def __FindValue(self,
Var='Pres',
Oper='min',
Comp='<',
M=None,
limits=[-5, 5]):
'''
DESCRIPTION:
Method that find the minimum or maximum value in the series
and calculates the changes.
_________________________________________________________________________
INPUT:
:param Var: An str, str of the dictionary of the Attribute Series.
:param VarC: An str, str of the dictionary of the Attribute Series when
comparation.
:param Oper: A str, Function that would search the value ('max','min').
:param Extract: A list, List with the limits of the extracted values
in minutes.
:param Comp: A str, string with the comparation that would be made.
:param M: A int or None, Integer with the position of the maximum of
minimum, if None it searches in the whole series.
:param limits: A list or None, List with the limits of search from the
value of M.
_________________________________________________________________________
OUTPUT:
'''
# Constants
dt = self.dt
Extract = self.Extract
# Determine Operation
O = self.Oper[Oper.lower()]
Comp = DM.Oper_Det(Comp)
flagRange = True
if M is None:
flagRange = False
if flagRange:
MVal = O(self.Series[Var][int(limits[0] / dt) + M:M +
int(limits[1] / dt)])
# Validation of the data
if Comp(MVal, self.Limits[Var]['MV']):
# Se extrae el evento
Event = self.Series[Var][-int(Extract[0] / dt) +
M:int(Extract[1] / dt) + M].copy()
if -int(self.Extract[0]) + M < 0:
self.Series[Var][0:int(self.Erase) + M] = np.nan
return -2
elif int(self.Extract[1]) + M > len(self.Series[Var]):
self.Series[Var][-int(self.Erase) +
M:len(self.Series[Var])] = np.nan
return -2
else:
self.Series[Var][-int(self.Erase) + M:int(self.Erase) +
M] = np.nan
q = sum(~np.isnan(Event))
if q < 0.9 * len(Event):
return -2
MP = np.where(Event == MVal)[0][0]
# Se calculan los cambios
Changes = BP.C_Rates_Changes(Event, dt=dt, MP=MP, MaxMin=Oper)
if Changes['VRateB'] < self.Limits[Var]['VRateB'] or np.isnan(
Changes['VRateB']):
return -2
try:
VarC = list(Changes)
for V in VarC:
a = len(self.Changes[Var][V])
self.Changes[Var][V] = np.hstack(
(self.Changes[Var][V], Changes[V]))
self.SeriesC[Var + 'C'] = np.vstack(
(self.SeriesC[Var + 'C'], Event))
except KeyError:
self.Changes[Var] = Changes.copy()
self.SeriesC[Var + 'C'] = Event.copy()
self.M[Var].append(MP)
else:
return -2
else:
# Finding the value
MVal = O(self.Series[Var])
M = np.where(self.Series[Var] == MVal)[0][0]
# Validation of the data
if Comp(MVal, self.Limits[Var]['MV']):
# Se extrae el evento
Event = self.Series[Var][-int(Extract[0] / dt) +
M:int(Extract[1] / dt) + M].copy()
if -int(self.Extract[0]) + M < 0:
self.Series[Var][0:int(self.Erase) + M] = np.nan
return -2
elif int(self.Extract[1]) + M > len(self.Series[Var]):
self.Series[Var][-int(self.Erase) +
M:len(self.Series[Var])] = np.nan
return -2
else:
self.Series[Var][-int(self.Erase) + M:int(self.Erase) +
M] = np.nan
q = sum(~np.isnan(Event))
if q < 0.9 * len(Event):
return -2
MP = np.where(Event == MVal)[0][0]
# Se calculan los cambios
Changes = BP.C_Rates_Changes(Event, dt=dt, MP=MP, MaxMin=Oper)
if Changes['VRateB'] < self.Limits[Var]['VRateB'] or np.isnan(
Changes['VRateB']):
return -2
Dates = self.Series['Dates'].datetime[-int(Extract[0] / dt) +
M:int(Extract[1] / dt) +
M]
try:
VarC = list(Changes)
for V in VarC:
a = len(self.Changes[Var][V])
self.Changes[Var][V] = np.hstack(
(self.Changes[Var][V], Changes[V]))
self.SeriesC['DatesP'] = np.vstack(
(self.SeriesC['DatesP'], Dates))
self.SeriesC['Dates'] = np.vstack(
(self.SeriesC['Dates'],
self.Series['Dates'].str[-int(Extract[0] / dt) +
M:int(Extract[1] / dt) + M]))
self.SeriesC[Var + 'C'] = np.vstack(
(self.SeriesC[Var + 'C'], Event))
except KeyError:
self.Changes[Var] = Changes.copy()
self.SeriesC['DatesP'] = self.Series['Dates'].datetime[
-int(Extract[0] / dt) + M:int(Extract[1] / dt) + M]
self.SeriesC['Dates'] = self.Series['Dates'].str[
-int(Extract[0] / dt) + M:int(Extract[1] / dt) + M]
self.SeriesC[Var + 'C'] = Event.copy()
self.MT.append(M)
self.M[Var].append(MP)
else:
return -1
return 1
def LoadStations(self):
'''
DESCRIPTION:
This method loads the information of the different stations.
_________________________________________________________________
INPUT:
'''
# ----------------
# Constants
# ----------------
lenArch = len(self.SC.Arch)
lenData = int((self.DateE-self.DateI).seconds/60)
self.DataSt = {}
self.PrecCount = {}
self.Changes = {}
self.IDC = []
dt = 1
# Horas atras
TBef = 1
# minutos hacia adelante
TAft = 30
Labels = ['FechaC','FechaCP','Prec','Pres_F','T_F','HR_F','W_F','q_F']
LabCh = ['Pres_F','T_F','HR_F']
opers = {'Pres_F':np.nanmin,'T_F':np.nanmax,'HR_F':np.nanmin}
MaxMin = {'Pres_F':'min','T_F':'max','HR_F':'min'}
self.vmax = {}
self.vmin = {}
for Lab in Labels[1:]:
self.vmax[Lab] = []
self.vmin[Lab] = []
# ----------------
# Extract Data
# ----------------
for iar in range(lenArch):
self.DataSt[self.ID[iar]] = {}
self.SC.LoadData(irow=iar)
xi = np.where(self.SC.f['FechaCP'] == self.DateI)[0]
xf = np.where(self.SC.f['FechaCP'] == self.DateE)[0]
if len(xi) == 0 or len(xf) == 0:
for Lab in Labels:
self.DataSt[self.ID[iar]][Lab] = np.array(lenData)
continue
for iLab,Lab in enumerate(Labels):
self.DataSt[self.ID[iar]][Lab] = self.SC.f[Lab][xi:xf+1]
if iLab > 1:
self.vmax[Lab].append(np.nanmax(self.SC.f[Lab][xi:xf+1]))
self.vmin[Lab].append(np.nanmin(self.SC.f[Lab][xi:xf+1]))
# Changes
if np.nanmax(self.DataSt[self.ID[iar]]['Prec']) == 0 or np.isnan(np.nanmax(self.DataSt[self.ID[iar]]['Prec'])):
continue
xMax = np.where(self.DataSt[self.ID[iar]]['Prec'] == np.nanmax(self.DataSt[self.ID[iar]]['Prec']))[0][0]
# Precipitation
self.PrecCount[self.ID[iar]] = HyMF.PrecCount(self.DataSt[self.ID[iar]]['Prec'],self.DataSt[self.ID[iar]]['FechaC'],dt=1,M=xMax)
try:
flag = np.isnan(self.PrecCount[self.ID[iar]]['DatesEvst'][0])
except TypeError:
flag = False
if flag:
continue
# Change First
xEv = np.where(self.DataSt[self.ID[iar]]['FechaCP'] == self.PrecCount[self.ID[iar]]['DatesEvst'][0])[0][0]
Bef = xEv-int(60/dt*TBef)
Aft = xEv+int(60/dt*(TAft/60))
self.Changes[self.ID[iar]] = dict()
for Var in LabCh:
oper = opers[Var]
Min = oper(self.DataSt[self.ID[iar]][Var][Bef:Aft])
xMin1 = np.where(self.DataSt[self.ID[iar]][Var][:Aft]==Min)[0][-1]
self.Changes[self.ID[iar]][Var] = BP.C_Rates_Changes(self.DataSt[self.ID[iar]][Var],dt=dt,
MP=xMin1,MaxMin=MaxMin[Var],flagTop=False)
self.IDC.append(self.ID[iar])
self.vmax2 = self.vmax.copy()
self.vmin2 = self.vmin.copy()
for Lab in Labels[2:]:
self.vmax[Lab] = np.nanmax(self.vmax[Lab])+0.1
if Lab == 'Prec':
self.vmin[Lab] = np.nanmin(self.vmin[Lab])
else:
self.vmin[Lab] = np.nanmin(self.vmin[Lab])-0.1
return