BUG MAINT TST #779 pvi update

README.md

@@ -204,7 +204,7 @@ $ pip install pandas_ta[full]
 
 ## Development Version
 
-The _development_ version, _0.4.13b_, includes _numerous_ bug fixes, speed improvements and better documentation since release, _0.3.14b_.
+The _development_ version, _0.4.14b_, includes _numerous_ bug fixes, speed improvements and better documentation since release, _0.3.14b_.
 
 ```sh
 $ pip install -U git+https://github.com/twopirllc/pandas-ta.git@development

pandas_ta/overlap/sma.py

@@ -68,7 +68,7 @@ def sma(
         sma = SMA(close, length)
     else:
         np_close = close.to_numpy()
-        sma = np_sma(np_close, length)
+        sma = nb_sma(np_close, length)
         sma = Series(sma, index=close.index)
 
     # Offset

pandas_ta/overlap/ssf3.py

@@ -79,7 +79,7 @@ def ssf3(
 
     # Calculate
     np_close = close.to_numpy()
-    ssf = np_ssf3(np_close, length, pi, sqrt3)
+    ssf = nb_ssf3(np_close, length, pi, sqrt3)
     ssf = Series(ssf, index=close.index)
 
     # Offset

pandas_ta/overlap/wma.py

@@ -77,7 +77,7 @@ def wma(
         wma = WMA(close, length)
     else:
         np_close = close.to_numpy()
-        wma_ = np_wma(np_close, length, asc, True)
+        wma_ = nb_wma(np_close, length, asc, True)
         wma = Series(wma_, index=close.index)
 
     # Offset

pandas_ta/trend/alphatrend.py

@@ -128,7 +128,7 @@ def alphatrend(
 
     np_upper_atr, np_lower_atr = upper_atr.to_numpy(), lower_atr.to_numpy()
 
-    at = np_alpha(np_lower_atr, np_upper_atr, momo.to_numpy() >= threshold)
+    at = nb_alpha(np_lower_atr, np_upper_atr, momo.to_numpy() >= threshold)
     at = Series(at, index=close.index)
 
     atl = at.shift(lag)

pandas_ta/trend/ht_trendline.py

@@ -127,7 +127,7 @@ def ht_trendline(
         tl = HT_TRENDLINE(close)
     else:
         np_close = close.to_numpy()
-        np_tl = np_ht_trendline(np_close)
+        np_tl = nb_ht_trendline(np_close)
 
         if prenan > 0:
             np_tl[:prenan] = nan

pandas_ta/trend/trendflex.py

@@ -93,7 +93,7 @@ def trendflex(
 
     # Calculate
     np_close = close.to_numpy()
-    result = np_trendflex(np_close, length, smooth, alpha, pi, sqrt2)
+    result = nb_trendflex(np_close, length, smooth, alpha, pi, sqrt2)
     result[:length] = nan
     result = Series(result, index=close.index)
 

pandas_ta/volatility/atrts.py

@@ -123,7 +123,7 @@ def atrts(
     ma_ = _ma(mamode, close, length=ma_length, talib=mode_tal)
 
     np_close, np_ma, np_atr = close.to_numpy(), ma_.to_numpy(), atr_.to_numpy()
-    np_atrts_, _, _ = np_atrts(np_close, np_ma, np_atr, length, ma_length)
+    np_atrts_, _, _ = nb_atrts(np_close, np_ma, np_atr, length, ma_length)
 
     percent = kwargs.pop("percent", False)
     if percent:

pandas_ta/volume/pvi.py

@@ -14,7 +14,7 @@
 
 
 @njit(cache=True)
-def np_pvi(np_close, np_volume, initial):
+def nb_pvi(np_close, np_volume, initial):
     result = zeros_like(np_close, dtype=float64)
     result[0] = initial
 
@@ -77,7 +77,7 @@ def pvi(
 
     # Calculate
     np_close, np_volume = close.to_numpy(), volume.to_numpy()
-    _pvi = np_pvi(np_close, np_volume, initial)
+    _pvi = nb_pvi(np_close, np_volume, initial)
 
     pvi = Series(_pvi, index=close.index)
     pvi_ma = ma(mamode, pvi, length=length)
@@ -102,7 +102,9 @@ def pvi(
     pvi_ma.name = f"PVI{_props}"
     pvi.category = pvi_ma.category = "volume"
 
-    data = { pvi.name: pvi,  pvi_ma.name: pvi_ma }
+    data = { pvi.name: pvi}
+    if np_close.size > length + 1:
+        data[pvi_ma.name] = pvi_ma
     df = DataFrame(data, index=close.index)
 
     # Name and Category

setup.py

@@ -19,7 +19,7 @@
         "pandas_ta.volatility",
         "pandas_ta.volume"
     ],
-    version=".".join(("0", "4", "13b")),
+    version=".".join(("0", "4", "14b")),
     description=long_description,
     long_description=long_description,
     author="Kevin Johnson",

tests/conftest.py

@@ -9,7 +9,7 @@
 
 from pandas import read_csv
 
-TEST_ROWS = 300
+TEST_ROWS = 200
 TEST_CSV = f"data/SPY_D.csv"
 
 BEEP = False

tests/test_studies.py

@@ -9,7 +9,7 @@
 [pytest.param(ta.CommonStudy, id="common"), pytest.param(ta.AllStudy, id="all")]
 
 # +/- when adding/removing indicators
-ALL_COLUMNS = 325
+ALL_COLUMNS = 323
 
 
 def test_all_study_props(all_study):
@@ -33,7 +33,7 @@ def test_common_study_props(common_study):
 @pytest.mark.parametrize("category,columns", [
     ("candles", 70), ("cycles", 2), ("momentum", 78), ("overlap", 56),
     ("performance", 2), ("statistics", 16), ("transform", 5), ("trend", 31),
-    ("volatility", 36), ("volume", 29),
+    ("volatility", 36), ("volume", 27),
     pytest.param(ta.AllStudy, ALL_COLUMNS, id=f"all-{ALL_COLUMNS}"),
     pytest.param(ta.CommonStudy, 5, id="common-5"),
 ])
@@ -89,7 +89,7 @@ def test_study_custom_e_talib(df, custom_study_e, talib):
 
 @pytest.mark.parametrize("talib", [False, True])
 def test_study_all_multirun_talib(df, all_study, talib):
-    new_columns = 619  # +/- when adding/removing indicators
+    new_columns = 613  # +/- when adding/removing indicators
     initial_columns = df.shape[1]
     df.ta.study(all_study, length=10, cores=0, talib=talib)
     df.ta.study(all_study, length=50, cores=0, talib=talib)