis_limits['z_max'], @@ -2077,6 +2092,9 @@ class Nes(object): netcdf.createDimension('lon', len(self._lon['data'])) netcdf.createDimension('lat', len(self._lat['data'])) + # Create string length dimension + netcdf.createDimension('strlen', self.strlen) + return None def _create_dimension_variables(self, netcdf): @@ -2199,7 +2217,10 @@ class Nes(object): if var_dict['data'] is not None: # Get dimensions - var_dims = ('time', 'lev',) + self._var_dim + if var_dict['data'].shape == 4: + var_dims = ('time', 'lev',) + self._var_dim + else: + var_dims = self._var_dim # Get data type if 'dtype' in var_dict.keys(): @@ -2222,28 +2243,29 @@ class Nes(object): var_dict['data'] = var_dict['data'].astype(str) var_dtype = var_dict['data'].dtype - print(var_dtype) - print(var_dict['data'].dtype) - print('first', var_dict['data']) # Convert list of strings to chars for parallelization try: unicode_type = len(max(var_dict['data'].flatten(), key=len)) - print('Unicode', unicode_type) - print(var_dict['data'].dtype == np.dtype('" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots(1, figsize=(20, 7))\n", "\n", @@ -1616,9 +1031,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "fig, ax = plt.subplots(1, figsize=(20, 7))\n", "\n", -- GitLab From e03c30b9d3bd0a5c41e2efd294e02f77e22fda7f Mon Sep 17 00:00:00 2001 From: Alba Vilanova Date: Wed, 8 Mar 2023 13:16:20 +0100 Subject: [PATCH 11/43] Add final prints to tests --- tests/1.1-test_read_write_projection.py | 4 ++++ tests/1.2-test_create_projection.py | 1 + tests/1.3-test_selecting.py | 2 +- tests/2.1-test_spatial_join.py | 1 + tests/2.2-test_create_shapefile.py | 1 + tests/2.3-test_bounds.py | 1 + tests/2.4-test_cell_area.py | 2 +- tests/3.1-test_vertical_interp.py | 1 + tests/3.2-test_horiz_interp_bilinear.py | 1 + tests/4.1-test_daily_stats.py | 1 + 10 files changed, 13 insertions(+), 2 deletions(-) diff --git a/tests/1.1-test_read_write_projection.py b/tests/1.1-test_read_write_projection.py index 37fd9cd..cd47aae 100644 --- a/tests/1.1-test_read_write_projection.py +++ b/tests/1.1-test_read_write_projection.py @@ -214,3 +214,7 @@ comm.Barrier() if rank == 0: print(result.loc[:, test_name]) sys.stdout.flush() + +if rank == 0: + result.to_csv(result_path) + print("TEST PASSED SUCCESSFULLY!!!!!") diff --git a/tests/1.2-test_create_projection.py b/tests/1.2-test_create_projection.py index f6e4125..fe6f0ba 100644 --- a/tests/1.2-test_create_projection.py +++ b/tests/1.2-test_create_projection.py @@ -187,3 +187,4 @@ sys.stdout.flush() if rank == 0: result.to_csv(result_path) + print("TEST PASSED SUCCESSFULLY!!!!!") diff --git a/tests/1.3-test_selecting.py b/tests/1.3-test_selecting.py index 72ab997..68106b1 100644 --- a/tests/1.3-test_selecting.py +++ b/tests/1.3-test_selecting.py @@ -181,4 +181,4 @@ sys.stdout.flush() if rank == 0: result.to_csv(result_path) - print("TEST PASSED SUCCESSFULLY") + print("TEST PASSED SUCCESSFULLY!!!!!") diff --git a/tests/2.1-test_spatial_join.py b/tests/2.1-test_spatial_join.py index 8aedaba..2daab1e 100644 --- a/tests/2.1-test_spatial_join.py +++ b/tests/2.1-test_spatial_join.py @@ -212,3 +212,4 @@ sys.stdout.flush() if rank == 0: result.to_csv(result_path) + print("TEST PASSED SUCCESSFULLY!!!!!") diff --git a/tests/2.2-test_create_shapefile.py b/tests/2.2-test_create_shapefile.py index d41b593..4c44ff0 100644 --- a/tests/2.2-test_create_shapefile.py +++ b/tests/2.2-test_create_shapefile.py @@ -198,3 +198,4 @@ sys.stdout.flush() if rank == 0: result.to_csv(result_path) + print("TEST PASSED SUCCESSFULLY!!!!!") diff --git a/tests/2.3-test_bounds.py b/tests/2.3-test_bounds.py index 3bee2ed..3e1c85a 100644 --- a/tests/2.3-test_bounds.py +++ b/tests/2.3-test_bounds.py @@ -158,3 +158,4 @@ sys.stdout.flush() if rank == 0: result.to_csv(result_path) + print("TEST PASSED SUCCESSFULLY!!!!!") diff --git a/tests/2.4-test_cell_area.py b/tests/2.4-test_cell_area.py index fde0b56..49821a0 100644 --- a/tests/2.4-test_cell_area.py +++ b/tests/2.4-test_cell_area.py @@ -192,4 +192,4 @@ del nessy if rank == 0: result.to_csv(result_path) - + print("TEST PASSED SUCCESSFULLY!!!!!") diff --git a/tests/3.1-test_vertical_interp.py b/tests/3.1-test_vertical_interp.py index d7bcfed..a285675 100644 --- a/tests/3.1-test_vertical_interp.py +++ b/tests/3.1-test_vertical_interp.py @@ -63,3 +63,4 @@ sys.stdout.flush() if rank == 0: result.to_csv(result_path) + print("TEST PASSED SUCCESSFULLY!!!!!") diff --git a/tests/3.2-test_horiz_interp_bilinear.py b/tests/3.2-test_horiz_interp_bilinear.py index 097085a..3018881 100644 --- a/tests/3.2-test_horiz_interp_bilinear.py +++ b/tests/3.2-test_horiz_interp_bilinear.py @@ -218,3 +218,4 @@ sys.stdout.flush() if rank == 0: result.to_csv(result_path) + print("TEST PASSED SUCCESSFULLY!!!!!") diff --git a/tests/4.1-test_daily_stats.py b/tests/4.1-test_daily_stats.py index dd00407..a32ed62 100644 --- a/tests/4.1-test_daily_stats.py +++ b/tests/4.1-test_daily_stats.py @@ -58,3 +58,4 @@ sys.stdout.flush() if rank == 0: result.to_csv(result_path) + print("TEST PASSED SUCCESSFULLY!!!!!") -- GitLab From ecf8aab99b2289abe2245280b4ae961c9c29cdfc Mon Sep 17 00:00:00 2001 From: Alba Vilanova Date: Wed, 8 Mar 2023 13:19:38 +0100 Subject: [PATCH 12/43] Update CHANGELOG --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index c79027a..02a976a 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -3,6 +3,7 @@ ### 1.1.1 * Release date: ??? * Changes and new features: + * Write 2D string data to save variables from shapefiles after doing a spatial join * Bugs fixing: * Bug on `cell_methods` serial write ([#53](https://earth.bsc.es/gitlab/es/NES/-/issues/53)) * Horizontal Interpolation Conservative: Improvement on memory usage when calculating the weight matrix ([#54](https://earth.bsc.es/gitlab/es/NES/-/issues/54)) -- GitLab From 558abbdb5e8d20e35231453e2b61ff3b6af7f604 Mon Sep 17 00:00:00 2001 From: Alba Vilanova Date: Wed, 8 Mar 2023 13:20:58 +0100 Subject: [PATCH 13/43] Remove import stringtochar --- nes/nc_projections/default_nes.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py index 38a8f8a..7b07598 100644 --- a/nes/nc_projections/default_nes.py +++ b/nes/nc_projections/default_nes.py @@ -6,7 +6,7 @@ import warnings import numpy as np import pandas as pd from xarray import open_dataset -from netCDF4 import Dataset, num2date, date2num, stringtochar +from netCDF4 import Dataset, num2date, date2num from mpi4py import MPI from cfunits import Units from numpy.ma.core import MaskError -- GitLab From 0f4be009ee6b57b4699fc28c00ffc9c4128312aa Mon Sep 17 00:00:00 2001 From: ctena Date: Wed, 8 Mar 2023 17:43:02 +0100 Subject: [PATCH 14/43] Write 2D string data (CHANGELOG) --- CHANGELOG.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 02a976a..8561210 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -3,7 +3,7 @@ ### 1.1.1 * Release date: ??? * Changes and new features: - * Write 2D string data to save variables from shapefiles after doing a spatial join + * Write 2D string data to save variables from shapefiles after doing a spatial join ([#49](https://earth.bsc.es/gitlab/es/NES/-/issues/49)) * Bugs fixing: * Bug on `cell_methods` serial write ([#53](https://earth.bsc.es/gitlab/es/NES/-/issues/53)) * Horizontal Interpolation Conservative: Improvement on memory usage when calculating the weight matrix ([#54](https://earth.bsc.es/gitlab/es/NES/-/issues/54)) -- GitLab From d578e0c1e1a973e133f06a8ff87008afe909deb6 Mon Sep 17 00:00:00 2001 From: ctena Date: Thu, 9 Mar 2023 14:45:39 +0100 Subject: [PATCH 15/43] default_nes.py Clean code style --- nes/nc_projections/default_nes.py | 205 +++++++++++++++--------------- 1 file changed, 102 insertions(+), 103 deletions(-) diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py index 7b07598..6c0ad88 100644 --- a/nes/nc_projections/default_nes.py +++ b/nes/nc_projections/default_nes.py @@ -9,7 +9,6 @@ from xarray import open_dataset from netCDF4 import Dataset, num2date, date2num from mpi4py import MPI from cfunits import Units -from numpy.ma.core import MaskError import geopandas as gpd from shapely.geometry import Polygon, Point from copy import deepcopy, copy @@ -69,7 +68,7 @@ class Nes(object): write_axis_limits : dict Dictionary with the 4D limits of the rank data to write. t_min, t_max, z_min, z_max, y_min, y_max, x_min and x_max. - time : List + time : List[datetime] List of time steps of the rank data. lev : dict Vertical levels dictionary with the portion of 'data' corresponding to the rank values. @@ -79,12 +78,12 @@ class Nes(object): Longitudes dictionary with the portion of 'data' corresponding to the rank values. global_attrs : dict Global attributes with the attribute name as key and data as values. - _var_dim : None, tuple - Tuple with the name of the Y and X dimensions for the variables. - _lat_dim : None, tuple - Tuple with the name of the dimensions of the Latitude values. - _lon_dim : None, tuple - Tuple with the name of the dimensions of the Longitude values. + _var_dim : None or tuple + Name of the Y and X dimensions for the variables. + _lat_dim : None or tuple + Name of the dimensions of the Latitude values. + _lon_dim : None or tuple + Name of the dimensions of the Longitude values. """ def __init__(self, comm=None, path=None, info=False, dataset=None, xarray=False, parallel_method='Y', avoid_first_hours=0, avoid_last_hours=0, first_level=0, last_level=None, create_nes=False, @@ -102,7 +101,7 @@ class Nes(object): Indicates if you want to get reading/writing info. dataset: Dataset NetCDF4-python Dataset to initialize the class. - xarray: bool: + xarray: bool (Not working) Indicates if you want to use xarray as default. parallel_method : str Indicates the parallelization method that you want. Default over Y axis @@ -116,11 +115,11 @@ class Nes(object): Number of hours to remove from last time steps. first_level : int Index of the first level to use. - last_level : int, None + last_level : int or None Index of the last level to use. None if it is the last. create_nes : bool Indicates if you want to create the object from scratch (True) or through an existing file. - times : List, None + times : List[datetime] or None List of times to substitute the current ones while creation. kwargs : Projection dependent parameters to create it from scratch @@ -272,7 +271,7 @@ class Nes(object): Indicates if you want to get reading/writing info. dataset: Dataset NetCDF4-python Dataset to initialize the class. - xarray: bool: + xarray: bool (Not working) Indicates if you want to use xarray as default. avoid_first_hours : int Number of hours to remove from first time steps. @@ -286,7 +285,7 @@ class Nes(object): Balanced dataset cannot be written in chunking mode. first_level : int Index of the first level to use. - last_level : int, None + last_level : int or None Index of the last level to use. None if it is the last. create_nes : bool Indicates if you want to create the object from scratch (True) or through an existing file. @@ -494,7 +493,8 @@ class Nes(object): return None - def create_single_spatial_bounds(self, coordinates, inc, spatial_nv=2, inverse=False): + @staticmethod + def create_single_spatial_bounds(coordinates, inc, spatial_nv=2, inverse=False): """ Calculate the vertices coordinates. @@ -505,7 +505,7 @@ class Nes(object): inc : float Increment between centre values. spatial_nv : int - Non mandatory parameter that informs the number of vertices that the boundaries must have. Default: 2. + Non-mandatory parameter that informs the number of vertices that the boundaries must have. Default: 2. inverse : bool For some grid latitudes. @@ -544,20 +544,14 @@ class Nes(object): inc_lat = np.abs(np.mean(np.diff(self._lat['data']))) lat_bnds = self.create_single_spatial_bounds(self._lat['data'], inc_lat, spatial_nv=2) - self._lat_bnds = {} - self._lat_bnds['data'] = deepcopy(lat_bnds) - self.lat_bnds = {} - self.lat_bnds['data'] = lat_bnds[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'], - :] + self._lat_bnds = {'data': deepcopy(lat_bnds)} + self.lat_bnds = {'data': lat_bnds[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'], :]} inc_lon = np.abs(np.mean(np.diff(self._lon['data']))) lon_bnds = self.create_single_spatial_bounds(self._lon['data'], inc_lon, spatial_nv=2) - self._lon_bnds = {} - self._lon_bnds['data'] = deepcopy(lon_bnds) - self.lon_bnds = {} - self.lon_bnds['data'] = lon_bnds[self.read_axis_limits['x_min']:self.read_axis_limits['x_max'], - :] + self._lon_bnds = {'data': deepcopy(lon_bnds)} + self.lon_bnds = {'data': lon_bnds[self.read_axis_limits['x_min']:self.read_axis_limits['x_max'], :]} return None @@ -569,9 +563,9 @@ class Nes(object): Returns ------- - lon_bnds_mesh : numpy.array + lon_bnds_mesh : numpy.ndarray Longitude boundaries in the mesh format - lat_bnds_mesh : numpy.array + lat_bnds_mesh : numpy.ndarray Latitude boundaries in the mesh format """ if self.size > 1: @@ -601,17 +595,17 @@ class Nes(object): lon_bnds_mesh[1:, 1:] = self.lon_bnds['data'][:, :, 2] lon_bnds_mesh[1:, :-1] = self.lon_bnds['data'][:, :, 3] else: - raise RuntimeError("Invalid number of vertices: {0}".format(self.lat_bnds['data'].shape[-1] )) + raise RuntimeError("Invalid number of vertices: {0}".format(self.lat_bnds['data'].shape[-1])) return lon_bnds_mesh, lat_bnds_mesh def free_vars(self, var_list): """ - Erase the selected variables from the variables information. + Erase the selected variables from the variables' information. Parameters ---------- - var_list : List, str, list + var_list : List or str List (or single string) of the variables to be loaded. """ @@ -638,7 +632,7 @@ class Nes(object): Parameters ---------- - var_list : List, str + var_list : List or str List (or single string) of the variables to be loaded. """ @@ -666,7 +660,7 @@ class Nes(object): return time_interval - def sel_time(self, time, copy=False): + def sel_time(self, time, do_copy=False): """ To select only one time step. @@ -674,7 +668,7 @@ class Nes(object): ---------- time : datetime.datetime Time stamp to select. - copy : bool + do_copy : bool Indicates if you want a copy with the selected time step (True) or to modify te existing one (False). Returns @@ -683,7 +677,7 @@ class Nes(object): Nes object with the data (and metadata) of the selected time step. """ - if copy: + if do_copy: aux_nessy = self.copy(copy_vars=False) aux_nessy.comm = self.comm else: @@ -982,7 +976,7 @@ class Nes(object): self.set_time_bnds(aux_time_bounds) elif type_op == 'withoutt0': - for var_name, var_info in self.variables.items (): + for var_name, var_info in self.variables.items(): if var_info['data'] is None: self.load(var_name) if op == 'mean': @@ -1197,10 +1191,10 @@ class Nes(object): rows_sum = 0 for proc in range(self.size): - fid_dist[proc] = {'x_min': None, 'x_max': None, - 'y_min': None, 'y_max': None, - 'z_min': None, 'z_max': None, - 't_min': None, 't_max': None} + fid_dist[proc] = {'x_min': 0, 'x_max': None, + 'y_min': 0, 'y_max': None, + 'z_min': 0, 'z_max': None, + 't_min': 0, 't_max': None} if proc < procs_rows_extended: aux_rows = procs_len + 1 else: @@ -1542,9 +1536,9 @@ class Nes(object): Returns ------- lat_bnds : List - List of latitude bounds of the NetCDF data. + Latitude bounds of the NetCDF data. lon_bnds : List - List of longitude bounds of the NetCDF data. + Longitude bounds of the NetCDF data. """ if self.is_xarray: @@ -1554,13 +1548,11 @@ class Nes(object): if self.master: if not create_nes: if 'lat_bnds' in self.netcdf.variables.keys(): - lat_bnds = {} - lat_bnds['data'] = self.netcdf.variables['lat_bnds'][:] + lat_bnds = {'data': self.netcdf.variables['lat_bnds'][:]} else: lat_bnds = None if 'lon_bnds' in self.netcdf.variables.keys(): - lon_bnds = {} - lon_bnds['data'] = self.netcdf.variables['lon_bnds'][:] + lon_bnds = {'data': self.netcdf.variables['lon_bnds'][:]} else: lon_bnds = None else: @@ -1587,21 +1579,21 @@ class Nes(object): Returns ------- - cell_measures : dict + dict Dictionary of cell measures of the NetCDF data. """ - cell_measures = {} + c_measures = {} if self.master: if not create_nes: if 'cell_area' in self.netcdf.variables.keys(): - cell_measures['cell_area'] = {} - cell_measures['cell_area']['data'] = self.netcdf.variables['cell_area'][:] - cell_measures = self.comm.bcast(cell_measures, root=0) + c_measures['cell_area'] = {} + c_measures['cell_area']['data'] = self.netcdf.variables['cell_area'][:] + c_measures = self.comm.bcast(c_measures, root=0) self.free_vars(['cell_area']) - return cell_measures + return c_measures def _get_coordinate_dimension(self, possible_names): """ @@ -1789,6 +1781,7 @@ class Nes(object): data: np.array Portion of the variable data corresponding to the rank. """ + # from numpy.ma.core import MaskError nc_var = self.netcdf.variables[var_name] var_dims = nc_var.dimensions @@ -1810,7 +1803,7 @@ class Nes(object): for lon_n in range(data.shape[1]): data_aux[lat_n, lon_n] = ''.join( data[lat_n, lon_n].tostring().decode('ascii').replace('\x00', '')) - data = data_aux.reshape(1, 1, data_aux.shape[-2], data_aux.shape[-1]) + data = data_aux.reshape((1, 1, data_aux.shape[-2], data_aux.shape[-1])) else: data = nc_var[self.read_axis_limits['t_min']:self.read_axis_limits['t_max'], self.read_axis_limits['y_min']:self.read_axis_limits['y_max'], @@ -1830,11 +1823,11 @@ class Nes(object): else: raise NotImplementedError('Error with {0}. Only can be read netCDF with 4 dimensions or less'.format( var_name)) - # Missing to nan - try: - data[data.shapefile == True] = np.nan - except (AttributeError, MaskError, ValueError): - pass + # # Missing to nan + # try: + # data[data.shapefile == True] = np.nan + # except (AttributeError, MaskError, ValueError): + # pass return data @@ -2042,10 +2035,10 @@ class Nes(object): rows_sum = 0 for proc in range(self.size): - fid_dist[proc] = {'x_min': None, 'x_max': None, - 'y_min': None, 'y_max': None, - 'z_min': None, 'z_max': None, - 't_min': None, 't_max': None} + fid_dist[proc] = {'x_min': 0, 'x_max': None, + 'y_min': 0, 'y_max': None, + 'z_min': 0, 'z_max': None, + 't_min': 0, 't_max': None} if proc < procs_rows_extended: aux_rows = procs_len + 1 else: @@ -2109,7 +2102,7 @@ class Nes(object): # TIMES time_var = netcdf.createVariable('time', np.float64, ('time',), zlib=self.zip_lvl > 0, complevel=self.zip_lvl) - time_var.units = 'hours since {0}'.format( self._time[0].strftime('%Y-%m-%d %H:%M:%S')) + time_var.units = 'hours since {0}'.format(self._time[0].strftime('%Y-%m-%d %H:%M:%S')) time_var.standard_name = 'time' time_var.calendar = 'standard' time_var.long_name = 'time' @@ -2228,13 +2221,13 @@ class Nes(object): if var_dtype != var_dict['data'].dtype: msg = "WARNING!!! " msg += "Different data types for variable {0}. ".format(var_name) - msg += "Input dtype={0}. Data dtype={1}.".format(var_dtype, - var_dict['data'].dtype) + msg += "Input dtype={0}. Data dtype={1}.".format(var_dtype, var_dict['data'].dtype) warnings.warn(msg) try: var_dict['data'] = var_dict['data'].astype(var_dtype) except Exception as e: # TODO: Detect exception - raise e("It was not possible to cast the data to the input dtype.") + print(e) + raise TypeError("It was not possible to cast the data to the input dtype.") else: var_dtype = var_dict['data'].dtype @@ -2246,8 +2239,8 @@ class Nes(object): # Convert list of strings to chars for parallelization try: unicode_type = len(max(var_dict['data'].flatten(), key=len)) - if ((var_dict['data'].dtype == np.dtype(' 1: - data = self._gather_data() + data = self._gather_data(self.variables) + c_measures = self._gather_data(self.cell_measures) if self.master: new_nc = self.copy(copy_vars=False) new_nc.set_communicator(MPI.COMM_SELF) new_nc.variables = data + new_nc.cell_measures = c_measures new_nc.__to_grib2(path, grib_keys, grib_template_path, lat_flip=lat_flip, info=info) else: self.__to_grib2(path, grib_keys, grib_template_path, lat_flip=lat_flip, info=info) @@ -2779,8 +2775,8 @@ class Nes(object): """ Add variables data to shapefile. - var_list : List - List (or single string) of the variables to be loaded and saved in the shapefile. + var_list : List or str + Variables to be loaded and saved in the shapefile. idx_lev : int Index of vertical level for which the data will be saved in the shapefile. idx_time : int @@ -2798,12 +2794,14 @@ class Nes(object): Parameters ---------- - ext_shp : GeoPandasDataFrame, str + ext_shp : GeoPandasDataFrame or str File or path from where the data will be obtained on the intersection. method : str Overlay method. Accepted values: ['nearest', 'intersection', 'centroid']. - var_list : List, None + var_list : List or None Variables that will be included in the resulting shapefile. + info : bool + Indicates if you want to print the process info or no """ if isinstance(ext_shp, str): ext_shp = gpd.read_file(ext_shp) @@ -2837,7 +2835,8 @@ class Nes(object): # From geodetic coordinates (e.g. 4326) to meters (e.g. 4328) to use sjoin_nearest # TODO: Check if the projection 4328 does not distort the coordinates too much - # https://gis.stackexchange.com/questions/372564/userwarning-when-trying-to-get-centroid-from-a-polygon-geopandas + # https://gis.stackexchange.com/questions/372564/ + # userwarning-when-trying-to-get-centroid-from-a-polygon-geopandas ext_shp = ext_shp.to_crs('EPSG:4328') centroids_gdf = centroids_gdf.to_crs('EPSG:4328') @@ -2875,7 +2874,7 @@ class Nes(object): intersection.loc[:, 'weight'] = 1. for i, row in intersection.iterrows(): - if i % 1000 == 0 and self.master and info: + if isinstance(i, int) and i % 1000 == 0 and self.master and info: print('\tRank {0:03d}: {1:.3f} %'.format(self.rank, i*100 / len(intersection))) # Filter to do not calculate percentages over 100% grid cells spatial joint if counts[row['FID']] > 1: @@ -3029,7 +3028,7 @@ class Nes(object): print("Gathering {0}".format(var_name)) shp_len = len(data_list[var_name]['data'].shape) try: - # Collect local array sizes using the high-level mpi4py gather + # Collect local array sizes using the gather communication pattern rank_shapes = np.array(self.comm.gather(data_list[var_name]['data'].shape, root=0)) sendbuf = data_list[var_name]['data'].flatten() sendcounts = np.array(self.comm.gather(len(sendbuf), root=0)) @@ -3151,7 +3150,7 @@ class Nes(object): self : Nes Source Nes object. new_levels : List - List of new vertical levels. + New vertical levels. new_src_vertical kind : str Vertical methods type. -- GitLab From 1ef8a0e9d22eb3f981f9230e42743ac5a3ac9877 Mon Sep 17 00:00:00 2001 From: ctena Date: Thu, 9 Mar 2023 16:40:33 +0100 Subject: [PATCH 16/43] Added sum --- nes/nc_projections/default_nes.py | 47 +++++++++++++--- tests/4.2-test_sum.py | 76 ++++++++++++++++++++++++++ tests/run_scalability_tests_nord3v2.sh | 2 +- tests/test_bash_mn4.cmd | 3 +- tests/test_bash_nord3v2.cmd | 3 +- 5 files changed, 121 insertions(+), 10 deletions(-) create mode 100644 tests/4.2-test_sum.py diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py index 6c0ad88..ec6e3bf 100644 --- a/nes/nc_projections/default_nes.py +++ b/nes/nc_projections/default_nes.py @@ -363,6 +363,35 @@ class Nes(object): return None + def __add__(self, other): + """ + Sum two NES objects + + Parameters + ---------- + other : Nes + Nes to be summed + + Returns + ------- + Nes + Summed Nes object + """ + nessy = self.copy(copy_vars=True) + for var_name in other.variables.keys(): + if var_name not in nessy.variables.keys(): + # Create New variable + nessy.variables[var_name] = deepcopy(other.variables[var_name]) + else: + nessy.variables[var_name]['data'] += other.variables[var_name]['data'] + return nessy + + def __radd__(self, other): + if other == 0 or other is None: + return self + else: + return self.__add__(other) + def copy(self, copy_vars=False): """ Copy the Nes object. @@ -382,7 +411,8 @@ class Nes(object): nessy = deepcopy(self) nessy.netcdf = None if copy_vars: - nessy.variables = nessy._get_lazy_variables() + nessy.set_communicator(self.comm) + nessy.variables = deepcopy(self.variables) nessy.cell_measures = deepcopy(self.cell_measures) else: nessy.variables = {} @@ -2210,7 +2240,7 @@ class Nes(object): if var_dict['data'] is not None: # Get dimensions - if var_dict['data'].shape == 4: + if len(var_dict['data'].shape) == 4: var_dims = ('time', 'lev',) + self._var_dim else: var_dims = self._var_dim @@ -2298,11 +2328,14 @@ class Nes(object): self.write_axis_limits['z_min']:self.write_axis_limits['z_max'], self.write_axis_limits['y_min']:self.write_axis_limits['y_max'], self.write_axis_limits['x_min']:self.write_axis_limits['x_max']] = att_value - except ValueError: - var[self.write_axis_limits['t_min']:self.write_axis_limits['t_max'], - 0, - self.write_axis_limits['y_min']:self.write_axis_limits['y_max'], - self.write_axis_limits['x_min']:self.write_axis_limits['x_max']] = att_value + except ValueError as e: + print(var) + print(att_value) + raise e + # var[self.write_axis_limits['t_min']:self.write_axis_limits['t_max'], + # 0, + # self.write_axis_limits['y_min']:self.write_axis_limits['y_max'], + # self.write_axis_limits['x_min']:self.write_axis_limits['x_max']] = att_value except IndexError: raise IndexError("Different shapes. out_shape={0}, data_shp={1}".format( diff --git a/tests/4.2-test_sum.py b/tests/4.2-test_sum.py new file mode 100644 index 0000000..92302a3 --- /dev/null +++ b/tests/4.2-test_sum.py @@ -0,0 +1,76 @@ +#!/usr/bin/env python + +import sys +from mpi4py import MPI +import pandas as pd +import timeit +import numpy as np +from nes import * + +comm = MPI.COMM_WORLD +rank = comm.Get_rank() +size = comm.Get_size() + +parallel_method = 'Y' + +result_path = "Times_test_4.2_sum_{0}_{1:03d}.csv".format(parallel_method, size) +result = pd.DataFrame(index=['read', 'calculate', 'write'], + columns=['4.2.1.Sum']) + +# ====================================================================================================================== +# =================================== CENTROID FROM NEW FILE =================================================== +# ====================================================================================================================== + +test_name = '4.2.1.Sum' + +if rank == 0: + print(test_name) + +# DEFINE PROJECTION +st_time = timeit.default_timer() +projection = 'regular' +lat_orig = 41.1 +lon_orig = 1.8 +inc_lat = 0.2 +inc_lon = 0.2 +n_lat = 100 +n_lon = 100 + +# SPATIAL JOIN +# Method can be centroid, nearest and intersection +nessy = create_nes(projection=projection, lat_orig=lat_orig, lon_orig=lon_orig, inc_lat=inc_lat, inc_lon=inc_lon, + n_lat=n_lat, n_lon=n_lon) +nessy.variables = {'var_aux': {'data': np.ones((len(nessy.time), len(nessy.lev['data']), + nessy.lat['data'].shape[0], nessy.lon['data'].shape[-1]))}} + +nessy_2 = nessy.copy(copy_vars=True) + +for var_name in nessy_2.variables.keys(): + nessy_2.variables[var_name]['data'] *= 2 + +comm.Barrier() +result.loc['read', test_name] = timeit.default_timer() - st_time + +st_time = timeit.default_timer() +nessy_3 = nessy + nessy_2 +print(nessy_3.time) +print(nessy_3.lev) +print(nessy_3.variables['var_aux']['data'].shape) + +comm.Barrier() +result.loc['calcul', test_name] = timeit.default_timer() - st_time + +# WRITE +st_time = timeit.default_timer() +nessy_3.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True) +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +if rank == 0: + result.to_csv(result_path) + print("TEST PASSED SUCCESSFULLY!!!!!") diff --git a/tests/run_scalability_tests_nord3v2.sh b/tests/run_scalability_tests_nord3v2.sh index 4e6a8fe..c75e601 100644 --- a/tests/run_scalability_tests_nord3v2.sh +++ b/tests/run_scalability_tests_nord3v2.sh @@ -8,7 +8,7 @@ module load Python/3.7.4-GCCcore-8.3.0 module load NES/1.0.0-nord3-v2-foss-2019b-Python-3.7.4 -for EXE in "1.1-test_read_write_projection.py" "1.2-test_create_projection.py" "1.3-test_selecting.py" "2.1-test_spatial_join.py" "2.2-test_create_shapefile.py" "2.3-test_bounds.py" "2.4-test_cell_area.py" "3.1-test_vertical_interp.py" "3.2-test_horiz_interp_bilinear.py" "3.3-test_horiz_interp_conservative.py" "4.1-test_daily_stats.py" +for EXE in "1.1-test_read_write_projection.py" "1.2-test_create_projection.py" "1.3-test_selecting.py" "2.1-test_spatial_join.py" "2.2-test_create_shapefile.py" "2.3-test_bounds.py" "2.4-test_cell_area.py" "3.1-test_vertical_interp.py" "3.2-test_horiz_interp_bilinear.py" "3.3-test_horiz_interp_conservative.py" "4.1-test_daily_stats.py" "4.2-test_sum.py" do for nprocs in 1 2 4 8 16 do diff --git a/tests/test_bash_mn4.cmd b/tests/test_bash_mn4.cmd index f8663a1..491076a 100644 --- a/tests/test_bash_mn4.cmd +++ b/tests/test_bash_mn4.cmd @@ -18,7 +18,7 @@ module use /gpfs/projects/bsc32/software/suselinux/11/modules/all module load NES/1.1.0-mn4-foss-2019b-Python-3.7.4 module load OpenMPI/4.0.5-GCC-8.3.0-mn4 -cd /gpfs/projects/bsc32/models/NES_master/tests +cd /gpfs/projects/bsc32/models/NES_master/tests || exit mpirun --mca mpi_warn_on_fork 0 -np 4 python 1.1-test_read_write_projection.py mpirun --mca mpi_warn_on_fork 0 -np 4 python 1.2-test_create_projection.py @@ -34,3 +34,4 @@ mpirun --mca mpi_warn_on_fork 0 -np 4 python 3.2-test_horiz_interp_bilinear.py mpirun --mca mpi_warn_on_fork 0 -np 4 python 3.3-test_horiz_interp_conservative.py mpirun --mca mpi_warn_on_fork 0 -np 4 python 4.1-test_daily_stats.py +mpirun --mca mpi_warn_on_fork 0 -np 4 python 4.2-test_sum.py diff --git a/tests/test_bash_nord3v2.cmd b/tests/test_bash_nord3v2.cmd index 15f22c9..45a2e3d 100644 --- a/tests/test_bash_nord3v2.cmd +++ b/tests/test_bash_nord3v2.cmd @@ -16,7 +16,7 @@ module purge module load NES/1.1.0-nord3-v2-foss-2019b-Python-3.7.4 -cd /gpfs/projects/bsc32/models/NES_master/tests +cd /gpfs/projects/bsc32/models/NES_master/tests || exit mpirun --mca mpi_warn_on_fork 0 -np 4 python 1.1-test_read_write_projection.py mpirun --mca mpi_warn_on_fork 0 -np 4 python 1.2-test_create_projection.py @@ -32,3 +32,4 @@ mpirun --mca mpi_warn_on_fork 0 -np 4 python 3.2-test_horiz_interp_bilinear.py mpirun --mca mpi_warn_on_fork 0 -np 4 python 3.3-test_horiz_interp_conservative.py mpirun --mca mpi_warn_on_fork 0 -np 4 python 4.1-test_daily_stats.py +mpirun --mca mpi_warn_on_fork 0 -np 4 python 4.2-test_sum.py -- GitLab From ed42eef8207fb7114da527a6f3b819660bb4dd1a Mon Sep 17 00:00:00 2001 From: ctena Date: Fri, 10 Mar 2023 14:11:13 +0100 Subject: [PATCH 17/43] Bugfix on a5rh testing suite experiment case --- nes/nc_projections/default_nes.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py index ec6e3bf..5025d08 100644 --- a/nes/nc_projections/default_nes.py +++ b/nes/nc_projections/default_nes.py @@ -2471,7 +2471,7 @@ class Nes(object): return to_netcdf_cams_ra(self, path) def to_netcdf(self, path, compression_level=0, serial=False, info=False, - chunking=False, nc_type='NES'): + chunking=False, type='NES'): """ Write the netCDF output file. @@ -2487,10 +2487,10 @@ class Nes(object): Indicates if you want to print the information of each writing step by stdout Default: False. chunking : bool Indicates if you want a chunked netCDF output. Only available with non-serial writes. Default: False. - nc_type : str + type : str Type to NetCDf to write. 'CAMS_RA' or 'NES' """ - + nc_type = type old_info = self.info self.info = info @@ -2975,7 +2975,7 @@ class Nes(object): return centroids_gdf - def __gather_data_py_object(self): + def __gather_data_py_object(self, data_to_gather): """ Gather all the variable data into the MPI rank 0 to perform a serial write. @@ -2985,7 +2985,7 @@ class Nes(object): Variables dictionary with all the data from all the ranks. """ - data_list = deepcopy(self.variables) + data_list = deepcopy(data_to_gather) for var_name in data_list.keys(): try: # noinspection PyArgumentList @@ -3066,7 +3066,8 @@ class Nes(object): sendbuf = data_list[var_name]['data'].flatten() sendcounts = np.array(self.comm.gather(len(sendbuf), root=0)) if self.master: - recvbuf = np.empty(sum(sendcounts), dtype=type(sendbuf[0])) + # recvbuf = np.empty(sum(sendcounts), dtype=type(sendbuf[0])) + recvbuf = np.empty(sum(sendcounts), dtype=type(sendbuf.max())) else: recvbuf = None self.comm.Gatherv(sendbuf=sendbuf, recvbuf=(recvbuf, sendcounts), root=0) -- GitLab From 579d372ed2c30ce8035bf5fa96c255757c0af9cb Mon Sep 17 00:00:00 2001 From: Alba Vilanova Date: Mon, 13 Mar 2023 09:53:52 +0100 Subject: [PATCH 18/43] Temporal --- tests/4.2-test_sum.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/4.2-test_sum.py b/tests/4.2-test_sum.py index 92302a3..4d4f87c 100644 --- a/tests/4.2-test_sum.py +++ b/tests/4.2-test_sum.py @@ -58,7 +58,7 @@ print(nessy_3.lev) print(nessy_3.variables['var_aux']['data'].shape) comm.Barrier() -result.loc['calcul', test_name] = timeit.default_timer() - st_time +result.loc['calculate', test_name] = timeit.default_timer() - st_time # WRITE st_time = timeit.default_timer() -- GitLab From 8d5558cfd8887da44f2a355beb257653b3c2117a Mon Sep 17 00:00:00 2001 From: Alba Vilanova Date: Mon, 13 Mar 2023 11:31:41 +0100 Subject: [PATCH 19/43] Add tutorial and fix error in string to char method --- nes/nc_projections/default_nes.py | 52 ++++--- nes/nc_projections/points_nes.py | 40 +++-- nes/nc_projections/points_nes_ghost.py | 40 +++-- nes/nc_projections/points_nes_providentia.py | 40 +++-- tests/4.2-test_sum.py | 14 +- tutorials/3.Statistics/3.2.Sum.ipynb | 154 +++++++++++++++++++ 6 files changed, 269 insertions(+), 71 deletions(-) create mode 100644 tutorials/3.Statistics/3.2.Sum.ipynb diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py index 5025d08..2d05637 100644 --- a/nes/nc_projections/default_nes.py +++ b/nes/nc_projections/default_nes.py @@ -2267,29 +2267,37 @@ class Nes(object): var_dtype = var_dict['data'].dtype # Convert list of strings to chars for parallelization - try: - unicode_type = len(max(var_dict['data'].flatten(), key=len)) - if ((var_dict['data'].dtype == np.dtype(' Date: Mon, 13 Mar 2023 18:43:09 +0100 Subject: [PATCH 20/43] Improved concatenation function --- nes/load_nes.py | 63 ++++++++++++++++++++++++++++++++++++++++--------- 1 file changed, 52 insertions(+), 11 deletions(-) diff --git a/nes/load_nes.py b/nes/load_nes.py index 5bfd333..8de2346 100644 --- a/nes/load_nes.py +++ b/nes/load_nes.py @@ -4,9 +4,12 @@ import os from mpi4py import MPI from netCDF4 import Dataset import warnings - +import numpy as np from .nc_projections import * +DIM_VAR_NAMES = ['lat', 'latitude', 'lat_bnds', 'lon', 'longitude', 'lon_bnds', 'time', 'time_bnds', 'lev', 'level', + 'cell_area', 'crs', 'rotated_pole', 'x', 'y', 'rlat', 'rlon', 'Lambert_conformal', 'mercator'] + def open_netcdf(path, comm=None, xarray=False, info=False, parallel_method='Y', avoid_first_hours=0, avoid_last_hours=0, first_level=0, last_level=None, balanced=False): @@ -270,15 +273,53 @@ def concatenate_netcdfs(nessy_list, comm=None, info=False, parallel_method='Y', nessy_first = nessy_list[0] for i, aux_nessy in enumerate(nessy_list[1:]): if isinstance(aux_nessy, str): - aux_nessy = open_netcdf(aux_nessy, - comm=comm, - parallel_method=parallel_method, - avoid_first_hours=avoid_first_hours, - avoid_last_hours=avoid_last_hours, - first_level=first_level, - last_level=last_level, - balanced=balanced - ) - nessy_first.concatenate(aux_nessy) + nc_add = Dataset(filename=aux_nessy, mode='r') + for var_name, var_info in nc_add.variables.items(): + if var_name not in DIM_VAR_NAMES: + nessy_first.variables[var_name] = {} + var_dims = var_info.dimensions + # Read data in 4 dimensions + if len(var_dims) < 2: + data = var_info[:] + elif len(var_dims) == 2: + data = var_info[nessy_first.read_axis_limits['y_min']:nessy_first.read_axis_limits['y_max'], + nessy_first.read_axis_limits['x_min']:nessy_first.read_axis_limits['x_max']] + data = data.reshape(1, 1, data.shape[-2], data.shape[-1]) + elif len(var_dims) == 3: + if 'strlen' in var_dims: + data = var_info[nessy_first.read_axis_limits['y_min']:nessy_first.read_axis_limits['y_max'], + nessy_first.read_axis_limits['x_min']:nessy_first.read_axis_limits['x_max'], + :] + data_aux = np.empty(shape=(data.shape[0], data.shape[1]), dtype=np.object) + for lat_n in range(data.shape[0]): + for lon_n in range(data.shape[1]): + data_aux[lat_n, lon_n] = ''.join( + data[lat_n, lon_n].tostring().decode('ascii').replace('\x00', '')) + data = data_aux.reshape((1, 1, data_aux.shape[-2], data_aux.shape[-1])) + else: + data = var_info[nessy_first.read_axis_limits['t_min']:nessy_first.read_axis_limits['t_max'], + nessy_first.read_axis_limits['y_min']:nessy_first.read_axis_limits['y_max'], + nessy_first.read_axis_limits['x_min']:nessy_first.read_axis_limits['x_max']] + data = data.reshape(data.shape[-3], 1, data.shape[-2], data.shape[-1]) + elif len(var_dims) == 4: + data = var_info[nessy_first.read_axis_limits['t_min']:nessy_first.read_axis_limits['t_max'], + nessy_first.read_axis_limits['z_min']:nessy_first.read_axis_limits['z_max'], + nessy_first.read_axis_limits['y_min']:nessy_first.read_axis_limits['y_max'], + nessy_first.read_axis_limits['x_min']:nessy_first.read_axis_limits['x_max']] + else: + raise TypeError("{} data shape is nto accepted".format(var_dims)) + + nessy_first.variables[var_name]['data'] = data + # Avoid some attributes + for attrname in var_info.ncattrs(): + if attrname not in ['missing_value', '_FillValue']: + value = getattr(var_info, attrname) + if value in ['unitless', '-']: + value = '' + nessy_first.variables[var_name][attrname] = value + nc_add.close() + + else: + nessy_first.concatenate(aux_nessy) return nessy_first -- GitLab From 74ac5b0cf2cdaf59d61b4cda25c06a6178c96056 Mon Sep 17 00:00:00 2001 From: ctena Date: Mon, 13 Mar 2023 18:44:47 +0100 Subject: [PATCH 21/43] Write: Corrected str write bug & capability to write all 0 data only with an integer provided --- nes/nc_projections/default_nes.py | 126 ++++++++++++++++-------------- 1 file changed, 68 insertions(+), 58 deletions(-) diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py index 2d05637..9585c79 100644 --- a/nes/nc_projections/default_nes.py +++ b/nes/nc_projections/default_nes.py @@ -2238,66 +2238,68 @@ class Nes(object): for i, (var_name, var_dict) in enumerate(self.variables.items()): if var_dict['data'] is not None: - - # Get dimensions - if len(var_dict['data'].shape) == 4: + if isinstance(var_dict['data'], int) and var_dict['data'] == 0: var_dims = ('time', 'lev',) + self._var_dim + var_dtype = np.float32 else: - var_dims = self._var_dim - - # Get data type - if 'dtype' in var_dict.keys(): - var_dtype = var_dict['dtype'] - if var_dtype != var_dict['data'].dtype: - msg = "WARNING!!! " - msg += "Different data types for variable {0}. ".format(var_name) - msg += "Input dtype={0}. Data dtype={1}.".format(var_dtype, var_dict['data'].dtype) - warnings.warn(msg) + # Get dimensions + if len(var_dict['data'].shape) == 4: + var_dims = ('time', 'lev',) + self._var_dim + else: + var_dims = self._var_dim + + # Get data type + if 'dtype' in var_dict.keys(): + var_dtype = var_dict['dtype'] + if var_dtype != var_dict['data'].dtype: + msg = "WARNING!!! " + msg += "Different data types for variable {0}. ".format(var_name) + msg += "Input dtype={0}. Data dtype={1}.".format(var_dtype, var_dict['data'].dtype) + warnings.warn(msg) + try: + var_dict['data'] = var_dict['data'].astype(var_dtype) + except Exception as e: # TODO: Detect exception + print(e) + raise TypeError("It was not possible to cast the data to the input dtype.") + else: + var_dtype = var_dict['data'].dtype + # Transform objects into strings + if var_dtype == np.dtype(object): + var_dict['data'] = var_dict['data'].astype(str) + var_dtype = var_dict['data'].dtype + + # Convert list of strings to chars for parallelization + if not np.issubdtype(var_dict['data'].dtype, np.number): try: - var_dict['data'] = var_dict['data'].astype(var_dtype) - except Exception as e: # TODO: Detect exception + # Get unicode + unicode_type = len(max(var_dict['data'].flatten(), key=len)) + + if ((var_dict['data'].dtype == np.dtype(' Date: Tue, 14 Mar 2023 12:02:39 +0100 Subject: [PATCH 22/43] Correct str write bug in points --- nes/nc_projections/points_nes.py | 2 +- nes/nc_projections/points_nes_ghost.py | 2 +- nes/nc_projections/points_nes_providentia.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/nes/nc_projections/points_nes.py b/nes/nc_projections/points_nes.py index 318302e..f542bac 100644 --- a/nes/nc_projections/points_nes.py +++ b/nes/nc_projections/points_nes.py @@ -368,7 +368,7 @@ class PointsNes(Nes): var_dims = ('time',) + self._var_dim # Convert list of strings to chars for parallelization - if (var_dict['data'].dtype != float) and (var_dict['data'].dtype != int): + if not np.issubdtype(var_dict['data'].dtype, np.number): try: # Get unicode unicode_type = len(max(var_dict['data'].flatten(), key=len)) diff --git a/nes/nc_projections/points_nes_ghost.py b/nes/nc_projections/points_nes_ghost.py index 695ac4b..936cbca 100644 --- a/nes/nc_projections/points_nes_ghost.py +++ b/nes/nc_projections/points_nes_ghost.py @@ -350,7 +350,7 @@ class PointsNesGHOST(PointsNes): var_dims = self._var_dim + ('time',) # Convert list of strings to chars for parallelization - if (var_dict['data'].dtype != float) and (var_dict['data'].dtype != int): + if not np.issubdtype(var_dict['data'].dtype, np.number): try: # Get unicode unicode_type = len(max(var_dict['data'].flatten(), key=len)) diff --git a/nes/nc_projections/points_nes_providentia.py b/nes/nc_projections/points_nes_providentia.py index 3e45308..937d7e6 100644 --- a/nes/nc_projections/points_nes_providentia.py +++ b/nes/nc_projections/points_nes_providentia.py @@ -386,7 +386,7 @@ class PointsNesProvidentia(PointsNes): var_dims = self._var_dim + ('time',) # Convert list of strings to chars for parallelization - if (var_dict['data'].dtype != float) and (var_dict['data'].dtype != int): + if not np.issubdtype(var_dict['data'].dtype, np.number): try: # Get unicode unicode_type = len(max(var_dict['data'].flatten(), key=len)) -- GitLab From 934c2acc1da9bea8ad611cafaba695e054aef486 Mon Sep 17 00:00:00 2001 From: ctena Date: Tue, 14 Mar 2023 16:09:49 +0100 Subject: [PATCH 23/43] Avoiding Numpy MPI gather if KeyError is raised --- nes/nc_projections/default_nes.py | 149 +++++++++++++++--------------- 1 file changed, 74 insertions(+), 75 deletions(-) diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py index 9585c79..afcc874 100644 --- a/nes/nc_projections/default_nes.py +++ b/nes/nc_projections/default_nes.py @@ -2223,6 +2223,7 @@ class Nes(object): for var_name in self.variables.keys(): self.variables[var_name]['cell_measures'] = 'area: cell_area' + return None def _create_variables(self, netcdf, chunking=False): """ @@ -2515,8 +2516,14 @@ class Nes(object): else: # if serial: if serial and self.size > 1: - data = self._gather_data(self.variables) - c_measures = self._gather_data(self.cell_measures) + try: + data = self._gather_data(self.variables) + except KeyError: + data = self.__gather_data_py_object(self.variables) + try: + c_measures = self._gather_data(self.cell_measures) + except KeyError: + c_measures = self.__gather_data_py_object(self.cell_measures) if self.master: new_nc = self.copy(copy_vars=False) new_nc.set_communicator(MPI.COMM_SELF) @@ -2659,8 +2666,14 @@ class Nes(object): # if serial: if self.parallel_method in ['X', 'Y'] and self.size > 1: - data = self._gather_data(self.variables) - c_measures = self._gather_data(self.cell_measures) + try: + data = self._gather_data(self.variables) + except KeyError: + data = self.__gather_data_py_object(self.variables) + try: + c_measures = self._gather_data(self.cell_measures) + except KeyError: + c_measures = self.__gather_data_py_object(self.cell_measures) if self.master: new_nc = self.copy(copy_vars=False) new_nc.set_communicator(MPI.COMM_SELF) @@ -3074,79 +3087,65 @@ class Nes(object): for var_name in data_list.keys(): if self.info and self.master: print("Gathering {0}".format(var_name)) - try: - shp_len = len(data_list[var_name]['data'].shape) - # Collect local array sizes using the gather communication pattern - rank_shapes = np.array(self.comm.gather(data_list[var_name]['data'].shape, root=0)) - sendbuf = data_list[var_name]['data'].flatten() - sendcounts = np.array(self.comm.gather(len(sendbuf), root=0)) - if self.master: - # recvbuf = np.empty(sum(sendcounts), dtype=type(sendbuf[0])) - recvbuf = np.empty(sum(sendcounts), dtype=type(sendbuf.max())) - else: - recvbuf = None - self.comm.Gatherv(sendbuf=sendbuf, recvbuf=(recvbuf, sendcounts), root=0) - if self.master: - recvbuf = np.split(recvbuf, np.cumsum(sendcounts)) - # TODO ask - # I don't understand why it is giving one more split - if len(recvbuf) > len(sendcounts): - recvbuf = recvbuf[:-1] - for i, shape in enumerate(rank_shapes): - recvbuf[i] = recvbuf[i].reshape(shape) - add_dimension = False # to Add a dimension - if self.parallel_method == 'Y': - if shp_len == 2: - # if is a 2D concatenate over first axis - axis = 0 - elif shp_len == 3: - # if is a 3D concatenate over second axis - axis = 1 - else: - # if is a 4D concatenate over third axis - axis = 2 - elif self.parallel_method == 'X': - if shp_len == 2: - # if is a 2D concatenate over second axis - axis = 1 - elif shp_len == 3: - # if is a 3D concatenate over third axis - axis = 2 - else: - # if is a 4D concatenate over forth axis - axis = 3 - elif self.parallel_method == 'T': - if shp_len == 2: - # if is a 2D add dimension - add_dimension = True - axis = None # Not used - elif shp_len == 3: - # if is a 3D concatenate over first axis - axis = 0 - else: - # if is a 4D concatenate over second axis - axis = 0 + shp_len = len(data_list[var_name]['data'].shape) + # Collect local array sizes using the gather communication pattern + rank_shapes = np.array(self.comm.gather(data_list[var_name]['data'].shape, root=0)) + sendbuf = data_list[var_name]['data'].flatten() + sendcounts = np.array(self.comm.gather(len(sendbuf), root=0)) + if self.master: + # recvbuf = np.empty(sum(sendcounts), dtype=type(sendbuf[0])) + recvbuf = np.empty(sum(sendcounts), dtype=type(sendbuf.max())) + else: + recvbuf = None + self.comm.Gatherv(sendbuf=sendbuf, recvbuf=(recvbuf, sendcounts), root=0) + if self.master: + recvbuf = np.split(recvbuf, np.cumsum(sendcounts)) + # TODO ask + # I don't understand why it is giving one more split + if len(recvbuf) > len(sendcounts): + recvbuf = recvbuf[:-1] + for i, shape in enumerate(rank_shapes): + recvbuf[i] = recvbuf[i].reshape(shape) + add_dimension = False # to Add a dimension + if self.parallel_method == 'Y': + if shp_len == 2: + # if is a 2D concatenate over first axis + axis = 0 + elif shp_len == 3: + # if is a 3D concatenate over second axis + axis = 1 else: - raise NotImplementedError( - "Parallel method '{meth}' is not implemented. Use one of these: {accept}".format( - meth=self.parallel_method, accept=['X', 'Y', 'T'])) - if add_dimension: - data_list[var_name]['data'] = np.stack(recvbuf) + # if is a 4D concatenate over third axis + axis = 2 + elif self.parallel_method == 'X': + if shp_len == 2: + # if is a 2D concatenate over second axis + axis = 1 + elif shp_len == 3: + # if is a 3D concatenate over third axis + axis = 2 else: - data_list[var_name]['data'] = np.concatenate(recvbuf, axis=axis) - - except AttributeError: - data_list[var_name]['data'] = 0 - except Exception as e: - print("**ERROR** an error has occurred while gathering the '{0}' variable.\n".format(var_name)) - sys.stderr.write("**ERROR** an error has occurred while gathering the '{0}' variable.\n".format( - var_name)) - print(e) - sys.stderr.write(str(e)) - # print(e, file=sys.stderr) - sys.stderr.flush() - self.comm.Abort(1) - raise e + # if is a 4D concatenate over forth axis + axis = 3 + elif self.parallel_method == 'T': + if shp_len == 2: + # if is a 2D add dimension + add_dimension = True + axis = None # Not used + elif shp_len == 3: + # if is a 3D concatenate over first axis + axis = 0 + else: + # if is a 4D concatenate over second axis + axis = 0 + else: + raise NotImplementedError( + "Parallel method '{meth}' is not implemented. Use one of these: {accept}".format( + meth=self.parallel_method, accept=['X', 'Y', 'T'])) + if add_dimension: + data_list[var_name]['data'] = np.stack(recvbuf) + else: + data_list[var_name]['data'] = np.concatenate(recvbuf, axis=axis) return data_list -- GitLab From 27718292fec4988e300d5bba81baa3e74b93264e Mon Sep 17 00:00:00 2001 From: ctena Date: Tue, 14 Mar 2023 16:25:41 +0100 Subject: [PATCH 24/43] Created set_strlen function to customize the maximum size of the string data --- nes/nc_projections/default_nes.py | 12 ++++++++++++ 1 file changed, 12 insertions(+) diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py index afcc874..010c006 100644 --- a/nes/nc_projections/default_nes.py +++ b/nes/nc_projections/default_nes.py @@ -297,6 +297,18 @@ class Nes(object): return new + def set_strlen(self, strlen=75): + """ + Set the strlen + + Parameters + ---------- + strlen : int + Max length of the string + """ + self.strlen = strlen + return None + def __del__(self): """ To delete the Nes object and close all the open datasets. -- GitLab From a53d898f6bc52fa3d80fe765dc9adaea24b56b24 Mon Sep 17 00:00:00 2001 From: Alba Vilanova Date: Wed, 15 Mar 2023 11:22:59 +0100 Subject: [PATCH 25/43] Fix arguments in gather for points datasets --- nes/nc_projections/default_nes.py | 4 ++-- nes/nc_projections/points_nes.py | 8 ++++---- nes/nc_projections/points_nes_ghost.py | 8 ++++---- nes/nc_projections/points_nes_providentia.py | 8 ++++---- 4 files changed, 14 insertions(+), 14 deletions(-) diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py index 010c006..92220e7 100644 --- a/nes/nc_projections/default_nes.py +++ b/nes/nc_projections/default_nes.py @@ -3091,8 +3091,8 @@ class Nes(object): Returns ------- - data_list: dict - Variables dictionary with all the data from all the ranks. + data_to_gather: dict + Variables to gather. """ data_list = deepcopy(data_to_gather) diff --git a/nes/nc_projections/points_nes.py b/nes/nc_projections/points_nes.py index f542bac..3d55257 100644 --- a/nes/nc_projections/points_nes.py +++ b/nes/nc_projections/points_nes.py @@ -489,16 +489,16 @@ class PointsNes(Nes): return None - def _gather_data(self): + def _gather_data(self, data_to_gather): """ Gather all the variable data into the MPI rank 0 to perform a serial write. Returns ------- - data_list: dict - Variables dictionary with all the data from all the ranks. + data_to_gather: dict + Variables to gather. """ - data_list = deepcopy(self.variables) + data_list = deepcopy(data_to_gather) for var_name, var_info in data_list.items(): try: # noinspection PyArgumentList diff --git a/nes/nc_projections/points_nes_ghost.py b/nes/nc_projections/points_nes_ghost.py index 936cbca..978f4d5 100644 --- a/nes/nc_projections/points_nes_ghost.py +++ b/nes/nc_projections/points_nes_ghost.py @@ -481,17 +481,17 @@ class PointsNesGHOST(PointsNes): return None - def _gather_data(self): + def _gather_data(self, data_to_gather): """ Gather all the variable data into the MPI rank 0 to perform a serial write. Returns ------- - data_list: dict - Variables dictionary with all the data from all the ranks. + data_to_gather: dict + Variables to gather. """ - data_list = deepcopy(self.variables) + data_list = deepcopy(data_to_gather) for var_name, var_info in data_list.items(): try: # noinspection PyArgumentList diff --git a/nes/nc_projections/points_nes_providentia.py b/nes/nc_projections/points_nes_providentia.py index 937d7e6..91229da 100644 --- a/nes/nc_projections/points_nes_providentia.py +++ b/nes/nc_projections/points_nes_providentia.py @@ -517,17 +517,17 @@ class PointsNesProvidentia(PointsNes): return None - def _gather_data(self): + def _gather_data(self, data_to_gather): """ Gather all the variable data into the MPI rank 0 to perform a serial write. Returns ------- - data_list: dict - Variables dictionary with all the data from all the ranks. + data_to_gather: dict + Variables to gather. """ - data_list = deepcopy(self.variables) + data_list = deepcopy(data_to_gather) for var_name, var_info in data_list.items(): try: # noinspection PyArgumentList -- GitLab From 1580fa26851b4b56e857e0525e2a02115927da79 Mon Sep 17 00:00:00 2001 From: ctena Date: Wed, 15 Mar 2023 12:35:45 +0100 Subject: [PATCH 26/43] - CHANGELOG Update - Reverse renaming of sel_time copy argument --- CHANGELOG.md | 2 ++ nes/nc_projections/default_nes.py | 6 +++--- 2 files changed, 5 insertions(+), 3 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 8561210..fc07441 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -3,6 +3,8 @@ ### 1.1.1 * Release date: ??? * Changes and new features: + * Improved time on **concatenate_netcdfs** function ([#55](https://earth.bsc.es/gitlab/es/NES/-/issues/55)) + * Sum of Nes objects ([#48](https://earth.bsc.es/gitlab/es/NES/-/issues/48)) * Write 2D string data to save variables from shapefiles after doing a spatial join ([#49](https://earth.bsc.es/gitlab/es/NES/-/issues/49)) * Bugs fixing: * Bug on `cell_methods` serial write ([#53](https://earth.bsc.es/gitlab/es/NES/-/issues/53)) diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py index 92220e7..060bb6a 100644 --- a/nes/nc_projections/default_nes.py +++ b/nes/nc_projections/default_nes.py @@ -702,7 +702,7 @@ class Nes(object): return time_interval - def sel_time(self, time, do_copy=False): + def sel_time(self, time, copy=False): """ To select only one time step. @@ -710,7 +710,7 @@ class Nes(object): ---------- time : datetime.datetime Time stamp to select. - do_copy : bool + copy : bool Indicates if you want a copy with the selected time step (True) or to modify te existing one (False). Returns @@ -719,7 +719,7 @@ class Nes(object): Nes object with the data (and metadata) of the selected time step. """ - if do_copy: + if copy: aux_nessy = self.copy(copy_vars=False) aux_nessy.comm = self.comm else: -- GitLab From 1a4581217567dd236e9e1d65980dad8a0e7a4905 Mon Sep 17 00:00:00 2001 From: ctena Date: Thu, 16 Mar 2023 18:03:20 +0100 Subject: [PATCH 27/43] Write by time step done --- nes/nc_projections/default_nes.py | 443 ++++++++++--------- tests/4.3-test_write_time_step.py | 149 +++++++ tutorials/6.Others/6.4.WriteByTimeStep.ipynb | 158 +++++++ 3 files changed, 550 insertions(+), 200 deletions(-) create mode 100644 tests/4.3-test_write_time_step.py create mode 100644 tutorials/6.Others/6.4.WriteByTimeStep.ipynb diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py index 060bb6a..0e4de4a 100644 --- a/nes/nc_projections/default_nes.py +++ b/nes/nc_projections/default_nes.py @@ -153,6 +153,7 @@ class Nes(object): # Define parallel method self.parallel_method = parallel_method + self.serial_nc = None # Place to store temporally the serial Nes instance # Get minor and major axes of Earth self.earth_radius = self.get_earth_radius('WGS84') @@ -503,6 +504,22 @@ class Nes(object): return None + def set_time(self, time_list): + """ + Modify the original level values with new ones. + + Parameters + ---------- + time_list : List[datetime] + List of time steps + """ + if self.parallel_method == 'T': + raise TypeError("Cannot set time on a 'T' parallel method") + self._time = deepcopy(time_list) + self.time = deepcopy(time_list) + + return None + def set_time_bnds(self, time_bnds): """ Modify the original time bounds values with new ones. @@ -1401,7 +1418,10 @@ class Nes(object): """ Close the NetCDF with netcdf4-python. """ - + if (hasattr(self, 'serial_nc')) and (self.serial_nc is not None): + if self.master: + self.serial_nc.close() + self.serial_nc = None if (hasattr(self, 'netcdf')) and (self.netcdf is not None): self.netcdf.close() self.netcdf = None @@ -2250,161 +2270,176 @@ class Nes(object): """ for i, (var_name, var_dict) in enumerate(self.variables.items()): - if var_dict['data'] is not None: - if isinstance(var_dict['data'], int) and var_dict['data'] == 0: + if isinstance(var_dict['data'], int) and var_dict['data'] == 0: + var_dims = ('time', 'lev',) + self._var_dim + var_dtype = np.float32 + else: + # Get dimensions + if var_dict['data'] is None or len(var_dict['data'].shape) == 4: var_dims = ('time', 'lev',) + self._var_dim - var_dtype = np.float32 else: - # Get dimensions - if len(var_dict['data'].shape) == 4: - var_dims = ('time', 'lev',) + self._var_dim - else: - var_dims = self._var_dim - - # Get data type - if 'dtype' in var_dict.keys(): - var_dtype = var_dict['dtype'] - if var_dtype != var_dict['data'].dtype: - msg = "WARNING!!! " - msg += "Different data types for variable {0}. ".format(var_name) - msg += "Input dtype={0}. Data dtype={1}.".format(var_dtype, var_dict['data'].dtype) - warnings.warn(msg) - try: - var_dict['data'] = var_dict['data'].astype(var_dtype) - except Exception as e: # TODO: Detect exception - print(e) - raise TypeError("It was not possible to cast the data to the input dtype.") - else: - var_dtype = var_dict['data'].dtype - # Transform objects into strings - if var_dtype == np.dtype(object): - var_dict['data'] = var_dict['data'].astype(str) - var_dtype = var_dict['data'].dtype - - # Convert list of strings to chars for parallelization - if not np.issubdtype(var_dict['data'].dtype, np.number): + var_dims = self._var_dim + + # Get data type + if 'dtype' in var_dict.keys(): + var_dtype = var_dict['dtype'] + if var_dict['data'] is not None and var_dtype != var_dict['data'].dtype: + msg = "WARNING!!! " + msg += "Different data types for variable {0}. ".format(var_name) + msg += "Input dtype={0}. Data dtype={1}.".format(var_dtype, var_dict['data'].dtype) + warnings.warn(msg) try: - # Get unicode - unicode_type = len(max(var_dict['data'].flatten(), key=len)) - - if ((var_dict['data'].dtype == np.dtype(' 0, complevel=self.zip_lvl) + else: + if self.balanced: + raise NotImplementedError("A balanced data cannot be chunked.") + if self.master: + chunk_size = var_dict['data'].shape + else: + chunk_size = None + chunk_size = self.comm.bcast(chunk_size, root=0) + var = netcdf.createVariable(var_name, var_dtype, var_dims, + zlib=self.zip_lvl > 0, complevel=self.zip_lvl, + chunksizes=chunk_size) + if self.info: + print("Rank {0:03d}: Var {1} created ({2}/{3})".format( + self.rank, var_name, i + 1, len(self.variables))) + if self.size > 1: + var.set_collective(True) if self.info: - print("Rank {0:03d}: Writing {1} var ({2}/{3})".format( + print("Rank {0:03d}: Var {1} collective ({2}/{3})".format( self.rank, var_name, i + 1, len(self.variables))) - try: - if not chunking: - var = netcdf.createVariable(var_name, var_dtype, var_dims, - zlib=self.zip_lvl > 0, complevel=self.zip_lvl) - else: - if self.balanced: - raise NotImplementedError("A balanced data cannot be chunked.") - if self.master: - chunk_size = var_dict['data'].shape - else: - chunk_size = None - chunk_size = self.comm.bcast(chunk_size, root=0) - var = netcdf.createVariable(var_name, var_dtype, var_dims, - zlib=self.zip_lvl > 0, complevel=self.zip_lvl, - chunksizes=chunk_size) - if self.info: - print("Rank {0:03d}: Var {1} created ({2}/{3})".format( - self.rank, var_name, i + 1, len(self.variables))) - if self.size > 1: - var.set_collective(True) + + for att_name, att_value in var_dict.items(): + if att_name == 'data': + if att_value is not None: + if self.info: + print("Rank {0:03d}: Filling {1})".format(self.rank, var_name)) + if isinstance(att_value, int) and att_value == 0: + var[self.write_axis_limits['t_min']:self.write_axis_limits['t_max'], + self.write_axis_limits['z_min']:self.write_axis_limits['z_max'], + self.write_axis_limits['y_min']:self.write_axis_limits['y_max'], + self.write_axis_limits['x_min']:self.write_axis_limits['x_max']] = 0 + elif len(att_value.shape) == 4: + var[self.write_axis_limits['t_min']:self.write_axis_limits['t_max'], + self.write_axis_limits['z_min']:self.write_axis_limits['z_max'], + self.write_axis_limits['y_min']:self.write_axis_limits['y_max'], + self.write_axis_limits['x_min']:self.write_axis_limits['x_max']] = att_value + + elif len(att_value.shape) == 3: + if 'strlen' in var_dims: + var[self.write_axis_limits['y_min']:self.write_axis_limits['y_max'], + self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], + :] = att_value + else: + raise NotImplementedError('It is not possible to write 3D variables.') if self.info: - print("Rank {0:03d}: Var {1} collective ({2}/{3})".format( + print("Rank {0:03d}: Var {1} data ({2}/{3})".format( self.rank, var_name, i + 1, len(self.variables))) + elif att_name not in ['chunk_size', 'var_dims', 'dimensions', 'dtype']: + var.setncattr(att_name, att_value) + + self._set_var_crs(var) + if self.info: + print("Rank {0:03d}: Var {1} completed ({2}/{3})".format( + self.rank, var_name, i + 1, len(self.variables))) + return None - for att_name, att_value in var_dict.items(): - if att_name == 'data': + def append_time_step_data(self, i_time): + """ + Fill the netCDF data for the indicated index time. + + Parameters + ---------- + i_time : int + index of the time step to write + """ + if self.serial_nc is not None: + try: + data = self._gather_data(self.variables) + except KeyError: + data = self.__gather_data_py_object(self.variables) + if self.master: + self.serial_nc.variables = data + self.serial_nc.append_time_step_data(i_time) + self.comm.Barrier() + else: + for i, (var_name, var_dict) in enumerate(self.variables.items()): + for att_name, att_value in var_dict.items(): + if att_name == 'data': + if att_value is not None: if self.info: print("Rank {0:03d}: Filling {1})".format(self.rank, var_name)) + var = self.netcdf.variables[var_name] if isinstance(att_value, int) and att_value == 0: - var[self.write_axis_limits['t_min']:self.write_axis_limits['t_max'], + var[i_time, self.write_axis_limits['z_min']:self.write_axis_limits['z_max'], self.write_axis_limits['y_min']:self.write_axis_limits['y_max'], self.write_axis_limits['x_min']:self.write_axis_limits['x_max']] = 0 elif len(att_value.shape) == 4: - try: - var[self.write_axis_limits['t_min']:self.write_axis_limits['t_max'], - self.write_axis_limits['z_min']:self.write_axis_limits['z_max'], - self.write_axis_limits['y_min']:self.write_axis_limits['y_max'], - self.write_axis_limits['x_min']:self.write_axis_limits['x_max']] = att_value - except ValueError as e: - print(var) - print(att_value) - raise e - # var[self.write_axis_limits['t_min']:self.write_axis_limits['t_max'], - # 0, - # self.write_axis_limits['y_min']:self.write_axis_limits['y_max'], - # self.write_axis_limits['x_min']:self.write_axis_limits['x_max']] = att_value - - except IndexError: - raise IndexError("Different shapes. out_shape={0}, data_shp={1}".format( - var[self.write_axis_limits['t_min']:self.write_axis_limits['t_max'], - self.write_axis_limits['z_min']:self.write_axis_limits['z_max'], - self.write_axis_limits['y_min']:self.write_axis_limits['y_max'], - self.write_axis_limits['x_min']:self.write_axis_limits['x_max']].shape, - att_value.shape)) + var[i_time, + self.write_axis_limits['z_min']:self.write_axis_limits['z_max'], + self.write_axis_limits['y_min']:self.write_axis_limits['y_max'], + self.write_axis_limits['x_min']:self.write_axis_limits['x_max']] = att_value + elif len(att_value.shape) == 3: - if 'strlen' in var_dims: - try: - var[self.write_axis_limits['y_min']:self.write_axis_limits['y_max'], - self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], - :] = att_value - except IndexError: - raise IndexError("Different shapes. out_shape={0}, data_shp={1}".format( - var[self.write_axis_limits['y_min']:self.write_axis_limits['y_max'], - self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], - :].shape, - att_value.shape)) - else: - raise NotImplementedError('It is not possible to write 3D variables.') + raise NotImplementedError('It is not possible to write 3D variables.') if self.info: print("Rank {0:03d}: Var {1} data ({2}/{3})".format( self.rank, var_name, i + 1, len(self.variables))) - elif att_name not in ['chunk_size', 'var_dims', 'dimensions']: - var.setncattr(att_name, att_value) - - self._set_var_crs(var) - if self.info: - print("Rank {0:03d}: Var {1} completed ({2}/{3})".format( - self.rank, var_name, i + 1, len(self.variables))) - except Exception as e: - print("**ERROR** an error has occurred while writing the '{0}' variable".format(var_name)) - # print("**ERROR** an error has occurredred while writing the '{0}' variable".format(var_name), - # file=sys.stderr) - raise e - else: - msg = 'WARNING!!! ' - msg += 'Variable {0} was not loaded. It will not be written.'.format(var_name) - warnings.warn(msg) + else: + # Metadata already writen + pass return None @@ -2444,7 +2479,7 @@ class Nes(object): return None - def __to_netcdf_py(self, path, chunking=False): + def __to_netcdf_py(self, path, chunking=False, keep_open=False): """ Create the NetCDF using netcdf4-python methods. @@ -2491,15 +2526,18 @@ class Nes(object): netcdf.setncattr(att_name, att_value) netcdf.setncattr('Conventions', 'CF-1.7') - netcdf.close() + if keep_open: + self.netcdf = netcdf + else: + netcdf.close() return None def __to_netcdf_cams_ra(self, path): return to_netcdf_cams_ra(self, path) - def to_netcdf(self, path, compression_level=0, serial=False, info=False, - chunking=False, type='NES'): + def to_netcdf(self, path, compression_level=0, serial=False, info=False, chunking=False, type='NES', + keep_open=False): """ Write the netCDF output file. @@ -2521,7 +2559,7 @@ class Nes(object): nc_type = type old_info = self.info self.info = info - + self.serial_nc = None self.zip_lvl = compression_level if self.is_xarray: raise NotImplementedError("Writing with xarray not implemented") @@ -2542,15 +2580,18 @@ class Nes(object): new_nc.variables = data new_nc.cell_measures = c_measures if type == 'NES': - new_nc.__to_netcdf_py(path) + new_nc.__to_netcdf_py(path, keep_open=keep_open) elif type == 'CAMS_RA': new_nc.__to_netcdf_cams_ra(path) else: raise ValueError( "Unknown NetCDF type '{0}'. Use 'CAMS_RA' or 'NES'; default='NES'".format(nc_type)) + self.serial_nc = new_nc + else: + self.serial_nc = True else: if nc_type == 'NES': - self.__to_netcdf_py(path, chunking=chunking) + self.__to_netcdf_py(path, chunking=chunking, keep_open=keep_open) elif nc_type == 'CAMS_RA': self.__to_netcdf_cams_ra(path) else: @@ -3099,66 +3140,68 @@ class Nes(object): for var_name in data_list.keys(): if self.info and self.master: print("Gathering {0}".format(var_name)) - shp_len = len(data_list[var_name]['data'].shape) - # Collect local array sizes using the gather communication pattern - rank_shapes = np.array(self.comm.gather(data_list[var_name]['data'].shape, root=0)) - sendbuf = data_list[var_name]['data'].flatten() - sendcounts = np.array(self.comm.gather(len(sendbuf), root=0)) - if self.master: - # recvbuf = np.empty(sum(sendcounts), dtype=type(sendbuf[0])) - recvbuf = np.empty(sum(sendcounts), dtype=type(sendbuf.max())) - else: - recvbuf = None - self.comm.Gatherv(sendbuf=sendbuf, recvbuf=(recvbuf, sendcounts), root=0) - if self.master: - recvbuf = np.split(recvbuf, np.cumsum(sendcounts)) - # TODO ask - # I don't understand why it is giving one more split - if len(recvbuf) > len(sendcounts): - recvbuf = recvbuf[:-1] - for i, shape in enumerate(rank_shapes): - recvbuf[i] = recvbuf[i].reshape(shape) - add_dimension = False # to Add a dimension - if self.parallel_method == 'Y': - if shp_len == 2: - # if is a 2D concatenate over first axis - axis = 0 - elif shp_len == 3: - # if is a 3D concatenate over second axis - axis = 1 - else: - # if is a 4D concatenate over third axis - axis = 2 - elif self.parallel_method == 'X': - if shp_len == 2: - # if is a 2D concatenate over second axis - axis = 1 - elif shp_len == 3: - # if is a 3D concatenate over third axis - axis = 2 + if data_list[var_name]['data'] is not None: + shp_len = len(data_list[var_name]['data'].shape) + # Collect local array sizes using the gather communication pattern + rank_shapes = np.array(self.comm.gather(data_list[var_name]['data'].shape, root=0)) + sendbuf = data_list[var_name]['data'].flatten() + sendcounts = np.array(self.comm.gather(len(sendbuf), root=0)) + if self.master: + # recvbuf = np.empty(sum(sendcounts), dtype=type(sendbuf[0])) + recvbuf = np.empty(sum(sendcounts), dtype=type(sendbuf.max())) + else: + recvbuf = None + self.comm.Gatherv(sendbuf=sendbuf, recvbuf=(recvbuf, sendcounts), root=0) + if self.master: + recvbuf = np.split(recvbuf, np.cumsum(sendcounts)) + # TODO ask + # I don't understand why it is giving one more split + if len(recvbuf) > len(sendcounts): + recvbuf = recvbuf[:-1] + for i, shape in enumerate(rank_shapes): + recvbuf[i] = recvbuf[i].reshape(shape) + add_dimension = False # to Add a dimension + if self.parallel_method == 'Y': + if shp_len == 2: + # if is a 2D concatenate over first axis + axis = 0 + elif shp_len == 3: + # if is a 3D concatenate over second axis + axis = 1 + else: + # if is a 4D concatenate over third axis + axis = 2 + elif self.parallel_method == 'X': + if shp_len == 2: + # if is a 2D concatenate over second axis + axis = 1 + elif shp_len == 3: + # if is a 3D concatenate over third axis + axis = 2 + else: + # if is a 4D concatenate over forth axis + axis = 3 + elif self.parallel_method == 'T': + if shp_len == 2: + # if is a 2D add dimension + add_dimension = True + axis = None # Not used + elif shp_len == 3: + # if is a 3D concatenate over first axis + axis = 0 + else: + # if is a 4D concatenate over second axis + axis = 0 else: - # if is a 4D concatenate over forth axis - axis = 3 - elif self.parallel_method == 'T': - if shp_len == 2: - # if is a 2D add dimension - add_dimension = True - axis = None # Not used - elif shp_len == 3: - # if is a 3D concatenate over first axis - axis = 0 + raise NotImplementedError( + "Parallel method '{meth}' is not implemented. Use one of these: {accept}".format( + meth=self.parallel_method, accept=['X', 'Y', 'T'])) + if add_dimension: + data_list[var_name]['data'] = np.stack(recvbuf) else: - # if is a 4D concatenate over second axis - axis = 0 - else: - raise NotImplementedError( - "Parallel method '{meth}' is not implemented. Use one of these: {accept}".format( - meth=self.parallel_method, accept=['X', 'Y', 'T'])) - if add_dimension: - data_list[var_name]['data'] = np.stack(recvbuf) - else: - data_list[var_name]['data'] = np.concatenate(recvbuf, axis=axis) - + data_list[var_name]['data'] = np.concatenate(recvbuf, axis=axis) + else: + data_list[var_name]['data'] = None return data_list # ================================================================================================================== diff --git a/tests/4.3-test_write_time_step.py b/tests/4.3-test_write_time_step.py new file mode 100644 index 0000000..45dd2b4 --- /dev/null +++ b/tests/4.3-test_write_time_step.py @@ -0,0 +1,149 @@ +#!/usr/bin/env python + +import sys +from mpi4py import MPI +import pandas as pd +import timeit +from datetime import datetime, timedelta +import numpy as np +from nes import * + +comm = MPI.COMM_WORLD +rank = comm.Get_rank() +size = comm.Get_size() + +parallel_method = 'Y' + +result_path = "Times_test_4.3_write_time_step_{0}_{1:03d}.csv".format(parallel_method, size) +result = pd.DataFrame(index=['read', 'calculate', 'write'], + columns=['4.3.1.ParallelWrite', '4.3.2.SerialWrite']) + +# ====================================================================================================================== +# =================================== PARALLEL WRITE =================================================== +# ====================================================================================================================== + +test_name = '4.3.1.ParallelWrite' + +if rank == 0: + print(test_name) + +st_time = timeit.default_timer() +# CREATE GRID +centre_lat = 51 +centre_lon = 10 +west_boundary = -35 +south_boundary = -27 +inc_rlat = 0.2 +inc_rlon = 0.2 +nessy = create_nes(comm=None, info=False, projection='rotated', + centre_lat=centre_lat, centre_lon=centre_lon, + west_boundary=west_boundary, south_boundary=south_boundary, + inc_rlat=inc_rlat, inc_rlon=inc_rlon) + +# ADD VARIABLES +nessy.variables = {'var1': {'data': None, 'units': 'kg.s-1', 'dtype': np.float32}, + 'var2': {'data': None, 'units': 'kg.s-1', 'dtype': np.float32}} +time_list = [datetime(year=2023, month=1, day=1) + timedelta(hours=x) for x in range(24)] +nessy.set_time(time_list) + +comm.Barrier() +result.loc['read', test_name] = timeit.default_timer() - st_time + +# CREATE +st_time = timeit.default_timer() +nessy.to_netcdf(test_name + '.nc', keep_open=True, info=False) + +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + +# CALCUL & APPEND +result.loc['calcul', test_name] = 0 + +for i_time, time_aux in enumerate(time_list): + # CALCUL + st_time = timeit.default_timer() + + nessy.variables['var1']['data'] = np.ones((1, 1, nessy.lat['data'].shape[0], nessy.lon['data'].shape[-1])) * i_time + nessy.variables['var2']['data'] = np.ones((1, 1, nessy.lat['data'].shape[0], nessy.lon['data'].shape[-1])) * i_time + + comm.Barrier() + result.loc['calcul', test_name] += timeit.default_timer() - st_time + # APPEND + st_time = timeit.default_timer() + nessy.append_time_step_data(i_time) + comm.Barrier() + if i_time == len(time_list) - 1: + nessy.close() + result.loc['write', test_name] = timeit.default_timer() - st_time + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +# ====================================================================================================================== +# =================================== SERIAL WRITE =================================================== +# ====================================================================================================================== + +test_name = '4.3.2.SerialWrite' + +if rank == 0: + print(test_name) + +st_time = timeit.default_timer() +# CREATE GRID +centre_lat = 51 +centre_lon = 10 +west_boundary = -35 +south_boundary = -27 +inc_rlat = 0.2 +inc_rlon = 0.2 +nessy = create_nes(comm=None, info=False, projection='rotated', + centre_lat=centre_lat, centre_lon=centre_lon, + west_boundary=west_boundary, south_boundary=south_boundary, + inc_rlat=inc_rlat, inc_rlon=inc_rlon) + +# ADD VARIABLES +nessy.variables = {'var1': {'data': None, 'units': 'kg.s-1', 'dtype': np.float32}, + 'var2': {'data': None, 'units': 'kg.s-1', 'dtype': np.float32}} +time_list = [datetime(year=2023, month=1, day=1) + timedelta(hours=x) for x in range(24)] +nessy.set_time(time_list) + +comm.Barrier() +result.loc['read', test_name] = timeit.default_timer() - st_time + +# CREATE +st_time = timeit.default_timer() +nessy.to_netcdf(test_name + '.nc', keep_open=True, info=False, serial=True) + +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + +# CALCUL & APPEND +result.loc['calcul', test_name] = 0 + +for i_time, time_aux in enumerate(time_list): + # CALCUL + st_time = timeit.default_timer() + + nessy.variables['var1']['data'] = np.ones((1, 1, nessy.lat['data'].shape[0], nessy.lon['data'].shape[-1])) * i_time + nessy.variables['var2']['data'] = np.ones((1, 1, nessy.lat['data'].shape[0], nessy.lon['data'].shape[-1])) * i_time + + comm.Barrier() + result.loc['calcul', test_name] += timeit.default_timer() - st_time + # APPEND + st_time = timeit.default_timer() + nessy.append_time_step_data(i_time) + comm.Barrier() + if i_time == len(time_list) - 1: + nessy.close() + result.loc['write', test_name] = timeit.default_timer() - st_time + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() + +if rank == 0: + result.to_csv(result_path) + print("TEST PASSED SUCCESSFULLY!!!!!") diff --git a/tutorials/6.Others/6.4.WriteByTimeStep.ipynb b/tutorials/6.Others/6.4.WriteByTimeStep.ipynb new file mode 100644 index 0000000..7f3ea27 --- /dev/null +++ b/tutorials/6.Others/6.4.WriteByTimeStep.ipynb @@ -0,0 +1,158 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from nes import *\n", + "import numpy as np\n", + "from datetime import datetime, timedelta" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "centre_lat = 51\n", + "centre_lon = 10\n", + "west_boundary = -35\n", + "south_boundary = -27\n", + "inc_rlat = 0.2\n", + "inc_rlon = 0.2\n", + "rotated_grid = create_nes(comm=None, info=False, projection='rotated',\n", + " centre_lat=centre_lat, centre_lon=centre_lon,\n", + " west_boundary=west_boundary, south_boundary=south_boundary,\n", + " inc_rlat=inc_rlat, inc_rlon=inc_rlon)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "rotated_grid.variables = {'var1': {'data': None, 'units': 'kg.s-1', 'dtype': np.float32},\n", + " 'var2': {'data': None, 'units': 'kg.s-1', 'dtype': np.float32}}" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[datetime.datetime(1996, 12, 31, 0, 0)]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rotated_grid.time" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "time_list = [datetime(year=2023, month=1, day=1) + timedelta(hours=x) for x in range (2)]" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "rotated_grid.set_time(time_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "rotated_grid.to_netcdf('rotated.nc', keep_open=True, info=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "rotated_grid.variables['var1']['data'] = np.ones((1, 1, rotated_grid.lat['data'].shape[0], rotated_grid.lon['data'].shape[-1]))\n", + "rotated_grid.variables['var2']['data'] = np.ones((1, 1, rotated_grid.lat['data'].shape[0], rotated_grid.lon['data'].shape[-1]))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "rotated_grid.append_time_step_data(0)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "rotated_grid.variables['var1']['data'] = np.ones((1, 1, rotated_grid.lat['data'].shape[0], rotated_grid.lon['data'].shape[-1])) *2\n", + "rotated_grid.variables['var2']['data'] = np.ones((1, 1, rotated_grid.lat['data'].shape[0], rotated_grid.lon['data'].shape[-1])) *2\n", + "\n", + "rotated_grid.append_time_step_data(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "rotated_grid.close()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.4" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} -- GitLab From a3d752c2a68c4f75a6b11bcd445f308c144bd55e Mon Sep 17 00:00:00 2001 From: Alba Vilanova Date: Fri, 17 Mar 2023 10:14:45 +0100 Subject: [PATCH 28/43] Update CHANGELOG, test and tutorial --- CHANGELOG.md | 1 + ...ime_step.py => 4.3-test_write_timestep.py} | 24 +++--- tests/run_scalability_tests_nord3v2.sh | 2 +- tests/test_bash_mn4.cmd | 1 + tests/test_bash_nord3v2.cmd | 1 + tutorials/6.Others/6.2.Selecting.ipynb | 18 ++-- ...Step.ipynb => 6.4.Write_By_Timestep.ipynb} | 84 ++++++++++++++++--- 7 files changed, 99 insertions(+), 32 deletions(-) rename tests/{4.3-test_write_time_step.py => 4.3-test_write_timestep.py} (90%) rename tutorials/6.Others/{6.4.WriteByTimeStep.ipynb => 6.4.Write_By_Timestep.ipynb} (77%) diff --git a/CHANGELOG.md b/CHANGELOG.md index fc07441..7ef3c82 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -6,6 +6,7 @@ * Improved time on **concatenate_netcdfs** function ([#55](https://earth.bsc.es/gitlab/es/NES/-/issues/55)) * Sum of Nes objects ([#48](https://earth.bsc.es/gitlab/es/NES/-/issues/48)) * Write 2D string data to save variables from shapefiles after doing a spatial join ([#49](https://earth.bsc.es/gitlab/es/NES/-/issues/49)) + * Write by time step to avoid memory issues ([#57](https://earth.bsc.es/gitlab/es/NES/-/issues/57)) * Bugs fixing: * Bug on `cell_methods` serial write ([#53](https://earth.bsc.es/gitlab/es/NES/-/issues/53)) * Horizontal Interpolation Conservative: Improvement on memory usage when calculating the weight matrix ([#54](https://earth.bsc.es/gitlab/es/NES/-/issues/54)) diff --git a/tests/4.3-test_write_time_step.py b/tests/4.3-test_write_timestep.py similarity index 90% rename from tests/4.3-test_write_time_step.py rename to tests/4.3-test_write_timestep.py index 45dd2b4..77539e7 100644 --- a/tests/4.3-test_write_time_step.py +++ b/tests/4.3-test_write_timestep.py @@ -16,13 +16,13 @@ parallel_method = 'Y' result_path = "Times_test_4.3_write_time_step_{0}_{1:03d}.csv".format(parallel_method, size) result = pd.DataFrame(index=['read', 'calculate', 'write'], - columns=['4.3.1.ParallelWrite', '4.3.2.SerialWrite']) + columns=['4.3.1.Parallel_Write', '4.3.2.Serial_Write']) # ====================================================================================================================== # =================================== PARALLEL WRITE =================================================== # ====================================================================================================================== -test_name = '4.3.1.ParallelWrite' +test_name = '4.3.1.Parallel_Write' if rank == 0: print(test_name) @@ -56,18 +56,19 @@ nessy.to_netcdf(test_name + '.nc', keep_open=True, info=False) comm.Barrier() result.loc['write', test_name] = timeit.default_timer() - st_time -# CALCUL & APPEND -result.loc['calcul', test_name] = 0 +# CALCULATE & APPEND +result.loc['calculate', test_name] = 0 for i_time, time_aux in enumerate(time_list): - # CALCUL + # CALCULATE st_time = timeit.default_timer() nessy.variables['var1']['data'] = np.ones((1, 1, nessy.lat['data'].shape[0], nessy.lon['data'].shape[-1])) * i_time nessy.variables['var2']['data'] = np.ones((1, 1, nessy.lat['data'].shape[0], nessy.lon['data'].shape[-1])) * i_time comm.Barrier() - result.loc['calcul', test_name] += timeit.default_timer() - st_time + result.loc['calculate', test_name] += timeit.default_timer() - st_time + # APPEND st_time = timeit.default_timer() nessy.append_time_step_data(i_time) @@ -85,7 +86,7 @@ sys.stdout.flush() # =================================== SERIAL WRITE =================================================== # ====================================================================================================================== -test_name = '4.3.2.SerialWrite' +test_name = '4.3.2.Serial_Write' if rank == 0: print(test_name) @@ -119,18 +120,19 @@ nessy.to_netcdf(test_name + '.nc', keep_open=True, info=False, serial=True) comm.Barrier() result.loc['write', test_name] = timeit.default_timer() - st_time -# CALCUL & APPEND -result.loc['calcul', test_name] = 0 +# CALCULATE & APPEND +result.loc['calculate', test_name] = 0 for i_time, time_aux in enumerate(time_list): - # CALCUL + # CALCULATEATE st_time = timeit.default_timer() nessy.variables['var1']['data'] = np.ones((1, 1, nessy.lat['data'].shape[0], nessy.lon['data'].shape[-1])) * i_time nessy.variables['var2']['data'] = np.ones((1, 1, nessy.lat['data'].shape[0], nessy.lon['data'].shape[-1])) * i_time comm.Barrier() - result.loc['calcul', test_name] += timeit.default_timer() - st_time + result.loc['calculate', test_name] += timeit.default_timer() - st_time + # APPEND st_time = timeit.default_timer() nessy.append_time_step_data(i_time) diff --git a/tests/run_scalability_tests_nord3v2.sh b/tests/run_scalability_tests_nord3v2.sh index c75e601..214fecd 100644 --- a/tests/run_scalability_tests_nord3v2.sh +++ b/tests/run_scalability_tests_nord3v2.sh @@ -8,7 +8,7 @@ module load Python/3.7.4-GCCcore-8.3.0 module load NES/1.0.0-nord3-v2-foss-2019b-Python-3.7.4 -for EXE in "1.1-test_read_write_projection.py" "1.2-test_create_projection.py" "1.3-test_selecting.py" "2.1-test_spatial_join.py" "2.2-test_create_shapefile.py" "2.3-test_bounds.py" "2.4-test_cell_area.py" "3.1-test_vertical_interp.py" "3.2-test_horiz_interp_bilinear.py" "3.3-test_horiz_interp_conservative.py" "4.1-test_daily_stats.py" "4.2-test_sum.py" +for EXE in "1.1-test_read_write_projection.py" "1.2-test_create_projection.py" "1.3-test_selecting.py" "2.1-test_spatial_join.py" "2.2-test_create_shapefile.py" "2.3-test_bounds.py" "2.4-test_cell_area.py" "3.1-test_vertical_interp.py" "3.2-test_horiz_interp_bilinear.py" "3.3-test_horiz_interp_conservative.py" "4.1-test_daily_stats.py" "4.2-test_sum.py" "4.3-test_write_timestep.py" do for nprocs in 1 2 4 8 16 do diff --git a/tests/test_bash_mn4.cmd b/tests/test_bash_mn4.cmd index 491076a..f7450c7 100644 --- a/tests/test_bash_mn4.cmd +++ b/tests/test_bash_mn4.cmd @@ -35,3 +35,4 @@ mpirun --mca mpi_warn_on_fork 0 -np 4 python 3.3-test_horiz_interp_conservative. mpirun --mca mpi_warn_on_fork 0 -np 4 python 4.1-test_daily_stats.py mpirun --mca mpi_warn_on_fork 0 -np 4 python 4.2-test_sum.py +mpirun --mca mpi_warn_on_fork 0 -np 4 python 4.3-test_write_timestep.py diff --git a/tests/test_bash_nord3v2.cmd b/tests/test_bash_nord3v2.cmd index 45a2e3d..1ef30c0 100644 --- a/tests/test_bash_nord3v2.cmd +++ b/tests/test_bash_nord3v2.cmd @@ -33,3 +33,4 @@ mpirun --mca mpi_warn_on_fork 0 -np 4 python 3.3-test_horiz_interp_conservative. mpirun --mca mpi_warn_on_fork 0 -np 4 python 4.1-test_daily_stats.py mpirun --mca mpi_warn_on_fork 0 -np 4 python 4.2-test_sum.py +mpirun --mca mpi_warn_on_fork 0 -np 4 python 4.3-test_write_timestep.py \ No newline at end of file diff --git a/tutorials/6.Others/6.2.Selecting.ipynb b/tutorials/6.Others/6.2.Selecting.ipynb index f4c28b4..7118c4d 100644 --- a/tutorials/6.Others/6.2.Selecting.ipynb +++ b/tutorials/6.Others/6.2.Selecting.ipynb @@ -31,7 +31,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Time" + "## 1. Select data by time" ] }, { @@ -170,7 +170,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Level" + "## 2. Select data by level" ] }, { @@ -216,7 +216,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Coordinates" + "## 3. Select data by coordinates" ] }, { @@ -256,20 +256,20 @@ ] }, { - "cell_type": "code", - "execution_count": 9, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "nessy.to_netcdf(\"test_sel.nc\")" + "## 4. Write dataset" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "nessy.to_netcdf(\"test_sel.nc\")" + ] } ], "metadata": { diff --git a/tutorials/6.Others/6.4.WriteByTimeStep.ipynb b/tutorials/6.Others/6.4.Write_By_Timestep.ipynb similarity index 77% rename from tutorials/6.Others/6.4.WriteByTimeStep.ipynb rename to tutorials/6.Others/6.4.Write_By_Timestep.ipynb index 7f3ea27..9080e0c 100644 --- a/tutorials/6.Others/6.4.WriteByTimeStep.ipynb +++ b/tutorials/6.Others/6.4.Write_By_Timestep.ipynb @@ -1,5 +1,12 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Write by timestep (to avoid memory issues)" + ] + }, { "cell_type": "code", "execution_count": 1, @@ -11,6 +18,13 @@ "from datetime import datetime, timedelta" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 1. Create grid" + ] + }, { "cell_type": "code", "execution_count": 2, @@ -29,6 +43,13 @@ " inc_rlat=inc_rlat, inc_rlon=inc_rlon)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Create variables" + ] + }, { "cell_type": "code", "execution_count": 3, @@ -39,6 +60,20 @@ " 'var2': {'data': None, 'units': 'kg.s-1', 'dtype': np.float32}}" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 3. Change time" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Old time" + ] + }, { "cell_type": "code", "execution_count": 4, @@ -59,6 +94,13 @@ "rotated_grid.time" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### New time" + ] + }, { "cell_type": "code", "execution_count": 5, @@ -81,6 +123,33 @@ "cell_type": "code", "execution_count": 7, "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[datetime.datetime(2023, 1, 1, 0, 0), datetime.datetime(2023, 1, 1, 1, 0)]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "rotated_grid.time" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Write dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, "outputs": [], "source": [ "rotated_grid.to_netcdf('rotated.nc', keep_open=True, info=False)" @@ -88,7 +157,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -98,7 +167,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -107,7 +176,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -119,19 +188,12 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "rotated_grid.close()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { -- GitLab From 65ffbb4c2e387b05459b838c28c57ff82cc43ca0 Mon Sep 17 00:00:00 2001 From: ctena Date: Fri, 17 Mar 2023 14:01:07 +0100 Subject: [PATCH 29/43] Write by time step: added error messages --- nes/nc_projections/default_nes.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py index 0e4de4a..285c9f0 100644 --- a/nes/nc_projections/default_nes.py +++ b/nes/nc_projections/default_nes.py @@ -2417,6 +2417,7 @@ class Nes(object): for i, (var_name, var_dict) in enumerate(self.variables.items()): for att_name, att_value in var_dict.items(): if att_name == 'data': + if att_value is not None: if self.info: print("Rank {0:03d}: Filling {1})".format(self.rank, var_name)) @@ -2434,9 +2435,13 @@ class Nes(object): elif len(att_value.shape) == 3: raise NotImplementedError('It is not possible to write 3D variables.') + else: + raise NotImplementedError("SHAPE APPEND ERROR: {0}".format(att_value.shape)) if self.info: print("Rank {0:03d}: Var {1} data ({2}/{3})".format( self.rank, var_name, i + 1, len(self.variables))) + else: + raise ValueError("Cannot append None Data for {0}".format(var_name)) else: # Metadata already writen pass -- GitLab From 9b732380afd7fffe594d2365718ad6c579403823 Mon Sep 17 00:00:00 2001 From: ctena Date: Fri, 17 Mar 2023 15:03:39 +0100 Subject: [PATCH 30/43] Write data as 0. Bugfix on gathering the integer --- nes/nc_projections/default_nes.py | 10 +++++++--- 1 file changed, 7 insertions(+), 3 deletions(-) diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py index 285c9f0..0cdb8a7 100644 --- a/nes/nc_projections/default_nes.py +++ b/nes/nc_projections/default_nes.py @@ -2408,6 +2408,7 @@ class Nes(object): try: data = self._gather_data(self.variables) except KeyError: + # Key Error means string data data = self.__gather_data_py_object(self.variables) if self.master: self.serial_nc.variables = data @@ -3145,7 +3146,11 @@ class Nes(object): for var_name in data_list.keys(): if self.info and self.master: print("Gathering {0}".format(var_name)) - if data_list[var_name]['data'] is not None: + if data_list[var_name]['data'] is None: + data_list[var_name]['data'] = None + elif isinstance(data_list[var_name]['data'], int) and data_list[var_name]['data'] == 0: + data_list[var_name]['data'] = 0 + else: shp_len = len(data_list[var_name]['data'].shape) # Collect local array sizes using the gather communication pattern rank_shapes = np.array(self.comm.gather(data_list[var_name]['data'].shape, root=0)) @@ -3205,8 +3210,7 @@ class Nes(object): data_list[var_name]['data'] = np.stack(recvbuf) else: data_list[var_name]['data'] = np.concatenate(recvbuf, axis=axis) - else: - data_list[var_name]['data'] = None + return data_list # ================================================================================================================== -- GitLab From 5e83954f0aef4737cf33c24ec0fd18e0e984e778 Mon Sep 17 00:00:00 2001 From: ctena Date: Tue, 21 Mar 2023 12:42:05 +0100 Subject: [PATCH 31/43] Bugfixing spin-up. --- nes/nc_projections/default_nes.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py index 0cdb8a7..1c442dc 100644 --- a/nes/nc_projections/default_nes.py +++ b/nes/nc_projections/default_nes.py @@ -256,6 +256,16 @@ class Nes(object): self.vertical_var_name = None + # Filtering (portion of the filter coordinates function) + idx = self.get_idx_intervals() + self._time = self._time[idx['idx_t_min']:idx['idx_t_max']] + self._lev['data'] = self._lev['data'][idx['idx_z_min']:idx['idx_z_max']] + + self.hours_start = 0 + self.hours_end = 0 + self.last_level = None + self.first_level = None + @staticmethod def new(comm=None, path=None, info=False, dataset=None, xarray=False, create_nes=False, balanced=False, parallel_method='Y', avoid_first_hours=0, avoid_last_hours=0, first_level=0, last_level=None): -- GitLab From 802789bcb8bba3885112fa5883da1510bf7326d3 Mon Sep 17 00:00:00 2001 From: ctena Date: Tue, 21 Mar 2023 13:03:45 +0100 Subject: [PATCH 32/43] CHANGELOG update --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 7ef3c82..987d0b8 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -10,6 +10,7 @@ * Bugs fixing: * Bug on `cell_methods` serial write ([#53](https://earth.bsc.es/gitlab/es/NES/-/issues/53)) * Horizontal Interpolation Conservative: Improvement on memory usage when calculating the weight matrix ([#54](https://earth.bsc.es/gitlab/es/NES/-/issues/54)) + * Bug on avoid_first_hours that where not filtered after read the dimensions ([#59](https://earth.bsc.es/gitlab/es/NES/-/issues/59)) ### 1.1.0 * Release date: 2023/03/02 -- GitLab From 85275f74a26e0350d9ad35587d70beb029fe7d82 Mon Sep 17 00:00:00 2001 From: ctena Date: Wed, 22 Mar 2023 17:04:38 +0100 Subject: [PATCH 33/43] - Added stdout.flush() to each warning - Improved spatial_join --- nes/create_nes.py | 2 + nes/load_nes.py | 2 + nes/methods/__init__.py | 1 + nes/methods/horizontal_interpolation.py | 4 + nes/methods/spatial_join.py | 277 +++++++++++++++ nes/nc_projections/default_nes.py | 159 ++------- nes/nc_projections/lcc_nes.py | 2 + nes/nc_projections/mercator_nes.py | 2 + nes/nc_projections/points_nes.py | 2 + nes/nc_projections/points_nes_ghost.py | 2 + nes/nc_projections/points_nes_providentia.py | 2 + nes/nc_projections/rotated_nes.py | 2 + nes/nes_formats/cams_ra_format.py | 1 + tests/2.1-test_spatial_join.py | 342 ++++++++++--------- 14 files changed, 508 insertions(+), 292 deletions(-) create mode 100644 nes/methods/spatial_join.py diff --git a/nes/create_nes.py b/nes/create_nes.py index 33f0bf6..da7d29c 100644 --- a/nes/create_nes.py +++ b/nes/create_nes.py @@ -1,6 +1,7 @@ #!/usr/bin/env python import warnings +import sys from netCDF4 import num2date from mpi4py import MPI import geopandas as gpd @@ -87,6 +88,7 @@ def create_nes(comm=None, info=False, projection=None, parallel_method='Y', bala if projection is None: if parallel_method == 'Y': warnings.warn("Parallel method cannot be 'Y' to create points NES. Setting it to 'X'") + sys.stderr.flush() parallel_method = 'X' elif parallel_method == 'T': raise NotImplementedError("Parallel method T not implemented yet") diff --git a/nes/load_nes.py b/nes/load_nes.py index 8de2346..2113b48 100644 --- a/nes/load_nes.py +++ b/nes/load_nes.py @@ -1,6 +1,7 @@ #!/usr/bin/env python import os +import sys from mpi4py import MPI from netCDF4 import Dataset import warnings @@ -70,6 +71,7 @@ def open_netcdf(path, comm=None, xarray=False, info=False, parallel_method='Y', elif __is_points(dataset): if parallel_method == 'Y': warnings.warn("Parallel method cannot be 'Y' to create points NES. Setting it to 'X'") + sys.stderr.flush() parallel_method = 'X' if __is_points_ghost(dataset): # Points - GHOST diff --git a/nes/methods/__init__.py b/nes/methods/__init__.py index 22c351a..772adac 100644 --- a/nes/methods/__init__.py +++ b/nes/methods/__init__.py @@ -1,3 +1,4 @@ from .vertical_interpolation import add_4d_vertical_info from .vertical_interpolation import interpolate_vertical from .horizontal_interpolation import interpolate_horizontal +from .spatial_join import spatial_join diff --git a/nes/methods/horizontal_interpolation.py b/nes/methods/horizontal_interpolation.py index 58a2abc..e33edd4 100644 --- a/nes/methods/horizontal_interpolation.py +++ b/nes/methods/horizontal_interpolation.py @@ -143,6 +143,7 @@ def interpolate_horizontal(self, dst_grid, weight_matrix_path=None, kind='Neares else: msg = "The final projection must be points to interpolate an experiment and get it in Providentia format." warnings.warn(msg) + sys.stderr.flush() else: # Convert dimensions (time, lev, lat, lon) or (time, lat, lon) to (time, station) for interpolated variables # and reshape data @@ -238,6 +239,7 @@ def get_weights_idx_t_axis(self, dst_grid, weight_matrix_path, kind, n_neighbour if len(weight_matrix.lev['data']) != n_neighbours: warn("The selected weight matrix does not have the same number of nearest neighbours." + "Re-calculating again but not saving it.") + sys.stderr.flush() weight_matrix = create_nn_weight_matrix(self, dst_grid, n_neighbours=n_neighbours) else: weight_matrix = True @@ -320,6 +322,7 @@ def get_weights_idx_xy_axis(self, dst_grid, weight_matrix_path, kind, n_neighbou if isinstance(dst_grid, nes.PointsNes) and weight_matrix_path is not None: if self.master: warn("To point weight matrix cannot be saved.") + sys.stderr.flush() weight_matrix_path = None if wm is not None: @@ -337,6 +340,7 @@ def get_weights_idx_xy_axis(self, dst_grid, weight_matrix_path, kind, n_neighbou if len(weight_matrix.lev['data']) != n_neighbours: warn("The selected weight matrix does not have the same number of nearest neighbours." + "Re-calculating again but not saving it.") + sys.stderr.flush() weight_matrix = create_nn_weight_matrix(self, dst_grid, n_neighbours=n_neighbours) else: weight_matrix = True diff --git a/nes/methods/spatial_join.py b/nes/methods/spatial_join.py new file mode 100644 index 0000000..6695cf0 --- /dev/null +++ b/nes/methods/spatial_join.py @@ -0,0 +1,277 @@ +#!/usr/bin/env python + +import sys +import warnings +import geopandas as gpd +from geopandas import GeoDataFrame +import nes +import numpy as np +import pandas as pd +from shapely.geos import TopologicalError + + +def spatial_join(self, ext_shp, method=None, var_list=None, info=False): + """ + Compute overlay intersection of two GeoPandasDataFrames. + + Parameters + ---------- + self : nes.Nes + ext_shp : GeoPandasDataFrame or str + File or path from where the data will be obtained on the intersection. + method : str + Overlay method. Accepted values: ['nearest', 'intersection', 'centroid']. + var_list : List or None or str + Variables that will be included in the resulting shapefile. + info : bool + Indicates if you want to print the process info or no + """ + if self.master and info: + print("Starting spatial join") + if isinstance(var_list, str): + # Transforming string (variable name) to a list with length 0 + var_list = [var_list] + + # Create source shapefile if it does not exist + if self.shapefile is None: + if self.master and info: + print("\tCreating shapefile") + sys.stdout.flush() + self.create_shapefile() + + ext_shp = prepare_external_shapefile(self, ext_shp=ext_shp, var_list=var_list, info=info) + + if method == 'nearest': + # Nearest centroids to the shapefile polygons + spatial_join_nearest(self, ext_shp=ext_shp, info=info) + elif method == 'intersection': + # Intersect the areas of the shapefile polygons, outside the shapefile there will be NaN + spatial_join_intersection(self, ext_shp=ext_shp, info=info) + elif method == 'centroid': + # Centroids that fall on the shapefile polygons, outside the shapefile there will be NaN + spatial_join_centroid(self, ext_shp=ext_shp, info=info) + + else: + accepted_values = ['nearest', 'intersection', 'centroid'] + raise NotImplementedError('{0} is not implemented. Choose from: {1}'.format(method, accepted_values)) + + return None + + +def prepare_external_shapefile(self, ext_shp, var_list, info=False): + """ + Prepare the external shapefile. + + It is high recommended to pass ext_shp parameter as string because it will clip the external shapefile to the rank. + + 1. Read if it is not already read + 2. Filter variables list + 3. Standardize projections + + Parameters + ---------- + self : nes.Nes + ext_shp : GeoDataFrame or str + External shapefile or path to it + var_list : List[str] or None + External shapefile variables to be computed + info : bool + Indicates if you want to print the information + + Returns + ------- + GeoDataFrame + External shapefile + """ + if isinstance(ext_shp, str): + # Reading external shapefile + if self.master and info: + print("\tReading external shapefile") + # ext_shp = gpd.read_file(ext_shp, include_fields=var_list, mask=self.shapefile.geometry) + ext_shp = gpd.read_file(ext_shp, include_fields=var_list, bbox=get_bbox(self)) + else: + msg = "WARNING!!! " + msg += "External shapefile already read. If you pass the path to the shapefile instead of the opened shapefile " + msg += "a best usage of memory is performed because the external shape will be clipped while reading." + warnings.warn(msg) + sys.stderr.flush() + if var_list is not None: + ext_shp = ext_shp.loc[:, var_list + ['geometry']] + self.comm.Barrier() + if self.master and info: + print("\t\tReading external shapefile done!") + # Standardizing projection + ext_shp = ext_shp.to_crs(self.shapefile.crs) + + return ext_shp + + +def get_bbox(self): + """ + Obtain the bounding box of the rank data + + (lon_min, lat_min, lon_max, lat_max) + + Parameters + ---------- + self : nes.Nes + + Returns + ------- + tuple + Bounding box + """ + bbox = (self.lon_bnds['data'].min(), self.lat_bnds['data'].min(), + self.lon_bnds['data'].max(), self.lat_bnds['data'].max(), ) + return bbox + + +def spatial_join_nearest(self, ext_shp, info=False): + """ + Perform the spatial join using the nearest method + + Parameters + ---------- + self : nes.Nes + ext_shp : GeoDataFrame + External shapefile + info : bool + Indicates if you want to print the information + """ + if self.master and info: + print("\tNearest spatial joint") + sys.stdout.flush() + grid_shp = self.get_centroids_from_coordinates() + # From geodetic coordinates (e.g. 4326) to meters (e.g. 4328) to use sjoin_nearest + # TODO: Check if the projection 4328 does not distort the coordinates too much + # https://gis.stackexchange.com/questions/372564/ + # userwarning-when-trying-to-get-centroid-from-a-polygon-geopandas + # ext_shp = ext_shp.to_crs('EPSG:4328') + # grid_shp = grid_shp.to_crs('EPSG:4328') + + # Calculate spatial joint by distance + aux_grid = gpd.sjoin_nearest(grid_shp, ext_shp, distance_col='distance') + + # Get data from closest shapes to centroids + del aux_grid['geometry'], aux_grid['index_right'] + self.shapefile.loc[aux_grid.index, aux_grid.columns] = aux_grid + + var_list = list(ext_shp.columns) + var_list.remove('geometry') + for var_name in var_list: + self.shapefile.loc[:, var_name] = np.array(self.shapefile.loc[:, var_name], dtype=ext_shp[var_name].dtype) + + return None + + +def spatial_join_centroid(self, ext_shp, info=False): + """ + Perform the spatial join using the centroid method + + Parameters + ---------- + self : nes.Nes + ext_shp : GeoDataFrame + External shapefile + info : bool + Indicates if you want to print the information + """ + if self.master and info: + print("\tCentroid spatial join") + sys.stdout.flush() + if info and self.master: + print("\t\tCalculating centroids") + sys.stdout.flush() + # Get centroids + grid_shp = self.get_centroids_from_coordinates() + + # Calculate spatial joint + if info and self.master: + print("\t\tCalculating centroid spatial join") + sys.stdout.flush() + aux_grid = gpd.sjoin(grid_shp, ext_shp, predicate='within') + + # Get data from shapes where there are centroids, rest will be NaN + del aux_grid['geometry'], aux_grid['index_right'] + self.shapefile.loc[aux_grid.index, aux_grid.columns] = aux_grid + + var_list = list(ext_shp.columns) + var_list.remove('geometry') + for var_name in var_list: + self.shapefile.loc[:, var_name] = np.array(self.shapefile.loc[:, var_name], dtype=ext_shp[var_name].dtype) + + return None + + +def spatial_join_intersection(self, ext_shp, info=False): + """ + Perform the spatial join using the intersection method + + Parameters + ---------- + self : nes.Nes + ext_shp : GeoDataFrame + External shapefile + info : bool + Indicates if you want to print the information + """ + var_list = list(ext_shp.columns) + var_list.remove('geometry') + + grid_shp = self.shapefile + + grid_shp['FID_grid'] = grid_shp.index + grid_shp = grid_shp.reset_index() + # Get intersected areas + # inp, res = ext_shp.sindex.query_bulk(grid_shp.geometry, predicate='intersects') + inp, res = grid_shp.sindex.query_bulk(ext_shp.geometry, predicate='intersects') + + if info: + print('\t\tRank {0:03d}: {1} intersected areas found'.format(self.rank, len(inp))) + sys.stdout.flush() + # Calculate intersected areas and fractions + intersection = pd.DataFrame(columns=['FID', 'ext_shp_id', 'weight']) + intersection['FID'] = np.array(grid_shp.loc[res, 'FID_grid'], dtype=np.uint32) + intersection['ext_shp_id'] = np.array(inp, dtype=np.uint32) + + if len(intersection) > 0: + if True: + # No Warnings Zone + counts = intersection['FID'].value_counts() + warnings.filterwarnings('ignore') + intersection.loc[:, 'weight'] = 1. + + for i, row in intersection.iterrows(): + if isinstance(i, int) and i % 1000 == 0 and info: + print('\t\t\tRank {0:03d}: {1:.3f} %'.format(self.rank, i * 100 / len(intersection))) + sys.stdout.flush() + # Filter to do not calculate percentages over 100% grid cells spatial joint + if counts[row['FID']] > 1: + try: + intersection.loc[i, 'weight'] = grid_shp.loc[res[i], 'geometry'].intersection( + ext_shp.loc[inp[i], 'geometry']).area / grid_shp.loc[res[i], 'geometry'].area + except TopologicalError: + # If for some reason the geometry is corrupted it should work with the buffer function + ext_shp.loc[[inp[i]], 'geometry'] = ext_shp.loc[[inp[i]], 'geometry'].buffer(0) + intersection.loc[i, 'weight'] = grid_shp.loc[res[i], 'geometry'].intersection( + ext_shp.loc[inp[i], 'geometry']).area / grid_shp.loc[res[i], 'geometry'].area + # intersection['intersect_area'] = intersection.apply( + # lambda x: x['geometry_grid'].intersection(x['geometry_ext']).area, axis=1) + intersection.drop(intersection[intersection['weight'] <= 0].index, inplace=True) + + warnings.filterwarnings('default') + + # Choose the biggest area from intersected areas with multiple options + intersection.sort_values('weight', ascending=False, inplace=True) + intersection = intersection.drop_duplicates(subset='FID', keep="first") + intersection = intersection.sort_values('FID').set_index('FID') + + for var_name in var_list: + self.shapefile.loc[intersection.index, var_name] = np.array( + ext_shp.loc[intersection['ext_shp_id'], var_name]) + else: + for var_name in var_list: + self.shapefile.loc[:, var_name] = np.nan + for var_name in var_list: + self.shapefile.loc[:, var_name] = np.array(self.shapefile.loc[:, var_name], dtype=ext_shp[var_name].dtype) + return None diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py index 0cdb8a7..6c1972a 100644 --- a/nes/nc_projections/default_nes.py +++ b/nes/nc_projections/default_nes.py @@ -9,14 +9,13 @@ from xarray import open_dataset from netCDF4 import Dataset, num2date, date2num from mpi4py import MPI from cfunits import Units +from shapely.geos import TopologicalError import geopandas as gpd from shapely.geometry import Polygon, Point from copy import deepcopy, copy import datetime import pyproj -from ..methods import vertical_interpolation -from ..methods import horizontal_interpolation -from ..methods import cell_measures +from ..methods import vertical_interpolation, horizontal_interpolation, cell_measures, spatial_join from ..nes_formats import to_netcdf_cams_ra @@ -544,11 +543,13 @@ class Nes(object): msg += "The given time bounds list has a different length than the time array. " msg += "(time:{0}, bnds:{1}). Time bounds will not be set.".format(len(self._time), len(time_bnds)) warnings.warn(msg) + sys.stderr.flush() else: msg = 'WARNING!!! ' msg += 'There is at least one element in the time bounds to be set that is not a datetime object. ' msg += 'Time bounds will not be set.' warnings.warn(msg) + sys.stderr.flush() return None @@ -2255,6 +2256,9 @@ class Nes(object): for var_name in self.variables.keys(): self.variables[var_name]['cell_measures'] = 'area: cell_area' + + if self.info: + print("Rank {0:03d}: Cell measures done".format(self.rank)) return None def _create_variables(self, netcdf, chunking=False): @@ -2288,6 +2292,7 @@ class Nes(object): msg += "Different data types for variable {0}. ".format(var_name) msg += "Input dtype={0}. Data dtype={1}.".format(var_dtype, var_dict['data'].dtype) warnings.warn(msg) + sys.stderr.flush() try: var_dict['data'] = var_dict['data'].astype(var_dtype) except Exception as e: # TODO: Detect exception @@ -2517,8 +2522,6 @@ class Nes(object): # Create cell measures self._create_cell_measures(netcdf) - if self.info: - print("Rank {0:03d}: Cell measures done".format(self.rank)) # Create variables self._create_variables(netcdf, chunking=chunking) @@ -2864,6 +2867,7 @@ class Nes(object): if lev is None: msg = 'No vertical level has been specified. The first one will be selected.' warnings.warn(msg) + sys.stderr.flush() idx_lev = 0 else: if lev not in self.lev['data']: @@ -2874,6 +2878,7 @@ class Nes(object): if time is None: msg = 'No time has been specified. The first one will be selected.' warnings.warn(msg) + sys.stderr.flush() idx_time = 0 else: if time not in self.time: @@ -2908,132 +2913,6 @@ class Nes(object): return None - def spatial_join(self, ext_shp, method=None, var_list=None, info=False): - """ - Compute overlay intersection of two GeoPandasDataFrames. - - Parameters - ---------- - ext_shp : GeoPandasDataFrame or str - File or path from where the data will be obtained on the intersection. - method : str - Overlay method. Accepted values: ['nearest', 'intersection', 'centroid']. - var_list : List or None - Variables that will be included in the resulting shapefile. - info : bool - Indicates if you want to print the process info or no - """ - if isinstance(ext_shp, str): - ext_shp = gpd.read_file(ext_shp) - - # Create shapefile if it does not exist - if self.shapefile is None: - msg = 'Shapefile does not exist. It will be created now.' - warnings.warn(msg) - grid_shp = self.create_shapefile() - else: - grid_shp = self.shapefile - grid_shp = copy(grid_shp) - - # Get variables of interest from shapefile - if var_list is not None: - if isinstance(var_list, str): - var_list = [var_list] - ext_shp = ext_shp.loc[:, var_list + ['geometry']] - else: - var_list = ext_shp.columns.to_list() - var_list.remove('geometry') - - ext_shp = ext_shp.to_crs(grid_shp.crs) - - # Nearest centroids to the shapefile polygons - if method == 'nearest': - if self.master and info: - print("Nearest spatial joint") - # Get centroids - centroids_gdf = self.get_centroids_from_coordinates() - - # From geodetic coordinates (e.g. 4326) to meters (e.g. 4328) to use sjoin_nearest - # TODO: Check if the projection 4328 does not distort the coordinates too much - # https://gis.stackexchange.com/questions/372564/ - # userwarning-when-trying-to-get-centroid-from-a-polygon-geopandas - ext_shp = ext_shp.to_crs('EPSG:4328') - centroids_gdf = centroids_gdf.to_crs('EPSG:4328') - - # Calculate spatial joint by distance - aux_grid = gpd.sjoin_nearest(centroids_gdf, ext_shp, distance_col='distance') - - # Get data from closest shapes to centroids - del aux_grid['geometry'], aux_grid['index_right'] - self.shapefile.loc[aux_grid.index, aux_grid.columns] = aux_grid - - # Intersect the areas of the shapefile polygons, outside the shapefile there will be NaN - elif method == 'intersection': - if self.master and info: - print("Intersection spatial joint") - grid_shp['FID_grid'] = grid_shp.index - grid_shp = grid_shp.reset_index() - # Get intersected areas - # inp, res = shapefile.sindex.query_bulk(self.shapefile.geometry, predicate='intersects') - inp, res = grid_shp.sindex.query_bulk(ext_shp.geometry, predicate='intersects') - - if self.master and info: - print('Rank {0:03d}: {1} intersected areas found'.format(self.rank, len(inp))) - - # Calculate intersected areas and fractions - intersection = pd.DataFrame() - intersection['FID'] = np.array(grid_shp.loc[res, 'FID_grid'], dtype=np.uint32) - intersection['ext_shp_id'] = np.array(inp, dtype=np.uint32) - - # intersection['geometry_ext'] = ext_shp.loc[inp, 'geometry'].values - # intersection['geometry_grid'] = grid_shp.loc[res, 'geometry'].values - if True: - # No Warnings Zone - counts = intersection['FID'].value_counts() - warnings.filterwarnings('ignore') - - intersection.loc[:, 'weight'] = 1. - for i, row in intersection.iterrows(): - if isinstance(i, int) and i % 1000 == 0 and self.master and info: - print('\tRank {0:03d}: {1:.3f} %'.format(self.rank, i*100 / len(intersection))) - # Filter to do not calculate percentages over 100% grid cells spatial joint - if counts[row['FID']] > 1: - intersection.loc[i, 'weight'] = grid_shp.loc[res[i], 'geometry'].intersection( - ext_shp.loc[inp[i], 'geometry']).area / grid_shp.loc[res[i], 'geometry'].area - # intersection['intersect_area'] = intersection.apply( - # lambda x: x['geometry_grid'].intersection(x['geometry_ext']).area, axis=1) - intersection.drop(intersection[intersection['weight'] <= 0].index, inplace=True) - - warnings.filterwarnings('default') - - # Choose the biggest area from intersected areas with multiple options - intersection.sort_values('weight', ascending=False, inplace=True) - intersection = intersection.drop_duplicates(subset='FID', keep="first") - intersection = intersection.sort_values('FID').set_index('FID') - - for var_name in var_list: - self.shapefile.loc[intersection.index, var_name] = np.array( - ext_shp.loc[intersection['ext_shp_id'], var_name]) - - # Centroids that fall on the shapefile polygons, outside the shapefile there will be NaN - elif method == 'centroid': - - # Get centroids - centroids_gdf = self.get_centroids_from_coordinates() - - # Calculate spatial joint - aux_grid = gpd.sjoin(centroids_gdf, ext_shp, predicate='within') - - # Get data from shapes where there are centroids, rest will be NaN - del aux_grid['geometry'], aux_grid['index_right'] - self.shapefile.loc[aux_grid.index, aux_grid.columns] = aux_grid - - else: - accepted_values = ['nearest', 'intersection', 'centroid'] - raise NotImplementedError('{0} is not implemented. Choose from: {1}'.format(method, accepted_values)) - - return None - def get_centroids_from_coordinates(self): """ Get centroids from geographical coordinates. @@ -3307,6 +3186,24 @@ class Nes(object): self, dst_grid, weight_matrix_path=weight_matrix_path, kind=kind, n_neighbours=n_neighbours, info=info, to_providentia=to_providentia, only_create_wm=only_create_wm, wm=wm) + def spatial_join(self, ext_shp, method=None, var_list=None, info=False): + """ + Compute overlay intersection of two GeoPandasDataFrames. + + Parameters + ---------- + ext_shp : GeoPandasDataFrame or str + File or path from where the data will be obtained on the intersection. + method : str + Overlay method. Accepted values: ['nearest', 'intersection', 'centroid']. + var_list : List or None + Variables that will be included in the resulting shapefile. + info : bool + Indicates if you want to print the process info or no + """ + + return spatial_join(self, ext_shp=ext_shp, method=method, var_list=var_list, info=info) + def calculate_grid_area(self): """ Get coordinate bounds and call function to calculate the area of each cell of a grid. diff --git a/nes/nc_projections/lcc_nes.py b/nes/nc_projections/lcc_nes.py index 585acbe..a049b50 100644 --- a/nes/nc_projections/lcc_nes.py +++ b/nes/nc_projections/lcc_nes.py @@ -1,6 +1,7 @@ #!/usr/bin/env python import warnings +import sys import numpy as np import pandas as pd from cfunits import Units @@ -168,6 +169,7 @@ class LCCNes(Nes): else: msg = 'There is no variable called Lambert_conformal, projection has not been defined.' warnings.warn(msg) + sys.stderr.flush() projection_data = None return projection_data diff --git a/nes/nc_projections/mercator_nes.py b/nes/nc_projections/mercator_nes.py index e6ccf18..d100e6f 100644 --- a/nes/nc_projections/mercator_nes.py +++ b/nes/nc_projections/mercator_nes.py @@ -1,6 +1,7 @@ #!/usr/bin/env python import warnings +import sys import numpy as np import pandas as pd from cfunits import Units @@ -164,6 +165,7 @@ class MercatorNes(Nes): else: msg = 'There is no variable called mercator, projection has not been defined.' warnings.warn(msg) + sys.stderr.flush() projection_data = None return projection_data diff --git a/nes/nc_projections/points_nes.py b/nes/nc_projections/points_nes.py index 3d55257..708f2b1 100644 --- a/nes/nc_projections/points_nes.py +++ b/nes/nc_projections/points_nes.py @@ -343,6 +343,7 @@ class PointsNes(Nes): msg += "Input dtype={0}. Data dtype={1}.".format(var_dtype, var_dict['data'].dtype) warnings.warn(msg) + sys.stderr.flush() try: var_dict['data'] = var_dict['data'].astype(var_dtype) except Exception as e: # TODO: Detect exception @@ -486,6 +487,7 @@ class PointsNes(Nes): msg = "WARNING!!! " msg += "Variable {0} was not loaded. It will not be written.".format(var_name) warnings.warn(msg) + sys.stderr.flush() return None diff --git a/nes/nc_projections/points_nes_ghost.py b/nes/nc_projections/points_nes_ghost.py index 978f4d5..79dd468 100644 --- a/nes/nc_projections/points_nes_ghost.py +++ b/nes/nc_projections/points_nes_ghost.py @@ -478,6 +478,7 @@ class PointsNesGHOST(PointsNes): msg = 'WARNING!!! ' msg += 'Variable {0} was not loaded. It will not be written.'.format(var_name) warnings.warn(msg) + sys.stderr.flush() return None @@ -603,6 +604,7 @@ class PointsNesGHOST(PointsNes): msg += 'GHOST datasets cannot be written in parallel yet. ' msg += 'Changing to serial mode.' warnings.warn(msg) + sys.stderr.flush() super(PointsNesGHOST, self).to_netcdf(path, compression_level=compression_level, serial=True, info=info, chunking=chunking) diff --git a/nes/nc_projections/points_nes_providentia.py b/nes/nc_projections/points_nes_providentia.py index 91229da..81a771b 100644 --- a/nes/nc_projections/points_nes_providentia.py +++ b/nes/nc_projections/points_nes_providentia.py @@ -514,6 +514,7 @@ class PointsNesProvidentia(PointsNes): msg = 'WARNING!!! ' msg += 'Variable {0} was not loaded. It will not be written.'.format(var_name) warnings.warn(msg) + sys.stderr.flush() return None @@ -605,6 +606,7 @@ class PointsNesProvidentia(PointsNes): msg += 'Providentia datasets cannot be written in parallel yet. ' msg += 'Changing to serial mode.' warnings.warn(msg) + sys.stderr.flush() super(PointsNesProvidentia, self).to_netcdf(path, compression_level=compression_level, serial=True, info=info, chunking=chunking) diff --git a/nes/nc_projections/rotated_nes.py b/nes/nc_projections/rotated_nes.py index ac66c76..7176da7 100644 --- a/nes/nc_projections/rotated_nes.py +++ b/nes/nc_projections/rotated_nes.py @@ -1,6 +1,7 @@ #!/usr/bin/env python import warnings +import sys import numpy as np import pandas as pd import math @@ -164,6 +165,7 @@ class RotatedNes(Nes): else: msg = 'There is no variable called rotated_pole, projection has not been defined.' warnings.warn(msg) + sys.stderr.flush() projection_data = None return projection_data diff --git a/nes/nes_formats/cams_ra_format.py b/nes/nes_formats/cams_ra_format.py index a286d5d..d5b8a6b 100644 --- a/nes/nes_formats/cams_ra_format.py +++ b/nes/nes_formats/cams_ra_format.py @@ -187,6 +187,7 @@ def create_variables(self, netcdf, i_lev): msg = 'WARNING!!! ' msg += 'Variable {0} was not loaded. It will not be written.'.format(var_name) warnings.warn(msg) + sys.stderr.flush() return None diff --git a/tests/2.1-test_spatial_join.py b/tests/2.1-test_spatial_join.py index ede7851..c377965 100644 --- a/tests/2.1-test_spatial_join.py +++ b/tests/2.1-test_spatial_join.py @@ -12,6 +12,7 @@ rank = comm.Get_rank() size = comm.Get_size() parallel_method = 'Y' +serial_write = False result_path = "Times_test_2.1_spatial_join_{0}_{1:03d}.csv".format(parallel_method, size) result = pd.DataFrame(index=['read', 'calculate', 'write'], @@ -20,11 +21,20 @@ result = pd.DataFrame(index=['read', 'calculate', 'write'], '2.1.5.Existing_file_intersection', '2.1.6.New_file_intersection']) # ===== PATH TO MASK ===== # -shapefile_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/timezones_2021c/timezones_2021c.shp' +# Timezones +# shapefile_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/timezones_2021c/timezones_2021c.shp' +# shapefile_var_list = ['tzid'] +# str_len = 32 +# Country ISO codes +shapefile_path = "/gpfs/projects/bsc32/models/NES_tutorial_data/gadm_country_mask/gadm_country_ISO3166.shp" +shapefile_var_list = ['ISO'] +str_len = 3 # Original path: /gpfs/scratch/bsc32/bsc32538/original_files/franco_interp.nc # Regular lat-lon grid from MONARCH original_path = '/gpfs/projects/bsc32/models/NES_tutorial_data/franco_interp.nc' +# CAMS_Global +# original_path = "/gpfs/projects/bsc32/models/NES_tutorial_data/nox_no_201505.nc" # ====================================================================================================================== # =================================== CENTROID EXISTING FILE =================================================== @@ -37,73 +47,27 @@ if rank == 0: # READ st_time = timeit.default_timer() nessy = open_netcdf(original_path, parallel_method=parallel_method) +nessy.variables = {} +nessy.create_shapefile() comm.Barrier() result.loc['read', test_name] = timeit.default_timer() - st_time # SPATIAL JOIN # Method can be centroid, nearest and intersection st_time = timeit.default_timer() -nessy.spatial_join(shapefile_path, method='centroid') +nessy.spatial_join(shapefile_path, method='centroid', var_list=shapefile_var_list, info=True) comm.Barrier() -result.loc['calculate', test_name] = timeit.default_timer() - st_time -print('SPATIAL JOIN - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) - -# ADD TIMEZONES -timezones = nessy.shapefile['tzid'].values.reshape([nessy.lat['data'].shape[0], - nessy.lon['data'].shape[-1]]) -nessy.variables['tz'] = {'data': timezones,} - -# WRITE -st_time = timeit.default_timer() -nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True) -comm.Barrier() -result.loc['write', test_name] = timeit.default_timer() - st_time - -# REOPEN -nessy = open_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), parallel_method=parallel_method) -nessy.load() - -comm.Barrier() -if rank == 0: - print(result.loc[:, test_name]) -sys.stdout.flush() - -# ====================================================================================================================== -# =================================== CENTROID FROM NEW FILE =================================================== -# ====================================================================================================================== - -test_name = '2.1.2.New_file_centroid' -if rank == 0: - print(test_name) +result.loc['calcul', test_name] = timeit.default_timer() - st_time -# DEFINE PROJECTION st_time = timeit.default_timer() -projection = 'regular' -lat_orig = 41.1 -lon_orig = 1.8 -inc_lat = 0.2 -inc_lon = 0.2 -n_lat = 100 -n_lon = 100 -# SPATIAL JOIN -# Method can be centroid, nearest and intersection -nessy = from_shapefile(shapefile_path, method='centroid', projection=projection, - lat_orig=lat_orig, lon_orig=lon_orig, - inc_lat=inc_lat, inc_lon=inc_lon, - n_lat=n_lat, n_lon=n_lon) +# ADD Var +for var_name in shapefile_var_list: + data = nessy.shapefile[var_name].values.reshape([nessy.lat['data'].shape[0], nessy.lon['data'].shape[-1]]) + nessy.variables[var_name] = {'data': data, 'dtype': str} +nessy.set_strlen(str_len) comm.Barrier() -result.loc['calculate', test_name] = timeit.default_timer() - st_time -print('FROM SHAPEFILE - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) - -# ADD TIMEZONES -timezones = nessy.shapefile['tzid'].values.reshape([nessy.lat['data'].shape[0], - nessy.lon['data'].shape[-1]]) -nessy.variables['tz'] = {'data': timezones,} - -# WRITE -st_time = timeit.default_timer() -nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True) +nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), serial=serial_write) comm.Barrier() result.loc['write', test_name] = timeit.default_timer() - st_time @@ -116,6 +80,55 @@ if rank == 0: print(result.loc[:, test_name]) sys.stdout.flush() +# # ====================================================================================================================== +# # =================================== CENTROID FROM NEW FILE =================================================== +# # ====================================================================================================================== +# +# test_name = '2.1.2.New_file_centroid' +# if rank == 0: +# print(test_name) +# +# # DEFINE PROJECTION +# st_time = timeit.default_timer() +# projection = 'regular' +# lat_orig = 41.1 +# lon_orig = 1.8 +# inc_lat = 0.2 +# inc_lon = 0.2 +# n_lat = 100 +# n_lon = 100 +# +# # SPATIAL JOIN +# # Method can be centroid, nearest and intersection +# nessy = from_shapefile(shapefile_path, method='centroid', projection=projection, +# lat_orig=lat_orig, lon_orig=lon_orig, +# inc_lat=inc_lat, inc_lon=inc_lon, +# n_lat=n_lat, n_lon=n_lon) +# comm.Barrier() +# result.loc['calculate', test_name] = timeit.default_timer() - st_time +# print('FROM SHAPEFILE - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) +# +# # ADD Var +# for var_name in shapefile_var_list: +# data = nessy.shapefile[var_name].values.reshape([nessy.lat['data'].shape[0], nessy.lon['data'].shape[-1]]) +# nessy.variables[var_name] = {'data': data, 'dtype': str} +# nessy.set_strlen(str_len) +# +# # WRITE +# st_time = timeit.default_timer() +# nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), serial=serial_write, info=True) +# comm.Barrier() +# result.loc['write', test_name] = timeit.default_timer() - st_time +# +# # REOPEN +# nessy = open_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), parallel_method=parallel_method) +# nessy.load() +# +# comm.Barrier() +# if rank == 0: +# print(result.loc[:, test_name]) +# sys.stdout.flush() + # ====================================================================================================================== # =================================== NEAREST EXISTING FILE =================================================== # ====================================================================================================================== @@ -127,25 +140,28 @@ if rank == 0: # READ st_time = timeit.default_timer() nessy = open_netcdf(original_path, parallel_method=parallel_method) +nessy.variables = {} +nessy.create_shapefile() comm.Barrier() result.loc['read', test_name] = timeit.default_timer() - st_time # SPATIAL JOIN # Method can be centroid, nearest and intersection st_time = timeit.default_timer() -nessy.spatial_join(shapefile_path, method='nearest') +nessy.spatial_join(shapefile_path, method='nearest', var_list=shapefile_var_list, info=True) comm.Barrier() result.loc['calculate', test_name] = timeit.default_timer() - st_time print('SPATIAL JOIN - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) -# ADD TIMEZONES -timezones = nessy.shapefile['tzid'].values.reshape([nessy.lat['data'].shape[0], - nessy.lon['data'].shape[-1]]) -nessy.variables['tz'] = {'data': timezones,} +# ADD Var +for var_name in shapefile_var_list: + data = nessy.shapefile[var_name].values.reshape([nessy.lat['data'].shape[0], nessy.lon['data'].shape[-1]]) + nessy.variables[var_name] = {'data': data, 'dtype': str} +nessy.set_strlen(str_len) # WRITE st_time = timeit.default_timer() -nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True) +nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), serial=serial_write, info=True) comm.Barrier() result.loc['write', test_name] = timeit.default_timer() - st_time @@ -158,53 +174,54 @@ if rank == 0: print(result.loc[:, test_name]) sys.stdout.flush() -# ====================================================================================================================== -# =================================== NEAREST FROM NEW FILE =================================================== -# ====================================================================================================================== - -test_name = '2.1.4.New_file_nearest' -if rank == 0: - print(test_name) - -# DEFINE PROJECTION -st_time = timeit.default_timer() -projection = 'regular' -lat_orig = 41.1 -lon_orig = 1.8 -inc_lat = 0.2 -inc_lon = 0.2 -n_lat = 100 -n_lon = 100 - -# SPATIAL JOIN -# Method can be centroid, nearest and intersection -nessy = from_shapefile(shapefile_path, method='nearest', projection=projection, - lat_orig=lat_orig, lon_orig=lon_orig, - inc_lat=inc_lat, inc_lon=inc_lon, - n_lat=n_lat, n_lon=n_lon) -comm.Barrier() -result.loc['calculate', test_name] = timeit.default_timer() - st_time -print('FROM SHAPEFILE - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) - -# ADD TIMEZONES -timezones = nessy.shapefile['tzid'].values.reshape([nessy.lat['data'].shape[0], - nessy.lon['data'].shape[-1]]) -nessy.variables['tz'] = {'data': timezones,} - -# WRITE -st_time = timeit.default_timer() -nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True) -comm.Barrier() -result.loc['write', test_name] = timeit.default_timer() - st_time - -# REOPEN -nessy = open_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), parallel_method=parallel_method) -nessy.load() - -comm.Barrier() -if rank == 0: - print(result.loc[:, test_name]) -sys.stdout.flush() +# # ====================================================================================================================== +# # =================================== NEAREST FROM NEW FILE =================================================== +# # ====================================================================================================================== +# +# test_name = '2.1.4.New_file_nearest' +# if rank == 0: +# print(test_name) +# +# # DEFINE PROJECTION +# st_time = timeit.default_timer() +# projection = 'regular' +# lat_orig = 41.1 +# lon_orig = 1.8 +# inc_lat = 0.2 +# inc_lon = 0.2 +# n_lat = 100 +# n_lon = 100 +# +# # SPATIAL JOIN +# # Method can be centroid, nearest and intersection +# nessy = from_shapefile(shapefile_path, method='nearest', projection=projection, +# lat_orig=lat_orig, lon_orig=lon_orig, +# inc_lat=inc_lat, inc_lon=inc_lon, +# n_lat=n_lat, n_lon=n_lon) +# comm.Barrier() +# result.loc['calculate', test_name] = timeit.default_timer() - st_time +# print('FROM SHAPEFILE - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) +# +# # ADD Var +# for var_name in shapefile_var_list: +# data = nessy.shapefile[var_name].values.reshape([nessy.lat['data'].shape[0], nessy.lon['data'].shape[-1]]) +# nessy.variables[var_name] = {'data': data, 'dtype': str} +# nessy.set_strlen(str_len) +# +# # WRITE +# st_time = timeit.default_timer() +# nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), serial=serial_write, info=True) +# comm.Barrier() +# result.loc['write', test_name] = timeit.default_timer() - st_time +# +# # REOPEN +# nessy = open_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), parallel_method=parallel_method) +# nessy.load() +# +# comm.Barrier() +# if rank == 0: +# print(result.loc[:, test_name]) +# sys.stdout.flush() # ====================================================================================================================== @@ -214,29 +231,33 @@ sys.stdout.flush() test_name = '2.1.5.Existing_file_intersection' if rank == 0: print(test_name) - + # READ st_time = timeit.default_timer() nessy = open_netcdf(original_path, parallel_method=parallel_method) +nessy.variables = {} +nessy.create_shapefile() comm.Barrier() result.loc['read', test_name] = timeit.default_timer() - st_time # SPATIAL JOIN # Method can be centroid, nearest and intersection st_time = timeit.default_timer() -nessy.spatial_join(shapefile_path, method='intersection') +nessy.spatial_join(shapefile_path, method='intersection', var_list=shapefile_var_list, info=True) comm.Barrier() result.loc['calculate', test_name] = timeit.default_timer() - st_time print('SPATIAL JOIN - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) -# ADD TIMEZONES -timezones = nessy.shapefile['tzid'].values.reshape([nessy.lat['data'].shape[0], - nessy.lon['data'].shape[-1]]) -nessy.variables['tz'] = {'data': timezones,} +# ADD Var +for var_name in shapefile_var_list: + data = nessy.shapefile[var_name].values.reshape([nessy.lat['data'].shape[0], nessy.lon['data'].shape[-1]]) + nessy.variables[var_name] = {'data': data, 'dtype': str} +nessy.set_strlen(str_len) # WRITE st_time = timeit.default_timer() -nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True) +nessy.set_strlen(strlen=str_len) +nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), serial=serial_write, info=True) comm.Barrier() result.loc['write', test_name] = timeit.default_timer() - st_time @@ -249,54 +270,53 @@ if rank == 0: print(result.loc[:, test_name]) sys.stdout.flush() - # ====================================================================================================================== # =================================== INTERSECTION FROM NEW FILE =================================================== # ====================================================================================================================== -test_name = '2.1.6.New_file_intersection' -if rank == 0: - print(test_name) - -# DEFINE PROJECTION -st_time = timeit.default_timer() -projection = 'regular' -lat_orig = 41.1 -lon_orig = 1.8 -inc_lat = 0.2 -inc_lon = 0.2 -n_lat = 100 -n_lon = 100 - -# SPATIAL JOIN -# Method can be centroid, nearest and intersection -nessy = from_shapefile(shapefile_path, method='intersection', projection=projection, - lat_orig=lat_orig, lon_orig=lon_orig, - inc_lat=inc_lat, inc_lon=inc_lon, - n_lat=n_lat, n_lon=n_lon) -comm.Barrier() -result.loc['calculate', test_name] = timeit.default_timer() - st_time -print('FROM SHAPEFILE - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) - -# ADD TIMEZONES -timezones = nessy.shapefile['tzid'].values.reshape([nessy.lat['data'].shape[0], - nessy.lon['data'].shape[-1]]) -nessy.variables['tz'] = {'data': timezones,} - -# WRITE -st_time = timeit.default_timer() -nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), info=True) -comm.Barrier() -result.loc['write', test_name] = timeit.default_timer() - st_time - -# REOPEN -nessy = open_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), parallel_method=parallel_method) -nessy.load() - -comm.Barrier() -if rank == 0: - print(result.loc[:, test_name]) -sys.stdout.flush() +# test_name = '2.1.6.New_file_intersection' +# if rank == 0: +# print(test_name) +# +# # DEFINE PROJECTION +# st_time = timeit.default_timer() +# projection = 'regular' +# lat_orig = 41.1 +# lon_orig = 1.8 +# inc_lat = 0.2 +# inc_lon = 0.2 +# n_lat = 100 +# n_lon = 100 +# +# # SPATIAL JOIN +# # Method can be centroid, nearest and intersection +# nessy = from_shapefile(shapefile_path, method='intersection', projection=projection, +# lat_orig=lat_orig, lon_orig=lon_orig, +# inc_lat=inc_lat, inc_lon=inc_lon, +# n_lat=n_lat, n_lon=n_lon) +# comm.Barrier() +# result.loc['calculate', test_name] = timeit.default_timer() - st_time +# print('FROM SHAPEFILE - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) +# +# # ADD Var +# for var_name in shapefile_var_list: +# data = nessy.shapefile[var_name].values.reshape([nessy.lat['data'].shape[0], nessy.lon['data'].shape[-1]]) +# nessy.variables[var_name] = {'data': data, 'dtype': str} +# nessy.set_strlen(str_len) +# # WRITE +# st_time = timeit.default_timer() +# nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), serial=serial_write, info=True) +# comm.Barrier() +# result.loc['write', test_name] = timeit.default_timer() - st_time +# +# # REOPEN +# nessy = open_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), parallel_method=parallel_method) +# nessy.load() +# +# comm.Barrier() +# if rank == 0: +# print(result.loc[:, test_name]) +# sys.stdout.flush() if rank == 0: result.to_csv(result_path) -- GitLab From a23ab9c0d3537e9f3ff1e468385ae634262271b7 Mon Sep 17 00:00:00 2001 From: ctena Date: Fri, 24 Mar 2023 12:43:10 +0100 Subject: [PATCH 34/43] - spatial_join added rewrite to spatial join test --- tests/2.1-test_spatial_join.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tests/2.1-test_spatial_join.py b/tests/2.1-test_spatial_join.py index c377965..283cfb8 100644 --- a/tests/2.1-test_spatial_join.py +++ b/tests/2.1-test_spatial_join.py @@ -74,6 +74,7 @@ result.loc['write', test_name] = timeit.default_timer() - st_time # REOPEN nessy = open_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), parallel_method=parallel_method) nessy.load() +nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}_2.nc".format(size), serial=serial_write) comm.Barrier() if rank == 0: -- GitLab From 41fc08890b4fadfb4edc3fe6a3d1fe265b62de01 Mon Sep 17 00:00:00 2001 From: Alba Vilanova Cortezon Date: Mon, 27 Mar 2023 16:17:28 +0200 Subject: [PATCH 35/43] Fix issue on 5D data with strlen and update shapefile functions --- nes/nc_projections/default_nes.py | 178 ++++++++++------ nes/nc_projections/lcc_nes.py | 62 +++--- nes/nc_projections/mercator_nes.py | 61 +++--- nes/nc_projections/points_nes.py | 11 +- nes/nc_projections/points_nes_ghost.py | 2 +- nes/nc_projections/rotated_nes.py | 57 ++--- tests/2.1-test_spatial_join.py | 284 +++++++++++++------------ 7 files changed, 370 insertions(+), 285 deletions(-) diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py index 8bd50e5..d479a19 100644 --- a/nes/nc_projections/default_nes.py +++ b/nes/nc_projections/default_nes.py @@ -1887,15 +1887,33 @@ class Nes(object): self.read_axis_limits['z_min']:self.read_axis_limits['z_max'], self.read_axis_limits['y_min']:self.read_axis_limits['y_max'], self.read_axis_limits['x_min']:self.read_axis_limits['x_max']] - # elif len(var_dims) == 5: - # data = nc_var[self.read_axis_limits['t_min']:self.read_axis_limits['t_max'], - # :, - # self.read_axis_limits['z_min']:self.read_axis_limits['z_max'], - # self.read_axis_limits['y_min']:self.read_axis_limits['y_max'], - # self.read_axis_limits['x_min']:self.read_axis_limits['x_max']] + elif len(var_dims) == 5: + if 'strlen' in var_dims: + data = nc_var[self.read_axis_limits['t_min']:self.read_axis_limits['t_max'], + self.read_axis_limits['z_min']:self.read_axis_limits['z_max'], + self.read_axis_limits['y_min']:self.read_axis_limits['y_max'], + self.read_axis_limits['x_min']:self.read_axis_limits['x_max'], + :] + data_aux = np.empty(shape=(data.shape[0], data.shape[1], data.shape[2], data.shape[3]), dtype=np.object) + for time_n in range(data.shape[0]): + for lev_n in range(data.shape[1]): + for lat_n in range(data.shape[2]): + for lon_n in range(data.shape[3]): + data_aux[time_n, lev_n, lat_n, lon_n] = ''.join( + data[time_n, lev_n, lat_n, lon_n].tostring().decode('ascii').replace('\x00', '')) + data = data_aux + else: + # data = nc_var[self.read_axis_limits['t_min']:self.read_axis_limits['t_max'], + # :, + # self.read_axis_limits['z_min']:self.read_axis_limits['z_max'], + # self.read_axis_limits['y_min']:self.read_axis_limits['y_max'], + # self.read_axis_limits['x_min']:self.read_axis_limits['x_max']] + raise NotImplementedError('Error with {0}. Only can be read netCDF with 4 dimensions or less'.format( + var_name)) else: raise NotImplementedError('Error with {0}. Only can be read netCDF with 4 dimensions or less'.format( var_name)) + # # Missing to nan # try: # data[data.shapefile == True] = np.nan @@ -2284,6 +2302,7 @@ class Nes(object): """ for i, (var_name, var_dict) in enumerate(self.variables.items()): + if isinstance(var_dict['data'], int) and var_dict['data'] == 0: var_dims = ('time', 'lev',) + self._var_dim var_dtype = np.float32 @@ -2330,15 +2349,33 @@ class Nes(object): var_dims += ('strlen',) # Split strings into chars (S1) - data_aux = np.chararray(shape=(var_dict['data'].shape[0], - var_dict['data'].shape[1], - self.strlen)) - for lat_n in range(var_dict['data'].shape[0]): - for lon_n in range(var_dict['data'].shape[1]): - chars = [v for v in var_dict['data'][lat_n][lon_n]] - data = chars + ['']*(self.strlen-len(chars)) - for char_n, char in enumerate(data): - data_aux[lat_n, lon_n, char_n] = char.encode('ascii', 'ignore') + if len(var_dict['data'].shape) == 2: + data_aux = np.chararray(shape=(var_dict['data'].shape[0], + var_dict['data'].shape[1], + self.strlen)) + for lat_n in range(var_dict['data'].shape[0]): + for lon_n in range(var_dict['data'].shape[1]): + chars = [v for v in var_dict['data'][lat_n][lon_n]] + data = chars + ['']*(self.strlen-len(chars)) + for char_n, char in enumerate(data): + data_aux[lat_n, lon_n, char_n] = char.encode('ascii', 'ignore') + + elif len(var_dict['data'].shape) == 4: + data_aux = np.chararray(shape=(var_dict['data'].shape[0], + var_dict['data'].shape[1], + var_dict['data'].shape[2], + var_dict['data'].shape[3], + self.strlen)) + for time_n in range(var_dict['data'].shape[0]): + for lev_n in range(var_dict['data'].shape[1]): + for lat_n in range(var_dict['data'].shape[2]): + for lon_n in range(var_dict['data'].shape[3]): + chars = [v for v in var_dict['data'][time_n][lev_n][lat_n][lon_n]] + data = chars + ['']*(self.strlen-len(chars)) + for char_n, char in enumerate(data): + data_aux[time_n, lev_n, lat_n, lon_n, char_n] = char.encode('ascii', + 'ignore') + var_dict['data'] = data_aux var_dtype = 'S1' @@ -2377,6 +2414,7 @@ class Nes(object): for att_name, att_value in var_dict.items(): if att_name == 'data': + if att_value is not None: if self.info: print("Rank {0:03d}: Filling {1})".format(self.rank, var_name)) @@ -2385,6 +2423,17 @@ class Nes(object): self.write_axis_limits['z_min']:self.write_axis_limits['z_max'], self.write_axis_limits['y_min']:self.write_axis_limits['y_max'], self.write_axis_limits['x_min']:self.write_axis_limits['x_max']] = 0 + + elif len(att_value.shape) == 5: + if 'strlen' in var_dims: + var[self.write_axis_limits['t_min']:self.write_axis_limits['t_max'], + self.write_axis_limits['z_min']:self.write_axis_limits['z_max'], + self.write_axis_limits['y_min']:self.write_axis_limits['y_max'], + self.write_axis_limits['x_min']:self.write_axis_limits['x_max'], + :] = att_value + else: + raise NotImplementedError('It is not possible to write 5D variables.') + elif len(att_value.shape) == 4: var[self.write_axis_limits['t_min']:self.write_axis_limits['t_max'], self.write_axis_limits['z_min']:self.write_axis_limits['z_max'], @@ -2398,6 +2447,7 @@ class Nes(object): :] = att_value else: raise NotImplementedError('It is not possible to write 3D variables.') + if self.info: print("Rank {0:03d}: Var {1} data ({2}/{3})".format( self.rank, var_name, i + 1, len(self.variables))) @@ -2766,50 +2816,55 @@ class Nes(object): shapefile : GeoPandasDataFrame Shapefile dataframe. """ + + if self.shapefile is None: - if self._lat_bnds is None or self._lon_bnds is None: - self.create_spatial_bounds() - - # Reshape arrays to create geometry - aux_shape = (self.lat_bnds['data'].shape[0], self.lon_bnds['data'].shape[0], 4) - lon_bnds_aux = np.empty(aux_shape) - lon_bnds_aux[:, :, 0] = self.lon_bnds['data'][np.newaxis, :, 0] - lon_bnds_aux[:, :, 1] = self.lon_bnds['data'][np.newaxis, :, 1] - lon_bnds_aux[:, :, 2] = self.lon_bnds['data'][np.newaxis, :, 1] - lon_bnds_aux[:, :, 3] = self.lon_bnds['data'][np.newaxis, :, 0] - - lon_bnds = lon_bnds_aux - del lon_bnds_aux - - lat_bnds_aux = np.empty(aux_shape) - lat_bnds_aux[:, :, 0] = self.lat_bnds['data'][:, np.newaxis, 0] - lat_bnds_aux[:, :, 1] = self.lat_bnds['data'][:, np.newaxis, 0] - lat_bnds_aux[:, :, 2] = self.lat_bnds['data'][:, np.newaxis, 1] - lat_bnds_aux[:, :, 3] = self.lat_bnds['data'][:, np.newaxis, 1] - - lat_bnds = lat_bnds_aux - del lat_bnds_aux - - aux_b_lats = lat_bnds.reshape((lat_bnds.shape[0] * lat_bnds.shape[1], lat_bnds.shape[2])) - aux_b_lons = lon_bnds.reshape((lon_bnds.shape[0] * lon_bnds.shape[1], lon_bnds.shape[2])) - - # Create dataframe cointaining all polygons - geometry = [] - for i in range(aux_b_lons.shape[0]): - geometry.append(Polygon([(aux_b_lons[i, 0], aux_b_lats[i, 0]), - (aux_b_lons[i, 1], aux_b_lats[i, 1]), - (aux_b_lons[i, 2], aux_b_lats[i, 2]), - (aux_b_lons[i, 3], aux_b_lats[i, 3]), - (aux_b_lons[i, 0], aux_b_lats[i, 0])])) - fids = np.arange(len(self._lat['data']) * len(self._lon['data'])) - fids = fids.reshape((len(self._lat['data']), len(self._lon['data']))) - fids = fids[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'], - self.read_axis_limits['x_min']:self.read_axis_limits['x_max']] - gdf = gpd.GeoDataFrame(index=pd.Index(name='FID', data=fids.ravel()), - geometry=geometry, - crs="EPSG:4326") - self.shapefile = gdf - + if self._lat_bnds is None or self._lon_bnds is None: + self.create_spatial_bounds() + + # Reshape arrays to create geometry + aux_shape = (self.lat_bnds['data'].shape[0], self.lon_bnds['data'].shape[0], 4) + lon_bnds_aux = np.empty(aux_shape) + lon_bnds_aux[:, :, 0] = self.lon_bnds['data'][np.newaxis, :, 0] + lon_bnds_aux[:, :, 1] = self.lon_bnds['data'][np.newaxis, :, 1] + lon_bnds_aux[:, :, 2] = self.lon_bnds['data'][np.newaxis, :, 1] + lon_bnds_aux[:, :, 3] = self.lon_bnds['data'][np.newaxis, :, 0] + + lon_bnds = lon_bnds_aux + del lon_bnds_aux + + lat_bnds_aux = np.empty(aux_shape) + lat_bnds_aux[:, :, 0] = self.lat_bnds['data'][:, np.newaxis, 0] + lat_bnds_aux[:, :, 1] = self.lat_bnds['data'][:, np.newaxis, 0] + lat_bnds_aux[:, :, 2] = self.lat_bnds['data'][:, np.newaxis, 1] + lat_bnds_aux[:, :, 3] = self.lat_bnds['data'][:, np.newaxis, 1] + + lat_bnds = lat_bnds_aux + del lat_bnds_aux + + aux_b_lats = lat_bnds.reshape((lat_bnds.shape[0] * lat_bnds.shape[1], lat_bnds.shape[2])) + aux_b_lons = lon_bnds.reshape((lon_bnds.shape[0] * lon_bnds.shape[1], lon_bnds.shape[2])) + + # Create dataframe cointaining all polygons + geometry = [] + for i in range(aux_b_lons.shape[0]): + geometry.append(Polygon([(aux_b_lons[i, 0], aux_b_lats[i, 0]), + (aux_b_lons[i, 1], aux_b_lats[i, 1]), + (aux_b_lons[i, 2], aux_b_lats[i, 2]), + (aux_b_lons[i, 3], aux_b_lats[i, 3]), + (aux_b_lons[i, 0], aux_b_lats[i, 0])])) + fids = np.arange(len(self._lat['data']) * len(self._lon['data'])) + fids = fids.reshape((len(self._lat['data']), len(self._lon['data']))) + fids = fids[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'], + self.read_axis_limits['x_min']:self.read_axis_limits['x_max']] + gdf = gpd.GeoDataFrame(index=pd.Index(name='FID', data=fids.ravel()), + geometry=geometry, + crs="EPSG:4326") + self.shapefile = gdf + + else: + gdf = self.shapefile + return gdf def write_shapefile(self, path): @@ -3224,8 +3279,11 @@ class Nes(object): Source projection Nes Object. """ - grid_area = cell_measures.calculate_grid_area(self) - + if 'cell_area' not in self.cell_measures.keys(): + grid_area = cell_measures.calculate_grid_area(self) + else: + grid_area = self.cell_measures['cell_area']['data'] + return grid_area @staticmethod diff --git a/nes/nc_projections/lcc_nes.py b/nes/nc_projections/lcc_nes.py index a049b50..8c9c436 100644 --- a/nes/nc_projections/lcc_nes.py +++ b/nes/nc_projections/lcc_nes.py @@ -467,35 +467,40 @@ class LCCNes(Nes): Shapefile dataframe. """ - # Get latitude and longitude cell boundaries - if self._lat_bnds is None or self._lon_bnds is None: - self.create_spatial_bounds() - - # Reshape arrays to create geometry - aux_b_lats = self.lat_bnds['data'].reshape((self.lat_bnds['data'].shape[0] * self.lat_bnds['data'].shape[1], - self.lat_bnds['data'].shape[2])) - aux_b_lons = self.lon_bnds['data'].reshape((self.lon_bnds['data'].shape[0] * self.lon_bnds['data'].shape[1], - self.lon_bnds['data'].shape[2])) - - # Get polygons from bounds - geometry = [] - for i in range(aux_b_lons.shape[0]): - geometry.append(Polygon([(aux_b_lons[i, 0], aux_b_lats[i, 0]), - (aux_b_lons[i, 1], aux_b_lats[i, 1]), - (aux_b_lons[i, 2], aux_b_lats[i, 2]), - (aux_b_lons[i, 3], aux_b_lats[i, 3]), - (aux_b_lons[i, 0], aux_b_lats[i, 0])])) - - # Create dataframe cointaining all polygons - fids = np.arange(self._lat['data'].shape[0] * self._lat['data'].shape[1]) - fids = fids.reshape((self._lat['data'].shape[0], self._lat['data'].shape[1])) - fids = fids[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'], - self.read_axis_limits['x_min']:self.read_axis_limits['x_max']] - gdf = gpd.GeoDataFrame(index=pd.Index(name='FID', data=fids.ravel()), - geometry=geometry, - crs="EPSG:4326") - self.shapefile = gdf + if self.shapefile is None: + + # Get latitude and longitude cell boundaries + if self._lat_bnds is None or self._lon_bnds is None: + self.create_spatial_bounds() + + # Reshape arrays to create geometry + aux_b_lats = self.lat_bnds['data'].reshape((self.lat_bnds['data'].shape[0] * self.lat_bnds['data'].shape[1], + self.lat_bnds['data'].shape[2])) + aux_b_lons = self.lon_bnds['data'].reshape((self.lon_bnds['data'].shape[0] * self.lon_bnds['data'].shape[1], + self.lon_bnds['data'].shape[2])) + + # Get polygons from bounds + geometry = [] + for i in range(aux_b_lons.shape[0]): + geometry.append(Polygon([(aux_b_lons[i, 0], aux_b_lats[i, 0]), + (aux_b_lons[i, 1], aux_b_lats[i, 1]), + (aux_b_lons[i, 2], aux_b_lats[i, 2]), + (aux_b_lons[i, 3], aux_b_lats[i, 3]), + (aux_b_lons[i, 0], aux_b_lats[i, 0])])) + + # Create dataframe cointaining all polygons + fids = np.arange(self._lat['data'].shape[0] * self._lat['data'].shape[1]) + fids = fids.reshape((self._lat['data'].shape[0], self._lat['data'].shape[1])) + fids = fids[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'], + self.read_axis_limits['x_min']:self.read_axis_limits['x_max']] + gdf = gpd.GeoDataFrame(index=pd.Index(name='FID', data=fids.ravel()), + geometry=geometry, + crs="EPSG:4326") + self.shapefile = gdf + else: + gdf = self.shapefile + return gdf def get_centroids_from_coordinates(self): @@ -530,6 +535,7 @@ class LCCNes(Nes): """ Get coordinate bounds and call function to calculate the area of each cell of a grid. """ + grid_area = super(LCCNes, self).calculate_grid_area() self.cell_measures['cell_area'] = {'data': grid_area.reshape([self.lon_bnds['data'].shape[0], self.lon_bnds['data'].shape[1]])} diff --git a/nes/nc_projections/mercator_nes.py b/nes/nc_projections/mercator_nes.py index d100e6f..af1433b 100644 --- a/nes/nc_projections/mercator_nes.py +++ b/nes/nc_projections/mercator_nes.py @@ -443,35 +443,40 @@ class MercatorNes(Nes): Shapefile dataframe. """ - # Get latitude and longitude cell boundaries - if self._lat_bnds is None or self._lon_bnds is None: - self.create_spatial_bounds() - - # Reshape arrays to create geometry - aux_b_lats = self.lat_bnds['data'].reshape((self.lat_bnds['data'].shape[0] * self.lat_bnds['data'].shape[1], - self.lat_bnds['data'].shape[2])) - aux_b_lons = self.lon_bnds['data'].reshape((self.lon_bnds['data'].shape[0] * self.lon_bnds['data'].shape[1], - self.lon_bnds['data'].shape[2])) - - # Get polygons from bounds - geometry = [] - for i in range(aux_b_lons.shape[0]): - geometry.append(Polygon([(aux_b_lons[i, 0], aux_b_lats[i, 0]), - (aux_b_lons[i, 1], aux_b_lats[i, 1]), - (aux_b_lons[i, 2], aux_b_lats[i, 2]), - (aux_b_lons[i, 3], aux_b_lats[i, 3]), - (aux_b_lons[i, 0], aux_b_lats[i, 0])])) - - # Create dataframe cointaining all polygons - fids = np.arange(self._lat['data'].shape[0] * self._lat['data'].shape[1]) - fids = fids.reshape((self._lat['data'].shape[0], self._lat['data'].shape[1])) - fids = fids[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'], - self.read_axis_limits['x_min']:self.read_axis_limits['x_max']] - gdf = gpd.GeoDataFrame(index=pd.Index(name='FID', data=fids.ravel()), - geometry=geometry, - crs="EPSG:4326") - self.shapefile = gdf + if self.shapefile is None: + + # Get latitude and longitude cell boundaries + if self._lat_bnds is None or self._lon_bnds is None: + self.create_spatial_bounds() + + # Reshape arrays to create geometry + aux_b_lats = self.lat_bnds['data'].reshape((self.lat_bnds['data'].shape[0] * self.lat_bnds['data'].shape[1], + self.lat_bnds['data'].shape[2])) + aux_b_lons = self.lon_bnds['data'].reshape((self.lon_bnds['data'].shape[0] * self.lon_bnds['data'].shape[1], + self.lon_bnds['data'].shape[2])) + + # Get polygons from bounds + geometry = [] + for i in range(aux_b_lons.shape[0]): + geometry.append(Polygon([(aux_b_lons[i, 0], aux_b_lats[i, 0]), + (aux_b_lons[i, 1], aux_b_lats[i, 1]), + (aux_b_lons[i, 2], aux_b_lats[i, 2]), + (aux_b_lons[i, 3], aux_b_lats[i, 3]), + (aux_b_lons[i, 0], aux_b_lats[i, 0])])) + + # Create dataframe cointaining all polygons + fids = np.arange(self._lat['data'].shape[0] * self._lat['data'].shape[1]) + fids = fids.reshape((self._lat['data'].shape[0], self._lat['data'].shape[1])) + fids = fids[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'], + self.read_axis_limits['x_min']:self.read_axis_limits['x_max']] + gdf = gpd.GeoDataFrame(index=pd.Index(name='FID', data=fids.ravel()), + geometry=geometry, + crs="EPSG:4326") + self.shapefile = gdf + else: + gdf = self.shapefile + return gdf def get_centroids_from_coordinates(self): diff --git a/nes/nc_projections/points_nes.py b/nes/nc_projections/points_nes.py index 708f2b1..ab0ae1e 100644 --- a/nes/nc_projections/points_nes.py +++ b/nes/nc_projections/points_nes.py @@ -669,10 +669,15 @@ class PointsNes(Nes): Shapefile dataframe. """ - # Create dataframe cointaining all points - gdf = self.get_centroids_from_coordinates() - self.shapefile = gdf + if self.shapefile is None: + + # Create dataframe cointaining all points + gdf = self.get_centroids_from_coordinates() + self.shapefile = gdf + else: + gdf = self.shapefile + return gdf def get_centroids_from_coordinates(self): diff --git a/nes/nc_projections/points_nes_ghost.py b/nes/nc_projections/points_nes_ghost.py index 79dd468..1f4f4b4 100644 --- a/nes/nc_projections/points_nes_ghost.py +++ b/nes/nc_projections/points_nes_ghost.py @@ -341,7 +341,7 @@ class PointsNesGHOST(PointsNes): else: var_dtype = var_dict['data'].dtype - # Get dimensions + # Get dimensions if len(var_dict['data'].shape) == 1: # Metadata var_dims = self._var_dim diff --git a/nes/nc_projections/rotated_nes.py b/nes/nc_projections/rotated_nes.py index 7176da7..9d58434 100644 --- a/nes/nc_projections/rotated_nes.py +++ b/nes/nc_projections/rotated_nes.py @@ -494,33 +494,38 @@ class RotatedNes(Nes): Shapefile dataframe. """ - if self._lat_bnds is None or self._lon_bnds is None: - self.create_spatial_bounds() - - # Reshape arrays to create geometry - aux_b_lats = self.lat_bnds['data'].reshape((self.lat_bnds['data'].shape[0] * self.lat_bnds['data'].shape[1], - self.lat_bnds['data'].shape[2])) - aux_b_lons = self.lon_bnds['data'].reshape((self.lon_bnds['data'].shape[0] * self.lon_bnds['data'].shape[1], - self.lon_bnds['data'].shape[2])) + if self.shapefile is None: + + if self._lat_bnds is None or self._lon_bnds is None: + self.create_spatial_bounds() + + # Reshape arrays to create geometry + aux_b_lats = self.lat_bnds['data'].reshape((self.lat_bnds['data'].shape[0] * self.lat_bnds['data'].shape[1], + self.lat_bnds['data'].shape[2])) + aux_b_lons = self.lon_bnds['data'].reshape((self.lon_bnds['data'].shape[0] * self.lon_bnds['data'].shape[1], + self.lon_bnds['data'].shape[2])) + + # Get polygons from bounds + geometry = [] + for i in range(aux_b_lons.shape[0]): + geometry.append(Polygon([(aux_b_lons[i, 0], aux_b_lats[i, 0]), + (aux_b_lons[i, 1], aux_b_lats[i, 1]), + (aux_b_lons[i, 2], aux_b_lats[i, 2]), + (aux_b_lons[i, 3], aux_b_lats[i, 3]), + (aux_b_lons[i, 0], aux_b_lats[i, 0])])) - # Get polygons from bounds - geometry = [] - for i in range(aux_b_lons.shape[0]): - geometry.append(Polygon([(aux_b_lons[i, 0], aux_b_lats[i, 0]), - (aux_b_lons[i, 1], aux_b_lats[i, 1]), - (aux_b_lons[i, 2], aux_b_lats[i, 2]), - (aux_b_lons[i, 3], aux_b_lats[i, 3]), - (aux_b_lons[i, 0], aux_b_lats[i, 0])])) - - # Create dataframe cointaining all polygons - fids = np.arange(self._lat['data'].shape[0] * self._lat['data'].shape[1]) - fids = fids.reshape((self._lat['data'].shape[0], self._lat['data'].shape[1])) - fids = fids[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'], - self.read_axis_limits['x_min']:self.read_axis_limits['x_max']] - gdf = gpd.GeoDataFrame(index=pd.Index(name='FID', data=fids.ravel()), - geometry=geometry, - crs="EPSG:4326") - self.shapefile = gdf + # Create dataframe cointaining all polygons + fids = np.arange(self._lat['data'].shape[0] * self._lat['data'].shape[1]) + fids = fids.reshape((self._lat['data'].shape[0], self._lat['data'].shape[1])) + fids = fids[self.read_axis_limits['y_min']:self.read_axis_limits['y_max'], + self.read_axis_limits['x_min']:self.read_axis_limits['x_max']] + gdf = gpd.GeoDataFrame(index=pd.Index(name='FID', data=fids.ravel()), + geometry=geometry, + crs="EPSG:4326") + self.shapefile = gdf + + else: + gdf = self.shapefile return gdf diff --git a/tests/2.1-test_spatial_join.py b/tests/2.1-test_spatial_join.py index 283cfb8..dfac69b 100644 --- a/tests/2.1-test_spatial_join.py +++ b/tests/2.1-test_spatial_join.py @@ -74,61 +74,67 @@ result.loc['write', test_name] = timeit.default_timer() - st_time # REOPEN nessy = open_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), parallel_method=parallel_method) nessy.load() + +# REWRITE nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}_2.nc".format(size), serial=serial_write) +# REOPEN +nessy = open_netcdf(test_name.replace(' ', '_') + "_{0:03d}_2.nc".format(size), parallel_method=parallel_method) +nessy.load() + comm.Barrier() if rank == 0: print(result.loc[:, test_name]) sys.stdout.flush() -# # ====================================================================================================================== -# # =================================== CENTROID FROM NEW FILE =================================================== -# # ====================================================================================================================== -# -# test_name = '2.1.2.New_file_centroid' -# if rank == 0: -# print(test_name) -# -# # DEFINE PROJECTION -# st_time = timeit.default_timer() -# projection = 'regular' -# lat_orig = 41.1 -# lon_orig = 1.8 -# inc_lat = 0.2 -# inc_lon = 0.2 -# n_lat = 100 -# n_lon = 100 -# -# # SPATIAL JOIN -# # Method can be centroid, nearest and intersection -# nessy = from_shapefile(shapefile_path, method='centroid', projection=projection, -# lat_orig=lat_orig, lon_orig=lon_orig, -# inc_lat=inc_lat, inc_lon=inc_lon, -# n_lat=n_lat, n_lon=n_lon) -# comm.Barrier() -# result.loc['calculate', test_name] = timeit.default_timer() - st_time -# print('FROM SHAPEFILE - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) -# -# # ADD Var -# for var_name in shapefile_var_list: -# data = nessy.shapefile[var_name].values.reshape([nessy.lat['data'].shape[0], nessy.lon['data'].shape[-1]]) -# nessy.variables[var_name] = {'data': data, 'dtype': str} -# nessy.set_strlen(str_len) -# -# # WRITE -# st_time = timeit.default_timer() -# nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), serial=serial_write, info=True) -# comm.Barrier() -# result.loc['write', test_name] = timeit.default_timer() - st_time -# -# # REOPEN -# nessy = open_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), parallel_method=parallel_method) -# nessy.load() -# -# comm.Barrier() -# if rank == 0: -# print(result.loc[:, test_name]) -# sys.stdout.flush() +# ====================================================================================================================== +# =================================== CENTROID FROM NEW FILE =================================================== +# ====================================================================================================================== + +test_name = '2.1.2.New_file_centroid' +if rank == 0: + print(test_name) + +# DEFINE PROJECTION +st_time = timeit.default_timer() +projection = 'regular' +lat_orig = 41.1 +lon_orig = 1.8 +inc_lat = 0.2 +inc_lon = 0.2 +n_lat = 100 +n_lon = 100 + +# SPATIAL JOIN +# Method can be centroid, nearest and intersection +nessy = from_shapefile(shapefile_path, method='centroid', projection=projection, + lat_orig=lat_orig, lon_orig=lon_orig, + inc_lat=inc_lat, inc_lon=inc_lon, + n_lat=n_lat, n_lon=n_lon) +comm.Barrier() +result.loc['calculate', test_name] = timeit.default_timer() - st_time +print('FROM SHAPEFILE - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) + +# ADD Var +for var_name in shapefile_var_list: + data = nessy.shapefile[var_name].values.reshape([nessy.lat['data'].shape[0], nessy.lon['data'].shape[-1]]) + nessy.variables[var_name] = {'data': data, 'dtype': str} +nessy.set_strlen(str_len) + +# WRITE +st_time = timeit.default_timer() +nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), serial=serial_write, info=True) +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + +# REOPEN +nessy = open_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), parallel_method=parallel_method) +nessy.load() + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() # ====================================================================================================================== # =================================== NEAREST EXISTING FILE =================================================== @@ -175,54 +181,54 @@ if rank == 0: print(result.loc[:, test_name]) sys.stdout.flush() -# # ====================================================================================================================== -# # =================================== NEAREST FROM NEW FILE =================================================== -# # ====================================================================================================================== -# -# test_name = '2.1.4.New_file_nearest' -# if rank == 0: -# print(test_name) -# -# # DEFINE PROJECTION -# st_time = timeit.default_timer() -# projection = 'regular' -# lat_orig = 41.1 -# lon_orig = 1.8 -# inc_lat = 0.2 -# inc_lon = 0.2 -# n_lat = 100 -# n_lon = 100 -# -# # SPATIAL JOIN -# # Method can be centroid, nearest and intersection -# nessy = from_shapefile(shapefile_path, method='nearest', projection=projection, -# lat_orig=lat_orig, lon_orig=lon_orig, -# inc_lat=inc_lat, inc_lon=inc_lon, -# n_lat=n_lat, n_lon=n_lon) -# comm.Barrier() -# result.loc['calculate', test_name] = timeit.default_timer() - st_time -# print('FROM SHAPEFILE - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) -# -# # ADD Var -# for var_name in shapefile_var_list: -# data = nessy.shapefile[var_name].values.reshape([nessy.lat['data'].shape[0], nessy.lon['data'].shape[-1]]) -# nessy.variables[var_name] = {'data': data, 'dtype': str} -# nessy.set_strlen(str_len) -# -# # WRITE -# st_time = timeit.default_timer() -# nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), serial=serial_write, info=True) -# comm.Barrier() -# result.loc['write', test_name] = timeit.default_timer() - st_time -# -# # REOPEN -# nessy = open_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), parallel_method=parallel_method) -# nessy.load() -# -# comm.Barrier() -# if rank == 0: -# print(result.loc[:, test_name]) -# sys.stdout.flush() +# ====================================================================================================================== +# =================================== NEAREST FROM NEW FILE =================================================== +# ====================================================================================================================== + +test_name = '2.1.4.New_file_nearest' +if rank == 0: + print(test_name) + +# DEFINE PROJECTION +st_time = timeit.default_timer() +projection = 'regular' +lat_orig = 41.1 +lon_orig = 1.8 +inc_lat = 0.2 +inc_lon = 0.2 +n_lat = 100 +n_lon = 100 + +# SPATIAL JOIN +# Method can be centroid, nearest and intersection +nessy = from_shapefile(shapefile_path, method='nearest', projection=projection, + lat_orig=lat_orig, lon_orig=lon_orig, + inc_lat=inc_lat, inc_lon=inc_lon, + n_lat=n_lat, n_lon=n_lon) +comm.Barrier() +result.loc['calculate', test_name] = timeit.default_timer() - st_time +print('FROM SHAPEFILE - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) + +# ADD Var +for var_name in shapefile_var_list: + data = nessy.shapefile[var_name].values.reshape([nessy.lat['data'].shape[0], nessy.lon['data'].shape[-1]]) + nessy.variables[var_name] = {'data': data, 'dtype': str} +nessy.set_strlen(str_len) + +# WRITE +st_time = timeit.default_timer() +nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), serial=serial_write, info=True) +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + +# REOPEN +nessy = open_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), parallel_method=parallel_method) +nessy.load() + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() # ====================================================================================================================== @@ -275,49 +281,49 @@ sys.stdout.flush() # =================================== INTERSECTION FROM NEW FILE =================================================== # ====================================================================================================================== -# test_name = '2.1.6.New_file_intersection' -# if rank == 0: -# print(test_name) -# -# # DEFINE PROJECTION -# st_time = timeit.default_timer() -# projection = 'regular' -# lat_orig = 41.1 -# lon_orig = 1.8 -# inc_lat = 0.2 -# inc_lon = 0.2 -# n_lat = 100 -# n_lon = 100 -# -# # SPATIAL JOIN -# # Method can be centroid, nearest and intersection -# nessy = from_shapefile(shapefile_path, method='intersection', projection=projection, -# lat_orig=lat_orig, lon_orig=lon_orig, -# inc_lat=inc_lat, inc_lon=inc_lon, -# n_lat=n_lat, n_lon=n_lon) -# comm.Barrier() -# result.loc['calculate', test_name] = timeit.default_timer() - st_time -# print('FROM SHAPEFILE - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) -# -# # ADD Var -# for var_name in shapefile_var_list: -# data = nessy.shapefile[var_name].values.reshape([nessy.lat['data'].shape[0], nessy.lon['data'].shape[-1]]) -# nessy.variables[var_name] = {'data': data, 'dtype': str} -# nessy.set_strlen(str_len) -# # WRITE -# st_time = timeit.default_timer() -# nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), serial=serial_write, info=True) -# comm.Barrier() -# result.loc['write', test_name] = timeit.default_timer() - st_time -# -# # REOPEN -# nessy = open_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), parallel_method=parallel_method) -# nessy.load() -# -# comm.Barrier() -# if rank == 0: -# print(result.loc[:, test_name]) -# sys.stdout.flush() +test_name = '2.1.6.New_file_intersection' +if rank == 0: + print(test_name) + +# DEFINE PROJECTION +st_time = timeit.default_timer() +projection = 'regular' +lat_orig = 41.1 +lon_orig = 1.8 +inc_lat = 0.2 +inc_lon = 0.2 +n_lat = 100 +n_lon = 100 + +# SPATIAL JOIN +# Method can be centroid, nearest and intersection +nessy = from_shapefile(shapefile_path, method='intersection', projection=projection, + lat_orig=lat_orig, lon_orig=lon_orig, + inc_lat=inc_lat, inc_lon=inc_lon, + n_lat=n_lat, n_lon=n_lon) +comm.Barrier() +result.loc['calculate', test_name] = timeit.default_timer() - st_time +print('FROM SHAPEFILE - Rank {0:03d} - Shapefile: \n{1}'.format(rank, nessy.shapefile)) + +# ADD Var +for var_name in shapefile_var_list: + data = nessy.shapefile[var_name].values.reshape([nessy.lat['data'].shape[0], nessy.lon['data'].shape[-1]]) + nessy.variables[var_name] = {'data': data, 'dtype': str} +nessy.set_strlen(str_len) +# WRITE +st_time = timeit.default_timer() +nessy.to_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), serial=serial_write, info=True) +comm.Barrier() +result.loc['write', test_name] = timeit.default_timer() - st_time + +# REOPEN +nessy = open_netcdf(test_name.replace(' ', '_') + "_{0:03d}.nc".format(size), parallel_method=parallel_method) +nessy.load() + +comm.Barrier() +if rank == 0: + print(result.loc[:, test_name]) +sys.stdout.flush() if rank == 0: result.to_csv(result_path) -- GitLab From dff748a4ebb3d8e3595005cde7c323adf0013100 Mon Sep 17 00:00:00 2001 From: ctena Date: Tue, 28 Mar 2023 16:12:52 +0200 Subject: [PATCH 36/43] BugFix while reading masked data --- CHANGELOG.md | 1 + nes/nc_projections/default_nes.py | 9 ++++----- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 987d0b8..ed9d369 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -11,6 +11,7 @@ * Bug on `cell_methods` serial write ([#53](https://earth.bsc.es/gitlab/es/NES/-/issues/53)) * Horizontal Interpolation Conservative: Improvement on memory usage when calculating the weight matrix ([#54](https://earth.bsc.es/gitlab/es/NES/-/issues/54)) * Bug on avoid_first_hours that where not filtered after read the dimensions ([#59](https://earth.bsc.es/gitlab/es/NES/-/issues/59)) + * Bug while reading masked data. ### 1.1.0 * Release date: 2023/03/02 diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py index 1c442dc..9d3faea 100644 --- a/nes/nc_projections/default_nes.py +++ b/nes/nc_projections/default_nes.py @@ -1895,11 +1895,10 @@ class Nes(object): else: raise NotImplementedError('Error with {0}. Only can be read netCDF with 4 dimensions or less'.format( var_name)) - # # Missing to nan - # try: - # data[data.shapefile == True] = np.nan - # except (AttributeError, MaskError, ValueError): - # pass + # Missing to nan + if np.ma.is_masked(data): + # This operation is done because sometimes the missing value is lost during the calculation + data[data.mask] = np.nan return data -- GitLab From 9e8bca6ecf0f5e8851e4919657893ba41d979b15 Mon Sep 17 00:00:00 2001 From: Alba Vilanova Date: Tue, 28 Mar 2023 16:20:57 +0200 Subject: [PATCH 37/43] Fix mask error --- nes/nc_projections/default_nes.py | 1 - nes/nc_projections/points_nes.py | 8 +++----- nes/nc_projections/points_nes_ghost.py | 8 +++----- nes/nc_projections/points_nes_providentia.py | 8 +++----- 4 files changed, 9 insertions(+), 16 deletions(-) diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py index 88cb5f0..f50fc31 100644 --- a/nes/nc_projections/default_nes.py +++ b/nes/nc_projections/default_nes.py @@ -1854,7 +1854,6 @@ class Nes(object): data: np.array Portion of the variable data corresponding to the rank. """ - # from numpy.ma.core import MaskError nc_var = self.netcdf.variables[var_name] var_dims = nc_var.dimensions diff --git a/nes/nc_projections/points_nes.py b/nes/nc_projections/points_nes.py index ab0ae1e..563cb34 100644 --- a/nes/nc_projections/points_nes.py +++ b/nes/nc_projections/points_nes.py @@ -7,7 +7,6 @@ import pandas as pd from copy import deepcopy import geopandas as gpd from netCDF4 import date2num, stringtochar -from numpy.ma.core import MaskError from .default_nes import Nes @@ -311,10 +310,9 @@ class PointsNes(Nes): var_name)) # Missing to nan - try: - data[data.mask == True] = np.nan - except (AttributeError, MaskError, ValueError): - pass + if np.ma.is_masked(data): + # This operation is done because sometimes the missing value is lost during the calculation + data[data.mask] = np.nan return data diff --git a/nes/nc_projections/points_nes_ghost.py b/nes/nc_projections/points_nes_ghost.py index 1f4f4b4..11c47ad 100644 --- a/nes/nc_projections/points_nes_ghost.py +++ b/nes/nc_projections/points_nes_ghost.py @@ -3,7 +3,6 @@ import sys import warnings import numpy as np -from numpy.ma.core import MaskError from netCDF4 import stringtochar, date2num from copy import deepcopy from .points_nes import PointsNes @@ -305,10 +304,9 @@ class PointsNesGHOST(PointsNes): var_name)) # Missing to nan - try: - data[data.mask == True] = np.nan - except (AttributeError, MaskError, ValueError): - pass + if np.ma.is_masked(data): + # This operation is done because sometimes the missing value is lost during the calculation + data[data.mask] = np.nan return data diff --git a/nes/nc_projections/points_nes_providentia.py b/nes/nc_projections/points_nes_providentia.py index 81a771b..8241a81 100644 --- a/nes/nc_projections/points_nes_providentia.py +++ b/nes/nc_projections/points_nes_providentia.py @@ -4,7 +4,6 @@ import sys import warnings import numpy as np from copy import deepcopy -from numpy.ma.core import MaskError from netCDF4 import stringtochar from .points_nes import PointsNes @@ -341,10 +340,9 @@ class PointsNesProvidentia(PointsNes): var_name)) # Missing to nan - try: - data[data.mask == True] = np.nan - except (AttributeError, MaskError, ValueError): - pass + if np.ma.is_masked(data): + # This operation is done because sometimes the missing value is lost during the calculation + data[data.mask] = np.nan return data -- GitLab From 6241d8102881461fca55d55ab0de535f4cc81e42 Mon Sep 17 00:00:00 2001 From: Alba Vilanova Date: Tue, 28 Mar 2023 16:49:48 +0200 Subject: [PATCH 38/43] Create function to get strlen --- nes/create_nes.py | 2 +- nes/nc_projections/default_nes.py | 31 +++++++++++++++++--- nes/nc_projections/points_nes.py | 6 +--- nes/nc_projections/points_nes_providentia.py | 2 +- tests/2.1-test_spatial_join.py | 2 +- 5 files changed, 31 insertions(+), 12 deletions(-) diff --git a/nes/create_nes.py b/nes/create_nes.py index da7d29c..10ee6de 100644 --- a/nes/create_nes.py +++ b/nes/create_nes.py @@ -9,7 +9,7 @@ from .nc_projections import * def create_nes(comm=None, info=False, projection=None, parallel_method='Y', balanced=False, - strlen=75, times=None, avoid_first_hours=0, avoid_last_hours=0, first_level=0, last_level=None, + times=None, avoid_first_hours=0, avoid_last_hours=0, first_level=0, last_level=None, **kwargs): """ Create a Nes class from scratch. diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py index f50fc31..e5a72f9 100644 --- a/nes/nc_projections/default_nes.py +++ b/nes/nc_projections/default_nes.py @@ -86,7 +86,7 @@ class Nes(object): """ def __init__(self, comm=None, path=None, info=False, dataset=None, xarray=False, parallel_method='Y', avoid_first_hours=0, avoid_last_hours=0, first_level=0, last_level=None, create_nes=False, - balanced=False, times=None, strlen=75, **kwargs): + balanced=False, times=None, **kwargs): """ Initialize the Nes class @@ -190,6 +190,10 @@ class Nes(object): # Set NetCDF attributes self.global_attrs = self.__get_global_attributes(create_nes) + # Set string length + # 75 is the standard value used in GHOST data + self.strlen = 75 + else: if dataset is not None: @@ -242,12 +246,12 @@ class Nes(object): # Set NetCDF attributes self.global_attrs = self.__get_global_attributes() + # Get string length + self.strlen = self._get_strlen() + # Writing options self.zip_lvl = 0 - # Get string length - self.strlen = strlen - # Dimensions information self._var_dim = None self._lat_dim = None @@ -307,6 +311,23 @@ class Nes(object): return new + def _get_strlen(self, strlen=75): + """ + Get the strlen + + Parameters + ---------- + strlen : int + Max length of the string + """ + + if 'strlen' in self.netcdf.dimensions: + strlen = self.netcdf.dimensions['strlen'].size + else: + strlen = strlen + + return strlen + def set_strlen(self, strlen=75): """ Set the strlen @@ -316,7 +337,9 @@ class Nes(object): strlen : int Max length of the string """ + self.strlen = strlen + return None def __del__(self): diff --git a/nes/nc_projections/points_nes.py b/nes/nc_projections/points_nes.py index 563cb34..756fe3c 100644 --- a/nes/nc_projections/points_nes.py +++ b/nes/nc_projections/points_nes.py @@ -30,7 +30,7 @@ class PointsNes(Nes): """ def __init__(self, comm=None, path=None, info=False, dataset=None, xarray=False, parallel_method='X', avoid_first_hours=0, avoid_last_hours=0, first_level=0, last_level=None, create_nes=False, - times=None, strlen=75, **kwargs): + times=None, **kwargs): """ Initialize the PointsNes class. @@ -67,10 +67,6 @@ class PointsNes(Nes): # Dimensions screening self.lat = self._get_coordinate_values(self._lat, 'X') self.lon = self._get_coordinate_values(self._lon, 'X') - self.strlen = strlen - else: - # Dimensions screening - self.strlen = self._get_strlen() # Complete dimensions self._station = {'data': np.arange(len(self._lon['data']))} diff --git a/nes/nc_projections/points_nes_providentia.py b/nes/nc_projections/points_nes_providentia.py index 8241a81..84c6be2 100644 --- a/nes/nc_projections/points_nes_providentia.py +++ b/nes/nc_projections/points_nes_providentia.py @@ -110,7 +110,7 @@ class PointsNesProvidentia(PointsNes): self.grid_edge_lat = self._get_coordinate_values(self._grid_edge_lat, '') # Set strlen to be None (avoid default strlen inherited from points) - self.strlen = None + self.set_strlen(None) @staticmethod def new(comm=None, path=None, info=False, dataset=None, xarray=False, create_nes=False, balanced=False, diff --git a/tests/2.1-test_spatial_join.py b/tests/2.1-test_spatial_join.py index dfac69b..98a482d 100644 --- a/tests/2.1-test_spatial_join.py +++ b/tests/2.1-test_spatial_join.py @@ -57,7 +57,7 @@ result.loc['read', test_name] = timeit.default_timer() - st_time st_time = timeit.default_timer() nessy.spatial_join(shapefile_path, method='centroid', var_list=shapefile_var_list, info=True) comm.Barrier() -result.loc['calcul', test_name] = timeit.default_timer() - st_time +result.loc['calculate', test_name] = timeit.default_timer() - st_time st_time = timeit.default_timer() -- GitLab From 0a42da98310d9fa94fc847c3b95b74acc9720901 Mon Sep 17 00:00:00 2001 From: Alba Vilanova Date: Tue, 28 Mar 2023 17:05:44 +0200 Subject: [PATCH 39/43] Remove cell measures code from proj files --- nes/nc_projections/default_nes.py | 2 ++ nes/nc_projections/latlon_nes.py | 11 ----------- nes/nc_projections/lcc_nes.py | 10 ---------- nes/nc_projections/mercator_nes.py | 11 ----------- nes/nc_projections/rotated_nes.py | 10 ---------- 5 files changed, 2 insertions(+), 42 deletions(-) diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py index e5a72f9..2677ec1 100644 --- a/nes/nc_projections/default_nes.py +++ b/nes/nc_projections/default_nes.py @@ -3302,6 +3302,8 @@ class Nes(object): if 'cell_area' not in self.cell_measures.keys(): grid_area = cell_measures.calculate_grid_area(self) + self.cell_measures['cell_area'] = {'data': grid_area.reshape([self.lon_bnds['data'].shape[0], + self.lon_bnds['data'].shape[1]])} else: grid_area = self.cell_measures['cell_area']['data'] diff --git a/nes/nc_projections/latlon_nes.py b/nes/nc_projections/latlon_nes.py index cfcb11d..b044d98 100644 --- a/nes/nc_projections/latlon_nes.py +++ b/nes/nc_projections/latlon_nes.py @@ -272,14 +272,3 @@ class LatLonNes(Nes): """ return super(LatLonNes, self).to_grib2(path, grib_keys, grib_template_path, lat_flip=lat_flip, info=info) - - def calculate_grid_area(self): - """ - Get coordinate bounds and call function to calculate the area of each cell of a grid. - - """ - grid_area = super().calculate_grid_area() - self.cell_measures['cell_area'] = {'data': grid_area.reshape([self.lon_bnds['data'].shape[0], - self.lon_bnds['data'].shape[1]])} - - return None diff --git a/nes/nc_projections/lcc_nes.py b/nes/nc_projections/lcc_nes.py index 8c9c436..b5a679e 100644 --- a/nes/nc_projections/lcc_nes.py +++ b/nes/nc_projections/lcc_nes.py @@ -531,13 +531,3 @@ class LCCNes(Nes): return centroids_gdf - def calculate_grid_area(self): - """ - Get coordinate bounds and call function to calculate the area of each cell of a grid. - """ - - grid_area = super(LCCNes, self).calculate_grid_area() - self.cell_measures['cell_area'] = {'data': grid_area.reshape([self.lon_bnds['data'].shape[0], - self.lon_bnds['data'].shape[1]])} - - return None diff --git a/nes/nc_projections/mercator_nes.py b/nes/nc_projections/mercator_nes.py index af1433b..b752e8d 100644 --- a/nes/nc_projections/mercator_nes.py +++ b/nes/nc_projections/mercator_nes.py @@ -506,14 +506,3 @@ class MercatorNes(Nes): crs="EPSG:4326") return centroids_gdf - - def calculate_grid_area(self): - """ - Get coordinate bounds and call function to calculate the area of each cell of a grid. - """ - - grid_area = super(MercatorNes, self).calculate_grid_area() - self.cell_measures['cell_area'] = {'data': grid_area.reshape([self.lon_bnds['data'].shape[0], - self.lon_bnds['data'].shape[1]])} - - return None diff --git a/nes/nc_projections/rotated_nes.py b/nes/nc_projections/rotated_nes.py index 9d58434..66dc2b7 100644 --- a/nes/nc_projections/rotated_nes.py +++ b/nes/nc_projections/rotated_nes.py @@ -557,13 +557,3 @@ class RotatedNes(Nes): return centroids_gdf - def calculate_grid_area(self): - """ - Get coordinate bounds and call function to calculate the area of each cell of a grid. - """ - - grid_area = super(RotatedNes, self).calculate_grid_area() - self.cell_measures['cell_area'] = {'data': grid_area.reshape([self.lon_bnds['data'].shape[0], - self.lon_bnds['data'].shape[1]])} - - return None -- GitLab From ec2769d91f38f2164f512887ec0a9a9d16cc55ad Mon Sep 17 00:00:00 2001 From: ctena Date: Tue, 28 Mar 2023 17:55:57 +0200 Subject: [PATCH 40/43] from_shapefile function improved to use less amount of memory --- nes/create_nes.py | 8 ++------ 1 file changed, 2 insertions(+), 6 deletions(-) diff --git a/nes/create_nes.py b/nes/create_nes.py index 10ee6de..cfb0205 100644 --- a/nes/create_nes.py +++ b/nes/create_nes.py @@ -128,8 +128,7 @@ def from_shapefile(path, method=None, parallel_method='Y', **kwargs): 1. Create NES grid. 2. Create shapefile for grid. - 3. Read shapefile from mask. - 4. Spatial join to add shapefile variables to NES variables. + 3. Spatial join to add shapefile variables to NES variables. Parameters ---------- @@ -150,10 +149,7 @@ def from_shapefile(path, method=None, parallel_method='Y', **kwargs): # Create shapefile for grid nessy.create_shapefile() - # Read shapefile - shapefile = gpd.read_file(path) - # Make spatial join - nessy.spatial_join(shapefile, method=method) + nessy.spatial_join(path, method=method) return nessy -- GitLab From 7c77001957d514336bc7b8e5bea1afb34c121a9c Mon Sep 17 00:00:00 2001 From: ctena Date: Wed, 29 Mar 2023 14:46:53 +0200 Subject: [PATCH 41/43] Minor fixes and new flux feature: - Fix on spatial_join.py - Fix on cell_area return 2D array - Flux conservative interpolation flag --- nes/methods/horizontal_interpolation.py | 51 +- nes/methods/spatial_join.py | 1 + nes/nc_projections/default_nes.py | 10 +- .../4.3.Conservative_Interpolation.ipynb | 607 +++++++++++++----- 4 files changed, 489 insertions(+), 180 deletions(-) diff --git a/nes/methods/horizontal_interpolation.py b/nes/methods/horizontal_interpolation.py index e33edd4..ec3840f 100644 --- a/nes/methods/horizontal_interpolation.py +++ b/nes/methods/horizontal_interpolation.py @@ -23,7 +23,7 @@ CONSERVATIVE_OPTS = ['Conservative', 'Area_Conservative', 'cons', 'conservative' def interpolate_horizontal(self, dst_grid, weight_matrix_path=None, kind='NearestNeighbour', n_neighbours=4, - info=False, to_providentia=False, only_create_wm=False, wm=None): + info=False, to_providentia=False, only_create_wm=False, wm=None, flux=False): """ Horizontal methods from one grid to another one. @@ -47,16 +47,18 @@ def interpolate_horizontal(self, dst_grid, weight_matrix_path=None, kind='Neares Indicates if you want to only create the Weight Matrix. wm : Nes Weight matrix Nes File + flux : bool + Indicates if you want to calculate the weight matrix for flux variables """ if info and self.master: print("Creating Weight Matrix") # Obtain weight matrix if self.parallel_method == 'T': weights, idx = get_weights_idx_t_axis(self, dst_grid, weight_matrix_path, kind, n_neighbours, - only_create_wm, wm) + only_create_wm, wm, flux) elif self.parallel_method in ['Y', 'X']: weights, idx = get_weights_idx_xy_axis(self, dst_grid, weight_matrix_path, kind, n_neighbours, - only_create_wm, wm) + only_create_wm, wm, flux) else: raise NotImplemented("Parallel method {0} is not implemented yet for horizontal interpolations. Use 'T'".format( self.parallel_method)) @@ -198,7 +200,7 @@ def get_src_data(comm, var_data, idx, parallel_method): # noinspection DuplicatedCode -def get_weights_idx_t_axis(self, dst_grid, weight_matrix_path, kind, n_neighbours, only_create, wm): +def get_weights_idx_t_axis(self, dst_grid, weight_matrix_path, kind, n_neighbours, only_create, wm, flux): """ To obtain the weights and source data index through the T axis. @@ -218,6 +220,8 @@ def get_weights_idx_t_axis(self, dst_grid, weight_matrix_path, kind, n_neighbour Indicates if you want to only create the Weight Matrix. wm : Nes Weight matrix Nes File + flux : bool + Indicates if you want to calculate the weight matrix for flux variables Returns ------- @@ -250,8 +254,8 @@ def get_weights_idx_t_axis(self, dst_grid, weight_matrix_path, kind, n_neighbour weight_matrix = create_nn_weight_matrix(self, dst_grid, n_neighbours=n_neighbours, wm_path=weight_matrix_path) elif kind in CONSERVATIVE_OPTS: - weight_matrix = create_area_conservative_weight_matrix(self, dst_grid, - wm_path=weight_matrix_path) + weight_matrix = create_area_conservative_weight_matrix( + self, dst_grid, wm_path=weight_matrix_path, flux=flux) else: raise NotImplementedError(kind) else: @@ -264,7 +268,7 @@ def get_weights_idx_t_axis(self, dst_grid, weight_matrix_path, kind, n_neighbour if kind in NEAREST_OPTS: weight_matrix = create_nn_weight_matrix(self, dst_grid, n_neighbours=n_neighbours) elif kind in CONSERVATIVE_OPTS: - weight_matrix = create_area_conservative_weight_matrix(self, dst_grid) + weight_matrix = create_area_conservative_weight_matrix(self, dst_grid, flux=flux) else: raise NotImplementedError(kind) else: @@ -293,7 +297,7 @@ def get_weights_idx_t_axis(self, dst_grid, weight_matrix_path, kind, n_neighbour # noinspection DuplicatedCode -def get_weights_idx_xy_axis(self, dst_grid, weight_matrix_path, kind, n_neighbours, only_create, wm): +def get_weights_idx_xy_axis(self, dst_grid, weight_matrix_path, kind, n_neighbours, only_create, wm, flux): """ To obtain the weights and source data index through the X or Y axis. @@ -313,6 +317,8 @@ def get_weights_idx_xy_axis(self, dst_grid, weight_matrix_path, kind, n_neighbou Indicates if you want to only create the Weight Matrix. wm : Nes Weight matrix Nes File + flux : bool + Indicates if you want to calculate the weight matrix for flux variables Returns ------- @@ -352,7 +358,8 @@ def get_weights_idx_xy_axis(self, dst_grid, weight_matrix_path, kind, n_neighbou else: weight_matrix = True elif kind in CONSERVATIVE_OPTS: - weight_matrix = create_area_conservative_weight_matrix(self, dst_grid, wm_path=weight_matrix_path) + weight_matrix = create_area_conservative_weight_matrix( + self, dst_grid, wm_path=weight_matrix_path, flux=flux) else: raise NotImplementedError(kind) @@ -362,7 +369,7 @@ def get_weights_idx_xy_axis(self, dst_grid, weight_matrix_path, kind, n_neighbou if kind in NEAREST_OPTS: weight_matrix = create_nn_weight_matrix(self, dst_grid, n_neighbours=n_neighbours) elif kind in CONSERVATIVE_OPTS: - weight_matrix = create_area_conservative_weight_matrix(self, dst_grid) + weight_matrix = create_area_conservative_weight_matrix(self, dst_grid, flux=flux) else: raise NotImplementedError(kind) @@ -441,6 +448,8 @@ def create_nn_weight_matrix(self, dst_grid, n_neighbours=4, wm_path=None, info=F Final projection Nes object. n_neighbours : int Used if kind == NearestNeighbour. Number of nearest neighbours to interpolate. Default: 4. + wm_path : str + Path where write the weight matrix info: bool Indicates if you want to print extra info during the methods process. @@ -523,7 +532,7 @@ def create_nn_weight_matrix(self, dst_grid, n_neighbours=4, wm_path=None, info=F return weight_matrix -def create_area_conservative_weight_matrix(self, dst_nes, wm_path=None, info=False): +def create_area_conservative_weight_matrix(self, dst_nes, wm_path=None, flux=False, info=False): """ To create the weight matrix with the area conservative method. @@ -535,7 +544,8 @@ def create_area_conservative_weight_matrix(self, dst_nes, wm_path=None, info=Fal Final projection Nes object. wm_path : str Path where write the weight matrix - + flux : bool + Indicates if you want to calculate the weight matrix for flux variables info: bool Indicates if you want to print extra info during the methods process. @@ -602,9 +612,20 @@ def create_area_conservative_weight_matrix(self, dst_nes, wm_path=None, info=Fal if True: # No Warnings Zone warnings.filterwarnings('ignore') - intersection_df['weight'] = np.array(intersection_df.apply( - lambda x: x['geometry_src'].intersection(x['geometry_dst']).buffer(0).area / x['geometry_src'].area, - axis=1), dtype=np.float64) + # intersection_df['weight'] = np.array(intersection_df.apply( + # lambda x: x['geometry_src'].intersection(x['geometry_dst']).buffer(0).area / x['geometry_src'].area, + # axis=1), dtype=np.float64) + if flux: + intersection_df['weight'] = np.array(intersection_df.apply( + lambda x: (x['geometry_src'].intersection(x['geometry_dst']).buffer(0).area / x['geometry_src'].area) * + (nes.Nes.calculate_geometry_area([x['geometry_src']])[0] / + nes.Nes.calculate_geometry_area([x['geometry_dst']])[0]), + axis=1), dtype=np.float64) + else: + intersection_df['weight'] = np.array(intersection_df.apply( + lambda x: x['geometry_src'].intersection(x['geometry_dst']).buffer(0).area / x['geometry_src'].area, + axis=1), dtype=np.float64) + intersection_df.drop(columns=["geometry_src", "geometry_dst"], inplace=True) gc.collect() diff --git a/nes/methods/spatial_join.py b/nes/methods/spatial_join.py index 6695cf0..33df091 100644 --- a/nes/methods/spatial_join.py +++ b/nes/methods/spatial_join.py @@ -95,6 +95,7 @@ def prepare_external_shapefile(self, ext_shp, var_list, info=False): msg += "a best usage of memory is performed because the external shape will be clipped while reading." warnings.warn(msg) sys.stderr.flush() + ext_shp.reset_index(inplace=True) if var_list is not None: ext_shp = ext_shp.loc[:, var_list + ['geometry']] self.comm.Barrier() diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py index 2677ec1..ab85ea1 100644 --- a/nes/nc_projections/default_nes.py +++ b/nes/nc_projections/default_nes.py @@ -3244,7 +3244,7 @@ class Nes(object): self, new_levels, new_src_vertical=new_src_vertical, kind=kind, extrapolate=extrapolate, info=info) def interpolate_horizontal(self, dst_grid, weight_matrix_path=None, kind='NearestNeighbour', n_neighbours=4, - info=False, to_providentia=False, only_create_wm=False, wm=None): + info=False, to_providentia=False, only_create_wm=False, wm=None, flux=False): """ Horizontal methods from the current grid to another one. @@ -3266,11 +3266,13 @@ class Nes(object): Indicates if you want to only create the Weight Matrix. wm : Nes Weight matrix Nes File + flux : bool + Indicates if you want to calculate the weight matrix for flux variables """ return horizontal_interpolation.interpolate_horizontal( self, dst_grid, weight_matrix_path=weight_matrix_path, kind=kind, n_neighbours=n_neighbours, info=info, - to_providentia=to_providentia, only_create_wm=only_create_wm, wm=wm) + to_providentia=to_providentia, only_create_wm=only_create_wm, wm=wm, flux=flux) def spatial_join(self, ext_shp, method=None, var_list=None, info=False): """ @@ -3302,8 +3304,8 @@ class Nes(object): if 'cell_area' not in self.cell_measures.keys(): grid_area = cell_measures.calculate_grid_area(self) - self.cell_measures['cell_area'] = {'data': grid_area.reshape([self.lon_bnds['data'].shape[0], - self.lon_bnds['data'].shape[1]])} + grid_area = grid_area.reshape([self.lat['data'].shape[0], self.lon['data'].shape[-1]]) + self.cell_measures['cell_area'] = {'data': grid_area} else: grid_area = self.cell_measures['cell_area']['data'] diff --git a/tutorials/4.Interpolation/4.3.Conservative_Interpolation.ipynb b/tutorials/4.Interpolation/4.3.Conservative_Interpolation.ipynb index 48b0216..fc602af 100644 --- a/tutorials/4.Interpolation/4.3.Conservative_Interpolation.ipynb +++ b/tutorials/4.Interpolation/4.3.Conservative_Interpolation.ipynb @@ -13,7 +13,26 @@ "metadata": {}, "outputs": [], "source": [ - "from nes import *" + "from nes import *\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib\n", + "import numpy as np\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_data(nessy, var_name):\n", + " nessy.create_spatial_bounds()\n", + " lon_bnds, lat_bnds = nessy.get_spatial_bounds_mesh_format()\n", + " ax = plt.axes()\n", + " plt.pcolormesh(lon_bnds, lat_bnds, nessy.variables[var_name]['data'][0,0], cmap='jet', norm=matplotlib.colors.LogNorm())\n", + " " ] }, { @@ -25,123 +44,225 @@ }, { "cell_type": "code", - 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POLYGON ((-179.79999 -90.00000, -179.70001 -90...\n", + "3 POLYGON ((-179.69998 -90.00000, -179.60001 -90...\n", + "4 POLYGON ((-179.60001 -90.00000, -179.50000 -90...\n", + "... ...\n", + "6479995 POLYGON ((179.50000 89.89999, 179.60001 89.899...\n", + "6479996 POLYGON ((179.60001 89.89999, 179.69998 89.899...\n", + "6479997 POLYGON ((179.70001 89.89999, 179.79999 89.899...\n", + "6479998 POLYGON ((179.80002 89.89999, 179.89999 89.899...\n", + "6479999 POLYGON ((179.89999 89.89999, 180.00000 89.899...\n", + "\n", + "[6480000 rows x 1 columns]" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "source_grid.lat" + "source_grid = open_netcdf(path=source_path)\n", + "source_grid.create_shapefile()" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Flux units: m-2.kg.s-1\n" + ] + } + ], + "source": [ + "source_grid.keep_vars(var_name)\n", + "print('Flux units: {0}'.format(source_grid.variables[var_name]['units']))\n", + "source_grid.load()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { + "image/png": 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\n", "text/plain": [ - "{'data': masked_array(\n", - " data=[[-22.181265, -22.016672, -21.851799, ..., 41.851795, 42.016666,\n", - " 42.18126 ],\n", - " [-22.27818 , -22.113186, -21.947905, ..., 41.9479 , 42.113174,\n", - " 42.27817 ],\n", - " [-22.375267, -22.209873, -22.04419 , ..., 42.044186, 42.209873,\n", - " 42.375263],\n", - " ...,\n", - " [-67.57767 , -67.397064, -67.21535 , ..., 87.21534 , 87.39706 ,\n", - " 87.57766 ],\n", - " [-67.90188 , -67.72247 , -67.54194 , ..., 87.54194 , 87.72246 ,\n", - " 87.90187 ],\n", - " [-68.228035, -68.04982 , -67.870514, ..., 87.87051 , 88.04982 ,\n", - " 88.228035]],\n", - " mask=False,\n", - " fill_value=1e+20,\n", - " dtype=float32),\n", - " 'dimensions': ('rlat', 'rlon'),\n", - " 'long_name': 'longitude',\n", - " 'units': 'degrees_east',\n", - " 'standard_name': 'longitude',\n", - " 'coordinates': 'lon lat'}" + "
" ] }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "source_grid.lon" + "plot_data(source_grid, var_name)" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Rank 000: Loading O3 var (1/1)\n", - "Rank 000: Loaded O3 var ((37, 24, 271, 351))\n" + "nox_no total flux: 1.0672498547137366e-06\n", + "nox_no total mass: 113.08470916748047\n" ] } ], "source": [ - "source_grid.keep_vars('O3')\n", - "source_grid.load()" + "# Flux to mass\n", + "cell_area = source_grid.calculate_grid_area()\n", + "\n", + "for var_aux in source_grid.variables.keys():\n", + " print(\"{0} total flux: {1}\".format(var_aux, source_grid.variables[var_aux]['data'].sum()))\n", + " source_grid.variables[var_aux]['data'] *= cell_area\n", + " source_grid.variables[var_aux]['units'] = 'kg.s-1'\n", + " print(\"{0} total mass: {1}\".format(var_aux, source_grid.variables[var_aux]['data'].sum()))\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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geometry
FID
0POLYGON ((-11.58393 32.47507, -11.54169 32.478...
1POLYGON ((-11.54169 32.47851, -11.49944 32.481...
2POLYGON ((-11.49944 32.48192, -11.45719 32.485...
3POLYGON ((-11.45719 32.48533, -11.41494 32.488...
4POLYGON ((-11.41494 32.48871, -11.37268 32.492...
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157604POLYGON ((6.95490 46.70274, 7.00684 46.69873, ...
157605POLYGON ((7.00684 46.69873, 7.05878 46.69470, ...
157606POLYGON ((7.05878 46.69470, 7.11071 46.69066, ...
157607POLYGON ((7.11071 46.69066, 7.16264 46.68659, ...
157608POLYGON ((7.16264 46.68659, 7.21456 46.68250, ...
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157609 rows × 1 columns

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" + ], + "text/plain": [ + " geometry\n", + "FID \n", + "0 POLYGON ((-11.58393 32.47507, -11.54169 32.478...\n", + "1 POLYGON ((-11.54169 32.47851, -11.49944 32.481...\n", + "2 POLYGON ((-11.49944 32.48192, -11.45719 32.485...\n", + "3 POLYGON ((-11.45719 32.48533, -11.41494 32.488...\n", + "4 POLYGON ((-11.41494 32.48871, -11.37268 32.492...\n", + "... ...\n", + "157604 POLYGON ((6.95490 46.70274, 7.00684 46.69873, ...\n", + "157605 POLYGON ((7.00684 46.69873, 7.05878 46.69470, ...\n", + "157606 POLYGON ((7.05878 46.69470, 7.11071 46.69066, ...\n", + "157607 POLYGON ((7.11071 46.69066, 7.16264 46.68659, ...\n", + "157608 POLYGON ((7.16264 46.68659, 7.21456 46.68250, ...\n", + "\n", + "[157609 rows x 1 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "lat_1 = 37\n", "lat_2 = 43\n", @@ -168,7 +390,8 @@ "x_0 = -807847.688\n", "y_0 = -797137.125\n", "dst_nes = create_nes(comm=None, info=False, projection='lcc', lat_1=lat_1, lat_2=lat_2, lon_0=lon_0, lat_0=lat_0,\n", - " nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0, times=source_grid.get_full_times())" + " nx=nx, ny=ny, inc_x=inc_x, inc_y=inc_y, x_0=x_0, y_0=y_0, times=source_grid.get_full_times())\n", + "dst_nes.create_shapefile()\n" ] }, { @@ -187,75 +410,60 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 1min 48s, sys: 1.7 s, total: 1min 49s\n", + "Wall time: 1min 50s\n" + ] + } + ], "source": [ - "interp_nes = source_grid.interpolate_horizontal(dst_grid=dst_nes, kind='Conservative')" + "%time interp_nes = source_grid.interpolate_horizontal(dst_grid=dst_nes, kind='Conservative')" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { + "image/png": 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\n", "text/plain": [ - "{'data': array([[32.49460816, 32.49803615, 32.50144726, ..., 32.52460207,\n", - " 32.52130788, 32.51799681],\n", - " [32.53024662, 32.5336765 , 32.5370895 , ..., 32.5602571 ,\n", - " 32.55696109, 32.55364819],\n", - " [32.5658877 , 32.56931947, 32.57273435, ..., 32.59591474,\n", - " 32.59261692, 32.58930218],\n", - " ...,\n", - " [46.60231473, 46.60653579, 46.6107361 , ..., 46.63924881,\n", - " 46.63519229, 46.63111499],\n", - " [46.63791957, 46.64214277, 46.6463452 , ..., 46.67487234,\n", - " 46.67081377, 46.66673441],\n", - " [46.67352141, 46.67774674, 46.68195131, ..., 46.71049289,\n", - " 46.70643226, 46.70235083]])}" + "
" ] }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "interp_nes.lat" + "plot_data(interp_nes, var_name)" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 13, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "{'data': array([[-11.56484687, -11.52259289, -11.48033507, ..., 5.18764453,\n", - " 5.22992847, 5.27220869],\n", - " [-11.56892309, -11.52664925, -11.48437156, ..., 5.1915433 ,\n", - " 5.23384715, 5.27614727],\n", - " [-11.57300318, -11.53070946, -11.48841188, ..., 5.19544578,\n", - " 5.23776955, 5.2800896 ],\n", - " ...,\n", - " [-13.53865445, -13.48682654, -13.4349915 , ..., 7.07590089,\n", - " 7.12778439, 7.17966098],\n", - " [-13.54481681, -13.49295916, -13.44109437, ..., 7.08179741,\n", - " 7.13371074, 7.18561716],\n", - " [-13.55098635, -13.49909893, -13.44720434, ..., 7.0877008 ,\n", - " 7.139644 , 7.19158028]])}" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "nox_no total mass: 113.08470916748047\n" + ] } ], "source": [ - "interp_nes.lon" + "for var_aux in source_grid.variables.keys():\n", + " print(\"{0} total mass: {1}\".format(var_aux, source_grid.variables[var_aux]['data'].sum()))" ] }, { @@ -267,108 +475,185 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Creating Weight Matrix\n", + "Weight Matrix done!\n", + "CPU times: user 1min 50s, sys: 2.37 s, total: 1min 52s\n", + "Wall time: 1min 53s\n" + ] + } + ], + "source": [ + "%time wm_nes = source_grid.interpolate_horizontal(dst_grid=dst_nes, kind='Conservative', info=True, weight_matrix_path=\"CONS_WM_NAMEE_to_IP.nc\", only_create_wm=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Creating Weight Matrix\n" + "CPU times: user 209 ms, sys: 14.9 ms, total: 224 ms\n", + "Wall time: 223 ms\n" ] - }, + } + ], + "source": [ + "%time interp_nes = source_grid.interpolate_horizontal(dst_grid=dst_nes, kind='Conservative', wm=wm_nes)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plot_data(interp_nes, var_name)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "/esarchive/scratch/avilanova/software/NES/nes/nc_projections/default_nes.py:2041: DeprecationWarning: tostring() is deprecated. Use tobytes() instead.\n", - " time_var.units, time_var.calendar)\n" + "nox_no total mass: 1.4103307834025396\n" ] - }, + } + ], + "source": [ + "for var_aux in source_grid.variables.keys():\n", + " print(\"{0} total mass: {1}\".format(var_aux, interp_nes.variables[var_aux]['data'].sum()))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 4. Flux conservative interpolation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.0 Using previous example to put the data as flux" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Weight Matrix done!\n" + "nox_no total mass: 1.4103307834025396\n", + "nox_no total flux: 8.809495172462768e-08\n" ] } ], "source": [ - "wm_nes = source_grid.interpolate_horizontal(dst_grid=dst_nes, kind='Conservative', info=True,\n", - " weight_matrix_path=\"CONS_WM_NAMEE_to_IP.nc\", only_create_wm=True)" + "# Mass to Flux\n", + "cell_area = interp_nes.calculate_grid_area()\n", + "\n", + "for var_aux in interp_nes.variables.keys():\n", + " print(\"{0} total mass: {1}\".format(var_aux, interp_nes.variables[var_aux]['data'].sum()))\n", + " interp_nes.variables[var_aux]['data'] /= cell_area\n", + " interp_nes.variables[var_aux]['units'] = 'm-2.kg.s-1'\n", + " print(\"{0} total flux: {1}\".format(var_aux, interp_nes.variables[var_aux]['data'].sum()))\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 4.1 Interpolation" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "CPU times: user 6min 14s, sys: 2.7 s, total: 6min 17s\n", + "Wall time: 6min 18s\n" + ] + } + ], "source": [ - "interp_nes = source_grid.interpolate_horizontal(dst_grid=dst_nes, kind='Conservative', wm=wm_nes)" + "%time interp_nes = source_grid.interpolate_horizontal(dst_grid=dst_nes, kind='Conservative', flux=True)" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 20, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "{'data': array([[32.49460816, 32.49803615, 32.50144726, ..., 32.52460207,\n", - " 32.52130788, 32.51799681],\n", - " [32.53024662, 32.5336765 , 32.5370895 , ..., 32.5602571 ,\n", - " 32.55696109, 32.55364819],\n", - " [32.5658877 , 32.56931947, 32.57273435, ..., 32.59591474,\n", - " 32.59261692, 32.58930218],\n", - " ...,\n", - " [46.60231473, 46.60653579, 46.6107361 , ..., 46.63924881,\n", - " 46.63519229, 46.63111499],\n", - " [46.63791957, 46.64214277, 46.6463452 , ..., 46.67487234,\n", - " 46.67081377, 46.66673441],\n", - " [46.67352141, 46.67774674, 46.68195131, ..., 46.71049289,\n", - " 46.70643226, 46.70235083]])}" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "nox_no total flux: 8.08686122275172\n" + ] } ], "source": [ - "interp_nes.lat" + "for var_aux in interp_nes.variables.keys():\n", + " print(\"{0} total flux: {1}\".format(var_aux, interp_nes.variables[var_aux]['data'].sum()))" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 21, "metadata": {}, "outputs": [ { "data": { + "image/png": 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\n", "text/plain": [ - "{'data': array([[-11.56484687, -11.52259289, -11.48033507, ..., 5.18764453,\n", - " 5.22992847, 5.27220869],\n", - " [-11.56892309, -11.52664925, -11.48437156, ..., 5.1915433 ,\n", - " 5.23384715, 5.27614727],\n", - " [-11.57300318, -11.53070946, -11.48841188, ..., 5.19544578,\n", - " 5.23776955, 5.2800896 ],\n", - " ...,\n", - " [-13.53865445, -13.48682654, -13.4349915 , ..., 7.07590089,\n", - " 7.12778439, 7.17966098],\n", - " [-13.54481681, -13.49295916, -13.44109437, ..., 7.08179741,\n", - " 7.13371074, 7.18561716],\n", - " [-13.55098635, -13.49909893, -13.44720434, ..., 7.0877008 ,\n", - " 7.139644 , 7.19158028]])}" + "
" ] }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "interp_nes.lon" + "plot_data(interp_nes, var_name)" ] } ], -- GitLab From bcc2df81d1fc21423f9c6edd73ab078d69fa7afa Mon Sep 17 00:00:00 2001 From: ctena Date: Wed, 29 Mar 2023 14:55:20 +0200 Subject: [PATCH 42/43] CHANGELOG.md update --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index ed9d369..6616fa3 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -7,6 +7,7 @@ * Sum of Nes objects ([#48](https://earth.bsc.es/gitlab/es/NES/-/issues/48)) * Write 2D string data to save variables from shapefiles after doing a spatial join ([#49](https://earth.bsc.es/gitlab/es/NES/-/issues/49)) * Write by time step to avoid memory issues ([#57](https://earth.bsc.es/gitlab/es/NES/-/issues/57)) + * Flux conservative horizontal interpolation ([#60](https://earth.bsc.es/gitlab/es/NES/-/issues/60)) * Bugs fixing: * Bug on `cell_methods` serial write ([#53](https://earth.bsc.es/gitlab/es/NES/-/issues/53)) * Horizontal Interpolation Conservative: Improvement on memory usage when calculating the weight matrix ([#54](https://earth.bsc.es/gitlab/es/NES/-/issues/54)) -- GitLab From 26e86a3682a506528e9693f618729e4d7e2002f0 Mon Sep 17 00:00:00 2001 From: ctena Date: Wed, 29 Mar 2023 15:28:40 +0200 Subject: [PATCH 43/43] update 4.3.Conservative_Interpolation.ipynb tutorial & CHANGELOG.md --- .../4.3.Conservative_Interpolation.ipynb | 61 ++++++++++++++----- 1 file changed, 47 insertions(+), 14 deletions(-) diff --git a/tutorials/4.Interpolation/4.3.Conservative_Interpolation.ipynb b/tutorials/4.Interpolation/4.3.Conservative_Interpolation.ipynb index fc602af..e52d406 100644 --- a/tutorials/4.Interpolation/4.3.Conservative_Interpolation.ipynb +++ b/tutorials/4.Interpolation/4.3.Conservative_Interpolation.ipynb @@ -239,8 +239,7 @@ " print(\"{0} total flux: {1}\".format(var_aux, source_grid.variables[var_aux]['data'].sum()))\n", " source_grid.variables[var_aux]['data'] *= cell_area\n", " source_grid.variables[var_aux]['units'] = 'kg.s-1'\n", - " print(\"{0} total mass: {1}\".format(var_aux, source_grid.variables[var_aux]['data'].sum()))\n", - "\n" + " print(\"{0} total mass: {1}\".format(var_aux, source_grid.variables[var_aux]['data'].sum()))" ] }, { @@ -417,8 +416,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 1min 48s, sys: 1.7 s, total: 1min 49s\n", - "Wall time: 1min 50s\n" + "CPU times: user 1min 48s, sys: 1.42 s, total: 1min 49s\n", + "Wall time: 1min 49s\n" ] } ], @@ -484,8 +483,8 @@ "text": [ "Creating Weight Matrix\n", "Weight Matrix done!\n", - "CPU times: user 1min 50s, sys: 2.37 s, total: 1min 52s\n", - "Wall time: 1min 53s\n" + "CPU times: user 1min 49s, sys: 1.9 s, total: 1min 51s\n", + "Wall time: 1min 52s\n" ] } ], @@ -502,8 +501,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 209 ms, sys: 14.9 ms, total: 224 ms\n", - "Wall time: 223 ms\n" + "CPU times: user 202 ms, sys: 26.9 ms, total: 229 ms\n", + "Wall time: 228 ms\n" ] } ], @@ -607,13 +606,29 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 6min 14s, sys: 2.7 s, total: 6min 17s\n", - "Wall time: 6min 18s\n" + "Flux units: m-2.kg.s-1\n" ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "%time interp_nes = source_grid.interpolate_horizontal(dst_grid=dst_nes, kind='Conservative', flux=True)" + "source_grid = open_netcdf(path=source_path)\n", + "source_grid.create_shapefile()\n", + "source_grid.keep_vars(var_name)\n", + "print('Flux units: {0}'.format(source_grid.variables[var_name]['units']))\n", + "source_grid.load()\n", + "plot_data(source_grid, var_name)" ] }, { @@ -625,7 +640,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "nox_no total flux: 8.08686122275172\n" + "CPU times: user 6min 12s, sys: 2.3 s, total: 6min 15s\n", + "Wall time: 6min 16s\n" + ] + } + ], + "source": [ + "%time interp_nes = source_grid.interpolate_horizontal(dst_grid=dst_nes, kind='Conservative', flux=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "nox_no total flux: 8.805639456704551e-08\n" ] } ], @@ -636,12 +669,12 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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