def plot_SQ(self):
ks = self.ks
param = self.param
length = self.length
alpha = self.alpha
m = 5.0 / 3
ie0 = 5.0
dot_s = np.arange(0, 1.0, 0.01)
for k in range(0, len(ks)):
ie1 = ie0 / ks[k]
min_q = ie1 * length
max_q = ie0 * length
min_s = m / (m + 1) * (ie1 * length**(m + 1) / alpha) * (1 / m)
max_s = m / (m + 1) * (ie0 * length**(m + 1) / alpha) * (1 / m)
_str = 'k=' + str(ks[k])
p = param[_str]
sq = bf.gen(dot_s, p)
sq[:, 1] = sq[:, 1] * (max_q - min_q) + min_q
sq[:, 0] = sq[:, 0] * (max_s - min_s) + min_s
plt.plot(sq[:, 1], sq[:, 0])
plt.plot(self.curve[k][:, 0], self.curve[k][:, 1], '--b')
plt.savefig('Falling_Fitting.png')
plt.close()
def SQ_curve(self, k):
P = self.get_P(k)
t = np.arange(0., 1.00001, 1. / 100)
curve = bf.gen(t, len(P) - 1, P)
return curve
def plot_SQ(self):
# max_k = self.max_k
param = self.param
length = self.length
alpha = self.alpha
ks = self.ks
m = 5.0 / 3
ie0 = 5.0
dot_s = np.arange(0, 1.0, 0.01)
for k in range(0, len(ks)):
ie1 = ie0 * ks[k]
max_q = ie1 * length
max_s = m / (m + 1) * (ie1 * length**(m + 1) / alpha) * (1 / m)
min_s = m / (m + 1) * (ie0 * length**(m + 1) / alpha) * (1 / m)
p = param[k]
dot_s = np.arange(0, 1.001, 0.01)
dot_q = bf.gen(dot_s, 6, p)
dot_s = (dot_s * (max_s - min_s) / max_s + min_s / max_s) * max_s
q = (dot_q * (1.0 - 1.0 / k) + 1.0 / k) * max_q
plt.plot(q, dot_s)
plt.savefig('Rising_Fitting.png')
plt.close()
def interpCurve(self, p):
Pts, midPoint = self.nearestNeighbor(p)
Pts = np.array(Pts)
midPoint = np.array(midPoint)
"""
xrbf = Rbf(Pts[:, 0], Pts[:, 1], Pts[:, 2], Pts[:, 3], midPoint[:, 0])
yrbf = Rbf(Pts[:, 0], Pts[:, 1], Pts[:, 2], Pts[:, 3], midPoint[:, 1])
P1X = xrbf(p[0], p[1], p[2], p[3])*p[0]
P1Y = yrbf(p[0], p[1], p[2], p[3])*p[1]"""
P1X, P1Y = self.inverseDist(p, Pts, midPoint)
P1X = P1X*p[0]
P1Y = P1Y*p[1]
print(midPoint)
print(P1X, P1Y)
curvePoints = [[p[0], p[1]], [P1X, P1Y], [p[2], p[3]]]
T = np.arange(0., 1.0001, 1./100)
curve = bezeir_fit.gen(T, self.degC, np.array(curvePoints))
return curve, T
def plot_norm_SQ(self):
param = self.param
for j in range(0, len(param)):
t = np.arange(0, 1.00001, 1. / 100)
p = param[j]
curve = bf.gen(t, len(p) - 1, p)
_curve = np.array(self.curve[j])
plt.plot(curve[:, 0], curve[:, 1], '-r')
plt.plot(_curve[:, 0], _curve[:, 1], '--b')
plt.savefig('Falling_Fitting.png', dpi=150)
plt.close()
def interpCurve(self, qBD, sBD):
PPlist = self.PPlist
curvePoints = [[1, 1]]
for i in range(0, len(PPlist)):
fX = PPlist[i][0]
fY = PPlist[i][1]
p = [fX(qBD, sBD), fY(qBD, sBD)]
curvePoints.append(p)
# p[-1] = [qBD, sBD]
T = np.arange(0., 1.0001, 1./100)
curve = bezeir_fit.gen(T, self.degC, np.array(curvePoints))
return curve, T
def run():
REC = pickle.load(open('Steady_fit/ks1_Rise_CurveRec.flow'))
param = pickle.load(open('Steady_fit/fit_1_RiseLimb.pick'))
key_str = 'k=10.0'
sto = REC['S'][key_str]
out = REC['Q'][key_str]
sto = sto[0:len(out), :]
out[:, 1] = (out[:, 1] - min(out[:, 1])) / (1.0 - min(out[:, 1]))
sto[:, 1] = (sto[:, 1] - min(sto[:, 1])) / (1.0 - min(sto[:, 1]))
lst = np.arange(1, len(out), len(out) / 10)
lst = lst.tolist()
lst.pop(-1)
plt.figure
plt.plot(out[:, 1],
sto[0:len(out), 1],
color='dodgerblue',
lw=3.0,
label='Analytic')
p = param['param']
p = p['k=10.0']
new_t = np.arange(0, 1.001, 0.01)
new_t = new_t.tolist()
new_t.append(1.0)
new_t = np.array(new_t)
y2 = bf.gen(new_t, 6, p)
plt.plot(y2[:, 1], y2[:, 0], '--r', label='Bezeir-fit')
counter = 0
for point in p:
plt.plot(point[0], point[1], marker='s', color='k')
plt.text(point[0] + 0.02, point[1] + 0.01, 'P' + str(counter))
counter += 1
plt.legend(loc=4)
plt.xlabel('$Q_{{}*{}}$')
plt.ylabel('$S_{{}*{}}$')
plt.title('k=10 Analytical S-Q fit (n=6)')
plt.savefig('s-q fit test.png')
plt.close()
def bzCurve(self):
# Start from zero
self.steady_q.tolist().insert(0, 0.)
self.steady_s.tolist().insert(0, 0.)
t = np.arange(0., 1.0001, 1. / 10)
Q = np.interp(t, self.ie / max(self.ie), self.steady_q)
S = np.interp(t, self.ie / max(self.ie), self.steady_s)
std_SQ = np.array([zip(Q, S)])[0]
p, t = bf.fit(std_SQ, 2, 1.0E-8)
print(p)
p[0] = [0, 0]
p[-1] = std_SQ[-1]
t = np.arange(0., 1.0001, 1. / 100)
f_std_SQ = bf.gen(t, 2, p)
return f_std_SQ, t
def eff_coeifficent(self):
self.eff = list()
param = self.param
for j in range(0, len(param)):
t = np.arange(0, 1.00001, 1. / 100)
p = param[j]
curve = bf.gen(t, len(p) - 1, p)
_curve = np.array(self.curve[j])
for j in range(0, len(_curve[:, 1])):
if _curve[j, 1] < 0:
_curve[j, 1] = 0.
fQ = interp1d(curve[:, 1], curve[:, 0])
Q = fQ(_curve[:, 1])
eff = 1 - np.sum((Q - _curve[:, 0])**2) / np.sum(
(Q - np.mean(_curve[:, 0]))**2)
self.eff.append(eff)
plt.plot(1. / np.array(self.ks), self.eff, 'b+')
def plot_norm_SQ(self):
# max_k = self.max_k
param = self.param
ks = self.header
curve = self.curve
dot_s = np.arange(0, 0.981, 0.01)
dot_s = dot_s.tolist()
dot_s.append(1.0)
dot_s - np.array(dot_s)
for k in range(0, len(ks)):
p = param[k]
out_s = curve[k][:, 1]
out_q = curve[k][:, 0]
dot_q = bf.gen(dot_s, self.bez_deg, p)
plt.plot(dot_q[:, 1], dot_q[:, 0], '-b')
plt.plot(out_q[:, 1], out_s[0:len(out_q[:, 1]), 1], '--r')
plt.savefig('Falling_Fitting.png', dpi=150)
plt.close()
def testFitSDR():
a = pickle.load(open('SDRrec.pick', 'r'))
header = a['header']
PT = a['PT']
Plist = list()
p2List = list()
p3List = list()
for j in range(0, len(PT)):
Plist.append(PT[j][0])
for k in range(0, len(Plist)):
p2List.append(Plist[k][1])
p3List.append(Plist[k][2])
p2List = np.array(p2List)
p3List = np.array(p3List)
P2, T2 = bezeir_fit.fit(p2List, 4, 1.0E-8)
P3, T3 = bezeir_fit.fit(p3List, 4, 1.0E-8)
T = np.arange(0., 1.001, 1./50)
P2_fit = bezeir_fit.gen(T, 4, P2)
P3_fit = bezeir_fit.gen(T, 4, P3)
plt.figure
plt.plot(P2_fit[:, 0], P2_fit[:, 1], label='P2', lw=3.0)
plt.plot(p2List[:, 0], p2List[:, 1], ls='--', lw=3.0)
plt.plot(P3_fit[:, 0], P3_fit[:, 1], label='P3')
plt.plot(p3List[:, 0], p3List[:, 1], ls='--')
plt.legend()
plt.savefig('p2p3fit.png')
plt.close()
t2 = np.interp([1.277], np.fliplr([header])[0], np.fliplr([T2])[0])
t3 = np.interp([1.277], np.fliplr([header])[0], np.fliplr([T3])[0])
_p2 = bezeir_fit.gen(t2, 4, P2)[0]
_p3 = bezeir_fit.gen(t3, 4, P3)[0]
_dat1 = bezeir_fit.gen(T, 2, [[1, 1], _p2, _p3])
_dat2 = bezeir_fit.gen(T, 2, PT[9][0])
plt.figure
plt.plot(_dat1[:, 0], _dat1[:, 1], label='gen')
plt.plot(_dat2[:, 0], _dat2[:, 1], label='rec')
plt.legend()
plt.show()
def run():
_file = "/home/room801/anaoverland/Steady_fit/ks1_Rise_CurveRec.flow"
RiseRec = pickle.load(open(_file, 'r'))
_file = "/home/room801/anaoverland/Steady_fit/ks1_Fall_CurveRec.flow"
FallRec = pickle.load(open(_file, 'r'))
RiseParam = pickle.load(
open("/home/room801/anaoverland/Steady_fit/fit_1_RiseLimb.pick", 'r'))
FallParam = pickle.load(
open("/home/room801/anaoverland/Steady_fit/fit_1_FallLimb.pick", 'r'))
plt.figure
ks = [
1.01, 1.05, 1.1, 1.2, 1.6, 2.0, 2.5, 3.0, 3.5, 4.0, 4.5, 5.0, 6.0, 7.0,
8.0, 9.0, 10.0, 15.0, 20.0, 30.0, 40.0, 50.0, 60.0, 70.0, 80.0, 90.0,
100.0
]
# ks = [2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 15.0, 20.0]
k_and_NS = list()
for k in ks:
keyStr = 'k=' + str(float(k))
out_s = RiseRec['S'][keyStr]
out_q = RiseRec['Q'][keyStr]
# Normalized curve to [0, 1]
out_s[:, 1] = (out_s[:, 1] - min(out_s[:, 1])) / (max(out_s[:, 1]) -
min(out_s[:, 1]))
out_q[:, 1] = (out_q[:, 1] - min(out_q[:, 1])) / (max(out_q[:, 1]) -
min(out_q[:, 1]))
# Interpolation on flowrate record
standard_s = np.arange(0, 1.001, 0.01)
standard_q = np.interp(standard_s, out_s[0:len(out_q), 1], out_q[:, 1])
rec_sq = np.zeros([len(standard_s), 2])
rec_sq[:, 0] = standard_s
rec_sq[:, 1] = standard_q
t = bf.aff_angle(rec_sq)
P = RiseParam.ref_points(k)
sq = bf.gen(t, 6, P)
R2 = 0.0
for j in range(0, len(sq)):
R2 = R2 + ((sq[j, 0] - rec_sq[j, 0])**2 +
(sq[j, 1] - rec_sq[j, 1])**2)
R2 = sqrt(R2)
plt.plot(out_q[:, 1],
out_s[0:len(out_q), 1],
color='aqua',
linewidth='1.2')
plt.plot(sq[:, 1], sq[:, 0], '-b')
print k, R2
k_and_NS.append([k, R2])
for k in ks:
keyStr = 'k=' + str(float(k))
out_s = FallRec['S'][keyStr]
out_q = FallRec['Q'][keyStr]
# Normalization to [0, 1]
out_s[:, 1] = (out_s[:, 1] - min(out_s[:, 1])) / (max(out_s[:, 1]) -
min(out_s[:, 1]))
out_q[:, 1] = (out_q[:, 1] - min(out_q[:, 1])) / (max(out_q[:, 1]) -
min(out_q[:, 1]))
standard_s = np.arange(0, 1.001, 0.01)
standard_q = np.interp(standard_s, mat_flip(out_s[0:len(out_q), 1]),
mat_flip(out_q[:, 1]))
rec_sq = np.zeros([len(standard_s), 2])
rec_sq[:, 0] = mat_flip(standard_s)
rec_sq[:, 1] = mat_flip(standard_q)
t = bf.aff_angle(rec_sq)
P = FallParam.ref_points(1. / k)
P[0] = [1.0, 1.0]
P[-1] = [0.0, 0.0]
sq = bf.gen(t, 6, P)
R2 = 0.0
for j in range(0, len(sq)):
R2 = R2 + ((sq[j, 0] - rec_sq[j, 0])**2 +
(sq[j, 1] - rec_sq[j, 1])**2)
R2 = sqrt(R2)
plt.plot(out_q[:, 1],
out_s[0:len(out_q), 1],
color='darksalmon',
lw=1.2)
plt.plot(sq[:, 1], sq[:, 0], '-r')
k_and_NS.append([1. / k, R2])
writeTxt('k_and_NS.txt', k_and_NS)
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.0])
plt.xlabel('$q^{{}*{}}$')
plt.ylabel('$s^{{}*{}}$')
plt.show()
def SWRFit(i0, i1, i2, L, x_space, dt, n, slope, deg):
c1 = list()
qBDRatio = list()
sBDRatio = list()
ptList = list()
plt.figure
plt.figure(figsize=(10, 8))
ax = plt.subplot(111)
dat_And_Indicator = list()
# Index 0: qB/qD
# Index 1: sB/sD
# Index 2: C1
# Index 3: [p, t] Secondary Curve Fit
for i in range(0, len(i1)):
ie1 = i1[i]
N = 5./3 # Manning Eq
alpha = 1./n*sqrt(slope) # Manning n
steady_time = ((ie1/3600./1000.)**(1-N)*L/alpha)**(1/N)
ToC = [steady_time*0., steady_time*0.001, steady_time*0.01,
steady_time*0.05, steady_time*0.1, steady_time*0.3,
steady_time*0.5, steady_time*0.55, steady_time*0.6,
steady_time*0.65, steady_time*0.7, steady_time*0.75,
steady_time*0.8, steady_time*0.9, steady_time*0.97,
steady_time*0.99]
for j in range(0, len(i2[i])):
ie2 = i2[i][j]
q2 = ie2/3600./1000.*L
s2 = 1./(N+1)*alpha/(ie2/3600./1000.)*(
(ie2/3600./1000.)*L/alpha)**((N+1)/N)
for k in range(0, len(ToC)):
d = ToC[k]
ca = two_step.two_step_overland(i0, ie1, ie2, L, x_space, dt,
n, slope)
ca.ie1_duration(d)
ca.run()
SWR = bcdPoints(ca) # Clip Secondary Curve
SWR = np.array(SWR)
qBD = SWR[-1, 1]/SWR[0, 1]
sBD = SWR[-1, 2]/SWR[0, 2]
# #
# Fit Second Drying Curve From Above #
# #
SWR[:, 0] = (SWR[:, 0] - SWR[0, 0])/(SWR[-1, 0] - SWR[0, 0])
SWR[:, 1] = SWR[:, 1]/SWR[0, 1]
SWR[:, 2] = SWR[:, 2]/SWR[0, 2]
t = np.arange(0, 1.01, 1./20)
_SWR = np.zeros([21, 2])
_SWR[:, 0] = np.interp(t, SWR[:, 0], SWR[:, 1])
_SWR[:, 1] = np.interp(t, SWR[:, 0], SWR[:, 2])
# Fitting
[p, z] = bezeir_fit.fit(_SWR, deg, 1.0E-8)
p[0] = [1.0, 1.0]
p[-1] = [SWR[-1, 1], SWR[-1, 2]]
n_SWR = bezeir_fit.gen(t, deg, p)
dat_And_Indicator.append([qBD,
sBD,
ie1/ie2,
[p, z]])
color = randColor()
labelString = ('$q_B/q_D$=' +
'{:4.3f}'.format(qBD) + ', ' +
'$c_1$=' +
'{:4.3f}'.format(ie1/ie2))
print(labelString)
ax.plot(SWR[:, 1], SWR[:, 2], lw=2.0, ls='-',
label=labelString, color=color)
ax.plot(n_SWR[:, 0], n_SWR[:, 1], lw=1.0, ls='--', color=color)
# dat_And_Indicator = sorted(dat_And_Indicator, key=itemgetter(2))
for i in range(0, len(dat_And_Indicator)):
qBDRatio.append(dat_And_Indicator[i][0])
sBDRatio.append(dat_And_Indicator[i][1])
c1.append(dat_And_Indicator[i][2])
ptList.append(dat_And_Indicator[i][3])
box = ax.get_position()
ax.set_position([box.x0, box.y0, box.width * 0.8, box.height])
ax.legend(loc='upper left', bbox_to_anchor=(1.05, 1.0), ncol=2)
ax.set_xlabel('q')
ax.set_ylabel('s')
plt.title('Secondary Wetting Curve - Normalized and Fitting')
plt.savefig('SWR_fitting.png')
plt.close()
f = open('ks1_SWRrec.pick', 'wb')
pickle.dump({'qBD': qBDRatio, 'sBD': sBDRatio, 'PT': ptList, 'C1': c1}, f)
f.close()