diff --git a/diffmah/fitting_helpers.py b/diffmah/fitting_helpers.py index 541dab0..368663a 100644 --- a/diffmah/fitting_helpers.py +++ b/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 @@ -137,12 +137,12 @@ 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)) @@ -150,7 +150,7 @@ def get_outline(root_indx, loss_data, u_p_best, loss_best, 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