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import itertools
import numpy as np
import pandas as pd
import pyproj
import xarray as xr
[docs]def new_cube(title='Test Cube',
width=360,
height=180,
x_name='lon',
y_name='lat',
x_dtype='float64',
y_dtype=None,
x_units='degrees_east',
y_units='degrees_north',
x_res=1.0,
y_res=None,
x_start=-180.0,
y_start=-90.0,
inverse_y=False,
time_name='time',
time_dtype='datetime64[s]',
time_units='seconds since 1970-01-01T00:00:00',
time_calendar='proleptic_gregorian',
time_periods=5,
time_freq="D",
time_start='2010-01-01T00:00:00',
use_cftime=False,
drop_bounds=False,
variables=None,
crs=None):
"""
Create a new empty cube. Useful for creating cubes templates with
predefined coordinate variables and metadata. The function is also
heavily used by xcube's unit tests.
The values of the *variables* dictionary can be either constants,
array-like objects, or functions that compute their return value from
passed coordinate indexes. The expected signature is:::
def my_func(time: int, y: int, x: int) -> Union[bool, int, float]
:param title: A title. Defaults to 'Test Cube'.
:param width: Horizontal number of grid cells. Defaults to 360.
:param height: Vertical number of grid cells. Defaults to 180.
:param x_name: Name of the x coordinate variable. Defaults to 'lon'.
:param y_name: Name of the y coordinate variable. Defaults to 'lat'.
:param x_dtype: Data type of x coordinates. Defaults to 'float64'.
:param y_dtype: Data type of y coordinates. Defaults to 'float64'.
:param x_units: Units of the x coordinates. Defaults to 'degrees_east'.
:param y_units: Units of the y coordinates. Defaults to 'degrees_north'.
:param x_start: Minimum x value. Defaults to -180.
:param y_start: Minimum y value. Defaults to -90.
:param x_res: Spatial resolution in x-direction. Defaults to 1.0.
:param y_res: Spatial resolution in y-direction. Defaults to 1.0.
:param inverse_y: Whether to create an inverse y axis. Defaults to False.
:param time_name: Name of the time coordinate variable. Defaults to 'time'.
:param time_periods: Number of time steps. Defaults to 5.
:param time_freq: Duration of each time step. Defaults to `1D'.
:param time_start: First time value. Defaults to '2010-01-01T00:00:00'.
:param time_dtype: Numpy data type for time coordinates.
Defaults to 'datetime64[s]'.
If used, parameter 'use_cftime' must be False.
:param time_units: Units for time coordinates.
Defaults to 'seconds since 1970-01-01T00:00:00'.
:param time_calendar: Calender for time coordinates.
Defaults to 'proleptic_gregorian'.
:param use_cftime: If True, the time will be given as data types
according to the 'cftime' package. If used, the time_calendar
parameter must be also be given with an appropriate value
such as 'gregorian' or 'julian'. If used, parameter 'time_dtype'
must be None.
:param drop_bounds: If True, coordinate bounds variables are not created.
Defaults to False.
:param variables: Dictionary of data variables to be added.
None by default.
:param crs: pyproj-compatible CRS string or instance
of pyproj.CRS or None
:return: A cube instance
"""
y_dtype = y_dtype if y_dtype is not None else y_dtype
y_res = y_res if y_res is not None else x_res
if width < 0 or height < 0 or x_res <= 0.0 or y_res <= 0.0:
raise ValueError()
if time_periods < 0:
raise ValueError()
if use_cftime and time_dtype is not None:
raise ValueError('If "use_cftime" is True,'
' "time_dtype" must not be set.')
x_is_lon = x_name == 'lon' or x_units == 'degrees_east'
y_is_lat = y_name == 'lat' or y_units == 'degrees_north'
x_end = x_start + width * x_res
y_end = y_start + height * y_res
x_res_05 = 0.5 * x_res
y_res_05 = 0.5 * y_res
x_data = np.linspace(x_start + x_res_05, x_end - x_res_05,
width, dtype=x_dtype)
y_data = np.linspace(y_start + y_res_05, y_end - y_res_05,
height, dtype=y_dtype)
x_var = xr.DataArray(x_data, dims=x_name, attrs=dict(units=x_units))
y_var = xr.DataArray(y_data, dims=y_name, attrs=dict(units=y_units))
if inverse_y:
y_var = y_var[::-1]
if x_is_lon:
x_var.attrs.update(long_name='longitude',
standard_name='longitude')
else:
x_var.attrs.update(long_name='x coordinate of projection',
standard_name='projection_x_coordinate')
if y_is_lat:
y_var.attrs.update(long_name='latitude',
standard_name='latitude')
else:
y_var.attrs.update(long_name='y coordinate of projection',
standard_name='projection_y_coordinate')
if use_cftime:
time_data_p1 = xr.cftime_range(start=time_start,
periods=time_periods + 1,
freq=time_freq,
calendar=time_calendar).values
else:
time_data_p1 = pd.date_range(start=time_start,
periods=time_periods + 1,
freq=time_freq).values
time_data_p1 = time_data_p1.astype(dtype=time_dtype)
time_delta = time_data_p1[1] - time_data_p1[0]
time_data = time_data_p1[0:-1] + time_delta // 2
time_var = xr.DataArray(time_data, dims=time_name)
time_var.encoding['units'] = time_units
time_var.encoding['calendar'] = time_calendar
coords = {x_name: x_var, y_name: y_var, time_name: time_var}
if not drop_bounds:
x_bnds_name = f'{x_name}_bnds'
y_bnds_name = f'{y_name}_bnds'
time_bnds_name = f'{time_name}_bnds'
bnds_dim = 'bnds'
x_bnds_data = np.zeros((width, 2), dtype=np.float64)
x_bnds_data[:, 0] = np.linspace(x_start, x_end - x_res,
width, dtype=x_dtype)
x_bnds_data[:, 1] = np.linspace(x_start + x_res, x_end,
width, dtype=x_dtype)
y_bnds_data = np.zeros((height, 2), dtype=np.float64)
y_bnds_data[:, 0] = np.linspace(y_start, y_end - x_res,
height, dtype=y_dtype)
y_bnds_data[:, 1] = np.linspace(y_start + x_res, y_end,
height, dtype=y_dtype)
if inverse_y:
y_bnds_data = y_bnds_data[::-1, ::-1]
x_bnds_var = xr.DataArray(x_bnds_data,
dims=(x_name, bnds_dim),
attrs=dict(units=x_units))
y_bnds_var = xr.DataArray(y_bnds_data,
dims=(y_name, bnds_dim),
attrs=dict(units=y_units))
x_var.attrs['bounds'] = x_bnds_name
y_var.attrs['bounds'] = y_bnds_name
time_bnds_data = np.zeros((time_periods, 2),
dtype=time_data_p1.dtype)
time_bnds_data[:, 0] = time_data_p1[:-1]
time_bnds_data[:, 1] = time_data_p1[1:]
time_bnds_var = xr.DataArray(time_bnds_data,
dims=(time_name, bnds_dim))
time_bnds_var.encoding['units'] = time_units
time_bnds_var.encoding['calendar'] = time_calendar
time_var.attrs['bounds'] = time_bnds_name
coords.update({x_bnds_name: x_bnds_var,
y_bnds_name: y_bnds_var,
time_bnds_name: time_bnds_var})
attrs = dict(Conventions="CF-1.7",
title=title,
time_coverage_start=str(time_data_p1[0]),
time_coverage_end=str(time_data_p1[-1]))
if x_is_lon:
attrs.update(dict(geospatial_lon_min=x_start,
geospatial_lon_max=x_end,
geospatial_lon_units=x_units))
if y_is_lat:
attrs.update(dict(geospatial_lat_min=y_start,
geospatial_lat_max=y_end,
geospatial_lat_units=y_units))
data_vars = {}
if variables:
dims = (time_name, y_name, x_name)
shape = (time_periods, height, width)
size = time_periods * height * width
for var_name, data in variables.items():
if isinstance(data, xr.DataArray):
data_vars[var_name] = data
elif isinstance(data, int) \
or isinstance(data, float) \
or isinstance(data, bool):
data_vars[var_name] = xr.DataArray(
np.full(shape, data), dims=dims
)
elif callable(data):
func = data
data = np.zeros(shape)
for index in itertools.product(*map(range, shape)):
data[index] = func(*index)
data_vars[var_name] = xr.DataArray(data, dims=dims)
elif data is None:
data_vars[var_name] = xr.DataArray(
np.random.uniform(0.0, 1.0, size).reshape(shape),
dims=dims
)
else:
data_vars[var_name] = xr.DataArray(data, dims=dims)
if isinstance(crs, str):
crs = pyproj.CRS.from_string(crs)
if isinstance(crs, pyproj.CRS):
data_vars['crs'] = xr.DataArray(0, attrs=crs.to_cf())
return xr.Dataset(data_vars=data_vars, coords=coords, attrs=attrs)