def eidw(self,
power=2.0,
anisotropy=5.0,
smoothie=0,
plane='sn',
plot=False):
"""
Performs Elliptical Inverse Distance Weighting (EIDW) interpolation to the instance's data
Kwargs: power <float> : power parameter, should be above 2.0
anisotropy <float> : anisotropy parameter: if >1.0, it brings points closer in the
longitudinal (s) direction, if <1.0 it brings points closer
in the transverse (n) direction
smoothie <int> : smoothing degree (Gaussian window)
plane <str> : chooses plane for interpolation; 'xy' for Cartesian, 'sn' for flow-oriented
plot <boolean> : decides to plot or not the resulting EIDW values
"""
gc.enable()
print "Calculating: Elliptical Inverse Distance Weighting [EIDW]"
#check
if power < 2.0:
print "Warning: Power parameter too small; set to 2.0."
power = 2.0
eidw = deepcopy(self.z) #avoid overwrite
#calculate EIDW (radius='None' executes global EIDW)
if plane == 'sn':
eidw[self.nodata] = self.eidw_interpol(self.s[self.isdata],
self.n[self.isdata],
eidw[self.isdata],
self.s[self.nodata],
self.n[self.nodata], power,
anisotropy, 'None')
elif plane == 'xy':
x = self.x.flatten()
y = self.y.flatten()
eidw[self.nodata] = self.eidw_interpol(
x[self.isdata], y[self.isdata], eidw[self.isdata],
x[self.nodata], y[self.nodata], power, anisotropy, 'None')
else:
print "Error: Plane for interpolation not correctly specified. No interpolation performed."
return
#grid (matrix) notation
eidw = common.to_grid(eidw, self.rows, self.cols)
#add pre-defined banks
eidw = self.__add_banks(eidw)
#smoothen
for i in range(self.rows):
eidw[i, :] = common.smooth(eidw[i, :], smoothie)
print "Finished [EIDW]."
self.z_interpol = eidw
del eidw
#plot
if plot:
self.plot(fignum='EIDW-GLOBAL')
def error_map(self, x, y, clim, plotname):
import matplotlib.pyplot as plt
err = self.GT.flatten() - self.P.flatten()
levels = np.linspace(clim[0], clim[1], 11)
n, m = self.GT.shape
plt.figure("Error Map - " + str(plotname))
err = common.to_grid(err, n, m)
Gx = common.to_grid(x, n, m)
Gy = common.to_grid(y, n, m)
#If more than half the points are unknown, the dataset to plot logically must be a testing dataset
if np.sum(np.isnan(err)) > err.shape[0] * err.shape[1] / 2.0:
print "-> Assuming plot of Testing Dataset."
cs = plt.scatter(Gx,
Gy,
s=10,
c=err,
cmap=plt.cm.bwr,
vmin=clim[0],
vmax=clim[1],
edgecolors='none')
else:
cs = plt.contourf(Gx,
Gy,
err,
levels,
cmap=plt.cm.bwr,
extend='both')
cs.cmap.set_over('deeppink')
cs.cmap.set_under('k')
cs.set_clim(np.floor(levels[0]), np.ceil(levels[-1]))
cbar = plt.colorbar(cs)
cbar.set_label('Bed Level Difference (m)',
labelpad=15,
weight='bold',
size='14')
plt.axis('equal')
plt.ion()
plt.show()
def rbf(self,
method='linear',
anisotropy=5.0,
smoothie=0,
plane='sn',
plot=False):
"""
Performs Radial Basis Interpolation using the defined method (linear or
thin plate spline)
Kwargs: method <str> : choice of RBF interpolation ['linear' or 'tps']
anisotropy <float> : anisotropy parameter: if >1.0, it brings points closer in the
longitudinal (s) direction, if <1.0 it brings points closer
in the transverse (n) direction
smoothie <int> : smoothing degree (Gaussian window)
plane <str> : chooses plane for interpolation; 'xy' for Cartesian, 'sn' for flow-oriented
plot <boolean> : decides to plot or not the resulting RBF values
"""
gc.enable()
print "Calculating: Radial Basis Function (RBF) with {0} method".format(
method)
rbf = deepcopy(self.z) #avoid overwrite
#choice of plane and rbf interpolation
if plane == 'sn':
rbf[self.nodata] = self.rbf_solver(
self.s[self.isdata], self.n[self.isdata], rbf[self.isdata],
self.s[self.nodata], self.n[self.nodata], anisotropy, method)
elif plane == 'xy':
x = self.x.flatten()
y = self.y.flatten()
rbf[self.nodata] = self.rbf_solver(x[self.isdata], y[self.isdata],
rbf[self.isdata],
x[self.nodata], y[self.nodata],
anisotropy, method)
else:
print "Error: Plane for interpolation not correctly specified. No interpolation performed."
return
rbf = common.to_grid(rbf, self.rows, self.cols) #griddify
#add pre-defined banks
rbf = self.__add_banks(rbf)
#smoothen
for i in range(self.rows):
rbf[i, :] = common.smooth(rbf[i, :], smoothie)
print "Finished [RBF]."
self.z_interpol = rbf
del rbf
#plot
if plot:
self.plot(fignum='RADIAL-BASIS[{0}]'.format(method.upper()))
def idw(self, power=2.0, smoothie=0, plane='sn', plot=False):
"""
Performs Inverse Distance Weighting (IDW) interpolation to the instance's data
Kwargs: power <float> : power parameter, should be above 2.0
smoothie <int> : smoothing degree (Gaussian window)
plane <str> : chooses plane for interpolation; 'xy' for Cartesian, 'sn' for flow-oriented
plot <boolean> : decides to plot or not the resulting IDW values
"""
gc.enable()
print "Calculating: Inverse Distance Weighting"
#check
if power < 2.0:
print "Warning: Power parameter too small; set to 2.0."
power = 2.0
idw = deepcopy(self.z) #avoid overwrite
#calculate IDW (when anisotropy ratio is 1, we have IDW; radius='None' executes global IDW)
if plane == 'sn':
idw[self.nodata] = self.eidw_interpol(
self.s[self.isdata], self.n[self.isdata], idw[self.isdata],
self.s[self.nodata], self.n[self.nodata], power, 1.0, 'None')
elif plane == 'xy':
x = self.x.flatten()
y = self.y.flatten()
idw[self.nodata] = self.eidw_interpol(
x[self.isdata], y[self.isdata], idw[self.isdata],
x[self.nodata], y[self.nodata], power, 1.0, 'None')
else:
print "Error: Plane for interpolation not correctly specified. No interpolation performed."
return
#grid (matrix) notation
idw = common.to_grid(idw, self.rows, self.cols)
#add pre-defined banks
idw = self.__add_banks(idw)
#smoothen
for i in range(self.rows):
idw[i, :] = common.smooth(idw[i, :], smoothie)
print "Finished [IDW]."
self.z_interpol = idw
del idw
#plot
if plot:
self.plot(fignum='IDW-GLOBAL')
def gridify_samples(self, branch):
"""
Use to set the sample data on the grid, if it is known that the samples
respond to the specified grid.
"""
if self.samples:
try:
data = zip(*self.samples)[2] #retrive only z values
b = str(branch)
s = self.gr.grid[b]['x'].shape
self.griddeddata[b] = common.to_grid(data, s[0], s[1])
print "Success: Sample data set to grid notation (matrix).\n"
except:
print "ERROR: Could not transform samples onto gridded data."
else:
print "No data samples loaded."
def cubic(self, smoothie=0, plane='sn', fill_nans=True, plot=False):
"""
Performs Cubic interpolation to the instance's data (piecewise cubic,
continuously differentiable and approximately curvature-minimizing
polynomial surface).
WARNING: High memory usage, usually in \'xy\' plane
Kwargs: smoothie <int> : smoothing degree (Gaussian window)
plane <str> : chooses plane for interpolation; 'xy' for Cartesian, 'sn' for flow-oriented
fill_nans <str> : decides to fill NaN values in a linear manner or keep NaN values
plot <boolean> : decides to plot or not the resulting CUBIC values
"""
gc.enable()
print "Calculating: Cubic Interpolation"
cubic = deepcopy(self.z) #avoid overwrite
#choice of plane
if plane == 'sn':
pnts = np.array(zip(self.s, self.n))
elif plane == 'xy':
pnts = np.array(zip(self.x.flatten(), self.y.flatten()))
else:
print "Error: Plane for interpolation not correctly specified. No interpolation performed."
return
#cubic interpolation
cubic[self.nodata] = griddata(pnts[self.isdata],
cubic[self.isdata],
pnts[self.nodata],
method='cubic',
fill_value=np.nan)
cubic = common.to_grid(cubic, self.rows, self.cols) #griddify
if fill_nans:
cubic = self.__fill_nans(cubic) #linear fill of nan values
#add pre-defined banks
cubic = self.__add_banks(cubic)
#smoothen
for i in range(self.rows):
cubic[i, :] = common.smooth(cubic[i, :], smoothie)
print "Finished [CUBIC]."
self.z_interpol = cubic
del cubic
#plot
if plot:
self.plot(fignum='CUBIC')
def linear(self, smoothie=0, plane='sn', fill_nans=True, plot=False):
"""
Performs Linear Barycentric interpolation to the instance's data
Kwargs: smoothie <int> : smoothing degree (Gaussian window)
plane <str> : chooses plane for interpolation; 'xy' for Cartesian, 'sn' for flow-oriented
fill_nans <str> : decides to fill NaN values in a linear manner or keep NaN values
plot <boolean> : decides to plot or not the resulting LINEAR values
"""
gc.enable()
print "Calculating: Linear Barycentric Interpolation"
linear = deepcopy(self.z) #avoid overwrite
#choice of plane
if plane == 'sn':
pnts = np.array(zip(self.s, self.n))
elif plane == 'xy':
pnts = np.array(zip(self.x.flatten(), self.y.flatten()))
else:
print "Error: Plane for interpolation not correctly specified. No interpolation performed."
return
#linear interpolation
linear[self.nodata] = griddata(pnts[self.isdata],
linear[self.isdata],
pnts[self.nodata],
method='linear',
fill_value=np.nan)
linear = common.to_grid(linear, self.rows, self.cols) #griddify
if fill_nans:
linear = self.__fill_nans(linear) #linear fill of nan values
#add pre-defined banks
linear = self.__add_banks(linear)
#smoothen
for i in range(self.rows):
linear[i, :] = common.smooth(linear[i, :], smoothie)
print "Finished [LINEAR]."
self.z_interpol = linear
del linear
#plot
if plot:
self.plot(fignum='LINEAR-BARYCENTRIC')
def near(self, smoothie=0, plane='sn', plot=False):
"""
Fills values of unsampled data points with the values of the closest
sampled data point of the instance's data.
Kwargs: smoothie <int> : smoothing degree (Gaussian window)
plane <str> : chooses plane for interpolation; 'xy' for Cartesian, 'sn' for flow-oriented
plot <boolean> : decides to plot or not the result NEAREST values
"""
gc.enable()
print "Calculating: Nearest-Neighbour \"Interpolation\""
near = deepcopy(self.z) #avoid overwrite
#choice of plane
if plane == 'sn':
pnts = np.array(zip(self.s, self.n))
elif plane == 'xy':
pnts = np.array(zip(self.x.flatten(), self.y.flatten()))
else:
print "Error: Plane for interpolation not correctly specified. No interpolation performed."
return
#find nearest values
near[self.nodata] = griddata(pnts[self.isdata],
near[self.isdata],
pnts[self.nodata],
method='nearest')
near = common.to_grid(near, self.rows, self.cols) #griddify
#add pre-defined banks
near = self.__add_banks(near)
#smoothen
for i in range(self.rows):
near[i, :] = common.smooth(near[i, :], smoothie)
print "Finished [NEAR]."
self.z_interpol = near
del near
#plot
if plot:
self.plot(fignum='NEAREST')
def fuse(self, ftype='free-form', dthres=0.0, anisotropy=1.0, smoothie=0):
gc.enable()
print "Calculating: Fusion method (" + ftype + ")"
if ftype not in ['free-form', 'cross-sections']:
print "Warning: Please specify testing dataset type as: \'free-form\' for trackline or free-form data, or \'cross-sections\' for cross-sectional data."
print "Assuming free-form data."
ftype = 'free-form'
s, n = self.s.flatten(), self.n.flatten()
isdata = self.isdata.flatten()
nodata = self.nodata.flatten()
#if samples in free-form (ie tracklines)
if ftype == 'free-form':
#check
dthres = abs(float(dthres))
#distance matrix:
s0, n0 = s[isdata], n[isdata] #sampled
s1, n1 = s[nodata], n[nodata] #unsampled
dm = common.distance_matrix(s0, n0, s1, n1, anisotropy) #matrix
#if no threshold is defined, calculate the furthest possible point
# = this is where bathymetry has the most impact
dmins = dm.min(axis=0)
if dthres <= 0:
dthres = dmins.max()
print "Threshold Distance:", dthres
#weights
w_bth = dmins / dthres #weights for model dataset
w_bth[w_bth > 1.0] = 1.0 #adjust to range of [0,1]
w_int = 1.0 - w_bth #weights for interpolation dataset
del dm, dmins, s0, n0, s1, n1 #clear
#if samples in cross-sections
else:
#find cross-sections and the points where there is data (clustering of data):
cs_where = []
for j in range(self.cols):
if np.nansum(self.isdata[:, j]) != 0:
cs_where.append(j)
print "Cross-section at: ", j
#Find minimum distances from 2 different cross-sections
ratios = np.ones((self.rows * self.cols, 2)) * np.inf
for j in cs_where:
dist_from_cs = common.distance_matrix(
self.s[:, j][self.isdata[:, j]],
self.n[:, j][self.isdata[:, j]], s, n, anisotropy)
cs_mindist = np.min(dist_from_cs, axis=0)
change = cs_mindist < ratios[:, 1]
ratios[:, 1][change] = cs_mindist[change]
ratios = np.sort(ratios,
axis=1) #holds min distances from 2 cs
del dist_from_cs
#weights
w_int = []
w_bth = []
for i in range(len(ratios)):
#real distances
d1 = ratios[i, 0] #distance from one cs
d2 = ratios[i, 1] #distance from another cs
if d1 == 0.0:
d1 = 1e-20 #avoid division by 0
if d2 == 0.0:
d2 = 1e-20 #avoid division by 0
hd = (d1 + d2) / 2.0 #half-distance
if d1 < hd:
d2 -= hd
r1 = 1. / (1. + (d2 / d1)**2)
else:
d1 -= hd
r1 = 1. / (1. + (d1 / d2)**2)
if r1 > 1.0:
r1 = 1.0
r2 = 1.0 - r1
w_bth.append(r1)
w_int.append(r2)
w_int = np.array(w_int)[nodata]
w_bth = np.array(w_bth)[nodata]
#In both cases, fused dataset is the sum of the weighted datasets
I = self.inter.flatten()
M = self.model.flatten()
fusion = self.z.flatten() #initialize, so to keep measured data values
fusion[nodata] = I[nodata] * w_int + M[nodata] * w_bth
fusion = common.to_grid(fusion, self.rows,
self.cols) #grid (matrix) notation
del w_bth, w_int #clear
#smoothen the longitudinals, if required
if smoothie > 0:
for i in range(self.rows):
fusion[i, :] = common.smooth(fusion[i, :], smoothie)
if smoothie > int(fusion.shape[0] / 4):
smoothie = int(fusion.shape[0] / 4)
for i in range(self.cols):
fusion[:, i] = common.smooth(fusion[:, i], smoothie)
print "Finished [FUSION]."
self.fused = fusion
del fusion
def plot(self, fignum, clim='auto', classes=100, system='xy', hold=False):
#append nan values
Gz = deepcopy(self.z_interpol)
Gz[Gz == -999.0] = np.nan
if system == 'xy':
Gx = self.x
Gy = self.y
elif system == 'sn':
Gx = common.to_grid(self.s, self.rows, self.cols)
Gy = common.to_grid(self.n, self.rows, self.cols)
elif system == 'mn':
Gx, Gy = np.meshgrid(self.cols, self.rows)
else:
print "Wrong coordinate system chosen. Please specify input system as \'xy\', \'sn\' or \'mn\'."
#plt.close(fignum)
plt.figure(fignum)
#plt.clf()
# set colorlimits
minn = np.nanmin(Gz)
maxx = np.nanmax(Gz)
if clim == 'auto':
mean = np.mean(np.ma.masked_array(Gz, np.isnan(Gz)))
classes = int(classes)
if classes % 2 == 1:
classes += 1
dl = np.linspace(minn, mean, classes / 2)
dr = np.linspace(mean, maxx, classes / 2)
d = 10.0
flag = True
c = 0
while flag:
levels = np.round(np.append(dl, dr[1:]) / d) * d
if c % 2 == 0:
d = d / 2.0
else:
d = d - 4 * d / 5.0
c += 1
flag = len(levels) != len(set(levels))
elif clim == 'minmax':
levels = np.linspace(minn, maxx, classes)
else:
levels = np.linspace(clim[0], clim[1], classes)
#If more than half the points are unknown, the dataset to plot logically must be a testing dataset
if np.sum(np.isnan(Gz)) > Gz.shape[0] * Gz.shape[1] / 2.0:
print "-> Assuming plot of Testing Dataset."
cs = plt.scatter(Gx,
Gy,
s=10,
c=Gz,
cmap=plt.cm.jet,
vmin=clim[0],
vmax=clim[1],
edgecolors='none')
else:
cs = plt.contourf(Gx,
Gy,
Gz,
levels,
cmap=plt.cm.jet,
extend="both")
cs.cmap.set_under('k')
cs.set_clim(np.floor(levels[0]), np.ceil(levels[-1]))
cbar = plt.colorbar(cs)
cbar.set_label('Bed Level (m)', labelpad=15, weight='bold', size='14')
plt.axis('equal')
plt.hold(hold) #used when the data needs to be "on top" of other data
plt.ion()
plt.show()
def natneigh(self, anisotropy=1.0, smoothie=0, plane='sn', plot=False):
"""
Performs Natural Neighbor (NN) interpolation with defined anisotropy
to the instance's data. The algorithm assumes a brute-force way of
calculating the Voronoi cells every time an unsampled point's value is
computed.
Kwargs: anisotropy <float> : anisotropy parameter: if >1.0, it brings points closer in the
longitudinal (s) direction, if <1.0 it brings points closer
in the transverse (n) direction
smoothie <int> : smoothing degree (Gaussian window)
plane <str> : chooses plane for interpolation; 'xy' for Cartesian, 'sn' for flow-oriented
plot <boolean> : decides to plot or not the resulting NN values
"""
from scipy.spatial import voronoi_plot_2d
import matplotlib.pyplot as plt
gc.enable()
print "Calculating: Natural Neighbour [NN]"
nn = deepcopy(self.z) #avoid overwrite
#choose plane
if plane == 'sn':
x = self.s * 1. / anisotropy #already flattened
y = self.n #already flattened
elif plane == 'xy':
x = self.x.flatten() * 1. / anisotropy
y = self.y.flatten()
else:
print "Error: Plane for interpolation not correctly specified. No interpolation performed."
return
#DEFINE BOUNDARY FOR INTERPOLATION
#griddify points to choose boundary easier:
Gx = common.to_grid(x, self.rows, self.cols)
Gy = common.to_grid(y, self.rows, self.cols)
bx = np.hstack((Gx[0, :], Gx[1:-1, -1], Gx[-1, :][::-1], Gx[1:-1,
0][::-1]))
by = np.hstack((Gy[0, :], Gy[1:-1, -1], Gy[-1, :][::-1], Gy[1:-1,
0][::-1]))
#define boundary:
boundary = np.array(zip(bx, by))
#VORONOI OF SAMPLED DATA POINTS:
vorpoints = np.array(zip(x[self.isdata], y[self.isdata]))
#shift points around central point of the dataset (for precision purposes)
center = vorpoints.mean(axis=0)
vorpoints -= center
#construct Voronoi diagram from (centered) sampled data points
vor = Voronoi(vorpoints)
vor.close()
"""
#TODO: delete:
voronoi_plot_2d(vor)
plt.ion()
plt.axis('equal')
plt.show()
"""
#calculate areas of sampled dataset Voronoi cells
original_areas, vor = self.__find_areas(vor, boundary - center)
"""
#TODO: delete:
# colorize
for region in vor.regions[1:]:
polygon = vor.vertices[region]
plt.fill(*zip(*polygon), alpha=0.4)
plt.plot(vorpoints[:,0], vorpoints[:,1], 'ko')
plt.xlim(vor.min_bound[0] - 0.1, vor.max_bound[0] + 0.1)
plt.ylim(vor.min_bound[1] - 0.1, vor.max_bound[1] + 0.1)
plt.axis('equal')
plt.ion()
plt.show()
"""
#ITERATE THROUGH UNSAMPLED DATA POINTS:
# ~ For each unsampled point, construct the new Voronoi diagram consisting of the
# ~ sampled data and the current unsampled point. Then calculate the new areas and
# ~ find the normalized weights based on how much of each area is "stolen away" from
# ~ each sampled dataset Voronoi cell (https://en.wikipedia.org/wiki/Natural_neighbor).
# ~ The areas are always bounded by the river polygon (based on the grid defined).
unknown = []
for i in range(len(x[self.nodata])):
if i % 1000 == 0:
print i, "out of ", len(x[self.nodata])
#add new point shifted around central point
varys = np.vstack((vorpoints, [
x[self.nodata][i] - center[0], y[self.nodata][i] - center[1]
]))
#calculate new Voronoi
pntvor = Voronoi(varys)
pntvor.close()
#calculate areas
new_areas, pntvor = self.__find_areas(pntvor, boundary - center)
new_areas = new_areas[:-1] #exclude new point's area
w = new_areas / original_areas
w[w >
1.0] = 1.0 #make sure that no area is larger than initial areas
areaweight = 1.0 - w
normalize = np.nansum(areaweight)
if normalize == 0.0: #to avoid division by 0
normalize = 1e-12
areaweight /= normalize
unknown.append(np.nansum(areaweight * self.z[self.isdata]))
nn[self.nodata] = np.array(unknown)
#grid (matrix) notation
nn = common.to_grid(nn, self.rows, self.cols)
#add pre-defined banks
nn = self.__add_banks(nn)
#smoothen
for i in range(self.rows):
nn[i, :] = common.smooth(nn[i, :], smoothie)
print "Finished [NN]."
self.z_interpol = nn
del nn
#plot
if plot:
self.plot(fignum='NN')
def uk(self,
sill,
vmodel='spherical',
anisotropy=1.0,
smoothie=0,
plane='sn',
plot=False):
"""
Performs Universal Kriging (UK) interpolation with defined anisotropy
to the instance's data using the specified model for its semivariogram.
The sill for the semivariogram needs to be defined. The range used is
equal to the sill times the anisotropy defined.
Args:
sill <float> : Sill value for semivariogram in UK
Kwargs: vmodel <str> : Semivariogram model (default: 'spherical')
anisotropy <float> : anisotropy parameter: if >1.0, it brings points closer in the
longitudinal (s) direction, if <1.0 it brings points closer
in the transverse (n) direction
smoothie <int> : smoothing degree (Gaussian window)
plane <str> : chooses plane for interpolation; 'xy' for Cartesian, 'sn' for flow-oriented
plot <boolean> : decides to plot or not the resulting UK values
"""
gc.enable()
print "Calculating: Universal Kriging [UK]"
uk = deepcopy(self.z) #avoid overwrite
#calculate UK with anisotropy defined
if plane == 'sn':
UK = UniversalKriging(
self.s[self.isdata],
self.n[self.isdata],
uk[self.isdata],
anisotropy_scaling=anisotropy,
anisotropy_angle=0.0,
nlags=self.cols,
variogram_model=vmodel,
variogram_parameters=[sill, sill * anisotropy, 0.0],
drift_terms=['functional'],
functional_drift=[self.uk_funct],
verbose=True,
enable_plotting=True)
uk[self.nodata], sigmas = UK.execute('points', self.s[self.nodata],
self.n[self.nodata])
elif plane == 'xy':
x = self.x.flatten()
y = self.y.flatten()
UK = UniversalKriging(
x[self.isdata],
y[self.isdata],
uk[self.isdata],
anisotropy_scaling=anisotropy,
anisotropy_angle=0.0,
nlags=self.cols,
variogram_model=vmodel,
variogram_parameters=[sill, sill * anisotropy, 0.0],
drift_terms=['regional_linear'],
functional_drift=[self.uk_funct],
verbose=False,
enable_plotting=False)
uk[self.nodata], sigmas = UK.execute('points', x[self.nodata],
y[self.nodata])
else:
print "Error: Plane for interpolation not correctly specified. No interpolation performed."
return
#grid (matrix) notation
uk = common.to_grid(uk, self.rows, self.cols)
#add pre-defined banks
uk = self.__add_banks(uk)
#smoothen
for i in range(self.rows):
uk[i, :] = common.smooth(uk[i, :], smoothie)
print "Finished [UK]."
self.z_interpol = uk
del uk
#plot
if plot:
self.plot(fignum='UK')
def hw_slope(self, widths, lengths, plotname=False):
import matplotlib.pyplot as plt
#take half-width:
k = int(self.GT.shape[0] / 4.0)
CDhw = self.P[k:-k, :] #computed dataset half-width
GThw = self.GT[k:-k, :] #ground-truth dataset half-width
numlines = CDhw.shape[0]
#TODO: find a better way to fill this
#filling of ground-truth half-widths // by nearest grid neighbour
z = GThw.flatten()
m = range(CDhw.shape[1]) * CDhw.shape[0]
n = sorted(m)
gpnts = np.vstack((m, n)).T
znear = griddata(gpnts[~np.isnan(z)],
z[~np.isnan(z)],
gpnts[np.isnan(z)],
method='nearest')
z[np.isnan(z)] = znear
GThw = common.to_grid(z, CDhw.shape[0], CDhw.shape[1])
#real half-width
w = (float(numlines - 1) / (self.P.shape[0] - 1)) * np.array(widths)
slopeDiff = []
a = []
gt_a = []
testNHWS = []
gtNHWS = []
for j in range(CDhw.shape[1]): #for each cross-section
excl = ~np.isnan(GThw[:, j]) * ~np.isnan(
CDhw[:, j]) #mutual exclusion
cs_x = np.linspace(0.0, 1.0, numlines) * w[j]
#test
cs_y = CDhw[:, j]
testslope = np.polyfit(cs_x[excl], cs_y[excl], 1) #f(x) = ax+b
#ground-truth
cs_y = GThw[:, j]
gtslope = np.polyfit(cs_x[excl], cs_y[excl], 1) #f(x) = ax+b
if np.isnan(gtslope[0]):
#print gtslope
#print cs_x[excl],cs_y[excl]
print "CHECK"
#slope difference
a.append(testslope[0])
gt_a.append(gtslope[0])
slopeDiff.append(abs(a[-1] - gt_a[-1]))
#Normalized Half-Width Slope
H = testslope[0] * cs_x[numlines / 2] + testslope[1]
Hgt = gtslope[0] * cs_x[numlines / 2] + gtslope[1]
testNHWS.append(testslope[0] * 1. / H)
gtNHWS.append(gtslope[0] * 1. / Hgt)
#//end for-loop
#slope's mae (not necessary):
print "SLOPE metrics:"
slopeDiff = np.array(slopeDiff)
sub = np.sum(np.isnan(slopeDiff))
Csum = np.nansum(slopeDiff)
Dim = len(slopeDiff) - sub
slope_mae = Csum / Dim
print "mae:", slope_mae
slopeDiff = np.array(slopeDiff)**2
sub = np.sum(np.isnan(slopeDiff))
Csum = np.nansum(slopeDiff)
Dim = len(slopeDiff) - sub
slope_rmse = np.sqrt(Csum / Dim)
print "rmse:", slope_rmse
nsd = np.sum(
np.abs(np.array(gtNHWS) - np.array(testNHWS))) / len(gtNHWS)
print "nsdiff:", nsd
#PLOTS:
if plotname:
#alpha "normalized"
plt.close(plotname + ' NHWS')
plt.figure(plotname + ' NHWS')
plt.xlabel('River span (m)', weight='bold', size='14')
plt.ylabel('Normalized Half-Width Slope (1/m)',
weight='bold',
size='14')
plt.hold(True)
plt.plot(lengths, testNHWS, 'r.-')
plt.plot(lengths, gtNHWS, 'b.-')
plt.hold(False)
#return calculations; in order of output:
#[slope of dataset to be tested, slope of ground-truth data, MAE of slope for dataset to be tested,
# RMSE of slope for dataset to be tested, absolute slope difference]
return [testNHWS, gtNHWS, slope_mae, slope_rmse, nsd]
