本文整理汇总了Python中molusce.algorithms.dataprovider.Raster.getGeodata方法的典型用法代码示例。如果您正苦于以下问题：Python Raster.getGeodata方法的具体用法？Python Raster.getGeodata怎么用？Python Raster.getGeodata使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类molusce.algorithms.dataprovider.Raster的用法示例。
在下文中一共展示了Raster.getGeodata方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: TestSimulator
# 需要导入模块: from molusce.algorithms.dataprovider import Raster [as 别名]
# 或者: from molusce.algorithms.dataprovider.Raster import getGeodata [as 别名]
class TestSimulator(unittest.TestCase):
def setUp(self):
# Raster1:
# ~ [1, 1, 3,],
# ~ [3, 2, 1,],
# ~ [0, 3, 1,]
self.raster1 = Raster("../../examples/multifact.tif")
self.raster1.resetMask([0])
self.X = np.array([[1, 2, 3], [3, 2, 1], [0, 1, 1]])
self.X = np.ma.array(self.X, mask=(self.X == 0))
self.raster2 = Raster()
self.raster2.create([self.X], self.raster1.getGeodata())
self.aa = AreaAnalyst(self.raster1, self.raster2)
self.crosstab = CrossTableManager(self.raster1, self.raster2)
# Simple model
self.model = Model(state=self.raster1)
def test_compute_table(self):
# print self.crosstab.getCrosstable().getCrosstable()
# CrossTab:
# [[ 3. 1. 0.]
# [ 0. 1. 0.]
# [ 1. 0. 2.]]
prediction = self.model.getPrediction(self.raster1)
# print prediction.getBand(1)
# prediction = [[1.0 1.0 6.0]
# [6.0 5.0 1.0]
# [-- 6.0 1.0]]
# confidence = self.model.getConfidence()
# print confidence.getBand(1)
# confidence = [[1.0 0.5 0.33]
# [0.5 0.33 0.25]
# [-- 0.25 0.2]]
result = np.array([[2.0, 1.0, 3.0], [1.0, 2.0, 1.0], [0, 3.0, 1.0]])
result = np.ma.array(result, mask=(result == 0))
simulator = Simulator(
state=self.raster1, factors=None, model=self.model, crosstable=self.crosstab
) # The model does't use factors
simulator.setIterationCount(1)
simulator.simN()
state = simulator.getState().getBand(1)
assert_array_equal(result, state)
result = np.array([[2.0, 1.0, 1.0], [2.0, 2.0, 1.0], [0, 3.0, 1.0]])
result = np.ma.array(result, mask=(result == 0))
simulator = Simulator(self.raster1, None, self.model, self.crosstab)
simulator.setIterationCount(2)
simulator.simN()
state = simulator.getState().getBand(1)
assert_array_equal(result, state)示例2: test_WoeManager
# 需要导入模块: from molusce.algorithms.dataprovider import Raster [as 别名]
# 或者: from molusce.algorithms.dataprovider.Raster import getGeodata [as 别名]
def test_WoeManager(self):
aa = AreaAnalyst(self.sites, self.sites)
w1 = WoeManager([self.factor], aa)
p = w1.getPrediction(self.sites).getBand(1)
assert_array_equal(p, self.sites.getBand(1))
initState = Raster("../../examples/data.tif")
finalState = Raster("../../examples/data1.tif")
aa = AreaAnalyst(initState, finalState)
w = WoeManager([initState], aa)
p = w.getPrediction(initState).getBand(1)
# Calculate by hands:
# 1->1 transition raster:
r11 = [[1, 1, 0, 0], [0, 1, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
# 1->2 raster:
r12 = [[0, 0, 1, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
# 1->3 raster:
r13 = [[0, 0, 0, 1], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
# 2->1
r21 = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
# 2->2
r22 = [[0, 0, 0, 0], [0, 0, 1, 0], [0, 0, 0, 0], [0, 0, 0, 0]]
# 2->3
r23 = [[0, 0, 0, 0], [0, 0, 0, 1], [1, 1, 1, 1], [0, 0, 0, 0]]
# 3->1
r31 = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [1, 1, 0, 0]]
# 3->2
r32 = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 1]]
# 3->3
r33 = [[0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 0], [0, 0, 1, 0]]
geodata = initState.getGeodata()
sites = {"11": r11, "12": r12, "13": r13, "21": r21, "22": r22, "23": r23, "31": r31, "32": r32, "33": r33}
woeDict = {} # WoE of transitions
for k in sites.keys(): #
if k != "21": # !!! r21 is zero
x = Raster()
x.create([np.ma.array(data=sites[k])], geodata)
sites[k] = x
woeDict[k] = woe(initState.getBand(1), x.getBand(1))
# w1max = np.maximum(woeDict['11'], woeDict['12'], woeDict['13'])
# w2max = np.maximum(woeDict['22'], woeDict['23'])
# w3max = np.maximum(woeDict['31'], woeDict['32'], woeDict['33'])
# Answer is index of finalClass that maximizes weights of transiotion initClass -> finalClass
answer = [[1, 1, 1, 1], [1, 1, 3, 3], [3, 3, 3, 3], [1, 1, 1, 1]]
assert_array_equal(p, answer)
w = WoeManager([initState], aa, bins={0: [[2]]})
p = w.getPrediction(initState).getBand(1)示例3: TestRaster
# 需要导入模块: from molusce.algorithms.dataprovider import Raster [as 别名]
# 或者: from molusce.algorithms.dataprovider.Raster import getGeodata [as 别名]
class TestRaster (unittest.TestCase):
def setUp(self):
self.r1 = Raster('examples/multifact.tif')
self.r2 = Raster('examples/sites.tif')
self.r3 = Raster('examples/two_band.tif')
# r1
data1 = np.array(
[
[1,1,3],
[3,2,1],
[0,3,1]
])
# r2
data2 = np.array(
[
[1,2,1],
[1,2,1],
[0,1,2]
])
mask = [
[False, False, False],
[False, False, False],
[False, False, False]
]
self.data1 = ma.array(data=data1, mask=mask)
self.data2 = ma.array(data=data2, mask=mask)
def test_RasterInit(self):
self.assertEqual(self.r1.getBandsCount(), 1)
band = self.r1.getBand(1)
shape = band.shape
x = self.r1.getXSize()
y = self.r1.getYSize()
self.assertEqual(shape, (x,y))
self.assertEqual(self.r2.getBandsCount(), 1)
band = self.r2.getBand(1)
assert_array_equal(band, self.data2)
self.assertTrue(self.r1.geoDataMatch(self.r2))
self.assertTrue(self.r1.isMetricProj())
def test_getBandStat(self):
stat = self.r1.getBandStat(1)
self.assertAlmostEqual(stat['mean'], 15.0/9)
self.assertAlmostEqual(stat['std'], np.sqrt(10.0/9))
def test_normalize(self):
multifact = [
[1,1,3],
[3,2,1],
[0,3,1],
]
# Normalize using std and mean
r1 = Raster('examples/multifact.tif')
r1.normalize()
r1.denormalize()
assert_array_equal(r1.getBand(1), multifact)
# Normalize using min and max
r1 = Raster('examples/multifact.tif')
r1.normalize(mode='maxmin')
r1.denormalize()
assert_array_equal(r1.getBand(1), multifact)
# Two normalization procedures
r1 = Raster('examples/multifact.tif')
r1.normalize()
r1.normalize(mode='maxmin')
r1.denormalize()
assert_array_equal(r1.getBand(1), multifact)
r1 = Raster('examples/multifact.tif')
r1.normalize(mode='maxmin')
r1.normalize()
r1.denormalize()
assert_array_equal(r1.getBand(1), multifact)
def test_getNeighbours(self):
neighbours = self.r2.getNeighbours(row=1,col=0, size=0)
self.assertEqual(neighbours, [[1]])
neighbours = self.r2.getNeighbours(row=1,col=1, size=1)
assert_array_equal(neighbours, [self.data2])
neighbours = self.r3.getNeighbours(row=1,col=1, size=1)
assert_array_equal(neighbours, [self.data2, self.data1])
# Check pixel on the raster bound and nonzero neighbour size
self.assertRaises(ProviderError, self.r2.getNeighbours, col=1, row=0, size=1)
self.assertRaises(ProviderError, self.r2.getNeighbours, col=1, row=1, size=2)
def test_geodata(self):
geodata = self.r1.getGeodata()
self.r1.setGeoData(geodata)
geodata['xSize'] = geodata['xSize'] + 10
#.........这里部分代码省略.........
示例4: test_WoeManager
# 需要导入模块: from molusce.algorithms.dataprovider import Raster [as 别名]
# 或者: from molusce.algorithms.dataprovider.Raster import getGeodata [as 别名]
def test_WoeManager(self):
aa = AreaAnalyst(self.sites, self.sites)
w1 = WoeManager([self.factor], aa)
w1.train()
p = w1.getPrediction(self.sites).getBand(1)
answer = [[0,3,0], [0,3,0], [9,0,3]]
answer = ma.array(data = answer, mask = self.mask)
assert_array_equal(p, answer)
initState = Raster('../../examples/data.tif')
#~ [1,1,1,1],
#~ [1,1,2,2],
#~ [2,2,2,2],
#~ [3,3,3,3]
finalState = Raster('../../examples/data1.tif')
#~ [1,1,2,3],
#~ [3,1,2,3],
#~ [3,3,3,3],
#~ [1,1,3,2]
aa = AreaAnalyst(initState, finalState)
w = WoeManager([initState], aa)
w.train()
#print w.woe
p = w.getPrediction(initState).getBand(1)
self.assertEquals(p.dtype, np.uint8)
# Calculate by hands:
#1->1 transition raster:
r11 = [
[1, 1, 0, 0],
[0, 1, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]
]
#1->2 raster:
r12 = [
[0, 0, 1, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]
]
#1->3 raster:
r13 = [
[0, 0, 0, 1],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]
]
# 2->1
r21 = [
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]
]
# 2->2
r22 = [
[0, 0, 0, 0],
[0, 0, 1, 0],
[0, 0, 0, 0],
[0, 0, 0, 0]
]
# 2->3
r23 = [
[0, 0, 0, 0],
[0, 0, 0, 1],
[1, 1, 1, 1],
[0, 0, 0, 0]
]
# 3->1
r31 = [
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[1, 1, 0, 0]
]
# 3->2
r32 = [
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 1]
]
# 3->3
r33 = [
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 0, 1, 0]
]
geodata = initState.getGeodata()
sites = {'11': r11, '12': r12, '13': r13, '21': r21, '22': r22, '23': r23, '31': r31, '32': r32, '33': r33}
woeDict = {} # WoE of transitions
for k in sites.keys(): #
if k !='21' : # !!! r21 is zero
x = Raster()
x.create([np.ma.array(data=sites[k])], geodata)
sites[k] = x
woeDict[k] = woe(initState.getBand(1), x.getBand(1))
#w1max = np.maximum(woeDict['11'], woeDict['12'], woeDict['13'])
#.........这里部分代码省略.........