更新相关计算方法，拆分错误提示

func/Module_approximately_align.py

@@ -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())
+        if not Path(Config["Path"]["Input_orgBcg"]).exists():
-                or (not Path(Config["Path"]["Input_Abd"]).exists())):
+            return Result().failure(
-            return Result().failure(info=Constants.INPUT_FAILURE + Constants.APPROXIMATELY_ALIGN_FAILURE_REASON["Data_Path_Not_Exist"])
+                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
+            a *= MULTIPLE_FACTOER
-            v *= 100
+            v *= MULTIPLE_FACTOER
             a = a.astype(int64)
             v = v.astype(int64)
-            a = pad(a, (len(v) - 1, len(v) - 1), mode='constant')
+            tho_relate = correlate(a, v, mode='full')
-            tho_relate = zeros(len(a) - len(v) - 1, dtype=int64)
+            tho_relate = tho_relate / (MULTIPLE_FACTOER ** 2)
-            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_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 = correlate(a, v, mode='full')
-
-            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 = 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