class SandboxSource(Object): lat = Float.T(default=0.0, help="Latitude in [deg]") lon = Float.T(default=0.0, help="Longitude in [deg]") easting = Float.T(default=0.0, help="Easting in [m]") northing = Float.T(default=0.0, help="Northing in [m]") depth = Float.T(default=1.0 * km, help="Depth in [m]") def __init__(self, *args, **kwargs): Object.__init__(self, *args, **kwargs) self._cached_result = None self.evParametersChanged = Subject() self._sandbox = None def parametersUpdated(self): self._cached_result = None self.evParametersChanged.notify() def getSandboxOffset(self): if not self._sandbox or (self.lat == 0.0 and self.lon == 0.0): return 0.0, 0.0 return od.latlon_to_ne_numpy( self._sandbox.frame.llLat, self._sandbox.frame.llLon, self.lat, self.lon ) def getParametersArray(self): raise NotImplementedError
class SandboxSource(Object): easting = Float.T(help='Easting in [m]') northing = Float.T(help='Northing in [m]') depth = Float.T(help='Depth in [m]') def __init__(self, *args, **kwargs): Object.__init__(self, *args, **kwargs) self._cached_result = None self.evParametersChanged = Subject() def parametersUpdated(self): self._cached_result = None self.evParametersChanged.notify() def getParametersArray(self): raise NotImplemented
class Frame(object): """ Frame holding geographical references for :class:`kite.scene.Scene` The pixel spacing is given by ``dE`` and ``dN`` which can meters or degree. """ def __init__(self, scene, config=None): self.evChanged = Subject() self._scene = scene self._log = scene._log.getChild('Frame') self.N = None self.E = None self.llEutm = None self.llNutm = None self.utm_zone = None self.utm_zone_letter = None self._meter_grid = None self._updateConfig(config or FrameConfig()) self._scene.evConfigChanged.subscribe(self._updateConfig) self._scene.evChanged.subscribe(self.updateExtent) def _updateConfig(self, config=None): if config is not None: self.config = config elif self.config != self._scene.config.frame: self.config = self._scene.config.frame else: return if self.config.old_import: self._log.warning('Importing an old kite format...') self._log.warning('Please check your pixel spacing - dE, dN!') self.updateExtent() def updateExtent(self): if self._scene.cols == 0 or self._scene.rows == 0: return self.cols = self._scene.cols self.rows = self._scene.rows (self.llEutm, self.llNutm, self.utm_zone, self.utm_zone_letter) = utm.from_latlon(self.llLat, self.llLon) self.E = None self.N = None self.gridE = None self.gridN = None self._meter_grid = None self.coordinates = None self.config.regularize() self.evChanged.notify() @property def llLat(self): return self.config.llLat @llLat.setter def llLat(self, llLat): self.config.llLat = llLat self.updateExtent() @property def llLon(self): return self.config.llLon @llLon.setter def llLon(self, llLon): self.config.llLon = llLon self.updateExtent() @property def dN(self): return self.config.dN @dN.setter def dN(self, dN): self.config.dN = dN self.updateExtent() @property def dE(self): return self.config.dE @dE.setter def dE(self, dE): self.config.dE = dE self.updateExtent() @property def dEmeter(self): if self.isMeter(): return self.dE _, dEmeter = latlon_to_ne(self.llLat, self.llLon, self.llLat, self.llLon + self.dE * self.cols) return dEmeter / self.cols @property def dNmeter(self): if self.isMeter(): return self.dN dNmeter, _ = latlon_to_ne(self.llLat, self.llLon, self.llLat + self.dN * self.rows, self.llLon) return dNmeter / self.rows @property def dEdegree(self): if self.isDegree(): return self.dE lat, lon = ne_to_latlon(self.llLat, self.llLon, 0., self.dE * self.cols) distLon = lon - self.llLon return distLon / self.cols @property def dNdegree(self): if self.isDegree(): return self.dE lat, lon = ne_to_latlon(self.llLat, self.llLon, self.dN * self.rows, 0.) distLat = lat - self.llLat return distLat / self.rows @property def spacing(self): return self.config.spacing @spacing.setter def spacing(self, unit): self.config.spacing = unit @property_cached def E(self): return num.arange(self.cols) * self.dE @property_cached def Emeter(self): return num.arange(self.cols) * self.dEmeter @property_cached def N(self): return num.arange(self.rows) * self.dN @property def lengthE(self): return self.cols * self.dE @property def lengthN(self): return self.rows * self.dN @property_cached def Nmeter(self): return num.arange(self.rows) * self.dNmeter @property_cached def gridE(self): """ Grid holding local east coordinates of all pixels in ``NxM`` matrix of :attr:`~kite.Scene.displacement`. :type: :class:`numpy.ndarray`, size ``NxM`` """ valid_data = num.isnan(self._scene.displacement) gridE = num.repeat(self.E[num.newaxis, :], self.rows, axis=0) return num.ma.masked_array(gridE, valid_data, fill_value=num.nan) @property_cached def gridN(self): """ Grid holding local north coordinates of all pixels in ``NxM`` matrix of :attr:`~kite.Scene.displacement`. :type: :class:`numpy.ndarray`, size ``NxM`` """ valid_data = num.isnan(self._scene.displacement) gridN = num.repeat(self.N[:, num.newaxis], self.cols, axis=1) return num.ma.masked_array(gridN, valid_data, fill_value=num.nan) def _calculateMeterGrid(self): if self.isMeter(): raise ValueError('Frame is defined in meter! ' 'Use gridE and gridN for meter grids') if self._meter_grid is None: self._log.debug('Transforming latlon grid to meters...') gridN, gridE = latlon_to_ne_numpy( self.llLat, self.llLon, self.llLat + self.gridN.data.ravel(), self.llLon + self.gridE.data.ravel()) valid_data = num.isnan(self._scene.displacement) gridE = num.ma.masked_array(gridE.reshape(self.gridE.shape), valid_data, fill_value=num.nan) gridN = num.ma.masked_array(gridN.reshape(self.gridN.shape), valid_data, fill_value=num.nan) self._meter_grid = (gridE, gridN) return self._meter_grid @property_cached def gridEmeter(self): if self.isMeter(): return self.gridE return self._calculateMeterGrid()[0] @property_cached def gridNmeter(self): if self.isMeter(): return self.gridN return self._calculateMeterGrid()[1] @property_cached def coordinates(self): """ Local east and north coordinates of all pixels in ``Nx2`` matrix. :type: :class:`numpy.ndarray`, size ``Nx2`` """ coords = num.empty((self.rows * self.cols, 2)) coords[:, 0] = num.repeat(self.E[num.newaxis, :], self.rows, axis=0).flatten() coords[:, 1] = num.repeat(self.N[:, num.newaxis], self.cols, axis=1).flatten() if self.isMeter(): coords = ne_to_latlon(self.llLat, self.llLon, *coords.T) coords = num.array(coords).T else: coords[:, 0] += self.llLon coords[:, 1] += self.llLat return coords @property_cached def coordinatesMeter(self): """ Local east and north coordinates [m] of all pixels in ``NxM`` matrix. :type: :class:`numpy.ndarray`, size ``NxM`` """ coords = num.empty((self.rows * self.cols, 2)) coords[:, 0] = num.repeat(self.Emeter[num.newaxis, :], self.rows, axis=0).flatten() coords[:, 1] = num.repeat(self.Nmeter[:, num.newaxis], self.cols, axis=1).flatten() return coords def mapENMatrix(self, E, N): """ Local map coordinates in east and north to matrix row and column :param E: Easting in local coordinates :type E: float :param N: Northing in local coordinates :type N: float :returns: Row and column :rtype: tuple (int), (row, column) """ row = round(E / self.dE) if E > 0 else 0 col = round(N / self.dN) if N > 0 else 0 return int(row), int(col) @property def shape(self): return self._scene.shape def isMeter(self): return self.config.spacing == 'meter' def isDegree(self): return self.config.spacing == 'degree' @property def npixel(self): return self.cols * self.rows def __eq__(self, other): return self.llLat == other.llLat and\ self.llLon == other.llLon and\ self.dE == other.dE and\ self.dN == other.dN and\ self.rows == other.rows and\ self.cols == other.cols
class BaseScene(object): def __init__(self, **kwargs): self._log = logging.getLogger(self.__class__.__name__) self.evChanged = Subject() self.evConfigChanged = Subject() self._displacement = None self._displacement_px_var = None self._phi = None self._theta = None self._los_factors = None self.cols = 0 self.rows = 0 self.los = LOSUnitVectors(scene=self) self._elevation = {} frame_config = kwargs.pop('frame_config', FrameConfig()) for fattr in ('llLat', 'llLon', 'dLat', 'dLon'): coord = kwargs.pop(fattr, None) if coord is not None: frame_config.__setattr__(fattr, coord) self.frame = Frame(scene=self, config=frame_config) for attr in ('displacement', 'displacement_px_var', 'theta', 'phi'): data = kwargs.pop(attr, None) if data is not None: self.__setattr__(attr, data) @property def displacement(self): """Surface displacement in meter on a regular grid. :setter: Set the unwrapped InSAR displacement. :getter: Return the displacement matrix. :type: :class:`numpy.ndarray`, ``NxM`` """ return self._displacement @displacement.setter def displacement(self, value): _setDataNumpy(self, '_displacement', value) self.rows, self.cols = self._displacement.shape self.evChanged.notify() @property def displacement_px_var(self): """ Variance of the surface displacement per pixel. Same dimension as displacement. :setter: Set standard deviation of of the displacement. :getter: Return the standard deviation matrix. :type: :class:`numpy.ndarray`, ``NxM`` """ return self._displacement_px_var @displacement_px_var.setter def displacement_px_var(self, value): self._displacement_px_var = value @property def displacement_mask(self): """ Displacement :attr:`numpy.nan` mask :type: :class:`numpy.ndarray`, dtype :class:`numpy.bool` """ return ~num.isfinite(self.displacement) @property def shape(self): return self.displacement.shape @property def phi(self): """ Horizontal angle towards satellite :abbr:`line of sight (LOS)` in radians counter-clockwise from East. .. important :: Kite's convention is: * :math:`0` is **East** * :math:`\\frac{\\pi}{2}` is **North**! :setter: Set the phi matrix for scene's displacement, can be ``int`` for static look vector. :type: :class:`numpy.ndarray`, size same as :attr:`~kite.Scene.displacement` or int """ return self._phi @phi.setter def phi(self, value): if isinstance(value, float): self._phi = value else: _setDataNumpy(self, '_phi', value) self.phiDeg = None self.los_rotation_factors = None self.evChanged.notify() @property def theta(self): """ Theta is the look vector elevation angle towards satellite from the horizon in radians. Matrix of theta towards satellite's :abbr:`line of sight (LOS)`. .. important :: Kite convention! * :math:`-\\frac{\\pi}{2}` is **Down** * :math:`\\frac{\\pi}{2}` is **Up** :setter: Set the theta matrix for scene's displacement, can be ``int`` for static look vector. :type: :class:`numpy.ndarray`, size same as :attr:`~kite.Scene.displacement` or int """ return self._theta @theta.setter def theta(self, value): if isinstance(value, float): self._theta = value else: _setDataNumpy(self, '_theta', value) self.thetaDeg = None self.los_rotation_factors = None self.evChanged.notify() @property_cached def thetaDeg(self): """ LOS elevation angle in degree, ``NxM`` matrix like :class:`kite.Scene.theta` :type: :class:`numpy.ndarray` """ return num.rad2deg(self.theta) @property_cached def phiDeg(self): """ LOS horizontal orientation angle in degree, counter-clockwise from East,``NxM`` matrix like :class:`kite.Scene.phi` :type: :class:`numpy.ndarray` """ return num.rad2deg(self.phi) @property_cached def los_rotation_factors(self): """ Trigonometric factors to rotate displacement matrices towards LOS Rotation is as follows: .. displacement_los =\ (los_rotation_factors[:, :, 0] * -down + los_rotation_factors[:, :, 1] * east + los_rotation_factors[:, :, 2] * north) :returns: Factors for rotation :rtype: :class:`numpy.ndarray`, ``NxMx3`` :raises: AttributeError """ if (self.theta.size != self.phi.size): raise AttributeError('LOS angles inconsistent with provided' ' coordinate shape.') if self._los_factors is None: self._los_factors = num.empty( (self.theta.shape[0], self.theta.shape[1], 3)) self._los_factors[:, :, 0] = num.sin(self.theta) self._los_factors[:, :, 1] = num.cos(self.theta)\ * num.cos(self.phi) self._los_factors[:, :, 2] = num.cos(self.theta)\ * num.sin(self.phi) return self._los_factors def get_elevation(self, interpolation='nearest_neighbor'): assert interpolation in ('nearest_neighbor', 'bivariate') if self._elevation.get(interpolation, None) is None: self._log.debug('Getting elevation...') # region = llLon, urLon, llLat, urLon coords = self.frame.coordinates lons = coords[:, 0] lats = coords[:, 1] region = (lons.min(), lons.max(), lats.min(), lats.max()) if not srtmgl3.covers(region): raise AssertionError( 'Region is outside of SRTMGL3 topo dataset') tile = srtmgl3.get(region) if not tile: raise AssertionError('Cannot get SRTMGL3 topo dataset') if interpolation == 'nearest_neighbor': iy = num.rint((lats - tile.ymin) / tile.dy).astype(num.intp) ix = num.rint((lons - tile.xmin) / tile.dx).astype(num.intp) elevation = tile.data[(iy, ix)] elif interpolation == 'bivariate': interp = interpolate.RectBivariateSpline( tile.y(), tile.x(), tile.data) elevation = interp(lats, lons, grid=False) elevation = elevation.reshape(self.rows, self.cols) self._elevation[interpolation] = elevation return self._elevation[interpolation] def __neg__(self): ret = copy.deepcopy(self) ret.displacement *= -1 return ret def __add__(self, other, copy_obj=True): if copy_obj: ret = copy.deepcopy(self) else: ret = self if not ret.frame == other.frame: raise ValueError('Scene frames do not align!') ret.displacement += other.displacement tmin = ret.meta.time_master \ if ret.meta.time_master < other.meta.time_master \ else other.meta.time_master tmax = ret.meta.time_slave \ if ret.meta.time_slave > other.meta.time_slave \ else other.meta.time_slave ret.meta.time_master = tmin ret.meta.time_slave = tmax return ret def __sub__(self, other): return self.__add__(-other) def __isub__(self, scene): return self.__add__(-scene, copy_obj=False) def __iadd__(self, scene): return self.__add__(scene, copy_obj=False)
class Scene(BaseScene): """Scene of unwrapped InSAR ground displacements measurements :param config: Configuration object :type config: :class:`~kite.scene.SceneConfig`, optional Optional parameters :param displacement: Displacement in [m] :type displacement: :class:`numpy.ndarray`, NxM, optional :param theta: Theta look angle, see :attr:`BaseScene.theta` :type theta: :class:`numpy.ndarray`, NxM, optional :param phi: Phi look angle, see :attr:`BaseScene.phi` :type phi: :class:`numpy.ndarray`, NxM, optional :param llLat: Lower left latitude in [deg] :type llLat: float, optional :param llLon: Lower left longitude in [deg] :type llLon: float, optional :param dLat: Pixel spacing in latitude [deg] :type dLat: float, optional :param dLon: Pixel spacing in longitude [deg] :type dLon: float, optional """ def __init__(self, config=SceneConfig(), **kwargs): self.evChanged = Subject() self.evConfigChanged = Subject() self.config = config self.meta = self.config.meta BaseScene.__init__(self, frame_config=self.config.frame, **kwargs) # wiring special methods self.import_data = self._import_data self.load = self._load @property_cached def quadtree(self): """ Instantiates the scene's quadtree. :type: :class:`kite.quadtree.Quadtree` """ self._log.debug('Creating kite.Quadtree instance') from kite.quadtree import Quadtree return Quadtree(scene=self, config=self.config.quadtree) @property_cached def covariance(self): """ Instantiates the scene's covariance attribute. :type: :class:`kite.covariance.Covariance` """ self._log.debug('Creating kite.Covariance instance') from kite.covariance import Covariance return Covariance(scene=self, config=self.config.covariance) @property_cached def plot(self): """ Shows a simple plot of the scene's displacement """ self._log.debug('Creating kite.ScenePlot instance') from kite.plot2d import ScenePlot return ScenePlot(self) def spool(self): """ Start the spool user interface :class:`~kite.spool.Spool` to inspect the scene. """ if self.displacement is None: raise SceneError('Can not display an empty scene.') from kite.spool import spool spool(scene=self) def _testImport(self): try: self.frame.E self.frame.N self.frame.gridE self.frame.gridN self.frame.dE self.frame.dN self.displacement self.theta self.phi except Exception as e: print(e) raise ImportError('Something went wrong during import - ' 'see Exception!') def save(self, filename=None): """ Save kite scene to kite file structure Saves the current scene meta information and UTM frame to a YAML (``.yml``) file. Numerical data (:attr:`~kite.Scene.displacement`, :attr:`~kite.Scene.theta` and :attr:`~kite.Scene.phi`) are saved as binary files from :class:`numpy.ndarray`. :param filename: Filenames to save scene to, defaults to ' :attr:`~kite.Scene.meta.scene_id` ``_`` :attr:`~kite.Scene.meta.scene_view` :type filename: str, optional """ filename = filename or '%s_%s' % (self.meta.scene_id, self.meta.scene_view) _file, ext = op.splitext(filename) filename = _file if ext in ['.yml', '.npz'] else filename components = ['displacement', 'theta', 'phi'] self._log.debug('Saving scene data to %s.npz' % filename) num.savez('%s.npz' % (filename), *[getattr(self, arr) for arr in components]) self.saveConfig('%s.yml' % filename) def saveConfig(self, filename): _file, ext = op.splitext(filename) filename = filename if ext in ['.yml'] else filename + '.yml' self._log.debug('Saving scene config to %s' % filename) self.config.regularize() self.config.dump(filename='%s' % filename, header='kite.Scene YAML Config') @dynamicmethod def _load(self, filename): """ Load a kite scene from file ``filename.[npz,yml]`` structure. :param filename: Filenames the scene data is saved under :type filename: str :returns: Scene object from data resources :rtype: :class:`~kite.Scene` """ scene = self components = ['displacement', 'theta', 'phi'] basename = op.splitext(filename)[0] scene._log.debug('Loading from %s[.npz,.yml]' % basename) try: data = num.load('%s.npz' % basename) for i, comp in enumerate(components): scene.__setattr__(comp, data['arr_%d' % i]) except IOError: raise UserIOWarning('Could not load data from %s.npz' % basename) try: scene.load_config('%s.yml' % basename) except IOError: raise UserIOWarning('Could not load %s.yml' % basename) scene.meta.filename = op.basename(filename) scene._testImport() return scene load = staticmethod(_load) def load_config(self, filename): self._log.debug('Loading config from %s' % filename) self.config = guts.load(filename=filename) self.meta = self.config.meta self.evConfigChanged.notify() @dynamicmethod def _import_data(self, path, **kwargs): """ Import displacement data from foreign file format. :param path: Filename of resource to import :type path: str :param kwargs: keyword arguments passed to import function :type kwargs: dict :returns: Scene from path :rtype: :class:`~kite.Scene` :raises: TypeError """ scene = self if not op.isfile(path) or op.isdir(path): raise ImportError('File %s does not exist!' % path) data = None for mod in scene_io.__all__: module = eval('scene_io.%s(scene)' % mod) if module.validate(path, **kwargs): scene._log.debug('Importing %s using %s module' % (path, mod)) data = module.read(path, **kwargs) break if data is None: raise ImportError('Could not recognize format for %s' % path) scene.meta.filename = op.basename(path) return scene._import_from_dict(scene, data) _import_data.__doc__ += \ '\nSupported import modules are **%s**.\n'\ % (', ').join(scene_io.__all__) for mod in scene_io.__all__: _import_data.__doc__ += '\n**%s**\n\n' % mod _import_data.__doc__ += eval('scene_io.%s.__doc__' % mod) import_data = staticmethod(_import_data) @staticmethod def _import_from_dict(scene, data): for sk in ['theta', 'phi', 'displacement']: setattr(scene, sk, data[sk]) for fk, fv in data['frame'].items(): setattr(scene.frame, fk, fv) for mk, mv in data['meta'].items(): if mv is not None: setattr(scene.meta, mk, mv) scene.meta.extra.update(data['extra']) scene.frame.updateExtent() scene._testImport() return scene def __str__(self): return self.config.__str__()
class Covariance(object): """Construct the variance-covariance matrix of quadtree subsampled data. Variance and covariance estimates are used to construct the weighting matrix to be used later in an optimization. Two different methods exist to propagate full-resolution data variances and covariances of :class:`kite.Scene.displacement` to the covariance matrix of the subsampled dataset: 1. The distance between :py:class:`kite.quadtree.QuadNode` leaf focal points, :py:class:`kite.covariance.Covariance.matrix_focal` defines the approximate covariance of the quadtree leaf pair. 2. The _accurate_ propagation of covariances by taking the mean of every node pair pixel covariances. This process is computational very expensive and can take a few minutes. :py:class:`kite.covariance.Covariance.matrix_focal` :param quadtree: Quadtree to work on :type quadtree: :class:`~kite.Quadtree` :param config: Config object :type config: :class:`~kite.covariance.CovarianceConfig` """ def __init__(self, scene, config=CovarianceConfig()): self.evChanged = Subject() self.evConfigChanged = Subject() self.frame = scene.frame self.quadtree = scene.quadtree self.scene = scene self.nthreads = 0 self._noise_data = None self._powerspec1d_cached = None self._powerspec2d_cached = None self._powerspec3d_cached = None self._noise_data_grid = None self._initialized = False self._log = scene._log.getChild("Covariance") self.setConfig(config) self.quadtree.evChanged.subscribe(self._clear) self.scene.evConfigChanged.subscribe(self.setConfig) def __call__(self, *args, **kwargs): return self.getLeafCovariance(*args, **kwargs) def setConfig(self, config=None): """Sets and updated the config of the instance :param config: New config instance, defaults to configuration provided by parent :class:`~kite.Scene` :type config: :class:`~kite.covariance.CovarianceConfig`, optional """ if config is None: config = self.scene.config.covariance if self.scene.config.old_import: self._log.warning("Old format - resetting noise patch coordinates") config.covariance_matrix = None config.noise_coord = None self.config = config if config.noise_coord is None and (config.model_coefficients is not None or config.variance is not None): self.noise_data # init data array self.config.model_coefficients = config.model_coefficients self.config.variance = config.variance self._clear(config=False) self.evConfigChanged.notify() def _clear(self, config=True, spectrum=True): if config: self.config.model_coefficients = None self.config.variance = None self.config.covariance_matrix = None if spectrum: self.structure_spectral = None self._powerspec1d_cached = None self._powerspec2d_cached = None self._noise_data_grid = None self.covariance_matrix = None self.covariance_matrix_focal = None self.covariance_spectral = None self.covariance_spatial = None self.structure_spatial = None self.weight_matrix = None self.weight_matrix_focal = None self._initialized = False self.evChanged.notify() @property def finished_combinations(self): return covariance_ext.get_finished_combinations() @property def noise_coord(self): """Coordinates of the noise patch in local coordinates. :setter: Set the noise coordinates :getter: Get the noise coordinates :type: :class:`numpy.ndarray`, ``[llE, llN, sizeE, sizeN]`` """ if self.config.noise_coord is None: self.noise_data return self.config.noise_coord @noise_coord.setter def noise_coord(self, values): self.config.noise_coord = num.array(values) @property def noise_patch_size_km2(self): """ :getter: Noise patch size in :math:`km^2`. :type: float """ if self.noise_coord is None: return 0.0 size = (self.noise_coord[2] * self.noise_coord[3]) * 1e-6 if self.noise_data.size < self.NOISE_PATCH_MIN_PX: self._log.warning("Defined noise patch is instably small") return size @property def noise_data(self, data): """Noise data we process to estimate the covariance :setter: Set the noise patch to analyze the covariance. :getter: If the noise data has not been set manually, we grab data through :func:`~kite.Covariance.selectNoiseNode`. :type: :class:`numpy.ndarray` """ return self._noise_data @noise_data.getter def noise_data(self): if self._noise_data is not None: return self._noise_data elif self.config.noise_coord is not None: self._log.debug("Selecting noise_data from config...") llE, llN = self.scene.frame.mapENMatrix( *self.config.noise_coord[:2]) sE, sN = self.scene.frame.mapENMatrix(*self.config.noise_coord[2:]) slice_E = slice(llE, llE + sE) slice_N = slice(llN, llN + sN) covariance_matrix = self.config.covariance_matrix self.noise_data = self.scene.displacement[slice_N, slice_E] self.config.covariance_matrix = covariance_matrix else: self._log.debug("Selecting noise_data from Quadtree...") node = self.selectNoiseNode() self.noise_data = node.displacement self.noise_coord = [node.llE, node.llN, node.sizeE, node.sizeN] return self._noise_data @noise_data.setter def noise_data(self, data): data = data.copy() data = derampMatrix(trimMatrix(data)) data[num.isnan(data)] = 0.0 self._noise_data = data self._clear() @property def noise_data_gridE(self): return self._get_noise_data_grid()[0] @property def noise_data_gridN(self): return self._get_noise_data_grid()[1] def _get_noise_data_grid(self): if self._noise_data_grid is None: scene = self.scene llE, llN = scene.frame.mapENMatrix(*self.noise_coord[:2]) sE, sN = scene.frame.mapENMatrix(*self.noise_coord[2:]) slice_E = slice(llE, llE + sE + 1) slice_N = slice(llN, llN + sN + 1) gridE = scene.frame.gridEmeter[slice_N, slice_E] gridN = scene.frame.gridNmeter[slice_N, slice_E] gridE = trimMatrix(self.noise_data, data=gridE) gridN = trimMatrix(self.noise_data, data=gridN) self._noise_data_grid = (gridE, gridN) return self._noise_data_grid def selectNoiseNode(self): """Choose noise node from quadtree the biggest :class:`~kite.quadtree.QuadNode` from :class:`~kite.Quadtree`. :returns: A quadnode with the least signal. :rtype: :class:`~kite.quadtree.QuadNode` """ t0 = time.time() node_selection = [ n for n in self.quadtree.nodes if n.npixel > NOISE_PATCH_MIN_PX and n.nan_fraction < NOISE_PATCH_MAX_NAN ] if not node_selection: node_selection = self.quadtree.leaves stdmax = max([n.std for n in node_selection]) lmax = max([n.std for n in node_selection]) def costFunction(n): nl = num.log2(n.length) / num.log2(lmax) ns = n.std / stdmax return nl * (1.0 - ns) * (1.0 - n.nan_fraction) fitness = num.array([costFunction(n) for n in node_selection]) self._log.debug("Fetched noise from Quadtree.nodes [%0.4f s]" % (time.time() - t0)) node = node_selection[num.argmin(fitness)] return node def _mapLeaves(self, nx, ny): """Helper function returning appropriate :class:`~kite.quadtree.QuadNode` and for maintaining the internal mapping with the matrices. :param nx: matrix x position :type nx: int :param ny: matrix y position :type ny: int :returns: tuple of :class:`~kite.quadtree.QuadNode` s for ``nx`` and ``ny`` :rtype: tuple """ leaf1 = self.quadtree.leaves[nx] leaf2 = self.quadtree.leaves[ny] self._leaf_mapping[leaf1.id] = nx self._leaf_mapping[leaf2.id] = ny return leaf1, leaf2 def isFullCovarianceCalculated(self): if self.config.covariance_matrix is None: return False return True @property_cached def covariance_matrix(self): """Covariance matrix calculated from mean of all pixel pairs inside the node pairs (full and accurate propagation). :type: :class:`numpy.ndarray`, size (:class:`~kite.Quadtree.nleaves` x :class:`~kite.Quadtree.nleaves`) """ if not isinstance(self.config.covariance_matrix, num.ndarray): self.config.covariance_matrix = self._calcCovarianceMatrix( method="full") self.evChanged.notify() elif self.config.covariance_matrix.ndim == 1: try: nl = self.quadtree.nleaves self.config.covariance_matrix = self.config.covariance_matrix.reshape( nl, nl) except ValueError: self.config.covariance_matrix = None return self.covariance_matrix return self.config.covariance_matrix @property_cached def covariance_matrix_focal(self): """Approximate Covariance matrix from quadtree leaf pair distance only. Fast, use for intermediate steps only and finallly use approach :attr:`~kite.Covariance.covariance_matrix`. :type: :class:`numpy.ndarray`, size (:class:`~kite.Quadtree.nleaves` x :class:`~kite.Quadtree.nleaves`) """ return self._calcCovarianceMatrix(method="focal") @property_cached def weight_matrix(self): """Weight matrix from full covariance :math:`cov^{-1}`. :type: :class:`numpy.ndarray`, size (:class:`~kite.Quadtree.nleaves` x :class:`~kite.Quadtree.nleaves`) """ return num.linalg.inv(self.covariance_matrix) @property_cached def weight_matrix_L2(self): """Weight matrix from full covariance :math:`\\sqrt{cov^{-1}}`. :type: :class:`numpy.ndarray`, size (:class:`~kite.Quadtree.nleaves` x :class:`~kite.Quadtree.nleaves`) """ incov = num.linalg.inv(self.covariance_matrix) return sp.linalg.sqrtm(incov) @property_cached def weight_matrix_focal(self): """Approximated weight matrix from fast focal method :math:`cov_{focal}^{-1}`. :type: :class:`numpy.ndarray`, size (:class:`~kite.Quadtree.nleaves` x :class:`~kite.Quadtree.nleaves`) """ try: return num.linalg.inv(self.covariance_matrix_focal) except num.linalg.LinAlgError as e: self._log.exception(e) return num.eye(self.covariance_matrix_focal.shape[0]) @property_cached def weight_vector(self): """Weight vector from full covariance :math:`cov^{-1}`. :type: :class:`numpy.ndarray`, size (:class:`~kite.Quadtree.nleaves`) """ return num.sum(self.weight_matrix, axis=1) @property_cached def weight_vector_focal(self): """Weight vector from fast focal method :math:`\\sqrt{cov_{focal}^{-1}}`. :type: :class:`numpy.ndarray`, size (:class:`~kite.Quadtree.nleaves`) """ return num.sum(self.weight_matrix_focal, axis=1) def _calcCovarianceMatrix(self, method="focal", nthreads=None): """Constructs the covariance matrix. :param method: Either ``focal`` point distances are used - this is quick but only an approximation. Or ``full``, where the full quadtree pixel distances matrices are calculated , defaults to ``focal`` :type method: str, optional :returns: Covariance matrix :rtype: thon:numpy.ndarray """ self._initialized = True nthreads = nthreads or self.nthreads nl = len(self.quadtree.leaves) self._leaf_mapping = {} t0 = time.time() if method == "focal": model = self.getModelFunction() coords = self.quadtree.leaf_focal_points_meter dist_matrix = num.sqrt( (coords[:, 0] - coords[:, 0, num.newaxis])**2 + (coords[:, 1] - coords[:, 1, num.newaxis])**2) cov_matrix = model(dist_matrix, *self.covariance_model) # adding variance if self.variance < cov_matrix.max(): variance = cov_matrix.max() else: variance = self.variance if self.quadtree.leaf_mean_px_var is not None: self._log.debug( "Adding variance from scene.displacement_px_var") variance += self.quadtree.leaf_mean_px_var num.fill_diagonal(cov_matrix, variance) for nx, ny in num.nditer(num.triu_indices_from(dist_matrix)): self._mapLeaves(nx, ny) elif method == "full": leaf_map = num.empty((len(self.quadtree.leaves), 4), dtype=num.uint32) for nl, leaf in enumerate(self.quadtree.leaves): leaf, _ = self._mapLeaves(nl, nl) leaf_map[nl, 0], leaf_map[nl, 1] = ( leaf._slice_rows.start, leaf._slice_rows.stop, ) leaf_map[nl, 2], leaf_map[nl, 3] = ( leaf._slice_cols.start, leaf._slice_cols.stop, ) nleaves = self.quadtree.nleaves cov_matrix = covariance_ext.covariance_matrix( self.scene.frame.gridEmeter.filled(), self.scene.frame.gridNmeter.filled(), leaf_map, self.covariance_model, self.variance, nthreads, self.config.adaptive_subsampling, ).reshape(nleaves, nleaves) if self.quadtree.leaf_mean_px_var is not None: self._log.debug( "Adding variance from scene.displacement_px_var") cov_matrix[num.diag_indices_from( cov_matrix)] += self.quadtree.leaf_mean_px_var else: raise TypeError("Covariance calculation %s method not defined!" % method) self._log.debug("Created covariance matrix - %s mode [%0.4f s]" % (method, time.time() - t0)) return cov_matrix def isMatrixPosDefinite(self, full=False): self._log.debug("Checking whether matrix is positive-definite") if full: matrix = self.covariance_matrix else: matrix = self.covariance_matrix_focal try: chol_decomp = num.linalg.cholesky(matrix) except num.linalg.linalg.LinAlgError: pos_def = False else: pos_def = ~num.all(num.iscomplex(chol_decomp)) finally: if not pos_def: self._log.warning("Covariance matrix is not positiv definite!") return pos_def @staticmethod def _leafFocalDistance(leaf1, leaf2): return num.sqrt((leaf1.focal_point[0] - leaf2.focal_point[0])**2 + (leaf1.focal_point[1] - leaf2.focal_point[1])**2) def _leafMapping(self, leaf1, leaf2): if not isinstance(leaf1, str): leaf1 = leaf1.id if not isinstance(leaf2, str): leaf2 = leaf2.id if not self._initialized: self.covariance_matrix_focal try: return self._leaf_mapping[leaf1], self._leaf_mapping[leaf2] except KeyError as e: raise KeyError("Unknown quadtree leaf with id %s" % e) def getLeafCovariance(self, leaf1, leaf2): """Get the covariance between ``leaf1`` and ``leaf2`` from distances. :param leaf1: Leaf one :type leaf1: str of `leaf.id` or :class:`~kite.quadtree.QuadNode` :param leaf2: Leaf two :type leaf2: str of `leaf.id` or :class:`~kite.quadtree.QuadNode` :returns: Covariance between ``leaf1`` and ``leaf2`` :rtype: float """ return self.covariance_matrix[self._leafMapping(leaf1, leaf2)] def getLeafWeight(self, leaf, model="focal"): """Get the total weight of ``leaf``, which is the summation of all single pair weights of :attr:`kite.Covariance.weight_matrix`. .. math :: w_{x} = \\sum_i W_{x,i} :param model: ``Focal`` or ``full``, default ``focal`` :type model: str :param leaf: A leaf from :class:`~kite.Quadtree` :type leaf: :class:`~kite.quadtree.QuadNode` :returns: Weight of the leaf :rtype: float """ (nl, _) = self._leafMapping(leaf, leaf) weight_mat = self.weight_matrix_focal return num.mean(weight_mat, axis=0)[nl] def syntheticNoise(self, shape=(1024, 1024), dEdN=None, anisotropic=False, rstate=None): """Create random synthetic noise from data noise power spectrum. This function uses the power spectrum of the data noise (:attr:`noise_data`) (:func:`powerspecNoise`) to create synthetic noise, e.g. to use it for data pertubation in optinmizations. The default sampling distances are taken from :attr:`kite.scene.Frame.dE` and :attr:`kite.scene.Frame.dN`. They can be overwritten. :param shape: shape of the desired noise patch. Pixels in northing and easting (`nE`, `nN`), defaults to `(1024, 1024)`. :type shape: tuple, optional :param dEdN: The sampling distance in east and north [m], defaults to (:attr:`kite.scene.Frame.dEmeter`, :attr:`kite.scene.Frame.dNmeter`). :type dEdN: tuple, floats :returns: synthetic noise patch :rtype: :class:`numpy.ndarray` """ if (shape[0] + shape[1]) % 2 != 0: # self._log.warning('Patch dimensions must be even, ' # 'ceiling dimensions!') pass nE = shape[1] + (shape[1] % 2) nN = shape[0] + (shape[0] % 2) if rstate is None: rstate = num.random.RandomState() rfield = rstate.rand(nN, nE) spec = num.fft.fft2(rfield) if not dEdN: dE, dN = (self.scene.frame.dEmeter, self.scene.frame.dNmeter) kE = num.fft.fftfreq(nE, dE) kN = num.fft.fftfreq(nN, dN) k_rad = num.sqrt(kN[:, num.newaxis]**2 + kE[num.newaxis, :]**2) amp = num.zeros_like(k_rad) if not anisotropic: noise_pspec, k, _, _, _, _ = self.powerspecNoise2D() k_bin = num.insert(k + k[0] / 2, 0, 0) for i in range(k.size): k_min = k_bin[i] k_max = k_bin[i + 1] r = num.logical_and(k_rad > k_min, k_rad <= k_max) if i == (k.size - 1): r = k_rad > k_min if not num.any(r): continue amp[r] = noise_pspec[i] amp[k_rad == 0.0] = self.variance amp[k_rad > k.max()] = noise_pspec[num.argmax(k)] amp = num.sqrt(amp * self.noise_data.size * num.pi * 4) elif anisotropic: interp_pspec, _, _, _, skE, skN = self.powerspecNoise3D() kE = num.fft.fftshift(kE) kN = num.fft.fftshift(kN) mkE = num.logical_and(kE >= skE.min(), kE <= skE.max()) mkN = num.logical_and(kN >= skN.min(), kN <= skN.max()) mkRad = num.where( # noqa k_rad < num.sqrt(kN[mkN].max()**2 + kE[mkE].max()**2)) res = interp_pspec(kN[mkN, num.newaxis], kE[num.newaxis, mkE], grid=True) amp = res amp = num.fft.fftshift(amp) spec *= amp noise = num.abs(num.fft.ifft2(spec)) noise -= num.mean(noise) # remove shape % 2 padding return noise[:shape[0], :shape[1]] def getQuadtreeNoise(self, rstate=None, gather=num.nanmedian): """Create noise for a :class:`~kite.quadtree.Quadtree` Use :meth:`~kite.covariance.Covariance.getSyntheticNoise` to create data-driven noise on each quadtree leaf, summarized by :param gather: Function gathering leaf's noise realisation, defaults to num.median. :type normalisation: callable, optional :returns: Array of noise level at each quadtree leaf. :rtype: :class:`numpy.ndarray` """ qt = self.quadtree syn_noise = self.syntheticNoise(shape=self.scene.displacement.shape, rstate=rstate) syn_noise[self.scene.displacement_mask] = num.nan noise_quadtree_arr = num.full(qt.nleaves, num.nan) for il, lv in enumerate(qt.leaves): noise_quadtree_arr[il] = gather(syn_noise[lv._slice_rows, lv._slice_cols]) return noise_quadtree_arr def powerspecNoise1D(self, data=None, ndeg=512, nk=512): if self._powerspec1d_cached is None: self._powerspec1d_cached = self._powerspecNoise(data, norm="1d", ndeg=ndeg, nk=nk) return self._powerspec1d_cached def powerspecNoise2D(self, data=None, ndeg=512, nk=512): if self._powerspec2d_cached is None: self._powerspec2d_cached = self._powerspecNoise(data, norm="2d", ndeg=ndeg, nk=nk) return self._powerspec2d_cached def powerspecNoise3D(self, data=None): if self._powerspec3d_cached is None: self._powerspec3d_cached = self._powerspecNoise(data, norm="3d") return self._powerspec3d_cached def _powerspecNoise(self, data=None, norm="1d", ndeg=512, nk=512): """Get the noise power spectrum from :attr:`kite.Covariance.noise_data`. :param data: Overwrite Covariance.noise_data, defaults to `None` :type data: :class:`numpy.ndarray`, optional :returns: `(power_spec, k, f_spectrum, kN, kE)` :rtype: tuple """ if data is None: noise = self.noise_data else: noise = data.copy() if norm not in ("1d", "2d", "3d"): raise AttributeError("norm must be 1d, 2d or 3d") # noise = squareMatrix(noise) shift = num.fft.fftshift spectrum = shift(num.fft.fft2(noise, axes=(0, 1), norm=None)) power_spec = (num.abs(spectrum) / spectrum.size)**2 kE = shift( num.fft.fftfreq(power_spec.shape[1], d=self.quadtree.frame.dEmeter)) kN = shift( num.fft.fftfreq(power_spec.shape[0], d=self.quadtree.frame.dNmeter)) k_rad = num.sqrt(kN[:, num.newaxis]**2 + kE[num.newaxis, :]**2) power_spec[k_rad == 0.0] = 0.0 power_interp = sp.interpolate.RectBivariateSpline(kN, kE, power_spec) # def power1d(k): # theta = num.linspace(-num.pi, num.pi, ndeg, False) # power = num.empty_like(k) # for i in range(k.size): # kE = num.cos(theta) * k[i] # kN = num.sin(theta) * k[i] # power[i] = num.median(power_interp.ev(kN, kE)) * k[i]\ # * num.pi * 4 # return power def power1d(k): theta = num.linspace(-num.pi, num.pi, ndeg, False) power = num.empty_like(k) cos_theta = num.cos(theta) sin_theta = num.sin(theta) for i in range(k.size): kE = cos_theta * k[i] kN = sin_theta * k[i] power[i] = num.mean(power_interp.ev(kN, kE)) power *= 2 * num.pi return power def power2d(k): """Mean 2D Power works!""" theta = num.linspace(-num.pi, num.pi, ndeg, False) power = num.empty_like(k) cos_theta = num.cos(theta) sin_theta = num.sin(theta) for i in range(k.size): kE = sin_theta * k[i] kN = cos_theta * k[i] power[i] = num.median(power_interp.ev(kN, kE)) # Median is more stable than the mean here return power def power3d(k): return power_interp power = power1d if norm == "2d": power = power2d elif norm == "3d": power = power3d k_rad = num.sqrt(kN[:, num.newaxis]**2 + kE[num.newaxis, :]**2) k = num.linspace(k_rad[k_rad > 0].min(), k_rad.max(), nk) dk = 1.0 / (k[1] - k[0]) / (2 * nk) return power(k), k, dk, spectrum, kE, kN def _powerCosineTransform(self, p_spec): """Calculating the cosine transform of the power spectrum. The cosine transform of the power spectrum is an estimate of the data covariance (see Hanssen, 2001).""" cos = fft.idct(p_spec, type=3) return cos def setSamplingMethod(self, method): """Set the sampling method""" assert method in CovarianceConfig.sampling_method.choices self.config.sampling_method = method self._clear(config=True, spectrum=False) self.evChanged.notify() self._log.debug("Changed sampling method to %s" % method) def setSpatialBins(self, nbins): """Set number of spatial bins""" self.config.spatial_bins = nbins self._clear(config=True, spectrum=False) self.evChanged.notify() self._log.debug("Changed spatial distance bins to %s" % nbins) def setSpatialPairs(self, npairs): """Set number of random spatial pairs""" self.config.spatial_pairs = npairs self._clear(config=True, spectrum=False) self.evChanged.notify() self._log.debug("Changed random pairs to %s" % npairs) def setModelFunction(self, model): assert model in CovarianceConfig.model_function.choices self.config.model_function = model self._clear(config=True, spectrum=True) self.evChanged.notify() self._log.debug("Changed model function to %s" % model) def getModelFunction(self): if self.config.model_function == "exponential": return modelCovarianceExponential if self.config.model_function == "exponential_cosine": return modelCovarianceExponentialCosine @property_cached def covariance_spectral(self): """Covariance function estimated directly from the power spectrum of displacement noise patch using the cosine transform. :type: tuple, :class:`numpy.ndarray` (covariance, distance)""" power_spec, k, dk, _, _, _ = self.powerspecNoise1D() # power_spec -= self.variance d = num.arange(1, power_spec.size + 1) * dk cov = self._powerCosineTransform(power_spec) return cov, d @property_cached def covariance_spatial(self): self._log.debug("Estimating covariance (spatial)...") nbins = self.config.spatial_bins npairs = self.config.spatial_pairs noise_data = self.noise_data.ravel() noise_data -= noise_data.mean() grdE = self.noise_data_gridE grdN = self.noise_data_gridN max_distance = min(abs(grdE.min() - grdE.max()), abs(grdN.min() - grdN.max())) dist_bins = num.linspace(0, max_distance, nbins + 1) grdE = grdE.ravel() grdN = grdN.ravel() # Select random coordinates rstate = num.random.RandomState(noise_data.size) rand_idx = rstate.randint(0, noise_data.size, (2, npairs)) idx0 = rand_idx[0, :] idx1 = rand_idx[1, :] distances = num.sqrt((grdN[idx0] - grdN[idx1])**2 + (grdE[idx0] - grdE[idx1])**2) cov_all = noise_data[idx0] * noise_data[idx1] vario_all = (noise_data[idx0] - noise_data[idx1])**2 bins = num.digitize(distances, dist_bins, right=True) bin_distances = dist_bins[1:] - dist_bins[1] / 2 covariance = num.full_like(bin_distances, fill_value=num.nan) variance = num.full_like(bin_distances, fill_value=num.nan) for ib in range(nbins): selection = bins == ib if selection.sum() != 0: covariance[ib] = num.nanmean(cov_all[selection]) variance[ib] = num.nanmean(vario_all[selection]) / 2 self._structure_spatial = ( variance[~num.isnan(variance)], bin_distances[~num.isnan(variance)], ) covariance[0] = num.nan return ( covariance[~num.isnan(covariance)], bin_distances[~num.isnan(covariance)], ) def getCovariance(self): """Calculate the covariance function :return: The covariance and distance :rtype: tuple """ if self.config.sampling_method == "spatial": return self.covariance_spatial elif self.config.sampling_method == "spectral": return self.covariance_spectral @property def covariance_model(self, regime=0): """Covariance model parameters for :func:`~kite.covariance.modelCovariance` retrieved from :attr:`~kite.Covariance.getCovarianceFunction`. .. note:: using this function implies several model fits: (1) fit of the spectrum and (2) the cosine transform. Not sure about the consequences, if this is useful and/or meaningful. :getter: Get the parameters. :type: tuple, ``a`` and ``b`` """ if self.config.model_coefficients is None: covariance, distance = self.getCovariance() model = self.getModelFunction() if self.config.model_function == "exponential": coeff = (num.mean(covariance), num.mean(distance)) elif self.config.model_function == "exponential_cosine": coeff = ( num.mean(covariance), num.mean(distance), num.mean(distance) * -0.1, 0.1, ) func = self.getModelFunction() # Testing penalty function def model(*args): distance, a, b, c, d = args res = func(*args) penalty = 0.0 if distance[-1] / b > (distance[-1] + c) / d: penalty = (b - d) * coeff[0] self._log.warning("Penalty %f" % penalty) return res + penalty # Overwrite with pure model function model = self.getModelFunction() # noqa try: coeff, _ = sp.optimize.curve_fit(model, distance, covariance, p0=coeff) except (RuntimeError, TypeError) as e: self._log.exception(e) self._log.warning("Could not fit the %s covariance model", self.config.model_function) finally: self.config.model_coefficients = tuple(map(float, coeff)) return self.config.model_coefficients @property def covariance_model_rms(self): """ :getter: RMS missfit between :class:`~kite.Covariance.covariance_model` and :class:`~kite.Covariance.getCovarianceFunction` :type: float """ cov, d = self.getCovariance() model = self.getModelFunction() cov_mod = model(d, *self.covariance_model) return num.sqrt(num.mean((cov - cov_mod)**2)) @property_cached def structure_spatial(self): self.covariance_spatial return self._structure_spatial @property_cached def structure_spectral(self): """Structure function derived from ``noise_patch`` :type: tuple, :class:`numpy.ndarray` (structure_spectral, distance) Adapted from http://clouds.eos.ubc.ca/~phil/courses/atsc500/docs/strfun.pdf """ power_spec, k, dk, _, _, _ = self.powerspecNoise1D() d = num.arange(1, power_spec.size + 1) * dk def structure_spectral(power_spec, d, k): struc_func = num.zeros_like(k) for i, d in enumerate(d): for ik, tk in enumerate(k): # struc_func[i] += (1. - num.cos(tk*d))*power_spec[ik] struc_func[i] += (1.0 - sp.special.j0(tk * d)) * power_spec[ik] struc_func *= 2.0 / 1 return struc_func struc_func = structure_spectral(power_spec, d, k) return struc_func, d def getStructure(self, method=None): """Get the structure function :param method: Either `spatial` or `spectral`, if `None` the method is taken from config :type method: str (optional) :return: (variance, distance) :rtype: tuple """ if method is None: method = self.config.sampling_method if method == "spatial": return self.structure_spatial elif method == "spectral": return self.structure_spectral @property def variance(self): """Variance of data noise estimated from the high-frequency end of power spectrum. :setter: Set the variance manually :getter: Retrieve the variance :type: float """ return self.config.variance @variance.setter def variance(self, value): self.config.variance = float(value) # self._clear(config=False, spectrum=False, spatial=False) self.evChanged.notify() @variance.getter def variance(self): if self.config.variance is None and self.config.sampling_method == "spatial": structure_spatial, dist = self.structure_spatial last_20p = -int(structure_spatial.size * 0.2) self.config.variance = float( num.mean(structure_spatial[(last_20p):])) elif self.config.variance is None and self.config.sampling_method == "spectral": power_spec, k, dk, spectrum, _, _ = self.powerspecNoise1D() cov, _ = self.covariance_spectral ma = self.covariance_model[0] # print(cov[1]) ps = power_spec * spectrum.size # print(spectrum.size) # print(num.mean(ps[-int(ps.size/9.):-1])) var = num.median(ps[-int(ps.size / 9.0):]) + ma self.config.variance = float(var) return self.config.variance def export_weight_matrix(self, filename): """Export the full :attr:`~kite.Covariance.weight_matrix` to an ASCII file. The data can be loaded through :func:`numpy.loadtxt`. :param filename: path to export to :type filename: str """ self._log.debug("Exporting Covariance.weight_matrix to %s" % filename) header = ("Exported kite.Covariance.weight_matrix, " "for more information visit https://pyrocko.org\n" "\nThe matrix is symmetric and ordered by QuadNode.id:\n") header += ", ".join([l.id for l in self.quadtree.leaves]) num.savetxt(filename, self.weight_matrix, header=header) def get_state_hash(self): sha = sha1() sha.update(str(self.config).encode()) return sha.digest().hex() @property_cached def plot(self): """Simple overview plot to summarize the covariance estimations.""" from kite.plot2d import CovariancePlot return CovariancePlot(self) @property_cached def plot_syntheticNoise(self): """Simple overview plot to summarize the covariance estimations.""" from kite.plot2d import SyntheticNoisePlot return SyntheticNoisePlot(self)
class SandboxScene(BaseScene): def __init__(self, config=None, **kwargs): self.evChanged = Subject() self.evModelUpdated = Subject() self.evConfigChanged = Subject() self._initialised = False self.config = config if config else SandboxSceneConfig() BaseScene.__init__(self, frame_config=self.config.frame, **kwargs) self.reference = None self._los_factors = None for attr in ['theta', 'phi']: data = kwargs.pop(attr, None) if data is not None: self.__setattr__(attr, data) self.setExtent(self.config.extent_east, self.config.extent_north) if self.config.reference_scene is not None: self.loadReferenceScene(self.config.reference_scene) @property def sources(self): """ :returns: List of sources attached sandbox :rtype: list """ return self.config.sources def setExtent(self, east, north): """Set the sandbox's extent in pixels :param east: Pixels in East :type east: int :param north: Pixels in North :type north: int """ if self.reference is not None: self._log.warning('Cannot change a referenced model!') return self._log.debug('Changing model extent to %d px by %d px' % (east, north)) self.cols = east self.rows = north self._north = num.zeros((self.rows, self.cols)) self._east = num.zeros_like(self._north) self._down = num.zeros_like(self._north) self.theta = num.zeros_like(self._north) self.phi = num.zeros_like(self._north) self.theta.fill(num.pi/2) self.phi.fill(0.) self.config.extent_east = east self.config.extent_north = north self.frame.updateExtent() self._clearModel() self.processSources() self.evChanged.notify() @property def north(self): if not self._initialised: self.processSources() return self._north @property def east(self): if not self._initialised: self.processSources() return self._east @property def down(self): if not self._initialised: self.processSources() return self._down @property_cached def displacement(self): """ Displacement projected to LOS """ self.processSources() los_factors = self.los_rotation_factors self._displacement =\ (los_factors[:, :, 0] * -self._down + los_factors[:, :, 1] * self._east + los_factors[:, :, 2] * self._north) return self._displacement @property_cached def max_horizontal_displacement(self): """ Maximum horizontal displacement """ return num.sqrt(self._north**2 + self._east**2).max() def addSource(self, source): """Add displacement source to sandbox :param source: Displacement Source :type source: :class:`kite.sources.meta.SandboxSource` """ if source not in self.sources: self.sources.append(source) source.evParametersChanged.subscribe(self._clearModel) self._clearModel() self._log.debug('Source %s added' % source.__class__.__name__) def removeSource(self, source): """Remove displacement source from sandbox :param source: Displacement Source :type source: :class:`kite.sources.meta.SandboxSource` """ source.evParametersChanged.unsubscribe(self._clearModel) self.sources.remove(source) self._log.debug('Source %s removed' % source.__class__.__name__) del source self._clearModel() def processSources(self): """ Process displacement sources and update displacements """ result = self._process( self.frame.coordinates, self.sources) self._north += result['north'].reshape(self.rows, self.cols) self._east += result['east'].reshape(self.rows, self.cols) self._down += result['down'].reshape(self.rows, self.cols) self._initialised = True def processCustom(self, coordinates, sources, result_dict=None): return self._process(coordinates, sources, result_dict) def _process(self, coordinates, sources, result=None): if result is None: result = num.zeros( coordinates.shape[0], dtype=[('north', num.float64), ('east', num.float64), ('down', num.float64)]) avail_processors = {} for proc in __processors__: avail_processors[proc.__implements__] = proc for impl in set([src.__implements__ for src in sources]): proc_sources = [src for src in sources if src.__implements__ == impl and src._cached_result is None] if not proc_sources: continue processor = avail_processors.get(impl, None) if processor is None: self._log.warning( 'Could not find source processor for %s' % impl) continue t0 = time.time() proc_result = processor(self).process( proc_sources, coordinates, nthreads=0) src_type = proc_sources[0].__class__.__name__ self._log.debug('Processed %s (nsources:%d) using %s [%.4f s]' % (src_type, len(proc_sources), processor.__name__, time.time() - t0)) result['north'] += proc_result['displacement.n'] result['east'] += proc_result['displacement.e'] result['down'] += proc_result['displacement.d'] return result def loadReferenceScene(self, filename): """Load a reference kite scene container into the sandbox A reference scene could be actually measured InSAR displacements. :param filename: filename of the scene container to load [.npy, .yml] :type filename: str """ from .scene import Scene self._log.debug('Loading reference scene from %s' % filename) scene = Scene.load(filename) self.setReferenceScene(scene) self.config.reference_scene = filename def setReferenceScene(self, scene): """Set a reference scene. A reference scene could be actually measured InSAR displacements. :param scene: Kite scene :type scene: :class:`kite.Scene` """ self.frame._updateConfig(scene.frame.config) self.setExtent(scene.cols, scene.rows) self.phi = scene.phi self.theta = scene.theta self.reference = Reference(self, scene) self._log.debug('Reference scene set to scene.id:%s' % scene.meta.scene_id) self._clearModel() def getKiteScene(self): """Return a :class:`kite.Scene` from current model. :returns: Scene :rtype: :class:`Scene` """ from .scene import Scene, SceneConfig self._log.debug('Creating kite.Scene from SandboxScene') config = SceneConfig() config.frame = self.frame.config config.meta.scene_id = 'Exported SandboxScene' return Scene( displacement=self.displacement, theta=self.theta, phi=self.phi, config=config) def _clearModel(self): for arr in [self._north, self._east, self._down]: arr.fill(0.) self.displacement = None self._los_factors = None self._initialised = False self.max_horizontal_displacement = None self.evModelUpdated.notify() def save(self, filename): """Save the sandbox as kite scene container :param filename: filename to save under :type filename: str """ _file, ext = op.splitext(filename) filename = filename if ext in ['.yml'] else filename + '.yml' self._log.debug('Saving model scene to %s' % filename) for source in self.sources: source.regularize() self.config.dump(filename='%s' % filename, header='kite.SandboxScene YAML Config') @classmethod def load(cls, filename): """Load a :class:`kite.SandboxScene` :param filename: Config file to load [.yml] :type filename: str :returns: A sandbox from config file :rtype: :class:`kite.SandboxScene` """ config = guts.load(filename=filename) sandbox_scene = cls(config=config) sandbox_scene._log.debug('Loading config from %s' % filename) for source in sandbox_scene.sources: sandbox_scene.addSource(source) return sandbox_scene
class Plot2D(object): """Base class for matplotlib 2D plots """ def __init__(self, scene, **kwargs): self.evPlotChanged = Subject() self._scene = scene self._data = None self.fig = None self.ax = None self._show_plt = False self._colormap_symmetric = True self.title = 'unnamed' self._log = logging.getLogger(self.__class__.__name__) def __call__(self, *args, **kwargs): return self.plot(*args, **kwargs) def setCanvas(self, **kwargs): """Set canvas to plot in :param figure: Matplotlib figure to plot in :type figure: :py:class:`matplotlib.Figure` :param axes: Matplotlib axes to plot in :type axes: :py:class:`matplotlib.Axes` :raises: TypeError """ axes = kwargs.get('axes', None) figure = kwargs.get('figure', None) if isinstance(axes, plt.Axes): self.fig, self.ax = axes.get_figure(), axes self._show_plt = False elif isinstance(figure, plt.Figure): self.fig, self.ax = figure, figure.gca() self._show_plt = False elif axes is None and figure is None and self.fig is None: self.fig, self.ax = plt.subplots(1, 1) self._show_plt = True else: raise TypeError('axes has to be of type matplotlib.Axes. ' 'figure has to be of type matplotlib.Figure') self.image = AxesImage(self.ax) self.ax.add_artist(self.image) @property def data(self): """ Data passed to matplotlib.image.AxesImage """ return self._data @data.setter def data(self, value): self._data = value self.image.set_data(self.data) self.colormapAdjust() @data.getter def data(self): if self._data is None: return num.empty((50, 50)) return self._data def _initImagePlot(self, **kwargs): """ Initiate the plot :param figure: Matplotlib figure to plot in :type figure: :py:class:`matplotlib.Figure` :param axes: Matplotlib axes to plot in :type axes: :py:class:`matplotlib.Axes` """ self.setCanvas(**kwargs) self.setColormap(kwargs.get('cmap', 'RdBu')) self.colormapAdjust() self.ax.set_xlim((0, self._scene.frame.E.size)) self.ax.set_ylim((0, self._scene.frame.N.size)) self.ax.set_aspect('equal') self.ax.invert_yaxis() self.ax.set_title(self.title) def close_figure(ev): self.fig = None self.ax = None try: self.fig.canvas.mpl_connect('close_event', close_figure) except Exception: pass def plot(self, **kwargs): """Placeholder in prototype class :param figure: Matplotlib figure to plot in :type figure: :py:class:`matplotlib.Figure` :param axes: Matplotlib axes to plot in :type axes: :py:class:`matplotlib.Axes` :param **kwargs: kwargs are passed into `plt.imshow` :type **kwargs: dict :raises: NotImplemented """ raise NotImplementedError() self._initImagePlot(**kwargs) if self._show_plt: plt.show() def _updateImage(self): self.image.set_data(self.data) def setColormap(self, cmap='RdBu'): """Set matplotlib colormap :param cmap: matplotlib colormap name, defaults to 'RdBu' :type cmap: str, optional """ self.image.set_cmap(cmap) self.evPlotChanged.notify() def colormapAdjust(self): """Set colormap limits automatically :param symmetric: symmetric colormap around 0, defaults to True :type symmetric: bool, optional """ vmax = num.nanmax(self.data) vmin = num.nanmin(self.data) self.colormap_limits = (vmin, vmax) @property def colormap_symmetric(self): return self._colormap_symmetric @colormap_symmetric.setter def colormap_symmetric(self, value): self._colormap_symmetric = value self.colormapAdjust() @property def colormap_limits(self): return self.image.get_clim() @colormap_limits.setter def colormap_limits(self, limits): if not isinstance(limits, tuple): raise AttributeError('Limits have to be a tuple (vmin, vmax)') vmin, vmax = limits if self.colormap_symmetric: _max = max(abs(vmin), abs(vmax)) vmin, vmax = -_max, _max self.image.set_clim(vmin, vmax) self.evPlotChanged.notify() @staticmethod def _colormapsAvailable(): return [ # ('Perceptually Uniform Sequential', # ['viridis', 'inferno', 'plasma', 'magma']), # ('Sequential', ['Blues', 'BuGn', 'BuPu', # 'GnBu', 'Greens', 'Greys', 'Oranges', 'OrRd', # 'PuBu', 'PuBuGn', 'PuRd', 'Purples', 'RdPu', # 'Reds', 'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd']), # ('Sequential (2)', ['afmhot', 'autumn', 'bone', 'cool', # 'copper', 'gist_heat', 'gray', 'hot', # 'pink', 'spring', 'summer', 'winter']), ('Diverging', [ 'BrBG', 'bwr', 'coolwarm', 'PiYG', 'PRGn', 'RdBu', 'RdGy', 'RdYlBu', 'RdYlGn', 'Spectral', 'seismic', 'PuOr' ]), ('Qualitative', [ 'Accent', 'Dark2', 'Paired', 'Pastel1', 'Pastel2', 'Set1', 'Set2', 'Set3' ]), # ('Miscellaneous', ['gist_earth', 'terrain', 'ocean', # 'brg', 'CMRmap', 'cubehelix', 'gist_stern', # 'gnuplot', 'gnuplot2', 'gist_ncar', # 'nipy_spectral', 'jet', 'rainbow', # 'gist_rainbow', 'hsv', 'flag', 'prism']) ]
class BaseScene(object): def __init__(self, **kwargs): self._initLogging() self.evChanged = Subject() self.evConfigChanged = Subject() self._displacement = None self._displacement_px_var = None self._phi = None self._theta = None self._los_factors = None self.cols = 0 self.rows = 0 self.los = LOSUnitVectors(scene=self) frame_config = kwargs.pop('frame_config', FrameConfig()) for fattr in ('llLat', 'llLon', 'dLat', 'dLon'): coord = kwargs.pop(fattr, None) if coord is not None: frame_config.__setattr__(fattr, coord) self.frame = Frame(scene=self, config=frame_config) for attr in ('displacement', 'displacement_px_var', 'theta', 'phi'): data = kwargs.pop(attr, None) if data is not None: self.__setattr__(attr, data) def _initLogging(self): self._log = logging.getLogger(self.__class__.__name__) @property def displacement(self): """Surface displacement in meter on a regular grid. :setter: Set the unwrapped InSAR displacement. :getter: Return the displacement matrix. :type: :class:`numpy.ndarray`, ``NxM`` """ return self._displacement @displacement.setter def displacement(self, value): _setDataNumpy(self, '_displacement', value) self.rows, self.cols = self._displacement.shape self.displacement_mask = None self.evChanged.notify() @property def displacement_px_var(self): """ Variance of the surface displacement per pixel. Same dimension as displacement. :setter: Set standard deviation of of the displacement. :getter: Return the standard deviation matrix. :type: :class:`numpy.ndarray`, ``NxM`` """ return self._displacement_px_var @displacement_px_var.setter def displacement_px_var(self, value): self._displacement_px_var = value @property_cached def displacement_mask(self): """ Displacement :attr:`numpy.nan` mask :type: :class:`numpy.ndarray`, dtype :class:`numpy.bool` """ return ~num.isfinite(self.displacement) @property def shape(self): return self._displacement.shape @property def phi(self): """ Horizontal angle towards satellite :abbr:`line of sight (LOS)` in radians counter-clockwise from East. .. important :: Kite's convention is: * :math:`0` is **East** * :math:`\\frac{\\pi}{2}` is **North**! :setter: Set the phi matrix for scene's displacement, can be ``int`` for static look vector. :type: :class:`numpy.ndarray`, size same as :attr:`~kite.Scene.displacement` or int """ return self._phi @phi.setter def phi(self, value): if isinstance(value, float): self._phi = value else: _setDataNumpy(self, '_phi', value) self.phiDeg = None self.los_rotation_factors = None self.evChanged.notify() @property def theta(self): """ Theta is the look vector elevation angle towards satellite from the horizon in radians. Matrix of theta towards satellite's :abbr:`line of sight (LOS)`. .. important :: Kite convention! * :math:`-\\frac{\\pi}{2}` is **Down** * :math:`\\frac{\\pi}{2}` is **Up** :setter: Set the theta matrix for scene's displacement, can be ``int`` for static look vector. :type: :class:`numpy.ndarray`, size same as :attr:`~kite.Scene.displacement` or int """ return self._theta @theta.setter def theta(self, value): if isinstance(value, float): self._theta = value else: _setDataNumpy(self, '_theta', value) self.thetaDeg = None self.los_rotation_factors = None self.evChanged.notify() @property_cached def thetaDeg(self): """ LOS elevation angle in degree, ``NxM`` matrix like :class:`kite.Scene.theta` :type: :class:`numpy.ndarray` """ return num.rad2deg(self.theta) @property_cached def phiDeg(self): """ LOS horizontal orientation angle in degree, counter-clockwise from East,``NxM`` matrix like :class:`kite.Scene.phi` :type: :class:`numpy.ndarray` """ return num.rad2deg(self.phi) @property_cached def los_rotation_factors(self): """ Trigonometric factors to rotate displacement matrices towards LOS Rotation is as follows: .. displacement_los =\ (los_rotation_factors[:, :, 0] * -down + los_rotation_factors[:, :, 1] * east + los_rotation_factors[:, :, 2] * north) :returns: Factors for rotation :rtype: :class:`numpy.ndarray`, ``NxMx3`` :raises: AttributeError """ if (self.theta.size != self.phi.size): raise AttributeError('LOS angles inconsistent with provided' ' coordinate shape.') if self._los_factors is None: self._los_factors = num.empty((self.theta.shape[0], self.theta.shape[1], 3)) self._los_factors[:, :, 0] = num.sin(self.theta) self._los_factors[:, :, 1] = num.cos(self.theta)\ * num.cos(self.phi) self._los_factors[:, :, 2] = num.cos(self.theta)\ * num.sin(self.phi) return self._los_factors def get_ramp_coefficients(self): '''Fit plane through the displacement data. :returns: Mean of the displacement and slopes in easting coefficients of the fitted plane. The array hold ``[offset_e, offset_n, slope_e, slope_n]``. :rtype: :class:`numpy.ndarray` ''' msk = ~self.displacement_mask displacement = self.displacement[msk] coords = self.frame.coordinates[msk.flatten()] # Add ones for the offset coords = num.hstack(( num.ones_like(coords), coords)) coeffs, res, _, _ = num.linalg.lstsq( coords, displacement, rcond=None) return coeffs def displacement_deramp(self, demean=True, inplace=True): '''Fit a plane onto the displacement data and substract it :param demean: Demean the displacement :type demean: bool :param inplace: Replace data of the scene (default: True) :type inplace: bool :return: ``None`` if ``inplace=True`` else a new Scene :rtype: ``None`` or :class:`~kite.Scene` ''' self._log.debug('De-ramping scene...') coeffs = self.get_ramp_coefficients() msk = self.displacement_mask coords = self.frame.coordinates ramp = coeffs[2:] * coords if demean: ramp += coeffs[:2] ramp = ramp.sum(axis=1).reshape(self.shape) ramp[msk] = num.nan if inplace: self.displacement -= ramp self.evChanged.notify() else: return self.__class__( config=self.config, theta=self.theta, phi=self.phi, displacement=self.displacement - ramp) def __neg__(self): ret = copy.deepcopy(self) ret.displacement *= -1 return ret def __add__(self, other, copy_obj=True): if copy_obj: ret = copy.deepcopy(self) else: ret = self if not ret.frame == other.frame: raise ValueError('Scene frames do not align!') ret.displacement += other.displacement tmin = ret.meta.time_master \ if ret.meta.time_master < other.meta.time_master \ else other.meta.time_master tmax = ret.meta.time_slave \ if ret.meta.time_slave > other.meta.time_slave \ else other.meta.time_slave ret.meta.time_master = tmin ret.meta.time_slave = tmax return ret def __sub__(self, other): return self.__add__(-other) def __isub__(self, scene): return self.__add__(-scene, copy_obj=False) def __iadd__(self, scene): return self.__add__(scene, copy_obj=False)