baseclasses.py 71.7 KB
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# -*- coding: utf-8 -*-

import os
import warnings
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from importlib import util
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from collections import OrderedDict

import numpy as np
from matplotlib import pyplot as plt
from osgeo import gdal_array
# custom
from shapely.geometry import Polygon
from shapely.wkt import loads as shply_loads
from six import PY3

# mpl_toolkits.basemap -> imported when GeoArray.show_map() is used
# dill -> imported when dumping GeoArray

try:
    from osgeo import gdal
    from osgeo import gdalnumeric
except ImportError:
    import gdal
    import gdalnumeric
from geopandas import GeoDataFrame, GeoSeries
from pandas import DataFrame
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from py_tools_ds.convenience.object_oriented import alias_property
from py_tools_ds.geo.coord_calc import get_corner_coordinates
from py_tools_ds.geo.coord_grid import snap_bounds_to_pixGrid
from py_tools_ds.geo.coord_trafo import mapXY2imXY, imXY2mapXY, transform_any_prj, reproject_shapelyGeometry
from py_tools_ds.geo.projection import prj_equal, WKT2EPSG, EPSG2WKT
from py_tools_ds.geo.raster.conversion import raster2polygon
from py_tools_ds.geo.vector.topology \
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    import get_footprint_polygon, polyVertices_outside_poly, fill_holes_within_poly
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from py_tools_ds.geo.vector.geometry import boxObj
from py_tools_ds.io.raster.gdal import get_GDAL_ds_inmem
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from py_tools_ds.compatibility.gdal import get_gdal_func
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from py_tools_ds.numeric.numbers import is_number
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#  internal imports
from .subsetting import get_array_at_mapPos

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if PY3:
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    # noinspection PyCompatibility
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    from builtins import TimeoutError, FileNotFoundError
else:
    from py_tools_ds.compatibility.python.exceptions import TimeoutError, FileNotFoundError
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__author__ = 'Daniel Scheffler'
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class GeoArray(object):
    def __init__(self, path_or_array, geotransform=None, projection=None, bandnames=None, nodata=None, progress=True,
                 q=False):
        # type: (any, tuple, str, list, float, bool, bool) -> None
        """This class creates a fast Python interface for geodata - either on disk or in memory. It can be instanced with
        a file path or with a numpy array and the corresponding geoinformation. Instances can always be indexed like
        normal numpy arrays, no matter if GeoArray has been instanced from file or from an in-memory array. GeoArray
        provides a wide range of geo-related attributes belonging to the dataset as well as some functions for quickly
        visualizing the data as a map, a simple image or an interactive image.

        :param path_or_array:   a numpy.ndarray or a valid file path
        :param geotransform:    GDAL geotransform of the given array or file on disk
        :param projection:      projection of the given array or file on disk as WKT string
                                (only needed if GeoArray is instanced with an array)
        :param bandnames:       names of the bands within the input array, e.g. ['mask_1bit', 'mask_clouds'],
                                (default: ['B1', 'B2', 'B3', ...])
        :param nodata:          nodata value
        :param progress:        show progress bars (default: True)
        :param q:               quiet mode (default: False)
        """

        # TODO implement compatibility to GDAL VRTs
        if not (isinstance(path_or_array, (str, np.ndarray, GeoArray)) or
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           issubclass(getattr(path_or_array, '__class__'), GeoArray)):
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            raise ValueError("%s parameter 'arg' takes only string, np.ndarray or GeoArray(and subclass) instances. "
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                             "Got %s." % (self.__class__.__name__, type(path_or_array)))
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        if path_or_array is None:
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            raise ValueError("The %s parameter 'path_or_array' must not be None!" % self.__class__.__name__)
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        if isinstance(path_or_array, str):
            assert ' ' not in path_or_array, "The given path contains whitespaces. This is not supported by GDAL."

            if not os.path.exists(path_or_array):
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                raise FileNotFoundError(path_or_array)
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        if isinstance(path_or_array, GeoArray) or issubclass(getattr(path_or_array, '__class__'), GeoArray):
            self.__dict__ = path_or_array.__dict__.copy()
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            self._initParams = dict([x for x in locals().items() if x[0] != "self"])
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            self.geotransform = geotransform or self.geotransform
            self.projection = projection or self.projection
            self.bandnames = bandnames or list(self.bandnames.values())
            self._nodata = nodata if nodata is not None else self._nodata
            self.progress = False if progress is False else self.progress
            self.q = q if q is not None else self.q
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        else:
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            self._initParams = dict([x for x in locals().items() if x[0] != "self"])
            self.arg = path_or_array
            self._arr = path_or_array if isinstance(path_or_array, np.ndarray) else None
            self.filePath = path_or_array if isinstance(path_or_array, str) and path_or_array else None
            self.basename = os.path.splitext(os.path.basename(self.filePath))[0] if not self.is_inmem else 'IN_MEM'
            self.progress = progress
            self.q = q
            self._arr_cache = None  # dict containing key 'pos' and 'arr_cached'
            self._geotransform = None
            self._projection = None
            self._shape = None
            self._dtype = None
            self._nodata = nodata
            self._mask_nodata = None
            self._mask_baddata = None
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            self._footprint_poly = None
            self._gdalDataset_meta_already_set = False
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            self._metadata = None
            self._bandnames = None
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            if bandnames:
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                self.bandnames = bandnames  # use property in order to validate given value
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            if geotransform:
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                self.geotransform = geotransform  # use property in order to validate given value
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            if projection:
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                self.projection = projection  # use property in order to validate given value
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            if self.filePath:
                self.set_gdalDataset_meta()

    @property
    def arr(self):
        return self._arr

    @arr.setter
    def arr(self, ndarray):
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        assert isinstance(ndarray, np.ndarray), "'arr' can only be set to a numpy array! Got %s." % type(ndarray)
        # assert ndarray.shape == self.shape, "'arr' can only be set to a numpy array with shape %s. Received %s. " \
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        #                                    "If you need to change the dimensions, create a new instance of %s." \
        #                                    %(self.shape, ndarray.shape, self.__class__.__name__)
        #  THIS would avoid warping like this: geoArr.arr, geoArr.gt, geoArr.prj = warp(...)

        if ndarray.shape != self.shape:
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            self.flush_cache()  # the cached array is not useful anymore
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        self._arr = ndarray

    @property
    def bandnames(self):
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        if self._bandnames and len(self._bandnames) == self.bands:
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            return self._bandnames
        else:
            self._bandnames = OrderedDict(('B%s' % band, i) for i, band in enumerate(range(1, self.bands + 1)))
            return self._bandnames

    @bandnames.setter
    def bandnames(self, list_bandnames):
        # type: (list) -> None

        if list_bandnames:
            assert isinstance(list_bandnames, list), "A list must be given when setting the 'bandnames' attribute. " \
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                                                     "Received %s." % type(list_bandnames)
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            assert len(list_bandnames) == self.bands, \
                'Number of given bandnames does not match number of bands in array.'
            assert len(list(set([type(b) for b in list_bandnames]))) == 1 and type(list_bandnames[0] == 'str'), \
                "'bandnames must be a set of strings. Got other datetypes in there.'"
            bN_dict = OrderedDict((band, i) for i, band in enumerate(list_bandnames))
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            assert len(
                bN_dict) == self.bands, 'Bands must not have the same name. Received band list: %s' % list_bandnames
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            self._bandnames = bN_dict

    @property
    def is_inmem(self):
        """Check if associated image array is completely loaded into memory."""

        return isinstance(self.arr, np.ndarray)

    @property
    def shape(self):
        """Get the array shape of the associated image array."""

        if self.is_inmem:
            return self.arr.shape
        else:
            if self._shape:
                return self._shape
            else:
                self.set_gdalDataset_meta()
                return self._shape

    @property
    def ndim(self):
        """Get the number dimensions of the associated image array."""
        return len(self.shape)

    @property
    def rows(self):
        """Get the number of rows of the associated image array."""

        return self.shape[0]

    @property
    def columns(self):
        """Get the number of columns of the associated image array."""

        return self.shape[1]

    cols = alias_property('columns')

    @property
    def bands(self):
        """Get the number of bands of the associated image array."""

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        return self.shape[2] if len(self.shape) > 2 else 1
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    @property
    def dtype(self):
        """Get the numpy data type of the associated image array."""

        if self._dtype:
            return self._dtype
        elif self.is_inmem:
            return self.arr.dtype
        else:
            self.set_gdalDataset_meta()
            return self._dtype

    @property
    def geotransform(self):
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        """Get the GDAL GeoTransform of the associated image, e.g., (283500.0, 5.0, 0.0, 4464500.0, 0.0, -5.0)"""
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        if self._geotransform:
            return self._geotransform
        elif not self.is_inmem:
            self.set_gdalDataset_meta()
            return self._geotransform
        else:
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            return [0, 1, 0, 0, 0, -1]
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    @geotransform.setter
    def geotransform(self, gt):
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        assert isinstance(gt, (list, tuple)) and len(gt) == 6,\
            'geotransform must be a list with 6 numbers. Got %s.' % str(gt)
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        for i in gt:
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            assert is_number(i), "geotransform must contain only numbers. Got '%s' (type: %s)." % (i, type(i))
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        self._geotransform = gt
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    gt = alias_property('geotransform')

    @property
    def xgsd(self):
        """Get the X resolution in units of the given or detected projection."""

        return self.geotransform[1]

    @property
    def ygsd(self):
        """Get the Y resolution in units of the given or detected projection."""

        return abs(self.geotransform[5])

    @property
    def xygrid_specs(self):
        """
        Get the specifications for the X/Y coordinate grid, e.g. [[15,30], [0,30]] for a coordinate with its origin
        at X/Y[15,0] and a GSD of X/Y[15,30].
        """

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        def get_grid(gt, xgsd, ygsd): return [[gt[0], gt[0] + xgsd], [gt[3], gt[3] - ygsd]]
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        return get_grid(self.geotransform, self.xgsd, self.ygsd)

    @property
    def projection(self):
        """
        Get the projection of the associated image. Setting the projection is only allowed if GeoArray has been
        instanced from memory or the associated file on disk has no projection.
        """

        if self._projection:
            return self._projection
        elif not self.is_inmem:
            self.set_gdalDataset_meta()
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            return self._projection  # or "LOCAL_CS[\"MAP\"]"
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        else:
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            return ''  # '"LOCAL_CS[\"MAP\"]"
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    @projection.setter
    def projection(self, prj):
        if self.filePath:
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            assert self.projection is None or prj_equal(self.projection, prj), \
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                "Cannot set %s.projection to the given value because it does not match the projection from the file " \
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                "on disk." % self.__class__.__name__
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        else:
            self._projection = prj

    prj = alias_property('projection')

    @property
    def epsg(self):
        """Get the EPSG code of the projection of the GeoArray."""

        return WKT2EPSG(self.projection)

    @epsg.setter
    def epsg(self, epsg_code):
        self.projection = EPSG2WKT(epsg_code)

    @property
    def box(self):
        mapPoly = get_footprint_polygon(get_corner_coordinates(gt=self.geotransform, cols=self.columns, rows=self.rows))
        return boxObj(gt=self.geotransform, prj=self.projection, mapPoly=mapPoly)

    @property
    def nodata(self):
        """
        Get the nodata value of the GeoArray. If GeoArray has been instanced with a file path the file is checked
        for an existing nodata value. Otherwise (if no value is exlicitly given during object instanciation) the nodata
        value is tried to be automatically detected.
        """

        if self._nodata is not None:
            return self._nodata
        else:
            # try to get nodata value from file
            if not self.is_inmem:
                self.set_gdalDataset_meta()
            if self._nodata is None:
Daniel Scheffler's avatar
Bugfix    
Daniel Scheffler committed
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                self._nodata = self.find_noDataVal()
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                if self._nodata == 'ambiguous':
                    warnings.warn('Nodata value could not be clearly identified. It has been set to None.')
                    self._nodata = None
                else:
                    if self._nodata is not None and not self.q:
                        print("Automatically detected nodata value for %s '%s': %s"
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                              % (self.__class__.__name__, self.basename, self._nodata))
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            return self._nodata

    @nodata.setter
    def nodata(self, value):
        self._nodata = value

    @property
    def mask_nodata(self):
        """
        Get the nodata mask of the associated image array. It is calculated using all image bands.
        """

        if self._mask_nodata is not None:
            return self._mask_nodata
        else:
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            self.calc_mask_nodata()  # sets self._mask_nodata
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            return self._mask_nodata

    @mask_nodata.setter
    def mask_nodata(self, mask):
        """Set bad data mask.

        :param mask:    Can be a file path, a numpy array or an instance o GeoArray.
        """

        if mask is not None:
            from .masks import NoDataMask
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            geoArr_mask = NoDataMask(mask, progress=self.progress, q=self.q)
            geoArr_mask.gt = geoArr_mask.gt if geoArr_mask.gt not in [None, [0, 1, 0, 0, 0, -1]] else self.gt
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            geoArr_mask.prj = geoArr_mask.prj if geoArr_mask.prj else self.prj
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            imName = "the %s '%s'" % (self.__class__.__name__, self.basename)
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            assert geoArr_mask.bands == 1, \
                'Expected one single band as nodata mask for %s. Got %s bands.' % (self.basename, geoArr_mask.bands)
            assert geoArr_mask.shape[:2] == self.shape[:2], 'The provided nodata mask must have the same number of ' \
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                                                            'rows and columns as the %s itself.' % imName
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            assert geoArr_mask.gt == self.gt, \
                'The geotransform of the given nodata mask for %s must match the geotransform of the %s itself. ' \
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                'Got %s.' % (imName, self.__class__.__name__, geoArr_mask.gt)
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            assert not geoArr_mask.prj or prj_equal(geoArr_mask.prj, self.prj), \
                'The projection of the given nodata mask for the %s must match the projection of the %s itself.' \
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                % (imName, self.__class__.__name__)
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            self._mask_nodata = geoArr_mask
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        else:
            del self.mask_nodata

    @mask_nodata.deleter
    def mask_nodata(self):
        self._mask_nodata = None
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    @property
    def mask_baddata(self):
        """
        Returns the bad data mask for the associated image array if it has been explicitly previously. It can be set
         by passing a file path, a numpy array or an instance of GeoArray to the setter of this property.
        """

        return self._mask_baddata

    @mask_baddata.setter
    def mask_baddata(self, mask):
        """Set bad data mask.

        :param mask:    Can be a file path, a numpy array or an instance o GeoArray.
        """

        if mask is not None:
            from .masks import BadDataMask
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            geoArr_mask = BadDataMask(mask, progress=self.progress, q=self.q)
            geoArr_mask.gt = geoArr_mask.gt if geoArr_mask.gt not in [None, [0, 1, 0, 0, 0, -1]] else self.gt
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            geoArr_mask.prj = geoArr_mask.prj if geoArr_mask.prj else self.prj
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            imName = "the %s '%s'" % (self.__class__.__name__, self.basename)
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            assert geoArr_mask.bands == 1, \
                'Expected one single band as bad data mask for %s. Got %s bands.' % (self.basename, geoArr_mask.bands)
            assert geoArr_mask.shape[:2] == self.shape[:2], 'The provided bad data mask must have the same number of ' \
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                                                            'rows and columns as the %s itself.' % imName
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            assert geoArr_mask.gt == self.gt, \
                'The geotransform of the given bad data mask for %s must match the geotransform of the %s itself. ' \
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                'Got %s.' % (imName, self.__class__.__name__, geoArr_mask.gt)
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            assert prj_equal(geoArr_mask.prj, self.prj), \
                'The projection of the given bad data mask for the %s must match the projection of the %s itself.' \
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                % (imName, self.__class__.__name__)
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            self._mask_baddata = geoArr_mask
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        else:
            del self.mask_baddata

    @mask_baddata.deleter
    def mask_baddata(self):
        self._mask_baddata = None
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    @property
    def footprint_poly(self):
        # FIXME should return polygon in image coordinates if no projection is available
        """
        Get the footprint polygon of the associated image array (returns an instance of shapely.geometry.Polygon.
        """

        if self._footprint_poly is None:
            assert self.mask_nodata is not None, 'A nodata mask is needed for calculating the footprint polygon. '
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            if False in self.mask_nodata[:]:
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                # do not run raster2polygon if whole image is filled with data
                self._footprint_poly = self.box.mapPoly
            else:
                try:
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                    multipolygon = raster2polygon(self.mask_nodata.astype(np.uint8), self.gt, self.prj, exact=False,
                                                  progress=self.progress, q=self.q, maxfeatCount=10, timeout=3)
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                    self._footprint_poly = fill_holes_within_poly(multipolygon)
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                except (RuntimeError, TimeoutError):
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                    if not self.q:
                        warnings.warn("\nCalculation of footprint polygon failed for %s '%s'. Using outer bounds. One "
                                      "reason could be that the nodata value appears within the actual image (not only "
                                      "as fill value). To avoid this use another nodata value. Current nodata value is "
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                                      "%s." % (self.__class__.__name__, self.basename, self.nodata))
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                    self._footprint_poly = self.box.mapPoly

            # validation
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            assert not polyVertices_outside_poly(self._footprint_poly, self.box.mapPoly), \
                "Computing footprint polygon for %s '%s' failed. The resulting polygon is partly or completely " \
                "outside of the image bounds." % (self.__class__.__name__, self.basename)
            # assert self._footprint_poly
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            # for XY in self.corner_coord:
            #    assert self.GeoArray.box.mapPoly.contains(Point(XY)) or self.GeoArray.box.mapPoly.touches(Point(XY)), \
            #        "The corner position '%s' is outside of the %s." % (XY, self.imName)

        return self._footprint_poly

    @footprint_poly.setter
    def footprint_poly(self, poly):
        if isinstance(poly, Polygon):
            self._footprint_poly = poly
        elif isinstance(poly, str):
            self._footprint_poly = shply_loads(poly)
        else:
            raise ValueError("'footprint_poly' can only be set from a shapely polygon or a WKT string.")

    @property
    def metadata(self):
        """
        Returns a GeoDataFrame containing all available metadata (read from file if available).
        Use 'metadata[band_index].to_dict()' to get a metadata dictionary for a specific band.
        Use 'metadata.loc[row_name].to_dict()' to get all metadata values of the same key for all bands as dictionary.
        Use 'metadata.loc[row_name, band_index] = value' to set a new value.

        :return:  geopandas.GeoDataFrame
        """

        if self._metadata is not None:
            return self._metadata
        else:
            default = GeoDataFrame(columns=range(self.bands))
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            # for bn,idx in self.bandnames.items():
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            #    default.loc['band_index',bn] = idx
            self._metadata = default
            if not self.is_inmem:
                self.set_gdalDataset_meta()
                return self._metadata
            else:
                return self._metadata

    @metadata.setter
    def metadata(self, GDF):
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        assert isinstance(GDF, (GeoDataFrame, DataFrame)) and len(GDF.columns) == self.bands, \
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            "%s.metadata can only be set with an instance of geopandas.GeoDataFrame of which the column number " \
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            "corresponds to the band number of %s." % (self.__class__.__name__, self.__class__.__name__)
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        self._metadata = GDF

    meta = alias_property('metadata')

    def __getitem__(self, given):
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        if isinstance(given, (int, float, slice)) and self.ndim == 3:
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            # handle 'given' as index for 3rd (bands) dimension
            if self.is_inmem:
                return self.arr[:, :, given]
            else:
                return self.from_path(self.arg, [given])

        elif isinstance(given, str):
            # behave like a dictionary and return the corresponding band
            if self.bandnames:
                if given not in self.bandnames:
                    raise ValueError("'%s' is not a known band. Known bands are: %s"
                                     % (given, ', '.join(list(self.bandnames.keys()))))
                if self.is_inmem:
523
                    return self.arr if self.ndim == 2 else self.arr[:, :, self.bandnames[given]]
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                else:
                    return self.from_path(self.arg, [self.bandnames[given]])
            else:
                raise ValueError('String indices are only supported if %s has been instanced with bandnames given.'
528
                                 % self.__class__.__name__)
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        elif isinstance(given, (tuple, list)):
            # handle requests like geoArr[[1,2],[3,4]  -> not implemented in from_path if array is not in mem
            types = [type(i) for i in given]
            if list in types or tuple in types:
                self.to_mem()

536
            if len(given) == 3:
537
538

                # handle strings in the 3rd dim of 'given' -> convert them to a band index
539
                if isinstance(given[2], str):
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                    if self.bandnames:
                        if given[2] not in self.bandnames:
                            raise ValueError("'%s' is not a known band. Known bands are: %s"
                                             % (given[2], ', '.join(list(self.bandnames.keys()))))

                        band_idx = self.bandnames[given[2]]
                        # NOTE: the string in the 3rd is ignored if ndim==2 and band_idx==0
                        if self.is_inmem:
548
                            return self.arr if (self.ndim == 2 and band_idx == 0) else self.arr[:, :, band_idx]
549
                        else:
550
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                            getitem_params = \
                                given[:2] if (self.ndim == 2 and band_idx == 0) else given[:2] + (band_idx,)
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                            return self.from_path(self.arg, getitem_params)
                    else:
                        raise ValueError(
                            'String indices are only supported if %s has been instanced with bandnames given.'
                            % self.__class__.__name__)

                # in case a third dim is requested from 2D-array -> ignore 3rd dim if 3rd dim is 0
559
                elif self.ndim == 2 and given[2] == 0:
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                    if self.is_inmem:
                        return self.arr[given[:2]]
                    else:
                        return self.from_path(self.arg, given[:2])

        # if nothing has been returned until here -> behave like a numpy array
        if self.is_inmem:
            return self.arr[given]
        else:
            getitem_params = [given] if isinstance(given, slice) else given
            return self.from_path(self.arg, getitem_params)

    def __setitem__(self, idx, array2set):
        """Overwrites the pixel values of GeoArray.arr with the given array.

        :param idx:         <int, list, slice> the index position to overwrite
        :param array2set:   <np.ndarray> array to be set. Must be compatible to the given index position.
        :return:
        """

        if self.is_inmem:
            self.arr[idx] = array2set
        else:
            raise NotImplementedError('Item assignment for %s instances that are not in memory is not yet supported.'
584
                                      % self.__class__.__name__)
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    def __getattr__(self, attr):
        # check if the requested attribute can not be present because GeoArray has been instanced with an array
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        if attr not in self.__dir__() and not self.is_inmem and attr in ['shape', 'dtype', 'geotransform',
                                                                         'projection']:
590
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            self.set_gdalDataset_meta()

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        if attr in self.__dir__():  # __dir__() includes also methods and properties
            return self.__getattribute__(attr)  # __getattribute__ avoids infinite loop
        elif hasattr(np.array([]), attr):
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            return self[:].__getattribute__(attr)
        else:
597
            raise AttributeError("%s object has no attribute '%s'." % (self.__class__.__name__, attr))
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    def __getstate__(self):
        """Defines how the attributes of GMS object are pickled."""

        # clean array cache in order to avoid cache pickling
        self.flush_cache()

        return self.__dict__

    def __setstate__(self, state):
        """Defines how the attributes of GMS object are unpickled.
        NOTE: This method has been implemented because otherwise pickled and unpickled instances show recursion errors
        within __getattr__ when requesting any attribute.
        """

        self.__dict__ = state

    def calc_mask_nodata(self, fromBand=None, overwrite=False):
        """Calculates a no data mask with (values: 0=nodata; 1=data)

        :param fromBand:  <int> index of the band to be used (if None, all bands are used)
        :param overwrite: <bool> whether to overwrite existing nodata mask that has already been calculated
        :return:
        """

        if self._mask_nodata is None or overwrite:
            assert self.ndim in [2, 3], "Only 2D or 3D arrays are supported. Got a %sD array." % self.ndim
625
            arr = self[:, :, fromBand] if self.ndim == 3 and fromBand is not None else self[:]
626
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628

            if self.nodata is None:
                self.mask_nodata = np.ones((self.rows, self.cols), np.bool)
629
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            elif np.isnan(self.nodata):
                self.mask_nodata = \
                    np.invert(np.isnan(arr)) if arr.ndim == 2 else \
                    np.all(np.invert(np.isnan(arr)), axis=2)
633
            else:
634
                self.mask_nodata = \
635
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                    np.ma.masked_not_equal(arr, self.nodata).mask if arr.ndim == 2 else \
                    np.all(np.ma.masked_not_equal(arr, self.nodata).mask, axis=2)
637

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    def find_noDataVal(self, bandIdx=0, sz=3):
        """Tries to derive no data value from homogenious corner pixels within 3x3 windows (by default).
        :param bandIdx:
        :param sz: window size in which corner pixels are analysed
        """
        wins = [self[0:sz, 0:sz, bandIdx], self[0:sz, -sz:, bandIdx],
                self[-sz:, -sz:, bandIdx], self[-sz:, 0:sz, bandIdx]]  # UL, UR, LR, LL

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        means, stds = [np.mean(win) for win in wins], [np.std(win) for win in wins]
        possVals = [mean for mean, std in zip(means, stds) if std == 0 or np.isnan(std)]
648
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650
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        # possVals==[]: all corners are filled with data; np.std(possVals)==0: noDataVal clearly identified

        if possVals:
            if np.std(possVals) != 0:
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                if np.isnan(np.std(possVals)):
                    # at least one of the possible values is np.nan
                    nodata = np.nan
                else:
                    # different possible nodata values have been found in the image corner
                    nodata = 'ambiguous'
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            else:
                if len(possVals) <= 2:
                    # each window in each corner
                    warnings.warn("\nAutomatic nodata value detection returned the value %s for GeoArray '%s' but this "
                                  "seems to be unreliable (occurs in only %s). To avoid automatic detection, just pass "
663
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                                  "the correct nodata value."
                                  % (possVals[0], self.basename, ('2 image corners' if len(possVals) == 2 else
                                                                  '1 image corner')))
666
                nodata = possVals[0]
667
        else:
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            nodata = None

        return nodata
671

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    def set_gdalDataset_meta(self):
        """Retrieves GDAL metadata from file. This function is only executed once to avoid overwriting of user defined
         attributes, that are defined after object instanciation.

        :return:
        """

        if not self._gdalDataset_meta_already_set:
            assert self.filePath
            ds = gdal.Open(self.filePath)
682
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            if not ds:
                raise Exception('Error reading file:  ' + gdal.GetLastErrorMsg())

685
            # set private class variables (in order to avoid recursion error)
686
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            self._shape = tuple([ds.RasterYSize, ds.RasterXSize] + ([ds.RasterCount] if ds.RasterCount > 1 else []))
            self._dtype = gdal_array.GDALTypeCodeToNumericTypeCode(ds.GetRasterBand(1).DataType)
688
            self._geotransform = list(ds.GetGeoTransform())
689
690

            # for some reason GDAL reads arbitrary geotransforms as (0, 1, 0, 0, 0, 1) instead of (0, 1, 0, 0, 0, -1)
691
            self._geotransform[5] = -abs(self._geotransform[5])  # => force ygsd to be negative
692

693
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            # temp conversion to EPSG needed because GDAL seems to modify WKT string when writing file to disk
            # (e.g. using gdal_merge) -> conversion to EPSG and back undos that
            self._projection = EPSG2WKT(WKT2EPSG(ds.GetProjection()))
696

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699
700
            if 'nodata' not in self._initParams or self._initParams['nodata'] is None:
                band = ds.GetRasterBand(1)
                # FIXME this does not support different nodata values within the same file
                self._nodata = band.GetNoDataValue()
701

702
            # read global domain metadata
703
704
            # TODO check to specifically use the 'ENVI' metadata domain ds.GetMetadata('ENVI')
            global_meta = ds.GetMetadata()
705

706
707
            # read band domain metadata
            for b in range(self.bands):
708
709
                band = ds.GetRasterBand(b + 1)
                meta_gs = GeoSeries(band.GetMetadata())
710

711
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                # add band names if available
                if 'Band_%s' % str(b + 1) in global_meta.keys():
                    meta_gs['band_name'] = global_meta['Band_%s' % str(b + 1)]

                # TODO add the remaining global metadata

717
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721
                # avoid double-call of set_gdalDataset_meta by setting self._metadata to default value
                self._metadata = \
                    self._metadata if self._metadata is not None else GeoDataFrame(columns=range(self.bands))

                # fill metadata
722
                self.metadata[b] = meta_gs
723
                del band
724

725
            del ds
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733
734
735
736
737
738
739

        self._gdalDataset_meta_already_set = True

    def from_path(self, path, getitem_params=None):
        # type: (str, list) -> np.ndarray
        """Read a GDAL compatible raster image from disk, with respect to the given image position.
        NOTE: If the requested array position is already in cache, it is returned from there.

        :param path:            <str> the file path of the image to read
        :param getitem_params:  <list> a list of slices in the form [row_slice, col_slice, band_slice]
        :return out_arr:        <np.ndarray> the output array
        """

        ds = gdal.Open(path)
740
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742
        if not ds:
            raise Exception('Error reading file:  ' + gdal.GetLastErrorMsg())

743
        R, C, B = ds.RasterYSize, ds.RasterXSize, ds.RasterCount
744
        del ds
745

746
        # convert getitem_params to subset area to be read ##
747
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        rS, rE, cS, cE, bS, bE, bL = [None] * 7

        # populate rS, rE, cS, cE, bS, bE, bL
        if getitem_params:
            if len(getitem_params) >= 2:
                givenR, givenC = getitem_params[:2]
                if isinstance(givenR, slice):
                    rS = givenR.start
                    rE = givenR.stop - 1 if givenR.stop is not None else None
                elif isinstance(givenR, int):
                    rS = givenR
                    rE = givenR
                if isinstance(givenC, slice):
                    cS = givenC.start
                    cE = givenC.stop - 1 if givenC.stop is not None else None
                elif isinstance(givenC, int):
                    cS = givenC
                    cE = givenC
            if len(getitem_params) in [1, 3]:
                givenB = getitem_params[2] if len(getitem_params) == 3 else getitem_params[0]
                if isinstance(givenB, slice):
                    bS = givenB.start
                    bE = givenB.stop - 1 if givenB.stop is not None else None
                elif isinstance(givenB, int):
                    bS = givenB
                    bE = givenB
773
                elif isinstance(givenB, (tuple, list)):
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                    typesInGivenB = [type(i) for i in givenB]
                    assert len(list(set(typesInGivenB))) == 1, \
                        'Mixed data types within the list of bands are not supported.'
                    if isinstance(givenB[0], int):
                        bL = list(givenB)
                    elif isinstance(givenB[0], str):
                        bL = [self.bandnames[i] for i in givenB]
                elif type(givenB) in [str]:
                    bL = [self.bandnames[givenB]]

        # set defaults for not given values
        rS = rS if rS is not None else 0
        rE = rE if rE is not None else R - 1
        cS = cS if cS is not None else 0
        cE = cE if cE is not None else C - 1
        bS = bS if bS is not None else 0
        bE = bE if bE is not None else B - 1
        bL = list(range(bS, bE + 1)) if not bL else bL

        # convert negative to positive ones
        rS = rS if rS >= 0 else self.rows + rS
        rE = rE if rE >= 0 else self.rows + rE
        cS = cS if cS >= 0 else self.columns + cS
        cE = cE if cE >= 0 else self.columns + cE
        bS = bS if bS >= 0 else self.bands + bS
        bE = bE if bE >= 0 else self.bands + bE
800
        bL = [b if b >= 0 else (self.bands + b) for b in bL]
801
802

        # validate subset area bounds to be read
803
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805
806
807
808
809
        def msg(v, idx, sz):
            # FIXME numpy raises that error ONLY for the 2nd axis
            return '%s is out of bounds for axis %s with size %s' % (v, idx, sz)

        for val, axIdx, axSize in zip([rS, rE, cS, cE, bS, bE], [0, 0, 1, 1, 2, 2], [R, R, C, C, B, B]):
            if not 0 <= val <= axSize - 1:
                raise ValueError(msg(val, axIdx, axSize))
810
811

        # summarize requested array position in arr_pos
812
        # NOTE: # bandlist must be string because truth value of an array with more than one element is ambiguous
813
814
815
        arr_pos = dict(rS=rS, rE=rE, cS=cS, cE=cE, bS=bS, bE=bE, bL=bL)

        # check if the requested array position is already in cache -> if yes, return it from there
816
        if self._arr_cache is not None and self._arr_cache['pos'] == arr_pos:
817
            out_arr = self._arr_cache['arr_cached']
818
819
820
821
822
823
824

        else:
            # TODO insert a multiprocessing.Lock here in order to prevent IO bottlenecks?
            # read subset area from disk
            if bL == list(range(0, B)):
                tempArr = gdalnumeric.LoadFile(path, cS, rS, cE - cS + 1, rE - rS + 1)
                out_arr = np.swapaxes(np.swapaxes(tempArr, 0, 2), 0, 1) if B > 1 else tempArr
825
826
                if out_arr is None:
                    raise Exception('Error reading file:  ' + gdal.GetLastErrorMsg())
827
828
829
830
            else:
                ds = gdal.Open(path)
                if len(bL) == 1:
                    band = ds.GetRasterBand(bL[0] + 1)
831
                    out_arr = band.ReadAsArray(cS, rS, cE - cS + 1, rE - rS + 1)
832
833
                    if out_arr is None:
                        raise Exception('Error reading file:  ' + gdal.GetLastErrorMsg())
834
                    del band
835
836
837
838
839
                else:
                    out_arr = np.empty((rE - rS + 1, cE - cS + 1, len(bL)))
                    for i, bIdx in enumerate(bL):
                        band = ds.GetRasterBand(bIdx + 1)
                        out_arr[:, :, i] = band.ReadAsArray(cS, rS, cE - cS + 1, rE - rS + 1)
840
841
                        if out_arr is None:
                            raise Exception('Error reading file:  ' + gdal.GetLastErrorMsg())
842
                        del band
843

844
                del ds
845
846

            # only set self.arr if the whole cube has been read (in order to avoid sudden shape changes)
847
            if out_arr.shape == self.shape:
848
849
850
851
852
                self.arr = out_arr

            # write _arr_cache
            self._arr_cache = dict(pos=arr_pos, arr_cached=out_arr)

853
854
        return out_arr  # TODO implement check of returned datatype (e.g. NoDataMask should always return np.bool
        # TODO -> would be np.int8 if an int8 file is read from disk
855
856
857
858
859
860
861
862

    def save(self, out_path, fmt='ENVI', creationOptions=None):
        # type: (str, str, list) -> None
        """Write the raster data to disk.

        :param out_path:        <str> output path
        :param fmt:             <str> the output format / GDAL driver code to be used for output creation, e.g. 'ENVI'
                                Refer to http://www.gdal.org/formats_list.html to get a full list of supported formats.
863
864
        :param creationOptions: <list> GDAL creation options,
                                e.g., ["QUALITY=80", "REVERSIBLE=YES", "WRITE_METADATA=YES"]
865
866
867
        """

        if not self.q:
868
869
            print('Writing GeoArray of size %s to %s.' % (self.shape, out_path))
        assert self.ndim in [2, 3], 'Only 2D- or 3D arrays are supported.'
870
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872
873
874
875
876
877
878
879

        driver = gdal.GetDriverByName(fmt)
        if driver is None:
            raise Exception("'%s' is not a supported GDAL driver. Refer to www.gdal.org/formats_list.html for full "
                            "list of GDAL driver codes." % fmt)

        if not os.path.isdir(os.path.dirname(out_path)):
            os.makedirs(os.path.dirname(out_path))

        if self.is_inmem:
880
881
            ds = get_GDAL_ds_inmem(self.arr, self.geotransform, self.projection,
                                   self.nodata)  # expects rows,columns,bands
882
883
884

            # set metadata
            if not self.metadata.empty:
885
886
887
                global_meta = {}

                # set band domain metadata
888
                for bidx in range(self.bands):
889
                    band = ds.GetRasterBand(bidx + 1)
890
                    meta2write = self.metadata[bidx].to_dict()
891
                    meta2write = dict((k, v) for k, v in meta2write.items() if v is not np.nan)
892
893

                    if 'band_name' in meta2write:
894
                        global_meta['Band_%s' % str(bidx + 1)] = meta2write['band_name']
895
896
                        del meta2write['band_name']

897
                    band.SetMetadata(meta2write)
898
                    del band
899

900
901
902
                # set global domain metadata
                ds.SetMetadata(global_meta)

903
904
905
906
907
                # get ENVI metadata domain
                # ds_orig = gdal.Open(self.filePath)
                # envi_meta_domain = ds_orig.GetMetadata('ENVI')
                # ds.SetMetadata(envi_meta_domain, 'ENVI')
                # ds_orig = None
908

909
910
            driver.CreateCopy(out_path, ds, options=creationOptions if creationOptions else [])

911
912
913
914
            # rows, columns, bands => bands, rows, columns
            # out_arr = self.arr if self.ndim == 2 else np.swapaxes(np.swapaxes(self.arr, 0, 2), 1, 2)
            # gdalnumeric.SaveArray(out_arr, out_path, format=fmt, prototype=ds) # expects bands,rows,columns
            del ds
915
916
917

        else:
            src_ds = gdal.Open(self.filePath)
918
919
920
            if not src_ds:
                raise Exception('Error reading file:  ' + gdal.GetLastErrorMsg())

921
922
            gdal_Translate = get_gdal_func('Translate')
            gdal_Translate(out_path, src_ds, format=fmt, creationOptions=creationOptions)
923
            del src_ds
924
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926
927
928
929
930
931
932

        if not os.path.exists(out_path):
            raise Exception(gdal.GetLastErrorMsg())

    def dump(self, out_path):
        # type: (str) -> None
        """Serialize the whole object instance to disk using dill."""

        import dill
933
934
        with open(out_path, 'wb') as outF:
            dill.dump(self, outF)
935
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937
938
939
940
941
942
943

    def _get_plottable_image(self, xlim=None, ylim=None, band=None, boundsMap=None, boundsMapPrj=None, res_factor=None,
                             nodataVal=None, out_prj=None):
        # handle limits
        if boundsMap:
            boundsMapPrj = boundsMapPrj if boundsMapPrj else self.prj
            image2plot, gt, prj = self.get_mapPos(boundsMap, boundsMapPrj, band2get=band,
                                                  fillVal=nodataVal if nodataVal is not None else self.nodata)
        else:
944
945
            cS, cE = xlim if isinstance(xlim, (tuple, list)) else (0, self.columns)
            rS, rE = ylim if isinstance(ylim, (tuple, list)) else (0, self.rows)
946
947

            image2plot = self[rS:rE, cS:cE, band] if band is not None else self[rS:rE, cS:cE]
948
            gt, prj = self.geotransform, self.projection
949

950
        transOpt = ['SRC_METHOD=NO_GEOTRANSFORM'] if tuple(gt) == (0, 1, 0, 0, 0, -1) else None
951
        xdim, ydim = None, None
952
        nodataVal = nodataVal if nodataVal is not None else self.nodata
953
954

        if res_factor != 1. and image2plot.shape[0] * image2plot.shape[1] > 1e6:  # shape > 1000*1000
955
956
957
958
            # sample image down / normalize
            xdim, ydim = \
                (self.columns * res_factor, self.rows * res_factor) if res_factor else \
                tuple((np.array([self.columns, self.rows]) / (np.array([self.columns, self.rows]).max() / 1000)))
959
960
961
            xdim, ydim = int(xdim), int(ydim)

        if xdim or ydim or out_prj:
962
            from py_tools_ds.geo.raster.reproject import warp_ndarray
963
964
965
966
967
            image2plot, gt, prj = warp_ndarray(image2plot, self.geotransform, self.projection,
                                               out_XYdims=(xdim, ydim), in_nodata=nodataVal, out_nodata=nodataVal,
                                               transformerOptions=transOpt, out_prj=out_prj, q=True)
            if transOpt and 'NO_GEOTRANSFORM' in ','.join(transOpt):
                image2plot = np.flipud(image2plot)
968
969
                gt = list(gt)
                gt[3] = 0
970
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972
973
974
975
976

            if xdim or ydim:
                print('Note: array has been downsampled to %s x %s for faster visualization.' % (xdim, ydim))

        return image2plot, gt, prj

    def show(self, xlim=None, ylim=None, band=None, boundsMap=None, boundsMapPrj=None, figsize=None,
977
978
             interpolation='none', vmin=None, vmax=None, pmin=2, pmax=98, cmap=None, nodataVal=None,
             res_factor=None, interactive=False):
979
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987
988
        """Plots the desired array position into a figure.

        :param xlim:            [start_column, end_column]
        :param ylim:            [start_row, end_row]
        :param band:            the band index of the band to be plotted (if None and interactive==True all bands are
                                shown, otherwise the first band is chosen)
        :param boundsMap:       xmin, ymin, xmax, ymax
        :param boundsMapPrj:
        :param figsize:
        :param interpolation:
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        :param vmin:            darkest pixel value to be included in stretching
        :param vmax:            brightest pixel value to be included in stretching
        :param pmin:            percentage to be used for excluding the darkest pixels from stretching (default: 2)
        :param pmax:            percentage to be used for excluding the brightest pixels from stretching (default: 98)
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        :param cmap:
        :param nodataVal:
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        :param res_factor:      <float> resolution factor for downsampling of the image to be plotted in order to save
                                plotting time and memory (default=None -> downsampling is performed to 1000x1000)
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        :param interactive:     <bool> activates interactive plotting based on 'holoviews' library.
                                NOTE: this deactivates the magic '% matplotlib inline' in Jupyter Notebook
        :return:
        """

        band = (band if band is not None else 0) if not interactive else band

        # get image to plot
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        nodataVal = nodataVal if nodataVal is not None else self.nodata
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        image2plot, gt, prj = \
            self._get_plottable_image(xlim, ylim, band, boundsMap=boundsMap, boundsMapPrj=boundsMapPrj,
                                      res_factor=res_factor, nodataVal=nodataVal)
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        # set color palette
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        palette = cmap if cmap else plt.cm.gray
        if nodataVal is not None and np.std(image2plot) != 0:  # do not show nodata
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            image2plot = np.ma.masked_equal(image2plot, nodataVal)
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            vmin_auto, vmax_auto = \
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                np.nanpercentile(image2plot.compressed(), pmin), np.nanpercentile(image2plot.compressed(), pmax)
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            palette.set_bad('aqua', 0)
        else:
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            vmin_auto, vmax_auto = np.nanpercentile(image2plot, pmin), np.nanpercentile(image2plot, pmax)
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        vmin = vmin if vmin is not None else vmin_auto
        vmax = vmax if vmax is not None else vmax_auto

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        palette.set_over('1')
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        palette.set_under('0')

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        # check availability of holoviews
        if not util.find_spec('holoviews'):
            warnings.warn("Interactive mode requires holoviews. Install it by running, e.g., "
                          "'conda install --yes -c ioam bokeh holoviews'. Using non-interactive mode.")
            interactive = False

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        if interactive and image2plot.ndim == 3:
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            import holoviews as hv
            from skimage.exposure import rescale_intensity
            hv.notebook_extension('matplotlib')

            cS, cE = xlim if isinstance(xlim, (tuple, list)) else (0, self.columns - 1)
            rS, rE = ylim if isinstance(ylim, (tuple, list)) else (0, self.rows - 1)

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            image2plot = np.array(rescale_intensity(image2plot, in_range=(vmin, vmax)))
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            def get_hv_image(b):
                # FIXME ylabels have the wrong order
                return hv.Image(image2plot[:, :, b] if b is not None else image2plot,
                                bounds=(cS, rS, cE, rE))(
                    style={'cmap': 'gray'}, plot={'fig_inches': 4 if figsize is None else figsize, 'show_grid': True})

            # hvIm = hv.Image(image2plot)(style={'cmap': 'gray'}, figure_inches=figsize)
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            hmap = hv.HoloMap([(band, get_hv_image(band)) for band in range(image2plot.shape[2])], kdims=['band'])

            return hmap

        else:
            if interactive:
                warnings.warn('Currently there is no interactive mode for single-band arrays. '
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                              'Switching to standard matplotlib figure..')  # TODO implement zoomable fig
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            # show image
            plt.figure(figsize=figsize)
            rows, cols = image2plot.shape[:2]
            plt.imshow(image2plot, palette, interpolation=interpolation, extent=(0, cols, rows, 0),
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                       vmin=vmin, vmax=vmax, )  # compressed excludes nodata values
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            plt.show()

    def show_map(self, xlim=None, ylim=None, band=0, boundsMap=None, boundsMapPrj=None, ax=None, figsize=None,
                 interpolation='none', vmin=None, vmax=None, cmap=None, nodataVal=None, res_factor=None,
                 return_map=False, zoomable=False):
        """

        :param xlim:
        :param ylim:
        :param band:            band index (starting with 0)
        :param boundsMap:       xmin, ymin, xmax, ymax
        :param boundsMapPrj:
        :param ax:              allows to pass a matplotlib axis object where figure is plotted into
        :param figsize:
        :param interpolation:
        :param vmin:
        :param vmax:
        :param cmap:
        :param nodataVal:
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        :param res_factor:      <float> resolution factor for downsampling of the image to be plotted in order to save
                                plotting time and memory (default=None -> downsampling is performed to 1000x1000)
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        :param return_map:
        :param zoomable:        <bool> enable or disable zooming via mpld3
        :return:
        """

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        if not util.find_spec('mpl_toolkits.basemap'):
            raise ImportError('This function requires Basemap. You need to install basemap manually (see www./'
                              'matplotlib.org/basemap) if you want to plot maps. It is not automatically installed.')

        from mpl_toolkits.basemap import Basemap

        mpld3_avl = util.find_spec('mpld3')
        if not mpld3_avl:
            warnings.warn('mpld3 is not available. Zooming disabled.')
            zoomable = False

        if zoomable:
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            import mpld3
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            mpld3.enable_notebook()
        elif mpld3_avl:
            import mpld3
            # noinspection PyBroadException
            try:
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                mpld3.disable_notebook()
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            except Exception:
                pass
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        assert self.geotransform and tuple(self.geotransform) != (0, 1, 0, 0, 0, -1), \
            'A valid geotransform is needed for a map visualization. Got %s.' % list(self.geotransform)
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        assert self.projection, "A projection is needed for a map visualization. Got '%s'." % self.projection
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        # get image to plot
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        nodataVal = nodataVal if nodataVal is not None else self.nodata
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        image2plot, gt, prj = self._get_plottable_image(xlim, ylim, band, boundsMap=boundsMap,
                                                        boundsMapPrj=boundsMapPrj, res_factor=res_factor,
                                                        nodataVal=nodataVal, out_prj='epsg:4326')

        # calculate corner coordinates of plot
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        # if boundsMap:
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        #    boundsMapPrj = boundsMapPrj if boundsMapPrj else self.prj
        #    if not prj_equal(boundsMapPrj, 4326):
        #        boundsMap = reproject_shapelyGeometry(box(*boundsMap), boundsMapPrj, 4626).bounds
        #    xmin, ymin, xmax, ymax = boundsMap
        #    UL_XY, UR_XY, LR_XY, LL_XY = (xmin,ymax), (xmax, ymax), (xmax,ymin), (xmin, ymin)
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        # else:
        UL_XY, UR_XY, LR_XY, LL_XY = [(YX[1], YX[0]) for YX in GeoArray(image2plot, gt, prj).box.boxMapYX]
        center_lon, center_lat = (UL_XY[0] + UR_XY[0]) / 2., (UL_XY[1] + LL_XY[1]) / 2.
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        # create map
        fig = plt.figure(figsize=figsize)
        plt.subplots_adjust(left=0.05, right=0.95, top=0.90, bottom=0.05, wspace=0.15, hspace=0.05)
        ax = ax if ax is not None else plt.subplot(111)

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        m = Basemap(projection='tmerc', resolution=None, lon_0=center_lon, lat_0=center_lat,
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                    urcrnrlon=UR_XY[0], urcrnrlat=UR_XY[1], llcrnrlon=LL_XY[0], llcrnrlat=LL_XY[1])

        # set color palette
        palette = cmap if cmap else plt.cm.gray
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        if nodataVal is not None and np.std(image2plot) != 0:  # do not show nodata
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            image2plot = np.ma.masked_equal(image2plot, nodataVal)
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            vmin_auto, vmax_auto = \
                np.nanpercentile(image2plot.compressed(), 2), np.nanpercentile(image2plot.compressed(), 98)
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            palette.set_bad('aqua', 0)
        else:
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            vmin_auto, vmax_auto = np.nanpercentile(image2plot, 2), np.nanpercentile(image2plot, 98)
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        vmin = vmin if vmin is not None else vmin_auto
        vmax = vmax if vmax is not None else vmax_auto
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        palette.set_over('1')
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        palette.set_under('0')

        # add image to map (y-axis must be inverted for basemap)
        if zoomable:
            m.imshow(image2plot, palette, interpolation=interpolation, vmin=vmin, vmax=vmax)
        else:
            m.imshow(np.flipud(image2plot), palette, interpolation=interpolation, vmin=vmin, vmax=vmax)

        # add coordinate grid lines
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        parallels = np.arange(-90, 90., 0.25)  # TODO make this adjustable
        # parallels = np.arange(-90, 90., 0.1)
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        m.drawparallels(parallels, labels=[1, 0, 0, 0], fontsize=12, linewidth=0.4)

        meridians = np.arange(-180., 180., 0.25)
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        # meridians = np.arange(-180., 180., 0.1)
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        m.drawmeridians(meridians, labels=[0, 0, 0, 1], fontsize=12, linewidth=0.4)

        if return_map:
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            return fig, ax, m
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        else:
            plt.show()

    def show_map_utm(self, xlim=None, ylim=None, band=0, figsize=None, interpolation='none', cmap=None,
                     nodataVal=None, vmin=None, vmax=None, res_factor=None, return_map=False):

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        if not util.find_spec('mpl_toolkits.basemap'):
            raise ImportError('This function requires Basemap. You need to install basemap manually (see www./'
                              'matplotlib.org/basemap) if you want to plot maps. It is not automatically installed.')

        from mpl_toolkits.basemap import Basemap

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        warnings.warn(UserWarning('This function is still under construction and may not work as expected!'))
        # TODO debug this function

        # get image to plot
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        nodataVal = nodataVal if nodataVal is not None else self.nodata
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        image2plot, gt, prj = self._get_plottable_image(xlim, ylim, band, res_factor, nodataVal)

        # calculate corner coordinates of plot
        box2plot = GeoArray(image2plot, gt, prj).box
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        # UL_XY, UR_XY, LR_XY, LL_XY = [(YX[1], YX[0]) for YX in GeoArray(image2plot, gt, prj).box.boxMapYX]
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        # Xarr, Yarr = self.box.get_coordArray_MapXY(prj=EPSG2WKT(4326))
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        UL_XY, UR_XY, LR_XY, LL_XY = [transform_any_prj(self.projection, 'epsg:4326', x, y) for y, x in
                                      box2plot.boxMapYX]
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        center_X, center_Y = (UL_XY[0] + UR_XY[0]) / 2., (UL_XY[1] + LL_XY[1]) / 2.
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        center_lon, center_lat = transform_any_prj(prj, 'epsg:4326', center_X, center_Y)
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        print(center_lon, center_lat)

        # create map
        fig = plt.figure(figsize=figsize)
        plt.subplots_adjust(left=0.05, right=0.95, top=0.90, bottom=0.05, wspace=0.15, hspace=0.05)
        ax = plt.subplot(111)
        print(UL_XY, UR_XY, LR_XY, LL_XY)
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        #        m = Basemap(projection='tmerc', resolution=None, lon_0=center_lon, lat_0=center_lat,
        #                    urcrnrx=UR_XY[0], urcrnry=UR_XY[1], llcrnrx=LL_XY[0], llcrnry=LL_XY[1])
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        m = Basemap(projection='tmerc', resolution=None, lon_0=center_lon, lat_0=center_lat,
                    urcrnrlon=UR_XY[0], urcrnrlat=UR_XY[1], llcrnrlon=LL_XY[0], llcrnrlat=LL_XY[1],
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                    k_0=0.9996, rsphere=(6378137.00, 6356752.314245179), suppress_ticks=False)
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        # m.pcolormesh(Xarr, Yarr, self[:], cmap=plt.cm.jet)

        # set color palette
        palette = cmap if cmap else plt.cm.gray
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        if nodataVal is not None:  # do not show nodata
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            image2plot = np.ma.masked_equal(image2plot, nodataVal)
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            vmin_auto, vmax_auto = \
                np.nanpercentile(image2plot.compressed(), 2), np.nanpercentile(image2plot.compressed(), 98)
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            palette.set_bad('aqua', 0)
        else:
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            vmin_auto, vmax_auto = np.nanpercentile(image2plot, 2), np.nanpercentile(image2plot, 98)
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        vmin = vmin if vmin is not None else vmin_auto
        vmax = vmax if vmax is not None else vmax_auto
        palette.set_over('1')
        palette.set_under('0')

        # add image to map (y-axis must be inverted for basemap)
        m.imshow(np.flipud(image2plot), palette, interpolation=interpolation, vmin=vmin, vmax=vmax)

        # add coordinate grid lines
        parallels = np.arange(-90, 90., 0.25)
        m.drawparallels(parallels, labels=[1, 0, 0, 0], fontsize=12, linewidth=0.4)

        meridians = np.arange(-180., 180., 0.25)
        m.drawmeridians(meridians, labels=[0, 0, 0, 1], fontsize=12, linewidth=0.4)

        if return_map:
            return fig, ax, m
        else:
            plt.show()

    def show_footprint(self):
        """This method is intended to be called from Jupyter Notebook and shows a web map containing the calculated
        footprint of GeoArray.
        """

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        if not util.find_spec('folium') or not util.find_spec('geojson'):
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            raise ImportError(
                "This method requires the libraries 'folium' and 'geojson'. They can be installed with "
                "the shell command 'pip install folium geojson'.")

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        import folium
        im