generate_plots: fix formatting and star count

generate_latex_table.py

@@ -1,9 +1,19 @@
 import pandas as pd
+import numpy as np
 from main.astrodatagui.db.StarsDB import StarDB
 
 db: StarDB = StarDB.getInstance("stars.db")
 starMainIDs = db.getAllStars()
-res = []
+resFull = []
+resUsed = []
+resUnused = []
+fullData = pd.read_pickle("datav5.1.cff")
+
+usedMstars = pd.read_csv("../large sized plots/2025-5-31-sine/M/M_starlist.csv", header=0, names=["StarName"])
+usedKstars = pd.read_csv("../large sized plots/2025-5-31-sine/K/K_starlist.csv", header=0, names=["StarName"])
+usedGstars = pd.read_csv("../large sized plots/2025-5-31-sine/G/G_starlist.csv", header=0, names=["StarName"])
+usedFstars = pd.read_csv("../large sized plots/2025-5-31-sine/F/F_starlist.csv", header=0, names=["StarName"])
+
 for mainID in starMainIDs:
     altNames = db.getStarAltNames(mainID)
     infos = db.getStarInfos(mainID)
@@ -16,16 +26,59 @@ for mainID in starMainIDs:
         if name[0].startswith("KIC"):
             kicName = name[0]
     spType = infos["SpType"]
-    res.append({"MainID": mainID,
-    "Spectral Type": spType,
-    "TIC": ticName,
-    "KIC": kicName})
+    
+    hasValidSineFit = fullData[(fullData["StarName"] == mainID) & (fullData["FitType"] == "sine")]["isValidFold"].any()
+    hasValidPolyFit = fullData[(fullData["StarName"] == mainID) & (fullData["FitType"] == "poly")]["isValidFold"].any()
+    hasValidLinearFit = fullData[(fullData["StarName"] == mainID) & (fullData["FitType"] == "linear")]["isValidFold"].any()
+    
+    fitTypeString = []
+    if(hasValidSineFit): fitTypeString.append("sine")
+    if(hasValidPolyFit): fitTypeString.append("poly")
+    if(hasValidLinearFit): fitTypeString.append("linear")
+    fitTypeString = ', '.join(fitTypeString)
 
-resDF = pd.DataFrame(res)
-resDF.sort_values(by=["Spectral Type", "MainID"]) #.to_latex(index=False)
+    resFull.append({"MainID": mainID,
+                    "Spectral Type": spType,
+                    "TIC": ticName,
+                    "KIC": kicName,
+                    "Fit Types": fitTypeString})
+
+    if((usedMstars["StarName"] == mainID).any() or (usedKstars["StarName"] == mainID).any() or 
+       (usedGstars["StarName"] == mainID).any() or (usedFstars["StarName"] == mainID).any()):
+        resUsed.append({"MainID": mainID,
+                        "Spectral Type": spType,
+                        "TIC": ticName,
+                        "KIC": kicName,
+                        "Fit Types": fitTypeString})
+    else:
+        resUnused.append({"MainID": mainID,
+                          "Spectral Type": spType,
+                          "TIC": ticName,
+                          "KIC": kicName,
+                          "Fit Types": fitTypeString})
+
+
+
+resFull = pd.DataFrame(resFull)
+resFull.sort_values(by=["Spectral Type", "MainID"])
+
+resUsed = pd.DataFrame(resUsed)
+resUsed.sort_values(by=["Spectral Type", "MainID"])
+resUnused = pd.DataFrame(resUnused)
+resUnused.sort_values(by=["Spectral Type", "MainID"])
 
 for sptype in ["M", "K", "G", "F"]:
-    texFile = open(f"table_{sptype}.tex", "w")
-    tex = resDF[resDF["Spectral Type"].str.startswith(sptype)].sort_values(by=["Spectral Type", "MainID"]).to_latex(index=False)
+    texFile = open(f"table_{sptype}_used.tex", "w")
+    tex = resUsed[resUsed["Spectral Type"].str.startswith(sptype)].sort_values(by=["Spectral Type", "MainID"]).to_latex(index=False)
     texFile.write(tex)
-    texFile.close()
+    texFile.close()
+
+texFile = open(f"table_full.tex", "w")
+tex = resFull.sort_values(by=["Spectral Type", "MainID"]).to_latex(index=False)
+texFile.write(tex)
+texFile.close()
+
+texFile = open(f"table_unused.tex", "w")
+tex = resUnused.sort_values(by=["Spectral Type", "MainID"]).to_latex(index=False)
+texFile.write(tex)
+texFile.close()

generate_plots.py

@@ -85,7 +85,7 @@ def plotBinsHistogram(PDCSAPdataList, PDCSAPlabelList, PDCSAPcolorList, bins, ti
     axHisto.set_title(title, wrap=True)
     axHisto.xaxis.set_major_formatter(tck.FormatStrFormatter('%g $\pi$'))
     axHisto.xaxis.set_major_locator(MaxNLocator(5))
-    axHisto.legend()
+    axHisto.legend(loc="lower right")
 
     axHistoPhase = axHisto.twinx()
     if(foldedFits is None):
@@ -149,7 +149,7 @@ def plotFlarePeaks(plotdata, filters, labels, colors, maxY, title, filename):
     axFlarePeaks.set_title(title, wrap=True)
     axFlarePeaks.xaxis.set_major_formatter(tck.FormatStrFormatter('%g $\pi$'))
     axFlarePeaks.xaxis.set_major_locator(MaxNLocator(5))
-    axFlarePeaks.legend()
+    axFlarePeaks.legend(loc="lower right")
     plt.savefig(filename, bbox_inches="tight")
     plt.close()
 
@@ -322,6 +322,101 @@ def plotStarPeriod(data, showSourceFilter, folderPath, starName):
 
 
 def plotCombo(data, showSourceFilter, folderPath, combo):
+    # all flare peaks
+    finalDataAllFlarePeaks = pd.DataFrame()
+    if("M" in combo):
+        Mfilter = data["SpType"].str.startswith("M")
+        Mfilter &= showSourceFilter
+        finalDataAllFlarePeaks = pd.concat([finalDataAllFlarePeaks, data[Mfilter]], ignore_index=True)
+    if("K" in combo):
+        Kfilter = data["SpType"].str.startswith("K")
+        Kfilter &= showSourceFilter
+        finalDataAllFlarePeaks = pd.concat([finalDataAllFlarePeaks, data[Kfilter]], ignore_index=True)
+    if("G" in combo):
+        Gfilter = data["SpType"].str.startswith("G")
+        Gfilter &= showSourceFilter
+        finalDataAllFlarePeaks = pd.concat([finalDataAllFlarePeaks, data[Gfilter]], ignore_index=True)
+    if("F" in combo):
+        Ffilter = data["SpType"].str.startswith("F")
+        Ffilter &= showSourceFilter
+        finalDataAllFlarePeaks = pd.concat([finalDataAllFlarePeaks, data[Ffilter]], ignore_index=True)
+
+    pdcsapbinningDataAllFlarePeaks = []
+    locFolder = f"{folderPath}/{''.join(combo)}/"
+    mkdir_p(f"{locFolder}/")
+    csvFile = open(f"{locFolder}/{''.join(combo)}.csv", "a")
+    csvFile.write("Star Name,Spectral Type,Source,File,Flare Time,Flare Peak,Period,Normalized Phase of Peak,Peak in Period")
+    csvFile.write("\n")
+    for ind, row in finalDataAllFlarePeaks.reset_index().iterrows():
+        PDCSAPminOrigPhase = row["pdcsapFoldedFitPhaseStarEnd"][0]
+        PDCSAPmaxOrigPhase = row["pdcsapFoldedFitPhaseStarEnd"][1]
+        if(len(row["pdcsapFoldedPeaksPhasePair"]) > 0):
+                pdcsapVals = pd.DataFrame(row["pdcsapFoldedPeaksPhasePair"])
+                for td, peak, pv in zip(pdcsapVals["Phase"], pdcsapVals["Peak"], row["pdcsapPeaks"]):
+                    normPhase = normalizePhase(td.value, np.abs(PDCSAPminOrigPhase), np.abs(PDCSAPmaxOrigPhase))
+                    csvFile.write(f"{row['StarName']},{row['SpType']},{row['Source']},{row['FilePath']},{pv['FlarePeakTime']},{pv['FlarePeak']},{row['pdcsapPeriod']},{normPhase},{peak['FlarePeak']}")
+                    csvFile.write("\n")
+                    pdcsapbinningDataAllFlarePeaks.append({"SpType": row["SpType"][0],
+                                            "PDCSAPNormPhase": normPhase,
+                                            "Peak": peak["FlarePeak"],
+                                            "StarName": row['StarName']})
+                    if(peak["FlarePeak"] > 100):
+                        print(row["StarName"], "has over 100 peak")
+
+    csvFile.close()
+    if(len(pdcsapbinningDataAllFlarePeaks) > 0):
+        pdcsapbinningDataAllFlarePeaks = pd.DataFrame(pdcsapbinningDataAllFlarePeaks)
+        numStarsAllFLarePeaks = len(set(pdcsapbinningDataAllFlarePeaks["StarName"]))
+        pd.DataFrame(set(pdcsapbinningDataAllFlarePeaks["StarName"])).to_csv(f"{locFolder}/{''.join(combo)}_starlist.csv")
+        PDCSAPdataListDataAllFlarePeaks = []
+        PDCSAPlabelListDataAllFlarePeaks = []
+        PDCSAPcolorListDataAllFlarePeaks = []
+        PDCSAPdataList2dhistPhaseDataAllFlarePeaks = []
+        PDCSAPdataList2dhistPeakDataAllFlarePeaks = []
+        PDCSAPlabelList2dhistDataAllFlarePeaks = []
+        PDCSAPcolorList2dhistDataAllFlarePeaks = []
+        plotFiltersDataAllFlarePeaks = []
+        if("M" in combo):
+            MfilterDataAllFlarePeaks = pdcsapbinningDataAllFlarePeaks["SpType"] == "M"
+            PDCSAPdataList2dhistPhaseDataAllFlarePeaks.append(pdcsapbinningDataAllFlarePeaks[MfilterDataAllFlarePeaks]["PDCSAPNormPhase"])
+            PDCSAPdataList2dhistPeakDataAllFlarePeaks.append(pdcsapbinningDataAllFlarePeaks[MfilterDataAllFlarePeaks]["Peak"])
+            PDCSAPdataListDataAllFlarePeaks.append(pdcsapbinningDataAllFlarePeaks[MfilterDataAllFlarePeaks]["PDCSAPNormPhase"])
+            PDCSAPlabelListDataAllFlarePeaks.append("M Stars")
+            PDCSAPcolorListDataAllFlarePeaks.append("red")
+            plotFiltersDataAllFlarePeaks.append(MfilterDataAllFlarePeaks)
+        if("K" in combo):
+            KfilterDataAllFlarePeaks = pdcsapbinningDataAllFlarePeaks["SpType"] == "K"
+            PDCSAPdataList2dhistPhaseDataAllFlarePeaks.append(pdcsapbinningDataAllFlarePeaks[KfilterDataAllFlarePeaks]["PDCSAPNormPhase"])
+            PDCSAPdataList2dhistPeakDataAllFlarePeaks.append(pdcsapbinningDataAllFlarePeaks[KfilterDataAllFlarePeaks]["Peak"])
+            PDCSAPdataListDataAllFlarePeaks.append(pdcsapbinningDataAllFlarePeaks[KfilterDataAllFlarePeaks]["PDCSAPNormPhase"])
+            PDCSAPlabelListDataAllFlarePeaks.append("K Stars")
+            PDCSAPcolorListDataAllFlarePeaks.append("orange")
+            plotFiltersDataAllFlarePeaks.append(KfilterDataAllFlarePeaks)
+        if("G" in combo):
+            GfilterDataAllFlarePeaks = pdcsapbinningDataAllFlarePeaks["SpType"] == "G"
+            PDCSAPdataList2dhistPhaseDataAllFlarePeaks.append(pdcsapbinningDataAllFlarePeaks[GfilterDataAllFlarePeaks]["PDCSAPNormPhase"])
+            PDCSAPdataList2dhistPeakDataAllFlarePeaks.append(pdcsapbinningDataAllFlarePeaks[GfilterDataAllFlarePeaks]["Peak"])
+            PDCSAPdataListDataAllFlarePeaks.append(pdcsapbinningDataAllFlarePeaks[GfilterDataAllFlarePeaks]["PDCSAPNormPhase"])
+            PDCSAPlabelListDataAllFlarePeaks.append("G Stars")
+            PDCSAPcolorListDataAllFlarePeaks.append("yellow")
+            plotFiltersDataAllFlarePeaks.append(GfilterDataAllFlarePeaks)
+        if("F" in combo):
+            FfilterDataAllFlarePeaks = pdcsapbinningDataAllFlarePeaks["SpType"] == "F"
+            PDCSAPdataList2dhistPhaseDataAllFlarePeaks.append(pdcsapbinningDataAllFlarePeaks[FfilterDataAllFlarePeaks]["PDCSAPNormPhase"])
+            PDCSAPdataList2dhistPeakDataAllFlarePeaks.append(pdcsapbinningDataAllFlarePeaks[FfilterDataAllFlarePeaks]["Peak"])
+            PDCSAPdataListDataAllFlarePeaks.append(pdcsapbinningDataAllFlarePeaks[FfilterDataAllFlarePeaks]["PDCSAPNormPhase"])
+            PDCSAPlabelListDataAllFlarePeaks.append("F Stars")
+            PDCSAPcolorListDataAllFlarePeaks.append("greenyellow")
+            plotFiltersDataAllFlarePeaks.append(FfilterDataAllFlarePeaks)
+
+        xDataDataAllFlarePeaks, yDataDataAllFlarePeaks, _ = setupxyDataAndDataList(PDCSAPdataList2dhistPhaseDataAllFlarePeaks, PDCSAPdataList2dhistPeakDataAllFlarePeaks,
+                                                    pdcsapbinningDataAllFlarePeaks, "PDCSAPNormPhase")
+
+        generatePlots(PDCSAPdataListDataAllFlarePeaks, PDCSAPlabelListDataAllFlarePeaks, PDCSAPcolorListDataAllFlarePeaks, f"Flare count per phase of {', '.join(combo)} type stars with @bins bins ({numStarsAllFLarePeaks} stars)", f"{locFolder}/{''.join(combo)}-Flarecount-@bins_Bins.png",
+                        xDataDataAllFlarePeaks, yDataDataAllFlarePeaks, f"Flare peak per phase histogram of {', '.join(combo)} type stars with @bins bins ({numStarsAllFLarePeaks} stars)", f"{locFolder}/{''.join(combo)}-Flarepeaks-@bins_Bins_maxY-@maxY.png",
+                        pdcsapbinningDataAllFlarePeaks, plotFiltersDataAllFlarePeaks, PDCSAPlabelListDataAllFlarePeaks, PDCSAPcolorListDataAllFlarePeaks, f"Flare peaks per phase of {', '.join(combo)} type stars ({numStarsAllFLarePeaks} stars)", f"{locFolder}/{''.join(combo)}-Flarepeaks_maxY-@maxY.png")
+        
+
     for maxFlarePeak in [1.01, 1.05, 1.1, 1.25, 1.5]:
         # max Flare Peak cut
         finalDataMaxFlarePeak = pd.DataFrame()
@@ -366,7 +461,8 @@ def plotCombo(data, showSourceFilter, folderPath, combo):
                             csvFileU.write("\n")
                             pdcsapbinningDataU.append({"SpType": row["SpType"][0],
                                                     "PDCSAPNormPhase": normPhase,
-                                                    "Peak": peak["FlarePeak"]})
+                                                    "Peak": peak["FlarePeak"],
+                                                    "StarName": row['StarName']})
                             if(peak["FlarePeak"] > 100):
                                 print(row["StarName"], "has over 100 peak")
                         else:
@@ -375,7 +471,8 @@ def plotCombo(data, showSourceFilter, folderPath, combo):
                             csvFileO.write("\n")
                             pdcsapbinningDataO.append({"SpType": row["SpType"][0],
                                                     "PDCSAPNormPhase": normPhase,
-                                                    "Peak": peak["FlarePeak"]})
+                                                    "Peak": peak["FlarePeak"],
+                                                    "StarName": row['StarName']})
                             if(peak["FlarePeak"] > 100):
                                 print(row["StarName"], "has over 100 peak")
 
@@ -384,6 +481,7 @@ def plotCombo(data, showSourceFilter, folderPath, combo):
         if(len(pdcsapbinningDataU) > 0):
             pdcsapbinningDataU = pd.DataFrame(pdcsapbinningDataU)
             numStarsFlarePeakU = len(set(pdcsapbinningDataU["StarName"]))
+            pd.DataFrame(set(pdcsapbinningDataU["StarName"])).to_csv(f"{locFolderU}/{''.join(combo)}_starlist.csv")
             PDCSAPdataListU = []
             PDCSAPlabelListU = []
             PDCSAPcolorListU = []
@@ -432,6 +530,7 @@ def plotCombo(data, showSourceFilter, folderPath, combo):
         if(len(pdcsapbinningDataO) > 0):
             pdcsapbinningDataO = pd.DataFrame(pdcsapbinningDataO)
             numStarsFlarePeakO = len(set(pdcsapbinningDataO["StarName"]))
+            pd.DataFrame(set(pdcsapbinningDataO["StarName"])).to_csv(f"{locFolderO}/{''.join(combo)}_starlist.csv")
             PDCSAPdataListO = []
             PDCSAPlabelListO = []
             PDCSAPcolorListO = []
@@ -476,99 +575,7 @@ def plotCombo(data, showSourceFilter, folderPath, combo):
             generatePlots(PDCSAPdataListO, PDCSAPlabelListO, PDCSAPcolorListO, f"Flare count per phase of {', '.join(combo)} type stars with @bins bins ({numStarsFlarePeakO} stars)", f"{locFolderO}/{''.join(combo)}_minFlarePeak_{maxFlarePeak}-Flarecount-@bins_Bins.png",
                             xData, yData, f"Flare peak per phase histogram of {', '.join(combo)} type stars with @bins bins ({numStarsFlarePeakO} stars)", f"{locFolderO}/{''.join(combo)}_minFlarePeak_{maxFlarePeak}-Flarepeaks-@bins_Bins_maxY-@maxY.png",
                             pdcsapbinningDataO, plotFiltersO, PDCSAPlabelListO, PDCSAPcolorListO, f"Flare peaks per phase of {', '.join(combo)} type stars ({numStarsFlarePeakO} stars)", f"{locFolderO}/{''.join(combo)}_minFlarePeak_{maxFlarePeak}-Flarepeaks_maxY-@maxY.png")
-    # all flare peaks
-    finalDataAllFlarePeaks = pd.DataFrame()
-    if("M" in combo):
-        Mfilter = data["SpType"].str.startswith("M")
-        Mfilter &= showSourceFilter
-        finalDataAllFlarePeaks = pd.concat([finalDataAllFlarePeaks, data[Mfilter]], ignore_index=True)
-    if("K" in combo):
-        Kfilter = data["SpType"].str.startswith("K")
-        Kfilter &= showSourceFilter
-        finalDataAllFlarePeaks = pd.concat([finalDataAllFlarePeaks, data[Kfilter]], ignore_index=True)
-    if("G" in combo):
-        Gfilter = data["SpType"].str.startswith("G")
-        Gfilter &= showSourceFilter
-        finalDataAllFlarePeaks = pd.concat([finalDataAllFlarePeaks, data[Gfilter]], ignore_index=True)
-    if("F" in combo):
-        Ffilter = data["SpType"].str.startswith("F")
-        Ffilter &= showSourceFilter
-        finalDataAllFlarePeaks = pd.concat([finalDataAllFlarePeaks, data[Ffilter]], ignore_index=True)
-
-    pdcsapbinningData = []
-    locFolder = f"{folderPath}/{''.join(combo)}/"
-    mkdir_p(f"{locFolder}/")
-    csvFile = open(f"{locFolder}/{''.join(combo)}.csv", "a")
-    csvFile.write("Star Name,Spectral Type,Source,File,Flare Time,Flare Peak,Period,Normalized Phase of Peak,Peak in Period")
-    csvFile.write("\n")
-    for ind, row in finalDataAllFlarePeaks.reset_index().iterrows():
-        PDCSAPminOrigPhase = row["pdcsapFoldedFitPhaseStarEnd"][0]
-        PDCSAPmaxOrigPhase = row["pdcsapFoldedFitPhaseStarEnd"][1]
-        if(len(row["pdcsapFoldedPeaksPhasePair"]) > 0):
-                pdcsapVals = pd.DataFrame(row["pdcsapFoldedPeaksPhasePair"])
-                for td, peak, pv in zip(pdcsapVals["Phase"], pdcsapVals["Peak"], row["pdcsapPeaks"]):
-                    normPhase = normalizePhase(td.value, np.abs(PDCSAPminOrigPhase), np.abs(PDCSAPmaxOrigPhase))
-                    csvFile.write(f"{row['StarName']},{row['SpType']},{row['Source']},{row['FilePath']},{pv['FlarePeakTime']},{pv['FlarePeak']},{row['pdcsapPeriod']},{normPhase},{peak['FlarePeak']}")
-                    csvFile.write("\n")
-                    pdcsapbinningData.append({"SpType": row["SpType"][0],
-                                            "PDCSAPNormPhase": normPhase,
-                                            "Peak": peak["FlarePeak"]})
-                    if(peak["FlarePeak"] > 100):
-                        print(row["StarName"], "has over 100 peak")
-
-    csvFile.close()
-    if(len(pdcsapbinningData) > 0):
-        pdcsapbinningData = pd.DataFrame(pdcsapbinningData)
-        numStarsAllFLarePeaks = len(set(pdcsapbinningData["StarName"]))
-        PDCSAPdataList = []
-        PDCSAPlabelList = []
-        PDCSAPcolorList = []
-        PDCSAPdataList2dhistPhase = []
-        PDCSAPdataList2dhistPeak = []
-        PDCSAPlabelList2dhist = []
-        PDCSAPcolorList2dhist = []
-        plotFilters = []
-        if("M" in combo):
-            Mfilter = pdcsapbinningData["SpType"] == "M"
-            PDCSAPdataList2dhistPhase.append(pdcsapbinningData[Mfilter]["PDCSAPNormPhase"])
-            PDCSAPdataList2dhistPeak.append(pdcsapbinningData[Mfilter]["Peak"])
-            PDCSAPdataList.append(pdcsapbinningData[Mfilter]["PDCSAPNormPhase"])
-            PDCSAPlabelList.append("M Stars")
-            PDCSAPcolorList.append("red")
-            plotFilters.append(Mfilter)
-        if("K" in combo):
-            Kfilter = pdcsapbinningData["SpType"] == "K"
-            PDCSAPdataList2dhistPhase.append(pdcsapbinningData[Kfilter]["PDCSAPNormPhase"])
-            PDCSAPdataList2dhistPeak.append(pdcsapbinningData[Kfilter]["Peak"])
-            PDCSAPdataList.append(pdcsapbinningData[Kfilter]["PDCSAPNormPhase"])
-            PDCSAPlabelList.append("K Stars")
-            PDCSAPcolorList.append("orange")
-            plotFilters.append(Kfilter)
-        if("G" in combo):
-            Gfilter = pdcsapbinningData["SpType"] == "G"
-            PDCSAPdataList2dhistPhase.append(pdcsapbinningData[Gfilter]["PDCSAPNormPhase"])
-            PDCSAPdataList2dhistPeak.append(pdcsapbinningData[Gfilter]["Peak"])
-            PDCSAPdataList.append(pdcsapbinningData[Gfilter]["PDCSAPNormPhase"])
-            PDCSAPlabelList.append("G Stars")
-            PDCSAPcolorList.append("yellow")
-            plotFilters.append(Gfilter)
-        if("F" in combo):
-            Ffilter = pdcsapbinningData["SpType"] == "F"
-            PDCSAPdataList2dhistPhase.append(pdcsapbinningData[Ffilter]["PDCSAPNormPhase"])
-            PDCSAPdataList2dhistPeak.append(pdcsapbinningData[Ffilter]["Peak"])
-            PDCSAPdataList.append(pdcsapbinningData[Ffilter]["PDCSAPNormPhase"])
-            PDCSAPlabelList.append("F Stars")
-            PDCSAPcolorList.append("greenyellow")
-            plotFilters.append(Ffilter)
-
-        xData, yData, _ = setupxyDataAndDataList(PDCSAPdataList2dhistPhase, PDCSAPdataList2dhistPeak,
-                                                    pdcsapbinningData, "PDCSAPNormPhase",)
-        plotdata = pdcsapbinningData
-        filters = plotFilters
-        generatePlots(PDCSAPdataList, PDCSAPlabelList, PDCSAPcolorList, f"Flare count per phase of {', '.join(combo)} type stars with @bins bins ({numStarsAllFLarePeaks} stars)", f"{locFolder}/{''.join(combo)}-Flarecount-@bins_Bins.png",
-                        xData, yData, f"Flare peak per phase histogram of {', '.join(combo)} type stars with @bins bins ({numStarsAllFLarePeaks} stars)", f"{locFolder}/{''.join(combo)}-Flarepeaks-@bins_Bins_maxY-@maxY.png",
-                        plotdata, filters, PDCSAPlabelList, PDCSAPcolorList, f"Flare peaks per phase of {', '.join(combo)} type stars ({numStarsAllFLarePeaks} stars)", f"{locFolder}/{''.join(combo)}-Flarepeaks_maxY-@maxY.png")
-        
+    
     if(len(combo) == 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"]
@@ -606,13 +613,15 @@ def plotCombo(data, showSourceFilter, folderPath, combo):
                             csvFile.write("\n")
                             pdcsapbinningData.append({"SpType": f'{row["SpType"][0]}{row["SpType"][1]}',
                                                     "PDCSAPNormPhase": normPhase,
-                                                    "Peak": peak["FlarePeak"]})
+                                                    "Peak": peak["FlarePeak"],
+                                                    "StarName": row['StarName']})
                             if(peak["FlarePeak"] > 100):
                                 print(row["StarName"], "has over 100 peak")
             csvFile.close()
             if(len(pdcsapbinningData) > 0):
                 pdcsapbinningData = pd.DataFrame(pdcsapbinningData)
-                numStarsAccSpType = len(set(pdcsapbinningData[typeFilter]["StarName"]))
+                numStarsAccSpType = len(set(pdcsapbinningData["StarName"]))
+                pd.DataFrame(set(pdcsapbinningData["StarName"])).to_csv(f"{locFolder}/{spTyp}_starlist.csv")
                 PDCSAPdataList = []
                 PDCSAPlabelList = []
                 PDCSAPcolorList = []
@@ -685,13 +694,15 @@ def plotCombo(data, showSourceFilter, folderPath, combo):
                             csvFileU.write("\n")
                             pdcsapbinningDataU.append({"SpType": f'{row["SpType"][0]}',
                                                         "PDCSAPNormPhase": normPhase,
-                                                        "Peak": peak["FlarePeak"]})
+                                                        "Peak": peak["FlarePeak"],
+                                                        "StarName": row['StarName']})
                         else:
                             csvFileO.write(f"{row['StarName']},{row['SpType']},{row['Source']},{row['FilePath']},{pv['FlarePeakTime']},{pv['FlarePeak']},{row['pdcsapPeriod']},{row['MeanPeriod']},{normPhase},{peak['FlarePeak']}")
                             csvFileO.write("\n")
                             pdcsapbinningDataO.append({"SpType": f'{row["SpType"][0]}',
                                                         "PDCSAPNormPhase": normPhase,
-                                                        "Peak": peak["FlarePeak"]})
+                                                        "Peak": peak["FlarePeak"],
+                                                        "StarName": row['StarName']})
                         if(peak["FlarePeak"] > 100):
                             print(row["StarName"], "has over 100 peak")
         csvFileU.close()
@@ -705,9 +716,6 @@ def plotCombo(data, showSourceFilter, folderPath, combo):
         PDCSAPdataList2dhistPhaseO = []; PDCSAPdataList2dhistPeakO = []
         plotFiltersU = []; plotFiltersO = [];
 
-        numStarsPeriodU = len(set(pdcsapbinningDataU[typeFilter]["StarName"]))
-        numStarsPeriodO = len(set(pdcsapbinningDataO[typeFilter]["StarName"]))
-
         if("M" in combo):
             PDCSAPlabelList.append("M Stars")
             PDCSAPcolorList.append("red")
@@ -722,6 +730,8 @@ def plotCombo(data, showSourceFilter, folderPath, combo):
             PDCSAPcolorList.append("greenyellow")
 
         if(len(pdcsapbinningDataU) > 0):
+            numStarsPeriodU = len(set(pdcsapbinningDataU["StarName"]))
+            pd.DataFrame(set(pdcsapbinningDataU["StarName"])).to_csv(f"{locFolder}/under_{periodCut}_starlist.csv")
             if("M" in combo):
                 MfilterU = pdcsapbinningDataU["SpType"] == "M"
                 PDCSAPdataList2dhistPhaseU.append(pdcsapbinningDataU[MfilterU]["PDCSAPNormPhase"])
@@ -748,6 +758,8 @@ def plotCombo(data, showSourceFilter, folderPath, combo):
                 plotFiltersU.append(FfilterU)
 
         if(len(pdcsapbinningDataO) > 0):
+            numStarsPeriodO = len(set(pdcsapbinningDataO["StarName"]))
+            pd.DataFrame(set(pdcsapbinningDataO["StarName"])).to_csv(f"{locFolder}/over_{periodCut}_starlist.csv")
             if("M" in combo):
                 MfilterO = pdcsapbinningDataO["SpType"] == "M"
                 PDCSAPdataList2dhistPhaseO.append(pdcsapbinningDataO[MfilterO]["PDCSAPNormPhase"])
@@ -851,7 +863,7 @@ if __name__ == "__main__":
             validStarPeriodMap = pd.DataFrame(validStarPeriodMap)
 
             data = pd.merge(data, validStarPeriodMap, on="StarName")
-
+            
             #starPlotFunc = partial(plotStar, data, showSourceFilter, folderPath)
             #list(pool.map(starPlotFunc, allStars)) # wrap in list, to force evaluation
             

main/astrodatagui/CalcAllFlaresThread.py

@@ -15,6 +15,18 @@ from errno import EEXIST
 from os import makedirs, path
 from datetime import datetime
 
+SMALL_SIZE = 16
+MEDIUM_SIZE = 18
+BIGGER_SIZE = 20
+
+plt.rc('font', size=SMALL_SIZE)          # controls default text sizes
+plt.rc('axes', titlesize=MEDIUM_SIZE)     # fontsize of the axes title
+plt.rc('axes', labelsize=MEDIUM_SIZE)    # fontsize of the x and y labels
+plt.rc('xtick', labelsize=SMALL_SIZE)    # fontsize of the tick labels
+plt.rc('ytick', labelsize=SMALL_SIZE)    # fontsize of the tick labels
+plt.rc('legend', fontsize=SMALL_SIZE)    # legend fontsize
+plt.rc('figure', titlesize=BIGGER_SIZE)  # fontsize of the figure title
+
 def mkdir_p(mypath):
     '''Creates a directory. equivalent to using mkdir -p on the command line'''
 
@@ -48,13 +60,13 @@ def getFlareCount(filesDict):
 
         lc.plot()
         plt.title(f"{starName} - normalized lightcurve")
-        plt.savefig(f"{starFolder}/{starNameR}_{source}-{sequence}-lc.png")
+        plt.savefig(f"{starFolder}/{starNameR}_{source}-{sequence}-lc.png", bbox_inches="tight")
         plt.close()
 
         flattenedLc = lc.flatten()
         flattenedLc.plot()
         plt.title(f"{starName} - flattened lightcurve")
-        plt.savefig(f"{starFolder}/{starNameR}_{source}-{sequence}-flattened_lc.png")
+        plt.savefig(f"{starFolder}/{starNameR}_{source}-{sequence}-flattened_lc.png", bbox_inches="tight")
         plt.close()
 
         pdcsapPeaks, pdcsapFits = calculateFlareFitsForLightcurve(flattenedLc, normalizedLC=lc)
@@ -64,7 +76,7 @@ def getFlareCount(filesDict):
             plt.plot(p["FlarePeakTime"].value, lc.flux[p["StandardIndex"]], "x", color="red")
         plt.plot([], [], "x", color="red", label="Flare peaks")
         plt.legend()
-        plt.savefig(f"{starFolder}/{starNameR}_{source}-{sequence}-lc-marked_flares.png")
+        plt.savefig(f"{starFolder}/{starNameR}_{source}-{sequence}-lc-marked_flares.png", bbox_inches="tight")
         plt.close()
 
         flattenedLc.plot()
@@ -76,7 +88,7 @@ def getFlareCount(filesDict):
             plt.plot(p["FlarePeakTime"].value, p["FlarePeak"], "x", color="red")
         plt.plot([], [], "x", color="red", label="Flare peaks")
         plt.legend()
-        plt.savefig(f"{starFolder}/{starNameR}_{source}-{sequence}-flattened_lc-marked_flares.png")
+        plt.savefig(f"{starFolder}/{starNameR}_{source}-{sequence}-flattened_lc-marked_flares.png", bbox_inches="tight")
         plt.close()
 
         pdcsapValSec, pdcsapTds = getTotalValidDataInSeconds(lc, "pdcsap_flux")
@@ -86,7 +98,7 @@ def getFlareCount(filesDict):
 
         pdcsapPeriodogram.plot(view="period")
         plt.plot(pdcsapPeakPeriod, pdcsapPeriodogram.power[maxPeriodIndex], "x", color="red")
-        plt.savefig(f"{starFolder}/{starNameR}_{source}-{sequence}-periodogram-marked_max.png")
+        plt.savefig(f"{starFolder}/{starNameR}_{source}-{sequence}-periodogram-marked_max.png", bbox_inches="tight")
         plt.close()
 
         #pdcsapEpochTime = getEpochTime(lc)
@@ -108,7 +120,7 @@ def getFlareCount(filesDict):
                      optimizedFit["foldedLC"].flux[optimizedFit["foldedLC"].cycle == cycle][foldedIndex], "x", color="red")
         plt.plot([], [], "x", color="red", label="Flare peaks")
         plt.legend()
-        plt.savefig(f"{starFolder}/{starNameR}_{source}-{sequence}-foldedLC-marked_fit_flares.png")
+        plt.savefig(f"{starFolder}/{starNameR}_{source}-{sequence}-foldedLC-marked_fit_flares.png", bbox_inches="tight")
         plt.close()
 
         if(optimizedFit["periodFoldedLC"] is not None):
@@ -123,7 +135,7 @@ def getFlareCount(filesDict):
                          optimizedFit["periodFoldedLC"].flux[optimizedFit["periodFoldedLC"].cycle == cycle][foldedIndex], "x", color="red")
             plt.plot([], [], "x", color="red", label="Flare peaks")
             plt.legend()
-            plt.savefig(f"{starFolder}/{starNameR}_{source}-{sequence}-periodFoldedLC-marked_fit_flares.png")
+            plt.savefig(f"{starFolder}/{starNameR}_{source}-{sequence}-periodFoldedLC-marked_fit_flares.png", bbox_inches="tight")
             plt.close()
         
         filesDict["pdcsapPeaks"] = pdcsapPeaks

main/astrodatagui/ui/FlaredetectorWidget.py

@@ -301,8 +301,8 @@ class FlaredetectorWidget(QtWidgets.QWidget):
                     phase, sineFit, _ = getFoldedBestFit(lc, fitType=self.foldedFitType)
                     self.figureAxis.plot(phase, sineFit, color="red")
                     print(phase, sineFit)
-                    minPhasesBoundsIndices, maxPhasesBoundsIndices = getPhaseRangesNearPeak(getFoldedFitPeakValley(sineFit), phase)
-                    plotPhaseRangesNearPeak((minPhasesBoundsIndices, maxPhasesBoundsIndices), phase, ax=self.figureAxis)
+                    #minPhasesBoundsIndices, maxPhasesBoundsIndices = getPhaseRangesNearPeak(getFoldedFitPeakValley(sineFit), phase)
+                    #plotPhaseRangesNearPeak((minPhasesBoundsIndices, maxPhasesBoundsIndices), phase, ax=self.figureAxis)
                 except Exception as e:
                     print("Failed to get fit")
                     print(e)