From af9f38a4d6997683cabc08b6cd55e671f5036054 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Al=C3=A1n=20Mu=C3=B1oz?= <alan.munoz@ed.ac.uk>
Date: Tue, 27 Sep 2022 13:25:53 +0100
Subject: [PATCH] refactor(dsignal): speedup using bottleneck
---
src/postprocessor/core/processes/dsignal.py | 24 +++++++++++++++------
1 file changed, 17 insertions(+), 7 deletions(-)
diff --git a/src/postprocessor/core/processes/dsignal.py b/src/postprocessor/core/processes/dsignal.py
index 26575b59..12631958 100644
--- a/src/postprocessor/core/processes/dsignal.py
+++ b/src/postprocessor/core/processes/dsignal.py
@@ -1,6 +1,8 @@
+import bottleneck as bn
+import numpy as np
import pandas as pd
-from agora.abc import ParametersABC
+from agora.abc import ParametersABC
from postprocessor.core.abc import PostProcessABC
@@ -9,20 +11,28 @@ class dsignalParameters(ParametersABC):
:window: Number of timepoints to consider for signal.
"""
- _defaults = {"window": 3}
+ _defaults = {"window": 10, "min_count": 5}
class dsignal(PostProcessABC):
"""
- Calculate the change in a signal depending on a window
+ Calculate the change in a signal using the mean of a moving window.
"""
def __init__(self, parameters: dsignalParameters):
super().__init__(parameters)
def run(self, signal: pd.DataFrame):
- return (
- signal.rolling(window=self.parameters.window, axis=1)
- .mean()
- .diff(axis=1)
+ matrix = np.diff(
+ bn.move_mean(
+ signal,
+ window=self.parameters.window,
+ min_count=self.parameters.min_count,
+ axis=1,
+ ),
+ axis=1,
)
+
+ # Pad values to keep the same signal shape
+ matrix = np.pad(matrix, ((0, 0), (0, 1)), constant_values=np.nan)
+ return pd.DataFrame(matrix, index=signal.index, columns=signal.columns)
--
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