def polygonize(raster_path: str, band: int, out_layer_path: str, epsg: int = None):
# mapping between gdal type and ogr field type
type_mapping = {
gdal.GDT_Byte: ogr.OFTInteger,
gdal.GDT_UInt16: ogr.OFTInteger,
gdal.GDT_Int16: ogr.OFTInteger,
gdal.GDT_UInt32: ogr.OFTInteger,
gdal.GDT_Int32: ogr.OFTInteger,
gdal.GDT_Float32: ogr.OFTReal,
gdal.GDT_Float64: ogr.OFTReal,
gdal.GDT_CInt16: ogr.OFTInteger,
gdal.GDT_CInt32: ogr.OFTInteger,
gdal.GDT_CFloat32: ogr.OFTReal,
gdal.GDT_CFloat64: ogr.OFTReal
}
with get_shp_or_gpkg(out_layer_path, write=True) as out_layer:
out_layer.create_layer(ogr.wkbPolygon, epsg=epsg)
src_ds = gdal.Open(raster_path)
src_band = src_ds.GetRasterBand(band)
out_layer.create_field('id', field_type=type_mapping[src_band.DataType])
progbar = ProgressBar(100, 50, "Polygonizing raster")
def poly_progress(progress, _msg, _data):
# double dfProgress, char const * pszMessage=None, void * pData=None
progbar.update(int(progress * 100))
gdal.Polygonize(src_band, src_ds.GetRasterBand(band), out_layer.ogr_layer, 0, [], callback=poly_progress)
progbar.finish()
src_ds = None
def quicksave(gpkg, name, geoms, geom_type):
with GeopackageLayer(gpkg, name, write=True) as out_lyr:
out_lyr.create_layer(geom_type, epsg=4326)
progbar = ProgressBar(len(geoms), 50,
f"saving {out_lyr.ogr_layer_name} features")
counter = 0
for shape in geoms:
progbar.update(counter)
counter += 1
out_lyr.create_feature(shape)
def dissolve_by_points(groups, polys):
progbar = ProgressBar(len(groups), 50, "Dissolving Polygons...")
counter = 0
progbar.update(counter)
dissolved_polys = {}
# Original method
# for key, group in groups.items():
# counter += 1
# progbar.update(counter)
# intersected = [p for p in polys if any([p.contains(pt.point) for pt in group])]
# dissolved_polys[key] = unary_union(intersected)
# This method gradulally speeds up processing by removing polygons from the list.
for key, group in groups.items():
counter += 1
progbar.update(counter)
intersected = []
indexes = []
for i, p in enumerate(polys):
if any([p.contains(pt.point) for pt in group]):
intersected.append(p)
indexes.append(i)
dissolved_polys[key] = unary_union(
intersected) # MultiPolygon(intersected) #intersected
polys = [p for i, p in enumerate(polys) if i not in indexes]
progbar.finish()
return dissolved_polys
def get_geometry_union(inpath, epsg, attribute_filter=None):
"""
TODO: Remove this method and replace all references to the get_geometry_unary_union method below
Load all features from a ShapeFile and union them together into a single geometry
:param inpath: Path to a ShapeFile
:param epsg: Desired output spatial reference
:return: Single Shapely geometry of all unioned features
"""
log = Logger('Shapefile')
driver = ogr.GetDriverByName("ESRI Shapefile")
data_source = driver.Open(inpath, 0)
layer = data_source.GetLayer()
in_spatial_ref = layer.GetSpatialRef()
if attribute_filter:
layer.SetAttributeFilter(attribute_filter)
_out_spatial_ref, transform = get_transform_from_epsg(in_spatial_ref, epsg)
geom = None
progbar = ProgressBar(layer.GetFeatureCount(), 50, "Unioning features")
counter = 0
for feature in layer:
counter += 1
progbar.update(counter)
new_geom = feature.GetGeometryRef()
if new_geom is None:
progbar.erase() # get around the progressbar
log.warning('Feature with FID={} has no geometry. Skipping'.format(
feature.GetFID()))
continue
new_geom.Transform(transform)
new_shape = wkbload(new_geom.ExportToWkb())
try:
geom = geom.union(new_shape) if geom else new_shape
except Exception as e:
progbar.erase() # get around the progressbar
log.warning(
'Union failed for shape with FID={} and will be ignored'.
format(feature.GetFID()))
progbar.finish()
data_source = None
return geom
def polygonize(raster_path, band, out_shp_path, epsg):
# mapping between gdal type and ogr field type
type_mapping = {
gdal.GDT_Byte: ogr.OFTInteger,
gdal.GDT_UInt16: ogr.OFTInteger,
gdal.GDT_Int16: ogr.OFTInteger,
gdal.GDT_UInt32: ogr.OFTInteger,
gdal.GDT_Int32: ogr.OFTInteger,
gdal.GDT_Float32: ogr.OFTReal,
gdal.GDT_Float64: ogr.OFTReal,
gdal.GDT_CInt16: ogr.OFTInteger,
gdal.GDT_CInt32: ogr.OFTInteger,
gdal.GDT_CFloat32: ogr.OFTReal,
gdal.GDT_CFloat64: ogr.OFTReal
}
src_ds = gdal.Open(raster_path)
src_band = src_ds.GetRasterBand(band)
driver = ogr.GetDriverByName("ESRI Shapefile")
if os.path.exists(out_shp_path):
driver.DeleteDataSource(out_shp_path)
outDataSource = driver.CreateDataSource(out_shp_path)
out_spatial_ref = osr.SpatialReference()
out_spatial_ref.ImportFromEPSG(epsg)
outLayer = outDataSource.CreateLayer("polygonized",
out_spatial_ref,
geom_type=ogr.wkbPolygon)
raster_field = ogr.FieldDefn('id', type_mapping[src_band.DataType])
outLayer.CreateField(raster_field)
progbar = ProgressBar(100, 50, "Polygonizing raster")
def poly_progress(progress, msg, data):
# double dfProgress, char const * pszMessage=None, void * pData=None
progbar.update(int(progress * 100))
gdal.Polygonize(src_band,
src_ds.GetRasterBand(band),
outLayer,
0, [],
callback=poly_progress)
progbar.finish()
outDataSource.Destroy()
src_ds = None
def clip_polygons(clip_poly, polys):
progbar = ProgressBar(len(polys), 50, "Clipping Polygons...")
counter = 0
progbar.update(counter)
out_polys = {}
for key, poly in polys.items():
counter += 1
progbar.update(counter)
out_polys[key] = clip_poly.intersection(poly.buffer(0))
progbar.finish()
return out_polys
def merge_geometries(feature_classes, epsg):
"""
Load all features from multiple feature classes into a single list of geometries
:param feature_classes:
:param epsg:
:return:
"""
log = Logger('Shapefile')
driver = ogr.GetDriverByName("ESRI Shapefile")
union = ogr.Geometry(ogr.wkbMultiLineString)
fccount = 0
for fc in feature_classes:
fccount += 1
log.info("Merging Geometries for feature class {}/{}".format(
fccount, len(feature_classes)))
data_source = driver.Open(fc, 0)
layer = data_source.GetLayer()
in_spatial_ref = layer.GetSpatialRef()
out_spatial_ref, transform = get_transform_from_epsg(
in_spatial_ref, epsg)
progbar = ProgressBar(layer.GetFeatureCount(), 50,
"Merging Geometries")
counter = 0
for feature in layer:
counter += 1
progbar.update(counter)
geom = feature.GetGeometryRef()
if geom is None:
progbar.erase() # get around the progressbar
log.warning(
'Feature with FID={} has no geoemtry. Skipping'.format(
feature.GetFID()))
continue
geom.Transform(transform)
union.AddGeometry(geom)
progbar.finish()
data_source = None
return union
def dissolve_by_intersection(lines, polys):
progbar = ProgressBar(len(polys), 50, "Dissolving Polygons...")
counter = 0
progbar.update(counter)
dissolved_polys = []
for line in lines:
counter += 1
progbar.update(counter)
intersected = [p for p in polys if line.intersects(p)]
dissolved_polys.append(unary_union(intersected))
return dissolved_polys
def extract_mean_values_by_polygon(polys, rasters, reference_raster):
log = Logger('extract_mean_values_by_polygon')
progbar = ProgressBar(len(polys), 50, "Extracting Mean values...")
counter = 0
with rasterio.open(reference_raster) as dataset:
output_mean = {}
output_unique = {}
for reachid, poly in polys.items():
counter += 1
progbar.update(counter)
if poly.geom_type in ["Polygon", "MultiPolygon"] and poly.area > 0:
values_mean = {}
values_unique = {}
reach_raster = np.ma.masked_invalid(
features.rasterize(
[poly],
out_shape=dataset.shape,
transform=dataset.transform,
all_touched=True,
fill=np.nan))
for key, raster in rasters.items():
if raster is not None:
current_raster = np.ma.masked_array(raster, mask=reach_raster.mask)
values_mean[key] = np.ma.mean(current_raster)
values_unique[key] = np.unique(np.ma.filled(current_raster, fill_value=0), return_counts=True)
else:
values_mean[key] = 0.0
values_unique[key] = []
output_mean[reachid] = values_mean
output_unique[reachid] = values_unique
# log.debug(f"Reach: {reachid} | {sum([v for v in values.values() if v is not None]):.2f}")
else:
progbar.erase()
log.warning(f"Reach: {reachid} | WARNING no geom")
progbar.finish()
return output_mean, output_unique
def calculate_neighbours(self):
self.region_neighbour = []
# Find which regions are next to which other regions
progbar = ProgressBar(len(self._vor.regions), 50, "baking in region adjacency")
counter = 0
for idx, reg in enumerate(self._vor.regions):
counter += 1
progbar.update(counter)
adj = []
for idy, reg2 in enumerate(self._vor.regions):
# Adjacent if we have two matching vertices (neighbours share a wall)
if idx != idy and len(set(reg) - (set(reg) - set(reg2))) >= 2:
adj.append(idy)
self.region_neighbour.append(adj)
progbar.finish()
def admin_agency(database, reaches, ownership, results):
log = Logger('Conflict')
log.info(
'Calculating land ownership administrating agency for {:,} reach(es)'.
format(len(reaches)))
# Load the agency lookups
with SQLiteCon(database) as database:
database.curs.execute(
'SELECT AgencyID, Name, Abbreviation FROM Agencies')
agencies = {
row['Abbreviation']: {
'AgencyID': row['AgencyID'],
'Name': row['Name'],
'RawGeometries': [],
'GeometryUnion': None
}
for row in database.curs.fetchall()
}
with get_shp_or_gpkg(ownership) as ownership_lyr:
progbar = ProgressBar(len(reaches), 50, "Calc administration agency")
counter = 0
# Loop over stream reaches and assign agency
for reach_id, polyline in reaches.items():
counter += 1
progbar.update(counter)
if reach_id not in results:
results[reach_id] = {}
mid_point = polyline.interpolate(0.5, normalized=True)
results[reach_id]['AgencyID'] = None
for feature, _counter, _progbar in ownership_lyr.iterate_features(
clip_shape=mid_point):
agency = feature.GetField('ADMIN_AGEN')
if agency not in agencies:
raise Exception(
'The ownership agency "{}" is not found in the BRAT SQLite database'
.format(agency))
results[reach_id]['AgencyID'] = agencies[agency]['AgencyID']
progbar.finish()
log.info('Adminstration agency assignment complete')
def simple_save(list_geoms, ogr_type, srs, layer_name, gpkg_path):
with GeopackageLayer(gpkg_path, layer_name, write=True) as lyr:
lyr.create_layer(ogr_type, spatial_ref=srs)
progbar = ProgressBar(len(list_geoms), 50, f"Saving {gpkg_path}/{layer_name}")
counter = 0
progbar.update(counter)
lyr.ogr_layer.StartTransaction()
for geom in list_geoms:
counter += 1
progbar.update(counter)
feature = ogr.Feature(lyr.ogr_layer_def)
geom_ogr = VectorBase.shapely2ogr(geom)
feature.SetGeometry(geom_ogr)
# if attributes:
# for field, value in attributes.items():
# feature.SetField(field, value)
lyr.ogr_layer.CreateFeature(feature)
feature = None
progbar.finish()
lyr.ogr_layer.CommitTransaction()
def rasterize(in_lyr_path, out_raster_path, template_path):
"""Rasterizing an input
Args:
in_lyr_path ([type]): [description]
out_raster_ ([type]): [description]
template_path ([type]): [description]
"""
log = Logger('VBETRasterize')
ds_path, lyr_path = VectorBase.path_sorter(in_lyr_path)
progbar = ProgressBar(100, 50, "Rasterizing ")
with rasterio.open(template_path) as raster:
t = raster.transform
raster_bounds = raster.bounds
def poly_progress(progress, _msg, _data):
progbar.update(int(progress * 100))
# Rasterize the features (roads, rail etc) and calculate a raster of Euclidean distance from these features
progbar.update(0)
# Rasterize the polygon to a temporary file
with TempRaster('vbet_rasterize') as tempfile:
log.debug('Temporary file: {}'.format(tempfile.filepath))
gdal.Rasterize(
tempfile.filepath,
ds_path,
layers=[lyr_path],
xRes=t[0],
yRes=t[4],
burnValues=1,
outputType=gdal.GDT_Int16,
creationOptions=['COMPRESS=LZW'],
# outputBounds --- assigned output bounds: [minx, miny, maxx, maxy]
outputBounds=[
raster_bounds.left, raster_bounds.bottom, raster_bounds.right,
raster_bounds.top
],
callback=poly_progress)
progbar.finish()
# Now mask the output correctly
mask_rasters_nodata(tempfile.filepath, template_path, out_raster_path)
def translate(vrtpath_in: str, raster_out_path: str, band: int):
"""GDAL translate Operation from VRT
Args:
vrtpath_in ([type]): [description]
raster_out_path ([type]): [description]
band ([int]): raster band
"""
log = Logger('translate')
tmr = Timer()
progbar = ProgressBar(100, 50, "Translating ")
def translate_progress(progress, _msg, _data):
progbar.update(int(progress * 100))
translateoptions = gdal.TranslateOptions(
gdal.ParseCommandLine(
"-of Gtiff -b {} -co COMPRESS=DEFLATE".format(band)))
gdal.Translate(raster_out_path,
vrtpath_in,
options=translateoptions,
callback=translate_progress)
log.info('completed in {}'.format(tmr.toString()))
def load_attributes(network, id_field, fields):
"""
Load ShapeFile attributes fields into a dictionary keyed by the id_field
:param network: Full, absolute path to a ShapeFile
:param id_field: Field that uniquely identifies each feature
:param fields: List of fields to load into the dictionary
:return: Dictionary with id_field as key and each feature as dictionary of values keyed by the field name
"""
# Get the input network
driver = ogr.GetDriverByName('ESRI Shapefile')
dataset = driver.Open(network, 0)
layer = dataset.GetLayer()
# Verify that all the fields are present or throw an exception
[verify_field(layer, field) for field in fields]
# Only calculate the combined FIS where all the inputs exist
# [networkLr.SetAttributeFilter('{} is not null'.format(field)) for field in [veg_field, drain_field, hydq2_field, hydlow_field, length_field, slope_field]]
# layer.SetAttributeFilter("iGeo_Slope > 0 and iGeo_DA > 0")
driver = ogr.GetDriverByName("ESRI Shapefile")
data_source = driver.Open(network, 0)
layer = data_source.GetLayer()
print('{:,} features in polygon ShapeFile {}'.format(
layer.GetFeatureCount(), network))
feature_values = {}
progbar = ProgressBar(layer.GetFeatureCount(), 50, "Loading features")
counter = 0
for inFeature in layer:
counter += 1
progbar.update(counter)
reach = inFeature.GetField(id_field)
feature_values[reach] = {}
for field in fields:
feature_values[reach][field] = inFeature.GetField(field)
progbar.finish()
return feature_values
def raster_buffer_stats2(polygons, raster):
log = Logger('Buffer Stats')
# Open the raster and then loop over all polyline features
results = {}
with rasterio.open(raster) as src:
log.info('Looping over {:,} polygon features...'.format(len(polygons)))
progbar = ProgressBar(len(polygons), 50, "Buffer Stats")
counter = 0
for reach_id, polygon in polygons.items():
counter += 1
progbar.update(counter)
# print('ReachID {}'.format(feature.GetField('ReachID')))
# retrieve an array for the cells under the polygon
raw_raster, _out_transform = mask(src, [polygon], crop=True)
mask_raster = np.ma.masked_values(raw_raster, src.nodata)
# print(mask_raster)
mean = None
maximum = None
minimum = None
if not mask_raster.mask.all():
mean = float(mask_raster.mean())
maximum = float(mask_raster.max())
minimum = float(mask_raster.min())
count = int(mask_raster.count())
rsum = float(mask_raster.sum())
results[reach_id] = {
'Mean': mean,
'Maximum': maximum,
'Minimum': minimum,
'Count': count,
'Sum': rsum
}
progbar.finish()
log.info('Process completed successfully.')
return results
def threshold(evidence_raster_path: str, thr_val: float,
thresh_raster_path: str):
"""Threshold a raster to greater than or equal to a threshold value
Args:
evidence_raster_path (str): [description]
thr_val (float): [description]
thresh_raster_path (str): [description]
"""
log = Logger('threshold')
with rasterio.open(evidence_raster_path) as fval_src:
out_meta = fval_src.meta
out_meta['count'] = 1
out_meta['compress'] = 'deflate'
out_meta['dtype'] = rasterio.uint8
out_meta['nodata'] = 0
log.info('Thresholding at {}'.format(thr_val))
with rasterio.open(thresh_raster_path, "w", **out_meta) as dest:
progbar = ProgressBar(len(list(fval_src.block_windows(1))), 50,
"Thresholding at {}".format(thr_val))
counter = 0
for ji, window in fval_src.block_windows(1):
progbar.update(counter)
counter += 1
fval_data = fval_src.read(1, window=window, masked=True)
# Fill an array with "1" values to give us a nice mask for polygonize
fvals_mask = np.full(fval_data.shape, np.uint8(1))
# Create a raster with 1.0 as a value everywhere in the same shape as fvals
new_fval_mask = np.ma.mask_or(fval_data.mask,
fval_data < thr_val)
masked_arr = np.ma.array(fvals_mask,
mask=[new_fval_mask
]) # & ch_data.mask])
dest.write(np.ma.filled(masked_arr, out_meta['nodata']),
window=window,
indexes=1)
progbar.finish()
def hand_rasterize(in_lyr_path: str, template_dem_path: str,
out_raster_path: str):
log = Logger('hand_rasterize')
ds_path, lyr_path = VectorBase.path_sorter(in_lyr_path)
g = gdal.Open(template_dem_path)
geo_t = g.GetGeoTransform()
width, height = g.RasterXSize, g.RasterYSize
xmin = min(geo_t[0], geo_t[0] + width * geo_t[1])
xmax = max(geo_t[0], geo_t[0] + width * geo_t[1])
ymin = min(geo_t[3], geo_t[3] + geo_t[-1] * height)
ymax = max(geo_t[3], geo_t[3] + geo_t[-1] * height)
# Close our dataset
g = None
progbar = ProgressBar(100, 50, "Rasterizing for HAND")
def poly_progress(progress, _msg, _data):
progbar.update(int(progress * 100))
# https://gdal.org/programs/gdal_rasterize.html
# https://gdal.org/python/osgeo.gdal-module.html#RasterizeOptions
gdal.Rasterize(
out_raster_path,
ds_path,
layers=[lyr_path],
height=height,
width=width,
burnValues=1,
outputType=gdal.GDT_CFloat32,
creationOptions=['COMPRESS=LZW'],
# outputBounds --- assigned output bounds: [minx, miny, maxx, maxy]
outputBounds=[xmin, ymin, xmax, ymax],
callback=poly_progress)
progbar.finish()
# Rasterize the features (roads, rail etc) and calculate a raster of Euclidean distance from these features
progbar.update(0)
def write_values_to_csv(csv_file, cols, values):
# cols = list(next(iter(values)).keys())
# # Remove the date related columns
# for unwanted_col in ['updated_on', 'created_on']:
# if unwanted_col in cols:
# del cols[cols.index(unwanted_col)]
output_cols = [snake_to_pascal(col) for col in cols]
progBar = ProgressBar(len(values), 50, 'Writing to {}'.format(os.path.basename(csv_file)))
with open(csv_file, 'w') as file:
writer = csv.writer(file)
writer.writerow(output_cols)
counter = 0
for vals in values:
counter += 1
progBar.update(counter)
writer.writerow([vals[col] for col in cols])
progBar.finish()
def inverse_mask(nodata_raster_path, out_raster_path):
"""Apply the nodata values of one raster to another of identical size
Args:
in_raster_path ([type]): [description]
nodata_raster_path ([type]): [description]
out_raster_path ([type]): [description]
"""
log = Logger('mask_rasters_nodata')
with rasterio.open(nodata_raster_path) as nd_src:
# All 3 rasters should have the same extent and properties. They differ only in dtype
out_meta = nd_src.meta
if 'nodata' not in out_meta or out_meta['nodata'] is None:
out_meta['nodata'] = -9999
out_meta['compress'] = 'deflate'
with rasterio.open(out_raster_path, 'w', **out_meta) as out_src:
progbar = ProgressBar(len(list(nd_src.block_windows(1))), 50,
"Applying inverse nodata mask")
counter = 0
# Again, these rasters should be orthogonal so their windows should also line up
for ji, window in nd_src.block_windows(1):
progbar.update(counter)
counter += 1
# These rasterizations don't begin life with a mask.
mask = nd_src.read(1, window=window, masked=True).mask
# Fill everywhere the mask reads true with a nodata value
mask_vals = np.full(mask.shape, 1)
output = np.ma.masked_array(mask_vals, np.logical_not(mask))
out_src.write(output.filled(out_meta['nodata']).astype(
out_meta['dtype']),
window=window,
indexes=1)
progbar.finish()
log.info('Complete')
def calculate_combined_fis(feature_values: dict, veg_fis_field: str,
capacity_field: str, dam_count_field: str,
max_drainage_area: float):
"""
Calculate dam capacity and density using combined FIS
:param feature_values: Dictionary of features keyed by ReachID and values are dictionaries of attributes
:param veg_fis_field: Attribute containing the output of the vegetation FIS
:param com_capacity_field: Attribute used to store the capacity result in feature_values
:param com_density_field: Attribute used to store the capacity results in feature_values
:param max_drainage_area: Reaches with drainage area greater than this threshold will have zero capacity
:return: Insert the dam capacity and density values to the feature_values dictionary
"""
log = Logger('Combined FIS')
log.info('Initializing Combined FIS')
if not max_drainage_area:
log.warning(
'Missing max drainage area. Calculating combined FIS without max drainage threshold.'
)
# get arrays for fields of interest
feature_count = len(feature_values)
reachid_array = np.zeros(feature_count, np.int64)
veg_array = np.zeros(feature_count, np.float64)
hydq2_array = np.zeros(feature_count, np.float64)
hydlow_array = np.zeros(feature_count, np.float64)
slope_array = np.zeros(feature_count, np.float64)
drain_array = np.zeros(feature_count, np.float64)
counter = 0
for reach_id, values in feature_values.items():
reachid_array[counter] = reach_id
veg_array[counter] = values[veg_fis_field]
hydlow_array[counter] = values['iHyd_SPLow']
hydq2_array[counter] = values['iHyd_SP2']
slope_array[counter] = values['iGeo_Slope']
drain_array[counter] = values['iGeo_DA']
counter += 1
# Adjust inputs to be within FIS membership range
veg_array[veg_array < 0] = 0
veg_array[veg_array > 45] = 45
hydq2_array[hydq2_array < 0] = 0.0001
hydq2_array[hydq2_array > 10000] = 10000
hydlow_array[hydlow_array < 0] = 0.0001
hydlow_array[hydlow_array > 10000] = 10000
slope_array[slope_array > 1] = 1
# create antecedent (input) and consequent (output) objects to hold universe variables and membership functions
ovc = ctrl.Antecedent(np.arange(0, 45, 0.01), 'input1')
sp2 = ctrl.Antecedent(np.arange(0, 10000, 1), 'input2')
splow = ctrl.Antecedent(np.arange(0, 10000, 1), 'input3')
slope = ctrl.Antecedent(np.arange(0, 1, 0.0001), 'input4')
density = ctrl.Consequent(np.arange(0, 45, 0.01), 'result')
# build membership functions for each antecedent and consequent object
ovc['none'] = fuzz.trimf(ovc.universe, [0, 0, 0.1])
ovc['rare'] = fuzz.trapmf(ovc.universe, [0, 0.1, 0.5, 1.5])
ovc['occasional'] = fuzz.trapmf(ovc.universe, [0.5, 1.5, 4, 8])
ovc['frequent'] = fuzz.trapmf(ovc.universe, [4, 8, 12, 25])
ovc['pervasive'] = fuzz.trapmf(ovc.universe, [12, 25, 45, 45])
sp2['persists'] = fuzz.trapmf(sp2.universe, [0, 0, 1000, 1200])
sp2['breach'] = fuzz.trimf(sp2.universe, [1000, 1200, 1600])
sp2['oblowout'] = fuzz.trimf(sp2.universe, [1200, 1600, 2400])
sp2['blowout'] = fuzz.trapmf(sp2.universe, [1600, 2400, 10000, 10000])
splow['can'] = fuzz.trapmf(splow.universe, [0, 0, 150, 175])
splow['probably'] = fuzz.trapmf(splow.universe, [150, 175, 180, 190])
splow['cannot'] = fuzz.trapmf(splow.universe, [180, 190, 10000, 10000])
slope['flat'] = fuzz.trapmf(slope.universe, [0, 0, 0.0002, 0.005])
slope['can'] = fuzz.trapmf(slope.universe, [0.0002, 0.005, 0.12, 0.15])
slope['probably'] = fuzz.trapmf(slope.universe, [0.12, 0.15, 0.17, 0.23])
slope['cannot'] = fuzz.trapmf(slope.universe, [0.17, 0.23, 1, 1])
density['none'] = fuzz.trimf(density.universe, [0, 0, 0.1])
density['rare'] = fuzz.trapmf(density.universe, [0, 0.1, 0.5, 1.5])
density['occasional'] = fuzz.trapmf(density.universe, [0.5, 1.5, 4, 8])
density['frequent'] = fuzz.trapmf(density.universe, [4, 8, 12, 25])
density['pervasive'] = fuzz.trapmf(density.universe, [12, 25, 45, 45])
# build fis rule table
log.info('Building FIS rule table')
comb_ctrl = ctrl.ControlSystem([
ctrl.Rule(ovc['none'], density['none']),
ctrl.Rule(splow['cannot'], density['none']),
ctrl.Rule(slope['cannot'], density['none']),
ctrl.Rule(
ovc['rare'] & sp2['persists'] & splow['can'] & ~slope['cannot'],
density['rare']),
ctrl.Rule(
ovc['rare'] & sp2['persists'] & splow['probably']
& ~slope['cannot'], density['rare']),
ctrl.Rule(
ovc['rare'] & sp2['breach'] & splow['can'] & ~slope['cannot'],
density['rare']),
ctrl.Rule(
ovc['rare'] & sp2['breach'] & splow['probably'] & ~slope['cannot'],
density['rare']),
ctrl.Rule(
ovc['rare'] & sp2['oblowout'] & splow['can'] & ~slope['cannot'],
density['rare']),
ctrl.Rule(
ovc['rare'] & sp2['oblowout'] & splow['probably']
& ~slope['cannot'], density['rare']),
ctrl.Rule(
ovc['rare'] & sp2['blowout'] & splow['can'] & ~slope['cannot'],
density['none']),
ctrl.Rule(
ovc['rare'] & sp2['blowout'] & splow['probably']
& ~slope['cannot'], density['none']),
ctrl.Rule(
ovc['occasional'] & sp2['persists'] & splow['can']
& ~slope['cannot'], density['occasional']),
ctrl.Rule(
ovc['occasional'] & sp2['persists'] & splow['probably']
& ~slope['cannot'], density['occasional']),
ctrl.Rule(
ovc['occasional'] & sp2['breach'] & splow['can']
& ~slope['cannot'], density['occasional']),
ctrl.Rule(
ovc['occasional'] & sp2['breach'] & splow['probably']
& ~slope['cannot'], density['occasional']),
ctrl.Rule(
ovc['occasional'] & sp2['oblowout'] & splow['can']
& ~slope['cannot'], density['occasional']),
ctrl.Rule(
ovc['occasional'] & sp2['oblowout'] & splow['probably']
& ~slope['cannot'], density['occasional']),
ctrl.Rule(
ovc['occasional'] & sp2['blowout'] & splow['can']
& ~slope['cannot'], density['rare']),
ctrl.Rule(
ovc['occasional'] & sp2['blowout'] & splow['probably']
& ~slope['cannot'], density['rare']),
ctrl.Rule(
ovc['frequent'] & sp2['persists'] & splow['can'] & slope['flat'],
density['occasional']),
ctrl.Rule(
ovc['frequent'] & sp2['persists'] & splow['can'] & slope['can'],
density['frequent']),
ctrl.Rule(
ovc['frequent'] & sp2['persists'] & splow['can']
& slope['probably'], density['occasional']),
ctrl.Rule(
ovc['frequent'] & sp2['persists'] & splow['probably']
& slope['flat'], density['occasional']),
ctrl.Rule(
ovc['frequent'] & sp2['persists'] & splow['probably']
& slope['can'], density['frequent']),
ctrl.Rule(
ovc['frequent'] & sp2['persists'] & splow['probably']
& slope['probably'], density['occasional']),
ctrl.Rule(
ovc['frequent'] & sp2['breach'] & splow['can'] & slope['flat'],
density['occasional']),
ctrl.Rule(
ovc['frequent'] & sp2['breach'] & splow['can'] & slope['can'],
density['frequent']),
ctrl.Rule(
ovc['frequent'] & sp2['breach'] & splow['can'] & slope['probably'],
density['occasional']),
ctrl.Rule(
ovc['frequent'] & sp2['breach'] & splow['probably']
& slope['flat'], density['occasional']),
ctrl.Rule(
ovc['frequent'] & sp2['breach'] & splow['probably'] & slope['can'],
density['frequent']),
ctrl.Rule(
ovc['frequent'] & sp2['breach'] & splow['probably']
& slope['probably'], density['occasional']),
ctrl.Rule(
ovc['frequent'] & sp2['oblowout'] & splow['can'] & slope['flat'],
density['occasional']),
ctrl.Rule(
ovc['frequent'] & sp2['oblowout'] & splow['can'] & slope['can'],
density['frequent']),
ctrl.Rule(
ovc['frequent'] & sp2['oblowout'] & splow['can']
& slope['probably'], density['occasional']),
ctrl.Rule(
ovc['frequent'] & sp2['oblowout'] & splow['probably']
& slope['flat'], density['rare']),
ctrl.Rule(
ovc['frequent'] & sp2['oblowout'] & splow['probably']
& slope['can'], density['occasional']),
ctrl.Rule(
ovc['frequent'] & sp2['oblowout'] & splow['probably']
& slope['probably'], density['rare']),
ctrl.Rule(
ovc['frequent'] & sp2['blowout'] & splow['can'] & slope['flat'],
density['rare']),
ctrl.Rule(
ovc['frequent'] & sp2['blowout'] & splow['can'] & slope['can'],
density['rare']),
ctrl.Rule(
ovc['frequent'] & sp2['blowout'] & splow['can']
& slope['probably'], density['rare']),
ctrl.Rule(
ovc['frequent'] & sp2['blowout'] & splow['probably']
& slope['flat'], density['rare']),
ctrl.Rule(
ovc['frequent'] & sp2['blowout'] & splow['probably']
& slope['can'], density['rare']),
ctrl.Rule(
ovc['frequent'] & sp2['blowout'] & splow['probably']
& slope['probably'], density['rare']),
ctrl.Rule(
ovc['pervasive'] & sp2['persists'] & splow['can'] & slope['flat'],
density['frequent']),
ctrl.Rule(
ovc['pervasive'] & sp2['persists'] & splow['can'] & slope['can'],
density['pervasive']),
ctrl.Rule(
ovc['pervasive'] & sp2['persists'] & splow['can']
& slope['probably'], density['frequent']),
ctrl.Rule(
ovc['pervasive'] & sp2['persists'] & splow['probably']
& slope['flat'], density['frequent']),
ctrl.Rule(
ovc['pervasive'] & sp2['persists'] & splow['probably']
& slope['can'], density['pervasive']),
ctrl.Rule(
ovc['pervasive'] & sp2['persists'] & splow['probably']
& slope['probably'], density['frequent']),
ctrl.Rule(
ovc['pervasive'] & sp2['breach'] & splow['can'] & slope['flat'],
density['frequent']),
ctrl.Rule(
ovc['pervasive'] & sp2['breach'] & splow['can'] & slope['can'],
density['pervasive']),
ctrl.Rule(
ovc['pervasive'] & sp2['breach'] & splow['can']
& slope['probably'], density['frequent']),
ctrl.Rule(
ovc['pervasive'] & sp2['breach'] & splow['probably']
& slope['flat'], density['frequent']),
ctrl.Rule(
ovc['pervasive'] & sp2['breach'] & splow['probably']
& slope['can'], density['pervasive']),
ctrl.Rule(
ovc['pervasive'] & sp2['breach'] & splow['probably']
& slope['probably'], density['frequent']),
ctrl.Rule(
ovc['pervasive'] & sp2['oblowout'] & splow['can'] & slope['flat'],
density['frequent']),
ctrl.Rule(
ovc['pervasive'] & sp2['oblowout'] & splow['can'] & slope['can'],
density['pervasive']),
ctrl.Rule(
ovc['pervasive'] & sp2['oblowout'] & splow['can']
& slope['probably'], density['frequent']),
ctrl.Rule(
ovc['pervasive'] & sp2['oblowout'] & splow['probably']
& slope['flat'], density['occasional']),
ctrl.Rule(
ovc['pervasive'] & sp2['oblowout'] & splow['probably']
& slope['can'], density['frequent']),
ctrl.Rule(
ovc['pervasive'] & sp2['oblowout'] & splow['probably']
& slope['probably'], density['occasional']),
ctrl.Rule(
ovc['pervasive'] & sp2['blowout'] & splow['can'] & slope['flat'],
density['occasional']),
ctrl.Rule(
ovc['pervasive'] & sp2['blowout'] & splow['can'] & slope['can'],
density['occasional']),
ctrl.Rule(
ovc['pervasive'] & sp2['blowout'] & splow['can']
& slope['probably'], density['rare']),
ctrl.Rule(
ovc['pervasive'] & sp2['blowout'] & splow['probably']
& slope['flat'], density['occasional']),
ctrl.Rule(
ovc['pervasive'] & sp2['blowout'] & splow['probably']
& slope['can'], density['occasional']),
ctrl.Rule(
ovc['pervasive'] & sp2['blowout'] & splow['probably']
& slope['probably'], density['rare'])
])
comb_fis = ctrl.ControlSystemSimulation(comb_ctrl)
# calculate defuzzified centroid value for density 'none' MF group
# this will be used to re-classify output values that fall in this group
# important: will need to update the array (x) and MF values (mfx) if the
# density 'none' values are changed in the model
x_vals = np.arange(0, 45, 0.01)
mfx = fuzz.trimf(x_vals, [0, 0, 0.1])
defuzz_centroid = round(fuzz.defuzz(x_vals, mfx, 'centroid'), 6)
progbar = ProgressBar(len(reachid_array), 50, "Combined FIS")
counter = 0
for i, reach_id in enumerate(reachid_array):
capacity = 0.0
# Only compute FIS if the reach has less than user-defined max drainage area.
# this enforces a stream size threshold above which beaver dams won't persist and/or won't be built
if not max_drainage_area or drain_array[i] < max_drainage_area:
comb_fis.input['input1'] = veg_array[i]
comb_fis.input['input2'] = hydq2_array[i]
comb_fis.input['input3'] = hydlow_array[i]
comb_fis.input['input4'] = slope_array[i]
comb_fis.compute()
capacity = comb_fis.output['result']
# Combined FIS result cannot be higher than limiting vegetation FIS result
if capacity > veg_array[i]:
capacity = veg_array[i]
if round(capacity, 6) == defuzz_centroid:
capacity = 0.0
count = capacity * (feature_values[reach_id]['iGeo_Len'] / 1000.0)
count = 1.0 if 0 < count < 1 else count
feature_values[reach_id][capacity_field] = round(capacity, 2)
feature_values[reach_id][dam_count_field] = round(count, 2)
counter += 1
progbar.update(counter)
progbar.finish()
log.info('Done')
def update_watersheds(curs, watershed_csv):
# Load all the watersheds from the database in a PREDICTABLE ORDER (so git diff is useful for previewing changes)
curs.execute("""SELECT * FROM watersheds ORDER BY watershed_id""")
watersheds = [row for row in curs.fetchall()]
# Validate the hydrologic equations. The following dictionary will be keyed by python exception concatenated to produce
# a unique string for each type of error for each equation. These will get printed to the screen for easy cut and paste
# into a GitHub issue for USU to resolve.
unique_errors = {}
for q in ['qlow', 'q2']:
progbar = ProgressBar(len(watersheds), 50, 'Verifying {} equations'.format(q))
counter = 0
for values in watersheds:
watershed = values['watershed_id']
counter += 1
progbar.update(counter)
# proceed if the watershed has a hydrologic formula defined
if not values[q]:
continue
# Load the hydrologic parameters for this watershed and substitute a placeholder for drainage area
curs.execute('SELECT * FROM vw_watershed_hydro_params WHERE watershed_id = %s', [watershed])
params = {row['name']: row['value'] for row in curs.fetchall()}
params['DRNAREA'] = 1.0
try:
equation = values[q]
equation = equation.replace('^', '**')
value = eval(equation, {'__builtins__': None}, params)
_float_val = float(value)
except Exception as ex:
# NoneType is not subscriptable means a watershed parameter is missing.
exception_id = repr(ex) + values[q]
if exception_id in unique_errors:
unique_errors[exception_id]['watersheds'][watershed] = params
else:
unique_errors[exception_id] = {
'watersheds': {watershed: params},
'exception': repr(ex),
'equation': values[q],
}
progbar.finish()
if len(unique_errors) > 0:
for exception_id, values in unique_errors.items():
print('\n## Hydrologic equation Error\n```')
print('Equation:', values['equation'])
print('Exception:', values['exception'])
print('Watersheds:')
for watershed, params in values['watersheds'].items():
print('\t{}:'.format(watershed))
[print('\t\t{}: {}'.format(key, val)) for key, val in params.items()]
print('```')
raise Exception('Aborting due to {} hydrology equation errors'.format(len(unique_errors)))
cols = list(next(iter(watersheds)).keys())
del cols[cols.index('updated_on')]
del cols[cols.index('created_on')]
del cols[cols.index('geom')]
write_values_to_csv(watershed_csv, cols, watersheds)
def load_idaho(shapefile, database):
conn = sqlite3.connect(database)
# Clear the database first
conn.execute('DELETE FROM Reaches')
conn.commit()
lookup = {
'oPBRC_CR': load_lookup(database, 'DamOpportunities', 'OpportunityID'),
'oPBRC_UI': load_lookup(database, 'DamRisks', 'RiskID'),
'oPBRC_UD': load_lookup(database, 'DamLimitations', 'LimitationID'),
'ADMIN_AGEN': load_lookup(database, 'Agencies', 'AgencyID', 'Abbreviation')
}
db_fields = []
for field in fields:
if field == 'HUC_ID':
db_fields.append('WatershedID')
elif field == 'FCode':
db_fields.append('ReachCode')
elif field == 'ADMIN_AGEN':
db_fields.append('AgencyID')
elif field == 'oPBRC_CR':
db_fields.append('OpportunityID')
elif field == 'oPBRC_UI':
db_fields.append('RiskID')
elif field == 'oPBRC_UD':
db_fields.append('LimitationID')
else:
db_fields.append(field)
driver = ogr.GetDriverByName('ESRI Shapefile')
dataset = driver.Open(shapefile, 0)
layer = dataset.GetLayer()
sr_idaho = layer.GetSpatialRef()
out_spatial_ref, transform = get_transform_from_epsg(sr_idaho, 4326)
progbar = ProgressBar(layer.GetFeatureCount(), 50, "Loading features")
counter = 0
for feature in layer:
counter += 1
progbar.update(counter)
geom = feature.GetGeometryRef()
geom.Transform(transform)
reach_values = [geom.ExportToJson()]
for field in fields:
if field in lookup:
value = feature.GetField(field)
if field == 'oPBRC_UI' and str.isdigit(value[0]):
value = value[1:]
elif value == '4PVT':
value = 'PVT'
reach_values.append(lookup[field][value])
else:
reach_values.append(feature.GetField(field))
conn.execute('INSERT INTO Reaches (Geometry, {}) Values (?, {})'.format(','.join(db_fields), ','.join('?' * len(db_fields))), reach_values)
conn.commit()
progbar.finish()
print('Process complete')
def import_shapefile(shapefile, host, port, user_name, password, database):
log = Logger('Import')
conn = psycopg2.connect(user=user_name, password=password, host=host, port=port, database=database)
curs = conn.cursor()
driver = ogr.GetDriverByName('ESRI ShapeFile')
data_source = driver.Open(shapefile)
layer = data_source.GetLayer()
total_features = layer.GetFeatureCount()
_spatial_ref, transform = get_transform_from_epsg( layer.GetSpatialRef(), 4326)
curs.execute('INSERT INTO uploads (added_by, file_name, remarks) VALUES (%s, %s, %s) RETURNING id',
[getpass.getuser(), os.path.basename(shapefile), 'Python Script Import'])
upload_id = curs.fetchone()[0]
curs.execute('SELECT name, id FROM observation_types')
obs_types = {row[0].replace('Dam', '').replace(' ', '').lower(): row[1] for row in curs.fetchall()}
certainties = {'Low': 1, 'Medium': 2, 'High': 3}
# Reach statistics for each reach in our batch
progbar = ProgressBar(total_features, 50, "Importing features")
try:
observations = []
for feature in layer:
geom = feature.GetGeometryRef()
geom.Transform(transform)
geom.FlattenTo2D()
metadata = {field: feature.GetField(field) for field in [
'Feature_Ty', 'Certainty', 'Year', 'Dam_Type', 'CreationDa',
'Creator', 'EditDate', 'Editor', 'Snapped', 'Imagery_Ye'] }
# print(metadata)
clean_type = feature.GetField('Feature_Ty').replace('_', '').replace('Dam', '').lower() if feature.GetField('Feature_Ty') else 'Unknown'.lower()
obs_date = datetime.datetime.strptime(feature.GetField('CreationDa'), '%Y/%m/%d')
year = int(feature.GetField('Year')) if feature.GetField('Year') else None
certainty = certainties[feature.GetField('Certainty')] if feature.GetField('Certainty') in certainties else 0
observations.append((
upload_id,
geom.ExportToWkb(),
obs_date,
feature.GetField('Creator'),
year,
obs_types[clean_type],
True,
certainty,
json.dumps(metadata)
))
progbar.update(len(observations))
progbar.finish()
curs.executemany("""
INSERT INTO observations (
upload_id,
geom,
obs_date,
observer,
obs_year,
obs_type_id,
is_public,
confidence,
metadata
) VALUES (%s, ST_GeomFromWKB(%s, 4326), %s, %s, %s, %s, %s, %s, %s)""", observations)
conn.commit()
except Exception as ex:
conn.rollback()
log.error(ex)
log.info('Shapefile import complete')
def vbet(huc, flowlines_orig, flowareas_orig, orig_slope, json_transforms,
orig_dem, hillshade, max_hand, min_hole_area_m, project_folder,
reach_codes: List[str], meta: Dict[str, str]):
"""[summary]
Args:
huc ([type]): [description]
flowlines_orig ([type]): [description]
flowareas_orig ([type]): [description]
orig_slope ([type]): [description]
json_transforms ([type]): [description]
orig_dem ([type]): [description]
hillshade ([type]): [description]
max_hand ([type]): [description]
min_hole_area_m ([type]): [description]
project_folder ([type]): [description]
reach_codes (List[int]): NHD reach codes for features to include in outputs
meta (Dict[str,str]): dictionary of riverscapes metadata key: value pairs
"""
log = Logger('VBET')
log.info('Starting VBET v.{}'.format(cfg.version))
project, _realization, proj_nodes = create_project(huc, project_folder)
# Incorporate project metadata to the riverscapes project
if meta is not None:
project.add_metadata(meta)
# Copy the inp
_proj_slope_node, proj_slope = project.add_project_raster(
proj_nodes['Inputs'], LayerTypes['SLOPE_RASTER'], orig_slope)
_proj_dem_node, proj_dem = project.add_project_raster(
proj_nodes['Inputs'], LayerTypes['DEM'], orig_dem)
_hillshade_node, hillshade = project.add_project_raster(
proj_nodes['Inputs'], LayerTypes['HILLSHADE'], hillshade)
# Copy input shapes to a geopackage
inputs_gpkg_path = os.path.join(project_folder,
LayerTypes['INPUTS'].rel_path)
intermediates_gpkg_path = os.path.join(
project_folder, LayerTypes['INTERMEDIATES'].rel_path)
flowlines_path = os.path.join(
inputs_gpkg_path,
LayerTypes['INPUTS'].sub_layers['FLOWLINES'].rel_path)
flowareas_path = os.path.join(
inputs_gpkg_path,
LayerTypes['INPUTS'].sub_layers['FLOW_AREA'].rel_path)
# Make sure we're starting with a fresh slate of new geopackages
GeopackageLayer.delete(inputs_gpkg_path)
GeopackageLayer.delete(intermediates_gpkg_path)
copy_feature_class(flowlines_orig, flowlines_path, epsg=cfg.OUTPUT_EPSG)
copy_feature_class(flowareas_orig, flowareas_path, epsg=cfg.OUTPUT_EPSG)
project.add_project_geopackage(proj_nodes['Inputs'], LayerTypes['INPUTS'])
# Create a copy of the flow lines with just the perennial and also connectors inside flow areas
network_path = os.path.join(
intermediates_gpkg_path,
LayerTypes['INTERMEDIATES'].sub_layers['VBET_NETWORK'].rel_path)
vbet_network(flowlines_path, flowareas_path, network_path, cfg.OUTPUT_EPSG,
reach_codes)
# Generate HAND from dem and vbet_network
# TODO make a place for this temporary folder. it can be removed after hand is generated.
temp_hand_dir = os.path.join(project_folder, "intermediates",
"hand_processing")
safe_makedirs(temp_hand_dir)
hand_raster = os.path.join(project_folder,
LayerTypes['HAND_RASTER'].rel_path)
create_hand_raster(proj_dem, network_path, temp_hand_dir, hand_raster)
project.add_project_raster(proj_nodes['Intermediates'],
LayerTypes['HAND_RASTER'])
# Build Transformation Tables
with sqlite3.connect(intermediates_gpkg_path) as conn:
cursor = conn.cursor()
# Build tables
with open(
os.path.join(os.path.abspath(os.path.dirname(__file__)), '..',
'database', 'vbet_schema.sql')) as sqlfile:
sql_commands = sqlfile.read()
cursor.executescript(sql_commands)
conn.commit()
# Load tables
for sqldata in glob.glob(os.path.join(
os.path.abspath(os.path.dirname(__file__)), '..', 'database',
'data', '**', '*.sql'),
recursive=True):
with open(sqldata) as sqlfile:
sql_commands = sqlfile.read()
cursor.executescript(sql_commands)
conn.commit()
# Load transforms from table
transforms = load_transform_functions(json_transforms,
intermediates_gpkg_path)
# Get raster resolution as min buffer and apply bankfull width buffer to reaches
with rasterio.open(proj_slope) as raster:
t = raster.transform
min_buffer = (t[0] + abs(t[4])) / 2
log.info("Buffering Polyine by bankfull width buffers")
network_path_buffered = os.path.join(
intermediates_gpkg_path, LayerTypes['INTERMEDIATES'].
sub_layers['VBET_NETWORK_BUFFERED'].rel_path)
buffer_by_field(network_path, network_path_buffered, "BFwidth",
cfg.OUTPUT_EPSG, min_buffer)
# Rasterize the channel polygon and write to raster
log.info('Writing channel raster using slope as a template')
flow_area_raster = os.path.join(project_folder,
LayerTypes['FLOW_AREA_RASTER'].rel_path)
channel_buffer_raster = os.path.join(
project_folder, LayerTypes['CHANNEL_BUFFER_RASTER'].rel_path)
rasterize(network_path_buffered, channel_buffer_raster, proj_slope)
project.add_project_raster(proj_nodes['Intermediates'],
LayerTypes['CHANNEL_BUFFER_RASTER'])
rasterize(flowareas_path, flow_area_raster, proj_slope)
project.add_project_raster(proj_nodes['Intermediates'],
LayerTypes['FLOW_AREA_RASTER'])
channel_dist_raster = os.path.join(project_folder,
LayerTypes['CHANNEL_DISTANCE'].rel_path)
fa_dist_raster = os.path.join(project_folder,
LayerTypes['FLOW_AREA_DISTANCE'].rel_path)
proximity_raster(channel_buffer_raster, channel_dist_raster)
proximity_raster(flow_area_raster, fa_dist_raster)
project.add_project_raster(proj_nodes["Intermediates"],
LayerTypes['CHANNEL_DISTANCE'])
project.add_project_raster(proj_nodes["Intermediates"],
LayerTypes['FLOW_AREA_DISTANCE'])
slope_transform_raster = os.path.join(
project_folder, LayerTypes['NORMALIZED_SLOPE'].rel_path)
hand_transform_raster = os.path.join(
project_folder, LayerTypes['NORMALIZED_HAND'].rel_path)
chan_dist_transform_raster = os.path.join(
project_folder, LayerTypes['NORMALIZED_CHANNEL_DISTANCE'].rel_path)
fa_dist_transform_raster = os.path.join(
project_folder, LayerTypes['NORMALIZED_FLOWAREA_DISTANCE'].rel_path)
topo_evidence_raster = os.path.join(project_folder,
LayerTypes['EVIDENCE_TOPO'].rel_path)
channel_evidence_raster = os.path.join(
project_folder, LayerTypes['EVIDENCE_CHANNEL'].rel_path)
evidence_raster = os.path.join(project_folder,
LayerTypes['VBET_EVIDENCE'].rel_path)
# Open evidence rasters concurrently. We're looping over windows so this shouldn't affect
# memory consumption too much
with rasterio.open(proj_slope) as slp_src, \
rasterio.open(hand_raster) as hand_src, \
rasterio.open(channel_dist_raster) as cdist_src, \
rasterio.open(fa_dist_raster) as fadist_src:
# All 3 rasters should have the same extent and properties. They differ only in dtype
out_meta = slp_src.meta
# Rasterio can't write back to a VRT so rest the driver and number of bands for the output
out_meta['driver'] = 'GTiff'
out_meta['count'] = 1
out_meta['compress'] = 'deflate'
# out_meta['dtype'] = rasterio.uint8
# We use this to buffer the output
cell_size = abs(slp_src.get_transform()[1])
with rasterio.open(evidence_raster, 'w', **out_meta) as dest_evidence, \
rasterio.open(topo_evidence_raster, "w", **out_meta) as dest, \
rasterio.open(channel_evidence_raster, 'w', **out_meta) as dest_channel, \
rasterio.open(slope_transform_raster, "w", **out_meta) as slope_ev_out, \
rasterio.open(hand_transform_raster, 'w', **out_meta) as hand_ev_out, \
rasterio.open(chan_dist_transform_raster, 'w', **out_meta) as chan_dist_ev_out, \
rasterio.open(fa_dist_transform_raster, 'w', **out_meta) as fa_dist_ev_out:
progbar = ProgressBar(len(list(slp_src.block_windows(1))), 50,
"Calculating evidence layer")
counter = 0
# Again, these rasters should be orthogonal so their windows should also line up
for _ji, window in slp_src.block_windows(1):
progbar.update(counter)
counter += 1
slope_data = slp_src.read(1, window=window, masked=True)
hand_data = hand_src.read(1, window=window, masked=True)
cdist_data = cdist_src.read(1, window=window, masked=True)
fadist_data = fadist_src.read(1, window=window, masked=True)
slope_transform = np.ma.MaskedArray(transforms["Slope"](
slope_data.data),
mask=slope_data.mask)
hand_transform = np.ma.MaskedArray(transforms["HAND"](
hand_data.data),
mask=hand_data.mask)
channel_dist_transform = np.ma.MaskedArray(
transforms["Channel"](cdist_data.data),
mask=cdist_data.mask)
fa_dist_transform = np.ma.MaskedArray(transforms["Flow Areas"](
fadist_data.data),
mask=fadist_data.mask)
fvals_topo = slope_transform * hand_transform
fvals_channel = np.maximum(channel_dist_transform,
fa_dist_transform)
fvals_evidence = np.maximum(fvals_topo, fvals_channel)
# Fill the masked values with the appropriate nodata vals
# Unthresholded in the base band (mostly for debugging)
dest.write(np.ma.filled(np.float32(fvals_topo),
out_meta['nodata']),
window=window,
indexes=1)
slope_ev_out.write(slope_transform.astype('float32').filled(
out_meta['nodata']),
window=window,
indexes=1)
hand_ev_out.write(hand_transform.astype('float32').filled(
out_meta['nodata']),
window=window,
indexes=1)
chan_dist_ev_out.write(
channel_dist_transform.astype('float32').filled(
out_meta['nodata']),
window=window,
indexes=1)
fa_dist_ev_out.write(
fa_dist_transform.astype('float32').filled(
out_meta['nodata']),
window=window,
indexes=1)
dest_channel.write(np.ma.filled(np.float32(fvals_channel),
out_meta['nodata']),
window=window,
indexes=1)
dest_evidence.write(np.ma.filled(np.float32(fvals_evidence),
out_meta['nodata']),
window=window,
indexes=1)
progbar.finish()
# The remaining rasters get added to the project
project.add_project_raster(proj_nodes["Intermediates"],
LayerTypes['NORMALIZED_SLOPE'])
project.add_project_raster(proj_nodes["Intermediates"],
LayerTypes['NORMALIZED_HAND'])
project.add_project_raster(proj_nodes["Intermediates"],
LayerTypes['NORMALIZED_CHANNEL_DISTANCE'])
project.add_project_raster(proj_nodes["Intermediates"],
LayerTypes['NORMALIZED_FLOWAREA_DISTANCE'])
project.add_project_raster(proj_nodes['Intermediates'],
LayerTypes['EVIDENCE_TOPO'])
project.add_project_raster(proj_nodes['Intermediates'],
LayerTypes['EVIDENCE_CHANNEL'])
project.add_project_raster(proj_nodes['Outputs'],
LayerTypes['VBET_EVIDENCE'])
# Get the length of a meter (roughly)
degree_factor = GeopackageLayer.rough_convert_metres_to_raster_units(
proj_slope, 1)
buff_dist = cell_size
min_hole_degrees = min_hole_area_m * (degree_factor**2)
# Get the full paths to the geopackages
intermed_gpkg_path = os.path.join(project_folder,
LayerTypes['INTERMEDIATES'].rel_path)
vbet_path = os.path.join(project_folder,
LayerTypes['VBET_OUTPUTS'].rel_path)
for str_val, thr_val in thresh_vals.items():
plgnize_id = 'THRESH_{}'.format(str_val)
with TempRaster('vbet_raw_thresh_{}'.format(plgnize_id)) as tmp_raw_thresh, \
TempRaster('vbet_cleaned_thresh_{}'.format(plgnize_id)) as tmp_cleaned_thresh:
log.debug('Temporary threshold raster: {}'.format(
tmp_raw_thresh.filepath))
threshold(evidence_raster, thr_val, tmp_raw_thresh.filepath)
raster_clean(tmp_raw_thresh.filepath,
tmp_cleaned_thresh.filepath,
buffer_pixels=1)
plgnize_lyr = RSLayer('Raw Threshold at {}%'.format(str_val),
plgnize_id, 'Vector', plgnize_id.lower())
# Add a project node for this thresholded vector
LayerTypes['INTERMEDIATES'].add_sub_layer(plgnize_id, plgnize_lyr)
vbet_id = 'VBET_{}'.format(str_val)
vbet_lyr = RSLayer('Threshold at {}%'.format(str_val), vbet_id,
'Vector', vbet_id.lower())
# Add a project node for this thresholded vector
LayerTypes['VBET_OUTPUTS'].add_sub_layer(vbet_id, vbet_lyr)
# Now polygonize the raster
log.info('Polygonizing')
polygonize(
tmp_cleaned_thresh.filepath, 1,
'{}/{}'.format(intermed_gpkg_path,
plgnize_lyr.rel_path), cfg.OUTPUT_EPSG)
log.info('Done')
# Now the final sanitization
sanitize(str_val, '{}/{}'.format(intermed_gpkg_path,
plgnize_lyr.rel_path),
'{}/{}'.format(vbet_path,
vbet_lyr.rel_path), buff_dist, network_path)
log.info('Completed thresholding at {}'.format(thr_val))
# Now add our Geopackages to the project XML
project.add_project_geopackage(proj_nodes['Intermediates'],
LayerTypes['INTERMEDIATES'])
project.add_project_geopackage(proj_nodes['Outputs'],
LayerTypes['VBET_OUTPUTS'])
report_path = os.path.join(project.project_dir,
LayerTypes['REPORT'].rel_path)
project.add_report(proj_nodes['Outputs'],
LayerTypes['REPORT'],
replace=True)
report = VBETReport(report_path, project)
report.write()
log.info('VBET Completed Successfully')
def rvd(huc: int, flowlines_orig: Path, existing_veg_orig: Path, historic_veg_orig: Path,
valley_bottom_orig: Path, output_folder: Path, reach_codes: List[str], flow_areas_orig: Path, waterbodies_orig: Path, meta=None):
"""[Generate segmented reaches on flowline network and calculate RVD from historic and existing vegetation rasters
Args:
huc (integer): Watershed ID
flowlines_orig (Path): Segmented flowlines feature layer
existing_veg_orig (Path): LANDFIRE version 2.00 evt raster, with adjacent xml metadata file
historic_veg_orig (Path): LANDFIRE version 2.00 bps raster, with adjacent xml metadata file
valley_bottom_orig (Path): Vbet polygon feature layer
output_folder (Path): destination folder for project output
reach_codes (List[int]): NHD reach codes for features to include in outputs
flow_areas_orig (Path): NHD flow area polygon feature layer
waterbodies (Path): NHD waterbodies polygon feature layer
meta (Dict[str,str]): dictionary of riverscapes metadata key: value pairs
"""
log = Logger("RVD")
log.info('RVD v.{}'.format(cfg.version))
try:
int(huc)
except ValueError:
raise Exception('Invalid HUC identifier "{}". Must be an integer'.format(huc))
if not (len(huc) == 4 or len(huc) == 8):
raise Exception('Invalid HUC identifier. Must be four digit integer')
safe_makedirs(output_folder)
project, _realization, proj_nodes = create_project(huc, output_folder)
# Incorporate project metadata to the riverscapes project
if meta is not None:
project.add_metadata(meta)
log.info('Adding inputs to project')
_prj_existing_path_node, prj_existing_path = project.add_project_raster(proj_nodes['Inputs'], LayerTypes['EXVEG'], existing_veg_orig)
_prj_historic_path_node, prj_historic_path = project.add_project_raster(proj_nodes['Inputs'], LayerTypes['HISTVEG'], historic_veg_orig)
# TODO: Don't forget the att_filter
# _prj_flowlines_node, prj_flowlines = project.add_project_geopackage(proj_nodes['Inputs'], LayerTypes['INPUTS'], flowlines, att_filter="\"ReachCode\" Like '{}%'".format(huc))
# Copy in the vectors we need
inputs_gpkg_path = os.path.join(output_folder, LayerTypes['INPUTS'].rel_path)
intermediates_gpkg_path = os.path.join(output_folder, LayerTypes['INTERMEDIATES'].rel_path)
outputs_gpkg_path = os.path.join(output_folder, LayerTypes['OUTPUTS'].rel_path)
# Make sure we're starting with empty/fresh geopackages
GeopackageLayer.delete(inputs_gpkg_path)
GeopackageLayer.delete(intermediates_gpkg_path)
GeopackageLayer.delete(outputs_gpkg_path)
# Copy our input layers and also find the difference in the geometry for the valley bottom
flowlines_path = os.path.join(inputs_gpkg_path, LayerTypes['INPUTS'].sub_layers['FLOWLINES'].rel_path)
vbottom_path = os.path.join(inputs_gpkg_path, LayerTypes['INPUTS'].sub_layers['VALLEY_BOTTOM'].rel_path)
copy_feature_class(flowlines_orig, flowlines_path, epsg=cfg.OUTPUT_EPSG)
copy_feature_class(valley_bottom_orig, vbottom_path, epsg=cfg.OUTPUT_EPSG)
with GeopackageLayer(flowlines_path) as flow_lyr:
# Set the output spatial ref as this for the whole project
out_srs = flow_lyr.spatial_ref
meter_conversion = flow_lyr.rough_convert_metres_to_vector_units(1)
distance_buffer = flow_lyr.rough_convert_metres_to_vector_units(1)
# Transform issues reading 102003 as espg id. Using sr wkt seems to work, however arcgis has problems loading feature classes with this method...
raster_srs = ogr.osr.SpatialReference()
ds = gdal.Open(prj_existing_path, 0)
raster_srs.ImportFromWkt(ds.GetProjectionRef())
raster_srs.SetAxisMappingStrategy(osr.OAMS_TRADITIONAL_GIS_ORDER)
transform_shp_to_raster = VectorBase.get_transform(out_srs, raster_srs)
gt = ds.GetGeoTransform()
cell_area = ((gt[1] / meter_conversion) * (-gt[5] / meter_conversion))
# Create the output feature class fields
with GeopackageLayer(outputs_gpkg_path, layer_name='ReachGeometry', delete_dataset=True) as out_lyr:
out_lyr.create_layer(ogr.wkbMultiLineString, spatial_ref=out_srs, options=['FID=ReachID'], fields={
'GNIS_NAME': ogr.OFTString,
'ReachCode': ogr.OFTString,
'TotDASqKm': ogr.OFTReal,
'NHDPlusID': ogr.OFTReal,
'WatershedID': ogr.OFTInteger
})
metadata = {
'RVD_DateTime': datetime.datetime.now().isoformat(),
'Reach_Codes': reach_codes
}
# Execute the SQL to create the lookup tables in the RVD geopackage SQLite database
watershed_name = create_database(huc, outputs_gpkg_path, metadata, cfg.OUTPUT_EPSG, os.path.join(os.path.abspath(os.path.dirname(__file__)), '..', 'database', 'rvd_schema.sql'))
project.add_metadata({'Watershed': watershed_name})
geom_vbottom = get_geometry_unary_union(vbottom_path, spatial_ref=raster_srs)
flowareas_path = None
if flow_areas_orig:
flowareas_path = os.path.join(inputs_gpkg_path, LayerTypes['INPUTS'].sub_layers['FLOW_AREA'].rel_path)
copy_feature_class(flow_areas_orig, flowareas_path, epsg=cfg.OUTPUT_EPSG)
geom_flow_areas = get_geometry_unary_union(flowareas_path)
# Difference with existing vbottom
geom_vbottom = geom_vbottom.difference(geom_flow_areas)
else:
del LayerTypes['INPUTS'].sub_layers['FLOW_AREA']
waterbodies_path = None
if waterbodies_orig:
waterbodies_path = os.path.join(inputs_gpkg_path, LayerTypes['INPUTS'].sub_layers['WATERBODIES'].rel_path)
copy_feature_class(waterbodies_orig, waterbodies_path, epsg=cfg.OUTPUT_EPSG)
geom_waterbodies = get_geometry_unary_union(waterbodies_path)
# Difference with existing vbottom
geom_vbottom = geom_vbottom.difference(geom_waterbodies)
else:
del LayerTypes['INPUTS'].sub_layers['WATERBODIES']
# Add the inputs to the XML
_nd, _in_gpkg_path, _sublayers = project.add_project_geopackage(proj_nodes['Inputs'], LayerTypes['INPUTS'])
# Filter the flow lines to just the required features and then segment to desired length
# TODO: These are brat methods that need to be refactored to use VectorBase layers
cleaned_path = os.path.join(outputs_gpkg_path, 'ReachGeometry')
build_network(flowlines_path, flowareas_path, cleaned_path, waterbodies_path=waterbodies_path, epsg=cfg.OUTPUT_EPSG, reach_codes=reach_codes, create_layer=False)
# Generate Voroni polygons
log.info("Calculating Voronoi Polygons...")
# Add all the points (including islands) to the list
flowline_thiessen_points_groups = centerline_points(cleaned_path, distance_buffer, transform_shp_to_raster)
flowline_thiessen_points = [pt for group in flowline_thiessen_points_groups.values() for pt in group]
simple_save([pt.point for pt in flowline_thiessen_points], ogr.wkbPoint, raster_srs, "Thiessen_Points", intermediates_gpkg_path)
# Exterior is the shell and there is only ever 1
myVorL = NARVoronoi(flowline_thiessen_points)
# Generate Thiessen Polys
myVorL.createshapes()
# Dissolve by flowlines
log.info("Dissolving Thiessen Polygons")
dissolved_polys = myVorL.dissolve_by_property('fid')
# Clip Thiessen Polys
log.info("Clipping Thiessen Polygons to Valley Bottom")
clipped_thiessen = clip_polygons(geom_vbottom, dissolved_polys)
# Save Intermediates
simple_save(clipped_thiessen.values(), ogr.wkbPolygon, raster_srs, "Thiessen", intermediates_gpkg_path)
simple_save(dissolved_polys.values(), ogr.wkbPolygon, raster_srs, "ThiessenPolygonsDissolved", intermediates_gpkg_path)
simple_save(myVorL.polys, ogr.wkbPolygon, raster_srs, "ThiessenPolygonsRaw", intermediates_gpkg_path)
_nd, _inter_gpkg_path, _sublayers = project.add_project_geopackage(proj_nodes['Intermediates'], LayerTypes['INTERMEDIATES'])
# OLD METHOD FOR AUDIT
# dissolved_polys2 = dissolve_by_points(flowline_thiessen_points_groups, myVorL.polys)
# simple_save(dissolved_polys2.values(), ogr.wkbPolygon, out_srs, "ThiessenPolygonsDissolved_OLD", intermediates_gpkg_path)
# Load Vegetation Rasters
log.info(f"Loading Existing and Historic Vegetation Rasters")
vegetation = {}
vegetation["EXISTING"] = load_vegetation_raster(prj_existing_path, outputs_gpkg_path, True, output_folder=os.path.join(output_folder, 'Intermediates'))
vegetation["HISTORIC"] = load_vegetation_raster(prj_historic_path, outputs_gpkg_path, False, output_folder=os.path.join(output_folder, 'Intermediates'))
for epoch in vegetation.keys():
for name in vegetation[epoch].keys():
if not f"{epoch}_{name}" == "HISTORIC_LUI":
project.add_project_raster(proj_nodes['Intermediates'], LayerTypes[f"{epoch}_{name}"])
if vegetation["EXISTING"]["RAW"].shape != vegetation["HISTORIC"]["RAW"].shape:
raise Exception('Vegetation raster shapes are not equal Existing={} Historic={}. Cannot continue'.format(vegetation["EXISTING"]["RAW"].shape, vegetation["HISTORIC"]["RAW"].shape))
# Vegetation zone calculations
riparian_zone_arrays = {}
riparian_zone_arrays["RIPARIAN_ZONES"] = ((vegetation["EXISTING"]["RIPARIAN"] + vegetation["HISTORIC"]["RIPARIAN"]) > 0) * 1
riparian_zone_arrays["NATIVE_RIPARIAN_ZONES"] = ((vegetation["EXISTING"]["NATIVE_RIPARIAN"] + vegetation["HISTORIC"]["NATIVE_RIPARIAN"]) > 0) * 1
riparian_zone_arrays["VEGETATION_ZONES"] = ((vegetation["EXISTING"]["VEGETATED"] + vegetation["HISTORIC"]["VEGETATED"]) > 0) * 1
# Save Intermediate Rasters
for name, raster in riparian_zone_arrays.items():
save_intarr_to_geotiff(raster, os.path.join(output_folder, "Intermediates", f"{name}.tif"), prj_existing_path)
project.add_project_raster(proj_nodes['Intermediates'], LayerTypes[name])
# Calculate Riparian Departure per Reach
riparian_arrays = {f"{epoch.capitalize()}{(name.capitalize()).replace('Native_riparian', 'NativeRiparian')}Mean": array for epoch, arrays in vegetation.items() for name, array in arrays.items() if name in ["RIPARIAN", "NATIVE_RIPARIAN"]}
# Vegetation Cell Counts
raw_arrays = {f"{epoch}": array for epoch, arrays in vegetation.items() for name, array in arrays.items() if name == "RAW"}
# Generate Vegetation Conversions
vegetation_change = (vegetation["HISTORIC"]["CONVERSION"] - vegetation["EXISTING"]["CONVERSION"])
save_intarr_to_geotiff(vegetation_change, os.path.join(output_folder, "Intermediates", "Conversion_Raster.tif"), prj_existing_path)
project.add_project_raster(proj_nodes['Intermediates'], LayerTypes['VEGETATION_CONVERSION'])
# load conversion types dictionary from database
conn = sqlite3.connect(outputs_gpkg_path)
conn.row_factory = dict_factory
curs = conn.cursor()
curs.execute('SELECT * FROM ConversionTypes')
conversion_classifications = curs.fetchall()
curs.execute('SELECT * FROM vwConversions')
conversion_ids = curs.fetchall()
# Split vegetation change classes into binary arrays
vegetation_change_arrays = {
c['FieldName']: (vegetation_change == int(c["TypeValue"])) * 1 if int(c["TypeValue"]) in np.unique(vegetation_change) else None
for c in conversion_classifications
}
# Calcuate average and unique cell counts per reach
progbar = ProgressBar(len(clipped_thiessen.keys()), 50, "Extracting array values by reach...")
counter = 0
discarded = 0
with rasterio.open(prj_existing_path) as dataset:
unique_vegetation_counts = {}
reach_average_riparian = {}
reach_average_change = {}
for reachid, poly in clipped_thiessen.items():
counter += 1
progbar.update(counter)
# we can discount a lot of shapes here.
if not poly.is_valid or poly.is_empty or poly.area == 0 or poly.geom_type not in ["Polygon", "MultiPolygon"]:
discarded += 1
continue
raw_values_unique = {}
change_values_mean = {}
riparian_values_mean = {}
reach_raster = np.ma.masked_invalid(
features.rasterize(
[poly],
out_shape=dataset.shape,
transform=dataset.transform,
all_touched=True,
fill=np.nan))
for raster_name, raster in raw_arrays.items():
if raster is not None:
current_raster = np.ma.masked_array(raster, mask=reach_raster.mask)
raw_values_unique[raster_name] = np.unique(np.ma.filled(current_raster, fill_value=0), return_counts=True)
else:
raw_values_unique[raster_name] = []
for raster_name, raster in riparian_arrays.items():
if raster is not None:
current_raster = np.ma.masked_array(raster, mask=reach_raster.mask)
riparian_values_mean[raster_name] = np.ma.mean(current_raster)
else:
riparian_values_mean[raster_name] = 0.0
for raster_name, raster in vegetation_change_arrays.items():
if raster is not None:
current_raster = np.ma.masked_array(raster, mask=reach_raster.mask)
change_values_mean[raster_name] = np.ma.mean(current_raster)
else:
change_values_mean[raster_name] = 0.0
unique_vegetation_counts[reachid] = raw_values_unique
reach_average_riparian[reachid] = riparian_values_mean
reach_average_change[reachid] = change_values_mean
progbar.finish()
with SQLiteCon(outputs_gpkg_path) as gpkg:
# Ensure all reaches are present in the ReachAttributes table before storing RVD output values
gpkg.curs.execute('INSERT INTO ReachAttributes (ReachID) SELECT ReachID FROM ReachGeometry;')
errs = 0
for reachid, epochs in unique_vegetation_counts.items():
for epoch in epochs.values():
insert_values = [[reachid, int(vegetationid), float(count * cell_area), int(count)] for vegetationid, count in zip(epoch[0], epoch[1]) if vegetationid != 0]
try:
gpkg.curs.executemany('''INSERT INTO ReachVegetation (
ReachID,
VegetationID,
Area,
CellCount)
VALUES (?,?,?,?)''', insert_values)
# Sqlite can't report on SQL errors so we have to print good log messages to help intuit what the problem is
except sqlite3.IntegrityError as err:
# THis is likely a constraint error.
errstr = "Integrity Error when inserting records: ReachID: {} VegetationIDs: {}".format(reachid, str(list(epoch[0])))
log.error(errstr)
errs += 1
except sqlite3.Error as err:
# This is any other kind of error
errstr = "SQL Error when inserting records: ReachID: {} VegetationIDs: {} ERROR: {}".format(reachid, str(list(epoch[0])), str(err))
log.error(errstr)
errs += 1
if errs > 0:
raise Exception('Errors were found inserting records into the database. Cannot continue.')
gpkg.conn.commit()
# load RVD departure levels from DepartureLevels database table
with SQLiteCon(outputs_gpkg_path) as gpkg:
gpkg.curs.execute('SELECT LevelID, MaxRVD FROM DepartureLevels ORDER BY MaxRVD ASC')
departure_levels = gpkg.curs.fetchall()
# Calcuate Average Departure for Riparian and Native Riparian
riparian_departure_values = riparian_departure(reach_average_riparian, departure_levels)
write_db_attributes(outputs_gpkg_path, riparian_departure_values, departure_type_columns)
# Add Conversion Code, Type to Vegetation Conversion
with SQLiteCon(outputs_gpkg_path) as gpkg:
gpkg.curs.execute('SELECT LevelID, MaxValue, NAME FROM ConversionLevels ORDER BY MaxValue ASC')
conversion_levels = gpkg.curs.fetchall()
reach_values_with_conversion_codes = classify_conversions(reach_average_change, conversion_ids, conversion_levels)
write_db_attributes(outputs_gpkg_path, reach_values_with_conversion_codes, rvd_columns)
# # Write Output to GPKG table
# log.info('Insert values to GPKG tables')
# # TODO move this to write_attirubtes method
# with get_shp_or_gpkg(outputs_gpkg_path, layer_name='ReachAttributes', write=True, ) as in_layer:
# # Create each field and store the name and index in a list of tuples
# field_indices = [(field, in_layer.create_field(field, field_type)) for field, field_type in {
# "FromConifer": ogr.OFTReal,
# "FromDevegetated": ogr.OFTReal,
# "FromGrassShrubland": ogr.OFTReal,
# "FromDeciduous": ogr.OFTReal,
# "NoChange": ogr.OFTReal,
# "Deciduous": ogr.OFTReal,
# "GrassShrubland": ogr.OFTReal,
# "Devegetation": ogr.OFTReal,
# "Conifer": ogr.OFTReal,
# "Invasive": ogr.OFTReal,
# "Development": ogr.OFTReal,
# "Agriculture": ogr.OFTReal,
# "ConversionCode": ogr.OFTInteger,
# "ConversionType": ogr.OFTString}.items()]
# for feature, _counter, _progbar in in_layer.iterate_features("Writing Attributes", write_layers=[in_layer]):
# reach = feature.GetFID()
# if reach not in reach_values_with_conversion_codes:
# continue
# # Set all the field values and then store the feature
# for field, _idx in field_indices:
# if field in reach_values_with_conversion_codes[reach]:
# if not reach_values_with_conversion_codes[reach][field]:
# feature.SetField(field, None)
# else:
# feature.SetField(field, reach_values_with_conversion_codes[reach][field])
# in_layer.ogr_layer.SetFeature(feature)
# # Create each field and store the name and index in a list of tuples
# field_indices = [(field, in_layer.create_field(field, field_type)) for field, field_type in {
# "EXISTING_RIPARIAN_MEAN": ogr.OFTReal,
# "HISTORIC_RIPARIAN_MEAN": ogr.OFTReal,
# "RIPARIAN_DEPARTURE": ogr.OFTReal,
# "EXISTING_NATIVE_RIPARIAN_MEAN": ogr.OFTReal,
# "HISTORIC_NATIVE_RIPARIAN_MEAN": ogr.OFTReal,
# "NATIVE_RIPARIAN_DEPARTURE": ogr.OFTReal, }.items()]
# for feature, _counter, _progbar in in_layer.iterate_features("Writing Attributes", write_layers=[in_layer]):
# reach = feature.GetFID()
# if reach not in riparian_departure_values:
# continue
# # Set all the field values and then store the feature
# for field, _idx in field_indices:
# if field in riparian_departure_values[reach]:
# if not riparian_departure_values[reach][field]:
# feature.SetField(field, None)
# else:
# feature.SetField(field, riparian_departure_values[reach][field])
# in_layer.ogr_layer.SetFeature(feature)
# with sqlite3.connect(outputs_gpkg_path) as conn:
# cursor = conn.cursor()
# errs = 0
# for reachid, epochs in unique_vegetation_counts.items():
# for epoch in epochs.values():
# insert_values = [[reachid, int(vegetationid), float(count * cell_area), int(count)] for vegetationid, count in zip(epoch[0], epoch[1]) if vegetationid != 0]
# try:
# cursor.executemany('''INSERT INTO ReachVegetation (
# ReachID,
# VegetationID,
# Area,
# CellCount)
# VALUES (?,?,?,?)''', insert_values)
# # Sqlite can't report on SQL errors so we have to print good log messages to help intuit what the problem is
# except sqlite3.IntegrityError as err:
# # THis is likely a constraint error.
# errstr = "Integrity Error when inserting records: ReachID: {} VegetationIDs: {}".format(reachid, str(list(epoch[0])))
# log.error(errstr)
# errs += 1
# except sqlite3.Error as err:
# # This is any other kind of error
# errstr = "SQL Error when inserting records: ReachID: {} VegetationIDs: {} ERROR: {}".format(reachid, str(list(epoch[0])), str(err))
# log.error(errstr)
# errs += 1
# if errs > 0:
# raise Exception('Errors were found inserting records into the database. Cannot continue.')
# conn.commit()
# Add intermediates and the report to the XML
# project.add_project_geopackage(proj_nodes['Intermediates'], LayerTypes['INTERMEDIATES']) already
# added above
project.add_project_geopackage(proj_nodes['Outputs'], LayerTypes['OUTPUTS'])
# Add the report to the XML
report_path = os.path.join(project.project_dir, LayerTypes['REPORT'].rel_path)
project.add_report(proj_nodes['Outputs'], LayerTypes['REPORT'], replace=True)
report = RVDReport(report_path, project)
report.write()
log.info('RVD complete')
def verify_areas(raster_path, boundary_shp):
"""[summary]
Arguments:
raster_path {[type]} -- path
boundary_shp {[type]} -- path
Raises:
Exception: [description] if raster area is zero
Exception: [description] if shapefile area is zero
Returns:
[type] -- rastio of raster area over shape file area
"""
log = Logger('Verify Areas')
log.info('Verifying raster and shape areas')
# This comes back in the raster's unit
raster_area = 0
with rasterio.open(raster_path) as ds:
cell_count = 0
gt = ds.get_transform()
cell_area = math.fabs(gt[1]) * math.fabs(gt[5])
# Incrememntally add the area of a block to the count
progbar = ProgressBar(len(list(ds.block_windows(1))), 50,
"Calculating Area")
progcount = 0
for _ji, window in ds.block_windows(1):
r = ds.read(1, window=window, masked=True)
progbar.update(progcount)
cell_count += r.count()
progcount += 1
progbar.finish()
# Multiply the count by the area of a given cell
raster_area = cell_area * cell_count
log.debug('raster area {}'.format(raster_area))
if (raster_area == 0):
raise Exception('Raster has zero area: {}'.format(raster_path))
# We could just use Rasterio's CRS object but it doesn't seem to play nice with GDAL so....
raster_ds = gdal.Open(raster_path)
raster_srs = osr.SpatialReference(wkt=raster_ds.GetProjection())
# Load and transform ownership polygons by adminstration agency
driver = ogr.GetDriverByName("ESRI Shapefile")
data_source = driver.Open(boundary_shp, 0)
layer = data_source.GetLayer()
in_spatial_ref = layer.GetSpatialRef()
# https://github.com/OSGeo/gdal/issues/1546
raster_srs.SetAxisMappingStrategy(in_spatial_ref.GetAxisMappingStrategy())
transform = osr.CoordinateTransformation(in_spatial_ref, raster_srs)
shape_area = 0
for polygon in layer:
geom = polygon.GetGeometryRef()
geom.Transform(transform)
shape_area = shape_area + geom.GetArea()
log.debug('shape file area {}'.format(shape_area))
if (shape_area == 0):
raise Exception('Shapefile has zero area: {}'.format(boundary_shp))
area_ratio = raster_area / shape_area
if (area_ratio < 0.99 and area_ratio > 0.9):
log.warning('Raster Area covers only {0:.2f}% of the shapefile'.format(
area_ratio * 100))
if (area_ratio <= 0.9):
log.error('Raster Area covers only {0:.2f}% of the shapefile'.format(
area_ratio * 100))
else:
log.info('Raster Area covers {0:.2f}% of the shapefile'.format(
area_ratio * 100))
return area_ratio
def floodplain_connectivity(vbet_network: Path,
vbet_polygon: Path,
roads: Path,
railroads: Path,
output_dir: Path,
debug_gpkg: Path = None):
"""[summary]
Args:
vbet_network (Path): Filtered Flowline network used to generate VBET. Final selection is based on this intersection.
vbet_polygon (Path): Vbet polygons with clipped NHD Catchments
roads (Path): Road network
railroads (Path): railroad network
out_polygon (Path): Output path and layer name for floodplain polygons
debug_gpkg (Path, optional): geopackage for saving debug layers (may substantially increase processing time). Defaults to None.
"""
log = Logger('Floodplain Connectivity')
log.info("Starting Floodplain Connectivity Script")
out_polygon = os.path.join(output_dir, 'fconn.gpkg/outputs')
# Prepare vbet and catchments
geom_vbet = get_geometry_unary_union(vbet_polygon)
geoms_raw_vbet = list(load_geometries(vbet_polygon, None).values())
listgeoms = []
for geom in geoms_raw_vbet:
if geom.geom_type == "MultiPolygon":
for g in geom:
listgeoms.append(g)
else:
listgeoms.append(geom)
geoms_vbet = MultiPolygon(listgeoms)
# Clip Transportation Network by VBET
log.info("Merging Transportation Networks")
# merge_feature_classes([roads, railroads], geom_vbet, os.path.join(debug_gpkg, "Transportation")) TODO: error when calling this method
geom_roads = get_geometry_unary_union(roads)
geom_railroads = get_geometry_unary_union(railroads)
geom_transportation = geom_roads.union(
geom_railroads) if geom_railroads is not None else geom_roads
log.info("Clipping Transportation Network by VBET")
geom_transportation_clipped = geom_vbet.intersection(geom_transportation)
if debug_gpkg:
quicksave(debug_gpkg, "Clipped_Transportation",
geom_transportation_clipped, ogr.wkbLineString)
# Split Valley Edges at transportation intersections
log.info("Splitting Valley Edges at transportation network intersections")
geom_vbet_edges = MultiLineString(
[geom.exterior for geom in geoms_vbet] +
[g for geom in geoms_vbet for g in geom.interiors])
geom_vbet_interior_pts = MultiPoint([
Polygon(g).representative_point() for geom in geom_vbet
for g in geom.interiors
])
if debug_gpkg:
quicksave(debug_gpkg, "Valley_Edges_Raw", geom_vbet_edges,
ogr.wkbLineString)
vbet_splitpoints = []
vbet_splitlines = []
counter = 0
for geom_edge in geom_vbet_edges:
counter += 1
log.info('Splitting edge features {}/{}'.format(
counter, len(geom_vbet_edges)))
if geom_edge.is_valid:
if not geom_edge.intersects(geom_transportation):
vbet_splitlines = vbet_splitlines + [geom_edge]
continue
pts = geom_transportation.intersection(geom_edge)
if pts.is_empty:
vbet_splitlines = vbet_splitlines + [geom_edge]
continue
if isinstance(pts, Point):
pts = [pts]
geom_boundaries = [geom_edge]
progbar = ProgressBar(len(geom_boundaries), 50, "Processing")
counter = 0
for pt in pts:
# TODO: I tried to break this out but I'm not sure
new_boundaries = []
for line in geom_boundaries:
if line is not None:
split_line = line_splitter(line, pt)
progbar.total += len(split_line)
for new_line in split_line:
counter += 1
progbar.update(counter)
if new_line is not None:
new_boundaries.append(new_line)
geom_boundaries = new_boundaries
# TODO: Not sure this is having the intended effect
# geom_boundaries = [new_line for line in geom_boundaries if line is not None for new_line in line_splitter(line, pt) if new_line is not None]
progbar.finish()
vbet_splitlines = vbet_splitlines + geom_boundaries
vbet_splitpoints = vbet_splitpoints + [pt for pt in pts]
if debug_gpkg:
quicksave(debug_gpkg, "Split_Points", vbet_splitpoints, ogr.wkbPoint)
quicksave(debug_gpkg, "Valley_Edges_Split", vbet_splitlines,
ogr.wkbLineString)
# Generate Polygons from lines
log.info("Generating Floodplain Polygons")
geom_lines = unary_union(
vbet_splitlines + [geom_tc for geom_tc in geom_transportation_clipped])
geoms_areas = [
geom for geom in polygonize(geom_lines)
if not any(geom.contains(pt) for pt in geom_vbet_interior_pts)
]
if debug_gpkg:
quicksave(debug_gpkg, "Split_Polygons", geoms_areas, ogr.wkbPolygon)
# Select Polygons by flowline intersection
log.info("Selecting connected floodplains")
geom_vbet_network = get_geometry_unary_union(vbet_network)
geoms_connected = []
geoms_disconnected = []
progbar = ProgressBar(len(geoms_areas), 50, f"Running polygon selection")
counter = 0
for geom in geoms_areas:
progbar.update(counter)
counter += 1
if geom_vbet_network.intersects(geom):
geoms_connected.append(geom)
else:
geoms_disconnected.append(geom)
log.info("Union connected floodplains")
geoms_connected_output = [
geom for geom in list(unary_union(geoms_connected))
]
geoms_disconnected_output = [
geom for geom in list(unary_union(geoms_disconnected))
]
# Save Outputs
log.info("Save Floodplain Output")
with GeopackageLayer(out_polygon, write=True) as out_lyr:
out_lyr.create_layer(ogr.wkbPolygon, epsg=4326)
out_lyr.create_field("Connected", ogr.OFTInteger)
progbar = ProgressBar(
len(geoms_connected_output) + len(geoms_disconnected_output), 50,
f"saving {out_lyr.ogr_layer_name} features")
counter = 0
for shape in geoms_connected_output:
progbar.update(counter)
counter += 1
out_lyr.create_feature(shape, attributes={"Connected": 1})
for shape in geoms_disconnected_output:
progbar.update(counter)
counter += 1
out_lyr.create_feature(shape, attributes={"Connected": 0})
def output_vegetation_raster(gpkg_path, raster_path, output_path, epoch,
prefix, ecoregion):
"""Output a vegetation suitability raster. This has no direct use in the process
but it's useful as a reference layer and visual aid.
Arguments:
database {str} -- Path to BRAT SQLite database
raster_path {str} -- path to input raster
output_path {str} -- path to output raster
epoch {str} -- Label identifying either 'existing' or 'historic'. Used for log messages only.
prefix {str} -- Either 'EX' for existing or 'HPE' for historic.
ecoregion {int} -- Database ID of the ecoregion associated with the watershed
"""
log = Logger('Veg Suitability Rasters')
log.info('Epoch: {}'.format(epoch))
with SQLiteCon(gpkg_path) as database:
# Get the database epoch that has the prefix 'EX' or 'HPE' in the metadata
database.curs.execute('SELECT EpochID FROM Epochs WHERE Metadata = ?',
[prefix])
epochid = database.curs.fetchone()['EpochID']
if not epochid:
raise Exception(
'Missing epoch in database with metadata value of "{}"'.format(
epoch))
database.curs.execute(
'SELECT VegetationID, EffectiveSuitability '
'FROM vwVegetationSuitability '
'WHERE EpochID = ? AND EcoregionID = ?', [epochid, ecoregion])
results = {
row['VegetationID']: row['EffectiveSuitability']
for row in database.curs.fetchall()
}
def translate_suit(in_val, in_nodata, out_nodata):
if in_val == in_nodata:
return out_nodata
elif in_val in results:
return results[in_val]
log.warning('Could not find {} VegetationID={}'.format(prefix, in_val))
return -1
vector = np.vectorize(translate_suit)
with rasterio.open(raster_path) as source_ds:
out_meta = source_ds.meta
out_meta['dtype'] = 'int16'
out_meta['nodata'] = -9999
out_meta['compress'] = 'deflate'
with rasterio.open(output_path, "w", **out_meta) as dest_ds:
progbar = ProgressBar(
len(list(source_ds.block_windows(1))), 50,
"Writing Vegetation Raster: {}".format(epoch))
counter = 0
for ji, window in dest_ds.block_windows(1):
progbar.update(counter)
counter += 1
in_data = source_ds.read(1, window=window, masked=True)
# Fill the masked values with the appropriate nodata vals
# Unthresholded in the base band (mostly for debugging)
out_data = vector(in_data, source_ds.meta['nodata'],
out_meta['nodata'])
dest_ds.write(np.int16(out_data), window=window, indexes=1)
progbar.finish()
def raster_clean(in_raster_path: str, out_raster_path: str, buffer_pixels=1):
"""This method grows and shrinks the raster by n pixels
Args:
in_raster_path (str): [description]
out_raster_path (str): [description]
buffer_pixels (int, optional): [description]. Defaults to 1.
"""
log = Logger('raster_clean')
with TempRaster('vbet_clean_prox_out') as tmp_prox_out, \
TempRaster('vbet_clean_buff_out') as tmp_buff_out, \
TempRaster('vbet_clean_prox_in') as tmp_prox_in, \
TempRaster('vbet_clean_mask') as inv_mask:
# 1. Find the proximity raster
proximity_raster(in_raster_path,
tmp_prox_out.filepath,
preserve_nodata=False)
# 2. Logical and the prox > 1 with the mask for the input raster
with rasterio.open(tmp_prox_out.filepath) as prox_out_src, \
rasterio.open(in_raster_path) as in_data_src:
# All 3 rasters should have the same extent and properties. They differ only in dtype
out_meta = in_data_src.meta
# Rasterio can't write back to a VRT so rest the driver and number of bands for the output
out_meta['driver'] = 'GTiff'
out_meta['count'] = 1
out_meta['compress'] = 'deflate'
with rasterio.open(tmp_buff_out.filepath, 'w',
**out_meta) as out_data:
progbar = ProgressBar(
len(list(out_data.block_windows(1))), 50,
"Growing the raster by {} pixels".format(buffer_pixels))
counter = 0
# Again, these rasters should be orthogonal so their windows should also line up
for _ji, window in out_data.block_windows(1):
progbar.update(counter)
counter += 1
prox_out_block = prox_out_src.read(1, window=window)
in_data_block = in_data_src.read(1,
window=window,
masked=True)
new_data = np.full(in_data_block.shape, 1)
new_mask = np.logical_and(in_data_block.mask,
prox_out_block > buffer_pixels)
output = np.ma.masked_array(new_data, new_mask)
out_data.write(output.filled(out_meta['nodata']).astype(
out_meta['dtype']),
window=window,
indexes=1)
progbar.finish()
# 3. Invert the product of (2) and find the inwards proximity
inverse_mask(tmp_buff_out.filepath, inv_mask.filepath)
proximity_raster(inv_mask.filepath,
tmp_prox_in.filepath,
preserve_nodata=False)
# 4. Now do the final logical and to shrink back a pixel
with rasterio.open(tmp_prox_in.filepath) as prox_in_src:
# Note: we reuse outmeta from before
with rasterio.open(out_raster_path, 'w',
**out_meta) as out_data_src:
progbar = ProgressBar(
len(list(out_data.block_windows(1))), 50,
"Shrinking the raster by {} pixels".format(buffer_pixels))
counter = 0
# Again, these rasters should be orthogonal so their windows should also line up
for _ji, window in out_data.block_windows(1):
progbar.update(counter)
counter += 1
prox_in_block = prox_in_src.read(1, window=window)
new_data = np.full(prox_in_block.shape, 1)
new_mask = np.logical_not(prox_in_block > buffer_pixels)
output = np.ma.masked_array(new_data, new_mask)
out_data_src.write(output.filled(
out_meta['nodata']).astype(out_meta['dtype']),
window=window,
indexes=1)
progbar.finish()
log.info('Cleaning finished')
