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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() +
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+ + + + \ No newline at end of file diff --git a/2.14.0a0+a270171/_modules/podaac/subsetter/subset.html b/2.14.0a0+a270171/_modules/podaac/subsetter/subset.html new file mode 100644 index 00000000..ee117de9 --- /dev/null +++ b/2.14.0a0+a270171/_modules/podaac/subsetter/subset.html @@ -0,0 +1,1563 @@ + + + + + + + + podaac.subsetter.subset — l2ss-py 2.14.0a0+a270171 documentation + + + + + + + + + + + + + + + +
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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 + + return None
+ + + +
+[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 + ) + + if not time_var_names and (min_time or max_time): + raise ValueError('Could not determine time variable') + + 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/2.14.0a0+a270171/_modules/podaac/subsetter/subset_harmony.html b/2.14.0a0+a270171/_modules/podaac/subsetter/subset_harmony.html new file mode 100644 index 00000000..357ae038 --- /dev/null +++ b/2.14.0a0+a270171/_modules/podaac/subsetter/subset_harmony.html @@ -0,0 +1,418 @@ + + + + + + + + podaac.subsetter.subset_harmony — l2ss-py 2.14.0a0+a270171 documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
  • + + +
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+
+
+
+
+ +

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
+import traceback
+from typing import List, Union
+
+import pystac
+from pystac import Asset
+
+import harmony_service_lib
+import numpy as np
+from harmony_service_lib import BaseHarmonyAdapter
+from harmony_service_lib.util import download, stage, generate_output_filename, bbox_to_geometry
+from harmony_service_lib.exceptions import HarmonyException
+
+from podaac.subsetter import subset
+from podaac.subsetter.subset import SERVICE_NAME
+
+DATA_DIRECTORY_ENV = "DATA_DIRECTORY"
+
+
+
+[docs] +class L2SSException(HarmonyException): + """Base class for exceptions in the Harmony GDAL Adapter.""" + +
+[docs] + def __init__(self, original_exception): + # Extract the last traceback entry (most recent call) for the error location + tb = traceback.extract_tb(original_exception.__traceback__)[-1] + + # Get the error details: file, line, function, and message + filename = tb.filename + lineno = tb.lineno + funcname = tb.name + error_msg = str(original_exception) + + # Format the error message to be more readable + readable_message = (f"Error in file '{filename}', line {lineno}, in function '{funcname}': " + f"{error_msg}") + + super().__init__(readable_message, 'podaac/l2-subsetter')
+
+ + + +
+[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_service_lib.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 + except Exception as ex: + raise L2SSException(ex) from ex + 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_service_lib.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_service_lib.setup_cli(parser) + args = parser.parse_args() + if harmony_service_lib.is_harmony_cli(args): + harmony_service_lib.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/2.14.0a0+a270171/_modules/podaac/subsetter/xarray_enhancements.html b/2.14.0a0+a270171/_modules/podaac/subsetter/xarray_enhancements.html new file mode 100644 index 00000000..43758475 --- /dev/null +++ b/2.14.0a0+a270171/_modules/podaac/subsetter/xarray_enhancements.html @@ -0,0 +1,417 @@ + + + + + + + + podaac.subsetter.xarray_enhancements — l2ss-py 2.14.0a0+a270171 documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
  • + + +
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  • +
+
+
+
+
+ +

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 = dim_grid = False + ndim = cond.values.squeeze().ndim + + # Determine axes and flags + if ndim == 2: + x_axis, y_axis = 1, 0 + else: + if 'xtrack' in cond.dims and 'atrack' in cond.dims: + x_axis, y_axis = cond.dims.index('xtrack'), cond.dims.index('atrack') + transpose = True + elif 'xdim_grid' in cond.dims and 'ydim_grid' in cond.dims: + x_axis, y_axis = cond.dims.index('xdim_grid'), cond.dims.index('ydim_grid') + dim_grid = x_axis == 1 and y_axis == 0 + else: + x_axis, y_axis = 2, 1 + + # Compute rows and columns + squeezed_values = cond.values.squeeze() + rows = np.any(squeezed_values, axis=x_axis) + cols = np.any(squeezed_values, axis=y_axis) if cut else np.ones(len(squeezed_values[0])) + + # Log information about subsetted area + if np.all(rows) and np.all(cols): + logging.info("Subsetted area equal to the original granule.") + if not np.any(rows) or not np.any(cols): + logging.info("No data within the given bounding box.") + + # Determine dimensions and clean them up + cond_dims = list(cond.dims) + cond_shape = list(cond.shape) + cond_dims = [dim for dim, size in zip(cond_dims, cond_shape) if size > 1] + + # Adjust for 3D data + if rows.ndim > 1: + if transpose: + rows, cols = rows.transpose()[0], cols.transpose()[0] + elif not dim_grid: + rows, cols = rows[0], cols[0] + + # Generate indexers + indexers = { + cond_dims[y_axis]: np.where(rows)[0], + cond_dims[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/2.14.0a0+a270171/_sources/index.rst.txt b/2.14.0a0+a270171/_sources/index.rst.txt new file mode 100644 index 00000000..04262c66 --- /dev/null +++ b/2.14.0a0+a270171/_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/2.14.0a0+a270171/_static/_sphinx_javascript_frameworks_compat.js b/2.14.0a0+a270171/_static/_sphinx_javascript_frameworks_compat.js new file mode 100644 index 00000000..81415803 --- /dev/null +++ b/2.14.0a0+a270171/_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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+ document.dispatchEvent(event); + }); +}); \ No newline at end of file diff --git a/2.14.0a0+a270171/_static/language_data.js b/2.14.0a0+a270171/_static/language_data.js new file mode 100644 index 00000000..c7fe6c6f --- /dev/null +++ b/2.14.0a0+a270171/_static/language_data.js @@ -0,0 +1,192 @@ +/* + * This script contains the language-specific data used by searchtools.js, + * namely the list of stopwords, stemmer, scorer and splitter. + */ + +var stopwords = ["a", "and", "are", "as", "at", "be", "but", "by", "for", "if", "in", "into", "is", "it", "near", "no", "not", "of", "on", "or", "such", "that", "the", "their", "then", "there", "these", "they", "this", "to", "was", "will", "with"]; + + +/* Non-minified version is copied as a separate JS file, if available */ + +/** + * Porter Stemmer + */ +var Stemmer = function() { + + var step2list = { + ational: 'ate', + tional: 'tion', + enci: 'ence', + anci: 'ance', + izer: 'ize', + bli: 'ble', + alli: 'al', + entli: 'ent', + eli: 'e', + ousli: 'ous', + ization: 'ize', + ation: 'ate', + ator: 'ate', + alism: 'al', + iveness: 'ive', + fulness: 'ful', + ousness: 'ous', + aliti: 'al', + iviti: 'ive', + biliti: 'ble', + logi: 'log' + }; 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font-style: italic } /* Literal.String.Doc */ +.highlight .s2 { color: #BA2121 } /* Literal.String.Double */ +.highlight .se { color: #AA5D1F; 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/2.14.0a0+a270171/_static/searchtools.js b/2.14.0a0+a270171/_static/searchtools.js new file mode 100644 index 00000000..2c774d17 --- /dev/null +++ b/2.14.0a0+a270171/_static/searchtools.js @@ -0,0 +1,632 @@ +/* + * Sphinx JavaScript utilities for the full-text search. + */ +"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, kind] = 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, + }; +} + +// Global search result kind enum, used by themes to style search results. +class SearchResultKind { + static get index() { return "index"; } + static get object() { return "object"; } + static get text() { return "text"; } + static get title() { return "title"; } +} + +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, "\\$&"); // $& means the whole matched string + +const _displayItem = (item, searchTerms, highlightTerms) => { + const docBuilder = DOCUMENTATION_OPTIONS.BUILDER; + const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX; + const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX; + const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY; + const contentRoot = document.documentElement.dataset.content_root; + + const [docName, title, anchor, descr, score, _filename, kind] = item; + + let listItem = document.createElement("li"); + // Add a class representing the item's type: + // can be used by a theme's CSS selector for styling + // See SearchResultKind for the class names. + listItem.classList.add(`kind-${kind}`); + 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 = Documentation.ngettext( + "Search finished, found one page matching the search query.", + "Search finished, found ${resultCount} pages matching the search query.", + resultCount, + ).replace('${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, kind]. +// 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.setAttribute("role", "list"); + 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, kind]. + 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], + SearchResultKind.title, + ]); + } + } + } + + // 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], + SearchResultKind.index, + ]; + 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]], + SearchResultKind.object, + ]); + }; + 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], + SearchResultKind.text, + ]); + } + 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/2.14.0a0+a270171/_static/sphinx_highlight.js b/2.14.0a0+a270171/_static/sphinx_highlight.js new file mode 100644 index 00000000..8a96c69a --- /dev/null +++ b/2.14.0a0+a270171/_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. 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+ + +
+ +
+
+
+
    +
  • + +
  • +
  • +
+
+
+
+
+ + +

Index

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

_

+ + +
+ +

A

+ + +
+ +

B

+ + +
+ +

C

+ + + +
+ +

D

+ + +
+ +

F

+ + +
+ +

G

+ + + +
+ +

H

+ + +
+ +

I

+ + + +
+ +

L

+ + + +
+ +

M

+ + +
+ +

O

+ + + +
+ +

P

+ + + +
+ +

R

+ + + +
+ +

S

+ + + +
+ +

T

+ + + +
+ +

W

+ + +
+ + + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/2.14.0a0+a270171/index.html b/2.14.0a0+a270171/index.html new file mode 100644 index 00000000..cde56d78 --- /dev/null +++ b/2.14.0a0+a270171/index.html @@ -0,0 +1,1008 @@ + + + + + + + + + Reference Guide for l2ss-py — l2ss-py 2.14.0a0+a270171 documentation + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ +
+

Reference Guide for l2ss-py

+
+
+
+
+

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.

+
+
+
+exception podaac.subsetter.subset_harmony.L2SSException(original_exception)[source]
+

Base class for exceptions in the Harmony GDAL Adapter.

+
+
+__init__(original_exception)[source]
+
+ +
+ +
+
+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_service_lib.Message) – The Harmony input which needs acting upon

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

  • +
  • config (harmony_service_lib.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/2.14.0a0+a270171/objects.inv b/2.14.0a0+a270171/objects.inv new file mode 100644 index 00000000..c93c5133 Binary files /dev/null and b/2.14.0a0+a270171/objects.inv differ diff --git a/2.14.0a0+a270171/py-modindex.html b/2.14.0a0+a270171/py-modindex.html new file mode 100644 index 00000000..c5022791 --- /dev/null +++ b/2.14.0a0+a270171/py-modindex.html @@ -0,0 +1,137 @@ + + + + + + + + Python Module Index — l2ss-py 2.14.0a0+a270171 documentation + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
  • + +
  • +
  • +
+
+
+
+
+ + +

Python Module Index

+ +
+ p +
+ + + + + + + + + + + + + + + + + + + +
 
+ p
+ podaac +
    + podaac.subsetter.run_subsetter +
    + podaac.subsetter.subset +
    + podaac.subsetter.subset_harmony +
    + podaac.subsetter.xarray_enhancements +
+ + +
+
+ +
+
+
+
+ + + + \ No newline at end of file diff --git a/2.14.0a0+a270171/search.html b/2.14.0a0+a270171/search.html new file mode 100644 index 00000000..fef33a2c --- /dev/null +++ b/2.14.0a0+a270171/search.html @@ -0,0 +1,117 @@ + + + + + + + + Search — l2ss-py 2.14.0a0+a270171 documentation + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+
    +
  • + +
  • +
  • +
+
+
+
+
+ + + + +
+ +
+ +
+
+ +
+
+
+
+ + + + + + + + + \ No newline at end of file diff --git a/2.14.0a0+a270171/searchindex.js b/2.14.0a0+a270171/searchindex.js new file mode 100644 index 00000000..ed152025 --- /dev/null +++ b/2.14.0a0+a270171/searchindex.js @@ -0,0 +1 @@ +Search.setIndex({"alltitles": {"Indices and tables": [[0, "indices-and-tables"]], "Module Documentation": [[0, "module-podaac.subsetter.subset"]], "Reference Guide for l2ss-py": [[0, null]], "subset.py": [[0, "subset-py"]], "subset_harmony.py": [[0, "subset-harmony-py"]], "xarray_enhancements.py": [[0, "xarray-enhancements-py"]]}, "docnames": ["index"], "envversion": {"sphinx": 64, "sphinx.domains.c": 3, "sphinx.domains.changeset": 1, "sphinx.domains.citation": 1, "sphinx.domains.cpp": 9, "sphinx.domains.index": 1, "sphinx.domains.javascript": 3, "sphinx.domains.math": 2, "sphinx.domains.python": 4, "sphinx.domains.rst": 2, "sphinx.domains.std": 2, "sphinx.ext.viewcode": 1}, "filenames": ["index.rst"], "indexentries": {"__init__() 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