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# Copyright (c) 2008-2016 MetPy Developers. |
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# Distributed under the terms of the BSD 3-Clause License. |
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# SPDX-License-Identifier: BSD-3-Clause |
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""" |
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=================== |
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Point Interpolation |
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=================== |
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Compares different point interpolation approaches. |
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""" |
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import cartopy |
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import cartopy.crs as ccrs |
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from matplotlib.colors import BoundaryNorm |
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import matplotlib.pyplot as plt |
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import numpy as np |
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from metpy.cbook import get_test_data |
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from metpy.gridding.gridding_functions import (interpolate, remove_nan_observations, |
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remove_repeat_coordinates) |
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########################################### |
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def basic_map(map_proj): |
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"""Make our basic default map for plotting""" |
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fig = plt.figure(figsize=(15, 10)) |
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view = fig.add_axes([0, 0, 1, 1], projection=to_proj) |
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view.set_extent([-120, -70, 20, 50]) |
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view.add_feature(cartopy.feature.NaturalEarthFeature(category='cultural', |
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name='admin_1_states_provinces_lakes', |
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scale='50m', facecolor='none')) |
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view.add_feature(cartopy.feature.OCEAN) |
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view.add_feature(cartopy.feature.COASTLINE) |
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view.add_feature(cartopy.feature.BORDERS, linestyle=':') |
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return view |
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View Code Duplication |
def station_test_data(variable_names, proj_from=None, proj_to=None): |
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f = get_test_data('station_data.txt') |
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all_data = np.loadtxt(f, skiprows=1, delimiter=',', |
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usecols=(1, 2, 3, 4, 5, 6, 7, 17, 18, 19), |
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dtype=np.dtype([('stid', '3S'), ('lat', 'f'), ('lon', 'f'), |
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('slp', 'f'), ('air_temperature', 'f'), |
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('cloud_fraction', 'f'), ('dewpoint', 'f'), |
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('weather', '16S'), |
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('wind_dir', 'f'), ('wind_speed', 'f')])) |
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all_stids = [s.decode('ascii') for s in all_data['stid']] |
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data = np.concatenate([all_data[all_stids.index(site)].reshape(1, ) for site in all_stids]) |
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value = data[variable_names] |
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lon = data['lon'] |
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lat = data['lat'] |
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if proj_from is not None and proj_to is not None: |
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try: |
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proj_points = proj_to.transform_points(proj_from, lon, lat) |
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return proj_points[:, 0], proj_points[:, 1], value |
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except Exception as e: |
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print(e) |
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return None |
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return lon, lat, value |
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from_proj = ccrs.Geodetic() |
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to_proj = ccrs.AlbersEqualArea(central_longitude=-97.0000, central_latitude=38.0000) |
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levels = list(range(-20, 20, 1)) |
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cmap = plt.get_cmap('magma') |
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norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True) |
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x, y, temp = station_test_data('air_temperature', from_proj, to_proj) |
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x, y, temp = remove_nan_observations(x, y, temp) |
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x, y, temp = remove_repeat_coordinates(x, y, temp) |
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########################################### |
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# ------------------------ |
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# Scipy.interpolate linear |
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# ------------------------ |
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gx, gy, img = interpolate(x, y, temp, interp_type='linear', hres=75000) |
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img = np.ma.masked_where(np.isnan(img), img) |
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view = basic_map(to_proj) |
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mmb = view.pcolormesh(gx, gy, img, cmap=cmap, norm=norm) |
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plt.colorbar(mmb, shrink=.4, pad=0, boundaries=levels) |
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########################################### |
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# ----------------------------------------------------- |
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# Natural neighbor interpolation (MetPy implementation) |
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# ----------------------------------------------------- |
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# `Reference <https://github.com/Unidata/MetPy/files/138653/cwp-657.pdf>`_ |
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gx, gy, img = interpolate(x, y, temp, interp_type='natural_neighbor', hres=75000) |
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img = np.ma.masked_where(np.isnan(img), img) |
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view = basic_map(to_proj) |
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mmb = view.pcolormesh(gx, gy, img, cmap=cmap, norm=norm) |
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plt.colorbar(mmb, shrink=.4, pad=0, boundaries=levels) |
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########################################### |
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# ---------------------- |
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# Cressman interpolation |
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# ---------------------- |
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# search_radius = 100 km |
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# |
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# grid resolution = 25 km |
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# |
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# min_neighbors = 1 |
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gx, gy, img = interpolate(x, y, temp, interp_type='cressman', minimum_neighbors=1, hres=75000, |
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search_radius=100000) |
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img = np.ma.masked_where(np.isnan(img), img) |
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view = basic_map(to_proj) |
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mmb = view.pcolormesh(gx, gy, img, cmap=cmap, norm=norm) |
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plt.colorbar(mmb, shrink=.4, pad=0, boundaries=levels) |
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########################################### |
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# -------------------- |
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# Barnes Interpolation |
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# -------------------- |
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# search_radius = 100km |
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# |
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# min_neighbors = 3 |
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gx, gy, img1 = interpolate(x, y, temp, interp_type='barnes', hres=75000, search_radius=100000) |
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img1 = np.ma.masked_where(np.isnan(img1), img1) |
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view = basic_map(to_proj) |
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mmb = view.pcolormesh(gx, gy, img1, cmap=cmap, norm=norm) |
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plt.colorbar(mmb, shrink=.4, pad=0, boundaries=levels) |
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########################################### |
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# ------------------------------------ |
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# Radial basis function interpolation |
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# ------------------------------------ |
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# linear |
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gx, gy, img = interpolate(x, y, temp, interp_type='rbf', hres=75000, rbf_func='linear', |
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rbf_smooth=0) |
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img = np.ma.masked_where(np.isnan(img), img) |
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view = basic_map(to_proj) |
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mmb = view.pcolormesh(gx, gy, img, cmap=cmap, norm=norm) |
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plt.colorbar(mmb, shrink=.4, pad=0, boundaries=levels) |
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plt.show() |
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Unidata /
MetPy
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