Added "pandas_offset_by_version" to address deprecated offset codes.

* Some pandas offset codes changed in pandas 2.2.  If <2.2 offset
  codes are with >2.2 throws deprecation warnings and if >2.2 codes
  used with <2.2 throws value errors.  Using the new
  "pandas_offset_by_version" function will used the >2.2 codes and
  change to the older codes if run with pandas <2.2.
* Fixed to_numeric(..., errors="ignore", ...) deprecation warnings.
* Fixed resample(..., kind="timestamp", ...) deprecation warnings.
* Removed unused FILES block entry for WDM2 and fixed comments
  ("***" has to be in columns less than 80) in
  tests/ZRW_WestIndian/HSPF.uci and removed the unused
  tests/ZRW_WestIndian/HSPF.WDM.
* Added missing WDM file in tests/ipwater.

src/hsp2/hsp2/utilities.py

@@ -224,7 +224,7 @@ def transform(ts, name, how, siminfo):
 
     if freq == tsfreq:
         pass
-    elif tsfreq == None:  # Sparse time base, frequency not defined
+    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
@@ -618,7 +618,7 @@ def get_gener_timeseries(
             if link.SVOLNO in gener_instances:
                 gener = gener_instances[link.SVOLNO]
                 series = zeros(len(gener.ts_output)) + gener.ts_output
-                if type(link.MFACTOR) == float and link.MFACTOR != 1:
+                if isinstance(link.MFACTOR, float) and link.MFACTOR != 1:
                     series *= link.MFACTOR
 
                 key = f"{link.TMEMN}{link.TMEMSB1} {link.TMEMSB2}".rstrip()
@@ -700,3 +700,53 @@ def clean_name(TMEMN, TMEMSB):
             tname = TMEMN + "1"
 
     return tname
+
+
+def pandas_offset_by_version(new_offset: str) -> str:
+    """
+    Convert the time offset code to match the version of pandas.
+
+    Parameters
+    ----------
+    offset
+        The offset to convert.
+
+    Returns
+    -------
+    offset_by_version
+        The offset converted to the correct version of pandas.
+    """
+    new_to_old_freq = {}
+    major, minor = pd.__version__.split(".")[:2]
+    if (int(major) + int(minor) / 10) < 2.2:
+        new_to_old_freq = {
+            "Y": "A",
+            "ME": "M",
+            "BME": "BM",
+            "SME": "SM",
+            "CBME": "CBM",
+            "QE": "Q",
+            "BQE": "BQ",
+            "BYE": "BY",
+            "h": "H",
+            "bh": "BH",
+            "cbh": "CBH",
+            "min": "T",
+            "s": "S",
+            "ms": "L",
+            "us": "U",
+            "ns": "N",
+            "YE-JAN": "A-JAN",
+            "YE-FEB": "A-FEB",
+            "YE-MAR": "A-MAR",
+            "YE-APR": "A-APR",
+            "YE-MAY": "A-MAY",
+            "YE-JUN": "A-JUN",
+            "YE-JUL": "A-JUL",
+            "YE-AUG": "A-AUG",
+            "YE-SEP": "A-SEP",
+            "YE-OCT": "A-OCT",
+            "YE-NOV": "A-NOV",
+            "YE-DEC": "A-DEC",
+        }
+    return new_to_old_freq.get(new_offset, new_offset)

src/hsp2/hsp2io/io.py

@@ -1,9 +1,9 @@
 from typing import List, Union
 
 import pandas as pd
-from pandas.core.frame import DataFrame
 
 from hsp2.hsp2.uci import UCI
+from hsp2.hsp2.utilities import pandas_offset_by_version
 from hsp2.hsp2io.protocols import (
     Category,
     SupportsReadTS,
@@ -86,11 +86,14 @@ def write_ts(
         if drop_columns:
             data_frame = data_frame.drop(columns=drop_columns)
 
+        if not isinstance(data_frame.index, pd.core.indexes.datetimes.DatetimeIndex):
+            data_frame = data_frame.to_timestamp()
+
         if outstep == 3:
             # change time step of output to daily
-            sumdf1 = data_frame.resample("D", kind="timestamp", origin="start").sum()
-            lastdf2 = data_frame.resample("D", kind="timestamp", origin="start").last()
-            meandf3 = data_frame.resample("D", kind="timestamp", origin="start").mean()
+            sumdf1 = data_frame.resample("D", origin="start").sum()
+            lastdf2 = data_frame.resample("D", origin="start").last()
+            meandf3 = data_frame.resample("D", origin="start").mean()
             data_frame = pd.merge(
                 lastdf2.add_suffix("_last"),
                 sumdf1.add_suffix("_sum"),
@@ -105,9 +108,15 @@ def write_ts(
             )
         elif outstep == 4:
             # change to monthly
-            sumdf1 = data_frame.resample("M", kind="timestamp", origin="start").sum()
-            lastdf2 = data_frame.resample("M", kind="timestamp", origin="start").last()
-            meandf3 = data_frame.resample("M", kind="timestamp", origin="start").mean()
+            sumdf1 = data_frame.resample(
+                pandas_offset_by_version("ME"), origin="start"
+            ).sum()
+            lastdf2 = data_frame.resample(
+                pandas_offset_by_version("ME"), origin="start"
+            ).last()
+            meandf3 = data_frame.resample(
+                pandas_offset_by_version("ME"), origin="start"
+            ).mean()
             data_frame = pd.merge(
                 lastdf2.add_suffix("_last"),
                 sumdf1.add_suffix("_sum"),
@@ -122,9 +131,15 @@ def write_ts(
             )
         elif outstep == 5:
             # change to annual
-            sumdf1 = data_frame.resample("Y", kind="timestamp", origin="start").sum()
-            lastdf2 = data_frame.resample("Y", kind="timestamp", origin="start").last()
-            meandf3 = data_frame.resample("Y", kind="timestamp", origin="start").mean()
+            sumdf1 = data_frame.resample(
+                pandas_offset_by_version("YE"), origin="start"
+            ).sum()
+            lastdf2 = data_frame.resample(
+                pandas_offset_by_version("YE"), origin="start"
+            ).last()
+            meandf3 = data_frame.resample(
+                pandas_offset_by_version("YE"), origin="start"
+            ).mean()
             data_frame = pd.merge(
                 lastdf2.add_suffix("_last"),
                 sumdf1.add_suffix("_sum"),

src/hsp2/hsp2tools/readUCI.py

@@ -168,7 +168,11 @@ def fix_df(df, op, save, ddfaults, valid):
                 pass
     cols = [c.replace("(", "").replace(")", "") for c in df.columns]
     df.columns = cols
-    df = df.apply(pd.to_numeric, errors="ignore")  # todo: 'ignore' is deprecated.
+    for col in df.columns:
+        try:
+            df[col] = pd.to_numeric(df[col])
+        except ValueError:
+            pass
     return df
 
 

tests/ZRW_WestIndian/HSPF.uci

@@ -17,7 +17,6 @@ FILES
 MESSU      24   HSPF.ECH
            25   HSPF.OUT
 WDM1       26   Met4.WDM
-WDM2       27   HSPF.WDM
 WDM3       28   ZUMBROSCEN.WDM
 BINO       92   HSPF.HBN
 END FILES
@@ -4378,8 +4377,8 @@ RCHRES
   END PLNK-PARM1
 
   PLNK-PARM2
-    RCHRES     CMMLT      CMMN     CMMNP      CMMP    TALGRH    TALGRL    TALGRM ***
-    # -  #     y/min      mg/l      mg/l      mg/l     deg F     deg F     degF ***
+*** RCHRES     CMMLT      CMMN     CMMNP      CMMP    TALGRH    TALGRL    TALGRM
+*** # -  #     y/min      mg/l      mg/l      mg/l     deg F     deg F     degF
     1  109      .033     0.045       .01      .015       95.       20.       77
   110           .033     0.045      .005      .025       95.       20.       67
   111  506      .033     0.045       .01      .015       95.       20.       77