Commit 9e325cdd authored by Daniel Scheffler's avatar Daniel Scheffler
Browse files

Split sensormapgeo module into transformer_2d and transformer_3d.



Signed-off-by: Daniel Scheffler's avatarDaniel Scheffler <danschef@gfz-potsdam.de>
parent c73a6aa8
......@@ -24,7 +24,8 @@
"""Top-level package for sensormapgeo."""
from .sensormapgeo import SensorMapGeometryTransformer, SensorMapGeometryTransformer3D
from .transformer_2d import SensorMapGeometryTransformer
from .transformer_3d import SensorMapGeometryTransformer3D
from .version import __version__, __versionalias__ # noqa (E402 + F401)
__all__ = [
......
......@@ -22,13 +22,10 @@
# You should have received a copy of the GNU Lesser General Public License along
# with this program. If not, see <http://www.gnu.org/licenses/>.
"""Main module."""
"""Module to transform 2D arrays between sensor and map geometry."""
from typing import Union, List, Tuple, Optional
import os
import multiprocessing
import sys
import warnings
from tempfile import TemporaryDirectory
import numpy as np
......@@ -373,24 +370,6 @@ class SensorMapGeometryTransformer(object):
return data_sensorgeo
_global_shared_lats = None
_global_shared_lons = None
_global_shared_data = None
def _initializer(lats, lons, data):
"""Declare global variables needed for SensorMapGeometryTransformer3D.to_map_geometry and to_sensor_geometry.
:param lats:
:param lons:
:param data:
"""
global _global_shared_lats, _global_shared_lons, _global_shared_data
_global_shared_lats = lats
_global_shared_lons = lons
_global_shared_data = data
def _corner_coords_lonlat_to_extent(corner_coords_ll: List, tgt_epsg: int):
corner_coords_tgt_prj = [transform_any_prj(EPSG2WKT(4326), EPSG2WKT(tgt_epsg), x, y)
for x, y in corner_coords_ll]
......@@ -400,206 +379,3 @@ def _corner_coords_lonlat_to_extent(corner_coords_ll: List, tgt_epsg: int):
tgt_extent = [np.min(x_coords), np.min(y_coords), np.max(x_coords), np.max(y_coords)]
return tgt_extent
class SensorMapGeometryTransformer3D(object):
def __init__(self,
lons: np.ndarray,
lats: np.ndarray,
resamp_alg: str = 'nearest',
radius_of_influence: int = 30,
mp_alg: str = 'auto',
**opts) -> None:
"""Get an instance of SensorMapGeometryTransformer.
:param lons: 3D longitude array corresponding to the 3D sensor geometry array
:param lats: 3D latitude array corresponding to the 3D sensor geometry array
:Keyword Arguments: (further documentation here: https://pyresample.readthedocs.io/en/latest/swath.html)
- resamp_alg: resampling algorithm ('nearest', 'bilinear', 'gauss', 'custom')
- radius_of_influence: <float> Cut off distance in meters (default: 30)
NOTE: keyword is named 'radius' in case of bilinear resampling
- mp_alg multiprocessing algorithm
'bands': parallelize over bands using multiprocessing lib
'tiles': parallelize over tiles using OpenMP
'auto': automatically choose the algorithm
- sigmas: <list of floats or float> [ONLY 'gauss'] List of sigmas to use for the gauss
weighting of each channel 1 to k, w_k = exp(-dist^2/sigma_k^2). If only one channel
is resampled sigmas is a single float value.
- neighbours: <int> [ONLY 'bilinear', 'gauss'] Number of neighbours to consider for each grid
point when searching the closest corner points
- epsilon: <float> Allowed uncertainty in meters. Increasing uncertainty reduces execution time
- weight_funcs: <list of function objects or function object> [ONLY 'custom'] List of weight
functions f(dist) to use for the weighting of each channel 1 to k. If only one
channel is resampled weight_funcs is a single function object.
- fill_value: <int or None> Set undetermined pixels to this value (default: 0).
If fill_value is None a masked array is returned with undetermined pixels masked
- reduce_data: <bool> Perform initial coarse reduction of source dataset in order to reduce
execution time
- nprocs: <int>, Number of processor cores to be used
- segments: <int or None> Number of segments to use when resampling.
If set to None an estimate will be calculated
- with_uncert: <bool> [ONLY 'gauss' and 'custom'] Calculate uncertainty estimates
NOTE: resampling function has 3 return values instead of 1: result, stddev, count
"""
# validation
if lons.ndim != 3:
raise ValueError('Expected a 3D longitude array. Received a %dD array.' % lons.ndim)
if lats.ndim != 3:
raise ValueError('Expected a 3D latitude array. Received a %dD array.' % lats.ndim)
if lons.shape != lats.shape:
raise ValueError((lons.shape, lats.shape), "'lons' and 'lats' are expected to have the same shape.")
self.lats = lats
self.lons = lons
self.resamp_alg = resamp_alg
self.radius_of_influence = radius_of_influence
self.opts = opts
# define number of CPUs to use (but avoid sub-multiprocessing)
# -> parallelize either over bands or over image tiles
# bands: multiprocessing uses multiprocessing.Pool, implemented in to_map_geometry / to_sensor_geometry
# tiles: multiprocessing uses OpenMP implemented in pykdtree which is used by pyresample
self.opts['nprocs'] = opts.get('nprocs', multiprocessing.cpu_count())
self.mp_alg = ('bands' if self.lons.shape[2] >= opts['nprocs'] else 'tiles') if mp_alg == 'auto' else mp_alg
# override self.mp_alg if SensorMapGeometryTransformer3D is called by nosetest or unittest
is_called_by_nose_cmd = 'nosetest' in sys.argv[0]
if self.opts['nprocs'] > 1 and self.mp_alg == 'bands' and is_called_by_nose_cmd:
warnings.warn("mp_alg='bands' causes deadlocks if SensorMapGeometryTransformer3D is called within a "
"nosetest console call. Using mp_alg='tiles'.")
self.mp_alg = 'tiles'
@staticmethod
def _to_map_geometry_2D(kwargs_dict: dict) -> Tuple[np.ndarray, tuple, str, int]:
assert [var is not None for var in (_global_shared_lons, _global_shared_lats, _global_shared_data)]
SMGT2D = SensorMapGeometryTransformer(lons=_global_shared_lons[:, :, kwargs_dict['band_idx']],
lats=_global_shared_lats[:, :, kwargs_dict['band_idx']],
resamp_alg=kwargs_dict['resamp_alg'],
radius_of_influence=kwargs_dict['radius_of_influence'],
**kwargs_dict['init_opts'])
data_mapgeo, out_gt, out_prj = SMGT2D.to_map_geometry(data=_global_shared_data[:, :, kwargs_dict['band_idx']],
tgt_prj=kwargs_dict['tgt_prj'],
tgt_extent=kwargs_dict['tgt_extent'],
tgt_res=kwargs_dict['tgt_res'])
return data_mapgeo, out_gt, out_prj, kwargs_dict['band_idx']
def _get_common_target_extent(self, tgt_epsg):
corner_coords_ll = [[self.lons[0, 0, :].min(), self.lats[0, 0, :].max()], # common UL_xy
[self.lons[0, -1, :].max(), self.lats[0, -1, :].max()], # common UR_xy
[self.lons[-1, 0, :].min(), self.lats[-1, 0, :].min()], # common LL_xy
[self.lons[-1, -1, :].max(), self.lats[-1, -1, :].min()], # common LR_xy
]
tgt_extent = _corner_coords_lonlat_to_extent(corner_coords_ll, tgt_epsg)
return tgt_extent
def to_map_geometry(self,
data: np.ndarray,
tgt_prj: Union[str, int],
tgt_extent: Tuple[float, float, float, float] = None,
tgt_res: Tuple = None) -> Tuple[np.ndarray, tuple, str]:
"""Transform the input sensor geometry array into map geometry.
:param data: 3D numpy array (representing sensor geometry) to be warped to map geometry
:param tgt_prj: target projection (WKT or 'epsg:1234' or <EPSG_int>)
:param tgt_extent: extent coordinates of output map geometry array (LL_x, LL_y, UR_x, UR_y) in the tgt_prj
:param tgt_res: target X/Y resolution (e.g., (30, 30))
"""
if data.ndim != 3:
raise ValueError(data.ndim, "'data' must have 3 dimensions.")
if data.shape != self.lons.shape:
raise ValueError(data.shape, 'Expected a sensor geometry data array with %d rows, %d columns and %d bands.'
% self.lons.shape)
# get common target extent
tgt_epsg = WKT2EPSG(proj4_to_WKT(get_proj4info(proj=tgt_prj)))
tgt_extent = tgt_extent or self._get_common_target_extent(tgt_epsg)
init_opts = self.opts.copy()
if self.mp_alg == 'bands':
del init_opts['nprocs'] # avoid sub-multiprocessing
args = [dict(
resamp_alg=self.resamp_alg,
radius_of_influence=self.radius_of_influence,
init_opts=init_opts,
tgt_prj=tgt_prj,
tgt_extent=tgt_extent,
tgt_res=tgt_res,
band_idx=band
) for band in range(data.shape[2])]
if self.opts['nprocs'] > 1 and self.mp_alg == 'bands':
with multiprocessing.Pool(self.opts['nprocs'],
initializer=_initializer,
initargs=(self.lats, self.lons, data)) as pool:
result = pool.map(self._to_map_geometry_2D, args)
else:
_initializer(self.lats, self.lons, data)
result = [self._to_map_geometry_2D(argsdict) for argsdict in args]
band_inds = list(np.array(result)[:, -1])
data_mapgeo = np.dstack([result[band_inds.index(i)][0] for i in range(data.shape[2])])
out_gt = result[0][1]
out_prj = result[0][2]
return data_mapgeo, out_gt, out_prj
@staticmethod
def _to_sensor_geometry_2D(kwargs_dict: dict) -> (np.ndarray, int):
assert [var is not None for var in (_global_shared_lons, _global_shared_lats, _global_shared_data)]
SMGT2D = SensorMapGeometryTransformer(lons=_global_shared_lons[:, :, kwargs_dict['band_idx']],
lats=_global_shared_lats[:, :, kwargs_dict['band_idx']],
resamp_alg=kwargs_dict['resamp_alg'],
radius_of_influence=kwargs_dict['radius_of_influence'],
**kwargs_dict['init_opts'])
data_sensorgeo = SMGT2D.to_sensor_geometry(data=_global_shared_data[:, :, kwargs_dict['band_idx']],
src_prj=kwargs_dict['src_prj'],
src_extent=kwargs_dict['src_extent'])
return data_sensorgeo, kwargs_dict['band_idx']
def to_sensor_geometry(self,
data: np.ndarray,
src_prj: Union[str, int],
src_extent: List[float, float, float, float]) -> np.ndarray:
"""Transform the input map geometry array into sensor geometry
:param data: 3D numpy array (representing map geometry) to be warped to sensor geometry
:param src_prj: projection of the input map geometry array (WKT or 'epsg:1234' or <EPSG_int>)
:param src_extent: extent coordinates of input map geometry array (LL_x, LL_y, UR_x, UR_y) in the src_prj
"""
if data.ndim != 3:
raise ValueError(data.ndim, "'data' must have 3 dimensions.")
init_opts = self.opts.copy()
if self.mp_alg == 'bands':
del init_opts['nprocs'] # avoid sub-multiprocessing
args = [dict(
resamp_alg=self.resamp_alg,
radius_of_influence=self.radius_of_influence,
init_opts=init_opts,
src_prj=src_prj,
src_extent=src_extent,
band_idx=band
) for band in range(data.shape[2])]
if self.opts['nprocs'] > 1 and self.mp_alg == 'bands':
with multiprocessing.Pool(self.opts['nprocs'],
initializer=_initializer,
initargs=(self.lats, self.lons, data)) as pool:
result = pool.map(self._to_sensor_geometry_2D, args)
else:
_initializer(self.lats, self.lons, data)
result = [self._to_sensor_geometry_2D(argsdict) for argsdict in args]
band_inds = list(np.array(result)[:, -1])
data_sensorgeo = np.dstack([result[band_inds.index(i)][0] for i in range(data.shape[2])])
return data_sensorgeo
# -*- coding: utf-8 -*-
# sensormapgeo, Transform remote sensing images between sensor and map geometry.
#
# Copyright (C) 2020 Daniel Scheffler (GFZ Potsdam, danschef@gfz-potsdam.de)
#
# This software was developed within the context of the EnMAP project supported
# by the DLR Space Administration with funds of the German Federal Ministry of
# Economic Affairs and Energy (on the basis of a decision by the German Bundestag:
# 50 EE 1529) and contributions from DLR, GFZ and OHB System AG.
#
# This program is free software: you can redistribute it and/or modify it under
# the terms of the GNU Lesser General Public License as published by the Free
# Software Foundation, either version 3 of the License, or (at your option) any
# later version.
#
# This program is distributed in the hope that it will be useful, but WITHOUT
# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
# FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more
# details.
#
# You should have received a copy of the GNU Lesser General Public License along
# with this program. If not, see <http://www.gnu.org/licenses/>.
"""Module to transform 3D arrays between sensor and map geometry (using band-wise Lon/Lat arrays)."""
from typing import Union, List, Tuple
import multiprocessing
import sys
import warnings
import numpy as np
from py_tools_ds.geo.projection import WKT2EPSG, proj4_to_WKT
from py_tools_ds.geo.coord_trafo import get_proj4info
from .transformer_2d import SensorMapGeometryTransformer, _corner_coords_lonlat_to_extent
class SensorMapGeometryTransformer3D(object):
def __init__(self,
lons: np.ndarray,
lats: np.ndarray,
resamp_alg: str = 'nearest',
radius_of_influence: int = 30,
mp_alg: str = 'auto',
**opts) -> None:
"""Get an instance of SensorMapGeometryTransformer.
:param lons: 3D longitude array corresponding to the 3D sensor geometry array
:param lats: 3D latitude array corresponding to the 3D sensor geometry array
:Keyword Arguments: (further documentation here: https://pyresample.readthedocs.io/en/latest/swath.html)
- resamp_alg: resampling algorithm ('nearest', 'bilinear', 'gauss', 'custom')
- radius_of_influence: <float> Cut off distance in meters (default: 30)
NOTE: keyword is named 'radius' in case of bilinear resampling
- mp_alg multiprocessing algorithm
'bands': parallelize over bands using multiprocessing lib
'tiles': parallelize over tiles using OpenMP
'auto': automatically choose the algorithm
- sigmas: <list of floats or float> [ONLY 'gauss'] List of sigmas to use for the gauss
weighting of each channel 1 to k, w_k = exp(-dist^2/sigma_k^2). If only one channel
is resampled sigmas is a single float value.
- neighbours: <int> [ONLY 'bilinear', 'gauss'] Number of neighbours to consider for each grid
point when searching the closest corner points
- epsilon: <float> Allowed uncertainty in meters. Increasing uncertainty reduces execution time
- weight_funcs: <list of function objects or function object> [ONLY 'custom'] List of weight
functions f(dist) to use for the weighting of each channel 1 to k. If only one
channel is resampled weight_funcs is a single function object.
- fill_value: <int or None> Set undetermined pixels to this value (default: 0).
If fill_value is None a masked array is returned with undetermined pixels masked
- reduce_data: <bool> Perform initial coarse reduction of source dataset in order to reduce
execution time
- nprocs: <int>, Number of processor cores to be used
- segments: <int or None> Number of segments to use when resampling.
If set to None an estimate will be calculated
- with_uncert: <bool> [ONLY 'gauss' and 'custom'] Calculate uncertainty estimates
NOTE: resampling function has 3 return values instead of 1: result, stddev, count
"""
# validation
if lons.ndim != 3:
raise ValueError('Expected a 3D longitude array. Received a %dD array.' % lons.ndim)
if lats.ndim != 3:
raise ValueError('Expected a 3D latitude array. Received a %dD array.' % lats.ndim)
if lons.shape != lats.shape:
raise ValueError((lons.shape, lats.shape), "'lons' and 'lats' are expected to have the same shape.")
self.lats = lats
self.lons = lons
self.resamp_alg = resamp_alg
self.radius_of_influence = radius_of_influence
self.opts = opts
# define number of CPUs to use (but avoid sub-multiprocessing)
# -> parallelize either over bands or over image tiles
# bands: multiprocessing uses multiprocessing.Pool, implemented in to_map_geometry / to_sensor_geometry
# tiles: multiprocessing uses OpenMP implemented in pykdtree which is used by pyresample
self.opts['nprocs'] = opts.get('nprocs', multiprocessing.cpu_count())
self.mp_alg = ('bands' if self.lons.shape[2] >= opts['nprocs'] else 'tiles') if mp_alg == 'auto' else mp_alg
# override self.mp_alg if SensorMapGeometryTransformer3D is called by nosetest or unittest
is_called_by_nose_cmd = 'nosetest' in sys.argv[0]
if self.opts['nprocs'] > 1 and self.mp_alg == 'bands' and is_called_by_nose_cmd:
warnings.warn("mp_alg='bands' causes deadlocks if SensorMapGeometryTransformer3D is called within a "
"nosetest console call. Using mp_alg='tiles'.")
self.mp_alg = 'tiles'
@staticmethod
def _to_map_geometry_2D(kwargs_dict: dict) -> Tuple[np.ndarray, tuple, str, int]:
assert [var is not None for var in (_global_shared_lons, _global_shared_lats, _global_shared_data)]
SMGT2D = SensorMapGeometryTransformer(lons=_global_shared_lons[:, :, kwargs_dict['band_idx']],
lats=_global_shared_lats[:, :, kwargs_dict['band_idx']],
resamp_alg=kwargs_dict['resamp_alg'],
radius_of_influence=kwargs_dict['radius_of_influence'],
**kwargs_dict['init_opts'])
data_mapgeo, out_gt, out_prj = SMGT2D.to_map_geometry(data=_global_shared_data[:, :, kwargs_dict['band_idx']],
tgt_prj=kwargs_dict['tgt_prj'],
tgt_extent=kwargs_dict['tgt_extent'],
tgt_res=kwargs_dict['tgt_res'])
return data_mapgeo, out_gt, out_prj, kwargs_dict['band_idx']
def _get_common_target_extent(self, tgt_epsg):
corner_coords_ll = [[self.lons[0, 0, :].min(), self.lats[0, 0, :].max()], # common UL_xy
[self.lons[0, -1, :].max(), self.lats[0, -1, :].max()], # common UR_xy
[self.lons[-1, 0, :].min(), self.lats[-1, 0, :].min()], # common LL_xy
[self.lons[-1, -1, :].max(), self.lats[-1, -1, :].min()], # common LR_xy
]
tgt_extent = _corner_coords_lonlat_to_extent(corner_coords_ll, tgt_epsg)
return tgt_extent
def to_map_geometry(self,
data: np.ndarray,
tgt_prj: Union[str, int],
tgt_extent: Tuple[float, float, float, float] = None,
tgt_res: Tuple = None) -> Tuple[np.ndarray, tuple, str]:
"""Transform the input sensor geometry array into map geometry.
:param data: 3D numpy array (representing sensor geometry) to be warped to map geometry
:param tgt_prj: target projection (WKT or 'epsg:1234' or <EPSG_int>)
:param tgt_extent: extent coordinates of output map geometry array (LL_x, LL_y, UR_x, UR_y) in the tgt_prj
:param tgt_res: target X/Y resolution (e.g., (30, 30))
"""
if data.ndim != 3:
raise ValueError(data.ndim, "'data' must have 3 dimensions.")
if data.shape != self.lons.shape:
raise ValueError(data.shape, 'Expected a sensor geometry data array with %d rows, %d columns and %d bands.'
% self.lons.shape)
# get common target extent
tgt_epsg = WKT2EPSG(proj4_to_WKT(get_proj4info(proj=tgt_prj)))
tgt_extent = tgt_extent or self._get_common_target_extent(tgt_epsg)
init_opts = self.opts.copy()
if self.mp_alg == 'bands':
del init_opts['nprocs'] # avoid sub-multiprocessing
args = [dict(
resamp_alg=self.resamp_alg,
radius_of_influence=self.radius_of_influence,
init_opts=init_opts,
tgt_prj=tgt_prj,
tgt_extent=tgt_extent,
tgt_res=tgt_res,
band_idx=band
) for band in range(data.shape[2])]
if self.opts['nprocs'] > 1 and self.mp_alg == 'bands':
with multiprocessing.Pool(self.opts['nprocs'],
initializer=_initializer,
initargs=(self.lats, self.lons, data)) as pool:
result = pool.map(self._to_map_geometry_2D, args)
else:
_initializer(self.lats, self.lons, data)
result = [self._to_map_geometry_2D(argsdict) for argsdict in args]
band_inds = list(np.array(result)[:, -1])
data_mapgeo = np.dstack([result[band_inds.index(i)][0] for i in range(data.shape[2])])
out_gt = result[0][1]
out_prj = result[0][2]
return data_mapgeo, out_gt, out_prj
@staticmethod
def _to_sensor_geometry_2D(kwargs_dict: dict) -> (np.ndarray, int):
assert [var is not None for var in (_global_shared_lons, _global_shared_lats, _global_shared_data)]
SMGT2D = SensorMapGeometryTransformer(lons=_global_shared_lons[:, :, kwargs_dict['band_idx']],
lats=_global_shared_lats[:, :, kwargs_dict['band_idx']],
resamp_alg=kwargs_dict['resamp_alg'],
radius_of_influence=kwargs_dict['radius_of_influence'],
**kwargs_dict['init_opts'])
data_sensorgeo = SMGT2D.to_sensor_geometry(data=_global_shared_data[:, :, kwargs_dict['band_idx']],
src_prj=kwargs_dict['src_prj'],
src_extent=kwargs_dict['src_extent'])
return data_sensorgeo, kwargs_dict['band_idx']
def to_sensor_geometry(self,
data: np.ndarray,
src_prj: Union[str, int],
src_extent: List[float, float, float, float]) -> np.ndarray:
"""Transform the input map geometry array into sensor geometry
:param data: 3D numpy array (representing map geometry) to be warped to sensor geometry
:param src_prj: projection of the input map geometry array (WKT or 'epsg:1234' or <EPSG_int>)
:param src_extent: extent coordinates of input map geometry array (LL_x, LL_y, UR_x, UR_y) in the src_prj
"""
if data.ndim != 3:
raise ValueError(data.ndim, "'data' must have 3 dimensions.")
init_opts = self.opts.copy()
if self.mp_alg == 'bands':
del init_opts['nprocs'] # avoid sub-multiprocessing
args = [dict(
resamp_alg=self.resamp_alg,
radius_of_influence=self.radius_of_influence,
init_opts=init_opts,
src_prj=src_prj,
src_extent=src_extent,
band_idx=band
) for band in range(data.shape[2])]
if self.opts['nprocs'] > 1 and self.mp_alg == 'bands':
with multiprocessing.Pool(self.opts['nprocs'],
initializer=_initializer,
initargs=(self.lats, self.lons, data)) as pool:
result = pool.map(self._to_sensor_geometry_2D, args)
else:
_initializer(self.lats, self.lons, data)
result = [self._to_sensor_geometry_2D(argsdict) for argsdict in args]
band_inds = list(np.array(result)[:, -1])
data_sensorgeo = np.dstack([result[band_inds.index(i)][0] for i in range(data.shape[2])])
return data_sensorgeo
_global_shared_lats = None
_global_shared_lons = None
_global_shared_data = None
def _initializer(lats, lons, data):
"""Declare global variables needed for SensorMapGeometryTransformer3D.to_map_geometry and to_sensor_geometry.
:param lats:
:param lons:
:param data:
"""
global _global_shared_lats, _global_shared_lons, _global_shared_data
_global_shared_lats = lats
_global_shared_lons = lons
_global_shared_data = data
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