update

Analyser/FFTAnalyser.py

@@ -7,49 +7,44 @@ from datetime import datetime
 import xrft
 import finufft
 
-
-class FFTAnalyser():
     
-    def __init__(self) -> None:
-        pass
+def fft(dataArray, **kwargs):
+    return xrft.fft(dataArray, **kwargs)
+
+def ifft(dataArray, **kwargs):
+    return xrft.ifft(dataArray, **kwargs)
+
+def fft_nutou(dataArray, modeNum, **kwargs):
     
-    def fft(self, dataArray, **kwargs):
-        return xrft.fft(dataArray, **kwargs)
+    data = dataArray.to_numpy()
+    data = data.astype('complex128')
     
-    def ifft(self, dataArray, **kwargs):
-        return xrft.ifft(dataArray, **kwargs)
+    time = dataArray[dataArray.dims[0]].to_numpy()
     
-    def fft_nutou(self, dataArray, modeNum):
-        
-        data = dataArray.to_numpy()
-        data = data.astype('complex128')
-        
-        time = dataArray[dataArray.dims[0]].to_numpy()
+    if isinstance(time[0], type(np.datetime64(500,'s'))):
+        time = time.astype(float)
+        time = time - time[0]
+        freqUpLim = 1 / np.min(np.abs(time - np.roll(time, 1))) * 1e9
+    else:
+        time = time.astype(float)
+        time = time - time[0]
+        freqUpLim = 1 / np.min(np.abs(time - np.roll(time, 1)))
         
-        if isinstance(time[0], type(np.datetime64(500,'s'))):
-            time = time.astype(float)
-            time = time - time[0]
-            freqUpLim = 1 / np.min(np.abs(time - np.roll(time, 1))) * 1e9
-        else:
-            time = time.astype(float)
-            time = time - time[0]
-            freqUpLim = 1 / np.min(np.abs(time - np.roll(time, 1)))
-            
-        print(freqUpLim)
-        
-        time = time / time.max() * 2 * np.pi
-
-        # calculate the transform
-        res = xr.DataArray(
-            data=finufft.nufft1d1(time, data, modeNum),
-            dims=['freq'],
-            coords={
-                "freq":np.linspace(-freqUpLim/2, freqUpLim/2, modeNum)
-            }
-        )
-
-        return res
+    print(freqUpLim)
     
-    def ifft_nutou(self, dataArray, modeNum):
-        pass
+    time = time / time.max() * 2 * np.pi
+
+    # calculate the transform
+    res = xr.DataArray(
+        data=finufft.nufft1d1(time, data, modeNum, **kwargs),
+        dims=['freq'],
+        coords={
+            "freq":np.linspace(-freqUpLim/2, freqUpLim/2, modeNum)
+        }
+    )
+
+    return res
+
+def ifft_nutou(dataArray, modeNum):
+    pass