def nt(model_name, dat, let):
"""
Read the number of time steps (used in observation file)
"""
# Define path and file
output_path = rm.make_output_dirs(model_name, dat, let)[0]
input_file = rm.make_file_dir_names(model_name)[2]
# Read records (number lines in time step input)
num_time_records = rm.read_records_input(output_path + '/' + input_file,
'# time periods')
# Read hashtag input
lines = rm.read_hashtag_input(output_path + '/' + input_file,
'# time periods', num_time_records)
# Split hashtag input by white space
split_lines = str.split(lines)
# Read entry 3/4 in each line
right_entries = split_lines[2::4]
# Sum of the entries
# -> Adding one accounts for the first time step at time = 0
nt = sum([int(right_entries[i]) for i in range(len(right_entries))]) + 1
return nt
def units(model_name, dat, let, uindex, pindex):
"""
Read parameters pindex from unit uindex
pindex:
1 - Porosity
4 - Permeability
8 - Thermal conductivity
10 - Volumetric heat capacity
"""
# Define path and file
output_path = rm.make_output_dirs(model_name, dat, let)[0]
input_file = rm.make_file_dir_names(model_name)[2]
# Read hashtag input
lines = rm.read_hashtag_input(output_path + '/' + input_file, '# units',
uindex)
# Split hashtag input by white space
split_lines = str.split(lines)
# Find the number of parameters per line
num_ps = len(split_lines) / uindex
# Calculate the index of the right parameter
right_index = (uindex - 1) * num_ps + pindex
right_entry = split_lines[right_index]
return right_entry
def delz(model_name, dat, let):
"""
Read model name, output date and letter and
return the corresponding array of cell lengths
in z-direction.
"""
output_dir = rm.make_output_dirs(model_name, dat, let)[0]
input_file = rm.make_file_dir_names(model_name)[2]
arr_filename = pm.py_output_filename('specs', 'delz',
specl(model_name, dat, let), 'npy')
if rm.check_hashtag(output_dir, input_file, "# delz"):
line = rm.read_hashtag_input(output_dir + '/' + input_file, '# delz',
1)
else:
input_file = rm.make_file_dir_names(model_name)[4]
if rm.check_hashtag(output_dir, input_file, "# delz"):
line = rm.read_hashtag_input(output_dir + '/' + input_file,
'# delz', 1)
else:
if os.path.isfile(arr_filename):
return np.load(arr_filename)
else:
raise IOError('Input files, hashtags or' +
' npy file not found:\n\n' + output_dir +
'\n\n' + input_file + '\n\n' + arr_filename)
num_entries = len(str.split(line))
nums = [
int(str.split(entry, "*")[0]) if len(str.split(entry, "*")) > 1 else 1
for entry in str.split(line)
]
lens = [
float(str.split(entry, "*")[1])
if len(str.split(entry, "*")) > 1 else float(str.split(entry, "*")[0])
for entry in str.split(line)
]
vec = [[lens[i] for j in range(nums[i])] for i in range(num_entries)]
arr = np.array([num for elem in vec for num in elem])
np.save(arr_filename, arr)
return arr
def nrobs_int(model_name, dat, let):
"""
Number of observation intervals for model_name.
"""
output_path = rm.make_output_dirs(model_name, dat, let)[0]
enkf_input_file = rm.make_file_dir_names(model_name)[3]
line = rm.read_hashtag_input(output_path + '/' + enkf_input_file,
'# nrobs_int', 1)
first_entry = str.split(line)[0]
nrobs_int = int(first_entry)
return nrobs_int
def nrens(model_name, dat, let):
"""
Number of ensemble members for model_name
"""
output_path = rm.make_output_dirs(model_name, dat, let)[0]
input_file = rm.make_file_dir_names(model_name)[2]
line = rm.read_hashtag_input(output_path + '/' + input_file, '# simulate',
1)
first_entry = str.split(line)[0]
nrens = int(first_entry)
return nrens
def monitor_locs(model_name, dat, let):
"""
Monitor point locations of model_name.
"""
output_path = rm.make_output_dirs(model_name, dat, let)[0]
true_input_file = rm.make_file_dir_names(model_name)[4]
n_mons = num_mons(model_name, dat, let)
lines = rm.read_hashtag_input(output_path + '/' + true_input_file,
'# monitor', n_mons)
locs = [[int(str.split(lines)[j]) for j in range(i, i + 3)]
for i in range(0, 4 * n_mons, 4)]
return locs
def single_cell_locs(model_name, dat, let):
"""
Single cell locations of model_name, dat and lets
"""
output_path = rm.make_output_dirs(model_name, dat, let)[0]
enkf_input_file = rm.make_file_dir_names(model_name)[3]
n_sc = num_single_cell(model_name, dat, let)
lines = rm.read_hashtag_input(output_path + '/' + enkf_input_file,
'# single cell output, ', n_sc)
locs = [[int(str.split(lines)[j]) for j in range(i, i + 3)]
for i in range(0, 5 * n_sc, 5)]
return locs
def single_cell_variables(model_name, dat, let):
"""
Single cell variable of model_name.
1: head, 2:temp, 3:conc, 4:kz, 5:lz, 6:por (inside mem)
11: head, 12:temp, 13:conc, 14:kz, 15:lz, 16:por (not inside mem)
"""
output_path = rm.make_output_dirs(model_name, dat, let)[0]
enkf_input_file = rm.make_file_dir_names(model_name)[3]
n_sc = num_single_cell(model_name, dat, let)
lines = rm.read_hashtag_input(output_path + '/' + enkf_input_file,
'# single cell output, ', n_sc)
variables = [int(str.split(lines)[i]) for i in range(3, 5 * n_sc, 5)]
return variables