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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright (c) 2009, 2010, 2011, 2012, 2013.
# SMHI,
# Folkborgsvägen 1,
# Norrköping,
# Sweden
# Author(s):
# Martin Raspaud <martin.raspaud@smhi.se>
# Adam Dybbroe <adam.dybbroe@smhi.se>
# This file is part of mpop.
# mpop is free software: you can redistribute it and/or modify it
# under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
# mpop 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
# General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with mpop. If not, see <http://www.gnu.org/licenses/>.
"""This module handles coverage objects. Such objects are used to
transform area projected data by changing either the area or the
projection or both. A typical usage is to transform one large area in
satellite projection to an area of interest in polar projection for
example.
"""
import os
import ConfigParser
import logging
import numpy as np
from pyresample import image, utils, geometry, kd_tree
from mpop import CONFIG_PATH
CONF = ConfigParser.ConfigParser()
CONF.read(os.path.join(CONFIG_PATH, "mpop.cfg"))
logger = logging.getLogger(__name__)
try:
AREA_FILE = os.path.join(CONF.get("projector", "area_directory") or
CONFIG_PATH,
CONF.get("projector", "area_file"))
except ConfigParser.NoSectionError:
AREA_FILE = ""
logger.warning("Couldn't find the mpop.cfg file. "
"Do you have one ? is it in $PPP_CONFIG_DIR ?")
def get_area_def(area_name):
"""Get the definition of *area_name* from file. The file is defined to use
is to be placed in the $PPP_CONFIG_DIR directory, and its name is defined
in mpop's configuration file.
"""
return utils.parse_area_file(AREA_FILE, area_name)[0]
def _get_area_hash(area):
"""Calculate a (close to) unique hash value for a given area.
"""
if isinstance(area, geometry.AreaDefinition):
return hash(str(area))
elif isinstance(area, geometry.SwathDefinition):
return hash(area.lons.tostring() + area.lats.tostring())
elif isinstance(area, np.ndarray):
# probaly not needed.
return hash(area.tostring())
else:
logger.warning("Cannot hash area, beware of duplicate area names.")
class Projector(object):
"""This class define projector objects. They contain the mapping
information necessary for projection purposes. For efficiency reasons,
generated projectors can be saved to disk for later reuse. Use the
:meth:`save` method for this.
To define a projector object, on has to specify *in_area* and *out_area*,
and can also input the *in_lonlats* or the *mode* ('quick' which works only
if both in- and out-areas are AreaDefinitions, or 'nearest'). *radius*
defines the radius of influence for nearest neighbour search in 'nearest'
mode.
"""
in_area = None
out_area = None
_cache = None
_filename = None
mode = "quick"
def __init__(self, in_area, out_area,
in_latlons=None, mode=None,
radius=10000):
# TODO:
# - Rework so that in_area and out_area can be lonlats.
# - Add a recompute flag ?
# Setting up the input area
try:
self.in_area = get_area_def(in_area)
in_id = in_area
except (utils.AreaNotFound, AttributeError):
if isinstance(in_area, geometry.AreaDefinition):
self.in_area = in_area
in_id = in_area.area_id
elif isinstance(in_area, geometry.SwathDefinition):
self.in_area = in_area
in_id = in_area.area_id
elif in_latlons is not None:
self.in_area = geometry.SwathDefinition(lons=in_latlons[0],
lats=in_latlons[1])
in_id = in_area
else:
raise utils.AreaNotFound("Input area " +
str(in_area) +
" must be defined in " +
AREA_FILE + ", be an area object"
" or longitudes/latitudes must be "
"provided.")
# Setting up the output area
try:
self.out_area = get_area_def(out_area)
out_id = out_area
except (utils.AreaNotFound, AttributeError):
if isinstance(out_area, (geometry.AreaDefinition,
geometry.SwathDefinition)):
self.out_area = out_area
out_id = out_area.area_id
else:
raise utils.AreaNotFound("Output area " +
str(out_area) +
" must be defined in " +
AREA_FILE + " or "
"be an area object.")
if self.in_area == self.out_area:
return
# choosing the right mode if necessary
if(mode is None):
if (isinstance(in_area, geometry.AreaDefinition) and
isinstance(out_area, geometry.AreaDefinition)):
self.mode = "quick"
else:
self.mode = "nearest"
else:
self.mode = mode
filename = (in_id + "2" + out_id + "_" +
str(_get_area_hash(self.in_area)) + "to" +
str(_get_area_hash(self.out_area)) + "_" +
self.mode + ".npz")
projections_directory = "/var/tmp"
try:
projections_directory = CONF.get("projector",
"projections_directory")
except ConfigParser.NoSectionError:
pass
self._filename = os.path.join(projections_directory, filename)
if(not os.path.exists(self._filename)):
logger.info("Computing projection from %s to %s..."
%(in_id, out_id))
if self.mode == "nearest":
valid_index, valid_output_index, index_array, distance_array = \
kd_tree.get_neighbour_info(self.in_area,
self.out_area,
radius,
neighbours=1)
del distance_array
self._cache = {}
self._cache['valid_index'] = valid_index
self._cache['valid_output_index'] = valid_output_index
self._cache['index_array'] = index_array
elif self.mode == "quick":
ridx, cidx = \
utils.generate_quick_linesample_arrays(self.in_area,
self.out_area)
self._cache = {}
self._cache['row_idx'] = ridx
self._cache['col_idx'] = cidx
else:
raise ValueError("Unrecognised mode " + str(self.mode) + ".")
else:
self._cache = {}
self._file_cache = np.load(self._filename)
def save(self, resave=False):
"""Save the precomputation to disk, and overwrite existing file in case
*resave* is true.
"""
if (not os.path.exists(self._filename)) or resave:
logger.info("Saving projection to " +
self._filename)
np.savez(self._filename, **self._cache)
def project_array(self, data):
"""Project an array *data* along the given Projector object.
"""
if self.in_area == self.out_area:
return data
if self.mode == "nearest":
if not 'valid_index' in self._cache:
self._cache['valid_index'] = self._file_cache['valid_index']
self._cache['valid_output_index'] = \
self._file_cache['valid_output_index']
self._cache['index_array'] = self._file_cache['index_array']
valid_index, valid_output_index, index_array = \
(self._cache['valid_index'],
self._cache['valid_output_index'],
self._cache['index_array'])
res = kd_tree.get_sample_from_neighbour_info('nn',
self.out_area.shape,
data,
valid_index,
valid_output_index,
index_array,
fill_value=None)
elif self.mode == "quick":
if not 'row_idx' in self._cache:
self._cache['row_idx'] = self._file_cache['row_idx']
self._cache['col_idx'] = self._file_cache['col_idx']
row_idx, col_idx = self._cache['row_idx'], self._cache['col_idx']
img = image.ImageContainer(data, self.in_area, fill_value=None)
res = np.ma.array(img.get_array_from_linesample(row_idx, col_idx),
dtype = data.dtype)
return res
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