Update flame_wave/analysis readme and docs for flame_speed.py

Exec/science/flame_wave/analysis/README.md

@@ -24,20 +24,19 @@ user to specify framerate.
 
 ### front_tracker.py
 Script for measuring the location of a flame or shock front over a sequence of snapshots. Allows the
-user to specify the metrics (will only track 1 / 1000th the local enuc maximum by default), whether
-to use a global or local maximum, domain bounds, and a few other things. For a usage description and
-a full list of valid parameters, type `./front_tracker.py -h`. Should work for any dataset, but has
-only been tested on flame wave ones. Restrictions: Currently only tracks along one dimension (the
-user can tell it how to eliminate the others - either through slicing or averaging), and only tracks
-percentages of the maximum of some field. Outputs to space-delimited data file called
-front_tracking.dat by default.
+user to specify the metrics (will only track 1 / 1000th the enuc maximum by default), domain bounds,
+and a few other things. For a usage description and a full list of valid parameters, type
+`./front_tracker.py -h`. Should work for any dataset, but has only been tested on flame wave ones.
+Restrictions: Currently only tracks along one dimension (the user can tell it how to eliminate the
+others - either through slicing or averaging), and only tracks percentages of the maximum of some
+field. Outputs to space-delimited data file called front_tracking.dat by default.
 
 ### flame_speed.py
-Script for reading in the front tracking dataset, plotting it, and fitting a line to some portion of
-it. Usage: `./flame_speed.py data_file starting_index`, where starting index is the index of the
-first datapoint to consider when fitting the line. The script prints out the slope of the line, the
-r-squared value, and the fit error. The plot will appear squashed when it pops up - the window needs
-to be enlarged. Uses `scipy` and `pandas`.
+Script for reading in the front tracking dataset generated with `front_tracker.py`, plotting it, and
+fitting a line to some portion of it.
+Usage: `./flame_speed.py [--tmin TMIN] [--tmax TMAX] data_file column [column...]`, where `TMIN` and
+`TMAX` specify the times to consider when fitting the line. The script prints out the slope of the
+line, the r-squared value, and the fit error. Uses `scipy` and `pandas`.
 
 ### multirays.py
 Plot 1D vertical slices of axisymmetric datasets. It generates 3 ortho rays - one at each end of

Exec/science/flame_wave/analysis/flame_speed.py

@@ -22,9 +22,7 @@
 def measure_and_plot(dat, args):  # pylint: disable=too-many-locals, too-many-statements
     """
     Plots front_location vs. time on the current pyplot figure, as well as any
-    reasonable linear fits. *tmin* and *tmax* should give a range of times for
-    which the flame front has stabilized, and the slope obtained from linear
-    regression will yield an accurate measurement of the front speed.
+    reasonable linear fits.
     """
 
     slopes = {}
@@ -123,19 +121,20 @@ def measure_and_plot(dat, args):  # pylint: disable=too-many-locals, too-many-st
 
 
 def main():
-    parser = argparse.ArgumentParser()
+    parser = argparse.ArgumentParser(
+        description="Script for plotting and fitting the flame front tracking data generated by front_tracker.py.",
+        epilog="""TMIN and TMAX should give a range of times for which the flame front
+        has stabilized, and the slope obtained from linear regression will
+        yield an accurate measurement of the front speed.""",
+    )
     parser.add_argument("data_file", type=argparse.FileType("r"))
     parser.add_argument(
-        "--tmin",
-        type=float,
-        help="ignore everything before this time for the linear regression",
+        "--tmin", type=float, help="minimum time for the linear regression"
     )
     parser.add_argument(
-        "--tmax",
-        type=float,
-        help="ignore everything after this time for the linear regression",
+        "--tmax", type=float, help="maximum time for the linear regression"
     )
-    parser.add_argument("columns", nargs="+")
+    parser.add_argument("columns", nargs="+", metavar="column")
     args = parser.parse_args()
 
     dat = pd.read_csv(args.data_file, sep=r"\s+")