generate_plots: bring uptodate for changes, add max flare peak plots

generate_plots.py

@@ -27,7 +27,7 @@ def mkdir_p(mypath):
             pass
         else: raise
 
-fileName = "datav5.cff"
+fileName = "datav5.1.cff"
 data = pd.read_pickle(fileName)
 
 binList = [10, 20, 30]
@@ -51,13 +51,13 @@ if(useTESS):
 current = datetime.now()
 date = f"{current.year}-{current.month}-{current.day}"
 time = f"{current.hour}-{current.minute}-{current.second}"
-folderPath = f"../{date}/"
+folderPath = f"../{date}-sine-poly/"
 #folderPath = f"G:/Meine Ablage/Masterthesis/{date}/"
 mkdir_p(folderPath)
 
 # remove any data that has no period
 #data = data[(data["FitType"] == "sine") | (data["FitType"] == "poly")]
-data = data[(data["FitType"] == "sine")]
+data = data[((data["FitType"] == "sine") | (data["FitType"] == "poly")) & (data["isValidFold"])]
 
 validStarPeriodMap = []
 starList = set(list(data["StarName"]))
@@ -155,18 +155,19 @@ def plotFlarePeaks(plotdata, filters, labels, colors, maxY, title, filename):
     if(isinstance(labels, list) and isinstance(colors, list)):
         _, ((axFlarePeaks)) = plt.subplots(nrows=1, ncols=1)
         for f, l, c in zip(filters, labels, colors):
-            axFlarePeaks.scatter(plotdata[f]["PDCSAPNormPhase"],
+            axFlarePeaks.scatter(plotdata[f]["PDCSAPNormPhase"] if "PDCSAPNormPhase" in plotdata[f].columns else plotdata[f]["PDCSAPNormPhasePeriod"],
-                                 plotdata[f]["Peak"],
+                                 plotdata[f]["Peak"] if "Peak" in plotdata[f].columns else plotdata[f]["PeakPeriod"],
                                  label=l, color=c)
     elif(isinstance(labels, str) and isinstance(colors, str)):
         _, ((axFlarePeaks)) = plt.subplots(nrows=1, ncols=1)
         if(filters is None):
-            axFlarePeaks.scatter(plotdata[:]["PDCSAPNormPhase"],
+            axFlarePeaks.scatter(plotdata[:]["PDCSAPNormPhase"] if "PDCSAPNormPhase" in plotdata[:].columns else plotdata[:]["PDCSAPNormPhasePeriod"],
-                                plotdata[:]["Peak"],
+                                
+                                 plotdata[:]["Peak"] if "Peak" in plotdata[:].columns else plotdata[:]["PeakPeriod"],
                                 label=labels, color=colors)
         else:
-            axFlarePeaks.scatter(plotdata[filters]["PDCSAPNormPhase"],
+            axFlarePeaks.scatter(plotdata[filters]["PDCSAPNormPhase"] if "PDCSAPNormPhase" in plotdata[filters].columns else plotdata[filters]["PDCSAPNormPhasePeriod"],
-                                plotdata[filters]["Peak"],
+                                 plotdata[filters]["Peak"] if "Peak" in plotdata[filters].columns else plotdata[filters]["PeakPeriod"],
                                  label=labels, color=colors)
     else:
         return
@@ -313,8 +314,8 @@ for starName in starDB.getAllStars():
         else:
             color = "gray"
 
-        PDCSAPdataList2dhistPhase.append(pdcsapbinningDataSpotModDiffPeriod[:]["PDCSAPNormPhase"])
+        PDCSAPdataList2dhistPhase.append(pdcsapbinningDataSpotModDiffPeriod[:]["PDCSAPNormPhasePeriod"])
-        PDCSAPdataList2dhistPeak.append(pdcsapbinningDataSpotModDiffPeriod[:]["Peak"])
+        PDCSAPdataList2dhistPeak.append(pdcsapbinningDataSpotModDiffPeriod[:]["PeakPeriod"])
 
         xData, yData, PDCSAPdataList = setupxyDataAndDataList(PDCSAPdataList2dhistPhase, PDCSAPdataList2dhistPeak, PDCSAPdataList)
 
@@ -525,6 +526,7 @@ for comboLength in range(1, len(spType) + 1):
                             print(row["StarName"], "has over 100 peak")
 
         csvFile.close()
+        if(len(pdcsapbinningData) > 0):
             pdcsapbinningData = pd.DataFrame(pdcsapbinningData)
             PDCSAPdataList = []
             PDCSAPlabelList = []
@@ -576,7 +578,7 @@ for comboLength in range(1, len(spType) + 1):
         
         if(comboLength == 1):
             mainSpType = combo[0]
-            spTypes = [f"{mainSpType}0", f"{mainSpType}1", f"{mainSpType}2", f"{mainSpType}3", f"{mainSpType}4", f"{mainSpType}5", f"{mainSpType}6", f"{mainSpType}7", f"{mainSpType}8" f"{mainSpType}9"]
+            spTypes = [f"{mainSpType}0", f"{mainSpType}1", f"{mainSpType}2", f"{mainSpType}3", f"{mainSpType}4", f"{mainSpType}5", f"{mainSpType}6", f"{mainSpType}7", f"{mainSpType}8", f"{mainSpType}9"]
             match mainSpType:
                 case "M":
                     color = "red"
@@ -616,6 +618,7 @@ for comboLength in range(1, len(spType) + 1):
                                 if(peak["FlarePeak"] > 100):
                                     print(row["StarName"], "has over 100 peak")
                 csvFile.close()
+                if(len(pdcsapbinningData) > 0):
                     pdcsapbinningData = pd.DataFrame(pdcsapbinningData)
                     PDCSAPdataList = []
                     PDCSAPlabelList = []

main/astrodatagui/CalcAllFlaresThread.py

@@ -150,6 +150,8 @@ def getFlareCount(filesDict):
         filesDict["pdcsapPeriodFoldedPeaksPhasePair"] = pdcsapPeriodFoldedPeaksPhasePair
         filesDict['periodFitType'] = optimizedFit["periodFitType"]
 
+        filesDict["isValidFold"] = optimizedFit["isValid"]
+
         csvFile = open(f"{starFolder}/{starNameR}_{source}-{sequence}.csv", "a")
         csvFile.write("StarName,Spectral Type,Rotational Velocity,Rotenional Velocity Unit,Distance,Distance Unit,Source,Sequence,File Path,Initial folded Fit Type,Used folded Fit Type")
         csvFile.write(f"{filesDict['StarName']},{filesDict['SpType']},{filesDict['RotVel']},{filesDict['RotVelUnit']},{filesDict['Distance']},{filesDict['DistanceUnit']},{filesDict['Source']},{filesDict['Sequence']},{filesDict['FilePath']},{filesDict['FitType']},{optimizedFit['fitType']}")

main/flaredetector/util.py

@@ -429,7 +429,9 @@ def getOptimizedFold(normalizedLC, preferedFoldedFitType):
         spotModulationBefore = spotModulation
         if(len(fitMaximaArgs) > 1):
             print("fitMaximaArgs > 1: ", fitMaximaArgs)
-            if(len(fitMaximaArgs) == 2 and fitMaximaArgs[0] > len(phase)*0.1 and fitMaximaArgs[1] < len(phase)*0.1):
+            print(fitMaximaArgs[0], len(phase)*0.1, fitMaximaArgs[1], len(phase)*0.9)
+            print(fitMaximaArgs[0] > len(phase)*0.1, fitMaximaArgs[1] < len(phase)*0.9)
+            if(len(fitMaximaArgs) == 2 and fitMaximaArgs[0] > len(phase)*0.1 and fitMaximaArgs[1] < len(phase)*0.9):
                 halfPeriod = period/2
                 for lsP, blsP in zip(lsPeriods, blsPeriods):
                     if(abs(lsP.value - halfPeriod) < period*0.05):
@@ -454,9 +456,23 @@ def getOptimizedFold(normalizedLC, preferedFoldedFitType):
 
         fitMinimaArgs = argrelextrema(np.asarray(fit), np.less)[0]
         fitMinima = phase[0]
+        fitMinimaArg = 0
+
+        newFitMinimaArgs = []
         for args in fitMinimaArgs:
-            if(abs(phase[args]) < abs(fitMinima)):
+            print("Phases: ", fit[0], fit[len(phase)-1], fit[args])
+            if(fit[0] < fit[args] and fit[len(fit)-1] < fit[args]):
+                print("Found new minima")
+                newFitMinimaArgs.append(0)
+            newFitMinimaArgs.append(args)
+
+        newFitMinimaArgs = set(newFitMinimaArgs)
+        print(newFitMinimaArgs)
+
+        for args in newFitMinimaArgs:
+            if(abs(phase[args]) < abs(fitMinima) and fit[args] < fit[fitMinimaArg]):
                 fitMinima = phase[args]
+                fitMinimaArg = args
 
         if(abs(fitMinima) < phaseLength*0.01):
             isValid = True