From b33e3450866e0b343dd3112676060a9edfa32da7 Mon Sep 17 00:00:00 2001
From: Eddy Comyn-Platt <53045993+EddyCMWF@users.noreply.github.com>
Date: Thu, 24 Aug 2023 13:49:41 +0000
Subject: [PATCH] Shape aggregation (#4)
* geopandas based shape aggregation added
---
.gitignore | 3 +-
Makefile | 8 +-
ci/combined-environment-ci.yml | 18 +
docs/conf.py | 2 +-
docs/index.md | 2 +-
earthkit/climate/__init__.py | 29 +-
earthkit/climate/_language.py | 13 -
earthkit/climate/aggregate.py | 155 +-
earthkit/climate/climatology.py | 23 +-
earthkit/climate/shapes.py | 425 +++
earthkit/climate/{_options.py => tools.py} | 97 +-
environment.yml | 3 +
notebooks/shapes.ipynb | 2713 +++++++++++++++++++
notebooks/test.ipynb | 2716 ++++++++++++++++++++
notebooks/test.nc | Bin 0 -> 243609 bytes
pyproject.toml | 4 +-
tests/test_10_shapes.py | 27 +
17 files changed, 6145 insertions(+), 93 deletions(-)
create mode 100644 ci/combined-environment-ci.yml
delete mode 100644 earthkit/climate/_language.py
create mode 100644 earthkit/climate/shapes.py
rename earthkit/climate/{_options.py => tools.py} (62%)
create mode 100644 notebooks/shapes.ipynb
create mode 100644 notebooks/test.ipynb
create mode 100644 notebooks/test.nc
create mode 100644 tests/test_10_shapes.py
diff --git a/.gitignore b/.gitignore
index d06cdf5..6b49a98 100644
--- a/.gitignore
+++ b/.gitignore
@@ -11,6 +11,7 @@ docs/_api/
# gitignore template for Jupyter Notebooks
# website: http://jupyter.org/
+dev-notebook.ipynb
.ipynb_checkpoints
*/.ipynb_checkpoints/*
@@ -213,7 +214,7 @@ docs/_build/
target/
# Jupyter Notebook
-notebooks/test_data
+test_data
# IPython
diff --git a/Makefile b/Makefile
index 476971c..24c1f79 100644
--- a/Makefile
+++ b/Makefile
@@ -4,15 +4,19 @@ CONDAFLAGS :=
COV_REPORT := html
default: qa unit-tests
-
qa:
pre-commit run --all-files
unit-tests:
python -m pytest -vv --cov=. --cov-report=$(COV_REPORT) --doctest-glob="*.md" --doctest-glob="*.rst"
+type-check:
+ python -m mypy . --no-namespace-packages
+
conda-env-update:
- $(CONDA) env update $(CONDAFLAGS) -f ci/environment-ci.yml
+ $(CONDA) install -y -c conda-forge conda-merge
+ $(CONDA) run conda-merge environment.yml ci/environment-ci.yml > ci/combined-environment-ci.yml
+ $(CONDA) env update $(CONDAFLAGS) -f ci/combined-environment-ci.yml
docker-build:
docker build -t $(PROJECT) .
diff --git a/ci/combined-environment-ci.yml b/ci/combined-environment-ci.yml
new file mode 100644
index 0000000..1defef4
--- /dev/null
+++ b/ci/combined-environment-ci.yml
@@ -0,0 +1,18 @@
+channels:
+- conda-forge
+- nodefaults
+dependencies:
+- earthkit-data
+- geopandas
+- make
+- mypy
+- myst-parser
+- numpy
+- pip
+- pre-commit
+- pydata-sphinx-theme
+- pytest
+- pytest-cov
+- sphinx
+- sphinx-autoapi
+- xarray
diff --git a/docs/conf.py b/docs/conf.py
index e9067bf..e8ccc2b 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -9,7 +9,7 @@
import os
import sys
-from earthkit import climate
+import earthkit.climate as climate
sys.path.insert(0, os.path.abspath("../"))
diff --git a/docs/index.md b/docs/index.md
index 79a6ae1..bb44d83 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -6,7 +6,7 @@ Statistical analysis tools for meteorological and climate data..
:caption: 'Contents:'
:maxdepth: 2
-API Reference <_api/earthkit-climate/index>
+API Reference <_api/index>
```
# Indices and tables
diff --git a/earthkit/climate/__init__.py b/earthkit/climate/__init__.py
index 8a107ab..9e45b0b 100644
--- a/earthkit/climate/__init__.py
+++ b/earthkit/climate/__init__.py
@@ -17,15 +17,30 @@
try:
# NOTE: the `version.py` file must not be present in the git repository
# as it is generated by setuptools at install time
- from .version import __version__
+ from earthkit.climate.version import __version__
except ImportError: # pragma: no cover
# Local copy or not installed with setuptools
__version__ = "999"
-from . import aggregate, climatology
-__all__ = [
- "__version__",
- "aggregate",
- "climatology",
-]
+from earthkit.climate import aggregate, climatology, shapes
+
+# try:
+# from earthkit.data.utils.module_inputs_wrappers import transform_module_inputs
+# except ImportError:
+# pass
+# else:
+from earthkit.data.utils.module_inputs_wrapper import transform_module_inputs
+
+KWARG_TYPES = {
+ # "dataarray": xr.DataArray,
+ # "dataset": xr.Dataset,
+}
+
+aggregate = transform_module_inputs(aggregate, kwarg_types=KWARG_TYPES)
+
+climatology = transform_module_inputs(climatology, kwarg_types=KWARG_TYPES)
+
+shapes = transform_module_inputs(shapes, kwarg_types=KWARG_TYPES)
+
+__all__ = ["__version__", "aggregate", "climatology", "shapes"]
diff --git a/earthkit/climate/_language.py b/earthkit/climate/_language.py
deleted file mode 100644
index eb2f2aa..0000000
--- a/earthkit/climate/_language.py
+++ /dev/null
@@ -1,13 +0,0 @@
-def _list_to_human(
- iterable: list,
- conjunction: str = "and",
- oxford_comma: bool = False,
-) -> str:
- list_of_strs = [str(item) for item in iterable]
-
- if len(list_of_strs) > 2:
- list_of_strs = [", ".join(list_of_strs[:-1]), list_of_strs[-1]]
- if oxford_comma:
- list_of_strs[0] += ","
-
- return f" {conjunction} ".join(list_of_strs)
diff --git a/earthkit/climate/aggregate.py b/earthkit/climate/aggregate.py
index 4b49a52..87f2ac1 100644
--- a/earthkit/climate/aggregate.py
+++ b/earthkit/climate/aggregate.py
@@ -1,8 +1,14 @@
-"""Module that contains generalised methods for aggregating xarray objects."""
+import typing as T
+from copy import deepcopy
+import numpy as np
import xarray as xr
-from ._options import ALLOWED_LIBS, HOW_DICT, WEIGHT_DICT
+from .tools import (
+ WEIGHTED_HOW_METHODS,
+ WEIGHTS_DICT,
+ get_how,
+)
#: Mapping from pandas frequency strings to xarray time groups
_PANDAS_FREQUENCIES = {
@@ -138,7 +144,7 @@ def _groupby_time(
frequency: str = None,
bin_widths: int = None,
squeeze: bool = True,
- time_dim="time",
+ time_dim: str = "time",
):
if frequency is None:
try:
@@ -199,68 +205,120 @@ def _pandas_frequency_and_bins(
return freq, bins
-def reduce(data, how="mean", how_weights=None, how_dropna=False, **kwargs):
+def _reduce_dataarray(
+ dataarray: xr.DataArray,
+ how: T.Union[T.Callable, str] = "mean",
+ weights: T.Union[None, str, np.ndarray] = None,
+ how_label: str = "",
+ how_dropna=False,
+ **kwargs,
+):
"""
Reduce an xarray.dataarray or xarray.dataset using a specified `how` method.
With the option to apply weights either directly or using a specified
- `how_weights` method.
+ `weights` method.
Parameters
----------
- data : xr.DataArray or xr.Dataset
+ dataarray : xr.DataArray or xr.Dataset
Data object to reduce
how: str or callable
Method used to reduce data. Default='mean', which will implement the xarray in-built mean.
If string, it must be an in-built xarray reduce method, a earthkit how method or any numpy method.
In the case of duplicate names, method selection is first in the order: xarray, earthkit, numpy.
- Otherwise it can be any function which can be called in the form f(x, axis=axis, **kwargs)
+ Otherwise it can be any function which can be called in the form `f(x, axis=axis, **kwargs)`
to return the result of reducing an np.ndarray over an integer valued axis
- how_weights (optional): str
- Choose a recognised method to apply weighting. Currently availble methods are; ['latitude']
- how_dropna (optional): str
+ weights : str
+ Choose a recognised method to apply weighting. Currently availble methods are; 'latitude'
+ how_dropna : str
Choose how to drop nan values.
Default is None and na values are preserved. Options are 'any' and 'all'.
- **kwargs:
+ **kwargs :
kwargs recognised by the how :func: `reduce`
Returns
-------
- A data array with dimensions [features] + [data.dims not in ['lat','lon']].
- Each slice of layer corresponds to a feature in layer.
+ A data array with reduce dimensions removed.
"""
- # If latitude_weighted, build array of weights based on latitude.
- if how_weights is not None:
- weights = WEIGHT_DICT.get(how_weights)(data)
- kwargs.update(dict(weights=weights))
-
- in_built_how_methods = [
- method for method in dir(data) if not method.startswith("_")
- ]
- # If how is string, fetch function from dictionary:
- if isinstance(how, str):
- if how in in_built_how_methods:
- return data.__getattribute__(how)(**kwargs)
- else:
- try:
- how_method = HOW_DICT[how]
- except KeyError:
- try:
- module, function = how.split(".")
- how_method = getattr(globals()[ALLOWED_LIBS[module]], function)
- except KeyError:
- raise ValueError(f"method must come from one of {ALLOWED_LIBS}")
- except AttributeError:
- raise AttributeError(
- f"module '{module}' has no attribute " f"'{function}'"
- )
+ # If weighted, use xarray weighted methods
+ if weights is not None:
+ # Create any standard weights, i.e. latitude
+ if isinstance(weights, str):
+ weights = WEIGHTS_DICT[weights](dataarray)
+ # We ensure the callable is always a string
+ if callable(how):
+ how = how.__name__
+ # map any alias methods:
+ how = WEIGHTED_HOW_METHODS.get(how, how)
+
+ dataarray = dataarray.weighted(weights)
+
+ red_array = dataarray.__getattribute__(how)(**kwargs)
+
else:
- how_method = how
+ # If how is string, fetch function from dictionary:
+ if isinstance(how, str):
+ if how in dir(dataarray):
+ red_array = dataarray.__getattribute__(how)(**kwargs)
+ else:
+ how_label = deepcopy(how)
+ how = get_how(how)
+ assert isinstance(how, T.Callable), f"how method not recognised: {how}"
+
+ red_array = dataarray.reduce(how, **kwargs)
+
+ if how_label:
+ red_array = red_array.rename(f"{red_array.name}_{how_label}")
+
+ if how_dropna:
+ red_array = red_array.dropna(how_dropna)
+
+ return red_array
+
+
+def reduce(
+ dataarray: T.Union[xr.DataArray, xr.Dataset],
+ **kwargs,
+):
+ """
+ Reduce an xarray.dataarray or xarray.dataset using a specified `how` method.
+
+ With the option to apply weights either directly or using a specified
+ `weights` method.
- reduced = data.reduce(how_method, **kwargs)
+ Parameters
+ ----------
+ dataarray : xr.DataArray or xr.Dataset
+ Data object to reduce
+ how: str or callable
+ Method used to reduce data. Default='mean', which will implement the xarray in-built mean.
+ If string, it must be an in-built xarray reduce method, a earthkit how method or any numpy method.
+ In the case of duplicate names, method selection is first in the order: xarray, earthkit, numpy.
+ Otherwise it can be any function which can be called in the form `f(x, axis=axis, **kwargs)`
+ to return the result of reducing an np.ndarray over an integer valued axis
+ weights : str
+ Choose a recognised method to apply weighting. Currently availble methods are; 'latitude'
+ how_label : str
+ Label to append to the name of the variable in the reduced object
+ how_dropna : str
+ Choose how to drop nan values.
+ Default is None and na values are preserved. Options are 'any' and 'all'.
+ **kwargs :
+ kwargs recognised by the how :func: `reduce`
- return reduced
+ Returns
+ -------
+ A data array with reduce dimensions removed.
+
+ """
+ if isinstance(dataarray, xr.DataArray):
+ return _reduce_dataarray(dataarray, **kwargs)
+ else:
+ return xr.Dataset(
+ [_reduce_dataarray(dataarray[var], **kwargs) for var in dataarray.data_vars]
+ )
def rolling_reduce(
@@ -272,18 +330,19 @@ def rolling_reduce(
----------
dataarray : xr.DataArray
Data over which the moving window is applied according to the reduction method.
- **windows : (see documentation for xarray.dataarray.rolling)
+ windows :
windows for the rolling groups, for example `time=10` to perform a reduction
in the time dimension with a bin size of 10. the rolling groups can be defined
- over any number of dimensions.
- min_periods (optional) : integer (see documentation for xarray.dataarray.rolling)
+ over any number of dimensions. **see documentation for xarray.dataarray.rolling**.
+ min_periods : integer
The minimum number of observations in the window required to have a value
(otherwise result is NaN). Default is to set **min_periods** equal to the size of the window.
- center (optional): bool (see documentation for xarray.dataarray.rolling)
- Set the labels at the centre of the window.
- how_reduce (optional) : str,
+ **see documentation for xarray.dataarray.rolling**
+ center : bool
+ Set the labels at the centre of the window, **see documentation for xarray.dataarray.rolling**.
+ how_reduce : str,
Function to be applied for reduction. Default is 'mean'.
- how_dropna (optional): str
+ how_dropna : str
Determine if dimension is removed from the output when we have at least one NaN or
all NaN. **how_dropna** can be 'None', 'any' or 'all'. Default is 'any'.
**kwargs :
diff --git a/earthkit/climate/climatology.py b/earthkit/climate/climatology.py
index 6d67167..54be8b6 100644
--- a/earthkit/climate/climatology.py
+++ b/earthkit/climate/climatology.py
@@ -1,5 +1,3 @@
-"""Module that contains methods for calculating climatological metrics from xarray objects."""
-
import xarray as xr
from . import aggregate
@@ -9,6 +7,7 @@ def mean(
dataarray: xr.DataArray,
frequency: str = None,
bin_widths: int = None,
+ time_dim: str = "time",
) -> xr.DataArray:
"""
Calculate the climatological mean.
@@ -29,14 +28,17 @@ def mean(
-------
xr.DataArray
"""
- grouped_data = aggregate._groupby_time(dataarray, frequency, bin_widths)
- return aggregate.reduce(grouped_data, dim="time")
+ grouped_data = aggregate._groupby_time(
+ dataarray, frequency=frequency, bin_widths=bin_widths, time_dim=time_dim
+ )
+ return aggregate.reduce(grouped_data, dim=time_dim)
def stdev(
dataarray: xr.DataArray,
frequency: str = None,
bin_widths: int = None,
+ time_dim: str = "time",
) -> xr.DataArray:
"""
Calculate of the climatological standard deviation.
@@ -58,7 +60,7 @@ def stdev(
xr.DataArray
"""
grouped_data = aggregate._groupby_time(dataarray, frequency, bin_widths)
- return aggregate.reduce(grouped_data, how="std", dim="time")
+ return aggregate.reduce(grouped_data, how="std", dim=time_dim)
def median(dataarray: xr.DataArray, **kwargs) -> xr.DataArray:
@@ -89,6 +91,7 @@ def max(
dataarray: xr.DataArray,
frequency: str = None,
bin_widths: int = None,
+ time_dim: str = "time",
) -> xr.DataArray:
"""
Calculate the climatological maximum.
@@ -110,13 +113,14 @@ def max(
xr.DataArray
"""
grouped_data = aggregate._groupby_time(dataarray, frequency, bin_widths)
- return aggregate.reduce(grouped_data, how="max", dim="time")
+ return aggregate.reduce(grouped_data, how="max", dim=time_dim)
def min(
dataarray: xr.DataArray,
frequency: str = None,
bin_widths: int = None,
+ time_dim: str = "time",
) -> xr.DataArray:
"""
Calculate the climatological minimum.
@@ -138,7 +142,7 @@ def min(
xr.DataArray
"""
grouped_data = aggregate._groupby_time(dataarray, frequency, bin_widths)
- return aggregate.reduce(grouped_data, how="min", dim="time")
+ return aggregate.reduce(grouped_data, how="min", dim=time_dim)
def quantiles(
@@ -147,6 +151,7 @@ def quantiles(
frequency: str = None,
bin_widths: int = None,
skipna: bool = False,
+ time_dim: str = "time",
**kwargs,
) -> xr.DataArray:
"""
@@ -171,14 +176,14 @@ def quantiles(
xr.DataArray
"""
grouped_data = aggregate._groupby_time(
- dataarray.chunk({"time": -1}), frequency, bin_widths
+ dataarray.chunk({time_dim: -1}), frequency, bin_widths
)
results = []
for quantile in quantiles:
results.append(
grouped_data.quantile(
q=quantile,
- dim="time",
+ dim=time_dim,
skipna=skipna,
**kwargs,
)
diff --git a/earthkit/climate/shapes.py b/earthkit/climate/shapes.py
new file mode 100644
index 0000000..3713768
--- /dev/null
+++ b/earthkit/climate/shapes.py
@@ -0,0 +1,425 @@
+import typing as T
+from copy import deepcopy
+
+import geopandas as gpd
+import numpy as np
+import xarray as xr
+
+from earthkit.climate.tools import (
+ WEIGHTS_DICT,
+ get_dim_key,
+ get_how,
+ get_spatial_dims,
+)
+
+
+def _transform_from_latlon(lat, lon):
+ """
+ Return an Affine transformation of input 1D arrays of lat and lon.
+
+ This assumes that both lat and lon are regular and contiguous.
+
+ Parameters
+ ----------
+ lat/lon : arrays or lists of latitude and longitude
+ """
+ from affine import Affine
+
+ trans = Affine.translation(
+ lon[0] - (lon[1] - lon[0]) / 2, lat[0] - (lat[1] - lat[0]) / 2
+ )
+ scale = Affine.scale(lon[1] - lon[0], lat[1] - lat[0])
+
+ return trans * scale
+
+
+def rasterize(
+ shape_list: T.List,
+ coords: xr.core.coordinates.Coordinates,
+ lat_key: str = "latitude",
+ lon_key: str = "longitude",
+ dtype: type = int,
+ **kwargs,
+):
+ """
+ Rasterize a list of geometries onto the given xarray coordinates.
+ This only works for regular and contiguous latitude and longitude grids.
+
+ Parameters
+ ----------
+ shape_list (affine.Affine): List of geometries
+ coords (xarray.coords): Coordinates of dataarray to be masked
+
+ lat_key/lon_key: name of the latitude/longitude variables in the coordinates object
+
+ fill: value to fill points which are not within the shape_list, default is 0
+ dtype: datatype of the returned mask, default is `int`
+
+ kwargs: Any other kwargs accepted by rasterio.features.rasterize
+
+ Returns
+ -------
+ xr.DataArray mask where points not inside the shape_list are set to `fill` value
+
+
+ """
+ from rasterio import features
+
+ transform = _transform_from_latlon(coords[lat_key], coords[lon_key])
+ out_shape = (len(coords[lat_key]), len(coords[lon_key]))
+ raster = features.rasterize(
+ shape_list, out_shape=out_shape, transform=transform, dtype=dtype, **kwargs
+ )
+ spatial_coords = {lat_key: coords[lat_key], lon_key: coords[lon_key]}
+ return xr.DataArray(raster, coords=spatial_coords, dims=(lat_key, lon_key))
+
+
+def mask_contains_points(shape_list, coords, lat_key="lat", lon_key="lon", **kwargs):
+ """
+ Return a mask array for the spatial points of data that lie within shapes in shape_list.
+
+
+ Function uses matplotlib.Path so can accept a list of points,
+ this is much faster than shapely.
+ It was initially included for use with irregular data but has been
+ constructed to also accept regular data and return in the same
+ format as the rasterize function.
+ """
+ import matplotlib.path as mpltPath
+
+ lat_dims = coords[lat_key].dims
+ lon_dims = coords[lon_key].dims
+ # Assert that latitude and longitude have the same dimensions
+ # (irregular data, e.g. x,y or obs)
+ # or the dimensions are themselves (regular data) but we will probably
+ # just use the rasterize function for the regular case
+ assert (lat_dims == lon_dims) or (lat_dims == (lat_key,) and lon_dims == (lon_key,))
+ if lat_dims == (lat_key,) and lon_dims == (lon_key,):
+ lon_full, lat_full = np.meshgrid(
+ coords[lon_key].values,
+ coords[lat_key].values,
+ )
+ else:
+ lon_full, lat_full = (
+ coords[lon_key].values,
+ coords[lat_key].values,
+ )
+ # convert lat lon pairs to to points:
+ points = list(
+ zip(
+ lon_full.flat,
+ lat_full.flat,
+ )
+ )
+
+ # get spatial dims and create output array:
+ spatial_dims = list(set(lat_dims + lon_dims))
+ outdata_shape = [len(coords[dim]) for dim in spatial_dims]
+ outdata = np.zeros(outdata_shape).astype(bool) * np.nan
+ # loop over shapes and mask any point that is in the shape
+ for shape in shape_list:
+ for shp in shape[0]:
+ shape_exterior = shp.exterior.coords.xy
+ shape_exterior = list(
+ zip(
+ list(shape_exterior[0]), # longitudes
+ list(shape_exterior[1]), # latitudes
+ )
+ )
+ path = mpltPath.Path(shape_exterior)
+ outdata.flat[path.contains_points(points)] = True
+
+ out_coords = {coord: coords[coord] for coord in spatial_dims}
+ outarray = xr.DataArray(outdata, coords=out_coords, dims=spatial_dims)
+
+ return outarray
+
+
+def _geopandas_to_shape_list(geodataframe):
+ """Iterate over rows of a geodataframe."""
+ return [row[1]["geometry"] for row in geodataframe.iterrows()]
+
+
+def _shape_mask_iterator(shapes, target, regular_grid=True, **kwargs):
+ """Method which iterates over shape mask methods."""
+ if isinstance(shapes, gpd.GeoDataFrame):
+ shapes = _geopandas_to_shape_list(shapes)
+ if regular_grid:
+ mask_function = rasterize
+ else:
+ mask_function = mask_contains_points
+ for shape in shapes:
+ shape_da = mask_function([shape], target.coords, **kwargs)
+ yield shape_da
+
+
+def shapes_to_mask(shapes, target, regular_grid=True, **kwargs):
+ """
+ Method which creates a list of mask dataarrays.
+
+ If possible use the shape_mask_iterator.
+ """
+ if isinstance(shapes, gpd.GeoDataFrame):
+ shapes = _geopandas_to_shape_list(shapes)
+ if regular_grid:
+ mask_function = rasterize
+ else:
+ mask_function = mask_contains_points
+
+ return [mask_function([shape], target.coords, **kwargs) for shape in shapes]
+
+
+def masks(
+ dataarray: T.Union[xr.DataArray, xr.Dataset],
+ geodataframe: gpd.geodataframe.GeoDataFrame,
+ mask_dim: str = "FID",
+ # regular_grid: bool = True,
+ **kwargs,
+):
+ """
+ Apply multiple shape masks to some gridded data.
+
+ Each feauture in shape is treated as an individual mask to apply to
+ data. The data provided is returned with an additional dimension equal in
+ length to the number of features in the shape object, this can result in very
+ large files which will slow down your workflow. It may be better to loop
+ over individual features, or directly apply the mask with the ct.shapes.average
+ or ct.shapes.reduce functions.
+
+ Parameters
+ ----------
+ dataarray :
+ Xarray data object (must have geospatial coordinates).
+ geodataframe :
+ Geopandas Dataframe containing the polygons for aggregations
+ how :
+ method used to apply mask. Default='mean', which calls np.nanmean
+ weights :
+ Provide weights for aggregation, also accepts recognised keys for weights, e.g.
+ 'latitude'
+
+ Returns
+ -------
+ A masked data array with dimensions [feautre_id] + [data.dims].
+ Each slice of layer corresponds to a feature in layer.
+ """
+ masked_arrays = []
+ for mask in _shape_mask_iterator(geodataframe, dataarray, **kwargs):
+ masked_arrays.append(dataarray.where(mask))
+
+ if isinstance(mask_dim, str):
+ mask_dim_values = geodataframe.get(
+ mask_dim, np.arange(len(masked_arrays))
+ ).to_numpy()
+ elif isinstance(mask_dim, dict):
+ assert (
+ len(mask_dim) == 1
+ ), "If provided as a dictionary, mask_dim should have onlly one key value pair"
+ mask_dim, mask_dim_values = mask_dim.items()
+ else:
+ raise ValueError(
+ "Unrecognised format for mask_dim, should be a string or length one dictionary"
+ )
+
+ out = xr.concat(masked_arrays, dim=mask_dim)
+ out = out.assign_coords({mask_dim: mask_dim_values})
+
+ out.attrs.update(geodataframe.attrs)
+
+ return out
+
+
+def reduce(
+ dataarray: T.Union[xr.DataArray, xr.Dataset],
+ geodataframe: gpd.GeoDataFrame,
+ **kwargs,
+):
+ """
+ Apply a shape object to an xarray.DataArray object using the specified 'how' method.
+
+ Geospatial coordinates are reduced to a dimension representing the list of features in the shape object.
+
+ Parameters
+ ----------
+ dataarray :
+ Xarray data object (must have geospatial coordinates).
+ geodataframe :
+ Geopandas Dataframe containing the polygons for aggregations
+ how :
+ method used to apply mask. Default='mean', which calls np.nanmean
+ weights :
+ Provide weights for aggregation, also accepts recognised keys for weights, e.g.
+ 'latitude'
+ lat_key/lon_key :
+ key for latitude/longitude variable, default behaviour is to detect variable keys.
+ extra_reduce_dims :
+ any additional dimensions to aggregate over when reducing over spatial dimensions
+ mask_dim :
+ dimension that will be created after the reduction of the spatial dimensions, default = `FID`
+ return_as :
+ what format to return the data object, `pandas` or `xarray`. Work In Progress
+ how_label :
+ label to append to variable name in returned object, default is `how`
+ kwargs :
+ kwargs recognised by the how function
+
+ Returns
+ -------
+ A data array with dimensions `features` + `data.dims not in 'lat','lon'`.
+ Each slice of layer corresponds to a feature in layer.
+
+ """
+ # kwargs.update({"how": how})
+ if isinstance(dataarray, xr.DataArray):
+ return _reduce_dataarray(dataarray, geodataframe, **kwargs)
+ else:
+ if kwargs.get("return_as", "pandas") in ["xarray"]:
+ return xr.Dataset(
+ [
+ _reduce_dataarray(dataarray[var], geodataframe, **kwargs)
+ for var in dataarray.data_vars
+ ]
+ )
+ else:
+ out = geodataframe
+ for var in dataarray.data_vars:
+ out = _reduce_dataarray(dataarray[var], geodataframe, **kwargs)
+ return out
+
+
+def _reduce_dataarray(
+ dataarray: xr.DataArray,
+ geodataframe: gpd.GeoDataFrame,
+ how: T.Union[T.Callable, str] = "mean",
+ weights: T.Union[None, str, np.ndarray] = None,
+ lat_key: T.Union[None, str] = None,
+ lon_key: T.Union[None, str] = None,
+ extra_reduce_dims: T.Union[list, str] = [],
+ mask_dim: str = "FID",
+ return_as: str = "pandas",
+ how_label: T.Union[str, None] = None,
+ squeeze: bool = True,
+ **kwargs,
+):
+ """
+ Apply a shape object to an xarray.DataArray object using the specified 'how' method.
+
+ Geospatial coordinates are reduced to a dimension representing the list of features in the shape object.
+
+ Parameters
+ ----------
+ dataarray :
+ Xarray data object (must have geospatial coordinates).
+ geodataframe :
+ Geopandas Dataframe containing the polygons for aggregations
+ how :
+ method used to apply mask. Default='mean', which calls np.nanmean
+ weights :
+ Provide weights for aggregation, also accepts recognised keys for weights, e.g.
+ 'latitude'
+ lat_key/lon_key :
+ key for latitude/longitude variable, default behaviour is to detect variable keys.
+ extra_reduce_dims :
+ any additional dimensions to aggregate over when reducing over spatial dimensions
+ mask_dim :
+ dimension that will be created after the reduction of the spatial dimensions, default = `"FID"`
+ return_as :
+ what format to return the data object, `"pandas"` or `"xarray"`. Work In Progress
+ how_label :
+ label to append to variable name in returned object, default is `how`
+ kwargs :
+ kwargs recognised by the how function
+
+ Returns
+ -------
+ A data array with dimensions [features] + [data.dims not in ['lat','lon']].
+ Each slice of layer corresponds to a feature in layer.
+
+ """
+ # If how is string, fetch function from dictionary:
+ if isinstance(how, str):
+ how_label = deepcopy(how)
+ how = get_how(how)
+ assert isinstance(how, T.Callable), f"how must be a callable: {how}"
+
+ if isinstance(extra_reduce_dims, str):
+ extra_reduce_dims = [extra_reduce_dims]
+
+ if lat_key is None:
+ lat_key = get_dim_key(dataarray, "y")
+ if lon_key is None:
+ lon_key = get_dim_key(dataarray, "x")
+
+ spatial_dims = get_spatial_dims(dataarray, lat_key, lon_key)
+
+ # Create any standard weights, i.e. latitude
+ if isinstance(weights, str):
+ weights = WEIGHTS_DICT[weights](dataarray)
+
+ red_kwargs = {}
+ reduced_list = []
+ for mask in _shape_mask_iterator(geodataframe, dataarray, **kwargs):
+ this = dataarray.where(mask, other=np.nan)
+
+ # If weighted, use xarray weighted arrays which correctly handle missing values etc.
+ if weights is not None:
+ dataarray.weighted(weights)
+
+ reduced = this.reduce(
+ how, dim=spatial_dims + extra_reduce_dims, **red_kwargs
+ ).compute()
+ reduced = reduced.assign_attrs(dataarray.attrs)
+ reduced_list.append(reduced)
+ # context.debug(f"Shapes.average reduced ({i}): {reduced} \n{i}")
+
+ if isinstance(mask_dim, str):
+ mask_dim_values = geodataframe.get(
+ mask_dim, np.arange(len(reduced_list))
+ ).to_numpy()
+ elif isinstance(mask_dim, dict):
+ assert (
+ len(mask_dim) == 1
+ ), "If provided as a dictionary, mask_dim should have only one key value pair"
+ mask_dim, mask_dim_values = mask_dim.items()
+ else:
+ raise ValueError(
+ "Unrecognised format for mask_dim, should be a string or length one dictionary"
+ )
+
+ if squeeze:
+ reduced_list = [red_data.squeeze() for red_data in reduced_list]
+
+ if return_as in ["xarray"]:
+ out = xr.concat(reduced_list, dim=mask_dim)
+ out = out.assign_coords(
+ **{
+ mask_dim: (mask_dim, mask_dim_values),
+ # TODO: the following creates an xarray that cannot be saved to netCDF
+ # "geometry": (mask_dim, [geom for geom in geodataframe["geometry"]]),
+ }
+ )
+ out = out.assign_attrs(geodataframe.attrs)
+ else:
+ how_label = f"{dataarray.name}_{how_label or how.__name__}"
+ if how_label in geodataframe:
+ how_label += "_reduced"
+
+ # Out dims for attributes:
+ out_dims = {
+ dim: dataarray.coords.get(dim).values if dim in dataarray.coords else None
+ for dim in reduced_list[0].dims
+ }
+ # # If all dataarrays are single valued, convert to integer values
+ # if all([not red.shape for red in reduced_list]):
+ reduced_list = [red.values for red in reduced_list]
+ # reduced_list = [red.to_dataframe() for red in reduced_list]
+
+ out = geodataframe.assign(**{how_label: reduced_list})
+ out.attrs.update(
+ {
+ f"{dataarray.name}_attrs": dataarray.attrs,
+ f"{how_label}_dims": out_dims,
+ }
+ )
+
+ return out
diff --git a/earthkit/climate/_options.py b/earthkit/climate/tools.py
similarity index 62%
rename from earthkit/climate/_options.py
rename to earthkit/climate/tools.py
index fe52d55..02dac79 100644
--- a/earthkit/climate/_options.py
+++ b/earthkit/climate/tools.py
@@ -1,3 +1,5 @@
+import typing as T
+
import numpy as np
import xarray as xr
@@ -109,18 +111,19 @@ def _latitude_weights(dataarray: xr.DataArray, lat_dim_names=["latitude", "lat"]
Detects the spatial dimensions latitude must be a coordinate of the dataarray.
"""
- data_shape = dataarray.shape
+ # data_shape = dataarray.shape
for lat in lat_dim_names:
- lat_array = dataarray.coords.get(lat, None)
+ lat_array = dataarray.coords.get(lat)
if lat_array is not None:
- break
- lat_dim_indices = [dataarray.dims.index(dim) for dim in lat_array.dims]
- return latitude_weights(
- lat_array.values, data_shape=data_shape, lat_dims=lat_dim_indices
- )
+ return np.cos(np.radians(lat_array.latitude))
+ # break
+ # lat_dim_indices = [dataarray.dims.index(dim) for dim in lat_array.dims]
+ # return latitude_weights(
+ # lat_array.values, data_shape=data_shape, lat_dims=lat_dim_indices
+ # )
-HOW_DICT = {
+HOW_METHODS = {
"average": nanaverage,
"mean": np.nanmean,
"stddev": np.nanstd,
@@ -137,13 +140,87 @@ def _latitude_weights(dataarray: xr.DataArray, lat_dim_names=["latitude", "lat"]
}
+WEIGHTED_HOW_METHODS = {
+ "average": "mean",
+ # "mean": "mean",
+ "nanmean": "mean",
+ "stddev": "std",
+ # "std": "std",
+ "stdev": "std",
+ # "sum": "sum",
+ # "sum_of_squares": "sum_of_squares",
+ # "sum_of_weights": "sum_of_weights",
+ "q": "quantile",
+ # "quantile": "quantile",
+ # "percentile": np.nanpercentile,
+ # "p": np.nanpercentile,
+}
+
+
# Libraries which are usable with reduce
ALLOWED_LIBS = {
"numpy": "np",
}
-
# Dictionary containing recognised weight functions.
-WEIGHT_DICT = {
+WEIGHTS_DICT = {
"latitude": _latitude_weights,
}
+
+
+def get_how(how: str, how_methods=HOW_METHODS):
+ try:
+ how = how_methods[how]
+ except KeyError:
+ try:
+ module, function = how.split(".")
+ how = getattr(globals()[ALLOWED_LIBS[module]], function)
+ except KeyError:
+ raise ValueError(f"method must come from one of {ALLOWED_LIBS}")
+ except AttributeError:
+ raise AttributeError(f"module '{module}' has no attribute " f"'{function}'")
+ return how
+
+
+STANDARD_AXIS_KEYS = {
+ "y": ["lat", "latitude"],
+ "x": ["lon", "long", "longitude"],
+ "t": ["time", "valid_time"],
+}
+
+
+def get_dim_key(
+ dataarray: T.Union[xr.DataArray, xr.Dataset],
+ axis: str,
+):
+ """Return the key of the dimension."""
+ # First check if the axis value is in any dim:
+ for dim in dataarray.dims:
+ if (
+ "axis" in dataarray[dim].attrs
+ and dataarray[dim].attrs["axis"].lower() == axis.lower()
+ ):
+ return dim
+
+ # Then check if any dims match our "standard" axis
+ for dim in dataarray.dims:
+ if dim in STANDARD_AXIS_KEYS.get(axis.lower()):
+ return dim
+
+ # We have not been able to detect, so return the axis key
+ return axis
+
+
+def get_spatial_dims(dataarray, lat_key, lon_key):
+ # Get the geospatial dimensions of the data. In the case of regular data this
+ # will be 'lat' and 'lon'. For irregular data it could be any dimensions
+ lat_dims = dataarray.coords[lat_key].dims
+ lon_dims = dataarray.coords[lon_key].dims
+
+ # Assert that latitude and longitude have the same dimensions
+ # (irregular data, e.g. x&y or obs)
+ # or the dimensions are themselves (regular data, 'lat'&'lon')
+ assert (lat_dims == lon_dims) or (
+ (lat_dims == (lat_key,)) and (lon_dims) == (lon_key,)
+ )
+ return list(set(lat_dims + lon_dims))
diff --git a/environment.yml b/environment.yml
index f1c50f2..4c51d51 100644
--- a/environment.yml
+++ b/environment.yml
@@ -18,3 +18,6 @@ dependencies:
# DO NOT EDIT ABOVE THIS LINE, ADD DEPENDENCIES BELOW AS SHOWN IN THE EXAMPLE
- numpy
- xarray
+- pip
+- geopandas
+- earthkit-data
diff --git a/notebooks/shapes.ipynb b/notebooks/shapes.ipynb
new file mode 100644
index 0000000..473f70e
--- /dev/null
+++ b/notebooks/shapes.ipynb
@@ -0,0 +1,2713 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "# Masking and reducing datacubes using geometry objects"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "from earthkit import climate as ek_climate\n",
+ "from earthkit import data as ek_data\n",
+ "\n",
+ "from earthkit.data.testing import earthkit_remote_test_data_file"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "
\n",
+ "\n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ "
<xarray.Dataset>\n",
+ "Dimensions: (number: 1, time: 1460, step: 1, surface: 1, latitude: 201,\n",
+ " longitude: 281)\n",
+ "Coordinates:\n",
+ " * number (number) int64 0\n",
+ " * time (time) datetime64[ns] 2015-01-01 ... 2015-12-31T18:00:00\n",
+ " * step (step) timedelta64[ns] 00:00:00\n",
+ " * surface (surface) float64 0.0\n",
+ " * latitude (latitude) float64 80.0 79.75 79.5 79.25 ... 30.5 30.25 30.0\n",
+ " * longitude (longitude) float64 -10.0 -9.75 -9.5 -9.25 ... 59.5 59.75 60.0\n",
+ " valid_time (time, step) datetime64[ns] ...\n",
+ "Data variables:\n",
+ " t2m (number, time, step, surface, latitude, longitude) float32 ...\n",
+ "Attributes:\n",
+ " GRIB_edition: 1\n",
+ " GRIB_centre: ecmf\n",
+ " GRIB_centreDescription: European Centre for Medium-Range Weather Forecasts\n",
+ " GRIB_subCentre: 0\n",
+ " Conventions: CF-1.7\n",
+ " institution: European Centre for Medium-Range Weather Forecasts\n",
+ " history: 2023-08-24T14:12 GRIB to CDM+CF via cfgrib-0.9.1... Dimensions: number : 1time : 1460step : 1surface : 1latitude : 201longitude : 281
Coordinates: (7)
number
(number)
int64
0
long_name : ensemble member numerical id units : 1 standard_name : realization time
(time)
datetime64[ns]
2015-01-01 ... 2015-12-31T18:00:00
long_name : initial time of forecast standard_name : forecast_reference_time array(['2015-01-01T00:00:00.000000000', '2015-01-01T06:00:00.000000000',\n",
+ " '2015-01-01T12:00:00.000000000', ..., '2015-12-31T06:00:00.000000000',\n",
+ " '2015-12-31T12:00:00.000000000', '2015-12-31T18:00:00.000000000'],\n",
+ " dtype='datetime64[ns]') step
(step)
timedelta64[ns]
00:00:00
long_name : time since forecast_reference_time standard_name : forecast_period array([0], dtype='timedelta64[ns]') surface
(surface)
float64
0.0
long_name : original GRIB coordinate for key: level(surface) units : 1 latitude
(latitude)
float64
80.0 79.75 79.5 ... 30.5 30.25 30.0
units : degrees_north standard_name : latitude long_name : latitude stored_direction : decreasing array([80. , 79.75, 79.5 , ..., 30.5 , 30.25, 30. ]) longitude
(longitude)
float64
-10.0 -9.75 -9.5 ... 59.75 60.0
units : degrees_east standard_name : longitude long_name : longitude array([-10. , -9.75, -9.5 , ..., 59.5 , 59.75, 60. ]) valid_time
(time, step)
datetime64[ns]
...
standard_name : time long_name : time [1460 values with dtype=datetime64[ns]] Data variables: (1)
Indexes: (6)
PandasIndex
PandasIndex(Index([0], dtype='int64', name='number')) PandasIndex
PandasIndex(DatetimeIndex(['2015-01-01 00:00:00', '2015-01-01 06:00:00',\n",
+ " '2015-01-01 12:00:00', '2015-01-01 18:00:00',\n",
+ " '2015-01-02 00:00:00', '2015-01-02 06:00:00',\n",
+ " '2015-01-02 12:00:00', '2015-01-02 18:00:00',\n",
+ " '2015-01-03 00:00:00', '2015-01-03 06:00:00',\n",
+ " ...\n",
+ " '2015-12-29 12:00:00', '2015-12-29 18:00:00',\n",
+ " '2015-12-30 00:00:00', '2015-12-30 06:00:00',\n",
+ " '2015-12-30 12:00:00', '2015-12-30 18:00:00',\n",
+ " '2015-12-31 00:00:00', '2015-12-31 06:00:00',\n",
+ " '2015-12-31 12:00:00', '2015-12-31 18:00:00'],\n",
+ " dtype='datetime64[ns]', name='time', length=1460, freq=None)) PandasIndex
PandasIndex(TimedeltaIndex(['0 days'], dtype='timedelta64[ns]', name='step', freq=None)) PandasIndex
PandasIndex(Index([0.0], dtype='float64', name='surface')) PandasIndex
PandasIndex(Index([ 80.0, 79.75, 79.5, 79.25, 79.0, 78.75, 78.5, 78.25, 78.0, 77.75,\n",
+ " ...\n",
+ " 32.25, 32.0, 31.75, 31.5, 31.25, 31.0, 30.75, 30.5, 30.25, 30.0],\n",
+ " dtype='float64', name='latitude', length=201)) PandasIndex
PandasIndex(Index([-10.0, -9.75, -9.5, -9.25, -9.0, -8.75, -8.5, -8.25, -8.0, -7.75,\n",
+ " ...\n",
+ " 57.75, 58.0, 58.25, 58.5, 58.75, 59.0, 59.25, 59.5, 59.75, 60.0],\n",
+ " dtype='float64', name='longitude', length=281)) Attributes: (7)
GRIB_edition : 1 GRIB_centre : ecmf GRIB_centreDescription : European Centre for Medium-Range Weather Forecasts GRIB_subCentre : 0 Conventions : CF-1.7 institution : European Centre for Medium-Range Weather Forecasts history : 2023-08-24T14:12 GRIB to CDM+CF via cfgrib-0.9.10.3/ecCodes-2.28.0 with {"source": "N/A", "filter_by_keys": {}, "encode_cf": ["parameter", "time", "geography", "vertical"]} "
+ ],
+ "text/plain": [
+ "\n",
+ "Dimensions: (number: 1, time: 1460, step: 1, surface: 1, latitude: 201,\n",
+ " longitude: 281)\n",
+ "Coordinates:\n",
+ " * number (number) int64 0\n",
+ " * time (time) datetime64[ns] 2015-01-01 ... 2015-12-31T18:00:00\n",
+ " * step (step) timedelta64[ns] 00:00:00\n",
+ " * surface (surface) float64 0.0\n",
+ " * latitude (latitude) float64 80.0 79.75 79.5 79.25 ... 30.5 30.25 30.0\n",
+ " * longitude (longitude) float64 -10.0 -9.75 -9.5 -9.25 ... 59.5 59.75 60.0\n",
+ " valid_time (time, step) datetime64[ns] ...\n",
+ "Data variables:\n",
+ " t2m (number, time, step, surface, latitude, longitude) float32 ...\n",
+ "Attributes:\n",
+ " GRIB_edition: 1\n",
+ " GRIB_centre: ecmf\n",
+ " GRIB_centreDescription: European Centre for Medium-Range Weather Forecasts\n",
+ " GRIB_subCentre: 0\n",
+ " Conventions: CF-1.7\n",
+ " institution: European Centre for Medium-Range Weather Forecasts\n",
+ " history: 2023-08-24T14:12 GRIB to CDM+CF via cfgrib-0.9.1..."
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Get some demonstration ERA5 data, this could be any url or path to an ERA5 grib or netCDF file.\n",
+ "remote_era5_file = earthkit_remote_test_data_file(\"test-data\", \"era5_temperature_europe_2015.grib\")\n",
+ "era5_data = ek_data.from_source(\"url\", remote_era5_file)\n",
+ "era5_data.to_xarray()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "GeojsonReader(represented as a geopandas object): \n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " id \n",
+ " NUTS_ID \n",
+ " LEVL_CODE \n",
+ " CNTR_CODE \n",
+ " NAME_LATN \n",
+ " NUTS_NAME \n",
+ " MOUNT_TYPE \n",
+ " URBN_TYPE \n",
+ " COAST_TYPE \n",
+ " FID \n",
+ " geometry \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " DK \n",
+ " DK \n",
+ " 0 \n",
+ " DK \n",
+ " Danmark \n",
+ " Danmark \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " DK \n",
+ " MULTIPOLYGON (((15.16290 55.09370, 15.09400 54... \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " RS \n",
+ " RS \n",
+ " 0 \n",
+ " RS \n",
+ " Serbia \n",
+ " Srbija/Сpбија \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " RS \n",
+ " POLYGON ((21.47920 45.19300, 21.35850 44.82160... \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " EE \n",
+ " EE \n",
+ " 0 \n",
+ " EE \n",
+ " Eesti \n",
+ " Eesti \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " EE \n",
+ " MULTIPOLYGON (((27.35700 58.78710, 27.64490 57... \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " EL \n",
+ " EL \n",
+ " 0 \n",
+ " EL \n",
+ " Elláda \n",
+ " Ελλάδα \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " EL \n",
+ " MULTIPOLYGON (((28.07770 36.11820, 27.86060 35... \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " ES \n",
+ " ES \n",
+ " 0 \n",
+ " ES \n",
+ " España \n",
+ " España \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " ES \n",
+ " MULTIPOLYGON (((4.39100 39.86170, 4.19070 39.7... \n",
+ " \n",
+ " \n",
+ " 5 \n",
+ " FI \n",
+ " FI \n",
+ " 0 \n",
+ " FI \n",
+ " Suomi/Finland \n",
+ " Suomi/Finland \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " FI \n",
+ " MULTIPOLYGON (((28.89670 69.04260, 28.47820 68... \n",
+ " \n",
+ " \n",
+ " 6 \n",
+ " FR \n",
+ " FR \n",
+ " 0 \n",
+ " FR \n",
+ " France \n",
+ " France \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " FR \n",
+ " MULTIPOLYGON (((55.84980 -21.18580, 55.78580 -... \n",
+ " \n",
+ " \n",
+ " 7 \n",
+ " HR \n",
+ " HR \n",
+ " 0 \n",
+ " HR \n",
+ " Hrvatska \n",
+ " Hrvatska \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " HR \n",
+ " MULTIPOLYGON (((17.65150 45.84780, 17.91210 45... \n",
+ " \n",
+ " \n",
+ " 8 \n",
+ " HU \n",
+ " HU \n",
+ " 0 \n",
+ " HU \n",
+ " Magyarország \n",
+ " Magyarország \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " HU \n",
+ " POLYGON ((22.12110 48.37830, 22.15530 48.40340... \n",
+ " \n",
+ " \n",
+ " 9 \n",
+ " IE \n",
+ " IE \n",
+ " 0 \n",
+ " IE \n",
+ " Éire/Ireland \n",
+ " Éire/Ireland \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " IE \n",
+ " POLYGON ((-7.18850 54.33770, -6.86420 54.33020... \n",
+ " \n",
+ " \n",
+ " 10 \n",
+ " IS \n",
+ " IS \n",
+ " 0 \n",
+ " IS \n",
+ " Ísland \n",
+ " Ísland \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " IS \n",
+ " POLYGON ((-22.12550 64.04060, -21.75690 64.325... \n",
+ " \n",
+ " \n",
+ " 11 \n",
+ " IT \n",
+ " IT \n",
+ " 0 \n",
+ " IT \n",
+ " Italia \n",
+ " Italia \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " IT \n",
+ " MULTIPOLYGON (((12.24070 47.06920, 12.21170 46... \n",
+ " \n",
+ " \n",
+ " 12 \n",
+ " LI \n",
+ " LI \n",
+ " 0 \n",
+ " LI \n",
+ " Liechtenstein \n",
+ " Liechtenstein \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " LI \n",
+ " POLYGON ((9.62060 47.15160, 9.60710 47.06080, ... \n",
+ " \n",
+ " \n",
+ " 13 \n",
+ " LT \n",
+ " LT \n",
+ " 0 \n",
+ " LT \n",
+ " Lietuva \n",
+ " Lietuva \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " LT \n",
+ " POLYGON ((22.92050 56.39910, 23.15540 56.32880... \n",
+ " \n",
+ " \n",
+ " 14 \n",
+ " LU \n",
+ " LU \n",
+ " 0 \n",
+ " LU \n",
+ " Luxembourg \n",
+ " Luxembourg \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " LU \n",
+ " POLYGON ((6.13770 50.13000, 6.47500 49.82130, ... \n",
+ " \n",
+ " \n",
+ " 15 \n",
+ " LV \n",
+ " LV \n",
+ " 0 \n",
+ " LV \n",
+ " Latvija \n",
+ " Latvija \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " LV \n",
+ " POLYGON ((27.69080 57.37060, 28.15460 56.16980... \n",
+ " \n",
+ " \n",
+ " 16 \n",
+ " ME \n",
+ " ME \n",
+ " 0 \n",
+ " ME \n",
+ " Crna Gora \n",
+ " Црна Гора \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " ME \n",
+ " POLYGON ((20.06390 43.00680, 20.35290 42.83340... \n",
+ " \n",
+ " \n",
+ " 17 \n",
+ " MK \n",
+ " MK \n",
+ " 0 \n",
+ " MK \n",
+ " Severna Makedonija \n",
+ " Северна Македонија \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " MK \n",
+ " POLYGON ((22.96830 41.51980, 22.92760 41.33850... \n",
+ " \n",
+ " \n",
+ " 18 \n",
+ " MT \n",
+ " MT \n",
+ " 0 \n",
+ " MT \n",
+ " Malta \n",
+ " Malta \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " MT \n",
+ " MULTIPOLYGON (((14.64590 35.93330, 14.43340 35... \n",
+ " \n",
+ " \n",
+ " 19 \n",
+ " SE \n",
+ " SE \n",
+ " 0 \n",
+ " SE \n",
+ " Sverige \n",
+ " Sverige \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " SE \n",
+ " MULTIPOLYGON (((20.54860 69.06000, 23.40780 68... \n",
+ " \n",
+ " \n",
+ " 20 \n",
+ " SI \n",
+ " SI \n",
+ " 0 \n",
+ " SI \n",
+ " Slovenija \n",
+ " Slovenija \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " SI \n",
+ " POLYGON ((16.37080 46.72220, 16.59680 46.47590... \n",
+ " \n",
+ " \n",
+ " 21 \n",
+ " SK \n",
+ " SK \n",
+ " 0 \n",
+ " SK \n",
+ " Slovensko \n",
+ " Slovensko \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " SK \n",
+ " POLYGON ((19.46740 49.61380, 19.88390 49.20420... \n",
+ " \n",
+ " \n",
+ " 22 \n",
+ " TR \n",
+ " TR \n",
+ " 0 \n",
+ " TR \n",
+ " Türkiye \n",
+ " Türkiye \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " TR \n",
+ " MULTIPOLYGON (((35.51370 41.63600, 35.94940 41... \n",
+ " \n",
+ " \n",
+ " 23 \n",
+ " UK \n",
+ " UK \n",
+ " 0 \n",
+ " UK \n",
+ " United Kingdom \n",
+ " United Kingdom \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " UK \n",
+ " MULTIPOLYGON (((-0.11020 51.50960, -0.02470 51... \n",
+ " \n",
+ " \n",
+ " 24 \n",
+ " NL \n",
+ " NL \n",
+ " 0 \n",
+ " NL \n",
+ " Nederland \n",
+ " Nederland \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " NL \n",
+ " MULTIPOLYGON (((7.20280 53.11330, 7.09270 52.8... \n",
+ " \n",
+ " \n",
+ " 25 \n",
+ " PL \n",
+ " PL \n",
+ " 0 \n",
+ " PL \n",
+ " Polska \n",
+ " Polska \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " PL \n",
+ " POLYGON ((18.54170 54.58450, 18.95000 54.35830... \n",
+ " \n",
+ " \n",
+ " 26 \n",
+ " PT \n",
+ " PT \n",
+ " 0 \n",
+ " PT \n",
+ " Portugal \n",
+ " Portugal \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " PT \n",
+ " MULTIPOLYGON (((-8.19900 42.15440, -8.16510 41... \n",
+ " \n",
+ " \n",
+ " 27 \n",
+ " RO \n",
+ " RO \n",
+ " 0 \n",
+ " RO \n",
+ " România \n",
+ " România \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " RO \n",
+ " POLYGON ((27.39120 47.58940, 28.11380 46.83840... \n",
+ " \n",
+ " \n",
+ " 28 \n",
+ " AL \n",
+ " AL \n",
+ " 0 \n",
+ " AL \n",
+ " Shqipëria \n",
+ " Shqipëria \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " AL \n",
+ " POLYGON ((20.07630 42.55580, 20.26490 42.39290... \n",
+ " \n",
+ " \n",
+ " 29 \n",
+ " AT \n",
+ " AT \n",
+ " 0 \n",
+ " AT \n",
+ " Österreich \n",
+ " Österreich \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " AT \n",
+ " POLYGON ((16.94030 48.61720, 16.94980 48.53580... \n",
+ " \n",
+ " \n",
+ " 30 \n",
+ " BE \n",
+ " BE \n",
+ " 0 \n",
+ " BE \n",
+ " Belgique/België \n",
+ " Belgique/België \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " BE \n",
+ " POLYGON ((5.56630 51.22080, 5.79830 51.05990, ... \n",
+ " \n",
+ " \n",
+ " 31 \n",
+ " BG \n",
+ " BG \n",
+ " 0 \n",
+ " BG \n",
+ " Bulgaria \n",
+ " България \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " BG \n",
+ " POLYGON ((22.96640 44.09830, 22.99720 43.80790... \n",
+ " \n",
+ " \n",
+ " 32 \n",
+ " CH \n",
+ " CH \n",
+ " 0 \n",
+ " CH \n",
+ " Schweiz/Suisse/Svizzera \n",
+ " Schweiz/Suisse/Svizzera \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " CH \n",
+ " POLYGON ((9.18220 47.65590, 9.49560 47.55150, ... \n",
+ " \n",
+ " \n",
+ " 33 \n",
+ " CY \n",
+ " CY \n",
+ " 0 \n",
+ " CY \n",
+ " Kýpros \n",
+ " Κύπρος \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " CY \n",
+ " POLYGON ((33.62510 34.85110, 32.94170 34.64180... \n",
+ " \n",
+ " \n",
+ " 34 \n",
+ " CZ \n",
+ " CZ \n",
+ " 0 \n",
+ " CZ \n",
+ " Česko \n",
+ " Česko \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " CZ \n",
+ " POLYGON ((14.49120 51.04350, 14.61880 50.85780... \n",
+ " \n",
+ " \n",
+ " 35 \n",
+ " DE \n",
+ " DE \n",
+ " 0 \n",
+ " DE \n",
+ " Deutschland \n",
+ " Deutschland \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " DE \n",
+ " MULTIPOLYGON (((9.11310 54.87360, 9.27360 54.8... \n",
+ " \n",
+ " \n",
+ " 36 \n",
+ " NO \n",
+ " NO \n",
+ " 0 \n",
+ " NO \n",
+ " Norge \n",
+ " Norge \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " NO \n",
+ " MULTIPOLYGON (((28.89670 69.04260, 29.15370 69... \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ "GeojsonReader(/var/folders/l2/529q7bzs665bnrn7_wjx1nsr0000gn/T/earthkit-data-edwardcomyn-platt/url-91b60c4aab9c1aec060eb0cb5db6a0ad03603f07b88e723453b62163ce787548.geojson)"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Use some demonstration polygons stored, this could be any url or path to geojson file\n",
+ "remote_nuts_url = earthkit_remote_test_data_file(\"test-data\", \"NUTS_RG_60M_2021_4326_LEVL_0.geojson\")\n",
+ "nuts_data = ek_data.from_source(\"url\", remote_nuts_url)\n",
+ "nuts_data"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Mask dataarray with geodataframe"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ "
<xarray.DataArray 't2m' (FID: 37, number: 1, time: 1460, step: 1, surface: 1,\n",
+ " latitude: 201, longitude: 281)>\n",
+ "array([[[[[[[nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
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+ " [[[[nan, nan, nan, ..., nan, nan, nan],\n",
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+ " ...,\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan]]]],\n",
+ "\n",
+ "\n",
+ "\n",
+ "...\n",
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+ "\n",
+ " [[[[nan, nan, nan, ..., nan, nan, nan],\n",
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+ " [nan, nan, nan, ..., nan, nan, nan],\n",
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+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan]]]],\n",
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+ " [[[[nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " ...,\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan]]]]]]], dtype=float32)\n",
+ "Coordinates:\n",
+ " * number (number) int64 0\n",
+ " * time (time) datetime64[ns] 2015-01-01 ... 2015-12-31T18:00:00\n",
+ " * step (step) timedelta64[ns] 00:00:00\n",
+ " * surface (surface) float64 0.0\n",
+ " * latitude (latitude) float64 80.0 79.75 79.5 79.25 ... 30.5 30.25 30.0\n",
+ " * longitude (longitude) float64 -10.0 -9.75 -9.5 -9.25 ... 59.5 59.75 60.0\n",
+ " valid_time (time, step) datetime64[ns] 2015-01-01 ... 2015-12-31T18:00:00\n",
+ " * FID (FID) object 'DK' 'RS' 'EE' 'EL' 'ES' ... 'CY' 'CZ' 'DE' 'NO'\n",
+ "Attributes: (12/30)\n",
+ " GRIB_paramId: 167\n",
+ " GRIB_dataType: an\n",
+ " GRIB_numberOfPoints: 56481\n",
+ " GRIB_typeOfLevel: surface\n",
+ " GRIB_stepUnits: 1\n",
+ " GRIB_stepType: instant\n",
+ " ... ...\n",
+ " GRIB_shortName: 2t\n",
+ " GRIB_totalNumber: 0\n",
+ " GRIB_units: K\n",
+ " long_name: 2 metre temperature\n",
+ " units: K\n",
+ " standard_name: unknown nan nan nan nan nan nan nan nan ... nan nan nan nan nan nan nan nan
array([[[[[[[nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " ...,\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan]]]],\n",
+ "\n",
+ "\n",
+ "\n",
+ " [[[[nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
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+ " ...,\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan]]]],\n",
+ "\n",
+ "\n",
+ "\n",
+ "...\n",
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+ "\n",
+ "\n",
+ " [[[[nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " ...,\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan]]]],\n",
+ "\n",
+ "\n",
+ "\n",
+ " [[[[nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " ...,\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan]]]]]]], dtype=float32) Coordinates: (8)
number
(number)
int64
0
long_name : ensemble member numerical id units : 1 standard_name : realization time
(time)
datetime64[ns]
2015-01-01 ... 2015-12-31T18:00:00
long_name : initial time of forecast standard_name : forecast_reference_time array(['2015-01-01T00:00:00.000000000', '2015-01-01T06:00:00.000000000',\n",
+ " '2015-01-01T12:00:00.000000000', ..., '2015-12-31T06:00:00.000000000',\n",
+ " '2015-12-31T12:00:00.000000000', '2015-12-31T18:00:00.000000000'],\n",
+ " dtype='datetime64[ns]') step
(step)
timedelta64[ns]
00:00:00
long_name : time since forecast_reference_time standard_name : forecast_period array([0], dtype='timedelta64[ns]') surface
(surface)
float64
0.0
long_name : original GRIB coordinate for key: level(surface) units : 1 latitude
(latitude)
float64
80.0 79.75 79.5 ... 30.5 30.25 30.0
units : degrees_north standard_name : latitude long_name : latitude stored_direction : decreasing array([80. , 79.75, 79.5 , ..., 30.5 , 30.25, 30. ]) longitude
(longitude)
float64
-10.0 -9.75 -9.5 ... 59.75 60.0
units : degrees_east standard_name : longitude long_name : longitude array([-10. , -9.75, -9.5 , ..., 59.5 , 59.75, 60. ]) valid_time
(time, step)
datetime64[ns]
2015-01-01 ... 2015-12-31T18:00:00
standard_name : time long_name : time array([['2015-01-01T00:00:00.000000000'],\n",
+ " ['2015-01-01T06:00:00.000000000'],\n",
+ " ['2015-01-01T12:00:00.000000000'],\n",
+ " ...,\n",
+ " ['2015-12-31T06:00:00.000000000'],\n",
+ " ['2015-12-31T12:00:00.000000000'],\n",
+ " ['2015-12-31T18:00:00.000000000']], dtype='datetime64[ns]') FID
(FID)
object
'DK' 'RS' 'EE' ... 'CZ' 'DE' 'NO'
array(['DK', 'RS', 'EE', 'EL', 'ES', 'FI', 'FR', 'HR', 'HU', 'IE', 'IS', 'IT',\n",
+ " 'LI', 'LT', 'LU', 'LV', 'ME', 'MK', 'MT', 'SE', 'SI', 'SK', 'TR', 'UK',\n",
+ " 'NL', 'PL', 'PT', 'RO', 'AL', 'AT', 'BE', 'BG', 'CH', 'CY', 'CZ', 'DE',\n",
+ " 'NO'], dtype=object) Indexes: (7)
PandasIndex
PandasIndex(Index([0], dtype='int64', name='number')) PandasIndex
PandasIndex(DatetimeIndex(['2015-01-01 00:00:00', '2015-01-01 06:00:00',\n",
+ " '2015-01-01 12:00:00', '2015-01-01 18:00:00',\n",
+ " '2015-01-02 00:00:00', '2015-01-02 06:00:00',\n",
+ " '2015-01-02 12:00:00', '2015-01-02 18:00:00',\n",
+ " '2015-01-03 00:00:00', '2015-01-03 06:00:00',\n",
+ " ...\n",
+ " '2015-12-29 12:00:00', '2015-12-29 18:00:00',\n",
+ " '2015-12-30 00:00:00', '2015-12-30 06:00:00',\n",
+ " '2015-12-30 12:00:00', '2015-12-30 18:00:00',\n",
+ " '2015-12-31 00:00:00', '2015-12-31 06:00:00',\n",
+ " '2015-12-31 12:00:00', '2015-12-31 18:00:00'],\n",
+ " dtype='datetime64[ns]', name='time', length=1460, freq=None)) PandasIndex
PandasIndex(TimedeltaIndex(['0 days'], dtype='timedelta64[ns]', name='step', freq=None)) PandasIndex
PandasIndex(Index([0.0], dtype='float64', name='surface')) PandasIndex
PandasIndex(Index([ 80.0, 79.75, 79.5, 79.25, 79.0, 78.75, 78.5, 78.25, 78.0, 77.75,\n",
+ " ...\n",
+ " 32.25, 32.0, 31.75, 31.5, 31.25, 31.0, 30.75, 30.5, 30.25, 30.0],\n",
+ " dtype='float64', name='latitude', length=201)) PandasIndex
PandasIndex(Index([-10.0, -9.75, -9.5, -9.25, -9.0, -8.75, -8.5, -8.25, -8.0, -7.75,\n",
+ " ...\n",
+ " 57.75, 58.0, 58.25, 58.5, 58.75, 59.0, 59.25, 59.5, 59.75, 60.0],\n",
+ " dtype='float64', name='longitude', length=281)) PandasIndex
PandasIndex(Index(['DK', 'RS', 'EE', 'EL', 'ES', 'FI', 'FR', 'HR', 'HU', 'IE', 'IS', 'IT',\n",
+ " 'LI', 'LT', 'LU', 'LV', 'ME', 'MK', 'MT', 'SE', 'SI', 'SK', 'TR', 'UK',\n",
+ " 'NL', 'PL', 'PT', 'RO', 'AL', 'AT', 'BE', 'BG', 'CH', 'CY', 'CZ', 'DE',\n",
+ " 'NO'],\n",
+ " dtype='object', name='FID')) Attributes: (30)
GRIB_paramId : 167 GRIB_dataType : an GRIB_numberOfPoints : 56481 GRIB_typeOfLevel : surface GRIB_stepUnits : 1 GRIB_stepType : instant GRIB_gridType : regular_ll GRIB_NV : 0 GRIB_Nx : 281 GRIB_Ny : 201 GRIB_cfName : unknown GRIB_cfVarName : t2m GRIB_gridDefinitionDescription : Latitude/Longitude Grid GRIB_iDirectionIncrementInDegrees : 0.25 GRIB_iScansNegatively : 0 GRIB_jDirectionIncrementInDegrees : 0.25 GRIB_jPointsAreConsecutive : 0 GRIB_jScansPositively : 0 GRIB_latitudeOfFirstGridPointInDegrees : 80.0 GRIB_latitudeOfLastGridPointInDegrees : 30.0 GRIB_longitudeOfFirstGridPointInDegrees : -10.0 GRIB_longitudeOfLastGridPointInDegrees : 60.0 GRIB_missingValue : 9999 GRIB_name : 2 metre temperature GRIB_shortName : 2t GRIB_totalNumber : 0 GRIB_units : K long_name : 2 metre temperature units : K standard_name : unknown "
+ ],
+ "text/plain": [
+ "\n",
+ "array([[[[[[[nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " ...,\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan]]]],\n",
+ "\n",
+ "\n",
+ "\n",
+ " [[[[nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " ...,\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan]]]],\n",
+ "\n",
+ "\n",
+ "\n",
+ "...\n",
+ "\n",
+ "\n",
+ "\n",
+ " [[[[nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " ...,\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan]]]],\n",
+ "\n",
+ "\n",
+ "\n",
+ " [[[[nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " ...,\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan],\n",
+ " [nan, nan, nan, ..., nan, nan, nan]]]]]]], dtype=float32)\n",
+ "Coordinates:\n",
+ " * number (number) int64 0\n",
+ " * time (time) datetime64[ns] 2015-01-01 ... 2015-12-31T18:00:00\n",
+ " * step (step) timedelta64[ns] 00:00:00\n",
+ " * surface (surface) float64 0.0\n",
+ " * latitude (latitude) float64 80.0 79.75 79.5 79.25 ... 30.5 30.25 30.0\n",
+ " * longitude (longitude) float64 -10.0 -9.75 -9.5 -9.25 ... 59.5 59.75 60.0\n",
+ " valid_time (time, step) datetime64[ns] 2015-01-01 ... 2015-12-31T18:00:00\n",
+ " * FID (FID) object 'DK' 'RS' 'EE' 'EL' 'ES' ... 'CY' 'CZ' 'DE' 'NO'\n",
+ "Attributes: (12/30)\n",
+ " GRIB_paramId: 167\n",
+ " GRIB_dataType: an\n",
+ " GRIB_numberOfPoints: 56481\n",
+ " GRIB_typeOfLevel: surface\n",
+ " GRIB_stepUnits: 1\n",
+ " GRIB_stepType: instant\n",
+ " ... ...\n",
+ " GRIB_shortName: 2t\n",
+ " GRIB_totalNumber: 0\n",
+ " GRIB_units: K\n",
+ " long_name: 2 metre temperature\n",
+ " units: K\n",
+ " standard_name: unknown"
+ ]
+ },
+ "execution_count": 14,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "masked_data = ek_climate.shapes.masks(era5_data, nuts_data)\n",
+ "masked_data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "Text(0.5, 1.0, 'Masked Germany Zoom')"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, axes = plt.subplots(1, 3, figsize=(15,3))\n",
+ "era5_data.to_xarray().t2m.mean(dim='time').plot(ax=axes[0])\n",
+ "axes[0].set_title('Original data')\n",
+ "masked_data.sel(FID='DE').mean(dim='time').plot(ax=axes[1])\n",
+ "axes[1].set_title('Masked for Germany')\n",
+ "germany_data = masked_data.sel(FID='DE').dropna(dim='latitude', how='all').dropna(dim='longitude', how='all')\n",
+ "germany_data.mean(dim='time').plot(ax=axes[2])\n",
+ "axes[2].set_title('Masked Germany Zoom')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Reduce data\n",
+ "### Default behaviour inserts reduced data into geodataframe\n",
+ "[An] additional column[s] is[/are] added to the geodataframe which contains an xarray.DataArray of the reduced data. The column header is constructed from the variable name and the how method applied"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
\n",
+ " \n",
+ " \n",
+ " \n",
+ " id \n",
+ " NUTS_ID \n",
+ " LEVL_CODE \n",
+ " CNTR_CODE \n",
+ " NAME_LATN \n",
+ " NUTS_NAME \n",
+ " MOUNT_TYPE \n",
+ " URBN_TYPE \n",
+ " COAST_TYPE \n",
+ " FID \n",
+ " geometry \n",
+ " t2m_mean \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " 0 \n",
+ " DK \n",
+ " DK \n",
+ " 0 \n",
+ " DK \n",
+ " Danmark \n",
+ " Danmark \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " DK \n",
+ " MULTIPOLYGON (((15.16290 55.09370, 15.09400 54... \n",
+ " [278.70923, 279.765, 279.77222, 279.57568, 279... \n",
+ " \n",
+ " \n",
+ " 1 \n",
+ " RS \n",
+ " RS \n",
+ " 0 \n",
+ " RS \n",
+ " Serbia \n",
+ " Srbija/Сpбија \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " RS \n",
+ " POLYGON ((21.47920 45.19300, 21.35850 44.82160... \n",
+ " [257.4673, 256.88065, 264.83875, 263.44513, 26... \n",
+ " \n",
+ " \n",
+ " 2 \n",
+ " EE \n",
+ " EE \n",
+ " 0 \n",
+ " EE \n",
+ " Eesti \n",
+ " Eesti \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " EE \n",
+ " MULTIPOLYGON (((27.35700 58.78710, 27.64490 57... \n",
+ " [275.7629, 275.43472, 275.91312, 276.8525, 277... \n",
+ " \n",
+ " \n",
+ " 3 \n",
+ " EL \n",
+ " EL \n",
+ " 0 \n",
+ " EL \n",
+ " Elláda \n",
+ " Ελλάδα \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " EL \n",
+ " MULTIPOLYGON (((28.07770 36.11820, 27.86060 35... \n",
+ " [274.514, 273.25146, 275.95157, 273.73346, 272... \n",
+ " \n",
+ " \n",
+ " 4 \n",
+ " ES \n",
+ " ES \n",
+ " 0 \n",
+ " ES \n",
+ " España \n",
+ " España \n",
+ " 0 \n",
+ " 0 \n",
+ " 0 \n",
+ " ES \n",
+ " MULTIPOLYGON (((4.39100 39.86170, 4.19070 39.7... \n",
+ " [273.99857, 272.70166, 282.6711, 278.778, 274.... \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " id NUTS_ID LEVL_CODE CNTR_CODE NAME_LATN NUTS_NAME MOUNT_TYPE \\\n",
+ "0 DK DK 0 DK Danmark Danmark 0 \n",
+ "1 RS RS 0 RS Serbia Srbija/Сpбија 0 \n",
+ "2 EE EE 0 EE Eesti Eesti 0 \n",
+ "3 EL EL 0 EL Elláda Ελλάδα 0 \n",
+ "4 ES ES 0 ES España España 0 \n",
+ "\n",
+ " URBN_TYPE COAST_TYPE FID \\\n",
+ "0 0 0 DK \n",
+ "1 0 0 RS \n",
+ "2 0 0 EE \n",
+ "3 0 0 EL \n",
+ "4 0 0 ES \n",
+ "\n",
+ " geometry \\\n",
+ "0 MULTIPOLYGON (((15.16290 55.09370, 15.09400 54... \n",
+ "1 POLYGON ((21.47920 45.19300, 21.35850 44.82160... \n",
+ "2 MULTIPOLYGON (((27.35700 58.78710, 27.64490 57... \n",
+ "3 MULTIPOLYGON (((28.07770 36.11820, 27.86060 35... \n",
+ "4 MULTIPOLYGON (((4.39100 39.86170, 4.19070 39.7... \n",
+ "\n",
+ " t2m_mean \n",
+ "0 [278.70923, 279.765, 279.77222, 279.57568, 279... \n",
+ "1 [257.4673, 256.88065, 264.83875, 263.44513, 26... \n",
+ "2 [275.7629, 275.43472, 275.91312, 276.8525, 277... \n",
+ "3 [274.514, 273.25146, 275.95157, 273.73346, 272... \n",
+ "4 [273.99857, 272.70166, 282.6711, 278.778, 274.... "
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "reduced_data = ek_climate.shapes.reduce(era5_data, nuts_data)\n",
+ "reduced_data.iloc[:5]"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "{'t2m_attrs': {'GRIB_paramId': 167,\n",
+ " 'GRIB_dataType': 'an',\n",
+ " 'GRIB_numberOfPoints': 56481,\n",
+ " 'GRIB_typeOfLevel': 'surface',\n",
+ " 'GRIB_stepUnits': 1,\n",
+ " 'GRIB_stepType': 'instant',\n",
+ " 'GRIB_gridType': 'regular_ll',\n",
+ " 'GRIB_NV': 0,\n",
+ " 'GRIB_Nx': 281,\n",
+ " 'GRIB_Ny': 201,\n",
+ " 'GRIB_cfName': 'unknown',\n",
+ " 'GRIB_cfVarName': 't2m',\n",
+ " 'GRIB_gridDefinitionDescription': 'Latitude/Longitude Grid',\n",
+ " 'GRIB_iDirectionIncrementInDegrees': 0.25,\n",
+ " 'GRIB_iScansNegatively': 0,\n",
+ " 'GRIB_jDirectionIncrementInDegrees': 0.25,\n",
+ " 'GRIB_jPointsAreConsecutive': 0,\n",
+ " 'GRIB_jScansPositively': 0,\n",
+ " 'GRIB_latitudeOfFirstGridPointInDegrees': 80.0,\n",
+ " 'GRIB_latitudeOfLastGridPointInDegrees': 30.0,\n",
+ " 'GRIB_longitudeOfFirstGridPointInDegrees': -10.0,\n",
+ " 'GRIB_longitudeOfLastGridPointInDegrees': 60.0,\n",
+ " 'GRIB_missingValue': 9999,\n",
+ " 'GRIB_name': '2 metre temperature',\n",
+ " 'GRIB_shortName': '2t',\n",
+ " 'GRIB_totalNumber': 0,\n",
+ " 'GRIB_units': 'K',\n",
+ " 'long_name': '2 metre temperature',\n",
+ " 'units': 'K',\n",
+ " 'standard_name': 'unknown'},\n",
+ " 't2m_mean_dims': {'time': array(['2015-01-01T00:00:00.000000000', '2015-01-01T06:00:00.000000000',\n",
+ " '2015-01-01T12:00:00.000000000', ...,\n",
+ " '2015-12-31T06:00:00.000000000', '2015-12-31T12:00:00.000000000',\n",
+ " '2015-12-31T18:00:00.000000000'], dtype='datetime64[ns]')}}"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "reduced_data.attrs"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "plot_var = \"t2m_mean\"\n",
+ "plot_x_vals = reduced_data.attrs[f\"{plot_var}_dims\"][\"time\"]\n",
+ "fig, ax = plt.subplots(1)\n",
+ "for i, feature in reduced_data.iterrows():\n",
+ " ax.plot(plot_x_vals, feature['t2m_mean'].squeeze(), label=feature['FID'])\n",
+ " # feature['t2m_mean'].plot(ax=ax, label=feature['FID'])\n",
+ " if i>5:\n",
+ " break\n",
+ "fig.legend()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Can provide additional dimensions to reduce along\n",
+ "\n",
+ "This is advisable with such analysis as it ensures correctly handled and weihted missing values"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "
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+ " \n",
+ " \n",
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+ " MOUNT_TYPE \n",
+ " URBN_TYPE \n",
+ " COAST_TYPE \n",
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+ " 0 \n",
+ " ES \n",
+ " MULTIPOLYGON (((4.39100 39.86170, 4.19070 39.7... \n",
+ " 287.7985 \n",
+ " \n",
+ " \n",
+ "
\n",
+ "
"
+ ],
+ "text/plain": [
+ " id NUTS_ID LEVL_CODE CNTR_CODE NAME_LATN NUTS_NAME MOUNT_TYPE \\\n",
+ "0 DK DK 0 DK Danmark Danmark 0 \n",
+ "1 RS RS 0 RS Serbia Srbija/Сpбија 0 \n",
+ "2 EE EE 0 EE Eesti Eesti 0 \n",
+ "3 EL EL 0 EL Elláda Ελλάδα 0 \n",
+ "4 ES ES 0 ES España España 0 \n",
+ "\n",
+ " URBN_TYPE COAST_TYPE FID \\\n",
+ "0 0 0 DK \n",
+ "1 0 0 RS \n",
+ "2 0 0 EE \n",
+ "3 0 0 EL \n",
+ "4 0 0 ES \n",
+ "\n",
+ " geometry t2m_mean \n",
+ "0 MULTIPOLYGON (((15.16290 55.09370, 15.09400 54... 282.48444 \n",
+ "1 POLYGON ((21.47920 45.19300, 21.35850 44.82160... 285.00317 \n",
+ "2 MULTIPOLYGON (((27.35700 58.78710, 27.64490 57... 280.56302 \n",
+ "3 MULTIPOLYGON (((28.07770 36.11820, 27.86060 35... 288.2147 \n",
+ "4 MULTIPOLYGON (((4.39100 39.86170, 4.19070 39.7... 287.7985 "
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "reduced_data = ek_climate.shapes.reduce(era5_data, nuts_data, extra_reduce_dims='time')\n",
+ "reduced_data.iloc[:5]"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### TODO: Use earthkit polygon plotting here"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Can provide weights for reduction\n",
+ "\n",
+ "Or use predefined weights options, i.e. `latitude`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import numpy as np\n",
+ "\n",
+ "reduced_data_xr = ek_climate.shapes.reduce(era5_data, nuts_data, weights='latitude')\n",
+ "reduced_data_xr"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Can return the object as an xarray\n",
+ "\n",
+ "TODO: how to attach to original geometry?"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "/Users/edwardcomyn-platt/miniconda3/envs/earthkit/lib/python3.10/site-packages/xarray/core/variable.py:2002: RuntimeWarning: All-NaN slice encountered\n",
+ " data = func(self.data, axis=axis, **kwargs)\n",
+ "/Users/edwardcomyn-platt/miniconda3/envs/earthkit/lib/python3.10/site-packages/xarray/core/variable.py:2002: RuntimeWarning: All-NaN slice encountered\n",
+ " data = func(self.data, axis=axis, **kwargs)\n"
+ ]
+ },
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ "
<xarray.DataArray 't2m' (FID: 37, time: 1460)>\n",
+ "array([[279.88135, 280.61102, 281.33185, ..., 277.7212 , 280.78748,\n",
+ " 281.04398],\n",
+ " [264.86768, 266.3454 , 269.08966, ..., 268.93408, 273.9906 ,\n",
+ " 269.51077],\n",
+ " [277.11377, 277.34735, 277.97247, ..., 271.02783, 271.52966,\n",
+ " 270.31937],\n",
+ " ...,\n",
+ " [274.09814, 275.03876, 276.59552, ..., 269.32666, 273.7621 ,\n",
+ " 271.876 ],\n",
+ " [280.16846, 279.87665, 278.94513, ..., 281.21338, 283.03357,\n",
+ " 282.04788],\n",
+ " [281.73486, 282.1579 , 281.6502 , ..., 281.2251 , 280.1527 ,\n",
+ " 280.6592 ]], dtype=float32)\n",
+ "Coordinates:\n",
+ " number int64 0\n",
+ " * time (time) datetime64[ns] 2015-01-01 ... 2015-12-31T18:00:00\n",
+ " step timedelta64[ns] 00:00:00\n",
+ " surface float64 0.0\n",
+ " valid_time (time) datetime64[ns] 2015-01-01 ... 2015-12-31T18:00:00\n",
+ " * FID (FID) object 'DK' 'RS' 'EE' 'EL' 'ES' ... 'CY' 'CZ' 'DE' 'NO'\n",
+ "Attributes: (12/30)\n",
+ " GRIB_paramId: 167\n",
+ " GRIB_dataType: an\n",
+ " GRIB_numberOfPoints: 56481\n",
+ " GRIB_typeOfLevel: surface\n",
+ " GRIB_stepUnits: 1\n",
+ " GRIB_stepType: instant\n",
+ " ... ...\n",
+ " GRIB_shortName: 2t\n",
+ " GRIB_totalNumber: 0\n",
+ " GRIB_units: K\n",
+ " long_name: 2 metre temperature\n",
+ " units: K\n",
+ " standard_name: unknown 279.9 280.6 281.3 281.1 281.8 283.0 ... 285.6 283.5 281.2 280.2 280.7
array([[279.88135, 280.61102, 281.33185, ..., 277.7212 , 280.78748,\n",
+ " 281.04398],\n",
+ " [264.86768, 266.3454 , 269.08966, ..., 268.93408, 273.9906 ,\n",
+ " 269.51077],\n",
+ " [277.11377, 277.34735, 277.97247, ..., 271.02783, 271.52966,\n",
+ " 270.31937],\n",
+ " ...,\n",
+ " [274.09814, 275.03876, 276.59552, ..., 269.32666, 273.7621 ,\n",
+ " 271.876 ],\n",
+ " [280.16846, 279.87665, 278.94513, ..., 281.21338, 283.03357,\n",
+ " 282.04788],\n",
+ " [281.73486, 282.1579 , 281.6502 , ..., 281.2251 , 280.1527 ,\n",
+ " 280.6592 ]], dtype=float32) Coordinates: (6)
number
()
int64
0
long_name : ensemble member numerical id units : 1 standard_name : realization time
(time)
datetime64[ns]
2015-01-01 ... 2015-12-31T18:00:00
long_name : initial time of forecast standard_name : forecast_reference_time array(['2015-01-01T00:00:00.000000000', '2015-01-01T06:00:00.000000000',\n",
+ " '2015-01-01T12:00:00.000000000', ..., '2015-12-31T06:00:00.000000000',\n",
+ " '2015-12-31T12:00:00.000000000', '2015-12-31T18:00:00.000000000'],\n",
+ " dtype='datetime64[ns]') step
()
timedelta64[ns]
00:00:00
long_name : time since forecast_reference_time standard_name : forecast_period array(0, dtype='timedelta64[ns]') surface
()
float64
0.0
long_name : original GRIB coordinate for key: level(surface) units : 1 valid_time
(time)
datetime64[ns]
2015-01-01 ... 2015-12-31T18:00:00
standard_name : time long_name : time array(['2015-01-01T00:00:00.000000000', '2015-01-01T06:00:00.000000000',\n",
+ " '2015-01-01T12:00:00.000000000', ...,\n",
+ " '2015-12-31T06:00:00.000000000', '2015-12-31T12:00:00.000000000',\n",
+ " '2015-12-31T18:00:00.000000000'], dtype='datetime64[ns]') FID
(FID)
object
'DK' 'RS' 'EE' ... 'CZ' 'DE' 'NO'
array(['DK', 'RS', 'EE', 'EL', 'ES', 'FI', 'FR', 'HR', 'HU', 'IE', 'IS', 'IT',\n",
+ " 'LI', 'LT', 'LU', 'LV', 'ME', 'MK', 'MT', 'SE', 'SI', 'SK', 'TR', 'UK',\n",
+ " 'NL', 'PL', 'PT', 'RO', 'AL', 'AT', 'BE', 'BG', 'CH', 'CY', 'CZ', 'DE',\n",
+ " 'NO'], dtype=object) Indexes: (2)
PandasIndex
PandasIndex(DatetimeIndex(['2015-01-01 00:00:00', '2015-01-01 06:00:00',\n",
+ " '2015-01-01 12:00:00', '2015-01-01 18:00:00',\n",
+ " '2015-01-02 00:00:00', '2015-01-02 06:00:00',\n",
+ " '2015-01-02 12:00:00', '2015-01-02 18:00:00',\n",
+ " '2015-01-03 00:00:00', '2015-01-03 06:00:00',\n",
+ " ...\n",
+ " '2015-12-29 12:00:00', '2015-12-29 18:00:00',\n",
+ " '2015-12-30 00:00:00', '2015-12-30 06:00:00',\n",
+ " '2015-12-30 12:00:00', '2015-12-30 18:00:00',\n",
+ " '2015-12-31 00:00:00', '2015-12-31 06:00:00',\n",
+ " '2015-12-31 12:00:00', '2015-12-31 18:00:00'],\n",
+ " dtype='datetime64[ns]', name='time', length=1460, freq=None)) PandasIndex
PandasIndex(Index(['DK', 'RS', 'EE', 'EL', 'ES', 'FI', 'FR', 'HR', 'HU', 'IE', 'IS', 'IT',\n",
+ " 'LI', 'LT', 'LU', 'LV', 'ME', 'MK', 'MT', 'SE', 'SI', 'SK', 'TR', 'UK',\n",
+ " 'NL', 'PL', 'PT', 'RO', 'AL', 'AT', 'BE', 'BG', 'CH', 'CY', 'CZ', 'DE',\n",
+ " 'NO'],\n",
+ " dtype='object', name='FID')) Attributes: (30)
GRIB_paramId : 167 GRIB_dataType : an GRIB_numberOfPoints : 56481 GRIB_typeOfLevel : surface GRIB_stepUnits : 1 GRIB_stepType : instant GRIB_gridType : regular_ll GRIB_NV : 0 GRIB_Nx : 281 GRIB_Ny : 201 GRIB_cfName : unknown GRIB_cfVarName : t2m GRIB_gridDefinitionDescription : Latitude/Longitude Grid GRIB_iDirectionIncrementInDegrees : 0.25 GRIB_iScansNegatively : 0 GRIB_jDirectionIncrementInDegrees : 0.25 GRIB_jPointsAreConsecutive : 0 GRIB_jScansPositively : 0 GRIB_latitudeOfFirstGridPointInDegrees : 80.0 GRIB_latitudeOfLastGridPointInDegrees : 30.0 GRIB_longitudeOfFirstGridPointInDegrees : -10.0 GRIB_longitudeOfLastGridPointInDegrees : 60.0 GRIB_missingValue : 9999 GRIB_name : 2 metre temperature GRIB_shortName : 2t GRIB_totalNumber : 0 GRIB_units : K long_name : 2 metre temperature units : K standard_name : unknown "
+ ],
+ "text/plain": [
+ "\n",
+ "array([[279.88135, 280.61102, 281.33185, ..., 277.7212 , 280.78748,\n",
+ " 281.04398],\n",
+ " [264.86768, 266.3454 , 269.08966, ..., 268.93408, 273.9906 ,\n",
+ " 269.51077],\n",
+ " [277.11377, 277.34735, 277.97247, ..., 271.02783, 271.52966,\n",
+ " 270.31937],\n",
+ " ...,\n",
+ " [274.09814, 275.03876, 276.59552, ..., 269.32666, 273.7621 ,\n",
+ " 271.876 ],\n",
+ " [280.16846, 279.87665, 278.94513, ..., 281.21338, 283.03357,\n",
+ " 282.04788],\n",
+ " [281.73486, 282.1579 , 281.6502 , ..., 281.2251 , 280.1527 ,\n",
+ " 280.6592 ]], dtype=float32)\n",
+ "Coordinates:\n",
+ " number int64 0\n",
+ " * time (time) datetime64[ns] 2015-01-01 ... 2015-12-31T18:00:00\n",
+ " step timedelta64[ns] 00:00:00\n",
+ " surface float64 0.0\n",
+ " valid_time (time) datetime64[ns] 2015-01-01 ... 2015-12-31T18:00:00\n",
+ " * FID (FID) object 'DK' 'RS' 'EE' 'EL' 'ES' ... 'CY' 'CZ' 'DE' 'NO'\n",
+ "Attributes: (12/30)\n",
+ " GRIB_paramId: 167\n",
+ " GRIB_dataType: an\n",
+ " GRIB_numberOfPoints: 56481\n",
+ " GRIB_typeOfLevel: surface\n",
+ " GRIB_stepUnits: 1\n",
+ " GRIB_stepType: instant\n",
+ " ... ...\n",
+ " GRIB_shortName: 2t\n",
+ " GRIB_totalNumber: 0\n",
+ " GRIB_units: K\n",
+ " long_name: 2 metre temperature\n",
+ " units: K\n",
+ " standard_name: unknown"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "\n",
+ "reduced_data_xr = ek_climate.shapes.reduce(era5_data, nuts_data, how=np.nanmax, weights='latitude', return_as='xarray')\n",
+ "reduced_data_xr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "reduced_data_xr.to_netcdf('test_data/test_reduced.nc')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ ""
+ ]
+ },
+ "execution_count": 13,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "fig, ax = plt.subplots(1)\n",
+ "for feature in nuts_data[:4]:\n",
+ " reduced_data_xr.sel(FID=feature[\"FID\"]).plot(ax=ax, label=feature[\"NUTS_NAME\"])\n",
+ "\n",
+ "fig.legend()\n"
+ ]
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "earthkit",
+ "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": "3.10.8"
+ },
+ "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/test.ipynb b/notebooks/test.ipynb
new file mode 100644
index 0000000..ec66126
--- /dev/null
+++ b/notebooks/test.ipynb
@@ -0,0 +1,2716 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import xarray as xr"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ " \n",
+ "
<xarray.Dataset>\n",
+ "Dimensions: (time: 1460, latitude: 201, longitude: 281)\n",
+ "Coordinates:\n",
+ " number int64 ...\n",
+ " * time (time) datetime64[ns] 2015-01-01 ... 2015-12-31T18:00:00\n",
+ " step timedelta64[ns] ...\n",
+ " surface float64 ...\n",
+ " * latitude (latitude) float64 80.0 79.75 79.5 79.25 ... 30.5 30.25 30.0\n",
+ " * longitude (longitude) float64 -10.0 -9.75 -9.5 -9.25 ... 59.5 59.75 60.0\n",
+ " valid_time (time) datetime64[ns] dask.array<chunksize=(48,), meta=np.ndarray>\n",
+ "Data variables:\n",
+ " t2m (time, latitude, longitude) float32 dask.array<chunksize=(48, 201, 281), meta=np.ndarray>\n",
+ "Attributes:\n",
+ " GRIB_edition: 1\n",
+ " GRIB_centre: ecmf\n",
+ " GRIB_centreDescription: European Centre for Medium-Range Weather Forecasts\n",
+ " GRIB_subCentre: 0\n",
+ " Conventions: CF-1.7\n",
+ " institution: European Centre for Medium-Range Weather Forecasts\n",
+ " history: 2023-08-23T17:14 GRIB to CDM+CF via cfgrib-0.9.1... Dimensions: time : 1460latitude : 201longitude : 281
Coordinates: (7)
number
()
int64
...
long_name : ensemble member numerical id units : 1 standard_name : realization [1 values with dtype=int64] time
(time)
datetime64[ns]
2015-01-01 ... 2015-12-31T18:00:00
long_name : initial time of forecast standard_name : forecast_reference_time array(['2015-01-01T00:00:00.000000000', '2015-01-01T06:00:00.000000000',\n",
+ " '2015-01-01T12:00:00.000000000', ..., '2015-12-31T06:00:00.000000000',\n",
+ " '2015-12-31T12:00:00.000000000', '2015-12-31T18:00:00.000000000'],\n",
+ " dtype='datetime64[ns]') step
()
timedelta64[ns]
...
long_name : time since forecast_reference_time standard_name : forecast_period [1 values with dtype=timedelta64[ns]] surface
()
float64
...
long_name : original GRIB coordinate for key: level(surface) units : 1 [1 values with dtype=float64] latitude
(latitude)
float64
80.0 79.75 79.5 ... 30.5 30.25 30.0
units : degrees_north standard_name : latitude long_name : latitude stored_direction : decreasing array([80. , 79.75, 79.5 , ..., 30.5 , 30.25, 30. ]) longitude
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float64
-10.0 -9.75 -9.5 ... 59.75 60.0
units : degrees_east standard_name : longitude long_name : longitude array([-10. , -9.75, -9.5 , ..., 59.5 , 59.75, 60. ]) valid_time
(time)
datetime64[ns]
dask.array<chunksize=(48,), meta=np.ndarray>
standard_name : time long_name : time \n",
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+ " 1460 \n",
+ " 1 \n",
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Data variables: (1)
t2m
(time, latitude, longitude)
float32
dask.array<chunksize=(48, 201, 281), meta=np.ndarray>
GRIB_paramId : 167 GRIB_dataType : an GRIB_numberOfPoints : 56481 GRIB_typeOfLevel : surface GRIB_stepUnits : 1 GRIB_stepType : instant GRIB_gridType : regular_ll GRIB_NV : 0 GRIB_Nx : 281 GRIB_Ny : 201 GRIB_cfName : unknown GRIB_cfVarName : t2m GRIB_gridDefinitionDescription : Latitude/Longitude Grid GRIB_iDirectionIncrementInDegrees : 0.25 GRIB_iScansNegatively : 0 GRIB_jDirectionIncrementInDegrees : 0.25 GRIB_jPointsAreConsecutive : 0 GRIB_jScansPositively : 0 GRIB_latitudeOfFirstGridPointInDegrees : 80.0 GRIB_latitudeOfLastGridPointInDegrees : 30.0 GRIB_longitudeOfFirstGridPointInDegrees : -10.0 GRIB_longitudeOfLastGridPointInDegrees : 60.0 GRIB_missingValue : 3.4028234663852886e+38 GRIB_name : 2 metre temperature GRIB_shortName : 2t GRIB_totalNumber : 0 GRIB_units : K long_name : 2 metre temperature units : K standard_name : unknown \n",
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Indexes: (3)
PandasIndex
PandasIndex(DatetimeIndex(['2015-01-01 00:00:00', '2015-01-01 06:00:00',\n",
+ " '2015-01-01 12:00:00', '2015-01-01 18:00:00',\n",
+ " '2015-01-02 00:00:00', '2015-01-02 06:00:00',\n",
+ " '2015-01-02 12:00:00', '2015-01-02 18:00:00',\n",
+ " '2015-01-03 00:00:00', '2015-01-03 06:00:00',\n",
+ " ...\n",
+ " '2015-12-29 12:00:00', '2015-12-29 18:00:00',\n",
+ " '2015-12-30 00:00:00', '2015-12-30 06:00:00',\n",
+ " '2015-12-30 12:00:00', '2015-12-30 18:00:00',\n",
+ " '2015-12-31 00:00:00', '2015-12-31 06:00:00',\n",
+ " '2015-12-31 12:00:00', '2015-12-31 18:00:00'],\n",
+ " dtype='datetime64[ns]', name='time', length=1460, freq=None)) PandasIndex
PandasIndex(Index([ 80.0, 79.75, 79.5, 79.25, 79.0, 78.75, 78.5, 78.25, 78.0, 77.75,\n",
+ " ...\n",
+ " 32.25, 32.0, 31.75, 31.5, 31.25, 31.0, 30.75, 30.5, 30.25, 30.0],\n",
+ " dtype='float64', name='latitude', length=201)) PandasIndex
PandasIndex(Index([-10.0, -9.75, -9.5, -9.25, -9.0, -8.75, -8.5, -8.25, -8.0, -7.75,\n",
+ " ...\n",
+ " 57.75, 58.0, 58.25, 58.5, 58.75, 59.0, 59.25, 59.5, 59.75, 60.0],\n",
+ " dtype='float64', name='longitude', length=281)) Attributes: (7)
GRIB_edition : 1 GRIB_centre : ecmf GRIB_centreDescription : European Centre for Medium-Range Weather Forecasts GRIB_subCentre : 0 Conventions : CF-1.7 institution : European Centre for Medium-Range Weather Forecasts history : 2023-08-23T17:14 GRIB to CDM+CF via cfgrib-0.9.10.3/ecCodes-2.28.0 with {"source": "notebooks/test_data/test_gridded_data.grib", "filter_by_keys": {}, "encode_cf": ["parameter", "time", "geography", "vertical"]} "
+ ],
+ "text/plain": [
+ "\n",
+ "Dimensions: (time: 1460, latitude: 201, longitude: 281)\n",
+ "Coordinates:\n",
+ " number int64 ...\n",
+ " * time (time) datetime64[ns] 2015-01-01 ... 2015-12-31T18:00:00\n",
+ " step timedelta64[ns] ...\n",
+ " surface float64 ...\n",
+ " * latitude (latitude) float64 80.0 79.75 79.5 79.25 ... 30.5 30.25 30.0\n",
+ " * longitude (longitude) float64 -10.0 -9.75 -9.5 -9.25 ... 59.5 59.75 60.0\n",
+ " valid_time (time) datetime64[ns] dask.array\n",
+ "Data variables:\n",
+ " t2m (time, latitude, longitude) float32 dask.array\n",
+ "Attributes:\n",
+ " GRIB_edition: 1\n",
+ " GRIB_centre: ecmf\n",
+ " GRIB_centreDescription: European Centre for Medium-Range Weather Forecasts\n",
+ " GRIB_subCentre: 0\n",
+ " Conventions: CF-1.7\n",
+ " institution: European Centre for Medium-Range Weather Forecasts\n",
+ " history: 2023-08-23T17:14 GRIB to CDM+CF via cfgrib-0.9.1..."
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "grib_file = \"notebooks/test_data/test_gridded_data.grib\"\n",
+ "ds = xr.open_dataset(grib_file, chunks={'time': 48})\n",
+ "ds"
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<xarray.DataArray 't2m' (time: 1460, latitude: 201, longitude: 281)>\n",
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+ " GRIB_paramId: 167\n",
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+ " GRIB_stepUnits: 1\n",
+ " GRIB_stepType: instant\n",
+ " ... ...\n",
+ " GRIB_shortName: 2t\n",
+ " GRIB_totalNumber: 0\n",
+ " GRIB_units: K\n",
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Coordinates: (7)
number
()
int64
...
long_name : ensemble member numerical id units : 1 standard_name : realization [1 values with dtype=int64] time
(time)
datetime64[ns]
2015-01-01 ... 2015-12-31T18:00:00
long_name : initial time of forecast standard_name : forecast_reference_time array(['2015-01-01T00:00:00.000000000', '2015-01-01T06:00:00.000000000',\n",
+ " '2015-01-01T12:00:00.000000000', ..., '2015-12-31T06:00:00.000000000',\n",
+ " '2015-12-31T12:00:00.000000000', '2015-12-31T18:00:00.000000000'],\n",
+ " dtype='datetime64[ns]') step
()
timedelta64[ns]
...
long_name : time since forecast_reference_time standard_name : forecast_period [1 values with dtype=timedelta64[ns]] surface
()
float64
...
long_name : original GRIB coordinate for key: level(surface) units : 1 [1 values with dtype=float64] latitude
(latitude)
float64
80.0 79.75 79.5 ... 30.5 30.25 30.0
units : degrees_north standard_name : latitude long_name : latitude stored_direction : decreasing array([80. , 79.75, 79.5 , ..., 30.5 , 30.25, 30. ]) longitude
(longitude)
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-10.0 -9.75 -9.5 ... 59.75 60.0
units : degrees_east standard_name : longitude long_name : longitude array([-10. , -9.75, -9.5 , ..., 59.5 , 59.75, 60. ]) valid_time
(time)
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dask.array<chunksize=(48,), meta=np.ndarray>
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Indexes: (3)
PandasIndex
PandasIndex(DatetimeIndex(['2015-01-01 00:00:00', '2015-01-01 06:00:00',\n",
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+ " '2015-01-02 00:00:00', '2015-01-02 06:00:00',\n",
+ " '2015-01-02 12:00:00', '2015-01-02 18:00:00',\n",
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+ " '2015-12-29 12:00:00', '2015-12-29 18:00:00',\n",
+ " '2015-12-30 00:00:00', '2015-12-30 06:00:00',\n",
+ " '2015-12-30 12:00:00', '2015-12-30 18:00:00',\n",
+ " '2015-12-31 00:00:00', '2015-12-31 06:00:00',\n",
+ " '2015-12-31 12:00:00', '2015-12-31 18:00:00'],\n",
+ " dtype='datetime64[ns]', name='time', length=1460, freq=None)) PandasIndex
PandasIndex(Index([ 80.0, 79.75, 79.5, 79.25, 79.0, 78.75, 78.5, 78.25, 78.0, 77.75,\n",
+ " ...\n",
+ " 32.25, 32.0, 31.75, 31.5, 31.25, 31.0, 30.75, 30.5, 30.25, 30.0],\n",
+ " dtype='float64', name='latitude', length=201)) PandasIndex
PandasIndex(Index([-10.0, -9.75, -9.5, -9.25, -9.0, -8.75, -8.5, -8.25, -8.0, -7.75,\n",
+ " ...\n",
+ " 57.75, 58.0, 58.25, 58.5, 58.75, 59.0, 59.25, 59.5, 59.75, 60.0],\n",
+ " dtype='float64', name='longitude', length=281)) Attributes: (30)
GRIB_paramId : 167 GRIB_dataType : an GRIB_numberOfPoints : 56481 GRIB_typeOfLevel : surface GRIB_stepUnits : 1 GRIB_stepType : instant GRIB_gridType : regular_ll GRIB_NV : 0 GRIB_Nx : 281 GRIB_Ny : 201 GRIB_cfName : unknown GRIB_cfVarName : t2m GRIB_gridDefinitionDescription : Latitude/Longitude Grid GRIB_iDirectionIncrementInDegrees : 0.25 GRIB_iScansNegatively : 0 GRIB_jDirectionIncrementInDegrees : 0.25 GRIB_jPointsAreConsecutive : 0 GRIB_jScansPositively : 0 GRIB_latitudeOfFirstGridPointInDegrees : 80.0 GRIB_latitudeOfLastGridPointInDegrees : 30.0 GRIB_longitudeOfFirstGridPointInDegrees : -10.0 GRIB_longitudeOfLastGridPointInDegrees : 60.0 GRIB_missingValue : 3.4028234663852886e+38 GRIB_name : 2 metre temperature GRIB_shortName : 2t GRIB_totalNumber : 0 GRIB_units : K long_name : 2 metre temperature units : K standard_name : unknown "
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+ " * time (time) datetime64[ns] 2015-01-01 ... 2015-12-31T18:00:00\n",
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+ " surface float64 ...\n",
+ " * latitude (latitude) float64 80.0 79.75 79.5 79.25 ... 30.5 30.25 30.0\n",
+ " * longitude (longitude) float64 -10.0 -9.75 -9.5 -9.25 ... 59.5 59.75 60.0\n",
+ " valid_time (time) datetime64[ns] dask.array\n",
+ "Attributes: (12/30)\n",
+ " GRIB_paramId: 167\n",
+ " GRIB_dataType: an\n",
+ " GRIB_numberOfPoints: 56481\n",
+ " GRIB_typeOfLevel: surface\n",
+ " GRIB_stepUnits: 1\n",
+ " GRIB_stepType: instant\n",
+ " ... ...\n",
+ " GRIB_shortName: 2t\n",
+ " GRIB_totalNumber: 0\n",
+ " GRIB_units: K\n",
+ " long_name: 2 metre temperature\n",
+ " units: K\n",
+ " standard_name: unknown"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
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+ " GRIB_subCentre: 0\n",
+ " Conventions: CF-1.7\n",
+ " institution: European Centre for Medium-Range Weather Forecasts\n",
+ " history: 2023-08-23T17:14 GRIB to CDM+CF via cfgrib-0.9.1... Dimensions: number : 1time : 1460step : 1surface : 1latitude : 201longitude : 281
Coordinates: (7)
number
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2015-01-01 ... 2015-12-31T18:00:00
long_name : initial time of forecast standard_name : forecast_reference_time array(['2015-01-01T00:00:00.000000000', '2015-01-01T06:00:00.000000000',\n",
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(step)
timedelta64[ns]
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long_name : time since forecast_reference_time standard_name : forecast_period array([0], dtype='timedelta64[ns]') surface
(surface)
float64
0.0
long_name : original GRIB coordinate for key: level(surface) units : 1 latitude
(latitude)
float64
80.0 79.75 79.5 ... 30.5 30.25 30.0
units : degrees_north standard_name : latitude long_name : latitude stored_direction : decreasing array([80. , 79.75, 79.5 , ..., 30.5 , 30.25, 30. ]) longitude
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units : degrees_east standard_name : longitude long_name : longitude array([-10. , -9.75, -9.5 , ..., 59.5 , 59.75, 60. ]) valid_time
(time, step)
datetime64[ns]
dask.array<chunksize=(48, 1), meta=np.ndarray>
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Data variables: (1)
t2m
(number, time, step, surface, latitude, longitude)
float32
dask.array<chunksize=(1, 48, 1, 1, 201, 281), meta=np.ndarray>
GRIB_paramId : 167 GRIB_dataType : an GRIB_numberOfPoints : 56481 GRIB_typeOfLevel : surface GRIB_stepUnits : 1 GRIB_stepType : instant GRIB_gridType : regular_ll GRIB_NV : 0 GRIB_Nx : 281 GRIB_Ny : 201 GRIB_cfName : unknown GRIB_cfVarName : t2m GRIB_gridDefinitionDescription : Latitude/Longitude Grid GRIB_iDirectionIncrementInDegrees : 0.25 GRIB_iScansNegatively : 0 GRIB_jDirectionIncrementInDegrees : 0.25 GRIB_jPointsAreConsecutive : 0 GRIB_jScansPositively : 0 GRIB_latitudeOfFirstGridPointInDegrees : 80.0 GRIB_latitudeOfLastGridPointInDegrees : 30.0 GRIB_longitudeOfFirstGridPointInDegrees : -10.0 GRIB_longitudeOfLastGridPointInDegrees : 60.0 GRIB_missingValue : 9999 GRIB_name : 2 metre temperature GRIB_shortName : 2t GRIB_totalNumber : 0 GRIB_units : K long_name : 2 metre temperature units : K standard_name : unknown \n",
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Indexes: (6)
PandasIndex
PandasIndex(Index([0], dtype='int64', name='number')) PandasIndex
PandasIndex(DatetimeIndex(['2015-01-01 00:00:00', '2015-01-01 06:00:00',\n",
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+ " '2015-01-03 00:00:00', '2015-01-03 06:00:00',\n",
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+ " '2015-12-30 12:00:00', '2015-12-30 18:00:00',\n",
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+ " '2015-12-31 12:00:00', '2015-12-31 18:00:00'],\n",
+ " dtype='datetime64[ns]', name='time', length=1460, freq=None)) PandasIndex
PandasIndex(TimedeltaIndex(['0 days'], dtype='timedelta64[ns]', name='step', freq=None)) PandasIndex
PandasIndex(Index([0.0], dtype='float64', name='surface')) PandasIndex
PandasIndex(Index([ 80.0, 79.75, 79.5, 79.25, 79.0, 78.75, 78.5, 78.25, 78.0, 77.75,\n",
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+ " 32.25, 32.0, 31.75, 31.5, 31.25, 31.0, 30.75, 30.5, 30.25, 30.0],\n",
+ " dtype='float64', name='latitude', length=201)) PandasIndex
PandasIndex(Index([-10.0, -9.75, -9.5, -9.25, -9.0, -8.75, -8.5, -8.25, -8.0, -7.75,\n",
+ " ...\n",
+ " 57.75, 58.0, 58.25, 58.5, 58.75, 59.0, 59.25, 59.5, 59.75, 60.0],\n",
+ " dtype='float64', name='longitude', length=281)) Attributes: (7)
GRIB_edition : 1 GRIB_centre : ecmf GRIB_centreDescription : European Centre for Medium-Range Weather Forecasts GRIB_subCentre : 0 Conventions : CF-1.7 institution : European Centre for Medium-Range Weather Forecasts history : 2023-08-23T17:14 GRIB to CDM+CF via cfgrib-0.9.10.3/ecCodes-2.28.0 with {"source": "N/A", "filter_by_keys": {}, "encode_cf": ["parameter", "time", "geography", "vertical"]} "
+ ],
+ "text/plain": [
+ "\n",
+ "Dimensions: (number: 1, time: 1460, step: 1, surface: 1, latitude: 201,\n",
+ " longitude: 281)\n",
+ "Coordinates:\n",
+ " * number (number) int64 0\n",
+ " * time (time) datetime64[ns] 2015-01-01 ... 2015-12-31T18:00:00\n",
+ " * step (step) timedelta64[ns] 00:00:00\n",
+ " * surface (surface) float64 0.0\n",
+ " * latitude (latitude) float64 80.0 79.75 79.5 79.25 ... 30.5 30.25 30.0\n",
+ " * longitude (longitude) float64 -10.0 -9.75 -9.5 -9.25 ... 59.5 59.75 60.0\n",
+ " valid_time (time, step) datetime64[ns] dask.array\n",
+ "Data variables:\n",
+ " t2m (number, time, step, surface, latitude, longitude) float32 dask.array\n",
+ "Attributes:\n",
+ " GRIB_edition: 1\n",
+ " GRIB_centre: ecmf\n",
+ " GRIB_centreDescription: European Centre for Medium-Range Weather Forecasts\n",
+ " GRIB_subCentre: 0\n",
+ " Conventions: CF-1.7\n",
+ " institution: European Centre for Medium-Range Weather Forecasts\n",
+ " history: 2023-08-23T17:14 GRIB to CDM+CF via cfgrib-0.9.1..."
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "nc_file = \"notebooks/test_data/test_gridded_data.nc\"\n",
+ "ds_nc = xr.open_dataset(nc_file, chunks={'time': 48})\n",
+ "ds_nc"
+ ]
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+ {
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Coordinates: (7)
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Indexes: (6)
PandasIndex
PandasIndex(Index([0], dtype='int64', name='number')) PandasIndex
PandasIndex(DatetimeIndex(['2015-01-01 00:00:00', '2015-01-01 06:00:00',\n",
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+ " '2015-01-02 12:00:00', '2015-01-02 18:00:00',\n",
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+ " '2015-12-30 00:00:00', '2015-12-30 06:00:00',\n",
+ " '2015-12-30 12:00:00', '2015-12-30 18:00:00',\n",
+ " '2015-12-31 00:00:00', '2015-12-31 06:00:00',\n",
+ " '2015-12-31 12:00:00', '2015-12-31 18:00:00'],\n",
+ " dtype='datetime64[ns]', name='time', length=1460, freq=None)) PandasIndex
PandasIndex(TimedeltaIndex(['0 days'], dtype='timedelta64[ns]', name='step', freq=None)) PandasIndex
PandasIndex(Index([0.0], dtype='float64', name='surface')) PandasIndex
PandasIndex(Index([ 80.0, 79.75, 79.5, 79.25, 79.0, 78.75, 78.5, 78.25, 78.0, 77.75,\n",
+ " ...\n",
+ " 32.25, 32.0, 31.75, 31.5, 31.25, 31.0, 30.75, 30.5, 30.25, 30.0],\n",
+ " dtype='float64', name='latitude', length=201)) PandasIndex
PandasIndex(Index([-10.0, -9.75, -9.5, -9.25, -9.0, -8.75, -8.5, -8.25, -8.0, -7.75,\n",
+ " ...\n",
+ " 57.75, 58.0, 58.25, 58.5, 58.75, 59.0, 59.25, 59.5, 59.75, 60.0],\n",
+ " dtype='float64', name='longitude', length=281)) Attributes: (30)
GRIB_paramId : 167 GRIB_dataType : an GRIB_numberOfPoints : 56481 GRIB_typeOfLevel : surface GRIB_stepUnits : 1 GRIB_stepType : instant GRIB_gridType : regular_ll GRIB_NV : 0 GRIB_Nx : 281 GRIB_Ny : 201 GRIB_cfName : unknown GRIB_cfVarName : t2m GRIB_gridDefinitionDescription : Latitude/Longitude Grid GRIB_iDirectionIncrementInDegrees : 0.25 GRIB_iScansNegatively : 0 GRIB_jDirectionIncrementInDegrees : 0.25 GRIB_jPointsAreConsecutive : 0 GRIB_jScansPositively : 0 GRIB_latitudeOfFirstGridPointInDegrees : 80.0 GRIB_latitudeOfLastGridPointInDegrees : 30.0 GRIB_longitudeOfFirstGridPointInDegrees : -10.0 GRIB_longitudeOfLastGridPointInDegrees : 60.0 GRIB_missingValue : 9999 GRIB_name : 2 metre temperature GRIB_shortName : 2t GRIB_totalNumber : 0 GRIB_units : K long_name : 2 metre temperature units : K standard_name : unknown "
+ ],
+ "text/plain": [
+ "\n",
+ "dask.array\n",
+ "Coordinates:\n",
+ " * number (number) int64 0\n",
+ " * time (time) datetime64[ns] 2015-01-01 ... 2015-12-31T18:00:00\n",
+ " * step (step) timedelta64[ns] 00:00:00\n",
+ " * surface (surface) float64 0.0\n",
+ " * latitude (latitude) float64 80.0 79.75 79.5 79.25 ... 30.5 30.25 30.0\n",
+ " * longitude (longitude) float64 -10.0 -9.75 -9.5 -9.25 ... 59.5 59.75 60.0\n",
+ " valid_time (time, step) datetime64[ns] dask.array\n",
+ "Attributes: (12/30)\n",
+ " GRIB_paramId: 167\n",
+ " GRIB_dataType: an\n",
+ " GRIB_numberOfPoints: 56481\n",
+ " GRIB_typeOfLevel: surface\n",
+ " GRIB_stepUnits: 1\n",
+ " GRIB_stepType: instant\n",
+ " ... ...\n",
+ " GRIB_shortName: 2t\n",
+ " GRIB_totalNumber: 0\n",
+ " GRIB_units: K\n",
+ " long_name: 2 metre temperature\n",
+ " units: K\n",
+ " standard_name: unknown"
+ ]
+ },
+ "execution_count": 9,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "ds_nc.t2m"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "earthkit",
+ "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": "3.10.8"
+ },
+ "orig_nbformat": 4
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/test.nc b/notebooks/test.nc
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