Merge remote-tracking branch 'github/master'

src/m_photoi_helmh.f90

@@ -55,7 +55,7 @@ subroutine photoi_helmh_initialize(tree, cfg, is_used, eta)
     real(dp)                   :: frac_O2, dummy(0)
 
     call CFG_add_get(cfg, "photoi_helmh%author", author, &
-         "Can be Luque (default), Bourdon-2, Bourdon-3 or custom")
+         "Can be Bourdon-3 (default), Bourdon-2, Luque or custom")
     call CFG_add(cfg, "photoi_helmh%lambdas", dummy, &
          "Lambdas to use in Helmholtz eq; unit 1/(m bar)", &
          .true.)
@@ -124,6 +124,7 @@ subroutine photoi_helmh_initialize(tree, cfg, is_used, eta)
        coeffs  = coeffs * (frac_O2 * gas_pressure)**2 ! 1/m^2
     case ("custom")
        call CFG_get_size(cfg, "photoi_helmh%lambdas", n_modes)
+       if (n_modes < 1) error stop "Custom photoionization lambdas and coeffs missing."
        allocate(lambdas(n_modes))
        allocate(coeffs(n_modes))
        call CFG_get(cfg, "photoi_helmh%lambdas", lambdas)

tools/sensitivity_analyze_results.py

@@ -12,15 +12,35 @@
     description='''Script to analyze results from a sensitivity study
     Authors: Hemaditya Malla, Jannis Teunissen''')
 parser.add_argument('logs', type=str, nargs='+',
-                    help='Log files')
+                    help='Log/amounts files')
 parser.add_argument('-y', type=str, nargs='+', default=["sum(n_e)"],
                     help='Variables in the log files to compare')
 parser.add_argument('-time_index', type=int, default=-1,
                     help='Which time index in the log files to consider')
 args = parser.parse_args()
 
 logs = sorted(args.logs)
-logs_df = [pd.read_csv(f, delim_whitespace=True) for f in args.logs]
+
+# Determine whether we analyse log files(pandas dataframe) or species amounts(txt files)
+if not all([x.endswith('amounts.txt') for x in logs]):
+    logs_df = [pd.read_csv(f, delim_whitespace=True) for f in args.logs]
+    base_name = logs[0].replace('_log.txt', '')
+else:
+    # Can we use the below variable elsewhere below?
+    analyse_chemistry = True
+    # Make sure that the default argument is changed
+    if args.y[0] == "sum(n_e)":
+        args.y = ["e"]
+
+    # Loading the species list and appending the time column to it
+    base_name = logs[0].replace('_amounts.txt', '')
+    with open(base_name + '_species.txt', 'r') as f:
+        species_list = [x.strip() for x in f.readlines() if x.strip()]
+    species_list.insert(0, "time")
+
+    # Load amounts of species and create a dataframe of them so that the below code wont break
+    logs_df = [pd.DataFrame(np.loadtxt(f), columns=species_list) for f in args.logs]
+
 
 log_sizes = np.array([len(df) for df in logs_df])
 max_size, min_size = log_sizes.max(), log_sizes.min()
@@ -59,7 +79,9 @@
 print(f'R{"#":<4} {"variable":15} {"mu":>15} {"mustar":>15} {"sigma":>15}')
 
 for i, ix in enumerate(reaction_ix):
-    values = np.array([df[args.y] for _, df in all_cases[ix]])
+    # print("test: ", args.y)
+    # test = [df[args.y] for _, df in all_cases[ix]]
+    values = np.array([df[args.y].to_numpy() for _, df in all_cases[ix]])
     factors = np.array([f for f, _ in all_cases[ix]])
 
     # Get values at time index
@@ -89,7 +111,7 @@
 print(f'{"rank":<6} R{"#":<6} {"reaction_list":40} {"max(mustar)":15}')
 
 # Load reaction names
-base_name = logs[0].replace('_log.txt', '')
+# base_name = logs[0].replace('_log.txt', '')
 with open(base_name + '_reactions.txt', 'r') as f:
     reactions_list = [x.strip() for x in f.readlines() if x.strip()]