def _axis_subset(self, crs_axis, nc_file):
"""
Returns an axis subset using the given crs axis in the context of the nc file
:param crs_axis:
:param nc_file:
:return:
"""
user_axis = self._user_axis(self._get_user_axis_by_crs_axis_name(crs_axis.label), nc_file)
high = user_axis.interval.high if user_axis.interval.high else user_axis.interval.low
if not user_axis.irregular:
geo_axis = RegularAxis(crs_axis.label, crs_axis.uom, user_axis.interval.low, high, user_axis.interval.low,
crs_axis)
else:
geo_axis = IrregularAxis(crs_axis.label, crs_axis.uom, user_axis.interval.low, high, user_axis.interval.low,
[0], crs_axis)
if user_axis.type == UserAxisType.DATE:
grid_low = 0
grid_high = 0
else:
grid_low = 0
number_of_geopixels = user_axis.interval.high - user_axis.interval.low
grid_high = int(math.fabs(math.floor(grid_low + number_of_geopixels / user_axis.resolution)))
if grid_high > grid_low:
grid_high -= 1
grid_axis = GridAxis(user_axis.order, crs_axis.label, user_axis.resolution, grid_low, grid_high)
if crs_axis.is_easting():
geo_axis.origin = geo_axis.low + user_axis.resolution / 2
elif crs_axis.is_northing():
geo_axis.origin = geo_axis.high + user_axis.resolution / 2
elif crs_axis.is_future():
geo_axis.origin = stringify(geo_axis.origin)
geo_axis.low = stringify(geo_axis.low)
if geo_axis.high is not None:
geo_axis.high = stringify(geo_axis.high)
user_axis.interval.low = stringify(user_axis.interval.low)
if user_axis.interval.high is not None:
user_axis.interval.high = stringify(user_axis.interval.high)
return AxisSubset(CoverageAxis(geo_axis, grid_axis, user_axis.dataBound),
Interval(user_axis.interval.low, user_axis.interval.high))
def _axis_subset(self, crs_axis, evaluator_slice, resolution=None):
"""
Returns an axis subset using the given crs axis in the context of the gdal file
:param CRSAxis crs_axis: the crs definition of the axis
:param GDALEvaluatorSlice evaluator_slice: the evaluator for GDAL file
:param resolution: Known axis resolution, no need to evaluate sentence expression from ingredient file (e.g: Sentinel2 recipe)
:rtype AxisSubset
"""
user_axis = self._user_axis(
self._get_user_axis_by_crs_axis_name(crs_axis.label),
evaluator_slice)
if resolution is not None:
user_axis.resolution = resolution
high = user_axis.interval.high if user_axis.interval.high is not None else user_axis.interval.low
if user_axis.type == UserAxisType.DATE:
# it must translate datetime string to float by arrow for calculating later
user_axis.interval.low = arrow.get(
user_axis.interval.low).float_timestamp
if user_axis.interval.high is not None:
user_axis.interval.high = arrow.get(
user_axis.interval.high).float_timestamp
if isinstance(user_axis, RegularUserAxis):
geo_axis = RegularAxis(crs_axis.label, crs_axis.uom,
user_axis.interval.low, high,
user_axis.interval.low, crs_axis)
else:
# Irregular axis (coefficients must be number, not datetime string)
if user_axis.type == UserAxisType.DATE:
if crs_axis.is_time_day_axis():
coefficients = self._translate_day_date_direct_position_to_coefficients(
user_axis.interval.low, user_axis.directPositions)
else:
coefficients = self._translate_seconds_date_direct_position_to_coefficients(
user_axis.interval.low, user_axis.directPositions)
else:
coefficients = self._translate_number_direct_position_to_coefficients(
user_axis.interval.low, user_axis.directPositions)
self._update_for_slice_group_size(self.coverage_id, user_axis,
crs_axis, coefficients)
geo_axis = IrregularAxis(crs_axis.label, crs_axis.uom,
user_axis.interval.low, high,
user_axis.interval.low, coefficients,
crs_axis)
if not crs_axis.is_x_axis() and not crs_axis.is_y_axis():
# GDAL model is 2D so on any axis except x/y we expect to have only one value
grid_low = 0
grid_high = None
if user_axis.interval.high is not None:
grid_high = 0
else:
grid_low = 0
number_of_grid_points = decimal.Decimal(str(user_axis.interval.high)) \
- decimal.Decimal(str(user_axis.interval.low))
# number_of_grid_points = (geo_max - geo_min) / resolution
grid_high = grid_low + number_of_grid_points / decimal.Decimal(
user_axis.resolution)
grid_high = HighPixelAjuster.adjust_high(grid_high)
# Negative axis, e.g: Latitude (min <--- max)
if user_axis.resolution < 0:
grid_high = int(abs(math.floor(grid_high)))
else:
# Positive axis, e.g: Longitude (min --> max)
grid_high = int(abs(math.ceil(grid_high)))
# NOTE: Grid Coverage uses the direct intervals as in Rasdaman
if self.grid_coverage is False and grid_high is not None:
if grid_high > grid_low:
grid_high -= 1
grid_axis = GridAxis(user_axis.order, crs_axis.label,
user_axis.resolution, grid_low, grid_high)
geo_axis.origin = PointPixelAdjuster.get_origin(user_axis, crs_axis)
if user_axis.type == UserAxisType.DATE:
self._translate_decimal_to_datetime(user_axis, geo_axis)
# NOTE: current, gdal recipe supports only has 2 axes which are "bounded" (i.e: they exist as 2D axes in file)
# and 1 or more another axes gotten (i.e: from fileName) which are not "bounded" to create 3D+ coverage.
data_bound = crs_axis.is_y_axis() or crs_axis.is_x_axis()
return AxisSubset(
CoverageAxis(geo_axis, grid_axis, data_bound),
Interval(user_axis.interval.low, user_axis.interval.high))
def _axis_subset(self, crs_axis, gdal_file):
"""
Returns an axis subset using the given crs axis in the context of the gdal file
:param CRSAxis crs_axis: the crs definition of the axis
:param File gdal_file: the gdal file
:rtype AxisSubset
"""
user_axis = self._user_axis(
self._get_user_axis_by_crs_axis_name(crs_axis.label),
GDALEvaluatorSlice(GDALGmlUtil(gdal_file.get_filepath())))
high = user_axis.interval.high if user_axis.interval.high else user_axis.interval.low
if isinstance(user_axis, RegularUserAxis):
geo_axis = RegularAxis(crs_axis.label, crs_axis.uom,
user_axis.interval.low, high,
user_axis.interval.low, crs_axis)
else:
# if irregular axis value is fetched from fileName so the coefficient is [0] as slicing
if user_axis.directPositions == AbstractToCoverageConverter.DIRECT_POSITIONS_SLICING:
user_axis.directPositions = AbstractToCoverageConverter.COEFFICIENT_SLICING
geo_axis = IrregularAxis(crs_axis.label, crs_axis.uom,
user_axis.interval.low, high,
user_axis.interval.low,
user_axis.directPositions, crs_axis)
if not crs_axis.is_easting() and not crs_axis.is_northing():
# GDAL model is 2D so on any axis except x/y we expect to have only one value
grid_low = 0
grid_high = 0
else:
grid_low = 0
number_of_grid_points = decimal.Decimal(str(user_axis.interval.high)) \
- decimal.Decimal(str(user_axis.interval.low))
# number_of_grid_points = (geo_max - geo_min) / resolution
grid_high = grid_low + number_of_grid_points / decimal.Decimal(
user_axis.resolution)
grid_high = HighPixelAjuster.adjust_high(grid_high)
# Negative axis, e.g: Latitude (min <--- max)
if user_axis.resolution < 0:
grid_high = int(abs(math.floor(grid_high)))
else:
# Positive axis, e.g: Longitude (min --> max)
grid_high = int(abs(math.ceil(grid_high)))
# NOTE: Grid Coverage uses the direct intervals as in Rasdaman
if self.grid_coverage is False:
if grid_high > grid_low:
grid_high -= 1
grid_axis = GridAxis(user_axis.order, crs_axis.label,
user_axis.resolution, grid_low, grid_high)
geo_axis.origin = PointPixelAdjuster.get_origin(user_axis, crs_axis)
if user_axis.type == UserAxisType.DATE:
self._translate_decimal_to_datetime(user_axis, geo_axis)
# NOTE: current, gdal recipe supports only has 2 axes which are "bounded" (i.e: they exist as 2D axes in file)
# and 1 or more another axes gotten (i.e: from fileName) which are not "bounded" to create 3D+ coverage.
data_bound = crs_axis.is_northing() or crs_axis.is_easting()
return AxisSubset(
CoverageAxis(geo_axis, grid_axis, data_bound),
Interval(user_axis.interval.low, user_axis.interval.high))