treewide: add minimal lightcurve support

main/astrodatagui/CalcAllFlaresThread.py

@@ -1,18 +1,18 @@
 from PyQt5.QtCore import pyqtSignal, QThread
 import multiprocessing
 import concurrent.futures
-import lightkurve as lk
+from ..util.MinimalLightCurve import read
 import pandas as pd
 from ..flaredetector.flaredetector import calculateFlareFitsForLightcurve
 
 def getFlareCount(filesDict):
-    lc = lk.read(filesDict["FilePath"])
+    lc = read(filesDict["FilePath"])
     lc.flux = lc["sap_flux"]
     lc.flux_err = lc["sap_flux_err"]
-    sapPeaks, sapFits = calculateFlareFitsForLightcurve(lc.flatten())
+    sapPeaks, sapFits = calculateFlareFitsForLightcurve(lc.flatten(), minimalLC=True)
     lc.flux = lc["pdcsap_flux"]
     lc.flux_err = lc["pdcsap_flux_err"]
-    pdcsapPeaks, pdcsapFits = calculateFlareFitsForLightcurve(lc.flatten())
+    pdcsapPeaks, pdcsapFits = calculateFlareFitsForLightcurve(lc.flatten(), minimalLC=True)
 
     filesDict["sapPeaks"] = sapPeaks
     filesDict["sapPeaksCount"] = len(sapPeaks)
@@ -21,7 +21,8 @@ def getFlareCount(filesDict):
     filesDict["pdcsapPeaksCount"] = len(pdcsapPeaks)
     filesDict["pdcsapFits"] = pdcsapFits
     del lc
-
+    if(filesDict["StarName"] == "CD-51 13128" and filesDict["Sequence"] == 1):
+        print(filesDict)
     return filesDict
 
 class CalcAllFlaresThread(QThread):
@@ -35,7 +36,6 @@ class CalcAllFlaresThread(QThread):
     #@pyqtSlot()
     def run(self):
         cpuCount = multiprocessing.cpu_count()
-        splitList = [self.allFlaresDictList[i:i + cpuCount] for i in range(0, len(self.allFlaresDictList), cpuCount)]
         executor = concurrent.futures.ProcessPoolExecutor(cpuCount)
 
         resFrame = pd.DataFrame(executor.map(getFlareCount, self.allFlaresDictList.to_dict(orient="records")))

main/flaredetector/flaredetector.py

@@ -40,8 +40,9 @@ def calculateFitForFlare(lightcurve, peakIndex):
     flareFitDataPoints = flareFitDataPoints + peakIndex
     return np.array(Peak+1), flareFitDataPoints, np.array(fit+1)
 
-def calculateFlareFitsForLightcurve(lightcurve, num=100, distance=1, height=0.05):
-    maxIndices = findMaxIndices(lightcurve, num, distance=distance, height=height, sortByHighest=True)
+def calculateFlareFitsForLightcurve(lightcurve, num=100, distance=1, height=0.05, minimalLC=False):
+    maxIndices = findMaxIndices(lightcurve, num, distance=distance, height=height,
+                                sortByHighest=True, minimalLC=minimalLC)
     peaks = []
     fits = []
     for index in maxIndices:
@@ -57,7 +58,6 @@ def calculateFlareFitsForLightcurve(lightcurve, num=100, distance=1, height=0.05
                          "Quality": lightcurve.quality[flareFitDataPoints]})
         except Exception as error:
             pass
-            #print("Flare fit exception: ", error)
 
     return peaks, fits
 

main/flaredetector/util.py

@@ -1,16 +1,19 @@
 import numpy as np
 from scipy.signal import find_peaks
 
-from lightkurve.periodogram import Periodogram
-from lightkurve.lightcurve import LightCurve
-
-def findMaxIndices(lc, num=3, distance=100, height=(None, None), sortByHighest=False):
-    if(isinstance(lc, Periodogram)):
-        data = lc.power
-    elif(isinstance(lc, LightCurve)):
+def findMaxIndices(lc, num=3, distance=100, height=(None, None), sortByHighest=False,
+                   minimalLC=False):
+    if(minimalLC):
         data = lc.flux
     else:
-        return np.zeros(num)-1
+        from lightkurve.periodogram import Periodogram as OrigPG
+        from lightkurve.lightcurve import LightCurve as OrigLC
+        if(isinstance(lc, OrigPG)):
+            data = lc.power
+        elif(isinstance(lc, OrigLC)):
+            data = lc.flux
+        else:
+            return np.zeros(num)-1
     peak_indices, peak_dict = find_peaks(data, height=height,
                                          distance=distance)
     peak_heights = peak_dict["peak_heights"]