def _create_gridded_product(self,
product_name,
grid_data,
base_product=None,
**kwargs):
from polar2grid.core.containers import GriddedProduct
if base_product is None:
base_product = kwargs
else:
base_product = base_product.copy(as_dict=True)
base_product.update(kwargs)
base_product["product_name"] = product_name
base_product["grid_data"] = grid_data
if base_product.get("grid_definition") is None:
msg = "No grid definition provided to base composite on (use `base_product` or `grid_definition`)"
LOG.error(msg)
raise ValueError(msg)
return GriddedProduct(**base_product)
def _remap_scene_nearest(self,
swath_scene,
grid_def,
share_dynamic_grids=True,
share_remap_mask=True,
**kwargs):
# TODO: Make methods more flexible than just a function call
gridded_scene = GriddedScene()
grid_name = grid_def["grid_name"]
# Group products together that shared the same geolocation
product_groups = defaultdict(list)
for product_name, swath_product in swath_scene.items():
swath_def = swath_product["swath_definition"]
geo_id = swath_def["swath_name"]
product_groups[geo_id].append(product_name)
grid_coverage = kwargs.get("grid_coverage", GRID_COVERAGE)
orig_grid_def = grid_def
for geo_id, product_names in product_groups.items():
pp_names = "\n\t".join(product_names)
LOG.debug(
"Running ll2cr on the geolocation data for the following products:\n\t%s",
pp_names)
LOG.debug("Swath name: %s", geo_id)
# TODO: Move into it's own function if this gets complicated
# TODO: Add some multiprocessing
try:
swath_def = swath_scene[product_names[0]]["swath_definition"]
if not share_dynamic_grids:
grid_def = orig_grid_def.copy()
cols_fn, rows_fn = self.run_ll2cr(swath_def, grid_def)
except StandardError:
LOG.error("Remapping error")
if self.exit_on_error:
raise
continue
LOG.debug(
"Running nearest neighbor for the following products:\n\t%s",
"\n\t".join(product_names))
edge_res = swath_def.get("limb_resolution", None)
if kwargs.get("distance_upper_bound", None) is None:
if edge_res is not None:
if grid_def.is_latlong:
distance_upper_bound = (edge_res /
2) / grid_def.cell_width_meters
else:
distance_upper_bound = (edge_res /
2) / grid_def["cell_width"]
LOG.debug("Distance upper bound dynamically set to %f",
distance_upper_bound)
else:
distance_upper_bound = 3.0
kwargs["distance_upper_bound"] = distance_upper_bound
try:
grid_x, grid_y = numpy.mgrid[:grid_def["height"], :
grid_def["width"]]
# we need flattened versions of these
shape = (swath_def["swath_rows"] *
swath_def["swath_columns"], )
cols_array = numpy.memmap(cols_fn,
shape=shape,
dtype=swath_def["data_type"])
rows_array = numpy.memmap(rows_fn,
shape=shape,
dtype=swath_def["data_type"])
good_mask = ~mask_helper(cols_array, swath_def["fill_value"])
if share_remap_mask:
for product_name in product_names:
LOG.debug(
"Combining data masks before building KDTree for nearest neighbor: %s",
product_name)
good_mask &= ~swath_scene[product_name].get_data_mask(
).ravel()
x = _ndim_coords_from_arrays(
(cols_array[good_mask], rows_array[good_mask]))
xi = _ndim_coords_from_arrays((grid_y, grid_x))
dist, i = cKDTree(x).query(
xi, distance_upper_bound=kwargs["distance_upper_bound"])
except StandardError:
LOG.debug("Remapping exception: ", exc_info=True)
LOG.error("Remapping error")
if self.exit_on_error:
self._clear_ll2cr_cache()
raise
continue
product_filepaths = swath_scene.get_data_filepaths(product_names)
output_filepaths = self._add_prefix("grid_%s_" % (grid_name, ),
*product_filepaths)
# Prepare the products
fill_value = numpy.nan
for product_name, output_fn in izip(product_names,
output_filepaths):
LOG.debug(
"Running nearest neighbor on '%s' with search distance %f",
product_name, kwargs["distance_upper_bound"])
if os.path.isfile(output_fn):
if not self.overwrite_existing:
LOG.error(
"Intermediate remapping file already exists: %s" %
(output_fn, ))
raise RuntimeError(
"Intermediate remapping file already exists: %s" %
(output_fn, ))
else:
LOG.warning(
"Intermediate remapping file already exists, will overwrite: %s",
output_fn)
try:
image_array = swath_scene[product_name].get_data_array(
).ravel()
values = numpy.append(image_array[good_mask],
image_array.dtype.type(fill_value))
output_array = values[i]
output_array.tofile(output_fn)
# Give the gridded product ownership of the remapped data
swath_product = swath_scene[product_name]
gridded_product = GriddedProduct()
gridded_product.from_swath_product(swath_product)
gridded_product["grid_definition"] = grid_def
gridded_product["fill_value"] = fill_value
gridded_product["grid_data"] = output_fn
# Check grid coverage
valid_points = numpy.count_nonzero(
~gridded_product.get_data_mask())
grid_covered_ratio = valid_points / float(
grid_def["width"] * grid_def["height"])
grid_covered = grid_covered_ratio > grid_coverage
if not grid_covered:
msg = "Nearest neighbor resampling only found %f%% of the grid covered (need %f%%) for %s" % (
grid_covered_ratio * 100, grid_coverage * 100,
product_name)
LOG.warning(msg)
continue
LOG.debug(
"Nearest neighbor resampling found %f%% of the grid covered for %s"
% (grid_covered_ratio * 100, product_name))
gridded_scene[product_name] = gridded_product
# hopefully force garbage collection
del output_array
except StandardError:
LOG.debug("Remapping exception: ", exc_info=True)
LOG.error("Remapping error")
self._safe_remove(output_fn)
if self.exit_on_error:
self._clear_ll2cr_cache()
raise
continue
LOG.debug("Done running nearest neighbor on '%s'",
product_name)
# Remove ll2cr files now that we are done with them
self._clear_ll2cr_cache()
if not gridded_scene:
raise RuntimeError(
"Nearest neighbor resampling could not remap any of the data to grid '%s'"
% (grid_name, ))
return gridded_scene
def _remap_scene_nearest(self, swath_scene, grid_def, share_dynamic_grids=True, share_remap_mask=True, **kwargs):
# TODO: Make methods more flexible than just a function call
gridded_scene = GriddedScene()
grid_name = grid_def["grid_name"]
# Group products together that shared the same geolocation
product_groups = defaultdict(list)
for product_name, swath_product in swath_scene.items():
swath_def = swath_product["swath_definition"]
geo_id = swath_def["swath_name"]
product_groups[geo_id].append(product_name)
grid_coverage = kwargs.get("grid_coverage", GRID_COVERAGE)
orig_grid_def = grid_def
for geo_id, product_names in product_groups.items():
pp_names = "\n\t".join(product_names)
LOG.debug("Running ll2cr on the geolocation data for the following products:\n\t%s", pp_names)
LOG.debug("Swath name: %s", geo_id)
# TODO: Move into it's own function if this gets complicated
# TODO: Add some multiprocessing
try:
swath_def = swath_scene[product_names[0]]["swath_definition"]
if not share_dynamic_grids:
grid_def = orig_grid_def.copy()
cols_fn, rows_fn = self.run_ll2cr(swath_def, grid_def)
except StandardError:
LOG.error("Remapping error")
if self.exit_on_error:
raise
continue
LOG.debug("Running nearest neighbor for the following products:\n\t%s", "\n\t".join(product_names))
edge_res = swath_def.get("limb_resolution", None)
if kwargs.get("distance_upper_bound", None) is None:
if edge_res is not None:
if grid_def.is_latlong:
distance_upper_bound = (edge_res / 2) / grid_def.cell_width_meters
else:
distance_upper_bound = (edge_res / 2) / grid_def["cell_width"]
LOG.debug("Distance upper bound dynamically set to %f", distance_upper_bound)
else:
distance_upper_bound = 3.0
kwargs["distance_upper_bound"] = distance_upper_bound
try:
grid_x, grid_y = numpy.mgrid[:grid_def["height"], :grid_def["width"]]
# we need flattened versions of these
shape = (swath_def["swath_rows"] * swath_def["swath_columns"],)
cols_array = numpy.memmap(cols_fn, shape=shape, dtype=swath_def["data_type"])
rows_array = numpy.memmap(rows_fn, shape=shape, dtype=swath_def["data_type"])
good_mask = ~mask_helper(cols_array, swath_def["fill_value"])
if share_remap_mask:
for product_name in product_names:
LOG.debug("Combining data masks before building KDTree for nearest neighbor: %s", product_name)
good_mask &= ~swath_scene[product_name].get_data_mask().ravel()
x = _ndim_coords_from_arrays((cols_array[good_mask], rows_array[good_mask]))
xi = _ndim_coords_from_arrays((grid_y, grid_x))
dist, i = cKDTree(x).query(xi, distance_upper_bound=kwargs["distance_upper_bound"])
except StandardError:
LOG.debug("Remapping exception: ", exc_info=True)
LOG.error("Remapping error")
if self.exit_on_error:
self._clear_ll2cr_cache()
raise
continue
product_filepaths = swath_scene.get_data_filepaths(product_names)
output_filepaths = self._add_prefix("grid_%s_" % (grid_name,), *product_filepaths)
# Prepare the products
fill_value = numpy.nan
for product_name, output_fn in izip(product_names, output_filepaths):
LOG.debug("Running nearest neighbor on '%s' with search distance %f", product_name, kwargs["distance_upper_bound"])
if os.path.isfile(output_fn):
if not self.overwrite_existing:
LOG.error("Intermediate remapping file already exists: %s" % (output_fn,))
raise RuntimeError("Intermediate remapping file already exists: %s" % (output_fn,))
else:
LOG.warning("Intermediate remapping file already exists, will overwrite: %s", output_fn)
try:
image_array = swath_scene[product_name].get_data_array().ravel()
values = numpy.append(image_array[good_mask], image_array.dtype.type(fill_value))
output_array = values[i]
output_array.tofile(output_fn)
# Give the gridded product ownership of the remapped data
swath_product = swath_scene[product_name]
gridded_product = GriddedProduct()
gridded_product.from_swath_product(swath_product)
gridded_product["grid_definition"] = grid_def
gridded_product["fill_value"] = fill_value
gridded_product["grid_data"] = output_fn
# Check grid coverage
valid_points = numpy.count_nonzero(~gridded_product.get_data_mask())
grid_covered_ratio = valid_points / float(grid_def["width"] * grid_def["height"])
grid_covered = grid_covered_ratio > grid_coverage
if not grid_covered:
msg = "Nearest neighbor resampling only found %f%% of the grid covered (need %f%%) for %s" % (grid_covered_ratio * 100, grid_coverage * 100, product_name)
LOG.warning(msg)
continue
LOG.debug("Nearest neighbor resampling found %f%% of the grid covered for %s" % (grid_covered_ratio * 100, product_name))
gridded_scene[product_name] = gridded_product
# hopefully force garbage collection
del output_array
except StandardError:
LOG.debug("Remapping exception: ", exc_info=True)
LOG.error("Remapping error")
self._safe_remove(output_fn)
if self.exit_on_error:
self._clear_ll2cr_cache()
raise
continue
LOG.debug("Done running nearest neighbor on '%s'", product_name)
# Remove ll2cr files now that we are done with them
self._clear_ll2cr_cache()
if not gridded_scene:
raise RuntimeError("Nearest neighbor resampling could not remap any of the data to grid '%s'" % (grid_name,))
return gridded_scene
def _remap_scene_ewa(self,
swath_scene,
grid_def,
share_dynamic_grids=True,
**kwargs):
# TODO: Make methods more flexible than just a function call
gridded_scene = GriddedScene()
grid_name = grid_def["grid_name"]
# Group products together that shared the same geolocation
product_groups = defaultdict(list)
for product_name, swath_product in swath_scene.items():
swath_def = swath_product["swath_definition"]
is_cat = swath_product.get('flag_meanings') is not None
geo_id = swath_def["swath_name"]
product_groups[(is_cat, geo_id)].append(product_name)
# keep a copy of the original grid definition
# if a shared grid definition isn't used then
# we start from the original
orig_grid_def = grid_def
for (is_cat, geo_id), product_names in product_groups.items():
try:
LOG.debug(
"Running ll2cr on the geolocation data for the following products:\n\t%s",
"\n\t".join(sorted(product_names)))
swath_def = swath_scene[product_names[0]]["swath_definition"]
if not share_dynamic_grids:
grid_def = orig_grid_def.copy()
cols_fn, rows_fn = self.run_ll2cr(swath_def,
grid_def,
swath_usage=kwargs.get(
"swath_usage",
SWATH_USAGE))
except StandardError:
LOG.error("Remapping error")
if self.exit_on_error:
raise
continue
# Run fornav for all of the products at once
LOG.debug("Running fornav for the following products:\n\t%s",
"\n\t".join(sorted(product_names)))
# XXX: May have to do something smarter if there are float products and integer products together (is_category property on SwathProduct?)
product_filepaths = list(
swath_scene.get_data_filepaths(product_names))
fornav_filepaths = self._add_prefix("grid_%s_" % (grid_name, ),
*product_filepaths)
for fp in fornav_filepaths:
if os.path.isfile(fp):
if not self.overwrite_existing:
LOG.error(
"Intermediate remapping file already exists: %s" %
(fp, ))
raise RuntimeError(
"Intermediate remapping file already exists: %s" %
(fp, ))
else:
LOG.warning(
"Intermediate remapping file already exists, will overwrite: %s",
fp)
rows_per_scan = swath_def.get("rows_per_scan", 0)
if rows_per_scan < 2:
LOG.warning(
"Data has less than 2 rows per scan, this is not optimal for the EWA resampling algorithm. All rows will be used as one scan"
)
rows_per_scan = swath_def['swath_rows']
edge_res = swath_def.get("limb_resolution", None)
fornav_D = kwargs.get("fornav_D", None)
if fornav_D is None:
if edge_res is not None:
if grid_def.is_latlong:
fornav_D = (edge_res / 2) / grid_def.cell_width_meters
else:
fornav_D = (edge_res / 2) / grid_def["cell_width"]
LOG.debug("Fornav 'D' option dynamically set to %f",
fornav_D)
else:
fornav_D = 10.0
mwm = kwargs.get('maximum_weight_mode', False)
if is_cat and not mwm:
LOG.debug(
"Turning on maximum weight mode in EWA resampling for category products"
)
mwm = True
try:
# fornav.ms2gt_fornav(
# len(product_filepaths),
# swath_def["swath_columns"],
# swath_def["swath_rows"]/rows_per_scan,
# rows_per_scan,
# cols_fn,
# rows_fn,
# product_filepaths,
# grid_def["width"],
# grid_def["height"],
# fornav_filepaths,
# swath_data_type_1="f4",
# swath_fill_1=swath_scene.get_fill_value(product_names),
# grid_fill_1=numpy.nan,
# weight_delta_max=fornav_D,
# weight_distance_max=kwargs.get("fornav_d", None),
# maximum_weight_mode=kwargs.get("maximum_weight_mode", None),
# start_scan=(0, 0),
# )
cols_array = numpy.memmap(cols_fn,
dtype=numpy.float32,
mode='r',
shape=(swath_def["swath_rows"],
swath_def["swath_columns"]))
rows_array = numpy.memmap(rows_fn,
dtype=numpy.float32,
mode='r',
shape=(swath_def["swath_rows"],
swath_def["swath_columns"]))
# Assumed that all share the same fill value and data type
input_dtype = [
swath_scene[pn]["data_type"] for pn in product_names
]
input_fill = [
swath_scene[pn]["fill_value"] for pn in product_names
]
LOG.debug("Running fornav with D={} and d={}".format(
fornav_D, kwargs.get('fornav_d', 1.0)))
valid_list = fornav.fornav(cols_array,
rows_array,
rows_per_scan,
product_filepaths,
input_dtype=input_dtype,
input_fill=input_fill,
output_arrays=fornav_filepaths,
grid_cols=grid_def["width"],
grid_rows=grid_def["height"],
weight_delta_max=fornav_D,
weight_distance_max=kwargs.get(
"fornav_d", 1.0),
maximum_weight_mode=mwm,
use_group_size=True)
except StandardError:
LOG.debug("Remapping exception: ", exc_info=True)
LOG.error("Remapping error")
self._safe_remove(*fornav_filepaths)
if self.exit_on_error:
self._clear_ll2cr_cache()
raise
continue
# Give the gridded product ownership of the remapped data
for product_name, fornav_fp, valid_points in zip(
product_names, fornav_filepaths, valid_list):
swath_product = swath_scene[product_name]
gridded_product = GriddedProduct()
gridded_product.from_swath_product(swath_product)
gridded_product["grid_definition"] = grid_def
gridded_product["fill_value"] = numpy.nan
gridded_product["grid_data"] = fornav_fp
grid_coverage = kwargs.get("grid_coverage", GRID_COVERAGE)
grid_covered_ratio = valid_points / float(
grid_def["width"] * grid_def["height"])
grid_covered = grid_covered_ratio > grid_coverage
if not grid_covered:
msg = "EWA resampling only found %f%% of the grid covered (need %f%%) for %s" % (
grid_covered_ratio * 100, grid_coverage * 100,
product_name)
LOG.warning(msg)
continue
LOG.debug(
"EWA resampling found %f%% of the grid covered for %s" %
(grid_covered_ratio * 100, product_name))
gridded_scene[product_name] = gridded_product
self._clear_ll2cr_cache()
if not gridded_scene:
self._safe_remove(*fornav_filepaths)
raise RuntimeError(
"EWA resampling could not remap any of the data to grid '%s'" %
(grid_name, ))
return gridded_scene
def _remap_scene_ewa(self, swath_scene, grid_def, share_dynamic_grids=True, **kwargs):
# TODO: Make methods more flexible than just a function call
gridded_scene = GriddedScene()
grid_name = grid_def["grid_name"]
# Group products together that shared the same geolocation
product_groups = defaultdict(list)
for product_name, swath_product in swath_scene.items():
swath_def = swath_product["swath_definition"]
is_cat = swath_product.get('flag_meanings') is not None
geo_id = swath_def["swath_name"]
product_groups[(is_cat, geo_id)].append(product_name)
# keep a copy of the original grid definition
# if a shared grid definition isn't used then
# we start from the original
orig_grid_def = grid_def
for (is_cat, geo_id), product_names in product_groups.items():
try:
LOG.debug("Running ll2cr on the geolocation data for the following products:\n\t%s", "\n\t".join(sorted(product_names)))
swath_def = swath_scene[product_names[0]]["swath_definition"]
if not share_dynamic_grids:
grid_def = orig_grid_def.copy()
cols_fn, rows_fn = self.run_ll2cr(swath_def, grid_def,
swath_usage=kwargs.get("swath_usage", SWATH_USAGE))
except StandardError:
LOG.error("Remapping error")
if self.exit_on_error:
raise
continue
# Run fornav for all of the products at once
LOG.debug("Running fornav for the following products:\n\t%s", "\n\t".join(sorted(product_names)))
# XXX: May have to do something smarter if there are float products and integer products together (is_category property on SwathProduct?)
product_filepaths = list(swath_scene.get_data_filepaths(product_names))
fornav_filepaths = self._add_prefix("grid_%s_" % (grid_name,), *product_filepaths)
for fp in fornav_filepaths:
if os.path.isfile(fp):
if not self.overwrite_existing:
LOG.error("Intermediate remapping file already exists: %s" % (fp,))
raise RuntimeError("Intermediate remapping file already exists: %s" % (fp,))
else:
LOG.warning("Intermediate remapping file already exists, will overwrite: %s", fp)
rows_per_scan = swath_def.get("rows_per_scan", 0)
if rows_per_scan < 2:
LOG.warning("Data has less than 2 rows per scan, this is not optimal for the EWA resampling algorithm. All rows will be used as one scan")
rows_per_scan = swath_def['swath_rows']
edge_res = swath_def.get("limb_resolution", None)
fornav_D = kwargs.get("fornav_D", None)
if fornav_D is None:
if edge_res is not None:
if grid_def.is_latlong:
fornav_D = (edge_res / 2) / grid_def.cell_width_meters
else:
fornav_D = (edge_res / 2) / grid_def["cell_width"]
LOG.debug("Fornav 'D' option dynamically set to %f", fornav_D)
else:
fornav_D = 10.0
mwm = kwargs.get('maximum_weight_mode', False)
if is_cat and not mwm:
LOG.debug("Turning on maximum weight mode in EWA resampling for category products")
mwm = True
try:
# fornav.ms2gt_fornav(
# len(product_filepaths),
# swath_def["swath_columns"],
# swath_def["swath_rows"]/rows_per_scan,
# rows_per_scan,
# cols_fn,
# rows_fn,
# product_filepaths,
# grid_def["width"],
# grid_def["height"],
# fornav_filepaths,
# swath_data_type_1="f4",
# swath_fill_1=swath_scene.get_fill_value(product_names),
# grid_fill_1=numpy.nan,
# weight_delta_max=fornav_D,
# weight_distance_max=kwargs.get("fornav_d", None),
# maximum_weight_mode=kwargs.get("maximum_weight_mode", None),
# start_scan=(0, 0),
# )
cols_array = numpy.memmap(cols_fn, dtype=numpy.float32, mode='r', shape=(swath_def["swath_rows"], swath_def["swath_columns"]))
rows_array = numpy.memmap(rows_fn, dtype=numpy.float32, mode='r', shape=(swath_def["swath_rows"], swath_def["swath_columns"]))
# Assumed that all share the same fill value and data type
input_dtype = [swath_scene[pn]["data_type"] for pn in product_names]
input_fill = [swath_scene[pn]["fill_value"] for pn in product_names]
LOG.debug("Running fornav with D={} and d={}".format(fornav_D, kwargs.get('fornav_d', 1.0)))
valid_list = fornav.fornav(cols_array,
rows_array,
rows_per_scan,
product_filepaths,
input_dtype=input_dtype,
input_fill=input_fill,
output_arrays=fornav_filepaths,
grid_cols=grid_def["width"],
grid_rows=grid_def["height"],
weight_delta_max=fornav_D,
weight_distance_max=kwargs.get("fornav_d", 1.0),
maximum_weight_mode=mwm,
use_group_size=True
)
except StandardError:
LOG.debug("Remapping exception: ", exc_info=True)
LOG.error("Remapping error")
self._safe_remove(*fornav_filepaths)
if self.exit_on_error:
self._clear_ll2cr_cache()
raise
continue
# Give the gridded product ownership of the remapped data
for product_name, fornav_fp, valid_points in zip(product_names, fornav_filepaths, valid_list):
swath_product = swath_scene[product_name]
gridded_product = GriddedProduct()
gridded_product.from_swath_product(swath_product)
gridded_product["grid_definition"] = grid_def
gridded_product["fill_value"] = numpy.nan
gridded_product["grid_data"] = fornav_fp
grid_coverage = kwargs.get("grid_coverage", GRID_COVERAGE)
grid_covered_ratio = valid_points / float(grid_def["width"] * grid_def["height"])
grid_covered = grid_covered_ratio > grid_coverage
if not grid_covered:
msg = "EWA resampling only found %f%% of the grid covered (need %f%%) for %s" % (grid_covered_ratio * 100, grid_coverage * 100, product_name)
LOG.warning(msg)
continue
LOG.debug("EWA resampling found %f%% of the grid covered for %s" % (grid_covered_ratio * 100, product_name))
gridded_scene[product_name] = gridded_product
self._clear_ll2cr_cache()
if not gridded_scene:
self._safe_remove(*fornav_filepaths)
raise RuntimeError("EWA resampling could not remap any of the data to grid '%s'" % (grid_name,))
return gridded_scene
