Changeset 2164 in lmdz_wrf for trunk/tools/generic_tools.py

Timestamp:

Oct 4, 2018, 7:24:00 PM (6 years ago)

Author:

lfita

Message:

Workin version of:

• `time_slices': Function to return temporal slices of a series of times for a given amount of periods

File:

• trunk/tools/generic_tools.py (modified) (4 diffs)

trunk/tools/generic_tools.py

@@ -175 +175 @@
 # stringList_dictKeysVals: Function to provide a dictionary with keys which contain values of lists from a string
 # subbasin_point: Function to provide sub-basins given a grid point following a matrix of trips
+# time_slices: Function to return temporal slices of a series of times for a given amount of periods
 # timestep_conform: Function to provide the time-step in seconds which conforms 1 temporal unit and the resultant number
 #   of time-steps for a whole period is multiple of a given number
@@ -14208 +14209 @@
         stmatdates.append(imdate)
 
+    elif per == 'day':
+        # Arranging accordingly beginning/ending statistics periods
+        istats[3:5] = 0
+        dmon = days_month(estats[0], estats[1])
+        estats[2] = estats[2]+1
+        if estats[2] > dmon: 
+            estats[1] = estats[1] + 1
+            if estats[1] > 12:
+                estats[0] = estats[0] + 1
+                estats[1] = 1
+        estats[3:5] = 0
+
+        istdate = dt.datetime(istats[0], istats[1], istats[2], istats[3], istats[4], \
+          istats[5])
+        estdate = dt.datetime(estats[0], estats[1], estats[2], estats[3], estats[4], \
+          estats[5])
+
+        # Getting statistics periods
+        stmatdates = [istats]
+        idate = istdate
+        imdate = istats.copy()
+        while idate < estdate:
+            imdate = imdate.copy()
+            dmon = days_month(imdate[0], imdate[1])
+            imdate[2] = imdate[2] + amount
+            if imdate[2] > dmon:
+                imdate[2] = imdate[2] - dmon
+                imdate[1] = imdate[1] + 1
+                if imdate[1] > 12:
+                    imdate[0] = imdate[0] + 1
+                    imdate[1] = 1
+            idate= dt.datetime(imdate[0], imdate[1], imdate[2], imdate[3], imdate[4],\
+              imdate[5])
+            if idate < estdate: stmatdates.append(imdate)
+            else: break
+        stmatdates.append(imdate)
+
     else:
         print errormsg
@@ -14257 +14295 @@
         timeslice[1] = it
         icfstup = np.min([icfst+1, Nslices-1])
-        print cfstdates[icfst], '<=', tv[it], '<', cfstdates[icfstup]
         if not(tv[it] >= cfstdates[icfst] and tv[it] < cfstdates[icfstup]):
             slices.append(timeslice)
@@ -14270 +14307 @@
     return slices, Nslices
 
-tv = []
-values = []
-totT = 31.
-dT = 7.
-for it in range(0,10):
-   for itt in range(3):
-        tv.append(it*totT+dT*itt)
-        values.append(it*1.)
-
-vals = np.array(values)
-itdim = 0
-tu = 'days since 1949-12-01 00:00:00'
-per = 'month'
-calend = 'standard'
-amount = 1
-
-tslc, Ntslc = time_slices(tv, tu, calend, per, amount)
-
-statn = 'mean'
-tstat = np.ones((Ntslc), dtype=np.float)
-for islc in range(Ntslc):
-    timeslcv = tslc[islc]
-    timeslc = [slice(timeslcv[0],timeslcv[1],timeslcv[2])]
-    tvals = vals[tuple(timeslc)]
-    if statn == 'min':
-        tstat[islc] = np.min(tvals, axis=itdim)
-    elif statn == 'max':
-        tstat[islc] = np.max(tvals, axis=itdim)
-    elif statn == 'mean':
-        tstat[islc] = np.mean(tvals, axis=itdim)
-    elif statn == 'mean2':
-        tstat[islc] = np.mean(tvals**2, axis=itdim)
-    elif statn == 'std':
-        tstat[islc] = np.std(tvals, axis=itdim)
-
-    timeslc = tslc[islc]
-    itime = tv[timeslc[0]]
-    etime = tv[timeslc[1]]
-    print islc, ':', itime, '-', etime, '<>', tvals, '=', tstat[islc]
-
+#tv = []
+#values = []
+#totT = 24.*60.
+#dT = 7.
+#for it in range(0,10):
+#   for itt in range(3):
+#        tv.append(29*24*60.+it*totT+dT*itt)
+#        values.append(it*1.)
+
+#vals = np.array(values)
+#itdim = 0
+#tu = 'minutes since 1949-12-01 00:00:00'
+#per = 'day'
+#calend = 'standard'
+#amount = 1
+
+#tslc, Ntslc = time_slices(tv, tu, calend, per, amount)
+
+#statn = 'mean'
+#tstat = np.ones((Ntslc), dtype=np.float)
+#for islc in range(Ntslc):
+#    timeslcv = tslc[islc]
+#    timeslc = [slice(timeslcv[0],timeslcv[1],timeslcv[2])]
+#    tvals = vals[tuple(timeslc)]
+#    if statn == 'min':
+#        tstat[islc] = np.min(tvals, axis=itdim)
+#    elif statn == 'max':
+#        tstat[islc] = np.max(tvals, axis=itdim)
+#    elif statn == 'mean':
+#        tstat[islc] = np.mean(tvals, axis=itdim)
+#    elif statn == 'mean2':
+#        tstat[islc] = np.mean(tvals**2, axis=itdim)
+#    elif statn == 'std':
+#        tstat[islc] = np.std(tvals, axis=itdim)
+
+#    timeslc = tslc[islc]
+#    itime = tv[timeslc[0]]
+#    etime = tv[timeslc[1]]
+#    print islc, ':', itime, '-', etime, '<>', tvals, '=', tstat[islc]
    
 #quit()