RequestHandler): def get(self): self.write("Hello, world") def make_app(): return tornado.web.Application([ (r"/", MainHandler), ]) async def main(): app = make_app() app.listen(8888) await asyncio.Event().wait() AsyncHTTPClient() my_future = Future() fetch_future = http_client.fetch(url) def on_fetch(f): my_future.set_result(f.result().body) fetch_future.add_done_callback(on_fetch) return my_future Notice that the # It returns when the next yield is reached future = self.gen.send(self.next) def callback(f): self.next = f.result() self.run() future.add_done_callback(callback) The decorator receives a Future from

. . . . . . . . . . . . . . . 455 36.21 Don't write a trivial anonymous function x->f(x) for a named function f . . . . . . . 455 36.22 Avoid using floats for numeric literals in generic code when possible from MATLAB . . . . . . . . . . . . . . . . . . . . . . . . . 478 38.2 Noteworthy differences from R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 480 38.3 Noteworthy differences from Python DOCUMENTATION 3 Because Julia's compiler is different from the interpreters used for languages like Python or R, you may find that Julia's performance is unintuitive at first. If you find that something is slow

. . . . . . . . . . . . . . . 455 36.21 Don't write a trivial anonymous function x->f(x) for a named function f . . . . . . . 455 36.22 Avoid using floats for numeric literals in generic code when possible from MATLAB . . . . . . . . . . . . . . . . . . . . . . . . . 478 38.2 Noteworthy differences from R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 480 38.3 Noteworthy differences from Python DOCUMENTATION 3 Because Julia's compiler is different from the interpreters used for languages like Python or R, you may find that Julia's performance is unintuitive at first. If you find that something is slow

. . . . . . . . . . . . . . . 489 37.22 Don't write a trivial anonymous function x->f(x) for a named function f . . . . . . . 489 37.23 Avoid using floats for numeric literals in generic code when possible from MATLAB . . . . . . . . . . . . . . . . . . . . . . . . . 513 39.2 Noteworthy differences from R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 515 39.3 Noteworthy differences from Python languages. Because Julia's compiler is different from the interpreters used for languages like Python or R, you may find that Julia's performance is unintuitive at first. If you find that something is slow

. . . . . . . . . . . . . . . 489 37.22 Don't write a trivial anonymous function x->f(x) for a named function f . . . . . . . 489 37.23 Avoid using floats for numeric literals in generic code when possible from MATLAB . . . . . . . . . . . . . . . . . . . . . . . . . 513 39.2 Noteworthy differences from R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 515 39.3 Noteworthy differences from Python languages. Because Julia's compiler is different from the interpreters used for languages like Python or R, you may find that Julia's performance is unintuitive at first. If you find that something is slow

. . . . . . . . . . . . . . . 489 37.22 Don't write a trivial anonymous function x->f(x) for a named function f . . . . . . . 489 37.23 Avoid using floats for numeric literals in generic code when possible from MATLAB . . . . . . . . . . . . . . . . . . . . . . . . . 513 39.2 Noteworthy differences from R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 515 39.3 Noteworthy differences from Python languages. Because Julia's compiler is different from the interpreters used for languages like Python or R, you may find that Julia's performance is unintuitive at first. If you find that something is slow

self.write("Hello, world") def make_app(): return tornado.web.Application([ (r"/", MainHandler), ]) async def main(): app = make_app() app.listen(8888) await asyncio AsyncHTTPClient() my_future = Future() fetch_future = http_client.fetch(url) def on_fetch(f):my_future.set_result(f.result().body) fetch_future.add_done_callback(on_fetch) return my_future Notice returns when the next yield is reached future = self.gen.send(self.next) def callback(f):self.next = f.result() self.run() future.add_done_callback(callback) The decorator receives

. . . . . . . . . . . . . . . 500 37.23 Don't write a trivial anonymous function x->f(x) for a named function f . . . . . . . 500 37.24 Avoid using floats for numeric literals in generic code when possible from MATLAB . . . . . . . . . . . . . . . . . . . . . . . . . 524 39.2 Noteworthy differences from R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 526 39.3 Noteworthy differences from Python languages. Because Julia's compiler is different from the interpreters used for languages like Python or R, you may find that Julia's performance is unintuitive at first. If you find that something is slow

. . . . . . . . . . . . . . . 501 37.22 Don't write a trivial anonymous function x->f(x) for a named function f . . . . . . . 501 37.23 Avoid using floats for numeric literals in generic code when possible from MATLAB . . . . . . . . . . . . . . . . . . . . . . . . . 525 39.2 Noteworthy differences from R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 527 39.3 Noteworthy differences from Python languages. Because Julia's compiler is different from the interpreters used for languages like Python or R, you may find that Julia's performance is unintuitive at first. If you find that something is slow

. . . . . . . . . . . . . . . 500 37.22 Don't write a trivial anonymous function x->f(x) for a named function f . . . . . . . 500 37.23 Avoid using floats for numeric literals in generic code when possible from MATLAB . . . . . . . . . . . . . . . . . . . . . . . . . 524 39.2 Noteworthy differences from R . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 526 39.3 Noteworthy differences from Python languages. Because Julia's compiler is different from the interpreters used for languages like Python or R, you may find that Julia's performance is unintuitive at first. If you find that something is slow