import pyinterp.backends.xarray
import xarray
try:
# When generating the documentation the variable DATASET points to the data
# path.
DATASET = pathlib.Path(os.environ['DATASET'])
except KeyError:
# Otherwise, the relative folder path is used.
DATASET = pathlib.Path("..", "..", "tests", "dataset")
TCW = DATASET.joinpath("tcw.nc")
#%%
# The first step is to load the data into memory and create the interpolator
# object:
ds = xarray.open_dataset(TCW)
interpolator = pyinterp.backends.xarray.Grid3D(ds.tcw)
#%%
# We will build a new grid that will be used to build a new interpolated grid.
#
# .. note ::
#
# The coordinates used for interpolation are shifted to avoid using the
# points of the trivariate function.
#
# .. warning ::
#
# When using a time axis, care must be taken to use the same unit of dates,
# between the axis defined and the dates supplied during interpolation. The
# function :py:meth:`pyinterp.TemporalAxis.safe_cast` automates this task and
# tensor data so that it is properly stored in memory for pyinterp.
#%%
# * The shape of the tensor must be (len(x_axis), len(y_axis), len(t_axis))
tcw = tcw.T
#%%
# * The undefined values must be set to nan.
tcw[tcw.mask] = float("nan")
#%%
# Now we can build the object handling the regular 3-dimensional grid.
#
# .. note::
# Grid data are not copied, the Grid3D class just keeps a reference on the
# handled array. Axis data are copied for non-uniform axes, and only examined
# for regular axes.
grid_3d = pyinterp.Grid3D(x_axis, y_axis, t_axis, tcw)
grid_3d
#%%
# xarray backend
# ##############
#
# The construction of these objects manipulating the :py:class:`regular grids
# <pyinterp.backends.xarray.RegularGridInterpolator>` can be done more easily
# using the `xarray <http://xarray.pydata.org/>`_ library and `CF
# <https://cfconventions.org/>`_ convention usually found in NetCDF files.
interpolator = pyinterp.backends.xarray.RegularGridInterpolator(
xarray.open_dataset(TCW).tcw)
interpolator.grid
import pyinterp.backends.xarray
import xarray
try:
# When generating the documentation the variable DATASET points to the data
# path.
DATASET = pathlib.Path(os.environ['DATASET'])
except KeyError:
# Otherwise, the relative folder path is used.
DATASET = pathlib.Path("..", "..", "tests", "dataset")
AOML = DATASET.joinpath("aoml_v2019.nc")
#%%
# The first step is to load the data into memory and create the interpolator
# object:
ds = xarray.open_dataset(AOML)
#%%
# Let's start by calculating the standard for vectors u and v.
norm = (ds.ud**2 + ds.vd**2)**0.5
#%%
# Now, we will describe the grid used to calculate our :py:class:`binned
# <pyinterp.Binning2D>` statics.
binning = pyinterp.Binning2D(
pyinterp.Axis(numpy.arange(27, 42, 0.3), is_circle=True),
pyinterp.Axis(numpy.arange(40, 47, 0.3)))
binning
#%%
# We push the loaded data into the different defined bins using :ref:`simple
# .. note::
#
# In the case of an interpolation of the nearest neighbor the undefined values
# have no impact because no arithmetic operation is done on the grid values:
# we just return the value of the nearest point.
#
# LOESS
# =====
#
# The :py:func:`first <pyinterp.fill.loess>` method applies a weighted local
# regression to extrapolate the boundary between defined and undefined values. The
# user must indicate the number of pixels on the X and Y axes to be considered in
# the calculation.
#
# Let's start by building the object handling our grid.
ds = xarray.open_dataset(MSS)
grid = pyinterp.backends.xarray.Grid2D(ds.mss)
#%%
# The function filling the holes near the mask is called
filled = pyinterp.fill.loess(grid, nx=3, ny=3)
#%%
# The image below illustrates the result:
fig = matplotlib.pyplot.figure()
ax1 = fig.add_subplot(
211, projection=cartopy.crs.PlateCarree(central_longitude=180))
lons, lats = numpy.meshgrid(grid.x, grid.y, indexing='ij')
pcm = ax1.pcolormesh(lons,
lats,
ds.mss.T,
import pyinterp.backends.xarray
import xarray
try:
# When generating the documentation the variable DATASET points to the data
# path.
DATASET = pathlib.Path(os.environ['DATASET'])
except KeyError:
# Otherwise, the relative folder path is used.
DATASET = pathlib.Path("..", "..", "tests", "dataset")
PRES_TEMP = DATASET.joinpath("pres_temp_4D.nc")
#%%
# The first step is to load the data into memory and create the interpolator
# object:
ds = xarray.open_dataset(PRES_TEMP)
interpolator = pyinterp.backends.xarray.Grid4D(ds.pressure)
#%%
# We will build a new grid that will be used to build a new interpolated grid.
#
# .. warning ::
#
# When using a time axis, care must be taken to use the same unit of dates,
# between the axis defined and the dates supplied during interpolation. The
# function :py:meth:`pyinterp.TemporalAxis.safe_cast` automates this task and
# will warn you if there is an inconsistency during the date conversion.
mx, my, mz, mu = numpy.meshgrid(numpy.arange(-125, -70, 0.5),
numpy.arange(25, 50, 0.5),
numpy.datetime64("2000-01-01T12:00"),
0.5,