def prune(freqSet, H, S, rules, minConf):
prunedH = []
for i in range(np.shape(H)[0]):
conseq = H[i, :] # get a consequent
ante = np.setdiff(
freqSet, conseq) # antecedent is the rest of items in the itemset
freqSetSup = S[str(freqSet)]
# support for freqSet
anteSup = S[str(ante)] # support for antecedent
conseqSup = S[str(conseq)] # support for consequent
conf = freqSetSup / anteSup # confidence
lift = freqSetSup / (anteSup * conseqSup) # and lift
if conf >= minConf:
prunedH = np.concatenate((prunedH, conseq), axis=1)
rule = {
'Antecedent': ante,
'Consequent': conseq,
'Conf': conf,
'Lift': lift,
'Sup': freqSetSup
}
if np.isempty(rules):
rules = rule
else:
rules = np.concatenate((rules, rule), axis=1)
return ([prunedH, rules])
def delete(self):
# delete class destructor
#
# Syntax:
# delete(self)
#
# Description:
# Loops through parameters and, if not an object, empties them. Else, calls
# the sub-object's destructor.
#
# Input:
# obj - model object
#
# Output:
# (none)
#
# Author:
# Dr. Tim Peterson, The Department of Infrastructure Engineering,
# The University of Melbourne.
#
# Date:
# 24 Aug 2016
##
propNames = properties(self)
for i in range(len(propNames)):
if np.isempty(self.propNames[i])):
continue
if isobject(self.propNames[i])):
delete(self.propNames[i]))
else:
self.propNames[i]) = []
def generate(freqSet, H, S, rules, minConf):
# if frequent itemset is longer than consequent by more than 1
(m, n) = np.shape(H)
if len(freqSet) > (m + 1):
if m == 1:
a, rules = prune(freqSet, H, S, rules,
minConf) # prune 1-item consequents
Hm1 = aprioriGen(
H, m + 1) # use aprioriGen to generate longer consequents
[Hm1, rules] = prune(freqSet, Hm1, S, rules,
minConf) # prune consequents
if not np.isempty(Hm1): # recursive if more consequents
rules = generate(freqSet, Hm1, S, rules, minConf)
return (rules)
def generateFreqItemsets(transactions, minSup):
transactions = pd.Series(transactions,
index=np.arange(0, len(transactions)))
items_generate = [
item for transaction in transactions.values for item in transaction
]
uniqItems, index, oneItemsets = np.unique(
items_generate, return_inverse=True,
return_counts=True) # index has indices of original items
N = len(transactions)
C1 = uniqItems
supk = (oneItemsets) / N # support for all candidates
S = {}
for j in range(0, len(C1)):
S[str(j)] = supk[j]
Lk = {
'items': uniqItems[(supk >= minSup).ravel().nonzero()][0],
'index': (supk >= minSup).ravel().nonzero()
} # must be >= minimal support
L = [Lk]
# get all frequent k-itemsets where k >= 2
k = 2
while True:
# Ck: candidate itemsets
p = L[k - 2]['index'][0]
Ck = aprioriGen(p, k)
support = np.zeros(np.shape(Ck)[0])
for i in range(N): # walk through all transactions
t = sorted([items.index(item) for item in transactions.values[i]
]) # which item in ith transaction, returns item index
support[myall(ismember(
Ck, t))] = support[myall(ismember(Ck, t))] + (
1 / N
) #if all the values in t are members, then it will return 1
Lk = {
'items': items[Ck[support >= minSup, :]],
'index': Ck[support >= minSup, :]
}
if not np.isempty(support):
mapS = {}
for i in range(len(support)):
mapS[str(Ck[i, :])] = support[i]
S = np.concatenate((S, mapS), axis=0)
else:
break
if not np.isempty(Lk):
L[k] = Lk
k = k + 1
else:
break
return ([L, S, items])
def main():
#Definitions of the event fetched from config.cfg
config = ConfigParser.RawConfigParser()
config.read("config.cfg")
startdate = config.get("input", "startdate")
enddate = config.get("input", "enddate")
n = config.getint("input", "n")
diacorr = config.getfloat("input", "diacorr")
#Convert dates to datetime
try:
startdt = datetime.strptime(startdate, "%Y%m%d%H%M")
enddt = datetime.strptime(enddate, "%Y%m%d%H%M")
except Exception:
traceback.print_exc()
foldername = os.path.join(config.get("input", "dataloc"),
datetime.strftime(startdt, '%Y%m%d'))
if not os.path.exists(foldername):
os.mkdir(foldername)
paraname = os.path.join(
foldername,
datetime.strftime(startdt, '%H%M') + '_' +
datetime.strftime(enddt, '%H%M'))
print(paraname)
parafile = shelve.open(paraname)
#Flags for optional data
pluvio200 = config.getboolean("flags", "pluvio200")
pluvio400 = config.getboolean("flags", "pluvio400")
wind_GILL = config.getboolean("flags", "wind_GILL")
FMI_met = config.getboolean("flags", "FMI_met")
PIP_psd = config.getboolean("flags", "PIP_psd")
PIP_vel = config.getboolean("flags", "PIP_vel")
PIP_par = config.getboolean("flags", "PIP_par")
PIP_parrel = config.getboolean("flags", "PIP_parrel")
PIP_mass = config.getboolean("flags", "PIP_mass")
PIP_minute = config.getboolean("flags", "PIP_minute")
imag = config.getboolean("flags", "imag")
version = config.getboolean("flags", "version")
#Read precipitation data
if (pluvio200 == 1 or pluvio400 == 1):
print("Reading precipitation data (PLUVIO)")
tv_PL200, PL200_acc, PL200_rr, tv_PL400, PL400_acc, PL400_rr = PluvioIntensity_GPM(
n, startdt, enddt)
else:
tv_PL200 = []
PL200_acc = []
PL200_rr = []
tv_PL400 = []
PL400_acc = []
PL400_rr = []
#Convert dates to datetime
try:
startdt = datetime.strptime(startdate, "%Y%m%d%H%M")
enddt = datetime.strptime(enddate, "%Y%m%d%H%M")
except Exception:
traceback.print_exc()
#Save parameters to file for later examination
##parafile.write(tv_PL200, ' ' , PL200_acc, ' ' , PL200_rr , ' ' , tv_PL400 , ' ' , PL400_acc , ' ' , PL400_rr )
#shelving(parafile)
#Read data from FMI-met
if (FMI_met == 1):
print("Reading FMI MET data")
tv_FMI, temp_FMI, rh_FMI, sn_FMI, rr_FMI, press_FMI = Read_FMIstation_GPM(
startdt, enddt, n)
else:
tv_FMI = []
temp_FMI = []
rh_FMI = []
sn_FMI = []
rr_FMI = []
press_FMI = []
#parafile.write(tv_FMI , ' ' , temp_FMI, ' ' , rh_FMI, ' ' , sn_FMI, ' ' , rr_FMI, ' ' , press_FMI)
#shelving(parafile)
#Read and plot GILL wind data
##TODO: Sanity check
if (wind_GILL == 1):
print("Reading wind data (GILL)")
time_vector_GILL, mean_vel_GILL, mode_dir_GILL = WindComparison_GPM(
n, startdt, enddt)
else:
time_vector_GILL = []
mean_vel_GILL = []
mode_dir_GILL = []
#parafile.write(time_vector_GILL, ' ', mean_vel_GILL, ' ', mode_dir_GILL)
#shelving(parafile)
#Read the PSD tables (size distribution)
if (PIP_psd == 1):
print("Reading PSD tables")
D_PIP, PIP_PSD, PIPtime_psd, N_mean_PIP, Dm - PIP, N0_PIP, lambda_PIP,
mu_PIP, Nw_PIP, D02_exp_PIP, lambda2_exp_PIP, PIPtime_N = ReadPIP_PSD_GPM(
startdt, enddt, n, foldername)
else:
D_PIP = []
PIP_PSD = []
PIPtime_psd = []
N_mean_PIP = []
Dm_PIP = []
N0_PIP = []
lambda_PIP = []
mu_PIP = []
Nw_PIP = []
D02_exp_PIP = []
lambda2_exp_PIP = []
PIPtime_N = []
#parafile.write(D_PIP, ' ', PIP_PSD, ' ', PIPtime_psd, ' ',N_mean_PIP , ' ',Dm_PIP , ' ',N0_PIP , ' ',
# ' ',lambda_PIP , ' ',mu_PIP , ' ',Nw_PIP , ' ',D02_exp_PIP , ' ',lambda2_exp_PIP , ' ',PIPtime_N)
#shelving(parafile)
#Read the velocity tables and perform a fit as function of D_PIP
if (PIP_vel == 1):
print("Reading the velocity tables (PIP_vel)")
D_PIP, PIPtime_vel, PIPD_vel, PIPV_vel = ReadPIP_vel_GPM(
startdt, enddt)
avel_DPIP_vel, bvel_DPIP_vel, PIPtime_vel_n = PIPVelRel_GPM(
startdt, enddt, D_PIP, PIPtime_vel, PIPD_vel, PIPV_vel, n,
foldername)
else:
PIPtime_vel = []
PIPD_vel = []
PIPV_vel = []
avel_DPIP = []
bvel_DPIP = []
DPIP_V_vel_n = []
PIPtime_vel_n = []
#parafile.write(D_PIP, ' ', PIPtime_vel, ' ', PIPD_vel, ' ', PIPV_vel, ' ', avel_DPIP_vel, ' ', bvel_DPIP_vel, ' ', PIPtime_vel_n)
#shelving(parafile)
#Read the particle tables
if (PIP_par == 1):
D_PIP, PIPD_par, PIPV_par, PIPtime_par, PIPEmaj, PIPEmajmax, PIPEmin, PIPAR, PIPOR, PIPLen, PIPHig = ReadPIP_par_GPM(
startdt, enddt)
temp_PIPD = PIPD_par
temp_Dmax = 2 * PIPEmajmax
temp_PIPD[np.isnan(tempDmax)] = []
temp_Dmax[np.isnan(tempDmax)] = []
temp_PIPD[np.isinf(tempDmax)] = []
temp_Dmax[np.isinf(tempDmax)] = []
temp_Dmax[np.isnan(temp_PIPD)] = []
temp_PIPD[np.isnan(temp_PIPD)] = []
temp_Dmax[np.isinf(temp_PIPD)] = []
temp_PIPD[np.isinf(temp_PIPD)] = []
#Area equivalent of whole snow event
##TODO
#C1 =
#kD = C1[1]
else:
D_PIP = []
PIPD_par = []
PIPV_par = []
PIPtime_par = []
PIPEmaj = []
PIPEmajmax = []
PIPEmin = []
PIPAR = []
PIPlonX = []
PIPDia = []
PIPOR = []
PIPLen = []
PIPHig = []
kD = []
#parafile.write(D_PIP, ' ', PIPtime_vel, ' ', PIPD_vel, ' ', PIPV_vel, ' ', avel_DPIP_vel, ' ', bvel_DPIP_vel, ' ', PIPtime_vel_n)
#shelving(parafile)
if (PIP_mass == 1):
temp_FMI[np.isnan(rh_FMI)] = []
press_FMI[np.isnan(rh_FMI)] = []
tv_FMI[np.isnan(rh_FMI)] = []
rh_FMI[np.isnan(rh_FMI)] = []
press_FMI[np.isnan(temp_FMI)] = []
tv_FMI[np.isnan(temp_FMI)] = []
rh_FMI[np.isnan(temp_FMI)] = []
temp_FMI[np.isnan(temp_FMI)] = []
tv_FMI[np.isnan(press_FMI)] = []
rh_FMI[np.isnan(press_FMI)] = []
temp_FMI[np.isnan(press_FMI)] = []
press_FMI[np.isnan(press_FMI)] = []
envr.temp = temp_FMI
envr.press = press_FMI
envr.time = tv_FMI
envr.rh = rh_FMI / 100
time_mass, amass_PIP, bmass_PIP = MassEstimate(n, D_PIP, PIPtime_par,
envr, PIPD_par,
PIPV_par, PIPEmajmax,
PIPAR, kD, diacorr,
foldername)
else:
time_mass = []
amass_PIP = []
bmass_PIP = []
"""
Part 2
"""
#Calculate the accumulation and reflectivity from the mass estimate
master_time_vector = np.arange(startdt, enddt, timedelta(minutes=n))
#PSD properties
N0_mtv = []
lambda_mtv = []
mu_mtv = []
N02_exp_mtv = []
lambda2_exp_mtv = []
D02_exp_mtv = []
N_mean_PIP_mtv = []
Dmax_mtv = []
#Selected a, b (mass factors) and av, bv (velocity factors)
amass_mtv = []
bmass_mtv = []
avel_mtv_max = []
bvel_mtv_max = []
"""
#Choose used diameter correction
if diacorr == 0.9:
time = time_mass.MH05_maxmaxD_corrconst08
amass = amass_PIP.MH05_maxmaxD_corrconst08
bmass = bmass_PIP.MH05_maxmaxD_corrconst08
elif diacorr == 0.82:
time = time_mass.MH05_maxmaxD_corrconst06
amass = amass_PIP.MH05_maxmaxD_corrconst06
bmass = bmass_PIP.MH05_maxmaxD_corrconst06
elif diacorr == 0.7:
time = time_mass.MH05_maxmaxD_corrconst04
amass = amass_PIP.MH05_maxmaxD_corrconst04
bmass = bmass_PIP.MH05_maxmaxD_corrconst04
else:
time_mtv = time_mass.MH05_maxmaxD
amass = amass_PIP.MH05_maxmaxD
bmass = bmass_PIP.MH05_maxmaxD
"""
accum_PIP_fac_MH05_maxmaxD = []
accum_PIP_fac_MH05_maxmaxD_corrconst08 = []
accum_PIP_fac_MH05_maxmaxD_corrconst06 = []
accum_PIP_fac_MH05_maxmaxD_corrconst04 = []
Ze_PIP_fac_MH05_maxmaxD = []
Ze_PIP_fac_MH05_maxmaxD_corrconst08 = []
Ze_PIP_fac_MH05_maxmaxD_corrconst06 = []
Ze_PIP_fac_MH05_maxmaxD_corrconst04 = []
diff_PIP = np.zeros(np.shape(D_PIP))
diff_PIP[0] = D_PIP[0]
diff_PIP[1:] = np.diff(D_PIP)
diacorr_no = kD / 1
diacorr08 = kD / 0.9
diacorr06 = kD / 0.82
diacorr04 = kD / 0.70
for dd in (np.arange(1, np.shape(master_time_vector)[1])):
d_ind = np.where(time_mass.MH05_maxmaxD >
(master_time_vector[dd] - 30 * (1 / (24 * 3600)))
and time_mass.MH05_maxmaxD <
(master_time_vector[dd] + 30 * (1 / (24 * 3600))))
#d_ind = np.where(time_mass.MH05_maxmaxD > (master_time_vector[dd]-30*(1/(24*3600))) & time_mass.MH05_maxmaxD < (master_time_vector[dd]+30*(1/(24*3600))));
dn_ind = np.where(PIPtime_n >
(master_time_vector[dd] - 30 * (1 / 24 * 3600))
and PIPtime_n <
(master_time_vector[dd] + 30 * (1 / (24 * 3600))))
#dn_ind = np.where(PIPtime_n > (master_time_vector(dd)-30*(1/(24*3600))) & PIPtime_n < (master_time_vector(dd)+30*(1/(24*3600))));
dv_ind = np.where(time_vector_parrel_max > (
master_time_vector[dd] - 30 *
(1 / (24 * 3600)) and time_vector_parrel_max <
(master_time_vector[dd] + 30 * (1 / (24 * 3600)))))
#dv_ind = np.where(time_vector_parrel_max > (master_time_vector(dd)-30*(1/(24*3600))) & time_vector_parrel_max < (master_time_vector(dd)+30*(1/(24*3600))));
if (np.isempty(d_ind) == 0 and np.isempty(dn_ind) == 0
and np.isempty(dv_ind) == 0):
#PSD parameters
N0_mtv = np.column_stack(N0_mtv, N0_PIP(dn_ind))
lambda_mtv = np.column_stack(lambda_mtv, lambda_PIP(dn_ind))
mu_mtv = np.column_stack(mu_mtv, mu_PIP(dn_ind))
N02_exp_mtv = np.column_stack(N02_exp_mtv, N02_exp_PIP[dn_ind])
lambda2_exp_mtv = np.column_stack(lambda2_exp_mtv,
lambda2_exp_PIP[dn_ind])
D02_exp_mtv = np.column_stack(D02_exp_mtv, D02_exp_PIP[dn_ind])
N_mean_PIP_mtv = np.row_stack(N_mean_PIP_mtv,
N_mean_PIP[dn_ind, :])
Dmax_mtv = np.column_stack(Dmax_mtv, Dmax_PIP(dn_ind))
#particle relations
avel_mtv_max = np.column_stack(avel_mtv_max,
avel_PIP_par_max(dv_ind))
bvel_mtv_max = np.column_stack(bvel_mtv_max,
bvel_PIP_par_max(dv_ind))
accum_PIP_fac_MH05_maxmaxD = np.column_stack(
accum_PIP_fac_MH05_maxmaxD, n * 60 * 10 ^ (-3) *
np.nansum(amass_PIP.MH05_maxmaxD[d_ind] *
(diacorr_no * 0.1 * D_PIP) ^
(bmass_PIP.MH05_maxmaxD[d_ind]) *
avel_PIP_par_max[dv_ind] *
(diacorr_no * D_PIP) ^ (bvel_PIP_par[dv_ind]) *
N_mean_PIP[dn_ind, :] * diff_PIP))
if (bmass_PIP.MH05_maxmaxD(d_ind) >= 1
and bmass_PIP.MH05_maxmaxD(d_ind) < 3.5) and (
bvel_PIP_par_max(dv_ind) >= 0):
Ze_PIP_fac_MH05_maxmaxD = np.column_stack(
Ze_PIP_fac_MH05_maxmaxD,
10 ^ 6 * 1.2076 * 0.2 / 0.93 * (6 / pi)
^ 2 * nansum((amass_PIP.MH05_maxmaxD(d_ind) *
(diacorr_no * 0.1 * D_PIP) ^
(bmass_PIP.MH05_maxmaxD(d_ind)))
^ 2 * N_mean_PIP[dn_ind, :] * diff_PIP))
amass_mtv = np.column_stack(amass_mtv, amass(d_ind))
bmass_mtv = np.column_stack(bmass_mtv, bmass(d_ind))
else:
Ze_PIP_fac_MH05_maxmaxD = np.column_stack(
Ze_PIP_fac_MH05_maxmaxD, 0)
amass_mtv = np.column_stack(amass_mtv, 0)
bmass_mtv = np.column_stack(bmass_mtv, 0)
else:
accum_PIP_fac_MH05_maxmaxD = np.column_stack(
accum_PIP_fac_MH05_maxmaxD, 0)
Ze_PIP_fac_MH05_maxmaxD = np.column_stack(Ze_PIP_fac_MH05_maxmaxD,
0)
amass_mtv = np.column_stack(mass_mtv, 0)
bmass_mtv = np.column_stack(bmass_mtv, 0)
N0_mtv = np.column_stack(N0_mtv, 0)
lambda_mtv = np.column_stack(lambda_mtv, 0)
mu_mtv = np.column_stack(mu_mtv, 0)
N02_exp_mtv = np.column_stack(N02_exp_mtv, 0)
D02_exp_mtv = np.column_stack(D02_exp_mtv, 0)
lambda2_exp_mtv = np.column_stack(lambda2_exp_mtv, 0)
N_mean_PIP_mtv = np.row_stack(N_mean_PIP_mtv,
np.zeros(size(D_PIP)))
Dmax_mtv = np.column_stack(Dmax_mtv, 0)
avel_mtv_max = np.column_stack(avel_mtv_max, 0)
bvel_mtv_max = np.column_stack(bvel_mtv_max, 0)
d_ind = np.where(time_mass.MH05_maxmaxD_corrconst08 >
(master_time_vector[dd] - 30 * (1 / (24 * 3600)))
and time_mass.MH05_maxmaxD_corrconst08 <
(master_time_vector[dd] + 30 * (1 / (24 * 3600))))
dn_ind = np.where(
PIPtime_n > (master_time_vector(dd) - 30 * (1 / (24 * 3600)))
& PIPtime_n < (master_time_vector(dd) + 30 * (1 / (24 * 3600))))
dv_ind = np.where(
time_vector_parrel_max > (master_time_vector(dd) - 30 *
(1 / (24 * 3600)))
& time_vector_parrel_max < (master_time_vector(dd) + 30 *
(1 / (24 * 3600))))
if np.isempty(d_ind) == 0 and np.isempty(dn_ind) == 0 and np.isempty(
dv_ind) == 0:
accum_PIP_fac_MH05_maxmaxD_corrconst08 = np.column_stack(
accum_PIP_fac_MH05_maxmaxD_corrconst08, n * 60 * 10 ^ (-3) *
np.nansum(amass_PIP.MH05_maxmaxD_corrconst08[d_ind] *
(diacorr08 * 0.1 * D_PIP) ^
(bmass_PIP.MH05_maxmaxD_corrconst08[d_ind]) *
avel_PIP_par_max[dv_ind] *
(D_PIP * diacorr08) ^ (bvel_PIP_par_max[dv_ind]) *
N_mean_PIP[dn_ind, :] * diff_PIP))
if (bmass_PIP.MH05_maxmaxD_corrconst08(d_ind) >= 1
and bmass_PIP.MH05_maxmaxD_corrconst08(d_ind) < 3.5) and (
bvel_PIP_par_max(dv_ind) >= 0):
Ze_PIP_fac_MH05_maxmaxD_corrconst08 = np.column_stack(
Ze_PIP_fac_MH05_maxmaxD_corrconst08,
10 ^ 6 * 1.2076 * 0.2 / 0.93 * (6 / pi) ^
2 * np.nansum((amass_PIP.MH05_maxmaxD_corrconst08[d_ind] *
(diacorr08 * 0.1 * D_PIP) ^
(bmass_PIP.MH05_maxmaxD_corrconst08[d_ind]))
^ 2 * N_mean_PIP[dn_ind, :] * diff_PIP))
else:
Ze_PIP_fac_MH05_maxmaxD_corrconst08 = np.column_stack(
Ze_PIP_fac_MH05_maxmaxD_corrconst08, 0)
else:
accum_PIP_fac_MH05_maxmaxD_corrconst08 = np.column_stack(
accum_PIP_fac_MH05_maxmaxD_corrconst08, 0)
Ze_PIP_fac_MH05_maxmaxD_corrconst08 = np.column_stack(
Ze_PIP_fac_MH05_maxmaxD_corrconst08, 0)
d_ind = np.where(time_mass.MH05_maxmaxD_corrconst06 >
(master_time_vector[dd] - 30 * (1 / (24 * 3600)))
and time_mass.MH05_maxmaxD_corrconst06 <
(master_time_vector[dd] + 30 * (1 / (24 * 3600))))
dn_ind = np.where(PIPtime_n >
(master_time_vector[dd] - 30 * (1 / (24 * 3600)))
and PIPtime_n <
(master_time_vector[dd] + 30 * (1 / (24 * 3600))))
dv_ind = np.where(time_vector_parrel_max >
(master_time_vector[dd] - 30 * (1 / (24 * 3600)))
and time_vector_parrel_max <
(master_time_vector[dd] + 30 * (1 / (24 * 3600))))
if np.isempty(d_ind) == 0 and np.isempty(dn_ind) == 0 and np.isempty(
dv_ind) == 0:
accum_PIP_fac_MH05_maxmaxD_corrconst06 = np.column_stack(
accum_PIP_fac_MH05_maxmaxD_corrconst06, n * 60 * 10 ^ (-3) *
np.nansum(amass_PIP.MH05_maxmaxD_corrconst06[d_ind] *
(diacorr06 * 0.1 * D_PIP) ^
(bmass_PIP.MH05_maxmaxD_corrconst06[d_ind]) *
avel_PIP_par_max[dv_ind] *
(diacorr06 * D_PIP) ^ (bvel_PIP_par_max[dv_ind]) *
N_mean_PIP[dn_ind, :] * diff_PIP))
if (bmass_PIP.MH05_maxmaxD_corrconst06[d_ind] >= 1
and bmass_PIP.MH05_maxmaxD_corrconst06[d_ind] < 3.5) and (
bvel_PIP_par_max[dv_ind] >= 0):
Ze_PIP_fac_MH05_maxmaxD_corrconst06 = np.column_stack(
Ze_PIP_fac_MH05_maxmaxD_corrconst06,
10 ^ 6 * 1.2076 * 0.2 / 0.93 * (6 / pi) ^
2 * np.nansum((amass_PIP.MH05_maxmaxD_corrconst06[d_ind] *
(diacorr06 * 0.1 * D_PIP) ^
(bmass_PIP.MH05_maxmaxD_corrconst06[d_ind]))
^ 2 * N_mean_PIP[dn_ind, :] * diff_PIP))
else:
Ze_PIP_fac_MH05_maxmaxD_corrconst06 = np.column_stack(
Ze_PIP_fac_MH05_maxmaxD_corrconst06, 0)
else:
accum_PIP_fac_MH05_maxmaxD_corrconst06 = np.column_stack(
accum_PIP_fac_MH05_maxmaxD_corrconst06, 0)
Ze_PIP_fac_MH05_maxmaxD_corrconst06 = np.column_stack(
Ze_PIP_fac_MH05_maxmaxD_corrconst06, 0)
d_ind = np.where(time_mass.MH05_maxmaxD_corrconst04 >
(master_time_vector[dd] - 30 * (1 / (24 * 3600)))
and time_mass.MH05_maxmaxD_corrconst04 <
(master_time_vector[dd] + 30 * (1 / (24 * 3600))))
dn_ind = np.where(
PIPtime_n > (master_time_vector[dd] - 30 * (1 / (24 * 3600)))
& PIPtime_n < (master_time_vector[dd] + 30 * (1 / (24 * 3600))))
dv_ind = np.where(time_vector_parrel_max >
(master_time_vector[dd] - 30 * (1 / (24 * 3600)))
and time_vector_parrel_max <
(master_time_vector[dd] + 30 * (1 / (24 * 3600))))
if (np.isempty(d_ind) == 0 and np.isempty(dn_ind) == 0
and np.isempty(dv_ind) == 0):
accum_PIP_fac_MH05_maxmaxD_corrconst04 = np.column_stack(
accum_PIP_fac_MH05_maxmaxD_corrconst04, n * 60 * 10 ^
(-3) * np.nansum(amass_PIP.MH05_maxmaxD_corrconst04[d_ind] *
(diacorr04 * 0.1 * D_PIP) ^
(bmass_PIP.MH05_maxmaxD_corrconst04[d_ind]) *
avel_PIP_par_max[dv_ind] *
(diacorr04 * D_PIP) ^ (bvel_PIP_par[dv_ind]) *
N_mean_PIP[dn_ind, :] * diff_PIP))
if (bmass_PIP.MH05_maxmaxD_corrconst04[d_ind] >= 1
and bmass_PIP.MH05_maxmaxD_corrconst04[d_ind] < 3.5) and (
bvel_PIP_par_max[dv_ind] >= 0):
Ze_PIP_fac_MH05_maxmaxD_corrconst04 = np.column_stack(
Ze_PIP_fac_MH05_maxmaxD_corrconst04,
10 ^ 6 * 1.2076 * 0.2 / 0.93 * (6 / pi) ^
2 * np.nansum((amass_PIP.MH05_maxmaxD_corrconst04[d_ind] *
(diacorr04 * 0.1 * D_PIP) ^
(bmass_PIP.MH05_maxmaxD_corrconst04[d_ind]))
^ 2 * N_mean_PIP[dn_ind, :] * diff_PIP))
else:
Ze_PIP_fac_MH05_maxmaxD_corrconst04 = np.column_stack(
Ze_PIP_fac_MH05_maxmaxD_corrconst04, 0)
else:
accum_PIP_fac_MH05_maxmaxD_corrconst04 = np.column_stack(
accum_PIP_fac_MH05_maxmaxD_corrconst04, 0)
Ze_PIP_fac_MH05_maxmaxD_corrconst04 = np.column_stack(
Ze_PIP_fac_MH05_maxmaxD_corrconst04, 0)
#shelving(parafile)
#Plot the summary of the event (IMPORTANT)
Plot_EventSummary_GPM()
#Define Z(S) with Rayleigh approximation
#Choose the relation
if (diacorr == 0.9):
accum = accum_PIP_fac_MH05_maxmaxD_corrconst08
Ze = Ze_PIP_fac_MH05_maxmaxD_corrconst08
fname1 = os.path.join(foldername, '\ZeS_corrconst08_', startdt, '_',
enddt)
fname2 = os.path.join(foldername, '\ZeS_timeseries_corrconst08_',
startdt, '_', enddt)
elif (diacorr == 0.82):
accum = accum_PIP_fac_MH05_maxmaxD_corrconst06
Ze = Ze_PIP_fac_MH05_maxmaxD_corrconst06
fname1 = os.path.join(foldername, '\ZeS_corrconst06_',
datestr(event_start_time, 30), '_',
datestr(event_end_time, 30))
fname2 = os.path.join(foldername, '\ZeS_timeseries_corrconst06_',
datestr(event_start_time, 30), '_',
datestr(event_end_time, 30))
elif (diacorr == 0.7):
accum = accum_PIP_fac_MH05_maxmaxD_corrconst04
Ze = Ze_PIP_fac_MH05_maxmaxD_corrconst04
fname1 = (foldername, '\ZeS_corrconst04_',
datestr(event_start_time,
30), '_', datestr(event_end_time, 30))
fname2 = (foldername, '\ZeS_timeseries_corrconst04_',
datestr(event_start_time,
30), '_', datestr(event_end_time, 30))
else:
accum = accum_PIP_fac_MH05_maxmaxD
Ze = Ze_PIP_fac_MH05_maxmaxD
fname1 = (foldername, '\ZeS_nocorr_', datestr(event_start_time,
30), '_',
datestr(event_end_time, 30))
fname2 = (foldername, '\ZeS_timeseries_nocorr_',
datestr(event_start_time,
30), '_', datestr(event_end_time, 30))
parafile.close()
def outlierDetection(headData, isOutlier, nSigma_threshold):
# Initialise outputs
noise_sigma = np.inf
x_opt = []
model_calib = []
# Initialise 'isOutliers' if it's not supplied by the user
if np.isempty(isOutlier):
isOutlier = False(np.shape([headData,1]))
isNewOutlier = False(np.shape([headData,1]))
isOutlier_input = isOutlier
# Build inputs for exponential smoothing model
t = headData[:,1]
h_obs = headData[:,2]
dummyBoreID = 'BoreID_123'
coordinates = [dummyBoreID, -999, -999 'Precip', -999, -999]
forcingData = [t[1]-10 : t[end]+10]
forcingData = table(year(forcingData), month(forcingData), day(forcingData), np.zeros(np.shape([forcingData,1]),1), 'VariableNames', ['Year', 'Month', 'Day', 'Precip'])
h_obs_model = [year(t), month(t), day(t), hour(t), minute(t), second(t), h_obs]
# Calibrate exponential smoothing model
summaryStr = []
noise_sigma = 0
i = 1
doFinalCalibration = False
el = 0
while (i==1) | (np.sum(isNewOutlier)>0) | (doFinalCalibration==True):
# Build model
model_calib = HydroSightModel('Outlier detection', dummyBoreID, 'ExpSmooth', h_obs_model[~isOutlier,:], -999, forcingData, coordinates, False)
# Calibrate model
calibrateModel(model_calib, [], 0, np.inf, 'SPUCI', 2)
# Get the standard deviation of the noise.
noise_sigma = model_calib.model.variables.sigma_n
# Exit if the this loop is being undertaken
if doFinalCalibration==True:
break
# Store calibrated parameters
alpha = model_calib.model.parameters.alpha
beta = model_calib.model.parameters.beta
gamma = model_calib.model.parameters.gamma
meanHead_calib = model_calib.model.variables.meanHead_calib
initialHead = model_calib.model.variables.initialHead
initialTrend = model_calib.model.variables.initialTrend
# Loop through each non-outlier observation to omit it from the
# simulation. This is done to exclude a possible outlier point from
# the smoothed estimate and the resulting calculation of the
# noise. If the difference between the current obs point and the
# forcast is greater than this noise estimate, then it is denoted
# as an outlier. Importantly, when calculating the noise the min and
# max points are also excluded.
isNewOutlier = False(np.shape(isOutlier))
filt = isOutlier
ind = find(~isOutlier) # <<< find ?
for k,j in enumerate(ind[2:end]):
# Get a vector of obs points excluding the current obs point, point ind[j].
filt[j] = True
time_points_trim = headData[~filt, 1]
delta_t = headData[j, 1] - headData[j-1, 1]
h_obs_trim = headData[~filt, 2]
h_obs_trim = [year(time_points_trim), month(time_points_trim), day(time_points_trim),
hour(time_points_trim), minute(time_points_trim), second(time_points_trim), h_obs_trim]
# Rebuild the model without the current time point, assign the
# calibrated parameters and solve the model.
model = HydroSightModel('Outlier detection', dummyBoreID, 'ExpSmooth', h_obs_trim, -999, forcingData, coordinates, False)
model.model.parameters.alpha = alpha
model.model.parameters.beta = beta
model.model.parameters.gamma = gamma
model.model.variables.meanHead_calib = meanHead_calib
model.model.variables.calibraion_time_points = time_points_trim
model.model.variables.initialHead = initialHead
model.model.variables.initialTrend = initialTrend
# Add current point back in the simulation. Note, when
# the simulation is undertaken for a point does not exist in
# model, then it is forecast.
filt[j] = False
time_points_trimExtended = headData[~filt,1]
h_mod_trim = solveModel(model, time_points_trimExtended, [], 'NoLabel', False)
h_forecast_trim = model.model.variables.h_forecast
# Create a filter to remove the current point from the forecast
# and then calculate the residuals
obs_filt = [1:k-1, k+1:len(ind)]
resid_trim = h_obs_trim[:,end] - h_forecast_trim[obs_filt]
# To minimise the impacts of outliers as yet identified, create
# a filter to remove the most negative and posative values from
# the residuals
resid_filt = resid_trim[(resid_trim>np.min(resid_trim)) & (resid_trim<np.max(resid_trim))]
resid_trim = resid_trim[resid_filt]
time_points_trim = time_points_trim[resid_filt]
# Calculate innovations
innov = resid_trim[2:end] - resid_trim[1:end-1] .* np.exp(-10.**model.model.parameters.beta .* np.diff(time_points_trim))
# Calculate st. dev. of residuals noise
#sigma_n_trimmed = sqrt(mean(innov.^2 ./ (1 - exp( -2 .* 10.^model.model.parameters.beta .* diff(time_points_trim) ))))
# Calculate st. dev. of residual for the current forecast only
# Note: estimate of sigma_n at a spacific time step is derived
# from von Asmuth 2015 doi:10.1029/2004WR00372 eqn A7 but with
# the innovations at t, v_t, replaced with the mean. The was
# undertaken so that sigma_n,t is independent from the residual forecast.
sigma_n_trimmed = np.sqrt(np.mean(innov.**2.) ./ (1.-np.exp(-2 .* 10.**model.model.parameters.beta .* delta_t)))
# Calculate residual for omitted obs point.
resid_point = h_obs[j] - h_forecast_trim[k]
# Break for-loop if an outlier is detected.
if np.abs(resid_point) >= nSigma_threshold*sigma_n_trimmed:
isNewOutlier[j] = True
el +=1
summaryStr[el] = ['Date : ', str(t[j]),', Head : ', str(h_obs[j]), ', Smoothed forecast head : ', str(h_forecast_trim[k]),
', Residual head : ', str(resid_point), ', St. dev of noise : ', str(sigma_n_trimmed)]
break
# Aggregate new outliers with previously detected outliers
isOutlier = [isOutlier, isNewOutlier]
# If the while loop is to exit, then set flag to do one last
# calibration so that the noise is best estimated.
if np.sum(isNewOutlier)==0:
doFinalCalibration = True
#update counter
#i=i+1 # needed twice ?
# Assign the final parameters
x_opt = getParameters(model_calib.model)
# Exclude input outliers from those input. That is, only return the
# outliers identified from the exponential smoothing model
isOutlier(isOutlier_input) = False
# Print summary:
print 'Summary of Outliers Detected'
print '----------------------------'
for i in range(el):
print summaryStr[i]
print '----------------------------'
return isOutlier, noise_sigma, x_opt, model_calib
def qc_kk(II, QI, IQ, QQ, Ihigh, Qhigh, Ilow, Qlow, Ierr, Qerr):
erfcinv = lambda x: erfinv(1 - x)
tval = lambda x: 2**.5 * erfcinv(2 * x)
rel_pwr = lambda Xhi, Xlo: 1.819745692478292 / (tval(Xhi) + tval(Xlo))**2
relPwrQ = rel_pwr(Qhigh, Qlow)
relPwrI = rel_pwr(Ihigh, Ilow)
qc_power = (relPwrQ * relPwrI)**.5
x1 = np.array([Qhigh, Ihigh, Ihigh, Qhigh])
x2 = np.array([Qlow, Ilow, Ilow, Qlow])
y1 = np.array([Qhigh, Ihigh, Qhigh, Ihigh])
y2 = np.array([Qlow, Ilow, Qlow, Ilow])
Xh, Xl, Yh, Yl = tval(x1), -tval(x2), tval(y1), -tval(y2)
Xh = np.tile(Xh, (len(II), 1))
Xl = np.tile(Xl, (len(II), 1))
Yh = np.tile(Yh, (len(II), 1))
Yl = np.tile(Yl, (len(II), 1))
sqrt2 = 2**-.5
x1 = erfc(sqrt2 * Xh)
x2 = erfc(-sqrt2 * Xl)
y1 = erfc(sqrt2 * Yh)
y2 = erfc(-sqrt2 * Yl)
Cxy = np.array([QQ, II, IQ, QI]).transpose()
TC0 = .25 * (x1 * y1 + x2 * y2 - x1 * y2 - x2 * y1)
TC1=1/(2*np.pi)*(\
(np.exp(-0.5*Xh*Xh)+np.exp(-0.5*Xl*Xl))*\
(np.exp(-0.5*Yh*Yh)+np.exp(-0.5*Yl*Yl)))
TC2=1/(4*np.pi)*(\
(Xh*np.exp(-0.5*Xh*Xh)+Xl*np.exp(-0.5*Xl*Xl))*\
(Yh*np.exp(-0.5*Yh*Yh)+Yl*np.exp(-0.5*Yl*Yl)))
TC3=1/(12*np.pi)*(\
((1-Xh*Xh)*np.exp(-0.5*Xh*Xh)+(1-Xl*Xl)*np.exp(-0.5*Xl*Xl))*\
((1-Yh*Yh)*np.exp(-0.5*Yh*Yh)+(1-Yl*Yl)*np.exp(-0.5*Yl*Yl)))
# FIND ROOTS TO 3rd DEGREE POLYNOMIAL
a, b, c = TC2 / TC3, TC1 / TC3, (TC0 - Cxy) / TC3
p, q = b - (1 / 3) * (a**2), c + (1 / 27) * (2 * (a**3) - 9 * a * b)
K = 0.5 * q + np.sign(q) * np.sqrt(0.25 * (q**2) + (1 / 27) * (p**3))
qc_data = Cxy * 0
#Handle NaNs
map_nan = np.isnan(K)
qc_data[map_nan] = Cxy[map_nan]
map_real = (K.imag == 0) & ~map_nan
#Handle case K=0
map_real_0 = (K == 0) & map_real
qc_data[map_real_0] = -a[map_real_0] / 3
#Handle optimization of roots
map_opt = map_real == ~map_real_0
x = K[map_opt].transpose()
r_opt = 0.5 * x
r2 = r_opt * 1.
e = x * 0 + 1.
run = (e == 1)
max_steps = 10000
err_thres = 1e-10
# Iterate to find roots
while np.any(run) and (max_steps > 0):
max_steps -= 1
r2[run] = r_opt[run]
r_opt[run] = 0.5 * (x[run] / (r_opt[run]**2) + r_opt[run])
e[run] = np.abs(r2[run] - r_opt[run])
run[run] = e[run] > err_thres
# Insert found roots
qc_data[map_opt] = p[map_opt] / (3 * r_opt) - r_opt - a[map_opt] / 3
# Handle case imaginary roots
map_imag = K.imag != 0 & ~map_nan
if np.any(map_imag):
coeff4 = TC3[map_imag]
coeff3 = TC2[map_imag]
coeff2 = TC1[map_imag]
coeff1 = TC0[map_imag] - Cxy[map_imag]
corr_compressed = Cxy[map_imag]
res = coeff1 * 0
for i in range(len(coeff1)):
r_roots = np.roots([coeff4[i], coeff3[i], coeff2[i], coeff1[i]])
if len(r_roots
) == 1 & np.isreal(r_roots) & r_roots < 1 & r_roots > -1:
res[i] = r_roots
else:
# Throw away complex roots and out-of-range values
tmp = r_roots[(np.abs(r_roots.imag) < 1e-10)
& (np.abs(r_roots.real) < 1)].real
if len(tmp) == 1: # case one root in the range
res[i] = tmp
elif len(
tmp
) > 1: # case multiple roots in the range -> invalid solution
(trash, I) = np.min(np.abs(tmp - corr_compressed[i]))
res[i] = tmp[I]
elif np.isempty(tmp): # case no roots
# This happens for large rho values. Set it equal to +/-1.
# Normally only happens for the first autocorr. values.
res[i] = np.sign(corr_compressed[i])
qc_data[map_imag] = res
# Handle big values
map_big = np.abs(qc_data) > 1
qc_data[map_big] = np.sign(Cxy[map_big])
QQqq = qc_data[:, 0]
IIqq = qc_data[:, 1]
IQqq = qc_data[:, 2]
QIqq = qc_data[:, 3]
# Rqq=IIqq+QQqq+1j*(IQqq-QIqq) # complex version
# real version
Rqq = np.zeros((QQqq.size * 2, ))
QIqq[0] = 0
QIqq = np.roll(QIqq, -1)
Rqq[::2] = IIqq + QQqq
Rqq[1::2] = IQqq + QIqq
# 0.5 correlation in Q and I channels => divide autocorrelation function
# by 2 for it to be strictly correct
Rqq = Rqq / 2
w = np.hanning(Rqq.size * 2)
yqq = qc_power * np.absolute(np.fft.hfft(Rqq * w[Rqq.size:]))
#yqq=qc_power*np.absolute(np.fft.hfft(Rqq))
yqq[0] = yqq[1]
yqq = yqq[yqq.size // 2:]
return yqq