def train(self,
targets,
training_dict,
total_epochs=10,
learning_rate=0.1):
"""Create the training loop"""
# Construct the optimizer
params = [
self.w_comb1, self.w_comb2, self.w_t, self.w_l, self.w_r,
self.b_conv, self.w_hidden, self.b_hidden
]
optimizer = torch.optim.SGD(params, lr=learning_rate)
criterion = nn.BCELoss()
print('The correct value of the files is: ', targets)
for epoch in range(total_epochs):
# Time
start = time()
outputs = self.forward(training_dict)
try:
loss = criterion(outputs, targets)
except AttributeError:
print(
f'The size of outputs is {len(outputs)} and is of type {type(outputs)}'
)
print('Check that the path is a folder and not a file')
raise AttributeError
# zero the parameter gradients
print('outputs: \n', outputs)
#print('Matrix w_r_conv: \n', params[4])
optimizer.zero_grad()
# Calculates the derivative
loss.backward(retain_graph=True)
# Update parameters
optimizer.step() #w_r = w_r - lr * w_r.grad
#Time
end = time()
print('Epoch: ', epoch, ', Time: ', end - start, ', Loss: ', loss)
message = f'''
The loss we have for the training network is: {loss}
'''
writer(message)
self.save()
def cartoonize(color, rep):
"""draw a cartoon representation of glycans"""
stored.ResiduesNumber = []
cmd.iterate('name c1', 'stored.ResiduesNumber.append((resi))')
resn_list = [int(i) for i in stored.ResiduesNumber]
bonds = get_glyco_bonds(resn_list[0], resn_list[-1]+1)
con_matrix = writer(bonds)
#con_matrix = writer2(bonds)
rings = find_rings(resn_list)
rings_coords = get_ring_coords(resn_list, rings)
bonds_coords = get_bonds_coords(resn_list, con_matrix)
colors = get_colors_c1(resn_list, color)
bonds_colors = get_bonds_colors(resn_list, con_matrix, color)
cmd.set('suspend_updates', 'on')
for state, coords in enumerate(rings_coords):
obj = []
if rep == 'beads':
radius_s = 1.8
radius_b = 0.18
obj = beads(obj, coords, colors[state], radius_s)
obj = cylinder(obj, bonds_coords[state], bonds_colors[state], radius_b)
else:
if rep == 'cartoon':
radius = 0.075
else:
radius = 0.035
obj = hexagon(obj, coords, colors[state], rep, radius)
obj = cylinder(obj, bonds_coords[state], bonds_colors[state], radius)
cmd.load_cgo(obj,'cgo01', state+1)
cmd.select('glycan', 'byres name C1')
cmd.delete('glycan')
cmd.delete('tmp')
cmd.set('two_sided_lighting', 1)
cmd.set('suspend_updates', 'off')
def faults(apic, **kwargs):
if 'filename' in kwargs:
wb = kwargs['filename']
else:
wb = 'discovery.xlsx'
if 'writer' in kwargs:
writer = kwargs['writer']
else:
writer = utils.writer(wb)
faultsUri = '/api/node/class/faultSummary.json?query-target-filter=' \
'and()&order-by=faultSummary.severity'
faultsUrl = apic.baseUrl + faultsUri
faultsResp = apic.session.get(faultsUrl, verify=False)
utils.responseCheck(faultsResp)
faultsJson = json.loads(faultsResp.text)
data = []
try:
for fault in faultsJson['imdata']:
data.append(fault['faultSummary']['attributes'])
except:
logging.critical('Error iterating through faults. Exiting')
apic.session.close()
sys.exit()
apic.session.close()
utils.dictDumpTwo(writer,
data,
list(data[0].keys()),
'faultSummary')
def validation(self,
targets,
validation_dict,
learning_rate=0.3,
momentum=0,
l2_penalty=0,
epoch_first=45):
"""Create the validation loop"""
print('########################################')
print(
'\n\n\nFinished training process. Entering validation process\n\n\n'
)
print("The correct value of the files is: ", targets)
# We calculate the predictions
predicts = self.prediction(validation_dict, learning_rate, momentum,
l2_penalty, epoch_first)
# print the predictions
print('predictions: \n', predicts)
# Loss function
criterion = nn.BCELoss()
loss = criterion(predicts, targets)
# TODO Build the accuracy evaluation method for each file
# Confusion matrix
conf_matrix = self.conf_matrix(predicts, targets)
print(conf_matrix)
plot_confusion_matrix(conf_matrix, ['no generator', 'generator'])
message = f'''
For the validation set we have the following results:
loss: {loss}
confusion_matrix:
{conf_matrix}
'''
writer(message)
print('Loss validation: ', loss)
# correct += (predicted == labels).sum()
accuracy = self.accuracy(predicts, targets)
print('accuracy: ', accuracy)
def validation(self, validation_path, validation_dict):
"""Create the validation loop"""
print('########################################')
print(
'\n\n\nFinished training process. Entering validation process\n\n\n'
)
### Validation set
# this is to have all the information of each file in the folder contained in a dictionary
#validation_dict = self.validation_dict_set_up(validation_path)
# this is the tensor with all target values associated to the validation set
targets = self.target_tensor_set_up(validation_path, validation_dict)
# We calculate the predictions
predicts = self.prediction(validation_dict)
# print the predictions
print('predictions: \n', predicts)
# Loss function
criterion = nn.BCELoss()
loss = criterion(predicts, targets)
# TODO Build the accuracy evaluation method for each file
# Confusion matrix
conf_matrix = self.conf_matrix(predicts, targets)
print(conf_matrix)
plot_confusion_matrix(conf_matrix, ['no generator', 'generator'])
message = f'''
For the validation set we have the following results:
loss: {loss}
confusion_matrix:
{conf_matrix}
'''
writer(message)
print('Loss validation: ', loss)
# correct += (predicted == labels).sum()
accuracy = self.accuracy(predicts, targets)
print('accuracy: ', accuracy)
def __init__(self, config, resume: bool, model, loss_function, optimizer,
scheduler, lr_override, test):
self.optimizer = optimizer
self.scheduler = scheduler
self.loss_function = loss_function
self.lr_override = lr_override
self.test = test
self.gpu_ids = config['gpu_ids']
self.model = model.to(self.gpu_ids[0])
if len(self.gpu_ids) > 1:
self.model = torch.nn.DataParallel(self.model,
device_ids=self.gpu_ids)
# Trainer
self.epochs = config["trainer"]["epochs"]
# The following args is not in the config file. We will update it if the resume is True in later.
self.start_epoch = 0
self.best_score = np.inf
self.root_dir = Path(config["root"]).expanduser().absolute(
) / "experiments" / config["experiment_name"]
self.checkpoints_dir = self.root_dir / "checkpoints"
self.logs_dir = self.root_dir / "logs"
prepare_empty_dir([self.checkpoints_dir, self.logs_dir], resume=resume)
if not self.test:
self.writer = writer(self.logs_dir.as_posix())
self.writer.add_text(
tag="Configuration",
text_string=
f"<pre> \n{json5.dumps(config, indent=4, sort_keys=False)} \n</pre>",
global_step=1)
# print("Configurations are as follows: ")
# print(json5.dumps(config, indent=2, sort_keys=False))
with open(
(self.root_dir /
f"{time.strftime('%Y-%m-%d-%H-%M-%S')}.json").as_posix(),
"w") as handle:
json5.dump(config, handle, indent=2, sort_keys=False)
if resume: self._resume_checkpoint()
self._print_networks([self.model])
def mcm(pose, mc_steps, SASA, randomize):
################################# MCM Parameters ##########################
T = 300. # Temperature
k = 0.0019872041 # Boltzmann constant
angles_prob = [1/3, 1/3, 1/3] # probability to sample phi, psi or chi
accepted = 0
############################################################################
#
first, last = pose_from_pdb(pose)
if first or last:
print('Starting MCM')
from energy import minimize, set_sasa, get_sasa
cmd.set('suspend_updates', 'on')
cmd.feedback('disable', 'executive', 'everything') ##uncomment for debugging
cmd.set('pdb_conect_all', 1)
glyco_bonds = get_glyco_bonds(first, last)
con_matrix = writer(glyco_bonds)
# Remove previous pdb files
prev_files = glob.glob('mcm_*.pdb')
for prev_file in prev_files:
os.remove(prev_file)
# set all paramenters for sasa-energy computation
if SASA:
params, points, const = set_sasa(n=1000)
## randomize initial conformation
if randomize:
for i in range(len(con_matrix)-1):
bond = con_matrix[i]
angle_values = np.random.uniform(-180, 180, size=2)
set_psi(pose, bond, angle_values[0])
set_phi(pose, bond, angle_values[1])
for i in range(6):
set_chi(pose, bond)
# minimize energy of starting conformation and save it
NRG_old = minimize(pose, nsteps=5000, rigid_geometry=False)
NRG_min = NRG_old
cmd.save('mcm_%08d.pdb' % accepted)
## start MCM routine
fd = open("mcm_log.txt", "w")
print('# iterations remaining = %s' % (mc_steps))
for i in range(1, mc_steps+1):
if i % (mc_steps//10) == 0:
print('#remaining iterations = %s' % (mc_steps-i))
if True:
sample_uniform(pose, con_matrix, angles_prob)
NRG_new = minimize('tmp', nsteps=100, rigid_geometry=False)
if SASA:
solvatation_nrg = get_sasa(params, points, const, selection='all',
probe=0)[0]
NRG_new = NRG_new + solvatation_nrg
if NRG_new < NRG_old:
NRG_old = NRG_new
fd.write('%8d%10.2f\n' % (accepted, NRG_new))
cmd.copy(pose, 'tmp')
cmd.delete('tmp')
cmd.save('mcm_%08d.pdb' % accepted)
accepted += 1
else:
delta = np.exp(-(NRG_new-NRG_old)/(T*k))
if delta > np.random.uniform(0, 1):
NRG_old = NRG_new
fd.write('%8d%10.2f\n' % (accepted, NRG_new))
cmd.copy(pose, 'tmp')
cmd.delete('tmp')
cmd.save('mcm_%08d.pdb' % accepted)
accepted += 1
cmd.delete('tmp')
if NRG_new < NRG_min:
NRG_min = NRG_new
cmd.save('mcm_min.pdb')
fd.close()
cmd.delete('all')
print('Savings all accepted conformations on a single file')
cmd.set('defer_builds_mode', 5)
for i in range(0, accepted):
cmd.load('mcm_%08d.pdb' % i, 'mcm_trace')
cmd.save('mcm_trace.pdb', 'all', state=0)
cmd.delete('all')
cmd.load('mcm_trace.pdb')
cmd.intra_fit('mcm_trace')
print(' MCM completed')
cmd.set('suspend_updates', 'off')
remover()
for vector_size in vector_size_ls:
for learning_rate in learning_rate_ls:
for momentum in momentum_ls:
for learning_rate2 in [0.1]:
for feature_size in feature_size_ls:
for l2_penalty in l2_penalty_ls:
message = f'''
########################################
The parameters we're using are the following:
vector_size = {vector_size}
learning_rate = {learning_rate}
momentum = {momentum}
learning_rate2 = {learning_rate2}
feature_size = {feature_size}
number of epochs for second neural network: {epoch}
'''
# We append the results in a results.txt file
writer(message)
main(vector_size, learning_rate, momentum, epoch_first, learning_rate2,\
feature_size, epoch, pooling, l2_penalty)
def mcm(pose, mc_steps, SASA, randomize):
################################# MCM Parameters ##########################
T = 300. # Temperature
k = 0.0019872041 # Boltzmann constant
kT = k * T
# probability to sample phi, psi or chi
angles_prob = [1 / 3, 1 / 3, 1 / 3]
accepted = 0
##########################################################################
#
first, last = pose_from_pdb(pose)
if first or last:
print('Starting MCM')
from energy import minimize, set_sasa, get_sasa
sus_updates = cmd.get('suspend_updates')
cmd.set('suspend_updates', 'on')
# uncomment for debugging
cmd.feedback('disable', 'executive', 'everything')
pdb_conect = cmd.get('pdb_conect_all')
cmd.set('pdb_conect_all', 1)
glyco_bonds = get_glyco_bonds(first, last)
con_matrix = writer(glyco_bonds)
# Remove previous pdb files
prev_files = glob.glob('mcm_*.pdb')
for prev_file in prev_files:
os.remove(prev_file)
# set all paramenters for sasa-energy computation
if SASA:
params, points, const = set_sasa(n=1000)
# randomize initial conformation
if randomize:
for i in range(len(con_matrix) - 1):
bond = con_matrix[i]
angle_values = np.random.uniform(-180, 180, size=2)
set_psi(pose, bond, angle_values[0])
set_phi(pose, bond, angle_values[1])
for i in range(6):
set_chi(pose, bond)
# minimize energy of starting conformation and save it
NRG_old = minimize(pose, nsteps=5000, rigid_geometry=False)
NRG_min = NRG_old
cmd.save('mcm_%08d.pdb' % accepted)
# start MCM routine
fd = open("mcm_log.txt", "w")
print('# iterations remaining = %s' % (mc_steps))
for i in range(1, mc_steps + 1):
if i % (mc_steps // 10) == 0:
print('#remaining iterations = %s' % (mc_steps - i))
if True:
sample_uniform(pose, con_matrix, angles_prob)
NRG_new = minimize('tmp', nsteps=100, rigid_geometry=False)
if SASA:
solvatation_nrg = get_sasa(params,
points,
const,
selection='all',
probe=0)[0]
NRG_new = NRG_new + solvatation_nrg
if NRG_new < NRG_old:
NRG_old = NRG_new
fd.write('%8d%10.2f\n' % (accepted, NRG_new))
cmd.copy(pose, 'tmp')
cmd.delete('tmp')
cmd.save('mcm_%08d.pdb' % accepted)
accepted += 1
else:
delta = np.exp(-(NRG_new - NRG_old) / (kT))
if delta > np.random.uniform(0, 1):
NRG_old = NRG_new
fd.write('%8d%10.2f\n' % (accepted, NRG_new))
cmd.copy(pose, 'tmp')
cmd.delete('tmp')
cmd.save('mcm_%08d.pdb' % accepted)
accepted += 1
cmd.delete('tmp')
if NRG_new < NRG_min:
NRG_min = NRG_new
cmd.save('mcm_min.pdb')
fd.close()
cmd.delete('all')
print('Savings all accepted conformations on a single file')
de_builds = cmd.get('defer_builds_mode')
cmd.set('defer_builds_mode', 5)
for i in range(0, accepted):
cmd.load('mcm_%08d.pdb' % i, 'mcm_trace')
cmd.save('mcm_trace.pdb', 'all', state=0)
cmd.delete('all')
cmd.load('mcm_trace.pdb')
cmd.intra_fit('mcm_trace')
print('MCM completed')
# restore settings
cmd.set('suspend_updates', sus_updates)
cmd.set('pdb_conect_all', pdb_conect)
cmd.set('defer_builds_mode', de_builds)
def write_header(self, outfile):
insr = self.instru_info.instru_name
vers = self.instru_info.version
outwriter = utils.writer(outfile, delimiter=self.delimiter)
outwriter.writerow([insr, vers])
def main():
parser = argparse.ArgumentParser()
parser.add_argument('cmd')
parser.add_argument('--params',help='Parameters for models.')
parser.add_argument('--dset',help='Corpus path for hypernymy extraction.')
parser.add_argument('--output',help='Output path for hypernymy pairs.')
parser.add_argument('--evaluate',help='Evaluation for hypernymy extraction models.')
args = parser.parse_args()
if args.cmd == 'hearst':
hp = hearst.HearstPatterns(extended=args.params)
if args.dset == None:
pass
else:
data = utils.reader(args.dset)
pair_list = hp.find_hyponyms(data)
if args.output is not None:
utils.writer(args.output,pair_list)
print('Finished ...')
else:
print('Do not specifiy output file, but still show 5 of the results here.')
for i in range(5):
print(pair_list[i])
print('Finished ...')
elif args.cmd == 'PPMI':
# Read prediction from precomputed file
shutil.copyfile("./patternBased/result_ppmi.txt", args.output)
with open(args.output) as f:
print("======== PPMI : score ======== ")
print("Print out first 5 examples.")
for i in range(6):
print(f.readline())
elif args.cmd == 'PPMISVD':
path = "result_SvdPpmi_"+str(args.params)+".txt"
shutil.copyfile("./patternBased/"+path, args.output)
with open(args.output) as f:
print("======== PPMI-SVD : score ======== ")
print("k = ", args.params)
print("Print out first 5 examples.")
for i in range(6):
print(f.readline())
elif args.cmd == 'dist':
if args.evaluate is not None:
res = pd.read_table("./distributional/data/results.txt", sep="\t")
print("======== Distributional Model : score ======== ")
print(res.head(6))
elif args.cmd == 'embed':
shutil.copyfile("./termEmbed/term_embed_result.txt", args.output)
if args.evaluate is not None:
res = pd.read_table("./termEmbed/results.txt", sep="\t")
print("======== Term Embedding : score ======== ")
print(res.head(10))
elif args.cmd == 'Projection':
if args.output == None:
pass
else:
print("Loading model...\n")
model = train_model('./projection/dumped.data','./projection/modLog.txt', 9510, use_gpu = False)
# save_model('./projection/projectMod.pt', model)
print("Loading test data...\n")
data=joblib.load('./projection/dumped.data')
candidates = data["candidates"]
test_q_cand_ids = data["test_query_cand_ids"]
test_q_embed = make_embedder(data["test_query_embeds"], grad=False,
cuda=model.use_cuda, sparse=False)
print("Writing predictions...")
test_eval = Evaluator(model, test_q_embed, test_q_cand_ids)
test_eval.write_predictions(args.output, list(candidates))
print("Done.\n")
if args.evaluate == 'True':
Dev_score={}
Dev_score['DevMAP'] = [];Dev_score['DevAP'] = [];Dev_score['DevMRR'] = []
for line in open('./projection/modLog.txt',"r"):
if line.split()[0] == 'Epoch':
# header, skip
continue
else:
Dev_score['DevMAP'].append(float(line.split()[5]))
Dev_score['DevAP'].append(float(line.split()[6]))
Dev_score['DevMRR'].append(float(line.split()[7]))
print("======== Projection Learning : score ======== ")
print("SemEval2018 Task9.")
print("DevMAP\tDevAP\tDevMRR")
print(str(max(Dev_score['DevMAP']))+"\t"+str(max(Dev_score['DevAP']))+"\t"+str(max(Dev_score['DevMRR'])))
elif args.cmd == 'BiLSTM':
# use default test data
if args.output == None:
pass
else:
if args.evaluate is not None:
print("======== BiLSTM Model : f1 score ======== ")
with open("./BiLSTM/data/res.score.txt", "r") as score:
print(score.read())
def write_header(self, outfile):
insr = self.instrument_name
vers = self.version
outwriter = utils.writer(outfile, delimiter=self.delimiter)
outwriter.writerow([insr, vers])
def main(**kwargs):
# Set Variables
date = time.strftime("%Y%m%d")
if 'filename' in kwargs:
wb = date + '-' + kwargs['filename']
else:
wb = date + '-discovery.xlsx'
tnData = []
tnCols = ['descr', 'dn', 'name', 'nameAlias', 'ownerKey', 'ownerTag']
bdData = []
bdCols = [
'tenant', 'bdName', 'subnetIp', 'subnetScope', 'primaryIp', 'virtualIp'
]
epgData = []
epgCols = [
'tenant', 'app', 'epg', 'nameAlias', 'bd', 'bdTenant',
'prefGroupMember'
]
dhcpData = []
dhcpCols = [
'podId', 'fabricId', 'nodeId', 'model', 'name', 'nameAlias',
'configNodeRole', 'nodeRole', 'nodeType', 'ip', 'spineLevel', 'fwVer',
'runningVer', 'supported', 'extPoolId', 'decommissioned',
'configIssues'
]
nodePData = []
nodePCols = ['name', 'descr', 'dn', 'leafSelector']
rsAccPortPData = []
rsAccPortPCols = ['nodeP', 'interfaceProfile', 'dn']
# Create login session to APIC
apic = utils.apicSession()
# Get Fabric Info
dhcpClientData = get(apic,
apic.baseUrl + '/api/node/class/dhcpClient.json')
dhcpClientList = utils.cleanListDict(dhcpClientData)
for node in dhcpClientList:
dhcpData.append(
(node['podId'], node['fabricId'], node['nodeId'], node['model'],
node['name'], node['nameAlias'], node['configNodeRole'],
node['nodeRole'], node['nodeType'], node['ip'],
node['spineLevel'], node['fwVer'], node['runningVer'],
node['supported'], node['extPoolId'], node['decomissioned'],
node['configIssues']))
# Get the current tenants
tenantsResp = get(apic, apic.baseUrl + '/api/class/fvTenant.json?' \
'rsp-prop-include=config-only')
'''
Loop through tenants and collect additional information. Including:
1) BDs
a) Subnets within BDs
2) EPGs
a) EPG Details
b) Related Domains
c) Related BD
d) Related Consumed Contracts
e) Related Provided Contracts
'''
tenantList = utils.cleanListDict(tenantsResp)
for tenant in tenantList:
tnData.append(tenant)
bdResp = get(apic, apic.baseUrl + '/api/class/fvBD.json?' \
'query-target-filter=wcard(fvBD.dn,"{}")&rsp-prop-' \
'include=config-only'.format(tenant['name']))
bdList = utils.cleanListDict(bdResp)
for bd in bdList:
subnetResp = get(apic,
apic.baseUrl + '/api/class/fvSubnet.json?query-target-filter=' \
'wcard(fvSubnet.dn,"tn-{}/BD-{}/")'.format(tenant['name'],
bd['name']))
subnetList = utils.cleanListDict(subnetResp)
if not subnetList:
bdData.append((tenant['name'], bd['name'], 'Null', 'Null',
'Null', 'Null'))
else:
for sub in subnetList:
bdData.append(
(tenant['name'], bd['name'], sub['ip'], sub['scope'],
sub['preferred'], sub['virtual']))
apResp = get(apic, apic.baseUrl + '/api/class/fvAp.json?query-target-filter=wcard('\
'fvAp.dn,"tn-{}")'.format(tenant['name']))
if not apResp:
pass
else:
appList = utils.cleanListDict(apResp)
for app in appList:
epgResp = get(apic,
apic.baseUrl + '/api/class/fvAEPg.json?' \
'query-target-filter=wcard(fvAEPg.dn, ' \
'"tn-{}/ap-{}/")&rsp-prop-include=config-only'.format(
tenant['name'],
app['name']))
if not epgResp:
pass
else:
epgList = utils.cleanListDict(epgResp)
for epg in epgList:
rsBd = get(apic,
apic.baseUrl + '/api/node/mo/uni/tn-{}/ap-{}/epg-{}.json?' \
'query-target=children&target-subtree-class=fvRsBd'.format(
tenant['name'],
app['name'],
epg['name']))
if not rsBd:
epgData.append(
(tenant['name'], app['name'], epg['name'],
epg['nameAlias'], '', '', epg['prefGrMemb']))
else:
rsBd = utils.cleanListDict(rsBd)
epgData.append(
(tenant['name'], app['name'], epg['name'],
epg['nameAlias'], rsBd[0]['tnFvBDName'],
rsBd[0]['dn'].split('tn-')[1].split('/')[0],
epg['prefGrMemb']))
'''
get access policy information
1) To include
-Switch Profiles
-Interface Profiles
-AAEPs
-Interface Policy Groups
-Interface Policies
'''
nodePResp = get(apic, apic.baseUrl + '/api/mo/uni/infra.json?query-target=children&target-subtree-class=infraNodeP&' \
'rsp-prop-include=config-only')
nodePList = utils.cleanListDict(nodePResp)
for nodeP in nodePList:
infraLeafSResp = get(apic, apic.baseUrl + '/api/mo/' + nodeP['dn'] + '.json?query-target=children&target-subtree' \
'-class=infraLeafS')
if infraLeafSResp:
for infraLeafS in infraLeafSResp:
nodeBlkResp = get(apic, apic.baseUrl + '/api/mo/uni/infra.json?query-target=children&target-subtree-class=' \
'infraNodeBlk&query-target-filter=wcard(infraNodeBlk.dn,"{}")'.format(
infraLeafS['infraLeafS']['attributes']['dn']))
nodeBlkList = utils.cleanListDict(nodeBlkResp)
for nodeBlk in nodeBlkList:
logging.info(nodeBlk['from_'], nodeBlk['to_'])
nodeTup = (nodeP['name'], nodeP['descr'], nodeP['dn'],
infraLeafS['infraLeafS']['attributes']['name'])
nodePData.append(nodeTup)
elif not infraLeafSResp:
nodeTup = (nodeP['name'], nodeP['descr'], nodeP['dn'], '')
nodePData.append(nodeTup)
rsAccPortPResp = get(apic, apic.baseUrl + '/api/mo/' + nodeP['dn'] + '.json?query-target=children&target-subtree' \
'-class=infraRsAccPortP')
if rsAccPortPResp:
rsAccPortPList = utils.cleanListDict(rsAccPortPResp)
for rsAccPortP in rsAccPortPList:
rsAccPortPTup = (nodeP['name'],
rsAccPortP['tDn'].split('accportprof-')[1],
rsAccPortP['dn'])
rsAccPortPData.append(rsAccPortPTup)
elif not rsAccPortPResp:
pass
writer = utils.writer(wb)
try:
faults(apic, writer=writer)
utils.dictDumpTwo(writer, dhcpData, dhcpCols, 'dhcpClient')
utils.dictDumpTwo(writer, nodePData, nodePCols, 'nodeProfiles')
utils.dictDumpTwo(writer, rsAccPortPData, rsAccPortPCols,
'interfaceProfiles')
utils.dictDumpTwo(writer, tnData, tnCols, 'fvTenant')
utils.dictDumpTwo(writer, bdData, bdCols, 'fvBD')
utils.dictDumpTwo(writer, epgData, epgCols, 'fvAEPg')
apic.session.close()
except AssertionError:
raise AssertionError
except Exception as ex:
utils.exceptTempl(ex)