Improved docs

Fixed several bugs
Added convenience methods in soil.analysis
history 0.10
J. Fernando Sánchez 7 years ago
parent 78364d89d5
commit a7c51742f6

@ -3,7 +3,7 @@
Soil is an extensible and user-friendly Agent-based Social Simulator for Social Networks.
Learn how to run your own simulations with our [documentation](http://soilsim.readthedocs.io).
Follow our [tutorial](notebooks/soil_tutorial.ipynb) to develop your own agent models.
Follow our [tutorial](examples/tutorial/soil_tutorial.ipynb) to develop your own agent models.
If you use Soil in your research, don't forget to cite this paper:

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@ -34,13 +34,14 @@ If you use Soil in your research, do not forget to cite this paper:
.. toctree::
:maxdepth: 2
:maxdepth: 0
:caption: Learn more about soil:
installation
quickstart
Tutorial - Spreading news
Tutorial <soil_tutorial>
..
.. Indices and tables

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@ -1,17 +0,0 @@
default_state: {}
environment_agents: []
environment_params: {prob_neighbor_spread: 0.0, prob_tv_spread: 0.01}
interval: 1
max_time: 20
name: Sim_prob_0
network_agents:
- agent_type: NewsSpread
state: {has_tv: false}
weight: 1
- agent_type: NewsSpread
state: {has_tv: true}
weight: 2
network_params: {generator: erdos_renyi_graph, n: 500, p: 0.1}
num_trials: 1
states:
- {has_tv: true}

@ -1,20 +0,0 @@
import soil
import random
class NewsSpread(soil.agents.FSM):
@soil.agents.default_state
@soil.agents.state
def neutral(self):
r = random.random()
if self['has_tv'] and r < self.env['prob_tv_spread']:
return self.infected
return
@soil.agents.state
def infected(self):
prob_infect = self.env['prob_neighbor_spread']
for neighbor in self.get_neighboring_agents(state_id=self.neutral.id):
r = random.random()
if r < prob_infect:
neighbor.state['id'] = self.infected.id
return

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@ -0,0 +1,138 @@
---
default_state: {}
load_module: newsspread
environment_agents: []
environment_params:
prob_neighbor_spread: 0.0
prob_tv_spread: 0.01
interval: 1
max_time: 30
name: Sim_all_dumb
network_agents:
- agent_type: DumbViewer
state:
has_tv: false
weight: 1
- agent_type: DumbViewer
state:
has_tv: true
weight: 1
network_params:
generator: barabasi_albert_graph
n: 500
m: 5
num_trials: 50
---
default_state: {}
load_module: newsspread
environment_agents: []
environment_params:
prob_neighbor_spread: 0.0
prob_tv_spread: 0.01
interval: 1
max_time: 30
name: Sim_half_herd
network_agents:
- agent_type: DumbViewer
state:
has_tv: false
weight: 1
- agent_type: DumbViewer
state:
has_tv: true
weight: 1
- agent_type: HerdViewer
state:
has_tv: false
weight: 1
- agent_type: HerdViewer
state:
has_tv: true
weight: 1
network_params:
generator: barabasi_albert_graph
n: 500
m: 5
num_trials: 50
---
default_state: {}
load_module: newsspread
environment_agents: []
environment_params:
prob_neighbor_spread: 0.0
prob_tv_spread: 0.01
interval: 1
max_time: 30
name: Sim_all_herd
network_agents:
- agent_type: HerdViewer
state:
has_tv: true
id: infected
weight: 1
- agent_type: HerdViewer
state:
has_tv: true
id: neutral
weight: 1
network_params:
generator: barabasi_albert_graph
n: 500
m: 5
num_trials: 50
---
default_state: {}
load_module: newsspread
environment_agents: []
environment_params:
prob_neighbor_spread: 0.0
prob_tv_spread: 0.01
prob_neighbor_cure: 0.1
interval: 1
max_time: 30
name: Sim_wise_herd
network_agents:
- agent_type: HerdViewer
state:
has_tv: true
id: infected
weight: 1
- agent_type: WiseViewer
state:
has_tv: true
weight: 1
network_params:
generator: barabasi_albert_graph
n: 500
m: 5
num_trials: 50
---
default_state: {}
load_module: newsspread
environment_agents: []
environment_params:
prob_neighbor_spread: 0.0
prob_tv_spread: 0.01
prob_neighbor_cure: 0.1
interval: 1
max_time: 30
name: Sim_all_wise
network_agents:
- agent_type: WiseViewer
state:
has_tv: true
id: infected
weight: 1
- agent_type: WiseViewer
state:
has_tv: true
weight: 1
network_params:
generator: barabasi_albert_graph
n: 500
m: 5
network_params:
generator: barabasi_albert_graph
n: 500
m: 5
num_trials: 50

@ -0,0 +1,79 @@
from soil.agents import BaseAgent,FSM, state, default_state
import random
import logging
class DumbViewer(FSM):
'''
A viewer that gets infected via TV (if it has one) and tries to infect
its neighbors once it's infected.
'''
defaults = {
'prob_neighbor_spread': 0.5,
'prob_neighbor_cure': 0.25,
}
@default_state
@state
def neutral(self):
r = random.random()
if self['has_tv'] and r < self.env['prob_tv_spread']:
self.infect()
return
@state
def infected(self):
for neighbor in self.get_neighboring_agents(state_id=self.neutral.id):
prob_infect = self.env['prob_neighbor_spread']
r = random.random()
if r < prob_infect:
self.set_state(self.infected.id)
neighbor.infect()
return
def infect(self):
self.set_state(self.infected)
class HerdViewer(DumbViewer):
'''
A viewer whose probability of infection depends on the state of its neighbors.
'''
level = logging.DEBUG
def infect(self):
infected = self.count_neighboring_agents(state_id=self.infected.id)
total = self.count_neighboring_agents()
prob_infect = self.env['prob_neighbor_spread'] * infected/total
self.debug('prob_infect', prob_infect)
r = random.random()
if r < prob_infect:
self.set_state(self.infected.id)
class WiseViewer(HerdViewer):
'''
A viewer that can change its mind.
'''
@state
def cured(self):
prob_cure = self.env['prob_neighbor_cure']
for neighbor in self.get_neighboring_agents(state_id=self.infected.id):
r = random.random()
if r < prob_cure:
try:
neighbor.cure()
except AttributeError:
self.debug('Viewer {} cannot be cured'.format(neighbor.id))
return
def cure(self):
self.set_state(self.cured.id)
@state
def infected(self):
prob_cure = self.env['prob_neighbor_cure']
r = random.random()
if r < prob_cure:
self.cure()
return
return super().infected()

@ -1,4 +1,4 @@
from soil.agents import NetworkAgent, FSM, state, default_state, BaseAgent
from soil.agents import FSM, state, default_state, BaseAgent
from enum import Enum
from random import random, choice
from itertools import islice
@ -11,7 +11,7 @@ class Genders(Enum):
female = 'female'
class RabbitModel(NetworkAgent, FSM):
class RabbitModel(FSM):
level = logging.INFO
@ -26,7 +26,7 @@ class RabbitModel(NetworkAgent, FSM):
life_expectancy = 365 * 3
gestation = 33
pregnancy = -1
max_females = 2
max_females = 5
@default_state
@state
@ -71,6 +71,7 @@ class RabbitModel(NetworkAgent, FSM):
self.debug('Pregnancy: {}'.format(self['pregnancy']))
if self['pregnancy'] >= self.gestation:
number_of_babies = int(8+4*random())
self.info('Having {} babies'.format(number_of_babies))
for i in range(number_of_babies):
state = {}
state['gender'] = choice(list(Genders)).value
@ -79,13 +80,13 @@ class RabbitModel(NetworkAgent, FSM):
self.env.add_edge(self['mate'], child.id)
# self.add_edge()
self.debug('A BABY IS COMING TO LIFE')
self.env['rabbits_alive'] = self.env.get('rabbits_alive', 0)+1
self.env['rabbits_alive'] = self.env.get('rabbits_alive', self.global_topology.number_of_nodes())+1
self.debug('Rabbits alive: {}'.format(self.env['rabbits_alive']))
self['offspring'] += 1
self.env.get_agent(self['mate'])['offspring'] += 1
del self['mate']
self['pregnancy'] = -1
return self.fertile
del self['mate']
self['pregnancy'] = -1
return self.fertile
@state
def dead(self):
@ -98,12 +99,12 @@ class RabbitModel(NetworkAgent, FSM):
class RandomAccident(BaseAgent):
level = logging.INFO
level = logging.DEBUG
def step(self):
rabbits_total = self.global_topology.number_of_nodes()
rabbits_alive = self.env.get('rabbits_alive', rabbits_total)
prob_death = self.env.get('prob_death', 1e-100)*math.log(max(1, rabbits_alive))
prob_death = self.env.get('prob_death', 1e-100)*math.floor(math.log10(max(1, rabbits_alive)))
self.debug('Killing some rabbits with prob={}!'.format(prob_death))
for i in self.env.network_agents:
if i.state['id'] == i.dead.id:

@ -1,14 +1,14 @@
---
load_module: rabbit_agents
name: rabbits_example
max_time: 1500
max_time: 1200
interval: 1
seed: MySeed
agent_type: RabbitModel
environment_agents:
- agent_type: RandomAccident
environment_params:
prob_death: 0.0001
prob_death: 0.001
default_state:
mating_prob: 0.01
topology:

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@ -4,15 +4,20 @@ import os
import pdb
import logging
__version__ = "0.9.7"
__version__ = "0.10"
try:
basestring
except NameError:
basestring = str
logging.basicConfig()#format=FORMAT)
logging.basicConfig()
from . import agents
from . import simulation
from . import environment
from . import utils
from . import analysis
def main():

@ -1,8 +1,8 @@
import random
from . import NetworkAgent
from . import BaseAgent
class BassModel(NetworkAgent):
class BassModel(BaseAgent):
"""
Settings:
innovation_prob

@ -1,8 +1,8 @@
import random
from . import NetworkAgent
from . import BaseAgent
class BigMarketModel(NetworkAgent):
class BigMarketModel(BaseAgent):
"""
Settings:
Names:

@ -1,7 +1,7 @@
from . import NetworkAgent
from . import BaseAgent
class CounterModel(NetworkAgent):
class CounterModel(BaseAgent):
"""
Dummy behaviour. It counts the number of nodes in the network and neighbors
in each step and adds it to its state.
@ -16,7 +16,7 @@ class CounterModel(NetworkAgent):
self.state['total'] = total
class AggregatedCounter(NetworkAgent):
class AggregatedCounter(BaseAgent):
"""
Dummy behaviour. It counts the number of nodes in the network and neighbors
in each step and adds it to its state.
@ -28,4 +28,5 @@ class AggregatedCounter(NetworkAgent):
neighbors = len(list(self.get_neighboring_agents()))
self.state['times'] = self.state.get('times', 0) + 1
self.state['neighbors'] = self.state.get('neighbors', 0) + neighbors
self.state['total'] = self.state.get('total', 0) + total
self.state['total'] = total = self.state.get('total', 0) + total
self.debug('Running for step: {}. Total: {}'.format(self.now, total))

@ -1,9 +1,9 @@
import random
import numpy as np
from . import NetworkAgent
from . import BaseAgent
class SpreadModelM2(NetworkAgent):
class SpreadModelM2(BaseAgent):
"""
Settings:
prob_neutral_making_denier
@ -104,7 +104,7 @@ class SpreadModelM2(NetworkAgent):
neighbor.state['id'] = 2 # Cured
class ControlModelM2(NetworkAgent):
class ControlModelM2(BaseAgent):
"""
Settings:
prob_neutral_making_denier

@ -1,9 +1,9 @@
import random
import numpy as np
from . import FSM, NetworkAgent, state
from . import FSM, state
class SISaModel(FSM, NetworkAgent):
class SISaModel(FSM):
"""
Settings:
neutral_discontent_spon_prob

@ -1,8 +1,8 @@
import random
from . import NetworkAgent
from . import BaseAgent
class SentimentCorrelationModel(NetworkAgent):
class SentimentCorrelationModel(BaseAgent):
"""
Settings:
outside_effects_prob

@ -72,9 +72,10 @@ class BaseAgent(nxsim.BaseAgent, metaclass=MetaAgent):
return None
def run(self):
interval = self.env.interval
while self.alive:
res = self.step()
yield res or self.env.timeout(self.env.interval)
yield res or self.env.timeout(interval)
def die(self, remove=False):
self.alive = False
@ -99,7 +100,10 @@ class BaseAgent(nxsim.BaseAgent, metaclass=MetaAgent):
count += 1
return count
def get_agents(self, state_id=None, limit_neighbors=False, **kwargs):
def count_neighboring_agents(self, state_id=None):
return len(super().get_agents(state_id, limit_neighbors=True))
def get_agents(self, state_id=None, limit_neighbors=False, iterator=False, **kwargs):
if limit_neighbors:
agents = super().get_agents(state_id, limit_neighbors)
else:
@ -113,9 +117,13 @@ class BaseAgent(nxsim.BaseAgent, metaclass=MetaAgent):
return False
return True
return filter(matches_all, agents)
f = filter(matches_all, agents)
if iterator:
return f
return list(f)
def log(self, message, level=logging.INFO, **kwargs):
def log(self, message, *args, level=logging.INFO, **kwargs):
message = message + " ".join(str(i) for i in args)
message = "\t@{:>5}:\t{}".format(self.now, message)
for k, v in kwargs:
message += " {k}={v} ".format(k, v)
@ -130,11 +138,6 @@ class BaseAgent(nxsim.BaseAgent, metaclass=MetaAgent):
def info(self, *args, **kwargs):
return self.log(*args, level=logging.INFO, **kwargs)
class NetworkAgent(BaseAgent, nxsim.BaseNetworkAgent):
def count_neighboring_agents(self, state_id=None):
return self.count_agents(state_id, limit_neighbors=True)
def state(func):
@ -150,7 +153,7 @@ def state(func):
try:
self.state['id'] = next_state.id
except AttributeError:
raise NotImplemented('State id %s is not valid.' % next_state)
raise ValueError('State id %s is not valid.' % next_state)
return when
func_wrapper.id = func.__name__

@ -4,20 +4,175 @@ import glob
import yaml
from os.path import join
from . import utils
def get_data(pattern, process=True, attributes=None):
def read_data(*args, group=False, **kwargs):
iterable = _read_data(*args, **kwargs)
if group:
return group_trials(iterable)
else:
return list(iterable)
def _read_data(pattern, keys=None, convert_types=False,
process=None, from_csv=False, **kwargs):
for folder in glob.glob(pattern):
config_file = glob.glob(join(folder, '*.yml'))[0]
config = yaml.load(open(config_file))
for trial_data in sorted(glob.glob(join(folder, '*.environment.csv'))):
df = pd.read_csv(trial_data)
if process:
if attributes is not None:
df = df[df['attribute'].isin(attributes)]
df = df.pivot_table(values='attribute', index='tstep', columns=['value'], aggfunc='count').fillna(0)
yield config_file, df, config
df = None
if from_csv:
for trial_data in sorted(glob.glob(join(folder,
'*.environment.csv'))):
df = read_csv(trial_data, convert_types=convert_types)
if process:
df = process(df, **kwargs)
yield config_file, df, config
else:
for trial_data in sorted(glob.glob(join(folder, '*.db.sqlite'))):
df = read_sql(trial_data, convert_types=convert_types,
keys=keys)
if process:
df = process(df, **kwargs)
yield config_file, df, config
def read_csv(filename, keys=None, convert_types=False, **kwargs):
'''
Read a CSV in canonical form: ::
<agent_id, t_step, key, value, value_type>
'''
df = pd.read_csv(filename)
if convert_types:
df = convert_types_slow(df)
if keys:
df = df[df['key'].isin(keys)]
return df
def read_sql(filename, keys=None, convert_types=False, limit=-1):
condition = ''
if keys:
k = map(lambda x: "\'{}\'".format(x), keys)
condition = 'where key in ({})'.format(','.join(k))
query = 'select * from history {} limit {}'.format(condition, limit)
df = pd.read_sql_query(query, 'sqlite:///{}'.format(filename))
if convert_types:
df = convert_types_slow(df)
return df
def convert_row(row):
row['value'] = utils.convert(row['value'], row['value_type'])
return row
def convert_types_slow(df):
'''This is a slow operation.'''
dtypes = get_types(df)
for k, v in dtypes.items():
t = df[df['key']==k]
t['value'] = t['value'].astype(v)
df = df.apply(convert_row, axis=1)
return df
def split_df(df):
'''
Split a dataframe in two dataframes: one with the history of agents,
and one with the environment history
'''
envmask = (df['agent_id'] == 'env')
n_env = envmask.sum()
if n_env == len(df):
return df, None
elif n_env == 0:
return None, df
agents, env = [x for _, x in df.groupby(envmask)]
return env, agents
def process(df, **kwargs):
'''
Process a dataframe in canonical form ``(t_step, agent_id, key, value, value_type)`` into
two dataframes with a column per key: one with the history of the agents, and one for the
history of the environment.
'''
env, agents = split_df(df)
return process_one(env, **kwargs), process_one(agents, **kwargs)
def get_types(df):
dtypes = df.groupby(by=['key'])['value_type'].unique()
return {k:v[0] for k,v in dtypes.iteritems()}
def process_one(df, *keys, columns=['key'], values='value',
index=['t_step', 'agent_id'], aggfunc='first', **kwargs):
'''
Process a dataframe in canonical form ``(t_step, agent_id, key, value, value_type)`` into
a dataframe with a column per key
'''
if df is None:
return df
if keys:
df = df[df['key'].isin(keys)]
dtypes = get_types(df)
df = df.pivot_table(values=values, index=index, columns=columns,
aggfunc=aggfunc, **kwargs)
df = df.fillna(0).astype(dtypes)
return df
def get_count_processed(df, *keys):
if keys:
df = df[list(keys)]
# p = df.groupby(level=0).apply(pd.Series.value_counts)
p = df.unstack().apply(pd.Series.value_counts, axis=1)
return p
def get_count(df, *keys):
if keys:
df = df[df['key'].isin(keys)]
p = df.groupby(by=['t_step', 'key', 'value']).size().unstack(level=[1,2]).fillna(0)
return p
def get_value(df, *keys, aggfunc='sum'):
if keys:
df = df[df['key'].isin(keys)]
p = process_one(df, *keys)
p = p.groupby(level='t_step').agg(aggfunc)
return p
def plot_all(*args, **kwargs):
for config_file, df, config in sorted(get_data(*args, **kwargs)):
'''
Read all the trial data and plot the result of applying a function on them.
'''
dfs = do_all(*args, **kwargs)
ps = []
for line in dfs:
f, df, config = line
df.plot(title=config['name'])
ps.append(df)
return ps
def do_all(pattern, func, *keys, include_env=False, **kwargs):
for config_file, df, config in read_data(pattern, keys=keys):
p = func(df, *keys, **kwargs)
p.plot(title=config['name'])
yield config_file, p, config
def group_trials(trials, aggfunc=['mean', 'min', 'max', 'std']):
trials = list(trials)
trials = list(map(lambda x: x[1] if isinstance(x, tuple) else x, trials))
return pd.concat(trials).groupby(level=0).agg(aggfunc).reorder_levels([2, 0,1] ,axis=1)

@ -41,17 +41,20 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
# executed before network agents
self['SEED'] = seed or time.time()
random.seed(self['SEED'])
self.process(self.save_state())
self.environment_agents = environment_agents or []
self.network_agents = network_agents or []
self.process(self.save_state())
if self.dump:
self._db_path = os.path.join(self.get_path(), 'db.sqlite')
self._db_path = os.path.join(self.get_path(), '{}.db.sqlite'.format(self.name))
else:
self._db_path = ":memory:"
self.create_db(self._db_path)
def create_db(self, db_path=None):
db_path = db_path or self._db_path
if os.path.exists(db_path):
newname = db_path.replace('db.sqlite', 'backup{}.sqlite'.format(time.time()))
os.rename(db_path, newname)
self._db = sqlite3.connect(db_path)
with self._db:
self._db.execute('''CREATE TABLE IF NOT EXISTS history (agent_id text, t_step int, key text, value text, value_type text)''')
@ -118,24 +121,25 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
return self.G.add_edge(agent1, agent2)
def run(self, *args, **kwargs):
self._save_state()
super().run(*args, **kwargs)
self._save_state()
def _save_state(self, now=None):
# for agent in self.agents:
# agent.save_state()
utils.logger.debug('Saving state @{}'.format(self.now))
with self._db:
self._db.executemany("insert into history(agent_id, t_step, key, value, value_type) values (?, ?, ?, ?, ?)", self.state_to_tuples(now=now))
def save_state(self):
self._save_state()
while self.peek() != simpy.core.Infinity:
utils.logger.info('Step: {}'.format(self.now))
delay = max(self.peek() - self.now, self.interval)
utils.logger.debug('Step: {}'.format(self.now))
ev = self.event()
ev._ok = True
# Schedule the event with minimum priority so
# that it executes after all agents are done
self.schedule(ev, -1, self.peek())
# that it executes before all agents
self.schedule(ev, -999, delay)
yield ev
self._save_state()
@ -215,7 +219,7 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
with open(csv_name, 'w') as f:
cr = csv.writer(f)
cr.writerow(('agent_id', 'tstep', 'attribute', 'value'))
cr.writerow(('agent_id', 't_step', 'key', 'value', 'value_type'))
for i in self.history_to_tuples():
cr.writerow(i)
@ -229,14 +233,16 @@ class SoilEnvironment(nxsim.NetworkEnvironment):
if now is None:
now = self.now
for k, v in self.environment_params.items():
yield 'env', now, k, v, type(v).__name__
v, v_t = utils.repr(v)
yield 'env', now, k, v, v_t
for agent in self.agents:
for k, v in agent.state.items():
yield agent.id, now, k, v, type(v).__name__
v, v_t = utils.repr(v)
yield agent.id, now, k, v, v_t
def history_to_tuples(self):
with self._db:
res = self._db.execute("select agent_id, t_step, key, value from history ").fetchall()
res = self._db.execute("select agent_id, t_step, key, value, value_type from history ").fetchall()
yield from res
def history_to_graph(self):

@ -67,7 +67,7 @@ class SoilSimulation(NetworkSimulation):
self.default_state = default_state or {}
self.dir_path = dir_path or os.getcwd()
self.interval = interval
self.seed = seed
self.seed = str(seed) or str(time.time())
self.dump = dump
self.environment_params = environment_params or {}
@ -168,7 +168,7 @@ class SoilSimulation(NetworkSimulation):
env_name = '{}_trial_{}'.format(self.name, trial_id)
env = environment.SoilEnvironment(name=env_name,
topology=self.topology.copy(),
seed=self.seed,
seed=self.seed+env_name,
initial_time=0,
dump=self.dump,
interval=self.interval,

@ -13,7 +13,6 @@ from contextlib import contextmanager
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
logger.addHandler(logging.StreamHandler())
def load_network(network_params, dir_path=None):
@ -86,6 +85,12 @@ def agent_from_distribution(distribution, value=-1):
raise Exception('Distribution for value {} not found in: {}'.format(value, distribution))
def repr(v):
if isinstance(v, bool):
v = "true" if v else ""
return v, bool.__name__
return v, type(v).__name__
def convert(value, type_):
import importlib
try:

@ -60,7 +60,7 @@ class TestMain(TestCase):
'network_params': {
'path': join(ROOT, 'test.gexf')
},
'agent_type': 'NetworkAgent',
'agent_type': 'BaseAgent',
'environment_params': {
}
}
@ -119,7 +119,7 @@ class TestMain(TestCase):
def test_custom_agent(self):
"""Allow for search of neighbors with a certain state_id"""
class CustomAgent(agents.NetworkAgent):
class CustomAgent(agents.BaseAgent):
def step(self):
self.state['neighbors'] = self.count_agents(state_id=0,
limit_neighbors=True)
@ -208,7 +208,7 @@ class TestMain(TestCase):
res = list(env.history_to_tuples())
assert len(res) == len(env.environment_params)
assert ('env', 0, 'test', 'test_value') in res
assert ('env', 0, 'test', 'test_value', 'str') in res
env['test'] = 'second_value'
env._save_state(now=1)

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