def check_regions_fracture(params, prefix):
""" Checks that the number of regions is each family is correct. Checks that the region index is a known index.
Parameters
-------------
params : dict
parameter dictionary
prefix : string
either 'e' or 'r' for ellipse or rectangle
Returns
---------
None
Notes
---------
Exits program is inconsistencies are found.
"""
key = prefix + "Region"
shape = "ellipse" if prefix == 'e' else "rectangle"
num_families = params['nFamEll']['value'] if prefix == 'e' else params[
'nFamRect']['value']
hf.check_none(key, params[key]['value'])
hf.check_length(key, params[key]['value'], num_families)
hf.check_values(key, params[key]['value'], 0,
params["numOfRegions"]['value'])
def check_p32_targets(params):
""" Check the number of polygons if stopCondition is set to 1, else check the p32 target parameters.
Parameters
-------------
params : dict
parameter dictionary
Returns
---------
None
Notes
---------
Exits program is inconsistencies are found.
"""
# Check P32 inputs for ellipses
if params['nFamEll']['value'] > 0:
hf.check_none('e_p32Targets', params['e_p32Targets']['value'])
hf.check_length('e_p32Targets', params['e_p32Targets']['value'],params['nFamEll']['value'])
hf.check_values('e_p32Targets',params['e_p32Targets']['value'],0)
# Check P32 inputs for rectangles
if params['nFamRect']['value'] > 0:
hf.check_none('r_p32Targets', params['r_p32Targets']['value'])
hf.check_length('r_p32Targets', params['r_p32Targets']['value'],params['nFamRect']['value'])
hf.check_values('r_p32Targets',params['r_p32Targets']['value'],0)
def check_aspect(params, prefix):
""" Check the aspect of the rectangle or ellipse families matches the number of families requested and is a positive value
Parameters
-------------
params : dict
parameter dictionary
prefix : string
either 'e' or 'r' for ellipse or rectangle
Returns
---------
None
Notes
---------
Exits program is inconsistencies are found.
"""
key = prefix + "aspect"
shape = "ellipse" if prefix == 'e' else "rectangle"
num_families = params['nFamEll']['value'] if prefix == 'e' else params[
'nFamRect']['value']
hf.check_none(key, params[key]['value'])
hf.check_length(key, params[key]['value'], num_families)
hf.check_values(key, params[key]['value'], 0)
def check_constant_dist(params, prefix, shape, cnt):
"""
Verifies parameters for constant distribution of fractures
"""
# print(f"checking constant {shape}")
for i in range(cnt):
value = params[prefix + "const"]["value"][i]
hf.check_values(prefix + "const", value, 0)
hf.check_min_frac_size(params, value)
def check_aperture(params):
""" Checks how apertures are being defined. This feature will be removed in the future and apertures will be defined by family..
Parameters
-------------
params : dict
parameter dictionary
Returns
---------
None
Notes
---------
Exits program is inconsistencies are found.
"""
if params['aperture']['value'] == 1:
hf.check_none('meanAperture', params['meanAperture']['value'])
hf.check_none('stdAperture', params['stdAperture']['value'])
hf.check_values('stdAperture', params['stdAperture']['value'], 0)
elif params['aperture']['value'] == 2:
hf.check_none('apertureFromTransmissivity',
params['apertureFromTransmissivity']['value'])
hf.check_length('apertureFromTransmissivity',
params['apertureFromTransmissivity']['value'], 2)
if params['apertureFromTransmissivity']['value'][0] == 0:
hf.print_error(
"\"apertureFromTransmissivity\"'s first value cannot be 0.")
if params['apertureFromTransmissivity']['value'][1] == 0:
hf.print_warning(
"\"apertureFromTransmissivity\"'s second value is 0, which will result in a constant aperture."
)
elif params['aperture']['value'] == 3:
hf.check_none('constantAperture', params['constantAperture']['value'])
hf.check_values('constantAperture',
params['constantAperture']['value'], 0)
elif params['aperture']['value'] == 4:
hf.check_none('lengthCorrelatedAperture',
params['lengthCorrelatedAperture']['value'])
hf.check_length('lengthCorrelatedAperture',
params['lengthCorrelatedAperture']['value'], 2)
if params['lengthCorrelatedAperture']['value'][0] == 0:
hf.print_error(
"\"lengthCorrelatedAperture\"'s first value cannot be 0.")
if params['lengthCorrelatedAperture']['value'][1] == 0:
hf.print_warning(
"\"lengthCorrelatedAperture\"'s second value is 0, which will result in a constant aperture."
)
else:
hf.print_error("\"aperture\" must only be option 1 (log-normal), 2 (from transmissivity), "\
"3 (constant), or 4 (length correlated).")
def check_lognormal_dist(params, prefix, shape, cnt):
"""
Verifies all logNormal Parameters for ellipses and Rectangles.
Parameters
-------------
params : dict
parameter dictionary
prefix : string
either 'e' or 'r' for ellipse or rectangle
Returns
---------
None
Notes
---------
Exits program is inconsistencies are found.
"""
#print(f"checking log normal {shape}")
for i in range(cnt):
min_val = params[prefix + "LogMin"]["value"][i]
max_val = params[prefix + "LogMax"]["value"][i]
mean_val = params[prefix + "LogMean"]["value"][i]
std_val = params[prefix + "sd"]["value"][i]
hf.check_values(prefix + "LogMax", min_val, 0)
hf.check_values(prefix + "LogMin", max_val, 0)
hf.check_min_max(min_val, max_val, i, f"{shape} log-normal")
if std_val <= 0:
hf.print_error(
f"A standard deviation equal of {std_val} was provided for {shape} log-normal entry {i+1}. Value must be post"
)
# Get the mean and variance of the final distribution.
# Note these are the actual values, not the input variables
mu = np.exp(mean_val + std_val**2 / 2)
variance = (np.exp(std_val**2) - 1) * np.exp(2 * mean_val + std_val**2)
if mu <= min_val:
hf.print_error(
f"Requested mean value of final {shape} log-normal {mu} is smaller than minimum value {min_val}. Note that the mean value is determined by {prefix+'LogMean'} and {prefix+'sd'}. See documentation for more details."
)
if mu >= max_val:
hf.print_error(
f"Requested mean value of {shape} log-normal {mu} is larger than maximum value {max_val}. Note that the mean value is determined by {prefix+'LogMean'} and {prefix+'sd'}. See documentation for more details."
)
hf.check_min_frac_size(params, min_val)
def check_enum_points(params):
""" Check that the value of enumPoints for each ellipse family is an integer greater than 4
Parameters
-------------
params : dict
parameter dictionary
Returns
---------
None
Notes
---------
Exits program is inconsistencies are found.
"""
key = 'enumPoints'
hf.check_none(key, params[key]['value'])
hf.check_length(key, params[key]['value'], params['nFamEll']['value'])
hf.check_values(key, params[key]['value'], 4)
def check_permeability(params):
"""Verify the float used for permeability, if permOption is set to 1
Parameters
-------------
params : dict
parameter dictionary
Returns
---------
None
Notes
---------
Exits program is inconsistencies are found.
"""
if params['permOption']['value'] == 1:
hf.check_none('constantPermeability',
params['constantPermeability']['value'])
hf.check_values('constantPermeability',
params['constantPermeability']['value'], 0)
def check_exponential_dist(params, prefix, shape, cnt):
"""
Verifies parameters for exponential distribution of fractures.
"""
#print(f"checking exp {shape}")
for i in range(cnt):
min_val = params[prefix + "ExpMin"]["value"][i]
max_val = params[prefix + "ExpMax"]["value"][i]
mean_val = params[prefix + "ExpMean"]["value"][i]
hf.check_values(prefix + "min", min_val, 0)
hf.check_values(prefix + "max", max_val, 0)
hf.check_values(prefix + "ExpMean", mean_val, 0)
hf.check_values(prefix + "ExpMean",
mean_val,
min_val=min_val,
max_val=max_val)
hf.check_min_max(min_val, max_val, i, f"{shape} exponential")
hf.check_min_frac_size(params, min_val)
def check_tpl_dist(params, prefix, shape, cnt):
"""
Verifies parameters for truncated power law distribution of fractures.
"""
#print(f"checking tpl {shape}")
for i in range(cnt):
min_val = params[prefix + "min"]["value"][i]
max_val = params[prefix + "max"]["value"][i]
alpha = params[prefix + "alpha"]["value"][i]
hf.check_values(prefix + "min", min_val, 0)
hf.check_values(prefix + "max", max_val, 0)
hf.check_values(prefix + "alpha", alpha, 0)
hf.check_min_max(min_val, max_val, i, f"{shape} Truncated Power law")
hf.check_min_frac_size(params, min_val)
def check_distributions(params, prefix):
"""
Verifies "edistr" and "rdistr" making sure one distribution is defined per family and each distribution is either 1 (log-normal), 2 (Truncated Power Law), 3 (Exponential), or 4 (constant).
Parameters
-------------
params : dict
parameter dictionary
prefix : string
either 'e' or 'r' for ellipse or rectangle
Returns
---------
None
Notes
---------
Exits program is inconsistencies are found.
"""
key = prefix + "distr"
shape = "ellipse" if prefix == 'e' else "rectangle"
num_families = params['nFamEll']['value'] if prefix == 'e' else params[
'nFamRect']['value']
hf.check_none(key, params[key]['value'])
hf.check_length(key, params[key]['value'], num_families)
hf.check_values(key, params[key]['value'], 1, 4)
#check lengths
for i in params[key]['value']:
cnt = params[key]['value'].count(i)
if i == 1:
## logNormal
dist_keys = [
prefix + name
for name in ["LogMean", "sd", "LogMin", "LogMax"]
]
for dist_key in dist_keys:
hf.check_none(dist_key, params[dist_key]['value'])
hf.check_length(dist_key, params[dist_key]['value'], cnt)
check_lognormal_dist(params, prefix, shape, cnt)
elif i == 2:
# TPL
dist_keys = [prefix + name for name in ["min", "max", "alpha"]]
for dist_key in dist_keys:
hf.check_none(dist_key, params[dist_key]['value'])
hf.check_length(dist_key, params[dist_key]['value'], cnt)
check_tpl_dist(params, prefix, shape, cnt)
elif i == 3:
# Exp
dist_keys = [
prefix + name for name in ["ExpMean", "ExpMin", "ExpMax"]
]
for dist_key in dist_keys:
hf.check_none(dist_key, params[dist_key]['value'])
hf.check_length(dist_key, params[dist_key]['value'], cnt)
check_exponential_dist(params, prefix, shape, cnt)
elif i == 4:
# constant
dist_keys = [prefix + name for name in ["const"]]
for dist_key in dist_keys:
hf.check_none(dist_key, params[dist_key]['value'])
hf.check_length(dist_key, params[dist_key]['value'], cnt)
check_constant_dist(params, prefix, shape, cnt)
def check_orientations(params, prefix):
""" Checks orientation options. If using trend/plunge, degrees must be used. If using spherical, radians are okay as well. Checks that values are within acceptable ranges.
Parameters
-------------
params : dict
parameter dictionary
prefix : string
either 'e' or 'r' for ellipse or rectangle
Returns
---------
None
Notes
---------
Exits program is inconsistencies are found.
"""
shape = "ellipse" if prefix == 'e' else "rectangle"
num_families = params['nFamEll']['value'] if prefix == 'e' else params[
'nFamRect']['value']
angle_option_key = prefix + 'AngleOption'
if params["orientationOption"]["value"] == 0:
#print("--> Using Spherical Coordinates")
keys = [prefix + name for name in ["theta", "phi"]]
elif params["orientationOption"]["value"] == 1:
#print("--> Using Trend and Plunge")
# 0 is radians, 1 is degrees
if params[angle_option_key]['value'] == 0:
hf.print_error(
f"Using Trend and Plunge but {prefix + 'AngleOption'} is set to use radians. Trend and plunge must use degrees. Set {prefix + 'AngleOption'} = 1. "
)
keys = [prefix + name for name in ["trend", "plunge"]]
elif params["orientationOption"]["value"] == 2:
# using dip / strike
if params[angle_option_key]['value'] == 0:
hf.print_error(
f"Using Dip and Strike but {prefix + 'AngleOption'} is set to use radians. Trend and plunge must use degrees. Set {prefix + 'AngleOption'} = 1. "
)
keys = [prefix + name for name in ["dip", "strike"]]
else:
hf.print_error(f"Unknown orientation option. Value must be 0,1, or 2.")
for key in keys:
hf.check_none(key, params[key]['value'])
hf.check_length(key, params[key]['value'], num_families)
for i, val in enumerate(params[key]['value']):
# check radians
if params[angle_option_key]['value'] == 0:
if val < 0 or val > 2 * pi:
hf.print_error(
f"\"{key}\" entry {i+1} has value {val} which is outside of acceptable parameter range [0,2*pi). If you want to use degrees, please use {angle_option_key} = 1. "
)
# check degrees
else:
if val < 0 or val > 365:
hf.print_error(
f"\"{key}\" entry {i+1} has value {val} which is outside of acceptable parameter range [0,365). "
)
#check kappa
key = prefix + 'kappa'
hf.check_none(key, params[key]['value'])
hf.check_length(key, params[key]['value'], num_families)
hf.check_values(key, params[key]['value'], 0, 100)