Updated script that can be controled by Nodejs web app

lib/python3.13/site-packages/pandas/core/_numba/kernels/min_max_.py

@@ -0,0 +1,125 @@
+"""
+Numba 1D min/max kernels that can be shared by
+* Dataframe / Series
+* groupby
+* rolling / expanding
+
+Mirrors pandas/_libs/window/aggregation.pyx
+"""
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+import numba
+import numpy as np
+
+if TYPE_CHECKING:
+    from pandas._typing import npt
+
+
+@numba.jit(nopython=True, nogil=True, parallel=False)
+def sliding_min_max(
+    values: np.ndarray,
+    result_dtype: np.dtype,
+    start: np.ndarray,
+    end: np.ndarray,
+    min_periods: int,
+    is_max: bool,
+) -> tuple[np.ndarray, list[int]]:
+    N = len(start)
+    nobs = 0
+    output = np.empty(N, dtype=result_dtype)
+    na_pos = []
+    # Use deque once numba supports it
+    # https://github.com/numba/numba/issues/7417
+    Q: list = []
+    W: list = []
+    for i in range(N):
+        curr_win_size = end[i] - start[i]
+        if i == 0:
+            st = start[i]
+        else:
+            st = end[i - 1]
+
+        for k in range(st, end[i]):
+            ai = values[k]
+            if not np.isnan(ai):
+                nobs += 1
+            elif is_max:
+                ai = -np.inf
+            else:
+                ai = np.inf
+            # Discard previous entries if we find new min or max
+            if is_max:
+                while Q and ((ai >= values[Q[-1]]) or values[Q[-1]] != values[Q[-1]]):
+                    Q.pop()
+            else:
+                while Q and ((ai <= values[Q[-1]]) or values[Q[-1]] != values[Q[-1]]):
+                    Q.pop()
+            Q.append(k)
+            W.append(k)
+
+        # Discard entries outside and left of current window
+        while Q and Q[0] <= start[i] - 1:
+            Q.pop(0)
+        while W and W[0] <= start[i] - 1:
+            if not np.isnan(values[W[0]]):
+                nobs -= 1
+            W.pop(0)
+
+        # Save output based on index in input value array
+        if Q and curr_win_size > 0 and nobs >= min_periods:
+            output[i] = values[Q[0]]
+        else:
+            if values.dtype.kind != "i":
+                output[i] = np.nan
+            else:
+                na_pos.append(i)
+
+    return output, na_pos
+
+
+@numba.jit(nopython=True, nogil=True, parallel=False)
+def grouped_min_max(
+    values: np.ndarray,
+    result_dtype: np.dtype,
+    labels: npt.NDArray[np.intp],
+    ngroups: int,
+    min_periods: int,
+    is_max: bool,
+) -> tuple[np.ndarray, list[int]]:
+    N = len(labels)
+    nobs = np.zeros(ngroups, dtype=np.int64)
+    na_pos = []
+    output = np.empty(ngroups, dtype=result_dtype)
+
+    for i in range(N):
+        lab = labels[i]
+        val = values[i]
+        if lab < 0:
+            continue
+
+        if values.dtype.kind == "i" or not np.isnan(val):
+            nobs[lab] += 1
+        else:
+            # NaN value cannot be a min/max value
+            continue
+
+        if nobs[lab] == 1:
+            # First element in group, set output equal to this
+            output[lab] = val
+            continue
+
+        if is_max:
+            if val > output[lab]:
+                output[lab] = val
+        else:
+            if val < output[lab]:
+                output[lab] = val
+
+    # Set labels that don't satisfy min_periods as np.nan
+    for lab, count in enumerate(nobs):
+        if count < min_periods:
+            na_pos.append(lab)
+
+    return output, na_pos