def test_dict2csv_convert(self):
(handle, file_name) = tempfile.mkstemp('.csv', 'test_csv_interface_')
os.close(handle)
attribute_dic = {'LONGITUDE': self.LONGITUDE,
'LATITUDE': self.LATITUDE,
'WALLS': self.WALLS}
# This is actually assumed to be a list in the code...
title_index_dic = {'LONGITUDE': 0,
'LATITUDE': 1,
'WALLS': 2}
convert = {'LONGITUDE': float,
'LATITUDE': float,
'WALLS' :str}
util.dict2csv(file_name, title_index_dic, attribute_dic)
attribute_dic_new, title_index_dic_new = \
csvi.csv2dict(file_name, title_check_list=title_index_dic,
convert=convert)
self.assert_(attribute_dic_new['LONGITUDE'] == self.LONGITUDE)
self.assert_(attribute_dic_new['LATITUDE'] == self.LATITUDE)
self.assert_(attribute_dic_new['WALLS'] == self.WALLS)
self.assert_(attribute_dic_new == attribute_dic)
self.assert_(title_index_dic_new == title_index_dic_new)
os.remove(file_name)
def test_dict2csv_bad(self):
(handle, file_name) = tempfile.mkstemp('.csv', 'test_csv_interface_')
os.close(handle)
attribute_dic = {'LONGITUDE': self.LONGITUDE,
'LATITUDE': self.LATITUDE,
'WALLS': self.WALLS}
title_index_dic = {'LONGITUDE': 0,
'LATITUDE': 1,
'WALLS': 2}
util.dict2csv(file_name, title_index_dic, attribute_dic)
try:
attribute_dic_new, title_index_dic_new = \
csvi.csv2dict(file_name, title_check_list={'wire': 8})
except IOError:
os.remove(file_name)
else:
os.remove(file_name)
self.failUnless(False, "Error not thrown")
def test_dict2csv_bad(self):
(handle, file_name) = tempfile.mkstemp('.csv', 'test_csv_interface_')
os.close(handle)
attribute_dic = {
'LONGITUDE': self.LONGITUDE,
'LATITUDE': self.LATITUDE,
'WALLS': self.WALLS
}
title_index_dic = {'LONGITUDE': 0, 'LATITUDE': 1, 'WALLS': 2}
util.dict2csv(file_name, title_index_dic, attribute_dic)
try:
attribute_dic_new, title_index_dic_new = \
csvi.csv2dict(file_name, title_check_list={'wire': 8})
except IOError:
os.remove(file_name)
else:
os.remove(file_name)
self.failUnless(False, "Error not thrown")
def load_site_class2Vs30(file_name):
"""
Load the data used to convert a site class to a Vs30 value.
args:
file_name: The file to be opened, with extension, or
a file handle.
Return:
class2Vs30_dict: A dic of 'site_class' and 'Vs30' keys.
Raises:
IOError if the file is not present or the file does not have
site_class and Vs30 in the header.
"""
csv_columns_dict, _ = csv2dict(file_name, ('vs30', 'site_class'),
convert={'vs30': float})
site_class2Vs30_dict = {}
for siteclass, Vs30 in map(None, csv_columns_dict['site_class'],
csv_columns_dict['vs30']):
site_class2Vs30_dict[siteclass] = Vs30
return site_class2Vs30_dict
def load_site_class2Vs30(file_name):
"""
Load the data used to convert a site class to a Vs30 value.
args:
file_name: The file to be opened, with extension, or
a file handle.
Return:
class2Vs30_dict: A dic of 'site_class' and 'Vs30' keys.
Raises:
IOError if the file is not present or the file does not have
site_class and Vs30 in the header.
"""
csv_columns_dict, _ = csv2dict(file_name, ('vs30', 'site_class'),
convert={'vs30': float})
site_class2Vs30_dict = {}
for siteclass, Vs30 in map(None, csv_columns_dict['site_class'],
csv_columns_dict['vs30']):
site_class2Vs30_dict[siteclass] = Vs30
return site_class2Vs30_dict
def test_dict2csv(self):
(handle, file_name) = tempfile.mkstemp('.csv', 'test_csv_interface_')
os.close(handle)
attribute_dic = {'LONGITUDE': self.LONGITUDE,
'LATITUDE': self.LATITUDE,
'WALLS': self.WALLS}
title_index_dic = {'LONGITUDE': 0,
'LATITUDE': 1,
'WALLS': 2}
util.dict2csv(file_name, title_index_dic, attribute_dic)
(attribute_dic_new, title_index_dic_new) = \
csvi.csv2dict(file_name, title_check_list=title_index_dic)
attribute_dic_new['LONGITUDE'] = \
[float(x) for x in attribute_dic_new['LONGITUDE']]
attribute_dic_new['LATITUDE'] = \
[float(x) for x in attribute_dic_new['LATITUDE']]
self.assert_(attribute_dic_new == attribute_dic)
self.assert_(title_index_dic_new == title_index_dic_new)
os.remove(file_name)
def test_dict2csv_convert(self):
(handle, file_name) = tempfile.mkstemp('.csv', 'test_csv_interface_')
os.close(handle)
attribute_dic = {
'LONGITUDE': self.LONGITUDE,
'LATITUDE': self.LATITUDE,
'WALLS': self.WALLS
}
# This is actually assumed to be a list in the code...
title_index_dic = {'LONGITUDE': 0, 'LATITUDE': 1, 'WALLS': 2}
convert = {'LONGITUDE': float, 'LATITUDE': float, 'WALLS': str}
util.dict2csv(file_name, title_index_dic, attribute_dic)
attribute_dic_new, title_index_dic_new = \
csvi.csv2dict(file_name, title_check_list=title_index_dic,
convert=convert)
self.assert_(attribute_dic_new['LONGITUDE'] == self.LONGITUDE)
self.assert_(attribute_dic_new['LATITUDE'] == self.LATITUDE)
self.assert_(attribute_dic_new['WALLS'] == self.WALLS)
self.assert_(attribute_dic_new == attribute_dic)
self.assert_(title_index_dic_new == title_index_dic_new)
os.remove(file_name)
def test_dict2csv(self):
(handle, file_name) = tempfile.mkstemp('.csv', 'test_csv_interface_')
os.close(handle)
attribute_dic = {
'LONGITUDE': self.LONGITUDE,
'LATITUDE': self.LATITUDE,
'WALLS': self.WALLS
}
title_index_dic = {'LONGITUDE': 0, 'LATITUDE': 1, 'WALLS': 2}
util.dict2csv(file_name, title_index_dic, attribute_dic)
(attribute_dic_new, title_index_dic_new) = \
csvi.csv2dict(file_name, title_check_list=title_index_dic)
attribute_dic_new['LONGITUDE'] = \
[float(x) for x in attribute_dic_new['LONGITUDE']]
attribute_dic_new['LATITUDE'] = \
[float(x) for x in attribute_dic_new['LATITUDE']]
self.assert_(attribute_dic_new == attribute_dic)
self.assert_(title_index_dic_new == title_index_dic_new)
os.remove(file_name)
def build_replacement_ratios(usage_per_struct, buildings_usage_classification):
"""Return an array of the building components replacement cost ratios.
shape (# of buildings, 3 (# of components in a structure))
A structure has 3 components;
structural
nonstructural acceleration sensitive
nonstructural drift sensitive
The ratio of the replacement cost for each component differs for
different usage types.
Also, there are two scales of usage types;
Functional classification of buildings (FCB)(ABS usage)
HAZUS usage
Parameters:
usage_per_struct: An array, shape (# of buildings) of usage
values for each structure.
buildings_usage_classification: The usage scale used.
Return:
A dictionary of cost ratios for the strucutures.
The keys are the structural components;
'structural', 'nonstructural drift sensitive',
'nonstructural acceleration sensitive'
Note: This should only be used once, since it is loading a file.
"""
usage_column = 'Functional classification of buildings (ABS usage)'
components = ['structural', 'nonstructural drift sensitive',
'nonstructural acceleration sensitive']
convert = {}
# extract usage dependent parameters
if buildings_usage_classification is 'HAZUS': # HAZUS_USAGE
file = 'replacement_cost_ratios_wrt_HAZUS_Usage.csv'
elif buildings_usage_classification is 'FCB': # FCB_USAGE
file = 'replacement_cost_ratios_wrt_FCB_Usage.csv'
convert[usage_column] = int
else:
msg = ('b_usage_type_flat = ' + str(buildings_usage_classification)
+ ' not "FCB" or "HAZUS"')
raise ValueError(msg)
for comp in components:
convert[comp] = float
root_dir = determine_eqrm_path()
data_dir = join(root_dir, 'resources', 'data')
(att_dic, _) = csv2dict(join(data_dir, file), convert=convert)
# This way does not have an assert
# Build a dict with
# key (usage, component), value = cost ratio
# cost_ratios = {}
# for comp in components:
# tmp = {}
# for i,usage in enumerate(att_dic[usage_column]):
# tmp[usage] = att_dic[comp][i]
# cost_ratios[comp] = tmp
# replacement_cost_ratios = {}
# for comp in components:
# this_cost_ratios = cost_ratios[comp]
# tmp = [this_cost_ratios[usage] for usage in usage_per_struct]
# replacement_cost_ratios[comp] = array(tmp)
cost_ratios = {}
for (i, usage) in enumerate(att_dic[usage_column]):
cost_ratios[usage] = [att_dic[components[0]][i],
att_dic[components[1]][i],
att_dic[components[2]][i]]
rcp_array = asarray([cost_ratios[usage] for usage in usage_per_struct])
assert allclose(sum(rcp_array), rcp_array.shape[0], 0.0000001)
replacement_cost_ratios = {}
for (i, comp) in enumerate(components):
replacement_cost_ratios[comp] = rcp_array[:, i]
return replacement_cost_ratios
def build_replacement_ratios(usage_per_struct, buildings_usage_classification):
"""Return an array of the building components replacement cost ratios.
shape (# of buildings, 3 (# of components in a structure))
A structure has 3 components;
structural
nonstructural acceleration sensitive
nonstructural drift sensitive
The ratio of the replacement cost for each component differs for
different usage types.
Also, there are two scales of usage types;
Functional classification of buildings (FCB)(ABS usage)
HAZUS usage
Parameters:
usage_per_struct: An array, shape (# of buildings) of usage
values for each structure.
buildings_usage_classification: The usage scale used.
Return:
A dictionary of cost ratios for the strucutures.
The keys are the structural components;
'structural', 'nonstructural drift sensitive',
'nonstructural acceleration sensitive'
Note: This should only be used once, since it is loading a file.
"""
usage_column = 'Functional classification of buildings (ABS usage)'
components = [
'structural', 'nonstructural drift sensitive',
'nonstructural acceleration sensitive'
]
convert = {}
# extract usage dependent parameters
if buildings_usage_classification is 'HAZUS': # HAZUS_USAGE
file = 'replacement_cost_ratios_wrt_HAZUS_Usage.csv'
elif buildings_usage_classification is 'FCB': # FCB_USAGE
file = 'replacement_cost_ratios_wrt_FCB_Usage.csv'
convert[usage_column] = int
else:
msg = ('b_usage_type_flat = ' + str(buildings_usage_classification) +
' not "FCB" or "HAZUS"')
raise ValueError(msg)
for comp in components:
convert[comp] = float
root_dir = determine_eqrm_path()
data_dir = join(root_dir, 'resources', 'data')
(att_dic, _) = csv2dict(join(data_dir, file), convert=convert)
# This way does not have an assert
# Build a dict with
# key (usage, component), value = cost ratio
# cost_ratios = {}
# for comp in components:
# tmp = {}
# for i,usage in enumerate(att_dic[usage_column]):
# tmp[usage] = att_dic[comp][i]
# cost_ratios[comp] = tmp
# replacement_cost_ratios = {}
# for comp in components:
# this_cost_ratios = cost_ratios[comp]
# tmp = [this_cost_ratios[usage] for usage in usage_per_struct]
# replacement_cost_ratios[comp] = array(tmp)
cost_ratios = {}
for (i, usage) in enumerate(att_dic[usage_column]):
cost_ratios[usage] = [
att_dic[components[0]][i], att_dic[components[1]][i],
att_dic[components[2]][i]
]
rcp_array = asarray([cost_ratios[usage] for usage in usage_per_struct])
assert allclose(sum(rcp_array), rcp_array.shape[0], 0.0000001)
replacement_cost_ratios = {}
for (i, comp) in enumerate(components):
replacement_cost_ratios[comp] = rcp_array[:, i]
return replacement_cost_ratios
