class TestToCrs(TestCase):
def setUp(self) -> None:
# test inputs
predictors = [
nc.band1, nc.band2, nc.band3, nc.band4, nc.band5, nc.band7
]
self.stack = Raster(predictors)
# test results
self.stack_prj = None
def tearDown(self) -> None:
self.stack.close()
self.stack_prj.close()
def test_to_crs_defaults(self):
self.stack_prj = self.stack.to_crs({"init": "EPSG:4326"})
# check raster object
self.assertIsInstance(self.stack_prj, Raster)
self.assertEqual(self.stack_prj.count, self.stack.count)
self.assertEqual(self.stack_prj.read(masked=True).count(), 1012061)
# test nodata value is recognized
self.assertEqual(
self.stack_prj.read(masked=True).min(),
self.stack.read(masked=True).min())
self.assertEqual(
self.stack_prj.read(masked=True).max(),
self.stack.read(masked=True).max())
def test_to_crs_custom_nodata(self):
self.stack_prj = self.stack.to_crs({"init": "EPSG:4326"}, nodata=-999)
# check raster object
self.assertIsInstance(self.stack_prj, Raster)
self.assertEqual(self.stack_prj.count, self.stack.count)
self.assertEqual(self.stack_prj.read(masked=True).count(), 1012061)
# test nodata value is recognized
self.assertEqual(
self.stack_prj.read(masked=True).min(),
self.stack.read(masked=True).min())
self.assertEqual(
self.stack_prj.read(masked=True).max(),
self.stack.read(masked=True).max())
def test_to_crs_in_memory(self):
self.stack_prj = self.stack.to_crs({"init": "EPSG:4326"},
in_memory=True)
# check raster object
self.assertIsInstance(self.stack_prj, Raster)
class TestSample(TestCase):
def setUp(self) -> None:
predictors = [nc.band1, nc.band2, nc.band3, nc.band4, nc.band5, nc.band7]
self.stack = Raster(predictors)
self.strata = Raster(nc.strata)
def tearDown(self) -> None:
self.stack.close()
self.strata.close()
def test_sample_strata(self):
# extract using a strata raster and returning two arrays
size = 100
categories = self.strata.read(masked=True).flatten()
categories = categories[~categories.mask]
n_categories = np.unique(categories).shape[0]
n_samples = size * n_categories
X, xy = self.stack.sample(size=size, strata=self.strata, return_array=True)
self.assertEqual(X.shape, (n_samples, 6))
self.assertEqual(xy.shape, (n_samples, 2))
# extract using a strata raster and returning a dataframe
samples = self.stack.sample(size=size, strata=self.strata, return_array=False)
self.assertEqual(samples.shape, (n_samples, 7))
def test_sample_no_strata(self):
size = 100
X, xy = self.stack.sample(size=size, return_array=True)
self.assertEqual(X.shape, (size, 6))
self.assertEqual(xy.shape, (size, 2))
samples = self.stack.sample(size=size, return_array=False)
self.assertEqual(samples.shape, (size, 7))
# spatial cross-validation from sklearn.cluster import KMeans # create 10 spatial clusters based on clustering of the training data point x,y coordinates clusters = KMeans(n_clusters=34, n_jobs=-1) clusters.fit(df_polygons.geometry.bounds.iloc[:, 0:2]) # cross validate scores = cross_validate( lr, X, y, groups=clusters.labels_, scoring='accuracy', cv=3, n_jobs=1) scores['test_score'].mean() # prediction df = stack.read(as_df=True, masked=True) result = stack.predict(estimator=lr, dtype='int16', nodata=0) result = stack.predict(estimator=lr, dtype='int16', nodata=0, as_df=True) result_prob = stack.predict_proba(estimator=lr) result.names result_prob.names result.plot() plt.show() result_prob.plot() plt.show() # sampling # extract training data using a random sample
ep.plot_rgb(rgb432, extent=extent, ax=axes[0,0], stretch=True, title='Verdadero color')
manzana.boundary.plot(ax=axes[0,0], color='white', alpha=0.5, linewidth=1)
# Plot falso color
ep.plot_rgb(rgb843, extent=extent, ax=axes[0,1], stretch=True, title='Falso color')
manzana.boundary.plot(ax=axes[0,1], color='white', alpha=0.5, linewidth=1)
# Plot Class Keras
ep.plot_bands(result_keras.read(), extent=extent, ax=axes[1,0], cmap='RdYlGn',
alpha=0.8, title="Clasificación ANN", cbar=False)
manzana.boundary.plot(ax=axes[1,0], color='white', alpha=0.5, linewidth=1)
# Plot class KNeigh
# Plot Class Keras
ep.plot_bands(result_neigh.read(), extent=extent, ax=axes[1,1], cmap='RdYlGn',
alpha=0.8, title="Clasificación KNeighbors", cbar=False)
manzana.boundary.plot(ax=axes[1,1], color='white', alpha=0.5, linewidth=1)
# Plot ndvi
ep.plot_bands(raster_ndvi.read(), extent=extent, ax=axes[2,0], cmap='RdYlGn',
alpha=0.8, title="NDVI", cbar=False)
manzana.boundary.plot(ax=axes[2,0], color='white', alpha=0.5, linewidth=1)
# Plot EVI
ep.plot_bands(raster_evi.read(), extent=extent, ax=axes[2,1], cmap='RdYlGn',
alpha=0.8, title="EVI", cbar=False)
manzana.boundary.plot(ax=axes[2,1], color='white', alpha=0.5, linewidth=1)
plt.tight_layout()
plt.show()
# %%
df_ndvi = raster_ndvi.extract_vector(training)
df_ndvi.rename(columns={df_ndvi.columns[0]: 'id'}, inplace=True)
df_evi = raster_evi.extract_vector(training)
df_evi.rename(columns={df_evi.columns[0]: 'id'}, inplace=True)