def _pysheds_handler(request, response):
# Create pysheds Grid object
dem_fn = request.inputs['dem'][0].file
dir_fn = request.inputs['dir'][0].file
if dem_fn:
grid = Grid.from_raster(dem_fn, 'dem')
if not dir_fn:
grid.flowdir('dem', out_name='dir', nodata=nodata)
else:
grid.read_raster(dir_fn, 'dir')
else:
grid = Grid.from_raster(dir_fn, 'dir')
lat = request.inputs['latitude'][0].data
lon = request.inputs['longitude'][0].data
# name = request.inputs['name'][0].data
grid.catchment(data='dir', x=lon, y=lat, dirmap=dirmap, out_name='catch', nodata_in=nodata, xytype='label')
# This returns multiple polygons, and it's not clear which we should use.
catch = grid.polygonize(grid.catch.astype('int32'), connectivity=8)
for p, val in catch:
if val == 0: # No idea if this is right or not
poly = p
break
# TODO: Select correct polygon
# TODO: Decide which output formats should be supported.
response.outputs['boundary'].data = json.dumps(poly)
return response
def test_from_raster():
grid.clip_to('catch')
grid.to_raster('dir', 'test_dir.tif', view=False, apply_mask=False, blockxsize=16, blockysize=16)
newgrid = Grid.from_raster('test_dir.tif', 'dir_output')
newgrid.clip_to('dir_output')
assert ((newgrid.dir_output == grid.dir).all())
grid.to_raster('dir', 'test_dir.tif', view=True, apply_mask=True, blockxsize=16, blockysize=16)
newgrid = Grid.from_raster('test_dir.tif', 'dir_output')
assert((newgrid.dir_output == grid.view('catch')).all())
def test_mask_geometry():
grid = Grid.from_raster(dem_path,'dem', mask_geometry=feature_geometry)
rows = np.array([225, 226, 227, 228, 229, 230, 231, 232] * 7)
cols = np.array([np.arange(98,105)] * 8).T.reshape(1,56)
masked_cols, masked_rows = grid.mask.nonzero()
assert (masked_cols == cols).all()
assert (masked_rows == rows).all()
with warnings.catch_warnings(record=True) as warn:
warnings.simplefilter("always")
grid = Grid.from_raster(dem_path,'dem', mask_geometry=out_of_bounds)
assert len(warn) == 1
assert issubclass(warn[-1].category, UserWarning)
assert "does not fall within the bounds" in str(warn[-1].message)
assert grid.mask.all(), "mask should be returned to all True as normal"
def compute_ridges(tiff_image):
grid = Grid.from_raster(str(tiff_image), data_name='dem')
grid.dem = grid.dem.max() - grid.dem
peaks = grid.detect_depressions('dem')
grid.fill_depressions(data='dem', out_name='flooded_dem')
flats = grid.detect_flats('flooded_dem')
grid.resolve_flats(data='flooded_dem', out_name='inflated_dem')
# Compute flow direction based on corrected DEM
dirmap = (64, 128, 1, 2, 4, 8, 16, 32)
grid.flowdir(data='inflated_dem', out_name='dir', dirmap=dirmap)
# Compute flow accumulation based on computed flow direction
grid.accumulation(data='dir', out_name='acc', dirmap=dirmap)
downsampled_ridges = np.log(grid.view('acc') + 1)
upsampled_peaks = cv2.pyrUp(np.array(peaks, dtype=np.uint8),
dstsize=(225, 225))
upsampled_ridges = cv2.pyrUp(downsampled_ridges, dstsize=(225, 225))
upsampled_ridges = (upsampled_ridges - np.amin(upsampled_ridges)) / \
(np.amax(upsampled_ridges) - np.amin(upsampled_ridges))
upsampled_ridges = np.array(upsampled_ridges * 255, dtype=np.uint8)
_, thresholded_ridges = cv2.threshold(upsampled_ridges, 150, 255,
cv2.THRESH_BINARY)
thresholded_ridges[thresholded_ridges == 255] = 1
skeletonized_ridges = skeletonize(thresholded_ridges)
return np.expand_dims(skeletonized_ridges,
axis=-1), np.expand_dims(upsampled_peaks, axis=-1)
def split_catchment(self, flow_dir, geom, x, y):
#method to use catchment bounding box instead of exact geom
minX, maxX, minY, maxY = geom.GetEnvelope()
print('HERE', minX, minY, maxX, maxY)
gdal.Warp('/vsimem/fdr.tif',
flow_dir,
outputBounds=[minX, minY, maxX, maxY])
#start pysheds catchment delineation
grid = Grid.from_raster('/vsimem/fdr.tif', data_name='dir')
#get catchment with pysheds
grid.catchment(data='dir',
x=x,
y=y,
out_name='catch',
recursionlimit=15000,
xytype='label')
# Clip the bounding box to the catchment
grid.clip_to('catch')
#some sort of strange raster to polygon conversion using rasterio method
shapes = grid.polygonize()
#get split Catchment geometry
print('Split catchment complete')
split_geom = ogr.Geometry(ogr.wkbPolygon)
for shape in shapes:
split_geom = split_geom.Union(
ogr.CreateGeometryFromJson(json.dumps(shape[0])))
return split_geom
def testing():
"""I understand we need both elevation and drainage direction to delineate the watershed.
We can compute the drainage direction from the DEM using flowdir.
Downloaded the au and ca files from hydrosheds at 30sec resolution.
Issues with affine transformation.
Flow directions seem to be saved in int16 to limit file size. I assume this is why dirmap takes
powers of 2 to indicate directions.
Catchment delineation does not seem to work.
"""
from matplotlib import pyplot as plt
dem_fn = "../../tests/testdata/ca_dem_30s/ca_dem_30s/"
dir_fn = "../../tests/testdata/ca_dir_30s/ca_dir_30s/"
fv = -32768
grid = Grid.from_raster(dir_fn, "dir", nodata=fv)
grid.read_raster(dem_fn, "dem", nodata=fv)
lon, lat = -99.0619, 20.933
fig, (ax1, ax2) = plt.subplots(1, 2)
idem = ax1.imshow(grid.view("dem"),
extent=grid.extent,
cmap="cubehelix",
zorder=1,
vmin=0)
plt.colorbar(idem, ax=ax1, label="Elevation (m)")
idir = ax2.imshow(grid.view("dir"),
extent=grid.extent,
cmap="viridis",
zorder=2,
vmin=0)
boundaries = [0] + sorted(list(dirmap))
plt.colorbar(idir, ax=ax2, boundaries=boundaries, values=sorted(dirmap))
grid.catchment(
data="dir",
x=lon,
y=lat,
dirmap=dirmap,
out_name="catch",
xytype="label",
nodata_in=nodata,
)
catch = grid.polygonize(grid.catch.astype("int32"), connectivity=8)
grid.clip_to("catch")
for (p, v) in catch:
poly = geometry.asShape(p)
ax1.plot(*poly.exterior.xy, color="white")
plt.show()
def make_watersheds(self, point):
#>>> grid = Grid.from_raster('../data/dem.tif', data_name='dem')
grid = Grid.from_raster(self.input_dem, data_name='dem')
print(grid.crs)
#grid.read_raster(os.path.split(self.input_dem)[0], data_name='dir')
# Read raw DEM
#grid = Grid.from_raster('../data/roi_10m', data_name='dem')
# Fill depressions
grid.fill_depressions(data='dem', out_name='flooded_dem')
# Resolve flats
grid.resolve_flats(data='flooded_dem', out_name='inflated_dem')
# Determine D8 flow directions from DEM
# ----------------------
# Resolve flats in DEM
#grid.resolve_flats('dem', out_name='inflated_dem')
# Specify directional mapping
dirmap = (64, 128, 1, 2, 4, 8, 16, 32)
# Compute flow directions
# -------------------------------------
grid.flowdir(data='dem', out_name='dir') #, dirmap=dirmap)
#return grid,output
#grid.view('dir')
#plt.imshow(grid.view('dir'))
#def make_watersheds(self,point):
# Specify pour point
x, y = point
print('x is: ', x)
print('y is: ', y)
#input_flow_dir=self.get_dem_and_infill()[1]
# Delineate the catchment
grid.catchment(data='dir',
x=x,
y=y,
out_name='catch',
recursionlimit=10000,
xytype='label',
dirmap=dirmap)
demView = grid.view('catch', nodata=np.nan)
#grid.to_raster(demView, self.output_directory+'dem_watershed_pysheds_catchment.tif')
# Plot the result
#grid.clip_to('catch')
plt.imshow(grid.view('catch'))
plt.show()
plt.close()
def watershed_15s(path):
'''
From path to the project a 15s watershed is generated and added to
the geopackage.
'''
grid = Grid.from_raster(os.path.join(path, 'dir_15s.tif'), data_name='dir')
grid.read_raster(os.path.join(path, 'acc_15s.tif'), data_name='acc')
with fiona.open(os.path.join(path, 'HydroSHEDS_15s.gpkg'),
layer='pour_point_15s') as source:
crs = source.crs
for pt in source:
coord = pt['geometry']['coordinates']
x, y = coord[0], coord[1]
xy = np.column_stack([x, y])
new_xy = grid.snap_to_mask(grid.acc > 100, xy, return_dist=False)
new_x, new_y = new_xy[0][0], new_xy[0][1]
#N NE E SE S SW W NW
dirmap = (64, 128, 1, 2, 4, 8, 16, 32)
grid.catchment(data='dir', x=new_x, y=new_y, dirmap=dirmap,
out_name='catch',recursionlimit=15000, xytype='label')
grid.clip_to('catch')
shapes = grid.polygonize()
for item in shapes:
geom = item[0]
schema = {'geometry': 'Polygon',
'properties': OrderedDict([])}
with fiona.open(os.path.join(path, 'HydroSHEDS_15s.gpkg'),
'w', layer='catchment_15s', driver='GPKG',
schema=schema, crs=crs) as sink:
data = dict()
data['geometry'] = geom
data['properties'] = OrderedDict()
sink.write(data)
def split_catchment(self, bounds, x, y):
"""Use catchment bounding box to clip NHD Plus v2 flow direction raster, and product split catchment delienation from X,Y"""
print('test bounds:', bounds)
RasterFormat = 'GTiff'
PixelRes = 30
#method to use catchment bounding box instead of exact geom
gdal.Warp(OUT_FDR, IN_FDR, format=RasterFormat, outputBounds=bounds, xRes=PixelRes, yRes=PixelRes, dstSRS=self.Projection, resampleAlg=gdal.GRA_NearestNeighbour, options=['COMPRESS=DEFLATE'])
#start pysheds catchment delineation
grid = Grid.from_raster(OUT_FDR, data_name='dir')
#compute flow accumulation to snap to
dirmap = (64, 128, 1, 2, 4, 8, 16, 32)
grid.accumulation(data='dir', dirmap=dirmap, out_name='acc', apply_mask=False)
grid.to_raster('acc', OUT_PATH + 'acc.tif', view=False, blockxsize=16, blockysize=16)
#snap the pourpoint to
xy = (x, y)
new_xy = grid.snap_to_mask(grid.acc > 100, xy, return_dist=False)
#get catchment with pysheds
grid.catchment(data='dir', x=new_xy[0], y=new_xy[1], out_name='catch', recursionlimit=15000, xytype='label')
# Clip the bounding box to the catchment
grid.clip_to('catch')
#some sort of strange raster to polygon conversion using rasterio method
shapes = grid.polygonize()
#get split Catchment geometry
print('Split catchment complete')
split_geom = ogr.Geometry(ogr.wkbPolygon)
for shape in shapes:
split_geom = split_geom.Union(ogr.CreateGeometryFromJson(json.dumps(shape[0])))
return split_geom
def __call__(self):
self._prepare()
out_shps = [self.gage_shp_moved, self.dem_net, self.watersheds_shp]
for shp in out_shps:
if not os_exists(shp):
continue
in_vec = ogr.Open(shp)
out_driver = in_vec.GetDriver()
in_vec.Destroy()
out_driver.DeleteDataSource(shp)
self._grid = Grid.from_raster(self.raw_dem_path, data_name='dem')
ftns = [
self._deps,
self._flats,
self._fdrs,
self._slopes,
self._facs,
self._threshs,
self._snaps,
self._streams_net,
# self._streams,
]
for ftn in ftns:
beg_time = timeit.default_timer()
ftn()
end_time = timeit.default_timer()
if self.verbose:
print(f'Took {end_time - beg_time:0.2f} seconds.\n')
return
def load_dem_data(basin_geom):
basin_bounds = basin_geom.bounds
basin_bbox = tuple((basin_bounds['minx'].values[0] - 0.001,
basin_bounds['miny'].values[0] - 0.001,
basin_bounds['maxx'].values[0] + 0.001,
basin_bounds['maxy'].values[0] + 0.001))
grid = Grid.from_raster(path=FP_DIR + '/na_dem_15s/na_dem_15s',
data_name='dem',
window=basin_bbox,
window_crs=Proj(4326))
xs = [c[1] for c in grid.view('dem').coords]
ys = [c[0] for c in grid.view('dem').coords]
elevations = np.array(grid.view('dem')).flatten()
dem_df = pd.DataFrame()
dem_df['x'] = xs
dem_df['y'] = ys
dem_df['elevation'] = elevations
dem_df['geometry'] = [Point(a[0], a[1]) for a in grid.view('dem').coords]
return dem_df
@author: joempie
"""
import os
from pysheds.grid import Grid
import richdem as rd
filledDemPath = '../data/N09E037.tif'
x, y = 37.15251, 9.55677
if (not os.path.isfile(filledDemPath)):
dem = rd.LoadGDAL('../data/N09E037.hgt')
rd.FillDepressions(dem, epsilon=True, in_place=True)
rd.SaveGDAL(filledDemPath, dem)
grid = Grid.from_raster(filledDemPath, data_name='infl_dem')
# grid.fill_depressions(data='dem',out_name='flooded_dem')
# grid.resolve_flats(data='flooded_dem')
# Specify directional mapping
dirmap = (64, 128, 1, 2, 4, 8, 16, 32)
grid.flowdir(data='infl_dem', out_name='dir', dirmap=dirmap)
# Delineate the catchment
grid.catchment(data='dir',
x=x,
y=y,
dirmap=dirmap,
out_name='catch',
recursionlimit=15000,
xytype='label')
def test_windowed_reading():
# TODO: Write test for windowed reading
newgrid = Grid.from_raster('test_dir.tif',
window=grid.bbox,
window_crs=grid.crs)
32.84167873121935),
(-97.29304093486502, 32.84167861026064), (-97.29304075342363,
32.847513357726825)), )
}]
class Datasets():
pass
# Initialize dataset holder
d = Datasets()
# Initialize grid
crs = pyproj.Proj('epsg:4326', preserve_units=True)
grid = Grid.from_raster(dem_path)
fdir = grid.read_ascii(dir_path, dtype=np.uint8, crs=grid.crs)
dem = grid.read_raster(dem_path)
roi = grid.read_raster(roi_path)
# Add datasets to dataset holder
d.dem = dem
d.fdir = fdir
d.roi = roi
# Initialize parameters
dirmap = (64, 128, 1, 2, 4, 8, 16, 32)
acc_in_frame = 77261
cells_in_catch = 11422
catch_shape = (159, 169)
max_distance_d8 = 209
# with rasterio.open(out_fp, "w", **out_meta) as dest:
# dest.write(mosaic)
#
#
# # Re-Project Conditioned Raster to NAD 83 Albers Equal Area
#
# print("Reprojecting raster to NAD83 Albers Equal Area...")
# os.chdir("D:\GitHub\pysheds\data\conditioned\output")
# os.system('gdalwarp mosaic.tif reproject.tif -t_srs "+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=23 +lon_0=-96 '
# '+x_0=0 +y_0=0 +ellps=GRS9- +towgs84=0,0,0,0,0,0,0 +units=m _no_defs"')
# os.chdir("D:\GitHub\pysheds\examples") # Change working directory back to script location
# Read in Conditioned (mosaiced + reprojected) raster
print("Reading grid of conditioned raster ...")
grid = Grid.from_raster(os.path.join('..', 'data', 'raster', 'grdn31w098_13'),
data_name='dem')
fig, ax = plt.subplots(figsize=(8, 6))
fig.patch.set_alpha(0)
plt.imshow(grid.dem,
vmin=0,
vmax=3000,
extent=grid.extent,
cmap='cubehelix',
zorder=1)
plt.colorbar(label='Elevation (m)')
plt.grid(zorder=0)
plt.title('Digital elevation map')
plt.xlabel('Longitude')
plt.ylabel('Latitude')
plt.tight_layout()
#Importamos librerías a usar
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as colors
from pysheds.grid import Grid
import seaborn as sns
import warnings
import os
warnings.filterwarnings('ignore')
#Indicamos el directorio de trabajo:
os.chdir("C:\\Users\\Julio\\Documents\\EVENTOS\\UNSCH-Julio 2020\\Pysheds")
#Indicamos el nombre del archivo ráster
grid = Grid.from_raster('n30w100_con', data_name='dem')
#Configuramos espacio de gráfico
fig, ax = plt.subplots(figsize=(8, 6))
fig.patch.set_alpha(0)
#Generamos gráfico de DEM
plt.imshow(grid.dem, extent=grid.extent, cmap='cubehelix', zorder=1)
plt.colorbar(label='Elevation (m)')
plt.grid(zorder=0)
plt.title('Digital elevation map')
plt.xlabel('Longitude')
plt.ylabel('Latitude')
plt.tight_layout()
plt.savefig('dem', bbox_inches='tight')
def getdem(filename):
grid = Grid.from_raster(filename, data_name='dem')
return grid, grid.dem
def create_FlowAccu_tif_file(shapefile, path_dict):
topo_tif_lst = glob.glob(path_dict['topography_path'] + '*.tif')
# shpdir_lst = glob.glob(path_dict['path_shp'] + '*')
# for shpdir in shpdir_lst[2:3]:
# shpfile_lst = glob.glob(shpdir + '/' + '*.shp')
# for shapefile in shpfile_lst:
shape = gpd.read_file(shapefile)
shape = shape.to_crs('epsg:4269')
shape_path_temp = os.path.join(path_dict['tempFolder_path'],
'watershed.shp')
shape.to_file(shape_path_temp)
with fiona.open(shape_path_temp) as shp:
shape_geo = [feature['geometry'] for feature in shp]
rs_lst = []
for tif_file in topo_tif_lst:
with rasterio.open(tif_file) as tile:
try:
out_image, out_transform = rasterio.mask.mask(tile,
shape_geo,
crop=True)
except ValueError:
continue
else: # it means there was not any except
rs_lst.append(tif_file)
temp_mosaic_path = os.path.join(path_dict['tempFolder_path'],
'temp_mosaic.tif')
### to release the memory
tile = None
if len(rs_lst) > 1: # need to mosaic
### firstly, doing clip, and then mosaic the clipped files
#read shapefile
with fiona.open(shape_path_temp, "r") as shpfile:
shapes = [feature["geometry"] for feature in shpfile]
# read raster files one by one
temp_path = []
for i, rs in enumerate(rs_lst):
with rasterio.open(rs) as src:
bbox = box(
np.maximum(src.bounds[0], shpfile.bounds[0]), # left
np.maximum(src.bounds[1], shpfile.bounds[1]), # bottom
np.minimum(src.bounds[2], shpfile.bounds[2]), # right
np.minimum(src.bounds[3], shpfile.bounds[3]) # top
)
geo = gpd.GeoDataFrame({'geometry': bbox},
index=[0],
crs="EPSG:4269")
geo = geo.to_crs(crs=src.crs.data['init'])
coords = getFeatures(geo)
out_img, out_transform = rasterio.mask.mask(dataset=src,
shapes=coords,
crop=True)
out_meta = src.meta.copy()
# we need to parse the epsg value from the CRS so that we can create a proj4 -string using pyCRS
# library (to ensure that the projection information is saved correctly)
epsg_code = int(src.crs.data['init'][5:])
# print(epsg_code)
out_meta.update({
"driver":
"GTiff",
"height":
out_img.shape[1],
"width":
out_img.shape[2],
"transform":
out_transform,
"crs":
pycrs.parse.from_epsg_code(epsg_code).to_proj4()
})
out_tif = os.path.join(path_dict['tempFolder_path'],
'topo_temp_clip_' + str(i) + '.tif')
temp_path.append(out_tif)
with rasterio.open(out_tif, "w", **out_meta) as dest:
dest.write(out_img)
# clipped = rasterio.open(out_tif)
# show(clipped, cmap='terrain')
dest.close()
### to release the memory
dest = None
out_img = None
# out_image, out_transform = rasterio.mask.mask(src, shapes, crop=True)
# out_meta = src.meta
# # make a new file
# out_meta.update({"driver": "GTiff",
# "height": out_image.shape[1],
# "width": out_image.shape[2],
# "transform": out_transform,
# "crs": "epsg:4269"})
# path = os.path.join(path_dict['tempFolder_path'], 'topo_temp_clip_' + str(i) + '.tif')
# temp_path.append(path)
# with rasterio.open(path, "w", **out_meta) as dest:
# dest.write(out_image)
# dest.close()
# ### to release the memory
# dest = None
# out_image = None
#### now we do mosaic:
src_files_to_mosaic = []
for fp in temp_path:
src = rasterio.open(fp)
src_files_to_mosaic.append(src)
out_image_mos, out_trans = rasterio.merge.merge(src_files_to_mosaic)
# make a new file
out_meta = src.meta.copy()
out_meta.update({
"driver": "GTiff",
"height": out_image_mos.shape[1],
"width": out_image_mos.shape[2],
"transform": out_trans,
"crs": "epsg:4269"
})
with rasterio.open(temp_mosaic_path, "w", **out_meta) as dest:
dest.write(out_image_mos)
dest.close()
### to release memory
dest = None
out_image_mos = None
AccuProcess = True
###########################################
# src_files_to_mosaic = []
# for fp in rs_lst:
# src = rasterio.open(fp)
# src_files_to_mosaic.append(src)
# out_image_mos, out_trans = rasterio.merge.merge(src_files_to_mosaic)
# # make a new file
# out_meta = src.meta.copy()
# out_meta.update(
# {
# "driver": "GTiff",
# "height": out_image_mos.shape[1],
# "width": out_image_mos.shape[2],
# "transform": out_trans,
# "crs": "epsg:4269"
# }
# )
# with rasterio.open(temp_mosaic_path, "w", **out_meta) as dest:
# dest.write(out_image_mos)
# dest.close()
# ### to release memory
# dest = None
# out_image_mos = None
# AccuProcess = True
elif len(rs_lst) == 1:
# copy the tif file to tempfolder to work on
# shutil.copyfile(rs_lst[0], temp_mosaic_path)
rasterio.shutil.copy(rs_lst[0], temp_mosaic_path)
# make a new file
AccuProcess = True
else:
print(
"There was no Topography .TIFF file for watershed: " +
os.path.split(shapefile)[1], '\n')
AccuProcess = False
return temp_mosaic_path, AccuProcess ## it means the code stops here. because there is not any .tif file
## clip shapefile and mosaic
try:
with fiona.open(shape_path_temp, "r") as shpfile:
shapes = [feature["geometry"] for feature in shpfile]
with rasterio.open(temp_mosaic_path) as src:
out_image, out_transform = rasterio.mask.mask(src,
shapes,
crop=True)
out_meta = src.meta
# make a new file
out_meta.update({
"driver": "GTiff",
"height": out_image.shape[1],
"width": out_image.shape[2],
"transform": out_transform,
"crs": "epsg:4269"
})
topo_clip_temp_path = os.path.join(
path_dict['tempFolder_path'], 'topo_temp_clip.tif')
with rasterio.open(topo_clip_temp_path, "w",
**out_meta) as dest:
dest.write(out_image)
dest.close()
### to release the memory
dest = None
out_image = None
except:
AccuProcess = False
return temp_mosaic_path, AccuProcess
# calculating flow direction and flow accumulation (using pysheds package)
try:
grid = Grid.from_raster(topo_clip_temp_path, data_name='dem')
grid.fill_depressions(data='dem', out_name='flooded_dem')
grid.dem = None
grid.resolve_flats(data='flooded_dem', out_name='inflated_dem')
grid.flooded_dem = None
# dirmap = (1, 2, 3, 4, 5, 6, 7, 8)
dirmap = (64, 128, 1, 2, 4, 8, 16, 32) # ESRI default
grid.flowdir(data='inflated_dem', out_name='dir', dirmap=dirmap)
grid.inflated_dem = None
grid.accumulation(data='dir', out_name='acc')
grid.dir = None
flowAccu_temp_path = os.path.join(path_dict['tempFolder_path'],
'flowAccu_temp.tif')
# grid.to_raster('dir', flowAccu_temp_path)
grid.to_raster('acc', flowAccu_temp_path, dtype=np.int32)
grid = None
return flowAccu_temp_path, AccuProcess
except:
AccuProcess = False
return flowAccu_temp_path, AccuProcess
def catch(x1,y1,x2,y2,data_url,out_url):
# os.chdir(r'D:\Data\SRTM_TR')
# os.chdir(r'D:\ASTER')
grid = Grid.from_raster(data_url, data_name='dem')
distance = 1
window = (x1,y1,x2,y2)
grid.set_bbox(window)
grid.read_raster(data_url, data_name='dem', window=grid.bbox,
window_crs=grid.crs)
dirmap = (64, 128, 1, 2, 4, 8, 16, 32)
grid.fill_pits(data='dem', out_name='pit')
grid.fill_depressions(data='pit', out_name='dep')
grid.resolve_flats(data='dep', out_name='flat')
grid.flowdir(data='flat', out_name='dir', dirmap=dirmap)
grid.accumulation(data='dir', out_name='acc')
# grid.to_raster('dir', "dir.tif", blockxsize=16, blockysize=16)
# grid.to_raster('flat', "dem.tif", blockxsize=16, blockysize=16)
# grid.to_raster('acc', "acc.tif", blockxsize=16, blockysize=16)
# xy = np.column_stack([x, y])
# new = grid.snap_to_mask(grid.acc > 500, xy, return_dist=False)
# x = new[:, 0]
# y = new[:, 1]
ynew, xnew = np.unravel_index(np.argsort(grid.acc.ravel())[-2], grid.acc.shape)
# grid.catchment(x=x, y=y, data='dir', dirmap=dirmap, out_name='catch', recursionlimit=15000, xytype='label')
grid.catchment(x=xnew, y=ynew, data='dir', dirmap=dirmap, out_name='catch', recursionlimit=15000, xytype='index')
nodat = grid.catch.nodata
grid.clip_to('catch')
shapes = grid.polygonize()
schema = {
'geometry': 'Polygon',
'properties': {'LABEL': 'float:16'}
}
i = 0
in_projection = Proj(init='epsg:4326')
out_projection = Proj(init='epsg:4326')
last_value = 0
coords = [[]]
# print('Tranformation Started !')
# transformation_time = datetime.datetime.now()
for shape, value in shapes:
if last_value < len(shape['coordinates'][0]):
coords[0] = []
for index, val in enumerate(shape['coordinates'][0]):
coords[0].append([transform(in_projection, out_projection, val[0], val[1])[0],
transform(in_projection, out_projection, val[0], val[1])[1]])
last_value = len(shape['coordinates'][0])
else:
pass
# print('Transformation Finished : ', datetime.datetime.now()-transformation_time)
# print(Polygon(coords))
poly = Polygon(coords)
with fiona.open(os.path.join(out_url, "catchment.shp"), 'w',
driver='ESRI Shapefile',
crs=grid.crs.srs,
schema=schema) as c:
i = 0
for shape, value in shapes:
rec = {}
rec['geometry'] = shape
rec['properties'] = {'LABEL': str(value)}
rec['id'] = str(i)
c.write(rec)
i += 1
grid.catch.nodata = -9223372036854775808
grid.clip_to('catch')
grid.accumulation(data='catch', dirmap=dirmap, pad_inplace=False, out_name='acc')
grid.catch.nodata = -9223372036854775808
branches = grid.extract_river_network('catch', 'acc', threshold=50, dirmap=dirmap)
# def saveDict(dic, file):
# f = open(file, 'w')
# f.write(str(dic))
# f.close()
#
# saveDict(branches, 'branches.geojson')
# streamNet = gpd.read_file('branches.geojson')
# streamNet.crs = {'init': 'epsg:23036'}
schema = {
'geometry': 'LineString',
'properties': {}
}
os.path.join(out_url, "rivers.shp")
with fiona.open(os.path.join(out_url, "rivers.shp"), 'w',
driver='ESRI Shapefile',
crs=grid.crs.srs,
schema=schema) as c:
i = 0
for branch in branches['features']:
rec = {}
rec['geometry'] = branch['geometry']
rec['properties'] = {}
rec['id'] = str(i)
c.write(rec)
i += 1
def convert_wgs_to_utm(lon, lat):
utm_band = str((math.floor((lon + 180) / 6) % 60) + 1)
if len(utm_band) == 1:
utm_band = '0' + utm_band
if lat >= 0:
epsg_code = '326' + utm_band
else:
epsg_code = '327' + utm_band
return epsg_code
input_lon, input_lat = x1, y1
utm_code = convert_wgs_to_utm(input_lon, input_lat)
crs_wgs = pyproj.Proj(init='epsg:4326') # assuming you're using WGS84 geographic
crs_utm = pyproj.Proj(init='epsg:{0}'.format(utm_code))
# a = gpd.read_file(out_url)
# data_proj = a.copy()
# data_proj['geometry'] = data_proj['geometry'].to_crs(epsg=utm_code)
# Area = data_proj.area[0] / 1e6
Area = area(poly) / 1e6
file_name = 'rivers.zip'
with zipfile.ZipFile(os.path.join(out_url, file_name), 'w') as file:
file.write(os.path.join(out_url, 'rivers.cpg'))
file.write(os.path.join(out_url, 'rivers.dbf'))
file.write(os.path.join(out_url, 'rivers.prj'))
file.write(os.path.join(out_url, 'rivers.shp'))
file.write(os.path.join(out_url, 'rivers.shx'))
return Area,utm_code,branches,poly
print("Writing file...")
with rasterio.open(out_fp, "w", **out_meta) as dest:
kwargs = src.meta
kwargs.update(bigtiff='YES', dtype=rasterio.uint16)
dest.write(mosaic)
# Re-Project Conditioned Raster to NAD 83 Albers Equal Area
# print("Reprojecting raster to NAD83 Albers Equal Area...")
# os.chdir("D:\\Projects\\Watersheds\\pysheds\\08108780\\mosaic")
# os.system('gdalwarp mosaic_34.tif reproject.tif -t_srs "+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=23 +lon_0=-96 '
# '+x_0=0 +y_0=0 +ellps=GRS9- +towgs84=0,0,0,0,0,0,0 +units=m _no_defs"')
#
grid = Grid.from_raster(os.path.join('pysheds', 'data', 'conditioned',
'original', 'n30w100_con', 'n30w100_con'),
data_name='dem')
grid.read_raster(os.path.join('pysheds', 'data', 'flowdir', 'original',
'n30w100_dir_bil', 'n30w100_dir.bil'),
data_name='dir')
# grid = Grid.from_raster(os.path.join('pysheds', 'data', 'conditioned', 'reproject', '30_100_con'), data_name='dem')
# grid.read_raster(os.path.join('pysheds', 'data', 'flowdir', 'reproject', '30_100_reproj'), data_name='dir')
dirmap = (64, 128, 1, 2, 4, 8, 16, 32)
grid.accumulation(data='dir', dirmap=dirmap, out_name='acc')
print("Writing flow direction raster ...")
os.chdir('D:\Projects\Watersheds\pysheds\data\processed_DEM')
grid.to_ascii('dir',
file_name='dir.ascii',
view=True,
apply_mask=False,
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as colors
from pysheds.grid import Grid
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
from PIL import Image
# -------------------------------- #
# <<< Instatiate a grid from a DEM raster >>>
# C:/Users/Rahul/Documents/Geany/files/cach2/n30w100_con_grid/n30w100_con/n30w100_con
grid = Grid.from_raster(
'C:/Users/Rahul/Documents/Geany/files/Cat_DEM/SRTM_90m_AP_srikakulam_5km.tif',
data_name='dem')
fig, ax = plt.subplots(figsize=(8, 6))
fig.patch.set_alpha(1)
plt.imshow(grid.dem, extent=grid.extent, cmap='cubehelix', zorder=1)
plt.colorbar(label='Elevation (m)')
plt.grid(zorder=0)
plt.title('Digital elevation map')
plt.xlabel('Longitude')
plt.ylabel('Latitude')
plt.tight_layout()
plt.savefig('C:/Users/Rahul/Documents/Geany/files/cach2/conditioned_dem.png',
bbox_inches='tight')
#im1=Image.open('C:/Users/Rahul/Documents/Geany/files/cach2/conditioned_dem.png')
#im1.show()
# -------------------------------- #
# dest.write(mosaic)
#
#
# # Re-Project Conditioned Raster to NAD 83 Albers Equal Area
#
# print("Reprojecting raster to NAD83 Albers Equal Area...")
# os.chdir("D:\GitHub\pysheds\data\conditioned\output")
# os.system('gdalwarp mosaic.tif reproject.tif -t_srs "+proj=aea +lat_1=29.5 +lat_2=45.5 +lat_0=23 +lon_0=-96 '
# '+x_0=0 +y_0=0 +ellps=GRS9- +towgs84=0,0,0,0,0,0,0 +units=m _no_defs"')
# os.chdir("D:\GitHub\pysheds\examples") # Change working directory back to script location
#
# Read in Conditioned (mosaiced + reprojected) raster
print("Reading grid of conditioned raster ...")
grid = Grid.from_raster(os.path.join('..', 'data', 'conditioned',
'n25w100_con', 'n25w100_con'),
data_name='dem')
# grid = Grid.from_raster(os.path.join('..', 'data', 'conditioned', 'output', 'reproject.tif'), data_name='dem')
# grid = Grid.from_raster(os.path.join('..', 'data', 'conditioned', 'n30w095_con', 'n30w095_con'), data_name='dem')
# grid = Grid.from_raster(os.path.join('..', 'data', 'conditioned', 'n30w100_con', 'n30w100_con'), data_name='dem')
fig, ax = plt.subplots(figsize=(8, 6))
fig.patch.set_alpha(0)
plt.imshow(grid.dem,
vmin=0,
vmax=3000,
extent=grid.extent,
cmap='cubehelix',
zorder=1)
plt.colorbar(label='Elevation (m)')
plt.grid(zorder=0)
plt.title('Digital elevation map')
x, y = stn_info['Longitude'].values[0], stn_info['Latitude'].values[0]
# expand the bounding box slightly
# 0.01 decimal degrees equals approximately 1.1132 km
basin_bbox = tuple((basin_bounds['minx'].values[0] - 0.2,
basin_bounds['miny'].values[0] - 0.2,
basin_bounds['maxx'].values[0] + 0.2,
basin_bounds['maxy'].values[0] + 0.2))
print(basin_bbox)
# get the DEM data
t1 = time.time()
print('{}/{} {} basin geometry loaded in {:.2f}s'.format(
i, len(all_sites), stn, t1 - t0))
grid = Grid.from_raster(path=FP_DIR + '/na_dem_15s/na_dem_15s',
data_name='dem',
window=basin_bbox,
nodata=np.nan)
print(grid.crs)
# grid.read_raster(GDIR_DIR + '/na_dir_15s/na_dir_15s',
# data_name='dem')#, window=basin_bbox, nodata=np.nan)#,
# window_crs=Proj('epsg:4326'))
print(grid.view('dem'))
print(grid.crs)
# reset the nodata from -32768 to 0
grid.catchment(data='dem',
x=x,
y=y,
out_name='catch',
recursionlimit=15000,
xytype='label',
nodata_out=0)
import pysheds
import gdal
import matplotlib.pyplot as plt
import matplotlib.colors as colors
from pysheds.grid import Grid
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
# functions
#%% read DEM
DEM = Grid.from_raster('DEM/n30w100_con/n30w100_con', data_name='dem')
#grid = Grid.from_raster('DEM/dem_2000.tif', data_name='dem')
#fig, ax = plt.subplots(figsize=(8,6))
#fig.patch.set_alpha(0)
#
#plt.imshow(DEM.dem, extent=DEM.extent, cmap='cubehelix', zorder=1)
#plt.colorbar(label='Elevation (m)')
#plt.grid(zorder=0)
#plt.title('Digital elevation map')
#plt.xlabel('Longitude')
#plt.ylabel('Latitude')
#plt.tight_layout()
#plt.savefig('img/conditioned_dem.png', bbox_inches='tight')
#%%
flow_dir=Grid.flowdir(DEM.dem, data=None, out_name='dir', nodata_in=None, nodata_out=0,
pits=-1, flats=-1, dirmap= (64, 128, 1, 2, 4, 8, 16, 32),
def init_grid(self):
self.grid = Grid.from_raster(self.basin_dem,
data_name=self.basin_data_dem_name)
def flow_acc(path_dem):
grid = Grid.from_raster(path_dem, data_name='dem')
print("The raster has been read")
# plt.figure(figsize=(12,10))
# plt.imshow(grid.dem, extent=grid.extent, cmap='Blues')
# plt.imshow(data, cmap=cmap)
# plt.colorbar(label='Elevation (m)')
# plt.grid()
# plotFigure(grid.dem, 'Elevation (m)')
depressions = grid.detect_depressions('dem')
# Plot pits
# fig, ax = plt.subplots(figsize=(8,8))
# ax.imshow(depressions, cmap='cubehelix', zorder=1)
# ax.set_yticklabels([])
# ax.set_xticklabels([])
# plt.title('Depressions', size=14)
grid.fill_depressions(data='dem', out_name='flooded_dem')
flats = grid.detect_flats('flooded_dem')
# Plot flats
# fig, ax = plt.subplots(figsize=(8,8))
# plt.imshow(flats, cmap='cubehelix', zorder=1)
# ax.set_yticklabels([])
# ax.set_xticklabels([])
# plt.title('Flats', size=14)
grid.resolve_flats(data='flooded_dem', out_name='inflated_dem')
# plt.figure(figsize=(12,10))
# plt.imshow(grid.inflated_dem, extent=grid.extent, cmap='cubehelix')
# plt.imshow(data, cmap=cmap)
# plt.colorbar(label='Flats')
# plt.grid()
# plotFigure(grid.inflated_dem, 'flats', 'cubehelix')
flats_test = grid.detect_flats('inflated_dem')
print("The Depressions have been filled and the Flats have been resolved")
# Plot flats
# fig, ax = plt.subplots(figsize=(8,8))
# plt.imshow(flats_test, cmap='cubehelix', zorder=1)
# ax.set_yticklabels([])
# ax.set_xticklabels([])
# plt.title('Flats', size=14)
#N NE E SE S SW W NW
dirmap = (64, 128, 1, 2, 4, 8, 16, 32)
# Compute flow direction based on corrected DEM
grid.flowdir(data='inflated_dem', out_name='dir', dirmap=dirmap)
print("Flow direction is computed")
# plt.figure(figsize=(12,10))
# plt.imshow(grid.dir, extent=grid.extent, cmap='viridis')
# plt.imshow(data, cmap=cmap)
# plt.colorbar(label='flow_dir')
# plt.grid()
# plotFigure(grid.dir , 'flow dir', 'viridis')
# Compute flow accumulation based on computed flow direction
grid.accumulation(data='dir', out_name='acc', dirmap=dirmap)
accView = grid.view('acc', nodata=np.nan)
print("Flow accumulation is computed")
# plt.figure(figsize=(12,10))
# plt.imshow(accView, extent=grid.extent, cmap='PuRd')
# plt.imshow(data, cmap=cmap)
# plt.colorbar(label='Cell_number')
# plt.grid()
# plotFigure(accView,"Cell Number",'PuRd')
# Delineate catchment at point of high accumulation
y, x = np.unravel_index(np.argsort(grid.acc.ravel())[-2], grid.acc.shape)
grid.catchment(x,
y,
data='dir',
out_name='catch',
dirmap=dirmap,
xytype='index')
print("Catchment Delineated")
streams = grid.extract_river_network('catch',
'acc',
args["threshold"],
dirmap=dirmap)
print(streams["features"][:2])
print("River network has been extracted")
f = open('OUTPUT/streams.geojson', 'w')
f.write(str(streams))
f.close()
lines.append('\n\n')
lines.append('[COORDINATES]')
lines.append('\n')
lines.append(self.coordinates.to_csv(sep='\t', header=None, index=None))
lines.append('\n\n')
lines.append('[POLYGONS]')
lines.append('\n')
lines.append(self.polygons.to_csv(sep='\t', header=None, index=None))
self.lines = lines
def to_file(self, filename, **kwargs):
with open(filename, 'w') as outfile:
outfile.writelines(self.lines)
if __name__ == "__main__":
grid = Grid.from_raster('../data/n30w100_con',
data_name='dem', input_type='ascii')
grid.read_raster('../data/n30w100_dir',
data_name='dir', input_type='ascii')
dirmap = (64, 128, 1, 2, 4, 8, 16, 32)
# Specify pour point
x, y = -97.2937, 32.7371
# Delineate the catchment
grid.catchment(x, y, data='dir', dirmap=dirmap, out_name='catch',
recursionlimit=15000, xytype='label')
# Clip the bounding box to the catchment
grid.clip_to('catch', precision=5)
grid.accumulation(data='catch', dirmap=dirmap, pad_inplace=False, out_name='acc')
projection = pyproj.Proj('+init=epsg:3083')
node_depths = pd.read_csv('../../eecs-598-a455/notebooks/other/node_depths.csv', index_col=0)
node_depths = node_depths.iloc[-1]
link_flows = pd.read_csv('../../eecs-598-a455/notebooks/other/link_flows.csv', index_col=0)
def test_windowed_reading():
newgrid = Grid.from_raster('test_dir.tif',
'dir_output',
window=grid.bbox,
window_crs=grid.crs)
"/data2/elilouis/hsfm-geomorph/data/mt_hood_eliot_glacier/rasters/2009_5m_flowdir.tif"
)
# %%
ref_dem_raster = rix.open_rasterio(reference_dem_fn)
ref_dem_raster_downsampled = ref_dem_raster.rio.reproject(
ref_dem_raster.rio.crs, resolution=5)
ref_dem_raster_downsampled.rio.to_raster(downsampled_reference_dem)
# %% [markdown]
# Create drainage raster with downsampled reference_dem
# %%
from pysheds.grid import Grid
grid = Grid.from_raster(downsampled_reference_dem, data_name='dem')
grid.fill_depressions(data='dem', out_name='filled')
grid.resolve_flats(data='filled', out_name='dem_inflated')
grid.flowdir('dem_inflated', out_name='dir', routing='d8')
grid.accumulation(data='dir', out_name='acc')
# %%
from matplotlib import colors, cm, pyplot as plt
# norm = colors.LogNorm(acc.min(), acc.max())
plt.imshow(grid.acc, cmap=cm.gray, norm=colors.LogNorm())
plt.colorbar()
# %%
grid.to_raster('acc', downsampled_reference_flowdir, dtype='float32')
# %% [markdown]
