update

Author: zhichen3

Diff for: Exec/science/xrb_spherical/analysis/front_tracker.py

@@ -6,40 +6,68 @@
 import numpy as np
 from yt.frontends.boxlib.api import CastroDataset
 
-'''
-This file tracks the flame front and writes them into a txt file.
 
-Usage: ./front_tracker.py plotFiles_*
-'''
-
-def track_flame_front(ds):
+def track_flame_front(ds, metric):
     '''
-    This script tracks the flame front position for a given dataset.
-    It returns a tuple of the form: (Time, Theta)
-    Note: time is in milisecond.
+    This function tracks the flame front position for a given dataset.
+    It returns a tuple of the form: (Time (in ms), Theta)
+
+    Procedure to determine flame front:
+    1) User selects e a quantity to use as a metric: enuc or Temp
+    2) Determine the global max of that quantity
+    3) Determine the theta position of the cell that contains the global max
+    4) Do a radial average of the data set to convert to 1D as a function of theta
+    5) Determine flame front at theta where the metric quantity drops to
+       metric_number * global_max of that quantity.
     '''
 
     time = ds.current_time.in_units("ms")
 
-    # How to determine the flame front?
+
+    ad = ds.all_data()
+
+
     # 1) Global max temperature: this can be used to track initial
     # detonation resulted from the initial temperature perturbation
-    # 2) Use vertically averaged max enuc to determine the actual flame front
+    # 2) Use radially averaged enuc then find flame front is determined by
+    # when the averaged enuc drops below some percentage of max global enuc
 
 
     return timeTheta
 
 
 if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="""
+    This file tracks the flame front and writes them into a txt file.
+    """)
 
-    ts = sys.argv[1:]
+    parser.add_argument('--fnames', nargs='+', type=str,
+                        help="Dataset file names for tracking flame front.")
+    parser.add_argument('--field', '-f', default="enuc", type=str,
+                        metavar="{enuc, Temp}",
+                        help="""field parameter used as metric to determine flame front.
+                        Choose between {enuc, Temp}""")
+    parser.add_argument('--percent', '-p', default=0.001, type=float,
+                        help="""Float number between (0, 1]. Representing the percent of
+                        the global maximum of the field quantity used to track the flame.""")
 
+    args = parser.parse_args()
+
+    metric_quantities = ["enuc", "Temp"]
+    if args.field not in metric_quantities:
+        parser.error("field must be either enuc or Temp")
+
+    if args.percent <= 0.0 or args.percent > 1.0:
+        parser.error("metric must be a float between (0, 1]")
+
+    metric = (args.field, args.percent)
     timeThetaArray = []
-    for fname in ts:
+
+    for fname in args.fnames:
         ds = CastroDataset(fname)
 
         # Get tuple in form (theta, time)
-        timeTheta= track_flame_front(ds)
+        timeTheta= track_flame_front(ds, metric)
         timeThetaArray.append(timeTheta)
 
     # Sort array by time and write to file

Diff for: Exec/science/xrb_spherical/analysis/r_profile.py

@@ -41,6 +41,8 @@
     for theta in thetas:
         # simply go from (rmin, theta) -> (rmax, theta). Doesn't need to convert to physical R-Z
         ray = ds.ray((rmin, theta, 0*cm), (rmax, theta, 0*cm))
+
+        # sort by "t", which goes from 0 to 1 representing the spatial order.
         isrt = np.argsort(ray["t"])
         axes[i].plot(ray['r'][isrt], ray[f][isrt], label=r"$\theta$ = {:.4f}".format(float(theta)))