generate_plots: fix multithreaded plot generation

generate_plots.py

@@ -184,9 +184,7 @@ def plotStar(data, showSourceFilter, folderPath, starName):
     finalData = pd.concat([finalData, data[nameFilter]], ignore_index=True)
 
     pdcsapbinningData = []
-    pdcsapbinningDataSpotModDiffPeriod = []
     foldedFits = []
-    foldedPeriodFits = []
     locFolder = f"{folderPath}/stars/{starNameR}/"
     mkdir_p(f"{locFolder}/")
     csvFile = open(f"{locFolder}/{starNameR}.csv", "a")
@@ -208,19 +206,9 @@ def plotStar(data, showSourceFilter, folderPath, starName):
                     if(peak["FlarePeak"] > 100):
                         print(row["StarName"], "has over 100 peak")
                 foldedFits.append([normalizePhase(row["pdcsapFoldedFitPhase"]), row["pdcsapFoldedFit"]])
-                if(row["pdcsapPeriodFoldedPhase"] is not None):
+              
-                    periodPdcsapVals = pd.DataFrame(row["pdcsapPeriodFoldedPeaksPhasePair"])
-                    for td, peak in zip(periodPdcsapVals["Phase"], periodPdcsapVals["Peak"]):
-                        normPhasePeriod = normalizePhase(td.value, np.abs(row["pdcsapPeriodFoldedFitPhaseStarEnd"][0]), np.abs(row["pdcsapPeriodFoldedFitPhaseStarEnd"][1]))
-                        pdcsapbinningDataSpotModDiffPeriod.append({"SpType": f'{row["SpType"][0:2] if len(row["SpType"]) > 1 else row["SpType"][0]}',
-                                                                    "PDCSAPNormPhasePeriod": normPhasePeriod,
-                                                                    "PeakPeriod": peak["FlarePeak"]})
-                        if(peak["FlarePeak"] > 100):
-                            print(row["StarName"], "has over 100 peak")
-                    foldedPeriodFits.append([normalizePhase(row["pdcsapPeriodFoldedFitPhase"]), row["pdcsapPeriodFoldedFit"]])
     csvFile.close()
     pdcsapbinningData = pd.DataFrame(pdcsapbinningData)
-    pdcsapbinningDataSpotModDiffPeriod = pd.DataFrame(pdcsapbinningDataSpotModDiffPeriod) if len(pdcsapbinningDataSpotModDiffPeriod) > 0 else None
     PDCSAPdataList = []
     PDCSAPlabelList = []
     PDCSAPcolorList = []
@@ -251,6 +239,42 @@ def plotStar(data, showSourceFilter, folderPath, starName):
                         pdcsapbinningData, None, f"{starName}", color, f"Flare peaks per phase of {starName}", f"{locFolder}/{starNameR}-Flarepeaks_maxY-@maxY.png",
                         foldedFits)
 
+def plotStarPeriod(data, showSourceFilter, folderPath, starName):
+    finalData = pd.DataFrame()
+    starNameR = starName.replace('*', '_star_')
+    nameFilter = data["StarName"] == starName
+    nameFilter &= showSourceFilter
+    finalData = pd.concat([finalData, data[nameFilter]], ignore_index=True)
+
+    pdcsapbinningData = []
+    pdcsapbinningDataSpotModDiffPeriod = []
+    foldedFits = []
+    foldedPeriodFits = []
+    locFolder = f"{folderPath}/stars/{starNameR}/"
+    mkdir_p(f"{locFolder}/")
+    csvFile = open(f"{locFolder}/{starNameR}_Period.csv", "a")
+    csvFile.write("Star Name,Spectral Type,Source,File,Flare Time,Flare Peak,Period,Spot Modulation,Normalized Phase of Peak,Peak in Period")
+    csvFile.write("\n")
+    for ind, row in finalData.reset_index().iterrows():
+        PDCSAPminOrigPhase = row["pdcsapPeriodFoldedFitPhaseStarEnd"][0]
+        PDCSAPmaxOrigPhase = row["pdcsapPeriodFoldedFitPhaseStarEnd"][1]
+
+        if(len(row["pdcsapPeriodFoldedPeaksPhasePair"]) > 0):
+                pdcsapVals = pd.DataFrame(row["pdcsapPeriodFoldedPeaksPhasePair"])
+                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']},{row['pdcsapSpotModulation']},{normPhase},{peak['FlarePeak']}")
+                    csvFile.write("\n")
+                    pdcsapbinningData.append({"SpType": f'{row["SpType"][0:2] if len(row["SpType"]) > 1 else row["SpType"][0]}',
+                                            "PDCSAPNormPhasePeriod": normPhase,
+                                            "PeakPeriod": peak["FlarePeak"]})
+                    if(peak["FlarePeak"] > 100):
+                        print(row["StarName"], "has over 100 peak")
+                foldedFits.append([normalizePhase(row["pdcsapFoldedFitPhase"]), row["pdcsapFoldedFit"]])
+
+    csvFile.close()
+    pdcsapbinningDataSpotModDiffPeriod = pd.DataFrame(pdcsapbinningDataSpotModDiffPeriod) if len(pdcsapbinningDataSpotModDiffPeriod) > 0 else None
+
     if(pdcsapbinningDataSpotModDiffPeriod is not None):
         PDCSAPdataListPeriod = []
         PDCSAPlabelList = []
@@ -284,6 +308,7 @@ def plotStar(data, showSourceFilter, folderPath, starName):
                         pdcsapbinningDataSpotModDiffPeriod, None, f"{starName}", color, f"Flare peaks per phase of {starName}", f"{locFolder}/{starNameR}-Flarepeaks_maxY-@maxY_Period.png",
                         foldedPeriodFits)
 
+
 def plotCombo(data, showSourceFilter, folderPath, combo):
     for maxFlarePeak in [1.01, 1.05, 1.1, 1.25, 1.5]:
         # max Flare Peak cut
@@ -751,14 +776,15 @@ def plotCombo(data, showSourceFilter, folderPath, combo):
                                 xDataO, yDataO, f"Flare peak per phase histogram of {', '.join(combo)} type stars with @bins bins (Rot. Period over {periodCut} days)", f"{locFolder}/{''.join(combo)}-Flarepeaks-Period_o_{periodCut}-@bins_Bins_maxY-@maxY.png",
                                 pdcsapbinningDataO, plotFiltersO, PDCSAPlabelList, PDCSAPcolorList, f"Flare peaks per phase of {', '.join(combo)} type stars (Rot. Period over {periodCut} days)", f"{locFolder}/{''.join(combo)}-Flarepeaks-Period_o_{periodCut}-maxY_@maxY.png")
 
-
 binList = [10, 20, 30]
 spType = ["M", "K", "G", "F"]
+periodsCutList = [0.5, 1, 1.5, 2, 5, 10, 15, 20]
 
 if __name__ == "__main__":
     fileName = "datav5.1.cff"
     fullData = pd.read_pickle(fileName)
     starDB: StarDB = StarDB.getInstance("stars.db")
+    allStars = starDB.getAllStars()
 
     useKepler = True
     useK2 = True
@@ -769,8 +795,7 @@ if __name__ == "__main__":
     time = f"{current.hour}-{current.minute}-{current.second}"
 
     cpuCount = multiprocessing.cpu_count()
-    executor = concurrent.futures.ProcessPoolExecutor(cpuCount)
+    pool = multiprocessing.Pool(processes=cpuCount)
-    #executor = concurrent.futures.ThreadPoolExecutor(cpuCount)
 
     foldedFitTypes = ["sine", "poly"]
     for foldedFitTypesLength in range(1, len(foldedFitTypes)+1):
@@ -778,12 +803,16 @@ if __name__ == "__main__":
             folderPath = f"../{date}-{'-'.join(foldedFitTypeCombo)}/"
             mkdir_p(folderPath)
             foldedFitTypeComboFilter = np.full(len(fullData), False)
+            foldedFitTypeComboFilterPeriod = np.full(len(fullData), False)
             if("sine" in foldedFitTypeCombo):
                 foldedFitTypeComboFilter |= fullData["FitType"] == "sine"
+                foldedFitTypeComboFilterPeriod |= fullData["periodFitType"] == "sine"
             if("poly" in foldedFitTypeCombo):
                 foldedFitTypeComboFilter |= fullData["FitType"] == "poly"
+                foldedFitTypeComboFilterPeriod |= fullData["periodFitType"] == "poly"
 
             data = fullData[(foldedFitTypeComboFilter) & (fullData["isValidFold"])]
+            dataPeriod = fullData[(foldedFitTypeComboFilterPeriod) & (fullData["isValidFold"])]
 
             showSourceFilter = np.full(len(data), False)
             if(useKepler):
@@ -807,12 +836,14 @@ if __name__ == "__main__":
                                            "PeriodWithinStd": allValuesWithin3Std(periods)})
 
             validStarPeriodMap = pd.DataFrame(validStarPeriodMap)
-            periodsCutList = [0.5, 1, 1.5, 2, 5, 10, 15, 20]
 
             data = pd.merge(data, validStarPeriodMap, on="StarName")
 
             starPlotFunc = partial(plotStar, data, showSourceFilter, folderPath)
-            list(executor.map(starPlotFunc, starDB.getAllStars())) # wrap in list, to force evaluation
+            list(pool.map(starPlotFunc, allStars)) # wrap in list, to force evaluation
+            
+            starPlotPeriodFunc = partial(plotStarPeriod, dataPeriod, showSourceFilter, folderPath)
+            list(pool.map(starPlotPeriodFunc, allStars)) # wrap in list, to force evaluation
 
             combos = []
             for comboLength in range(1, len(spType) + 1):
@@ -820,4 +851,4 @@ if __name__ == "__main__":
                     combos.append(combo)
 
             plotComboFunc = partial(plotCombo, data, showSourceFilter, folderPath)
-            list(executor.map(plotComboFunc, combos))
+            list(pool.map(plotComboFunc, combos)) # wrap in list, to force evaluation