Yorusora/Signal_Label_Reborn
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更新相关计算方法，拆分错误提示

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marques
2025-05-14 21:26:26 +08:00
parent 517ad55517
commit 2b5cc322d7
1 changed files with 21 additions and 34 deletions
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55
func/Module_approximately_align.py
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@ -7,12 +7,11 @@ from PySide6.QtWidgets import QMessageBox, QMainWindow, QApplication
from matplotlib.backends.backend_qt import NavigationToolbar2QT
from matplotlib.backends.backend_qtagg import FigureCanvasQTAgg as FigureCanvas
from matplotlib.figure import Figure
from numba import prange, njit
from numpy import repeat, convolve, ones, mean, std, empty, int64, sum as np_sum, pad, zeros, array, argmax, linspace, \
diff
from overrides import overrides
from pandas import read_csv, DataFrame
from scipy.signal import find_peaks, resample, butter, sosfiltfilt
from scipy.signal import find_peaks, resample, butter, sosfiltfilt, correlate
from yaml import dump, load, FullLoader
from func.utils.PublicFunc import PublicFunc
@ -856,10 +855,19 @@ class Data:
self.processed_Abd = None
def open_file(self):
if ((not Path(Config["Path"]["Input_orgBcg"]).exists())
or (not Path(Config["Path"]["Input_Tho"]).exists())
or (not Path(Config["Path"]["Input_Abd"]).exists())):
return Result().failure(info=Constants.INPUT_FAILURE + Constants.APPROXIMATELY_ALIGN_FAILURE_REASON["Data_Path_Not_Exist"])
if not Path(Config["Path"]["Input_orgBcg"]).exists():
return Result().failure(
info=Constants.INPUT_FAILURE + "orgBcg: " + Constants.APPROXIMATELY_ALIGN_FAILURE_REASON[
"Data_Path_Not_Exist"])
if not Path(Config["Path"]["Input_Tho"]).exists():
return Result().failure(
info=Constants.INPUT_FAILURE + "Tho: " + Constants.APPROXIMATELY_ALIGN_FAILURE_REASON[
"Data_Path_Not_Exist"])
if not Path(Config["Path"]["Input_Abd"]).exists():
return Result().failure(
info=Constants.INPUT_FAILURE + "Abd: " + Constants.APPROXIMATELY_ALIGN_FAILURE_REASON[
"Data_Path_Not_Exist"])
try:
self.raw_orgBcg = read_csv(Config["Path"]["Input_orgBcg"],
@ -1008,20 +1016,16 @@ class Data:
def calculate_correlation1(self):
# 计算互相关1/2
try:
# 计算因子
MULTIPLE_FACTOER = ConfigParams.APPROXIMATELY_ALIGN_CONFIG_NEW_CONTENT["Multiple_Factor"]
a = self.processed_Tho[Config["PSGConfig"]["PreCut"]:len(self.processed_Tho) - Config["PSGConfig"]["PostCut"]].copy()
v = self.processed_orgBcg[Config["orgBcgConfig"]["PreCut"]:len(self.processed_orgBcg) - Config["orgBcgConfig"]["PostCut"]].copy()
a *= 100
v *= 100
a *= MULTIPLE_FACTOER
v *= MULTIPLE_FACTOER
a = a.astype(int64)
v = v.astype(int64)
a = pad(a, (len(v) - 1, len(v) - 1), mode='constant')
tho_relate = zeros(len(a) - len(v) - 1, dtype=int64)
len_calc = len(a) - len(v) - 1
# 将序列分成一百份来计算
for i in range(100):
tho_relate[i * len_calc // 100:(i + 1) * len_calc // 100] = Data.get_Correlate(a, v, array(
[i * len_calc // 100, (i + 1) * len_calc // 100], dtype=int64))
tho_relate = tho_relate / 10000
tho_relate = correlate(a, v, mode='full')
tho_relate = tho_relate / (MULTIPLE_FACTOER ** 2)
tho_relate2 = - tho_relate
result = {"tho_relate": tho_relate, "tho_relate2": tho_relate2}
@ -1039,14 +1043,7 @@ class Data:
v *= 100
a = a.astype(int64)
v = v.astype(int64)
a = pad(a, (len(v) - 1, len(v) - 1), mode='constant')
abd_relate = zeros(len(a) - len(v) - 1, dtype=int64)
len_calc = len(a) - len(v) - 1
# 将序列分成一百份来计算
for i in range(100):
abd_relate[i * len_calc // 100:(i + 1) * len_calc // 100] = Data.get_Correlate(a, v, array(
[i * len_calc // 100, (i + 1) * len_calc // 100], dtype=int64))
abd_relate = correlate(a, v, mode='full')
abd_relate = abd_relate / 10000
abd_relate2 = - abd_relate
@ -1086,13 +1083,3 @@ class Data:
return Result().success(info=Constants.APPROXIMATELY_EPOCH_GET_FINISHED, data=result)
@staticmethod
@njit("int64[:](int64[:],int64[:], int64[:])", nogil=True, parallel=True)
def get_Correlate(a, v, between):
begin, end = between
if end == 0:
end = len(a) - len(v) - 1
result = empty(end - begin, dtype=int64)
for i in prange(end - begin):
result[i] = np_sum(a[begin + i: begin + i + len(v)] * v)
return result