def main():
"""
RichDEM flat resolution: give a gentle slope
"""
# lazy import RICHDEM
try:
import richdem as rd
except:
g.message(
flags="e",
message=("RichDEM not detected. Install pip3 and " +
"then type at the command prompt: " +
'"pip3 install richdem".'),
)
_input = options["input"]
_output = options["output"]
_method = options["method"]
_exponent = options["exponent"]
_weights = options["weights"]
if (_method == "Holmgren") or (_method == "Freeman"):
if _exponent == "":
g.message(
flags="w",
message=("Exponent must be defined for " +
"Holmgren or Freeman methods. " + "Exiting."),
)
return
else:
_exponent = float(_exponent)
else:
_exponent = None
if _weights == "":
rd_weights = None
else:
g_weights = garray.array(_weights, null=np.nan)
rd_weights = rd.rdarray(g_weights, no_data=np.nan)
dem = garray.array(_input, null=np.nan)
mask = dem * 0 + 1
rd_input = rd.rdarray(dem, no_data=np.nan)
del dem
rd_output = rd.FlowAccumulation(
dem=rd_input,
method=_method,
exponent=_exponent,
weights=rd_weights,
in_place=False,
)
rd_output *= mask
accum = garray.array()
accum[:] = rd_output[:]
accum.write(_output, overwrite=gscript.overwrite())
def main():
"""
RichDEM flat resolution: give a gentle slope
"""
# lazy import RICHDEM
try:
import richdem as rd
except:
g.message(flags='e',
message=('RichDEM not detected. Install pip3 and ' +
'then type at the command prompt: ' +
'"pip3 install richdem".'))
options, flags = gscript.parser()
_input = options['input']
_output = options['output']
_method = options['method']
_exponent = options['exponent']
_weights = options['weights']
if (_method == 'Holmgren') or (_method == 'Freeman'):
if _exponent == '':
g.message(flags='w',
message=('Exponent must be defined for ' +
'Holmgren or Freeman methods. ' + 'Exiting.'))
return
else:
_exponent = float(_exponent)
else:
_exponent = None
if _weights == '':
rd_weights = None
else:
g_weights = garray.array()
g_weights.read(_weights, null=np.nan)
rd_weights = rd.rdarray(g_weights, no_data=np.nan)
dem = garray.array()
dem.read(_input, null=np.nan)
mask = dem * 0 + 1
rd_input = rd.rdarray(dem, no_data=np.nan)
del dem
rd_output = rd.FlowAccumulation(dem=rd_input,
method=_method,
exponent=_exponent,
weights=rd_weights,
in_place=False)
rd_output *= mask
accum = garray.array()
accum[:] = rd_output[:]
accum.write(_output, overwrite=gscript.overwrite())
def _accumulate_flow_RD(self, props_Pf, hill_flow=False):
"""
Function to accumualte flow using the richdem package
Parameters
----------
props_Pf : float
flow proportions calcualte with the RichDEM package using the
FlowProportions function
hill_flow : Boolean, optional
Defines which instance of flow accumulation is updated.
If FALSE, the first, default instance is updated.
If TRUE, the second, hillslope, instance is updated.
The default is False.
Returns
-------
None.
"""
if not hill_flow:
a = self._drainage_area
q = self._discharges
else:
a = self._hill_drainage_area
q = self._hill_discharges
# Create weight for flow accum: both open (status ==1) and closed nodes (status ==4) will have zero weight
wg = np.full(self.grid.number_of_nodes, self.grid.dx**2)
# Only core nodes (status == 0) need to receive a weight
wg[self._grid.status_at_node != NodeStatus.CORE] = 0
wg = rd.rdarray(
wg.reshape(self.grid.shape),
no_data=-9999,
)
wg.geotransform = [0, 1, 0, 0, 0, -1]
with self._suppress_output():
a[:] = np.array(
rd.FlowAccumFromProps(props=props_Pf, weights=wg).reshape(
self.grid.number_of_nodes
)
)
if any(self.grid.at_node["water__unit_flux_in"] != 1):
wg = self.grid.at_node["water__unit_flux_in"] * self.grid.dx * self.grid.dx
# Only core nodes (status == 0) need to receive a weight
wg[self._grid.status_at_node != NodeStatus.CORE] = 0
wg = rd.rdarray(wg.reshape(self.grid.shape), no_data=-9999)
wg.geotransform = [0, 1, 0, 0, 0, -1]
with self._suppress_output():
q_pf = rd.FlowAccumFromProps(props=props_Pf, weights=wg)
q[:] = np.array(q_pf.reshape(self.grid.number_of_nodes))
else:
q[:] = self._drainage_area
def main():
"""
RichDEM depression breaching
"""
# lazy import RICHDEM
try:
import richdem as rd
except:
g.message(
flags="e",
message=("RichDEM not detected. Install pip3 and " +
"then type at the command prompt: " +
'"pip3 install richdem".'),
)
_input = options["input"]
_output = options["output"]
_topology = options["topology"]
dem = garray.array()
dem.read(_input, null=np.nan)
rd_inout = rd.rdarray(dem, no_data=np.nan)
rd.BreachDepressions(dem=rd_inout, in_place=True, topology=_topology)
dem[:] = rd_inout[:]
dem.write(_output, overwrite=gscript.overwrite())
def __init__(self,
dtm_path,
manning_path,
cellsize=5,
radius=2000,
nodata=-99,
mute=True):
self.dtm_ds = gtif.openf(dtm_path)
self.dtm_gt = self.dtm_ds.GetGeoTransform()
self.mannings_ds = gtif.openf(manning_path)
self.mannings_gt = self.mannings_ds.GetGeoTransform()
Matrix.__init__(self, gtif.as_array(self.dtm_ds))
self.cellsize = cellsize
self.radius = radius
self.nodata = nodata
self.dtm = rd.rdarray(self.dtm, no_data=nodata)
rd.FillDepressions(self.dtm, in_place=True)
self.mannings = self.array(gtif.as_array(self.mannings_ds))
self.mannings = GeoTransformFit(self.mannings, self.mannings_gt,
self.dtm_gt)
self.mute = mute
def main():
"""
RichDEM depression filling
"""
options, flags = gscript.parser()
_input = options['input']
_output = options['output']
_epsilon = options['epsilon']
_topology = options['topology']
if _epsilon == 'true':
epsilon = True
else:
epsilon = False
dem = garray.array()
dem.read(_input, null=np.nan)
rd_inout = rd.rdarray(dem, no_data=np.nan)
rd.FillDepressions(dem=rd_inout, epsilon=epsilon, in_place=True,
topology=_topology)
dem[:] = rd_inout[:]
dem.write(_output, overwrite=gscript.overwrite())
def main():
"""
RichDEM depression filling
"""
# lazy import RICHDEM
try:
import richdem as rd
except:
g.message(flags='e',
message=('RichDEM not detected. Install pip3 and ' +
'then type at the command prompt: ' +
'"pip3 install richdem".'))
_input = options['input']
_output = options['output']
_epsilon = options['epsilon']
_topology = options['topology']
if _epsilon == 'true':
epsilon = True
else:
epsilon = False
dem = garray.array()
dem.read(_input, null=np.nan)
rd_inout = rd.rdarray(dem, no_data=np.nan)
rd.FillDepressions(dem=rd_inout,
epsilon=epsilon,
in_place=True,
topology=_topology)
dem[:] = rd_inout[:]
dem.write(_output, overwrite=gscript.overwrite())
def main():
"""
RichDEM flat resolution: give a gentle slope
"""
# lazy import RICHDEM
try:
import richdem as rd
except:
g.message(
flags="e",
message=("RichDEM not detected. Install pip3 and " +
"then type at the command prompt: " +
'"pip3 install richdem".'),
)
_input = options["input"]
_output = options["output"]
_attribute = options["attribute"]
_zscale = float(options["zscale"])
dem = garray.array()
dem.read(_input, null=np.nan)
rd_input = rd.rdarray(dem, no_data=np.nan)
del dem
rd_output = rd.TerrainAttribute(dem=rd_input,
attrib=_attribute,
zscale=_zscale)
outarray = garray.array()
outarray[:] = rd_output[:]
outarray.write(_output, overwrite=gscript.overwrite())
def main():
"""
RichDEM flat resolution: give a gentle slope
"""
# lazy import RICHDEM
try:
import richdem as rd
except:
g.message(flags='e',
message=('RichDEM not detected. Install pip3 and ' +
'then type at the command prompt: ' +
'"pip3 install richdem".'))
_input = options['input']
_output = options['output']
# Check for overwrite
_rasters = np.array(gscript.parse_command('g.list', type='raster').keys())
if (_rasters == _output).any():
g.message(flags='e', message="output would overwrite " + _output)
dem = garray.array()
dem.read(_input, null=np.nan)
rd_input = rd.rdarray(dem, no_data=np.nan)
rd_output = rd.ResolveFlats(rd_input)
dem[:] = rd_output[:]
dem.write(_output, overwrite=gscript.overwrite())
def get_slope(self):
""" TODO """
beau = rd.rdarray(np.load('imgs/beauford.npz')['beauford'], no_data=-9999)
slope = rd.TerrainAttribute(beau, attrib='slope_riserun')
rd.rdShow(slope, axes=False, cmap='jet', figsize=(8,5.5))
pass
def _numpy2richdem(self, in_array, no_data=-9999):
'''
slope and aspect calculations are performed by richdem, which
requires data in rdarray format. this function converts numpy
arrays into rdarrays.
'''
out_array = rd.rdarray(in_array, no_data=no_data)
out_array.projection = self.projection
cell_scale = np.around(a=self.side_len / self.resolution, decimals=5)
out_array.geotransform = [0, cell_scale, 0, 0, 0, cell_scale]
return out_array
def filled_dem(ctx, cropped_dem_fp, filled_dem_fp):
logger = ctx.obj['LOGGER']
logger.info("Cropping DEM to watershed extent")
with rasterio.open(cropped_dem_fp) as dataset:
dem = dataset.read(1)
rdem = richdem.rdarray(dem, no_data=dataset.nodata)
filled_dem = richdem.FillDepressions(rdem)
logger.info("Filled {} pixels".format(np.sum(filled_dem != rdem)))
out_meta = dataset.meta
with rasterio.open(filled_dem_fp, 'w', **out_meta) as out_dataset:
out_dataset.write(filled_dem, indexes=1)
logger.info("DONE")
def _create_richdem_properties(self):
self._depression_free_dem = cp.deepcopy(
rd.rdarray(
self.grid.at_node["topographic__elevation"].reshape(self.grid.shape),
no_data=-9999,
)
)
self._depression_free_dem.geotransform = [0, 1, 0, 0, 0, -1]
# Calculate SQUARED length adjacent
self.grid.at_node["squared_length_adjacent"] = np.concatenate(
(
np.ones((self.grid.number_of_nodes, 4)),
2 * np.ones((self.grid.number_of_nodes, 4)),
),
axis=1,
)
self._closed = np.zeros(self._grid.number_of_nodes)
self._closed[self._grid.status_at_node == NodeStatus.CLOSED] = 1
self._closed = rd.rdarray(self._closed.reshape(self._grid.shape), no_data=-9999)
self._closed.geotransform = [0, 1, 0, 0, 0, -1]
def terrain(elvDs, variable="elevation", fillDem=True, flowMethod='D8'):
out = elvDs.copy()
zScales = {
0: 0.00000898,
10: 0.00000912,
20: 0.00000956,
30: 0.00001036,
40: 0.00001171,
50: 0.00001395,
60: 0.00001792,
70: 0.00002619,
80: 0.00005156
}
# calculat the geospatial information from the dataset
elvDim = [elvDs.dims['lon'], elvDs.dims["lat"]]
xres = float((elvDs.lon[1] - elvDs.lon[0]).values)
yres = float((elvDs.lat[1] - elvDs.lat[0]).values)
xinit = float((elvDs.lon[0]).values) - (xres / 2)
yinit = float((elvDs.lat[0]).values) + (yres / 2)
# get elevation values as np.ndarray
elvVals = np.squeeze(elvDs[variable].values)
rdem = rd.rdarray(elvVals, no_data=np.nan)
rdem.projection = '''GEOGCS["WGS 84",
DATUM["WGS_1984",
SPHEROID["WGS 84",6378137,298.257223563,
AUTHORITY["EPSG","7030"]],
AUTHORITY["EPSG","6326"]],
PRIMEM["Greenwich",0,
AUTHORITY["EPSG","8901"]],
UNIT["degree",0.0174532925199433,
AUTHORITY["EPSG","9122"]],
AUTHORITY["EPSG","4326"]]
'''
rdem.geotransform = (xinit, xres, 0, yinit, 0, yres)
if fillDem:
filled = rd.FillDepressions(rdem,
epsilon=True,
in_place=False,
topology='D8')
rdem = rd.ResolveFlats(filled, in_place=False)
zScale = zScales[np.around(yinit, decimals=-1)]
slope = rd.TerrainAttribute(rdem, attrib='slope_percentage', zscale=zScale)
accum = rd.FlowAccumulation(rdem, method=flowMethod)
out["slope"] = (('time', 'lat', 'lon'), slope[np.newaxis, :, :])
out["flowAcc"] = (('time', 'lat', 'lon'), accum[np.newaxis, :, :])
return out
def remove_depressions(self):
self._depression_free_dem = cp.deepcopy(
rd.rdarray(
self.grid.at_node["topographic__elevation"].reshape(self.grid.shape),
no_data=-9999,
)
)
self._depression_free_dem.geotransform = [0, 1, 0, 0, 0, -1]
with self._suppress_output():
self._depression_handler(self._depression_free_dem)
self._sort[:] = np.argsort(
np.array(self._depression_free_dem.reshape(self.grid.number_of_nodes))
)
self.grid.at_node["depression_free_elevation"] = self._depression_free_dem
def np2rdarray(in_array, no_data, projection, geotransform):
"""Converts an numpy array to rdarray.
Args:
in_array (np.array): The input numpy array.
no_data (float): The no_data value of the array.
projection (str): The projection of the image.
geotransform (str): The geotransform of the image.
Returns:
object: The richDEM array.
"""
out_array = rd.rdarray(in_array, no_data=no_data)
out_array.projection = projection
out_array.geotransform = geotransform
return out_array
def np2rdarray(in_array, no_data, projection, geotransform):
"""Converts numpy array to rdarray.
Args:
in_array (np.array): The input numpy array containing the image.
no_data (float): The no_data value of the image.
projection (str): The projection coordinate system of the image.
geotransform (str): The geotransform of the image.
Returns:
rdarray: The richDEM array containing the image.
"""
out_array = rd.rdarray(in_array, no_data=no_data)
out_array.projection = projection
out_array.geotransform = geotransform
return out_array
def main():
"""
RichDEM depression breaching
"""
options, flags = gscript.parser()
_input = options['input']
_output = options['output']
_topology = options['topology']
dem = garray.array()
dem.read(_input, null=np.nan)
rd_inout = rd.rdarray(dem, no_data=np.nan)
rd.BreachDepressions(dem=rd_inout, in_place=True, topology=_topology)
dem[:] = rd_inout[:]
dem.write(_output, overwrite=gscript.overwrite())
def main():
"""
RichDEM flat resolution: give a gentle slope
"""
options, flags = gscript.parser()
_input = options['input']
_output = options['output']
_attribute = options['attribute']
_zscale = float(options['zscale'])
dem = garray.array()
dem.read(_input, null=np.nan)
rd_input = rd.rdarray(dem, no_data=np.nan)
del dem
rd_output = rd.TerrainAttribute(dem=rd_input, attrib=_attribute,
zscale=_zscale)
outarray = garray.array()
outarray[:] = rd_output[:]
outarray.write(_output, overwrite=gscript.overwrite())
def main():
"""
RichDEM flat resolution: give a gentle slope
"""
options, flags = gscript.parser()
_input = options['input']
_output = options['output']
# Check for overwrite
_rasters = np.array(gscript.parse_command('g.list', type='raster').keys())
if (_rasters == _output).any():
g.message(flags='e', message="output would overwrite " + _output)
dem = garray.array()
dem.read(_input, null=np.nan)
rd_input = rd.rdarray(dem, no_data=np.nan)
rd_output = rd.ResolveFlats(rd_input)
dem[:] = rd_output[:]
dem.write(_output, overwrite=gscript.overwrite())
def process_dem(self, global_dem=''):
''' Download DEM from AWS, calculate slope
'''
# Download DEM
if not os.path.exists(self.dem_file) and global_dem == '':
tPrint("Downloading DEM")
elevation.clip(bounds=self.inD.total_bounds, max_download_tiles=90000, output=self.dem_file, product='SRTM3')
if not os.path.exists(self.dem_file) and not global_dem == '':
tPrint("Downloading DEM")
rMisc.clipRaster(rasterio.open(global_dem), self.inD, self.dem_file)
# Calculate slope
if not os.path.exists(self.slope_file) and os.path.exists(self.dem_file):
tPrint("Calculating slope")
in_dem = rasterio.open(self.dem_file)
in_dem_data = in_dem.read()
beau = richdem.rdarray(in_dem_data[0,:,:], no_data=in_dem.meta['nodata'])
slope = richdem.TerrainAttribute(beau, attrib='slope_riserun')
meta = in_dem.meta.copy()
meta.update(dtype = slope.dtype)
with rasterio.open(self.slope_file, 'w', **meta) as outR:
outR.write_band(1, slope)
def generate_slope_aspect():
"""Generate slope and aspect from DEM."""
dem_ds = xr.open_dataset(
'/Users/kenzatazi/Downloads/GMTED2010_15n015_00625deg.nc')
dem_ds = dem_ds.assign_coords({
'nlat': dem_ds.latitude,
'nlon': dem_ds.longitude
})
dem_ds = dem_ds.sel(nlat=slice(29, 34), nlon=slice(75, 83))
elev_arr = dem_ds.elevation.values
elev_rd_arr = rd.rdarray(elev_arr, no_data=np.nan)
slope_rd_arr = rd.TerrainAttribute(elev_rd_arr, attrib='slope_riserun')
slope_arr = np.array(slope_rd_arr)
aspect_rd_arr = rd.TerrainAttribute(elev_rd_arr, attrib='aspect')
aspect_arr = np.array(aspect_rd_arr)
dem_ds['slope'] = (('nlat', 'nlon'), slope_arr)
dem_ds['aspect'] = (('nlat', 'nlon'), aspect_arr)
streamlined_dem_ds = dem_ds[['elevation', 'slope', 'aspect']]
streamlined_dem_ds.to_netcdf('_Data/SRTM_data.nc')
#del pre_φdtm
φdtm[φdtm == 100000] = np.NaN
plt.imshow(
φdtm,
cmap='Greys') # displays better if first φdtm[φdtm == 100000] = np.NaN
plt.title(f'c = {c}')
#%% [markdown]
# Next we calculate the slope surface ```φslope```; max slope parameter, ```Slmin```; and min slope parameter,```Slmax```. In this case, Slmin
# and Slmax are established from the 65% and 90% quantiles of the cell
# values of the slope surface. We will use the handy TerrainAttribute function from RichDEM to find. ```φslope``'.
#%%
rda = rd.rdarray(φdtm, no_data=np.NaN)
φslope = rd.TerrainAttribute(rda, attrib='slope_riserun')
Slmin, Slmax = np.nanpercentile(φslope, [65, 90])
#%% [markdown]
# Then we classify points as ground if they are within δh of the reference surface given
# by φdtm, and calculate the penetrability surfac, eφpnt.#%% [markdown]
# Then we classify points as ground if they are within δh of the reference surface given
# by φdtm, and calculate the penetrability surfac, eφpnt.
#%%
# Classify as ground (2) if δi <= δh
S['Classification'] = (S['Z'] - φdtm[S['gridX'], S['gridY']] <= δh) * 2
# create φpnt
#φpnt = np.full_like(φdtm, np.NaN)
def transform_slope(self):
self.dem_data = rd.rdarray(self.dem_data, no_data=-9999)
self.dem_data = rd.TerrainAttribute(self.dem_data,
attrib='slope_riserun')
def loadfigures(pathascii, pathvis16R, pathxy, filename):
'''
pathascii = 'D:/ANALYSIS/SIMPLECRATERS_MOON/SLDEM_2013_COLDSPOTS/ascii/'
pathvis8R = 'D:/ANALYSIS/SIMPLECRATERS_MOON/SLDEM_2013_COLDSPOTS/ascii_visible8R/'
pathvis32R = 'D:/ANALYSIS/SIMPLECRATERS_MOON/SLDEM_2013_COLDSPOTS/ascii_visible/'
pathxy = 'D:/ANALYSIS/SIMPLECRATERS_MOON/SLDEM_2013_COLDSPOTS/double_detrending/data/'
pathascii = '/uio/kant/geo-ceed-u1/nilscp/Desktop/astra/TMP_DOWNLOAD/ascii/'
pathvis8R = '/uio/kant/geo-ceed-u1/nilscp/Desktop/astra/TMP_DOWNLOAD/ascii_visible8R/'
pathvis32R = '/uio/kant/geo-ceed-u1/nilscp/Desktop/astra/TMP_DOWNLOAD/ascii_visible/'
pathxy = 'D:/ANALYSIS/SIMPLECRATERS_MOON/SLDEM_2013_COLDSPOTS/double_detrending/data/'
filename = 'cpcrater0000'
'''
name_ascii = pathascii + filename + '.asc'
name_crater_txt = pathxy + filename + 'XY.txt'
name_vis16R = pathvis16R + filename + '_visible.asc'
# should be good this way
data = readASCII(name_ascii)
datavis16R = readASCII(name_vis16R)
# load data and constrain size of the array
(xc, yc, data, ncenterx,
ncentery) = constrainASCII(pathascii, name_ascii, data)
(xcv1, ycv1, datav1, ncenterxv1,
ncenteryv1) = constrainASCII(pathvis16R, name_vis16R, datavis16R)
# load xy
dataxy = np.loadtxt(name_crater_txt, skiprows=1, delimiter=";")
datax = dataxy[:, 0]
datay = dataxy[:, 1]
# slope
datareload = rd.rdarray(data, no_data=-9999)
slope = rd.TerrainAttribute(datareload, attrib='slope_riserun')
# profile_curvatyre
pfc = rd.TerrainAttribute(datareload, attrib='profile_curvature')
# planform curvature
pfc2 = rd.TerrainAttribute(datareload, attrib='planform_curvature')
# curvature
pfc3 = rd.TerrainAttribute(datareload, attrib='curvature')
# I could calculate the slope and the curvature and then plot it
# plot figures (1) visible 32R, (1) visible 8R, (2) DTM 8R, (3) Slope 8R, (4) Curvature 8R
# when (4) is open, engage
fig2 = plt.figure(1)
plt.pcolormesh(xcv1, ycv1, datav1)
plt.colorbar()
fig3 = plt.figure(2)
plt.pcolormesh(xc, yc, data)
plt.colorbar()
fig4 = plt.figure(3)
plt.pcolormesh(xc, yc, slope)
plt.colorbar()
return fig4
def display_slope(self):
self.dem_data = rd.rdarray(self.dem_data, no_data=-9999)
rd.rdShow(self.dem_data, axes=False, cmap='magma', figsize=(8, 5.5))
bbox = [args.ulx, args.uly, args.lrx, args.lry]
jimdem = pj.Jim(Path(demfn),
bbox=bbox,
tileindex=args.tileindex,
tiletotal=args.tiletotal,
overlap=args.overlap)
else:
jimdem = pj.Jim(Path(demfn),
tileindex=args.tileindex,
tiletotal=args.tiletotal,
overlap=args.overlap)
if args.t_srs is not None:
jimdem.geometry.warp(args.t_srs)
if args.output_dem is not None:
jimdem.io.write(args.output_dem, co=['COMPRESS=LZW', 'TILED=YES'])
jimdem.pixops.convert('GDT_Float32')
jimdem[jimdem <= 0] = -9999
dem_richdem = rd.rdarray(jimdem.np(), no_data=-9999)
dem_richdem.geotransform = jimdem.properties.getGeoTransform()
dem_richdem.projection = jimdem.properties.getProjection()
slope = rd.TerrainAttribute(dem_richdem, attrib=args.attribute)
jimdem.np()[:] = slope
jimdem.properties.setNoDataVals(-9999)
jimdem.io.write(args.output, co=['COMPRESS=LZW', 'TILED=YES'])
def get_d8_without_depression(self, dem_img, pad=3):
dem_rd = rd.rdarray(dem_img, no_data=-9999)
dem_filled_rd = np.array(rd.FillDepressions(dem_rd, in_place=False))
d8_without_depression = self.get_d8(dem_filled_rd, pad=pad)
return d8_without_depression
def np2rdarray(in_array, no_data, projection, geotransform):
out_array = rd.rdarray(in_array, no_data=no_data)
out_array.projection = projection
out_array.geotransform = geotransform
return out_array
return np.sqrt(dx * dx + dy * dy)
def np_curvature(x, y, z):
d = y[1, 0] - y[0, 0]
dy, dx = np.gradient(z, d)
dz = np.sqrt(dx * dx + dy * dy)
dy, dx = np.gradient(dz, d)
return np.sqrt(dx * dx + dy * dy)
# Gaussian hill
n = 234
x, y, z = gaussian_hill_elevation(n)
d = y[1, 0] - y[0, 0]
sca = rd.FlowAccumulation(rd.rdarray(z, no_data=-9999),
method='Freeman',
exponent=1.1)
sca *= d
fg, ax = pl.subplots(1, 3)
im = ax[0].imshow(z, cmap=pl.cm.cividis_r)
cb = fg.colorbar(im, ax=ax[0], orientation='horizontal')
cb.set_label('Elevation')
im = ax[1].imshow(sca, cmap=pl.cm.viridis_r)
cb = fg.colorbar(im, ax=ax[1], orientation='horizontal')
cb.set_label('SCA (Freeman 1991 flow accumulation, MFD)')
v = gaussian_hill_sca(n) - sca
vmin = v.min()
