Python Basemap.shiftdata方法代码示例

# 需要导入模块: from mpl_toolkits.basemap import Basemap [as 别名]
# 或者: from mpl_toolkits.basemap.Basemap import shiftdata [as 别名]
    def test_less_than_n_by_3_points_should_work(self):
        bm = Basemap(llcrnrlon=0, llcrnrlat=-80, urcrnrlon=360, urcrnrlat=80, projection='mill')
        lons_expected = self._get_2d_lons([10, 15, 20])

        # nothing should change
        lonsout = bm.shiftdata(lons_expected)
        assert_almost_equal(lons_expected, lonsout)

        # shift n x 3 and n x 2 grids and compare results over overlapping region
        lonsin = self._get_2d_lons([10, 361, 362])
        lonsout_expected = bm.shiftdata(lonsin[:])[:, :2]
        lonsout = bm.shiftdata(lonsin[:, :2])
        assert_almost_equal(lonsout_expected, lonsout)

# 需要导入模块: from mpl_toolkits.basemap import Basemap [as 别名]
# 或者: from mpl_toolkits.basemap.Basemap import shiftdata [as 别名]
    def test_2_points_should_work(self):
        """
        Shiftdata should work with 2 points
        """
        bm = Basemap(llcrnrlon=0, llcrnrlat=-80, urcrnrlon=360, urcrnrlat=80, projection='mill')

        lons_expected = [10, 15, 20]
        lonsout = bm.shiftdata(lons_expected[:])
        assert_almost_equal(lons_expected, lonsout)

        lonsout_expected = bm.shiftdata([10, 361, 362])
        lonsout = bm.shiftdata([10, 361])
        assert_almost_equal(lonsout_expected[:len(lonsout)], lonsout)

# 需要导入模块: from mpl_toolkits.basemap import Basemap [as 别名]
# 或者: from mpl_toolkits.basemap.Basemap import shiftdata [as 别名]
    def test_1_point_should_work(self):
        bm = Basemap(llcrnrlon=0, llcrnrlat=-80, urcrnrlon=360, urcrnrlat=80, projection='mill')

        # should not fail
        lonsout = bm.shiftdata([361])
        assert_almost_equal(lonsout, [1.0,])

        lonsout = bm.shiftdata([10])
        assert_almost_equal(lonsout, [10.0,])

        lonsin = np.array([361.0])
        lonsin.shape = (1, 1)
        lonsout = bm.shiftdata(lonsin[:])
        assert_almost_equal(lonsout.squeeze(), [1.0,])

# 需要导入模块: from mpl_toolkits.basemap import Basemap [as 别名]
# 或者: from mpl_toolkits.basemap.Basemap import shiftdata [as 别名]
    def test_shiftdata_on_monotonous_lons(self):
        """
        Test that shiftdata with fix_wrap_around keyword added works as before,
        when it is True
        """

        bm = Basemap(lon_0=0)

        lons_in = [120, 140, 160, 180, 200, 220]
        lons_out_expect = [-160, -140,  120,  140,  160,  180]
        lons_out = bm.shiftdata(lons_in, fix_wrap_around=True)

        assert_almost_equal(lons_out, lons_out_expect)

# 需要导入模块: from mpl_toolkits.basemap import Basemap [as 别名]
# 或者: from mpl_toolkits.basemap.Basemap import shiftdata [as 别名]
    def test_non_monotonous_longitudes(self):
        """
        when called for scatter, the longitudes passed to shiftdata are
        not necessarily monotonous...
        """
        lons = [179, 180, 180, 0, 290, 10, 320, -150, 350, -250, 250]
        bm = Basemap(lon_0=0)

        # before, having several break points would cause the exception,
        # inside the shiftdata method called from scatter method.
        self.assertRaises(ValueError, bm.shiftdata, lons, fix_wrap_around=True)

        lons_new = bm.shiftdata(lons, fix_wrap_around=False)

        # Check if the modified longitudes are inside of the projection region
        for lon in lons_new:
            assert lon >= bm.projparams["lon_0"] - 180
            assert lon <= bm.projparams["lon_0"] + 180

# 需要导入模块: from mpl_toolkits.basemap import Basemap [as 别名]
# 或者: from mpl_toolkits.basemap.Basemap import shiftdata [as 别名]
from mpl_toolkits.basemap import Basemap
import matplotlib.pyplot as plt
import numpy as np

map = Basemap(projection='sinu', 
              lat_0=0, lon_0=0)

lons = np.arange(30, 390, 30)
lats = np.arange(0, 100, 10)

data = np.indices((lats.shape[0], lons.shape[0]))
data = data[0] + data[1]

print data

print lons

lons, data = map.shiftdata(lons, datain = data, lon_0=0)

print lons

llons, llats = np.meshgrid(lons, lats)

x, y = map(llons, llats)

map.contourf(x, y, data)

map.drawcoastlines()
plt.show()

# 需要导入模块: from mpl_toolkits.basemap import Basemap [as 别名]
# 或者: from mpl_toolkits.basemap.Basemap import shiftdata [as 别名]
def main():
    erainterim_075_folder = "/HOME/data/Validation/ERA-Interim_0.75/Offline_driving_data/3h_Forecast"

    vname = "PR"
    start_year = 1980
    end_year = 2010

    season_key = "summer"
    season_labels = {season_key: "Summer"}
    season_to_months = OrderedDict([(season_key, [6, 7, 8])])

    # Validate temperature and precip
    model_vars = ["TT", "PR"]
    obs_vars = ["tmp", "pre"]

    obs_paths = [
        "/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.tmp.dat.nc",
        "/HOME/data/Validation/CRU_TS_3.1/Original_files_gzipped/cru_ts_3_10.1901.2009.pre.dat.nc",
    ]

    model_var_to_obs_var = dict(zip(model_vars, obs_vars))
    model_var_to_obs_path = dict(zip(model_vars, obs_paths))

    obs_path = model_var_to_obs_path[vname]

    cru = CRUDataManager(var_name=model_var_to_obs_var[vname], path=obs_path)

    # Calculate seasonal means for CRU
    seasonal_clim_fields_cru = cru.get_seasonal_means(
        season_name_to_months=season_to_months, start_year=start_year, end_year=end_year
    )

    # Calculate seasonal mean for erai
    flist = get_files_for_season(
        erainterim_075_folder, start_year=start_year, end_year=end_year, months=season_to_months[season_key]
    )

    rpf = MultiRPN(flist)
    date_to_field_erai075 = rpf.get_all_time_records_for_name_and_level(varname=vname, level=-1)

    # Convert to mm/day
    era075 = np.mean([field for field in date_to_field_erai075.values()], axis=0) * 24 * 3600 * 1000
    lons_era, lats_era = rpf.get_longitudes_and_latitudes_of_the_last_read_rec()

    seasonal_clim_fields_cru_interp = OrderedDict()

    # Calculate biases
    for season, cru_field in seasonal_clim_fields_cru.items():
        seasonal_clim_fields_cru_interp[season] = cru.interpolate_data_to(
            cru_field, lons2d=lons_era, lats2d=lats_era, nneighbours=1
        )

    # Do the plotting ------------------------------------------------------------------------------
    plot_utils.apply_plot_params()
    fig = plt.figure()

    b = Basemap()
    gs = gridspec.GridSpec(nrows=3, ncols=1)

    ax = fig.add_subplot(gs[0, 0])
    xx, yy = b(cru.lons2d, cru.lats2d)
    cs = b.contourf(xx, yy, seasonal_clim_fields_cru[season_key], 20)
    b.drawcoastlines(ax=ax)
    ax.set_title("CRU")
    plt.colorbar(cs, ax=ax)

    ax = fig.add_subplot(gs[1, 0])

    lons_era[lons_era > 180] -= 360
    lons_era, era075 = b.shiftdata(lons_era, datain=era075, lon_0=0)
    xx, yy = b(lons_era, lats_era)

    # mask oceans in the era plot as well
    era075 = maskoceans(lons_era, lats_era, era075)

    cs = b.contourf(xx, yy, era075, levels=cs.levels, norm=cs.norm, cmap=cs.cmap, ax=ax)
    b.drawcoastlines(ax=ax)
    ax.set_title("ERA-Interim 0.75")
    plt.colorbar(cs, ax=ax)

    # differences
    ax = fig.add_subplot(gs[2, 0])
    diff = era075 - seasonal_clim_fields_cru_interp[season_key]
    delta = np.percentile(np.abs(diff)[~diff.mask], 90)
    clevs = np.linspace(-delta, delta, 20)

    cs = b.contourf(xx, yy, diff, levels=clevs, cmap="RdBu_r", extend="both")
    b.drawcoastlines(ax=ax)
    ax.set_title("ERA-Interim 0.75 - CRU")
    plt.colorbar(cs, ax=ax)

    plt.show()

    fig.savefig(os.path.join(img_folder, "erai0.75_vs_cru_precip.png"), bbox_inches="tight")

# 需要导入模块: from mpl_toolkits.basemap import Basemap [as 别名]
# 或者: from mpl_toolkits.basemap.Basemap import shiftdata [as 别名]

# convert the sliced data to a pandas dataframe
df = hur_lev.to_dataframe()

# this is a simple way to rotate the coordinates of longitudes from 0-360 to -180-180
new_lons = ((ds.lon.data + 180) % 360) - 180

lons_greenwich = ((lons_pacific + 180) % 360) - 180

# see also:
# basemap shiftdata
from mpl_toolkits.basemap import Basemap, addcyclic, shiftgrid
m = Basemap( epsg=4326 )
new_arr = np.rollaxis( hur_lev.values.T, -1 )
test = m.shiftdata( xds.lon.data, hur_lev.values[1,...][:].T, lon_0=0.0 )

llons, llats = np.meshgrid( test[0], ds.lat.data )
x, y = m(llons, llats)
# m.contourf(x, y, test[1])
# m.drawcoastlines()
m.imshow( test[1] )
plt.savefig('test.png')


scp [email protected]:/home/malindgren/Documents/hur/test.png ~/Documents

# from frank
gdalwarp -multi -overwrite -of NetCDF -t_srs WGS84 hur_Amon_GFDL-CM3_historical_r1i1p1_186001-186412.nc test.nc -wo SOURCE_EXTRA=1000 -wo NUM_THREADS=ALL_CPUS --config CENTER_LONG 0

	# test file