root_indx --> tree_root

diffmah/fitting_helpers.py

@@ -21,7 +21,7 @@
 
 DLOGM_CUT = 2.5
 T_FIT_MIN = 1.0
-HEADER = "# root_indx logm0 logtc early_index late_index t_peak loss n_points_per_fit fit_algo\n"
+HEADER = "# tree_root logm0 logtc early_index late_index t_peak loss n_points_per_fit fit_algo\n"
 DEFAULT_NCHUNKS = 50
 
 LJ_Om = 0.310
@@ -31,7 +31,7 @@
 def write_collated_data(outname, fit_data_strings, chunk_arr=None):
     import h5py
 
-    root_indx = fit_data_strings[:, 0].astype(int)
+    tree_root = fit_data_strings[:, 0].astype(int)
     logm0 = fit_data_strings[:, 1].astype(float)
     logtc = fit_data_strings[:, 2].astype(float)
     early_index = fit_data_strings[:, 3].astype(float)
@@ -42,7 +42,7 @@ def write_collated_data(outname, fit_data_strings, chunk_arr=None):
     fit_algo = fit_data_strings[:, 8].astype(int)
 
     with h5py.File(outname, "w") as hdf:
-        hdf["root_indx"] = root_indx
+        hdf["tree_root"] = tree_root
         hdf["logm0"] = logm0
         hdf["logtc"] = logtc
         hdf["early_index"] = early_index
@@ -128,7 +128,7 @@ def log_mah_loss_uparams(u_params, loss_data):
 loss_and_grads_kern = jjit(value_and_grad(log_mah_loss_uparams))
 
 
-def get_outline_bad_fit(root_indx, loss_data, npts_mah, algo):
+def get_outline_bad_fit(tree_root, loss_data, npts_mah, algo):
     log_mah_target = loss_data[1]
     logm0 = log_mah_target[-1]
     logtc, early, late = -1.0, -1.0, -1.0
@@ -137,20 +137,20 @@ def get_outline_bad_fit(root_indx, loss_data, npts_mah, algo):
     _floats = (logm0, logtc, early, late, t_peak, loss_best)
     out_list = ["{:.5e}".format(float(x)) for x in _floats]
     out_list = [str(x) for x in out_list]
-    out_list = [str(root_indx), *out_list, str(npts_mah), str(algo)]
+    out_list = [str(tree_root), *out_list, str(npts_mah), str(algo)]
     outline = " ".join(out_list) + "\n"
     return outline
 
 
-def get_outline(root_indx, loss_data, u_p_best, loss_best, npts_mah, algo):
+def get_outline(tree_root, loss_data, u_p_best, loss_best, npts_mah, algo):
     """Return the string storing fitting results that will be written to disk"""
     t_peak = loss_data[2]
     p_best = get_bounded_mah_params(DiffmahUParams(*u_p_best))
     logm0, logtc, early, late = p_best
     _floats = (logm0, logtc, early, late, t_peak, loss_best)
     out_list = ["{:.5e}".format(float(x)) for x in _floats]
     out_list = [str(x) for x in out_list]
-    out_list = [str(root_indx), *out_list, str(npts_mah), str(algo)]
+    out_list = [str(tree_root), *out_list, str(npts_mah), str(algo)]
     outline = " ".join(out_list) + "\n"
     return outline