diff --git a/2.12.0a2/.buildinfo b/2.12.0a2/.buildinfo new file mode 100644 index 00000000..869dd4e8 --- /dev/null +++ b/2.12.0a2/.buildinfo @@ -0,0 +1,4 @@ +# Sphinx build info version 1 +# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done. +config: b979dbf08076d6409ffdd024f384581c +tags: 645f666f9bcd5a90fca523b33c5a78b7 diff --git a/2.12.0a2/.doctrees/environment.pickle b/2.12.0a2/.doctrees/environment.pickle new file mode 100644 index 00000000..3d1b411d Binary files /dev/null and b/2.12.0a2/.doctrees/environment.pickle differ diff --git a/2.12.0a2/.doctrees/index.doctree b/2.12.0a2/.doctrees/index.doctree new file mode 100644 index 00000000..4036bce8 Binary files /dev/null and b/2.12.0a2/.doctrees/index.doctree differ diff --git a/2.12.0a2/_modules/index.html b/2.12.0a2/_modules/index.html new file mode 100644 index 00000000..e368754a --- /dev/null +++ b/2.12.0a2/_modules/index.html @@ -0,0 +1,106 @@ + + + + + + Overview: module code — l2ss-py 2.12.0a2 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Overview: module code
    • +
  • +
    • +
+
+
+
+ +
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/2.12.0a2/_modules/podaac/subsetter/run_subsetter.html b/2.12.0a2/_modules/podaac/subsetter/run_subsetter.html new file mode 100644 index 00000000..0b91094d --- /dev/null +++ b/2.12.0a2/_modules/podaac/subsetter/run_subsetter.html @@ -0,0 +1,232 @@ + + + + + + podaac.subsetter.run_subsetter — l2ss-py 2.12.0a2 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • podaac.subsetter.run_subsetter
    • +
  • +
    • +
+
+
+
+
+ +

Source code for podaac.subsetter.run_subsetter

+"""
+run_subsetter.py
+
+This script runs L2SS-Py on the given granule.
+"""
+import argparse
+import logging
+import os
+import sys
+
+import numpy as np
+
+from podaac.subsetter import subset
+
+
+

+[docs]
+def parse_args(args: list) -> tuple:
+    """
+    Parse args for this script.
+
+    Returns
+    -------
+    tuple
+        input_file, output_file, bbox, variables, min_time, max_time
+    """
+    parser = argparse.ArgumentParser(description='Run l2ss-py')
+    parser.add_argument(
+        'input_file',
+        type=str,
+        help='File to subset'
+    )
+    parser.add_argument(
+        'output_file',
+        type=str,
+        help='Output file'
+    )
+    parser.add_argument(
+        '--bbox',
+        type=float,
+        default=[-180, -90, 180, 90],
+        nargs=4,
+        action='store',
+        help='Bounding box in the form min_lon min_lat max_lon max_lat'
+    )
+    parser.add_argument(
+        '--variables',
+        type=str,
+        default=None,
+        nargs='+',
+        help='Variables, only include if variable subset is desired. '
+             'Should be a space separated list of variable names e.g. '
+             'sst wind_dir sst_error ...'
+    )
+    parser.add_argument(
+        '--min-time',
+        type=str,
+        default=None,
+        help='Min time. Should be ISO-8601 format. Only include if '
+             'temporal subset is desired.'
+    )
+    parser.add_argument(
+        '--max-time',
+        type=str,
+        default=None,
+        help='Max time. Should be ISO-8601 format. Only include if '
+             'temporal subset is desired.'
+    )
+    parser.add_argument(
+        '--cut',
+        default=False,
+        action='store_true',
+        help='If provided, scanline will be cut'
+    )
+    parser.add_argument(
+        '--shapefile',
+        type=str,
+        default=None,
+        help='Path to either shapefile or geojson file used to subset '
+             'the provided input granule'
+    )
+
+    args = parser.parse_args(args=args)
+    bbox = np.array([[args.bbox[0], args.bbox[2]], [args.bbox[1], args.bbox[3]]])
+
+    return args.input_file, args.output_file, bbox, args.variables, \
+        args.min_time, args.max_time, args.cut, args.shapefile

+
+
+
+

+[docs]
+def run_subsetter(args: list) -> None:
+    """
+    Parse arguments and run subsetter on the specified input file
+    """
+    input_file, output_file, bbox, variables, min_time, max_time, cut, shapefile = parse_args(args)
+
+    logging.info('Executing subset on %s...', input_file)
+    subset.subset(
+        file_to_subset=input_file,
+        bbox=bbox,
+        output_file=output_file,
+        variables=variables,
+        cut=cut,
+        min_time=min_time,
+        max_time=max_time,
+        origin_source=os.path.basename(input_file),
+        shapefile=shapefile
+    )
+    logging.info('Subset complete. Result in %s', output_file)

+
+
+
+

+[docs]
+def main() -> None:
+    """Entry point to the script"""
+    logging.basicConfig(
+        stream=sys.stdout,
+        format='[%(asctime)s] {%(filename)s:%(lineno)d} %(levelname)s - %(message)s',
+        level=logging.DEBUG
+    )
+    run_subsetter(sys.argv[1:])

+
+
+
+if __name__ == '__main__':
+    main()
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/2.12.0a2/_modules/podaac/subsetter/subset.html b/2.12.0a2/_modules/podaac/subsetter/subset.html new file mode 100644 index 00000000..163a4f7f --- /dev/null +++ b/2.12.0a2/_modules/podaac/subsetter/subset.html @@ -0,0 +1,1565 @@ + + + + + + podaac.subsetter.subset — l2ss-py 2.12.0a2 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +

Source code for podaac.subsetter.subset

+# Copyright 2019, by the California Institute of Technology.
+# ALL RIGHTS RESERVED. United States Government Sponsorship acknowledged.
+# Any commercial use must be negotiated with the Office of Technology
+# Transfer at the California Institute of Technology.
+#
+# This software may be subject to U.S. export control laws. By accepting
+# this software, the user agrees to comply with all applicable U.S. export
+# laws and regulations. User has the responsibility to obtain export
+# licenses, or other export authority as may be required before exporting
+# such information to foreign countries or providing access to foreign
+# persons.
+
+"""
+=========
+subset.py
+=========
+
+Functions related to subsetting a NetCDF file.
+"""
+
+import datetime
+import functools
+import json
+import operator
+import os
+import re
+from itertools import zip_longest
+from typing import List, Optional, Tuple, Union
+import dateutil
+from dateutil import parser
+
+import cf_xarray as cfxr
+import cftime
+import geopandas as gpd
+import importlib_metadata
+import julian
+import netCDF4 as nc
+import numpy as np
+import pandas as pd
+import xarray as xr
+import xarray.coding.times
+from shapely.geometry import Point, Polygon, MultiPolygon
+from shapely.ops import transform
+
+from podaac.subsetter import gpm_cleanup as gc
+from podaac.subsetter import time_converting as tc
+from podaac.subsetter import dimension_cleanup as dc
+from podaac.subsetter import xarray_enhancements as xre
+from podaac.subsetter.group_handling import GROUP_DELIM, transform_grouped_dataset, recombine_grouped_datasets, \
+    h5file_transform
+
+SERVICE_NAME = 'l2ss-py'
+
+
+

+[docs]
+def apply_scale_offset(scale: float, offset: float, value: float) -> float:
+    """Apply scale and offset to the given value"""
+    return (value + offset) / scale

+
+
+
+

+[docs]
+def remove_scale_offset(value: float, scale: float, offset: float) -> float:
+    """Remove scale and offset from the given value"""
+    return (value * scale) - offset

+
+
+
+

+[docs]
+def convert_bound(bound: np.ndarray, coord_max: int, coord_var: xr.DataArray) -> np.ndarray:
+    """
+    This function will return a converted bound, which matches the
+    range of the given input file.
+
+    Parameters
+    ----------
+    bound : np.array
+        1-dimensional 2-element numpy array which represent the lower
+        and upper bounding box on this coordinate, respectively.
+    coord_max : integer
+        The max value which is possible given this coordinate. For
+        example, the max for longitude is 360.
+    coord_var : xarray.DataArray
+        The xarray variable for some coordinate.
+
+    Returns
+    -------
+    np.array
+        1-dimensional 2-element number array which represents the lower
+        and upper bounding box on this coordinate and has been converted
+        based on the valid bounds coordinate range of the dataset.
+
+    Notes
+    -----
+    Assumption that 0 is always on the prime meridian/equator.
+    """
+
+    scale = coord_var.attrs.get('scale_factor', 1.0)
+    offset = coord_var.attrs.get('add_offset', 0.0)
+    valid_min = coord_var.attrs.get('valid_min', None)
+
+    if valid_min is None or valid_min > 0:
+        # If coord var doesn't contain valid min, attempt to find
+        # manually. Note: Given the perfect storm, this could still fail
+        # to find the actual bounds.
+
+        # Filter out _FillValue from data before calculating min and max
+        fill_value = coord_var.attrs.get('_FillValue', None)
+        var_values = coord_var.values
+        if fill_value:
+            var_values = np.where(var_values != fill_value, var_values, np.nan)
+        var_min = np.nanmin(var_values)
+        var_max = np.nanmax(var_values)
+
+        if 0 <= var_min <= var_max <= (coord_max / scale):
+            valid_min = 0
+
+    # If the file coords are 0 --> max
+    if valid_min == 0:
+        bound = (bound + coord_max) % coord_max
+
+        # If the right/top bound is 0, set to max.
+        if bound[1] == 0:
+            bound[1] = coord_max
+
+        # If edges are the same, assume it wraps and return all
+        if bound[0] == bound[1]:
+            bound = np.array([0, coord_max])
+
+    # If the file longitude is -coord_max/2 --> coord_max/2
+    if valid_min != 0:
+        # If edges are the same, assume it wraps and return all
+        if bound[0] == bound[1]:
+            bound = np.array([-(coord_max / 2), coord_max / 2])
+
+    # Calculate scale and offset so the bounds match the coord data
+    return apply_scale_offset(scale, offset, bound)

+
+
+
+

+[docs]
+def convert_bbox(bbox: np.ndarray, dataset: xr.Dataset, lat_var_name: str, lon_var_name: str) -> np.ndarray:
+    """
+    This function will return a converted bbox which matches the range
+    of the given input file. This will convert both the latitude and
+    longitude range. For example, an input dataset can have a valid
+    longitude range of -180 --> 180 or of 0 --> 360.
+
+    Parameters
+    ----------
+    bbox : np.array
+        The bounding box
+    dataset : xarray.Dataset
+        The dataset which is being subset.
+    lat_var_name : str
+        Name of the lat variable in the given dataset
+    lon_var_name : str
+        Name of the lon variable in the given dataset
+
+    Returns
+    -------
+    bbox : np.array
+        The new bbox which matches latitude and longitude ranges of the
+        input file.
+
+    Notes
+    -----
+    Assumption that the provided bounding box is always between
+    -180 --> 180 for longitude and -90, 90 for latitude.
+    """
+    return np.array([convert_bound(bbox[0], 360, dataset[lon_var_name]),
+                     convert_bound(bbox[1], 180, dataset[lat_var_name])])

+
+
+
+

+[docs]
+def set_json_history(dataset: xr.Dataset, cut: bool, file_to_subset: str,
+                     bbox: np.ndarray = None, shapefile: str = None, origin_source=None) -> None:
+    """
+    Set the 'json_history' metadata header of the granule to reflect the
+    current version of the subsetter, as well as the parameters used
+    to call the subsetter. This will append an json array to the json_history of
+    the following format:
+
+    Parameters
+    ----------
+    dataset : xarray.Dataset
+        The dataset to change the header of
+    cut : boolean
+        True to cut the scanline
+    file_to_subset : string
+        The filepath of the file which was used to subset
+    bbox : np.ndarray
+        The requested bounding box
+    shapefile : str
+        Name of the shapefile to include in the version history
+    TODO: add docstring and type hint for `origin_source` parameter.
+    """
+
+    params = f'cut={cut}'
+    if bbox is not None:
+        params = f'bbox={bbox.tolist()} {params}'
+    elif shapefile is not None:
+        params = f'shapefile={shapefile} {params}'
+
+    history_json = dataset.attrs.get('history_json', [])
+    if history_json:
+        history_json = json.loads(history_json)
+
+    if origin_source:
+        derived_from = origin_source
+    else:
+        derived_from = os.path.basename(file_to_subset)
+
+    new_history_json = {
+        "date_time": datetime.datetime.now(tz=datetime.timezone.utc).isoformat(),
+        "derived_from": derived_from,
+        "program": SERVICE_NAME,
+        "version": importlib_metadata.distribution(SERVICE_NAME).version,
+        "parameters": params,
+        "program_ref": "https://cmr.earthdata.nasa.gov:443/search/concepts/S1962070864-POCLOUD",
+        "$schema": "https://harmony.earthdata.nasa.gov/schemas/history/0.1.0/history-v0.1.0.json"
+    }
+
+    history_json.append(new_history_json)
+    dataset.attrs['history_json'] = json.dumps(history_json)

+
+
+
+

+[docs]
+def set_version_history(dataset: xr.Dataset, cut: bool, bbox: np.ndarray = None, shapefile: str = None) -> None:
+    """
+    Set the 'history' metadata header of the granule to reflect the
+    current version of the subsetter, as well as the parameters used
+    to call the subsetter. This will append a line to the history of
+    the following format:
+
+    TIMESTAMP podaac.subsetter VERSION (PARAMS)
+
+    Parameters
+    ----------
+    dataset : xarray.Dataset
+        The dataset to change the header of
+    cut : boolean
+        True to cut the scanline
+    bbox : np.ndarray
+        The requested bounding box
+    shapefile : str
+        Name of the shapefile to include in the version history
+
+    """
+
+    version = importlib_metadata.distribution(SERVICE_NAME).version
+    history = dataset.attrs.get('history', "")
+    timestamp = datetime.datetime.utcnow()
+    params = f'cut={cut}'
+    if bbox is not None:
+        params = f'bbox={bbox.tolist()} {params}'
+    elif shapefile is not None:
+        params = f'shapefile={shapefile} {params}'
+
+    history += f"\n{timestamp} {SERVICE_NAME} v{version} ({params})"
+    dataset.attrs['history'] = history.strip()

+
+
+
+

+[docs]
+def calculate_chunks(dataset: xr.Dataset) -> dict:
+    """
+    For the given dataset, calculate if the size on any dimension is
+    worth chunking. Any dimension larger than 4000 will be chunked. This
+    is done to ensure that the variable can fit in memory.
+
+    Parameters
+    ----------
+    dataset : xarray.Dataset
+        The dataset to calculate chunks for.
+
+    Returns
+    -------
+    dict
+        The chunk dictionary, where the key is the dimension and the
+        value is 4000 or 500 depending on how many dimensions.
+    """
+    if len(dataset.dims) <= 3:
+        chunk = {dim: 4000 for dim in dataset.dims
+                 if dataset.sizes[dim] > 4000
+                 and len(dataset.dims) > 1}
+    else:
+        chunk = {dim: 500 for dim in dataset.dims
+                 if dataset.sizes[dim] > 500}
+
+    return chunk

+
+
+
+

+[docs]
+def find_matching_coords(dataset: xr.Dataset, match_list: List[str]) -> List[str]:
+    """
+    As a backup for finding a coordinate var, look at the 'coordinates'
+    metadata attribute of all data vars in the granule. Return any
+    coordinate vars that have name matches with the provided
+    'match_list'
+
+    Parameters
+    ----------
+    dataset : xr.Dataset
+        Dataset to search data variable coordinate metadata attribute
+    match_list : list (str)
+        List of possible matches to search for. For example,
+        ['lat', 'latitude'] would search for variables in the
+        'coordinates' metadata attribute containing either 'lat'
+        or 'latitude'
+
+    Returns
+    -------
+    list (str)
+        List of matching coordinate variables names
+    """
+    coord_attrs = [
+        var.attrs['coordinates'] for var_name, var in dataset.data_vars.items()
+        if 'coordinates' in var.attrs
+    ]
+    coord_attrs = list(set(coord_attrs))
+    match_coord_vars = []
+    for coord_attr in coord_attrs:
+        coords = coord_attr.split(' ')
+        match_vars = [
+            coord for coord in coords
+            if any(coord_cand in coord for coord_cand in match_list)
+        ]
+        if match_vars and match_vars[0] in dataset:
+            # Check if the var actually exists in the dataset
+            match_coord_vars.append(match_vars[0])
+    return match_coord_vars

+
+
+
+

+[docs]
+def compute_coordinate_variable_names(dataset: xr.Dataset) -> Tuple[Union[List[str], str], Union[List[str], str]]:
+    """
+    Given a dataset, determine the coordinate variable from a list
+    of options
+
+    Parameters
+    ----------
+    dataset: xr.Dataset
+        The dataset to find the coordinate variables for
+
+    Returns
+    -------
+    tuple, str
+        Tuple of strings (or list of strings), where the first element is the lat coordinate
+        name and the second element is the lon coordinate name
+    """
+
+    dataset = xr.decode_cf(dataset)
+
+    # look for lon and lat using standard name in coordinates and axes
+    custom_criteria = {
+        "latitude": {
+            "standard_name": "latitude|projection_y_coordinate",
+        },
+        "longitude": {
+            "standard_name": "longitude|projection_x_coordinate",
+        }
+    }
+
+    possible_lat_coord_names = ['lat', 'latitude', 'y']
+    possible_lon_coord_names = ['lon', 'longitude', 'x']
+
+    def var_is_coord(var_name, possible_coord_names):
+        var_name = var_name.strip(GROUP_DELIM).split(GROUP_DELIM)[-1]
+        return var_name.lower() in possible_coord_names
+
+    lat_coord_names = list(filter(
+        lambda var_name: var_is_coord(var_name, possible_lat_coord_names), dataset.variables))
+    lon_coord_names = list(filter(
+        lambda var_name: var_is_coord(var_name, possible_lon_coord_names), dataset.variables))
+
+    if len(lat_coord_names) < 1 or len(lon_coord_names) < 1:
+        lat_coord_names = find_matching_coords(dataset, possible_lat_coord_names)
+        lon_coord_names = find_matching_coords(dataset, possible_lon_coord_names)
+
+    # Couldn't find lon lat in data variables look in coordinates
+    if len(lat_coord_names) < 1 or len(lon_coord_names) < 1:
+        with cfxr.set_options(custom_criteria=custom_criteria):
+            lat_coord_names = dataset.cf.coordinates.get('latitude', [])
+            lon_coord_names = dataset.cf.coordinates.get('longitude', [])
+
+        if len(lat_coord_names) < 1 or len(lon_coord_names) < 1:
+            try:
+                lat_coord_names = [dataset.cf["latitude"].name]
+                lon_coord_names = [dataset.cf["longitude"].name]
+            except KeyError:
+                pass
+
+    if len(lat_coord_names) < 1 or len(lon_coord_names) < 1:
+        raise ValueError('Could not determine coordinate variables')
+
+    return lat_coord_names, lon_coord_names

+
+
+
+

+[docs]
+def is_360(lon_var: xr.DataArray, scale: float, offset: float) -> bool:
+    """
+    Determine if given dataset is a '360' dataset or not.
+
+    Parameters
+    ----------
+    lon_var : xr.DataArray
+        The lon variable from the xarray Dataset
+    scale : float
+        Used to remove scale and offset for easier calculation
+    offset : float
+        Used to remove scale and offset for easier calculation
+
+    Returns
+    -------
+    bool
+        True if dataset is 360, False if not. Defaults to False.
+    """
+    valid_min = lon_var.attrs.get('valid_min', None)
+
+    if valid_min is None or valid_min > 0:
+        var_min = remove_scale_offset(np.amin(lon_var.values), scale, offset)
+        var_max = remove_scale_offset(np.amax(lon_var.values), scale, offset)
+
+        if var_min < 0:
+            return False
+        if var_max > 180:
+            return True
+
+    if valid_min == 0:
+        return True
+    if valid_min < 0:
+        return False
+
+    return False

+
+
+
+

+[docs]
+def get_spatial_bounds(dataset: xr.Dataset, lat_var_names: str, lon_var_names: str) -> Union[np.ndarray, None]:
+    """
+    Get the spatial bounds for this dataset. These values are masked
+    and scaled.
+
+    Parameters
+    ----------
+    dataset : xr.Dataset
+        Dataset to retrieve spatial bounds for
+    lat_var_names : str
+        Name of the lat variable
+    lon_var_names : str
+        Name of the lon variable
+
+    Returns
+    -------
+    np.array
+        [[lon min, lon max], [lat min, lat max]]
+    """
+
+    lat_var_name = lat_var_names[0] if len(lat_var_names) == 1 else [
+        lat_name for lat_name in lat_var_names if lat_name in dataset.data_vars.keys()
+    ][0]
+    lon_var_name = lon_var_names[0] if len(lon_var_names) == 1 else [
+        lon_name for lon_name in lon_var_names if lon_name in dataset.data_vars.keys()
+    ][0]
+
+    # Get scale from coordinate variable metadata attributes
+    lat_scale = dataset[lat_var_name].attrs.get('scale_factor', 1.0)
+    lon_scale = dataset[lon_var_name].attrs.get('scale_factor', 1.0)
+    lat_offset = dataset[lat_var_name].attrs.get('add_offset', 0.0)
+    lon_offset = dataset[lon_var_name].attrs.get('add_offset', 0.0)
+    lon_valid_min = dataset[lon_var_name].attrs.get('valid_min', None)
+    lat_fill_value = dataset[lat_var_name].attrs.get('_FillValue', None)
+    lon_fill_value = dataset[lon_var_name].attrs.get('_FillValue', None)
+
+    # Apply mask and scale to min/max coordinate variables to get
+    # spatial bounds
+
+    # Remove fill value. Might cause errors when getting min and max
+    lats = dataset[lat_var_name].values.flatten()
+    lons = dataset[lon_var_name].values.flatten()
+
+    if lat_fill_value:
+        lats = list(filter(lambda a: not a == lat_fill_value, lats))
+    if lon_fill_value:
+        lons = list(filter(lambda a: not a == lon_fill_value, lons))
+
+    if len(lats) == 0 or len(lons) == 0:
+        return None
+
+    min_lat = remove_scale_offset(np.nanmin(lats), lat_scale, lat_offset)
+    max_lat = remove_scale_offset(np.nanmax(lats), lat_scale, lat_offset)
+    min_lon = remove_scale_offset(np.nanmin(lons), lon_scale, lon_offset)
+    max_lon = remove_scale_offset(np.nanmax(lons), lon_scale, lon_offset)
+
+    min_lat = round(min_lat, 1)
+    max_lat = round(max_lat, 1)
+    min_lon = round(min_lon, 1)
+    max_lon = round(max_lon, 1)
+
+    # Convert longitude to [-180,180] format
+    if lon_valid_min == 0 or 0 <= min_lon <= max_lon <= 360:
+        if min_lon > 180:
+            min_lon -= 360
+        if max_lon > 180:
+            max_lon -= 360
+        if min_lon == max_lon:
+            min_lon = -180
+            max_lon = 180
+
+    return np.array([[min_lon, max_lon], [min_lat, max_lat]])

+
+
+
+

+[docs]
+def compute_time_variable_name(dataset: xr.Dataset, lat_var: xr.Variable, total_time_vars: list) -> str:
+    """
+    Try to determine the name of the 'time' variable. This is done as
+    follows:
+
+    - The variable name contains 'time'
+    - The variable dimensions match the dimensions of the given lat var
+    - The variable that hasn't already been found
+
+    Parameters
+    ----------
+    dataset : xr.Dataset
+        xarray dataset to find time variable from
+    lat_var : xr.Variable
+        Lat variable for this dataset
+
+    Returns
+    -------
+    str
+        The name of the variable
+
+    Raises
+    ------
+    ValueError
+        If the time variable could not be determined
+    """
+
+    time_vars = find_matching_coords(dataset, ['time'])
+    if time_vars:
+        # There should only be one time var match (this is called once
+        # per lat var)
+        return time_vars[0]
+
+    # Filter variables with 'time' in the name to avoid extra work
+    time_vars = list(filter(lambda var_name: 'time' in var_name, dataset.sizes.keys()))
+
+    for var_name in time_vars:
+        if var_name not in total_time_vars and "time" in var_name and dataset[var_name].squeeze().dims == lat_var.squeeze().dims:
+            return var_name
+
+    # first check if any variables are named 'time'
+    for var_name in list(dataset.data_vars.keys()):
+        var_name_time = var_name.strip(GROUP_DELIM).split(GROUP_DELIM)[-1]
+        if len(dataset[var_name].squeeze().dims) == 0:
+            continue
+        if var_name not in total_time_vars and ('time' == var_name_time.lower() or 'timeMidScan' == var_name_time) and dataset[var_name].squeeze().dims[0] in lat_var.squeeze().dims:
+            return var_name
+
+    time_units_pattern = re.compile(r"(days|d|hours|hr|h|minutes|min|m|seconds|sec|s) since \d{4}-\d{2}-\d{2}( \d{2}:\d{2}:\d{2})?")
+    # Check variables for common time variable indicators
+    for var_name, var in dataset.variables.items():
+        # pylint: disable=too-many-boolean-expressions
+        if ((('standard_name' in var.attrs and var.attrs['standard_name'] == 'time') or
+           ('axis' in var.attrs and var.attrs['axis'] == 'T') or
+           ('units' in var.attrs and time_units_pattern.match(var.attrs['units'])))) and var_name not in total_time_vars:
+            return var_name
+
+    # then check if any variables have 'time' in the string if the above loop doesn't return anything
+    for var_name in list(dataset.data_vars.keys()):
+        var_name_time = var_name.strip(GROUP_DELIM).split(GROUP_DELIM)[-1]
+        if len(dataset[var_name].squeeze().dims) == 0:
+            continue
+        if var_name not in total_time_vars and 'time' in var_name_time.lower() and dataset[var_name].squeeze().dims[0] in lat_var.squeeze().dims:
+            return var_name
+
+    raise ValueError('Unable to determine time variable')

+
+
+
+

+[docs]
+def compute_utc_name(dataset: xr.Dataset) -> Union[str, None]:
+    """
+    Get the name of the utc variable if it is there to determine origine time
+    """
+    for var_name in list(dataset.data_vars.keys()):
+        if 'utc' in var_name.lower() and 'time' in var_name.lower():
+            return var_name
+
+    return None

+
+
+
+

+[docs]
+def translate_longitude(geometry):
+    """
+    Translates the longitude values of a Shapely geometry from the range [-180, 180) to [0, 360).
+
+    Parameters
+    ----------
+    geometry : shapely.geometry.base.BaseGeometry
+        The input shape geometry to be translated
+
+    Returns
+    -------
+    geometry
+        The translated shape geometry
+    """
+
+    def translate_point(point):
+        # Translate the point's x-coordinate (longitude) by adding 360
+        return Point((point.x + 360) % 360, point.y)
+
+    def translate_polygon(polygon):
+        def translate_coordinates(coords):
+            if len(coords[0]) == 2:
+                return [((x + 360) % 360, y) for x, y in coords]
+            if len(coords[0]) == 3:
+                return [((x + 360) % 360, y, z) for x, y, z in coords]
+            return coords
+
+        exterior = translate_coordinates(polygon.exterior.coords)
+
+        interiors = [
+            translate_coordinates(ring.coords)
+            for ring in polygon.interiors
+        ]
+
+        return Polygon(exterior, interiors)
+
+    if isinstance(geometry, (Point, Polygon)):  # pylint: disable=no-else-return
+        return translate_point(geometry) if isinstance(geometry, Point) else translate_polygon(geometry)
+    elif isinstance(geometry, MultiPolygon):
+        # Translate each polygon in the MultiPolygon
+        translated_polygons = [translate_longitude(subgeometry) for subgeometry in geometry.geoms]
+        return MultiPolygon(translated_polygons)
+    else:
+        # Handle other geometry types as needed
+        return geometry

+
+
+
+

+[docs]
+def get_time_epoch_var(dataset: xr.Dataset, time_var_name: str) -> str:
+    """
+    Get the name of the epoch time var. This is only needed in the case
+    where there is a single time var (of size 1) that contains the time
+    epoch used by the actual time var.
+
+    Parameters
+    ----------
+    dataset : xr.Dataset
+        Dataset that contains time var
+    time_var_name : str
+        The name of the actual time var (with matching dims to the
+        coord vars)
+
+    Returns
+    -------
+    str
+        The name of the epoch time variable
+    """
+    time_var = dataset[time_var_name]
+
+    if 'comment' in time_var.attrs:
+        epoch_var_name = time_var.attrs['comment'].split('plus')[0].strip()
+    elif 'time' in dataset.variables.keys() and time_var_name != 'time':
+        epoch_var_name = 'time'
+    elif any('time' in s for s in list(dataset.variables.keys())) and time_var_name != 'time':
+        for i in list(dataset.variables.keys()):
+            group_list = i.split(GROUP_DELIM)
+            if group_list[-1] == 'time':
+                epoch_var_name = i
+                break
+        return epoch_var_name
+    else:
+        raise ValueError('Unable to determine time variables')
+
+    return epoch_var_name

+
+
+
+

+[docs]
+def is_time_mjd(dataset: xr.Dataset, time_var_name: str) -> bool:
+    """
+    Check to see if the time format is a time delta from a modified julian date.
+
+    Parameters
+    ----------
+    dataset : xr.Dataset
+        Dataset that contains time var
+    time_var_name : str
+        The name of the actual time var (with matching dims to the
+        coord vars)
+
+    Returns
+    -------
+    boolean
+        is time delta format in modified julian date
+    """
+    time_var = dataset[time_var_name]
+    if 'comment' in time_var.attrs:
+        if 'Modified Julian Day' in time_var.attrs['comment']:
+            return True
+    return False

+
+
+
+

+[docs]
+def translate_timestamp(str_timestamp: str) -> datetime.datetime:
+    """
+    Translate timestamp to datetime object
+
+    Parameters
+    ----------
+    str_timestamp : str
+        Timestamp string. ISO or RFC
+
+    Returns
+    -------
+    datetime
+        Constructed Datetime object
+    """
+    allowed_ts_formats = [
+        '%Y-%m-%dT%H:%M:%SZ',
+        '%Y-%m-%dT%H:%M:%S%Z',
+        '%Y-%m-%dT%H:%M:%S.%fZ',
+        '%Y-%m-%dT%H:%M:%S.%f%Z',
+        '%Y-%m-%d %H:%M:%S',
+    ]
+
+    for timestamp_format in allowed_ts_formats:
+        try:
+            return datetime.datetime.strptime(str_timestamp, timestamp_format)
+        except ValueError:
+            pass
+    return datetime.datetime.fromisoformat(str_timestamp)

+
+
+
+

+[docs]
+def datetime_from_mjd(dataset: xr.Dataset, time_var_name: str) -> Union[datetime.datetime, None]:
+    """
+    Translate the modified julian date from the long name in the time attribute.
+
+    Parameters
+    ----------
+    dataset : xr.Dataset
+        Dataset that contains time var
+    time_var_name : str
+        The name of the actual time var (with matching dims to the
+        coord vars)
+
+    Returns
+    -------
+    datetime
+        the datetime of the modified julian date
+    """
+
+    time_var = dataset[time_var_name]
+    if 'long_name' in time_var.attrs:
+        mdj_string = time_var.attrs['long_name']
+        mjd = mdj_string[mdj_string.find("(") + 1:mdj_string.find(")")].split("= ")[1]
+        try:
+            mjd_float = float(mjd)
+        except ValueError:
+            return None
+        mjd_datetime = julian.from_jd(mjd_float, fmt='mjd')
+        return mjd_datetime
+
+    return None

+
+
+
+

+[docs]
+def build_temporal_cond(min_time: str, max_time: str, dataset: xr.Dataset, time_var_name: str
+                        ) -> Union[np.ndarray, bool]:
+    """
+    Build the temporal condition used in the xarray 'where' call which
+    drops data not in the given bounds. If the data in the time var is
+    of type 'datetime', assume this is a normal case where the time var
+    uses the epoch from the 'units' metadata attribute to get epoch. If
+    the data in the time var is of type 'timedelta', the epoch var is
+    needed to calculate the datetime.
+
+    Parameters
+    ----------
+    min_time : str
+        ISO timestamp representing the lower temporal bound
+    max_time : str
+        ISO timestamp representing the upper temporal bound
+    dataset : xr.Dataset
+        Dataset to build the condition off of
+    time_var_name : str
+        Name of the time variable
+
+    Returns
+    -------
+    np.array or boolean
+        If temporally subsetted, returns a boolean ND-array the shape
+        of which matches the dimensions of the coordinate vars. 'True'
+        is essentially a noop.
+    """
+
+    def build_cond(str_timestamp, compare):
+        timestamp = translate_timestamp(str_timestamp)
+        if np.issubdtype(dataset[time_var_name].dtype, np.dtype(np.datetime64)):
+            timestamp = pd.to_datetime(timestamp)
+        if np.issubdtype(dataset[time_var_name].dtype, np.dtype(np.timedelta64)):
+            if is_time_mjd(dataset, time_var_name):
+                mjd_datetime = datetime_from_mjd(dataset, time_var_name)
+                if mjd_datetime is None:
+                    raise ValueError('Unable to get datetime from dataset to calculate time delta')
+
+                # timedelta between timestamp and mjd
+                timestamp = np.datetime64(timestamp) - np.datetime64(mjd_datetime)
+            else:
+                epoch_time_var_name = get_time_epoch_var(dataset, time_var_name)
+                epoch_datetime = dataset[epoch_time_var_name].values[0]
+                timestamp = np.datetime64(timestamp) - epoch_datetime
+
+        time_data = dataset[time_var_name]
+        if getattr(time_data, 'long_name', None) == "reference time of sst file":
+            timedelta_seconds = dataset['sst_dtime'].astype('timedelta64[s]')
+            time_data = time_data + timedelta_seconds
+
+        return compare(time_data, timestamp)
+
+    temporal_conds = []
+    if min_time:
+        comparison_op = operator.ge
+        temporal_conds.append(build_cond(min_time, comparison_op))
+    if max_time:
+        comparison_op = operator.le
+        temporal_conds.append(build_cond(max_time, comparison_op))
+    temporal_cond = True
+    if min_time or max_time:
+        temporal_cond = functools.reduce(lambda cond_a, cond_b: cond_a & cond_b, temporal_conds)
+    return temporal_cond

+
+
+
+

+[docs]
+def get_base_group_names(lats: List[str]) -> Tuple[List[str], List[Union[int, str]]]:  # pylint: disable=too-many-branches
+    """Latitude groups may be at different depths. This function gets the level
+    number that makes each latitude group unique from the other latitude names"""
+    unique_groups = []
+    group_list = [lat.strip(GROUP_DELIM).split(GROUP_DELIM) for lat in lats]
+
+    # make all lists of group levels the same length
+    group_list = list(zip(*zip_longest(*group_list, fillvalue='')))
+
+    # put the groups in the same levels in the same list
+    group_list_transpose = np.array(group_list).T.tolist()
+
+    diff_count = ['' for _ in range(len(group_list))]
+    group_count = 0
+    # loop through each group level
+    for my_list in group_list_transpose:
+        for i in range(len(my_list)):  # pylint: disable=consider-using-enumerate
+            count = 0
+            for j in range(len(my_list)):  # pylint: disable=consider-using-enumerate
+                # go through each lat name and compare the level names
+                if my_list[i] == my_list[j] and not isinstance(diff_count[j], int):
+                    count += 1
+            # if the lat names is equivalent to only itself then insert the level number
+            if count == 1:
+                if isinstance(diff_count[i], int):
+                    continue
+                if 'lat' in my_list[i].lower():  # if we get to the end of the list, go to the previous level
+                    diff_count[i] = group_count - 1
+                    continue
+
+                diff_count[i] = group_count
+
+        group_count += 1
+
+    # go back and re-put together the unique groups
+    for lat in enumerate(lats):
+        unique_groups.append(f'{GROUP_DELIM}{GROUP_DELIM.join(lat[1].strip(GROUP_DELIM).split(GROUP_DELIM)[:(diff_count[lat[0]]+1)])}')
+    return unique_groups, diff_count

+
+
+
+

+[docs]
+def subset_with_bbox(dataset: xr.Dataset,  # pylint: disable=too-many-branches
+                     lat_var_names: list,
+                     lon_var_names: list,
+                     time_var_names: list,
+                     variables: Optional[List[str]] = None,
+                     bbox: np.ndarray = None,
+                     cut: bool = True,
+                     min_time: str = None,
+                     max_time: str = None) -> np.ndarray:
+    """
+    Subset an xarray Dataset using a spatial bounding box.
+
+    Parameters
+    ----------
+    dataset : xr.Dataset
+        Dataset to subset
+    lat_var_names : list
+        Name of the latitude variables in the given dataset
+    lon_var_names : list
+        Name of the longitude variables in the given dataset
+    time_var_names : list
+        Name of the time variables in the given dataset
+    variables : list[str]
+        List of variables to include in the result
+    bbox : np.array
+        Spatial bounding box to subset Dataset with.
+    cut : bool
+        True if scanline should be cut.
+    min_time : str
+        ISO timestamp of min temporal bound
+    max_time : str
+        ISO timestamp of max temporal bound
+    TODO: add docstring and type hint for `variables` parameter.
+
+    Returns
+    -------
+    np.array
+        Spatial bounds of Dataset after subset operation
+    TODO - fix this docstring type and the type hint to match code (currently returning a list[xr.Dataset])
+    """
+    lon_bounds, lat_bounds = convert_bbox(bbox, dataset, lat_var_names[0], lon_var_names[0])
+    # condition should be 'or' instead of 'and' when bbox lon_min > lon_max
+    oper = operator.and_
+
+    if lon_bounds[0] > lon_bounds[1]:
+        oper = operator.or_
+
+    # get unique group names for latitude coordinates
+    diff_count = [-1]
+    if len(lat_var_names) > 1:
+        unique_groups, diff_count = get_base_group_names(lat_var_names)
+    else:
+        unique_groups = [f'{GROUP_DELIM}{GROUP_DELIM.join(x.strip(GROUP_DELIM).split(GROUP_DELIM)[:-1])}' for x in lat_var_names]
+
+    datasets = []
+    total_list = []  # don't include repeated variables
+    for lat_var_name, lon_var_name, time_var_name, diffs in zip(  # pylint: disable=too-many-nested-blocks
+            lat_var_names, lon_var_names, time_var_names, diff_count
+    ):
+        if GROUP_DELIM in lat_var_name:
+            lat_var_prefix = GROUP_DELIM.join(lat_var_name.strip(GROUP_DELIM).split(GROUP_DELIM)[:(diffs+1)])
+
+            if diffs == -1:  # if the lat name is in the root group: take only the root group vars
+                group_vars = list(dataset.data_vars.keys())
+                # include the coordinate variables if user asks for
+                group_vars.extend([
+                        var for var in list(dataset.coords.keys())
+                        if var in variables and var not in group_vars
+                    ])
+            else:
+                group_vars = [
+                    var for var in dataset.data_vars.keys()
+                    if GROUP_DELIM.join(var.strip(GROUP_DELIM).split(GROUP_DELIM)[:(diffs+1)]) == lat_var_prefix
+                ]
+                # include variables that aren't in a latitude group
+                if variables:
+                    group_vars.extend([
+                        var for var in dataset.variables.keys()
+                        if (var in variables and
+                            var not in group_vars and
+                            var not in total_list and
+                            not var.startswith(tuple(unique_groups))
+                            )
+                    ])
+                else:
+                    group_vars.extend([
+                        var for var in dataset.data_vars.keys()
+                        if (var not in group_vars and
+                            var not in total_list and
+                            not var.startswith(tuple(unique_groups))
+                            )
+                        ])
+
+                # group dimensions do not get carried over if unused by data variables (MLS nTotalTimes var)
+                # get all dimensions from data variables
+                dim_list = []
+                for var in group_vars:
+                    dim_list.extend(list(list(dataset[var].dims)))
+                # get all group dimensions
+                group_dims = [
+                    dim for dim in list(dataset.coords.keys())
+                    if GROUP_DELIM.join(dim.strip(GROUP_DELIM).split(GROUP_DELIM)[:(diffs+1)]) == lat_var_prefix
+                ]
+                # include any group dimensions that aren't accounted for in variable dimensions
+                var_included = list(set(group_dims) - set(dim_list))
+                group_vars.extend(var_included)
+
+        else:
+            group_vars = list(dataset.keys())
+
+        group_dataset = dataset[group_vars]
+
+        # Calculate temporal conditions
+        temporal_cond = build_temporal_cond(min_time, max_time, group_dataset, time_var_name)
+
+        group_dataset = xre.where(
+            group_dataset,
+            oper(
+                (group_dataset[lon_var_name] >= lon_bounds[0]),
+                (group_dataset[lon_var_name] <= lon_bounds[1])
+            ) &
+            (group_dataset[lat_var_name] >= lat_bounds[0]) &
+            (group_dataset[lat_var_name] <= lat_bounds[1]) &
+            temporal_cond,
+            cut
+        )
+
+        datasets.append(group_dataset)
+        total_list.extend(group_vars)
+        if diffs == -1:
+            return datasets
+    dim_cleaned_datasets = dc.recreate_pixcore_dimensions(datasets)
+    return dim_cleaned_datasets

+
+
+
+

+[docs]
+def subset_with_shapefile(dataset: xr.Dataset,
+                          lat_var_name: str,
+                          lon_var_name: str,
+                          shapefile: str,
+                          cut: bool,
+                          chunks) -> np.ndarray:
+    """
+    Subset an xarray Dataset using a shapefile
+
+    Parameters
+    ----------
+    dataset : xr.Dataset
+        Dataset to subset
+    lat_var_name : str
+        Name of the latitude variable in the given dataset
+    lon_var_name : str
+        Name of the longitude variable in the given dataset
+    shapefile : str
+        Absolute path to the shapefile used to subset the given dataset
+    cut : bool
+        True if scanline should be cut.
+    TODO: add docstring and type hint for `chunks` parameter.
+
+    Returns
+    -------
+    np.array
+        Spatial bounds of Dataset after shapefile subset operation
+    TODO - fix this docstring type and the type hint to match code (currently returning a xr.Dataset)
+    """
+    shapefile_df = gpd.read_file(shapefile)
+
+    lat_scale = dataset[lat_var_name].attrs.get('scale_factor', 1.0)
+    lon_scale = dataset[lon_var_name].attrs.get('scale_factor', 1.0)
+    lat_offset = dataset[lat_var_name].attrs.get('add_offset', 0.0)
+    lon_offset = dataset[lon_var_name].attrs.get('add_offset', 0.0)
+
+    # If data is '360', convert shapefile to '360' as well. There is an
+    # assumption that the shapefile is -180,180.
+    if is_360(dataset[lon_var_name], lon_scale, lon_offset):
+        # Transform
+        shapefile_df.geometry = shapefile_df['geometry'].apply(translate_longitude)
+
+    # Mask and scale shapefile
+    def scale(lon, lat, extra=None):  # pylint: disable=unused-argument
+        lon = tuple(map(functools.partial(apply_scale_offset, lon_scale, lon_offset), lon))
+        lat = tuple(map(functools.partial(apply_scale_offset, lat_scale, lat_offset), lat))
+        return lon, lat
+
+    geometries = [transform(scale, geometry) for geometry in shapefile_df.geometry]
+    shapefile_df.geometry = geometries
+
+    def in_shape(lon, lat):
+        point = Point(lon, lat)
+        point_in_shapefile = shapefile_df.contains(point)
+        return point_in_shapefile.array[0]
+
+    dask = "forbidden"
+    if chunks:
+        dask = "allowed"
+
+    in_shape_vec = np.vectorize(in_shape)
+    boolean_mask = xr.apply_ufunc(in_shape_vec, dataset[lon_var_name], dataset[lat_var_name], dask=dask)
+    return xre.where(dataset, boolean_mask, cut)

+
+
+
+

+[docs]
+def get_coordinate_variable_names(dataset: xr.Dataset,
+                                  lat_var_names: list = None,
+                                  lon_var_names: list = None,
+                                  time_var_names: list = None):
+    """
+    Retrieve coordinate variables for this dataset. If coordinate
+    variables are provided, use those, Otherwise, attempt to determine
+    coordinate variables manually.
+
+    Parameters
+    ----------
+    dataset : xr.Dataset
+        xarray Dataset used to compute coordinate variables manually.
+        Only used if lat, lon, or time vars are not provided.
+    lat_var_names : list
+        List of latitude coordinate variables.
+    lon_var_names : list
+        List of longitude coordinate variables.
+    time_var_names : list
+        List of time coordinate variables.
+
+    Returns
+    -------
+    TODO: add return type docstring and type hint.
+    """
+
+    if not lat_var_names or not lon_var_names:
+        lat_var_names, lon_var_names = compute_coordinate_variable_names(dataset)
+    if not time_var_names:
+        time_var_names = []
+        for lat_var_name in lat_var_names:
+            time_var_names.append(compute_time_variable_name(dataset,
+                                                             dataset[lat_var_name],
+                                                             time_var_names))
+
+        time_var_names.append(compute_utc_name(dataset))
+        time_var_names = [x for x in time_var_names if x is not None]  # remove Nones and any duplicates
+
+    return lat_var_names, lon_var_names, time_var_names

+
+
+
+

+[docs]
+def open_as_nc_dataset(filepath: str) -> Tuple[nc.Dataset, bool]:
+    """Open netcdf file, and flatten groups if they exist."""
+    hdf_type = None
+    # Open dataset with netCDF4 first, so we can get group info
+    try:
+        nc_dataset = nc.Dataset(filepath, mode='r')
+        has_groups = bool(nc_dataset.groups)
+        # If dataset has groups, transform to work with xarray
+        if has_groups:
+            nc_dataset = transform_grouped_dataset(nc_dataset, filepath)
+
+    except OSError:
+        nc_dataset, has_groups, hdf_type = h5file_transform(filepath)
+
+    nc_dataset = dc.remove_duplicate_dims(nc_dataset)
+
+    return nc_dataset, has_groups, hdf_type

+
+
+
+

+[docs]
+def override_decode_cf_datetime() -> None:
+    """
+    WARNING !!! REMOVE AT EARLIEST XARRAY FIX, this is a override to xarray override_decode_cf_datetime function.
+    xarray has problems decoding time units with format `seconds since 2000-1-1 0:0:0 0`, this solves by testing
+    the unit to see if its parsable, if it is use original function, if not format unit into a parsable format.
+
+    https://github.com/pydata/xarray/issues/7210
+    """
+
+    orig_decode_cf_datetime = xarray.coding.times.decode_cf_datetime
+
+    def decode_cf_datetime(num_dates, units, calendar=None, use_cftime=None):
+        try:
+            parser.parse(units.split('since')[-1])
+            return orig_decode_cf_datetime(num_dates, units, calendar, use_cftime)
+        except dateutil.parser.ParserError:
+            reference_time = cftime.num2date(0, units, calendar)
+            units = f"{units.split('since')[0]} since {reference_time}"
+            return orig_decode_cf_datetime(num_dates, units, calendar, use_cftime)
+
+    xarray.coding.times.decode_cf_datetime = decode_cf_datetime

+
+
+
+

+[docs]
+def test_access_sst_dtime_values(datafile):
+    """
+    Test accessing values of 'sst_dtime' variable in a NetCDF file.
+
+    Parameters
+    ----------
+    datafile (str): Path to the NetCDF file.
+
+    Returns
+    -------
+    access_successful (bool): True if 'sst_dtime' values are accessible, False otherwise.
+    """
+
+    nc_dataset, _, _ = open_as_nc_dataset(datafile)
+    args = {
+        'decode_coords': False,
+        'mask_and_scale': True,
+        'decode_times': True
+    }
+    try:
+        with xr.open_dataset(
+                xr.backends.NetCDF4DataStore(nc_dataset),
+                **args
+        ) as dataset:
+            # pylint: disable=pointless-statement
+            for var_name in dataset.variables:
+                dataset[var_name].values
+    except (TypeError, ValueError, KeyError):
+        return False
+    return True

+
+
+
+

+[docs]
+def subset(file_to_subset: str, bbox: np.ndarray, output_file: str,
+           variables: Union[List[str], str, None] = (),
+           # pylint: disable=too-many-branches, disable=too-many-statements
+           cut: bool = True, shapefile: str = None, min_time: str = None, max_time: str = None,
+           origin_source: str = None,
+           lat_var_names: List[str] = (), lon_var_names: List[str] = (), time_var_names: List[str] = ()
+           ) -> Union[np.ndarray, None]:
+    """
+    Subset a given NetCDF file given a bounding box
+
+    Parameters
+    ----------
+    file_to_subset : string
+        The location of the file which will be subset
+    bbox : np.ndarray
+        The chosen bounding box. This is a tuple of tuples formatted
+        as such: ((west, east), (south, north)). The assumption is that
+        the valid range is ((-180, 180), (-90, 90)). This will be
+        transformed as appropriate if the actual longitude range is
+        0-360.
+    output_file : string
+        The file path for the output of the subsetting operation.
+    variables : list, str, optional
+        List of variables to include in the resulting data file.
+        NOTE: This will remove ALL variables which are not included
+        in this list, including coordinate variables!
+    cut : boolean
+        True if the scanline should be cut, False if the scanline should
+        not be cut. Defaults to True.
+    shapefile : str
+        Name of local shapefile used to subset given file.
+    min_time : str
+        ISO timestamp representing the lower bound of the temporal
+        subset to be performed. If this value is not provided, the
+        granule will not be subset temporally on the lower bound.
+    max_time : str
+        ISO timestamp representing the upper bound of the temporal
+        subset to be performed. If this value is not provided, the
+        granule will not be subset temporally on the upper bound.
+    origin_source : str
+        Original location or filename of data to be used in "derived from"
+        history element.
+    lat_var_names : list
+        List of variables that represent the latitude coordinate
+        variables for this granule. This list will only contain more
+        than one value in the case where there are multiple groups and
+        different coordinate variables for each group.
+    lon_var_names : list
+        List of variables that represent the longitude coordinate
+        variables for this granule. This list will only contain more
+        than one value in the case where there are multiple groups and
+        different coordinate variables for each group.
+    time_var_names : list
+        List of variables that represent the time coordinate
+        variables for this granule. This list will only contain more
+        than one value in the case where there are multiple groups and
+        different coordinate variables for each group.
+    """
+    file_extension = os.path.splitext(file_to_subset)[1]
+    nc_dataset, has_groups, hdf_type = open_as_nc_dataset(file_to_subset)
+
+    override_decode_cf_datetime()
+
+    if has_groups:
+        # Make sure all variables start with '/'
+        if variables:
+            variables = ['/' + var if not var.startswith('/') else var for var in variables]
+        lat_var_names = ['/' + var if not var.startswith('/') else var for var in lat_var_names]
+        lon_var_names = ['/' + var if not var.startswith('/') else var for var in lon_var_names]
+        time_var_names = ['/' + var if not var.startswith('/') else var for var in time_var_names]
+        # Replace all '/' with GROUP_DELIM
+        if variables:
+            variables = [var.replace('/', GROUP_DELIM) for var in variables]
+        lat_var_names = [var.replace('/', GROUP_DELIM) for var in lat_var_names]
+        lon_var_names = [var.replace('/', GROUP_DELIM) for var in lon_var_names]
+        time_var_names = [var.replace('/', GROUP_DELIM) for var in time_var_names]
+
+    if '.HDF5' == file_extension:
+        # GPM files will have a ScanTime group
+        if 'ScanTime' in [var.split('__')[-2] for var in list(nc_dataset.variables.keys())]:
+            gc.change_var_dims(nc_dataset, variables)
+            hdf_type = 'GPM'
+    args = {
+        'decode_coords': False,
+        'mask_and_scale': False,
+        'decode_times': False
+    }
+    # clean up time variable in SNDR before decode_times
+    # SNDR.AQUA files have ascending node time blank
+    if any('__asc_node_tai93' in i for i in list(nc_dataset.variables)):
+        asc_time_var = nc_dataset.variables['__asc_node_tai93']
+        if not asc_time_var[:] > 0:
+            del nc_dataset.variables['__asc_node_tai93']
+
+    if min_time or max_time:
+        args['decode_times'] = True
+        float_dtypes = ['float64', 'float32']
+        fill_value_f8 = nc.default_fillvals.get('f8')
+
+        for time_variable in (v for v in nc_dataset.variables.keys() if 'time' in v):
+            time_var = nc_dataset[time_variable]
+
+            if (getattr(time_var, '_FillValue', None) == fill_value_f8 and time_var.dtype in float_dtypes) or \
+               (getattr(time_var, 'long_name', None) == "reference time of sst file"):
+                args['mask_and_scale'] = True
+                if getattr(time_var, 'long_name', None) == "reference time of sst file":
+                    args['mask_and_scale'] = test_access_sst_dtime_values(file_to_subset)
+                break
+
+    if hdf_type == 'GPM':
+        args['decode_times'] = False
+
+    with xr.open_dataset(
+            xr.backends.NetCDF4DataStore(nc_dataset),
+            **args
+    ) as dataset:
+
+        original_dataset = dataset
+
+        lat_var_names, lon_var_names, time_var_names = get_coordinate_variable_names(
+            dataset=dataset,
+            lat_var_names=lat_var_names,
+            lon_var_names=lon_var_names,
+            time_var_names=time_var_names
+        )
+
+        start_date = None
+        if hdf_type and (min_time or max_time):
+            dataset, start_date = tc.convert_to_datetime(dataset, time_var_names, hdf_type)
+
+        chunks = calculate_chunks(dataset)
+        if chunks:
+            dataset = dataset.chunk(chunks)
+        if variables:
+            # Drop variables that aren't explicitly requested, except lat_var_name and
+            # lon_var_name which are needed for subsetting
+            variables_upper = [variable.upper() for variable in variables]
+            vars_to_drop = [
+                var_name for var_name, var in dataset.data_vars.items()
+                if var_name.upper() not in variables_upper
+                and var_name not in lat_var_names
+                and var_name not in lon_var_names
+                and var_name not in time_var_names
+            ]
+
+            dataset = dataset.drop_vars(vars_to_drop)
+        if shapefile:
+            datasets = [
+                subset_with_shapefile(dataset, lat_var_names[0], lon_var_names[0], shapefile, cut, chunks)
+            ]
+        elif bbox is not None:
+            datasets = subset_with_bbox(
+                dataset=dataset,
+                lat_var_names=lat_var_names,
+                lon_var_names=lon_var_names,
+                time_var_names=time_var_names,
+                variables=variables,
+                bbox=bbox,
+                cut=cut,
+                min_time=min_time,
+                max_time=max_time
+            )
+        else:
+            raise ValueError('Either bbox or shapefile must be provided')
+
+        spatial_bounds = []
+
+        for dataset in datasets:
+            set_version_history(dataset, cut, bbox, shapefile)
+            set_json_history(dataset, cut, file_to_subset, bbox, shapefile, origin_source)
+            if has_groups:
+                spatial_bounds.append(get_spatial_bounds(
+                    dataset=dataset,
+                    lat_var_names=lat_var_names,
+                    lon_var_names=lon_var_names
+                ))
+            else:
+                for var in dataset.data_vars:
+                    if dataset[var].dtype == 'S1' and isinstance(dataset[var].attrs.get('_FillValue'), bytes):
+                        dataset[var].attrs['_FillValue'] = dataset[var].attrs['_FillValue'].decode('UTF-8')
+
+                    var_encoding = {
+                        "zlib": True,
+                        "complevel": 5,
+                        "_FillValue": original_dataset[var].encoding.get('_FillValue')
+                    }
+
+                    data_var = dataset[var].copy()
+                    data_var.load().to_netcdf(output_file, 'a', encoding={var: var_encoding})
+                    del data_var
+
+                with nc.Dataset(output_file, 'a') as dataset_attr:
+                    dataset_attr.setncatts(dataset.attrs)
+
+        if has_groups:
+            recombine_grouped_datasets(datasets, output_file, start_date, time_var_names)
+            # Check if the spatial bounds are all 'None'. This means the
+            # subset result is empty.
+            if any(bound is None for bound in spatial_bounds):
+                return None
+            return np.array([[
+                min(lon[0][0][0] for lon in zip(spatial_bounds)),
+                max(lon[0][0][1] for lon in zip(spatial_bounds))
+            ], [
+                min(lat[0][1][0] for lat in zip(spatial_bounds)),
+                max(lat[0][1][1] for lat in zip(spatial_bounds))
+            ]])
+
+        return get_spatial_bounds(
+            dataset=dataset,
+            lat_var_names=lat_var_names,
+            lon_var_names=lon_var_names
+        )

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/2.12.0a2/_modules/podaac/subsetter/subset_harmony.html b/2.12.0a2/_modules/podaac/subsetter/subset_harmony.html new file mode 100644 index 00000000..8d5be29a --- /dev/null +++ b/2.12.0a2/_modules/podaac/subsetter/subset_harmony.html @@ -0,0 +1,393 @@ + + + + + + podaac.subsetter.subset_harmony — l2ss-py 2.12.0a2 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • podaac.subsetter.subset_harmony
    • +
  • +
    • +
+
+
+
+
+ +

Source code for podaac.subsetter.subset_harmony

+# Copyright 2019, by the California Institute of Technology.
+# ALL RIGHTS RESERVED. United States Government Sponsorship acknowledged.
+# Any commercial use must be negotiated with the Office of Technology
+# Transfer at the California Institute of Technology.
+#
+# This software may be subject to U.S. export control laws. By accepting
+# this software, the user agrees to comply with all applicable U.S. export
+# laws and regulations. User has the responsibility to obtain export
+# licenses, or other export authority as may be required before exporting
+# such information to foreign countries or providing access to foreign
+# persons.
+
+"""
+=================
+subset_harmony.py
+=================
+
+Implementation of harmony-service-lib that invokes the Level 2 subsetter.
+"""
+import argparse
+import os
+import subprocess
+import shutil
+from tempfile import mkdtemp
+from typing import List, Union
+
+import pystac
+from pystac import Asset
+
+import harmony
+import numpy as np
+from harmony import BaseHarmonyAdapter
+from harmony.util import download, stage, generate_output_filename, bbox_to_geometry
+
+from podaac.subsetter import subset
+from podaac.subsetter.subset import SERVICE_NAME
+
+DATA_DIRECTORY_ENV = "DATA_DIRECTORY"
+
+
+

+[docs]
+def podaac_to_harmony_bbox(bbox: np.ndarray) -> Union[np.ndarray, float]:
+    """
+    Convert PO.DAAC bbox ((west, east), (south, north))
+    to Harmony bbox (west, south, east, north)
+
+    Parameters
+    ----------
+    bbox : np.array
+        Podaac bbox
+
+    Returns
+    -------
+    array, int or float
+        Harmony bbox
+    TODO - fix this docstring type, type hint, and code to match (code currently returns a list)
+    """
+
+    return_box = [bbox.item(0), bbox.item(2), bbox.item(1), bbox.item(3)]
+    return return_box

+
+
+
+

+[docs]
+def harmony_to_podaac_bbox(bbox: list) -> np.ndarray:
+    """
+    Convert Harmony bbox (west, south, east, north)
+    to PO.DAAC bbox ((west, east), (south, north))
+
+    Parameters
+    ----------
+    bbox : list
+        Harmony bbox
+
+    Returns
+    -------
+    np.array
+        PO.DAAC bbox
+    """
+    return np.array(((bbox[0], bbox[2]),
+                     (bbox[1], bbox[3])))

+
+
+
+

+[docs]
+class L2SubsetterService(BaseHarmonyAdapter):
+    """
+    See https://github.com/nasa/harmony-service-lib-py
+    for documentation and examples.
+    """
+
+

+[docs]
+    def __init__(self, message, catalog=None, config=None):
+        super().__init__(message, catalog, config)
+
+        self.data_dir = os.getenv(DATA_DIRECTORY_ENV, '/home/dockeruser/data')

+
+
+

+[docs]
+    def process_item(self, item: pystac.Item, source: harmony.message.Source) -> pystac.Item:
+        """
+        Performs variable and bounding box subsetting on the input STAC Item's data, returning
+        an output STAC item
+
+        Parameters
+        ----------
+        item : pystac.Item
+            the item that should be subset
+        source : harmony.message.Source
+            the input source defining the variables, if any, to subset from the item
+
+        Returns
+        -------
+        pystac.Item
+            a STAC item describing the output of the subsetter
+        """
+        result = item.clone()
+        result.assets = {}
+
+        # Create a temporary dir for processing we may do
+        temp_dir = mkdtemp()
+        output_dir = self.data_dir
+        self.prepare_output_dir(output_dir)
+
+        try:
+            # Get the data file
+            asset = next(v for k, v in item.assets.items() if 'data' in (v.roles or []))
+            input_filename = download(asset.href,
+                                      temp_dir,
+                                      logger=self.logger,
+                                      access_token=self.message.accessToken,
+                                      cfg=self.config)
+
+            message = self.message
+
+            # Dictionary of keywords params that will be passed into
+            # the l2ss-py subset function
+            subset_params = {}
+
+            # Transform params to PO.DAAC subsetter arguments and invoke the subsetter
+            harmony_bbox = [-180, -90, 180, 90]
+
+            if message.subset and message.subset.bbox:
+                harmony_bbox = message.subset.bbox
+
+            if message.subset and message.subset.shape:
+                subset_params['shapefile'] = download(
+                    message.subset.shape.href,
+                    temp_dir,
+                    logger=self.logger,
+                    access_token=self.message.accessToken,
+                    cfg=self.config
+                )
+
+            if message.temporal:
+                subset_params['min_time'] = message.temporal.start
+                subset_params['max_time'] = message.temporal.end
+
+            subset_params['bbox'] = harmony_to_podaac_bbox(harmony_bbox)
+
+            try:
+                subset_params['cut'] = message.extraArgs['cut']
+            except (KeyError, AttributeError, TypeError):
+                pass
+
+            if source.variables:
+                subset_params['variables'] = [variable.name for variable in source.process('variables')]
+
+            if source.coordinateVariables:
+                coordinate_variables = list(
+                    filter(lambda var: var.type and var.subtype, source.coordinateVariables)
+                )
+
+                def filter_by_subtype(variables, subtype):
+                    return list(map(lambda var: var.name, filter(
+                        lambda var: var.subtype == subtype, variables
+                    )))
+                subset_params['lat_var_names'] = filter_by_subtype(coordinate_variables, 'LATITUDE')
+                subset_params['lon_var_names'] = filter_by_subtype(coordinate_variables, 'LONGITUDE')
+                subset_params['time_var_names'] = filter_by_subtype(coordinate_variables, 'TIME')
+
+            subset_params['output_file'] = f'{output_dir}/{os.path.basename(input_filename)}'
+            subset_params['file_to_subset'] = input_filename
+            subset_params['origin_source'] = asset.href
+
+            self.logger.info('Calling l2ss-py subset with params %s', subset_params)
+            result_bbox = subset.subset(**subset_params)
+
+            # Stage the output file with a conventional filename
+            mime = 'application/x-netcdf4'
+            operations = {
+                "variable_subset": subset_params.get('variables'),
+                "is_subsetted": bool(result_bbox is not None)
+            }
+            staged_filename = generate_output_filename(asset.href, '.nc4', **operations)
+
+            url = stage(subset_params['output_file'],
+                        staged_filename,
+                        mime,
+                        location=message.stagingLocation,
+                        logger=self.logger,
+                        cfg=self.config)
+
+            # Update the STAC record
+            asset = Asset(url, title=staged_filename, media_type=mime, roles=['data'])
+            result.assets['data'] = asset
+            if result_bbox is not None:
+                if message.subset:
+                    message.subset.process('bbox')
+                    bounding_box_array = np.array(podaac_to_harmony_bbox(result_bbox))
+                    if not np.all(np.isnan(bounding_box_array)):
+                        result.bbox = podaac_to_harmony_bbox(result_bbox)
+                        result.geometry = bbox_to_geometry(result.bbox)
+
+            # Return the STAC record
+            return result
+        finally:
+            # Clean up any intermediate resources
+            shutil.rmtree(temp_dir)

+
+
+

+[docs]
+    def prepare_output_dir(self, output_dir: str) -> None:
+        """
+        Deletes (if present) and recreates the given output_dir, ensuring it exists
+        and is empty
+
+        Parameters
+        ----------
+        output_dir : string
+            the directory to delete and recreate
+        """
+        self.cmd('rm', '-rf', output_dir)
+        self.cmd('mkdir', '-p', output_dir)

+
+
+

+[docs]
+    def cmd(self, *args) -> List[str]:
+        """
+        Logs and then runs command.
+
+        Parameters
+        ----------
+        args Command and args to run
+
+        Returns
+        -------
+        Command output
+        """
+        self.logger.info("%s %s", args[0], " ".join(["'{}'".format(arg) for arg in args[1:]]))  # pylint: disable=C0209
+        result_str = subprocess.check_output(args).decode("utf-8")
+        return result_str.split("\n")

+

+
+
+
+

+[docs]
+def main(config: harmony.util.Config = None) -> None:
+    """Parse command line arguments and invoke the service to respond to
+    them.
+
+    Parameters
+    ----------
+    config : harmony.util.Config
+
+    Returns
+    -------
+    None
+    """
+    parser = argparse.ArgumentParser(prog=SERVICE_NAME,
+                                     description='Run the l2_subsetter service')
+    harmony.setup_cli(parser)
+    args = parser.parse_args()
+    if harmony.is_harmony_cli(args):
+        harmony.run_cli(parser, args, L2SubsetterService, cfg=config)
+    else:
+        parser.error("Only --harmony CLIs are supported")

+
+
+
+if __name__ == "__main__":
+    main()
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/2.12.0a2/_modules/podaac/subsetter/xarray_enhancements.html b/2.12.0a2/_modules/podaac/subsetter/xarray_enhancements.html new file mode 100644 index 00000000..2be715db --- /dev/null +++ b/2.12.0a2/_modules/podaac/subsetter/xarray_enhancements.html @@ -0,0 +1,429 @@ + + + + + + podaac.subsetter.xarray_enhancements — l2ss-py 2.12.0a2 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Module code
    • +
  • podaac.subsetter.xarray_enhancements
    • +
  • +
    • +
+
+
+
+
+ +

Source code for podaac.subsetter.xarray_enhancements

+# Copyright 2019, by the California Institute of Technology.
+# ALL RIGHTS RESERVED. United States Government Sponsorship acknowledged.
+# Any commercial use must be negotiated with the Office of Technology
+# Transfer at the California Institute of Technology.
+#
+# This software may be subject to U.S. export control laws. By accepting
+# this software, the user agrees to comply with all applicable U.S. export
+# laws and regulations. User has the responsibility to obtain export
+# licenses, or other export authority as may be required before exporting
+# such information to foreign countries or providing access to foreign
+# persons.
+
+"""
+======================
+xarray_enhancements.py
+======================
+
+Functions which improve upon existing xarray functionality, optimized
+for this specific use-case.
+"""
+
+import logging
+from typing import Union
+
+import numpy as np
+import xarray as xr
+from podaac.subsetter import dimension_cleanup as dc
+
+
+

+[docs]
+def get_indexers_from_1d(cond: xr.Dataset) -> dict:
+    """
+    Get indexers from a dataset with 1 dimension.
+
+    Parameters
+    ----------
+    cond : xarray.Dataset
+        Contains the result of the initial lat lon condition.
+
+    Returns
+    -------
+    dict
+        Indexer dictionary for the provided condition.
+    """
+    cols = cond.values
+
+    if not cols.any():
+        logging.info("No data within the given bounding box.")
+
+    indexers = {
+        cond.dims[0]: np.where(cols)[0]
+    }
+    return indexers

+
+
+
+

+[docs]
+def get_indexers_from_nd(cond: xr.Dataset, cut: bool) -> dict:
+    """
+    Get indexers from a dataset with more than one dimension.
+
+    Parameters
+    ----------
+    cond : xarray.Dataset
+        Contains the result of the initial lat lon condition.
+    cut : bool
+        True if the scanline should be cut.
+
+    Returns
+    -------
+    dict
+        Indexer dictionary for the provided condition.
+    """
+    # check if the lat/lon coordinate numpy array has 2 or more dimensions
+    transpose = False
+    if cond.values.squeeze().ndim == 2:
+        x_axis = 1
+        y_axis = 0
+    else:
+        if any('xtrack' in dim for dim in list(cond.dims)) and\
+           any('atrack' in dim for dim in list(cond.dims)):
+            x_axis = list(cond.dims).index('xtrack')
+            y_axis = list(cond.dims).index('atrack')
+            transpose = True
+        else:
+            x_axis = 2
+            y_axis = 1
+
+    rows = np.any(cond.values.squeeze(), axis=x_axis)
+    if cut:
+        cols = np.any(cond.values.squeeze(), axis=y_axis)
+    else:
+        cols = np.ones(len(cond.values[0]))
+
+    # If the subsetted area is equal to the original area
+    if np.all(rows) & np.all(cols):
+        logging.info("Subsetted area equal to the original granule.")
+
+    # If the subsetted area is empty
+    if not np.any(rows) | np.any(cols):
+        logging.info("No data within the given bounding box.")
+
+    cond_shape_list = list(cond.shape)
+    cond_list = list(cond.dims)
+    output = [idx for idx, element in enumerate(cond_shape_list) if element == 1]
+    for i in output:
+        cond_list.pop(i)
+
+    if rows.ndim == 1:
+        indexers = {
+            cond_list[0]: np.where(rows)[0],
+            cond_list[1]: np.where(cols)[0]
+        }
+    else:
+        # if the lat/lon had 3 dimensions the conditional array was identical in the z direction - taking the first
+        if transpose:
+            rows = rows.transpose()[0]
+            cols = cols.transpose()[0]
+        else:
+            rows = rows[0]
+            cols = cols[0]
+        indexers = {
+            cond_list[y_axis]: np.where(rows)[0],
+            cond_list[x_axis]: np.where(cols)[0]
+        }
+
+    return indexers

+
+
+
+

+[docs]
+def copy_empty_dataset(dataset: xr.Dataset) -> xr.Dataset:
+    """
+    Copy a dataset into a new, empty dataset. This dataset should:
+        * Contain the same structure as the input dataset (only include
+          requested variables, if variable subset)
+        * Contain the same global metadata as the input dataset
+        * Contain a history field which describes this subset operation.
+
+    Parameters
+    ----------
+    dataset: xarray.Dataset
+        The dataset to copy into a empty dataset.
+
+    Returns
+    -------
+    xarray.Dataset
+        The new dataset which has no data.
+    """
+    # Create a dict object where each key is a variable in the dataset and the value is an
+    # array initialized to the fill value for that variable or NaN if there is no fill value
+    # attribute for the variable
+
+    empty_data = {k: np.full(v.shape, dataset.variables[k].attrs.get('_FillValue', np.nan), dtype=v.dtype) for k, v in dataset.items()}
+
+    # Create a copy of the dataset filled with the empty data. Then select the first index along each
+    # dimension and return the result
+    return dataset.copy(data=empty_data).isel({dim: slice(0, 1, 1) for dim in dataset.dims})

+
+
+
+

+[docs]
+def cast_type(var: xr.DataArray, var_type: str) -> xr.DataArray:
+    """
+    Type cast a variable into a var type.
+
+    Parameters
+    ----------
+    var: xr.DataArray
+        The dataarray to be type casted.
+    var_type: string
+        New type the variable will be type casted to.
+    Returns
+    -------
+    xr.DataArray
+        The newly type casted variable.
+    """
+
+    return var.astype(var_type)

+
+
+
+

+[docs]
+def get_variables_with_indexers(dataset, indexers):
+    """
+    returns a list of variables with bounding box dimensions and variables that
+    don't have bounding box dimensions
+    """
+    index_list = list(indexers.keys())
+    subset_vars = []
+    no_subset_vars = []
+    for i in list(dataset.variables.keys()):
+        variable_dims = list(dataset[i].dims)
+        if any(item in index_list for item in variable_dims):
+            subset_vars.append(i)
+        else:
+            no_subset_vars.append(i)
+
+    return subset_vars, no_subset_vars

+
+
+
+

+[docs]
+def where(dataset: xr.Dataset, cond: Union[xr.Dataset, xr.DataArray], cut: bool) -> xr.Dataset:
+    """
+    Return a dataset which meets the given condition.
+
+    This is a modification of the existing xarray 'where' function.
+    https://github.com/pydata/xarray/blob/master/xarray/core/common.py#L999
+
+    Parameters
+    ----------
+    dataset : xarray.Dataset
+        The dataset to filter and return.
+    cond : DataArray or Dataset with boolean dtype
+        Locations at which to preserve this object's values.
+    cut : boolean
+        True if the scanline should be cut, False if the scanline should
+        not be cut.
+
+    Returns
+    -------
+    xarray.Dataset
+        The filtered Dataset
+
+    Notes
+    -----
+    The `cond` variable contains a boolean mask of valid data indices.
+    However in that mask, True represents valid data and False
+    represents invalid data.
+    """
+    if cond.values.ndim == 1:
+        indexers = get_indexers_from_1d(cond)
+    else:
+        indexers = get_indexers_from_nd(cond, cut)
+    # If any of the indexer dimensions are empty, return an empty dataset
+    if not all(len(value) > 0 for value in indexers.values()):
+        return copy_empty_dataset(dataset)
+
+    # This will be true if any variables in the dataset have a partial
+    # overlap with the coordinate dims. If so, the cond should be
+    # applied per-variable rather than to the entire dataset.
+    partial_dim_in_in_vars = np.any(
+        [len(set(indexers.keys()).intersection(var.dims)) > 0 and len(
+            indexers.keys() - var.dims) > 0 for _, var in dataset.variables.items()]
+    )
+
+    indexed_cond = cond.isel(**indexers)
+    indexed_ds = dataset.isel(**indexers)
+    subset_vars, non_subset_vars = get_variables_with_indexers(dataset, indexers)
+
+    # dataset with variables that need to be subsetted
+    new_dataset_sub = indexed_ds[subset_vars].where(indexed_cond)
+    # data with variables that shouldn't be subsetted
+    new_dataset_non_sub = indexed_ds[non_subset_vars]
+
+    # merge the datasets
+    new_dataset = xr.merge([new_dataset_non_sub, new_dataset_sub])
+
+    # Cast all variables to their original type
+    for variable_name, variable in new_dataset.data_vars.items():
+        original_type = indexed_ds[variable_name].dtype
+        new_type = variable.dtype
+        indexed_var = indexed_ds[variable_name]
+
+        if partial_dim_in_in_vars and (indexers.keys() - dataset[variable_name].dims) and set(
+                indexers.keys()).intersection(dataset[variable_name].dims):
+
+            missing_dim = (indexers.keys() - dataset[variable_name].dims).pop()  # Assume only 1
+            var_indexers = {
+                dim_name: dim_value for dim_name, dim_value in indexers.items()
+                if dim_name in dataset[variable_name].dims
+            }
+
+            var_cond = cond.any(axis=cond.dims.index(missing_dim)).isel(**var_indexers)
+            indexed_var = dataset[variable_name].isel(**var_indexers)
+            new_dataset[variable_name] = indexed_var.where(var_cond)
+            variable = new_dataset[variable_name]
+        elif partial_dim_in_in_vars and (indexers.keys() - dataset[variable_name].dims) and set(
+                indexers.keys()).intersection(new_dataset[variable_name].dims):
+            new_dataset[variable_name] = indexed_var
+
+            new_dataset[variable_name].attrs = indexed_var.attrs
+            variable.attrs = indexed_var.attrs
+        # Check if variable has no _FillValue. If so, use original data
+        if '_FillValue' not in variable.attrs or len(indexed_var.shape) == 0:
+
+            if original_type != new_type:
+                new_dataset[variable_name] = xr.apply_ufunc(cast_type, variable,
+                                                            str(original_type), dask='allowed',
+                                                            keep_attrs=True)
+
+            # Replace nans with values from original dataset. If the
+            # variable has more than one dimension, copy the entire
+            # variable over, otherwise use a NaN mask to copy over the
+            # relevant values.
+            new_dataset[variable_name] = indexed_var
+
+            new_dataset[variable_name].attrs = indexed_var.attrs
+            variable.attrs = indexed_var.attrs
+            new_dataset[variable_name].encoding['_FillValue'] = None
+            variable.encoding['_FillValue'] = None
+
+        else:
+            # Manually replace nans with FillValue
+            # If variable represents time, cast _FillValue to datetime
+            fill_value = new_dataset[variable_name].attrs.get('_FillValue')
+            if np.issubdtype(new_dataset[variable_name].dtype, np.dtype(np.datetime64)):
+                fill_value = np.datetime64('nat')
+            if np.issubdtype(new_dataset[variable_name].dtype, np.dtype(np.timedelta64)):
+                fill_value = np.timedelta64('nat')
+            new_dataset[variable_name] = new_dataset[variable_name].fillna(fill_value)
+            if original_type != new_type:
+                new_dataset[variable_name] = xr.apply_ufunc(cast_type, new_dataset[variable_name],
+                                                            str(original_type), dask='allowed',
+                                                            keep_attrs=True)
+
+    dc.sync_dims_inplace(dataset, new_dataset)
+    return new_dataset

+
+
+ +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/2.12.0a2/_sources/index.rst.txt b/2.12.0a2/_sources/index.rst.txt new file mode 100644 index 00000000..04262c66 --- /dev/null +++ b/2.12.0a2/_sources/index.rst.txt @@ -0,0 +1,40 @@ +Reference Guide for l2ss-py +====================================== + +.. toctree:: + :maxdepth: 2 + :caption: Contents: + +.. mdinclude:: ../README.md + +Module Documentation +==================== + +.. automodule:: podaac.subsetter.subset + :members: + :special-members: + :private-members: + +.. automodule:: podaac.subsetter.subset_harmony + :members: + :special-members: + :private-members: + +.. automodule:: podaac.subsetter.run_subsetter + :members: + :special-members: + :private-members: + +.. automodule:: podaac.subsetter.xarray_enhancements + :members: + :special-members: + :private-members: + + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` + diff --git a/2.12.0a2/_static/_sphinx_javascript_frameworks_compat.js b/2.12.0a2/_static/_sphinx_javascript_frameworks_compat.js new file mode 100644 index 00000000..81415803 --- /dev/null +++ b/2.12.0a2/_static/_sphinx_javascript_frameworks_compat.js @@ -0,0 +1,123 @@ +/* Compatability shim for jQuery and underscores.js. + * + * Copyright Sphinx contributors + * Released under the two clause BSD licence + */ + +/** + * small helper function to urldecode strings + * + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/decodeURIComponent#Decoding_query_parameters_from_a_URL + */ +jQuery.urldecode = function(x) { + if (!x) { + return x + } + return decodeURIComponent(x.replace(/\+/g, ' ')); +}; + +/** + * small helper function to urlencode strings + */ +jQuery.urlencode = encodeURIComponent; + +/** + * This function returns the parsed url parameters of the + * current request. 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span; + var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.className = className; + } + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + node.parentNode.insertBefore(span, node.parentNode.insertBefore( + document.createTextNode(val.substr(pos + text.length)), + node.nextSibling)); + node.nodeValue = val.substr(0, pos); + if (isInSVG) { + var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect"); + var bbox = node.parentElement.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute('class', className); + addItems.push({ + "parent": node.parentNode, + "target": rect}); + } + } + } + else if (!jQuery(node).is("button, select, textarea")) { + jQuery.each(node.childNodes, function() { + highlight(this, addItems); + }); + } + } + var addItems = []; + var result = this.each(function() { + highlight(this, addItems); + }); + for (var i = 0; i < addItems.length; ++i) { + jQuery(addItems[i].parent).before(addItems[i].target); + } + return result; +}; + +/* + * backward compatibility for jQuery.browser + * This will be supported until firefox bug is fixed. + */ +if (!jQuery.browser) { + jQuery.uaMatch = function(ua) { + ua = ua.toLowerCase(); + + var match = /(chrome)[ \/]([\w.]+)/.exec(ua) || + /(webkit)[ \/]([\w.]+)/.exec(ua) || + /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) || + /(msie) ([\w.]+)/.exec(ua) || + ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) || + []; + + return { + browser: match[ 1 ] || "", + version: match[ 2 ] || "0" + }; + }; + jQuery.browser = {}; + jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true; +} diff --git a/2.12.0a2/_static/basic.css b/2.12.0a2/_static/basic.css new file mode 100644 index 00000000..f316efcb --- /dev/null +++ b/2.12.0a2/_static/basic.css @@ -0,0 +1,925 @@ +/* + * basic.css + * ~~~~~~~~~ + * + * Sphinx stylesheet -- basic theme. + * + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ + +/* -- main layout ----------------------------------------------------------- */ + +div.clearer { + clear: both; +} + +div.section::after { + display: block; + content: ''; + clear: left; +} + +/* -- relbar ---------------------------------------------------------------- */ + +div.related { + width: 100%; + font-size: 90%; +} + +div.related h3 { + display: none; +} + +div.related ul { + margin: 0; + padding: 0 0 0 10px; + list-style: none; +} + +div.related li { + display: inline; +} + +div.related li.right { + float: right; + margin-right: 5px; +} + +/* -- sidebar --------------------------------------------------------------- */ + +div.sphinxsidebarwrapper { + padding: 10px 5px 0 10px; +} + +div.sphinxsidebar { + float: left; + width: 230px; + margin-left: -100%; + font-size: 90%; + word-wrap: break-word; + overflow-wrap : break-word; +} + +div.sphinxsidebar ul { + list-style: none; +} + +div.sphinxsidebar ul ul, +div.sphinxsidebar ul.want-points { + margin-left: 20px; + list-style: square; +} + +div.sphinxsidebar ul ul { + margin-top: 0; + margin-bottom: 0; +} + +div.sphinxsidebar form { + margin-top: 10px; +} + +div.sphinxsidebar input { + border: 1px solid #98dbcc; + font-family: sans-serif; + font-size: 1em; +} + +div.sphinxsidebar #searchbox form.search { + overflow: hidden; +} + +div.sphinxsidebar #searchbox input[type="text"] { + float: left; + width: 80%; + padding: 0.25em; + box-sizing: border-box; +} + +div.sphinxsidebar #searchbox input[type="submit"] { + float: left; + width: 20%; + border-left: none; + padding: 0.25em; + box-sizing: border-box; +} + + +img { + border: 0; + max-width: 100%; +} + +/* -- search page ----------------------------------------------------------- */ + +ul.search { + margin: 10px 0 0 20px; + padding: 0; +} + +ul.search li { + padding: 5px 0 5px 20px; + background-image: url(file.png); + background-repeat: no-repeat; + background-position: 0 7px; +} + +ul.search li a { + font-weight: bold; +} + +ul.search li p.context { + color: #888; + margin: 2px 0 0 30px; + text-align: left; +} + +ul.keywordmatches li.goodmatch a { + font-weight: bold; +} + +/* -- index page ------------------------------------------------------------ */ + +table.contentstable { + width: 90%; + margin-left: auto; + margin-right: auto; +} + +table.contentstable p.biglink { + line-height: 150%; +} + +a.biglink { + font-size: 1.3em; +} + +span.linkdescr { + font-style: italic; + padding-top: 5px; + font-size: 90%; +} + +/* -- general index --------------------------------------------------------- */ + +table.indextable { + width: 100%; +} + +table.indextable td { + text-align: left; + vertical-align: top; +} + +table.indextable ul { + margin-top: 0; + margin-bottom: 0; + list-style-type: none; +} + +table.indextable > tbody > tr > td > ul { + padding-left: 0em; +} + +table.indextable tr.pcap { + height: 10px; +} + +table.indextable tr.cap { + margin-top: 10px; + background-color: #f2f2f2; +} + +img.toggler { + margin-right: 3px; + margin-top: 3px; + cursor: pointer; +} + +div.modindex-jumpbox { + border-top: 1px solid #ddd; + border-bottom: 1px solid #ddd; + margin: 1em 0 1em 0; + padding: 0.4em; +} + +div.genindex-jumpbox { + border-top: 1px solid #ddd; + border-bottom: 1px solid #ddd; + margin: 1em 0 1em 0; + padding: 0.4em; +} + +/* -- domain module index --------------------------------------------------- */ + +table.modindextable td { + padding: 2px; + border-collapse: collapse; +} + +/* -- general body styles --------------------------------------------------- */ + +div.body { + min-width: 360px; + max-width: 800px; +} + +div.body p, div.body dd, div.body li, div.body blockquote { + -moz-hyphens: auto; + -ms-hyphens: auto; + -webkit-hyphens: auto; + hyphens: auto; +} + +a.headerlink { + visibility: hidden; +} + +a:visited { + color: #551A8B; +} + +h1:hover > a.headerlink, +h2:hover > a.headerlink, +h3:hover > a.headerlink, +h4:hover > a.headerlink, +h5:hover > a.headerlink, +h6:hover > a.headerlink, +dt:hover > a.headerlink, +caption:hover > a.headerlink, +p.caption:hover > a.headerlink, +div.code-block-caption:hover > a.headerlink { + visibility: visible; +} + +div.body p.caption { + text-align: inherit; +} + +div.body td { + text-align: left; +} + +.first { + margin-top: 0 !important; +} + +p.rubric { + margin-top: 30px; + font-weight: bold; +} + +img.align-left, figure.align-left, .figure.align-left, object.align-left { + clear: left; + float: left; + margin-right: 1em; +} + +img.align-right, figure.align-right, .figure.align-right, object.align-right { + clear: right; + float: right; + margin-left: 1em; +} + +img.align-center, 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+ padding: 7px; + margin: 10px 0 10px 0; +} + +p.topic-title { + font-size: 1.1em; + font-weight: bold; + margin-top: 10px; +} + +/* -- admonitions ----------------------------------------------------------- */ + +div.admonition { + margin-top: 10px; + margin-bottom: 10px; + padding: 7px; +} + +div.admonition dt { + font-weight: bold; +} + +p.admonition-title { + margin: 0px 10px 5px 0px; + font-weight: bold; +} + +div.body p.centered { + text-align: center; + margin-top: 25px; +} + +/* -- content of sidebars/topics/admonitions -------------------------------- */ + +div.sidebar > :last-child, +aside.sidebar > :last-child, +nav.contents > :last-child, +aside.topic > :last-child, +div.topic > :last-child, +div.admonition > :last-child { + margin-bottom: 0; +} + +div.sidebar::after, +aside.sidebar::after, +nav.contents::after, +aside.topic::after, +div.topic::after, +div.admonition::after, +blockquote::after { + display: block; + content: ''; + clear: both; +} + +/* -- tables 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font-weight: bold } /* Literal.String.Escape */ +.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */ +.highlight .si { color: #A45A77; font-weight: bold } /* Literal.String.Interpol */ +.highlight .sx { color: #008000 } /* Literal.String.Other */ +.highlight .sr { color: #A45A77 } /* Literal.String.Regex */ +.highlight .s1 { color: #BA2121 } /* Literal.String.Single */ +.highlight .ss { color: #19177C } /* Literal.String.Symbol */ +.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */ +.highlight .fm { color: #0000FF } /* Name.Function.Magic */ +.highlight .vc { color: #19177C } /* Name.Variable.Class */ +.highlight .vg { color: #19177C } /* Name.Variable.Global */ +.highlight .vi { color: #19177C } /* Name.Variable.Instance */ +.highlight .vm { color: #19177C } /* Name.Variable.Magic */ +.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */ \ No newline at end of file diff --git a/2.12.0a2/_static/searchtools.js b/2.12.0a2/_static/searchtools.js new file mode 100644 index 00000000..b08d58c9 --- /dev/null +++ b/2.12.0a2/_static/searchtools.js @@ -0,0 +1,620 @@ +/* + * searchtools.js + * ~~~~~~~~~~~~~~~~ + * + * Sphinx JavaScript utilities for the full-text search. + * + * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS. + * :license: BSD, see LICENSE for details. + * + */ +"use strict"; + +/** + * Simple result scoring code. + */ +if (typeof Scorer === "undefined") { + var Scorer = { + // Implement the following function to further tweak the score for each result + // The function takes a result array [docname, title, anchor, descr, score, filename] + // and returns the new score. + /* + score: result => { + const [docname, title, anchor, descr, score, filename] = result + return score + }, + */ + + // query matches the full name of an object + objNameMatch: 11, + // or matches in the last dotted part of the object name + objPartialMatch: 6, + // Additive scores depending on the priority of the object + objPrio: { + 0: 15, // used to be importantResults + 1: 5, // used to be objectResults + 2: -5, // used to be unimportantResults + }, + // Used when the priority is not in the mapping. + objPrioDefault: 0, + + // query found in title + title: 15, + partialTitle: 7, + // query found in terms + term: 5, + partialTerm: 2, + }; +} + +const _removeChildren = (element) => { + while (element && element.lastChild) element.removeChild(element.lastChild); +}; + +/** + * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping + */ +const _escapeRegExp = (string) => + string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms, highlightTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + const contentRoot = document.documentElement.dataset.content_root; + + const [docName, title, anchor, descr, score, _filename] = item; + + let listItem = document.createElement("li"); + let requestUrl; + let linkUrl; + if (docBuilder === "dirhtml") { + // dirhtml builder + let dirname = docName + "/"; + if (dirname.match(/\/index\/$/)) + dirname = dirname.substring(0, dirname.length - 6); + else if (dirname === "index/") dirname = ""; + requestUrl = contentRoot + dirname; + linkUrl = requestUrl; + } else { + // normal html builders + requestUrl = contentRoot + docName + docFileSuffix; + linkUrl = docName + docLinkSuffix; + } + let linkEl = listItem.appendChild(document.createElement("a")); + linkEl.href = linkUrl + anchor; + linkEl.dataset.score = score; + linkEl.innerHTML = title; + if (descr) { + listItem.appendChild(document.createElement("span")).innerHTML = + " (" + descr + ")"; + // highlight search terms in the description + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + } + else if (showSearchSummary) + fetch(requestUrl) + .then((responseData) => responseData.text()) + .then((data) => { + if (data) + listItem.appendChild( + Search.makeSearchSummary(data, searchTerms, anchor) + ); + // highlight search terms in the summary + if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js + highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted")); + }); + Search.output.appendChild(listItem); +}; +const _finishSearch = (resultCount) => { + Search.stopPulse(); + Search.title.innerText = _("Search Results"); + if (!resultCount) + Search.status.innerText = Documentation.gettext( + "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories." + ); + else + Search.status.innerText = _( + "Search finished, found ${resultCount} page(s) matching the search query." + ).replace('${resultCount}', resultCount); +}; +const _displayNextItem = ( + results, + resultCount, + searchTerms, + highlightTerms, +) => { + // results left, load the summary and display it + // this is intended to be dynamic (don't sub resultsCount) + if (results.length) { + _displayItem(results.pop(), searchTerms, highlightTerms); + setTimeout( + () => _displayNextItem(results, resultCount, searchTerms, highlightTerms), + 5 + ); + } + // search finished, update title and status message + else _finishSearch(resultCount); +}; +// Helper function used by query() to order search results. +// Each input is an array of [docname, title, anchor, descr, score, filename]. +// Order the results by score (in opposite order of appearance, since the +// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically. +const _orderResultsByScoreThenName = (a, b) => { + const leftScore = a[4]; + const rightScore = b[4]; + if (leftScore === rightScore) { + // same score: sort alphabetically + const leftTitle = a[1].toLowerCase(); + const rightTitle = b[1].toLowerCase(); + if (leftTitle === rightTitle) return 0; + return leftTitle > rightTitle ? -1 : 1; // inverted is intentional + } + return leftScore > rightScore ? 1 : -1; +}; + +/** + * Default splitQuery function. Can be overridden in ``sphinx.search`` with a + * custom function per language. + * + * The regular expression works by splitting the string on consecutive characters + * that are not Unicode letters, numbers, underscores, or emoji characters. + * This is the same as ``\W+`` in Python, preserving the surrogate pair area. + */ +if (typeof splitQuery === "undefined") { + var splitQuery = (query) => query + .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu) + .filter(term => term) // remove remaining empty strings +} + +/** + * Search Module + */ +const Search = { + _index: null, + _queued_query: null, + _pulse_status: -1, + + htmlToText: (htmlString, anchor) => { + const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html'); + for (const removalQuery of [".headerlink", "script", "style"]) { + htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() }); + } + if (anchor) { + const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`); + if (anchorContent) return anchorContent.textContent; + + console.warn( + `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.` + ); + } + + // if anchor not specified or not found, fall back to main content + const docContent = htmlElement.querySelector('[role="main"]'); + if (docContent) return docContent.textContent; + + console.warn( + "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template." + ); + return ""; + }, + + init: () => { + const query = new URLSearchParams(window.location.search).get("q"); + document + .querySelectorAll('input[name="q"]') + .forEach((el) => (el.value = query)); + if (query) Search.performSearch(query); + }, + + loadIndex: (url) => + (document.body.appendChild(document.createElement("script")).src = url), + + setIndex: (index) => { + Search._index = index; + if (Search._queued_query !== null) { + const query = Search._queued_query; + Search._queued_query = null; + Search.query(query); + } + }, + + hasIndex: () => Search._index !== null, + + deferQuery: (query) => (Search._queued_query = query), + + stopPulse: () => (Search._pulse_status = -1), + + startPulse: () => { + if (Search._pulse_status >= 0) return; + + const pulse = () => { + Search._pulse_status = (Search._pulse_status + 1) % 4; + Search.dots.innerText = ".".repeat(Search._pulse_status); + if (Search._pulse_status >= 0) window.setTimeout(pulse, 500); + }; + pulse(); + }, + + /** + * perform a search for something (or wait until index is loaded) + */ + performSearch: (query) => { + // create the required interface elements + const searchText = document.createElement("h2"); + searchText.textContent = _("Searching"); + const searchSummary = document.createElement("p"); + searchSummary.classList.add("search-summary"); + searchSummary.innerText = ""; + const searchList = document.createElement("ul"); + searchList.classList.add("search"); + + const out = document.getElementById("search-results"); + Search.title = out.appendChild(searchText); + Search.dots = Search.title.appendChild(document.createElement("span")); + Search.status = out.appendChild(searchSummary); + Search.output = out.appendChild(searchList); + + const searchProgress = document.getElementById("search-progress"); + // Some themes don't use the search progress node + if (searchProgress) { + searchProgress.innerText = _("Preparing search..."); + } + Search.startPulse(); + + // index already loaded, the browser was quick! + if (Search.hasIndex()) Search.query(query); + else Search.deferQuery(query); + }, + + _parseQuery: (query) => { + // stem the search terms and add them to the correct list + const stemmer = new Stemmer(); + const searchTerms = new Set(); + const excludedTerms = new Set(); + const highlightTerms = new Set(); + const objectTerms = new Set(splitQuery(query.toLowerCase().trim())); + splitQuery(query.trim()).forEach((queryTerm) => { + const queryTermLower = queryTerm.toLowerCase(); + + // maybe skip this "word" + // stopwords array is from language_data.js + if ( + stopwords.indexOf(queryTermLower) !== -1 || + queryTerm.match(/^\d+$/) + ) + return; + + // stem the word + let word = stemmer.stemWord(queryTermLower); + // select the correct list + if (word[0] === "-") excludedTerms.add(word.substr(1)); + else { + searchTerms.add(word); + highlightTerms.add(queryTermLower); + } + }); + + if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js + localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" ")) + } + + // console.debug("SEARCH: searching for:"); + // console.info("required: ", [...searchTerms]); + // console.info("excluded: ", [...excludedTerms]); + + return [query, searchTerms, excludedTerms, highlightTerms, objectTerms]; + }, + + /** + * execute search (requires search index to be loaded) + */ + _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + const allTitles = Search._index.alltitles; + const indexEntries = Search._index.indexentries; + + // Collect multiple result groups to be sorted separately and then ordered. + // Each is an array of [docname, title, anchor, descr, score, filename]. + const normalResults = []; + const nonMainIndexResults = []; + + _removeChildren(document.getElementById("search-progress")); + + const queryLower = query.toLowerCase().trim(); + for (const [title, foundTitles] of Object.entries(allTitles)) { + if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) { + for (const [file, id] of foundTitles) { + const score = Math.round(Scorer.title * queryLower.length / title.length); + const boost = titles[file] === title ? 1 : 0; // add a boost for document titles + normalResults.push([ + docNames[file], + titles[file] !== title ? `${titles[file]} > ${title}` : title, + id !== null ? "#" + id : "", + null, + score + boost, + filenames[file], + ]); + } + } + } + + // search for explicit entries in index directives + for (const [entry, foundEntries] of Object.entries(indexEntries)) { + if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) { + for (const [file, id, isMain] of foundEntries) { + const score = Math.round(100 * queryLower.length / entry.length); + const result = [ + docNames[file], + titles[file], + id ? "#" + id : "", + null, + score, + filenames[file], + ]; + if (isMain) { + normalResults.push(result); + } else { + nonMainIndexResults.push(result); + } + } + } + } + + // lookup as object + objectTerms.forEach((term) => + normalResults.push(...Search.performObjectSearch(term, objectTerms)) + ); + + // lookup as search terms in fulltext + normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms)); + + // let the scorer override scores with a custom scoring function + if (Scorer.score) { + normalResults.forEach((item) => (item[4] = Scorer.score(item))); + nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item))); + } + + // Sort each group of results by score and then alphabetically by name. + normalResults.sort(_orderResultsByScoreThenName); + nonMainIndexResults.sort(_orderResultsByScoreThenName); + + // Combine the result groups in (reverse) order. + // Non-main index entries are typically arbitrary cross-references, + // so display them after other results. + let results = [...nonMainIndexResults, ...normalResults]; + + // remove duplicate search results + // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept + let seen = new Set(); + results = results.reverse().reduce((acc, result) => { + let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(','); + if (!seen.has(resultStr)) { + acc.push(result); + seen.add(resultStr); + } + return acc; + }, []); + + return results.reverse(); + }, + + query: (query) => { + const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query); + const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms); + + // for debugging + //Search.lastresults = results.slice(); // a copy + // console.info("search results:", Search.lastresults); + + // print the results + _displayNextItem(results, results.length, searchTerms, highlightTerms); + }, + + /** + * search for object names + */ + performObjectSearch: (object, objectTerms) => { + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const objects = Search._index.objects; + const objNames = Search._index.objnames; + const titles = Search._index.titles; + + const results = []; + + const objectSearchCallback = (prefix, match) => { + const name = match[4] + const fullname = (prefix ? prefix + "." : "") + name; + const fullnameLower = fullname.toLowerCase(); + if (fullnameLower.indexOf(object) < 0) return; + + let score = 0; + const parts = fullnameLower.split("."); + + // check for different match types: exact matches of full name or + // "last name" (i.e. last dotted part) + if (fullnameLower === object || parts.slice(-1)[0] === object) + score += Scorer.objNameMatch; + else if (parts.slice(-1)[0].indexOf(object) > -1) + score += Scorer.objPartialMatch; // matches in last name + + const objName = objNames[match[1]][2]; + const title = titles[match[0]]; + + // If more than one term searched for, we require other words to be + // found in the name/title/description + const otherTerms = new Set(objectTerms); + otherTerms.delete(object); + if (otherTerms.size > 0) { + const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase(); + if ( + [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0) + ) + return; + } + + let anchor = match[3]; + if (anchor === "") anchor = fullname; + else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname; + + const descr = objName + _(", in ") + title; + + // add custom score for some objects according to scorer + if (Scorer.objPrio.hasOwnProperty(match[2])) + score += Scorer.objPrio[match[2]]; + else score += Scorer.objPrioDefault; + + results.push([ + docNames[match[0]], + fullname, + "#" + anchor, + descr, + score, + filenames[match[0]], + ]); + }; + Object.keys(objects).forEach((prefix) => + objects[prefix].forEach((array) => + objectSearchCallback(prefix, array) + ) + ); + return results; + }, + + /** + * search for full-text terms in the index + */ + performTermsSearch: (searchTerms, excludedTerms) => { + // prepare search + const terms = Search._index.terms; + const titleTerms = Search._index.titleterms; + const filenames = Search._index.filenames; + const docNames = Search._index.docnames; + const titles = Search._index.titles; + + const scoreMap = new Map(); + const fileMap = new Map(); + + // perform the search on the required terms + searchTerms.forEach((word) => { + const files = []; + const arr = [ + { files: terms[word], score: Scorer.term }, + { files: titleTerms[word], score: Scorer.title }, + ]; + // add support for partial matches + if (word.length > 2) { + const escapedWord = _escapeRegExp(word); + if (!terms.hasOwnProperty(word)) { + Object.keys(terms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: terms[term], score: Scorer.partialTerm }); + }); + } + if (!titleTerms.hasOwnProperty(word)) { + Object.keys(titleTerms).forEach((term) => { + if (term.match(escapedWord)) + arr.push({ files: titleTerms[term], score: Scorer.partialTitle }); + }); + } + } + + // no match but word was a required one + if (arr.every((record) => record.files === undefined)) return; + + // found search word in contents + arr.forEach((record) => { + if (record.files === undefined) return; + + let recordFiles = record.files; + if (recordFiles.length === undefined) recordFiles = [recordFiles]; + files.push(...recordFiles); + + // set score for the word in each file + recordFiles.forEach((file) => { + if (!scoreMap.has(file)) scoreMap.set(file, {}); + scoreMap.get(file)[word] = record.score; + }); + }); + + // create the mapping + files.forEach((file) => { + if (!fileMap.has(file)) fileMap.set(file, [word]); + else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word); + }); + }); + + // now check if the files don't contain excluded terms + const results = []; + for (const [file, wordList] of fileMap) { + // check if all requirements are matched + + // as search terms with length < 3 are discarded + const filteredTermCount = [...searchTerms].filter( + (term) => term.length > 2 + ).length; + if ( + wordList.length !== searchTerms.size && + wordList.length !== filteredTermCount + ) + continue; + + // ensure that none of the excluded terms is in the search result + if ( + [...excludedTerms].some( + (term) => + terms[term] === file || + titleTerms[term] === file || + (terms[term] || []).includes(file) || + (titleTerms[term] || []).includes(file) + ) + ) + break; + + // select one (max) score for the file. + const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w])); + // add result to the result list + results.push([ + docNames[file], + titles[file], + "", + null, + score, + filenames[file], + ]); + } + return results; + }, + + /** + * helper function to return a node containing the + * search summary for a given text. keywords is a list + * of stemmed words. + */ + makeSearchSummary: (htmlText, keywords, anchor) => { + const text = Search.htmlToText(htmlText, anchor); + if (text === "") return null; + + const textLower = text.toLowerCase(); + const actualStartPosition = [...keywords] + .map((k) => textLower.indexOf(k.toLowerCase())) + .filter((i) => i > -1) + .slice(-1)[0]; + const startWithContext = Math.max(actualStartPosition - 120, 0); + + const top = startWithContext === 0 ? "" : "..."; + const tail = startWithContext + 240 < text.length ? "..." : ""; + + let summary = document.createElement("p"); + summary.classList.add("context"); + summary.textContent = top + text.substr(startWithContext, 240).trim() + tail; + + return summary; + }, +}; + +_ready(Search.init); diff --git a/2.12.0a2/_static/sphinx_highlight.js b/2.12.0a2/_static/sphinx_highlight.js new file mode 100644 index 00000000..8a96c69a --- /dev/null +++ b/2.12.0a2/_static/sphinx_highlight.js @@ -0,0 +1,154 @@ +/* Highlighting utilities for Sphinx HTML documentation. */ +"use strict"; + +const SPHINX_HIGHLIGHT_ENABLED = true + +/** + * highlight a given string on a node by wrapping it in + * span elements with the given class name. + */ +const _highlight = (node, addItems, text, className) => { + if (node.nodeType === Node.TEXT_NODE) { + const val = node.nodeValue; + const parent = node.parentNode; + const pos = val.toLowerCase().indexOf(text); + if ( + pos >= 0 && + !parent.classList.contains(className) && + !parent.classList.contains("nohighlight") + ) { + let span; + + const closestNode = parent.closest("body, svg, foreignObject"); + const isInSVG = closestNode && closestNode.matches("svg"); + if (isInSVG) { + span = document.createElementNS("http://www.w3.org/2000/svg", "tspan"); + } else { + span = document.createElement("span"); + span.classList.add(className); + } + + span.appendChild(document.createTextNode(val.substr(pos, text.length))); + const rest = document.createTextNode(val.substr(pos + text.length)); + parent.insertBefore( + span, + parent.insertBefore( + rest, + node.nextSibling + ) + ); + node.nodeValue = val.substr(0, pos); + /* There may be more occurrences of search term in this node. So call this + * function recursively on the remaining fragment. + */ + _highlight(rest, addItems, text, className); + + if (isInSVG) { + const rect = document.createElementNS( + "http://www.w3.org/2000/svg", + "rect" + ); + const bbox = parent.getBBox(); + rect.x.baseVal.value = bbox.x; + rect.y.baseVal.value = bbox.y; + rect.width.baseVal.value = bbox.width; + rect.height.baseVal.value = bbox.height; + rect.setAttribute("class", className); + addItems.push({ parent: parent, target: rect }); + } + } + } else if (node.matches && !node.matches("button, select, textarea")) { + node.childNodes.forEach((el) => _highlight(el, addItems, text, className)); + } +}; +const _highlightText = (thisNode, text, className) => { + let addItems = []; + _highlight(thisNode, addItems, text, className); + addItems.forEach((obj) => + obj.parent.insertAdjacentElement("beforebegin", obj.target) + ); +}; + +/** + * Small JavaScript module for the documentation. + */ +const SphinxHighlight = { + + /** + * highlight the search words provided in localstorage in the text + */ + highlightSearchWords: () => { + if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight + + // get and clear terms from localstorage + const url = new URL(window.location); + const highlight = + localStorage.getItem("sphinx_highlight_terms") + || url.searchParams.get("highlight") + || ""; + localStorage.removeItem("sphinx_highlight_terms") + url.searchParams.delete("highlight"); + window.history.replaceState({}, "", url); + + // get individual terms from highlight string + const terms = highlight.toLowerCase().split(/\s+/).filter(x => x); + if (terms.length === 0) return; // nothing to do + + // There should never be more than one element matching "div.body" + const divBody = document.querySelectorAll("div.body"); + const body = divBody.length ? divBody[0] : document.querySelector("body"); + window.setTimeout(() => { + terms.forEach((term) => _highlightText(body, term, "highlighted")); + }, 10); + + const searchBox = document.getElementById("searchbox"); + if (searchBox === null) return; + searchBox.appendChild( + document + .createRange() + .createContextualFragment( + '

' + + '' + + _("Hide Search Matches") + + "

" + ) + ); + }, + + /** + * helper function to hide the search marks again + */ + hideSearchWords: () => { + document + .querySelectorAll("#searchbox .highlight-link") + .forEach((el) => el.remove()); + document + .querySelectorAll("span.highlighted") + .forEach((el) => el.classList.remove("highlighted")); + localStorage.removeItem("sphinx_highlight_terms") + }, + + initEscapeListener: () => { + // only install a listener if it is really needed + if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) return; + + document.addEventListener("keydown", (event) => { + // bail for input elements + if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return; + // bail with special keys + if (event.shiftKey || event.altKey || event.ctrlKey || event.metaKey) return; + if (DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS && (event.key === "Escape")) { + SphinxHighlight.hideSearchWords(); + event.preventDefault(); + } + }); + }, +}; + +_ready(() => { + /* Do not call highlightSearchWords() when we are on the search page. + * It will highlight words from the *previous* search query. + */ + if (typeof Search === "undefined") SphinxHighlight.highlightSearchWords(); + SphinxHighlight.initEscapeListener(); +}); diff --git a/2.12.0a2/genindex.html b/2.12.0a2/genindex.html new file mode 100644 index 00000000..86811307 --- /dev/null +++ b/2.12.0a2/genindex.html @@ -0,0 +1,373 @@ + + + + + + Index — l2ss-py 2.12.0a2 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Index
    • +
  • +
    • +
+
+
+
+
+ + +

Index

+ +
+ _ + | A + | B + | C + | D + | F + | G + | H + | I + | L + | M + | O + | P + | R + | S + | T + | W + +
+

_

+ + +
+ +

A

+ + +
+ +

B

+ + +
+ +

C

+ + + +
+ +

D

+ + +
+ +

F

+ + +
+ +

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+ + + +
+ +

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+ + +
+ +

I

+ + + +
+ +

L

+ + +
+ +

M

+ + +
+ +

O

+ + + +
+ +

P

+ + + +
+ +

R

+ + + +
+ +

S

+ + + +
+ +

T

+ + + +
+ +

W

+ + +
+ + + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/2.12.0a2/index.html b/2.12.0a2/index.html new file mode 100644 index 00000000..96da5dae --- /dev/null +++ b/2.12.0a2/index.html @@ -0,0 +1,1144 @@ + + + + + + + Reference Guide for l2ss-py — l2ss-py 2.12.0a2 documentation + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Reference Guide for l2ss-py

+
+
+
+
+

l2ss-py

+Coverage +


 +develop: +.. image:: https://github.com/podaac/l2ss-py/actions/workflows/build-pipeline.yml/badge.svg?branch=develop&event=push

+
+
+


 +main: +.. image:: https://github.com/podaac/l2ss-py/actions/workflows/build-pipeline.yml/badge.svg?branch=main&event=push

+
+
+

Harmony service for subsetting L2 data. l2ss-py supports:

+
    +

  • Spatial subsetting

    +
      +

    • Bounding box

      • +

    • Shapefile subsetting

      • +

    • GeoJSON subsetting

      • +
    +
    • +

  • Temporal subsetting

    • +

  • Variable subsetting

    • +
+

If you would like to contribute to l2ss-py, refer to the contribution document.

+
+

Initial setup, with poetry

+
    +

  1. Follow the instructions for installing poetry here.

    2. +

  2. Install l2ss-py, with its dependencies, by running the following from the repository directory:

    3. +
+
poetry install
+
+
+

**Note:** l2ss-py can be installed as above and run without any dependency on harmony. +However, to additionally test the harmony adapter layer, +extra dependencies can be installed with poetry install -E harmony.

+
+
+

How to test l2ss-py locally

+
+

Unit tests

+

There are comprehensive unit tests for l2ss-py. The tests can be run as follows:

+
poetry run pytest -m "not aws and not integration" tests/
+
+
+

You can generate coverage reports as follows:

+
poetry run pytest --junitxml=build/reports/pytest.xml --cov=podaac/ --cov-report=html -m "not aws and not integration" tests/
+
+
+

**Note:** The majority of the tests execute core functionality of l2ss-py without ever interacting with the harmony python modules. +The test_subset_harmony tests, however, are explicitly for testing the harmony adapter layer +and do require the harmony optional dependencies be installed, +as described above with the -E harmony argument.

+
+
+

l2ss-py script

+

You can run l2ss-py on a single granule without using Harmony. In order +to run this, the l2ss-py package must be installed in your current +Python interpreter

+
$ l2ss-py --help
+usage: run_subsetter.py [-h] [--bbox BBOX BBOX BBOX BBOX]
+                        [--variables VARIABLES [VARIABLES ...]]
+                        [--min-time MIN_TIME] [--max-time MAX_TIME] [--cut]
+                        input_file output_file
+
+Run l2ss-py
+
+positional arguments:
+  input_file            File to subset
+  output_file           Output file
+
+optional arguments:
+  -h, --help            show this help message and exit
+  --bbox BBOX BBOX BBOX BBOX
+                        Bounding box in the form min_lon min_lat max_lon
+                        max_lat
+  --variables VARIABLES [VARIABLES ...]
+                        Variables, only include if variable subset is desired.
+                        Should be a space separated list of variable names
+                        e.g. sst wind_dir sst_error ...
+  --min-time MIN_TIME   Min time. Should be ISO-8601 format. Only include if
+                        temporal subset is desired.
+  --max-time MAX_TIME   Max time. Should be ISO-8601 format. Only include if
+                        temporal subset is desired.
+  --cut                 If provided, scanline will be cut
+  --shapefile SHAPEFILE
+                        Path to either shapefile or geojson file used to subset the provided input granule
+
+
+

For example:

+
l2ss-py /path/to/input.nc /path/to/output.nc --bbox -50 -10 50 10 --variables wind_speed wind_dir ice_age time --min-time '2015-07-02T09:00:00' --max-time '2015-07-02T10:00:00' --cut
+
+
+

An addition to providing a bounding box, spatial subsetting can be achieved by passing in a shapefile or a geojson file.

+

```shell script +poetry run l2ss-py /path/to/input.nc /path/to/output.nc –shapefile /path/to/test.shp

+
or
+
+```shell script
+poetry run l2ss-py /path/to/input.nc /path/to/output.nc --shapefile /path/to/test.geojson
+
+
+
+
+

Running Harmony locally

+

In order to fully test l2ss-py with Harmony, you can run Harmony locally. This requires the data exists in UAT Earthdata Cloud.

+
    +

  1. Set up local Harmony instance. Instructions here

    2. +

  2. Add concept ID for your data to services.yml

    3. +

  3. Execute a local Harmony l2ss-py request. For example:

    +
    localhost:3000/YOUR_COLLECTION_ID/ogc-api-coverages/1.0.0/collections/all/coverage/rangeset?format=application%2Fx-netcdf4&subset=lat(-10%3A10)&subset=lon(-10%3A10)&maxResults=2
+
    +
    +
    4. +
+
+
+
+
+

Module Documentation

+
+

subset.py

+

Functions related to subsetting a NetCDF file.

+
+
+
+podaac.subsetter.subset.apply_scale_offset(scale: float, offset: float, value: float) → float[source]
+

Apply scale and offset to the given value

+
+ +
+
+podaac.subsetter.subset.build_temporal_cond(min_time: str, max_time: str, dataset: Dataset, time_var_name: str) → ndarray | bool[source]
+

Build the temporal condition used in the xarray ‘where’ call which +drops data not in the given bounds. If the data in the time var is +of type ‘datetime’, assume this is a normal case where the time var +uses the epoch from the ‘units’ metadata attribute to get epoch. If +the data in the time var is of type ‘timedelta’, the epoch var is +needed to calculate the datetime.

+
+
Parameters:
+
    +

  • min_time (str) – ISO timestamp representing the lower temporal bound

    • +

  • max_time (str) – ISO timestamp representing the upper temporal bound

    • +

  • dataset (xr.Dataset) – Dataset to build the condition off of

    • +

  • time_var_name (str) – Name of the time variable

    • +
+
+
Returns:
+

If temporally subsetted, returns a boolean ND-array the shape +of which matches the dimensions of the coordinate vars. ‘True’ +is essentially a noop.

+
+
Return type:
+

np.array or boolean

+
+
+
+ +
+
+podaac.subsetter.subset.calculate_chunks(dataset: Dataset) → dict[source]
+

For the given dataset, calculate if the size on any dimension is +worth chunking. Any dimension larger than 4000 will be chunked. This +is done to ensure that the variable can fit in memory.

+
+
Parameters:
+

dataset (xarray.Dataset) – The dataset to calculate chunks for.

+
+
Returns:
+

The chunk dictionary, where the key is the dimension and the +value is 4000 or 500 depending on how many dimensions.

+
+
Return type:
+

dict

+
+
+
+ +
+
+podaac.subsetter.subset.compute_coordinate_variable_names(dataset: Dataset) → Tuple[List[str] | str, List[str] | str][source]
+

Given a dataset, determine the coordinate variable from a list +of options

+
+
Parameters:
+

dataset (xr.Dataset) – The dataset to find the coordinate variables for

+
+
Returns:
+

Tuple of strings (or list of strings), where the first element is the lat coordinate +name and the second element is the lon coordinate name

+
+
Return type:
+

tuple, str

+
+
+
+ +
+
+podaac.subsetter.subset.compute_time_variable_name(dataset: Dataset, lat_var: Variable, total_time_vars: list) → str[source]
+

Try to determine the name of the ‘time’ variable. This is done as +follows:

+
    +

  • The variable name contains ‘time’

    • +

  • The variable dimensions match the dimensions of the given lat var

    • +

  • The variable that hasn’t already been found

    • +
+
+
Parameters:
+
    +

  • dataset (xr.Dataset) – xarray dataset to find time variable from

    • +

  • lat_var (xr.Variable) – Lat variable for this dataset

    • +
+
+
Returns:
+

The name of the variable

+
+
Return type:
+

str

+
+
Raises:
+

ValueError – If the time variable could not be determined

+
+
+
+ +
+
+podaac.subsetter.subset.compute_utc_name(dataset: Dataset) → str | None[source]
+

Get the name of the utc variable if it is there to determine origine time

+
+ +
+
+podaac.subsetter.subset.convert_bbox(bbox: ndarray, dataset: Dataset, lat_var_name: str, lon_var_name: str) → ndarray[source]
+

This function will return a converted bbox which matches the range +of the given input file. This will convert both the latitude and +longitude range. For example, an input dataset can have a valid +longitude range of -180 –> 180 or of 0 –> 360.

+
+
Parameters:
+
    +

  • bbox (np.array) – The bounding box

    • +

  • dataset (xarray.Dataset) – The dataset which is being subset.

    • +

  • lat_var_name (str) – Name of the lat variable in the given dataset

    • +

  • lon_var_name (str) – Name of the lon variable in the given dataset

    • +
+
+
Returns:
+

bbox – The new bbox which matches latitude and longitude ranges of the +input file.

+
+
Return type:
+

np.array

+
+
+

Notes

+

Assumption that the provided bounding box is always between +-180 –> 180 for longitude and -90, 90 for latitude.

+
+ +
+
+podaac.subsetter.subset.convert_bound(bound: ndarray, coord_max: int, coord_var: DataArray) → ndarray[source]
+

This function will return a converted bound, which matches the +range of the given input file.

+
+
Parameters:
+
    +

  • bound (np.array) – 1-dimensional 2-element numpy array which represent the lower +and upper bounding box on this coordinate, respectively.

    • +

  • coord_max (integer) – The max value which is possible given this coordinate. For +example, the max for longitude is 360.

    • +

  • coord_var (xarray.DataArray) – The xarray variable for some coordinate.

    • +
+
+
Returns:
+

1-dimensional 2-element number array which represents the lower +and upper bounding box on this coordinate and has been converted +based on the valid bounds coordinate range of the dataset.

+
+
Return type:
+

np.array

+
+
+

Notes

+

Assumption that 0 is always on the prime meridian/equator.

+
+ +
+
+podaac.subsetter.subset.datetime_from_mjd(dataset: Dataset, time_var_name: str) → datetime | None[source]
+

Translate the modified julian date from the long name in the time attribute.

+
+
Parameters:
+
    +

  • dataset (xr.Dataset) – Dataset that contains time var

    • +

  • time_var_name (str) – The name of the actual time var (with matching dims to the +coord vars)

    • +
+
+
Returns:
+

the datetime of the modified julian date

+
+
Return type:
+

datetime

+
+
+
+ +
+
+podaac.subsetter.subset.find_matching_coords(dataset: Dataset, match_list: List[str]) → List[str][source]
+

As a backup for finding a coordinate var, look at the ‘coordinates’ +metadata attribute of all data vars in the granule. Return any +coordinate vars that have name matches with the provided +‘match_list’

+
+
Parameters:
+
    +

  • dataset (xr.Dataset) – Dataset to search data variable coordinate metadata attribute

    • +

  • match_list (list (str)) – List of possible matches to search for. For example, +[‘lat’, ‘latitude’] would search for variables in the +‘coordinates’ metadata attribute containing either ‘lat’ +or ‘latitude’

    • +
+
+
Returns:
+

List of matching coordinate variables names

+
+
Return type:
+

list (str)

+
+
+
+ +
+
+podaac.subsetter.subset.get_base_group_names(lats: List[str]) → Tuple[List[str], List[str | int]][source]
+

Latitude groups may be at different depths. This function gets the level +number that makes each latitude group unique from the other latitude names

+
+ +
+
+podaac.subsetter.subset.get_coordinate_variable_names(dataset: Dataset, lat_var_names: list | None = None, lon_var_names: list | None = None, time_var_names: list | None = None)[source]
+

Retrieve coordinate variables for this dataset. If coordinate +variables are provided, use those, Otherwise, attempt to determine +coordinate variables manually.

+
+
Parameters:
+
    +

  • dataset (xr.Dataset) – xarray Dataset used to compute coordinate variables manually. +Only used if lat, lon, or time vars are not provided.

    • +

  • lat_var_names (list) – List of latitude coordinate variables.

    • +

  • lon_var_names (list) – List of longitude coordinate variables.

    • +

  • time_var_names (list) – List of time coordinate variables.

    • +
+
+
Returns:
+

TODO

+
+
Return type:
+

add return type docstring and type hint.

+
+
+
+ +
+
+podaac.subsetter.subset.get_spatial_bounds(dataset: Dataset, lat_var_names: str, lon_var_names: str) → ndarray | None[source]
+

Get the spatial bounds for this dataset. These values are masked +and scaled.

+
+
Parameters:
+
    +

  • dataset (xr.Dataset) – Dataset to retrieve spatial bounds for

    • +

  • lat_var_names (str) – Name of the lat variable

    • +

  • lon_var_names (str) – Name of the lon variable

    • +
+
+
Returns:
+

[[lon min, lon max], [lat min, lat max]]

+
+
Return type:
+

np.array

+
+
+
+ +
+
+podaac.subsetter.subset.get_time_epoch_var(dataset: Dataset, time_var_name: str) → str[source]
+

Get the name of the epoch time var. This is only needed in the case +where there is a single time var (of size 1) that contains the time +epoch used by the actual time var.

+
+
Parameters:
+
    +

  • dataset (xr.Dataset) – Dataset that contains time var

    • +

  • time_var_name (str) – The name of the actual time var (with matching dims to the +coord vars)

    • +
+
+
Returns:
+

The name of the epoch time variable

+
+
Return type:
+

str

+
+
+
+ +
+
+podaac.subsetter.subset.is_360(lon_var: DataArray, scale: float, offset: float) → bool[source]
+

Determine if given dataset is a ‘360’ dataset or not.

+
+
Parameters:
+
    +

  • lon_var (xr.DataArray) – The lon variable from the xarray Dataset

    • +

  • scale (float) – Used to remove scale and offset for easier calculation

    • +

  • offset (float) – Used to remove scale and offset for easier calculation

    • +
+
+
Returns:
+

True if dataset is 360, False if not. Defaults to False.

+
+
Return type:
+

bool

+
+
+
+ +
+
+podaac.subsetter.subset.is_time_mjd(dataset: Dataset, time_var_name: str) → bool[source]
+

Check to see if the time format is a time delta from a modified julian date.

+
+
Parameters:
+
    +

  • dataset (xr.Dataset) – Dataset that contains time var

    • +

  • time_var_name (str) – The name of the actual time var (with matching dims to the +coord vars)

    • +
+
+
Returns:
+

is time delta format in modified julian date

+
+
Return type:
+

boolean

+
+
+
+ +
+
+podaac.subsetter.subset.open_as_nc_dataset(filepath: str) → Tuple[Dataset, bool][source]
+

Open netcdf file, and flatten groups if they exist.

+
+ +
+
+podaac.subsetter.subset.override_decode_cf_datetime() → None[source]
+

WARNING !!! REMOVE AT EARLIEST XARRAY FIX, this is a override to xarray override_decode_cf_datetime function. +xarray has problems decoding time units with format seconds since 2000-1-1 0:0:0 0, this solves by testing +the unit to see if its parsable, if it is use original function, if not format unit into a parsable format.

+

https://github.com/pydata/xarray/issues/7210

+
+ +
+
+podaac.subsetter.subset.remove_scale_offset(value: float, scale: float, offset: float) → float[source]
+

Remove scale and offset from the given value

+
+ +
+
+podaac.subsetter.subset.set_json_history(dataset: Dataset, cut: bool, file_to_subset: str, bbox: ndarray | None = None, shapefile: str | None = None, origin_source=None) → None[source]
+

Set the ‘json_history’ metadata header of the granule to reflect the +current version of the subsetter, as well as the parameters used +to call the subsetter. This will append an json array to the json_history of +the following format:

+
+
Parameters:
+
    +

  • dataset (xarray.Dataset) – The dataset to change the header of

    • +

  • cut (boolean) – True to cut the scanline

    • +

  • file_to_subset (string) – The filepath of the file which was used to subset

    • +

  • bbox (np.ndarray) – The requested bounding box

    • +

  • shapefile (str) – Name of the shapefile to include in the version history

    • +

  • TODO (add docstring and type hint for origin_source parameter.)

    • +
+
+
+
+ +
+
+podaac.subsetter.subset.set_version_history(dataset: Dataset, cut: bool, bbox: ndarray | None = None, shapefile: str | None = None) → None[source]
+

Set the ‘history’ metadata header of the granule to reflect the +current version of the subsetter, as well as the parameters used +to call the subsetter. This will append a line to the history of +the following format:

+

TIMESTAMP podaac.subsetter VERSION (PARAMS)

+
+
Parameters:
+
    +

  • dataset (xarray.Dataset) – The dataset to change the header of

    • +

  • cut (boolean) – True to cut the scanline

    • +

  • bbox (np.ndarray) – The requested bounding box

    • +

  • shapefile (str) – Name of the shapefile to include in the version history

    • +
+
+
+
+ +
+
+podaac.subsetter.subset.subset(file_to_subset: str, bbox: ndarray, output_file: str, variables: List[str] | str | None = (), cut: bool = True, shapefile: str | None = None, min_time: str | None = None, max_time: str | None = None, origin_source: str | None = None, lat_var_names: List[str] = (), lon_var_names: List[str] = (), time_var_names: List[str] = ()) → ndarray | None[source]
+

Subset a given NetCDF file given a bounding box

+
+
Parameters:
+
    +

  • file_to_subset (string) – The location of the file which will be subset

    • +

  • bbox (np.ndarray) – The chosen bounding box. This is a tuple of tuples formatted +as such: ((west, east), (south, north)). The assumption is that +the valid range is ((-180, 180), (-90, 90)). This will be +transformed as appropriate if the actual longitude range is +0-360.

    • +

  • output_file (string) – The file path for the output of the subsetting operation.

    • +

  • variables (list, str, optional) – List of variables to include in the resulting data file. +NOTE: This will remove ALL variables which are not included +in this list, including coordinate variables!

    • +

  • cut (boolean) – True if the scanline should be cut, False if the scanline should +not be cut. Defaults to True.

    • +

  • shapefile (str) – Name of local shapefile used to subset given file.

    • +

  • min_time (str) – ISO timestamp representing the lower bound of the temporal +subset to be performed. If this value is not provided, the +granule will not be subset temporally on the lower bound.

    • +

  • max_time (str) – ISO timestamp representing the upper bound of the temporal +subset to be performed. If this value is not provided, the +granule will not be subset temporally on the upper bound.

    • +

  • origin_source (str) – Original location or filename of data to be used in “derived from” +history element.

    • +

  • lat_var_names (list) – List of variables that represent the latitude coordinate +variables for this granule. This list will only contain more +than one value in the case where there are multiple groups and +different coordinate variables for each group.

    • +

  • lon_var_names (list) – List of variables that represent the longitude coordinate +variables for this granule. This list will only contain more +than one value in the case where there are multiple groups and +different coordinate variables for each group.

    • +

  • time_var_names (list) – List of variables that represent the time coordinate +variables for this granule. This list will only contain more +than one value in the case where there are multiple groups and +different coordinate variables for each group.

    • +
+
+
+
+ +
+
+podaac.subsetter.subset.subset_with_bbox(dataset: Dataset, lat_var_names: list, lon_var_names: list, time_var_names: list, variables: List[str] | None = None, bbox: ndarray | None = None, cut: bool = True, min_time: str | None = None, max_time: str | None = None) → ndarray[source]
+

Subset an xarray Dataset using a spatial bounding box.

+
+
Parameters:
+
    +

  • dataset (xr.Dataset) – Dataset to subset

    • +

  • lat_var_names (list) – Name of the latitude variables in the given dataset

    • +

  • lon_var_names (list) – Name of the longitude variables in the given dataset

    • +

  • time_var_names (list) – Name of the time variables in the given dataset

    • +

  • variables (list[str]) – List of variables to include in the result

    • +

  • bbox (np.array) – Spatial bounding box to subset Dataset with.

    • +

  • cut (bool) – True if scanline should be cut.

    • +

  • min_time (str) – ISO timestamp of min temporal bound

    • +

  • max_time (str) – ISO timestamp of max temporal bound

    • +

  • TODO (add docstring and type hint for variables parameter.)

    • +
+
+
Returns:
+

    +

  • np.array – Spatial bounds of Dataset after subset operation

    • +

  • TODO - fix this docstring type and the type hint to match code (currently returning a list[xr.Dataset])

    • +
+

+
+
+
+ +
+
+podaac.subsetter.subset.subset_with_shapefile(dataset: Dataset, lat_var_name: str, lon_var_name: str, shapefile: str, cut: bool, chunks) → ndarray[source]
+

Subset an xarray Dataset using a shapefile

+
+
Parameters:
+
    +

  • dataset (xr.Dataset) – Dataset to subset

    • +

  • lat_var_name (str) – Name of the latitude variable in the given dataset

    • +

  • lon_var_name (str) – Name of the longitude variable in the given dataset

    • +

  • shapefile (str) – Absolute path to the shapefile used to subset the given dataset

    • +

  • cut (bool) – True if scanline should be cut.

    • +

  • TODO (add docstring and type hint for chunks parameter.)

    • +
+
+
Returns:
+

    +

  • np.array – Spatial bounds of Dataset after shapefile subset operation

    • +

  • TODO - fix this docstring type and the type hint to match code (currently returning a xr.Dataset)

    • +
+

+
+
+
+ +
+
+podaac.subsetter.subset.test_access_sst_dtime_values(datafile)[source]
+

Test accessing values of ‘sst_dtime’ variable in a NetCDF file.

+
+
Parameters:
+

(str) (datafile)

+
+
Returns:
+

access_successful (bool)

+
+
Return type:
+

True if ‘sst_dtime’ values are accessible, False otherwise.

+
+
+
+ +
+
+podaac.subsetter.subset.translate_longitude(geometry)[source]
+

Translates the longitude values of a Shapely geometry from the range [-180, 180) to [0, 360).

+
+
Parameters:
+

geometry (shapely.geometry.base.BaseGeometry) – The input shape geometry to be translated

+
+
Returns:
+

The translated shape geometry

+
+
Return type:
+

geometry

+
+
+
+ +
+
+podaac.subsetter.subset.translate_timestamp(str_timestamp: str) → datetime[source]
+

Translate timestamp to datetime object

+
+
Parameters:
+

str_timestamp (str) – Timestamp string. ISO or RFC

+
+
Returns:
+

Constructed Datetime object

+
+
Return type:
+

datetime

+
+
+
+ +
+

subset_harmony.py

+

Implementation of harmony-service-lib that invokes the Level 2 subsetter.

+
+
+
+class podaac.subsetter.subset_harmony.L2SubsetterService(message, catalog=None, config=None)[source]
+

See https://github.com/nasa/harmony-service-lib-py +for documentation and examples.

+
+
+__init__(message, catalog=None, config=None)[source]
+

Constructs the adapter

+
+
Parameters:
+
    +

  • message (harmony.Message) – The Harmony input which needs acting upon

    • +

  • catalog (pystac.Catalog) – A STAC catalog containing the files on which to act

    • +

  • config (harmony.util.Config) – The configuration values for this runtime environment.

    • +
+
+
+
+ +
+
+cmd(*args) → List[str][source]
+

Logs and then runs command.

+
+
Parameters:
+

run (args Command and args to)

+
+
Return type:
+

Command output

+
+
+
+ +
+
+prepare_output_dir(output_dir: str) → None[source]
+

Deletes (if present) and recreates the given output_dir, ensuring it exists +and is empty

+
+
Parameters:
+

output_dir (string) – the directory to delete and recreate

+
+
+
+ +
+
+process_item(item: Item, source: Source) → Item[source]
+

Performs variable and bounding box subsetting on the input STAC Item’s data, returning +an output STAC item

+
+
Parameters:
+
    +

  • item (pystac.Item) – the item that should be subset

    • +

  • source (harmony.message.Source) – the input source defining the variables, if any, to subset from the item

    • +
+
+
Returns:
+

a STAC item describing the output of the subsetter

+
+
Return type:
+

pystac.Item

+
+
+
+ +
+ +
+
+podaac.subsetter.subset_harmony.harmony_to_podaac_bbox(bbox: list) → ndarray[source]
+

Convert Harmony bbox (west, south, east, north) +to PO.DAAC bbox ((west, east), (south, north))

+
+
Parameters:
+

bbox (list) – Harmony bbox

+
+
Returns:
+

PO.DAAC bbox

+
+
Return type:
+

np.array

+
+
+
+ +
+
+podaac.subsetter.subset_harmony.main(config: Config | None = None) → None[source]
+

Parse command line arguments and invoke the service to respond to +them.

+
+
Parameters:
+

config (harmony.util.Config)

+
+
Return type:
+

None

+
+
+
+ +
+
+podaac.subsetter.subset_harmony.podaac_to_harmony_bbox(bbox: ndarray) → ndarray | float[source]
+

Convert PO.DAAC bbox ((west, east), (south, north)) +to Harmony bbox (west, south, east, north)

+
+
Parameters:
+

bbox (np.array) – Podaac bbox

+
+
Returns:
+

    +

  • array, int or float – Harmony bbox

    • +

  • TODO - fix this docstring type, type hint, and code to match (code currently returns a list)

    • +
+

+
+
+
+ +

run_subsetter.py

+

This script runs L2SS-Py on the given granule.

+
+
+podaac.subsetter.run_subsetter.main() → None[source]
+

Entry point to the script

+
+ +
+
+podaac.subsetter.run_subsetter.parse_args(args: list) → tuple[source]
+

Parse args for this script.

+
+
Returns:
+

input_file, output_file, bbox, variables, min_time, max_time

+
+
Return type:
+

tuple

+
+
+
+ +
+
+podaac.subsetter.run_subsetter.run_subsetter(args: list) → None[source]
+

Parse arguments and run subsetter on the specified input file

+
+ +
+

xarray_enhancements.py

+

Functions which improve upon existing xarray functionality, optimized +for this specific use-case.

+
+
+
+podaac.subsetter.xarray_enhancements.cast_type(var: DataArray, var_type: str) → DataArray[source]
+

Type cast a variable into a var type.

+
+
Parameters:
+
    +

  • var (xr.DataArray) – The dataarray to be type casted.

    • +

  • var_type (string) – New type the variable will be type casted to.

    • +
+
+
Returns:
+

The newly type casted variable.

+
+
Return type:
+

xr.DataArray

+
+
+
+ +
+
+podaac.subsetter.xarray_enhancements.copy_empty_dataset(dataset: Dataset) → Dataset[source]
+
+
Copy a dataset into a new, empty dataset. This dataset should:
    +

  • Contain the same structure as the input dataset (only include +requested variables, if variable subset)

    • +

  • Contain the same global metadata as the input dataset

    • +

  • Contain a history field which describes this subset operation.

    • +
+
+
+
+
Parameters:
+

dataset (xarray.Dataset) – The dataset to copy into a empty dataset.

+
+
Returns:
+

The new dataset which has no data.

+
+
Return type:
+

xarray.Dataset

+
+
+
+ +
+
+podaac.subsetter.xarray_enhancements.get_indexers_from_1d(cond: Dataset) → dict[source]
+

Get indexers from a dataset with 1 dimension.

+
+
Parameters:
+

cond (xarray.Dataset) – Contains the result of the initial lat lon condition.

+
+
Returns:
+

Indexer dictionary for the provided condition.

+
+
Return type:
+

dict

+
+
+
+ +
+
+podaac.subsetter.xarray_enhancements.get_indexers_from_nd(cond: Dataset, cut: bool) → dict[source]
+

Get indexers from a dataset with more than one dimension.

+
+
Parameters:
+
    +

  • cond (xarray.Dataset) – Contains the result of the initial lat lon condition.

    • +

  • cut (bool) – True if the scanline should be cut.

    • +
+
+
Returns:
+

Indexer dictionary for the provided condition.

+
+
Return type:
+

dict

+
+
+
+ +
+
+podaac.subsetter.xarray_enhancements.get_variables_with_indexers(dataset, indexers)[source]
+

returns a list of variables with bounding box dimensions and variables that +don’t have bounding box dimensions

+
+ +
+
+podaac.subsetter.xarray_enhancements.where(dataset: Dataset, cond: Dataset | DataArray, cut: bool) → Dataset[source]
+

Return a dataset which meets the given condition.

+

This is a modification of the existing xarray ‘where’ function. +https://github.com/pydata/xarray/blob/master/xarray/core/common.py#L999

+
+
Parameters:
+
    +

  • dataset (xarray.Dataset) – The dataset to filter and return.

    • +

  • cond (DataArray or Dataset with boolean dtype) – Locations at which to preserve this object’s values.

    • +

  • cut (boolean) – True if the scanline should be cut, False if the scanline should +not be cut.

    • +
+
+
Returns:
+

The filtered Dataset

+
+
Return type:
+

xarray.Dataset

+
+
+

Notes

+

The cond variable contains a boolean mask of valid data indices. +However in that mask, True represents valid data and False +represents invalid data.

+
+ +
+
+

Indices and tables

+ +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/2.12.0a2/objects.inv b/2.12.0a2/objects.inv new file mode 100644 index 00000000..b562a49c Binary files /dev/null and b/2.12.0a2/objects.inv differ diff --git a/2.12.0a2/py-modindex.html b/2.12.0a2/py-modindex.html new file mode 100644 index 00000000..fe9176ed --- /dev/null +++ b/2.12.0a2/py-modindex.html @@ -0,0 +1,142 @@ + + + + + + Python Module Index — l2ss-py 2.12.0a2 documentation + + + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
    • +
  • Python Module Index
    • +
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    • +
+
+
+
+
+ + +

Python Module Index

+ +
+ p +
+ + + + + + + + + + + + + + + + + + + +
 
+ p
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+ + +
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+ +
+
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+
+ + + + \ No newline at end of file diff --git a/2.12.0a2/search.html b/2.12.0a2/search.html new file mode 100644 index 00000000..e5ea152a --- /dev/null +++ b/2.12.0a2/search.html @@ -0,0 +1,122 @@ + + + + + + Search — l2ss-py 2.12.0a2 documentation + + + + + + + + + + + + + + + + + + + + +
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