def compilable_transformation_dictionary(feature_var, sentinel_im, geometry_transformation=None):
t_dict=\
{'feature':{'object':feature_var}, \
'feature_id':{'function':(lambda y: y.get_id() if type(y)!=tridy.Feature else y.get_data()['pixel_valu']),'parameters':['feature']},\
'feature_data':{'function':(lambda y: y.get_data()),'parameters':['feature']},\
'feature_geometry':{'function':(lambda y: y.get_geometry()),'parameters':['feature']},\
'sentinel_im':{'object': sentinel_im}, \
'im_band2_cropped':{'function':(lambda y, z: Imagee(*y.get_raster_map()['im_band2'].clip_by_shape(z)) ),'parameters':['sentinel_im','feature_geometry']},\
'im_band4_cropped':{'function':(lambda y, z: Imagee(*y.get_raster_map()['im_band4'].clip_by_shape(z)) ),'parameters':['sentinel_im','feature_geometry']},\
'im_band8_cropped':{'function':(lambda y, z: Imagee(*y.get_raster_map()['im_band8'].clip_by_shape(z)) ),'parameters':['sentinel_im','feature_geometry']},\
'cri2': {'function':(lambda y, z: Imagee((1 / (y.get_data()/10000) ) - (1 / (z.get_data()/10000) ), y.get_metadata() ) ),'parameters':['im_band2_cropped', 'im_band8_cropped']},\
'msr': {'function':(lambda x, y, z: Imagee( (z.get_data()-x.get_data())/(y.get_data()-x.get_data()) , y.get_metadata() ) ),'parameters':['im_band2_cropped', 'im_band4_cropped', 'im_band8_cropped']},\
'geometry_transformation':{'object': geometry_transformation}, \
'transformed_geometry':{'function':(lambda y, z: transform_wkt_geometry(y,z) if z!=None else y ),'parameters':['feature_geometry','geometry_transformation']},\
'result_feature':{'function':(lambda v, w, x, y, z: FeatureWithID(data={**v, **{'cri2':y.get_statistics()}, **{'msr':z.get_statistics()}}, geom=w, id=x) ),'parameters':['feature_data','transformed_geometry','feature_id','cri2', 'msr']},\
}
return t_dict
def get_twi(im_object):
with TemporaryDirectory() as tempdir:
im_object.export_as_tif('%s/dem.tif' % tempdir)
routing.fill_pits(('%s/dem.tif' % tempdir, 1), '%s/cdem.tif' % tempdir)
routing.flow_dir_mfd(('%s/cdem.tif' % tempdir, 1),
'%s/dir.tif' % tempdir)
routing.flow_accumulation_mfd(('%s/dir.tif' % tempdir, 1),
'%s/twi.tif' % tempdir)
ds = gdal.Open('%s/twi.tif' % tempdir)
metadata_dict = im_object.get_metadata()
twi = Imagee(np.array(ds.GetRasterBand(1).ReadAsArray()),
metadata_dict)
return twi
def compilable_transformation_dictionary(feature_var, feature_geomorpho_var,
feature_soils):
t_dict=\
{'feature':{'object':feature_var}, \
'feature_id':{'function':(lambda y: y.get_id() if type(y)!=tridy.Feature else 1),'parameters':['feature']},\
'feature_data':{'function':(lambda y: y.get_data()),'parameters':['feature']},\
'feature_geometry':{'function':(lambda y: y.get_geometry()),'parameters':['feature']},\
'feature_geomorpho':{'object': feature_geomorpho_var}, \
'feature_dem':{'function':(lambda y, z: Imagee(*y.get_raster_map()['dem'].clip_by_shape(z)) ),'parameters':['feature_geomorpho','feature_geometry']},\
'feature_slope':{'function':(lambda y, z: Imagee(*y.get_raster_map()['slope'].clip_by_shape(z)) ),'parameters':['feature_geomorpho','feature_geometry']},\
'feature_orientation':{'function':(lambda y, z: Imagee(*y.get_raster_map()['orientation'].clip_by_shape(z)) ),'parameters':['feature_geomorpho','feature_geometry']},\
'feature_twi':{'function':(lambda y, z: Imagee(*y.get_raster_map()['twi'].clip_by_shape(z)) ),'parameters':['feature_geomorpho','feature_geometry']},\
'feature_soils':{'object': feature_soils}, \
'feature_aglim1':{'function':(lambda y, z: Imagee(*y.get_raster_map()['aglim1'].clip_by_shape(z)) ),'parameters':['feature_soils','feature_geometry']},\
'feature_aglim2':{'function':(lambda y, z: Imagee(*y.get_raster_map()['aglim2'].clip_by_shape(z)) ),'parameters':['feature_soils','feature_geometry']},\
'result_feature':{'function':(lambda r, s, t, u, v, w, x, y, z: FeatureWithRasterMap(data=r, geom=s,id=t,raster_map={'dem':u,'slope':v,'orientation':w,'twi':x,'aglim1':y,'aglim2':z}) ),'parameters':['feature_data','feature_geometry','feature_id','feature_dem', 'feature_slope', 'feature_orientation', 'feature_twi', 'feature_aglim1', 'feature_aglim2']},\
}
return t_dict
grid_clip = Grid(
np.array(grid.get_gridorigin()) +
np.array(tuple(grid.find_index(
(envelope[0], envelope[3])))) * np.array(grid.get_gridstepsize()),
(metadata_dict['affine_transformation'][1],
metadata_dict['affine_transformation'][5]))
# %%
clip_metadata_dict = {}
clip_metadata_dict[
'affine_transformation'] = grid_clip.get_affinetransformation()
clip_metadata_dict['nodata'] = float(np.min(a)) if np.min(a) < 0 else np.nan
clip_metadata_dict['proj_wkt'] = etrs3035_sr.ExportToWkt()
# %%
big_im = Imagee(a, clip_metadata_dict)
# %%
big_twi = get_twi(big_im)
big_orientation = Imagee(*big_im.calculate_azimuth())
big_slope = Imagee(*big_im.calculate_slope())
# %%
feature_geomorpho = FeatureWithRasterMap(
data={},
geom=('POLYGON ((%d %d, %d %d, %d %d, %d %d, %d %d))' %
(envelope[0], envelope[2], envelope[0], envelope[3], envelope[1],
envelope[3], envelope[1], envelope[2], envelope[0], envelope[2])),
id=1,
raster_map={
'dem': big_im,
def geomorphology_transformations(ogr_olu_object, attributes_table,
attributes2object_table, attributes_fid,
dem_im, slope_im, aspect_im, twi_im):
o_dict=\
{
'feature':{'object':ogr_olu_object},\
'feature_json':{'function':(lambda y: json.loads(y.ExportToJson())),'parameters':['feature']},\
'feature_geometry':{'function':(lambda y: y.GetGeometryRef().ExportToWkt() ),'parameters':['feature']},\
'feature_id':{'function':(lambda y: int(y['properties']['fid'])),'parameters':['feature_json']},\
'attributes_table':{'object':attributes_table},\
'attributes2object_table':{'object':attributes2object_table},\
'attributes_fid':{'object':attributes_fid},\
'dem_image':{'object':dem_im},\
'slope_image':{'object':slope_im},\
'azimuth_image':{'object':aspect_im},\
'twi_image':{'object':twi_im},\
'attribute_properties':{'function':(lambda u, v, w, x,y,z: {**{'fid':u},**{'dataset_id':5},**{'atts':{'elevation':Imagee(*w.clip_by_shape(v)).get_statistics()['mean'], 'slope':Imagee(*x.clip_by_shape(v)).get_statistics()['mean'], 'azimuth':Imagee(*y.clip_by_shape(v)).get_statistics()['mean'], 'twi':Imagee(*z.clip_by_shape(v)).get_statistics()['mean']} } }),'parameters':['attributes_fid','feature_geometry','dem_image', 'slope_image', 'azimuth_image', 'twi_image']},
'attributes_insert_statement':{'function':(lambda x, y,z: "INSERT INTO %s (fid,atts,dataset_fid) VALUES (%s,('%s'::json),%s)" % (x,y,json.dumps(z['atts']),z['dataset_id']) ),'parameters':['attributes_table','attributes_fid','attribute_properties']},\
'attributes2object_insert_statement':{'function':(lambda x,y,z: "INSERT INTO %s (object_fid,atts_fid,atts_origin) VALUES (%s,%s,4)" % (z,x,y) ),'parameters':['feature_id','attributes_fid','attributes2object_table']},\
'two_insert_statements':{'function':(lambda y,z: [y,z]),'parameters':['attributes_insert_statement','attributes2object_insert_statement']},\
}
return o_dict
size=tuple(np.array(grid.find_index((geom_utm_envelope[1],geom_utm_envelope[2])))-np.array(grid.find_index((geom_utm_envelope[0],geom_utm_envelope[3]))))+np.array([1,1])
# %%
a=np.array(ds_b2.GetRasterBand(1).ReadAsArray(xoff=int(origin[0]),yoff=int(origin[1]),win_xsize=int(size[0]),win_ysize=int(size[1])))
# %%
grid_clip=Grid(np.array(grid.get_gridorigin())+np.array(tuple(grid.find_index((geom_utm_envelope[0],geom_utm_envelope[3]))))*np.array(grid.get_gridstepsize()),(metadata_dict['affine_transformation'][1],metadata_dict['affine_transformation'][5]))
# %%
clip_metadata_dict={}
clip_metadata_dict['affine_transformation']=grid_clip.get_affinetransformation()
clip_metadata_dict['nodata']=float(np.min(a)) if np.min(a)<0 else np.nan
clip_metadata_dict['proj_wkt']=utm32635_sr.ExportToWkt()
# %%
im=Imagee(a,clip_metadata_dict)
# %%
im.export_as_tif('vyrez.tif')
# %%
a=np.array(ds_b4.GetRasterBand(1).ReadAsArray(xoff=int(origin[0]),yoff=int(origin[1]),win_xsize=int(size[0]),win_ysize=int(size[1])))
im_b4=Imagee(a,clip_metadata_dict)
im_b4.export_as_tif('vyrez_b4.tif')
# %%
a=np.array(ds_b8.GetRasterBand(1).ReadAsArray(xoff=int(origin[0]),yoff=int(origin[1]),win_xsize=int(size[0]),win_ysize=int(size[1])))
im_b8=Imagee(a,clip_metadata_dict)
im_b8.export_as_tif('vyrez_b8.tif')
# %%
data_source=eudem_cz_ds,
subgeoconcepts=[])
eudem_cz.set_raster_output_backend(
'',
eudem_cz.get_data_source().get_attributes()['local'])
f = admunit_cz.read_features_from_table_by_sqlcondition('id=538493', 1)
ff = next(f)[0]
ff.transform_geometry(sjtsk5514_to_wgs84)
ff.transform_geometry(wgs84_to_etrs3035)
bbox = ogr.CreateGeometryFromWkt(ff.get_geometry()).Buffer(10).GetEnvelope()
im = eudem_cz.read_raster_output_backend(1,
(bbox[0], bbox[3], bbox[1], bbox[2]))
im_slope = Imagee(*im.calculate_slope())
im_azimuth = Imagee(*im.calculate_azimuth())
im_twi = get_twi(im)
olu_atts_fid_maxvalue = dbs_olu.execute(
'SELECT max(fid) FROM olu2.olu_attribute_set')[0][0]
dbs_olu.execute("select setval('olu2.olu_attributes_fid_seq', %s)" %
olu_atts_fid_maxvalue)
olu_atts_id_gen = sequence_generator(
dbs_olu.execute('SELECT last_value FROM olu2.olu_attributes_fid_seq')[0]
[0])
dbs_olu_ogr_conn = dbs_olu.create_ogr_connection()
lyr = dbs_olu_ogr_conn.GetLayerByName('olu_object.538493')
np.array([-500, 500, 500, -500]))
cci__im = cci_africa.read_raster_output_backend(1, bbox_r3857)
colnames = ['id', 'lc_class', 'frequency']
df = pd.DataFrame(columns=colnames)
lyr.ResetReading()
bad_list = []
for c in range(lyr.GetFeatureCount()):
lyr_f = lyr.GetNextFeature()
row_d = {}
if lyr_f.GetGeometryRef().IsEmpty():
continue
try:
geom = lyr_f.GetGeometryRef()
geom.Transform(wgs84_to_mercator3857)
statistics = Imagee(
*cci__im.clip_by_shape(geom.ExportToWkt())).get_statistics(
categoric=True, percentage=True, full=True)
row_d['id'] = lyr_f.GetFieldAsInteger64(0)
row_d['lc_class'] = statistics['mode']
row_d['frequency'] = statistics['frequencies'][str(
statistics['mode'])]
df = df.append(row_d, ignore_index=True)
except:
bad_list.append(lyr_f.GetFieldAsInteger64(0))
df.set_index(df.id, inplace=True)
dbs.execute("create table %s.atts_%s () inherits (%s) " %
(country.lower(), table, olu_attribute_set_table))
for index, row in df.iterrows():
atts_fid = next(olu_atts_id_gen)
dbs.execute(
'insert into %s.atts_%s (fid,atts,dataset_fid,clc_id) select %d as fid, \'{"frequency":%s}\'::jsonb as atts, %d as dataset_fid, %s as clc_id '
tuple(
grid.find_index((feature_geometry3035.GetEnvelope()[0],
feature_geometry3035.GetEnvelope()[3])))) *
np.array(grid.get_gridstepsize()),
(metadata_dict['affine_transformation'][1],
metadata_dict['affine_transformation'][5]))
# %%
clip_metadata_dict = {}
clip_metadata_dict[
'affine_transformation'] = grid_clip.get_affinetransformation()
clip_metadata_dict['nodata'] = float(np.min(a)) if np.min(a) < 0 else np.nan
clip_metadata_dict['proj_wkt'] = etrs3035_sr.ExportToWkt()
# %%
im = Imagee(a, clip_metadata_dict)
# %%
im.export_as_tif('Mnichovice.tif')
# %%
G = apply_function(compilable_tree_dictionary(admunit_cz), 'admunit__tree')
# %%
list(find_neighbors_level(G.reverse(), str(f.get_data()['Kod']), 4))
# %%
features_lpis = lpis_cz.get_subgeoconcept_by_name(
'697541').read_features_from_table(1)
# %%
lyr.GetExtent()[1],
lyr.GetExtent()[2]
]
cci20__im = cci20.read_raster_output_backend(1, bbox_r)
colnames = ['id', 'lc_class', 'frequency']
df = pd.DataFrame(columns=colnames)
lyr.ResetReading()
bad_list = []
for c in range(lyr.GetFeatureCount()):
lyr_f = lyr.GetNextFeature()
row_d = {}
if lyr_f.GetGeometryRef().IsEmpty():
continue
try:
statistics = Imagee(*cci20__im.clip_by_shape(
lyr_f.GetGeometryRef().ExportToWkt())).get_statistics(
categoric=True, percentage=True, full=True)
row_d['id'] = lyr_f.GetFieldAsInteger64(0)
row_d['lc_class'] = statistics['mode']
row_d['frequency'] = statistics['frequencies'][str(
statistics['mode'])]
df = df.append(row_d, ignore_index=True)
except:
bad_list.append(lyr_f.GetFieldAsInteger64(0))
df.set_index(df.id, inplace=True)
all_args = ''
for index, row in df.iterrows():
args = '),('.join([
str(int(index)) + ',' + str(int(row['lc_class'])) + ',' +
str(float(row['frequency']))
])
tuple(
grid.find_index((feature_geometry_buffer.GetEnvelope()[0],
feature_geometry_buffer.GetEnvelope()[3])))) *
np.array(grid.get_gridstepsize()),
(metadata_dict['affine_transformation'][1],
metadata_dict['affine_transformation'][5]))
# %%
clip_metadata_dict = {}
clip_metadata_dict[
'affine_transformation'] = grid_clip.get_affinetransformation()
clip_metadata_dict['nodata'] = float(np.min(a)) if np.min(a) < 0 else np.nan
clip_metadata_dict['proj_wkt'] = etrs3035_sr.ExportToWkt()
# %%
im = Imagee(a, clip_metadata_dict)
# %%
im.export_as_tif('pole_s_bufrem500.tif')
# %%
im_twi = get_twi(im)
# %%
im_twi.export_as_tif('akumulace_vody_na_poli_s_bufrem500.tif')
# %%
cropped_im = Imagee(*im.clip_by_shape(feature_geometry.ExportToWkt()))
# %%
plt.imshow(cropped_im.get_data(),