From a11794e54b83ff7b6a5562feb8d2a24632a746e3 Mon Sep 17 00:00:00 2001
From: Ryuichi Arafune <ryuichi.arafune@gmail.com>
Date: Wed, 22 Nov 2023 12:45:22 +0900
Subject: [PATCH] =?UTF-8?q?=F0=9F=94=A5=20=20remove=20function:=20propagat?=
 =?UTF-8?q?e=5Fstatistical=5Ferror=20&=20derivative?=
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---
 arpes/plotting/spin.py  |  4 ----
 arpes/utilities/math.py | 35 +----------------------------------
 2 files changed, 1 insertion(+), 38 deletions(-)

diff --git a/arpes/plotting/spin.py b/arpes/plotting/spin.py
index 7e21bbd5..e9451045 100644
--- a/arpes/plotting/spin.py
+++ b/arpes/plotting/spin.py
@@ -16,7 +16,6 @@
 from arpes.analysis.statistics import mean_and_deviation
 from arpes.bootstrap import bootstrap
 from arpes.provenance import save_plot_provenance
-from arpes.utilities.math import polarization, propagate_statistical_error
 
 from .tof import scatter_with_std
 from .utils import label_for_dim, path_for_plot, polarization_colorbar, savefig
@@ -35,9 +34,6 @@
 )
 
 
-test_polarization = propagate_statistical_error(polarization)
-
-
 @save_plot_provenance
 def spin_colored_spectrum(
     spin_dr: xr.Dataset,
diff --git a/arpes/utilities/math.py b/arpes/utilities/math.py
index 73fb808e..97e16d20 100644
--- a/arpes/utilities/math.py
+++ b/arpes/utilities/math.py
@@ -2,7 +2,7 @@
 from __future__ import annotations
 
 import itertools
-from collections.abc import Callable, Iterable
+from collections.abc import Iterable
 from typing import TYPE_CHECKING
 
 import numpy as np
@@ -16,44 +16,11 @@
     from numpy.typing import NDArray
 
 
-def derivative(f: Callable[..., float], arg_idx: int = 0) -> float:
-    """Defines a simple midpoint derivative."""
-
-    def d(*args: Incomplete):
-        args = list(args)
-        ref_arg = args[arg_idx]
-        d = ref_arg / 100
-        args[arg_idx] = ref_arg + d
-        high = f(*args)
-        args[arg_idx] = ref_arg - d
-        low = f(*args)
-        return (high - low) / (2 * d)
-
-    return d
-
-
 def polarization(up: NDArray[np.float_], down: NDArray[np.float_]) -> NDArray[np.float_]:
     """The equivalent normalized difference for a two component signal."""
     return (up - down) / (up + down)
 
 
-def propagate_statistical_error(f):
-    """To compute a function which propagates statistical error.
-
-    It Uses numerical derivatives and sampling.
-    """
-
-    def compute_propagated_error(*args):
-        running_sum = 0
-        for i, arg in enumerate(args):
-            df_darg_i = derivative(f, i)
-            running_sum += df_darg_i(*args) ** 2 * arg
-
-        return np.sqrt(running_sum)
-
-    return compute_propagated_error
-
-
 def shift_by(
     arr: NDArray[np.float_],
     value: xr.DataArray | NDArray[np.float_],