from typing import Any, Dict, Tuple, List, Optional
import numpy as np
from numpy import ndarray
from openghg.types import optionalPathType, ArrayLikeMatch, ArrayLike, XrDataLike, XrDataLikeMatch
__all__ = ["get_domain_info", "find_domain", "convert_longitude"]
def get_domain_info(domain_filepath: optionalPathType = None) -> Dict[str, Any]:
"""Extract data from domain info JSON file as a dictionary.
This uses the data stored within openghg_defs/domain_info JSON file by default.
Args:
domain_filepath: Alternative domain info file.
Returns:
dict: Data from domain JSON file
"""
from openghg_defs import domain_info_file
from openghg.util import load_json
if domain_filepath is None:
domain_info_json = load_json(path=domain_info_file)
else:
domain_info_json = load_json(path=domain_filepath)
return domain_info_json
[docs]def find_domain(domain: str, domain_filepath: optionalPathType = None) -> Tuple[ndarray, ndarray]:
"""Finds the latitude and longitude values in degrees associated
with a given domain name.
Args:
domain: Pre-defined domain name
domain_filepath: Alternative domain info file. Defaults to openghg_defs input.
Returns:
array, array : Latitude and longitude values for the domain in degrees.
"""
domain_info = get_domain_info(domain_filepath)
# Look for domain in domain_info file
if domain in domain_info:
domain_data = domain_info[domain]
elif domain.upper() in domain_info:
domain = domain.upper()
domain_data = domain_info[domain]
else:
raise ValueError(f"Pre-defined domain '{domain}' not found")
# Extract or create latitude and longitude data
latitude = _get_coord_data("latitude", domain_data, domain)
longitude = _get_coord_data("longitude", domain_data, domain)
return latitude, longitude
def _get_coord_data(coord: str, data: Dict[str, Any], domain: str) -> ndarray:
"""Attempts to extract or derive coordinate (typically latitude/longitude)
values for a domain from provided data dictionary (typically
this can be derived from 'domain_info.json' file).
This looks for:
- File containing coordinate values (in degrees)
- Looks for "{coord}_file" attribute e.g. "latitude_file"
- OR for a file within "domain" subfolder called "{domain}_{coord}.dat"
e.g. "EUROPE_latitude.dat"
- "{coord}_range" and "{coord}_increment" attributes to use to construct
the coordinate values e.g. "latitude_range" to include the start and
end (inclusive) range and "latitude_increment" for the step in degrees.
Args:
coord: Name of coordinate (e.g. latitude, longitude)
data: Data dictionary containing details of domain
(e.g. derived from 'domain_info.json')
domain: Name of domain
Returns:
array: Extracted or derived coordinate values
"""
from openghg_defs import data_path
# Look for explicit file keyword in data e.g. "latitude_file"
# Extract data from file if found and return
filename_str = f"{coord}_file"
if filename_str in data:
full_filename = data_path / data[filename_str]
coord_data: ndarray = np.loadtxt(full_filename)
return coord_data
# If no explicit file name defined, look within known location to see
# if data is present by looking for file of form "domain/{domain}_{coord}.csv"
# e.g. "domain/EUROPE_latitude.csv" (within "openghg/openghg/data" folder)
try:
full_filename = data_path / "domain" / f"{domain}_{coord}.dat"
coord_data = np.loadtxt(full_filename)
except OSError:
pass
else:
return coord_data
# If no data files can be found, look for coordinate range and increment values
# If present, create the coordinate data. If not raise a ValueError.
try:
coord_range = data[f"{coord}_range"]
increment = data[f"{coord}_increment"]
except KeyError:
raise ValueError(f"Unable to get {coord} coordinate data for domain: {domain}")
coord_min = float(coord_range[0])
coord_max = float(coord_range[-1])
increment = float(increment)
coord_data = np.arange(coord_min, coord_max + increment, increment)
return coord_data
def find_coord_name(data: XrDataLike, options: List[str]) -> Optional[str]:
"""
Find the name of a coordinate based on input options.
Only the first found value will be returned.
Args:
data: xarray Data structure
options: List of options to check. Will be checked in order.
Returns:
str / None: Name of coordinate if located within data. None otherwise.
"""
for option in options:
if option in data.coords:
name = option
break
else:
return None
return name
[docs]def convert_longitude(longitude: ArrayLikeMatch) -> ArrayLikeMatch:
"""
Convert longitude extent from (0 to 360) to (-180 to 180).
This does *not* reorder the values.
Args:
longitude: Valid longitude values in degrees.
Returns:
ndarray / DataArray : Updated longitude values in the same order.
"""
# Check range of longitude values and convert to -180 - +180
longitude = ((longitude - 180) % 360) - 180
return longitude
def convert_internal_longitude(data: XrDataLikeMatch,
lon_name: Optional[str] = None,
reorder: bool = True) -> XrDataLikeMatch:
"""
Convert longitude coordinate within an xarray data structure (DataArray or Dataset).
Args:
data: Data with longitude values to convert.
lon_name: By default will look a coord called "lon" or "longitude".
Otherwise must be specified.
reorder: Whether to reorder the data based on the converted longitude values.
Returns:
DataArray / Dataset: Input data with updated longitude values
"""
if lon_name is None:
lon_options = ["lon", "longitude"]
lon_name = find_coord_name(data, lon_options)
if lon_name is None:
raise ValueError("Please specify 'lon_name'.")
longitude = data[lon_name]
longitude = convert_longitude(longitude)
data = data.assign_coords({lon_name: longitude})
if reorder:
data = data.sortby(lon_name)
return data
def cut_data_extent(data: XrDataLikeMatch,
lat_out: ArrayLike,
lon_out: ArrayLike,
lat_name: Optional[str] = None,
lon_name: Optional[str] = None,
copy: bool = False) -> XrDataLikeMatch:
"""
Cut down extent of data within an xarray data structure (DataArray or Dataset)
against an output latitude and longitude range.
A buffer based on the maximum difference along the lon_out and lat_out dimensions
will be added when the data is cut.
Args:
data: Data to be cut down
lat_out: Array containing output latitude values
lon_out: Array containing output longitude values
lat_name: Name of latitude dimension. Must be specified if not "lat" or "latitude".
lon_name: Name of longitude dimension. Must be specified if not "lon" or "longitude".
copy: Whether to explicitly copy the data.
Returns:
xarray.DataArray / xarray.Dataset: data with reduced lat, lon ranges.
"""
if lat_name is None:
lat_options = ["lat", "latitude"]
lat_name = find_coord_name(data, lat_options)
if lat_name is None:
raise ValueError("Please specify 'lat_name'.")
if lon_name is None:
lon_options = ["lon", "longitude"]
lon_name = find_coord_name(data, lon_options)
if lon_name is None:
raise ValueError("Please specify 'lon_name'.")
if isinstance(lat_out, np.ndarray):
lat_out.sort()
else:
lat_out = lat_out.sortby(lat_out[lat_name])
if isinstance(lon_out, np.ndarray):
lon_out.sort()
else:
lon_out = lon_out.sortby(lon_out[lon_name])
lat_diff = (lat_out[1:] - lat_out[:-1]).max()
lon_diff = (lon_out[1:] - lon_out[:-1]).max()
lat_low = np.min(lat_out) - lat_diff
lat_high = np.max(lat_out) + lat_diff
lon_low = np.min(lon_out) - lon_diff
lon_high = np.max(lon_out) + lon_diff
lat_cut_wide_range = slice(lat_low, lat_high)
lon_cut_wide_range = slice(lon_low, lon_high)
if copy:
data_cut = data.copy()
else:
data_cut = data
data_cut = data_cut.sel({lat_name: lat_cut_wide_range, lon_name: lon_cut_wide_range})
return data_cut