def load_bridge_data():
"""Preload bridge damage data from external files."""
global ClassificationDamageParams, EQCoefficients
# check dictionaries - if empty, load from file
if ClassificationDamageParams is None:
eqrm_dir = determine_eqrm_path(__file__)
path = os.path.join(eqrm_dir, DataFilePath,
ClassificationDamageParamsCSVFile)
ClassificationDamageParams = csvi.csv2rowdict(path,
columns=['CLASS', 'K3D', 'Ishape',
'Slight', 'Moderate',
'Extensive', 'Complete'],
convert={'Ishape': int,
'Slight': float,
'Moderate': float,
'Extensive': float,
'Complete': float})
if EQCoefficients is None:
eqrm_dir = determine_eqrm_path(__file__)
path = os.path.join(eqrm_dir, DataFilePath, EQCoefficientsCSVFile)
EQCoefficients = csvi.csv2rowdict(path,
columns=['Equation','A','B'],
convert={'A': float, 'B': int})
def load_external_data(fp):
"""Load mean and sigma values from an external file.
Data is placed in a numpy array where row number is the state
number and the columns are the ppf() function for each pf value
That is, if the source data file contains:
State,StateIndex,Mean,Sigma
none,0,0.0,0.00001
slight,1,0.6,0.6
moderate,2,2.5,2.7
extensive,3,75.0,42.0
complete,4,230.0,110.0
and the fp value is [10, 20] then the resulting array will be:
fp(10) fp(20)
vvv vvv
[[ 0 0 ] <- state 0 (none)
[ 1 1 ] <- state 1 (slight)
[ 1 2 ] <- state 2 (moderate)
[ 40 53 ] <- state 3 (extensive)
[ 138 173 ]] <- state 4 (complete)
All 'none' state days will be 0 days. All other states have values
computed with ceil() and if less than 1 changed to 1.
"""
eqrm_dir = determine_eqrm_path(__file__)
path = os.path.join(eqrm_dir,
eqrm_filesystem.Resources_Data_Path,
MeanSigmaValuesCSVFile)
dict = csvi.csv2rowdict(path,
columns=['StateIndex', 'Mean', 'Sigma'],
convert={'StateIndex': int,
'Mean': float,
'Sigma': float })
data = []
for i in range(5):
(mean, sigma) = dict[str(i)]
data.append(scipy.stats.norm.ppf(fp, mean, sigma))
data = numpy.array(data)
# 'normalise' results
# if value < 0, convert to 1
# if value < 1 and > 0, convert to 1
result = data.clip(1.0, 99999999999.0)
result = numpy.ceil(numpy.array(result))
result[0,:] = 0.0 # force all 'none' times to 0
return result
def load_external_data(fp):
"""Load mean and sigma values from an external file.
Data is placed in a numpy array where row number is the state
number and the columns are the ppf() function for each pf value
That is, if the source data file contains:
State,StateIndex,Mean,Sigma
none,0,0.0,0.00001
slight,1,0.6,0.6
moderate,2,2.5,2.7
extensive,3,75.0,42.0
complete,4,230.0,110.0
and the fp value is [10, 20] then the resulting array will be:
fp(10) fp(20)
vvv vvv
[[ 0 0 ] <- state 0 (none)
[ 1 1 ] <- state 1 (slight)
[ 1 2 ] <- state 2 (moderate)
[ 40 53 ] <- state 3 (extensive)
[ 138 173 ]] <- state 4 (complete)
All 'none' state days will be 0 days. All other states have values
computed with ceil() and if less than 1 changed to 1.
"""
eqrm_dir = determine_eqrm_path(__file__)
path = os.path.join(eqrm_dir, eqrm_filesystem.Resources_Data_Path,
MeanSigmaValuesCSVFile)
dict = csvi.csv2rowdict(path,
columns=['StateIndex', 'Mean', 'Sigma'],
convert={
'StateIndex': int,
'Mean': float,
'Sigma': float
})
data = []
for i in range(5):
(mean, sigma) = dict[str(i)]
data.append(scipy.stats.norm.ppf(fp, mean, sigma))
data = numpy.array(data)
# 'normalise' results
# if value < 0, convert to 1
# if value < 1 and > 0, convert to 1
result = data.clip(1.0, 99999999999.0)
result = numpy.ceil(numpy.array(result))
result[0, :] = 0.0 # force all 'none' times to 0
return result
