a brief comment to trigger git

src/hsp2/hsp2/main.py

@@ -704,23 +704,23 @@ def get_flows(
                         t = data_frame[smemn].astype(float64).to_numpy()[0:steps]
 
                     if MFname in ts and AFname in ts:
-                        t *= ts[MFname][:steps] * ts[AFname][0:steps]
+                        t = t * ts[MFname][:steps] * ts[AFname][0:steps]
                         msg(4, f"MFACTOR modified by timeseries {MFname}")
                         msg(4, f"AFACTR modified by timeseries {AFname}")
                     elif MFname in ts:
-                        t *= afactr * ts[MFname][0:steps]
+                        t = t * afactr * ts[MFname][0:steps]
                         msg(4, f"MFACTOR modified by timeseries {MFname}")
                     elif AFname in ts:
-                        t *= mfactor * ts[AFname][0:steps]
+                        t = t * mfactor * ts[AFname][0:steps]
                         msg(4, f"AFACTR modified by timeseries {AFname}")
                     else:
-                        t *= factor
+                        t = t * factor
 
                     # if poht to iheat, imprecision in hspf conversion factor requires a slight adjustment
                     if (smemn == "POHT" or smemn == "SOHT") and tmemn == "IHEAT":
-                        t *= 0.998553
+                        t = t * 0.998553
                     if (smemn == "PODOXM" or smemn == "SODOXM") and tmemn == "OXIF1":
-                        t *= 1.000565
+                        t = t * 1.000565
 
                     # ??? ISSUE: can fetched data be at different frequency - don't know how to transform.
                     if tmemn in ts:

src/hsp2/hsp2/utilities.py

@@ -14,7 +14,7 @@
 from numba import types
 from numba.typed import Dict
 from numpy import float64, full, tile, zeros
-from pandas import Series, date_range
+from pandas import Series, date_range, Timedelta
 from pandas.tseries.offsets import Minute
 
 from hsp2.hsp2io.protocols import Category, SupportsReadTS, SupportsWriteTS
@@ -213,8 +213,9 @@ def transform(ts, name, how, siminfo):
     NOTE: these routines work for both regular and sparse timeseries input
     """
 
-    tsfreq = ts.index.freq
-    freq = Minute(siminfo["delt"])
+    tsfreq = Timedelta("1 " + ts.index.freqstr)
+    fmins = Minute(siminfo["delt"])
+    freq = Timedelta(fmins).to_timedelta64()
     stop = siminfo["stop"]
 
     # append duplicate of last point to force processing last full interval
@@ -226,7 +227,7 @@ def transform(ts, name, how, siminfo):
     elif tsfreq is None:  # Sparse time base, frequency not defined
         ts = ts.reindex(siminfo["tbase"]).ffill().bfill()
     elif how == "SAME":
-        ts = ts.resample(freq).ffill()  # tsfreq >= freq assumed, or bad user choice
+        ts = ts.resample(fmins).ffill()  # tsfreq >= freq assumed, or bad user choice
     elif not how:
         if name in flowtype:
             if "Y" in str(tsfreq) or "M" in str(tsfreq) or tsfreq > freq:
@@ -236,24 +237,24 @@ def transform(ts, name, how, siminfo):
                     ratio = 1.0 / 8766.0
                 else:
                     ratio = freq / tsfreq
-                ts = (ratio * ts).resample(freq).ffill()  # HSP2 how = div
+                ts = (ratio * ts).resample(fmins).ffill()  # HSP2 how = div
             else:
-                ts = ts.resample(freq).sum()
+                ts = ts.resample(fmins).sum()
         else:
             if "Y" in str(tsfreq) or "M" in str(tsfreq) or tsfreq > freq:
-                ts = ts.resample(freq).ffill()
+                ts = ts.resample(fmins).ffiio_managerll()
             else:
-                ts = ts.resample(freq).mean()
+                ts = ts.resample(fmins).mean()
     elif how == "MEAN":
-        ts = ts.resample(freq).mean()
+        ts = ts.resample(fmins).mean()
     elif how == "SUM":
-        ts = ts.resample(freq).sum()
+        ts = ts.resample(fmins).sum()
     elif how == "MAX":
-        ts = ts.resample(freq).max()
+        ts = ts.resample(fmins).max()
     elif how == "MIN":
-        ts = ts.resample(freq).min()
+        ts = ts.resample(fmins).min()
     elif how == "LAST":
-        ts = ts.resample(freq).ffill()
+        ts = ts.resample(fmins).ffill()
     elif how == "DIV":
         if "Y" in str(tsfreq) or "M" in str(tsfreq):
             mult = 1
@@ -268,13 +269,13 @@ def transform(ts, name, how, siminfo):
                 ratio = 1.0 / (8766.0 * mult)
             else:
                 ratio = freq / tsfreq
-            ts = (ratio * ts).resample(freq).ffill()  # HSP2 how = div
+            ts = (ratio * ts).resample(fmins).ffill()  # HSP2 how = div
         else:
-            ts = (ts * (freq / ts.index.freq)).resample(freq).ffill()
+            ts = (ts * (freq / tsfreq)).resample(fmins).ffill()
     elif how == "ZEROFILL":
-        ts = ts.resample(freq).fillna(0.0)
+        ts = ts.resample(fmins).fillna(0.0)
     elif how == "INTERPOLATE":
-        ts = ts.resample(freq).interpolate()
+        ts = ts.resample(fmins).interpolate()
     else:
         print(f"UNKNOWN method in TRANS, {how}")
         return zeros(1)
@@ -287,7 +288,8 @@ def hoursval(siminfo, hours24, dofirst=False, lapselike=False):
     """create hours flags, flag on the hour or lapse table over full simulation"""
     start = siminfo["start"]
     stop = siminfo["stop"]
-    freq = Minute(siminfo["delt"])
+    fmins = Minute(siminfo["delt"])
+    freq = Timedelta(fmins).to_timedelta64()
 
     dr = date_range(
         start=f"{start.year}-01-01", end=f"{stop.year}-12-31", freq=Minute(60)
@@ -297,16 +299,17 @@ def hoursval(siminfo, hours24, dofirst=False, lapselike=False):
         hours[0] = 1
 
     ts = Series(hours[0 : len(dr)], dr)
+    tsfreq = Timedelta("1 " + ts.index.freqstr)
     if lapselike:
-        if ts.index.freq > freq:  # upsample
-            ts = ts.resample(freq).asfreq().ffill()
-        elif ts.index.freq < freq:  # downsample
-            ts = ts.resample(freq).mean()
+        if tsfreq > freq:  # upsample
+            ts = ts.resample(fmins).asfreq().ffill()
+        elif tsfreq < freq:  # downsample
+            ts = ts.resample(fmins).mean()
     else:
-        if ts.index.freq > freq:  # upsample
-            ts = ts.resample(freq).asfreq().fillna(0.0)
-        elif ts.index.freq < freq:  # downsample
-            ts = ts.resample(freq).max()
+        if tsfreq > freq:  # upsample
+            ts = ts.resample(fmins).asfreq().fillna(0.0)
+        elif tsfreq < freq:  # downsample
+            ts = ts.resample(fmins).max()
     return ts.truncate(start, stop).to_numpy()
 
 
@@ -321,16 +324,18 @@ def monthval(siminfo, monthly):
     """returns value at start of month for all times within the month"""
     start = siminfo["start"]
     stop = siminfo["stop"]
-    freq = Minute(siminfo["delt"])
+    fmins = Minute(siminfo["delt"])
+    freq = Timedelta(fmins).to_timedelta64()
 
     months = tile(monthly, stop.year - start.year + 1).astype(float)
     dr = date_range(start=f"{start.year}-01-01", end=f"{stop.year}-12-31", freq="MS")
     ts = Series(months, index=dr).resample("D").ffill()
+    tsfreq = Timedelta("1 " + ts.index.freqstr)
 
-    if ts.index.freq > freq:  # upsample
-        ts = ts.resample(freq).asfreq().ffill()
-    elif ts.index.freq < freq:  # downsample
-        ts = ts.resample(freq).mean()
+    if tsfreq > freq:  # upsample
+        ts = ts.resample(fmins).asfreq().ffill()
+    elif tsfreq < freq:  # downsample
+        ts = ts.resample(fmins).mean()
     return ts.truncate(start, stop).to_numpy()
 
 
@@ -339,16 +344,19 @@ def dayval(siminfo, monthly):
     interpolation to day, but constant within day"""
     start = siminfo["start"]
     stop = siminfo["stop"]
-    freq = Minute(siminfo["delt"])
+    fmins = Minute(siminfo["delt"])
+    freq = Timedelta(fmins).to_timedelta64()
 
     months = tile(monthly, stop.year - start.year + 1).astype(float)
     dr = date_range(start=f"{start.year}-01-01", end=f"{stop.year}-12-31", freq="MS")
     ts = Series(months, index=dr).resample("D").interpolate("time")
+    tsfreq = Timedelta("1 " + ts.index.freqstr)
 
-    if ts.index.freq > freq:  # upsample
-        ts = ts.resample(freq).ffill()
-    elif ts.index.freq < freq:  # downsample
-        ts = ts.resample(freq).mean()
+
+    if tsfreq > freq:  # upsample
+        ts = ts.resample(fmins).ffill()
+    elif tsfreq < freq:  # downsample
+        ts = ts.resample(fmins).mean()
     return ts.truncate(start, stop).to_numpy()
 
 

src/hsp2/hsp2io/hdf.py

@@ -13,9 +13,12 @@ def __init__(self, file_path: str) -> None:
         self._store = pd.HDFStore(file_path)
         None
 
-    def __del__(self):
+    def close(self):
         self._store.close()
 
+    def __del__(self):
+        self.close()
+
     def __enter__(self):
         return self
 

src/hsp2/hsp2tools/commands.py

@@ -24,6 +24,7 @@ def run(h5file, saveall=True, compress=True):
     hdf5_instance = HDF5(h5file)
     io_manager = IOManager(hdf5_instance)
     main(io_manager, saveall=saveall, jupyterlab=compress)
+    hdf5_instance.close()
 
 
 def import_uci(ucifile, h5file):