import os from eiler_SA.forwardmodeling_sa import make_params_dict, write_params_file chloe_model_params = '/home/gabriel/PycharmProjects/FastGrainBoundary-DiffusionSolver/sensitivity_analysis/original_eiler/Eiler94_Amphibolite.txt' output = os.path.split(chloe_model_params)[0] output = os.path.join(output, 'mode_length_temp') fwd_model_parameters = make_params_dict(chloe_model_params) print(fwd_model_parameters) # # # TODO - first change the parameters of the plag vs hornblende modalities print(fwd_model_parameters['Min1-Mode']) # 0.3 - base case plag_mode_key = 'Min1-Mode' plagioclase_mode = float(fwd_model_parameters[plag_mode_key]) plagioclase_mode_start = 0.1 # 0.6 - base case hornb_mode_key = 'Min2-Mode' hornblende_mode = float(fwd_model_parameters[hornb_mode_key]) hornblende_mode_start = 0.8 plag_length_key = 'Min1-W' plag_length = float(fwd_model_parameters[plag_length_key]) plag_length_start = 200.0 horn_length_key = 'Min2-R' horn_length = float(fwd_model_parameters[horn_length_key])
# root = '/home/gabriel/Documents/Euler_SA/euler_modality'
# root = '/home/gabriel/Documents/FGB_model/JohnEiler/modality_SF/'
root = '/home/gabriel/PycharmProjects/FastGrainBoundary-DiffusionSolver/sensitivity_analysis/mode_length_chloe_cooling/blockD/'
outroot = '/home/gabriel/PycharmProjects/FastGrainBoundary-DiffusionSolver/sensitivity_analysis/modality/modality_len_results_fwd/blockD/'
if not os.path.exists(outroot):
os.mkdir(outroot)
# root = '/home/gabriel/PycharmProjects/FastGrainBoundary-DiffusionSolver/sensitivity_analysis/mode_length_configs/'
# outroot = '/home/gabriel/PycharmProjects/FastGrainBoundary-DiffusionSolver/sensitivity_analysis/modality/modality_fwd_results/'
# root = '/home/gabriel/PycharmProjects/FastGrainBoundary-DiffusionSolver/sensitivity_analysis/mode_length_configs/'
for dir in os.listdir(root):
print('dir', dir)
param_path = os.path.join(root, dir)
print('path', param_path)
save_name = dir.split('.')[0]
fwd_model_params = make_params_dict(params=param_path)
print('running the slow model with a cooling file')
xresult, yresult, timeresult = forward_model_slow_bulk(
fwd_model_params, coolfile=True)
# print('running the slow model with no cooling file')
# xresult, yresult, timeresult = forward_model_slow_bulk(fwd_model_params)
# Save each array as a .npy file
save(os.path.join(outroot, '{}_x.npy'.format(save_name)), xresult)
save(os.path.join(outroot, '{}_y.npy'.format(save_name)), yresult)
save(os.path.join(outroot, '{}_time.npy'.format(save_name)),
timeresult)
# year in MA we want to see
year_ma = 44
time_step = 0.0005
# convert the year into the index for the y array. Subtract one bc the y array starts at index 0
year_index = int(round(((year_ma / time_step) - 1), 1))
print('year index {}'.format(year_index))
x_path = os.path.join(root, 'Eiler94_Amphibolite_x.npy')
y_path = os.path.join(root, 'Eiler94_Amphibolite_y.npy')
time_path = os.path.join(root, 'Eiler94_Amphibolite_time.npy')
param_path = os.path.join(root, 'Eiler94_Amphibolite.txt')
param_dict = make_params_dict(param_path)
x_arr = np.load(x_path)
y_arr = np.load(y_path)
t_arr = np.load(time_path)
print('x shape: {}, y shape {}, t shape {}'.format(x_arr.shape, y_arr.shape,
t_arr.shape))
# quartz
plt.plot(x_arr[:, 0], y_arr[0, year_index, :])
#plt.show()
# TODO - construct inverse thingy
# 1) Start with a known time-temp history - in our case linear