diff --git a/CHANGELOG.md b/CHANGELOG.md index f09840a328dfaa4aebee4ccfefb96492b347fbd2..4c7f45e6ce645ce1f2867c20cedbb73a7ac58708 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -9,6 +9,7 @@ * Function create_shapefile() can now be used in parallel. * Function calculate_grid_area() to calculate the area of each cell in a grid. * Function calculate_geometry_area() to calculate the area of each cell given a set of geometries. + * Function get_spatial_bounds_mesh_format() to get the lon-lat boundaries in a mesh format. (used in pcolormesh) * Bugs fixing: * Correct the dimensions of the resulting points datasets from any interpolation. * Amend the interpolation method to take into account the cases in which the distance among points equals zero. diff --git a/nes/nc_projections/default_nes.py b/nes/nc_projections/default_nes.py index 1c80b9be21453d8ae0b18f4c9f22c445aaacb34c..5520c91372ca08a38c33163bb7b16ec1f5a8e890 100644 --- a/nes/nc_projections/default_nes.py +++ b/nes/nc_projections/default_nes.py @@ -558,6 +558,50 @@ class Nes(object): return None + def get_spatial_bounds_mesh_format(self): + """ + Get the spatial bounds in the pcolormesh format: + + see: https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.pcolormesh.html + + Returns + ------- + lon_bnds_mesh : numpy.array + Longitude boundaries in the mesh format + lat_bnds_mesh : numpy.array + Latitude boundaries in the mesh format + """ + if self.size > 1: + raise RuntimeError("NES.get_spatial_bounds_mesh_format() function only works in serial mode.") + if self.lat_bnds is None: + self.create_spatial_bounds() + + if self.lat_bnds['data'].shape[-1] == 2: + # get the lat_b and lon_b first rows + lat_b_0 = np.append(self.lat_bnds['data'][:, 0], self.lat_bnds['data'][-1, -1]) + lon_b_0 = np.append(self.lon_bnds['data'][:, 0], self.lon_bnds['data'][-1, -1]) + # expand lat_band lon_b in 2D + lat_bnds_mesh = np.tile(lat_b_0, (len(self.lon['data']) + 1, 1)).transpose() + lon_bnds_mesh = np.tile(lon_b_0, (len(self.lat['data']) + 1, 1)) + + elif self.lat_bnds['data'].shape[-1] == 4: + # Irregular quadrilateral polygon cell definition + lat_bnds_mesh = np.empty((self.lat['data'].shape[0] + 1, self.lat['data'].shape[1] + 1)) + lat_bnds_mesh[:-1, :-1] = self.lat_bnds['data'][:, :, 0] + lat_bnds_mesh[:-1, 1:] = self.lat_bnds['data'][:, :, 1] + lat_bnds_mesh[1:, 1:] = self.lat_bnds['data'][:, :, 2] + lat_bnds_mesh[1:, :-1] = self.lat_bnds['data'][:, :, 3] + + lon_bnds_mesh = np.empty((self.lat['data'].shape[0] + 1, self.lat['data'].shape[1] + 1)) + lon_bnds_mesh[:-1, :-1] = self.lon_bnds['data'][:, :, 0] + lon_bnds_mesh[:-1, 1:] = self.lon_bnds['data'][:, :, 1] + 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] )) + + return lon_bnds_mesh, lat_bnds_mesh + def free_vars(self, var_list): """ Erase the selected variables from the variables information. @@ -763,7 +807,7 @@ class Nes(object): else: idx['idx_y_min'] = self.get_coordinate_id(self._lat['data'], self.lat_min, axis=0) if self.lat_max is None: - idx['idx_y_max'] = self._lat['data'].shape[0] + 1 + idx['idx_y_max'] = self._lat['data'].shape[0] else: idx['idx_y_max'] = self.get_coordinate_id(self._lat['data'], self.lat_max, axis=0) + 1 @@ -783,7 +827,7 @@ class Nes(object): axis = 1 idx['idx_x_min'] = self.get_coordinate_id(self._lon['data'], self.lon_min, axis=axis) if self.lon_max is None: - idx['idx_x_max'] = self._lon['data'].shape[-1] + 1 + idx['idx_x_max'] = self._lon['data'].shape[-1] else: if len(self._lon['data'].shape) == 1: axis = 0 @@ -993,7 +1037,6 @@ class Nes(object): Dictionary with the 4D limits of the rank data to read. t_min, t_max, z_min, z_max, y_min, y_max, x_min and x_max. """ - if self.balanced: return self.get_read_axis_limits_balanced() else: @@ -1016,7 +1059,6 @@ class Nes(object): 't_min': None, 't_max': None} idx = self.get_idx_intervals() - if self.parallel_method == 'Y': y_len = idx['idx_y_max'] - idx['idx_y_min'] if y_len < self.size: @@ -1106,6 +1148,7 @@ class Nes(object): min_axis = 'y_min' max_axis = 'y_max' to_add = idx['idx_y_min'] + elif self.parallel_method == 'X': len_to_split = idx['idx_x_max'] - idx['idx_x_min'] if len_to_split < self.size: diff --git a/nes/nc_projections/latlon_nes.py b/nes/nc_projections/latlon_nes.py index 32756f13f7ff91b39bb5bcd05e16169ad5b89192..02ac978a372ca74f068f530eee88ee668c1077dc 100644 --- a/nes/nc_projections/latlon_nes.py +++ b/nes/nc_projections/latlon_nes.py @@ -143,7 +143,7 @@ class LatLonNes(Nes): else: lat_orig = kwargs['lat_orig'] lon_orig = kwargs['lon_orig'] - n_lat = kwargs['n_lon'] + n_lat = kwargs['n_lat'] n_lon = kwargs['n_lon'] # Calculate centre latitudes diff --git a/tutorials/6.Others/6.3.Plot_PcolorMesh.ipynb b/tutorials/6.Others/6.3.Plot_PcolorMesh.ipynb new file mode 100644 index 0000000000000000000000000000000000000000..18950994eb9bf4a634657725cb8a318657de2806 --- /dev/null +++ b/tutorials/6.Others/6.3.Plot_PcolorMesh.ipynb @@ -0,0 +1,98 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 5.7.Plot_PcolorMesh" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from nes import *\n", + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "fn = \"/gpfs/projects/bsc32/models/NES_tutorial_data/sconcno2-000_2019010100.nc\"\n", + "nessy = open_netcdf(fn)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "lon_b, lat_b = nessy.get_spatial_bounds_mesh_format()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "nessy.load()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "ax = plt.axes()\n", + "\n", + "plt.pcolormesh(lon_b, lat_b, nessy.variables['sconcno2']['data'][0,0])\n", + "\n", + "plt.show()" + ] + } + ], + "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 +}