Updates (e0c3fc93) · Commits · aflab / astrogeology / Plio

environment.yml

+1 −0

Original line number	Diff line number	Diff line
		@@ -10,3 +10,4 @@ dependencies:
		- pandas
		- sqlalchemy
		- pyyaml
		- networkx

plio/examples/loggers/logging.json

+19 −19

Original line number	Diff line number	Diff line
		@@ -20,7 +20,7 @@
		"info_file_handler": {
		"formatter": "simple",
		"backupCount": 10,
		"level": "INFO",
		"level": "DEBUG",
		"encoding": "utf8",
		"class": "logging.handlers.RotatingFileHandler",
		"maxBytes": 10485760,

plio/io/io_controlnetwork.py

+21 −17

Original line number	Diff line number	Diff line
		@@ -145,7 +145,7 @@ class IsisStore(object):

		self._path = path
		if not mode:
		mode = 'a' # pragma: no cover
		mode = 'wb' # pragma: no cover
		self._mode = mode
		self._handle = None

		@@ -170,13 +170,13 @@ class IsisStore(object):
		self._handle.seek(offset)
		self._handle.write(data)

		def create_points(self, obj, pointid_prefix, pointid_suffix):
		def create_points(self, graphs, pointid_prefix, pointid_suffix):
		"""
		Step through a control network (C) and return protocol buffer point objects

		Parameters
		----------
		obj : list
		graphs : list
		of iterable objects (dataframes) with the appropriate
		attributes: point_id, point_type, serial, measure_type, x, y required.
		The entries in the list must support grouping by the point_id attribute.
		@@ -197,20 +197,21 @@ class IsisStore(object):
		# TODO: Rewrite using apply syntax for performance
		point_sizes = []
		point_messages = []
		for df in obj:
		for i, g in df.groupby('point_id'):
		for graph in graphs:
		for i, control_point_id in enumerate(graph.point_nodes):
		control_point = graph.node[control_point_id]
		point_spec = cnf.ControlPointFileEntryV0002()
		point_spec.id = _set_pid(self.pointid)

		point_spec.id = _set_pid(control_point['pointid'])
		for attr, attrtype in self.point_attrs:
		if attr in g.columns:
		if attr in control_point.keys():
		# As per protobuf docs for assigning to a repeated field.
		if attr == 'aprioriCovar':
		arr = g.iloc[0]['aprioriCovar']
		arr = control_point['aprioriCovar']
		point_spec.aprioriCovar.extend(arr.ravel().tolist())
		else:
		setattr(point_spec, attr, attrtype(g.iloc[0][attr]))
		point_spec.type = int(g.point_type.iat[0])
		# The third argument casts the value to the correct type
		setattr(point_spec, attr, attrtype(control_point[attr]))
		point_spec.type = int(control_point.get('point_type', 2))

		# The reference index should always be the image with the lowest index
		point_spec.referenceIndex = 0
		@@ -218,14 +219,19 @@ class IsisStore(object):
		# A single extend call is cheaper than many add calls to pack points
		measure_iterable = []

		for node_id, m in g.iterrows():
		for j, control_measure_id in graph.edges(control_point_id):
		control_measure = graph.node[control_measure_id]
		if control_measure['node_type'] != 'correspondence':
		continue
		measure_spec = point_spec.Measure()
		# For all of the attributes, set if they are an dict accessible attr of the obj.
		for attr, attrtype in self.measure_attrs:
		if attr in g.columns:
		setattr(measure_spec, attr, attrtype(m[attr]))
		measure_spec.type = int(m.measure_type)
		if attr in control_measure.keys():
		setattr(measure_spec, attr, attrtype(control_measure[attr]))

		measure_spec.type = int(control_measure['measure_type'])
		measure_spec.line = float(control_measure['y'])
		measure_spec.sample = float(control_measure['x'])
		measure_iterable.append(measure_spec)
		self.nmeasures += 1
		point_spec.measures.extend(measure_iterable)
		@@ -364,5 +370,3 @@ class IsisStore(object):
		if self._handle is not None:
		self._handle.close()
		self._handle = None

plio/io/io_gdal.py

+9 −5

Original line number	Diff line number	Diff line
		@@ -198,6 +198,12 @@ class GeoDataset(object):
		self._gcs = self._srs.CloneGeogCS()
		return self._srs

		@property
		def nbands(self):
		if not getattr(self, '_nbands', None):
		self._nbands = self.dataset.RasterCount
		return self._nbands

		@property
		def geospatial_coordinate_system(self):
		if not getattr(self, '_gcs', None):
		@@ -321,10 +327,8 @@ class GeoDataset(object):
		@property
		def coordinate_transformation(self):
		if not getattr(self, '_ct', None):
		print('Getting CT')
		self._ct = osr.CoordinateTransformation(self.spatial_reference,
		self.geospatial_coordinate_system)
		print('CT', self._ct)
		return self._ct

		@property
		@@ -332,7 +336,6 @@ class GeoDataset(object):
		if not getattr(self, '_ict', None):
		self._ict = osr.CoordinateTransformation(self.geospatial_coordinate_system,
		self.spatial_reference)
		print(self._ict)
		return self._ict

		@property
		@@ -558,6 +561,7 @@ def match_rasters(match_to, match_from, destination,
		GRA_Cubic, GRA_CubicSpline, GRA_Lanczos, GRA_Average,
		GRA_Mode}
		"""

		import gdalconst # import here so Sphinx can build the docos, mocking is not working
		# TODO: If a destination is not provided create an in-memory GeoDataSet object
		match_to_srs = match_to.dataset.GetProjection()
		@@ -567,8 +571,8 @@ def match_rasters(match_to, match_from, destination,
		match_from__srs = match_from.dataset.GetProjection()
		match_from__gt = match_from.geotransform

		dst = gdal.GetDriverByName('GTiff').Create(destination, width, height, match_from.RasterCount,
		gdalconst.GDT_Float32)
		dst = gdal.GetDriverByName('GTiff').Create(destination, width, height, 1,
		gdalconst.GDT_Float64)
		dst.SetGeoTransform(match_to_gt)
		dst.SetProjection(match_to_srs)

plio/io/io_krc.py

+6 −3

Original line number	Diff line number	Diff line
		@@ -364,9 +364,9 @@ class ReadBin52(object):
		self.nseasons)
		caseidx = np.repeat(np.arange(self.ncases), self.nseasons)
		seasonidx = np.repeat(np.arange(self.nseasons), self.ncases)
		flt_seasitems = seasitems.reshape(len(self.vlabels),
		flt_seasitems = seasitems.reshape(len(columns),
		self.ncases * self.nseasons)
		self.seasons = pd.DataFrame(flt_seasitems,
		self.seasons = pd.DataFrame(flt_seasitems.T,
		index=[caseidx,seasonidx],
		columns=columns)
		self.seasons.index.names = ['Case', 'Season']
		@@ -432,6 +432,9 @@ class ReadBin52(object):
		#Extract the hourly temperature data
		hourly2dataframe()

		# Extract the seasons
		season2dataframe()

		# Extract by layer data from the data cube
		layeritems = self.bin52data[: , self.ndx: , : , 5: 7, : ]
		latitems2dataframe()