- Added Residual to noise ratio

- Fixed a bug with figure
- Nyquist frequency is more stable (when most common value is 0)

smurfs/_version.py

@@ -1 +1 @@
-__version__ = "0.4.0"
+__version__ = "0.4.2"

smurfs/analysis/analysis.py

@@ -70,7 +70,9 @@ def run(file: str, snrCriterion: float, windowSize: float, **kwargs):
             tList.append(t)
             iList.append(i)
 
-            result[(data[0][0], max(data[0]))] = frequencyList
+            print(term.format(f"Residual noise {frequencyList[-1][4]}", term.Color.GREEN))
+
+            result[(data[0][0], max(data[0]),frequencyList[-1][4])] = frequencyList
             if defines.dieGracefully:
                 break
 
@@ -83,5 +85,5 @@ def run(file: str, snrCriterion: float, windowSize: float, **kwargs):
 
 
     waitForProcessesFinished()
-    writeResults("results/results.txt",result,nyquistFrequency(fileData))
+    writeResults("results/results.csv",result,nyquistFrequency(fileData))
 

smurfs/files/dataHandler.py

@@ -173,6 +173,7 @@ def writeResults(file: str, data: Dict[Tuple[float,float],List[Tuple[float,float
         f.write("Lowerrange;Upperrange;f(c/d);f_err(c/d);snr;amp;amp_err;phase;phase_err\n")
         for key,value in data.items():
             f.write(f"{key[0]};{key[1]};;;;;;;\n")
+            f.write(f"Residuals to noise;{key[2]};;;;;;;\n")
 
             for i in value:
                 text = ";"
@@ -206,4 +207,4 @@ def saveAmpSpectrumAndImage(ampSpectrum: np.ndarray, path: str, name: str):
         plotData = {name:(ampSpectrum, '-')}
         p = plotCustom(name,plotData,xLabel="Frequency(c/d)",yLabel="Amplitude")
         p.savefig(name+".pdf")
-        pl.close(fig=p)
+        pl.close()

smurfs/timeseries/timerseries.py

@@ -88,6 +88,7 @@ def findMostCommonDiff(time: np.ndarray) -> float:
     :return:
     """
     realDiffX = time[1:len(time)] - time[0:len(time) - 1]
+    realDiffX = realDiffX[realDiffX!=0]
     (values, counts) = np.unique(realDiffX, return_counts=True)
     mostCommon = values[np.argmax(counts)]
     return mostCommon
@@ -300,7 +301,8 @@ def recursiveFrequencyFinder(data: np.ndarray, snrCriterion: float, windowSize:
 
         while (snr > snrCriterion):
             try:
-                frequencyList.append((fit[1], snr, fit[0], fit[2],))
+                resNoise = np.mean(data[1])
+                frequencyList.append((fit[1], snr, fit[0], fit[2],resNoise))
                 saveStuff = True if kwargs['mode'] == 'Full' else False
             except:
                 saveStuff = True
@@ -323,7 +325,7 @@ def recursiveFrequencyFinder(data: np.ndarray, snrCriterion: float, windowSize:
 
             if not cutoffCriterion(frequencyList):
                 break
-
+        resNoise = np.mean(amp)
         try:
             if kwargs['mode'] == 'Normal':
                 pass
@@ -333,11 +335,9 @@ def recursiveFrequencyFinder(data: np.ndarray, snrCriterion: float, windowSize:
     except KeyboardInterrupt:
         print(term.format("Interrupted Run", term.Color.RED))
         defines.dieGracefully = True
+    finally:
         return frequencyList, f, t, i
 
-    return frequencyList, f, t, i
-
-
 def combineDatasets(fList: List[np.ndarray], tList: List[np.ndarray], iList: List[np.ndarray]) -> Tuple[
     np.ndarray, np.ndarray, np.ndarray]:
     if fList == [] or tList == [] or iList == []: