Loading plio/io/hcube.pydeleted 100644 → 0 +0 −208 Original line number Diff line number Diff line import numpy as np import gdal from ..utils.indexing import _LocIndexer, _iLocIndexer from libpyhat.transform.continuum import continuum_correction from libpyhat.transform.continuum import polynomial, linear, regression class HCube(object): """ A Mixin class for use with the io_gdal.GeoDataset class to optionally add support for spectral labels, label based indexing, and lazy loading for reads. """ def __init__(self, data = [], wavelengths = []): if len(data) != 0: self._data = data if len(wavelengths) != 0: self._wavelengths = wavelengths @property def wavelengths(self): if not hasattr(self, '_wavelengths'): try: info = gdal.Info(self.file_name, format='json') wavelengths = [float(j) for i, j in sorted(info['metadata'][''].items(), key=lambda x: float(x[0].split('_')[-1]))] self._original_wavelengths = wavelengths self._wavelengths = np.round(wavelengths, self.tolerance) except: self._wavelengths = [] return self._wavelengths @property def data(self): if not hasattr(self, '_data'): try: key = (slice(None, None, None), slice(None, None, None), slice(None, None, None)) data = self._read(key) except Exception as e: print(e) data = [] self._data = data return self._data @property def tolerance(self): return getattr(self, '_tolerance', 2) @tolerance.setter def tolerance(self, val): if isinstance(val, int): self._tolerance = val self._reindex() else: raise TypeError def _reindex(self): if self._original_wavelengths is not None: self._wavelengths = np.round(self._original_wavelengths, decimals=self.tolerance) def __getitem__(self, key): i = _iLocIndexer(self) return i[key] @property def loc(self): return _LocIndexer(self) @property def iloc(self): return _iLocIndexer(self) def reduce(self, how = np.mean, axis = (1, 2)): """ Parameters ---------- how : function Function to apply across along axises of the hcube axis : tuple List of axis to apply a given function along Returns ------- new_hcube : Object A new hcube object with the reduced data set """ res = how(self.data, axis = axis) new_hcube = HCube(res, self.wavelengths) return new_hcube def continuum_correct(self, nodes, correction_nodes = np.array([]), correction = linear, axis=0, adaptive=False, window=3, **kwargs): """ Parameters ---------- nodes : list A list of wavelengths for the continuum to be corrected along correction_nodes : list A list of nodes to limit the correction between correction : function Function specifying the type of correction to perform along the continuum axis : int Axis to apply the continuum correction on adaptive : boolean ? window : int ? Returns ------- new_hcube : Object A new hcube object with the corrected dataset """ continuum_data = continuum_correction(self.data, self.wavelengths, nodes = nodes, correction_nodes = correction_nodes, correction = correction, axis = axis, adaptive = adaptive, window = window, **kwargs) new_hcube = HCube(continuum_data[0], self.wavelengths) return new_hcube def clip_roi(self, x, y, band, tolerance=2): """ Parameters ---------- x : tuple Lower and upper bound along the x axis for clipping y : tuple Lower and upper bound along the y axis for clipping band : tuple Lower and upper band along the z axis for clipping tolerance : int Tolerance given for trying to find wavelengths between the upper and lower bound Returns ------- new_hcube : Object A new hcube object with the clipped dataset """ wavelength_clip = [] for wavelength in self.wavelengths: wavelength_upper = wavelength + tolerance wavelength_lower = wavelength - tolerance if wavelength_upper > band[0] and wavelength_lower < band[1]: wavelength_clip.append(wavelength) key = (wavelength_clip, slice(*x), slice(*y)) data_clip = _LocIndexer(self)[key] new_hcube = HCube(np.copy(data_clip), np.array(wavelength_clip)) return new_hcube def _read(self, key): ifnone = lambda a, b: b if a is None else a y = key[1] x = key[2] if isinstance(x, slice): xstart = ifnone(x.start,0) xstop = ifnone(x.stop,self.raster_size[0]) xstep = xstop - xstart else: raise TypeError("Loc style access elements must be slices, e.g., [:] or [10:100]") if isinstance(y, slice): ystart = ifnone(y.start, 0) ystop = ifnone(y.stop, self.raster_size[1]) ystep = ystop - ystart else: raise TypeError("Loc style access elements must be slices, e.g., [:] or [10:100]") pixels = (xstart, ystart, xstep, ystep) if isinstance(key[0], (int, np.integer)): return self.read_array(band=int(key[0]+1), pixels=pixels) elif isinstance(key[0], slice): # Given some slice iterate over the bands and get the bands and pixel space requested arrs = [] for band in list(list(range(1, self.nbands + 1))[key[0]]): arrs.append(self.read_array(band, pixels = pixels)) return np.stack(arrs) else: arrs = [] for b in key[0]: arrs.append(self.read_array(band=int(b+1), pixels=pixels)) return np.stack(arrs) plio/io/io_crism.pydeleted 100644 → 0 +0 −61 Original line number Diff line number Diff line import os import numpy as np from .io_gdal import GeoDataset from .hcube import HCube try: from libpyhat.derived import crism from libpyhat.derived.utils import get_derived_funcs libpyhat_enabled = True except: print('No libpyhat module. Unable to attach derived product functions') libpyhat_enabled = False import gdal class Crism(GeoDataset, HCube): """ An Crism specific reader with the spectral mixin. """ def __init__(self, file_name): GeoDataset.__init__(self, file_name) HCube.__init__(self) self.derived_funcs = {} if libpyhat_enabled: self.derived_funcs = get_derived_funcs(crism) def __getattr__(self, name): try: func = self.derived_funcs[name] setattr(self, name, func.__get__(self)) return getattr(self, name) except KeyError as keyerr: raise AttributeError("'Crism' object has no attribute '{}'".format(name)) from None @property def wavelengths(self): if not hasattr(self, '_wavelengths'): try: info = gdal.Info(self.file_name, format='json') wv = dict((k,v) for (k,v) in info['metadata'][''].items() if 'Band' in k) # Only get the 'Band_###' keys wavelengths = [float(j.split(" ")[0]) for i, j in sorted(wv.items(), key=lambda x: int(x[0].split('_')[-1]))] self._original_wavelengths = wavelengths self._wavelengths = np.round(wavelengths, self.tolerance) except: self._wavelengths = [] return self._wavelengths def open(input_data): if os.path.splitext(input_data)[-1] == 'hdr': # GDAL wants the img, but many users aim at the .hdr input_data = os.path.splitext(input_data)[:-1] + '.img' ds = Crism(input_data) return ds plio/io/io_moon_minerology_mapper.pydeleted 100644 → 0 +0 −67 Original line number Diff line number Diff line import os import numpy as np from .io_gdal import GeoDataset from .hcube import HCube try: from libpyhat.derived import m3 from libpyhat.derived.utils import get_derived_funcs libpyhat_enabled = True except: print('No libpyhat module. Unable to attach derived product functions') libpyhat_enabled = False import gdal class M3(GeoDataset, HCube): """ An M3 specific reader with the spectral mixin. """ def __init__(self, file_name): GeoDataset.__init__(self, file_name) HCube.__init__(self) self.derived_funcs = {} if libpyhat_enabled: self.derived_funcs = get_derived_funcs(m3) def __getattr__(self, name): try: func = self.derived_funcs[name] setattr(self, name, func.__get__(self)) return getattr(self, name) except KeyError as keyerr: raise AttributeError("'M3' object has no attribute '{}'".format(name)) from None @property def wavelengths(self): if not hasattr(self, '_wavelengths'): try: info = gdal.Info(self.file_name, format='json') if 'Resize' in info['metadata']['']['Band_1']: wavelengths = [float(j.split(' ')[-1].replace('(','').replace(')', '')) for\ i,j in sorted(info['metadata'][''].items(), key=lambda x: float(x[0].split('_')[-1]))] # This is a geotiff translated from the PDS IMG else: # This is a PDS IMG wavelengths = [float(j) for i, j in sorted(info['metadata'][''].items(), key=lambda x: float(x[0].split('_')[-1]))] self._original_wavelengths = wavelengths self._wavelengths = np.round(wavelengths, self.tolerance) except: self._wavelengths = [] return self._wavelengths def open(input_data): if os.path.splitext(input_data)[-1] == 'hdr': # GDAL wants the img, but many users aim at the .hdr input_data = os.path.splitext(input_data)[:-1] + '.img' ds = M3(input_data) return ds Loading
plio/io/hcube.pydeleted 100644 → 0 +0 −208 Original line number Diff line number Diff line import numpy as np import gdal from ..utils.indexing import _LocIndexer, _iLocIndexer from libpyhat.transform.continuum import continuum_correction from libpyhat.transform.continuum import polynomial, linear, regression class HCube(object): """ A Mixin class for use with the io_gdal.GeoDataset class to optionally add support for spectral labels, label based indexing, and lazy loading for reads. """ def __init__(self, data = [], wavelengths = []): if len(data) != 0: self._data = data if len(wavelengths) != 0: self._wavelengths = wavelengths @property def wavelengths(self): if not hasattr(self, '_wavelengths'): try: info = gdal.Info(self.file_name, format='json') wavelengths = [float(j) for i, j in sorted(info['metadata'][''].items(), key=lambda x: float(x[0].split('_')[-1]))] self._original_wavelengths = wavelengths self._wavelengths = np.round(wavelengths, self.tolerance) except: self._wavelengths = [] return self._wavelengths @property def data(self): if not hasattr(self, '_data'): try: key = (slice(None, None, None), slice(None, None, None), slice(None, None, None)) data = self._read(key) except Exception as e: print(e) data = [] self._data = data return self._data @property def tolerance(self): return getattr(self, '_tolerance', 2) @tolerance.setter def tolerance(self, val): if isinstance(val, int): self._tolerance = val self._reindex() else: raise TypeError def _reindex(self): if self._original_wavelengths is not None: self._wavelengths = np.round(self._original_wavelengths, decimals=self.tolerance) def __getitem__(self, key): i = _iLocIndexer(self) return i[key] @property def loc(self): return _LocIndexer(self) @property def iloc(self): return _iLocIndexer(self) def reduce(self, how = np.mean, axis = (1, 2)): """ Parameters ---------- how : function Function to apply across along axises of the hcube axis : tuple List of axis to apply a given function along Returns ------- new_hcube : Object A new hcube object with the reduced data set """ res = how(self.data, axis = axis) new_hcube = HCube(res, self.wavelengths) return new_hcube def continuum_correct(self, nodes, correction_nodes = np.array([]), correction = linear, axis=0, adaptive=False, window=3, **kwargs): """ Parameters ---------- nodes : list A list of wavelengths for the continuum to be corrected along correction_nodes : list A list of nodes to limit the correction between correction : function Function specifying the type of correction to perform along the continuum axis : int Axis to apply the continuum correction on adaptive : boolean ? window : int ? Returns ------- new_hcube : Object A new hcube object with the corrected dataset """ continuum_data = continuum_correction(self.data, self.wavelengths, nodes = nodes, correction_nodes = correction_nodes, correction = correction, axis = axis, adaptive = adaptive, window = window, **kwargs) new_hcube = HCube(continuum_data[0], self.wavelengths) return new_hcube def clip_roi(self, x, y, band, tolerance=2): """ Parameters ---------- x : tuple Lower and upper bound along the x axis for clipping y : tuple Lower and upper bound along the y axis for clipping band : tuple Lower and upper band along the z axis for clipping tolerance : int Tolerance given for trying to find wavelengths between the upper and lower bound Returns ------- new_hcube : Object A new hcube object with the clipped dataset """ wavelength_clip = [] for wavelength in self.wavelengths: wavelength_upper = wavelength + tolerance wavelength_lower = wavelength - tolerance if wavelength_upper > band[0] and wavelength_lower < band[1]: wavelength_clip.append(wavelength) key = (wavelength_clip, slice(*x), slice(*y)) data_clip = _LocIndexer(self)[key] new_hcube = HCube(np.copy(data_clip), np.array(wavelength_clip)) return new_hcube def _read(self, key): ifnone = lambda a, b: b if a is None else a y = key[1] x = key[2] if isinstance(x, slice): xstart = ifnone(x.start,0) xstop = ifnone(x.stop,self.raster_size[0]) xstep = xstop - xstart else: raise TypeError("Loc style access elements must be slices, e.g., [:] or [10:100]") if isinstance(y, slice): ystart = ifnone(y.start, 0) ystop = ifnone(y.stop, self.raster_size[1]) ystep = ystop - ystart else: raise TypeError("Loc style access elements must be slices, e.g., [:] or [10:100]") pixels = (xstart, ystart, xstep, ystep) if isinstance(key[0], (int, np.integer)): return self.read_array(band=int(key[0]+1), pixels=pixels) elif isinstance(key[0], slice): # Given some slice iterate over the bands and get the bands and pixel space requested arrs = [] for band in list(list(range(1, self.nbands + 1))[key[0]]): arrs.append(self.read_array(band, pixels = pixels)) return np.stack(arrs) else: arrs = [] for b in key[0]: arrs.append(self.read_array(band=int(b+1), pixels=pixels)) return np.stack(arrs)
plio/io/io_crism.pydeleted 100644 → 0 +0 −61 Original line number Diff line number Diff line import os import numpy as np from .io_gdal import GeoDataset from .hcube import HCube try: from libpyhat.derived import crism from libpyhat.derived.utils import get_derived_funcs libpyhat_enabled = True except: print('No libpyhat module. Unable to attach derived product functions') libpyhat_enabled = False import gdal class Crism(GeoDataset, HCube): """ An Crism specific reader with the spectral mixin. """ def __init__(self, file_name): GeoDataset.__init__(self, file_name) HCube.__init__(self) self.derived_funcs = {} if libpyhat_enabled: self.derived_funcs = get_derived_funcs(crism) def __getattr__(self, name): try: func = self.derived_funcs[name] setattr(self, name, func.__get__(self)) return getattr(self, name) except KeyError as keyerr: raise AttributeError("'Crism' object has no attribute '{}'".format(name)) from None @property def wavelengths(self): if not hasattr(self, '_wavelengths'): try: info = gdal.Info(self.file_name, format='json') wv = dict((k,v) for (k,v) in info['metadata'][''].items() if 'Band' in k) # Only get the 'Band_###' keys wavelengths = [float(j.split(" ")[0]) for i, j in sorted(wv.items(), key=lambda x: int(x[0].split('_')[-1]))] self._original_wavelengths = wavelengths self._wavelengths = np.round(wavelengths, self.tolerance) except: self._wavelengths = [] return self._wavelengths def open(input_data): if os.path.splitext(input_data)[-1] == 'hdr': # GDAL wants the img, but many users aim at the .hdr input_data = os.path.splitext(input_data)[:-1] + '.img' ds = Crism(input_data) return ds
plio/io/io_moon_minerology_mapper.pydeleted 100644 → 0 +0 −67 Original line number Diff line number Diff line import os import numpy as np from .io_gdal import GeoDataset from .hcube import HCube try: from libpyhat.derived import m3 from libpyhat.derived.utils import get_derived_funcs libpyhat_enabled = True except: print('No libpyhat module. Unable to attach derived product functions') libpyhat_enabled = False import gdal class M3(GeoDataset, HCube): """ An M3 specific reader with the spectral mixin. """ def __init__(self, file_name): GeoDataset.__init__(self, file_name) HCube.__init__(self) self.derived_funcs = {} if libpyhat_enabled: self.derived_funcs = get_derived_funcs(m3) def __getattr__(self, name): try: func = self.derived_funcs[name] setattr(self, name, func.__get__(self)) return getattr(self, name) except KeyError as keyerr: raise AttributeError("'M3' object has no attribute '{}'".format(name)) from None @property def wavelengths(self): if not hasattr(self, '_wavelengths'): try: info = gdal.Info(self.file_name, format='json') if 'Resize' in info['metadata']['']['Band_1']: wavelengths = [float(j.split(' ')[-1].replace('(','').replace(')', '')) for\ i,j in sorted(info['metadata'][''].items(), key=lambda x: float(x[0].split('_')[-1]))] # This is a geotiff translated from the PDS IMG else: # This is a PDS IMG wavelengths = [float(j) for i, j in sorted(info['metadata'][''].items(), key=lambda x: float(x[0].split('_')[-1]))] self._original_wavelengths = wavelengths self._wavelengths = np.round(wavelengths, self.tolerance) except: self._wavelengths = [] return self._wavelengths def open(input_data): if os.path.splitext(input_data)[-1] == 'hdr': # GDAL wants the img, but many users aim at the .hdr input_data = os.path.splitext(input_data)[:-1] + '.img' ds = M3(input_data) return ds
