def test_raster_bbox(self):
raster_io = RasterFileIO(
uri=os.path.join(testfile_path, 'satellite_test_data',
'LC81070352015218LGN00_B5.TIF'))
bbox = raster_io.get_bbox()
self.assertEquals(bbox, [
307366.7298195886, 3869968.931078481, 539974.1151507461,
4107482.3190209507
])
def __init__(self, calc=None, **kwargs):
"""
Initialize a RasterMathProcess object.
:param calc: A text representation of the calculation to make.
:param kwargs: Other keyword arguments
"""
self.calc = calc
super(RasterMathProcess, self).__init__(**kwargs)
if not self.output:
self.output = RasterFileIO(name='result', uri=self.get_outpath())
def test_raster_footprint(self):
raster_io = RasterFileIO(
uri=os.path.join(testfile_path, 'satellite_test_data',
'LC81070352015218LGN00_B5.TIF'))
footprint = raster_io.get_footprint()
self.assertEquals(footprint, [(354404.6484867488, 4107482.3190209507),
(307366.7298195886, 3915415.660631991),
(492936.19648358587, 3869968.931078481),
(539974.1151507461, 4062035.5894674407),
(354404.6484867488, 4107482.3190209507)])
def test_validationInputsNoMax(self):
"""
Test the GaiaProcess.validate() function - pass on no max input types
"""
raster_io1 = RasterFileIO(uri='/fake/path1')
raster_io2 = RasterFileIO(uri='/fake/path2')
try:
geo.RasterMathProcess(inputs=[raster_io1, raster_io2], calc='A+B')
except geo.GaiaException:
self.fail("Multiple inputs should have passed validation")
def __init__(self, **kwargs):
"""
Create a process to subset a raster by a vector polygon
:param clip_io: IO object containing vector polygon data
:param raster_io: IO object containing raster data
:param kwargs:
:return: SubsetProcess object
"""
super(SubsetProcess, self).__init__(**kwargs)
if not self.output:
self.output = RasterFileIO(name='result', uri=self.get_outpath())
def test_rastermath_add(self):
"""
Test adding two rasters together
"""
raster1_io = RasterFileIO(name='A',
uri=os.path.join(testfile_path,
'globalairtemp.tif'))
raster2_io = RasterFileIO(name='B',
uri=os.path.join(testfile_path,
'globalprecip.tif'))
calc = 'A + B'
bands = [1, 1]
process = geo.RasterMathProcess(inputs=[raster1_io, raster2_io],
calc=calc,
bands=bands)
try:
process.compute()
self.assertTrue(os.path.exists(process.output.uri))
oraster, raster1, raster2 = [
x.read() for x in (process.output, raster1_io, raster2_io)
]
# Output raster should be same dimensions as raster 1
self.assertEquals((oraster.RasterXSize, oraster.RasterYSize),
(raster1.RasterXSize, raster1.RasterYSize))
orb, r1b, r2b = [
x.GetRasterBand(1) for x in (oraster, raster1, raster2)
]
# Min value of output should be >= the max minimum of inputs
self.assertGreaterEqual(
orb.GetStatistics(False, True)[0],
max(
r1b.GetStatistics(False, True)[0],
r2b.GetStatistics(False, True)[0]))
# Max value of output >= max(minimum)+min(maximum) of inputs
self.assertGreaterEqual(
orb.GetStatistics(False, True)[1],
max(
r1b.GetStatistics(False, True)[0],
r2b.GetStatistics(False, True)[0]) + min(
r1b.GetStatistics(False, True)[1],
r2b.GetStatistics(False, True)[1]))
finally:
if process:
process.purge()
class SubsetProcess(GaiaProcess):
"""
Generates a raster dataset representing the portion of the input raster
dataset that is contained within a vector polygon.
"""
#: Tuple of required inputs; name, type , max # of each; None = no max
required_inputs = [
{'description': 'Image to subset',
'type': types.RASTER,
'max': 1
},
{'description': 'Subset area:',
'type': types.VECTOR,
'max': 1
}
]
#: Default output format for the process
default_output = formats.RASTER
def __init__(self, **kwargs):
"""
Create a process to subset a raster by a vector polygon
:param clip_io: IO object containing vector polygon data
:param raster_io: IO object containing raster data
:param kwargs:
:return: SubsetProcess object
"""
super(SubsetProcess, self).__init__(**kwargs)
if not self.output:
self.output = RasterFileIO(name='result', uri=self.get_outpath())
def compute(self):
"""
Runs the subset computation, creating a raster dataset as output.
"""
raster, clip = self.inputs[0], self.inputs[1]
raster_img = raster.read()
clip_df = clip.read(epsg=raster.get_epsg())
# Merge all features in vector input
raster_output = self.output.uri
self.output.create_output_dir(raster_output)
clip_json = clip_df.geometry.unary_union.__geo_interface__
self.output.data = gdal_clip(raster_img, raster_output, clip_json)
def test_validationInputsOrder(self):
"""
Test the GaiaProcess.validate() function - fail on incorrect order
"""
raster_iO = RasterFileIO(uri='/fake/path1')
vector_io = VectorFileIO(uri='/fake/path2')
with self.assertRaises(geo.GaiaException) as ge:
geo.ZonalStatsProcess(inputs=[vector_io, raster_iO])
self.assertIn('Input #1 is of incorrect type.', str(ge.exception))
def test_validationInputsPass(self):
"""
Test the GaiaProcess.validate() function - pass on valid input
"""
raster_io = RasterFileIO(uri='/fake/path')
vector_io = VectorFileIO(uri='/fake/path')
try:
geo.ZonalStatsProcess(inputs=[raster_io, vector_io])
except geo.GaiaException:
self.fail("ZonalProcess should have passed validation but did not")
def __init__(self, **kwargs):
"""
Create a process to merge rasters.
:param inputs: Images to merge.
:param kwargs: Other keyword arguments.
:return: MergeProcess object
"""
super(MergeProcess, self).__init__(**kwargs)
if not self.output:
self.output = RasterFileIO(name='result', uri=self.get_outpath())
def test_zonalstats(self):
vector_io = FeatureIO(features=[{
"type": "Feature",
"geometry": {
"type":
"Polygon",
"coordinates": [[[100.0, 0.0], [120.0, 0.0], [120.0, 30.0],
[100.0, 30.0], [100.0, 0.0]]]
},
"properties": {
"prop0": "value1",
"prop1": {
"this": "that"
}
}
}, {
"type": "Feature",
"geometry": {
"type":
"Polygon",
"coordinates": [[[-100.0, 0.0], [-120.0, 0.0], [-120.0, -30.0],
[100.0, -30.0], [100.0, 0.0]]]
},
"properties": {
"prop0": "value0",
"prop1": {
"this": "other thing"
}
}
}])
raster_io = RasterFileIO(name='temp',
uri=os.path.join(testfile_path,
'globalairtemp.tif'))
process = geo.ZonalStatsProcess(inputs=[raster_io, vector_io])
try:
process.compute()
with open(
os.path.join(testfile_path,
'zonalstats_process_results.json')) as exp:
expected_json = json.load(exp)
actual_json = json.loads(
process.output.read(format=geo.formats.JSON))
self.assertEquals(len(expected_json['features']),
len(actual_json['features']))
finally:
pass
if process:
process.purge()
def test_rastermath_multiply_by_value(self):
"""
Test multiplying a raster by a value,
and specifying output type (Float32)
"""
raster1_io = RasterFileIO(name='A',
uri=os.path.join(testfile_path,
'globalprecip.tif'))
calc = 'A * 2'
output_type = 'Float32'
process = geo.RasterMathProcess(inputs=[
raster1_io,
],
calc=calc,
output_type=output_type)
try:
process.compute()
self.assertTrue(os.path.exists(process.output.uri))
oraster, raster1 = [x.read() for x in (process.output, raster1_io)]
# Output raster should be same dimensions as raster 1
self.assertEquals((oraster.RasterXSize, oraster.RasterYSize),
(raster1.RasterXSize, raster1.RasterYSize))
orb, r1b = [x.GetRasterBand(1) for x in (oraster, raster1)]
# Maximum value of output should be 2x the max of input raster
self.assertEqual(
orb.GetStatistics(False, True)[1],
r1b.GetStatistics(False, True)[1] * 2)
# Datatype of band should be Float32 (== gdal.GDT_Float32 == 6)
self.assertEquals(6, orb.DataType)
self.assertEquals(1.175494351E-38, orb.GetNoDataValue())
# Each pixel of output raster should equal 2x input raster
# unless it is a nodata value
ora, r1a = [x.ReadAsArray() for x in (orb, r1b)]
for x in range(raster1.RasterXSize):
for y in range(raster1.RasterYSize):
if r1a[y, x] != r1b.GetNoDataValue():
self.assertEquals(ora[y, x], r1a[y, x] * 2)
finally:
if process:
process.purge()
def test_rastermath_logical_operators(self):
"""
Test creation of a masked raster based on logical operators
"""
raster1_io = RasterFileIO(name='A',
uri=os.path.join(testfile_path,
'globalairtemp.tif'))
calc = 'logical_or(logical_and(A >= 27000, A <= 28000), ' \
'logical_and(A >= 30000, A <= 31000))'
process = geo.RasterMathProcess(inputs=[
raster1_io,
], calc=calc)
try:
process.compute()
self.assertTrue(os.path.exists(process.output.uri))
oraster, raster1 = [x.read() for x in (process.output, raster1_io)]
# Output raster should be same dimensions as raster 1
self.assertEquals((oraster.RasterXSize, oraster.RasterYSize),
(raster1.RasterXSize, raster1.RasterYSize))
orb, r1b = [x.GetRasterBand(1) for x in (oraster, raster1)]
# Maximum value of output should be 1
self.assertEqual(orb.GetStatistics(False, True)[1], 1)
# Minimum value of output should be 0
self.assertEqual(orb.GetStatistics(False, True)[0], 0)
# Pixels should be 1 where source is between 27K-28K or 30-31K
ora, r1a = [x.ReadAsArray() for x in (orb, r1b)]
self.assertTrue(ora[90, 10] == 1 and r1a[90, 10] == 30083)
self.assertTrue(ora[160, 10] == 1 and r1a[160, 10] == 27074)
# Pixels should be 0 where source is not between 27K-28K or 30-31K
ora, r1a = [x.ReadAsArray() for x in (orb, r1b)]
self.assertTrue(ora[120, 10] == 0 and r1a[120, 10] == 29623)
self.assertTrue(ora[175, 10] == 0 and r1a[175, 10] == 23928)
finally:
if process:
process.purge()
def test_subset_raster(self):
"""
Test SubsetProcess for vector & raster inputs
"""
zipfile = ZipFile(os.path.join(testfile_path, '2states.zip'), 'r')
zipfile.extract('2states.geojson', testfile_path)
vector_io = VectorFileIO(
uri=os.path.join(testfile_path, '2states.geojson'))
raster_io = RasterFileIO(
uri=os.path.join(testfile_path, 'globalairtemp.tif'))
process = geo.SubsetProcess(inputs=[raster_io, vector_io])
try:
process.compute()
self.assertEquals(type(process.output.data).__name__, 'Dataset')
self.assertTrue(os.path.exists(process.output.uri))
self.assertIsNotNone(process.id)
self.assertIn(process.id, process.output.uri)
finally:
testfile = os.path.join(testfile_path, '2states.geojson')
if os.path.exists(testfile):
os.remove(testfile)
if process:
process.purge()
class RasterMathProcess(GaiaProcess):
"""
Performs raster math/algebra based on supplied arguments.
Inputs should consist of at least one raster IO object.
Required arg is 'calc', an equation for the input rasters.
Example: "A + B / (C * 2.4)". The letters in the equation
should correspond to the names of the inputs.
"""
#: Tuple of required inputs; name, type , max # of each; None = no max
required_inputs = [{
'description': 'Image',
'type': types.RASTER,
'max': None
}]
#: Required arguments for the process
required_args = [{
'name': 'calc',
'title': 'Calculation',
'description': 'Equation to apply to rasters (ex: "(A+B)/2"',
'type': str
}]
#: Default output format for the process
default_output = formats.RASTER
#: Default input raster bands to process
bands = None
#: Default NODATA value for raster input
nodata = None
#: Use all bands in raster input (default: False)
all_bands = False
#: Default data type for raster (UInt32, Float, etc)
output_type = None
#: Image output format (default='GTiff')
output_format = 'GTiff'
def __init__(self, calc=None, **kwargs):
"""
Initialize a RasterMathProcess object.
:param calc: A text representation of the calculation to make.
:param kwargs: Other keyword arguments
"""
self.calc = calc
super(RasterMathProcess, self).__init__(**kwargs)
if not self.output:
self.output = RasterFileIO(name='result', uri=self.get_outpath())
def compute(self):
"""
Run the RasterMath process, generating a raster output dataset.
"""
first = self.inputs[0]
epsg = first.get_epsg()
rasters = [x.read(epsg=epsg) for x in self.inputs]
bands = self.bands
nodata = self.nodata
all_bands = self.all_bands
otype = self.output_type
self.output.create_output_dir(self.output.uri)
self.output.data = gdal_calc(self.calc,
self.output.uri,
rasters,
bands=bands,
nodata=nodata,
allBands=all_bands,
output_type=otype,
format=self.output_format)
