class WSHandler(WebSocketHandler):
def open(self):
print("WebSocket opened")
# A Subject is both an observable and observer, so we can both subscribe
# to it and also feed (on_next) it with new values
self.stream = Subject()
# Get all distinct key up events from the input and only fire if long enough and distinct
query = (
self.stream.map(lambda x: x["term"])
.filter(lambda text: len(text) > 2) # Only if the text is longer than 2 characters
.debounce(0.750, scheduler=scheduler) # Pause for 750ms
.distinct_until_changed()
) # Only if the value has changed
searcher = query.flat_map_latest(search_wikipedia)
def send_response(x):
self.write_message(x.body)
def on_error(ex):
print(ex)
searcher.subscribe(send_response, on_error)
def on_message(self, message):
obj = json_decode(message)
self.stream.on_next(obj)
def on_close(self):
print("WebSocket closed")
class WSHandler(WebSocketHandler):
def open(self):
print("WebSocket opened")
# A Subject is both an observable and observer, so we can both subscribe
# to it and also feed (on_next) it with new values
self.subject = Subject()
# Now we take on our magic glasses and project the stream of bytes into
# a ...
query = self.subject.map(
lambda obj: obj["keycode"] # 1. stream of keycodes
).window_with_count(
10,
1 # 2. stream of windows (10 ints long)
).select_many(
# 3. stream of booleans, True or False
lambda win: win.sequence_equal(codes)).filter(
lambda equal: equal # 4. stream of Trues
)
# 4. we then subscribe to the Trues, and signal Konami! if we see any
query.subscribe(lambda x: self.write_message("Konami!"))
def on_message(self, message):
obj = json_decode(message)
self.subject.on_next(obj)
def on_close(self):
print("WebSocket closed")
class WSHandler(WebSocketHandler):
def open(self):
print("WebSocket opened")
# A Subject is both an observable and observer, so we can both subscribe
# to it and also feed (send) it with new values
self.stream = Subject()
# Get all distinct key up events from the input and only fire if long enough and distinct
query = (
self.stream.map(lambda x: x["term"]).filter(
lambda text: len(text) > 2
) # Only if the text is longer than 2 characters
.debounce(0.750) # Pause for 750ms
.distinct_until_changed() # Only if the value has changed
)
searcher = query.flat_map_latest(search_wikipedia)
def send_response(x):
self.write_message(x.body)
def on_error(ex):
print(ex)
searcher.subscribe_(send_response, on_error, scheduler=scheduler)
def on_message(self, message):
obj = json_decode(message)
self.stream.on_next(obj)
def on_close(self):
print("WebSocket closed")
class WSHandler(WebSocketHandler):
def open(self):
print("WebSocket opened")
# A Subject is both an observable and observer, so we can both subscribe
# to it and also feed (on_next) it with new values
self.subject = Subject()
# Now we take on our magic glasses and project the stream of bytes into
# a ...
query = self.subject.map(
lambda obj: obj["keycode"] # 1. stream of keycodes
).window_with_count(
10, 1 # 2. stream of windows (10 ints long)
).select_many(
# 3. stream of booleans, True or False
lambda win: win.sequence_equal(codes)
).filter(
lambda equal: equal # 4. stream of Trues
)
# 4. we then subscribe to the Trues, and signal Konami! if we see any
query.subscribe(lambda x: self.write_message("Konami!"))
def on_message(self, message):
obj = json_decode(message)
self.subject.on_next(obj)
def on_close(self):
print("WebSocket closed")
# -*- coding: utf-8 -*-
import logging
from rx.subjects import Subject
from src.core.models import ChatRoomMessage
logger = logging.getLogger(__name__)
# 收到微信的原始 dict 数据
raw_stream = Subject()
# 收指定微信群的数据
stream = raw_stream.map(lambda message_data: ChatRoomMessage(message_data)
).filter(lambda message: message.chatroom)
def subscribe(observable):
""" 订阅装饰器，cls订阅observable """
def wrapper(cls):
observable.subscribe(cls())
return cls
return wrapper
def no_exception(func):
""" 无异常装饰器，直接打log """
def wrapper(*args, **kwargs):
try:
return func(*args, **kwargs)
class OptiRx(object):
def __init__(self, chr_contr, executor, gen0=None, opts=default_options):
self.gen0 = gen0
self.chr_contr = chr_contr
self.pauser = Pauser()
self.executor = executor
self.executor.kpp_fun = self.pauser.kpp
self.sub_chr_best = ReplaySubject()
self.sub_gen_solved = Subject()
self.sub_gen_unsolved = Subject()
self.subj_done = Subject()
self.su_genetic = StepUpGenetic(chr_contr)
self.opts = dict(default_options)
self.init_opts(opts)
self.stop_flag = True
self.gen_flag = True
self.init_logic()
def init_opts(self, opts):
for k in opts:
self.opts[k] = opts[k]
self.su_genetic.pop_count = self.opts['pop_count']
self.su_genetic.prob_mut = self.opts['prob_mut']
self.su_genetic.prob_mut_gene = self.opts['prob_mut_gene']
self.su_genetic.prob_cross = self.opts['prob_cross']
self.su_genetic.elite_count = self.opts['elite_count']
def init_logic(self):
# (fit, op)...
def best_fun(gener):
fit, op = gener.get_best(True)
return gener.num_g, fit, op
best_stream = self.sub_gen_solved.map(best_fun).publish()
improve_best = best_stream.distinct(lambda tp: tp[1])
improve_best.subscribe(self.sub_chr_best)
def wind_switch(beep_inds):
beep_set = set(beep_inds)
def inner(s):
inds = Observable.interval(0)
return s.zip(inds, lambda x, ind: ind) \
.filter(lambda ind: ind in beep_set)
return inner
switch = best_stream\
.window(improve_best.skip(1))\
.flat_map(wind_switch([self.opts['n_gener_switch'], self.opts['n_gener_done']-2]))
done = best_stream\
.window(improve_best.skip(1))\
.flat_map(wind_switch([self.opts['n_gener_done']]))
def stop(ind):
self.stop_flag = True
def switch_f(ind):
self.gen_flag = False
switch.subscribe(switch_f)
done.subscribe(stop)
best_stream.skip(self.opts['n_gener_max'] - 1).subscribe(stop)
best_stream.connect()
def paused(self):
return self.pauser.paused
def pause(self):
self.pauser.pause()
def unpause(self):
self.pauser.play()
def run_sync(self):
if self.gen0:
gen0 = self.gen0
else:
gen0 = Generation(self.chr_contr, 0)
gen0.get_init_pop(self.opts['pop_count'], self.opts['seed0'])
self.stop_flag = False
self.gen_flag = False
while not self.stop_flag:
self.sub_gen_unsolved.on_next(gen0)
if self.gen_flag:
gen0 = self.stepup_gen(gen0)
else:
gen0 = self.stepup_slsqp(gen0)
self.gen_flag = True
self.subj_done.on_completed()
def run(self):
cs1 = threading.Thread(name='calc_thread', target=self.run_sync)
cs1.start()
def stepup_gen(self, gen, seed=None):
self.calc_gen(gen)
self.sub_gen_solved.on_next(gen)
gen_dict = self.su_genetic.step_up(gen.pop, seed)
gen2 = Generation(self.chr_contr, gen.num_g + 1, gen_dict)
return gen2
def calc_gen(self, gen):
fitnessless = gen.get_fitlessness()
if len(fitnessless) == 0:
return
tick_tack = ReplaySubject()
fset = seq(fitnessless) \
.map(lambda wchr: (wchr['name'], wchr['id'])) \
.to_set()
results = []
def remove_fset(result):
wchr, fit = result
tp = wchr['name'], wchr['id']
if tp in fset:
fset.discard(tp)
results.append(result)
if len(fset) == 0:
tick_tack.on_next(0)
s1 = self.executor.sub_compl.subscribe(on_next=remove_fset)
for fn in fitnessless:
self.executor.sub_in.on_next(fn)
if len(fset) == 0:
tick_tack.on_next(0)
tick_tack.to_blocking().first()
tick_tack.dispose()
s1.dispose()
gen.init_fitnesses(results)
if len(gen.get_fitlessness()) > 0:
raise AssertionError("Посчитались не все гены в хромосоме")
def stepup_slsqp(self, gen0):
self.calc_gen(gen0)
lst = reduce_pop(gen0.pop_list, self.opts['n_op_direct'])
opti_lst = [
OptiSLSQP(op, self.executor.sub_in, self.executor.sub_compl)
for op in lst
]
tp = ThreadPoolScheduler()
calc_stream = Observable \
.from_(opti_lst) \
.flat_map(lambda op: Observable.just(op).observe_on(tp).map(lambda op: op.run()))
calc_stream.to_blocking().last_or_default(0)
for op in lst:
op.remove_exept_best()
for o_sls in opti_lst:
o_sls.dispose_conn()
gen1 = Generation(self.chr_contr, gen0.num_g + 1, lst)
self.sub_gen_solved.on_next(gen1)
gen1.fill_pop(self.opts['pop_count'])
return gen1
import math
from rx import Observable
from rx.subjects import Subject
# import needed for to_backpressure operator
import rxbackpressure
# time value samples recorded by some device
time_value_record = Subject()
# separate time and value
time = time_value_record.map(lambda pair: pair[0])
signal = time_value_record.map(lambda pair: pair[1])
# timebase synchronization
sync1 = Subject()
sync2 = Subject()
# synchronize time samples to two timebases
time_sync2_bp = time.to_backpressure().zip(sync2.repeat_first(),
lambda t, sync_time: t + sync_time)
time_sync1_bp = time.to_backpressure().zip(sync1.repeat_first(),
lambda t, sync_time: t + sync_time)
# pairing time value observables
time_sync1_bp.to_observable().zip(signal, lambda t, v:
(t, v)).unsafe_subscribe()
time_sync2_bp.to_observable().zip(signal, lambda t, v:
(t, v)).unsafe_subscribe(
print,