def order_vars(self):
"""
Return the order variables object.
This instance is generated when data is ordered from the NSIDC.
See Also
--------
variables.Variables
Examples
--------
>>> reg_a = ipx.Query('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) # doctest: +SKIP
>>> reg_a.earthdata_login(user_id,user_email) # doctest: +SKIP
Earthdata Login password: ········
>>> reg_a.order_vars # doctest: +SKIP
<icepyx.core.variables.Variables at [location]>
"""
if not hasattr(self, "_order_vars"):
if self._source == "order":
# DevGoal: check for active session here
if hasattr(self, "_cust_options"):
self._order_vars = Variables(
self._source,
session=self._session,
product=self.product,
avail=self._cust_options["variables"],
)
else:
self._order_vars = Variables(
self._source,
session=self._session,
product=self.product,
version=self._version,
)
# I think this is where property setters come in, and one should be used here? Right now order_vars.avail is only filled in
# if _cust_options exists when the class is initialized, but not if _cust_options is filled in prior to another call to order_vars
# if self._order_vars.avail == None and hasattr(self, '_cust_options'):
# print('got into the loop')
# self._order_vars.avail = self._cust_options['variables']
return self._order_vars
def vars(self):
"""
Return the variables object associated with the data being read in.
This instance is generated from the source file or first file in a list of input files (when source is a directory).
See Also
--------
variables.Variables
Examples
--------
>>> reader = ipx.Read(path_root, "ATL06", pattern) # doctest: +SKIP
>>> reader.vars # doctest: +SKIP
<icepyx.core.variables.Variables at [location]>
"""
if not hasattr(self, "_read_vars"):
self._read_vars = Variables("file",
path=self._filelist[0],
product=self._prod)
return self._read_vars
def file_vars(self):
"""
Return the file variables object.
This instance is generated when files are used to create the data object (not yet implemented).
See Also
--------
variables.Variables
Examples
--------
>>> reg_a = ipx.Query('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) # doctest: +SKIP
>>> reg_a.earthdata_login(user_id,user_email) # doctest: +SKIP
Earthdata Login password: ········
>>> reg_a.file_vars # doctest: +SKIP
<icepyx.core.variables.Variables at [location]>
"""
if not hasattr(self, "_file_vars"):
if self._source == "file":
self._file_vars = Variables(self._source, product=self.product)
return self._file_vars
def file_vars(self):
"""
Return the file variables object.
This instance is generated when files are used to create the data object (not yet implemented).
See Also
--------
variables.Variables
Examples
--------
>>> reg_a = icepyx.icesat2data.Icesat2Data('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28'])
>>> reg_a.earthdata_login(user_id,user_email)
Earthdata Login password: ········
>>> reg_a.file_vars
<icepyx.core.variables.Variables at [location]>
"""
if not hasattr(self, '_file_vars'):
if self._source == 'file':
self._file_vars = Variables(self._source, dataset=self.dataset)
return self._file_vars
def show_custom_options(self, dictview=False):
"""
Display customization/subsetting options available for this product.
Parameters
----------
dictview : boolean, default False
Show the variable portion of the custom options list as a dictionary with key:value
pairs representing variable:paths-to-variable rather than as a long list of full
variable paths.
Examples
--------
>>> reg_a = ipx.Query('ATL06',[-55, 68, -48, 71],['2019-02-20','2019-02-28']) # doctest: +SKIP
>>> reg_a.earthdata_login(user_id,user_email) # doctest: +SKIP
Earthdata Login password: ········
>>> reg_a.show_custom_options(dictview=True) # doctest: +SKIP
Subsetting options
[{'id': 'ICESAT2',
'maxGransAsyncRequest': '2000',
'maxGransSyncRequest': '100',
'spatialSubsetting': 'true',
'spatialSubsettingShapefile': 'true',
'temporalSubsetting': 'true',
'type': 'both'}]
Data File Formats (Reformatting Options)
['TABULAR_ASCII', 'NetCDF4-CF', 'Shapefile', 'NetCDF-3']
Reprojection Options
[]
Data File (Reformatting) Options Supporting Reprojection
['TABULAR_ASCII', 'NetCDF4-CF', 'Shapefile', 'NetCDF-3', 'No reformatting']
Data File (Reformatting) Options NOT Supporting Reprojection
[]
Data Variables (also Subsettable)
['ancillary_data/atlas_sdp_gps_epoch',
'ancillary_data/control',
'ancillary_data/data_end_utc',
.
.
.
'quality_assessment/gt3r/signal_selection_source_fraction_3']
"""
headers = [
"Subsetting options",
"Data File Formats (Reformatting Options)",
"Reprojection Options",
"Data File (Reformatting) Options Supporting Reprojection",
"Data File (Reformatting) Options NOT Supporting Reprojection",
"Data Variables (also Subsettable)",
]
keys = [
"options",
"fileformats",
"reprojectionONLY",
"formatreproj",
"noproj",
"variables",
]
try:
all(key in self._cust_options.keys() for key in keys)
except AttributeError or KeyError:
self._cust_options = is2ref._get_custom_options(
self._session, self.product, self._version
)
for h, k in zip(headers, keys):
print(h)
if k == "variables" and dictview:
vgrp, paths = Variables.parse_var_list(self._cust_options[k])
pprint.pprint(vgrp)
else:
pprint.pprint(self._cust_options[k])
def _build_single_file_dataset(self, file, groups_list):
"""
Create a single xarray dataset with all of the wanted variables/groups from the wanted var list for a single data file/url.
Parameters
----------
file : str
Full path to ICESat-2 data file.
Currently tested for locally downloaded files; untested but hopefully works for s3 stored cloud files.
groups_list : list of strings
List of full paths to data variables within the file.
e.g. ['orbit_info/sc_orient', 'gt1l/land_ice_segments/h_li', 'gt1l/land_ice_segments/latitude', 'gt1l/land_ice_segments/longitude']
Returns
-------
Xarray Dataset
"""
file_product = self._read_single_var(
file, "/").attrs["identifier_product_type"]
assert (
file_product == self._prod
), "Your product specification does not match the product specification within your files."
# I think the below method might NOT read the file into memory as the above might?
# import h5py
# with h5py.File(filepath,'r') as h5pt:
# prod_id = h5pt.attrs["identifier_product_type"]
# DEVNOTE: does not actually apply wanted variable list, and has not been tested for merging multiple files into one ds
# if a gridded product
if self._prod in [
"ATL14",
"ATL15",
"ATL16",
"ATL17",
"ATL18",
"ATL19",
"ATL20",
"ATL21",
]:
is2ds = xr.open_dataset(file)
else:
is2ds = self._build_dataset_template(file)
# returns the wanted groups as a single list of full group path strings
wanted_dict, wanted_groups = Variables.parse_var_list(groups_list,
tiered=False)
wanted_groups_set = set(wanted_groups)
# orbit_info is used automatically as the first group path so the info is available for the rest of the groups
wanted_groups_set.remove("orbit_info")
# returns the wanted groups as a list of lists with group path string elements separated
_, wanted_groups_tiered = Variables.parse_var_list(groups_list,
tiered=True)
for grp_path in ["orbit_info"] + list(wanted_groups_set):
ds = self._read_single_var(file, grp_path)
is2ds = Read._add_var_to_ds(is2ds, ds, grp_path,
wanted_groups_tiered, wanted_dict)
return is2ds
