# sort
ratios_sort = contrast_ratios2.sort_values(['BL_tau'])
ratios_sort.head()
############################ GET TTS OUTPUTS FOR EA. CASE ############################
# --------------- ver 1: UT = RF06, BL = IN SHEAR
# need to drop any row with a nan value
idx = ratios_sort['RF06_In Shear'].index[ratios_sort['RF06_In Shear'].apply(
np.isnan)]
ratios_sort_dirty = ratios_sort
ratios_sort_dirty = ratios_sort_dirty.dropna()
utbl_dirty = ratios_sort_dirty['RF06_In Shear'].values
tau_dirty = ratios_sort_dirty['BL_tau'].values
t_dirty, exp_decay_matrix_dirty, LT_dirty = tts_mod.prep_for_tts(tau_dirty)
my_mustar_dirty, my_r2_dirty, my_gf_dirty, my_t_dirty, mean_age_dirty, mode_age_dirty, best_k_dirty = tts_mod.get_tts(
utbl_dirty, tau_dirty, t_dirty, exp_decay_matrix_dirty, LT_dirty)
# --------------- ver 1: UT = RF06, BL = IN SHEAR
# need to drop any row with a nan value
idx = ratios_sort['RF06_S of Shear'].index[
ratios_sort['RF06_S of Shear'].apply(np.isnan)]
ratios_sort_clean = ratios_sort
ratios_sort_clean = ratios_sort_clean.dropna()
utbl_clean = ratios_sort_clean['RF06_S of Shear'].values
tau_clean = ratios_sort_clean['BL_tau'].values
t_clean, exp_decay_matrix_clean, LT_clean = tts_mod.prep_for_tts(tau_clean)
my_mustar_clean, my_r2_clean, my_gf_clean, my_t_clean, mean_age_clean, mode_age_clean, best_k_clean = tts_mod.get_tts(
tau_rf05[col] = ''
# lgnth of G(t), usually 275999 but make higher so it doesnt fail
topg = 276000
for col in ratios_sort[idx_rf05]:
print(col)
# ----- remove nans or else tts function fails
utbl_full = np.array(ratios_sort[col].values, dtype=np.float64)
utbl_not_null_idx = np.argwhere(~np.isnan(utbl_full))
# ----- inputs without nans
my_utbl = utbl_full[utbl_not_null_idx]
my_tau = tau[utbl_not_null_idx]
#
# ----- run tts function
t, exp_decay_matrix, LT = tts_mod.prep_for_tts(my_tau)
my_mustar, my_r2, my_gf, my_t, mean_age, mode_age, best_k = tts_mod.get_tts(
my_utbl, my_tau, t, exp_decay_matrix, LT)
#
# ----- fill to make mu*, tau all length 52
diffa = len(utbl_full) - len(my_utbl)
if (diffa > 0):
filla = np.empty((1, diffa))
filla.fill(np.nan)
my_tau = np.append(my_tau, filla)
my_mustar = np.append(my_mustar, filla)
my_utbl = np.append(my_utbl, filla)
# ----- fill to make gf, t all length 275999
diffb = 275999 - len(my_gf)
if (diffb > 0):
fillb = np.empty((1, diffb))
# read in ratios dataframe
contrast_ratios = pd.read_pickle(
"/UTLS/schelpon/TTS_2020/get_ratios/contrast_ratios.pkl")
ratios_sort = contrast_ratios.sort_values(['BL_tau'])
ratios_sort.head()
# utbl, same for all
utbl = ratios_sort['All RF']
############################ GET TTS OUTPUTS FOR EA. CASE ############################
# --------------- ver 1: BL TAU
tau_bl = ratios_sort['BL_tau'].values
# get inputs
t_bl, exp_decay_matrix_bl, LT_bl = tts_mod.prep_for_tts(tau_bl)
# get tts
my_mustar_bl, my_r2_bl, my_gf_bl, my_t_bl, mean_age_bl, mode_age_bl, best_k_bl = tts_mod.get_tts(
utbl, tau_bl, t_bl, exp_decay_matrix_bl, LT_bl)
# save campaign average for later use
campaign_avg_figa = pd.DataFrame()
campaign_avg_figa['tau'] = tau_bl
campaign_avg_figa['utbl'] = utbl.values
campaign_avg_figa['mustar'] = my_mustar_bl
campaign_avg_figb = pd.DataFrame()
campaign_avg_figb['t'] = t_bl / 86400
campaign_avg_figb['gf'] = my_gf_bl
campaign_avg_stats = pd.DataFrame({