fix: Docs, fixes because of changed memory_optimize

Docstrings.

Several fixes because tsutils.memory_optimize is less aggressive.

Horizontal and vertical lines in `plot`

Most of butterworth filter in `filter`

setup.py

@@ -85,5 +85,5 @@
         "console_scripts": ["{pkg_name}={pkg_name}.{pkg_name}:main".format(**locals())]
     },
     test_suite="tests",
-    python_requires=">=3.6",
+    python_requires=">=3.7.1",
 )

tstoolbox/functions/accumulate.py

@@ -37,13 +37,13 @@ def accumulate_cli(
 
     Parameters
     ----------
-    statistic : str
-        [optional, default is 'sum', transformation]
+    statistic : Union(str, list(str))
+        [optional, default is "sum", transformation]
 
-        Any of 'sum', 'max', 'min', 'prod' or list of same.
+        OneOrMore("sum", "max", "min", "prod")
 
         Python example::
-            statistic=['sum', 'max']
+            statistic=["sum", "max"]
 
         Command line example::
             --statistic=sum,max

tstoolbox/functions/aggregate.py

@@ -36,6 +36,7 @@ def aggregate_cli(
     target_units=None,
     print_input=False,
     tablefmt="csv",
+    skipna=True,
 ):
     """Take a time series and aggregate to specified frequency.
 
@@ -74,6 +75,12 @@ def aggregate_cli(
     {target_units}
     {print_input}
     {tablefmt}
+    skipna : bool
+        [optional, defaults to True, transformation]
+
+        If False will return a NaN for any aggregation group that has a NaN.
+        If True will fill all NaNs with 0 before aggregation.  Ignored if
+        `groupby` is "all" or `groupby` is "months_across_years".
     agg_interval :
         DEPRECATED:
         Use the 'groupby' option instead.
@@ -100,6 +107,7 @@ def aggregate_cli(
             source_units=source_units,
             target_units=target_units,
             print_input=print_input,
+            skipna=skipna,
         ),
         tablefmt=tablefmt,
     )
@@ -144,6 +152,7 @@ def aggregate(
     source_units=None,
     target_units=None,
     print_input=False,
+    skipna=True,
 ):
     """Take a time series and aggregate to specified frequency."""
     aggd = {"hourly": "H", "daily": "D", "monthly": "M", "yearly": "A", "all": "all"}
@@ -235,7 +244,9 @@ def aggregate(
             tmptsd.index = list(range(1, 13))
         else:
             tmptsd = eval(
-                """tsd.resample('{0}{1}').{2}()""".format(ninterval, groupby, method)
+                """tsd.resample('{0}{1}').agg(pd.Series.{2}, skipna={3})""".format(
+                    ninterval, groupby, method, skipna
+                )
             )
         tmptsd.columns = [tsutils.renamer(i, method) for i in tmptsd.columns]
         newts = newts.join(tmptsd, how="outer")

tstoolbox/functions/date_offset.py

@@ -116,7 +116,7 @@ def date_offset(
         clean=clean,
     )
 
-    return tsd.tshift(intervals, offset)
+    return tsd.shift(intervals, offset)
 
 
 date_offset.__doc__ = date_offset_cli.__doc__

tstoolbox/functions/equation.py

@@ -254,21 +254,21 @@ def returnval(t, x, testeval, nequation):
         tsearch, nsearch, testeval, nequation = _parse_equation(eqn)
         if tsearch and nsearch:
             y = pd.DataFrame(
-                pd.Series(index=x.index, dtype="float64"),
+                pd.Series(index=x.index, dtype="Float64"),
                 columns=["_"],
-                dtype="float64",
+                dtype="Float64",
             )
             for t in range(len(x)):
                 y.iloc[t, 0] = returnval(t, x, testeval, nequation)
         elif tsearch:
-            y = x.copy()
+            y = x.astype("Float64")
             for t in range(len(x)):
                 y.iloc[t, :] = returnval(t, x, testeval, nequation)
         elif nsearch:
             y = pd.DataFrame(
-                pd.Series(index=x.index, dtype="float64"),
+                pd.Series(index=x.index, dtype="Float64"),
                 columns=["_"],
-                dtype="float64",
+                dtype="Float64",
             )
             try:
                 y.iloc[:, 0] = eval(nequation)
@@ -286,7 +286,7 @@ def returnval(t, x, testeval, nequation):
 
         else:
             y = eval(eqn)
-        y.columns = [tsutils.renamer(i, "equation.{0}".format(cnt)) for i in y.columns]
+        y.columns = [tsutils.renamer(i, "equation{0}_".format(cnt)) for i in y.columns]
         newy = newy.join(y, how="outer")
 
     newy = tsutils.memory_optimize(newy)