def requeue(self, _=None):
run = lambda: self._query_services(wait=POLL_WAIT, debounce=True)
if unix_timestamp() - self.last_queued < MIN_QUERY_PERIOD:
d = deferLater(reactor, MIN_QUERY_PERIOD, run)
else:
d = run()
self.last_queued = unix_timestamp()
d.addCallback(self.requeue)
d.addErrback(self._handle_api_error)
def generate_schedule_commands(schedule):
# FUTURE
# We could support more than 10 items using more than one profile sequence
# Just add more commands in the returned list of commands with different profile sequences/ids (which one?)
profile_id = 1
profile_sequence = 0
dali_address = 254 # broadcast
days = 254 # all days, minus holidays
steps = []
for time, _cmd_id, cmd_name in schedule:
try:
if cmd_name.startswith('set_dim_level_'):
level = int(cmd_name[len('set_dim_level_'):])
steps.append((time, level))
except:
pass
set_schedule_cmd = {
'data': encode_profile_configuration_message(profile_id, profile_sequence, dali_address, days, steps).decode(),
'fport': 50
}
set_clock_cmd = {
'data': encode_time_settings_configuration_message(int(unix_timestamp()) + 3*60*60).decode(),
'fport': 50
}
return [
set_schedule_cmd,
set_clock_cmd
]
def convert_to_saccade_node(trial_node):
n = trial_node
return {
'name': n['name'] + 'saccade/',
'timestamp': int(unix_timestamp()),
'format': 'gazelle/v1/saccade/',
'meta': {},
'function': ['saccade_em'],
'function_version': [], # virgin
'input': [n['name']],
'input_timestamp': [], # virgin
'output': [], # virgin
}
def convert_to_project_node(session_nodes):
'''
Combine sessions to get an aggregate of all trial sequences and trials.
'''
if len(session_nodes) < 1:
raise Exception('Should receive at least one session node.')
# More general name: remove method and participant
ses_name = session_nodes[0]['name']
node_name = '/'.join(ses_name.split('/')[:-8]) + '/'
return {
'name': node_name,
'timestamp': int(unix_timestamp()),
'format': 'gazelle/v1/project/simple/',
'meta': {},
'function': ['trial_project'],
'function_version': [], # virgin
'input': map(lambda n: n['name'], session_nodes),
'input_timestamp': [], # virgin
'output': [], # virgin
}
def convert_to_session_node(sequence_nodes):
'''
Combine sequences
'''
if len(sequence_nodes) < 1:
raise Exception('Should receive at least one sequence node.')
# More general name: remove SRT id
seq_name = sequence_nodes[0]['name']
node_name = '/'.join(seq_name.split('/')[:-2]) + '/'
return {
'name': node_name,
'timestamp': int(unix_timestamp()),
'format': 'gazelle/v1/session/simple/',
'meta': {},
'function': ['trial_session'],
'function_version': [], # virgin
'input': map(lambda n: n['name'], sequence_nodes),
'input_timestamp': [], # virgin
'output': [], # virgin
}
def by_prototype(baby_node):
'''
Generates valid node from minimal node dictionary.
Required keys are 'name' and 'format'.
'''
b = baby_node # alias
adult_node = {}
required_keys_and_types = {
'name': str,
'format': str
}
keys_and_defaults = {
'function': [],
'function_version': [],
'input': [],
'input_timestamp': [],
'function_version': [],
'output': None,
'meta': {},
'timestamp': int(unix_timestamp()),
}
for k, t in required_keys_and_types.items():
if k not in b:
raise InvalidNodeCandidate('Node does not have required keys.')
if isinstance(b[k], t):
raise InvalidNodeCandidate('Node key ' + k + ' has invalid type.')
adult_node[k] = b[k]
for k, default in keys_and_defaults.items():
if k in baby_node:
adult_node[k] = baby_node[k]
else:
adult_node[k] = default
return adult_node
def compute_node(db, node, policy='init'):
'''
Ensure that node is up-to-date and if not then execute it's function.
Return the ensured node.
Computation policies:
policy='init' (default)
compute only if not computed before.
policy='ensure'
ensure that cache is up-to-date and execute only if necessary.
policy='force'
compute always, disregard any caches.
'''
print 'Computing node: ' + node['name']
#import pdb; pdb.set_trace()
if policy not in ['init', 'ensure', 'force']:
raise ValueError('Unknown policy: ' + policy)
# Node might not have been found
if node is None:
raise InvalidNodeError('Invalid node: None')
# Ensure that the object really has sufficient properties
if any(map(lambda x: x not in node, ['name', 'function', 'output'])):
raise InvalidNodeError('Node does not have required attributes.')
# Aliases to simplify names.
input_names = node['input']
input_times = node['input_timestamp']
fn_versions = node['function_version']
output = node['output']
fn_names = node['function']
# Ensure that the object really has sufficient properties
if not isinstance(fn_versions, list):
raise InvalidNodeError('Node\'s function versions should be a list.')
if not isinstance(fn_names, list):
raise InvalidNodeError('Node\'s function should be a list.')
# Node without function is a data node, and does not need execution
if len(fn_names) < 1:
# A data node, no functions to execute
return node
# If node has been run before, return it without recomputation
if policy == 'init' and len(fn_versions) == len(fn_names):
return node
# Gather the data for execution. Load input nodes:
input_nodes = map(lambda n: get_node.by_name_computed(db, n, policy), input_names)
# Import functions.
fn_modules = []
for fn_name in fn_names:
# Import the function
try:
modu = import_module('functions.' + fn_name)
except ImportError as e:
raise InvalidNodeFunctionError('No node function ' + fn_name +
' can be found or there is problem in the module: ' + str(e))
# Assert: import successful
if not hasattr(modu, 'execute') or not hasattr(modu, 'version'):
raise InvalidNodeFunctionAPIError('Node function should have an ' +
'execute method and version property.')
fn_modules.append(modu)
# If the function versions have remained the same
# and cached output has same timestamp as the input nodes,
# then do not recompute. However, under 'force' policy, compute
# anyway. Under 'init' policy, if we got this far, nothing is up-to-date.
if policy != 'force':
if len(input_times) == len(input_nodes): # Test if virgin
if all(map(lambda nt: nt[0]['timestamp'] == nt[1],
zip(input_nodes, input_times))):
# Output is up to date
if len(fn_names) == len(fn_versions):
if all(map(lambda fv: fv[0].version == fv[1],
zip(fn_modules, fn_versions))):
# no reason to recompute
return node
# Execute functions from left to right, feeding the prev output to
# next input. Give input nodes as parameters to the first.
input_args = input_nodes
for modu in fn_modules:
# Execute the function TODO function composition
input_args = modu.execute(input_args)
# The last input_args is the final output.
# Store results and update timestamps. Like a cache.
node['output'] = input_args
node['timestamp'] = int(unix_timestamp())
node['input_timestamp'] = map(lambda n: n['timestamp'], input_nodes)
node['function_version'] = map(lambda m: m.version, fn_modules)
#pdb.set_trace()
db.nodes.replace_one({'_id': node['_id']}, node)
return node
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'asg_base_name',
help=(
'The base name of the ASG. Turquoise will look for an ASG a ' +
'equal to the base name or the base name plus a hyphen plus a ' +
'unix timestamp (e.g., "my-asg-1234567").'
),
)
parser.add_argument(
'ami_id',
help='The ID of the AMI you would like to deploy.',
)
args = parser.parse_args()
asg_client = boto3.client('autoscaling')
elb_client = boto3.client('elb')
asg_new_name = args.asg_base_name + '-' + str(int(unix_timestamp()))
print('Searching for ASG...')
asg = find_asg(asg_client, args.asg_base_name)
if asg is None:
raise Exception('Could not find ASG.')
print('Loading LC...')
lc = asg_client.describe_launch_configurations(
LaunchConfigurationNames=[asg['LaunchConfigurationName']],
)['LaunchConfigurations'][0]
print('Suspending ASG...')
asg_client.suspend_processes(
AutoScalingGroupName=asg['AutoScalingGroupName'],
)
print('Creating new launch configuration...')
new_lc = clone_lc(asg_client, lc, asg_new_name, args.ami_id)
print('Creating new ASG...')
new_asg = clone_asg(
asg_client,
asg,
asg_new_name,
new_lc['LaunchConfigurationName'],
)
has_lbs = len(asg['LoadBalancerNames']) > 0
if has_lbs:
asg_client.attach_load_balancers(
AutoScalingGroupName=asg_new_name,
LoadBalancerNames=asg['LoadBalancerNames'],
)
print('Waiting for instances to boot...')
wait_for_instances(
asg_client, new_asg, 'InService', 'Healthy',
new_asg['DesiredCapacity'],
)
if has_lbs:
# Wait for LB health checks
print('Waiting for ELB health checks...')
for lb_name in asg['LoadBalancerNames']:
wait_for_lb_instances(elb_client, lb_name)
print('Scaling down and deleting old ASG...')
delete_asg(asg_client, asg)
print('Deleting old LC...')
asg_client.delete_launch_configuration(
LaunchConfigurationName=asg['LaunchConfigurationName'],
)
def put(action,timestamp,nick,message):
if(len(storage) > qsize):
storage.pop(0)
storage.append({'action':action,'timestamp':timestamp,'nick':nick,'message':message,'unix_timestamp':unix_timestamp()})
writeJSON()
def convert_to_trial_nodes(gazedata_path, config_path):
'''
Return
list of nodes
'''
# Open file
config = loadjson(config_path)
g = loadCsvAsJson(gazedata_path, dialect='excel-tab')
# Collect meta from filename
meta = rec.recognize_from_filename(gazedata_path)
# Pick config id from filemeta
config_id = meta['trial_configuration_id']
# Pick meta from config. Find correct config by config id.
trialseq_config = filter(lambda x: x['name'] == config_id,
config)[0]
aoi_rects = trialseq_config['aois']
# Create entries (and collections) for each + entry for source file
# For each trial, create a trial node
trials = gazedata.getTrials(g)
nodes = []
for trial in trials:
# Collect trial meta
relevant_g = gazedata.getSaccadeWindow(trial)
rel_start, rel_end = gazedata.getSaccadeWindowRange(trial)
aoi_xy = gazedata.getTargetLocation(relevant_g, aoi_rects)
num_valid_p = gazedata.countValidGazepoints(trial)
num_valid_sac_p = gazedata.countValidGazepoints(relevant_g)
if len(trial) > 0:
validity_ratio = float(num_valid_p) / len(trial)
else:
validity_ratio = 0
trial_num = gazedata.getTrialNumber(trial)
trial_num_str = str(trial_num).zfill(2)
age_str = str(meta['participant_age_months']) + 'mo'
# Normalize gazepoints
norm_g = map(tobii_to_gazelle, trial)
name_root = '/'.join([
'icl',
'cg',
'person',
meta['participant_id'],
'age',
age_str,
'method',
meta['method_version'],
'calib' if meta['calibration_successful'] else 'nocalib',
meta['trial_configuration_id'].lower(),
'trial',
trial_num_str,
])
trial_node = {
'name': name_root + '/',
'timestamp': int(unix_timestamp()),
'format': 'gazelle/v1/trial/simple/',
'meta': {
'date': gazedata.get_trial_date(trial),
'participant_id': meta['participant_id'],
'method_version': meta['method_version'],
'participant_age_months': meta['participant_age_months'],
'calibration_successful': meta['calibration_successful'],
'trial_configuration_id': meta['trial_configuration_id'],
'trial_number': trial_num,
'num_points': len(norm_g),
'num_valid_points': num_valid_p,
'validity_ratio': validity_ratio,
'aoi_x_rel': aoi_xy[0],
'aoi_y_rel': aoi_xy[1],
'source_file': os.path.basename(gazedata_path),
'tags': {
'target': {'range': [rel_start, -1]},
'saccade_window': {'range': [rel_start, rel_end]},
},
},
'function': [],
'function_version': [],
'input': [],
'input_timestamp': [],
'output': norm_g,
}
nodes.append(trial_node)
return nodes
